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b/data/conda_envs/evalclaw/lib/python3.11/site-packages/pytz/zoneinfo/America/North_Dakota/New_Salem differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_VariableFunctions.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_VariableFunctions.pyi new file mode 100644 index 0000000000000000000000000000000000000000..efe58f1e400f5048ec7bf06fc7c7f8917953ce90 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_VariableFunctions.pyi @@ -0,0 +1,25699 @@ +# @generated by tools/pyi/gen_pyi.py from torch/_C/_VariableFunctions.pyi.in +# mypy: disable-error-code="type-arg" +# mypy: allow-untyped-defs + +import builtins +from typing import ( + Any, + Callable, + ContextManager, + Iterator, + List, + Literal, + NamedTuple, + Optional, + overload, + Sequence, + Tuple, + TypeVar, + Union, +) + +import torch +from torch import contiguous_format, Generator, inf, memory_format, strided, SymInt, Tensor +from torch.types import ( + _bool, + _complex, + _device, + _dtype, + _float, + _int, + _layout, + _qscheme, + _size, + Device, + Number, +) + +from torch._prims_common import DeviceLikeType + +@overload +def __and__(input: Tensor, other: Tensor) -> Tensor: ... +@overload +def __and__(input: Tensor, other: Union[Number, _complex]) -> Tensor: ... +@overload +def __lshift__(input: Tensor, other: Tensor) -> Tensor: ... +@overload +def __lshift__(input: Tensor, other: Union[Number, _complex]) -> Tensor: ... +@overload +def __or__(input: Tensor, other: Tensor) -> Tensor: ... +@overload +def __or__(input: Tensor, other: Union[Number, _complex]) -> Tensor: ... +@overload +def __rshift__(input: Tensor, other: Tensor) -> Tensor: ... +@overload +def __rshift__(input: Tensor, other: Union[Number, _complex]) -> Tensor: ... +@overload +def __xor__(input: Tensor, other: Tensor) -> Tensor: ... +@overload +def __xor__(input: Tensor, other: Union[Number, _complex]) -> Tensor: ... +def _adaptive_avg_pool2d(input: Tensor, output_size: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]]) -> Tensor: ... +def _adaptive_avg_pool3d(input: Tensor, output_size: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]]) -> Tensor: ... +def _add_batch_dim(input: Tensor, batch_dim: _int, level: _int) -> Tensor: ... +@overload +def _add_relu(input: Tensor, other: Tensor, *, alpha: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: ... +@overload +def _add_relu(input: Tensor, other: Union[Number, _complex], alpha: Union[Number, _complex] = 1) -> Tensor: ... +@overload +def _add_relu_(input: Tensor, other: Tensor, *, alpha: Union[Number, _complex] = 1) -> Tensor: ... +@overload +def _add_relu_(input: Tensor, other: Union[Number, _complex], alpha: Union[Number, _complex] = 1) -> Tensor: ... +def _addmm_activation(input: Tensor, mat1: Tensor, mat2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1, use_gelu: _bool = False, out: Optional[Tensor] = None) -> Tensor: ... +@overload +def _aminmax(input: Tensor) -> Tuple[Tensor, Tensor]: ... +@overload +def _aminmax(input: Tensor, dim: _int, keepdim: _bool = False) -> Tuple[Tensor, Tensor]: ... +def _amp_foreach_non_finite_check_and_unscale_(self: Union[Tuple[Tensor, ...], List[Tensor]], found_inf: Tensor, inv_scale: Tensor) -> None: ... +def _amp_update_scale_(input: Tensor, growth_tracker: Tensor, found_inf: Tensor, scale_growth_factor: _float, scale_backoff_factor: _float, growth_interval: _int) -> Tensor: ... +@overload +def _assert_async(input: Tensor) -> None: + r""" + _assert_async(tensor) -> void + + Asynchronously assert that the contents of tensor are nonzero. For CPU tensors, + this is equivalent to ``assert tensor`` or ``assert tensor.is_nonzero()``; for + CUDA tensors, we DO NOT synchronize and you may only find out the assertion + failed at a later CUDA kernel launch. Asynchronous assertion can be helpful for + testing invariants in CUDA tensors without giving up performance. This function + is NOT intended to be used for regular error checking, as it will trash your CUDA + context if the assert fails (forcing you to restart your PyTorch process.) + + Args: + tensor (Tensor): a one element tensor to test to see if it is nonzero. Zero + elements (including False for boolean tensors) cause an assertion failure + to be raised. + """ + ... +@overload +def _assert_async(input: Tensor, assert_msg: str) -> None: + r""" + _assert_async(tensor) -> void + + Asynchronously assert that the contents of tensor are nonzero. For CPU tensors, + this is equivalent to ``assert tensor`` or ``assert tensor.is_nonzero()``; for + CUDA tensors, we DO NOT synchronize and you may only find out the assertion + failed at a later CUDA kernel launch. Asynchronous assertion can be helpful for + testing invariants in CUDA tensors without giving up performance. This function + is NOT intended to be used for regular error checking, as it will trash your CUDA + context if the assert fails (forcing you to restart your PyTorch process.) + + Args: + tensor (Tensor): a one element tensor to test to see if it is nonzero. Zero + elements (including False for boolean tensors) cause an assertion failure + to be raised. + """ + ... +def _assert_scalar(self: Union[Number, _complex], assert_msg: str) -> None: ... +def _assert_tensor_metadata(a: Tensor, size: Optional[Sequence[Union[_int, SymInt]]] = None, stride: Optional[Sequence[Union[_int, SymInt]]] = None, dtype: Optional[_dtype] = None) -> None: ... +def _batch_norm_impl_index(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], running_mean: Optional[Tensor], running_var: Optional[Tensor], training: _bool, momentum: _float, eps: _float, cudnn_enabled: _bool) -> Tuple[Tensor, Tensor, Tensor, Tensor, _int]: ... +def _cast_Byte(input: Tensor, non_blocking: _bool = False) -> Tensor: ... +def _cast_Char(input: Tensor, non_blocking: _bool = False) -> Tensor: ... +def _cast_Double(input: Tensor, non_blocking: _bool = False) -> Tensor: ... +def _cast_Float(input: Tensor, non_blocking: _bool = False) -> Tensor: ... +def _cast_Half(input: Tensor, non_blocking: _bool = False) -> Tensor: ... +def _cast_Int(input: Tensor, non_blocking: _bool = False) -> Tensor: ... +def _cast_Long(input: Tensor, non_blocking: _bool = False) -> Tensor: ... +def _cast_Short(input: Tensor, non_blocking: _bool = False) -> Tensor: ... +def _choose_qparams_per_tensor(input: Tensor, reduce_range: _bool = False) -> Tuple[_float, _int]: ... +def _chunk_cat(tensors: Union[Tuple[Tensor, ...], List[Tensor]], dim: _int, num_chunks: _int, *, out: Optional[Tensor] = None) -> Tensor: ... +def _coalesce(input: Tensor) -> Tensor: ... +def _compute_linear_combination(input: Tensor, coefficients: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def _conj(input: Tensor) -> Tensor: ... +def _conj_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def _conj_physical(input: Tensor) -> Tensor: ... +def _convert_indices_from_coo_to_csr(input: Tensor, size: _int, *, out_int32: _bool = False, out: Optional[Tensor] = None) -> Tensor: ... +def _convert_indices_from_csr_to_coo(crow_indices: Tensor, col_indices: Tensor, *, out_int32: _bool = False, transpose: _bool = False, out: Optional[Tensor] = None) -> Tensor: ... +def _convert_weight_to_int4pack(input: Tensor, innerKTiles: _int) -> Tensor: ... +@overload +def _convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], stride: Sequence[Union[_int, SymInt]], padding: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], transposed: _bool, output_padding: _size, groups: Union[_int, SymInt], benchmark: _bool, deterministic: _bool, cudnn_enabled: _bool) -> Tensor: ... +@overload +def _convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], stride: Sequence[Union[_int, SymInt]], padding: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], transposed: _bool, output_padding: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt], benchmark: _bool, deterministic: _bool, cudnn_enabled: _bool, allow_tf32: _bool) -> Tensor: ... +def _convolution_mode(input: Tensor, weight: Tensor, bias: Optional[Tensor], stride: Sequence[Union[_int, SymInt]], padding: str, dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt]) -> Tensor: ... +def _copy_from(input: Tensor, dst: Tensor, non_blocking: _bool = False) -> Tensor: ... +def _copy_from_and_resize(input: Tensor, dst: Tensor) -> Tensor: ... +def _cslt_compress(input: Tensor) -> Tensor: ... +def _cslt_sparse_mm(compressed_A: Tensor, dense_B: Tensor, bias: Optional[Tensor] = None, alpha: Optional[Tensor] = None, out_dtype: Optional[_dtype] = None, transpose_result: _bool = False, alg_id: _int = 0) -> Tensor: ... +def _cslt_sparse_mm_search(compressed_A: Tensor, dense_B: Tensor, bias: Optional[Tensor] = None, alpha: Optional[Tensor] = None, out_dtype: Optional[_dtype] = None, transpose_result: _bool = False) -> _int: ... +@overload +def _ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: _size, target_lengths: _size, blank: _int = 0, zero_infinity: _bool = False) -> Tuple[Tensor, Tensor]: ... +@overload +def _ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: Tensor, target_lengths: Tensor, blank: _int = 0, zero_infinity: _bool = False) -> Tuple[Tensor, Tensor]: ... +@overload +def _cudnn_ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: _size, target_lengths: _size, blank: _int, deterministic: _bool, zero_infinity: _bool) -> Tuple[Tensor, Tensor]: ... +@overload +def _cudnn_ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: Tensor, target_lengths: Tensor, blank: _int, deterministic: _bool, zero_infinity: _bool) -> Tuple[Tensor, Tensor]: ... +def _cudnn_init_dropout_state(dropout: _float, train: _bool, dropout_seed: _int, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... +def _cudnn_rnn(input: Tensor, weight: Union[Tuple[Tensor, ...], List[Tensor]], weight_stride0: _int, weight_buf: Optional[Tensor], hx: Tensor, cx: Optional[Tensor], mode: _int, hidden_size: Union[_int, SymInt], proj_size: Union[_int, SymInt], num_layers: _int, batch_first: _bool, dropout: _float, train: _bool, bidirectional: _bool, batch_sizes: Sequence[Union[_int, SymInt]], dropout_state: Optional[Tensor]) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: ... +def _cudnn_rnn_flatten_weight(weight_arr: Union[Tuple[Tensor, ...], List[Tensor]], weight_stride0: _int, input_size: Union[_int, SymInt], mode: _int, hidden_size: Union[_int, SymInt], proj_size: Union[_int, SymInt], num_layers: _int, batch_first: _bool, bidirectional: _bool) -> Tensor: ... +def _cufft_clear_plan_cache(device_index: _int) -> None: ... +def _cufft_get_plan_cache_max_size(device_index: _int) -> _int: ... +def _cufft_get_plan_cache_size(device_index: _int) -> _int: ... +def _cufft_set_plan_cache_max_size(device_index: _int, max_size: _int) -> None: ... +def _cummax_helper(input: Tensor, values: Tensor, indices: Tensor, dim: _int) -> None: ... +def _cummin_helper(input: Tensor, values: Tensor, indices: Tensor, dim: _int) -> None: ... +def _debug_has_internal_overlap(input: Tensor) -> _int: ... +def _dim_arange(like: Tensor, dim: _int) -> Tensor: ... +def _dirichlet_grad(x: Tensor, alpha: Tensor, total: Tensor) -> Tensor: ... +def _disable_functionalization(): ... +@overload +def _efficientzerotensor(size: Sequence[Union[_int, SymInt]], *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... +@overload +def _efficientzerotensor(*size: Union[_int, SymInt], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... +def _embedding_bag(weight: Tensor, indices: Tensor, offsets: Tensor, scale_grad_by_freq: _bool = False, mode: _int = 0, sparse: _bool = False, per_sample_weights: Optional[Tensor] = None, include_last_offset: _bool = False, padding_idx: _int = -1) -> Tuple[Tensor, Tensor, Tensor, Tensor]: ... +def _embedding_bag_forward_only(weight: Tensor, indices: Tensor, offsets: Tensor, scale_grad_by_freq: _bool = False, mode: _int = 0, sparse: _bool = False, per_sample_weights: Optional[Tensor] = None, include_last_offset: _bool = False, padding_idx: _int = -1) -> Tuple[Tensor, Tensor, Tensor, Tensor]: ... +@overload +def _empty_affine_quantized(size: Sequence[Union[_int, SymInt]], *, scale: _float = 1, zero_point: _int = 0, memory_format: Optional[memory_format] = contiguous_format, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... +@overload +def _empty_affine_quantized(*size: Union[_int, SymInt], scale: _float = 1, zero_point: _int = 0, memory_format: Optional[memory_format] = contiguous_format, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... +@overload +def _empty_per_channel_affine_quantized(size: Sequence[Union[_int, SymInt]], *, scales: Tensor, zero_points: Tensor, axis: _int, memory_format: Optional[memory_format] = contiguous_format, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... +@overload +def _empty_per_channel_affine_quantized(*size: Union[_int, SymInt], scales: Tensor, zero_points: Tensor, axis: _int, memory_format: Optional[memory_format] = contiguous_format, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... +def _enable_functionalization(*, reapply_views: _bool = False): ... +def _euclidean_dist(x1: Tensor, x2: Tensor) -> Tensor: ... +def _fake_quantize_learnable_per_channel_affine(input: Tensor, scale: Tensor, zero_point: Tensor, axis: _int, quant_min: _int, quant_max: _int, grad_factor: _float = 1.0) -> Tensor: ... +def _fake_quantize_learnable_per_tensor_affine(input: Tensor, scale: Tensor, zero_point: Tensor, quant_min: _int, quant_max: _int, grad_factor: _float = 1.0) -> Tensor: ... +def _fake_quantize_per_tensor_affine_cachemask_tensor_qparams(input: Tensor, scale: Tensor, zero_point: Tensor, fake_quant_enabled: Tensor, quant_min: _int, quant_max: _int) -> torch.return_types._fake_quantize_per_tensor_affine_cachemask_tensor_qparams: ... +def _fft_c2c(input: Tensor, dim: Sequence[Union[_int, SymInt]], normalization: _int, forward: _bool, *, out: Optional[Tensor] = None) -> Tensor: ... +def _fft_c2r(input: Tensor, dim: _size, normalization: _int, last_dim_size: Union[_int, SymInt], *, out: Optional[Tensor] = None) -> Tensor: ... +def _fft_r2c(input: Tensor, dim: _size, normalization: _int, onesided: _bool, *, out: Optional[Tensor] = None) -> Tensor: ... +def _fill_mem_eff_dropout_mask_(input: Tensor, dropout_p: _float, seed: _int, offset: _int) -> Tensor: ... +def _foobar(input: Tensor, arg1: _bool = True, arg2: _bool = True, *, arg3: _bool = True) -> Tensor: ... +def _foreach_abs(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_abs(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.abs` to each Tensor of the input list. + """ + ... +def _foreach_abs_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_abs_(self: List[Tensor]) -> None + + Apply :func:`torch.abs` to each Tensor of the input list. + """ + ... +def _foreach_acos(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_acos(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.acos` to each Tensor of the input list. + """ + ... +def _foreach_acos_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_acos_(self: List[Tensor]) -> None + + Apply :func:`torch.acos` to each Tensor of the input list. + """ + ... +@overload +def _foreach_add(self: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[Union[Number, _complex]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_add(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Union[Tuple[Tensor, ...], List[Tensor]], *, alpha: Union[Number, _complex] = 1) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_add(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Tensor, *, alpha: Union[Number, _complex] = 1) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_add(self: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Union[Number, _complex]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_add_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_add_(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Union[Tuple[Tensor, ...], List[Tensor]], *, alpha: Union[Number, _complex] = 1) -> None: ... +@overload +def _foreach_add_(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Tensor, *, alpha: Union[Number, _complex] = 1) -> None: ... +@overload +def _foreach_add_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Union[Number, _complex]) -> None: ... +@overload +def _foreach_addcdiv(self: Union[Tuple[Tensor, ...], List[Tensor]], tensor1: Union[Tuple[Tensor, ...], List[Tensor]], tensor2: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[Union[Number, _complex]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_addcdiv(self: Union[Tuple[Tensor, ...], List[Tensor]], tensor1: Union[Tuple[Tensor, ...], List[Tensor]], tensor2: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Tensor) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_addcdiv(self: Union[Tuple[Tensor, ...], List[Tensor]], tensor1: Union[Tuple[Tensor, ...], List[Tensor]], tensor2: Union[Tuple[Tensor, ...], List[Tensor]], value: Union[Number, _complex] = 1) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_addcdiv_(self: Union[Tuple[Tensor, ...], List[Tensor]], tensor1: Union[Tuple[Tensor, ...], List[Tensor]], tensor2: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_addcdiv_(self: Union[Tuple[Tensor, ...], List[Tensor]], tensor1: Union[Tuple[Tensor, ...], List[Tensor]], tensor2: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Tensor) -> None: ... +@overload +def _foreach_addcdiv_(self: Union[Tuple[Tensor, ...], List[Tensor]], tensor1: Union[Tuple[Tensor, ...], List[Tensor]], tensor2: Union[Tuple[Tensor, ...], List[Tensor]], value: Union[Number, _complex] = 1) -> None: ... +@overload +def _foreach_addcmul(self: Union[Tuple[Tensor, ...], List[Tensor]], tensor1: Union[Tuple[Tensor, ...], List[Tensor]], tensor2: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[Union[Number, _complex]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_addcmul(self: Union[Tuple[Tensor, ...], List[Tensor]], tensor1: Union[Tuple[Tensor, ...], List[Tensor]], tensor2: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Tensor) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_addcmul(self: Union[Tuple[Tensor, ...], List[Tensor]], tensor1: Union[Tuple[Tensor, ...], List[Tensor]], tensor2: Union[Tuple[Tensor, ...], List[Tensor]], value: Union[Number, _complex] = 1) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_addcmul_(self: Union[Tuple[Tensor, ...], List[Tensor]], tensor1: Union[Tuple[Tensor, ...], List[Tensor]], tensor2: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_addcmul_(self: Union[Tuple[Tensor, ...], List[Tensor]], tensor1: Union[Tuple[Tensor, ...], List[Tensor]], tensor2: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Tensor) -> None: ... +@overload +def _foreach_addcmul_(self: Union[Tuple[Tensor, ...], List[Tensor]], tensor1: Union[Tuple[Tensor, ...], List[Tensor]], tensor2: Union[Tuple[Tensor, ...], List[Tensor]], value: Union[Number, _complex] = 1) -> None: ... +def _foreach_asin(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_asin(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.asin` to each Tensor of the input list. + """ + ... +def _foreach_asin_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_asin_(self: List[Tensor]) -> None + + Apply :func:`torch.asin` to each Tensor of the input list. + """ + ... +def _foreach_atan(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_atan(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.atan` to each Tensor of the input list. + """ + ... +def _foreach_atan_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_atan_(self: List[Tensor]) -> None + + Apply :func:`torch.atan` to each Tensor of the input list. + """ + ... +def _foreach_ceil(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_ceil(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.ceil` to each Tensor of the input list. + """ + ... +def _foreach_ceil_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_ceil_(self: List[Tensor]) -> None + + Apply :func:`torch.ceil` to each Tensor of the input list. + """ + ... +@overload +def _foreach_clamp_max(self: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[Union[Number, _complex]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_clamp_max(self: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Union[Number, _complex]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_clamp_max(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_clamp_max_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_clamp_max_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Union[Number, _complex]) -> None: ... +@overload +def _foreach_clamp_max_(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ... +@overload +def _foreach_clamp_min(self: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[Union[Number, _complex]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_clamp_min(self: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Union[Number, _complex]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_clamp_min(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_clamp_min_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_clamp_min_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Union[Number, _complex]) -> None: ... +@overload +def _foreach_clamp_min_(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ... +def _foreach_copy_(self: Union[Tuple[Tensor, ...], List[Tensor]], src: Union[Tuple[Tensor, ...], List[Tensor]], non_blocking: _bool = False) -> None: ... +def _foreach_cos(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_cos(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.cos` to each Tensor of the input list. + """ + ... +def _foreach_cos_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_cos_(self: List[Tensor]) -> None + + Apply :func:`torch.cos` to each Tensor of the input list. + """ + ... +def _foreach_cosh(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_cosh(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.cosh` to each Tensor of the input list. + """ + ... +def _foreach_cosh_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_cosh_(self: List[Tensor]) -> None + + Apply :func:`torch.cosh` to each Tensor of the input list. + """ + ... +@overload +def _foreach_div(self: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[Union[Number, _complex]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_div(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Tensor) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_div(self: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Union[Number, _complex]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_div(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_div_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_div_(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Tensor) -> None: ... +@overload +def _foreach_div_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Union[Number, _complex]) -> None: ... +@overload +def _foreach_div_(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ... +def _foreach_erf(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_erf(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.erf` to each Tensor of the input list. + """ + ... +def _foreach_erf_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_erf_(self: List[Tensor]) -> None + + Apply :func:`torch.erf` to each Tensor of the input list. + """ + ... +def _foreach_erfc(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_erfc(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.erfc` to each Tensor of the input list. + """ + ... +def _foreach_erfc_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_erfc_(self: List[Tensor]) -> None + + Apply :func:`torch.erfc` to each Tensor of the input list. + """ + ... +def _foreach_exp(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_exp(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.exp` to each Tensor of the input list. + """ + ... +def _foreach_exp_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_exp_(self: List[Tensor]) -> None + + Apply :func:`torch.exp` to each Tensor of the input list. + """ + ... +def _foreach_expm1(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_expm1(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.expm1` to each Tensor of the input list. + """ + ... +def _foreach_expm1_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_expm1_(self: List[Tensor]) -> None + + Apply :func:`torch.expm1` to each Tensor of the input list. + """ + ... +def _foreach_floor(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_floor(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.floor` to each Tensor of the input list. + """ + ... +def _foreach_floor_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_floor_(self: List[Tensor]) -> None + + Apply :func:`torch.floor` to each Tensor of the input list. + """ + ... +def _foreach_frac(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_frac(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.frac` to each Tensor of the input list. + """ + ... +def _foreach_frac_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_frac_(self: List[Tensor]) -> None + + Apply :func:`torch.frac` to each Tensor of the input list. + """ + ... +@overload +def _foreach_lerp(self: Union[Tuple[Tensor, ...], List[Tensor]], tensors1: Union[Tuple[Tensor, ...], List[Tensor]], weight: Union[Number, _complex]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_lerp(self: Union[Tuple[Tensor, ...], List[Tensor]], tensors1: Union[Tuple[Tensor, ...], List[Tensor]], weights: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_lerp_(self: Union[Tuple[Tensor, ...], List[Tensor]], tensors1: Union[Tuple[Tensor, ...], List[Tensor]], weight: Union[Number, _complex]) -> None: ... +@overload +def _foreach_lerp_(self: Union[Tuple[Tensor, ...], List[Tensor]], tensors1: Union[Tuple[Tensor, ...], List[Tensor]], weights: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ... +def _foreach_lgamma(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_lgamma(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.lgamma` to each Tensor of the input list. + """ + ... +def _foreach_lgamma_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_lgamma_(self: List[Tensor]) -> None + + Apply :func:`torch.lgamma` to each Tensor of the input list. + """ + ... +def _foreach_log(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_log(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.log` to each Tensor of the input list. + """ + ... +def _foreach_log10(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_log10(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.log10` to each Tensor of the input list. + """ + ... +def _foreach_log10_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_log10_(self: List[Tensor]) -> None + + Apply :func:`torch.log10` to each Tensor of the input list. + """ + ... +def _foreach_log1p(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_log1p(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.log1p` to each Tensor of the input list. + """ + ... +def _foreach_log1p_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_log1p_(self: List[Tensor]) -> None + + Apply :func:`torch.log1p` to each Tensor of the input list. + """ + ... +def _foreach_log2(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_log2(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.log2` to each Tensor of the input list. + """ + ... +def _foreach_log2_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_log2_(self: List[Tensor]) -> None + + Apply :func:`torch.log2` to each Tensor of the input list. + """ + ... +def _foreach_log_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_log_(self: List[Tensor]) -> None + + Apply :func:`torch.log` to each Tensor of the input list. + """ + ... +def _foreach_max(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_maximum(self: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[Union[Number, _complex]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_maximum(self: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Union[Number, _complex]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_maximum(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_maximum_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_maximum_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Union[Number, _complex]) -> None: ... +@overload +def _foreach_maximum_(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ... +@overload +def _foreach_minimum(self: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[Union[Number, _complex]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_minimum(self: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Union[Number, _complex]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_minimum(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_minimum_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_minimum_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Union[Number, _complex]) -> None: ... +@overload +def _foreach_minimum_(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ... +@overload +def _foreach_mul(self: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[Union[Number, _complex]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_mul(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Tensor) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_mul(self: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Union[Number, _complex]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_mul(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_mul_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_mul_(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Tensor) -> None: ... +@overload +def _foreach_mul_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Union[Number, _complex]) -> None: ... +@overload +def _foreach_mul_(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ... +def _foreach_neg(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_neg(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.neg` to each Tensor of the input list. + """ + ... +def _foreach_neg_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_neg_(self: List[Tensor]) -> None + + Apply :func:`torch.neg` to each Tensor of the input list. + """ + ... +def _foreach_norm(self: Union[Tuple[Tensor, ...], List[Tensor]], ord: Union[Number, _complex] = 2, dtype: Optional[_dtype] = None) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_pow(self: Union[Tuple[Tensor, ...], List[Tensor]], exponent: Sequence[Union[Number, _complex]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_pow(self: Union[Tuple[Tensor, ...], List[Tensor]], exponent: Union[Number, _complex]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_pow(self: Union[Tuple[Tensor, ...], List[Tensor]], exponent: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_pow(self: Union[Number, _complex], exponent: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_pow_(self: Union[Tuple[Tensor, ...], List[Tensor]], exponent: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_pow_(self: Union[Tuple[Tensor, ...], List[Tensor]], exponent: Union[Number, _complex]) -> None: ... +@overload +def _foreach_pow_(self: Union[Tuple[Tensor, ...], List[Tensor]], exponent: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ... +def _foreach_reciprocal(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_reciprocal(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.reciprocal` to each Tensor of the input list. + """ + ... +def _foreach_reciprocal_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_reciprocal_(self: List[Tensor]) -> None + + Apply :func:`torch.reciprocal` to each Tensor of the input list. + """ + ... +def _foreach_round(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_round(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.round` to each Tensor of the input list. + """ + ... +def _foreach_round_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_round_(self: List[Tensor]) -> None + + Apply :func:`torch.round` to each Tensor of the input list. + """ + ... +def _foreach_sigmoid(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_sigmoid(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.sigmoid` to each Tensor of the input list. + """ + ... +def _foreach_sigmoid_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_sigmoid_(self: List[Tensor]) -> None + + Apply :func:`torch.sigmoid` to each Tensor of the input list. + """ + ... +def _foreach_sign(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: ... +def _foreach_sign_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ... +def _foreach_sin(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_sin(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.sin` to each Tensor of the input list. + """ + ... +def _foreach_sin_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_sin_(self: List[Tensor]) -> None + + Apply :func:`torch.sin` to each Tensor of the input list. + """ + ... +def _foreach_sinh(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_sinh(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.sinh` to each Tensor of the input list. + """ + ... +def _foreach_sinh_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_sinh_(self: List[Tensor]) -> None + + Apply :func:`torch.sinh` to each Tensor of the input list. + """ + ... +def _foreach_sqrt(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_sqrt(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.sqrt` to each Tensor of the input list. + """ + ... +def _foreach_sqrt_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_sqrt_(self: List[Tensor]) -> None + + Apply :func:`torch.sqrt` to each Tensor of the input list. + """ + ... +@overload +def _foreach_sub(self: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[Union[Number, _complex]]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_sub(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Union[Tuple[Tensor, ...], List[Tensor]], *, alpha: Union[Number, _complex] = 1) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_sub(self: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Union[Number, _complex]) -> Tuple[Tensor, ...]: ... +@overload +def _foreach_sub_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_sub_(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Union[Tuple[Tensor, ...], List[Tensor]], *, alpha: Union[Number, _complex] = 1) -> None: ... +@overload +def _foreach_sub_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Union[Number, _complex]) -> None: ... +def _foreach_tan(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_tan(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.tan` to each Tensor of the input list. + """ + ... +def _foreach_tan_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_tan_(self: List[Tensor]) -> None + + Apply :func:`torch.tan` to each Tensor of the input list. + """ + ... +def _foreach_tanh(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_tanh(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.tanh` to each Tensor of the input list. + """ + ... +def _foreach_tanh_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_tanh_(self: List[Tensor]) -> None + + Apply :func:`torch.tanh` to each Tensor of the input list. + """ + ... +def _foreach_trunc(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + _foreach_trunc(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.trunc` to each Tensor of the input list. + """ + ... +def _foreach_trunc_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_trunc_(self: List[Tensor]) -> None + + Apply :func:`torch.trunc` to each Tensor of the input list. + """ + ... +def _foreach_zero_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: + r""" + _foreach_zero_(self: List[Tensor]) -> None + + Apply :func:`torch.zero` to each Tensor of the input list. + """ + ... +def _from_functional_tensor(t: Tensor) -> Tensor: ... +def _functional_assert_async(input: Tensor, assert_msg: str, dep_token: Tensor) -> Tensor: ... +def _functional_assert_scalar(self: Union[Number, _complex], assert_msg: str, dep_token: Tensor) -> Tensor: ... +def _functional_sym_constrain_range(size: Union[Number, _complex], min: Optional[_int], max: Optional[_int], dep_token: Tensor) -> Tensor: ... +def _functional_sym_constrain_range_for_size(size: Union[Number, _complex], min: Optional[_int], max: Optional[_int], dep_token: Tensor) -> Tensor: ... +def _functionalize_apply_view_metas(tensor: Tensor, base: Tensor) -> Tensor: ... +def _functionalize_are_all_mutations_hidden_from_autograd(t: Tensor) -> _bool: ... +def _functionalize_are_all_mutations_under_no_grad_or_inference_mode(t: Tensor) -> _bool: ... +def _functionalize_commit_update(t: Tensor) -> None: ... +def _functionalize_has_metadata_mutation(tensor: Tensor) -> _bool: ... +def _functionalize_is_symbolic(tensor: Tensor) -> _bool: ... +def _functionalize_mark_mutation_hidden_from_autograd(t: Tensor) -> None: ... +def _functionalize_replace(self_: Tensor, other: Tensor) -> None: ... +def _functionalize_set_storage_changed(tensor: Tensor) -> _bool: ... +def _functionalize_sync(t: Tensor) -> None: ... +def _functionalize_unsafe_set(dst: Tensor, src: Tensor) -> None: ... +def _functionalize_was_inductor_storage_resized(t: Tensor) -> _bool: ... +def _functionalize_was_storage_changed(tensor: Tensor) -> _bool: ... +def _fused_adagrad_(self: Union[Tuple[Tensor, ...], List[Tensor]], grads: Union[Tuple[Tensor, ...], List[Tensor]], state_sums: Union[Tuple[Tensor, ...], List[Tensor]], state_steps: Union[Tuple[Tensor, ...], List[Tensor]], *, lr: _float, lr_decay: _float, weight_decay: _float, eps: _float, maximize: _bool, grad_scale: Optional[Tensor] = None, found_inf: Optional[Tensor] = None) -> None: ... +@overload +def _fused_adam_(self: Union[Tuple[Tensor, ...], List[Tensor]], grads: Union[Tuple[Tensor, ...], List[Tensor]], exp_avgs: Union[Tuple[Tensor, ...], List[Tensor]], exp_avg_sqs: Union[Tuple[Tensor, ...], List[Tensor]], max_exp_avg_sqs: Union[Tuple[Tensor, ...], List[Tensor]], state_steps: Union[Tuple[Tensor, ...], List[Tensor]], *, lr: Tensor, beta1: _float, beta2: _float, weight_decay: _float, eps: _float, amsgrad: _bool, maximize: _bool, grad_scale: Optional[Tensor] = None, found_inf: Optional[Tensor] = None) -> None: ... +@overload +def _fused_adam_(self: Union[Tuple[Tensor, ...], List[Tensor]], grads: Union[Tuple[Tensor, ...], List[Tensor]], exp_avgs: Union[Tuple[Tensor, ...], List[Tensor]], exp_avg_sqs: Union[Tuple[Tensor, ...], List[Tensor]], max_exp_avg_sqs: Union[Tuple[Tensor, ...], List[Tensor]], state_steps: Union[Tuple[Tensor, ...], List[Tensor]], *, lr: _float, beta1: _float, beta2: _float, weight_decay: _float, eps: _float, amsgrad: _bool, maximize: _bool, grad_scale: Optional[Tensor] = None, found_inf: Optional[Tensor] = None) -> None: ... +@overload +def _fused_adamw_(self: Union[Tuple[Tensor, ...], List[Tensor]], grads: Union[Tuple[Tensor, ...], List[Tensor]], exp_avgs: Union[Tuple[Tensor, ...], List[Tensor]], exp_avg_sqs: Union[Tuple[Tensor, ...], List[Tensor]], max_exp_avg_sqs: Union[Tuple[Tensor, ...], List[Tensor]], state_steps: Union[Tuple[Tensor, ...], List[Tensor]], *, lr: Tensor, beta1: _float, beta2: _float, weight_decay: _float, eps: _float, amsgrad: _bool, maximize: _bool, grad_scale: Optional[Tensor] = None, found_inf: Optional[Tensor] = None) -> None: ... +@overload +def _fused_adamw_(self: Union[Tuple[Tensor, ...], List[Tensor]], grads: Union[Tuple[Tensor, ...], List[Tensor]], exp_avgs: Union[Tuple[Tensor, ...], List[Tensor]], exp_avg_sqs: Union[Tuple[Tensor, ...], List[Tensor]], max_exp_avg_sqs: Union[Tuple[Tensor, ...], List[Tensor]], state_steps: Union[Tuple[Tensor, ...], List[Tensor]], *, lr: _float, beta1: _float, beta2: _float, weight_decay: _float, eps: _float, amsgrad: _bool, maximize: _bool, grad_scale: Optional[Tensor] = None, found_inf: Optional[Tensor] = None) -> None: ... +def _fused_dropout(input: Tensor, p: _float, generator: Optional[Generator] = None) -> Tuple[Tensor, Tensor]: ... +def _fused_moving_avg_obs_fq_helper(input: Tensor, observer_on: Tensor, fake_quant_on: Tensor, running_min: Tensor, running_max: Tensor, scale: Tensor, zero_point: Tensor, averaging_const: _float, quant_min: _int, quant_max: _int, ch_axis: _int, per_row_fake_quant: _bool = False, symmetric_quant: _bool = False) -> torch.return_types._fused_moving_avg_obs_fq_helper: ... +def _fused_sdp_choice(query: Tensor, key: Tensor, value: Tensor, attn_mask: Optional[Tensor] = None, dropout_p: _float = 0.0, is_causal: _bool = False, *, scale: Optional[_float] = None, enable_gqa: _bool = False) -> _int: ... +@overload +def _fused_sgd_(self: Union[Tuple[Tensor, ...], List[Tensor]], grads: Union[Tuple[Tensor, ...], List[Tensor]], momentum_buffer_list: Union[Tuple[Tensor, ...], List[Tensor]], *, weight_decay: _float, momentum: _float, lr: Tensor, dampening: _float, nesterov: _bool, maximize: _bool, is_first_step: _bool, grad_scale: Optional[Tensor] = None, found_inf: Optional[Tensor] = None) -> None: ... +@overload +def _fused_sgd_(self: Union[Tuple[Tensor, ...], List[Tensor]], grads: Union[Tuple[Tensor, ...], List[Tensor]], momentum_buffer_list: Union[Tuple[Tensor, ...], List[Tensor]], *, weight_decay: _float, momentum: _float, lr: _float, dampening: _float, nesterov: _bool, maximize: _bool, is_first_step: _bool, grad_scale: Optional[Tensor] = None, found_inf: Optional[Tensor] = None) -> None: ... +def _fw_primal_copy(input: Tensor, level: _int, *, out: Optional[Tensor] = None) -> Tensor: ... +def _grid_sampler_2d_cpu_fallback(input: Tensor, grid: Tensor, interpolation_mode: _int, padding_mode: _int, align_corners: _bool) -> Tensor: ... +def _has_compatible_shallow_copy_type(input: Tensor, from_: Tensor) -> _bool: ... +def _histogramdd_bin_edges(input: Tensor, bins: _size, *, range: Optional[Sequence[_float]] = None, weight: Optional[Tensor] = None, density: _bool = False) -> Tuple[Tensor, ...]: ... +def _histogramdd_from_bin_cts(input: Tensor, bins: _size, *, range: Optional[Sequence[_float]] = None, weight: Optional[Tensor] = None, density: _bool = False) -> Tensor: ... +def _histogramdd_from_bin_tensors(input: Tensor, bins: Union[Tuple[Tensor, ...], List[Tensor]], *, weight: Optional[Tensor] = None, density: _bool = False) -> Tensor: ... +def _index_put_impl_(input: Tensor, indices: Optional[Union[Tuple[Tensor, ...], List[Tensor]]], values: Tensor, accumulate: _bool = False, unsafe: _bool = False) -> Tensor: ... +def _indices_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def _int_mm(input: Tensor, mat2: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def _is_all_true(input: Tensor) -> Tensor: ... +def _is_any_true(input: Tensor) -> Tensor: ... +def _is_functional_tensor(t: Tensor) -> _bool: ... +def _is_functional_tensor_base(t: Tensor) -> _bool: ... +def _is_zerotensor(input: Tensor) -> _bool: ... +def _lazy_clone(input: Tensor) -> Tensor: ... +def _linalg_check_errors(info: Tensor, api_name: str, *, is_matrix: _bool) -> None: ... +def _linalg_det(A: Tensor, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types._linalg_det: ... +def _linalg_eigh(A: Tensor, UPLO: str = "L", compute_v: _bool = True, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types._linalg_eigh: ... +def _linalg_slogdet(A: Tensor, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types._linalg_slogdet: ... +def _linalg_solve_ex(A: Tensor, B: Tensor, *, left: _bool = True, check_errors: _bool = False, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types._linalg_solve_ex: ... +def _linalg_svd(A: Tensor, full_matrices: _bool = False, compute_uv: _bool = True, *, driver: Optional[str] = None, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types._linalg_svd: ... +def _log_softmax(input: Tensor, dim: _int, half_to_float: _bool, *, out: Optional[Tensor] = None) -> Tensor: ... +def _log_softmax_backward_data(grad_output: Tensor, output: Tensor, dim: _int, input_dtype: _dtype, *, out: Optional[Tensor] = None) -> Tensor: ... +def _logcumsumexp(input: Tensor, dim: _int, *, out: Optional[Tensor] = None) -> Tensor: ... +def _lstm_mps(input: Tensor, hx: Union[Tuple[Tensor, ...], List[Tensor]], params: Union[Tuple[Tensor, ...], List[Tensor]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool, batch_first: _bool) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]: ... +def _lu_with_info(input: Tensor, pivot: _bool = True, check_errors: _bool = True) -> torch.return_types._lu_with_info: ... +def _make_dep_token(*, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... +def _make_dual(primal: Tensor, tangent: Tensor, level: _int) -> Tensor: ... +def _make_dual_copy(primal: Tensor, tangent: Tensor, level: _int, *, out: Optional[Tensor] = None) -> Tensor: ... +def _make_per_channel_quantized_tensor(input: Tensor, scale: Tensor, zero_point: Tensor, axis: _int) -> Tensor: ... +def _make_per_tensor_quantized_tensor(input: Tensor, scale: _float, zero_point: _int) -> Tensor: ... +def _masked_scale(input: Tensor, mask: Tensor, scale: _float) -> Tensor: ... +def _masked_softmax(input: Tensor, mask: Tensor, dim: Optional[_int] = None, mask_type: Optional[_int] = None) -> Tensor: ... +def _mixed_dtypes_linear(input: Tensor, weight: Tensor, scale: Tensor, *, bias: Optional[Tensor] = None, activation: Optional[str] = None) -> Tensor: ... +def _mkldnn_reshape(input: Tensor, shape: _size) -> Tensor: ... +def _mkldnn_transpose(input: Tensor, dim0: _int, dim1: _int) -> Tensor: ... +def _mkldnn_transpose_(input: Tensor, dim0: _int, dim1: _int) -> Tensor: ... +def _mps_convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt]) -> Tensor: ... +def _mps_convolution_transpose(input: Tensor, weight: Tensor, padding: Sequence[Union[_int, SymInt]], output_padding: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt]) -> Tensor: ... +@overload +def _native_batch_norm_legit(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], running_mean: Tensor, running_var: Tensor, training: _bool, momentum: _float, eps: _float, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> Tuple[Tensor, Tensor, Tensor]: ... +@overload +def _native_batch_norm_legit(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], training: _bool, momentum: _float, eps: _float, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> Tuple[Tensor, Tensor, Tensor]: ... +def _native_batch_norm_legit_no_training(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], running_mean: Tensor, running_var: Tensor, momentum: _float, eps: _float) -> Tuple[Tensor, Tensor, Tensor]: ... +def _native_multi_head_attention(query: Tensor, key: Tensor, value: Tensor, embed_dim: _int, num_head: _int, qkv_weight: Tensor, qkv_bias: Tensor, proj_weight: Tensor, proj_bias: Tensor, mask: Optional[Tensor] = None, need_weights: _bool = True, average_attn_weights: _bool = True, mask_type: Optional[_int] = None) -> Tuple[Tensor, Tensor]: ... +def _neg_view(input: Tensor) -> Tensor: ... +def _neg_view_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def _nested_compute_contiguous_strides_offsets(nested_size: Tensor) -> Tuple[Tensor, Tensor]: ... +def _nested_from_padded(padded: Tensor, cpu_nested_shape_example: Tensor, fuse_transform_0213: _bool = False) -> Tensor: ... +def _nested_from_padded_and_nested_example(padded: Tensor, nt_example: Tensor) -> Tensor: ... +def _nested_get_jagged_dummy(any: Tensor) -> Tensor: ... +def _nested_get_lengths(input: Tensor) -> Tensor: ... +def _nested_get_max_seqlen(input: Tensor) -> Tensor: ... +def _nested_get_min_seqlen(input: Tensor) -> Tensor: ... +def _nested_get_offsets(input: Tensor) -> Tensor: ... +def _nested_get_ragged_idx(input: Tensor) -> _int: ... +def _nested_get_values(input: Tensor) -> Tensor: ... +def _nested_get_values_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def _nested_tensor_from_mask(t: Tensor, mask: Tensor, mask_check: _bool = True) -> Tensor: ... +def _nested_tensor_from_mask_left_aligned(t: Tensor, mask: Tensor) -> _bool: ... +def _nested_tensor_from_tensor_list(list: Union[Tuple[Tensor, ...], List[Tensor]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = None) -> Tensor: ... +def _nested_tensor_softmax_with_shape(input: Tensor, query: Tensor) -> Tensor: ... +def _nested_view_from_buffer(input: Tensor, nested_size: Tensor, nested_strides: Tensor, offsets: Tensor) -> Tensor: ... +def _nested_view_from_buffer_copy(input: Tensor, nested_size: Tensor, nested_strides: Tensor, offsets: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def _nested_view_from_jagged(input: Tensor, offsets: Tensor, dummy: Tensor, lengths: Optional[Tensor] = None, ragged_idx: _int = 1, min_seqlen: Optional[Tensor] = None, max_seqlen: Optional[Tensor] = None) -> Tensor: ... +def _nested_view_from_jagged_copy(input: Tensor, offsets: Tensor, dummy: Tensor, lengths: Optional[Tensor] = None, ragged_idx: _int = 1, min_seqlen: Optional[Tensor] = None, max_seqlen: Optional[Tensor] = None, *, out: Optional[Tensor] = None) -> Tensor: ... +def _nnpack_available() -> _bool: ... +def _nnpack_spatial_convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]], stride: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1) -> Tensor: ... +def _pack_padded_sequence(input: Tensor, lengths: Tensor, batch_first: _bool) -> Tuple[Tensor, Tensor]: ... +def _pad_packed_sequence(data: Tensor, batch_sizes: Tensor, batch_first: _bool, padding_value: Union[Number, _complex], total_length: _int) -> Tuple[Tensor, Tensor]: ... +def _pin_memory(input: Tensor, device: Optional[Optional[DeviceLikeType]] = None) -> Tensor: ... +def _prelu_kernel(input: Tensor, weight: Tensor) -> Tensor: ... +def _print(s: str) -> None: ... +def _propagate_xla_data(input: Tensor, output: Tensor) -> None: ... +def _remove_batch_dim(input: Tensor, level: _int, batch_size: _int, out_dim: _int) -> Tensor: ... +def _reshape_alias_copy(input: Tensor, size: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], *, out: Optional[Tensor] = None) -> Tensor: ... +def _reshape_from_tensor(input: Tensor, shape: Tensor) -> Tensor: ... +def _resize_output_(input: Tensor, size: Sequence[Union[_int, SymInt]], device: Optional[DeviceLikeType]) -> Tensor: ... +def _rowwise_prune(weight: Tensor, mask: Tensor, compressed_indices_dtype: _dtype) -> Tuple[Tensor, Tensor]: ... +def _safe_softmax(input: Tensor, dim: _int, dtype: Optional[_dtype] = None) -> Tensor: ... +def _sample_dirichlet(input: Tensor, generator: Optional[Generator] = None) -> Tensor: ... +def _saturate_weight_to_fp16(weight: Tensor) -> Tensor: ... +def _scaled_dot_product_attention_math(query: Tensor, key: Tensor, value: Tensor, attn_mask: Optional[Tensor] = None, dropout_p: _float = 0.0, is_causal: _bool = False, dropout_mask: Optional[Tensor] = None, *, scale: Optional[_float] = None, enable_gqa: _bool = False) -> Tuple[Tensor, Tensor]: ... +def _scaled_dot_product_attention_math_for_mps(query: Tensor, key: Tensor, value: Tensor, attn_mask: Optional[Tensor] = None, dropout_p: _float = 0.0, is_causal: _bool = False, dropout_mask: Optional[Tensor] = None, *, scale: Optional[_float] = None) -> Tuple[Tensor, Tensor]: ... +def _scaled_dot_product_cudnn_attention(query: Tensor, key: Tensor, value: Tensor, attn_bias: Optional[Tensor], compute_log_sumexp: _bool, dropout_p: _float = 0.0, is_causal: _bool = False, return_debug_mask: _bool = False, *, scale: Optional[_float] = None) -> torch.return_types._scaled_dot_product_cudnn_attention: ... +def _scaled_dot_product_efficient_attention(query: Tensor, key: Tensor, value: Tensor, attn_bias: Optional[Tensor], compute_log_sumexp: _bool, dropout_p: _float = 0.0, is_causal: _bool = False, *, scale: Optional[_float] = None) -> torch.return_types._scaled_dot_product_efficient_attention: ... +def _scaled_dot_product_flash_attention(query: Tensor, key: Tensor, value: Tensor, dropout_p: _float = 0.0, is_causal: _bool = False, return_debug_mask: _bool = False, *, scale: Optional[_float] = None) -> torch.return_types._scaled_dot_product_flash_attention: ... +def _scaled_dot_product_flash_attention_for_cpu(query: Tensor, key: Tensor, value: Tensor, dropout_p: _float = 0.0, is_causal: _bool = False, *, attn_mask: Optional[Tensor] = None, scale: Optional[_float] = None) -> torch.return_types._scaled_dot_product_flash_attention_for_cpu: ... +def _scaled_mm(input: Tensor, mat2: Tensor, scale_a: Tensor, scale_b: Tensor, bias: Optional[Tensor] = None, scale_result: Optional[Tensor] = None, out_dtype: Optional[_dtype] = None, use_fast_accum: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: ... +def _shape_as_tensor(input: Tensor) -> Tensor: ... +def _sobol_engine_draw(quasi: Tensor, n: _int, sobolstate: Tensor, dimension: _int, num_generated: _int, dtype: Optional[_dtype]) -> Tuple[Tensor, Tensor]: ... +def _sobol_engine_ff_(input: Tensor, n: _int, sobolstate: Tensor, dimension: _int, num_generated: _int) -> Tensor: ... +def _sobol_engine_initialize_state_(input: Tensor, dimension: _int) -> Tensor: ... +def _sobol_engine_scramble_(input: Tensor, ltm: Tensor, dimension: _int) -> Tensor: ... +def _softmax(input: Tensor, dim: _int, half_to_float: _bool, *, out: Optional[Tensor] = None) -> Tensor: ... +def _softmax_backward_data(grad_output: Tensor, output: Tensor, dim: _int, input_dtype: _dtype, *, grad_input: Optional[Tensor] = None) -> Tensor: ... +def _sparse_broadcast_to(input: Tensor, size: _size) -> Tensor: ... +def _sparse_broadcast_to_copy(input: Tensor, size: _size, *, out: Optional[Tensor] = None) -> Tensor: ... +def _sparse_csr_prod(input: Tensor, dim: Union[_int, _size], keepdim: _bool = False, *, dtype: Optional[_dtype] = None) -> Tensor: ... +def _sparse_csr_sum(input: Tensor, dim: Union[_int, _size], keepdim: _bool = False, *, dtype: Optional[_dtype] = None) -> Tensor: ... +def _sparse_log_softmax_backward_data(grad_output: Tensor, output: Tensor, dim: _int, input: Tensor) -> Tensor: ... +def _sparse_semi_structured_addmm(input: Tensor, mat1: Tensor, mat1_meta: Tensor, mat2: Tensor, *, alpha: Union[Number, _complex] = 1, beta: Union[Number, _complex] = 1, out_dtype: Optional[_dtype] = None) -> Tensor: ... +def _sparse_semi_structured_apply(input: Tensor, thread_masks: Tensor) -> Tuple[Tensor, Tensor]: ... +def _sparse_semi_structured_apply_dense(input: Tensor, thread_masks: Tensor) -> Tensor: ... +def _sparse_semi_structured_linear(input: Tensor, weight: Tensor, meta: Tensor, *, bias: Optional[Tensor] = None, activation: Optional[str] = None, out_dtype: Optional[_dtype] = None) -> Tensor: ... +def _sparse_semi_structured_mm(mat1: Tensor, mat1_meta: Tensor, mat2: Tensor, *, out_dtype: Optional[_dtype] = None) -> Tensor: ... +def _sparse_semi_structured_tile(input: Tensor, algorithm: str = "", use_cutlass: _bool = True) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: ... +def _sparse_softmax_backward_data(grad_output: Tensor, output: Tensor, dim: _int, input: Tensor) -> Tensor: ... +def _sparse_sparse_matmul(input: Tensor, other: Tensor) -> Tensor: ... +@overload +def _sparse_sum(input: Tensor) -> Tensor: ... +@overload +def _sparse_sum(input: Tensor, *, dtype: _dtype) -> Tensor: ... +@overload +def _sparse_sum(input: Tensor, dim: Union[_int, _size]) -> Tensor: ... +@overload +def _sparse_sum(input: Tensor, dim: Union[_int, _size], *, dtype: _dtype) -> Tensor: ... +def _stack(tensors: Union[Tuple[Tensor, ...], List[Tensor]], dim: _int = 0, *, out: Optional[Tensor] = None) -> Tensor: ... +def _standard_gamma(input: Tensor, generator: Optional[Generator] = None) -> Tensor: ... +def _standard_gamma_grad(input: Tensor, output: Tensor) -> Tensor: ... +def _sync(t: Tensor) -> None: ... +@overload +def _test_autograd_multiple_dispatch(input: Tensor) -> Tensor: ... +@overload +def _test_autograd_multiple_dispatch(input: Tensor, b: _bool) -> Tensor: ... +def _test_autograd_multiple_dispatch_view(input: Tensor) -> Tensor: ... +def _test_autograd_multiple_dispatch_view_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def _test_check_tensor(input: Tensor) -> Tensor: ... +def _test_functorch_fallback(input: Tensor, other: Tensor) -> Tensor: ... +def _test_parallel_materialize(input: Tensor, num_parallel: _int, skip_first: _bool = False) -> Tensor: ... +def _test_serialization_subcmul(input: Tensor, other: Tensor, alpha: Union[Number, _complex] = 1) -> Tensor: ... +def _to_cpu(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: ... +def _to_functional_tensor(t: Tensor) -> Tensor: ... +def _to_sparse_semi_structured(dense: Tensor) -> Tuple[Tensor, Tensor]: ... +def _transform_bias_rescale_qkv(qkv: Tensor, qkv_bias: Tensor, num_heads: _int) -> Tuple[Tensor, Tensor, Tensor]: ... +def _transformer_encoder_layer_fwd(src: Tensor, embed_dim: _int, num_heads: _int, qkv_weight: Tensor, qkv_bias: Tensor, proj_weight: Tensor, proj_bias: Tensor, use_gelu: _bool, norm_first: _bool, eps: _float, norm_weight_1: Tensor, norm_bias_1: Tensor, norm_weight_2: Tensor, norm_bias_2: Tensor, ffn_weight_1: Tensor, ffn_bias_1: Tensor, ffn_weight_2: Tensor, ffn_bias_2: Tensor, mask: Optional[Tensor] = None, mask_type: Optional[_int] = None) -> Tensor: ... +def _trilinear(i1: Tensor, i2: Tensor, i3: Tensor, expand1: _size, expand2: _size, expand3: _size, sumdim: _size, unroll_dim: _int = 1) -> Tensor: ... +def _triton_multi_head_attention(query: Tensor, key: Tensor, value: Tensor, embed_dim: _int, num_head: _int, qkv_weight: Tensor, qkv_bias: Tensor, proj_weight: Tensor, proj_bias: Tensor, mask: Optional[Tensor] = None) -> Tensor: ... +def _triton_scaled_dot_attention(q: Tensor, k: Tensor, v: Tensor, dropout_p: _float = 0.0) -> Tensor: ... +def _unique(input: Tensor, sorted: _bool = True, return_inverse: _bool = False) -> Tuple[Tensor, Tensor]: ... +def _unique2(input: Tensor, sorted: _bool = True, return_inverse: _bool = False, return_counts: _bool = False) -> Tuple[Tensor, Tensor, Tensor]: ... +def _unpack_dual(dual: Tensor, level: _int) -> torch.return_types._unpack_dual: ... +def _unsafe_index(input: Tensor, indices: Optional[Union[Tuple[Tensor, ...], List[Tensor]]]) -> Tensor: ... +def _unsafe_index_put(input: Tensor, indices: Optional[Union[Tuple[Tensor, ...], List[Tensor]]], values: Tensor, accumulate: _bool = False) -> Tensor: ... +def _unsafe_masked_index(input: Tensor, mask: Tensor, indices: Optional[Union[Tuple[Tensor, ...], List[Tensor]]], fill: Union[Number, _complex]) -> Tensor: ... +def _unsafe_masked_index_put_accumulate(input: Tensor, mask: Tensor, indices: Optional[Union[Tuple[Tensor, ...], List[Tensor]]], values: Tensor) -> Tensor: ... +@overload +def _use_cudnn_ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: Tensor, target_lengths: Tensor, blank: _int) -> _bool: ... +@overload +def _use_cudnn_ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: _size, target_lengths: _size, blank: _int) -> _bool: ... +def _use_cudnn_rnn_flatten_weight() -> _bool: ... +def _validate_compressed_sparse_indices(is_crow: _bool, compressed_idx: Tensor, plain_idx: Tensor, cdim: _int, dim: _int, nnz: _int) -> None: ... +def _validate_sparse_bsc_tensor_args(ccol_indices: Tensor, row_indices: Tensor, values: Tensor, size: _size) -> None: ... +def _validate_sparse_bsr_tensor_args(crow_indices: Tensor, col_indices: Tensor, values: Tensor, size: _size) -> None: ... +def _validate_sparse_compressed_tensor_args(compressed_indices: Tensor, plain_indices: Tensor, values: Tensor, size: _size, layout: _layout) -> None: ... +def _validate_sparse_coo_tensor_args(indices: Tensor, values: Tensor, size: _size, is_coalesced: Optional[_bool] = None) -> None: ... +def _validate_sparse_csc_tensor_args(ccol_indices: Tensor, row_indices: Tensor, values: Tensor, size: _size) -> None: ... +def _validate_sparse_csr_tensor_args(crow_indices: Tensor, col_indices: Tensor, values: Tensor, size: _size) -> None: ... +def _values_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def _weight_int4pack_mm(input: Tensor, mat2: Tensor, qGroupSize: _int, qScaleAndZeros: Tensor) -> Tensor: ... +def _weight_int8pack_mm(input: Tensor, mat2: Tensor, scales: Tensor) -> Tensor: ... +def _weight_norm(v: Tensor, g: Tensor, dim: _int = 0) -> Tensor: ... +def _weight_norm_interface(v: Tensor, g: Tensor, dim: _int = 0) -> Tuple[Tensor, Tensor]: ... +def _wrapped_linear_prepack(weight: Tensor, weight_scale: Tensor, weight_zero_point: Tensor, bias: Tensor) -> Tensor: ... +def _wrapped_quantized_linear_prepacked(input: Tensor, input_scale: Tensor, input_zero_point: Tensor, packed_weight: Tensor, output_scale: Tensor, output_zero_point: Tensor, out_channel: _int) -> Tensor: ... +def abs(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + abs(input, *, out=None) -> Tensor + + Computes the absolute value of each element in :attr:`input`. + + .. math:: + \text{out}_{i} = |\text{input}_{i}| + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.abs(torch.tensor([-1, -2, 3])) + tensor([ 1, 2, 3]) + """ + ... +def abs_(input: Tensor) -> Tensor: ... +def absolute(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + absolute(input, *, out=None) -> Tensor + + Alias for :func:`torch.abs` + """ + ... +def acos(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + acos(input, *, out=None) -> Tensor + + Computes the inverse cosine of each element in :attr:`input`. + + .. math:: + \text{out}_{i} = \cos^{-1}(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.3348, -0.5889, 0.2005, -0.1584]) + >>> torch.acos(a) + tensor([ 1.2294, 2.2004, 1.3690, 1.7298]) + """ + ... +def acos_(input: Tensor) -> Tensor: ... +def acosh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + acosh(input, *, out=None) -> Tensor + + Returns a new tensor with the inverse hyperbolic cosine of the elements of :attr:`input`. + + .. math:: + \text{out}_{i} = \cosh^{-1}(\text{input}_{i}) + + Note: + The domain of the inverse hyperbolic cosine is `[1, inf)` and values outside this range + will be mapped to ``NaN``, except for `+ INF` for which the output is mapped to `+ INF`. + + Args: + input (Tensor): the input tensor. + + Keyword arguments: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4).uniform_(1, 2) + >>> a + tensor([ 1.3192, 1.9915, 1.9674, 1.7151 ]) + >>> torch.acosh(a) + tensor([ 0.7791, 1.3120, 1.2979, 1.1341 ]) + """ + ... +def acosh_(input: Tensor) -> Tensor: ... +def adaptive_avg_pool1d(input: Tensor, output_size: Union[_int, _size]) -> Tensor: ... +def adaptive_max_pool1d(input: Tensor, output_size: Union[_int, _size]) -> Tuple[Tensor, Tensor]: ... +@overload +def add(input: Union[Tensor, Number, _complex], other: Union[Tensor, Number, _complex], *, alpha: Optional[Union[Number, _complex]] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + add(input, other, *, alpha=1, out=None) -> Tensor + + Adds :attr:`other`, scaled by :attr:`alpha`, to :attr:`input`. + + .. math:: + \text{{out}}_i = \text{{input}}_i + \text{{alpha}} \times \text{{other}}_i + + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer, float, and complex inputs. + + Args: + input (Tensor): the input tensor. + other (Tensor or Number): the tensor or number to add to :attr:`input`. + + Keyword arguments: + alpha (Number): the multiplier for :attr:`other`. + out (Tensor, optional): the output tensor. + + Examples:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.0202, 1.0985, 1.3506, -0.6056]) + >>> torch.add(a, 20) + tensor([ 20.0202, 21.0985, 21.3506, 19.3944]) + + >>> b = torch.randn(4) + >>> b + tensor([-0.9732, -0.3497, 0.6245, 0.4022]) + >>> c = torch.randn(4, 1) + >>> c + tensor([[ 0.3743], + [-1.7724], + [-0.5811], + [-0.8017]]) + >>> torch.add(b, c, alpha=10) + tensor([[ 2.7695, 3.3930, 4.3672, 4.1450], + [-18.6971, -18.0736, -17.0994, -17.3216], + [ -6.7845, -6.1610, -5.1868, -5.4090], + [ -8.9902, -8.3667, -7.3925, -7.6147]]) + """ + ... +@overload +def add(self: Tensor, alpha: Union[Number, _complex], other: Tensor) -> Tensor: + r""" + add(input, other, *, alpha=1, out=None) -> Tensor + + Adds :attr:`other`, scaled by :attr:`alpha`, to :attr:`input`. + + .. math:: + \text{{out}}_i = \text{{input}}_i + \text{{alpha}} \times \text{{other}}_i + + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer, float, and complex inputs. + + Args: + input (Tensor): the input tensor. + other (Tensor or Number): the tensor or number to add to :attr:`input`. + + Keyword arguments: + alpha (Number): the multiplier for :attr:`other`. + out (Tensor, optional): the output tensor. + + Examples:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.0202, 1.0985, 1.3506, -0.6056]) + >>> torch.add(a, 20) + tensor([ 20.0202, 21.0985, 21.3506, 19.3944]) + + >>> b = torch.randn(4) + >>> b + tensor([-0.9732, -0.3497, 0.6245, 0.4022]) + >>> c = torch.randn(4, 1) + >>> c + tensor([[ 0.3743], + [-1.7724], + [-0.5811], + [-0.8017]]) + >>> torch.add(b, c, alpha=10) + tensor([[ 2.7695, 3.3930, 4.3672, 4.1450], + [-18.6971, -18.0736, -17.0994, -17.3216], + [ -6.7845, -6.1610, -5.1868, -5.4090], + [ -8.9902, -8.3667, -7.3925, -7.6147]]) + """ + ... +@overload +def add(self: Tensor, alpha: Union[Number, _complex], other: Tensor, *, out: Tensor) -> Tensor: + r""" + add(input, other, *, alpha=1, out=None) -> Tensor + + Adds :attr:`other`, scaled by :attr:`alpha`, to :attr:`input`. + + .. math:: + \text{{out}}_i = \text{{input}}_i + \text{{alpha}} \times \text{{other}}_i + + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer, float, and complex inputs. + + Args: + input (Tensor): the input tensor. + other (Tensor or Number): the tensor or number to add to :attr:`input`. + + Keyword arguments: + alpha (Number): the multiplier for :attr:`other`. + out (Tensor, optional): the output tensor. + + Examples:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.0202, 1.0985, 1.3506, -0.6056]) + >>> torch.add(a, 20) + tensor([ 20.0202, 21.0985, 21.3506, 19.3944]) + + >>> b = torch.randn(4) + >>> b + tensor([-0.9732, -0.3497, 0.6245, 0.4022]) + >>> c = torch.randn(4, 1) + >>> c + tensor([[ 0.3743], + [-1.7724], + [-0.5811], + [-0.8017]]) + >>> torch.add(b, c, alpha=10) + tensor([[ 2.7695, 3.3930, 4.3672, 4.1450], + [-18.6971, -18.0736, -17.0994, -17.3216], + [ -6.7845, -6.1610, -5.1868, -5.4090], + [ -8.9902, -8.3667, -7.3925, -7.6147]]) + """ + ... +@overload +def addbmm(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], batch1: Tensor, batch2: Tensor) -> Tensor: + r""" + addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices stored + in :attr:`batch1` and :attr:`batch2`, + with a reduced add step (all matrix multiplications get accumulated + along the first dimension). + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the + same number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, :attr:`input` must be + :ref:`broadcastable ` with a :math:`(n \times p)` tensor + and :attr:`out` will be a :math:`(n \times p)` tensor. + + .. math:: + out = \beta\ \text{input} + \alpha\ (\sum_{i=0}^{b-1} \text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and :attr:`alpha` + must be real numbers, otherwise they should be integers. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + input (Tensor): matrix to be added + alpha (Number, optional): multiplier for `batch1 @ batch2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(3, 5) + >>> batch1 = torch.randn(10, 3, 4) + >>> batch2 = torch.randn(10, 4, 5) + >>> torch.addbmm(M, batch1, batch2) + tensor([[ 6.6311, 0.0503, 6.9768, -12.0362, -2.1653], + [ -4.8185, -1.4255, -6.6760, 8.9453, 2.5743], + [ -3.8202, 4.3691, 1.0943, -1.1109, 5.4730]]) + """ + ... +@overload +def addbmm(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: + r""" + addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices stored + in :attr:`batch1` and :attr:`batch2`, + with a reduced add step (all matrix multiplications get accumulated + along the first dimension). + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the + same number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, :attr:`input` must be + :ref:`broadcastable ` with a :math:`(n \times p)` tensor + and :attr:`out` will be a :math:`(n \times p)` tensor. + + .. math:: + out = \beta\ \text{input} + \alpha\ (\sum_{i=0}^{b-1} \text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and :attr:`alpha` + must be real numbers, otherwise they should be integers. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + input (Tensor): matrix to be added + alpha (Number, optional): multiplier for `batch1 @ batch2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(3, 5) + >>> batch1 = torch.randn(10, 3, 4) + >>> batch2 = torch.randn(10, 4, 5) + >>> torch.addbmm(M, batch1, batch2) + tensor([[ 6.6311, 0.0503, 6.9768, -12.0362, -2.1653], + [ -4.8185, -1.4255, -6.6760, 8.9453, 2.5743], + [ -3.8202, 4.3691, 1.0943, -1.1109, 5.4730]]) + """ + ... +@overload +def addbmm(input: Tensor, batch1: Tensor, batch2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices stored + in :attr:`batch1` and :attr:`batch2`, + with a reduced add step (all matrix multiplications get accumulated + along the first dimension). + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the + same number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, :attr:`input` must be + :ref:`broadcastable ` with a :math:`(n \times p)` tensor + and :attr:`out` will be a :math:`(n \times p)` tensor. + + .. math:: + out = \beta\ \text{input} + \alpha\ (\sum_{i=0}^{b-1} \text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and :attr:`alpha` + must be real numbers, otherwise they should be integers. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + input (Tensor): matrix to be added + alpha (Number, optional): multiplier for `batch1 @ batch2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(3, 5) + >>> batch1 = torch.randn(10, 3, 4) + >>> batch2 = torch.randn(10, 4, 5) + >>> torch.addbmm(M, batch1, batch2) + tensor([[ 6.6311, 0.0503, 6.9768, -12.0362, -2.1653], + [ -4.8185, -1.4255, -6.6760, 8.9453, 2.5743], + [ -3.8202, 4.3691, 1.0943, -1.1109, 5.4730]]) + """ + ... +@overload +def addbmm(beta: Union[Number, _complex], self: Tensor, batch1: Tensor, batch2: Tensor) -> Tensor: + r""" + addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices stored + in :attr:`batch1` and :attr:`batch2`, + with a reduced add step (all matrix multiplications get accumulated + along the first dimension). + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the + same number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, :attr:`input` must be + :ref:`broadcastable ` with a :math:`(n \times p)` tensor + and :attr:`out` will be a :math:`(n \times p)` tensor. + + .. math:: + out = \beta\ \text{input} + \alpha\ (\sum_{i=0}^{b-1} \text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and :attr:`alpha` + must be real numbers, otherwise they should be integers. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + input (Tensor): matrix to be added + alpha (Number, optional): multiplier for `batch1 @ batch2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(3, 5) + >>> batch1 = torch.randn(10, 3, 4) + >>> batch2 = torch.randn(10, 4, 5) + >>> torch.addbmm(M, batch1, batch2) + tensor([[ 6.6311, 0.0503, 6.9768, -12.0362, -2.1653], + [ -4.8185, -1.4255, -6.6760, 8.9453, 2.5743], + [ -3.8202, 4.3691, 1.0943, -1.1109, 5.4730]]) + """ + ... +@overload +def addbmm(beta: Union[Number, _complex], self: Tensor, batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: + r""" + addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices stored + in :attr:`batch1` and :attr:`batch2`, + with a reduced add step (all matrix multiplications get accumulated + along the first dimension). + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the + same number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, :attr:`input` must be + :ref:`broadcastable ` with a :math:`(n \times p)` tensor + and :attr:`out` will be a :math:`(n \times p)` tensor. + + .. math:: + out = \beta\ \text{input} + \alpha\ (\sum_{i=0}^{b-1} \text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and :attr:`alpha` + must be real numbers, otherwise they should be integers. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + input (Tensor): matrix to be added + alpha (Number, optional): multiplier for `batch1 @ batch2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(3, 5) + >>> batch1 = torch.randn(10, 3, 4) + >>> batch2 = torch.randn(10, 4, 5) + >>> torch.addbmm(M, batch1, batch2) + tensor([[ 6.6311, 0.0503, 6.9768, -12.0362, -2.1653], + [ -4.8185, -1.4255, -6.6760, 8.9453, 2.5743], + [ -3.8202, 4.3691, 1.0943, -1.1109, 5.4730]]) + """ + ... +@overload +def addcdiv(self: Tensor, value: Union[Number, _complex], tensor1: Tensor, tensor2: Tensor) -> Tensor: + r""" + addcdiv(input, tensor1, tensor2, *, value=1, out=None) -> Tensor + + Performs the element-wise division of :attr:`tensor1` by :attr:`tensor2`, + multiplies the result by the scalar :attr:`value` and adds it to :attr:`input`. + + .. warning:: + Integer division with addcdiv is no longer supported, and in a future + release addcdiv will perform a true division of tensor1 and tensor2. + The historic addcdiv behavior can be implemented as + (input + value * torch.trunc(tensor1 / tensor2)).to(input.dtype) + for integer inputs and as (input + value * tensor1 / tensor2) for float inputs. + The future addcdiv behavior is just the latter implementation: + (input + value * tensor1 / tensor2), for all dtypes. + + .. math:: + \text{out}_i = \text{input}_i + \text{value} \times \frac{\text{tensor1}_i}{\text{tensor2}_i} + + + The shapes of :attr:`input`, :attr:`tensor1`, and :attr:`tensor2` must be + :ref:`broadcastable `. + + For inputs of type `FloatTensor` or `DoubleTensor`, :attr:`value` must be + a real number, otherwise an integer. + + Args: + input (Tensor): the tensor to be added + tensor1 (Tensor): the numerator tensor + tensor2 (Tensor): the denominator tensor + + Keyword args: + value (Number, optional): multiplier for :math:`\text{tensor1} / \text{tensor2}` + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.randn(1, 3) + >>> t1 = torch.randn(3, 1) + >>> t2 = torch.randn(1, 3) + >>> torch.addcdiv(t, t1, t2, value=0.1) + tensor([[-0.2312, -3.6496, 0.1312], + [-1.0428, 3.4292, -0.1030], + [-0.5369, -0.9829, 0.0430]]) + """ + ... +@overload +def addcdiv(self: Tensor, value: Union[Number, _complex], tensor1: Tensor, tensor2: Tensor, *, out: Tensor) -> Tensor: + r""" + addcdiv(input, tensor1, tensor2, *, value=1, out=None) -> Tensor + + Performs the element-wise division of :attr:`tensor1` by :attr:`tensor2`, + multiplies the result by the scalar :attr:`value` and adds it to :attr:`input`. + + .. warning:: + Integer division with addcdiv is no longer supported, and in a future + release addcdiv will perform a true division of tensor1 and tensor2. + The historic addcdiv behavior can be implemented as + (input + value * torch.trunc(tensor1 / tensor2)).to(input.dtype) + for integer inputs and as (input + value * tensor1 / tensor2) for float inputs. + The future addcdiv behavior is just the latter implementation: + (input + value * tensor1 / tensor2), for all dtypes. + + .. math:: + \text{out}_i = \text{input}_i + \text{value} \times \frac{\text{tensor1}_i}{\text{tensor2}_i} + + + The shapes of :attr:`input`, :attr:`tensor1`, and :attr:`tensor2` must be + :ref:`broadcastable `. + + For inputs of type `FloatTensor` or `DoubleTensor`, :attr:`value` must be + a real number, otherwise an integer. + + Args: + input (Tensor): the tensor to be added + tensor1 (Tensor): the numerator tensor + tensor2 (Tensor): the denominator tensor + + Keyword args: + value (Number, optional): multiplier for :math:`\text{tensor1} / \text{tensor2}` + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.randn(1, 3) + >>> t1 = torch.randn(3, 1) + >>> t2 = torch.randn(1, 3) + >>> torch.addcdiv(t, t1, t2, value=0.1) + tensor([[-0.2312, -3.6496, 0.1312], + [-1.0428, 3.4292, -0.1030], + [-0.5369, -0.9829, 0.0430]]) + """ + ... +@overload +def addcdiv(input: Tensor, tensor1: Tensor, tensor2: Tensor, *, value: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + addcdiv(input, tensor1, tensor2, *, value=1, out=None) -> Tensor + + Performs the element-wise division of :attr:`tensor1` by :attr:`tensor2`, + multiplies the result by the scalar :attr:`value` and adds it to :attr:`input`. + + .. warning:: + Integer division with addcdiv is no longer supported, and in a future + release addcdiv will perform a true division of tensor1 and tensor2. + The historic addcdiv behavior can be implemented as + (input + value * torch.trunc(tensor1 / tensor2)).to(input.dtype) + for integer inputs and as (input + value * tensor1 / tensor2) for float inputs. + The future addcdiv behavior is just the latter implementation: + (input + value * tensor1 / tensor2), for all dtypes. + + .. math:: + \text{out}_i = \text{input}_i + \text{value} \times \frac{\text{tensor1}_i}{\text{tensor2}_i} + + + The shapes of :attr:`input`, :attr:`tensor1`, and :attr:`tensor2` must be + :ref:`broadcastable `. + + For inputs of type `FloatTensor` or `DoubleTensor`, :attr:`value` must be + a real number, otherwise an integer. + + Args: + input (Tensor): the tensor to be added + tensor1 (Tensor): the numerator tensor + tensor2 (Tensor): the denominator tensor + + Keyword args: + value (Number, optional): multiplier for :math:`\text{tensor1} / \text{tensor2}` + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.randn(1, 3) + >>> t1 = torch.randn(3, 1) + >>> t2 = torch.randn(1, 3) + >>> torch.addcdiv(t, t1, t2, value=0.1) + tensor([[-0.2312, -3.6496, 0.1312], + [-1.0428, 3.4292, -0.1030], + [-0.5369, -0.9829, 0.0430]]) + """ + ... +@overload +def addcmul(self: Tensor, value: Union[Number, _complex], tensor1: Tensor, tensor2: Tensor) -> Tensor: + r""" + addcmul(input, tensor1, tensor2, *, value=1, out=None) -> Tensor + + Performs the element-wise multiplication of :attr:`tensor1` + by :attr:`tensor2`, multiplies the result by the scalar :attr:`value` + and adds it to :attr:`input`. + + .. math:: + \text{out}_i = \text{input}_i + \text{value} \times \text{tensor1}_i \times \text{tensor2}_i + + The shapes of :attr:`tensor`, :attr:`tensor1`, and :attr:`tensor2` must be + :ref:`broadcastable `. + + For inputs of type `FloatTensor` or `DoubleTensor`, :attr:`value` must be + a real number, otherwise an integer. + + Args: + input (Tensor): the tensor to be added + tensor1 (Tensor): the tensor to be multiplied + tensor2 (Tensor): the tensor to be multiplied + + Keyword args: + value (Number, optional): multiplier for :math:`tensor1 .* tensor2` + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.randn(1, 3) + >>> t1 = torch.randn(3, 1) + >>> t2 = torch.randn(1, 3) + >>> torch.addcmul(t, t1, t2, value=0.1) + tensor([[-0.8635, -0.6391, 1.6174], + [-0.7617, -0.5879, 1.7388], + [-0.8353, -0.6249, 1.6511]]) + """ + ... +@overload +def addcmul(self: Tensor, value: Union[Number, _complex], tensor1: Tensor, tensor2: Tensor, *, out: Tensor) -> Tensor: + r""" + addcmul(input, tensor1, tensor2, *, value=1, out=None) -> Tensor + + Performs the element-wise multiplication of :attr:`tensor1` + by :attr:`tensor2`, multiplies the result by the scalar :attr:`value` + and adds it to :attr:`input`. + + .. math:: + \text{out}_i = \text{input}_i + \text{value} \times \text{tensor1}_i \times \text{tensor2}_i + + The shapes of :attr:`tensor`, :attr:`tensor1`, and :attr:`tensor2` must be + :ref:`broadcastable `. + + For inputs of type `FloatTensor` or `DoubleTensor`, :attr:`value` must be + a real number, otherwise an integer. + + Args: + input (Tensor): the tensor to be added + tensor1 (Tensor): the tensor to be multiplied + tensor2 (Tensor): the tensor to be multiplied + + Keyword args: + value (Number, optional): multiplier for :math:`tensor1 .* tensor2` + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.randn(1, 3) + >>> t1 = torch.randn(3, 1) + >>> t2 = torch.randn(1, 3) + >>> torch.addcmul(t, t1, t2, value=0.1) + tensor([[-0.8635, -0.6391, 1.6174], + [-0.7617, -0.5879, 1.7388], + [-0.8353, -0.6249, 1.6511]]) + """ + ... +@overload +def addcmul(input: Tensor, tensor1: Tensor, tensor2: Tensor, *, value: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + addcmul(input, tensor1, tensor2, *, value=1, out=None) -> Tensor + + Performs the element-wise multiplication of :attr:`tensor1` + by :attr:`tensor2`, multiplies the result by the scalar :attr:`value` + and adds it to :attr:`input`. + + .. math:: + \text{out}_i = \text{input}_i + \text{value} \times \text{tensor1}_i \times \text{tensor2}_i + + The shapes of :attr:`tensor`, :attr:`tensor1`, and :attr:`tensor2` must be + :ref:`broadcastable `. + + For inputs of type `FloatTensor` or `DoubleTensor`, :attr:`value` must be + a real number, otherwise an integer. + + Args: + input (Tensor): the tensor to be added + tensor1 (Tensor): the tensor to be multiplied + tensor2 (Tensor): the tensor to be multiplied + + Keyword args: + value (Number, optional): multiplier for :math:`tensor1 .* tensor2` + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.randn(1, 3) + >>> t1 = torch.randn(3, 1) + >>> t2 = torch.randn(1, 3) + >>> torch.addcmul(t, t1, t2, value=0.1) + tensor([[-0.8635, -0.6391, 1.6174], + [-0.7617, -0.5879, 1.7388], + [-0.8353, -0.6249, 1.6511]]) + """ + ... +@overload +def addmm(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], mat1: Tensor, mat2: Tensor) -> Tensor: + r""" + addmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a matrix multiplication of the matrices :attr:`mat1` and :attr:`mat2`. + The matrix :attr:`input` is added to the final result. + + If :attr:`mat1` is a :math:`(n \times m)` tensor, :attr:`mat2` is a + :math:`(m \times p)` tensor, then :attr:`input` must be + :ref:`broadcastable ` with a :math:`(n \times p)` tensor + and :attr:`out` will be a :math:`(n \times p)` tensor. + + :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between + :attr:`mat1` and :attr:`mat2` and the added matrix :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{mat1}_i \mathbin{@} \text{mat2}_i) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + This operation has support for arguments with :ref:`sparse layouts`. If + :attr:`input` is sparse the result will have the same layout and if :attr:`out` + is provided it must have the same layout as :attr:`input`. + + + .. warning:: + Sparse support is a beta feature and some layout(s)/dtype/device combinations may not be supported, + or may not have autograd support. If you notice missing functionality please + open a feature request. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): matrix to be added + mat1 (Tensor): the first matrix to be matrix multiplied + mat2 (Tensor): the second matrix to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(2, 3) + >>> mat1 = torch.randn(2, 3) + >>> mat2 = torch.randn(3, 3) + >>> torch.addmm(M, mat1, mat2) + tensor([[-4.8716, 1.4671, -1.3746], + [ 0.7573, -3.9555, -2.8681]]) + """ + ... +@overload +def addmm(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], mat1: Tensor, mat2: Tensor, *, out: Tensor) -> Tensor: + r""" + addmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a matrix multiplication of the matrices :attr:`mat1` and :attr:`mat2`. + The matrix :attr:`input` is added to the final result. + + If :attr:`mat1` is a :math:`(n \times m)` tensor, :attr:`mat2` is a + :math:`(m \times p)` tensor, then :attr:`input` must be + :ref:`broadcastable ` with a :math:`(n \times p)` tensor + and :attr:`out` will be a :math:`(n \times p)` tensor. + + :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between + :attr:`mat1` and :attr:`mat2` and the added matrix :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{mat1}_i \mathbin{@} \text{mat2}_i) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + This operation has support for arguments with :ref:`sparse layouts`. If + :attr:`input` is sparse the result will have the same layout and if :attr:`out` + is provided it must have the same layout as :attr:`input`. + + + .. warning:: + Sparse support is a beta feature and some layout(s)/dtype/device combinations may not be supported, + or may not have autograd support. If you notice missing functionality please + open a feature request. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): matrix to be added + mat1 (Tensor): the first matrix to be matrix multiplied + mat2 (Tensor): the second matrix to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(2, 3) + >>> mat1 = torch.randn(2, 3) + >>> mat2 = torch.randn(3, 3) + >>> torch.addmm(M, mat1, mat2) + tensor([[-4.8716, 1.4671, -1.3746], + [ 0.7573, -3.9555, -2.8681]]) + """ + ... +@overload +def addmm(input: Tensor, mat1: Tensor, mat2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + addmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a matrix multiplication of the matrices :attr:`mat1` and :attr:`mat2`. + The matrix :attr:`input` is added to the final result. + + If :attr:`mat1` is a :math:`(n \times m)` tensor, :attr:`mat2` is a + :math:`(m \times p)` tensor, then :attr:`input` must be + :ref:`broadcastable ` with a :math:`(n \times p)` tensor + and :attr:`out` will be a :math:`(n \times p)` tensor. + + :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between + :attr:`mat1` and :attr:`mat2` and the added matrix :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{mat1}_i \mathbin{@} \text{mat2}_i) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + This operation has support for arguments with :ref:`sparse layouts`. If + :attr:`input` is sparse the result will have the same layout and if :attr:`out` + is provided it must have the same layout as :attr:`input`. + + + .. warning:: + Sparse support is a beta feature and some layout(s)/dtype/device combinations may not be supported, + or may not have autograd support. If you notice missing functionality please + open a feature request. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): matrix to be added + mat1 (Tensor): the first matrix to be matrix multiplied + mat2 (Tensor): the second matrix to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(2, 3) + >>> mat1 = torch.randn(2, 3) + >>> mat2 = torch.randn(3, 3) + >>> torch.addmm(M, mat1, mat2) + tensor([[-4.8716, 1.4671, -1.3746], + [ 0.7573, -3.9555, -2.8681]]) + """ + ... +@overload +def addmm(beta: Union[Number, _complex], self: Tensor, mat1: Tensor, mat2: Tensor) -> Tensor: + r""" + addmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a matrix multiplication of the matrices :attr:`mat1` and :attr:`mat2`. + The matrix :attr:`input` is added to the final result. + + If :attr:`mat1` is a :math:`(n \times m)` tensor, :attr:`mat2` is a + :math:`(m \times p)` tensor, then :attr:`input` must be + :ref:`broadcastable ` with a :math:`(n \times p)` tensor + and :attr:`out` will be a :math:`(n \times p)` tensor. + + :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between + :attr:`mat1` and :attr:`mat2` and the added matrix :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{mat1}_i \mathbin{@} \text{mat2}_i) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + This operation has support for arguments with :ref:`sparse layouts`. If + :attr:`input` is sparse the result will have the same layout and if :attr:`out` + is provided it must have the same layout as :attr:`input`. + + + .. warning:: + Sparse support is a beta feature and some layout(s)/dtype/device combinations may not be supported, + or may not have autograd support. If you notice missing functionality please + open a feature request. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): matrix to be added + mat1 (Tensor): the first matrix to be matrix multiplied + mat2 (Tensor): the second matrix to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(2, 3) + >>> mat1 = torch.randn(2, 3) + >>> mat2 = torch.randn(3, 3) + >>> torch.addmm(M, mat1, mat2) + tensor([[-4.8716, 1.4671, -1.3746], + [ 0.7573, -3.9555, -2.8681]]) + """ + ... +@overload +def addmm(beta: Union[Number, _complex], self: Tensor, mat1: Tensor, mat2: Tensor, *, out: Tensor) -> Tensor: + r""" + addmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a matrix multiplication of the matrices :attr:`mat1` and :attr:`mat2`. + The matrix :attr:`input` is added to the final result. + + If :attr:`mat1` is a :math:`(n \times m)` tensor, :attr:`mat2` is a + :math:`(m \times p)` tensor, then :attr:`input` must be + :ref:`broadcastable ` with a :math:`(n \times p)` tensor + and :attr:`out` will be a :math:`(n \times p)` tensor. + + :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between + :attr:`mat1` and :attr:`mat2` and the added matrix :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{mat1}_i \mathbin{@} \text{mat2}_i) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + This operation has support for arguments with :ref:`sparse layouts`. If + :attr:`input` is sparse the result will have the same layout and if :attr:`out` + is provided it must have the same layout as :attr:`input`. + + + .. warning:: + Sparse support is a beta feature and some layout(s)/dtype/device combinations may not be supported, + or may not have autograd support. If you notice missing functionality please + open a feature request. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): matrix to be added + mat1 (Tensor): the first matrix to be matrix multiplied + mat2 (Tensor): the second matrix to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(2, 3) + >>> mat1 = torch.randn(2, 3) + >>> mat2 = torch.randn(3, 3) + >>> torch.addmm(M, mat1, mat2) + tensor([[-4.8716, 1.4671, -1.3746], + [ 0.7573, -3.9555, -2.8681]]) + """ + ... +@overload +def addmv(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], mat: Tensor, vec: Tensor) -> Tensor: + r""" + addmv(input, mat, vec, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a matrix-vector product of the matrix :attr:`mat` and + the vector :attr:`vec`. + The vector :attr:`input` is added to the final result. + + If :attr:`mat` is a :math:`(n \times m)` tensor, :attr:`vec` is a 1-D tensor of + size `m`, then :attr:`input` must be + :ref:`broadcastable ` with a 1-D tensor of size `n` and + :attr:`out` will be 1-D tensor of size `n`. + + :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between + :attr:`mat` and :attr:`vec` and the added tensor :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{mat} \mathbin{@} \text{vec}) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + Args: + input (Tensor): vector to be added + mat (Tensor): matrix to be matrix multiplied + vec (Tensor): vector to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat @ vec` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(2) + >>> mat = torch.randn(2, 3) + >>> vec = torch.randn(3) + >>> torch.addmv(M, mat, vec) + tensor([-0.3768, -5.5565]) + """ + ... +@overload +def addmv(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], mat: Tensor, vec: Tensor, *, out: Tensor) -> Tensor: + r""" + addmv(input, mat, vec, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a matrix-vector product of the matrix :attr:`mat` and + the vector :attr:`vec`. + The vector :attr:`input` is added to the final result. + + If :attr:`mat` is a :math:`(n \times m)` tensor, :attr:`vec` is a 1-D tensor of + size `m`, then :attr:`input` must be + :ref:`broadcastable ` with a 1-D tensor of size `n` and + :attr:`out` will be 1-D tensor of size `n`. + + :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between + :attr:`mat` and :attr:`vec` and the added tensor :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{mat} \mathbin{@} \text{vec}) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + Args: + input (Tensor): vector to be added + mat (Tensor): matrix to be matrix multiplied + vec (Tensor): vector to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat @ vec` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(2) + >>> mat = torch.randn(2, 3) + >>> vec = torch.randn(3) + >>> torch.addmv(M, mat, vec) + tensor([-0.3768, -5.5565]) + """ + ... +@overload +def addmv(input: Tensor, mat: Tensor, vec: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + addmv(input, mat, vec, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a matrix-vector product of the matrix :attr:`mat` and + the vector :attr:`vec`. + The vector :attr:`input` is added to the final result. + + If :attr:`mat` is a :math:`(n \times m)` tensor, :attr:`vec` is a 1-D tensor of + size `m`, then :attr:`input` must be + :ref:`broadcastable ` with a 1-D tensor of size `n` and + :attr:`out` will be 1-D tensor of size `n`. + + :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between + :attr:`mat` and :attr:`vec` and the added tensor :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{mat} \mathbin{@} \text{vec}) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + Args: + input (Tensor): vector to be added + mat (Tensor): matrix to be matrix multiplied + vec (Tensor): vector to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat @ vec` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(2) + >>> mat = torch.randn(2, 3) + >>> vec = torch.randn(3) + >>> torch.addmv(M, mat, vec) + tensor([-0.3768, -5.5565]) + """ + ... +@overload +def addmv(beta: Union[Number, _complex], self: Tensor, mat: Tensor, vec: Tensor) -> Tensor: + r""" + addmv(input, mat, vec, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a matrix-vector product of the matrix :attr:`mat` and + the vector :attr:`vec`. + The vector :attr:`input` is added to the final result. + + If :attr:`mat` is a :math:`(n \times m)` tensor, :attr:`vec` is a 1-D tensor of + size `m`, then :attr:`input` must be + :ref:`broadcastable ` with a 1-D tensor of size `n` and + :attr:`out` will be 1-D tensor of size `n`. + + :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between + :attr:`mat` and :attr:`vec` and the added tensor :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{mat} \mathbin{@} \text{vec}) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + Args: + input (Tensor): vector to be added + mat (Tensor): matrix to be matrix multiplied + vec (Tensor): vector to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat @ vec` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(2) + >>> mat = torch.randn(2, 3) + >>> vec = torch.randn(3) + >>> torch.addmv(M, mat, vec) + tensor([-0.3768, -5.5565]) + """ + ... +@overload +def addmv(beta: Union[Number, _complex], self: Tensor, mat: Tensor, vec: Tensor, *, out: Tensor) -> Tensor: + r""" + addmv(input, mat, vec, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a matrix-vector product of the matrix :attr:`mat` and + the vector :attr:`vec`. + The vector :attr:`input` is added to the final result. + + If :attr:`mat` is a :math:`(n \times m)` tensor, :attr:`vec` is a 1-D tensor of + size `m`, then :attr:`input` must be + :ref:`broadcastable ` with a 1-D tensor of size `n` and + :attr:`out` will be 1-D tensor of size `n`. + + :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between + :attr:`mat` and :attr:`vec` and the added tensor :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{mat} \mathbin{@} \text{vec}) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + Args: + input (Tensor): vector to be added + mat (Tensor): matrix to be matrix multiplied + vec (Tensor): vector to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat @ vec` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(2) + >>> mat = torch.randn(2, 3) + >>> vec = torch.randn(3) + >>> torch.addmv(M, mat, vec) + tensor([-0.3768, -5.5565]) + """ + ... +@overload +def addmv_(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], mat: Tensor, vec: Tensor) -> Tensor: ... +@overload +def addmv_(input: Tensor, mat: Tensor, vec: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: ... +@overload +def addmv_(beta: Union[Number, _complex], self: Tensor, mat: Tensor, vec: Tensor) -> Tensor: ... +@overload +def addr(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], vec1: Tensor, vec2: Tensor) -> Tensor: + r""" + addr(input, vec1, vec2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs the outer-product of vectors :attr:`vec1` and :attr:`vec2` + and adds it to the matrix :attr:`input`. + + Optional values :attr:`beta` and :attr:`alpha` are scaling factors on the + outer product between :attr:`vec1` and :attr:`vec2` and the added matrix + :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{vec1} \otimes \text{vec2}) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + If :attr:`vec1` is a vector of size `n` and :attr:`vec2` is a vector + of size `m`, then :attr:`input` must be + :ref:`broadcastable ` with a matrix of size + :math:`(n \times m)` and :attr:`out` will be a matrix of size + :math:`(n \times m)`. + + Args: + input (Tensor): matrix to be added + vec1 (Tensor): the first vector of the outer product + vec2 (Tensor): the second vector of the outer product + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{vec1} \otimes \text{vec2}` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> vec1 = torch.arange(1., 4.) + >>> vec2 = torch.arange(1., 3.) + >>> M = torch.zeros(3, 2) + >>> torch.addr(M, vec1, vec2) + tensor([[ 1., 2.], + [ 2., 4.], + [ 3., 6.]]) + """ + ... +@overload +def addr(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], vec1: Tensor, vec2: Tensor, *, out: Tensor) -> Tensor: + r""" + addr(input, vec1, vec2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs the outer-product of vectors :attr:`vec1` and :attr:`vec2` + and adds it to the matrix :attr:`input`. + + Optional values :attr:`beta` and :attr:`alpha` are scaling factors on the + outer product between :attr:`vec1` and :attr:`vec2` and the added matrix + :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{vec1} \otimes \text{vec2}) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + If :attr:`vec1` is a vector of size `n` and :attr:`vec2` is a vector + of size `m`, then :attr:`input` must be + :ref:`broadcastable ` with a matrix of size + :math:`(n \times m)` and :attr:`out` will be a matrix of size + :math:`(n \times m)`. + + Args: + input (Tensor): matrix to be added + vec1 (Tensor): the first vector of the outer product + vec2 (Tensor): the second vector of the outer product + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{vec1} \otimes \text{vec2}` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> vec1 = torch.arange(1., 4.) + >>> vec2 = torch.arange(1., 3.) + >>> M = torch.zeros(3, 2) + >>> torch.addr(M, vec1, vec2) + tensor([[ 1., 2.], + [ 2., 4.], + [ 3., 6.]]) + """ + ... +@overload +def addr(input: Tensor, vec1: Tensor, vec2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + addr(input, vec1, vec2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs the outer-product of vectors :attr:`vec1` and :attr:`vec2` + and adds it to the matrix :attr:`input`. + + Optional values :attr:`beta` and :attr:`alpha` are scaling factors on the + outer product between :attr:`vec1` and :attr:`vec2` and the added matrix + :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{vec1} \otimes \text{vec2}) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + If :attr:`vec1` is a vector of size `n` and :attr:`vec2` is a vector + of size `m`, then :attr:`input` must be + :ref:`broadcastable ` with a matrix of size + :math:`(n \times m)` and :attr:`out` will be a matrix of size + :math:`(n \times m)`. + + Args: + input (Tensor): matrix to be added + vec1 (Tensor): the first vector of the outer product + vec2 (Tensor): the second vector of the outer product + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{vec1} \otimes \text{vec2}` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> vec1 = torch.arange(1., 4.) + >>> vec2 = torch.arange(1., 3.) + >>> M = torch.zeros(3, 2) + >>> torch.addr(M, vec1, vec2) + tensor([[ 1., 2.], + [ 2., 4.], + [ 3., 6.]]) + """ + ... +@overload +def addr(beta: Union[Number, _complex], self: Tensor, vec1: Tensor, vec2: Tensor) -> Tensor: + r""" + addr(input, vec1, vec2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs the outer-product of vectors :attr:`vec1` and :attr:`vec2` + and adds it to the matrix :attr:`input`. + + Optional values :attr:`beta` and :attr:`alpha` are scaling factors on the + outer product between :attr:`vec1` and :attr:`vec2` and the added matrix + :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{vec1} \otimes \text{vec2}) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + If :attr:`vec1` is a vector of size `n` and :attr:`vec2` is a vector + of size `m`, then :attr:`input` must be + :ref:`broadcastable ` with a matrix of size + :math:`(n \times m)` and :attr:`out` will be a matrix of size + :math:`(n \times m)`. + + Args: + input (Tensor): matrix to be added + vec1 (Tensor): the first vector of the outer product + vec2 (Tensor): the second vector of the outer product + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{vec1} \otimes \text{vec2}` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> vec1 = torch.arange(1., 4.) + >>> vec2 = torch.arange(1., 3.) + >>> M = torch.zeros(3, 2) + >>> torch.addr(M, vec1, vec2) + tensor([[ 1., 2.], + [ 2., 4.], + [ 3., 6.]]) + """ + ... +@overload +def addr(beta: Union[Number, _complex], self: Tensor, vec1: Tensor, vec2: Tensor, *, out: Tensor) -> Tensor: + r""" + addr(input, vec1, vec2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs the outer-product of vectors :attr:`vec1` and :attr:`vec2` + and adds it to the matrix :attr:`input`. + + Optional values :attr:`beta` and :attr:`alpha` are scaling factors on the + outer product between :attr:`vec1` and :attr:`vec2` and the added matrix + :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{vec1} \otimes \text{vec2}) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + If :attr:`vec1` is a vector of size `n` and :attr:`vec2` is a vector + of size `m`, then :attr:`input` must be + :ref:`broadcastable ` with a matrix of size + :math:`(n \times m)` and :attr:`out` will be a matrix of size + :math:`(n \times m)`. + + Args: + input (Tensor): matrix to be added + vec1 (Tensor): the first vector of the outer product + vec2 (Tensor): the second vector of the outer product + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{vec1} \otimes \text{vec2}` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> vec1 = torch.arange(1., 4.) + >>> vec2 = torch.arange(1., 3.) + >>> M = torch.zeros(3, 2) + >>> torch.addr(M, vec1, vec2) + tensor([[ 1., 2.], + [ 2., 4.], + [ 3., 6.]]) + """ + ... +def adjoint(input: Tensor) -> Tensor: + r""" + adjoint(Tensor) -> Tensor + Returns a view of the tensor conjugated and with the last two dimensions transposed. + + ``x.adjoint()`` is equivalent to ``x.transpose(-2, -1).conj()`` for complex tensors and + to ``x.transpose(-2, -1)`` for real tensors. + + Example:: + >>> x = torch.arange(4, dtype=torch.float) + >>> A = torch.complex(x, x).reshape(2, 2) + >>> A + tensor([[0.+0.j, 1.+1.j], + [2.+2.j, 3.+3.j]]) + >>> A.adjoint() + tensor([[0.-0.j, 2.-2.j], + [1.-1.j, 3.-3.j]]) + >>> (A.adjoint() == A.mH).all() + tensor(True) + """ + ... +def affine_grid_generator(theta: Tensor, size: Sequence[Union[_int, SymInt]], align_corners: _bool) -> Tensor: ... +def alias_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.alias`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +@overload +def all(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + all(input) -> Tensor + + Tests if all elements in :attr:`input` evaluate to `True`. + + .. note:: This function matches the behaviour of NumPy in returning + output of dtype `bool` for all supported dtypes except `uint8`. + For `uint8` the dtype of output is `uint8` itself. + + Example:: + + >>> a = torch.rand(1, 2).bool() + >>> a + tensor([[False, True]], dtype=torch.bool) + >>> torch.all(a) + tensor(False, dtype=torch.bool) + >>> a = torch.arange(0, 3) + >>> a + tensor([0, 1, 2]) + >>> torch.all(a) + tensor(False) + + .. function:: all(input, dim, keepdim=False, *, out=None) -> Tensor + :noindex: + + For each row of :attr:`input` in the given dimension :attr:`dim`, + returns `True` if all elements in the row evaluate to `True` and `False` otherwise. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.rand(4, 2).bool() + >>> a + tensor([[True, True], + [True, False], + [True, True], + [True, True]], dtype=torch.bool) + >>> torch.all(a, dim=1) + tensor([ True, False, True, True], dtype=torch.bool) + >>> torch.all(a, dim=0) + tensor([ True, False], dtype=torch.bool) + """ + ... +@overload +def all(input: Tensor, dim: Optional[_size] = None, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + all(input) -> Tensor + + Tests if all elements in :attr:`input` evaluate to `True`. + + .. note:: This function matches the behaviour of NumPy in returning + output of dtype `bool` for all supported dtypes except `uint8`. + For `uint8` the dtype of output is `uint8` itself. + + Example:: + + >>> a = torch.rand(1, 2).bool() + >>> a + tensor([[False, True]], dtype=torch.bool) + >>> torch.all(a) + tensor(False, dtype=torch.bool) + >>> a = torch.arange(0, 3) + >>> a + tensor([0, 1, 2]) + >>> torch.all(a) + tensor(False) + + .. function:: all(input, dim, keepdim=False, *, out=None) -> Tensor + :noindex: + + For each row of :attr:`input` in the given dimension :attr:`dim`, + returns `True` if all elements in the row evaluate to `True` and `False` otherwise. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.rand(4, 2).bool() + >>> a + tensor([[True, True], + [True, False], + [True, True], + [True, True]], dtype=torch.bool) + >>> torch.all(a, dim=1) + tensor([ True, False, True, True], dtype=torch.bool) + >>> torch.all(a, dim=0) + tensor([ True, False], dtype=torch.bool) + """ + ... +@overload +def all(input: Tensor, dim: _int, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + all(input) -> Tensor + + Tests if all elements in :attr:`input` evaluate to `True`. + + .. note:: This function matches the behaviour of NumPy in returning + output of dtype `bool` for all supported dtypes except `uint8`. + For `uint8` the dtype of output is `uint8` itself. + + Example:: + + >>> a = torch.rand(1, 2).bool() + >>> a + tensor([[False, True]], dtype=torch.bool) + >>> torch.all(a) + tensor(False, dtype=torch.bool) + >>> a = torch.arange(0, 3) + >>> a + tensor([0, 1, 2]) + >>> torch.all(a) + tensor(False) + + .. function:: all(input, dim, keepdim=False, *, out=None) -> Tensor + :noindex: + + For each row of :attr:`input` in the given dimension :attr:`dim`, + returns `True` if all elements in the row evaluate to `True` and `False` otherwise. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.rand(4, 2).bool() + >>> a + tensor([[True, True], + [True, False], + [True, True], + [True, True]], dtype=torch.bool) + >>> torch.all(a, dim=1) + tensor([ True, False, True, True], dtype=torch.bool) + >>> torch.all(a, dim=0) + tensor([ True, False], dtype=torch.bool) + """ + ... +@overload +def all(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + all(input) -> Tensor + + Tests if all elements in :attr:`input` evaluate to `True`. + + .. note:: This function matches the behaviour of NumPy in returning + output of dtype `bool` for all supported dtypes except `uint8`. + For `uint8` the dtype of output is `uint8` itself. + + Example:: + + >>> a = torch.rand(1, 2).bool() + >>> a + tensor([[False, True]], dtype=torch.bool) + >>> torch.all(a) + tensor(False, dtype=torch.bool) + >>> a = torch.arange(0, 3) + >>> a + tensor([0, 1, 2]) + >>> torch.all(a) + tensor(False) + + .. function:: all(input, dim, keepdim=False, *, out=None) -> Tensor + :noindex: + + For each row of :attr:`input` in the given dimension :attr:`dim`, + returns `True` if all elements in the row evaluate to `True` and `False` otherwise. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.rand(4, 2).bool() + >>> a + tensor([[True, True], + [True, False], + [True, True], + [True, True]], dtype=torch.bool) + >>> torch.all(a, dim=1) + tensor([ True, False, True, True], dtype=torch.bool) + >>> torch.all(a, dim=0) + tensor([ True, False], dtype=torch.bool) + """ + ... +def allclose(input: Tensor, other: Tensor, rtol: _float = 1e-05, atol: _float = 1e-08, equal_nan: _bool = False) -> _bool: + r""" + allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) -> bool + + This function checks if :attr:`input` and :attr:`other` satisfy the condition: + + .. math:: + \lvert \text{input} - \text{other} \rvert \leq \texttt{atol} + \texttt{rtol} \times \lvert \text{other} \rvert + + elementwise, for all elements of :attr:`input` and :attr:`other`. The behaviour of this function is analogous to + `numpy.allclose `_ + + Args: + input (Tensor): first tensor to compare + other (Tensor): second tensor to compare + atol (float, optional): absolute tolerance. Default: 1e-08 + rtol (float, optional): relative tolerance. Default: 1e-05 + equal_nan (bool, optional): if ``True``, then two ``NaN`` s will be considered equal. Default: ``False`` + + Example:: + + >>> torch.allclose(torch.tensor([10000., 1e-07]), torch.tensor([10000.1, 1e-08])) + False + >>> torch.allclose(torch.tensor([10000., 1e-08]), torch.tensor([10000.1, 1e-09])) + True + >>> torch.allclose(torch.tensor([1.0, float('nan')]), torch.tensor([1.0, float('nan')])) + False + >>> torch.allclose(torch.tensor([1.0, float('nan')]), torch.tensor([1.0, float('nan')]), equal_nan=True) + True + """ + ... +def alpha_dropout(input: Tensor, p: _float, train: _bool) -> Tensor: ... +def alpha_dropout_(input: Tensor, p: _float, train: _bool) -> Tensor: ... +def amax(input: Tensor, dim: Union[_int, _size] = (), keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + amax(input, dim, keepdim=False, *, out=None) -> Tensor + + Returns the maximum value of each slice of the :attr:`input` tensor in the given + dimension(s) :attr:`dim`. + + .. note:: + The difference between ``max``/``min`` and ``amax``/``amin`` is: + - ``amax``/``amin`` supports reducing on multiple dimensions, + - ``amax``/``amin`` does not return indices, + - ``amax``/``amin`` evenly distributes gradient between equal values, + while ``max(dim)``/``min(dim)`` propagates gradient only to a single + index in the source tensor. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.8177, 1.4878, -0.2491, 0.9130], + [-0.7158, 1.1775, 2.0992, 0.4817], + [-0.0053, 0.0164, -1.3738, -0.0507], + [ 1.9700, 1.1106, -1.0318, -1.0816]]) + >>> torch.amax(a, 1) + tensor([1.4878, 2.0992, 0.0164, 1.9700]) + """ + ... +def amin(input: Tensor, dim: Union[_int, _size] = (), keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + amin(input, dim, keepdim=False, *, out=None) -> Tensor + + Returns the minimum value of each slice of the :attr:`input` tensor in the given + dimension(s) :attr:`dim`. + + .. note:: + The difference between ``max``/``min`` and ``amax``/``amin`` is: + - ``amax``/``amin`` supports reducing on multiple dimensions, + - ``amax``/``amin`` does not return indices, + - ``amax``/``amin`` evenly distributes gradient between equal values, + while ``max(dim)``/``min(dim)`` propagates gradient only to a single + index in the source tensor. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.6451, -0.4866, 0.2987, -1.3312], + [-0.5744, 1.2980, 1.8397, -0.2713], + [ 0.9128, 0.9214, -1.7268, -0.2995], + [ 0.9023, 0.4853, 0.9075, -1.6165]]) + >>> torch.amin(a, 1) + tensor([-1.3312, -0.5744, -1.7268, -1.6165]) + """ + ... +def aminmax(input: Tensor, *, dim: Optional[_int] = None, keepdim: _bool = False, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.aminmax: + r""" + aminmax(input, *, dim=None, keepdim=False, out=None) -> (Tensor min, Tensor max) + + Computes the minimum and maximum values of the :attr:`input` tensor. + + Args: + input (Tensor): + The input tensor + + Keyword Args: + dim (Optional[int]): + The dimension along which to compute the values. If `None`, + computes the values over the entire :attr:`input` tensor. + Default is `None`. + keepdim (bool): + If `True`, the reduced dimensions will be kept in the output + tensor as dimensions with size 1 for broadcasting, otherwise + they will be removed, as if calling (:func:`torch.squeeze`). + Default is `False`. + out (Optional[Tuple[Tensor, Tensor]]): + Optional tensors on which to write the result. Must have the same + shape and dtype as the expected output. + Default is `None`. + + Returns: + A named tuple `(min, max)` containing the minimum and maximum values. + + Raises: + RuntimeError + If any of the dimensions to compute the values over has size 0. + + .. note:: + NaN values are propagated to the output if at least one value is NaN. + + .. seealso:: + :func:`torch.amin` computes just the minimum value + :func:`torch.amax` computes just the maximum value + + Example:: + + >>> torch.aminmax(torch.tensor([1, -3, 5])) + torch.return_types.aminmax( + min=tensor(-3), + max=tensor(5)) + + >>> # aminmax propagates NaNs + >>> torch.aminmax(torch.tensor([1, -3, 5, torch.nan])) + torch.return_types.aminmax( + min=tensor(nan), + max=tensor(nan)) + + >>> t = torch.arange(10).view(2, 5) + >>> t + tensor([[0, 1, 2, 3, 4], + [5, 6, 7, 8, 9]]) + >>> t.aminmax(dim=0, keepdim=True) + torch.return_types.aminmax( + min=tensor([[0, 1, 2, 3, 4]]), + max=tensor([[5, 6, 7, 8, 9]])) + """ + ... +def angle(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + angle(input, *, out=None) -> Tensor + + Computes the element-wise angle (in radians) of the given :attr:`input` tensor. + + .. math:: + \text{out}_{i} = angle(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + .. note:: Starting in PyTorch 1.8, angle returns pi for negative real numbers, + zero for non-negative real numbers, and propagates NaNs. Previously + the function would return zero for all real numbers and not propagate + floating-point NaNs. + + Example:: + + >>> torch.angle(torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j]))*180/3.14159 + tensor([ 135., 135, -45]) + """ + ... +@overload +def any(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + any(input) -> Tensor + + Tests if any element in :attr:`input` evaluates to `True`. + + .. note:: This function matches the behaviour of NumPy in returning + output of dtype `bool` for all supported dtypes except `uint8`. + For `uint8` the dtype of output is `uint8` itself. + + Example:: + + >>> a = torch.rand(1, 2).bool() + >>> a + tensor([[False, True]], dtype=torch.bool) + >>> torch.any(a) + tensor(True, dtype=torch.bool) + >>> a = torch.arange(0, 3) + >>> a + tensor([0, 1, 2]) + >>> torch.any(a) + tensor(True) + + .. function:: any(input, dim, keepdim=False, *, out=None) -> Tensor + :noindex: + + For each row of :attr:`input` in the given dimension :attr:`dim`, + returns `True` if any element in the row evaluate to `True` and `False` otherwise. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4, 2) < 0 + >>> a + tensor([[ True, True], + [False, True], + [ True, True], + [False, False]]) + >>> torch.any(a, 1) + tensor([ True, True, True, False]) + >>> torch.any(a, 0) + tensor([True, True]) + """ + ... +@overload +def any(input: Tensor, dim: Optional[_size] = None, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + any(input) -> Tensor + + Tests if any element in :attr:`input` evaluates to `True`. + + .. note:: This function matches the behaviour of NumPy in returning + output of dtype `bool` for all supported dtypes except `uint8`. + For `uint8` the dtype of output is `uint8` itself. + + Example:: + + >>> a = torch.rand(1, 2).bool() + >>> a + tensor([[False, True]], dtype=torch.bool) + >>> torch.any(a) + tensor(True, dtype=torch.bool) + >>> a = torch.arange(0, 3) + >>> a + tensor([0, 1, 2]) + >>> torch.any(a) + tensor(True) + + .. function:: any(input, dim, keepdim=False, *, out=None) -> Tensor + :noindex: + + For each row of :attr:`input` in the given dimension :attr:`dim`, + returns `True` if any element in the row evaluate to `True` and `False` otherwise. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4, 2) < 0 + >>> a + tensor([[ True, True], + [False, True], + [ True, True], + [False, False]]) + >>> torch.any(a, 1) + tensor([ True, True, True, False]) + >>> torch.any(a, 0) + tensor([True, True]) + """ + ... +@overload +def any(input: Tensor, dim: _int, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + any(input) -> Tensor + + Tests if any element in :attr:`input` evaluates to `True`. + + .. note:: This function matches the behaviour of NumPy in returning + output of dtype `bool` for all supported dtypes except `uint8`. + For `uint8` the dtype of output is `uint8` itself. + + Example:: + + >>> a = torch.rand(1, 2).bool() + >>> a + tensor([[False, True]], dtype=torch.bool) + >>> torch.any(a) + tensor(True, dtype=torch.bool) + >>> a = torch.arange(0, 3) + >>> a + tensor([0, 1, 2]) + >>> torch.any(a) + tensor(True) + + .. function:: any(input, dim, keepdim=False, *, out=None) -> Tensor + :noindex: + + For each row of :attr:`input` in the given dimension :attr:`dim`, + returns `True` if any element in the row evaluate to `True` and `False` otherwise. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4, 2) < 0 + >>> a + tensor([[ True, True], + [False, True], + [ True, True], + [False, False]]) + >>> torch.any(a, 1) + tensor([ True, True, True, False]) + >>> torch.any(a, 0) + tensor([True, True]) + """ + ... +@overload +def any(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + any(input) -> Tensor + + Tests if any element in :attr:`input` evaluates to `True`. + + .. note:: This function matches the behaviour of NumPy in returning + output of dtype `bool` for all supported dtypes except `uint8`. + For `uint8` the dtype of output is `uint8` itself. + + Example:: + + >>> a = torch.rand(1, 2).bool() + >>> a + tensor([[False, True]], dtype=torch.bool) + >>> torch.any(a) + tensor(True, dtype=torch.bool) + >>> a = torch.arange(0, 3) + >>> a + tensor([0, 1, 2]) + >>> torch.any(a) + tensor(True) + + .. function:: any(input, dim, keepdim=False, *, out=None) -> Tensor + :noindex: + + For each row of :attr:`input` in the given dimension :attr:`dim`, + returns `True` if any element in the row evaluate to `True` and `False` otherwise. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4, 2) < 0 + >>> a + tensor([[ True, True], + [False, True], + [ True, True], + [False, False]]) + >>> torch.any(a, 1) + tensor([ True, True, True, False]) + >>> torch.any(a, 0) + tensor([True, True]) + """ + ... +@overload +def arange(start: Number, end: Number, step: Number, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + arange(start=0, end, step=1, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a 1-D tensor of size :math:`\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil` + with values from the interval ``[start, end)`` taken with common difference + :attr:`step` beginning from `start`. + + Note that non-integer :attr:`step` is subject to floating point rounding errors when + comparing against :attr:`end`; to avoid inconsistency, we advise subtracting a small epsilon from :attr:`end` + in such cases. + + .. math:: + \text{out}_{{i+1}} = \text{out}_{i} + \text{step} + + Args: + start (Number): the starting value for the set of points. Default: ``0``. + end (Number): the ending value for the set of points + step (Number): the gap between each pair of adjacent points. Default: ``1``. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). If `dtype` is not given, infer the data type from the other input + arguments. If any of `start`, `end`, or `stop` are floating-point, the + `dtype` is inferred to be the default dtype, see + :meth:`~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to + be `torch.int64`. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.arange(5) + tensor([ 0, 1, 2, 3, 4]) + >>> torch.arange(1, 4) + tensor([ 1, 2, 3]) + >>> torch.arange(1, 2.5, 0.5) + tensor([ 1.0000, 1.5000, 2.0000]) + """ + ... +@overload +def arange(start: Number, end: Number, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + arange(start=0, end, step=1, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a 1-D tensor of size :math:`\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil` + with values from the interval ``[start, end)`` taken with common difference + :attr:`step` beginning from `start`. + + Note that non-integer :attr:`step` is subject to floating point rounding errors when + comparing against :attr:`end`; to avoid inconsistency, we advise subtracting a small epsilon from :attr:`end` + in such cases. + + .. math:: + \text{out}_{{i+1}} = \text{out}_{i} + \text{step} + + Args: + start (Number): the starting value for the set of points. Default: ``0``. + end (Number): the ending value for the set of points + step (Number): the gap between each pair of adjacent points. Default: ``1``. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). If `dtype` is not given, infer the data type from the other input + arguments. If any of `start`, `end`, or `stop` are floating-point, the + `dtype` is inferred to be the default dtype, see + :meth:`~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to + be `torch.int64`. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.arange(5) + tensor([ 0, 1, 2, 3, 4]) + >>> torch.arange(1, 4) + tensor([ 1, 2, 3]) + >>> torch.arange(1, 2.5, 0.5) + tensor([ 1.0000, 1.5000, 2.0000]) + """ + ... +@overload +def arange(end: Number, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + arange(start=0, end, step=1, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a 1-D tensor of size :math:`\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil` + with values from the interval ``[start, end)`` taken with common difference + :attr:`step` beginning from `start`. + + Note that non-integer :attr:`step` is subject to floating point rounding errors when + comparing against :attr:`end`; to avoid inconsistency, we advise subtracting a small epsilon from :attr:`end` + in such cases. + + .. math:: + \text{out}_{{i+1}} = \text{out}_{i} + \text{step} + + Args: + start (Number): the starting value for the set of points. Default: ``0``. + end (Number): the ending value for the set of points + step (Number): the gap between each pair of adjacent points. Default: ``1``. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). If `dtype` is not given, infer the data type from the other input + arguments. If any of `start`, `end`, or `stop` are floating-point, the + `dtype` is inferred to be the default dtype, see + :meth:`~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to + be `torch.int64`. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.arange(5) + tensor([ 0, 1, 2, 3, 4]) + >>> torch.arange(1, 4) + tensor([ 1, 2, 3]) + >>> torch.arange(1, 2.5, 0.5) + tensor([ 1.0000, 1.5000, 2.0000]) + """ + ... +@overload +def arange(end: Union[Number, _complex], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + arange(start=0, end, step=1, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a 1-D tensor of size :math:`\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil` + with values from the interval ``[start, end)`` taken with common difference + :attr:`step` beginning from `start`. + + Note that non-integer :attr:`step` is subject to floating point rounding errors when + comparing against :attr:`end`; to avoid inconsistency, we advise subtracting a small epsilon from :attr:`end` + in such cases. + + .. math:: + \text{out}_{{i+1}} = \text{out}_{i} + \text{step} + + Args: + start (Number): the starting value for the set of points. Default: ``0``. + end (Number): the ending value for the set of points + step (Number): the gap between each pair of adjacent points. Default: ``1``. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). If `dtype` is not given, infer the data type from the other input + arguments. If any of `start`, `end`, or `stop` are floating-point, the + `dtype` is inferred to be the default dtype, see + :meth:`~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to + be `torch.int64`. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.arange(5) + tensor([ 0, 1, 2, 3, 4]) + >>> torch.arange(1, 4) + tensor([ 1, 2, 3]) + >>> torch.arange(1, 2.5, 0.5) + tensor([ 1.0000, 1.5000, 2.0000]) + """ + ... +@overload +def arange(start: Union[Number, _complex], end: Union[Number, _complex], *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + arange(start=0, end, step=1, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a 1-D tensor of size :math:`\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil` + with values from the interval ``[start, end)`` taken with common difference + :attr:`step` beginning from `start`. + + Note that non-integer :attr:`step` is subject to floating point rounding errors when + comparing against :attr:`end`; to avoid inconsistency, we advise subtracting a small epsilon from :attr:`end` + in such cases. + + .. math:: + \text{out}_{{i+1}} = \text{out}_{i} + \text{step} + + Args: + start (Number): the starting value for the set of points. Default: ``0``. + end (Number): the ending value for the set of points + step (Number): the gap between each pair of adjacent points. Default: ``1``. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). If `dtype` is not given, infer the data type from the other input + arguments. If any of `start`, `end`, or `stop` are floating-point, the + `dtype` is inferred to be the default dtype, see + :meth:`~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to + be `torch.int64`. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.arange(5) + tensor([ 0, 1, 2, 3, 4]) + >>> torch.arange(1, 4) + tensor([ 1, 2, 3]) + >>> torch.arange(1, 2.5, 0.5) + tensor([ 1.0000, 1.5000, 2.0000]) + """ + ... +@overload +def arange(start: Union[Number, _complex], end: Union[Number, _complex], step: Union[Number, _complex] = 1, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + arange(start=0, end, step=1, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a 1-D tensor of size :math:`\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil` + with values from the interval ``[start, end)`` taken with common difference + :attr:`step` beginning from `start`. + + Note that non-integer :attr:`step` is subject to floating point rounding errors when + comparing against :attr:`end`; to avoid inconsistency, we advise subtracting a small epsilon from :attr:`end` + in such cases. + + .. math:: + \text{out}_{{i+1}} = \text{out}_{i} + \text{step} + + Args: + start (Number): the starting value for the set of points. Default: ``0``. + end (Number): the ending value for the set of points + step (Number): the gap between each pair of adjacent points. Default: ``1``. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). If `dtype` is not given, infer the data type from the other input + arguments. If any of `start`, `end`, or `stop` are floating-point, the + `dtype` is inferred to be the default dtype, see + :meth:`~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to + be `torch.int64`. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.arange(5) + tensor([ 0, 1, 2, 3, 4]) + >>> torch.arange(1, 4) + tensor([ 1, 2, 3]) + >>> torch.arange(1, 2.5, 0.5) + tensor([ 1.0000, 1.5000, 2.0000]) + """ + ... +def arccos(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + arccos(input, *, out=None) -> Tensor + + Alias for :func:`torch.acos`. + """ + ... +def arccos_(input: Tensor) -> Tensor: ... +def arccosh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + arccosh(input, *, out=None) -> Tensor + + Alias for :func:`torch.acosh`. + """ + ... +def arccosh_(input: Tensor) -> Tensor: ... +def arcsin(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + arcsin(input, *, out=None) -> Tensor + + Alias for :func:`torch.asin`. + """ + ... +def arcsin_(input: Tensor) -> Tensor: ... +def arcsinh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + arcsinh(input, *, out=None) -> Tensor + + Alias for :func:`torch.asinh`. + """ + ... +def arcsinh_(input: Tensor) -> Tensor: ... +def arctan(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + arctan(input, *, out=None) -> Tensor + + Alias for :func:`torch.atan`. + """ + ... +def arctan2(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + arctan2(input, other, *, out=None) -> Tensor + Alias for :func:`torch.atan2`. + """ + ... +def arctan_(input: Tensor) -> Tensor: ... +def arctanh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + arctanh(input, *, out=None) -> Tensor + + Alias for :func:`torch.atanh`. + """ + ... +def arctanh_(input: Tensor) -> Tensor: ... +def argmax(input: Tensor, dim: Optional[_int] = None, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + argmax(input) -> LongTensor + + Returns the indices of the maximum value of all elements in the :attr:`input` tensor. + + This is the second value returned by :meth:`torch.max`. See its + documentation for the exact semantics of this method. + + .. note:: If there are multiple maximal values then the indices of the first maximal value are returned. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 1.3398, 0.2663, -0.2686, 0.2450], + [-0.7401, -0.8805, -0.3402, -1.1936], + [ 0.4907, -1.3948, -1.0691, -0.3132], + [-1.6092, 0.5419, -0.2993, 0.3195]]) + >>> torch.argmax(a) + tensor(0) + + .. function:: argmax(input, dim, keepdim=False) -> LongTensor + :noindex: + + Returns the indices of the maximum values of a tensor across a dimension. + + This is the second value returned by :meth:`torch.max`. See its + documentation for the exact semantics of this method. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. If ``None``, the argmax of the flattened input is returned. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 1.3398, 0.2663, -0.2686, 0.2450], + [-0.7401, -0.8805, -0.3402, -1.1936], + [ 0.4907, -1.3948, -1.0691, -0.3132], + [-1.6092, 0.5419, -0.2993, 0.3195]]) + >>> torch.argmax(a, dim=1) + tensor([ 0, 2, 0, 1]) + """ + ... +def argmin(input: Tensor, dim: Optional[_int] = None, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + argmin(input, dim=None, keepdim=False) -> LongTensor + + Returns the indices of the minimum value(s) of the flattened tensor or along a dimension + + This is the second value returned by :meth:`torch.min`. See its + documentation for the exact semantics of this method. + + .. note:: If there are multiple minimal values then the indices of the first minimal value are returned. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. If ``None``, the argmin of the flattened input is returned. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.1139, 0.2254, -0.1381, 0.3687], + [ 1.0100, -1.1975, -0.0102, -0.4732], + [-0.9240, 0.1207, -0.7506, -1.0213], + [ 1.7809, -1.2960, 0.9384, 0.1438]]) + >>> torch.argmin(a) + tensor(13) + >>> torch.argmin(a, dim=1) + tensor([ 2, 1, 3, 1]) + >>> torch.argmin(a, dim=1, keepdim=True) + tensor([[2], + [1], + [3], + [1]]) + """ + ... +@overload +def argsort(input: Tensor, *, stable: _bool, dim: _int = -1, descending: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + argsort(input, dim=-1, descending=False, stable=False) -> Tensor + + Returns the indices that sort a tensor along a given dimension in ascending + order by value. + + This is the second value returned by :meth:`torch.sort`. See its documentation + for the exact semantics of this method. + + If :attr:`stable` is ``True`` then the sorting routine becomes stable, preserving + the order of equivalent elements. If ``False``, the relative order of values + which compare equal is not guaranteed. ``True`` is slower. + + Args: + input (Tensor): the input tensor. + dim (int, optional): the dimension to sort along + descending (bool, optional): controls the sorting order (ascending or descending) + stable (bool, optional): controls the relative order of equivalent elements + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.0785, 1.5267, -0.8521, 0.4065], + [ 0.1598, 0.0788, -0.0745, -1.2700], + [ 1.2208, 1.0722, -0.7064, 1.2564], + [ 0.0669, -0.2318, -0.8229, -0.9280]]) + + + >>> torch.argsort(a, dim=1) + tensor([[2, 0, 3, 1], + [3, 2, 1, 0], + [2, 1, 0, 3], + [3, 2, 1, 0]]) + """ + ... +@overload +def argsort(input: Tensor, dim: _int = -1, descending: _bool = False) -> Tensor: + r""" + argsort(input, dim=-1, descending=False, stable=False) -> Tensor + + Returns the indices that sort a tensor along a given dimension in ascending + order by value. + + This is the second value returned by :meth:`torch.sort`. See its documentation + for the exact semantics of this method. + + If :attr:`stable` is ``True`` then the sorting routine becomes stable, preserving + the order of equivalent elements. If ``False``, the relative order of values + which compare equal is not guaranteed. ``True`` is slower. + + Args: + input (Tensor): the input tensor. + dim (int, optional): the dimension to sort along + descending (bool, optional): controls the sorting order (ascending or descending) + stable (bool, optional): controls the relative order of equivalent elements + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.0785, 1.5267, -0.8521, 0.4065], + [ 0.1598, 0.0788, -0.0745, -1.2700], + [ 1.2208, 1.0722, -0.7064, 1.2564], + [ 0.0669, -0.2318, -0.8229, -0.9280]]) + + + >>> torch.argsort(a, dim=1) + tensor([[2, 0, 3, 1], + [3, 2, 1, 0], + [2, 1, 0, 3], + [3, 2, 1, 0]]) + """ + ... +@overload +def argsort(input: Tensor, dim: Union[str, ellipsis, None], descending: _bool = False) -> Tensor: + r""" + argsort(input, dim=-1, descending=False, stable=False) -> Tensor + + Returns the indices that sort a tensor along a given dimension in ascending + order by value. + + This is the second value returned by :meth:`torch.sort`. See its documentation + for the exact semantics of this method. + + If :attr:`stable` is ``True`` then the sorting routine becomes stable, preserving + the order of equivalent elements. If ``False``, the relative order of values + which compare equal is not guaranteed. ``True`` is slower. + + Args: + input (Tensor): the input tensor. + dim (int, optional): the dimension to sort along + descending (bool, optional): controls the sorting order (ascending or descending) + stable (bool, optional): controls the relative order of equivalent elements + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.0785, 1.5267, -0.8521, 0.4065], + [ 0.1598, 0.0788, -0.0745, -1.2700], + [ 1.2208, 1.0722, -0.7064, 1.2564], + [ 0.0669, -0.2318, -0.8229, -0.9280]]) + + + >>> torch.argsort(a, dim=1) + tensor([[2, 0, 3, 1], + [3, 2, 1, 0], + [2, 1, 0, 3], + [3, 2, 1, 0]]) + """ + ... +def argwhere(input: Tensor) -> Tensor: + r""" + argwhere(input) -> Tensor + + Returns a tensor containing the indices of all non-zero elements of + :attr:`input`. Each row in the result contains the indices of a non-zero + element in :attr:`input`. The result is sorted lexicographically, with + the last index changing the fastest (C-style). + + If :attr:`input` has :math:`n` dimensions, then the resulting indices tensor + :attr:`out` is of size :math:`(z \times n)`, where :math:`z` is the total number of + non-zero elements in the :attr:`input` tensor. + + .. note:: + This function is similar to NumPy's `argwhere`. + + When :attr:`input` is on CUDA, this function causes host-device synchronization. + + Args: + {input} + + Example:: + + >>> t = torch.tensor([1, 0, 1]) + >>> torch.argwhere(t) + tensor([[0], + [2]]) + >>> t = torch.tensor([[1, 0, 1], [0, 1, 1]]) + >>> torch.argwhere(t) + tensor([[0, 0], + [0, 2], + [1, 1], + [1, 2]]) + """ + ... +def as_strided(input: Tensor, size: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], storage_offset: Optional[Union[_int, SymInt]] = None) -> Tensor: + r""" + as_strided(input, size, stride, storage_offset=None) -> Tensor + + Create a view of an existing `torch.Tensor` :attr:`input` with specified + :attr:`size`, :attr:`stride` and :attr:`storage_offset`. + + .. warning:: + Prefer using other view functions, like :meth:`torch.Tensor.expand`, + to setting a view's strides manually with `as_strided`, as this + function's behavior depends on the implementation of a tensor's storage. + The constructed view of the storage must only refer to elements within + the storage or a runtime error will be thrown, and if the view is + "overlapped" (with multiple indices referring to the same element in + memory) its behavior is undefined. + + Args: + input (Tensor): the input tensor. + size (tuple or ints): the shape of the output tensor + stride (tuple or ints): the stride of the output tensor + storage_offset (int, optional): the offset in the underlying storage of the output tensor. + If ``None``, the storage_offset of the output tensor will match the input tensor. + + Example:: + + >>> x = torch.randn(3, 3) + >>> x + tensor([[ 0.9039, 0.6291, 1.0795], + [ 0.1586, 2.1939, -0.4900], + [-0.1909, -0.7503, 1.9355]]) + >>> t = torch.as_strided(x, (2, 2), (1, 2)) + >>> t + tensor([[0.9039, 1.0795], + [0.6291, 0.1586]]) + >>> t = torch.as_strided(x, (2, 2), (1, 2), 1) + tensor([[0.6291, 0.1586], + [1.0795, 2.1939]]) + """ + ... +def as_strided_(input: Tensor, size: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], storage_offset: Optional[Union[_int, SymInt]] = None) -> Tensor: ... +def as_strided_copy(input: Tensor, size: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], storage_offset: Optional[Union[_int, SymInt]] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.as_strided`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def as_strided_scatter(input: Tensor, src: Tensor, size: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], storage_offset: Optional[Union[_int, SymInt]] = None) -> Tensor: + r""" + as_strided_scatter(input, src, size, stride, storage_offset=None) -> Tensor + + Embeds the values of the :attr:`src` tensor into :attr:`input` along + the elements corresponding to the result of calling + input.as_strided(size, stride, storage_offset). + + This function returns a tensor with fresh storage; it does not + return a view. + + Args: + input (Tensor): the input tensor. + size (tuple or ints): the shape of the output tensor + stride (tuple or ints): the stride of the output tensor + storage_offset (int, optional): the offset in the underlying storage of the output tensor + + .. note:: + + :attr:`src` must be of the proper size in order to be embedded + into :attr:`input`. Specifically, it should have the same shape as + `torch.as_strided(input, size, stride, storage_offset)` + + Example:: + + >>> a = torch.arange(4).reshape(2, 2) + 1 + >>> a + tensor([[1, 2], + [3, 4]]) + >>> b = torch.zeros(3, 3) + >>> b + tensor([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]) + >>> torch.as_strided_scatter(b, a, (2, 2), (1, 2)) + tensor([[1., 3., 2.], + [4., 0., 0.], + [0., 0., 0.]]) + """ + ... +def as_tensor(data: Any, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None) -> Tensor: + r""" + as_tensor(data, dtype=None, device=None) -> Tensor + + Converts :attr:`data` into a tensor, sharing data and preserving autograd + history if possible. + + If :attr:`data` is already a tensor with the requested dtype and device + then :attr:`data` itself is returned, but if :attr:`data` is a + tensor with a different dtype or device then it's copied as if using + `data.to(dtype=dtype, device=device)`. + + If :attr:`data` is a NumPy array (an ndarray) with the same dtype and device then a + tensor is constructed using :func:`torch.from_numpy`. + + If :attr:`data` is a CuPy array, the returned tensor will be located on the same device as the CuPy array unless + specifically overwritten by :attr:`device` or a default device. + + .. seealso:: + + :func:`torch.tensor` never shares its data and creates a new "leaf tensor" (see :doc:`/notes/autograd`). + + + Args: + data (array_like): Initial data for the tensor. Can be a list, tuple, + NumPy ``ndarray``, scalar, and other types. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, infers data type from :attr:`data`. + device (:class:`torch.device`, optional): the device of the constructed tensor. If None and data is a tensor + then the device of data is used. If None and data is not a tensor then + the result tensor is constructed on the current device. + + + Example:: + + >>> a = numpy.array([1, 2, 3]) + >>> t = torch.as_tensor(a) + >>> t + tensor([ 1, 2, 3]) + >>> t[0] = -1 + >>> a + array([-1, 2, 3]) + + >>> a = numpy.array([1, 2, 3]) + >>> t = torch.as_tensor(a, device=torch.device('cuda')) + >>> t + tensor([ 1, 2, 3]) + >>> t[0] = -1 + >>> a + array([1, 2, 3]) + """ + ... +def asarray(obj: Any, *, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, copy: Optional[_bool] = None, requires_grad: _bool = False) -> Tensor: + r""" + asarray(obj, *, dtype=None, device=None, copy=None, requires_grad=False) -> Tensor + + Converts :attr:`obj` to a tensor. + + :attr:`obj` can be one of: + + 1. a tensor + 2. a NumPy array or a NumPy scalar + 3. a DLPack capsule + 4. an object that implements Python's buffer protocol + 5. a scalar + 6. a sequence of scalars + + When :attr:`obj` is a tensor, NumPy array, or DLPack capsule the returned tensor will, + by default, not require a gradient, have the same datatype as :attr:`obj`, be on the + same device, and share memory with it. These properties can be controlled with the + :attr:`dtype`, :attr:`device`, :attr:`copy`, and :attr:`requires_grad` keyword arguments. + If the returned tensor is of a different datatype, on a different device, or a copy is + requested then it will not share its memory with :attr:`obj`. If :attr:`requires_grad` + is ``True`` then the returned tensor will require a gradient, and if :attr:`obj` is + also a tensor with an autograd history then the returned tensor will have the same history. + + When :attr:`obj` is not a tensor, NumPy array, or DLPack capsule but implements Python's + buffer protocol then the buffer is interpreted as an array of bytes grouped according to + the size of the datatype passed to the :attr:`dtype` keyword argument. (If no datatype is + passed then the default floating point datatype is used, instead.) The returned tensor + will have the specified datatype (or default floating point datatype if none is specified) + and, by default, be on the CPU device and share memory with the buffer. + + When :attr:`obj` is a NumPy scalar, the returned tensor will be a 0-dimensional tensor on + the CPU and that doesn't share its memory (i.e. ``copy=True``). By default datatype will + be the PyTorch datatype corresponding to the NumPy's scalar's datatype. + + When :attr:`obj` is none of the above but a scalar, or a sequence of scalars then the + returned tensor will, by default, infer its datatype from the scalar values, be on the + current default device, and not share its memory. + + .. seealso:: + + :func:`torch.tensor` creates a tensor that always copies the data from the input object. + :func:`torch.from_numpy` creates a tensor that always shares memory from NumPy arrays. + :func:`torch.frombuffer` creates a tensor that always shares memory from objects that + implement the buffer protocol. + :func:`torch.from_dlpack` creates a tensor that always shares memory from + DLPack capsules. + + Args: + obj (object): a tensor, NumPy array, DLPack Capsule, object that implements Python's + buffer protocol, scalar, or sequence of scalars. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the datatype of the returned tensor. + Default: ``None``, which causes the datatype of the returned tensor to be + inferred from :attr:`obj`. + copy (bool, optional): controls whether the returned tensor shares memory with :attr:`obj`. + Default: ``None``, which causes the returned tensor to share memory with :attr:`obj` + whenever possible. If ``True`` then the returned tensor does not share its memory. + If ``False`` then the returned tensor shares its memory with :attr:`obj` and an + error is thrown if it cannot. + device (:class:`torch.device`, optional): the device of the returned tensor. + Default: ``None``, which causes the device of :attr:`obj` to be used. Or, if + :attr:`obj` is a Python sequence, the current default device will be used. + requires_grad (bool, optional): whether the returned tensor requires grad. + Default: ``False``, which causes the returned tensor not to require a gradient. + If ``True``, then the returned tensor will require a gradient, and if :attr:`obj` + is also a tensor with an autograd history then the returned tensor will have + the same history. + + Example:: + + >>> a = torch.tensor([1, 2, 3]) + >>> # Shares memory with tensor 'a' + >>> b = torch.asarray(a) + >>> a.data_ptr() == b.data_ptr() + True + >>> # Forces memory copy + >>> c = torch.asarray(a, copy=True) + >>> a.data_ptr() == c.data_ptr() + False + + >>> a = torch.tensor([1., 2., 3.], requires_grad=True) + >>> b = a + 2 + >>> b + tensor([3., 4., 5.], grad_fn=) + >>> # Shares memory with tensor 'b', with no grad + >>> c = torch.asarray(b) + >>> c + tensor([3., 4., 5.]) + >>> # Shares memory with tensor 'b', retaining autograd history + >>> d = torch.asarray(b, requires_grad=True) + >>> d + tensor([3., 4., 5.], grad_fn=) + + >>> array = numpy.array([1, 2, 3]) + >>> # Shares memory with array 'array' + >>> t1 = torch.asarray(array) + >>> array.__array_interface__['data'][0] == t1.data_ptr() + True + >>> # Copies memory due to dtype mismatch + >>> t2 = torch.asarray(array, dtype=torch.float32) + >>> array.__array_interface__['data'][0] == t2.data_ptr() + False + + >>> scalar = numpy.float64(0.5) + >>> torch.asarray(scalar) + tensor(0.5000, dtype=torch.float64) + """ + ... +def asin(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + asin(input, *, out=None) -> Tensor + + Returns a new tensor with the arcsine of the elements of :attr:`input`. + + .. math:: + \text{out}_{i} = \sin^{-1}(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-0.5962, 1.4985, -0.4396, 1.4525]) + >>> torch.asin(a) + tensor([-0.6387, nan, -0.4552, nan]) + """ + ... +def asin_(input: Tensor) -> Tensor: ... +def asinh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + asinh(input, *, out=None) -> Tensor + + Returns a new tensor with the inverse hyperbolic sine of the elements of :attr:`input`. + + .. math:: + \text{out}_{i} = \sinh^{-1}(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword arguments: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.1606, -1.4267, -1.0899, -1.0250 ]) + >>> torch.asinh(a) + tensor([ 0.1599, -1.1534, -0.9435, -0.8990 ]) + """ + ... +def asinh_(input: Tensor) -> Tensor: ... +def atan(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + atan(input, *, out=None) -> Tensor + + Returns a new tensor with the arctangent of the elements of :attr:`input`. + + .. math:: + \text{out}_{i} = \tan^{-1}(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.2341, 0.2539, -0.6256, -0.6448]) + >>> torch.atan(a) + tensor([ 0.2299, 0.2487, -0.5591, -0.5727]) + """ + ... +def atan2(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + atan2(input, other, *, out=None) -> Tensor + + Element-wise arctangent of :math:`\text{input}_{i} / \text{other}_{i}` + with consideration of the quadrant. Returns a new tensor with the signed angles + in radians between vector :math:`(\text{other}_{i}, \text{input}_{i})` + and vector :math:`(1, 0)`. (Note that :math:`\text{other}_{i}`, the second + parameter, is the x-coordinate, while :math:`\text{input}_{i}`, the first + parameter, is the y-coordinate.) + + The shapes of ``input`` and ``other`` must be + :ref:`broadcastable `. + + Args: + input (Tensor): the first input tensor + other (Tensor): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.9041, 0.0196, -0.3108, -2.4423]) + >>> torch.atan2(a, torch.randn(4)) + tensor([ 0.9833, 0.0811, -1.9743, -1.4151]) + """ + ... +def atan_(input: Tensor) -> Tensor: ... +def atanh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + atanh(input, *, out=None) -> Tensor + + Returns a new tensor with the inverse hyperbolic tangent of the elements of :attr:`input`. + + Note: + The domain of the inverse hyperbolic tangent is `(-1, 1)` and values outside this range + will be mapped to ``NaN``, except for the values `1` and `-1` for which the output is + mapped to `+/-INF` respectively. + + .. math:: + \text{out}_{i} = \tanh^{-1}(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword arguments: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4).uniform_(-1, 1) + >>> a + tensor([ -0.9385, 0.2968, -0.8591, -0.1871 ]) + >>> torch.atanh(a) + tensor([ -1.7253, 0.3060, -1.2899, -0.1893 ]) + """ + ... +def atanh_(input: Tensor) -> Tensor: ... +def avg_pool1d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size] = (), padding: Union[_int, _size] = 0, ceil_mode: _bool = False, count_include_pad: _bool = True) -> Tensor: ... +@overload +def baddbmm(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], batch1: Tensor, batch2: Tensor) -> Tensor: + r""" + baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices in :attr:`batch1` + and :attr:`batch2`. + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the same + number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, then :attr:`input` must be + :ref:`broadcastable ` with a + :math:`(b \times n \times p)` tensor and :attr:`out` will be a + :math:`(b \times n \times p)` tensor. Both :attr:`alpha` and :attr:`beta` mean the + same as the scaling factors used in :meth:`torch.addbmm`. + + .. math:: + \text{out}_i = \beta\ \text{input}_i + \alpha\ (\text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): the tensor to be added + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{batch1} \mathbin{@} \text{batch2}` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(10, 3, 5) + >>> batch1 = torch.randn(10, 3, 4) + >>> batch2 = torch.randn(10, 4, 5) + >>> torch.baddbmm(M, batch1, batch2).size() + torch.Size([10, 3, 5]) + """ + ... +@overload +def baddbmm(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: + r""" + baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices in :attr:`batch1` + and :attr:`batch2`. + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the same + number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, then :attr:`input` must be + :ref:`broadcastable ` with a + :math:`(b \times n \times p)` tensor and :attr:`out` will be a + :math:`(b \times n \times p)` tensor. Both :attr:`alpha` and :attr:`beta` mean the + same as the scaling factors used in :meth:`torch.addbmm`. + + .. math:: + \text{out}_i = \beta\ \text{input}_i + \alpha\ (\text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): the tensor to be added + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{batch1} \mathbin{@} \text{batch2}` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(10, 3, 5) + >>> batch1 = torch.randn(10, 3, 4) + >>> batch2 = torch.randn(10, 4, 5) + >>> torch.baddbmm(M, batch1, batch2).size() + torch.Size([10, 3, 5]) + """ + ... +@overload +def baddbmm(input: Tensor, batch1: Tensor, batch2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices in :attr:`batch1` + and :attr:`batch2`. + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the same + number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, then :attr:`input` must be + :ref:`broadcastable ` with a + :math:`(b \times n \times p)` tensor and :attr:`out` will be a + :math:`(b \times n \times p)` tensor. Both :attr:`alpha` and :attr:`beta` mean the + same as the scaling factors used in :meth:`torch.addbmm`. + + .. math:: + \text{out}_i = \beta\ \text{input}_i + \alpha\ (\text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): the tensor to be added + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{batch1} \mathbin{@} \text{batch2}` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(10, 3, 5) + >>> batch1 = torch.randn(10, 3, 4) + >>> batch2 = torch.randn(10, 4, 5) + >>> torch.baddbmm(M, batch1, batch2).size() + torch.Size([10, 3, 5]) + """ + ... +@overload +def baddbmm(beta: Union[Number, _complex], self: Tensor, batch1: Tensor, batch2: Tensor) -> Tensor: + r""" + baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices in :attr:`batch1` + and :attr:`batch2`. + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the same + number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, then :attr:`input` must be + :ref:`broadcastable ` with a + :math:`(b \times n \times p)` tensor and :attr:`out` will be a + :math:`(b \times n \times p)` tensor. Both :attr:`alpha` and :attr:`beta` mean the + same as the scaling factors used in :meth:`torch.addbmm`. + + .. math:: + \text{out}_i = \beta\ \text{input}_i + \alpha\ (\text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): the tensor to be added + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{batch1} \mathbin{@} \text{batch2}` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(10, 3, 5) + >>> batch1 = torch.randn(10, 3, 4) + >>> batch2 = torch.randn(10, 4, 5) + >>> torch.baddbmm(M, batch1, batch2).size() + torch.Size([10, 3, 5]) + """ + ... +@overload +def baddbmm(beta: Union[Number, _complex], self: Tensor, batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: + r""" + baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices in :attr:`batch1` + and :attr:`batch2`. + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the same + number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, then :attr:`input` must be + :ref:`broadcastable ` with a + :math:`(b \times n \times p)` tensor and :attr:`out` will be a + :math:`(b \times n \times p)` tensor. Both :attr:`alpha` and :attr:`beta` mean the + same as the scaling factors used in :meth:`torch.addbmm`. + + .. math:: + \text{out}_i = \beta\ \text{input}_i + \alpha\ (\text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): the tensor to be added + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{batch1} \mathbin{@} \text{batch2}` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(10, 3, 5) + >>> batch1 = torch.randn(10, 3, 4) + >>> batch2 = torch.randn(10, 4, 5) + >>> torch.baddbmm(M, batch1, batch2).size() + torch.Size([10, 3, 5]) + """ + ... +@overload +def bartlett_window(window_length: _int, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + bartlett_window(window_length, periodic=True, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Bartlett window function. + + .. math:: + w[n] = 1 - \left| \frac{2n}{N-1} - 1 \right| = \begin{cases} + \frac{2n}{N - 1} & \text{if } 0 \leq n \leq \frac{N - 1}{2} \\ + 2 - \frac{2n}{N - 1} & \text{if } \frac{N - 1}{2} < n < N \\ + \end{cases}, + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window and is + ready to be used as a periodic window with functions like + :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have + ``torch.bartlett_window(L, periodic=True)`` equal to + ``torch.bartlett_window(L + 1, periodic=False)[:-1])``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported. + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window + """ + ... +@overload +def bartlett_window(window_length: _int, periodic: _bool, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + bartlett_window(window_length, periodic=True, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Bartlett window function. + + .. math:: + w[n] = 1 - \left| \frac{2n}{N-1} - 1 \right| = \begin{cases} + \frac{2n}{N - 1} & \text{if } 0 \leq n \leq \frac{N - 1}{2} \\ + 2 - \frac{2n}{N - 1} & \text{if } \frac{N - 1}{2} < n < N \\ + \end{cases}, + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window and is + ready to be used as a periodic window with functions like + :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have + ``torch.bartlett_window(L, periodic=True)`` equal to + ``torch.bartlett_window(L + 1, periodic=False)[:-1])``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported. + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window + """ + ... +def batch_norm(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], running_mean: Optional[Tensor], running_var: Optional[Tensor], training: _bool, momentum: _float, eps: _float, cudnn_enabled: _bool) -> Tensor: ... +def batch_norm_backward_elemt(grad_out: Tensor, input: Tensor, mean: Tensor, invstd: Tensor, weight: Optional[Tensor], sum_dy: Tensor, sum_dy_xmu: Tensor, count: Tensor) -> Tensor: ... +def batch_norm_backward_reduce(grad_out: Tensor, input: Tensor, mean: Tensor, invstd: Tensor, weight: Optional[Tensor], input_g: _bool, weight_g: _bool, bias_g: _bool) -> Tuple[Tensor, Tensor, Tensor, Tensor]: ... +def batch_norm_elemt(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], mean: Tensor, invstd: Tensor, eps: _float, *, out: Optional[Tensor] = None) -> Tensor: ... +def batch_norm_gather_stats(input: Tensor, mean: Tensor, invstd: Tensor, running_mean: Optional[Tensor], running_var: Optional[Tensor], momentum: _float, eps: _float, count: _int) -> Tuple[Tensor, Tensor]: ... +def batch_norm_gather_stats_with_counts(input: Tensor, mean: Tensor, invstd: Tensor, running_mean: Optional[Tensor], running_var: Optional[Tensor], momentum: _float, eps: _float, counts: Tensor) -> Tuple[Tensor, Tensor]: ... +def batch_norm_stats(input: Tensor, eps: _float) -> Tuple[Tensor, Tensor]: ... +def batch_norm_update_stats(input: Tensor, running_mean: Optional[Tensor], running_var: Optional[Tensor], momentum: _float) -> Tuple[Tensor, Tensor]: ... +@overload +def bernoulli(input: Tensor, *, generator: Optional[Generator] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + bernoulli(input, *, generator=None, out=None) -> Tensor + + Draws binary random numbers (0 or 1) from a Bernoulli distribution. + + The :attr:`input` tensor should be a tensor containing probabilities + to be used for drawing the binary random number. + Hence, all values in :attr:`input` have to be in the range: + :math:`0 \leq \text{input}_i \leq 1`. + + The :math:`\text{i}^{th}` element of the output tensor will draw a + value :math:`1` according to the :math:`\text{i}^{th}` probability value given + in :attr:`input`. + + .. math:: + \text{out}_{i} \sim \mathrm{Bernoulli}(p = \text{input}_{i}) + + The returned :attr:`out` tensor only has values 0 or 1 and is of the same + shape as :attr:`input`. + + :attr:`out` can have integral ``dtype``, but :attr:`input` must have floating + point ``dtype``. + + Args: + input (Tensor): the input tensor of probability values for the Bernoulli distribution + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.empty(3, 3).uniform_(0, 1) # generate a uniform random matrix with range [0, 1] + >>> a + tensor([[ 0.1737, 0.0950, 0.3609], + [ 0.7148, 0.0289, 0.2676], + [ 0.9456, 0.8937, 0.7202]]) + >>> torch.bernoulli(a) + tensor([[ 1., 0., 0.], + [ 0., 0., 0.], + [ 1., 1., 1.]]) + + >>> a = torch.ones(3, 3) # probability of drawing "1" is 1 + >>> torch.bernoulli(a) + tensor([[ 1., 1., 1.], + [ 1., 1., 1.], + [ 1., 1., 1.]]) + >>> a = torch.zeros(3, 3) # probability of drawing "1" is 0 + >>> torch.bernoulli(a) + tensor([[ 0., 0., 0.], + [ 0., 0., 0.], + [ 0., 0., 0.]]) + """ + ... +@overload +def bernoulli(input: Tensor, p: _float, *, generator: Optional[Generator] = None) -> Tensor: + r""" + bernoulli(input, *, generator=None, out=None) -> Tensor + + Draws binary random numbers (0 or 1) from a Bernoulli distribution. + + The :attr:`input` tensor should be a tensor containing probabilities + to be used for drawing the binary random number. + Hence, all values in :attr:`input` have to be in the range: + :math:`0 \leq \text{input}_i \leq 1`. + + The :math:`\text{i}^{th}` element of the output tensor will draw a + value :math:`1` according to the :math:`\text{i}^{th}` probability value given + in :attr:`input`. + + .. math:: + \text{out}_{i} \sim \mathrm{Bernoulli}(p = \text{input}_{i}) + + The returned :attr:`out` tensor only has values 0 or 1 and is of the same + shape as :attr:`input`. + + :attr:`out` can have integral ``dtype``, but :attr:`input` must have floating + point ``dtype``. + + Args: + input (Tensor): the input tensor of probability values for the Bernoulli distribution + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.empty(3, 3).uniform_(0, 1) # generate a uniform random matrix with range [0, 1] + >>> a + tensor([[ 0.1737, 0.0950, 0.3609], + [ 0.7148, 0.0289, 0.2676], + [ 0.9456, 0.8937, 0.7202]]) + >>> torch.bernoulli(a) + tensor([[ 1., 0., 0.], + [ 0., 0., 0.], + [ 1., 1., 1.]]) + + >>> a = torch.ones(3, 3) # probability of drawing "1" is 1 + >>> torch.bernoulli(a) + tensor([[ 1., 1., 1.], + [ 1., 1., 1.], + [ 1., 1., 1.]]) + >>> a = torch.zeros(3, 3) # probability of drawing "1" is 0 + >>> torch.bernoulli(a) + tensor([[ 0., 0., 0.], + [ 0., 0., 0.], + [ 0., 0., 0.]]) + """ + ... +def bilinear(input1: Tensor, input2: Tensor, weight: Tensor, bias: Optional[Tensor] = None) -> Tensor: ... +def binary_cross_entropy_with_logits(input: Tensor, target: Tensor, weight: Optional[Tensor] = None, pos_weight: Optional[Tensor] = None, reduction: _int = 1) -> Tensor: ... +def bincount(input: Tensor, weights: Optional[Tensor] = None, minlength: _int = 0) -> Tensor: + r""" + bincount(input, weights=None, minlength=0) -> Tensor + + Count the frequency of each value in an array of non-negative ints. + + The number of bins (size 1) is one larger than the largest value in + :attr:`input` unless :attr:`input` is empty, in which case the result is a + tensor of size 0. If :attr:`minlength` is specified, the number of bins is at least + :attr:`minlength` and if :attr:`input` is empty, then the result is tensor of size + :attr:`minlength` filled with zeros. If ``n`` is the value at position ``i``, + ``out[n] += weights[i]`` if :attr:`weights` is specified else + ``out[n] += 1``. + + Note: + This operation may produce nondeterministic gradients when given tensors on a CUDA device. See :doc:`/notes/randomness` for more information. + + Arguments: + input (Tensor): 1-d int tensor + weights (Tensor): optional, weight for each value in the input tensor. + Should be of same size as input tensor. + minlength (int): optional, minimum number of bins. Should be non-negative. + + Returns: + output (Tensor): a tensor of shape ``Size([max(input) + 1])`` if + :attr:`input` is non-empty, else ``Size(0)`` + + Example:: + + >>> input = torch.randint(0, 8, (5,), dtype=torch.int64) + >>> weights = torch.linspace(0, 1, steps=5) + >>> input, weights + (tensor([4, 3, 6, 3, 4]), + tensor([ 0.0000, 0.2500, 0.5000, 0.7500, 1.0000]) + + >>> torch.bincount(input) + tensor([0, 0, 0, 2, 2, 0, 1]) + + >>> input.bincount(weights) + tensor([0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 0.0000, 0.5000]) + """ + ... +def binomial(count: Tensor, prob: Tensor, generator: Optional[Generator] = None) -> Tensor: ... +@overload +def bitwise_and(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_and(input, other, *, out=None) -> Tensor + + Computes the bitwise AND of :attr:`input` and :attr:`other`. The input tensor must be of + integral or Boolean types. For bool tensors, it computes the logical AND. + + Args: + input: the first input tensor + other: the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_and(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([1, 0, 3], dtype=torch.int8) + >>> torch.bitwise_and(torch.tensor([True, True, False]), torch.tensor([False, True, False])) + tensor([ False, True, False]) + """ + ... +@overload +def bitwise_and(self: Union[Number, _complex], other: Tensor) -> Tensor: + r""" + bitwise_and(input, other, *, out=None) -> Tensor + + Computes the bitwise AND of :attr:`input` and :attr:`other`. The input tensor must be of + integral or Boolean types. For bool tensors, it computes the logical AND. + + Args: + input: the first input tensor + other: the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_and(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([1, 0, 3], dtype=torch.int8) + >>> torch.bitwise_and(torch.tensor([True, True, False]), torch.tensor([False, True, False])) + tensor([ False, True, False]) + """ + ... +@overload +def bitwise_and(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_and(input, other, *, out=None) -> Tensor + + Computes the bitwise AND of :attr:`input` and :attr:`other`. The input tensor must be of + integral or Boolean types. For bool tensors, it computes the logical AND. + + Args: + input: the first input tensor + other: the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_and(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([1, 0, 3], dtype=torch.int8) + >>> torch.bitwise_and(torch.tensor([True, True, False]), torch.tensor([False, True, False])) + tensor([ False, True, False]) + """ + ... +@overload +def bitwise_left_shift(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_left_shift(input, other, *, out=None) -> Tensor + + Computes the left arithmetic shift of :attr:`input` by :attr:`other` bits. + The input tensor must be of integral type. This operator supports + :ref:`broadcasting to a common shape ` and + :ref:`type promotion `. + + The operation applied is: + + .. math:: + \text{out}_i = \text{input}_i << \text{other}_i + + Args: + input (Tensor or Scalar): the first input tensor + other (Tensor or Scalar): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_left_shift(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-2, -2, 24], dtype=torch.int8) + """ + ... +@overload +def bitwise_left_shift(self: Union[Number, _complex], other: Tensor) -> Tensor: + r""" + bitwise_left_shift(input, other, *, out=None) -> Tensor + + Computes the left arithmetic shift of :attr:`input` by :attr:`other` bits. + The input tensor must be of integral type. This operator supports + :ref:`broadcasting to a common shape ` and + :ref:`type promotion `. + + The operation applied is: + + .. math:: + \text{out}_i = \text{input}_i << \text{other}_i + + Args: + input (Tensor or Scalar): the first input tensor + other (Tensor or Scalar): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_left_shift(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-2, -2, 24], dtype=torch.int8) + """ + ... +@overload +def bitwise_left_shift(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_left_shift(input, other, *, out=None) -> Tensor + + Computes the left arithmetic shift of :attr:`input` by :attr:`other` bits. + The input tensor must be of integral type. This operator supports + :ref:`broadcasting to a common shape ` and + :ref:`type promotion `. + + The operation applied is: + + .. math:: + \text{out}_i = \text{input}_i << \text{other}_i + + Args: + input (Tensor or Scalar): the first input tensor + other (Tensor or Scalar): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_left_shift(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-2, -2, 24], dtype=torch.int8) + """ + ... +def bitwise_not(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_not(input, *, out=None) -> Tensor + + Computes the bitwise NOT of the given input tensor. The input tensor must be of + integral or Boolean types. For bool tensors, it computes the logical NOT. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_not(torch.tensor([-1, -2, 3], dtype=torch.int8)) + tensor([ 0, 1, -4], dtype=torch.int8) + """ + ... +@overload +def bitwise_or(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_or(input, other, *, out=None) -> Tensor + + Computes the bitwise OR of :attr:`input` and :attr:`other`. The input tensor must be of + integral or Boolean types. For bool tensors, it computes the logical OR. + + Args: + input: the first input tensor + other: the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_or(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-1, -2, 3], dtype=torch.int8) + >>> torch.bitwise_or(torch.tensor([True, True, False]), torch.tensor([False, True, False])) + tensor([ True, True, False]) + """ + ... +@overload +def bitwise_or(self: Union[Number, _complex], other: Tensor) -> Tensor: + r""" + bitwise_or(input, other, *, out=None) -> Tensor + + Computes the bitwise OR of :attr:`input` and :attr:`other`. The input tensor must be of + integral or Boolean types. For bool tensors, it computes the logical OR. + + Args: + input: the first input tensor + other: the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_or(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-1, -2, 3], dtype=torch.int8) + >>> torch.bitwise_or(torch.tensor([True, True, False]), torch.tensor([False, True, False])) + tensor([ True, True, False]) + """ + ... +@overload +def bitwise_or(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_or(input, other, *, out=None) -> Tensor + + Computes the bitwise OR of :attr:`input` and :attr:`other`. The input tensor must be of + integral or Boolean types. For bool tensors, it computes the logical OR. + + Args: + input: the first input tensor + other: the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_or(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-1, -2, 3], dtype=torch.int8) + >>> torch.bitwise_or(torch.tensor([True, True, False]), torch.tensor([False, True, False])) + tensor([ True, True, False]) + """ + ... +@overload +def bitwise_right_shift(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_right_shift(input, other, *, out=None) -> Tensor + + Computes the right arithmetic shift of :attr:`input` by :attr:`other` bits. + The input tensor must be of integral type. This operator supports + :ref:`broadcasting to a common shape ` and + :ref:`type promotion `. + In any case, if the value of the right operand is negative or is greater + or equal to the number of bits in the promoted left operand, the behavior is undefined. + + The operation applied is: + + .. math:: + \text{out}_i = \text{input}_i >> \text{other}_i + + Args: + input (Tensor or Scalar): the first input tensor + other (Tensor or Scalar): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_right_shift(torch.tensor([-2, -7, 31], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-1, -7, 3], dtype=torch.int8) + """ + ... +@overload +def bitwise_right_shift(self: Union[Number, _complex], other: Tensor) -> Tensor: + r""" + bitwise_right_shift(input, other, *, out=None) -> Tensor + + Computes the right arithmetic shift of :attr:`input` by :attr:`other` bits. + The input tensor must be of integral type. This operator supports + :ref:`broadcasting to a common shape ` and + :ref:`type promotion `. + In any case, if the value of the right operand is negative or is greater + or equal to the number of bits in the promoted left operand, the behavior is undefined. + + The operation applied is: + + .. math:: + \text{out}_i = \text{input}_i >> \text{other}_i + + Args: + input (Tensor or Scalar): the first input tensor + other (Tensor or Scalar): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_right_shift(torch.tensor([-2, -7, 31], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-1, -7, 3], dtype=torch.int8) + """ + ... +@overload +def bitwise_right_shift(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_right_shift(input, other, *, out=None) -> Tensor + + Computes the right arithmetic shift of :attr:`input` by :attr:`other` bits. + The input tensor must be of integral type. This operator supports + :ref:`broadcasting to a common shape ` and + :ref:`type promotion `. + In any case, if the value of the right operand is negative or is greater + or equal to the number of bits in the promoted left operand, the behavior is undefined. + + The operation applied is: + + .. math:: + \text{out}_i = \text{input}_i >> \text{other}_i + + Args: + input (Tensor or Scalar): the first input tensor + other (Tensor or Scalar): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_right_shift(torch.tensor([-2, -7, 31], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-1, -7, 3], dtype=torch.int8) + """ + ... +@overload +def bitwise_xor(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_xor(input, other, *, out=None) -> Tensor + + Computes the bitwise XOR of :attr:`input` and :attr:`other`. The input tensor must be of + integral or Boolean types. For bool tensors, it computes the logical XOR. + + Args: + input: the first input tensor + other: the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_xor(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-2, -2, 0], dtype=torch.int8) + >>> torch.bitwise_xor(torch.tensor([True, True, False]), torch.tensor([False, True, False])) + tensor([ True, False, False]) + """ + ... +@overload +def bitwise_xor(self: Union[Number, _complex], other: Tensor) -> Tensor: + r""" + bitwise_xor(input, other, *, out=None) -> Tensor + + Computes the bitwise XOR of :attr:`input` and :attr:`other`. The input tensor must be of + integral or Boolean types. For bool tensors, it computes the logical XOR. + + Args: + input: the first input tensor + other: the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_xor(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-2, -2, 0], dtype=torch.int8) + >>> torch.bitwise_xor(torch.tensor([True, True, False]), torch.tensor([False, True, False])) + tensor([ True, False, False]) + """ + ... +@overload +def bitwise_xor(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_xor(input, other, *, out=None) -> Tensor + + Computes the bitwise XOR of :attr:`input` and :attr:`other`. The input tensor must be of + integral or Boolean types. For bool tensors, it computes the logical XOR. + + Args: + input: the first input tensor + other: the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_xor(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-2, -2, 0], dtype=torch.int8) + >>> torch.bitwise_xor(torch.tensor([True, True, False]), torch.tensor([False, True, False])) + tensor([ True, False, False]) + """ + ... +@overload +def blackman_window(window_length: _int, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + blackman_window(window_length, periodic=True, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Blackman window function. + + .. math:: + w[n] = 0.42 - 0.5 \cos \left( \frac{2 \pi n}{N - 1} \right) + 0.08 \cos \left( \frac{4 \pi n}{N - 1} \right) + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window and is + ready to be used as a periodic window with functions like + :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have + ``torch.blackman_window(L, periodic=True)`` equal to + ``torch.blackman_window(L + 1, periodic=False)[:-1])``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported. + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window + """ + ... +@overload +def blackman_window(window_length: _int, periodic: _bool, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + blackman_window(window_length, periodic=True, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Blackman window function. + + .. math:: + w[n] = 0.42 - 0.5 \cos \left( \frac{2 \pi n}{N - 1} \right) + 0.08 \cos \left( \frac{4 \pi n}{N - 1} \right) + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window and is + ready to be used as a periodic window with functions like + :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have + ``torch.blackman_window(L, periodic=True)`` equal to + ``torch.blackman_window(L + 1, periodic=False)[:-1])``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported. + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window + """ + ... +def bmm(input: Tensor, mat2: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + bmm(input, mat2, *, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices stored in :attr:`input` + and :attr:`mat2`. + + :attr:`input` and :attr:`mat2` must be 3-D tensors each containing + the same number of matrices. + + If :attr:`input` is a :math:`(b \times n \times m)` tensor, :attr:`mat2` is a + :math:`(b \times m \times p)` tensor, :attr:`out` will be a + :math:`(b \times n \times p)` tensor. + + .. math:: + \text{out}_i = \text{input}_i \mathbin{@} \text{mat2}_i + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + .. note:: This function does not :ref:`broadcast `. + For broadcasting matrix products, see :func:`torch.matmul`. + + Args: + input (Tensor): the first batch of matrices to be multiplied + mat2 (Tensor): the second batch of matrices to be multiplied + + Keyword Args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> input = torch.randn(10, 3, 4) + >>> mat2 = torch.randn(10, 4, 5) + >>> res = torch.bmm(input, mat2) + >>> res.size() + torch.Size([10, 3, 5]) + """ + ... +def broadcast_to(input: Tensor, size: Sequence[Union[_int, SymInt]]) -> Tensor: + r""" + broadcast_to(input, shape) -> Tensor + + Broadcasts :attr:`input` to the shape :attr:`\shape`. + Equivalent to calling ``input.expand(shape)``. See :meth:`~Tensor.expand` for details. + + Args: + input (Tensor): the input tensor. + shape (list, tuple, or :class:`torch.Size`): the new shape. + + Example:: + + >>> x = torch.tensor([1, 2, 3]) + >>> torch.broadcast_to(x, (3, 3)) + tensor([[1, 2, 3], + [1, 2, 3], + [1, 2, 3]]) + """ + ... +@overload +def bucketize(input: Tensor, boundaries: Tensor, *, out_int32: _bool = False, right: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + bucketize(input, boundaries, *, out_int32=False, right=False, out=None) -> Tensor + + Returns the indices of the buckets to which each value in the :attr:`input` belongs, where the + boundaries of the buckets are set by :attr:`boundaries`. Return a new tensor with the same size + as :attr:`input`. If :attr:`right` is False (default), then the left boundary is open. Note that + this behavior is opposite the behavior of + `numpy.digitize `_. + More formally, the returned index satisfies the following rules: + + .. list-table:: + :widths: 15 85 + :header-rows: 1 + + * - :attr:`right` + - *returned index satisfies* + * - False + - ``boundaries[i-1] < input[m][n]...[l][x] <= boundaries[i]`` + * - True + - ``boundaries[i-1] <= input[m][n]...[l][x] < boundaries[i]`` + + Args: + input (Tensor or Scalar): N-D tensor or a Scalar containing the search value(s). + boundaries (Tensor): 1-D tensor, must contain a strictly increasing sequence, or the return value is undefined. + + Keyword args: + out_int32 (bool, optional): indicate the output data type. torch.int32 if True, torch.int64 otherwise. + Default value is False, i.e. default output data type is torch.int64. + right (bool, optional): if False, return the first suitable location that is found. If True, return the + last such index. If no suitable index found, return 0 for non-numerical value + (eg. nan, inf) or the size of :attr:`boundaries` (one pass the last index). + In other words, if False, gets the lower bound index for each value in :attr:`input` + from :attr:`boundaries`. If True, gets the upper bound index instead. + Default value is False. + out (Tensor, optional): the output tensor, must be the same size as :attr:`input` if provided. + + + Example:: + + >>> boundaries = torch.tensor([1, 3, 5, 7, 9]) + >>> boundaries + tensor([1, 3, 5, 7, 9]) + >>> v = torch.tensor([[3, 6, 9], [3, 6, 9]]) + >>> v + tensor([[3, 6, 9], + [3, 6, 9]]) + >>> torch.bucketize(v, boundaries) + tensor([[1, 3, 4], + [1, 3, 4]]) + >>> torch.bucketize(v, boundaries, right=True) + tensor([[2, 3, 5], + [2, 3, 5]]) + """ + ... +@overload +def bucketize(self: Union[Number, _complex], boundaries: Tensor, *, out_int32: _bool = False, right: _bool = False) -> Tensor: + r""" + bucketize(input, boundaries, *, out_int32=False, right=False, out=None) -> Tensor + + Returns the indices of the buckets to which each value in the :attr:`input` belongs, where the + boundaries of the buckets are set by :attr:`boundaries`. Return a new tensor with the same size + as :attr:`input`. If :attr:`right` is False (default), then the left boundary is open. Note that + this behavior is opposite the behavior of + `numpy.digitize `_. + More formally, the returned index satisfies the following rules: + + .. list-table:: + :widths: 15 85 + :header-rows: 1 + + * - :attr:`right` + - *returned index satisfies* + * - False + - ``boundaries[i-1] < input[m][n]...[l][x] <= boundaries[i]`` + * - True + - ``boundaries[i-1] <= input[m][n]...[l][x] < boundaries[i]`` + + Args: + input (Tensor or Scalar): N-D tensor or a Scalar containing the search value(s). + boundaries (Tensor): 1-D tensor, must contain a strictly increasing sequence, or the return value is undefined. + + Keyword args: + out_int32 (bool, optional): indicate the output data type. torch.int32 if True, torch.int64 otherwise. + Default value is False, i.e. default output data type is torch.int64. + right (bool, optional): if False, return the first suitable location that is found. If True, return the + last such index. If no suitable index found, return 0 for non-numerical value + (eg. nan, inf) or the size of :attr:`boundaries` (one pass the last index). + In other words, if False, gets the lower bound index for each value in :attr:`input` + from :attr:`boundaries`. If True, gets the upper bound index instead. + Default value is False. + out (Tensor, optional): the output tensor, must be the same size as :attr:`input` if provided. + + + Example:: + + >>> boundaries = torch.tensor([1, 3, 5, 7, 9]) + >>> boundaries + tensor([1, 3, 5, 7, 9]) + >>> v = torch.tensor([[3, 6, 9], [3, 6, 9]]) + >>> v + tensor([[3, 6, 9], + [3, 6, 9]]) + >>> torch.bucketize(v, boundaries) + tensor([[1, 3, 4], + [1, 3, 4]]) + >>> torch.bucketize(v, boundaries, right=True) + tensor([[2, 3, 5], + [2, 3, 5]]) + """ + ... +def can_cast(from_: _dtype, to: _dtype) -> _bool: + r""" + can_cast(from_, to) -> bool + + Determines if a type conversion is allowed under PyTorch casting rules + described in the type promotion :ref:`documentation `. + + Args: + from\_ (dtype): The original :class:`torch.dtype`. + to (dtype): The target :class:`torch.dtype`. + + Example:: + + >>> torch.can_cast(torch.double, torch.float) + True + >>> torch.can_cast(torch.float, torch.int) + False + """ + ... +@overload +def cat(tensors: Union[Tuple[Tensor, ...], List[Tensor]], dim: _int = 0, *, out: Optional[Tensor] = None) -> Tensor: + r""" + cat(tensors, dim=0, *, out=None) -> Tensor + + Concatenates the given sequence of :attr:`seq` tensors in the given dimension. + All tensors must either have the same shape (except in the concatenating + dimension) or be a 1-D empty tensor with size ``(0,)``. + + :func:`torch.cat` can be seen as an inverse operation for :func:`torch.split` + and :func:`torch.chunk`. + + :func:`torch.cat` can be best understood via examples. + + .. seealso:: + + :func:`torch.stack` concatenates the given sequence along a new dimension. + + Args: + tensors (sequence of Tensors): any python sequence of tensors of the same type. + Non-empty tensors provided must have the same shape, except in the + cat dimension. + dim (int, optional): the dimension over which the tensors are concatenated + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> x = torch.randn(2, 3) + >>> x + tensor([[ 0.6580, -1.0969, -0.4614], + [-0.1034, -0.5790, 0.1497]]) + >>> torch.cat((x, x, x), 0) + tensor([[ 0.6580, -1.0969, -0.4614], + [-0.1034, -0.5790, 0.1497], + [ 0.6580, -1.0969, -0.4614], + [-0.1034, -0.5790, 0.1497], + [ 0.6580, -1.0969, -0.4614], + [-0.1034, -0.5790, 0.1497]]) + >>> torch.cat((x, x, x), 1) + tensor([[ 0.6580, -1.0969, -0.4614, 0.6580, -1.0969, -0.4614, 0.6580, + -1.0969, -0.4614], + [-0.1034, -0.5790, 0.1497, -0.1034, -0.5790, 0.1497, -0.1034, + -0.5790, 0.1497]]) + """ + ... +@overload +def cat(tensors: Union[Tuple[Tensor, ...], List[Tensor]], dim: Union[str, ellipsis, None], *, out: Optional[Tensor] = None) -> Tensor: + r""" + cat(tensors, dim=0, *, out=None) -> Tensor + + Concatenates the given sequence of :attr:`seq` tensors in the given dimension. + All tensors must either have the same shape (except in the concatenating + dimension) or be a 1-D empty tensor with size ``(0,)``. + + :func:`torch.cat` can be seen as an inverse operation for :func:`torch.split` + and :func:`torch.chunk`. + + :func:`torch.cat` can be best understood via examples. + + .. seealso:: + + :func:`torch.stack` concatenates the given sequence along a new dimension. + + Args: + tensors (sequence of Tensors): any python sequence of tensors of the same type. + Non-empty tensors provided must have the same shape, except in the + cat dimension. + dim (int, optional): the dimension over which the tensors are concatenated + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> x = torch.randn(2, 3) + >>> x + tensor([[ 0.6580, -1.0969, -0.4614], + [-0.1034, -0.5790, 0.1497]]) + >>> torch.cat((x, x, x), 0) + tensor([[ 0.6580, -1.0969, -0.4614], + [-0.1034, -0.5790, 0.1497], + [ 0.6580, -1.0969, -0.4614], + [-0.1034, -0.5790, 0.1497], + [ 0.6580, -1.0969, -0.4614], + [-0.1034, -0.5790, 0.1497]]) + >>> torch.cat((x, x, x), 1) + tensor([[ 0.6580, -1.0969, -0.4614, 0.6580, -1.0969, -0.4614, 0.6580, + -1.0969, -0.4614], + [-0.1034, -0.5790, 0.1497, -0.1034, -0.5790, 0.1497, -0.1034, + -0.5790, 0.1497]]) + """ + ... +def ccol_indices_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def ceil(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + ceil(input, *, out=None) -> Tensor + + Returns a new tensor with the ceil of the elements of :attr:`input`, + the smallest integer greater than or equal to each element. + + For integer inputs, follows the array-api convention of returning a + copy of the input tensor. + + .. math:: + \text{out}_{i} = \left\lceil \text{input}_{i} \right\rceil + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-0.6341, -1.4208, -1.0900, 0.5826]) + >>> torch.ceil(a) + tensor([-0., -1., -1., 1.]) + """ + ... +def ceil_(input: Tensor) -> Tensor: ... +def celu(input: Tensor, alpha: Union[Number, _complex] = 1.0) -> Tensor: ... +def celu_(input: Tensor, alpha: Union[Number, _complex] = 1.0) -> Tensor: ... +def channel_shuffle(input: Tensor, groups: Union[_int, SymInt]) -> Tensor: ... +def cholesky(input: Tensor, upper: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + cholesky(input, upper=False, *, out=None) -> Tensor + + Computes the Cholesky decomposition of a symmetric positive-definite + matrix :math:`A` or for batches of symmetric positive-definite matrices. + + If :attr:`upper` is ``True``, the returned matrix ``U`` is upper-triangular, and + the decomposition has the form: + + .. math:: + + A = U^TU + + If :attr:`upper` is ``False``, the returned matrix ``L`` is lower-triangular, and + the decomposition has the form: + + .. math:: + + A = LL^T + + If :attr:`upper` is ``True``, and :math:`A` is a batch of symmetric positive-definite + matrices, then the returned tensor will be composed of upper-triangular Cholesky factors + of each of the individual matrices. Similarly, when :attr:`upper` is ``False``, the returned + tensor will be composed of lower-triangular Cholesky factors of each of the individual + matrices. + + .. warning:: + + :func:`torch.cholesky` is deprecated in favor of :func:`torch.linalg.cholesky` + and will be removed in a future PyTorch release. + + ``L = torch.cholesky(A)`` should be replaced with + + .. code:: python + + L = torch.linalg.cholesky(A) + + ``U = torch.cholesky(A, upper=True)`` should be replaced with + + .. code:: python + + U = torch.linalg.cholesky(A).mH + + This transform will produce equivalent results for all valid (symmetric positive definite) inputs. + + Args: + input (Tensor): the input tensor :math:`A` of size :math:`(*, n, n)` where `*` is zero or more + batch dimensions consisting of symmetric positive-definite matrices. + upper (bool, optional): flag that indicates whether to return a + upper or lower triangular matrix. Default: ``False`` + + Keyword args: + out (Tensor, optional): the output matrix + + Example:: + + >>> a = torch.randn(3, 3) + >>> a = a @ a.mT + 1e-3 # make symmetric positive-definite + >>> l = torch.cholesky(a) + >>> a + tensor([[ 2.4112, -0.7486, 1.4551], + [-0.7486, 1.3544, 0.1294], + [ 1.4551, 0.1294, 1.6724]]) + >>> l + tensor([[ 1.5528, 0.0000, 0.0000], + [-0.4821, 1.0592, 0.0000], + [ 0.9371, 0.5487, 0.7023]]) + >>> l @ l.mT + tensor([[ 2.4112, -0.7486, 1.4551], + [-0.7486, 1.3544, 0.1294], + [ 1.4551, 0.1294, 1.6724]]) + >>> a = torch.randn(3, 2, 2) # Example for batched input + >>> a = a @ a.mT + 1e-03 # make symmetric positive-definite + >>> l = torch.cholesky(a) + >>> z = l @ l.mT + >>> torch.dist(z, a) + tensor(2.3842e-07) + """ + ... +def cholesky_inverse(input: Tensor, upper: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + cholesky_inverse(L, upper=False, *, out=None) -> Tensor + + Computes the inverse of a complex Hermitian or real symmetric + positive-definite matrix given its Cholesky decomposition. + + Let :math:`A` be a complex Hermitian or real symmetric positive-definite matrix, + and :math:`L` its Cholesky decomposition such that: + + .. math:: + + A = LL^{\text{H}} + + where :math:`L^{\text{H}}` is the conjugate transpose when :math:`L` is complex, + and the transpose when :math:`L` is real-valued. + + Computes the inverse matrix :math:`A^{-1}`. + + Supports input of float, double, cfloat and cdouble dtypes. + Also supports batches of matrices, and if :math:`A` is a batch of matrices + then the output has the same batch dimensions. + + Args: + L (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions + consisting of lower or upper triangular Cholesky decompositions of + symmetric or Hermitian positive-definite matrices. + upper (bool, optional): flag that indicates whether :math:`L` is lower triangular + or upper triangular. Default: ``False`` + + Keyword args: + out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`. + + Example:: + + >>> A = torch.randn(3, 3) + >>> A = A @ A.T + torch.eye(3) * 1e-3 # Creates a symmetric positive-definite matrix + >>> L = torch.linalg.cholesky(A) # Extract Cholesky decomposition + >>> torch.cholesky_inverse(L) + tensor([[ 1.9314, 1.2251, -0.0889], + [ 1.2251, 2.4439, 0.2122], + [-0.0889, 0.2122, 0.1412]]) + >>> A.inverse() + tensor([[ 1.9314, 1.2251, -0.0889], + [ 1.2251, 2.4439, 0.2122], + [-0.0889, 0.2122, 0.1412]]) + + >>> A = torch.randn(3, 2, 2, dtype=torch.complex64) + >>> A = A @ A.mH + torch.eye(2) * 1e-3 # Batch of Hermitian positive-definite matrices + >>> L = torch.linalg.cholesky(A) + >>> torch.dist(torch.inverse(A), torch.cholesky_inverse(L)) + tensor(5.6358e-7) + """ + ... +def cholesky_solve(input: Tensor, input2: Tensor, upper: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + cholesky_solve(B, L, upper=False, *, out=None) -> Tensor + + Computes the solution of a system of linear equations with complex Hermitian + or real symmetric positive-definite lhs given its Cholesky decomposition. + + Let :math:`A` be a complex Hermitian or real symmetric positive-definite matrix, + and :math:`L` its Cholesky decomposition such that: + + .. math:: + + A = LL^{\text{H}} + + where :math:`L^{\text{H}}` is the conjugate transpose when :math:`L` is complex, + and the transpose when :math:`L` is real-valued. + + Returns the solution :math:`X` of the following linear system: + + .. math:: + + AX = B + + Supports inputs of float, double, cfloat and cdouble dtypes. + Also supports batches of matrices, and if :math:`A` or :math:`B` is a batch of matrices + then the output has the same batch dimensions. + + Args: + B (Tensor): right-hand side tensor of shape `(*, n, k)` + where :math:`*` is zero or more batch dimensions + L (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions + consisting of lower or upper triangular Cholesky decompositions of + symmetric or Hermitian positive-definite matrices. + upper (bool, optional): flag that indicates whether :math:`L` is lower triangular + or upper triangular. Default: ``False``. + + Keyword args: + out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`. + + Example:: + + >>> A = torch.randn(3, 3) + >>> A = A @ A.T + torch.eye(3) * 1e-3 # Creates a symmetric positive-definite matrix + >>> L = torch.linalg.cholesky(A) # Extract Cholesky decomposition + >>> B = torch.randn(3, 2) + >>> torch.cholesky_solve(B, L) + tensor([[ -8.1625, 19.6097], + [ -5.8398, 14.2387], + [ -4.3771, 10.4173]]) + >>> A.inverse() @ B + tensor([[ -8.1626, 19.6097], + [ -5.8398, 14.2387], + [ -4.3771, 10.4173]]) + + >>> A = torch.randn(3, 2, 2, dtype=torch.complex64) + >>> A = A @ A.mH + torch.eye(2) * 1e-3 # Batch of Hermitian positive-definite matrices + >>> L = torch.linalg.cholesky(A) + >>> B = torch.randn(2, 1, dtype=torch.complex64) + >>> X = torch.cholesky_solve(B, L) + >>> torch.dist(X, A.inverse() @ B) + tensor(1.6881e-5) + """ + ... +def choose_qparams_optimized(input: Tensor, numel: _int, n_bins: _int, ratio: _float, bit_width: _int) -> Tuple[Tensor, Tensor]: ... +def chunk(input: Tensor, chunks: _int, dim: _int = 0) -> Tuple[Tensor, ...]: + r""" + chunk(input, chunks, dim=0) -> List of Tensors + + Attempts to split a tensor into the specified number of chunks. Each chunk is a view of + the input tensor. + + + .. note:: + + This function may return fewer than the specified number of chunks! + + .. seealso:: + + :func:`torch.tensor_split` a function that always returns exactly the specified number of chunks + + If the tensor size along the given dimension :attr:`dim` is divisible by :attr:`chunks`, + all returned chunks will be the same size. + If the tensor size along the given dimension :attr:`dim` is not divisible by :attr:`chunks`, + all returned chunks will be the same size, except the last one. + If such division is not possible, this function may return fewer + than the specified number of chunks. + + Arguments: + input (Tensor): the tensor to split + chunks (int): number of chunks to return + dim (int): dimension along which to split the tensor + + Example: + >>> torch.arange(11).chunk(6) + (tensor([0, 1]), + tensor([2, 3]), + tensor([4, 5]), + tensor([6, 7]), + tensor([8, 9]), + tensor([10])) + >>> torch.arange(12).chunk(6) + (tensor([0, 1]), + tensor([2, 3]), + tensor([4, 5]), + tensor([6, 7]), + tensor([8, 9]), + tensor([10, 11])) + >>> torch.arange(13).chunk(6) + (tensor([0, 1, 2]), + tensor([3, 4, 5]), + tensor([6, 7, 8]), + tensor([ 9, 10, 11]), + tensor([12])) + """ + ... +@overload +def clamp(input: Tensor, min: Optional[Tensor] = None, max: Optional[Tensor] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + clamp(input, min=None, max=None, *, out=None) -> Tensor + + Clamps all elements in :attr:`input` into the range `[` :attr:`min`, :attr:`max` `]`. + Letting min_value and max_value be :attr:`min` and :attr:`max`, respectively, this returns: + + .. math:: + y_i = \min(\max(x_i, \text{min\_value}_i), \text{max\_value}_i) + + If :attr:`min` is ``None``, there is no lower bound. + Or, if :attr:`max` is ``None`` there is no upper bound. + + + .. note:: + If :attr:`min` is greater than :attr:`max` :func:`torch.clamp(..., min, max) ` + sets all elements in :attr:`input` to the value of :attr:`max`. + + Args: + input (Tensor): the input tensor. + min (Number or Tensor, optional): lower-bound of the range to be clamped to + max (Number or Tensor, optional): upper-bound of the range to be clamped to + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-1.7120, 0.1734, -0.0478, -0.0922]) + >>> torch.clamp(a, min=-0.5, max=0.5) + tensor([-0.5000, 0.1734, -0.0478, -0.0922]) + + >>> min = torch.linspace(-1, 1, steps=4) + >>> torch.clamp(a, min=min) + tensor([-1.0000, 0.1734, 0.3333, 1.0000]) + """ + ... +@overload +def clamp(input: Tensor, min: Optional[Union[Number, _complex]] = None, max: Optional[Union[Number, _complex]] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + clamp(input, min=None, max=None, *, out=None) -> Tensor + + Clamps all elements in :attr:`input` into the range `[` :attr:`min`, :attr:`max` `]`. + Letting min_value and max_value be :attr:`min` and :attr:`max`, respectively, this returns: + + .. math:: + y_i = \min(\max(x_i, \text{min\_value}_i), \text{max\_value}_i) + + If :attr:`min` is ``None``, there is no lower bound. + Or, if :attr:`max` is ``None`` there is no upper bound. + + + .. note:: + If :attr:`min` is greater than :attr:`max` :func:`torch.clamp(..., min, max) ` + sets all elements in :attr:`input` to the value of :attr:`max`. + + Args: + input (Tensor): the input tensor. + min (Number or Tensor, optional): lower-bound of the range to be clamped to + max (Number or Tensor, optional): upper-bound of the range to be clamped to + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-1.7120, 0.1734, -0.0478, -0.0922]) + >>> torch.clamp(a, min=-0.5, max=0.5) + tensor([-0.5000, 0.1734, -0.0478, -0.0922]) + + >>> min = torch.linspace(-1, 1, steps=4) + >>> torch.clamp(a, min=min) + tensor([-1.0000, 0.1734, 0.3333, 1.0000]) + """ + ... +@overload +def clamp_(input: Tensor, min: Optional[Tensor] = None, max: Optional[Tensor] = None) -> Tensor: ... +@overload +def clamp_(input: Tensor, min: Optional[Union[Number, _complex]] = None, max: Optional[Union[Number, _complex]] = None) -> Tensor: ... +@overload +def clamp_max(input: Tensor, max: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +@overload +def clamp_max(input: Tensor, max: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: ... +@overload +def clamp_max_(input: Tensor, max: Tensor) -> Tensor: ... +@overload +def clamp_max_(input: Tensor, max: Union[Number, _complex]) -> Tensor: ... +@overload +def clamp_min(input: Tensor, min: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +@overload +def clamp_min(input: Tensor, min: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: ... +@overload +def clamp_min_(input: Tensor, min: Tensor) -> Tensor: ... +@overload +def clamp_min_(input: Tensor, min: Union[Number, _complex]) -> Tensor: ... +@overload +def clip(input: Tensor, min: Optional[Tensor] = None, max: Optional[Tensor] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + clip(input, min=None, max=None, *, out=None) -> Tensor + + Alias for :func:`torch.clamp`. + """ + ... +@overload +def clip(input: Tensor, min: Optional[Union[Number, _complex]] = None, max: Optional[Union[Number, _complex]] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + clip(input, min=None, max=None, *, out=None) -> Tensor + + Alias for :func:`torch.clamp`. + """ + ... +@overload +def clip_(input: Tensor, min: Optional[Tensor] = None, max: Optional[Tensor] = None) -> Tensor: ... +@overload +def clip_(input: Tensor, min: Optional[Union[Number, _complex]] = None, max: Optional[Union[Number, _complex]] = None) -> Tensor: ... +def clone(input: Tensor, *, memory_format: Optional[memory_format] = None) -> Tensor: + r""" + clone(input, *, memory_format=torch.preserve_format) -> Tensor + + Returns a copy of :attr:`input`. + + .. note:: + + This function is differentiable, so gradients will flow back from the + result of this operation to :attr:`input`. To create a tensor without an + autograd relationship to :attr:`input` see :meth:`~Tensor.detach`. + + Args: + input (Tensor): the input tensor. + + Keyword args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned tensor. Default: ``torch.preserve_format``. + """ + ... +def col_indices_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.col_indices`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def column_stack(tensors: Union[Tuple[Tensor, ...], List[Tensor]], *, out: Optional[Tensor] = None) -> Tensor: + r""" + column_stack(tensors, *, out=None) -> Tensor + + Creates a new tensor by horizontally stacking the tensors in :attr:`tensors`. + + Equivalent to ``torch.hstack(tensors)``, except each zero or one dimensional tensor ``t`` + in :attr:`tensors` is first reshaped into a ``(t.numel(), 1)`` column before being stacked horizontally. + + Args: + tensors (sequence of Tensors): sequence of tensors to concatenate + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([1, 2, 3]) + >>> b = torch.tensor([4, 5, 6]) + >>> torch.column_stack((a, b)) + tensor([[1, 4], + [2, 5], + [3, 6]]) + >>> a = torch.arange(5) + >>> b = torch.arange(10).reshape(5, 2) + >>> torch.column_stack((a, b, b)) + tensor([[0, 0, 1, 0, 1], + [1, 2, 3, 2, 3], + [2, 4, 5, 4, 5], + [3, 6, 7, 6, 7], + [4, 8, 9, 8, 9]]) + """ + ... +def combinations(input: Tensor, r: _int = 2, with_replacement: _bool = False) -> Tensor: + r""" + combinations(input, r=2, with_replacement=False) -> seq + + Compute combinations of length :math:`r` of the given tensor. The behavior is similar to + python's `itertools.combinations` when `with_replacement` is set to `False`, and + `itertools.combinations_with_replacement` when `with_replacement` is set to `True`. + + Arguments: + input (Tensor): 1D vector. + r (int, optional): number of elements to combine + with_replacement (bool, optional): whether to allow duplication in combination + + Returns: + Tensor: A tensor equivalent to converting all the input tensors into lists, do + `itertools.combinations` or `itertools.combinations_with_replacement` on these + lists, and finally convert the resulting list into tensor. + + Example:: + + >>> a = [1, 2, 3] + >>> list(itertools.combinations(a, r=2)) + [(1, 2), (1, 3), (2, 3)] + >>> list(itertools.combinations(a, r=3)) + [(1, 2, 3)] + >>> list(itertools.combinations_with_replacement(a, r=2)) + [(1, 1), (1, 2), (1, 3), (2, 2), (2, 3), (3, 3)] + >>> tensor_a = torch.tensor(a) + >>> torch.combinations(tensor_a) + tensor([[1, 2], + [1, 3], + [2, 3]]) + >>> torch.combinations(tensor_a, r=3) + tensor([[1, 2, 3]]) + >>> torch.combinations(tensor_a, with_replacement=True) + tensor([[1, 1], + [1, 2], + [1, 3], + [2, 2], + [2, 3], + [3, 3]]) + """ + ... +def complex(real: Tensor, imag: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + complex(real, imag, *, out=None) -> Tensor + + Constructs a complex tensor with its real part equal to :attr:`real` and its + imaginary part equal to :attr:`imag`. + + Args: + real (Tensor): The real part of the complex tensor. Must be half, float or double. + imag (Tensor): The imaginary part of the complex tensor. Must be same dtype + as :attr:`real`. + + Keyword args: + out (Tensor): If the inputs are ``torch.float32``, must be + ``torch.complex64``. If the inputs are ``torch.float64``, must be + ``torch.complex128``. + + Example:: + + >>> real = torch.tensor([1, 2], dtype=torch.float32) + >>> imag = torch.tensor([3, 4], dtype=torch.float32) + >>> z = torch.complex(real, imag) + >>> z + tensor([(1.+3.j), (2.+4.j)]) + >>> z.dtype + torch.complex64 + """ + ... +@overload +def concat(tensors: Union[Tuple[Tensor, ...], List[Tensor]], dim: _int = 0, *, out: Optional[Tensor] = None) -> Tensor: + r""" + concat(tensors, dim=0, *, out=None) -> Tensor + + Alias of :func:`torch.cat`. + """ + ... +@overload +def concat(tensors: Union[Tuple[Tensor, ...], List[Tensor]], dim: Union[str, ellipsis, None], *, out: Optional[Tensor] = None) -> Tensor: + r""" + concat(tensors, dim=0, *, out=None) -> Tensor + + Alias of :func:`torch.cat`. + """ + ... +@overload +def concatenate(tensors: Union[Tuple[Tensor, ...], List[Tensor]], dim: _int = 0, *, out: Optional[Tensor] = None) -> Tensor: + r""" + concatenate(tensors, axis=0, out=None) -> Tensor + + Alias of :func:`torch.cat`. + """ + ... +@overload +def concatenate(tensors: Union[Tuple[Tensor, ...], List[Tensor]], dim: Union[str, ellipsis, None], *, out: Optional[Tensor] = None) -> Tensor: + r""" + concatenate(tensors, axis=0, out=None) -> Tensor + + Alias of :func:`torch.cat`. + """ + ... +def conj(input: Tensor) -> Tensor: + r""" + conj(input) -> Tensor + + Returns a view of :attr:`input` with a flipped conjugate bit. If :attr:`input` has a non-complex dtype, + this function just returns :attr:`input`. + + .. note:: + :func:`torch.conj` performs a lazy conjugation, but the actual conjugated tensor can be materialized + at any time using :func:`torch.resolve_conj`. + + .. warning:: In the future, :func:`torch.conj` may return a non-writeable view for an :attr:`input` of + non-complex dtype. It's recommended that programs not modify the tensor returned by :func:`torch.conj_physical` + when :attr:`input` is of non-complex dtype to be compatible with this change. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> x = torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j]) + >>> x.is_conj() + False + >>> y = torch.conj(x) + >>> y.is_conj() + True + """ + ... +def conj_physical(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + conj_physical(input, *, out=None) -> Tensor + + Computes the element-wise conjugate of the given :attr:`input` tensor. + If :attr:`input` has a non-complex dtype, this function just returns :attr:`input`. + + .. note:: + This performs the conjugate operation regardless of the fact conjugate bit is set or not. + + .. warning:: In the future, :func:`torch.conj_physical` may return a non-writeable view for an :attr:`input` of + non-complex dtype. It's recommended that programs not modify the tensor returned by :func:`torch.conj_physical` + when :attr:`input` is of non-complex dtype to be compatible with this change. + + .. math:: + \text{out}_{i} = conj(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.conj_physical(torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j])) + tensor([-1 - 1j, -2 - 2j, 3 + 3j]) + """ + ... +def conj_physical_(input: Tensor) -> Tensor: ... +def constant_pad_nd(input: Tensor, pad: Sequence[Union[_int, SymInt]], value: Union[Number, _complex] = 0) -> Tensor: ... +@overload +def conv1d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, padding: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 0, dilation: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, groups: Union[_int, SymInt] = 1) -> Tensor: ... +@overload +def conv1d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, padding: str = "valid", dilation: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, groups: Union[_int, SymInt] = 1) -> Tensor: ... +@overload +def conv2d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, padding: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 0, dilation: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, groups: Union[_int, SymInt] = 1) -> Tensor: ... +@overload +def conv2d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, padding: str = "valid", dilation: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, groups: Union[_int, SymInt] = 1) -> Tensor: ... +@overload +def conv3d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, padding: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 0, dilation: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, groups: Union[_int, SymInt] = 1) -> Tensor: ... +@overload +def conv3d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, padding: str = "valid", dilation: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, groups: Union[_int, SymInt] = 1) -> Tensor: ... +def conv_tbc(input: Tensor, weight: Tensor, bias: Tensor, pad: _int = 0) -> Tensor: ... +def conv_transpose1d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, padding: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 0, output_padding: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 0, groups: Union[_int, SymInt] = 1, dilation: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1) -> Tensor: ... +def conv_transpose2d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, padding: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 0, output_padding: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 0, groups: Union[_int, SymInt] = 1, dilation: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1) -> Tensor: ... +def conv_transpose3d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, padding: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 0, output_padding: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 0, groups: Union[_int, SymInt] = 1, dilation: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1) -> Tensor: ... +def convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], stride: Sequence[Union[_int, SymInt]], padding: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], transposed: _bool, output_padding: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt]) -> Tensor: ... +@overload +def copysign(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + copysign(input, other, *, out=None) -> Tensor + + Create a new floating-point tensor with the magnitude of :attr:`input` and the sign of :attr:`other`, elementwise. + + .. math:: + \text{out}_{i} = \begin{cases} + -|\text{input}_{i}| & \text{if } \text{other}_{i} \leq -0.0 \\ + |\text{input}_{i}| & \text{if } \text{other}_{i} \geq 0.0 \\ + \end{cases} + + + Supports :ref:`broadcasting to a common shape `, + and integer and float inputs. + + Args: + input (Tensor): magnitudes. + other (Tensor or Number): contains value(s) whose signbit(s) are + applied to the magnitudes in :attr:`input`. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(5) + >>> a + tensor([-1.2557, -0.0026, -0.5387, 0.4740, -0.9244]) + >>> torch.copysign(a, 1) + tensor([1.2557, 0.0026, 0.5387, 0.4740, 0.9244]) + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.7079, 0.2778, -1.0249, 0.5719], + [-0.0059, -0.2600, -0.4475, -1.3948], + [ 0.3667, -0.9567, -2.5757, -0.1751], + [ 0.2046, -0.0742, 0.2998, -0.1054]]) + >>> b = torch.randn(4) + tensor([ 0.2373, 0.3120, 0.3190, -1.1128]) + >>> torch.copysign(a, b) + tensor([[ 0.7079, 0.2778, 1.0249, -0.5719], + [ 0.0059, 0.2600, 0.4475, -1.3948], + [ 0.3667, 0.9567, 2.5757, -0.1751], + [ 0.2046, 0.0742, 0.2998, -0.1054]]) + >>> a = torch.tensor([1.]) + >>> b = torch.tensor([-0.]) + >>> torch.copysign(a, b) + tensor([-1.]) + + .. note:: + copysign handles signed zeros. If the other argument has a negative zero (-0), + the corresponding output value will be negative. + """ + ... +@overload +def copysign(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + copysign(input, other, *, out=None) -> Tensor + + Create a new floating-point tensor with the magnitude of :attr:`input` and the sign of :attr:`other`, elementwise. + + .. math:: + \text{out}_{i} = \begin{cases} + -|\text{input}_{i}| & \text{if } \text{other}_{i} \leq -0.0 \\ + |\text{input}_{i}| & \text{if } \text{other}_{i} \geq 0.0 \\ + \end{cases} + + + Supports :ref:`broadcasting to a common shape `, + and integer and float inputs. + + Args: + input (Tensor): magnitudes. + other (Tensor or Number): contains value(s) whose signbit(s) are + applied to the magnitudes in :attr:`input`. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(5) + >>> a + tensor([-1.2557, -0.0026, -0.5387, 0.4740, -0.9244]) + >>> torch.copysign(a, 1) + tensor([1.2557, 0.0026, 0.5387, 0.4740, 0.9244]) + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.7079, 0.2778, -1.0249, 0.5719], + [-0.0059, -0.2600, -0.4475, -1.3948], + [ 0.3667, -0.9567, -2.5757, -0.1751], + [ 0.2046, -0.0742, 0.2998, -0.1054]]) + >>> b = torch.randn(4) + tensor([ 0.2373, 0.3120, 0.3190, -1.1128]) + >>> torch.copysign(a, b) + tensor([[ 0.7079, 0.2778, 1.0249, -0.5719], + [ 0.0059, 0.2600, 0.4475, -1.3948], + [ 0.3667, 0.9567, 2.5757, -0.1751], + [ 0.2046, 0.0742, 0.2998, -0.1054]]) + >>> a = torch.tensor([1.]) + >>> b = torch.tensor([-0.]) + >>> torch.copysign(a, b) + tensor([-1.]) + + .. note:: + copysign handles signed zeros. If the other argument has a negative zero (-0), + the corresponding output value will be negative. + """ + ... +def corrcoef(input: Tensor) -> Tensor: + r""" + corrcoef(input) -> Tensor + + Estimates the Pearson product-moment correlation coefficient matrix of the variables given by the :attr:`input` matrix, + where rows are the variables and columns are the observations. + + .. note:: + + The correlation coefficient matrix R is computed using the covariance matrix C as given by + :math:`R_{ij} = \frac{ C_{ij} } { \sqrt{ C_{ii} * C_{jj} } }` + + .. note:: + + Due to floating point rounding, the resulting array may not be Hermitian and its diagonal elements may not be 1. + The real and imaginary values are clipped to the interval [-1, 1] in an attempt to improve this situation. + + Args: + input (Tensor): A 2D matrix containing multiple variables and observations, or a + Scalar or 1D vector representing a single variable. + + Returns: + (Tensor) The correlation coefficient matrix of the variables. + + .. seealso:: + + :func:`torch.cov` covariance matrix. + + Example:: + + >>> x = torch.tensor([[0, 1, 2], [2, 1, 0]]) + >>> torch.corrcoef(x) + tensor([[ 1., -1.], + [-1., 1.]]) + >>> x = torch.randn(2, 4) + >>> x + tensor([[-0.2678, -0.0908, -0.3766, 0.2780], + [-0.5812, 0.1535, 0.2387, 0.2350]]) + >>> torch.corrcoef(x) + tensor([[1.0000, 0.3582], + [0.3582, 1.0000]]) + >>> torch.corrcoef(x[0]) + tensor(1.) + """ + ... +def cos(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + cos(input, *, out=None) -> Tensor + + Returns a new tensor with the cosine of the elements of :attr:`input`. + + .. math:: + \text{out}_{i} = \cos(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 1.4309, 1.2706, -0.8562, 0.9796]) + >>> torch.cos(a) + tensor([ 0.1395, 0.2957, 0.6553, 0.5574]) + """ + ... +def cos_(input: Tensor) -> Tensor: ... +def cosh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + cosh(input, *, out=None) -> Tensor + + Returns a new tensor with the hyperbolic cosine of the elements of + :attr:`input`. + + .. math:: + \text{out}_{i} = \cosh(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.1632, 1.1835, -0.6979, -0.7325]) + >>> torch.cosh(a) + tensor([ 1.0133, 1.7860, 1.2536, 1.2805]) + + .. note:: + When :attr:`input` is on the CPU, the implementation of torch.cosh may use + the Sleef library, which rounds very large results to infinity or negative + infinity. See `here `_ for details. + """ + ... +def cosh_(input: Tensor) -> Tensor: ... +def cosine_embedding_loss(input1: Tensor, input2: Tensor, target: Tensor, margin: _float = 0.0, reduction: _int = 1) -> Tensor: ... +def cosine_similarity(x1: Tensor, x2: Tensor, dim: _int = 1, eps: _float = 1e-08) -> Tensor: ... +@overload +def count_nonzero(input: Tensor, dim: Optional[_int] = None) -> Tensor: + r""" + count_nonzero(input, dim=None) -> Tensor + + Counts the number of non-zero values in the tensor :attr:`input` along the given :attr:`dim`. + If no dim is specified then all non-zeros in the tensor are counted. + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints, optional): Dim or tuple of dims along which to count non-zeros. + + Example:: + + >>> x = torch.zeros(3,3) + >>> x[torch.randn(3,3) > 0.5] = 1 + >>> x + tensor([[0., 1., 1.], + [0., 0., 0.], + [0., 0., 1.]]) + >>> torch.count_nonzero(x) + tensor(3) + >>> torch.count_nonzero(x, dim=0) + tensor([0, 1, 2]) + """ + ... +@overload +def count_nonzero(input: Tensor, dim: _size) -> Tensor: + r""" + count_nonzero(input, dim=None) -> Tensor + + Counts the number of non-zero values in the tensor :attr:`input` along the given :attr:`dim`. + If no dim is specified then all non-zeros in the tensor are counted. + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints, optional): Dim or tuple of dims along which to count non-zeros. + + Example:: + + >>> x = torch.zeros(3,3) + >>> x[torch.randn(3,3) > 0.5] = 1 + >>> x + tensor([[0., 1., 1.], + [0., 0., 0.], + [0., 0., 1.]]) + >>> torch.count_nonzero(x) + tensor(3) + >>> torch.count_nonzero(x, dim=0) + tensor([0, 1, 2]) + """ + ... +def cov(input: Tensor, *, correction: _int = 1, fweights: Optional[Tensor] = None, aweights: Optional[Tensor] = None) -> Tensor: + r""" + cov(input, *, correction=1, fweights=None, aweights=None) -> Tensor + + Estimates the covariance matrix of the variables given by the :attr:`input` matrix, where rows are + the variables and columns are the observations. + + A covariance matrix is a square matrix giving the covariance of each pair of variables. The diagonal contains + the variance of each variable (covariance of a variable with itself). By definition, if :attr:`input` represents + a single variable (Scalar or 1D) then its variance is returned. + + The sample covariance of the variables :math:`x` and :math:`y` is given by: + + .. math:: + \text{cov}(x,y) = \frac{\sum^{N}_{i = 1}(x_{i} - \bar{x})(y_{i} - \bar{y})}{\max(0,~N~-~\delta N)} + + where :math:`\bar{x}` and :math:`\bar{y}` are the simple means of the :math:`x` and :math:`y` respectively, and + :math:`\delta N` is the :attr:`correction`. + + If :attr:`fweights` and/or :attr:`aweights` are provided, the weighted covariance + is calculated, which is given by: + + .. math:: + \text{cov}_w(x,y) = \frac{\sum^{N}_{i = 1}w_i(x_{i} - \mu_x^*)(y_{i} - \mu_y^*)} + {\max(0,~\sum^{N}_{i = 1}w_i~-~\frac{\sum^{N}_{i = 1}w_ia_i}{\sum^{N}_{i = 1}w_i}~\delta N)} + + where :math:`w` denotes :attr:`fweights` or :attr:`aweights` (``f`` and ``a`` for brevity) based on whichever is + provided, or :math:`w = f \times a` if both are provided, and + :math:`\mu_x^* = \frac{\sum^{N}_{i = 1}w_ix_{i} }{\sum^{N}_{i = 1}w_i}` is the weighted mean of the variable. If not + provided, ``f`` and/or ``a`` can be seen as a :math:`\mathbb{1}` vector of appropriate size. + + Args: + input (Tensor): A 2D matrix containing multiple variables and observations, or a + Scalar or 1D vector representing a single variable. + + Keyword Args: + correction (int, optional): difference between the sample size and sample degrees of freedom. + Defaults to Bessel's correction, ``correction = 1`` which returns the unbiased estimate, + even if both :attr:`fweights` and :attr:`aweights` are specified. ``correction = 0`` + will return the simple average. Defaults to ``1``. + fweights (tensor, optional): A Scalar or 1D tensor of observation vector frequencies representing the number of + times each observation should be repeated. Its numel must equal the number of columns of :attr:`input`. + Must have integral dtype. Ignored if ``None``. Defaults to ``None``. + aweights (tensor, optional): A Scalar or 1D array of observation vector weights. + These relative weights are typically large for observations considered "important" and smaller for + observations considered less "important". Its numel must equal the number of columns of :attr:`input`. + Must have floating point dtype. Ignored if ``None``. Defaults to ``None``. + + Returns: + (Tensor) The covariance matrix of the variables. + + .. seealso:: + + :func:`torch.corrcoef` normalized covariance matrix. + + Example:: + >>> x = torch.tensor([[0, 2], [1, 1], [2, 0]]).T + >>> x + tensor([[0, 1, 2], + [2, 1, 0]]) + >>> torch.cov(x) + tensor([[ 1., -1.], + [-1., 1.]]) + >>> torch.cov(x, correction=0) + tensor([[ 0.6667, -0.6667], + [-0.6667, 0.6667]]) + >>> fw = torch.randint(1, 10, (3,)) + >>> fw + tensor([1, 6, 9]) + >>> aw = torch.rand(3) + >>> aw + tensor([0.4282, 0.0255, 0.4144]) + >>> torch.cov(x, fweights=fw, aweights=aw) + tensor([[ 0.4169, -0.4169], + [-0.4169, 0.4169]]) + """ + ... +def cross(input: Tensor, other: Tensor, dim: Optional[_int] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + cross(input, other, dim=None, *, out=None) -> Tensor + + + Returns the cross product of vectors in dimension :attr:`dim` of :attr:`input` + and :attr:`other`. + + Supports input of float, double, cfloat and cdouble dtypes. Also supports batches + of vectors, for which it computes the product along the dimension :attr:`dim`. + In this case, the output has the same batch dimensions as the inputs. + + .. warning:: + If :attr:`dim` is not given, it defaults to the first dimension found + with the size 3. Note that this might be unexpected. + + This behavior is deprecated and will be changed to match that of :func:`torch.linalg.cross` + in a future release. + + .. seealso:: + :func:`torch.linalg.cross` which has dim=-1 as default. + + + Args: + input (Tensor): the input tensor. + other (Tensor): the second input tensor + dim (int, optional): the dimension to take the cross-product in. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4, 3) + >>> a + tensor([[-0.3956, 1.1455, 1.6895], + [-0.5849, 1.3672, 0.3599], + [-1.1626, 0.7180, -0.0521], + [-0.1339, 0.9902, -2.0225]]) + >>> b = torch.randn(4, 3) + >>> b + tensor([[-0.0257, -1.4725, -1.2251], + [-1.1479, -0.7005, -1.9757], + [-1.3904, 0.3726, -1.1836], + [-0.9688, -0.7153, 0.2159]]) + >>> torch.cross(a, b, dim=1) + tensor([[ 1.0844, -0.5281, 0.6120], + [-2.4490, -1.5687, 1.9792], + [-0.8304, -1.3037, 0.5650], + [-1.2329, 1.9883, 1.0551]]) + >>> torch.cross(a, b) + tensor([[ 1.0844, -0.5281, 0.6120], + [-2.4490, -1.5687, 1.9792], + [-0.8304, -1.3037, 0.5650], + [-1.2329, 1.9883, 1.0551]]) + """ + ... +def crow_indices_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.crow_indices`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +@overload +def ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: _size, target_lengths: _size, blank: _int = 0, reduction: _int = 1, zero_infinity: _bool = False) -> Tensor: ... +@overload +def ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: Tensor, target_lengths: Tensor, blank: _int = 0, reduction: _int = 1, zero_infinity: _bool = False) -> Tensor: ... +def cudnn_affine_grid_generator(theta: Tensor, N: _int, C: _int, H: _int, W: _int) -> Tensor: ... +def cudnn_batch_norm(input: Tensor, weight: Tensor, bias: Optional[Tensor], running_mean: Optional[Tensor], running_var: Optional[Tensor], training: _bool, exponential_average_factor: _float, epsilon: _float) -> Tuple[Tensor, Tensor, Tensor, Tensor]: ... +def cudnn_convolution(input: Tensor, weight: Tensor, padding: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt], benchmark: _bool, deterministic: _bool, allow_tf32: _bool, *, out: Optional[Tensor] = None) -> Tensor: ... +def cudnn_convolution_add_relu(input: Tensor, weight: Tensor, z: Tensor, alpha: Optional[Union[Number, _complex]], bias: Optional[Tensor], stride: Sequence[Union[_int, SymInt]], padding: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt]) -> Tensor: ... +def cudnn_convolution_relu(input: Tensor, weight: Tensor, bias: Optional[Tensor], stride: Sequence[Union[_int, SymInt]], padding: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt]) -> Tensor: ... +def cudnn_convolution_transpose(input: Tensor, weight: Tensor, padding: Sequence[Union[_int, SymInt]], output_padding: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt], benchmark: _bool, deterministic: _bool, allow_tf32: _bool) -> Tensor: ... +def cudnn_grid_sampler(input: Tensor, grid: Tensor) -> Tensor: ... +def cudnn_is_acceptable(input: Tensor) -> _bool: ... +@overload +def cummax(input: Tensor, dim: _int, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.cummax: + r""" + cummax(input, dim, *, out=None) -> (Tensor, LongTensor) + Returns a namedtuple ``(values, indices)`` where ``values`` is the cumulative maximum of + elements of :attr:`input` in the dimension :attr:`dim`. And ``indices`` is the index + location of each maximum value found in the dimension :attr:`dim`. + + .. math:: + y_i = max(x_1, x_2, x_3, \dots, x_i) + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to do the operation over + + Keyword args: + out (tuple, optional): the result tuple of two output tensors (values, indices) + + Example:: + + >>> a = torch.randn(10) + >>> a + tensor([-0.3449, -1.5447, 0.0685, -1.5104, -1.1706, 0.2259, 1.4696, -1.3284, + 1.9946, -0.8209]) + >>> torch.cummax(a, dim=0) + torch.return_types.cummax( + values=tensor([-0.3449, -0.3449, 0.0685, 0.0685, 0.0685, 0.2259, 1.4696, 1.4696, + 1.9946, 1.9946]), + indices=tensor([0, 0, 2, 2, 2, 5, 6, 6, 8, 8])) + """ + ... +@overload +def cummax(input: Tensor, dim: Union[str, ellipsis, None], *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.cummax: + r""" + cummax(input, dim, *, out=None) -> (Tensor, LongTensor) + Returns a namedtuple ``(values, indices)`` where ``values`` is the cumulative maximum of + elements of :attr:`input` in the dimension :attr:`dim`. And ``indices`` is the index + location of each maximum value found in the dimension :attr:`dim`. + + .. math:: + y_i = max(x_1, x_2, x_3, \dots, x_i) + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to do the operation over + + Keyword args: + out (tuple, optional): the result tuple of two output tensors (values, indices) + + Example:: + + >>> a = torch.randn(10) + >>> a + tensor([-0.3449, -1.5447, 0.0685, -1.5104, -1.1706, 0.2259, 1.4696, -1.3284, + 1.9946, -0.8209]) + >>> torch.cummax(a, dim=0) + torch.return_types.cummax( + values=tensor([-0.3449, -0.3449, 0.0685, 0.0685, 0.0685, 0.2259, 1.4696, 1.4696, + 1.9946, 1.9946]), + indices=tensor([0, 0, 2, 2, 2, 5, 6, 6, 8, 8])) + """ + ... +@overload +def cummin(input: Tensor, dim: _int, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.cummin: + r""" + cummin(input, dim, *, out=None) -> (Tensor, LongTensor) + Returns a namedtuple ``(values, indices)`` where ``values`` is the cumulative minimum of + elements of :attr:`input` in the dimension :attr:`dim`. And ``indices`` is the index + location of each maximum value found in the dimension :attr:`dim`. + + .. math:: + y_i = min(x_1, x_2, x_3, \dots, x_i) + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to do the operation over + + Keyword args: + out (tuple, optional): the result tuple of two output tensors (values, indices) + + Example:: + + >>> a = torch.randn(10) + >>> a + tensor([-0.2284, -0.6628, 0.0975, 0.2680, -1.3298, -0.4220, -0.3885, 1.1762, + 0.9165, 1.6684]) + >>> torch.cummin(a, dim=0) + torch.return_types.cummin( + values=tensor([-0.2284, -0.6628, -0.6628, -0.6628, -1.3298, -1.3298, -1.3298, -1.3298, + -1.3298, -1.3298]), + indices=tensor([0, 1, 1, 1, 4, 4, 4, 4, 4, 4])) + """ + ... +@overload +def cummin(input: Tensor, dim: Union[str, ellipsis, None], *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.cummin: + r""" + cummin(input, dim, *, out=None) -> (Tensor, LongTensor) + Returns a namedtuple ``(values, indices)`` where ``values`` is the cumulative minimum of + elements of :attr:`input` in the dimension :attr:`dim`. And ``indices`` is the index + location of each maximum value found in the dimension :attr:`dim`. + + .. math:: + y_i = min(x_1, x_2, x_3, \dots, x_i) + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to do the operation over + + Keyword args: + out (tuple, optional): the result tuple of two output tensors (values, indices) + + Example:: + + >>> a = torch.randn(10) + >>> a + tensor([-0.2284, -0.6628, 0.0975, 0.2680, -1.3298, -0.4220, -0.3885, 1.1762, + 0.9165, 1.6684]) + >>> torch.cummin(a, dim=0) + torch.return_types.cummin( + values=tensor([-0.2284, -0.6628, -0.6628, -0.6628, -1.3298, -1.3298, -1.3298, -1.3298, + -1.3298, -1.3298]), + indices=tensor([0, 1, 1, 1, 4, 4, 4, 4, 4, 4])) + """ + ... +@overload +def cumprod(input: Tensor, dim: _int, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + cumprod(input, dim, *, dtype=None, out=None) -> Tensor + + Returns the cumulative product of elements of :attr:`input` in the dimension + :attr:`dim`. + + For example, if :attr:`input` is a vector of size N, the result will also be + a vector of size N, with elements. + + .. math:: + y_i = x_1 \times x_2\times x_3\times \dots \times x_i + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to do the operation over + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(10) + >>> a + tensor([ 0.6001, 0.2069, -0.1919, 0.9792, 0.6727, 1.0062, 0.4126, + -0.2129, -0.4206, 0.1968]) + >>> torch.cumprod(a, dim=0) + tensor([ 0.6001, 0.1241, -0.0238, -0.0233, -0.0157, -0.0158, -0.0065, + 0.0014, -0.0006, -0.0001]) + + >>> a[5] = 0.0 + >>> torch.cumprod(a, dim=0) + tensor([ 0.6001, 0.1241, -0.0238, -0.0233, -0.0157, -0.0000, -0.0000, + 0.0000, -0.0000, -0.0000]) + """ + ... +@overload +def cumprod(input: Tensor, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + cumprod(input, dim, *, dtype=None, out=None) -> Tensor + + Returns the cumulative product of elements of :attr:`input` in the dimension + :attr:`dim`. + + For example, if :attr:`input` is a vector of size N, the result will also be + a vector of size N, with elements. + + .. math:: + y_i = x_1 \times x_2\times x_3\times \dots \times x_i + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to do the operation over + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(10) + >>> a + tensor([ 0.6001, 0.2069, -0.1919, 0.9792, 0.6727, 1.0062, 0.4126, + -0.2129, -0.4206, 0.1968]) + >>> torch.cumprod(a, dim=0) + tensor([ 0.6001, 0.1241, -0.0238, -0.0233, -0.0157, -0.0158, -0.0065, + 0.0014, -0.0006, -0.0001]) + + >>> a[5] = 0.0 + >>> torch.cumprod(a, dim=0) + tensor([ 0.6001, 0.1241, -0.0238, -0.0233, -0.0157, -0.0000, -0.0000, + 0.0000, -0.0000, -0.0000]) + """ + ... +@overload +def cumsum(input: Tensor, dim: _int, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + cumsum(input, dim, *, dtype=None, out=None) -> Tensor + + Returns the cumulative sum of elements of :attr:`input` in the dimension + :attr:`dim`. + + For example, if :attr:`input` is a vector of size N, the result will also be + a vector of size N, with elements. + + .. math:: + y_i = x_1 + x_2 + x_3 + \dots + x_i + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to do the operation over + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randint(1, 20, (10,)) + >>> a + tensor([13, 7, 3, 10, 13, 3, 15, 10, 9, 10]) + >>> torch.cumsum(a, dim=0) + tensor([13, 20, 23, 33, 46, 49, 64, 74, 83, 93]) + """ + ... +@overload +def cumsum(input: Tensor, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + cumsum(input, dim, *, dtype=None, out=None) -> Tensor + + Returns the cumulative sum of elements of :attr:`input` in the dimension + :attr:`dim`. + + For example, if :attr:`input` is a vector of size N, the result will also be + a vector of size N, with elements. + + .. math:: + y_i = x_1 + x_2 + x_3 + \dots + x_i + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to do the operation over + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randint(1, 20, (10,)) + >>> a + tensor([13, 7, 3, 10, 13, 3, 15, 10, 9, 10]) + >>> torch.cumsum(a, dim=0) + tensor([13, 20, 23, 33, 46, 49, 64, 74, 83, 93]) + """ + ... +@overload +def cumulative_trapezoid(y: Tensor, x: Tensor, *, dim: _int = -1) -> Tensor: + r""" + cumulative_trapezoid(y, x=None, *, dx=None, dim=-1) -> Tensor + + Cumulatively computes the `trapezoidal rule `_ + along :attr:`dim`. By default the spacing between elements is assumed to be 1, but + :attr:`dx` can be used to specify a different constant spacing, and :attr:`x` can be + used to specify arbitrary spacing along :attr:`dim`. + + For more details, please read :func:`torch.trapezoid`. The difference between :func:`torch.trapezoid` + and this function is that, :func:`torch.trapezoid` returns a value for each integration, + where as this function returns a cumulative value for every spacing within the integration. This + is analogous to how `.sum` returns a value and `.cumsum` returns a cumulative sum. + + Arguments: + y (Tensor): Values to use when computing the trapezoidal rule. + x (Tensor): If specified, defines spacing between values as specified above. + + Keyword arguments: + dx (float): constant spacing between values. If neither :attr:`x` or :attr:`dx` + are specified then this defaults to 1. Effectively multiplies the result by its value. + dim (int): The dimension along which to compute the trapezoidal rule. + The last (inner-most) dimension by default. + + Examples:: + + >>> # Cumulatively computes the trapezoidal rule in 1D, spacing is implicitly 1. + >>> y = torch.tensor([1, 5, 10]) + >>> torch.cumulative_trapezoid(y) + tensor([3., 10.5]) + + >>> # Computes the same trapezoidal rule directly up to each element to verify + >>> (1 + 5) / 2 + 3.0 + >>> (1 + 10 + 10) / 2 + 10.5 + + >>> # Cumulatively computes the trapezoidal rule in 1D with constant spacing of 2 + >>> # NOTE: the result is the same as before, but multiplied by 2 + >>> torch.cumulative_trapezoid(y, dx=2) + tensor([6., 21.]) + + >>> # Cumulatively computes the trapezoidal rule in 1D with arbitrary spacing + >>> x = torch.tensor([1, 3, 6]) + >>> torch.cumulative_trapezoid(y, x) + tensor([6., 28.5]) + + >>> # Computes the same trapezoidal rule directly up to each element to verify + >>> ((3 - 1) * (1 + 5)) / 2 + 6.0 + >>> ((3 - 1) * (1 + 5) + (6 - 3) * (5 + 10)) / 2 + 28.5 + + >>> # Cumulatively computes the trapezoidal rule for each row of a 3x3 matrix + >>> y = torch.arange(9).reshape(3, 3) + tensor([[0, 1, 2], + [3, 4, 5], + [6, 7, 8]]) + >>> torch.cumulative_trapezoid(y) + tensor([[ 0.5, 2.], + [ 3.5, 8.], + [ 6.5, 14.]]) + + >>> # Cumulatively computes the trapezoidal rule for each column of the matrix + >>> torch.cumulative_trapezoid(y, dim=0) + tensor([[ 1.5, 2.5, 3.5], + [ 6.0, 8.0, 10.0]]) + + >>> # Cumulatively computes the trapezoidal rule for each row of a 3x3 ones matrix + >>> # with the same arbitrary spacing + >>> y = torch.ones(3, 3) + >>> x = torch.tensor([1, 3, 6]) + >>> torch.cumulative_trapezoid(y, x) + tensor([[2., 5.], + [2., 5.], + [2., 5.]]) + + >>> # Cumulatively computes the trapezoidal rule for each row of a 3x3 ones matrix + >>> # with different arbitrary spacing per row + >>> y = torch.ones(3, 3) + >>> x = torch.tensor([[1, 2, 3], [1, 3, 5], [1, 4, 7]]) + >>> torch.cumulative_trapezoid(y, x) + tensor([[1., 2.], + [2., 4.], + [3., 6.]]) + """ + ... +@overload +def cumulative_trapezoid(y: Tensor, *, dx: Union[Number, _complex] = 1, dim: _int = -1) -> Tensor: + r""" + cumulative_trapezoid(y, x=None, *, dx=None, dim=-1) -> Tensor + + Cumulatively computes the `trapezoidal rule `_ + along :attr:`dim`. By default the spacing between elements is assumed to be 1, but + :attr:`dx` can be used to specify a different constant spacing, and :attr:`x` can be + used to specify arbitrary spacing along :attr:`dim`. + + For more details, please read :func:`torch.trapezoid`. The difference between :func:`torch.trapezoid` + and this function is that, :func:`torch.trapezoid` returns a value for each integration, + where as this function returns a cumulative value for every spacing within the integration. This + is analogous to how `.sum` returns a value and `.cumsum` returns a cumulative sum. + + Arguments: + y (Tensor): Values to use when computing the trapezoidal rule. + x (Tensor): If specified, defines spacing between values as specified above. + + Keyword arguments: + dx (float): constant spacing between values. If neither :attr:`x` or :attr:`dx` + are specified then this defaults to 1. Effectively multiplies the result by its value. + dim (int): The dimension along which to compute the trapezoidal rule. + The last (inner-most) dimension by default. + + Examples:: + + >>> # Cumulatively computes the trapezoidal rule in 1D, spacing is implicitly 1. + >>> y = torch.tensor([1, 5, 10]) + >>> torch.cumulative_trapezoid(y) + tensor([3., 10.5]) + + >>> # Computes the same trapezoidal rule directly up to each element to verify + >>> (1 + 5) / 2 + 3.0 + >>> (1 + 10 + 10) / 2 + 10.5 + + >>> # Cumulatively computes the trapezoidal rule in 1D with constant spacing of 2 + >>> # NOTE: the result is the same as before, but multiplied by 2 + >>> torch.cumulative_trapezoid(y, dx=2) + tensor([6., 21.]) + + >>> # Cumulatively computes the trapezoidal rule in 1D with arbitrary spacing + >>> x = torch.tensor([1, 3, 6]) + >>> torch.cumulative_trapezoid(y, x) + tensor([6., 28.5]) + + >>> # Computes the same trapezoidal rule directly up to each element to verify + >>> ((3 - 1) * (1 + 5)) / 2 + 6.0 + >>> ((3 - 1) * (1 + 5) + (6 - 3) * (5 + 10)) / 2 + 28.5 + + >>> # Cumulatively computes the trapezoidal rule for each row of a 3x3 matrix + >>> y = torch.arange(9).reshape(3, 3) + tensor([[0, 1, 2], + [3, 4, 5], + [6, 7, 8]]) + >>> torch.cumulative_trapezoid(y) + tensor([[ 0.5, 2.], + [ 3.5, 8.], + [ 6.5, 14.]]) + + >>> # Cumulatively computes the trapezoidal rule for each column of the matrix + >>> torch.cumulative_trapezoid(y, dim=0) + tensor([[ 1.5, 2.5, 3.5], + [ 6.0, 8.0, 10.0]]) + + >>> # Cumulatively computes the trapezoidal rule for each row of a 3x3 ones matrix + >>> # with the same arbitrary spacing + >>> y = torch.ones(3, 3) + >>> x = torch.tensor([1, 3, 6]) + >>> torch.cumulative_trapezoid(y, x) + tensor([[2., 5.], + [2., 5.], + [2., 5.]]) + + >>> # Cumulatively computes the trapezoidal rule for each row of a 3x3 ones matrix + >>> # with different arbitrary spacing per row + >>> y = torch.ones(3, 3) + >>> x = torch.tensor([[1, 2, 3], [1, 3, 5], [1, 4, 7]]) + >>> torch.cumulative_trapezoid(y, x) + tensor([[1., 2.], + [2., 4.], + [3., 6.]]) + """ + ... +def deg2rad(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + deg2rad(input, *, out=None) -> Tensor + + Returns a new tensor with each of the elements of :attr:`input` + converted from angles in degrees to radians. + + Args: + input (Tensor): the input tensor. + + Keyword arguments: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([[180.0, -180.0], [360.0, -360.0], [90.0, -90.0]]) + >>> torch.deg2rad(a) + tensor([[ 3.1416, -3.1416], + [ 6.2832, -6.2832], + [ 1.5708, -1.5708]]) + """ + ... +def deg2rad_(input: Tensor) -> Tensor: ... +@overload +def dequantize(input: Tensor) -> Tensor: + r""" + dequantize(tensor) -> Tensor + + Returns an fp32 Tensor by dequantizing a quantized Tensor + + Args: + tensor (Tensor): A quantized Tensor + + .. function:: dequantize(tensors) -> sequence of Tensors + :noindex: + + Given a list of quantized Tensors, dequantize them and return a list of fp32 Tensors + + Args: + tensors (sequence of Tensors): A list of quantized Tensors + """ + ... +@overload +def dequantize(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Tuple[Tensor, ...]: + r""" + dequantize(tensor) -> Tensor + + Returns an fp32 Tensor by dequantizing a quantized Tensor + + Args: + tensor (Tensor): A quantized Tensor + + .. function:: dequantize(tensors) -> sequence of Tensors + :noindex: + + Given a list of quantized Tensors, dequantize them and return a list of fp32 Tensors + + Args: + tensors (sequence of Tensors): A list of quantized Tensors + """ + ... +def det(input: Tensor) -> Tensor: + r""" + det(input) -> Tensor + + Alias for :func:`torch.linalg.det` + """ + ... +def detach(input: Tensor) -> Tensor: ... +def detach_(input: Tensor) -> Tensor: ... +def detach_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.detach`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def diag(input: Tensor, diagonal: _int = 0, *, out: Optional[Tensor] = None) -> Tensor: + r""" + diag(input, diagonal=0, *, out=None) -> Tensor + + - If :attr:`input` is a vector (1-D tensor), then returns a 2-D square tensor + with the elements of :attr:`input` as the diagonal. + - If :attr:`input` is a matrix (2-D tensor), then returns a 1-D tensor with + the diagonal elements of :attr:`input`. + + The argument :attr:`diagonal` controls which diagonal to consider: + + - If :attr:`diagonal` = 0, it is the main diagonal. + - If :attr:`diagonal` > 0, it is above the main diagonal. + - If :attr:`diagonal` < 0, it is below the main diagonal. + + Args: + input (Tensor): the input tensor. + diagonal (int, optional): the diagonal to consider + + Keyword args: + out (Tensor, optional): the output tensor. + + .. seealso:: + + :func:`torch.diagonal` always returns the diagonal of its input. + + :func:`torch.diagflat` always constructs a tensor with diagonal elements + specified by the input. + + Examples: + + Get the square matrix where the input vector is the diagonal:: + + >>> a = torch.randn(3) + >>> a + tensor([ 0.5950,-0.0872, 2.3298]) + >>> torch.diag(a) + tensor([[ 0.5950, 0.0000, 0.0000], + [ 0.0000,-0.0872, 0.0000], + [ 0.0000, 0.0000, 2.3298]]) + >>> torch.diag(a, 1) + tensor([[ 0.0000, 0.5950, 0.0000, 0.0000], + [ 0.0000, 0.0000,-0.0872, 0.0000], + [ 0.0000, 0.0000, 0.0000, 2.3298], + [ 0.0000, 0.0000, 0.0000, 0.0000]]) + + Get the k-th diagonal of a given matrix:: + + >>> a = torch.randn(3, 3) + >>> a + tensor([[-0.4264, 0.0255,-0.1064], + [ 0.8795,-0.2429, 0.1374], + [ 0.1029,-0.6482,-1.6300]]) + >>> torch.diag(a, 0) + tensor([-0.4264,-0.2429,-1.6300]) + >>> torch.diag(a, 1) + tensor([ 0.0255, 0.1374]) + """ + ... +def diag_embed(input: Tensor, offset: _int = 0, dim1: _int = -2, dim2: _int = -1) -> Tensor: + r""" + diag_embed(input, offset=0, dim1=-2, dim2=-1) -> Tensor + + Creates a tensor whose diagonals of certain 2D planes (specified by + :attr:`dim1` and :attr:`dim2`) are filled by :attr:`input`. + To facilitate creating batched diagonal matrices, the 2D planes formed by + the last two dimensions of the returned tensor are chosen by default. + + The argument :attr:`offset` controls which diagonal to consider: + + - If :attr:`offset` = 0, it is the main diagonal. + - If :attr:`offset` > 0, it is above the main diagonal. + - If :attr:`offset` < 0, it is below the main diagonal. + + The size of the new matrix will be calculated to make the specified diagonal + of the size of the last input dimension. + Note that for :attr:`offset` other than :math:`0`, the order of :attr:`dim1` + and :attr:`dim2` matters. Exchanging them is equivalent to changing the + sign of :attr:`offset`. + + Applying :meth:`torch.diagonal` to the output of this function with + the same arguments yields a matrix identical to input. However, + :meth:`torch.diagonal` has different default dimensions, so those + need to be explicitly specified. + + Args: + input (Tensor): the input tensor. Must be at least 1-dimensional. + offset (int, optional): which diagonal to consider. Default: 0 + (main diagonal). + dim1 (int, optional): first dimension with respect to which to + take diagonal. Default: -2. + dim2 (int, optional): second dimension with respect to which to + take diagonal. Default: -1. + + Example:: + + >>> a = torch.randn(2, 3) + >>> torch.diag_embed(a) + tensor([[[ 1.5410, 0.0000, 0.0000], + [ 0.0000, -0.2934, 0.0000], + [ 0.0000, 0.0000, -2.1788]], + + [[ 0.5684, 0.0000, 0.0000], + [ 0.0000, -1.0845, 0.0000], + [ 0.0000, 0.0000, -1.3986]]]) + + >>> torch.diag_embed(a, offset=1, dim1=0, dim2=2) + tensor([[[ 0.0000, 1.5410, 0.0000, 0.0000], + [ 0.0000, 0.5684, 0.0000, 0.0000]], + + [[ 0.0000, 0.0000, -0.2934, 0.0000], + [ 0.0000, 0.0000, -1.0845, 0.0000]], + + [[ 0.0000, 0.0000, 0.0000, -2.1788], + [ 0.0000, 0.0000, 0.0000, -1.3986]], + + [[ 0.0000, 0.0000, 0.0000, 0.0000], + [ 0.0000, 0.0000, 0.0000, 0.0000]]]) + """ + ... +def diagflat(input: Tensor, offset: _int = 0) -> Tensor: + r""" + diagflat(input, offset=0) -> Tensor + + - If :attr:`input` is a vector (1-D tensor), then returns a 2-D square tensor + with the elements of :attr:`input` as the diagonal. + - If :attr:`input` is a tensor with more than one dimension, then returns a + 2-D tensor with diagonal elements equal to a flattened :attr:`input`. + + The argument :attr:`offset` controls which diagonal to consider: + + - If :attr:`offset` = 0, it is the main diagonal. + - If :attr:`offset` > 0, it is above the main diagonal. + - If :attr:`offset` < 0, it is below the main diagonal. + + Args: + input (Tensor): the input tensor. + offset (int, optional): the diagonal to consider. Default: 0 (main + diagonal). + + Examples:: + + >>> a = torch.randn(3) + >>> a + tensor([-0.2956, -0.9068, 0.1695]) + >>> torch.diagflat(a) + tensor([[-0.2956, 0.0000, 0.0000], + [ 0.0000, -0.9068, 0.0000], + [ 0.0000, 0.0000, 0.1695]]) + >>> torch.diagflat(a, 1) + tensor([[ 0.0000, -0.2956, 0.0000, 0.0000], + [ 0.0000, 0.0000, -0.9068, 0.0000], + [ 0.0000, 0.0000, 0.0000, 0.1695], + [ 0.0000, 0.0000, 0.0000, 0.0000]]) + + >>> a = torch.randn(2, 2) + >>> a + tensor([[ 0.2094, -0.3018], + [-0.1516, 1.9342]]) + >>> torch.diagflat(a) + tensor([[ 0.2094, 0.0000, 0.0000, 0.0000], + [ 0.0000, -0.3018, 0.0000, 0.0000], + [ 0.0000, 0.0000, -0.1516, 0.0000], + [ 0.0000, 0.0000, 0.0000, 1.9342]]) + """ + ... +@overload +def diagonal(input: Tensor, offset: _int = 0, dim1: _int = 0, dim2: _int = 1) -> Tensor: + r""" + diagonal(input, offset=0, dim1=0, dim2=1) -> Tensor + + Returns a partial view of :attr:`input` with the its diagonal elements + with respect to :attr:`dim1` and :attr:`dim2` appended as a dimension + at the end of the shape. + + The argument :attr:`offset` controls which diagonal to consider: + + - If :attr:`offset` = 0, it is the main diagonal. + - If :attr:`offset` > 0, it is above the main diagonal. + - If :attr:`offset` < 0, it is below the main diagonal. + + Applying :meth:`torch.diag_embed` to the output of this function with + the same arguments yields a diagonal matrix with the diagonal entries + of the input. However, :meth:`torch.diag_embed` has different default + dimensions, so those need to be explicitly specified. + + Args: + input (Tensor): the input tensor. Must be at least 2-dimensional. + offset (int, optional): which diagonal to consider. Default: 0 + (main diagonal). + dim1 (int, optional): first dimension with respect to which to + take diagonal. Default: 0. + dim2 (int, optional): second dimension with respect to which to + take diagonal. Default: 1. + + .. note:: To take a batch diagonal, pass in dim1=-2, dim2=-1. + + Examples:: + + >>> a = torch.randn(3, 3) + >>> a + tensor([[-1.0854, 1.1431, -0.1752], + [ 0.8536, -0.0905, 0.0360], + [ 0.6927, -0.3735, -0.4945]]) + + + >>> torch.diagonal(a, 0) + tensor([-1.0854, -0.0905, -0.4945]) + + + >>> torch.diagonal(a, 1) + tensor([ 1.1431, 0.0360]) + + + >>> x = torch.randn(2, 5, 4, 2) + >>> torch.diagonal(x, offset=-1, dim1=1, dim2=2) + tensor([[[-1.2631, 0.3755, -1.5977, -1.8172], + [-1.1065, 1.0401, -0.2235, -0.7938]], + + [[-1.7325, -0.3081, 0.6166, 0.2335], + [ 1.0500, 0.7336, -0.3836, -1.1015]]]) + """ + ... +@overload +def diagonal(input: Tensor, *, outdim: Union[str, ellipsis, None], dim1: Union[str, ellipsis, None], dim2: Union[str, ellipsis, None], offset: _int = 0) -> Tensor: + r""" + diagonal(input, offset=0, dim1=0, dim2=1) -> Tensor + + Returns a partial view of :attr:`input` with the its diagonal elements + with respect to :attr:`dim1` and :attr:`dim2` appended as a dimension + at the end of the shape. + + The argument :attr:`offset` controls which diagonal to consider: + + - If :attr:`offset` = 0, it is the main diagonal. + - If :attr:`offset` > 0, it is above the main diagonal. + - If :attr:`offset` < 0, it is below the main diagonal. + + Applying :meth:`torch.diag_embed` to the output of this function with + the same arguments yields a diagonal matrix with the diagonal entries + of the input. However, :meth:`torch.diag_embed` has different default + dimensions, so those need to be explicitly specified. + + Args: + input (Tensor): the input tensor. Must be at least 2-dimensional. + offset (int, optional): which diagonal to consider. Default: 0 + (main diagonal). + dim1 (int, optional): first dimension with respect to which to + take diagonal. Default: 0. + dim2 (int, optional): second dimension with respect to which to + take diagonal. Default: 1. + + .. note:: To take a batch diagonal, pass in dim1=-2, dim2=-1. + + Examples:: + + >>> a = torch.randn(3, 3) + >>> a + tensor([[-1.0854, 1.1431, -0.1752], + [ 0.8536, -0.0905, 0.0360], + [ 0.6927, -0.3735, -0.4945]]) + + + >>> torch.diagonal(a, 0) + tensor([-1.0854, -0.0905, -0.4945]) + + + >>> torch.diagonal(a, 1) + tensor([ 1.1431, 0.0360]) + + + >>> x = torch.randn(2, 5, 4, 2) + >>> torch.diagonal(x, offset=-1, dim1=1, dim2=2) + tensor([[[-1.2631, 0.3755, -1.5977, -1.8172], + [-1.1065, 1.0401, -0.2235, -0.7938]], + + [[-1.7325, -0.3081, 0.6166, 0.2335], + [ 1.0500, 0.7336, -0.3836, -1.1015]]]) + """ + ... +def diagonal_copy(input: Tensor, offset: _int = 0, dim1: _int = 0, dim2: _int = 1, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.diagonal`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def diagonal_scatter(input: Tensor, src: Tensor, offset: _int = 0, dim1: _int = 0, dim2: _int = 1) -> Tensor: + r""" + diagonal_scatter(input, src, offset=0, dim1=0, dim2=1) -> Tensor + + Embeds the values of the :attr:`src` tensor into :attr:`input` along + the diagonal elements of :attr:`input`, with respect to :attr:`dim1` + and :attr:`dim2`. + + This function returns a tensor with fresh storage; it does not + return a view. + + The argument :attr:`offset` controls which diagonal to consider: + + - If :attr:`offset` = 0, it is the main diagonal. + - If :attr:`offset` > 0, it is above the main diagonal. + - If :attr:`offset` < 0, it is below the main diagonal. + + Args: + input (Tensor): the input tensor. Must be at least 2-dimensional. + src (Tensor): the tensor to embed into :attr:`input`. + offset (int, optional): which diagonal to consider. Default: 0 + (main diagonal). + dim1 (int, optional): first dimension with respect to which to + take diagonal. Default: 0. + dim2 (int, optional): second dimension with respect to which to + take diagonal. Default: 1. + + .. note:: + + :attr:`src` must be of the proper size in order to be embedded + into :attr:`input`. Specifically, it should have the same shape as + ``torch.diagonal(input, offset, dim1, dim2)`` + + Examples:: + + >>> a = torch.zeros(3, 3) + >>> a + tensor([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]) + + >>> torch.diagonal_scatter(a, torch.ones(3), 0) + tensor([[1., 0., 0.], + [0., 1., 0.], + [0., 0., 1.]]) + + >>> torch.diagonal_scatter(a, torch.ones(2), 1) + tensor([[0., 1., 0.], + [0., 0., 1.], + [0., 0., 0.]]) + """ + ... +def diff(input: Tensor, n: _int = 1, dim: _int = -1, prepend: Optional[Tensor] = None, append: Optional[Tensor] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + diff(input, n=1, dim=-1, prepend=None, append=None) -> Tensor + + Computes the n-th forward difference along the given dimension. + + The first-order differences are given by `out[i] = input[i + 1] - input[i]`. Higher-order + differences are calculated by using :func:`torch.diff` recursively. + + Args: + input (Tensor): the tensor to compute the differences on + n (int, optional): the number of times to recursively compute the difference + dim (int, optional): the dimension to compute the difference along. + Default is the last dimension. + prepend, append (Tensor, optional): values to prepend or append to + :attr:`input` along :attr:`dim` before computing the difference. + Their dimensions must be equivalent to that of input, and their shapes + must match input's shape except on :attr:`dim`. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([1, 3, 2]) + >>> torch.diff(a) + tensor([ 2, -1]) + >>> b = torch.tensor([4, 5]) + >>> torch.diff(a, append=b) + tensor([ 2, -1, 2, 1]) + >>> c = torch.tensor([[1, 2, 3], [3, 4, 5]]) + >>> torch.diff(c, dim=0) + tensor([[2, 2, 2]]) + >>> torch.diff(c, dim=1) + tensor([[1, 1], + [1, 1]]) + """ + ... +def digamma(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + digamma(input, *, out=None) -> Tensor + + Alias for :func:`torch.special.digamma`. + """ + ... +def dist(input: Tensor, other: Tensor, p: Union[Number, _complex] = 2) -> Tensor: + r""" + dist(input, other, p=2) -> Tensor + + Returns the p-norm of (:attr:`input` - :attr:`other`) + + The shapes of :attr:`input` and :attr:`other` must be + :ref:`broadcastable `. + + Args: + input (Tensor): the input tensor. + other (Tensor): the Right-hand-side input tensor + p (float, optional): the norm to be computed + + Example:: + + >>> x = torch.randn(4) + >>> x + tensor([-1.5393, -0.8675, 0.5916, 1.6321]) + >>> y = torch.randn(4) + >>> y + tensor([ 0.0967, -1.0511, 0.6295, 0.8360]) + >>> torch.dist(x, y, 3.5) + tensor(1.6727) + >>> torch.dist(x, y, 3) + tensor(1.6973) + >>> torch.dist(x, y, 0) + tensor(4.) + >>> torch.dist(x, y, 1) + tensor(2.6537) + """ + ... +def div(input: Union[Tensor, Number], other: Union[Tensor, Number], *, rounding_mode: Optional[str] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + div(input, other, *, rounding_mode=None, out=None) -> Tensor + + Divides each element of the input ``input`` by the corresponding element of + :attr:`other`. + + .. math:: + \text{out}_i = \frac{\text{input}_i}{\text{other}_i} + + .. note:: + By default, this performs a "true" division like Python 3. + See the :attr:`rounding_mode` argument for floor division. + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer, float, and complex inputs. + Always promotes integer types to the default scalar type. + + Args: + input (Tensor): the dividend + other (Tensor or Number): the divisor + + Keyword args: + rounding_mode (str, optional): Type of rounding applied to the result: + + * None - default behavior. Performs no rounding and, if both :attr:`input` and + :attr:`other` are integer types, promotes the inputs to the default scalar type. + Equivalent to true division in Python (the ``/`` operator) and NumPy's ``np.true_divide``. + * ``"trunc"`` - rounds the results of the division towards zero. + Equivalent to C-style integer division. + * ``"floor"`` - rounds the results of the division down. + Equivalent to floor division in Python (the ``//`` operator) and NumPy's ``np.floor_divide``. + + out (Tensor, optional): the output tensor. + + Examples:: + + >>> x = torch.tensor([ 0.3810, 1.2774, -0.2972, -0.3719, 0.4637]) + >>> torch.div(x, 0.5) + tensor([ 0.7620, 2.5548, -0.5944, -0.7438, 0.9274]) + + >>> a = torch.tensor([[-0.3711, -1.9353, -0.4605, -0.2917], + ... [ 0.1815, -1.0111, 0.9805, -1.5923], + ... [ 0.1062, 1.4581, 0.7759, -1.2344], + ... [-0.1830, -0.0313, 1.1908, -1.4757]]) + >>> b = torch.tensor([ 0.8032, 0.2930, -0.8113, -0.2308]) + >>> torch.div(a, b) + tensor([[-0.4620, -6.6051, 0.5676, 1.2639], + [ 0.2260, -3.4509, -1.2086, 6.8990], + [ 0.1322, 4.9764, -0.9564, 5.3484], + [-0.2278, -0.1068, -1.4678, 6.3938]]) + + >>> torch.div(a, b, rounding_mode='trunc') + tensor([[-0., -6., 0., 1.], + [ 0., -3., -1., 6.], + [ 0., 4., -0., 5.], + [-0., -0., -1., 6.]]) + + >>> torch.div(a, b, rounding_mode='floor') + tensor([[-1., -7., 0., 1.], + [ 0., -4., -2., 6.], + [ 0., 4., -1., 5.], + [-1., -1., -2., 6.]]) + """ + ... +@overload +def divide(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + divide(input, other, *, rounding_mode=None, out=None) -> Tensor + + Alias for :func:`torch.div`. + """ + ... +@overload +def divide(input: Tensor, other: Tensor, *, rounding_mode: Optional[str], out: Optional[Tensor] = None) -> Tensor: + r""" + divide(input, other, *, rounding_mode=None, out=None) -> Tensor + + Alias for :func:`torch.div`. + """ + ... +@overload +def divide(input: Tensor, other: Union[Number, _complex], *, rounding_mode: Optional[str]) -> Tensor: + r""" + divide(input, other, *, rounding_mode=None, out=None) -> Tensor + + Alias for :func:`torch.div`. + """ + ... +@overload +def divide(input: Tensor, other: Union[Number, _complex]) -> Tensor: + r""" + divide(input, other, *, rounding_mode=None, out=None) -> Tensor + + Alias for :func:`torch.div`. + """ + ... +def dot(input: Tensor, tensor: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + dot(input, tensor, *, out=None) -> Tensor + + Computes the dot product of two 1D tensors. + + .. note:: + + Unlike NumPy's dot, torch.dot intentionally only supports computing the dot product + of two 1D tensors with the same number of elements. + + Args: + input (Tensor): first tensor in the dot product, must be 1D. + tensor (Tensor): second tensor in the dot product, must be 1D. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.dot(torch.tensor([2, 3]), torch.tensor([2, 1])) + tensor(7) + + >>> t1, t2 = torch.tensor([0, 1]), torch.tensor([2, 3]) + >>> torch.dot(t1, t2) + tensor(3) + """ + ... +def dropout(input: Tensor, p: _float, train: _bool) -> Tensor: ... +def dropout_(input: Tensor, p: _float, train: _bool) -> Tensor: ... +def dsmm(input: Tensor, mat2: Tensor) -> Tensor: ... +@overload +def dsplit(input: Tensor, sections: _int) -> Tuple[Tensor, ...]: + r""" + dsplit(input, indices_or_sections) -> List of Tensors + + Splits :attr:`input`, a tensor with three or more dimensions, into multiple tensors + depthwise according to :attr:`indices_or_sections`. Each split is a view of + :attr:`input`. + + This is equivalent to calling torch.tensor_split(input, indices_or_sections, dim=2) + (the split dimension is 2), except that if :attr:`indices_or_sections` is an integer + it must evenly divide the split dimension or a runtime error will be thrown. + + This function is based on NumPy's :func:`numpy.dsplit`. + + Args: + input (Tensor): tensor to split. + indices_or_sections (int or list or tuple of ints): See argument in :func:`torch.tensor_split`. + + Example:: + >>> t = torch.arange(16.0).reshape(2, 2, 4) + >>> t + tensor([[[ 0., 1., 2., 3.], + [ 4., 5., 6., 7.]], + [[ 8., 9., 10., 11.], + [12., 13., 14., 15.]]]) + >>> torch.dsplit(t, 2) + (tensor([[[ 0., 1.], + [ 4., 5.]], + [[ 8., 9.], + [12., 13.]]]), + tensor([[[ 2., 3.], + [ 6., 7.]], + [[10., 11.], + [14., 15.]]])) + + >>> torch.dsplit(t, [3, 6]) + (tensor([[[ 0., 1., 2.], + [ 4., 5., 6.]], + [[ 8., 9., 10.], + [12., 13., 14.]]]), + tensor([[[ 3.], + [ 7.]], + [[11.], + [15.]]]), + tensor([], size=(2, 2, 0))) + """ + ... +@overload +def dsplit(input: Tensor, indices: _size) -> Tuple[Tensor, ...]: + r""" + dsplit(input, indices_or_sections) -> List of Tensors + + Splits :attr:`input`, a tensor with three or more dimensions, into multiple tensors + depthwise according to :attr:`indices_or_sections`. Each split is a view of + :attr:`input`. + + This is equivalent to calling torch.tensor_split(input, indices_or_sections, dim=2) + (the split dimension is 2), except that if :attr:`indices_or_sections` is an integer + it must evenly divide the split dimension or a runtime error will be thrown. + + This function is based on NumPy's :func:`numpy.dsplit`. + + Args: + input (Tensor): tensor to split. + indices_or_sections (int or list or tuple of ints): See argument in :func:`torch.tensor_split`. + + Example:: + >>> t = torch.arange(16.0).reshape(2, 2, 4) + >>> t + tensor([[[ 0., 1., 2., 3.], + [ 4., 5., 6., 7.]], + [[ 8., 9., 10., 11.], + [12., 13., 14., 15.]]]) + >>> torch.dsplit(t, 2) + (tensor([[[ 0., 1.], + [ 4., 5.]], + [[ 8., 9.], + [12., 13.]]]), + tensor([[[ 2., 3.], + [ 6., 7.]], + [[10., 11.], + [14., 15.]]])) + + >>> torch.dsplit(t, [3, 6]) + (tensor([[[ 0., 1., 2.], + [ 4., 5., 6.]], + [[ 8., 9., 10.], + [12., 13., 14.]]]), + tensor([[[ 3.], + [ 7.]], + [[11.], + [15.]]]), + tensor([], size=(2, 2, 0))) + """ + ... +def dstack(tensors: Union[Tuple[Tensor, ...], List[Tensor]], *, out: Optional[Tensor] = None) -> Tensor: + r""" + dstack(tensors, *, out=None) -> Tensor + + Stack tensors in sequence depthwise (along third axis). + + This is equivalent to concatenation along the third axis after 1-D and 2-D tensors have been reshaped by :func:`torch.atleast_3d`. + + Args: + tensors (sequence of Tensors): sequence of tensors to concatenate + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([1, 2, 3]) + >>> b = torch.tensor([4, 5, 6]) + >>> torch.dstack((a,b)) + tensor([[[1, 4], + [2, 5], + [3, 6]]]) + >>> a = torch.tensor([[1],[2],[3]]) + >>> b = torch.tensor([[4],[5],[6]]) + >>> torch.dstack((a,b)) + tensor([[[1, 4]], + [[2, 5]], + [[3, 6]]]) + """ + ... +def embedding(weight: Tensor, indices: Tensor, padding_idx: Union[_int, SymInt] = -1, scale_grad_by_freq: _bool = False, sparse: _bool = False) -> Tensor: ... +@overload +def embedding_bag(weight: Tensor, indices: Tensor, offsets: Tensor, scale_grad_by_freq: _bool, mode: _int, sparse: _bool, per_sample_weights: Optional[Tensor], include_last_offset: _bool, padding_idx: Optional[_int]) -> Tuple[Tensor, Tensor, Tensor, Tensor]: ... +@overload +def embedding_bag(weight: Tensor, indices: Tensor, offsets: Tensor, scale_grad_by_freq: _bool = False, mode: _int = 0, sparse: _bool = False, per_sample_weights: Optional[Tensor] = None, include_last_offset: _bool = False) -> Tuple[Tensor, Tensor, Tensor, Tensor]: ... +def embedding_renorm_(input: Tensor, indices: Tensor, max_norm: _float, norm_type: _float) -> Tensor: ... +@overload +def empty(size: Sequence[Union[_int, SymInt]], *, memory_format: Optional[memory_format] = None, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + empty(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False, memory_format=torch.contiguous_format) -> Tensor + + Returns a tensor filled with uninitialized data. The shape of the tensor is + defined by the variable argument :attr:`size`. + + .. note:: + If :func:`torch.use_deterministic_algorithms()` and + :attr:`torch.utils.deterministic.fill_uninitialized_memory` are both set to + ``True``, the output tensor is initialized to prevent any possible + nondeterministic behavior from using the data as an input to an operation. + Floating point and complex tensors are filled with NaN, and integer tensors + are filled with the maximum value. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.contiguous_format``. + + Example:: + + >>> torch.empty((2,3), dtype=torch.int64) + tensor([[ 9.4064e+13, 2.8000e+01, 9.3493e+13], + [ 7.5751e+18, 7.1428e+18, 7.5955e+18]]) + """ + ... +@overload +def empty(*size: Union[_int, SymInt], memory_format: Optional[memory_format] = None, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + empty(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False, memory_format=torch.contiguous_format) -> Tensor + + Returns a tensor filled with uninitialized data. The shape of the tensor is + defined by the variable argument :attr:`size`. + + .. note:: + If :func:`torch.use_deterministic_algorithms()` and + :attr:`torch.utils.deterministic.fill_uninitialized_memory` are both set to + ``True``, the output tensor is initialized to prevent any possible + nondeterministic behavior from using the data as an input to an operation. + Floating point and complex tensors are filled with NaN, and integer tensors + are filled with the maximum value. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.contiguous_format``. + + Example:: + + >>> torch.empty((2,3), dtype=torch.int64) + tensor([[ 9.4064e+13, 2.8000e+01, 9.3493e+13], + [ 7.5751e+18, 7.1428e+18, 7.5955e+18]]) + """ + ... +@overload +def empty(size: _size, *, names: Optional[Sequence[Union[str, ellipsis, None]]], memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + empty(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False, memory_format=torch.contiguous_format) -> Tensor + + Returns a tensor filled with uninitialized data. The shape of the tensor is + defined by the variable argument :attr:`size`. + + .. note:: + If :func:`torch.use_deterministic_algorithms()` and + :attr:`torch.utils.deterministic.fill_uninitialized_memory` are both set to + ``True``, the output tensor is initialized to prevent any possible + nondeterministic behavior from using the data as an input to an operation. + Floating point and complex tensors are filled with NaN, and integer tensors + are filled with the maximum value. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.contiguous_format``. + + Example:: + + >>> torch.empty((2,3), dtype=torch.int64) + tensor([[ 9.4064e+13, 2.8000e+01, 9.3493e+13], + [ 7.5751e+18, 7.1428e+18, 7.5955e+18]]) + """ + ... +@overload +def empty(*size: _int, names: Optional[Sequence[Union[str, ellipsis, None]]], memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + empty(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False, memory_format=torch.contiguous_format) -> Tensor + + Returns a tensor filled with uninitialized data. The shape of the tensor is + defined by the variable argument :attr:`size`. + + .. note:: + If :func:`torch.use_deterministic_algorithms()` and + :attr:`torch.utils.deterministic.fill_uninitialized_memory` are both set to + ``True``, the output tensor is initialized to prevent any possible + nondeterministic behavior from using the data as an input to an operation. + Floating point and complex tensors are filled with NaN, and integer tensors + are filled with the maximum value. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.contiguous_format``. + + Example:: + + >>> torch.empty((2,3), dtype=torch.int64) + tensor([[ 9.4064e+13, 2.8000e+01, 9.3493e+13], + [ 7.5751e+18, 7.1428e+18, 7.5955e+18]]) + """ + ... +def empty_like(input: Tensor, *, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + empty_like(input, *, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor + + Returns an uninitialized tensor with the same size as :attr:`input`. + ``torch.empty_like(input)`` is equivalent to + ``torch.empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``. + + .. note:: + If :func:`torch.use_deterministic_algorithms()` and + :attr:`torch.utils.deterministic.fill_uninitialized_memory` are both set to + ``True``, the output tensor is initialized to prevent any possible + nondeterministic behavior from using the data as an input to an operation. + Floating point and complex tensors are filled with NaN, and integer tensors + are filled with the maximum value. + + Args: + input (Tensor): the size of :attr:`input` will determine size of the output tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. + Default: if ``None``, defaults to the dtype of :attr:`input`. + layout (:class:`torch.layout`, optional): the desired layout of returned tensor. + Default: if ``None``, defaults to the layout of :attr:`input`. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, defaults to the device of :attr:`input`. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + + Example:: + + >>> a=torch.empty((2,3), dtype=torch.int32, device = 'cuda') + >>> torch.empty_like(a) + tensor([[0, 0, 0], + [0, 0, 0]], device='cuda:0', dtype=torch.int32) + """ + ... +def empty_permuted(size: Sequence[Union[_int, SymInt]], physical_layout: _size, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + empty_permuted(size, physical_layout, *, dtype=None, layout=None, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Creates an uninitialized, non-overlapping and dense tensor with the + specified :attr:`size`, with :attr:`physical_layout` specifying how the + dimensions are physically laid out in memory (each logical dimension is listed + from outermost to innermost). :attr:`physical_layout` is a generalization + of NCHW/NHWC notation: if each dimension is assigned a number according to + what order they occur in size (N=0, C=1, H=2, W=3), then NCHW is ``(0, 1, 2, 3)`` + while NHWC is ``(0, 2, 3, 1)``. Equivalently, the strides of the output + tensor ``t`` are such that ``t.stride(physical_layout[i]) == contiguous_strides[i]`` + (notably, this function is *not* equivalent to ``torch.empty(size).permute(physical_layout)``). + + Unlike :func:`torch.empty_strided`, this is guaranteed to produce a dense + tensor with no overlaps. If possible, prefer using this function over + :func:`torch.empty_strided` or manual use of :func:`torch.as_strided`. + + .. note:: + If :func:`torch.use_deterministic_algorithms()` and + :attr:`torch.utils.deterministic.fill_uninitialized_memory` are both set to + ``True``, the output tensor is initialized to prevent any possible + nondeterministic behavior from using the data as an input to an operation. + Floating point and complex tensors are filled with NaN, and integer tensors + are filled with the maximum value. + + Args: + size (tuple of int): the shape of the output tensor + physical_layout (tuple of int): the ordering of dimensions physically in memory + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Examples: + + >>> torch.empty((2, 3, 5, 7)).stride() + (105, 35, 7, 1) + >>> torch.empty_permuted((2, 3, 5, 7), (0, 1, 2, 3)).stride() + (105, 35, 7, 1) + >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).stride() + (105, 1, 21, 3) + >>> torch.empty_permuted((2, 3, 5, 7), (0, 2, 3, 1)).stride() + (105, 1, 21, 3) + >>> torch.empty_permuted((2, 3, 5, 7), (0, 2, 3, 1)).dim_order() + (0, 2, 3, 1) + """ + ... +def empty_quantized(size: _size, qtensor: Tensor, *, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... +def empty_strided(size: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + empty_strided(size, stride, *, dtype=None, layout=None, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Creates a tensor with the specified :attr:`size` and :attr:`stride` and filled with undefined data. + + .. warning:: + If the constructed tensor is "overlapped" (with multiple indices referring to the same element + in memory) its behavior is undefined. + + .. note:: + If :func:`torch.use_deterministic_algorithms()` and + :attr:`torch.utils.deterministic.fill_uninitialized_memory` are both set to + ``True``, the output tensor is initialized to prevent any possible + nondeterministic behavior from using the data as an input to an operation. + Floating point and complex tensors are filled with NaN, and integer tensors + are filled with the maximum value. + + Args: + size (tuple of int): the shape of the output tensor + stride (tuple of int): the strides of the output tensor + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> a = torch.empty_strided((2, 3), (1, 2)) + >>> a + tensor([[8.9683e-44, 4.4842e-44, 5.1239e+07], + [0.0000e+00, 0.0000e+00, 3.0705e-41]]) + >>> a.stride() + (1, 2) + >>> a.size() + torch.Size([2, 3]) + """ + ... +@overload +def eq(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + eq(input, other, *, out=None) -> Tensor + + Computes element-wise equality + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or float): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is equal to :attr:`other` and False elsewhere + + Example:: + + >>> torch.eq(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[ True, False], + [False, True]]) + """ + ... +@overload +def eq(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + eq(input, other, *, out=None) -> Tensor + + Computes element-wise equality + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or float): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is equal to :attr:`other` and False elsewhere + + Example:: + + >>> torch.eq(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[ True, False], + [False, True]]) + """ + ... +def equal(input: Tensor, other: Tensor) -> _bool: + r""" + equal(input, other) -> bool + + ``True`` if two tensors have the same size and elements, ``False`` otherwise. + + Example:: + + >>> torch.equal(torch.tensor([1, 2]), torch.tensor([1, 2])) + True + """ + ... +def erf(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + erf(input, *, out=None) -> Tensor + + Alias for :func:`torch.special.erf`. + """ + ... +def erf_(input: Tensor) -> Tensor: ... +def erfc(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + erfc(input, *, out=None) -> Tensor + + Alias for :func:`torch.special.erfc`. + """ + ... +def erfc_(input: Tensor) -> Tensor: ... +def erfinv(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + erfinv(input, *, out=None) -> Tensor + + Alias for :func:`torch.special.erfinv`. + """ + ... +def exp(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + exp(input, *, out=None) -> Tensor + + Returns a new tensor with the exponential of the elements + of the input tensor :attr:`input`. + + .. math:: + y_{i} = e^{x_{i}} + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.exp(torch.tensor([0, math.log(2.)])) + tensor([ 1., 2.]) + """ + ... +def exp2(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + exp2(input, *, out=None) -> Tensor + + Alias for :func:`torch.special.exp2`. + """ + ... +def exp2_(input: Tensor) -> Tensor: ... +def exp_(input: Tensor) -> Tensor: ... +def expand_copy(input: Tensor, size: Sequence[Union[_int, SymInt]], *, implicit: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.expand`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def expm1(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + expm1(input, *, out=None) -> Tensor + + Alias for :func:`torch.special.expm1`. + """ + ... +def expm1_(input: Tensor) -> Tensor: ... +@overload +def eye(n: Union[_int, SymInt], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + eye(n, m=None, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a 2-D tensor with ones on the diagonal and zeros elsewhere. + + Args: + n (int): the number of rows + m (int, optional): the number of columns with default being :attr:`n` + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 2-D tensor with ones on the diagonal and zeros elsewhere + + Example:: + + >>> torch.eye(3) + tensor([[ 1., 0., 0.], + [ 0., 1., 0.], + [ 0., 0., 1.]]) + """ + ... +@overload +def eye(n: Union[_int, SymInt], m: Union[_int, SymInt], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + eye(n, m=None, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a 2-D tensor with ones on the diagonal and zeros elsewhere. + + Args: + n (int): the number of rows + m (int, optional): the number of columns with default being :attr:`n` + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 2-D tensor with ones on the diagonal and zeros elsewhere + + Example:: + + >>> torch.eye(3) + tensor([[ 1., 0., 0.], + [ 0., 1., 0.], + [ 0., 0., 1.]]) + """ + ... +def fake_quantize_per_channel_affine(input: Tensor, scale: Tensor, zero_point: Tensor, axis: _int, quant_min: _int, quant_max: _int) -> Tensor: + r""" + fake_quantize_per_channel_affine(input, scale, zero_point, axis, quant_min, quant_max) -> Tensor + + Returns a new tensor with the data in :attr:`input` fake quantized per channel using :attr:`scale`, + :attr:`zero_point`, :attr:`quant_min` and :attr:`quant_max`, across the channel specified by :attr:`axis`. + + .. math:: + \text{output} = ( + min( + \text{quant\_max}, + max( + \text{quant\_min}, + \text{std::nearby\_int}(\text{input} / \text{scale}) + \text{zero\_point} + ) + ) - \text{zero\_point} + ) \times \text{scale} + + Args: + input (Tensor): the input value(s), in ``torch.float32`` + scale (Tensor): quantization scale, per channel in ``torch.float32`` + zero_point (Tensor): quantization zero_point, per channel in ``torch.int32`` or ``torch.half`` or ``torch.float32`` + axis (int32): channel axis + quant_min (int64): lower bound of the quantized domain + quant_max (int64): upper bound of the quantized domain + + Returns: + Tensor: A newly fake_quantized per channel ``torch.float32`` tensor + + Example:: + + >>> x = torch.randn(2, 2, 2) + >>> x + tensor([[[-0.2525, -0.0466], + [ 0.3491, -0.2168]], + + [[-0.5906, 1.6258], + [ 0.6444, -0.0542]]]) + >>> scales = (torch.randn(2) + 1) * 0.05 + >>> scales + tensor([0.0475, 0.0486]) + >>> zero_points = torch.zeros(2).to(torch.int32) + >>> zero_points + tensor([0, 0]) + >>> torch.fake_quantize_per_channel_affine(x, scales, zero_points, 1, 0, 255) + tensor([[[0.0000, 0.0000], + [0.3405, 0.0000]], + + [[0.0000, 1.6134], + [0.6323, 0.0000]]]) + """ + ... +@overload +def fake_quantize_per_tensor_affine(input: Tensor, scale: _float, zero_point: _int, quant_min: _int, quant_max: _int) -> Tensor: + r""" + fake_quantize_per_tensor_affine(input, scale, zero_point, quant_min, quant_max) -> Tensor + + Returns a new tensor with the data in :attr:`input` fake quantized using :attr:`scale`, + :attr:`zero_point`, :attr:`quant_min` and :attr:`quant_max`. + + .. math:: + \text{output} = ( + min( + \text{quant\_max}, + max( + \text{quant\_min}, + \text{std::nearby\_int}(\text{input} / \text{scale}) + \text{zero\_point} + ) + ) - \text{zero\_point} + ) \times \text{scale} + + Args: + input (Tensor): the input value(s), ``torch.float32`` tensor + scale (double scalar or ``float32`` Tensor): quantization scale + zero_point (int64 scalar or ``int32`` Tensor): quantization zero_point + quant_min (int64): lower bound of the quantized domain + quant_max (int64): upper bound of the quantized domain + + Returns: + Tensor: A newly fake_quantized ``torch.float32`` tensor + + Example:: + + >>> x = torch.randn(4) + >>> x + tensor([ 0.0552, 0.9730, 0.3973, -1.0780]) + >>> torch.fake_quantize_per_tensor_affine(x, 0.1, 0, 0, 255) + tensor([0.1000, 1.0000, 0.4000, 0.0000]) + >>> torch.fake_quantize_per_tensor_affine(x, torch.tensor(0.1), torch.tensor(0), 0, 255) + tensor([0.1000, 1.0000, 0.4000, 0.0000]) + """ + ... +@overload +def fake_quantize_per_tensor_affine(input: Tensor, scale: Tensor, zero_point: Tensor, quant_min: _int, quant_max: _int) -> Tensor: + r""" + fake_quantize_per_tensor_affine(input, scale, zero_point, quant_min, quant_max) -> Tensor + + Returns a new tensor with the data in :attr:`input` fake quantized using :attr:`scale`, + :attr:`zero_point`, :attr:`quant_min` and :attr:`quant_max`. + + .. math:: + \text{output} = ( + min( + \text{quant\_max}, + max( + \text{quant\_min}, + \text{std::nearby\_int}(\text{input} / \text{scale}) + \text{zero\_point} + ) + ) - \text{zero\_point} + ) \times \text{scale} + + Args: + input (Tensor): the input value(s), ``torch.float32`` tensor + scale (double scalar or ``float32`` Tensor): quantization scale + zero_point (int64 scalar or ``int32`` Tensor): quantization zero_point + quant_min (int64): lower bound of the quantized domain + quant_max (int64): upper bound of the quantized domain + + Returns: + Tensor: A newly fake_quantized ``torch.float32`` tensor + + Example:: + + >>> x = torch.randn(4) + >>> x + tensor([ 0.0552, 0.9730, 0.3973, -1.0780]) + >>> torch.fake_quantize_per_tensor_affine(x, 0.1, 0, 0, 255) + tensor([0.1000, 1.0000, 0.4000, 0.0000]) + >>> torch.fake_quantize_per_tensor_affine(x, torch.tensor(0.1), torch.tensor(0), 0, 255) + tensor([0.1000, 1.0000, 0.4000, 0.0000]) + """ + ... +def fbgemm_linear_fp16_weight(input: Tensor, packed_weight: Tensor, bias: Tensor) -> Tensor: ... +def fbgemm_linear_fp16_weight_fp32_activation(input: Tensor, packed_weight: Tensor, bias: Tensor) -> Tensor: ... +def fbgemm_linear_int8_weight(input: Tensor, weight: Tensor, packed: Tensor, col_offsets: Tensor, weight_scale: Union[Number, _complex], weight_zero_point: Union[Number, _complex], bias: Tensor) -> Tensor: ... +def fbgemm_linear_int8_weight_fp32_activation(input: Tensor, weight: Tensor, packed: Tensor, col_offsets: Tensor, weight_scale: Union[Number, _complex], weight_zero_point: Union[Number, _complex], bias: Tensor) -> Tensor: ... +def fbgemm_linear_quantize_weight(input: Tensor) -> Tuple[Tensor, Tensor, _float, _int]: ... +def fbgemm_pack_gemm_matrix_fp16(input: Tensor) -> Tensor: ... +@overload +def fbgemm_pack_quantized_matrix(input: Tensor) -> Tensor: ... +@overload +def fbgemm_pack_quantized_matrix(input: Tensor, K: _int, N: _int) -> Tensor: ... +def feature_alpha_dropout(input: Tensor, p: _float, train: _bool) -> Tensor: ... +def feature_alpha_dropout_(input: Tensor, p: _float, train: _bool) -> Tensor: ... +def feature_dropout(input: Tensor, p: _float, train: _bool) -> Tensor: ... +def feature_dropout_(input: Tensor, p: _float, train: _bool) -> Tensor: ... +@overload +def fill(input: Tensor, value: Tensor) -> Tensor: ... +@overload +def fill(input: Tensor, value: Union[Number, _complex]) -> Tensor: ... +@overload +def fill_(input: Tensor, value: Tensor) -> Tensor: ... +@overload +def fill_(input: Tensor, value: Union[Number, _complex]) -> Tensor: ... +def fix(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + fix(input, *, out=None) -> Tensor + + Alias for :func:`torch.trunc` + """ + ... +def fix_(input: Tensor) -> Tensor: ... +@overload +def flatten(input: Tensor, start_dim: _int = 0, end_dim: _int = -1) -> Tensor: + r""" + flatten(input, start_dim=0, end_dim=-1) -> Tensor + + Flattens :attr:`input` by reshaping it into a one-dimensional tensor. If :attr:`start_dim` or :attr:`end_dim` + are passed, only dimensions starting with :attr:`start_dim` and ending with :attr:`end_dim` are flattened. + The order of elements in :attr:`input` is unchanged. + + Unlike NumPy's flatten, which always copies input's data, this function may return the original object, a view, + or copy. If no dimensions are flattened, then the original object :attr:`input` is returned. Otherwise, if input can + be viewed as the flattened shape, then that view is returned. Finally, only if the input cannot be viewed as the + flattened shape is input's data copied. See :meth:`torch.Tensor.view` for details on when a view will be returned. + + .. note:: + Flattening a zero-dimensional tensor will return a one-dimensional view. + + Args: + input (Tensor): the input tensor. + start_dim (int): the first dim to flatten + end_dim (int): the last dim to flatten + + Example:: + + >>> t = torch.tensor([[[1, 2], + ... [3, 4]], + ... [[5, 6], + ... [7, 8]]]) + >>> torch.flatten(t) + tensor([1, 2, 3, 4, 5, 6, 7, 8]) + >>> torch.flatten(t, start_dim=1) + tensor([[1, 2, 3, 4], + [5, 6, 7, 8]]) + """ + ... +@overload +def flatten(input: Tensor, start_dim: _int, end_dim: _int, out_dim: Union[str, ellipsis, None]) -> Tensor: + r""" + flatten(input, start_dim=0, end_dim=-1) -> Tensor + + Flattens :attr:`input` by reshaping it into a one-dimensional tensor. If :attr:`start_dim` or :attr:`end_dim` + are passed, only dimensions starting with :attr:`start_dim` and ending with :attr:`end_dim` are flattened. + The order of elements in :attr:`input` is unchanged. + + Unlike NumPy's flatten, which always copies input's data, this function may return the original object, a view, + or copy. If no dimensions are flattened, then the original object :attr:`input` is returned. Otherwise, if input can + be viewed as the flattened shape, then that view is returned. Finally, only if the input cannot be viewed as the + flattened shape is input's data copied. See :meth:`torch.Tensor.view` for details on when a view will be returned. + + .. note:: + Flattening a zero-dimensional tensor will return a one-dimensional view. + + Args: + input (Tensor): the input tensor. + start_dim (int): the first dim to flatten + end_dim (int): the last dim to flatten + + Example:: + + >>> t = torch.tensor([[[1, 2], + ... [3, 4]], + ... [[5, 6], + ... [7, 8]]]) + >>> torch.flatten(t) + tensor([1, 2, 3, 4, 5, 6, 7, 8]) + >>> torch.flatten(t, start_dim=1) + tensor([[1, 2, 3, 4], + [5, 6, 7, 8]]) + """ + ... +@overload +def flatten(input: Tensor, start_dim: Union[str, ellipsis, None], end_dim: Union[str, ellipsis, None], out_dim: Union[str, ellipsis, None]) -> Tensor: + r""" + flatten(input, start_dim=0, end_dim=-1) -> Tensor + + Flattens :attr:`input` by reshaping it into a one-dimensional tensor. If :attr:`start_dim` or :attr:`end_dim` + are passed, only dimensions starting with :attr:`start_dim` and ending with :attr:`end_dim` are flattened. + The order of elements in :attr:`input` is unchanged. + + Unlike NumPy's flatten, which always copies input's data, this function may return the original object, a view, + or copy. If no dimensions are flattened, then the original object :attr:`input` is returned. Otherwise, if input can + be viewed as the flattened shape, then that view is returned. Finally, only if the input cannot be viewed as the + flattened shape is input's data copied. See :meth:`torch.Tensor.view` for details on when a view will be returned. + + .. note:: + Flattening a zero-dimensional tensor will return a one-dimensional view. + + Args: + input (Tensor): the input tensor. + start_dim (int): the first dim to flatten + end_dim (int): the last dim to flatten + + Example:: + + >>> t = torch.tensor([[[1, 2], + ... [3, 4]], + ... [[5, 6], + ... [7, 8]]]) + >>> torch.flatten(t) + tensor([1, 2, 3, 4, 5, 6, 7, 8]) + >>> torch.flatten(t, start_dim=1) + tensor([[1, 2, 3, 4], + [5, 6, 7, 8]]) + """ + ... +@overload +def flatten(input: Tensor, dims: Sequence[Union[str, ellipsis, None]], out_dim: Union[str, ellipsis, None]) -> Tensor: + r""" + flatten(input, start_dim=0, end_dim=-1) -> Tensor + + Flattens :attr:`input` by reshaping it into a one-dimensional tensor. If :attr:`start_dim` or :attr:`end_dim` + are passed, only dimensions starting with :attr:`start_dim` and ending with :attr:`end_dim` are flattened. + The order of elements in :attr:`input` is unchanged. + + Unlike NumPy's flatten, which always copies input's data, this function may return the original object, a view, + or copy. If no dimensions are flattened, then the original object :attr:`input` is returned. Otherwise, if input can + be viewed as the flattened shape, then that view is returned. Finally, only if the input cannot be viewed as the + flattened shape is input's data copied. See :meth:`torch.Tensor.view` for details on when a view will be returned. + + .. note:: + Flattening a zero-dimensional tensor will return a one-dimensional view. + + Args: + input (Tensor): the input tensor. + start_dim (int): the first dim to flatten + end_dim (int): the last dim to flatten + + Example:: + + >>> t = torch.tensor([[[1, 2], + ... [3, 4]], + ... [[5, 6], + ... [7, 8]]]) + >>> torch.flatten(t) + tensor([1, 2, 3, 4, 5, 6, 7, 8]) + >>> torch.flatten(t, start_dim=1) + tensor([[1, 2, 3, 4], + [5, 6, 7, 8]]) + """ + ... +def flip(input: Tensor, dims: _size) -> Tensor: + r""" + flip(input, dims) -> Tensor + + Reverse the order of an n-D tensor along given axis in dims. + + .. note:: + `torch.flip` makes a copy of :attr:`input`'s data. This is different from NumPy's `np.flip`, + which returns a view in constant time. Since copying a tensor's data is more work than viewing that data, + `torch.flip` is expected to be slower than `np.flip`. + + Args: + input (Tensor): the input tensor. + dims (a list or tuple): axis to flip on + + Example:: + + >>> x = torch.arange(8).view(2, 2, 2) + >>> x + tensor([[[ 0, 1], + [ 2, 3]], + + [[ 4, 5], + [ 6, 7]]]) + >>> torch.flip(x, [0, 1]) + tensor([[[ 6, 7], + [ 4, 5]], + + [[ 2, 3], + [ 0, 1]]]) + """ + ... +def fliplr(input: Tensor) -> Tensor: + r""" + fliplr(input) -> Tensor + + Flip tensor in the left/right direction, returning a new tensor. + + Flip the entries in each row in the left/right direction. + Columns are preserved, but appear in a different order than before. + + Note: + Requires the tensor to be at least 2-D. + + .. note:: + `torch.fliplr` makes a copy of :attr:`input`'s data. This is different from NumPy's `np.fliplr`, + which returns a view in constant time. Since copying a tensor's data is more work than viewing that data, + `torch.fliplr` is expected to be slower than `np.fliplr`. + + Args: + input (Tensor): Must be at least 2-dimensional. + + Example:: + + >>> x = torch.arange(4).view(2, 2) + >>> x + tensor([[0, 1], + [2, 3]]) + >>> torch.fliplr(x) + tensor([[1, 0], + [3, 2]]) + """ + ... +def flipud(input: Tensor) -> Tensor: + r""" + flipud(input) -> Tensor + + Flip tensor in the up/down direction, returning a new tensor. + + Flip the entries in each column in the up/down direction. + Rows are preserved, but appear in a different order than before. + + Note: + Requires the tensor to be at least 1-D. + + .. note:: + `torch.flipud` makes a copy of :attr:`input`'s data. This is different from NumPy's `np.flipud`, + which returns a view in constant time. Since copying a tensor's data is more work than viewing that data, + `torch.flipud` is expected to be slower than `np.flipud`. + + Args: + input (Tensor): Must be at least 1-dimensional. + + Example:: + + >>> x = torch.arange(4).view(2, 2) + >>> x + tensor([[0, 1], + [2, 3]]) + >>> torch.flipud(x) + tensor([[2, 3], + [0, 1]]) + """ + ... +@overload +def float_power(input: Tensor, exponent: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + float_power(input, exponent, *, out=None) -> Tensor + + Raises :attr:`input` to the power of :attr:`exponent`, elementwise, in double precision. + If neither input is complex returns a ``torch.float64`` tensor, + and if one or more inputs is complex returns a ``torch.complex128`` tensor. + + .. note:: + This function always computes in double precision, unlike :func:`torch.pow`, + which implements more typical :ref:`type promotion `. + This is useful when the computation needs to be performed in a wider or more precise dtype, + or the results of the computation may contain fractional values not representable in the input dtypes, + like when an integer base is raised to a negative integer exponent. + + Args: + input (Tensor or Number): the base value(s) + exponent (Tensor or Number): the exponent value(s) + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randint(10, (4,)) + >>> a + tensor([6, 4, 7, 1]) + >>> torch.float_power(a, 2) + tensor([36., 16., 49., 1.], dtype=torch.float64) + + >>> a = torch.arange(1, 5) + >>> a + tensor([ 1, 2, 3, 4]) + >>> exp = torch.tensor([2, -3, 4, -5]) + >>> exp + tensor([ 2, -3, 4, -5]) + >>> torch.float_power(a, exp) + tensor([1.0000e+00, 1.2500e-01, 8.1000e+01, 9.7656e-04], dtype=torch.float64) + """ + ... +@overload +def float_power(self: Union[Number, _complex], exponent: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + float_power(input, exponent, *, out=None) -> Tensor + + Raises :attr:`input` to the power of :attr:`exponent`, elementwise, in double precision. + If neither input is complex returns a ``torch.float64`` tensor, + and if one or more inputs is complex returns a ``torch.complex128`` tensor. + + .. note:: + This function always computes in double precision, unlike :func:`torch.pow`, + which implements more typical :ref:`type promotion `. + This is useful when the computation needs to be performed in a wider or more precise dtype, + or the results of the computation may contain fractional values not representable in the input dtypes, + like when an integer base is raised to a negative integer exponent. + + Args: + input (Tensor or Number): the base value(s) + exponent (Tensor or Number): the exponent value(s) + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randint(10, (4,)) + >>> a + tensor([6, 4, 7, 1]) + >>> torch.float_power(a, 2) + tensor([36., 16., 49., 1.], dtype=torch.float64) + + >>> a = torch.arange(1, 5) + >>> a + tensor([ 1, 2, 3, 4]) + >>> exp = torch.tensor([2, -3, 4, -5]) + >>> exp + tensor([ 2, -3, 4, -5]) + >>> torch.float_power(a, exp) + tensor([1.0000e+00, 1.2500e-01, 8.1000e+01, 9.7656e-04], dtype=torch.float64) + """ + ... +@overload +def float_power(input: Tensor, exponent: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + float_power(input, exponent, *, out=None) -> Tensor + + Raises :attr:`input` to the power of :attr:`exponent`, elementwise, in double precision. + If neither input is complex returns a ``torch.float64`` tensor, + and if one or more inputs is complex returns a ``torch.complex128`` tensor. + + .. note:: + This function always computes in double precision, unlike :func:`torch.pow`, + which implements more typical :ref:`type promotion `. + This is useful when the computation needs to be performed in a wider or more precise dtype, + or the results of the computation may contain fractional values not representable in the input dtypes, + like when an integer base is raised to a negative integer exponent. + + Args: + input (Tensor or Number): the base value(s) + exponent (Tensor or Number): the exponent value(s) + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randint(10, (4,)) + >>> a + tensor([6, 4, 7, 1]) + >>> torch.float_power(a, 2) + tensor([36., 16., 49., 1.], dtype=torch.float64) + + >>> a = torch.arange(1, 5) + >>> a + tensor([ 1, 2, 3, 4]) + >>> exp = torch.tensor([2, -3, 4, -5]) + >>> exp + tensor([ 2, -3, 4, -5]) + >>> torch.float_power(a, exp) + tensor([1.0000e+00, 1.2500e-01, 8.1000e+01, 9.7656e-04], dtype=torch.float64) + """ + ... +def floor(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + floor(input, *, out=None) -> Tensor + + Returns a new tensor with the floor of the elements of :attr:`input`, + the largest integer less than or equal to each element. + + For integer inputs, follows the array-api convention of returning a + copy of the input tensor. + + .. math:: + \text{out}_{i} = \left\lfloor \text{input}_{i} \right\rfloor + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-0.8166, 1.5308, -0.2530, -0.2091]) + >>> torch.floor(a) + tensor([-1., 1., -1., -1.]) + """ + ... +def floor_(input: Tensor) -> Tensor: ... +def floor_divide(input: Union[Tensor, Number], other: Union[Tensor, Number], *, out: Optional[Tensor] = None) -> Tensor: + r""" + floor_divide(input, other, *, out=None) -> Tensor + + .. note:: + + Before PyTorch 1.13 :func:`torch.floor_divide` incorrectly performed + truncation division. To restore the previous behavior use + :func:`torch.div` with ``rounding_mode='trunc'``. + + Computes :attr:`input` divided by :attr:`other`, elementwise, and floors + the result. + + .. math:: + \text{{out}}_i = \text{floor} \left( \frac{{\text{{input}}_i}}{{\text{{other}}_i}} \right) + + + + Supports broadcasting to a common shape, type promotion, and integer and float inputs. + + Args: + input (Tensor or Number): the dividend + other (Tensor or Number): the divisor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([4.0, 3.0]) + >>> b = torch.tensor([2.0, 2.0]) + >>> torch.floor_divide(a, b) + tensor([2.0, 1.0]) + >>> torch.floor_divide(a, 1.4) + tensor([2.0, 2.0]) + """ + ... +def fmax(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + fmax(input, other, *, out=None) -> Tensor + + Computes the element-wise maximum of :attr:`input` and :attr:`other`. + + This is like :func:`torch.maximum` except it handles NaNs differently: + if exactly one of the two elements being compared is a NaN then the non-NaN element is taken as the maximum. + Only if both elements are NaN is NaN propagated. + + This function is a wrapper around C++'s ``std::fmax`` and is similar to NumPy's ``fmax`` function. + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer and floating-point inputs. + + Args: + input (Tensor): the input tensor. + other (Tensor): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([9.7, float('nan'), 3.1, float('nan')]) + >>> b = torch.tensor([-2.2, 0.5, float('nan'), float('nan')]) + >>> torch.fmax(a, b) + tensor([9.7000, 0.5000, 3.1000, nan]) + """ + ... +def fmin(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + fmin(input, other, *, out=None) -> Tensor + + Computes the element-wise minimum of :attr:`input` and :attr:`other`. + + This is like :func:`torch.minimum` except it handles NaNs differently: + if exactly one of the two elements being compared is a NaN then the non-NaN element is taken as the minimum. + Only if both elements are NaN is NaN propagated. + + This function is a wrapper around C++'s ``std::fmin`` and is similar to NumPy's ``fmin`` function. + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer and floating-point inputs. + + Args: + input (Tensor): the input tensor. + other (Tensor): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([2.2, float('nan'), 2.1, float('nan')]) + >>> b = torch.tensor([-9.3, 0.1, float('nan'), float('nan')]) + >>> torch.fmin(a, b) + tensor([-9.3000, 0.1000, 2.1000, nan]) + """ + ... +@overload +def fmod(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + fmod(input, other, *, out=None) -> Tensor + + Applies C++'s `std::fmod `_ entrywise. + The result has the same sign as the dividend :attr:`input` and its absolute value + is less than that of :attr:`other`. + + This function may be defined in terms of :func:`torch.div` as + + .. code:: python + + torch.fmod(a, b) == a - a.div(b, rounding_mode="trunc") * b + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer and float inputs. + + .. note:: + + When the divisor is zero, returns ``NaN`` for floating point dtypes + on both CPU and GPU; raises ``RuntimeError`` for integer division by + zero on CPU; Integer division by zero on GPU may return any value. + + .. note:: + + Complex inputs are not supported. In some cases, it is not mathematically + possible to satisfy the definition of a modulo operation with complex numbers. + + .. seealso:: + + :func:`torch.remainder` which implements Python's modulus operator. + This one is defined using division rounding down the result. + + Args: + input (Tensor): the dividend + other (Tensor or Scalar): the divisor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.fmod(torch.tensor([-3., -2, -1, 1, 2, 3]), 2) + tensor([-1., -0., -1., 1., 0., 1.]) + >>> torch.fmod(torch.tensor([1, 2, 3, 4, 5]), -1.5) + tensor([1.0000, 0.5000, 0.0000, 1.0000, 0.5000]) + """ + ... +@overload +def fmod(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + fmod(input, other, *, out=None) -> Tensor + + Applies C++'s `std::fmod `_ entrywise. + The result has the same sign as the dividend :attr:`input` and its absolute value + is less than that of :attr:`other`. + + This function may be defined in terms of :func:`torch.div` as + + .. code:: python + + torch.fmod(a, b) == a - a.div(b, rounding_mode="trunc") * b + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer and float inputs. + + .. note:: + + When the divisor is zero, returns ``NaN`` for floating point dtypes + on both CPU and GPU; raises ``RuntimeError`` for integer division by + zero on CPU; Integer division by zero on GPU may return any value. + + .. note:: + + Complex inputs are not supported. In some cases, it is not mathematically + possible to satisfy the definition of a modulo operation with complex numbers. + + .. seealso:: + + :func:`torch.remainder` which implements Python's modulus operator. + This one is defined using division rounding down the result. + + Args: + input (Tensor): the dividend + other (Tensor or Scalar): the divisor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.fmod(torch.tensor([-3., -2, -1, 1, 2, 3]), 2) + tensor([-1., -0., -1., 1., 0., 1.]) + >>> torch.fmod(torch.tensor([1, 2, 3, 4, 5]), -1.5) + tensor([1.0000, 0.5000, 0.0000, 1.0000, 0.5000]) + """ + ... +def frac(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + frac(input, *, out=None) -> Tensor + + Computes the fractional portion of each element in :attr:`input`. + + .. math:: + \text{out}_{i} = \text{input}_{i} - \left\lfloor |\text{input}_{i}| \right\rfloor * \operatorname{sgn}(\text{input}_{i}) + + Example:: + + >>> torch.frac(torch.tensor([1, 2.5, -3.2])) + tensor([ 0.0000, 0.5000, -0.2000]) + """ + ... +def frac_(input: Tensor) -> Tensor: ... +def frexp(input: Tensor, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.frexp: + r""" + frexp(input, *, out=None) -> (Tensor mantissa, Tensor exponent) + + Decomposes :attr:`input` into mantissa and exponent tensors + such that :math:`\text{input} = \text{mantissa} \times 2^{\text{exponent}}`. + + The range of mantissa is the open interval (-1, 1). + + Supports float inputs. + + Args: + input (Tensor): the input tensor + + + Keyword args: + out (tuple, optional): the output tensors + + Example:: + + >>> x = torch.arange(9.) + >>> mantissa, exponent = torch.frexp(x) + >>> mantissa + tensor([0.0000, 0.5000, 0.5000, 0.7500, 0.5000, 0.6250, 0.7500, 0.8750, 0.5000]) + >>> exponent + tensor([0, 1, 2, 2, 3, 3, 3, 3, 4], dtype=torch.int32) + >>> torch.ldexp(mantissa, exponent) + tensor([0., 1., 2., 3., 4., 5., 6., 7., 8.]) + """ + ... +def frobenius_norm(input: Tensor, dim: Union[_int, _size], keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: ... +def from_file(filename: str, shared: Optional[_bool] = None, size: Optional[_int] = 0, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + from_file(filename, shared=None, size=0, *, dtype=None, layout=None, device=None, pin_memory=False) + + Creates a CPU tensor with a storage backed by a memory-mapped file. + + If ``shared`` is True, then memory is shared between processes. All changes are written to the file. + If ``shared`` is False, then changes to the tensor do not affect the file. + + ``size`` is the number of elements in the Tensor. If ``shared`` is ``False``, then the file must contain + at least ``size * sizeof(dtype)`` bytes. If ``shared`` is ``True`` the file will be created if needed. + + .. note:: + Only CPU tensors can be mapped to files. + + .. note:: + For now, tensors with storages backed by a memory-mapped file cannot be created in pinned memory. + + + Args: + filename (str): file name to map + shared (bool): whether to share memory (whether ``MAP_SHARED`` or ``MAP_PRIVATE`` is passed to the + underlying `mmap(2) call `_) + size (int): number of elements in the tensor + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + >>> t = torch.randn(2, 5, dtype=torch.float64) + >>> t.numpy().tofile('storage.pt') + >>> t_mapped = torch.from_file('storage.pt', shared=False, size=10, dtype=torch.float64) + """ + ... +def from_numpy(ndarray) -> Tensor: + r""" + from_numpy(ndarray) -> Tensor + + Creates a :class:`Tensor` from a :class:`numpy.ndarray`. + + The returned tensor and :attr:`ndarray` share the same memory. Modifications to + the tensor will be reflected in the :attr:`ndarray` and vice versa. The returned + tensor is not resizable. + + It currently accepts :attr:`ndarray` with dtypes of ``numpy.float64``, + ``numpy.float32``, ``numpy.float16``, ``numpy.complex64``, ``numpy.complex128``, + ``numpy.int64``, ``numpy.int32``, ``numpy.int16``, ``numpy.int8``, ``numpy.uint8``, + and ``bool``. + + .. warning:: + Writing to a tensor created from a read-only NumPy array is not supported and will result in undefined behavior. + + Example:: + + >>> a = numpy.array([1, 2, 3]) + >>> t = torch.from_numpy(a) + >>> t + tensor([ 1, 2, 3]) + >>> t[0] = -1 + >>> a + array([-1, 2, 3]) + """ + ... +def frombuffer(buffer: Any, *, dtype: _dtype, count: int = -1, offset: int = 0, requires_grad: _bool = False) -> Tensor: + r""" + frombuffer(buffer, *, dtype, count=-1, offset=0, requires_grad=False) -> Tensor + + Creates a 1-dimensional :class:`Tensor` from an object that implements + the Python buffer protocol. + + Skips the first :attr:`offset` bytes in the buffer, and interprets the rest of + the raw bytes as a 1-dimensional tensor of type :attr:`dtype` with :attr:`count` + elements. + + Note that either of the following must be true: + + 1. :attr:`count` is a positive non-zero number, and the total number of bytes + in the buffer is more than :attr:`offset` plus :attr:`count` times the size + (in bytes) of :attr:`dtype`. + + 2. :attr:`count` is negative, and the length (number of bytes) of the buffer + subtracted by the :attr:`offset` is a multiple of the size (in bytes) of + :attr:`dtype`. + + The returned tensor and buffer share the same memory. Modifications to + the tensor will be reflected in the buffer and vice versa. The returned + tensor is not resizable. + + .. note:: + This function increments the reference count for the object that + owns the shared memory. Therefore, such memory will not be deallocated + before the returned tensor goes out of scope. + + .. warning:: + This function's behavior is undefined when passed an object implementing + the buffer protocol whose data is not on the CPU. Doing so is likely to + cause a segmentation fault. + + .. warning:: + This function does not try to infer the :attr:`dtype` (hence, it is not + optional). Passing a different :attr:`dtype` than its source may result + in unexpected behavior. + + Args: + buffer (object): a Python object that exposes the buffer interface. + + Keyword args: + dtype (:class:`torch.dtype`): the desired data type of returned tensor. + count (int, optional): the number of desired elements to be read. + If negative, all the elements (until the end of the buffer) will be + read. Default: -1. + offset (int, optional): the number of bytes to skip at the start of + the buffer. Default: 0. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> import array + >>> a = array.array('i', [1, 2, 3]) + >>> t = torch.frombuffer(a, dtype=torch.int32) + >>> t + tensor([ 1, 2, 3]) + >>> t[0] = -1 + >>> a + array([-1, 2, 3]) + + >>> # Interprets the signed char bytes as 32-bit integers. + >>> # Each 4 signed char elements will be interpreted as + >>> # 1 signed 32-bit integer. + >>> import array + >>> a = array.array('b', [-1, 0, 0, 0]) + >>> torch.frombuffer(a, dtype=torch.int32) + tensor([255], dtype=torch.int32) + """ + ... +@overload +def full(size: _size, fill_value: Union[Number, _complex], *, out: Optional[Tensor] = None, layout: _layout = strided, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + full(size, fill_value, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Creates a tensor of size :attr:`size` filled with :attr:`fill_value`. The + tensor's dtype is inferred from :attr:`fill_value`. + + Args: + size (int...): a list, tuple, or :class:`torch.Size` of integers defining the + shape of the output tensor. + fill_value (Scalar): the value to fill the output tensor with. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.full((2, 3), 3.141592) + tensor([[ 3.1416, 3.1416, 3.1416], + [ 3.1416, 3.1416, 3.1416]]) + """ + ... +@overload +def full(size: _size, fill_value: Union[Number, _complex], *, names: List[Union[str, None]], layout: _layout = strided, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + full(size, fill_value, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Creates a tensor of size :attr:`size` filled with :attr:`fill_value`. The + tensor's dtype is inferred from :attr:`fill_value`. + + Args: + size (int...): a list, tuple, or :class:`torch.Size` of integers defining the + shape of the output tensor. + fill_value (Scalar): the value to fill the output tensor with. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.full((2, 3), 3.141592) + tensor([[ 3.1416, 3.1416, 3.1416], + [ 3.1416, 3.1416, 3.1416]]) + """ + ... +@overload +def full(size: Sequence[Union[_int, SymInt]], fill_value: Union[Number, _complex], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + full(size, fill_value, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Creates a tensor of size :attr:`size` filled with :attr:`fill_value`. The + tensor's dtype is inferred from :attr:`fill_value`. + + Args: + size (int...): a list, tuple, or :class:`torch.Size` of integers defining the + shape of the output tensor. + fill_value (Scalar): the value to fill the output tensor with. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.full((2, 3), 3.141592) + tensor([[ 3.1416, 3.1416, 3.1416], + [ 3.1416, 3.1416, 3.1416]]) + """ + ... +@overload +def full(size: _size, fill_value: Union[Number, _complex], *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + full(size, fill_value, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Creates a tensor of size :attr:`size` filled with :attr:`fill_value`. The + tensor's dtype is inferred from :attr:`fill_value`. + + Args: + size (int...): a list, tuple, or :class:`torch.Size` of integers defining the + shape of the output tensor. + fill_value (Scalar): the value to fill the output tensor with. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.full((2, 3), 3.141592) + tensor([[ 3.1416, 3.1416, 3.1416], + [ 3.1416, 3.1416, 3.1416]]) + """ + ... +def full_like(input: Tensor, fill_value: Union[Number, _complex], *, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + full_like(input, fill_value, \*, dtype=None, layout=torch.strided, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor + + Returns a tensor with the same size as :attr:`input` filled with :attr:`fill_value`. + ``torch.full_like(input, fill_value)`` is equivalent to + ``torch.full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device)``. + + Args: + input (Tensor): the size of :attr:`input` will determine size of the output tensor. + fill_value: the number to fill the output tensor with. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. + Default: if ``None``, defaults to the dtype of :attr:`input`. + layout (:class:`torch.layout`, optional): the desired layout of returned tensor. + Default: if ``None``, defaults to the layout of :attr:`input`. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, defaults to the device of :attr:`input`. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... +def fused_moving_avg_obs_fake_quant(input: Tensor, observer_on: Tensor, fake_quant_on: Tensor, running_min: Tensor, running_max: Tensor, scale: Tensor, zero_point: Tensor, averaging_const: _float, quant_min: _int, quant_max: _int, ch_axis: _int, per_row_fake_quant: _bool = False, symmetric_quant: _bool = False) -> Tensor: ... +@overload +def gather(input: Tensor, dim: _int, index: Tensor, *, sparse_grad: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + gather(input, dim, index, *, sparse_grad=False, out=None) -> Tensor + + Gathers values along an axis specified by `dim`. + + For a 3-D tensor the output is specified by:: + + out[i][j][k] = input[index[i][j][k]][j][k] # if dim == 0 + out[i][j][k] = input[i][index[i][j][k]][k] # if dim == 1 + out[i][j][k] = input[i][j][index[i][j][k]] # if dim == 2 + + :attr:`input` and :attr:`index` must have the same number of dimensions. + It is also required that ``index.size(d) <= input.size(d)`` for all + dimensions ``d != dim``. :attr:`out` will have the same shape as :attr:`index`. + Note that ``input`` and ``index`` do not broadcast against each other. + + Args: + input (Tensor): the source tensor + dim (int): the axis along which to index + index (LongTensor): the indices of elements to gather + + Keyword arguments: + sparse_grad (bool, optional): If ``True``, gradient w.r.t. :attr:`input` will be a sparse tensor. + out (Tensor, optional): the destination tensor + + Example:: + + >>> t = torch.tensor([[1, 2], [3, 4]]) + >>> torch.gather(t, 1, torch.tensor([[0, 0], [1, 0]])) + tensor([[ 1, 1], + [ 4, 3]]) + """ + ... +@overload +def gather(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, *, sparse_grad: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + gather(input, dim, index, *, sparse_grad=False, out=None) -> Tensor + + Gathers values along an axis specified by `dim`. + + For a 3-D tensor the output is specified by:: + + out[i][j][k] = input[index[i][j][k]][j][k] # if dim == 0 + out[i][j][k] = input[i][index[i][j][k]][k] # if dim == 1 + out[i][j][k] = input[i][j][index[i][j][k]] # if dim == 2 + + :attr:`input` and :attr:`index` must have the same number of dimensions. + It is also required that ``index.size(d) <= input.size(d)`` for all + dimensions ``d != dim``. :attr:`out` will have the same shape as :attr:`index`. + Note that ``input`` and ``index`` do not broadcast against each other. + + Args: + input (Tensor): the source tensor + dim (int): the axis along which to index + index (LongTensor): the indices of elements to gather + + Keyword arguments: + sparse_grad (bool, optional): If ``True``, gradient w.r.t. :attr:`input` will be a sparse tensor. + out (Tensor, optional): the destination tensor + + Example:: + + >>> t = torch.tensor([[1, 2], [3, 4]]) + >>> torch.gather(t, 1, torch.tensor([[0, 0], [1, 0]])) + tensor([[ 1, 1], + [ 4, 3]]) + """ + ... +def gcd(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + gcd(input, other, *, out=None) -> Tensor + + Computes the element-wise greatest common divisor (GCD) of :attr:`input` and :attr:`other`. + + Both :attr:`input` and :attr:`other` must have integer types. + + .. note:: + This defines :math:`gcd(0, 0) = 0`. + + Args: + input (Tensor): the input tensor. + other (Tensor): the second input tensor + + Keyword arguments: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([5, 10, 15]) + >>> b = torch.tensor([3, 4, 5]) + >>> torch.gcd(a, b) + tensor([1, 2, 5]) + >>> c = torch.tensor([3]) + >>> torch.gcd(a, c) + tensor([1, 1, 3]) + """ + ... +def gcd_(input: Tensor, other: Tensor) -> Tensor: ... +@overload +def ge(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + ge(input, other, *, out=None) -> Tensor + + Computes :math:`\text{input} \geq \text{other}` element-wise. + + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or float): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is greater than or equal to :attr:`other` and False elsewhere + + Example:: + + >>> torch.ge(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[True, True], [False, True]]) + """ + ... +@overload +def ge(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + ge(input, other, *, out=None) -> Tensor + + Computes :math:`\text{input} \geq \text{other}` element-wise. + + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or float): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is greater than or equal to :attr:`other` and False elsewhere + + Example:: + + >>> torch.ge(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[True, True], [False, True]]) + """ + ... +def geqrf(input: Tensor, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.geqrf: + r""" + geqrf(input, *, out=None) -> (Tensor, Tensor) + + This is a low-level function for calling LAPACK's geqrf directly. This function + returns a namedtuple (a, tau) as defined in `LAPACK documentation for geqrf`_ . + + Computes a QR decomposition of :attr:`input`. + Both `Q` and `R` matrices are stored in the same output tensor `a`. + The elements of `R` are stored on and above the diagonal. + Elementary reflectors (or Householder vectors) implicitly defining matrix `Q` + are stored below the diagonal. + The results of this function can be used together with :func:`torch.linalg.householder_product` + to obtain the `Q` matrix or + with :func:`torch.ormqr`, which uses an implicit representation of the `Q` matrix, + for an efficient matrix-matrix multiplication. + + See `LAPACK documentation for geqrf`_ for further details. + + .. note:: + See also :func:`torch.linalg.qr`, which computes Q and R matrices, and :func:`torch.linalg.lstsq` + with the ``driver="gels"`` option for a function that can solve matrix equations using a QR decomposition. + + Args: + input (Tensor): the input matrix + + Keyword args: + out (tuple, optional): the output tuple of (Tensor, Tensor). Ignored if `None`. Default: `None`. + + .. _LAPACK documentation for geqrf: + http://www.netlib.org/lapack/explore-html/df/dc5/group__variants_g_ecomputational_ga3766ea903391b5cf9008132f7440ec7b.html + """ + ... +def ger(input: Tensor, vec2: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + ger(input, vec2, *, out=None) -> Tensor + + Alias of :func:`torch.outer`. + + .. warning:: + This function is deprecated and will be removed in a future PyTorch release. + Use :func:`torch.outer` instead. + """ + ... +def get_default_dtype() -> _dtype: + r""" + get_default_dtype() -> torch.dtype + + Get the current default floating point :class:`torch.dtype`. + + Example:: + + >>> torch.get_default_dtype() # initial default for floating point is torch.float32 + torch.float32 + >>> torch.set_default_dtype(torch.float64) + >>> torch.get_default_dtype() # default is now changed to torch.float64 + torch.float64 + """ + ... +def get_num_interop_threads() -> _int: + r""" + get_num_interop_threads() -> int + + Returns the number of threads used for inter-op parallelism on CPU + (e.g. in JIT interpreter) + """ + ... +def get_num_threads() -> _int: + r""" + get_num_threads() -> int + + Returns the number of threads used for parallelizing CPU operations + """ + ... +@overload +def gradient(input: Tensor, *, spacing: Optional[Union[Number, _complex]] = None, dim: Optional[_int] = None, edge_order: _int = 1) -> Tuple[Tensor, ...]: + r""" + gradient(input, *, spacing=1, dim=None, edge_order=1) -> List of Tensors + + Estimates the gradient of a function :math:`g : \mathbb{R}^n \rightarrow \mathbb{R}` in + one or more dimensions using the `second-order accurate central differences method + `_ and + either first or second order estimates at the boundaries. + + The gradient of :math:`g` is estimated using samples. By default, when :attr:`spacing` is not + specified, the samples are entirely described by :attr:`input`, and the mapping of input coordinates + to an output is the same as the tensor's mapping of indices to values. For example, for a three-dimensional + :attr:`input` the function described is :math:`g : \mathbb{R}^3 \rightarrow \mathbb{R}`, and + :math:`g(1, 2, 3)\ == input[1, 2, 3]`. + + When :attr:`spacing` is specified, it modifies the relationship between :attr:`input` and input coordinates. + This is detailed in the "Keyword Arguments" section below. + + The gradient is estimated by estimating each partial derivative of :math:`g` independently. This estimation is + accurate if :math:`g` is in :math:`C^3` (it has at least 3 continuous derivatives), and the estimation can be + improved by providing closer samples. Mathematically, the value at each interior point of a partial derivative + is estimated using `Taylor's theorem with remainder `_. + Letting :math:`x` be an interior point with :math:`x-h_l` and :math:`x+h_r` be points neighboring + it to the left and right respectively, :math:`f(x+h_r)` and :math:`f(x-h_l)` can be estimated using: + + .. math:: + \begin{aligned} + f(x+h_r) = f(x) + h_r f'(x) + {h_r}^2 \frac{f''(x)}{2} + {h_r}^3 \frac{f'''(\xi_1)}{6}, \xi_1 \in (x, x+h_r) \\ + f(x-h_l) = f(x) - h_l f'(x) + {h_l}^2 \frac{f''(x)}{2} - {h_l}^3 \frac{f'''(\xi_2)}{6}, \xi_2 \in (x, x-h_l) \\ + \end{aligned} + + Using the fact that :math:`f \in C^3` and solving the linear system, we derive: + + .. math:: + f'(x) \approx \frac{ {h_l}^2 f(x+h_r) - {h_r}^2 f(x-h_l) + + ({h_r}^2-{h_l}^2 ) f(x) }{ {h_r} {h_l}^2 + {h_r}^2 {h_l} } + + .. note:: + We estimate the gradient of functions in complex domain + :math:`g : \mathbb{C}^n \rightarrow \mathbb{C}` in the same way. + + The value of each partial derivative at the boundary points is computed differently. See edge_order below. + + Args: + input (``Tensor``): the tensor that represents the values of the function + + Keyword args: + spacing (``scalar``, ``list of scalar``, ``list of Tensor``, optional): :attr:`spacing` can be used to modify + how the :attr:`input` tensor's indices relate to sample coordinates. If :attr:`spacing` is a scalar then + the indices are multiplied by the scalar to produce the coordinates. For example, if :attr:`spacing=2` the + indices (1, 2, 3) become coordinates (2, 4, 6). If :attr:`spacing` is a list of scalars then the corresponding + indices are multiplied. For example, if :attr:`spacing=(2, -1, 3)` the indices (1, 2, 3) become coordinates (2, -2, 9). + Finally, if :attr:`spacing` is a list of one-dimensional tensors then each tensor specifies the coordinates for + the corresponding dimension. For example, if the indices are (1, 2, 3) and the tensors are (t0, t1, t2), then + the coordinates are (t0[1], t1[2], t2[3]) + + dim (``int``, ``list of int``, optional): the dimension or dimensions to approximate the gradient over. By default + the partial gradient in every dimension is computed. Note that when :attr:`dim` is specified the elements of + the :attr:`spacing` argument must correspond with the specified dims." + + edge_order (``int``, optional): 1 or 2, for `first-order + `_ or + `second-order `_ + estimation of the boundary ("edge") values, respectively. + + Examples:: + + >>> # Estimates the gradient of f(x)=x^2 at points [-2, -1, 2, 4] + >>> coordinates = (torch.tensor([-2., -1., 1., 4.]),) + >>> values = torch.tensor([4., 1., 1., 16.], ) + >>> torch.gradient(values, spacing = coordinates) + (tensor([-3., -2., 2., 5.]),) + + >>> # Estimates the gradient of the R^2 -> R function whose samples are + >>> # described by the tensor t. Implicit coordinates are [0, 1] for the outermost + >>> # dimension and [0, 1, 2, 3] for the innermost dimension, and function estimates + >>> # partial derivative for both dimensions. + >>> t = torch.tensor([[1, 2, 4, 8], [10, 20, 40, 80]]) + >>> torch.gradient(t) + (tensor([[ 9., 18., 36., 72.], + [ 9., 18., 36., 72.]]), + tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]])) + + >>> # A scalar value for spacing modifies the relationship between tensor indices + >>> # and input coordinates by multiplying the indices to find the + >>> # coordinates. For example, below the indices of the innermost + >>> # 0, 1, 2, 3 translate to coordinates of [0, 2, 4, 6], and the indices of + >>> # the outermost dimension 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = 2.0) # dim = None (implicitly [0, 1]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.5000, 0.7500, 1.5000, 2.0000], + [ 5.0000, 7.5000, 15.0000, 20.0000]])) + >>> # doubling the spacing between samples halves the estimated partial gradients. + + >>> + >>> # Estimates only the partial derivative for dimension 1 + >>> torch.gradient(t, dim = 1) # spacing = None (implicitly 1.) + (tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]]),) + + >>> # When spacing is a list of scalars, the relationship between the tensor + >>> # indices and input coordinates changes based on dimension. + >>> # For example, below, the indices of the innermost dimension 0, 1, 2, 3 translate + >>> # to coordinates of [0, 3, 6, 9], and the indices of the outermost dimension + >>> # 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = [3., 2.]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + + >>> # The following example is a replication of the previous one with explicit + >>> # coordinates. + >>> coords = (torch.tensor([0, 2]), torch.tensor([0, 3, 6, 9])) + >>> torch.gradient(t, spacing = coords) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + """ + ... +@overload +def gradient(input: Tensor, *, spacing: Sequence[Union[Number, _complex]], dim: Optional[_int] = None, edge_order: _int = 1) -> Tuple[Tensor, ...]: + r""" + gradient(input, *, spacing=1, dim=None, edge_order=1) -> List of Tensors + + Estimates the gradient of a function :math:`g : \mathbb{R}^n \rightarrow \mathbb{R}` in + one or more dimensions using the `second-order accurate central differences method + `_ and + either first or second order estimates at the boundaries. + + The gradient of :math:`g` is estimated using samples. By default, when :attr:`spacing` is not + specified, the samples are entirely described by :attr:`input`, and the mapping of input coordinates + to an output is the same as the tensor's mapping of indices to values. For example, for a three-dimensional + :attr:`input` the function described is :math:`g : \mathbb{R}^3 \rightarrow \mathbb{R}`, and + :math:`g(1, 2, 3)\ == input[1, 2, 3]`. + + When :attr:`spacing` is specified, it modifies the relationship between :attr:`input` and input coordinates. + This is detailed in the "Keyword Arguments" section below. + + The gradient is estimated by estimating each partial derivative of :math:`g` independently. This estimation is + accurate if :math:`g` is in :math:`C^3` (it has at least 3 continuous derivatives), and the estimation can be + improved by providing closer samples. Mathematically, the value at each interior point of a partial derivative + is estimated using `Taylor's theorem with remainder `_. + Letting :math:`x` be an interior point with :math:`x-h_l` and :math:`x+h_r` be points neighboring + it to the left and right respectively, :math:`f(x+h_r)` and :math:`f(x-h_l)` can be estimated using: + + .. math:: + \begin{aligned} + f(x+h_r) = f(x) + h_r f'(x) + {h_r}^2 \frac{f''(x)}{2} + {h_r}^3 \frac{f'''(\xi_1)}{6}, \xi_1 \in (x, x+h_r) \\ + f(x-h_l) = f(x) - h_l f'(x) + {h_l}^2 \frac{f''(x)}{2} - {h_l}^3 \frac{f'''(\xi_2)}{6}, \xi_2 \in (x, x-h_l) \\ + \end{aligned} + + Using the fact that :math:`f \in C^3` and solving the linear system, we derive: + + .. math:: + f'(x) \approx \frac{ {h_l}^2 f(x+h_r) - {h_r}^2 f(x-h_l) + + ({h_r}^2-{h_l}^2 ) f(x) }{ {h_r} {h_l}^2 + {h_r}^2 {h_l} } + + .. note:: + We estimate the gradient of functions in complex domain + :math:`g : \mathbb{C}^n \rightarrow \mathbb{C}` in the same way. + + The value of each partial derivative at the boundary points is computed differently. See edge_order below. + + Args: + input (``Tensor``): the tensor that represents the values of the function + + Keyword args: + spacing (``scalar``, ``list of scalar``, ``list of Tensor``, optional): :attr:`spacing` can be used to modify + how the :attr:`input` tensor's indices relate to sample coordinates. If :attr:`spacing` is a scalar then + the indices are multiplied by the scalar to produce the coordinates. For example, if :attr:`spacing=2` the + indices (1, 2, 3) become coordinates (2, 4, 6). If :attr:`spacing` is a list of scalars then the corresponding + indices are multiplied. For example, if :attr:`spacing=(2, -1, 3)` the indices (1, 2, 3) become coordinates (2, -2, 9). + Finally, if :attr:`spacing` is a list of one-dimensional tensors then each tensor specifies the coordinates for + the corresponding dimension. For example, if the indices are (1, 2, 3) and the tensors are (t0, t1, t2), then + the coordinates are (t0[1], t1[2], t2[3]) + + dim (``int``, ``list of int``, optional): the dimension or dimensions to approximate the gradient over. By default + the partial gradient in every dimension is computed. Note that when :attr:`dim` is specified the elements of + the :attr:`spacing` argument must correspond with the specified dims." + + edge_order (``int``, optional): 1 or 2, for `first-order + `_ or + `second-order `_ + estimation of the boundary ("edge") values, respectively. + + Examples:: + + >>> # Estimates the gradient of f(x)=x^2 at points [-2, -1, 2, 4] + >>> coordinates = (torch.tensor([-2., -1., 1., 4.]),) + >>> values = torch.tensor([4., 1., 1., 16.], ) + >>> torch.gradient(values, spacing = coordinates) + (tensor([-3., -2., 2., 5.]),) + + >>> # Estimates the gradient of the R^2 -> R function whose samples are + >>> # described by the tensor t. Implicit coordinates are [0, 1] for the outermost + >>> # dimension and [0, 1, 2, 3] for the innermost dimension, and function estimates + >>> # partial derivative for both dimensions. + >>> t = torch.tensor([[1, 2, 4, 8], [10, 20, 40, 80]]) + >>> torch.gradient(t) + (tensor([[ 9., 18., 36., 72.], + [ 9., 18., 36., 72.]]), + tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]])) + + >>> # A scalar value for spacing modifies the relationship between tensor indices + >>> # and input coordinates by multiplying the indices to find the + >>> # coordinates. For example, below the indices of the innermost + >>> # 0, 1, 2, 3 translate to coordinates of [0, 2, 4, 6], and the indices of + >>> # the outermost dimension 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = 2.0) # dim = None (implicitly [0, 1]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.5000, 0.7500, 1.5000, 2.0000], + [ 5.0000, 7.5000, 15.0000, 20.0000]])) + >>> # doubling the spacing between samples halves the estimated partial gradients. + + >>> + >>> # Estimates only the partial derivative for dimension 1 + >>> torch.gradient(t, dim = 1) # spacing = None (implicitly 1.) + (tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]]),) + + >>> # When spacing is a list of scalars, the relationship between the tensor + >>> # indices and input coordinates changes based on dimension. + >>> # For example, below, the indices of the innermost dimension 0, 1, 2, 3 translate + >>> # to coordinates of [0, 3, 6, 9], and the indices of the outermost dimension + >>> # 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = [3., 2.]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + + >>> # The following example is a replication of the previous one with explicit + >>> # coordinates. + >>> coords = (torch.tensor([0, 2]), torch.tensor([0, 3, 6, 9])) + >>> torch.gradient(t, spacing = coords) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + """ + ... +@overload +def gradient(input: Tensor, *, spacing: Sequence[Union[Number, _complex]], dim: _size, edge_order: _int = 1) -> Tuple[Tensor, ...]: + r""" + gradient(input, *, spacing=1, dim=None, edge_order=1) -> List of Tensors + + Estimates the gradient of a function :math:`g : \mathbb{R}^n \rightarrow \mathbb{R}` in + one or more dimensions using the `second-order accurate central differences method + `_ and + either first or second order estimates at the boundaries. + + The gradient of :math:`g` is estimated using samples. By default, when :attr:`spacing` is not + specified, the samples are entirely described by :attr:`input`, and the mapping of input coordinates + to an output is the same as the tensor's mapping of indices to values. For example, for a three-dimensional + :attr:`input` the function described is :math:`g : \mathbb{R}^3 \rightarrow \mathbb{R}`, and + :math:`g(1, 2, 3)\ == input[1, 2, 3]`. + + When :attr:`spacing` is specified, it modifies the relationship between :attr:`input` and input coordinates. + This is detailed in the "Keyword Arguments" section below. + + The gradient is estimated by estimating each partial derivative of :math:`g` independently. This estimation is + accurate if :math:`g` is in :math:`C^3` (it has at least 3 continuous derivatives), and the estimation can be + improved by providing closer samples. Mathematically, the value at each interior point of a partial derivative + is estimated using `Taylor's theorem with remainder `_. + Letting :math:`x` be an interior point with :math:`x-h_l` and :math:`x+h_r` be points neighboring + it to the left and right respectively, :math:`f(x+h_r)` and :math:`f(x-h_l)` can be estimated using: + + .. math:: + \begin{aligned} + f(x+h_r) = f(x) + h_r f'(x) + {h_r}^2 \frac{f''(x)}{2} + {h_r}^3 \frac{f'''(\xi_1)}{6}, \xi_1 \in (x, x+h_r) \\ + f(x-h_l) = f(x) - h_l f'(x) + {h_l}^2 \frac{f''(x)}{2} - {h_l}^3 \frac{f'''(\xi_2)}{6}, \xi_2 \in (x, x-h_l) \\ + \end{aligned} + + Using the fact that :math:`f \in C^3` and solving the linear system, we derive: + + .. math:: + f'(x) \approx \frac{ {h_l}^2 f(x+h_r) - {h_r}^2 f(x-h_l) + + ({h_r}^2-{h_l}^2 ) f(x) }{ {h_r} {h_l}^2 + {h_r}^2 {h_l} } + + .. note:: + We estimate the gradient of functions in complex domain + :math:`g : \mathbb{C}^n \rightarrow \mathbb{C}` in the same way. + + The value of each partial derivative at the boundary points is computed differently. See edge_order below. + + Args: + input (``Tensor``): the tensor that represents the values of the function + + Keyword args: + spacing (``scalar``, ``list of scalar``, ``list of Tensor``, optional): :attr:`spacing` can be used to modify + how the :attr:`input` tensor's indices relate to sample coordinates. If :attr:`spacing` is a scalar then + the indices are multiplied by the scalar to produce the coordinates. For example, if :attr:`spacing=2` the + indices (1, 2, 3) become coordinates (2, 4, 6). If :attr:`spacing` is a list of scalars then the corresponding + indices are multiplied. For example, if :attr:`spacing=(2, -1, 3)` the indices (1, 2, 3) become coordinates (2, -2, 9). + Finally, if :attr:`spacing` is a list of one-dimensional tensors then each tensor specifies the coordinates for + the corresponding dimension. For example, if the indices are (1, 2, 3) and the tensors are (t0, t1, t2), then + the coordinates are (t0[1], t1[2], t2[3]) + + dim (``int``, ``list of int``, optional): the dimension or dimensions to approximate the gradient over. By default + the partial gradient in every dimension is computed. Note that when :attr:`dim` is specified the elements of + the :attr:`spacing` argument must correspond with the specified dims." + + edge_order (``int``, optional): 1 or 2, for `first-order + `_ or + `second-order `_ + estimation of the boundary ("edge") values, respectively. + + Examples:: + + >>> # Estimates the gradient of f(x)=x^2 at points [-2, -1, 2, 4] + >>> coordinates = (torch.tensor([-2., -1., 1., 4.]),) + >>> values = torch.tensor([4., 1., 1., 16.], ) + >>> torch.gradient(values, spacing = coordinates) + (tensor([-3., -2., 2., 5.]),) + + >>> # Estimates the gradient of the R^2 -> R function whose samples are + >>> # described by the tensor t. Implicit coordinates are [0, 1] for the outermost + >>> # dimension and [0, 1, 2, 3] for the innermost dimension, and function estimates + >>> # partial derivative for both dimensions. + >>> t = torch.tensor([[1, 2, 4, 8], [10, 20, 40, 80]]) + >>> torch.gradient(t) + (tensor([[ 9., 18., 36., 72.], + [ 9., 18., 36., 72.]]), + tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]])) + + >>> # A scalar value for spacing modifies the relationship between tensor indices + >>> # and input coordinates by multiplying the indices to find the + >>> # coordinates. For example, below the indices of the innermost + >>> # 0, 1, 2, 3 translate to coordinates of [0, 2, 4, 6], and the indices of + >>> # the outermost dimension 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = 2.0) # dim = None (implicitly [0, 1]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.5000, 0.7500, 1.5000, 2.0000], + [ 5.0000, 7.5000, 15.0000, 20.0000]])) + >>> # doubling the spacing between samples halves the estimated partial gradients. + + >>> + >>> # Estimates only the partial derivative for dimension 1 + >>> torch.gradient(t, dim = 1) # spacing = None (implicitly 1.) + (tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]]),) + + >>> # When spacing is a list of scalars, the relationship between the tensor + >>> # indices and input coordinates changes based on dimension. + >>> # For example, below, the indices of the innermost dimension 0, 1, 2, 3 translate + >>> # to coordinates of [0, 3, 6, 9], and the indices of the outermost dimension + >>> # 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = [3., 2.]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + + >>> # The following example is a replication of the previous one with explicit + >>> # coordinates. + >>> coords = (torch.tensor([0, 2]), torch.tensor([0, 3, 6, 9])) + >>> torch.gradient(t, spacing = coords) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + """ + ... +@overload +def gradient(input: Tensor, *, spacing: Union[Tuple[Tensor, ...], List[Tensor]], dim: Optional[_int] = None, edge_order: _int = 1) -> Tuple[Tensor, ...]: + r""" + gradient(input, *, spacing=1, dim=None, edge_order=1) -> List of Tensors + + Estimates the gradient of a function :math:`g : \mathbb{R}^n \rightarrow \mathbb{R}` in + one or more dimensions using the `second-order accurate central differences method + `_ and + either first or second order estimates at the boundaries. + + The gradient of :math:`g` is estimated using samples. By default, when :attr:`spacing` is not + specified, the samples are entirely described by :attr:`input`, and the mapping of input coordinates + to an output is the same as the tensor's mapping of indices to values. For example, for a three-dimensional + :attr:`input` the function described is :math:`g : \mathbb{R}^3 \rightarrow \mathbb{R}`, and + :math:`g(1, 2, 3)\ == input[1, 2, 3]`. + + When :attr:`spacing` is specified, it modifies the relationship between :attr:`input` and input coordinates. + This is detailed in the "Keyword Arguments" section below. + + The gradient is estimated by estimating each partial derivative of :math:`g` independently. This estimation is + accurate if :math:`g` is in :math:`C^3` (it has at least 3 continuous derivatives), and the estimation can be + improved by providing closer samples. Mathematically, the value at each interior point of a partial derivative + is estimated using `Taylor's theorem with remainder `_. + Letting :math:`x` be an interior point with :math:`x-h_l` and :math:`x+h_r` be points neighboring + it to the left and right respectively, :math:`f(x+h_r)` and :math:`f(x-h_l)` can be estimated using: + + .. math:: + \begin{aligned} + f(x+h_r) = f(x) + h_r f'(x) + {h_r}^2 \frac{f''(x)}{2} + {h_r}^3 \frac{f'''(\xi_1)}{6}, \xi_1 \in (x, x+h_r) \\ + f(x-h_l) = f(x) - h_l f'(x) + {h_l}^2 \frac{f''(x)}{2} - {h_l}^3 \frac{f'''(\xi_2)}{6}, \xi_2 \in (x, x-h_l) \\ + \end{aligned} + + Using the fact that :math:`f \in C^3` and solving the linear system, we derive: + + .. math:: + f'(x) \approx \frac{ {h_l}^2 f(x+h_r) - {h_r}^2 f(x-h_l) + + ({h_r}^2-{h_l}^2 ) f(x) }{ {h_r} {h_l}^2 + {h_r}^2 {h_l} } + + .. note:: + We estimate the gradient of functions in complex domain + :math:`g : \mathbb{C}^n \rightarrow \mathbb{C}` in the same way. + + The value of each partial derivative at the boundary points is computed differently. See edge_order below. + + Args: + input (``Tensor``): the tensor that represents the values of the function + + Keyword args: + spacing (``scalar``, ``list of scalar``, ``list of Tensor``, optional): :attr:`spacing` can be used to modify + how the :attr:`input` tensor's indices relate to sample coordinates. If :attr:`spacing` is a scalar then + the indices are multiplied by the scalar to produce the coordinates. For example, if :attr:`spacing=2` the + indices (1, 2, 3) become coordinates (2, 4, 6). If :attr:`spacing` is a list of scalars then the corresponding + indices are multiplied. For example, if :attr:`spacing=(2, -1, 3)` the indices (1, 2, 3) become coordinates (2, -2, 9). + Finally, if :attr:`spacing` is a list of one-dimensional tensors then each tensor specifies the coordinates for + the corresponding dimension. For example, if the indices are (1, 2, 3) and the tensors are (t0, t1, t2), then + the coordinates are (t0[1], t1[2], t2[3]) + + dim (``int``, ``list of int``, optional): the dimension or dimensions to approximate the gradient over. By default + the partial gradient in every dimension is computed. Note that when :attr:`dim` is specified the elements of + the :attr:`spacing` argument must correspond with the specified dims." + + edge_order (``int``, optional): 1 or 2, for `first-order + `_ or + `second-order `_ + estimation of the boundary ("edge") values, respectively. + + Examples:: + + >>> # Estimates the gradient of f(x)=x^2 at points [-2, -1, 2, 4] + >>> coordinates = (torch.tensor([-2., -1., 1., 4.]),) + >>> values = torch.tensor([4., 1., 1., 16.], ) + >>> torch.gradient(values, spacing = coordinates) + (tensor([-3., -2., 2., 5.]),) + + >>> # Estimates the gradient of the R^2 -> R function whose samples are + >>> # described by the tensor t. Implicit coordinates are [0, 1] for the outermost + >>> # dimension and [0, 1, 2, 3] for the innermost dimension, and function estimates + >>> # partial derivative for both dimensions. + >>> t = torch.tensor([[1, 2, 4, 8], [10, 20, 40, 80]]) + >>> torch.gradient(t) + (tensor([[ 9., 18., 36., 72.], + [ 9., 18., 36., 72.]]), + tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]])) + + >>> # A scalar value for spacing modifies the relationship between tensor indices + >>> # and input coordinates by multiplying the indices to find the + >>> # coordinates. For example, below the indices of the innermost + >>> # 0, 1, 2, 3 translate to coordinates of [0, 2, 4, 6], and the indices of + >>> # the outermost dimension 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = 2.0) # dim = None (implicitly [0, 1]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.5000, 0.7500, 1.5000, 2.0000], + [ 5.0000, 7.5000, 15.0000, 20.0000]])) + >>> # doubling the spacing between samples halves the estimated partial gradients. + + >>> + >>> # Estimates only the partial derivative for dimension 1 + >>> torch.gradient(t, dim = 1) # spacing = None (implicitly 1.) + (tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]]),) + + >>> # When spacing is a list of scalars, the relationship between the tensor + >>> # indices and input coordinates changes based on dimension. + >>> # For example, below, the indices of the innermost dimension 0, 1, 2, 3 translate + >>> # to coordinates of [0, 3, 6, 9], and the indices of the outermost dimension + >>> # 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = [3., 2.]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + + >>> # The following example is a replication of the previous one with explicit + >>> # coordinates. + >>> coords = (torch.tensor([0, 2]), torch.tensor([0, 3, 6, 9])) + >>> torch.gradient(t, spacing = coords) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + """ + ... +@overload +def gradient(input: Tensor, *, spacing: Union[Number, _complex], dim: _size, edge_order: _int = 1) -> Tuple[Tensor, ...]: + r""" + gradient(input, *, spacing=1, dim=None, edge_order=1) -> List of Tensors + + Estimates the gradient of a function :math:`g : \mathbb{R}^n \rightarrow \mathbb{R}` in + one or more dimensions using the `second-order accurate central differences method + `_ and + either first or second order estimates at the boundaries. + + The gradient of :math:`g` is estimated using samples. By default, when :attr:`spacing` is not + specified, the samples are entirely described by :attr:`input`, and the mapping of input coordinates + to an output is the same as the tensor's mapping of indices to values. For example, for a three-dimensional + :attr:`input` the function described is :math:`g : \mathbb{R}^3 \rightarrow \mathbb{R}`, and + :math:`g(1, 2, 3)\ == input[1, 2, 3]`. + + When :attr:`spacing` is specified, it modifies the relationship between :attr:`input` and input coordinates. + This is detailed in the "Keyword Arguments" section below. + + The gradient is estimated by estimating each partial derivative of :math:`g` independently. This estimation is + accurate if :math:`g` is in :math:`C^3` (it has at least 3 continuous derivatives), and the estimation can be + improved by providing closer samples. Mathematically, the value at each interior point of a partial derivative + is estimated using `Taylor's theorem with remainder `_. + Letting :math:`x` be an interior point with :math:`x-h_l` and :math:`x+h_r` be points neighboring + it to the left and right respectively, :math:`f(x+h_r)` and :math:`f(x-h_l)` can be estimated using: + + .. math:: + \begin{aligned} + f(x+h_r) = f(x) + h_r f'(x) + {h_r}^2 \frac{f''(x)}{2} + {h_r}^3 \frac{f'''(\xi_1)}{6}, \xi_1 \in (x, x+h_r) \\ + f(x-h_l) = f(x) - h_l f'(x) + {h_l}^2 \frac{f''(x)}{2} - {h_l}^3 \frac{f'''(\xi_2)}{6}, \xi_2 \in (x, x-h_l) \\ + \end{aligned} + + Using the fact that :math:`f \in C^3` and solving the linear system, we derive: + + .. math:: + f'(x) \approx \frac{ {h_l}^2 f(x+h_r) - {h_r}^2 f(x-h_l) + + ({h_r}^2-{h_l}^2 ) f(x) }{ {h_r} {h_l}^2 + {h_r}^2 {h_l} } + + .. note:: + We estimate the gradient of functions in complex domain + :math:`g : \mathbb{C}^n \rightarrow \mathbb{C}` in the same way. + + The value of each partial derivative at the boundary points is computed differently. See edge_order below. + + Args: + input (``Tensor``): the tensor that represents the values of the function + + Keyword args: + spacing (``scalar``, ``list of scalar``, ``list of Tensor``, optional): :attr:`spacing` can be used to modify + how the :attr:`input` tensor's indices relate to sample coordinates. If :attr:`spacing` is a scalar then + the indices are multiplied by the scalar to produce the coordinates. For example, if :attr:`spacing=2` the + indices (1, 2, 3) become coordinates (2, 4, 6). If :attr:`spacing` is a list of scalars then the corresponding + indices are multiplied. For example, if :attr:`spacing=(2, -1, 3)` the indices (1, 2, 3) become coordinates (2, -2, 9). + Finally, if :attr:`spacing` is a list of one-dimensional tensors then each tensor specifies the coordinates for + the corresponding dimension. For example, if the indices are (1, 2, 3) and the tensors are (t0, t1, t2), then + the coordinates are (t0[1], t1[2], t2[3]) + + dim (``int``, ``list of int``, optional): the dimension or dimensions to approximate the gradient over. By default + the partial gradient in every dimension is computed. Note that when :attr:`dim` is specified the elements of + the :attr:`spacing` argument must correspond with the specified dims." + + edge_order (``int``, optional): 1 or 2, for `first-order + `_ or + `second-order `_ + estimation of the boundary ("edge") values, respectively. + + Examples:: + + >>> # Estimates the gradient of f(x)=x^2 at points [-2, -1, 2, 4] + >>> coordinates = (torch.tensor([-2., -1., 1., 4.]),) + >>> values = torch.tensor([4., 1., 1., 16.], ) + >>> torch.gradient(values, spacing = coordinates) + (tensor([-3., -2., 2., 5.]),) + + >>> # Estimates the gradient of the R^2 -> R function whose samples are + >>> # described by the tensor t. Implicit coordinates are [0, 1] for the outermost + >>> # dimension and [0, 1, 2, 3] for the innermost dimension, and function estimates + >>> # partial derivative for both dimensions. + >>> t = torch.tensor([[1, 2, 4, 8], [10, 20, 40, 80]]) + >>> torch.gradient(t) + (tensor([[ 9., 18., 36., 72.], + [ 9., 18., 36., 72.]]), + tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]])) + + >>> # A scalar value for spacing modifies the relationship between tensor indices + >>> # and input coordinates by multiplying the indices to find the + >>> # coordinates. For example, below the indices of the innermost + >>> # 0, 1, 2, 3 translate to coordinates of [0, 2, 4, 6], and the indices of + >>> # the outermost dimension 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = 2.0) # dim = None (implicitly [0, 1]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.5000, 0.7500, 1.5000, 2.0000], + [ 5.0000, 7.5000, 15.0000, 20.0000]])) + >>> # doubling the spacing between samples halves the estimated partial gradients. + + >>> + >>> # Estimates only the partial derivative for dimension 1 + >>> torch.gradient(t, dim = 1) # spacing = None (implicitly 1.) + (tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]]),) + + >>> # When spacing is a list of scalars, the relationship between the tensor + >>> # indices and input coordinates changes based on dimension. + >>> # For example, below, the indices of the innermost dimension 0, 1, 2, 3 translate + >>> # to coordinates of [0, 3, 6, 9], and the indices of the outermost dimension + >>> # 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = [3., 2.]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + + >>> # The following example is a replication of the previous one with explicit + >>> # coordinates. + >>> coords = (torch.tensor([0, 2]), torch.tensor([0, 3, 6, 9])) + >>> torch.gradient(t, spacing = coords) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + """ + ... +@overload +def gradient(input: Tensor, *, spacing: Union[Tuple[Tensor, ...], List[Tensor]], dim: _size, edge_order: _int = 1) -> Tuple[Tensor, ...]: + r""" + gradient(input, *, spacing=1, dim=None, edge_order=1) -> List of Tensors + + Estimates the gradient of a function :math:`g : \mathbb{R}^n \rightarrow \mathbb{R}` in + one or more dimensions using the `second-order accurate central differences method + `_ and + either first or second order estimates at the boundaries. + + The gradient of :math:`g` is estimated using samples. By default, when :attr:`spacing` is not + specified, the samples are entirely described by :attr:`input`, and the mapping of input coordinates + to an output is the same as the tensor's mapping of indices to values. For example, for a three-dimensional + :attr:`input` the function described is :math:`g : \mathbb{R}^3 \rightarrow \mathbb{R}`, and + :math:`g(1, 2, 3)\ == input[1, 2, 3]`. + + When :attr:`spacing` is specified, it modifies the relationship between :attr:`input` and input coordinates. + This is detailed in the "Keyword Arguments" section below. + + The gradient is estimated by estimating each partial derivative of :math:`g` independently. This estimation is + accurate if :math:`g` is in :math:`C^3` (it has at least 3 continuous derivatives), and the estimation can be + improved by providing closer samples. Mathematically, the value at each interior point of a partial derivative + is estimated using `Taylor's theorem with remainder `_. + Letting :math:`x` be an interior point with :math:`x-h_l` and :math:`x+h_r` be points neighboring + it to the left and right respectively, :math:`f(x+h_r)` and :math:`f(x-h_l)` can be estimated using: + + .. math:: + \begin{aligned} + f(x+h_r) = f(x) + h_r f'(x) + {h_r}^2 \frac{f''(x)}{2} + {h_r}^3 \frac{f'''(\xi_1)}{6}, \xi_1 \in (x, x+h_r) \\ + f(x-h_l) = f(x) - h_l f'(x) + {h_l}^2 \frac{f''(x)}{2} - {h_l}^3 \frac{f'''(\xi_2)}{6}, \xi_2 \in (x, x-h_l) \\ + \end{aligned} + + Using the fact that :math:`f \in C^3` and solving the linear system, we derive: + + .. math:: + f'(x) \approx \frac{ {h_l}^2 f(x+h_r) - {h_r}^2 f(x-h_l) + + ({h_r}^2-{h_l}^2 ) f(x) }{ {h_r} {h_l}^2 + {h_r}^2 {h_l} } + + .. note:: + We estimate the gradient of functions in complex domain + :math:`g : \mathbb{C}^n \rightarrow \mathbb{C}` in the same way. + + The value of each partial derivative at the boundary points is computed differently. See edge_order below. + + Args: + input (``Tensor``): the tensor that represents the values of the function + + Keyword args: + spacing (``scalar``, ``list of scalar``, ``list of Tensor``, optional): :attr:`spacing` can be used to modify + how the :attr:`input` tensor's indices relate to sample coordinates. If :attr:`spacing` is a scalar then + the indices are multiplied by the scalar to produce the coordinates. For example, if :attr:`spacing=2` the + indices (1, 2, 3) become coordinates (2, 4, 6). If :attr:`spacing` is a list of scalars then the corresponding + indices are multiplied. For example, if :attr:`spacing=(2, -1, 3)` the indices (1, 2, 3) become coordinates (2, -2, 9). + Finally, if :attr:`spacing` is a list of one-dimensional tensors then each tensor specifies the coordinates for + the corresponding dimension. For example, if the indices are (1, 2, 3) and the tensors are (t0, t1, t2), then + the coordinates are (t0[1], t1[2], t2[3]) + + dim (``int``, ``list of int``, optional): the dimension or dimensions to approximate the gradient over. By default + the partial gradient in every dimension is computed. Note that when :attr:`dim` is specified the elements of + the :attr:`spacing` argument must correspond with the specified dims." + + edge_order (``int``, optional): 1 or 2, for `first-order + `_ or + `second-order `_ + estimation of the boundary ("edge") values, respectively. + + Examples:: + + >>> # Estimates the gradient of f(x)=x^2 at points [-2, -1, 2, 4] + >>> coordinates = (torch.tensor([-2., -1., 1., 4.]),) + >>> values = torch.tensor([4., 1., 1., 16.], ) + >>> torch.gradient(values, spacing = coordinates) + (tensor([-3., -2., 2., 5.]),) + + >>> # Estimates the gradient of the R^2 -> R function whose samples are + >>> # described by the tensor t. Implicit coordinates are [0, 1] for the outermost + >>> # dimension and [0, 1, 2, 3] for the innermost dimension, and function estimates + >>> # partial derivative for both dimensions. + >>> t = torch.tensor([[1, 2, 4, 8], [10, 20, 40, 80]]) + >>> torch.gradient(t) + (tensor([[ 9., 18., 36., 72.], + [ 9., 18., 36., 72.]]), + tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]])) + + >>> # A scalar value for spacing modifies the relationship between tensor indices + >>> # and input coordinates by multiplying the indices to find the + >>> # coordinates. For example, below the indices of the innermost + >>> # 0, 1, 2, 3 translate to coordinates of [0, 2, 4, 6], and the indices of + >>> # the outermost dimension 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = 2.0) # dim = None (implicitly [0, 1]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.5000, 0.7500, 1.5000, 2.0000], + [ 5.0000, 7.5000, 15.0000, 20.0000]])) + >>> # doubling the spacing between samples halves the estimated partial gradients. + + >>> + >>> # Estimates only the partial derivative for dimension 1 + >>> torch.gradient(t, dim = 1) # spacing = None (implicitly 1.) + (tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]]),) + + >>> # When spacing is a list of scalars, the relationship between the tensor + >>> # indices and input coordinates changes based on dimension. + >>> # For example, below, the indices of the innermost dimension 0, 1, 2, 3 translate + >>> # to coordinates of [0, 3, 6, 9], and the indices of the outermost dimension + >>> # 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = [3., 2.]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + + >>> # The following example is a replication of the previous one with explicit + >>> # coordinates. + >>> coords = (torch.tensor([0, 2]), torch.tensor([0, 3, 6, 9])) + >>> torch.gradient(t, spacing = coords) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + """ + ... +@overload +def gradient(input: Tensor, *, dim: _size, edge_order: _int = 1) -> Tuple[Tensor, ...]: + r""" + gradient(input, *, spacing=1, dim=None, edge_order=1) -> List of Tensors + + Estimates the gradient of a function :math:`g : \mathbb{R}^n \rightarrow \mathbb{R}` in + one or more dimensions using the `second-order accurate central differences method + `_ and + either first or second order estimates at the boundaries. + + The gradient of :math:`g` is estimated using samples. By default, when :attr:`spacing` is not + specified, the samples are entirely described by :attr:`input`, and the mapping of input coordinates + to an output is the same as the tensor's mapping of indices to values. For example, for a three-dimensional + :attr:`input` the function described is :math:`g : \mathbb{R}^3 \rightarrow \mathbb{R}`, and + :math:`g(1, 2, 3)\ == input[1, 2, 3]`. + + When :attr:`spacing` is specified, it modifies the relationship between :attr:`input` and input coordinates. + This is detailed in the "Keyword Arguments" section below. + + The gradient is estimated by estimating each partial derivative of :math:`g` independently. This estimation is + accurate if :math:`g` is in :math:`C^3` (it has at least 3 continuous derivatives), and the estimation can be + improved by providing closer samples. Mathematically, the value at each interior point of a partial derivative + is estimated using `Taylor's theorem with remainder `_. + Letting :math:`x` be an interior point with :math:`x-h_l` and :math:`x+h_r` be points neighboring + it to the left and right respectively, :math:`f(x+h_r)` and :math:`f(x-h_l)` can be estimated using: + + .. math:: + \begin{aligned} + f(x+h_r) = f(x) + h_r f'(x) + {h_r}^2 \frac{f''(x)}{2} + {h_r}^3 \frac{f'''(\xi_1)}{6}, \xi_1 \in (x, x+h_r) \\ + f(x-h_l) = f(x) - h_l f'(x) + {h_l}^2 \frac{f''(x)}{2} - {h_l}^3 \frac{f'''(\xi_2)}{6}, \xi_2 \in (x, x-h_l) \\ + \end{aligned} + + Using the fact that :math:`f \in C^3` and solving the linear system, we derive: + + .. math:: + f'(x) \approx \frac{ {h_l}^2 f(x+h_r) - {h_r}^2 f(x-h_l) + + ({h_r}^2-{h_l}^2 ) f(x) }{ {h_r} {h_l}^2 + {h_r}^2 {h_l} } + + .. note:: + We estimate the gradient of functions in complex domain + :math:`g : \mathbb{C}^n \rightarrow \mathbb{C}` in the same way. + + The value of each partial derivative at the boundary points is computed differently. See edge_order below. + + Args: + input (``Tensor``): the tensor that represents the values of the function + + Keyword args: + spacing (``scalar``, ``list of scalar``, ``list of Tensor``, optional): :attr:`spacing` can be used to modify + how the :attr:`input` tensor's indices relate to sample coordinates. If :attr:`spacing` is a scalar then + the indices are multiplied by the scalar to produce the coordinates. For example, if :attr:`spacing=2` the + indices (1, 2, 3) become coordinates (2, 4, 6). If :attr:`spacing` is a list of scalars then the corresponding + indices are multiplied. For example, if :attr:`spacing=(2, -1, 3)` the indices (1, 2, 3) become coordinates (2, -2, 9). + Finally, if :attr:`spacing` is a list of one-dimensional tensors then each tensor specifies the coordinates for + the corresponding dimension. For example, if the indices are (1, 2, 3) and the tensors are (t0, t1, t2), then + the coordinates are (t0[1], t1[2], t2[3]) + + dim (``int``, ``list of int``, optional): the dimension or dimensions to approximate the gradient over. By default + the partial gradient in every dimension is computed. Note that when :attr:`dim` is specified the elements of + the :attr:`spacing` argument must correspond with the specified dims." + + edge_order (``int``, optional): 1 or 2, for `first-order + `_ or + `second-order `_ + estimation of the boundary ("edge") values, respectively. + + Examples:: + + >>> # Estimates the gradient of f(x)=x^2 at points [-2, -1, 2, 4] + >>> coordinates = (torch.tensor([-2., -1., 1., 4.]),) + >>> values = torch.tensor([4., 1., 1., 16.], ) + >>> torch.gradient(values, spacing = coordinates) + (tensor([-3., -2., 2., 5.]),) + + >>> # Estimates the gradient of the R^2 -> R function whose samples are + >>> # described by the tensor t. Implicit coordinates are [0, 1] for the outermost + >>> # dimension and [0, 1, 2, 3] for the innermost dimension, and function estimates + >>> # partial derivative for both dimensions. + >>> t = torch.tensor([[1, 2, 4, 8], [10, 20, 40, 80]]) + >>> torch.gradient(t) + (tensor([[ 9., 18., 36., 72.], + [ 9., 18., 36., 72.]]), + tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]])) + + >>> # A scalar value for spacing modifies the relationship between tensor indices + >>> # and input coordinates by multiplying the indices to find the + >>> # coordinates. For example, below the indices of the innermost + >>> # 0, 1, 2, 3 translate to coordinates of [0, 2, 4, 6], and the indices of + >>> # the outermost dimension 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = 2.0) # dim = None (implicitly [0, 1]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.5000, 0.7500, 1.5000, 2.0000], + [ 5.0000, 7.5000, 15.0000, 20.0000]])) + >>> # doubling the spacing between samples halves the estimated partial gradients. + + >>> + >>> # Estimates only the partial derivative for dimension 1 + >>> torch.gradient(t, dim = 1) # spacing = None (implicitly 1.) + (tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]]),) + + >>> # When spacing is a list of scalars, the relationship between the tensor + >>> # indices and input coordinates changes based on dimension. + >>> # For example, below, the indices of the innermost dimension 0, 1, 2, 3 translate + >>> # to coordinates of [0, 3, 6, 9], and the indices of the outermost dimension + >>> # 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = [3., 2.]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + + >>> # The following example is a replication of the previous one with explicit + >>> # coordinates. + >>> coords = (torch.tensor([0, 2]), torch.tensor([0, 3, 6, 9])) + >>> torch.gradient(t, spacing = coords) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + """ + ... +@overload +def greater(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + greater(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.gt`. + """ + ... +@overload +def greater(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + greater(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.gt`. + """ + ... +@overload +def greater_equal(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + greater_equal(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.ge`. + """ + ... +@overload +def greater_equal(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + greater_equal(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.ge`. + """ + ... +def grid_sampler(input: Tensor, grid: Tensor, interpolation_mode: _int, padding_mode: _int, align_corners: _bool) -> Tensor: ... +def grid_sampler_2d(input: Tensor, grid: Tensor, interpolation_mode: _int, padding_mode: _int, align_corners: _bool) -> Tensor: ... +def grid_sampler_3d(input: Tensor, grid: Tensor, interpolation_mode: _int, padding_mode: _int, align_corners: _bool) -> Tensor: ... +def group_norm(input: Tensor, num_groups: _int, weight: Optional[Tensor] = None, bias: Optional[Tensor] = None, eps: _float = 1e-05, cudnn_enabled: _bool = True) -> Tensor: ... +@overload +def gru(data: Tensor, batch_sizes: Tensor, hx: Tensor, params: Union[Tuple[Tensor, ...], List[Tensor]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool) -> Tuple[Tensor, Tensor]: ... +@overload +def gru(input: Tensor, hx: Tensor, params: Union[Tuple[Tensor, ...], List[Tensor]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool, batch_first: _bool) -> Tuple[Tensor, Tensor]: ... +def gru_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Optional[Tensor] = None, b_hh: Optional[Tensor] = None) -> Tensor: ... +@overload +def gt(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + gt(input, other, *, out=None) -> Tensor + + Computes :math:`\text{input} > \text{other}` element-wise. + + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or float): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is greater than :attr:`other` and False elsewhere + + Example:: + + >>> torch.gt(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[False, True], [False, False]]) + """ + ... +@overload +def gt(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + gt(input, other, *, out=None) -> Tensor + + Computes :math:`\text{input} > \text{other}` element-wise. + + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or float): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is greater than :attr:`other` and False elsewhere + + Example:: + + >>> torch.gt(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[False, True], [False, False]]) + """ + ... +@overload +def hamming_window(window_length: _int, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Hamming window function. + + .. math:: + w[n] = \alpha - \beta\ \cos \left( \frac{2 \pi n}{N - 1} \right), + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window and is + ready to be used as a periodic window with functions like + :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have + ``torch.hamming_window(L, periodic=True)`` equal to + ``torch.hamming_window(L + 1, periodic=False)[:-1])``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + .. note:: + This is a generalized version of :meth:`torch.hann_window`. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + alpha (float, optional): The coefficient :math:`\alpha` in the equation above + beta (float, optional): The coefficient :math:`\beta` in the equation above + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported. + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window. + """ + ... +@overload +def hamming_window(window_length: _int, periodic: _bool, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Hamming window function. + + .. math:: + w[n] = \alpha - \beta\ \cos \left( \frac{2 \pi n}{N - 1} \right), + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window and is + ready to be used as a periodic window with functions like + :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have + ``torch.hamming_window(L, periodic=True)`` equal to + ``torch.hamming_window(L + 1, periodic=False)[:-1])``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + .. note:: + This is a generalized version of :meth:`torch.hann_window`. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + alpha (float, optional): The coefficient :math:`\alpha` in the equation above + beta (float, optional): The coefficient :math:`\beta` in the equation above + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported. + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window. + """ + ... +@overload +def hamming_window(window_length: _int, periodic: _bool, alpha: _float, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Hamming window function. + + .. math:: + w[n] = \alpha - \beta\ \cos \left( \frac{2 \pi n}{N - 1} \right), + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window and is + ready to be used as a periodic window with functions like + :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have + ``torch.hamming_window(L, periodic=True)`` equal to + ``torch.hamming_window(L + 1, periodic=False)[:-1])``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + .. note:: + This is a generalized version of :meth:`torch.hann_window`. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + alpha (float, optional): The coefficient :math:`\alpha` in the equation above + beta (float, optional): The coefficient :math:`\beta` in the equation above + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported. + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window. + """ + ... +@overload +def hamming_window(window_length: _int, periodic: _bool, alpha: _float, beta: _float, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Hamming window function. + + .. math:: + w[n] = \alpha - \beta\ \cos \left( \frac{2 \pi n}{N - 1} \right), + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window and is + ready to be used as a periodic window with functions like + :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have + ``torch.hamming_window(L, periodic=True)`` equal to + ``torch.hamming_window(L + 1, periodic=False)[:-1])``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + .. note:: + This is a generalized version of :meth:`torch.hann_window`. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + alpha (float, optional): The coefficient :math:`\alpha` in the equation above + beta (float, optional): The coefficient :math:`\beta` in the equation above + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported. + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window. + """ + ... +@overload +def hann_window(window_length: _int, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + hann_window(window_length, periodic=True, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Hann window function. + + .. math:: + w[n] = \frac{1}{2}\ \left[1 - \cos \left( \frac{2 \pi n}{N - 1} \right)\right] = + \sin^2 \left( \frac{\pi n}{N - 1} \right), + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window and is + ready to be used as a periodic window with functions like + :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have + ``torch.hann_window(L, periodic=True)`` equal to + ``torch.hann_window(L + 1, periodic=False)[:-1])``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported. + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window + """ + ... +@overload +def hann_window(window_length: _int, periodic: _bool, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + hann_window(window_length, periodic=True, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Hann window function. + + .. math:: + w[n] = \frac{1}{2}\ \left[1 - \cos \left( \frac{2 \pi n}{N - 1} \right)\right] = + \sin^2 \left( \frac{\pi n}{N - 1} \right), + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window and is + ready to be used as a periodic window with functions like + :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have + ``torch.hann_window(L, periodic=True)`` equal to + ``torch.hann_window(L + 1, periodic=False)[:-1])``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported. + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window + """ + ... +def hardshrink(input: Tensor, lambd: Union[Number, _complex] = 0.5, *, out: Optional[Tensor] = None) -> Tensor: ... +def heaviside(input: Tensor, values: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + heaviside(input, values, *, out=None) -> Tensor + + Computes the Heaviside step function for each element in :attr:`input`. + The Heaviside step function is defined as: + + .. math:: + \text{{heaviside}}(input, values) = \begin{cases} + 0, & \text{if input < 0}\\ + values, & \text{if input == 0}\\ + 1, & \text{if input > 0} + \end{cases} + + + Args: + input (Tensor): the input tensor. + values (Tensor): The values to use where :attr:`input` is zero. + + Keyword arguments: + out (Tensor, optional): the output tensor. + + Example:: + + >>> input = torch.tensor([-1.5, 0, 2.0]) + >>> values = torch.tensor([0.5]) + >>> torch.heaviside(input, values) + tensor([0.0000, 0.5000, 1.0000]) + >>> values = torch.tensor([1.2, -2.0, 3.5]) + >>> torch.heaviside(input, values) + tensor([0., -2., 1.]) + """ + ... +def hinge_embedding_loss(input: Tensor, target: Tensor, margin: _float = 1.0, reduction: _int = 1) -> Tensor: ... +def histc(input: Tensor, bins: _int = 100, min: Union[Number, _complex] = 0, max: Union[Number, _complex] = 0, *, out: Optional[Tensor] = None) -> Tensor: + r""" + histc(input, bins=100, min=0, max=0, *, out=None) -> Tensor + + Computes the histogram of a tensor. + + The elements are sorted into equal width bins between :attr:`min` and + :attr:`max`. If :attr:`min` and :attr:`max` are both zero, the minimum and + maximum values of the data are used. + + Elements lower than min and higher than max and ``NaN`` elements are ignored. + + Args: + input (Tensor): the input tensor. + bins (int): number of histogram bins + min (Scalar): lower end of the range (inclusive) + max (Scalar): upper end of the range (inclusive) + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + Tensor: Histogram represented as a tensor + + Example:: + + >>> torch.histc(torch.tensor([1., 2, 1]), bins=4, min=0, max=3) + tensor([ 0., 2., 1., 0.]) + """ + ... +@overload +def histogram(input: Tensor, bins: Tensor, *, weight: Optional[Tensor] = None, density: _bool = False, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.histogram: + r""" + histogram(input, bins, *, range=None, weight=None, density=False, out=None) -> (Tensor, Tensor) + + Computes a histogram of the values in a tensor. + + :attr:`bins` can be an integer or a 1D tensor. + + If :attr:`bins` is an int, it specifies the number of equal-width bins. + By default, the lower and upper range of the bins is determined by the + minimum and maximum elements of the input tensor. The :attr:`range` + argument can be provided to specify a range for the bins. + + If :attr:`bins` is a 1D tensor, it specifies the sequence of bin edges + including the rightmost edge. It should contain at least 2 elements + and its elements should be increasing. + + Args: + input (Tensor): the input tensor. + bins: int or 1D Tensor. If int, defines the number of equal-width bins. If tensor, + defines the sequence of bin edges including the rightmost edge. + + Keyword args: + range (tuple of float): Defines the range of the bins. + weight (Tensor): If provided, weight should have the same shape as input. Each value in + input contributes its associated weight towards its bin's result. + density (bool): If False, the result will contain the count (or total weight) in each bin. + If True, the result is the value of the probability density function over the bins, + normalized such that the integral over the range of the bins is 1. + out (Tensor, optional): the output tensor. (tuple, optional): The result tuple of two output tensors (hist, bin_edges). + + Returns: + hist (Tensor): 1D Tensor containing the values of the histogram. + bin_edges(Tensor): 1D Tensor containing the edges of the histogram bins. + + Example:: + + >>> torch.histogram(torch.tensor([1., 2, 1]), bins=4, range=(0., 3.), weight=torch.tensor([1., 2., 4.])) + (tensor([ 0., 5., 2., 0.]), tensor([0., 0.75, 1.5, 2.25, 3.])) + >>> torch.histogram(torch.tensor([1., 2, 1]), bins=4, range=(0., 3.), weight=torch.tensor([1., 2., 4.]), density=True) + (tensor([ 0., 0.9524, 0.3810, 0.]), tensor([0., 0.75, 1.5, 2.25, 3.])) + """ + ... +@overload +def histogram(input: Tensor, bins: _int = 100, *, range: Optional[Sequence[_float]] = None, weight: Optional[Tensor] = None, density: _bool = False, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.histogram: + r""" + histogram(input, bins, *, range=None, weight=None, density=False, out=None) -> (Tensor, Tensor) + + Computes a histogram of the values in a tensor. + + :attr:`bins` can be an integer or a 1D tensor. + + If :attr:`bins` is an int, it specifies the number of equal-width bins. + By default, the lower and upper range of the bins is determined by the + minimum and maximum elements of the input tensor. The :attr:`range` + argument can be provided to specify a range for the bins. + + If :attr:`bins` is a 1D tensor, it specifies the sequence of bin edges + including the rightmost edge. It should contain at least 2 elements + and its elements should be increasing. + + Args: + input (Tensor): the input tensor. + bins: int or 1D Tensor. If int, defines the number of equal-width bins. If tensor, + defines the sequence of bin edges including the rightmost edge. + + Keyword args: + range (tuple of float): Defines the range of the bins. + weight (Tensor): If provided, weight should have the same shape as input. Each value in + input contributes its associated weight towards its bin's result. + density (bool): If False, the result will contain the count (or total weight) in each bin. + If True, the result is the value of the probability density function over the bins, + normalized such that the integral over the range of the bins is 1. + out (Tensor, optional): the output tensor. (tuple, optional): The result tuple of two output tensors (hist, bin_edges). + + Returns: + hist (Tensor): 1D Tensor containing the values of the histogram. + bin_edges(Tensor): 1D Tensor containing the edges of the histogram bins. + + Example:: + + >>> torch.histogram(torch.tensor([1., 2, 1]), bins=4, range=(0., 3.), weight=torch.tensor([1., 2., 4.])) + (tensor([ 0., 5., 2., 0.]), tensor([0., 0.75, 1.5, 2.25, 3.])) + >>> torch.histogram(torch.tensor([1., 2, 1]), bins=4, range=(0., 3.), weight=torch.tensor([1., 2., 4.]), density=True) + (tensor([ 0., 0.9524, 0.3810, 0.]), tensor([0., 0.75, 1.5, 2.25, 3.])) + """ + ... +@overload +def histogramdd(input: Tensor, bins: _int, range: Optional[Sequence[_float]] = None, weight: Optional[Tensor] = None, density: _bool = False) -> torch.return_types.histogramdd: + r""" + histogramdd(input, bins, *, range=None, weight=None, density=False, out=None) -> (Tensor, Tensor[]) + + Computes a multi-dimensional histogram of the values in a tensor. + + Interprets the elements of an input tensor whose innermost dimension has size N + as a collection of N-dimensional points. Maps each of the points into a set of + N-dimensional bins and returns the number of points (or total weight) in each bin. + + :attr:`input` must be a tensor with at least 2 dimensions. + If input has shape (M, N), each of its M rows defines a point in N-dimensional space. + If input has three or more dimensions, all but the last dimension are flattened. + + Each dimension is independently associated with its own strictly increasing sequence + of bin edges. Bin edges may be specified explicitly by passing a sequence of 1D + tensors. Alternatively, bin edges may be constructed automatically by passing a + sequence of integers specifying the number of equal-width bins in each dimension. + + For each N-dimensional point in input: + - Each of its coordinates is binned independently among the bin edges + corresponding to its dimension + - Binning results are combined to identify the N-dimensional bin (if any) + into which the point falls + - If the point falls into a bin, the bin's count (or total weight) is incremented + - Points which do not fall into any bin do not contribute to the output + + :attr:`bins` can be a sequence of N 1D tensors, a sequence of N ints, or a single int. + + If :attr:`bins` is a sequence of N 1D tensors, it explicitly specifies the N sequences + of bin edges. Each 1D tensor should contain a strictly increasing sequence with at + least one element. A sequence of K bin edges defines K-1 bins, explicitly specifying + the left and right edges of all bins. Every bin is exclusive of its left edge. Only + the rightmost bin is inclusive of its right edge. + + If :attr:`bins` is a sequence of N ints, it specifies the number of equal-width bins + in each dimension. By default, the leftmost and rightmost bin edges in each dimension + are determined by the minimum and maximum elements of the input tensor in the + corresponding dimension. The :attr:`range` argument can be provided to manually + specify the leftmost and rightmost bin edges in each dimension. + + If :attr:`bins` is an int, it specifies the number of equal-width bins for all dimensions. + + .. note:: + See also :func:`torch.histogram`, which specifically computes 1D histograms. + While :func:`torch.histogramdd` infers the dimensionality of its bins and + binned values from the shape of :attr:`input`, :func:`torch.histogram` + accepts and flattens :attr:`input` of any shape. + + Args: + input (Tensor): the input tensor. + bins: Tensor[], int[], or int. + If Tensor[], defines the sequences of bin edges. + If int[], defines the number of equal-width bins in each dimension. + If int, defines the number of equal-width bins for all dimensions. + Keyword args: + range (sequence of float): Defines the leftmost and rightmost bin edges + in each dimension. + weight (Tensor): By default, each value in the input has weight 1. If a weight + tensor is passed, each N-dimensional coordinate in input + contributes its associated weight towards its bin's result. + The weight tensor should have the same shape as the :attr:`input` + tensor excluding its innermost dimension N. + density (bool): If False (default), the result will contain the count (or total weight) + in each bin. If True, each count (weight) is divided by the total count + (total weight), then divided by the volume of its associated bin. + Returns: + hist (Tensor): N-dimensional Tensor containing the values of the histogram. + bin_edges(Tensor[]): sequence of N 1D Tensors containing the bin edges. + + Example:: + >>> torch.histogramdd(torch.tensor([[0., 1.], [1., 0.], [2., 0.], [2., 2.]]), bins=[3, 3], + ... weight=torch.tensor([1., 2., 4., 8.])) + torch.return_types.histogramdd( + hist=tensor([[0., 1., 0.], + [2., 0., 0.], + [4., 0., 8.]]), + bin_edges=(tensor([0.0000, 0.6667, 1.3333, 2.0000]), + tensor([0.0000, 0.6667, 1.3333, 2.0000]))) + + >>> torch.histogramdd(torch.tensor([[0., 0.], [1., 1.], [2., 2.]]), bins=[2, 2], + ... range=[0., 1., 0., 1.], density=True) + torch.return_types.histogramdd( + hist=tensor([[2., 0.], + [0., 2.]]), + bin_edges=(tensor([0.0000, 0.5000, 1.0000]), + tensor([0.0000, 0.5000, 1.0000]))) + """ + ... +@overload +def histogramdd(input: Tensor, bins: _size, range: Optional[Sequence[_float]] = None, weight: Optional[Tensor] = None, density: _bool = False) -> torch.return_types.histogramdd: + r""" + histogramdd(input, bins, *, range=None, weight=None, density=False, out=None) -> (Tensor, Tensor[]) + + Computes a multi-dimensional histogram of the values in a tensor. + + Interprets the elements of an input tensor whose innermost dimension has size N + as a collection of N-dimensional points. Maps each of the points into a set of + N-dimensional bins and returns the number of points (or total weight) in each bin. + + :attr:`input` must be a tensor with at least 2 dimensions. + If input has shape (M, N), each of its M rows defines a point in N-dimensional space. + If input has three or more dimensions, all but the last dimension are flattened. + + Each dimension is independently associated with its own strictly increasing sequence + of bin edges. Bin edges may be specified explicitly by passing a sequence of 1D + tensors. Alternatively, bin edges may be constructed automatically by passing a + sequence of integers specifying the number of equal-width bins in each dimension. + + For each N-dimensional point in input: + - Each of its coordinates is binned independently among the bin edges + corresponding to its dimension + - Binning results are combined to identify the N-dimensional bin (if any) + into which the point falls + - If the point falls into a bin, the bin's count (or total weight) is incremented + - Points which do not fall into any bin do not contribute to the output + + :attr:`bins` can be a sequence of N 1D tensors, a sequence of N ints, or a single int. + + If :attr:`bins` is a sequence of N 1D tensors, it explicitly specifies the N sequences + of bin edges. Each 1D tensor should contain a strictly increasing sequence with at + least one element. A sequence of K bin edges defines K-1 bins, explicitly specifying + the left and right edges of all bins. Every bin is exclusive of its left edge. Only + the rightmost bin is inclusive of its right edge. + + If :attr:`bins` is a sequence of N ints, it specifies the number of equal-width bins + in each dimension. By default, the leftmost and rightmost bin edges in each dimension + are determined by the minimum and maximum elements of the input tensor in the + corresponding dimension. The :attr:`range` argument can be provided to manually + specify the leftmost and rightmost bin edges in each dimension. + + If :attr:`bins` is an int, it specifies the number of equal-width bins for all dimensions. + + .. note:: + See also :func:`torch.histogram`, which specifically computes 1D histograms. + While :func:`torch.histogramdd` infers the dimensionality of its bins and + binned values from the shape of :attr:`input`, :func:`torch.histogram` + accepts and flattens :attr:`input` of any shape. + + Args: + input (Tensor): the input tensor. + bins: Tensor[], int[], or int. + If Tensor[], defines the sequences of bin edges. + If int[], defines the number of equal-width bins in each dimension. + If int, defines the number of equal-width bins for all dimensions. + Keyword args: + range (sequence of float): Defines the leftmost and rightmost bin edges + in each dimension. + weight (Tensor): By default, each value in the input has weight 1. If a weight + tensor is passed, each N-dimensional coordinate in input + contributes its associated weight towards its bin's result. + The weight tensor should have the same shape as the :attr:`input` + tensor excluding its innermost dimension N. + density (bool): If False (default), the result will contain the count (or total weight) + in each bin. If True, each count (weight) is divided by the total count + (total weight), then divided by the volume of its associated bin. + Returns: + hist (Tensor): N-dimensional Tensor containing the values of the histogram. + bin_edges(Tensor[]): sequence of N 1D Tensors containing the bin edges. + + Example:: + >>> torch.histogramdd(torch.tensor([[0., 1.], [1., 0.], [2., 0.], [2., 2.]]), bins=[3, 3], + ... weight=torch.tensor([1., 2., 4., 8.])) + torch.return_types.histogramdd( + hist=tensor([[0., 1., 0.], + [2., 0., 0.], + [4., 0., 8.]]), + bin_edges=(tensor([0.0000, 0.6667, 1.3333, 2.0000]), + tensor([0.0000, 0.6667, 1.3333, 2.0000]))) + + >>> torch.histogramdd(torch.tensor([[0., 0.], [1., 1.], [2., 2.]]), bins=[2, 2], + ... range=[0., 1., 0., 1.], density=True) + torch.return_types.histogramdd( + hist=tensor([[2., 0.], + [0., 2.]]), + bin_edges=(tensor([0.0000, 0.5000, 1.0000]), + tensor([0.0000, 0.5000, 1.0000]))) + """ + ... +@overload +def histogramdd(input: Tensor, bins: Union[Tuple[Tensor, ...], List[Tensor]], range: Optional[Sequence[_float]] = None, weight: Optional[Tensor] = None, density: _bool = False) -> torch.return_types.histogramdd: + r""" + histogramdd(input, bins, *, range=None, weight=None, density=False, out=None) -> (Tensor, Tensor[]) + + Computes a multi-dimensional histogram of the values in a tensor. + + Interprets the elements of an input tensor whose innermost dimension has size N + as a collection of N-dimensional points. Maps each of the points into a set of + N-dimensional bins and returns the number of points (or total weight) in each bin. + + :attr:`input` must be a tensor with at least 2 dimensions. + If input has shape (M, N), each of its M rows defines a point in N-dimensional space. + If input has three or more dimensions, all but the last dimension are flattened. + + Each dimension is independently associated with its own strictly increasing sequence + of bin edges. Bin edges may be specified explicitly by passing a sequence of 1D + tensors. Alternatively, bin edges may be constructed automatically by passing a + sequence of integers specifying the number of equal-width bins in each dimension. + + For each N-dimensional point in input: + - Each of its coordinates is binned independently among the bin edges + corresponding to its dimension + - Binning results are combined to identify the N-dimensional bin (if any) + into which the point falls + - If the point falls into a bin, the bin's count (or total weight) is incremented + - Points which do not fall into any bin do not contribute to the output + + :attr:`bins` can be a sequence of N 1D tensors, a sequence of N ints, or a single int. + + If :attr:`bins` is a sequence of N 1D tensors, it explicitly specifies the N sequences + of bin edges. Each 1D tensor should contain a strictly increasing sequence with at + least one element. A sequence of K bin edges defines K-1 bins, explicitly specifying + the left and right edges of all bins. Every bin is exclusive of its left edge. Only + the rightmost bin is inclusive of its right edge. + + If :attr:`bins` is a sequence of N ints, it specifies the number of equal-width bins + in each dimension. By default, the leftmost and rightmost bin edges in each dimension + are determined by the minimum and maximum elements of the input tensor in the + corresponding dimension. The :attr:`range` argument can be provided to manually + specify the leftmost and rightmost bin edges in each dimension. + + If :attr:`bins` is an int, it specifies the number of equal-width bins for all dimensions. + + .. note:: + See also :func:`torch.histogram`, which specifically computes 1D histograms. + While :func:`torch.histogramdd` infers the dimensionality of its bins and + binned values from the shape of :attr:`input`, :func:`torch.histogram` + accepts and flattens :attr:`input` of any shape. + + Args: + input (Tensor): the input tensor. + bins: Tensor[], int[], or int. + If Tensor[], defines the sequences of bin edges. + If int[], defines the number of equal-width bins in each dimension. + If int, defines the number of equal-width bins for all dimensions. + Keyword args: + range (sequence of float): Defines the leftmost and rightmost bin edges + in each dimension. + weight (Tensor): By default, each value in the input has weight 1. If a weight + tensor is passed, each N-dimensional coordinate in input + contributes its associated weight towards its bin's result. + The weight tensor should have the same shape as the :attr:`input` + tensor excluding its innermost dimension N. + density (bool): If False (default), the result will contain the count (or total weight) + in each bin. If True, each count (weight) is divided by the total count + (total weight), then divided by the volume of its associated bin. + Returns: + hist (Tensor): N-dimensional Tensor containing the values of the histogram. + bin_edges(Tensor[]): sequence of N 1D Tensors containing the bin edges. + + Example:: + >>> torch.histogramdd(torch.tensor([[0., 1.], [1., 0.], [2., 0.], [2., 2.]]), bins=[3, 3], + ... weight=torch.tensor([1., 2., 4., 8.])) + torch.return_types.histogramdd( + hist=tensor([[0., 1., 0.], + [2., 0., 0.], + [4., 0., 8.]]), + bin_edges=(tensor([0.0000, 0.6667, 1.3333, 2.0000]), + tensor([0.0000, 0.6667, 1.3333, 2.0000]))) + + >>> torch.histogramdd(torch.tensor([[0., 0.], [1., 1.], [2., 2.]]), bins=[2, 2], + ... range=[0., 1., 0., 1.], density=True) + torch.return_types.histogramdd( + hist=tensor([[2., 0.], + [0., 2.]]), + bin_edges=(tensor([0.0000, 0.5000, 1.0000]), + tensor([0.0000, 0.5000, 1.0000]))) + """ + ... +def hsmm(input: Tensor, mat2: Tensor) -> Tensor: ... +@overload +def hsplit(input: Tensor, sections: _int) -> Tuple[Tensor, ...]: + r""" + hsplit(input, indices_or_sections) -> List of Tensors + + Splits :attr:`input`, a tensor with one or more dimensions, into multiple tensors + horizontally according to :attr:`indices_or_sections`. Each split is a view of + :attr:`input`. + + If :attr:`input` is one dimensional this is equivalent to calling + torch.tensor_split(input, indices_or_sections, dim=0) (the split dimension is + zero), and if :attr:`input` has two or more dimensions it's equivalent to calling + torch.tensor_split(input, indices_or_sections, dim=1) (the split dimension is 1), + except that if :attr:`indices_or_sections` is an integer it must evenly divide + the split dimension or a runtime error will be thrown. + + This function is based on NumPy's :func:`numpy.hsplit`. + + Args: + input (Tensor): tensor to split. + indices_or_sections (int or list or tuple of ints): See argument in :func:`torch.tensor_split`. + + Example:: + >>> t = torch.arange(16.0).reshape(4,4) + >>> t + tensor([[ 0., 1., 2., 3.], + [ 4., 5., 6., 7.], + [ 8., 9., 10., 11.], + [12., 13., 14., 15.]]) + >>> torch.hsplit(t, 2) + (tensor([[ 0., 1.], + [ 4., 5.], + [ 8., 9.], + [12., 13.]]), + tensor([[ 2., 3.], + [ 6., 7.], + [10., 11.], + [14., 15.]])) + >>> torch.hsplit(t, [3, 6]) + (tensor([[ 0., 1., 2.], + [ 4., 5., 6.], + [ 8., 9., 10.], + [12., 13., 14.]]), + tensor([[ 3.], + [ 7.], + [11.], + [15.]]), + tensor([], size=(4, 0))) + """ + ... +@overload +def hsplit(input: Tensor, indices: _size) -> Tuple[Tensor, ...]: + r""" + hsplit(input, indices_or_sections) -> List of Tensors + + Splits :attr:`input`, a tensor with one or more dimensions, into multiple tensors + horizontally according to :attr:`indices_or_sections`. Each split is a view of + :attr:`input`. + + If :attr:`input` is one dimensional this is equivalent to calling + torch.tensor_split(input, indices_or_sections, dim=0) (the split dimension is + zero), and if :attr:`input` has two or more dimensions it's equivalent to calling + torch.tensor_split(input, indices_or_sections, dim=1) (the split dimension is 1), + except that if :attr:`indices_or_sections` is an integer it must evenly divide + the split dimension or a runtime error will be thrown. + + This function is based on NumPy's :func:`numpy.hsplit`. + + Args: + input (Tensor): tensor to split. + indices_or_sections (int or list or tuple of ints): See argument in :func:`torch.tensor_split`. + + Example:: + >>> t = torch.arange(16.0).reshape(4,4) + >>> t + tensor([[ 0., 1., 2., 3.], + [ 4., 5., 6., 7.], + [ 8., 9., 10., 11.], + [12., 13., 14., 15.]]) + >>> torch.hsplit(t, 2) + (tensor([[ 0., 1.], + [ 4., 5.], + [ 8., 9.], + [12., 13.]]), + tensor([[ 2., 3.], + [ 6., 7.], + [10., 11.], + [14., 15.]])) + >>> torch.hsplit(t, [3, 6]) + (tensor([[ 0., 1., 2.], + [ 4., 5., 6.], + [ 8., 9., 10.], + [12., 13., 14.]]), + tensor([[ 3.], + [ 7.], + [11.], + [15.]]), + tensor([], size=(4, 0))) + """ + ... +def hspmm(mat1: Tensor, mat2: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + hspmm(mat1, mat2, *, out=None) -> Tensor + + Performs a matrix multiplication of a :ref:`sparse COO matrix + ` :attr:`mat1` and a strided matrix :attr:`mat2`. The + result is a (1 + 1)-dimensional :ref:`hybrid COO matrix + `. + + Args: + mat1 (Tensor): the first sparse matrix to be matrix multiplied + mat2 (Tensor): the second strided matrix to be matrix multiplied + + Keyword args: + out (Tensor, optional): the output tensor. + """ + ... +def hstack(tensors: Union[Tuple[Tensor, ...], List[Tensor]], *, out: Optional[Tensor] = None) -> Tensor: + r""" + hstack(tensors, *, out=None) -> Tensor + + Stack tensors in sequence horizontally (column wise). + + This is equivalent to concatenation along the first axis for 1-D tensors, and along the second axis for all other tensors. + + Args: + tensors (sequence of Tensors): sequence of tensors to concatenate + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([1, 2, 3]) + >>> b = torch.tensor([4, 5, 6]) + >>> torch.hstack((a,b)) + tensor([1, 2, 3, 4, 5, 6]) + >>> a = torch.tensor([[1],[2],[3]]) + >>> b = torch.tensor([[4],[5],[6]]) + >>> torch.hstack((a,b)) + tensor([[1, 4], + [2, 5], + [3, 6]]) + """ + ... +def hypot(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + hypot(input, other, *, out=None) -> Tensor + + Given the legs of a right triangle, return its hypotenuse. + + .. math:: + \text{out}_{i} = \sqrt{\text{input}_{i}^{2} + \text{other}_{i}^{2}} + + The shapes of ``input`` and ``other`` must be + :ref:`broadcastable `. + + Args: + input (Tensor): the first input tensor + other (Tensor): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.hypot(torch.tensor([4.0]), torch.tensor([3.0, 4.0, 5.0])) + tensor([5.0000, 5.6569, 6.4031]) + """ + ... +def i0(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + i0(input, *, out=None) -> Tensor + + Alias for :func:`torch.special.i0`. + """ + ... +def i0_(input: Tensor) -> Tensor: ... +def igamma(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + igamma(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.special.gammainc`. + """ + ... +def igammac(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + igammac(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.special.gammaincc`. + """ + ... +def imag(input: Tensor) -> Tensor: + r""" + imag(input) -> Tensor + + Returns a new tensor containing imaginary values of the :attr:`self` tensor. + The returned tensor and :attr:`self` share the same underlying storage. + + .. warning:: + :func:`imag` is only supported for tensors with complex dtypes. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> x=torch.randn(4, dtype=torch.cfloat) + >>> x + tensor([(0.3100+0.3553j), (-0.5445-0.7896j), (-1.6492-0.0633j), (-0.0638-0.8119j)]) + >>> x.imag + tensor([ 0.3553, -0.7896, -0.0633, -0.8119]) + """ + ... +@overload +def index_add(input: Tensor, dim: _int, index: Tensor, source: Tensor, *, alpha: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + index_add(input, dim, index, source, *, alpha=1, out=None) -> Tensor + + See :meth:`~Tensor.index_add_` for function description. + """ + ... +@overload +def index_add(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, source: Tensor, *, alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + index_add(input, dim, index, source, *, alpha=1, out=None) -> Tensor + + See :meth:`~Tensor.index_add_` for function description. + """ + ... +@overload +def index_copy(input: Tensor, dim: _int, index: Tensor, source: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + index_copy(input, dim, index, source, *, out=None) -> Tensor + + See :meth:`~Tensor.index_add_` for function description. + """ + ... +@overload +def index_copy(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, source: Tensor) -> Tensor: + r""" + index_copy(input, dim, index, source, *, out=None) -> Tensor + + See :meth:`~Tensor.index_add_` for function description. + """ + ... +@overload +def index_fill(input: Tensor, dim: _int, index: Tensor, value: Tensor) -> Tensor: ... +@overload +def index_fill(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, value: Tensor) -> Tensor: ... +@overload +def index_fill(input: Tensor, dim: _int, index: Tensor, value: Union[Number, _complex]) -> Tensor: ... +@overload +def index_fill(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, value: Union[Number, _complex]) -> Tensor: ... +def index_put(input: Tensor, indices: Optional[Union[Tuple[Tensor, ...], List[Tensor]]], values: Tensor, accumulate: _bool = False) -> Tensor: ... +def index_put_(input: Tensor, indices: Optional[Union[Tuple[Tensor, ...], List[Tensor]]], values: Tensor, accumulate: _bool = False) -> Tensor: ... +def index_reduce(input: Tensor, dim: _int, index: Tensor, source: Tensor, reduce: str, *, include_self: _bool = True, out: Optional[Tensor] = None) -> Tensor: + r""" + index_reduce(input, dim, index, source, reduce, *, include_self=True, out=None) -> Tensor + + See :meth:`~Tensor.index_reduce_` for function description. + """ + ... +@overload +def index_select(input: Tensor, dim: _int, index: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + index_select(input, dim, index, *, out=None) -> Tensor + + Returns a new tensor which indexes the :attr:`input` tensor along dimension + :attr:`dim` using the entries in :attr:`index` which is a `LongTensor`. + + The returned tensor has the same number of dimensions as the original tensor + (:attr:`input`). The :attr:`dim`\ th dimension has the same size as the length + of :attr:`index`; other dimensions have the same size as in the original tensor. + + .. note:: The returned tensor does **not** use the same storage as the original + tensor. If :attr:`out` has a different shape than expected, we + silently change it to the correct shape, reallocating the underlying + storage if necessary. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension in which we index + index (IntTensor or LongTensor): the 1-D tensor containing the indices to index + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> x = torch.randn(3, 4) + >>> x + tensor([[ 0.1427, 0.0231, -0.5414, -1.0009], + [-0.4664, 0.2647, -0.1228, -1.1068], + [-1.1734, -0.6571, 0.7230, -0.6004]]) + >>> indices = torch.tensor([0, 2]) + >>> torch.index_select(x, 0, indices) + tensor([[ 0.1427, 0.0231, -0.5414, -1.0009], + [-1.1734, -0.6571, 0.7230, -0.6004]]) + >>> torch.index_select(x, 1, indices) + tensor([[ 0.1427, -0.5414], + [-0.4664, -0.1228], + [-1.1734, 0.7230]]) + """ + ... +@overload +def index_select(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + index_select(input, dim, index, *, out=None) -> Tensor + + Returns a new tensor which indexes the :attr:`input` tensor along dimension + :attr:`dim` using the entries in :attr:`index` which is a `LongTensor`. + + The returned tensor has the same number of dimensions as the original tensor + (:attr:`input`). The :attr:`dim`\ th dimension has the same size as the length + of :attr:`index`; other dimensions have the same size as in the original tensor. + + .. note:: The returned tensor does **not** use the same storage as the original + tensor. If :attr:`out` has a different shape than expected, we + silently change it to the correct shape, reallocating the underlying + storage if necessary. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension in which we index + index (IntTensor or LongTensor): the 1-D tensor containing the indices to index + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> x = torch.randn(3, 4) + >>> x + tensor([[ 0.1427, 0.0231, -0.5414, -1.0009], + [-0.4664, 0.2647, -0.1228, -1.1068], + [-1.1734, -0.6571, 0.7230, -0.6004]]) + >>> indices = torch.tensor([0, 2]) + >>> torch.index_select(x, 0, indices) + tensor([[ 0.1427, 0.0231, -0.5414, -1.0009], + [-1.1734, -0.6571, 0.7230, -0.6004]]) + >>> torch.index_select(x, 1, indices) + tensor([[ 0.1427, -0.5414], + [-0.4664, -0.1228], + [-1.1734, 0.7230]]) + """ + ... +def indices_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.indices`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def init_num_threads() -> None: ... +def inner(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + inner(input, other, *, out=None) -> Tensor + + Computes the dot product for 1D tensors. For higher dimensions, sums the product + of elements from :attr:`input` and :attr:`other` along their last dimension. + + .. note:: + + If either :attr:`input` or :attr:`other` is a scalar, the result is equivalent + to `torch.mul(input, other)`. + + If both :attr:`input` and :attr:`other` are non-scalars, the size of their last + dimension must match and the result is equivalent to `torch.tensordot(input, + other, dims=([-1], [-1]))` + + Args: + input (Tensor): First input tensor + other (Tensor): Second input tensor + + Keyword args: + out (Tensor, optional): Optional output tensor to write result into. The output + shape is `input.shape[:-1] + other.shape[:-1]`. + + Example:: + + # Dot product + >>> torch.inner(torch.tensor([1, 2, 3]), torch.tensor([0, 2, 1])) + tensor(7) + + # Multidimensional input tensors + >>> a = torch.randn(2, 3) + >>> a + tensor([[0.8173, 1.0874, 1.1784], + [0.3279, 0.1234, 2.7894]]) + >>> b = torch.randn(2, 4, 3) + >>> b + tensor([[[-0.4682, -0.7159, 0.1506], + [ 0.4034, -0.3657, 1.0387], + [ 0.9892, -0.6684, 0.1774], + [ 0.9482, 1.3261, 0.3917]], + + [[ 0.4537, 0.7493, 1.1724], + [ 0.2291, 0.5749, -0.2267], + [-0.7920, 0.3607, -0.3701], + [ 1.3666, -0.5850, -1.7242]]]) + >>> torch.inner(a, b) + tensor([[[-0.9837, 1.1560, 0.2907, 2.6785], + [ 2.5671, 0.5452, -0.6912, -1.5509]], + + [[ 0.1782, 2.9843, 0.7366, 1.5672], + [ 3.5115, -0.4864, -1.2476, -4.4337]]]) + + # Scalar input + >>> torch.inner(a, torch.tensor(2)) + tensor([[1.6347, 2.1748, 2.3567], + [0.6558, 0.2469, 5.5787]]) + """ + ... +def instance_norm(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], running_mean: Optional[Tensor], running_var: Optional[Tensor], use_input_stats: _bool, momentum: _float, eps: _float, cudnn_enabled: _bool) -> Tensor: ... +def int_repr(input: Tensor) -> Tensor: ... +def inverse(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + inverse(input, *, out=None) -> Tensor + + Alias for :func:`torch.linalg.inv` + """ + ... +def is_complex(input: Tensor) -> _bool: + r""" + is_complex(input) -> (bool) + + Returns True if the data type of :attr:`input` is a complex data type i.e., + one of ``torch.complex64``, and ``torch.complex128``. + + Args: + input (Tensor): the input tensor. + """ + ... +def is_conj(input: Tensor) -> _bool: + r""" + is_conj(input) -> (bool) + + Returns True if the :attr:`input` is a conjugated tensor, i.e. its conjugate bit is set to `True`. + + Args: + input (Tensor): the input tensor. + """ + ... +def is_distributed(input: Tensor) -> _bool: ... +def is_floating_point(input: Tensor) -> _bool: + r""" + is_floating_point(input) -> (bool) + + Returns True if the data type of :attr:`input` is a floating point data type i.e., + one of ``torch.float64``, ``torch.float32``, ``torch.float16``, and ``torch.bfloat16``. + + Args: + input (Tensor): the input tensor. + """ + ... +def is_grad_enabled() -> _bool: + r""" + is_grad_enabled() -> (bool) + + Returns True if grad mode is currently enabled. + """ + ... +def is_inference(input: Tensor) -> _bool: + r""" + is_inference(input) -> (bool) + + Returns True if :attr:`input` is an inference tensor. + + A non-view tensor is an inference tensor if and only if it was + allocated during inference mode. A view tensor is an inference + tensor if and only if the tensor it is a view of is an inference tensor. + + For details on inference mode please see + `Inference Mode `_. + + Args: + input (Tensor): the input tensor. + """ + ... +def is_inference_mode_enabled() -> _bool: + r""" + is_inference_mode_enabled() -> (bool) + + Returns True if inference mode is currently enabled. + """ + ... +def is_neg(input: Tensor) -> _bool: ... +def is_nonzero(input: Tensor) -> _bool: + r""" + is_nonzero(input) -> (bool) + + Returns True if the :attr:`input` is a single element tensor which is not equal to zero + after type conversions. + i.e. not equal to ``torch.tensor([0.])`` or ``torch.tensor([0])`` or + ``torch.tensor([False])``. + Throws a ``RuntimeError`` if ``torch.numel() != 1`` (even in case + of sparse tensors). + + Args: + input (Tensor): the input tensor. + + Examples:: + + >>> torch.is_nonzero(torch.tensor([0.])) + False + >>> torch.is_nonzero(torch.tensor([1.5])) + True + >>> torch.is_nonzero(torch.tensor([False])) + False + >>> torch.is_nonzero(torch.tensor([3])) + True + >>> torch.is_nonzero(torch.tensor([1, 3, 5])) + Traceback (most recent call last): + ... + RuntimeError: bool value of Tensor with more than one value is ambiguous + >>> torch.is_nonzero(torch.tensor([])) + Traceback (most recent call last): + ... + RuntimeError: bool value of Tensor with no values is ambiguous + """ + ... +def is_same_size(input: Tensor, other: Tensor) -> _bool: ... +def is_signed(input: Tensor) -> _bool: ... +def is_vulkan_available() -> _bool: ... +def isclose(input: Tensor, other: Tensor, rtol: _float = 1e-05, atol: _float = 1e-08, equal_nan: _bool = False) -> Tensor: + r""" + isclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) -> Tensor + + Returns a new tensor with boolean elements representing if each element of + :attr:`input` is "close" to the corresponding element of :attr:`other`. + Closeness is defined as: + + .. math:: + \lvert \text{input} - \text{other} \rvert \leq \texttt{atol} + \texttt{rtol} \times \lvert \text{other} \rvert + + + where :attr:`input` and :attr:`other` are finite. Where :attr:`input` + and/or :attr:`other` are nonfinite they are close if and only if + they are equal, with NaNs being considered equal to each other when + :attr:`equal_nan` is True. + + Args: + input (Tensor): first tensor to compare + other (Tensor): second tensor to compare + atol (float, optional): absolute tolerance. Default: 1e-08 + rtol (float, optional): relative tolerance. Default: 1e-05 + equal_nan (bool, optional): if ``True``, then two ``NaN`` s will be considered equal. Default: ``False`` + + Examples:: + + >>> torch.isclose(torch.tensor((1., 2, 3)), torch.tensor((1 + 1e-10, 3, 4))) + tensor([ True, False, False]) + >>> torch.isclose(torch.tensor((float('inf'), 4)), torch.tensor((float('inf'), 6)), rtol=.5) + tensor([True, True]) + """ + ... +def isfinite(input: Tensor) -> Tensor: + r""" + isfinite(input) -> Tensor + + Returns a new tensor with boolean elements representing if each element is `finite` or not. + + Real values are finite when they are not NaN, negative infinity, or infinity. + Complex values are finite when both their real and imaginary parts are finite. + + Args: + input (Tensor): the input tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is finite and False elsewhere + + Example:: + + >>> torch.isfinite(torch.tensor([1, float('inf'), 2, float('-inf'), float('nan')])) + tensor([True, False, True, False, False]) + """ + ... +@overload +def isin(elements: Tensor, test_elements: Tensor, *, assume_unique: _bool = False, invert: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + isin(elements, test_elements, *, assume_unique=False, invert=False) -> Tensor + + Tests if each element of :attr:`elements` is in :attr:`test_elements`. Returns + a boolean tensor of the same shape as :attr:`elements` that is True for elements + in :attr:`test_elements` and False otherwise. + + .. note:: + One of :attr:`elements` or :attr:`test_elements` can be a scalar, but not both. + + Args: + elements (Tensor or Scalar): Input elements + test_elements (Tensor or Scalar): Values against which to test for each input element + assume_unique (bool, optional): If True, assumes both :attr:`elements` and + :attr:`test_elements` contain unique elements, which can speed up the + calculation. Default: False + invert (bool, optional): If True, inverts the boolean return tensor, resulting in True + values for elements *not* in :attr:`test_elements`. Default: False + + Returns: + A boolean tensor of the same shape as :attr:`elements` that is True for elements in + :attr:`test_elements` and False otherwise + + Example: + >>> torch.isin(torch.tensor([[1, 2], [3, 4]]), torch.tensor([2, 3])) + tensor([[False, True], + [ True, False]]) + """ + ... +@overload +def isin(element: Union[Number, _complex], test_elements: Tensor, *, assume_unique: _bool = False, invert: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + isin(elements, test_elements, *, assume_unique=False, invert=False) -> Tensor + + Tests if each element of :attr:`elements` is in :attr:`test_elements`. Returns + a boolean tensor of the same shape as :attr:`elements` that is True for elements + in :attr:`test_elements` and False otherwise. + + .. note:: + One of :attr:`elements` or :attr:`test_elements` can be a scalar, but not both. + + Args: + elements (Tensor or Scalar): Input elements + test_elements (Tensor or Scalar): Values against which to test for each input element + assume_unique (bool, optional): If True, assumes both :attr:`elements` and + :attr:`test_elements` contain unique elements, which can speed up the + calculation. Default: False + invert (bool, optional): If True, inverts the boolean return tensor, resulting in True + values for elements *not* in :attr:`test_elements`. Default: False + + Returns: + A boolean tensor of the same shape as :attr:`elements` that is True for elements in + :attr:`test_elements` and False otherwise + + Example: + >>> torch.isin(torch.tensor([[1, 2], [3, 4]]), torch.tensor([2, 3])) + tensor([[False, True], + [ True, False]]) + """ + ... +@overload +def isin(elements: Tensor, test_element: Union[Number, _complex], *, assume_unique: _bool = False, invert: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + isin(elements, test_elements, *, assume_unique=False, invert=False) -> Tensor + + Tests if each element of :attr:`elements` is in :attr:`test_elements`. Returns + a boolean tensor of the same shape as :attr:`elements` that is True for elements + in :attr:`test_elements` and False otherwise. + + .. note:: + One of :attr:`elements` or :attr:`test_elements` can be a scalar, but not both. + + Args: + elements (Tensor or Scalar): Input elements + test_elements (Tensor or Scalar): Values against which to test for each input element + assume_unique (bool, optional): If True, assumes both :attr:`elements` and + :attr:`test_elements` contain unique elements, which can speed up the + calculation. Default: False + invert (bool, optional): If True, inverts the boolean return tensor, resulting in True + values for elements *not* in :attr:`test_elements`. Default: False + + Returns: + A boolean tensor of the same shape as :attr:`elements` that is True for elements in + :attr:`test_elements` and False otherwise + + Example: + >>> torch.isin(torch.tensor([[1, 2], [3, 4]]), torch.tensor([2, 3])) + tensor([[False, True], + [ True, False]]) + """ + ... +def isinf(input: Tensor) -> Tensor: + r""" + isinf(input) -> Tensor + + Tests if each element of :attr:`input` is infinite + (positive or negative infinity) or not. + + .. note:: + Complex values are infinite when their real or imaginary part is + infinite. + + Args: + input (Tensor): the input tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is infinite and False elsewhere + + Example:: + + >>> torch.isinf(torch.tensor([1, float('inf'), 2, float('-inf'), float('nan')])) + tensor([False, True, False, True, False]) + """ + ... +def isnan(input: Tensor) -> Tensor: + r""" + isnan(input) -> Tensor + + Returns a new tensor with boolean elements representing if each element of :attr:`input` + is NaN or not. Complex values are considered NaN when either their real + and/or imaginary part is NaN. + + Arguments: + input (Tensor): the input tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is NaN and False elsewhere + + Example:: + + >>> torch.isnan(torch.tensor([1, float('nan'), 2])) + tensor([False, True, False]) + """ + ... +def isneginf(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + isneginf(input, *, out=None) -> Tensor + Tests if each element of :attr:`input` is negative infinity or not. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([-float('inf'), float('inf'), 1.2]) + >>> torch.isneginf(a) + tensor([ True, False, False]) + """ + ... +def isposinf(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + isposinf(input, *, out=None) -> Tensor + Tests if each element of :attr:`input` is positive infinity or not. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([-float('inf'), float('inf'), 1.2]) + >>> torch.isposinf(a) + tensor([False, True, False]) + """ + ... +def isreal(input: Tensor) -> Tensor: + r""" + isreal(input) -> Tensor + + Returns a new tensor with boolean elements representing if each element of :attr:`input` is real-valued or not. + All real-valued types are considered real. Complex values are considered real when their imaginary part is 0. + + Arguments: + input (Tensor): the input tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is real and False elsewhere + + Example:: + + >>> torch.isreal(torch.tensor([1, 1+1j, 2+0j])) + tensor([True, False, True]) + """ + ... +def istft(input: Tensor, n_fft: _int, hop_length: Optional[_int] = None, win_length: Optional[_int] = None, window: Optional[Tensor] = None, center: _bool = True, normalized: _bool = False, onesided: Optional[_bool] = None, length: Optional[_int] = None, return_complex: _bool = False) -> Tensor: ... +@overload +def kaiser_window(window_length: _int, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + kaiser_window(window_length, periodic=True, beta=12.0, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Computes the Kaiser window with window length :attr:`window_length` and shape parameter :attr:`beta`. + + Let I_0 be the zeroth order modified Bessel function of the first kind (see :func:`torch.i0`) and + ``N = L - 1`` if :attr:`periodic` is False and ``L`` if :attr:`periodic` is True, + where ``L`` is the :attr:`window_length`. This function computes: + + .. math:: + out_i = I_0 \left( \beta \sqrt{1 - \left( {\frac{i - N/2}{N/2}} \right) ^2 } \right) / I_0( \beta ) + + Calling ``torch.kaiser_window(L, B, periodic=True)`` is equivalent to calling + ``torch.kaiser_window(L + 1, B, periodic=False)[:-1])``. + The :attr:`periodic` argument is intended as a helpful shorthand + to produce a periodic window as input to functions like :func:`torch.stft`. + + .. note:: + If :attr:`window_length` is one, then the returned window is a single element tensor containing a one. + + + Args: + window_length (int): length of the window. + periodic (bool, optional): If True, returns a periodic window suitable for use in spectral analysis. + If False, returns a symmetric window suitable for use in filter design. + beta (float, optional): shape parameter for the window. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + """ + ... +@overload +def kaiser_window(window_length: _int, periodic: _bool, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + kaiser_window(window_length, periodic=True, beta=12.0, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Computes the Kaiser window with window length :attr:`window_length` and shape parameter :attr:`beta`. + + Let I_0 be the zeroth order modified Bessel function of the first kind (see :func:`torch.i0`) and + ``N = L - 1`` if :attr:`periodic` is False and ``L`` if :attr:`periodic` is True, + where ``L`` is the :attr:`window_length`. This function computes: + + .. math:: + out_i = I_0 \left( \beta \sqrt{1 - \left( {\frac{i - N/2}{N/2}} \right) ^2 } \right) / I_0( \beta ) + + Calling ``torch.kaiser_window(L, B, periodic=True)`` is equivalent to calling + ``torch.kaiser_window(L + 1, B, periodic=False)[:-1])``. + The :attr:`periodic` argument is intended as a helpful shorthand + to produce a periodic window as input to functions like :func:`torch.stft`. + + .. note:: + If :attr:`window_length` is one, then the returned window is a single element tensor containing a one. + + + Args: + window_length (int): length of the window. + periodic (bool, optional): If True, returns a periodic window suitable for use in spectral analysis. + If False, returns a symmetric window suitable for use in filter design. + beta (float, optional): shape parameter for the window. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + """ + ... +@overload +def kaiser_window(window_length: _int, periodic: _bool, beta: _float, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + kaiser_window(window_length, periodic=True, beta=12.0, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Computes the Kaiser window with window length :attr:`window_length` and shape parameter :attr:`beta`. + + Let I_0 be the zeroth order modified Bessel function of the first kind (see :func:`torch.i0`) and + ``N = L - 1`` if :attr:`periodic` is False and ``L`` if :attr:`periodic` is True, + where ``L`` is the :attr:`window_length`. This function computes: + + .. math:: + out_i = I_0 \left( \beta \sqrt{1 - \left( {\frac{i - N/2}{N/2}} \right) ^2 } \right) / I_0( \beta ) + + Calling ``torch.kaiser_window(L, B, periodic=True)`` is equivalent to calling + ``torch.kaiser_window(L + 1, B, periodic=False)[:-1])``. + The :attr:`periodic` argument is intended as a helpful shorthand + to produce a periodic window as input to functions like :func:`torch.stft`. + + .. note:: + If :attr:`window_length` is one, then the returned window is a single element tensor containing a one. + + + Args: + window_length (int): length of the window. + periodic (bool, optional): If True, returns a periodic window suitable for use in spectral analysis. + If False, returns a symmetric window suitable for use in filter design. + beta (float, optional): shape parameter for the window. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + """ + ... +def kl_div(input: Tensor, target: Tensor, reduction: _int = 1, *, log_target: _bool = False) -> Tensor: ... +def kron(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + kron(input, other, *, out=None) -> Tensor + + Computes the Kronecker product, denoted by :math:`\otimes`, of :attr:`input` and :attr:`other`. + + If :attr:`input` is a :math:`(a_0 \times a_1 \times \dots \times a_n)` tensor and :attr:`other` is a + :math:`(b_0 \times b_1 \times \dots \times b_n)` tensor, the result will be a + :math:`(a_0*b_0 \times a_1*b_1 \times \dots \times a_n*b_n)` tensor with the following entries: + + .. math:: + (\text{input} \otimes \text{other})_{k_0, k_1, \dots, k_n} = + \text{input}_{i_0, i_1, \dots, i_n} * \text{other}_{j_0, j_1, \dots, j_n}, + + where :math:`k_t = i_t * b_t + j_t` for :math:`0 \leq t \leq n`. + If one tensor has fewer dimensions than the other it is unsqueezed until it has the same number of dimensions. + + Supports real-valued and complex-valued inputs. + + .. note:: + This function generalizes the typical definition of the Kronecker product for two matrices to two tensors, + as described above. When :attr:`input` is a :math:`(m \times n)` matrix and :attr:`other` is a + :math:`(p \times q)` matrix, the result will be a :math:`(p*m \times q*n)` block matrix: + + .. math:: + \mathbf{A} \otimes \mathbf{B}=\begin{bmatrix} + a_{11} \mathbf{B} & \cdots & a_{1 n} \mathbf{B} \\ + \vdots & \ddots & \vdots \\ + a_{m 1} \mathbf{B} & \cdots & a_{m n} \mathbf{B} \end{bmatrix} + + where :attr:`input` is :math:`\mathbf{A}` and :attr:`other` is :math:`\mathbf{B}`. + + Arguments: + input (Tensor) + other (Tensor) + + Keyword args: + out (Tensor, optional): The output tensor. Ignored if ``None``. Default: ``None`` + + Examples:: + + >>> mat1 = torch.eye(2) + >>> mat2 = torch.ones(2, 2) + >>> torch.kron(mat1, mat2) + tensor([[1., 1., 0., 0.], + [1., 1., 0., 0.], + [0., 0., 1., 1.], + [0., 0., 1., 1.]]) + + >>> mat1 = torch.eye(2) + >>> mat2 = torch.arange(1, 5).reshape(2, 2) + >>> torch.kron(mat1, mat2) + tensor([[1., 2., 0., 0.], + [3., 4., 0., 0.], + [0., 0., 1., 2.], + [0., 0., 3., 4.]]) + """ + ... +@overload +def kthvalue(input: Tensor, k: _int, dim: _int = -1, keepdim: _bool = False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.kthvalue: + r""" + kthvalue(input, k, dim=None, keepdim=False, *, out=None) -> (Tensor, LongTensor) + + Returns a namedtuple ``(values, indices)`` where ``values`` is the :attr:`k` th + smallest element of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`. And ``indices`` is the index location of each element found. + + If :attr:`dim` is not given, the last dimension of the `input` is chosen. + + If :attr:`keepdim` is ``True``, both the :attr:`values` and :attr:`indices` tensors + are the same size as :attr:`input`, except in the dimension :attr:`dim` where + they are of size 1. Otherwise, :attr:`dim` is squeezed + (see :func:`torch.squeeze`), resulting in both the :attr:`values` and + :attr:`indices` tensors having 1 fewer dimension than the :attr:`input` tensor. + + .. note:: + When :attr:`input` is a CUDA tensor and there are multiple valid + :attr:`k` th values, this function may nondeterministically return + :attr:`indices` for any of them. + + Args: + input (Tensor): the input tensor. + k (int): k for the k-th smallest element + dim (int, optional): the dimension to find the kth value along + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (tuple, optional): the output tuple of (Tensor, LongTensor) + can be optionally given to be used as output buffers + + Example:: + + >>> x = torch.arange(1., 6.) + >>> x + tensor([ 1., 2., 3., 4., 5.]) + >>> torch.kthvalue(x, 4) + torch.return_types.kthvalue(values=tensor(4.), indices=tensor(3)) + + >>> x=torch.arange(1.,7.).resize_(2,3) + >>> x + tensor([[ 1., 2., 3.], + [ 4., 5., 6.]]) + >>> torch.kthvalue(x, 2, 0, True) + torch.return_types.kthvalue(values=tensor([[4., 5., 6.]]), indices=tensor([[1, 1, 1]])) + """ + ... +@overload +def kthvalue(input: Tensor, k: _int, dim: Union[str, ellipsis, None], keepdim: _bool = False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.kthvalue: + r""" + kthvalue(input, k, dim=None, keepdim=False, *, out=None) -> (Tensor, LongTensor) + + Returns a namedtuple ``(values, indices)`` where ``values`` is the :attr:`k` th + smallest element of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`. And ``indices`` is the index location of each element found. + + If :attr:`dim` is not given, the last dimension of the `input` is chosen. + + If :attr:`keepdim` is ``True``, both the :attr:`values` and :attr:`indices` tensors + are the same size as :attr:`input`, except in the dimension :attr:`dim` where + they are of size 1. Otherwise, :attr:`dim` is squeezed + (see :func:`torch.squeeze`), resulting in both the :attr:`values` and + :attr:`indices` tensors having 1 fewer dimension than the :attr:`input` tensor. + + .. note:: + When :attr:`input` is a CUDA tensor and there are multiple valid + :attr:`k` th values, this function may nondeterministically return + :attr:`indices` for any of them. + + Args: + input (Tensor): the input tensor. + k (int): k for the k-th smallest element + dim (int, optional): the dimension to find the kth value along + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (tuple, optional): the output tuple of (Tensor, LongTensor) + can be optionally given to be used as output buffers + + Example:: + + >>> x = torch.arange(1., 6.) + >>> x + tensor([ 1., 2., 3., 4., 5.]) + >>> torch.kthvalue(x, 4) + torch.return_types.kthvalue(values=tensor(4.), indices=tensor(3)) + + >>> x=torch.arange(1.,7.).resize_(2,3) + >>> x + tensor([[ 1., 2., 3.], + [ 4., 5., 6.]]) + >>> torch.kthvalue(x, 2, 0, True) + torch.return_types.kthvalue(values=tensor([[4., 5., 6.]]), indices=tensor([[1, 1, 1]])) + """ + ... +def layer_norm(input: Tensor, normalized_shape: Sequence[Union[_int, SymInt]], weight: Optional[Tensor] = None, bias: Optional[Tensor] = None, eps: _float = 1e-05, cudnn_enable: _bool = True) -> Tensor: ... +def lcm(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + lcm(input, other, *, out=None) -> Tensor + + Computes the element-wise least common multiple (LCM) of :attr:`input` and :attr:`other`. + + Both :attr:`input` and :attr:`other` must have integer types. + + .. note:: + This defines :math:`lcm(0, 0) = 0` and :math:`lcm(0, a) = 0`. + + Args: + input (Tensor): the input tensor. + other (Tensor): the second input tensor + + Keyword arguments: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([5, 10, 15]) + >>> b = torch.tensor([3, 4, 5]) + >>> torch.lcm(a, b) + tensor([15, 20, 15]) + >>> c = torch.tensor([3]) + >>> torch.lcm(a, c) + tensor([15, 30, 15]) + """ + ... +def lcm_(input: Tensor, other: Tensor) -> Tensor: ... +def ldexp(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + ldexp(input, other, *, out=None) -> Tensor + + Multiplies :attr:`input` by 2 ** :attr:`other`. + + .. math:: + \text{{out}}_i = \text{{input}}_i * 2^\text{{other}}_i + + + Typically this function is used to construct floating point numbers by multiplying + mantissas in :attr:`input` with integral powers of two created from the exponents + in :attr:`other`. + + Args: + input (Tensor): the input tensor. + other (Tensor): a tensor of exponents, typically integers. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.ldexp(torch.tensor([1.]), torch.tensor([1])) + tensor([2.]) + >>> torch.ldexp(torch.tensor([1.0]), torch.tensor([1, 2, 3, 4])) + tensor([ 2., 4., 8., 16.]) + """ + ... +def ldexp_(input: Tensor, other: Tensor) -> Tensor: ... +@overload +def le(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + le(input, other, *, out=None) -> Tensor + + Computes :math:`\text{input} \leq \text{other}` element-wise. + + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or Scalar): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is less than or equal to + :attr:`other` and False elsewhere + + Example:: + + >>> torch.le(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[True, False], [True, True]]) + """ + ... +@overload +def le(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + le(input, other, *, out=None) -> Tensor + + Computes :math:`\text{input} \leq \text{other}` element-wise. + + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or Scalar): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is less than or equal to + :attr:`other` and False elsewhere + + Example:: + + >>> torch.le(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[True, False], [True, True]]) + """ + ... +@overload +def lerp(input: Tensor, end: Tensor, weight: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + lerp(input, end, weight, *, out=None) + + Does a linear interpolation of two tensors :attr:`start` (given by :attr:`input`) and :attr:`end` based + on a scalar or tensor :attr:`weight` and returns the resulting :attr:`out` tensor. + + .. math:: + \text{out}_i = \text{start}_i + \text{weight}_i \times (\text{end}_i - \text{start}_i) + + The shapes of :attr:`start` and :attr:`end` must be + :ref:`broadcastable `. If :attr:`weight` is a tensor, then + the shapes of :attr:`weight`, :attr:`start`, and :attr:`end` must be :ref:`broadcastable `. + + Args: + input (Tensor): the tensor with the starting points + end (Tensor): the tensor with the ending points + weight (float or tensor): the weight for the interpolation formula + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> start = torch.arange(1., 5.) + >>> end = torch.empty(4).fill_(10) + >>> start + tensor([ 1., 2., 3., 4.]) + >>> end + tensor([ 10., 10., 10., 10.]) + >>> torch.lerp(start, end, 0.5) + tensor([ 5.5000, 6.0000, 6.5000, 7.0000]) + >>> torch.lerp(start, end, torch.full_like(start, 0.5)) + tensor([ 5.5000, 6.0000, 6.5000, 7.0000]) + """ + ... +@overload +def lerp(input: Tensor, end: Tensor, weight: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + lerp(input, end, weight, *, out=None) + + Does a linear interpolation of two tensors :attr:`start` (given by :attr:`input`) and :attr:`end` based + on a scalar or tensor :attr:`weight` and returns the resulting :attr:`out` tensor. + + .. math:: + \text{out}_i = \text{start}_i + \text{weight}_i \times (\text{end}_i - \text{start}_i) + + The shapes of :attr:`start` and :attr:`end` must be + :ref:`broadcastable `. If :attr:`weight` is a tensor, then + the shapes of :attr:`weight`, :attr:`start`, and :attr:`end` must be :ref:`broadcastable `. + + Args: + input (Tensor): the tensor with the starting points + end (Tensor): the tensor with the ending points + weight (float or tensor): the weight for the interpolation formula + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> start = torch.arange(1., 5.) + >>> end = torch.empty(4).fill_(10) + >>> start + tensor([ 1., 2., 3., 4.]) + >>> end + tensor([ 10., 10., 10., 10.]) + >>> torch.lerp(start, end, 0.5) + tensor([ 5.5000, 6.0000, 6.5000, 7.0000]) + >>> torch.lerp(start, end, torch.full_like(start, 0.5)) + tensor([ 5.5000, 6.0000, 6.5000, 7.0000]) + """ + ... +@overload +def less(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + less(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.lt`. + """ + ... +@overload +def less(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + less(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.lt`. + """ + ... +@overload +def less_equal(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + less_equal(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.le`. + """ + ... +@overload +def less_equal(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + less_equal(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.le`. + """ + ... +def lgamma(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + lgamma(input, *, out=None) -> Tensor + + Computes the natural logarithm of the absolute value of the gamma function on :attr:`input`. + + .. math:: + \text{out}_{i} = \ln |\Gamma(\text{input}_{i})| + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.arange(0.5, 2, 0.5) + >>> torch.lgamma(a) + tensor([ 0.5724, 0.0000, -0.1208]) + """ + ... +@overload +def linspace(start: Number, end: Number, steps: Optional[_int] = None, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + linspace(start, end, steps, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :attr:`start` to :attr:`end`, inclusive. That is, the value are: + + .. math:: + (\text{start}, + \text{start} + \frac{\text{end} - \text{start}}{\text{steps} - 1}, + \ldots, + \text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{\text{steps} - 1}, + \text{end}) + + + From PyTorch 1.11 linspace requires the steps argument. Use steps=100 to restore the previous behavior. + + Args: + start (float or Tensor): the starting value for the set of points. If `Tensor`, it must be 0-dimensional + end (float or Tensor): the ending value for the set of points. If `Tensor`, it must be 0-dimensional + steps (int): size of the constructed tensor + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (torch.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see torch.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + + Example:: + + >>> torch.linspace(3, 10, steps=5) + tensor([ 3.0000, 4.7500, 6.5000, 8.2500, 10.0000]) + >>> torch.linspace(-10, 10, steps=5) + tensor([-10., -5., 0., 5., 10.]) + >>> torch.linspace(start=-10, end=10, steps=5) + tensor([-10., -5., 0., 5., 10.]) + >>> torch.linspace(start=-10, end=10, steps=1) + tensor([-10.]) + """ + ... +@overload +def linspace(start: Tensor, end: Tensor, steps: _int, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + linspace(start, end, steps, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :attr:`start` to :attr:`end`, inclusive. That is, the value are: + + .. math:: + (\text{start}, + \text{start} + \frac{\text{end} - \text{start}}{\text{steps} - 1}, + \ldots, + \text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{\text{steps} - 1}, + \text{end}) + + + From PyTorch 1.11 linspace requires the steps argument. Use steps=100 to restore the previous behavior. + + Args: + start (float or Tensor): the starting value for the set of points. If `Tensor`, it must be 0-dimensional + end (float or Tensor): the ending value for the set of points. If `Tensor`, it must be 0-dimensional + steps (int): size of the constructed tensor + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (torch.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see torch.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + + Example:: + + >>> torch.linspace(3, 10, steps=5) + tensor([ 3.0000, 4.7500, 6.5000, 8.2500, 10.0000]) + >>> torch.linspace(-10, 10, steps=5) + tensor([-10., -5., 0., 5., 10.]) + >>> torch.linspace(start=-10, end=10, steps=5) + tensor([-10., -5., 0., 5., 10.]) + >>> torch.linspace(start=-10, end=10, steps=1) + tensor([-10.]) + """ + ... +@overload +def linspace(start: Union[Number, _complex], end: Tensor, steps: _int, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + linspace(start, end, steps, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :attr:`start` to :attr:`end`, inclusive. That is, the value are: + + .. math:: + (\text{start}, + \text{start} + \frac{\text{end} - \text{start}}{\text{steps} - 1}, + \ldots, + \text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{\text{steps} - 1}, + \text{end}) + + + From PyTorch 1.11 linspace requires the steps argument. Use steps=100 to restore the previous behavior. + + Args: + start (float or Tensor): the starting value for the set of points. If `Tensor`, it must be 0-dimensional + end (float or Tensor): the ending value for the set of points. If `Tensor`, it must be 0-dimensional + steps (int): size of the constructed tensor + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (torch.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see torch.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + + Example:: + + >>> torch.linspace(3, 10, steps=5) + tensor([ 3.0000, 4.7500, 6.5000, 8.2500, 10.0000]) + >>> torch.linspace(-10, 10, steps=5) + tensor([-10., -5., 0., 5., 10.]) + >>> torch.linspace(start=-10, end=10, steps=5) + tensor([-10., -5., 0., 5., 10.]) + >>> torch.linspace(start=-10, end=10, steps=1) + tensor([-10.]) + """ + ... +@overload +def linspace(start: Tensor, end: Union[Number, _complex], steps: _int, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + linspace(start, end, steps, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :attr:`start` to :attr:`end`, inclusive. That is, the value are: + + .. math:: + (\text{start}, + \text{start} + \frac{\text{end} - \text{start}}{\text{steps} - 1}, + \ldots, + \text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{\text{steps} - 1}, + \text{end}) + + + From PyTorch 1.11 linspace requires the steps argument. Use steps=100 to restore the previous behavior. + + Args: + start (float or Tensor): the starting value for the set of points. If `Tensor`, it must be 0-dimensional + end (float or Tensor): the ending value for the set of points. If `Tensor`, it must be 0-dimensional + steps (int): size of the constructed tensor + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (torch.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see torch.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + + Example:: + + >>> torch.linspace(3, 10, steps=5) + tensor([ 3.0000, 4.7500, 6.5000, 8.2500, 10.0000]) + >>> torch.linspace(-10, 10, steps=5) + tensor([-10., -5., 0., 5., 10.]) + >>> torch.linspace(start=-10, end=10, steps=5) + tensor([-10., -5., 0., 5., 10.]) + >>> torch.linspace(start=-10, end=10, steps=1) + tensor([-10.]) + """ + ... +@overload +def linspace(start: Union[Number, _complex], end: Union[Number, _complex], steps: _int, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + linspace(start, end, steps, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :attr:`start` to :attr:`end`, inclusive. That is, the value are: + + .. math:: + (\text{start}, + \text{start} + \frac{\text{end} - \text{start}}{\text{steps} - 1}, + \ldots, + \text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{\text{steps} - 1}, + \text{end}) + + + From PyTorch 1.11 linspace requires the steps argument. Use steps=100 to restore the previous behavior. + + Args: + start (float or Tensor): the starting value for the set of points. If `Tensor`, it must be 0-dimensional + end (float or Tensor): the ending value for the set of points. If `Tensor`, it must be 0-dimensional + steps (int): size of the constructed tensor + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (torch.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see torch.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + + Example:: + + >>> torch.linspace(3, 10, steps=5) + tensor([ 3.0000, 4.7500, 6.5000, 8.2500, 10.0000]) + >>> torch.linspace(-10, 10, steps=5) + tensor([-10., -5., 0., 5., 10.]) + >>> torch.linspace(start=-10, end=10, steps=5) + tensor([-10., -5., 0., 5., 10.]) + >>> torch.linspace(start=-10, end=10, steps=1) + tensor([-10.]) + """ + ... +def log(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + log(input, *, out=None) -> Tensor + + Returns a new tensor with the natural logarithm of the elements + of :attr:`input`. + + .. math:: + y_{i} = \log_{e} (x_{i}) + + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.rand(5) * 5 + >>> a + tensor([4.7767, 4.3234, 1.2156, 0.2411, 4.5739]) + >>> torch.log(a) + tensor([ 1.5637, 1.4640, 0.1952, -1.4226, 1.5204]) + """ + ... +def log10(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + log10(input, *, out=None) -> Tensor + + Returns a new tensor with the logarithm to the base 10 of the elements + of :attr:`input`. + + .. math:: + y_{i} = \log_{10} (x_{i}) + + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.rand(5) + >>> a + tensor([ 0.5224, 0.9354, 0.7257, 0.1301, 0.2251]) + + + >>> torch.log10(a) + tensor([-0.2820, -0.0290, -0.1392, -0.8857, -0.6476]) + """ + ... +def log10_(input: Tensor) -> Tensor: ... +def log1p(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + log1p(input, *, out=None) -> Tensor + + Returns a new tensor with the natural logarithm of (1 + :attr:`input`). + + .. math:: + y_i = \log_{e} (x_i + 1) + + .. note:: This function is more accurate than :func:`torch.log` for small + values of :attr:`input` + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(5) + >>> a + tensor([-1.0090, -0.9923, 1.0249, -0.5372, 0.2492]) + >>> torch.log1p(a) + tensor([ nan, -4.8653, 0.7055, -0.7705, 0.2225]) + """ + ... +def log1p_(input: Tensor) -> Tensor: ... +def log2(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + log2(input, *, out=None) -> Tensor + + Returns a new tensor with the logarithm to the base 2 of the elements + of :attr:`input`. + + .. math:: + y_{i} = \log_{2} (x_{i}) + + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.rand(5) + >>> a + tensor([ 0.8419, 0.8003, 0.9971, 0.5287, 0.0490]) + + + >>> torch.log2(a) + tensor([-0.2483, -0.3213, -0.0042, -0.9196, -4.3504]) + """ + ... +def log2_(input: Tensor) -> Tensor: ... +def log_(input: Tensor) -> Tensor: ... +@overload +def log_softmax(input: Tensor, dim: _int, dtype: Optional[_dtype] = None, *, out: Optional[Tensor] = None) -> Tensor: ... +@overload +def log_softmax(input: Tensor, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype] = None) -> Tensor: ... +def logaddexp(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + logaddexp(input, other, *, out=None) -> Tensor + + Logarithm of the sum of exponentiations of the inputs. + + Calculates pointwise :math:`\log\left(e^x + e^y\right)`. This function is useful + in statistics where the calculated probabilities of events may be so small as to + exceed the range of normal floating point numbers. In such cases the logarithm + of the calculated probability is stored. This function allows adding + probabilities stored in such a fashion. + + This op should be disambiguated with :func:`torch.logsumexp` which performs a + reduction on a single tensor. + + Args: + input (Tensor): the input tensor. + other (Tensor): the second input tensor + + Keyword arguments: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.logaddexp(torch.tensor([-1.0]), torch.tensor([-1.0, -2, -3])) + tensor([-0.3069, -0.6867, -0.8731]) + >>> torch.logaddexp(torch.tensor([-100.0, -200, -300]), torch.tensor([-1.0, -2, -3])) + tensor([-1., -2., -3.]) + >>> torch.logaddexp(torch.tensor([1.0, 2000, 30000]), torch.tensor([-1.0, -2, -3])) + tensor([1.1269e+00, 2.0000e+03, 3.0000e+04]) + """ + ... +def logaddexp2(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + logaddexp2(input, other, *, out=None) -> Tensor + + Logarithm of the sum of exponentiations of the inputs in base-2. + + Calculates pointwise :math:`\log_2\left(2^x + 2^y\right)`. See + :func:`torch.logaddexp` for more details. + + Args: + input (Tensor): the input tensor. + other (Tensor): the second input tensor + + Keyword arguments: + out (Tensor, optional): the output tensor. + """ + ... +@overload +def logcumsumexp(input: Tensor, dim: _int, *, out: Optional[Tensor] = None) -> Tensor: + r""" + logcumsumexp(input, dim, *, out=None) -> Tensor + Returns the logarithm of the cumulative summation of the exponentiation of + elements of :attr:`input` in the dimension :attr:`dim`. + + For summation index :math:`j` given by `dim` and other indices :math:`i`, the result is + + .. math:: + \text{logcumsumexp}(x)_{ij} = \log \sum\limits_{j=0}^{i} \exp(x_{ij}) + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to do the operation over + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(10) + >>> torch.logcumsumexp(a, dim=0) + tensor([-0.42296738, -0.04462666, 0.86278635, 0.94622083, 1.05277811, + 1.39202815, 1.83525007, 1.84492621, 2.06084887, 2.06844475])) + """ + ... +@overload +def logcumsumexp(input: Tensor, dim: Union[str, ellipsis, None], *, out: Optional[Tensor] = None) -> Tensor: + r""" + logcumsumexp(input, dim, *, out=None) -> Tensor + Returns the logarithm of the cumulative summation of the exponentiation of + elements of :attr:`input` in the dimension :attr:`dim`. + + For summation index :math:`j` given by `dim` and other indices :math:`i`, the result is + + .. math:: + \text{logcumsumexp}(x)_{ij} = \log \sum\limits_{j=0}^{i} \exp(x_{ij}) + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to do the operation over + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(10) + >>> torch.logcumsumexp(a, dim=0) + tensor([-0.42296738, -0.04462666, 0.86278635, 0.94622083, 1.05277811, + 1.39202815, 1.83525007, 1.84492621, 2.06084887, 2.06844475])) + """ + ... +def logdet(input: Tensor) -> Tensor: + r""" + logdet(input) -> Tensor + + Calculates log determinant of a square matrix or batches of square matrices. + + It returns ``-inf`` if the input has a determinant of zero, and ``NaN`` if it has + a negative determinant. + + .. note:: + Backward through :meth:`logdet` internally uses SVD results when :attr:`input` + is not invertible. In this case, double backward through :meth:`logdet` will + be unstable in when :attr:`input` doesn't have distinct singular values. See + :func:`torch.linalg.svd` for details. + + .. seealso:: + + :func:`torch.linalg.slogdet` computes the sign (resp. angle) and natural logarithm of the + absolute value of the determinant of real-valued (resp. complex) square matrices. + + Arguments: + input (Tensor): the input tensor of size ``(*, n, n)`` where ``*`` is zero or more + batch dimensions. + + Example:: + + >>> A = torch.randn(3, 3) + >>> torch.det(A) + tensor(0.2611) + >>> torch.logdet(A) + tensor(-1.3430) + >>> A + tensor([[[ 0.9254, -0.6213], + [-0.5787, 1.6843]], + + [[ 0.3242, -0.9665], + [ 0.4539, -0.0887]], + + [[ 1.1336, -0.4025], + [-0.7089, 0.9032]]]) + >>> A.det() + tensor([1.1990, 0.4099, 0.7386]) + >>> A.det().log() + tensor([ 0.1815, -0.8917, -0.3031]) + """ + ... +def logical_and(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + logical_and(input, other, *, out=None) -> Tensor + + Computes the element-wise logical AND of the given input tensors. Zeros are treated as ``False`` and nonzeros are + treated as ``True``. + + Args: + input (Tensor): the input tensor. + other (Tensor): the tensor to compute AND with + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.logical_and(torch.tensor([True, False, True]), torch.tensor([True, False, False])) + tensor([ True, False, False]) + >>> a = torch.tensor([0, 1, 10, 0], dtype=torch.int8) + >>> b = torch.tensor([4, 0, 1, 0], dtype=torch.int8) + >>> torch.logical_and(a, b) + tensor([False, False, True, False]) + >>> torch.logical_and(a.double(), b.double()) + tensor([False, False, True, False]) + >>> torch.logical_and(a.double(), b) + tensor([False, False, True, False]) + >>> torch.logical_and(a, b, out=torch.empty(4, dtype=torch.bool)) + tensor([False, False, True, False]) + """ + ... +def logical_not(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + logical_not(input, *, out=None) -> Tensor + + Computes the element-wise logical NOT of the given input tensor. If not specified, the output tensor will have the bool + dtype. If the input tensor is not a bool tensor, zeros are treated as ``False`` and non-zeros are treated as ``True``. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.logical_not(torch.tensor([True, False])) + tensor([False, True]) + >>> torch.logical_not(torch.tensor([0, 1, -10], dtype=torch.int8)) + tensor([ True, False, False]) + >>> torch.logical_not(torch.tensor([0., 1.5, -10.], dtype=torch.double)) + tensor([ True, False, False]) + >>> torch.logical_not(torch.tensor([0., 1., -10.], dtype=torch.double), out=torch.empty(3, dtype=torch.int16)) + tensor([1, 0, 0], dtype=torch.int16) + """ + ... +def logical_or(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + logical_or(input, other, *, out=None) -> Tensor + + Computes the element-wise logical OR of the given input tensors. Zeros are treated as ``False`` and nonzeros are + treated as ``True``. + + Args: + input (Tensor): the input tensor. + other (Tensor): the tensor to compute OR with + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.logical_or(torch.tensor([True, False, True]), torch.tensor([True, False, False])) + tensor([ True, False, True]) + >>> a = torch.tensor([0, 1, 10, 0], dtype=torch.int8) + >>> b = torch.tensor([4, 0, 1, 0], dtype=torch.int8) + >>> torch.logical_or(a, b) + tensor([ True, True, True, False]) + >>> torch.logical_or(a.double(), b.double()) + tensor([ True, True, True, False]) + >>> torch.logical_or(a.double(), b) + tensor([ True, True, True, False]) + >>> torch.logical_or(a, b, out=torch.empty(4, dtype=torch.bool)) + tensor([ True, True, True, False]) + """ + ... +def logical_xor(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + logical_xor(input, other, *, out=None) -> Tensor + + Computes the element-wise logical XOR of the given input tensors. Zeros are treated as ``False`` and nonzeros are + treated as ``True``. + + Args: + input (Tensor): the input tensor. + other (Tensor): the tensor to compute XOR with + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.logical_xor(torch.tensor([True, False, True]), torch.tensor([True, False, False])) + tensor([False, False, True]) + >>> a = torch.tensor([0, 1, 10, 0], dtype=torch.int8) + >>> b = torch.tensor([4, 0, 1, 0], dtype=torch.int8) + >>> torch.logical_xor(a, b) + tensor([ True, True, False, False]) + >>> torch.logical_xor(a.double(), b.double()) + tensor([ True, True, False, False]) + >>> torch.logical_xor(a.double(), b) + tensor([ True, True, False, False]) + >>> torch.logical_xor(a, b, out=torch.empty(4, dtype=torch.bool)) + tensor([ True, True, False, False]) + """ + ... +def logit(input: Tensor, eps: Optional[_float] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + logit(input, eps=None, *, out=None) -> Tensor + + Alias for :func:`torch.special.logit`. + """ + ... +def logit_(input: Tensor, eps: Optional[_float] = None) -> Tensor: ... +@overload +def logspace(start: Number, end: Number, steps: Optional[_int] = None, base: _float = 10.0, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + logspace(start, end, steps, base=10.0, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :math:`{{\text{{base}}}}^{{\text{{start}}}}` to + :math:`{{\text{{base}}}}^{{\text{{end}}}}`, inclusive, on a logarithmic scale + with base :attr:`base`. That is, the values are: + + .. math:: + (\text{base}^{\text{start}}, + \text{base}^{(\text{start} + \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \ldots, + \text{base}^{(\text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \text{base}^{\text{end}}) + + + + From PyTorch 1.11 logspace requires the steps argument. Use steps=100 to restore the previous behavior. + + Args: + start (float or Tensor): the starting value for the set of points. If `Tensor`, it must be 0-dimensional + end (float or Tensor): the ending value for the set of points. If `Tensor`, it must be 0-dimensional + steps (int): size of the constructed tensor + base (float, optional): base of the logarithm function. Default: ``10.0``. + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (torch.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see torch.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.logspace(start=-10, end=10, steps=5) + tensor([ 1.0000e-10, 1.0000e-05, 1.0000e+00, 1.0000e+05, 1.0000e+10]) + >>> torch.logspace(start=0.1, end=1.0, steps=5) + tensor([ 1.2589, 2.1135, 3.5481, 5.9566, 10.0000]) + >>> torch.logspace(start=0.1, end=1.0, steps=1) + tensor([1.2589]) + >>> torch.logspace(start=2, end=2, steps=1, base=2) + tensor([4.0]) + """ + ... +@overload +def logspace(start: Tensor, end: Tensor, steps: _int, base: _float = 10.0, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + logspace(start, end, steps, base=10.0, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :math:`{{\text{{base}}}}^{{\text{{start}}}}` to + :math:`{{\text{{base}}}}^{{\text{{end}}}}`, inclusive, on a logarithmic scale + with base :attr:`base`. That is, the values are: + + .. math:: + (\text{base}^{\text{start}}, + \text{base}^{(\text{start} + \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \ldots, + \text{base}^{(\text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \text{base}^{\text{end}}) + + + + From PyTorch 1.11 logspace requires the steps argument. Use steps=100 to restore the previous behavior. + + Args: + start (float or Tensor): the starting value for the set of points. If `Tensor`, it must be 0-dimensional + end (float or Tensor): the ending value for the set of points. If `Tensor`, it must be 0-dimensional + steps (int): size of the constructed tensor + base (float, optional): base of the logarithm function. Default: ``10.0``. + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (torch.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see torch.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.logspace(start=-10, end=10, steps=5) + tensor([ 1.0000e-10, 1.0000e-05, 1.0000e+00, 1.0000e+05, 1.0000e+10]) + >>> torch.logspace(start=0.1, end=1.0, steps=5) + tensor([ 1.2589, 2.1135, 3.5481, 5.9566, 10.0000]) + >>> torch.logspace(start=0.1, end=1.0, steps=1) + tensor([1.2589]) + >>> torch.logspace(start=2, end=2, steps=1, base=2) + tensor([4.0]) + """ + ... +@overload +def logspace(start: Union[Number, _complex], end: Tensor, steps: _int, base: _float = 10.0, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + logspace(start, end, steps, base=10.0, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :math:`{{\text{{base}}}}^{{\text{{start}}}}` to + :math:`{{\text{{base}}}}^{{\text{{end}}}}`, inclusive, on a logarithmic scale + with base :attr:`base`. That is, the values are: + + .. math:: + (\text{base}^{\text{start}}, + \text{base}^{(\text{start} + \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \ldots, + \text{base}^{(\text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \text{base}^{\text{end}}) + + + + From PyTorch 1.11 logspace requires the steps argument. Use steps=100 to restore the previous behavior. + + Args: + start (float or Tensor): the starting value for the set of points. If `Tensor`, it must be 0-dimensional + end (float or Tensor): the ending value for the set of points. If `Tensor`, it must be 0-dimensional + steps (int): size of the constructed tensor + base (float, optional): base of the logarithm function. Default: ``10.0``. + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (torch.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see torch.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.logspace(start=-10, end=10, steps=5) + tensor([ 1.0000e-10, 1.0000e-05, 1.0000e+00, 1.0000e+05, 1.0000e+10]) + >>> torch.logspace(start=0.1, end=1.0, steps=5) + tensor([ 1.2589, 2.1135, 3.5481, 5.9566, 10.0000]) + >>> torch.logspace(start=0.1, end=1.0, steps=1) + tensor([1.2589]) + >>> torch.logspace(start=2, end=2, steps=1, base=2) + tensor([4.0]) + """ + ... +@overload +def logspace(start: Tensor, end: Union[Number, _complex], steps: _int, base: _float = 10.0, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + logspace(start, end, steps, base=10.0, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :math:`{{\text{{base}}}}^{{\text{{start}}}}` to + :math:`{{\text{{base}}}}^{{\text{{end}}}}`, inclusive, on a logarithmic scale + with base :attr:`base`. That is, the values are: + + .. math:: + (\text{base}^{\text{start}}, + \text{base}^{(\text{start} + \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \ldots, + \text{base}^{(\text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \text{base}^{\text{end}}) + + + + From PyTorch 1.11 logspace requires the steps argument. Use steps=100 to restore the previous behavior. + + Args: + start (float or Tensor): the starting value for the set of points. If `Tensor`, it must be 0-dimensional + end (float or Tensor): the ending value for the set of points. If `Tensor`, it must be 0-dimensional + steps (int): size of the constructed tensor + base (float, optional): base of the logarithm function. Default: ``10.0``. + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (torch.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see torch.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.logspace(start=-10, end=10, steps=5) + tensor([ 1.0000e-10, 1.0000e-05, 1.0000e+00, 1.0000e+05, 1.0000e+10]) + >>> torch.logspace(start=0.1, end=1.0, steps=5) + tensor([ 1.2589, 2.1135, 3.5481, 5.9566, 10.0000]) + >>> torch.logspace(start=0.1, end=1.0, steps=1) + tensor([1.2589]) + >>> torch.logspace(start=2, end=2, steps=1, base=2) + tensor([4.0]) + """ + ... +@overload +def logspace(start: Union[Number, _complex], end: Union[Number, _complex], steps: _int, base: _float = 10.0, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + logspace(start, end, steps, base=10.0, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :math:`{{\text{{base}}}}^{{\text{{start}}}}` to + :math:`{{\text{{base}}}}^{{\text{{end}}}}`, inclusive, on a logarithmic scale + with base :attr:`base`. That is, the values are: + + .. math:: + (\text{base}^{\text{start}}, + \text{base}^{(\text{start} + \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \ldots, + \text{base}^{(\text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \text{base}^{\text{end}}) + + + + From PyTorch 1.11 logspace requires the steps argument. Use steps=100 to restore the previous behavior. + + Args: + start (float or Tensor): the starting value for the set of points. If `Tensor`, it must be 0-dimensional + end (float or Tensor): the ending value for the set of points. If `Tensor`, it must be 0-dimensional + steps (int): size of the constructed tensor + base (float, optional): base of the logarithm function. Default: ``10.0``. + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (torch.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see torch.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.logspace(start=-10, end=10, steps=5) + tensor([ 1.0000e-10, 1.0000e-05, 1.0000e+00, 1.0000e+05, 1.0000e+10]) + >>> torch.logspace(start=0.1, end=1.0, steps=5) + tensor([ 1.2589, 2.1135, 3.5481, 5.9566, 10.0000]) + >>> torch.logspace(start=0.1, end=1.0, steps=1) + tensor([1.2589]) + >>> torch.logspace(start=2, end=2, steps=1, base=2) + tensor([4.0]) + """ + ... +@overload +def logsumexp(input: Tensor, dim: Union[_int, _size], keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + logsumexp(input, dim, keepdim=False, *, out=None) + + Returns the log of summed exponentials of each row of the :attr:`input` + tensor in the given dimension :attr:`dim`. The computation is numerically + stabilized. + + For summation index :math:`j` given by `dim` and other indices :math:`i`, the result is + + .. math:: + \text{logsumexp}(x)_{i} = \log \sum_j \exp(x_{ij}) + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(3, 3) + >>> torch.logsumexp(a, 1) + tensor([1.4907, 1.0593, 1.5696]) + >>> torch.dist(torch.logsumexp(a, 1), torch.log(torch.sum(torch.exp(a), 1))) + tensor(1.6859e-07) + """ + ... +@overload +def logsumexp(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + logsumexp(input, dim, keepdim=False, *, out=None) + + Returns the log of summed exponentials of each row of the :attr:`input` + tensor in the given dimension :attr:`dim`. The computation is numerically + stabilized. + + For summation index :math:`j` given by `dim` and other indices :math:`i`, the result is + + .. math:: + \text{logsumexp}(x)_{i} = \log \sum_j \exp(x_{ij}) + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(3, 3) + >>> torch.logsumexp(a, 1) + tensor([1.4907, 1.0593, 1.5696]) + >>> torch.dist(torch.logsumexp(a, 1), torch.log(torch.sum(torch.exp(a), 1))) + tensor(1.6859e-07) + """ + ... +@overload +def lstm(data: Tensor, batch_sizes: Tensor, hx: Union[Tuple[Tensor, ...], List[Tensor]], params: Union[Tuple[Tensor, ...], List[Tensor]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool) -> Tuple[Tensor, Tensor, Tensor]: ... +@overload +def lstm(input: Tensor, hx: Union[Tuple[Tensor, ...], List[Tensor]], params: Union[Tuple[Tensor, ...], List[Tensor]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool, batch_first: _bool) -> Tuple[Tensor, Tensor, Tensor]: ... +def lstm_cell(input: Tensor, hx: Union[Tuple[Tensor, ...], List[Tensor]], w_ih: Tensor, w_hh: Tensor, b_ih: Optional[Tensor] = None, b_hh: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]: ... +@overload +def lt(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + lt(input, other, *, out=None) -> Tensor + + Computes :math:`\text{input} < \text{other}` element-wise. + + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or float): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is less than :attr:`other` and False elsewhere + + Example:: + + >>> torch.lt(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[False, False], [True, False]]) + """ + ... +@overload +def lt(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + lt(input, other, *, out=None) -> Tensor + + Computes :math:`\text{input} < \text{other}` element-wise. + + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or float): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is less than :attr:`other` and False elsewhere + + Example:: + + >>> torch.lt(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[False, False], [True, False]]) + """ + ... +def lu_solve(input: Tensor, LU_data: Tensor, LU_pivots: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + lu_solve(b, LU_data, LU_pivots, *, out=None) -> Tensor + + Returns the LU solve of the linear system :math:`Ax = b` using the partially pivoted + LU factorization of A from :func:`~linalg.lu_factor`. + + This function supports ``float``, ``double``, ``cfloat`` and ``cdouble`` dtypes for :attr:`input`. + + .. warning:: + + :func:`torch.lu_solve` is deprecated in favor of :func:`torch.linalg.lu_solve`. + :func:`torch.lu_solve` will be removed in a future PyTorch release. + ``X = torch.lu_solve(B, LU, pivots)`` should be replaced with + + .. code:: python + + X = linalg.lu_solve(LU, pivots, B) + + Arguments: + b (Tensor): the RHS tensor of size :math:`(*, m, k)`, where :math:`*` + is zero or more batch dimensions. + LU_data (Tensor): the pivoted LU factorization of A from :meth:`~linalg.lu_factor` of size :math:`(*, m, m)`, + where :math:`*` is zero or more batch dimensions. + LU_pivots (IntTensor): the pivots of the LU factorization from :meth:`~linalg.lu_factor` of size :math:`(*, m)`, + where :math:`*` is zero or more batch dimensions. + The batch dimensions of :attr:`LU_pivots` must be equal to the batch dimensions of + :attr:`LU_data`. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> A = torch.randn(2, 3, 3) + >>> b = torch.randn(2, 3, 1) + >>> LU, pivots = torch.linalg.lu_factor(A) + >>> x = torch.lu_solve(b, LU, pivots) + >>> torch.dist(A @ x, b) + tensor(1.00000e-07 * + 2.8312) + """ + ... +def lu_unpack(LU_data: Tensor, LU_pivots: Tensor, unpack_data: _bool = True, unpack_pivots: _bool = True, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.lu_unpack: + r""" + lu_unpack(LU_data, LU_pivots, unpack_data=True, unpack_pivots=True, *, out=None) -> (Tensor, Tensor, Tensor) + + Unpacks the LU decomposition returned by :func:`~linalg.lu_factor` into the `P, L, U` matrices. + + .. seealso:: + + :func:`~linalg.lu` returns the matrices from the LU decomposition. Its gradient formula is more efficient + than that of doing :func:`~linalg.lu_factor` followed by :func:`~linalg.lu_unpack`. + + Args: + LU_data (Tensor): the packed LU factorization data + LU_pivots (Tensor): the packed LU factorization pivots + unpack_data (bool): flag indicating if the data should be unpacked. + If ``False``, then the returned ``L`` and ``U`` are empty tensors. + Default: ``True`` + unpack_pivots (bool): flag indicating if the pivots should be unpacked into a permutation matrix ``P``. + If ``False``, then the returned ``P`` is an empty tensor. + Default: ``True`` + + Keyword args: + out (tuple, optional): output tuple of three tensors. Ignored if `None`. + + Returns: + A namedtuple ``(P, L, U)`` + + Examples:: + + >>> A = torch.randn(2, 3, 3) + >>> LU, pivots = torch.linalg.lu_factor(A) + >>> P, L, U = torch.lu_unpack(LU, pivots) + >>> # We can recover A from the factorization + >>> A_ = P @ L @ U + >>> torch.allclose(A, A_) + True + + >>> # LU factorization of a rectangular matrix: + >>> A = torch.randn(2, 3, 2) + >>> LU, pivots = torch.linalg.lu_factor(A) + >>> P, L, U = torch.lu_unpack(LU, pivots) + >>> # P, L, U are the same as returned by linalg.lu + >>> P_, L_, U_ = torch.linalg.lu(A) + >>> torch.allclose(P, P_) and torch.allclose(L, L_) and torch.allclose(U, U_) + True + """ + ... +def margin_ranking_loss(input1: Tensor, input2: Tensor, target: Tensor, margin: _float = 0.0, reduction: _int = 1) -> Tensor: ... +@overload +def masked_fill(input: Tensor, mask: Tensor, value: Tensor) -> Tensor: ... +@overload +def masked_fill(input: Tensor, mask: Tensor, value: Union[Number, _complex]) -> Tensor: ... +def masked_scatter(input: Tensor, mask: Tensor, source: Tensor) -> Tensor: ... +def masked_select(input: Tensor, mask: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + masked_select(input, mask, *, out=None) -> Tensor + + Returns a new 1-D tensor which indexes the :attr:`input` tensor according to + the boolean mask :attr:`mask` which is a `BoolTensor`. + + The shapes of the :attr:`mask` tensor and the :attr:`input` tensor don't need + to match, but they must be :ref:`broadcastable `. + + .. note:: The returned tensor does **not** use the same storage + as the original tensor + + Args: + input (Tensor): the input tensor. + mask (BoolTensor): the tensor containing the binary mask to index with + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> x = torch.randn(3, 4) + >>> x + tensor([[ 0.3552, -2.3825, -0.8297, 0.3477], + [-1.2035, 1.2252, 0.5002, 0.6248], + [ 0.1307, -2.0608, 0.1244, 2.0139]]) + >>> mask = x.ge(0.5) + >>> mask + tensor([[False, False, False, False], + [False, True, True, True], + [False, False, False, True]]) + >>> torch.masked_select(x, mask) + tensor([ 1.2252, 0.5002, 0.6248, 2.0139]) + """ + ... +def matmul(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + matmul(input, other, *, out=None) -> Tensor + + Matrix product of two tensors. + + The behavior depends on the dimensionality of the tensors as follows: + + - If both tensors are 1-dimensional, the dot product (scalar) is returned. + - If both arguments are 2-dimensional, the matrix-matrix product is returned. + - If the first argument is 1-dimensional and the second argument is 2-dimensional, + a 1 is prepended to its dimension for the purpose of the matrix multiply. + After the matrix multiply, the prepended dimension is removed. + - If the first argument is 2-dimensional and the second argument is 1-dimensional, + the matrix-vector product is returned. + - If both arguments are at least 1-dimensional and at least one argument is + N-dimensional (where N > 2), then a batched matrix multiply is returned. If the first + argument is 1-dimensional, a 1 is prepended to its dimension for the purpose of the + batched matrix multiply and removed after. If the second argument is 1-dimensional, a + 1 is appended to its dimension for the purpose of the batched matrix multiple and removed after. + The non-matrix (i.e. batch) dimensions are :ref:`broadcasted ` (and thus + must be broadcastable). For example, if :attr:`input` is a + :math:`(j \times 1 \times n \times n)` tensor and :attr:`other` is a :math:`(k \times n \times n)` + tensor, :attr:`out` will be a :math:`(j \times k \times n \times n)` tensor. + + Note that the broadcasting logic only looks at the batch dimensions when determining if the inputs + are broadcastable, and not the matrix dimensions. For example, if :attr:`input` is a + :math:`(j \times 1 \times n \times m)` tensor and :attr:`other` is a :math:`(k \times m \times p)` + tensor, these inputs are valid for broadcasting even though the final two dimensions (i.e. the + matrix dimensions) are different. :attr:`out` will be a :math:`(j \times k \times n \times p)` tensor. + + This operation has support for arguments with :ref:`sparse layouts`. In particular the + matrix-matrix (both arguments 2-dimensional) supports sparse arguments with the same restrictions + as :func:`torch.mm` + + + .. warning:: + Sparse support is a beta feature and some layout(s)/dtype/device combinations may not be supported, + or may not have autograd support. If you notice missing functionality please + open a feature request. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + .. note:: + + The 1-dimensional dot product version of this function does not support an :attr:`out` parameter. + + Arguments: + input (Tensor): the first tensor to be multiplied + other (Tensor): the second tensor to be multiplied + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> # vector x vector + >>> tensor1 = torch.randn(3) + >>> tensor2 = torch.randn(3) + >>> torch.matmul(tensor1, tensor2).size() + torch.Size([]) + >>> # matrix x vector + >>> tensor1 = torch.randn(3, 4) + >>> tensor2 = torch.randn(4) + >>> torch.matmul(tensor1, tensor2).size() + torch.Size([3]) + >>> # batched matrix x broadcasted vector + >>> tensor1 = torch.randn(10, 3, 4) + >>> tensor2 = torch.randn(4) + >>> torch.matmul(tensor1, tensor2).size() + torch.Size([10, 3]) + >>> # batched matrix x batched matrix + >>> tensor1 = torch.randn(10, 3, 4) + >>> tensor2 = torch.randn(10, 4, 5) + >>> torch.matmul(tensor1, tensor2).size() + torch.Size([10, 3, 5]) + >>> # batched matrix x broadcasted matrix + >>> tensor1 = torch.randn(10, 3, 4) + >>> tensor2 = torch.randn(4, 5) + >>> torch.matmul(tensor1, tensor2).size() + torch.Size([10, 3, 5]) + """ + ... +def matrix_exp(input: Tensor) -> Tensor: + r""" + matrix_exp(A) -> Tensor + + Alias for :func:`torch.linalg.matrix_exp`. + """ + ... +def matrix_power(input: Tensor, n: _int, *, out: Optional[Tensor] = None) -> Tensor: + r""" + matrix_power(input, n, *, out=None) -> Tensor + + Alias for :func:`torch.linalg.matrix_power` + """ + ... +@overload +def max(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + max(input) -> Tensor + + Returns the maximum value of all elements in the ``input`` tensor. + + .. warning:: + This function produces deterministic (sub)gradients unlike ``max(dim=0)`` + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.6763, 0.7445, -2.2369]]) + >>> torch.max(a) + tensor(0.7445) + + .. function:: max(input, dim, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` is the maximum + value of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`. And ``indices`` is the index location of each maximum value found + (argmax). + + If ``keepdim`` is ``True``, the output tensors are of the same size + as ``input`` except in the dimension ``dim`` where they are of size 1. + Otherwise, ``dim`` is squeezed (see :func:`torch.squeeze`), resulting + in the output tensors having 1 fewer dimension than ``input``. + + .. note:: If there are multiple maximal values in a reduced row then + the indices of the first maximal value are returned. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. Default: ``False``. + + Keyword args: + out (tuple, optional): the result tuple of two output tensors (max, max_indices) + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-1.2360, -0.2942, -0.1222, 0.8475], + [ 1.1949, -1.1127, -2.2379, -0.6702], + [ 1.5717, -0.9207, 0.1297, -1.8768], + [-0.6172, 1.0036, -0.6060, -0.2432]]) + >>> torch.max(a, 1) + torch.return_types.max(values=tensor([0.8475, 1.1949, 1.5717, 1.0036]), indices=tensor([3, 0, 0, 1])) + + .. function:: max(input, other, *, out=None) -> Tensor + :noindex: + + See :func:`torch.maximum`. + """ + ... +@overload +def max(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + max(input) -> Tensor + + Returns the maximum value of all elements in the ``input`` tensor. + + .. warning:: + This function produces deterministic (sub)gradients unlike ``max(dim=0)`` + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.6763, 0.7445, -2.2369]]) + >>> torch.max(a) + tensor(0.7445) + + .. function:: max(input, dim, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` is the maximum + value of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`. And ``indices`` is the index location of each maximum value found + (argmax). + + If ``keepdim`` is ``True``, the output tensors are of the same size + as ``input`` except in the dimension ``dim`` where they are of size 1. + Otherwise, ``dim`` is squeezed (see :func:`torch.squeeze`), resulting + in the output tensors having 1 fewer dimension than ``input``. + + .. note:: If there are multiple maximal values in a reduced row then + the indices of the first maximal value are returned. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. Default: ``False``. + + Keyword args: + out (tuple, optional): the result tuple of two output tensors (max, max_indices) + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-1.2360, -0.2942, -0.1222, 0.8475], + [ 1.1949, -1.1127, -2.2379, -0.6702], + [ 1.5717, -0.9207, 0.1297, -1.8768], + [-0.6172, 1.0036, -0.6060, -0.2432]]) + >>> torch.max(a, 1) + torch.return_types.max(values=tensor([0.8475, 1.1949, 1.5717, 1.0036]), indices=tensor([3, 0, 0, 1])) + + .. function:: max(input, other, *, out=None) -> Tensor + :noindex: + + See :func:`torch.maximum`. + """ + ... +@overload +def max(input: Tensor, dim: _int, keepdim: _bool = False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.max: + r""" + max(input) -> Tensor + + Returns the maximum value of all elements in the ``input`` tensor. + + .. warning:: + This function produces deterministic (sub)gradients unlike ``max(dim=0)`` + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.6763, 0.7445, -2.2369]]) + >>> torch.max(a) + tensor(0.7445) + + .. function:: max(input, dim, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` is the maximum + value of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`. And ``indices`` is the index location of each maximum value found + (argmax). + + If ``keepdim`` is ``True``, the output tensors are of the same size + as ``input`` except in the dimension ``dim`` where they are of size 1. + Otherwise, ``dim`` is squeezed (see :func:`torch.squeeze`), resulting + in the output tensors having 1 fewer dimension than ``input``. + + .. note:: If there are multiple maximal values in a reduced row then + the indices of the first maximal value are returned. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. Default: ``False``. + + Keyword args: + out (tuple, optional): the result tuple of two output tensors (max, max_indices) + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-1.2360, -0.2942, -0.1222, 0.8475], + [ 1.1949, -1.1127, -2.2379, -0.6702], + [ 1.5717, -0.9207, 0.1297, -1.8768], + [-0.6172, 1.0036, -0.6060, -0.2432]]) + >>> torch.max(a, 1) + torch.return_types.max(values=tensor([0.8475, 1.1949, 1.5717, 1.0036]), indices=tensor([3, 0, 0, 1])) + + .. function:: max(input, other, *, out=None) -> Tensor + :noindex: + + See :func:`torch.maximum`. + """ + ... +@overload +def max(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool = False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.max: + r""" + max(input) -> Tensor + + Returns the maximum value of all elements in the ``input`` tensor. + + .. warning:: + This function produces deterministic (sub)gradients unlike ``max(dim=0)`` + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.6763, 0.7445, -2.2369]]) + >>> torch.max(a) + tensor(0.7445) + + .. function:: max(input, dim, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` is the maximum + value of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`. And ``indices`` is the index location of each maximum value found + (argmax). + + If ``keepdim`` is ``True``, the output tensors are of the same size + as ``input`` except in the dimension ``dim`` where they are of size 1. + Otherwise, ``dim`` is squeezed (see :func:`torch.squeeze`), resulting + in the output tensors having 1 fewer dimension than ``input``. + + .. note:: If there are multiple maximal values in a reduced row then + the indices of the first maximal value are returned. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. Default: ``False``. + + Keyword args: + out (tuple, optional): the result tuple of two output tensors (max, max_indices) + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-1.2360, -0.2942, -0.1222, 0.8475], + [ 1.1949, -1.1127, -2.2379, -0.6702], + [ 1.5717, -0.9207, 0.1297, -1.8768], + [-0.6172, 1.0036, -0.6060, -0.2432]]) + >>> torch.max(a, 1) + torch.return_types.max(values=tensor([0.8475, 1.1949, 1.5717, 1.0036]), indices=tensor([3, 0, 0, 1])) + + .. function:: max(input, other, *, out=None) -> Tensor + :noindex: + + See :func:`torch.maximum`. + """ + ... +def max_pool1d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size] = (), padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: _bool = False) -> Tensor: ... +def max_pool1d_with_indices(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size] = (), padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: _bool = False) -> Tuple[Tensor, Tensor]: ... +def max_pool2d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size] = (), padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: _bool = False) -> Tensor: ... +def max_pool3d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size] = (), padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: _bool = False) -> Tensor: ... +def maximum(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + maximum(input, other, *, out=None) -> Tensor + + Computes the element-wise maximum of :attr:`input` and :attr:`other`. + + .. note:: + If one of the elements being compared is a NaN, then that element is returned. + :func:`maximum` is not supported for tensors with complex dtypes. + + Args: + input (Tensor): the input tensor. + other (Tensor): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor((1, 2, -1)) + >>> b = torch.tensor((3, 0, 4)) + >>> torch.maximum(a, b) + tensor([3, 2, 4]) + """ + ... +@overload +def mean(input: Tensor, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + mean(input, *, dtype=None) -> Tensor + + Returns the mean value of all elements in the :attr:`input` tensor. Input must be floating point or complex. + + Args: + input (Tensor): + the input tensor, either of floating point or complex dtype + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.2294, -0.5481, 1.3288]]) + >>> torch.mean(a) + tensor(0.3367) + + .. function:: mean(input, dim, keepdim=False, *, dtype=None, out=None) -> Tensor + :noindex: + + Returns the mean value of each row of the :attr:`input` tensor in the given + dimension :attr:`dim`. If :attr:`dim` is a list of dimensions, + reduce over all of them. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + out (Tensor, optional): the output tensor. + + .. seealso:: + + :func:`torch.nanmean` computes the mean value of `non-NaN` elements. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-0.3841, 0.6320, 0.4254, -0.7384], + [-0.9644, 1.0131, -0.6549, -1.4279], + [-0.2951, -1.3350, -0.7694, 0.5600], + [ 1.0842, -0.9580, 0.3623, 0.2343]]) + >>> torch.mean(a, 1) + tensor([-0.0163, -0.5085, -0.4599, 0.1807]) + >>> torch.mean(a, 1, True) + tensor([[-0.0163], + [-0.5085], + [-0.4599], + [ 0.1807]]) + """ + ... +@overload +def mean(input: Tensor, dim: Optional[Union[_int, _size]], keepdim: _bool = False, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + mean(input, *, dtype=None) -> Tensor + + Returns the mean value of all elements in the :attr:`input` tensor. Input must be floating point or complex. + + Args: + input (Tensor): + the input tensor, either of floating point or complex dtype + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.2294, -0.5481, 1.3288]]) + >>> torch.mean(a) + tensor(0.3367) + + .. function:: mean(input, dim, keepdim=False, *, dtype=None, out=None) -> Tensor + :noindex: + + Returns the mean value of each row of the :attr:`input` tensor in the given + dimension :attr:`dim`. If :attr:`dim` is a list of dimensions, + reduce over all of them. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + out (Tensor, optional): the output tensor. + + .. seealso:: + + :func:`torch.nanmean` computes the mean value of `non-NaN` elements. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-0.3841, 0.6320, 0.4254, -0.7384], + [-0.9644, 1.0131, -0.6549, -1.4279], + [-0.2951, -1.3350, -0.7694, 0.5600], + [ 1.0842, -0.9580, 0.3623, 0.2343]]) + >>> torch.mean(a, 1) + tensor([-0.0163, -0.5085, -0.4599, 0.1807]) + >>> torch.mean(a, 1, True) + tensor([[-0.0163], + [-0.5085], + [-0.4599], + [ 0.1807]]) + """ + ... +@overload +def mean(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], keepdim: _bool = False, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + mean(input, *, dtype=None) -> Tensor + + Returns the mean value of all elements in the :attr:`input` tensor. Input must be floating point or complex. + + Args: + input (Tensor): + the input tensor, either of floating point or complex dtype + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.2294, -0.5481, 1.3288]]) + >>> torch.mean(a) + tensor(0.3367) + + .. function:: mean(input, dim, keepdim=False, *, dtype=None, out=None) -> Tensor + :noindex: + + Returns the mean value of each row of the :attr:`input` tensor in the given + dimension :attr:`dim`. If :attr:`dim` is a list of dimensions, + reduce over all of them. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + out (Tensor, optional): the output tensor. + + .. seealso:: + + :func:`torch.nanmean` computes the mean value of `non-NaN` elements. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-0.3841, 0.6320, 0.4254, -0.7384], + [-0.9644, 1.0131, -0.6549, -1.4279], + [-0.2951, -1.3350, -0.7694, 0.5600], + [ 1.0842, -0.9580, 0.3623, 0.2343]]) + >>> torch.mean(a, 1) + tensor([-0.0163, -0.5085, -0.4599, 0.1807]) + >>> torch.mean(a, 1, True) + tensor([[-0.0163], + [-0.5085], + [-0.4599], + [ 0.1807]]) + """ + ... +@overload +def median(input: Tensor) -> Tensor: + r""" + median(input) -> Tensor + + Returns the median of the values in :attr:`input`. + + .. note:: + The median is not unique for :attr:`input` tensors with an even number + of elements. In this case the lower of the two medians is returned. To + compute the mean of both medians, use :func:`torch.quantile` with ``q=0.5`` instead. + + .. warning:: + This function produces deterministic (sub)gradients unlike ``median(dim=0)`` + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 1.5219, -1.5212, 0.2202]]) + >>> torch.median(a) + tensor(0.2202) + + .. function:: median(input, dim=-1, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` contains the median of each row of :attr:`input` + in the dimension :attr:`dim`, and ``indices`` contains the index of the median values found in the dimension :attr:`dim`. + + By default, :attr:`dim` is the last dimension of the :attr:`input` tensor. + + If :attr:`keepdim` is ``True``, the output tensors are of the same size + as :attr:`input` except in the dimension :attr:`dim` where they are of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in + the outputs tensor having 1 fewer dimension than :attr:`input`. + + .. note:: + The median is not unique for :attr:`input` tensors with an even number + of elements in the dimension :attr:`dim`. In this case the lower of the + two medians is returned. To compute the mean of both medians in + :attr:`input`, use :func:`torch.quantile` with ``q=0.5`` instead. + + .. warning:: + ``indices`` does not necessarily contain the first occurrence of each + median value found, unless it is unique. + The exact implementation details are device-specific. + Do not expect the same result when run on CPU and GPU in general. + For the same reason do not expect the gradients to be deterministic. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out ((Tensor, Tensor), optional): The first tensor will be populated with the median values and the second + tensor, which must have dtype long, with their indices in the dimension + :attr:`dim` of :attr:`input`. + + Example:: + + >>> a = torch.randn(4, 5) + >>> a + tensor([[ 0.2505, -0.3982, -0.9948, 0.3518, -1.3131], + [ 0.3180, -0.6993, 1.0436, 0.0438, 0.2270], + [-0.2751, 0.7303, 0.2192, 0.3321, 0.2488], + [ 1.0778, -1.9510, 0.7048, 0.4742, -0.7125]]) + >>> torch.median(a, 1) + torch.return_types.median(values=tensor([-0.3982, 0.2270, 0.2488, 0.4742]), indices=tensor([1, 4, 4, 3])) + """ + ... +@overload +def median(input: Tensor, dim: _int, keepdim: _bool = False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.median: + r""" + median(input) -> Tensor + + Returns the median of the values in :attr:`input`. + + .. note:: + The median is not unique for :attr:`input` tensors with an even number + of elements. In this case the lower of the two medians is returned. To + compute the mean of both medians, use :func:`torch.quantile` with ``q=0.5`` instead. + + .. warning:: + This function produces deterministic (sub)gradients unlike ``median(dim=0)`` + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 1.5219, -1.5212, 0.2202]]) + >>> torch.median(a) + tensor(0.2202) + + .. function:: median(input, dim=-1, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` contains the median of each row of :attr:`input` + in the dimension :attr:`dim`, and ``indices`` contains the index of the median values found in the dimension :attr:`dim`. + + By default, :attr:`dim` is the last dimension of the :attr:`input` tensor. + + If :attr:`keepdim` is ``True``, the output tensors are of the same size + as :attr:`input` except in the dimension :attr:`dim` where they are of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in + the outputs tensor having 1 fewer dimension than :attr:`input`. + + .. note:: + The median is not unique for :attr:`input` tensors with an even number + of elements in the dimension :attr:`dim`. In this case the lower of the + two medians is returned. To compute the mean of both medians in + :attr:`input`, use :func:`torch.quantile` with ``q=0.5`` instead. + + .. warning:: + ``indices`` does not necessarily contain the first occurrence of each + median value found, unless it is unique. + The exact implementation details are device-specific. + Do not expect the same result when run on CPU and GPU in general. + For the same reason do not expect the gradients to be deterministic. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out ((Tensor, Tensor), optional): The first tensor will be populated with the median values and the second + tensor, which must have dtype long, with their indices in the dimension + :attr:`dim` of :attr:`input`. + + Example:: + + >>> a = torch.randn(4, 5) + >>> a + tensor([[ 0.2505, -0.3982, -0.9948, 0.3518, -1.3131], + [ 0.3180, -0.6993, 1.0436, 0.0438, 0.2270], + [-0.2751, 0.7303, 0.2192, 0.3321, 0.2488], + [ 1.0778, -1.9510, 0.7048, 0.4742, -0.7125]]) + >>> torch.median(a, 1) + torch.return_types.median(values=tensor([-0.3982, 0.2270, 0.2488, 0.4742]), indices=tensor([1, 4, 4, 3])) + """ + ... +@overload +def median(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool = False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.median: + r""" + median(input) -> Tensor + + Returns the median of the values in :attr:`input`. + + .. note:: + The median is not unique for :attr:`input` tensors with an even number + of elements. In this case the lower of the two medians is returned. To + compute the mean of both medians, use :func:`torch.quantile` with ``q=0.5`` instead. + + .. warning:: + This function produces deterministic (sub)gradients unlike ``median(dim=0)`` + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 1.5219, -1.5212, 0.2202]]) + >>> torch.median(a) + tensor(0.2202) + + .. function:: median(input, dim=-1, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` contains the median of each row of :attr:`input` + in the dimension :attr:`dim`, and ``indices`` contains the index of the median values found in the dimension :attr:`dim`. + + By default, :attr:`dim` is the last dimension of the :attr:`input` tensor. + + If :attr:`keepdim` is ``True``, the output tensors are of the same size + as :attr:`input` except in the dimension :attr:`dim` where they are of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in + the outputs tensor having 1 fewer dimension than :attr:`input`. + + .. note:: + The median is not unique for :attr:`input` tensors with an even number + of elements in the dimension :attr:`dim`. In this case the lower of the + two medians is returned. To compute the mean of both medians in + :attr:`input`, use :func:`torch.quantile` with ``q=0.5`` instead. + + .. warning:: + ``indices`` does not necessarily contain the first occurrence of each + median value found, unless it is unique. + The exact implementation details are device-specific. + Do not expect the same result when run on CPU and GPU in general. + For the same reason do not expect the gradients to be deterministic. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out ((Tensor, Tensor), optional): The first tensor will be populated with the median values and the second + tensor, which must have dtype long, with their indices in the dimension + :attr:`dim` of :attr:`input`. + + Example:: + + >>> a = torch.randn(4, 5) + >>> a + tensor([[ 0.2505, -0.3982, -0.9948, 0.3518, -1.3131], + [ 0.3180, -0.6993, 1.0436, 0.0438, 0.2270], + [-0.2751, 0.7303, 0.2192, 0.3321, 0.2488], + [ 1.0778, -1.9510, 0.7048, 0.4742, -0.7125]]) + >>> torch.median(a, 1) + torch.return_types.median(values=tensor([-0.3982, 0.2270, 0.2488, 0.4742]), indices=tensor([1, 4, 4, 3])) + """ + ... +@overload +def min(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + min(input) -> Tensor + + Returns the minimum value of all elements in the :attr:`input` tensor. + + .. warning:: + This function produces deterministic (sub)gradients unlike ``min(dim=0)`` + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.6750, 1.0857, 1.7197]]) + >>> torch.min(a) + tensor(0.6750) + + .. function:: min(input, dim, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` is the minimum + value of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`. And ``indices`` is the index location of each minimum value found + (argmin). + + If :attr:`keepdim` is ``True``, the output tensors are of the same size as + :attr:`input` except in the dimension :attr:`dim` where they are of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in + the output tensors having 1 fewer dimension than :attr:`input`. + + .. note:: If there are multiple minimal values in a reduced row then + the indices of the first minimal value are returned. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (tuple, optional): the tuple of two output tensors (min, min_indices) + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-0.6248, 1.1334, -1.1899, -0.2803], + [-1.4644, -0.2635, -0.3651, 0.6134], + [ 0.2457, 0.0384, 1.0128, 0.7015], + [-0.1153, 2.9849, 2.1458, 0.5788]]) + >>> torch.min(a, 1) + torch.return_types.min(values=tensor([-1.1899, -1.4644, 0.0384, -0.1153]), indices=tensor([2, 0, 1, 0])) + + .. function:: min(input, other, *, out=None) -> Tensor + :noindex: + + See :func:`torch.minimum`. + """ + ... +@overload +def min(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + min(input) -> Tensor + + Returns the minimum value of all elements in the :attr:`input` tensor. + + .. warning:: + This function produces deterministic (sub)gradients unlike ``min(dim=0)`` + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.6750, 1.0857, 1.7197]]) + >>> torch.min(a) + tensor(0.6750) + + .. function:: min(input, dim, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` is the minimum + value of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`. And ``indices`` is the index location of each minimum value found + (argmin). + + If :attr:`keepdim` is ``True``, the output tensors are of the same size as + :attr:`input` except in the dimension :attr:`dim` where they are of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in + the output tensors having 1 fewer dimension than :attr:`input`. + + .. note:: If there are multiple minimal values in a reduced row then + the indices of the first minimal value are returned. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (tuple, optional): the tuple of two output tensors (min, min_indices) + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-0.6248, 1.1334, -1.1899, -0.2803], + [-1.4644, -0.2635, -0.3651, 0.6134], + [ 0.2457, 0.0384, 1.0128, 0.7015], + [-0.1153, 2.9849, 2.1458, 0.5788]]) + >>> torch.min(a, 1) + torch.return_types.min(values=tensor([-1.1899, -1.4644, 0.0384, -0.1153]), indices=tensor([2, 0, 1, 0])) + + .. function:: min(input, other, *, out=None) -> Tensor + :noindex: + + See :func:`torch.minimum`. + """ + ... +@overload +def min(input: Tensor, dim: _int, keepdim: _bool = False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.min: + r""" + min(input) -> Tensor + + Returns the minimum value of all elements in the :attr:`input` tensor. + + .. warning:: + This function produces deterministic (sub)gradients unlike ``min(dim=0)`` + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.6750, 1.0857, 1.7197]]) + >>> torch.min(a) + tensor(0.6750) + + .. function:: min(input, dim, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` is the minimum + value of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`. And ``indices`` is the index location of each minimum value found + (argmin). + + If :attr:`keepdim` is ``True``, the output tensors are of the same size as + :attr:`input` except in the dimension :attr:`dim` where they are of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in + the output tensors having 1 fewer dimension than :attr:`input`. + + .. note:: If there are multiple minimal values in a reduced row then + the indices of the first minimal value are returned. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (tuple, optional): the tuple of two output tensors (min, min_indices) + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-0.6248, 1.1334, -1.1899, -0.2803], + [-1.4644, -0.2635, -0.3651, 0.6134], + [ 0.2457, 0.0384, 1.0128, 0.7015], + [-0.1153, 2.9849, 2.1458, 0.5788]]) + >>> torch.min(a, 1) + torch.return_types.min(values=tensor([-1.1899, -1.4644, 0.0384, -0.1153]), indices=tensor([2, 0, 1, 0])) + + .. function:: min(input, other, *, out=None) -> Tensor + :noindex: + + See :func:`torch.minimum`. + """ + ... +@overload +def min(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool = False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.min: + r""" + min(input) -> Tensor + + Returns the minimum value of all elements in the :attr:`input` tensor. + + .. warning:: + This function produces deterministic (sub)gradients unlike ``min(dim=0)`` + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.6750, 1.0857, 1.7197]]) + >>> torch.min(a) + tensor(0.6750) + + .. function:: min(input, dim, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` is the minimum + value of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`. And ``indices`` is the index location of each minimum value found + (argmin). + + If :attr:`keepdim` is ``True``, the output tensors are of the same size as + :attr:`input` except in the dimension :attr:`dim` where they are of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in + the output tensors having 1 fewer dimension than :attr:`input`. + + .. note:: If there are multiple minimal values in a reduced row then + the indices of the first minimal value are returned. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (tuple, optional): the tuple of two output tensors (min, min_indices) + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-0.6248, 1.1334, -1.1899, -0.2803], + [-1.4644, -0.2635, -0.3651, 0.6134], + [ 0.2457, 0.0384, 1.0128, 0.7015], + [-0.1153, 2.9849, 2.1458, 0.5788]]) + >>> torch.min(a, 1) + torch.return_types.min(values=tensor([-1.1899, -1.4644, 0.0384, -0.1153]), indices=tensor([2, 0, 1, 0])) + + .. function:: min(input, other, *, out=None) -> Tensor + :noindex: + + See :func:`torch.minimum`. + """ + ... +def minimum(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + minimum(input, other, *, out=None) -> Tensor + + Computes the element-wise minimum of :attr:`input` and :attr:`other`. + + .. note:: + If one of the elements being compared is a NaN, then that element is returned. + :func:`minimum` is not supported for tensors with complex dtypes. + + Args: + input (Tensor): the input tensor. + other (Tensor): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor((1, 2, -1)) + >>> b = torch.tensor((3, 0, 4)) + >>> torch.minimum(a, b) + tensor([1, 0, -1]) + """ + ... +def miopen_batch_norm(input: Tensor, weight: Tensor, bias: Optional[Tensor], running_mean: Optional[Tensor], running_var: Optional[Tensor], training: _bool, exponential_average_factor: _float, epsilon: _float) -> Tuple[Tensor, Tensor, Tensor]: ... +def miopen_convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt], benchmark: _bool, deterministic: _bool) -> Tensor: ... +def miopen_convolution_add_relu(input: Tensor, weight: Tensor, z: Tensor, alpha: Optional[Union[Number, _complex]], bias: Optional[Tensor], stride: Sequence[Union[_int, SymInt]], padding: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt]) -> Tensor: ... +def miopen_convolution_relu(input: Tensor, weight: Tensor, bias: Optional[Tensor], stride: Sequence[Union[_int, SymInt]], padding: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt]) -> Tensor: ... +def miopen_convolution_transpose(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: Sequence[Union[_int, SymInt]], output_padding: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt], benchmark: _bool, deterministic: _bool) -> Tensor: ... +def miopen_depthwise_convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt], benchmark: _bool, deterministic: _bool) -> Tensor: ... +def miopen_rnn(input: Tensor, weight: Union[Tuple[Tensor, ...], List[Tensor]], weight_stride0: _int, hx: Tensor, cx: Optional[Tensor], mode: _int, hidden_size: _int, num_layers: _int, batch_first: _bool, dropout: _float, train: _bool, bidirectional: _bool, batch_sizes: _size, dropout_state: Optional[Tensor]) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: ... +def mkldnn_adaptive_avg_pool2d(input: Tensor, output_size: Union[_int, _size], *, out: Optional[Tensor] = None) -> Tensor: ... +def mkldnn_convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt]) -> Tensor: ... +def mkldnn_linear_backward_weights(grad_output: Tensor, input: Tensor, weight: Tensor, bias_defined: _bool) -> Tuple[Tensor, Tensor]: ... +def mkldnn_max_pool2d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size] = (), padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: _bool = False) -> Tensor: ... +def mkldnn_max_pool3d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size] = (), padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: _bool = False) -> Tensor: ... +def mkldnn_rnn_layer(input: Tensor, weight0: Tensor, weight1: Tensor, weight2: Tensor, weight3: Tensor, hx_: Tensor, cx_: Tensor, reverse: _bool, batch_sizes: _size, mode: _int, hidden_size: _int, num_layers: _int, has_biases: _bool, bidirectional: _bool, batch_first: _bool, train: _bool) -> Tuple[Tensor, Tensor, Tensor, Tensor]: ... +def mm(input: Tensor, mat2: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + mm(input, mat2, *, out=None) -> Tensor + + Performs a matrix multiplication of the matrices :attr:`input` and :attr:`mat2`. + + If :attr:`input` is a :math:`(n \times m)` tensor, :attr:`mat2` is a + :math:`(m \times p)` tensor, :attr:`out` will be a :math:`(n \times p)` tensor. + + .. note:: This function does not :ref:`broadcast `. + For broadcasting matrix products, see :func:`torch.matmul`. + + Supports strided and sparse 2-D tensors as inputs, autograd with + respect to strided inputs. + + This operation has support for arguments with :ref:`sparse layouts`. + If :attr:`out` is provided its layout will be used. Otherwise, the result + layout will be deduced from that of :attr:`input`. + + + .. warning:: + Sparse support is a beta feature and some layout(s)/dtype/device combinations may not be supported, + or may not have autograd support. If you notice missing functionality please + open a feature request. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): the first matrix to be matrix multiplied + mat2 (Tensor): the second matrix to be matrix multiplied + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> mat1 = torch.randn(2, 3) + >>> mat2 = torch.randn(3, 3) + >>> torch.mm(mat1, mat2) + tensor([[ 0.4851, 0.5037, -0.3633], + [-0.0760, -3.6705, 2.4784]]) + """ + ... +@overload +def mode(input: Tensor, dim: _int = -1, keepdim: _bool = False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.mode: + r""" + mode(input, dim=-1, keepdim=False, *, out=None) -> (Tensor, LongTensor) + + Returns a namedtuple ``(values, indices)`` where ``values`` is the mode + value of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`, i.e. a value which appears most often + in that row, and ``indices`` is the index location of each mode value found. + + By default, :attr:`dim` is the last dimension of the :attr:`input` tensor. + + If :attr:`keepdim` is ``True``, the output tensors are of the same size as + :attr:`input` except in the dimension :attr:`dim` where they are of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting + in the output tensors having 1 fewer dimension than :attr:`input`. + + .. note:: This function is not defined for ``torch.cuda.Tensor`` yet. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (tuple, optional): the result tuple of two output tensors (values, indices) + + Example:: + + >>> b = torch.tensor([[0, 0, 0, 2, 0, 0, 2], + ... [0, 3, 0, 0, 2, 0, 1], + ... [2, 2, 2, 0, 0, 0, 3], + ... [2, 2, 3, 0, 1, 1, 0], + ... [1, 1, 0, 0, 2, 0, 2]]) + >>> torch.mode(b, 0) + torch.return_types.mode( + values=tensor([0, 2, 0, 0, 0, 0, 2]), + indices=tensor([1, 3, 4, 4, 2, 4, 4])) + """ + ... +@overload +def mode(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool = False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.mode: + r""" + mode(input, dim=-1, keepdim=False, *, out=None) -> (Tensor, LongTensor) + + Returns a namedtuple ``(values, indices)`` where ``values`` is the mode + value of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`, i.e. a value which appears most often + in that row, and ``indices`` is the index location of each mode value found. + + By default, :attr:`dim` is the last dimension of the :attr:`input` tensor. + + If :attr:`keepdim` is ``True``, the output tensors are of the same size as + :attr:`input` except in the dimension :attr:`dim` where they are of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting + in the output tensors having 1 fewer dimension than :attr:`input`. + + .. note:: This function is not defined for ``torch.cuda.Tensor`` yet. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (tuple, optional): the result tuple of two output tensors (values, indices) + + Example:: + + >>> b = torch.tensor([[0, 0, 0, 2, 0, 0, 2], + ... [0, 3, 0, 0, 2, 0, 1], + ... [2, 2, 2, 0, 0, 0, 3], + ... [2, 2, 3, 0, 1, 1, 0], + ... [1, 1, 0, 0, 2, 0, 2]]) + >>> torch.mode(b, 0) + torch.return_types.mode( + values=tensor([0, 2, 0, 0, 0, 0, 2]), + indices=tensor([1, 3, 4, 4, 2, 4, 4])) + """ + ... +@overload +def moveaxis(input: Tensor, source: _int, destination: _int) -> Tensor: + r""" + moveaxis(input, source, destination) -> Tensor + + Alias for :func:`torch.movedim`. + + This function is equivalent to NumPy's moveaxis function. + + Examples:: + + >>> t = torch.randn(3,2,1) + >>> t + tensor([[[-0.3362], + [-0.8437]], + + [[-0.9627], + [ 0.1727]], + + [[ 0.5173], + [-0.1398]]]) + >>> torch.moveaxis(t, 1, 0).shape + torch.Size([2, 3, 1]) + >>> torch.moveaxis(t, 1, 0) + tensor([[[-0.3362], + [-0.9627], + [ 0.5173]], + + [[-0.8437], + [ 0.1727], + [-0.1398]]]) + >>> torch.moveaxis(t, (1, 2), (0, 1)).shape + torch.Size([2, 1, 3]) + >>> torch.moveaxis(t, (1, 2), (0, 1)) + tensor([[[-0.3362, -0.9627, 0.5173]], + + [[-0.8437, 0.1727, -0.1398]]]) + """ + ... +@overload +def moveaxis(input: Tensor, source: _size, destination: _size) -> Tensor: + r""" + moveaxis(input, source, destination) -> Tensor + + Alias for :func:`torch.movedim`. + + This function is equivalent to NumPy's moveaxis function. + + Examples:: + + >>> t = torch.randn(3,2,1) + >>> t + tensor([[[-0.3362], + [-0.8437]], + + [[-0.9627], + [ 0.1727]], + + [[ 0.5173], + [-0.1398]]]) + >>> torch.moveaxis(t, 1, 0).shape + torch.Size([2, 3, 1]) + >>> torch.moveaxis(t, 1, 0) + tensor([[[-0.3362], + [-0.9627], + [ 0.5173]], + + [[-0.8437], + [ 0.1727], + [-0.1398]]]) + >>> torch.moveaxis(t, (1, 2), (0, 1)).shape + torch.Size([2, 1, 3]) + >>> torch.moveaxis(t, (1, 2), (0, 1)) + tensor([[[-0.3362, -0.9627, 0.5173]], + + [[-0.8437, 0.1727, -0.1398]]]) + """ + ... +@overload +def movedim(input: Tensor, source: _int, destination: _int) -> Tensor: + r""" + movedim(input, source, destination) -> Tensor + + Moves the dimension(s) of :attr:`input` at the position(s) in :attr:`source` + to the position(s) in :attr:`destination`. + + Other dimensions of :attr:`input` that are not explicitly moved remain in + their original order and appear at the positions not specified in :attr:`destination`. + + Args: + input (Tensor): the input tensor. + source (int or tuple of ints): Original positions of the dims to move. These must be unique. + destination (int or tuple of ints): Destination positions for each of the original dims. These must also be unique. + + Examples:: + + >>> t = torch.randn(3,2,1) + >>> t + tensor([[[-0.3362], + [-0.8437]], + + [[-0.9627], + [ 0.1727]], + + [[ 0.5173], + [-0.1398]]]) + >>> torch.movedim(t, 1, 0).shape + torch.Size([2, 3, 1]) + >>> torch.movedim(t, 1, 0) + tensor([[[-0.3362], + [-0.9627], + [ 0.5173]], + + [[-0.8437], + [ 0.1727], + [-0.1398]]]) + >>> torch.movedim(t, (1, 2), (0, 1)).shape + torch.Size([2, 1, 3]) + >>> torch.movedim(t, (1, 2), (0, 1)) + tensor([[[-0.3362, -0.9627, 0.5173]], + + [[-0.8437, 0.1727, -0.1398]]]) + """ + ... +@overload +def movedim(input: Tensor, source: _size, destination: _size) -> Tensor: + r""" + movedim(input, source, destination) -> Tensor + + Moves the dimension(s) of :attr:`input` at the position(s) in :attr:`source` + to the position(s) in :attr:`destination`. + + Other dimensions of :attr:`input` that are not explicitly moved remain in + their original order and appear at the positions not specified in :attr:`destination`. + + Args: + input (Tensor): the input tensor. + source (int or tuple of ints): Original positions of the dims to move. These must be unique. + destination (int or tuple of ints): Destination positions for each of the original dims. These must also be unique. + + Examples:: + + >>> t = torch.randn(3,2,1) + >>> t + tensor([[[-0.3362], + [-0.8437]], + + [[-0.9627], + [ 0.1727]], + + [[ 0.5173], + [-0.1398]]]) + >>> torch.movedim(t, 1, 0).shape + torch.Size([2, 3, 1]) + >>> torch.movedim(t, 1, 0) + tensor([[[-0.3362], + [-0.9627], + [ 0.5173]], + + [[-0.8437], + [ 0.1727], + [-0.1398]]]) + >>> torch.movedim(t, (1, 2), (0, 1)).shape + torch.Size([2, 1, 3]) + >>> torch.movedim(t, (1, 2), (0, 1)) + tensor([[[-0.3362, -0.9627, 0.5173]], + + [[-0.8437, 0.1727, -0.1398]]]) + """ + ... +def msort(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + msort(input, *, out=None) -> Tensor + + Sorts the elements of the :attr:`input` tensor along its first dimension + in ascending order by value. + + .. note:: `torch.msort(t)` is equivalent to `torch.sort(t, dim=0)[0]`. + See also :func:`torch.sort`. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.randn(3, 4) + >>> t + tensor([[-0.1321, 0.4370, -1.2631, -1.1289], + [-2.0527, -1.1250, 0.2275, 0.3077], + [-0.0881, -0.1259, -0.5495, 1.0284]]) + >>> torch.msort(t) + tensor([[-2.0527, -1.1250, -1.2631, -1.1289], + [-0.1321, -0.1259, -0.5495, 0.3077], + [-0.0881, 0.4370, 0.2275, 1.0284]]) + """ + ... +def mul(input: Union[Tensor, Number, _complex], other: Union[Tensor, Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + mul(input, other, *, out=None) -> Tensor + + Multiplies :attr:`input` by :attr:`other`. + + + .. math:: + \text{out}_i = \text{input}_i \times \text{other}_i + + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer, float, and complex inputs. + + Args: + input (Tensor): the input tensor. + other (Tensor or Number) - the tensor or number to multiply input by. + + Keyword args: + out (Tensor, optional): the output tensor. + + Examples:: + + >>> a = torch.randn(3) + >>> a + tensor([ 0.2015, -0.4255, 2.6087]) + >>> torch.mul(a, 100) + tensor([ 20.1494, -42.5491, 260.8663]) + + >>> b = torch.randn(4, 1) + >>> b + tensor([[ 1.1207], + [-0.3137], + [ 0.0700], + [ 0.8378]]) + >>> c = torch.randn(1, 4) + >>> c + tensor([[ 0.5146, 0.1216, -0.5244, 2.2382]]) + >>> torch.mul(b, c) + tensor([[ 0.5767, 0.1363, -0.5877, 2.5083], + [-0.1614, -0.0382, 0.1645, -0.7021], + [ 0.0360, 0.0085, -0.0367, 0.1567], + [ 0.4312, 0.1019, -0.4394, 1.8753]]) + """ + ... +def multinomial(input: Tensor, num_samples: _int, replacement: _bool = False, *, generator: Optional[Generator] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + multinomial(input, num_samples, replacement=False, *, generator=None, out=None) -> LongTensor + + Returns a tensor where each row contains :attr:`num_samples` indices sampled + from the multinomial (a stricter definition would be multivariate, + refer to :class:`torch.distributions.multinomial.Multinomial` for more details) + probability distribution located in the corresponding row + of tensor :attr:`input`. + + .. note:: + The rows of :attr:`input` do not need to sum to one (in which case we use + the values as weights), but must be non-negative, finite and have + a non-zero sum. + + Indices are ordered from left to right according to when each was sampled + (first samples are placed in first column). + + If :attr:`input` is a vector, :attr:`out` is a vector of size :attr:`num_samples`. + + If :attr:`input` is a matrix with `m` rows, :attr:`out` is an matrix of shape + :math:`(m \times \text{num\_samples})`. + + If replacement is ``True``, samples are drawn with replacement. + + If not, they are drawn without replacement, which means that when a + sample index is drawn for a row, it cannot be drawn again for that row. + + .. note:: + When drawn without replacement, :attr:`num_samples` must be lower than + number of non-zero elements in :attr:`input` (or the min number of non-zero + elements in each row of :attr:`input` if it is a matrix). + + Args: + input (Tensor): the input tensor containing probabilities + num_samples (int): number of samples to draw + replacement (bool, optional): whether to draw with replacement or not + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + + Example:: + + >>> weights = torch.tensor([0, 10, 3, 0], dtype=torch.float) # create a tensor of weights + >>> torch.multinomial(weights, 2) + tensor([1, 2]) + >>> torch.multinomial(weights, 5) # ERROR! + RuntimeError: cannot sample n_sample > prob_dist.size(-1) samples without replacement + >>> torch.multinomial(weights, 4, replacement=True) + tensor([ 2, 1, 1, 1]) + """ + ... +@overload +def multiply(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + multiply(input, other, *, out=None) + + Alias for :func:`torch.mul`. + """ + ... +@overload +def multiply(input: Tensor, other: Union[Number, _complex]) -> Tensor: + r""" + multiply(input, other, *, out=None) + + Alias for :func:`torch.mul`. + """ + ... +def mv(input: Tensor, vec: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + mv(input, vec, *, out=None) -> Tensor + + Performs a matrix-vector product of the matrix :attr:`input` and the vector + :attr:`vec`. + + If :attr:`input` is a :math:`(n \times m)` tensor, :attr:`vec` is a 1-D tensor of + size :math:`m`, :attr:`out` will be 1-D of size :math:`n`. + + .. note:: This function does not :ref:`broadcast `. + + Args: + input (Tensor): matrix to be multiplied + vec (Tensor): vector to be multiplied + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> mat = torch.randn(2, 3) + >>> vec = torch.randn(3) + >>> torch.mv(mat, vec) + tensor([ 1.0404, -0.6361]) + """ + ... +def mvlgamma(input: Tensor, p: _int, *, out: Optional[Tensor] = None) -> Tensor: + r""" + mvlgamma(input, p, *, out=None) -> Tensor + + Alias for :func:`torch.special.multigammaln`. + """ + ... +def nan_to_num(input: Tensor, nan: Optional[_float] = None, posinf: Optional[_float] = None, neginf: Optional[_float] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None) -> Tensor + + Replaces :literal:`NaN`, positive infinity, and negative infinity values in :attr:`input` + with the values specified by :attr:`nan`, :attr:`posinf`, and :attr:`neginf`, respectively. + By default, :literal:`NaN`\ s are replaced with zero, positive infinity is replaced with the + greatest finite value representable by :attr:`input`'s dtype, and negative infinity + is replaced with the least finite value representable by :attr:`input`'s dtype. + + Args: + input (Tensor): the input tensor. + nan (Number, optional): the value to replace :literal:`NaN`\s with. Default is zero. + posinf (Number, optional): if a Number, the value to replace positive infinity values with. + If None, positive infinity values are replaced with the greatest finite value representable by :attr:`input`'s dtype. + Default is None. + neginf (Number, optional): if a Number, the value to replace negative infinity values with. + If None, negative infinity values are replaced with the lowest finite value representable by :attr:`input`'s dtype. + Default is None. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> x = torch.tensor([float('nan'), float('inf'), -float('inf'), 3.14]) + >>> torch.nan_to_num(x) + tensor([ 0.0000e+00, 3.4028e+38, -3.4028e+38, 3.1400e+00]) + >>> torch.nan_to_num(x, nan=2.0) + tensor([ 2.0000e+00, 3.4028e+38, -3.4028e+38, 3.1400e+00]) + >>> torch.nan_to_num(x, nan=2.0, posinf=1.0) + tensor([ 2.0000e+00, 1.0000e+00, -3.4028e+38, 3.1400e+00]) + """ + ... +def nan_to_num_(input: Tensor, nan: Optional[_float] = None, posinf: Optional[_float] = None, neginf: Optional[_float] = None) -> Tensor: ... +def nanmean(input: Tensor, dim: Optional[Union[_int, _size]] = None, keepdim: _bool = False, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + nanmean(input, dim=None, keepdim=False, *, dtype=None, out=None) -> Tensor + + Computes the mean of all `non-NaN` elements along the specified dimensions. + Input must be floating point or complex. + + This function is identical to :func:`torch.mean` when there are no `NaN` values + in the :attr:`input` tensor. In the presence of `NaN`, :func:`torch.mean` will + propagate the `NaN` to the output whereas :func:`torch.nanmean` will ignore the + `NaN` values (`torch.nanmean(a)` is equivalent to `torch.mean(a[~a.isnan()])`). + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor, either of floating point or complex dtype + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + out (Tensor, optional): the output tensor. + + .. seealso:: + + :func:`torch.mean` computes the mean value, propagating `NaN`. + + Example:: + + >>> x = torch.tensor([[torch.nan, 1, 2], [1, 2, 3]]) + >>> x.mean() + tensor(nan) + >>> x.nanmean() + tensor(1.8000) + >>> x.mean(dim=0) + tensor([ nan, 1.5000, 2.5000]) + >>> x.nanmean(dim=0) + tensor([1.0000, 1.5000, 2.5000]) + + # If all elements in the reduced dimensions are NaN then the result is NaN + >>> torch.tensor([torch.nan]).nanmean() + tensor(nan) + """ + ... +@overload +def nanmedian(input: Tensor) -> Tensor: + r""" + nanmedian(input) -> Tensor + + Returns the median of the values in :attr:`input`, ignoring ``NaN`` values. + + This function is identical to :func:`torch.median` when there are no ``NaN`` values in :attr:`input`. + When :attr:`input` has one or more ``NaN`` values, :func:`torch.median` will always return ``NaN``, + while this function will return the median of the non-``NaN`` elements in :attr:`input`. + If all the elements in :attr:`input` are ``NaN`` it will also return ``NaN``. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.tensor([1, float('nan'), 3, 2]) + >>> a.median() + tensor(nan) + >>> a.nanmedian() + tensor(2.) + + .. function:: nanmedian(input, dim=-1, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` contains the median of each row of :attr:`input` + in the dimension :attr:`dim`, ignoring ``NaN`` values, and ``indices`` contains the index of the median values + found in the dimension :attr:`dim`. + + This function is identical to :func:`torch.median` when there are no ``NaN`` values in a reduced row. When a reduced row has + one or more ``NaN`` values, :func:`torch.median` will always reduce it to ``NaN``, while this function will reduce it to the + median of the non-``NaN`` elements. If all the elements in a reduced row are ``NaN`` then it will be reduced to ``NaN``, too. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out ((Tensor, Tensor), optional): The first tensor will be populated with the median values and the second + tensor, which must have dtype long, with their indices in the dimension + :attr:`dim` of :attr:`input`. + + Example:: + + >>> a = torch.tensor([[2, 3, 1], [float('nan'), 1, float('nan')]]) + >>> a + tensor([[2., 3., 1.], + [nan, 1., nan]]) + >>> a.median(0) + torch.return_types.median(values=tensor([nan, 1., nan]), indices=tensor([1, 1, 1])) + >>> a.nanmedian(0) + torch.return_types.nanmedian(values=tensor([2., 1., 1.]), indices=tensor([0, 1, 0])) + """ + ... +@overload +def nanmedian(input: Tensor, dim: _int, keepdim: _bool = False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.nanmedian: + r""" + nanmedian(input) -> Tensor + + Returns the median of the values in :attr:`input`, ignoring ``NaN`` values. + + This function is identical to :func:`torch.median` when there are no ``NaN`` values in :attr:`input`. + When :attr:`input` has one or more ``NaN`` values, :func:`torch.median` will always return ``NaN``, + while this function will return the median of the non-``NaN`` elements in :attr:`input`. + If all the elements in :attr:`input` are ``NaN`` it will also return ``NaN``. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.tensor([1, float('nan'), 3, 2]) + >>> a.median() + tensor(nan) + >>> a.nanmedian() + tensor(2.) + + .. function:: nanmedian(input, dim=-1, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` contains the median of each row of :attr:`input` + in the dimension :attr:`dim`, ignoring ``NaN`` values, and ``indices`` contains the index of the median values + found in the dimension :attr:`dim`. + + This function is identical to :func:`torch.median` when there are no ``NaN`` values in a reduced row. When a reduced row has + one or more ``NaN`` values, :func:`torch.median` will always reduce it to ``NaN``, while this function will reduce it to the + median of the non-``NaN`` elements. If all the elements in a reduced row are ``NaN`` then it will be reduced to ``NaN``, too. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out ((Tensor, Tensor), optional): The first tensor will be populated with the median values and the second + tensor, which must have dtype long, with their indices in the dimension + :attr:`dim` of :attr:`input`. + + Example:: + + >>> a = torch.tensor([[2, 3, 1], [float('nan'), 1, float('nan')]]) + >>> a + tensor([[2., 3., 1.], + [nan, 1., nan]]) + >>> a.median(0) + torch.return_types.median(values=tensor([nan, 1., nan]), indices=tensor([1, 1, 1])) + >>> a.nanmedian(0) + torch.return_types.nanmedian(values=tensor([2., 1., 1.]), indices=tensor([0, 1, 0])) + """ + ... +@overload +def nanmedian(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool = False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.nanmedian: + r""" + nanmedian(input) -> Tensor + + Returns the median of the values in :attr:`input`, ignoring ``NaN`` values. + + This function is identical to :func:`torch.median` when there are no ``NaN`` values in :attr:`input`. + When :attr:`input` has one or more ``NaN`` values, :func:`torch.median` will always return ``NaN``, + while this function will return the median of the non-``NaN`` elements in :attr:`input`. + If all the elements in :attr:`input` are ``NaN`` it will also return ``NaN``. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.tensor([1, float('nan'), 3, 2]) + >>> a.median() + tensor(nan) + >>> a.nanmedian() + tensor(2.) + + .. function:: nanmedian(input, dim=-1, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` contains the median of each row of :attr:`input` + in the dimension :attr:`dim`, ignoring ``NaN`` values, and ``indices`` contains the index of the median values + found in the dimension :attr:`dim`. + + This function is identical to :func:`torch.median` when there are no ``NaN`` values in a reduced row. When a reduced row has + one or more ``NaN`` values, :func:`torch.median` will always reduce it to ``NaN``, while this function will reduce it to the + median of the non-``NaN`` elements. If all the elements in a reduced row are ``NaN`` then it will be reduced to ``NaN``, too. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out ((Tensor, Tensor), optional): The first tensor will be populated with the median values and the second + tensor, which must have dtype long, with their indices in the dimension + :attr:`dim` of :attr:`input`. + + Example:: + + >>> a = torch.tensor([[2, 3, 1], [float('nan'), 1, float('nan')]]) + >>> a + tensor([[2., 3., 1.], + [nan, 1., nan]]) + >>> a.median(0) + torch.return_types.median(values=tensor([nan, 1., nan]), indices=tensor([1, 1, 1])) + >>> a.nanmedian(0) + torch.return_types.nanmedian(values=tensor([2., 1., 1.]), indices=tensor([0, 1, 0])) + """ + ... +@overload +def nanquantile(input: Tensor, q: Tensor, dim: Optional[_int] = None, keepdim: _bool = False, *, interpolation: str = "linear", out: Optional[Tensor] = None) -> Tensor: + r""" + nanquantile(input, q, dim=None, keepdim=False, *, interpolation='linear', out=None) -> Tensor + + This is a variant of :func:`torch.quantile` that "ignores" ``NaN`` values, + computing the quantiles :attr:`q` as if ``NaN`` values in :attr:`input` did + not exist. If all values in a reduced row are ``NaN`` then the quantiles for + that reduction will be ``NaN``. See the documentation for :func:`torch.quantile`. + + Args: + input (Tensor): the input tensor. + q (float or Tensor): a scalar or 1D tensor of quantile values in the range [0, 1] + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword arguments: + interpolation (str): interpolation method to use when the desired quantile lies between two data points. + Can be ``linear``, ``lower``, ``higher``, ``midpoint`` and ``nearest``. + Default is ``linear``. + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.tensor([float('nan'), 1, 2]) + >>> t.quantile(0.5) + tensor(nan) + >>> t.nanquantile(0.5) + tensor(1.5000) + >>> t = torch.tensor([[float('nan'), float('nan')], [1, 2]]) + >>> t + tensor([[nan, nan], + [1., 2.]]) + >>> t.nanquantile(0.5, dim=0) + tensor([1., 2.]) + >>> t.nanquantile(0.5, dim=1) + tensor([ nan, 1.5000]) + """ + ... +@overload +def nanquantile(input: Tensor, q: _float, dim: Optional[_int] = None, keepdim: _bool = False, *, interpolation: str = "linear", out: Optional[Tensor] = None) -> Tensor: + r""" + nanquantile(input, q, dim=None, keepdim=False, *, interpolation='linear', out=None) -> Tensor + + This is a variant of :func:`torch.quantile` that "ignores" ``NaN`` values, + computing the quantiles :attr:`q` as if ``NaN`` values in :attr:`input` did + not exist. If all values in a reduced row are ``NaN`` then the quantiles for + that reduction will be ``NaN``. See the documentation for :func:`torch.quantile`. + + Args: + input (Tensor): the input tensor. + q (float or Tensor): a scalar or 1D tensor of quantile values in the range [0, 1] + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword arguments: + interpolation (str): interpolation method to use when the desired quantile lies between two data points. + Can be ``linear``, ``lower``, ``higher``, ``midpoint`` and ``nearest``. + Default is ``linear``. + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.tensor([float('nan'), 1, 2]) + >>> t.quantile(0.5) + tensor(nan) + >>> t.nanquantile(0.5) + tensor(1.5000) + >>> t = torch.tensor([[float('nan'), float('nan')], [1, 2]]) + >>> t + tensor([[nan, nan], + [1., 2.]]) + >>> t.nanquantile(0.5, dim=0) + tensor([1., 2.]) + >>> t.nanquantile(0.5, dim=1) + tensor([ nan, 1.5000]) + """ + ... +def nansum(input: Tensor, dim: Optional[Union[_int, _size]] = None, keepdim: _bool = False, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + nansum(input, *, dtype=None) -> Tensor + + Returns the sum of all elements, treating Not a Numbers (NaNs) as zero. + + Args: + input (Tensor): the input tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.tensor([1., 2., float('nan'), 4.]) + >>> torch.nansum(a) + tensor(7.) + + .. function:: nansum(input, dim, keepdim=False, *, dtype=None) -> Tensor + :noindex: + + Returns the sum of each row of the :attr:`input` tensor in the given + dimension :attr:`dim`, treating Not a Numbers (NaNs) as zero. + If :attr:`dim` is a list of dimensions, reduce over all of them. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> torch.nansum(torch.tensor([1., float("nan")])) + 1.0 + >>> a = torch.tensor([[1, 2], [3., float("nan")]]) + >>> torch.nansum(a) + tensor(6.) + >>> torch.nansum(a, dim=0) + tensor([4., 2.]) + >>> torch.nansum(a, dim=1) + tensor([3., 3.]) + """ + ... +@overload +def narrow(input: Tensor, dim: _int, start: Tensor, length: Union[_int, SymInt]) -> Tensor: + r""" + narrow(input, dim, start, length) -> Tensor + + Returns a new tensor that is a narrowed version of :attr:`input` tensor. The + dimension :attr:`dim` is input from :attr:`start` to ``start + length``. The + returned tensor and :attr:`input` tensor share the same underlying storage. + + Args: + input (Tensor): the tensor to narrow + dim (int): the dimension along which to narrow + start (int or Tensor): index of the element to start the narrowed dimension + from. Can be negative, which means indexing from the end of `dim`. If + `Tensor`, it must be an 0-dim integral `Tensor` (bools not allowed) + length (int): length of the narrowed dimension, must be weakly positive + + Example:: + + >>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + >>> torch.narrow(x, 0, 0, 2) + tensor([[ 1, 2, 3], + [ 4, 5, 6]]) + >>> torch.narrow(x, 1, 1, 2) + tensor([[ 2, 3], + [ 5, 6], + [ 8, 9]]) + >>> torch.narrow(x, -1, torch.tensor(-1), 1) + tensor([[3], + [6], + [9]]) + """ + ... +@overload +def narrow(input: Tensor, dim: _int, start: Union[_int, SymInt], length: Union[_int, SymInt]) -> Tensor: + r""" + narrow(input, dim, start, length) -> Tensor + + Returns a new tensor that is a narrowed version of :attr:`input` tensor. The + dimension :attr:`dim` is input from :attr:`start` to ``start + length``. The + returned tensor and :attr:`input` tensor share the same underlying storage. + + Args: + input (Tensor): the tensor to narrow + dim (int): the dimension along which to narrow + start (int or Tensor): index of the element to start the narrowed dimension + from. Can be negative, which means indexing from the end of `dim`. If + `Tensor`, it must be an 0-dim integral `Tensor` (bools not allowed) + length (int): length of the narrowed dimension, must be weakly positive + + Example:: + + >>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + >>> torch.narrow(x, 0, 0, 2) + tensor([[ 1, 2, 3], + [ 4, 5, 6]]) + >>> torch.narrow(x, 1, 1, 2) + tensor([[ 2, 3], + [ 5, 6], + [ 8, 9]]) + >>> torch.narrow(x, -1, torch.tensor(-1), 1) + tensor([[3], + [6], + [9]]) + """ + ... +def narrow_copy(input: Tensor, dim: _int, start: Union[_int, SymInt], length: Union[_int, SymInt], *, out: Optional[Tensor] = None) -> Tensor: + r""" + narrow_copy(input, dim, start, length, *, out=None) -> Tensor + + Same as :meth:`Tensor.narrow` except this returns a copy rather + than shared storage. This is primarily for sparse tensors, which + do not have a shared-storage narrow method. + + Args: + input (Tensor): the tensor to narrow + dim (int): the dimension along which to narrow + start (int): index of the element to start the narrowed dimension from. Can + be negative, which means indexing from the end of `dim` + length (int): length of the narrowed dimension, must be weakly positive + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + >>> torch.narrow_copy(x, 0, 0, 2) + tensor([[ 1, 2, 3], + [ 4, 5, 6]]) + >>> torch.narrow_copy(x, 1, 1, 2) + tensor([[ 2, 3], + [ 5, 6], + [ 8, 9]]) + >>> s = torch.arange(16).reshape(2, 2, 2, 2).to_sparse(2) + >>> torch.narrow_copy(s, 0, 0, 1) + tensor(indices=tensor([[0, 0], + [0, 1]]), + values=tensor([[[0, 1], + [2, 3]], + + [[4, 5], + [6, 7]]]), + size=(1, 2, 2, 2), nnz=2, layout=torch.sparse_coo) + + .. seealso:: + + :func:`torch.narrow` for a non copy variant + """ + ... +def native_batch_norm(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], running_mean: Optional[Tensor], running_var: Optional[Tensor], training: _bool, momentum: _float, eps: _float, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> Tuple[Tensor, Tensor, Tensor]: ... +def native_channel_shuffle(input: Tensor, groups: Union[_int, SymInt]) -> Tensor: ... +def native_dropout(input: Tensor, p: _float, train: Optional[_bool]) -> Tuple[Tensor, Tensor]: ... +def native_group_norm(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], N: Union[_int, SymInt], C: Union[_int, SymInt], HxW: Union[_int, SymInt], group: _int, eps: _float) -> Tuple[Tensor, Tensor, Tensor]: ... +def native_layer_norm(input: Tensor, normalized_shape: Sequence[Union[_int, SymInt]], weight: Optional[Tensor], bias: Optional[Tensor], eps: _float) -> Tuple[Tensor, Tensor, Tensor]: ... +@overload +def native_norm(input: Tensor, p: Optional[Union[Number, _complex]], dim: Union[_int, _size], keepdim: _bool, dtype: Optional[_dtype]) -> Tensor: ... +@overload +def native_norm(input: Tensor, p: Union[Number, _complex] = 2) -> Tensor: ... +@overload +def ne(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + ne(input, other, *, out=None) -> Tensor + + Computes :math:`\text{input} \neq \text{other}` element-wise. + + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or float): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is not equal to :attr:`other` and False elsewhere + + Example:: + + >>> torch.ne(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[False, True], [True, False]]) + """ + ... +@overload +def ne(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + ne(input, other, *, out=None) -> Tensor + + Computes :math:`\text{input} \neq \text{other}` element-wise. + + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or float): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is not equal to :attr:`other` and False elsewhere + + Example:: + + >>> torch.ne(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[False, True], [True, False]]) + """ + ... +def neg(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + neg(input, *, out=None) -> Tensor + + Returns a new tensor with the negative of the elements of :attr:`input`. + + .. math:: + \text{out} = -1 \times \text{input} + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(5) + >>> a + tensor([ 0.0090, -0.2262, -0.0682, -0.2866, 0.3940]) + >>> torch.neg(a) + tensor([-0.0090, 0.2262, 0.0682, 0.2866, -0.3940]) + """ + ... +def neg_(input: Tensor) -> Tensor: ... +def negative(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + negative(input, *, out=None) -> Tensor + + Alias for :func:`torch.neg` + """ + ... +def negative_(input: Tensor) -> Tensor: ... +def nextafter(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + nextafter(input, other, *, out=None) -> Tensor + + Return the next floating-point value after :attr:`input` towards :attr:`other`, elementwise. + + The shapes of ``input`` and ``other`` must be + :ref:`broadcastable `. + + Args: + input (Tensor): the first input tensor + other (Tensor): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> eps = torch.finfo(torch.float32).eps + >>> torch.nextafter(torch.tensor([1.0, 2.0]), torch.tensor([2.0, 1.0])) == torch.tensor([eps + 1, 2 - eps]) + tensor([True, True]) + """ + ... +@overload +def nonzero(input: Tensor, *, as_tuple: Literal[False] = False, out: Optional[Tensor] = None) -> Tensor: + r""" + nonzero(input, *, out=None, as_tuple=False) -> LongTensor or tuple of LongTensors + + .. note:: + :func:`torch.nonzero(..., as_tuple=False) ` (default) returns a + 2-D tensor where each row is the index for a nonzero value. + + :func:`torch.nonzero(..., as_tuple=True) ` returns a tuple of 1-D + index tensors, allowing for advanced indexing, so ``x[x.nonzero(as_tuple=True)]`` + gives all nonzero values of tensor ``x``. Of the returned tuple, each index tensor + contains nonzero indices for a certain dimension. + + See below for more details on the two behaviors. + + When :attr:`input` is on CUDA, :func:`torch.nonzero() ` causes + host-device synchronization. + + **When** :attr:`as_tuple` **is** ``False`` **(default)**: + + Returns a tensor containing the indices of all non-zero elements of + :attr:`input`. Each row in the result contains the indices of a non-zero + element in :attr:`input`. The result is sorted lexicographically, with + the last index changing the fastest (C-style). + + If :attr:`input` has :math:`n` dimensions, then the resulting indices tensor + :attr:`out` is of size :math:`(z \times n)`, where :math:`z` is the total number of + non-zero elements in the :attr:`input` tensor. + + **When** :attr:`as_tuple` **is** ``True``: + + Returns a tuple of 1-D tensors, one for each dimension in :attr:`input`, + each containing the indices (in that dimension) of all non-zero elements of + :attr:`input` . + + If :attr:`input` has :math:`n` dimensions, then the resulting tuple contains :math:`n` + tensors of size :math:`z`, where :math:`z` is the total number of + non-zero elements in the :attr:`input` tensor. + + As a special case, when :attr:`input` has zero dimensions and a nonzero scalar + value, it is treated as a one-dimensional tensor with one element. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (LongTensor, optional): the output tensor containing indices + + Returns: + LongTensor or tuple of LongTensor: If :attr:`as_tuple` is ``False``, the output + tensor containing indices. If :attr:`as_tuple` is ``True``, one 1-D tensor for + each dimension, containing the indices of each nonzero element along that + dimension. + + Example:: + + >>> torch.nonzero(torch.tensor([1, 1, 1, 0, 1])) + tensor([[ 0], + [ 1], + [ 2], + [ 4]]) + >>> torch.nonzero(torch.tensor([[0.6, 0.0, 0.0, 0.0], + ... [0.0, 0.4, 0.0, 0.0], + ... [0.0, 0.0, 1.2, 0.0], + ... [0.0, 0.0, 0.0,-0.4]])) + tensor([[ 0, 0], + [ 1, 1], + [ 2, 2], + [ 3, 3]]) + >>> torch.nonzero(torch.tensor([1, 1, 1, 0, 1]), as_tuple=True) + (tensor([0, 1, 2, 4]),) + >>> torch.nonzero(torch.tensor([[0.6, 0.0, 0.0, 0.0], + ... [0.0, 0.4, 0.0, 0.0], + ... [0.0, 0.0, 1.2, 0.0], + ... [0.0, 0.0, 0.0,-0.4]]), as_tuple=True) + (tensor([0, 1, 2, 3]), tensor([0, 1, 2, 3])) + >>> torch.nonzero(torch.tensor(5), as_tuple=True) + (tensor([0]),) + """ + ... +@overload +def nonzero(input: Tensor, *, as_tuple: Literal[True]) -> Tuple[Tensor, ...]: + r""" + nonzero(input, *, out=None, as_tuple=False) -> LongTensor or tuple of LongTensors + + .. note:: + :func:`torch.nonzero(..., as_tuple=False) ` (default) returns a + 2-D tensor where each row is the index for a nonzero value. + + :func:`torch.nonzero(..., as_tuple=True) ` returns a tuple of 1-D + index tensors, allowing for advanced indexing, so ``x[x.nonzero(as_tuple=True)]`` + gives all nonzero values of tensor ``x``. Of the returned tuple, each index tensor + contains nonzero indices for a certain dimension. + + See below for more details on the two behaviors. + + When :attr:`input` is on CUDA, :func:`torch.nonzero() ` causes + host-device synchronization. + + **When** :attr:`as_tuple` **is** ``False`` **(default)**: + + Returns a tensor containing the indices of all non-zero elements of + :attr:`input`. Each row in the result contains the indices of a non-zero + element in :attr:`input`. The result is sorted lexicographically, with + the last index changing the fastest (C-style). + + If :attr:`input` has :math:`n` dimensions, then the resulting indices tensor + :attr:`out` is of size :math:`(z \times n)`, where :math:`z` is the total number of + non-zero elements in the :attr:`input` tensor. + + **When** :attr:`as_tuple` **is** ``True``: + + Returns a tuple of 1-D tensors, one for each dimension in :attr:`input`, + each containing the indices (in that dimension) of all non-zero elements of + :attr:`input` . + + If :attr:`input` has :math:`n` dimensions, then the resulting tuple contains :math:`n` + tensors of size :math:`z`, where :math:`z` is the total number of + non-zero elements in the :attr:`input` tensor. + + As a special case, when :attr:`input` has zero dimensions and a nonzero scalar + value, it is treated as a one-dimensional tensor with one element. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (LongTensor, optional): the output tensor containing indices + + Returns: + LongTensor or tuple of LongTensor: If :attr:`as_tuple` is ``False``, the output + tensor containing indices. If :attr:`as_tuple` is ``True``, one 1-D tensor for + each dimension, containing the indices of each nonzero element along that + dimension. + + Example:: + + >>> torch.nonzero(torch.tensor([1, 1, 1, 0, 1])) + tensor([[ 0], + [ 1], + [ 2], + [ 4]]) + >>> torch.nonzero(torch.tensor([[0.6, 0.0, 0.0, 0.0], + ... [0.0, 0.4, 0.0, 0.0], + ... [0.0, 0.0, 1.2, 0.0], + ... [0.0, 0.0, 0.0,-0.4]])) + tensor([[ 0, 0], + [ 1, 1], + [ 2, 2], + [ 3, 3]]) + >>> torch.nonzero(torch.tensor([1, 1, 1, 0, 1]), as_tuple=True) + (tensor([0, 1, 2, 4]),) + >>> torch.nonzero(torch.tensor([[0.6, 0.0, 0.0, 0.0], + ... [0.0, 0.4, 0.0, 0.0], + ... [0.0, 0.0, 1.2, 0.0], + ... [0.0, 0.0, 0.0,-0.4]]), as_tuple=True) + (tensor([0, 1, 2, 3]), tensor([0, 1, 2, 3])) + >>> torch.nonzero(torch.tensor(5), as_tuple=True) + (tensor([0]),) + """ + ... +def nonzero_static(input: Tensor, *, size: _int, fill_value: _int = -1, out: Optional[Tensor] = None) -> Tensor: ... +def norm_except_dim(v: Tensor, pow: _int = 2, dim: _int = 0) -> Tensor: ... +@overload +def normal(mean: Tensor, std: Tensor, *, generator: Optional[Generator] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + normal(mean, std, *, generator=None, out=None) -> Tensor + + Returns a tensor of random numbers drawn from separate normal distributions + whose mean and standard deviation are given. + + The :attr:`mean` is a tensor with the mean of + each output element's normal distribution + + The :attr:`std` is a tensor with the standard deviation of + each output element's normal distribution + + The shapes of :attr:`mean` and :attr:`std` don't need to match, but the + total number of elements in each tensor need to be the same. + + .. note:: When the shapes do not match, the shape of :attr:`mean` + is used as the shape for the returned output tensor + + .. note:: When :attr:`std` is a CUDA tensor, this function synchronizes + its device with the CPU. + + Args: + mean (Tensor): the tensor of per-element means + std (Tensor): the tensor of per-element standard deviations + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(mean=torch.arange(1., 11.), std=torch.arange(1, 0, -0.1)) + tensor([ 1.0425, 3.5672, 2.7969, 4.2925, 4.7229, 6.2134, + 8.0505, 8.1408, 9.0563, 10.0566]) + + .. function:: normal(mean=0.0, std, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the means are shared among all drawn + elements. + + Args: + mean (float, optional): the mean for all distributions + std (Tensor): the tensor of per-element standard deviations + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(mean=0.5, std=torch.arange(1., 6.)) + tensor([-1.2793, -1.0732, -2.0687, 5.1177, -1.2303]) + + .. function:: normal(mean, std=1.0, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the standard deviations are shared among + all drawn elements. + + Args: + mean (Tensor): the tensor of per-element means + std (float, optional): the standard deviation for all distributions + + Keyword args: + out (Tensor, optional): the output tensor + + Example:: + + >>> torch.normal(mean=torch.arange(1., 6.)) + tensor([ 1.1552, 2.6148, 2.6535, 5.8318, 4.2361]) + + .. function:: normal(mean, std, size, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the means and standard deviations are shared + among all drawn elements. The resulting tensor has size given by :attr:`size`. + + Args: + mean (float): the mean for all distributions + std (float): the standard deviation for all distributions + size (int...): a sequence of integers defining the shape of the output tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(2, 3, size=(1, 4)) + tensor([[-1.3987, -1.9544, 3.6048, 0.7909]]) + """ + ... +@overload +def normal(mean: Tensor, std: _float = 1, *, generator: Optional[Generator] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + normal(mean, std, *, generator=None, out=None) -> Tensor + + Returns a tensor of random numbers drawn from separate normal distributions + whose mean and standard deviation are given. + + The :attr:`mean` is a tensor with the mean of + each output element's normal distribution + + The :attr:`std` is a tensor with the standard deviation of + each output element's normal distribution + + The shapes of :attr:`mean` and :attr:`std` don't need to match, but the + total number of elements in each tensor need to be the same. + + .. note:: When the shapes do not match, the shape of :attr:`mean` + is used as the shape for the returned output tensor + + .. note:: When :attr:`std` is a CUDA tensor, this function synchronizes + its device with the CPU. + + Args: + mean (Tensor): the tensor of per-element means + std (Tensor): the tensor of per-element standard deviations + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(mean=torch.arange(1., 11.), std=torch.arange(1, 0, -0.1)) + tensor([ 1.0425, 3.5672, 2.7969, 4.2925, 4.7229, 6.2134, + 8.0505, 8.1408, 9.0563, 10.0566]) + + .. function:: normal(mean=0.0, std, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the means are shared among all drawn + elements. + + Args: + mean (float, optional): the mean for all distributions + std (Tensor): the tensor of per-element standard deviations + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(mean=0.5, std=torch.arange(1., 6.)) + tensor([-1.2793, -1.0732, -2.0687, 5.1177, -1.2303]) + + .. function:: normal(mean, std=1.0, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the standard deviations are shared among + all drawn elements. + + Args: + mean (Tensor): the tensor of per-element means + std (float, optional): the standard deviation for all distributions + + Keyword args: + out (Tensor, optional): the output tensor + + Example:: + + >>> torch.normal(mean=torch.arange(1., 6.)) + tensor([ 1.1552, 2.6148, 2.6535, 5.8318, 4.2361]) + + .. function:: normal(mean, std, size, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the means and standard deviations are shared + among all drawn elements. The resulting tensor has size given by :attr:`size`. + + Args: + mean (float): the mean for all distributions + std (float): the standard deviation for all distributions + size (int...): a sequence of integers defining the shape of the output tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(2, 3, size=(1, 4)) + tensor([[-1.3987, -1.9544, 3.6048, 0.7909]]) + """ + ... +@overload +def normal(mean: _float, std: Tensor, *, generator: Optional[Generator] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + normal(mean, std, *, generator=None, out=None) -> Tensor + + Returns a tensor of random numbers drawn from separate normal distributions + whose mean and standard deviation are given. + + The :attr:`mean` is a tensor with the mean of + each output element's normal distribution + + The :attr:`std` is a tensor with the standard deviation of + each output element's normal distribution + + The shapes of :attr:`mean` and :attr:`std` don't need to match, but the + total number of elements in each tensor need to be the same. + + .. note:: When the shapes do not match, the shape of :attr:`mean` + is used as the shape for the returned output tensor + + .. note:: When :attr:`std` is a CUDA tensor, this function synchronizes + its device with the CPU. + + Args: + mean (Tensor): the tensor of per-element means + std (Tensor): the tensor of per-element standard deviations + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(mean=torch.arange(1., 11.), std=torch.arange(1, 0, -0.1)) + tensor([ 1.0425, 3.5672, 2.7969, 4.2925, 4.7229, 6.2134, + 8.0505, 8.1408, 9.0563, 10.0566]) + + .. function:: normal(mean=0.0, std, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the means are shared among all drawn + elements. + + Args: + mean (float, optional): the mean for all distributions + std (Tensor): the tensor of per-element standard deviations + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(mean=0.5, std=torch.arange(1., 6.)) + tensor([-1.2793, -1.0732, -2.0687, 5.1177, -1.2303]) + + .. function:: normal(mean, std=1.0, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the standard deviations are shared among + all drawn elements. + + Args: + mean (Tensor): the tensor of per-element means + std (float, optional): the standard deviation for all distributions + + Keyword args: + out (Tensor, optional): the output tensor + + Example:: + + >>> torch.normal(mean=torch.arange(1., 6.)) + tensor([ 1.1552, 2.6148, 2.6535, 5.8318, 4.2361]) + + .. function:: normal(mean, std, size, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the means and standard deviations are shared + among all drawn elements. The resulting tensor has size given by :attr:`size`. + + Args: + mean (float): the mean for all distributions + std (float): the standard deviation for all distributions + size (int...): a sequence of integers defining the shape of the output tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(2, 3, size=(1, 4)) + tensor([[-1.3987, -1.9544, 3.6048, 0.7909]]) + """ + ... +@overload +def normal(mean: _float, std: _float, size: Sequence[Union[_int, SymInt]], *, generator: Optional[Generator] = None, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + normal(mean, std, *, generator=None, out=None) -> Tensor + + Returns a tensor of random numbers drawn from separate normal distributions + whose mean and standard deviation are given. + + The :attr:`mean` is a tensor with the mean of + each output element's normal distribution + + The :attr:`std` is a tensor with the standard deviation of + each output element's normal distribution + + The shapes of :attr:`mean` and :attr:`std` don't need to match, but the + total number of elements in each tensor need to be the same. + + .. note:: When the shapes do not match, the shape of :attr:`mean` + is used as the shape for the returned output tensor + + .. note:: When :attr:`std` is a CUDA tensor, this function synchronizes + its device with the CPU. + + Args: + mean (Tensor): the tensor of per-element means + std (Tensor): the tensor of per-element standard deviations + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(mean=torch.arange(1., 11.), std=torch.arange(1, 0, -0.1)) + tensor([ 1.0425, 3.5672, 2.7969, 4.2925, 4.7229, 6.2134, + 8.0505, 8.1408, 9.0563, 10.0566]) + + .. function:: normal(mean=0.0, std, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the means are shared among all drawn + elements. + + Args: + mean (float, optional): the mean for all distributions + std (Tensor): the tensor of per-element standard deviations + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(mean=0.5, std=torch.arange(1., 6.)) + tensor([-1.2793, -1.0732, -2.0687, 5.1177, -1.2303]) + + .. function:: normal(mean, std=1.0, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the standard deviations are shared among + all drawn elements. + + Args: + mean (Tensor): the tensor of per-element means + std (float, optional): the standard deviation for all distributions + + Keyword args: + out (Tensor, optional): the output tensor + + Example:: + + >>> torch.normal(mean=torch.arange(1., 6.)) + tensor([ 1.1552, 2.6148, 2.6535, 5.8318, 4.2361]) + + .. function:: normal(mean, std, size, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the means and standard deviations are shared + among all drawn elements. The resulting tensor has size given by :attr:`size`. + + Args: + mean (float): the mean for all distributions + std (float): the standard deviation for all distributions + size (int...): a sequence of integers defining the shape of the output tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(2, 3, size=(1, 4)) + tensor([[-1.3987, -1.9544, 3.6048, 0.7909]]) + """ + ... +@overload +def not_equal(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + not_equal(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.ne`. + """ + ... +@overload +def not_equal(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + not_equal(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.ne`. + """ + ... +@overload +def nuclear_norm(input: Tensor, dim: Union[_int, _size], keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: ... +@overload +def nuclear_norm(input: Tensor, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: ... +def numel(self: Tensor) -> _int: + r""" + numel(input) -> int + + Returns the total number of elements in the :attr:`input` tensor. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 2, 3, 4, 5) + >>> torch.numel(a) + 120 + >>> a = torch.zeros(4,4) + >>> torch.numel(a) + 16 + """ + ... +@overload +def ones(size: Sequence[Union[_int, SymInt]], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + ones(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with the scalar value `1`, with the shape defined + by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.ones(2, 3) + tensor([[ 1., 1., 1.], + [ 1., 1., 1.]]) + + >>> torch.ones(5) + tensor([ 1., 1., 1., 1., 1.]) + """ + ... +@overload +def ones(*size: Union[_int, SymInt], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + ones(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with the scalar value `1`, with the shape defined + by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.ones(2, 3) + tensor([[ 1., 1., 1.], + [ 1., 1., 1.]]) + + >>> torch.ones(5) + tensor([ 1., 1., 1., 1., 1.]) + """ + ... +@overload +def ones(size: _size, *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + ones(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with the scalar value `1`, with the shape defined + by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.ones(2, 3) + tensor([[ 1., 1., 1.], + [ 1., 1., 1.]]) + + >>> torch.ones(5) + tensor([ 1., 1., 1., 1., 1.]) + """ + ... +@overload +def ones(*size: _int, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + ones(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with the scalar value `1`, with the shape defined + by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.ones(2, 3) + tensor([[ 1., 1., 1.], + [ 1., 1., 1.]]) + + >>> torch.ones(5) + tensor([ 1., 1., 1., 1., 1.]) + """ + ... +def ones_like(input: Tensor, *, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + ones_like(input, *, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor + + Returns a tensor filled with the scalar value `1`, with the same size as + :attr:`input`. ``torch.ones_like(input)`` is equivalent to + ``torch.ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``. + + .. warning:: + As of 0.4, this function does not support an :attr:`out` keyword. As an alternative, + the old ``torch.ones_like(input, out=output)`` is equivalent to + ``torch.ones(input.size(), out=output)``. + + Args: + input (Tensor): the size of :attr:`input` will determine size of the output tensor. + + Keyword arguments: + dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. + Default: if ``None``, defaults to the dtype of :attr:`input`. + layout (:class:`torch.layout`, optional): the desired layout of returned tensor. + Default: if ``None``, defaults to the layout of :attr:`input`. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, defaults to the device of :attr:`input`. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + + Example:: + + >>> input = torch.empty(2, 3) + >>> torch.ones_like(input) + tensor([[ 1., 1., 1.], + [ 1., 1., 1.]]) + """ + ... +def orgqr(input: Tensor, input2: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + orgqr(input, tau) -> Tensor + + Alias for :func:`torch.linalg.householder_product`. + """ + ... +def ormqr(input: Tensor, input2: Tensor, input3: Tensor, left: _bool = True, transpose: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + ormqr(input, tau, other, left=True, transpose=False, *, out=None) -> Tensor + + Computes the matrix-matrix multiplication of a product of Householder matrices with a general matrix. + + Multiplies a :math:`m \times n` matrix `C` (given by :attr:`other`) with a matrix `Q`, + where `Q` is represented using Householder reflectors `(input, tau)`. + See `Representation of Orthogonal or Unitary Matrices`_ for further details. + + If :attr:`left` is `True` then `op(Q)` times `C` is computed, otherwise the result is `C` times `op(Q)`. + When :attr:`left` is `True`, the implicit matrix `Q` has size :math:`m \times m`. + It has size :math:`n \times n` otherwise. + If :attr:`transpose` is `True` then `op` is the conjugate transpose operation, otherwise it's a no-op. + + Supports inputs of float, double, cfloat and cdouble dtypes. + Also supports batched inputs, and, if the input is batched, the output is batched with the same dimensions. + + .. seealso:: + :func:`torch.geqrf` can be used to form the Householder representation `(input, tau)` of matrix `Q` + from the QR decomposition. + + .. note:: + This function supports backward but it is only fast when ``(input, tau)`` do not require gradients + and/or ``tau.size(-1)`` is very small. + `` + + Args: + input (Tensor): tensor of shape `(*, mn, k)` where `*` is zero or more batch dimensions + and `mn` equals to `m` or `n` depending on the :attr:`left`. + tau (Tensor): tensor of shape `(*, min(mn, k))` where `*` is zero or more batch dimensions. + other (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions. + left (bool): controls the order of multiplication. + transpose (bool): controls whether the matrix `Q` is conjugate transposed or not. + + Keyword args: + out (Tensor, optional): the output Tensor. Ignored if `None`. Default: `None`. + + .. _Representation of Orthogonal or Unitary Matrices: + https://www.netlib.org/lapack/lug/node128.html + """ + ... +def outer(input: Tensor, vec2: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + outer(input, vec2, *, out=None) -> Tensor + + Outer product of :attr:`input` and :attr:`vec2`. + If :attr:`input` is a vector of size :math:`n` and :attr:`vec2` is a vector of + size :math:`m`, then :attr:`out` must be a matrix of size :math:`(n \times m)`. + + .. note:: This function does not :ref:`broadcast `. + + Args: + input (Tensor): 1-D input vector + vec2 (Tensor): 1-D input vector + + Keyword args: + out (Tensor, optional): optional output matrix + + Example:: + + >>> v1 = torch.arange(1., 5.) + >>> v2 = torch.arange(1., 4.) + >>> torch.outer(v1, v2) + tensor([[ 1., 2., 3.], + [ 2., 4., 6.], + [ 3., 6., 9.], + [ 4., 8., 12.]]) + """ + ... +def pairwise_distance(x1: Tensor, x2: Tensor, p: _float = 2, eps: _float = 1e-06, keepdim: _bool = False) -> Tensor: ... +def pdist(input: Tensor, p: _float = 2) -> Tensor: ... +def permute(input: Tensor, dims: _size) -> Tensor: + r""" + permute(input, dims) -> Tensor + + Returns a view of the original tensor :attr:`input` with its dimensions permuted. + + Args: + input (Tensor): the input tensor. + dims (tuple of int): The desired ordering of dimensions + + Example: + >>> x = torch.randn(2, 3, 5) + >>> x.size() + torch.Size([2, 3, 5]) + >>> torch.permute(x, (2, 0, 1)).size() + torch.Size([5, 2, 3]) + """ + ... +def permute_copy(input: Tensor, dims: _size, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.permute`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def pinverse(input: Tensor, rcond: _float = 1e-15) -> Tensor: + r""" + pinverse(input, rcond=1e-15) -> Tensor + + Alias for :func:`torch.linalg.pinv` + """ + ... +def pixel_shuffle(input: Tensor, upscale_factor: _int) -> Tensor: ... +def pixel_unshuffle(input: Tensor, downscale_factor: _int) -> Tensor: ... +def poisson(input: Tensor, generator: Optional[Generator] = None) -> Tensor: + r""" + poisson(input, generator=None) -> Tensor + + Returns a tensor of the same size as :attr:`input` with each element + sampled from a Poisson distribution with rate parameter given by the corresponding + element in :attr:`input` i.e., + + .. math:: + \text{out}_i \sim \text{Poisson}(\text{input}_i) + + :attr:`input` must be non-negative. + + Args: + input (Tensor): the input tensor containing the rates of the Poisson distribution + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + + Example:: + + >>> rates = torch.rand(4, 4) * 5 # rate parameter between 0 and 5 + >>> torch.poisson(rates) + tensor([[9., 1., 3., 5.], + [8., 6., 6., 0.], + [0., 4., 5., 3.], + [2., 1., 4., 2.]]) + """ + ... +def poisson_nll_loss(input: Tensor, target: Tensor, log_input: _bool, full: _bool, eps: _float, reduction: _int) -> Tensor: ... +def polar(abs: Tensor, angle: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + polar(abs, angle, *, out=None) -> Tensor + + Constructs a complex tensor whose elements are Cartesian coordinates + corresponding to the polar coordinates with absolute value :attr:`abs` and angle + :attr:`angle`. + + .. math:: + \text{out} = \text{abs} \cdot \cos(\text{angle}) + \text{abs} \cdot \sin(\text{angle}) \cdot j + + .. note:: + `torch.polar` is similar to + `std::polar `_ + and does not compute the polar decomposition + of a complex tensor like Python's `cmath.polar` and SciPy's `linalg.polar` do. + The behavior of this function is undefined if `abs` is negative or NaN, or if `angle` is + infinite. + + + Args: + abs (Tensor): The absolute value the complex tensor. Must be float or double. + angle (Tensor): The angle of the complex tensor. Must be same dtype as + :attr:`abs`. + + Keyword args: + out (Tensor): If the inputs are ``torch.float32``, must be + ``torch.complex64``. If the inputs are ``torch.float64``, must be + ``torch.complex128``. + + Example:: + + >>> import numpy as np + >>> abs = torch.tensor([1, 2], dtype=torch.float64) + >>> angle = torch.tensor([np.pi / 2, 5 * np.pi / 4], dtype=torch.float64) + >>> z = torch.polar(abs, angle) + >>> z + tensor([(0.0000+1.0000j), (-1.4142-1.4142j)], dtype=torch.complex128) + """ + ... +def polygamma(n: _int, input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + polygamma(n, input, *, out=None) -> Tensor + + Alias for :func:`torch.special.polygamma`. + """ + ... +def positive(input: Tensor) -> Tensor: + r""" + positive(input) -> Tensor + + Returns :attr:`input`. + Throws a runtime error if :attr:`input` is a bool tensor. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> t = torch.randn(5) + >>> t + tensor([ 0.0090, -0.2262, -0.0682, -0.2866, 0.3940]) + >>> torch.positive(t) + tensor([ 0.0090, -0.2262, -0.0682, -0.2866, 0.3940]) + """ + ... +@overload +def pow(input: Tensor, exponent: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + pow(input, exponent, *, out=None) -> Tensor + + Takes the power of each element in :attr:`input` with :attr:`exponent` and + returns a tensor with the result. + + :attr:`exponent` can be either a single ``float`` number or a `Tensor` + with the same number of elements as :attr:`input`. + + When :attr:`exponent` is a scalar value, the operation applied is: + + .. math:: + \text{out}_i = x_i ^ \text{exponent} + + When :attr:`exponent` is a tensor, the operation applied is: + + .. math:: + \text{out}_i = x_i ^ {\text{exponent}_i} + + When :attr:`exponent` is a tensor, the shapes of :attr:`input` + and :attr:`exponent` must be :ref:`broadcastable `. + + Args: + input (Tensor): the input tensor. + exponent (float or tensor): the exponent value + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.4331, 1.2475, 0.6834, -0.2791]) + >>> torch.pow(a, 2) + tensor([ 0.1875, 1.5561, 0.4670, 0.0779]) + >>> exp = torch.arange(1., 5.) + + >>> a = torch.arange(1., 5.) + >>> a + tensor([ 1., 2., 3., 4.]) + >>> exp + tensor([ 1., 2., 3., 4.]) + >>> torch.pow(a, exp) + tensor([ 1., 4., 27., 256.]) + + .. function:: pow(self, exponent, *, out=None) -> Tensor + :noindex: + + :attr:`self` is a scalar ``float`` value, and :attr:`exponent` is a tensor. + The returned tensor :attr:`out` is of the same shape as :attr:`exponent` + + The operation applied is: + + .. math:: + \text{out}_i = \text{self} ^ {\text{exponent}_i} + + Args: + self (float): the scalar base value for the power operation + exponent (Tensor): the exponent tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> exp = torch.arange(1., 5.) + >>> base = 2 + >>> torch.pow(base, exp) + tensor([ 2., 4., 8., 16.]) + """ + ... +@overload +def pow(self: Union[Number, _complex], exponent: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + pow(input, exponent, *, out=None) -> Tensor + + Takes the power of each element in :attr:`input` with :attr:`exponent` and + returns a tensor with the result. + + :attr:`exponent` can be either a single ``float`` number or a `Tensor` + with the same number of elements as :attr:`input`. + + When :attr:`exponent` is a scalar value, the operation applied is: + + .. math:: + \text{out}_i = x_i ^ \text{exponent} + + When :attr:`exponent` is a tensor, the operation applied is: + + .. math:: + \text{out}_i = x_i ^ {\text{exponent}_i} + + When :attr:`exponent` is a tensor, the shapes of :attr:`input` + and :attr:`exponent` must be :ref:`broadcastable `. + + Args: + input (Tensor): the input tensor. + exponent (float or tensor): the exponent value + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.4331, 1.2475, 0.6834, -0.2791]) + >>> torch.pow(a, 2) + tensor([ 0.1875, 1.5561, 0.4670, 0.0779]) + >>> exp = torch.arange(1., 5.) + + >>> a = torch.arange(1., 5.) + >>> a + tensor([ 1., 2., 3., 4.]) + >>> exp + tensor([ 1., 2., 3., 4.]) + >>> torch.pow(a, exp) + tensor([ 1., 4., 27., 256.]) + + .. function:: pow(self, exponent, *, out=None) -> Tensor + :noindex: + + :attr:`self` is a scalar ``float`` value, and :attr:`exponent` is a tensor. + The returned tensor :attr:`out` is of the same shape as :attr:`exponent` + + The operation applied is: + + .. math:: + \text{out}_i = \text{self} ^ {\text{exponent}_i} + + Args: + self (float): the scalar base value for the power operation + exponent (Tensor): the exponent tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> exp = torch.arange(1., 5.) + >>> base = 2 + >>> torch.pow(base, exp) + tensor([ 2., 4., 8., 16.]) + """ + ... +@overload +def pow(input: Tensor, exponent: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + pow(input, exponent, *, out=None) -> Tensor + + Takes the power of each element in :attr:`input` with :attr:`exponent` and + returns a tensor with the result. + + :attr:`exponent` can be either a single ``float`` number or a `Tensor` + with the same number of elements as :attr:`input`. + + When :attr:`exponent` is a scalar value, the operation applied is: + + .. math:: + \text{out}_i = x_i ^ \text{exponent} + + When :attr:`exponent` is a tensor, the operation applied is: + + .. math:: + \text{out}_i = x_i ^ {\text{exponent}_i} + + When :attr:`exponent` is a tensor, the shapes of :attr:`input` + and :attr:`exponent` must be :ref:`broadcastable `. + + Args: + input (Tensor): the input tensor. + exponent (float or tensor): the exponent value + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.4331, 1.2475, 0.6834, -0.2791]) + >>> torch.pow(a, 2) + tensor([ 0.1875, 1.5561, 0.4670, 0.0779]) + >>> exp = torch.arange(1., 5.) + + >>> a = torch.arange(1., 5.) + >>> a + tensor([ 1., 2., 3., 4.]) + >>> exp + tensor([ 1., 2., 3., 4.]) + >>> torch.pow(a, exp) + tensor([ 1., 4., 27., 256.]) + + .. function:: pow(self, exponent, *, out=None) -> Tensor + :noindex: + + :attr:`self` is a scalar ``float`` value, and :attr:`exponent` is a tensor. + The returned tensor :attr:`out` is of the same shape as :attr:`exponent` + + The operation applied is: + + .. math:: + \text{out}_i = \text{self} ^ {\text{exponent}_i} + + Args: + self (float): the scalar base value for the power operation + exponent (Tensor): the exponent tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> exp = torch.arange(1., 5.) + >>> base = 2 + >>> torch.pow(base, exp) + tensor([ 2., 4., 8., 16.]) + """ + ... +def prelu(input: Tensor, weight: Tensor) -> Tensor: ... +@overload +def prod(input: Tensor, *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + prod(input, *, dtype=None) -> Tensor + + Returns the product of all elements in the :attr:`input` tensor. + + Args: + input (Tensor): the input tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[-0.8020, 0.5428, -1.5854]]) + >>> torch.prod(a) + tensor(0.6902) + + .. function:: prod(input, dim, keepdim=False, *, dtype=None) -> Tensor + :noindex: + + Returns the product of each row of the :attr:`input` tensor in the given + dimension :attr:`dim`. + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in + the output tensor having 1 fewer dimension than :attr:`input`. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(4, 2) + >>> a + tensor([[ 0.5261, -0.3837], + [ 1.1857, -0.2498], + [-1.1646, 0.0705], + [ 1.1131, -1.0629]]) + >>> torch.prod(a, 1) + tensor([-0.2018, -0.2962, -0.0821, -1.1831]) + """ + ... +@overload +def prod(input: Tensor, dim: _int, keepdim: _bool = False, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + prod(input, *, dtype=None) -> Tensor + + Returns the product of all elements in the :attr:`input` tensor. + + Args: + input (Tensor): the input tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[-0.8020, 0.5428, -1.5854]]) + >>> torch.prod(a) + tensor(0.6902) + + .. function:: prod(input, dim, keepdim=False, *, dtype=None) -> Tensor + :noindex: + + Returns the product of each row of the :attr:`input` tensor in the given + dimension :attr:`dim`. + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in + the output tensor having 1 fewer dimension than :attr:`input`. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(4, 2) + >>> a + tensor([[ 0.5261, -0.3837], + [ 1.1857, -0.2498], + [-1.1646, 0.0705], + [ 1.1131, -1.0629]]) + >>> torch.prod(a, 1) + tensor([-0.2018, -0.2962, -0.0821, -1.1831]) + """ + ... +@overload +def prod(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool = False, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + prod(input, *, dtype=None) -> Tensor + + Returns the product of all elements in the :attr:`input` tensor. + + Args: + input (Tensor): the input tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[-0.8020, 0.5428, -1.5854]]) + >>> torch.prod(a) + tensor(0.6902) + + .. function:: prod(input, dim, keepdim=False, *, dtype=None) -> Tensor + :noindex: + + Returns the product of each row of the :attr:`input` tensor in the given + dimension :attr:`dim`. + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in + the output tensor having 1 fewer dimension than :attr:`input`. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(4, 2) + >>> a + tensor([[ 0.5261, -0.3837], + [ 1.1857, -0.2498], + [-1.1646, 0.0705], + [ 1.1131, -1.0629]]) + >>> torch.prod(a, 1) + tensor([-0.2018, -0.2962, -0.0821, -1.1831]) + """ + ... +def promote_types(type1: _dtype, type2: _dtype) -> _dtype: + r""" + promote_types(type1, type2) -> dtype + + Returns the :class:`torch.dtype` with the smallest size and scalar kind that is + not smaller nor of lower kind than either `type1` or `type2`. See type promotion + :ref:`documentation ` for more information on the type + promotion logic. + + Args: + type1 (:class:`torch.dtype`) + type2 (:class:`torch.dtype`) + + Example:: + + >>> torch.promote_types(torch.int32, torch.float32) + torch.float32 + >>> torch.promote_types(torch.uint8, torch.long) + torch.long + """ + ... +def put(input: Tensor, index: Tensor, source: Tensor, accumulate: _bool = False) -> Tensor: ... +def q_per_channel_axis(input: Tensor) -> _int: ... +def q_per_channel_scales(input: Tensor) -> Tensor: ... +def q_per_channel_zero_points(input: Tensor) -> Tensor: ... +def q_scale(input: Tensor) -> _float: ... +def q_zero_point(input: Tensor) -> _int: ... +def qr(input: Tensor, some: _bool = True, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.qr: + r""" + qr(input, some=True, *, out=None) -> (Tensor, Tensor) + + Computes the QR decomposition of a matrix or a batch of matrices :attr:`input`, + and returns a namedtuple (Q, R) of tensors such that :math:`\text{input} = Q R` + with :math:`Q` being an orthogonal matrix or batch of orthogonal matrices and + :math:`R` being an upper triangular matrix or batch of upper triangular matrices. + + If :attr:`some` is ``True``, then this function returns the thin (reduced) QR factorization. + Otherwise, if :attr:`some` is ``False``, this function returns the complete QR factorization. + + .. warning:: + + :func:`torch.qr` is deprecated in favor of :func:`torch.linalg.qr` + and will be removed in a future PyTorch release. The boolean parameter :attr:`some` has been + replaced with a string parameter :attr:`mode`. + + ``Q, R = torch.qr(A)`` should be replaced with + + .. code:: python + + Q, R = torch.linalg.qr(A) + + ``Q, R = torch.qr(A, some=False)`` should be replaced with + + .. code:: python + + Q, R = torch.linalg.qr(A, mode="complete") + + .. warning:: + If you plan to backpropagate through QR, note that the current backward implementation + is only well-defined when the first :math:`\min(input.size(-1), input.size(-2))` + columns of :attr:`input` are linearly independent. + This behavior will probably change once QR supports pivoting. + + .. note:: This function uses LAPACK for CPU inputs and MAGMA for CUDA inputs, + and may produce different (valid) decompositions on different device types + or different platforms. + + Args: + input (Tensor): the input tensor of size :math:`(*, m, n)` where `*` is zero or more + batch dimensions consisting of matrices of dimension :math:`m \times n`. + some (bool, optional): Set to ``True`` for reduced QR decomposition and ``False`` for + complete QR decomposition. If `k = min(m, n)` then: + + * ``some=True`` : returns `(Q, R)` with dimensions (m, k), (k, n) (default) + + * ``'some=False'``: returns `(Q, R)` with dimensions (m, m), (m, n) + + Keyword args: + out (tuple, optional): tuple of `Q` and `R` tensors. + The dimensions of `Q` and `R` are detailed in the description of :attr:`some` above. + + Example:: + + >>> a = torch.tensor([[12., -51, 4], [6, 167, -68], [-4, 24, -41]]) + >>> q, r = torch.qr(a) + >>> q + tensor([[-0.8571, 0.3943, 0.3314], + [-0.4286, -0.9029, -0.0343], + [ 0.2857, -0.1714, 0.9429]]) + >>> r + tensor([[ -14.0000, -21.0000, 14.0000], + [ 0.0000, -175.0000, 70.0000], + [ 0.0000, 0.0000, -35.0000]]) + >>> torch.mm(q, r).round() + tensor([[ 12., -51., 4.], + [ 6., 167., -68.], + [ -4., 24., -41.]]) + >>> torch.mm(q.t(), q).round() + tensor([[ 1., 0., 0.], + [ 0., 1., -0.], + [ 0., -0., 1.]]) + >>> a = torch.randn(3, 4, 5) + >>> q, r = torch.qr(a, some=False) + >>> torch.allclose(torch.matmul(q, r), a) + True + >>> torch.allclose(torch.matmul(q.mT, q), torch.eye(5)) + True + """ + ... +@overload +def quantile(input: Tensor, q: Tensor, dim: Optional[_int] = None, keepdim: _bool = False, *, interpolation: str = "linear", out: Optional[Tensor] = None) -> Tensor: + r""" + quantile(input, q, dim=None, keepdim=False, *, interpolation='linear', out=None) -> Tensor + + Computes the q-th quantiles of each row of the :attr:`input` tensor along the dimension :attr:`dim`. + + To compute the quantile, we map q in [0, 1] to the range of indices [0, n] to find the location + of the quantile in the sorted input. If the quantile lies between two data points ``a < b`` with + indices ``i`` and ``j`` in the sorted order, result is computed according to the given + :attr:`interpolation` method as follows: + + - ``linear``: ``a + (b - a) * fraction``, where ``fraction`` is the fractional part of the computed quantile index. + - ``lower``: ``a``. + - ``higher``: ``b``. + - ``nearest``: ``a`` or ``b``, whichever's index is closer to the computed quantile index (rounding down for .5 fractions). + - ``midpoint``: ``(a + b) / 2``. + + If :attr:`q` is a 1D tensor, the first dimension of the output represents the quantiles and has size + equal to the size of :attr:`q`, the remaining dimensions are what remains from the reduction. + + .. note:: + By default :attr:`dim` is ``None`` resulting in the :attr:`input` tensor being flattened before computation. + + Args: + input (Tensor): the input tensor. + q (float or Tensor): a scalar or 1D tensor of values in the range [0, 1]. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword arguments: + interpolation (str): interpolation method to use when the desired quantile lies between two data points. + Can be ``linear``, ``lower``, ``higher``, ``midpoint`` and ``nearest``. + Default is ``linear``. + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(2, 3) + >>> a + tensor([[ 0.0795, -1.2117, 0.9765], + [ 1.1707, 0.6706, 0.4884]]) + >>> q = torch.tensor([0.25, 0.5, 0.75]) + >>> torch.quantile(a, q, dim=1, keepdim=True) + tensor([[[-0.5661], + [ 0.5795]], + + [[ 0.0795], + [ 0.6706]], + + [[ 0.5280], + [ 0.9206]]]) + >>> torch.quantile(a, q, dim=1, keepdim=True).shape + torch.Size([3, 2, 1]) + >>> a = torch.arange(4.) + >>> a + tensor([0., 1., 2., 3.]) + >>> torch.quantile(a, 0.6, interpolation='linear') + tensor(1.8000) + >>> torch.quantile(a, 0.6, interpolation='lower') + tensor(1.) + >>> torch.quantile(a, 0.6, interpolation='higher') + tensor(2.) + >>> torch.quantile(a, 0.6, interpolation='midpoint') + tensor(1.5000) + >>> torch.quantile(a, 0.6, interpolation='nearest') + tensor(2.) + >>> torch.quantile(a, 0.4, interpolation='nearest') + tensor(1.) + """ + ... +@overload +def quantile(input: Tensor, q: _float, dim: Optional[_int] = None, keepdim: _bool = False, *, interpolation: str = "linear", out: Optional[Tensor] = None) -> Tensor: + r""" + quantile(input, q, dim=None, keepdim=False, *, interpolation='linear', out=None) -> Tensor + + Computes the q-th quantiles of each row of the :attr:`input` tensor along the dimension :attr:`dim`. + + To compute the quantile, we map q in [0, 1] to the range of indices [0, n] to find the location + of the quantile in the sorted input. If the quantile lies between two data points ``a < b`` with + indices ``i`` and ``j`` in the sorted order, result is computed according to the given + :attr:`interpolation` method as follows: + + - ``linear``: ``a + (b - a) * fraction``, where ``fraction`` is the fractional part of the computed quantile index. + - ``lower``: ``a``. + - ``higher``: ``b``. + - ``nearest``: ``a`` or ``b``, whichever's index is closer to the computed quantile index (rounding down for .5 fractions). + - ``midpoint``: ``(a + b) / 2``. + + If :attr:`q` is a 1D tensor, the first dimension of the output represents the quantiles and has size + equal to the size of :attr:`q`, the remaining dimensions are what remains from the reduction. + + .. note:: + By default :attr:`dim` is ``None`` resulting in the :attr:`input` tensor being flattened before computation. + + Args: + input (Tensor): the input tensor. + q (float or Tensor): a scalar or 1D tensor of values in the range [0, 1]. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword arguments: + interpolation (str): interpolation method to use when the desired quantile lies between two data points. + Can be ``linear``, ``lower``, ``higher``, ``midpoint`` and ``nearest``. + Default is ``linear``. + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(2, 3) + >>> a + tensor([[ 0.0795, -1.2117, 0.9765], + [ 1.1707, 0.6706, 0.4884]]) + >>> q = torch.tensor([0.25, 0.5, 0.75]) + >>> torch.quantile(a, q, dim=1, keepdim=True) + tensor([[[-0.5661], + [ 0.5795]], + + [[ 0.0795], + [ 0.6706]], + + [[ 0.5280], + [ 0.9206]]]) + >>> torch.quantile(a, q, dim=1, keepdim=True).shape + torch.Size([3, 2, 1]) + >>> a = torch.arange(4.) + >>> a + tensor([0., 1., 2., 3.]) + >>> torch.quantile(a, 0.6, interpolation='linear') + tensor(1.8000) + >>> torch.quantile(a, 0.6, interpolation='lower') + tensor(1.) + >>> torch.quantile(a, 0.6, interpolation='higher') + tensor(2.) + >>> torch.quantile(a, 0.6, interpolation='midpoint') + tensor(1.5000) + >>> torch.quantile(a, 0.6, interpolation='nearest') + tensor(2.) + >>> torch.quantile(a, 0.4, interpolation='nearest') + tensor(1.) + """ + ... +def quantize_per_channel(input: Tensor, scales: Tensor, zero_points: Tensor, axis: _int, dtype: _dtype) -> Tensor: + r""" + quantize_per_channel(input, scales, zero_points, axis, dtype) -> Tensor + + Converts a float tensor to a per-channel quantized tensor with given scales and zero points. + + Arguments: + input (Tensor): float tensor to quantize + scales (Tensor): float 1D tensor of scales to use, size should match ``input.size(axis)`` + zero_points (int): integer 1D tensor of offset to use, size should match ``input.size(axis)`` + axis (int): dimension on which apply per-channel quantization + dtype (:class:`torch.dtype`): the desired data type of returned tensor. + Has to be one of the quantized dtypes: ``torch.quint8``, ``torch.qint8``, ``torch.qint32`` + + Returns: + Tensor: A newly quantized tensor + + Example:: + + >>> x = torch.tensor([[-1.0, 0.0], [1.0, 2.0]]) + >>> torch.quantize_per_channel(x, torch.tensor([0.1, 0.01]), torch.tensor([10, 0]), 0, torch.quint8) + tensor([[-1., 0.], + [ 1., 2.]], size=(2, 2), dtype=torch.quint8, + quantization_scheme=torch.per_channel_affine, + scale=tensor([0.1000, 0.0100], dtype=torch.float64), + zero_point=tensor([10, 0]), axis=0) + >>> torch.quantize_per_channel(x, torch.tensor([0.1, 0.01]), torch.tensor([10, 0]), 0, torch.quint8).int_repr() + tensor([[ 0, 10], + [100, 200]], dtype=torch.uint8) + """ + ... +@overload +def quantize_per_tensor(input: Tensor, scale: Tensor, zero_point: Tensor, dtype: _dtype) -> Tensor: + r""" + quantize_per_tensor(input, scale, zero_point, dtype) -> Tensor + + Converts a float tensor to a quantized tensor with given scale and zero point. + + Arguments: + input (Tensor): float tensor or list of tensors to quantize + scale (float or Tensor): scale to apply in quantization formula + zero_point (int or Tensor): offset in integer value that maps to float zero + dtype (:class:`torch.dtype`): the desired data type of returned tensor. + Has to be one of the quantized dtypes: ``torch.quint8``, ``torch.qint8``, ``torch.qint32`` + + Returns: + Tensor: A newly quantized tensor or list of quantized tensors. + + Example:: + + >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8) + tensor([-1., 0., 1., 2.], size=(4,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.1, zero_point=10) + >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8).int_repr() + tensor([ 0, 10, 20, 30], dtype=torch.uint8) + >>> torch.quantize_per_tensor([torch.tensor([-1.0, 0.0]), torch.tensor([-2.0, 2.0])], + >>> torch.tensor([0.1, 0.2]), torch.tensor([10, 20]), torch.quint8) + (tensor([-1., 0.], size=(2,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.1, zero_point=10), + tensor([-2., 2.], size=(2,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.2, zero_point=20)) + >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), torch.tensor(0.1), torch.tensor(10), torch.quint8) + tensor([-1., 0., 1., 2.], size=(4,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.10, zero_point=10) + """ + ... +@overload +def quantize_per_tensor(input: Tensor, scale: _float, zero_point: _int, dtype: _dtype) -> Tensor: + r""" + quantize_per_tensor(input, scale, zero_point, dtype) -> Tensor + + Converts a float tensor to a quantized tensor with given scale and zero point. + + Arguments: + input (Tensor): float tensor or list of tensors to quantize + scale (float or Tensor): scale to apply in quantization formula + zero_point (int or Tensor): offset in integer value that maps to float zero + dtype (:class:`torch.dtype`): the desired data type of returned tensor. + Has to be one of the quantized dtypes: ``torch.quint8``, ``torch.qint8``, ``torch.qint32`` + + Returns: + Tensor: A newly quantized tensor or list of quantized tensors. + + Example:: + + >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8) + tensor([-1., 0., 1., 2.], size=(4,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.1, zero_point=10) + >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8).int_repr() + tensor([ 0, 10, 20, 30], dtype=torch.uint8) + >>> torch.quantize_per_tensor([torch.tensor([-1.0, 0.0]), torch.tensor([-2.0, 2.0])], + >>> torch.tensor([0.1, 0.2]), torch.tensor([10, 20]), torch.quint8) + (tensor([-1., 0.], size=(2,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.1, zero_point=10), + tensor([-2., 2.], size=(2,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.2, zero_point=20)) + >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), torch.tensor(0.1), torch.tensor(10), torch.quint8) + tensor([-1., 0., 1., 2.], size=(4,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.10, zero_point=10) + """ + ... +@overload +def quantize_per_tensor(tensors: Union[Tuple[Tensor, ...], List[Tensor]], scales: Tensor, zero_points: Tensor, dtype: _dtype) -> Tuple[Tensor, ...]: + r""" + quantize_per_tensor(input, scale, zero_point, dtype) -> Tensor + + Converts a float tensor to a quantized tensor with given scale and zero point. + + Arguments: + input (Tensor): float tensor or list of tensors to quantize + scale (float or Tensor): scale to apply in quantization formula + zero_point (int or Tensor): offset in integer value that maps to float zero + dtype (:class:`torch.dtype`): the desired data type of returned tensor. + Has to be one of the quantized dtypes: ``torch.quint8``, ``torch.qint8``, ``torch.qint32`` + + Returns: + Tensor: A newly quantized tensor or list of quantized tensors. + + Example:: + + >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8) + tensor([-1., 0., 1., 2.], size=(4,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.1, zero_point=10) + >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8).int_repr() + tensor([ 0, 10, 20, 30], dtype=torch.uint8) + >>> torch.quantize_per_tensor([torch.tensor([-1.0, 0.0]), torch.tensor([-2.0, 2.0])], + >>> torch.tensor([0.1, 0.2]), torch.tensor([10, 20]), torch.quint8) + (tensor([-1., 0.], size=(2,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.1, zero_point=10), + tensor([-2., 2.], size=(2,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.2, zero_point=20)) + >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), torch.tensor(0.1), torch.tensor(10), torch.quint8) + tensor([-1., 0., 1., 2.], size=(4,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.10, zero_point=10) + """ + ... +def quantize_per_tensor_dynamic(input: Tensor, dtype: _dtype, reduce_range: _bool) -> Tensor: + r""" + quantize_per_tensor_dynamic(input, dtype, reduce_range) -> Tensor + + Converts a float tensor to a quantized tensor with scale and zero_point calculated + dynamically based on the input. + + Arguments: + input (Tensor): float tensor or list of tensors to quantize + dtype (:class:`torch.dtype`): the desired data type of returned tensor. + Has to be one of the quantized dtypes: ``torch.quint8``, ``torch.qint8`` + reduce_range (bool): a flag to indicate whether to reduce the range of quantized + data by 1 bit, it's required to avoid instruction overflow for some hardwares + + Returns: + Tensor: A newly (dynamically) quantized tensor + + Example:: + + >>> t = torch.quantize_per_tensor_dynamic(torch.tensor([-1.0, 0.0, 1.0, 2.0]), torch.quint8, False) + >>> print(t) + tensor([-1., 0., 1., 2.], size=(4,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.011764705882352941, + zero_point=85) + >>> t.int_repr() + tensor([ 0, 85, 170, 255], dtype=torch.uint8) + """ + ... +def quantized_batch_norm(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], mean: Tensor, var: Tensor, eps: _float, output_scale: _float, output_zero_point: _int) -> Tensor: + r""" + quantized_batch_norm(input, weight=None, bias=None, mean, var, eps, output_scale, output_zero_point) -> Tensor + + Applies batch normalization on a 4D (NCHW) quantized tensor. + + .. math:: + + y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta + + Arguments: + input (Tensor): quantized tensor + weight (Tensor): float tensor that corresponds to the gamma, size C + bias (Tensor): float tensor that corresponds to the beta, size C + mean (Tensor): float mean value in batch normalization, size C + var (Tensor): float tensor for variance, size C + eps (float): a value added to the denominator for numerical stability. + output_scale (float): output quantized tensor scale + output_zero_point (int): output quantized tensor zero_point + + Returns: + Tensor: A quantized tensor with batch normalization applied. + + Example:: + + >>> qx = torch.quantize_per_tensor(torch.rand(2, 2, 2, 2), 1.5, 3, torch.quint8) + >>> torch.quantized_batch_norm(qx, torch.ones(2), torch.zeros(2), torch.rand(2), torch.rand(2), 0.00001, 0.2, 2) + tensor([[[[-0.2000, -0.2000], + [ 1.6000, -0.2000]], + + [[-0.4000, -0.4000], + [-0.4000, 0.6000]]], + + + [[[-0.2000, -0.2000], + [-0.2000, -0.2000]], + + [[ 0.6000, -0.4000], + [ 0.6000, -0.4000]]]], size=(2, 2, 2, 2), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.2, zero_point=2) + """ + ... +def quantized_gru_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Tensor, b_hh: Tensor, packed_ih: Tensor, packed_hh: Tensor, col_offsets_ih: Tensor, col_offsets_hh: Tensor, scale_ih: Union[Number, _complex], scale_hh: Union[Number, _complex], zero_point_ih: Union[Number, _complex], zero_point_hh: Union[Number, _complex]) -> Tensor: ... +def quantized_lstm_cell(input: Tensor, hx: Union[Tuple[Tensor, ...], List[Tensor]], w_ih: Tensor, w_hh: Tensor, b_ih: Tensor, b_hh: Tensor, packed_ih: Tensor, packed_hh: Tensor, col_offsets_ih: Tensor, col_offsets_hh: Tensor, scale_ih: Union[Number, _complex], scale_hh: Union[Number, _complex], zero_point_ih: Union[Number, _complex], zero_point_hh: Union[Number, _complex]) -> Tuple[Tensor, Tensor]: ... +def quantized_max_pool1d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size] = (), padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: _bool = False) -> Tensor: + r""" + quantized_max_pool1d(input, kernel_size, stride=[], padding=0, dilation=1, ceil_mode=False) -> Tensor + + Applies a 1D max pooling over an input quantized tensor composed of several input planes. + + Arguments: + input (Tensor): quantized tensor + kernel_size (list of int): the size of the sliding window + stride (``list of int``, optional): the stride of the sliding window + padding (``list of int``, optional): padding to be added on both sides, must be >= 0 and <= kernel_size / 2 + dilation (``list of int``, optional): The stride between elements within a sliding window, must be > 0. Default 1 + ceil_mode (bool, optional): If True, will use ceil instead of floor to compute the output shape. + Defaults to False. + + + Returns: + Tensor: A quantized tensor with max_pool1d applied. + + Example:: + + >>> qx = torch.quantize_per_tensor(torch.rand(2, 2), 1.5, 3, torch.quint8) + >>> torch.quantized_max_pool1d(qx, [2]) + tensor([[0.0000], + [1.5000]], size=(2, 1), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=1.5, zero_point=3) + """ + ... +def quantized_max_pool2d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size] = (), padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: _bool = False) -> Tensor: + r""" + quantized_max_pool2d(input, kernel_size, stride=[], padding=0, dilation=1, ceil_mode=False) -> Tensor + + Applies a 2D max pooling over an input quantized tensor composed of several input planes. + + Arguments: + input (Tensor): quantized tensor + kernel_size (``list of int``): the size of the sliding window + stride (``list of int``, optional): the stride of the sliding window + padding (``list of int``, optional): padding to be added on both sides, must be >= 0 and <= kernel_size / 2 + dilation (``list of int``, optional): The stride between elements within a sliding window, must be > 0. Default 1 + ceil_mode (bool, optional): If True, will use ceil instead of floor to compute the output shape. + Defaults to False. + + + Returns: + Tensor: A quantized tensor with max_pool2d applied. + + Example:: + + >>> qx = torch.quantize_per_tensor(torch.rand(2, 2, 2, 2), 1.5, 3, torch.quint8) + >>> torch.quantized_max_pool2d(qx, [2,2]) + tensor([[[[1.5000]], + + [[1.5000]]], + + + [[[0.0000]], + + [[0.0000]]]], size=(2, 2, 1, 1), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=1.5, zero_point=3) + """ + ... +def quantized_max_pool3d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size] = (), padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: _bool = False) -> Tensor: ... +def quantized_rnn_relu_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Tensor, b_hh: Tensor, packed_ih: Tensor, packed_hh: Tensor, col_offsets_ih: Tensor, col_offsets_hh: Tensor, scale_ih: Union[Number, _complex], scale_hh: Union[Number, _complex], zero_point_ih: Union[Number, _complex], zero_point_hh: Union[Number, _complex]) -> Tensor: ... +def quantized_rnn_tanh_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Tensor, b_hh: Tensor, packed_ih: Tensor, packed_hh: Tensor, col_offsets_ih: Tensor, col_offsets_hh: Tensor, scale_ih: Union[Number, _complex], scale_hh: Union[Number, _complex], zero_point_ih: Union[Number, _complex], zero_point_hh: Union[Number, _complex]) -> Tensor: ... +def rad2deg(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + rad2deg(input, *, out=None) -> Tensor + + Returns a new tensor with each of the elements of :attr:`input` + converted from angles in radians to degrees. + + Args: + input (Tensor): the input tensor. + + Keyword arguments: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([[3.142, -3.142], [6.283, -6.283], [1.570, -1.570]]) + >>> torch.rad2deg(a) + tensor([[ 180.0233, -180.0233], + [ 359.9894, -359.9894], + [ 89.9544, -89.9544]]) + """ + ... +def rad2deg_(input: Tensor) -> Tensor: ... +@overload +def rand(size: Sequence[Union[_int, SymInt]], *, generator: Optional[Generator], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + rand(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Returns a tensor filled with random numbers from a uniform distribution + on the interval :math:`[0, 1)` + + The shape of the tensor is defined by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.rand(4) + tensor([ 0.5204, 0.2503, 0.3525, 0.5673]) + >>> torch.rand(2, 3) + tensor([[ 0.8237, 0.5781, 0.6879], + [ 0.3816, 0.7249, 0.0998]]) + """ + ... +@overload +def rand(*size: Union[_int, SymInt], generator: Optional[Generator], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + rand(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Returns a tensor filled with random numbers from a uniform distribution + on the interval :math:`[0, 1)` + + The shape of the tensor is defined by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.rand(4) + tensor([ 0.5204, 0.2503, 0.3525, 0.5673]) + >>> torch.rand(2, 3) + tensor([[ 0.8237, 0.5781, 0.6879], + [ 0.3816, 0.7249, 0.0998]]) + """ + ... +@overload +def rand(size: Sequence[Union[_int, SymInt]], *, generator: Optional[Generator], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + rand(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Returns a tensor filled with random numbers from a uniform distribution + on the interval :math:`[0, 1)` + + The shape of the tensor is defined by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.rand(4) + tensor([ 0.5204, 0.2503, 0.3525, 0.5673]) + >>> torch.rand(2, 3) + tensor([[ 0.8237, 0.5781, 0.6879], + [ 0.3816, 0.7249, 0.0998]]) + """ + ... +@overload +def rand(*size: Union[_int, SymInt], generator: Optional[Generator], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + rand(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Returns a tensor filled with random numbers from a uniform distribution + on the interval :math:`[0, 1)` + + The shape of the tensor is defined by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.rand(4) + tensor([ 0.5204, 0.2503, 0.3525, 0.5673]) + >>> torch.rand(2, 3) + tensor([[ 0.8237, 0.5781, 0.6879], + [ 0.3816, 0.7249, 0.0998]]) + """ + ... +@overload +def rand(size: Sequence[Union[_int, SymInt]], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + rand(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Returns a tensor filled with random numbers from a uniform distribution + on the interval :math:`[0, 1)` + + The shape of the tensor is defined by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.rand(4) + tensor([ 0.5204, 0.2503, 0.3525, 0.5673]) + >>> torch.rand(2, 3) + tensor([[ 0.8237, 0.5781, 0.6879], + [ 0.3816, 0.7249, 0.0998]]) + """ + ... +@overload +def rand(*size: Union[_int, SymInt], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + rand(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Returns a tensor filled with random numbers from a uniform distribution + on the interval :math:`[0, 1)` + + The shape of the tensor is defined by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.rand(4) + tensor([ 0.5204, 0.2503, 0.3525, 0.5673]) + >>> torch.rand(2, 3) + tensor([[ 0.8237, 0.5781, 0.6879], + [ 0.3816, 0.7249, 0.0998]]) + """ + ... +@overload +def rand(size: Sequence[Union[_int, SymInt]], *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + rand(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Returns a tensor filled with random numbers from a uniform distribution + on the interval :math:`[0, 1)` + + The shape of the tensor is defined by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.rand(4) + tensor([ 0.5204, 0.2503, 0.3525, 0.5673]) + >>> torch.rand(2, 3) + tensor([[ 0.8237, 0.5781, 0.6879], + [ 0.3816, 0.7249, 0.0998]]) + """ + ... +@overload +def rand(*size: Union[_int, SymInt], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + rand(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Returns a tensor filled with random numbers from a uniform distribution + on the interval :math:`[0, 1)` + + The shape of the tensor is defined by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.rand(4) + tensor([ 0.5204, 0.2503, 0.3525, 0.5673]) + >>> torch.rand(2, 3) + tensor([[ 0.8237, 0.5781, 0.6879], + [ 0.3816, 0.7249, 0.0998]]) + """ + ... +def rand_like(input: Tensor, *, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + rand_like(input, *, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor + + Returns a tensor with the same size as :attr:`input` that is filled with + random numbers from a uniform distribution on the interval :math:`[0, 1)`. + ``torch.rand_like(input)`` is equivalent to + ``torch.rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``. + + Args: + input (Tensor): the size of :attr:`input` will determine size of the output tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. + Default: if ``None``, defaults to the dtype of :attr:`input`. + layout (:class:`torch.layout`, optional): the desired layout of returned tensor. + Default: if ``None``, defaults to the layout of :attr:`input`. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, defaults to the device of :attr:`input`. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... +@overload +def randint(low: _int, high: _int, size: _size, *, generator: Optional[Generator] = None, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + randint(low=0, high, size, \*, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with random integers generated uniformly + between :attr:`low` (inclusive) and :attr:`high` (exclusive). + + The shape of the tensor is defined by the variable argument :attr:`size`. + + .. note:: + With the global dtype default (``torch.float32``), this function returns + a tensor with dtype ``torch.int64``. + + Args: + low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. + high (int): One above the highest integer to be drawn from the distribution. + size (tuple): a tuple defining the shape of the output tensor. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (`torch.dtype`, optional) - the desired data type of returned tensor. Default: if ``None``, + this function returns a tensor with dtype ``torch.int64``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.randint(3, 5, (3,)) + tensor([4, 3, 4]) + + + >>> torch.randint(10, (2, 2)) + tensor([[0, 2], + [5, 5]]) + + + >>> torch.randint(3, 10, (2, 2)) + tensor([[4, 5], + [6, 7]]) + """ + ... +@overload +def randint(high: _int, size: _size, *, generator: Optional[Generator] = None, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + randint(low=0, high, size, \*, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with random integers generated uniformly + between :attr:`low` (inclusive) and :attr:`high` (exclusive). + + The shape of the tensor is defined by the variable argument :attr:`size`. + + .. note:: + With the global dtype default (``torch.float32``), this function returns + a tensor with dtype ``torch.int64``. + + Args: + low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. + high (int): One above the highest integer to be drawn from the distribution. + size (tuple): a tuple defining the shape of the output tensor. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (`torch.dtype`, optional) - the desired data type of returned tensor. Default: if ``None``, + this function returns a tensor with dtype ``torch.int64``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.randint(3, 5, (3,)) + tensor([4, 3, 4]) + + + >>> torch.randint(10, (2, 2)) + tensor([[0, 2], + [5, 5]]) + + + >>> torch.randint(3, 10, (2, 2)) + tensor([[4, 5], + [6, 7]]) + """ + ... +@overload +def randint(high: Union[_int, SymInt], size: Sequence[Union[_int, SymInt]], *, generator: Optional[Generator], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randint(low=0, high, size, \*, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with random integers generated uniformly + between :attr:`low` (inclusive) and :attr:`high` (exclusive). + + The shape of the tensor is defined by the variable argument :attr:`size`. + + .. note:: + With the global dtype default (``torch.float32``), this function returns + a tensor with dtype ``torch.int64``. + + Args: + low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. + high (int): One above the highest integer to be drawn from the distribution. + size (tuple): a tuple defining the shape of the output tensor. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (`torch.dtype`, optional) - the desired data type of returned tensor. Default: if ``None``, + this function returns a tensor with dtype ``torch.int64``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.randint(3, 5, (3,)) + tensor([4, 3, 4]) + + + >>> torch.randint(10, (2, 2)) + tensor([[0, 2], + [5, 5]]) + + + >>> torch.randint(3, 10, (2, 2)) + tensor([[4, 5], + [6, 7]]) + """ + ... +@overload +def randint(high: Union[_int, SymInt], size: Sequence[Union[_int, SymInt]], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randint(low=0, high, size, \*, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with random integers generated uniformly + between :attr:`low` (inclusive) and :attr:`high` (exclusive). + + The shape of the tensor is defined by the variable argument :attr:`size`. + + .. note:: + With the global dtype default (``torch.float32``), this function returns + a tensor with dtype ``torch.int64``. + + Args: + low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. + high (int): One above the highest integer to be drawn from the distribution. + size (tuple): a tuple defining the shape of the output tensor. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (`torch.dtype`, optional) - the desired data type of returned tensor. Default: if ``None``, + this function returns a tensor with dtype ``torch.int64``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.randint(3, 5, (3,)) + tensor([4, 3, 4]) + + + >>> torch.randint(10, (2, 2)) + tensor([[0, 2], + [5, 5]]) + + + >>> torch.randint(3, 10, (2, 2)) + tensor([[4, 5], + [6, 7]]) + """ + ... +@overload +def randint(low: Union[_int, SymInt], high: Union[_int, SymInt], size: Sequence[Union[_int, SymInt]], *, generator: Optional[Generator], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randint(low=0, high, size, \*, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with random integers generated uniformly + between :attr:`low` (inclusive) and :attr:`high` (exclusive). + + The shape of the tensor is defined by the variable argument :attr:`size`. + + .. note:: + With the global dtype default (``torch.float32``), this function returns + a tensor with dtype ``torch.int64``. + + Args: + low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. + high (int): One above the highest integer to be drawn from the distribution. + size (tuple): a tuple defining the shape of the output tensor. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (`torch.dtype`, optional) - the desired data type of returned tensor. Default: if ``None``, + this function returns a tensor with dtype ``torch.int64``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.randint(3, 5, (3,)) + tensor([4, 3, 4]) + + + >>> torch.randint(10, (2, 2)) + tensor([[0, 2], + [5, 5]]) + + + >>> torch.randint(3, 10, (2, 2)) + tensor([[4, 5], + [6, 7]]) + """ + ... +@overload +def randint(low: Union[_int, SymInt], high: Union[_int, SymInt], size: Sequence[Union[_int, SymInt]], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randint(low=0, high, size, \*, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with random integers generated uniformly + between :attr:`low` (inclusive) and :attr:`high` (exclusive). + + The shape of the tensor is defined by the variable argument :attr:`size`. + + .. note:: + With the global dtype default (``torch.float32``), this function returns + a tensor with dtype ``torch.int64``. + + Args: + low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. + high (int): One above the highest integer to be drawn from the distribution. + size (tuple): a tuple defining the shape of the output tensor. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (`torch.dtype`, optional) - the desired data type of returned tensor. Default: if ``None``, + this function returns a tensor with dtype ``torch.int64``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.randint(3, 5, (3,)) + tensor([4, 3, 4]) + + + >>> torch.randint(10, (2, 2)) + tensor([[0, 2], + [5, 5]]) + + + >>> torch.randint(3, 10, (2, 2)) + tensor([[4, 5], + [6, 7]]) + """ + ... +@overload +def randint_like(input: Tensor, high: Union[_int, SymInt], *, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randint_like(input, low=0, high, \*, dtype=None, layout=torch.strided, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor + + Returns a tensor with the same shape as Tensor :attr:`input` filled with + random integers generated uniformly between :attr:`low` (inclusive) and + :attr:`high` (exclusive). + + .. note: + With the global dtype default (``torch.float32``), this function returns + a tensor with dtype ``torch.int64``. + + Args: + input (Tensor): the size of :attr:`input` will determine size of the output tensor. + low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. + high (int): One above the highest integer to be drawn from the distribution. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. + Default: if ``None``, defaults to the dtype of :attr:`input`. + layout (:class:`torch.layout`, optional): the desired layout of returned tensor. + Default: if ``None``, defaults to the layout of :attr:`input`. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, defaults to the device of :attr:`input`. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... +@overload +def randint_like(input: Tensor, low: Union[_int, SymInt], high: Union[_int, SymInt], *, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randint_like(input, low=0, high, \*, dtype=None, layout=torch.strided, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor + + Returns a tensor with the same shape as Tensor :attr:`input` filled with + random integers generated uniformly between :attr:`low` (inclusive) and + :attr:`high` (exclusive). + + .. note: + With the global dtype default (``torch.float32``), this function returns + a tensor with dtype ``torch.int64``. + + Args: + input (Tensor): the size of :attr:`input` will determine size of the output tensor. + low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. + high (int): One above the highest integer to be drawn from the distribution. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. + Default: if ``None``, defaults to the dtype of :attr:`input`. + layout (:class:`torch.layout`, optional): the desired layout of returned tensor. + Default: if ``None``, defaults to the layout of :attr:`input`. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, defaults to the device of :attr:`input`. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... +@overload +def randn(size: Sequence[Union[_int, SymInt]], *, generator: Optional[Generator], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randn(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + + Returns a tensor filled with random numbers from a normal distribution + with mean `0` and variance `1` (also called the standard normal + distribution). + + .. math:: + \text{out}_{i} \sim \mathcal{N}(0, 1) + + For complex dtypes, the tensor is i.i.d. sampled from a `complex normal distribution`_ with zero mean and + unit variance as + + .. math:: + \text{out}_{i} \sim \mathcal{CN}(0, 1) + + This is equivalent to separately sampling the real :math:`(\operatorname{Re})` and imaginary + :math:`(\operatorname{Im})` part of :math:`\text{out}_i` as + + .. math:: + \operatorname{Re}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}),\quad + \operatorname{Im}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}) + + The shape of the tensor is defined by the variable argument :attr:`size`. + + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.randn(4) + tensor([-2.1436, 0.9966, 2.3426, -0.6366]) + >>> torch.randn(2, 3) + tensor([[ 1.5954, 2.8929, -1.0923], + [ 1.1719, -0.4709, -0.1996]]) + + .. _complex normal distribution: https://en.wikipedia.org/wiki/Complex_normal_distribution + """ + ... +@overload +def randn(*size: Union[_int, SymInt], generator: Optional[Generator], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randn(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + + Returns a tensor filled with random numbers from a normal distribution + with mean `0` and variance `1` (also called the standard normal + distribution). + + .. math:: + \text{out}_{i} \sim \mathcal{N}(0, 1) + + For complex dtypes, the tensor is i.i.d. sampled from a `complex normal distribution`_ with zero mean and + unit variance as + + .. math:: + \text{out}_{i} \sim \mathcal{CN}(0, 1) + + This is equivalent to separately sampling the real :math:`(\operatorname{Re})` and imaginary + :math:`(\operatorname{Im})` part of :math:`\text{out}_i` as + + .. math:: + \operatorname{Re}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}),\quad + \operatorname{Im}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}) + + The shape of the tensor is defined by the variable argument :attr:`size`. + + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.randn(4) + tensor([-2.1436, 0.9966, 2.3426, -0.6366]) + >>> torch.randn(2, 3) + tensor([[ 1.5954, 2.8929, -1.0923], + [ 1.1719, -0.4709, -0.1996]]) + + .. _complex normal distribution: https://en.wikipedia.org/wiki/Complex_normal_distribution + """ + ... +@overload +def randn(size: Sequence[Union[_int, SymInt]], *, generator: Optional[Generator], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randn(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + + Returns a tensor filled with random numbers from a normal distribution + with mean `0` and variance `1` (also called the standard normal + distribution). + + .. math:: + \text{out}_{i} \sim \mathcal{N}(0, 1) + + For complex dtypes, the tensor is i.i.d. sampled from a `complex normal distribution`_ with zero mean and + unit variance as + + .. math:: + \text{out}_{i} \sim \mathcal{CN}(0, 1) + + This is equivalent to separately sampling the real :math:`(\operatorname{Re})` and imaginary + :math:`(\operatorname{Im})` part of :math:`\text{out}_i` as + + .. math:: + \operatorname{Re}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}),\quad + \operatorname{Im}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}) + + The shape of the tensor is defined by the variable argument :attr:`size`. + + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.randn(4) + tensor([-2.1436, 0.9966, 2.3426, -0.6366]) + >>> torch.randn(2, 3) + tensor([[ 1.5954, 2.8929, -1.0923], + [ 1.1719, -0.4709, -0.1996]]) + + .. _complex normal distribution: https://en.wikipedia.org/wiki/Complex_normal_distribution + """ + ... +@overload +def randn(*size: Union[_int, SymInt], generator: Optional[Generator], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randn(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + + Returns a tensor filled with random numbers from a normal distribution + with mean `0` and variance `1` (also called the standard normal + distribution). + + .. math:: + \text{out}_{i} \sim \mathcal{N}(0, 1) + + For complex dtypes, the tensor is i.i.d. sampled from a `complex normal distribution`_ with zero mean and + unit variance as + + .. math:: + \text{out}_{i} \sim \mathcal{CN}(0, 1) + + This is equivalent to separately sampling the real :math:`(\operatorname{Re})` and imaginary + :math:`(\operatorname{Im})` part of :math:`\text{out}_i` as + + .. math:: + \operatorname{Re}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}),\quad + \operatorname{Im}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}) + + The shape of the tensor is defined by the variable argument :attr:`size`. + + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.randn(4) + tensor([-2.1436, 0.9966, 2.3426, -0.6366]) + >>> torch.randn(2, 3) + tensor([[ 1.5954, 2.8929, -1.0923], + [ 1.1719, -0.4709, -0.1996]]) + + .. _complex normal distribution: https://en.wikipedia.org/wiki/Complex_normal_distribution + """ + ... +@overload +def randn(size: Sequence[Union[_int, SymInt]], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randn(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + + Returns a tensor filled with random numbers from a normal distribution + with mean `0` and variance `1` (also called the standard normal + distribution). + + .. math:: + \text{out}_{i} \sim \mathcal{N}(0, 1) + + For complex dtypes, the tensor is i.i.d. sampled from a `complex normal distribution`_ with zero mean and + unit variance as + + .. math:: + \text{out}_{i} \sim \mathcal{CN}(0, 1) + + This is equivalent to separately sampling the real :math:`(\operatorname{Re})` and imaginary + :math:`(\operatorname{Im})` part of :math:`\text{out}_i` as + + .. math:: + \operatorname{Re}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}),\quad + \operatorname{Im}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}) + + The shape of the tensor is defined by the variable argument :attr:`size`. + + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.randn(4) + tensor([-2.1436, 0.9966, 2.3426, -0.6366]) + >>> torch.randn(2, 3) + tensor([[ 1.5954, 2.8929, -1.0923], + [ 1.1719, -0.4709, -0.1996]]) + + .. _complex normal distribution: https://en.wikipedia.org/wiki/Complex_normal_distribution + """ + ... +@overload +def randn(*size: Union[_int, SymInt], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randn(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + + Returns a tensor filled with random numbers from a normal distribution + with mean `0` and variance `1` (also called the standard normal + distribution). + + .. math:: + \text{out}_{i} \sim \mathcal{N}(0, 1) + + For complex dtypes, the tensor is i.i.d. sampled from a `complex normal distribution`_ with zero mean and + unit variance as + + .. math:: + \text{out}_{i} \sim \mathcal{CN}(0, 1) + + This is equivalent to separately sampling the real :math:`(\operatorname{Re})` and imaginary + :math:`(\operatorname{Im})` part of :math:`\text{out}_i` as + + .. math:: + \operatorname{Re}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}),\quad + \operatorname{Im}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}) + + The shape of the tensor is defined by the variable argument :attr:`size`. + + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.randn(4) + tensor([-2.1436, 0.9966, 2.3426, -0.6366]) + >>> torch.randn(2, 3) + tensor([[ 1.5954, 2.8929, -1.0923], + [ 1.1719, -0.4709, -0.1996]]) + + .. _complex normal distribution: https://en.wikipedia.org/wiki/Complex_normal_distribution + """ + ... +@overload +def randn(size: Sequence[Union[_int, SymInt]], *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randn(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + + Returns a tensor filled with random numbers from a normal distribution + with mean `0` and variance `1` (also called the standard normal + distribution). + + .. math:: + \text{out}_{i} \sim \mathcal{N}(0, 1) + + For complex dtypes, the tensor is i.i.d. sampled from a `complex normal distribution`_ with zero mean and + unit variance as + + .. math:: + \text{out}_{i} \sim \mathcal{CN}(0, 1) + + This is equivalent to separately sampling the real :math:`(\operatorname{Re})` and imaginary + :math:`(\operatorname{Im})` part of :math:`\text{out}_i` as + + .. math:: + \operatorname{Re}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}),\quad + \operatorname{Im}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}) + + The shape of the tensor is defined by the variable argument :attr:`size`. + + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.randn(4) + tensor([-2.1436, 0.9966, 2.3426, -0.6366]) + >>> torch.randn(2, 3) + tensor([[ 1.5954, 2.8929, -1.0923], + [ 1.1719, -0.4709, -0.1996]]) + + .. _complex normal distribution: https://en.wikipedia.org/wiki/Complex_normal_distribution + """ + ... +@overload +def randn(*size: Union[_int, SymInt], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randn(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + + Returns a tensor filled with random numbers from a normal distribution + with mean `0` and variance `1` (also called the standard normal + distribution). + + .. math:: + \text{out}_{i} \sim \mathcal{N}(0, 1) + + For complex dtypes, the tensor is i.i.d. sampled from a `complex normal distribution`_ with zero mean and + unit variance as + + .. math:: + \text{out}_{i} \sim \mathcal{CN}(0, 1) + + This is equivalent to separately sampling the real :math:`(\operatorname{Re})` and imaginary + :math:`(\operatorname{Im})` part of :math:`\text{out}_i` as + + .. math:: + \operatorname{Re}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}),\quad + \operatorname{Im}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}) + + The shape of the tensor is defined by the variable argument :attr:`size`. + + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.randn(4) + tensor([-2.1436, 0.9966, 2.3426, -0.6366]) + >>> torch.randn(2, 3) + tensor([[ 1.5954, 2.8929, -1.0923], + [ 1.1719, -0.4709, -0.1996]]) + + .. _complex normal distribution: https://en.wikipedia.org/wiki/Complex_normal_distribution + """ + ... +def randn_like(input: Tensor, *, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randn_like(input, *, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor + + Returns a tensor with the same size as :attr:`input` that is filled with + random numbers from a normal distribution with mean 0 and variance 1. Please refer to :func:`torch.randn` for the + sampling process of complex dtypes. ``torch.randn_like(input)`` is equivalent to + ``torch.randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``. + + Args: + input (Tensor): the size of :attr:`input` will determine size of the output tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. + Default: if ``None``, defaults to the dtype of :attr:`input`. + layout (:class:`torch.layout`, optional): the desired layout of returned tensor. + Default: if ``None``, defaults to the layout of :attr:`input`. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, defaults to the device of :attr:`input`. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... +@overload +def randperm(n: Union[_int, SymInt], *, generator: Optional[Generator], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randperm(n, *, generator=None, out=None, dtype=torch.int64,layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Returns a random permutation of integers from ``0`` to ``n - 1``. + + Args: + n (int): the upper bound (exclusive) + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: ``torch.int64``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.randperm(4) + tensor([2, 1, 0, 3]) + """ + ... +@overload +def randperm(n: Union[_int, SymInt], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randperm(n, *, generator=None, out=None, dtype=torch.int64,layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Returns a random permutation of integers from ``0`` to ``n - 1``. + + Args: + n (int): the upper bound (exclusive) + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: ``torch.int64``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.randperm(4) + tensor([2, 1, 0, 3]) + """ + ... +def range(start: Number, end: Number, step: Number = 1, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + range(start=0, end, step=1, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a 1-D tensor of size :math:`\left\lfloor \frac{\text{end} - \text{start}}{\text{step}} \right\rfloor + 1` + with values from :attr:`start` to :attr:`end` with step :attr:`step`. Step is + the gap between two values in the tensor. + + .. math:: + \text{out}_{i+1} = \text{out}_i + \text{step}. + + .. warning:: + This function is deprecated and will be removed in a future release because its behavior is inconsistent with + Python's range builtin. Instead, use :func:`torch.arange`, which produces values in [start, end). + + Args: + start (float): the starting value for the set of points. Default: ``0``. + end (float): the ending value for the set of points + step (float): the gap between each pair of adjacent points. Default: ``1``. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). If `dtype` is not given, infer the data type from the other input + arguments. If any of `start`, `end`, or `step` are floating-point, the + `dtype` is inferred to be the default dtype, see + :meth:`~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to + be `torch.int64`. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.range(1, 4) + tensor([ 1., 2., 3., 4.]) + >>> torch.range(1, 4, 0.5) + tensor([ 1.0000, 1.5000, 2.0000, 2.5000, 3.0000, 3.5000, 4.0000]) + """ + ... +def ravel(input: Tensor) -> Tensor: + r""" + ravel(input) -> Tensor + + Return a contiguous flattened tensor. A copy is made only if needed. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> t = torch.tensor([[[1, 2], + ... [3, 4]], + ... [[5, 6], + ... [7, 8]]]) + >>> torch.ravel(t) + tensor([1, 2, 3, 4, 5, 6, 7, 8]) + """ + ... +def real(input: Tensor) -> Tensor: + r""" + real(input) -> Tensor + + Returns a new tensor containing real values of the :attr:`self` tensor. + The returned tensor and :attr:`self` share the same underlying storage. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> x=torch.randn(4, dtype=torch.cfloat) + >>> x + tensor([(0.3100+0.3553j), (-0.5445-0.7896j), (-1.6492-0.0633j), (-0.0638-0.8119j)]) + >>> x.real + tensor([ 0.3100, -0.5445, -1.6492, -0.0638]) + """ + ... +def reciprocal(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + reciprocal(input, *, out=None) -> Tensor + + Returns a new tensor with the reciprocal of the elements of :attr:`input` + + .. math:: + \text{out}_{i} = \frac{1}{\text{input}_{i}} + + .. note:: + Unlike NumPy's reciprocal, torch.reciprocal supports integral inputs. Integral + inputs to reciprocal are automatically :ref:`promoted ` to + the default scalar type. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-0.4595, -2.1219, -1.4314, 0.7298]) + >>> torch.reciprocal(a) + tensor([-2.1763, -0.4713, -0.6986, 1.3702]) + """ + ... +def reciprocal_(input: Tensor) -> Tensor: ... +def relu(input: Tensor) -> Tensor: ... +def relu_(input: Tensor) -> Tensor: ... +@overload +def remainder(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + remainder(input, other, *, out=None) -> Tensor + + Computes + `Python's modulus operation `_ + entrywise. The result has the same sign as the divisor :attr:`other` and its absolute value + is less than that of :attr:`other`. + + It may also be defined in terms of :func:`torch.div` as + + .. code:: python + + torch.remainder(a, b) == a - a.div(b, rounding_mode="floor") * b + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer and float inputs. + + .. note:: + Complex inputs are not supported. In some cases, it is not mathematically + possible to satisfy the definition of a modulo operation with complex numbers. + See :func:`torch.fmod` for how division by zero is handled. + + .. seealso:: + + :func:`torch.fmod` which implements C++'s `std::fmod `_. + This one is defined in terms of division rounding towards zero. + + Args: + input (Tensor or Scalar): the dividend + other (Tensor or Scalar): the divisor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.remainder(torch.tensor([-3., -2, -1, 1, 2, 3]), 2) + tensor([ 1., 0., 1., 1., 0., 1.]) + >>> torch.remainder(torch.tensor([1, 2, 3, 4, 5]), -1.5) + tensor([ -0.5000, -1.0000, 0.0000, -0.5000, -1.0000 ]) + """ + ... +@overload +def remainder(self: Union[Number, _complex], other: Tensor) -> Tensor: + r""" + remainder(input, other, *, out=None) -> Tensor + + Computes + `Python's modulus operation `_ + entrywise. The result has the same sign as the divisor :attr:`other` and its absolute value + is less than that of :attr:`other`. + + It may also be defined in terms of :func:`torch.div` as + + .. code:: python + + torch.remainder(a, b) == a - a.div(b, rounding_mode="floor") * b + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer and float inputs. + + .. note:: + Complex inputs are not supported. In some cases, it is not mathematically + possible to satisfy the definition of a modulo operation with complex numbers. + See :func:`torch.fmod` for how division by zero is handled. + + .. seealso:: + + :func:`torch.fmod` which implements C++'s `std::fmod `_. + This one is defined in terms of division rounding towards zero. + + Args: + input (Tensor or Scalar): the dividend + other (Tensor or Scalar): the divisor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.remainder(torch.tensor([-3., -2, -1, 1, 2, 3]), 2) + tensor([ 1., 0., 1., 1., 0., 1.]) + >>> torch.remainder(torch.tensor([1, 2, 3, 4, 5]), -1.5) + tensor([ -0.5000, -1.0000, 0.0000, -0.5000, -1.0000 ]) + """ + ... +@overload +def remainder(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + remainder(input, other, *, out=None) -> Tensor + + Computes + `Python's modulus operation `_ + entrywise. The result has the same sign as the divisor :attr:`other` and its absolute value + is less than that of :attr:`other`. + + It may also be defined in terms of :func:`torch.div` as + + .. code:: python + + torch.remainder(a, b) == a - a.div(b, rounding_mode="floor") * b + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer and float inputs. + + .. note:: + Complex inputs are not supported. In some cases, it is not mathematically + possible to satisfy the definition of a modulo operation with complex numbers. + See :func:`torch.fmod` for how division by zero is handled. + + .. seealso:: + + :func:`torch.fmod` which implements C++'s `std::fmod `_. + This one is defined in terms of division rounding towards zero. + + Args: + input (Tensor or Scalar): the dividend + other (Tensor or Scalar): the divisor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.remainder(torch.tensor([-3., -2, -1, 1, 2, 3]), 2) + tensor([ 1., 0., 1., 1., 0., 1.]) + >>> torch.remainder(torch.tensor([1, 2, 3, 4, 5]), -1.5) + tensor([ -0.5000, -1.0000, 0.0000, -0.5000, -1.0000 ]) + """ + ... +def renorm(input: Tensor, p: Union[Number, _complex], dim: _int, maxnorm: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + renorm(input, p, dim, maxnorm, *, out=None) -> Tensor + + Returns a tensor where each sub-tensor of :attr:`input` along dimension + :attr:`dim` is normalized such that the `p`-norm of the sub-tensor is lower + than the value :attr:`maxnorm` + + .. note:: If the norm of a row is lower than `maxnorm`, the row is unchanged + + Args: + input (Tensor): the input tensor. + p (float): the power for the norm computation + dim (int): the dimension to slice over to get the sub-tensors + maxnorm (float): the maximum norm to keep each sub-tensor under + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> x = torch.ones(3, 3) + >>> x[1].fill_(2) + tensor([ 2., 2., 2.]) + >>> x[2].fill_(3) + tensor([ 3., 3., 3.]) + >>> x + tensor([[ 1., 1., 1.], + [ 2., 2., 2.], + [ 3., 3., 3.]]) + >>> torch.renorm(x, 1, 0, 5) + tensor([[ 1.0000, 1.0000, 1.0000], + [ 1.6667, 1.6667, 1.6667], + [ 1.6667, 1.6667, 1.6667]]) + """ + ... +@overload +def repeat_interleave(input: Tensor, repeats: Tensor, dim: Optional[_int] = None, *, output_size: Optional[Union[_int, SymInt]] = None) -> Tensor: + r""" + repeat_interleave(input, repeats, dim=None, *, output_size=None) -> Tensor + + Repeat elements of a tensor. + + .. warning:: + + This is different from :meth:`torch.Tensor.repeat` but similar to ``numpy.repeat``. + + Args: + input (Tensor): the input tensor. + repeats (Tensor or int): The number of repetitions for each element. + repeats is broadcasted to fit the shape of the given axis. + dim (int, optional): The dimension along which to repeat values. + By default, use the flattened input array, and return a flat output + array. + + Keyword args: + output_size (int, optional): Total output size for the given axis + ( e.g. sum of repeats). If given, it will avoid stream synchronization + needed to calculate output shape of the tensor. + + Returns: + Tensor: Repeated tensor which has the same shape as input, except along the given axis. + + Example:: + + >>> x = torch.tensor([1, 2, 3]) + >>> x.repeat_interleave(2) + tensor([1, 1, 2, 2, 3, 3]) + >>> y = torch.tensor([[1, 2], [3, 4]]) + >>> torch.repeat_interleave(y, 2) + tensor([1, 1, 2, 2, 3, 3, 4, 4]) + >>> torch.repeat_interleave(y, 3, dim=1) + tensor([[1, 1, 1, 2, 2, 2], + [3, 3, 3, 4, 4, 4]]) + >>> torch.repeat_interleave(y, torch.tensor([1, 2]), dim=0) + tensor([[1, 2], + [3, 4], + [3, 4]]) + >>> torch.repeat_interleave(y, torch.tensor([1, 2]), dim=0, output_size=3) + tensor([[1, 2], + [3, 4], + [3, 4]]) + + If the `repeats` is `tensor([n1, n2, n3, ...])`, then the output will be + `tensor([0, 0, ..., 1, 1, ..., 2, 2, ..., ...])` where `0` appears `n1` times, + `1` appears `n2` times, `2` appears `n3` times, etc. + + .. function:: repeat_interleave(repeats, *) -> Tensor + :noindex: + + Repeats 0 repeats[0] times, 1 repeats[1] times, 2 repeats[2] times, etc. + + Args: + repeats (Tensor): The number of repetitions for each element. + + Returns: + Tensor: Repeated tensor of size `sum(repeats)`. + + Example:: + + >>> torch.repeat_interleave(torch.tensor([1, 2, 3])) + tensor([0, 1, 1, 2, 2, 2]) + """ + ... +@overload +def repeat_interleave(repeats: Tensor, *, output_size: Optional[Union[_int, SymInt]] = None) -> Tensor: + r""" + repeat_interleave(input, repeats, dim=None, *, output_size=None) -> Tensor + + Repeat elements of a tensor. + + .. warning:: + + This is different from :meth:`torch.Tensor.repeat` but similar to ``numpy.repeat``. + + Args: + input (Tensor): the input tensor. + repeats (Tensor or int): The number of repetitions for each element. + repeats is broadcasted to fit the shape of the given axis. + dim (int, optional): The dimension along which to repeat values. + By default, use the flattened input array, and return a flat output + array. + + Keyword args: + output_size (int, optional): Total output size for the given axis + ( e.g. sum of repeats). If given, it will avoid stream synchronization + needed to calculate output shape of the tensor. + + Returns: + Tensor: Repeated tensor which has the same shape as input, except along the given axis. + + Example:: + + >>> x = torch.tensor([1, 2, 3]) + >>> x.repeat_interleave(2) + tensor([1, 1, 2, 2, 3, 3]) + >>> y = torch.tensor([[1, 2], [3, 4]]) + >>> torch.repeat_interleave(y, 2) + tensor([1, 1, 2, 2, 3, 3, 4, 4]) + >>> torch.repeat_interleave(y, 3, dim=1) + tensor([[1, 1, 1, 2, 2, 2], + [3, 3, 3, 4, 4, 4]]) + >>> torch.repeat_interleave(y, torch.tensor([1, 2]), dim=0) + tensor([[1, 2], + [3, 4], + [3, 4]]) + >>> torch.repeat_interleave(y, torch.tensor([1, 2]), dim=0, output_size=3) + tensor([[1, 2], + [3, 4], + [3, 4]]) + + If the `repeats` is `tensor([n1, n2, n3, ...])`, then the output will be + `tensor([0, 0, ..., 1, 1, ..., 2, 2, ..., ...])` where `0` appears `n1` times, + `1` appears `n2` times, `2` appears `n3` times, etc. + + .. function:: repeat_interleave(repeats, *) -> Tensor + :noindex: + + Repeats 0 repeats[0] times, 1 repeats[1] times, 2 repeats[2] times, etc. + + Args: + repeats (Tensor): The number of repetitions for each element. + + Returns: + Tensor: Repeated tensor of size `sum(repeats)`. + + Example:: + + >>> torch.repeat_interleave(torch.tensor([1, 2, 3])) + tensor([0, 1, 1, 2, 2, 2]) + """ + ... +@overload +def repeat_interleave(input: Tensor, repeats: Union[_int, SymInt], dim: Optional[_int] = None, *, output_size: Optional[Union[_int, SymInt]] = None) -> Tensor: + r""" + repeat_interleave(input, repeats, dim=None, *, output_size=None) -> Tensor + + Repeat elements of a tensor. + + .. warning:: + + This is different from :meth:`torch.Tensor.repeat` but similar to ``numpy.repeat``. + + Args: + input (Tensor): the input tensor. + repeats (Tensor or int): The number of repetitions for each element. + repeats is broadcasted to fit the shape of the given axis. + dim (int, optional): The dimension along which to repeat values. + By default, use the flattened input array, and return a flat output + array. + + Keyword args: + output_size (int, optional): Total output size for the given axis + ( e.g. sum of repeats). If given, it will avoid stream synchronization + needed to calculate output shape of the tensor. + + Returns: + Tensor: Repeated tensor which has the same shape as input, except along the given axis. + + Example:: + + >>> x = torch.tensor([1, 2, 3]) + >>> x.repeat_interleave(2) + tensor([1, 1, 2, 2, 3, 3]) + >>> y = torch.tensor([[1, 2], [3, 4]]) + >>> torch.repeat_interleave(y, 2) + tensor([1, 1, 2, 2, 3, 3, 4, 4]) + >>> torch.repeat_interleave(y, 3, dim=1) + tensor([[1, 1, 1, 2, 2, 2], + [3, 3, 3, 4, 4, 4]]) + >>> torch.repeat_interleave(y, torch.tensor([1, 2]), dim=0) + tensor([[1, 2], + [3, 4], + [3, 4]]) + >>> torch.repeat_interleave(y, torch.tensor([1, 2]), dim=0, output_size=3) + tensor([[1, 2], + [3, 4], + [3, 4]]) + + If the `repeats` is `tensor([n1, n2, n3, ...])`, then the output will be + `tensor([0, 0, ..., 1, 1, ..., 2, 2, ..., ...])` where `0` appears `n1` times, + `1` appears `n2` times, `2` appears `n3` times, etc. + + .. function:: repeat_interleave(repeats, *) -> Tensor + :noindex: + + Repeats 0 repeats[0] times, 1 repeats[1] times, 2 repeats[2] times, etc. + + Args: + repeats (Tensor): The number of repetitions for each element. + + Returns: + Tensor: Repeated tensor of size `sum(repeats)`. + + Example:: + + >>> torch.repeat_interleave(torch.tensor([1, 2, 3])) + tensor([0, 1, 1, 2, 2, 2]) + """ + ... +def reshape(input: Tensor, shape: Sequence[Union[_int, SymInt]]) -> Tensor: + r""" + reshape(input, shape) -> Tensor + + Returns a tensor with the same data and number of elements as :attr:`input`, + but with the specified shape. When possible, the returned tensor will be a view + of :attr:`input`. Otherwise, it will be a copy. Contiguous inputs and inputs + with compatible strides can be reshaped without copying, but you should not + depend on the copying vs. viewing behavior. + + See :meth:`torch.Tensor.view` on when it is possible to return a view. + + A single dimension may be -1, in which case it's inferred from the remaining + dimensions and the number of elements in :attr:`input`. + + Args: + input (Tensor): the tensor to be reshaped + shape (tuple of int): the new shape + + Example:: + + >>> a = torch.arange(4.) + >>> torch.reshape(a, (2, 2)) + tensor([[ 0., 1.], + [ 2., 3.]]) + >>> b = torch.tensor([[0, 1], [2, 3]]) + >>> torch.reshape(b, (-1,)) + tensor([ 0, 1, 2, 3]) + """ + ... +def resize_as_(input: Tensor, the_template: Tensor, *, memory_format: Optional[memory_format] = None) -> Tensor: ... +def resize_as_sparse_(input: Tensor, the_template: Tensor) -> Tensor: ... +def resolve_conj(input: Tensor) -> Tensor: + r""" + resolve_conj(input) -> Tensor + + Returns a new tensor with materialized conjugation if :attr:`input`'s conjugate bit is set to `True`, + else returns :attr:`input`. The output tensor will always have its conjugate bit set to `False`. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> x = torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j]) + >>> y = x.conj() + >>> y.is_conj() + True + >>> z = y.resolve_conj() + >>> z + tensor([-1 - 1j, -2 - 2j, 3 + 3j]) + >>> z.is_conj() + False + """ + ... +def resolve_neg(input: Tensor) -> Tensor: + r""" + resolve_neg(input) -> Tensor + + Returns a new tensor with materialized negation if :attr:`input`'s negative bit is set to `True`, + else returns :attr:`input`. The output tensor will always have its negative bit set to `False`. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> x = torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j]) + >>> y = x.conj() + >>> z = y.imag + >>> z.is_neg() + True + >>> out = z.resolve_neg() + >>> out + tensor([-1., -2., 3.]) + >>> out.is_neg() + False + """ + ... +@overload +def result_type(tensor: Tensor, other: Tensor) -> _dtype: + r""" + result_type(tensor1, tensor2) -> dtype + + Returns the :class:`torch.dtype` that would result from performing an arithmetic + operation on the provided input tensors. See type promotion :ref:`documentation ` + for more information on the type promotion logic. + + Args: + tensor1 (Tensor or Number): an input tensor or number + tensor2 (Tensor or Number): an input tensor or number + + Example:: + + >>> torch.result_type(torch.tensor([1, 2], dtype=torch.int), 1.0) + torch.float32 + >>> torch.result_type(torch.tensor([1, 2], dtype=torch.uint8), torch.tensor(1)) + torch.uint8 + """ + ... +@overload +def result_type(scalar: Union[Number, _complex], tensor: Tensor) -> _dtype: + r""" + result_type(tensor1, tensor2) -> dtype + + Returns the :class:`torch.dtype` that would result from performing an arithmetic + operation on the provided input tensors. See type promotion :ref:`documentation ` + for more information on the type promotion logic. + + Args: + tensor1 (Tensor or Number): an input tensor or number + tensor2 (Tensor or Number): an input tensor or number + + Example:: + + >>> torch.result_type(torch.tensor([1, 2], dtype=torch.int), 1.0) + torch.float32 + >>> torch.result_type(torch.tensor([1, 2], dtype=torch.uint8), torch.tensor(1)) + torch.uint8 + """ + ... +@overload +def result_type(tensor: Tensor, other: Union[Number, _complex]) -> _dtype: + r""" + result_type(tensor1, tensor2) -> dtype + + Returns the :class:`torch.dtype` that would result from performing an arithmetic + operation on the provided input tensors. See type promotion :ref:`documentation ` + for more information on the type promotion logic. + + Args: + tensor1 (Tensor or Number): an input tensor or number + tensor2 (Tensor or Number): an input tensor or number + + Example:: + + >>> torch.result_type(torch.tensor([1, 2], dtype=torch.int), 1.0) + torch.float32 + >>> torch.result_type(torch.tensor([1, 2], dtype=torch.uint8), torch.tensor(1)) + torch.uint8 + """ + ... +@overload +def result_type(scalar1: Union[Number, _complex], scalar2: Union[Number, _complex]) -> _dtype: + r""" + result_type(tensor1, tensor2) -> dtype + + Returns the :class:`torch.dtype` that would result from performing an arithmetic + operation on the provided input tensors. See type promotion :ref:`documentation ` + for more information on the type promotion logic. + + Args: + tensor1 (Tensor or Number): an input tensor or number + tensor2 (Tensor or Number): an input tensor or number + + Example:: + + >>> torch.result_type(torch.tensor([1, 2], dtype=torch.int), 1.0) + torch.float32 + >>> torch.result_type(torch.tensor([1, 2], dtype=torch.uint8), torch.tensor(1)) + torch.uint8 + """ + ... +def rms_norm(input: Tensor, normalized_shape: _size, weight: Optional[Tensor] = None, eps: Optional[_float] = None) -> Tensor: ... +@overload +def rnn_relu(data: Tensor, batch_sizes: Tensor, hx: Tensor, params: Union[Tuple[Tensor, ...], List[Tensor]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool) -> Tuple[Tensor, Tensor]: ... +@overload +def rnn_relu(input: Tensor, hx: Tensor, params: Union[Tuple[Tensor, ...], List[Tensor]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool, batch_first: _bool) -> Tuple[Tensor, Tensor]: ... +def rnn_relu_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Optional[Tensor] = None, b_hh: Optional[Tensor] = None) -> Tensor: ... +@overload +def rnn_tanh(data: Tensor, batch_sizes: Tensor, hx: Tensor, params: Union[Tuple[Tensor, ...], List[Tensor]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool) -> Tuple[Tensor, Tensor]: ... +@overload +def rnn_tanh(input: Tensor, hx: Tensor, params: Union[Tuple[Tensor, ...], List[Tensor]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool, batch_first: _bool) -> Tuple[Tensor, Tensor]: ... +def rnn_tanh_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Optional[Tensor] = None, b_hh: Optional[Tensor] = None) -> Tensor: ... +def roll(input: Tensor, shifts: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]], dims: Union[_int, _size] = ()) -> Tensor: + r""" + roll(input, shifts, dims=None) -> Tensor + + Roll the tensor :attr:`input` along the given dimension(s). Elements that are + shifted beyond the last position are re-introduced at the first position. If + :attr:`dims` is `None`, the tensor will be flattened before rolling and then + restored to the original shape. + + Args: + input (Tensor): the input tensor. + shifts (int or tuple of ints): The number of places by which the elements + of the tensor are shifted. If shifts is a tuple, dims must be a tuple of + the same size, and each dimension will be rolled by the corresponding + value + dims (int or tuple of ints): Axis along which to roll + + Example:: + + >>> x = torch.tensor([1, 2, 3, 4, 5, 6, 7, 8]).view(4, 2) + >>> x + tensor([[1, 2], + [3, 4], + [5, 6], + [7, 8]]) + >>> torch.roll(x, 1) + tensor([[8, 1], + [2, 3], + [4, 5], + [6, 7]]) + >>> torch.roll(x, 1, 0) + tensor([[7, 8], + [1, 2], + [3, 4], + [5, 6]]) + >>> torch.roll(x, -1, 0) + tensor([[3, 4], + [5, 6], + [7, 8], + [1, 2]]) + >>> torch.roll(x, shifts=(2, 1), dims=(0, 1)) + tensor([[6, 5], + [8, 7], + [2, 1], + [4, 3]]) + """ + ... +def rot90(input: Tensor, k: _int = 1, dims: _size = (0, 1)) -> Tensor: + r""" + rot90(input, k=1, dims=(0, 1)) -> Tensor + + Rotate an n-D tensor by 90 degrees in the plane specified by dims axis. + Rotation direction is from the first towards the second axis if k > 0, and from the second towards the first for k < 0. + + Args: + input (Tensor): the input tensor. + k (int): number of times to rotate. Default value is 1 + dims (a list or tuple): axis to rotate. Default value is [0, 1] + + Example:: + + >>> x = torch.arange(4).view(2, 2) + >>> x + tensor([[0, 1], + [2, 3]]) + >>> torch.rot90(x, 1, [0, 1]) + tensor([[1, 3], + [0, 2]]) + + >>> x = torch.arange(8).view(2, 2, 2) + >>> x + tensor([[[0, 1], + [2, 3]], + + [[4, 5], + [6, 7]]]) + >>> torch.rot90(x, 1, [1, 2]) + tensor([[[1, 3], + [0, 2]], + + [[5, 7], + [4, 6]]]) + """ + ... +@overload +def round(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + round(input, *, decimals=0, out=None) -> Tensor + + Rounds elements of :attr:`input` to the nearest integer. + + For integer inputs, follows the array-api convention of returning a + copy of the input tensor. + The return type of output is same as that of input's dtype. + + .. note:: + This function implements the "round half to even" to + break ties when a number is equidistant from two + integers (e.g. `round(2.5)` is 2). + + When the :attr:\`decimals\` argument is specified the + algorithm used is similar to NumPy's `around`. This + algorithm is fast but inexact and it can easily + overflow for low precision dtypes. + Eg. `round(tensor([10000], dtype=torch.float16), decimals=3)` is `inf`. + + .. seealso:: + :func:`torch.ceil`, which rounds up. + :func:`torch.floor`, which rounds down. + :func:`torch.trunc`, which rounds towards zero. + + Args: + input (Tensor): the input tensor. + decimals (int): Number of decimal places to round to (default: 0). + If decimals is negative, it specifies the number of positions + to the left of the decimal point. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.round(torch.tensor((4.7, -2.3, 9.1, -7.7))) + tensor([ 5., -2., 9., -8.]) + + >>> # Values equidistant from two integers are rounded towards the + >>> # the nearest even value (zero is treated as even) + >>> torch.round(torch.tensor([-0.5, 0.5, 1.5, 2.5])) + tensor([-0., 0., 2., 2.]) + + >>> # A positive decimals argument rounds to the to that decimal place + >>> torch.round(torch.tensor([0.1234567]), decimals=3) + tensor([0.1230]) + + >>> # A negative decimals argument rounds to the left of the decimal + >>> torch.round(torch.tensor([1200.1234567]), decimals=-3) + tensor([1000.]) + """ + ... +@overload +def round(input: Tensor, *, decimals: _int, out: Optional[Tensor] = None) -> Tensor: + r""" + round(input, *, decimals=0, out=None) -> Tensor + + Rounds elements of :attr:`input` to the nearest integer. + + For integer inputs, follows the array-api convention of returning a + copy of the input tensor. + The return type of output is same as that of input's dtype. + + .. note:: + This function implements the "round half to even" to + break ties when a number is equidistant from two + integers (e.g. `round(2.5)` is 2). + + When the :attr:\`decimals\` argument is specified the + algorithm used is similar to NumPy's `around`. This + algorithm is fast but inexact and it can easily + overflow for low precision dtypes. + Eg. `round(tensor([10000], dtype=torch.float16), decimals=3)` is `inf`. + + .. seealso:: + :func:`torch.ceil`, which rounds up. + :func:`torch.floor`, which rounds down. + :func:`torch.trunc`, which rounds towards zero. + + Args: + input (Tensor): the input tensor. + decimals (int): Number of decimal places to round to (default: 0). + If decimals is negative, it specifies the number of positions + to the left of the decimal point. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.round(torch.tensor((4.7, -2.3, 9.1, -7.7))) + tensor([ 5., -2., 9., -8.]) + + >>> # Values equidistant from two integers are rounded towards the + >>> # the nearest even value (zero is treated as even) + >>> torch.round(torch.tensor([-0.5, 0.5, 1.5, 2.5])) + tensor([-0., 0., 2., 2.]) + + >>> # A positive decimals argument rounds to the to that decimal place + >>> torch.round(torch.tensor([0.1234567]), decimals=3) + tensor([0.1230]) + + >>> # A negative decimals argument rounds to the left of the decimal + >>> torch.round(torch.tensor([1200.1234567]), decimals=-3) + tensor([1000.]) + """ + ... +@overload +def round_(input: Tensor) -> Tensor: ... +@overload +def round_(input: Tensor, *, decimals: _int) -> Tensor: ... +def row_indices_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def row_stack(tensors: Union[Tuple[Tensor, ...], List[Tensor]], *, out: Optional[Tensor] = None) -> Tensor: + r""" + row_stack(tensors, *, out=None) -> Tensor + + Alias of :func:`torch.vstack`. + """ + ... +def rrelu(input: Tensor, lower: Union[Number, _complex] = 0.125, upper: Union[Number, _complex] = 0.3333333333333333, training: _bool = False, generator: Optional[Generator] = None) -> Tensor: ... +def rrelu_(input: Tensor, lower: Union[Number, _complex] = 0.125, upper: Union[Number, _complex] = 0.3333333333333333, training: _bool = False, generator: Optional[Generator] = None) -> Tensor: ... +def rsqrt(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + rsqrt(input, *, out=None) -> Tensor + + Returns a new tensor with the reciprocal of the square-root of each of + the elements of :attr:`input`. + + .. math:: + \text{out}_{i} = \frac{1}{\sqrt{\text{input}_{i}}} + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-0.0370, 0.2970, 1.5420, -0.9105]) + >>> torch.rsqrt(a) + tensor([ nan, 1.8351, 0.8053, nan]) + """ + ... +def rsqrt_(input: Tensor) -> Tensor: ... +@overload +def rsub(input: Tensor, other: Tensor, *, alpha: Union[Number, _complex] = 1) -> Tensor: ... +@overload +def rsub(input: Tensor, other: Union[Number, _complex], alpha: Union[Number, _complex] = 1) -> Tensor: ... +def saddmm(input: Tensor, mat1: Tensor, mat2: Tensor, *, beta: Number = 1, alpha: Number = 1, out: Optional[Tensor] = None) -> Tensor: ... +def scalar_tensor(s: Union[Number, _complex], *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... +@overload +def scatter(input: Tensor, dim: _int, index: Tensor, src: Tensor, *, reduce: str, out: Optional[Tensor] = None) -> Tensor: + r""" + scatter(input, dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_` + """ + ... +@overload +def scatter(input: Tensor, dim: _int, index: Tensor, src: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + scatter(input, dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_` + """ + ... +@overload +def scatter(input: Tensor, dim: _int, index: Tensor, value: Union[Number, _complex], *, reduce: str, out: Optional[Tensor] = None) -> Tensor: + r""" + scatter(input, dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_` + """ + ... +@overload +def scatter(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, src: Tensor) -> Tensor: + r""" + scatter(input, dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_` + """ + ... +@overload +def scatter(input: Tensor, dim: _int, index: Tensor, value: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + scatter(input, dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_` + """ + ... +@overload +def scatter(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, value: Union[Number, _complex]) -> Tensor: + r""" + scatter(input, dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_` + """ + ... +@overload +def scatter_add(input: Tensor, dim: _int, index: Tensor, src: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + scatter_add(input, dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_add_` + """ + ... +@overload +def scatter_add(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, src: Tensor) -> Tensor: + r""" + scatter_add(input, dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_add_` + """ + ... +def scatter_reduce(input: Tensor, dim: _int, index: Tensor, src: Tensor, reduce: str, *, include_self: _bool = True, out: Optional[Tensor] = None) -> Tensor: + r""" + scatter_reduce(input, dim, index, src, reduce, *, include_self=True) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_reduce_` + """ + ... +@overload +def searchsorted(sorted_sequence: Tensor, input: Tensor, *, out_int32: _bool = False, right: _bool = False, side: Optional[str] = None, sorter: Optional[Tensor] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + searchsorted(sorted_sequence, values, *, out_int32=False, right=False, side=None, out=None, sorter=None) -> Tensor + + Find the indices from the *innermost* dimension of :attr:`sorted_sequence` such that, if the + corresponding values in :attr:`values` were inserted before the indices, when sorted, the order + of the corresponding *innermost* dimension within :attr:`sorted_sequence` would be preserved. + Return a new tensor with the same size as :attr:`values`. More formally, + the returned index satisfies the following rules: + + .. list-table:: + :widths: 12 10 78 + :header-rows: 1 + + * - :attr:`sorted_sequence` + - :attr:`right` + - *returned index satisfies* + * - 1-D + - False + - ``sorted_sequence[i-1] < values[m][n]...[l][x] <= sorted_sequence[i]`` + * - 1-D + - True + - ``sorted_sequence[i-1] <= values[m][n]...[l][x] < sorted_sequence[i]`` + * - N-D + - False + - ``sorted_sequence[m][n]...[l][i-1] < values[m][n]...[l][x] <= sorted_sequence[m][n]...[l][i]`` + * - N-D + - True + - ``sorted_sequence[m][n]...[l][i-1] <= values[m][n]...[l][x] < sorted_sequence[m][n]...[l][i]`` + + Args: + sorted_sequence (Tensor): N-D or 1-D tensor, containing monotonically increasing sequence on the *innermost* + dimension unless :attr:`sorter` is provided, in which case the sequence does not + need to be sorted + values (Tensor or Scalar): N-D tensor or a Scalar containing the search value(s). + + Keyword args: + out_int32 (bool, optional): indicate the output data type. torch.int32 if True, torch.int64 otherwise. + Default value is False, i.e. default output data type is torch.int64. + right (bool, optional): if False, return the first suitable location that is found. If True, return the + last such index. If no suitable index found, return 0 for non-numerical value + (eg. nan, inf) or the size of *innermost* dimension within :attr:`sorted_sequence` + (one pass the last index of the *innermost* dimension). In other words, if False, + gets the lower bound index for each value in :attr:`values` on the corresponding + *innermost* dimension of the :attr:`sorted_sequence`. If True, gets the upper + bound index instead. Default value is False. :attr:`side` does the same and is + preferred. It will error if :attr:`side` is set to "left" while this is True. + side (str, optional): the same as :attr:`right` but preferred. "left" corresponds to False for :attr:`right` + and "right" corresponds to True for :attr:`right`. It will error if this is set to + "left" while :attr:`right` is True. Default value is None. + out (Tensor, optional): the output tensor, must be the same size as :attr:`values` if provided. + sorter (LongTensor, optional): if provided, a tensor matching the shape of the unsorted + :attr:`sorted_sequence` containing a sequence of indices that sort it in the + ascending order on the innermost dimension + + + Example:: + + >>> sorted_sequence = torch.tensor([[1, 3, 5, 7, 9], [2, 4, 6, 8, 10]]) + >>> sorted_sequence + tensor([[ 1, 3, 5, 7, 9], + [ 2, 4, 6, 8, 10]]) + >>> values = torch.tensor([[3, 6, 9], [3, 6, 9]]) + >>> values + tensor([[3, 6, 9], + [3, 6, 9]]) + >>> torch.searchsorted(sorted_sequence, values) + tensor([[1, 3, 4], + [1, 2, 4]]) + >>> torch.searchsorted(sorted_sequence, values, side='right') + tensor([[2, 3, 5], + [1, 3, 4]]) + + >>> sorted_sequence_1d = torch.tensor([1, 3, 5, 7, 9]) + >>> sorted_sequence_1d + tensor([1, 3, 5, 7, 9]) + >>> torch.searchsorted(sorted_sequence_1d, values) + tensor([[1, 3, 4], + [1, 3, 4]]) + """ + ... +@overload +def searchsorted(sorted_sequence: Tensor, self: Union[Number, _complex], *, out_int32: _bool = False, right: _bool = False, side: Optional[str] = None, sorter: Optional[Tensor] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + searchsorted(sorted_sequence, values, *, out_int32=False, right=False, side=None, out=None, sorter=None) -> Tensor + + Find the indices from the *innermost* dimension of :attr:`sorted_sequence` such that, if the + corresponding values in :attr:`values` were inserted before the indices, when sorted, the order + of the corresponding *innermost* dimension within :attr:`sorted_sequence` would be preserved. + Return a new tensor with the same size as :attr:`values`. More formally, + the returned index satisfies the following rules: + + .. list-table:: + :widths: 12 10 78 + :header-rows: 1 + + * - :attr:`sorted_sequence` + - :attr:`right` + - *returned index satisfies* + * - 1-D + - False + - ``sorted_sequence[i-1] < values[m][n]...[l][x] <= sorted_sequence[i]`` + * - 1-D + - True + - ``sorted_sequence[i-1] <= values[m][n]...[l][x] < sorted_sequence[i]`` + * - N-D + - False + - ``sorted_sequence[m][n]...[l][i-1] < values[m][n]...[l][x] <= sorted_sequence[m][n]...[l][i]`` + * - N-D + - True + - ``sorted_sequence[m][n]...[l][i-1] <= values[m][n]...[l][x] < sorted_sequence[m][n]...[l][i]`` + + Args: + sorted_sequence (Tensor): N-D or 1-D tensor, containing monotonically increasing sequence on the *innermost* + dimension unless :attr:`sorter` is provided, in which case the sequence does not + need to be sorted + values (Tensor or Scalar): N-D tensor or a Scalar containing the search value(s). + + Keyword args: + out_int32 (bool, optional): indicate the output data type. torch.int32 if True, torch.int64 otherwise. + Default value is False, i.e. default output data type is torch.int64. + right (bool, optional): if False, return the first suitable location that is found. If True, return the + last such index. If no suitable index found, return 0 for non-numerical value + (eg. nan, inf) or the size of *innermost* dimension within :attr:`sorted_sequence` + (one pass the last index of the *innermost* dimension). In other words, if False, + gets the lower bound index for each value in :attr:`values` on the corresponding + *innermost* dimension of the :attr:`sorted_sequence`. If True, gets the upper + bound index instead. Default value is False. :attr:`side` does the same and is + preferred. It will error if :attr:`side` is set to "left" while this is True. + side (str, optional): the same as :attr:`right` but preferred. "left" corresponds to False for :attr:`right` + and "right" corresponds to True for :attr:`right`. It will error if this is set to + "left" while :attr:`right` is True. Default value is None. + out (Tensor, optional): the output tensor, must be the same size as :attr:`values` if provided. + sorter (LongTensor, optional): if provided, a tensor matching the shape of the unsorted + :attr:`sorted_sequence` containing a sequence of indices that sort it in the + ascending order on the innermost dimension + + + Example:: + + >>> sorted_sequence = torch.tensor([[1, 3, 5, 7, 9], [2, 4, 6, 8, 10]]) + >>> sorted_sequence + tensor([[ 1, 3, 5, 7, 9], + [ 2, 4, 6, 8, 10]]) + >>> values = torch.tensor([[3, 6, 9], [3, 6, 9]]) + >>> values + tensor([[3, 6, 9], + [3, 6, 9]]) + >>> torch.searchsorted(sorted_sequence, values) + tensor([[1, 3, 4], + [1, 2, 4]]) + >>> torch.searchsorted(sorted_sequence, values, side='right') + tensor([[2, 3, 5], + [1, 3, 4]]) + + >>> sorted_sequence_1d = torch.tensor([1, 3, 5, 7, 9]) + >>> sorted_sequence_1d + tensor([1, 3, 5, 7, 9]) + >>> torch.searchsorted(sorted_sequence_1d, values) + tensor([[1, 3, 4], + [1, 3, 4]]) + """ + ... +def segment_reduce(data: Tensor, reduce: str, *, lengths: Optional[Tensor] = None, indices: Optional[Tensor] = None, offsets: Optional[Tensor] = None, axis: _int = 0, unsafe: _bool = False, initial: Optional[Union[Number, _complex]] = None) -> Tensor: ... +@overload +def select(input: Tensor, dim: _int, index: Union[_int, SymInt]) -> Tensor: + r""" + select(input, dim, index) -> Tensor + + Slices the :attr:`input` tensor along the selected dimension at the given index. + This function returns a view of the original tensor with the given dimension removed. + + .. note:: If :attr:`input` is a sparse tensor and returning a view of + the tensor is not possible, a RuntimeError exception is + raised. In this is the case, consider using + :func:`torch.select_copy` function. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to slice + index (int): the index to select with + + .. note:: + + :meth:`select` is equivalent to slicing. For example, + ``tensor.select(0, index)`` is equivalent to ``tensor[index]`` and + ``tensor.select(2, index)`` is equivalent to ``tensor[:,:,index]``. + """ + ... +@overload +def select(input: Tensor, dim: Union[str, ellipsis, None], index: _int) -> Tensor: + r""" + select(input, dim, index) -> Tensor + + Slices the :attr:`input` tensor along the selected dimension at the given index. + This function returns a view of the original tensor with the given dimension removed. + + .. note:: If :attr:`input` is a sparse tensor and returning a view of + the tensor is not possible, a RuntimeError exception is + raised. In this is the case, consider using + :func:`torch.select_copy` function. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to slice + index (int): the index to select with + + .. note:: + + :meth:`select` is equivalent to slicing. For example, + ``tensor.select(0, index)`` is equivalent to ``tensor[index]`` and + ``tensor.select(2, index)`` is equivalent to ``tensor[:,:,index]``. + """ + ... +def select_copy(input: Tensor, dim: _int, index: Union[_int, SymInt], *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.select`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def select_scatter(input: Tensor, src: Tensor, dim: _int, index: Union[_int, SymInt]) -> Tensor: + r""" + select_scatter(input, src, dim, index) -> Tensor + + Embeds the values of the :attr:`src` tensor into :attr:`input` at the given index. + This function returns a tensor with fresh storage; it does not create a view. + + + Args: + input (Tensor): the input tensor. + src (Tensor): The tensor to embed into :attr:`input` + dim (int): the dimension to insert the slice into. + index (int): the index to select with + + .. note:: + + :attr:`src` must be of the proper size in order to be embedded + into :attr:`input`. Specifically, it should have the same shape as + ``torch.select(input, dim, index)`` + + Example:: + + >>> a = torch.zeros(2, 2) + >>> b = torch.ones(2) + >>> a.select_scatter(b, 0, 0) + tensor([[1., 1.], + [0., 0.]]) + """ + ... +def selu(input: Tensor) -> Tensor: ... +def selu_(input: Tensor) -> Tensor: ... +def set_flush_denormal(mode: _bool) -> _bool: + r""" + set_flush_denormal(mode) -> bool + + Disables denormal floating numbers on CPU. + + Returns ``True`` if your system supports flushing denormal numbers and it + successfully configures flush denormal mode. :meth:`~torch.set_flush_denormal` + is supported on x86 architectures supporting SSE3 and AArch64 architecture. + + Args: + mode (bool): Controls whether to enable flush denormal mode or not + + Example:: + + >>> torch.set_flush_denormal(True) + True + >>> torch.tensor([1e-323], dtype=torch.float64) + tensor([ 0.], dtype=torch.float64) + >>> torch.set_flush_denormal(False) + True + >>> torch.tensor([1e-323], dtype=torch.float64) + tensor(9.88131e-324 * + [ 1.0000], dtype=torch.float64) + """ + ... +def set_num_interop_threads(num: _int) -> None: + r""" + set_num_interop_threads(int) + + Sets the number of threads used for interop parallelism + (e.g. in JIT interpreter) on CPU. + + .. warning:: + Can only be called once and before any inter-op parallel work + is started (e.g. JIT execution). + """ + ... +def set_num_threads(num: _int) -> None: + r""" + set_num_threads(int) + + Sets the number of threads used for intraop parallelism on CPU. + + .. warning:: + To ensure that the correct number of threads is used, set_num_threads + must be called before running eager, JIT or autograd code. + """ + ... +def sgn(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + sgn(input, *, out=None) -> Tensor + + This function is an extension of torch.sign() to complex tensors. + It computes a new tensor whose elements have + the same angles as the corresponding elements of :attr:`input` and + absolute values (i.e. magnitudes) of one for complex tensors and + is equivalent to torch.sign() for non-complex tensors. + + .. math:: + \text{out}_{i} = \begin{cases} + 0 & |\text{{input}}_i| == 0 \\ + \frac{{\text{{input}}_i}}{|{\text{{input}}_i}|} & \text{otherwise} + \end{cases} + + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.tensor([3+4j, 7-24j, 0, 1+2j]) + >>> t.sgn() + tensor([0.6000+0.8000j, 0.2800-0.9600j, 0.0000+0.0000j, 0.4472+0.8944j]) + """ + ... +def sigmoid(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + sigmoid(input, *, out=None) -> Tensor + + Alias for :func:`torch.special.expit`. + """ + ... +def sigmoid_(input: Tensor) -> Tensor: ... +def sign(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + sign(input, *, out=None) -> Tensor + + Returns a new tensor with the signs of the elements of :attr:`input`. + + .. math:: + \text{out}_{i} = \operatorname{sgn}(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([0.7, -1.2, 0., 2.3]) + >>> a + tensor([ 0.7000, -1.2000, 0.0000, 2.3000]) + >>> torch.sign(a) + tensor([ 1., -1., 0., 1.]) + """ + ... +def signbit(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + signbit(input, *, out=None) -> Tensor + + Tests if each element of :attr:`input` has its sign bit set or not. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([0.7, -1.2, 0., 2.3]) + >>> torch.signbit(a) + tensor([ False, True, False, False]) + >>> a = torch.tensor([-0.0, 0.0]) + >>> torch.signbit(a) + tensor([ True, False]) + + .. note:: + signbit handles signed zeros, so negative zero (-0) returns True. + """ + ... +def sin(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + sin(input, *, out=None) -> Tensor + + Returns a new tensor with the sine of the elements of :attr:`input`. + + .. math:: + \text{out}_{i} = \sin(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-0.5461, 0.1347, -2.7266, -0.2746]) + >>> torch.sin(a) + tensor([-0.5194, 0.1343, -0.4032, -0.2711]) + """ + ... +def sin_(input: Tensor) -> Tensor: ... +def sinc(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + sinc(input, *, out=None) -> Tensor + + Alias for :func:`torch.special.sinc`. + """ + ... +def sinc_(input: Tensor) -> Tensor: ... +def sinh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + sinh(input, *, out=None) -> Tensor + + Returns a new tensor with the hyperbolic sine of the elements of + :attr:`input`. + + .. math:: + \text{out}_{i} = \sinh(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.5380, -0.8632, -0.1265, 0.9399]) + >>> torch.sinh(a) + tensor([ 0.5644, -0.9744, -0.1268, 1.0845]) + + .. note:: + When :attr:`input` is on the CPU, the implementation of torch.sinh may use + the Sleef library, which rounds very large results to infinity or negative + infinity. See `here `_ for details. + """ + ... +def sinh_(input: Tensor) -> Tensor: ... +def slice_copy(input: Tensor, dim: _int = 0, start: Optional[Union[_int, SymInt]] = None, end: Optional[Union[_int, SymInt]] = None, step: Union[_int, SymInt] = 1, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.slice`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def slice_inverse(input: Tensor, src: Tensor, dim: _int = 0, start: Optional[Union[_int, SymInt]] = None, end: Optional[Union[_int, SymInt]] = None, step: Union[_int, SymInt] = 1) -> Tensor: ... +def slice_scatter(input: Tensor, src: Tensor, dim: _int = 0, start: Optional[Union[_int, SymInt]] = None, end: Optional[Union[_int, SymInt]] = None, step: Union[_int, SymInt] = 1, *, out: Optional[Tensor] = None) -> Tensor: + r""" + slice_scatter(input, src, dim=0, start=None, end=None, step=1) -> Tensor + + Embeds the values of the :attr:`src` tensor into :attr:`input` at the given + dimension. + This function returns a tensor with fresh storage; it does not create a view. + + + Args: + input (Tensor): the input tensor. + src (Tensor): The tensor to embed into :attr:`input` + dim (int): the dimension to insert the slice into + start (Optional[int]): the start index of where to insert the slice + end (Optional[int]): the end index of where to insert the slice + step (int): the how many elements to skip in + + Example:: + + >>> a = torch.zeros(8, 8) + >>> b = torch.ones(2, 8) + >>> a.slice_scatter(b, start=6) + tensor([[0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0.], + [1., 1., 1., 1., 1., 1., 1., 1.], + [1., 1., 1., 1., 1., 1., 1., 1.]]) + + >>> b = torch.ones(8, 2) + >>> a.slice_scatter(b, dim=1, start=2, end=6, step=2) + tensor([[0., 0., 1., 0., 1., 0., 0., 0.], + [0., 0., 1., 0., 1., 0., 0., 0.], + [0., 0., 1., 0., 1., 0., 0., 0.], + [0., 0., 1., 0., 1., 0., 0., 0.], + [0., 0., 1., 0., 1., 0., 0., 0.], + [0., 0., 1., 0., 1., 0., 0., 0.], + [0., 0., 1., 0., 1., 0., 0., 0.], + [0., 0., 1., 0., 1., 0., 0., 0.]]) + """ + ... +def slogdet(input: Tensor, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.slogdet: + r""" + slogdet(input) -> (Tensor, Tensor) + + Alias for :func:`torch.linalg.slogdet` + """ + ... +def smm(input: Tensor, mat2: Tensor) -> Tensor: + r""" + smm(input, mat) -> Tensor + + Performs a matrix multiplication of the sparse matrix :attr:`input` + with the dense matrix :attr:`mat`. + + Args: + input (Tensor): a sparse matrix to be matrix multiplied + mat (Tensor): a dense matrix to be matrix multiplied + """ + ... +@overload +def softmax(input: Tensor, dim: _int, dtype: Optional[_dtype] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + softmax(input, dim, *, dtype=None) -> Tensor + + Alias for :func:`torch.nn.functional.softmax`. + """ + ... +@overload +def softmax(input: Tensor, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + softmax(input, dim, *, dtype=None) -> Tensor + + Alias for :func:`torch.nn.functional.softmax`. + """ + ... +@overload +def sort(input: Tensor, *, stable: Optional[_bool], dim: _int = -1, descending: _bool = False, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.sort: + r""" + sort(input, dim=-1, descending=False, stable=False, *, out=None) -> (Tensor, LongTensor) + + Sorts the elements of the :attr:`input` tensor along a given dimension + in ascending order by value. + + If :attr:`dim` is not given, the last dimension of the `input` is chosen. + + If :attr:`descending` is ``True`` then the elements are sorted in descending + order by value. + + If :attr:`stable` is ``True`` then the sorting routine becomes stable, preserving + the order of equivalent elements. + + A namedtuple of (values, indices) is returned, where the `values` are the + sorted values and `indices` are the indices of the elements in the original + `input` tensor. + + Args: + input (Tensor): the input tensor. + dim (int, optional): the dimension to sort along + descending (bool, optional): controls the sorting order (ascending or descending) + stable (bool, optional): makes the sorting routine stable, which guarantees that the order + of equivalent elements is preserved. + + Keyword args: + out (tuple, optional): the output tuple of (`Tensor`, `LongTensor`) that can + be optionally given to be used as output buffers + + Example:: + + >>> x = torch.randn(3, 4) + >>> sorted, indices = torch.sort(x) + >>> sorted + tensor([[-0.2162, 0.0608, 0.6719, 2.3332], + [-0.5793, 0.0061, 0.6058, 0.9497], + [-0.5071, 0.3343, 0.9553, 1.0960]]) + >>> indices + tensor([[ 1, 0, 2, 3], + [ 3, 1, 0, 2], + [ 0, 3, 1, 2]]) + + >>> sorted, indices = torch.sort(x, 0) + >>> sorted + tensor([[-0.5071, -0.2162, 0.6719, -0.5793], + [ 0.0608, 0.0061, 0.9497, 0.3343], + [ 0.6058, 0.9553, 1.0960, 2.3332]]) + >>> indices + tensor([[ 2, 0, 0, 1], + [ 0, 1, 1, 2], + [ 1, 2, 2, 0]]) + >>> x = torch.tensor([0, 1] * 9) + >>> x.sort() + torch.return_types.sort( + values=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]), + indices=tensor([ 2, 16, 4, 6, 14, 8, 0, 10, 12, 9, 17, 15, 13, 11, 7, 5, 3, 1])) + >>> x.sort(stable=True) + torch.return_types.sort( + values=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]), + indices=tensor([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 1, 3, 5, 7, 9, 11, 13, 15, 17])) + """ + ... +@overload +def sort(input: Tensor, dim: _int = -1, descending: _bool = False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.sort: + r""" + sort(input, dim=-1, descending=False, stable=False, *, out=None) -> (Tensor, LongTensor) + + Sorts the elements of the :attr:`input` tensor along a given dimension + in ascending order by value. + + If :attr:`dim` is not given, the last dimension of the `input` is chosen. + + If :attr:`descending` is ``True`` then the elements are sorted in descending + order by value. + + If :attr:`stable` is ``True`` then the sorting routine becomes stable, preserving + the order of equivalent elements. + + A namedtuple of (values, indices) is returned, where the `values` are the + sorted values and `indices` are the indices of the elements in the original + `input` tensor. + + Args: + input (Tensor): the input tensor. + dim (int, optional): the dimension to sort along + descending (bool, optional): controls the sorting order (ascending or descending) + stable (bool, optional): makes the sorting routine stable, which guarantees that the order + of equivalent elements is preserved. + + Keyword args: + out (tuple, optional): the output tuple of (`Tensor`, `LongTensor`) that can + be optionally given to be used as output buffers + + Example:: + + >>> x = torch.randn(3, 4) + >>> sorted, indices = torch.sort(x) + >>> sorted + tensor([[-0.2162, 0.0608, 0.6719, 2.3332], + [-0.5793, 0.0061, 0.6058, 0.9497], + [-0.5071, 0.3343, 0.9553, 1.0960]]) + >>> indices + tensor([[ 1, 0, 2, 3], + [ 3, 1, 0, 2], + [ 0, 3, 1, 2]]) + + >>> sorted, indices = torch.sort(x, 0) + >>> sorted + tensor([[-0.5071, -0.2162, 0.6719, -0.5793], + [ 0.0608, 0.0061, 0.9497, 0.3343], + [ 0.6058, 0.9553, 1.0960, 2.3332]]) + >>> indices + tensor([[ 2, 0, 0, 1], + [ 0, 1, 1, 2], + [ 1, 2, 2, 0]]) + >>> x = torch.tensor([0, 1] * 9) + >>> x.sort() + torch.return_types.sort( + values=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]), + indices=tensor([ 2, 16, 4, 6, 14, 8, 0, 10, 12, 9, 17, 15, 13, 11, 7, 5, 3, 1])) + >>> x.sort(stable=True) + torch.return_types.sort( + values=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]), + indices=tensor([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 1, 3, 5, 7, 9, 11, 13, 15, 17])) + """ + ... +@overload +def sort(input: Tensor, *, stable: Optional[_bool], dim: Union[str, ellipsis, None], descending: _bool = False, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.sort: + r""" + sort(input, dim=-1, descending=False, stable=False, *, out=None) -> (Tensor, LongTensor) + + Sorts the elements of the :attr:`input` tensor along a given dimension + in ascending order by value. + + If :attr:`dim` is not given, the last dimension of the `input` is chosen. + + If :attr:`descending` is ``True`` then the elements are sorted in descending + order by value. + + If :attr:`stable` is ``True`` then the sorting routine becomes stable, preserving + the order of equivalent elements. + + A namedtuple of (values, indices) is returned, where the `values` are the + sorted values and `indices` are the indices of the elements in the original + `input` tensor. + + Args: + input (Tensor): the input tensor. + dim (int, optional): the dimension to sort along + descending (bool, optional): controls the sorting order (ascending or descending) + stable (bool, optional): makes the sorting routine stable, which guarantees that the order + of equivalent elements is preserved. + + Keyword args: + out (tuple, optional): the output tuple of (`Tensor`, `LongTensor`) that can + be optionally given to be used as output buffers + + Example:: + + >>> x = torch.randn(3, 4) + >>> sorted, indices = torch.sort(x) + >>> sorted + tensor([[-0.2162, 0.0608, 0.6719, 2.3332], + [-0.5793, 0.0061, 0.6058, 0.9497], + [-0.5071, 0.3343, 0.9553, 1.0960]]) + >>> indices + tensor([[ 1, 0, 2, 3], + [ 3, 1, 0, 2], + [ 0, 3, 1, 2]]) + + >>> sorted, indices = torch.sort(x, 0) + >>> sorted + tensor([[-0.5071, -0.2162, 0.6719, -0.5793], + [ 0.0608, 0.0061, 0.9497, 0.3343], + [ 0.6058, 0.9553, 1.0960, 2.3332]]) + >>> indices + tensor([[ 2, 0, 0, 1], + [ 0, 1, 1, 2], + [ 1, 2, 2, 0]]) + >>> x = torch.tensor([0, 1] * 9) + >>> x.sort() + torch.return_types.sort( + values=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]), + indices=tensor([ 2, 16, 4, 6, 14, 8, 0, 10, 12, 9, 17, 15, 13, 11, 7, 5, 3, 1])) + >>> x.sort(stable=True) + torch.return_types.sort( + values=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]), + indices=tensor([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 1, 3, 5, 7, 9, 11, 13, 15, 17])) + """ + ... +@overload +def sort(input: Tensor, dim: Union[str, ellipsis, None], descending: _bool = False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.sort: + r""" + sort(input, dim=-1, descending=False, stable=False, *, out=None) -> (Tensor, LongTensor) + + Sorts the elements of the :attr:`input` tensor along a given dimension + in ascending order by value. + + If :attr:`dim` is not given, the last dimension of the `input` is chosen. + + If :attr:`descending` is ``True`` then the elements are sorted in descending + order by value. + + If :attr:`stable` is ``True`` then the sorting routine becomes stable, preserving + the order of equivalent elements. + + A namedtuple of (values, indices) is returned, where the `values` are the + sorted values and `indices` are the indices of the elements in the original + `input` tensor. + + Args: + input (Tensor): the input tensor. + dim (int, optional): the dimension to sort along + descending (bool, optional): controls the sorting order (ascending or descending) + stable (bool, optional): makes the sorting routine stable, which guarantees that the order + of equivalent elements is preserved. + + Keyword args: + out (tuple, optional): the output tuple of (`Tensor`, `LongTensor`) that can + be optionally given to be used as output buffers + + Example:: + + >>> x = torch.randn(3, 4) + >>> sorted, indices = torch.sort(x) + >>> sorted + tensor([[-0.2162, 0.0608, 0.6719, 2.3332], + [-0.5793, 0.0061, 0.6058, 0.9497], + [-0.5071, 0.3343, 0.9553, 1.0960]]) + >>> indices + tensor([[ 1, 0, 2, 3], + [ 3, 1, 0, 2], + [ 0, 3, 1, 2]]) + + >>> sorted, indices = torch.sort(x, 0) + >>> sorted + tensor([[-0.5071, -0.2162, 0.6719, -0.5793], + [ 0.0608, 0.0061, 0.9497, 0.3343], + [ 0.6058, 0.9553, 1.0960, 2.3332]]) + >>> indices + tensor([[ 2, 0, 0, 1], + [ 0, 1, 1, 2], + [ 1, 2, 2, 0]]) + >>> x = torch.tensor([0, 1] * 9) + >>> x.sort() + torch.return_types.sort( + values=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]), + indices=tensor([ 2, 16, 4, 6, 14, 8, 0, 10, 12, 9, 17, 15, 13, 11, 7, 5, 3, 1])) + >>> x.sort(stable=True) + torch.return_types.sort( + values=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]), + indices=tensor([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 1, 3, 5, 7, 9, 11, 13, 15, 17])) + """ + ... +def sparse_bsc_tensor(ccol_indices: Union[Tensor, List], row_indices: Union[Tensor, List], values: Union[Tensor, List], size: Optional[_size] = None, *, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, check_invariants: Optional[_bool] = None) -> Tensor: + r""" + sparse_bsc_tensor(ccol_indices, row_indices, values, size=None, *, dtype=None, device=None, pin_memory=False, requires_grad=False, check_invariants=None) -> Tensor + + Constructs a :ref:`sparse tensor in BSC (Block Compressed Sparse + Column)) ` with specified 2-dimensional blocks at the + given :attr:`ccol_indices` and :attr:`row_indices`. Sparse matrix + multiplication operations in BSC format are typically faster than that + for sparse tensors in COO format. Make you have a look at :ref:`the + note on the data type of the indices `. + + .. note:: + + If the ``device`` argument is not specified the device of the given + :attr:`values` and indices tensor(s) must match. If, however, the + argument is specified the input Tensors will be converted to the + given device and in turn determine the device of the constructed + sparse tensor. + + Args: + ccol_indices (array_like): (B+1)-dimensional array of size + ``(*batchsize, ncolblocks + 1)``. The last element of each + batch is the number of non-zeros. This tensor encodes the + index in values and row_indices depending on where the given + column starts. Each successive number in the tensor subtracted + by the number before it denotes the number of elements in a + given column. + row_indices (array_like): Row block co-ordinates of each block in + values. (B+1)-dimensional tensor with the same length + as values. + values (array_list): Initial blocks for the tensor. Can be a list, + tuple, NumPy ``ndarray``, and other types that + represents a (1 + 2 + K)-dimensional tensor where ``K`` is the + number of dense dimensions. + size (list, tuple, :class:`torch.Size`, optional): Size of the + sparse tensor: ``(*batchsize, nrows * blocksize[0], ncols * + blocksize[1], *densesize)`` If not provided, the size will be + inferred as the minimum size big enough to hold all non-zero + blocks. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of + returned tensor. Default: if None, infers data type from + :attr:`values`. + device (:class:`torch.device`, optional): the desired device of + returned tensor. Default: if None, uses the current device + for the default tensor type (see + :func:`torch.set_default_device`). :attr:`device` will be + the CPU for CPU tensor types and the current CUDA device for + CUDA tensor types. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + check_invariants (bool, optional): If sparse tensor invariants are checked. + Default: as returned by :func:`torch.sparse.check_sparse_tensor_invariants.is_enabled`, + initially False. + + Example:: + >>> ccol_indices = [0, 1, 2] + >>> row_indices = [0, 1] + >>> values = [[[1, 2], [3, 4]], [[5, 6], [7, 8]]] + >>> torch.sparse_bsc_tensor(torch.tensor(ccol_indices, dtype=torch.int64), + ... torch.tensor(row_indices, dtype=torch.int64), + ... torch.tensor(values), dtype=torch.double) + tensor(ccol_indices=tensor([0, 1, 2]), + row_indices=tensor([0, 1]), + values=tensor([[[1., 2.], + [3., 4.]], + [[5., 6.], + [7., 8.]]]), size=(2, 2), nnz=2, dtype=torch.float64, + layout=torch.sparse_bsc) + """ + ... +def sparse_bsr_tensor(crow_indices: Union[Tensor, List], col_indices: Union[Tensor, List], values: Union[Tensor, List], size: Optional[_size] = None, *, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, check_invariants: Optional[_bool] = None) -> Tensor: + r""" + sparse_bsr_tensor(crow_indices, col_indices, values, size=None, *, dtype=None, device=None, pin_memory=False, requires_grad=False, check_invariants=None) -> Tensor + + Constructs a :ref:`sparse tensor in BSR (Block Compressed Sparse Row)) + ` with specified 2-dimensional blocks at the given + :attr:`crow_indices` and :attr:`col_indices`. Sparse matrix + multiplication operations in BSR format are typically faster than that + for sparse tensors in COO format. Make you have a look at :ref:`the + note on the data type of the indices `. + + .. note:: + + If the ``device`` argument is not specified the device of the given + :attr:`values` and indices tensor(s) must match. If, however, the + argument is specified the input Tensors will be converted to the + given device and in turn determine the device of the constructed + sparse tensor. + + Args: + crow_indices (array_like): (B+1)-dimensional array of size + ``(*batchsize, nrowblocks + 1)``. The last element of each + batch is the number of non-zeros. This tensor encodes the + block index in values and col_indices depending on where the + given row block starts. Each successive number in the tensor + subtracted by the number before it denotes the number of + blocks in a given row. + col_indices (array_like): Column block co-ordinates of each block + in values. (B+1)-dimensional tensor with the same length as + values. + values (array_list): Initial values for the tensor. Can be a list, + tuple, NumPy ``ndarray``, scalar, and other types that + represents a (1 + 2 + K)-dimensional tensor where ``K`` is the + number of dense dimensions. + size (list, tuple, :class:`torch.Size`, optional): Size of the + sparse tensor: ``(*batchsize, nrows * blocksize[0], ncols * + blocksize[1], *densesize)`` where ``blocksize == + values.shape[1:3]``. If not provided, the size will be + inferred as the minimum size big enough to hold all non-zero + blocks. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of + returned tensor. Default: if None, infers data type from + :attr:`values`. + device (:class:`torch.device`, optional): the desired device of + returned tensor. Default: if None, uses the current device + for the default tensor type (see + :func:`torch.set_default_device`). :attr:`device` will be + the CPU for CPU tensor types and the current CUDA device for + CUDA tensor types. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + check_invariants (bool, optional): If sparse tensor invariants are checked. + Default: as returned by :func:`torch.sparse.check_sparse_tensor_invariants.is_enabled`, + initially False. + + Example:: + >>> crow_indices = [0, 1, 2] + >>> col_indices = [0, 1] + >>> values = [[[1, 2], [3, 4]], [[5, 6], [7, 8]]] + >>> torch.sparse_bsr_tensor(torch.tensor(crow_indices, dtype=torch.int64), + ... torch.tensor(col_indices, dtype=torch.int64), + ... torch.tensor(values), dtype=torch.double) + tensor(crow_indices=tensor([0, 1, 2]), + col_indices=tensor([0, 1]), + values=tensor([[[1., 2.], + [3., 4.]], + [[5., 6.], + [7., 8.]]]), size=(2, 2), nnz=2, dtype=torch.float64, + layout=torch.sparse_bsr) + """ + ... +def sparse_compressed_tensor(compressed_indices: Union[Tensor, List], plain_indices: Union[Tensor, List], values: Union[Tensor, List], size: Optional[_size] = None, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, check_invariants: Optional[_bool] = None) -> Tensor: + r""" + sparse_compressed_tensor(compressed_indices, plain_indices, values, size=None, *, dtype=None, layout=None, device=None, pin_memory=False, requires_grad=False, check_invariants=None) -> Tensor + + Constructs a :ref:`sparse tensor in Compressed Sparse format - CSR, + CSC, BSR, or BSC - ` with specified values at + the given :attr:`compressed_indices` and :attr:`plain_indices`. Sparse + matrix multiplication operations in Compressed Sparse format are + typically faster than that for sparse tensors in COO format. Make you + have a look at :ref:`the note on the data type of the indices + `. + + .. note:: + + If the ``device`` argument is not specified the device of the given + :attr:`values` and indices tensor(s) must match. If, however, the + argument is specified the input Tensors will be converted to the + given device and in turn determine the device of the constructed + sparse tensor. + + Args: + compressed_indices (array_like): (B+1)-dimensional array of size + ``(*batchsize, compressed_dim_size + 1)``. The last element of + each batch is the number of non-zero elements or blocks. This + tensor encodes the index in ``values`` and ``plain_indices`` + depending on where the given compressed dimension (row or + column) starts. Each successive number in the tensor + subtracted by the number before it denotes the number of + elements or blocks in a given compressed dimension. + plain_indices (array_like): Plain dimension (column or row) + co-ordinates of each element or block in values. (B+1)-dimensional + tensor with the same length as values. + + values (array_list): Initial values for the tensor. Can be a list, + tuple, NumPy ``ndarray``, scalar, and other types. that + represents a (1+K)-dimensional (for CSR and CSC layouts) or + (1+2+K)-dimensional tensor (for BSR and BSC layouts) where + ``K`` is the number of dense dimensions. + size (list, tuple, :class:`torch.Size`, optional): Size of the + sparse tensor: ``(*batchsize, nrows * blocksize[0], ncols * + blocksize[1], *densesize)`` where ``blocksize[0] == + blocksize[1] == 1`` for CSR and CSC formats. If not provided, + the size will be inferred as the minimum size big enough to + hold all non-zero elements or blocks. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of + returned tensor. Default: if None, infers data type from + :attr:`values`. + layout (:class:`torch.layout`, required): the desired layout of + returned tensor: :attr:`torch.sparse_csr`, + :attr:`torch.sparse_csc`, :attr:`torch.sparse_bsr`, or + :attr:`torch.sparse_bsc`. + device (:class:`torch.device`, optional): the desired device of + returned tensor. Default: if None, uses the current device + for the default tensor type (see + :func:`torch.set_default_device`). :attr:`device` will be + the CPU for CPU tensor types and the current CUDA device for + CUDA tensor types. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + check_invariants (bool, optional): If sparse tensor invariants are checked. + Default: as returned by :func:`torch.sparse.check_sparse_tensor_invariants.is_enabled`, + initially False. + + Example:: + >>> compressed_indices = [0, 2, 4] + >>> plain_indices = [0, 1, 0, 1] + >>> values = [1, 2, 3, 4] + >>> torch.sparse_compressed_tensor(torch.tensor(compressed_indices, dtype=torch.int64), + ... torch.tensor(plain_indices, dtype=torch.int64), + ... torch.tensor(values), dtype=torch.double, layout=torch.sparse_csr) + tensor(crow_indices=tensor([0, 2, 4]), + col_indices=tensor([0, 1, 0, 1]), + values=tensor([1., 2., 3., 4.]), size=(2, 2), nnz=4, + dtype=torch.float64, layout=torch.sparse_csr) + """ + ... +def sparse_coo_tensor(indices: Tensor, values: Union[Tensor, List], size: Optional[_size] = None, *, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, check_invariants: Optional[_bool] = None, is_coalesced: Optional[_bool] = None) -> Tensor: + r""" + sparse_coo_tensor(indices, values, size=None, *, dtype=None, device=None, pin_memory=False, requires_grad=False, check_invariants=None, is_coalesced=None) -> Tensor + + Constructs a :ref:`sparse tensor in COO(rdinate) format + ` with specified values at the given + :attr:`indices`. + + .. note:: + + This function returns an :ref:`uncoalesced tensor + ` when :attr:`is_coalesced` is + unspecified or ``None``. + + .. note:: + + If the ``device`` argument is not specified the device of the given + :attr:`values` and indices tensor(s) must match. If, however, the + argument is specified the input Tensors will be converted to the + given device and in turn determine the device of the constructed + sparse tensor. + + Args: + indices (array_like): Initial data for the tensor. Can be a list, tuple, + NumPy ``ndarray``, scalar, and other types. Will be cast to a :class:`torch.LongTensor` + internally. The indices are the coordinates of the non-zero values in the matrix, and thus + should be two-dimensional where the first dimension is the number of tensor dimensions and + the second dimension is the number of non-zero values. + values (array_like): Initial values for the tensor. Can be a list, tuple, + NumPy ``ndarray``, scalar, and other types. + size (list, tuple, or :class:`torch.Size`, optional): Size of the sparse tensor. If not + provided the size will be inferred as the minimum size big enough to hold all non-zero + elements. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if None, infers data type from :attr:`values`. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if None, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + check_invariants (bool, optional): If sparse tensor invariants are checked. + Default: as returned by :func:`torch.sparse.check_sparse_tensor_invariants.is_enabled`, + initially False. + is_coalesced (bool, optional): When``True``, the caller is + responsible for providing tensor indices that correspond to a + coalesced tensor. If the :attr:`check_invariants` flag is + False, no error will be raised if the prerequisites are not + met and this will lead to silently incorrect results. To force + coalescion please use :meth:`coalesce` on the resulting + Tensor. + Default: None: except for trivial cases (e.g. nnz < 2) the + resulting Tensor has is_coalesced set to ``False```. + + Example:: + + >>> i = torch.tensor([[0, 1, 1], + ... [2, 0, 2]]) + >>> v = torch.tensor([3, 4, 5], dtype=torch.float32) + >>> torch.sparse_coo_tensor(i, v, [2, 4]) + tensor(indices=tensor([[0, 1, 1], + [2, 0, 2]]), + values=tensor([3., 4., 5.]), + size=(2, 4), nnz=3, layout=torch.sparse_coo) + + >>> torch.sparse_coo_tensor(i, v) # Shape inference + tensor(indices=tensor([[0, 1, 1], + [2, 0, 2]]), + values=tensor([3., 4., 5.]), + size=(2, 3), nnz=3, layout=torch.sparse_coo) + + >>> torch.sparse_coo_tensor(i, v, [2, 4], + ... dtype=torch.float64, + ... device=torch.device('cuda:0')) + tensor(indices=tensor([[0, 1, 1], + [2, 0, 2]]), + values=tensor([3., 4., 5.]), + device='cuda:0', size=(2, 4), nnz=3, dtype=torch.float64, + layout=torch.sparse_coo) + + # Create an empty sparse tensor with the following invariants: + # 1. sparse_dim + dense_dim = len(SparseTensor.shape) + # 2. SparseTensor._indices().shape = (sparse_dim, nnz) + # 3. SparseTensor._values().shape = (nnz, SparseTensor.shape[sparse_dim:]) + # + # For instance, to create an empty sparse tensor with nnz = 0, dense_dim = 0 and + # sparse_dim = 1 (hence indices is a 2D tensor of shape = (1, 0)) + >>> S = torch.sparse_coo_tensor(torch.empty([1, 0]), [], [1]) + tensor(indices=tensor([], size=(1, 0)), + values=tensor([], size=(0,)), + size=(1,), nnz=0, layout=torch.sparse_coo) + + # and to create an empty sparse tensor with nnz = 0, dense_dim = 1 and + # sparse_dim = 1 + >>> S = torch.sparse_coo_tensor(torch.empty([1, 0]), torch.empty([0, 2]), [1, 2]) + tensor(indices=tensor([], size=(1, 0)), + values=tensor([], size=(0, 2)), + size=(1, 2), nnz=0, layout=torch.sparse_coo) + + .. _torch.sparse: https://pytorch.org/docs/stable/sparse.html + """ + ... +def sparse_csc_tensor(ccol_indices: Union[Tensor, List], row_indices: Union[Tensor, List], values: Union[Tensor, List], size: Optional[_size] = None, *, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, check_invariants: Optional[_bool] = None) -> Tensor: + r""" + sparse_csc_tensor(ccol_indices, row_indices, values, size=None, *, dtype=None, device=None, pin_memory=False, requires_grad=False, check_invariants=None) -> Tensor + + Constructs a :ref:`sparse tensor in CSC (Compressed Sparse Column) + ` with specified values at the given + :attr:`ccol_indices` and :attr:`row_indices`. Sparse matrix + multiplication operations in CSC format are typically faster than that + for sparse tensors in COO format. Make you have a look at :ref:`the + note on the data type of the indices `. + + .. note:: + + If the ``device`` argument is not specified the device of the given + :attr:`values` and indices tensor(s) must match. If, however, the + argument is specified the input Tensors will be converted to the + given device and in turn determine the device of the constructed + sparse tensor. + + Args: + ccol_indices (array_like): (B+1)-dimensional array of size + ``(*batchsize, ncols + 1)``. The last element of each batch + is the number of non-zeros. This tensor encodes the index in + values and row_indices depending on where the given column + starts. Each successive number in the tensor subtracted by the + number before it denotes the number of elements in a given + column. + row_indices (array_like): Row co-ordinates of each element in + values. (B+1)-dimensional tensor with the same length as + values. + values (array_list): Initial values for the tensor. Can be a list, + tuple, NumPy ``ndarray``, scalar, and other types that + represents a (1+K)-dimensional tensor where ``K`` is the number + of dense dimensions. + size (list, tuple, :class:`torch.Size`, optional): Size of the + sparse tensor: ``(*batchsize, nrows, ncols, *densesize)``. If + not provided, the size will be inferred as the minimum size + big enough to hold all non-zero elements. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of + returned tensor. Default: if None, infers data type from + :attr:`values`. + device (:class:`torch.device`, optional): the desired device of + returned tensor. Default: if None, uses the current device + for the default tensor type (see + :func:`torch.set_default_device`). :attr:`device` will be + the CPU for CPU tensor types and the current CUDA device for + CUDA tensor types. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + check_invariants (bool, optional): If sparse tensor invariants are checked. + Default: as returned by :func:`torch.sparse.check_sparse_tensor_invariants.is_enabled`, + initially False. + + Example:: + >>> ccol_indices = [0, 2, 4] + >>> row_indices = [0, 1, 0, 1] + >>> values = [1, 2, 3, 4] + >>> torch.sparse_csc_tensor(torch.tensor(ccol_indices, dtype=torch.int64), + ... torch.tensor(row_indices, dtype=torch.int64), + ... torch.tensor(values), dtype=torch.double) + tensor(ccol_indices=tensor([0, 2, 4]), + row_indices=tensor([0, 1, 0, 1]), + values=tensor([1., 2., 3., 4.]), size=(2, 2), nnz=4, + dtype=torch.float64, layout=torch.sparse_csc) + """ + ... +def sparse_csr_tensor(crow_indices: Union[Tensor, List], col_indices: Union[Tensor, List], values: Union[Tensor, List], size: Optional[_size] = None, *, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, check_invariants: Optional[_bool] = None) -> Tensor: + r""" + sparse_csr_tensor(crow_indices, col_indices, values, size=None, *, dtype=None, device=None, pin_memory=False, requires_grad=False, check_invariants=None) -> Tensor + + Constructs a :ref:`sparse tensor in CSR (Compressed Sparse Row) ` with specified + values at the given :attr:`crow_indices` and :attr:`col_indices`. Sparse matrix multiplication operations + in CSR format are typically faster than that for sparse tensors in COO format. Make you have a look + at :ref:`the note on the data type of the indices `. + + .. note:: + + If the ``device`` argument is not specified the device of the given + :attr:`values` and indices tensor(s) must match. If, however, the + argument is specified the input Tensors will be converted to the + given device and in turn determine the device of the constructed + sparse tensor. + + Args: + crow_indices (array_like): (B+1)-dimensional array of size + ``(*batchsize, nrows + 1)``. The last element of each batch + is the number of non-zeros. This tensor encodes the index in + values and col_indices depending on where the given row + starts. Each successive number in the tensor subtracted by the + number before it denotes the number of elements in a given + row. + col_indices (array_like): Column co-ordinates of each element in + values. (B+1)-dimensional tensor with the same length + as values. + values (array_list): Initial values for the tensor. Can be a list, + tuple, NumPy ``ndarray``, scalar, and other types that + represents a (1+K)-dimensional tensor where ``K`` is the number + of dense dimensions. + size (list, tuple, :class:`torch.Size`, optional): Size of the + sparse tensor: ``(*batchsize, nrows, ncols, *densesize)``. If + not provided, the size will be inferred as the minimum size + big enough to hold all non-zero elements. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of + returned tensor. Default: if None, infers data type from + :attr:`values`. + device (:class:`torch.device`, optional): the desired device of + returned tensor. Default: if None, uses the current device + for the default tensor type (see + :func:`torch.set_default_device`). :attr:`device` will be + the CPU for CPU tensor types and the current CUDA device for + CUDA tensor types. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + check_invariants (bool, optional): If sparse tensor invariants are checked. + Default: as returned by :func:`torch.sparse.check_sparse_tensor_invariants.is_enabled`, + initially False. + + Example:: + >>> crow_indices = [0, 2, 4] + >>> col_indices = [0, 1, 0, 1] + >>> values = [1, 2, 3, 4] + >>> torch.sparse_csr_tensor(torch.tensor(crow_indices, dtype=torch.int64), + ... torch.tensor(col_indices, dtype=torch.int64), + ... torch.tensor(values), dtype=torch.double) + tensor(crow_indices=tensor([0, 2, 4]), + col_indices=tensor([0, 1, 0, 1]), + values=tensor([1., 2., 3., 4.]), size=(2, 2), nnz=4, + dtype=torch.float64, layout=torch.sparse_csr) + """ + ... +def split_copy(input: Tensor, split_size: Union[_int, SymInt], dim: _int = 0, *, out: Union[Tuple[Tensor, ...], List[Tensor], None] = None) -> None: + r""" + Performs the same operation as :func:`torch.split`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def split_with_sizes(input: Tensor, split_sizes: Sequence[Union[_int, SymInt]], dim: _int = 0) -> Tuple[Tensor, ...]: ... +def split_with_sizes_copy(input: Tensor, split_sizes: Sequence[Union[_int, SymInt]], dim: _int = 0, *, out: Union[Tuple[Tensor, ...], List[Tensor], None] = None) -> None: + r""" + Performs the same operation as :func:`torch.split_with_sizes`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def spmm(input: Tensor, mat2: Tensor) -> Tensor: ... +def sqrt(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + sqrt(input, *, out=None) -> Tensor + + Returns a new tensor with the square-root of the elements of :attr:`input`. + + .. math:: + \text{out}_{i} = \sqrt{\text{input}_{i}} + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-2.0755, 1.0226, 0.0831, 0.4806]) + >>> torch.sqrt(a) + tensor([ nan, 1.0112, 0.2883, 0.6933]) + """ + ... +def sqrt_(input: Tensor) -> Tensor: ... +def square(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + square(input, *, out=None) -> Tensor + + Returns a new tensor with the square of the elements of :attr:`input`. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-2.0755, 1.0226, 0.0831, 0.4806]) + >>> torch.square(a) + tensor([ 4.3077, 1.0457, 0.0069, 0.2310]) + """ + ... +def square_(input: Tensor) -> Tensor: ... +@overload +def squeeze(input: Tensor) -> Tensor: + r""" + squeeze(input, dim=None) -> Tensor + + Returns a tensor with all specified dimensions of :attr:`input` of size `1` removed. + + For example, if `input` is of shape: + :math:`(A \times 1 \times B \times C \times 1 \times D)` then the `input.squeeze()` + will be of shape: :math:`(A \times B \times C \times D)`. + + When :attr:`dim` is given, a squeeze operation is done only in the given + dimension(s). If `input` is of shape: :math:`(A \times 1 \times B)`, + ``squeeze(input, 0)`` leaves the tensor unchanged, but ``squeeze(input, 1)`` + will squeeze the tensor to the shape :math:`(A \times B)`. + + .. note:: The returned tensor shares the storage with the input tensor, + so changing the contents of one will change the contents of the other. + + .. warning:: If the tensor has a batch dimension of size 1, then `squeeze(input)` + will also remove the batch dimension, which can lead to unexpected + errors. Consider specifying only the dims you wish to be squeezed. + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints, optional): if given, the input will be squeezed + only in the specified dimensions. + + .. versionchanged:: 2.0 + :attr:`dim` now accepts tuples of dimensions. + + Example:: + + >>> x = torch.zeros(2, 1, 2, 1, 2) + >>> x.size() + torch.Size([2, 1, 2, 1, 2]) + >>> y = torch.squeeze(x) + >>> y.size() + torch.Size([2, 2, 2]) + >>> y = torch.squeeze(x, 0) + >>> y.size() + torch.Size([2, 1, 2, 1, 2]) + >>> y = torch.squeeze(x, 1) + >>> y.size() + torch.Size([2, 2, 1, 2]) + >>> y = torch.squeeze(x, (1, 2, 3)) + torch.Size([2, 2, 2]) + """ + ... +@overload +def squeeze(input: Tensor, dim: _int) -> Tensor: + r""" + squeeze(input, dim=None) -> Tensor + + Returns a tensor with all specified dimensions of :attr:`input` of size `1` removed. + + For example, if `input` is of shape: + :math:`(A \times 1 \times B \times C \times 1 \times D)` then the `input.squeeze()` + will be of shape: :math:`(A \times B \times C \times D)`. + + When :attr:`dim` is given, a squeeze operation is done only in the given + dimension(s). If `input` is of shape: :math:`(A \times 1 \times B)`, + ``squeeze(input, 0)`` leaves the tensor unchanged, but ``squeeze(input, 1)`` + will squeeze the tensor to the shape :math:`(A \times B)`. + + .. note:: The returned tensor shares the storage with the input tensor, + so changing the contents of one will change the contents of the other. + + .. warning:: If the tensor has a batch dimension of size 1, then `squeeze(input)` + will also remove the batch dimension, which can lead to unexpected + errors. Consider specifying only the dims you wish to be squeezed. + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints, optional): if given, the input will be squeezed + only in the specified dimensions. + + .. versionchanged:: 2.0 + :attr:`dim` now accepts tuples of dimensions. + + Example:: + + >>> x = torch.zeros(2, 1, 2, 1, 2) + >>> x.size() + torch.Size([2, 1, 2, 1, 2]) + >>> y = torch.squeeze(x) + >>> y.size() + torch.Size([2, 2, 2]) + >>> y = torch.squeeze(x, 0) + >>> y.size() + torch.Size([2, 1, 2, 1, 2]) + >>> y = torch.squeeze(x, 1) + >>> y.size() + torch.Size([2, 2, 1, 2]) + >>> y = torch.squeeze(x, (1, 2, 3)) + torch.Size([2, 2, 2]) + """ + ... +@overload +def squeeze(input: Tensor, dim: _size) -> Tensor: + r""" + squeeze(input, dim=None) -> Tensor + + Returns a tensor with all specified dimensions of :attr:`input` of size `1` removed. + + For example, if `input` is of shape: + :math:`(A \times 1 \times B \times C \times 1 \times D)` then the `input.squeeze()` + will be of shape: :math:`(A \times B \times C \times D)`. + + When :attr:`dim` is given, a squeeze operation is done only in the given + dimension(s). If `input` is of shape: :math:`(A \times 1 \times B)`, + ``squeeze(input, 0)`` leaves the tensor unchanged, but ``squeeze(input, 1)`` + will squeeze the tensor to the shape :math:`(A \times B)`. + + .. note:: The returned tensor shares the storage with the input tensor, + so changing the contents of one will change the contents of the other. + + .. warning:: If the tensor has a batch dimension of size 1, then `squeeze(input)` + will also remove the batch dimension, which can lead to unexpected + errors. Consider specifying only the dims you wish to be squeezed. + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints, optional): if given, the input will be squeezed + only in the specified dimensions. + + .. versionchanged:: 2.0 + :attr:`dim` now accepts tuples of dimensions. + + Example:: + + >>> x = torch.zeros(2, 1, 2, 1, 2) + >>> x.size() + torch.Size([2, 1, 2, 1, 2]) + >>> y = torch.squeeze(x) + >>> y.size() + torch.Size([2, 2, 2]) + >>> y = torch.squeeze(x, 0) + >>> y.size() + torch.Size([2, 1, 2, 1, 2]) + >>> y = torch.squeeze(x, 1) + >>> y.size() + torch.Size([2, 2, 1, 2]) + >>> y = torch.squeeze(x, (1, 2, 3)) + torch.Size([2, 2, 2]) + """ + ... +@overload +def squeeze(input: Tensor, dim: Union[str, ellipsis, None]) -> Tensor: + r""" + squeeze(input, dim=None) -> Tensor + + Returns a tensor with all specified dimensions of :attr:`input` of size `1` removed. + + For example, if `input` is of shape: + :math:`(A \times 1 \times B \times C \times 1 \times D)` then the `input.squeeze()` + will be of shape: :math:`(A \times B \times C \times D)`. + + When :attr:`dim` is given, a squeeze operation is done only in the given + dimension(s). If `input` is of shape: :math:`(A \times 1 \times B)`, + ``squeeze(input, 0)`` leaves the tensor unchanged, but ``squeeze(input, 1)`` + will squeeze the tensor to the shape :math:`(A \times B)`. + + .. note:: The returned tensor shares the storage with the input tensor, + so changing the contents of one will change the contents of the other. + + .. warning:: If the tensor has a batch dimension of size 1, then `squeeze(input)` + will also remove the batch dimension, which can lead to unexpected + errors. Consider specifying only the dims you wish to be squeezed. + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints, optional): if given, the input will be squeezed + only in the specified dimensions. + + .. versionchanged:: 2.0 + :attr:`dim` now accepts tuples of dimensions. + + Example:: + + >>> x = torch.zeros(2, 1, 2, 1, 2) + >>> x.size() + torch.Size([2, 1, 2, 1, 2]) + >>> y = torch.squeeze(x) + >>> y.size() + torch.Size([2, 2, 2]) + >>> y = torch.squeeze(x, 0) + >>> y.size() + torch.Size([2, 1, 2, 1, 2]) + >>> y = torch.squeeze(x, 1) + >>> y.size() + torch.Size([2, 2, 1, 2]) + >>> y = torch.squeeze(x, (1, 2, 3)) + torch.Size([2, 2, 2]) + """ + ... +@overload +def squeeze_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.squeeze`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +@overload +def squeeze_copy(input: Tensor, dim: _int, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.squeeze`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +@overload +def squeeze_copy(input: Tensor, dim: _size, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.squeeze`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +@overload +def sspaddmm(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], mat1: Tensor, mat2: Tensor) -> Tensor: + r""" + sspaddmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor + + Matrix multiplies a sparse tensor :attr:`mat1` with a dense tensor + :attr:`mat2`, then adds the sparse tensor :attr:`input` to the result. + + Note: This function is equivalent to :func:`torch.addmm`, except + :attr:`input` and :attr:`mat1` are sparse. + + Args: + input (Tensor): a sparse matrix to be added + mat1 (Tensor): a sparse matrix to be matrix multiplied + mat2 (Tensor): a dense matrix to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`mat` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + """ + ... +@overload +def sspaddmm(input: Tensor, mat1: Tensor, mat2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + sspaddmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor + + Matrix multiplies a sparse tensor :attr:`mat1` with a dense tensor + :attr:`mat2`, then adds the sparse tensor :attr:`input` to the result. + + Note: This function is equivalent to :func:`torch.addmm`, except + :attr:`input` and :attr:`mat1` are sparse. + + Args: + input (Tensor): a sparse matrix to be added + mat1 (Tensor): a sparse matrix to be matrix multiplied + mat2 (Tensor): a dense matrix to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`mat` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + """ + ... +@overload +def sspaddmm(beta: Union[Number, _complex], self: Tensor, mat1: Tensor, mat2: Tensor) -> Tensor: + r""" + sspaddmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor + + Matrix multiplies a sparse tensor :attr:`mat1` with a dense tensor + :attr:`mat2`, then adds the sparse tensor :attr:`input` to the result. + + Note: This function is equivalent to :func:`torch.addmm`, except + :attr:`input` and :attr:`mat1` are sparse. + + Args: + input (Tensor): a sparse matrix to be added + mat1 (Tensor): a sparse matrix to be matrix multiplied + mat2 (Tensor): a dense matrix to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`mat` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + """ + ... +def stack(tensors: Union[Tuple[Tensor, ...], List[Tensor]], dim: _int = 0, *, out: Optional[Tensor] = None) -> Tensor: + r""" + stack(tensors, dim=0, *, out=None) -> Tensor + + Concatenates a sequence of tensors along a new dimension. + + All tensors need to be of the same size. + + .. seealso:: + + :func:`torch.cat` concatenates the given sequence along an existing dimension. + + Arguments: + tensors (sequence of Tensors): sequence of tensors to concatenate + dim (int, optional): dimension to insert. Has to be between 0 and the number + of dimensions of concatenated tensors (inclusive). Default: 0 + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> x = torch.randn(2, 3) + >>> x + tensor([[ 0.3367, 0.1288, 0.2345], + [ 0.2303, -1.1229, -0.1863]]) + >>> torch.stack((x, x)) # same as torch.stack((x, x), dim=0) + tensor([[[ 0.3367, 0.1288, 0.2345], + [ 0.2303, -1.1229, -0.1863]], + + [[ 0.3367, 0.1288, 0.2345], + [ 0.2303, -1.1229, -0.1863]]]) + >>> torch.stack((x, x)).size() + torch.Size([2, 2, 3]) + >>> torch.stack((x, x), dim=1) + tensor([[[ 0.3367, 0.1288, 0.2345], + [ 0.3367, 0.1288, 0.2345]], + + [[ 0.2303, -1.1229, -0.1863], + [ 0.2303, -1.1229, -0.1863]]]) + >>> torch.stack((x, x), dim=2) + tensor([[[ 0.3367, 0.3367], + [ 0.1288, 0.1288], + [ 0.2345, 0.2345]], + + [[ 0.2303, 0.2303], + [-1.1229, -1.1229], + [-0.1863, -0.1863]]]) + >>> torch.stack((x, x), dim=-1) + tensor([[[ 0.3367, 0.3367], + [ 0.1288, 0.1288], + [ 0.2345, 0.2345]], + + [[ 0.2303, 0.2303], + [-1.1229, -1.1229], + [-0.1863, -0.1863]]]) + """ + ... +@overload +def std(input: Tensor, dim: Optional[Union[_int, _size]], unbiased: _bool = True, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + std(input, dim=None, *, correction=1, keepdim=False, out=None) -> Tensor + + Calculates the standard deviation over the dimensions specified by :attr:`dim`. + :attr:`dim` can be a single dimension, list of dimensions, or ``None`` to + reduce over all dimensions. + + The standard deviation (:math:`\sigma`) is calculated as + + .. math:: \sigma = \sqrt{\frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2} + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.std(a, dim=1, keepdim=True) + tensor([[1.0311], + [0.7477], + [1.2204], + [0.9087]]) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def std(input: Tensor, dim: Optional[Union[_int, _size]] = None, *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + std(input, dim=None, *, correction=1, keepdim=False, out=None) -> Tensor + + Calculates the standard deviation over the dimensions specified by :attr:`dim`. + :attr:`dim` can be a single dimension, list of dimensions, or ``None`` to + reduce over all dimensions. + + The standard deviation (:math:`\sigma`) is calculated as + + .. math:: \sigma = \sqrt{\frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2} + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.std(a, dim=1, keepdim=True) + tensor([[1.0311], + [0.7477], + [1.2204], + [0.9087]]) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def std(input: Tensor, unbiased: _bool = True) -> Tensor: + r""" + std(input, dim=None, *, correction=1, keepdim=False, out=None) -> Tensor + + Calculates the standard deviation over the dimensions specified by :attr:`dim`. + :attr:`dim` can be a single dimension, list of dimensions, or ``None`` to + reduce over all dimensions. + + The standard deviation (:math:`\sigma`) is calculated as + + .. math:: \sigma = \sqrt{\frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2} + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.std(a, dim=1, keepdim=True) + tensor([[1.0311], + [0.7477], + [1.2204], + [0.9087]]) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def std(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + std(input, dim=None, *, correction=1, keepdim=False, out=None) -> Tensor + + Calculates the standard deviation over the dimensions specified by :attr:`dim`. + :attr:`dim` can be a single dimension, list of dimensions, or ``None`` to + reduce over all dimensions. + + The standard deviation (:math:`\sigma`) is calculated as + + .. math:: \sigma = \sqrt{\frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2} + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.std(a, dim=1, keepdim=True) + tensor([[1.0311], + [0.7477], + [1.2204], + [0.9087]]) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def std(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], unbiased: _bool = True, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + std(input, dim=None, *, correction=1, keepdim=False, out=None) -> Tensor + + Calculates the standard deviation over the dimensions specified by :attr:`dim`. + :attr:`dim` can be a single dimension, list of dimensions, or ``None`` to + reduce over all dimensions. + + The standard deviation (:math:`\sigma`) is calculated as + + .. math:: \sigma = \sqrt{\frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2} + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.std(a, dim=1, keepdim=True) + tensor([[1.0311], + [0.7477], + [1.2204], + [0.9087]]) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def std_mean(input: Tensor, dim: Optional[Union[_int, _size]], unbiased: _bool = True, keepdim: _bool = False) -> Tuple[Tensor, Tensor]: + r""" + std_mean(input, dim=None, *, correction=1, keepdim=False, out=None) -> (Tensor, Tensor) + + Calculates the standard deviation and mean over the dimensions specified by + :attr:`dim`. :attr:`dim` can be a single dimension, list of dimensions, or + ``None`` to reduce over all dimensions. + + The standard deviation (:math:`\sigma`) is calculated as + + .. math:: \sigma = \sqrt{\frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2} + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Returns: + A tuple (std, mean) containing the standard deviation and mean. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.std_mean(a, dim=0, keepdim=True) + (tensor([[1.2620, 1.0028, 1.0957, 0.6038]]), + tensor([[ 0.0645, 0.4485, 0.8707, -0.0665]])) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def std_mean(input: Tensor, dim: Optional[Union[_int, _size]] = None, *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False) -> Tuple[Tensor, Tensor]: + r""" + std_mean(input, dim=None, *, correction=1, keepdim=False, out=None) -> (Tensor, Tensor) + + Calculates the standard deviation and mean over the dimensions specified by + :attr:`dim`. :attr:`dim` can be a single dimension, list of dimensions, or + ``None`` to reduce over all dimensions. + + The standard deviation (:math:`\sigma`) is calculated as + + .. math:: \sigma = \sqrt{\frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2} + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Returns: + A tuple (std, mean) containing the standard deviation and mean. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.std_mean(a, dim=0, keepdim=True) + (tensor([[1.2620, 1.0028, 1.0957, 0.6038]]), + tensor([[ 0.0645, 0.4485, 0.8707, -0.0665]])) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def std_mean(input: Tensor, unbiased: _bool = True) -> Tuple[Tensor, Tensor]: + r""" + std_mean(input, dim=None, *, correction=1, keepdim=False, out=None) -> (Tensor, Tensor) + + Calculates the standard deviation and mean over the dimensions specified by + :attr:`dim`. :attr:`dim` can be a single dimension, list of dimensions, or + ``None`` to reduce over all dimensions. + + The standard deviation (:math:`\sigma`) is calculated as + + .. math:: \sigma = \sqrt{\frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2} + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Returns: + A tuple (std, mean) containing the standard deviation and mean. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.std_mean(a, dim=0, keepdim=True) + (tensor([[1.2620, 1.0028, 1.0957, 0.6038]]), + tensor([[ 0.0645, 0.4485, 0.8707, -0.0665]])) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def std_mean(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False) -> Tuple[Tensor, Tensor]: + r""" + std_mean(input, dim=None, *, correction=1, keepdim=False, out=None) -> (Tensor, Tensor) + + Calculates the standard deviation and mean over the dimensions specified by + :attr:`dim`. :attr:`dim` can be a single dimension, list of dimensions, or + ``None`` to reduce over all dimensions. + + The standard deviation (:math:`\sigma`) is calculated as + + .. math:: \sigma = \sqrt{\frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2} + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Returns: + A tuple (std, mean) containing the standard deviation and mean. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.std_mean(a, dim=0, keepdim=True) + (tensor([[1.2620, 1.0028, 1.0957, 0.6038]]), + tensor([[ 0.0645, 0.4485, 0.8707, -0.0665]])) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def std_mean(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], unbiased: _bool = True, keepdim: _bool = False) -> Tuple[Tensor, Tensor]: + r""" + std_mean(input, dim=None, *, correction=1, keepdim=False, out=None) -> (Tensor, Tensor) + + Calculates the standard deviation and mean over the dimensions specified by + :attr:`dim`. :attr:`dim` can be a single dimension, list of dimensions, or + ``None`` to reduce over all dimensions. + + The standard deviation (:math:`\sigma`) is calculated as + + .. math:: \sigma = \sqrt{\frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2} + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Returns: + A tuple (std, mean) containing the standard deviation and mean. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.std_mean(a, dim=0, keepdim=True) + (tensor([[1.2620, 1.0028, 1.0957, 0.6038]]), + tensor([[ 0.0645, 0.4485, 0.8707, -0.0665]])) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def sub(input: Union[Tensor, Number, _complex], other: Union[Tensor, Number, _complex], *, alpha: Optional[Union[Number, _complex]] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + sub(input, other, *, alpha=1, out=None) -> Tensor + + Subtracts :attr:`other`, scaled by :attr:`alpha`, from :attr:`input`. + + .. math:: + \text{{out}}_i = \text{{input}}_i - \text{{alpha}} \times \text{{other}}_i + + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer, float, and complex inputs. + + Args: + input (Tensor): the input tensor. + other (Tensor or Number): the tensor or number to subtract from :attr:`input`. + + Keyword args: + alpha (Number): the multiplier for :attr:`other`. + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor((1, 2)) + >>> b = torch.tensor((0, 1)) + >>> torch.sub(a, b, alpha=2) + tensor([1, 0]) + """ + ... +@overload +def sub(self: Tensor, alpha: Union[Number, _complex], other: Tensor) -> Tensor: + r""" + sub(input, other, *, alpha=1, out=None) -> Tensor + + Subtracts :attr:`other`, scaled by :attr:`alpha`, from :attr:`input`. + + .. math:: + \text{{out}}_i = \text{{input}}_i - \text{{alpha}} \times \text{{other}}_i + + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer, float, and complex inputs. + + Args: + input (Tensor): the input tensor. + other (Tensor or Number): the tensor or number to subtract from :attr:`input`. + + Keyword args: + alpha (Number): the multiplier for :attr:`other`. + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor((1, 2)) + >>> b = torch.tensor((0, 1)) + >>> torch.sub(a, b, alpha=2) + tensor([1, 0]) + """ + ... +@overload +def sub(self: Tensor, alpha: Union[Number, _complex], other: Tensor, *, out: Tensor) -> Tensor: + r""" + sub(input, other, *, alpha=1, out=None) -> Tensor + + Subtracts :attr:`other`, scaled by :attr:`alpha`, from :attr:`input`. + + .. math:: + \text{{out}}_i = \text{{input}}_i - \text{{alpha}} \times \text{{other}}_i + + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer, float, and complex inputs. + + Args: + input (Tensor): the input tensor. + other (Tensor or Number): the tensor or number to subtract from :attr:`input`. + + Keyword args: + alpha (Number): the multiplier for :attr:`other`. + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor((1, 2)) + >>> b = torch.tensor((0, 1)) + >>> torch.sub(a, b, alpha=2) + tensor([1, 0]) + """ + ... +@overload +def subtract(input: Tensor, other: Tensor, *, alpha: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + subtract(input, other, *, alpha=1, out=None) -> Tensor + + Alias for :func:`torch.sub`. + """ + ... +@overload +def subtract(input: Tensor, other: Union[Number, _complex], alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + subtract(input, other, *, alpha=1, out=None) -> Tensor + + Alias for :func:`torch.sub`. + """ + ... +@overload +def sum(input: Tensor, *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + sum(input, *, dtype=None) -> Tensor + + Returns the sum of all elements in the :attr:`input` tensor. + + Args: + input (Tensor): the input tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.1133, -0.9567, 0.2958]]) + >>> torch.sum(a) + tensor(-0.5475) + + .. function:: sum(input, dim, keepdim=False, *, dtype=None) -> Tensor + :noindex: + + Returns the sum of each row of the :attr:`input` tensor in the given + dimension :attr:`dim`. If :attr:`dim` is a list of dimensions, + reduce over all of them. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.0569, -0.2475, 0.0737, -0.3429], + [-0.2993, 0.9138, 0.9337, -1.6864], + [ 0.1132, 0.7892, -0.1003, 0.5688], + [ 0.3637, -0.9906, -0.4752, -1.5197]]) + >>> torch.sum(a, 1) + tensor([-0.4598, -0.1381, 1.3708, -2.6217]) + >>> b = torch.arange(4 * 5 * 6).view(4, 5, 6) + >>> torch.sum(b, (2, 1)) + tensor([ 435., 1335., 2235., 3135.]) + """ + ... +@overload +def sum(input: Tensor, dim: Optional[Union[_int, _size]], keepdim: _bool = False, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + sum(input, *, dtype=None) -> Tensor + + Returns the sum of all elements in the :attr:`input` tensor. + + Args: + input (Tensor): the input tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.1133, -0.9567, 0.2958]]) + >>> torch.sum(a) + tensor(-0.5475) + + .. function:: sum(input, dim, keepdim=False, *, dtype=None) -> Tensor + :noindex: + + Returns the sum of each row of the :attr:`input` tensor in the given + dimension :attr:`dim`. If :attr:`dim` is a list of dimensions, + reduce over all of them. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.0569, -0.2475, 0.0737, -0.3429], + [-0.2993, 0.9138, 0.9337, -1.6864], + [ 0.1132, 0.7892, -0.1003, 0.5688], + [ 0.3637, -0.9906, -0.4752, -1.5197]]) + >>> torch.sum(a, 1) + tensor([-0.4598, -0.1381, 1.3708, -2.6217]) + >>> b = torch.arange(4 * 5 * 6).view(4, 5, 6) + >>> torch.sum(b, (2, 1)) + tensor([ 435., 1335., 2235., 3135.]) + """ + ... +@overload +def sum(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], keepdim: _bool = False, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + sum(input, *, dtype=None) -> Tensor + + Returns the sum of all elements in the :attr:`input` tensor. + + Args: + input (Tensor): the input tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.1133, -0.9567, 0.2958]]) + >>> torch.sum(a) + tensor(-0.5475) + + .. function:: sum(input, dim, keepdim=False, *, dtype=None) -> Tensor + :noindex: + + Returns the sum of each row of the :attr:`input` tensor in the given + dimension :attr:`dim`. If :attr:`dim` is a list of dimensions, + reduce over all of them. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.0569, -0.2475, 0.0737, -0.3429], + [-0.2993, 0.9138, 0.9337, -1.6864], + [ 0.1132, 0.7892, -0.1003, 0.5688], + [ 0.3637, -0.9906, -0.4752, -1.5197]]) + >>> torch.sum(a, 1) + tensor([-0.4598, -0.1381, 1.3708, -2.6217]) + >>> b = torch.arange(4 * 5 * 6).view(4, 5, 6) + >>> torch.sum(b, (2, 1)) + tensor([ 435., 1335., 2235., 3135.]) + """ + ... +def svd(input: Tensor, some: _bool = True, compute_uv: _bool = True, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.svd: + r""" + svd(input, some=True, compute_uv=True, *, out=None) -> (Tensor, Tensor, Tensor) + + Computes the singular value decomposition of either a matrix or batch of + matrices :attr:`input`. The singular value decomposition is represented as a + namedtuple `(U, S, V)`, such that :attr:`input` :math:`= U \text{diag}(S) V^{\text{H}}`. + where :math:`V^{\text{H}}` is the transpose of `V` for real inputs, + and the conjugate transpose of `V` for complex inputs. + If :attr:`input` is a batch of matrices, then `U`, `S`, and `V` are also + batched with the same batch dimensions as :attr:`input`. + + If :attr:`some` is `True` (default), the method returns the reduced singular + value decomposition. In this case, if the last two dimensions of :attr:`input` are + `m` and `n`, then the returned `U` and `V` matrices will contain only + `min(n, m)` orthonormal columns. + + If :attr:`compute_uv` is `False`, the returned `U` and `V` will be + zero-filled matrices of shape `(m, m)` and `(n, n)` + respectively, and the same device as :attr:`input`. The argument :attr:`some` + has no effect when :attr:`compute_uv` is `False`. + + Supports :attr:`input` of float, double, cfloat and cdouble data types. + The dtypes of `U` and `V` are the same as :attr:`input`'s. `S` will + always be real-valued, even if :attr:`input` is complex. + + .. warning:: + + :func:`torch.svd` is deprecated in favor of :func:`torch.linalg.svd` + and will be removed in a future PyTorch release. + + ``U, S, V = torch.svd(A, some=some, compute_uv=True)`` (default) should be replaced with + + .. code:: python + + U, S, Vh = torch.linalg.svd(A, full_matrices=not some) + V = Vh.mH + + ``_, S, _ = torch.svd(A, some=some, compute_uv=False)`` should be replaced with + + .. code:: python + + S = torch.linalg.svdvals(A) + + .. note:: Differences with :func:`torch.linalg.svd`: + + * :attr:`some` is the opposite of + :func:`torch.linalg.svd`'s :attr:`full_matrices`. Note that + default value for both is `True`, so the default behavior is + effectively the opposite. + * :func:`torch.svd` returns `V`, whereas :func:`torch.linalg.svd` returns + `Vh`, that is, :math:`V^{\text{H}}`. + * If :attr:`compute_uv` is `False`, :func:`torch.svd` returns zero-filled + tensors for `U` and `Vh`, whereas :func:`torch.linalg.svd` returns + empty tensors. + + .. note:: The singular values are returned in descending order. If :attr:`input` is a batch of matrices, + then the singular values of each matrix in the batch are returned in descending order. + + .. note:: The `S` tensor can only be used to compute gradients if :attr:`compute_uv` is `True`. + + .. note:: When :attr:`some` is `False`, the gradients on `U[..., :, min(m, n):]` + and `V[..., :, min(m, n):]` will be ignored in the backward pass, as those vectors + can be arbitrary bases of the corresponding subspaces. + + .. note:: The implementation of :func:`torch.linalg.svd` on CPU uses LAPACK's routine `?gesdd` + (a divide-and-conquer algorithm) instead of `?gesvd` for speed. Analogously, + on GPU, it uses cuSOLVER's routines `gesvdj` and `gesvdjBatched` on CUDA 10.1.243 + and later, and MAGMA's routine `gesdd` on earlier versions of CUDA. + + .. note:: The returned `U` will not be contiguous. The matrix (or batch of matrices) will + be represented as a column-major matrix (i.e. Fortran-contiguous). + + .. warning:: The gradients with respect to `U` and `V` will only be finite when the input does not + have zero nor repeated singular values. + + .. warning:: If the distance between any two singular values is close to zero, the gradients with respect to + `U` and `V` will be numerically unstable, as they depends on + :math:`\frac{1}{\min_{i \neq j} \sigma_i^2 - \sigma_j^2}`. The same happens when the matrix + has small singular values, as these gradients also depend on `S^{-1}`. + + .. warning:: For complex-valued :attr:`input` the singular value decomposition is not unique, + as `U` and `V` may be multiplied by an arbitrary phase factor :math:`e^{i \phi}` on every column. + The same happens when :attr:`input` has repeated singular values, where one may multiply + the columns of the spanning subspace in `U` and `V` by a rotation matrix + and `the resulting vectors will span the same subspace`_. + Different platforms, like NumPy, or inputs on different device types, + may produce different `U` and `V` tensors. + + Args: + input (Tensor): the input tensor of size `(*, m, n)` where `*` is zero or more + batch dimensions consisting of `(m, n)` matrices. + some (bool, optional): controls whether to compute the reduced or full decomposition, and + consequently, the shape of returned `U` and `V`. Default: `True`. + compute_uv (bool, optional): controls whether to compute `U` and `V`. Default: `True`. + + Keyword args: + out (tuple, optional): the output tuple of tensors + + Example:: + + >>> a = torch.randn(5, 3) + >>> a + tensor([[ 0.2364, -0.7752, 0.6372], + [ 1.7201, 0.7394, -0.0504], + [-0.3371, -1.0584, 0.5296], + [ 0.3550, -0.4022, 1.5569], + [ 0.2445, -0.0158, 1.1414]]) + >>> u, s, v = torch.svd(a) + >>> u + tensor([[ 0.4027, 0.0287, 0.5434], + [-0.1946, 0.8833, 0.3679], + [ 0.4296, -0.2890, 0.5261], + [ 0.6604, 0.2717, -0.2618], + [ 0.4234, 0.2481, -0.4733]]) + >>> s + tensor([2.3289, 2.0315, 0.7806]) + >>> v + tensor([[-0.0199, 0.8766, 0.4809], + [-0.5080, 0.4054, -0.7600], + [ 0.8611, 0.2594, -0.4373]]) + >>> torch.dist(a, torch.mm(torch.mm(u, torch.diag(s)), v.t())) + tensor(8.6531e-07) + >>> a_big = torch.randn(7, 5, 3) + >>> u, s, v = torch.svd(a_big) + >>> torch.dist(a_big, torch.matmul(torch.matmul(u, torch.diag_embed(s)), v.mT)) + tensor(2.6503e-06) + + .. _the resulting vectors will span the same subspace: + (https://en.wikipedia.org/wiki/Singular_value_decomposition#Singular_values,_singular_vectors,_and_their_relation_to_the_SVD) + """ + ... +def swapaxes(input: Tensor, axis0: _int, axis1: _int) -> Tensor: + r""" + swapaxes(input, axis0, axis1) -> Tensor + + Alias for :func:`torch.transpose`. + + This function is equivalent to NumPy's swapaxes function. + + Examples:: + + >>> x = torch.tensor([[[0,1],[2,3]],[[4,5],[6,7]]]) + >>> x + tensor([[[0, 1], + [2, 3]], + + [[4, 5], + [6, 7]]]) + >>> torch.swapaxes(x, 0, 1) + tensor([[[0, 1], + [4, 5]], + + [[2, 3], + [6, 7]]]) + >>> torch.swapaxes(x, 0, 2) + tensor([[[0, 4], + [2, 6]], + + [[1, 5], + [3, 7]]]) + """ + ... +def swapdims(input: Tensor, dim0: _int, dim1: _int) -> Tensor: + r""" + swapdims(input, dim0, dim1) -> Tensor + + Alias for :func:`torch.transpose`. + + This function is equivalent to NumPy's swapaxes function. + + Examples:: + + >>> x = torch.tensor([[[0,1],[2,3]],[[4,5],[6,7]]]) + >>> x + tensor([[[0, 1], + [2, 3]], + + [[4, 5], + [6, 7]]]) + >>> torch.swapdims(x, 0, 1) + tensor([[[0, 1], + [4, 5]], + + [[2, 3], + [6, 7]]]) + >>> torch.swapdims(x, 0, 2) + tensor([[[0, 4], + [2, 6]], + + [[1, 5], + [3, 7]]]) + """ + ... +def sym_constrain_range(size: Union[Number, _complex], *, min: Optional[_int] = None, max: Optional[_int] = None) -> None: ... +def sym_constrain_range_for_size(size: Union[Number, _complex], *, min: Optional[_int] = None, max: Optional[_int] = None) -> None: ... +def t(input: Tensor) -> Tensor: + r""" + t(input) -> Tensor + + Expects :attr:`input` to be <= 2-D tensor and transposes dimensions 0 + and 1. + + 0-D and 1-D tensors are returned as is. When input is a 2-D tensor this + is equivalent to ``transpose(input, 0, 1)``. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> x = torch.randn(()) + >>> x + tensor(0.1995) + >>> torch.t(x) + tensor(0.1995) + >>> x = torch.randn(3) + >>> x + tensor([ 2.4320, -0.4608, 0.7702]) + >>> torch.t(x) + tensor([ 2.4320, -0.4608, 0.7702]) + >>> x = torch.randn(2, 3) + >>> x + tensor([[ 0.4875, 0.9158, -0.5872], + [ 0.3938, -0.6929, 0.6932]]) + >>> torch.t(x) + tensor([[ 0.4875, 0.3938], + [ 0.9158, -0.6929], + [-0.5872, 0.6932]]) + + See also :func:`torch.transpose`. + """ + ... +def t_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.t`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def take(input: Tensor, index: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + take(input, index) -> Tensor + + Returns a new tensor with the elements of :attr:`input` at the given indices. + The input tensor is treated as if it were viewed as a 1-D tensor. The result + takes the same shape as the indices. + + Args: + input (Tensor): the input tensor. + index (LongTensor): the indices into tensor + + Example:: + + >>> src = torch.tensor([[4, 3, 5], + ... [6, 7, 8]]) + >>> torch.take(src, torch.tensor([0, 2, 5])) + tensor([ 4, 5, 8]) + """ + ... +def take_along_dim(input: Tensor, indices: Tensor, dim: Optional[_int] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + take_along_dim(input, indices, dim=None, *, out=None) -> Tensor + + Selects values from :attr:`input` at the 1-dimensional indices from :attr:`indices` along the given :attr:`dim`. + + If :attr:`dim` is None, the input array is treated as if it has been flattened to 1d. + + Functions that return indices along a dimension, like :func:`torch.argmax` and :func:`torch.argsort`, + are designed to work with this function. See the examples below. + + .. note:: + This function is similar to NumPy's `take_along_axis`. + See also :func:`torch.gather`. + + Args: + input (Tensor): the input tensor. + indices (tensor): the indices into :attr:`input`. Must have long dtype. + dim (int, optional): dimension to select along. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.tensor([[10, 30, 20], [60, 40, 50]]) + >>> max_idx = torch.argmax(t) + >>> torch.take_along_dim(t, max_idx) + tensor([60]) + >>> sorted_idx = torch.argsort(t, dim=1) + >>> torch.take_along_dim(t, sorted_idx, dim=1) + tensor([[10, 20, 30], + [40, 50, 60]]) + """ + ... +def tan(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + tan(input, *, out=None) -> Tensor + + Returns a new tensor with the tangent of the elements of :attr:`input`. + + .. math:: + \text{out}_{i} = \tan(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-1.2027, -1.7687, 0.4412, -1.3856]) + >>> torch.tan(a) + tensor([-2.5930, 4.9859, 0.4722, -5.3366]) + """ + ... +def tan_(input: Tensor) -> Tensor: ... +def tanh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + tanh(input, *, out=None) -> Tensor + + Returns a new tensor with the hyperbolic tangent of the elements + of :attr:`input`. + + .. math:: + \text{out}_{i} = \tanh(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.8986, -0.7279, 1.1745, 0.2611]) + >>> torch.tanh(a) + tensor([ 0.7156, -0.6218, 0.8257, 0.2553]) + """ + ... +def tanh_(input: Tensor) -> Tensor: ... +def tensor(data: Any, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + tensor(data, *, dtype=None, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Constructs a tensor with no autograd history (also known as a "leaf tensor", see :doc:`/notes/autograd`) by copying :attr:`data`. + + .. warning:: + + When working with tensors prefer using :func:`torch.Tensor.clone`, + :func:`torch.Tensor.detach`, and :func:`torch.Tensor.requires_grad_` for + readability. Letting `t` be a tensor, ``torch.tensor(t)`` is equivalent to + ``t.clone().detach()``, and ``torch.tensor(t, requires_grad=True)`` + is equivalent to ``t.clone().detach().requires_grad_(True)``. + + .. seealso:: + + :func:`torch.as_tensor` preserves autograd history and avoids copies where possible. + :func:`torch.from_numpy` creates a tensor that shares storage with a NumPy array. + + Args: + data (array_like): Initial data for the tensor. Can be a list, tuple, + NumPy ``ndarray``, scalar, and other types. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, infers data type from :attr:`data`. + device (:class:`torch.device`, optional): the device of the constructed tensor. If None and data is a tensor + then the device of data is used. If None and data is not a tensor then + the result tensor is constructed on the current device. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + + Example:: + + >>> torch.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]]) + tensor([[ 0.1000, 1.2000], + [ 2.2000, 3.1000], + [ 4.9000, 5.2000]]) + + >>> torch.tensor([0, 1]) # Type inference on data + tensor([ 0, 1]) + + >>> torch.tensor([[0.11111, 0.222222, 0.3333333]], + ... dtype=torch.float64, + ... device=torch.device('cuda:0')) # creates a double tensor on a CUDA device + tensor([[ 0.1111, 0.2222, 0.3333]], dtype=torch.float64, device='cuda:0') + + >>> torch.tensor(3.14159) # Create a zero-dimensional (scalar) tensor + tensor(3.1416) + + >>> torch.tensor([]) # Create an empty tensor (of size (0,)) + tensor([]) + """ + ... +@overload +def tensor_split(input: Tensor, tensor_indices_or_sections: Tensor, dim: _int = 0) -> Tuple[Tensor, ...]: + r""" + tensor_split(input, indices_or_sections, dim=0) -> List of Tensors + + Splits a tensor into multiple sub-tensors, all of which are views of :attr:`input`, + along dimension :attr:`dim` according to the indices or number of sections specified + by :attr:`indices_or_sections`. This function is based on NumPy's + :func:`numpy.array_split`. + + Args: + input (Tensor): the tensor to split + indices_or_sections (Tensor, int or list or tuple of ints): + If :attr:`indices_or_sections` is an integer ``n`` or a zero dimensional long tensor + with value ``n``, :attr:`input` is split into ``n`` sections along dimension :attr:`dim`. + If :attr:`input` is divisible by ``n`` along dimension :attr:`dim`, each + section will be of equal size, :code:`input.size(dim) / n`. If :attr:`input` + is not divisible by ``n``, the sizes of the first :code:`int(input.size(dim) % n)` + sections will have size :code:`int(input.size(dim) / n) + 1`, and the rest will + have size :code:`int(input.size(dim) / n)`. + + If :attr:`indices_or_sections` is a list or tuple of ints, or a one-dimensional long + tensor, then :attr:`input` is split along dimension :attr:`dim` at each of the indices + in the list, tuple or tensor. For instance, :code:`indices_or_sections=[2, 3]` and :code:`dim=0` + would result in the tensors :code:`input[:2]`, :code:`input[2:3]`, and :code:`input[3:]`. + + If :attr:`indices_or_sections` is a tensor, it must be a zero-dimensional or one-dimensional + long tensor on the CPU. + + dim (int, optional): dimension along which to split the tensor. Default: ``0`` + + Example:: + + >>> x = torch.arange(8) + >>> torch.tensor_split(x, 3) + (tensor([0, 1, 2]), tensor([3, 4, 5]), tensor([6, 7])) + + >>> x = torch.arange(7) + >>> torch.tensor_split(x, 3) + (tensor([0, 1, 2]), tensor([3, 4]), tensor([5, 6])) + >>> torch.tensor_split(x, (1, 6)) + (tensor([0]), tensor([1, 2, 3, 4, 5]), tensor([6])) + + >>> x = torch.arange(14).reshape(2, 7) + >>> x + tensor([[ 0, 1, 2, 3, 4, 5, 6], + [ 7, 8, 9, 10, 11, 12, 13]]) + >>> torch.tensor_split(x, 3, dim=1) + (tensor([[0, 1, 2], + [7, 8, 9]]), + tensor([[ 3, 4], + [10, 11]]), + tensor([[ 5, 6], + [12, 13]])) + >>> torch.tensor_split(x, (1, 6), dim=1) + (tensor([[0], + [7]]), + tensor([[ 1, 2, 3, 4, 5], + [ 8, 9, 10, 11, 12]]), + tensor([[ 6], + [13]])) + """ + ... +@overload +def tensor_split(input: Tensor, sections: Union[_int, SymInt], dim: _int = 0) -> Tuple[Tensor, ...]: + r""" + tensor_split(input, indices_or_sections, dim=0) -> List of Tensors + + Splits a tensor into multiple sub-tensors, all of which are views of :attr:`input`, + along dimension :attr:`dim` according to the indices or number of sections specified + by :attr:`indices_or_sections`. This function is based on NumPy's + :func:`numpy.array_split`. + + Args: + input (Tensor): the tensor to split + indices_or_sections (Tensor, int or list or tuple of ints): + If :attr:`indices_or_sections` is an integer ``n`` or a zero dimensional long tensor + with value ``n``, :attr:`input` is split into ``n`` sections along dimension :attr:`dim`. + If :attr:`input` is divisible by ``n`` along dimension :attr:`dim`, each + section will be of equal size, :code:`input.size(dim) / n`. If :attr:`input` + is not divisible by ``n``, the sizes of the first :code:`int(input.size(dim) % n)` + sections will have size :code:`int(input.size(dim) / n) + 1`, and the rest will + have size :code:`int(input.size(dim) / n)`. + + If :attr:`indices_or_sections` is a list or tuple of ints, or a one-dimensional long + tensor, then :attr:`input` is split along dimension :attr:`dim` at each of the indices + in the list, tuple or tensor. For instance, :code:`indices_or_sections=[2, 3]` and :code:`dim=0` + would result in the tensors :code:`input[:2]`, :code:`input[2:3]`, and :code:`input[3:]`. + + If :attr:`indices_or_sections` is a tensor, it must be a zero-dimensional or one-dimensional + long tensor on the CPU. + + dim (int, optional): dimension along which to split the tensor. Default: ``0`` + + Example:: + + >>> x = torch.arange(8) + >>> torch.tensor_split(x, 3) + (tensor([0, 1, 2]), tensor([3, 4, 5]), tensor([6, 7])) + + >>> x = torch.arange(7) + >>> torch.tensor_split(x, 3) + (tensor([0, 1, 2]), tensor([3, 4]), tensor([5, 6])) + >>> torch.tensor_split(x, (1, 6)) + (tensor([0]), tensor([1, 2, 3, 4, 5]), tensor([6])) + + >>> x = torch.arange(14).reshape(2, 7) + >>> x + tensor([[ 0, 1, 2, 3, 4, 5, 6], + [ 7, 8, 9, 10, 11, 12, 13]]) + >>> torch.tensor_split(x, 3, dim=1) + (tensor([[0, 1, 2], + [7, 8, 9]]), + tensor([[ 3, 4], + [10, 11]]), + tensor([[ 5, 6], + [12, 13]])) + >>> torch.tensor_split(x, (1, 6), dim=1) + (tensor([[0], + [7]]), + tensor([[ 1, 2, 3, 4, 5], + [ 8, 9, 10, 11, 12]]), + tensor([[ 6], + [13]])) + """ + ... +@overload +def tensor_split(input: Tensor, indices: Sequence[Union[_int, SymInt]], dim: _int = 0) -> Tuple[Tensor, ...]: + r""" + tensor_split(input, indices_or_sections, dim=0) -> List of Tensors + + Splits a tensor into multiple sub-tensors, all of which are views of :attr:`input`, + along dimension :attr:`dim` according to the indices or number of sections specified + by :attr:`indices_or_sections`. This function is based on NumPy's + :func:`numpy.array_split`. + + Args: + input (Tensor): the tensor to split + indices_or_sections (Tensor, int or list or tuple of ints): + If :attr:`indices_or_sections` is an integer ``n`` or a zero dimensional long tensor + with value ``n``, :attr:`input` is split into ``n`` sections along dimension :attr:`dim`. + If :attr:`input` is divisible by ``n`` along dimension :attr:`dim`, each + section will be of equal size, :code:`input.size(dim) / n`. If :attr:`input` + is not divisible by ``n``, the sizes of the first :code:`int(input.size(dim) % n)` + sections will have size :code:`int(input.size(dim) / n) + 1`, and the rest will + have size :code:`int(input.size(dim) / n)`. + + If :attr:`indices_or_sections` is a list or tuple of ints, or a one-dimensional long + tensor, then :attr:`input` is split along dimension :attr:`dim` at each of the indices + in the list, tuple or tensor. For instance, :code:`indices_or_sections=[2, 3]` and :code:`dim=0` + would result in the tensors :code:`input[:2]`, :code:`input[2:3]`, and :code:`input[3:]`. + + If :attr:`indices_or_sections` is a tensor, it must be a zero-dimensional or one-dimensional + long tensor on the CPU. + + dim (int, optional): dimension along which to split the tensor. Default: ``0`` + + Example:: + + >>> x = torch.arange(8) + >>> torch.tensor_split(x, 3) + (tensor([0, 1, 2]), tensor([3, 4, 5]), tensor([6, 7])) + + >>> x = torch.arange(7) + >>> torch.tensor_split(x, 3) + (tensor([0, 1, 2]), tensor([3, 4]), tensor([5, 6])) + >>> torch.tensor_split(x, (1, 6)) + (tensor([0]), tensor([1, 2, 3, 4, 5]), tensor([6])) + + >>> x = torch.arange(14).reshape(2, 7) + >>> x + tensor([[ 0, 1, 2, 3, 4, 5, 6], + [ 7, 8, 9, 10, 11, 12, 13]]) + >>> torch.tensor_split(x, 3, dim=1) + (tensor([[0, 1, 2], + [7, 8, 9]]), + tensor([[ 3, 4], + [10, 11]]), + tensor([[ 5, 6], + [12, 13]])) + >>> torch.tensor_split(x, (1, 6), dim=1) + (tensor([[0], + [7]]), + tensor([[ 1, 2, 3, 4, 5], + [ 8, 9, 10, 11, 12]]), + tensor([[ 6], + [13]])) + """ + ... +def threshold(input: Tensor, threshold: Union[Number, _complex], value: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: ... +def threshold_(input: Tensor, threshold: Union[Number, _complex], value: Union[Number, _complex]) -> Tensor: ... +def tile(input: Tensor, dims: Sequence[Union[_int, SymInt]]) -> Tensor: + r""" + tile(input, dims) -> Tensor + + Constructs a tensor by repeating the elements of :attr:`input`. + The :attr:`dims` argument specifies the number of repetitions + in each dimension. + + If :attr:`dims` specifies fewer dimensions than :attr:`input` has, then + ones are prepended to :attr:`dims` until all dimensions are specified. + For example, if :attr:`input` has shape (8, 6, 4, 2) and :attr:`dims` + is (2, 2), then :attr:`dims` is treated as (1, 1, 2, 2). + + Analogously, if :attr:`input` has fewer dimensions than :attr:`dims` + specifies, then :attr:`input` is treated as if it were unsqueezed at + dimension zero until it has as many dimensions as :attr:`dims` specifies. + For example, if :attr:`input` has shape (4, 2) and :attr:`dims` + is (3, 3, 2, 2), then :attr:`input` is treated as if it had the + shape (1, 1, 4, 2). + + .. note:: + + This function is similar to NumPy's tile function. + + Args: + input (Tensor): the tensor whose elements to repeat. + dims (tuple): the number of repetitions per dimension. + + Example:: + + >>> x = torch.tensor([1, 2, 3]) + >>> x.tile((2,)) + tensor([1, 2, 3, 1, 2, 3]) + >>> y = torch.tensor([[1, 2], [3, 4]]) + >>> torch.tile(y, (2, 2)) + tensor([[1, 2, 1, 2], + [3, 4, 3, 4], + [1, 2, 1, 2], + [3, 4, 3, 4]]) + """ + ... +def topk(input: Tensor, k: Union[_int, SymInt], dim: _int = -1, largest: _bool = True, sorted: _bool = True, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.topk: + r""" + topk(input, k, dim=None, largest=True, sorted=True, *, out=None) -> (Tensor, LongTensor) + + Returns the :attr:`k` largest elements of the given :attr:`input` tensor along + a given dimension. + + If :attr:`dim` is not given, the last dimension of the `input` is chosen. + + If :attr:`largest` is ``False`` then the `k` smallest elements are returned. + + A namedtuple of `(values, indices)` is returned with the `values` and + `indices` of the largest `k` elements of each row of the `input` tensor in the + given dimension `dim`. + + The boolean option :attr:`sorted` if ``True``, will make sure that the returned + `k` elements are themselves sorted + + Args: + input (Tensor): the input tensor. + k (int): the k in "top-k" + dim (int, optional): the dimension to sort along + largest (bool, optional): controls whether to return largest or + smallest elements + sorted (bool, optional): controls whether to return the elements + in sorted order + + Keyword args: + out (tuple, optional): the output tuple of (Tensor, LongTensor) that can be + optionally given to be used as output buffers + + Example:: + + >>> x = torch.arange(1., 6.) + >>> x + tensor([ 1., 2., 3., 4., 5.]) + >>> torch.topk(x, 3) + torch.return_types.topk(values=tensor([5., 4., 3.]), indices=tensor([4, 3, 2])) + """ + ... +def trace(input: Tensor) -> Tensor: + r""" + trace(input) -> Tensor + + Returns the sum of the elements of the diagonal of the input 2-D matrix. + + Example:: + + >>> x = torch.arange(1., 10.).view(3, 3) + >>> x + tensor([[ 1., 2., 3.], + [ 4., 5., 6.], + [ 7., 8., 9.]]) + >>> torch.trace(x) + tensor(15.) + """ + ... +@overload +def transpose(input: Tensor, dim0: _int, dim1: _int) -> Tensor: + r""" + transpose(input, dim0, dim1) -> Tensor + + Returns a tensor that is a transposed version of :attr:`input`. + The given dimensions :attr:`dim0` and :attr:`dim1` are swapped. + + If :attr:`input` is a strided tensor then the resulting :attr:`out` + tensor shares its underlying storage with the :attr:`input` tensor, so + changing the content of one would change the content of the other. + + If :attr:`input` is a :ref:`sparse tensor ` then the + resulting :attr:`out` tensor *does not* share the underlying storage + with the :attr:`input` tensor. + + If :attr:`input` is a :ref:`sparse tensor ` with compressed + layout (SparseCSR, SparseBSR, SparseCSC or SparseBSC) the arguments + :attr:`dim0` and :attr:`dim1` must be both batch dimensions, or must + both be sparse dimensions. The batch dimensions of a sparse tensor are the + dimensions preceding the sparse dimensions. + + .. note:: + Transpositions which interchange the sparse dimensions of a `SparseCSR` + or `SparseCSC` layout tensor will result in the layout changing between + the two options. Transposition of the sparse dimensions of a ` SparseBSR` + or `SparseBSC` layout tensor will likewise generate a result with the + opposite layout. + + + Args: + input (Tensor): the input tensor. + dim0 (int): the first dimension to be transposed + dim1 (int): the second dimension to be transposed + + Example:: + + >>> x = torch.randn(2, 3) + >>> x + tensor([[ 1.0028, -0.9893, 0.5809], + [-0.1669, 0.7299, 0.4942]]) + >>> torch.transpose(x, 0, 1) + tensor([[ 1.0028, -0.1669], + [-0.9893, 0.7299], + [ 0.5809, 0.4942]]) + + See also :func:`torch.t`. + """ + ... +@overload +def transpose(input: Tensor, dim0: Union[str, ellipsis, None], dim1: Union[str, ellipsis, None]) -> Tensor: + r""" + transpose(input, dim0, dim1) -> Tensor + + Returns a tensor that is a transposed version of :attr:`input`. + The given dimensions :attr:`dim0` and :attr:`dim1` are swapped. + + If :attr:`input` is a strided tensor then the resulting :attr:`out` + tensor shares its underlying storage with the :attr:`input` tensor, so + changing the content of one would change the content of the other. + + If :attr:`input` is a :ref:`sparse tensor ` then the + resulting :attr:`out` tensor *does not* share the underlying storage + with the :attr:`input` tensor. + + If :attr:`input` is a :ref:`sparse tensor ` with compressed + layout (SparseCSR, SparseBSR, SparseCSC or SparseBSC) the arguments + :attr:`dim0` and :attr:`dim1` must be both batch dimensions, or must + both be sparse dimensions. The batch dimensions of a sparse tensor are the + dimensions preceding the sparse dimensions. + + .. note:: + Transpositions which interchange the sparse dimensions of a `SparseCSR` + or `SparseCSC` layout tensor will result in the layout changing between + the two options. Transposition of the sparse dimensions of a ` SparseBSR` + or `SparseBSC` layout tensor will likewise generate a result with the + opposite layout. + + + Args: + input (Tensor): the input tensor. + dim0 (int): the first dimension to be transposed + dim1 (int): the second dimension to be transposed + + Example:: + + >>> x = torch.randn(2, 3) + >>> x + tensor([[ 1.0028, -0.9893, 0.5809], + [-0.1669, 0.7299, 0.4942]]) + >>> torch.transpose(x, 0, 1) + tensor([[ 1.0028, -0.1669], + [-0.9893, 0.7299], + [ 0.5809, 0.4942]]) + + See also :func:`torch.t`. + """ + ... +def transpose_copy(input: Tensor, dim0: _int, dim1: _int, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.transpose`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +@overload +def trapezoid(y: Tensor, x: Tensor, *, dim: _int = -1) -> Tensor: + r""" + trapezoid(y, x=None, *, dx=None, dim=-1) -> Tensor + + Computes the `trapezoidal rule `_ along + :attr:`dim`. By default the spacing between elements is assumed to be 1, but + :attr:`dx` can be used to specify a different constant spacing, and :attr:`x` can be + used to specify arbitrary spacing along :attr:`dim`. + + + Assuming :attr:`y` is a one-dimensional tensor with elements :math:`{y_0, y_1, ..., y_n}`, + the default computation is + + .. math:: + \begin{aligned} + \sum_{i = 1}^{n-1} \frac{1}{2} (y_i + y_{i-1}) + \end{aligned} + + When :attr:`dx` is specified the computation becomes + + .. math:: + \begin{aligned} + \sum_{i = 1}^{n-1} \frac{\Delta x}{2} (y_i + y_{i-1}) + \end{aligned} + + effectively multiplying the result by :attr:`dx`. When :attr:`x` is specified, + assuming :attr:`x` is also a one-dimensional tensor with + elements :math:`{x_0, x_1, ..., x_n}`, the computation becomes + + .. math:: + \begin{aligned} + \sum_{i = 1}^{n-1} \frac{(x_i - x_{i-1})}{2} (y_i + y_{i-1}) + \end{aligned} + + When :attr:`x` and :attr:`y` have the same size, the computation is as described above and no broadcasting is needed. + The broadcasting behavior of this function is as follows when their sizes are different. For both :attr:`x` + and :attr:`y`, the function computes the difference between consecutive elements along + dimension :attr:`dim`. This effectively creates two tensors, `x_diff` and `y_diff`, that have + the same shape as the original tensors except their lengths along the dimension :attr:`dim` is reduced by 1. + After that, those two tensors are broadcast together to compute final output as part of the trapezoidal rule. + See the examples below for details. + + .. note:: + The trapezoidal rule is a technique for approximating the definite integral of a function + by averaging its left and right Riemann sums. The approximation becomes more accurate as + the resolution of the partition increases. + + Arguments: + y (Tensor): Values to use when computing the trapezoidal rule. + x (Tensor): If specified, defines spacing between values as specified above. + + Keyword arguments: + dx (float): constant spacing between values. If neither :attr:`x` or :attr:`dx` + are specified then this defaults to 1. Effectively multiplies the result by its value. + dim (int): The dimension along which to compute the trapezoidal rule. + The last (inner-most) dimension by default. + + Examples:: + + >>> # Computes the trapezoidal rule in 1D, spacing is implicitly 1 + >>> y = torch.tensor([1, 5, 10]) + >>> torch.trapezoid(y) + tensor(10.5) + + >>> # Computes the same trapezoidal rule directly to verify + >>> (1 + 10 + 10) / 2 + 10.5 + + >>> # Computes the trapezoidal rule in 1D with constant spacing of 2 + >>> # NOTE: the result is the same as before, but multiplied by 2 + >>> torch.trapezoid(y, dx=2) + 21.0 + + >>> # Computes the trapezoidal rule in 1D with arbitrary spacing + >>> x = torch.tensor([1, 3, 6]) + >>> torch.trapezoid(y, x) + 28.5 + + >>> # Computes the same trapezoidal rule directly to verify + >>> ((3 - 1) * (1 + 5) + (6 - 3) * (5 + 10)) / 2 + 28.5 + + >>> # Computes the trapezoidal rule for each row of a 3x3 matrix + >>> y = torch.arange(9).reshape(3, 3) + tensor([[0, 1, 2], + [3, 4, 5], + [6, 7, 8]]) + >>> torch.trapezoid(y) + tensor([ 2., 8., 14.]) + + >>> # Computes the trapezoidal rule for each column of the matrix + >>> torch.trapezoid(y, dim=0) + tensor([ 6., 8., 10.]) + + >>> # Computes the trapezoidal rule for each row of a 3x3 ones matrix + >>> # with the same arbitrary spacing + >>> y = torch.ones(3, 3) + >>> x = torch.tensor([1, 3, 6]) + >>> torch.trapezoid(y, x) + array([5., 5., 5.]) + + >>> # Computes the trapezoidal rule for each row of a 3x3 ones matrix + >>> # with different arbitrary spacing per row + >>> y = torch.ones(3, 3) + >>> x = torch.tensor([[1, 2, 3], [1, 3, 5], [1, 4, 7]]) + >>> torch.trapezoid(y, x) + array([2., 4., 6.]) + """ + ... +@overload +def trapezoid(y: Tensor, *, dx: Union[Number, _complex] = 1, dim: _int = -1) -> Tensor: + r""" + trapezoid(y, x=None, *, dx=None, dim=-1) -> Tensor + + Computes the `trapezoidal rule `_ along + :attr:`dim`. By default the spacing between elements is assumed to be 1, but + :attr:`dx` can be used to specify a different constant spacing, and :attr:`x` can be + used to specify arbitrary spacing along :attr:`dim`. + + + Assuming :attr:`y` is a one-dimensional tensor with elements :math:`{y_0, y_1, ..., y_n}`, + the default computation is + + .. math:: + \begin{aligned} + \sum_{i = 1}^{n-1} \frac{1}{2} (y_i + y_{i-1}) + \end{aligned} + + When :attr:`dx` is specified the computation becomes + + .. math:: + \begin{aligned} + \sum_{i = 1}^{n-1} \frac{\Delta x}{2} (y_i + y_{i-1}) + \end{aligned} + + effectively multiplying the result by :attr:`dx`. When :attr:`x` is specified, + assuming :attr:`x` is also a one-dimensional tensor with + elements :math:`{x_0, x_1, ..., x_n}`, the computation becomes + + .. math:: + \begin{aligned} + \sum_{i = 1}^{n-1} \frac{(x_i - x_{i-1})}{2} (y_i + y_{i-1}) + \end{aligned} + + When :attr:`x` and :attr:`y` have the same size, the computation is as described above and no broadcasting is needed. + The broadcasting behavior of this function is as follows when their sizes are different. For both :attr:`x` + and :attr:`y`, the function computes the difference between consecutive elements along + dimension :attr:`dim`. This effectively creates two tensors, `x_diff` and `y_diff`, that have + the same shape as the original tensors except their lengths along the dimension :attr:`dim` is reduced by 1. + After that, those two tensors are broadcast together to compute final output as part of the trapezoidal rule. + See the examples below for details. + + .. note:: + The trapezoidal rule is a technique for approximating the definite integral of a function + by averaging its left and right Riemann sums. The approximation becomes more accurate as + the resolution of the partition increases. + + Arguments: + y (Tensor): Values to use when computing the trapezoidal rule. + x (Tensor): If specified, defines spacing between values as specified above. + + Keyword arguments: + dx (float): constant spacing between values. If neither :attr:`x` or :attr:`dx` + are specified then this defaults to 1. Effectively multiplies the result by its value. + dim (int): The dimension along which to compute the trapezoidal rule. + The last (inner-most) dimension by default. + + Examples:: + + >>> # Computes the trapezoidal rule in 1D, spacing is implicitly 1 + >>> y = torch.tensor([1, 5, 10]) + >>> torch.trapezoid(y) + tensor(10.5) + + >>> # Computes the same trapezoidal rule directly to verify + >>> (1 + 10 + 10) / 2 + 10.5 + + >>> # Computes the trapezoidal rule in 1D with constant spacing of 2 + >>> # NOTE: the result is the same as before, but multiplied by 2 + >>> torch.trapezoid(y, dx=2) + 21.0 + + >>> # Computes the trapezoidal rule in 1D with arbitrary spacing + >>> x = torch.tensor([1, 3, 6]) + >>> torch.trapezoid(y, x) + 28.5 + + >>> # Computes the same trapezoidal rule directly to verify + >>> ((3 - 1) * (1 + 5) + (6 - 3) * (5 + 10)) / 2 + 28.5 + + >>> # Computes the trapezoidal rule for each row of a 3x3 matrix + >>> y = torch.arange(9).reshape(3, 3) + tensor([[0, 1, 2], + [3, 4, 5], + [6, 7, 8]]) + >>> torch.trapezoid(y) + tensor([ 2., 8., 14.]) + + >>> # Computes the trapezoidal rule for each column of the matrix + >>> torch.trapezoid(y, dim=0) + tensor([ 6., 8., 10.]) + + >>> # Computes the trapezoidal rule for each row of a 3x3 ones matrix + >>> # with the same arbitrary spacing + >>> y = torch.ones(3, 3) + >>> x = torch.tensor([1, 3, 6]) + >>> torch.trapezoid(y, x) + array([5., 5., 5.]) + + >>> # Computes the trapezoidal rule for each row of a 3x3 ones matrix + >>> # with different arbitrary spacing per row + >>> y = torch.ones(3, 3) + >>> x = torch.tensor([[1, 2, 3], [1, 3, 5], [1, 4, 7]]) + >>> torch.trapezoid(y, x) + array([2., 4., 6.]) + """ + ... +@overload +def trapz(y: Tensor, *, dx: _float = 1, dim: _int = -1) -> Tensor: + r""" + trapz(y, x, *, dim=-1) -> Tensor + + Alias for :func:`torch.trapezoid`. + """ + ... +@overload +def trapz(y: Tensor, x: Tensor, *, dim: _int = -1) -> Tensor: + r""" + trapz(y, x, *, dim=-1) -> Tensor + + Alias for :func:`torch.trapezoid`. + """ + ... +def triangular_solve(input: Tensor, A: Tensor, upper: _bool = True, transpose: _bool = False, unitriangular: _bool = False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None] = None) -> torch.return_types.triangular_solve: + r""" + triangular_solve(b, A, upper=True, transpose=False, unitriangular=False, *, out=None) -> (Tensor, Tensor) + + Solves a system of equations with a square upper or lower triangular invertible matrix :math:`A` + and multiple right-hand sides :math:`b`. + + In symbols, it solves :math:`AX = b` and assumes :math:`A` is square upper-triangular + (or lower-triangular if :attr:`upper`\ `= False`) and does not have zeros on the diagonal. + + `torch.triangular_solve(b, A)` can take in 2D inputs `b, A` or inputs that are + batches of 2D matrices. If the inputs are batches, then returns + batched outputs `X` + + If the diagonal of :attr:`A` contains zeros or elements that are very close to zero and + :attr:`unitriangular`\ `= False` (default) or if the input matrix is badly conditioned, + the result may contain `NaN` s. + + Supports input of float, double, cfloat and cdouble data types. + + .. warning:: + + :func:`torch.triangular_solve` is deprecated in favor of :func:`torch.linalg.solve_triangular` + and will be removed in a future PyTorch release. + :func:`torch.linalg.solve_triangular` has its arguments reversed and does not return a + copy of one of the inputs. + + ``X = torch.triangular_solve(B, A).solution`` should be replaced with + + .. code:: python + + X = torch.linalg.solve_triangular(A, B) + + Args: + b (Tensor): multiple right-hand sides of size :math:`(*, m, k)` where + :math:`*` is zero of more batch dimensions + A (Tensor): the input triangular coefficient matrix of size :math:`(*, m, m)` + where :math:`*` is zero or more batch dimensions + upper (bool, optional): whether :math:`A` is upper or lower triangular. Default: ``True``. + transpose (bool, optional): solves `op(A)X = b` where `op(A) = A^T` if this flag is ``True``, + and `op(A) = A` if it is ``False``. Default: ``False``. + unitriangular (bool, optional): whether :math:`A` is unit triangular. + If True, the diagonal elements of :math:`A` are assumed to be + 1 and not referenced from :math:`A`. Default: ``False``. + + Keyword args: + out ((Tensor, Tensor), optional): tuple of two tensors to write + the output to. Ignored if `None`. Default: `None`. + + Returns: + A namedtuple `(solution, cloned_coefficient)` where `cloned_coefficient` + is a clone of :math:`A` and `solution` is the solution :math:`X` to :math:`AX = b` + (or whatever variant of the system of equations, depending on the keyword arguments.) + + Examples:: + + >>> A = torch.randn(2, 2).triu() + >>> A + tensor([[ 1.1527, -1.0753], + [ 0.0000, 0.7986]]) + >>> b = torch.randn(2, 3) + >>> b + tensor([[-0.0210, 2.3513, -1.5492], + [ 1.5429, 0.7403, -1.0243]]) + >>> torch.triangular_solve(b, A) + torch.return_types.triangular_solve( + solution=tensor([[ 1.7841, 2.9046, -2.5405], + [ 1.9320, 0.9270, -1.2826]]), + cloned_coefficient=tensor([[ 1.1527, -1.0753], + [ 0.0000, 0.7986]])) + """ + ... +def tril(input: Tensor, diagonal: _int = 0, *, out: Optional[Tensor] = None) -> Tensor: + r""" + tril(input, diagonal=0, *, out=None) -> Tensor + + Returns the lower triangular part of the matrix (2-D tensor) or batch of matrices + :attr:`input`, the other elements of the result tensor :attr:`out` are set to 0. + + The lower triangular part of the matrix is defined as the elements on and + below the diagonal. + + The argument :attr:`diagonal` controls which diagonal to consider. If + :attr:`diagonal` = 0, all elements on and below the main diagonal are + retained. A positive value includes just as many diagonals above the main + diagonal, and similarly a negative value excludes just as many diagonals below + the main diagonal. The main diagonal are the set of indices + :math:`\lbrace (i, i) \rbrace` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` where + :math:`d_{1}, d_{2}` are the dimensions of the matrix. + + Args: + input (Tensor): the input tensor. + diagonal (int, optional): the diagonal to consider + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(3, 3) + >>> a + tensor([[-1.0813, -0.8619, 0.7105], + [ 0.0935, 0.1380, 2.2112], + [-0.3409, -0.9828, 0.0289]]) + >>> torch.tril(a) + tensor([[-1.0813, 0.0000, 0.0000], + [ 0.0935, 0.1380, 0.0000], + [-0.3409, -0.9828, 0.0289]]) + + >>> b = torch.randn(4, 6) + >>> b + tensor([[ 1.2219, 0.5653, -0.2521, -0.2345, 1.2544, 0.3461], + [ 0.4785, -0.4477, 0.6049, 0.6368, 0.8775, 0.7145], + [ 1.1502, 3.2716, -1.1243, -0.5413, 0.3615, 0.6864], + [-0.0614, -0.7344, -1.3164, -0.7648, -1.4024, 0.0978]]) + >>> torch.tril(b, diagonal=1) + tensor([[ 1.2219, 0.5653, 0.0000, 0.0000, 0.0000, 0.0000], + [ 0.4785, -0.4477, 0.6049, 0.0000, 0.0000, 0.0000], + [ 1.1502, 3.2716, -1.1243, -0.5413, 0.0000, 0.0000], + [-0.0614, -0.7344, -1.3164, -0.7648, -1.4024, 0.0000]]) + >>> torch.tril(b, diagonal=-1) + tensor([[ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], + [ 0.4785, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], + [ 1.1502, 3.2716, 0.0000, 0.0000, 0.0000, 0.0000], + [-0.0614, -0.7344, -1.3164, 0.0000, 0.0000, 0.0000]]) + """ + ... +def tril_indices(row: _int, col: _int, offset: _int = 0, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + tril_indices(row, col, offset=0, *, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor + + Returns the indices of the lower triangular part of a :attr:`row`-by- + :attr:`col` matrix in a 2-by-N Tensor, where the first row contains row + coordinates of all indices and the second row contains column coordinates. + Indices are ordered based on rows and then columns. + + The lower triangular part of the matrix is defined as the elements on and + below the diagonal. + + The argument :attr:`offset` controls which diagonal to consider. If + :attr:`offset` = 0, all elements on and below the main diagonal are + retained. A positive value includes just as many diagonals above the main + diagonal, and similarly a negative value excludes just as many diagonals below + the main diagonal. The main diagonal are the set of indices + :math:`\lbrace (i, i) \rbrace` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` + where :math:`d_{1}, d_{2}` are the dimensions of the matrix. + + .. note:: + When running on CUDA, ``row * col`` must be less than :math:`2^{59}` to + prevent overflow during calculation. + + Args: + row (``int``): number of rows in the 2-D matrix. + col (``int``): number of columns in the 2-D matrix. + offset (``int``): diagonal offset from the main diagonal. + Default: if not provided, 0. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, ``torch.long``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + layout (:class:`torch.layout`, optional): currently only support ``torch.strided``. + + Example:: + + >>> a = torch.tril_indices(3, 3) + >>> a + tensor([[0, 1, 1, 2, 2, 2], + [0, 0, 1, 0, 1, 2]]) + + >>> a = torch.tril_indices(4, 3, -1) + >>> a + tensor([[1, 2, 2, 3, 3, 3], + [0, 0, 1, 0, 1, 2]]) + + >>> a = torch.tril_indices(4, 3, 1) + >>> a + tensor([[0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3], + [0, 1, 0, 1, 2, 0, 1, 2, 0, 1, 2]]) + """ + ... +def triplet_margin_loss(anchor: Tensor, positive: Tensor, negative: Tensor, margin: _float = 1.0, p: _float = 2, eps: _float = 1e-06, swap: _bool = False, reduction: _int = 1) -> Tensor: ... +def triu(input: Tensor, diagonal: _int = 0, *, out: Optional[Tensor] = None) -> Tensor: + r""" + triu(input, diagonal=0, *, out=None) -> Tensor + + Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices + :attr:`input`, the other elements of the result tensor :attr:`out` are set to 0. + + The upper triangular part of the matrix is defined as the elements on and + above the diagonal. + + The argument :attr:`diagonal` controls which diagonal to consider. If + :attr:`diagonal` = 0, all elements on and above the main diagonal are + retained. A positive value excludes just as many diagonals above the main + diagonal, and similarly a negative value includes just as many diagonals below + the main diagonal. The main diagonal are the set of indices + :math:`\lbrace (i, i) \rbrace` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` where + :math:`d_{1}, d_{2}` are the dimensions of the matrix. + + Args: + input (Tensor): the input tensor. + diagonal (int, optional): the diagonal to consider + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(3, 3) + >>> a + tensor([[ 0.2309, 0.5207, 2.0049], + [ 0.2072, -1.0680, 0.6602], + [ 0.3480, -0.5211, -0.4573]]) + >>> torch.triu(a) + tensor([[ 0.2309, 0.5207, 2.0049], + [ 0.0000, -1.0680, 0.6602], + [ 0.0000, 0.0000, -0.4573]]) + >>> torch.triu(a, diagonal=1) + tensor([[ 0.0000, 0.5207, 2.0049], + [ 0.0000, 0.0000, 0.6602], + [ 0.0000, 0.0000, 0.0000]]) + >>> torch.triu(a, diagonal=-1) + tensor([[ 0.2309, 0.5207, 2.0049], + [ 0.2072, -1.0680, 0.6602], + [ 0.0000, -0.5211, -0.4573]]) + + >>> b = torch.randn(4, 6) + >>> b + tensor([[ 0.5876, -0.0794, -1.8373, 0.6654, 0.2604, 1.5235], + [-0.2447, 0.9556, -1.2919, 1.3378, -0.1768, -1.0857], + [ 0.4333, 0.3146, 0.6576, -1.0432, 0.9348, -0.4410], + [-0.9888, 1.0679, -1.3337, -1.6556, 0.4798, 0.2830]]) + >>> torch.triu(b, diagonal=1) + tensor([[ 0.0000, -0.0794, -1.8373, 0.6654, 0.2604, 1.5235], + [ 0.0000, 0.0000, -1.2919, 1.3378, -0.1768, -1.0857], + [ 0.0000, 0.0000, 0.0000, -1.0432, 0.9348, -0.4410], + [ 0.0000, 0.0000, 0.0000, 0.0000, 0.4798, 0.2830]]) + >>> torch.triu(b, diagonal=-1) + tensor([[ 0.5876, -0.0794, -1.8373, 0.6654, 0.2604, 1.5235], + [-0.2447, 0.9556, -1.2919, 1.3378, -0.1768, -1.0857], + [ 0.0000, 0.3146, 0.6576, -1.0432, 0.9348, -0.4410], + [ 0.0000, 0.0000, -1.3337, -1.6556, 0.4798, 0.2830]]) + """ + ... +def triu_indices(row: _int, col: _int, offset: _int = 0, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + triu_indices(row, col, offset=0, *, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor + + Returns the indices of the upper triangular part of a :attr:`row` by + :attr:`col` matrix in a 2-by-N Tensor, where the first row contains row + coordinates of all indices and the second row contains column coordinates. + Indices are ordered based on rows and then columns. + + The upper triangular part of the matrix is defined as the elements on and + above the diagonal. + + The argument :attr:`offset` controls which diagonal to consider. If + :attr:`offset` = 0, all elements on and above the main diagonal are + retained. A positive value excludes just as many diagonals above the main + diagonal, and similarly a negative value includes just as many diagonals below + the main diagonal. The main diagonal are the set of indices + :math:`\lbrace (i, i) \rbrace` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` + where :math:`d_{1}, d_{2}` are the dimensions of the matrix. + + .. note:: + When running on CUDA, ``row * col`` must be less than :math:`2^{59}` to + prevent overflow during calculation. + + Args: + row (``int``): number of rows in the 2-D matrix. + col (``int``): number of columns in the 2-D matrix. + offset (``int``): diagonal offset from the main diagonal. + Default: if not provided, 0. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, ``torch.long``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + layout (:class:`torch.layout`, optional): currently only support ``torch.strided``. + + Example:: + + >>> a = torch.triu_indices(3, 3) + >>> a + tensor([[0, 0, 0, 1, 1, 2], + [0, 1, 2, 1, 2, 2]]) + + >>> a = torch.triu_indices(4, 3, -1) + >>> a + tensor([[0, 0, 0, 1, 1, 1, 2, 2, 3], + [0, 1, 2, 0, 1, 2, 1, 2, 2]]) + + >>> a = torch.triu_indices(4, 3, 1) + >>> a + tensor([[0, 0, 1], + [1, 2, 2]]) + """ + ... +def true_divide(input: Union[Tensor, Number], other: Union[Tensor, Number], *, out: Optional[Tensor] = None) -> Tensor: + r""" + true_divide(dividend, divisor, *, out) -> Tensor + + Alias for :func:`torch.div` with ``rounding_mode=None``. + """ + ... +def trunc(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + trunc(input, *, out=None) -> Tensor + + Returns a new tensor with the truncated integer values of + the elements of :attr:`input`. + + For integer inputs, follows the array-api convention of returning a + copy of the input tensor. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 3.4742, 0.5466, -0.8008, -0.9079]) + >>> torch.trunc(a) + tensor([ 3., 0., -0., -0.]) + """ + ... +def trunc_(input: Tensor) -> Tensor: ... +@overload +def unbind(input: Tensor, dim: _int = 0) -> Tuple[Tensor, ...]: + r""" + unbind(input, dim=0) -> seq + + Removes a tensor dimension. + + Returns a tuple of all slices along a given dimension, already without it. + + Arguments: + input (Tensor): the tensor to unbind + dim (int): dimension to remove + + Example:: + + >>> torch.unbind(torch.tensor([[1, 2, 3], + >>> [4, 5, 6], + >>> [7, 8, 9]])) + (tensor([1, 2, 3]), tensor([4, 5, 6]), tensor([7, 8, 9])) + """ + ... +@overload +def unbind(input: Tensor, dim: Union[str, ellipsis, None]) -> Tuple[Tensor, ...]: + r""" + unbind(input, dim=0) -> seq + + Removes a tensor dimension. + + Returns a tuple of all slices along a given dimension, already without it. + + Arguments: + input (Tensor): the tensor to unbind + dim (int): dimension to remove + + Example:: + + >>> torch.unbind(torch.tensor([[1, 2, 3], + >>> [4, 5, 6], + >>> [7, 8, 9]])) + (tensor([1, 2, 3]), tensor([4, 5, 6]), tensor([7, 8, 9])) + """ + ... +def unbind_copy(input: Tensor, dim: _int = 0, *, out: Union[Tuple[Tensor, ...], List[Tensor], None] = None) -> None: + r""" + Performs the same operation as :func:`torch.unbind`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +@overload +def unflatten(input: Tensor, dim: Union[str, ellipsis, None], sizes: Sequence[Union[_int, SymInt]], names: Sequence[Union[str, ellipsis, None]]) -> Tensor: + r""" + unflatten(input, dim, sizes) -> Tensor + + Expands a dimension of the input tensor over multiple dimensions. + + .. seealso:: + + :func:`torch.flatten` the inverse of this function. It coalesces several dimensions into one. + + Args: + input (Tensor): the input tensor. + dim (int): Dimension to be unflattened, specified as an index into + ``input.shape``. + sizes (Tuple[int]): New shape of the unflattened dimension. + One of its elements can be `-1` in which case the corresponding output + dimension is inferred. Otherwise, the product of ``sizes`` *must* + equal ``input.shape[dim]``. + + Returns: + A View of input with the specified dimension unflattened. + + Examples:: + >>> torch.unflatten(torch.randn(3, 4, 1), 1, (2, 2)).shape + torch.Size([3, 2, 2, 1]) + >>> torch.unflatten(torch.randn(3, 4, 1), 1, (-1, 2)).shape + torch.Size([3, 2, 2, 1]) + >>> torch.unflatten(torch.randn(5, 12, 3), -2, (2, 2, 3, 1, 1)).shape + torch.Size([5, 2, 2, 3, 1, 1, 3]) + """ + ... +@overload +def unflatten(input: Tensor, dim: _int, sizes: Sequence[Union[_int, SymInt]]) -> Tensor: + r""" + unflatten(input, dim, sizes) -> Tensor + + Expands a dimension of the input tensor over multiple dimensions. + + .. seealso:: + + :func:`torch.flatten` the inverse of this function. It coalesces several dimensions into one. + + Args: + input (Tensor): the input tensor. + dim (int): Dimension to be unflattened, specified as an index into + ``input.shape``. + sizes (Tuple[int]): New shape of the unflattened dimension. + One of its elements can be `-1` in which case the corresponding output + dimension is inferred. Otherwise, the product of ``sizes`` *must* + equal ``input.shape[dim]``. + + Returns: + A View of input with the specified dimension unflattened. + + Examples:: + >>> torch.unflatten(torch.randn(3, 4, 1), 1, (2, 2)).shape + torch.Size([3, 2, 2, 1]) + >>> torch.unflatten(torch.randn(3, 4, 1), 1, (-1, 2)).shape + torch.Size([3, 2, 2, 1]) + >>> torch.unflatten(torch.randn(5, 12, 3), -2, (2, 2, 3, 1, 1)).shape + torch.Size([5, 2, 2, 3, 1, 1, 3]) + """ + ... +def unfold_copy(input: Tensor, dimension: _int, size: _int, step: _int, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.unfold`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def unique_dim(input: Tensor, dim: _int, sorted: _bool = True, return_inverse: _bool = False, return_counts: _bool = False) -> Tuple[Tensor, Tensor, Tensor]: ... +def unsafe_chunk(input: Tensor, chunks: _int, dim: _int = 0) -> Tuple[Tensor, ...]: + r""" + unsafe_chunk(input, chunks, dim=0) -> List of Tensors + + Works like :func:`torch.chunk` but without enforcing the autograd restrictions + on inplace modification of the outputs. + + .. warning:: + This function is safe to use as long as only the input, or only the outputs + are modified inplace after calling this function. It is user's + responsibility to ensure that is the case. If both the input and one or more + of the outputs are modified inplace, gradients computed by autograd will be + silently incorrect. + """ + ... +def unsafe_split(input: Tensor, split_size: Union[_int, SymInt], dim: _int = 0) -> Tuple[Tensor, ...]: + r""" + unsafe_split(tensor, split_size_or_sections, dim=0) -> List of Tensors + + Works like :func:`torch.split` but without enforcing the autograd restrictions + on inplace modification of the outputs. + + .. warning:: + This function is safe to use as long as only the input, or only the outputs + are modified inplace after calling this function. It is user's + responsibility to ensure that is the case. If both the input and one or more + of the outputs are modified inplace, gradients computed by autograd will be + silently incorrect. + """ + ... +def unsafe_split_with_sizes(input: Tensor, split_sizes: Sequence[Union[_int, SymInt]], dim: _int = 0) -> Tuple[Tensor, ...]: ... +def unsqueeze(input: Tensor, dim: _int) -> Tensor: + r""" + unsqueeze(input, dim) -> Tensor + + Returns a new tensor with a dimension of size one inserted at the + specified position. + + The returned tensor shares the same underlying data with this tensor. + + A :attr:`dim` value within the range ``[-input.dim() - 1, input.dim() + 1)`` + can be used. Negative :attr:`dim` will correspond to :meth:`unsqueeze` + applied at :attr:`dim` = ``dim + input.dim() + 1``. + + Args: + input (Tensor): the input tensor. + dim (int): the index at which to insert the singleton dimension + + Example:: + + >>> x = torch.tensor([1, 2, 3, 4]) + >>> torch.unsqueeze(x, 0) + tensor([[ 1, 2, 3, 4]]) + >>> torch.unsqueeze(x, 1) + tensor([[ 1], + [ 2], + [ 3], + [ 4]]) + """ + ... +def unsqueeze_copy(input: Tensor, dim: _int, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.unsqueeze`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def values_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.values`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def vander(x: Tensor, N: Optional[_int] = None, increasing: _bool = False) -> Tensor: + r""" + vander(x, N=None, increasing=False) -> Tensor + + Generates a Vandermonde matrix. + + The columns of the output matrix are elementwise powers of the input vector :math:`x^{(N-1)}, x^{(N-2)}, ..., x^0`. + If increasing is True, the order of the columns is reversed :math:`x^0, x^1, ..., x^{(N-1)}`. Such a + matrix with a geometric progression in each row is named for Alexandre-Theophile Vandermonde. + + Arguments: + x (Tensor): 1-D input tensor. + N (int, optional): Number of columns in the output. If N is not specified, + a square array is returned :math:`(N = len(x))`. + increasing (bool, optional): Order of the powers of the columns. If True, + the powers increase from left to right, if False (the default) they are reversed. + + Returns: + Tensor: Vandermonde matrix. If increasing is False, the first column is :math:`x^{(N-1)}`, + the second :math:`x^{(N-2)}` and so forth. If increasing is True, the columns + are :math:`x^0, x^1, ..., x^{(N-1)}`. + + Example:: + + >>> x = torch.tensor([1, 2, 3, 5]) + >>> torch.vander(x) + tensor([[ 1, 1, 1, 1], + [ 8, 4, 2, 1], + [ 27, 9, 3, 1], + [125, 25, 5, 1]]) + >>> torch.vander(x, N=3) + tensor([[ 1, 1, 1], + [ 4, 2, 1], + [ 9, 3, 1], + [25, 5, 1]]) + >>> torch.vander(x, N=3, increasing=True) + tensor([[ 1, 1, 1], + [ 1, 2, 4], + [ 1, 3, 9], + [ 1, 5, 25]]) + """ + ... +@overload +def var(input: Tensor, dim: Optional[Union[_int, _size]], unbiased: _bool = True, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + var(input, dim=None, *, correction=1, keepdim=False, out=None) -> Tensor + + Calculates the variance over the dimensions specified by :attr:`dim`. :attr:`dim` + can be a single dimension, list of dimensions, or ``None`` to reduce over all + dimensions. + + The variance (:math:`\sigma^2`) is calculated as + + .. math:: \sigma^2 = \frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2 + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.var(a, dim=1, keepdim=True) + tensor([[1.0631], + [0.5590], + [1.4893], + [0.8258]]) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def var(input: Tensor, dim: Optional[Union[_int, _size]] = None, *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + var(input, dim=None, *, correction=1, keepdim=False, out=None) -> Tensor + + Calculates the variance over the dimensions specified by :attr:`dim`. :attr:`dim` + can be a single dimension, list of dimensions, or ``None`` to reduce over all + dimensions. + + The variance (:math:`\sigma^2`) is calculated as + + .. math:: \sigma^2 = \frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2 + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.var(a, dim=1, keepdim=True) + tensor([[1.0631], + [0.5590], + [1.4893], + [0.8258]]) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def var(input: Tensor, unbiased: _bool = True) -> Tensor: + r""" + var(input, dim=None, *, correction=1, keepdim=False, out=None) -> Tensor + + Calculates the variance over the dimensions specified by :attr:`dim`. :attr:`dim` + can be a single dimension, list of dimensions, or ``None`` to reduce over all + dimensions. + + The variance (:math:`\sigma^2`) is calculated as + + .. math:: \sigma^2 = \frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2 + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.var(a, dim=1, keepdim=True) + tensor([[1.0631], + [0.5590], + [1.4893], + [0.8258]]) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def var(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + var(input, dim=None, *, correction=1, keepdim=False, out=None) -> Tensor + + Calculates the variance over the dimensions specified by :attr:`dim`. :attr:`dim` + can be a single dimension, list of dimensions, or ``None`` to reduce over all + dimensions. + + The variance (:math:`\sigma^2`) is calculated as + + .. math:: \sigma^2 = \frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2 + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.var(a, dim=1, keepdim=True) + tensor([[1.0631], + [0.5590], + [1.4893], + [0.8258]]) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def var(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], unbiased: _bool = True, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + var(input, dim=None, *, correction=1, keepdim=False, out=None) -> Tensor + + Calculates the variance over the dimensions specified by :attr:`dim`. :attr:`dim` + can be a single dimension, list of dimensions, or ``None`` to reduce over all + dimensions. + + The variance (:math:`\sigma^2`) is calculated as + + .. math:: \sigma^2 = \frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2 + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.var(a, dim=1, keepdim=True) + tensor([[1.0631], + [0.5590], + [1.4893], + [0.8258]]) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def var_mean(input: Tensor, dim: Optional[Union[_int, _size]], unbiased: _bool = True, keepdim: _bool = False) -> Tuple[Tensor, Tensor]: + r""" + var_mean(input, dim=None, *, correction=1, keepdim=False, out=None) -> (Tensor, Tensor) + + Calculates the variance and mean over the dimensions specified by :attr:`dim`. + :attr:`dim` can be a single dimension, list of dimensions, or ``None`` to + reduce over all dimensions. + + The variance (:math:`\sigma^2`) is calculated as + + .. math:: \sigma^2 = \frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2 + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Returns: + A tuple (var, mean) containing the variance and mean. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.var_mean(a, dim=0, keepdim=True) + (tensor([[1.5926, 1.0056, 1.2005, 0.3646]]), + tensor([[ 0.0645, 0.4485, 0.8707, -0.0665]])) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def var_mean(input: Tensor, dim: Optional[Union[_int, _size]] = None, *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False) -> Tuple[Tensor, Tensor]: + r""" + var_mean(input, dim=None, *, correction=1, keepdim=False, out=None) -> (Tensor, Tensor) + + Calculates the variance and mean over the dimensions specified by :attr:`dim`. + :attr:`dim` can be a single dimension, list of dimensions, or ``None`` to + reduce over all dimensions. + + The variance (:math:`\sigma^2`) is calculated as + + .. math:: \sigma^2 = \frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2 + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Returns: + A tuple (var, mean) containing the variance and mean. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.var_mean(a, dim=0, keepdim=True) + (tensor([[1.5926, 1.0056, 1.2005, 0.3646]]), + tensor([[ 0.0645, 0.4485, 0.8707, -0.0665]])) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def var_mean(input: Tensor, unbiased: _bool = True) -> Tuple[Tensor, Tensor]: + r""" + var_mean(input, dim=None, *, correction=1, keepdim=False, out=None) -> (Tensor, Tensor) + + Calculates the variance and mean over the dimensions specified by :attr:`dim`. + :attr:`dim` can be a single dimension, list of dimensions, or ``None`` to + reduce over all dimensions. + + The variance (:math:`\sigma^2`) is calculated as + + .. math:: \sigma^2 = \frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2 + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Returns: + A tuple (var, mean) containing the variance and mean. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.var_mean(a, dim=0, keepdim=True) + (tensor([[1.5926, 1.0056, 1.2005, 0.3646]]), + tensor([[ 0.0645, 0.4485, 0.8707, -0.0665]])) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def var_mean(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False) -> Tuple[Tensor, Tensor]: + r""" + var_mean(input, dim=None, *, correction=1, keepdim=False, out=None) -> (Tensor, Tensor) + + Calculates the variance and mean over the dimensions specified by :attr:`dim`. + :attr:`dim` can be a single dimension, list of dimensions, or ``None`` to + reduce over all dimensions. + + The variance (:math:`\sigma^2`) is calculated as + + .. math:: \sigma^2 = \frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2 + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Returns: + A tuple (var, mean) containing the variance and mean. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.var_mean(a, dim=0, keepdim=True) + (tensor([[1.5926, 1.0056, 1.2005, 0.3646]]), + tensor([[ 0.0645, 0.4485, 0.8707, -0.0665]])) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def var_mean(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], unbiased: _bool = True, keepdim: _bool = False) -> Tuple[Tensor, Tensor]: + r""" + var_mean(input, dim=None, *, correction=1, keepdim=False, out=None) -> (Tensor, Tensor) + + Calculates the variance and mean over the dimensions specified by :attr:`dim`. + :attr:`dim` can be a single dimension, list of dimensions, or ``None`` to + reduce over all dimensions. + + The variance (:math:`\sigma^2`) is calculated as + + .. math:: \sigma^2 = \frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2 + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Returns: + A tuple (var, mean) containing the variance and mean. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.var_mean(a, dim=0, keepdim=True) + (tensor([[1.5926, 1.0056, 1.2005, 0.3646]]), + tensor([[ 0.0645, 0.4485, 0.8707, -0.0665]])) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +def vdot(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + vdot(input, other, *, out=None) -> Tensor + + Computes the dot product of two 1D vectors along a dimension. + + In symbols, this function computes + + .. math:: + + \sum_{i=1}^n \overline{x_i}y_i. + + where :math:`\overline{x_i}` denotes the conjugate for complex + vectors, and it is the identity for real vectors. + + .. note:: + + Unlike NumPy's vdot, torch.vdot intentionally only supports computing the dot product + of two 1D tensors with the same number of elements. + + .. seealso:: + + :func:`torch.linalg.vecdot` computes the dot product of two batches of vectors along a dimension. + + Args: + input (Tensor): first tensor in the dot product, must be 1D. Its conjugate is used if it's complex. + other (Tensor): second tensor in the dot product, must be 1D. + + Keyword args: + + .. note:: out (Tensor, optional): the output tensor. + + + Example:: + + >>> torch.vdot(torch.tensor([2, 3]), torch.tensor([2, 1])) + tensor(7) + >>> a = torch.tensor((1 +2j, 3 - 1j)) + >>> b = torch.tensor((2 +1j, 4 - 0j)) + >>> torch.vdot(a, b) + tensor([16.+1.j]) + >>> torch.vdot(b, a) + tensor([16.-1.j]) + """ + ... +def view_as_complex(input: Tensor) -> Tensor: + r""" + view_as_complex(input) -> Tensor + + Returns a view of :attr:`input` as a complex tensor. For an input complex + tensor of :attr:`size` :math:`m1, m2, \dots, mi, 2`, this function returns a + new complex tensor of :attr:`size` :math:`m1, m2, \dots, mi` where the last + dimension of the input tensor is expected to represent the real and imaginary + components of complex numbers. + + .. warning:: + :func:`view_as_complex` is only supported for tensors with + :class:`torch.dtype` ``torch.float64`` and ``torch.float32``. The input is + expected to have the last dimension of :attr:`size` 2. In addition, the + tensor must have a `stride` of 1 for its last dimension. The strides of all + other dimensions must be even numbers. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> x=torch.randn(4, 2) + >>> x + tensor([[ 1.6116, -0.5772], + [-1.4606, -0.9120], + [ 0.0786, -1.7497], + [-0.6561, -1.6623]]) + >>> torch.view_as_complex(x) + tensor([(1.6116-0.5772j), (-1.4606-0.9120j), (0.0786-1.7497j), (-0.6561-1.6623j)]) + """ + ... +def view_as_complex_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.view_as_complex`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def view_as_real(input: Tensor) -> Tensor: + r""" + view_as_real(input) -> Tensor + + Returns a view of :attr:`input` as a real tensor. For an input complex tensor of + :attr:`size` :math:`m1, m2, \dots, mi`, this function returns a new + real tensor of size :math:`m1, m2, \dots, mi, 2`, where the last dimension of size 2 + represents the real and imaginary components of complex numbers. + + .. warning:: + :func:`view_as_real` is only supported for tensors with ``complex dtypes``. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> x=torch.randn(4, dtype=torch.cfloat) + >>> x + tensor([(0.4737-0.3839j), (-0.2098-0.6699j), (0.3470-0.9451j), (-0.5174-1.3136j)]) + >>> torch.view_as_real(x) + tensor([[ 0.4737, -0.3839], + [-0.2098, -0.6699], + [ 0.3470, -0.9451], + [-0.5174, -1.3136]]) + """ + ... +def view_as_real_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.view_as_real`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +@overload +def view_copy(input: Tensor, dtype: _dtype, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.view`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +@overload +def view_copy(input: Tensor, size: Sequence[Union[_int, SymInt]], *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.view`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +@overload +def vsplit(input: Tensor, sections: _int) -> Tuple[Tensor, ...]: + r""" + vsplit(input, indices_or_sections) -> List of Tensors + + Splits :attr:`input`, a tensor with two or more dimensions, into multiple tensors + vertically according to :attr:`indices_or_sections`. Each split is a view of + :attr:`input`. + + This is equivalent to calling torch.tensor_split(input, indices_or_sections, dim=0) + (the split dimension is 0), except that if :attr:`indices_or_sections` is an integer + it must evenly divide the split dimension or a runtime error will be thrown. + + This function is based on NumPy's :func:`numpy.vsplit`. + + Args: + input (Tensor): tensor to split. + indices_or_sections (int or list or tuple of ints): See argument in :func:`torch.tensor_split`. + + Example:: + >>> t = torch.arange(16.0).reshape(4,4) + >>> t + tensor([[ 0., 1., 2., 3.], + [ 4., 5., 6., 7.], + [ 8., 9., 10., 11.], + [12., 13., 14., 15.]]) + >>> torch.vsplit(t, 2) + (tensor([[0., 1., 2., 3.], + [4., 5., 6., 7.]]), + tensor([[ 8., 9., 10., 11.], + [12., 13., 14., 15.]])) + >>> torch.vsplit(t, [3, 6]) + (tensor([[ 0., 1., 2., 3.], + [ 4., 5., 6., 7.], + [ 8., 9., 10., 11.]]), + tensor([[12., 13., 14., 15.]]), + tensor([], size=(0, 4))) + """ + ... +@overload +def vsplit(input: Tensor, indices: _size) -> Tuple[Tensor, ...]: + r""" + vsplit(input, indices_or_sections) -> List of Tensors + + Splits :attr:`input`, a tensor with two or more dimensions, into multiple tensors + vertically according to :attr:`indices_or_sections`. Each split is a view of + :attr:`input`. + + This is equivalent to calling torch.tensor_split(input, indices_or_sections, dim=0) + (the split dimension is 0), except that if :attr:`indices_or_sections` is an integer + it must evenly divide the split dimension or a runtime error will be thrown. + + This function is based on NumPy's :func:`numpy.vsplit`. + + Args: + input (Tensor): tensor to split. + indices_or_sections (int or list or tuple of ints): See argument in :func:`torch.tensor_split`. + + Example:: + >>> t = torch.arange(16.0).reshape(4,4) + >>> t + tensor([[ 0., 1., 2., 3.], + [ 4., 5., 6., 7.], + [ 8., 9., 10., 11.], + [12., 13., 14., 15.]]) + >>> torch.vsplit(t, 2) + (tensor([[0., 1., 2., 3.], + [4., 5., 6., 7.]]), + tensor([[ 8., 9., 10., 11.], + [12., 13., 14., 15.]])) + >>> torch.vsplit(t, [3, 6]) + (tensor([[ 0., 1., 2., 3.], + [ 4., 5., 6., 7.], + [ 8., 9., 10., 11.]]), + tensor([[12., 13., 14., 15.]]), + tensor([], size=(0, 4))) + """ + ... +def vstack(tensors: Union[Tuple[Tensor, ...], List[Tensor]], *, out: Optional[Tensor] = None) -> Tensor: + r""" + vstack(tensors, *, out=None) -> Tensor + + Stack tensors in sequence vertically (row wise). + + This is equivalent to concatenation along the first axis after all 1-D tensors have been reshaped by :func:`torch.atleast_2d`. + + Args: + tensors (sequence of Tensors): sequence of tensors to concatenate + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([1, 2, 3]) + >>> b = torch.tensor([4, 5, 6]) + >>> torch.vstack((a,b)) + tensor([[1, 2, 3], + [4, 5, 6]]) + >>> a = torch.tensor([[1],[2],[3]]) + >>> b = torch.tensor([[4],[5],[6]]) + >>> torch.vstack((a,b)) + tensor([[1], + [2], + [3], + [4], + [5], + [6]]) + """ + ... +@overload +def where(condition: Tensor) -> Tuple[Tensor, ...]: + r""" + where(condition, input, other, *, out=None) -> Tensor + + Return a tensor of elements selected from either :attr:`input` or :attr:`other`, depending on :attr:`condition`. + + The operation is defined as: + + .. math:: + \text{out}_i = \begin{cases} + \text{input}_i & \text{if } \text{condition}_i \\ + \text{other}_i & \text{otherwise} \\ + \end{cases} + + .. note:: + The tensors :attr:`condition`, :attr:`input`, :attr:`other` must be :ref:`broadcastable `. + + Arguments: + condition (BoolTensor): When True (nonzero), yield input, otherwise yield other + input (Tensor or Scalar): value (if :attr:`input` is a scalar) or values selected at indices + where :attr:`condition` is ``True`` + other (Tensor or Scalar): value (if :attr:`other` is a scalar) or values selected at indices + where :attr:`condition` is ``False`` + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + Tensor: A tensor of shape equal to the broadcasted shape of :attr:`condition`, :attr:`input`, :attr:`other` + + Example:: + + >>> x = torch.randn(3, 2) + >>> y = torch.ones(3, 2) + >>> x + tensor([[-0.4620, 0.3139], + [ 0.3898, -0.7197], + [ 0.0478, -0.1657]]) + >>> torch.where(x > 0, 1.0, 0.0) + tensor([[0., 1.], + [1., 0.], + [1., 0.]]) + >>> torch.where(x > 0, x, y) + tensor([[ 1.0000, 0.3139], + [ 0.3898, 1.0000], + [ 0.0478, 1.0000]]) + >>> x = torch.randn(2, 2, dtype=torch.double) + >>> x + tensor([[ 1.0779, 0.0383], + [-0.8785, -1.1089]], dtype=torch.float64) + >>> torch.where(x > 0, x, 0.) + tensor([[1.0779, 0.0383], + [0.0000, 0.0000]], dtype=torch.float64) + + .. function:: where(condition) -> tuple of LongTensor + :noindex: + + ``torch.where(condition)`` is identical to + ``torch.nonzero(condition, as_tuple=True)``. + + .. note:: + See also :func:`torch.nonzero`. + """ + ... +@overload +def where(condition: Tensor, input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + where(condition, input, other, *, out=None) -> Tensor + + Return a tensor of elements selected from either :attr:`input` or :attr:`other`, depending on :attr:`condition`. + + The operation is defined as: + + .. math:: + \text{out}_i = \begin{cases} + \text{input}_i & \text{if } \text{condition}_i \\ + \text{other}_i & \text{otherwise} \\ + \end{cases} + + .. note:: + The tensors :attr:`condition`, :attr:`input`, :attr:`other` must be :ref:`broadcastable `. + + Arguments: + condition (BoolTensor): When True (nonzero), yield input, otherwise yield other + input (Tensor or Scalar): value (if :attr:`input` is a scalar) or values selected at indices + where :attr:`condition` is ``True`` + other (Tensor or Scalar): value (if :attr:`other` is a scalar) or values selected at indices + where :attr:`condition` is ``False`` + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + Tensor: A tensor of shape equal to the broadcasted shape of :attr:`condition`, :attr:`input`, :attr:`other` + + Example:: + + >>> x = torch.randn(3, 2) + >>> y = torch.ones(3, 2) + >>> x + tensor([[-0.4620, 0.3139], + [ 0.3898, -0.7197], + [ 0.0478, -0.1657]]) + >>> torch.where(x > 0, 1.0, 0.0) + tensor([[0., 1.], + [1., 0.], + [1., 0.]]) + >>> torch.where(x > 0, x, y) + tensor([[ 1.0000, 0.3139], + [ 0.3898, 1.0000], + [ 0.0478, 1.0000]]) + >>> x = torch.randn(2, 2, dtype=torch.double) + >>> x + tensor([[ 1.0779, 0.0383], + [-0.8785, -1.1089]], dtype=torch.float64) + >>> torch.where(x > 0, x, 0.) + tensor([[1.0779, 0.0383], + [0.0000, 0.0000]], dtype=torch.float64) + + .. function:: where(condition) -> tuple of LongTensor + :noindex: + + ``torch.where(condition)`` is identical to + ``torch.nonzero(condition, as_tuple=True)``. + + .. note:: + See also :func:`torch.nonzero`. + """ + ... +@overload +def where(condition: Tensor, self: Union[Number, _complex], other: Tensor) -> Tensor: + r""" + where(condition, input, other, *, out=None) -> Tensor + + Return a tensor of elements selected from either :attr:`input` or :attr:`other`, depending on :attr:`condition`. + + The operation is defined as: + + .. math:: + \text{out}_i = \begin{cases} + \text{input}_i & \text{if } \text{condition}_i \\ + \text{other}_i & \text{otherwise} \\ + \end{cases} + + .. note:: + The tensors :attr:`condition`, :attr:`input`, :attr:`other` must be :ref:`broadcastable `. + + Arguments: + condition (BoolTensor): When True (nonzero), yield input, otherwise yield other + input (Tensor or Scalar): value (if :attr:`input` is a scalar) or values selected at indices + where :attr:`condition` is ``True`` + other (Tensor or Scalar): value (if :attr:`other` is a scalar) or values selected at indices + where :attr:`condition` is ``False`` + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + Tensor: A tensor of shape equal to the broadcasted shape of :attr:`condition`, :attr:`input`, :attr:`other` + + Example:: + + >>> x = torch.randn(3, 2) + >>> y = torch.ones(3, 2) + >>> x + tensor([[-0.4620, 0.3139], + [ 0.3898, -0.7197], + [ 0.0478, -0.1657]]) + >>> torch.where(x > 0, 1.0, 0.0) + tensor([[0., 1.], + [1., 0.], + [1., 0.]]) + >>> torch.where(x > 0, x, y) + tensor([[ 1.0000, 0.3139], + [ 0.3898, 1.0000], + [ 0.0478, 1.0000]]) + >>> x = torch.randn(2, 2, dtype=torch.double) + >>> x + tensor([[ 1.0779, 0.0383], + [-0.8785, -1.1089]], dtype=torch.float64) + >>> torch.where(x > 0, x, 0.) + tensor([[1.0779, 0.0383], + [0.0000, 0.0000]], dtype=torch.float64) + + .. function:: where(condition) -> tuple of LongTensor + :noindex: + + ``torch.where(condition)`` is identical to + ``torch.nonzero(condition, as_tuple=True)``. + + .. note:: + See also :func:`torch.nonzero`. + """ + ... +@overload +def where(condition: Tensor, input: Tensor, other: Union[Number, _complex]) -> Tensor: + r""" + where(condition, input, other, *, out=None) -> Tensor + + Return a tensor of elements selected from either :attr:`input` or :attr:`other`, depending on :attr:`condition`. + + The operation is defined as: + + .. math:: + \text{out}_i = \begin{cases} + \text{input}_i & \text{if } \text{condition}_i \\ + \text{other}_i & \text{otherwise} \\ + \end{cases} + + .. note:: + The tensors :attr:`condition`, :attr:`input`, :attr:`other` must be :ref:`broadcastable `. + + Arguments: + condition (BoolTensor): When True (nonzero), yield input, otherwise yield other + input (Tensor or Scalar): value (if :attr:`input` is a scalar) or values selected at indices + where :attr:`condition` is ``True`` + other (Tensor or Scalar): value (if :attr:`other` is a scalar) or values selected at indices + where :attr:`condition` is ``False`` + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + Tensor: A tensor of shape equal to the broadcasted shape of :attr:`condition`, :attr:`input`, :attr:`other` + + Example:: + + >>> x = torch.randn(3, 2) + >>> y = torch.ones(3, 2) + >>> x + tensor([[-0.4620, 0.3139], + [ 0.3898, -0.7197], + [ 0.0478, -0.1657]]) + >>> torch.where(x > 0, 1.0, 0.0) + tensor([[0., 1.], + [1., 0.], + [1., 0.]]) + >>> torch.where(x > 0, x, y) + tensor([[ 1.0000, 0.3139], + [ 0.3898, 1.0000], + [ 0.0478, 1.0000]]) + >>> x = torch.randn(2, 2, dtype=torch.double) + >>> x + tensor([[ 1.0779, 0.0383], + [-0.8785, -1.1089]], dtype=torch.float64) + >>> torch.where(x > 0, x, 0.) + tensor([[1.0779, 0.0383], + [0.0000, 0.0000]], dtype=torch.float64) + + .. function:: where(condition) -> tuple of LongTensor + :noindex: + + ``torch.where(condition)`` is identical to + ``torch.nonzero(condition, as_tuple=True)``. + + .. note:: + See also :func:`torch.nonzero`. + """ + ... +@overload +def where(condition: Tensor, self: Union[Number, _complex], other: Union[Number, _complex]) -> Tensor: + r""" + where(condition, input, other, *, out=None) -> Tensor + + Return a tensor of elements selected from either :attr:`input` or :attr:`other`, depending on :attr:`condition`. + + The operation is defined as: + + .. math:: + \text{out}_i = \begin{cases} + \text{input}_i & \text{if } \text{condition}_i \\ + \text{other}_i & \text{otherwise} \\ + \end{cases} + + .. note:: + The tensors :attr:`condition`, :attr:`input`, :attr:`other` must be :ref:`broadcastable `. + + Arguments: + condition (BoolTensor): When True (nonzero), yield input, otherwise yield other + input (Tensor or Scalar): value (if :attr:`input` is a scalar) or values selected at indices + where :attr:`condition` is ``True`` + other (Tensor or Scalar): value (if :attr:`other` is a scalar) or values selected at indices + where :attr:`condition` is ``False`` + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + Tensor: A tensor of shape equal to the broadcasted shape of :attr:`condition`, :attr:`input`, :attr:`other` + + Example:: + + >>> x = torch.randn(3, 2) + >>> y = torch.ones(3, 2) + >>> x + tensor([[-0.4620, 0.3139], + [ 0.3898, -0.7197], + [ 0.0478, -0.1657]]) + >>> torch.where(x > 0, 1.0, 0.0) + tensor([[0., 1.], + [1., 0.], + [1., 0.]]) + >>> torch.where(x > 0, x, y) + tensor([[ 1.0000, 0.3139], + [ 0.3898, 1.0000], + [ 0.0478, 1.0000]]) + >>> x = torch.randn(2, 2, dtype=torch.double) + >>> x + tensor([[ 1.0779, 0.0383], + [-0.8785, -1.1089]], dtype=torch.float64) + >>> torch.where(x > 0, x, 0.) + tensor([[1.0779, 0.0383], + [0.0000, 0.0000]], dtype=torch.float64) + + .. function:: where(condition) -> tuple of LongTensor + :noindex: + + ``torch.where(condition)`` is identical to + ``torch.nonzero(condition, as_tuple=True)``. + + .. note:: + See also :func:`torch.nonzero`. + """ + ... +@overload +def xlogy(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + xlogy(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.special.xlogy`. + """ + ... +@overload +def xlogy(self: Union[Number, _complex], other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + xlogy(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.special.xlogy`. + """ + ... +@overload +def xlogy(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + xlogy(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.special.xlogy`. + """ + ... +@overload +def xlogy_(input: Tensor, other: Tensor) -> Tensor: ... +@overload +def xlogy_(input: Tensor, other: Union[Number, _complex]) -> Tensor: ... +def zero_(input: Tensor) -> Tensor: ... +@overload +def zeros(size: Sequence[Union[_int, SymInt]], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + zeros(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with the scalar value `0`, with the shape defined + by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.zeros(2, 3) + tensor([[ 0., 0., 0.], + [ 0., 0., 0.]]) + + >>> torch.zeros(5) + tensor([ 0., 0., 0., 0., 0.]) + """ + ... +@overload +def zeros(*size: Union[_int, SymInt], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + zeros(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with the scalar value `0`, with the shape defined + by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.zeros(2, 3) + tensor([[ 0., 0., 0.], + [ 0., 0., 0.]]) + + >>> torch.zeros(5) + tensor([ 0., 0., 0., 0., 0.]) + """ + ... +@overload +def zeros(size: _size, *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + zeros(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with the scalar value `0`, with the shape defined + by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.zeros(2, 3) + tensor([[ 0., 0., 0.], + [ 0., 0., 0.]]) + + >>> torch.zeros(5) + tensor([ 0., 0., 0., 0., 0.]) + """ + ... +@overload +def zeros(*size: _int, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + zeros(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with the scalar value `0`, with the shape defined + by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.zeros(2, 3) + tensor([[ 0., 0., 0.], + [ 0., 0., 0.]]) + + >>> torch.zeros(5) + tensor([ 0., 0., 0., 0., 0.]) + """ + ... +def zeros_like(input: Tensor, *, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + zeros_like(input, *, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor + + Returns a tensor filled with the scalar value `0`, with the same size as + :attr:`input`. ``torch.zeros_like(input)`` is equivalent to + ``torch.zeros(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``. + + .. warning:: + As of 0.4, this function does not support an :attr:`out` keyword. As an alternative, + the old ``torch.zeros_like(input, out=output)`` is equivalent to + ``torch.zeros(input.size(), out=output)``. + + Args: + input (Tensor): the size of :attr:`input` will determine size of the output tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. + Default: if ``None``, defaults to the dtype of :attr:`input`. + layout (:class:`torch.layout`, optional): the desired layout of returned tensor. + Default: if ``None``, defaults to the layout of :attr:`input`. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, defaults to the device of :attr:`input`. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + + Example:: + + >>> input = torch.empty(2, 3) + >>> torch.zeros_like(input) + tensor([[ 0., 0., 0.], + [ 0., 0., 0.]]) + """ + ... + +__all__ = ['__and__', '__lshift__', '__or__', '__rshift__', '__xor__', '_adaptive_avg_pool2d', + '_adaptive_avg_pool3d', '_add_batch_dim', '_add_relu', '_add_relu_', '_addmm_activation', + '_aminmax', '_amp_foreach_non_finite_check_and_unscale_', '_amp_update_scale_', '_assert_async', + '_assert_scalar', '_assert_tensor_metadata', '_batch_norm_impl_index', '_cast_Byte', '_cast_Char', + '_cast_Double', '_cast_Float', '_cast_Half', '_cast_Int', '_cast_Long', '_cast_Short', + '_choose_qparams_per_tensor', '_chunk_cat', '_coalesce', '_compute_linear_combination', '_conj', + '_conj_copy', '_conj_physical', '_convert_indices_from_coo_to_csr', + '_convert_indices_from_csr_to_coo', '_convert_weight_to_int4pack', '_convolution', + '_convolution_mode', '_copy_from', '_copy_from_and_resize', '_cslt_compress', '_cslt_sparse_mm', + '_cslt_sparse_mm_search', '_ctc_loss', '_cudnn_ctc_loss', '_cudnn_init_dropout_state', + '_cudnn_rnn', '_cudnn_rnn_flatten_weight', '_cufft_clear_plan_cache', + '_cufft_get_plan_cache_max_size', '_cufft_get_plan_cache_size', '_cufft_set_plan_cache_max_size', + '_cummax_helper', '_cummin_helper', '_debug_has_internal_overlap', '_dim_arange', + '_dirichlet_grad', '_disable_functionalization', '_efficientzerotensor', '_embedding_bag', + '_embedding_bag_forward_only', '_empty_affine_quantized', '_empty_per_channel_affine_quantized', + '_enable_functionalization', '_euclidean_dist', '_fake_quantize_learnable_per_channel_affine', + '_fake_quantize_learnable_per_tensor_affine', + '_fake_quantize_per_tensor_affine_cachemask_tensor_qparams', + '_fake_quantize_per_tensor_affine_cachemask_tensor_qparams', '_fft_c2c', '_fft_c2r', '_fft_r2c', + '_fill_mem_eff_dropout_mask_', '_foobar', '_foreach_abs', '_foreach_abs_', '_foreach_acos', + '_foreach_acos_', '_foreach_add', '_foreach_add_', '_foreach_addcdiv', '_foreach_addcdiv_', + '_foreach_addcmul', '_foreach_addcmul_', '_foreach_asin', '_foreach_asin_', '_foreach_atan', + '_foreach_atan_', '_foreach_ceil', '_foreach_ceil_', '_foreach_clamp_max', '_foreach_clamp_max_', + '_foreach_clamp_min', '_foreach_clamp_min_', '_foreach_copy_', '_foreach_cos', '_foreach_cos_', + '_foreach_cosh', '_foreach_cosh_', '_foreach_div', '_foreach_div_', '_foreach_erf', + '_foreach_erf_', '_foreach_erfc', '_foreach_erfc_', '_foreach_exp', '_foreach_exp_', + '_foreach_expm1', '_foreach_expm1_', '_foreach_floor', '_foreach_floor_', '_foreach_frac', + '_foreach_frac_', '_foreach_lerp', '_foreach_lerp_', '_foreach_lgamma', '_foreach_lgamma_', + '_foreach_log', '_foreach_log10', '_foreach_log10_', '_foreach_log1p', '_foreach_log1p_', + '_foreach_log2', '_foreach_log2_', '_foreach_log_', '_foreach_max', '_foreach_maximum', + '_foreach_maximum_', '_foreach_minimum', '_foreach_minimum_', '_foreach_mul', '_foreach_mul_', + '_foreach_neg', '_foreach_neg_', '_foreach_norm', '_foreach_pow', '_foreach_pow_', + '_foreach_reciprocal', '_foreach_reciprocal_', '_foreach_round', '_foreach_round_', + '_foreach_sigmoid', '_foreach_sigmoid_', '_foreach_sign', '_foreach_sign_', '_foreach_sin', + '_foreach_sin_', '_foreach_sinh', '_foreach_sinh_', '_foreach_sqrt', '_foreach_sqrt_', + '_foreach_sub', '_foreach_sub_', '_foreach_tan', '_foreach_tan_', '_foreach_tanh', + '_foreach_tanh_', '_foreach_trunc', '_foreach_trunc_', '_foreach_zero_', '_from_functional_tensor', + '_functional_assert_async', '_functional_assert_scalar', '_functional_sym_constrain_range', + '_functional_sym_constrain_range_for_size', '_functionalize_apply_view_metas', + '_functionalize_are_all_mutations_hidden_from_autograd', + '_functionalize_are_all_mutations_under_no_grad_or_inference_mode', '_functionalize_commit_update', + '_functionalize_has_metadata_mutation', '_functionalize_is_symbolic', + '_functionalize_mark_mutation_hidden_from_autograd', '_functionalize_replace', + '_functionalize_set_storage_changed', '_functionalize_sync', '_functionalize_unsafe_set', + '_functionalize_was_inductor_storage_resized', '_functionalize_was_storage_changed', + '_fused_adagrad_', '_fused_adam_', '_fused_adamw_', '_fused_dropout', + '_fused_moving_avg_obs_fq_helper', '_fused_moving_avg_obs_fq_helper', '_fused_sdp_choice', + '_fused_sgd_', '_fw_primal_copy', '_grid_sampler_2d_cpu_fallback', + '_has_compatible_shallow_copy_type', '_histogramdd_bin_edges', '_histogramdd_from_bin_cts', + '_histogramdd_from_bin_tensors', '_index_put_impl_', '_indices_copy', '_int_mm', '_is_all_true', + '_is_any_true', '_is_functional_tensor', '_is_functional_tensor_base', '_is_zerotensor', + '_lazy_clone', '_linalg_check_errors', '_linalg_det', '_linalg_det', '_linalg_eigh', + '_linalg_eigh', '_linalg_slogdet', '_linalg_slogdet', '_linalg_solve_ex', '_linalg_solve_ex', + '_linalg_svd', '_linalg_svd', '_log_softmax', '_log_softmax_backward_data', '_logcumsumexp', + '_lstm_mps', '_lu_with_info', '_lu_with_info', '_make_dep_token', '_make_dual', '_make_dual_copy', + '_make_per_channel_quantized_tensor', '_make_per_tensor_quantized_tensor', '_masked_scale', + '_masked_softmax', '_mixed_dtypes_linear', '_mkldnn_reshape', '_mkldnn_transpose', + '_mkldnn_transpose_', '_mps_convolution', '_mps_convolution_transpose', '_native_batch_norm_legit', + '_native_batch_norm_legit_no_training', '_native_multi_head_attention', '_neg_view', + '_neg_view_copy', '_nested_compute_contiguous_strides_offsets', '_nested_from_padded', + '_nested_from_padded_and_nested_example', '_nested_get_jagged_dummy', '_nested_get_lengths', + '_nested_get_max_seqlen', '_nested_get_min_seqlen', '_nested_get_offsets', + '_nested_get_ragged_idx', '_nested_get_values', '_nested_get_values_copy', + '_nested_tensor_from_mask', '_nested_tensor_from_mask_left_aligned', + '_nested_tensor_from_tensor_list', '_nested_tensor_softmax_with_shape', '_nested_view_from_buffer', + '_nested_view_from_buffer_copy', '_nested_view_from_jagged', '_nested_view_from_jagged_copy', + '_nnpack_available', '_nnpack_spatial_convolution', '_pack_padded_sequence', + '_pad_packed_sequence', '_pin_memory', '_prelu_kernel', '_print', '_propagate_xla_data', + '_remove_batch_dim', '_reshape_alias_copy', '_reshape_from_tensor', '_resize_output_', + '_rowwise_prune', '_safe_softmax', '_sample_dirichlet', '_saturate_weight_to_fp16', + '_scaled_dot_product_attention_math', '_scaled_dot_product_attention_math_for_mps', + '_scaled_dot_product_cudnn_attention', '_scaled_dot_product_cudnn_attention', + '_scaled_dot_product_efficient_attention', '_scaled_dot_product_efficient_attention', + '_scaled_dot_product_flash_attention', '_scaled_dot_product_flash_attention', + '_scaled_dot_product_flash_attention_for_cpu', '_scaled_dot_product_flash_attention_for_cpu', + '_scaled_mm', '_shape_as_tensor', '_sobol_engine_draw', '_sobol_engine_ff_', + '_sobol_engine_initialize_state_', '_sobol_engine_scramble_', '_softmax', '_softmax_backward_data', + '_sparse_broadcast_to', '_sparse_broadcast_to_copy', '_sparse_csr_prod', '_sparse_csr_sum', + '_sparse_log_softmax_backward_data', '_sparse_semi_structured_addmm', + '_sparse_semi_structured_apply', '_sparse_semi_structured_apply_dense', + '_sparse_semi_structured_linear', '_sparse_semi_structured_mm', '_sparse_semi_structured_tile', + '_sparse_softmax_backward_data', '_sparse_sparse_matmul', '_sparse_sum', '_stack', + '_standard_gamma', '_standard_gamma_grad', '_sync', '_test_autograd_multiple_dispatch', + '_test_autograd_multiple_dispatch_view', '_test_autograd_multiple_dispatch_view_copy', + '_test_check_tensor', '_test_functorch_fallback', '_test_parallel_materialize', + '_test_serialization_subcmul', '_to_cpu', '_to_functional_tensor', '_to_sparse_semi_structured', + '_transform_bias_rescale_qkv', '_transformer_encoder_layer_fwd', '_trilinear', + '_triton_multi_head_attention', '_triton_scaled_dot_attention', '_unique', '_unique2', + '_unpack_dual', '_unpack_dual', '_unsafe_index', '_unsafe_index_put', '_unsafe_masked_index', + '_unsafe_masked_index_put_accumulate', '_use_cudnn_ctc_loss', '_use_cudnn_rnn_flatten_weight', + '_validate_compressed_sparse_indices', '_validate_sparse_bsc_tensor_args', + '_validate_sparse_bsr_tensor_args', '_validate_sparse_compressed_tensor_args', + '_validate_sparse_coo_tensor_args', '_validate_sparse_csc_tensor_args', + '_validate_sparse_csr_tensor_args', '_values_copy', '_weight_int4pack_mm', '_weight_int8pack_mm', + '_weight_norm', '_weight_norm_interface', '_wrapped_linear_prepack', + '_wrapped_quantized_linear_prepacked', 'abs', 'abs_', 'absolute', 'acos', 'acos_', 'acosh', + 'acosh_', 'adaptive_avg_pool1d', 'adaptive_max_pool1d', 'add', 'addbmm', 'addcdiv', 'addcmul', + 'addmm', 'addmv', 'addmv_', 'addr', 'adjoint', 'affine_grid_generator', 'alias_copy', 'all', + 'allclose', 'alpha_dropout', 'alpha_dropout_', 'amax', 'amin', 'aminmax', 'aminmax', 'angle', + 'any', 'arange', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', + 'arcsinh_', 'arctan', 'arctan2', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', + 'argwhere', 'as_strided', 'as_strided_', 'as_strided_copy', 'as_strided_scatter', 'as_tensor', + 'asarray', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan_', 'atanh', 'atanh_', + 'avg_pool1d', 'baddbmm', 'bartlett_window', 'batch_norm', 'batch_norm_backward_elemt', + 'batch_norm_backward_reduce', 'batch_norm_elemt', 'batch_norm_gather_stats', + 'batch_norm_gather_stats_with_counts', 'batch_norm_stats', 'batch_norm_update_stats', 'bernoulli', + 'bilinear', 'binary_cross_entropy_with_logits', 'bincount', 'binomial', 'bitwise_and', + 'bitwise_left_shift', 'bitwise_not', 'bitwise_or', 'bitwise_right_shift', 'bitwise_xor', + 'blackman_window', 'bmm', 'broadcast_to', 'bucketize', 'can_cast', 'cat', 'ccol_indices_copy', + 'ceil', 'ceil_', 'celu', 'celu_', 'channel_shuffle', 'cholesky', 'cholesky_inverse', + 'cholesky_solve', 'choose_qparams_optimized', 'chunk', 'clamp', 'clamp_', 'clamp_max', + 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'col_indices_copy', + 'column_stack', 'combinations', 'complex', 'concat', 'concatenate', 'conj', 'conj_physical', + 'conj_physical_', 'constant_pad_nd', 'conv1d', 'conv2d', 'conv3d', 'conv_tbc', 'conv_transpose1d', + 'conv_transpose2d', 'conv_transpose3d', 'convolution', 'copysign', 'corrcoef', 'cos', 'cos_', + 'cosh', 'cosh_', 'cosine_embedding_loss', 'cosine_similarity', 'count_nonzero', 'cov', 'cross', + 'crow_indices_copy', 'ctc_loss', 'cudnn_affine_grid_generator', 'cudnn_batch_norm', + 'cudnn_convolution', 'cudnn_convolution_add_relu', 'cudnn_convolution_relu', + 'cudnn_convolution_transpose', 'cudnn_grid_sampler', 'cudnn_is_acceptable', 'cummax', 'cummax', + 'cummin', 'cummin', 'cumprod', 'cumsum', 'cumulative_trapezoid', 'deg2rad', 'deg2rad_', + 'dequantize', 'det', 'detach', 'detach_', 'detach_copy', 'diag', 'diag_embed', 'diagflat', + 'diagonal', 'diagonal_copy', 'diagonal_scatter', 'diff', 'digamma', 'dist', 'div', 'divide', 'dot', + 'dropout', 'dropout_', 'dsmm', 'dsplit', 'dstack', 'embedding', 'embedding_bag', + 'embedding_renorm_', 'empty', 'empty_like', 'empty_permuted', 'empty_quantized', 'empty_strided', + 'eq', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'exp', 'exp2', 'exp2_', 'exp_', + 'expand_copy', 'expm1', 'expm1_', 'eye', 'fake_quantize_per_channel_affine', + 'fake_quantize_per_tensor_affine', 'fbgemm_linear_fp16_weight', + 'fbgemm_linear_fp16_weight_fp32_activation', 'fbgemm_linear_int8_weight', + 'fbgemm_linear_int8_weight_fp32_activation', 'fbgemm_linear_quantize_weight', + 'fbgemm_pack_gemm_matrix_fp16', 'fbgemm_pack_quantized_matrix', 'feature_alpha_dropout', + 'feature_alpha_dropout_', 'feature_dropout', 'feature_dropout_', 'fill', 'fill_', 'fix', 'fix_', + 'flatten', 'flip', 'fliplr', 'flipud', 'float_power', 'floor', 'floor_', 'floor_divide', 'fmax', + 'fmin', 'fmod', 'frac', 'frac_', 'frexp', 'frexp', 'frobenius_norm', 'from_file', 'from_numpy', + 'frombuffer', 'full', 'full_like', 'fused_moving_avg_obs_fake_quant', 'gather', 'gcd', 'gcd_', + 'ge', 'geqrf', 'geqrf', 'ger', 'get_default_dtype', 'get_num_interop_threads', 'get_num_threads', + 'gradient', 'greater', 'greater_equal', 'grid_sampler', 'grid_sampler_2d', 'grid_sampler_3d', + 'group_norm', 'gru', 'gru_cell', 'gt', 'hamming_window', 'hann_window', 'hardshrink', 'heaviside', + 'hinge_embedding_loss', 'histc', 'histogram', 'histogram', 'histogramdd', 'histogramdd', 'hsmm', + 'hsplit', 'hspmm', 'hstack', 'hypot', 'i0', 'i0_', 'igamma', 'igammac', 'imag', 'index_add', + 'index_copy', 'index_fill', 'index_put', 'index_put_', 'index_reduce', 'index_select', + 'indices_copy', 'init_num_threads', 'inner', 'instance_norm', 'int_repr', 'inverse', 'is_complex', + 'is_conj', 'is_distributed', 'is_floating_point', 'is_grad_enabled', 'is_inference', + 'is_inference_mode_enabled', 'is_neg', 'is_nonzero', 'is_same_size', 'is_signed', + 'is_vulkan_available', 'isclose', 'isfinite', 'isin', 'isinf', 'isnan', 'isneginf', 'isposinf', + 'isreal', 'istft', 'kaiser_window', 'kl_div', 'kron', 'kthvalue', 'kthvalue', 'layer_norm', 'lcm', + 'lcm_', 'ldexp', 'ldexp_', 'le', 'lerp', 'less', 'less_equal', 'lgamma', 'linspace', 'log', + 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_softmax', 'logaddexp', + 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_not', 'logical_or', 'logical_xor', + 'logit', 'logit_', 'logspace', 'logsumexp', 'lstm', 'lstm_cell', 'lt', 'lu_solve', 'lu_unpack', + 'lu_unpack', 'margin_ranking_loss', 'masked_fill', 'masked_scatter', 'masked_select', 'matmul', + 'matrix_exp', 'matrix_power', 'max', 'max', 'max_pool1d', 'max_pool1d_with_indices', 'max_pool2d', + 'max_pool3d', 'maximum', 'mean', 'median', 'median', 'min', 'min', 'minimum', 'miopen_batch_norm', + 'miopen_convolution', 'miopen_convolution_add_relu', 'miopen_convolution_relu', + 'miopen_convolution_transpose', 'miopen_depthwise_convolution', 'miopen_rnn', + 'mkldnn_adaptive_avg_pool2d', 'mkldnn_convolution', 'mkldnn_linear_backward_weights', + 'mkldnn_max_pool2d', 'mkldnn_max_pool3d', 'mkldnn_rnn_layer', 'mm', 'mode', 'mode', 'moveaxis', + 'movedim', 'msort', 'mul', 'multinomial', 'multiply', 'mv', 'mvlgamma', 'nan_to_num', + 'nan_to_num_', 'nanmean', 'nanmedian', 'nanmedian', 'nanquantile', 'nansum', 'narrow', + 'narrow_copy', 'native_batch_norm', 'native_channel_shuffle', 'native_dropout', + 'native_group_norm', 'native_layer_norm', 'native_norm', 'ne', 'neg', 'neg_', 'negative', + 'negative_', 'nextafter', 'nonzero', 'nonzero_static', 'norm_except_dim', 'normal', 'not_equal', + 'nuclear_norm', 'numel', 'ones', 'ones_like', 'orgqr', 'ormqr', 'outer', 'pairwise_distance', + 'pdist', 'permute', 'permute_copy', 'pinverse', 'pixel_shuffle', 'pixel_unshuffle', 'poisson', + 'poisson_nll_loss', 'polar', 'polygamma', 'positive', 'pow', 'prelu', 'prod', 'promote_types', + 'put', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', + 'q_zero_point', 'qr', 'qr', 'quantile', 'quantize_per_channel', 'quantize_per_tensor', + 'quantize_per_tensor_dynamic', 'quantized_batch_norm', 'quantized_gru_cell', 'quantized_lstm_cell', + 'quantized_max_pool1d', 'quantized_max_pool2d', 'quantized_max_pool3d', 'quantized_rnn_relu_cell', + 'quantized_rnn_tanh_cell', 'rad2deg', 'rad2deg_', 'rand', 'rand_like', 'randint', 'randint_like', + 'randn', 'randn_like', 'randperm', 'range', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'relu', + 'relu_', 'remainder', 'renorm', 'repeat_interleave', 'reshape', 'resize_as_', 'resize_as_sparse_', + 'resolve_conj', 'resolve_neg', 'result_type', 'rms_norm', 'rnn_relu', 'rnn_relu_cell', 'rnn_tanh', + 'rnn_tanh_cell', 'roll', 'rot90', 'round', 'round_', 'row_indices_copy', 'row_stack', 'rrelu', + 'rrelu_', 'rsqrt', 'rsqrt_', 'rsub', 'saddmm', 'scalar_tensor', 'scatter', 'scatter_add', + 'scatter_reduce', 'searchsorted', 'segment_reduce', 'select', 'select_copy', 'select_scatter', + 'selu', 'selu_', 'set_flush_denormal', 'set_num_interop_threads', 'set_num_threads', 'sgn', + 'sigmoid', 'sigmoid_', 'sign', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', + 'slice_copy', 'slice_inverse', 'slice_scatter', 'slogdet', 'slogdet', 'smm', 'softmax', 'sort', + 'sort', 'sparse_bsc_tensor', 'sparse_bsr_tensor', 'sparse_compressed_tensor', 'sparse_coo_tensor', + 'sparse_csc_tensor', 'sparse_csr_tensor', 'split_copy', 'split_with_sizes', + 'split_with_sizes_copy', 'spmm', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_copy', + 'sspaddmm', 'stack', 'std', 'std_mean', 'sub', 'subtract', 'sum', 'svd', 'svd', 'swapaxes', + 'swapdims', 'sym_constrain_range', 'sym_constrain_range_for_size', 't', 't_copy', 'take', + 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor', 'tensor_split', 'threshold', + 'threshold_', 'tile', 'topk', 'topk', 'trace', 'transpose', 'transpose_copy', 'trapezoid', 'trapz', + 'triangular_solve', 'triangular_solve', 'tril', 'tril_indices', 'triplet_margin_loss', 'triu', + 'triu_indices', 'true_divide', 'trunc', 'trunc_', 'unbind', 'unbind_copy', 'unflatten', + 'unfold_copy', 'unique_dim', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', + 'unsqueeze', 'unsqueeze_copy', 'values_copy', 'vander', 'var', 'var_mean', 'vdot', + 'view_as_complex', 'view_as_complex_copy', 'view_as_real', 'view_as_real_copy', 'view_copy', + 'vsplit', 'vstack', 'where', 'xlogy', 'xlogy_', 'zero_', 'zeros', 'zeros_like'] diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/__init__.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/__init__.pyi new file mode 100644 index 0000000000000000000000000000000000000000..8449d454ce37cac9086952d87fb5caf9208125a1 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/__init__.pyi @@ -0,0 +1,11259 @@ +# @generated by tools/pyi/gen_pyi.py from torch/_C/__init__.pyi.in +# mypy: disable-error-code="type-arg" +# mypy: allow-untyped-defs + +import builtins +from enum import Enum, IntEnum +from pathlib import Path +from typing import ( + Any, + AnyStr, + BinaryIO, + Callable, + ContextManager, + Dict, + Generic, + Iterable, + Iterator, + List, + Literal, + NamedTuple, + Optional, + Protocol, + Sequence, + Set, + SupportsIndex, + Tuple, + Type as _Type, + TypeVar, + Union, + overload, + runtime_checkable, +) +from typing_extensions import ParamSpec, Self + +import numpy + +import torch +from torch import SymInt, Tensor, inf +from torch._prims_common import DeviceLikeType +from torch.autograd.graph import Node as _Node +from torch.fx.node import Node as FxNode +from torch.package import PackageExporter +from torch.storage import TypedStorage, UntypedStorage +from torch.types import ( + Device, + Number, + Storage, + IntLikeType, + _bool, + _bytes, + _complex, + _device, + _dispatchkey, + _dtype, + _float, + _int, + _layout, + _qscheme, + _size, + _str, + _symsize, +) +from torch.utils._python_dispatch import TorchDispatchMode + +from . import ( + _aoti, + _cpu, + _dynamo, + _functorch, + _lazy, + _lazy_ts_backend, + _nn, + _onnx, + _VariableFunctions, + _verbose, +) + +# This module is defined in torch/csrc/Module.cpp + +K = TypeVar("K") +T = TypeVar("T") +S = TypeVar("S", bound="torch.Tensor") +P = ParamSpec("P") +ReturnVal = TypeVar("ReturnVal", covariant=True) # return value (always covariant) +_T_co = TypeVar("_T_co", covariant=True) + + +@runtime_checkable +class _NestedSequence(Protocol[_T_co]): + """A protocol for representing nested sequences. + + References:: + `numpy._typing._NestedSequence` + + """ + + def __len__(self, /) -> builtins.int: ... + def __getitem__(self, index: builtins.int, /) -> _T_co | _NestedSequence[_T_co]: ... + def __contains__(self, x: builtins.object, /) -> builtins.bool: ... + def __iter__(self, /) -> Iterator[_T_co | _NestedSequence[_T_co]]: ... + def __reversed__(self, /) -> Iterator[_T_co | _NestedSequence[_T_co]]: ... + def count(self, value: Any, /) -> builtins.int: ... + def index(self, value: Any, /) -> builtins.int: ... + + +# Defined in torch/csrc/Device.cpp +class device: + type: str # THPDevice_type + index: _int # THPDevice_index + + def __get__(self, instance, owner=None) -> device: ... + + # THPDevice_pynew + @overload + def __init__(self, device: DeviceLikeType) -> None: ... + @overload + def __init__(self, type: str, index: _int) -> None: ... + + # Uncomment if we ever make torch.device a decorator + # def __call__(self, func: T) -> T: ... + + def __enter__(self) -> device: ... + def __exit__(self, exc_type, exc_val, exc_tb) -> None: ... + def __reduce__(self) -> Tuple[Any, ...]: ... # THPDevice_reduce + +# Defined in torch/csrc/Stream.cpp +class Stream: + stream_id: _int # Stream id + device_index: _int + device_type: _int + + device: _device # The device of the stream + + @overload + def __new__(self, device: Optional[DeviceLikeType] = None, *, priority: _int = 0) -> Stream: ... + @overload + def __new__(self, stream_id: _int, device_index: _int, device_type: _int, *, priority: _int = 0) -> Stream: ... + def query(self) -> _bool: ... + def synchronize(self) -> None: ... + def wait_event(self, event: Event) -> None: ... + def wait_stream(self, other: Stream) -> None: ... + def record_event(self, event: Optional[Event] = None) -> Event: ... + def __hash__(self) -> _int: ... + def __repr__(self) -> str: ... + def __eq__(self, other: object) -> _bool: ... + + +# Defined in torch/csrc/Event.cpp +class Event: + + device: _device # The device of the Event + event_id: _int # The raw event created by device backend + + def __new__(self, + device: Optional[DeviceLikeType] = None, + *, + enable_timing: _bool = False, + blocking: _bool = False, + interprocess: _bool = False) -> Event: ... + @classmethod + def from_ipc_handle(self, device: _device, ipc_handle: bytes) -> Event: ... + def record(self, stream: Optional[Stream] = None) -> None: ... + def wait(self, stream: Optional[Stream] = None) -> None: ... + def query(self) -> _bool: ... + def elapsed_time(self, other: Event) -> _float: ... + def synchronize(self) -> None: ... + def ipc_handle(self) -> bytes: ... + def __repr__(self) -> str: ... + + +# Defined in torch/csrc/Size.cpp +class Size(Tuple[_int, ...]): + # TODO: __reduce__ + + @overload # type: ignore[override] + def __getitem__(self: Size, key: _int) -> _int: ... + @overload + def __getitem__(self: Size, key: slice) -> Size: ... + def numel(self: Size) -> _int: ... + +# Defined in torch/csrc/Dtype.cpp +class dtype: + # TODO: __reduce__ + is_floating_point: _bool + is_complex: _bool + is_signed: _bool + itemsize: _int + def to_real(self) -> dtype: ... + def to_complex(self) -> dtype: ... + +# Defined in torch/csrc/TypeInfo.cpp +class iinfo: + bits: _int + min: _int + max: _int + dtype: str + + def __init__(self, dtype: _dtype) -> None: ... + +class finfo: + bits: _int + min: _float + max: _float + eps: _float + tiny: _float + smallest_normal: _float + resolution: _float + dtype: str + + @overload + def __init__(self, dtype: _dtype) -> None: ... + @overload + def __init__(self) -> None: ... + +float32: dtype = ... +float: dtype = ... +float64: dtype = ... +double: dtype = ... +float16: dtype = ... +bfloat16: dtype = ... +float8_e4m3fn: dtype = ... +float8_e4m3fnuz: dtype = ... +float8_e5m2: dtype = ... +float8_e5m2fnuz: dtype = ... +half: dtype = ... +uint8: dtype = ... +uint16: dtype = ... +uint32: dtype = ... +uint64: dtype = ... +int8: dtype = ... +int16: dtype = ... +short: dtype = ... +int32: dtype = ... +int: dtype = ... +int64: dtype = ... +long: dtype = ... +complex32: dtype = ... +complex64: dtype = ... +chalf: dtype = ... +cfloat: dtype = ... +complex128: dtype = ... +cdouble: dtype = ... +quint8: dtype = ... +qint8: dtype = ... +qint32: dtype = ... +bool: dtype = ... +quint4x2: dtype = ... +quint2x4: dtype = ... +bits1x8: dtype = ... +bits2x4: dtype = ... +bits4x2: dtype = ... +bits8: dtype = ... +bits16: dtype = ... + +# Defined in torch/csrc/Layout.cpp +class layout: ... + +# Defined in torch/csrc/utils/disable_torch_function.cpp +def DisableTorchFunction(): ... +def DisableTorchFunctionSubclass(): ... + +# Defined in torch/csrc/utils/tensor_layouts.cpp +strided: layout = ... +sparse_coo: layout = ... +sparse_csr: layout = ... +sparse_csc: layout = ... +sparse_bsr: layout = ... +sparse_bsc: layout = ... +_mkldnn: layout = ... +jagged: layout = ... + +# Defined in torch/csrc/MemoryFormat.cpp +class memory_format: ... + +# Defined in torch/csrc/utils/tensor_memoryformats.cpp +contiguous_format: memory_format = ... +channels_last: memory_format = ... +channels_last_3d: memory_format = ... +preserve_format: memory_format = ... + +# Defined in torch/csrc/QScheme.cpp +class qscheme: ... + +# Defined in torch/csrc/utils/tensor_qschemes.h +per_tensor_affine: qscheme = ... +per_channel_affine: qscheme = ... +per_tensor_symmetric: qscheme = ... +per_channel_symmetric: qscheme = ... +per_channel_affine_float_qparams: qscheme = ... + +# Defined in torch/csrc/autograd/python_function.cpp +class _FunctionBase: + saved_tensors: Tuple[Tensor] + _raw_saved_tensors: Tuple[Any] + next_functions: Tuple[Tuple[Any, _int], ...] + needs_input_grad: Tuple[_bool] + metadata: dict + _materialize_non_diff_grads: _bool + # skip adding type hints for the fields that have wrappers defined + # in torch/autograd/function.py + +# Defined in torch/csrc/autograd/python_legacy_variable.cpp +class _LegacyVariableBase(Tensor): # inherits from Tensor to appease mypy + def __init__( + self, + data: Optional[Tensor] = ..., + requires_grad: Optional[_bool] = ..., + volatile: Optional[_bool] = ..., + _grad_fn: Optional[_FunctionBase] = ..., + ) -> None: ... + +# Defined in torch/csrc/jit/python/init.cpp +class IODescriptor: ... +class JITException: ... + +class Future(Generic[T]): + def __init__(self, devices: List[device]) -> None: ... + def done(self) -> _bool: ... + def value(self) -> T: ... + def wait(self) -> T: ... + def add_done_callback(self, callback: Callable) -> None: ... + def then(self, callback: Callable) -> Future[T]: ... + def set_result(self, result: T) -> None: ... + def _set_unwrap_func(self, callback: Callable) -> None: ... + +class _Await: + def __init__(self) -> None: ... + def fn(self) -> Callable: ... + def args(self) -> Tuple[Any, ...]: ... + def is_nowait(self) -> _bool: ... + +def _jit_set_num_profiled_runs(num: _size) -> _size: ... + +# Defined in torch/csrc/jit/passes/mobile_optimizer_type.h +class _MobileOptimizerType: ... + +CONV_BN_FUSION: _MobileOptimizerType +INSERT_FOLD_PREPACK_OPS: _MobileOptimizerType +REMOVE_DROPOUT: _MobileOptimizerType +FUSE_ADD_RELU: _MobileOptimizerType +HOIST_CONV_PACKED_PARAMS: _MobileOptimizerType +VULKAN_AUTOMATIC_GPU_TRANSFER: _MobileOptimizerType + +def fork(*args: Any, **kwargs: Any) -> Future: ... +def wait(fut: Future) -> Any: ... +def _awaitable(*args: Any, **kwargs: Any) -> _Await: ... +def _awaitable_wait(aw: _Await) -> Any: ... +def _awaitable_nowait(x: Any) -> _Await: ... +def _collect_all(futures: List[Future]) -> Future: ... +def _set_print_stack_traces_on_fatal_signal(print: _bool) -> None: ... +def unify_type_list(types: List[JitType]) -> JitType: ... +def _freeze_module( + module: ScriptModule, + preserved_attrs: List[str] = [], + freeze_interfaces: _bool = True, + preserveParameters: _bool = True, +) -> ScriptModule: ... +def _jit_pass_optimize_frozen_graph(Graph, optimize_numerics: _bool = True) -> None: ... +def _jit_pass_optimize_for_inference( + module: torch.jit.ScriptModule, + other_methods: List[str] = [], +) -> None: ... +def _jit_pass_fold_frozen_conv_bn(graph: Graph): ... +def _jit_pass_fold_frozen_conv_add_or_sub(graph: Graph): ... +def _jit_pass_fold_frozen_conv_mul_or_div(graph: Graph): ... +def _jit_pass_fuse_frozen_conv_add_relu(graph: Graph): ... +def _jit_pass_concat_frozen_linear(graph: Graph): ... +def _jit_pass_convert_frozen_ops_to_mkldnn(graph: Graph): ... +def _jit_pass_transpose_frozen_linear(graph: Graph): ... +def _jit_pass_remove_dropout(module: torch.jit.ScriptModule): ... +def _is_tracing() -> _bool: ... +def _jit_init() -> _bool: ... +def _jit_flatten(arg: Any) -> Tuple[List[Tensor], IODescriptor]: ... +def _jit_unflatten(vars: List[Tensor], desc: IODescriptor) -> Any: ... +def _jit_get_operation(op_name: str) -> Tuple[Callable, List[str]]: ... +def _get_operation_overload( + op_name: str, + op_overload_name: str, +) -> Tuple[Callable, Callable, List[Any]]: ... +def _get_schema(op_name: str, overload_name: str) -> FunctionSchema: ... +def _jit_pass_optimize_for_mobile( + module: torch.jit.ScriptModule, + optimization_blocklist: Set[_MobileOptimizerType], + preserved_methods: List[AnyStr], +) -> torch.jit.ScriptModule: ... +def _clone_module_with_class( + module: torch.jit.ScriptModule, + ignored_methods: List[AnyStr], + ignored_attributes: List[AnyStr], +) -> torch.jit.ScriptModule: ... +def _jit_pass_vulkan_optimize_for_mobile( + module: torch.jit.ScriptModule, + optimization_blocklist: Set[_MobileOptimizerType], + preserved_methods: List[AnyStr], +) -> torch.jit.ScriptModule: ... +def _jit_pass_metal_optimize_for_mobile( + module: torch.jit.ScriptModule, + preserved_methods: List[AnyStr], +) -> torch.jit.ScriptModule: ... +def _jit_pass_inline(Graph) -> None: ... +def _jit_pass_constant_propagation(Graph) -> None: ... +def _jit_pass_propagate_shapes_on_graph(Graph) -> None: ... +def _jit_register_decomposition_for_schema(schema: FunctionSchema, Graph) -> None: ... +def _jit_erase_non_input_shape_information(Graph) -> None: ... +def _jit_get_schemas_for_operator(name: str) -> List[FunctionSchema]: ... +def _jit_get_all_schemas() -> List[FunctionSchema]: ... +def _jit_check_alias_annotation( + g: Graph, + args: Tuple[Any, ...], + unqualified_op_name: str, +): ... +def _jit_can_fuse_on_cpu() -> _bool: ... +def _jit_can_fuse_on_gpu() -> _bool: ... +def _jit_can_fuse_on_cpu_legacy() -> _bool: ... +def _debug_get_fusion_group_inlining() -> _bool: ... +def _debug_set_fusion_group_inlining(enable: _bool): ... +def _jit_texpr_fuser_enabled() -> _bool: ... +def _jit_nvfuser_enabled() -> _bool: ... +def _jit_llga_enabled() -> _bool: ... +def _jit_set_llga_enabled(enable: _bool): ... +def _llvm_enabled() -> _bool: ... +def _jit_override_can_fuse_on_cpu(override: _bool): ... +def _jit_override_can_fuse_on_gpu(override: _bool): ... +def _jit_override_can_fuse_on_cpu_legacy(override: _bool): ... +def _jit_set_symbolic_shapes_test_mode(override: _bool): ... +def _jit_symbolic_shapes_test_mode_enabled() -> _bool: ... +def _jit_set_texpr_fuser_enabled(enable: _bool): ... +def _jit_set_te_must_use_llvm_cpu(use_llvm: _bool): ... +def _jit_set_nvfuser_enabled(enable: _bool) -> _bool: ... +def _jit_cat_wo_conditionals(optimize_cat: _bool): ... +def _jit_opt_conditionals(opt_conds: _bool): ... +def _jit_pass_canonicalize(graph: Graph, keep_unique_names: _bool = True): ... +def _jit_pass_erase_shape_information(graph: Graph): ... +def _jit_pass_fold_convbn(module: torch.jit.ScriptModule): ... +def _jit_pass_insert_observers( + module: torch.jit.ScriptModule, + method_name: str, + qconfig_dict: Dict[str, Any], + inplace: _bool, + quant_type: _int, +): ... +def _jit_pass_insert_quant_dequant( + module: torch.jit.ScriptModule, + method_name: str, + inplace: _bool, + debug: _bool, + quant_type: _int, +): ... +def _jit_pass_insert_quant_dequant_for_ondevice_ptq( + module: torch.jit.ScriptModule, + method_name: str, + inplace: _bool, + debug: _bool, + quant_type: _int, +): ... +def _jit_pass_quant_finalize( + module: torch.jit.ScriptModule, + quant_type: _int, + preserved_attrs: Sequence[str], +): ... +def _jit_pass_quant_finalize_for_ondevice_ptq( + module: torch.jit.ScriptModule, + quant_type: _int, + method_name: str, +): ... +def _jit_pass_insert_observer_method_for_ondevice_ptq( + module: torch.jit.ScriptModule, + method_name: str, + qconfig_dict: Dict[str, Any], + inplace: _bool, + quant_type: _int, +): ... +def _jit_set_profiling_executor(profiling_flag: _bool) -> _bool: ... +def _jit_set_profiling_mode(profiling_flag: _bool) -> _bool: ... +def _jit_set_fusion_strategy( + strategy: List[Tuple[str, _int]], +) -> List[Tuple[str, _int]]: ... +def _jit_try_infer_type(obj: Any) -> InferredType: ... +def _jit_get_trigger_value(trigger_name: str) -> _int: ... + +# Defined in torch/csrc/jit/python/script_init.cpp +ResolutionCallback = Callable[[str], Callable[..., Any]] + +# Defined in torch/csrc/jit/python/script_init.cpp +# and torch/csrc/jit/python/init.cpp +def _maybe_call_torch_function_for_op_packet( + op_overload_packet: Any, + args: Any, + kwargs: Any, +) -> Any: ... +def _check_schema_allow_fake_script_object( + schema: FunctionSchema, + args: Any, + kwargs: Any, +) -> _bool: ... +def _create_function_from_graph(qualname: str, graph: Graph) -> ScriptFunction: ... +def _debug_set_autodiff_subgraph_inlining(disabled: _bool) -> None: ... +def _ivalue_tags_match(lhs: ScriptModule, rhs: ScriptModule) -> _bool: ... +def _jit_assert_is_instance(obj: Any, type: JitType): ... +def _jit_clear_class_registry() -> None: ... +def _jit_set_emit_hooks( + ModuleHook: Optional[Callable], + FunctionHook: Optional[Callable], +) -> None: ... +def _jit_get_emit_hooks() -> Tuple[Callable, Callable]: ... +def _load_for_lite_interpreter( + filename: Union[str, Path], + map_location: Optional[DeviceLikeType], +): ... +def _load_for_lite_interpreter_from_buffer( + buffer: BinaryIO, + map_location: Optional[DeviceLikeType], +): ... +def _export_operator_list(module: LiteScriptModule): ... +def _quantize_ondevice_ptq_dynamic(module: LiteScriptModule, method_name: str): ... +def _get_model_bytecode_version(filename: Union[str, Path]) -> _int: ... +def _get_model_bytecode_version_from_buffer(buffer: BinaryIO) -> _int: ... +def _backport_for_mobile( + filename_input: Union[str, Path], + filename_output: Union[str, Path], + to_version: _int, +) -> None: ... +def _backport_for_mobile_from_buffer( + buffer: BinaryIO, + filename_output: Union[str, Path], + to_version: _int, +) -> None: ... +def _backport_for_mobile_to_buffer( + filename_input: Union[str, Path], + to_version: _int, +) -> bytes: ... +def _backport_for_mobile_from_buffer_to_buffer( + buffer: BinaryIO, + to_version: _int, +) -> bytes: ... +def _get_model_ops_and_info(filename: Union[str, Path]): ... +def _get_model_ops_and_info_from_buffer(buffer: BinaryIO): ... +def _get_mobile_model_contained_types(filename: Union[str, Path]): ... +def _get_mobile_model_contained_types_from_buffer(buffer: BinaryIO): ... +def _logging_set_logger(logger: LoggerBase) -> LoggerBase: ... +def _get_graph_executor_optimize(optimize: Optional[_bool] = None) -> _bool: ... +def _set_graph_executor_optimize(optimize: _bool): ... +def _export_opnames(module: ScriptModule) -> List[str]: ... +def _create_function_from_trace( + qualname: str, + func: Callable[..., Any], + input_tuple: Tuple[Any, ...], + var_lookup_fn: Callable[[Tensor], str], + strict: _bool, + force_outplace: _bool, + argument_names: List[str], +) -> Tuple[Graph, Stack]: ... +def _create_function_from_trace_with_dict( + qualname: str, + func: Callable[..., Any], + input_dict: Dict[str, Any], + var_lookup_fn: Callable[[Tensor], str], + strict: _bool, + force_outplace: _bool, + argument_names: List[str], +) -> Tuple[Graph, Stack]: ... +def _jit_is_script_object(obj: Any) -> _bool: ... +def _last_executed_optimized_graph() -> Graph: ... +def parse_type_comment(comment: str) -> Decl: ... +def _get_upgraders_map_size() -> _int: ... +def _get_upgraders_entry_map() -> Dict[str, str]: ... +def _dump_upgraders_map() -> Dict[str, str]: ... +def _test_only_populate_upgraders(content: Dict[str, str]) -> None: ... +def _test_only_remove_upgraders(content: Dict[str, str]) -> None: ... +def merge_type_from_type_comment( + decl: Decl, + type_annotation_decl: Decl, + is_method: _bool, +) -> Decl: ... +def parse_ir(input: str, parse_tensor_constants: _bool = False) -> Graph: ... +def parse_schema(schema: str) -> FunctionSchema: ... +def get_device(input: Tensor) -> _int: ... +def _resolve_type_from_object( + obj: Any, + range: SourceRange, + rcb: ResolutionCallback, +) -> JitType: ... +def _create_module_with_type(ty: JitType) -> ScriptModule: ... +def _create_object_with_type(ty: ClassType) -> ScriptObject: ... +def _run_emit_module_hook(m: ScriptModule): ... +def _replace_overloaded_method_decl( + overload_decl: Decl, + implementation_def: Def, + new_name: str, +) -> Def: ... +def _jit_pass_lower_all_tuples(graph: Graph) -> None: ... +def _jit_pass_onnx_set_dynamic_input_shape( + graph: Graph, + dynamic_axes: Dict[str, Dict[_int, str]], + input_names: List[str], +) -> None: ... +def _jit_pass_onnx_graph_shape_type_inference( + graph: Graph, + params_dict: Dict[str, IValue], + opset_version: _int, +) -> None: ... +def _jit_pass_onnx_assign_output_shape( + graph: Graph, + tensors: List[Tensor], + desc: IODescriptor, + onnx_shape_inference: _bool, + is_script: _bool, + opset_version: _int, +) -> None: ... +def _jit_pass_onnx_remove_inplace_ops_for_onnx( + graph: Graph, + module: Optional[ScriptModule] = None, +) -> None: ... +def _jit_pass_remove_inplace_ops(graph: Graph) -> None: ... +def _jit_pass_canonicalize_graph_fuser_ops(graph: Graph) -> None: ... +def _jit_pass_peephole( + graph: Graph, + disable_shape_peepholes: _bool = False, +) -> None: ... +def _jit_pass_onnx_autograd_function_process(graph: Graph) -> None: ... +def _jit_pass_fuse_addmm(graph: Graph) -> None: ... +def _jit_pass_onnx_preprocess(graph: Graph) -> None: ... +def _jit_pass_prepare_division_for_onnx(graph: Graph) -> None: ... +def _jit_pass_onnx_remove_print(graph: Graph) -> None: ... +def _jit_pass_onnx_preprocess_caffe2(graph: Graph) -> None: ... +def _jit_pass_onnx_unpack_quantized_weights( + graph: Graph, + paramsDict: Dict[str, IValue], +) -> Dict[str, IValue]: ... +def _jit_pass_onnx_quantization_insert_permutes( + graph: Graph, + paramsDict: Dict[str, IValue], +) -> Dict[str, IValue]: ... +def _jit_pass_custom_pattern_based_rewrite_graph( + pattern: str, + fused_node_name: str, + graph: Graph, +) -> None: ... +def _jit_onnx_list_model_parameters( + module: ScriptModule, +) -> Tuple[ScriptModule, List[IValue]]: ... +def _jit_pass_erase_number_types(graph: Graph) -> None: ... +def _jit_pass_onnx_lint(graph: Graph) -> None: ... +def _jit_pass_onnx( + graph: Graph, + _jit_pass_onnx: _onnx.OperatorExportTypes, +) -> Graph: ... +def _jit_pass_onnx_scalar_type_analysis( + graph: Graph, + lowprecision_cast: _bool, + opset_version: _int, +) -> None: ... +def _jit_pass_onnx_peephole( + graph: Graph, + opset_version: _int, + fixed_batch_size: _bool, +) -> None: ... +def _jit_pass_dce_allow_deleting_nodes_with_side_effects(graph: Graph) -> None: ... +def _jit_pass_onnx_function_substitution(graph: Graph) -> None: ... +def _jit_pass_onnx_function_extraction( + graph: Graph, + module_names: Set[str], + param_names: List[str], +) -> Dict[Node, Dict[str, str]]: ... +def _jit_pass_onnx_clear_scope_records() -> None: ... +def _jit_pass_onnx_track_scope_attributes( + graph: Graph, + onnx_attrs: Dict[str, Any], +) -> None: ... +def _jit_is_onnx_log_enabled() -> _bool: ... +def _jit_set_onnx_log_enabled(enabled: _bool) -> None: ... +def _jit_set_onnx_log_output_stream(stream_name: str) -> None: ... +def _jit_onnx_log(*args: Any) -> None: ... +def _jit_pass_lower_graph(graph: Graph, m: Module) -> Tuple[Graph, List[IValue]]: ... +def _jit_pass_inline_fork_wait(graph: Graph) -> None: ... +def _jit_pass_onnx_deduplicate_initializers( + graph: Graph, + params_dict: Dict[str, IValue], + is_train: _bool, +) -> Dict[str, IValue]: ... +def _jit_pass_onnx_eval_peephole( + graph: Graph, + paramsDict: Dict[str, IValue], +) -> Dict[str, IValue]: ... +def _jit_pass_onnx_constant_fold( + graph: Graph, + paramsDict: Dict[str, IValue], + opset_version: _int, +) -> Dict[str, IValue]: ... +def _jit_pass_onnx_eliminate_unused_items( + graph: Graph, + paramsDict: Dict[str, IValue], +) -> Dict[str, IValue]: ... +def _jit_pass_onnx_cast_all_constant_to_floating(graph: Graph) -> None: ... +def _jit_pass_filter_non_tensor_arguments( + params: Dict[str, IValue], +) -> Dict[str, Tensor]: ... +def _jit_decay_packed_param_input_types(graph: Graph) -> None: ... +def _jit_pass_onnx_node_shape_type_inference( + n: Node, + paramsDict: Dict[str, IValue], + opset_version: _int, +) -> None: ... +def _jit_onnx_convert_pattern_from_subblock( + block: Block, + n: Node, + env: Dict[Value, Value], + values_in_env: Set[Value], +) -> List[Value]: ... +def _jit_pass_onnx_block( + old_block: Block, + new_block: Block, + operator_export_type: _onnx.OperatorExportTypes, + env: Dict[Value, Value], + values_in_env: Set[Value], + is_sub_block: _bool, +) -> Dict[Value, Value]: ... +def _jit_pass_onnx_assign_scoped_names_for_node_and_value(graph: Graph) -> None: ... +def _jit_pass_fixup_onnx_controlflow_node( + n: Node, + opset_version: _int, +) -> List[Value]: ... +def _jit_onnx_create_full_scope_name(class_name: str, variable_name: str) -> str: ... +def _compile_graph_to_code_table(name: str, graph: Graph) -> IValue: ... +def _generate_upgraders_graph() -> Dict[str, Graph]: ... +def _calculate_package_version_based_on_upgraders(val: _bool): ... +def _get_version_calculator_flag() -> _bool: ... +def _jit_script_interface_compile( + name: str, + class_def: ClassDef, + rcb: ResolutionCallback, + is_module: _bool, +): ... +def _jit_script_compile_overload( + qualname: str, + overload_decl: Decl, + implementation_def: Def, + rcb: ResolutionCallback, + implementation_defaults: Dict[str, Any], + signature: Any, +): ... +def _jit_script_compile( + qual_name: str, + definition: Def, + rcb: ResolutionCallback, + defaults: Dict[str, Any], +): ... +def _jit_script_class_compile( + qual_name: str, + definition: ClassDef, + defaults: Dict[str, Dict[str, Any]], + rcb: ResolutionCallback, +): ... +def _parse_source_def(src: str) -> Def: ... +def import_ir_module( + cu: CompilationUnit, + filename: Union[str, Path], + map_location: Optional[DeviceLikeType], + extra_files: Dict[str, Any], +) -> ScriptModule: ... +def import_ir_module_from_buffer( + cu: CompilationUnit, + buffer: BinaryIO, + map_location: Optional[DeviceLikeType], + extra_files: Dict[str, Any], +) -> ScriptModule: ... +def _import_ir_module_from_package( + cu: CompilationUnit, + reader: PyTorchFileReader, + storage_context: DeserializationStorageContext, + map_location: Optional[DeviceLikeType], + ts_id: str, +) -> ScriptModule: ... +def _assign_output_shapes(graph: Graph, inputs: List[Tensor]) -> Graph: ... +def _check_onnx_proto(proto: str) -> None: ... +def _propagate_and_assign_input_shapes( + graph: Graph, + inputs: Tuple[Tensor, ...], + param_count_list: List[_int], + with_grad: _bool, + propagate: _bool, +) -> Graph: ... + +# Defined in torch/csrc/jit/runtime/graph_executor.h +class GraphExecutorState: ... + +# Defined in torch/torch/csrc/jit/ir/alias_analysis.h +class AliasDb: + def __str__(self) -> str: ... + +class _InsertPoint: + def __enter__(self) -> None: ... + def __exit__(self, *args) -> None: ... + +# Defined in torch/csrc/jit/ir/ir.h +class Use: + @property + def user(self) -> Node: ... + @property + def offset(self) -> _int: ... + def isAfter(self, other: Use) -> _bool: ... + +# Defined in torch/csrc/jit/ir/ir.h +class Value: + def type(self) -> JitType: ... + def setType(self, t: JitType) -> Value: ... + def setTypeAs(self, other: Value) -> Value: ... + def inferTypeFrom(self, t: Tensor) -> None: ... + def debugName(self) -> str: ... + def setDebugName(self, name: str) -> None: ... + def unique(self) -> _int: ... + def offset(self) -> _int: ... + def node(self) -> Node: ... + def uses(self) -> List[Use]: ... + def replaceAllUsesWith(self, val: Value) -> None: ... + def replaceAllUsesAfterNodeWith(self, node: Node, val: Value) -> None: ... + def requires_grad(self) -> _bool: ... + def requiresGrad(self) -> _bool: ... + def copyMetadata(self, other: Value) -> Value: ... + def isCompleteTensor(self) -> _bool: ... + def toIValue(self) -> IValue: ... + +# Defined in torch/csrc/jit/ir/ir.h +class Block: + def inputs(self) -> Iterator[Value]: ... + def outputs(self) -> Iterator[Value]: ... + def nodes(self) -> Iterator[Node]: ... + def paramNode(self) -> Node: ... + def returnNode(self) -> Node: ... + def owningNode(self) -> Node: ... + def registerOutput(self, n: Value) -> _int: ... + def addNode(self, name: str, inputs: Sequence[Value]) -> Node: ... + +# Defined in torch/csrc/jit/ir/ir.h +class Node: + def __getitem__(self, key: str) -> Any: ... + def schema(self) -> str: ... + def input(self) -> Value: ... + def inputs(self) -> Iterator[Value]: ... + def inputsAt(self, idx: _int) -> Value: ... + def inputsSize(self) -> _int: ... + def output(self) -> Value: ... + def outputs(self) -> Iterator[Value]: ... + def outputsAt(self, idx: _int) -> Value: ... + def outputsSize(self) -> _int: ... + def hasMultipleOutputs(self) -> _bool: ... + def blocks(self) -> List[Block]: ... + def addBlock(self) -> Block: ... + def mustBeNone(self) -> _bool: ... + def matches(self, pattern: str) -> _bool: ... + def kind(self) -> str: ... + def kindOf(self, name: str) -> str: ... + def addInput(self, name: str) -> Value: ... + def replaceInput(self, i: _int, newValue: Value) -> Value: ... + def replaceInputWith(self, from_: Value, to: Value) -> None: ... + def replaceAllUsesWith(self, n: Node) -> None: ... + def insertBefore(self, n: Node) -> Node: ... + def insertAfter(self, n: Node) -> Node: ... + def isBefore(self, n: Node) -> _bool: ... + def isAfter(self, n: Node) -> _bool: ... + def moveBefore(self, n: Node) -> None: ... + def moveAfter(self, n: Node) -> None: ... + def removeInput(self, i: _int) -> None: ... + def removeAllInputs(self, i: _int) -> None: ... + def hasUses(self) -> _bool: ... + def eraseOutput(self, i: _int) -> None: ... + def addOutput(self) -> Value: ... + def scopeName(self) -> str: ... + def isNondeterministic(self) -> _bool: ... + def copyAttributes(self, rhs: Node) -> Node: ... + def copyMetadata(self, rhs: Node) -> Node: ... + def hasAttributes(self) -> _bool: ... + def hasAttribute(self, name: str) -> _bool: ... + def removeAttribute(self, attr: str) -> Node: ... + def namedInput(self, name: str) -> Value: ... + def sourceRange(self) -> SourceRange: ... + def owningBlock(self) -> Block: ... + def findNode(self, kind: str, recurse: _bool = True) -> Node: ... + def findAllNodes(self, kind: str, recurse: _bool = True) -> List[Node]: ... + def getModuleHierarchy(self) -> str: ... + def prev(self) -> Node: ... + def destroy(self) -> None: ... + def attributeNames(self) -> List[str]: ... + + # Accessors for attributes as types. + def f(self, name: str) -> _float: ... + def f_(self, name: str, val: _float) -> Node: ... + def fs(self, name: str) -> List[_float]: ... + def fs_(self, name: str, val: List[_float]) -> Node: ... + def c(self, name: str) -> complex: ... + def c_(self, name: str, val: complex) -> Node: ... + def s(self, name: str) -> str: ... + def s_(self, name: str, val: str) -> Node: ... + def ss(self, name: str) -> List[str]: ... + def ss_(self, name: str, val: List[str]) -> Node: ... + def i(self, name: str) -> _int: ... + def i_(self, name: str, val: _int) -> Node: ... + # Cannot define "is" like this because it's a reserved keyword in python. + # def is(self, name: str) -> List[_int]: ... + # def is_(self, name: str, val: List[_int]) -> Node: ... + def g(self, name: str) -> Graph: ... + def g_(self, name: str, val: Graph) -> Node: ... + def gs(self, name: str) -> List[Graph]: ... + def gs_(self, name: str, val: List[Graph]) -> Node: ... + def ival(self, name: str) -> IValue: ... + def ival_(self, name: str, val: IValue) -> Node: ... + def t(self, name: str) -> Tensor: ... + def t_(self, name: str, val: Tensor) -> Node: ... + def ts(self, name: str) -> List[Tensor]: ... + def ts_(self, name: str, val: List[Tensor]) -> Node: ... + def ty(self, name: str) -> JitType: ... + def ty_(self, name: str, val: JitType) -> Node: ... + def tys(self, name: str) -> List[JitType]: ... + def tys_(self, name: str, val: List[JitType]) -> Node: ... + +# Defined in torch/torch/csrc/jit/ir/ir.h +class Graph: + def inputs(self) -> Iterator[Value]: ... + def outputs(self) -> Iterator[Value]: ... + def nodes(self) -> Iterator[Node]: ... + def param_node(self) -> Node: ... + def return_node(self) -> Node: ... + def addInput(self, name: str = "") -> Value: ... + def eraseInput(self, i: _int) -> None: ... + def registerOutput(self, n: Value) -> _int: ... + def eraseOutput(self, i: _int) -> None: ... + def create(self, name: str, args, num_outputs: _int) -> Node: ... + def appendNode(self, n: Node) -> Node: ... + def prependNode(self, n: Node) -> Node: ... + def insertNode(self, n: Node) -> Node: ... + def block(self) -> Block: ... + def lint(self) -> None: ... + def alias_db(self) -> AliasDb: ... + def setInsertPoint(self, n: Union[Block, Node]) -> None: ... + def insert_point_guard(self, n: Union[Block, Node]) -> _InsertPoint: ... + def insertPoint(self) -> Node: ... + def insertGraph(self, callee: Graph, inputs: List[Value]) -> List[Value]: ... + def makeMultiOutputIntoTuple(self) -> None: ... + def copy(self) -> Graph: ... + +# Defined in torch/aten/src/ATen/core/alias_info.h +class AliasInfo: + is_write: _bool + before_set: Set[str] + after_set: Set[str] + +# Defined in torch/aten/src/ATen/core/function_schema.h +class Argument: + name: str + type: JitType + default_value: Optional[Any] + def has_default_value(self) -> _bool: ... + kwarg_only: _bool + is_out: _bool + alias_info: Optional[AliasInfo] + +class FunctionSchema: + arguments: List[Argument] + returns: List[Argument] + name: str + overload_name: str + is_mutable: _bool + +class _UpgraderEntry: + bumped_at_version: _int + upgrader_name: str + old_schema: str + def __init__( + self, + bumped_at_version: _int, + upgrader_name: str, + old_schema: str, + ) -> None: ... + +class _UpgraderRange: + min_version: _int + max_version: _int + +def _get_max_operator_version() -> _int: ... +def _get_operator_version_map() -> Dict[str, List[_UpgraderEntry]]: ... +def _get_upgrader_ranges(name: str) -> List[_UpgraderRange]: ... +def _test_only_add_entry_to_op_version(op_name: str, entry: _UpgraderEntry) -> None: ... +def _test_only_remove_entry_to_op_version(op_name: str) -> None: ... + +# Defined in torch/csrc/jit/python/script_init.cpp +class ScriptModuleSerializer: + def __init__(self, export_writer: PyTorchFileWriter) -> None: ... + def serialize(self, model: ScriptModule, script_module_id: _int) -> None: ... + def write_files(self) -> None: ... + def storage_context(self) -> SerializationStorageContext: ... + +# Defined in torch/csrc/jit/python/script_init.cpp +class SerializationStorageContext: + def __init__(self) -> None: ... + def has_storage(self, storage: Storage) -> _bool: ... + def get_or_add_storage(self, storage: Storage) -> _int: ... + +# Defined in torch/csrc/jit/python/script_init.cpp +class DeserializationStorageContext: + def __init__(self) -> None: ... + def get_storage(self, name: str, dtype: _dtype) -> Tensor: ... + def has_storage(self, name: str) -> _bool: ... + def add_storage(self, name: str, tensor: Tensor) -> _int: ... + +# Defined in torch/csrc/jit/python/script_init.cpp +class ConcreteModuleTypeBuilder: + def __init__(self, obj: Any) -> None: ... + def set_module_dict(self): ... + def set_module_list(self): ... + def set_parameter_list(self): ... + def set_parameter_dict(self): ... + def add_attribute( + self, + name: str, + ty: JitType, + is_param: _bool, + is_buffer: _bool, + ): ... + def add_module(self, name: str, meta: ConcreteModuleType): ... + def add_constant(self, name: str, value: Any): ... + def add_overload(self, method_name: str, overloaded_method_names: List[str]): ... + def add_builtin_function(self, name: str, symbol_name: str): ... + def add_failed_attribute(self, name: str, failure_reason: str): ... + def add_function_attribute( + self, + name: str, + ty: JitType, + func: Callable[..., Any], + ): ... + def add_ignored_attribute(self, name: str): ... + def add_ignored_attributes(self, names: List[str]): ... + def add_forward_hook(self, hook: Callable[..., Any]): ... + def add_forward_pre_hook(self, pre_hook: Callable[..., Any]): ... + +class ConcreteModuleType: + def get_constants(self) -> Dict[str, Any]: ... + def equals(self, other: ConcreteModuleType) -> _bool: ... + @staticmethod + def from_jit_type(ty: JitType) -> ConcreteModuleType: ... + +class CallStack: + def __init__(self, name: str, range: SourceRange): ... + +class ErrorReport: + def __init__(self, range: SourceRange) -> None: ... + def what(self) -> str: ... + @staticmethod + def call_stack() -> str: ... + +class CompilationUnit: + def __init__(self, lang: str = ..., _frames_up: _int = ...) -> None: ... + def find_function(self, name: str) -> ScriptFunction: ... + def __getattr__(self, name: str) -> ScriptFunction: ... + def define( + self, + script: str, + rcb: ResolutionCallback = ..., + _frames_up: _int = ..., + ): ... + def get_interface(self, name: str) -> InterfaceType: ... + def get_functions(self) -> List[ScriptFunction]: ... + def create_function( + self, + name: str, + graph: Graph, + shouldMangle: _bool = ..., + ) -> ScriptFunction: ... + def get_class(self, name: str) -> ClassType: ... + +class ScriptObject: + def setattr(self, name: str, value: Any): ... + +class ScriptModule(ScriptObject): + def _method_names(self) -> List[str]: ... + def _get_method(self, name: str) -> ScriptMethod: ... + +class LiteScriptModule: + def __call__(self, *input): ... + def find_method(self, method_name: str): ... + def forward(self, *input) -> List[str]: ... + def run_method(self, method_name: str, *input): ... + +# NOTE: switch to collections.abc.Callable in python 3.9 +class ScriptFunction(Generic[P, ReturnVal]): + def __call__(self, *args: P.args, **kwargs: P.kwargs) -> ReturnVal: ... + def save(self, filename: str, _extra_files: Dict[str, bytes]) -> None: ... + def save_to_buffer(self, _extra_files: Dict[str, bytes]) -> bytes: ... + @property + def graph(self) -> Graph: ... + def inlined_graph(self) -> Graph: ... + def schema(self) -> FunctionSchema: ... + def code(self) -> str: ... + def name(self) -> str: ... + @property + def qualified_name(self) -> str: ... + +# NOTE: switch to collections.abc.Callable in python 3.9 +class ScriptMethod(Generic[P, ReturnVal]): + graph: Graph + def __call__(self, *args: P.args, **kwargs: P.kwargs) -> ReturnVal: ... + @property + def owner(self) -> ScriptModule: ... + @property + def name(self) -> str: ... + +class ScriptDict(Generic[K, T]): + def __init__(self, dict: Dict[K, T]) -> None: ... + def __len__(self) -> _int: ... + def __contains__(self, key: K) -> _bool: ... + def __getitem__(self, key: K) -> T: ... + def __setitem__(self, key: K, value: T) -> None: ... + def __delitem__(self, key: K) -> None: ... + def __iter__(self) -> Iterator[K]: ... + def items(self) -> Iterator[tuple[K, T]]: ... + def keys(self) -> Iterator[K]: ... + +class ScriptList(Generic[T]): + def __init__(self, list: List[T]) -> None: ... + def __len__(self) -> _int: ... + def __contains__(self, item: T) -> _bool: ... + @overload + def __getitem__(self, idx: _int) -> T: ... + @overload + def __getitem__(self, idx: slice) -> ScriptList[T]: ... + @overload + def __setitem__(self, idx: _int, value: T) -> None: ... + @overload + def __setitem__(self, idx: slice, value: List[T]) -> None: ... + def __delitem__(self, idx: _int) -> None: ... + def __iter__(self) -> Iterator[T]: ... + def count(self, value: T) -> _int: ... + def remove(self, value: T) -> None: ... + def append(self, value: T) -> None: ... + def clear(self) -> None: ... + @overload + def extend(self, values: List[T]) -> None: ... + @overload + def extend(self, values: Iterable[T]) -> None: ... + @overload + def pop(self) -> T: ... + @overload + def pop(self, idx: _int) -> T: ... + +class ModuleDict: + def __init__(self, mod: ScriptModule) -> None: ... + def items(self) -> List[Tuple[str, Any]]: ... + +class ParameterDict: + def __init__(self, mod: ScriptModule) -> None: ... + +class BufferDict: + def __init__(self, mod: ScriptModule) -> None: ... + +# Defined in torch/csrc/jit/api/module.h +class Module: ... + +# Defined in torch/csrc/Module.cpp +def _initExtension(shm_manager_path: str) -> None: ... # THPModule_initExtension +def _autograd_init() -> _bool: ... # THPAutograd_initExtension +def _add_docstr(obj: T, doc_obj: str) -> T: ... # THPModule_addDocStr +def _init_names(arg: Sequence[_Type]) -> None: ... # THPModule_initNames +def _has_distributed() -> _bool: ... # THPModule_hasDistributed +def _set_default_tensor_type(type) -> None: ... # THPModule_setDefaultTensorType +def _set_default_dtype(d: _dtype) -> None: ... # THPModule_setDefaultDtype +def _infer_size(arg1: Size, arg2: Size) -> Size: ... # THPModule_inferSize +def _crash_if_csrc_asan() -> _int: ... # THPModule_crashIfCsrcASAN +def _crash_if_csrc_ubsan() -> _int: ... # THPModule_crashIfCsrcUBSAN +def _crash_if_aten_asan() -> _int: ... # THPModule_crashIfATenASAN +def _show_config() -> str: ... # THPModule_showConfig +def _cxx_flags() -> str: ... # THPModule_cxxFlags +def _parallel_info() -> str: ... # THPModule_parallelInfo +def _get_cpu_capability() -> str: ... # THPModule_getCpuCapability +def _set_backcompat_broadcast_warn( + arg: _bool, +) -> None: ... # THPModule_setBackcompatBroadcastWarn +def _get_backcompat_broadcast_warn() -> _bool: ... # THPModule_getBackcompatBroadcastWarn +def _set_backcompat_keepdim_warn( + arg: _bool, +) -> None: ... # THPModule_setBackcompatKeepdimWarn +def _get_backcompat_keepdim_warn() -> _bool: ... # THPModule_getBackcompatKeepdimWarn +def get_num_thread() -> _int: ... # THPModule_getNumThreads +def set_num_threads(nthreads: _int) -> None: ... # THPModule_setNumThreads +def get_num_interop_threads() -> _int: ... # THPModule_getNumInteropThreads +def set_num_interop_threads( + nthreads: _int, +) -> None: ... # THPModule_setNumInteropThreads +def _get_cudnn_enabled() -> _bool: ... # THPModule_userEnabledCuDNN +def _set_cudnn_enabled(arg: _bool) -> None: ... # THPModule_setUserEnabledCuDNN +def _get_flash_sdp_enabled() -> _bool: ... # THPModule_userEnabledFusedSDP +def _set_sdp_use_flash(arg: _bool) -> None: ... # THPModule_setSDPUseFlash +def _get_mem_efficient_sdp_enabled() -> _bool: ... # THPModule_userEnabledMathSDP +def _set_sdp_use_mem_efficient( + arg: _bool, +) -> None: ... # THPModule_setSDPUseMemEfficient +def _get_math_sdp_enabled() -> _bool: ... # THPModule_userEnabledMathSDP +def _set_sdp_use_math(arg: _bool) -> None: ... # THPModule_setSDPUseMath +def _get_math_sdp_allow_fp16_bf16_reduction() -> _bool: ... # THPModule_allowFP16BF16ReductionMathSDP +def _set_math_sdp_allow_fp16_bf16_reduction(arg: _bool) -> None: ... # THPModule_setAllowFP16BF16ReductionMathSDP +def _get_overrideable_sdp_enabled() -> _bool: ... # THPModule_userEnabledOverrideableSDP +def _set_sdp_use_overrideable(arg: _bool) -> None: ... # THPModule_setSDPUseOverrideable +def _get_cudnn_sdp_enabled() -> _bool: ... # THPModule_userEnabledMathSDP +def _set_sdp_use_cudnn(arg: _bool) -> None: ... # THPModule_setSDPUseMath +def _get_mkldnn_enabled() -> _bool: ... # THPModule_userEnabledMkldnn +def _set_mkldnn_enabled(arg: _bool) -> None: ... # THPModule_setUserEnabledMkldnn +def _get_cudnn_benchmark() -> _bool: ... # THPModule_benchmarkCuDNN +def _set_cudnn_benchmark(arg: _bool) -> None: ... # THPModule_setBenchmarkCuDNN +def _get_cudnn_deterministic() -> _bool: ... # THPModule_deterministicCuDNN +def _set_cudnn_deterministic(arg: _bool) -> None: ... # THPModule_setDeterministicCuDNN +def _get_mkldnn_deterministic() -> _bool: ... # THPModule_deterministicMkldnn +def _set_mkldnn_deterministic(arg: _bool) -> None: ... # THPModule_setDeterministicMkldnn +def _get_deterministic_algorithms() -> _bool: ... # THPModule_deterministicAlgorithms +def _get_deterministic_algorithms_warn_only() -> _bool: ... # THPModule_deterministicAlgorithmsWarnOnly +def _set_deterministic_algorithms( + mode: _bool, + *, + warn_only: _bool = ..., +) -> None: ... # THPModule_setDeterministicAlgorithms +def _get_deterministic_fill_uninitialized_memory() -> _bool: ... # THPModule_deterministicFillUninitializedMemory +def _set_deterministic_fill_uninitialized_memory(arg: _bool) -> None: ... # THPModule_setDeterministicFillUninitializedMemory +def _get_nnpack_enabled() -> _bool: ... # THPModule_userEnabledNNPACK +def _set_nnpack_enabled(arg: _bool) -> None: ... # THPModule_setUserEnabledNNPACK +def _get_warnAlways() -> _bool: ... # THPModule_warnAlways +def _set_warnAlways(arg: _bool) -> None: ... # THPModule_setWarnAlways +def _get_cudnn_allow_tf32() -> _bool: ... # THPModule_allowTF32CuDNN +def _set_cudnn_allow_tf32(arg: _bool) -> None: ... # THPModule_setAllowTF32CuDNN +def _get_cublas_allow_tf32() -> _bool: ... # THPModule_allowTF32CuBLAS +def _set_cublas_allow_tf32(arg: _bool) -> None: ... # THPModule_setAllowTF32CuBLAS +def _get_float32_matmul_precision() -> str: ... # THPModule_float32MatmulPrecision +def _set_float32_matmul_precision( + arg: str, +) -> None: ... # THPModule_setFloat32MatmulPrecision +def _get_cublas_allow_fp16_reduced_precision_reduction() -> _bool: ... # THPModule_allowFP16ReductionCuBLAS +def _set_cublas_allow_fp16_reduced_precision_reduction( + arg: _bool, +) -> None: ... # THPModule_setAllowFP16ReductionCuBLAS +def _get_cublas_allow_bf16_reduced_precision_reduction() -> _bool: ... # THPModule_allowBF16ReductionCuBLAS +def _set_cublas_allow_bf16_reduced_precision_reduction( + arg: _bool, +) -> None: ... # THPModule_setAllowBF16ReductionCuBLAS +def _set_conj(x: Tensor, conj: _bool) -> None: ... +def _set_neg(x: Tensor, neg: _bool) -> None: ... +def _set_meta_in_tls_dispatch_include(meta_in_tls: _bool) -> None: ... +def _meta_in_tls_dispatch_include() -> _bool: ... +def _stash_obj_in_tls(key: str, arg: Any) -> None: ... +def _get_obj_in_tls(key: str) -> Any: ... +def _is_key_in_tls(key: str) -> _bool: ... +def _select_batch_norm_backend(*args, **kwargs) -> BatchNormBackend: ... +def _select_conv_backend(*args, **kwargs) -> ConvBackend: ... +def _conv_determine_backend_memory_format( + input: Tensor, + weight: Tensor, + backend: ConvBackend, +) -> memory_format: ... +def _has_storage(x: Tensor) -> _bool: ... +def _construct_storage_from_data_pointer(data_ptr: _int, device: torch.device, size: _int) -> Storage: ... +def _should_allow_numbers_as_tensors(func_name: str) -> _bool: ... +def _group_tensors_by_device_and_dtype(nested_tensorlists: List[List[Optional[Tensor]]], with_indices: _bool = False) -> Dict[Tuple[torch.device, torch.dtype], Tuple[List[List[Optional[Tensor]]], List[_int]]]: ... + +# NB: There is no Capsule type in typing, see +# https://code.activestate.com/lists/python-dev/139675/ +def _to_dlpack(data: Tensor) -> Any: ... # THPModule_toDLPack +def _from_dlpack(data: Any) -> Tensor: ... # THPModule_fromDLPack +def _get_cpp_backtrace( + frames_to_skip: _int, + maximum_number_of_frames: _int, +) -> str: ... # THPModule_getCppBacktrace +def set_flush_denormal(arg: _bool) -> _bool: ... # THPModule_setFlushDenormal +def get_default_dtype() -> _dtype: ... # THPModule_getDefaultDtype +def _get_default_device() -> str: ... # THPModule_getDefaultDevice +def _get_qengine() -> _int: ... # THPModule_qEngine +def _set_qengine(qengine: _int) -> None: ... # THPModule_setQEngine +def _supported_qengines() -> List[_int]: ... # THPModule_supportedQEngines +def _is_xnnpack_enabled() -> _bool: ... # THPModule_isEnabledXNNPACK +def _check_sparse_tensor_invariants() -> _bool: ... # THPModule_checkSparseTensorInvariants +def _set_check_sparse_tensor_invariants( + arg: _bool, +) -> None: ... # THPModule_setCheckSparseTensorInvariants +def _set_default_mobile_cpu_allocator() -> None: ... # THPModule_setDefaultMobileCPUAllocator +def _unset_default_mobile_cpu_allocator() -> None: ... # THPModule_unsetDefaultMobileCPUAllocator +def _is_torch_function_enabled() -> _bool: ... # THPModule_isEnabledTorchFunction +def _is_torch_function_all_disabled() -> _bool: ... # THPModule_isAllDisabledTorchFunction +def _has_torch_function( + args: Iterable[Any], +) -> _bool: ... # THPModule_has_torch_function +def _has_torch_function_unary(Any) -> _bool: ... # THPModule_has_torch_function_unary +def _has_torch_function_variadic( + *args: Any, +) -> _bool: ... # THPModule_has_torch_function_variadic +def _vmapmode_increment_nesting() -> _int: ... # THPModule_vmapmode_increment_nesting +def _vmapmode_decrement_nesting() -> _int: ... # THPModule_vmapmode_decrement_nesting +def _log_api_usage_once(str) -> None: ... # LogAPIUsageOnceFromPython +def _log_api_usage_metadata(event: str, metadata_map: Dict[str, str]) -> None: ... # LogAPIUsageMetadataFromPython +def _demangle(str) -> str: ... # c10::demangle +def _disabled_torch_function_impl( + func: Callable, + types: Iterable[_Type], + args: Tuple, + kwargs: Dict, +) -> Any: ... # THPModule_disable_torch_function +def _disabled_torch_dispatch_impl( + func: Callable, + types: Iterable[_Type], + args: Tuple, + kwargs: Dict, +) -> Any: ... # THPModule_disable_dispatch_function +def _get_linalg_preferred_backend() -> torch._C._LinalgBackend: ... +def _set_linalg_preferred_backend(arg: torch._C._LinalgBackend): ... + +class _LinalgBackend: + Default: _LinalgBackend + Cusolver: _LinalgBackend + Magma: _LinalgBackend + +class BatchNormBackend(Enum): ... + +def _get_blas_preferred_backend() -> torch._C._BlasBackend: ... +def _set_blas_preferred_backend(arg: torch._C._BlasBackend): ... + +class _BlasBackend: + Cublas: _BlasBackend + Cublaslt: _BlasBackend + +class ConvBackend(Enum): ... + +class Tag(Enum): + core: _int = 0 + data_dependent_output: _int = 1 + dynamic_output_shape: _int = 2 + flexible_layout: _int = 3 + generated: _int = 4 + inplace_view: _int = 5 + needs_fixed_stride_order: _int = 6 + nondeterministic_bitwise: _int = 7 + nondeterministic_seeded: _int = 8 + pointwise: _int = 9 + pt2_compliant_tag: _int = 10 + view_copy: _int = 11 + +# Defined in `valgrind.h` and `callgrind.h` respectively. +def _valgrind_supported_platform() -> _bool: ... # NVALGRIND +def _valgrind_toggle() -> None: ... # CALLGRIND_TOGGLE_COLLECT +def _valgrind_toggle_and_dump_stats() -> None: ... # CALLGRIND_TOGGLE_COLLECT and CALLGRIND_DUMP_STATS + +has_openmp: _bool +has_mkl: _bool +_has_mps: _bool +has_lapack: _bool +_has_cuda: _bool +_has_magma: _bool +_has_xpu: _bool +_has_mkldnn: _bool +_has_cudnn: _bool +_has_cusparselt: _bool +has_spectral: _bool +_GLIBCXX_USE_CXX11_ABI: _bool +default_generator: Generator + +# Defined in torch/csrc/autograd/init.cpp +def _set_grad_enabled(enabled: _bool) -> None: ... +def is_grad_enabled() -> _bool: ... +def _set_fwd_grad_enabled(enabled: _bool) -> None: ... +def _is_fwd_grad_enabled() -> _bool: ... +def is_inference_mode_enabled() -> _bool: ... +@overload +def set_autocast_enabled(device_type: str, enabled: _bool) -> None: ... +@overload +def set_autocast_enabled(enabled: _bool) -> None: ... +@overload +def is_autocast_enabled(device_type: str) -> _bool: ... +@overload +def is_autocast_enabled() -> _bool: ... +def set_autocast_dtype(device_type: str, dtype: _dtype) -> None: ... +def get_autocast_dtype(device_type: str) -> _dtype: ... +def clear_autocast_cache() -> None: ... +def set_autocast_cpu_enabled(enabled: _bool) -> None: ... +def is_autocast_cpu_enabled() -> _bool: ... +def _is_any_autocast_enabled() -> _bool: ... +def _is_autocast_available(device_type: str) -> _bool: ... +def set_autocast_cpu_dtype(dtype: _dtype) -> None: ... +def set_autocast_gpu_dtype(dtype: _dtype) -> None: ... +def get_autocast_cpu_dtype() -> _dtype: ... +def get_autocast_gpu_dtype() -> _dtype: ... +def autocast_increment_nesting() -> _int: ... +def autocast_decrement_nesting() -> _int: ... +def is_autocast_cache_enabled() -> _bool: ... +def set_autocast_cache_enabled(enabled: _bool) -> None: ... +def _increment_version(tensors: Iterable[Tensor]) -> None: ... +def set_anomaly_enabled(enabled: _bool, check_nan: _bool = True) -> None: ... +def is_anomaly_enabled() -> _bool: ... +def is_anomaly_check_nan_enabled() -> _bool: ... +def _is_multithreading_enabled() -> _bool: ... +def _set_multithreading_enabled(enabled: _bool) -> None: ... +def _set_view_replay_enabled(enabled: _bool) -> None: ... +def _is_view_replay_enabled() -> _bool: ... +def _enter_dual_level() -> _int: ... +def _exit_dual_level(level: _int) -> None: ... +def _make_dual(tensor: Tensor, tangent: Tensor, level: _int) -> Tensor: ... +def _unpack_dual(tensor: Tensor, level: _int) -> Tensor: ... +def __set_forward_AD_enabled(enabled: _bool) -> None: ... +def __is_forward_AD_enabled() -> _bool: ... +def _register_default_hooks(pack_hook: Callable, unpack_hook: Callable) -> None: ... +def _reset_default_hooks() -> None: ... +def _is_torch_function_mode_enabled() -> _bool: ... +def _set_torch_function_mode(cls: Any) -> None: ... +def _push_on_torch_function_stack(cls: Any) -> None: ... +def _pop_torch_function_stack() -> Any: ... +def _get_function_stack_at(idx: _int) -> Any: ... +def _len_torch_function_stack() -> _int: ... +def _set_torch_dispatch_mode(cls: Any) -> None: ... +def _push_on_torch_dispatch_stack(cls: TorchDispatchMode) -> None: ... +def _pop_torch_dispatch_stack(mode_key: Optional[torch._C._TorchDispatchModeKey] = None) -> Any: ... +def _get_dispatch_mode(mode_key: Optional[torch._C._TorchDispatchModeKey]) -> Any: ... +def _unset_dispatch_mode(mode: torch._C._TorchDispatchModeKey) -> Optional[TorchDispatchMode]: ... +def _set_dispatch_mode(mode: TorchDispatchMode) -> None: ... +def _get_dispatch_stack_at(idx: _int) -> Any: ... +def _len_torch_dispatch_stack() -> _int: ... +def _activate_gpu_trace() -> None: ... + +class _DisableTorchDispatch: + def __init__(self): ... + def __enter__(self): ... + def __exit__(self, exc_type, exc_value, traceback): ... + +class _EnableTorchFunction: + def __init__(self): ... + def __enter__(self): ... + def __exit__(self, exc_type, exc_value, traceback): ... + +class _EnablePythonDispatcher: + def __init__(self): ... + def __enter__(self): ... + def __exit__(self, exc_type, exc_value, traceback): ... + +class _DisablePythonDispatcher: + def __init__(self): ... + def __enter__(self): ... + def __exit__(self, exc_type, exc_value, traceback): ... + +class _EnablePreDispatch: + def __init__(self): ... + def __enter__(self): ... + def __exit__(self, exc_type, exc_value, traceback): ... + +class _DisableFuncTorch: + def __init__(self): ... + def __enter__(self): ... + def __exit__(self, exc_type, exc_value, traceback): ... + +class _DisableAutocast: + def __init__(self): ... + def __enter__(self): ... + def __exit__(self, exc_type, exc_value, traceback): ... + +class _InferenceMode: + def __init__(self, enabled: _bool): ... + def __enter__(self): ... + def __exit__(self, exc_type, exc_value, traceback): ... + +def _set_autograd_fallback_mode(mode: str) -> None: ... +def _get_autograd_fallback_mode() -> str: ... + +# Defined in torch/csrc/jit/python/script_init.cpp +class LoggerBase: ... +class NoopLogger(LoggerBase): ... +class LockingLogger(LoggerBase): ... + +class AggregationType(Enum): + SUM = 0 + AVG = 1 + +class FileCheck: + def run(self, test_string: str) -> None: ... + def check(self, test_string: str) -> FileCheck: ... + def check_not(self, test_string: str) -> FileCheck: ... + def check_same(self, test_string: str) -> FileCheck: ... + def check_next(self, test_string: str) -> FileCheck: ... + def check_count( + self, + test_string: str, + count: _int, + exactly: _bool = False, + ) -> FileCheck: ... + def check_dag(self, test_string: str) -> FileCheck: ... + def check_source_highlighted(self, test_string: str) -> FileCheck: ... + def check_regex(self, test_string: str) -> FileCheck: ... + +# Defined in torch/csrc/jit/python/init.cpp +class PyTorchFileReader: + @overload + def __init__(self, name: str) -> None: ... + @overload + def __init__(self, buffer: BinaryIO) -> None: ... + def get_record(self, name: str) -> bytes: ... + def serialization_id(self) -> str: ... + +class PyTorchFileWriter: + @overload + def __init__(self, name: str) -> None: ... + @overload + def __init__(self, buffer: BinaryIO) -> None: ... + def write_record(self, name: str, data: Union[Storage, bytes, _int], size: _int) -> None: ... + def write_end_of_file(self) -> None: ... + def set_min_version(self, version: _int) -> None: ... + def get_all_written_records(self) -> List[str]: ... + def archive_name(self) -> str: ... + def serialization_id(self) -> str: ... + +def _jit_get_inline_everything_mode() -> _bool: ... +def _jit_set_inline_everything_mode(enabled: _bool) -> None: ... +def _jit_get_logging_option() -> str: ... +def _jit_set_logging_option(option: str) -> None: ... +def _jit_set_logging_stream(stream_name: str) -> None: ... +def _jit_pass_cse(Graph) -> _bool: ... +def _jit_pass_dce(Graph) -> None: ... +def _jit_pass_lint(Graph) -> None: ... + +# Defined in torch/csrc/jit/python/python_custom_class.cpp +def _get_custom_class_python_wrapper(name: str, attr: str) -> Any: ... + +# Defined in torch/csrc/Module.cpp +def _rename_privateuse1_backend(backend: str) -> None: ... +def _get_privateuse1_backend_name() -> str: ... + +# Defined in torch/csrc/Generator.cpp +class Generator: + device: _device + def __init__(self, device: Optional[DeviceLikeType] = None) -> None: ... + def __reduce__(self) -> Tuple[_Type[Generator], Tuple[_device], Tuple[_int, Optional[_int], Tensor]]: ... + def __setstate__(self, state: Tuple[_int, Optional[_int], Tensor]) -> None: ... + def get_state(self) -> Tensor: ... + def set_state(self, _new_state: Tensor) -> Generator: ... + def clone_state(self) -> Generator: ... + def graphsafe_get_state(self) -> Generator: ... + def graphsafe_set_state(self, _new_state: Generator) -> Generator: ... + def set_offset(self, offset: _int) -> Generator: ... + def get_offset(self) -> _int: ... + def manual_seed(self, seed: _int) -> Generator: ... + def seed(self) -> _int: ... + def initial_seed(self) -> _int: ... + +# Defined in torch/csrc/utils/python_dispatch.cpp + +class _DispatchOperatorHandle: + def schema(self) -> FunctionSchema: ... + def debug(self) -> str: ... + +class _DispatchModule: + def reset(self) -> None: ... + def def_(self, schema: str, alias: str = "") -> _DispatchModule: ... + def def_legacy(self, schema: str) -> _DispatchModule: ... + def def_name_t_t( + self, + name: str, + dispatch: str, + debug: str = "default_def_name_t_t", + ) -> _DispatchModule: ... + def def_schema_t_t( + self, + schema: str, + dispatch: str, + alias: str, + debug: str = "default_def_schema_t_t", + ) -> _DispatchModule: ... + def impl_t_t( + self, + name: str, + dispatch: str, + debug: str = "impl_t_t", + ) -> _DispatchModule: ... + def impl_with_aoti_compile( + self, + ns: str, + op_name_with_overload: str, + dispatch: _dispatchkey + ) -> None: ... + def impl(self, name: str, dispatch: _dispatchkey, func: Callable) -> None: ... + def define(self, schema: str, alias: str = "") -> str: ... + def fallback_fallthrough(self, dispatch: str = "") -> _DispatchModule: ... + def fallback(self, dispatch: _dispatchkey, func: Callable, with_keyset: _bool = False) -> None: ... + +_after_ADInplaceOrView_keyset: DispatchKeySet +_after_autograd_keyset: DispatchKeySet + +def _dispatch_library( + kind: str, + name: str, + dispatch: str, + file: str = "", + linenum: Any = 0, +) -> _DispatchModule: ... +def _dispatch_dump(name: str) -> str: ... +def _dispatch_dump_table(name: str) -> str: ... +def _dispatch_check_invariants(name: str) -> None: ... +def _dispatch_check_all_invariants() -> None: ... +def _dispatch_call_boxed(handle: _DispatchOperatorHandle, *args, **kwargs) -> Any: ... +def _dispatch_find_schema_or_throw(name: str, overload_name: str) -> _DispatchOperatorHandle: ... +def _dispatch_set_report_error_callback(handle: _DispatchOperatorHandle, callback: Callable) -> None: ... +def _dispatch_has_kernel(name: str) -> _bool: ... +def _dispatch_has_kernel_for_dispatch_key( + name: str, + dispatch: _dispatchkey, +) -> _bool: ... +def _dispatch_has_kernel_for_any_dispatch_key( + name: str, + dispatch_key_set: DispatchKeySet, +) -> _bool: ... +def _dispatch_kernel_for_dispatch_key_is_fallthrough( + name: str, + dispatch: _dispatchkey, +) -> _bool: ... +def _dispatch_has_computed_kernel_for_dispatch_key( + name: str, + dispatch: _dispatchkey, +) -> _bool: ... +def _dispatch_find_dangling_impls() -> List[str]: ... +def _dispatch_get_all_op_names() -> List[str]: ... +def _dispatch_tls_set_dispatch_key_excluded( + dispatch: _dispatchkey, + val: _bool, +) -> None: ... +def _dispatch_tls_is_dispatch_key_excluded(dispatch: _dispatchkey) -> _bool: ... +def _dispatch_tls_set_dispatch_key_included( + dispatch: _dispatchkey, + val: _bool, +) -> None: ... +def _dispatch_tls_is_dispatch_key_included(dispatch: _dispatchkey) -> _bool: ... +def _dispatch_isTensorSubclassLike(tensor: Tensor) -> _bool: ... +def _dispatch_key_name(dispatch: _dispatchkey) -> str: ... +def _dispatch_key_for_device(device_type: str) -> str: ... +def _parse_dispatch_key(key: str) -> Optional[DispatchKey]: ... +def _dispatch_key_parse(dispatch: _dispatchkey) -> DispatchKey: ... +def _dispatch_num_backends() -> _int: ... +def _dispatch_pystub(name: str, overload: str) -> Optional[Tuple[str, str]]: ... +def _dispatch_is_alias_key(dispatch: _dispatchkey) -> _bool: ... +def _functionality_to_backend_keys(dispatch: _dispatchkey) -> List[DispatchKey]: ... +def _functionalization_reapply_views_tls() -> _bool: ... +def _only_lift_cpu_tensors() -> _bool: ... +def _set_only_lift_cpu_tensors(value: _bool) -> None: ... +def _set_throw_on_mutable_data_ptr(tensor: Tensor) -> None: ... +def _set_warn_deprecated_on_mutable_data_ptr(tensor: Tensor) -> None: ... + +class DispatchKey(Enum): + Undefined: DispatchKey = ... + FPGA: DispatchKey = ... + MAIA: DispatchKey = ... + Vulkan: DispatchKey = ... + Metal: DispatchKey = ... + MKLDNN: DispatchKey = ... + OpenGL: DispatchKey = ... + OpenCL: DispatchKey = ... + IDEEP: DispatchKey = ... + CustomRNGKeyId: DispatchKey = ... + MkldnnCPU: DispatchKey = ... + Sparse: DispatchKey = ... + SparseCsr: DispatchKey = ... + NestedTensor: DispatchKey = ... + Dense: DispatchKey = ... + PythonTLSSnapshot: DispatchKey = ... + PreDispatch: DispatchKey = ... + PythonDispatcher: DispatchKey = ... + Python: DispatchKey = ... + FuncTorchDynamicLayerBackMode: DispatchKey = ... + ZeroTensor: DispatchKey = ... + Conjugate: DispatchKey = ... + Negative: DispatchKey = ... + BackendSelect: DispatchKey = ... + Named: DispatchKey = ... + AutogradOther: DispatchKey = ... + AutogradFunctionality: DispatchKey = ... + AutogradNestedTensor: DispatchKey = ... + Tracer: DispatchKey = ... + Autocast: DispatchKey = ... + AutocastCPU: DispatchKey = ... + AutocastCUDA: DispatchKey = ... + Batched: DispatchKey = ... + VmapMode: DispatchKey = ... + FuncTorchGradWrapper: DispatchKey = ... + FuncTorchBatched: DispatchKey = ... + BatchedNestedTensor: DispatchKey = ... + FuncTorchVmapMode: DispatchKey = ... + FuncTorchDynamicLayerFrontMode: DispatchKey = ... + Functionalize: DispatchKey = ... + TESTING_ONLY_GenericWrapper: DispatchKey = ... + TESTING_ONLY_GenericMode: DispatchKey = ... + ADInplaceOrView: DispatchKey = ... + Autograd: DispatchKey = ... + CompositeImplicitAutograd: DispatchKey = ... + CompositeImplicitAutogradNestedTensor: DispatchKey = ... + CompositeExplicitAutograd: DispatchKey = ... + CompositeExplicitAutogradNonFunctional: DispatchKey = ... + FuncTorchBatchedDecomposition: DispatchKey = ... + CPU: DispatchKey = ... + CUDA: DispatchKey = ... + HIP: DispatchKey = ... + XLA: DispatchKey = ... + MTIA: DispatchKey = ... + MPS: DispatchKey = ... + IPU: DispatchKey = ... + XPU: DispatchKey = ... + HPU: DispatchKey = ... + VE: DispatchKey = ... + Lazy: DispatchKey = ... + Meta: DispatchKey = ... + PrivateUse1: DispatchKey = ... + PrivateUse2: DispatchKey = ... + PrivateUse3: DispatchKey = ... + QuantizedCPU: DispatchKey = ... + QuantizedCUDA: DispatchKey = ... + QuantizedHIP: DispatchKey = ... + QuantizedXLA: DispatchKey = ... + QuantizedMTIA: DispatchKey = ... + QuantizedMPS: DispatchKey = ... + QuantizedIPU: DispatchKey = ... + QuantizedXPU: DispatchKey = ... + QuantizedHPU: DispatchKey = ... + QuantizedVE: DispatchKey = ... + QuantizedLazy: DispatchKey = ... + QuantizedMeta: DispatchKey = ... + QuantizedPrivateUse1: DispatchKey = ... + QuantizedPrivateUse2: DispatchKey = ... + QuantizedPrivateUse3: DispatchKey = ... + SparseCPU: DispatchKey = ... + SparseCUDA: DispatchKey = ... + SparseHIP: DispatchKey = ... + SparseXLA: DispatchKey = ... + SparseMTIA: DispatchKey = ... + SparseMPS: DispatchKey = ... + SparseIPU: DispatchKey = ... + SparseXPU: DispatchKey = ... + SparseHPU: DispatchKey = ... + SparseVE: DispatchKey = ... + SparseLazy: DispatchKey = ... + SparseMeta: DispatchKey = ... + SparsePrivateUse1: DispatchKey = ... + SparsePrivateUse2: DispatchKey = ... + SparsePrivateUse3: DispatchKey = ... + SparseCsrCPU: DispatchKey = ... + SparseCsrCUDA: DispatchKey = ... + SparseCsrHIP: DispatchKey = ... + SparseCsrXLA: DispatchKey = ... + SparseCsrMTIA: DispatchKey = ... + SparseCsrMPS: DispatchKey = ... + SparseCsrIPU: DispatchKey = ... + SparseCsrXPU: DispatchKey = ... + SparseCsrHPU: DispatchKey = ... + SparseCsrVE: DispatchKey = ... + SparseCsrLazy: DispatchKey = ... + SparseCsrMeta: DispatchKey = ... + SparseCsrPrivateUse1: DispatchKey = ... + SparseCsrPrivateUse2: DispatchKey = ... + SparseCsrPrivateUse3: DispatchKey = ... + NestedTensorCPU: DispatchKey = ... + NestedTensorCUDA: DispatchKey = ... + NestedTensorHIP: DispatchKey = ... + NestedTensorXLA: DispatchKey = ... + NestedTensorMTIA: DispatchKey = ... + NestedTensorMPS: DispatchKey = ... + NestedTensorIPU: DispatchKey = ... + NestedTensorXPU: DispatchKey = ... + NestedTensorHPU: DispatchKey = ... + NestedTensorVE: DispatchKey = ... + NestedTensorLazy: DispatchKey = ... + NestedTensorMeta: DispatchKey = ... + NestedTensorPrivateUse1: DispatchKey = ... + NestedTensorPrivateUse2: DispatchKey = ... + NestedTensorPrivateUse3: DispatchKey = ... + AutogradCPU: DispatchKey = ... + AutogradCUDA: DispatchKey = ... + AutogradHIP: DispatchKey = ... + AutogradXLA: DispatchKey = ... + AutogradMTIA: DispatchKey = ... + AutogradMPS: DispatchKey = ... + AutogradIPU: DispatchKey = ... + AutogradXPU: DispatchKey = ... + AutogradHPU: DispatchKey = ... + AutogradVE: DispatchKey = ... + AutogradLazy: DispatchKey = ... + AutogradMeta: DispatchKey = ... + AutogradPrivateUse1: DispatchKey = ... + AutogradPrivateUse2: DispatchKey = ... + AutogradPrivateUse3: DispatchKey = ... + +class DispatchKeySet: + def __init__(self, key: DispatchKey) -> None: ... + def __or__(self, other: DispatchKeySet) -> DispatchKeySet: ... + def __sub__(self, other: DispatchKeySet) -> DispatchKeySet: ... + def __and__(self, other: DispatchKeySet) -> DispatchKeySet: ... + def raw_repr(self) -> _int: ... + def highestPriorityTypeId(self) -> DispatchKey: ... + def has(self, k: _dispatchkey) -> _bool: ... + def add(self, k: _dispatchkey) -> DispatchKeySet: ... + def remove(self, k: _dispatchkey) -> DispatchKeySet: ... + def __repr__(self) -> str: ... + +_dispatch_autogradother_backends: DispatchKeySet +_additional_keys_to_prop_for_wrapper_tensors: DispatchKeySet + +def _dispatch_has_backend_fallback(dispatch: _dispatchkey) -> _bool: ... +def _dispatch_keyset_full_after(t: _dispatchkey) -> DispatchKeySet: ... +def _dispatch_keyset_full() -> DispatchKeySet: ... +def _dispatch_keyset_to_string(keyset: DispatchKeySet) -> str: ... +def _dispatch_get_backend_keyset_from_autograd( + dispatch: _dispatchkey, +) -> DispatchKeySet: ... +def _dispatch_keys(tensor: Tensor) -> DispatchKeySet: ... +def _dispatch_tls_local_exclude_set() -> DispatchKeySet: ... +def _dispatch_tls_local_include_set() -> DispatchKeySet: ... +def _dispatch_is_included_in_alias( + dispatch_a: _dispatchkey, + dispatch_b: _dispatchkey, +) -> _bool: ... +def _propagate_xla_data(a: Tensor, b: Tensor) -> None: ... +def _replace_(a: Tensor, b: Tensor) -> None: ... +def _commit_update(a: Tensor) -> None: ... + +class _ExcludeDispatchKeyGuard: + def __init__(self, keyset: DispatchKeySet): ... + def __enter__(self): ... + def __exit__(self, exc_type, exc_value, traceback): ... + +class _IncludeDispatchKeyGuard: + def __init__(self, k: DispatchKey): ... + def __enter__(self): ... + def __exit__(self, exc_type, exc_value, traceback): ... + +class _ForceDispatchKeyGuard: + def __init__(self, include: DispatchKeySet, exclude: DispatchKeySet): ... + def __enter__(self): ... + def __exit__(self, exc_type, exc_value, traceback): ... + +class _PreserveDispatchKeyGuard: + def __init__(self): ... + def __enter__(self): ... + def __exit__(self, exc_type, exc_value, traceback): ... + +class _AutoDispatchBelowAutograd: + def __init__(self): ... + def __enter__(self): ... + def __exit__(self, exc_type, exc_value, traceback): ... + +class _AutoDispatchBelowADInplaceOrView: + def __init__(self): ... + def __enter__(self): ... + def __exit__(self, exc_type, exc_value, traceback): ... + +def _dispatch_print_registrations_for_dispatch_key(dispatch_key: str = "") -> None: ... +def _dispatch_get_registrations_for_dispatch_key( + dispatch_key: str = "", +) -> List[str]: ... +def _are_functorch_transforms_active() -> _bool: ... + +# Define in torch/csrc/autograd/init.cpp +def _set_python_dispatcher(dispatcher: object) -> None: ... + +def _get_nested_int(id: _int, coeff: _int) -> SymInt: ... + +def _get_constant_bool_symnode(val: _bool) -> Any: ... + +class _TorchDispatchModeKey(Enum): + FAKE: _TorchDispatchModeKey = ... + PROXY: _TorchDispatchModeKey = ... + FUNCTIONAL: _TorchDispatchModeKey = ... + +class _SetExcludeDispatchKeyGuard: + def __init__(self, k: DispatchKey, enabled: _bool): ... + def __enter__(self): ... + def __exit__(self, exc_type, exc_value, traceback): ... + +# Defined in torch/csrc/utils/schema_info.h + +class _SchemaInfo: + def __init__(self, schema: _int) -> None: ... + + @overload + def is_mutable(self) -> _bool: ... + @overload + def is_mutable(self, name: str) -> _bool: ... + + def has_argument(self, name: str) -> _bool: ... + +# Defined in torch/csrc/utils/init.cpp +class BenchmarkConfig: + num_calling_threads: _int + num_worker_threads: _int + num_warmup_iters: _int + num_iters: _int + profiler_output_path: str + +class BenchmarkExecutionStats: + latency_avg_ms: _float + num_iters: _int + +class ThroughputBenchmark: + def __init__(self, module: Any) -> None: ... + def add_input(self, *args: Any, **kwargs: Any) -> None: ... + def run_once(self, *args: Any, **kwargs: Any) -> Any: ... + def benchmark(self, config: BenchmarkConfig) -> BenchmarkExecutionStats: ... + +# Defined in torch/csrc/Storage.cpp +class StorageBase(object): ... + +# TODO: where +class DoubleTensor(Tensor): ... +class FloatTensor(Tensor): ... +class BFloat16Tensor(Tensor): ... +class LongTensor(Tensor): ... +class IntTensor(Tensor): ... +class ShortTensor(Tensor): ... +class HalfTensor(Tensor): ... +class CharTensor(Tensor): ... +class ByteTensor(Tensor): ... +class BoolTensor(Tensor): ... + +# Defined in torch/csrc/autograd/python_engine.cpp +class _ImperativeEngine: + def queue_callback(self, callback: Callable[[], None]) -> None: ... + def run_backward(self, *args: Any, **kwargs: Any) -> Tuple[Tensor, ...]: ... + def is_checkpoint_valid(self) -> _bool: ... + +# Defined in torch/csrc/autograd/python_variable.cpp +class _TensorMeta(type): ... + +# Defined in torch/csrc/autograd/python_variable.cpp +class TensorBase(metaclass=_TensorMeta): + requires_grad: _bool + retains_grad: _bool + shape: Size + data: Tensor + names: List[str] + device: _device + dtype: _dtype + layout: _layout + real: Tensor + imag: Tensor + T: Tensor + H: Tensor + mT: Tensor + mH: Tensor + ndim: _int + output_nr: _int + _version: _int + _base: Optional[Tensor] + _cdata: _int + grad_fn: Optional[_Node] + _grad_fn: Any + _grad: Optional[Tensor] + grad: Optional[Tensor] + _backward_hooks: Optional[Dict[_int, Callable[[Tensor], Optional[Tensor]]]] + nbytes: _int + itemsize: _int + _has_symbolic_sizes_strides: _bool + + def _view_func_unsafe( + self, + new_base: Tensor, + symint_visitor_fn: Optional[Callable[[_int], _int]] = None, + tensor_visitor_fn: Optional[Callable[[Tensor], Tensor]] = None + ): + ... + + def __abs__(self) -> Tensor: ... + def __add__(self, other: Any) -> Tensor: ... + @overload + def __and__(self, other: Tensor) -> Tensor: ... + @overload + def __and__(self, other: Union[Number, _complex]) -> Tensor: ... + @overload + def __and__(self, other: Any) -> Tensor: ... + def __bool__(self) -> builtins.bool: ... + def __complex__(self) -> builtins.complex: ... + def __contains__(self, other: Any, /) -> _bool: ... + def __div__(self, other: Any) -> Tensor: ... + def __eq__(self, other: Any) -> Tensor: ... # type: ignore[override] + def __float__(self) -> builtins.float: ... + def __floordiv__(self, other: Any) -> Tensor: ... + def __ge__(self, other: Any) -> Tensor: ... + def __getitem__(self, indices: Union[Union[SupportsIndex, Union[None, _bool, _int, slice, ellipsis, Tensor], _NestedSequence[Union[None, _bool, _int, slice, ellipsis, Tensor]]], tuple[Union[SupportsIndex, Union[None, _bool, _int, slice, ellipsis, Tensor], _NestedSequence[Union[None, _bool, _int, slice, ellipsis, Tensor]]], ...]]) -> Tensor: ... + def __gt__(self, other: Any) -> Tensor: ... + def __iadd__(self, other: Any) -> Tensor: ... + @overload + def __iand__(self, other: Tensor) -> Tensor: ... + @overload + def __iand__(self, other: Union[Number, _complex]) -> Tensor: ... + @overload + def __iand__(self, other: Any) -> Tensor: ... + def __idiv__(self, other: Any) -> Tensor: ... + def __ifloordiv__(self, other: Any) -> Tensor: ... + @overload + def __ilshift__(self, other: Tensor) -> Tensor: ... + @overload + def __ilshift__(self, other: Union[Number, _complex]) -> Tensor: ... + @overload + def __ilshift__(self, other: Any) -> Tensor: ... + def __imod__(self, other: Any) -> Tensor: ... + def __imul__(self, other: Any) -> Tensor: ... + def __index__(self) -> builtins.int: ... + @overload + def __init__(self, *args: Any, device: Optional[DeviceLikeType] = None) -> None: ... + @overload + def __init__(self, storage: Storage) -> None: ... + @overload + def __init__(self, other: Tensor) -> None: ... + @overload + def __init__(self, size: _size, *, device: Optional[DeviceLikeType] = None) -> None: ... + def __int__(self) -> builtins.int: ... + def __invert__(self) -> Tensor: ... + @overload + def __ior__(self, other: Tensor) -> Tensor: ... + @overload + def __ior__(self, other: Union[Number, _complex]) -> Tensor: ... + @overload + def __ior__(self, other: Any) -> Tensor: ... + @overload + def __irshift__(self, other: Tensor) -> Tensor: ... + @overload + def __irshift__(self, other: Union[Number, _complex]) -> Tensor: ... + @overload + def __irshift__(self, other: Any) -> Tensor: ... + def __isub__(self, other: Any) -> Tensor: ... + @overload + def __ixor__(self, other: Tensor) -> Tensor: ... + @overload + def __ixor__(self, other: Union[Number, _complex]) -> Tensor: ... + @overload + def __ixor__(self, other: Any) -> Tensor: ... + def __le__(self, other: Any) -> Tensor: ... + def __long__(self) -> builtins.int: ... + @overload + def __lshift__(self, other: Tensor) -> Tensor: ... + @overload + def __lshift__(self, other: Union[Number, _complex]) -> Tensor: ... + @overload + def __lshift__(self, other: Any) -> Tensor: ... + def __lt__(self, other: Any) -> Tensor: ... + def __matmul__(self, other: Any) -> Tensor: ... + def __mod__(self, other: Any) -> Tensor: ... + def __mul__(self, other: Any) -> Tensor: ... + def __ne__(self, other: Any) -> Tensor: ... # type: ignore[override] + def __neg__(self) -> Tensor: ... + def __new__(cls, *args, **kwargs) -> Self: ... + def __nonzero__(self) -> builtins.bool: ... + @overload + def __or__(self, other: Tensor) -> Tensor: ... + @overload + def __or__(self, other: Union[Number, _complex]) -> Tensor: ... + @overload + def __or__(self, other: Any) -> Tensor: ... + def __pow__(self, other: Any) -> Tensor: ... + def __radd__(self, other: Any) -> Tensor: ... + def __rand__(self, other: Any) -> Tensor: ... + def __rfloordiv__(self, other: Any) -> Tensor: ... + def __rmul__(self, other: Any) -> Tensor: ... + def __ror__(self, other: Any) -> Tensor: ... + def __rpow__(self, other: Any) -> Tensor: ... + @overload + def __rshift__(self, other: Tensor) -> Tensor: ... + @overload + def __rshift__(self, other: Union[Number, _complex]) -> Tensor: ... + @overload + def __rshift__(self, other: Any) -> Tensor: ... + def __rsub__(self, other: Any) -> Tensor: ... + def __rtruediv__(self, other: Any) -> Tensor: ... + def __rxor__(self, other: Any) -> Tensor: ... + def __setitem__(self, indices: Union[Union[SupportsIndex, Union[None, _bool, _int, slice, ellipsis, Tensor], _NestedSequence[Union[None, _bool, _int, slice, ellipsis, Tensor]]], tuple[Union[SupportsIndex, Union[None, _bool, _int, slice, ellipsis, Tensor], _NestedSequence[Union[None, _bool, _int, slice, ellipsis, Tensor]]], ...]], val: Union[Tensor, Number]) -> None: ... + def __sub__(self, other: Any) -> Tensor: ... + def __truediv__(self, other: Any) -> Tensor: ... + @overload + def __xor__(self, other: Tensor) -> Tensor: ... + @overload + def __xor__(self, other: Union[Number, _complex]) -> Tensor: ... + @overload + def __xor__(self, other: Any) -> Tensor: ... + def _addmm_activation(self, mat1: Tensor, mat2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1, use_gelu: _bool = False) -> Tensor: ... + def _autocast_to_full_precision(self, cuda_enabled: _bool, cpu_enabled: _bool) -> Tensor: ... + def _autocast_to_reduced_precision(self, cuda_enabled: _bool, cpu_enabled: _bool, cuda_dtype: _dtype, cpu_dtype: _dtype) -> Tensor: ... + def _coalesced_(self, coalesced: _bool) -> Tensor: ... + def _conj(self) -> Tensor: ... + def _conj_physical(self) -> Tensor: ... + def _dimI(self) -> _int: ... + def _dimV(self) -> _int: ... + def _indices(self) -> Tensor: ... + def _is_all_true(self) -> Tensor: ... + def _is_any_true(self) -> Tensor: ... + def _is_view(self) -> _bool: ... + def _is_zerotensor(self) -> _bool: ... + def _lazy_clone(self) -> Tensor: ... + @staticmethod + def _make_subclass(cls: _Type[S], data: Tensor, require_grad: _bool = False, dispatch_strides: _bool = False, dispatch_device: _bool = False, device_for_backend_keys: Optional[_device] = None) -> S: ... + def _neg_view(self) -> Tensor: ... + def _nested_tensor_size(self) -> Tensor: ... + def _nested_tensor_storage_offsets(self) -> Tensor: ... + def _nested_tensor_strides(self) -> Tensor: ... + def _nnz(self) -> _int: ... + def _sparse_mask_projection(self, mask: Tensor, accumulate_matches: _bool = False) -> Tensor: ... + def _to_dense(self, dtype: Optional[_dtype] = None, masked_grad: Optional[_bool] = None) -> Tensor: ... + @overload + def _to_sparse(self, *, layout: Optional[_layout] = None, blocksize: Optional[Union[_int, _size]] = None, dense_dim: Optional[_int] = None) -> Tensor: ... + @overload + def _to_sparse(self, sparse_dim: _int) -> Tensor: ... + def _to_sparse_bsc(self, blocksize: Union[_int, _size], dense_dim: Optional[_int] = None) -> Tensor: ... + def _to_sparse_bsr(self, blocksize: Union[_int, _size], dense_dim: Optional[_int] = None) -> Tensor: ... + def _to_sparse_csc(self, dense_dim: Optional[_int] = None) -> Tensor: ... + def _to_sparse_csr(self, dense_dim: Optional[_int] = None) -> Tensor: ... + def _values(self) -> Tensor: ... + def abs(self) -> Tensor: + r""" + abs() -> Tensor + + See :func:`torch.abs` + """ + ... + def abs_(self) -> Tensor: + r""" + abs_() -> Tensor + + In-place version of :meth:`~Tensor.abs` + """ + ... + def absolute(self) -> Tensor: + r""" + absolute() -> Tensor + + Alias for :func:`abs` + """ + ... + def absolute_(self) -> Tensor: + r""" + absolute_() -> Tensor + + In-place version of :meth:`~Tensor.absolute` + Alias for :func:`abs_` + """ + ... + def acos(self) -> Tensor: + r""" + acos() -> Tensor + + See :func:`torch.acos` + """ + ... + def acos_(self) -> Tensor: + r""" + acos_() -> Tensor + + In-place version of :meth:`~Tensor.acos` + """ + ... + def acosh(self) -> Tensor: + r""" + acosh() -> Tensor + + See :func:`torch.acosh` + """ + ... + def acosh_(self) -> Tensor: + r""" + acosh_() -> Tensor + + In-place version of :meth:`~Tensor.acosh` + """ + ... + def add(self, other: Union[Tensor, Number, _complex, torch.SymInt, torch.SymFloat], *, alpha: Optional[Union[Number, _complex]] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + add(other, *, alpha=1) -> Tensor + + Add a scalar or tensor to :attr:`self` tensor. If both :attr:`alpha` + and :attr:`other` are specified, each element of :attr:`other` is scaled by + :attr:`alpha` before being used. + + When :attr:`other` is a tensor, the shape of :attr:`other` must be + :ref:`broadcastable ` with the shape of the underlying + tensor + + See :func:`torch.add` + """ + ... + def add_(self, other: Union[Tensor, Number, _complex, torch.SymInt, torch.SymFloat], *, alpha: Optional[Union[Number, _complex]] = 1) -> Tensor: + r""" + add_(other, *, alpha=1) -> Tensor + + In-place version of :meth:`~Tensor.add` + """ + ... + def addbmm(self, batch1: Tensor, batch2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + addbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor + + See :func:`torch.addbmm` + """ + ... + def addbmm_(self, batch1: Tensor, batch2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + addbmm_(batch1, batch2, *, beta=1, alpha=1) -> Tensor + + In-place version of :meth:`~Tensor.addbmm` + """ + ... + def addcdiv(self, tensor1: Tensor, tensor2: Tensor, *, value: Union[Number, _complex] = 1) -> Tensor: + r""" + addcdiv(tensor1, tensor2, *, value=1) -> Tensor + + See :func:`torch.addcdiv` + """ + ... + def addcdiv_(self, tensor1: Tensor, tensor2: Tensor, *, value: Union[Number, _complex] = 1) -> Tensor: + r""" + addcdiv_(tensor1, tensor2, *, value=1) -> Tensor + + In-place version of :meth:`~Tensor.addcdiv` + """ + ... + def addcmul(self, tensor1: Tensor, tensor2: Tensor, *, value: Union[Number, _complex] = 1) -> Tensor: + r""" + addcmul(tensor1, tensor2, *, value=1) -> Tensor + + See :func:`torch.addcmul` + """ + ... + def addcmul_(self, tensor1: Tensor, tensor2: Tensor, *, value: Union[Number, _complex] = 1) -> Tensor: + r""" + addcmul_(tensor1, tensor2, *, value=1) -> Tensor + + In-place version of :meth:`~Tensor.addcmul` + """ + ... + def addmm(self, mat1: Tensor, mat2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + addmm(mat1, mat2, *, beta=1, alpha=1) -> Tensor + + See :func:`torch.addmm` + """ + ... + def addmm_(self, mat1: Tensor, mat2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + addmm_(mat1, mat2, *, beta=1, alpha=1) -> Tensor + + In-place version of :meth:`~Tensor.addmm` + """ + ... + def addmv(self, mat: Tensor, vec: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + addmv(mat, vec, *, beta=1, alpha=1) -> Tensor + + See :func:`torch.addmv` + """ + ... + def addmv_(self, mat: Tensor, vec: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + addmv_(mat, vec, *, beta=1, alpha=1) -> Tensor + + In-place version of :meth:`~Tensor.addmv` + """ + ... + def addr(self, vec1: Tensor, vec2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + addr(vec1, vec2, *, beta=1, alpha=1) -> Tensor + + See :func:`torch.addr` + """ + ... + def addr_(self, vec1: Tensor, vec2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + addr_(vec1, vec2, *, beta=1, alpha=1) -> Tensor + + In-place version of :meth:`~Tensor.addr` + """ + ... + def adjoint(self) -> Tensor: + r""" + adjoint() -> Tensor + + Alias for :func:`adjoint` + """ + ... + def align_as(self, other: Tensor) -> Tensor: + r""" + align_as(other) -> Tensor + + Permutes the dimensions of the :attr:`self` tensor to match the dimension order + in the :attr:`other` tensor, adding size-one dims for any new names. + + This operation is useful for explicit broadcasting by names (see examples). + + All of the dims of :attr:`self` must be named in order to use this method. + The resulting tensor is a view on the original tensor. + + All dimension names of :attr:`self` must be present in ``other.names``. + :attr:`other` may contain named dimensions that are not in ``self.names``; + the output tensor has a size-one dimension for each of those new names. + + To align a tensor to a specific order, use :meth:`~Tensor.align_to`. + + Examples:: + + # Example 1: Applying a mask + >>> mask = torch.randint(2, [127, 128], dtype=torch.bool).refine_names('W', 'H') + >>> imgs = torch.randn(32, 128, 127, 3, names=('N', 'H', 'W', 'C')) + >>> imgs.masked_fill_(mask.align_as(imgs), 0) + + + # Example 2: Applying a per-channel-scale + >>> def scale_channels(input, scale): + >>> scale = scale.refine_names('C') + >>> return input * scale.align_as(input) + + >>> num_channels = 3 + >>> scale = torch.randn(num_channels, names=('C',)) + >>> imgs = torch.rand(32, 128, 128, num_channels, names=('N', 'H', 'W', 'C')) + >>> more_imgs = torch.rand(32, num_channels, 128, 128, names=('N', 'C', 'H', 'W')) + >>> videos = torch.randn(3, num_channels, 128, 128, 128, names=('N', 'C', 'H', 'W', 'D')) + + # scale_channels is agnostic to the dimension order of the input + >>> scale_channels(imgs, scale) + >>> scale_channels(more_imgs, scale) + >>> scale_channels(videos, scale) + + .. warning:: + The named tensor API is experimental and subject to change. + """ + ... + @overload + def align_to(self, order: Sequence[Union[str, ellipsis, None]], ellipsis_idx: _int) -> Tensor: ... + @overload + def align_to(self, names: Sequence[Union[str, ellipsis, None]]) -> Tensor: ... + @overload + def all(self) -> Tensor: + r""" + all(dim=None, keepdim=False) -> Tensor + + See :func:`torch.all` + """ + ... + @overload + def all(self, dim: Optional[_size] = None, keepdim: _bool = False) -> Tensor: + r""" + all(dim=None, keepdim=False) -> Tensor + + See :func:`torch.all` + """ + ... + @overload + def all(self, dim: _int, keepdim: _bool = False) -> Tensor: + r""" + all(dim=None, keepdim=False) -> Tensor + + See :func:`torch.all` + """ + ... + @overload + def all(self, dim: Union[str, ellipsis, None], keepdim: _bool = False) -> Tensor: + r""" + all(dim=None, keepdim=False) -> Tensor + + See :func:`torch.all` + """ + ... + def allclose(self, other: Tensor, rtol: _float = 1e-05, atol: _float = 1e-08, equal_nan: _bool = False) -> _bool: + r""" + allclose(other, rtol=1e-05, atol=1e-08, equal_nan=False) -> Tensor + + See :func:`torch.allclose` + """ + ... + def amax(self, dim: Union[_int, _size] = (), keepdim: _bool = False) -> Tensor: + r""" + amax(dim=None, keepdim=False) -> Tensor + + See :func:`torch.amax` + """ + ... + def amin(self, dim: Union[_int, _size] = (), keepdim: _bool = False) -> Tensor: + r""" + amin(dim=None, keepdim=False) -> Tensor + + See :func:`torch.amin` + """ + ... + def aminmax(self, *, dim: Optional[_int] = None, keepdim: _bool = False) -> torch.return_types.aminmax: + r""" + aminmax(*, dim=None, keepdim=False) -> (Tensor min, Tensor max) + + See :func:`torch.aminmax` + """ + ... + def angle(self) -> Tensor: + r""" + angle() -> Tensor + + See :func:`torch.angle` + """ + ... + @overload + def any(self) -> Tensor: + r""" + any(dim=None, keepdim=False) -> Tensor + + See :func:`torch.any` + """ + ... + @overload + def any(self, dim: Optional[_size] = None, keepdim: _bool = False) -> Tensor: + r""" + any(dim=None, keepdim=False) -> Tensor + + See :func:`torch.any` + """ + ... + @overload + def any(self, dim: _int, keepdim: _bool = False) -> Tensor: + r""" + any(dim=None, keepdim=False) -> Tensor + + See :func:`torch.any` + """ + ... + @overload + def any(self, dim: Union[str, ellipsis, None], keepdim: _bool = False) -> Tensor: + r""" + any(dim=None, keepdim=False) -> Tensor + + See :func:`torch.any` + """ + ... + def apply_(self, callable: Callable) -> Tensor: + r""" + apply_(callable) -> Tensor + + Applies the function :attr:`callable` to each element in the tensor, replacing + each element with the value returned by :attr:`callable`. + + .. note:: + + This function only works with CPU tensors and should not be used in code + sections that require high performance. + """ + ... + def arccos(self) -> Tensor: + r""" + arccos() -> Tensor + + See :func:`torch.arccos` + """ + ... + def arccos_(self) -> Tensor: + r""" + arccos_() -> Tensor + + In-place version of :meth:`~Tensor.arccos` + """ + ... + def arccosh(self) -> Tensor: + r""" + acosh() -> Tensor + + See :func:`torch.arccosh` + """ + ... + def arccosh_(self) -> Tensor: + r""" + acosh_() -> Tensor + + In-place version of :meth:`~Tensor.arccosh` + """ + ... + def arcsin(self) -> Tensor: + r""" + arcsin() -> Tensor + + See :func:`torch.arcsin` + """ + ... + def arcsin_(self) -> Tensor: + r""" + arcsin_() -> Tensor + + In-place version of :meth:`~Tensor.arcsin` + """ + ... + def arcsinh(self) -> Tensor: + r""" + arcsinh() -> Tensor + + See :func:`torch.arcsinh` + """ + ... + def arcsinh_(self) -> Tensor: + r""" + arcsinh_() -> Tensor + + In-place version of :meth:`~Tensor.arcsinh` + """ + ... + def arctan(self) -> Tensor: + r""" + arctan() -> Tensor + + See :func:`torch.arctan` + """ + ... + def arctan2(self, other: Tensor) -> Tensor: + r""" + arctan2(other) -> Tensor + + See :func:`torch.arctan2` + """ + ... + def arctan2_(self, other: Tensor) -> Tensor: + r""" + atan2_(other) -> Tensor + + In-place version of :meth:`~Tensor.arctan2` + """ + ... + def arctan_(self) -> Tensor: + r""" + arctan_() -> Tensor + + In-place version of :meth:`~Tensor.arctan` + """ + ... + def arctanh(self) -> Tensor: + r""" + arctanh() -> Tensor + + See :func:`torch.arctanh` + """ + ... + def arctanh_(self) -> Tensor: + r""" + arctanh_(other) -> Tensor + + In-place version of :meth:`~Tensor.arctanh` + """ + ... + def argmax(self, dim: Optional[_int] = None, keepdim: _bool = False) -> Tensor: + r""" + argmax(dim=None, keepdim=False) -> LongTensor + + See :func:`torch.argmax` + """ + ... + def argmin(self, dim: Optional[_int] = None, keepdim: _bool = False) -> Tensor: + r""" + argmin(dim=None, keepdim=False) -> LongTensor + + See :func:`torch.argmin` + """ + ... + @overload + def argsort(self, *, stable: _bool, dim: _int = -1, descending: _bool = False) -> Tensor: + r""" + argsort(dim=-1, descending=False) -> LongTensor + + See :func:`torch.argsort` + """ + ... + @overload + def argsort(self, dim: _int = -1, descending: _bool = False) -> Tensor: + r""" + argsort(dim=-1, descending=False) -> LongTensor + + See :func:`torch.argsort` + """ + ... + @overload + def argsort(self, dim: Union[str, ellipsis, None], descending: _bool = False) -> Tensor: + r""" + argsort(dim=-1, descending=False) -> LongTensor + + See :func:`torch.argsort` + """ + ... + def argwhere(self) -> Tensor: + r""" + argwhere() -> Tensor + + See :func:`torch.argwhere` + """ + ... + def as_strided(self, size: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], storage_offset: Optional[Union[_int, SymInt]] = None) -> Tensor: + r""" + as_strided(size, stride, storage_offset=None) -> Tensor + + See :func:`torch.as_strided` + """ + ... + def as_strided_(self, size: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], storage_offset: Optional[Union[_int, SymInt]] = None) -> Tensor: + r""" + as_strided_(size, stride, storage_offset=None) -> Tensor + + In-place version of :meth:`~Tensor.as_strided` + """ + ... + def as_strided_scatter(self, src: Tensor, size: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], storage_offset: Optional[Union[_int, SymInt]] = None) -> Tensor: + r""" + as_strided_scatter(src, size, stride, storage_offset=None) -> Tensor + + See :func:`torch.as_strided_scatter` + """ + ... + def as_subclass(self, cls: _Type[S]) -> S: + r""" + as_subclass(cls) -> Tensor + + Makes a ``cls`` instance with the same data pointer as ``self``. Changes + in the output mirror changes in ``self``, and the output stays attached + to the autograd graph. ``cls`` must be a subclass of ``Tensor``. + """ + ... + def asin(self) -> Tensor: + r""" + asin() -> Tensor + + See :func:`torch.asin` + """ + ... + def asin_(self) -> Tensor: + r""" + asin_() -> Tensor + + In-place version of :meth:`~Tensor.asin` + """ + ... + def asinh(self) -> Tensor: + r""" + asinh() -> Tensor + + See :func:`torch.asinh` + """ + ... + def asinh_(self) -> Tensor: + r""" + asinh_() -> Tensor + + In-place version of :meth:`~Tensor.asinh` + """ + ... + def atan(self) -> Tensor: + r""" + atan() -> Tensor + + See :func:`torch.atan` + """ + ... + def atan2(self, other: Tensor) -> Tensor: + r""" + atan2(other) -> Tensor + + See :func:`torch.atan2` + """ + ... + def atan2_(self, other: Tensor) -> Tensor: + r""" + atan2_(other) -> Tensor + + In-place version of :meth:`~Tensor.atan2` + """ + ... + def atan_(self) -> Tensor: + r""" + atan_() -> Tensor + + In-place version of :meth:`~Tensor.atan` + """ + ... + def atanh(self) -> Tensor: + r""" + atanh() -> Tensor + + See :func:`torch.atanh` + """ + ... + def atanh_(self) -> Tensor: + r""" + atanh_(other) -> Tensor + + In-place version of :meth:`~Tensor.atanh` + """ + ... + def baddbmm(self, batch1: Tensor, batch2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + baddbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor + + See :func:`torch.baddbmm` + """ + ... + def baddbmm_(self, batch1: Tensor, batch2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + baddbmm_(batch1, batch2, *, beta=1, alpha=1) -> Tensor + + In-place version of :meth:`~Tensor.baddbmm` + """ + ... + @overload + def bernoulli(self, *, generator: Optional[Generator] = None) -> Tensor: + r""" + bernoulli(*, generator=None) -> Tensor + + Returns a result tensor where each :math:`\texttt{result[i]}` is independently + sampled from :math:`\text{Bernoulli}(\texttt{self[i]})`. :attr:`self` must have + floating point ``dtype``, and the result will have the same ``dtype``. + + See :func:`torch.bernoulli` + """ + ... + @overload + def bernoulli(self, p: _float, *, generator: Optional[Generator] = None) -> Tensor: + r""" + bernoulli(*, generator=None) -> Tensor + + Returns a result tensor where each :math:`\texttt{result[i]}` is independently + sampled from :math:`\text{Bernoulli}(\texttt{self[i]})`. :attr:`self` must have + floating point ``dtype``, and the result will have the same ``dtype``. + + See :func:`torch.bernoulli` + """ + ... + @overload + def bernoulli_(self, p: Tensor, *, generator: Optional[Generator] = None) -> Tensor: + r""" + bernoulli_(p=0.5, *, generator=None) -> Tensor + + Fills each location of :attr:`self` with an independent sample from + :math:`\text{Bernoulli}(\texttt{p})`. :attr:`self` can have integral + ``dtype``. + + :attr:`p` should either be a scalar or tensor containing probabilities to be + used for drawing the binary random number. + + If it is a tensor, the :math:`\text{i}^{th}` element of :attr:`self` tensor + will be set to a value sampled from + :math:`\text{Bernoulli}(\texttt{p\_tensor[i]})`. In this case `p` must have + floating point ``dtype``. + + See also :meth:`~Tensor.bernoulli` and :func:`torch.bernoulli` + """ + ... + @overload + def bernoulli_(self, p: _float = 0.5, *, generator: Optional[Generator] = None) -> Tensor: + r""" + bernoulli_(p=0.5, *, generator=None) -> Tensor + + Fills each location of :attr:`self` with an independent sample from + :math:`\text{Bernoulli}(\texttt{p})`. :attr:`self` can have integral + ``dtype``. + + :attr:`p` should either be a scalar or tensor containing probabilities to be + used for drawing the binary random number. + + If it is a tensor, the :math:`\text{i}^{th}` element of :attr:`self` tensor + will be set to a value sampled from + :math:`\text{Bernoulli}(\texttt{p\_tensor[i]})`. In this case `p` must have + floating point ``dtype``. + + See also :meth:`~Tensor.bernoulli` and :func:`torch.bernoulli` + """ + ... + def bfloat16(self) -> Tensor: + r""" + bfloat16(memory_format=torch.preserve_format) -> Tensor + ``self.bfloat16()`` is equivalent to ``self.to(torch.bfloat16)``. See :func:`to`. + + Args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... + def bincount(self, weights: Optional[Tensor] = None, minlength: _int = 0) -> Tensor: + r""" + bincount(weights=None, minlength=0) -> Tensor + + See :func:`torch.bincount` + """ + ... + @overload + def bitwise_and(self, other: Tensor) -> Tensor: + r""" + bitwise_and() -> Tensor + + See :func:`torch.bitwise_and` + """ + ... + @overload + def bitwise_and(self, other: Union[Number, _complex]) -> Tensor: + r""" + bitwise_and() -> Tensor + + See :func:`torch.bitwise_and` + """ + ... + @overload + def bitwise_and_(self, other: Tensor) -> Tensor: + r""" + bitwise_and_() -> Tensor + + In-place version of :meth:`~Tensor.bitwise_and` + """ + ... + @overload + def bitwise_and_(self, other: Union[Number, _complex]) -> Tensor: + r""" + bitwise_and_() -> Tensor + + In-place version of :meth:`~Tensor.bitwise_and` + """ + ... + @overload + def bitwise_left_shift(self, other: Tensor) -> Tensor: + r""" + bitwise_left_shift(other) -> Tensor + + See :func:`torch.bitwise_left_shift` + """ + ... + @overload + def bitwise_left_shift(self, other: Union[Number, _complex]) -> Tensor: + r""" + bitwise_left_shift(other) -> Tensor + + See :func:`torch.bitwise_left_shift` + """ + ... + @overload + def bitwise_left_shift_(self, other: Tensor) -> Tensor: + r""" + bitwise_left_shift_(other) -> Tensor + + In-place version of :meth:`~Tensor.bitwise_left_shift` + """ + ... + @overload + def bitwise_left_shift_(self, other: Union[Number, _complex]) -> Tensor: + r""" + bitwise_left_shift_(other) -> Tensor + + In-place version of :meth:`~Tensor.bitwise_left_shift` + """ + ... + def bitwise_not(self) -> Tensor: + r""" + bitwise_not() -> Tensor + + See :func:`torch.bitwise_not` + """ + ... + def bitwise_not_(self) -> Tensor: + r""" + bitwise_not_() -> Tensor + + In-place version of :meth:`~Tensor.bitwise_not` + """ + ... + @overload + def bitwise_or(self, other: Tensor) -> Tensor: + r""" + bitwise_or() -> Tensor + + See :func:`torch.bitwise_or` + """ + ... + @overload + def bitwise_or(self, other: Union[Number, _complex]) -> Tensor: + r""" + bitwise_or() -> Tensor + + See :func:`torch.bitwise_or` + """ + ... + @overload + def bitwise_or_(self, other: Tensor) -> Tensor: + r""" + bitwise_or_() -> Tensor + + In-place version of :meth:`~Tensor.bitwise_or` + """ + ... + @overload + def bitwise_or_(self, other: Union[Number, _complex]) -> Tensor: + r""" + bitwise_or_() -> Tensor + + In-place version of :meth:`~Tensor.bitwise_or` + """ + ... + @overload + def bitwise_right_shift(self, other: Tensor) -> Tensor: + r""" + bitwise_right_shift(other) -> Tensor + + See :func:`torch.bitwise_right_shift` + """ + ... + @overload + def bitwise_right_shift(self, other: Union[Number, _complex]) -> Tensor: + r""" + bitwise_right_shift(other) -> Tensor + + See :func:`torch.bitwise_right_shift` + """ + ... + @overload + def bitwise_right_shift_(self, other: Tensor) -> Tensor: + r""" + bitwise_right_shift_(other) -> Tensor + + In-place version of :meth:`~Tensor.bitwise_right_shift` + """ + ... + @overload + def bitwise_right_shift_(self, other: Union[Number, _complex]) -> Tensor: + r""" + bitwise_right_shift_(other) -> Tensor + + In-place version of :meth:`~Tensor.bitwise_right_shift` + """ + ... + @overload + def bitwise_xor(self, other: Tensor) -> Tensor: + r""" + bitwise_xor() -> Tensor + + See :func:`torch.bitwise_xor` + """ + ... + @overload + def bitwise_xor(self, other: Union[Number, _complex]) -> Tensor: + r""" + bitwise_xor() -> Tensor + + See :func:`torch.bitwise_xor` + """ + ... + @overload + def bitwise_xor_(self, other: Tensor) -> Tensor: + r""" + bitwise_xor_() -> Tensor + + In-place version of :meth:`~Tensor.bitwise_xor` + """ + ... + @overload + def bitwise_xor_(self, other: Union[Number, _complex]) -> Tensor: + r""" + bitwise_xor_() -> Tensor + + In-place version of :meth:`~Tensor.bitwise_xor` + """ + ... + def bmm(self, mat2: Tensor) -> Tensor: + r""" + bmm(batch2) -> Tensor + + See :func:`torch.bmm` + """ + ... + def bool(self) -> Tensor: + r""" + bool(memory_format=torch.preserve_format) -> Tensor + + ``self.bool()`` is equivalent to ``self.to(torch.bool)``. See :func:`to`. + + Args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... + @overload + def broadcast_to(self, size: Sequence[Union[_int, SymInt]]) -> Tensor: + r""" + broadcast_to(shape) -> Tensor + + See :func:`torch.broadcast_to`. + """ + ... + @overload + def broadcast_to(self, *size: Union[_int, SymInt]) -> Tensor: + r""" + broadcast_to(shape) -> Tensor + + See :func:`torch.broadcast_to`. + """ + ... + def byte(self) -> Tensor: + r""" + byte(memory_format=torch.preserve_format) -> Tensor + + ``self.byte()`` is equivalent to ``self.to(torch.uint8)``. See :func:`to`. + + Args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... + def cauchy_(self, median: _float = 0, sigma: _float = 1, *, generator: Optional[Generator] = None) -> Tensor: + r""" + cauchy_(median=0, sigma=1, *, generator=None) -> Tensor + + Fills the tensor with numbers drawn from the Cauchy distribution: + + .. math:: + + f(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - \text{median})^2 + \sigma^2} + + .. note:: + Sigma (:math:`\sigma`) is used to denote the scale parameter in Cauchy distribution. + """ + ... + def ccol_indices(self) -> Tensor: ... + def ceil(self) -> Tensor: + r""" + ceil() -> Tensor + + See :func:`torch.ceil` + """ + ... + def ceil_(self) -> Tensor: + r""" + ceil_() -> Tensor + + In-place version of :meth:`~Tensor.ceil` + """ + ... + def chalf(self, *, memory_format: Optional[memory_format] = None) -> Tensor: + r""" + chalf(memory_format=torch.preserve_format) -> Tensor + + ``self.chalf()`` is equivalent to ``self.to(torch.complex32)``. See :func:`to`. + + Args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... + def char(self) -> Tensor: + r""" + char(memory_format=torch.preserve_format) -> Tensor + + ``self.char()`` is equivalent to ``self.to(torch.int8)``. See :func:`to`. + + Args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... + def cholesky(self, upper: _bool = False) -> Tensor: + r""" + cholesky(upper=False) -> Tensor + + See :func:`torch.cholesky` + """ + ... + def cholesky_inverse(self, upper: _bool = False) -> Tensor: + r""" + cholesky_inverse(upper=False) -> Tensor + + See :func:`torch.cholesky_inverse` + """ + ... + def cholesky_solve(self, input2: Tensor, upper: _bool = False) -> Tensor: + r""" + cholesky_solve(input2, upper=False) -> Tensor + + See :func:`torch.cholesky_solve` + """ + ... + def chunk(self, chunks: _int, dim: _int = 0) -> Tuple[Tensor, ...]: + r""" + chunk(chunks, dim=0) -> List of Tensors + + See :func:`torch.chunk` + """ + ... + @overload + def clamp(self, min: Optional[Tensor] = None, max: Optional[Tensor] = None) -> Tensor: + r""" + clamp(min=None, max=None) -> Tensor + + See :func:`torch.clamp` + """ + ... + @overload + def clamp(self, min: Optional[Union[Number, _complex]] = None, max: Optional[Union[Number, _complex]] = None) -> Tensor: + r""" + clamp(min=None, max=None) -> Tensor + + See :func:`torch.clamp` + """ + ... + @overload + def clamp_(self, min: Optional[Tensor] = None, max: Optional[Tensor] = None) -> Tensor: + r""" + clamp_(min=None, max=None) -> Tensor + + In-place version of :meth:`~Tensor.clamp` + """ + ... + @overload + def clamp_(self, min: Optional[Union[Number, _complex]] = None, max: Optional[Union[Number, _complex]] = None) -> Tensor: + r""" + clamp_(min=None, max=None) -> Tensor + + In-place version of :meth:`~Tensor.clamp` + """ + ... + @overload + def clamp_max(self, max: Tensor) -> Tensor: ... + @overload + def clamp_max(self, max: Union[Number, _complex]) -> Tensor: ... + @overload + def clamp_max_(self, max: Tensor) -> Tensor: ... + @overload + def clamp_max_(self, max: Union[Number, _complex]) -> Tensor: ... + @overload + def clamp_min(self, min: Tensor) -> Tensor: ... + @overload + def clamp_min(self, min: Union[Number, _complex]) -> Tensor: ... + @overload + def clamp_min_(self, min: Tensor) -> Tensor: ... + @overload + def clamp_min_(self, min: Union[Number, _complex]) -> Tensor: ... + @overload + def clip(self, min: Optional[Tensor] = None, max: Optional[Tensor] = None) -> Tensor: + r""" + clip(min=None, max=None) -> Tensor + + Alias for :meth:`~Tensor.clamp`. + """ + ... + @overload + def clip(self, min: Optional[Union[Number, _complex]] = None, max: Optional[Union[Number, _complex]] = None) -> Tensor: + r""" + clip(min=None, max=None) -> Tensor + + Alias for :meth:`~Tensor.clamp`. + """ + ... + @overload + def clip_(self, min: Optional[Tensor] = None, max: Optional[Tensor] = None) -> Tensor: + r""" + clip_(min=None, max=None) -> Tensor + + Alias for :meth:`~Tensor.clamp_`. + """ + ... + @overload + def clip_(self, min: Optional[Union[Number, _complex]] = None, max: Optional[Union[Number, _complex]] = None) -> Tensor: + r""" + clip_(min=None, max=None) -> Tensor + + Alias for :meth:`~Tensor.clamp_`. + """ + ... + def clone(self, *, memory_format: Optional[memory_format] = None) -> Tensor: + r""" + clone(*, memory_format=torch.preserve_format) -> Tensor + + See :func:`torch.clone` + """ + ... + def coalesce(self) -> Tensor: + r""" + coalesce() -> Tensor + + Returns a coalesced copy of :attr:`self` if :attr:`self` is an + :ref:`uncoalesced tensor `. + + Returns :attr:`self` if :attr:`self` is a coalesced tensor. + + .. warning:: + Throws an error if :attr:`self` is not a sparse COO tensor. + """ + ... + def col_indices(self) -> Tensor: + r""" + col_indices() -> IntTensor + + Returns the tensor containing the column indices of the :attr:`self` + tensor when :attr:`self` is a sparse CSR tensor of layout ``sparse_csr``. + The ``col_indices`` tensor is strictly of shape (:attr:`self`.nnz()) + and of type ``int32`` or ``int64``. When using MKL routines such as sparse + matrix multiplication, it is necessary to use ``int32`` indexing in order + to avoid downcasting and potentially losing information. + + Example:: + >>> csr = torch.eye(5,5).to_sparse_csr() + >>> csr.col_indices() + tensor([0, 1, 2, 3, 4], dtype=torch.int32) + """ + ... + def conj(self) -> Tensor: + r""" + conj() -> Tensor + + See :func:`torch.conj` + """ + ... + def conj_physical(self) -> Tensor: + r""" + conj_physical() -> Tensor + + See :func:`torch.conj_physical` + """ + ... + def conj_physical_(self) -> Tensor: + r""" + conj_physical_() -> Tensor + + In-place version of :meth:`~Tensor.conj_physical` + """ + ... + def contiguous(self, memory_format=torch.contiguous_format) -> Tensor: + r""" + contiguous(memory_format=torch.contiguous_format) -> Tensor + + Returns a contiguous in memory tensor containing the same data as :attr:`self` tensor. If + :attr:`self` tensor is already in the specified memory format, this function returns the + :attr:`self` tensor. + + Args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.contiguous_format``. + """ + ... + def copy_(self, src: Tensor, non_blocking: _bool = False) -> Tensor: + r""" + copy_(src, non_blocking=False) -> Tensor + + Copies the elements from :attr:`src` into :attr:`self` tensor and returns + :attr:`self`. + + The :attr:`src` tensor must be :ref:`broadcastable ` + with the :attr:`self` tensor. It may be of a different data type or reside on a + different device. + + Args: + src (Tensor): the source tensor to copy from + non_blocking (bool): if ``True`` and this copy is between CPU and GPU, + the copy may occur asynchronously with respect to the host. For other + cases, this argument has no effect. + """ + ... + @overload + def copysign(self, other: Tensor) -> Tensor: + r""" + copysign(other) -> Tensor + + See :func:`torch.copysign` + """ + ... + @overload + def copysign(self, other: Union[Number, _complex]) -> Tensor: + r""" + copysign(other) -> Tensor + + See :func:`torch.copysign` + """ + ... + @overload + def copysign_(self, other: Tensor) -> Tensor: + r""" + copysign_(other) -> Tensor + + In-place version of :meth:`~Tensor.copysign` + """ + ... + @overload + def copysign_(self, other: Union[Number, _complex]) -> Tensor: + r""" + copysign_(other) -> Tensor + + In-place version of :meth:`~Tensor.copysign` + """ + ... + def corrcoef(self) -> Tensor: + r""" + corrcoef() -> Tensor + + See :func:`torch.corrcoef` + """ + ... + def cos(self) -> Tensor: + r""" + cos() -> Tensor + + See :func:`torch.cos` + """ + ... + def cos_(self) -> Tensor: + r""" + cos_() -> Tensor + + In-place version of :meth:`~Tensor.cos` + """ + ... + def cosh(self) -> Tensor: + r""" + cosh() -> Tensor + + See :func:`torch.cosh` + """ + ... + def cosh_(self) -> Tensor: + r""" + cosh_() -> Tensor + + In-place version of :meth:`~Tensor.cosh` + """ + ... + @overload + def count_nonzero(self, dim: Optional[_int] = None) -> Tensor: + r""" + count_nonzero(dim=None) -> Tensor + + See :func:`torch.count_nonzero` + """ + ... + @overload + def count_nonzero(self, dim: _size) -> Tensor: + r""" + count_nonzero(dim=None) -> Tensor + + See :func:`torch.count_nonzero` + """ + ... + @overload + def count_nonzero(self, *dim: _int) -> Tensor: + r""" + count_nonzero(dim=None) -> Tensor + + See :func:`torch.count_nonzero` + """ + ... + def cov(self, *, correction: _int = 1, fweights: Optional[Tensor] = None, aweights: Optional[Tensor] = None) -> Tensor: + r""" + cov(*, correction=1, fweights=None, aweights=None) -> Tensor + + See :func:`torch.cov` + """ + ... + def cpu(self, memory_format: torch.memory_format = torch.preserve_format) -> Tensor: + r""" + cpu(memory_format=torch.preserve_format) -> Tensor + + Returns a copy of this object in CPU memory. + + If this object is already in CPU memory and on the correct device, + then no copy is performed and the original object is returned. + + Args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... + def cross(self, other: Tensor, dim: Optional[_int] = None) -> Tensor: + r""" + cross(other, dim=None) -> Tensor + + See :func:`torch.cross` + """ + ... + def crow_indices(self) -> Tensor: + r""" + crow_indices() -> IntTensor + + Returns the tensor containing the compressed row indices of the :attr:`self` + tensor when :attr:`self` is a sparse CSR tensor of layout ``sparse_csr``. + The ``crow_indices`` tensor is strictly of shape (:attr:`self`.size(0) + 1) + and of type ``int32`` or ``int64``. When using MKL routines such as sparse + matrix multiplication, it is necessary to use ``int32`` indexing in order + to avoid downcasting and potentially losing information. + + Example:: + >>> csr = torch.eye(5,5).to_sparse_csr() + >>> csr.crow_indices() + tensor([0, 1, 2, 3, 4, 5], dtype=torch.int32) + """ + ... + def cuda(self, device: Optional[Union[_device, _int, str]] = None, non_blocking: _bool = False, memory_format: torch.memory_format = torch.preserve_format) -> Tensor: + r""" + cuda(device=None, non_blocking=False, memory_format=torch.preserve_format) -> Tensor + + Returns a copy of this object in CUDA memory. + + If this object is already in CUDA memory and on the correct device, + then no copy is performed and the original object is returned. + + Args: + device (:class:`torch.device`): The destination GPU device. + Defaults to the current CUDA device. + non_blocking (bool): If ``True`` and the source is in pinned memory, + the copy will be asynchronous with respect to the host. + Otherwise, the argument has no effect. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... + @overload + def cummax(self, dim: _int) -> torch.return_types.cummax: + r""" + cummax(dim) -> (Tensor, Tensor) + + See :func:`torch.cummax` + """ + ... + @overload + def cummax(self, dim: Union[str, ellipsis, None]) -> torch.return_types.cummax: + r""" + cummax(dim) -> (Tensor, Tensor) + + See :func:`torch.cummax` + """ + ... + @overload + def cummin(self, dim: _int) -> torch.return_types.cummin: + r""" + cummin(dim) -> (Tensor, Tensor) + + See :func:`torch.cummin` + """ + ... + @overload + def cummin(self, dim: Union[str, ellipsis, None]) -> torch.return_types.cummin: + r""" + cummin(dim) -> (Tensor, Tensor) + + See :func:`torch.cummin` + """ + ... + @overload + def cumprod(self, dim: _int, *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + cumprod(dim, dtype=None) -> Tensor + + See :func:`torch.cumprod` + """ + ... + @overload + def cumprod(self, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + cumprod(dim, dtype=None) -> Tensor + + See :func:`torch.cumprod` + """ + ... + @overload + def cumprod_(self, dim: _int, *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + cumprod_(dim, dtype=None) -> Tensor + + In-place version of :meth:`~Tensor.cumprod` + """ + ... + @overload + def cumprod_(self, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + cumprod_(dim, dtype=None) -> Tensor + + In-place version of :meth:`~Tensor.cumprod` + """ + ... + @overload + def cumsum(self, dim: _int, *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + cumsum(dim, dtype=None) -> Tensor + + See :func:`torch.cumsum` + """ + ... + @overload + def cumsum(self, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + cumsum(dim, dtype=None) -> Tensor + + See :func:`torch.cumsum` + """ + ... + @overload + def cumsum_(self, dim: _int, *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + cumsum_(dim, dtype=None) -> Tensor + + In-place version of :meth:`~Tensor.cumsum` + """ + ... + @overload + def cumsum_(self, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + cumsum_(dim, dtype=None) -> Tensor + + In-place version of :meth:`~Tensor.cumsum` + """ + ... + def data_ptr(self) -> _int: + r""" + data_ptr() -> int + + Returns the address of the first element of :attr:`self` tensor. + """ + ... + def deg2rad(self) -> Tensor: + r""" + deg2rad() -> Tensor + + See :func:`torch.deg2rad` + """ + ... + def deg2rad_(self) -> Tensor: + r""" + deg2rad_() -> Tensor + + In-place version of :meth:`~Tensor.deg2rad` + """ + ... + def dense_dim(self) -> _int: + r""" + dense_dim() -> int + + Return the number of dense dimensions in a :ref:`sparse tensor ` :attr:`self`. + + .. note:: + Returns ``len(self.shape)`` if :attr:`self` is not a sparse tensor. + + See also :meth:`Tensor.sparse_dim` and :ref:`hybrid tensors `. + """ + ... + def dequantize(self) -> Tensor: + r""" + dequantize() -> Tensor + + Given a quantized Tensor, dequantize it and return the dequantized float Tensor. + """ + ... + def det(self) -> Tensor: + r""" + det() -> Tensor + + See :func:`torch.det` + """ + ... + def detach(self) -> Tensor: ... + def detach_(self) -> Tensor: ... + def diag(self, diagonal: _int = 0) -> Tensor: + r""" + diag(diagonal=0) -> Tensor + + See :func:`torch.diag` + """ + ... + def diag_embed(self, offset: _int = 0, dim1: _int = -2, dim2: _int = -1) -> Tensor: + r""" + diag_embed(offset=0, dim1=-2, dim2=-1) -> Tensor + + See :func:`torch.diag_embed` + """ + ... + def diagflat(self, offset: _int = 0) -> Tensor: + r""" + diagflat(offset=0) -> Tensor + + See :func:`torch.diagflat` + """ + ... + @overload + def diagonal(self, *, outdim: Union[str, ellipsis, None], dim1: Union[str, ellipsis, None], dim2: Union[str, ellipsis, None], offset: _int = 0) -> Tensor: + r""" + diagonal(offset=0, dim1=0, dim2=1) -> Tensor + + See :func:`torch.diagonal` + """ + ... + @overload + def diagonal(self, offset: _int = 0, dim1: _int = 0, dim2: _int = 1) -> Tensor: + r""" + diagonal(offset=0, dim1=0, dim2=1) -> Tensor + + See :func:`torch.diagonal` + """ + ... + def diagonal_scatter(self, src: Tensor, offset: _int = 0, dim1: _int = 0, dim2: _int = 1) -> Tensor: + r""" + diagonal_scatter(src, offset=0, dim1=0, dim2=1) -> Tensor + + See :func:`torch.diagonal_scatter` + """ + ... + def diff(self, n: _int = 1, dim: _int = -1, prepend: Optional[Tensor] = None, append: Optional[Tensor] = None) -> Tensor: + r""" + diff(n=1, dim=-1, prepend=None, append=None) -> Tensor + + See :func:`torch.diff` + """ + ... + def digamma(self) -> Tensor: + r""" + digamma() -> Tensor + + See :func:`torch.digamma` + """ + ... + def digamma_(self) -> Tensor: + r""" + digamma_() -> Tensor + + In-place version of :meth:`~Tensor.digamma` + """ + ... + def dim(self) -> _int: + r""" + dim() -> int + + Returns the number of dimensions of :attr:`self` tensor. + """ + ... + def dist(self, other: Tensor, p: Union[Number, _complex] = 2) -> Tensor: + r""" + dist(other, p=2) -> Tensor + + See :func:`torch.dist` + """ + ... + def div(self, other: Union[Tensor, Number], *, rounding_mode: Optional[str] = None) -> Tensor: + r""" + div(value, *, rounding_mode=None) -> Tensor + + See :func:`torch.div` + """ + ... + def div_(self, other: Union[Tensor, Number], *, rounding_mode: Optional[str] = None) -> Tensor: + r""" + div_(value, *, rounding_mode=None) -> Tensor + + In-place version of :meth:`~Tensor.div` + """ + ... + @overload + def divide(self, other: Tensor) -> Tensor: + r""" + divide(value, *, rounding_mode=None) -> Tensor + + See :func:`torch.divide` + """ + ... + @overload + def divide(self, other: Tensor, *, rounding_mode: Optional[str]) -> Tensor: + r""" + divide(value, *, rounding_mode=None) -> Tensor + + See :func:`torch.divide` + """ + ... + @overload + def divide(self, other: Union[Number, _complex], *, rounding_mode: Optional[str]) -> Tensor: + r""" + divide(value, *, rounding_mode=None) -> Tensor + + See :func:`torch.divide` + """ + ... + @overload + def divide(self, other: Union[Number, _complex]) -> Tensor: + r""" + divide(value, *, rounding_mode=None) -> Tensor + + See :func:`torch.divide` + """ + ... + @overload + def divide_(self, other: Tensor) -> Tensor: + r""" + divide_(value, *, rounding_mode=None) -> Tensor + + In-place version of :meth:`~Tensor.divide` + """ + ... + @overload + def divide_(self, other: Tensor, *, rounding_mode: Optional[str]) -> Tensor: + r""" + divide_(value, *, rounding_mode=None) -> Tensor + + In-place version of :meth:`~Tensor.divide` + """ + ... + @overload + def divide_(self, other: Union[Number, _complex], *, rounding_mode: Optional[str]) -> Tensor: + r""" + divide_(value, *, rounding_mode=None) -> Tensor + + In-place version of :meth:`~Tensor.divide` + """ + ... + @overload + def divide_(self, other: Union[Number, _complex]) -> Tensor: + r""" + divide_(value, *, rounding_mode=None) -> Tensor + + In-place version of :meth:`~Tensor.divide` + """ + ... + def dot(self, tensor: Tensor) -> Tensor: + r""" + dot(other) -> Tensor + + See :func:`torch.dot` + """ + ... + def double(self) -> Tensor: + r""" + double(memory_format=torch.preserve_format) -> Tensor + + ``self.double()`` is equivalent to ``self.to(torch.float64)``. See :func:`to`. + + Args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... + @overload + def dsplit(self, sections: _int) -> Tuple[Tensor, ...]: + r""" + dsplit(split_size_or_sections) -> List of Tensors + + See :func:`torch.dsplit` + """ + ... + @overload + def dsplit(self, indices: _size) -> Tuple[Tensor, ...]: + r""" + dsplit(split_size_or_sections) -> List of Tensors + + See :func:`torch.dsplit` + """ + ... + @overload + def dsplit(self, *indices: _int) -> Tuple[Tensor, ...]: + r""" + dsplit(split_size_or_sections) -> List of Tensors + + See :func:`torch.dsplit` + """ + ... + def element_size(self) -> _int: + r""" + element_size() -> int + + Returns the size in bytes of an individual element. + + Example:: + + >>> torch.tensor([]).element_size() + 4 + >>> torch.tensor([], dtype=torch.uint8).element_size() + 1 + """ + ... + @overload + def eq(self, other: Tensor) -> Tensor: + r""" + eq(other) -> Tensor + + See :func:`torch.eq` + """ + ... + @overload + def eq(self, other: Union[Number, _complex]) -> Tensor: + r""" + eq(other) -> Tensor + + See :func:`torch.eq` + """ + ... + @overload + def eq_(self, other: Tensor) -> Tensor: + r""" + eq_(other) -> Tensor + + In-place version of :meth:`~Tensor.eq` + """ + ... + @overload + def eq_(self, other: Union[Number, _complex]) -> Tensor: + r""" + eq_(other) -> Tensor + + In-place version of :meth:`~Tensor.eq` + """ + ... + def equal(self, other: Tensor) -> _bool: + r""" + equal(other) -> bool + + See :func:`torch.equal` + """ + ... + def erf(self) -> Tensor: + r""" + erf() -> Tensor + + See :func:`torch.erf` + """ + ... + def erf_(self) -> Tensor: + r""" + erf_() -> Tensor + + In-place version of :meth:`~Tensor.erf` + """ + ... + def erfc(self) -> Tensor: + r""" + erfc() -> Tensor + + See :func:`torch.erfc` + """ + ... + def erfc_(self) -> Tensor: + r""" + erfc_() -> Tensor + + In-place version of :meth:`~Tensor.erfc` + """ + ... + def erfinv(self) -> Tensor: + r""" + erfinv() -> Tensor + + See :func:`torch.erfinv` + """ + ... + def erfinv_(self) -> Tensor: + r""" + erfinv_() -> Tensor + + In-place version of :meth:`~Tensor.erfinv` + """ + ... + def exp(self) -> Tensor: + r""" + exp() -> Tensor + + See :func:`torch.exp` + """ + ... + def exp2(self) -> Tensor: + r""" + exp2() -> Tensor + + See :func:`torch.exp2` + """ + ... + def exp2_(self) -> Tensor: + r""" + exp2_() -> Tensor + + In-place version of :meth:`~Tensor.exp2` + """ + ... + def exp_(self) -> Tensor: + r""" + exp_() -> Tensor + + In-place version of :meth:`~Tensor.exp` + """ + ... + @overload + def expand(self, size: Sequence[Union[_int, SymInt]], *, implicit: _bool = False) -> Tensor: + r""" + expand(*sizes) -> Tensor + + Returns a new view of the :attr:`self` tensor with singleton dimensions expanded + to a larger size. + + Passing -1 as the size for a dimension means not changing the size of + that dimension. + + Tensor can be also expanded to a larger number of dimensions, and the + new ones will be appended at the front. For the new dimensions, the + size cannot be set to -1. + + Expanding a tensor does not allocate new memory, but only creates a + new view on the existing tensor where a dimension of size one is + expanded to a larger size by setting the ``stride`` to 0. Any dimension + of size 1 can be expanded to an arbitrary value without allocating new + memory. + + Args: + *sizes (torch.Size or int...): the desired expanded size + + .. warning:: + + More than one element of an expanded tensor may refer to a single + memory location. As a result, in-place operations (especially ones that + are vectorized) may result in incorrect behavior. If you need to write + to the tensors, please clone them first. + + Example:: + + >>> x = torch.tensor([[1], [2], [3]]) + >>> x.size() + torch.Size([3, 1]) + >>> x.expand(3, 4) + tensor([[ 1, 1, 1, 1], + [ 2, 2, 2, 2], + [ 3, 3, 3, 3]]) + >>> x.expand(-1, 4) # -1 means not changing the size of that dimension + tensor([[ 1, 1, 1, 1], + [ 2, 2, 2, 2], + [ 3, 3, 3, 3]]) + """ + ... + @overload + def expand(self, *size: Union[_int, SymInt], implicit: _bool = False) -> Tensor: + r""" + expand(*sizes) -> Tensor + + Returns a new view of the :attr:`self` tensor with singleton dimensions expanded + to a larger size. + + Passing -1 as the size for a dimension means not changing the size of + that dimension. + + Tensor can be also expanded to a larger number of dimensions, and the + new ones will be appended at the front. For the new dimensions, the + size cannot be set to -1. + + Expanding a tensor does not allocate new memory, but only creates a + new view on the existing tensor where a dimension of size one is + expanded to a larger size by setting the ``stride`` to 0. Any dimension + of size 1 can be expanded to an arbitrary value without allocating new + memory. + + Args: + *sizes (torch.Size or int...): the desired expanded size + + .. warning:: + + More than one element of an expanded tensor may refer to a single + memory location. As a result, in-place operations (especially ones that + are vectorized) may result in incorrect behavior. If you need to write + to the tensors, please clone them first. + + Example:: + + >>> x = torch.tensor([[1], [2], [3]]) + >>> x.size() + torch.Size([3, 1]) + >>> x.expand(3, 4) + tensor([[ 1, 1, 1, 1], + [ 2, 2, 2, 2], + [ 3, 3, 3, 3]]) + >>> x.expand(-1, 4) # -1 means not changing the size of that dimension + tensor([[ 1, 1, 1, 1], + [ 2, 2, 2, 2], + [ 3, 3, 3, 3]]) + """ + ... + def expand_as(self, other: Tensor) -> Tensor: + r""" + expand_as(other) -> Tensor + + Expand this tensor to the same size as :attr:`other`. + ``self.expand_as(other)`` is equivalent to ``self.expand(other.size())``. + + Please see :meth:`~Tensor.expand` for more information about ``expand``. + + Args: + other (:class:`torch.Tensor`): The result tensor has the same size + as :attr:`other`. + """ + ... + def expm1(self) -> Tensor: + r""" + expm1() -> Tensor + + See :func:`torch.expm1` + """ + ... + def expm1_(self) -> Tensor: + r""" + expm1_() -> Tensor + + In-place version of :meth:`~Tensor.expm1` + """ + ... + def exponential_(self, lambd: _float = 1, *, generator: Optional[Generator] = None) -> Tensor: + r""" + exponential_(lambd=1, *, generator=None) -> Tensor + + Fills :attr:`self` tensor with elements drawn from the PDF (probability density function): + + .. math:: + + f(x) = \lambda e^{-\lambda x}, x > 0 + + .. note:: + In probability theory, exponential distribution is supported on interval [0, :math:`\inf`) (i.e., :math:`x >= 0`) + implying that zero can be sampled from the exponential distribution. + However, :func:`torch.Tensor.exponential_` does not sample zero, + which means that its actual support is the interval (0, :math:`\inf`). + + Note that :func:`torch.distributions.exponential.Exponential` is supported on the interval [0, :math:`\inf`) and can sample zero. + """ + ... + @overload + def fill_(self, value: Tensor) -> Tensor: + r""" + fill_(value) -> Tensor + + Fills :attr:`self` tensor with the specified value. + """ + ... + @overload + def fill_(self, value: Union[Number, _complex]) -> Tensor: + r""" + fill_(value) -> Tensor + + Fills :attr:`self` tensor with the specified value. + """ + ... + def fill_diagonal_(self, fill_value: Union[Number, _complex], wrap: _bool = False) -> Tensor: + r""" + fill_diagonal_(fill_value, wrap=False) -> Tensor + + Fill the main diagonal of a tensor that has at least 2-dimensions. + When dims>2, all dimensions of input must be of equal length. + This function modifies the input tensor in-place, and returns the input tensor. + + Arguments: + fill_value (Scalar): the fill value + wrap (bool): the diagonal 'wrapped' after N columns for tall matrices. + + Example:: + + >>> a = torch.zeros(3, 3) + >>> a.fill_diagonal_(5) + tensor([[5., 0., 0.], + [0., 5., 0.], + [0., 0., 5.]]) + >>> b = torch.zeros(7, 3) + >>> b.fill_diagonal_(5) + tensor([[5., 0., 0.], + [0., 5., 0.], + [0., 0., 5.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]) + >>> c = torch.zeros(7, 3) + >>> c.fill_diagonal_(5, wrap=True) + tensor([[5., 0., 0.], + [0., 5., 0.], + [0., 0., 5.], + [0., 0., 0.], + [5., 0., 0.], + [0., 5., 0.], + [0., 0., 5.]]) + """ + ... + def fix(self) -> Tensor: + r""" + fix() -> Tensor + + See :func:`torch.fix`. + """ + ... + def fix_(self) -> Tensor: + r""" + fix_() -> Tensor + + In-place version of :meth:`~Tensor.fix` + """ + ... + @overload + def flatten(self, start_dim: _int = 0, end_dim: _int = -1) -> Tensor: + r""" + flatten(start_dim=0, end_dim=-1) -> Tensor + + See :func:`torch.flatten` + """ + ... + @overload + def flatten(self, start_dim: _int, end_dim: _int, out_dim: Union[str, ellipsis, None]) -> Tensor: + r""" + flatten(start_dim=0, end_dim=-1) -> Tensor + + See :func:`torch.flatten` + """ + ... + @overload + def flatten(self, start_dim: Union[str, ellipsis, None], end_dim: Union[str, ellipsis, None], out_dim: Union[str, ellipsis, None]) -> Tensor: + r""" + flatten(start_dim=0, end_dim=-1) -> Tensor + + See :func:`torch.flatten` + """ + ... + @overload + def flatten(self, dims: Sequence[Union[str, ellipsis, None]], out_dim: Union[str, ellipsis, None]) -> Tensor: + r""" + flatten(start_dim=0, end_dim=-1) -> Tensor + + See :func:`torch.flatten` + """ + ... + @overload + def flip(self, dims: _size) -> Tensor: + r""" + flip(dims) -> Tensor + + See :func:`torch.flip` + """ + ... + @overload + def flip(self, *dims: _int) -> Tensor: + r""" + flip(dims) -> Tensor + + See :func:`torch.flip` + """ + ... + def fliplr(self) -> Tensor: + r""" + fliplr() -> Tensor + + See :func:`torch.fliplr` + """ + ... + def flipud(self) -> Tensor: + r""" + flipud() -> Tensor + + See :func:`torch.flipud` + """ + ... + def float(self) -> Tensor: + r""" + float(memory_format=torch.preserve_format) -> Tensor + + ``self.float()`` is equivalent to ``self.to(torch.float32)``. See :func:`to`. + + Args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... + @overload + def float_power(self, exponent: Tensor) -> Tensor: + r""" + float_power(exponent) -> Tensor + + See :func:`torch.float_power` + """ + ... + @overload + def float_power(self, exponent: Union[Number, _complex]) -> Tensor: + r""" + float_power(exponent) -> Tensor + + See :func:`torch.float_power` + """ + ... + @overload + def float_power_(self, exponent: Tensor) -> Tensor: + r""" + float_power_(exponent) -> Tensor + + In-place version of :meth:`~Tensor.float_power` + """ + ... + @overload + def float_power_(self, exponent: Union[Number, _complex]) -> Tensor: + r""" + float_power_(exponent) -> Tensor + + In-place version of :meth:`~Tensor.float_power` + """ + ... + def floor(self) -> Tensor: + r""" + floor() -> Tensor + + See :func:`torch.floor` + """ + ... + def floor_(self) -> Tensor: + r""" + floor_() -> Tensor + + In-place version of :meth:`~Tensor.floor` + """ + ... + def floor_divide(self, other: Union[Tensor, Number, torch.SymInt, torch.SymFloat], *, out: Optional[Tensor] = None) -> Tensor: + r""" + floor_divide(value) -> Tensor + + See :func:`torch.floor_divide` + """ + ... + def floor_divide_(self, other: Union[Tensor, Number, torch.SymInt, torch.SymFloat]) -> Tensor: + r""" + floor_divide_(value) -> Tensor + + In-place version of :meth:`~Tensor.floor_divide` + """ + ... + def fmax(self, other: Tensor) -> Tensor: + r""" + fmax(other) -> Tensor + + See :func:`torch.fmax` + """ + ... + def fmin(self, other: Tensor) -> Tensor: + r""" + fmin(other) -> Tensor + + See :func:`torch.fmin` + """ + ... + @overload + def fmod(self, other: Tensor) -> Tensor: + r""" + fmod(divisor) -> Tensor + + See :func:`torch.fmod` + """ + ... + @overload + def fmod(self, other: Union[Number, _complex]) -> Tensor: + r""" + fmod(divisor) -> Tensor + + See :func:`torch.fmod` + """ + ... + @overload + def fmod_(self, other: Tensor) -> Tensor: + r""" + fmod_(divisor) -> Tensor + + In-place version of :meth:`~Tensor.fmod` + """ + ... + @overload + def fmod_(self, other: Union[Number, _complex]) -> Tensor: + r""" + fmod_(divisor) -> Tensor + + In-place version of :meth:`~Tensor.fmod` + """ + ... + def frac(self) -> Tensor: + r""" + frac() -> Tensor + + See :func:`torch.frac` + """ + ... + def frac_(self) -> Tensor: + r""" + frac_() -> Tensor + + In-place version of :meth:`~Tensor.frac` + """ + ... + def frexp(self) -> torch.return_types.frexp: + r""" + frexp(input) -> (Tensor mantissa, Tensor exponent) + + See :func:`torch.frexp` + """ + ... + @overload + def gather(self, dim: _int, index: Tensor, *, sparse_grad: _bool = False) -> Tensor: + r""" + gather(dim, index) -> Tensor + + See :func:`torch.gather` + """ + ... + @overload + def gather(self, dim: Union[str, ellipsis, None], index: Tensor, *, sparse_grad: _bool = False) -> Tensor: + r""" + gather(dim, index) -> Tensor + + See :func:`torch.gather` + """ + ... + def gcd(self, other: Tensor) -> Tensor: + r""" + gcd(other) -> Tensor + + See :func:`torch.gcd` + """ + ... + def gcd_(self, other: Tensor) -> Tensor: + r""" + gcd_(other) -> Tensor + + In-place version of :meth:`~Tensor.gcd` + """ + ... + @overload + def ge(self, other: Tensor) -> Tensor: + r""" + ge(other) -> Tensor + + See :func:`torch.ge`. + """ + ... + @overload + def ge(self, other: Union[Number, _complex]) -> Tensor: + r""" + ge(other) -> Tensor + + See :func:`torch.ge`. + """ + ... + @overload + def ge_(self, other: Tensor) -> Tensor: + r""" + ge_(other) -> Tensor + + In-place version of :meth:`~Tensor.ge`. + """ + ... + @overload + def ge_(self, other: Union[Number, _complex]) -> Tensor: + r""" + ge_(other) -> Tensor + + In-place version of :meth:`~Tensor.ge`. + """ + ... + def geometric_(self, p: _float, *, generator: Optional[Generator] = None) -> Tensor: + r""" + geometric_(p, *, generator=None) -> Tensor + + Fills :attr:`self` tensor with elements drawn from the geometric distribution: + + .. math:: + + P(X=k) = (1 - p)^{k - 1} p, k = 1, 2, ... + + .. note:: + :func:`torch.Tensor.geometric_` `k`-th trial is the first success hence draws samples in :math:`\{1, 2, \ldots\}`, whereas + :func:`torch.distributions.geometric.Geometric` :math:`(k+1)`-th trial is the first success + hence draws samples in :math:`\{0, 1, \ldots\}`. + """ + ... + def geqrf(self) -> torch.return_types.geqrf: + r""" + geqrf() -> (Tensor, Tensor) + + See :func:`torch.geqrf` + """ + ... + def ger(self, vec2: Tensor) -> Tensor: + r""" + ger(vec2) -> Tensor + + See :func:`torch.ger` + """ + ... + def get_device(self) -> _int: + r""" + get_device() -> Device ordinal (Integer) + + For CUDA tensors, this function returns the device ordinal of the GPU on which the tensor resides. + For CPU tensors, this function returns `-1`. + + Example:: + + >>> x = torch.randn(3, 4, 5, device='cuda:0') + >>> x.get_device() + 0 + >>> x.cpu().get_device() + -1 + """ + ... + @overload + def greater(self, other: Tensor) -> Tensor: + r""" + greater(other) -> Tensor + + See :func:`torch.greater`. + """ + ... + @overload + def greater(self, other: Union[Number, _complex]) -> Tensor: + r""" + greater(other) -> Tensor + + See :func:`torch.greater`. + """ + ... + @overload + def greater_(self, other: Tensor) -> Tensor: + r""" + greater_(other) -> Tensor + + In-place version of :meth:`~Tensor.greater`. + """ + ... + @overload + def greater_(self, other: Union[Number, _complex]) -> Tensor: + r""" + greater_(other) -> Tensor + + In-place version of :meth:`~Tensor.greater`. + """ + ... + @overload + def greater_equal(self, other: Tensor) -> Tensor: + r""" + greater_equal(other) -> Tensor + + See :func:`torch.greater_equal`. + """ + ... + @overload + def greater_equal(self, other: Union[Number, _complex]) -> Tensor: + r""" + greater_equal(other) -> Tensor + + See :func:`torch.greater_equal`. + """ + ... + @overload + def greater_equal_(self, other: Tensor) -> Tensor: + r""" + greater_equal_(other) -> Tensor + + In-place version of :meth:`~Tensor.greater_equal`. + """ + ... + @overload + def greater_equal_(self, other: Union[Number, _complex]) -> Tensor: + r""" + greater_equal_(other) -> Tensor + + In-place version of :meth:`~Tensor.greater_equal`. + """ + ... + @overload + def gt(self, other: Tensor) -> Tensor: + r""" + gt(other) -> Tensor + + See :func:`torch.gt`. + """ + ... + @overload + def gt(self, other: Union[Number, _complex]) -> Tensor: + r""" + gt(other) -> Tensor + + See :func:`torch.gt`. + """ + ... + @overload + def gt_(self, other: Tensor) -> Tensor: + r""" + gt_(other) -> Tensor + + In-place version of :meth:`~Tensor.gt`. + """ + ... + @overload + def gt_(self, other: Union[Number, _complex]) -> Tensor: + r""" + gt_(other) -> Tensor + + In-place version of :meth:`~Tensor.gt`. + """ + ... + def half(self) -> Tensor: + r""" + half(memory_format=torch.preserve_format) -> Tensor + + ``self.half()`` is equivalent to ``self.to(torch.float16)``. See :func:`to`. + + Args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... + def hardshrink(self, lambd: Union[Number, _complex] = 0.5) -> Tensor: + r""" + hardshrink(lambd=0.5) -> Tensor + + See :func:`torch.nn.functional.hardshrink` + """ + ... + def has_names(self) -> _bool: + r""" + Is ``True`` if any of this tensor's dimensions are named. Otherwise, is ``False``. + """ + ... + def heaviside(self, values: Tensor) -> Tensor: + r""" + heaviside(values) -> Tensor + + See :func:`torch.heaviside` + """ + ... + def heaviside_(self, values: Tensor) -> Tensor: + r""" + heaviside_(values) -> Tensor + + In-place version of :meth:`~Tensor.heaviside` + """ + ... + def histc(self, bins: _int = 100, min: Union[Number, _complex] = 0, max: Union[Number, _complex] = 0) -> Tensor: + r""" + histc(bins=100, min=0, max=0) -> Tensor + + See :func:`torch.histc` + """ + ... + @overload + def histogram(self, bins: Tensor, *, weight: Optional[Tensor] = None, density: _bool = False) -> torch.return_types.histogram: + r""" + histogram(input, bins, *, range=None, weight=None, density=False) -> (Tensor, Tensor) + + See :func:`torch.histogram` + """ + ... + @overload + def histogram(self, bins: _int = 100, *, range: Optional[Sequence[_float]] = None, weight: Optional[Tensor] = None, density: _bool = False) -> torch.return_types.histogram: + r""" + histogram(input, bins, *, range=None, weight=None, density=False) -> (Tensor, Tensor) + + See :func:`torch.histogram` + """ + ... + @overload + def hsplit(self, sections: _int) -> Tuple[Tensor, ...]: + r""" + hsplit(split_size_or_sections) -> List of Tensors + + See :func:`torch.hsplit` + """ + ... + @overload + def hsplit(self, indices: _size) -> Tuple[Tensor, ...]: + r""" + hsplit(split_size_or_sections) -> List of Tensors + + See :func:`torch.hsplit` + """ + ... + @overload + def hsplit(self, *indices: _int) -> Tuple[Tensor, ...]: + r""" + hsplit(split_size_or_sections) -> List of Tensors + + See :func:`torch.hsplit` + """ + ... + def hypot(self, other: Tensor) -> Tensor: + r""" + hypot(other) -> Tensor + + See :func:`torch.hypot` + """ + ... + def hypot_(self, other: Tensor) -> Tensor: + r""" + hypot_(other) -> Tensor + + In-place version of :meth:`~Tensor.hypot` + """ + ... + def i0(self) -> Tensor: + r""" + i0() -> Tensor + + See :func:`torch.i0` + """ + ... + def i0_(self) -> Tensor: + r""" + i0_() -> Tensor + + In-place version of :meth:`~Tensor.i0` + """ + ... + def igamma(self, other: Tensor) -> Tensor: + r""" + igamma(other) -> Tensor + + See :func:`torch.igamma` + """ + ... + def igamma_(self, other: Tensor) -> Tensor: + r""" + igamma_(other) -> Tensor + + In-place version of :meth:`~Tensor.igamma` + """ + ... + def igammac(self, other: Tensor) -> Tensor: + r""" + igammac(other) -> Tensor + See :func:`torch.igammac` + """ + ... + def igammac_(self, other: Tensor) -> Tensor: + r""" + igammac_(other) -> Tensor + In-place version of :meth:`~Tensor.igammac` + """ + ... + @overload + def index_add(self, dim: _int, index: Tensor, source: Tensor, *, alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + index_add(dim, index, source, *, alpha=1) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.index_add_`. + """ + ... + @overload + def index_add(self, dim: Union[str, ellipsis, None], index: Tensor, source: Tensor, *, alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + index_add(dim, index, source, *, alpha=1) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.index_add_`. + """ + ... + def index_add_(self, dim: _int, index: Tensor, source: Tensor, *, alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + index_add_(dim, index, source, *, alpha=1) -> Tensor + + Accumulate the elements of :attr:`alpha` times ``source`` into the :attr:`self` + tensor by adding to the indices in the order given in :attr:`index`. For example, + if ``dim == 0``, ``index[i] == j``, and ``alpha=-1``, then the ``i``\ th row of + ``source`` is subtracted from the ``j``\ th row of :attr:`self`. + + The :attr:`dim`\ th dimension of ``source`` must have the same size as the + length of :attr:`index` (which must be a vector), and all other dimensions must + match :attr:`self`, or an error will be raised. + + For a 3-D tensor the output is given as:: + + self[index[i], :, :] += alpha * src[i, :, :] # if dim == 0 + self[:, index[i], :] += alpha * src[:, i, :] # if dim == 1 + self[:, :, index[i]] += alpha * src[:, :, i] # if dim == 2 + + Note: + This operation may behave nondeterministically when given tensors on a CUDA device. See :doc:`/notes/randomness` for more information. + + Args: + dim (int): dimension along which to index + index (Tensor): indices of ``source`` to select from, + should have dtype either `torch.int64` or `torch.int32` + source (Tensor): the tensor containing values to add + + Keyword args: + alpha (Number): the scalar multiplier for ``source`` + + Example:: + + >>> x = torch.ones(5, 3) + >>> t = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float) + >>> index = torch.tensor([0, 4, 2]) + >>> x.index_add_(0, index, t) + tensor([[ 2., 3., 4.], + [ 1., 1., 1.], + [ 8., 9., 10.], + [ 1., 1., 1.], + [ 5., 6., 7.]]) + >>> x.index_add_(0, index, t, alpha=-1) + tensor([[ 1., 1., 1.], + [ 1., 1., 1.], + [ 1., 1., 1.], + [ 1., 1., 1.], + [ 1., 1., 1.]]) + """ + ... + @overload + def index_copy(self, dim: _int, index: Tensor, source: Tensor) -> Tensor: + r""" + index_copy(dim, index, tensor2) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.index_copy_`. + """ + ... + @overload + def index_copy(self, dim: Union[str, ellipsis, None], index: Tensor, source: Tensor) -> Tensor: + r""" + index_copy(dim, index, tensor2) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.index_copy_`. + """ + ... + @overload + def index_copy_(self, dim: _int, index: Tensor, source: Tensor) -> Tensor: + r""" + index_copy_(dim, index, tensor) -> Tensor + + Copies the elements of :attr:`tensor` into the :attr:`self` tensor by selecting + the indices in the order given in :attr:`index`. For example, if ``dim == 0`` + and ``index[i] == j``, then the ``i``\ th row of :attr:`tensor` is copied to the + ``j``\ th row of :attr:`self`. + + The :attr:`dim`\ th dimension of :attr:`tensor` must have the same size as the + length of :attr:`index` (which must be a vector), and all other dimensions must + match :attr:`self`, or an error will be raised. + + .. note:: + If :attr:`index` contains duplicate entries, multiple elements from + :attr:`tensor` will be copied to the same index of :attr:`self`. The result + is nondeterministic since it depends on which copy occurs last. + + Args: + dim (int): dimension along which to index + index (LongTensor): indices of :attr:`tensor` to select from + tensor (Tensor): the tensor containing values to copy + + Example:: + + >>> x = torch.zeros(5, 3) + >>> t = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float) + >>> index = torch.tensor([0, 4, 2]) + >>> x.index_copy_(0, index, t) + tensor([[ 1., 2., 3.], + [ 0., 0., 0.], + [ 7., 8., 9.], + [ 0., 0., 0.], + [ 4., 5., 6.]]) + """ + ... + @overload + def index_copy_(self, dim: Union[str, ellipsis, None], index: Tensor, source: Tensor) -> Tensor: + r""" + index_copy_(dim, index, tensor) -> Tensor + + Copies the elements of :attr:`tensor` into the :attr:`self` tensor by selecting + the indices in the order given in :attr:`index`. For example, if ``dim == 0`` + and ``index[i] == j``, then the ``i``\ th row of :attr:`tensor` is copied to the + ``j``\ th row of :attr:`self`. + + The :attr:`dim`\ th dimension of :attr:`tensor` must have the same size as the + length of :attr:`index` (which must be a vector), and all other dimensions must + match :attr:`self`, or an error will be raised. + + .. note:: + If :attr:`index` contains duplicate entries, multiple elements from + :attr:`tensor` will be copied to the same index of :attr:`self`. The result + is nondeterministic since it depends on which copy occurs last. + + Args: + dim (int): dimension along which to index + index (LongTensor): indices of :attr:`tensor` to select from + tensor (Tensor): the tensor containing values to copy + + Example:: + + >>> x = torch.zeros(5, 3) + >>> t = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float) + >>> index = torch.tensor([0, 4, 2]) + >>> x.index_copy_(0, index, t) + tensor([[ 1., 2., 3.], + [ 0., 0., 0.], + [ 7., 8., 9.], + [ 0., 0., 0.], + [ 4., 5., 6.]]) + """ + ... + @overload + def index_fill(self, dim: _int, index: Tensor, value: Tensor) -> Tensor: + r""" + index_fill(dim, index, value) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.index_fill_`. + """ + ... + @overload + def index_fill(self, dim: Union[str, ellipsis, None], index: Tensor, value: Tensor) -> Tensor: + r""" + index_fill(dim, index, value) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.index_fill_`. + """ + ... + @overload + def index_fill(self, dim: _int, index: Tensor, value: Union[Number, _complex]) -> Tensor: + r""" + index_fill(dim, index, value) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.index_fill_`. + """ + ... + @overload + def index_fill(self, dim: Union[str, ellipsis, None], index: Tensor, value: Union[Number, _complex]) -> Tensor: + r""" + index_fill(dim, index, value) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.index_fill_`. + """ + ... + @overload + def index_fill_(self, dim: _int, index: Tensor, value: Tensor) -> Tensor: + r""" + index_fill_(dim, index, value) -> Tensor + + Fills the elements of the :attr:`self` tensor with value :attr:`value` by + selecting the indices in the order given in :attr:`index`. + + Args: + dim (int): dimension along which to index + index (LongTensor): indices of :attr:`self` tensor to fill in + value (float): the value to fill with + + Example:: + >>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float) + >>> index = torch.tensor([0, 2]) + >>> x.index_fill_(1, index, -1) + tensor([[-1., 2., -1.], + [-1., 5., -1.], + [-1., 8., -1.]]) + """ + ... + @overload + def index_fill_(self, dim: Union[str, ellipsis, None], index: Tensor, value: Tensor) -> Tensor: + r""" + index_fill_(dim, index, value) -> Tensor + + Fills the elements of the :attr:`self` tensor with value :attr:`value` by + selecting the indices in the order given in :attr:`index`. + + Args: + dim (int): dimension along which to index + index (LongTensor): indices of :attr:`self` tensor to fill in + value (float): the value to fill with + + Example:: + >>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float) + >>> index = torch.tensor([0, 2]) + >>> x.index_fill_(1, index, -1) + tensor([[-1., 2., -1.], + [-1., 5., -1.], + [-1., 8., -1.]]) + """ + ... + @overload + def index_fill_(self, dim: _int, index: Tensor, value: Union[Number, _complex]) -> Tensor: + r""" + index_fill_(dim, index, value) -> Tensor + + Fills the elements of the :attr:`self` tensor with value :attr:`value` by + selecting the indices in the order given in :attr:`index`. + + Args: + dim (int): dimension along which to index + index (LongTensor): indices of :attr:`self` tensor to fill in + value (float): the value to fill with + + Example:: + >>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float) + >>> index = torch.tensor([0, 2]) + >>> x.index_fill_(1, index, -1) + tensor([[-1., 2., -1.], + [-1., 5., -1.], + [-1., 8., -1.]]) + """ + ... + @overload + def index_fill_(self, dim: Union[str, ellipsis, None], index: Tensor, value: Union[Number, _complex]) -> Tensor: + r""" + index_fill_(dim, index, value) -> Tensor + + Fills the elements of the :attr:`self` tensor with value :attr:`value` by + selecting the indices in the order given in :attr:`index`. + + Args: + dim (int): dimension along which to index + index (LongTensor): indices of :attr:`self` tensor to fill in + value (float): the value to fill with + + Example:: + >>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float) + >>> index = torch.tensor([0, 2]) + >>> x.index_fill_(1, index, -1) + tensor([[-1., 2., -1.], + [-1., 5., -1.], + [-1., 8., -1.]]) + """ + ... + def index_put(self, indices: Optional[Union[Tuple[Tensor, ...], List[Tensor]]], values: Tensor, accumulate: _bool = False) -> Tensor: + r""" + index_put(indices, values, accumulate=False) -> Tensor + + Out-place version of :meth:`~Tensor.index_put_`. + """ + ... + def index_put_(self, indices: Optional[Union[Tuple[Tensor, ...], List[Tensor]]], values: Tensor, accumulate: _bool = False) -> Tensor: + r""" + index_put_(indices, values, accumulate=False) -> Tensor + + Puts values from the tensor :attr:`values` into the tensor :attr:`self` using + the indices specified in :attr:`indices` (which is a tuple of Tensors). The + expression ``tensor.index_put_(indices, values)`` is equivalent to + ``tensor[indices] = values``. Returns :attr:`self`. + + If :attr:`accumulate` is ``True``, the elements in :attr:`values` are added to + :attr:`self`. If accumulate is ``False``, the behavior is undefined if indices + contain duplicate elements. + + Args: + indices (tuple of LongTensor): tensors used to index into `self`. + values (Tensor): tensor of same dtype as `self`. + accumulate (bool): whether to accumulate into self + """ + ... + def index_reduce(self, dim: _int, index: Tensor, source: Tensor, reduce: str, *, include_self: _bool = True) -> Tensor: ... + def index_reduce_(self, dim: _int, index: Tensor, source: Tensor, reduce: str, *, include_self: _bool = True) -> Tensor: + r""" + index_reduce_(dim, index, source, reduce, *, include_self=True) -> Tensor + + Accumulate the elements of ``source`` into the :attr:`self` + tensor by accumulating to the indices in the order given in :attr:`index` + using the reduction given by the ``reduce`` argument. For example, if ``dim == 0``, + ``index[i] == j``, ``reduce == prod`` and ``include_self == True`` then the ``i``\ th + row of ``source`` is multiplied by the ``j``\ th row of :attr:`self`. If + :obj:`include_self="True"`, the values in the :attr:`self` tensor are included + in the reduction, otherwise, rows in the :attr:`self` tensor that are accumulated + to are treated as if they were filled with the reduction identites. + + The :attr:`dim`\ th dimension of ``source`` must have the same size as the + length of :attr:`index` (which must be a vector), and all other dimensions must + match :attr:`self`, or an error will be raised. + + For a 3-D tensor with :obj:`reduce="prod"` and :obj:`include_self=True` the + output is given as:: + + self[index[i], :, :] *= src[i, :, :] # if dim == 0 + self[:, index[i], :] *= src[:, i, :] # if dim == 1 + self[:, :, index[i]] *= src[:, :, i] # if dim == 2 + + Note: + This operation may behave nondeterministically when given tensors on a CUDA device. See :doc:`/notes/randomness` for more information. + + .. note:: + + This function only supports floating point tensors. + + .. warning:: + + This function is in beta and may change in the near future. + + Args: + dim (int): dimension along which to index + index (Tensor): indices of ``source`` to select from, + should have dtype either `torch.int64` or `torch.int32` + source (FloatTensor): the tensor containing values to accumulate + reduce (str): the reduction operation to apply + (:obj:`"prod"`, :obj:`"mean"`, :obj:`"amax"`, :obj:`"amin"`) + + Keyword args: + include_self (bool): whether the elements from the ``self`` tensor are + included in the reduction + + Example:: + + >>> x = torch.empty(5, 3).fill_(2) + >>> t = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]], dtype=torch.float) + >>> index = torch.tensor([0, 4, 2, 0]) + >>> x.index_reduce_(0, index, t, 'prod') + tensor([[20., 44., 72.], + [ 2., 2., 2.], + [14., 16., 18.], + [ 2., 2., 2.], + [ 8., 10., 12.]]) + >>> x = torch.empty(5, 3).fill_(2) + >>> x.index_reduce_(0, index, t, 'prod', include_self=False) + tensor([[10., 22., 36.], + [ 2., 2., 2.], + [ 7., 8., 9.], + [ 2., 2., 2.], + [ 4., 5., 6.]]) + """ + ... + @overload + def index_select(self, dim: _int, index: Tensor) -> Tensor: + r""" + index_select(dim, index) -> Tensor + + See :func:`torch.index_select` + """ + ... + @overload + def index_select(self, dim: Union[str, ellipsis, None], index: Tensor) -> Tensor: + r""" + index_select(dim, index) -> Tensor + + See :func:`torch.index_select` + """ + ... + def indices(self) -> Tensor: + r""" + indices() -> Tensor + + Return the indices tensor of a :ref:`sparse COO tensor `. + + .. warning:: + Throws an error if :attr:`self` is not a sparse COO tensor. + + See also :meth:`Tensor.values`. + + .. note:: + This method can only be called on a coalesced sparse tensor. See + :meth:`Tensor.coalesce` for details. + """ + ... + def inner(self, other: Tensor) -> Tensor: + r""" + inner(other) -> Tensor + + See :func:`torch.inner`. + """ + ... + def int(self) -> Tensor: + r""" + int(memory_format=torch.preserve_format) -> Tensor + + ``self.int()`` is equivalent to ``self.to(torch.int32)``. See :func:`to`. + + Args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... + def int_repr(self) -> Tensor: + r""" + int_repr() -> Tensor + + Given a quantized Tensor, + ``self.int_repr()`` returns a CPU Tensor with uint8_t as data type that stores the + underlying uint8_t values of the given Tensor. + """ + ... + def inverse(self) -> Tensor: + r""" + inverse() -> Tensor + + See :func:`torch.inverse` + """ + ... + def is_coalesced(self) -> _bool: + r""" + is_coalesced() -> bool + + Returns ``True`` if :attr:`self` is a :ref:`sparse COO tensor + ` that is coalesced, ``False`` otherwise. + + .. warning:: + Throws an error if :attr:`self` is not a sparse COO tensor. + + See :meth:`coalesce` and :ref:`uncoalesced tensors `. + """ + ... + def is_complex(self) -> _bool: + r""" + is_complex() -> bool + + Returns True if the data type of :attr:`self` is a complex data type. + """ + ... + def is_conj(self) -> _bool: + r""" + is_conj() -> bool + + Returns True if the conjugate bit of :attr:`self` is set to true. + """ + ... + def is_contiguous(self, memory_format=torch.contiguous_format) -> _bool: + r""" + is_contiguous(memory_format=torch.contiguous_format) -> bool + + Returns True if :attr:`self` tensor is contiguous in memory in the order specified + by memory format. + + Args: + memory_format (:class:`torch.memory_format`, optional): Specifies memory allocation + order. Default: ``torch.contiguous_format``. + """ + ... + is_cpu: _bool + r"""Is ``True`` if the Tensor is stored on the CPU, ``False`` otherwise.""" + is_cuda: _bool + r"""Is ``True`` if the Tensor is stored on the GPU, ``False`` otherwise.""" + def is_distributed(self) -> _bool: ... + def is_floating_point(self) -> _bool: + r""" + is_floating_point() -> bool + + Returns True if the data type of :attr:`self` is a floating point data type. + """ + ... + def is_inference(self) -> _bool: + r""" + is_inference() -> bool + + See :func:`torch.is_inference` + """ + ... + is_ipu: _bool + r"""Is ``True`` if the Tensor is stored on the IPU, ``False`` otherwise.""" + is_leaf: _bool + r"""All Tensors that have :attr:`requires_grad` which is ``False`` will be leaf Tensors by convention. + + For Tensors that have :attr:`requires_grad` which is ``True``, they will be leaf Tensors if they were + created by the user. This means that they are not the result of an operation and so + :attr:`grad_fn` is None. + + Only leaf Tensors will have their :attr:`grad` populated during a call to :func:`backward`. + To get :attr:`grad` populated for non-leaf Tensors, you can use :func:`retain_grad`. + + Example:: + + >>> a = torch.rand(10, requires_grad=True) + >>> a.is_leaf + True + >>> b = torch.rand(10, requires_grad=True).cuda() + >>> b.is_leaf + False + # b was created by the operation that cast a cpu Tensor into a cuda Tensor + >>> c = torch.rand(10, requires_grad=True) + 2 + >>> c.is_leaf + False + # c was created by the addition operation + >>> d = torch.rand(10).cuda() + >>> d.is_leaf + True + # d does not require gradients and so has no operation creating it (that is tracked by the autograd engine) + >>> e = torch.rand(10).cuda().requires_grad_() + >>> e.is_leaf + True + # e requires gradients and has no operations creating it + >>> f = torch.rand(10, requires_grad=True, device="cuda") + >>> f.is_leaf + True + # f requires grad, has no operation creating it""" + is_maia: _bool + is_meta: _bool + r"""Is ``True`` if the Tensor is a meta tensor, ``False`` otherwise. Meta tensors + are like normal tensors, but they carry no data.""" + is_mkldnn: _bool + is_mps: _bool + r"""Is ``True`` if the Tensor is stored on the MPS device, ``False`` otherwise.""" + is_mtia: _bool + def is_neg(self) -> _bool: + r""" + is_neg() -> bool + + Returns True if the negative bit of :attr:`self` is set to true. + """ + ... + is_nested: _bool + def is_nonzero(self) -> _bool: ... + def is_pinned(self, device: Optional[Optional[DeviceLikeType]] = None) -> _bool: + r""" + Returns true if this tensor resides in pinned memory. + """ + ... + is_quantized: _bool + r"""Is ``True`` if the Tensor is quantized, ``False`` otherwise.""" + def is_same_size(self, other: Tensor) -> _bool: ... + def is_set_to(self, tensor: Tensor) -> _bool: + r""" + is_set_to(tensor) -> bool + + Returns True if both tensors are pointing to the exact same memory (same + storage, offset, size and stride). + """ + ... + def is_signed(self) -> _bool: + r""" + is_signed() -> bool + + Returns True if the data type of :attr:`self` is a signed data type. + """ + ... + is_sparse: _bool + r"""Is ``True`` if the Tensor uses sparse COO storage layout, ``False`` otherwise.""" + is_sparse_csr: _bool + r"""Is ``True`` if the Tensor uses sparse CSR storage layout, ``False`` otherwise.""" + is_vulkan: _bool + def isclose(self, other: Tensor, rtol: _float = 1e-05, atol: _float = 1e-08, equal_nan: _bool = False) -> Tensor: + r""" + isclose(other, rtol=1e-05, atol=1e-08, equal_nan=False) -> Tensor + + See :func:`torch.isclose` + """ + ... + def isfinite(self) -> Tensor: + r""" + isfinite() -> Tensor + + See :func:`torch.isfinite` + """ + ... + def isinf(self) -> Tensor: + r""" + isinf() -> Tensor + + See :func:`torch.isinf` + """ + ... + def isnan(self) -> Tensor: + r""" + isnan() -> Tensor + + See :func:`torch.isnan` + """ + ... + def isneginf(self) -> Tensor: + r""" + isneginf() -> Tensor + + See :func:`torch.isneginf` + """ + ... + def isposinf(self) -> Tensor: + r""" + isposinf() -> Tensor + + See :func:`torch.isposinf` + """ + ... + def isreal(self) -> Tensor: + r""" + isreal() -> Tensor + + See :func:`torch.isreal` + """ + ... + def istft(self, n_fft: _int, hop_length: Optional[_int] = None, win_length: Optional[_int] = None, window: Optional[Tensor] = None, center: _bool = True, normalized: _bool = False, onesided: Optional[_bool] = None, length: Optional[_int] = None, return_complex: _bool = False) -> Tensor: + r""" + istft(n_fft, hop_length=None, win_length=None, window=None, + center=True, normalized=False, onesided=True, length=None) -> Tensor + + See :func:`torch.istft` + """ + ... + def item(self) -> Number: + r""" + item() -> number + + Returns the value of this tensor as a standard Python number. This only works + for tensors with one element. For other cases, see :meth:`~Tensor.tolist`. + + This operation is not differentiable. + + Example:: + + >>> x = torch.tensor([1.0]) + >>> x.item() + 1.0 + """ + ... + def kron(self, other: Tensor) -> Tensor: + r""" + kron(other) -> Tensor + + See :func:`torch.kron` + """ + ... + @overload + def kthvalue(self, k: _int, dim: _int = -1, keepdim: _bool = False) -> torch.return_types.kthvalue: + r""" + kthvalue(k, dim=None, keepdim=False) -> (Tensor, LongTensor) + + See :func:`torch.kthvalue` + """ + ... + @overload + def kthvalue(self, k: _int, dim: Union[str, ellipsis, None], keepdim: _bool = False) -> torch.return_types.kthvalue: + r""" + kthvalue(k, dim=None, keepdim=False) -> (Tensor, LongTensor) + + See :func:`torch.kthvalue` + """ + ... + def lcm(self, other: Tensor) -> Tensor: + r""" + lcm(other) -> Tensor + + See :func:`torch.lcm` + """ + ... + def lcm_(self, other: Tensor) -> Tensor: + r""" + lcm_(other) -> Tensor + + In-place version of :meth:`~Tensor.lcm` + """ + ... + def ldexp(self, other: Tensor) -> Tensor: + r""" + ldexp(other) -> Tensor + + See :func:`torch.ldexp` + """ + ... + def ldexp_(self, other: Tensor) -> Tensor: + r""" + ldexp_(other) -> Tensor + + In-place version of :meth:`~Tensor.ldexp` + """ + ... + @overload + def le(self, other: Tensor) -> Tensor: + r""" + le(other) -> Tensor + + See :func:`torch.le`. + """ + ... + @overload + def le(self, other: Union[Number, _complex]) -> Tensor: + r""" + le(other) -> Tensor + + See :func:`torch.le`. + """ + ... + @overload + def le_(self, other: Tensor) -> Tensor: + r""" + le_(other) -> Tensor + + In-place version of :meth:`~Tensor.le`. + """ + ... + @overload + def le_(self, other: Union[Number, _complex]) -> Tensor: + r""" + le_(other) -> Tensor + + In-place version of :meth:`~Tensor.le`. + """ + ... + @overload + def lerp(self, end: Tensor, weight: Tensor) -> Tensor: + r""" + lerp(end, weight) -> Tensor + + See :func:`torch.lerp` + """ + ... + @overload + def lerp(self, end: Tensor, weight: Union[Number, _complex]) -> Tensor: + r""" + lerp(end, weight) -> Tensor + + See :func:`torch.lerp` + """ + ... + @overload + def lerp_(self, end: Tensor, weight: Tensor) -> Tensor: + r""" + lerp_(end, weight) -> Tensor + + In-place version of :meth:`~Tensor.lerp` + """ + ... + @overload + def lerp_(self, end: Tensor, weight: Union[Number, _complex]) -> Tensor: + r""" + lerp_(end, weight) -> Tensor + + In-place version of :meth:`~Tensor.lerp` + """ + ... + @overload + def less(self, other: Tensor) -> Tensor: + r""" + lt(other) -> Tensor + + See :func:`torch.less`. + """ + ... + @overload + def less(self, other: Union[Number, _complex]) -> Tensor: + r""" + lt(other) -> Tensor + + See :func:`torch.less`. + """ + ... + @overload + def less_(self, other: Tensor) -> Tensor: + r""" + less_(other) -> Tensor + + In-place version of :meth:`~Tensor.less`. + """ + ... + @overload + def less_(self, other: Union[Number, _complex]) -> Tensor: + r""" + less_(other) -> Tensor + + In-place version of :meth:`~Tensor.less`. + """ + ... + @overload + def less_equal(self, other: Tensor) -> Tensor: + r""" + less_equal(other) -> Tensor + + See :func:`torch.less_equal`. + """ + ... + @overload + def less_equal(self, other: Union[Number, _complex]) -> Tensor: + r""" + less_equal(other) -> Tensor + + See :func:`torch.less_equal`. + """ + ... + @overload + def less_equal_(self, other: Tensor) -> Tensor: + r""" + less_equal_(other) -> Tensor + + In-place version of :meth:`~Tensor.less_equal`. + """ + ... + @overload + def less_equal_(self, other: Union[Number, _complex]) -> Tensor: + r""" + less_equal_(other) -> Tensor + + In-place version of :meth:`~Tensor.less_equal`. + """ + ... + def lgamma(self) -> Tensor: + r""" + lgamma() -> Tensor + + See :func:`torch.lgamma` + """ + ... + def lgamma_(self) -> Tensor: + r""" + lgamma_() -> Tensor + + In-place version of :meth:`~Tensor.lgamma` + """ + ... + def log(self) -> Tensor: + r""" + log() -> Tensor + + See :func:`torch.log` + """ + ... + def log10(self) -> Tensor: + r""" + log10() -> Tensor + + See :func:`torch.log10` + """ + ... + def log10_(self) -> Tensor: + r""" + log10_() -> Tensor + + In-place version of :meth:`~Tensor.log10` + """ + ... + def log1p(self) -> Tensor: + r""" + log1p() -> Tensor + + See :func:`torch.log1p` + """ + ... + def log1p_(self) -> Tensor: + r""" + log1p_() -> Tensor + + In-place version of :meth:`~Tensor.log1p` + """ + ... + def log2(self) -> Tensor: + r""" + log2() -> Tensor + + See :func:`torch.log2` + """ + ... + def log2_(self) -> Tensor: + r""" + log2_() -> Tensor + + In-place version of :meth:`~Tensor.log2` + """ + ... + def log_(self) -> Tensor: + r""" + log_() -> Tensor + + In-place version of :meth:`~Tensor.log` + """ + ... + def log_normal_(self, mean: _float = 1, std: _float = 2, *, generator: Optional[Generator] = None) -> Tensor: + r""" + log_normal_(mean=1, std=2, *, generator=None) + + Fills :attr:`self` tensor with numbers samples from the log-normal distribution + parameterized by the given mean :math:`\mu` and standard deviation + :math:`\sigma`. Note that :attr:`mean` and :attr:`std` are the mean and + standard deviation of the underlying normal distribution, and not of the + returned distribution: + + .. math:: + + f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}} + """ + ... + @overload + def log_softmax(self, dim: _int, dtype: Optional[_dtype] = None) -> Tensor: ... + @overload + def log_softmax(self, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype] = None) -> Tensor: ... + def logaddexp(self, other: Tensor) -> Tensor: + r""" + logaddexp(other) -> Tensor + + See :func:`torch.logaddexp` + """ + ... + def logaddexp2(self, other: Tensor) -> Tensor: + r""" + logaddexp2(other) -> Tensor + + See :func:`torch.logaddexp2` + """ + ... + @overload + def logcumsumexp(self, dim: _int) -> Tensor: + r""" + logcumsumexp(dim) -> Tensor + + See :func:`torch.logcumsumexp` + """ + ... + @overload + def logcumsumexp(self, dim: Union[str, ellipsis, None]) -> Tensor: + r""" + logcumsumexp(dim) -> Tensor + + See :func:`torch.logcumsumexp` + """ + ... + def logdet(self) -> Tensor: + r""" + logdet() -> Tensor + + See :func:`torch.logdet` + """ + ... + def logical_and(self, other: Tensor) -> Tensor: + r""" + logical_and() -> Tensor + + See :func:`torch.logical_and` + """ + ... + def logical_and_(self, other: Tensor) -> Tensor: + r""" + logical_and_() -> Tensor + + In-place version of :meth:`~Tensor.logical_and` + """ + ... + def logical_not(self) -> Tensor: + r""" + logical_not() -> Tensor + + See :func:`torch.logical_not` + """ + ... + def logical_not_(self) -> Tensor: + r""" + logical_not_() -> Tensor + + In-place version of :meth:`~Tensor.logical_not` + """ + ... + def logical_or(self, other: Tensor) -> Tensor: + r""" + logical_or() -> Tensor + + See :func:`torch.logical_or` + """ + ... + def logical_or_(self, other: Tensor) -> Tensor: + r""" + logical_or_() -> Tensor + + In-place version of :meth:`~Tensor.logical_or` + """ + ... + def logical_xor(self, other: Tensor) -> Tensor: + r""" + logical_xor() -> Tensor + + See :func:`torch.logical_xor` + """ + ... + def logical_xor_(self, other: Tensor) -> Tensor: + r""" + logical_xor_() -> Tensor + + In-place version of :meth:`~Tensor.logical_xor` + """ + ... + def logit(self, eps: Optional[_float] = None) -> Tensor: + r""" + logit() -> Tensor + + See :func:`torch.logit` + """ + ... + def logit_(self, eps: Optional[_float] = None) -> Tensor: + r""" + logit_() -> Tensor + + In-place version of :meth:`~Tensor.logit` + """ + ... + @overload + def logsumexp(self, dim: Union[_int, _size], keepdim: _bool = False) -> Tensor: + r""" + logsumexp(dim, keepdim=False) -> Tensor + + See :func:`torch.logsumexp` + """ + ... + @overload + def logsumexp(self, dim: Sequence[Union[str, ellipsis, None]], keepdim: _bool = False) -> Tensor: + r""" + logsumexp(dim, keepdim=False) -> Tensor + + See :func:`torch.logsumexp` + """ + ... + def long(self) -> Tensor: + r""" + long(memory_format=torch.preserve_format) -> Tensor + + ``self.long()`` is equivalent to ``self.to(torch.int64)``. See :func:`to`. + + Args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... + @overload + def lt(self, other: Tensor) -> Tensor: + r""" + lt(other) -> Tensor + + See :func:`torch.lt`. + """ + ... + @overload + def lt(self, other: Union[Number, _complex]) -> Tensor: + r""" + lt(other) -> Tensor + + See :func:`torch.lt`. + """ + ... + @overload + def lt_(self, other: Tensor) -> Tensor: + r""" + lt_(other) -> Tensor + + In-place version of :meth:`~Tensor.lt`. + """ + ... + @overload + def lt_(self, other: Union[Number, _complex]) -> Tensor: + r""" + lt_(other) -> Tensor + + In-place version of :meth:`~Tensor.lt`. + """ + ... + def lu_solve(self, LU_data: Tensor, LU_pivots: Tensor) -> Tensor: + r""" + lu_solve(LU_data, LU_pivots) -> Tensor + + See :func:`torch.lu_solve` + """ + ... + def map2_(self, x: Tensor, y: Tensor, callable: Callable) -> Tensor: ... + def map_(self, tensor: Tensor, callable: Callable) -> Tensor: + r""" + map_(tensor, callable) + + Applies :attr:`callable` for each element in :attr:`self` tensor and the given + :attr:`tensor` and stores the results in :attr:`self` tensor. :attr:`self` tensor and + the given :attr:`tensor` must be :ref:`broadcastable `. + + The :attr:`callable` should have the signature:: + + def callable(a, b) -> number + """ + ... + @overload + def masked_fill(self, mask: Tensor, value: Tensor) -> Tensor: + r""" + masked_fill(mask, value) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.masked_fill_` + """ + ... + @overload + def masked_fill(self, mask: Tensor, value: Union[Number, _complex]) -> Tensor: + r""" + masked_fill(mask, value) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.masked_fill_` + """ + ... + @overload + def masked_fill_(self, mask: Tensor, value: Tensor) -> Tensor: + r""" + masked_fill_(mask, value) + + Fills elements of :attr:`self` tensor with :attr:`value` where :attr:`mask` is + True. The shape of :attr:`mask` must be + :ref:`broadcastable ` with the shape of the underlying + tensor. + + Args: + mask (BoolTensor): the boolean mask + value (float): the value to fill in with + """ + ... + @overload + def masked_fill_(self, mask: Tensor, value: Union[Number, _complex]) -> Tensor: + r""" + masked_fill_(mask, value) + + Fills elements of :attr:`self` tensor with :attr:`value` where :attr:`mask` is + True. The shape of :attr:`mask` must be + :ref:`broadcastable ` with the shape of the underlying + tensor. + + Args: + mask (BoolTensor): the boolean mask + value (float): the value to fill in with + """ + ... + def masked_scatter(self, mask: Tensor, source: Tensor) -> Tensor: + r""" + masked_scatter(mask, tensor) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.masked_scatter_` + + .. note:: + + The inputs :attr:`self` and :attr:`mask` + :ref:`broadcast `. + + Example: + + >>> self = torch.tensor([0, 0, 0, 0, 0]) + >>> mask = torch.tensor([[0, 0, 0, 1, 1], [1, 1, 0, 1, 1]], dtype=torch.bool) + >>> source = torch.tensor([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]) + >>> self.masked_scatter(mask, source) + tensor([[0, 0, 0, 0, 1], + [2, 3, 0, 4, 5]]) + """ + ... + def masked_scatter_(self, mask: Tensor, source: Tensor) -> Tensor: + r""" + masked_scatter_(mask, source) + + Copies elements from :attr:`source` into :attr:`self` tensor at positions where + the :attr:`mask` is True. Elements from :attr:`source` are copied into :attr:`self` + starting at position 0 of :attr:`source` and continuing in order one-by-one for each + occurrence of :attr:`mask` being True. + The shape of :attr:`mask` must be :ref:`broadcastable ` + with the shape of the underlying tensor. The :attr:`source` should have at least + as many elements as the number of ones in :attr:`mask`. + + Args: + mask (BoolTensor): the boolean mask + source (Tensor): the tensor to copy from + + .. note:: + + The :attr:`mask` operates on the :attr:`self` tensor, not on the given + :attr:`source` tensor. + + Example: + + >>> self = torch.tensor([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]) + >>> mask = torch.tensor([[0, 0, 0, 1, 1], [1, 1, 0, 1, 1]], dtype=torch.bool) + >>> source = torch.tensor([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]) + >>> self.masked_scatter_(mask, source) + tensor([[0, 0, 0, 0, 1], + [2, 3, 0, 4, 5]]) + """ + ... + def masked_select(self, mask: Tensor) -> Tensor: + r""" + masked_select(mask) -> Tensor + + See :func:`torch.masked_select` + """ + ... + def matmul(self, other: Tensor) -> Tensor: + r""" + matmul(tensor2) -> Tensor + + See :func:`torch.matmul` + """ + ... + def matrix_exp(self) -> Tensor: + r""" + matrix_exp() -> Tensor + + See :func:`torch.matrix_exp` + """ + ... + def matrix_power(self, n: _int) -> Tensor: + r""" + matrix_power(n) -> Tensor + + .. note:: :meth:`~Tensor.matrix_power` is deprecated, use :func:`torch.linalg.matrix_power` instead. + + Alias for :func:`torch.linalg.matrix_power` + """ + ... + @overload + def max(self) -> Tensor: + r""" + max(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor) + + See :func:`torch.max` + """ + ... + @overload + def max(self, other: Tensor) -> Tensor: + r""" + max(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor) + + See :func:`torch.max` + """ + ... + @overload + def max(self, dim: _int, keepdim: _bool = False) -> torch.return_types.max: + r""" + max(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor) + + See :func:`torch.max` + """ + ... + @overload + def max(self, dim: Union[str, ellipsis, None], keepdim: _bool = False) -> torch.return_types.max: + r""" + max(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor) + + See :func:`torch.max` + """ + ... + def maximum(self, other: Tensor) -> Tensor: + r""" + maximum(other) -> Tensor + + See :func:`torch.maximum` + """ + ... + @overload + def mean(self, *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + mean(dim=None, keepdim=False, *, dtype=None) -> Tensor + + See :func:`torch.mean` + """ + ... + @overload + def mean(self, dim: Optional[Union[_int, _size]], keepdim: _bool = False, *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + mean(dim=None, keepdim=False, *, dtype=None) -> Tensor + + See :func:`torch.mean` + """ + ... + @overload + def mean(self, dim: Sequence[Union[str, ellipsis, None]], keepdim: _bool = False, *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + mean(dim=None, keepdim=False, *, dtype=None) -> Tensor + + See :func:`torch.mean` + """ + ... + @overload + def median(self) -> Tensor: + r""" + median(dim=None, keepdim=False) -> (Tensor, LongTensor) + + See :func:`torch.median` + """ + ... + @overload + def median(self, dim: _int, keepdim: _bool = False) -> torch.return_types.median: + r""" + median(dim=None, keepdim=False) -> (Tensor, LongTensor) + + See :func:`torch.median` + """ + ... + @overload + def median(self, dim: Union[str, ellipsis, None], keepdim: _bool = False) -> torch.return_types.median: + r""" + median(dim=None, keepdim=False) -> (Tensor, LongTensor) + + See :func:`torch.median` + """ + ... + @overload + def min(self) -> Tensor: + r""" + min(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor) + + See :func:`torch.min` + """ + ... + @overload + def min(self, other: Tensor) -> Tensor: + r""" + min(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor) + + See :func:`torch.min` + """ + ... + @overload + def min(self, dim: _int, keepdim: _bool = False) -> torch.return_types.min: + r""" + min(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor) + + See :func:`torch.min` + """ + ... + @overload + def min(self, dim: Union[str, ellipsis, None], keepdim: _bool = False) -> torch.return_types.min: + r""" + min(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor) + + See :func:`torch.min` + """ + ... + def minimum(self, other: Tensor) -> Tensor: + r""" + minimum(other) -> Tensor + + See :func:`torch.minimum` + """ + ... + def mm(self, mat2: Tensor) -> Tensor: + r""" + mm(mat2) -> Tensor + + See :func:`torch.mm` + """ + ... + @overload + def mode(self, dim: _int = -1, keepdim: _bool = False) -> torch.return_types.mode: + r""" + mode(dim=None, keepdim=False) -> (Tensor, LongTensor) + + See :func:`torch.mode` + """ + ... + @overload + def mode(self, dim: Union[str, ellipsis, None], keepdim: _bool = False) -> torch.return_types.mode: + r""" + mode(dim=None, keepdim=False) -> (Tensor, LongTensor) + + See :func:`torch.mode` + """ + ... + @overload + def moveaxis(self, source: _int, destination: _int) -> Tensor: + r""" + moveaxis(source, destination) -> Tensor + + See :func:`torch.moveaxis` + """ + ... + @overload + def moveaxis(self, source: _size, destination: _size) -> Tensor: + r""" + moveaxis(source, destination) -> Tensor + + See :func:`torch.moveaxis` + """ + ... + @overload + def movedim(self, source: _int, destination: _int) -> Tensor: + r""" + movedim(source, destination) -> Tensor + + See :func:`torch.movedim` + """ + ... + @overload + def movedim(self, source: _size, destination: _size) -> Tensor: + r""" + movedim(source, destination) -> Tensor + + See :func:`torch.movedim` + """ + ... + def msort(self) -> Tensor: + r""" + msort() -> Tensor + + See :func:`torch.msort` + """ + ... + def mul(self, other: Union[Tensor, Number, _complex, torch.SymInt, torch.SymFloat], *, out: Optional[Tensor] = None) -> Tensor: + r""" + mul(value) -> Tensor + + See :func:`torch.mul`. + """ + ... + def mul_(self, other: Union[Tensor, Number, _complex, torch.SymInt, torch.SymFloat]) -> Tensor: + r""" + mul_(value) -> Tensor + + In-place version of :meth:`~Tensor.mul`. + """ + ... + def multinomial(self, num_samples: _int, replacement: _bool = False, *, generator: Optional[Generator] = None) -> Tensor: + r""" + multinomial(num_samples, replacement=False, *, generator=None) -> Tensor + + See :func:`torch.multinomial` + """ + ... + @overload + def multiply(self, other: Tensor) -> Tensor: + r""" + multiply(value) -> Tensor + + See :func:`torch.multiply`. + """ + ... + @overload + def multiply(self, other: Union[Number, _complex]) -> Tensor: + r""" + multiply(value) -> Tensor + + See :func:`torch.multiply`. + """ + ... + @overload + def multiply_(self, other: Tensor) -> Tensor: + r""" + multiply_(value) -> Tensor + + In-place version of :meth:`~Tensor.multiply`. + """ + ... + @overload + def multiply_(self, other: Union[Number, _complex]) -> Tensor: + r""" + multiply_(value) -> Tensor + + In-place version of :meth:`~Tensor.multiply`. + """ + ... + def mv(self, vec: Tensor) -> Tensor: + r""" + mv(vec) -> Tensor + + See :func:`torch.mv` + """ + ... + def mvlgamma(self, p: _int) -> Tensor: + r""" + mvlgamma(p) -> Tensor + + See :func:`torch.mvlgamma` + """ + ... + def mvlgamma_(self, p: _int) -> Tensor: + r""" + mvlgamma_(p) -> Tensor + + In-place version of :meth:`~Tensor.mvlgamma` + """ + ... + def nan_to_num(self, nan: Optional[_float] = None, posinf: Optional[_float] = None, neginf: Optional[_float] = None) -> Tensor: + r""" + nan_to_num(nan=0.0, posinf=None, neginf=None) -> Tensor + + See :func:`torch.nan_to_num`. + """ + ... + def nan_to_num_(self, nan: Optional[_float] = None, posinf: Optional[_float] = None, neginf: Optional[_float] = None) -> Tensor: + r""" + nan_to_num_(nan=0.0, posinf=None, neginf=None) -> Tensor + + In-place version of :meth:`~Tensor.nan_to_num`. + """ + ... + def nanmean(self, dim: Optional[Union[_int, _size]] = None, keepdim: _bool = False, *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + nanmean(dim=None, keepdim=False, *, dtype=None) -> Tensor + + See :func:`torch.nanmean` + """ + ... + @overload + def nanmedian(self) -> Tensor: + r""" + nanmedian(dim=None, keepdim=False) -> (Tensor, LongTensor) + + See :func:`torch.nanmedian` + """ + ... + @overload + def nanmedian(self, dim: _int, keepdim: _bool = False) -> torch.return_types.nanmedian: + r""" + nanmedian(dim=None, keepdim=False) -> (Tensor, LongTensor) + + See :func:`torch.nanmedian` + """ + ... + @overload + def nanmedian(self, dim: Union[str, ellipsis, None], keepdim: _bool = False) -> torch.return_types.nanmedian: + r""" + nanmedian(dim=None, keepdim=False) -> (Tensor, LongTensor) + + See :func:`torch.nanmedian` + """ + ... + @overload + def nanquantile(self, q: Tensor, dim: Optional[_int] = None, keepdim: _bool = False, *, interpolation: str = "linear") -> Tensor: + r""" + nanquantile(q, dim=None, keepdim=False, *, interpolation='linear') -> Tensor + + See :func:`torch.nanquantile` + """ + ... + @overload + def nanquantile(self, q: _float, dim: Optional[_int] = None, keepdim: _bool = False, *, interpolation: str = "linear") -> Tensor: + r""" + nanquantile(q, dim=None, keepdim=False, *, interpolation='linear') -> Tensor + + See :func:`torch.nanquantile` + """ + ... + def nansum(self, dim: Optional[Union[_int, _size]] = None, keepdim: _bool = False, *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + nansum(dim=None, keepdim=False, dtype=None) -> Tensor + + See :func:`torch.nansum` + """ + ... + @overload + def narrow(self, dim: _int, start: Tensor, length: Union[_int, SymInt]) -> Tensor: + r""" + narrow(dimension, start, length) -> Tensor + + See :func:`torch.narrow`. + """ + ... + @overload + def narrow(self, dim: _int, start: Union[_int, SymInt], length: Union[_int, SymInt]) -> Tensor: + r""" + narrow(dimension, start, length) -> Tensor + + See :func:`torch.narrow`. + """ + ... + def narrow_copy(self, dim: _int, start: Union[_int, SymInt], length: Union[_int, SymInt]) -> Tensor: + r""" + narrow_copy(dimension, start, length) -> Tensor + + See :func:`torch.narrow_copy`. + """ + ... + def ndimension(self) -> _int: + r""" + ndimension() -> int + + Alias for :meth:`~Tensor.dim()` + """ + ... + @overload + def ne(self, other: Tensor) -> Tensor: + r""" + ne(other) -> Tensor + + See :func:`torch.ne`. + """ + ... + @overload + def ne(self, other: Union[Number, _complex]) -> Tensor: + r""" + ne(other) -> Tensor + + See :func:`torch.ne`. + """ + ... + @overload + def ne_(self, other: Tensor) -> Tensor: + r""" + ne_(other) -> Tensor + + In-place version of :meth:`~Tensor.ne`. + """ + ... + @overload + def ne_(self, other: Union[Number, _complex]) -> Tensor: + r""" + ne_(other) -> Tensor + + In-place version of :meth:`~Tensor.ne`. + """ + ... + def neg(self) -> Tensor: + r""" + neg() -> Tensor + + See :func:`torch.neg` + """ + ... + def neg_(self) -> Tensor: + r""" + neg_() -> Tensor + + In-place version of :meth:`~Tensor.neg` + """ + ... + def negative(self) -> Tensor: + r""" + negative() -> Tensor + + See :func:`torch.negative` + """ + ... + def negative_(self) -> Tensor: + r""" + negative_() -> Tensor + + In-place version of :meth:`~Tensor.negative` + """ + ... + def nelement(self) -> _int: + r""" + nelement() -> int + + Alias for :meth:`~Tensor.numel` + """ + ... + @overload + def new(cls, *args: Any, device: Optional[DeviceLikeType] = None) -> Self: ... + @overload + def new(cls, storage: Storage) -> Self: ... + @overload + def new(cls, other: Tensor) -> Self: ... + @overload + def new(cls, size: _size, *, device: Optional[DeviceLikeType] = None) -> Self: ... + @overload + def new_empty(self, size: Sequence[Union[_int, SymInt]], *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + new_empty(size, *, dtype=None, device=None, requires_grad=False, layout=torch.strided, pin_memory=False) -> Tensor + + + Returns a Tensor of size :attr:`size` filled with uninitialized data. + By default, the returned Tensor has the same :class:`torch.dtype` and + :class:`torch.device` as this tensor. + + Args: + size (int...): a list, tuple, or :class:`torch.Size` of integers defining the + shape of the output tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired type of returned tensor. + Default: if None, same :class:`torch.dtype` as this tensor. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if None, same :class:`torch.device` as this tensor. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> tensor = torch.ones(()) + >>> tensor.new_empty((2, 3)) + tensor([[ 5.8182e-18, 4.5765e-41, -1.0545e+30], + [ 3.0949e-41, 4.4842e-44, 0.0000e+00]]) + """ + ... + @overload + def new_empty(self, *size: Union[_int, SymInt], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + new_empty(size, *, dtype=None, device=None, requires_grad=False, layout=torch.strided, pin_memory=False) -> Tensor + + + Returns a Tensor of size :attr:`size` filled with uninitialized data. + By default, the returned Tensor has the same :class:`torch.dtype` and + :class:`torch.device` as this tensor. + + Args: + size (int...): a list, tuple, or :class:`torch.Size` of integers defining the + shape of the output tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired type of returned tensor. + Default: if None, same :class:`torch.dtype` as this tensor. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if None, same :class:`torch.device` as this tensor. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> tensor = torch.ones(()) + >>> tensor.new_empty((2, 3)) + tensor([[ 5.8182e-18, 4.5765e-41, -1.0545e+30], + [ 3.0949e-41, 4.4842e-44, 0.0000e+00]]) + """ + ... + def new_empty_strided(self, size: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + new_empty_strided(size, stride, dtype=None, device=None, requires_grad=False, layout=torch.strided, pin_memory=False) -> Tensor + + + Returns a Tensor of size :attr:`size` and strides :attr:`stride` filled with + uninitialized data. By default, the returned Tensor has the same + :class:`torch.dtype` and :class:`torch.device` as this tensor. + + Args: + size (int...): a list, tuple, or :class:`torch.Size` of integers defining the + shape of the output tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired type of returned tensor. + Default: if None, same :class:`torch.dtype` as this tensor. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if None, same :class:`torch.device` as this tensor. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> tensor = torch.ones(()) + >>> tensor.new_empty_strided((2, 3), (3, 1)) + tensor([[ 5.8182e-18, 4.5765e-41, -1.0545e+30], + [ 3.0949e-41, 4.4842e-44, 0.0000e+00]]) + """ + ... + def new_full(self, size: Sequence[Union[_int, SymInt]], fill_value: Union[Number, _complex], *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + new_full(size, fill_value, *, dtype=None, device=None, requires_grad=False, layout=torch.strided, pin_memory=False) -> Tensor + + + Returns a Tensor of size :attr:`size` filled with :attr:`fill_value`. + By default, the returned Tensor has the same :class:`torch.dtype` and + :class:`torch.device` as this tensor. + + Args: + fill_value (scalar): the number to fill the output tensor with. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired type of returned tensor. + Default: if None, same :class:`torch.dtype` as this tensor. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if None, same :class:`torch.device` as this tensor. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> tensor = torch.ones((2,), dtype=torch.float64) + >>> tensor.new_full((3, 4), 3.141592) + tensor([[ 3.1416, 3.1416, 3.1416, 3.1416], + [ 3.1416, 3.1416, 3.1416, 3.1416], + [ 3.1416, 3.1416, 3.1416, 3.1416]], dtype=torch.float64) + """ + ... + @overload + def new_ones(self, size: _size, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + new_ones(size, *, dtype=None, device=None, requires_grad=False, layout=torch.strided, pin_memory=False) -> Tensor + + + Returns a Tensor of size :attr:`size` filled with ``1``. + By default, the returned Tensor has the same :class:`torch.dtype` and + :class:`torch.device` as this tensor. + + Args: + size (int...): a list, tuple, or :class:`torch.Size` of integers defining the + shape of the output tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired type of returned tensor. + Default: if None, same :class:`torch.dtype` as this tensor. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if None, same :class:`torch.device` as this tensor. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> tensor = torch.tensor((), dtype=torch.int32) + >>> tensor.new_ones((2, 3)) + tensor([[ 1, 1, 1], + [ 1, 1, 1]], dtype=torch.int32) + """ + ... + @overload + def new_ones(self, size: Sequence[Union[_int, SymInt]], *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + new_ones(size, *, dtype=None, device=None, requires_grad=False, layout=torch.strided, pin_memory=False) -> Tensor + + + Returns a Tensor of size :attr:`size` filled with ``1``. + By default, the returned Tensor has the same :class:`torch.dtype` and + :class:`torch.device` as this tensor. + + Args: + size (int...): a list, tuple, or :class:`torch.Size` of integers defining the + shape of the output tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired type of returned tensor. + Default: if None, same :class:`torch.dtype` as this tensor. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if None, same :class:`torch.device` as this tensor. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> tensor = torch.tensor((), dtype=torch.int32) + >>> tensor.new_ones((2, 3)) + tensor([[ 1, 1, 1], + [ 1, 1, 1]], dtype=torch.int32) + """ + ... + @overload + def new_ones(self, *size: Union[_int, SymInt], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + new_ones(size, *, dtype=None, device=None, requires_grad=False, layout=torch.strided, pin_memory=False) -> Tensor + + + Returns a Tensor of size :attr:`size` filled with ``1``. + By default, the returned Tensor has the same :class:`torch.dtype` and + :class:`torch.device` as this tensor. + + Args: + size (int...): a list, tuple, or :class:`torch.Size` of integers defining the + shape of the output tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired type of returned tensor. + Default: if None, same :class:`torch.dtype` as this tensor. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if None, same :class:`torch.device` as this tensor. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> tensor = torch.tensor((), dtype=torch.int32) + >>> tensor.new_ones((2, 3)) + tensor([[ 1, 1, 1], + [ 1, 1, 1]], dtype=torch.int32) + """ + ... + def new_tensor(self, data: Any, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + new_tensor(data, *, dtype=None, device=None, requires_grad=False, layout=torch.strided, pin_memory=False) -> Tensor + + + Returns a new Tensor with :attr:`data` as the tensor data. + By default, the returned Tensor has the same :class:`torch.dtype` and + :class:`torch.device` as this tensor. + + .. warning:: + + :func:`new_tensor` always copies :attr:`data`. If you have a Tensor + ``data`` and want to avoid a copy, use :func:`torch.Tensor.requires_grad_` + or :func:`torch.Tensor.detach`. + If you have a numpy array and want to avoid a copy, use + :func:`torch.from_numpy`. + + .. warning:: + + When data is a tensor `x`, :func:`new_tensor()` reads out 'the data' from whatever it is passed, + and constructs a leaf variable. Therefore ``tensor.new_tensor(x)`` is equivalent to ``x.clone().detach()`` + and ``tensor.new_tensor(x, requires_grad=True)`` is equivalent to ``x.clone().detach().requires_grad_(True)``. + The equivalents using ``clone()`` and ``detach()`` are recommended. + + Args: + data (array_like): The returned Tensor copies :attr:`data`. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired type of returned tensor. + Default: if None, same :class:`torch.dtype` as this tensor. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if None, same :class:`torch.device` as this tensor. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> tensor = torch.ones((2,), dtype=torch.int8) + >>> data = [[0, 1], [2, 3]] + >>> tensor.new_tensor(data) + tensor([[ 0, 1], + [ 2, 3]], dtype=torch.int8) + """ + ... + @overload + def new_zeros(self, size: Sequence[Union[_int, SymInt]], *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + new_zeros(size, *, dtype=None, device=None, requires_grad=False, layout=torch.strided, pin_memory=False) -> Tensor + + + Returns a Tensor of size :attr:`size` filled with ``0``. + By default, the returned Tensor has the same :class:`torch.dtype` and + :class:`torch.device` as this tensor. + + Args: + size (int...): a list, tuple, or :class:`torch.Size` of integers defining the + shape of the output tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired type of returned tensor. + Default: if None, same :class:`torch.dtype` as this tensor. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if None, same :class:`torch.device` as this tensor. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> tensor = torch.tensor((), dtype=torch.float64) + >>> tensor.new_zeros((2, 3)) + tensor([[ 0., 0., 0.], + [ 0., 0., 0.]], dtype=torch.float64) + """ + ... + @overload + def new_zeros(self, *size: Union[_int, SymInt], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + new_zeros(size, *, dtype=None, device=None, requires_grad=False, layout=torch.strided, pin_memory=False) -> Tensor + + + Returns a Tensor of size :attr:`size` filled with ``0``. + By default, the returned Tensor has the same :class:`torch.dtype` and + :class:`torch.device` as this tensor. + + Args: + size (int...): a list, tuple, or :class:`torch.Size` of integers defining the + shape of the output tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired type of returned tensor. + Default: if None, same :class:`torch.dtype` as this tensor. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if None, same :class:`torch.device` as this tensor. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> tensor = torch.tensor((), dtype=torch.float64) + >>> tensor.new_zeros((2, 3)) + tensor([[ 0., 0., 0.], + [ 0., 0., 0.]], dtype=torch.float64) + """ + ... + def nextafter(self, other: Tensor) -> Tensor: + r""" + nextafter(other) -> Tensor + See :func:`torch.nextafter` + """ + ... + def nextafter_(self, other: Tensor) -> Tensor: + r""" + nextafter_(other) -> Tensor + In-place version of :meth:`~Tensor.nextafter` + """ + ... + @overload + def nonzero(self, *, as_tuple: Literal[False] = False) -> Tensor: + r""" + nonzero() -> LongTensor + + See :func:`torch.nonzero` + """ + ... + @overload + def nonzero(self, *, as_tuple: Literal[True]) -> Tuple[Tensor, ...]: + r""" + nonzero() -> LongTensor + + See :func:`torch.nonzero` + """ + ... + def nonzero_static(self, *, size: _int, fill_value: _int = -1) -> Tensor: + r""" + nonzero_static(input, *, size, fill_value=-1) -> Tensor + + Returns a 2-D tensor where each row is the index for a non-zero value. + The returned Tensor has the same `torch.dtype` as `torch.nonzero()`. + + Args: + input (Tensor): the input tensor to count non-zero elements. + + Keyword args: + size (int): the size of non-zero elements expected to be included in the out + tensor. Pad the out tensor with `fill_value` if the `size` is larger + than total number of non-zero elements, truncate out tensor if `size` + is smaller. The size must be a non-negative integer. + fill_value (int): the value to fill the output tensor with when `size` is larger + than the total number of non-zero elements. Default is `-1` to represent + invalid index. + + Example: + + # Example 1: Padding + >>> input_tensor = torch.tensor([[1, 0], [3, 2]]) + >>> static_size = 4 + >>> t = torch.nonzero_static(input_tensor, size = static_size) + tensor([[ 0, 0], + [ 1, 0], + [ 1, 1], + [ -1, -1]], dtype=torch.int64) + + # Example 2: Truncating + >>> input_tensor = torch.tensor([[1, 0], [3, 2]]) + >>> static_size = 2 + >>> t = torch.nonzero_static(input_tensor, size = static_size) + tensor([[ 0, 0], + [ 1, 0]], dtype=torch.int64) + + # Example 3: 0 size + >>> input_tensor = torch.tensor([10]) + >>> static_size = 0 + >>> t = torch.nonzero_static(input_tensor, size = static_size) + tensor([], size=(0, 1), dtype=torch.int64) + + # Example 4: 0 rank input + >>> input_tensor = torch.tensor(10) + >>> static_size = 2 + >>> t = torch.nonzero_static(input_tensor, size = static_size) + tensor([], size=(2, 0), dtype=torch.int64) + """ + ... + def normal_(self, mean: _float = 0, std: _float = 1, *, generator: Optional[Generator] = None) -> Tensor: + r""" + normal_(mean=0, std=1, *, generator=None) -> Tensor + + Fills :attr:`self` tensor with elements samples from the normal distribution + parameterized by :attr:`mean` and :attr:`std`. + """ + ... + @overload + def not_equal(self, other: Tensor) -> Tensor: + r""" + not_equal(other) -> Tensor + + See :func:`torch.not_equal`. + """ + ... + @overload + def not_equal(self, other: Union[Number, _complex]) -> Tensor: + r""" + not_equal(other) -> Tensor + + See :func:`torch.not_equal`. + """ + ... + @overload + def not_equal_(self, other: Tensor) -> Tensor: + r""" + not_equal_(other) -> Tensor + + In-place version of :meth:`~Tensor.not_equal`. + """ + ... + @overload + def not_equal_(self, other: Union[Number, _complex]) -> Tensor: + r""" + not_equal_(other) -> Tensor + + In-place version of :meth:`~Tensor.not_equal`. + """ + ... + def numel(self) -> _int: + r""" + numel() -> int + + See :func:`torch.numel` + """ + ... + def numpy(self, *, force: _bool = False) -> numpy.ndarray: + r""" + numpy(*, force=False) -> numpy.ndarray + + Returns the tensor as a NumPy :class:`ndarray`. + + If :attr:`force` is ``False`` (the default), the conversion + is performed only if the tensor is on the CPU, does not require grad, + does not have its conjugate bit set, and is a dtype and layout that + NumPy supports. The returned ndarray and the tensor will share their + storage, so changes to the tensor will be reflected in the ndarray + and vice versa. + + If :attr:`force` is ``True`` this is equivalent to + calling ``t.detach().cpu().resolve_conj().resolve_neg().numpy()``. + If the tensor isn't on the CPU or the conjugate or negative bit is set, + the tensor won't share its storage with the returned ndarray. + Setting :attr:`force` to ``True`` can be a useful shorthand. + + Args: + force (bool): if ``True``, the ndarray may be a copy of the tensor + instead of always sharing memory, defaults to ``False``. + """ + ... + def orgqr(self, input2: Tensor) -> Tensor: + r""" + orgqr(input2) -> Tensor + + See :func:`torch.orgqr` + """ + ... + def ormqr(self, input2: Tensor, input3: Tensor, left: _bool = True, transpose: _bool = False) -> Tensor: + r""" + ormqr(input2, input3, left=True, transpose=False) -> Tensor + + See :func:`torch.ormqr` + """ + ... + def outer(self, vec2: Tensor) -> Tensor: + r""" + outer(vec2) -> Tensor + + See :func:`torch.outer`. + """ + ... + @overload + def permute(self, dims: _size) -> Tensor: + r""" + permute(*dims) -> Tensor + + See :func:`torch.permute` + """ + ... + @overload + def permute(self, *dims: _int) -> Tensor: + r""" + permute(*dims) -> Tensor + + See :func:`torch.permute` + """ + ... + def pin_memory(self, device: Optional[Optional[DeviceLikeType]] = None) -> Tensor: + r""" + pin_memory() -> Tensor + + Copies the tensor to pinned memory, if it's not already pinned. + """ + ... + def pinverse(self, rcond: _float = 1e-15) -> Tensor: + r""" + pinverse() -> Tensor + + See :func:`torch.pinverse` + """ + ... + def polygamma(self, n: _int) -> Tensor: + r""" + polygamma(n) -> Tensor + + See :func:`torch.polygamma` + """ + ... + def polygamma_(self, n: _int) -> Tensor: + r""" + polygamma_(n) -> Tensor + + In-place version of :meth:`~Tensor.polygamma` + """ + ... + def positive(self) -> Tensor: + r""" + positive() -> Tensor + + See :func:`torch.positive` + """ + ... + @overload + def pow(self, exponent: Tensor) -> Tensor: + r""" + pow(exponent) -> Tensor + + See :func:`torch.pow` + """ + ... + @overload + def pow(self, exponent: Union[Number, _complex]) -> Tensor: + r""" + pow(exponent) -> Tensor + + See :func:`torch.pow` + """ + ... + @overload + def pow_(self, exponent: Tensor) -> Tensor: + r""" + pow_(exponent) -> Tensor + + In-place version of :meth:`~Tensor.pow` + """ + ... + @overload + def pow_(self, exponent: Union[Number, _complex]) -> Tensor: + r""" + pow_(exponent) -> Tensor + + In-place version of :meth:`~Tensor.pow` + """ + ... + def prelu(self, weight: Tensor) -> Tensor: ... + @overload + def prod(self, *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + prod(dim=None, keepdim=False, dtype=None) -> Tensor + + See :func:`torch.prod` + """ + ... + @overload + def prod(self, dim: _int, keepdim: _bool = False, *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + prod(dim=None, keepdim=False, dtype=None) -> Tensor + + See :func:`torch.prod` + """ + ... + @overload + def prod(self, dim: Union[str, ellipsis, None], keepdim: _bool = False, *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + prod(dim=None, keepdim=False, dtype=None) -> Tensor + + See :func:`torch.prod` + """ + ... + def put(self, index: Tensor, source: Tensor, accumulate: _bool = False) -> Tensor: + r""" + put(input, index, source, accumulate=False) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.put_`. + `input` corresponds to `self` in :meth:`torch.Tensor.put_`. + """ + ... + def put_(self, index: Tensor, source: Tensor, accumulate: _bool = False) -> Tensor: + r""" + put_(index, source, accumulate=False) -> Tensor + + Copies the elements from :attr:`source` into the positions specified by + :attr:`index`. For the purpose of indexing, the :attr:`self` tensor is treated as if + it were a 1-D tensor. + + :attr:`index` and :attr:`source` need to have the same number of elements, but not necessarily + the same shape. + + If :attr:`accumulate` is ``True``, the elements in :attr:`source` are added to + :attr:`self`. If accumulate is ``False``, the behavior is undefined if :attr:`index` + contain duplicate elements. + + Args: + index (LongTensor): the indices into self + source (Tensor): the tensor containing values to copy from + accumulate (bool): whether to accumulate into self + + Example:: + + >>> src = torch.tensor([[4, 3, 5], + ... [6, 7, 8]]) + >>> src.put_(torch.tensor([1, 3]), torch.tensor([9, 10])) + tensor([[ 4, 9, 5], + [ 10, 7, 8]]) + """ + ... + def q_per_channel_axis(self) -> _int: + r""" + q_per_channel_axis() -> int + + Given a Tensor quantized by linear (affine) per-channel quantization, + returns the index of dimension on which per-channel quantization is applied. + """ + ... + def q_per_channel_scales(self) -> Tensor: + r""" + q_per_channel_scales() -> Tensor + + Given a Tensor quantized by linear (affine) per-channel quantization, + returns a Tensor of scales of the underlying quantizer. It has the number of + elements that matches the corresponding dimensions (from q_per_channel_axis) of + the tensor. + """ + ... + def q_per_channel_zero_points(self) -> Tensor: + r""" + q_per_channel_zero_points() -> Tensor + + Given a Tensor quantized by linear (affine) per-channel quantization, + returns a tensor of zero_points of the underlying quantizer. It has the number of + elements that matches the corresponding dimensions (from q_per_channel_axis) of + the tensor. + """ + ... + def q_scale(self) -> _float: + r""" + q_scale() -> float + + Given a Tensor quantized by linear(affine) quantization, + returns the scale of the underlying quantizer(). + """ + ... + def q_zero_point(self) -> _int: + r""" + q_zero_point() -> int + + Given a Tensor quantized by linear(affine) quantization, + returns the zero_point of the underlying quantizer(). + """ + ... + def qr(self, some: _bool = True) -> torch.return_types.qr: + r""" + qr(some=True) -> (Tensor, Tensor) + + See :func:`torch.qr` + """ + ... + def qscheme(self) -> _qscheme: + r""" + qscheme() -> torch.qscheme + + Returns the quantization scheme of a given QTensor. + """ + ... + @overload + def quantile(self, q: Tensor, dim: Optional[_int] = None, keepdim: _bool = False, *, interpolation: str = "linear") -> Tensor: + r""" + quantile(q, dim=None, keepdim=False, *, interpolation='linear') -> Tensor + + See :func:`torch.quantile` + """ + ... + @overload + def quantile(self, q: _float, dim: Optional[_int] = None, keepdim: _bool = False, *, interpolation: str = "linear") -> Tensor: + r""" + quantile(q, dim=None, keepdim=False, *, interpolation='linear') -> Tensor + + See :func:`torch.quantile` + """ + ... + def rad2deg(self) -> Tensor: + r""" + rad2deg() -> Tensor + + See :func:`torch.rad2deg` + """ + ... + def rad2deg_(self) -> Tensor: + r""" + rad2deg_() -> Tensor + + In-place version of :meth:`~Tensor.rad2deg` + """ + ... + @overload + def random_(self, *, generator: Optional[Generator] = None) -> Tensor: + r""" + random_(from=0, to=None, *, generator=None) -> Tensor + + Fills :attr:`self` tensor with numbers sampled from the discrete uniform + distribution over ``[from, to - 1]``. If not specified, the values are usually + only bounded by :attr:`self` tensor's data type. However, for floating point + types, if unspecified, range will be ``[0, 2^mantissa]`` to ensure that every + value is representable. For example, `torch.tensor(1, dtype=torch.double).random_()` + will be uniform in ``[0, 2^53]``. + """ + ... + @overload + def random_(self, from_: _int, to: Optional[_int], *, generator: Optional[Generator] = None) -> Tensor: + r""" + random_(from=0, to=None, *, generator=None) -> Tensor + + Fills :attr:`self` tensor with numbers sampled from the discrete uniform + distribution over ``[from, to - 1]``. If not specified, the values are usually + only bounded by :attr:`self` tensor's data type. However, for floating point + types, if unspecified, range will be ``[0, 2^mantissa]`` to ensure that every + value is representable. For example, `torch.tensor(1, dtype=torch.double).random_()` + will be uniform in ``[0, 2^53]``. + """ + ... + @overload + def random_(self, to: _int, *, generator: Optional[Generator] = None) -> Tensor: + r""" + random_(from=0, to=None, *, generator=None) -> Tensor + + Fills :attr:`self` tensor with numbers sampled from the discrete uniform + distribution over ``[from, to - 1]``. If not specified, the values are usually + only bounded by :attr:`self` tensor's data type. However, for floating point + types, if unspecified, range will be ``[0, 2^mantissa]`` to ensure that every + value is representable. For example, `torch.tensor(1, dtype=torch.double).random_()` + will be uniform in ``[0, 2^53]``. + """ + ... + def ravel(self) -> Tensor: + r""" + ravel() -> Tensor + + see :func:`torch.ravel` + """ + ... + def reciprocal(self) -> Tensor: + r""" + reciprocal() -> Tensor + + See :func:`torch.reciprocal` + """ + ... + def reciprocal_(self) -> Tensor: + r""" + reciprocal_() -> Tensor + + In-place version of :meth:`~Tensor.reciprocal` + """ + ... + def record_stream(self, s: Stream) -> None: + r""" + record_stream(stream) + + Marks the tensor as having been used by this stream. When the tensor + is deallocated, ensure the tensor memory is not reused for another tensor + until all work queued on :attr:`stream` at the time of deallocation is + complete. + + .. note:: + + The caching allocator is aware of only the stream where a tensor was + allocated. Due to the awareness, it already correctly manages the life + cycle of tensors on only one stream. But if a tensor is used on a stream + different from the stream of origin, the allocator might reuse the memory + unexpectedly. Calling this method lets the allocator know which streams + have used the tensor. + + .. warning:: + + This method is most suitable for use cases where you are providing a + function that created a tensor on a side stream, and want users to be able + to make use of the tensor without having to think carefully about stream + safety when making use of them. These safety guarantees come at some + performance and predictability cost (analogous to the tradeoff between GC + and manual memory management), so if you are in a situation where + you manage the full lifetime of your tensors, you may consider instead + manually managing CUDA events so that calling this method is not necessary. + In particular, when you call this method, on later allocations the + allocator will poll the recorded stream to see if all operations have + completed yet; you can potentially race with side stream computation and + non-deterministically reuse or fail to reuse memory for an allocation. + + You can safely use tensors allocated on side streams without + :meth:`~Tensor.record_stream`; you must manually ensure that + any non-creation stream uses of a tensor are synced back to the creation + stream before you deallocate the tensor. As the CUDA caching allocator + guarantees that the memory will only be reused with the same creation stream, + this is sufficient to ensure that writes to future reallocations of the + memory will be delayed until non-creation stream uses are done. + (Counterintuitively, you may observe that on the CPU side we have already + reallocated the tensor, even though CUDA kernels on the old tensor are + still in progress. This is fine, because CUDA operations on the new + tensor will appropriately wait for the old operations to complete, as they + are all on the same stream.) + + Concretely, this looks like this:: + + with torch.cuda.stream(s0): + x = torch.zeros(N) + + s1.wait_stream(s0) + with torch.cuda.stream(s1): + y = some_comm_op(x) + + ... some compute on s0 ... + + # synchronize creation stream s0 to side stream s1 + # before deallocating x + s0.wait_stream(s1) + del x + + Note that some discretion is required when deciding when to perform + ``s0.wait_stream(s1)``. In particular, if we were to wait immediately + after ``some_comm_op``, there wouldn't be any point in having the side + stream; it would be equivalent to have run ``some_comm_op`` on ``s0``. + Instead, the synchronization must be placed at some appropriate, later + point in time where you expect the side stream ``s1`` to have finished + work. This location is typically identified via profiling, e.g., using + Chrome traces produced + :meth:`torch.autograd.profiler.profile.export_chrome_trace`. If you + place the wait too early, work on s0 will block until ``s1`` has finished, + preventing further overlapping of communication and computation. If you + place the wait too late, you will use more memory than is strictly + necessary (as you are keeping ``x`` live for longer.) For a concrete + example of how this guidance can be applied in practice, see this post: + `FSDP and CUDACachingAllocator + `_. + """ + ... + def refine_names(self, names: Sequence[Union[str, ellipsis, None]]) -> Tensor: ... + def relu(self) -> Tensor: ... + def relu_(self) -> Tensor: ... + @overload + def remainder(self, other: Tensor) -> Tensor: + r""" + remainder(divisor) -> Tensor + + See :func:`torch.remainder` + """ + ... + @overload + def remainder(self, other: Union[Number, _complex]) -> Tensor: + r""" + remainder(divisor) -> Tensor + + See :func:`torch.remainder` + """ + ... + @overload + def remainder_(self, other: Tensor) -> Tensor: + r""" + remainder_(divisor) -> Tensor + + In-place version of :meth:`~Tensor.remainder` + """ + ... + @overload + def remainder_(self, other: Union[Number, _complex]) -> Tensor: + r""" + remainder_(divisor) -> Tensor + + In-place version of :meth:`~Tensor.remainder` + """ + ... + def rename(self, names: Optional[Sequence[Union[str, ellipsis, None]]]) -> Tensor: ... + def rename_(self, names: Optional[Sequence[Union[str, ellipsis, None]]]) -> Tensor: ... + def renorm(self, p: Union[Number, _complex], dim: _int, maxnorm: Union[Number, _complex]) -> Tensor: + r""" + renorm(p, dim, maxnorm) -> Tensor + + See :func:`torch.renorm` + """ + ... + def renorm_(self, p: Union[Number, _complex], dim: _int, maxnorm: Union[Number, _complex]) -> Tensor: + r""" + renorm_(p, dim, maxnorm) -> Tensor + + In-place version of :meth:`~Tensor.renorm` + """ + ... + @overload + def repeat(self, repeats: Sequence[Union[_int, SymInt]]) -> Tensor: + r""" + repeat(*repeats) -> Tensor + + Repeats this tensor along the specified dimensions. + + Unlike :meth:`~Tensor.expand`, this function copies the tensor's data. + + .. warning:: + + :meth:`~Tensor.repeat` behaves differently from + `numpy.repeat `_, + but is more similar to + `numpy.tile `_. + For the operator similar to `numpy.repeat`, see :func:`torch.repeat_interleave`. + + Args: + repeat (torch.Size, int..., tuple of int or list of int): The number of times to repeat this tensor along each dimension + + Example:: + + >>> x = torch.tensor([1, 2, 3]) + >>> x.repeat(4, 2) + tensor([[ 1, 2, 3, 1, 2, 3], + [ 1, 2, 3, 1, 2, 3], + [ 1, 2, 3, 1, 2, 3], + [ 1, 2, 3, 1, 2, 3]]) + >>> x.repeat(4, 2, 1).size() + torch.Size([4, 2, 3]) + """ + ... + @overload + def repeat(self, *repeats: Union[_int, SymInt]) -> Tensor: + r""" + repeat(*repeats) -> Tensor + + Repeats this tensor along the specified dimensions. + + Unlike :meth:`~Tensor.expand`, this function copies the tensor's data. + + .. warning:: + + :meth:`~Tensor.repeat` behaves differently from + `numpy.repeat `_, + but is more similar to + `numpy.tile `_. + For the operator similar to `numpy.repeat`, see :func:`torch.repeat_interleave`. + + Args: + repeat (torch.Size, int..., tuple of int or list of int): The number of times to repeat this tensor along each dimension + + Example:: + + >>> x = torch.tensor([1, 2, 3]) + >>> x.repeat(4, 2) + tensor([[ 1, 2, 3, 1, 2, 3], + [ 1, 2, 3, 1, 2, 3], + [ 1, 2, 3, 1, 2, 3], + [ 1, 2, 3, 1, 2, 3]]) + >>> x.repeat(4, 2, 1).size() + torch.Size([4, 2, 3]) + """ + ... + @overload + def repeat_interleave(self, repeats: Tensor, dim: Optional[_int] = None, *, output_size: Optional[Union[_int, SymInt]] = None) -> Tensor: + r""" + repeat_interleave(repeats, dim=None, *, output_size=None) -> Tensor + + See :func:`torch.repeat_interleave`. + """ + ... + @overload + def repeat_interleave(self, repeats: Union[_int, SymInt], dim: Optional[_int] = None, *, output_size: Optional[Union[_int, SymInt]] = None) -> Tensor: + r""" + repeat_interleave(repeats, dim=None, *, output_size=None) -> Tensor + + See :func:`torch.repeat_interleave`. + """ + ... + def requires_grad_(self, mode: _bool = True) -> Tensor: + r""" + requires_grad_(requires_grad=True) -> Tensor + + Change if autograd should record operations on this tensor: sets this tensor's + :attr:`requires_grad` attribute in-place. Returns this tensor. + + :func:`requires_grad_`'s main use case is to tell autograd to begin recording + operations on a Tensor ``tensor``. If ``tensor`` has ``requires_grad=False`` + (because it was obtained through a DataLoader, or required preprocessing or + initialization), ``tensor.requires_grad_()`` makes it so that autograd will + begin to record operations on ``tensor``. + + Args: + requires_grad (bool): If autograd should record operations on this tensor. + Default: ``True``. + + Example:: + + >>> # Let's say we want to preprocess some saved weights and use + >>> # the result as new weights. + >>> saved_weights = [0.1, 0.2, 0.3, 0.25] + >>> loaded_weights = torch.tensor(saved_weights) + >>> weights = preprocess(loaded_weights) # some function + >>> weights + tensor([-0.5503, 0.4926, -2.1158, -0.8303]) + + >>> # Now, start to record operations done to weights + >>> weights.requires_grad_() + >>> out = weights.pow(2).sum() + >>> out.backward() + >>> weights.grad + tensor([-1.1007, 0.9853, -4.2316, -1.6606]) + """ + ... + @overload + def reshape(self, shape: Sequence[Union[_int, SymInt]]) -> Tensor: + r""" + reshape(*shape) -> Tensor + + Returns a tensor with the same data and number of elements as :attr:`self` + but with the specified shape. This method returns a view if :attr:`shape` is + compatible with the current shape. See :meth:`torch.Tensor.view` on when it is + possible to return a view. + + See :func:`torch.reshape` + + Args: + shape (tuple of ints or int...): the desired shape + """ + ... + @overload + def reshape(self, *shape: Union[_int, SymInt]) -> Tensor: + r""" + reshape(*shape) -> Tensor + + Returns a tensor with the same data and number of elements as :attr:`self` + but with the specified shape. This method returns a view if :attr:`shape` is + compatible with the current shape. See :meth:`torch.Tensor.view` on when it is + possible to return a view. + + See :func:`torch.reshape` + + Args: + shape (tuple of ints or int...): the desired shape + """ + ... + def reshape_as(self, other: Tensor) -> Tensor: + r""" + reshape_as(other) -> Tensor + + Returns this tensor as the same shape as :attr:`other`. + ``self.reshape_as(other)`` is equivalent to ``self.reshape(other.sizes())``. + This method returns a view if ``other.sizes()`` is compatible with the current + shape. See :meth:`torch.Tensor.view` on when it is possible to return a view. + + Please see :meth:`reshape` for more information about ``reshape``. + + Args: + other (:class:`torch.Tensor`): The result tensor has the same shape + as :attr:`other`. + """ + ... + @overload + def resize_(self, size: Sequence[Union[_int, SymInt]], *, memory_format: Optional[memory_format] = None) -> Tensor: + r""" + resize_(*sizes, memory_format=torch.contiguous_format) -> Tensor + + Resizes :attr:`self` tensor to the specified size. If the number of elements is + larger than the current storage size, then the underlying storage is resized + to fit the new number of elements. If the number of elements is smaller, the + underlying storage is not changed. Existing elements are preserved but any new + memory is uninitialized. + + .. warning:: + + This is a low-level method. The storage is reinterpreted as C-contiguous, + ignoring the current strides (unless the target size equals the current + size, in which case the tensor is left unchanged). For most purposes, you + will instead want to use :meth:`~Tensor.view()`, which checks for + contiguity, or :meth:`~Tensor.reshape()`, which copies data if needed. To + change the size in-place with custom strides, see :meth:`~Tensor.set_()`. + + .. note:: + + If :func:`torch.use_deterministic_algorithms()` and + :attr:`torch.utils.deterministic.fill_uninitialized_memory` are both set to + ``True``, new elements are initialized to prevent nondeterministic behavior + from using the result as an input to an operation. Floating point and + complex values are set to NaN, and integer values are set to the maximum + value. + + Args: + sizes (torch.Size or int...): the desired size + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + Tensor. Default: ``torch.contiguous_format``. Note that memory format of + :attr:`self` is going to be unaffected if ``self.size()`` matches ``sizes``. + + Example:: + + >>> x = torch.tensor([[1, 2], [3, 4], [5, 6]]) + >>> x.resize_(2, 2) + tensor([[ 1, 2], + [ 3, 4]]) + """ + ... + @overload + def resize_(self, *size: Union[_int, SymInt], memory_format: Optional[memory_format] = None) -> Tensor: + r""" + resize_(*sizes, memory_format=torch.contiguous_format) -> Tensor + + Resizes :attr:`self` tensor to the specified size. If the number of elements is + larger than the current storage size, then the underlying storage is resized + to fit the new number of elements. If the number of elements is smaller, the + underlying storage is not changed. Existing elements are preserved but any new + memory is uninitialized. + + .. warning:: + + This is a low-level method. The storage is reinterpreted as C-contiguous, + ignoring the current strides (unless the target size equals the current + size, in which case the tensor is left unchanged). For most purposes, you + will instead want to use :meth:`~Tensor.view()`, which checks for + contiguity, or :meth:`~Tensor.reshape()`, which copies data if needed. To + change the size in-place with custom strides, see :meth:`~Tensor.set_()`. + + .. note:: + + If :func:`torch.use_deterministic_algorithms()` and + :attr:`torch.utils.deterministic.fill_uninitialized_memory` are both set to + ``True``, new elements are initialized to prevent nondeterministic behavior + from using the result as an input to an operation. Floating point and + complex values are set to NaN, and integer values are set to the maximum + value. + + Args: + sizes (torch.Size or int...): the desired size + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + Tensor. Default: ``torch.contiguous_format``. Note that memory format of + :attr:`self` is going to be unaffected if ``self.size()`` matches ``sizes``. + + Example:: + + >>> x = torch.tensor([[1, 2], [3, 4], [5, 6]]) + >>> x.resize_(2, 2) + tensor([[ 1, 2], + [ 3, 4]]) + """ + ... + def resize_as_(self, the_template: Tensor, *, memory_format: Optional[memory_format] = None) -> Tensor: + r""" + resize_as_(tensor, memory_format=torch.contiguous_format) -> Tensor + + Resizes the :attr:`self` tensor to be the same size as the specified + :attr:`tensor`. This is equivalent to ``self.resize_(tensor.size())``. + + Args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + Tensor. Default: ``torch.contiguous_format``. Note that memory format of + :attr:`self` is going to be unaffected if ``self.size()`` matches ``tensor.size()``. + """ + ... + def resize_as_sparse_(self, the_template: Tensor) -> Tensor: ... + def resolve_conj(self) -> Tensor: + r""" + resolve_conj() -> Tensor + + See :func:`torch.resolve_conj` + """ + ... + def resolve_neg(self) -> Tensor: + r""" + resolve_neg() -> Tensor + + See :func:`torch.resolve_neg` + """ + ... + def retain_grad(self) -> None: + r""" + retain_grad() -> None + + Enables this Tensor to have their :attr:`grad` populated during + :func:`backward`. This is a no-op for leaf tensors. + """ + ... + def roll(self, shifts: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]], dims: Union[_int, _size] = ()) -> Tensor: + r""" + roll(shifts, dims) -> Tensor + + See :func:`torch.roll` + """ + ... + def rot90(self, k: _int = 1, dims: _size = (0, 1)) -> Tensor: + r""" + rot90(k, dims) -> Tensor + + See :func:`torch.rot90` + """ + ... + @overload + def round(self) -> Tensor: + r""" + round(decimals=0) -> Tensor + + See :func:`torch.round` + """ + ... + @overload + def round(self, *, decimals: _int) -> Tensor: + r""" + round(decimals=0) -> Tensor + + See :func:`torch.round` + """ + ... + @overload + def round_(self) -> Tensor: + r""" + round_(decimals=0) -> Tensor + + In-place version of :meth:`~Tensor.round` + """ + ... + @overload + def round_(self, *, decimals: _int) -> Tensor: + r""" + round_(decimals=0) -> Tensor + + In-place version of :meth:`~Tensor.round` + """ + ... + def row_indices(self) -> Tensor: ... + def rsqrt(self) -> Tensor: + r""" + rsqrt() -> Tensor + + See :func:`torch.rsqrt` + """ + ... + def rsqrt_(self) -> Tensor: + r""" + rsqrt_() -> Tensor + + In-place version of :meth:`~Tensor.rsqrt` + """ + ... + @overload + def scatter(self, dim: _int, index: Tensor, src: Tensor) -> Tensor: + r""" + scatter(dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_` + """ + ... + @overload + def scatter(self, dim: _int, index: Tensor, src: Tensor, *, reduce: str) -> Tensor: + r""" + scatter(dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_` + """ + ... + @overload + def scatter(self, dim: _int, index: Tensor, value: Union[Number, _complex], *, reduce: str) -> Tensor: + r""" + scatter(dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_` + """ + ... + @overload + def scatter(self, dim: Union[str, ellipsis, None], index: Tensor, src: Tensor) -> Tensor: + r""" + scatter(dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_` + """ + ... + @overload + def scatter(self, dim: _int, index: Tensor, value: Union[Number, _complex]) -> Tensor: + r""" + scatter(dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_` + """ + ... + @overload + def scatter(self, dim: Union[str, ellipsis, None], index: Tensor, value: Union[Number, _complex]) -> Tensor: + r""" + scatter(dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_` + """ + ... + @overload + def scatter_(self, dim: _int, index: Tensor, src: Tensor) -> Tensor: + r""" + scatter_(dim, index, src, *, reduce=None) -> Tensor + + Writes all values from the tensor :attr:`src` into :attr:`self` at the indices + specified in the :attr:`index` tensor. For each value in :attr:`src`, its output + index is specified by its index in :attr:`src` for ``dimension != dim`` and by + the corresponding value in :attr:`index` for ``dimension = dim``. + + For a 3-D tensor, :attr:`self` is updated as:: + + self[index[i][j][k]][j][k] = src[i][j][k] # if dim == 0 + self[i][index[i][j][k]][k] = src[i][j][k] # if dim == 1 + self[i][j][index[i][j][k]] = src[i][j][k] # if dim == 2 + + This is the reverse operation of the manner described in :meth:`~Tensor.gather`. + + :attr:`self`, :attr:`index` and :attr:`src` (if it is a Tensor) should all have + the same number of dimensions. It is also required that + ``index.size(d) <= src.size(d)`` for all dimensions ``d``, and that + ``index.size(d) <= self.size(d)`` for all dimensions ``d != dim``. + Note that ``index`` and ``src`` do not broadcast. + + Moreover, as for :meth:`~Tensor.gather`, the values of :attr:`index` must be + between ``0`` and ``self.size(dim) - 1`` inclusive. + + .. warning:: + + When indices are not unique, the behavior is non-deterministic (one of the + values from ``src`` will be picked arbitrarily) and the gradient will be + incorrect (it will be propagated to all locations in the source that + correspond to the same index)! + + .. note:: + + The backward pass is implemented only for ``src.shape == index.shape``. + + Additionally accepts an optional :attr:`reduce` argument that allows + specification of an optional reduction operation, which is applied to all + values in the tensor :attr:`src` into :attr:`self` at the indices + specified in the :attr:`index`. For each value in :attr:`src`, the reduction + operation is applied to an index in :attr:`self` which is specified by + its index in :attr:`src` for ``dimension != dim`` and by the corresponding + value in :attr:`index` for ``dimension = dim``. + + Given a 3-D tensor and reduction using the multiplication operation, :attr:`self` + is updated as:: + + self[index[i][j][k]][j][k] *= src[i][j][k] # if dim == 0 + self[i][index[i][j][k]][k] *= src[i][j][k] # if dim == 1 + self[i][j][index[i][j][k]] *= src[i][j][k] # if dim == 2 + + Reducing with the addition operation is the same as using + :meth:`~torch.Tensor.scatter_add_`. + + .. warning:: + The reduce argument with Tensor ``src`` is deprecated and will be removed in + a future PyTorch release. Please use :meth:`~torch.Tensor.scatter_reduce_` + instead for more reduction options. + + Args: + dim (int): the axis along which to index + index (LongTensor): the indices of elements to scatter, can be either empty + or of the same dimensionality as ``src``. When empty, the operation + returns ``self`` unchanged. + src (Tensor): the source element(s) to scatter. + + Keyword args: + reduce (str, optional): reduction operation to apply, can be either + ``'add'`` or ``'multiply'``. + + Example:: + + >>> src = torch.arange(1, 11).reshape((2, 5)) + >>> src + tensor([[ 1, 2, 3, 4, 5], + [ 6, 7, 8, 9, 10]]) + >>> index = torch.tensor([[0, 1, 2, 0]]) + >>> torch.zeros(3, 5, dtype=src.dtype).scatter_(0, index, src) + tensor([[1, 0, 0, 4, 0], + [0, 2, 0, 0, 0], + [0, 0, 3, 0, 0]]) + >>> index = torch.tensor([[0, 1, 2], [0, 1, 4]]) + >>> torch.zeros(3, 5, dtype=src.dtype).scatter_(1, index, src) + tensor([[1, 2, 3, 0, 0], + [6, 7, 0, 0, 8], + [0, 0, 0, 0, 0]]) + + >>> torch.full((2, 4), 2.).scatter_(1, torch.tensor([[2], [3]]), + ... 1.23, reduce='multiply') + tensor([[2.0000, 2.0000, 2.4600, 2.0000], + [2.0000, 2.0000, 2.0000, 2.4600]]) + >>> torch.full((2, 4), 2.).scatter_(1, torch.tensor([[2], [3]]), + ... 1.23, reduce='add') + tensor([[2.0000, 2.0000, 3.2300, 2.0000], + [2.0000, 2.0000, 2.0000, 3.2300]]) + + .. function:: scatter_(dim, index, value, *, reduce=None) -> Tensor: + :noindex: + + Writes the value from :attr:`value` into :attr:`self` at the indices + specified in the :attr:`index` tensor. This operation is equivalent to the previous version, + with the :attr:`src` tensor filled entirely with :attr:`value`. + + Args: + dim (int): the axis along which to index + index (LongTensor): the indices of elements to scatter, can be either empty + or of the same dimensionality as ``src``. When empty, the operation + returns ``self`` unchanged. + value (Scalar): the value to scatter. + + Keyword args: + reduce (str, optional): reduction operation to apply, can be either + ``'add'`` or ``'multiply'``. + + Example:: + + >>> index = torch.tensor([[0, 1]]) + >>> value = 2 + >>> torch.zeros(3, 5).scatter_(0, index, value) + tensor([[2., 0., 0., 0., 0.], + [0., 2., 0., 0., 0.], + [0., 0., 0., 0., 0.]]) + """ + ... + @overload + def scatter_(self, dim: _int, index: Tensor, src: Tensor, *, reduce: str) -> Tensor: + r""" + scatter_(dim, index, src, *, reduce=None) -> Tensor + + Writes all values from the tensor :attr:`src` into :attr:`self` at the indices + specified in the :attr:`index` tensor. For each value in :attr:`src`, its output + index is specified by its index in :attr:`src` for ``dimension != dim`` and by + the corresponding value in :attr:`index` for ``dimension = dim``. + + For a 3-D tensor, :attr:`self` is updated as:: + + self[index[i][j][k]][j][k] = src[i][j][k] # if dim == 0 + self[i][index[i][j][k]][k] = src[i][j][k] # if dim == 1 + self[i][j][index[i][j][k]] = src[i][j][k] # if dim == 2 + + This is the reverse operation of the manner described in :meth:`~Tensor.gather`. + + :attr:`self`, :attr:`index` and :attr:`src` (if it is a Tensor) should all have + the same number of dimensions. It is also required that + ``index.size(d) <= src.size(d)`` for all dimensions ``d``, and that + ``index.size(d) <= self.size(d)`` for all dimensions ``d != dim``. + Note that ``index`` and ``src`` do not broadcast. + + Moreover, as for :meth:`~Tensor.gather`, the values of :attr:`index` must be + between ``0`` and ``self.size(dim) - 1`` inclusive. + + .. warning:: + + When indices are not unique, the behavior is non-deterministic (one of the + values from ``src`` will be picked arbitrarily) and the gradient will be + incorrect (it will be propagated to all locations in the source that + correspond to the same index)! + + .. note:: + + The backward pass is implemented only for ``src.shape == index.shape``. + + Additionally accepts an optional :attr:`reduce` argument that allows + specification of an optional reduction operation, which is applied to all + values in the tensor :attr:`src` into :attr:`self` at the indices + specified in the :attr:`index`. For each value in :attr:`src`, the reduction + operation is applied to an index in :attr:`self` which is specified by + its index in :attr:`src` for ``dimension != dim`` and by the corresponding + value in :attr:`index` for ``dimension = dim``. + + Given a 3-D tensor and reduction using the multiplication operation, :attr:`self` + is updated as:: + + self[index[i][j][k]][j][k] *= src[i][j][k] # if dim == 0 + self[i][index[i][j][k]][k] *= src[i][j][k] # if dim == 1 + self[i][j][index[i][j][k]] *= src[i][j][k] # if dim == 2 + + Reducing with the addition operation is the same as using + :meth:`~torch.Tensor.scatter_add_`. + + .. warning:: + The reduce argument with Tensor ``src`` is deprecated and will be removed in + a future PyTorch release. Please use :meth:`~torch.Tensor.scatter_reduce_` + instead for more reduction options. + + Args: + dim (int): the axis along which to index + index (LongTensor): the indices of elements to scatter, can be either empty + or of the same dimensionality as ``src``. When empty, the operation + returns ``self`` unchanged. + src (Tensor): the source element(s) to scatter. + + Keyword args: + reduce (str, optional): reduction operation to apply, can be either + ``'add'`` or ``'multiply'``. + + Example:: + + >>> src = torch.arange(1, 11).reshape((2, 5)) + >>> src + tensor([[ 1, 2, 3, 4, 5], + [ 6, 7, 8, 9, 10]]) + >>> index = torch.tensor([[0, 1, 2, 0]]) + >>> torch.zeros(3, 5, dtype=src.dtype).scatter_(0, index, src) + tensor([[1, 0, 0, 4, 0], + [0, 2, 0, 0, 0], + [0, 0, 3, 0, 0]]) + >>> index = torch.tensor([[0, 1, 2], [0, 1, 4]]) + >>> torch.zeros(3, 5, dtype=src.dtype).scatter_(1, index, src) + tensor([[1, 2, 3, 0, 0], + [6, 7, 0, 0, 8], + [0, 0, 0, 0, 0]]) + + >>> torch.full((2, 4), 2.).scatter_(1, torch.tensor([[2], [3]]), + ... 1.23, reduce='multiply') + tensor([[2.0000, 2.0000, 2.4600, 2.0000], + [2.0000, 2.0000, 2.0000, 2.4600]]) + >>> torch.full((2, 4), 2.).scatter_(1, torch.tensor([[2], [3]]), + ... 1.23, reduce='add') + tensor([[2.0000, 2.0000, 3.2300, 2.0000], + [2.0000, 2.0000, 2.0000, 3.2300]]) + + .. function:: scatter_(dim, index, value, *, reduce=None) -> Tensor: + :noindex: + + Writes the value from :attr:`value` into :attr:`self` at the indices + specified in the :attr:`index` tensor. This operation is equivalent to the previous version, + with the :attr:`src` tensor filled entirely with :attr:`value`. + + Args: + dim (int): the axis along which to index + index (LongTensor): the indices of elements to scatter, can be either empty + or of the same dimensionality as ``src``. When empty, the operation + returns ``self`` unchanged. + value (Scalar): the value to scatter. + + Keyword args: + reduce (str, optional): reduction operation to apply, can be either + ``'add'`` or ``'multiply'``. + + Example:: + + >>> index = torch.tensor([[0, 1]]) + >>> value = 2 + >>> torch.zeros(3, 5).scatter_(0, index, value) + tensor([[2., 0., 0., 0., 0.], + [0., 2., 0., 0., 0.], + [0., 0., 0., 0., 0.]]) + """ + ... + @overload + def scatter_(self, dim: _int, index: Tensor, value: Union[Number, _complex], *, reduce: str) -> Tensor: + r""" + scatter_(dim, index, src, *, reduce=None) -> Tensor + + Writes all values from the tensor :attr:`src` into :attr:`self` at the indices + specified in the :attr:`index` tensor. For each value in :attr:`src`, its output + index is specified by its index in :attr:`src` for ``dimension != dim`` and by + the corresponding value in :attr:`index` for ``dimension = dim``. + + For a 3-D tensor, :attr:`self` is updated as:: + + self[index[i][j][k]][j][k] = src[i][j][k] # if dim == 0 + self[i][index[i][j][k]][k] = src[i][j][k] # if dim == 1 + self[i][j][index[i][j][k]] = src[i][j][k] # if dim == 2 + + This is the reverse operation of the manner described in :meth:`~Tensor.gather`. + + :attr:`self`, :attr:`index` and :attr:`src` (if it is a Tensor) should all have + the same number of dimensions. It is also required that + ``index.size(d) <= src.size(d)`` for all dimensions ``d``, and that + ``index.size(d) <= self.size(d)`` for all dimensions ``d != dim``. + Note that ``index`` and ``src`` do not broadcast. + + Moreover, as for :meth:`~Tensor.gather`, the values of :attr:`index` must be + between ``0`` and ``self.size(dim) - 1`` inclusive. + + .. warning:: + + When indices are not unique, the behavior is non-deterministic (one of the + values from ``src`` will be picked arbitrarily) and the gradient will be + incorrect (it will be propagated to all locations in the source that + correspond to the same index)! + + .. note:: + + The backward pass is implemented only for ``src.shape == index.shape``. + + Additionally accepts an optional :attr:`reduce` argument that allows + specification of an optional reduction operation, which is applied to all + values in the tensor :attr:`src` into :attr:`self` at the indices + specified in the :attr:`index`. For each value in :attr:`src`, the reduction + operation is applied to an index in :attr:`self` which is specified by + its index in :attr:`src` for ``dimension != dim`` and by the corresponding + value in :attr:`index` for ``dimension = dim``. + + Given a 3-D tensor and reduction using the multiplication operation, :attr:`self` + is updated as:: + + self[index[i][j][k]][j][k] *= src[i][j][k] # if dim == 0 + self[i][index[i][j][k]][k] *= src[i][j][k] # if dim == 1 + self[i][j][index[i][j][k]] *= src[i][j][k] # if dim == 2 + + Reducing with the addition operation is the same as using + :meth:`~torch.Tensor.scatter_add_`. + + .. warning:: + The reduce argument with Tensor ``src`` is deprecated and will be removed in + a future PyTorch release. Please use :meth:`~torch.Tensor.scatter_reduce_` + instead for more reduction options. + + Args: + dim (int): the axis along which to index + index (LongTensor): the indices of elements to scatter, can be either empty + or of the same dimensionality as ``src``. When empty, the operation + returns ``self`` unchanged. + src (Tensor): the source element(s) to scatter. + + Keyword args: + reduce (str, optional): reduction operation to apply, can be either + ``'add'`` or ``'multiply'``. + + Example:: + + >>> src = torch.arange(1, 11).reshape((2, 5)) + >>> src + tensor([[ 1, 2, 3, 4, 5], + [ 6, 7, 8, 9, 10]]) + >>> index = torch.tensor([[0, 1, 2, 0]]) + >>> torch.zeros(3, 5, dtype=src.dtype).scatter_(0, index, src) + tensor([[1, 0, 0, 4, 0], + [0, 2, 0, 0, 0], + [0, 0, 3, 0, 0]]) + >>> index = torch.tensor([[0, 1, 2], [0, 1, 4]]) + >>> torch.zeros(3, 5, dtype=src.dtype).scatter_(1, index, src) + tensor([[1, 2, 3, 0, 0], + [6, 7, 0, 0, 8], + [0, 0, 0, 0, 0]]) + + >>> torch.full((2, 4), 2.).scatter_(1, torch.tensor([[2], [3]]), + ... 1.23, reduce='multiply') + tensor([[2.0000, 2.0000, 2.4600, 2.0000], + [2.0000, 2.0000, 2.0000, 2.4600]]) + >>> torch.full((2, 4), 2.).scatter_(1, torch.tensor([[2], [3]]), + ... 1.23, reduce='add') + tensor([[2.0000, 2.0000, 3.2300, 2.0000], + [2.0000, 2.0000, 2.0000, 3.2300]]) + + .. function:: scatter_(dim, index, value, *, reduce=None) -> Tensor: + :noindex: + + Writes the value from :attr:`value` into :attr:`self` at the indices + specified in the :attr:`index` tensor. This operation is equivalent to the previous version, + with the :attr:`src` tensor filled entirely with :attr:`value`. + + Args: + dim (int): the axis along which to index + index (LongTensor): the indices of elements to scatter, can be either empty + or of the same dimensionality as ``src``. When empty, the operation + returns ``self`` unchanged. + value (Scalar): the value to scatter. + + Keyword args: + reduce (str, optional): reduction operation to apply, can be either + ``'add'`` or ``'multiply'``. + + Example:: + + >>> index = torch.tensor([[0, 1]]) + >>> value = 2 + >>> torch.zeros(3, 5).scatter_(0, index, value) + tensor([[2., 0., 0., 0., 0.], + [0., 2., 0., 0., 0.], + [0., 0., 0., 0., 0.]]) + """ + ... + @overload + def scatter_(self, dim: _int, index: Tensor, value: Union[Number, _complex]) -> Tensor: + r""" + scatter_(dim, index, src, *, reduce=None) -> Tensor + + Writes all values from the tensor :attr:`src` into :attr:`self` at the indices + specified in the :attr:`index` tensor. For each value in :attr:`src`, its output + index is specified by its index in :attr:`src` for ``dimension != dim`` and by + the corresponding value in :attr:`index` for ``dimension = dim``. + + For a 3-D tensor, :attr:`self` is updated as:: + + self[index[i][j][k]][j][k] = src[i][j][k] # if dim == 0 + self[i][index[i][j][k]][k] = src[i][j][k] # if dim == 1 + self[i][j][index[i][j][k]] = src[i][j][k] # if dim == 2 + + This is the reverse operation of the manner described in :meth:`~Tensor.gather`. + + :attr:`self`, :attr:`index` and :attr:`src` (if it is a Tensor) should all have + the same number of dimensions. It is also required that + ``index.size(d) <= src.size(d)`` for all dimensions ``d``, and that + ``index.size(d) <= self.size(d)`` for all dimensions ``d != dim``. + Note that ``index`` and ``src`` do not broadcast. + + Moreover, as for :meth:`~Tensor.gather`, the values of :attr:`index` must be + between ``0`` and ``self.size(dim) - 1`` inclusive. + + .. warning:: + + When indices are not unique, the behavior is non-deterministic (one of the + values from ``src`` will be picked arbitrarily) and the gradient will be + incorrect (it will be propagated to all locations in the source that + correspond to the same index)! + + .. note:: + + The backward pass is implemented only for ``src.shape == index.shape``. + + Additionally accepts an optional :attr:`reduce` argument that allows + specification of an optional reduction operation, which is applied to all + values in the tensor :attr:`src` into :attr:`self` at the indices + specified in the :attr:`index`. For each value in :attr:`src`, the reduction + operation is applied to an index in :attr:`self` which is specified by + its index in :attr:`src` for ``dimension != dim`` and by the corresponding + value in :attr:`index` for ``dimension = dim``. + + Given a 3-D tensor and reduction using the multiplication operation, :attr:`self` + is updated as:: + + self[index[i][j][k]][j][k] *= src[i][j][k] # if dim == 0 + self[i][index[i][j][k]][k] *= src[i][j][k] # if dim == 1 + self[i][j][index[i][j][k]] *= src[i][j][k] # if dim == 2 + + Reducing with the addition operation is the same as using + :meth:`~torch.Tensor.scatter_add_`. + + .. warning:: + The reduce argument with Tensor ``src`` is deprecated and will be removed in + a future PyTorch release. Please use :meth:`~torch.Tensor.scatter_reduce_` + instead for more reduction options. + + Args: + dim (int): the axis along which to index + index (LongTensor): the indices of elements to scatter, can be either empty + or of the same dimensionality as ``src``. When empty, the operation + returns ``self`` unchanged. + src (Tensor): the source element(s) to scatter. + + Keyword args: + reduce (str, optional): reduction operation to apply, can be either + ``'add'`` or ``'multiply'``. + + Example:: + + >>> src = torch.arange(1, 11).reshape((2, 5)) + >>> src + tensor([[ 1, 2, 3, 4, 5], + [ 6, 7, 8, 9, 10]]) + >>> index = torch.tensor([[0, 1, 2, 0]]) + >>> torch.zeros(3, 5, dtype=src.dtype).scatter_(0, index, src) + tensor([[1, 0, 0, 4, 0], + [0, 2, 0, 0, 0], + [0, 0, 3, 0, 0]]) + >>> index = torch.tensor([[0, 1, 2], [0, 1, 4]]) + >>> torch.zeros(3, 5, dtype=src.dtype).scatter_(1, index, src) + tensor([[1, 2, 3, 0, 0], + [6, 7, 0, 0, 8], + [0, 0, 0, 0, 0]]) + + >>> torch.full((2, 4), 2.).scatter_(1, torch.tensor([[2], [3]]), + ... 1.23, reduce='multiply') + tensor([[2.0000, 2.0000, 2.4600, 2.0000], + [2.0000, 2.0000, 2.0000, 2.4600]]) + >>> torch.full((2, 4), 2.).scatter_(1, torch.tensor([[2], [3]]), + ... 1.23, reduce='add') + tensor([[2.0000, 2.0000, 3.2300, 2.0000], + [2.0000, 2.0000, 2.0000, 3.2300]]) + + .. function:: scatter_(dim, index, value, *, reduce=None) -> Tensor: + :noindex: + + Writes the value from :attr:`value` into :attr:`self` at the indices + specified in the :attr:`index` tensor. This operation is equivalent to the previous version, + with the :attr:`src` tensor filled entirely with :attr:`value`. + + Args: + dim (int): the axis along which to index + index (LongTensor): the indices of elements to scatter, can be either empty + or of the same dimensionality as ``src``. When empty, the operation + returns ``self`` unchanged. + value (Scalar): the value to scatter. + + Keyword args: + reduce (str, optional): reduction operation to apply, can be either + ``'add'`` or ``'multiply'``. + + Example:: + + >>> index = torch.tensor([[0, 1]]) + >>> value = 2 + >>> torch.zeros(3, 5).scatter_(0, index, value) + tensor([[2., 0., 0., 0., 0.], + [0., 2., 0., 0., 0.], + [0., 0., 0., 0., 0.]]) + """ + ... + @overload + def scatter_add(self, dim: _int, index: Tensor, src: Tensor) -> Tensor: + r""" + scatter_add(dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_add_` + """ + ... + @overload + def scatter_add(self, dim: Union[str, ellipsis, None], index: Tensor, src: Tensor) -> Tensor: + r""" + scatter_add(dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_add_` + """ + ... + def scatter_add_(self, dim: _int, index: Tensor, src: Tensor) -> Tensor: + r""" + scatter_add_(dim, index, src) -> Tensor + + Adds all values from the tensor :attr:`src` into :attr:`self` at the indices + specified in the :attr:`index` tensor in a similar fashion as + :meth:`~torch.Tensor.scatter_`. For each value in :attr:`src`, it is added to + an index in :attr:`self` which is specified by its index in :attr:`src` + for ``dimension != dim`` and by the corresponding value in :attr:`index` for + ``dimension = dim``. + + For a 3-D tensor, :attr:`self` is updated as:: + + self[index[i][j][k]][j][k] += src[i][j][k] # if dim == 0 + self[i][index[i][j][k]][k] += src[i][j][k] # if dim == 1 + self[i][j][index[i][j][k]] += src[i][j][k] # if dim == 2 + + :attr:`self`, :attr:`index` and :attr:`src` should have same number of + dimensions. It is also required that ``index.size(d) <= src.size(d)`` for all + dimensions ``d``, and that ``index.size(d) <= self.size(d)`` for all dimensions + ``d != dim``. Note that ``index`` and ``src`` do not broadcast. + + Note: + This operation may behave nondeterministically when given tensors on a CUDA device. See :doc:`/notes/randomness` for more information. + + .. note:: + + The backward pass is implemented only for ``src.shape == index.shape``. + + Args: + dim (int): the axis along which to index + index (LongTensor): the indices of elements to scatter and add, can be + either empty or of the same dimensionality as ``src``. When empty, the + operation returns ``self`` unchanged. + src (Tensor): the source elements to scatter and add + + Example:: + + >>> src = torch.ones((2, 5)) + >>> index = torch.tensor([[0, 1, 2, 0, 0]]) + >>> torch.zeros(3, 5, dtype=src.dtype).scatter_add_(0, index, src) + tensor([[1., 0., 0., 1., 1.], + [0., 1., 0., 0., 0.], + [0., 0., 1., 0., 0.]]) + >>> index = torch.tensor([[0, 1, 2, 0, 0], [0, 1, 2, 2, 2]]) + >>> torch.zeros(3, 5, dtype=src.dtype).scatter_add_(0, index, src) + tensor([[2., 0., 0., 1., 1.], + [0., 2., 0., 0., 0.], + [0., 0., 2., 1., 1.]]) + """ + ... + def scatter_reduce(self, dim: _int, index: Tensor, src: Tensor, reduce: str, *, include_self: _bool = True) -> Tensor: + r""" + scatter_reduce(dim, index, src, reduce, *, include_self=True) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_reduce_` + """ + ... + def scatter_reduce_(self, dim: _int, index: Tensor, src: Tensor, reduce: str, *, include_self: _bool = True) -> Tensor: + r""" + scatter_reduce_(dim, index, src, reduce, *, include_self=True) -> Tensor + + Reduces all values from the :attr:`src` tensor to the indices specified in + the :attr:`index` tensor in the :attr:`self` tensor using the applied reduction + defined via the :attr:`reduce` argument (:obj:`"sum"`, :obj:`"prod"`, :obj:`"mean"`, + :obj:`"amax"`, :obj:`"amin"`). For each value in :attr:`src`, it is reduced to an + index in :attr:`self` which is specified by its index in :attr:`src` for + ``dimension != dim`` and by the corresponding value in :attr:`index` for + ``dimension = dim``. If :obj:`include_self="True"`, the values in the :attr:`self` + tensor are included in the reduction. + + :attr:`self`, :attr:`index` and :attr:`src` should all have + the same number of dimensions. It is also required that + ``index.size(d) <= src.size(d)`` for all dimensions ``d``, and that + ``index.size(d) <= self.size(d)`` for all dimensions ``d != dim``. + Note that ``index`` and ``src`` do not broadcast. + + For a 3-D tensor with :obj:`reduce="sum"` and :obj:`include_self=True` the + output is given as:: + + self[index[i][j][k]][j][k] += src[i][j][k] # if dim == 0 + self[i][index[i][j][k]][k] += src[i][j][k] # if dim == 1 + self[i][j][index[i][j][k]] += src[i][j][k] # if dim == 2 + + Note: + This operation may behave nondeterministically when given tensors on a CUDA device. See :doc:`/notes/randomness` for more information. + + .. note:: + + The backward pass is implemented only for ``src.shape == index.shape``. + + .. warning:: + + This function is in beta and may change in the near future. + + Args: + dim (int): the axis along which to index + index (LongTensor): the indices of elements to scatter and reduce. + src (Tensor): the source elements to scatter and reduce + reduce (str): the reduction operation to apply for non-unique indices + (:obj:`"sum"`, :obj:`"prod"`, :obj:`"mean"`, :obj:`"amax"`, :obj:`"amin"`) + include_self (bool): whether elements from the :attr:`self` tensor are + included in the reduction + + Example:: + + >>> src = torch.tensor([1., 2., 3., 4., 5., 6.]) + >>> index = torch.tensor([0, 1, 0, 1, 2, 1]) + >>> input = torch.tensor([1., 2., 3., 4.]) + >>> input.scatter_reduce(0, index, src, reduce="sum") + tensor([5., 14., 8., 4.]) + >>> input.scatter_reduce(0, index, src, reduce="sum", include_self=False) + tensor([4., 12., 5., 4.]) + >>> input2 = torch.tensor([5., 4., 3., 2.]) + >>> input2.scatter_reduce(0, index, src, reduce="amax") + tensor([5., 6., 5., 2.]) + >>> input2.scatter_reduce(0, index, src, reduce="amax", include_self=False) + tensor([3., 6., 5., 2.]) + """ + ... + @overload + def select(self, dim: _int, index: Union[_int, SymInt]) -> Tensor: + r""" + select(dim, index) -> Tensor + + See :func:`torch.select` + """ + ... + @overload + def select(self, dim: Union[str, ellipsis, None], index: _int) -> Tensor: + r""" + select(dim, index) -> Tensor + + See :func:`torch.select` + """ + ... + def select_scatter(self, src: Tensor, dim: _int, index: Union[_int, SymInt]) -> Tensor: + r""" + select_scatter(src, dim, index) -> Tensor + + See :func:`torch.select_scatter` + """ + ... + @overload + def set_(self, storage: Union[Storage, TypedStorage, UntypedStorage], offset: IntLikeType, size: _symsize, stride: _symsize) -> Tensor: + r""" + set_(source=None, storage_offset=0, size=None, stride=None) -> Tensor + + Sets the underlying storage, size, and strides. If :attr:`source` is a tensor, + :attr:`self` tensor will share the same storage and have the same size and + strides as :attr:`source`. Changes to elements in one tensor will be reflected + in the other. + + If :attr:`source` is a :class:`~torch.Storage`, the method sets the underlying + storage, offset, size, and stride. + + Args: + source (Tensor or Storage): the tensor or storage to use + storage_offset (int, optional): the offset in the storage + size (torch.Size, optional): the desired size. Defaults to the size of the source. + stride (tuple, optional): the desired stride. Defaults to C-contiguous strides. + """ + ... + @overload + def set_(self, storage: Union[Storage, TypedStorage, UntypedStorage]) -> Tensor: + r""" + set_(source=None, storage_offset=0, size=None, stride=None) -> Tensor + + Sets the underlying storage, size, and strides. If :attr:`source` is a tensor, + :attr:`self` tensor will share the same storage and have the same size and + strides as :attr:`source`. Changes to elements in one tensor will be reflected + in the other. + + If :attr:`source` is a :class:`~torch.Storage`, the method sets the underlying + storage, offset, size, and stride. + + Args: + source (Tensor or Storage): the tensor or storage to use + storage_offset (int, optional): the offset in the storage + size (torch.Size, optional): the desired size. Defaults to the size of the source. + stride (tuple, optional): the desired stride. Defaults to C-contiguous strides. + """ + ... + def sgn(self) -> Tensor: + r""" + sgn() -> Tensor + + See :func:`torch.sgn` + """ + ... + def sgn_(self) -> Tensor: + r""" + sgn_() -> Tensor + + In-place version of :meth:`~Tensor.sgn` + """ + ... + def short(self) -> Tensor: + r""" + short(memory_format=torch.preserve_format) -> Tensor + + ``self.short()`` is equivalent to ``self.to(torch.int16)``. See :func:`to`. + + Args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... + def sigmoid(self) -> Tensor: + r""" + sigmoid() -> Tensor + + See :func:`torch.sigmoid` + """ + ... + def sigmoid_(self) -> Tensor: + r""" + sigmoid_() -> Tensor + + In-place version of :meth:`~Tensor.sigmoid` + """ + ... + def sign(self) -> Tensor: + r""" + sign() -> Tensor + + See :func:`torch.sign` + """ + ... + def sign_(self) -> Tensor: + r""" + sign_() -> Tensor + + In-place version of :meth:`~Tensor.sign` + """ + ... + def signbit(self) -> Tensor: + r""" + signbit() -> Tensor + + See :func:`torch.signbit` + """ + ... + def sin(self) -> Tensor: + r""" + sin() -> Tensor + + See :func:`torch.sin` + """ + ... + def sin_(self) -> Tensor: + r""" + sin_() -> Tensor + + In-place version of :meth:`~Tensor.sin` + """ + ... + def sinc(self) -> Tensor: + r""" + sinc() -> Tensor + + See :func:`torch.sinc` + """ + ... + def sinc_(self) -> Tensor: + r""" + sinc_() -> Tensor + + In-place version of :meth:`~Tensor.sinc` + """ + ... + def sinh(self) -> Tensor: + r""" + sinh() -> Tensor + + See :func:`torch.sinh` + """ + ... + def sinh_(self) -> Tensor: + r""" + sinh_() -> Tensor + + In-place version of :meth:`~Tensor.sinh` + """ + ... + @overload + def size(self, dim: None = None) -> Size: + r""" + size(dim=None) -> torch.Size or int + + Returns the size of the :attr:`self` tensor. If ``dim`` is not specified, + the returned value is a :class:`torch.Size`, a subclass of :class:`tuple`. + If ``dim`` is specified, returns an int holding the size of that dimension. + + Args: + dim (int, optional): The dimension for which to retrieve the size. + + Example:: + + >>> t = torch.empty(3, 4, 5) + >>> t.size() + torch.Size([3, 4, 5]) + >>> t.size(dim=1) + 4 + """ + ... + @overload + def size(self, dim: _int) -> _int: + r""" + size(dim=None) -> torch.Size or int + + Returns the size of the :attr:`self` tensor. If ``dim`` is not specified, + the returned value is a :class:`torch.Size`, a subclass of :class:`tuple`. + If ``dim`` is specified, returns an int holding the size of that dimension. + + Args: + dim (int, optional): The dimension for which to retrieve the size. + + Example:: + + >>> t = torch.empty(3, 4, 5) + >>> t.size() + torch.Size([3, 4, 5]) + >>> t.size(dim=1) + 4 + """ + ... + def slice_inverse(self, src: Tensor, dim: _int = 0, start: Optional[Union[_int, SymInt]] = None, end: Optional[Union[_int, SymInt]] = None, step: Union[_int, SymInt] = 1) -> Tensor: ... + def slice_scatter(self, src: Tensor, dim: _int = 0, start: Optional[Union[_int, SymInt]] = None, end: Optional[Union[_int, SymInt]] = None, step: Union[_int, SymInt] = 1) -> Tensor: + r""" + slice_scatter(src, dim=0, start=None, end=None, step=1) -> Tensor + + See :func:`torch.slice_scatter` + """ + ... + def slogdet(self) -> torch.return_types.slogdet: + r""" + slogdet() -> (Tensor, Tensor) + + See :func:`torch.slogdet` + """ + ... + def smm(self, mat2: Tensor) -> Tensor: + r""" + smm(mat) -> Tensor + + See :func:`torch.smm` + """ + ... + @overload + def softmax(self, dim: _int, dtype: Optional[_dtype] = None) -> Tensor: + r""" + softmax(dim) -> Tensor + + Alias for :func:`torch.nn.functional.softmax`. + """ + ... + @overload + def softmax(self, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + softmax(dim) -> Tensor + + Alias for :func:`torch.nn.functional.softmax`. + """ + ... + @overload + def sort(self, *, stable: Optional[_bool], dim: _int = -1, descending: _bool = False) -> torch.return_types.sort: + r""" + sort(dim=-1, descending=False) -> (Tensor, LongTensor) + + See :func:`torch.sort` + """ + ... + @overload + def sort(self, dim: _int = -1, descending: _bool = False) -> torch.return_types.sort: + r""" + sort(dim=-1, descending=False) -> (Tensor, LongTensor) + + See :func:`torch.sort` + """ + ... + @overload + def sort(self, *, stable: Optional[_bool], dim: Union[str, ellipsis, None], descending: _bool = False) -> torch.return_types.sort: + r""" + sort(dim=-1, descending=False) -> (Tensor, LongTensor) + + See :func:`torch.sort` + """ + ... + @overload + def sort(self, dim: Union[str, ellipsis, None], descending: _bool = False) -> torch.return_types.sort: + r""" + sort(dim=-1, descending=False) -> (Tensor, LongTensor) + + See :func:`torch.sort` + """ + ... + def sparse_dim(self) -> _int: + r""" + sparse_dim() -> int + + Return the number of sparse dimensions in a :ref:`sparse tensor ` :attr:`self`. + + .. note:: + Returns ``0`` if :attr:`self` is not a sparse tensor. + + See also :meth:`Tensor.dense_dim` and :ref:`hybrid tensors `. + """ + ... + def sparse_mask(self, mask: Tensor) -> Tensor: + r""" + sparse_mask(mask) -> Tensor + + Returns a new :ref:`sparse tensor ` with values from a + strided tensor :attr:`self` filtered by the indices of the sparse + tensor :attr:`mask`. The values of :attr:`mask` sparse tensor are + ignored. :attr:`self` and :attr:`mask` tensors must have the same + shape. + + .. note:: + + The returned sparse tensor might contain duplicate values if :attr:`mask` + is not coalesced. It is therefore advisable to pass ``mask.coalesce()`` + if such behavior is not desired. + + .. note:: + + The returned sparse tensor has the same indices as the sparse tensor + :attr:`mask`, even when the corresponding values in :attr:`self` are + zeros. + + Args: + mask (Tensor): a sparse tensor whose indices are used as a filter + + Example:: + + >>> nse = 5 + >>> dims = (5, 5, 2, 2) + >>> I = torch.cat([torch.randint(0, dims[0], size=(nse,)), + ... torch.randint(0, dims[1], size=(nse,))], 0).reshape(2, nse) + >>> V = torch.randn(nse, dims[2], dims[3]) + >>> S = torch.sparse_coo_tensor(I, V, dims).coalesce() + >>> D = torch.randn(dims) + >>> D.sparse_mask(S) + tensor(indices=tensor([[0, 0, 0, 2], + [0, 1, 4, 3]]), + values=tensor([[[ 1.6550, 0.2397], + [-0.1611, -0.0779]], + + [[ 0.2326, -1.0558], + [ 1.4711, 1.9678]], + + [[-0.5138, -0.0411], + [ 1.9417, 0.5158]], + + [[ 0.0793, 0.0036], + [-0.2569, -0.1055]]]), + size=(5, 5, 2, 2), nnz=4, layout=torch.sparse_coo) + """ + ... + def sparse_resize_(self, size: _size, sparse_dim: _int, dense_dim: _int) -> Tensor: + r""" + sparse_resize_(size, sparse_dim, dense_dim) -> Tensor + + Resizes :attr:`self` :ref:`sparse tensor ` to the desired + size and the number of sparse and dense dimensions. + + .. note:: + If the number of specified elements in :attr:`self` is zero, then + :attr:`size`, :attr:`sparse_dim`, and :attr:`dense_dim` can be any + size and positive integers such that ``len(size) == sparse_dim + + dense_dim``. + + If :attr:`self` specifies one or more elements, however, then each + dimension in :attr:`size` must not be smaller than the corresponding + dimension of :attr:`self`, :attr:`sparse_dim` must equal the number + of sparse dimensions in :attr:`self`, and :attr:`dense_dim` must + equal the number of dense dimensions in :attr:`self`. + + .. warning:: + Throws an error if :attr:`self` is not a sparse tensor. + + Args: + size (torch.Size): the desired size. If :attr:`self` is non-empty + sparse tensor, the desired size cannot be smaller than the + original size. + sparse_dim (int): the number of sparse dimensions + dense_dim (int): the number of dense dimensions + """ + ... + def sparse_resize_and_clear_(self, size: _size, sparse_dim: _int, dense_dim: _int) -> Tensor: + r""" + sparse_resize_and_clear_(size, sparse_dim, dense_dim) -> Tensor + + Removes all specified elements from a :ref:`sparse tensor + ` :attr:`self` and resizes :attr:`self` to the desired + size and the number of sparse and dense dimensions. + + .. warning: + Throws an error if :attr:`self` is not a sparse tensor. + + Args: + size (torch.Size): the desired size. + sparse_dim (int): the number of sparse dimensions + dense_dim (int): the number of dense dimensions + """ + ... + @overload + def split(self, split_size: _int, dim: _int = 0) -> Sequence[Tensor]: ... + @overload + def split(self, split_size: Tuple[_int, ...], dim: _int = 0) -> Sequence[Tensor]: ... + def split_with_sizes(self, split_sizes: Sequence[Union[_int, SymInt]], dim: _int = 0) -> Tuple[Tensor, ...]: ... + def sqrt(self) -> Tensor: + r""" + sqrt() -> Tensor + + See :func:`torch.sqrt` + """ + ... + def sqrt_(self) -> Tensor: + r""" + sqrt_() -> Tensor + + In-place version of :meth:`~Tensor.sqrt` + """ + ... + def square(self) -> Tensor: + r""" + square() -> Tensor + + See :func:`torch.square` + """ + ... + def square_(self) -> Tensor: + r""" + square_() -> Tensor + + In-place version of :meth:`~Tensor.square` + """ + ... + @overload + def squeeze(self) -> Tensor: + r""" + squeeze(dim=None) -> Tensor + + See :func:`torch.squeeze` + """ + ... + @overload + def squeeze(self, dim: _int) -> Tensor: + r""" + squeeze(dim=None) -> Tensor + + See :func:`torch.squeeze` + """ + ... + @overload + def squeeze(self, dim: _size) -> Tensor: + r""" + squeeze(dim=None) -> Tensor + + See :func:`torch.squeeze` + """ + ... + @overload + def squeeze(self, *dim: _int) -> Tensor: + r""" + squeeze(dim=None) -> Tensor + + See :func:`torch.squeeze` + """ + ... + @overload + def squeeze(self, dim: Union[str, ellipsis, None]) -> Tensor: + r""" + squeeze(dim=None) -> Tensor + + See :func:`torch.squeeze` + """ + ... + @overload + def squeeze_(self) -> Tensor: + r""" + squeeze_(dim=None) -> Tensor + + In-place version of :meth:`~Tensor.squeeze` + """ + ... + @overload + def squeeze_(self, dim: _int) -> Tensor: + r""" + squeeze_(dim=None) -> Tensor + + In-place version of :meth:`~Tensor.squeeze` + """ + ... + @overload + def squeeze_(self, dim: _size) -> Tensor: + r""" + squeeze_(dim=None) -> Tensor + + In-place version of :meth:`~Tensor.squeeze` + """ + ... + @overload + def squeeze_(self, *dim: _int) -> Tensor: + r""" + squeeze_(dim=None) -> Tensor + + In-place version of :meth:`~Tensor.squeeze` + """ + ... + @overload + def squeeze_(self, dim: Union[str, ellipsis, None]) -> Tensor: + r""" + squeeze_(dim=None) -> Tensor + + In-place version of :meth:`~Tensor.squeeze` + """ + ... + def sspaddmm(self, mat1: Tensor, mat2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + sspaddmm(mat1, mat2, *, beta=1, alpha=1) -> Tensor + + See :func:`torch.sspaddmm` + """ + ... + @overload + def std(self, dim: Optional[Union[_int, _size]], unbiased: _bool = True, keepdim: _bool = False) -> Tensor: + r""" + std(dim=None, *, correction=1, keepdim=False) -> Tensor + + See :func:`torch.std` + """ + ... + @overload + def std(self, dim: Optional[Union[_int, _size]] = None, *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False) -> Tensor: + r""" + std(dim=None, *, correction=1, keepdim=False) -> Tensor + + See :func:`torch.std` + """ + ... + @overload + def std(self, unbiased: _bool = True) -> Tensor: + r""" + std(dim=None, *, correction=1, keepdim=False) -> Tensor + + See :func:`torch.std` + """ + ... + @overload + def std(self, dim: Sequence[Union[str, ellipsis, None]], unbiased: _bool = True, keepdim: _bool = False) -> Tensor: + r""" + std(dim=None, *, correction=1, keepdim=False) -> Tensor + + See :func:`torch.std` + """ + ... + @overload + def std(self, dim: Sequence[Union[str, ellipsis, None]], *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False) -> Tensor: + r""" + std(dim=None, *, correction=1, keepdim=False) -> Tensor + + See :func:`torch.std` + """ + ... + def untyped_storage(self) -> UntypedStorage: ... + def storage_offset(self) -> Union[_int, SymInt]: + r""" + storage_offset() -> int + + Returns :attr:`self` tensor's offset in the underlying storage in terms of + number of storage elements (not bytes). + + Example:: + + >>> x = torch.tensor([1, 2, 3, 4, 5]) + >>> x.storage_offset() + 0 + >>> x[3:].storage_offset() + 3 + """ + ... + def storage_type(self) -> Storage: ... + @overload + def stride(self, dim: None = None) -> Tuple[_int, ...]: + r""" + stride(dim) -> tuple or int + + Returns the stride of :attr:`self` tensor. + + Stride is the jump necessary to go from one element to the next one in the + specified dimension :attr:`dim`. A tuple of all strides is returned when no + argument is passed in. Otherwise, an integer value is returned as the stride in + the particular dimension :attr:`dim`. + + Args: + dim (int, optional): the desired dimension in which stride is required + + Example:: + + >>> x = torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]) + >>> x.stride() + (5, 1) + >>> x.stride(0) + 5 + >>> x.stride(-1) + 1 + """ + ... + @overload + def stride(self, dim: _int) -> _int: + r""" + stride(dim) -> tuple or int + + Returns the stride of :attr:`self` tensor. + + Stride is the jump necessary to go from one element to the next one in the + specified dimension :attr:`dim`. A tuple of all strides is returned when no + argument is passed in. Otherwise, an integer value is returned as the stride in + the particular dimension :attr:`dim`. + + Args: + dim (int, optional): the desired dimension in which stride is required + + Example:: + + >>> x = torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]) + >>> x.stride() + (5, 1) + >>> x.stride(0) + 5 + >>> x.stride(-1) + 1 + """ + ... + def sub(self, other: Union[Tensor, Number, _complex, torch.SymInt, torch.SymFloat], *, alpha: Optional[Union[Number, _complex]] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + sub(other, *, alpha=1) -> Tensor + + See :func:`torch.sub`. + """ + ... + def sub_(self, other: Union[Tensor, Number, _complex, torch.SymInt, torch.SymFloat], *, alpha: Optional[Union[Number, _complex]] = 1) -> Tensor: + r""" + sub_(other, *, alpha=1) -> Tensor + + In-place version of :meth:`~Tensor.sub` + """ + ... + @overload + def subtract(self, other: Tensor, *, alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + subtract(other, *, alpha=1) -> Tensor + + See :func:`torch.subtract`. + """ + ... + @overload + def subtract(self, other: Union[Number, _complex], alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + subtract(other, *, alpha=1) -> Tensor + + See :func:`torch.subtract`. + """ + ... + @overload + def subtract_(self, other: Tensor, *, alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + subtract_(other, *, alpha=1) -> Tensor + + In-place version of :meth:`~Tensor.subtract`. + """ + ... + @overload + def subtract_(self, other: Union[Number, _complex], alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + subtract_(other, *, alpha=1) -> Tensor + + In-place version of :meth:`~Tensor.subtract`. + """ + ... + @overload + def sum(self, *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + sum(dim=None, keepdim=False, dtype=None) -> Tensor + + See :func:`torch.sum` + """ + ... + @overload + def sum(self, dim: Optional[Union[_int, _size]], keepdim: _bool = False, *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + sum(dim=None, keepdim=False, dtype=None) -> Tensor + + See :func:`torch.sum` + """ + ... + @overload + def sum(self, dim: Sequence[Union[str, ellipsis, None]], keepdim: _bool = False, *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + sum(dim=None, keepdim=False, dtype=None) -> Tensor + + See :func:`torch.sum` + """ + ... + @overload + def sum_to_size(self, size: Sequence[Union[_int, SymInt]]) -> Tensor: + r""" + sum_to_size(*size) -> Tensor + + Sum ``this`` tensor to :attr:`size`. + :attr:`size` must be broadcastable to ``this`` tensor size. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + """ + ... + @overload + def sum_to_size(self, *size: Union[_int, SymInt]) -> Tensor: + r""" + sum_to_size(*size) -> Tensor + + Sum ``this`` tensor to :attr:`size`. + :attr:`size` must be broadcastable to ``this`` tensor size. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + """ + ... + def svd(self, some: _bool = True, compute_uv: _bool = True) -> torch.return_types.svd: + r""" + svd(some=True, compute_uv=True) -> (Tensor, Tensor, Tensor) + + See :func:`torch.svd` + """ + ... + def swapaxes(self, axis0: _int, axis1: _int) -> Tensor: + r""" + swapaxes(axis0, axis1) -> Tensor + + See :func:`torch.swapaxes` + """ + ... + def swapaxes_(self, axis0: _int, axis1: _int) -> Tensor: + r""" + swapaxes_(axis0, axis1) -> Tensor + + In-place version of :meth:`~Tensor.swapaxes` + """ + ... + def swapdims(self, dim0: _int, dim1: _int) -> Tensor: + r""" + swapdims(dim0, dim1) -> Tensor + + See :func:`torch.swapdims` + """ + ... + def swapdims_(self, dim0: _int, dim1: _int) -> Tensor: + r""" + swapdims_(dim0, dim1) -> Tensor + + In-place version of :meth:`~Tensor.swapdims` + """ + ... + def t(self) -> Tensor: + r""" + t() -> Tensor + + See :func:`torch.t` + """ + ... + def t_(self) -> Tensor: + r""" + t_() -> Tensor + + In-place version of :meth:`~Tensor.t` + """ + ... + def take(self, index: Tensor) -> Tensor: + r""" + take(indices) -> Tensor + + See :func:`torch.take` + """ + ... + def take_along_dim(self, indices: Tensor, dim: Optional[_int] = None) -> Tensor: + r""" + take_along_dim(indices, dim) -> Tensor + + See :func:`torch.take_along_dim` + """ + ... + def tan(self) -> Tensor: + r""" + tan() -> Tensor + + See :func:`torch.tan` + """ + ... + def tan_(self) -> Tensor: + r""" + tan_() -> Tensor + + In-place version of :meth:`~Tensor.tan` + """ + ... + def tanh(self) -> Tensor: + r""" + tanh() -> Tensor + + See :func:`torch.tanh` + """ + ... + def tanh_(self) -> Tensor: + r""" + tanh_() -> Tensor + + In-place version of :meth:`~Tensor.tanh` + """ + ... + @overload + def tensor_split(self, indices: Sequence[Union[_int, SymInt]], dim: _int = 0) -> Tuple[Tensor, ...]: + r""" + tensor_split(indices_or_sections, dim=0) -> List of Tensors + + See :func:`torch.tensor_split` + """ + ... + @overload + def tensor_split(self, tensor_indices_or_sections: Tensor, dim: _int = 0) -> Tuple[Tensor, ...]: + r""" + tensor_split(indices_or_sections, dim=0) -> List of Tensors + + See :func:`torch.tensor_split` + """ + ... + @overload + def tensor_split(self, sections: Union[_int, SymInt], dim: _int = 0) -> Tuple[Tensor, ...]: + r""" + tensor_split(indices_or_sections, dim=0) -> List of Tensors + + See :func:`torch.tensor_split` + """ + ... + @overload + def tile(self, dims: Sequence[Union[_int, SymInt]]) -> Tensor: + r""" + tile(dims) -> Tensor + + See :func:`torch.tile` + """ + ... + @overload + def tile(self, *dims: Union[_int, SymInt]) -> Tensor: + r""" + tile(dims) -> Tensor + + See :func:`torch.tile` + """ + ... + @overload + def to(self, dtype: _dtype, non_blocking: _bool = False, copy: _bool = False, *, memory_format: Optional[torch.memory_format] = None) -> Tensor: + r""" + to(*args, **kwargs) -> Tensor + + Performs Tensor dtype and/or device conversion. A :class:`torch.dtype` and :class:`torch.device` are + inferred from the arguments of ``self.to(*args, **kwargs)``. + + .. note:: + + If the ``self`` Tensor already + has the correct :class:`torch.dtype` and :class:`torch.device`, then ``self`` is returned. + Otherwise, the returned tensor is a copy of ``self`` with the desired + :class:`torch.dtype` and :class:`torch.device`. + + Here are the ways to call ``to``: + + .. method:: to(dtype, non_blocking=False, copy=False, memory_format=torch.preserve_format) -> Tensor + :noindex: + + Returns a Tensor with the specified :attr:`dtype` + + Args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + + .. method:: to(device=None, dtype=None, non_blocking=False, copy=False, memory_format=torch.preserve_format) -> Tensor + :noindex: + + Returns a Tensor with the specified :attr:`device` and (optional) + :attr:`dtype`. If :attr:`dtype` is ``None`` it is inferred to be ``self.dtype``. + When :attr:`non_blocking`, tries to convert asynchronously with respect to + the host if possible, e.g., converting a CPU Tensor with pinned memory to a + CUDA Tensor. + When :attr:`copy` is set, a new Tensor is created even when the Tensor + already matches the desired conversion. + + Args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + + .. method:: to(other, non_blocking=False, copy=False) -> Tensor + :noindex: + + Returns a Tensor with same :class:`torch.dtype` and :class:`torch.device` as + the Tensor :attr:`other`. When :attr:`non_blocking`, tries to convert + asynchronously with respect to the host if possible, e.g., converting a CPU + Tensor with pinned memory to a CUDA Tensor. + When :attr:`copy` is set, a new Tensor is created even when the Tensor + already matches the desired conversion. + + Example:: + + >>> tensor = torch.randn(2, 2) # Initially dtype=float32, device=cpu + >>> tensor.to(torch.float64) + tensor([[-0.5044, 0.0005], + [ 0.3310, -0.0584]], dtype=torch.float64) + + >>> cuda0 = torch.device('cuda:0') + >>> tensor.to(cuda0) + tensor([[-0.5044, 0.0005], + [ 0.3310, -0.0584]], device='cuda:0') + + >>> tensor.to(cuda0, dtype=torch.float64) + tensor([[-0.5044, 0.0005], + [ 0.3310, -0.0584]], dtype=torch.float64, device='cuda:0') + + >>> other = torch.randn((), dtype=torch.float64, device=cuda0) + >>> tensor.to(other, non_blocking=True) + tensor([[-0.5044, 0.0005], + [ 0.3310, -0.0584]], dtype=torch.float64, device='cuda:0') + """ + ... + @overload + def to(self, device: Optional[DeviceLikeType] = None, dtype: Optional[_dtype] = None, non_blocking: _bool = False, copy: _bool = False, *, memory_format: Optional[torch.memory_format] = None) -> Tensor: + r""" + to(*args, **kwargs) -> Tensor + + Performs Tensor dtype and/or device conversion. A :class:`torch.dtype` and :class:`torch.device` are + inferred from the arguments of ``self.to(*args, **kwargs)``. + + .. note:: + + If the ``self`` Tensor already + has the correct :class:`torch.dtype` and :class:`torch.device`, then ``self`` is returned. + Otherwise, the returned tensor is a copy of ``self`` with the desired + :class:`torch.dtype` and :class:`torch.device`. + + Here are the ways to call ``to``: + + .. method:: to(dtype, non_blocking=False, copy=False, memory_format=torch.preserve_format) -> Tensor + :noindex: + + Returns a Tensor with the specified :attr:`dtype` + + Args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + + .. method:: to(device=None, dtype=None, non_blocking=False, copy=False, memory_format=torch.preserve_format) -> Tensor + :noindex: + + Returns a Tensor with the specified :attr:`device` and (optional) + :attr:`dtype`. If :attr:`dtype` is ``None`` it is inferred to be ``self.dtype``. + When :attr:`non_blocking`, tries to convert asynchronously with respect to + the host if possible, e.g., converting a CPU Tensor with pinned memory to a + CUDA Tensor. + When :attr:`copy` is set, a new Tensor is created even when the Tensor + already matches the desired conversion. + + Args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + + .. method:: to(other, non_blocking=False, copy=False) -> Tensor + :noindex: + + Returns a Tensor with same :class:`torch.dtype` and :class:`torch.device` as + the Tensor :attr:`other`. When :attr:`non_blocking`, tries to convert + asynchronously with respect to the host if possible, e.g., converting a CPU + Tensor with pinned memory to a CUDA Tensor. + When :attr:`copy` is set, a new Tensor is created even when the Tensor + already matches the desired conversion. + + Example:: + + >>> tensor = torch.randn(2, 2) # Initially dtype=float32, device=cpu + >>> tensor.to(torch.float64) + tensor([[-0.5044, 0.0005], + [ 0.3310, -0.0584]], dtype=torch.float64) + + >>> cuda0 = torch.device('cuda:0') + >>> tensor.to(cuda0) + tensor([[-0.5044, 0.0005], + [ 0.3310, -0.0584]], device='cuda:0') + + >>> tensor.to(cuda0, dtype=torch.float64) + tensor([[-0.5044, 0.0005], + [ 0.3310, -0.0584]], dtype=torch.float64, device='cuda:0') + + >>> other = torch.randn((), dtype=torch.float64, device=cuda0) + >>> tensor.to(other, non_blocking=True) + tensor([[-0.5044, 0.0005], + [ 0.3310, -0.0584]], dtype=torch.float64, device='cuda:0') + """ + ... + @overload + def to(self, other: Tensor, non_blocking: _bool = False, copy: _bool = False, *, memory_format: Optional[torch.memory_format] = None) -> Tensor: + r""" + to(*args, **kwargs) -> Tensor + + Performs Tensor dtype and/or device conversion. A :class:`torch.dtype` and :class:`torch.device` are + inferred from the arguments of ``self.to(*args, **kwargs)``. + + .. note:: + + If the ``self`` Tensor already + has the correct :class:`torch.dtype` and :class:`torch.device`, then ``self`` is returned. + Otherwise, the returned tensor is a copy of ``self`` with the desired + :class:`torch.dtype` and :class:`torch.device`. + + Here are the ways to call ``to``: + + .. method:: to(dtype, non_blocking=False, copy=False, memory_format=torch.preserve_format) -> Tensor + :noindex: + + Returns a Tensor with the specified :attr:`dtype` + + Args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + + .. method:: to(device=None, dtype=None, non_blocking=False, copy=False, memory_format=torch.preserve_format) -> Tensor + :noindex: + + Returns a Tensor with the specified :attr:`device` and (optional) + :attr:`dtype`. If :attr:`dtype` is ``None`` it is inferred to be ``self.dtype``. + When :attr:`non_blocking`, tries to convert asynchronously with respect to + the host if possible, e.g., converting a CPU Tensor with pinned memory to a + CUDA Tensor. + When :attr:`copy` is set, a new Tensor is created even when the Tensor + already matches the desired conversion. + + Args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + + .. method:: to(other, non_blocking=False, copy=False) -> Tensor + :noindex: + + Returns a Tensor with same :class:`torch.dtype` and :class:`torch.device` as + the Tensor :attr:`other`. When :attr:`non_blocking`, tries to convert + asynchronously with respect to the host if possible, e.g., converting a CPU + Tensor with pinned memory to a CUDA Tensor. + When :attr:`copy` is set, a new Tensor is created even when the Tensor + already matches the desired conversion. + + Example:: + + >>> tensor = torch.randn(2, 2) # Initially dtype=float32, device=cpu + >>> tensor.to(torch.float64) + tensor([[-0.5044, 0.0005], + [ 0.3310, -0.0584]], dtype=torch.float64) + + >>> cuda0 = torch.device('cuda:0') + >>> tensor.to(cuda0) + tensor([[-0.5044, 0.0005], + [ 0.3310, -0.0584]], device='cuda:0') + + >>> tensor.to(cuda0, dtype=torch.float64) + tensor([[-0.5044, 0.0005], + [ 0.3310, -0.0584]], dtype=torch.float64, device='cuda:0') + + >>> other = torch.randn((), dtype=torch.float64, device=cuda0) + >>> tensor.to(other, non_blocking=True) + tensor([[-0.5044, 0.0005], + [ 0.3310, -0.0584]], dtype=torch.float64, device='cuda:0') + """ + ... + def to_dense(self, dtype: Optional[_dtype] = None, *, masked_grad: Optional[_bool] = None) -> Tensor: + r""" + to_dense(dtype=None, *, masked_grad=True) -> Tensor + + Creates a strided copy of :attr:`self` if :attr:`self` is not a strided tensor, otherwise returns :attr:`self`. + + Keyword args: + {dtype} + masked_grad (bool, optional): If set to ``True`` (default) and + :attr:`self` has a sparse layout then the backward of + :meth:`to_dense` returns ``grad.sparse_mask(self)``. + + Example:: + + >>> s = torch.sparse_coo_tensor( + ... torch.tensor([[1, 1], + ... [0, 2]]), + ... torch.tensor([9, 10]), + ... size=(3, 3)) + >>> s.to_dense() + tensor([[ 0, 0, 0], + [ 9, 0, 10], + [ 0, 0, 0]]) + """ + ... + def to_mkldnn(self, dtype: Optional[_dtype] = None) -> Tensor: + r""" + to_mkldnn() -> Tensor + Returns a copy of the tensor in ``torch.mkldnn`` layout. + """ + ... + def to_padded_tensor(self, padding: _float, output_size: Optional[Sequence[Union[_int, SymInt]]] = None) -> Tensor: + r""" + to_padded_tensor(padding, output_size=None) -> Tensor + See :func:`to_padded_tensor` + """ + ... + @overload + def to_sparse(self, *, layout: Optional[_layout] = None, blocksize: Optional[Union[_int, _size]] = None, dense_dim: Optional[_int] = None) -> Tensor: + r""" + to_sparse(sparseDims) -> Tensor + + Returns a sparse copy of the tensor. PyTorch supports sparse tensors in + :ref:`coordinate format `. + + Args: + sparseDims (int, optional): the number of sparse dimensions to include in the new sparse tensor + + Example:: + + >>> d = torch.tensor([[0, 0, 0], [9, 0, 10], [0, 0, 0]]) + >>> d + tensor([[ 0, 0, 0], + [ 9, 0, 10], + [ 0, 0, 0]]) + >>> d.to_sparse() + tensor(indices=tensor([[1, 1], + [0, 2]]), + values=tensor([ 9, 10]), + size=(3, 3), nnz=2, layout=torch.sparse_coo) + >>> d.to_sparse(1) + tensor(indices=tensor([[1]]), + values=tensor([[ 9, 0, 10]]), + size=(3, 3), nnz=1, layout=torch.sparse_coo) + + .. method:: to_sparse(*, layout=None, blocksize=None, dense_dim=None) -> Tensor + :noindex: + + Returns a sparse tensor with the specified layout and blocksize. If + the :attr:`self` is strided, the number of dense dimensions could be + specified, and a hybrid sparse tensor will be created, with + `dense_dim` dense dimensions and `self.dim() - 2 - dense_dim` batch + dimension. + + .. note:: If the :attr:`self` layout and blocksize parameters match + with the specified layout and blocksize, return + :attr:`self`. Otherwise, return a sparse tensor copy of + :attr:`self`. + + Args: + + layout (:class:`torch.layout`, optional): The desired sparse + layout. One of ``torch.sparse_coo``, ``torch.sparse_csr``, + ``torch.sparse_csc``, ``torch.sparse_bsr``, or + ``torch.sparse_bsc``. Default: if ``None``, + ``torch.sparse_coo``. + + blocksize (list, tuple, :class:`torch.Size`, optional): Block size + of the resulting BSR or BSC tensor. For other layouts, + specifying the block size that is not ``None`` will result in a + RuntimeError exception. A block size must be a tuple of length + two such that its items evenly divide the two sparse dimensions. + + dense_dim (int, optional): Number of dense dimensions of the + resulting CSR, CSC, BSR or BSC tensor. This argument should be + used only if :attr:`self` is a strided tensor, and must be a + value between 0 and dimension of :attr:`self` tensor minus two. + + Example:: + + >>> x = torch.tensor([[1, 0], [0, 0], [2, 3]]) + >>> x.to_sparse(layout=torch.sparse_coo) + tensor(indices=tensor([[0, 2, 2], + [0, 0, 1]]), + values=tensor([1, 2, 3]), + size=(3, 2), nnz=3, layout=torch.sparse_coo) + >>> x.to_sparse(layout=torch.sparse_bsr, blocksize=(1, 2)) + tensor(crow_indices=tensor([0, 1, 1, 2]), + col_indices=tensor([0, 0]), + values=tensor([[[1, 0]], + [[2, 3]]]), size=(3, 2), nnz=2, layout=torch.sparse_bsr) + >>> x.to_sparse(layout=torch.sparse_bsr, blocksize=(2, 1)) + RuntimeError: Tensor size(-2) 3 needs to be divisible by blocksize[0] 2 + >>> x.to_sparse(layout=torch.sparse_csr, blocksize=(3, 1)) + RuntimeError: to_sparse for Strided to SparseCsr conversion does not use specified blocksize + + >>> x = torch.tensor([[[1], [0]], [[0], [0]], [[2], [3]]]) + >>> x.to_sparse(layout=torch.sparse_csr, dense_dim=1) + tensor(crow_indices=tensor([0, 1, 1, 3]), + col_indices=tensor([0, 0, 1]), + values=tensor([[1], + [2], + [3]]), size=(3, 2, 1), nnz=3, layout=torch.sparse_csr) + """ + ... + @overload + def to_sparse(self, sparse_dim: _int) -> Tensor: + r""" + to_sparse(sparseDims) -> Tensor + + Returns a sparse copy of the tensor. PyTorch supports sparse tensors in + :ref:`coordinate format `. + + Args: + sparseDims (int, optional): the number of sparse dimensions to include in the new sparse tensor + + Example:: + + >>> d = torch.tensor([[0, 0, 0], [9, 0, 10], [0, 0, 0]]) + >>> d + tensor([[ 0, 0, 0], + [ 9, 0, 10], + [ 0, 0, 0]]) + >>> d.to_sparse() + tensor(indices=tensor([[1, 1], + [0, 2]]), + values=tensor([ 9, 10]), + size=(3, 3), nnz=2, layout=torch.sparse_coo) + >>> d.to_sparse(1) + tensor(indices=tensor([[1]]), + values=tensor([[ 9, 0, 10]]), + size=(3, 3), nnz=1, layout=torch.sparse_coo) + + .. method:: to_sparse(*, layout=None, blocksize=None, dense_dim=None) -> Tensor + :noindex: + + Returns a sparse tensor with the specified layout and blocksize. If + the :attr:`self` is strided, the number of dense dimensions could be + specified, and a hybrid sparse tensor will be created, with + `dense_dim` dense dimensions and `self.dim() - 2 - dense_dim` batch + dimension. + + .. note:: If the :attr:`self` layout and blocksize parameters match + with the specified layout and blocksize, return + :attr:`self`. Otherwise, return a sparse tensor copy of + :attr:`self`. + + Args: + + layout (:class:`torch.layout`, optional): The desired sparse + layout. One of ``torch.sparse_coo``, ``torch.sparse_csr``, + ``torch.sparse_csc``, ``torch.sparse_bsr``, or + ``torch.sparse_bsc``. Default: if ``None``, + ``torch.sparse_coo``. + + blocksize (list, tuple, :class:`torch.Size`, optional): Block size + of the resulting BSR or BSC tensor. For other layouts, + specifying the block size that is not ``None`` will result in a + RuntimeError exception. A block size must be a tuple of length + two such that its items evenly divide the two sparse dimensions. + + dense_dim (int, optional): Number of dense dimensions of the + resulting CSR, CSC, BSR or BSC tensor. This argument should be + used only if :attr:`self` is a strided tensor, and must be a + value between 0 and dimension of :attr:`self` tensor minus two. + + Example:: + + >>> x = torch.tensor([[1, 0], [0, 0], [2, 3]]) + >>> x.to_sparse(layout=torch.sparse_coo) + tensor(indices=tensor([[0, 2, 2], + [0, 0, 1]]), + values=tensor([1, 2, 3]), + size=(3, 2), nnz=3, layout=torch.sparse_coo) + >>> x.to_sparse(layout=torch.sparse_bsr, blocksize=(1, 2)) + tensor(crow_indices=tensor([0, 1, 1, 2]), + col_indices=tensor([0, 0]), + values=tensor([[[1, 0]], + [[2, 3]]]), size=(3, 2), nnz=2, layout=torch.sparse_bsr) + >>> x.to_sparse(layout=torch.sparse_bsr, blocksize=(2, 1)) + RuntimeError: Tensor size(-2) 3 needs to be divisible by blocksize[0] 2 + >>> x.to_sparse(layout=torch.sparse_csr, blocksize=(3, 1)) + RuntimeError: to_sparse for Strided to SparseCsr conversion does not use specified blocksize + + >>> x = torch.tensor([[[1], [0]], [[0], [0]], [[2], [3]]]) + >>> x.to_sparse(layout=torch.sparse_csr, dense_dim=1) + tensor(crow_indices=tensor([0, 1, 1, 3]), + col_indices=tensor([0, 0, 1]), + values=tensor([[1], + [2], + [3]]), size=(3, 2, 1), nnz=3, layout=torch.sparse_csr) + """ + ... + def to_sparse_bsc(self, blocksize: Union[_int, _size], dense_dim: Optional[_int] = None) -> Tensor: + r""" + to_sparse_bsc(blocksize, dense_dim) -> Tensor + + Convert a tensor to a block sparse column (BSC) storage format of + given blocksize. If the :attr:`self` is strided, then the number of + dense dimensions could be specified, and a hybrid BSC tensor will be + created, with `dense_dim` dense dimensions and `self.dim() - 2 - + dense_dim` batch dimension. + + Args: + + blocksize (list, tuple, :class:`torch.Size`, optional): Block size + of the resulting BSC tensor. A block size must be a tuple of + length two such that its items evenly divide the two sparse + dimensions. + + dense_dim (int, optional): Number of dense dimensions of the + resulting BSC tensor. This argument should be used only if + :attr:`self` is a strided tensor, and must be a value between 0 + and dimension of :attr:`self` tensor minus two. + + Example:: + + >>> dense = torch.randn(10, 10) + >>> sparse = dense.to_sparse_csr() + >>> sparse_bsc = sparse.to_sparse_bsc((5, 5)) + >>> sparse_bsc.row_indices() + tensor([0, 1, 0, 1]) + + >>> dense = torch.zeros(4, 3, 1) + >>> dense[0:2, 0] = dense[0:2, 2] = dense[2:4, 1] = 1 + >>> dense.to_sparse_bsc((2, 1), 1) + tensor(ccol_indices=tensor([0, 1, 2, 3]), + row_indices=tensor([0, 1, 0]), + values=tensor([[[[1.]], + + [[1.]]], + + + [[[1.]], + + [[1.]]], + + + [[[1.]], + + [[1.]]]]), size=(4, 3, 1), nnz=3, + layout=torch.sparse_bsc) + """ + ... + def to_sparse_bsr(self, blocksize: Union[_int, _size], dense_dim: Optional[_int] = None) -> Tensor: + r""" + to_sparse_bsr(blocksize, dense_dim) -> Tensor + + Convert a tensor to a block sparse row (BSR) storage format of given + blocksize. If the :attr:`self` is strided, then the number of dense + dimensions could be specified, and a hybrid BSR tensor will be + created, with `dense_dim` dense dimensions and `self.dim() - 2 - + dense_dim` batch dimension. + + Args: + + blocksize (list, tuple, :class:`torch.Size`, optional): Block size + of the resulting BSR tensor. A block size must be a tuple of + length two such that its items evenly divide the two sparse + dimensions. + + dense_dim (int, optional): Number of dense dimensions of the + resulting BSR tensor. This argument should be used only if + :attr:`self` is a strided tensor, and must be a value between 0 + and dimension of :attr:`self` tensor minus two. + + Example:: + + >>> dense = torch.randn(10, 10) + >>> sparse = dense.to_sparse_csr() + >>> sparse_bsr = sparse.to_sparse_bsr((5, 5)) + >>> sparse_bsr.col_indices() + tensor([0, 1, 0, 1]) + + >>> dense = torch.zeros(4, 3, 1) + >>> dense[0:2, 0] = dense[0:2, 2] = dense[2:4, 1] = 1 + >>> dense.to_sparse_bsr((2, 1), 1) + tensor(crow_indices=tensor([0, 2, 3]), + col_indices=tensor([0, 2, 1]), + values=tensor([[[[1.]], + + [[1.]]], + + + [[[1.]], + + [[1.]]], + + + [[[1.]], + + [[1.]]]]), size=(4, 3, 1), nnz=3, + layout=torch.sparse_bsr) + """ + ... + def to_sparse_csc(self, dense_dim: Optional[_int] = None) -> Tensor: + r""" + to_sparse_csc() -> Tensor + + Convert a tensor to compressed column storage (CSC) format. Except + for strided tensors, only works with 2D tensors. If the :attr:`self` + is strided, then the number of dense dimensions could be specified, + and a hybrid CSC tensor will be created, with `dense_dim` dense + dimensions and `self.dim() - 2 - dense_dim` batch dimension. + + Args: + + dense_dim (int, optional): Number of dense dimensions of the + resulting CSC tensor. This argument should be used only if + :attr:`self` is a strided tensor, and must be a value between 0 + and dimension of :attr:`self` tensor minus two. + + Example:: + + >>> dense = torch.randn(5, 5) + >>> sparse = dense.to_sparse_csc() + >>> sparse._nnz() + 25 + + >>> dense = torch.zeros(3, 3, 1, 1) + >>> dense[0, 0] = dense[1, 2] = dense[2, 1] = 1 + >>> dense.to_sparse_csc(dense_dim=2) + tensor(ccol_indices=tensor([0, 1, 2, 3]), + row_indices=tensor([0, 2, 1]), + values=tensor([[[1.]], + + [[1.]], + + [[1.]]]), size=(3, 3, 1, 1), nnz=3, + layout=torch.sparse_csc) + """ + ... + def to_sparse_csr(self, dense_dim: Optional[_int] = None) -> Tensor: + r""" + to_sparse_csr(dense_dim=None) -> Tensor + + Convert a tensor to compressed row storage format (CSR). Except for + strided tensors, only works with 2D tensors. If the :attr:`self` is + strided, then the number of dense dimensions could be specified, and a + hybrid CSR tensor will be created, with `dense_dim` dense dimensions + and `self.dim() - 2 - dense_dim` batch dimension. + + Args: + + dense_dim (int, optional): Number of dense dimensions of the + resulting CSR tensor. This argument should be used only if + :attr:`self` is a strided tensor, and must be a value between 0 + and dimension of :attr:`self` tensor minus two. + + Example:: + + >>> dense = torch.randn(5, 5) + >>> sparse = dense.to_sparse_csr() + >>> sparse._nnz() + 25 + + >>> dense = torch.zeros(3, 3, 1, 1) + >>> dense[0, 0] = dense[1, 2] = dense[2, 1] = 1 + >>> dense.to_sparse_csr(dense_dim=2) + tensor(crow_indices=tensor([0, 1, 2, 3]), + col_indices=tensor([0, 2, 1]), + values=tensor([[[1.]], + + [[1.]], + + [[1.]]]), size=(3, 3, 1, 1), nnz=3, + layout=torch.sparse_csr) + """ + ... + def tolist(self) -> List: + r""" + tolist() -> list or number + + Returns the tensor as a (nested) list. For scalars, a standard + Python number is returned, just like with :meth:`~Tensor.item`. + Tensors are automatically moved to the CPU first if necessary. + + This operation is not differentiable. + + Examples:: + + >>> a = torch.randn(2, 2) + >>> a.tolist() + [[0.012766935862600803, 0.5415473580360413], + [-0.08909505605697632, 0.7729271650314331]] + >>> a[0,0].tolist() + 0.012766935862600803 + """ + ... + def topk(self, k: Union[_int, SymInt], dim: _int = -1, largest: _bool = True, sorted: _bool = True) -> torch.return_types.topk: + r""" + topk(k, dim=None, largest=True, sorted=True) -> (Tensor, LongTensor) + + See :func:`torch.topk` + """ + ... + def trace(self) -> Tensor: + r""" + trace() -> Tensor + + See :func:`torch.trace` + """ + ... + @overload + def transpose(self, dim0: _int, dim1: _int) -> Tensor: + r""" + transpose(dim0, dim1) -> Tensor + + See :func:`torch.transpose` + """ + ... + @overload + def transpose(self, dim0: Union[str, ellipsis, None], dim1: Union[str, ellipsis, None]) -> Tensor: + r""" + transpose(dim0, dim1) -> Tensor + + See :func:`torch.transpose` + """ + ... + def transpose_(self, dim0: _int, dim1: _int) -> Tensor: + r""" + transpose_(dim0, dim1) -> Tensor + + In-place version of :meth:`~Tensor.transpose` + """ + ... + def triangular_solve(self, A: Tensor, upper: _bool = True, transpose: _bool = False, unitriangular: _bool = False) -> torch.return_types.triangular_solve: + r""" + triangular_solve(A, upper=True, transpose=False, unitriangular=False) -> (Tensor, Tensor) + + See :func:`torch.triangular_solve` + """ + ... + def tril(self, diagonal: _int = 0) -> Tensor: + r""" + tril(diagonal=0) -> Tensor + + See :func:`torch.tril` + """ + ... + def tril_(self, diagonal: _int = 0) -> Tensor: + r""" + tril_(diagonal=0) -> Tensor + + In-place version of :meth:`~Tensor.tril` + """ + ... + def triu(self, diagonal: _int = 0) -> Tensor: + r""" + triu(diagonal=0) -> Tensor + + See :func:`torch.triu` + """ + ... + def triu_(self, diagonal: _int = 0) -> Tensor: + r""" + triu_(diagonal=0) -> Tensor + + In-place version of :meth:`~Tensor.triu` + """ + ... + def true_divide(self, other: Union[Tensor, Number, torch.SymInt, torch.SymFloat], *, out: Optional[Tensor] = None) -> Tensor: + r""" + true_divide(value) -> Tensor + + See :func:`torch.true_divide` + """ + ... + def true_divide_(self, other: Union[Tensor, Number, torch.SymInt, torch.SymFloat]) -> Tensor: + r""" + true_divide_(value) -> Tensor + + In-place version of :meth:`~Tensor.true_divide_` + """ + ... + def trunc(self) -> Tensor: + r""" + trunc() -> Tensor + + See :func:`torch.trunc` + """ + ... + def trunc_(self) -> Tensor: + r""" + trunc_() -> Tensor + + In-place version of :meth:`~Tensor.trunc` + """ + ... + @overload + def type(self, dtype: None = None, non_blocking: _bool = False) -> str: + r""" + type(dtype=None, non_blocking=False, **kwargs) -> str or Tensor + Returns the type if `dtype` is not provided, else casts this object to + the specified type. + + If this is already of the correct type, no copy is performed and the + original object is returned. + + Args: + dtype (dtype or string): The desired type + non_blocking (bool): If ``True``, and the source is in pinned memory + and destination is on the GPU or vice versa, the copy is performed + asynchronously with respect to the host. Otherwise, the argument + has no effect. + **kwargs: For compatibility, may contain the key ``async`` in place of + the ``non_blocking`` argument. The ``async`` arg is deprecated. + """ + ... + @overload + def type(self, dtype: Union[str, _dtype], non_blocking: _bool = False) -> Tensor: + r""" + type(dtype=None, non_blocking=False, **kwargs) -> str or Tensor + Returns the type if `dtype` is not provided, else casts this object to + the specified type. + + If this is already of the correct type, no copy is performed and the + original object is returned. + + Args: + dtype (dtype or string): The desired type + non_blocking (bool): If ``True``, and the source is in pinned memory + and destination is on the GPU or vice versa, the copy is performed + asynchronously with respect to the host. Otherwise, the argument + has no effect. + **kwargs: For compatibility, may contain the key ``async`` in place of + the ``non_blocking`` argument. The ``async`` arg is deprecated. + """ + ... + def type_as(self, other: Tensor) -> Tensor: + r""" + type_as(tensor) -> Tensor + + Returns this tensor cast to the type of the given tensor. + + This is a no-op if the tensor is already of the correct type. This is + equivalent to ``self.type(tensor.type())`` + + Args: + tensor (Tensor): the tensor which has the desired type + """ + ... + @overload + def unbind(self, dim: _int = 0) -> Tuple[Tensor, ...]: + r""" + unbind(dim=0) -> seq + + See :func:`torch.unbind` + """ + ... + @overload + def unbind(self, dim: Union[str, ellipsis, None]) -> Tuple[Tensor, ...]: + r""" + unbind(dim=0) -> seq + + See :func:`torch.unbind` + """ + ... + @overload + def unflatten(self, dim: Union[str, ellipsis, None], sizes: Sequence[Union[_int, SymInt]], names: Sequence[Union[str, ellipsis, None]]) -> Tensor: ... + @overload + def unflatten(self, dim: _int, sizes: Sequence[Union[_int, SymInt]]) -> Tensor: ... + def unfold(self, dimension: _int, size: _int, step: _int) -> Tensor: + r""" + unfold(dimension, size, step) -> Tensor + + Returns a view of the original tensor which contains all slices of size :attr:`size` from + :attr:`self` tensor in the dimension :attr:`dimension`. + + Step between two slices is given by :attr:`step`. + + If `sizedim` is the size of dimension :attr:`dimension` for :attr:`self`, the size of + dimension :attr:`dimension` in the returned tensor will be + `(sizedim - size) / step + 1`. + + An additional dimension of size :attr:`size` is appended in the returned tensor. + + Args: + dimension (int): dimension in which unfolding happens + size (int): the size of each slice that is unfolded + step (int): the step between each slice + + Example:: + + >>> x = torch.arange(1., 8) + >>> x + tensor([ 1., 2., 3., 4., 5., 6., 7.]) + >>> x.unfold(0, 2, 1) + tensor([[ 1., 2.], + [ 2., 3.], + [ 3., 4.], + [ 4., 5.], + [ 5., 6.], + [ 6., 7.]]) + >>> x.unfold(0, 2, 2) + tensor([[ 1., 2.], + [ 3., 4.], + [ 5., 6.]]) + """ + ... + def uniform_(self, from_: _float = 0, to: _float = 1, *, generator: Optional[Generator] = None) -> Tensor: + r""" + uniform_(from=0, to=1, *, generator=None) -> Tensor + + Fills :attr:`self` tensor with numbers sampled from the continuous uniform + distribution: + + .. math:: + f(x) = \dfrac{1}{\text{to} - \text{from}} + """ + ... + def unsafe_chunk(self, chunks: _int, dim: _int = 0) -> Tuple[Tensor, ...]: + r""" + unsafe_chunk(chunks, dim=0) -> List of Tensors + + See :func:`torch.unsafe_chunk` + """ + ... + def unsafe_split(self, split_size: Union[_int, SymInt], dim: _int = 0) -> Tuple[Tensor, ...]: + r""" + unsafe_split(split_size, dim=0) -> List of Tensors + + See :func:`torch.unsafe_split` + """ + ... + def unsafe_split_with_sizes(self, split_sizes: Sequence[Union[_int, SymInt]], dim: _int = 0) -> Tuple[Tensor, ...]: ... + def unsqueeze(self, dim: _int) -> Tensor: + r""" + unsqueeze(dim) -> Tensor + + See :func:`torch.unsqueeze` + """ + ... + def unsqueeze_(self, dim: _int) -> Tensor: + r""" + unsqueeze_(dim) -> Tensor + + In-place version of :meth:`~Tensor.unsqueeze` + """ + ... + def values(self) -> Tensor: + r""" + values() -> Tensor + + Return the values tensor of a :ref:`sparse COO tensor `. + + .. warning:: + Throws an error if :attr:`self` is not a sparse COO tensor. + + See also :meth:`Tensor.indices`. + + .. note:: + This method can only be called on a coalesced sparse tensor. See + :meth:`Tensor.coalesce` for details. + """ + ... + @overload + def var(self, dim: Optional[Union[_int, _size]], unbiased: _bool = True, keepdim: _bool = False) -> Tensor: + r""" + var(dim=None, *, correction=1, keepdim=False) -> Tensor + + See :func:`torch.var` + """ + ... + @overload + def var(self, dim: Optional[Union[_int, _size]] = None, *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False) -> Tensor: + r""" + var(dim=None, *, correction=1, keepdim=False) -> Tensor + + See :func:`torch.var` + """ + ... + @overload + def var(self, unbiased: _bool = True) -> Tensor: + r""" + var(dim=None, *, correction=1, keepdim=False) -> Tensor + + See :func:`torch.var` + """ + ... + @overload + def var(self, dim: Sequence[Union[str, ellipsis, None]], unbiased: _bool = True, keepdim: _bool = False) -> Tensor: + r""" + var(dim=None, *, correction=1, keepdim=False) -> Tensor + + See :func:`torch.var` + """ + ... + @overload + def var(self, dim: Sequence[Union[str, ellipsis, None]], *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False) -> Tensor: + r""" + var(dim=None, *, correction=1, keepdim=False) -> Tensor + + See :func:`torch.var` + """ + ... + def vdot(self, other: Tensor) -> Tensor: + r""" + vdot(other) -> Tensor + + See :func:`torch.vdot` + """ + ... + @overload + def view(self, dtype: _dtype) -> Tensor: + r""" + view(*shape) -> Tensor + + Returns a new tensor with the same data as the :attr:`self` tensor but of a + different :attr:`shape`. + + The returned tensor shares the same data and must have the same number + of elements, but may have a different size. For a tensor to be viewed, the new + view size must be compatible with its original size and stride, i.e., each new + view dimension must either be a subspace of an original dimension, or only span + across original dimensions :math:`d, d+1, \dots, d+k` that satisfy the following + contiguity-like condition that :math:`\forall i = d, \dots, d+k-1`, + + .. math:: + + \text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1] + + Otherwise, it will not be possible to view :attr:`self` tensor as :attr:`shape` + without copying it (e.g., via :meth:`contiguous`). When it is unclear whether a + :meth:`view` can be performed, it is advisable to use :meth:`reshape`, which + returns a view if the shapes are compatible, and copies (equivalent to calling + :meth:`contiguous`) otherwise. + + Args: + shape (torch.Size or int...): the desired size + + Example:: + + >>> x = torch.randn(4, 4) + >>> x.size() + torch.Size([4, 4]) + >>> y = x.view(16) + >>> y.size() + torch.Size([16]) + >>> z = x.view(-1, 8) # the size -1 is inferred from other dimensions + >>> z.size() + torch.Size([2, 8]) + + >>> a = torch.randn(1, 2, 3, 4) + >>> a.size() + torch.Size([1, 2, 3, 4]) + >>> b = a.transpose(1, 2) # Swaps 2nd and 3rd dimension + >>> b.size() + torch.Size([1, 3, 2, 4]) + >>> c = a.view(1, 3, 2, 4) # Does not change tensor layout in memory + >>> c.size() + torch.Size([1, 3, 2, 4]) + >>> torch.equal(b, c) + False + + + .. method:: view(dtype) -> Tensor + :noindex: + + Returns a new tensor with the same data as the :attr:`self` tensor but of a + different :attr:`dtype`. + + If the element size of :attr:`dtype` is different than that of ``self.dtype``, + then the size of the last dimension of the output will be scaled + proportionally. For instance, if :attr:`dtype` element size is twice that of + ``self.dtype``, then each pair of elements in the last dimension of + :attr:`self` will be combined, and the size of the last dimension of the output + will be half that of :attr:`self`. If :attr:`dtype` element size is half that + of ``self.dtype``, then each element in the last dimension of :attr:`self` will + be split in two, and the size of the last dimension of the output will be + double that of :attr:`self`. For this to be possible, the following conditions + must be true: + + * ``self.dim()`` must be greater than 0. + * ``self.stride(-1)`` must be 1. + + Additionally, if the element size of :attr:`dtype` is greater than that of + ``self.dtype``, the following conditions must be true as well: + + * ``self.size(-1)`` must be divisible by the ratio between the element + sizes of the dtypes. + * ``self.storage_offset()`` must be divisible by the ratio between the + element sizes of the dtypes. + * The strides of all dimensions, except the last dimension, must be + divisible by the ratio between the element sizes of the dtypes. + + If any of the above conditions are not met, an error is thrown. + + .. warning:: + + This overload is not supported by TorchScript, and using it in a Torchscript + program will cause undefined behavior. + + + Args: + dtype (:class:`torch.dtype`): the desired dtype + + Example:: + + >>> x = torch.randn(4, 4) + >>> x + tensor([[ 0.9482, -0.0310, 1.4999, -0.5316], + [-0.1520, 0.7472, 0.5617, -0.8649], + [-2.4724, -0.0334, -0.2976, -0.8499], + [-0.2109, 1.9913, -0.9607, -0.6123]]) + >>> x.dtype + torch.float32 + + >>> y = x.view(torch.int32) + >>> y + tensor([[ 1064483442, -1124191867, 1069546515, -1089989247], + [-1105482831, 1061112040, 1057999968, -1084397505], + [-1071760287, -1123489973, -1097310419, -1084649136], + [-1101533110, 1073668768, -1082790149, -1088634448]], + dtype=torch.int32) + >>> y[0, 0] = 1000000000 + >>> x + tensor([[ 0.0047, -0.0310, 1.4999, -0.5316], + [-0.1520, 0.7472, 0.5617, -0.8649], + [-2.4724, -0.0334, -0.2976, -0.8499], + [-0.2109, 1.9913, -0.9607, -0.6123]]) + + >>> x.view(torch.cfloat) + tensor([[ 0.0047-0.0310j, 1.4999-0.5316j], + [-0.1520+0.7472j, 0.5617-0.8649j], + [-2.4724-0.0334j, -0.2976-0.8499j], + [-0.2109+1.9913j, -0.9607-0.6123j]]) + >>> x.view(torch.cfloat).size() + torch.Size([4, 2]) + + >>> x.view(torch.uint8) + tensor([[ 0, 202, 154, 59, 182, 243, 253, 188, 185, 252, 191, 63, 240, 22, + 8, 191], + [227, 165, 27, 190, 128, 72, 63, 63, 146, 203, 15, 63, 22, 106, + 93, 191], + [205, 59, 30, 192, 112, 206, 8, 189, 7, 95, 152, 190, 12, 147, + 89, 191], + [ 43, 246, 87, 190, 235, 226, 254, 63, 111, 240, 117, 191, 177, 191, + 28, 191]], dtype=torch.uint8) + >>> x.view(torch.uint8).size() + torch.Size([4, 16]) + """ + ... + @overload + def view(self, size: Sequence[Union[_int, SymInt]]) -> Tensor: + r""" + view(*shape) -> Tensor + + Returns a new tensor with the same data as the :attr:`self` tensor but of a + different :attr:`shape`. + + The returned tensor shares the same data and must have the same number + of elements, but may have a different size. For a tensor to be viewed, the new + view size must be compatible with its original size and stride, i.e., each new + view dimension must either be a subspace of an original dimension, or only span + across original dimensions :math:`d, d+1, \dots, d+k` that satisfy the following + contiguity-like condition that :math:`\forall i = d, \dots, d+k-1`, + + .. math:: + + \text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1] + + Otherwise, it will not be possible to view :attr:`self` tensor as :attr:`shape` + without copying it (e.g., via :meth:`contiguous`). When it is unclear whether a + :meth:`view` can be performed, it is advisable to use :meth:`reshape`, which + returns a view if the shapes are compatible, and copies (equivalent to calling + :meth:`contiguous`) otherwise. + + Args: + shape (torch.Size or int...): the desired size + + Example:: + + >>> x = torch.randn(4, 4) + >>> x.size() + torch.Size([4, 4]) + >>> y = x.view(16) + >>> y.size() + torch.Size([16]) + >>> z = x.view(-1, 8) # the size -1 is inferred from other dimensions + >>> z.size() + torch.Size([2, 8]) + + >>> a = torch.randn(1, 2, 3, 4) + >>> a.size() + torch.Size([1, 2, 3, 4]) + >>> b = a.transpose(1, 2) # Swaps 2nd and 3rd dimension + >>> b.size() + torch.Size([1, 3, 2, 4]) + >>> c = a.view(1, 3, 2, 4) # Does not change tensor layout in memory + >>> c.size() + torch.Size([1, 3, 2, 4]) + >>> torch.equal(b, c) + False + + + .. method:: view(dtype) -> Tensor + :noindex: + + Returns a new tensor with the same data as the :attr:`self` tensor but of a + different :attr:`dtype`. + + If the element size of :attr:`dtype` is different than that of ``self.dtype``, + then the size of the last dimension of the output will be scaled + proportionally. For instance, if :attr:`dtype` element size is twice that of + ``self.dtype``, then each pair of elements in the last dimension of + :attr:`self` will be combined, and the size of the last dimension of the output + will be half that of :attr:`self`. If :attr:`dtype` element size is half that + of ``self.dtype``, then each element in the last dimension of :attr:`self` will + be split in two, and the size of the last dimension of the output will be + double that of :attr:`self`. For this to be possible, the following conditions + must be true: + + * ``self.dim()`` must be greater than 0. + * ``self.stride(-1)`` must be 1. + + Additionally, if the element size of :attr:`dtype` is greater than that of + ``self.dtype``, the following conditions must be true as well: + + * ``self.size(-1)`` must be divisible by the ratio between the element + sizes of the dtypes. + * ``self.storage_offset()`` must be divisible by the ratio between the + element sizes of the dtypes. + * The strides of all dimensions, except the last dimension, must be + divisible by the ratio between the element sizes of the dtypes. + + If any of the above conditions are not met, an error is thrown. + + .. warning:: + + This overload is not supported by TorchScript, and using it in a Torchscript + program will cause undefined behavior. + + + Args: + dtype (:class:`torch.dtype`): the desired dtype + + Example:: + + >>> x = torch.randn(4, 4) + >>> x + tensor([[ 0.9482, -0.0310, 1.4999, -0.5316], + [-0.1520, 0.7472, 0.5617, -0.8649], + [-2.4724, -0.0334, -0.2976, -0.8499], + [-0.2109, 1.9913, -0.9607, -0.6123]]) + >>> x.dtype + torch.float32 + + >>> y = x.view(torch.int32) + >>> y + tensor([[ 1064483442, -1124191867, 1069546515, -1089989247], + [-1105482831, 1061112040, 1057999968, -1084397505], + [-1071760287, -1123489973, -1097310419, -1084649136], + [-1101533110, 1073668768, -1082790149, -1088634448]], + dtype=torch.int32) + >>> y[0, 0] = 1000000000 + >>> x + tensor([[ 0.0047, -0.0310, 1.4999, -0.5316], + [-0.1520, 0.7472, 0.5617, -0.8649], + [-2.4724, -0.0334, -0.2976, -0.8499], + [-0.2109, 1.9913, -0.9607, -0.6123]]) + + >>> x.view(torch.cfloat) + tensor([[ 0.0047-0.0310j, 1.4999-0.5316j], + [-0.1520+0.7472j, 0.5617-0.8649j], + [-2.4724-0.0334j, -0.2976-0.8499j], + [-0.2109+1.9913j, -0.9607-0.6123j]]) + >>> x.view(torch.cfloat).size() + torch.Size([4, 2]) + + >>> x.view(torch.uint8) + tensor([[ 0, 202, 154, 59, 182, 243, 253, 188, 185, 252, 191, 63, 240, 22, + 8, 191], + [227, 165, 27, 190, 128, 72, 63, 63, 146, 203, 15, 63, 22, 106, + 93, 191], + [205, 59, 30, 192, 112, 206, 8, 189, 7, 95, 152, 190, 12, 147, + 89, 191], + [ 43, 246, 87, 190, 235, 226, 254, 63, 111, 240, 117, 191, 177, 191, + 28, 191]], dtype=torch.uint8) + >>> x.view(torch.uint8).size() + torch.Size([4, 16]) + """ + ... + @overload + def view(self, *size: Union[_int, SymInt]) -> Tensor: + r""" + view(*shape) -> Tensor + + Returns a new tensor with the same data as the :attr:`self` tensor but of a + different :attr:`shape`. + + The returned tensor shares the same data and must have the same number + of elements, but may have a different size. For a tensor to be viewed, the new + view size must be compatible with its original size and stride, i.e., each new + view dimension must either be a subspace of an original dimension, or only span + across original dimensions :math:`d, d+1, \dots, d+k` that satisfy the following + contiguity-like condition that :math:`\forall i = d, \dots, d+k-1`, + + .. math:: + + \text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1] + + Otherwise, it will not be possible to view :attr:`self` tensor as :attr:`shape` + without copying it (e.g., via :meth:`contiguous`). When it is unclear whether a + :meth:`view` can be performed, it is advisable to use :meth:`reshape`, which + returns a view if the shapes are compatible, and copies (equivalent to calling + :meth:`contiguous`) otherwise. + + Args: + shape (torch.Size or int...): the desired size + + Example:: + + >>> x = torch.randn(4, 4) + >>> x.size() + torch.Size([4, 4]) + >>> y = x.view(16) + >>> y.size() + torch.Size([16]) + >>> z = x.view(-1, 8) # the size -1 is inferred from other dimensions + >>> z.size() + torch.Size([2, 8]) + + >>> a = torch.randn(1, 2, 3, 4) + >>> a.size() + torch.Size([1, 2, 3, 4]) + >>> b = a.transpose(1, 2) # Swaps 2nd and 3rd dimension + >>> b.size() + torch.Size([1, 3, 2, 4]) + >>> c = a.view(1, 3, 2, 4) # Does not change tensor layout in memory + >>> c.size() + torch.Size([1, 3, 2, 4]) + >>> torch.equal(b, c) + False + + + .. method:: view(dtype) -> Tensor + :noindex: + + Returns a new tensor with the same data as the :attr:`self` tensor but of a + different :attr:`dtype`. + + If the element size of :attr:`dtype` is different than that of ``self.dtype``, + then the size of the last dimension of the output will be scaled + proportionally. For instance, if :attr:`dtype` element size is twice that of + ``self.dtype``, then each pair of elements in the last dimension of + :attr:`self` will be combined, and the size of the last dimension of the output + will be half that of :attr:`self`. If :attr:`dtype` element size is half that + of ``self.dtype``, then each element in the last dimension of :attr:`self` will + be split in two, and the size of the last dimension of the output will be + double that of :attr:`self`. For this to be possible, the following conditions + must be true: + + * ``self.dim()`` must be greater than 0. + * ``self.stride(-1)`` must be 1. + + Additionally, if the element size of :attr:`dtype` is greater than that of + ``self.dtype``, the following conditions must be true as well: + + * ``self.size(-1)`` must be divisible by the ratio between the element + sizes of the dtypes. + * ``self.storage_offset()`` must be divisible by the ratio between the + element sizes of the dtypes. + * The strides of all dimensions, except the last dimension, must be + divisible by the ratio between the element sizes of the dtypes. + + If any of the above conditions are not met, an error is thrown. + + .. warning:: + + This overload is not supported by TorchScript, and using it in a Torchscript + program will cause undefined behavior. + + + Args: + dtype (:class:`torch.dtype`): the desired dtype + + Example:: + + >>> x = torch.randn(4, 4) + >>> x + tensor([[ 0.9482, -0.0310, 1.4999, -0.5316], + [-0.1520, 0.7472, 0.5617, -0.8649], + [-2.4724, -0.0334, -0.2976, -0.8499], + [-0.2109, 1.9913, -0.9607, -0.6123]]) + >>> x.dtype + torch.float32 + + >>> y = x.view(torch.int32) + >>> y + tensor([[ 1064483442, -1124191867, 1069546515, -1089989247], + [-1105482831, 1061112040, 1057999968, -1084397505], + [-1071760287, -1123489973, -1097310419, -1084649136], + [-1101533110, 1073668768, -1082790149, -1088634448]], + dtype=torch.int32) + >>> y[0, 0] = 1000000000 + >>> x + tensor([[ 0.0047, -0.0310, 1.4999, -0.5316], + [-0.1520, 0.7472, 0.5617, -0.8649], + [-2.4724, -0.0334, -0.2976, -0.8499], + [-0.2109, 1.9913, -0.9607, -0.6123]]) + + >>> x.view(torch.cfloat) + tensor([[ 0.0047-0.0310j, 1.4999-0.5316j], + [-0.1520+0.7472j, 0.5617-0.8649j], + [-2.4724-0.0334j, -0.2976-0.8499j], + [-0.2109+1.9913j, -0.9607-0.6123j]]) + >>> x.view(torch.cfloat).size() + torch.Size([4, 2]) + + >>> x.view(torch.uint8) + tensor([[ 0, 202, 154, 59, 182, 243, 253, 188, 185, 252, 191, 63, 240, 22, + 8, 191], + [227, 165, 27, 190, 128, 72, 63, 63, 146, 203, 15, 63, 22, 106, + 93, 191], + [205, 59, 30, 192, 112, 206, 8, 189, 7, 95, 152, 190, 12, 147, + 89, 191], + [ 43, 246, 87, 190, 235, 226, 254, 63, 111, 240, 117, 191, 177, 191, + 28, 191]], dtype=torch.uint8) + >>> x.view(torch.uint8).size() + torch.Size([4, 16]) + """ + ... + def view_as(self, other: Tensor) -> Tensor: + r""" + view_as(other) -> Tensor + + View this tensor as the same size as :attr:`other`. + ``self.view_as(other)`` is equivalent to ``self.view(other.size())``. + + Please see :meth:`~Tensor.view` for more information about ``view``. + + Args: + other (:class:`torch.Tensor`): The result tensor has the same size + as :attr:`other`. + """ + ... + @overload + def vsplit(self, sections: _int) -> Tuple[Tensor, ...]: + r""" + vsplit(split_size_or_sections) -> List of Tensors + + See :func:`torch.vsplit` + """ + ... + @overload + def vsplit(self, indices: _size) -> Tuple[Tensor, ...]: + r""" + vsplit(split_size_or_sections) -> List of Tensors + + See :func:`torch.vsplit` + """ + ... + @overload + def vsplit(self, *indices: _int) -> Tuple[Tensor, ...]: + r""" + vsplit(split_size_or_sections) -> List of Tensors + + See :func:`torch.vsplit` + """ + ... + @overload + def where(self, condition: Tensor, other: Tensor) -> Tensor: + r""" + where(condition, y) -> Tensor + + ``self.where(condition, y)`` is equivalent to ``torch.where(condition, self, y)``. + See :func:`torch.where` + """ + ... + @overload + def where(self, condition: Tensor, other: Union[Number, _complex]) -> Tensor: + r""" + where(condition, y) -> Tensor + + ``self.where(condition, y)`` is equivalent to ``torch.where(condition, self, y)``. + See :func:`torch.where` + """ + ... + @overload + def xlogy(self, other: Tensor) -> Tensor: + r""" + xlogy(other) -> Tensor + + See :func:`torch.xlogy` + """ + ... + @overload + def xlogy(self, other: Union[Number, _complex]) -> Tensor: + r""" + xlogy(other) -> Tensor + + See :func:`torch.xlogy` + """ + ... + @overload + def xlogy_(self, other: Tensor) -> Tensor: + r""" + xlogy_(other) -> Tensor + + In-place version of :meth:`~Tensor.xlogy` + """ + ... + @overload + def xlogy_(self, other: Union[Number, _complex]) -> Tensor: + r""" + xlogy_(other) -> Tensor + + In-place version of :meth:`~Tensor.xlogy` + """ + ... + def xpu(self, device: Optional[Union[_device, _int, str]] = None, non_blocking: _bool = False, memory_format: torch.memory_format = torch.preserve_format) -> Tensor: + r""" + xpu(device=None, non_blocking=False, memory_format=torch.preserve_format) -> Tensor + + Returns a copy of this object in XPU memory. + + If this object is already in XPU memory and on the correct device, + then no copy is performed and the original object is returned. + + Args: + device (:class:`torch.device`): The destination XPU device. + Defaults to the current XPU device. + non_blocking (bool): If ``True`` and the source is in pinned memory, + the copy will be asynchronous with respect to the host. + Otherwise, the argument has no effect. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... + def zero_(self) -> Tensor: + r""" + zero_() -> Tensor + + Fills :attr:`self` tensor with zeros. + """ + ... + +_TensorBase = TensorBase + +# Defined in torch/csrc/multiprocessing/init.cpp +def _multiprocessing_init() -> None: ... +def _set_thread_name(name: str) -> None: ... +def _get_thread_name() -> str: ... + +# Defined in torch/csrc/Module.cpp +def _accelerator_hooks_device_count() -> _int: ... +def _accelerator_hooks_set_current_device(device_index: _int) -> None: ... +def _accelerator_hooks_get_current_device() -> _int: ... +def _accelerator_hooks_exchange_device(device_index: _int) -> _int: ... +def _accelerator_hooks_maybe_exchange_device(device_index: _int) -> _int: ... +def _get_accelerator(check: _bool = False) -> _device: ... + +# Defined in torch/csrc/mtia/Module.cpp +def _mtia_init() -> None: ... +def _mtia_isBuilt() -> _bool: ... +def _mtia_isInBadFork() -> _bool: ... +def _mtia_deviceSynchronize() -> None: ... +def _mtia_getCurrentStream(device: _int) -> Stream: ... +def _mtia_setCurrentStream(stream: Stream) -> None: ... +def _mtia_getDefaultStream(device: _int) -> Stream: ... +def _mtia_memoryStats(device: _int) -> Dict[str, Any]: ... + + +# Defined in torch/csrc/mps/Module.cpp +def _mps_deviceSynchronize() -> None: ... +def _mps_get_default_generator() -> Generator: ... +def _mps_emptyCache() -> None: ... +def _mps_setMemoryFraction(fraction: _float) -> None: ... +def _mps_currentAllocatedMemory() -> _int: ... +def _mps_driverAllocatedMemory() -> _int: ... +def _mps_recommendedMaxMemory() -> _int: ... +def _mps_is_available() -> _bool: ... +def _mps_is_on_macos_or_newer(major: _int, minor: _int) -> _bool: ... +def _mps_profilerStartTrace(mode: str, wait_until_completed: _bool) -> None: ... +def _mps_profilerStopTrace() -> None: ... +def _mps_acquireEvent(enable_timing: _bool) -> _int: ... +def _mps_releaseEvent(event_id: _int) -> None: ... +def _mps_recordEvent(event_id: _int) -> None: ... +def _mps_waitForEvent(event_id: _int) -> None: ... +def _mps_synchronizeEvent(event_id: _int) -> None: ... +def _mps_queryEvent(event_id: _int) -> _bool: ... +def _mps_elapsedTimeOfEvents(start_event_id: _int, end_event_id: _int) -> _float: ... + + +# Defined in torch/csrc/cuda/Module.cpp +def _cuda_getCurrentStream(device: _int) -> Tuple: ... +def _cuda_getCurrentRawStream(device: _int) -> _int: ... +def _cuda_getDefaultStream(device: _int) -> Tuple: ... +def _cuda_getCurrentBlasHandle() -> _int: ... +def _cuda_clearCublasWorkspaces() -> None: ... +def _cuda_setDevice(device: _int) -> None: ... +def _cuda_exchangeDevice(device: _int) -> _int: ... +def _cuda_maybeExchangeDevice(device: _int) -> _int: ... +def _cuda_getDevice() -> _int: ... +def _cuda_getDeviceCount() -> _int: ... +def _cuda_set_sync_debug_mode(warn_level: Union[_int, str]) -> None: ... +def _cuda_get_sync_debug_mode() -> _int: ... +def _cuda_sleep(cycles: _int) -> None: ... +def _cuda_synchronize() -> None: ... +def _cuda_ipc_collect() -> None: ... +def _cuda_getArchFlags() -> Optional[str]: ... +def _cuda_init() -> None: ... +def _cuda_setStream(stream_id: _int, device_index: _int, device_type: _int) -> None: ... +def _cuda_getCompiledVersion() -> _int: ... +def _cuda_cudaHostAllocator() -> _int: ... +def _cuda_cudaCachingAllocator_raw_alloc(size: _int, cuda_stream: _int) -> _int: ... +def _cuda_cudaCachingAllocator_raw_delete(ptr: _int) -> None: ... +def _cuda_cudaCachingAllocator_set_allocator_settings(env: str) -> None: ... +def _cuda_beginAllocateToPool(device: _int, mempool_id: Tuple[_int, _int]) -> None: ... +def _cuda_beginAllocateCurrentStreamToPool(device: _int, mempool_id: Tuple[_int, _int]) -> None: ... +def _cuda_endAllocateCurrentStreamToPool(device: _int, mempool_id: Tuple[_int, _int]) -> None: ... +def _cuda_releasePool(device: _int, mempool_id: Tuple[_int, _int]) -> None: ... +def _cuda_checkPoolLiveAllocations(device: _int, mempool_id: Tuple[_int, _int], expected_live_allocations: Set) -> _bool: ... +def _cuda_setCheckpointPoolState(device: _int, state: _cuda_CUDAAllocator_AllocatorState, stale_storages: List[_int], storages_to_add_deleters_to: List[_int]) -> None: ... +def _cuda_setMemoryFraction(fraction: _float, device: _int) -> None: ... +def _cuda_emptyCache() -> None: ... +def _cuda_memoryStats(device: _int) -> Dict[str, Any]: ... +def _cuda_resetAccumulatedMemoryStats(device: _int) -> None: ... +def _cuda_resetPeakMemoryStats(device: _int) -> None: ... +def _cuda_memorySnapshot() -> Dict[str, Any]: ... +def _cuda_record_memory_history_legacy( + enabled: _bool, + record_context: _bool, + record_context_cpp: _bool, + alloc_trace_max_entries: _int, + alloc_trace_record_context: _bool, +) -> None: ... +def _cuda_record_memory_history( + enabled: Optional[str], + context: Optional[str], + stacks: str, + max_entries +) -> None: ... +def _cuda_isHistoryEnabled() -> _bool: ... + +def _cuda_getAllocatorBackend() -> str: ... +class _cuda_CUDAAllocator_AllocatorState: + pass +def _cuda_getCheckpointState(device: _int, mempool: Tuple[_int, _int]) -> _cuda_CUDAAllocator_AllocatorState: ... +def _set_cached_tensors_enabled(enabled: _bool) -> None: ... +def _add_cached_tensor(t: Tensor) -> None: ... +def _remove_cached_tensor(t: Tensor) -> None: ... +def _tensors_data_ptrs_at_indices_equal(tensors: List[Union[Tensor, _int]], ptrs: List[Optional[_int]], indices: List[_int]) -> _bool: ... +def _construct_CUDA_Tensor_From_Storage_And_Metadata(metadata: dict, storage: Storage) -> Tensor: ... +def _storage_Use_Count(storage_ptr: _int) -> _int: ... +def _set_storage_access_error_msg(t: Tensor, s: str) -> None: ... +def _free_And_Remove_DeleterFn(storage_ptr: _int) -> None: ... +def _has_Standard_Deleter(storage_ptr: _int) -> _bool: ... + +class _cuda_CUDAAllocator: ... + +def _cuda_customAllocator(alloc_fn: _int, free_fn: _int) -> _cuda_CUDAAllocator: ... +def _cuda_changeCurrentAllocator(allocator: _cuda_CUDAAllocator) -> None: ... +def _cuda_getAllocator() -> _cuda_CUDAAllocator: ... +def _cuda_lock_mutex() -> None: ... +def _cuda_unlock_mutex() -> None: ... +def _cuda_canDeviceAccessPeer(device: _int, peer_device: _int) -> _bool: ... +def _cuda_jiterator_compile_and_launch_kernel( + code_string: str, + kernel_name: str, + return_by_ref: _bool, + num_outputs: _int, + tensors: Tuple, + kwargs: Dict[str, Union[_int, _float, _bool]], +) -> Tensor: ... +def _cuda_get_cudnn_benchmark_limit() -> _int: ... +def _cuda_set_cudnn_benchmark_limit(arg: _int) -> None: ... +def _cuda_get_conv_benchmark_empty_cache() -> _bool: ... +def _cudnn_set_conv_benchmark_empty_cache(enable: _bool) -> None: ... +def _nccl_version() -> _int: ... +def _nccl_version_suffix() -> bytes : ... +def _nccl_unique_id() -> bytes: ... +def _nccl_init_rank(nranks: _int, comm_id: bytes, rank: _int) -> object: ... +def _nccl_reduce( + input: Sequence[Tensor], + output: Tensor, + root: _int, + op: _int, + streams: Optional[Sequence[_CudaStreamBase]], + comms: Optional[Sequence[object]], +) -> None: ... +def _nccl_all_reduce( + input: Sequence[Tensor], + output: Sequence[Tensor], + op: _int, + streams: Optional[Sequence[_CudaStreamBase]], + comms: Optional[Sequence[object]], +) -> None: ... +def _nccl_broadcast( + input: Sequence[Tensor], + root: _int, + streams: Optional[Sequence[_CudaStreamBase]], + comms: Optional[Sequence[object]], +) -> None: ... +def _nccl_all_gather( + input: Sequence[Tensor], + output: Sequence[Tensor], + streams: Optional[Sequence[_CudaStreamBase]], + comms: Optional[Sequence[object]], +) -> None: ... +def _nccl_reduce_scatter( + input: Sequence[Tensor], + output: Sequence[Tensor], + op: _int, + streams: Optional[Sequence[_CudaStreamBase]], + comms: Optional[Sequence[object]], +) -> None: ... +def _rocm_is_backward_pass() -> _bool: ... +def _cuda_tunableop_enable(val: _bool) -> None: ... +def _cuda_tunableop_is_enabled() -> _bool: ... +def _cuda_tunableop_tuning_enable(val: _bool) -> None: ... +def _cuda_tunableop_tuning_is_enabled() -> _bool: ... +def _cuda_tunableop_set_max_tuning_duration(duration: _int) -> None: ... +def _cuda_tunableop_get_max_tuning_duration() -> _int: ... +def _cuda_tunableop_set_max_tuning_iterations(iterations: _int) -> None: ... +def _cuda_tunableop_get_max_tuning_iterations() -> _int: ... +def _cuda_tunableop_set_filename(filename: str, insert_device_ordinal: Optional[_bool]) -> None: ... +def _cuda_tunableop_get_filename() -> str: ... +def _cuda_tunableop_write_file(filename: Optional[str]) -> _bool: ... +def _cuda_tunableop_read_file(filename: Optional[str]) -> _bool: ... +def _cuda_tunableop_write_file_on_exit(val: _bool) -> None: ... +def _cuda_tunableop_get_results() -> Tuple[str, str, str, _float]: ... +def _cuda_tunableop_get_validators() -> Tuple[str, str]: ... + +class _CudaDeviceProperties: + name: str + major: _int + minor: _int + multi_processor_count: _int + total_memory: _int + is_integrated: _int + is_multi_gpu_board: _int + max_threads_per_multi_processor: _int + gcnArchName: str + warp_size: _int + uuid: str + L2_cache_size: _int + +# Functions related to SDPA +class _SDPAParams: + query: Tensor + key: Tensor + value: Tensor + attn_mask: Optional[Tensor] + dropout: _float + is_causal: _bool + enable_gqa: _bool + def __init__( + self, + query: Tensor, + key: Tensor, + value: Tensor, + attn_mask: Optional[Tensor], + dropout: _float, + is_causal: _bool, + enable_gqa: _bool) -> None: ... + +class _SDPBackend(Enum): + ERROR = -1 + MATH = 0 + FLASH_ATTENTION = 1 + EFFICIENT_ATTENTION = 2 + CUDNN_ATTENTION = 3 + +def _is_flash_attention_available() -> _bool: ... +def _can_use_cudnn_attention(params: _SDPAParams, debug: _bool) -> _bool: ... +def _can_use_flash_attention(params: _SDPAParams, debug: _bool) -> _bool: ... +def _can_use_mem_efficient_attention(params: _SDPAParams, debug: _bool) -> _bool: ... + +# Defined in torch/csrc/cuda/GdsFile.cpp +def _gds_register_buffer(t: Storage) -> None: ... +def _gds_deregister_buffer(t: Storage) -> None: ... +def _gds_register_handle(fd: _int) -> _int: ... +def _gds_deregister_handle(handle: _int) -> None: ... +def _gds_load_storage(handle: _int, s: Storage, offset: _int) -> None: ... +def _gds_save_storage(handle: _int, s: Storage, offset: _int) -> None: ... + +# Defined in torch/csrc/cuda/python_comm.cpp +def _broadcast(tensor: Tensor, devices: List[_int]) -> List[Tensor]: ... +def _broadcast_out(tensor: Tensor, out_tensors: List[Tensor]) -> List[Tensor]: ... +def _broadcast_coalesced( + tensors: List[Tensor], + devices: List[_int], + buffer_size: _int, +) -> List[List[Tensor]]: ... +def _scatter( + tensor: Tensor, + devices: List[_int], + chunk_sizes: Optional[List[_int]], + dim: _int, + streams: Optional[List[Stream]], +) -> List[Tensor]: ... +def _scatter_out( + tensor: Tensor, + out_tensors: List[Tensor], + dim: _int, + streams: Optional[List[Stream]], +) -> List[Tensor]: ... +def _gather( + tensors: List[Tensor], + dim: _int, + destination_index: Optional[_int], +) -> Tensor: ... +def _gather_out(tensors: List[Tensor], out_tensor: Tensor, dim: _int) -> Tensor: ... + +# Defined in torch/csrc/cuda/Stream.cpp +class _CudaStreamBase(Stream): + stream_id: _int + device_index: _int + device_type: _int + + device: _device + cuda_stream: _int + priority: _int + + def __new__( + self, + priority: _int = 0, + stream_id: _int = 0, + device_index: _int = 0, + stream_ptr: _int = 0, + ) -> _CudaStreamBase: ... + def query(self) -> _bool: ... + def synchronize(self) -> None: ... + def priority_range(self) -> Tuple[_int, _int]: ... + +# Defined in torch/csrc/cuda/Event.cpp +class _CudaEventBase: + device: _device + cuda_event: _int + + def __new__( + cls, + enable_timing: _bool = False, + blocking: _bool = False, + interprocess: _bool = False, + ) -> _CudaEventBase: ... + @classmethod + def from_ipc_handle(cls, device: _device, ipc_handle: bytes) -> _CudaEventBase: ... + def record(self, stream: _CudaStreamBase) -> None: ... + def wait(self, stream: _CudaStreamBase) -> None: ... + def query(self) -> _bool: ... + def elapsed_time(self, other: _CudaEventBase) -> _float: ... + def synchronize(self) -> None: ... + def ipc_handle(self) -> bytes: ... + +# Defined in torch/csrc/cuda/Graph.cpp +class _CUDAGraph: + def capture_begin(self, pool: Optional[Tuple[_int, _int]] = ..., capture_error_mode: str = "global") -> None: ... + def capture_end(self) -> None: ... + def register_generator_state(self, Generator) -> None: ... + def replay(self) -> None: ... + def reset(self) -> None: ... + def pool(self) -> Tuple[_int, _int]: ... + def enable_debug_mode(self) -> None: ... + def debug_dump(self, debug_path: str) -> None: ... + +# Defined in torch/csrc/cuda/MemPool.cpp +class _MemPool: + def __init__(self, allocator: Optional[_cuda_CUDAAllocator] = None, is_user_created: _bool = True) -> None: ... + @property + def id(self) -> Tuple[_int, _int]: ... + @property + def allocator(self) -> Optional[_cuda_CUDAAllocator]: ... + +class _MemPoolContext: + def __init__(self, pool: _MemPool) -> None: ... + @staticmethod + def active_pool() -> Optional[_MemPool]: ... + +def _cuda_isCurrentStreamCapturing() -> _bool: ... +def _graph_pool_handle() -> Tuple[_int, _int]: ... + +# Defined in torch/csrc/xpu/Module.cpp +def _xpu_setDevice(device: _int) -> None: ... +def _xpu_exchangeDevice(device: _int) -> _int: ... +def _xpu_maybeExchangeDevice(device: _int) -> _int: ... +def _xpu_getDevice() -> _int: ... +def _xpu_getDeviceCount() -> _int: ... +def _xpu_init() -> None: ... +def _xpu_setStream(stream_id: _int, device_index: _int, device_type: _int) -> None: ... +def _xpu_getCurrentStream(device: _int) -> Tuple: ... +def _xpu_getCurrentRawStream(device: _int) -> _int: ... +def _xpu_synchronize(device: _int) -> None: ... +def _xpu_emptyCache() -> None: ... +def _xpu_memoryStats(device: _int) -> Dict[str, Any]: ... +def _xpu_resetAccumulatedMemoryStats(device: _int) -> None: ... +def _xpu_resetPeakMemoryStats(device: _int) -> None: ... + +class _XpuDeviceProperties: + name: str + platform_name: str + vendor: str + driver_version: str + version: str + total_memory: _int + max_compute_units: _int + gpu_eu_count: _int + gpu_subslice_count: _int + max_work_group_size: _int + max_num_sub_groups: _int + sub_group_sizes: List[_int] + has_fp16: _bool + has_fp64: _bool + has_atomic64: _bool + type: str + +# Defined in torch/csrc/xpu/Stream.cpp +class _XpuStreamBase(Stream): + stream_id: _int + device_index: _int + device_type: _int + + device: _device + sycl_queue: _int + priority: _int + + def __new__( + cls, + priority: _int = 0, + stream_id: _int = 0, + device_index: _int = 0, + device_type: _int = 0, + ) -> _XpuStreamBase: ... + def query(self) -> _bool: ... + def synchronize(self) -> None: ... + @staticmethod + def priority_range() -> Tuple: ... + +# Defined in torch/csrc/xpu/Event.cpp +class _XpuEventBase: + device: _device + sycl_event: _int + + def __new__(cls, enable_timing: _bool = False) -> _XpuEventBase: ... + def record(self, stream: _XpuEventBase) -> None: ... + def wait(self, stream: _XpuStreamBase) -> None: ... + def query(self) -> _bool: ... + def elapsed_time(self, other: _XpuEventBase) -> _float: ... + def synchronize(self) -> None: ... + +# Defined in torch/csrc/DataLoader.cpp +def _set_worker_signal_handlers( + *arg: Any, +) -> None: ... # THPModule_setWorkerSignalHandlers +def _set_worker_pids( + key: _int, + child_pids: Tuple[_int, ...], +) -> None: ... # THPModule_setWorkerPIDs +def _remove_worker_pids(loader_id: _int) -> None: ... # THPModule_removeWorkerPIDs +def _error_if_any_worker_fails() -> None: ... # THPModule_errorIfAnyWorkerFails + +# Defined in torch/csrc/jit/python/python_tracer.cpp +class TracingState: + def push_scope(self, scope_name: str) -> None: ... + def pop_scope(self) -> None: ... + def current_scope(self) -> str: ... + def set_graph(self, graph: Graph) -> None: ... + def graph(self) -> Graph: ... + +def _create_graph_by_tracing( + func: Callable[..., Any], + inputs: Any, + var_name_lookup_fn: Callable[[Tensor], str], + strict: Any, + force_outplace: Any, + self: Any = None, + argument_names: List[str] = [], +) -> Tuple[Graph, Stack]: ... +def _tracer_warn_use_python(): ... +def _get_tracing_state() -> TracingState: ... + +# Defined in torch/csrc/jit/python/python_ir.cpp +# Not actually defined in python_ir.cpp, not sure where they are. +class IValue: ... + +Stack = List[IValue] + +class JitType: + annotation_str: str + def isSubtypeOf(self, other: JitType) -> _bool: ... + def with_dtype(self, dtype: _dtype) -> JitType: ... + def with_sizes(self, sizes: List[Optional[_int]]) -> JitType: ... + def kind(self) -> str: ... + def scalarType(self) -> Optional[str]: ... + def getElementType(self) -> JitType: ... + def dtype(self) -> Optional[_dtype]: ... + +class InferredType: + def __init__(self, arg: Union[JitType, str]): ... + def type(self) -> JitType: ... + def success(self) -> _bool: ... + def reason(self) -> str: ... + +R = TypeVar("R", bound=JitType) + +class Type(JitType): + def str(self) -> _str: ... + def containedTypes(self) -> List[JitType]: ... + def dim(self) -> Optional[_int]: ... + def undefined(self) -> Optional[_bool]: ... + def sizes(self) -> Optional[List[_int]]: ... + def symbol_sizes(self) -> Optional[List[_int]]: ... + def varyingSizes(self) -> Optional[List[Optional[_int]]]: ... + def strides(self) -> Optional[List[_int]]: ... + def contiguous(self) -> Self: ... + def device(self) -> Optional[_device]: ... + def __eq__(self, other: object) -> _bool: ... + __hash__ = None # type: ignore[assignment] + def is_interface_type(self) -> _bool: ... + def requires_grad(self) -> _bool: ... + @property + def annotation_string(self) -> _str: ... + +class AnyType(JitType): + @staticmethod + def get() -> AnyType: ... + +class NoneType(JitType): + @staticmethod + def get() -> NoneType: ... + +class BoolType(JitType): + @staticmethod + def get() -> BoolType: ... + +class FloatType(JitType): + @staticmethod + def get() -> FloatType: ... + +class ComplexType(JitType): + @staticmethod + def get() -> ComplexType: ... + +class IntType(JitType): + @staticmethod + def get() -> IntType: ... + +class SymIntType(JitType): + @staticmethod + def get() -> SymIntType: ... + +class SymBoolType(JitType): + @staticmethod + def get() -> SymBoolType: ... + +class NumberType(JitType): + @staticmethod + def get() -> NumberType: ... + +class StringType(JitType): + @staticmethod + def get() -> StringType: ... + +class DeviceObjType(JitType): + @staticmethod + def get() -> DeviceObjType: ... + +class _GeneratorType(JitType): + @staticmethod + def get() -> _GeneratorType: ... + +class StreamObjType(JitType): + @staticmethod + def get() -> StreamObjType: ... + +class ListType(JitType): + def __init__(self, a: JitType) -> None: ... + def getElementType(self) -> JitType: ... + @staticmethod + def ofInts() -> ListType: ... + @staticmethod + def ofTensors() -> ListType: ... + @staticmethod + def ofFloats() -> ListType: ... + @staticmethod + def ofComplexDoubles() -> ListType: ... + @staticmethod + def ofBools() -> ListType: ... + @staticmethod + def ofStrings() -> ListType: ... + +class DictType(JitType): + def __init__(self, key: JitType, value: JitType) -> None: ... + def getKeyType(self) -> JitType: ... + def getValueType(self) -> JitType: ... + +class TupleType(JitType): + def __init__(self, a: List[Optional[JitType]]) -> None: ... + def elements(self) -> List[JitType]: ... + +class UnionType(JitType): + def __init__(self, a: List[JitType]) -> None: ... + +class ClassType(JitType): + def __init__(self, qualified_name: str) -> None: ... + +class InterfaceType(JitType): + def __init__(self, qualified_name: str) -> None: ... + def getMethod(self, name: str) -> Optional[FunctionSchema]: ... + def getMethodNames(self) -> List[str]: ... + +class OptionalType(JitType, Generic[R]): + def __init__(self, a: JitType) -> None: ... + def getElementType(self) -> JitType: ... + @staticmethod + def ofTensor() -> OptionalType: ... + +class FutureType(JitType): + def __init__(self, a: JitType) -> None: ... + def getElementType(self) -> JitType: ... + +class AwaitType(JitType): + def __init__(self, a: JitType) -> None: ... + def getElementType(self) -> JitType: ... + +class RRefType(JitType): + def __init__(self, a: JitType) -> None: ... + +class EnumType(JitType): + def __init__( + self, + qualified_name: str, + value_type: JitType, + enum_names_values: List[Any], + ) -> None: ... + +class TensorType(JitType): + @classmethod + def get(cls) -> TensorType: ... + @classmethod + def getInferred(cls) -> TensorType: ... + def with_sizes(self, other: Optional[List[Optional[_int]]]) -> TensorType: ... + def sizes(self) -> Optional[List[_int]]: ... + def varyingSizes(self) -> Optional[List[Optional[_int]]]: ... + def strides(self) -> Optional[List[_int]]: ... + def device(self) -> Optional[_device]: ... + def dim(self) -> _int: ... + def dtype(self) -> Optional[_dtype]: ... + @staticmethod + def create_from_tensor(t: Tensor) -> TensorType: ... + +# Defined in torch/csrc/jit/python/python_tree_views.cpp +class SourceRange: ... +class TreeView: ... + +class Ident(TreeView): + @property + def name(self) -> str: ... + +class ClassDef(TreeView): ... + +class Def(TreeView): + def name(self) -> Ident: ... + +class Decl(TreeView): ... + +# Defined in torch/csrc/distributed/rpc/init.cpp +def _rpc_init() -> _bool: ... + +# Defined in torch/csrc/distributed/autograd/init.cpp +def _dist_autograd_init() -> _bool: ... + +# Defined in torch/csrc/distributed/c10d/init.cpp +def _c10d_init() -> _bool: ... + +# Defined in torch/csrc/distributed/rpc/testing/init.cpp +def _faulty_agent_init() -> _bool: ... +def _register_py_class_for_device(device: str, cls: Any) -> None: ... + +# Defined in torch/csrc/Module.cpp +def _current_graph_task_id() -> _int: ... +def _current_autograd_node() -> _Node: ... +def _will_engine_execute_node(node: _Node) -> _bool: ... +def _dispatch_key_set(tensor) -> str: ... + +# Defined in torch/csrc/Exceptions.cpp +class OutOfMemoryError(RuntimeError): ... +class _DistError(RuntimeError): ... +class _DistBackendError(RuntimeError): ... +class _DistStoreError(RuntimeError): ... +class _DistNetworkError(RuntimeError): ... + +# Defined in torch/csrc/profiler/init.cpp +class CapturedTraceback: + pass +def gather_traceback(python: _bool, script: _bool, cpp: _bool) -> CapturedTraceback: ... +def symbolize_tracebacks(tracebacks: List[CapturedTraceback]) -> List[Dict[str, Any]]: ... + +def _load_mobile_module_from_file(filename: str): ... +def _load_mobile_module_from_bytes(bytes_: bytes): ... +def _load_jit_module_from_file(filename: str): ... +def _load_jit_module_from_bytes(bytes_: bytes): ... +def _save_mobile_module(m: LiteScriptModule, filename: str): ... +def _save_jit_module(m: ScriptModule, filename: str, extra_files: Dict[str, Any]): ... +def _save_mobile_module_to_bytes(m: LiteScriptModule) -> bytes: ... +def _save_jit_module_to_bytes(m: ScriptModule, extra_files: Dict[str, Any]) -> bytes: ... +def _get_module_info_from_flatbuffer(data: bytes): ... +def _jit_resolve_packet(op_name: str, *args, **kwargs) -> str: ... +def _swap_tensor_impl(t1: Tensor, t2: Tensor): ... +def _pickle_save(obj: Any) -> bytes: ... +def _pickle_load_obj(bs: bytes) -> Any: ... + +# Defined in torch/csrc/jit/runtime/static/init.cpp +def _jit_to_static_module(graph_or_module: Union[Graph,ScriptModule]) -> Any: ... +def _fuse_to_static_module(graph_or_module: Union[Graph,ScriptModule], min_size: _int) -> Any: ... + +# Defined in torch/csrc/fx/node.cpp +class _NodeBase: + _erased: _bool + _prev: FxNode + _next: FxNode + +class _NodeIter(Iterator): + def __init__(self, root: FxNode, reversed: _bool) -> None: ... + def __iter__(self) -> Iterator[FxNode]: ... + def __next__(self) -> FxNode: ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_aoti.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_aoti.pyi new file mode 100644 index 0000000000000000000000000000000000000000..a5e782fe62123e6e6c141c80a10e70fd991ee654 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_aoti.pyi @@ -0,0 +1,20 @@ +from ctypes import c_void_p + +from torch import Tensor + +# Defined in torch/csrc/inductor/aoti_runner/pybind.cpp + +# Tensor to AtenTensorHandle +def unsafe_alloc_void_ptrs_from_tensors(tensors: list[Tensor]) -> list[c_void_p]: ... +def unsafe_alloc_void_ptr_from_tensor(tensor: Tensor) -> c_void_p: ... + +# AtenTensorHandle to Tensor +def alloc_tensors_by_stealing_from_void_ptrs( + handles: list[c_void_p], +) -> list[Tensor]: ... +def alloc_tensor_by_stealing_from_void_ptr( + handle: c_void_p, +) -> Tensor: ... + +class AOTIModelContainerRunnerCpu: ... +class AOTIModelContainerRunnerCuda: ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_autograd.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_autograd.pyi new file mode 100644 index 0000000000000000000000000000000000000000..f756828ed6c9bf23a901e7416474fc86215276f4 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_autograd.pyi @@ -0,0 +1,135 @@ +# mypy: allow-untyped-defs +from enum import Enum +from typing import Any, Callable + +import torch +from torch._C._profiler import ( + _ProfilerEvent, + ActiveProfilerType, + ProfilerActivity, + ProfilerConfig, +) + +# Defined in torch/csrc/autograd/init.cpp + +class DeviceType(Enum): + CPU = ... + CUDA = ... + XPU = ... + MKLDNN = ... + OPENGL = ... + OPENCL = ... + IDEEP = ... + HIP = ... + FPGA = ... + MAIA = ... + XLA = ... + MTIA = ... + MPS = ... + HPU = ... + Meta = ... + Vulkan = ... + Metal = ... + PrivateUse1 = ... + +class ProfilerEvent: + def cpu_elapsed_us(self, other: ProfilerEvent) -> float: ... + def cpu_memory_usage(self) -> int: ... + def cuda_elapsed_us(self, other: ProfilerEvent) -> float: ... + def privateuse1_elapsed_us(self, other: ProfilerEvent) -> float: ... + def cuda_memory_usage(self) -> int: ... + def device(self) -> int: ... + def handle(self) -> int: ... + def has_cuda(self) -> bool: ... + def is_remote(self) -> bool: ... + def kind(self) -> int: ... + def name(self) -> str: ... + def node_id(self) -> int: ... + def sequence_nr(self) -> int: ... + def shapes(self) -> list[list[int]]: ... + def thread_id(self) -> int: ... + def flops(self) -> float: ... + def is_async(self) -> bool: ... + +class _KinetoEvent: + def name(self) -> str: ... + def device_index(self) -> int: ... + def device_resource_id(self) -> int: ... + def start_ns(self) -> int: ... + def end_ns(self) -> int: ... + def duration_ns(self) -> int: ... + def is_async(self) -> bool: ... + def linked_correlation_id(self) -> int: ... + def shapes(self) -> list[list[int]]: ... + def dtypes(self) -> list[str]: ... + def concrete_inputs(self) -> list[Any]: ... + def kwinputs(self) -> dict[str, Any]: ... + def device_type(self) -> DeviceType: ... + def start_thread_id(self) -> int: ... + def end_thread_id(self) -> int: ... + def correlation_id(self) -> int: ... + def fwd_thread_id(self) -> int: ... + def stack(self) -> list[str]: ... + def scope(self) -> int: ... + def sequence_nr(self) -> int: ... + def flops(self) -> int: ... + def cuda_elapsed_us(self) -> int: ... + def privateuse1_elapsed_us(self) -> int: ... + def is_user_annotation(self) -> bool: ... + +class _ProfilerResult: + def events(self) -> list[_KinetoEvent]: ... + def legacy_events(self) -> list[list[ProfilerEvent]]: ... + def save(self, path: str) -> None: ... + def experimental_event_tree(self) -> list[_ProfilerEvent]: ... + def trace_start_ns(self) -> int: ... + +class SavedTensor: ... + +def _enable_profiler( + config: ProfilerConfig, + activities: set[ProfilerActivity], +) -> None: ... +def _prepare_profiler( + config: ProfilerConfig, + activities: set[ProfilerActivity], +) -> None: ... +def _toggle_collection_dynamic( + enable: bool, + activities: set[ProfilerActivity], +) -> None: ... +def _disable_profiler() -> _ProfilerResult: ... +def _profiler_enabled() -> bool: ... +def _add_metadata_json(key: str, value: str) -> None: ... +def _kineto_step() -> None: ... +def _get_current_graph_task_keep_graph() -> bool: ... +def _get_sequence_nr() -> int: ... +def kineto_available() -> bool: ... +def _record_function_with_args_enter(name: str, *args) -> torch.Tensor: ... +def _record_function_with_args_exit(handle: torch.Tensor) -> None: ... +def _supported_activities() -> set[ProfilerActivity]: ... +def _enable_record_function(enable: bool) -> None: ... +def _set_empty_test_observer(is_global: bool, sampling_prob: float) -> None: ... +def _push_saved_tensors_default_hooks( + pack_hook: Callable[[torch.Tensor], Any], + unpack_hook: Callable[[Any], torch.Tensor], +) -> None: ... +def _pop_saved_tensors_default_hooks() -> None: ... +def _unsafe_set_version_counter(t: torch.Tensor, prev_version: int) -> None: ... +def _enable_profiler_legacy(config: ProfilerConfig) -> None: ... +def _disable_profiler_legacy() -> list[list[ProfilerEvent]]: ... +def _profiler_type() -> ActiveProfilerType: ... +def _saved_tensors_hooks_enable() -> None: ... +def _saved_tensors_hooks_disable(message: str) -> None: ... +def _saved_tensors_hooks_get_disabled_error_message() -> str | None: ... +def _saved_tensors_hooks_set_tracing(is_tracing: bool) -> bool: ... + +class CreationMeta(Enum): + DEFAULT = ... + IN_CUSTOM_FUNCTION = ... + MULTI_OUTPUT_NODE = ... + NO_GRAD_MODE = ... + INFERENCE_MODE = ... + +def _set_creation_meta(t: torch.Tensor, creation_meta: CreationMeta) -> None: ... +def _get_creation_meta(t: torch.Tensor) -> CreationMeta: ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_cpu.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_cpu.pyi new file mode 100644 index 0000000000000000000000000000000000000000..6593222a119f4dfbd532859009ecb24ea4522323 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_cpu.pyi @@ -0,0 +1,12 @@ +from torch.types import _bool, _int + +# Defined in torch/csrc/cpu/Module.cpp + +def _is_avx2_supported() -> _bool: ... +def _is_avx512_supported() -> _bool: ... +def _is_avx512_vnni_supported() -> _bool: ... +def _is_avx512_bf16_supported() -> _bool: ... +def _is_amx_tile_supported() -> _bool: ... +def _init_amx() -> _bool: ... +def _L1d_cache_size() -> _int: ... +def _L2_cache_size() -> _int: ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_cudnn.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_cudnn.pyi new file mode 100644 index 0000000000000000000000000000000000000000..689c984b9d7de1ca98329495223dcb0a13a54f4e --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_cudnn.pyi @@ -0,0 +1,17 @@ +from enum import Enum + +from torch.types import _bool, Tuple + +# Defined in torch/csrc/cuda/shared/cudnn.cpp +is_cuda: _bool + +def getRuntimeVersion() -> Tuple[int, int, int]: ... +def getCompileVersion() -> Tuple[int, int, int]: ... +def getVersionInt() -> int: ... + +class RNNMode(int, Enum): + value: int + rnn_relu = ... + rnn_tanh = ... + lstm = ... + gru = ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_cusparselt.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_cusparselt.pyi new file mode 100644 index 0000000000000000000000000000000000000000..a1c4bbb217777d50b9a3384da11d9992db5e18f0 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_cusparselt.pyi @@ -0,0 +1 @@ +def getVersionInt() -> int: ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_distributed_autograd.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_distributed_autograd.pyi new file mode 100644 index 0000000000000000000000000000000000000000..d103a9ba067ee041ceca5f5ce92aa69a21e2f373 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_distributed_autograd.pyi @@ -0,0 +1,27 @@ +# mypy: allow-untyped-defs +from typing import Any + +import torch + +# This module is defined in torch/csrc/distributed/autograd/init.cpp + +class DistAutogradContext: + def _context_id(self) -> int: ... + def _recv_functions(self) -> dict[int, Any]: ... + def _send_functions(self) -> dict[int, Any]: ... + def _known_worker_ids(self) -> set[int]: ... + +def _new_context() -> DistAutogradContext: ... +def _release_context(context_id: int) -> None: ... +def _get_max_id() -> int: ... +def _is_valid_context(worker_id: int) -> bool: ... +def _retrieve_context(context_id: int) -> DistAutogradContext: ... +def _current_context() -> DistAutogradContext: ... +def _init(worker_id: int) -> None: ... +def _get_debug_info() -> dict[str, str]: ... +def backward( + context_id: int, + roots: list[torch.Tensor], + retain_graph=False, +) -> None: ... +def get_gradients(context_id: int) -> dict[torch.Tensor, torch.Tensor]: ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_distributed_c10d.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_distributed_c10d.pyi new file mode 100644 index 0000000000000000000000000000000000000000..94e8578bbfff62b37a9102e4c770feb74b1866db --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_distributed_c10d.pyi @@ -0,0 +1,699 @@ +# mypy: allow-untyped-defs +# mypy: disable-error-code="type-arg" +from datetime import timedelta +from enum import Enum +from typing import Any, Optional, overload + +import torch +from torch import Tensor +from torch._C import ScriptObject +from torch.futures import Future + +# This module is defined in torch/csrc/distributed/c10d/init.cpp + +_DEFAULT_FIRST_BUCKET_BYTES: int +_DEFAULT_NO_TIMEOUT: timedelta +_DEFAULT_PG_TIMEOUT: timedelta +_DEFAULT_PG_NCCL_TIMEOUT: timedelta + +class BuiltinCommHookType(Enum): + ALLREDUCE = ... + FP16_COMPRESS = ... + +def _register_comm_hook(reducer: Reducer, state: Any, comm_hook: Any): ... +def _register_builtin_comm_hook( + reducer: Reducer, + comm_hook_type: BuiltinCommHookType, +): ... +def _set_global_rank(rank: int) -> None: ... +def _hash_tensors(tensors: list[Tensor]) -> int: ... + +class GradBucket: + def index(self) -> int: ... + def buffer(self) -> Tensor: ... + def gradients(self) -> list[Tensor]: ... + def is_last(self) -> bool: ... + def set_buffer(self, tensor: Tensor) -> None: ... + def parameters(self) -> list[Tensor]: ... + +class Reducer: + def __init__( + self, + params: list[Tensor], + bucket_indices: list[list[int]], + per_bucket_size_limits: list[int], + process_group: ProcessGroup, + expect_sparse_gradients: list[bool] = ..., + bucket_bytes_cap: int = ..., # kDefaultBucketBytesCap in reducer.hpp + find_unused_parameters: bool = ..., + gradient_as_bucket_view: bool = ..., + param_to_name_mapping: dict[int, str] = ..., + first_bucket_types_cap: int = ..., # kDefaultFirstBucketBytes in reducer.hpp + ) -> None: ... + def prepare_for_forward(self) -> None: ... + def prepare_for_backward(self, output: list[Tensor]) -> None: ... + def get_backward_stats(self) -> list[int]: ... + def _install_post_backward_futures(self, futures: list[Future]) -> None: ... + def _rebuild_buckets(self) -> bool: ... + def _get_zeros_like_grad_buckets(self) -> list[GradBucket]: ... + def _push_all_rebuilt_params(self) -> None: ... + def _set_forward_pass_work_handle( + self, + work: Work, + use_static_world_size: bool, + ): ... + def _get_local_used_map(self) -> Tensor: ... + def _set_ddp_runtime_logging_sample_rate(self, sample_rate: int) -> None: ... + def _set_static_graph(self) -> None: ... + def _run_comm_hook(self, bucket: GradBucket) -> Future: ... + def set_logger(self, logger: Logger) -> None: ... + def _remove_autograd_hooks(self) -> None: ... + def _check_reducer_finalized(self) -> None: ... + def _set_sparse_metadata(self, global_unique_ids: dict[str, Tensor]) -> None: ... + def _reset_state(self) -> None: ... + def _update_process_group(self, new_process_group: ProcessGroup) -> None: ... + +class DDPLoggingData: + strs_map: dict[str, str] + ints_map: dict[str, int] + +class Logger: + def __init__(self, reducer: Reducer) -> None: ... + def set_construction_data_and_log( + self, + module_name: str, + device_ids: list[int], + output_device: int, + broadcast_buffers: bool, + has_sync_bn: bool, + static_graph: bool, + ): ... + def set_runtime_stats_and_log(self) -> None: ... + def set_error_and_log(self, error: str) -> None: ... + def _get_ddp_logging_data(self) -> DDPLoggingData: ... + def _set_comm_hook_name(self, comm_hook: str) -> None: ... + def _set_uneven_input_join(self) -> None: ... + def _set_static_graph(self) -> None: ... + +class _WorkerServer: + def __init__(self, socket_path: str) -> None: ... + def shutdown(self) -> None: ... + +def get_debug_level(): ... +def set_debug_level(): ... +def set_debug_level_from_env(): ... + +class DebugLevel(Enum): + OFF = ... + INFO = ... + DETAIL = ... + +class ReduceOp: + def __init__(self, op: RedOpType) -> None: ... + + SUM: RedOpType = ... + AVG: RedOpType = ... + PRODUCT: RedOpType = ... + MIN: RedOpType = ... + MAX: RedOpType = ... + BAND: RedOpType = ... + BOR: RedOpType = ... + BXOR: RedOpType = ... + PREMUL_SUM: RedOpType = ... + UNUSED: RedOpType = ... + + class RedOpType(Enum): ... + +class BroadcastOptions: + rootRank: int + rootTensor: int + timeout: timedelta + asyncOp: bool + +class AllreduceOptions: + reduceOp: ReduceOp + timeout: timedelta + +class AllreduceCoalescedOptions(AllreduceOptions): ... + +class ReduceOptions: + reduceOp: ReduceOp + rootRank: int + rootTensor: int + timeout: timedelta + +class AllgatherOptions: + timeout: timedelta + asyncOp: bool + +class GatherOptions: + rootRank: int + timeout: timedelta + +class ScatterOptions: + rootRank: int + timeout: timedelta + asyncOp: bool + +class ReduceScatterOptions: + reduceOp: ReduceOp + timeout: timedelta + asyncOp: bool + +class BarrierOptions: + device_ids: list[int] + device: torch.device + timeout: timedelta + +class AllToAllOptions: + timeout: timedelta + +class Store: + def set(self, key: str, value: str): ... + def get(self, key: str) -> bytes: ... + def add(self, key: str, value: int) -> int: ... + def compare_set( + self, + key: str, + expected_value: str, + desired_value: str, + ) -> bytes: ... + def delete_key(self, key: str) -> bool: ... + def num_keys(self) -> int: ... + def set_timeout(self, timeout: timedelta): ... + @overload + def wait(self, keys: list[str]): ... + @overload + def wait(self, keys: list[str], timeout: timedelta): ... + +class FileStore(Store): + def __init__(self, path: str, numWorkers: int = ...) -> None: ... + +class HashStore(Store): + def __init__(self) -> None: ... + +class TCPStore(Store): + def __init__( + self, + host_name: str, + port: int, + world_size: int | None = ..., + is_master: bool = ..., + timeout: timedelta = ..., + wait_for_workers: bool = ..., + multi_tenant: bool = ..., + master_listen_fd: int | None = ..., + use_libuv: bool | None = ..., + ) -> None: ... + @property + def host(self) -> str: ... + @property + def port(self) -> int: ... + +class PrefixStore(Store): + def __init__(self, prefix: str, store: Store) -> None: ... + @property + def underlying_store(self) -> Store: ... + +class _ControlCollectives: + def barrier(self, key: str, timeout: timedelta, blocking: bool) -> None: ... + def broadcast_send(self, key: str, data: str, timeout: timedelta) -> None: ... + def broadcast_recv(self, key: str, timeout: timedelta) -> str: ... + def gather_send(self, key: str, data: str, timeout: timedelta) -> None: ... + def gather_recv(self, key: str, timeout: timedelta) -> str: ... + def scatter_send(self, key: str, data: str, timeout: timedelta) -> None: ... + def scatter_recv(self, key: str, timeout: timedelta) -> str: ... + def all_gather(self, key: str, data: str, timeout: timedelta) -> str: ... + def all_sum(self, key: str, data: int, timeout: timedelta) -> int: ... + +class _StoreCollectives(_ControlCollectives): + def __init__(self, store: Store, rank: int, world_size: int) -> None: ... + +class _DistributedBackendOptions: + def __init__(self) -> None: ... + @property + def store(self) -> Store: ... + @store.setter + def store(self, store: Store) -> None: ... + @property + def group_rank(self) -> int: ... + @group_rank.setter + def group_rank(self, rank: int) -> None: ... + @property + def group_size(self) -> int: ... + @group_size.setter + def group_size(self, size: int) -> None: ... + @property + def timeout(self) -> timedelta: ... + @timeout.setter + def timeout(self, timeout: timedelta) -> None: ... + @property + def group_id(self) -> str: ... + @group_id.setter + def group_id(self, group_id: str) -> None: ... + @property + def global_ranks_in_group(self) -> list[int]: ... + @global_ranks_in_group.setter + def global_ranks_in_group(self, ranks: list[int]) -> None: ... + +class Work: + def is_completed(self) -> bool: ... + def is_success(self) -> bool: ... + def exception(self) -> Any: ... + def wait(self, timeout: timedelta = ...) -> bool: ... + def get_future(self) -> Future: ... + def source_rank(self) -> int: ... + def _source_rank(self) -> int: ... + def result(self) -> list[Tensor]: ... + def synchronize(self): ... + def boxed(self) -> ScriptObject: ... + @staticmethod + def unbox(obj: ScriptObject) -> Work: ... + +class Backend: + class Options: + def __init__(self, backend: str, timeout: timedelta = ...) -> None: ... + @property + def backend(self) -> str: ... + @property + def _timeout(self) -> timedelta: ... + @_timeout.setter + def _timeout(self, val: timedelta) -> None: ... + + def __init__( + self, + rank: int, + size: int, + ) -> None: ... + @property + def supports_splitting(self) -> bool: ... + @property + def options(self) -> Options: ... + def rank(self) -> int: ... + def size(self) -> int: ... + def eager_connect_single_device(self, device: torch.device | None) -> None: ... + def _set_sequence_number_for_group(self) -> None: ... + def _set_default_timeout(self, timeout: timedelta) -> None: ... + +class ProcessGroup: + class Options: + def __init__(self, backend: str, timeout: timedelta = ...) -> None: ... + @property + def backend(self) -> str: ... + @property + def _timeout(self) -> timedelta: ... + @_timeout.setter + def _timeout(self, val: timedelta) -> None: ... + + class BackendType(Enum): + UNDEFINED = ... + GLOO = ... + NCCL = ... + UCC = ... + MPI = ... + CUSTOM = ... + + def __init__( + self, + store: Store, + rank: int, + size: int, + options: Options, + ) -> None: ... + def rank(self) -> int: ... + def size(self) -> int: ... + @overload + def broadcast( + self, + tensors: list[Tensor], + opts=..., + ) -> Work: ... + @overload + def broadcast( + self, + tensor: Tensor, + root: int, + ) -> Work: ... + @overload + def allreduce( + self, + tensors: list[Tensor], + opts: AllreduceOptions = ..., + ) -> Work: ... + @overload + def allreduce( + self, + tensors: list[Tensor], + op=..., + ) -> Work: ... + @overload + def allreduce( + self, + tensor: Tensor, + op=..., + ) -> Work: ... + def allreduce_coalesced( + self, + tensors: list[Tensor], + opts=..., + ) -> Work: ... + def reduce_scatter_tensor_coalesced( + self, + outputTensors: list[Tensor], + inputTensors: list[Tensor], + opts: ReduceScatterOptions | None = None, + ) -> Work: ... + @overload + def reduce( + self, + tensors: list[Tensor], + opts=..., + ) -> Work: ... + @overload + def reduce( + self, + tensor: Tensor, + root: int, + op=..., + ) -> Work: ... + @overload + def allgather( + self, + output_tensors: list[list[Tensor]], + input_tensors: list[Tensor], + opts=..., + ) -> Work: ... + @overload + def allgather( + self, + output_tensors: list[Tensor], + input_tensor: Tensor, + ) -> Work: ... + def _allgather_base( + self, + output: Tensor, + input: Tensor, + opts=..., + ) -> Work: ... + def allgather_coalesced( + self, + output_lists: list[list[Tensor]], + input_list: list[Tensor], + opts=..., + ) -> Work: ... + def allgather_into_tensor_coalesced( + self, + output_lists: list[Tensor], + input_list: list[Tensor], + opts=..., + ) -> Work: ... + @overload + def gather( + self, + output_tensors: list[list[Tensor]], + input_tensors: list[Tensor], + opts=..., + ) -> Work: ... + @overload + def gather( + self, + output_tensors: list[Tensor], + input_tensor: Tensor, + root: int, + ) -> Work: ... + @overload + def scatter( + self, + output_tensors: list[Tensor], + input_tensors: list[list[Tensor]], + opts=..., + ) -> Work: ... + @overload + def scatter( + self, + output_tensor: Tensor, + input_tensors: list[Tensor], + root: int, + ) -> Work: ... + @overload + def reduce_scatter( + self, + output_tensors: list[Tensor], + input_tensors: list[list[Tensor]], + opts=..., + ) -> Work: ... + @overload + def reduce_scatter( + self, + output_tensors: Tensor, + input_tensor: list[Tensor], + ) -> Work: ... + def _reduce_scatter_base( + self, + outputTensor: Tensor, + inputTensor: Tensor, + opts: ReduceScatterOptions | None, + ) -> Work: ... + @overload + def alltoall_base( + self, + output_tensor: Tensor, + input_tensor: Tensor, + output_split_sizes: list[int], + input_split_sizes: list[int], + opts=..., + ) -> Work: ... + @overload + def alltoall_base( + self, + output: Tensor, + input: Tensor, + output_split_sizes: list[int], + input_split_sizes: list[int], + ) -> Work: ... + @overload + def alltoall( + self, + output_tensor: list[Tensor], + input_tensor: list[Tensor], + opts=..., + ) -> Work: ... + @overload + def alltoall( + self, + output: list[Tensor], + input: list[Tensor], + ) -> Work: ... + def send( + self, + tensors: list[Tensor], + dstRank: int, + tag: int, + ) -> Work: ... + def recv( + self, + tensors: list[Tensor], + srcRank: int, + tag: int, + ) -> Work: ... + def recv_anysource(self, tensors: list[Tensor], tag: int) -> Work: ... + def barrier(self, opts=...) -> Work: ... + def boxed(self) -> ScriptObject: ... + @staticmethod + def unbox(obj: ScriptObject) -> ProcessGroup: ... + def _start_coalescing(self, device: torch.device) -> None: ... + def _end_coalescing(self, device: torch.device) -> Work: ... + def _get_backend_name(self) -> str: ... + def _backend_id(self, backend_type: BackendType) -> int: ... + @property + def _device_types(self) -> list[torch.device]: ... + def _get_backend(self, device: torch.device) -> Backend: ... + def _register_backend( + self, + device: torch.device, + backend_type: BackendType, + backend: Backend | None, + ) -> None: ... + def _set_group_name(self, name: str) -> None: ... + def _set_group_desc(self, desc: str) -> None: ... + def name(self) -> str: ... + def _has_hooks(self) -> bool: ... + def _wait_for_pending_works(self) -> None: ... + def _set_sequence_number_for_group(self) -> None: ... + @property + def bound_device_id(self) -> torch.device | None: ... + @bound_device_id.setter + def bound_device_id(self, device: torch.device | None) -> None: ... + @property + def group_name(self) -> str: ... + @property + def group_desc(self) -> str: ... + +class ProcessGroupGloo(Backend): + class Device: ... + + class Options(ProcessGroup.Options): + devices: list[ProcessGroupGloo.Device] + threads: int + + def __init__(self): ... + + def __init__( + self, + store: Store, + rank: int, + size: int, + timeout: timedelta, + ) -> None: ... + @staticmethod + def create_device(hostname="", interface="") -> Device: ... + @staticmethod + def create_default_device() -> Device: ... + def _set_default_timeout(self, timeout) -> None: ... + +class _ProcessGroupWrapper(Backend): + def __init__(self, pg: Backend, gloo_pg: ProcessGroupGloo) -> None: ... + wrapped_pg: Backend + +class ProcessGroupNCCL(Backend): + class NCCLConfig: + blocking: int + cga_cluster_size: int + min_ctas: int + max_ctas: int + + class Options(ProcessGroup.Options): + config: ProcessGroupNCCL.NCCLConfig + is_high_priority_stream: bool + split_from: ProcessGroupNCCL + split_color: int + global_ranks_in_group: list[int] + group_name: str + + def __init__(self, is_high_priority_stream: bool = False): ... + + def __init__( + self, + store: Store, + rank: int, + size: int, + options: Options, + ) -> None: ... + def _group_start(self) -> None: ... + def _group_end(self) -> None: ... + def _set_default_timeout(self, timeout) -> None: ... + def _shutdown(self) -> None: ... + def perform_nocolor_split(self, device: torch.device) -> None: ... + def comm_split_count(self) -> int: ... + def _add_ephemeral_timeout(self, timeout: timedelta) -> None: ... + @property + def uid(self) -> int: ... + @property + def options(self) -> Options: ... # type: ignore[override] + +class ProcessGroupUCC(Backend): + def __init__( + self, + store: Store, + rank: int, + size: int, + timeout: timedelta, + ) -> None: ... + +class ProcessGroupMPI(Backend): + def __init__( + self, + rank: int, + size: int, + pgComm: int, + ) -> None: ... + @staticmethod + def create(ranks: list[int]) -> ProcessGroupMPI: ... + +def _compute_bucket_assignment_by_size( + tensors: list[Tensor], + bucket_size_limits: list[int], + expect_sparse_gradient: list[bool] = ..., + tensor_indices: list[int] = ..., +) -> tuple[list[list[int]], list[int]]: ... +def _broadcast_coalesced( + process_group: ProcessGroup, + tensors: list[Tensor], + buffer_size: int, + src: int, +): ... +def _test_python_store(store: Store): ... +def _verify_params_across_processes( + process_group: ProcessGroup, + params: list[Tensor], + logger: Logger | None, +): ... +def _make_nccl_premul_sum(factor: float | list[Tensor]) -> ReduceOp: ... +def _register_process_group( + group_name: str, + process_group: ProcessGroup, +) -> None: ... +def _resolve_process_group(group_name: str) -> ProcessGroup: ... +def _register_work(tensor: torch.Tensor, work: Work) -> ProcessGroup: ... +def _unregister_all_process_groups() -> None: ... +def _unregister_process_group(group_name: str) -> None: ... + +class _SymmetricMemory: + @staticmethod + def set_group_info( + group_name: str, + rank: int, + world_size: int, + store: Store, + ) -> None: ... + @staticmethod + def empty_strided_p2p( + size: torch.types._size, + stride: torch.types._size, + dtype: torch.dtype, + device: torch.device, + group_name: str, + ) -> torch.Tensor: ... + @property + def rank(self) -> int: ... + @property + def world_size(self) -> int: ... + @staticmethod + def rendezvous(tensor: torch.Tensor) -> _SymmetricMemory: ... + def get_buffer( + self, + rank: int, + sizes: torch.types._size, + dtype: torch.dtype, + storage_offset: int | None = 0, + ) -> torch.Tensor: ... + def barrier(self, channel: int = 0) -> None: ... + def put_signal(self, dst_rank: int, channel: int = 0) -> None: ... + def wait_signal(self, src_rank: int, channel: int = 0) -> None: ... + +class ProcessGroupCudaP2P(Backend): + class Options: + nccl_options: Optional[ProcessGroupNCCL.Options] + buffer_size: Optional[int] + + def __init__(self) -> None: ... + + def __init__( + self, + store: Store, + rank: int, + size: int, + options: ProcessGroupCudaP2P.Options, + ) -> None: ... + def is_p2p_available(self) -> bool: ... + def get_buffer_size(self) -> int: ... + def stream(self) -> torch.cuda.Stream: ... + def intra_node_barrier(self) -> Work: ... + def get_p2p_buffer( + self, + rank: int, + sizes: torch.Size, + dtype: torch.dtype, + storage_offset: Optional[int] = 0, + ) -> torch.Tensor: ... + def _shutdown(self) -> None: ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_distributed_rpc.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_distributed_rpc.pyi new file mode 100644 index 0000000000000000000000000000000000000000..48f636d8524637305cd9c758e9f22428d3b055cc --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_distributed_rpc.pyi @@ -0,0 +1,188 @@ +# mypy: allow-untyped-defs +# mypy: disable-error-code="type-arg" +from datetime import timedelta +from typing import Any, Generic, overload, TypeVar + +import torch +from torch._C import Future +from torch._C._autograd import ProfilerEvent +from torch._C._distributed_c10d import Store +from torch._C._profiler import ProfilerConfig + +# This module is defined in torch/csrc/distributed/rpc/init.cpp + +_DEFAULT_INIT_METHOD: str +_DEFAULT_NUM_WORKER_THREADS: int +_UNSET_RPC_TIMEOUT: float +_DEFAULT_RPC_TIMEOUT_SEC: float + +_T = TypeVar("_T") + +class RpcBackendOptions: + rpc_timeout: float + init_method: str + def __init__( + self, + rpc_timeout: float = ..., + init_method: str = ..., + ) -> None: ... + +class WorkerInfo: + def __init__(self, name: str, worker_id: int) -> None: ... + @property + def name(self) -> str: ... + @property + def id(self) -> int: ... + def __eq__(self, other: object) -> bool: ... + +class RpcAgent: + def join(self, shutdown: bool = False, timeout: float = 0): ... + def sync(self): ... + def shutdown(self): ... + @overload + def get_worker_info(self) -> WorkerInfo: ... + @overload + def get_worker_info(self, workerName: str) -> WorkerInfo: ... + def get_worker_infos(self) -> list[WorkerInfo]: ... + def _get_device_map(self, dst: WorkerInfo) -> dict[torch.device, torch.device]: ... + def get_debug_info(self) -> dict[str, str]: ... + def get_metrics(self) -> dict[str, str]: ... + +class PyRRef(Generic[_T]): + def __init__(self, value: _T, type_hint: Any = None) -> None: ... + def is_owner(self) -> bool: ... + def confirmed_by_owner(self) -> bool: ... + def owner(self) -> WorkerInfo: ... + def owner_name(self) -> str: ... + def to_here(self, timeout: float = ...) -> _T: ... + def local_value(self) -> Any: ... + def rpc_sync(self, timeout: float = ...) -> Any: ... + def rpc_async(self, timeout: float = ...) -> Any: ... + def remote(self, timeout: float = ...) -> Any: ... + def _serialize(self) -> tuple: ... + @staticmethod + def _deserialize(tp: tuple) -> PyRRef: ... + def _get_type(self) -> type[_T]: ... + def _get_future(self) -> Future[_T]: ... + def _get_profiling_future(self) -> Future[_T]: ... + def _set_profiling_future(self, profilingFuture: Future[_T]): ... + +class _TensorPipeRpcBackendOptionsBase(RpcBackendOptions): + num_worker_threads: int + device_maps: dict[str, dict[torch.device, torch.device]] + devices: list[torch.device] + def __init__( + self, + num_worker_threads: int, + _transports: list | None, + _channels: list | None, + rpc_timeout: float = ..., + init_method: str = ..., + device_maps: dict[str, dict[torch.device, torch.device]] = {}, # noqa: B006 + devices: list[torch.device] = [], # noqa: B006 + ) -> None: ... + def _set_device_map( + self, + to: str, + device_map: dict[torch.device, torch.device], + ): ... + +class TensorPipeAgent(RpcAgent): + def __init__( + self, + store: Store, + name: str, + worker_id: int, + world_size: int | None, + opts: _TensorPipeRpcBackendOptionsBase, + reverse_device_maps: dict[str, dict[torch.device, torch.device]], + devices: list[torch.device], + ) -> None: ... + def join(self, shutdown: bool = False, timeout: float = 0): ... + def shutdown(self): ... + @overload + def get_worker_info(self) -> WorkerInfo: ... + @overload + def get_worker_info(self, workerName: str) -> WorkerInfo: ... + @overload + def get_worker_info(self, id: int) -> WorkerInfo: ... + def get_worker_infos(self) -> list[WorkerInfo]: ... + def _get_device_map(self, dst: WorkerInfo) -> dict[torch.device, torch.device]: ... + def _update_group_membership( + self, + worker_info: WorkerInfo, + my_devices: list[torch.device], + reverse_device_map: dict[str, dict[torch.device, torch.device]], + is_join: bool, + ): ... + def _get_backend_options(self) -> _TensorPipeRpcBackendOptionsBase: ... + @property + def is_static_group(self) -> bool: ... + @property + def store(self) -> Store: ... + +def _is_current_rpc_agent_set() -> bool: ... +def _get_current_rpc_agent() -> RpcAgent: ... +def _set_and_start_rpc_agent(agent: RpcAgent): ... +def _reset_current_rpc_agent(): ... +def _delete_all_user_and_unforked_owner_rrefs(timeout: timedelta = ...): ... +def _destroy_rref_context(ignoreRRefLeak: bool): ... +def _rref_context_get_debug_info() -> dict[str, str]: ... +def _cleanup_python_rpc_handler(): ... +def _invoke_rpc_builtin( + dst: WorkerInfo, + opName: str, + rpcTimeoutSeconds: float, + *args: Any, + **kwargs: Any, +): ... +def _invoke_rpc_python_udf( + dst: WorkerInfo, + pickledPythonUDF: str, + tensors: list[torch.Tensor], + rpcTimeoutSeconds: float, + isAsyncExecution: bool, +): ... +def _invoke_rpc_torchscript( + dstWorkerName: str, + qualifiedNameStr: str, + argsTuple: tuple, + kwargsDict: dict, + rpcTimeoutSeconds: float, + isAsyncExecution: bool, +): ... +def _invoke_remote_builtin( + dst: WorkerInfo, + opName: str, + rpcTimeoutSeconds: float, + *args: Any, + **kwargs: Any, +): ... +def _invoke_remote_python_udf( + dst: WorkerInfo, + pickledPythonUDF: str, + tensors: list[torch.Tensor], + rpcTimeoutSeconds: float, + isAsyncExecution: bool, +): ... +def _invoke_remote_torchscript( + dstWorkerName: WorkerInfo, + qualifiedNameStr: str, + rpcTimeoutSeconds: float, + isAsyncExecution: bool, + *args: Any, + **kwargs: Any, +): ... +def get_rpc_timeout() -> float: ... +def enable_gil_profiling(flag: bool): ... +def _set_rpc_timeout(rpcTimeoutSeconds: float): ... + +class RemoteProfilerManager: + @staticmethod + def set_current_profiling_key(key: str): ... + +def _enable_server_process_global_profiler(new_config: ProfilerConfig): ... +def _disable_server_process_global_profiler() -> list[list[list[ProfilerEvent]]]: ... +def _set_profiler_node_id(default_node_id: int): ... +def _enable_jit_rref_pickle(): ... +def _disable_jit_rref_pickle(): ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_distributed_rpc_testing.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_distributed_rpc_testing.pyi new file mode 100644 index 0000000000000000000000000000000000000000..9313281027dbd89ae65ebe65c95a1d0c38593b80 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_distributed_rpc_testing.pyi @@ -0,0 +1,32 @@ +import torch +from torch._C._distributed_c10d import Store +from torch._C._distributed_rpc import _TensorPipeRpcBackendOptionsBase, TensorPipeAgent + +# This module is defined in torch/csrc/distributed/rpc/testing/init.cpp + +class FaultyTensorPipeRpcBackendOptions(_TensorPipeRpcBackendOptionsBase): + def __init__( + self, + num_worker_threads: int, + rpc_timeout: float, + init_method: str, + messages_to_fail: list[str], + messages_to_delay: dict[str, float], + num_fail_sends: int, + ) -> None: ... + num_send_recv_threads: int + messages_to_fail: list[str] + messages_to_delay: dict[str, float] + num_fail_sends: int + +class FaultyTensorPipeAgent(TensorPipeAgent): + def __init__( + self, + store: Store, + name: str, + rank: int, + world_size: int, + options: FaultyTensorPipeRpcBackendOptions, + reverse_device_maps: dict[str, dict[torch.device, torch.device]], + devices: list[torch.device], + ) -> None: ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_functions.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_functions.pyi new file mode 100644 index 0000000000000000000000000000000000000000..38a6bb8b39faa3e43f585b23a183e4c95b16479d --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_functions.pyi @@ -0,0 +1,11 @@ +from typing import AnyStr + +from torch import Tensor + +class UndefinedGrad: + def __init__(self) -> None: ... + def __call__(self, *inputs: Tensor) -> list[Tensor]: ... + +class DelayedError: + def __init__(self, msg: AnyStr, num_inputs: int) -> None: ... + def __call__(self, inputs: list[Tensor]) -> list[Tensor]: ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_functorch.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_functorch.pyi new file mode 100644 index 0000000000000000000000000000000000000000..c5edf2deea1c6748eae5f9aaa8d23ae224d292c5 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_functorch.pyi @@ -0,0 +1,83 @@ +# mypy: allow-untyped-defs +from enum import Enum + +from torch import Tensor + +# Defined in torch/csrc/functorch/init.cpp + +def _set_dynamic_layer_keys_included(included: bool) -> None: ... +def get_unwrapped(tensor: Tensor) -> Tensor: ... +def is_batchedtensor(tensor: Tensor) -> bool: ... +def is_functionaltensor(tensor: Tensor) -> bool: ... +def is_functorch_wrapped_tensor(tensor: Tensor) -> bool: ... +def is_gradtrackingtensor(tensor: Tensor) -> bool: ... +def is_legacy_batchedtensor(tensor: Tensor) -> bool: ... +def maybe_get_bdim(tensor: Tensor) -> int: ... +def maybe_get_level(tensor: Tensor) -> int: ... +def maybe_current_level() -> int | None: ... +def unwrap_if_dead(tensor: Tensor) -> Tensor: ... +def _unwrap_for_grad(tensor: Tensor, level: int) -> Tensor: ... +def _wrap_for_grad(tensor: Tensor, level: int) -> Tensor: ... +def _unwrap_batched(tensor: Tensor, level: int) -> tuple[Tensor, int | None]: ... +def current_level() -> int: ... +def count_jvp_interpreters() -> int: ... +def _add_batch_dim(tensor: Tensor, bdim: int, level: int) -> Tensor: ... +def set_single_level_autograd_function_allowed(allowed: bool) -> None: ... +def get_single_level_autograd_function_allowed() -> bool: ... +def _unwrap_functional_tensor(tensor: Tensor, reapply_views: bool) -> Tensor: ... +def _wrap_functional_tensor(tensor: Tensor, level: int) -> Tensor: ... +def _vmap_increment_nesting(batch_size: int, randomness: str) -> int: ... +def _vmap_decrement_nesting() -> int: ... +def _grad_increment_nesting() -> int: ... +def _grad_decrement_nesting() -> int: ... +def _jvp_increment_nesting() -> int: ... +def _jvp_decrement_nesting() -> int: ... + +# Defined in aten/src/ATen/functorch/Interpreter.h +class TransformType(Enum): + Torch: TransformType = ... + Vmap: TransformType = ... + Grad: TransformType = ... + Jvp: TransformType = ... + Functionalize: TransformType = ... + +class RandomnessType(Enum): + Error: TransformType = ... + Same: TransformType = ... + Different: TransformType = ... + +class CInterpreter: + def key(self) -> TransformType: ... + def level(self) -> int: ... + +class CGradInterpreterPtr: + def __init__(self, interpreter: CInterpreter) -> None: ... + def lift(self, Tensor) -> Tensor: ... + def prevGradMode(self) -> bool: ... + +class CJvpInterpreterPtr: + def __init__(self, interpreter: CInterpreter) -> None: ... + def lift(self, Tensor) -> Tensor: ... + def prevFwdGradMode(self) -> bool: ... + +class CFunctionalizeInterpreterPtr: + def __init__(self, interpreter: CInterpreter) -> None: ... + def key(self) -> TransformType: ... + def level(self) -> int: ... + def functionalizeAddBackViews(self) -> bool: ... + +class CVmapInterpreterPtr: + def __init__(self, interpreter: CInterpreter) -> None: ... + def key(self) -> TransformType: ... + def level(self) -> int: ... + def batchSize(self) -> int: ... + def randomness(self) -> RandomnessType: ... + +class DynamicLayer: ... + +def get_dynamic_layer_stack_depth() -> int: ... +def get_interpreter_stack() -> list[CInterpreter]: ... +def peek_interpreter_stack() -> CInterpreter: ... +def pop_dynamic_layer_stack() -> DynamicLayer: ... +def pop_dynamic_layer_stack_and_undo_to_depth(int) -> None: ... +def push_dynamic_layer_stack(dl: DynamicLayer) -> int: ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_instruction_counter.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_instruction_counter.pyi new file mode 100644 index 0000000000000000000000000000000000000000..4e3c27567eb228b8763a6d2578db827b1ffbde41 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_instruction_counter.pyi @@ -0,0 +1,4 @@ +# Defined in torch/csrc/instruction_counter/Module.cpp + +def start() -> int: ... +def end(id: int) -> int: ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_itt.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_itt.pyi new file mode 100644 index 0000000000000000000000000000000000000000..8a54437f527b994821133532f26598c951631b28 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_itt.pyi @@ -0,0 +1,5 @@ +# Defined in torch/csrc/itt.cpp +def is_available() -> None: ... +def rangePush(message: str) -> None: ... +def rangePop() -> None: ... +def mark(message: str) -> None: ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_lazy.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_lazy.pyi new file mode 100644 index 0000000000000000000000000000000000000000..6591aa0da843efd3a1811cd67d7ca741fd3351fc --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_lazy.pyi @@ -0,0 +1,27 @@ +# mypy: allow-untyped-defs +from torch import Tensor + +# defined in torch/csrc/lazy/python/init.cpp +def _mark_step(device: str, devices: list[str], wait: bool): ... +def _wait_device_ops(devices: list[str]): ... +def _reset_metrics(): ... +def _counter_names() -> list[str]: ... +def _counter_value(name: str) -> int: ... +def _metrics_report() -> str: ... +def _get_graph_hash(tensors: list[Tensor]) -> str: ... +def _sync_multi( + tensors: list[Tensor], + devices: list[str], + wait: bool = True, + sync_ltc_data: bool = True, +): ... +def _get_tensor_id(tensor: Tensor) -> int: ... +def _get_tensors_text(tensors: list[Tensor]) -> str: ... +def _get_tensors_dot(tensors: list[Tensor]) -> str: ... +def _get_tensors_backend(tensors: list[Tensor]) -> str: ... +def _get_force_fallback() -> str: ... +def _set_force_fallback(newval: str): ... +def _clear_ir_cache(): ... +def _dump_ir_cache(filename: str): ... +def _set_reuse_ir(val: bool): ... +def _get_default_device_type(): ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_lazy_ts_backend.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_lazy_ts_backend.pyi new file mode 100644 index 0000000000000000000000000000000000000000..cd1bef2de069dfc93cd2b6243ab97d550d75f086 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_lazy_ts_backend.pyi @@ -0,0 +1,12 @@ +# mypy: allow-untyped-defs +# defined in torch/csrc/lazy/python/init.cpp + +from typing import Any + +from torch import Tensor + +def _init(): ... +def _get_tensors_ts_device_data_node( + tensors: list[Tensor], +) -> tuple[list[int], list[Any]]: ... +def _run_cached_graph(hash_str: str, graph_inputs: list[Any]) -> list[Tensor]: ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_monitor.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_monitor.pyi new file mode 100644 index 0000000000000000000000000000000000000000..25485a864bba72cb2b6c9f9b75dc1d12018c4566 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_monitor.pyi @@ -0,0 +1,44 @@ +# Defined in torch/csrc/monitor/python_init.cpp + +import datetime +from enum import Enum +from typing import Callable + +class Aggregation(Enum): + VALUE = ... + MEAN = ... + COUNT = ... + SUM = ... + MAX = ... + MIN = ... + +class Stat: + name: str + count: int + def __init__( + self, + name: str, + aggregations: list[Aggregation], + window_size: int, + max_samples: int = -1, + ) -> None: ... + def add(self, v: float) -> None: ... + def get(self) -> dict[Aggregation, float]: ... + +class Event: + name: str + timestamp: datetime.datetime + data: dict[str, int | float | bool | str] + def __init__( + self, + name: str, + timestamp: datetime.datetime, + data: dict[str, int | float | bool | str], + ) -> None: ... + +def log_event(e: Event) -> None: ... + +class EventHandlerHandle: ... + +def register_event_handler(handler: Callable[[Event], None]) -> EventHandlerHandle: ... +def unregister_event_handler(handle: EventHandlerHandle) -> None: ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_nn.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_nn.pyi new file mode 100644 index 0000000000000000000000000000000000000000..8472a309b99c6e4d8f3fde14879ffaaf40b3e208 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_nn.pyi @@ -0,0 +1,89 @@ +# @generated by tools/pyi/gen_pyi.py from torch/_C/_nn.pyi.in +# mypy: disable-error-code="type-arg" + +from typing import List, Literal, Optional, overload, Sequence, Tuple, Union + +from torch import memory_format, Tensor +from torch.types import _bool, _device, _dtype, _int, _size + +# Defined in tools/autograd/templates/python_nn_functions.cpp + +def adaptive_max_pool2d(input: Tensor, output_size: Union[_int, _size]) -> Tuple[Tensor, Tensor]: ... +def adaptive_max_pool3d(input: Tensor, output_size: Union[_int, _size]) -> Tuple[Tensor, Tensor]: ... +def avg_pool2d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]] = None, padding: Union[_int, _size] = 0, ceil_mode: bool = False, count_include_pad: bool = True, divisor_override: Optional[int] = None) -> Tensor: ... +def avg_pool3d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]] = None, padding: Union[_int, _size] = 0, ceil_mode: bool = False, count_include_pad: bool = True, divisor_override: Optional[int] = None) -> Tensor: ... +def elu_(input: Tensor, alpha: float = ...) -> Tensor: ... +def fractional_max_pool2d(input: Tensor, kernel_size: Union[_int, _size], output_size: Union[_int, _size], _random_samples: Tensor) -> Tuple[Tensor, Tensor]: ... +def fractional_max_pool3d(input: Tensor, kernel_size: Union[_int, _size], output_size: Union[_int, _size], _random_samples: Tensor) -> Tuple[Tensor, Tensor]: ... +def gelu(input: Tensor, approximate: str = ...) -> Tensor: ... +def hardsigmoid(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def hardtanh(input: Tensor, min_val: float = ..., max_val: float = ..., *, out: Optional[Tensor] = None) -> Tensor: ... +def hardtanh_(input: Tensor, min_val: float = ..., max_val: float = ...) -> Tensor: ... +def leaky_relu(input: Tensor, negative_slope: float = ..., *, out: Optional[Tensor] = None) -> Tensor: ... +def leaky_relu_(input: Tensor, negative_slope: float = ...) -> Tensor: ... +def linear(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None) -> Tensor: ... +def log_sigmoid(input: Tensor) -> Tensor: ... +def one_hot(tensor: Tensor, num_classes: int = ...) -> Tensor: ... +def pad(input: Tensor, pad: Sequence[int], mode: str = ..., value: Optional[float] = None) -> Tensor: ... +def scaled_dot_product_attention(query: Tensor, key: Tensor, value: Tensor, attn_mask: Optional[Tensor] = None, dropout_p: float = 0.0, is_causal: bool = False, scale: Optional[float] = None, enable_gqa: bool = False) -> Tensor: ... +def softplus(input: Tensor, beta: float = ..., threshold: float = ...) -> Tensor: ... +def softshrink(input: Tensor, lambd: float = ...) -> Tensor: ... + +# Defined in aten/src/ATen/native/mkldnn/Linear.cpp +def mkldnn_linear(input: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor: ... + +# Defined at aten/src/ATen/native/mkldnn/MKLDNNConversions.cpp +def mkldnn_reorder_conv2d_weight( + self: Tensor, + padding: List, + stride: List, + dilatation: List, + groups: int, +) -> Tensor: ... +def mkldnn_reorder_conv3d_weight( + self: Tensor, + padding: List, + stride: List, + dilatation: List, + groups: int, +) -> Tensor: ... + +# Defined in aten/src/ATen/native/mkldnn/Prelu.cpp +def mkldnn_prelu(input: Tensor, weight: Tensor) -> Tensor: ... + +# Defined at tools/autograd/templates/python_nn_functions.cpp +@overload +def _parse_to( + device: _device, + dtype: _dtype, + non_blocking: _bool, + copy: _bool, + *, + memory_format: memory_format, +) -> Tuple[_device, _dtype, _bool, memory_format]: ... +@overload +def _parse_to( + dtype: _dtype, + non_blocking: _bool, + copy: _bool, + *, + memory_format: memory_format, +) -> Tuple[_device, _dtype, _bool, memory_format]: ... +@overload +def _parse_to( + tensor: Tensor, + non_blocking: _bool, + copy: _bool, + *, + memory_format: memory_format, +) -> Tuple[_device, _dtype, _bool, memory_format]: ... + +# Defined in aten/src/ATen/native/PackedSequence.cpp +def pad_sequence( + sequences: Union[List[Tensor], Tuple[Tensor, ...]], + batch_first: bool = False, + padding_value: float = 0.0, + padding_side: Union[Literal["left", "right"], str] = "right", +) -> Tensor: ... +def flatten_dense_tensors(tensors: List[Tensor]) -> Tensor: ... +def unflatten_dense_tensors(flat: Tensor, tensors: List[Tensor]) -> List[Tensor]: ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_nvtx.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_nvtx.pyi new file mode 100644 index 0000000000000000000000000000000000000000..79c9cc2c4b9bfe1d00a09c4d45e3d8e2c6f56995 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_nvtx.pyi @@ -0,0 +1,7 @@ +# mypy: allow-untyped-defs +# Defined in torch/csrc/cuda/shared/nvtx.cpp +def rangePushA(message: str) -> int: ... +def rangePop() -> int: ... +def rangeStartA(message: str) -> int: ... +def rangeEnd(int) -> None: ... +def markA(message: str) -> None: ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_onnx.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_onnx.pyi new file mode 100644 index 0000000000000000000000000000000000000000..349e0b9ad12f0dd9306fde89a40718f26b158f0e --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_onnx.pyi @@ -0,0 +1,39 @@ +# Defined in torch/csrc/onnx/init.cpp + +from enum import Enum + +PRODUCER_VERSION: str + +class TensorProtoDataType(Enum): + UNDEFINED = ... + FLOAT = ... + UINT8 = ... + INT8 = ... + UINT16 = ... + INT16 = ... + INT32 = ... + INT64 = ... + STRING = ... + BOOL = ... + FLOAT16 = ... + DOUBLE = ... + UINT32 = ... + UINT64 = ... + COMPLEX64 = ... + COMPLEX128 = ... + BFLOAT16 = ... + FLOAT8E5M2 = ... + FLOAT8E4M3FN = ... + FLOAT8E5M2FNUZ = ... + FLOAT8E4M3FNUZ = ... + +class OperatorExportTypes(Enum): + ONNX = ... + ONNX_ATEN = ... + ONNX_ATEN_FALLBACK = ... + ONNX_FALLTHROUGH = ... + +class TrainingMode(Enum): + EVAL = ... + PRESERVE = ... + TRAINING = ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_profiler.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_profiler.pyi new file mode 100644 index 0000000000000000000000000000000000000000..3c1b74c681a11e31f7581622b7379e48ff8af44c --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_profiler.pyi @@ -0,0 +1,244 @@ +from enum import Enum +from typing import Any, Literal +from typing_extensions import TypeAlias + +from torch._C import device, dtype, layout + +# defined in torch/csrc/profiler/python/init.cpp + +class RecordScope(Enum): + FUNCTION = ... + BACKWARD_FUNCTION = ... + TORCHSCRIPT_FUNCTION = ... + KERNEL_FUNCTION_DTYPE = ... + CUSTOM_CLASS = ... + BUILD_FEATURE = ... + LITE_INTERPRETER = ... + USER_SCOPE = ... + STATIC_RUNTIME_OP = ... + STATIC_RUNTIME_MODEL = ... + +class ProfilerState(Enum): + Disable = ... + CPU = ... + CUDA = ... + NVTX = ... + ITT = ... + KINETO = ... + KINETO_GPU_FALLBACK = ... + KINETO_PRIVATEUSE1_FALLBACK = ... + KINETO_PRIVATEUSE1 = ... + +class ActiveProfilerType(Enum): + NONE = ... + LEGACY = ... + KINETO = ... + NVTX = ... + ITT = ... + +class ProfilerActivity(Enum): + CPU = ... + CUDA = ... + XPU = ... + MTIA = ... + PrivateUse1 = ... + +class _EventType(Enum): + TorchOp = ... + Backend = ... + Allocation = ... + OutOfMemory = ... + PyCall = ... + PyCCall = ... + Kineto = ... + +class _ExperimentalConfig: + def __init__( + self, + profiler_metrics: list[str] = ..., + profiler_measure_per_kernel: bool = ..., + verbose: bool = ..., + performance_events: list[str] = ..., + enable_cuda_sync_events: bool = ..., + ) -> None: ... + +class ProfilerConfig: + def __init__( + self, + state: ProfilerState, + report_input_shapes: bool, + profile_memory: bool, + with_stack: bool, + with_flops: bool, + with_modules: bool, + experimental_config: _ExperimentalConfig, + ) -> None: ... + +class _ProfilerEvent: + start_tid: int + start_time_ns: int + children: list[_ProfilerEvent] + + # TODO(robieta): remove in favor of `self.typed` + extra_fields: ( + _ExtraFields_TorchOp + | _ExtraFields_Backend + | _ExtraFields_Allocation + | _ExtraFields_OutOfMemory + | _ExtraFields_PyCall + | _ExtraFields_PyCCall + | _ExtraFields_Kineto + ) + + @property + def typed( + self, + ) -> ( + tuple[Literal[_EventType.TorchOp], _ExtraFields_TorchOp] + | tuple[Literal[_EventType.Backend], _ExtraFields_Backend] + | tuple[Literal[_EventType.Allocation], _ExtraFields_Allocation] + | tuple[Literal[_EventType.OutOfMemory], _ExtraFields_OutOfMemory] + | tuple[Literal[_EventType.PyCall], _ExtraFields_PyCall] + | tuple[Literal[_EventType.PyCCall], _ExtraFields_PyCCall] + | tuple[Literal[_EventType.Kineto], _ExtraFields_Kineto] + ): ... + @property + def name(self) -> str: ... + @property + def tag(self) -> _EventType: ... + @property + def id(self) -> int: ... + @property + def parent(self) -> _ProfilerEvent | None: ... + @property + def correlation_id(self) -> int: ... + @property + def end_time_ns(self) -> int: ... + @property + def duration_time_ns(self) -> int: ... + +class _TensorMetadata: + impl_ptr: int | None + storage_data_ptr: int | None + id: int | None + + @property + def allocation_id(self) -> int | None: ... + @property + def layout(self) -> layout: ... + @property + def device(self) -> device: ... + @property + def dtype(self) -> dtype: ... + @property + def sizes(self) -> list[int]: ... + @property + def strides(self) -> list[int]: ... + +Scalar: TypeAlias = int | float | bool | complex +Input: TypeAlias = _TensorMetadata | list[_TensorMetadata] | Scalar | None + +class _ExtraFields_TorchOp: + name: str + sequence_number: int + allow_tf32_cublas: bool + + @property + def inputs(self) -> list[Input]: ... + @property + def scope(self) -> RecordScope: ... + +class _ExtraFields_Backend: ... + +class _ExtraFields_Allocation: + ptr: int + id: int | None + alloc_size: int + total_allocated: int + total_reserved: int + + @property + def allocation_id(self) -> int | None: ... + @property + def device(self) -> device: ... + +class _ExtraFields_OutOfMemory: ... + +class _PyFrameState: + line_number: int + function_name: str + + @property + def file_name(self) -> str: ... + +class _NNModuleInfo: + @property + def self_ptr(self) -> int: ... + @property + def cls_ptr(self) -> int: ... + @property + def cls_name(self) -> str: ... + @property + def parameters( + self, + ) -> list[tuple[str, _TensorMetadata, _TensorMetadata | None]]: ... + +class _OptimizerInfo: + @property + def parameters( + self, + ) -> list[ + tuple[ + # Parameter + _TensorMetadata, + # + # Gradient (if present during optimizer.step()) + _TensorMetadata | None, + # + # Optimizer state for Parameter as (name, tensor) pairs + list[tuple[str, _TensorMetadata]], + ] + ]: ... + +class _ExtraFields_PyCCall: + @property + def caller(self) -> _PyFrameState: ... + +class _ExtraFields_PyCall: + @property + def callsite(self) -> _PyFrameState: ... + @property + def caller(self) -> _PyFrameState: ... + @property + def module(self) -> _NNModuleInfo | None: ... + @property + def optimizer(self) -> _OptimizerInfo | None: ... + +class _ExtraFields_Kineto: ... + +def _add_execution_trace_observer(output_file_path: str) -> bool: ... +def _remove_execution_trace_observer() -> None: ... +def _enable_execution_trace_observer() -> None: ... +def _disable_execution_trace_observer() -> None: ... +def _set_record_concrete_inputs_enabled_val(val: bool) -> None: ... +def _set_fwd_bwd_enabled_val(val: bool) -> None: ... +def _set_cuda_sync_enabled_val(val: bool) -> None: ... + +class CapturedTraceback: ... + +def gather_traceback(python: bool, script: bool, cpp: bool) -> CapturedTraceback: ... + +# The Dict has name, filename, line +def symbolize_tracebacks( + to_symbolize: list[CapturedTraceback], +) -> list[list[dict[str, str]]]: ... + +class _RecordFunctionFast: + def __init__( + self, + name: str, + input_values: list | tuple | None = None, + keyword_values: dict | None = None, + ) -> None: ... + def __enter__(self) -> None: ... + def __exit__(self, *args: Any) -> None: ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_verbose.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_verbose.pyi new file mode 100644 index 0000000000000000000000000000000000000000..2388ce2bb8a5edd4c7640c374537e71607e5b72e --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_C/_verbose.pyi @@ -0,0 +1,3 @@ +# Defined in torch/csrc/utils/verbose.cpp +def mkl_set_verbose(enable: int) -> int: ... +def mkldnn_set_verbose(level: int) -> int: ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/__pycache__/_VF.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/__pycache__/_VF.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..26dd4ac4987c7cd3c7480c8b46aeb8160a8be825 Binary files /dev/null and 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0000000000000000000000000000000000000000..b08067bdcf45a17dbcf3e032b4156315d9e2981b --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_awaits/__init__.py @@ -0,0 +1,53 @@ +from __future__ import annotations + +from typing import Generic, TypeVar + +import torch + +__all__ = ['Await'] + +W = TypeVar("W") + +class _PyAwaitMeta(type(torch._C._Await), type(Generic)): # type: ignore[misc, no-redef] + pass + +class _Await(torch._C._Await, Generic[W], metaclass=_PyAwaitMeta): + r""" + Wrapper around a ``torch._C.Await`` which encapsulates delayed execution + of a callable. All manipulations happen with functions ``torch.jit._awaitable``, + ``torch.jit._awaitable_wait``, ``torch.jit._awaitable_nowait``. + + Torch scriptable manipulations: + ``torch.jit._awaitable(func, *args)`` + Creates ``Await[W]`` object, where W is return type of func. + + Returns: + ``torch.jit._awaitable_wait(Await[W])`` + Returns the result of the function, specified at ``_awaitable``, with specified arguments. + + Returns: + The result of type ``W`` of the function call. The result is owned by ``Await[W]`` + and returned on all following ``_awaitable_wait`` calls. + + + ``torch.jit._awaitable_nowait(W)`` + Returns: + Trivial ``Await[W]`` with specified result. + + + Only in eager mode: + ``fn() -> Callable[Tuple[Any], W]`` + Returns: + Specified at ``_awaitable`` python function ``func``. + + ``args() -> Tuple[Any]`` + Returns: + Specified at ``_awaitable`` python args. + + ``is_nowait() -> _bool`` + Returns: + ``True`` if this object was created via ``_awaitable_nowait`` call (trivial `Await[W]`). + + In eager mode ``Await[W]`` can be used as ``W`` i.e. attributes of W can be called on ``Await[W]``, + ``_awaitable_wait()`` call will be transparently added. + """ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..35e6d30150c06de97a855fc0326b2df27922d2db Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/__pycache__/__init__.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/__pycache__/autograd.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/__pycache__/autograd.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5688f35c0fe73fdc8a8aa31e91cebfaf11a2d14b Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/__pycache__/autograd.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/__pycache__/functional.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/__pycache__/functional.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6b6d694b3431e04c4a163855dd17d7827f96ddf3 Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/__pycache__/functional.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/__pycache__/impl.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/__pycache__/impl.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2c7eb3c1eccc17a1f43ac70ebb423331ab8011c3 Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/__pycache__/impl.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/autograd.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/autograd.py new file mode 100644 index 0000000000000000000000000000000000000000..35727197d03c1c4c1e00584d2c25e1830d6bcbd8 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/autograd.py @@ -0,0 +1,275 @@ +# mypy: allow-untyped-defs +import torch +import torch.utils._pytree as pytree +from collections import namedtuple +import functools + + +# NOTE [CustomOp autograd kernel indirection] +# We register `inner` as the autograd kernel for this custom_op. +# `inner` either calls the autograd formula registered by the user, +# or goes into an `autograd_not_implemented` kernel. +# +# The reason why this indirection exists is +# so that we can swap out the autograd kernel (the PyTorch dispatcher +# doesn't actually allow us to do this). By default, we want +# the `autograd_not_implemented` behavior, but then the user may come +# and register something that is actually a backward formula +def autograd_kernel_indirection(custom_op): + autograd_fallback = autograd_not_implemented(custom_op) + + def inner(*args, **kwargs): + if custom_op._has_impl('autograd'): + kernel = custom_op._get_impl('autograd').func + return kernel(*args, **kwargs) + # As explained in NOTE ["backward", "save_for_backward", and "autograd"], + # after the user gives us "backward" and "save_for_backward", we generate + # the "autograd" impl. If the user only provided one, then we tell + # the user they've done something wrong. + if custom_op._has_impl('save_for_backward') or custom_op._has_impl('backward'): + missing = ( + 'save_for_backward' if custom_op._has_impl('backward') + else 'backward' + ) + found = 'save_for_backward' if missing == 'backward' else 'backward' + loc = custom_op._get_impl(found).location + raise RuntimeError( + f"We found a '{found}' registration for {custom_op} at " + f"{loc} but were unable to find a '{missing}' registration. " + f"To use the CustomOp API to register a backward formula, " + f"please provide us both a backward function and a " + f"'save for backward' function via `impl_backward` and " + f"`impl_save_for_backward` respectively.") + return autograd_fallback(*args, **kwargs) + return inner + + +# TODO(#101191): Use the actual C++ autograd not implemented fallback, +# or change the default autograd fallback to the autograd not implemented fallback. +def autograd_not_implemented(custom_op): + def kernel(*args, **kwargs): + if torch.is_grad_enabled() and pytree.tree_any( + lambda x: isinstance(x, torch.Tensor) and x.requires_grad, (args, kwargs) + ): + raise RuntimeError("Autograd has not been implemented for operator") + with torch._C._AutoDispatchBelowAutograd(): + return custom_op(*args, **kwargs) + return kernel + + +def mark_non_differentiable(ctx, output, output_differentiability): + # Output types are restricted to be: + # - Tensor + # - Tensor[] + # - int, bool, Scalar, float + # See _check_can_register_backward + if output_differentiability is not None: + if not isinstance(output, tuple): + tuple_output = (output,) + else: + tuple_output = output # type: ignore[assignment] + assert len(output_differentiability) == len(tuple_output) + non_differentiable_tensors = [] + for idx, (differentiable, out) in enumerate(zip(output_differentiability, tuple_output)): + if isinstance(out, torch.Tensor): + if not differentiable: + non_differentiable_tensors.append(out) + continue + if isinstance(out, list): + if not differentiable: + non_differentiable_tensors.extend(out) + continue + if differentiable: + raise RuntimeError( + f"With output_differentiability={output_differentiability}. " + f"At idx {idx}, we received an object of type {type(out)} that " + f"is not a Tensor, so it cannot have be marked as differentiable in " + f"output_differentiability.") + if non_differentiable_tensors: + ctx.mark_non_differentiable(*non_differentiable_tensors) + + +def construct_autograd_kernel( + schema, + output_differentiability, + custom_op, + op_overload, + save_for_backward_fn, + backward_fn): + + def apply(*args): + flat_args, spec = pytree.tree_flatten(args) + out_spec = None + + def forward(ctx, *flat_args): + ctx.set_materialize_grads(True) + args = pytree.tree_unflatten(list(flat_args), spec) + with torch._C._AutoDispatchBelowAutograd(): + output = op_overload(*args) + + # We use the info about args to give better error messages in backward + args_info = namedtuple_args( + schema, pytree.tree_map(type, args)) + + save_for_backward_fn_inputs = namedtuple_args(schema, args) + to_save = save_for_backward_fn(save_for_backward_fn_inputs, output) + + save_pytree_for_backward(ctx, (to_save, args_info)) + mark_non_differentiable(ctx, output, output_differentiability) + + nonlocal out_spec + flat_output, out_spec = pytree.tree_flatten(output) + return tuple(flat_output) + + def backward(ctx, *flat_grad_output): + assert out_spec is not None + grads = pytree.tree_unflatten(list(flat_grad_output), out_spec) + saved, args_info = unpack_saved(ctx) + # There is nothing on the ctx object for now, it is just there so + # that we can add additional things in the future. + inner_ctx = object() + if not isinstance(grads, tuple): + grads = (grads,) + grad_inputs_dict = backward_fn(inner_ctx, saved, *grads) + + # Massage the grad_inputs_dict to a form acceptable by + # autograd.Function. + validate_grad_inputs_dict(grad_inputs_dict, custom_op, args_info) + return grad_inputs_dict_to_flat_tuple(grad_inputs_dict, args_info) + + generated_cls = gen_autograd_function( + custom_op._opname + '_customop', forward, backward) + + flat_output = generated_cls.apply(*flat_args) + assert out_spec is not None + return pytree.tree_unflatten(list(flat_output), out_spec) + return apply + + +def gen_autograd_function(name, forward, backward): + generated_cls = type( + name, + (torch.autograd.Function,), + { + 'forward': staticmethod(forward), + 'backward': staticmethod(backward), + } + ) + return generated_cls + + +@functools.lru_cache +def namedtuple_args_cls(schema): + attribs = [arg.name for arg in schema.arguments.flat_all] + name = str(schema.name) + "_args" + # mypy doesn't support dynamic namedtuple name + tuple_cls = namedtuple(name, attribs) # type: ignore[misc] + return tuple_cls + + +def namedtuple_args(schema, args): + assert isinstance(args, tuple) + tuple_cls = namedtuple_args_cls(schema) + return tuple_cls(*args) + + +def validate_grad_inputs_dict(grad_inputs_dict, forward_op, args_info): + def error(what): + backward = forward_op._get_impl('backward') + raise RuntimeError( + f"In the backward function defined for {forward_op} at " + f"{backward.location} using the CustomOp API, {what}") + + if not isinstance(grad_inputs_dict, dict): + error(f"expected the output of the backward function to be a dict but " + f"got {type(grad_inputs_dict)}") + + expected_keys = {arg.name for arg in forward_op._schema.arguments.flat_all + if arg.type.is_tensor_like()} + actual_keys = grad_inputs_dict.keys() + if expected_keys != actual_keys: + error(f"expected the returned grad_input dict to have keys " + f"{expected_keys} but got {actual_keys}. The backward " + f"function must return a gradient (can be None) for each arg " + f"to the CustomOp that may be a Tensor or Sequence[Tensor]. " + f"Args declared to be non-Tensor-like types should not appear " + f"in the grad_input dict") + + for name, grad in grad_inputs_dict.items(): + arg_info = getattr(args_info, name) + + if isinstance(arg_info, list): + if not isinstance(grad, (tuple, list)): + error(f"for input '{name}' expected the grad_input dict to " + f"hold a list of gradients but got object of type " + f"{type(grad)}.") + if not len(grad) == len(arg_info): + error(f"for input '{name}' expected the grad_input dict to " + f"hold a list of {len(arg_info)} gradients but got " + f"{len(grad)}") + for idx, (g, info) in enumerate(zip(grad, arg_info)): + if g is None: + continue + if not isinstance(g, torch.Tensor): + error(f"for input '{name}' expected the grad_input dict to " + f"hold a list of None or Tensor gradients but got " + f"object of {type(g)} at index {idx}") + if not issubclass(info, torch.Tensor): + error(f"for input '{name}', got a Tensor as the gradient " + f"for the {idx}-th value but expected None because " + f"the {idx}-th value was not a Tensor (it was " + f"type {arg_info}") + continue + + if grad is None: + continue + if not isinstance(grad, torch.Tensor): + error(f"got object of type {type(grad)} as the gradient for input " + f"'{name}', " + f"but expected the gradient to be either None or a Tensor") + if not issubclass(arg_info, torch.Tensor): + error(f"got a Tensor as the gradient for input '{name}' but " + f"expected None as the gradient because input '{name}' " + f"was not a Tensor (it was type {arg_info}).") + + +def grad_inputs_dict_to_flat_tuple(grad_inputs_dict, args_info): + result = [] + for name, arg_info in args_info._asdict().items(): + if name not in grad_inputs_dict: + result.append(pytree.tree_map(lambda x: None, arg_info)) + continue + result.append(grad_inputs_dict[name]) + return tuple(pytree.tree_leaves(result)) + +# Saves "stuff" (a pytree) onto the ctx object. Use unpack_saved to unpack it. +# autograd.Function prefers that users use ctx.save_for_backward to +# save Tensors (to avoid reference cycles) and for non-Tensors to go onto the +# ctx object. +def save_pytree_for_backward(ctx, stuff): + flat_stuff, spec = pytree.tree_flatten(stuff) + num_elts = len(flat_stuff) + tensor_idxs = [idx for idx, thing in enumerate(flat_stuff) + if isinstance(thing, torch.Tensor)] + non_tensor_idxs = [idx for idx, thing in enumerate(flat_stuff) + if not isinstance(thing, torch.Tensor)] + tensors = [thing for thing in flat_stuff if isinstance(thing, torch.Tensor)] + non_tensors = [thing for thing in flat_stuff if not isinstance(thing, torch.Tensor)] + + ctx.spec = spec + ctx.num_elts = num_elts + ctx.save_for_backward(*tensors) + ctx.tensor_idxs = tensor_idxs + ctx.saved_non_tensors = non_tensors + ctx.non_tensor_idxs = non_tensor_idxs + + +# Inverse operation to save_pytree_for_backward +def unpack_saved(ctx): + flat_stuff = [None] * ctx.num_elts + for tensor, idx in zip(ctx.saved_tensors, ctx.tensor_idxs): + flat_stuff[idx] = tensor + for non_tensor, idx in zip(ctx.saved_non_tensors, ctx.non_tensor_idxs): + flat_stuff[idx] = non_tensor + stuff = pytree.tree_unflatten(flat_stuff, ctx.spec) + return stuff diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/functional.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/functional.py new file mode 100644 index 0000000000000000000000000000000000000000..57ff351e2e2d53a217008e793c57b1e3867ebe54 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/functional.py @@ -0,0 +1,188 @@ +# mypy: allow-untyped-defs +import weakref + +import torch +import torch.utils._pytree as pytree +from torch._C import _ExcludeDispatchKeyGuard, DispatchKey, DispatchKeySet +from torch._ops import OpOverload +from torch.library import Library +from torchgen.model import ( + BaseTy, + BaseType, + FunctionSchema, + OperatorName, + OptionalType, + SchemaKind, +) + +from .autograd import autograd_not_implemented + + +def register_functional_op( + lib: Library, + new_op_name: str, + mutable_op: OpOverload, +) -> None: + """Given a mutable operator, registers the functional variant. + + This API also correctly links the functional variant with the mutable + operator for the purposes of functionalization. + + All of the new registrations are performed on the ``lib`` passed in. + + Arguments: + lib (Library): Should be a torch.library.Library object that has + the same namespace as ``mutable_op``'s namespace. + lib will be used to register the new functional op as well + as a functionalization kernel for the ``mutable_op`` + If you don't have a library handy, use + ``torch.library.Library(ns, 'FRAGMENT')`` to construct one. + new_op_name (str): The name of the functional operator (without the + namespace). If no namespace, the new functional variant will be + accessible under ``torch.ops.{lib.ns}.new_op_name``. + mutable_op (OpOverload): The mutable custom operator. Note + that you may need to add a `.default` to it, like + `torch.ops.aten.abs_.default`. + + """ + validate(mutable_op) + schema = functional_schema(new_op_name, mutable_op) + lib.define(schema) + + functional_impl = construct_functional_impl(mutable_op) + lib.impl(new_op_name, functional_impl, 'CompositeExplicitAutograd') + + functional_op = getattr(getattr(torch.ops, lib.ns), new_op_name).default + + # There's no easy way for us to generate the autograd kernel, so we + # use autograd_not_implemented. Also, this makes it so that the user + # is unable to register an autograd formula themselves. This shouldn't + # be a problem if the user doesn't use the functional op direclty + # in their program, but we may need to revist this in the future. + lib.impl(new_op_name, autograd_not_implemented(functional_op), 'Autograd') + + f_kernel = construct_functionalization_kernel(weakref.proxy(mutable_op), functional_op) + + lib.impl(mutable_op, f_kernel, 'Functionalize') + + +def construct_functional_impl(mutable_op): + def functional_impl(*args): + # Strategy: + # - clone args that would have been mutated + # - run mutable_op + # - return the cloned args as additional outputs + new_args = [] + extra_rets = [] + for is_write, arg in zip(mutable_args(mutable_op), args): + if is_write: + cloned = arg.clone() if arg is not None else None + new_args.append(cloned) + extra_rets.append(cloned) + else: + new_args.append(arg) + result = mutable_op(*new_args) + if result is None: + return tuple(extra_rets) + if isinstance(result, tuple): + return (*result, *extra_rets) + return (result, *extra_rets) + return functional_impl + + +def construct_functionalization_kernel(mutable_op, functional_op): + def kernel(*args): + # There's nothing to be functionalized! + # We can still end up here because DispatchKey::Functionalize is a mode key + if pytree.tree_all_only(torch.Tensor, lambda x: not torch._is_functional_tensor(x), args): + with _ExcludeDispatchKeyGuard(DispatchKeySet(DispatchKey.Functionalize)): + return mutable_op(*args) + + # NB: This differs from the codegen -- codegen handles cases where there + # are mixed FunctionalTensorWrapper and non-FunctionalTensorWrapper. + # This only really matters for XLA (mixed CPU-XLA tensors) and + # running functionalization without the PT2 stack (which guarantees to us that + # all tensors are FunctionalTensorWrapper). + if not pytree.tree_all_only(torch.Tensor, torch._is_functional_tensor, args): + raise RuntimeError("{mutable_op}: expected all args to be FunctionalTensorWrapper") + + unwrapped_args = [] + for arg in args: + if isinstance(arg, torch.Tensor) and torch._is_functional_tensor(arg): + torch._sync(arg) + unwrapped = torch._from_functional_tensor(arg) + unwrapped_args.append(unwrapped) + else: + unwrapped_args.append(arg) + + with _ExcludeDispatchKeyGuard(DispatchKeySet(DispatchKey.Functionalize)): + output = functional_op(*unwrapped_args) + + num_actual_output = len(mutable_op._schema.returns) + actual_output = pytree.tree_map( + torch._to_functional_tensor, output[:num_actual_output]) + + new_values_to_propagate = output[num_actual_output:] + inputs_to_replace = [arg for is_write, arg in zip(mutable_args(mutable_op), args) + if is_write] + assert len(new_values_to_propagate) == len(inputs_to_replace) + for new_value, arg in zip(new_values_to_propagate, inputs_to_replace): + if (arg is None and new_value is None) or (arg is not None and new_value is not None): + continue + torch._C._propagate_xla_data(arg, new_value) + torch._C._replace_(arg, new_value) + torch._C._commit_update(arg) + torch._sync(arg) + + if len(actual_output) == 1: + return actual_output[0] + elif len(actual_output) == 0: + return None + return actual_output + + return kernel + + +def validate(mutable_op: OpOverload): + if not isinstance(mutable_op, OpOverload): + raise TypeError( + f"register_functional_op(mutable_op): expected mutable_op to be instance of " + f"OpOverload but got {type(mutable_op)}") + + # There are generally three types of "in-place" or "mutable" ops. + # Each of them have their own conventions: + # - inplace (first input modified in-place and returned as only output) + # - out= (some args modified in-place and returned as outputs) + # - mutable (some args modified in-place but none of those returned as outputs) + # In theory we can support all three, but we'll just support the last + # option right now for simplicity. + schema = FunctionSchema.parse(str(mutable_op._schema)) + if not schema.kind() == SchemaKind.mutable: + raise RuntimeError("Expected op to be mutable (as opposed to functional, inplace or out)") + for ret in schema.returns: + # construct_functionalization_kernel assumes this for simplicity + if ret.annotation is not None: + raise NotImplementedError( + "NYI: register_functional_op(op) where op returns a mutated or aliased value. " + "Please file an issue (and as a workaround, modify your operator to " + "not return the mutated value or aliases)") + for arg in schema.arguments.flat_all: + # construct_functionalization_kernel assumes this for simplicity + if arg.type.is_tensor_like() and ( + arg.type != BaseType(BaseTy.Tensor) + and arg.type != OptionalType(BaseType(BaseTy.Tensor)) + ): + raise NotImplementedError( + "NYI: register_functional_op(op) where op has a List[Tensor] input." + "Please file an issue.") + + +def functional_schema(new_op_name, op: OpOverload): + schema = FunctionSchema.parse(str(op._schema)) + schema = schema.signature().with_name(OperatorName.parse(new_op_name)) + return str(schema) + + +def mutable_args(op: OpOverload): + return tuple(False if arg.alias_info is None else arg.alias_info.is_write + for arg in op._schema.arguments) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/impl.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/impl.py new file mode 100644 index 0000000000000000000000000000000000000000..c00e25ec7316b1dca66a8f77d9738c170b36d625 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_custom_op/impl.py @@ -0,0 +1,670 @@ +# mypy: allow-untyped-defs +import dataclasses +import functools +import inspect +import sys +import typing +import weakref +import warnings + +from torchgen.model import FunctionSchema, OperatorName, SchemaKind, BaseType, ListType, BaseTy + +import torch +import torch._C as _C +import torch.library as library +from torch.library import get_ctx + +from .autograd import autograd_kernel_indirection, construct_autograd_kernel +import torch._library.infer_schema +from torch._library.infer_schema import infer_schema + +""" +torch._custom_op is deprecated. We shipped a production-ready version of it into torch.library. +Please use those APIs instead. +""" + +__all__ = ["custom_op", "CustomOp", "get_ctx"] + + +SUPPORTED_DEVICE_TYPE_TO_KEY = { + "cpu": "CPU", + "cuda": "CUDA", +} + +# We will not let users register CustomOps with anything that could look like +# PyTorch internals to avoid confusion. +RESERVED_NS = { + "prim", + "prims", + "aten", + "at", + "torch", + "pytorch", +} + +def warn_deprecated(): + warnings.warn( + "torch._custom_op is deprecated and will be removed in PyTorch 2.6, please " + "use the equivalent torch.library API instead.", DeprecationWarning) + + +def custom_op( + qualname: str, manual_schema: typing.Optional[str] = None +) -> typing.Callable: + r""" + This API is deprecated, please use torch.library.custom_op instead + """ + warn_deprecated() + + def inner(func): + if not inspect.isfunction(func): + raise ValueError( + f"custom_op(...)(func): Expected `func` to be a Python " + f"function, got: {type(func)}" + ) + + ns, name = parse_qualname(qualname) + validate_namespace(ns) + if func.__name__ != name: + raise ValueError( + f"custom_op(qualname='{qualname}', ...)(func): expected `func` " + f"to have name '{name}' but got '{func.__name__}'. " + f"Please either change the name of `func` or the qualname that " + f"is passed to `custom_op`" + ) + + schema = infer_schema(func, mutates_args=()) if manual_schema is None else manual_schema + schema_str = f"{name}{schema}" + function_schema = FunctionSchema.parse(schema_str) + validate_schema(function_schema) + if manual_schema is not None: + validate_function_matches_schema(function_schema, func) + + lib = library.Library(ns, "FRAGMENT") + lib.define(schema_str) + ophandle = find_ophandle_or_throw(ns, function_schema.name) + result = CustomOp(lib, ns, function_schema, name, ophandle, _private_access=True) + + result.__name__ = func.__name__ + result.__module__ = func.__module__ + result.__doc__ = func.__doc__ + + library.impl(lib, result._opname, "Autograd")( + autograd_kernel_indirection(weakref.proxy(result)) + ) + + torch._C._dispatch_set_report_error_callback( + ophandle, functools.partial(report_error_callback, weakref.proxy(result)) + ) + + return result + + return inner + + +# Global dictionary holding references to all CustomOp objects +# Yes, it keeps all CustomOps alive (see NOTE [CustomOp lifetime]) +# Used to query the CustomOp associated with a specific C++ dispatcher operator. +# An example usage is FakeTensor: FakeTensor checks if a specific operator +# has an implementation registered via the CustomOp API. +# Indexed by qualname (e.g. aten::foo) +global_registry: typing.Dict[str, "CustomOp"] = {} + + +class CustomOp: + r""" + This API is deprecated, please use torch.library.custom_op instead + """ + + def __init__(self, lib, cpp_ns, schema, operator_name, ophandle, *, _private_access=False): + super().__init__() + warn_deprecated() + if not _private_access: + raise RuntimeError( + "The CustomOp constructor is private and we do not guarantee " + "BC for it. Please use custom_op(...) to create a CustomOp object" + ) + name = f"{cpp_ns}::{operator_name}" + self._schema = schema + self._cpp_ns = cpp_ns + self._lib: library.Library = lib + self._ophandle: _C._DispatchOperatorHandle = ophandle + # Has the name of the op, e.g. "foo". We cache here for convenience. + self._opname: str = operator_name + # this is _opname but with namespace. e.g. "custom::foo" + self._qualname: str = name + self.__name__ = None # mypy requires this + # NB: Some of these impls are registered as kernels to DispatchKeys. + # Modifying the _impls dict directly won't do anything in that case. + self._impls: typing.Dict[str, typing.Optional[FuncAndLocation]] = {} + # See NOTE [CustomOp autograd kernel indirection] + self._registered_autograd_kernel_indirection = False + + global_registry[self._qualname] = self + + def _register_autograd_kernel_indirection(self): + assert not self._registered_autograd_kernel_indirection + self._lib.impl(self._opname, autograd_kernel_indirection(weakref.proxy(self)), "Autograd") + self._registered_autograd_kernel_indirection = True + + # Records the impl and the source location in self._impls + # Note that this doesn't cause torch.library to use the impl, that + # needs to be done in a separate self._lib.impl call. + def _register_impl(self, kind, func, stacklevel=2): + if self._has_impl(kind): + func_and_location = self._impls[kind] + assert func_and_location is not None # Pacify mypy + location = func_and_location.location + raise RuntimeError( + f"Attempting to register a {kind} impl for operator {self._qualname} " + f"that already has a {kind} impl registered from Python at " + f"{location}. This is not supported." + ) + frame = inspect.getframeinfo(sys._getframe(stacklevel)) + location = f"{frame.filename}:{frame.lineno}" + self._impls[kind] = FuncAndLocation(func, location) + + def _get_impl(self, kind): + return self._impls[kind] + + def _has_impl(self, kind): + return kind in self._impls + + def _destroy(self): + # NOTE: [CustomOp lifetime] + # A CustomOp, once created, lives forever. The mechanism is that the + # global registry holds a reference to it. However, to make testing + # easier, we want to be able to destroy CustomOp objects. + # CustomOp._destroy does the job, though it leaves the CustomOp + # in a garbage state. + del self._lib + + opnamespace = getattr(torch.ops, self._cpp_ns) + if hasattr(opnamespace, self._opname): + delattr(opnamespace, self._opname) + + del global_registry[self._qualname] + + def __repr__(self): + return f'' + + def __call__(self, *args, **kwargs): + # Bypass torch.ops.* and directly do OperatorHandle::callBoxed. + # Using torch.ops.* is a bit of a pain (it can be slow and it has lifetime + # issues from caching operators that make testing CustomOp difficult). + result = _C._dispatch_call_boxed(self._ophandle, *args, **kwargs) + return result + + def impl( + self, device_types: typing.Union[str, typing.Iterable[str]], _stacklevel=2, + ) -> typing.Callable: + r""" + This API is deprecated, please use torch.library.custom_op instead + """ + if isinstance(device_types, str): + device_types = [device_types] + for device_type in device_types: + validate_device_type(device_type) + + def inner(f): + for device_type in set(device_types): + self._check_doesnt_have_library_impl(device_type) + self._register_impl(device_type, f, stacklevel=_stacklevel) + dispatch_key = SUPPORTED_DEVICE_TYPE_TO_KEY[device_type] + library.impl(self._lib, self._opname, dispatch_key)(f) + return f + + return inner + + def _check_doesnt_have_library_impl(self, device_type): + if self._has_impl(device_type): + return + key = SUPPORTED_DEVICE_TYPE_TO_KEY[device_type] + if _C._dispatch_has_computed_kernel_for_dispatch_key(self._qualname, key): + raise RuntimeError( + f"impl(..., device_types={device_type}): the operator {self._qualname} " + f"already has an implementation for this device type via a " + f"pre-existing torch.library or TORCH_LIBRARY registration.") + + def impl_factory(self) -> typing.Callable: + r"""Register an implementation for a factory function.""" + + def inner(f): + self._register_impl("factory", f) + library.impl(self._lib, self._opname, "BackendSelect")(f) + return f + + return inner + + def impl_abstract(self, _stacklevel=2) -> typing.Callable: + r""" + This API is deprecated, please use torch.library.custom_op instead + """ + + def inner(f): + self._check_doesnt_have_library_meta_impl() + self._register_impl("abstract", f, stacklevel=_stacklevel) + location = self._get_impl("abstract").location + + qualname = self._qualname + + # Handle DispatchKey.Meta registration + @functools.wraps(f) + def f_with_ctx(*args, **kwargs): + def error_on_ctx(): + raise RuntimeError( + f"Attempted to call get_ctx() for the meta implementation " + f"for {qualname}." + f"You have presumably called get_ctx() because the operator " + f"has a data-dependent output shape; if so, there is no " + f"such meta implementation and this error is the correct " + f"behavior. Otherwise, please remove the call to get_ctx() " + f"in the implementation registered with impl_abstract " + f"at {location}" + ) + + with torch._library.fake_impl.set_ctx_getter(error_on_ctx): + return f(*args, **kwargs) + + self._lib.impl(self._opname, f_with_ctx, "Meta") + return f + + return inner + + def _check_can_register_backward(self): + def error(detail): + raise RuntimeError( + f"Cannot use torch._custom_ops APIs to register backward " + f"formula for {detail}. Got operator " + f"{self._qualname} with schema: {schema}" + ) + + schema = self._schema + if schema.kind() != SchemaKind.functional: + error("non-functional operator") + + rets = schema.returns + if not schema.returns: + error("operator with no returns") + + assert len(rets) > 0 + is_non_mutating_view = any( + r.annotation is not None and not r.annotation.is_write for r in rets + ) + if is_non_mutating_view: + error("operator that returns views") + + # We make assumptions about the schema's return types. + allowed_return_types = { + BaseType(BaseTy.int): "int", + BaseType(BaseTy.SymInt): "SymInt", + BaseType(BaseTy.bool): "bool", + BaseType(BaseTy.float): "float", + BaseType(BaseTy.Tensor): "Tensor", + ListType(BaseType(BaseTy.Tensor), None): "List[Tensor]", + } + for ret in schema.returns: + if ret.type in allowed_return_types: + continue + error(f"operator with return not in {list(allowed_return_types.values())} (got {ret.type})") + + def _check_doesnt_have_library_autograd_impl(self): + if self._registered_autograd_kernel_indirection: + return + + if _C._dispatch_has_kernel_for_dispatch_key(self._qualname, "CompositeImplicitAutograd"): + raise RuntimeError( + f"impl_backward/impl_save_for_backward: the operator {self._qualname} " + f"already has an implementation for this device type via a " + f"pre-existing registration to DispatchKey::CompositeImplicitAutograd." + f"CompositeImplicitAutograd operators do not need an autograd formula; " + f"instead, the operator will decompose into its constituents and those " + f"can have autograd formulas defined on them.") + + # We can improve this by adding "all Autograd keys", but + # realistically people will just be using this API for CPU/CUDA for now. + for key in ["Autograd", "AutogradCPU", "AutogradCUDA"]: + if _C._dispatch_has_kernel_for_dispatch_key(self._qualname, key): + raise RuntimeError( + f"impl_backward/impl_save_for_backward: " + f"the operator {self._qualname} already has an Autograd kernel " + f"registered to DispatchKey::{key} vi a pre-existing " + f"torch.library or TORCH_LIBRARY registration. Please either " + f"remove those registrations or don't use the torch._custom_ops APIs") + + def _check_doesnt_have_library_meta_impl(self): + if self._has_impl("abstract"): + return + + # If the user's operator is CompositeExplicitAutograd, + # allow them to impl_abstract. This is being pragmatic + # (existing custom ops may have CompositeExplicitAutograd + # registration that don't work with Meta kernels, so this + # gives them an escape hatch). + if ( + _C._dispatch_has_kernel_for_dispatch_key(self._qualname, "CompositeExplicitAutograd") + and not _C._dispatch_has_kernel_for_dispatch_key(self._qualname, "Meta") + ): + return + + # Otherwise, if the user's already has a Meta kernel or their + # op is CompositeImplicitAutograd or some other alias dispatch key, + # raise. + + # Special case for CompositeImplicitAutograd + if _C._dispatch_has_kernel_for_dispatch_key(self._qualname, "CompositeImplicitAutograd"): + raise RuntimeError( + f"impl_abstract(...): the operator {self._qualname} " + f"already has an implementation for this device type via a " + f"pre-existing registration to DispatchKey::CompositeImplicitAutograd." + f"CompositeImplicitAutograd operators do not need an abstract impl; " + f"instead, the operator will decompose into its constituents and those " + f"can have abstract impls defined on them.") + + if _C._dispatch_has_kernel_for_dispatch_key(self._qualname, "Meta"): + raise RuntimeError( + f"impl_abstract(...): the operator {self._qualname} " + f"already has an DispatchKey::Meta implementation via a " + f"pre-existing torch.library or TORCH_LIBRARY registration. " + f"Please either remove that registration or don't call impl_abstract.") + + # NOTE ["backward", "save_for_backward", and "autograd"] + # As a part of the explicit autograd API, a user must provide us + # a "save_for_backward" function and a "backward" function. + # When both of these have been provided, then we automatically + # construct the "autograd" kernel. + def _register_autograd_kernel(self): + assert self._has_impl("backward") + assert self._has_impl("save_for_backward") + kernel = construct_autograd_kernel( + self._schema, + self._output_differentiability, + self, + get_op(self._qualname), + self._get_impl("save_for_backward").func, + self._get_impl("backward").func) + self._register_impl("autograd", kernel) + + def impl_save_for_backward(self, _stacklevel=2): + r"""Register a function that tells us what to save for backward. + + Please see impl_backward for more details. + """ + def inner(f): + self._check_can_register_backward() + self._check_doesnt_have_library_autograd_impl() + if not self._registered_autograd_kernel_indirection: + self._register_autograd_kernel_indirection() + self._register_impl("save_for_backward", f, stacklevel=_stacklevel) + if self._has_impl("backward"): + self._register_autograd_kernel() + return inner + + def impl_backward(self, output_differentiability=None, _stacklevel=2): + r""" + This API is deprecated, please use torch.library.custom_op instead + """ + if output_differentiability is not None: + def yell(): + raise RuntimeError( + f"impl_backward(output_differentiability): expected " + f"output_differentiability to be a list of bools with " + f"length equal to the number of outputs of this CustomOp " + f"got: {output_differentiability}") + + if not isinstance(output_differentiability, list): + yell() + for diff in output_differentiability: + if not isinstance(diff, bool): + yell() + if len(self._schema.returns) != len(output_differentiability): + yell() + + def inner(f): + self._check_can_register_backward() + self._check_doesnt_have_library_autograd_impl() + if not self._registered_autograd_kernel_indirection: + self._register_autograd_kernel_indirection() + self._register_impl("backward", f, stacklevel=_stacklevel) + self._output_differentiability = output_differentiability + if self._has_impl("save_for_backward"): + self._register_autograd_kernel() + return inner + + +@dataclasses.dataclass +class FuncAndLocation: + func: typing.Callable + location: str + + +def find_ophandle_or_throw(cpp_ns: str, operator_name: OperatorName): + overload_name = ( + "" if operator_name.overload_name is None else operator_name.overload_name + ) + return _C._dispatch_find_schema_or_throw( + f"{cpp_ns}::{str(operator_name.name)}", overload_name + ) + + +def validate_namespace(ns: str) -> None: + if "." in ns: + raise ValueError( + f'custom_op(..., ns="{ns}"): expected ns to not contain any . (and be a ' + f"valid variable name)" + ) + if ns in RESERVED_NS: + raise ValueError( + f"custom_op(..., ns='{ns}'): '{ns}' is a reserved namespace, " + f"please choose something else. " + ) + +def validate_schema(schema: FunctionSchema) -> None: + if not torch._library.utils.is_functional_schema(schema): + raise ValueError( + f"custom_op only supports functional operators " + f"(ops that do not mutate any inputs, do not return " + f"views of the inputs, and has at least one return). " + f"Got the following non-functional schema: {schema}" + ) + + # For simplicity: don't allow self arguments + if schema.arguments.self_arg is not None: + raise ValueError( + f"custom_op does not support arguments named 'self'. Please " + f"rename your argument. Got: {schema}" + ) + + +def parse_qualname(qualname: str) -> typing.Tuple[str, str]: + names = qualname.split("::", 1) + if len(names) != 2: + raise ValueError(f"Expected there to be a namespace in {qualname}, i.e. The " + f"operator name should look something like ns::foo") + if '.' in names[1]: + raise ValueError(f"The torch.custom_ops APIs do not handle overloads, " + f"i.e. operator names with '.' in them. " + f"Please name your operator something like ns::foo. " + f"Got: {qualname}") + return names[0], names[1] + + +def validate_device_type(device_type: str) -> None: + if device_type not in SUPPORTED_DEVICE_TYPE_TO_KEY: + raise ValueError( + f"CustomOp.impl(device_types=[{device_type}, ...]): we only support device_type " + f"in {SUPPORTED_DEVICE_TYPE_TO_KEY.keys()}." + ) + + +def supported_param(param: inspect.Parameter) -> bool: + return param.kind in ( + inspect.Parameter.POSITIONAL_OR_KEYWORD, + inspect.Parameter.KEYWORD_ONLY, + ) + + +def validate_function_matches_schema( + schema: FunctionSchema, func: typing.Callable +) -> None: + sig = inspect.signature(func) + + if not all(supported_param(p) for _, p in sig.parameters.items()): + raise ValueError( + f"custom_op(..., manual_schema)(func): positional-only args, " + f"varargs, and kwargs are not supported. Please rewrite `func` " + f"to not have them. Got `func` with signature: {sig}" + ) + + if ( + any( + p.annotation is not inspect.Parameter.empty + for _, p in sig.parameters.items() + ) + or sig.return_annotation is not inspect.Signature.empty + ): + raise ValueError( + f"custom_op(..., manual_schema)(func): When passing in a manual " + f"schema, we expect `func` to have no type annotations to avoid " + f"ambiguity. Got `func` with signature: {sig}" + ) + + positional = [ + (name, param) + for name, param in sig.parameters.items() + if param.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD + ] + kwargonly = [ + (name, param) + for name, param in sig.parameters.items() + if param.kind == inspect.Parameter.KEYWORD_ONLY + ] + + def error(): + raise ValueError( + f"custom_op(..., manual_schema)(func): When passing in a manual " + f"schema, we expect `func`'s signature to match `manual_schema` " + f"(aside from type annotations). " + f"func's signature: {sig}, manual_schema: {schema}" + ) + + def error_default_args(): + raise ValueError( + f"custom_op(..., manual_schema)(func): " + f"neither func nor manual_schema should have default " + f"arguments. Got " + f"func's signature: {sig}, manual_schema: {schema}" + ) + + def compare(sig_args, schema_args): + if len(sig_args) != len(schema_args): + error() + for (name, param), arg in zip(sig_args, schema_args): + if name != arg.name: + error() + if param.default is not inspect.Parameter.empty or arg.default is not None: + error_default_args() + + compare(positional, schema.arguments.flat_positional) + compare(kwargonly, schema.arguments.flat_kwarg_only) + + +def report_error_callback(custom_op: typing.Any, key: str) -> None: + if key == "Undefined": + raise NotImplementedError( + f"{custom_op}: There were no Tensor inputs to this operator " + f"(e.g. you passed an empty list of Tensors). If your operator is a " + f"factory function (that is, it takes no Tensors and constructs " + f"a new one), then please use CustomOp.impl_factory to register " + f"an implementation for it" + ) + if key == "Meta": + raise NotImplementedError( + f"{custom_op}: when running with device='Meta' tensors: there is no " + f"abstract impl registered for this CustomOp. Please register one via " + f"CustomOp.impl_abstract to get this CustomOp to work with Meta tensors" + ) + if key in ("CPU", "CUDA"): + device = key.lower() + raise NotImplementedError( + f"{custom_op}: when running with device='{device}' tensors: there is no " + f"{device} impl registered for this CustomOp. Please register one via " + f"CustomOp.impl(device_type='{device}')" + ) + raise NotImplementedError( + f"{custom_op}: No implementation for dispatch key {key}. It is likely " + f"that we have not added this functionality yet, please either open an " + f"issue or if you're feeling adventurous, use the low-level " + f"torch.library API" + ) + + +def custom_op_from_existing(op): + ns = op.namespace + lib = torch.library.Library(ns, "FRAGMENT") + name = op.name().split("::")[-1] + schema_str = str(op._schema) + # CustomOp expects the schema string without the namespace + schema_str = schema_str.split("::")[-1] + schema = FunctionSchema.parse(schema_str) + return CustomOp(lib, ns, schema, name, op, _private_access=True) + + +def get_op(qualname): + def error_not_found(): + raise ValueError( + f"Could not find the operator {qualname}. Please make sure you have " + f"already registered the operator and (if registered from C++) " + f"loaded it via torch.ops.load_library.") + + ns, name = parse_qualname(qualname) + if not hasattr(torch.ops, ns): + error_not_found() + opnamespace = getattr(torch.ops, ns) + if not hasattr(opnamespace, name): + error_not_found() + packet = getattr(opnamespace, name) + if not hasattr(packet, 'default'): + error_not_found() + return packet.default + + +def _find_custom_op(qualname, also_check_torch_library=False): + if qualname in global_registry: + return global_registry[qualname] + if not also_check_torch_library: + raise RuntimeError( + f'Could not find custom op "{qualname}". Did you register it via ' + f"the torch._custom_ops API?") + overload = get_op(qualname) + result = custom_op_from_existing(overload) + return result + + +def get_abstract_impl(qualname): + if qualname not in torch._custom_op.impl.global_registry: + return None + custom_op = torch._custom_op.impl.global_registry[qualname] + if custom_op is None: + return None + if not custom_op._has_impl("abstract"): + return None + return custom_op._get_impl("abstract").func + + +def _custom_op_with_schema(qualname, schema, needs_fixed_stride_order=True): + ns, name = qualname.split("::") + schema_str = f"{name}{schema}" + function_schema = FunctionSchema.parse(schema_str) + validate_schema(function_schema) + tags = [torch._C.Tag.needs_fixed_stride_order] if needs_fixed_stride_order else [] + lib = library.Library(ns, "FRAGMENT") + lib.define(schema_str, tags=tags) + ophandle = find_ophandle_or_throw(ns, function_schema.name) + result = CustomOp(lib, ns, function_schema, name, ophandle, _private_access=True) + result._register_autograd_kernel_indirection() + + torch._C._dispatch_set_report_error_callback( + ophandle, functools.partial(report_error_callback, weakref.proxy(result)) + ) + return get_op(qualname) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_decomp/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_decomp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..93bbec04a425be731a4958ea075703c8386e121a --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_decomp/__init__.py @@ -0,0 +1,484 @@ +# mypy: allow-untyped-defs +import inspect +from collections import defaultdict +from functools import wraps +from itertools import chain +from typing import Callable, Dict, List, Sequence, TypeVar, Union +from typing_extensions import ParamSpec + +import torch +import torch.library +from torch._ops import HigherOrderOperator, OpOverload, OpOverloadPacket +from torch._prims_common import CustomOutParamAnnotation +from torch.utils import _pytree as pytree + + +__all__ = [ + "decomposition_table", + "pre_autograd_decomposition_table", + "meta_table", + "register_decomposition", + "get_decompositions", + "core_aten_decompositions", +] + +_T = TypeVar("_T") +_P = ParamSpec("_P") + +# TODO: relax key type here; torch registrations should be possible to; but +# right now this type is accurate +global_decomposition_table: Dict[ + str, Dict[torch._ops.OperatorBase, Callable] +] = defaultdict(dict) + +decomposition_table = global_decomposition_table["post_autograd"] +pre_autograd_decomposition_table = global_decomposition_table["pre_autograd"] +meta_table = global_decomposition_table["meta"] + + +def _add_op_to_registry(registry, op, fn): + """ + This is an internal API for adding an op to the decomposition table. + + If op is OpOverload, it will be added to the registry directly. + If op is OpOverloadPacket, all the valid op_overloads in the packet will be added to the registry. + """ + overloads: List[Union[torch._ops.OperatorBase]] = [] + if isinstance(op, HigherOrderOperator): + # There's no concept of overloads for HigherOrderOperator + registry[op] = fn + return + elif isinstance(op, OpOverload): + overloads.append(op) + else: + assert isinstance(op, OpOverloadPacket) + for ol in op.overloads(): + overloads.append(getattr(op, ol)) + + for op_overload in overloads: + if op_overload in registry: + raise RuntimeError(f"duplicate registrations for {op_overload}") + # TorchScript dumps a bunch of extra nonsense overloads + # which don't have corresponding dispatcher entries, we need + # to filter those out, e.g aten.add.float_int + if torch._C._dispatch_has_kernel(op_overload.name()): + registry[op_overload] = fn + + +def _convert_out_params(f): + out_annotation = f.__annotations__.get("out") + + # If there are no out params, do not wrap the function. + if not out_annotation: + return f + + # Hack to detect when out is a Tuple. There seems to be no pretty way of doing this + if getattr(out_annotation, "__origin__", None) is tuple: + sig = inspect.signature(f) + out_names = sig.return_annotation._fields + # If out is a tuple, we need to register a function that unpacks all the out + # elements as this is what native_functions.yaml expects + + @wraps(f) + def _fn(*args, **kwargs): + out_kwargs = tuple(kwargs.pop(o, None) for o in out_names) + # Either all of the out kwargs are set or none of them + is_none = out_kwargs[0] is None + assert all((o is None) == is_none for o in out_kwargs) + return f(*args, **kwargs, out=None if is_none else out_kwargs) + + out_params = [ + inspect.Parameter( + o, + kind=inspect.Parameter.KEYWORD_ONLY, + default=None, + annotation=t, + ) + for o, t in zip(out_names, out_annotation.__args__) + ] + # Drop the out parameter and concatenate the new kwargs in the signature + params = chain((v for k, v in sig.parameters.items() if k != "out"), out_params) + _fn.__signature__ = inspect.Signature( # type: ignore[attr-defined] + parameters=params, return_annotation=sig.return_annotation # type: ignore[arg-type] + ) + # Drop the out parameter and concatenate the new kwargs in the annotations + _fn.__annotations__ = {k: v for k, v in f.__annotations__.items() if k != "out"} + for o in out_params: + _fn.__annotations__[o.name] = o.annotation + + # Propagate that this function is wrapped by `out_wrapper` + _fn._torch_decompositions_out_wrapper = f._torch_decompositions_out_wrapper # type: ignore[attr-defined] + + return _fn + + # Alternatively, there may be a single tensor out parameter with a name + # other than "out". This will need special treatment and is indicated by an + # annotation, which we will remove here so it is not exposed after wrapping. + custom_out_param_name = f.__annotations__.pop(CustomOutParamAnnotation, None) + if custom_out_param_name: + + @wraps(f) + def _fn(*args, **kwargs): + out_kwarg = kwargs.pop(custom_out_param_name, None) + return f(*args, **kwargs, out=out_kwarg) + + out_param = inspect.Parameter( + custom_out_param_name, + kind=inspect.Parameter.KEYWORD_ONLY, + default=None, + annotation=out_annotation, + ) + + # Drop the out parameter and concatenate the new kwarg in the signature + sig = inspect.signature(f) + params = chain( + (v for k, v in sig.parameters.items() if k != "out"), (out_param,) + ) + _fn.__signature__ = inspect.Signature( # type: ignore[attr-defined] + parameters=params, return_annotation=sig.return_annotation # type: ignore[arg-type] + ) + + # Drop the out parameter and concatenate the new kwargs in the annotations + _fn.__annotations__ = {k: v for k, v in f.__annotations__.items() if k != "out"} + _fn.__annotations__[out_param.name] = out_param.annotation + + return _fn + + return f + + +def register_decomposition( + aten_op, registry=None, *, type="post_autograd", unsafe=False +) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]: + """ + A decorator to register a function as a decomposition to the Python + decomposition table. Use it like this:: + + @register_decomposition(torch.ops.aten.clamp_min) + def clamp_min(x): + return torch.clamp(self, min=min) + + If you are writing a new decomposition, consider contributing it + directly to PyTorch in torch._decomp.decompositions. + + This API is experimental; we are almost certainly going to extend + the API when we make decompositions eligible for use in transforms (e.g., + autograd) and not just backend tracing, where we then need to know if a + decomposition can be used to simulate a transform. + + By default, we also will register it to the Meta key of dispatcher, + and replace the c++ Meta implementation if there is already one. + + unsafe kwarg is for reuse of this function for registering non-function + things + """ + + assert type in {"post_autograd", "pre_autograd", "meta"} + + def decomposition_decorator(fn: Callable[_P, _T]) -> Callable[_P, _T]: + orig_fn = fn + if not unsafe: + fn = _convert_out_params(fn) + + nonlocal registry + if registry is None: + registry = global_decomposition_table[type] + + def register(op): + _add_op_to_registry(registry, op, fn) + + # To handle allowing multiple aten_ops at once + pytree.tree_map_(register, aten_op) + return orig_fn + + return decomposition_decorator + + +def get_decompositions( + aten_ops: Sequence[Union[torch._ops.OperatorBase, OpOverloadPacket]], + type: str = "post_autograd", +) -> Dict[torch._ops.OperatorBase, Callable]: + """ + Retrieve a dictionary of decompositions corresponding to the list of + operator overloads and overload packets passed as input. Overload + packets will include all decomposed overloads in the packet. If there is + no decomposition for a requested operator, it is silently ignored. + + This API is experimental; we are almost certainly going to give an alternate, + more recommended formulation, where a user provides the set of operators + they know how to implement, and we provide decompositions for everything + not in this set. + """ + assert type in {"post_autograd", "pre_autograd", "meta"} + + registry = global_decomposition_table[type] + packets_to_overloads = defaultdict(list) + for opo in registry: + if isinstance(opo, (OpOverload, OpOverloadPacket)): + packets_to_overloads[opo.overloadpacket].append(opo) + decompositions: Dict[torch._ops.OperatorBase, Callable] = {} + for op in aten_ops: + if isinstance(op, OpOverloadPacket) and op in packets_to_overloads: + for op_overload in packets_to_overloads[op]: + decompositions[op_overload] = registry[op_overload] + elif isinstance(op, (torch._ops.OperatorBase)) and op in registry: + decompositions[op] = registry[op] + return decompositions + + +def remove_decompositions( + decompositions: Dict[torch._ops.OperatorBase, Callable], + aten_ops: Sequence[Union[OpOverload, OpOverloadPacket]], +) -> None: + """ + Given a dictionary of decompositions obtained from get_decompositions(), removes + operators associated with a list of operator overloads and overload packets passed + as input. If the decomposition dictionary does not contain a decomposition that is + specified to be removed, it is silently ignored. + """ + for op in aten_ops: + if isinstance(op, OpOverloadPacket): + for overload_name in op.overloads(): + opo = getattr(op, overload_name) + decompositions.pop(opo, None) + elif isinstance(op, OpOverload): + decompositions.pop(op, None) + + +# populate the table +import torch._decomp.decompositions +import torch._refs + + +# See NOTE [Core ATen Ops] +# +# list was copied from torch/_inductor/decomposition.py +# excluding decompositions that results in prim ops +# Resulting opset of decomposition is core aten ops +def core_aten_decompositions() -> Dict[torch._ops.OperatorBase, Callable]: + aten = torch.ops.aten + return get_decompositions( + [ + aten.addcdiv, + aten.addcdiv_, + aten.addcmul, + aten.addcmul_, + aten.addr, + aten.affine_grid_generator, + aten.alias_copy, + aten.all, + aten.aminmax, + aten.arange.default, + aten.arange.start, + aten.avg_pool2d_backward, + aten.baddbmm, + aten.binary_cross_entropy, + aten.binary_cross_entropy_backward, + aten.binary_cross_entropy_with_logits, + aten.block_diag, + aten.celu, + aten.celu_, + aten.channel_shuffle, + aten.clamp_max, + aten.clamp_min, + aten.col2im, + aten.count_nonzero, + aten.linalg_cross, + aten.cudnn_batch_norm, + aten.cudnn_batch_norm_backward, + aten.miopen_batch_norm_backward, + aten.deg2rad, + aten.deg2rad_, + aten.detach, + aten.diag_embed, + aten.diagonal_backward, + aten.dot, + aten.vdot, + aten.elu, + aten.elu_, + aten.elu_backward, + aten._embedding_bag, + aten.embedding_dense_backward, + aten.empty_like, + aten._euclidean_dist.default, + aten.expand_as, + aten.expand_copy, + aten.eye, + aten.fill, + aten.fill_, + aten.floor_divide, + aten.frac, + aten.frac_, + aten._fused_moving_avg_obs_fq_helper, + aten.gelu_, + aten.gelu_backward, + aten.glu, + aten.glu_backward, + aten.hardshrink, + aten.hardsigmoid, + aten.hardsigmoid_, + aten.hardsigmoid_backward, + aten.hardswish, + aten.hardswish_, + aten.hardswish_backward, + aten.hardtanh_, + aten.hardtanh_backward, + aten.heaviside, + aten.heaviside_, + aten.huber_loss, + aten.huber_loss_backward, + aten.im2col, + aten.index_add, + aten.index_add_, + aten.index_copy, + aten.index_copy_, + aten.index_fill, + aten.index_fill_, + aten.isin, + aten.isneginf, + aten.isposinf, + aten.l1_loss, + aten._lazy_clone, + aten._test_parallel_materialize, + aten.leaky_relu_, + aten.leaky_relu_backward, + aten.lerp, + aten.lerp_, + aten.linspace, + aten.logaddexp, + aten.logaddexp2, + aten.logit, + aten.logit_, + aten.logit_backward, + aten.log_sigmoid_backward, + aten.log_sigmoid_forward, + aten._log_softmax_backward_data, + aten.logspace, + aten.logsumexp.default, + aten.masked_fill, + aten.masked_fill_, + aten.mish, + aten.mish_, + aten.mse_loss, + aten.mse_loss_backward, + aten.multi_margin_loss, + aten.multilabel_margin_loss_forward, + aten.mv, + aten.mvlgamma, + aten.mvlgamma_, + aten.nansum, + aten.nan_to_num, + aten.nan_to_num_, + aten.narrow, + aten.native_batch_norm_backward, + aten.native_dropout_backward, + aten.native_group_norm_backward, + aten.native_layer_norm_backward, + aten.new_empty, + aten.new_full, + aten.new_ones, + aten.new_zeros, + aten.nll_loss2d_forward, + aten.nll_loss2d_backward, + aten.nll_loss_backward, + aten.nll_loss_forward, + aten.norm, + aten.ones, + aten.ones_like, + aten.pixel_shuffle, + aten.pixel_unshuffle, + aten._prelu_kernel, + aten._prelu_kernel_backward, + aten._reshape_alias, + aten.rad2deg, + aten.rad2deg_, + aten.reflection_pad1d, + aten.reflection_pad1d_backward, + aten.reflection_pad2d, + aten.reflection_pad2d_backward, + aten.reflection_pad3d, + aten.reflection_pad3d_backward, + aten.replication_pad1d, + aten.replication_pad2d, + aten.replication_pad3d, + aten.renorm, + aten.renorm_, + aten.replication_pad2d, + aten.resize_as, + aten.roll, + aten.rot90, + aten.rrelu_with_noise, + aten.rrelu_with_noise_, + aten.rsub, + aten._safe_softmax, + aten._scaled_dot_product_flash_attention_for_cpu.default, + aten.select_backward, + aten.select_scatter, + aten.sgn, + aten.sgn_, + aten.sigmoid_backward, + aten.silu, + aten.silu_, + aten.silu_backward, + aten.sinc, + aten.sinc_, + aten.slice_backward, + aten.smooth_l1_loss, + aten.smooth_l1_loss_backward, + aten.soft_margin_loss, + aten.soft_margin_loss_backward, + aten._softmax_backward_data, + aten.softplus, + aten.softplus_backward, + aten.softshrink, + aten.special_entr, + aten.special_log_ndtr, + aten.special_xlog1py, + aten.split.Tensor, + aten.split_with_sizes_copy, + aten.squeeze.default, + aten.squeeze.dim, + aten.std, + aten.std_mean, + aten.stack, + aten.sum.default, + aten.sum.out, + aten.t, + aten.t_copy, + aten.take, + aten.tanh_backward, + aten.threshold, + aten.threshold_, + aten.threshold_backward, + aten.trace, + aten.transpose.int, + aten.tril, + aten.tril_, + aten.triu, + aten.triu_, + aten.unbind, + aten.unfold_backward, + aten.unfold_copy, + aten._unsafe_index, + aten._unsafe_index_put, + aten._unsafe_masked_index, + aten._unsafe_masked_index_put_accumulate, + aten.unsafe_split.Tensor, + aten.unsafe_split_with_sizes, + aten.unsqueeze_copy, + aten._unsafe_view, + aten.upsample_linear1d, + aten.upsample_bilinear2d, + aten.upsample_trilinear3d, + aten.upsample_nearest2d_backward, + aten.view_as_complex, + aten.xlogy, + aten.xlogy_, + aten.zero, + aten.zero_, + aten.zeros, + aten.zeros_like, + aten._chunk_cat, + aten._weight_norm_interface, + ] + ) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_decomp/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_decomp/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e8e46feabd67e76192136795caf45ba6d433e932 Binary files /dev/null and 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Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_decomp/__pycache__/decompositions_for_rng.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_decomp/decompositions.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_decomp/decompositions.py new file mode 100644 index 0000000000000000000000000000000000000000..c35d7a72774f1643b3688446983e7ee27442ca00 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_decomp/decompositions.py @@ -0,0 +1,5113 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import functools +import itertools +import numbers +import operator +import sys +from enum import Enum +from functools import partial, reduce +from itertools import chain, product +from typing import Any, Callable, cast, Iterable, List, Optional, Tuple, Union + +import torch +import torch._meta_registrations +import torch._prims as prims +import torch._prims_common as utils +import torch.nn.functional as F +from torch import sym_float, sym_int, Tensor +from torch._decomp import register_decomposition +from torch._higher_order_ops.out_dtype import out_dtype +from torch._prims_common import ( + IntLike, + NumberType, + suggest_memory_format, + TensorLike, + TensorSequenceType, +) +from torch._prims_common.wrappers import ( + _maybe_convert_to_dtype, + _maybe_resize_out, + _safe_copy_out, + out_wrapper, +) +from torch.utils import _pytree as pytree +from torch.utils._pytree import tree_map + + +DispatchKey = torch._C.DispatchKey # type: ignore[attr-defined] + +# None of these functions are publicly accessible; get at them +# from torch._decomps +__all__: List[str] = [] + +aten = torch._ops.ops.aten + + +class Reduction(Enum): + NONE = 0 + MEAN = 1 + SUM = 2 + + +# This wraps a decomposition and performs various type promotion logic within it, depending on the strategy provided +# We're currently re-using ELEMENTWISE_TYPE_PROMOTION_KIND, although some of the usages are on non-elementwise ops +# Will need to validate the non-elementwise uses +def type_casts( + f: Callable, + type_promotion: utils.ELEMENTWISE_TYPE_PROMOTION_KIND, + compute_dtype_only: bool = False, +): + @functools.wraps(f) + def inner(*args, **kwargs): + flat_args = [ + x for x in pytree.arg_tree_leaves(*args, **kwargs) if isinstance(x, Tensor) + ] + computation_dtype, result_dtype = utils.elementwise_dtypes( + *flat_args, type_promotion_kind=type_promotion + ) + + # TODO: pretty sure this is not quite right + def increase_prec(x): + if isinstance(x, Tensor): + return x.to(computation_dtype) + else: + return x + + def decrease_prec(x): + if isinstance(x, Tensor): + return x.to(result_dtype) + else: + return x + + r = f(*tree_map(increase_prec, args), **tree_map(increase_prec, kwargs)) + if compute_dtype_only: + return r + else: + return tree_map(decrease_prec, r) + + return inner + + +compute_only_pw_cast_for_opmath = partial( + type_casts, + type_promotion=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, + compute_dtype_only=True, +) +pw_cast_for_opmath = partial( + type_casts, type_promotion=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT +) +pw_cast_for_int_to_real = partial( + type_casts, type_promotion=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT +) + + +# This expands x until x.dim() == dim. Might be useful as an operator +def _unsqueeze_to_dim(x: Tensor, dim: int) -> Tensor: + for _ in range(dim - x.dim()): + x = x.unsqueeze(-1) + return x + + +@register_decomposition(aten.tanh_backward) +@out_wrapper("grad_input") +@pw_cast_for_opmath +def tanh_backward(out_grad: Tensor, y: Tensor): + return out_grad * (1 - y * y).conj_physical() + + +@register_decomposition(aten.sigmoid_backward) +@out_wrapper("grad_input") +@pw_cast_for_opmath +def sigmoid_backward(out_grad: Tensor, y: Tensor): + return out_grad * (y * (1 - y)).conj_physical() + + +@register_decomposition(aten.softplus_backward) +@out_wrapper("grad_input") +@pw_cast_for_opmath +def softplus_backward(out_grad: Tensor, x: Tensor, beta: float, threshold: float): + z = (x * beta).exp() + return torch.where((x * beta) > threshold, out_grad, out_grad * z / (z + 1.0)) + + +@register_decomposition(aten.elu_backward) +@out_wrapper("grad_input") +@pw_cast_for_opmath +def elu_backward( + grad_output: Tensor, + alpha: float, + scale: float, + input_scale: float, + is_result: bool, + self_or_result: Tensor, +): + negcoef = alpha * scale + poscoef = scale + negiptcoef = input_scale + if is_result: + return torch.where( + self_or_result <= 0, + grad_output * negiptcoef * (self_or_result + negcoef), + grad_output * poscoef, + ) + else: + return torch.where( + self_or_result <= 0, + grad_output * negiptcoef * negcoef * torch.exp(self_or_result * negiptcoef), + grad_output * poscoef, + ) + + +@register_decomposition([aten.fill.Scalar]) +def fill_scalar(self, value): + return torch.full_like(self, value) + + +@register_decomposition([aten.fill.Tensor]) +def fill_tensor(self, value: Tensor): + torch._check( + value.dim() == 0, + lambda: f"fill only supports 0-dimension value tensor but got tensor with {value.dim()} dimensions", + ) + return aten.copy(self, value) + + +@register_decomposition(aten.hardsigmoid) +@out_wrapper() +@pw_cast_for_opmath +def hardsigmoid(self: Tensor) -> Tensor: + return torch.clamp(torch.clamp(self + 3, min=0), max=6) / 6 + + +@register_decomposition(aten.hardsigmoid_backward) +@out_wrapper("grad_input") +@pw_cast_for_opmath +def hardsigmoid_backward(grad_output: Tensor, self: Tensor): + return torch.where( + (self > -3.0) & (self < 3.0), + grad_output * (1.0 / 6.0), + 0.0, + ) + + +@register_decomposition(aten.hardtanh_backward) +@out_wrapper("grad_input") +def hardtanh_backward( + grad_output: Tensor, self: Tensor, min_val: float, max_val: float +): + return torch.where((self <= min_val) | (self >= max_val), 0.0, grad_output) + + +@register_decomposition(aten.hardswish) +@out_wrapper() +@pw_cast_for_opmath +def hardswish(self: Tensor) -> Tensor: + return self * torch.clamp(torch.clamp(self + 3, min=0), max=6) / 6 + + +@register_decomposition(aten.hardswish_backward) +@out_wrapper() +@pw_cast_for_opmath +def hardswish_backward(grad_output: Tensor, self: Tensor) -> Tensor: + return torch.where( + self < -3, + 0.0, + torch.where(self <= 3, grad_output * ((self / 3) + 0.5), grad_output), + ) + + +@register_decomposition(aten.threshold_backward) +@out_wrapper("grad_input") +def threshold_backward(grad_output: Tensor, self: Tensor, threshold: float): + return torch.where(self <= threshold, 0, grad_output) + + +@register_decomposition(aten.leaky_relu_backward) +@out_wrapper("grad_input") +@pw_cast_for_opmath +def leaky_relu_backward( + grad_output: Tensor, self: Tensor, negative_slope: float, self_is_result: bool +): + return torch.where(self > 0, grad_output, grad_output * negative_slope) + + +@register_decomposition(aten.gelu_backward) +@out_wrapper("grad_input") +@pw_cast_for_opmath +def gelu_backward(grad: Tensor, self: Tensor, approximate: str = "none"): + M_SQRT2 = 1.41421356237309504880 + M_SQRT1_2 = 0.70710678118654752440 + M_2_SQRTPI = 1.12837916709551257390 + if approximate == "tanh": + kBeta = M_SQRT2 * M_2_SQRTPI * 0.5 + kKappa = 0.044715 + x_sq = self * self + x_cube = x_sq * self + inner = kBeta * (self + kKappa * x_cube) + tanh_inner = torch.tanh(inner) + + left = 0.5 * self + right = 1 + tanh_inner + + left_derivative = 0.5 * right + + tanh_derivative = 1 - tanh_inner * tanh_inner + inner_derivative = kBeta * (1 + 3 * kKappa * x_sq) + right_derivative = left * tanh_derivative * inner_derivative + + return grad * (left_derivative + right_derivative) + else: + kAlpha = M_SQRT1_2 + kBeta = M_2_SQRTPI * M_SQRT1_2 * 0.5 + cdf = 0.5 * (1 + torch.erf(self * kAlpha)) + pdf = kBeta * torch.exp(self * self * -0.5) + return grad * (cdf + self * pdf) + + +@register_decomposition(aten.mish_backward) +@pw_cast_for_opmath +def mish_backward(grad_output: Tensor, input: Tensor): + input_tanh_softplus = torch.tanh(F.softplus(input)) + input_sigmoid = torch.sigmoid(input) + out = input * input_sigmoid * (1 - input_tanh_softplus * input_tanh_softplus) + return grad_output * (input_tanh_softplus + out) + + +@register_decomposition(aten.silu) +@out_wrapper() +@pw_cast_for_opmath +def silu(self: Tensor) -> Tensor: + return self * torch.sigmoid(self) + + +@register_decomposition(aten.silu_backward) +@out_wrapper("grad_input") +@pw_cast_for_opmath +def silu_backward(grad_output: Tensor, self: Tensor) -> Tensor: + sigmoid = 1 / (1 + torch.exp(-self)) + return grad_output * sigmoid * (1 + self * (1 - sigmoid)) + + +@register_decomposition(aten._prelu_kernel) +def _prelu_kernel(self: Tensor, weight: Tensor) -> Tensor: + return torch.where(self > 0, self, weight * self) + + +@register_decomposition(aten._prelu_kernel_backward) +def _prelu_kernel_backward( + grad_output: Tensor, + self: Tensor, + weight: Tensor, +) -> Tuple[Tensor, Tensor]: + input_grad = torch.where(self > 0, grad_output, weight * grad_output) + weight_grad = torch.where(self > 0, 0.0, self * grad_output) + return (input_grad, weight_grad) + + +@register_decomposition(aten.rrelu_with_noise) +@aten.rrelu_with_noise.default.py_impl(DispatchKey.AutogradCUDA) +@out_wrapper() +@pw_cast_for_opmath +def rrelu_with_noise( + self: Tensor, + noise: Tensor, + lower: float = 0.125, + upper: float = 0.3333333333333333, + training: bool = False, + generator: Optional[torch.Generator] = None, +) -> Tensor: + assert generator is None + if training: + not_positive = self <= 0 + r = aten.uniform(self, lower, upper) + output = torch.where(not_positive, self * r, self) + noise.copy_(torch.where(not_positive, r, 1)) + return output + else: + negative_slope = (lower + upper) / 2 + return aten.leaky_relu(self, negative_slope) + + +@register_decomposition(aten.rrelu_with_noise_) +@aten.rrelu_with_noise_.default.py_impl(DispatchKey.AutogradCUDA) +@pw_cast_for_opmath +def rrelu_with_noise_( + self: Tensor, + noise: Tensor, + lower: float = 0.125, + upper: float = 0.3333333333333333, + training: bool = False, + generator: Optional[torch.Generator] = None, +) -> Tensor: + return self.copy_(rrelu_with_noise(self, noise, lower, upper, training, generator)) + + +@register_decomposition(aten.rrelu_with_noise_backward) +@out_wrapper() +@pw_cast_for_opmath +def rrelu_with_noise_backward( + grad_output: Tensor, + self: Tensor, + noise: Tensor, + lower: float, + upper: float, + training: bool, + self_is_result: bool, +) -> Tensor: + if training and upper - lower > 1e-6: + return grad_output.mul(noise) + else: + negative_slope = (lower + upper) / 2 + return aten.leaky_relu_backward( + grad_output, self, negative_slope, self_is_result + ) + + +@register_decomposition(aten.log_sigmoid_backward) +@out_wrapper("grad_input") +@pw_cast_for_opmath +def log_sigmoid_backward(grad_output: Tensor, self: Tensor, buffer: Tensor) -> Tensor: + in_negative = self < 0 + max_deriv = torch.where(in_negative, 1, 0) + sign = torch.where(in_negative, 1, -1) + z = torch.exp(-torch.abs(self)) + return grad_output * (max_deriv - sign * (z / (1 + z))) + # CPU has a special formula that uses buffer, but disabled for convenience sake + # return (max_deriv - sign * (buffer / (1 + buffer))) * grad_output + + +def apply_loss_reduction(loss: Tensor, reduction: int): + if reduction == Reduction.MEAN.value: + return torch.mean(loss) + elif reduction == Reduction.SUM.value: + return torch.sum(loss) + else: + return loss + + +def to_real_dtype(dtype: torch.dtype): + if dtype == torch.complex32: + return torch.float16 + elif dtype == torch.complex64: + return torch.float32 + elif dtype == torch.complex128: + return torch.float64 + + +# TODO: None of these loss castings are quite correct, see +# https://github.com/pytorch/pytorch/issues/76870. Also, the ATen kernels +# perform the pointwise portion in opmath, but don't maintain it between the +# pointwise portion and the reduction + + +@register_decomposition(aten.mse_loss) +@out_wrapper() +@pw_cast_for_opmath +def mse_loss( + self: Tensor, target: Tensor, reduction: int = Reduction.MEAN.value +) -> Tensor: + loss = (self - target) ** 2 + return apply_loss_reduction(loss, reduction) + + +@register_decomposition(aten.mse_loss_backward) +@out_wrapper("grad_input") +@pw_cast_for_opmath +def mse_loss_backward( + grad_output: Tensor, input: Tensor, target: Tensor, reduction: int +): + norm = 2.0 / input.numel() if reduction == Reduction.MEAN.value else 2.0 + return norm * (input - target) * grad_output + + +@register_decomposition(aten._safe_softmax) +def safe_softmax(self, dim, dtype=None): + out = torch.softmax(self, dim=dim, dtype=dtype) + masked = self.eq(float("-inf")) + masked_rows = torch.all(masked, dim=dim, keepdim=True) + zeros = torch.zeros_like(out) + return torch.where(masked_rows, zeros, out) + + +@register_decomposition(aten.smooth_l1_loss) +@out_wrapper() +@pw_cast_for_opmath +def smooth_l1_loss( + self: Tensor, + target: Tensor, + reduction: int = Reduction.MEAN.value, + beta: float = 1.0, +): + loss = (self - target).abs() + loss = torch.where(loss < beta, 0.5 * loss**2 / beta, loss - 0.5 * beta) + return apply_loss_reduction(loss, reduction) + + +@register_decomposition(aten.smooth_l1_loss_backward.default) +@pw_cast_for_opmath +def smooth_l1_loss_backward( + grad_output: Tensor, self: Tensor, target: Tensor, reduction: int, beta: float +): + norm = 1.0 / self.numel() if reduction == Reduction.MEAN.value else 1.0 + x = self - target + abs_x = torch.abs(x) + norm_grad = norm * grad_output + return torch.where( + abs_x < beta, + norm_grad * x / beta, + norm_grad * torch.sign(x), + ) + + +@register_decomposition(aten.smooth_l1_loss_backward.grad_input) +@pw_cast_for_opmath +def smooth_l1_loss_backward_out( + grad_output: Tensor, + self: Tensor, + target: Tensor, + reduction: int, + beta: float, + grad_input: Tensor, +): + result = smooth_l1_loss_backward(grad_output, self, target, reduction, beta) + _maybe_resize_out(grad_input, result.shape) + return _safe_copy_out(copy_from=result, copy_to=grad_input, exact_dtype=True) + + +@register_decomposition(aten.huber_loss_backward.default) +@pw_cast_for_opmath +def huber_loss_backward( + grad_output: Tensor, self: Tensor, target: Tensor, reduction: int, delta: float +): + norm = 1.0 / self.numel() if reduction == Reduction.MEAN.value else 1.0 + x = self - target + return torch.where( + x < -delta, + -norm * grad_output * delta, + torch.where(x > delta, norm * grad_output * delta, norm * x * grad_output), + ) + + +# We cannot use @out_wrapper() here, because the output tensor is not named 'out', it's 'grad_input' +@register_decomposition(aten.huber_loss_backward.out) +@pw_cast_for_opmath +def huber_loss_backward_out( + grad_output: Tensor, + self: Tensor, + target: Tensor, + reduction: int, + delta: float, + grad_input: Tensor, +): + result = huber_loss_backward(grad_output, self, target, reduction, delta) + _maybe_resize_out(grad_input, result.shape) + return _safe_copy_out(copy_from=result, copy_to=grad_input, exact_dtype=True) + + +def _nll_loss_backward( + grad_output: Tensor, + self: Tensor, + target: Tensor, + weight: Optional[Tensor], + reduction: int, + ignore_index: int, + total_weight: Tensor, +) -> Tensor: + channel_dim = 0 if self.dim() < 2 else 1 + if reduction == Reduction.MEAN.value: + grad_output = grad_output / total_weight + + target = target.unsqueeze(channel_dim) + safe_target = torch.where(target != ignore_index, target, 0) + grad_input = torch.zeros_like(self) + grad_input = torch.scatter(grad_input, channel_dim, safe_target, -1.0) + + if grad_input.dim() > grad_output.dim() > 0: + grad_output = grad_output.unsqueeze(channel_dim) + + if weight is not None: + new_shape = [1 for _ in range(self.dim())] + new_shape[channel_dim] = weight.shape[0] + weight = weight.reshape(new_shape) + grad_output = grad_output * weight + + grad_output = torch.where(target != ignore_index, grad_output, 0) + + return grad_input * grad_output + + +@register_decomposition(aten.glu_backward) +@out_wrapper("grad_input") +@pw_cast_for_opmath +def glu_backward(grad_output: Tensor, self: Tensor, dim: int) -> Tensor: + assert self.dim() > 0, "glu does not support 0-dimensional tensors" + wrap_dim = utils.canonicalize_dim(self.dim(), dim) + nIn = self.size(wrap_dim) + assert ( + nIn % 2 == 0 + ), f"Halving dimension must be even, but dimension {wrap_dim} is size {nIn}" + inputSize = nIn // 2 + firstHalf = self.narrow(wrap_dim, 0, inputSize) + secondHalf = self.narrow(wrap_dim, inputSize, inputSize) + gradInputFirstHalf = torch.sigmoid(secondHalf) + gradInputSecondHalf = ( + (1.0 - gradInputFirstHalf) * gradInputFirstHalf * firstHalf * grad_output + ) + gradInputFirstHalf = gradInputFirstHalf * grad_output + return torch.cat([gradInputFirstHalf, gradInputSecondHalf], dim=wrap_dim) + + +@register_decomposition(aten.nll_loss_backward) +@out_wrapper("grad_input") +def nll_loss_backward( + grad_output: Tensor, + self: Tensor, + target: Tensor, + weight: Optional[Tensor], + reduction: int, + ignore_index: int, + total_weight: Tensor, +) -> Tensor: + assert 0 <= self.dim() <= 2, "input tensor should be 1D or 2D" + assert ( + target.dim() <= 1 + ), "0D or 1D target tensor expected, multi-target not supported" + + no_batch_dim = self.dim() == 1 and target.dim() == 0 + assert no_batch_dim or ( + self.shape[0] == target.shape[0] + ), f"size mismatch (got input: {self.shape}, target: {target.shape})" + assert total_weight.numel() == 1, ( + "expected total_weight to be a single element tensor, got: ", + f"{total_weight.shape} ({total_weight.numel()} elements)", + ) + + assert ( + weight is None or weight.numel() == self.shape[-1] + ), "weight tensor should be defined either for all or no classes" + + if reduction == Reduction.NONE.value and self.dim() == 2: + assert grad_output.dim() == 1 and grad_output.shape[0] == self.shape[0], ( + f"Expected a tensor of dimension 1 and tensor.size[0] == {self.shape[0]} but " + f"got: dimension {grad_output.dim()} and tensor.size[0] == {grad_output.shape[0]}" + ) + else: + assert ( + grad_output.dim() <= 1 and grad_output.numel() == 1 + ), f"Expected a single element grad_output tensor, but got: {grad_output.shape}" + + return _nll_loss_backward( + grad_output, self, target, weight, reduction, ignore_index, total_weight + ) + + +@register_decomposition(aten.nll_loss2d_backward) +@out_wrapper("grad_input") +def nll_loss2d_backward( + grad_output: Tensor, + self: Tensor, + target: Tensor, + weight: Optional[Tensor], + reduction: int, + ignore_index: int, + total_weight: Tensor, +) -> Tensor: + assert ( + self.dim() == 4 + ), f"only batches of spatial inputs supported (4D tensors), but got input of dimension: {self.dim()}" + + assert ( + target.dim() == 3 + ), f"only batches of spatial targets supported (3D tensors) but got targets of dimension: {target.dim()}" + + assert ( + self.shape[0] == target.shape[0] + and self.shape[2] == target.shape[1] + and self.shape[3] == target.shape[2] + ), f"size mismatch (got input: {self.shape}, target: {target.shape}" + + assert total_weight.numel() == 1, ( + "expected total_weight to be a single element tensor, " + f"got: {total_weight.shape} ( {total_weight.numel()}, elements)" + ) + + return _nll_loss_backward( + grad_output, self, target, weight, reduction, ignore_index, total_weight + ) + + +@register_decomposition(aten.binary_cross_entropy) +@out_wrapper() +@pw_cast_for_opmath +def binary_cross_entropy( + self: Tensor, + target: Tensor, + weight: Optional[Tensor] = None, + reduction: int = Reduction.MEAN.value, +) -> Tensor: + # We cannot currently model this without introducing data-dependent control flow + # TORCH_CHECK( + # (input_val >= 0) && (input_val <= 1), + # "all elements of input should be between 0 and 1" + # ) + loss = (target - 1) * torch.maximum( + torch.log1p(-self), self.new_full((), -100) + ) - target * torch.maximum(torch.log(self), self.new_full((), -100)) + if weight is not None: + loss = loss * weight + return apply_loss_reduction(loss, reduction) + + +@register_decomposition(aten.binary_cross_entropy_backward) +@out_wrapper("grad_input") +@pw_cast_for_opmath +def binary_cross_entropy_backward( + grad_output: Tensor, + self: Tensor, + target: Tensor, + weight: Optional[Tensor] = None, + reduction: int = Reduction.MEAN.value, +) -> Tensor: + EPSILON = 1e-12 + result = grad_output * (self - target) / torch.clamp(self * (1 - self), min=EPSILON) + if weight is not None: + result = result * weight + if reduction == Reduction.MEAN.value: + result = result / self.numel() + return result + + +@register_decomposition(aten.soft_margin_loss) +@out_wrapper() +@pw_cast_for_opmath +def soft_margin_loss( + input: Tensor, + target: Tensor, + reduction: int = Reduction.MEAN.value, +) -> Tensor: + loss = torch.log1p(torch.exp(-input * target)) + return apply_loss_reduction(loss, reduction) + + +@register_decomposition(aten.soft_margin_loss_backward) +@out_wrapper("grad_input") +@pw_cast_for_opmath +def soft_margin_loss_backward( + grad_output: Tensor, + self: Tensor, + target: Tensor, + reduction: int = Reduction.MEAN.value, +) -> Tensor: + grad_input = target * grad_output * (torch.sigmoid(target * self) - 1) + if reduction == Reduction.MEAN.value: + grad_input = grad_input / self.numel() + return grad_input + + +@register_decomposition(aten.dist) +@out_wrapper() +def dist(input: Tensor, other: Tensor, p: float = 2): + return aten.norm(input - other, p=p) + + +@register_decomposition(aten._euclidean_dist) +@out_wrapper() +def _euclidean_dist(x1: Tensor, x2: Tensor) -> Tensor: + x1_norm = x1.pow(2).sum(-1, True) + x1_pad = torch.ones_like(x1_norm, memory_format=torch.contiguous_format) + x2_norm = x2.pow(2).sum(-1, True) + x2_pad = torch.ones_like(x2_norm, memory_format=torch.contiguous_format) + x1_ = torch.cat([x1.mul(-2), x1_norm, x1_pad], -1) + x2_ = torch.cat([x2, x2_pad, x2_norm], -1) + result = x1_.matmul(x2_.mT) + return result.clamp_min(0).sqrt() + + +@register_decomposition(aten.slice_backward) +@out_wrapper() +def slice_backward( + grad_output: Tensor, + input_sizes: List[int], + dim: int, + start: int, + end: int, + step: int, +): + grad_input = grad_output.new_zeros(input_sizes) + return torch.slice_scatter(grad_input, grad_output, dim, start, end, step) + + +@register_decomposition(aten.slice.Tensor) +def slice_forward( + # Tensor(a) self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1 + self: Tensor, + dim: int = 0, + start: Optional[int] = None, + end: Optional[int] = None, + step: int = 1, +): + from torch.fx.experimental.symbolic_shapes import ( + guard_size_oblivious, + statically_known_true, + ) + + ndim = self.dim() + if ndim == 0: + raise RuntimeError("slice() cannot be applied to a 0-dim tensor.") + dim = utils.canonicalize_dim(self.dim(), dim) + sizes = list(self.size()) + strides = list(self.stride()) + + if step <= 0: + raise RuntimeError("slice step must be positive") + + start_val = start if start is not None else 0 + end_val = end if end is not None else sys.maxsize # 2^63 - 1 + + if guard_size_oblivious(start_val < 0): + start_val += sizes[dim] + + if guard_size_oblivious(end_val < 0): + end_val += sizes[dim] + + if guard_size_oblivious(start_val < 0): + start_val = 0 + elif guard_size_oblivious(start_val > sizes[dim]): + start_val = sizes[dim] + + if guard_size_oblivious(end_val < start_val): + end_val = start_val + elif statically_known_true(end_val == sys.maxsize) or guard_size_oblivious( + end_val > sizes[dim] + ): + end_val = sizes[dim] + + storage_offset = self.storage_offset() + start_val * strides[dim] + len = end_val - start_val + sizes[dim] = (len + step - 1) // step + strides[dim] *= step + + if self.is_quantized: + raise NotImplementedError( + "Slice decomposition for quantized tensors aren't implemented" + ) + else: + return self.as_strided(sizes, strides, storage_offset) + + +def _normalize_start_end( + x: Tensor, dim: int, start: Optional[int], end: Optional[int] +) -> Tuple[int, int]: + """ + Normalize start and end such that both are in the range + [0, x.get_size()[dim]] and start <= end. + """ + dim_size = x.shape[dim] + + def clamp_wrap(val, lower, upper, default) -> int: + if val is None: + return default + if val < 0: + val = val + dim_size + return min(max(val, lower), upper) + + start = clamp_wrap(start, 0, dim_size, 0) + end = clamp_wrap(end, start, dim_size, dim_size) + return start, end + + +# This is not in torch._refs because aten.index used by +# aten._unsafe_masked_index does not have a decomposition. +@register_decomposition(aten.slice_scatter) +@out_wrapper() +def slice_scatter( + input: Tensor, + src: Tensor, + dim: int = 0, + start: Optional[int] = None, + end: Optional[int] = None, + step: int = 1, +): + dim = utils.canonicalize_dim(input.ndim, dim) + dim_size = input.shape[dim] + start, end = _normalize_start_end(input, dim, start, end) + + src_size = list(input.shape) + src_size[dim] = (end - start + (step - 1)) // step + src = src.expand(src_size) + + if start == 0 and end == dim_size and step == 1: + return src.clone() + + indices = [None] * input.dim() + idx = torch.arange(dim_size, device=input.device) + indices[dim] = (idx - start) // step + + mask = torch.ones(dim_size, device=input.device, dtype=torch.bool) + if start != 0: + mask = torch.logical_and(mask, idx >= start) + + if end != dim_size: + mask = torch.logical_and(mask, idx < end) + + if step != 1: + mask = torch.logical_and(mask, (idx - start) % step == 0) + + mask_shape = [1] * input.dim() + mask_shape[dim] = -1 + mask = mask.view(mask_shape) + return aten.where(mask, aten._unsafe_masked_index(src, mask, indices, 0), input) + + +@register_decomposition(aten.select_backward) +@out_wrapper() +def select_backward(grad_output: Tensor, input_sizes: List[int], dim: int, index: int): + grad_input = grad_output.new_zeros(input_sizes) + return torch.select_scatter(grad_input, grad_output, dim, index) + + +@register_decomposition(aten.diagonal_backward) +@out_wrapper() +def diagonal_backward( + grad_output: Tensor, input_sizes: List[int], offset: int, dim1: int, dim2: int +): + grad_input = grad_output.new_zeros(input_sizes) + return torch.diagonal_scatter(grad_input, grad_output, offset, dim1, dim2) + + +def _cast_grad_to_input_dtype( + grad_output: Tensor, grad_input: Tensor, input_dtype: torch.dtype +): + if grad_output.dtype != input_dtype: + grad_input = grad_input.to(input_dtype) + return grad_input + + +@register_decomposition(aten._softmax_backward_data) +@out_wrapper("grad_input") +@compute_only_pw_cast_for_opmath +def _softmax_backward_data( + grad_output: Tensor, output: Tensor, dim: int, input_dtype: torch.dtype +): + new_grad_output = grad_output * output + grad_input = new_grad_output - output * torch.sum( + new_grad_output, dim=dim, keepdim=True + ) + + # CPU kernel doesn't respect input_dtype, but following check doesn't work for meta tensor + # if grad_output.device == torch.device("cpu"): + # return grad_input.contiguous() + + return _cast_grad_to_input_dtype(grad_output, grad_input, input_dtype).contiguous() + + +@register_decomposition(aten._log_softmax_backward_data) +@out_wrapper() +@compute_only_pw_cast_for_opmath +def _log_softmax_backward_data( + grad_output: Tensor, output: Tensor, dim: int, input_dtype: torch.dtype +): + grad_input = grad_output - torch.exp(output) * torch.sum( + grad_output, dim=dim, keepdim=True + ) + return _cast_grad_to_input_dtype(grad_output, grad_input, input_dtype) + + +def _im2col_col2im_indices_along_dim( + input_d, kernel_d, dilation_d, padding_d, stride_d, device +): + """Utility function to implement im2col and col2im""" + blocks_d = input_d + padding_d * 2 - dilation_d * (kernel_d - 1) + + arange_kw = partial(torch.arange, dtype=torch.int64, device=device) + + # Stride kernel over input and find starting indices along dim d + blocks_d_indices = arange_kw(0, blocks_d, stride_d).unsqueeze(0) + + # Apply dilation on kernel and find its indices along dim d + kernel_grid = arange_kw(0, kernel_d * dilation_d, dilation_d).unsqueeze(-1) + + # Broadcast and add kernel starting positions (indices) with + # kernel_grid along dim d, to get block indices along dim d + return blocks_d_indices + kernel_grid + + +@register_decomposition(aten.im2col) +@out_wrapper() +def im2col( + input: Tensor, + kernel_size: List[int], + dilation: List[int], + padding: List[int], + stride: List[int], +) -> Tensor: + torch._check(len(kernel_size) == 2, lambda: "im2col(): only 2D kernel supported") + torch._check(len(dilation) == 2, lambda: "im2col(): only 2D dilation supported") + torch._check(len(padding) == 2, lambda: "im2col(): only 2D padding supported") + torch._check(len(stride) == 2, lambda: "im2col(): only 2D stride supported") + + def check_positive(param, param_name, strict=True): + cond = all(p > 0 for p in param) if strict else all(p >= 0 for p in param) + torch._check( + cond, lambda: "{param_name} should be greater {'than' zero, but got {param}" + ) + + check_positive(kernel_size, "kernel_size") + check_positive(dilation, "dilation") + check_positive(dilation, "padding", strict=False) + check_positive(stride, "stride") + + shape = input.shape + ndim = len(shape) + torch._check( + ndim in (3, 4) and all(d != 0 for d in shape[-3:]), + lambda: "Expected 3D or 4D (batch mode) tensor for input with possible 0 batch size " + f"and non-zero dimensions, but got: {tuple(shape)}", + ) + output_size = tuple( + 1 + (out + 2 * pad - dil * (ker - 1) - 1) // st + for out, pad, dil, ker, st in zip( + shape[-2:], padding, dilation, kernel_size, stride + ) + ) + torch._check( + all(c > 0 for c in output_size), + lambda: f"Given an input with spacial size {tuple(shape[-2:])}, " + f"kernel_size={kernel_size}, dilation={dilation}, " + f"padding={padding}, stride={stride}, " + "the calculated shape of the array of sliding blocks " + f"is {output_size}, but its components must be at least one.", + ) + batched_input = ndim == 4 + if not batched_input: + input = input.unsqueeze(0) + + batch_dim, channel_dim, input_h, input_w = input.shape + + stride_h, stride_w = stride + padding_h, padding_w = padding + dilation_h, dilation_w = dilation + kernel_h, kernel_w = kernel_size + + blocks_row_indices = _im2col_col2im_indices_along_dim( + input_h, kernel_h, dilation_h, padding_h, stride_h, input.device + ) + blocks_col_indices = _im2col_col2im_indices_along_dim( + input_w, kernel_w, dilation_w, padding_w, stride_w, input.device + ) + + # Note that F.pad takes (padding_left, padding_right, padding_top, padding_bottom) + # ugh + padded_input = F.pad(input, (padding_w, padding_w, padding_h, padding_h)) + + blocks_row_indices = blocks_row_indices.unsqueeze(-1).unsqueeze(-1) + output = padded_input[:, :, blocks_row_indices, blocks_col_indices] + output = output.permute(0, 1, 2, 4, 3, 5) + num_blocks_row = blocks_row_indices.size(1) + num_blocks_col = blocks_col_indices.size(1) + output = output.reshape( + batch_dim, channel_dim * kernel_h * kernel_w, num_blocks_row * num_blocks_col + ) + + if not batched_input: + output = output.squeeze(0) + return output + + +@register_decomposition(aten.col2im) +@out_wrapper() +@pw_cast_for_opmath +def col2im( + input: Tensor, + output_size: List[int], + kernel_size: List[int], + dilation: List[int], + padding: List[int], + stride: List[int], +) -> Tensor: + torch._check(len(output_size) == 2, lambda: "only 2D output_size supported") + torch._check(len(kernel_size) == 2, lambda: "only 2D kernel supported") + torch._check(len(dilation) == 2, lambda: "only 2D dilation supported") + torch._check(len(padding) == 2, lambda: "only 2D padding supported") + torch._check(len(stride) == 2, lambda: "only 2D stride supported") + + def check_positive(param, param_name, strict=True): + cond = all(p > 0 for p in param) if strict else all(p >= 0 for p in param) + torch._check( + cond, lambda: "{param_name} should be greater than zero, but got {param}" + ) + + check_positive(kernel_size, "kernel_size") + check_positive(dilation, "dilation") + check_positive(padding, "padding", strict=False) + check_positive(stride, "stride") + check_positive(output_size, "output_size") + + shape = input.shape + ndim = len(shape) + torch._check( + ndim in (2, 3) and all(d != 0 for d in shape[-2:]), + lambda: "Expected 2D or 3D (batch mode) tensor for input with possible 0 batch size " + f"and non-zero dimensions, but got: {tuple(shape)}", + ) + prod_kernel_size = kernel_size[0] * kernel_size[1] + torch._check( + shape[-2] % prod_kernel_size == 0, + lambda: "Expected size of input's first non-batch dimension to be divisible by the " + f"product of kernel_size, but got input.shape[-2] = {shape[-2]} and " + f"kernel_size={kernel_size}", + ) + col = [ + 1 + (out + 2 * pad - dil * (ker - 1) - 1) // st + for out, pad, dil, ker, st in zip( + output_size, padding, dilation, kernel_size, stride + ) + ] + L = col[0] * col[1] + torch._check( + shape[-1] == L, + lambda: f"Given output_size={output_size}, kernel_size={kernel_size}, " + f"dilation={dilation}, padding={padding}, stride={stride}, " + f"expected input.size(-1) to be {L} but got {shape[-1]}.", + ) + torch._check( + L > 0, + lambda: f"Given output_size={output_size}, kernel_size={kernel_size}, " + f"dilation={dilation}, padding={padding}, stride={stride}, " + f"expected input.size(-1) to be {L} but got {shape[-1]}.", + ) + batched_input = ndim == 3 + if not batched_input: + input = input.unsqueeze(0) + + shape = input.shape + + out_h, out_w = output_size + stride_h, stride_w = stride + padding_h, padding_w = padding + dilation_h, dilation_w = dilation + kernel_h, kernel_w = kernel_size + + # col2im is defined as the backwards of im2col, so we differentiate its decomposition by hand + input = input.reshape([shape[0], shape[1] // prod_kernel_size] + kernel_size + col) + input = input.permute(0, 1, 2, 4, 3, 5) + + indices_row = _im2col_col2im_indices_along_dim( + out_h, kernel_h, dilation_h, padding_h, stride_h, input.device + ) + indices_row = _unsqueeze_to_dim(indices_row, 4) + indices_col = _im2col_col2im_indices_along_dim( + out_w, kernel_w, dilation_w, padding_w, stride_w, input.device + ) + + output_padded_size = [o + 2 * p for o, p in zip(output_size, padding)] + output = input.new_zeros( + [shape[0], shape[1] // prod(kernel_size)] + output_padded_size + ) + idx = (None, None, indices_row, indices_col) + output = aten._unsafe_index_put(output, idx, input, accumulate=True) + output = F.pad(output, (-padding_w, -padding_w, -padding_h, -padding_h)) + + if not batched_input: + output = output.squeeze(0) + return output + + +@register_decomposition(aten.native_dropout_backward) +@out_wrapper() +def native_dropout_backward(grad_output: Tensor, mask: Tensor, scale: float): + # According to the CUDA kernel implementation we should have this test; + # but it seems to fail tests! + # torch._check(mask.dtype == torch.bool, lambda: f"Mask should be Bool Scalar Type {mask.dtype}") + + # Mimicking CUDA kernel's behavior for output stride: output follow input's memory format + # This different from TensorIterator's behavior + r = (grad_output * (mask.type_as(grad_output) * scale)).clone( + memory_format=utils.suggest_memory_format(grad_output) + ) + return r + + +@register_decomposition(aten.unfold_backward) +@out_wrapper() +def unfold_backward( + grad: Tensor, input_size: List[int], dimension: int, size: int, step: int +) -> Tensor: + if len(input_size) == 0: + return torch.squeeze_copy(grad, 0) + dim = utils.canonicalize_dim(len(input_size), dimension) + idx = torch.arange(input_size[dim], device=grad.device, dtype=torch.int32) + idx = idx.unfold(0, size, step).flatten() + grad = grad.movedim(-1, dim + 1).flatten(dim, dim + 1) + # nb. At the moment this generates two kernels in triton + # It could potentially be fused into one call to scatter_reduce, + # in the case step <= size provided scatter_reduce generates 1 kernel + grad_input = grad.new_zeros(input_size) + index = (None,) * dim + (idx,) + return aten._unsafe_index_put(grad_input, index, grad, accumulate=True).contiguous() + + +@register_decomposition(aten.logit_backward.default) +@pw_cast_for_opmath +def logit_backward( + grad_output: Tensor, self: Tensor, eps: Optional[float] = None +) -> Tensor: + if eps is not None: + lo = eps + hi = 1.0 - lo + return torch.where( + torch.logical_and(self >= lo, self <= hi), + grad_output / (self * (1.0 - self)), + 0.0, + ) + else: + return torch.where( + torch.logical_and(self >= 0.0, self <= 1.0), + grad_output / (self * (1.0 - self)), + self.new_full((), float("nan")), + ) + + +@register_decomposition(aten.dropout) +@aten.dropout.default.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.dropout.default.py_impl(DispatchKey.Autograd) +def dropout(input: Tensor, p: float, train: Optional[bool]): + if train and p != 0: + return aten.native_dropout(input, p, train)[0] + else: + return input.clone() + + +@register_decomposition(aten.native_dropout) +@out_wrapper("out0", "out1") +def native_dropout(input: Tensor, p: float, train: Optional[bool]): + if train and p != 0: + if p == 1: + return (torch.zeros_like(input), torch.zeros_like(input, dtype=torch.bool)) + if not input.dtype.is_floating_point: + raise RuntimeError( + "result type Float can't be cast to the desired output type Long" + ) + bool_mask = torch.rand_like(input) > p + res = bool_mask * input * float(1.0 / (1.0 - p)) + return (res, bool_mask) + else: + return (input, torch.ones_like(input, dtype=torch.bool)) + + +@register_decomposition(aten._softmax) +@out_wrapper() +def _softmax(x: Tensor, dim: int, half_to_float: bool): + # eager softmax returns a contiguous tensor. Ensure that decomp also returns + # a contiguous tensor. + x = x.contiguous() + if half_to_float: + assert x.dtype == torch.half + computation_dtype, result_dtype = utils.elementwise_dtypes( + x, type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT + ) + x = x.to(computation_dtype) + if x.numel() == 0: + unnormalized = torch.exp(x) + else: + x_max = torch.amax(x, dim, keepdim=True) + unnormalized = torch.exp(x - x_max) + result = unnormalized / torch.sum(unnormalized, dim, keepdim=True) + if not half_to_float: + result = result.to(result_dtype) + return result + + +@register_decomposition(aten._log_softmax) +@out_wrapper() +def _log_softmax(x: Tensor, dim: int, half_to_float: bool): + # eager log_softmax returns a contiguous tensor. Ensure that decomp also + # returns a contiguous tensor. + x = x.contiguous() + if half_to_float: + assert x.dtype == torch.half + computation_dtype, result_dtype = utils.elementwise_dtypes( + x, type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT + ) + x = x.to(computation_dtype) + if x.numel() == 0: + shifted = x + else: + x_max = torch.amax(x, dim, keepdim=True) + shifted = x - x_max + shifted_logsumexp = torch.log(torch.sum(torch.exp(shifted), dim, keepdim=True)) + result = shifted - shifted_logsumexp + if not half_to_float: + result = result.to(result_dtype) + return result + + +@register_decomposition(aten.embedding) +@out_wrapper() +def embedding( + weight: Tensor, + indices: Tensor, + padding_idx: int = -1, + scale_grad_by_freq: bool = False, + sparse: bool = False, +) -> Tensor: + assert weight.dim() == 2, "'weight' must be 2-D" + # Nb. scale_grad_by_freq is not used in the forward + if indices.ndim <= 1: + # We need this one as weight[indices] calls item() in these cases + out = weight.index_select(0, indices) + if indices.ndim == 0: + out = out.squeeze(0) + return out + else: + return weight[indices] + + +@register_decomposition(aten.embedding_dense_backward) +@out_wrapper() +def embedding_dense_backward( + grad_output: Tensor, + indices: Tensor, + num_weights: int, + padding_idx: int, + scale_grad_by_freq: bool, +): + computation_dtype, result_dtype = utils.elementwise_dtypes( + grad_output, type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT + ) + grad_output = grad_output.to(computation_dtype) + indices = _maybe_convert_to_dtype(indices, torch.long) # type: ignore[assignment] + if scale_grad_by_freq: + counts = indices.new_zeros((num_weights,)) + ones = torch.ones_like(indices) + counts = aten._unsafe_index_put(counts, [indices], ones, accumulate=True) + grad_weights_scale = counts[indices] + grad_output = grad_output / grad_weights_scale.unsqueeze(-1) + + mask = _unsqueeze_to_dim(indices == padding_idx, grad_output.ndim) + grad = grad_output.masked_fill(mask, 0) + grad_weight = grad_output.new_zeros( + (num_weights,) + grad_output.shape[indices.ndim :] + ) + return aten._unsafe_index_put(grad_weight, [indices], grad, accumulate=True).to( + result_dtype + ) + + +def prod(x: List[int]): + r = 1 + for i in x: + r *= i + return r + + +def _pad_chunk( + tensors: List[Tensor], + dim: int, + num_chunks: int, +) -> List[Tensor]: + padded_tensors = [] + for tensor in tensors: + tensor_size = tensor.size() + pad_along_dim = (tensor_size[dim] + num_chunks - 1) // num_chunks * num_chunks + if pad_along_dim != tensor_size[dim]: + # Use aten.constant_pad_nd instead of copy_ for functionalization + pad = [0] * 2 * (tensor.ndim - dim - 1) + [ + 0, + pad_along_dim - tensor_size[dim], + ] + tensor = aten.constant_pad_nd(tensor, pad, 0) + view_size = tensor_size[:dim] + torch.Size([num_chunks, -1]) + padded_tensors.append(tensor.view(view_size)) + return padded_tensors + + +def have_same_ndims(tensors: List[Tensor]): + ndim = tensors[0].ndim + for tensor in tensors: + if tensor.ndim != ndim: + return False + return True + + +def leading_dimension_matches(tensors: List[Tensor], dim: int): + leading_dim_sizes = tensors[0].size()[:dim] + for tensor in tensors: + torch._check( + tensor.size()[:dim] == leading_dim_sizes, + lambda: "_chunk_cat expects same sizes of 0,...,dim-1 dimensions for all tensors", + ) + + +def _preprocess_chunk_cat_inputs( + tensors: List[Tensor], + dim: int, + num_chunks: int, +): + torch._check(num_chunks >= 1, lambda: "_chunk_cat expects positive num_chunks") + torch._check( + len(tensors) > 0, lambda: "_chunk_cat expects a non-empty input tensor list" + ) + expected_dtype = tensors[0].dtype + expected_device = tensors[0].device + for tensor in tensors: + torch._check(tensor.numel() > 0, lambda: "_chunk_cat expects non-empty tensor") + torch._check( + tensor.dtype == expected_dtype, + lambda: "_chunk_cat expects all input tensors with the same dtype", + ) + torch._check( + tensor.device == expected_device, + lambda: "_chunk_cat expects all inputs tensors on the same device", + ) + if have_same_ndims(tensors): + dim = utils.canonicalize_dim(tensors[0].dim(), dim) + else: + torch._check( + dim >= 0, + lambda: "_chunk_cat expects non-negative dim when input tensors have different ndims", + ) + for tensor in tensors: + torch._check( + dim < tensor.ndim, + lambda: "_chunk_cat expects dim < ndim for all input tensors", + ) + leading_dimension_matches(tensors, dim) + return dim + + +@register_decomposition([aten._chunk_cat.default, aten._chunk_cat.out]) +def _chunk_cat( + tensors: List[Tensor], + dim: int, + num_chunks: int, + out: Optional[Tensor] = None, +) -> Tensor: + dim = _preprocess_chunk_cat_inputs(tensors, dim, num_chunks) + padded_tensors = _pad_chunk(tensors, dim, num_chunks) + if out is None: + return torch.cat(padded_tensors, dim + 1) + else: + torch.cat(padded_tensors, dim + 1, out=out) + return out + + +@register_decomposition(aten.split_with_sizes) +def split_with_sizes( + self: Tensor, split_sizes: List[int], dim: int = 0 +) -> List[Tensor]: + # NB: Perform the check_is_size tests first so that the + # sum test does not try to do a replacement + for i in range(len(split_sizes)): + torch._check_is_size( + split_sizes[i], + lambda: "split_with_sizes expects split_sizes have only non-negative entries", + ) + torch._check_with( + ValueError, + sum(split_sizes) == self.shape[dim], + lambda: f"Split sizes add up to {sum(split_sizes)} but got the tensor's size of {self.shape[dim]}", + ) + num_splits = len(split_sizes) + splits = [] + start_idx = 0 + + for i in range(num_splits): + length = split_sizes[i] + splits.append(self.narrow(dim, start_idx, length)) + start_idx += length + return splits + + +# out_wrapper currently does not allow optional outputs +@register_decomposition( + [aten.split_with_sizes_copy.default, aten.split_with_sizes_copy.out] +) +def split_with_sizes_copy( + self: Tensor, + split_sizes: List[int], + dim: int = 0, + out: Optional[List[Tensor]] = None, +) -> Optional[List[Tensor]]: + splits = split_with_sizes(self, split_sizes, dim=dim) + if out is None: + return [s.clone(memory_format=torch.contiguous_format) for s in splits] + else: + for output, split in zip(out, splits): + _maybe_resize_out(output, split.shape) + _safe_copy_out(copy_from=split, copy_to=output, exact_dtype=True) + return None + + +@register_decomposition(aten.unsafe_split.Tensor) +def unsafe_split(input: Tensor, split_size: int, dim: int = 0) -> Tuple[Tensor, ...]: + return aten.split.Tensor(input, split_size, dim) + + +@register_decomposition(aten.unsafe_split_with_sizes.default) +def unsafe_split_with_sizes( + input: Tensor, split_sizes: List[int], dim: int = 0 +) -> Tuple[Tensor, ...]: + return aten.split_with_sizes.default(input, split_sizes, dim) + + +@register_decomposition(aten.split.Tensor) +def split(self: Tensor, split_size: int, dim: int = 0) -> Tuple[Tensor, ...]: + input_sizes = self.shape + dim_size = input_sizes[dim] + if split_size == 0: + assert dim_size == 0 + return (self,) + chunks = (dim_size + split_size - 1) // split_size + + # Avoid importing sympy at a module level + from torch.fx.experimental.symbolic_shapes import guard_int + + chunks = guard_int(chunks) + split_sizes = [split_size for i in range(chunks)] + split_sizes[-1] = split_size - (split_size * chunks - dim_size) + return torch.split(self, split_sizes, dim) + + +@aten.tensor_split.tensor_indices_or_sections.py_impl( + DispatchKey.CompositeImplicitAutograd +) +def tensor_split_tensor_indices_or_sections_py_impl( + self: Tensor, + tensor_indices_or_sections: Tensor, + dim: int = 0, +) -> Tuple[Tensor, ...]: + assert tensor_indices_or_sections.device.type == "cpu" + assert tensor_indices_or_sections.dtype == torch.int64 + split_dim = tensor_indices_or_sections.dim() + torch._check( + split_dim == 1 or split_dim == 0, + lambda: "tensor_split expected tensor_indices_or_sections to be a zero-dimensional " + f"or one-dimensional tensor, but got a tensor with {split_dim} dims", + ) + if split_dim == 0: + sections = tensor_indices_or_sections.item() + assert isinstance(sections, IntLike) + return self.tensor_split(sections, dim) + else: + indices = [i.item() for i in tensor_indices_or_sections] + # WARNING: Tempted to torch._check_is_size on the indices here? You + # can't: tensor_split works with negative values in indices: + # + # >>> torch.tensor_split(torch.randn(10), torch.tensor([-5, 5])) + # (tensor([ 0.3540, 2.1074, -0.8507, 1.1639, 0.3055]), tensor([]), + # tensor([-0.4285, 1.0692, -0.1776, 0.9362, 1.6143])) + # + # Sorry, I don't make the rules. Explicitly do the item call in user + # code if you KNOW that they are non-negative. + return self.tensor_split(indices, dim) + + +# TODO: this doesn't appear to have enough precision in bfloat16 +@register_decomposition(aten.addmm) +@out_wrapper() +@pw_cast_for_opmath +def addmm(self: Tensor, mat1: Tensor, mat2: Tensor, beta: int = 1, alpha: int = 1): + if not self.is_floating_point() and not self.is_complex(): + beta = int(beta) + alpha = int(alpha) + out = alpha * torch.mm(mat1, mat2) + if beta == 0: + return out + + # The output of aten.addmm is contiguous, we need to match this behavior in the decomposition. + # The original implementation 'beta * self + out' would return a strided tensor if `self` is strided. + # We thus use `out`, the output of torch.mm, which is always contiguous, as the first argument for addition. + # This is relying on TensorIterator's behavior that it takes higher precedence on the stride of first input. + # Alternative, we can write `(beta * self + out).contiguous()`, but it introduces another copy in some cases. + # This implementation is not ideal, and we should revisit this when we have a better solution. + return out + beta * self + + +@register_decomposition(aten._addmm_activation) +@out_wrapper() +@pw_cast_for_opmath +def _addmm_activation( + self: Tensor, + mat1: Tensor, + mat2: Tensor, + beta: int = 1, + alpha: int = 1, + use_gelu: bool = False, +): + out = addmm(self, mat1, mat2, beta, alpha) + if use_gelu: + if self.is_cuda: + return aten.gelu(out, approximate="tanh") + else: + return aten.gelu(out) + return aten.relu(out) + + +@register_decomposition(aten.addmv) +@out_wrapper() +@pw_cast_for_opmath +def addmv(self: Tensor, mat1: Tensor, vec: Tensor, beta: int = 1, alpha: int = 1): + if not self.is_floating_point() and not self.is_complex(): + beta = int(beta) + alpha = int(alpha) + out = alpha * torch.mv(mat1, vec) + if beta == 0: + return out + return out + beta * self + + +@register_decomposition(aten.native_group_norm_backward.default) +@pw_cast_for_opmath +def native_group_norm_backward( + grad_output: Tensor, + input: Tensor, + mean: Tensor, + rstd: Tensor, + gamma: Optional[Tensor], + N: int, + C: int, + HxW: int, + group: int, + output_mask: List[bool], +) -> Tuple[Optional[Tensor], Optional[Tensor], Optional[Tensor]]: + utils.check_same_device( + grad_output, input, mean, rstd, allow_cpu_scalar_tensors=False + ) + utils.check_same_shape(input, grad_output, allow_cpu_scalar_tensors=False) + utils.check_same_shape(mean, rstd, allow_cpu_scalar_tensors=False) + torch._check( + input.numel() == N * C * HxW, + lambda: f"Expect input to have {N * C * HxW} elements", + ) + torch._check( + mean.shape == (N, group), + lambda: f"Expect mean to have shape ({N}, {group}, but got {mean.shape}", + ) + torch._check( + gamma is None or gamma.numel() == C, + lambda: f"Expect gamma to have {C} elements but got {gamma.numel() if gamma is not None else -1}", + ) + + cpg, _rem = divmod(C, group) + torch._check( + _rem == 0, + lambda: f"Expect number of channels {C} to be evenly-divisible by number of groups {group}", + ) + + # Compute Internal gradients + ds = torch.mul(grad_output, input).view(N, C, HxW).sum(dim=[2]) + db = grad_output.view(N, C, HxW).sum(dim=[2]) + + d_input: Optional[Tensor] = None + d_gamma: Optional[Tensor] = None + d_bias: Optional[Tensor] = None + if output_mask[0]: + s = 1.0 / (HxW * cpg) + if gamma is not None: + ds_val = torch.mul(ds, gamma.unsqueeze(0)).reshape(N, group, cpg).sum(2) + db_val = torch.mul(db, gamma.unsqueeze(0)).reshape(N, group, cpg).sum(2) + c1 = torch.mul( + rstd.unsqueeze(-1), + gamma.reshape(1, group, cpg), + ) + else: + ds_val = ds.reshape(N, group, cpg).sum(2) + db_val = db.reshape(N, group, cpg).sum(2) + c1 = torch.mul( + rstd.unsqueeze(-1), + torch.ones((1, group, cpg), device=rstd.device), + ) + c2 = (db_val * mean - ds_val) * rstd * rstd * rstd * s + c3 = -c2 * mean - db_val * rstd * s + + c1 = c1.unsqueeze(-1) + c2 = _unsqueeze_to_dim(c2, 4) + c3 = _unsqueeze_to_dim(c3, 4) + d_input = ( + torch.mul(grad_output.reshape(N, group, cpg, HxW), c1) + + torch.mul(input.reshape(N, group, cpg, HxW), c2) + + c3 + ) + d_input = d_input.reshape(input.shape).to(input.dtype) + if output_mask[1]: + d_gamma = ( + ( + (ds.view(N, group, cpg) - db.view(N, group, cpg) * mean.unsqueeze(-1)) + * rstd.unsqueeze(-1) + ) + .sum(dim=[0]) + .reshape(C) + ) + if output_mask[2]: + d_bias = db.sum(dim=[0]) + + return (d_input, d_gamma, d_bias) + + +# out_wrapper currently does not allow optional outputs +@register_decomposition(aten.native_group_norm_backward.out) +def native_group_norm_backward_out( + grad_output: Tensor, + input: Tensor, + mean: Tensor, + rstd: Tensor, + gamma: Optional[Tensor], + N: int, + C: int, + HxW: int, + group: int, + output_mask: List[bool], + *, + out0: torch.Tensor, + out1: torch.Tensor, + out2: torch.Tensor, +) -> Tuple[Optional[Tensor], Optional[Tensor], Optional[Tensor]]: + result = native_group_norm_backward( + grad_output, input, mean, rstd, gamma, N, C, HxW, group, output_mask + ) + grad_input = (out0, out1, out2) + for i, r in enumerate(result): + if r is not None: + _maybe_resize_out(grad_input[i], r.shape) + _safe_copy_out(copy_from=r, copy_to=grad_input[i], exact_dtype=True) + + return grad_input + + +def _maybe_cast(x: Optional[Tensor], dtype) -> Optional[Tensor]: + if x is not None: + return x.to(dtype) + return x + + +# TODO: Take a closer look at the type promotion semantics +@register_decomposition(aten.native_layer_norm_backward.default) +def native_layer_norm_backward( + grad_out: Tensor, + input: Tensor, + normalized_shape: List[int], + mean: Tensor, + rstd: Tensor, + weight: Optional[Tensor], + bias: Optional[Tensor], + output_mask: List[bool], +) -> Tuple[Optional[Tensor], Optional[Tensor], Optional[Tensor]]: + input_shape = input.shape + input_ndim = input.dim() + computation_dtype = utils.get_computation_dtype(input.dtype) + grad_out_cast, input_cast, weight_cast, bias_cast = ( + x.to(computation_dtype).contiguous() if x is not None else x + for x in (grad_out, input, weight, bias) + ) + assert grad_out_cast is not None + + axis = input_ndim - len(normalized_shape) + inner_dims = input_shape[axis:] + outer_dims = input_shape[:axis] + inner_dim_indices: List[int] = [] + outer_dim_indices: List[int] = [] + for i in range(input_ndim): + if i >= axis: + inner_dim_indices.append(i) + else: + outer_dim_indices.append(i) + + N = prod(inner_dims) # type: ignore[arg-type] + M = prod(outer_dims) # type: ignore[arg-type] + if M <= 0 or N <= 0: + return ( + input.new_zeros(input_shape) if output_mask[0] else None, + input.new_zeros(input_shape[axis:]) if output_mask[1] else None, + input.new_zeros(input_shape[axis:]) if output_mask[2] else None, + ) + mean = _unsqueeze_to_dim(mean, input_cast.dim()) # type: ignore[union-attr] + rstd = _unsqueeze_to_dim(rstd, input_cast.dim()) # type: ignore[union-attr] + x_hat = (input_cast - mean) * rstd + if weight_cast is not None: + grad_x_hat = grad_out_cast * weight_cast + else: + grad_x_hat = grad_out_cast + a = grad_x_hat * N + b = torch.sum(grad_x_hat, inner_dim_indices, True) + c1 = torch.mul(grad_x_hat, x_hat) + c2 = torch.sum(c1, inner_dim_indices, True) + c3 = torch.mul(x_hat, c2) + + inner = a - b - c3 + d_input: Optional[Tensor] = None + d_weight: Optional[Tensor] = None + d_bias: Optional[Tensor] = None + if output_mask[0]: + d_input = (rstd / N) * inner + + if output_mask[1] and weight_cast is not None: + if len(outer_dim_indices) > 0: + d_weight = torch.sum(grad_out_cast * x_hat, outer_dim_indices, False) + else: + d_weight = grad_out_cast * x_hat + + if output_mask[2] and bias_cast is not None: + if len(outer_dim_indices) > 0: + d_bias = torch.sum(grad_out_cast, outer_dim_indices, False) + else: + d_bias = grad_out_cast.clone() + + return ( + _maybe_cast(d_input, input.dtype), + _maybe_cast(d_weight, input.dtype), + _maybe_cast(d_bias, input.dtype), + ) + + +# out_wrapper currently does not allow optional outputs +@register_decomposition(aten.native_layer_norm_backward.out) +def native_layer_norm_backward_out( + grad_out: Tensor, + input: Tensor, + normalized_shape: List[int], + mean: Tensor, + rstd: Tensor, + weight: Optional[Tensor], + bias: Optional[Tensor], + output_mask: List[bool], + *, + out0: torch.Tensor, + out1: torch.Tensor, + out2: torch.Tensor, +) -> Tuple[Optional[Tensor], Optional[Tensor], Optional[Tensor]]: + result = native_layer_norm_backward( + grad_out, input, normalized_shape, mean, rstd, weight, bias, output_mask + ) + grad_input = (out0, out1, out2) + for i, r in enumerate(result): + if r is not None: + _maybe_resize_out(grad_input[i], r.shape) + _safe_copy_out(copy_from=r, copy_to=grad_input[i], exact_dtype=True) + + return grad_input + + +def native_batch_norm_helper( + input: Tensor, + weight: Optional[Tensor], + bias: Optional[Tensor], + running_mean: Optional[Tensor], + running_var: Optional[Tensor], + training: bool, + momentum: float, + eps: float, + functional: bool, +) -> Tuple[Tensor, Tensor, Tensor, Optional[Tensor], Optional[Tensor]]: + reduction_dims = [0] + list(range(2, input.dim())) + computation_dtype = utils.get_computation_dtype(input.dtype) + new_running_mean = running_mean + new_running_var = running_var + if training: + computation_dtype = utils.get_computation_dtype(input.dtype) + input_acc = input.to(dtype=computation_dtype) + biased_var, mean = torch.var_mean( + input_acc, dim=reduction_dims, correction=0, keepdim=True + ) + rstd = torch.rsqrt(biased_var + eps) + + output = (input - mean) * rstd + + save_mean = torch.squeeze(mean, reduction_dims) + save_rstd = torch.squeeze(rstd, reduction_dims) + if running_mean is not None: + new_running_mean = momentum * save_mean + (1 - momentum) * running_mean + if not functional: + running_mean.copy_(new_running_mean) + if running_var is not None: + n = input.numel() / input.shape[1] + # This doesn't strictly match eager's numerics, which accumulates var sum and then directly applies the correction + # But... that would require re-implementing var here, for negligible numerics gain on a tensor whose + # numerics probably don't matter. + squeezed_var = torch.squeeze(biased_var, reduction_dims) + unbiased_var = squeezed_var * (n / (n - 1)) + new_running_var = momentum * unbiased_var + (1 - momentum) * running_var + if not functional: + running_var.copy_(new_running_var) + else: + assert running_mean is not None and running_var is not None + running_mean = running_mean.to(dtype=computation_dtype, copy=True) + new_running_mean = running_mean + running_var = running_var.to(dtype=computation_dtype, copy=True) + new_running_var = running_var + mean = running_mean + invstd = 1 / (torch.sqrt(running_var + eps)) + # Very annoying inconsistency where CPU and CUDA give different shapes + if input.device.type != "cpu": + save_mean = running_mean + save_rstd = invstd + else: + save_mean = input.new_zeros((0,)) + save_rstd = input.new_zeros((0,)) + mean = _unsqueeze_to_dim(mean, input.dim() - 1) + invstd = _unsqueeze_to_dim(invstd, input.dim() - 1) + output = (input - mean) * invstd + + if weight is not None: + weight = weight.flatten() + weight = _unsqueeze_to_dim(weight, input.dim() - 1) + output = output * weight + + if bias is not None: + bias = bias.flatten() + bias = _unsqueeze_to_dim(bias, input.dim() - 1) + output = output + bias + + if input.device.type == "cpu": + save_mean = save_mean.to(dtype=input.dtype) + save_rstd = save_rstd.to(dtype=input.dtype) + return ( + output.to(dtype=input.dtype), + save_mean, + save_rstd, + new_running_mean, + new_running_var, + ) + + +@register_decomposition(aten.native_batch_norm) +@out_wrapper("out", "save_mean", "save_invstd") +def native_batch_norm( + input: Tensor, + weight: Optional[Tensor], + bias: Optional[Tensor], + running_mean: Optional[Tensor], + running_var: Optional[Tensor], + training: bool, + momentum: float, + eps: float, +) -> Tuple[Tensor, Tensor, Tensor]: + output, save_mean, save_rstd, _, _ = native_batch_norm_helper( + input, weight, bias, running_mean, running_var, training, momentum, eps, False + ) + return output, save_mean, save_rstd + + +# TODO: this decomposition is NOT here to stay. We would much prefer replacing native_batch_norm +# with our new correctly schema'd _native_batch_norm_legit and its variants, but +# we cannot do that immediately in the C++ because it would be forwards incompatible +# with some mobile use cases. +# +# Since this change is most impactful for aot autograd/functionalization, we simply +# register this decomposition on the Autograd key for the python dispatcher (which is +# currently only used by aot autograd/functionalization and no one else, really). +# In two weeks or so, we should remove this decomposition and phase out the current native_batch_norm +# to be _native_batch_norm_legit and have the right schema (stating that there are input mutations). +@aten.native_batch_norm.default.py_impl(DispatchKey.Autograd) +@aten.native_batch_norm.default.py_impl(DispatchKey.CompositeImplicitAutograd) +def native_batch_norm_decomposition( + input: Tensor, + weight: Optional[Tensor], + bias: Optional[Tensor], + running_mean: Optional[Tensor], + running_var: Optional[Tensor], + training: bool, + momentum: float, + eps: float, +) -> Tuple[Tensor, Tensor, Tensor]: + if running_mean is None and running_var is None: + return aten._native_batch_norm_legit( + input, weight, bias, training, momentum, eps + ) + if running_mean is None: + raise RuntimeError( + "running_mean is None, but running_var is provided. " + "They should both be None or both be provided." + ) + if running_var is None: + raise RuntimeError( + "running_var is None, but running_mean is provided. " + "They should both be None or both be provided." + ) + if training: + # HACK: batch norm consolidation should clean this up so this op doesn't take in a training arg. + return aten._native_batch_norm_legit( + input, weight, bias, running_mean, running_var, training, momentum, eps + ) + else: + return aten._native_batch_norm_legit_no_training( + input, weight, bias, running_mean, running_var, momentum, eps + ) + + +@aten.unsafe_chunk.default.py_impl(DispatchKey.CompositeImplicitAutograd) +def unsafe_chunk_py_impl(tensor, chunks, dim=0) -> List[Tensor]: + dim_size = tensor.size(dim) + split_size = (dim_size + chunks - 1) // chunks + + if split_size == 0 and dim_size == 0: + split_sizes = [split_size for _ in chunks] + split_sizes[chunks - 1] = split_size - (split_size * chunks - dim_size) + return torch.ops.aten.unsafe_split_with_sizes.default(tensor, split_sizes, dim) + return torch.ops.aten.unsafe_split.Tensor(tensor, split_size, dim) + + +@register_decomposition(aten._native_batch_norm_legit_no_training.default) +def _native_batch_norm_legit_no_training( + input: Tensor, + weight: Optional[Tensor], + bias: Optional[Tensor], + running_mean: Tensor, + running_var: Tensor, + momentum: float, + eps: float, +) -> Tuple[Tensor, Tensor, Tensor]: + return aten._native_batch_norm_legit.default( + input, + weight, + bias, + running_mean, + running_var, + False, # training + momentum, + eps, + ) + + +@register_decomposition(aten._native_batch_norm_legit.default) +def _native_batch_norm_legit( + input: Tensor, + weight: Optional[Tensor], + bias: Optional[Tensor], + running_mean: Tensor, + running_var: Tensor, + training: bool, + momentum: float, + eps: float, +) -> Tuple[Tensor, Tensor, Tensor]: + output, save_mean, save_rstd, _, _ = native_batch_norm_helper( + input, weight, bias, running_mean, running_var, training, momentum, eps, False + ) + return output, save_mean, save_rstd + + +@register_decomposition(aten._native_batch_norm_legit.no_stats) +def _native_batch_norm_legit_no_stats( + input: Tensor, + weight: Optional[Tensor], + bias: Optional[Tensor], + training: bool, + momentum: float, + eps: float, +) -> Tuple[Tensor, Tensor, Tensor]: + output, save_mean, save_rstd, _, _ = native_batch_norm_helper( + input, weight, bias, None, None, training, momentum, eps, False + ) + return output, save_mean, save_rstd + + +@register_decomposition(aten._native_batch_norm_legit_functional.default) +def _native_batch_norm_legit_functional( + input: Tensor, + weight: Optional[Tensor], + bias: Optional[Tensor], + running_mean: Tensor, + running_var: Tensor, + training: bool, + momentum: float, + eps: float, +) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: + ( + output, + save_mean, + save_rstd, + new_running_mean, + new_running_var, + ) = native_batch_norm_helper( + input, weight, bias, running_mean, running_var, training, momentum, eps, True + ) + assert new_running_mean is not None, "new_running_mean should not be None" + assert new_running_var is not None, "new_running_var should not be None" + return output, save_mean, save_rstd, new_running_mean, new_running_var + + +def _get_batch_norm_reserve_tensor( + input: Tensor, + weight: Optional[Tensor], + bias: Optional[Tensor], + running_mean: Tensor, + running_var: Tensor, + eps: float, + training: bool, +) -> Tensor: + """ + Return a reserve tensor for batch norm, used only by cudnn to pass forward state to the + backward pass. This is needed for `_batch_norm_with_update` and `_batch_norm_no_update`, + which support a variety of backends including cudnn. We create this tensor here to get + the correct shape in the traced graph if we detect that will call the cudnn kernel, + and rely on DCE to avoid materializing this tensor. + """ + backend = torch._C._select_batch_norm_backend( # type: ignore[attr-defined] + input, weight, bias, running_mean, running_var, True, eps + ) + reserve_size = 0 + if backend == torch._C._BatchNormBackend.Cudnn: # type: ignore[attr-defined] + reserve_size = torch._C._get_cudnn_batch_norm_reserve_space_size(input, training) # type: ignore[attr-defined] + return torch.empty( + reserve_size, dtype=torch.uint8, layout=input.layout, device=input.device + ) + + +@register_decomposition(aten._batch_norm_with_update.default) +def _batch_norm_with_update( + input: Tensor, + weight: Optional[Tensor], + bias: Optional[Tensor], + running_mean: Tensor, + running_var: Tensor, + momentum: float, + eps: float, +) -> Tuple[Tensor, Tensor, Tensor, Tensor]: + output, save_mean, save_rstd, _, _ = native_batch_norm_helper( + input, + weight, + bias, + running_mean, + running_var, + True, # training + momentum, + eps, + False, # functional + ) + reserve = _get_batch_norm_reserve_tensor( + input, weight, bias, running_mean, running_var, eps, training=True + ) + return output, save_mean, save_rstd, reserve + + +@register_decomposition(aten._batch_norm_with_update_functional.default) +def _batch_norm_with_update_functional( + input: Tensor, + weight: Optional[Tensor], + bias: Optional[Tensor], + running_mean: Tensor, + running_var: Tensor, + momentum: float, + eps: float, +) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]: + ( + output, + save_mean, + save_rstd, + new_rm, + new_rv, + ) = native_batch_norm_helper( + input, weight, bias, running_mean, running_var, True, momentum, eps, True + ) + reserve = _get_batch_norm_reserve_tensor( + input, weight, bias, running_mean, running_var, eps, training=True + ) + assert new_rm is not None, "new_running_mean should not be None" + assert new_rv is not None, "new_running_var should not be None" + return (output, save_mean, save_rstd, reserve, new_rm, new_rv) + + +@register_decomposition(aten._batch_norm_no_update.default) +def _batch_norm_no_update( + input: Tensor, + weight: Optional[Tensor], + bias: Optional[Tensor], + running_mean: Tensor, + running_var: Tensor, + momentum: float, + eps: float, +) -> Tuple[Tensor, Tensor, Tensor, Tensor]: + output, save_mean, save_rstd, _, _ = native_batch_norm_helper( + input, + weight, + bias, + running_mean, + running_var, + False, # training + momentum, + eps, + False, # functional + ) + reserve = _get_batch_norm_reserve_tensor( + input, weight, bias, running_mean, running_var, eps, training=False + ) + return output, save_mean, save_rstd, reserve + + +@register_decomposition(aten._fused_dropout) +@out_wrapper("out0", "out1") +@pw_cast_for_opmath +def _fused_dropout_decomposition(input, p, generator=None): + assert generator is None + mask = (torch.rand_like(input) < p).to(dtype=torch.uint8) + res = mask.type_as(input) * input * (1.0 / p) + return (res, mask) + + +@register_decomposition(aten._to_copy) +@out_wrapper() +def _to_copy( + x: Union[Tensor, NumberType], + *, + dtype: Optional[torch.dtype] = None, + layout=None, + device: Optional[torch.device] = None, + pin_memory: bool = False, + non_blocking: bool = False, + memory_format: Optional[torch.memory_format] = None, +): + assert not layout or layout == torch.strided, "TODO" + assert not pin_memory, "TODO" + assert isinstance(x, (torch.Tensor, int, float, bool, complex)) + if device is None and dtype is None and memory_format is None: + if isinstance(x, torch.Tensor): + return x.clone() + else: + return x + dtype_converted = False + + if isinstance(x, torch.Tensor): + x_tensor = x + else: + x_tensor = torch.scalar_tensor(x) + + if device is not None and device != x_tensor.device: + # avoid conversions on cpu + if dtype is not None and device.type == "cpu": + x_tensor = torch._prims.convert_element_type(x_tensor, dtype) + dtype_converted = True + x_tensor = torch._prims.device_put(x_tensor, device) + + if dtype is not None and not dtype_converted: + x_tensor = torch._prims.convert_element_type(x_tensor, dtype) + dtype_converted = True + + if memory_format is not None: # no ref/prim for memory format + return torch.clone(x_tensor, memory_format=memory_format) + return x_tensor + + +# Questionable decompositions +# This is only valid if we're running the graph without autograd, such as if the backward pass has been traced. +# Note that this decomposition causes issues with in-place ops +@register_decomposition([aten.detach, aten.lift, aten.lift_fresh]) +@out_wrapper() +def nop_decomposition(x): + return aten.alias(x) + + +# Also register to the Autograd dispatch key, so this decomp can run above autograd. +# native_batch_norm needs to decompose into other ops before autograd. +@aten.cudnn_batch_norm.default.py_impl(DispatchKey.Autograd) +@register_decomposition(aten.cudnn_batch_norm) +@out_wrapper("out0", "out1", "out2", "out3") +def cudnn_batch_norm( + input: Tensor, + weight: Tensor, + bias: Optional[Tensor], + running_mean: Optional[Tensor], + running_var: Optional[Tensor], + training: bool, + exponential_average_factor: float, + epsilon: float, +): + a, b, c = aten.native_batch_norm( + input, + weight, + bias, + running_mean, + running_var, + training, + exponential_average_factor, + epsilon, + ) + # Cudnn return running mean and variance when training is True + if training: + return (a, b, c, input.new_zeros((0,), dtype=torch.uint8)) + return ( + a, + weight.new_zeros((0,)), + weight.new_zeros((0,)), + input.new_zeros((0,), dtype=torch.uint8), + ) + + +def _broadcast_batch_norm_backward(x, broadcast_mask): + for axis, mask in enumerate(broadcast_mask): + if mask == 1 and not (axis < x.ndim and x.shape[axis] == mask): + x = x.unsqueeze(axis) + return x + + +@register_decomposition(aten.batch_norm_backward.default) +def batch_norm_backward( + grad_out: Tensor, + input: Tensor, + weight: Optional[Tensor], + running_mean: Optional[Tensor], + running_var: Optional[Tensor], + save_mean: Optional[Tensor], + save_invstd: Optional[Tensor], + train: bool, + eps: float, + output_mask: List[bool], + reserve: Tensor, +) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor]]: + return native_batch_norm_backward( + grad_out, + input, + weight, + running_mean, + running_var, + save_mean, + save_invstd, + train, + eps, + output_mask, + ) + + +@register_decomposition(aten.native_batch_norm_backward.default) +def native_batch_norm_backward( + grad_out: Tensor, + input: Tensor, + weight: Optional[Tensor], + running_mean: Optional[Tensor], + running_var: Optional[Tensor], + save_mean: Optional[Tensor], + save_invstd: Optional[Tensor], + train: bool, + eps: float, + output_mask: List[bool], +) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor]]: + input_dtype = input.dtype + if weight is not None: + weight_dtype = weight.dtype + else: + weight_dtype = input_dtype + computation_dtype = utils.get_computation_dtype(input.dtype) + ( + grad_out_cast, + input_cast, + weight_cast, + running_mean_cast, + running_var_cast, + save_mean_cast, + save_invstd_cast, + ) = ( + x.to(computation_dtype) if x is not None else x + for x in ( + grad_out, + input, + weight, + running_mean, + running_var, + save_mean, + save_invstd, + ) + ) + input_shape = input.shape + input_rank = input.dim() + assert input_rank >= 2, "rank of the input must be at least 2" + + axis = 1 + num_features = prod(list(input_shape)) / input_shape[axis] + mean = save_mean_cast + invstd = save_invstd_cast + if train: + assert save_mean_cast is not None and save_invstd_cast is not None + else: + assert running_mean_cast is not None and running_var_cast is not None + mean = running_mean_cast + invstd = torch.rsqrt(running_var_cast + eps) + + broadcast_mask: List[int] = [1] * input_rank + broadcast_mask[axis] = input_shape[axis] + + reduction_axes: List[int] = [] + for i in range(input_rank): + if i != axis: + reduction_axes.append(i) + + mean = _broadcast_batch_norm_backward(mean, broadcast_mask) # type: ignore[arg-type] + norm = 1.0 / num_features + grad_output_sum = torch.sum(grad_out_cast, reduction_axes) # type: ignore[arg-type] + dot_p = torch.sum(grad_out_cast * (input_cast - mean), reduction_axes) # type: ignore[operator] + + grad_mean = _broadcast_batch_norm_backward(grad_output_sum * norm, broadcast_mask) + proj_scale = _broadcast_batch_norm_backward(torch.mul(dot_p * norm, invstd * invstd), broadcast_mask) # type: ignore[operator] + + if weight_cast is None: + grad_scale = _broadcast_batch_norm_backward(invstd, broadcast_mask) * 1.0 # type: ignore[arg-type] + else: + grad_scale = _broadcast_batch_norm_backward( + invstd * weight_cast, broadcast_mask + ) + + if train: + proj = (input_cast - mean) * proj_scale # type: ignore[operator] + grad_input = ((grad_out_cast - proj) - grad_mean) * grad_scale + else: + grad_input = grad_out_cast * grad_scale + + if output_mask[1]: + grad_weight = dot_p * invstd + else: + grad_weight = None # "None" doesn't work with vjp, should use zeros for vjp + + if output_mask[2]: + grad_bias = grad_output_sum + else: + grad_bias = None # "None" doesn't work with vjp, should use zeros for vjp + + return ( + grad_input.to(input_dtype), + _maybe_cast(grad_weight, weight_dtype), + _maybe_cast(grad_bias, weight_dtype), + ) + + +# out_wrapper currently does not allow optional outputs +@register_decomposition(aten.native_batch_norm_backward.out) +def native_batch_norm_backward_out( + grad_out: Tensor, + input: Tensor, + weight: Optional[Tensor], + running_mean: Optional[Tensor], + running_var: Optional[Tensor], + save_mean: Optional[Tensor], + save_invstd: Optional[Tensor], + train: bool, + eps: float, + output_mask: List[bool], + *, + out0: torch.Tensor, + out1: torch.Tensor, + out2: torch.Tensor, +) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor]]: + result = native_batch_norm_backward( + grad_out, + input, + weight, + running_mean, + running_var, + save_mean, + save_invstd, + train, + eps, + output_mask, + ) + grad_input = (out0, out1, out2) + for i, r in enumerate(result): + if r is not None: + _maybe_resize_out(grad_input[i], r.shape) + _safe_copy_out(copy_from=r, copy_to=grad_input[i], exact_dtype=True) + + return grad_input + + +@register_decomposition(aten.miopen_batch_norm_backward) +@out_wrapper("out0", "out1", "out2") +def miopen_batch_norm_backward( + input: Tensor, + grad_output: Tensor, + weight: Tensor, + running_mean: Optional[Tensor], + running_var: Optional[Tensor], + save_mean: Optional[Tensor], + save_var: Optional[Tensor], + epsilon: float, +): + return aten.native_batch_norm_backward( + grad_output, + input, + weight, + running_mean, + running_var, + save_mean, + save_var, + True, + epsilon, + [True, True, True], + ) + + +@register_decomposition(aten.cudnn_batch_norm_backward) +@out_wrapper("out0", "out1", "out2") +def cudnn_batch_norm_backward( + input: Tensor, + grad_output: Tensor, + weight: Tensor, + running_mean: Optional[Tensor], + running_var: Optional[Tensor], + save_mean: Optional[Tensor], + save_var: Optional[Tensor], + epsilon: float, + reserveSpace: Tensor, +): + return aten.native_batch_norm_backward( + grad_output, + input, + weight, + running_mean, + running_var, + save_mean, + save_var, + True, + epsilon, + [True, True, True], + ) + + +@register_decomposition(aten._adaptive_avg_pool2d) +@out_wrapper() +@pw_cast_for_opmath +def adaptive_avg_pool2d(input: Tensor, output_size: Tuple[int, int]): + # Preconditions + device = input.device + shape = input.shape + ndim = len(shape) + torch._check( + ndim in (3, 4), + lambda: f"adaptive_avg_pool2d(): Expected 3D or 4D tensor, but got {ndim}", + ) + for d in input.shape[-2:]: + torch._check( + d != 0, + lambda: "adaptive_avg_pool2d(): Expected input to have non-zero size for " + f"non-batch dimensions, but input has shape {tuple(shape)}.", + ) + + # Optimisation (we should also do this in the kernel implementation) + if shape[-2] % output_size[-2] == 0 and shape[-1] % output_size[-1] == 0: + stride = tuple(i // o for i, o in zip(shape[-2:], output_size)) + kernel = tuple( + i - (o - 1) * s for i, o, s in zip(shape[-2:], output_size, stride) + ) + return torch.nn.functional.avg_pool2d(input, kernel, stride) + + def start_index(a, b, c): + return torch.div(a * c, b, rounding_mode="trunc") + + def end_index(a, b, c): + return torch.div((a + 1) * c + b - 1, b, rounding_mode="trunc") + + def compute_idx(in_size, out_size): + orange = torch.arange(out_size, device=device, dtype=torch.int64) + i0 = start_index(orange, out_size, in_size) + # Let length = end_index - start_index, i.e. the length of the pooling kernels + # length.max() can be computed analytically as follows: + maxlength = in_size // out_size + 1 + in_size_mod = in_size % out_size + # adaptive = True iff there are kernels with different lengths + adaptive = not (in_size_mod == 0 or out_size % in_size_mod == 0) + if adaptive: + maxlength += 1 + elif in_size_mod == 0: + maxlength -= 1 + + range_max = torch.arange(maxlength, device=device, dtype=torch.int64) + idx = i0.unsqueeze(-1) + range_max + if adaptive: + # Need to clamp to avoid accessing out-of-bounds memory + # TODO make minimum accept scalars + maxval = torch.scalar_tensor( + in_size - 1, dtype=idx.dtype, device=idx.device + ) + idx = torch.minimum(idx, maxval) + + # Compute the length + i1 = end_index(orange, out_size, in_size) + length = i1 - i0 + else: + length = maxlength + return idx, length, range_max, adaptive + + # length is not None if it's constant, otherwise we'll need to compute it + idxh, length_h, range_max_h, adaptive_h = compute_idx(shape[-2], output_size[-2]) + idxw, length_w, range_max_w, adaptive_w = compute_idx(shape[-1], output_size[-1]) + + vals = input[..., _unsqueeze_to_dim(idxh, 4), idxw] + # Shortcut for the simpler case + if not adaptive_h and not adaptive_w: + return torch.mean(vals, dim=(-3, -1)) + + def maybe_mask(vals, length, range_max, adaptive, dim): + if isinstance(length, IntLike): + return vals, length + else: + # zero-out the things we didn't really want to select + assert dim < 0 + # hack + mask = range_max >= length.unsqueeze(-1) + if dim == -2: + mask = _unsqueeze_to_dim(mask, 4) + vals = torch.masked_fill(vals, mask, 0.0) + # Compute the length of each window + length = _unsqueeze_to_dim(length, -dim) + return vals, length + + vals, length_h = maybe_mask( + vals, length_h, range_max_h, adaptive=adaptive_h, dim=-2 + ) + vals, length_w = maybe_mask( + vals, length_w, range_max_w, adaptive=adaptive_w, dim=-1 + ) + + # We unroll the sum as we assume that the kernels are going to be small + ret = None + for i, j in product(range(vals.shape[-3]), range(vals.shape[-1])): + if ret is None: + ret = vals[..., i, :, j] + else: + ret = ret + vals[..., i, :, j] + return ret / (length_h * length_w) + + +@register_decomposition(aten.index_add_) +def index_add_( + x: TensorLike, + dim: int, + index: TensorLike, + tensor: TensorLike, + *, + alpha: NumberType = 1, +): + return _index_add(x, dim, index, tensor, inplace=True, alpha=alpha) + + +@register_decomposition(aten.index_add) +@out_wrapper() +def index_add( + x: TensorLike, + dim: int, + index: TensorLike, + tensor: TensorLike, + *, + alpha: NumberType = 1, +): + return _index_add(x, dim, index, tensor, inplace=False, alpha=alpha) + + +def _index_add( + x: TensorLike, + dim: int, + index: TensorLike, + tensor: TensorLike, + *, + inplace: bool, + alpha: NumberType = 1, +): + dim = utils.canonicalize_dims(x.ndim, dim) + torch._check( + index.ndim <= 1, + lambda: f"Index should have dimension 1 or 0 (got {index.ndim})", + ) + index_size = index.size(0) if index.ndim == 1 else 1 + tensor_size = tensor.size(dim) if tensor.ndim > 0 else 1 + torch._check( + tensor_size == index_size, + lambda: f"Number of indices ({index_size}) should be equal to tensor.size(dim) ({tensor_size}), for {dim=}", + ) + if alpha != 1: + python_type = utils.dtype_to_type(x.dtype) + torch._check( + python_type == bool + or utils.is_weakly_lesser_type(type(alpha), python_type), + lambda: f"alpha argument of type {type(alpha)} cannot be safely cast to type {python_type}!", + ) + tensor = tensor * alpha + # Treat scalars as elements of \R^1 + zero_dim = x.ndim == 0 + x1 = x.unsqueeze(0) if zero_dim else x + idx = (None,) * dim + (index,) + index_put = aten.index_put_ if inplace else aten.index_put + out = index_put(x1, idx, tensor, accumulate=True) + if inplace: + return x + else: + return out.squeeze(0) if zero_dim else out.contiguous() + + +@register_decomposition(aten.pad_sequence.default) +@aten.pad_sequence.default.py_impl(DispatchKey.CompositeImplicitAutograd) +def pad_sequence(sequences, batch_first=False, padding_value=0.0): + torch._check(len(sequences) > 0, lambda: "received an empty list of sequences") + sequences_size = len(sequences) + max_size = sequences[0].size() + trailing_dims = max_size[1:] + max_len = max(x.size(0) for x in sequences) + if batch_first: + out_dims = (sequences_size, max_len) + else: + out_dims = (max_len, sequences_size) + out_dims = out_dims + trailing_dims + out = sequences[0].new_full(out_dims, padding_value) + dim_paddings = (0, 0) * len(trailing_dims) + for i in range(sequences_size): + currseq = sequences[i] + row = aten.constant_pad_nd( + currseq, dim_paddings + (0, max_len - currseq.size(0)), padding_value + ) + if batch_first: + out = aten.select_scatter(out, row, dim=0, index=i) + else: + out = aten.select_scatter(out, row, dim=1, index=i) + return out + + +@register_decomposition(aten.index_copy_) +def index_copy_(x: TensorLike, dim: int, index: TensorLike, tensor: TensorLike): + return _index_copy(x, dim, index, tensor, inplace=True) + + +@register_decomposition(aten.index_copy) +@out_wrapper() +def index_copy(x: TensorLike, dim: int, index: TensorLike, tensor: TensorLike): + return _index_copy(x, dim, index, tensor, inplace=False) + + +def _index_copy( + x: TensorLike, dim: int, index: TensorLike, tensor: TensorLike, *, inplace: bool +): + dim = utils.canonicalize_dims(x.ndim, dim) + torch._check( + index.ndim <= 1, + lambda: f"Index should have dimension 1 or 0 (got {index.ndim})", + ) + # Treat scalars as elements of \R^1 + zero_dim = x.ndim == 0 + x1 = x.unsqueeze(0) if zero_dim else x + index = index.unsqueeze(0) if index.ndim == 0 else index + idx = (None,) * dim + (index,) + index_put = aten.index_put_ if inplace else aten.index_put + out = index_put(x1, idx, tensor) + if inplace: + return x + else: + return out.squeeze(0) if zero_dim else out.contiguous() + + +# nb: Should use acc_t, not op_math +@register_decomposition(aten.log_sigmoid_forward) +@out_wrapper("output", "buffer") +@pw_cast_for_opmath +def log_sigmoid_forward(self: Tensor) -> Tuple[Tensor, Tensor]: + min = torch.minimum(self.new_zeros(()), self) + z = torch.exp(-torch.abs(self)) + if self.is_cuda: + buffer = self.new_zeros((0,)) + else: + buffer = z + return min - torch.log1p(z), buffer + + +@register_decomposition(aten.uniform) +@out_wrapper() +def uniform( + x: Tensor, + low: Union[bool, int, float] = 0.0, + high: Union[bool, int, float] = 1.0, + generator: Optional[torch.Generator] = None, +): + return prims._uniform_helper( + x.shape, + low=sym_float(low), + high=sym_float(high), + dtype=x.dtype, + device=x.device, + generator=generator, + ) + + +@register_decomposition(aten.uniform_) +def uniform_(self, low=0, high=1, generator=None): + return self.copy_(uniform(self, low, high, generator)) + + +# aten/src/ATen/native/UpSample.cpp compute_output_size +def upsample_compute_output_size(input_size, output_size, scale_factors): + spatial_dimensions = len(input_size) - 2 + if output_size is not None: + torch._check( + scale_factors is None, + lambda: "Must specify exactly one of output_size and scale_factors", + ) + torch._check(len(output_size) == spatial_dimensions, lambda: "") + return output_size + if scale_factors is not None: + # NB: this isn't necessary lol + torch._check( + output_size is None, + lambda: "Must specify exactly one of output_size and scale_factors", + ) + torch._check(len(scale_factors) == spatial_dimensions, lambda: "") + output_size = [] + for i, s in enumerate(scale_factors): + if int(s) == s: + output_size.append(input_size[i + 2] * int(s)) + else: + output_size.append(sym_int(input_size[i + 2] * s)) + return output_size + torch._check( + False, lambda: "Must specify exactly one of output_size and scale_factors" + ) + + +def get_scale_value(scales, idx): + if scales is None: + return None + return scales[idx] + + +@register_decomposition(aten.upsample_nearest1d.vec) +@register_decomposition(aten.upsample_nearest2d.vec) +@register_decomposition(aten.upsample_nearest3d.vec) +@aten.upsample_nearest1d.vec.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.upsample_nearest1d.vec.py_impl(DispatchKey.Autograd) +@aten.upsample_nearest2d.vec.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.upsample_nearest2d.vec.py_impl(DispatchKey.Autograd) +@aten.upsample_nearest3d.vec.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.upsample_nearest3d.vec.py_impl(DispatchKey.Autograd) +def _upsample_nearest_vec( + input: Tensor, + output_size: Optional[List[int]], + scale_factors: Optional[List[float]], +) -> Tensor: + osize = upsample_compute_output_size(input.size(), output_size, scale_factors) + scales = ( + scale_factors if scale_factors else [None] * len(osize) # type: ignore[list-item] + ) + return _upsample_nearest(input, osize, scales) + + +@register_decomposition(aten._upsample_nearest_exact1d.vec) +@register_decomposition(aten._upsample_nearest_exact2d.vec) +@register_decomposition(aten._upsample_nearest_exact3d.vec) +@aten._upsample_nearest_exact1d.vec.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten._upsample_nearest_exact1d.vec.py_impl(DispatchKey.Autograd) +@aten._upsample_nearest_exact2d.vec.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten._upsample_nearest_exact2d.vec.py_impl(DispatchKey.Autograd) +@aten._upsample_nearest_exact3d.vec.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten._upsample_nearest_exact3d.vec.py_impl(DispatchKey.Autograd) +def _upsample_nearest_exact_vec( + input: Tensor, + output_size: Optional[List[int]], + scale_factors: Optional[List[float]], +) -> Tensor: + osize = upsample_compute_output_size(input.size(), output_size, scale_factors) + scales = ( + scale_factors if scale_factors else [None] * len(osize) # type: ignore[list-item] + ) + return _upsample_nearest(input, osize, scales, exact=True) + + +def _compute_upsample_nearest_indices(input, output_size, scales, exact=False): + # For each dim in output_size, compute the set of input indices used + # to produce the upsampled output. + indices = [] + num_spatial_dims = len(output_size) + offset = 0.5 if exact else 0.0 + + for d in range(num_spatial_dims): + # Math matches aten/src/ATen/native/cpu/UpSampleKernel.cpp + # + # Indices are computed as following: + # scale = isize / osize + # Case: exact=False + # input_index = floor(output_index * scale) + # Same as OpenCV INTER_NEAREST + # + # Case: exact=False + # index_f32 = (output_index + 0.5) * scale - 0.5 + # input_index = round(index_f32) + # Same as Pillow and Scikit-Image/Scipy ndi.zoom + osize = output_size[d] + isize = input.shape[-num_spatial_dims + d] + scale = isize / (isize * scales[d]) if scales[d] is not None else isize / osize + + output_indices = torch.arange(osize, dtype=torch.float32, device=input.device) + input_indices = ((output_indices + offset) * scale).to(torch.int64) + for _ in range(num_spatial_dims - 1 - d): + input_indices = input_indices.unsqueeze(-1) + indices.append(input_indices) + return indices + + +@register_decomposition([aten.upsample_nearest1d.default, aten.upsample_nearest1d.out]) +@aten.upsample_nearest1d.default.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.upsample_nearest1d.default.py_impl(DispatchKey.Autograd) +@out_wrapper(preserve_memory_format=True, exact_dtype=True) +def upsample_nearest1d( + input: Tensor, + output_size: List[int], + scales: Optional[float] = None, +) -> Tensor: + return _upsample_nearest(input, output_size, [scales]) + + +@register_decomposition( + [aten._upsample_nearest_exact1d.default, aten._upsample_nearest_exact1d.out] +) +@aten._upsample_nearest_exact1d.default.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten._upsample_nearest_exact1d.default.py_impl(DispatchKey.Autograd) +@out_wrapper(preserve_memory_format=True, exact_dtype=True) +def upsample_nearest_exact1d( + input: Tensor, + output_size: List[int], + scales: Optional[float] = None, +) -> Tensor: + return _upsample_nearest(input, output_size, [scales], exact=True) + + +@register_decomposition([aten.upsample_nearest2d.default, aten.upsample_nearest2d.out]) +@aten.upsample_nearest2d.default.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.upsample_nearest2d.default.py_impl(DispatchKey.Autograd) +@out_wrapper(preserve_memory_format=True, exact_dtype=True) +def upsample_nearest2d( + input: Tensor, + output_size: List[int], + scales_h: Optional[float] = None, + scales_w: Optional[float] = None, +) -> Tensor: + return _upsample_nearest(input, output_size, [scales_h, scales_w]) + + +@register_decomposition( + [aten._upsample_nearest_exact2d.default, aten._upsample_nearest_exact2d.out] +) +@aten._upsample_nearest_exact2d.default.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten._upsample_nearest_exact2d.default.py_impl(DispatchKey.Autograd) +@out_wrapper(preserve_memory_format=True, exact_dtype=True) +def _upsample_nearest_exact2d( + input: Tensor, + output_size: List[int], + scales_h: Optional[float] = None, + scales_w: Optional[float] = None, +) -> Tensor: + return _upsample_nearest(input, output_size, [scales_h, scales_w], exact=True) + + +@register_decomposition([aten.upsample_nearest3d.default, aten.upsample_nearest3d.out]) +@aten.upsample_nearest3d.default.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.upsample_nearest3d.default.py_impl(DispatchKey.Autograd) +@out_wrapper(preserve_memory_format=True, exact_dtype=True) +def upsample_nearest3d( + input: Tensor, + output_size: List[int], + scales_d: Optional[float] = None, + scales_h: Optional[float] = None, + scales_w: Optional[float] = None, +) -> Tensor: + return _upsample_nearest(input, output_size, [scales_d, scales_h, scales_w]) + + +@register_decomposition( + [aten._upsample_nearest_exact3d.default, aten._upsample_nearest_exact3d.out] +) +@aten._upsample_nearest_exact3d.default.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten._upsample_nearest_exact3d.default.py_impl(DispatchKey.Autograd) +@out_wrapper(preserve_memory_format=True, exact_dtype=True) +def _upsample_nearest_exact3d( + input: Tensor, + output_size: List[int], + scales_d: Optional[float] = None, + scales_h: Optional[float] = None, + scales_w: Optional[float] = None, +) -> Tensor: + return _upsample_nearest( + input, output_size, [scales_d, scales_h, scales_w], exact=True + ) + + +@pw_cast_for_opmath +def _upsample_nearest( + input: Tensor, + output_size: List[int], + scales: List[Optional[float]], + exact: bool = False, +) -> Tensor: + spatial_indices = _compute_upsample_nearest_indices( + input, output_size, scales, exact=exact + ) + + indices = [None, None] + spatial_indices + result = aten._unsafe_index(input, indices) + + if result.ndim == 4: + # convert output to correct memory format, if necessary + memory_format = utils.suggest_memory_format(input) + + # following "heuristic: only use channels_last path when it's faster than the contiguous path" + n_channels = input.shape[1] + if input.device.type == "cuda" and n_channels < 4: + memory_format = torch.contiguous_format + + result = result.contiguous(memory_format=memory_format) + return result + + +def gather_params(params, has_biases, has_projections): + if has_biases and has_projections: + group_size = 5 + elif has_biases: + group_size = 4 + elif has_projections: + group_size = 3 + else: + group_size = 2 + + assert len(params) % group_size == 0, len(params) + return [ + tuple(params[i : i + group_size]) for i in range(0, len(params), group_size) + ] + + +def params_hiddens(params, hiddens, i, bidirectional): + if bidirectional: + cur_params, cur_hidden = params[2 * i], hiddens[2 * i] + bidir_params, bidir_hidden = params[2 * i + 1], hiddens[2 * i + 1] + else: + cur_params, cur_hidden = params[i], hiddens[i] + bidir_params, bidir_hidden = None, None + + return cur_params, cur_hidden, bidir_params, bidir_hidden + + +def update_hidden_for_packed(cur_hidden, last_batch_size, batch_size, hiddens): + assert last_batch_size > batch_size + hiddens.append(cur_hidden.narrow(0, batch_size, last_batch_size - batch_size)) + return cur_hidden.narrow(0, 0, batch_size) + + +def update_hidden_for_packed_reverse( + cur_hidden, last_batch_size, batch_size, inp_hidden +): + if last_batch_size == batch_size: + return cur_hidden + assert last_batch_size < batch_size + return torch.concat( + ( + cur_hidden, + inp_hidden.narrow(0, last_batch_size, batch_size - last_batch_size), + ) + ) + + +def one_layer_rnn_data( + inp, hidden, params, has_biases, hidden_fn, batch_sizes, reverse=False +): + ih_weight = params[0] + hh_weight = params[1] + ih_bias = params[2] if has_biases else None + hh_bias = params[3] if has_biases else None + + step_output = [] + hiddens: List[torch.Tensor] = [] + + last_batch_size = batch_sizes[-1] if reverse else batch_sizes[0] + cur_hidden = hidden.narrow(0, 0, last_batch_size) + split_inp = torch.split(inp, list(batch_sizes)) + if reverse: + split_inp = split_inp[::-1] + for inp in split_inp: + i = inp.shape[0] + + if last_batch_size == i: + pass # don't update cur_hidden + # this will only happen when reverse=False, since batch sizes are sorted largest -> smallest + elif reverse: + cur_hidden = update_hidden_for_packed_reverse( + cur_hidden, last_batch_size, i, hidden + ) + else: + cur_hidden = update_hidden_for_packed( + cur_hidden, last_batch_size, i, hiddens + ) + + cur_hidden = hidden_fn(inp, cur_hidden, ih_weight, ih_bias, hh_weight, hh_bias) + last_batch_size = i + step_output.append(cur_hidden) + + if reverse: + step_output.reverse() + else: + hiddens.append(cur_hidden) + hiddens.reverse() + + out = torch.cat(step_output, 0) + hidden_out = torch.cat(hiddens, 0) if not reverse else cur_hidden + return out, hidden_out + + +def rnn_cell(nonlinearity): + def inner(i, cur_hidden, ih_weight, ih_bias, hh_weight, hh_bias): + return nonlinearity(F.linear(cur_hidden, hh_weight, hh_bias) + i) + + return inner + + +def rnn_cell_data(nonlinearity): + def inner(i, cur_hidden, ih_weight, ih_bias, hh_weight, hh_bias): + i = F.linear(i, ih_weight, ih_bias) + return nonlinearity(F.linear(cur_hidden, hh_weight, hh_bias) + i) + + return inner + + +def one_layer_rnn(inp, hidden, params, has_biases, hidden_fn, reverse=False): + ih_weight = params[0] + hh_weight = params[1] + ih_bias = params[2] if has_biases else None + hh_bias = params[3] if has_biases else None + + precomputed_input = F.linear(inp, ih_weight, ih_bias) + precomputed_input = precomputed_input.flip(0) if reverse else precomputed_input + cur_hidden = hidden.unsqueeze(0) + step_output = [] + for i in precomputed_input: + cur_hidden = hidden_fn(i, cur_hidden, ih_weight, ih_bias, hh_weight, hh_bias) + step_output.append(cur_hidden) + + if reverse: + step_output.reverse() + + out = torch.cat(step_output, 0) + + return out, cur_hidden.squeeze(0) + + +def mkldnn_one_layer_lstm(inp, hidden, params, has_biases, reverse=False): + w0 = params[0] + w1 = params[1] + if has_biases: + w2 = params[2] + w3 = params[3] + else: + w2 = torch.zeros(w0.size()) + w3 = torch.zeros(w1.size()) + + hx = hidden[0].unsqueeze(0) + cx = hidden[1].unsqueeze(0) + + batch_sizes: List[int] = [] + mode = 2 # third_party/ideep/include/ideep/abstract_types.hpp: ideep::rnn_kind::LSTM = 2 + hidden_size = hx.size(2) + num_layers = 1 + + # _rnn_helper already handles bidirectional and batch_first so we hard-code them to False here + bidirectional = False + batch_first = False + + train = False + # If batch_first, inp has been permuted in _rnn_helper. Convert to contiguous here. + # Same as aten/src/ATen/native/mkldnn/RNN.cpp: mkldnn_rnn: input = input.contiguous(); + inp = inp.contiguous() + hx = hx.contiguous() + cx = cx.contiguous() + outputs = torch.ops.aten.mkldnn_rnn_layer.default( + inp, + w0, + w1, + w2, + w3, + hx, + cx, + reverse, + batch_sizes, + mode, + hidden_size, + num_layers, + has_biases, + bidirectional, + batch_first, + train, + ) + y, hy, cy = outputs[0], outputs[1], outputs[2] + return y, (hy.squeeze(0), cy.squeeze(0)) + + +def _rnn_helper( + input, + hidden, + params, + has_biases, + num_layers, + dropout, + train, + bidirectional, + batch_first, + layer_fn, +): + input = input.transpose(0, 1) if batch_first else input + final_hiddens = [] + + for i in range(num_layers): + cur_params, cur_hidden, bidir_params, bidir_hidden = params_hiddens( + params, hidden, i, bidirectional + ) + dropout = dropout if (train and num_layers < i - 1) else 0.0 + fwd_inp, fwd_hidden = layer_fn(input, cur_hidden, cur_params, has_biases) + final_hiddens.append(fwd_hidden) + + if bidirectional: + bwd_inp, bwd_hidden = layer_fn( + input, bidir_hidden, bidir_params, has_biases, reverse=True + ) + final_hiddens.append(bwd_hidden) + + if bidirectional: + input = torch.cat([fwd_inp, bwd_inp], fwd_inp.dim() - 1) # type: ignore[possibly-undefined] + else: + input = fwd_inp + + if dropout != 0 and train and i < num_layers - 1: + input = torch.dropout(input, dropout, train=True) + + input = input.transpose(0, 1) if batch_first else input + return input, final_hiddens + + +@register_decomposition(aten.rnn_tanh.input) +@aten.rnn_tanh.input.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.rnn_tanh.input.py_impl(DispatchKey.Autograd) +def rnn_tanh_input( + input, + hx, + params, + has_biases, + num_layers, + dropout, + train, + bidirectional, + batch_first, +): + hidden = hx.unbind(0) + params = gather_params(params, has_biases, False) + out, final_hiddens = _rnn_helper( + input, + hidden, + params, + has_biases, + num_layers, + dropout, + train, + bidirectional, + batch_first, + partial(one_layer_rnn, hidden_fn=rnn_cell(torch.tanh)), + ) + return out, torch.stack(final_hiddens, 0) + + +@register_decomposition(aten.rnn_relu.input) +@aten.rnn_relu.input.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.rnn_relu.input.py_impl(DispatchKey.Autograd) +def rnn_relu_input( + input, + hx, + params, + has_biases, + num_layers, + dropout, + train, + bidirectional, + batch_first, +): + hidden = hx.unbind(0) + params = gather_params(params, has_biases, False) + out, final_hiddens = _rnn_helper( + input, + hidden, + params, + has_biases, + num_layers, + dropout, + train, + bidirectional, + batch_first, + partial(one_layer_rnn, hidden_fn=rnn_cell(torch.relu)), + ) + return out, torch.stack(final_hiddens, 0) + + +@register_decomposition(aten.rnn_relu.data) +@aten.rnn_relu.data.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.rnn_relu.data.py_impl(DispatchKey.Autograd) +def rnn_relu_data( + data, + batch_sizes, + hx, + params, + has_biases, + num_layers, + dropout, + train, + bidirectional, +): + hidden = hx.unbind(0) + params = gather_params(params, has_biases, False) + out, final_hiddens = _rnn_helper( + data, + hidden, + params, + has_biases, + num_layers, + dropout, + train, + bidirectional, + False, + partial( + one_layer_rnn_data, + batch_sizes=batch_sizes, + hidden_fn=rnn_cell_data(torch.relu), + ), + ) + return out, torch.stack(final_hiddens, 0) + + +@register_decomposition(aten.rnn_tanh.data) +@aten.rnn_tanh.data.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.rnn_tanh.data.py_impl(DispatchKey.Autograd) +def rnn_tanh_data( + data, + batch_sizes, + hx, + params, + has_biases, + num_layers, + dropout, + train, + bidirectional, +): + hidden = hx.unbind(0) + params = gather_params(params, has_biases, False) + out, final_hiddens = _rnn_helper( + data, + hidden, + params, + has_biases, + num_layers, + dropout, + train, + bidirectional, + False, + partial( + one_layer_rnn_data, + batch_sizes=batch_sizes, + hidden_fn=rnn_cell_data(torch.tanh), + ), + ) + return out, torch.stack(final_hiddens, 0) + + +def lstm_cell(inp, hx, cx, hh_weight, hh_bias, hr_weight, chunk_dim): + gates = F.linear(hx, hh_weight, hh_bias) + inp + chunked_gates = gates.chunk(4, chunk_dim) + in_gate = chunked_gates[0].sigmoid() + forget_gate = chunked_gates[1].sigmoid() + cell_gate = chunked_gates[2].tanh() + out_gate = chunked_gates[3].sigmoid() + cy = forget_gate * cx + (in_gate * cell_gate) + hy = out_gate * cy.tanh() + hy = hy if hr_weight is None else F.linear(hy, hr_weight, None) + + return hy, cy + + +def one_layer_lstm(inp, hidden, params, has_biases, reverse=False): + ih_weight = params[0] + hh_weight = params[1] + ih_bias = params[2] if has_biases else None + hh_bias = params[3] if has_biases else None + hr_weight = ( + params[4] if len(params) == 5 else params[2] if len(params) == 3 else None + ) + + hx = hidden[0].unsqueeze(0) + cx = hidden[1].unsqueeze(0) + + precomputed_input = F.linear(inp, ih_weight, ih_bias) + precomputed_input = precomputed_input.flip(0) if reverse else precomputed_input + step_output = [] + for inp in precomputed_input: + hx, cx = lstm_cell(inp, hx, cx, hh_weight, hh_bias, hr_weight, chunk_dim=2) + step_output.append(hx) + + if reverse: + step_output.reverse() + + out = torch.cat(step_output, 0) + + return out, (hx.squeeze(1), cx.squeeze(1)) + + +def one_layer_lstm_data(inp, hidden, params, has_biases, batch_sizes, reverse=False): + ih_weight = params[0] + hh_weight = params[1] + ih_bias = params[2] if has_biases else None + hh_bias = params[3] if has_biases else None + hr_weight = ( + params[4] if len(params) == 5 else params[2] if len(params) == 3 else None + ) + + step_output = [] + hiddens = [] + + last_batch_size = batch_sizes[-1] if reverse else batch_sizes[0] + split_inp = torch.split(inp, list(batch_sizes)) + if reverse: + split_inp = split_inp[::-1] + + orig_hx = hidden[0] + orig_cx = hidden[1] + hx, cx = orig_hx.narrow(0, 0, last_batch_size), orig_cx.narrow( + 0, 0, last_batch_size + ) + + for inp in split_inp: + i = inp.shape[0] + inp = F.linear(inp, ih_weight, ih_bias) + + # this will only happen when reverse=False, since batch sizes are sorted largest -> smallest + if i < last_batch_size: + hiddens.append( + ( + hx.narrow(0, i, last_batch_size - i), + cx.narrow(0, i, last_batch_size - i), + ) + ) + hx, cx = hx.narrow(0, 0, i), cx.narrow(0, 0, i) + + # this will only happen when reverse=True + if i > last_batch_size: + hx = torch.concat( + (hx, orig_hx.narrow(0, last_batch_size, i - last_batch_size)), 0 + ) + cx = torch.concat( + (cx, orig_cx.narrow(0, last_batch_size, i - last_batch_size)), 0 + ) + + hx, cx = lstm_cell(inp, hx, cx, hh_weight, hh_bias, hr_weight, chunk_dim=1) + last_batch_size = i + step_output.append(hx) + + if reverse: + step_output.reverse() + hidden_out = (hx, cx) + else: + hiddens.append((hx, cx)) + hiddens.reverse() + hidden0, hidden1 = zip(*hiddens) + hidden_out = torch.cat(hidden0, 0), torch.cat(hidden1, 0) + + out = torch.cat(step_output, 0) + return out, hidden_out + + +def select_one_layer_lstm_function(input, hx, params): + r"""Check whether we could use decompose lstm with mkldnn_rnn_layer. + All the below conditions need to be met: + * ``torch._C._get_mkldnn_enabled()`` returns ``True``. + * All the input args are on CPU. + * The dtypes of args are either torch.float or torch.bfloat16. + * Inference. + * ``has_projections`` returns ``False``. + + Args: + * input: the input sequence to LSTM + * hx: a tuple of the input hidden state and cell state ``(h_0, c_0)`` to LSTM + * params: the weight and bias tensors of LSTM + """ + + def use_mkldnn(input, hx, params): + if not torch._C._get_mkldnn_enabled(): + return False + + tensors = [input] + list(hx) + list(chain.from_iterable(params)) + devices = {t.device for t in tensors} + if len(devices) != 1: + return False + + device = devices.pop() + if device != torch.device("cpu"): + return False + # With autocast, possible to have mixed dtype here + dtypes = {t.dtype for t in tensors} + for dtype in dtypes: + if dtype not in [torch.float, torch.bfloat16]: + return False + + if input.requires_grad: + return False + + has_projections = hx[0].size(2) != hx[1].size(2) + if has_projections: + return False + + return True + + # mkldnn_one_layer_lstm does not depend on seq_len while one_layer_lstm + # will expand over the seq_len dim + if use_mkldnn(input, hx, params): + return mkldnn_one_layer_lstm + else: + return one_layer_lstm + + +@register_decomposition(aten.lstm.input) +@aten.lstm.input.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.lstm.input.py_impl(DispatchKey.Autograd) +def lstm_impl( + input, + hx, + params, + has_biases, + num_layers, + dropout, + train, + bidirectional, + batch_first, +): + assert len(hx) == 2, "lstm expects two hidden states" + params = gather_params(params, has_biases, hx[0].size(2) != hx[1].size(2)) + hidden = list(zip(hx[0], hx[1])) + layer_fn = select_one_layer_lstm_function(input, hx, params) + out, final_hiddens = _rnn_helper( + input, + hidden, + params, + has_biases, + num_layers, + dropout, + train, + bidirectional, + batch_first, + layer_fn, + ) + final_hiddens = list(zip(*final_hiddens)) + return out, torch.stack(final_hiddens[0], 0), torch.stack(final_hiddens[1], 0) + + +@register_decomposition(aten.lstm.data) +@aten.lstm.data.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.lstm.data.py_impl(DispatchKey.Autograd) +def lstm_data_impl( + data, + batch_sizes, + hx, + params, + has_biases, + num_layers, + dropout, + train, + bidirectional, +): + assert len(hx) == 2, "lstm expects two hidden states" + params = gather_params(params, has_biases, hx[0].size(2) != hx[1].size(2)) + hidden = list(zip(hx[0], hx[1])) + out, final_hiddens = _rnn_helper( + data, + hidden, + params, + has_biases, + num_layers, + dropout, + train, + bidirectional, + False, + partial(one_layer_lstm_data, batch_sizes=batch_sizes), + ) + final_hiddens = list(zip(*final_hiddens)) + return out, torch.stack(final_hiddens[0], 0), torch.stack(final_hiddens[1], 0) + + +def gru_cell(inp, cur_hidden, ih_weight, ih_bias, hh_weight, hh_bias): + chunked_igates = inp.chunk(3, 1) + chunked_hgates = F.linear(cur_hidden, hh_weight, hh_bias).chunk(3, 2) + reset_gate = (chunked_hgates[0] + chunked_igates[0]).sigmoid() + input_gate = (chunked_hgates[1] + chunked_igates[1]).sigmoid() + new_gate = (chunked_igates[2] + (chunked_hgates[2] * reset_gate)).tanh() + return (cur_hidden - new_gate) * input_gate + new_gate + + +def gru_cell_data(inp, cur_hidden, ih_weight, ih_bias, hh_weight, hh_bias): + chunked_igates = F.linear(inp, ih_weight, ih_bias).chunk(3, 1) + chunked_hgates = F.linear(cur_hidden, hh_weight, hh_bias).chunk(3, 1) + reset_gate = (chunked_hgates[0] + chunked_igates[0]).sigmoid() + input_gate = (chunked_hgates[1] + chunked_igates[1]).sigmoid() + new_gate = (chunked_igates[2] + (chunked_hgates[2] * reset_gate)).tanh() + return (cur_hidden - new_gate) * input_gate + new_gate + + +@register_decomposition(aten.gru.data) +@aten.gru.data.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.gru.data.py_impl(DispatchKey.Autograd) +def gru_impl_data( + data, + batch_sizes, + hx, + params, + has_biases, + num_layers, + dropout, + train, + bidirectional, +): + params = gather_params(params, has_biases, False) + out, final_hiddens = _rnn_helper( + data, + hx.unbind(0), + params, + has_biases, + num_layers, + dropout, + train, + bidirectional, + False, + partial(one_layer_rnn_data, batch_sizes=batch_sizes, hidden_fn=gru_cell_data), + ) + return out, torch.stack(final_hiddens, 0) + + +@register_decomposition(aten.gru.input) +@aten.gru.input.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.gru.input.py_impl(DispatchKey.Autograd) +def gru_impl( + input, + hx, + params, + has_biases, + num_layers, + dropout, + train, + bidirectional, + batch_first, +): + params = gather_params(params, has_biases, False) + out, final_hiddens = _rnn_helper( + input, + hx.unbind(0), + params, + has_biases, + num_layers, + dropout, + train, + bidirectional, + batch_first, + partial(one_layer_rnn, hidden_fn=gru_cell), + ) + return out, torch.stack(final_hiddens, 0) + + +@register_decomposition(aten._upsample_bilinear2d_aa.vec) +@aten._upsample_bilinear2d_aa.vec.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten._upsample_bilinear2d_aa.vec.py_impl(DispatchKey.Autograd) +def upsample_bilinear2d_aa_vec(input, output_size, align_corners, scale_factors): + osize = upsample_compute_output_size(input.size(), output_size, scale_factors) + scale_h = get_scale_value(scale_factors, 0) + scale_w = get_scale_value(scale_factors, 1) + return torch.ops.aten._upsample_bilinear2d_aa( + input, osize, align_corners, scale_h, scale_w + ) + + +@register_decomposition(aten._upsample_bicubic2d_aa.vec) +@aten._upsample_bicubic2d_aa.vec.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten._upsample_bicubic2d_aa.vec.py_impl(DispatchKey.Autograd) +def upsample_bicubic2d_aa_vec(input, output_size, align_corners, scale_factors): + osize = upsample_compute_output_size(input.size(), output_size, scale_factors) + scale_h = get_scale_value(scale_factors, 0) + scale_w = get_scale_value(scale_factors, 1) + return torch.ops.aten._upsample_bicubic2d_aa( + input, osize, align_corners, scale_h, scale_w + ) + + +@register_decomposition(aten.upsample_bilinear2d.vec) +@register_decomposition(aten.upsample_trilinear3d.vec) +@aten.upsample_linear1d.vec.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.upsample_linear1d.vec.py_impl(DispatchKey.Autograd) +@aten.upsample_bilinear2d.vec.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.upsample_bilinear2d.vec.py_impl(DispatchKey.Autograd) +@aten.upsample_trilinear3d.vec.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.upsample_trilinear3d.vec.py_impl(DispatchKey.Autograd) +def _upsample_linear_vec(input, output_size, align_corners, scale_factors): + osize = upsample_compute_output_size(input.size(), output_size, scale_factors) + scales = scale_factors if scale_factors else [None] * len(osize) + return _upsample_linear(input, osize, align_corners, scales) + + +@register_decomposition([aten.upsample_linear1d.default, aten.upsample_linear1d.out]) +@out_wrapper() +def upsample_linear1d( + input: Tensor, + output_size: List[int], + align_corners: bool, + scales_w: Optional[float] = None, +) -> Tensor: + return _upsample_linear(input, output_size, align_corners, [scales_w]) + + +@register_decomposition( + [aten.upsample_bilinear2d.default, aten.upsample_bilinear2d.out] +) +@aten.upsample_bilinear2d.default.py_impl(DispatchKey.Autograd) +@out_wrapper() +def upsample_bilinear2d( + input: Tensor, + output_size: List[int], + align_corners: bool, + scales_h: Optional[float] = None, + scales_w: Optional[float] = None, +) -> Tensor: + return _upsample_linear(input, output_size, align_corners, [scales_h, scales_w]) + + +@register_decomposition( + [aten.upsample_trilinear3d.default, aten.upsample_trilinear3d.out] +) +@out_wrapper() +def upsample_trilinear3d( + input: Tensor, + output_size: List[int], + align_corners: bool, + scales_d: Optional[float] = None, + scales_h: Optional[float] = None, + scales_w: Optional[float] = None, +) -> Tensor: + return _upsample_linear( + input, output_size, align_corners, [scales_d, scales_h, scales_w] + ) + + +def _compute_scale(in_size, out_size, align_corners, scale=None): + if align_corners: + return (in_size - 1.0) / (out_size - 1.0) if out_size > 1 else 0 + else: + return 1.0 / scale if scale is not None and scale > 0 else in_size / out_size + + +def _compute_source_index(scale, dst_index, align_corners): + if align_corners: + return scale * dst_index + else: + return scale * (dst_index + 0.5) - 0.5 + + +def _sum_tensors_uint8( + src: Iterable[Tensor], weights: Iterable[Tensor], weights_precision: Tensor +) -> Tensor: + output = _sum_tensors( + s.to(torch.int32) * c.to(torch.int32) for s, c in zip(src, weights) + ) + (1 << (weights_precision - 1)) + output = output >> weights_precision + return torch.clamp(output, 0, 255).to(torch.uint8) + + +def _compute_weight_precision(weights: TensorSequenceType) -> Tensor: + max_weight = torch.stack(weights).max() + max_weight_precision = 22 + precisions = torch.arange(max_weight_precision, device=max_weight.device) + values = 0.5 + max_weight * (1 << (precisions + 1)) + mask = values >= (1 << 15) + return max_weight_precision - mask.sum() + + +@pw_cast_for_opmath +def _upsample_linear( + input: Tensor, + output_size: List[int], + align_corners: bool, + scales: List[Optional[float]], +) -> Tensor: + # get dimensions of original image + n_batch, n_channels = input.shape[:2] + inp_sizes = input.shape[2:] + n_dims = len(inp_sizes) + + _, dtype = utils.elementwise_dtypes( + input, + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + ) + + def get_values(inp_size, out_size, scales, nsqueeze): + # First Calculate scaling factor + scale_factor = _compute_scale(inp_size, out_size, align_corners, scales) + # We have to create arange with int64 dtype and use .to in order to avoid + # additional kernels creation in inductor and get a perf slowdown + i = torch.arange(out_size, device=input.device).to(dtype=dtype) + + x_f32 = _compute_source_index(scale_factor, i, align_corners).clamp(min=0.0) + x_f32 = x_f32.reshape(x_f32.shape[0], *[1] * (nsqueeze)) + x = x_f32.to(torch.int64) + xp1 = (x + 1).clamp(max=inp_size - 1) + return x_f32, x, xp1 + + values = [ + get_values(inp_size, out_size, scales, n_dims - 1 - i) + for i, (inp_size, out_size, scales) in enumerate( + zip(inp_sizes, output_size, scales) + ) + ] + xs_f32, xs, xp1s = list(zip(*values)) + + vs = [] + for a in product(*[[0, 1]] * n_dims): + idx = [None, None] + [xs[k] if a[k] == 0 else xp1s[k] for k in range(n_dims)] + v = aten._unsafe_index(input, idx) + v = _maybe_convert_to_dtype(v, dtype) + vs.append(v) + + for i in reversed(range(n_dims)): + xscale = (xs_f32[i] - xs[i]).clamp(0.0, 1.0).to(dtype) + vs = [ + # x1 * (1 - alpha) + x2 * alpha == x1 + (x2 - x1) * alpha + v1 + torch.mul(v2 - v1, xscale) + for v1, v2 in zip(vs[::2], vs[1::2]) + ] + + assert len(vs) == 1 + result = vs[0] + + # convert output to correct memory format, if necessary + memory_format = utils.suggest_memory_format(input) + + # following "heuristic: only use channels_last path when it's faster than the contiguous path" + if input.device.type == "cuda" and n_channels < 16: + memory_format = torch.contiguous_format + + assert isinstance(result, torch.Tensor) + + result = result.contiguous(memory_format=memory_format) + + if not input.is_floating_point(): + result = result.round() + + return result + + +# We should be applying decompositions after all transformations +@register_decomposition(aten.is_same_size.default) +def is_same_size(a: Tensor, b: Tensor) -> bool: + return a.shape == b.shape + + +@register_decomposition([aten._reshape_alias, aten._unsafe_view]) +@out_wrapper() +def _reshape_alias(x, shape, *args): + return aten.view(x, shape) + + +@register_decomposition([aten._unsafe_index]) +def _unsafe_index(x, indices): + return aten.index(x, indices) + + +@register_decomposition([aten._unsafe_index_put]) +def _unsafe_index_put(x, indices, value, accumulate=False): + return aten.index_put(x, indices, value, accumulate) + + +@register_decomposition([aten._unsafe_masked_index]) +def _unsafe_masked_index(x, mask, indices, fill): + for index in indices: + if index is not None: + torch._check( + index.dtype in [torch.long, torch.int], + lambda: "tensors used as indices must be long or int tensors", + ) + + torch._check( + mask.dtype == torch.bool, + lambda: "tensors used as masks must be bool tensors", + ) + + if x.numel() == 0: + meta_result = torch._meta_registrations.meta_index_Tensor(x, indices) + return x.new_full(meta_result.shape, fill) + + for i in range(len(indices)): + index = indices[i] + if index is not None: + indices[i] = index.clamp(min=0, max=x.size(i) - 1) + + return aten._unsafe_index(x, indices).masked_fill(~mask, fill) + + +@register_decomposition([aten._unsafe_masked_index_put_accumulate]) +def _unsafe_masked_index_put_accumulate(x, mask, indices, values): + for index in indices: + if index is not None: + torch._check( + index.dtype in [torch.long, torch.int], + lambda: "tensors used as indices must be long or int tensors", + ) + + torch._check( + mask.dtype == torch.bool, + lambda: "tensors used as masks must be bool tensors", + ) + + if x.numel() == 0: + return x.clone() + + for i in range(len(indices)): + index = indices[i] + if index is not None: + indices[i] = index.clamp(min=-x.size(i), max=x.size(i) - 1) + + masked_value = values.masked_fill(~mask, 0) + return aten._unsafe_index_put(x, indices, masked_value, accumulate=True) + + +def _nll_loss_forward( + self: Tensor, + target: Tensor, + weight: Optional[Tensor], + reduction: int, + ignore_index: int, +) -> Tuple[Tensor, Tensor]: + # self can be [N, C] or [C] + # target can be [N] or [] + + n_dims = self.dim() + channel_dim = 1 + if n_dims < 2: + channel_dim = 0 + + if weight is not None: + if n_dims > 1: + shape = [ + 1, + ] * n_dims + shape[channel_dim] = weight.shape[0] + w = weight.view(shape) + else: + w = weight + self = self * w + safe_target = torch.where(target != ignore_index, target, 0) + safe_target_ = safe_target.unsqueeze(channel_dim) + # target can be [N, 1] or [1] + + result = -torch.gather(self, channel_dim, safe_target_).squeeze(channel_dim) + + result = torch.where(target != ignore_index, result, 0) + + if reduction == Reduction.NONE.value and n_dims > 1: + total_weight = self.new_full((), 0.0) + return result, total_weight + + if weight is not None: + w = w.expand(self.shape) + wsum = torch.gather(w, channel_dim, safe_target_).squeeze(channel_dim) + wsum = torch.where(target != ignore_index, wsum, 0) + total_weight = wsum.sum() + else: + total_weight = (target != ignore_index).sum().to(self) + + if reduction == Reduction.SUM.value: + result = result.sum() + elif reduction == Reduction.MEAN.value: + result = result.sum() / total_weight + + return result, total_weight + + +@register_decomposition(aten.nll_loss_forward) +@out_wrapper("output", "total_weight") +def nll_loss_forward( + self: Tensor, + target: Tensor, + weight: Optional[Tensor], + reduction: int, + ignore_index: int, +) -> Tuple[Tensor, Tensor]: + assert self.dim() > 0 and self.dim() <= 2, "input tensor should be 1D or 2D" + assert ( + target.dim() <= 1 + ), "0D or 1D target tensor expected, multi-target not supported" + + no_batch_dim = self.dim() == 1 and target.dim() == 0 + assert no_batch_dim or ( + self.shape[0] == target.shape[0] + ), f"size mismatch (got input: {self.shape}, target: {target.shape})" + + n_classes = self.shape[-1] + + assert weight is None or ( + weight.dim() == 1 and weight.numel() == n_classes + ), f"weight tensor should be defined either for all {n_classes} classes or no classes but got weight tensor of shape: {weight.shape}" # noqa: B950 + + return _nll_loss_forward(self, target, weight, reduction, ignore_index) + + +@register_decomposition(aten.nll_loss2d_forward) +@out_wrapper("output", "total_weight") +def nll_loss2d_forward( + self: Tensor, + target: Tensor, + weight: Optional[Tensor], + reduction: int, + ignore_index: int, +) -> Tuple[Tensor, Tensor]: + return _nll_loss_forward(self, target, weight, reduction, ignore_index) + + +# These are adapted from aten/src/ATen/native/UpSample.h, wich is based on +# https://en.wikipedia.org/wiki/Bicubic_interpolation#Bicubic_convolution_algorithm +def _upsample_cubic_convolution1(x: Tensor, A: float) -> Tensor: + return ((A + 2) * x - (A + 3)) * x * x + 1 + + +def _upsample_cubic_convolution2(x: Tensor, A: float) -> Tensor: + return ((A * x - 5 * A) * x + 8 * A) * x - 4 * A + + +def _upsample_get_cubic_coefficients(t: Tensor) -> TensorSequenceType: + A = -0.75 + + if t.device == torch.device("cpu"): + tt1 = torch.stack([t, 1.0 - t], dim=0) + tt2 = torch.stack([t + 1.0, 2.0 - t], dim=0) + w03 = _upsample_cubic_convolution2(tt2, A) + w12 = _upsample_cubic_convolution1(tt1, A) + w0, w3 = torch.unbind(w03, dim=0) + w1, w2 = torch.unbind(w12, dim=0) + return w0, w1, w2, w3 + else: + return ( + _upsample_cubic_convolution2(t + 1.0, A), + _upsample_cubic_convolution1(t, A), + _upsample_cubic_convolution1(1.0 - t, A), + _upsample_cubic_convolution2(2.0 - t, A), + ) + + +def _upsample_cubic_interp1d(coeffs: TensorSequenceType, ts: Tensor) -> Tensor: + coeffs2 = _upsample_get_cubic_coefficients(ts) + return _sum_tensors(c1 * c2 for (c1, c2) in zip(coeffs, coeffs2)) + + +# Need this instead of just sum() to keep mypy happy +def _sum_tensors(ts: Iterable[Tensor]) -> Tensor: + return reduce(torch.add, ts) + + +def _linspace_from_neg_one( + num_steps: int, align_corners: bool, dtype: torch.dtype, device: torch.device +): + if num_steps <= 1: + return torch.tensor(0, device=device, dtype=dtype) + + a = ((num_steps - 1) / num_steps) if not align_corners else 1 + return torch.linspace(-a, a, steps=num_steps, device=device, dtype=dtype) + + +def _make_base_grid_4d(theta: Tensor, h: int, w: int, align_corners: bool): + dtype = theta.dtype + device = theta.device + + # Using padding and summation generates a single kernel vs using torch.stack where 3 kernels generated + # corresponding to each individual tensor: grid_x, grid_y, grid_one + grid_x = _linspace_from_neg_one(w, align_corners, dtype, device).view(1, w, 1) + grid_y = _linspace_from_neg_one(h, align_corners, dtype, device).view(h, 1, 1) + grid_one = torch.ones((1, 1, 1), dtype=dtype, device=device) + + # this is just a temporary hack and we should use torch.stack here once #104480 is merged + grid_x = torch.nn.functional.pad(grid_x, pad=(0, 2), mode="constant", value=0) + grid_y = torch.nn.functional.pad(grid_y, pad=(1, 1), mode="constant", value=0) + grid_one = torch.nn.functional.pad(grid_one, pad=(2, 0), mode="constant", value=0) + return grid_x + grid_y + grid_one + + +def _make_base_grid_5d(theta: Tensor, d: int, h: int, w: int, align_corners: bool): + dtype = theta.dtype + device = theta.device + + grid_x = _linspace_from_neg_one(w, align_corners, dtype, device).view(1, 1, w, 1) + grid_y = _linspace_from_neg_one(h, align_corners, dtype, device).view(1, h, 1, 1) + grid_z = _linspace_from_neg_one(d, align_corners, dtype, device).view(d, 1, 1, 1) + grid_one = torch.ones((1, 1, 1, 1), dtype=dtype, device=device) + + # this is just a temporary hack and we should use torch.stack here once #104480 is merged + grid_x = torch.nn.functional.pad(grid_x, pad=(0, 3), mode="constant", value=0) + grid_y = torch.nn.functional.pad(grid_y, pad=(1, 2), mode="constant", value=0) + grid_z = torch.nn.functional.pad(grid_z, pad=(2, 1), mode="constant", value=0) + grid_one = torch.nn.functional.pad(grid_one, pad=(3, 0), mode="constant", value=0) + return grid_x + grid_y + grid_z + grid_one + + +def _affine_grid_generator_4d(theta: Tensor, size: List[int], align_corners: bool): + n, _, h, w = size + base_grid = _make_base_grid_4d(theta, h, w, align_corners=align_corners) + # base_grid shape is (h, w, 3) and theta shape is (n, 2, 3) + # We do manually a matrix multiplication which is faster than mm() + # (h * w, 3, 1) * (n, 1, 3, 2) -> (n, h * w, 2) + grid = (base_grid.view(-1, 3, 1) * theta.mT.unsqueeze(1)).sum(-2) + return grid.view(n, h, w, 2) + + +def _affine_grid_generator_5d(theta: Tensor, size: List[int], align_corners: bool): + n, _, d, h, w = size + base_grid = _make_base_grid_5d(theta, d, h, w, align_corners=align_corners) + # base_grid shape is (d, h, w, 4) and theta shape is (n, 3, 4) + # We do manually a matrix multiplication which is faster than mm() + # (d * h * w, 4, 1) * (n, 1, 4, 3) -> (n, h * w, 3) + grid = (base_grid.view(-1, 4, 1) * theta.mT.unsqueeze(1)).sum(-2) + return grid.view(n, d, h, w, 3) + + +@register_decomposition(aten.affine_grid_generator) +@out_wrapper() +@pw_cast_for_opmath +def affine_grid_generator(theta: Tensor, size: List[int], align_corners: bool): + torch._check( + len(size) in (4, 5), + lambda: "affine_grid_generator needs 4d (spatial) or 5d (volumetric) inputs.", + ) + if len(size) == 4: + return _affine_grid_generator_4d(theta, size, align_corners=align_corners) + else: + return _affine_grid_generator_5d(theta, size, align_corners=align_corners) + + +def _grid_sampler_2d( + a: Tensor, + grid: Tensor, + interpolation_mode: int = 0, + padding_mode: int = 0, + align_corners: bool = False, + _expand_grid: bool = True, +) -> Tensor: + # This method is a copy of grid_sampler_2d implementation and introduced with additional arg _expand_grid to + # optionally expand the input grid for performance reasons. + # Experimenting locally it was found that compiled CUDA code is accelerated by ~5x + # and CPU code by ~2x on bicubic mode, if we expand the grid from (N, H, W, 2) into (N, C, H, W, 2) + # However, this leads to a slowdown around ~0.8x on CPU bilinear mode, channels first. + # Thus we apply this hack to not expand the grid for this case. + + torch._check( + interpolation_mode in (0, 1, 2), + lambda: f"Invalid interpolation mode {interpolation_mode}", + ) + torch._check( + padding_mode in (0, 1, 2), lambda: f"Invalid padding mode {padding_mode}" + ) + + def unnormalize(coords: Tensor, size: int) -> Tensor: + # Rescale coordinates from [-1, 1] to: + # [0, size - 1] if align_corners is True + # [-.5, size -.5] if align_corners is False + mul = (size * 0.5 - 0.5) if align_corners else (size * 0.5) + ofs = size * 0.5 - 0.5 + return coords * mul + ofs + + # Reflects coordinates until they fall between low and high (inclusive). + # The bounds are passed as twice their value so that half-integer values + # can be represented as ints. + def reflect_coordinates(coords: Tensor, twice_low: int, twice_high: int) -> Tensor: + if twice_low == twice_high: + return torch.zeros_like(coords) + coords_min = twice_low / 2 + coords_span = (twice_high - twice_low) / 2 + coords2 = (coords - coords_min).abs() + extra = torch.fmod(coords2, coords_span) + flips = (coords2 / coords_span).floor().to(dtype=torch.int8) + return torch.where( + flips & 1 == 0, extra + coords_min, coords_span + coords_min - extra + ) + + def compute_coordinates(coords: Tensor, size: int) -> Tensor: + if padding_mode == 0: # Zero + return coords + elif padding_mode == 1: # Borders + return torch.clamp(coords, 0, size - 1) + else: # padding_mode == 2, Reflection + if align_corners: + coords_reflected = reflect_coordinates(coords, 0, 2 * (size - 1)) + else: + coords_reflected = reflect_coordinates(coords, -1, 2 * size - 1) + return torch.clamp(coords_reflected, 0, size - 1) + + def compute_source_index(coords: Tensor, size: int) -> Tensor: + coords_un = unnormalize(coords, size) + return compute_coordinates(coords_un, size) + + N, C, iH, iW = a.shape + _, oH, oW, two = grid.shape + assert two == 2 + + if _expand_grid: + # Let's expand grid to [N, C, oH, oW, 2] + # This allows to generate a single triton cuda kernel instead of two kernels. + # Two kernels are due source indices, weights have shape (N, 1, oH, oW), xnumel=N*oH*oW + # and output has shape (N, C, oH, oW), xnumel=N*C*oH*oW + # Expanding grid to (N, C, oH, oW, two) unifies xnumel to N*C*oH*oW + grid = grid.view(N, 1, oH, oW, two).expand(N, C, oH, oW, 2) + + def in_bounds_cond(xs: Tensor, ys: Tensor) -> Tensor: + return torch.logical_and( + 0 <= xs, torch.logical_and(xs < iW, torch.logical_and(0 <= ys, ys < iH)) + ) + + N_idx = torch.arange(N, device=a.device).view(N, 1, 1, 1) + C_idx = torch.arange(C, device=a.device).view(1, C, 1, 1) + + def clip(xs: Tensor, ys: Tensor, ws: Tensor) -> TensorSequenceType: + cond = in_bounds_cond(xs, ys) + # To clip to inside valid coordinates, we map the coordinates + # to (x, y) = (0, 0) and also set the weight to 0 + # We also change the shape of the tensor to the appropriate one for + # broadcasting with N_idx, C_idx for the purposes of advanced indexing + c = C if _expand_grid else 1 + return tuple( + torch.where(cond, t, 0).view(N, c, oH, oW) + for t in (xs.to(dtype=torch.int64), ys.to(dtype=torch.int64), ws) + ) + + def get_summand(ix: Tensor, iy: Tensor, w) -> Tensor: + # Perform clipping, index into input tensor and multiply by weight + idx_x, idx_y, w_ = clip(ix, iy, w) + return a[N_idx, C_idx, idx_y, idx_x] * w_ + + x = grid[..., 0] + y = grid[..., 1] + + if interpolation_mode == 0: # Bilinear + ix = compute_source_index(x, iW) + iy = compute_source_index(y, iH) + + ix_nw, iy_nw = ix.floor(), iy.floor() + ix_ne, iy_ne = ix_nw + 1, iy_nw + ix_sw, iy_sw = ix_nw, iy_nw + 1 + ix_se, iy_se = ix_ne, iy_sw + + w_nw = (ix_se - ix) * (iy_se - iy) + w_ne = (ix - ix_sw) * (iy_sw - iy) + w_sw = (ix_ne - ix) * (iy - iy_ne) + w_se = (ix - ix_nw) * (iy - iy_nw) + + return _sum_tensors( + get_summand(ix, iy, w) + for (ix, iy, w) in ( + (ix_nw, iy_nw, w_nw), + (ix_ne, iy_ne, w_ne), + (ix_sw, iy_sw, w_sw), + (ix_se, iy_se, w_se), + ) + ) + elif interpolation_mode == 1: # Nearest + ix = compute_source_index(x, iW) + iy = compute_source_index(y, iH) + + ix_nearest = ix.round() + iy_nearest = iy.round() + + return get_summand(ix_nearest, iy_nearest, 1) + else: # interpolation_mode == 2, Bicubic + ix = unnormalize(x, iW) + iy = unnormalize(y, iH) + + ix_nw = ix.floor() + iy_nw = iy.floor() + + tx = ix - ix_nw + ty = iy - iy_nw + + if not _expand_grid: + tx = tx.unsqueeze(1) + ty = ty.unsqueeze(1) + + def get_value_bounded(ix: Tensor, iy: Tensor) -> Tensor: + x = compute_coordinates(ix, iW) + y = compute_coordinates(iy, iH) + return get_summand(x, y, 1) + + def get_coeff(ofs: int) -> Tensor: + iy_ofs = iy_nw + (ofs - 1) + cs = ( + get_value_bounded(ix_nw - 1, iy_ofs), + get_value_bounded(ix_nw, iy_ofs), + get_value_bounded(ix_nw + 1, iy_ofs), + get_value_bounded(ix_nw + 2, iy_ofs), + ) + return _upsample_cubic_interp1d(cs, tx) + + coeffs = tuple(get_coeff(ofs) for ofs in range(4)) + return _upsample_cubic_interp1d(coeffs, ty) + + +@register_decomposition(aten.grid_sampler_2d) +@out_wrapper() +@pw_cast_for_opmath +def grid_sampler_2d( + a: Tensor, + grid: Tensor, + interpolation_mode: int = 0, + padding_mode: int = 0, + align_corners: bool = False, +) -> Tensor: + return _grid_sampler_2d( + a, + grid=grid, + interpolation_mode=interpolation_mode, + padding_mode=padding_mode, + align_corners=align_corners, + ) + + +@register_decomposition(aten.mv) +@out_wrapper() +@pw_cast_for_opmath +def mv(self, vec): + torch._check( + self.dim() == 2 and vec.dim() == 1, + lambda: f"matrix @ vector expected, got {self.dim()}, {vec.dim()}", + ) + torch._check( + self.size(1) == vec.size(0), + lambda: f"size mismatch, got input ({self.size(0)}x{self.size(1)}), vec ({vec.size(0)})", + ) + return (self * vec).sum(dim=1) + + +@register_decomposition(aten.binary_cross_entropy_with_logits) +@out_wrapper() +def binary_cross_entropy_with_logits( + self, target, weight=None, pos_weight=None, reduction=Reduction.MEAN.value +): + if pos_weight is not None: + log_weight = (pos_weight - 1) * target + 1 + loss = (1 - target) * self - (log_weight * F.logsigmoid(self)) + else: + loss = (1 - target) * self - F.logsigmoid(self) + + if weight is not None: + loss = loss * weight + + return apply_loss_reduction(loss, reduction) + + +def should_fold(tensor1: torch.Tensor, tensor2: torch.Tensor, is_out: bool) -> bool: + # For comments of the logic of this function see eager in /native/LinearAlgebra.cpp + + t1, t2 = (tensor1, tensor2) if tensor1.ndim >= tensor2.ndim else (tensor2, tensor1) + + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + if not (t1.ndim >= 3 and t2.ndim <= 2): + return False + if t2.requires_grad and not is_out: + return True + if tensor1.ndim == 2: + return False + if guard_size_oblivious(t1.numel() == 0): + return True + + t1_shape = t1.shape + t1_stride = t1.stride() + return all( + st1 == st2 * s2 + for (st1, st2, s2) in zip(t1_stride[:-2], t1_stride[1:-1], t1_shape[1:-1]) + ) + + +@aten.matmul.default.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.matmul.out.py_impl(DispatchKey.CompositeImplicitAutograd) +@out_wrapper(pass_is_out=True) +def matmul(tensor1, tensor2, *, is_out=False): + dim_tensor1 = tensor1.dim() + dim_tensor2 = tensor2.dim() + assert dim_tensor1 != 0 and dim_tensor2 != 0 + if dim_tensor1 == 1 and dim_tensor2 == 1: + return torch.dot(tensor1, tensor2) + elif dim_tensor1 == 2 and dim_tensor2 == 1: + return torch.mv(tensor1, tensor2) + elif dim_tensor1 == 1 and dim_tensor2 == 2: + return torch.squeeze(torch.mm(torch.unsqueeze(tensor1, 0), tensor2), 0) + elif dim_tensor1 == 2 and dim_tensor2 == 2: + return torch.mm(tensor1, tensor2) + elif should_fold(tensor1, tensor2, is_out): + # dim_tensor1 >=3 && (dim_tensor2 == 1 || dim_tensor2 == 2) || + # dim_tensor2 >=3 && (dim_tensor1 == 1 || dim_tensor1 == 2) + # and some condition on the strides is fulfilled + + # optimization: use mm instead of bmm by folding the batch of the larger tensor + # into its leading matrix dimension + transpose = dim_tensor2 > dim_tensor1 + t1 = tensor2.mT if transpose else tensor1 + t2 = ( + tensor2 if not transpose else (tensor1.t() if dim_tensor1 == 2 else tensor1) + ) + # Invariant: t1.dim() >= 3 && (t2.dim() == 1 || t2.dim() == 2) + # and t1 and t2 are matmul-compatible + + # Why not t1.view(-1, sizes_1[-1])? + # If the last dim is 0, then view(-1, 0) won't work because the -1 becomes ambiguous. + # This can happen in e.g. [3, 5, 0] @ [0, 0]. + sizes_1 = t1.shape + output_shape = list(sizes_1[:-1]) + folded_dim1 = reduce(operator.mul, output_shape) + + # Readjust output_shape if we are multiplying by a matrix + t2_is_matrix = t2.dim() == 2 + if t2_is_matrix: + output_shape.append(t2.shape[1]) + + # This will almost always be a view. + # It may not be a view if t2->requires_grad(). See should_fold in aten/ for an explanation + t1_folded = t1.reshape(folded_dim1, sizes_1[-1]) + if t2_is_matrix: + # This copies if we perform a 2D @ 3D and the first tensor requires_grad + # See should_fold native/LinearAlgebra.cpp for why. + output = t1_folded.mm(t2).view(output_shape) + return output.mT.contiguous() if transpose else output + else: + return t1_folded.mv(t2).view(output_shape) + + elif dim_tensor1 >= 1 and dim_tensor2 >= 1: + # We are multiplying b1 x n x m1 by x2 x m2 x p (where b1 can be a list); + # we track m1 vs m2 separately even though they must match for nicer error messages + n = tensor1.size(-2) if dim_tensor1 > 1 else 1 + m1 = tensor1.size(-1) + batch_tensor1 = tensor1.shape[:-2] + m2 = tensor2.size(-2) if dim_tensor2 > 1 else tensor2.size(-1) + p = tensor2.size(-1) if dim_tensor2 > 1 else 1 + + batch_tensor2: List[int] = [] + # TODO: handling of slice + for i in range(dim_tensor2 - 2): + batch_tensor2.append(tensor2.size(i)) + + # Same optimization for the gradients as that in should_fold + # If we're going to broadcast, we force it to go through the should_fold branch + if ( + dim_tensor1 == 3 + and dim_tensor2 == 3 + and batch_tensor1[0] != batch_tensor2[0] + ): + if batch_tensor1[0] == 1 and tensor1.requires_grad: + return matmul(tensor1.squeeze(0), tensor2) + if batch_tensor2[0] == 1 and tensor2.requires_grad: + return matmul(tensor1, tensor2.squeeze(0)) + + # expand the batch portion (i.e. cut off matrix dimensions and expand rest) + expand_batch_portion = list( + torch.broadcast_shapes(batch_tensor1, batch_tensor2) + ) + + tensor1_expand_size = expand_batch_portion + [n, m1] + + expand_batch_product = prod(expand_batch_portion) + + # HACK: We need reshape with symint support + tensor1_expanded = tensor1.expand(tensor1_expand_size).reshape( + expand_batch_product, n, m1 + ) + + vector_rhs = dim_tensor2 == 1 + if vector_rhs: + tensor2_expand_size = expand_batch_portion + [m2] + tensor2_expanded = ( + tensor2.expand(tensor2_expand_size) + .reshape(expand_batch_product, m2) + .unsqueeze(2) + ) + else: + tensor2_expand_size = expand_batch_portion + [m2, p] + tensor2_expanded = tensor2.expand(tensor2_expand_size).reshape( + expand_batch_product, m2, p + ) + + output_shape = expand_batch_portion + if dim_tensor1 > 1: + output_shape.append(n) + + if dim_tensor2 > 1: + output_shape.append(p) + + if vector_rhs: + return tensor1_expanded.bmm(tensor2_expanded).squeeze(-1).view(output_shape) + else: + return tensor1_expanded.bmm(tensor2_expanded).view(output_shape) + else: + torch._check(False, lambda: "both arguments to matmul need to be at least 1D") + + +@register_decomposition([aten.upsample_bicubic2d.default, aten.upsample_bicubic2d.out]) +@aten.upsample_bicubic2d.default.py_impl(DispatchKey.Autograd) +@out_wrapper() +@pw_cast_for_opmath +def upsample_bicubic2d_default( + input: Tensor, + output_size: Tuple[int, int], + align_corners: bool, + scale_h: Optional[float] = None, + scale_w: Optional[float] = None, +) -> Tensor: + # get dimensions of original image + _, _, in_h, in_w = input.shape + + # Calculate horizontal and vertical scaling factor + h_scale_factor = _compute_scale(in_h, output_size[0], align_corners, scale_h) + w_scale_factor = _compute_scale(in_w, output_size[1], align_corners, scale_w) + + _, dtype = utils.elementwise_dtypes( + input, type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT + ) + + # We have to create arange with int64 dtype and use .to in order to avoid + # additional kernels creation in inductor and get a perf slowdown + i = torch.arange(output_size[0], device=input.device).to(dtype=dtype) + j = torch.arange(output_size[1], device=input.device).to(dtype=dtype) + + x_float = _compute_source_index(w_scale_factor, j, align_corners) + y_float = _compute_source_index(h_scale_factor, i, align_corners) + y_float = y_float.unsqueeze(-1) + + x = x_float.floor() + y = y_float.floor() + + # We should also clamp xscale/yscale + # See guard_index_and_lambda in UpSample.h + yscale = (y_float - y).clamp(0.0, 1.0) + xscale = (x_float - x).clamp(0.0, 1.0) + x = x.to(torch.int64) + y = y.to(torch.int64) + + iys_ofs = (y - 1, y, y + 1, y + 2) + ixs_ofs = (x - 1, x, x + 1, x + 2) + + weights_x = _upsample_get_cubic_coefficients(xscale) + weights_y = _upsample_get_cubic_coefficients(yscale) + + weights_precision_x, weights_precision_y = None, None + if input.dtype == torch.uint8: + weights_precision_x = _compute_weight_precision(weights_x) + weights_precision_y = _compute_weight_precision(weights_y) + + weights_x = [ + (w * (1 << weights_precision_x) + torch.sign(w) * 0.5).to(torch.int16) + for w in weights_x + ] + weights_y = [ + (w * (1 << weights_precision_y) + torch.sign(w) * 0.5).to(torch.int16) + for w in weights_y + ] + + def load_bounded(ys, xs): + y_idx = torch.clamp(ys, 0, in_h - 1) + x_idx = torch.clamp(xs, 0, in_w - 1) + v = aten._unsafe_index(input, [None, None, y_idx, x_idx]) + return v + + def get_x_interp(y): + src_x = tuple(load_bounded(y, x_ofs) for x_ofs in ixs_ofs) + if input.dtype == torch.uint8: + assert weights_precision_x is not None + return _sum_tensors_uint8(src_x, weights_x, weights_precision_x) + return _sum_tensors(c1 * c2 for (c1, c2) in zip(src_x, weights_x)) + + src_y = tuple(get_x_interp(y_ofs) for y_ofs in iys_ofs) + if input.dtype == torch.uint8: + assert weights_precision_y is not None + result = _sum_tensors_uint8(src_y, weights_y, weights_precision_y) + else: + result = _sum_tensors(c1 * c2 for (c1, c2) in zip(src_y, weights_y)) + + # convert output to correct memory format, if necessary + memory_format = utils.suggest_memory_format(input) + result = result.contiguous(memory_format=memory_format) + return result + + +@register_decomposition(aten.upsample_bicubic2d.vec) +@aten.upsample_bicubic2d.vec.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.upsample_bicubic2d.vec.py_impl(DispatchKey.Autograd) +@out_wrapper() +@pw_cast_for_opmath +def upsample_bicubic2d_vec( + a: Tensor, + output_size: Optional[Tuple[int, int]], + align_corners: bool, + scale_factors: Optional[Tuple[float, float]] = None, +) -> Tensor: + torch._check( + bool(output_size) + bool(scale_factors) == 1, + lambda: "Must specify exactly one of output_size and scale_factors.", + ) + if output_size is None: + assert scale_factors is not None + output_size = cast( + Tuple[int, int], + tuple( + sym_int(sym_float(w) * scale) + for w, scale in zip(a.shape[2:], scale_factors) + ), + ) + scale_h, scale_w = scale_factors if scale_factors else (None, None) + return upsample_bicubic2d_default(a, output_size, align_corners, scale_h, scale_w) + + +@register_decomposition(aten.reflection_pad1d) +@register_decomposition(aten.reflection_pad2d) +@register_decomposition(aten.reflection_pad3d) +@pw_cast_for_opmath +@out_wrapper() +def _reflection_pad(a: Tensor, padding: Tuple[int, ...]) -> Tensor: + def idx(left, middle, right): + dim_idx = torch.arange(-left, middle + right, device=a.device) + return middle - 1 - (middle - 1 - dim_idx.abs()).abs() + + return _reflection_or_replication_pad( + a, + padding, + idx, + ) + + +@register_decomposition(aten.replication_pad1d) +@register_decomposition(aten.replication_pad2d) +@register_decomposition(aten.replication_pad3d) +@pw_cast_for_opmath +@out_wrapper() +def _replication_pad(a: Tensor, padding: Tuple[int, ...]) -> Tensor: + def idx(left, middle, right): + dim_idx = torch.arange(-left, middle + right, device=a.device) + return torch.clamp(dim_idx, 0, middle - 1) + + return _reflection_or_replication_pad( + a, + padding, + idx, + ) + + +def _reflection_or_replication_pad( + a: Tensor, + padding: Tuple[int, ...], + idx_fn: Callable[[int, int, int], Tensor], +) -> Tensor: + dim = len(padding) // 2 + torch._check( + a.dim() in (dim + 1, dim + 2), + lambda: f"reflection_pad{dim}d requires {dim + 1}D or {dim + 2}D input", + ) + inp_shape = a.shape[-dim:] + nc_dim = a.dim() - dim + + padding_left = [padding[2 * (dim - 1 - i)] for i in range(dim)] + padding_right = [padding[2 * (dim - 1 - i) + 1] for i in range(dim)] + + result = a + for i in range(dim): + idx: List[Any] = [None] * result.dim() + idx[i + nc_dim] = idx_fn(padding_left[i], inp_shape[i], padding_right[i]) + result = aten._unsafe_index(result, idx) + + # convert output to correct memory format, if necessary + memory_format = utils.suggest_memory_format(result) + result = result.contiguous(memory_format=memory_format) + return result + + +@register_decomposition(aten.reflection_pad1d_backward) +@register_decomposition(aten.reflection_pad2d_backward) +@register_decomposition(aten.reflection_pad3d_backward) +@out_wrapper("grad_input") +def _reflection_pad_backward(grad_output, x, padding): + dim = len(padding) // 2 + + dhw = [h - 1 for h in x.shape[-dim:]] + + padding_left = [padding[2 * (dim - 1 - i)] for i in range(dim)] + padding_right = [padding[2 * (dim - 1 - i) + 1] for i in range(dim)] + + indices = [] + for i in range(x.ndim): + view_shape = [1] * x.ndim + view_shape[i] = -1 + indices.append(torch.arange(x.shape[i], device=x.device).view(view_shape)) + + b = indices[:-dim] + xyz = indices[-dim:] + + def index_range_condition(index_range): + i, lb, ub = index_range + return torch.logical_and(i >= lb, i <= ub) + + # Areas after reflection: + # + # top-left | top | top-right + # ----------------------------------------- + # left | center | right + # ----------------------------------------- + # bottom-left | bottom | bottom-right + # + # The center area is the original matrix. Other areas are reflections. + + center = [xyz[i] + padding_left[i] for i in range(dim)] + left_reflect = [padding_left[i] - xyz[i] for i in range(dim)] + right_reflect = [2 * dhw[i] + padding_left[i] - xyz[i] for i in range(dim)] + + # Accumulate gradients from different areas + # If some of the padding is negative, center load is not always valid + range_c = [ + (center[i], 0, dhw[i] + padding_left[i] + padding_right[i]) for i in range(dim) + ] + cond = functools.reduce( + aten.logical_and, [index_range_condition(range_c[i]) for i in range(dim)] + ) + grad = aten._unsafe_masked_index(grad_output, cond, b + center, 0.0) + + def accumulate(grad, out, index_ranges): + # If the upper bound is less than the lower bound, we can get rid of one accumulation. + # This happens when the padding size is zero. + for i in range(dim): + upper_less_than_lower = index_ranges[i][2] < index_ranges[i][1] + if isinstance(upper_less_than_lower, bool) and upper_less_than_lower: + return grad + + cond = functools.reduce( + aten.logical_and, + [index_range_condition(index_range) for index_range in index_ranges], + ) + g = aten._unsafe_masked_index(grad_output, cond, b + out, 0.0) + return grad + g + + for area in itertools.product(*[[-1, 0, 1] for _ in range(dim)]): + if area == tuple([0] * dim): + # center, this is already done. + continue + + outs = [] + index_ranges = [] + + for i in range(dim): + if area[i] == 0: + out = center[i] + index_range = range_c[i] + elif area[i] == -1: + out = left_reflect[i] + index_range = (xyz[i], 1, padding_left[i]) + elif area[i] == 1: + out = right_reflect[i] + index_range = (xyz[i], dhw[i] - padding_right[i], dhw[i] - 1) + + outs.append(out) # type: ignore[possibly-undefined] + index_ranges.append(index_range) # type: ignore[possibly-undefined] + + grad = accumulate(grad, outs, index_ranges) + + return grad + + +@register_decomposition(aten.aminmax) +@out_wrapper("min", "max") +def aminmax(self, *, dim=None, keepdim=False): + amin = torch.amin(self, dim=dim, keepdim=keepdim) + amax = torch.amax(self, dim=dim, keepdim=keepdim) + return amin, amax + + +@register_decomposition(aten.nansum) +@out_wrapper() +def nansum(self, dim=None, keepdim=False, *, dtype=None): + return aten.sum(torch.where(torch.isnan(self), 0, self), dim, keepdim, dtype=dtype) + + +@register_decomposition([aten.arange.default, aten.arange.out]) +@out_wrapper() +def arange_default( + end: NumberType, + *, + dtype: Optional[torch.dtype] = None, + layout: torch.layout = torch.strided, + device: Optional[torch.device] = None, + pin_memory: bool = False, +): + return aten.arange.start_step( + 0, end, 1, dtype=dtype, layout=layout, device=device, pin_memory=pin_memory + ) + + +@register_decomposition([aten.arange.start]) +def arange_start( + start: NumberType, + end: NumberType, + *, + dtype: Optional[torch.dtype] = None, + layout: torch.layout = torch.strided, + device: Optional[torch.device] = None, + pin_memory: bool = False, +): + return aten.arange.start_step( + start, end, 1, dtype=dtype, layout=layout, device=device, pin_memory=pin_memory + ) + + +@register_decomposition(out_dtype) +def out_dtype_decomp(*args, **kwargs): + from torch._higher_order_ops.out_dtype import out_dtype_dense + + return out_dtype_dense(*args, **kwargs) + + +@register_decomposition(aten.multi_margin_loss) +@aten.multi_margin_loss.default.py_impl(DispatchKey.Autograd) +@out_wrapper() +def multi_margin_loss( + input: Tensor, + target: Tensor, + p: NumberType = 1, + margin: NumberType = 1, + weight: Optional[Tensor] = None, + reduction: int = Reduction.MEAN.value, +) -> Tensor: + input = torch.atleast_2d(input) + target = torch.atleast_1d(target) + nframe = input.shape[0] + dim = input.shape[1] + torch._check(p == 1 or p == 2, lambda: "only p == 1 and p == 2 supported") + torch._check( + input.ndim == 2 and dim != 0, + lambda: f"Expected non-empty vector or matrix with optional 0-dim batch size, but got: {input.shape}", + ) + torch._check( + target.ndim == 1 and target.numel() == nframe, + lambda: f"inconsistent target size, expected {nframe} but got {target.shape}", + ) + if weight is not None: + weight = torch.atleast_1d(weight) + torch._check( + weight.ndim == 1 and weight.numel() == dim, # type: ignore[union-attr] + lambda: f"inconsistent weight size, expected {dim} but got {weight.shape}", # type: ignore[union-attr] + ) + target = target.unsqueeze(1) + u = torch.gather(input, dim=1, index=target) + z = margin - u + input + z = z.clamp_min(0) + z = z if p == 1 else z * z + if weight is not None: + z = z * weight[target] + idx = torch.arange(dim, device=input.device) + z = torch.where(idx != target, z, 0) + if reduction == Reduction.MEAN.value: + return z.mean() + elif reduction == Reduction.SUM.value: + return z.sum() / z.shape[1] + else: + return z.mean(dim=1) + + +@register_decomposition(aten.multilabel_margin_loss_forward) +@aten.multilabel_margin_loss_forward.default.py_impl(DispatchKey.Autograd) +@out_wrapper("output", "is_target") +def multilabel_margin_loss_forward( + input: Tensor, + target: Tensor, + reduction: int, +) -> Tuple[Tensor, Tensor]: + orig_input_shape = input.shape + orig_target_shape = target.shape + input = torch.atleast_2d(input) + target = torch.atleast_2d(target) + dim = input.shape[1] + torch._check( + len(orig_input_shape) <= 2 and dim != 0, + lambda: f"Expected non-empty vector or matrix with optional 0-dim batch size, but got: {orig_input_shape}", + ) + torch._check( + len(orig_target_shape) <= 2 and orig_target_shape == orig_input_shape, + lambda: f"inconsistent target size: {orig_target_shape} for input of size: {orig_input_shape}", + ) + # ignores labels after the first -1, detects when -1 is not present + idx = torch.arange(dim, device=target.device) + is_end = target == -1 + end_idx = torch.amin(torch.where(is_end, idx, dim), dim=-1, keepdim=True) + # target indices + target_mask = idx < end_idx + # masks target to be able to use gather, which doesn't allow -1 + tidx0 = torch.where(target_mask, target, 0) + u = torch.gather(input, dim=-1, index=tidx0) + # is_target + tidx1 = torch.where(target_mask, target, -1) + is_target = torch.any(idx == tidx1.unsqueeze(dim=-1), dim=1) + # loss + z = 1.0 - u.T.unsqueeze(dim=-1) + input + z = z.clamp_min(0) + z = z / dim + # masks loss + z = torch.where(is_target, 0, z) + # reduction + if reduction == Reduction.MEAN.value: + z = z.sum(dim=(0, -1)).mean() + elif reduction == Reduction.SUM.value: + z = z.sum() + else: + z = z.sum(dim=(0, -1)) + # result + is_target = is_target.to(input.dtype).reshape(orig_target_shape) + return z, is_target + + +# scaled_dot_product_attention used to be decomposed in pre-autograd, given that +# it calls _scaled_dot_product_attention_math and +# _scaled_dot_product_attention_math only has a CompositeImplicitAutograd +# kernel. As a result it's decomposed into ops with finer granularity. +# However recent PRs (#103826 #105131 #115913) added new logic in +# scaled_dot_product_attention and now it calls +# _scaled_dot_product_flash_attention_for_cpu in export path. This results +# in _scaled_dot_product_flash_attention_for_cpu showing up in export result. +# This decomposition ensures scaled_dot_product_attention is still decomposed +# the same way as before, i.e., going through +# _scaled_dot_product_attention_math. Notice that this decomp rule should be +# excluded by inductor. +@register_decomposition(aten._scaled_dot_product_flash_attention_for_cpu.default) +def scaled_dot_product_flash_attention_for_cpu( + query: Tensor, + key: Tensor, + value: Tensor, + dropout_p: float = 0.0, + is_causal: bool = False, + *, + attn_mask: Optional[Tensor] = None, + scale: Optional[float] = None, +) -> Tuple[Tensor, Tensor]: + dtype = query.dtype + torch._check( + torch.is_floating_point(query), + lambda: f"query must be FP32, FP64, BF16, FP16 but got {query.dtype}", + ) + torch._check( + query.dim() == 4 and key.dim() == 4 and value.dim() == 4, + lambda: f"q, k, v must be a 4 dimensional tensor, got {query.dim()}, {key.dim()}, {value.dim()}", + ) + torch._check( + dropout_p == 0.0, lambda: f"dropout probability must be zero, got {dropout_p}" + ) + torch._check( + query.shape[3] == value.shape[3] and key.shape[3] == value.shape[3], + lambda: "q, k, v should have the same head size", + ) + + output, attn = aten._scaled_dot_product_attention_math.default( + query, + key, + value, + attn_mask=attn_mask, + dropout_p=dropout_p, + is_causal=is_causal, + dropout_mask=None, + scale=scale, + ) + # Why this change? + # In pre-dispatch export scaled_dot_product_attention is executed via + # * flash_attention. + # flash_attention allocates output tensor as (N, L, H, E) + # it then transposes that to get (N, H, L, E) which is supposed to be the return + # tensor dim for scaled_dot_product_attention + # assume x: [N, H, L, E] is the output sdpa + # In MHA code, this output is then permuted via (2, 0, 1, 3) to get + # (L, N, H, E) dim tensor + # x = x.permute(2, 0, 1, 3).contiguous() and the viewed via + # x = x.view(L * N, H * E) + # During pre autograd dispatch call to contiguous is not traced because + # flash_attention output after the x.permute is already contiguous + # on which the view is valid + # However, during 2nd stage export, post-dispatch, we run _match variant + # instead of flash* to get the decomposition. _match variant returns + # x: [N, H, L, E] applying x.permute(2, 0, 1, 3) returns + # x: [L, N, H, E] and without converting this to contiguous tensor + # subsequent view is not valid and the export fails + # solution is to maintain the return tensor view from the decomp to be + # exactly same as *flash* variant. + # flash variants output is contiguous as [N, L, H, E] + # _match variant out is contiguous as [N, H, L, E] + # out = out.transpose(1, 2).contiguous gets output as contiguous + # in [N, L, H, E]. + # Subsrequent transpose(1, 2) then returns a view on which + # aforementioned code snippet, as showm below, is valid + # x = x.permute(2, 0, 1, 3).contiguous() and the viewed via + # x = x.view(L * N, H * E) + + # Really the invariant you want to maintain is: + # pre-dispatch op-output and its decomposed representation must + # return tensor with same view and dims + output = output.transpose(1, 2).contiguous(memory_format=torch.contiguous_format) + return (output.transpose(1, 2), attn) + + +def register_inplace(aten_op, outplace_op): + @register_decomposition(aten_op) + def inplace_op(*args, **kwargs): + out = outplace_op(*args, **kwargs) + return args[0].copy_(out) + + return inplace_op + + +@register_decomposition([aten.baddbmm]) +@out_wrapper() +@pw_cast_for_opmath +def baddbmm(self, batch1, batch2, beta=1, alpha=1): + if not self.is_floating_point() and not self.is_complex(): + beta = int(beta) + alpha = int(alpha) + result = torch.bmm(batch1, batch2) + if not isinstance(alpha, numbers.Number) or alpha != 1: + result = result * alpha + if beta == 0: + return result + if not isinstance(beta, numbers.Number) or beta != 1: + self = self * beta + return self + result + + +@register_decomposition(aten.floor_divide) +@out_wrapper() +def floor_divide(self, other): + return torch.div(self, other, rounding_mode="floor") + + +@register_decomposition(aten.sym_numel) +def sym_numel(t): + return functools.reduce(operator.mul, t.shape, 1) + + +@register_decomposition([aten.sum.default, aten.sum.out]) +def sum_default( + self: Tensor, + *, + dtype: Optional[torch.dtype] = None, + out: Optional[Tensor] = None, +) -> Tensor: + if out is None: + return aten.sum.dim_IntList(self, [], dtype=dtype) + else: + return aten.sum.IntList_out(self, [], dtype=dtype, out=out) + + +@register_decomposition([aten.squeeze.default, aten.squeeze.dim]) +def squeeze_default(self: Tensor, dim: Optional[int] = None): + # handle a scalar directly + if not isinstance(self, torch.Tensor): + return self + # perform squeeze + if dim is None: + return aten.squeeze.dims(self, list(range(self.dim()))) + else: + return aten.squeeze.dims(self, [dim]) + + +@register_decomposition(torch.ops.aten._weight_norm_interface) +def _weight_norm_interface(v, g, dim=0): + # https://github.com/pytorch/pytorch/blob/852f8526c52190125446adc9a6ecbcc28fb66182/aten/src/ATen/native/WeightNorm.cpp#L58 + keep_dim = tuple(i for i in range(len(v.shape)) if i != dim) + # align with cuda behavior, keep norm in 'float' when g is 'bfloat16' + norm_dtype = torch.float if g.dtype == torch.bfloat16 else None + norm = v.norm(2, keep_dim, keepdim=True, dtype=norm_dtype) + return v * (g / norm.to(g.dtype)), norm + + +@register_decomposition(aten.isin) +@out_wrapper() +def isin(elements, test_elements, *, assume_unique=False, invert=False): + # handle when either elements or test_elements are Scalars (they can't both be) + if not isinstance(elements, torch.Tensor): + elements = torch.tensor(elements, device=test_elements.device) + if not isinstance(test_elements, torch.Tensor): + test_elements = torch.tensor(test_elements, device=elements.device) + + if test_elements.numel() < 10.0 * pow(elements.numel(), 0.145): + return isin_default(elements, test_elements, invert=invert) + else: + return isin_sorting( + elements, test_elements, assume_unique=assume_unique, invert=invert + ) + + +def isin_default(elements, test_elements, *, invert=False): + if elements.numel() == 0: + return torch.empty_like(elements, dtype=torch.bool) + + x = elements.view(*elements.shape, *((1,) * test_elements.ndim)) + if not invert: + cmp = x == test_elements + else: + cmp = x != test_elements + dim = tuple(range(-1, -test_elements.ndim - 1, -1)) + return cmp.any(dim=dim) + + +def isin_sorting(elements, test_elements, *, assume_unique=False, invert=False): + elements_flat = elements.flatten() + test_elements_flat = test_elements.flatten() + if assume_unique: + # This is the same as the aten implementation. For + # assume_unique=False, we cannot use unique() here, so we use a + # version with searchsorted instead. + all_elements = torch.cat([elements_flat, test_elements_flat]) + sorted_elements, sorted_order = torch.sort(all_elements, stable=True) + + duplicate_mask = sorted_elements[1:] == sorted_elements[:-1] + duplicate_mask = torch.constant_pad_nd(duplicate_mask, [0, 1], False) + + if invert: + duplicate_mask = duplicate_mask.logical_not() + + mask = torch.empty_like(duplicate_mask) + mask = mask.index_copy(0, sorted_order, duplicate_mask) + + return mask[0 : elements.numel()] + else: + sorted_test_elements, _ = torch.sort(test_elements_flat) + idx = torch.searchsorted(sorted_test_elements, elements_flat) + test_idx = torch.where(idx < sorted_test_elements.numel(), idx, 0) + cmp = sorted_test_elements[test_idx] == elements_flat + cmp = cmp.logical_not() if invert else cmp + return cmp.reshape(elements.shape) + + +@register_decomposition(aten.take) +@out_wrapper() +def take(self, index): + flattened = self.reshape(-1) + return flattened[index] + + +@register_decomposition(aten.resize_as) +def resize_as(self, other, memory_format=None): + if memory_format is None: + memory_format = torch.contiguous_format + if memory_format == torch.preserve_format: + memory_format = suggest_memory_format(other) + return aten.resize(self, other.shape, memory_format=memory_format) + + +register_inplace(aten.addbmm_, aten.addbmm) +register_inplace(aten.addmm_, aten.addmm) +register_inplace(aten.addmv_, aten.addmv) +register_inplace(aten.baddbmm_, aten.baddbmm) +register_inplace(aten.fill_, aten.fill) +register_inplace(aten.gelu_, aten.gelu) +register_inplace(aten.hardswish_, aten.hardswish) +register_inplace(aten.hardtanh_, aten.hardtanh) +register_inplace(aten.hardsigmoid_, aten.hardsigmoid) +register_inplace(aten.__iand__, aten.__and__) +register_inplace(aten.__ilshift__, aten.__lshift__) +register_inplace(aten.index_put_, aten.index_put) +register_inplace(aten.index_reduce_, aten.index_reduce) +register_inplace(aten.__ior__, aten.__or__) +register_inplace(aten.__irshift__, aten.__rshift__) +register_inplace(aten.__ixor__, aten.__xor__) +register_inplace(aten.leaky_relu_, aten.leaky_relu) +register_inplace(aten.logit_, aten.logit) +register_inplace(aten.relu_, aten.relu) +register_inplace(aten.renorm_, aten.renorm) +register_inplace(aten.round_, aten.round) +register_inplace(aten.scatter_, aten.scatter) +register_inplace(aten.scatter_add_, aten.scatter_add) +register_inplace(aten.scatter_reduce_, aten.scatter_reduce) +register_inplace(aten.silu_, aten.silu) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_decomp/decompositions_for_jvp.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_decomp/decompositions_for_jvp.py new file mode 100644 index 0000000000000000000000000000000000000000..b542b7c511c4ad10bdc3ab083a991145d0262de3 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_decomp/decompositions_for_jvp.py @@ -0,0 +1,335 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import inspect +from typing import Callable, Dict, List, Optional, Tuple + +import torch +import torch._decomp +from torch import Tensor +from torch._prims_common.wrappers import _maybe_remove_out_wrapper + + +decomposition_table = torch._decomp.decomposition_table +decomposition_table_for_jvp: Dict[torch._ops.OperatorBase, Callable] = {} +register_decomposition = torch._decomp.register_decomposition +aten = torch.ops.aten + +# NOTE: [forward-mode AD decompositions mechanism] +# +# The mechanism is in VariableType, +# IF any inputs have forward grad +# AND there is no forward AD formula implemented +# AND the functions is actually differentiable +# run the decomposition +# See run_jit_decomposition_with_args_for_jvp +# We currently use python decompositions that we torchscript. +# +# Note that we would be building the backward graph at the decomposed level +# too, but that is OK, because we would've errored out otherwise anyway. +# +# TODO: The mechanism we are using to register decompositions doesn't +# seem to be exclusively used for jvp. So open question here is whether +# torch/csrc/jit/runtime/decomposition_registry.cpp is being used for other things. +# If that is the case, we may go down the decomposition path unexpectedly +# (and possibly produce an unintelligible error) vs erroring out earlier and +# printing that the forward AD formula is not implemented. +# +# The solution to this may be to have a explicitly white list control when +# to enable the decomposition. + + +def maybe_register_decomposition(op): + def decorator(f): + try: + return register_decomposition(op)(f) + except Exception: + return f + + return decorator + + +# Functions where we need a special decomposition for jvp but there's another version that +# should be used more generally (ex. for jvp we need to recompute the mean and variance for +# the backwards of a normalization function. Without jvp, it should use the saved value) +decomposition_table_for_jvp = {} + + +def register_decomposition_for_jvp(fn): + return register_decomposition(fn, registry=decomposition_table_for_jvp) + + +def _register_jit_decomposition_for_jvp(decomp, use_python=False): + if decomp in decomposition_table_for_jvp: + decomposition_table_used = decomposition_table_for_jvp + elif decomp in decomposition_table: + decomposition_table_used = decomposition_table + else: + raise RuntimeError(f"could not find decomposition for {decomp}") + decomp_fn = decomposition_table_used[decomp] + + # `out_wrapper` extends a decompositions signature with + # an `out` parameter. However jit will use the unwrapped function's + # signature instead so we need to unwrap here to prevent an error + decomp_fn = _maybe_remove_out_wrapper(decomp_fn) + + if use_python: + decomp_fn = torch.jit.ignore(decomp_fn) + sig = inspect.signature(decomp_fn) + + # Create a string wrapping the function from the signature + # example output: + # def wrapped_decomp(x: torch.Tensor, y: int, z: int): + # return decomp_fn(x, y, z) + # Thanks copilot! + def get_function_def(sig): + param_def = [f"{param_str}" for param_str in sig.parameters.values()] + param_use = [f"{param_str}" for param_str in sig.parameters.keys()] + + return f"def wrapped_decomp({', '.join(param_def)}):\n return decomp_fn({', '.join(param_use)})\n" + + f_str = get_function_def(sig) + graph = torch.jit.CompilationUnit(f_str).wrapped_decomp.graph + else: + graph = torch.jit.script(decomp_fn).graph + torch.jit._register_decomposition(decomp, graph) + + +# The only decompositions here are temporary or hacks for the purposes of jvp + + +# TODO: do these also belong here? +@maybe_register_decomposition(aten.trace.default) +def trace(self: Tensor) -> Tensor: + return torch.sum(torch.diag(self)) + + +@maybe_register_decomposition(aten.log_sigmoid_forward.default) +def log_sigmoid_forward(self: Tensor) -> Tuple[Tensor, Tensor]: + min = torch.minimum(self.new_zeros(()), self) + z = torch.exp(-torch.abs(self)) + if self.is_cuda: + buffer = self.new_zeros((0,)) + else: + buffer = z + return min - torch.log1p(z), buffer + + +def recompute_mean_var( + input: Tensor, rstd: Tensor, inner_dim_indices: List[int], keepdim: bool +): + # for most norm decompositions, it will be the same as the core version except for here. + # We recompute the mean and variance so that they track gradients through input + + mean = torch.mean(input, dim=inner_dim_indices, keepdim=keepdim) + var = torch.var(input, dim=inner_dim_indices, unbiased=False, keepdim=keepdim) + eps = torch.pow(1 / rstd, 2) - var # this makes me so sad inside + eps = eps.detach() + rstd = 1 / torch.sqrt(var + eps) + return mean, rstd + + +@register_decomposition_for_jvp(aten.native_layer_norm_backward) +def native_layer_norm_backward( + grad_out: Tensor, + input: Tensor, + normalized_shape: List[int], + mean: Tensor, + rstd: Tensor, + weight: Optional[Tensor], + bias: Optional[Tensor], + output_mask: List[bool], +) -> Tuple[Optional[Tensor], Optional[Tensor], Optional[Tensor]]: + input_shape = input.shape + input_ndim = input.dim() + + axis = input_ndim - len(normalized_shape) + inner_dims = input_shape[axis:] + outer_dims = input_shape[:axis] + inner_dim_indices = list(range(axis, input_ndim)) + outer_dim_indices = list(range(0, axis)) + + N = 1 + for i in inner_dims: + N *= i + M = 1 + for i in outer_dims: + M *= i + if M <= 0 or N <= 0: + return ( + input.new_zeros(input_shape), + input.new_zeros(input_shape[axis:]), + input.new_zeros(input_shape[axis:]), + ) + + mean_, rstd_ = recompute_mean_var(input, rstd, inner_dim_indices, keepdim=True) + + x_hat = (input - mean_) * rstd_ + if weight is not None: + grad_x_hat = grad_out * weight + else: + grad_x_hat = grad_out + a = grad_x_hat * N + b = torch.sum(grad_x_hat, inner_dim_indices, True) + c1 = torch.mul(grad_x_hat, x_hat) + c2 = torch.sum(c1, inner_dim_indices, True) + c3 = torch.mul(x_hat, c2) + inner = a - b - c3 + + if output_mask[0]: + d_input: Optional[Tensor] = (rstd_ / N) * inner + else: + d_input = torch.zeros_like(input) # should be None but doesn't work with vjp + + if output_mask[1] and weight is not None: + if len(outer_dim_indices) > 0: + d_weight: Optional[Tensor] = torch.sum( + grad_out * x_hat, outer_dim_indices, False + ) + else: + d_weight = grad_out * x_hat + elif weight is not None: + d_weight = torch.zeros_like(weight) # should be None but doesn't work with vjp + else: + d_weight = torch.zeros(()) # should be None but doesn't work with vjp + + if output_mask[2] and bias is not None: + if len(outer_dim_indices) > 0: + d_bias: Optional[Tensor] = torch.sum(grad_out, outer_dim_indices, False) + else: + d_bias = grad_out.clone() + elif bias is not None: + d_bias = torch.zeros_like(bias) # should be None but doesn't work with vjp + else: + d_bias = torch.zeros(()) # should be None but doesn't work with vjp + + return (d_input, d_weight, d_bias) + + +def prod(x: List[int]): + r = 1 + for i in x: + r *= i + return r + + +@register_decomposition_for_jvp(aten.native_batch_norm_backward) +def native_batch_norm_backward( + grad_out: Tensor, + input: Tensor, + weight: Optional[Tensor], + running_mean: Optional[Tensor], + running_var: Optional[Tensor], + save_mean: Optional[Tensor], + save_invstd: Optional[Tensor], + train: bool, + eps: float, + output_mask: List[bool], +) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor]]: + input_shape = input.shape + input_rank = input.dim() + assert input_rank >= 2, "rank of the input must be at least 2" + + axis = 1 + num_features = prod(input_shape) / input_shape[axis] # type: ignore[arg-type] + mean = save_mean + invstd = save_invstd + if train: + assert ( + save_mean is not None and save_invstd is not None + ), "when train=True, save_mean and save_invstd are required" + + reduciton_dims = [0] + list(range(2, input.dim())) + assert invstd is not None # for typing + mean, invstd = recompute_mean_var(input, invstd, reduciton_dims, keepdim=False) + else: + assert running_mean is not None and running_var is not None + mean = running_mean + invstd = torch.rsqrt(running_var + eps) + + assert invstd is not None and mean is not None + + broadcast_mask = [1] * input_rank + broadcast_mask[axis] = input_shape[axis] + + reduction_axes: List[int] = [] + for i in range(input_rank): + if i != axis: + reduction_axes.append(i) + + mean = torch.reshape(mean, broadcast_mask) + norm = 1.0 / num_features + grad_output_sum = torch.sum(grad_out, reduction_axes) + dot_p = torch.sum(grad_out * (input - mean), reduction_axes) + + grad_mean = torch.reshape(grad_output_sum * norm, broadcast_mask) + proj_scale = torch.reshape(torch.mul(dot_p * norm, invstd * invstd), broadcast_mask) + + if weight is None: + grad_scale = torch.reshape(invstd, broadcast_mask) * 1.0 + else: + grad_scale = torch.reshape(invstd * weight, broadcast_mask) + + if train: + proj = (input - mean) * proj_scale + grad_input = ((grad_out - proj) - grad_mean) * grad_scale + else: + grad_input = grad_out * grad_scale + + if output_mask[1]: + grad_weight = dot_p * invstd + elif weight is not None: + grad_weight = torch.zeros_like( + weight + ) # should be None but doesn't work with vjp + else: + grad_weight = torch.zeros(()) # should be None but doesn't work with vjp + + if output_mask[2]: + grad_bias = grad_output_sum + else: + grad_bias = torch.zeros_like( + grad_output_sum + ) # should be None but doesn't work with vjp + + return (grad_input, grad_weight, grad_bias) + + +@register_decomposition_for_jvp(aten.batch_norm_backward) +def batch_norm_backward( + grad_out: Tensor, + input: Tensor, + weight: Tensor, + running_mean: Optional[Tensor], + running_var: Optional[Tensor], + save_mean: Optional[Tensor], + save_var: Optional[Tensor], + update: bool, + eps: float, + output_mask: List[bool], + reserve: Tensor, +) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor]]: + return native_batch_norm_backward( + grad_out, + input, + weight, + running_mean, + running_var, + save_mean, + save_var, + update, + eps, + output_mask, + ) + + +_register_jit_decomposition_for_jvp(torch.ops.aten.trace.default, use_python=True) +_register_jit_decomposition_for_jvp(torch.ops.aten.nll_loss_backward.default) +_register_jit_decomposition_for_jvp(torch.ops.aten.nll_loss2d_backward.default) +_register_jit_decomposition_for_jvp(torch.ops.aten._log_softmax_backward_data.default) +_register_jit_decomposition_for_jvp(torch.ops.aten._softmax_backward_data.default) +_register_jit_decomposition_for_jvp(torch.ops.aten.log_sigmoid_forward.default) +_register_jit_decomposition_for_jvp(torch.ops.aten.native_layer_norm_backward.default) +_register_jit_decomposition_for_jvp(torch.ops.aten.native_batch_norm_backward.default) +_register_jit_decomposition_for_jvp(torch.ops.aten.cudnn_batch_norm_backward.default) +_register_jit_decomposition_for_jvp(torch.ops.aten.batch_norm_backward.default) +_register_jit_decomposition_for_jvp(torch.ops.aten.miopen_batch_norm_backward.default) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_decomp/decompositions_for_rng.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_decomp/decompositions_for_rng.py new file mode 100644 index 0000000000000000000000000000000000000000..a62a28f783b7131dbccdae2ac9198aca13c1bf53 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_decomp/decompositions_for_rng.py @@ -0,0 +1,266 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import functools +from collections import defaultdict +from typing import Callable, Dict + +import torch +import torch._decomp as decomp +from torch._decomp import get_decompositions +from torch._ops import OpOverload + + +aten = torch.ops.aten + +rng_decompositions: Dict[str, Dict[OpOverload, Callable]] = defaultdict(dict) + + +def register_rng_decomposition(aten_op): + return decomp.register_decomposition(aten_op, rng_decompositions) + + +def throw_on_non_cuda(device): + raise RuntimeError( + f"You are trying to functionalize a {device.type} RNG operator but {device.type} does not " + f"use Philox/counter-based RNG. Therefore, functionalizing a {device.type} RNG operator is " + "not supported. We are discussing the possibility of a Philox-based RNG implementation for CPU." + ) + + +# TODO - We have to register many more distributions here, and also higher level +# ops like dropout which have fused implementation and can hide the rand inside. +@register_rng_decomposition(aten.rand) +def rand(shape, dtype=None, layout=torch.strided, device=None, pin_memory=False): + if device and device.type != "cuda": + throw_on_non_cuda(device) + seed, offset = PhiloxStateTracker.get_state_as_tuple() + dtype = dtype or torch.float32 + out, offset_jump = torch.ops.rngprims.philox_rand( + shape, seed, offset, None, device, dtype + ) + PhiloxStateTracker.advance_offset(offset_jump) + return out + + +@register_rng_decomposition(aten.rand_like) +def rand_like( + x: torch.Tensor, + dtype=None, + layout=None, + device=None, + pin_memory=False, + memory_format=torch.preserve_format, +): + device = device or x.device + if device.type != "cuda": + throw_on_non_cuda(device) + dtype = dtype or x.dtype + seed, offset = PhiloxStateTracker.get_state_as_tuple() + out, offset_jump = torch.ops.rngprims.philox_rand( + x.shape, seed, offset, None, device, dtype + ) + PhiloxStateTracker.advance_offset(offset_jump) + return out + + +class PhiloxState: + """ + Represents a PhiloxRngState - (seed, offset) where offset = base_offset + + relative_offset. seed and base_offset basically point to the rng state just + before tracing starts. relative offset tracks the totally consumed offset at + trace time. + """ + + def __init__(self) -> None: + self.reset() + + def reset(self): + self.seed = torch.tensor(()) + self.base_offset = torch.tensor(()) + self.relative_offset = 0 + self.offset_advanced_alteast_once = False + + def validate_state(self): + assert self.seed.numel() != 0 and self.base_offset.numel() != 0 + + def advance_offset(self, consumed_offset): + self.offset_advanced_alteast_once = True + self.relative_offset = self.relative_offset + consumed_offset + + def set_state(self, seed, base_offset, relative_offset=0): + self.seed = seed + self.base_offset = base_offset + self.relative_offset = relative_offset + + def get_state_as_tuple(self): + self.validate_state() + return (self.seed, self.base_offset + self.relative_offset) + + def get_state_as_tensor(self): + # Only needed because we override get_rng_state. + self.validate_state() + return torch.stack([self.seed, self.base_offset + self.relative_offset]) + + def set_state_from_tensor(self, state): + # Only needed because we override set_rng_state. + self.seed, self.base_offset = torch.unbind(state) + self.relative_offset = 0 + + +class PhiloxStateTracker: + """ + Singleton class to track the philox rng state during AOT Autograd tracing. + For each aot tracing instance, AOT Autograd resets this tracker and keeps + track of both forward and backward offsets. At runtime, we only care about + the total consumed forward and backward offsets. For dynamic shapes, these + offsets are a function of input shapes. Therefore, the AOT generated graphs + have additional outputs that compute total consumed forward and backward + offsets. + """ + + running_state: PhiloxState + fwd_state: PhiloxState + bwd_state: PhiloxState + + def __enter__(self): + PhiloxStateTracker.reset() + return self + + def __exit__(self, exc_type, exc_cal, exc_tb): + PhiloxStateTracker.reset() + + @classmethod + def reset(cls): + cls.running_state = PhiloxState() + cls.fwd_state = PhiloxState() + cls.bwd_state = PhiloxState() + + @classmethod + def mark_beginning_of_forward(cls): + # Tells the tracker to use fwd_state as the running state + cls.running_state = cls.fwd_state + + @classmethod + def mark_beginning_of_backward(cls): + # Tells the tracker to use bwd_state as the running state + cls.running_state = cls.bwd_state + + @classmethod + def record_state(cls, seed, offset, mode): + # Records the seed and offset tensors. These tensors are used to invoke + # the philox_rand functional primitives. + if mode == "forward": + cls.fwd_state.set_state(seed, offset) + cls.mark_beginning_of_forward() + else: + assert mode == "backward" + cls.bwd_state.set_state(seed, offset) + + @classmethod + def get_state_as_tensor(cls): + # The only reason this exists is because we override get_rng_state and + # set_rng_state during tracing. get_rng_state expects a tensor output, + # so return (seed, offset) tuple upset other parts of the program like + # ctx.saved_tensors. + + # A bad consequence is that if user saves and restores rng state, we + # have little bit of ugliness in the generated code, where we first + # concat the (seed, offset) to create a tensor for get_rng_state, and + # then split it back to get (seed, offset) tuple in set_rng_state. + + # TODO: Investigate if there is be a better way to wrap the tuple in a + # false Tensor object, and then desugar it later on. + return cls.running_state.get_state_as_tensor() + + @classmethod + def get_state_as_tuple(cls): + return cls.running_state.get_state_as_tuple() + + @classmethod + def set_state_from_tensor(cls, x): + # This is only needed because we override set_rng_state. Look at the + # comment in get_state_from_tensor method. + cls.running_state.set_state_from_tensor(x) + + @classmethod + def advance_offset(cls, consumed_offset): + cls.running_state.advance_offset(consumed_offset) + + @classmethod + def get_current_relative_offset(cls): + return cls.running_state.relative_offset + + @staticmethod + def multiple_of_4(offset): + # torch cuda rng state offset must be a multiple of 4. For inductor, as + # we sum up all the numel, the result might not be a multiple of 4. This + # method achieves that. + return (offset + 3) // 4 * 4 + + @classmethod + def get_updated_fwd_offset(cls): + # Short circuit if no rand ops were observed + if not cls.fwd_state.offset_advanced_alteast_once: + return cls.fwd_state.base_offset + return cls.multiple_of_4( + cls.fwd_state.base_offset + cls.fwd_state.relative_offset + ) + + @classmethod + def get_updated_bwd_offset(cls): + # Short circuit if no rand ops were observed + if not cls.bwd_state.offset_advanced_alteast_once: + return cls.bwd_state.base_offset + return cls.multiple_of_4( + cls.bwd_state.base_offset + cls.bwd_state.relative_offset + ) + + +# Adding more decompositions which eventually use rand_like inside decomps. +# Adding these in rng_decompositions ensures the functionalization of rand_like +# ops used in these decomps. The list is copied from inductor codebase, which +# uses it for similar purpose. +# +# Caution - These decomps do not have same accuracy as that of eager. However, +# we can't just disable them with a config flag like fallback_random, because +# for functionalization of rng ops, we have to decompose these ops. +extra_random_decomps = get_decompositions( + [ + aten.cauchy, + aten.cauchy_, + aten.exponential, + aten.exponential_, + aten.geometric, + aten.geometric_, + aten.native_dropout, + aten.normal, + aten.normal_, + aten.normal_functional, + aten.log_normal, + aten.log_normal_, + aten.rrelu_with_noise, + aten.rrelu_with_noise_, + aten.uniform_, + ] +) +register_extra_random_decomp = functools.partial( + decomp.register_decomposition, registry=extra_random_decomps +) + + +@register_extra_random_decomp([aten.bernoulli_]) +def bernoulli_(self, p=0.5): + if self.device == torch.device("cpu"): + return NotImplemented + return self.copy_(torch.rand_like(self, dtype=torch.float32) < p) + + +@register_extra_random_decomp([aten.bernoulli.p]) +def bernoulli_p(self, p=0.5, *, generator=None): + if self.device == torch.device("cpu"): + return NotImplemented + assert generator is None + return torch.rand_like(self, dtype=torch.float32) < p + + +rng_decompositions.update(extra_random_decomps) # type: ignore[arg-type] diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dispatch/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dispatch/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dispatch/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dispatch/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..73119b5bc4d9c8821ec99043ce190ade84796022 Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dispatch/__pycache__/__init__.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dispatch/__pycache__/python.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dispatch/__pycache__/python.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..436bcf5e2494284dd7d712589ea9372a7f0de0e0 Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dispatch/__pycache__/python.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dispatch/python.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dispatch/python.py new file mode 100644 index 0000000000000000000000000000000000000000..8b0eb69e9c3845ec5086a1f79aae4c1a6befa1ef --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dispatch/python.py @@ -0,0 +1,180 @@ +# mypy: allow-untyped-defs +import itertools +import unittest.mock +from contextlib import contextmanager +from typing import Iterator + +import torch +import torch._C +import torch._ops +import torch.utils._python_dispatch +import torch.utils._pytree as pytree + + +__all__ = ["enable_python_dispatcher", "no_python_dispatcher", "enable_pre_dispatch"] + +no_python_dispatcher = torch._C._DisablePythonDispatcher +enable_python_dispatcher = torch._C._EnablePythonDispatcher +enable_pre_dispatch = torch._C._EnablePreDispatch + +CROSSREF_FUNCTIONALIZE = False + + +def all_py_loaded_overloads() -> Iterator[torch._ops.OpOverload]: + """ + Warning: the set of overloads this will report is very subtle. It is precisely + the set of torch.ops functions that have actually been accessed from Python + (e.g., we actually called torch.ops.aten.blah at some point. This is DIFFERENT + from the set of registered operators, which will in general be a larger set, + as this would include all operators which we ran C++ static initializers or + Python operator registration on. This does not eagerly populate the list on + torch.ops.aten; this list is lazy! + + In other words, this is good for traversing over everything that has an + OpOverload object allocated in Python. We use it for cache invalidation, but + don't rely on this list being complete. + + Note that even if we did report all C++ registered overloads, this isn't guaranteed + to be complete either, as a subsequent lazy load of a library which triggers more + registrations could add more things to the set. + """ + for ns in torch.ops: + packets = getattr(torch.ops, ns) + for op_name in packets: + packet = getattr(packets, op_name) + for overload in packet: + yield getattr(packet, overload) + + +@contextmanager +def suspend_functionalization(): + f_tls = torch._C._dispatch_tls_is_dispatch_key_included( + torch._C.DispatchKey.Functionalize + ) + f_rv = torch._C._functionalization_reapply_views_tls() + if f_tls: + torch._disable_functionalization() + try: + yield + finally: + if f_tls: + torch._enable_functionalization(reapply_views=f_rv) + + +def check_tensor_metadata_matches(nv, rv, desc): + assert callable(desc) + assert nv.size() == rv.size(), f"{desc()}: sizes {nv.size()} != {rv.size()}" + assert nv.dtype == rv.dtype, f"{desc()}: dtype {nv.dtype} != {rv.dtype}" + same_strides, idx = torch._prims_common.check_significant_strides( + nv, rv, only_cuda=False + ) + assert ( + same_strides + ), f"{desc()}: strides {nv.stride()} != {rv.stride()} (mismatch at index {idx})" + + +def check_metadata_matches(n, r, desc): + assert callable(desc) + n_vals, n_spec = pytree.tree_flatten(n) + r_vals, r_spec = pytree.tree_flatten(r) + # TODO: test the specs match; empirically sometimes we have a tuple + # on one side and a list on the other + assert len(n_vals) == len(r_vals), f"{len(n_vals)} != {len(r_vals)}" + for i, nv, rv in zip(range(len(n_vals)), n_vals, r_vals): + if not isinstance(rv, torch.Tensor): + continue + check_tensor_metadata_matches(nv, rv, lambda: f"{desc()} output {i}") + + +class Lit: + def __init__(self, s): + self.s = s + + def __repr__(self): + return self.s + + +def _fmt(a: object) -> object: + if isinstance(a, torch.Tensor): + return Lit( + f"torch.empty_strided({tuple(a.size())}, {a.stride()}, dtype={a.dtype})" + ) + else: + return a + + +def make_crossref_functionalize(op, final_key): + from torch._subclasses.fake_tensor import FakeTensorMode + + # This case is pretty weird, suppress it for now + if op == torch.ops.aten.lift_fresh.default: + return final_key + + def handler(*args, **kwargs): + fake_mode = FakeTensorMode() + + def fakeify_defun(t): + if isinstance(t, torch.Tensor): + if torch._is_functional_tensor(t): + r = torch._from_functional_tensor(t) + # NB: This assumes that the inner tensor sizes/strides match + # the outer tensor sizes/strides. This doesn't necessarily have to + # be the case, see discussion at + # https://github.com/pytorch/pytorch/pull/87610/files/401ddeda1d769bedc88a12de332c7357b60e51a4#r1007264456 + assert t.size() == r.size() + assert t.stride() == r.stride() + else: + r = t + # TODO: suppress guards + return fake_mode.from_tensor(r) + return t + + def maybe_detach(t): + if isinstance(t, torch.Tensor): + return t.detach() + else: + return t + + # TODO: This probably does the wrong thing if you're running other + # substantive modes with the normal op outside here + with torch.utils._python_dispatch._disable_current_modes(), suspend_functionalization(): + f_args, f_kwargs = pytree.tree_map(fakeify_defun, (args, kwargs)) + orig_f_args, orig_f_kwargs = pytree.tree_map( + maybe_detach, (f_args, f_kwargs) + ) + with fake_mode: + f_r = op(*f_args, **f_kwargs) + r = op._op_dk(final_key, *args, **kwargs) + + def desc(): + fmt_args = ", ".join( + itertools.chain( + (repr(pytree.tree_map(_fmt, a)) for a in orig_f_args), + ( + f"{k}={pytree.tree_map(_fmt, v)}" + for k, v in orig_f_kwargs.items() + ), + ) + ) + return f"{op}({fmt_args})" + + check_metadata_matches(f_r, r, desc) + return r + + return handler + + +# NB: enabling this is slow, don't do it in a hot loop. This is purely +# for debugging purposes. +@contextmanager +def enable_crossref_functionalize(): + for op in all_py_loaded_overloads(): + op._uncache_dispatch(torch._C.DispatchKey.Functionalize) + try: + with enable_python_dispatcher(), unittest.mock.patch( + "torch._dispatch.python.CROSSREF_FUNCTIONALIZE", True + ): + yield + finally: + for op in all_py_loaded_overloads(): + op._uncache_dispatch(torch._C.DispatchKey.Functionalize) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7f58ba7f7bf7f1331b9e483e103ac57cbc0dee71 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/__init__.py @@ -0,0 +1,109 @@ +import torch + +from . import convert_frame, eval_frame, resume_execution +from .backends.registry import list_backends, lookup_backend, register_backend +from .callback import callback_handler, on_compile_end, on_compile_start +from .code_context import code_context +from .convert_frame import replay +from .decorators import ( + allow_in_graph, + assume_constant_result, + disable, + disallow_in_graph, + forbid_in_graph, + graph_break, + mark_dynamic, + mark_static, + mark_static_address, + maybe_mark_dynamic, + run, + substitute_in_graph, +) +from .eval_frame import ( + _reset_guarded_backend_cache, + explain, + export, + is_dynamo_supported, + is_inductor_supported, + optimize, + optimize_assert, + OptimizedModule, + reset_code, +) +from .external_utils import is_compiling +from .mutation_guard import GenerationTracker +from .utils import graph_break_reasons, guard_failures, orig_code_map, reset_frame_count + + +# Register polyfill functions +from .polyfills import loader as _ # usort: skip # noqa: F401 + + +__all__ = [ + "allow_in_graph", + "assume_constant_result", + "disallow_in_graph", + "forbid_in_graph", + "substitute_in_graph", + "graph_break", + "mark_dynamic", + "maybe_mark_dynamic", + "mark_static", + "mark_static_address", + "optimize", + "optimize_assert", + "export", + "explain", + "run", + "replay", + "disable", + "reset", + "OptimizedModule", + "is_compiling", + "register_backend", + "list_backends", + "lookup_backend", +] + +if torch.manual_seed is torch.random.manual_seed: + import torch.jit._builtins + + # Wrap manual_seed with the disable decorator. + # Can't do it at its implementation due to dependency issues. + torch.manual_seed = torch._disable_dynamo(torch.manual_seed) + # Add the new manual_seed to the builtin registry. + torch.jit._builtins._register_builtin(torch.manual_seed, "aten::manual_seed") + + +def reset() -> None: + """Clear all compile caches and restore initial state""" + with convert_frame.compile_lock: + reset_code_caches() + convert_frame.input_codes.clear() + convert_frame.output_codes.clear() + orig_code_map.clear() + guard_failures.clear() + graph_break_reasons.clear() + resume_execution.ContinueExecutionCache.cache.clear() + _reset_guarded_backend_cache() + reset_frame_count() + torch._C._dynamo.compiled_autograd.clear_cache() + convert_frame.FRAME_COUNTER = 0 + convert_frame.FRAME_COMPILE_COUNTER.clear() + callback_handler.clear() + GenerationTracker.clear() + torch._dynamo.utils.warn_once_cache.clear() + torch._dynamo.utils.user_obj_id_to_weakref.clear() + torch._C._autograd._saved_tensors_hooks_set_tracing(False) + + +def reset_code_caches() -> None: + """Clear compile caches that are keyed by code objects""" + with convert_frame.compile_lock: + for weak_code in ( + convert_frame.input_codes.seen + convert_frame.output_codes.seen + ): + code = weak_code() + if code: + reset_code(code) + code_context.clear() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/_trace_wrapped_higher_order_op.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/_trace_wrapped_higher_order_op.py new file mode 100644 index 0000000000000000000000000000000000000000..c698ded100943a6add2f80637e0873bfea5aa4b5 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/_trace_wrapped_higher_order_op.py @@ -0,0 +1,127 @@ +# mypy: allow-untyped-defs +import torch +from torch._C import DispatchKey +from torch._higher_order_ops.utils import autograd_not_implemented +from torch._ops import HigherOrderOperator +from torch._subclasses import FakeTensorMode +from torch.fx.experimental._backward_state import BackwardState +from torch.fx.experimental.proxy_tensor import ProxyTorchDispatchMode, track_tensor_tree +from torch.utils._python_dispatch import _get_current_dispatch_mode +from torch.utils._pytree import tree_map_only + + +__all__ = ["trace_wrapped"] + + +# trace_wrapped(*args, fn) is equivalent to fn(*args), but with a twist: +# if you make_fx trace through this call, we will not actually trace into fn; instead, +# we will directly insert it as a call_function to fn in the graph. +# (Unlike make_fx, Dynamo WILL inline into fn.) +# You can think of this as a one off allow_in_graph equivalent for proxy tensor tracing. +# +# Because proxy tensor tracing does not actually run the function, there are +# requirements on the behavior of fn. We are still figuring it out, but here is the current state: +# +# 1) fn SHOULD only take a single argument, which must be a tensor +# 2) fn MUST return a new tensor with the same metadata as the original tensor +# (e.g., zeros_like(input) is a permissible implementation of fn). +# This is verified via an extra assert that is inserted into the traced graph. +# 3) fn MAY have side effects, but it MAY NOT perform metadata mutation on other tensors +# participating in proxy tensor tracing (it MAY mutate other tensors, it MAY mutate Python state) +# These requirements stem from the requirement that we need to continue performing proxy tensor tracing, +# which assumes accurate fake tensor metadata, without actually running fn. +# In the future, we may allow for a "meta" function associated with fn to allow for more interesting input-output patterns. +# +# Note that tensors / Python state are allowed to be mutated. +# This is relaxed constraint is not always sound, but it is sound for backward tracing with fake +# tensors as it takes place in AOTAutograd, as the backward pass is guaranteed not to depend on concrete +# tensor values (via fake tensor) or Python state (because the autograd engine doesn't depend on Python). +# +# The intended use case for this function is to allow AOTAutograd to defer complex +# backward hooks to compiled autograd. AOTAutograd performs a make_fx trace which preserves +# the function call as is in the graph, and only when we Dynamo through the backward graph in +# compiled autograd do we inline into the function. + + +def trace_wrapped(*args, **kwargs): + with torch.no_grad(): + return _trace_wrapped_op(*args, **kwargs) + + +class TraceWrapped(HigherOrderOperator): + def __init__(self): + super().__init__("trace_wrapped") + + def __call__(self, *args, **kwargs): + return super().__call__(*args, **kwargs) + + +# TODO(jansel): need to ensure this does not get DCEed +_trace_wrapped_op = TraceWrapped() + + +def _assert_meta(grad, size, stride, dtype): + assert grad.size() == size, "size mismatch" + assert grad.stride() == stride, "stride mismatch" + assert grad.dtype == dtype, "dtype mismatch" + return grad + + +@_trace_wrapped_op.py_impl(ProxyTorchDispatchMode) +def inner_trace(mode, *args, bw_state=None, **kwargs): + def self_invoke(*args, **dyn_kwargs): + with torch.no_grad(): + return _trace_wrapped_op(*args, **dyn_kwargs, **kwargs) + + def unwrap_proxies(x): + if isinstance(x, torch.Tensor): + return mode.tracer.unwrap_proxy(x) + if isinstance(x, (list, tuple)): + return type(x)(map(unwrap_proxies, x)) + if x is None: + return None + raise AssertionError(f"unhandled type: {type(x)}") + + proxy_kwargs = {} + if bw_state is not None: + assert isinstance(bw_state, BackwardState) and bw_state.proxy is not None + proxy_kwargs["bw_state"] = bw_state.proxy + out_proxy = mode.tracer.create_proxy( + "call_function", + self_invoke, + unwrap_proxies(args), + proxy_kwargs, + name="trace_wrapped", + ) + + if args[0] is None: + grad = args[1] # module backward hooks + else: + grad = args[0] # other backward hooks + grad = tree_map_only(torch.Tensor, torch.empty_like, grad) + track_tensor_tree(grad, out_proxy, constant=None, tracer=mode.tracer) + return grad + + +@_trace_wrapped_op.py_impl(FakeTensorMode) +def inner_fake(*args, **kwargs): + raise RuntimeError("This op should never be invoked here") + + +@_trace_wrapped_op.py_impl(DispatchKey.CompositeExplicitAutograd) +def _trace_wrapped_op_dense(*args, fn, **kwargs): + mode = _get_current_dispatch_mode() + assert mode is None, "Mode should never be enabled for CPU/CUDA key" + return fn(*args, **kwargs) + + +_trace_wrapped_op.py_impl(DispatchKey.Autograd)( + autograd_not_implemented(_trace_wrapped_op, deferred_error=True) +) + + +@_trace_wrapped_op.py_functionalize_impl +def _trace_wrapped_functionalized(ctx, *args, **kwargs): + unwrapped_args = ctx.unwrap_tensors(args) + with ctx.redispatch_to_next(): + return ctx.wrap_tensors(_trace_wrapped_op(*unwrapped_args, **kwargs)) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/bytecode_analysis.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/bytecode_analysis.py new file mode 100644 index 0000000000000000000000000000000000000000..fe2ea31b09e558a6c0f5bf80a3bb037475e10b1a --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/bytecode_analysis.py @@ -0,0 +1,257 @@ +# mypy: allow-untyped-defs +import bisect +import dataclasses +import dis +import sys +from typing import Any, Set, Union + + +TERMINAL_OPCODES = { + dis.opmap["RETURN_VALUE"], + dis.opmap["JUMP_FORWARD"], + dis.opmap["RAISE_VARARGS"], + # TODO(jansel): double check exception handling +} +if sys.version_info >= (3, 9): + TERMINAL_OPCODES.add(dis.opmap["RERAISE"]) +if sys.version_info >= (3, 11): + TERMINAL_OPCODES.add(dis.opmap["JUMP_BACKWARD"]) + TERMINAL_OPCODES.add(dis.opmap["JUMP_FORWARD"]) +else: + TERMINAL_OPCODES.add(dis.opmap["JUMP_ABSOLUTE"]) +if sys.version_info >= (3, 12): + TERMINAL_OPCODES.add(dis.opmap["RETURN_CONST"]) +JUMP_OPCODES = set(dis.hasjrel + dis.hasjabs) +JUMP_OPNAMES = {dis.opname[opcode] for opcode in JUMP_OPCODES} +HASLOCAL = set(dis.haslocal) +HASFREE = set(dis.hasfree) + +stack_effect = dis.stack_effect + + +def get_indexof(insts): + """ + Get a mapping from instruction memory address to index in instruction list. + Additionally checks that each instruction only appears once in the list. + """ + indexof = {} + for i, inst in enumerate(insts): + assert inst not in indexof + indexof[inst] = i + return indexof + + +def remove_dead_code(instructions): + """Dead code elimination""" + indexof = get_indexof(instructions) + live_code = set() + + def find_live_code(start): + for i in range(start, len(instructions)): + if i in live_code: + return + live_code.add(i) + inst = instructions[i] + if inst.exn_tab_entry: + find_live_code(indexof[inst.exn_tab_entry.target]) + if inst.opcode in JUMP_OPCODES: + find_live_code(indexof[inst.target]) + if inst.opcode in TERMINAL_OPCODES: + return + + find_live_code(0) + + # change exception table entries if start/end instructions are dead + # assumes that exception table entries have been propagated, + # e.g. with bytecode_transformation.propagate_inst_exn_table_entries, + # and that instructions with an exn_tab_entry lies within its start/end. + if sys.version_info >= (3, 11): + live_idx = sorted(live_code) + for i, inst in enumerate(instructions): + if i in live_code and inst.exn_tab_entry: + # find leftmost live instruction >= start + start_idx = bisect.bisect_left( + live_idx, indexof[inst.exn_tab_entry.start] + ) + assert start_idx < len(live_idx) + # find rightmost live instruction <= end + end_idx = ( + bisect.bisect_right(live_idx, indexof[inst.exn_tab_entry.end]) - 1 + ) + assert end_idx >= 0 + assert live_idx[start_idx] <= i <= live_idx[end_idx] + inst.exn_tab_entry.start = instructions[live_idx[start_idx]] + inst.exn_tab_entry.end = instructions[live_idx[end_idx]] + + return [inst for i, inst in enumerate(instructions) if i in live_code] + + +def remove_pointless_jumps(instructions): + """Eliminate jumps to the next instruction""" + pointless_jumps = { + id(a) + for a, b in zip(instructions, instructions[1:]) + if a.opname == "JUMP_ABSOLUTE" and a.target is b + } + return [inst for inst in instructions if id(inst) not in pointless_jumps] + + +def propagate_line_nums(instructions): + """Ensure every instruction has line number set in case some are removed""" + cur_line_no = None + + def populate_line_num(inst): + nonlocal cur_line_no + if inst.starts_line: + cur_line_no = inst.starts_line + + inst.starts_line = cur_line_no + + for inst in instructions: + populate_line_num(inst) + + +def remove_extra_line_nums(instructions): + """Remove extra starts line properties before packing bytecode""" + + cur_line_no = None + + def remove_line_num(inst): + nonlocal cur_line_no + if inst.starts_line is None: + return + elif inst.starts_line == cur_line_no: + inst.starts_line = None + else: + cur_line_no = inst.starts_line + + for inst in instructions: + remove_line_num(inst) + + +@dataclasses.dataclass +class ReadsWrites: + reads: Set[Any] + writes: Set[Any] + visited: Set[Any] + + +def livevars_analysis(instructions, instruction): + indexof = get_indexof(instructions) + must = ReadsWrites(set(), set(), set()) + may = ReadsWrites(set(), set(), set()) + + def walk(state, start): + if start in state.visited: + return + state.visited.add(start) + + for i in range(start, len(instructions)): + inst = instructions[i] + if inst.opcode in HASLOCAL or inst.opcode in HASFREE: + if "LOAD" in inst.opname or "DELETE" in inst.opname: + if inst.argval not in must.writes: + state.reads.add(inst.argval) + elif "STORE" in inst.opname: + state.writes.add(inst.argval) + elif inst.opname == "MAKE_CELL": + pass + else: + raise NotImplementedError(f"unhandled {inst.opname}") + if inst.exn_tab_entry: + walk(may, indexof[inst.exn_tab_entry.target]) + if inst.opcode in JUMP_OPCODES: + walk(may, indexof[inst.target]) + state = may + if inst.opcode in TERMINAL_OPCODES: + return + + walk(must, indexof[instruction]) + return must.reads | may.reads + + +@dataclasses.dataclass +class FixedPointBox: + value: bool = True + + +@dataclasses.dataclass +class StackSize: + low: Union[int, float] + high: Union[int, float] + fixed_point: FixedPointBox + + def zero(self): + self.low = 0 + self.high = 0 + self.fixed_point.value = False + + def offset_of(self, other, n): + prior = (self.low, self.high) + self.low = min(self.low, other.low + n) + self.high = max(self.high, other.high + n) + if (self.low, self.high) != prior: + self.fixed_point.value = False + + def exn_tab_jump(self, depth): + prior = (self.low, self.high) + self.low = min(self.low, depth) + self.high = max(self.high, depth) + if (self.low, self.high) != prior: + self.fixed_point.value = False + + +def stacksize_analysis(instructions) -> Union[int, float]: + assert instructions + fixed_point = FixedPointBox() + stack_sizes = { + inst: StackSize(float("inf"), float("-inf"), fixed_point) + for inst in instructions + } + stack_sizes[instructions[0]].zero() + + for _ in range(100): + if fixed_point.value: + break + fixed_point.value = True + + for inst, next_inst in zip(instructions, instructions[1:] + [None]): + stack_size = stack_sizes[inst] + # CALL_FINALLY in Python 3.8 is handled differently when determining stack depth. + # See https://github.com/python/cpython/blob/3.8/Python/compile.c#L5450. + # Essentially, the stack effect of CALL_FINALLY is computed with jump=True, + # but the resulting stack depth is propagated to the next instruction, not the + # jump target. + is_call_finally = ( + sys.version_info < (3, 9) and inst.opcode == dis.opmap["CALL_FINALLY"] + ) + if inst.opcode not in TERMINAL_OPCODES: + assert next_inst is not None, f"missing next inst: {inst}" + # total stack effect of CALL_FINALLY and END_FINALLY in 3.8 is 0 + eff = ( + 0 + if is_call_finally + else stack_effect(inst.opcode, inst.arg, jump=False) + ) + stack_sizes[next_inst].offset_of(stack_size, eff) + if inst.opcode in JUMP_OPCODES and not is_call_finally: + stack_sizes[inst.target].offset_of( + stack_size, stack_effect(inst.opcode, inst.arg, jump=True) + ) + if inst.exn_tab_entry: + # see https://github.com/python/cpython/blob/3.11/Objects/exception_handling_notes.txt + # on why depth is computed this way. + depth = inst.exn_tab_entry.depth + int(inst.exn_tab_entry.lasti) + 1 + stack_sizes[inst.exn_tab_entry.target].exn_tab_jump(depth) + + if False: + for inst in instructions: + stack_size = stack_sizes[inst] + print(stack_size.low, stack_size.high, inst) + + low = min(x.low for x in stack_sizes.values()) + high = max(x.high for x in stack_sizes.values()) + + assert fixed_point.value, "failed to reach fixed point" + assert low >= 0 + return high diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/bytecode_transformation.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/bytecode_transformation.py new file mode 100644 index 0000000000000000000000000000000000000000..5c9a0ce5d4eb5aa2c32e6d0c433057b8c1afc9a6 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/bytecode_transformation.py @@ -0,0 +1,1503 @@ +# mypy: allow-untyped-defs +import copy +import dataclasses +import dis +import itertools +import sys +import types +from typing import Any, Callable, cast, Dict, Iterator, List, Optional, Tuple, Union + +from .bytecode_analysis import ( + get_indexof, + propagate_line_nums, + remove_extra_line_nums, + stacksize_analysis, +) + + +@dataclasses.dataclass +class InstructionExnTabEntry: + start: "Instruction" + end: "Instruction" + target: "Instruction" + depth: int + lasti: bool + + def __repr__(self) -> str: + return ( + f"InstructionExnTabEntry(start={self.start.short_inst_repr()}, " + f"end={self.end.short_inst_repr()}, " + f"target={self.target.short_inst_repr()}, " + f"depth={self.depth}, lasti={self.lasti})" + ) + + def __eq__(self, o) -> bool: + return ( + self.start is o.start + and self.end is o.end + and self.target is o.target + and self.depth == o.depth + and self.lasti == o.lasti + ) + + +@dataclasses.dataclass +class Instruction: + """A mutable version of dis.Instruction""" + + opcode: int + opname: str + arg: Optional[int] + argval: Any + offset: Optional[int] = None + starts_line: Optional[int] = None + is_jump_target: bool = False + positions: Optional["dis.Positions"] = None + # extra fields to make modification easier: + target: Optional["Instruction"] = None + exn_tab_entry: Optional[InstructionExnTabEntry] = None + + def __hash__(self) -> int: + return id(self) + + def __eq__(self, other) -> bool: + return id(self) == id(other) + + def short_inst_repr(self) -> str: + return f"Instruction(opname={self.opname}, offset={self.offset})" + + +def convert_instruction(i: dis.Instruction) -> Instruction: + if sys.version_info >= (3, 13): + starts_line = i.line_number + else: + starts_line = i.starts_line + return Instruction( + i.opcode, + i.opname, + i.arg, + i.argval, + i.offset, + starts_line, + i.is_jump_target, + getattr(i, "positions", None), + ) + + +class _NotProvided: + def __repr__(self) -> str: + return "_NotProvided" + + +def inst_has_op_bits(name): + return (sys.version_info >= (3, 11) and name == "LOAD_GLOBAL") or ( + sys.version_info >= (3, 12) and name in ("LOAD_ATTR", "LOAD_SUPER_ATTR") + ) + + +def create_instruction( + name, *, arg=None, argval=_NotProvided, target=None +) -> Instruction: + """ + At most one of `arg`, `argval`, and `target` can be not None/_NotProvided. + This is to prevent ambiguity, e.g. does + create_instruction("LOAD_CONST", 5) + mean load the constant at co_consts[5], or load the constant 5? + + If `arg` is not provided, it will be computed during assembly from + `argval` or `target`. + + Bits in the args of instructions LOAD_GLOBAL, LOAD_ATTR (3.12+), and LOAD_SUPER_ATTR + modify the behavior of the instruction. In this case, we allow both `arg` + and `argval` to be set. The value of `arg` here is expected to be the value of + the op bits and the true value of `arg` will be computed during assembly. + If `arg` is not set, the bits are assumed to be 0. + """ + + # allow for instructions with op bits to have both arg and argval specified + if inst_has_op_bits(name): + if target is not None: + raise RuntimeError("target cannot be specified for instruction") + if arg is None: + arg = 0 + else: + cnt = (arg is not None) + (argval is not _NotProvided) + (target is not None) + if cnt > 1: + raise RuntimeError( + "only one of arg, argval, and target can be not None/_NotProvided" + ) + if arg is not None and not isinstance(arg, int): + raise RuntimeError("instruction arg must be int or None") + return Instruction( + opcode=dis.opmap[name], opname=name, arg=arg, argval=argval, target=target + ) + + +# Python 3.11 remaps +def create_jump_absolute(target) -> Instruction: + inst = "JUMP_FORWARD" if sys.version_info >= (3, 11) else "JUMP_ABSOLUTE" + return create_instruction(inst, target=target) + + +def create_dup_top() -> Instruction: + if sys.version_info >= (3, 11): + return create_instruction("COPY", arg=1) + return create_instruction("DUP_TOP") + + +def create_rot_n(n) -> List[Instruction]: + """ + Returns a "simple" sequence of instructions that rotates TOS to the n-th + position in the stack. For Python < 3.11, returns a single ROT_* + instruction. If no such instruction exists, an error is raised and the + caller is expected to generate an equivalent sequence of instructions. + For Python >= 3.11, any rotation can be expressed as a simple sequence of + swaps. + """ + if n <= 1: + # don't rotate + return [] + + if sys.version_info >= (3, 11): + # rotate can be expressed as a sequence of swap operations + # e.g. rotate 3 is equivalent to swap 3, swap 2 + return [create_instruction("SWAP", arg=i) for i in range(n, 1, -1)] + + # ensure desired rotate function exists + if sys.version_info < (3, 8) and n >= 4: + raise AttributeError(f"rotate {n} not supported for Python < 3.8") + if sys.version_info < (3, 10) and n >= 5: + raise AttributeError(f"rotate {n} not supported for Python < 3.10") + + if n <= 4: + return [create_instruction("ROT_" + ["TWO", "THREE", "FOUR"][n - 2])] + return [create_instruction("ROT_N", arg=n)] + + +def add_push_null( + inst_or_insts: Union[Instruction, List[Instruction]], +) -> List[Instruction]: + """ + Appends or prepends a PUSH_NULL instruction to `inst_or_insts`, + depending on Python version. Used when you know that + `inst_or_insts` generates a callable that will be called. + + NOTE: Assumes `inst_or_insts` is a single instruction or sequence of + instructions that pushes exactly 1 object to the stack that is to + be called. It is important that you include ALL instructions that + construct the callable - not just the first instruction/a prefix. + + Will attempt to use the NULL push bit for instructions + with such bits (LOAD_GLOBAL 3.11+, LOAD_ATTR 3.12+, LOAD_SUPER_ATTR). + In this case, instructions WILL be modified. + """ + if isinstance(inst_or_insts, Instruction): + insts = [inst_or_insts] + else: + insts = inst_or_insts + + def inst_has_bit_set(idx): + assert insts[idx].arg is not None + return insts[idx].arg & 1 == 1 + + def set_inst_bit(idx): + assert insts[idx].arg is not None + insts[idx].arg |= 1 + + if sys.version_info >= (3, 13): + # In 3.13, NULL follows the callable + if inst_has_op_bits(insts[-1].opname) and not inst_has_bit_set(-1): + # All insts with op bits have the push_null bit as the last one. + # Only set the bit if it hasn't been set - otherwise, we need + # to add another PUSH_NULL. + set_inst_bit(-1) + else: + insts = insts + [create_instruction("PUSH_NULL")] + elif sys.version_info >= (3, 12): + # LOAD_ATTR/LOAD_SUPER_ATTR at the end + # We assume that `insts` will only load 1 object, so + # LOAD_GLOBAL at the end doesn't need to be checked + if inst_has_op_bits(insts[-1].opname) and not inst_has_bit_set(-1): + set_inst_bit(-1) + elif insts[0].opname == "LOAD_GLOBAL" and not inst_has_bit_set(0): + set_inst_bit(0) + else: + insts = [create_instruction("PUSH_NULL")] + insts + elif sys.version_info >= (3, 11): + # 3.11 introduced NULL preceding callable + if inst_has_op_bits(insts[0].opname) and not inst_has_bit_set(0): + set_inst_bit(0) + else: + insts = [create_instruction("PUSH_NULL")] + insts + return insts + + +def add_push_null_call_function_ex( + inst_or_insts: Union[Instruction, List[Instruction]], +) -> List[Instruction]: + """Like add_push_null, but the low bit of LOAD_ATTR/LOAD_SUPER_ATTR + is not set, due to an expected CALL_FUNCTION_EX instruction. + """ + if isinstance(inst_or_insts, Instruction): + insts = [inst_or_insts] + else: + insts = inst_or_insts + + if sys.version_info < (3, 11): + return insts + + idx = -1 if sys.version_info >= (3, 13) else 0 + if insts[idx].opname == "LOAD_GLOBAL": + assert insts[idx].arg is not None + if insts[idx].arg & 1 == 0: # type: ignore[operator] + insts[idx].arg |= 1 # type: ignore[operator] + return insts + + if sys.version_info >= (3, 13): + insts = insts + [create_instruction("PUSH_NULL")] + else: + insts = [create_instruction("PUSH_NULL")] + insts + + return insts + + +def create_call_function(nargs, push_null) -> List[Instruction]: + """ + Creates a sequence of instructions that makes a function call. + + `push_null` is used in Python 3.11+ only. It is used in codegen when + a function call is intended to be made with the NULL + fn convention, + and we know that the NULL has not been pushed yet. We will push a + NULL and rotate it to the correct position immediately before making + the function call. + + `push_null` should be True if no NULL is pushed for the callable. + Conversely, `push_null` should be False if a NULL was pushed for the callable. + Prefer using `push_null=False` when possible since we will not need to rotate + NULL to the right place, which is less efficient. + + Generally, you should codegen a function by using `add_push_null` then + `create_call_function` with `push_null=False`. + + Example of when to set push_null False: + + insts = [ + create_instruction("LOAD_GLOBAL", argval="torch"), + create_instruction("LOAD_ATTR", argval="nn"), + create_instruction("LOAD_ATTR", argval="functional"), + create_instruction("LOAD_ATTR", argval="relu"), + ] + insts = add_push_null(insts) + insts.append(create_instruction("LOAD_FAST", argval="x")) + insts.extend(create_call_function(1, False)) + + Example of when to set push_null True: + + insts = [create_instruction("LOAD_FAST", x)] + for should_wrap, wrapper_name in wrappers: + if should_wrap: + insts.extend([ + create_instruction("LOAD_GLOBAL", argval="wrapper1"), + create_instruction("SWAP", arg=2), + *create_call_function(1, True), + ) + """ + if sys.version_info >= (3, 11): + output = [] + if push_null: + output.append(create_instruction("PUSH_NULL")) + # 3.13 swapped NULL and callable + rots = nargs + 1 if sys.version_info >= (3, 13) else nargs + 2 + output.extend(create_rot_n(rots)) + if sys.version_info < (3, 12): + output.append(create_instruction("PRECALL", arg=nargs)) + output.append(create_instruction("CALL", arg=nargs)) + return output + return [create_instruction("CALL_FUNCTION", arg=nargs)] + + +def create_call_method(nargs) -> List[Instruction]: + if sys.version_info >= (3, 12): + return [create_instruction("CALL", arg=nargs)] + if sys.version_info >= (3, 11): + return [ + create_instruction("PRECALL", arg=nargs), + create_instruction("CALL", arg=nargs), + ] + return [create_instruction("CALL_METHOD", arg=nargs)] + + +def create_load_method(name) -> Instruction: + if sys.version_info >= (3, 12): + # in 3.12, create a LOAD_ATTR instruction with the low bit set + return create_instruction("LOAD_ATTR", arg=1, argval=name) + return create_instruction("LOAD_METHOD", argval=name) + + +def create_setup_with(target) -> Instruction: + opname = "BEFORE_WITH" if sys.version_info >= (3, 11) else "SETUP_WITH" + return create_instruction(opname, target=target) + + +def create_swap(n) -> List[Instruction]: + if sys.version_info >= (3, 11): + return [create_instruction("SWAP", arg=n)] + # in Python < 3.11, SWAP is a macro that expands to multiple instructions + if n == 1: + return [] + """ + e.g. swap "a" and "b" in this stack: + 0 a 1 2 3 b + 0 a [1 2 3 b] + 0 a [1 2 3 b] [1 2 3 b] + 0 a [1 2 3 b] [1 2 3 b] -1 + 0 a [1 2 3 b] b + 0 b a [1 2 3 b] + 0 b a [1 2 3 b] [1 2 3 b] + 0 b [1 2 3 b] a [1 2 3 b] + 0 b [1 2 3 b] a [1 2 3 b] -1 + 0 b [1 2 3 a] + 0 b [1 2 3 a] [1 2 3 a] + 0 b [1 2 3 a] [1 2 3 a] reverse + 0 b [a 3 2 1] None + 0 b [a 3 2 1] + 0 b 1 2 3 a + """ + return [ + create_instruction("BUILD_LIST", arg=n - 1), + create_instruction("DUP_TOP"), + create_instruction("LOAD_CONST", argval=-1), + create_instruction("BINARY_SUBSCR"), + create_instruction("ROT_THREE"), + create_instruction("DUP_TOP"), + create_instruction("ROT_THREE"), + create_instruction("LOAD_CONST", argval=-1), + create_instruction("STORE_SUBSCR"), + create_instruction("DUP_TOP"), + create_load_method("reverse"), + *create_call_method(0), + create_instruction("POP_TOP"), + create_instruction("UNPACK_SEQUENCE", arg=n - 1), + ] + + +def lnotab_writer( + lineno: int, byteno: int = 0 +) -> Tuple[List[int], Callable[[int, int], None]]: + """ + Used to create typing.CodeType.co_lnotab + See https://github.com/python/cpython/blob/main/Objects/lnotab_notes.txt + This is the internal format of the line number table if Python < 3.10 + """ + assert sys.version_info < (3, 10) + lnotab: List[int] = [] + + def update(lineno_new, byteno_new): + nonlocal byteno, lineno + while byteno_new != byteno or lineno_new != lineno: + byte_offset = max(0, min(byteno_new - byteno, 255)) + line_offset = max(-128, min(lineno_new - lineno, 127)) + assert byte_offset != 0 or line_offset != 0 + byteno += byte_offset + lineno += line_offset + lnotab.extend((byte_offset, line_offset & 0xFF)) + + return lnotab, update + + +def linetable_310_writer(first_lineno): + """ + Used to create typing.CodeType.co_linetable + See https://github.com/python/cpython/blob/main/Objects/lnotab_notes.txt + This is the internal format of the line number table for Python 3.10 + """ + assert sys.version_info >= (3, 10) and sys.version_info < (3, 11) + linetable: List[int] = [] + lineno = first_lineno + lineno_delta = 0 + byteno = 0 + + def _update(byteno_delta, lineno_delta): + while byteno_delta != 0 or lineno_delta != 0: + byte_offset = max(0, min(byteno_delta, 254)) + line_offset = max(-127, min(lineno_delta, 127)) + assert byte_offset != 0 or line_offset != 0 + byteno_delta -= byte_offset + lineno_delta -= line_offset + linetable.extend((byte_offset, line_offset & 0xFF)) + + def update(lineno_new, byteno_new): + nonlocal lineno, lineno_delta, byteno + byteno_delta = byteno_new - byteno + byteno = byteno_new + _update(byteno_delta, lineno_delta) + lineno_delta = lineno_new - lineno + lineno = lineno_new + + def end(total_bytes): + _update(total_bytes - byteno, lineno_delta) + + return linetable, update, end + + +def encode_varint(n: int) -> List[int]: + """ + 6-bit chunk encoding of an unsigned integer + See https://github.com/python/cpython/blob/3.11/Objects/locations.md + """ + assert n >= 0 + b = [n & 63] + n >>= 6 + while n > 0: + b[-1] |= 64 + b.append(n & 63) + n >>= 6 + return b + + +def linetable_311_writer(first_lineno: int): + """ + Used to create typing.CodeType.co_linetable + See https://github.com/python/cpython/blob/3.11/Objects/locations.md + This is the internal format of the line number table for Python 3.11 + """ + assert sys.version_info >= (3, 11) + linetable = [] + lineno = first_lineno + + def update(positions: "dis.Positions", inst_size): + nonlocal lineno + lineno_new = positions.lineno if positions else None + + def _update(delta, size): + assert 0 < size <= 8 + # first byte - use 13 (no column info) is positions is + # malformed, otherwise use 14 (long form) + other_varints: Tuple[int, ...] = () + if ( + positions + and positions.lineno is not None + and positions.end_lineno is not None + and positions.col_offset is not None + and positions.end_col_offset is not None + ): + linetable.append(0b1_1110_000 + size - 1) + # for whatever reason, column offset needs `+ 1` + # https://github.com/python/cpython/blob/1931c2a438c50e6250725c84dff94fc760b9b951/Python/compile.c#L7603 + other_varints = ( + positions.end_lineno - positions.lineno, + positions.col_offset + 1, + positions.end_col_offset + 1, + ) + else: + linetable.append(0b1_1101_000 + size - 1) + # encode signed int + if delta < 0: + delta = ((-delta) << 1) | 1 + else: + delta <<= 1 + # encode unsigned int + linetable.extend(encode_varint(delta)) + for n in other_varints: + linetable.extend(encode_varint(n)) + + if lineno_new is None: + lineno_delta = 0 + else: + lineno_delta = lineno_new - lineno + lineno = lineno_new + while inst_size > 8: + _update(lineno_delta, 8) + inst_size -= 8 + _update(lineno_delta, inst_size) + + return linetable, update + + +@dataclasses.dataclass +class ExceptionTableEntry: + start: int + end: int + target: int + depth: int + lasti: bool + + +def encode_exception_table_varint(n: int) -> List[int]: + """ + Similar to `encode_varint`, but the 6-bit chunks are ordered in reverse. + """ + assert n >= 0 + b = [n & 63] + n >>= 6 + while n > 0: + b.append(n & 63) + n >>= 6 + b.reverse() + for i in range(len(b) - 1): + b[i] |= 64 + return b + + +def decode_exception_table_varint(bytes_iter: Iterator[int]) -> int: + """ + Inverse of `encode_exception_table_varint`. + """ + b = next(bytes_iter) + val = b & 63 + while b & 64: + val <<= 6 + b = next(bytes_iter) + val |= b & 63 + return val + + +def check_exception_table(tab: List[ExceptionTableEntry]) -> None: + """ + Verifies that a list of ExceptionTableEntries will make a well-formed + jump table: entries are non-empty, sorted, and do not overlap. + """ + for i in range(len(tab) - 1): + assert ( + tab[i].start <= tab[i].end + and tab[i].end < tab[i + 1].start + and tab[i + 1].start <= tab[i + 1].end + ) + + +def parse_exception_table(exntab: bytes) -> List[ExceptionTableEntry]: + """ + Parse the exception table according to + https://github.com/python/cpython/blob/3.11/Objects/exception_handling_notes.txt + """ + exntab_iter = iter(exntab) + tab = [] + try: + while True: + start = decode_exception_table_varint(exntab_iter) * 2 + length = decode_exception_table_varint(exntab_iter) * 2 + end = start + length - 2 + target = decode_exception_table_varint(exntab_iter) * 2 + dl = decode_exception_table_varint(exntab_iter) + depth = dl >> 1 + lasti = bool(dl & 1) + tab.append(ExceptionTableEntry(start, end, target, depth, lasti)) + except StopIteration: + check_exception_table(tab) + return tab + + +def assemble_exception_table(tab: List[ExceptionTableEntry]) -> bytes: + """ + Inverse of parse_exception_table - encodes list of exception + table entries into bytes. + """ + b = [] + for entry in tab: + first_entry = encode_exception_table_varint(entry.start // 2) + first_entry[0] |= 1 << 7 + b.extend(first_entry) + length = entry.end - entry.start + 2 + b.extend(encode_exception_table_varint(length // 2)) + b.extend(encode_exception_table_varint(entry.target // 2)) + dl = (entry.depth << 1) + entry.lasti + b.extend(encode_exception_table_varint(dl)) + return bytes(b) + + +def assemble(instructions: List[Instruction], firstlineno: int) -> Tuple[bytes, bytes]: + """Do the opposite of dis.get_instructions()""" + code: List[int] = [] + if sys.version_info >= (3, 11): + lnotab, update_lineno = linetable_311_writer(firstlineno) + num_ext = 0 + for i, inst in enumerate(instructions): + if inst.opname == "EXTENDED_ARG": + inst_size = 1 + num_ext += 1 + # copy positions from the actual instruction + for j in (1, 2, 3): + if instructions[i + j].opname != "EXTENDED_ARG": + inst.positions = instructions[i + j].positions + break + else: + inst_size = instruction_size(inst) // 2 + num_ext + num_ext = 0 + update_lineno(inst.positions, inst_size) + num_ext = 0 + arg = inst.arg or 0 + code.extend((inst.opcode, arg & 0xFF)) + for _ in range(instruction_size(inst) // 2 - 1): + code.extend((0, 0)) + else: + if sys.version_info < (3, 10): + lnotab, update_lineno = lnotab_writer(firstlineno) + else: + lnotab, update_lineno, end = linetable_310_writer(firstlineno) + + for inst in instructions: + if inst.starts_line is not None: + update_lineno(inst.starts_line, len(code)) + arg = inst.arg or 0 + code.extend((inst.opcode, arg & 0xFF)) + + if sys.version_info >= (3, 10): + end(len(code)) + + return bytes(code), bytes(lnotab) + + +def _get_instruction_by_offset(offset_to_inst: Dict[int, Instruction], offset: int): + """ + Get the instruction located at a given offset, accounting for EXTENDED_ARGs + """ + for n in (0, 2, 4, 6): + if offset_to_inst[offset + n].opcode != dis.EXTENDED_ARG: + return offset_to_inst[offset + n] + return None + + +def virtualize_jumps(instructions) -> None: + """Replace jump targets with pointers to make editing easier""" + jump_targets = {inst.offset: inst for inst in instructions} + + for inst in instructions: + if inst.opcode in dis.hasjabs or inst.opcode in dis.hasjrel: + inst.target = _get_instruction_by_offset(jump_targets, inst.argval) + + +_REL_JUMPS = set(dis.hasjrel) + + +def flip_jump_direction(instruction: Instruction) -> None: + if sys.version_info < (3, 11): + raise RuntimeError("Cannot flip jump direction in Python < 3.11") + if "FORWARD" in instruction.opname: + instruction.opname = instruction.opname.replace("FORWARD", "BACKWARD") + elif "BACKWARD" in instruction.opname: + instruction.opname = instruction.opname.replace("BACKWARD", "FORWARD") + else: + raise AttributeError("Instruction is not a forward or backward jump") + instruction.opcode = dis.opmap[instruction.opname] + assert instruction.opcode in _REL_JUMPS + + +def _get_instruction_front(instructions: List[Instruction], idx: int): + """ + i.e. get the first EXTENDED_ARG instruction (if any) when targeting + instructions[idx] with a jump. + """ + target = instructions[idx] + for offset in (1, 2, 3): + if idx >= offset and instructions[idx - offset].opcode == dis.EXTENDED_ARG: + target = instructions[idx - offset] + else: + break + return target + + +def devirtualize_jumps(instructions): + """Fill in args for virtualized jump target after instructions may have moved""" + jumps = set(dis.hasjabs).union(set(dis.hasjrel)) + + # check for negative jump args and fix them + for inst in instructions: + if inst.opcode in jumps: + if inst.opcode not in dis.hasjabs: + if inst.target.offset < inst.offset: + if sys.version_info < (3, 11): + raise RuntimeError("Got negative jump offset for Python < 3.11") + # forward jumps become backward + if "FORWARD" in inst.opname: + flip_jump_direction(inst) + else: + # backward jumps become forward + if sys.version_info >= (3, 11) and "BACKWARD" in inst.opname: + flip_jump_direction(inst) + + # jump instruction size may have changed due to flips + update_offsets(instructions) + indexof = get_indexof(instructions) + + # compute jump instruction arg + for inst in instructions: + if inst.opcode in jumps: + target = _get_instruction_front(instructions, indexof[inst.target]) + if inst.opcode in dis.hasjabs: + if sys.version_info < (3, 10): + inst.arg = target.offset + elif sys.version_info < (3, 11): + # `arg` is expected to be bytecode offset, whereas `offset` is byte offset. + # Divide since bytecode is 2 bytes large. + inst.arg = int(target.offset / 2) + else: + raise RuntimeError("Python 3.11+ should not have absolute jumps") + else: # relative jump + # byte offset between target and next instruction + inst.arg = abs( + int(target.offset - inst.offset - instruction_size(inst)) + ) + if sys.version_info >= (3, 10): + # see bytecode size comment in the absolute jump case above + inst.arg //= 2 + inst.argval = target.offset + inst.argrepr = f"to {target.offset}" + + +def virtualize_exception_table(exn_tab_bytes: bytes, instructions: List[Instruction]): + """Replace exception table entries with pointers to make editing easier""" + exn_tab = parse_exception_table(exn_tab_bytes) + offset_to_inst = {cast(int, inst.offset): inst for inst in instructions} + offsets = sorted(offset_to_inst.keys()) + end_offset_idx = 0 + exn_tab_iter = iter(exn_tab) + try: + + def step(): + nonlocal end_offset_idx + entry = next(exn_tab_iter) + # find rightmost offset <= entry.end, since entry.end may not be + # an actual instruction, e.g. if the end instruction is LOAD_GLOBAL, + # which takes more than 2 bytes, then entry.end points to the end + # of the LOAD_GLOBAL instruction, not the beginning. + while ( + end_offset_idx < len(offsets) and offsets[end_offset_idx] <= entry.end + ): + end_offset_idx += 1 + assert end_offset_idx > 0 + end_offset = offsets[end_offset_idx - 1] + inst_entry = InstructionExnTabEntry( + _get_instruction_by_offset(offset_to_inst, entry.start), + _get_instruction_by_offset(offset_to_inst, end_offset), + _get_instruction_by_offset(offset_to_inst, entry.target), + entry.depth, + entry.lasti, + ) + return entry, inst_entry + + entry, inst_entry = step() + for inst in instructions: + while inst.offset > entry.end: + entry, inst_entry = step() + if inst.offset >= entry.start: + inst.exn_tab_entry = copy.copy(inst_entry) + except StopIteration: + pass + + +def compute_exception_table( + instructions: List[Instruction], +) -> List[ExceptionTableEntry]: + """Compute exception table in list format from instructions with exn_tab_entries""" + exn_dict: Dict[Tuple[int, int], Tuple[int, int, bool]] = {} + indexof = get_indexof(instructions) + + for inst in instructions: + if inst.exn_tab_entry: + # account for prefixed EXTENDED_ARGS + start = _get_instruction_front( + instructions, indexof[inst.exn_tab_entry.start] + ).offset + # point to the last 2 bytes of the end instruction + end = ( + cast(int, inst.exn_tab_entry.end.offset) + + instruction_size(inst.exn_tab_entry.end) + - 2 + ) + target = _get_instruction_front( + instructions, indexof[inst.exn_tab_entry.target] + ).offset + key = (start, end) + val = (target, inst.exn_tab_entry.depth, inst.exn_tab_entry.lasti) + if key in exn_dict: + assert exn_dict[key] == val + exn_dict[key] = val + + # Dynamo may construct nested exception table entries for convenience, + # but Python expects exception table entries to not overlap. + # NOTE: below, "keys" refer to old instruction entries' starts and ends, + # and "entries" refer to the generated exception table entries. + + # Sort keys by increasing start, then decreasing end + keys_sorted = sorted(exn_dict.keys(), key=lambda t: (t[0], -t[1])) + # smallest byte that the next exception table entry can start at + nexti = 0 + # stack of current nested keys + key_stack: List[Tuple[int, int]] = [] + exn_tab: List[ExceptionTableEntry] = [] + + def pop(): + """ + Pop the key_stack and append an exception table entry if possible. + """ + nonlocal nexti + if key_stack: + key = key_stack.pop() + if nexti <= key[1]: + exn_tab.append( + ExceptionTableEntry(max(key[0], nexti), key[1], *exn_dict[key]) + ) + nexti = key[1] + 2 + + for key in keys_sorted: + # pop keys that are no longer nested over the current key + while key_stack and key_stack[-1][1] < key[0]: + pop() + if key_stack: + # create an entry covering to the current key, if possible + assert key_stack[-1][0] <= key[0] <= key[1] <= key_stack[-1][1] + left = max(nexti, key_stack[-1][0]) + if left < key[0]: + exn_tab.append( + ExceptionTableEntry(left, key[0] - 2, *exn_dict[key_stack[-1]]) + ) + nexti = key[0] + key_stack.append(key) + while key_stack: + pop() + check_exception_table(exn_tab) + return exn_tab + + +def check_inst_exn_tab_entries_nested( + tab: List[InstructionExnTabEntry], indexof +) -> None: + """ + Checks `tab` is a properly sorted list of nested InstructionExnTabEntry's, + i.e. no entries partially overlap. + "Properly sorted" means entries are sorted by increasing starts, then + decreasing ends. + """ + entry_stack: List[Tuple[int, int]] = [] + for entry in tab: + key = (indexof[entry.start], indexof[entry.end]) + while entry_stack and entry_stack[-1][1] < key[0]: + entry_stack.pop() + if entry_stack: + assert entry_stack[-1][0] <= key[0] <= key[1] <= entry_stack[-1][1] + entry_stack.append(key) + + +def propagate_inst_exn_table_entries(instructions: List[Instruction]) -> None: + """ + Copies exception table entries to all instructions in an entry's range. + Supports nested exception table entries. + """ + indexof = get_indexof(instructions) + entries: Dict[Tuple[int, int], InstructionExnTabEntry] = {} + for inst in instructions: + if inst.exn_tab_entry: + key = ( + indexof[inst.exn_tab_entry.start], + indexof[inst.exn_tab_entry.end], + ) + if key in entries: + assert inst.exn_tab_entry == entries[key] + entries[key] = inst.exn_tab_entry + sorted_entries = [ + entries[key] for key in sorted(entries.keys(), key=lambda t: (t[0], -t[1])) + ] + check_inst_exn_tab_entries_nested(sorted_entries, indexof) + # Propagation of nested entries works since nested entries come later + # in sorted order. + for entry in sorted_entries: + for i in range(indexof[entry.start], indexof[entry.end] + 1): + instructions[i].exn_tab_entry = copy.copy(entry) + + +def check_inst_exn_tab_entries_valid(instructions: List[Instruction]): + """ + Checks that exn_tab_entries of instructions are valid. + An entry's start, end, and target must be in instructions. + Instructions with an exn_tab_entry are located within + the entry's start and end instructions. + Instructions do not share exn_tab_entries. + + Implicitly checks for no duplicate instructions. + """ + indexof = get_indexof(instructions) + exn_tab_entry_set = set() + for i, inst in enumerate(instructions): + if inst.exn_tab_entry: + assert sys.version_info >= (3, 11) + assert id(inst.exn_tab_entry) not in exn_tab_entry_set + exn_tab_entry_set.add(id(inst.exn_tab_entry)) + entry = inst.exn_tab_entry + assert entry.start in indexof + assert entry.end in indexof + assert entry.target in indexof + assert indexof[entry.start] <= i <= indexof[entry.end] + + +def strip_extended_args(instructions: List[Instruction]) -> None: + instructions[:] = [i for i in instructions if i.opcode != dis.EXTENDED_ARG] + + +def remove_load_call_method(instructions: List[Instruction]) -> List[Instruction]: + """LOAD_METHOD puts a NULL on the stack which causes issues, so remove it""" + assert sys.version_info < (3, 11) + rewrites = {"LOAD_METHOD": "LOAD_ATTR", "CALL_METHOD": "CALL_FUNCTION"} + for inst in instructions: + if inst.opname in rewrites: + inst.opname = rewrites[inst.opname] + inst.opcode = dis.opmap[inst.opname] + return instructions + + +def remove_jump_if_none(instructions: List[Instruction]) -> None: + new_insts = [] + for inst in instructions: + new_insts.append(inst) + if "_NONE" in inst.opname: + is_op = create_instruction("IS_OP", arg=int("NOT" in inst.opname)) + is_op.argval = is_op.arg + is_op.positions = inst.positions + if sys.version_info < (3, 12): + jump_op = create_instruction( + "POP_JUMP_FORWARD_IF_TRUE" + if "FORWARD" in inst.opname + else "POP_JUMP_BACKWARD_IF_TRUE", + target=inst.target, + ) + else: + jump_op = create_instruction("POP_JUMP_IF_TRUE", target=inst.target) + jump_op.positions = inst.positions + # update inst.exn_tab_entry.end if necessary + if inst.exn_tab_entry and inst.exn_tab_entry.end is inst: + inst.exn_tab_entry.end = jump_op + # preserve exception table entries + is_op.exn_tab_entry = copy.copy(inst.exn_tab_entry) + jump_op.exn_tab_entry = copy.copy(inst.exn_tab_entry) + # modify inst in-place to preserve jump target + inst.opcode = dis.opmap["LOAD_CONST"] + inst.opname = "LOAD_CONST" + inst.arg = None + inst.argval = None + new_insts.extend([is_op, jump_op]) + instructions[:] = new_insts + + +def remove_binary_store_slice(instructions: List[Instruction]) -> None: + new_insts = [] + for inst in instructions: + new_insts.append(inst) + if inst.opname in ("BINARY_SLICE", "STORE_SLICE"): + # new instruction + subscr_inst = create_instruction(inst.opname.replace("SLICE", "SUBSCR")) + if inst.exn_tab_entry and inst.exn_tab_entry.end is inst: + inst.exn_tab_entry.end = subscr_inst + subscr_inst.exn_tab_entry = copy.copy(inst.exn_tab_entry) + subscr_inst.positions = inst.positions + # modify inst in-place to preserve jump target + inst.opcode = dis.opmap["BUILD_SLICE"] + inst.opname = "BUILD_SLICE" + inst.arg = 2 + inst.argval = 2 + new_insts.append(subscr_inst) + instructions[:] = new_insts + + +FUSED_INSTS = { + "LOAD_FAST_LOAD_FAST": ("LOAD_FAST", "LOAD_FAST"), + "STORE_FAST_STORE_FAST": ("STORE_FAST", "STORE_FAST"), + "STORE_FAST_LOAD_FAST": ("STORE_FAST", "LOAD_FAST"), +} + + +def remove_fused_load_store(instructions: List[Instruction]) -> None: + new_insts = [] + for inst in instructions: + new_insts.append(inst) + if inst.opname in FUSED_INSTS: + inst0, inst1 = FUSED_INSTS[inst.opname] + argval0, argval1 = inst.argval + + # modify inst in-place to preserve jump target + inst.opcode = dis.opmap[inst0] + inst.opname = inst0 + inst.argval = argval0 + + new_inst = create_instruction(inst1, argval=argval1) + # update inst.exn_tab_entry.end if necessary + if inst.exn_tab_entry and inst.exn_tab_entry.end is inst: + inst.exn_tab_entry.end = new_inst + # preserve exception table entries + new_inst.exn_tab_entry = copy.copy(inst.exn_tab_entry) + + new_insts.append(new_inst) + instructions[:] = new_insts + + +def explicit_super(code: types.CodeType, instructions: List[Instruction]) -> None: + """convert super() with no args into explicit arg form""" + cell_and_free = (code.co_cellvars or ()) + (code.co_freevars or ()) + if not len(code.co_varnames): + # A function with no argument cannot contain a valid "super()" call + return + output = [] + for idx, inst in enumerate(instructions): + output.append(inst) + if inst.opname == "LOAD_GLOBAL" and inst.argval == "super": + nexti = instructions[idx + 1] + if nexti.arg == 0 and ( + (sys.version_info >= (3, 12) and nexti.opname == "CALL") + or ( + sys.version_info >= (3, 11) + and sys.version_info < (3, 12) + and nexti.opname == "PRECALL" + ) + or (sys.version_info < (3, 11) and nexti.opname == "CALL_FUNCTION") + ): + assert "__class__" in cell_and_free + output.append(create_instruction("LOAD_DEREF", argval="__class__")) + first_var = code.co_varnames[0] + if first_var in cell_and_free: + output.append(create_instruction("LOAD_DEREF", argval=first_var)) + else: + output.append(create_instruction("LOAD_FAST", argval=first_var)) + nexti.arg = 2 + nexti.argval = 2 + if nexti.opname == "PRECALL": + # also update the following CALL instruction + call_inst = instructions[idx + 2] + call_inst.arg = 2 + call_inst.argval = 2 + + instructions[:] = output + + +def fix_extended_args(instructions: List[Instruction]) -> int: + """Fill in correct argvals for EXTENDED_ARG ops""" + output: List[Instruction] = [] + + def maybe_pop_n(n): + for _ in range(n): + if output and output[-1].opcode == dis.EXTENDED_ARG: + output.pop() + + for inst in instructions: + if inst.opcode == dis.EXTENDED_ARG: + # Leave this instruction alone for now so we never shrink code + inst.arg = 0 + elif inst.arg and inst.arg > 0xFFFFFF: + maybe_pop_n(3) + output.append(create_instruction("EXTENDED_ARG", arg=inst.arg >> 24)) + output.append(create_instruction("EXTENDED_ARG", arg=inst.arg >> 16)) + output.append(create_instruction("EXTENDED_ARG", arg=inst.arg >> 8)) + elif inst.arg and inst.arg > 0xFFFF: + maybe_pop_n(2) + output.append(create_instruction("EXTENDED_ARG", arg=inst.arg >> 16)) + output.append(create_instruction("EXTENDED_ARG", arg=inst.arg >> 8)) + elif inst.arg and inst.arg > 0xFF: + maybe_pop_n(1) + output.append(create_instruction("EXTENDED_ARG", arg=inst.arg >> 8)) + output.append(inst) + + added = len(output) - len(instructions) + assert added >= 0 + instructions[:] = output + return added + + +def instruction_size(inst) -> int: + import torch + + if sys.version_info >= (3, 11): + return 2 * (torch._C._dynamo.eval_frame.py_opcode_caches[inst.opcode] + 1) + return 2 + + +def check_offsets(instructions) -> None: + offset = 0 + for inst in instructions: + assert inst.offset == offset + offset += instruction_size(inst) + + +def update_offsets(instructions) -> None: + offset = 0 + for inst in instructions: + inst.offset = offset + offset += instruction_size(inst) + + +def debug_bytes(*args) -> str: + index = range(max(map(len, args))) + result = [] + for arg in ( + [index] + list(args) + [[int(a != b) for a, b in zip(args[-1], args[-2])]] + ): + result.append(" ".join(f"{x:03}" for x in arg)) + + return "bytes mismatch\n" + "\n".join(result) + + +def debug_checks(code): + """Make sure our assembler produces same bytes as we start with""" + dode = transform_code_object(code, lambda x, y: None, safe=True) + assert code.co_code == dode.co_code, debug_bytes(code.co_code, dode.co_code) + assert code.co_lnotab == dode.co_lnotab, debug_bytes(code.co_lnotab, dode.co_lnotab) + + +HAS_LOCAL = set(dis.haslocal) +HAS_NAME = set(dis.hasname) +HAS_FREE = set(dis.hasfree) +HAS_CONST = set(dis.hasconst) + + +def get_const_index(code_options, val) -> int: + for i, v in enumerate(code_options["co_consts"]): + # NOTE: stronger comparison is required, since we have + # examples where two values compare equal but have + # different semantic meaning in some cases, e.g. + # 0.0 == -0.0 but have different effects in torch.copysign. + if val is v: + return i + code_options["co_consts"] += (val,) + return len(code_options["co_consts"]) - 1 + + +def fix_vars(instructions: List[Instruction], code_options, varname_from_oparg=None): + # compute instruction arg from argval if arg is not provided + names = {name: idx for idx, name in enumerate(code_options["co_names"])} + + def get_name_index(name) -> int: + try: + idx = names[name] + except KeyError: + # Add a missing item to co_names + idx = names[name] = len(names) + code_options["co_names"] = (*code_options["co_names"], name) + assert len(code_options["co_names"]) == len(names) + return idx + + if sys.version_info < (3, 11): + assert varname_from_oparg is None + varnames = {name: idx for idx, name in enumerate(code_options["co_varnames"])} + freenames = { + name: idx + for idx, name in enumerate( + code_options["co_cellvars"] + code_options["co_freevars"] + ) + } + else: + assert callable(varname_from_oparg) + allnames = {} + for idx in itertools.count(): + try: + name = varname_from_oparg(idx) + allnames[name] = idx + except IndexError: + break + varnames = {name: allnames[name] for name in code_options["co_varnames"]} + freenames = { + name: allnames[name] + for name in code_options["co_cellvars"] + code_options["co_freevars"] + } + for i in range(len(instructions)): + + def should_compute_arg(): + # argval is prioritized over arg + return instructions[i].argval is not _NotProvided + + if instructions[i].opname == "LOAD_GLOBAL": + # 3.11 LOAD_GLOBAL requires both arg and argval - see create_instruction + assert instructions[i].argval is not _NotProvided + if sys.version_info >= (3, 11): + assert instructions[i].arg is not None + instructions[i].arg = (get_name_index(instructions[i].argval) << 1) + ( + cast(int, instructions[i].arg) % 2 + ) + else: + instructions[i].arg = get_name_index(instructions[i].argval) + elif instructions[i].opname == "LOAD_ATTR": + # 3.12 LOAD_ATTR requires both arg and argval, like LOAD_GLOBAL + assert instructions[i].argval is not _NotProvided + if sys.version_info >= (3, 12): + assert instructions[i].arg is not None + instructions[i].arg = (get_name_index(instructions[i].argval) << 1) + ( + cast(int, instructions[i].arg) % 2 + ) + else: + instructions[i].arg = get_name_index(instructions[i].argval) + elif instructions[i].opname == "LOAD_SUPER_ATTR": + assert instructions[i].arg is not None + assert instructions[i].argval is not _NotProvided + # Copy low bit, force second bit on for explicit super (the "+ 2") + instructions[i].arg = ( + (get_name_index(instructions[i].argval) << 2) + + (cast(int, instructions[i].arg) % 2) + + 2 + ) + elif instructions[i].opcode in HAS_LOCAL: + if should_compute_arg(): + if ( + sys.version_info >= (3, 13) + and instructions[i].argval not in varnames + ): + # instructions like LOAD_FAST used for both local and free vars + instructions[i].arg = freenames[instructions[i].argval] + else: + instructions[i].arg = varnames[instructions[i].argval] + elif instructions[i].opcode in HAS_NAME: + if should_compute_arg(): + instructions[i].arg = get_name_index(instructions[i].argval) + elif instructions[i].opcode in HAS_FREE: + if should_compute_arg(): + instructions[i].arg = freenames[instructions[i].argval] + elif instructions[i].opcode in HAS_CONST: + # NOTE: only update argval if arg is not provided. This assumes + # that any additions to co_consts are appended. + if instructions[i].arg is None: + # cannot use a dictionary since consts may not be hashable + idx = get_const_index(code_options, instructions[i].argval) + assert idx >= 0 + instructions[i].arg = idx + + +def clear_instruction_args(instructions): + # Clear the instruction arg for instructions that have argvals. + # Useful for using dis'd bytecode within generated bytecode. + for inst in instructions: + if ( + inst.argval is not _NotProvided + and ( + inst.opcode in HAS_LOCAL + or inst.opcode in HAS_NAME + or inst.opcode in HAS_FREE + or inst.opcode in HAS_CONST + ) + and inst.opname not in ("LOAD_GLOBAL", "LOAD_ATTR", "LOAD_SUPER_ATTR") + ): + inst.arg = None + + +def get_code_keys() -> List[str]: + # Python 3.11 changes to code keys are not fully documented. + # See https://github.com/python/cpython/blob/3.11/Objects/clinic/codeobject.c.h#L24 + # for new format. + keys = ["co_argcount"] + keys.append("co_posonlyargcount") + keys.extend( + [ + "co_kwonlyargcount", + "co_nlocals", + "co_stacksize", + "co_flags", + "co_code", + "co_consts", + "co_names", + "co_varnames", + "co_filename", + "co_name", + ] + ) + if sys.version_info >= (3, 11): + keys.append("co_qualname") + keys.append("co_firstlineno") + if sys.version_info >= (3, 10): + keys.append("co_linetable") + else: + keys.append("co_lnotab") + if sys.version_info >= (3, 11): + # not documented, but introduced in https://github.com/python/cpython/issues/84403 + keys.append("co_exceptiontable") + keys.extend( + [ + "co_freevars", + "co_cellvars", + ] + ) + return keys + + +def transform_code_object(code, transformations, safe=False) -> types.CodeType: + keys = get_code_keys() + code_options = {k: getattr(code, k) for k in keys} + assert len(code_options["co_varnames"]) == code_options["co_nlocals"] + + instructions = cleaned_instructions(code, safe) + propagate_line_nums(instructions) + + transformations(instructions, code_options) + return clean_and_assemble_instructions(instructions, keys, code_options)[1] + + +def clean_and_assemble_instructions( + instructions: List[Instruction], keys: List[str], code_options: Dict[str, Any] +) -> Tuple[List[Instruction], types.CodeType]: + # also implicitly checks for no duplicate instructions + check_inst_exn_tab_entries_valid(instructions) + + code_options["co_nlocals"] = len(code_options["co_varnames"]) + varname_from_oparg = None + if sys.version_info >= (3, 11): + # temporary code object with updated names + tmp_code = types.CodeType(*[code_options[k] for k in keys]) + varname_from_oparg = tmp_code._varname_from_oparg # type: ignore[attr-defined] + fix_vars(instructions, code_options, varname_from_oparg=varname_from_oparg) + + dirty = True + while dirty: + update_offsets(instructions) + devirtualize_jumps(instructions) + # this pass might change offsets, if so we need to try again + dirty = bool(fix_extended_args(instructions)) + + remove_extra_line_nums(instructions) + bytecode, lnotab = assemble(instructions, code_options["co_firstlineno"]) + if sys.version_info < (3, 10): + code_options["co_lnotab"] = lnotab + else: + code_options["co_linetable"] = lnotab + + code_options["co_code"] = bytecode + code_options["co_stacksize"] = stacksize_analysis(instructions) + assert set(keys) - {"co_posonlyargcount"} == set(code_options.keys()) - { + "co_posonlyargcount" + } + if sys.version_info >= (3, 11): + code_options["co_exceptiontable"] = assemble_exception_table( + compute_exception_table(instructions) + ) + + return instructions, types.CodeType(*[code_options[k] for k in keys]) + + +def populate_kw_names_argval(instructions, consts): + for inst in instructions: + if inst.opname == "KW_NAMES": + inst.argval = consts[inst.arg] + + +def cleaned_instructions(code, safe=False) -> List[Instruction]: + instructions = list(map(convert_instruction, dis.get_instructions(code))) + check_offsets(instructions) + if sys.version_info >= (3, 11): + populate_kw_names_argval(instructions, code.co_consts) + virtualize_exception_table(code.co_exceptiontable, instructions) + virtualize_jumps(instructions) + strip_extended_args(instructions) + if not safe: + if sys.version_info < (3, 11): + remove_load_call_method(instructions) + if sys.version_info < (3, 12): + explicit_super(code, instructions) + if sys.version_info >= (3, 11): + remove_jump_if_none(instructions) + if sys.version_info >= (3, 12): + remove_binary_store_slice(instructions) + if sys.version_info >= (3, 13): + remove_fused_load_store(instructions) + update_offsets(instructions) + devirtualize_jumps(instructions) + return instructions + + +_unique_id_counter = itertools.count() + + +def unique_id(name) -> str: + return f"{name}_{next(_unique_id_counter)}" + + +def is_generator(code: types.CodeType) -> bool: + co_generator = 0x20 + return (code.co_flags & co_generator) > 0 + + +def bytecode_from_template(fn, varname_map=None, noreturn=True, noprefix=True): + """Generates bytecode from a template function `fn` for use in + dynamo bytecode generation. + + For example, we can generate Python-version-independent bytecode + for looping through a dictionary and copying the values to a new dictionary. + + def template(d1, d2): + for k, v in d1.items(): + d2[k] = v + + + or a try block: + + def template(): + try: + dummy1 + except: + dummy2 + raise + dummy3 + + Args: + fn: a function template to generate bytecode from + varname_map: a mapping of `fn`'s varnames to new names. This + map will be applied to the generated bytecode's varnames. + For example, local variables in `fn` can be replaced with + new names that are generated by `OutputGraph.new_var`. + noreturn: remove all RETURN_* bytecodes and replace them with a jump + to the end of the bytecode. + noprefix: remove prefix bytecodes (all bytecode before the first RESUME, inclusive). + """ + insts = cleaned_instructions(fn.__code__) + clear_instruction_args(insts) + + if noprefix: + for i, inst in enumerate(insts): + if inst.opname == "RESUME": + insts = insts[i + 1 :] + break + + for inst in insts: + # If we don't reset starts_line, then the generated + # bytecode's line number will be based on fn's. + inst.starts_line = None + if varname_map and inst.argval in varname_map: + inst.argval = varname_map[inst.argval] + + if noreturn: + if sys.version_info >= (3, 12): + # replace RETURN_CONST with LOAD_CONST RETURN_VALUE + new_insts = [] + for inst in insts: + if inst.opname == "RETURN_CONST": + inst.opcode = dis.opmap["LOAD_CONST"] + inst.opname = "LOAD_CONST" + new_insts.append(inst) + # no need to propagate target/exn table + new_insts.append(create_instruction("RETURN_VALUE")) + else: + new_insts.append(inst) + insts = new_insts + + returns = [] + for inst in insts: + if inst.opname == "RETURN_VALUE": + returns.append(inst) + + if len(returns) == 1 and returns[0] is insts[-1]: + # only 1 return at the end - just pop it + insts.pop(-1) + elif len(returns) > 0: + # create jump target - if the last inst is a return, + # we can replace it with a NOP and make that the jump target. + if insts[-1] is returns[-1]: + insts[-1].opname = "NOP" + insts[-1].opcode = dis.opmap["NOP"] + insts[-1].arg = None + insts[-1].argval = _NotProvided + returns.pop(-1) + else: + insts.append(create_instruction("NOP")) + + # replace returns with jumps + for inst in returns: + # don't replace inst with new instruction + # due to targetting/exn table/etc. + jump_inst = create_jump_absolute(insts[-1]) + inst.opname = jump_inst.opname + inst.opcode = jump_inst.opcode + inst.arg = jump_inst.arg + inst.argval = jump_inst.argval + inst.target = jump_inst.target + + return insts diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/cache_size.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/cache_size.py new file mode 100644 index 0000000000000000000000000000000000000000..c5a793aa06c47ebf800ae705d85cbcc484de9c46 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/cache_size.py @@ -0,0 +1,185 @@ +# mypy: allow-untyped-defs +import logging +import types +import weakref +from dataclasses import dataclass +from typing import Tuple + +from torch._guards import CompileId + +from . import config + + +log = logging.getLogger(__name__) +""" +[Note on cache size limit] + +Background - TorchDynamo cache is a linked list. Each cache entry is a +(check_fn, out_code, next pointer). These are stored on the f_code's co_extra +scratch space. When a frame is invoked, we walk this linked list and run +check_fn in each cache_entry to decide if the frame needs recompilation. If none +of the check_fn's returns True, we recompile and add a new entry. To ensure we +don't end up recompiling infinitely, we put limits on the cache size. + +There are two limits +1) cache_size_limit +2) accumulated_cache_size_limit + + +Earlier we used to have only limit - maximum number of entries in 1 cache line +(which is now represented by (2) above). So, why do we need two limits? Lets try +to understand that. + +In general, we want our cache limit value to be a small number (e.g. 8 or even +lower). This ensures that for frames that cause too many recompilation fall to +eager quickly. However, there is another problem that prevents us from lowering +the value of cache_size_limit. This is due to ID_MATCH'd guards. Today, we put +ID_MATCH guards on nn module if there is a graph break. This means we will have +many recompilations for the same code object because the ID_MATCH guard fails +for different instances of the nn module. This is a common pattern in how models +are authored. Therefore, this requires us to keep the cache_size_limit high. + +We resolve this by introducing these two limits. The first limit (1) limits the +number of cache entries that have an ID_MATCH'd guard for an nn module instance. +And, (2)nd limit becomes a safeguard mechanism to have a maximum compilations +for a code object. One important question is - what is the limit for the code +object that does not have any ID_MATCH guard? For such code objects, we choose +(1) as the cache size limit. + +Lets take an example to understand how these limits help. Suppose, we have 16 +instances of a nn module and we ID_MATCH on the self object. Further, suppose +the inputs to these functions have varying batch size, leading to one +recompilation. In total, there will be 32 recompilations, and therefore 32 cache +entries on the forward code object. In the older case when we had only 1 limit, +our cache size limit must be >= 32 to capture all these recompilations. Now, +suppose there is a separate function in the same program which is very dynamic +and unsuitable for compilation. Such a function will need to undergo 32 +compilations to burst the cache and fallback to eager. These 32 recompilations +are too many and we want to fallback for these compilation-unfriendly functions +sooner. + +In the new scenario, we can have (1) cache_size_limit = 2, (2) +accumulated_cache_size_limit = 32. This means that each ID_MATCH'd object can +have maximum of two cache entries, and the maximum number of cache entries +(irrespective of ID_MATCH obj) is 32. This covers the case of forward code +object which has 32 recompilations. For the other function, the one unsuitable +for recompilation, our limit is 2. So, we will burst the cache in just 2 +recompilations. In this manner, these 2 limits help us resolve the tension +mentioned earlier. +""" + + +@dataclass +class CacheSizeRelevantForFrame: + """ + We track the number of cache entries that have same id_match objects as the + given frame. + + TODO(janimesh) - Consider adding a map from tuple_of_match_ids to count - + https://github.com/pytorch/pytorch/pull/107496#discussion_r1304564682 - this + could be useful for debugging as well. + """ + + # Total number of CacheEntry objects in the Dynamo linked list + num_cache_entries: int = 0 + + # Number of CacheEntry objects having same ID_MATCH'd objects as given frame. + num_cache_entries_with_same_id_matched_objs: int = 0 + + def will_compilation_exceed(self, limit: int) -> bool: + # Checks if a compilation will exceed the given limit (thats why >=). + return ( + self.will_compilation_exceed_accumulated_limit() + or self.will_compilation_exceed_specific_limit(limit) + ) + + def will_compilation_exceed_accumulated_limit(self) -> bool: + return self.num_cache_entries >= config.accumulated_cache_size_limit + + def will_compilation_exceed_specific_limit(self, limit: int) -> bool: + return self.num_cache_entries_with_same_id_matched_objs >= limit + + +def _get_weakref_from_f_locals(frame: types.FrameType, local_name: str): + obj = frame.f_locals.get(local_name, None) + weak_id = None + try: + weak_id = weakref.ref(obj) + except TypeError: + pass # cannot weakref bool object + return weak_id + + +def _has_same_id_matched_objs(frame: types.FrameType, cache_entry) -> bool: + """ + Checks if the ID_MATCH'd objects saved on cache_entry are same as the ones + in frame.f_locals. + """ + if not cache_entry: + return False + + for ( + local_name, + weakref_from_cache_entry, + ) in cache_entry.check_fn.id_matched_objs.items(): + if weakref_from_cache_entry() is not None: + weakref_from_frame = _get_weakref_from_f_locals(frame, local_name) + if weakref_from_frame != weakref_from_cache_entry: + return False + + # Also covers the case where no ID_MATCH objects are saved in frame.f_locals + return True + + +def compute_cache_size( + frame: types.FrameType, cache_entry +) -> CacheSizeRelevantForFrame: + # Walk the linked list to calculate the cache size + num_cache_entries = 0 + num_cache_entries_with_same_id_matched_objs = 0 + + while cache_entry: + num_cache_entries += 1 + # Track the number of cache entries having same ID_MATCH'd objects as + # that of frame.f_locals. This will be used later to compare against the + # cache_size_limit. + if _has_same_id_matched_objs(frame, cache_entry): + num_cache_entries_with_same_id_matched_objs += 1 + cache_entry = cache_entry.next + + return CacheSizeRelevantForFrame( + num_cache_entries, num_cache_entries_with_same_id_matched_objs + ) + + +def is_recompilation(cache_size: CacheSizeRelevantForFrame) -> bool: + """ + If the frame (earlier parsed by compute_cache_size) has more than 1 cache + entry with same ID_MATCH'd objects, then its a recompilation. + """ + # Note that you can have multiple entries in the cache but still not a + # recompile, e.g., you can have 64 nn module instances, each one having an + # ID_MATCH guard, and each one having just 1 cache entry in the cache. In + # this case, we can have 64 entries in the cache, but no recompilation + # because there is only one entry for each id_matched_obj. + return cache_size.will_compilation_exceed(1) + + +def exceeds_cache_size_limit( + cache_size: CacheSizeRelevantForFrame, compile_id: CompileId +) -> Tuple[bool, str]: + """ + Checks if we are exceeding the cache size limit. + """ + if cache_size.will_compilation_exceed_accumulated_limit(): + return True, "accumulated_cache_size_limit" + if cache_size.will_compilation_exceed_specific_limit(config.cache_size_limit): + return True, "cache_size_limit" + # NOTE this check is needed in the case that the frame's cache doesn't grow + # and we keep recompiling. This can happen if the guard check_fn becomes invalidated, + # e.g. due to guarded objects being freed. This technically makes the + # will_compilation_exceed_accumulated_limit check unnecessary, but we will keep the + # check in case we have a better fix in the future. + if compile_id.frame_compile_id >= config.accumulated_cache_size_limit: + return True, "accumulated_cache_size_limit" + return False, "" diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/callback.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/callback.py new file mode 100644 index 0000000000000000000000000000000000000000..35f447a8034903833d142ad4225995bdded9e3a1 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/callback.py @@ -0,0 +1,83 @@ +# mypy: allow-untyped-defs +class CompilationCallbackHandler: + def __init__(self): + self.start_callbacks = [] + self.end_callbacks = [] + + def register_start_callback(self, callback): + """ + Register a callback function to be called when the compilation starts. + + Args: + - callback (callable): The callback function to register. + """ + self.start_callbacks.append(callback) + return callback + + def register_end_callback(self, callback): + """ + Register a callback function to be called when the compilation ends. + + Args: + - callback (callable): The callback function to register. + """ + self.end_callbacks.append(callback) + return callback + + def remove_start_callback(self, callback): + """ + Remove a registered start callback function. + + Args: + - callback (callable): The callback function to remove. + """ + self.start_callbacks.remove(callback) + + def remove_end_callback(self, callback): + """ + Remove a registered end callback function. + + Args: + - callback (callable): The callback function to remove. + """ + self.end_callbacks.remove(callback) + + def run_start_callbacks(self): + """ + Execute all registered start callbacks. + """ + for callback in self.start_callbacks: + callback() + + def run_end_callbacks(self): + """ + Execute all registered end callbacks. + """ + for callback in self.end_callbacks: + callback() + + def clear(self): + """ + Clear all registered callbacks. + """ + self.start_callbacks.clear() + self.end_callbacks.clear() + + +callback_handler = CompilationCallbackHandler() + + +def on_compile_start(callback): + """ + Decorator to register a callback function for the start of the compilation. + """ + callback_handler.register_start_callback(callback) + return callback + + +def on_compile_end(callback): + """ + Decorator to register a callback function for the end of the compilation. + """ + callback_handler.register_end_callback(callback) + return callback diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/code_context.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/code_context.py new file mode 100644 index 0000000000000000000000000000000000000000..727aad9349555f363a727c5200c22c044c0a5083 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/code_context.py @@ -0,0 +1,30 @@ +# mypy: allow-untyped-defs +import types + +from .utils import ExactWeakKeyDictionary + + +class CodeContextDict: + def __init__(self) -> None: + self.code_context = ExactWeakKeyDictionary() + + def has_context(self, code: types.CodeType): + return code in self.code_context + + def get_context(self, code: types.CodeType): + ctx = self.code_context.get(code) + if ctx is None: + ctx = {} + self.code_context[code] = ctx + return ctx + + def pop_context(self, code: types.CodeType): + ctx = self.get_context(code) + self.code_context._remove_id(id(code)) + return ctx + + def clear(self): + self.code_context.clear() + + +code_context = CodeContextDict() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/codegen.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/codegen.py new file mode 100644 index 0000000000000000000000000000000000000000..5cc8361a974eb65de316009be7dcae05626056eb --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/codegen.py @@ -0,0 +1,511 @@ +# mypy: allow-untyped-defs +import collections +import dataclasses +import re +import sys +import types +from typing import Counter, Dict, List, Optional + +import torch.nn + +from . import utils +from .bytecode_transformation import ( + add_push_null, + add_push_null_call_function_ex, + create_call_function, + create_call_method, + create_dup_top, + create_instruction, + create_load_method, + create_rot_n, + Instruction, +) +from .exc import unimplemented +from .source import AttrSource, Source +from .utils import is_safe_constant, rot_n_helper +from .variables.base import VariableTracker +from .variables.nn_module import NNModuleVariable +from .variables.tensor import ( + NumpyNdarrayVariable, + SymNodeVariable, + TensorVariable, + UnspecializedPythonVariable, +) +from .variables.torch_function import TensorWithTFOverrideVariable + + +@dataclasses.dataclass +class GraphOutputEntry: + index: int + variable: VariableTracker + + +class PyCodegen: + """ + Helper class uses for constructing Python bytecode + """ + + def __init__( + self, + tx=None, + root: Optional[torch.nn.Module] = None, + graph_output_var: Optional[str] = None, + tempvars=None, + ) -> None: + self.root = root + self.top_of_stack: Optional[VariableTracker] = None + self.uses: Counter[VariableTracker] = collections.Counter() + self.graph_outputs: Dict[int, GraphOutputEntry] = {} + self._output: List[Instruction] = [] + self.tempvars = tempvars or {} + self.tx = tx + self.graph_output_var = graph_output_var + self.code_options = self.tx.output.code_options + self.cell_and_freevars = self.tx.cell_and_freevars + self.new_var = self.tx.output.new_var + self.mutable_side_effects_from_source = False + self.value_from_source: bool = True + + def restore_stack(self, stack_values, *, value_from_source=True): + prior = self.mutable_side_effects_from_source + self.mutable_side_effects_from_source = True + prev = self.value_from_source + self.value_from_source &= value_from_source + try: + self.foreach(stack_values) + finally: + self.mutable_side_effects_from_source = prior + self.value_from_source = prev + + def graph_output_vars(self): + return [x.variable for x in self.graph_outputs.values()] + + def call_reconstruct(self, value): + res = value.reconstruct(self) + assert res is None, f"reconstruct!=None {value}" + + def add_push_null(self, gen_fn, call_function_ex=False): + """ + `gen_fn` generates instructions via PyCodegen methods + that push a single callable to the stack. + + `add_push_null` pushes a NULL to the stack before or after the + instructions generated by `gen_fn`, depending on Python version. + + Will attempt to use the NULL push bit for instructions + with such bits (LOAD_GLOBAL 3.11+, LOAD_ATTR 3.12+, LOAD_SUPER_ATTR). + """ + old_len = len(self._output) + if sys.version_info < (3, 13): + # gen_fn may DUP_TOP instead if TOS is not cleared. + # Will cause problems since NULL will be pushed right + # before the generated instructions in <= 3.12 + self.clear_tos() + gen_fn() + # inplace modify self._output + added_insts = self._output[old_len:] + del self._output[old_len:] + if call_function_ex: + self._output.extend(add_push_null_call_function_ex(added_insts)) + else: + self._output.extend(add_push_null(added_insts)) + if sys.version_info >= (3, 13): + # NULL will be at top of stack + self.clear_tos() + + def __call__(self, value, allow_cache=True): + """Generate code such that top-of-stack (TOS) is set to value""" + if isinstance(value, Source): + self.call_reconstruct(value) + self.clear_tos() + return + + assert isinstance(value, VariableTracker) + output = self._output + graph_outputs = self.graph_outputs + + if self.top_of_stack is value and allow_cache: + output.append(create_dup_top()) + return + + if self.mutable_side_effects_from_source: + # this is needed to get aliasing relationships right + # value.mutable_local.source will get mutated to hold `value` + # mutable_side_effects_from_source=False is used to codegen the mutation + # mutable_side_effects_from_source=True is used to codegen a reference + from .side_effects import MutableSideEffects + + if isinstance(value.mutable_local, MutableSideEffects): + self(value.mutable_local.source) + return + + if allow_cache: + if value.mutable_local and value.mutable_local in self.tempvars: + output.append(self.create_load(self.tempvars[value.mutable_local])) + self.top_of_stack = value + return + if self.tempvars.get(value) is not None: + output.append(self.create_load(self.tempvars[value])) + self.top_of_stack = value + return + + if value.source is not None and allow_cache and self.value_from_source: + self.call_reconstruct(value.source) + elif value.is_python_constant() and is_safe_constant( + value.as_python_constant() + ): + output.append(self.create_load_const(value.as_python_constant())) + elif isinstance(value, TensorWithTFOverrideVariable): + graph_outputs_key = self.add_graph_output(value) + + self.add_push_null( + lambda: self.load_import_from(utils.__name__, "to_subclass") + ) + self.load_graph_output(graph_outputs[graph_outputs_key].index) + output.append( + self.create_load_global( + value.global_mangled_class_name(self.tx), add=True + ) + ) + output.extend(create_call_function(2, False)) + elif ( + isinstance(value, SymNodeVariable) + and value.python_type() == float + and not self.tx.export + ): + # This is a little unusual; force the output convention to be a + # Tensor here. Don't do this for export because this is + # apparently load bearing for export tests (but I am a bit + # doubtful it actually works in the real world) + # NB: It works to add_graph_output on a computed expression + # as_tensor here, because we memoize as_tensor calls on + # SymNodeVariable! + graph_outputs_key = self.add_graph_output(value.as_tensor(self.tx)) + + def gen_fn(): + self.load_graph_output(graph_outputs[graph_outputs_key].index) + output.append(self.create_load_attr("item")) + + self.add_push_null(gen_fn) + output.extend(create_call_function(0, False)) + elif isinstance( + value, + ( + TensorVariable, + SymNodeVariable, + UnspecializedPythonVariable, + NumpyNdarrayVariable, + ), + ): + graph_outputs_key = self.add_graph_output(value) + + if isinstance(value, NumpyNdarrayVariable): + self.add_push_null( + lambda: self.load_import_from(utils.__name__, "to_numpy_helper") + ) + self.load_graph_output(graph_outputs[graph_outputs_key].index) + output.extend(create_call_function(1, False)) + elif isinstance(value, UnspecializedPythonVariable) and value.need_unwrap: + + def gen_fn(): + self.load_graph_output(graph_outputs[graph_outputs_key].index) + output.append(self.create_load_attr("item")) + + self.add_push_null(gen_fn) + output.extend(create_call_function(0, False)) + else: + self.load_graph_output(graph_outputs[graph_outputs_key].index) + elif isinstance(value, NNModuleVariable): + parts = value.module_key.split(".") + if parts[0] in self.code_options["co_varnames"]: + output.append(self.create_load(parts[0])) + parts = parts[1:] + else: + assert self.root is not None + output.append(self.create_load_output(self.root)) + for part in parts: + output.append(self.create_load_attr(part)) + else: + self.uses[value] += 1 + try: + self.call_reconstruct(value) + except NotImplementedError: + unimplemented(f"reconstruct: {value}") + if allow_cache and value in self.tempvars: + self._output.append(create_dup_top()) + self.add_cache(value) + + self.top_of_stack = value + + def add_graph_output(self, value): + graph_outputs_key = id(value.as_proxy()) + if graph_outputs_key not in self.graph_outputs: + self.graph_outputs[graph_outputs_key] = GraphOutputEntry( + len(self.graph_outputs), value + ) + return graph_outputs_key + + def load_graph_output(self, index): + output = self._output + output.append(self.create_load(self.graph_output_var)) + output.append(self._create_load_const(index)) + output.append(create_instruction("BINARY_SUBSCR")) + + def add_cache(self, value): + var = self.new_var() + self.tempvars[value] = var + if value.mutable_local: + self.tempvars[value.mutable_local] = var + self._output.append(self.create_store(var)) + + def foreach(self, items): + for i in items: + self(i) + + def setup_globally_cached(self, name, value): + """Store value in a new global""" + name = re.sub(r"[^a-zA-Z0-9_]+", "_", name) + f_globals = self.tx.f_globals + if name in f_globals: + assert id(f_globals[name]) == id(value) + else: + f_globals[name] = value + return [self.create_load_global(name, add=True)] + + def clear_tos(self): + self.top_of_stack = None + + def append_output(self, inst): + assert isinstance(inst, Instruction) + self._output.append(inst) + self.clear_tos() + + def extend_output(self, insts): + assert all(isinstance(x, Instruction) for x in insts) + self._output.extend(insts) + self.clear_tos() + + def get_instructions(self) -> List[Instruction]: + return self._output + + def create_load(self, name) -> Instruction: + if name in self.cell_and_freevars(): + return create_instruction("LOAD_DEREF", argval=name) + assert name in self.code_options["co_varnames"], f"{name} missing" + return create_instruction("LOAD_FAST", argval=name) + + def create_load_closure(self, name) -> Instruction: + assert name in self.cell_and_freevars() + inst_name = "LOAD_FAST" if sys.version_info >= (3, 13) else "LOAD_CLOSURE" + return create_instruction(inst_name, argval=name) + + def create_store(self, name) -> Instruction: + if name in self.cell_and_freevars(): + return create_instruction("STORE_DEREF", argval=name) + assert name in self.code_options["co_varnames"] + return create_instruction("STORE_FAST", argval=name) + + def create_load_global(self, name, add=False) -> Instruction: + if add: + self.tx.output.update_co_names(name) + assert name in self.code_options["co_names"], f"{name} not in co_names" + return create_instruction("LOAD_GLOBAL", argval=name) + + def create_load_const(self, value) -> Instruction: + assert is_safe_constant(value), f"unsafe constant {value}" + return self._create_load_const(value) + + def _create_load_const(self, value) -> Instruction: + return create_instruction("LOAD_CONST", argval=value) + + create_load_output = _create_load_const + + def load_method(self, name): + self.tx.output.update_co_names(name) + self.append_output(create_load_method(name)) + + def call_method(self, nargs): + self.extend_output(create_call_method(nargs)) + + def create_load_attr(self, name) -> Instruction: + if name not in self.code_options["co_names"]: + self.code_options["co_names"] += (name,) + return create_instruction("LOAD_ATTR", argval=name) + + def load_attr(self, name): + self.append_output(self.create_load_attr(name)) + + def create_load_attrs(self, names): + return [self.create_load_attr(name) for name in names.split(".")] + + def create_store_attr(self, name) -> Instruction: + if name not in self.code_options["co_names"]: + self.code_options["co_names"] += (name,) + return create_instruction("STORE_ATTR", argval=name) + + def store_attr(self, name): + self.append_output(self.create_store_attr(name)) + + def load_function_name(self, fn_name, push_null, num_on_stack=0): + """Load the global fn_name on the stack num_on_stack down""" + output = [] + if push_null and sys.version_info >= (3, 11): + output.extend(add_push_null(self.create_load_global(fn_name, add=True))) + if num_on_stack > 0: + output.extend( + [ + *self.rot_n(num_on_stack + 2), + *self.rot_n(num_on_stack + 2), + ] + ) + else: + output.extend( + [ + self.create_load_global(fn_name, add=True), + *self.rot_n(num_on_stack + 1), + ] + ) + return output + + def rot_n(self, n): + try: + return create_rot_n(n) + except AttributeError: + # desired rotate bytecode doesn't exist, generate equivalent bytecode + return [ + create_instruction("BUILD_TUPLE", arg=n), + self._create_load_const(rot_n_helper(n)), + *create_rot_n(2), + create_instruction("CALL_FUNCTION_EX", arg=0), + create_instruction("UNPACK_SEQUENCE", arg=n), + ] + + def pop_null(self): + # POP_TOP doesn't work for null, so we pop nulls by pushing in a + # nop function, calling it (which consumes the null), and popping the result. + assert sys.version_info >= (3, 11) + return [ + self._create_load_const(lambda: None), + # 3.13 swapped NULL and callable + *( + (create_instruction("SWAP", arg=2),) + if sys.version_info >= (3, 13) + else () + ), + *create_call_function(0, False), + create_instruction("POP_TOP"), + ] + + def pop_top(self): + self.append_output(create_instruction("POP_TOP")) + + def call_function(self, nargs: int, push_null: bool): + self.extend_output(create_call_function(nargs, push_null=push_null)) + + def dup_top(self): + self.append_output(create_dup_top()) + + def store(self, varname): + self.append_output(self.create_store(varname)) + + def make_function_with_closure( + self, fn_name: str, code: types.CodeType, push_null: bool, num_on_stack=0 + ): + freevars = code.co_freevars + assert freevars + output = self._output + + def gen_fn(): + for var in freevars: + assert var in self.cell_and_freevars() + inst_name = ( + "LOAD_FAST" if sys.version_info >= (3, 13) else "LOAD_CLOSURE" + ) + output.append(create_instruction(inst_name, argval=var)) + output.append(create_instruction("BUILD_TUPLE", arg=len(freevars))) + output.append(self.create_load_const(code)) + if sys.version_info < (3, 11): + output.append(self.create_load_const(fn_name)) + if sys.version_info >= (3, 13): + output.extend( + [ + create_instruction("MAKE_FUNCTION"), + create_instruction("SET_FUNCTION_ATTRIBUTE", arg=0x08), + ] + ) + else: + output.append(create_instruction("MAKE_FUNCTION", arg=0x08)) + + if push_null and sys.version_info >= (3, 11): + self.add_push_null(gen_fn) + output.extend(self.rot_n(num_on_stack + 2)) + output.extend(self.rot_n(num_on_stack + 2)) + else: + gen_fn() + output.extend(self.rot_n(num_on_stack + 1)) + self.clear_tos() + + def create_load_python_module(self, mod) -> Instruction: + """ + Generate a LOAD_GLOBAL instruction to fetch a given python module. + """ + output = self.tx.output + global_scope = output.global_scope + name = re.sub(r"^.*[.]", "", mod.__name__) + if global_scope.get(name, None) is mod: + return self.create_load_global(name, add=True) + prefix = f"___module_{name}" + global_name = self.tx.output.install_global_by_id(prefix, mod) + return self.create_load_global(global_name, add=True) + + def make_call_generated_code(self, fn_name: str) -> None: + """Call the generated code function stored in fn_name""" + self.extend_output(self.load_function_name(fn_name, True)) + + graphargs = self.tx.output.graphargs + for arg in graphargs: + if arg.pass_arg_as_tensor: + self.add_push_null( + lambda: self.extend_output( + [ + self.create_load_python_module(torch), + self.create_load_attr("as_tensor"), + ] + ) + ) + self.call_reconstruct(arg) + self.extend_output(create_call_function(1, False)) + else: + self.call_reconstruct(arg) + + self.extend_output(create_call_function(len(graphargs), False)) + + def load_import_from(self, module_name, object_name) -> None: + self(AttrSource(self.tx.import_source(module_name), object_name)) + + def create_call_function_kw(self, nargs, kw_names, push_null) -> List[Instruction]: + if sys.version_info >= (3, 13): + output = create_call_function(nargs, push_null) + assert output[-1].opname == "CALL" + output.insert(-1, self.create_load_const(kw_names)) + output[-1] = create_instruction("CALL_KW", arg=nargs) + return output + elif sys.version_info >= (3, 11): + output = create_call_function(nargs, push_null) + if sys.version_info >= (3, 12): + idx = -1 + expected_inst = "CALL" + else: + idx = -2 + expected_inst = "PRECALL" + assert output[idx].opname == expected_inst + kw_names_inst = create_instruction("KW_NAMES", argval=kw_names) + output.insert(idx, kw_names_inst) + return output + return [ + self.create_load_const(kw_names), + create_instruction("CALL_FUNCTION_KW", arg=nargs), + ] + + def create_delete(self, value) -> Instruction: + return create_instruction("DELETE_FAST", argval=value) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/compiled_autograd.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/compiled_autograd.py new file mode 100644 index 0000000000000000000000000000000000000000..e7c5d2414f6e2b8b5a4610ee4f2e192d89f1cca1 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/compiled_autograd.py @@ -0,0 +1,533 @@ +# mypy: allow-untyped-defs +import contextlib +import functools +from typing import Any, Dict, List, Optional, TYPE_CHECKING, Union + +import torch +from torch._dynamo.external_utils import ( + call_backward, + call_hook, + FakeCompiledAutogradEngine, +) +from torch._dynamo.source import GetItemSource, LocalSource +from torch._dynamo.utils import counters, lazy_format_graph_code, set_locals_to_steal +from torch._logging import getArtifactLogger, trace_structured +from torch._prims_common import clone_preserve_strides +from torch._subclasses import FakeTensorMode +from torch.fx import GraphModule +from torch.fx.experimental._backward_state import BackwardState +from torch.fx.experimental.proxy_tensor import ( + decompose, + disable_autocast_cache, + disable_proxy_modes_tracing, + fetch_object_proxy, + ProxyTorchDispatchMode, + PythonKeyTracer, + track_tensor_tree, +) +from torch.fx.experimental.symbolic_shapes import DimDynamic, ShapeEnv +from torch.fx.traceback import preserve_node_meta, set_stack_trace +from torch.utils._traceback import CapturedTraceback + + +if TYPE_CHECKING: + from torch.fx.proxy import Proxy + + +compiled_autograd_log = getArtifactLogger(__name__, "compiled_autograd") +verbose_log = getArtifactLogger(__name__, "compiled_autograd_verbose") + + +def snapshot_verbose_logging_enabled(): + return torch._logging._internal.log_state.is_artifact_enabled( + "compiled_autograd_verbose" + ) + + +def cpp_verbose_log_fn(msg: str) -> None: + verbose_log.debug(msg) + + +def snapshot_cudagraph_enabled(): + return torch._inductor.config.triton.cudagraphs + + +def maybe_clone(x): + if x is not None: + return clone_preserve_strides(x) + return x + + +class AutogradCompilerInstance: + def __init__(self, compiler_fn) -> None: + self.compiler_fn = compiler_fn + self.stack = contextlib.ExitStack() + self.close = self.stack.close + self.shape_env = ShapeEnv() + self.fake_tensor_mode = FakeTensorMode( + allow_fallback_kernels=True, + allow_non_fake_inputs=True, + shape_env=self.shape_env, + ) + self.fx_tracer = PythonKeyTracer() + self.proxy_mode = ProxyTorchDispatchMode(self.fx_tracer, "symbolic") + self.hooks_proxy: Optional[Proxy] = None + self.graph_placeholders = ["inputs", "sizes", "scalars", "hooks"] + + def wrap_fake(self, x, source): + assert isinstance(x, torch.Tensor) + return self.fake_tensor_mode.from_tensor(x, source=source) + + @staticmethod + def source(name, idx) -> GetItemSource: + return GetItemSource(LocalSource(name), idx) + + def begin_capture( + self, + inputs: List[torch.Tensor], + sizes: List[int], + scalars: List[Union[int, float]], + ): + counters["compiled_autograd"]["captures"] += 1 + self.aot_graph_cls_name: Optional[str] = None + self.aot_graph_infos: Dict[int, Dict[str, Any]] = {} + self.fx_tracer.root = torch.nn.Module() + self.fx_tracer.graph = torch.fx.Graph(tracer_cls=PythonKeyTracer) + self.fx_tracer.tensor_attrs = {} + args_proxy, sizes_proxy, scalars_proxy, self.hooks_proxy = ( + self.fx_tracer.create_proxy("placeholder", name, (), {}) + for name in self.graph_placeholders + ) + + # tensor inputs to fake tensors + inputs = [ + self.wrap_fake(x, self.source("inputs", idx)) + for idx, x in enumerate(inputs) + ] + self.bind_tensors_to_proxies(inputs, args_proxy) + + # size inputs to symints + sizes = [ + self.shape_env.create_unspecified_symint_and_symbol( + val, + self.source("sizes", idx), + DimDynamic.DYNAMIC, + ) + for idx, val in enumerate(sizes) + ] + self.bind_tensors_to_proxies(sizes, sizes_proxy) + + for idx, val in enumerate(scalars): + source = self.source("scalars", idx) + if isinstance(val, int): + scalars[idx] = self.shape_env.create_unspecified_symint_and_symbol( + val, + source, + DimDynamic.DYNAMIC, + ) + elif isinstance(val, float): + scalars[idx] = self.shape_env.create_symfloatnode( + self.shape_env.create_unspecified_symbol( + val, + source=source, + dynamic_dim=DimDynamic.DYNAMIC, + ), + hint=val, + source=source, + ) + else: + raise AssertionError("Unexpected scalar type: ", type(val)) + self.bind_tensors_to_proxies(scalars, scalars_proxy) + + # TODO(jansel): are all these modes needed? + self.stack.enter_context(decompose({})) + self.stack.enter_context(self.fake_tensor_mode) + self.stack.enter_context(self.proxy_mode) + self.stack.enter_context(disable_autocast_cache()) + self.stack.enter_context(preserve_node_meta()) + return inputs, sizes, scalars + + def proxy_call_backward( + self, + inputs, + output_metadatas, + saved_tensors, + backward_idx: int, + ): + assert self.hooks_proxy is not None + backward_c_function = self.hooks_proxy[backward_idx] # type: ignore[index] + proxies = self.fx_tracer.create_proxy( + kind="call_function", + target=call_backward, + args=( + backward_c_function, + self.to_proxy(saved_tensors), + *self.to_proxy(inputs), + ), + kwargs={}, + ) + + with disable_proxy_modes_tracing(): + # create fake Tensors + grad_ins: List[Optional[torch.Tensor]] = [] + for output_metadata in output_metadatas: + if output_metadata is None: + grad_ins.append(None) + continue + + layout, device, dtype, size = output_metadata + grad_ins.append( + torch.empty(size=size, dtype=dtype, layout=layout, device=device) + ) + self.bind_tensors_to_proxies(grad_ins, proxies) + return tuple(grad_ins) + + def proxy_call_hook(self, hook, *args, **kwargs): + return self.fx_tracer.create_proxy( + "call_function", + call_hook, + ( + hook, + *[self.to_proxy(x) for x in args], + ), + kwargs, + ) + + def tensor_pre_hook(self, inputs, hook_id, i: int): + assert self.hooks_proxy is not None + hook = self.hooks_proxy[hook_id] # type: ignore[index] + proxy = self.proxy_call_hook( + hook, + inputs[i], + hook_type="tensor_pre_hook", + ) + with disable_proxy_modes_tracing(): + inputs[i] = maybe_clone(inputs[i]) + self.bind_tensors_to_proxies([inputs[i]], [proxy]) + return inputs + + def pre_hook(self, inputs, hook_id): + assert self.hooks_proxy is not None + hook = self.hooks_proxy[hook_id] # type: ignore[index] + proxies = self.proxy_call_hook( + hook, + inputs, + hook_type="pre_hook", + ) + with disable_proxy_modes_tracing(): + inputs = [maybe_clone(x) for x in inputs] + self.bind_tensors_to_proxies(inputs, proxies) + return inputs + + def post_hook(self, outputs, inputs, hook_id): + assert self.hooks_proxy is not None + hook = self.hooks_proxy[hook_id] # type: ignore[index] + proxies = self.proxy_call_hook( + hook, + outputs, + inputs, + hook_type="post_hook", + ) + with disable_proxy_modes_tracing(): + outputs = [maybe_clone(x) for x in outputs] + self.bind_tensors_to_proxies(outputs, proxies) + return outputs + + def post_acc_grad_hook(self, input, hook_id): + assert isinstance(input, torch.Tensor) + assert self.hooks_proxy is not None + hook = self.hooks_proxy[hook_id] # type: ignore[index] + proxy = self.proxy_call_hook( + hook, + input, + hook_type="post_acc_grad_hook", + ) + with disable_proxy_modes_tracing(): + input = [maybe_clone(input)] + self.bind_tensors_to_proxies(input, [proxy]) + return input + + # Note: [Compiled autograd and cudagraphs] + # Eager autograd backward implements scalars as 0-dim tensors, see DivBackward0::other_. + # When compiled autograd traces those nodes, it lifts the scalar tensors, resulting in a graph + # with some cpu 0-dim tensor inputs. To prevent the entire graph from skipping cudagraph, we move the + # scalars tensors to cuda. This works because ATen/prims ops will accept cuda 0-dim tensors too. + def move_graph_nodes_to_cuda(self, graph) -> List[int]: + to_move: Dict[int, torch.fx.Node] = {} + has_cuda_inputs = False + nodes = list(graph.nodes) + assert nodes[0].target == "inputs" + inputs = nodes[0] + inputs_users = list(inputs.users.keys()) + # input access nodes should immediately follow placeholder nodes + first_getitem_idx = len(self.graph_placeholders) + assert nodes[first_getitem_idx] == inputs_users[0] + last_getitem_idx = first_getitem_idx + len(inputs_users) - 1 + assert nodes[last_getitem_idx] == inputs_users[-1] + for i, node in enumerate(inputs_users): + if not has_cuda_inputs and node.meta["val"].device.type == "cuda": + has_cuda_inputs = True + continue + + is_cpu = node.meta["val"].device.type == "cpu" + is_scalar = len(node.meta["val"].size()) == 0 + if is_cpu and is_scalar: + node_users = list(node.users.keys()) + if all( + isinstance(user.target, torch._ops.OpOverload) + and user.target.namespace in ("prims", "aten") + for user in node_users + ): + # all users are prims/aten, can move safely + to_move[i] = node + + # only move cpu scalars to cuda if there were cuda activations in this graph, + # this is to handle the case where cudagraphs is enabled on a cpu-only graph + if has_cuda_inputs: + for node in to_move.values(): + node.meta["val"] = node.meta["val"].cuda() + + # return runtime indices we need to move to cuda + return list(to_move.keys()) + + return [] + + def end_capture(self, outputs): + self.fx_tracer.create_proxy( + "call_function", + FakeCompiledAutogradEngine._exec_final_callbacks_stub, + (), + {}, + ) + self.stack.close() + self.fx_tracer.create_node( + "output", + "output", + (self.fx_tracer.create_arg(self.to_proxy(outputs)),), + {}, + ) + self.rename_aot_dispatcher_nodes() + self.reorder_accumulate_grad_nodes() + runtime_inputs_to_move: List[int] = [] + if snapshot_cudagraph_enabled(): + runtime_inputs_to_move = self.move_graph_nodes_to_cuda(self.fx_tracer.graph) + + graph = GraphModule( + self.fx_tracer.root, self.fx_tracer.graph, "CompiledAutograd" + ) + set_locals_to_steal(graph, ["inputs"]) + lazy_graph_code = lazy_format_graph_code( + "Compiled autograd graph", + graph, + include_device=True, + include_stride=True, + colored=True, + ) + compiled_autograd_log.info("%s", lazy_graph_code) + verbose_log.debug("%s", lazy_graph_code) + trace_structured( + "compiled_autograd_graph", + payload_fn=lambda: graph.print_readable(print_output=False), + ) + + def runtime_wrapper(compiled_fn, inputs, sizes, scalars, hooks): + global in_compiled_autograd_region + try: + in_compiled_autograd_region = True + for i in runtime_inputs_to_move: + inputs[i] = inputs[i].pin_memory().cuda(non_blocking=True) + + return compiled_fn(inputs, sizes, scalars, hooks) + finally: + in_compiled_autograd_region = False + + return runtime_wrapper, self.compiler_fn(graph) + + def rename_aot_dispatcher_nodes(self): + """ + Renames nodes as they appear in the AOTDispatcher backward graphs, prefixed by AOT id + e.g. AOTDispatcher backward graph X's `sin_Y` -> `aotX_sin_Y` + """ + if self.aot_graph_cls_name is None: + return + + def is_similar(a: torch.fx.node.Node, b: torch.fx.node.Node): + target_match = a.target == b.target + if not target_match: + target_match = ( + hasattr(a.target, "__name__") + and hasattr(b.target, "__name__") + and a.target.__name__ == b.target.__name__ + ) + return ( + target_match + and a.op == b.op + and a.type == b.type + and len(a.all_input_nodes) == len(b.all_input_nodes) + ) + + for nodecall_index, info in self.aot_graph_infos.items(): + ca_node_start_idx = info["ca_node_start_idx"] + aot_id = info["aot_id"] + aot_graph = info["aot_gm"].graph + + # 1. Find the first op from user code in the AOT graph + aot_it = iter(aot_graph.nodes) + aot_node = next(aot_it) + assert aot_node is not None + try: + while aot_node.op != "call_function": + aot_node = next(aot_it) + except StopIteration: + continue + + try: + # 2. Find the first op in the compiled autograd graph segment + ca_it = iter(self.fx_tracer.graph.nodes) + for _ in range(ca_node_start_idx): + next(ca_it) + ca_node = next(ca_it) + + # Graphs should all end with output node + while ca_node.op != "output" and not is_similar(ca_node, aot_node): + # The compiled autograd graph may contain lazily inserted ops + # We skip those when aligning nodes + ca_node = next(ca_it) + + # 3. Keep alligned and rename nodes + while aot_node.op != "output" and ca_node.op != "output": + if not ca_node.users: + # TODO: DCE for compiled autograd graph + ca_node = next(ca_it) + continue + + if not is_similar(aot_node, ca_node): + # There should be no lazily inserted ops in the middle of a match + # So any deviation is an error + raise StopIteration + + ca_node.name = f"aot{aot_id}_{aot_node.name}" + for i, inp in enumerate(aot_node.all_input_nodes): + ca_node.all_input_nodes[i].name = f"aot{aot_id}_{inp.name}" + + aot_node = next(aot_it) + ca_node = next(ca_it) + except StopIteration: + verbose_log.debug( + "Failed to match %s%s (NodeCall %s) nodes with AOT backward graph %s nodes", + self.aot_graph_cls_name, + aot_id, + nodecall_index, + aot_id, + ) + + def reorder_accumulate_grad_nodes(self): + """ + Usage of AOTAutograd causes all the accumulate_grad_ nodes to get pushed to the end of + the graph. This differs from eager mode, which schedules them as soon as possible. This + pass attempts to reorder the graph to mimic eager behavior. + """ + for node in self.fx_tracer.graph.find_nodes( + op="call_function", target=torch.ops.inductor.accumulate_grad_.default + ): + arg = max(node.args) # last arg + if arg is not node.prev and arg.op != "placeholder": + arg.append(node) + + def to_proxy(self, t): + if t is None: + return None + if isinstance(t, list): + return [self.to_proxy(x) for x in t] + if isinstance(t, tuple): + return tuple(self.to_proxy(x) for x in t) + # can it be torch.SymInt as the code used to imply? + assert isinstance(t, torch.Tensor) + proxy_tensor = fetch_object_proxy(self.fx_tracer, t) + assert isinstance(proxy_tensor, torch.fx.experimental.proxy_tensor._ProxyTensor) + return proxy_tensor.proxy + + def bind_tensors_to_proxies(self, tensors, proxies): + if isinstance(proxies, torch.fx.Proxy): + proxies = [proxies[i] for i in range(len(tensors))] # type: ignore[index] + assert len(tensors) == len(proxies) + track_tensor_tree(tensors, proxies, constant=None, tracer=self.fx_tracer) + + def bind_backward_state(self, index: int): + assert self.hooks_proxy is not None + proxy = self.hooks_proxy[index] # type: ignore[index] + bw_state = BackwardState() + track_tensor_tree(bw_state, proxy, constant=None, tracer=self.fx_tracer) + return bw_state + + def set_node_origin( + self, + node_name: str, + nodecall_index: int, + pyobj: Optional[torch.autograd.Function], + ): + maybe_aot_id = "" + if pyobj is not None: + forward_cls = pyobj._forward_cls # type: ignore[attr-defined] + if hasattr(forward_cls, "_aot_id"): + # backward was created by AOT Dispatcher + self.aot_graph_cls_name = node_name + maybe_aot_id = forward_cls._aot_id + self.aot_graph_infos[nodecall_index] = { + "ca_node_start_idx": len(self.fx_tracer.graph.nodes), + "aot_id": maybe_aot_id, + "aot_gm": forward_cls._lazy_backward_info.bw_module, + } + + new_code = f"{node_name}{maybe_aot_id} (NodeCall {nodecall_index})" + raw_stack_trace = CapturedTraceback.extract().format()[-1] + new_stack_trace = raw_stack_trace.replace( + "raw_stack_trace = CapturedTraceback.extract().format()[-1]", new_code + ) + set_stack_trace(new_stack_trace) + + +# state of the autograd engine dispatch, kept in sync by enable/disable context managers +compiled_autograd_enabled = False + +# global flag to check if we are processing graphs produced from a compiled autograd graph +in_compiled_autograd_region = False + + +@contextlib.contextmanager +def enable(compiler_fn): + prior = torch._C._dynamo.compiled_autograd.set_autograd_compiler( + functools.partial(AutogradCompilerInstance, compiler_fn) + ) + if snapshot_verbose_logging_enabled(): + torch._C._dynamo.compiled_autograd.set_verbose_logger(cpp_verbose_log_fn) + global compiled_autograd_enabled + compiled_autograd_enabled = True + try: + with torch.autograd.set_multithreading_enabled(False): + yield + finally: + if not prior: + compiled_autograd_enabled = False + torch._C._dynamo.compiled_autograd.set_autograd_compiler(prior) + + +@contextlib.contextmanager +def disable(): + prior = torch._C._dynamo.compiled_autograd.set_autograd_compiler(None) + global compiled_autograd_enabled + compiled_autograd_enabled = False + try: + yield + finally: + if prior: + compiled_autograd_enabled = True + torch._C._dynamo.compiled_autograd.set_autograd_compiler(prior) + + +# return to starting state of a new process +def reset() -> None: + compiled_autograd_enable = False + assert not in_compiled_autograd_region + torch._C._dynamo.compiled_autograd.set_autograd_compiler(None) + torch._C._dynamo.compiled_autograd.set_verbose_logger(None) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/comptime.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/comptime.py new file mode 100644 index 0000000000000000000000000000000000000000..972d79d48fa8b2e841aa491e315798c7e56e0a51 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/comptime.py @@ -0,0 +1,401 @@ +# mypy: allow-untyped-defs +# This file establishes the public comptime interface to Dynamo. +# This allows Dynamo users to execute arbitrary Python code while +# Dynamo is symbolically evaluating their original programs. +# +# The goal of the public API is to give users rope, without actually +# leaking private implementation details of Dynamo. + +import builtins +import dis +import time +import traceback +from typing import Optional, Union + +import torch +from torch.fx.experimental.symbolic_shapes import free_symbols + +from .exc import unimplemented +from .variables import NewCellVariable +from .variables.constant import ConstantVariable +from .variables.misc import ClosureVariable +from .variables.tensor import SymNodeVariable + + +class ComptimeVar: + """ + A ComptimeVar represents a Python value, at some particular point + in time, in the Python code we are symbolically evaluating with + torchdynamo. This must be distinguished from a runtime value, as + at compile-time there are some properties of the variable we + do not know (for example, if the ComptimeVar represents a Tensor, + we only know metadata about the tensor; we do NOT know what the + actual data in the Tensor is.) + """ + + def __init__(self, v) -> None: + self.__variable = v + + def as_proxy(self): + """ + Returns an fx.Proxy (or tuple/list of fx.Proxy) representing + this variable in the FX graph we are assembling to pass + to the user compiler. + + This method only works for variables we actually track in + the FX graph, aka Tensors (and ints, if you are compiling + with dynamic shapes). In particular, if you have a list + or tuple of tensors, you will get a list/tuple of proxies + (not a single proxy representing the entire list/tuple). + """ + return self.__variable.as_proxy() + + def is_proxy(self): + """ + Returns True if as_proxy() would succeed. + """ + return self.__variable.is_proxy() + + def as_fake(self): + """ + Returns a "fake" value (either a FakeTensor or a SymInt) + representing the variable in question. This only works + for variables that denote Tensor or int. You can use + this to query metadata; e.g., v.as_fake().size(0) will + tell you the compile-time known size of the tensor. + + WARNING: Do NOT mutate the returned tensor. + """ + return self.__variable.as_proxy().node.meta["example_value"] + + def size(self, dim: Optional[int] = None) -> Union[int, torch.SymInt]: + """ + Returns the size of the tensor (if dim is None) or the size + at the dimension dim. The returned size may be a SymInt. + """ + return self.as_fake().size(dim) + + def python_type(self): + """ + Returns what type(v) would have returned for the variable + at compile time. + """ + return self.__variable.python_type() + + def as_python_constant(self): + """ + Returns the Python value this variable would have, but only if it is + completely known at compile-time (e.g., it is constant). + + WARNING: Do NOT mutate the returned constant. The returned constant + may or may not correspond to the actual value this variable may take + on at runtime; for example, if the variable in question is a constant + list, we may return a copy of that list. + """ + return self.__variable.as_python_constant() + + def is_python_constant(self): + """ + Returns True if as_python_constant would succeed. + """ + return self.__variable.is_python_constant() + + def is_dynamic(self): + if isinstance(self.__variable, SymNodeVariable): + fs = free_symbols(self.__variable.sym_num) + return bool(fs) + return False + + def force_static(self): + """ + Forces that a value is static, inducing a guard on its specific value + """ + if isinstance(self.__variable, SymNodeVariable): + self.__variable.evaluate_expr() + elif isinstance(self.__variable, ConstantVariable): + # TODO: Maybe complain if this isn't a int/bool/float variable + pass + else: + raise AssertionError( + f"cannot force {self.__variable} ({type(self.__variable)}) static" + ) + + def _i_will_not_complain_if_bc_breaks_VariableTracker(self): + """ + Returns the internal data structure VariableTracker that Dynamo uses + to represent variables at compile time. There are no BC guarantees on + this API and WE RESERVE THE RIGHT TO BREAK YOUR CODE if you rely on + it. + """ + return self.__variable + + def __repr__(self) -> str: + return self.__variable.debug_repr() + + # TODO: API for adding a custom guard + + +class ComptimeContext: + """ + This context class provides access to a public API for Dynamo's internals. + If there is something here you would find useful that is missing, please + file a feature request at https://github.com/pytorch/pytorch/ + """ + + def __init__(self, tx) -> None: + self.__tx = tx + + def get_local(self, name: str, *, stacklevel=0) -> ComptimeVar: + """ + Retrieve the compile-time known information about a local. + """ + tx = self.__get_tx(stacklevel) + + # This is analogous to LOAD_DEREF + if hasattr(tx, "closure_cells") and name in tx.closure_cells: + cell = tx.closure_cells[name] + if isinstance(cell, ClosureVariable): + return ComptimeVar(tx.output.root_tx.symbolic_locals[cell.name]) + else: + return ComptimeVar(tx.output.side_effects.load_cell(cell)) + else: + r = tx.symbolic_locals[name] + if isinstance(r, NewCellVariable): + return ComptimeVar(tx.output.side_effects.load_cell(r)) + else: + return ComptimeVar(r) + + def graph_break(self, msg="ComptimeContext.graph_break"): + """ + Manually trigger a graph break + """ + unimplemented(msg) + + def graph(self): + """ + Retrieve the partially constructed FX graph that would be + passed to the user compiler after compilation. + """ + return self.__tx.output.graph + + def assert_static(self, val): + """ + Asserts that the int is static (and not dynamic, per dynamic shapes) + """ + assert ( + not val.is_dynamic() + ), "expected static but got dynamic (run with TORCH_LOGS=dynamic for more info)" + + def print_graph(self, *, verbose=True, file=None): + """ + Print the partially constructed FX graph that would be passed + to the user compiler after compilation. + """ + print( + self.__tx.output.graph.python_code("self", verbose=verbose).src, file=file + ) + + def parent(self): + return ComptimeContext(self.__tx.parent) + + def __get_tx(self, stacklevel): + tx = self.__tx + for _ in range(stacklevel): + tx = tx.parent + return tx + + def print(self, val, *, file=None): + print(repr(val), file=file) + + def print_disas(self, *, file=None, stacklevel=0): + """ + Print the current series of opcodes being executed (not including + parent frames), including where you are in the particular opcode + stream. + """ + tx = self.__get_tx(stacklevel) + print( + dis.Bytecode( + tx.f_code, + current_offset=tx.instructions[tx.instruction_pointer].offset, + ).dis(), + file=file, + ) + + def print_value_stack(self, *, file=None, stacklevel=0): + """ + Print the current Python value stack. Note that this is NOT the same + as the traceback; use print_bt() to print that. Note that at + stacklevel=0, this will typically be empty, as comptime cannot + currently be used in an expression context where there would be + intermediates on the stack. If you would find this useful, please + file a bug at https://github.com/pytorch/pytorch/ + + NB: Stack grows downwards in our print + """ + tx = self.__get_tx(stacklevel) + for s in tx.stack: + print(f"- {s.debug_repr()}", file=file) + + def print_locals(self, *, file=None, stacklevel=0): + """ + Print all of the locals available in the current context. + By default this view is very limited; you can get more information + about any individual local using get_local(). + """ + tx = self.__get_tx(stacklevel) + for k, v in tx.symbolic_locals.items(): + print(f"{k} = {v.debug_repr()}", file=file) + + def print_bt(self, *, file=None, stacklevel=0): + """ + Print the user code backtrace, starting at the beginning of the + frame Dynamo started evaluating. Note that this MAY NOT go all + the way to the torch.compile invocation, as we may have done + a graph break and are compiling an intermediate frame as the + starting point. If you think the other behavior would be better, + file a bug at https://github.com/pytorch/pytorch/ + """ + stack = [] + tx = self.__get_tx(stacklevel) + while tx is not None: + stack.append(tx.frame_summary()) + tx = getattr(tx, "parent", None) + print( + "".join(traceback.StackSummary.from_list(reversed(stack)).format()), + file=file, + ) + + def print_guards(self, *, file=None): + """ + Print the currently installed guards for the Dynamo context. + This does NOT include guards associated with variables that + may or may not be installed in the future if those variables + are used. + """ + # TODO: improve print format, current guard format is extremely + # verbose + print( + "\n".join(f"{repr(guard)}" for guard in sorted(self.__tx.output.guards)), + file=file, + ) + + def _i_will_not_complain_if_bc_breaks_InstructionTranslator(self): + """ + Returns the internal data structure InstructionTranslator that Dynamo + uses to track state of symbolic evaluation. There are no BC + guarantees on this API and WE RESERVE THE RIGHT TO BREAK YOUR CODE if + you rely on it. + """ + return self.__tx + + def sleep(self, sec): + time.sleep(sec) + + +class _Comptime: + @staticmethod + def __call__(fn, fallback_fn=lambda: None): + """fn gets called at compile time in TorchDynamo, calls fallback_fn otherwise""" + fallback_fn() + + # Convenience wrappers that are more compact to use + + @staticmethod + def graph_break(): + comptime(lambda ctx: ctx.graph_break()) + + @staticmethod + def print(e): + comptime(lambda ctx: ctx.print(ctx.get_local("e")), lambda: print(e)) + + @staticmethod + def print_graph(): + comptime(lambda ctx: ctx.print_graph()) + + @staticmethod + def print_disas(*, stacklevel=0): + comptime( + lambda ctx: ctx.print_disas( + stacklevel=ctx.get_local("stacklevel").as_python_constant() + 1 + ) + ) + + @staticmethod + def print_value_stack(*, stacklevel=0): + comptime( + lambda ctx: ctx.print_value_stack( + stacklevel=ctx.get_local("stacklevel").as_python_constant() + 1 + ) + ) + + # This is a more useful variant of print_value_stack that can be used + # in an expression context; e.g., x + print_value_stack_and_return(y + z), + # you will see x on the stack prior to the addition operation + @staticmethod + def print_value_stack_and_return(e, *, stacklevel=0): + comptime( + lambda ctx: ctx.print_value_stack( + stacklevel=ctx.get_local("stacklevel").as_python_constant() + 1 + ) + ) + return e + + @staticmethod + def print_locals(*, stacklevel=0): + comptime( + lambda ctx: ctx.print_locals( + stacklevel=ctx.get_local("stacklevel").as_python_constant() + 1 + ) + ) + + @staticmethod + def print_bt(*, stacklevel=0): + comptime( + lambda ctx: ctx.print_bt( + stacklevel=ctx.get_local("stacklevel").as_python_constant() + 1 + ) + ) + + @staticmethod + def print_guards(): + comptime(lambda ctx: ctx.print_guards()) + + @staticmethod + def assert_static(val): + comptime(lambda ctx: ctx.assert_static(ctx.get_local("val"))) + + @staticmethod + def force_static(val): + comptime(lambda ctx: ctx.get_local("val").force_static()) + + @staticmethod + def breakpoint(): + """ + Like pdb breakpoint(), but drop into pdb whenever this line + of code is compiled by dynamo. Use it by putting + this in your model code:: + + from torch._dynamo.comptime import comptime + comptime.breakpoint() + + And then, inside pdb, you can access 'ctx' to query things + about the compilation context:: + + (Pdb) !ctx.print_bt() + (Pdb) !ctx.print_locals() + (Pdb) p ctx.get_local("attention").as_fake() + """ + + def inner(inner_ctx): + ctx = inner_ctx.parent() + builtins.breakpoint() + + comptime(inner) + + @staticmethod + def sleep(sec): + comptime(lambda ctx: ctx.sleep(ctx.get_local("sec").as_python_constant())) + + +comptime = _Comptime() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/config.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/config.py new file mode 100644 index 0000000000000000000000000000000000000000..2ba29961af36e93b5c16d3ce02181fe989e0458c --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/config.py @@ -0,0 +1,490 @@ +# mypy: allow-untyped-defs +import getpass +import inspect +import os +import re +import sys +import tempfile +from os.path import abspath, dirname +from typing import Any, Callable, Dict, Optional, Set, Type, TYPE_CHECKING, Union + +import torch + + +def is_fbcode(): + return not hasattr(torch.version, "git_version") + + +# to configure logging for dynamo, aot, and inductor +# use the following API in the torch._logging module +# torch._logging.set_logs(dynamo=, aot=, inductor) +# or use the environment variable TORCH_LOGS="dynamo,aot,inductor" (use a prefix + to indicate higher verbosity) +# see this design doc for more detailed info +# Design doc: https://docs.google.com/document/d/1ZRfTWKa8eaPq1AxaiHrq4ASTPouzzlPiuquSBEJYwS8/edit# +# the name of a file to write the logs to +# [@compile_ignored: debug] +log_file_name: Optional[str] = None + +# [@compile_ignored: debug] Verbose will print full stack traces on warnings and errors +verbose = os.environ.get("TORCHDYNAMO_VERBOSE", "0") == "1" + +# [@compile_ignored: runtime_behaviour] verify the correctness of optimized backend +verify_correctness = False + +# need this many ops to create an FX graph +minimum_call_count = 1 + +# turn on/off DCE pass +dead_code_elimination = True + +# disable (for a function) when cache reaches this size + +# controls the maximum number of cache entries with a guard on same ID_MATCH'd +# object. It also controls the maximum size of cache entries if they don't have +# any ID_MATCH'd guards. +# [@compile_ignored: runtime_behaviour] +cache_size_limit = 8 + +# [@compile_ignored: runtime_behaviour] safeguarding to prevent horrible recomps +accumulated_cache_size_limit = 256 + +# [@compile_ignored: runtime_behaviour] skip tracing recursively if cache limit is hit +skip_code_recursive_on_cache_limit_hit = True + +# whether or not to specialize on int inputs. This only has an effect with +# dynamic_shapes; when dynamic_shapes is False, we ALWAYS specialize on int +# inputs. Note that assume_static_by_default will also cause ints to get +# specialized, so this is mostly useful for export, where we want inputs +# to be dynamic, but accesses to ints should NOT get promoted into inputs. +specialize_int = False + +# Whether or not to specialize on float inputs. Dynamo will always promote +# float inputs into Tensor inputs, but at the moment, backends inconsistently +# support codegen on float (this is to be fixed). +specialize_float = True + +# legacy config, does nothing now! +dynamic_shapes = True + +use_lazy_graph_module = ( + os.environ.get("TORCH_COMPILE_USE_LAZY_GRAPH_MODULE", "1") == "1" +) + +# This is a temporarily flag, which changes the behavior of dynamic_shapes=True. +# When assume_static_by_default is True, we only allocate symbols for shapes marked dynamic via mark_dynamic. +# NOTE - this flag can be removed once we can run dynamic_shapes=False w/ the mark_dynamic API +# see [Note - on the state of mark_dynamic] +assume_static_by_default = True + +# This flag changes how dynamic_shapes=True works, and is meant to be used in conjunction +# with assume_static_by_default=True. +# With this flag enabled, we always compile a frame as fully static for the first time, and, if we fail +# any guards due to wobbles in shape, we recompile with *all* the wobbled shapes as being marked dynamic. +automatic_dynamic_shapes = True + +# This flag changes how the shapes of parameters are treated. +# If this flag is set to True, then the shapes of torch.nn.Parameter as well as of torch.Tensor are attempted to be dynamic +# If this flag is set to False, then the shapes of torch.nn.Parameter are assumed to be static, +# while the shapes of torch.Tensor are assumed to be dynamic. +force_parameter_static_shapes = True + +# This flag ensures that the shapes of a nn module are always assumed to be static +# If the flag is set to True, then the shapes of a nn.module are assumed to be static +# If the flag is set to False, then the shapes of a nn.module can be dynamic +force_nn_module_property_static_shapes = True + +# Typically, if you mark_dynamic a dimension, we will error if the dimension +# actually ended up getting specialized. This knob changes the behavior so +# that we don't error at all. This is helpful for our CI where I'm using a +# heuristic to mark batch dimensions as dynamic and the heuristic may get it +# wrong. +allow_ignore_mark_dynamic = False + +# Set this to False to assume nn.Modules() contents are immutable (similar assumption as freezing) +guard_nn_modules = True + +# Uses CPython internal dictionary tags to detect mutation. There is some +# overlap between guard_nn_modules_using_dict_tags and guard_nn_modules flag. +# guard_nn_modules unspecializes the nn module instance and adds guard for each +# relevant member of the nn modules. On the other hand, +# guard_nn_modules_using_dict_tags specializes on each nn module instance but +# uses low overhead dict version matching to detect mutations, obviating the +# need to guard on members of the nn modules. With +# guard_nn_modules_using_dict_tags, the guard_nn_modules is not really required +# but kept around for debugging and discussing unspecializing nn module +# variables. +# TODO(janimesh, voz): Remove both of these flags (or atleast guard_nn_modules) +# once we have reached stability for the guard_nn_modules_using_dict_tags. +guard_nn_modules_using_dict_tags = True + +# This feature doesn't really work. We offer this flag for experimental +# purposes / if you want to help us build out support. +# +# torchdynamo has limited support for tensor subclasses that implement +# __torch_function__ see [Note: __torch_function__] in torch_function.py. +# Our current support is limited to tensor subclasses +# that DO NOT store metadata on the tensor (in general, dynamo does not +# support Python code that stores extra attributes on tensors at present). +# If your tensor subclass purely changes function call behavior via +# __torch_function__, you can allow torchdynamo to trace into it by +# adding it to traceable_tensor_subclasses. We don't do any safety checks, +# so it is up to you to ensure that your subclass is well behaved. See also +# https://github.com/pytorch/torchdynamo/issues/1948 +# +# We do NOT currently support __torch_dispatch__. The implementation is +# currently buggy, the main show stopper for nontrivial use is +# https://github.com/pytorch/torchdynamo/issues/1952 +traceable_tensor_subclasses: Set[Type[Any]] = set() + +# Suppress errors in torch._dynamo.optimize, instead forcing a fallback to eager. +# This is a good way to get your model to work one way or another, but you may +# lose optimization opportunities this way. Devs, if your benchmark model is failing +# this way, you should figure out why instead of suppressing it. +suppress_errors = bool(os.environ.get("TORCHDYNAMO_SUPPRESS_ERRORS", False)) + +# Record and write an execution record of the current frame to a file +# if an exception is encountered +# @compile_ignored[debug] +replay_record_enabled = os.environ.get("TORCH_COMPILE_REPLAY_RECORD", "0") == "1" + +# Rewrite assert statement in python with torch._assert +rewrite_assert_with_torch_assert = True + +# Disable dynamo +disable = os.environ.get("TORCH_COMPILE_DISABLE", False) + +# [@compile_ignored: runtime_behaviour] Get a cprofile trace of Dynamo +cprofile = os.environ.get("TORCH_COMPILE_CPROFILE", False) + +# legacy config, does nothing now! +skipfiles_inline_module_allowlist: Dict[Any, Any] = {} + +# If a string representing a PyTorch module is in this ignorelist, +# the `allowed_functions.is_allowed` function will not consider it +# when creating a list of PyTorch functions that will appear in +# FX IR. +allowed_functions_module_string_ignorelist = { + "torch.distributions", + "torch.testing", + "torch._refs", + "torch._prims", + "torch._decomp", +} + +# Debug Flag to try minifier at different stages. Possible values are {None, "aot", "dynamo"} +# None - Minifier is switched off +# dynamo - Runs minifier on the TorchDynamo produced graphs, if compilation fails +# aot - Runs minifier on the Aot Autograd produced graphs, if compilation fails +# [@compile_ignored: debug] +repro_after = os.environ.get("TORCHDYNAMO_REPRO_AFTER", None) + +# Compiler compilation debug info +# 1: Dumps the original graph out to repro.py if compilation fails +# 2: Dumps a minifier_launcher.py if compilation fails. +# 3: Always dumps a minifier_launcher.py. Good for segfaults. +# 4: Dumps a minifier_launcher.py if the accuracy fails. +# [@compile_ignored: debug] +repro_level = int(os.environ.get("TORCHDYNAMO_REPRO_LEVEL", 2)) + +# By default, we try to detect accuracy failure by running both forward +# and backward of a torchdynamo produced graph (if you are using repro_after +# 'dynamo'). This setting forces us to only test the forward graph and +# not the backward graph. This can be helpful if you're trying to debug +# an inference only problem, but the minifier seems to be choking on the +# backwards step +# TODO: Detect this situation automatically so the user doesn't need +# to manually configure this +# [@compile_ignored: debug] +repro_forward_only = os.environ.get("TORCHDYNAMO_REPRO_FORWARD_ONLY") == "1" + +# The tolerance we should use when testing if a compiled graph +# has diverged so that we should treat it as an accuracy failure +# [@compile_ignored: debug] +repro_tolerance = 1e-3 + + +# Whether to ignore non-floating point values when checking accuracy. +# Checking accuracy of non-floating point values such as boolean tensors +# can lead to false positives. +# [@compile_ignored: debug] +repro_ignore_non_fp = os.environ.get("TORCHDYNAMO_REPRO_IGNORE_NON_FP") == "1" + +# If True, when testing if two models are the same, we will test them against +# a third fp64 reference and only report a problem if the RMSE relative to the +# fp64 is greater. However, this will use more memory; you may disable this +# if memory usage is too high. +# [@compile_ignored: runtime_behaviour] +same_two_models_use_fp64 = True + +# Not all backends support scalars. Some calls on torch.Tensor (like .item()) return a scalar type. +# When this flag is set to False, we introduce a graph break instead of capturing. +# This requires dynamic_shapes to be True. +capture_scalar_outputs = os.environ.get("TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS") == "1" + +# Not all backends support operators that have dynamic output shape (e.g., +# nonzero, unique). When this flag is set to False, we introduce a graph +# break instead of capturing. This requires dynamic_shapes to be True. +# If you set this to True, you probably also want capture_scalar_outputs +# (these are separated for historical reasons). +capture_dynamic_output_shape_ops = ( + os.environ.get("TORCHDYNAMO_CAPTURE_DYNAMIC_OUTPUT_SHAPE_OPS", "0") == "1" +) + +# hybrid backed unbacked symints +prefer_deferred_runtime_asserts_over_guards = False + +# For complex dynamic shapes guards that we're unable to specify with dynamo/export's +# range constraints + dims + derived dims language, we raise constraint violation +# errors or specialize by default. If set to True, this flag avoids crashing/specialization, +# and allows complex guards as runtime assertions in the graph. +allow_complex_guards_as_runtime_asserts = False + +# By default, dynamo will treat all ints as backed SymInts, which means (1) it +# will wait to see the int change over multiple runs before generalizing and +# (2) it will still always 0/1 specialize an int. When true, this knob +# forces dynamo to treat _length_per_key and _offset_per_key on +# KeyedJaggedTensor from torchrec as size-like unbacked SymInts, so that +# they (1) generalize immediately and (2) unsoundly never compare equal to +# 0/1. This is not on by default as AOTAutograd/Inductor cannot currently +# compile this code; however, this can be useful for export. +force_unspec_int_unbacked_size_like_on_torchrec_kjt = False + +# Should almost always be true in prod. This relaxes the requirement that cond's true_fn and +# false_fn produces code with identical guards. +enforce_cond_guards_match = True + +# Specify how to optimize a compiled DDP module. The flag accepts a boolean +# value or a string. There are 4 modes. +# 1. "ddp_optimizer" (or True): with "ddp_ptimizer", Dynamo will automatically +# split model graph into pieces to match DDP bucket sizes to allow DDP +# comm/compute overlap. +# 2. "python_reducer" (experimental): this optimization requires the usage +# of compiled_autograd. With "python_reducer", DDP will disable the C++ reducer +# and use the Python reducer to allow compiled_autograd to trace the +# communication and allow comm/compute overlap without graph-breaks. +# 3. "python_reducer_without_compiled_forward" (experimental): this mode is +# similar to "python_reducer". One should only use this optimization mode +# when compiled_autograd is used but the DDP module is not compiled. +# 4. "no_optimization" (or False): Dynamo won't split the model graph, nor +# will Python reducer be used. With this mode, there will be no graph-breaks +# and the original DDP C++ reducer will be used. There will no comm/compute +# overlap. This mode CANNOT be used with compiled_autograd. +# Note that to avoid breaking the existing usage, mode 1 and mode 4 can be +# specified with a boolean value. True is using ddp_optimizer and False is +# no optimization. +optimize_ddp: Union[bool, str] = True + +# By default, Dynamo emits runtime asserts (e.g. torch._check, torch._check_is_size) in the graph. +# In some cases those asserts could be performance costly +# E.g. torch._check(tensor[0].item() > 2) for tensor on cuda will require cuda sync. +# Setting this to True keeps them hinting to symbolic shapes engine, +# but not be emitted in the graph. +do_not_emit_runtime_asserts: bool = ( + os.environ.get("TORCH_DYNAMO_DO_NOT_EMIT_RUNTIME_ASSERTS", "0") == "1" +) + +_ddp_optimization_mode = [ + "ddp_optimizer", + "python_reducer", # experimental mode + "python_reducer_without_compiled_forward", # experimental mode + "no_optimization", +] + + +def _get_optimize_ddp_mode(): + m = sys.modules[__name__] + if isinstance(m.optimize_ddp, bool): + if m.optimize_ddp: + mode = "ddp_optimizer" + else: + mode = "no_optimization" + elif isinstance(m.optimize_ddp, str): + mode = m.optimize_ddp + else: + raise ValueError(f"Invalid type, {type(optimize_ddp)=}") + + assert mode in m._ddp_optimization_mode, f"Invalid mode {mode=}" + return mode + + +# Skip tracing the torchrec files added to trace_rules.FBCODE_SKIP_DIRS +skip_torchrec = True + + +# No longer used +optimize_ddp_lazy_compile = False + +# Whether to skip guarding on FSDP-managed modules +skip_fsdp_guards = True +# Whether to apply torch._dynamo.disable() to FSDP2 hooks. +# Defaults to True. If Traceable FSDP2 is used, set this to False. +skip_fsdp_hooks = True + +# Make dynamo skip guarding on hooks on nn modules +# Note: unsafe: if your model actually has hooks and you remove them, or doesn't and you add them, +# dynamo will not notice and will execute whichever version you first compiled. +skip_nnmodule_hook_guards = True + +# If True, raises exception if TorchDynamo is called with a context manager +raise_on_ctx_manager_usage = True + +# If True, raise when aot autograd is unsafe to use +raise_on_unsafe_aot_autograd = False + +# If true, error if you torch.jit.trace over a dynamo-optimized function. +# If false, silently suppress dynamo +error_on_nested_jit_trace = True + +# If true, error with a better message if we symbolically trace over a +# dynamo-optimized function. If false, silently suppress dynamo. +error_on_nested_fx_trace = True + +# Disables graph breaking on rnn. YMMV with backends. +allow_rnn = False + +# If true, enables feature that captures PyTorch sparsity in the +# exported FX graph. This flag should become the default eventually +# and be removed, but currently provides a way to fall back to old +# graph breaking behavior. +capture_sparse_compute = False if is_fbcode() else True + +# If true, error if we try to compile a function that has +# been seen before. +# [@compile_ignored: runtime_behaviour] +error_on_recompile = False + +# [@compile_ignored: debug] Whether to report any guard failures (deprecated: does not do anything) +report_guard_failures = True + +# [@compile_ignored: debug] root folder of the project +base_dir = dirname(dirname(dirname(abspath(__file__)))) + +# Trace through NumPy or graphbreak +trace_numpy = True + +# Default NumPy dtypes when tracing with torch.compile +# We default to 64bits. For efficiency, one may want to change these to float32 +numpy_default_float = "float64" +numpy_default_complex = "complex128" +numpy_default_int = "int64" + +# use numpy's PRNG if True, pytorch otherwise +use_numpy_random_stream = False + +# Use C++ guard manager +enable_cpp_guard_manager = os.environ.get("TORCHDYNAMO_CPP_GUARD_MANAGER", "1") == "1" + +# Inline inbuilt nn modules +inline_inbuilt_nn_modules = not is_fbcode() + +# When set, total compile time instruction count is recorded using +# torch._dynamo.utilsCompileTimeInstructionCounter. +record_compile_time_instruction_count = False + + +def default_debug_dir_root(): + # [@compile_ignored: debug] + DEBUG_DIR_VAR_NAME = "TORCH_COMPILE_DEBUG_DIR" + if DEBUG_DIR_VAR_NAME in os.environ: + return os.path.join(os.environ[DEBUG_DIR_VAR_NAME], "torch_compile_debug") + elif is_fbcode(): + return os.path.join( + tempfile.gettempdir(), getpass.getuser(), "torch_compile_debug" + ) + else: + return os.path.join(os.getcwd(), "torch_compile_debug") + + +# [@compile_ignored: debug] +debug_dir_root = default_debug_dir_root() + +# [@compile_ignored: debug] +_save_config_ignore = { + "repro_after", + "repro_level", + # workaround: "cannot pickle PyCapsule" + "constant_functions", + # workaround: "cannot pickle module" + "skipfiles_inline_module_allowlist", +} + +# for backend="cudagraphs", mutations on input be sent to the cudagraph backend +# or replayed in aot_autograd epilogue. default is False because mutation on inputs +# can prevent cudagraphing. +cudagraph_backend_keep_input_mutation = False + +# enable cudagraph support for mutated inputs from prior cudagraph pool +cudagraph_backend_support_input_mutation = False + +# When True, only ops that have the torch.Tag.pt2_compliant tag +# will be allowed into the graph; all other ops will be disallowed +# and will fall back to eager-mode PyTorch. Useful to ensure +# correctness of custom ops. +only_allow_pt2_compliant_ops = False + +capture_autograd_function = True + +# enable/disable dynamo tracing for `torch.func` transforms +capture_func_transforms = True + +# If to log Dynamo compilation metrics into log files (for OSS) and Scuba tables (for fbcode). +log_compilation_metrics = True + +# A set of logging functions which will be reordered to the end of graph breaks, +# allowing dynamo to construct larget graph. Note that there are some +# limitations to this, such as how it does not correctly print objects that were +# mutated after the print statement. +reorderable_logging_functions: Set[Callable[[Any], None]] = set() + +# simulates what would happen if we didn't have support for BUILD_SET opcode, +# used for testing +inject_BUILD_SET_unimplemented_TESTING_ONLY = False + +_autograd_backward_strict_mode_banned_ops = [ + "stride", + "requires_grad", + "storage_offset", + "layout", + "data", +] + +_autograd_backward_strict_mode_banned_ops.extend( + [name for name, _ in inspect.getmembers(torch.Tensor) if re.match(r"^is_.*", name)] +) + +# Enables caching of dispatches to fake tensors. +fake_tensor_cache_enabled = ( + os.environ.get("TORCH_FAKE_TENSOR_DISPATCH_CACHE", "1") == "1" +) + +# Enables cross checking between the fake tensor cache and dispatch. +fake_tensor_cache_crosscheck_enabled = ( + os.environ.get("TORCH_FAKE_TENSOR_DISPATCH_CACHE_CROSSCHECK", "0") == "1" +) + +# Enables the Compiled Autograd engine to trace .backward() calls made under torch.compile(). +# Note: AOT Autograd will still trace joint graphs. +compiled_autograd = False + +# Enables use of collectives *during* compilation to synchronize behavior +# across ranks. Today, this is used solely to modify automatic_dynamic_shapes +# behavior, making it so that we infer that if an input is dynamic by +# inspecting whether or not its input size varies across ranks. Because +# this synchronization uses collectives, all ranks must run compilation at +# the same time; ranks must not diverge with graph breaks. This can be most +# reliably achieved by ensuring PT2 only is run on SPMD programs. If this +# invariant is inviolated, you will likely deadlock NCCL and encounter a +# NCCL timeout. +enable_compiler_collectives = os.environ.get("TORCH_COMPILER_COLLECTIVES", "0") == "1" + +if TYPE_CHECKING: + from torch.utils._config_typing import * # noqa: F401, F403 + + def _make_closure_patcher(**changes): + ... + + +from torch.utils._config_module import install_config_module + + +install_config_module(sys.modules[__name__]) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py new file mode 100644 index 0000000000000000000000000000000000000000..d15f5c4aa43dcffe4dcd343a895e546147dab2d0 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py @@ -0,0 +1,1277 @@ +# mypy: allow-untyped-decorators +from __future__ import annotations + +import collections +import contextlib +import cProfile +import dis +import functools +import itertools +import logging +import os +import pstats +import random +import subprocess +import sys +import threading +import time +import traceback +import typing +import weakref +from pathlib import Path +from types import CodeType, FrameType, FunctionType, ModuleType +from typing import Any, Callable, Dict, List, Optional, Set, TypeVar, Union +from typing_extensions import ParamSpec +from weakref import ReferenceType + +import torch +import torch._logging +from torch._C._dynamo.guards import GlobalStateGuard +from torch._dynamo.distributed import get_compile_pg +from torch._dynamo.utils import CompileTimeInstructionCounter +from torch._guards import compile_context, CompileContext, CompileId, tracing +from torch._logging import structured +from torch._utils_internal import ( + compile_time_strobelight_meta, + justknobs_check, + maybe_upload_prof_stats_to_manifold, + signpost_event, +) +from torch.fx._lazy_graph_module import _use_lazy_graph_module +from torch.fx.experimental.symbolic_shapes import ( + ConstraintViolationError, + GuardOnDataDependentSymNode, +) +from torch.fx.graph_module import _forward_from_src as original_forward_from_src +from torch.nn.parallel.distributed import DistributedDataParallel +from torch.utils._python_dispatch import ( + _disable_current_modes, + is_in_torch_dispatch_mode, +) +from torch.utils._traceback import CapturedTraceback, format_traceback_short + +from . import config, exc, trace_rules +from .bytecode_analysis import remove_dead_code, remove_pointless_jumps +from .bytecode_transformation import ( + check_inst_exn_tab_entries_valid, + Instruction, + is_generator, + propagate_inst_exn_table_entries, + transform_code_object, +) +from .cache_size import ( + CacheSizeRelevantForFrame, + compute_cache_size, + exceeds_cache_size_limit, + is_recompilation, +) +from .eval_frame import always_optimize_code_objects, skip_code, TorchPatcher +from .exc import ( + augment_exc_message, + BackendCompilerFailed, + CacheLimitExceeded, + format_error_msg, + InternalTorchDynamoError, + SkipCodeRecursiveException, + TorchRuntimeError, + UncapturedHigherOrderOpError, + unimplemented, + Unsupported, +) +from .guards import ( + CheckFunctionManager, + get_and_maybe_log_recompilation_reason, + GuardedCode, +) +from .hooks import Hooks +from .replay_record import ExecutionRecord +from .symbolic_convert import ( + DistributedState, + InstructionTranslator, + LocalState, + SpeculationLog, +) +from .trace_rules import is_numpy +from .utils import ( + CleanupManager, + CompilationMetrics, + counters, + dynamo_timed, + format_bytecode, + frame_phase_timing, + gen_record_file_name, + get_chromium_event_logger, + increment_frame, + is_namedtuple, + istype, + LazyString, + orig_code_map, + record_compilation_metrics, + reset_graph_break_dup_checker, + setup_compile_debug, + troubleshooting_url, + write_record_to_file, +) + + +np: Optional[ModuleType] +try: + import numpy as np +except ModuleNotFoundError: + np = None + + +if typing.TYPE_CHECKING: + from .backends.registry import CompilerFn + from .repro.after_dynamo import WrapBackendDebug + from .types import BytecodeHook, CacheEntry + from .variables.builder import FrameStateSizeEntry + + +log = logging.getLogger(__name__) +bytecode_log = torch._logging.getArtifactLogger(__name__, "bytecode") +graph_break_log = torch._logging.getArtifactLogger(__name__, "graph_breaks") + + +compile_lock = threading.RLock() + +_T = TypeVar("_T") +_P = ParamSpec("_P") + + +class TODO_UNKNOWN: + pass + + +class Tracker: + def __init__(self) -> None: + self.seen: List[ReferenceType[CodeType]] = [] + self.seen_ids: Set[int] = set() + + def add(self, strong_obj: CodeType) -> None: + idx = id(strong_obj) + if idx not in self.seen_ids: + obj = weakref.ref(strong_obj, lambda _: self.seen_ids.remove(idx)) + self.seen.append(obj) + self.seen_ids.add(idx) + + def __contains__(self, item: CodeType) -> bool: + return id(item) in self.seen_ids + + def clear(self) -> None: + self.seen.clear() + self.seen_ids.clear() + + +input_codes = Tracker() +output_codes = Tracker() + +initial_global_state: Optional[GlobalStateGuard] = None + + +@functools.wraps(original_forward_from_src) +def fx_forward_from_src_skip_result( + src: str, globals: Dict[str, Any], co_fields: Optional[Dict[str, str]] = None +) -> FunctionType: + # we monkey patch FX to prevent infinite loop of trying to convert + # our generated code + result = original_forward_from_src(src, globals, co_fields) + skip_code(result.__code__) + return result + + +def preserve_global_state(fn: Callable[_P, _T]) -> Callable[_P, _T]: + """ + Context manager to: + 1) Save/restore torch.is_grad_enabled() state + 2) Save/restore python random state + 3) Save/restore torch random state + 4) Monkey patch torch.fx.graph_module._forward_from_src + """ + + @functools.wraps(fn) + def _fn(*args: _P.args, **kwargs: _P.kwargs) -> _T: + guards = GlobalStateGuard() + prior_grad_mode = torch.is_grad_enabled() + # Just in case we get left in a bad dispatch state we want to restore + # it. This can happen because the dispatch bits aren't a true + # stack/counter - so we can't just increment/decrement them as we enter + # and leave. + with torch._C._PreserveDispatchKeyGuard(): + prior_inference_mode = torch.is_inference_mode_enabled() + prior_deterministic = torch.are_deterministic_algorithms_enabled() + prior_warn_only = torch.is_deterministic_algorithms_warn_only_enabled() + py_rng_state = random.getstate() + torch_rng_state = torch.random.get_rng_state() + cuda_rng_state = None + if torch.cuda.is_available(): + cuda_rng_state = torch.cuda.get_rng_state() + allow_tf32 = torch._C._get_cublas_allow_tf32() + prior_fwd_from_src = torch.fx.graph_module._forward_from_src + torch.fx.graph_module._forward_from_src = fx_forward_from_src_skip_result + cleanup = setup_compile_debug() + + exit_stack = contextlib.ExitStack() + exit_stack.enter_context( + torch.fx._symbolic_trace._maybe_revert_all_patches() + ) + try: + return fn(*args, **kwargs) + finally: + cleanup.close() + exit_stack.close() + torch._C._set_grad_enabled(prior_grad_mode) + torch.autograd.grad_mode._enter_inference_mode(prior_inference_mode) + torch.use_deterministic_algorithms( + prior_deterministic, warn_only=prior_warn_only + ) + random.setstate(py_rng_state) + torch.random.set_rng_state(torch_rng_state) + if cuda_rng_state is not None: + torch.cuda.set_rng_state(cuda_rng_state) + torch._C._set_cublas_allow_tf32(allow_tf32) + torch.fx.graph_module._forward_from_src = prior_fwd_from_src + assert ( + guards.check() + ), f"Global {guards.reason()}state changed while dynamo tracing, please report a bug" + + _fn._torchdynamo_orig_callable = fn # type: ignore[attr-defined] + return _fn + + +@TorchPatcher.suppress_torch_distributed_warnings +def has_tensor_in_frame(frame: FrameType) -> bool: + """Check if the frame has torch.* related bits""" + # Check if the function was decorated using torch._dynamo.optimize + if frame.f_code in always_optimize_code_objects: + return True + + # Check if there is global import of torch.* + for co_name in frame.f_code.co_names: + if co_name in frame.f_globals: + obj = frame.f_globals[co_name] + if isinstance(obj, ModuleType) and ( + obj.__name__.startswith("torch.") or obj is torch + ): + return True + # ... or a global import of numpy.* + if np and config.trace_numpy and (obj is np or is_numpy(obj)): + return True + + seen_ids: Dict[int, bool] = {} + + def has_tensor(obj: object) -> bool: + """Recursively check if the obj has a tensor""" + obj_id = id(obj) + if obj_id in seen_ids: + return seen_ids[obj_id] + seen_ids[obj_id] = False + + if isinstance(obj, (torch.Tensor, torch.nn.Module)) or ( + istype(obj, type) and issubclass(obj, torch.nn.Module) + ): + seen_ids[obj_id] = True + return seen_ids[obj_id] + elif ( + config.trace_numpy + and np + and (istype(obj, np.ndarray) or isinstance(obj, np.generic)) + ): + seen_ids[obj_id] = True + return seen_ids[obj_id] + elif istype(obj, (list, tuple)): + seen_ids[obj_id] = any(has_tensor(v) for v in obj) + return seen_ids[obj_id] + elif istype(obj, dict): + # Some packages like pytest can be updated during runtime. So, make a + # copy of values to avoid issues like "RuntimeError: dictionary + # changed size during iteration" + values = list(obj.values()) + seen_ids[obj_id] = any(has_tensor(v) for v in values) + return seen_ids[obj_id] + elif istype(obj, (str, int, float, type(None), bool)): + seen_ids[obj_id] = False + return seen_ids[obj_id] + elif is_namedtuple(obj) and hasattr(obj, "_fields"): + seen_ids[obj_id] = any(has_tensor(getattr(obj, v)) for v in obj._fields) + return seen_ids[obj_id] + else: + # if config.debug: + # print( + # f"Assuming that object of type {type(obj)} does not have a tensor" + # ) + return False + + # Check if the passed arguments are of type Tensor + for value in frame.f_locals.values(): + if has_tensor(value): + return True + + log.debug( + "skipping because no torch.* %s \ + %s %s", + frame.f_code.co_name, + frame.f_code.co_filename, + frame.f_code.co_firstlineno, + ) + + return False + + +def exception_handler( + e: Exception, + code: CodeType, + frame: Optional[FrameType] = None, + export: bool = False, +) -> None: + record_filename = None + if hasattr(e, "exec_record"): + record_filename = gen_record_file_name(e, code) + write_record_to_file(record_filename, e.exec_record) + e.record_filename = record_filename # type: ignore[attr-defined] + + augment_exc_message(e, export=export) + + +FRAME_COUNTER = 0 +FRAME_COMPILE_COUNTER: typing.Counter[ + Union[int, FrameStateSizeEntry] +] = collections.Counter() + + +def maybe_cprofile(func: Callable[_P, _T]) -> Callable[_P, _T]: + if config.cprofile: + return cprofile_wrapper(func) + return func + + +def cprofile_wrapper(func: Callable[_P, _T]) -> Callable[_P, _T]: + @functools.wraps(func) + def profile_wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _T: + trace_id = CompileContext.current_trace_id() + assert trace_id, "Trace id is None" + profile_path = Path( + f"/tmp/{func.__name__}_{str(trace_id).replace('/', '_')}.profile" + ) + prof = cProfile.Profile() + prof.enable() + start_ts = time.time() + retval = prof.runcall(func, *args, **kwargs) + profile_latency = time.time() - start_ts + prof.disable() + log.warning( + "### Cprofile for %s trace id [%s] took %.3f seconds ###", + func.__name__, + trace_id, + profile_latency, + ) + ps = pstats.Stats(prof) + try: + prof.dump_stats(profile_path) + except PermissionError: + log.exception("Cannot write to %s", profile_path) + log.warning("Raw profile at %s", profile_path) + svg_path = profile_path.with_suffix(".svg") + try: + gprof2dot_process = subprocess.Popen( + [ + "gprof2dot", + "-f", + "pstats", + "--node-label=total-time-percentage", + "--node-label=self-time-percentage", + "--node-label=total-time", + str(profile_path), + ], + stdout=subprocess.PIPE, + ) + subprocess.check_call( + ["dot", "-Tsvg", "-o", str(svg_path)], + stdin=gprof2dot_process.stdout, + ) + log.warning("Generated SVG from profile at %s", svg_path) + except FileNotFoundError: + log.warning( + "Failed to generate SVG from profile -- dumping stats instead." + "Try installing gprof2dot and dot for a better visualization" + ) + ps.sort_stats(pstats.SortKey.TIME).print_stats(20) + ps.sort_stats(pstats.SortKey.CUMULATIVE).print_stats(20) + + if manifold_link := maybe_upload_prof_stats_to_manifold( + str(profile_path) + ): # fb-only + torch._logging.trace_structured( + "link", + lambda: {"name": "cprofile_manifold_url", "url": manifold_link}, + ) + return retval + + return profile_wrapper + + +class ConvertFrameAssert: + def __init__( + self, + compiler_fn: CompilerFn, + one_graph: bool = True, + export: bool = False, + export_constraints: Optional[typing.Never] = None, + ) -> None: + # assert export_constraints is None + reset_graph_break_dup_checker() + self._torchdynamo_orig_callable = compiler_fn + self._one_graph = one_graph + self._export = export + self._export_constraints = export_constraints + + @property + def _clone_with_backend(self) -> Callable[[CompilerFn], ConvertFrameAssert]: + return lambda backend: convert_frame_assert( + backend, self._one_graph, self._export, self._export_constraints + ) + + def __call__( + self, + frame: FrameType, + cache_entry: Optional[CacheEntry], + hooks: Hooks, + frame_state: Dict[str, Union[int, FrameStateSizeEntry]], + *, + skip: int = 0, + ) -> Optional[GuardedCode]: + increment_frame() + + code = frame.f_code + + cache_size = compute_cache_size(frame, cache_entry) + input_codes.add(code) + if code in output_codes: + return None + if ( + os.environ.get("TORCHDYNAMO_DEBUG_FUNCTION") + and os.environ.get("TORCHDYNAMO_DEBUG_FUNCTION") != code.co_name + ): + return None + if code.co_name == "" and code.co_filename.endswith( + ( + "transformers/file_utils.py", + "transformers/utils/generic.py", + "diffusers/utils/outputs.py", + ) + ): + # not needed, but cleans up torchbench error stats + return None + if code.co_name == "__setattr__": + # setattr could be tricky to handle generally, + # but also not likely useful to compile- skip the whole frame + return None + if code.co_name == "__init__" and code.co_filename.startswith( + os.path.dirname(torch.optim.__file__) + ): + # optimizer support is still incomplete see + # test_state_dict in test/dynamo/test_optimizers.py + return None + + # Check if the frame is generated by an exec builtin call + # TODO - Running exec generated frame seems propagates f_globals to the + # next frames. + if code.co_name == "" and code.co_filename == "": + return None + + if ( + code.co_name == "" + and code.co_filename == "" + and not bool(frame.f_builtins) + ): + # namedtuple subclass constructor. Empty builtins cause issue with + # len keyword in LIST_LEN guard. + return None + + if is_generator(code): + unimplemented("generator") + + if not has_tensor_in_frame(frame): + return None + + global initial_global_state + initial_global_state = GlobalStateGuard() + + global FRAME_COUNTER + if "_id" not in frame_state: + frame_state["_id"] = FRAME_COUNTER + FRAME_COUNTER += 1 + frame_id = frame_state["_id"] + assert isinstance(frame_id, int) + + frame_compile_id = FRAME_COMPILE_COUNTER[frame_id] + FRAME_COMPILE_COUNTER[frame_id] += 1 + + compile_id = CompileId(frame_id, frame_compile_id) + + signpost_event( + "dynamo", + "_convert_frame_assert._compile", + { + "co_name": code.co_name, + "frame_id": frame_id, + "compile_id": str(compile_id), + "co_filename": code.co_filename, + "co_firstlineno": code.co_firstlineno, + "cache_size": cache_size.num_cache_entries_with_same_id_matched_objs, + "accumulated_cache_size": cache_size.num_cache_entries, + }, + ) + + return _compile( + frame.f_code, + frame.f_globals, + frame.f_locals, + frame.f_builtins, + self._torchdynamo_orig_callable, + self._one_graph, + self._export, + self._export_constraints, + hooks, + cache_entry, + cache_size, + frame, + frame_state=frame_state, + compile_id=compile_id, + skip=skip + 1, + ) + + +def convert_frame_assert( + compiler_fn: CompilerFn, + one_graph: bool = True, + export: bool = False, + export_constraints: Optional[typing.Never] = None, +) -> ConvertFrameAssert: + """Fully convert a frame into an FX graph""" + return ConvertFrameAssert(compiler_fn, one_graph, export, export_constraints) + + +from collections import OrderedDict + +from torch.utils.hooks import RemovableHandle + + +if typing.TYPE_CHECKING: + from .output_graph import OutputGraph + +# we have to use `OrderedDict` to make `RemovableHandle` work. +_bytecode_hooks: Dict[int, BytecodeHook] = OrderedDict() + + +def register_bytecode_hook(hook: BytecodeHook) -> RemovableHandle: + """Register hooks for bytecode generated by Dynamo. The hook can do some + logging, as well as return a new code object to be used. Please refer + to `BytecodeHook` for the hook signature. + """ + handle = RemovableHandle(_bytecode_hooks) + _bytecode_hooks[handle.id] = hook + return handle + + +def _compile( + code: CodeType, + globals: Dict[str, object], + locals: Dict[str, object], + builtins: Dict[str, object], + compiler_fn: CompilerFn, + one_graph: bool, + export: bool, + export_constraints: Optional[typing.Never], + hooks: Hooks, + cache_entry: Optional[CacheEntry], + cache_size: CacheSizeRelevantForFrame, + frame: Optional[FrameType] = None, + frame_state: Optional[Dict[str, Union[int, FrameStateSizeEntry]]] = None, + *, + compile_id: CompileId, + skip: int = 0, +) -> Optional[GuardedCode]: + from torch.fx.experimental.validator import ( + bisect, + BisectValidationException, + translation_validation_enabled, + ValidationException, + ) + + # Only nonlocal defs here please! + # Time spent compiling this frame before restarting or failing analysis + dynamo_time_before_restart: float = 0.0 + output: Optional[OutputGraph] = None + tracer: Optional[InstructionTranslator] = None + + @preserve_global_state + def transform( + instructions: List[Instruction], code_options: Dict[str, object] + ) -> None: + nonlocal output + nonlocal tracer + speculation_log.restart() + tracer = InstructionTranslator( + instructions, + code, + locals, + globals, + builtins, + code_options, + compiler_fn, + one_graph, + export, + export_constraints, + mutated_closure_cell_contents, + frame_state=frame_state, + speculation_log=speculation_log, + distributed_state=distributed_state, + ) + + try: + with tracing(tracer.output.tracing_context), tracer.set_current_tx(): + tracer.run() + except exc.UnspecializeRestartAnalysis: + speculation_log.clear() + raise + except (exc.SpeculationRestartAnalysis, exc.SkipFrame): + raise + except Exception: + if translation_validation_enabled(): + bisect(tracer.output.shape_env) + raise + finally: + tracer.output.call_cleanup_hooks() + + output = tracer.output + assert output is not None + assert output.output_instructions + instructions[:] = output.output_instructions + code_options.update(output.code_options) + + if config.dead_code_elimination: + propagate_inst_exn_table_entries(instructions) + check_inst_exn_tab_entries_valid(instructions) + instructions[:] = remove_pointless_jumps(remove_dead_code(instructions)) + + def compile_inner( + code: CodeType, + one_graph: bool, + hooks: Hooks, + transform: Callable[[List[Instruction], Dict[str, Any]], Any], + ) -> Optional[GuardedCode]: + with dynamo_timed("_compile.compile_inner", phase_name="entire_frame_compile"): + with CompileTimeInstructionCounter.record(): + return _compile_inner(code, one_graph, hooks, transform) + + @compile_time_strobelight_meta(phase_name="compile_inner") + @maybe_cprofile + def _compile_inner( + code: CodeType, + one_graph: bool, + hooks: Hooks, + transform: Callable[[List[Instruction], Dict[str, Any]], Any], + ) -> Optional[GuardedCode]: + nonlocal dynamo_time_before_restart + last_attempt_start_time = start_time = time.time() + + def log_bytecode( + prefix: str, name: str, filename: str, line_no: int, code: CodeType + ) -> None: + if bytecode_log.isEnabledFor(logging.DEBUG): + bytecode_log.debug( + format_bytecode(prefix, name, filename, line_no, code) + ) + + log_bytecode( + "ORIGINAL BYTECODE", + code.co_name, + code.co_filename, + code.co_firstlineno, + code, + ) + + out_code = None + for attempt in itertools.count(): + CompileContext.get().attempt = attempt + try: + out_code = transform_code_object(code, transform) + break + except exc.RestartAnalysis as e: + log.info( + "Restarting analysis due to %s", + LazyString(format_traceback_short, e.__traceback__), + ) + # If restart reason is None just log the type of the exception + restart_reasons.add(e.restart_reason or str(type(e))) + # We now have a new "last attempt", reset the clock + last_attempt_start_time = time.time() + if attempt > 100: + unimplemented("100+ RestartAnalysis() calls") + except exc.SkipFrame as e: + log.debug( + "Skipping frame %s %s \ + %s %s", + e, + code.co_name, + code.co_filename, + code.co_firstlineno, + ) + if one_graph: + log.debug("No graph captured with one_graph=True") + return None + + assert ( + distributed_state is None or distributed_state.all_states is not None + ), "compiler collective wasn't run before compilation completed" + + assert out_code is not None + log_bytecode( + "MODIFIED BYTECODE", + code.co_name, + code.co_filename, + code.co_firstlineno, + out_code, + ) + + for hook in _bytecode_hooks.values(): + hook_output = hook(code, out_code) + if hook_output is not None: + out_code = hook_output + + orig_code_map[out_code] = code + output_codes.add(out_code) + dynamo_time_before_restart = last_attempt_start_time - start_time + assert output is not None + + # Tests for new code objects. + # The rationale for these tests can be found in torch/csrc/dynamo/eval_frame.c + # Only test once the code object is created. + # They are not tested during runtime. + + def count_args(code: CodeType) -> int: + import inspect + + return ( + code.co_argcount + + code.co_kwonlyargcount + + bool(code.co_flags & inspect.CO_VARARGS) + + bool(code.co_flags & inspect.CO_VARKEYWORDS) + ) + + assert out_code is not None + + total_argcount_old = count_args(code) + total_argcount_new = count_args(out_code) + msg = "arg mismatch: " + msg += f"old code object has args {code.co_varnames[:total_argcount_old]}, " + msg += f"new code object has args {out_code.co_varnames[:total_argcount_new]}" + assert ( + code.co_varnames[:total_argcount_old] + == out_code.co_varnames[:total_argcount_new] + ), msg + + msg = "free var mismatch: " + msg += f"old code object has free var {code.co_freevars}, " + msg += f"new code object has free var {out_code.co_freevars}" + assert code.co_freevars == out_code.co_freevars, msg + + msg = "cell var mismatch: " + msg += f"old code object has cell var {code.co_cellvars}, " + msg += f"new code object has cell var {out_code.co_cellvars}" + assert code.co_cellvars == out_code.co_cellvars, msg + + # Skipping Dynamo on a frame without any extracted graph. + # This does not affect eager functionality. But this is necessary + # for export for cases where Dynamo-reconstructed bytecode can create + # new function frames, confusing export in thinking that there + # are extra graphs now. + + if output.export and output.is_empty_graph(): + return None + + assert output.guards is not None + CleanupManager.instance[out_code] = output.cleanups + check_fn = CheckFunctionManager( + output, + hooks.guard_fail_fn if hooks else None, + ) + + guarded_code = GuardedCode(out_code, check_fn.check_fn, compile_id) + + if not output.is_empty_graph() and hooks.guard_export_fn is not None: + # We should not run the guard_export_fn when Dynamo does not + # generate any graph. This can happen in export when TorchDynamo + # generated bytecode has some reconstruction logic for mutated + # variables which can trigger TorchDynamo on the children frames but + # they are benign and do not generate any new graphs. + hooks.guard_export_fn(output.guards) + + return guarded_code + + with _use_lazy_graph_module(config.use_lazy_graph_module), compile_context( + CompileContext(compile_id) + ): + restart_reasons: set[str] = set() + # This is shared across restarts + mutated_closure_cell_contents: Set[str] = set() + speculation_log = SpeculationLog() + if compile_pg := get_compile_pg(): + distributed_state = DistributedState(compile_pg, LocalState()) + else: + distributed_state = None + torch._dynamo.callback_handler.run_start_callbacks() + + # Check recompilations + recompile_reasons = None + if is_recompilation(cache_size) and frame: + recompile_reasons = get_and_maybe_log_recompilation_reason( + cache_entry, frame + ) + + exceeded, limit_type = exceeds_cache_size_limit(cache_size, compile_id) + if exceeded: + + def format_func_info(code: CodeType) -> str: + return f"'{code.co_name}' ({code.co_filename}:{code.co_firstlineno})" + + def format_guard_failures() -> str: + if not recompile_reasons: + return "Unable to find recompilation reasons" + return recompile_reasons[-1] + + log.warning( + "torch._dynamo hit config.%s (%s)\n" + " function: %s\n" + " last reason: %s\n" + 'To log all recompilation reasons, use TORCH_LOGS="recompiles".\n' + "To diagnose recompilation issues, see %s.", + limit_type, + getattr(config, limit_type), + format_func_info(code), + format_guard_failures(), + troubleshooting_url, + ) + if config.skip_code_recursive_on_cache_limit_hit and justknobs_check( + "pytorch/compiler:skip_code_recursive_on_cache_limit_hit" + ): + raise CacheLimitExceeded(f"{limit_type} reached") + else: + # do not recursively skip frames + unimplemented(f"{limit_type} reached") + + log.debug( + "torchdynamo start compiling %s %s:%s, stack (elided %s frames):\n%s", + code.co_name, + code.co_filename, + code.co_firstlineno, + skip + 2, + # -2: omit current frame, omit contextlib decorator + "".join(CapturedTraceback.extract(skip=2 + skip).format()), + ) + # -4: -2 as above, plus trace_structured frames + # + # NB: the frame looks like this: + # + # # handled by skip argument + # torch/_dynamo/convert_frame.py:1069 in catch_errors + # torch/_dynamo/convert_frame.py:910 in _convert_frame + # torch/_dynamo/convert_frame.py:464 in _convert_frame_assert + # torch/_utils_internal.py:70 in wrapper_function + # + # # 2 current frame and context lib + # env/lib/python3.10/contextlib.py:79 in inner + # torch/_dynamo/convert_frame.py:776 in _compile + # + # # 2 extra here + # torch/_logging/_internal.py:1064 in trace_structured + # torch/_dynamo/convert_frame.py:780 in + convert_frame_intern = structured.intern_string(__file__) + # Initialize the ChromiumEventLogger on start + chromium_event_log = get_chromium_event_logger() + chromium_event_log.reset() + torch._logging.trace_structured( + "dynamo_start", + lambda: { + "stack": list( + itertools.takewhile( + lambda f: f["filename"] != convert_frame_intern, + structured.from_traceback( + CapturedTraceback.extract(skip=4 + skip).summary() + ), + ) + ) + + [ + { + "line": code.co_firstlineno, + "name": code.co_name, + "filename": structured.intern_string(code.co_filename), + } + ] + }, + ) + start_time = time.time() + fail_type: Optional[str] = None + fail_reason: Optional[str] = None + fail_user_frame_filename: Optional[str] = None + fail_user_frame_lineno: Optional[int] = None + start_possibly_missed_reinplacing_opportunities = torch._dynamo.utils.counters[ + "inductor" + ]["possibly_missed_reinplacing_opportunities"] + guarded_code = None + try: + guarded_code = compile_inner(code, one_graph, hooks, transform) + return guarded_code + except Exception as e: + fail_type = type(e).__qualname__ + fail_reason = str(e) + # NB: e's msg is mutated here to add user stack, but we DON'T want + # that stack in the Scuba logged fail_reason + exception_handler(e, code, frame, export=export) + fail_user_frame_filename, fail_user_frame_lineno = exc.get_exc_message( + e, compile_id + ) + if isinstance( + e, + ( + Unsupported, + TorchRuntimeError, + BackendCompilerFailed, + AssertionError, + ConstraintViolationError, + GuardOnDataDependentSymNode, + ValidationException, + UncapturedHigherOrderOpError, + BisectValidationException, + ), + ): + raise + else: + # Rewrap for clarity + raise InternalTorchDynamoError( + f"{type(e).__qualname__}: {str(e)}" + ).with_traceback(e.__traceback__) from None + finally: + if tracer: + tracer.output.local_scope = {} + + from .utils import curr_frame + + frame_key = str(curr_frame) + if ( + fail_reason is None + and output is not None + and frame_key in frame_phase_timing + ): + guard_count = len(output.guards) + shape_env_guard_count = len(output.shape_env.guards) + graph_op_count = output.count_calls() + graph_node_count = len(output.graph.nodes) + graph_input_count = len(output.placeholders) + entire_frame_compile_time = frame_phase_timing[frame_key].get( + "entire_frame_compile", None + ) + backend_compile_time = frame_phase_timing[frame_key].get( + "backend_compile", None + ) + inductor_compile_time = frame_phase_timing[frame_key].get( + "inductor_compile", None + ) + code_gen_time = frame_phase_timing[frame_key].get("code_gen", None) + non_compliant_ops = {op.__qualname__ for op in output.non_compliant_ops} + compliant_custom_ops = { + op.__qualname__ for op in output.compliant_custom_ops + } + possibly_missed_reinplacing_opportunities = ( + torch._dynamo.utils.counters["inductor"][ + "possibly_missed_reinplacing_opportunities" + ] + - start_possibly_missed_reinplacing_opportunities + ) + else: + guard_count = None + shape_env_guard_count = None + graph_op_count = None + graph_node_count = None + graph_input_count = None + entire_frame_compile_time = None + backend_compile_time = None + inductor_compile_time = None + code_gen_time = None + non_compliant_ops = set({}) + compliant_custom_ops = set({}) + restart_reasons = set() + # If compilation failed, the entire time is wasted + dynamo_time_before_restart = time.time() - start_time + possibly_missed_reinplacing_opportunities = None + + metrics = CompilationMetrics( + str(compile_id), + frame_key, + code.co_name, + code.co_filename, + code.co_firstlineno, + cache_size.num_cache_entries_with_same_id_matched_objs, + cache_size.num_cache_entries, + guard_count, + shape_env_guard_count, + graph_op_count, + graph_node_count, + graph_input_count, + start_time, + entire_frame_compile_time, + backend_compile_time, + inductor_compile_time, + code_gen_time, + fail_type, + fail_reason, + fail_user_frame_filename, + fail_user_frame_lineno, + non_compliant_ops, + compliant_custom_ops, + restart_reasons, + dynamo_time_before_restart, + guarded_code is not None, + possibly_missed_reinplacing_opportunities, + ) + record_compilation_metrics(metrics) + torch._dynamo.callback_handler.run_end_callbacks() + + +class ConvertFrame: + def __init__(self, compiler_fn: CompilerFn, hooks: Hooks) -> None: + self._torchdynamo_orig_callable = compiler_fn + self._inner_convert = convert_frame_assert(compiler_fn, one_graph=False) + self._hooks = hooks + + @property + def _clone_with_backend(self) -> Callable[[WrapBackendDebug], ConvertFrame]: + return lambda backend: convert_frame(backend, self._hooks) + + def __call__( + self, + frame: FrameType, + cache_entry: Optional[CacheEntry], + hooks: Hooks, + frame_state: Dict[str, Union[int, FrameStateSizeEntry]], + skip: int = 0, + ) -> Optional[ + Union[GuardedCode, torch._C._dynamo.eval_frame.SkipCodeRecursiveFlag] + ]: + counters["frames"]["total"] += 1 + try: + result = self._inner_convert( + frame, cache_entry, hooks, frame_state, skip=skip + 1 + ) + counters["frames"]["ok"] += 1 + return result + except Exception as e: + # These two exception types are "soft" failure, in the sense that + # we know this is due to something we didn't implement all the + # way, scare the user less about it. That being said, if you + # are trying to understand why a graph break happened, it's still + # important to have this information, so offer it. + # + # NB: NotImplementedError used to be on this list, but actually + # it is impossible for it to reach here, as it is converted into + # InternalTorchDynamoError. This behavior seemed reasonable + # to me (ezyang, Aug 2023) so I kept it, but maybe at some point + # someone wanted these to also get suppressed. If so, you'll + # need to make these exceptions not get wrapped + + # We intentionally don't want to suppress error here. + if isinstance(e, UncapturedHigherOrderOpError): + raise + + soft_fail = isinstance(e, Unsupported) + + # This is a soft failure. In the sense, the code path reaches here + # when we do not support graph breaks on bytecodes like LOAD_ATTR, + # BUILD_SET etc. In such case, we can fallback to eager without + # scaring users. + if isinstance(e, Unsupported) and graph_break_log.isEnabledFor( + logging.DEBUG + ): + # Log this message in the graph break. Also use the string + # "skip: " to tell that the whole frame is falling back to + # eager. + if hasattr(e, "compile_id"): + with compile_context(CompileContext(e.compile_id)): # type: ignore[attr-defined] + user_stack = e.real_stack + user_stack_formatted = "".join( + traceback.format_list(user_stack) + ) + graph_break_log.debug( + "Graph break: skip: from user code at:\n%s", + user_stack_formatted, + exc_info=True, + ) + + if not config.suppress_errors and not soft_fail: + raise + + # Suppress the error. NB: It's very important to do the + # suppression logging HERE, where the actual suppression + # happens. Previously it was somewhere else and so it was + # possible to accidentally not log at all. + record_filename = getattr(e, "record_filename", None) + code = frame.f_code + error_msg = format_error_msg(e, code, record_filename, frame) + + if soft_fail: + log.info(error_msg, exc_info=True) + else: + log.warning(error_msg, exc_info=True) + + # If we encounter SkipCodeRecursiveException, return skip_code_recursive_flag + # to signal to Dynamo eval frame to skip the current frame and any recursive calls. + if isinstance(e, SkipCodeRecursiveException): + return torch._C._dynamo.eval_frame.skip_code_recursive_flag + + return None + + +def convert_frame(compiler_fn: CompilerFn, hooks: Hooks) -> ConvertFrame: + """Try to convert a frame into an FX graph, if error leave frame unmodified""" + return ConvertFrame(compiler_fn, hooks) + + +# TODO mlazos: add support for same args, or record them +def replay(filename: str) -> None: + from .backends.debugging import eager + + original_replay_val = config.replay_record_enabled + config.replay_record_enabled = False + with open(filename, "rb") as in_file: + record = ExecutionRecord.load(in_file) + record.globals = dict(itertools.chain(record.globals.items(), globals().items())) + + try: + _compile( + record.code, + record.globals, + record.locals, + record.builtins, + compiler_fn=eager, + one_graph=False, + export=False, + export_constraints=None, + hooks=Hooks(), + cache_size=CacheSizeRelevantForFrame(0, 0), + cache_entry=None, + frame=None, + frame_state={}, + compile_id=CompileId(42, 999), + ) + finally: + config.replay_record_enabled = original_replay_val + + +def first_real_inst_idx(code: CodeType) -> int: + if sys.version_info < (3, 11): + return 0 + for inst in dis.get_instructions(code): + if inst.opname == "RESUME": + return inst.offset // 2 + raise RuntimeError("RESUME instruction not found in code") + + +class ConvertFrameProtocol(typing.Protocol): + def __call__( + self, + frame: FrameType, + cache_entry: Optional[CacheEntry], + hooks: Hooks, + frame_state: Dict[str, Union[int, FrameStateSizeEntry]], + *, + skip: int = 0, + ) -> Optional[GuardedCode]: + ... + + +class CatchErrorsWrapper: + def __init__(self, callback: ConvertFrameProtocol, hooks: Hooks) -> None: + functools.wraps(callback)(self) + self._torchdynamo_orig_callable = callback + self.hooks = hooks + + def __call__( + self, + frame: FrameType, + cache_entry: Optional[CacheEntry], + frame_state: Dict[str, Union[int, FrameStateSizeEntry]], + ) -> Optional[GuardedCode]: + assert frame_state is not None + + is_skipfile = trace_rules.check(frame.f_code) + if sys.version_info >= (3, 13): + has_started_execution = frame.f_lasti > first_real_inst_idx(frame.f_code) + else: + has_started_execution = frame.f_lasti >= first_real_inst_idx(frame.f_code) + if ( + # TODO: the first condition is not covered by any test + has_started_execution + or is_skipfile + or config.disable + or ( + is_in_torch_dispatch_mode(include_infra_modes=False) + and not getattr(self._torchdynamo_orig_callable, "_export", False) + ) + ): + if log.isEnabledFor(logging.DEBUG): + print(frame.f_lasti, first_real_inst_idx(frame.f_code)) + + if has_started_execution: + skip_reason = "traced frame already" + elif trace_rules.check(frame.f_code): + skip_reason = "in skipfiles" + elif is_in_torch_dispatch_mode(include_infra_modes=False): + skip_reason = "non-infra torch dispatch mode present, this is not supported today in torch.compile" + else: + skip_reason = "dynamo tracing is disabled" + + log.debug( + "skipping: %s (reason: %s, file: %s)", + frame.f_code.co_name, + skip_reason, + frame.f_code.co_filename, + ) + return None + + if frame.f_code.co_filename == "" and frame.f_code.co_name == "__new__": + # nametuple constructor + return None + if config._get_optimize_ddp_mode() == "ddp_optimizer": + ddp_module = DistributedDataParallel._get_active_ddp_module() + if ddp_module: + with compile_lock: + from torch._dynamo.backends.distributed import DDPOptimizer + + ddp_optimizer = DDPOptimizer( + bucket_bytes_cap=ddp_module.bucket_bytes_cap, + backend_compile_fn=self._torchdynamo_orig_callable._torchdynamo_orig_callable, # type: ignore[attr-defined] + ) + assert hasattr( + self._torchdynamo_orig_callable, "_clone_with_backend" + ), "DDPOptimizer only supports callback fns that know how to clone themselves." + hijacked_callback = ( + self._torchdynamo_orig_callable._clone_with_backend( + ddp_optimizer.compile_fn, + ) + ) + return hijacked_callback( + frame, cache_entry, self.hooks, frame_state + ) + + with compile_lock, _disable_current_modes(): + # skip=1: skip this frame + return self._torchdynamo_orig_callable( + frame, cache_entry, self.hooks, frame_state, skip=1 + ) + + +def catch_errors_wrapper( + callback: ConvertFrameProtocol, hooks: Hooks +) -> CatchErrorsWrapper: + return CatchErrorsWrapper(callback, hooks) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/create_parameter_op.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/create_parameter_op.py new file mode 100644 index 0000000000000000000000000000000000000000..6661078859211c969287e646a3bc8f078e364df2 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/create_parameter_op.py @@ -0,0 +1,60 @@ +# mypy: allow-untyped-defs +import threading +from contextlib import contextmanager + +import torch + + +doc = """ +This is used when dynamo traces torch.nn.Parameter, which normally would not trace properly +with AOTAutograd. We instead create a placeholder torch.nn.Parameter before the graph, which +becomes a graph arg and has no storage backing it. At the point in the graph where the parameter +actually should be created we mutate this sacrificial placeholder into it. This allows gradients +to flow into the parameter as if it were an input to the graph (which is the only thing we are +allowed to compute gradients on). +""".strip() + + +class TracableCreateParameter(torch.autograd.Function): + @staticmethod + def forward(ctx, tensor, placeholder): + assert not tensor.requires_grad + return placeholder.set_(tensor) + + @staticmethod + def backward(ctx, grad): + return None, grad # grad flows to placeholder + + +def tracable_create_parameter(tensor, placeholder): + with torch.set_grad_enabled(placeholder.requires_grad): + out = TracableCreateParameter.apply(tensor, placeholder) + return out + + +def new_parameter_placeholder(size, dtype, device, requires_grad): + """Create a placeholder to be passed to the above functions""" + result = torch.nn.Parameter( + torch.empty(size, dtype=dtype, device=device), requires_grad=requires_grad + ) + # TODO(jansel): alloc followed by free is inefficient, need a way to allocate an unbacked tensor. + # Allocating a zero tensor would causes assert failures in autograd. + result.untyped_storage().resize_(0) + return result + + +_TLS = threading.local() + + +@contextmanager +def do_not_convert_to_tracable_parameter(): + old_flag = getattr(_TLS, "convert_tracable_parameter", True) + _TLS.convert_tracable_parameter = False + try: + yield False + finally: + _TLS.convert_tracable_parameter = old_flag + + +def can_convert_to_tracable_parameter(): + return getattr(_TLS, "convert_tracable_parameter", True) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/current_scope_id.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/current_scope_id.py new file mode 100644 index 0000000000000000000000000000000000000000..c0337b78462fa094447437ccd07004b9f4c525a8 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/current_scope_id.py @@ -0,0 +1,25 @@ +# mypy: allow-untyped-defs +import contextlib +import threading + + +# Global variable to identify which SubgraphTracer we are in. +# It is sometimes difficult to find an InstructionTranslator to use. +_current_scope_id = threading.local() + + +def current_scope_id(): + global _current_scope_id + if not hasattr(_current_scope_id, "value"): + _current_scope_id.value = 1 + return _current_scope_id.value + + +@contextlib.contextmanager +def enter_new_scope(): + global _current_scope_id + try: + _current_scope_id.value = current_scope_id() + 1 + yield + finally: + _current_scope_id.value = current_scope_id() - 1 diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/debug_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/debug_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..94687ff2747bf375899c5428836e0790e06bb2d3 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/debug_utils.py @@ -0,0 +1,824 @@ +# mypy: allow-untyped-defs +# mypy: disable-error-code="method-assign" +import atexit +import copy +import cProfile +import functools +import getpass +import inspect +import itertools +import logging +import os +import re +import subprocess +import sys +import tempfile +import textwrap +from collections import Counter +from importlib import import_module +from typing import Any, Callable, Dict, List, Optional, TypeVar + +import torch +import torch._prims_common as utils +import torch._subclasses.meta_utils +from torch import Tensor +from torch._dynamo.testing import rand_strided +from torch._prims_common import is_float_dtype +from torch.multiprocessing.reductions import StorageWeakRef +from torch.utils._content_store import ContentStoreReader, ContentStoreWriter + +from . import config +from .utils import clone_inputs, get_debug_dir + + +log = logging.getLogger(__name__) + +T = TypeVar("T") + + +inductor_config = import_module("torch._inductor.config") +use_buck = inductor_config.is_fbcode() + +if use_buck: + import libfb.py.build_info + + +extra_deps = [] +extra_imports = "" +if use_buck: + extra_deps = [ + "//caffe2/torch/fb/sparsenn:sparsenn_operators_gpu", + "//caffe2/torch/fb/sparsenn:sparsenn_operators", + "//deeplearning/fbgemm/fbgemm_gpu:sparse_ops_cpu", + "//deeplearning/fbgemm/fbgemm_gpu:sparse_ops", + ] + cur_target = libfb.py.build_info.BuildInfo.get_build_rule().replace("fbcode:", "//") # type: ignore[possibly-undefined] + extra_imports = "\n".join([f'torch.ops.load_library("{x}")' for x in extra_deps]) + + +BUCK_CMD_PREFIX = ["buck2", "run", "@mode/dev-nosan"] + + +class BuckTargetWriter: + def __init__(self, filename): + self.subdir, self.py_file = os.path.split(os.path.abspath(filename)) + self.target = self.py_file.replace(".py", "") + + # Get main_module path from fbcode + self.path = f'{self.subdir.replace("/", ".")}.{self.target}' + self.path = self.path[self.path.find("fbcode.") :] + self.path = self.path[7:] + + # Get cmd line path + tmp = self.subdir + tmp = tmp[tmp.find("fbcode/") :][7:] + self.cmd_line_path = f"//{tmp}:{self.target}" + + def build(self): + extra_cpp_deps = "\n".join([f' "{x}",' for x in extra_deps]) + return textwrap.dedent( + f""" +load("@fbcode_macros//build_defs:python_binary.bzl", "python_binary") + +python_binary( + name="{self.target}", + srcs = ["{self.py_file}"], + compile = False, + deps = [ + "//caffe2:torch", + "//caffe2/functorch:functorch", + "//triton:triton", + "{cur_target}", + ], + cpp_deps = [ +{extra_cpp_deps} + ], + main_module = "{self.path}", + par_style = "xar", +) +""" + ) + + def write(self, print_msg=True): + target_file = os.path.join(self.subdir, "TARGETS") + with open(target_file, "w") as fd: + fd.write(self.build()) + # log.warning("Wrote isolation TARGETS file at %s", target_file) + cmd_split = BUCK_CMD_PREFIX + [self.cmd_line_path] + if print_msg: + log.warning( + "Found an example that reproduces the error. Run this cmd to repro - %s", + " ".join(cmd_split), + ) + return cmd_split + + +def minifier_dir(): + path = os.path.join(get_debug_dir(), "minifier") + if path is None: + path = f"{tempfile.gettempdir()}/minifier_{getpass.getuser()}" + if not os.path.exists(path): + os.makedirs(path, exist_ok=True) + return path + + +MAX_CONSTANT_NUMEL_INLINE = 4 + + +class NNModuleToString: + safe_reprs = [ + torch.nn.Linear, + torch.nn.Conv1d, + torch.nn.Conv2d, + torch.nn.Conv3d, + torch.nn.BatchNorm1d, + torch.nn.BatchNorm2d, + torch.nn.BatchNorm3d, + torch.nn.LayerNorm, + torch.nn.Dropout, + torch.nn.Softmax, + torch.nn.ReLU, + torch.nn.GELU, + torch.nn.Identity, + torch.nn.MaxPool2d, + torch.nn.Embedding, + torch.nn.Tanh, + torch.nn.ConvTranspose1d, + torch.nn.GLU, + torch.nn.LSTM, + torch.nn.Flatten, + torch.nn.AdaptiveAvgPool2d, + ] + + @staticmethod + def can_convert_to_string(gm): + cant_convert = set() + for _, module in gm.named_children(): + if type(module) not in NNModuleToString.safe_reprs: + cant_convert.add(module) + + if len(cant_convert) > 0: + log.warning("We have not tested reprs of some modules - %s", cant_convert) + # TODO - Assuming that all modules can be safely repr'd. Check if that assumption is correct. + return True + + @staticmethod + def convert(gm): + from torch.nn.modules.module import _addindent + + tab = " " * 4 + + model_str = textwrap.dedent( + """ + from torch.nn import * + class Repro(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + """ + ) + + for module_name, module in gm.named_children(): + module_str = f"{module.__repr__()}" + # module should be a core torch.nn.Module, so all parameters + # should be on the same device. + example_param = next(module.parameters(), None) + if example_param is not None and example_param.is_cuda: + module_str = f"{module_str}.cuda()" + model_str += f"{tab*2}self.{module_name} = {module_str}\n" + + for buffer_name, buffer in gm._buffers.items(): + if buffer is None: + continue + # Serialize full data for small buffers + if buffer.numel() <= MAX_CONSTANT_NUMEL_INLINE: + from torch._tensor_str import PRINT_OPTS + + assert PRINT_OPTS.threshold >= MAX_CONSTANT_NUMEL_INLINE + tensor_str = repr(buffer) + elif torch.is_floating_point(buffer): + tensor_str = f"torch.randn({list(buffer.shape)}, dtype={buffer.dtype})" + else: + tensor_str = ( + f"torch.randint(1, size={list(buffer.shape)}, dtype={buffer.dtype})" + ) + if buffer.is_cuda: + tensor_str = f"{tensor_str}.cuda()" + model_str += f"{tab*2}self.register_buffer('{buffer_name}', {tensor_str})\n" + + for param_name, param in gm._parameters.items(): + if param is None: + continue + maybe_device = "" + if param.is_cuda: + maybe_device = ', device="cuda"' + tensor_str = f"torch.nn.Parameter(torch.randn({list(param.shape)}, dtype={param.dtype}{maybe_device}))" + model_str += f"{tab*2}self.{param_name} = {tensor_str}\n" + + # TODO - Keep this code for now. But, I don't think we will need this. + # attrs = dir(gm) + # for attr in attrs: + # if "_tensor_constant" in attr: + # val = getattr(gm, attr) + # model_str += f" {attr} = {val!r}\n" + + model_str += f"{_addindent(gm.code, 4)}\n" + return model_str + + +@functools.lru_cache(None) # subprocess is expensive +def _cuda_system_info_comment(): + if not torch.cuda.is_available(): + return "# torch.cuda.is_available()==False, no GPU info collected\n" + + model_str = "# CUDA Info: \n" + try: + cuda_version_out = subprocess.check_output(["nvcc", "--version"]) + cuda_version_lines = cuda_version_out.decode().split("\n") + comment = "".join([f"# {s} \n" for s in cuda_version_lines if s not in [""]]) + model_str += f"{comment}\n" + except (FileNotFoundError, subprocess.CalledProcessError): + model_str += "# nvcc not found\n" + + gpu_names = Counter( + torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count()) + ) + + model_str += "# GPU Hardware Info: \n" + for name, count in gpu_names.items(): + model_str += f"# {name} : {count} \n" + model_str += "\n" + return model_str + + +def generate_config_string(*, stable_output=False): + import torch._functorch.config + import torch._inductor.config + + if stable_output: + return "# config omitted due to stable_output=True" + + experimental_config = torch.fx.experimental._config.codegen_config() # type: ignore[attr-defined] + return f"""\ +import torch._dynamo.config +import torch._inductor.config +import torch._functorch.config +import torch.fx.experimental._config +{torch._dynamo.config.codegen_config()} +{torch._inductor.config.codegen_config()} +{torch._functorch.config.codegen_config()} +{experimental_config} +""" + + +def get_minifier_repro_path(): + return os.path.join(minifier_dir(), "minifier_launcher.py") + + +def helper_for_dump_minify(contents): + minified_repro_path = get_minifier_repro_path() + log.warning("Writing minified repro to:\n%s", minified_repro_path) + + if use_buck: + BuckTargetWriter(minified_repro_path).write() + try: + with open(minified_repro_path, "w") as fd: + fd.write(contents) + + except OSError as e: + log.exception("") + raise NotImplementedError("Could not write to {minified_repro_path}") from e + + +class AccuracyError(Exception): + pass + + +def clone_inputs_retaining_gradness(example_inputs): + """ + This clone inputs is different from utils clone_input. In case of minifier, + all the tensors are leaf tensors while creating a new graph. So, we set the + requires_grad field w/o checking the leafness of the tensor. + """ + cloned_inputs = clone_inputs(example_inputs) + for idx in range(len(example_inputs)): + if isinstance(cloned_inputs[idx], torch.Tensor): + cloned_inputs[idx].requires_grad_(example_inputs[idx].requires_grad) + return cloned_inputs + + +def run_fwd_maybe_bwd(gm, args, only_fwd=False, disable_clone=False): + """ + Runs a forward and possibly backward iteration for a given mod and args. + + When disable_clone is True, we will use args as-is without cloning. + This is higher fidelity but we may destroy the args in the process. + """ + from .testing import collect_results, reduce_to_scalar_loss, requires_bwd_pass + + gm = copy.deepcopy(gm) + if not disable_clone: + args = clone_inputs_retaining_gradness(args) + + if hasattr(gm, "zero_grad"): + gm.zero_grad(True) + + # TorchInductor returned callable expects lists. So, may need a boxed calling convention. + out = gm(args) if hasattr(gm, "_boxed_call") else gm(*args) + + if only_fwd: + return out + if requires_bwd_pass(out): + loss = reduce_to_scalar_loss(out) + loss.backward() + return collect_results(gm, out, None, args) + + +def same_two_models( + gm, + opt_gm, + example_inputs, + only_fwd=False, + *, + require_fp64=False, + ignore_non_fp=False, +): + """ + Check two models have same accuracy. + + require_fp64: if True, raise an error if we unable to calculate the fp64 reference + ignore_non_fp: if True, do not compare outputs which are not floating point. This + is mostly useful for the minifier (which wants to avoid quantizing floating point + error into integer/boolean error) + """ + from .utils import same + + ref = run_fwd_maybe_bwd(gm, example_inputs, only_fwd) + + fp64_ref = None + if config.same_two_models_use_fp64: + try: + fp64_model, fp64_examples = cast_to_fp64( + copy.deepcopy(gm), clone_inputs_retaining_gradness(example_inputs) + ) + fp64_ref = run_fwd_maybe_bwd(fp64_model, fp64_examples, only_fwd) + except Exception: + if require_fp64: + raise RuntimeError( # noqa: B904 + "Could not generate fp64 outputs, workaround with torch._dynamo.config.same_two_models_use_fp64 = False" + ) + log.warning("Could not generate fp64 outputs") + + try: + res = run_fwd_maybe_bwd(opt_gm, example_inputs, only_fwd) + except Exception as e: + # This means that the minified graph is bad/exposes a different problem. + # As we are checking accuracy here, lets log the exception and return True. + log.exception( + "While minifying the program in accuracy minification mode, " + "ran into a runtime exception which is likely an unrelated issue." + " Skipping this graph." + ) + return True + + passing = same( + ref, + res, + fp64_ref, + tol=config.repro_tolerance, + equal_nan=True, + ignore_non_fp=ignore_non_fp, + ) + return passing + + +def cast_dtype_args_to_fp64(model): + for node in model.graph.nodes: + if ( + node.op == "call_function" + and node.target == torch.ops.prims.convert_element_type.default + ): + assert len(node.args) == 2 + if is_float_dtype(node.args[1]) and node.args[1] != torch.float64: + node.args = (node.args[0], torch.float64) + if node.op == "call_function": + dtype = node.kwargs.get("dtype") + if dtype is not None and is_float_dtype(dtype): + new_kwargs = dict(node.kwargs) + new_kwargs["dtype"] = torch.float64 + node.kwargs = new_kwargs + + model.graph.lint() + model.recompile() + return model + + +def cast_to(dtype, model, inputs): + from torch.utils._pytree import tree_map + + model = model.to(dtype) + if dtype == torch.float64: + # If casting to fp64 for accuracy comparison, we need to + # replace dtype arguments embedded in the graph with fp64 + model = cast_dtype_args_to_fp64(model) + + inputs = tree_map( + lambda x: x.to(dtype) + if isinstance(x, torch.Tensor) and x.is_floating_point() + else x, + inputs, + ) + return model, inputs + + +def cast_to_fp64(model, inputs): + return cast_to(torch.float64, model, inputs) + + +def backend_accuracy_fails( + gm, + example_inputs, + compiler_fn, + only_fwd=False, + *, + require_fp64=False, + ignore_non_fp=False, +): + try: + compiled_gm = compiler_fn( + copy.deepcopy(gm), clone_inputs_retaining_gradness(example_inputs) + ) + return not same_two_models( + gm, + compiled_gm, + example_inputs, + only_fwd, + require_fp64=require_fp64, + ignore_non_fp=ignore_non_fp, + ) + except Exception as e: + # This means that the minified graph is bad/exposes a different problem. + # As we are checking accuracy here, lets log the exception and return False. + log.exception( + "While minifying the program in accuracy minification mode, " + "ran into a runtime exception which is likely an unrelated issue." + " Skipping this graph" + ) + return False + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # +# REPRO SUPPORT CODE +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # + + +# Helper functions for computing what the default values of tensor +# values should be. These all coincide with factory functions, e.g., torch.empty + + +def _stride_or_default( + stride: Optional["torch._prims_common.StrideType"], + *, + shape: "torch._prims_common.ShapeType", +) -> "torch._prims_common.StrideType": + return stride if stride is not None else utils.make_contiguous_strides_for(shape) + + +def _mk_defaulter(d: T) -> Callable[[Optional[T]], T]: + return lambda x: x if x is not None else d + + +_dtype_or_default = _mk_defaulter(torch.float32) +_device_or_default = _mk_defaulter(torch.device("cpu")) +_storage_offset_or_default = _mk_defaulter(0) +_requires_grad_or_default = _mk_defaulter(False) +_is_leaf_or_default = _mk_defaulter(False) + + +class NopInputReader: + def __init__(self) -> None: + self.total = 0 + + def storage(self, storage_hash, nbytes, *, device=None, dtype_hint=None): + self.total += 1 + + def tensor(self, *args, **kwargs): + pass + + def symint(self, *args, **kwargs): + pass + + +# TODO: Support bundling the entire repro into a zip file for ease of +# transferring around +class InputReader: + def __init__(self, save_dir=None, *, pbar=None): + # If None, we will generate random data instead. It's important + # to natively support this use case as it will allow people to + # share repros without including the real data, if the problem + # reproduces even on random data. + if save_dir is None: + log.warning("no save_dir specified, will generate random data") + self.store = ContentStoreReader(save_dir) if save_dir is not None else None + self.args = [] + self.pbar = pbar + + def storage(self, storage_hash, nbytes, *, device=None, dtype_hint=None): + if self.pbar is not None: + self.pbar.update(1) + device = _device_or_default(device) + dtype_hint = _dtype_or_default(dtype_hint) + if self.store is not None and storage_hash is not None: + try: + storage = self.store.read_storage(storage_hash) + except FileNotFoundError: + pass + else: + if device != storage.device: + log.warning("device mismatch: %s != %s", device, storage.device) + # TODO: transfer it to the right device? But failing this + # way would be very mysterious! Would have been better + # not to store device in the serialized format... + return storage + log.warning("could not load %s, generating random data instead", storage_hash) + shape = (nbytes // dtype_hint.itemsize,) + stride = _stride_or_default(None, shape=shape) + return rand_strided(shape, stride, dtype_hint, device).untyped_storage() + + def tensor( + self, + storage, + shape, + stride=None, + *, + storage_offset=None, + dtype=None, + requires_grad=None, + is_leaf=None, + **metadata, + ): + stride = _stride_or_default(stride, shape=shape) + storage_offset = _storage_offset_or_default(storage_offset) + dtype = _dtype_or_default(dtype) + is_leaf = _is_leaf_or_default(is_leaf) + requires_grad = _requires_grad_or_default(requires_grad) + t = torch.tensor( + [], dtype=dtype, device=storage.device, requires_grad=requires_grad + ) + with torch.no_grad(): + t.set_(storage, storage_offset, shape, stride) + if not is_leaf: + # Fake up some autograd history in a very naughty way + with torch.enable_grad(): + t = t.clone(memory_format=torch.preserve_format) + with torch.no_grad(): + t.set_(storage, storage_offset, shape, stride) + assert torch._subclasses.meta_utils.safe_is_leaf(t) == is_leaf + torch._utils.set_tensor_metadata(t, metadata) + self.args.append(t) + return t # for BC + + def symint(self, val): + self.args.append(val) + return val # for BC + + +# Here is our writer strategy: +# 1. We will stream all of the inputs to disk +# 2. You can now deterministically randomize the inputs, or reload +# the inputs from disk +# 3. You can YOLO run the script without the inputs, in which case +# we'll fill the inputs with random data and pray. This is the +# legacy behavior, but it's also useful if you want to find out +# if we're so broken even random inputs trigger it +# 4. We could offer an in process "check if the randomized thing +# works too" but this is delicate so we don't do it + + +class InputWriter: + def __init__(self, save_dir, *, stable_hash=False): + self._lines = [] + # TODO: consider ensuring tensor and storage counters line up? + self.storage_counter = itertools.count() + self.save_dir = save_dir + self.store = ( + ContentStoreWriter(save_dir, stable_hash=stable_hash) + if save_dir is not None + else None + ) + self.seen_storages = {} + + def lines(self): + r = [ + "def load_args(reader):", + ] + r.extend(f" {l}" for l in self._lines) + # In case we need to change the internal format of load_args + # in an FC-breaking way + r.append("load_args._version = 0") + return r + + # Storages are untyped, but we need to initialize them with data if + # we don't have the real data, so we give a hint saying what kind + # of initialization may be appropriate + # + # If we had a FakeTensor, device_hint tells us what device should be + def storage(self, untyped_storage, *, dtype_hint=None, device_hint=None) -> str: + ws = StorageWeakRef(untyped_storage) + v = self.seen_storages.get(ws) + if v is not None: + return v + v = f"buf{next(self.storage_counter)}" + maybe_dtype_hint = "" + if _dtype_or_default(None) != _dtype_or_default(dtype_hint): + maybe_dtype_hint = f", dtype_hint={dtype_hint!r}" + # TODO: being optional on device is kind of pointless as the default + # is CPU but most repros we care about are CUDA + maybe_device = "" + device = untyped_storage.device + if device.type == "meta": + assert device_hint is not None + device = device_hint + if _device_or_default(None) != device: + maybe_device = f", device={device!r}" + nbytes = untyped_storage.nbytes() + storage_hash = None + if self.store is not None and untyped_storage.device.type != "meta": + storage_hash = self.store.write_storage(untyped_storage) + self._lines.append( + f"{v} = reader.storage({storage_hash!r}, {nbytes!r}{maybe_device}{maybe_dtype_hint})" + ) + self.seen_storages[ws] = v + return v + + def tensor(self, name, t) -> None: + from torch.fx.experimental.symbolic_shapes import statically_known_true + + storage = self.storage( + t.untyped_storage(), dtype_hint=t.dtype, device_hint=t.device + ) + args = [] + # NB: this is positional, must come first + if _stride_or_default(None, shape=t.shape) != t.stride(): + args.append(str(tuple(t.stride()))) + if _dtype_or_default(None) != t.dtype: + args.append(f"dtype={t.dtype!r}") + if not statically_known_true( + _storage_offset_or_default(None) == t.storage_offset() + ): + args.append(f"storage_offset={t.storage_offset()!r}") + tensor_metadata = torch._utils.get_tensor_metadata(t) + if tensor_metadata: + args.extend(f"{k}={v!r}" for k, v in tensor_metadata.items()) + if _requires_grad_or_default(None) != t.requires_grad: + args.append(f"requires_grad={t.requires_grad!r}") + is_leaf = torch._subclasses.meta_utils.safe_is_leaf(t) + if _is_leaf_or_default(None) != is_leaf: + args.append(f"is_leaf={is_leaf!r}") + self._lines.append( + "reader.tensor(" + + ", ".join([storage, str(tuple(t.shape)), *args]) + + f") # {name}" + ) + + # TODO: this doesn't actually symint atm + def symint(self, name, val) -> None: + if isinstance(val, torch.SymInt): + val = val.node.hint + self._lines.append(f"reader.symint({val!r}) # {name}") + + +def aot_graph_input_parser( + func: Callable[[List[Tensor]], List[Tensor]], + device: str = "cuda", + sym_shapes: Optional[Dict[str, int]] = None, + default_sym_shape: Optional[int] = None, +) -> Dict[str, Any]: + """ + Takes in a function which has been printed with print_readable() and constructs kwargs to run it. + + Handles Tensor inputs, Symints, and a graph module which might have tensor constants. + + Consider a function `forward` defined as follows: + + def forward(self, primals_1: "f32[1001, 6]", primals_2: "f32[s0]", primals_3: "Sym(s0)",): + _tensor_constant0: "i64[4190]" = self._tensor_constant0 + # Further implementation + + kwargs = aot_graph_input_parser(forward) + forward(**kwargs) + """ + + from torch.fx.graph import dtype_abbrs + + dtype_map = {value: key for key, value in dtype_abbrs.items()} + dtype_pattern = "|".join(dtype_abbrs.values()) + + # Extracting the source code from the function + source = inspect.getsource(func) + + # Regular expressions + tensor_assignment_regex = rf"(_tensor_constant\d+): \"({dtype_pattern})\[\s*(.*?)\s*\]\" = self\.(_tensor_constant\d+)" + tensor_regex = rf"({dtype_pattern})\[\s*(.*?)\s*\]" + sym_shape_regex = r"Sym\((s\d+)\)" + + class TensorContainer: + "Container for tensors as attributes" + + # Dictionary for tensors from annotations + kwargs: Dict[str, Any] = {} + + sym_shapes = sym_shapes or {} + + def get_sym_int(symint): + torch._check( + symint in sym_shapes or default_sym_shape is not None, + lambda: f"{symint} not in symbolic_shapes and default sym shape not passed in", + ) + return sym_shapes.get(symint, default_sym_shape) + + def gen_tensor(shape, dtype) -> Tensor: + # Resolve symbolic shapes to concrete values + resolved_shape = [] + dynamic_dims = [] + for i, dim in enumerate(shape): + dim = dim.strip() + if "s" in dim: + s = get_sym_int(dim) + resolved_shape.append(s) + dynamic_dims.append(i) + else: + if dim: + resolved_shape.append(int(dim)) + + constructor = torch.randn if dtype.is_floating_point else torch.zeros + out = constructor(resolved_shape, dtype=dtype, device=device) # type: ignore[call-arg] + for d in dynamic_dims: + torch._dynamo.mark_dynamic(out, d) + return out + + # Parse function annotations for tensor generation + annotations = func.__annotations__ + for param, annotation in annotations.items(): + # Skip 'return' annotation + if param == "return": + continue + + match = re.search(tensor_regex, annotation) + if match: + data_type, shape_str = match.groups() + shape = tuple(shape_str.split(",")) + dtype = dtype_map[data_type] + kwargs[param] = gen_tensor(shape, dtype) + + match = re.search(sym_shape_regex, annotation) + if match: + kwargs[param] = get_sym_int(match.group(1)) + + if "self" in inspect.signature(func).parameters: + container = TensorContainer() + kwargs["self"] = container + for match in re.finditer(tensor_assignment_regex, source): + attr_name, data_type, shape_str, _ = match.groups() + shape = tuple(shape_str.split(",")) + dtype = dtype_map[data_type] + setattr(container, attr_name, gen_tensor(shape, dtype)) + + return kwargs + + +def profile_to_file(filename: str) -> Callable[[T], T]: + """ + Decorator to cProfile a given function and save the result to disk on process exit. + + Args: + filename: filename to save profile to + """ + prof = cProfile.Profile() + filename = os.path.abspath(os.path.expanduser(filename)) + + def decorator(fn): + @functools.wraps(fn) + def wrapper(*args, **kwargs): + prof.enable() + try: + return fn(*args, **kwargs) + finally: + prof.disable() + + return wrapper + + def save_it(): + prof.dump_stats(filename) + sys.stderr.write( + textwrap.dedent( + f"""\ + Wrote profile to {filename}, view with: + + snakeviz {filename} + + """ + ) + ) + + atexit.register(save_it) + return decorator diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/decorators.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/decorators.py new file mode 100644 index 0000000000000000000000000000000000000000..235db547dc7b1c251a23a18e9911250ed23abfd5 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/decorators.py @@ -0,0 +1,580 @@ +# mypy: allow-untyped-defs +# ruff: noqa: TCH004 +import functools +import inspect +from dataclasses import dataclass +from typing import Any, Callable, Dict, Type, TYPE_CHECKING, TypeVar + +import torch +from torch.utils._python_dispatch import is_traceable_wrapper_subclass + +from . import trace_rules, variables +from .comptime import comptime +from .eval_frame import DisableContext, innermost_fn, RunOnlyContext +from .exc import IncorrectUsage +from .external_utils import is_compiling +from .utils import is_function + + +if TYPE_CHECKING: + from types import FunctionType + + from torch._C._dynamo.eval_frame import ( # noqa: F401 + reset_code, + set_eval_frame, + set_guard_error_hook, + skip_code, + unsupported, + ) + + from .variables import VariableTracker +else: + for name in dir(torch._C._dynamo.eval_frame): + if name.startswith("__"): + continue + globals()[name] = getattr(torch._C._dynamo.eval_frame, name) + + +_F = TypeVar("_F", bound=Callable[..., Any]) + + +def run(fn=None): + """Don't do any dynamic compiles, just use prior optimizations""" + if fn is not None: + fn = innermost_fn(fn) + assert callable(fn) + return RunOnlyContext()(fn) + return RunOnlyContext() + + +def disable(fn=None, recursive=True): + """ + Decorator and context manager to disable TorchDynamo + + If recursive=True, Dynamo is completely skipped on the decorated function + frame as well as the recursively invoked functions. + + If recursive=False, Dynamo skips frames associated with the function code, + but still process recursively invoked frames. + """ + if recursive: + if fn is not None: + fn = innermost_fn(fn) + assert callable(fn) + return DisableContext()(fn) + return DisableContext() + else: + return skip(fn) + + +def skip(fn=None): + """ + Skip frames associated with the function code, but still process recursively + invoked frames + """ + if fn is None: + return skip + fn = innermost_fn(fn) + assert callable(fn) + skip_code(fn.__code__) + fn._torchdynamo_disable = True + return fn + + +def assume_constant_result(fn): + fn._dynamo_marked_constant = True + return fn + + +def allow_in_graph(fn): + """ + Tells the compiler frontend (Dynamo) to skip symbolic introspection of the function + and instead directly write it to the graph when encountered. + + See :func:`torch.compiler.allow_in_graph`'s docstring for the full documentation + + WARNING: this API can be a footgun, please read the documentation carefully. + """ + if isinstance(fn, (list, tuple)): + return [allow_in_graph(x) for x in fn] + assert callable(fn), "allow_in_graph expects a callable" + if trace_rules.lookup_callable(fn) != variables.TorchInGraphFunctionVariable: + trace_rules._disallowed_callable_ids.remove(id(fn)) + trace_rules._allowed_callable_ids.add(id(fn)) + return fn + + +def _disallow_in_graph_helper(throw_if_not_allowed): + def inner(fn): + if isinstance(fn, (list, tuple)): + return [disallow_in_graph(x) for x in fn] + assert callable(fn), "disallow_in_graph expects a callable" + if ( + throw_if_not_allowed + and trace_rules.lookup_callable(fn) + != variables.TorchInGraphFunctionVariable + and trace_rules.lookup(fn) != variables.TorchInGraphFunctionVariable + ): + raise IncorrectUsage( + "disallow_in_graph is expected to be used on an already allowed callable (like torch.* ops). " + "Allowed callables means callables that TorchDynamo puts as-is in the extracted graph." + ) + trace_rules._allowed_callable_ids.remove(id(fn)) + trace_rules._disallowed_callable_ids.add(id(fn)) + return fn + + return inner + + +def disallow_in_graph(fn): + """ + Customize which functions TorchDynamo will exclude in the generated + graph and force a graph break on. + :: + + torch._dynamo.disallow_in_graph(torch.sub) + + @torch._dynamo.optimize(...) + def fn(a): + x = torch.add(x, 1) + x = torch.sub(x, 1) + x = torch.add(x, 1) + return x + + fn(...) + + Will break the graph on `torch.sub`, and give two graphs each with a + single `torch.add()` op. + """ + return _disallow_in_graph_helper(throw_if_not_allowed=True)(fn) + + +@_disallow_in_graph_helper(throw_if_not_allowed=False) +def graph_break(): + """Force a graph break""" + + +def forbid_in_graph(fn): + """ + Customize which functions TorchDynamo will assert are not present while tracing. + + If you want a graph break on this function instead, use disallow_in_graph. + TODO(voz): We now have allow_in_graph, disallow_in_graph, forbid_in_graph - some more robust + documentation would not be amiss. + """ + if isinstance(fn, (list, tuple)): + return [forbid_in_graph(x) for x in fn] + assert callable(fn), "forbid_in_graph applies only to callables" + fn._dynamo_forbidden = True + return fn + + +def substitute_in_graph( + original_fn: _F, + *, + can_constant_fold_through: bool = False, + skip_signature_check: bool = False, + # type that is embedded in the Python interpreter + is_embedded_type: bool = False, # internal use only +) -> Callable[[_F], _F]: + """ + Register a polyfill handler for a function, usually a C function from the C extension, to be + used in place of the original function when inlining the original function in the graph. + + .. note:: + + The polyfill handler is only used when inlining the original function. It is not used when + the original function is called directly. In the eager mode, the decorated function calls + the performant C function rather than the polyfill handler. + + The polyfill handler is a function that will be called in place of the original function when + inlining the original function. The polyfill handler should have the same signature and the same + behavior as the original function. + + Args: + original_fn (callable): The original function, usually a C function, to register a polyfill + handler for. + can_constant_fold_through (bool, optional): Whether the polyfill handler can be constant + folded through. That is, if the polyfill handler is a pure function and its arguments + are constant, the result of the polyfill handler can be constant folded during the + compilation. Defaults to ``False``. + skip_signature_check (bool, optional): Whether to skip the signature check between the + original function and the polyfill handler. Defaults to ``False``. + + Returns: + A decorator that registers the polyfill handler for the original function. + + Example:: + + >>> # xdoctest: +SKIP("conflict with the tests: duplicate polyfill handlers") + >>> import operator + >>> operator.indexOf([1, 2, 3, 4, 5], 3) + 2 + >>> torch.compile(operator.indexOf, fullgraph=True)([1, 2, 3, 4, 5], 3) + Traceback (most recent call last): + ... + torch._dynamo.exc.Unsupported: ... + + >>> @torch.compiler.substitute_in_graph(operator.indexOf) + ... def indexOf(a, b, /): + ... for i, item in enumerate(a): + ... if item is b or item == b: + ... return i + ... raise ValueError("sequence.index(x): x not in sequence") + >>> + >>> torch.compile(operator.indexOf, fullgraph=True)([1, 2, 3, 4, 5], 3) + 2 + """ + if not is_function(original_fn) and not ( + is_embedded_type and inspect.isclass(original_fn) + ): + raise TypeError( + f"substitute_in_graph expects a function but got {type(original_fn)!r}" + ) + if is_embedded_type: + if not inspect.isclass(original_fn): + raise TypeError( + f"substitute_in_graph expects a class but got {type(original_fn)!r}" + ) + + from .variables.builder import ITERTOOLS_POLYFILLED_TYPE_IDS, ITERTOOLS_TYPE_IDS + + if id(original_fn) in ITERTOOLS_TYPE_IDS: + ITERTOOLS_POLYFILLED_TYPE_IDS.add(id(original_fn)) + + def wrapper(traceable_fn: _F) -> _F: + if not is_function(traceable_fn): + raise TypeError( + f"@substitute_in_graph(...) expects a function but got {type(traceable_fn)!r}" + ) + + if not skip_signature_check: + try: + original_sig = inspect.signature(original_fn) + except ValueError: + pass + else: + traceable_sig = inspect.signature(traceable_fn) + + def sig_ident(sig): + # Ignore annotations for parameters and return type + return ( + tuple( + p.name + for p in sig.parameters.values() + if ( + p.kind + not in { + p.KEYWORD_ONLY, + # the name of *args and **kwargs is not important + p.VAR_POSITIONAL, + p.VAR_KEYWORD, + } + ) + ), + { + p.name + for p in sig.parameters.values() + if p.kind == p.KEYWORD_ONLY + }, + { + p.name: p.default + for p in sig.parameters.values() + # the name of *args and **kwargs is not important + if p.kind not in {p.VAR_POSITIONAL, p.VAR_KEYWORD} + }, + ) + + wildcard_sig = inspect.signature(lambda *args, **kwargs: None) + + if ( + sig_ident(original_sig) != sig_ident(traceable_sig) + and sig_ident(original_sig) != sig_ident(wildcard_sig) + and sig_ident(traceable_sig) != sig_ident(wildcard_sig) + ): + raise TypeError( + f"Signature mismatch between {original_fn} and {traceable_fn}: " + f"{original_sig} != {traceable_sig}" + ) + + from torch._dynamo.guards import GuardBuilder + from torch._dynamo.trace_rules import get_torch_obj_rule_map + from torch._dynamo.variables import PolyfilledFunctionVariable + from torch._dynamo.variables.builder import VariableBuilder + + id_dispatch_map = VariableBuilder._id_dispatch() + if id(original_fn) in id_dispatch_map: + raise ValueError( + f"Duplicate dispatch rule for {original_fn}: " + "already registered in VariableBuilder's id dispatch map" + ) + + rule_map: Dict[Any, Type[VariableTracker]] = get_torch_obj_rule_map() + if original_fn in rule_map: + raise ValueError( + f"Duplicate object {original_fn} with different rules: " + f"{PolyfilledFunctionVariable}, {rule_map[original_fn]}" + ) + + polyfill_handlers: Dict[Callable[..., Any], FunctionType] + polyfill_handlers = PolyfilledFunctionVariable._get_polyfill_handlers() + if original_fn in polyfill_handlers: + raise ValueError( + f"Duplicate polyfill handlers for {original_fn}: " + f"already handled by {polyfill_handlers[original_fn]}" + ) + + # Need to wrap the function because we may cannot assign __torch_dynamo_polyfill__ to a + # C++ function. + @functools.wraps(traceable_fn) + def wrapped(*args, **kwargs): + return original_fn(*args, **kwargs) + + def dispatch_fn(self, value: _F) -> PolyfilledFunctionVariable: + return PolyfilledFunctionVariable( + value, + source=self.source, + **self.install_guards(GuardBuilder.FUNCTION_MATCH), + ) + + id_dispatch_map[id(original_fn)] = id_dispatch_map[id(wrapped)] = dispatch_fn + rule_map[original_fn] = rule_map[wrapped] = PolyfilledFunctionVariable + polyfill_handlers[original_fn] = polyfill_handlers[wrapped] = wrapped # type: ignore[assignment] + + wrapped.__torch_dynamo_original__ = original_fn # type: ignore[attr-defined] + wrapped.__torch_dynamo_polyfill__ = traceable_fn # type: ignore[attr-defined] + wrapped.__torch_dynamo_can_constant_fold_through__ = can_constant_fold_through # type: ignore[attr-defined] + + return wrapped # type: ignore[return-value] + + return wrapper + + +# Helper function to flatten a tensor subclass and apply a function to +# all inner tensors that match the outer dim. Used to reduce duplication +# across the various marking APIs. +def _apply_func_to_inner_tensors_of_same_dim(func, t, *args, **kwargs): + assert is_traceable_wrapper_subclass(t) + + attrs, ctx = t.__tensor_flatten__() + assert isinstance(t, torch.Tensor) + for attr in attrs: + inner = getattr(t, attr) + if inner.dim() == t.dim(): + func(inner, *args, **kwargs) + + +@dataclass(frozen=True) +class _DimRange: + """ + This represents an dimension of a tensor and the corresponding + min and max values it can take. Don't create this + class directly; instead, use :func:`mark_dynamic`. + """ + + dim: int + min: int + max: int + + +@forbid_in_graph +def mark_unbacked(t, index): + """ + Mark a tensor as having an unbacked dim. This changes the semantics of operations, + we will always report the size does not equal zero/one, we will turn asserts + on this index into runtime asserts, and if you try to get the real value we will + raise an exception. In other words, we will treat this dimension as if it was + data dependent (we do not know anything about its value.) + """ + # You could have copied the mark_dynamic behavior but I'm not convinced + # it's what you want + assert not is_traceable_wrapper_subclass(t), "not implemented yet" + + if isinstance(index, int): + if not hasattr(t, "_dynamo_unbacked_indices"): + t._dynamo_unbacked_indices = set() + t._dynamo_unbacked_indices.add(index) + return + + assert isinstance(index, (list, tuple)) + for i in index: + mark_unbacked(t, i) + + +@forbid_in_graph +def mark_dynamic(t, index, *, min=None, max=None): + """ + Mark a tensor as having a dynamic dim and set corresponding min and max range for the dim. + + [Note - on the state of mark_dynamic] + + The behavior of having a dynamic dimension on a tensor is governed by a few factors: + + 1) torch._dynamo.config dynamic_shapes True or False. + a) dynamic_shapes=True - dynamic_shapes must be True for mark_dynamic to work. + a) dynamic_shapes=False - This config will raise an exception when used in conjunction with + mark_dynamic. We will eventually support this. + + 2) If the dimension is fully constrained - as in, it does not allow more than a single value + in both eager (torch.compile, torch._dynamo.optimize) mode and export mode (torch._dynamo.export), + we will raise an error + + 3) If the dimension is partially constrained - allowing at least 2 values but not the full unbounded + range of shapes, in eager we will pass it through, but export will raise an error. + + 4) Attempts to trace this function will explicitly raise. As such, all calls to mark_dynamic must be made + before torch.compile. + + """ + if is_traceable_wrapper_subclass(t): + # default behavior: mirror mark_dynamic() on all inner tensors with same dim as t + # TODO: Make this configurable via a supported public API + _apply_func_to_inner_tensors_of_same_dim( + mark_dynamic, t, index, min=min, max=max + ) + + if isinstance(index, int): + if not hasattr(t, "_dynamo_dynamic_indices"): + t._dynamo_dynamic_indices = set() + t._dynamo_dynamic_range = set() + # TODO(voz): Should we bounds check? + t._dynamo_dynamic_indices.add(index) + t._dynamo_dynamic_range.add(_DimRange(index, min, max)) + return + + assert isinstance(index, (list, tuple)) + for i in index: + mark_dynamic(t, i, min=min, max=max) + + +@forbid_in_graph +def maybe_mark_dynamic(t, index): + """ + Mark a tensor as having a dynamic dim, but don't enforce it (i.e., if this + dimension ends up getting specialized, don't error). + """ + if is_traceable_wrapper_subclass(t): + # default behavior: mirror maybe_mark_dynamic() on all inner tensors with same dim as t + # TODO: Make this configurable via a supported public API + _apply_func_to_inner_tensors_of_same_dim(maybe_mark_dynamic, t, index) + + if isinstance(index, int): + if not hasattr(t, "_dynamo_weak_dynamic_indices"): + t._dynamo_weak_dynamic_indices = set() + # TODO(voz): Should we bounds check? + t._dynamo_weak_dynamic_indices.add(index) + return + + assert isinstance(index, (list, tuple)) + for i in index: + maybe_mark_dynamic(t, i) + + +def mark_static(t, index=None): + """ + Mark a tensor as having a static dim or mark a nn module class as static. + + For tensors + =========== + This will prevent us from attempting to compile it dynamically + when dynamic=True; this can improve trace-time performance. + + This has lower precedence than mark_dynamic. + + Unlike mark_dynamic, this can be done inside a graph, in which case it + induces specialization on the tensor. + + For nn.Module classes + ===================== + For static nn.Module classes, TorchDynamo assumes that the module instance + attributes will not be modified after compilation. This will ensure that + TorchDynamo keeps integer attributes CONSTANT and not symints. + + From TorchDynamo implementation side, the instances of static-marked + nn.Module class will be converted to UnspecializedBuiltinNNModuleVariable, + which have the same properties. + + Note that we still have to guard on the attributes, because different + instances of the nn.Module can have different values of the attributes. The + key point here is that the attributes are static. + """ + if is_compiling(): + if index is None: + for s in t.size(): + comptime.force_static(s) + else: + comptime.force_static(t.size(index)) + return + + if is_traceable_wrapper_subclass(t): + # default behavior: mirror mark_static() on all inner tensors with same dim as t + # TODO: Make this configurable via a supported public API + _apply_func_to_inner_tensors_of_same_dim(mark_static, t, index) + + if not isinstance(t, torch.Tensor) and issubclass(t, torch.nn.Module): + t._dynamo_marked_static = True + return t + + if not isinstance(t, torch.Tensor): + raise TypeError( + f"mark_static expects a tensor/nn.Module class but recieved {type(t)}" + ) + + if isinstance(index, int): + if not hasattr(t, "_dynamo_static_indices"): + t._dynamo_static_indices = set() # type: ignore[attr-defined] + # TODO(voz): Should we bounds check? + t._dynamo_static_indices.add(index) # type: ignore[attr-defined] + elif index is None: + for i in range(t.dim()): + mark_static(t, i) + else: + assert isinstance(index, (list, tuple)) + for i in index: + mark_static(t, i) + + +@forbid_in_graph +def mark_static_address(t, guard=True): + """ + Marks an input tensor whose data_ptr will not change across multiple calls + to a dynamo-compiled function. This indicates to cudagraphs that an extra allocation + is not needed for this input. The data_ptr will be guarded if guard=True. Note: + Tensors marked in this way will be kept alive until `torch._dynamo.reset()` is called. + """ + if not isinstance(t, torch.Tensor): + raise TypeError(f"mark_static_address expects a tensor but recieved {type(t)}") + + if guard: + t._dynamo_static_input_type = "guarded" # type: ignore[attr-defined] + else: + t._dynamo_static_input_type = "unguarded" # type: ignore[attr-defined] + + +# Note: this carefully avoids eagerly import einops. +# TODO: we should delete this whole _allow_in_graph_einops logic by approximately 2024 Q2 +def _allow_in_graph_einops(): + import einops + + try: + # requires einops > 0.6.1, torch >= 2.0 + from einops._torch_specific import ( # type: ignore[attr-defined] # noqa: F401 + _ops_were_registered_in_torchdynamo, + ) + + # einops > 0.6.1 will call the op registration logic as it is imported. + except ImportError: + # einops <= 0.6.1 + allow_in_graph(einops.rearrange) + allow_in_graph(einops.reduce) + if hasattr(einops, "repeat"): + allow_in_graph(einops.repeat) # available since einops 0.2.0 + if hasattr(einops, "einsum"): + allow_in_graph(einops.einsum) # available since einops 0.5.0 + if hasattr(einops, "pack"): + allow_in_graph(einops.pack) # available since einops 0.6.0 + if hasattr(einops, "unpack"): + allow_in_graph(einops.unpack) # available since einops 0.6.0 + + +trace_rules.add_module_init_func("einops", _allow_in_graph_einops) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/device_interface.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/device_interface.py new file mode 100644 index 0000000000000000000000000000000000000000..5670172c49c52c5f5db2e213d0428512fec83946 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/device_interface.py @@ -0,0 +1,330 @@ +# mypy: allow-untyped-defs +import inspect +from typing import Any, Callable, Dict, Iterable, Optional, Tuple, Type, Union + +import torch +from torch._streambase import _EventBase, _StreamBase + + +get_cuda_stream: Optional[Callable[[int], int]] +if torch.cuda._is_compiled(): + from torch._C import _cuda_getCurrentRawStream as get_cuda_stream +else: + get_cuda_stream = None + +_device_t = Union[torch.device, str, int, None] + +# Recording the device properties in the main process but used in worker process. +caching_worker_device_properties: Dict[str, Any] = {} +caching_worker_current_devices: Dict[str, int] = {} + + +class DeviceInterfaceMeta(type): + def __new__(metacls, *args, **kwargs): + class_member = args[2] + if "Event" in class_member: + assert inspect.isclass(class_member["Event"]) and issubclass( + class_member["Event"], _EventBase + ), "DeviceInterface member Event should be inherit from _EventBase" + if "Stream" in class_member: + assert inspect.isclass(class_member["Stream"]) and issubclass( + class_member["Stream"], _StreamBase + ), "DeviceInterface member Stream should be inherit from _StreamBase" + return super().__new__(metacls, *args, **kwargs) + + +class DeviceInterface(metaclass=DeviceInterfaceMeta): + """ + This is a simple device runtime interface for Inductor. It enables custom + backends to be integrated with Inductor in a device-agnostic semantic. + """ + + class device: + def __new__(cls, device: _device_t): + raise NotImplementedError + + class Worker: + """ + Worker API to query device properties that will work in multi processing + workers that cannot use the GPU APIs (due to processing fork() and + initialization time issues). Properties are recorded in the main process + before we fork the workers. + """ + + @staticmethod + def set_device(device: int): + raise NotImplementedError + + @staticmethod + def current_device() -> int: + raise NotImplementedError + + @staticmethod + def get_device_properties(device: _device_t = None): + raise NotImplementedError + + @staticmethod + def current_device(): + raise NotImplementedError + + @staticmethod + def set_device(device: _device_t): + raise NotImplementedError + + @staticmethod + def maybe_exchange_device(device: int) -> int: + raise NotImplementedError + + @staticmethod + def exchange_device(device: int) -> int: + raise NotImplementedError + + @staticmethod + def device_count(): + raise NotImplementedError + + @staticmethod + def is_available() -> bool: + raise NotImplementedError + + @staticmethod + def stream(stream: torch.Stream): + raise NotImplementedError + + @staticmethod + def current_stream(): + raise NotImplementedError + + @staticmethod + def set_stream(stream: torch.Stream): + raise NotImplementedError + + @staticmethod + def _set_stream_by_id(stream_id: int, device_index: int, device_type: int): + raise NotImplementedError + + @staticmethod + def get_raw_stream(device_idx: int) -> int: + raise NotImplementedError + + @staticmethod + def synchronize(device: _device_t = None): + raise NotImplementedError + + @staticmethod + def get_device_properties(device: _device_t = None): + raise NotImplementedError + + @staticmethod + def get_compute_capability(device: _device_t = None): + raise NotImplementedError + + @staticmethod + def is_bf16_supported(including_emulation: bool = False): + raise NotImplementedError + + +class DeviceGuard: + """ + This class provides a context manager for device switching. This is a stripped + down version of torch.{device_name}.device. + + The context manager changes the current device to the given device index + on entering the context and restores the original device on exiting. + The device is switched using the provided device interface. + """ + + def __init__( + self, device_interface: Type[DeviceInterface], index: Optional[int] + ) -> None: + self.device_interface = device_interface + self.idx = index + self.prev_idx = -1 + + def __enter__(self): + if self.idx is not None: + self.prev_idx = self.device_interface.exchange_device(self.idx) + + def __exit__(self, type: Any, value: Any, traceback: Any): + if self.idx is not None: + self.idx = self.device_interface.maybe_exchange_device(self.prev_idx) + return False + + +class CudaInterface(DeviceInterface): + device = torch.cuda.device + + # register Event and Stream class into the backend interface + # make sure Event and Stream are implemented and inherited from the _EventBase and _StreamBase + Event = torch.cuda.Event + Stream = torch.cuda.Stream + + class Worker: + @staticmethod + def set_device(device: int): + caching_worker_current_devices["cuda"] = device + + @staticmethod + def current_device() -> int: + if "cuda" in caching_worker_current_devices: + return caching_worker_current_devices["cuda"] + return torch.cuda.current_device() + + @staticmethod + def get_device_properties(device: _device_t = None): + if device is not None: + if isinstance(device, str): + device = torch.device(device) + assert device.type == "cuda" + if isinstance(device, torch.device): + device = device.index + if device is None: + device = CudaInterface.Worker.current_device() + + if "cuda" not in caching_worker_device_properties: + device_prop = [ + torch.cuda.get_device_properties(i) + for i in range(torch.cuda.device_count()) + ] + caching_worker_device_properties["cuda"] = device_prop + + return caching_worker_device_properties["cuda"][device] + + current_device = staticmethod(torch.cuda.current_device) + set_device = staticmethod(torch.cuda.set_device) + device_count = staticmethod(torch.cuda.device_count) + stream = staticmethod(torch.cuda.stream) # type: ignore[assignment] + current_stream = staticmethod(torch.cuda.current_stream) + set_stream = staticmethod(torch.cuda.set_stream) # type: ignore[assignment] + _set_stream_by_id = staticmethod(torch.cuda._set_stream_by_id) # type: ignore[assignment] + synchronize = staticmethod(torch.cuda.synchronize) + get_device_properties = staticmethod(torch.cuda.get_device_properties) # type: ignore[assignment] + get_raw_stream = staticmethod(get_cuda_stream) # type: ignore[assignment, arg-type] + exchange_device = staticmethod(torch.cuda._exchange_device) # type: ignore[arg-type] + maybe_exchange_device = staticmethod(torch.cuda._maybe_exchange_device) # type: ignore[arg-type] + is_bf16_supported = staticmethod(torch.cuda.is_bf16_supported) # type: ignore[arg-type] + + # Can be mock patched by @patch decorator. + @staticmethod + def is_available() -> bool: + return torch.cuda.is_available() + + @staticmethod + def get_compute_capability(device: _device_t = None): + if torch.version.hip is None: + major, min = torch.cuda.get_device_capability(device) + return major * 10 + min + else: + return torch.cuda.get_device_properties(device).gcnArchName.split(":", 1)[0] + + +get_xpu_stream: Optional[Callable[[int], int]] +if torch.xpu._is_compiled(): + from torch._C import _xpu_getCurrentRawStream as get_xpu_stream +else: + get_xpu_stream = None + + +class XpuInterface(DeviceInterface): + device = torch.xpu.device + Event = torch.xpu.Event + Stream = torch.xpu.Stream + + class Worker: + @staticmethod + def set_device(device: int): + caching_worker_current_devices["xpu"] = device + + @staticmethod + def current_device() -> int: + if "xpu" in caching_worker_current_devices: + return caching_worker_current_devices["xpu"] + return torch.xpu.current_device() + + @staticmethod + def get_device_properties(device: _device_t = None): + if device is not None: + if isinstance(device, str): + device = torch.device(device) + assert device.type == "xpu" + if isinstance(device, torch.device): + device = device.index + if device is None: + device = XpuInterface.Worker.current_device() + + if "xpu" not in caching_worker_device_properties: + device_prop = [ + torch.xpu.get_device_properties(i) + for i in range(torch.xpu.device_count()) + ] + caching_worker_device_properties["xpu"] = device_prop + + return caching_worker_device_properties["xpu"][device] + + current_device = staticmethod(torch.xpu.current_device) + set_device = staticmethod(torch.xpu.set_device) + device_count = staticmethod(torch.xpu.device_count) + stream = staticmethod(torch.xpu.stream) # type: ignore[assignment] + current_stream = staticmethod(torch.xpu.current_stream) + set_stream = staticmethod(torch.xpu.set_stream) # type: ignore[assignment] + _set_stream_by_id = staticmethod(torch.xpu._set_stream_by_id) # type: ignore[assignment] + synchronize = staticmethod(torch.xpu.synchronize) + get_device_properties = staticmethod(torch.xpu.get_device_properties) # type: ignore[assignment] + get_raw_stream = staticmethod(get_xpu_stream) # type: ignore[assignment, arg-type] + exchange_device = staticmethod(torch.xpu._exchange_device) # type: ignore[arg-type] + maybe_exchange_device = staticmethod(torch.xpu._maybe_exchange_device) # type: ignore[arg-type] + + # Can be mock patched by @patch decorator. + @staticmethod + def is_available() -> bool: + return torch.xpu.is_available() + + @staticmethod + def get_compute_capability(device: _device_t = None): + cc = torch.xpu.get_device_capability(device) + return cc + + @staticmethod + def is_bf16_supported(including_emulation: bool = False) -> bool: + return torch.xpu.is_bf16_supported() + + +device_interfaces: Dict[str, Type[DeviceInterface]] = {} +_device_initialized = False + + +def register_interface_for_device( + device: Union[str, torch.device], device_interface: Type[DeviceInterface] +): + if isinstance(device, torch.device): + device = str(device) + device_interfaces[device] = device_interface + + +def get_interface_for_device(device: Union[str, torch.device]) -> Type[DeviceInterface]: + if isinstance(device, torch.device): + device = str(device) + if not _device_initialized: + init_device_reg() + if device in device_interfaces: + return device_interfaces[device] + raise NotImplementedError(f"No interface for device {device}") + + +def get_registered_device_interfaces() -> Iterable[Tuple[str, Type[DeviceInterface]]]: + if not _device_initialized: + init_device_reg() + return device_interfaces.items() + + +def init_device_reg(): + global _device_initialized + register_interface_for_device("cuda", CudaInterface) + for i in range(torch.cuda.device_count()): + register_interface_for_device(f"cuda:{i}", CudaInterface) + + register_interface_for_device("xpu", XpuInterface) + for i in range(torch.xpu.device_count()): + register_interface_for_device(f"xpu:{i}", XpuInterface) + + _device_initialized = True diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/distributed.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/distributed.py new file mode 100644 index 0000000000000000000000000000000000000000..90a1376c7a13b83891cf3b80fc51b14fca13c3f7 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/distributed.py @@ -0,0 +1,25 @@ +from typing import Optional + +import torch.distributed as dist + +from . import config + + +_COMPILE_PG: Optional[dist.ProcessGroup] = None + + +def get_compile_pg() -> Optional[dist.ProcessGroup]: + if ( + config.enable_compiler_collectives + and dist.is_available() + and dist.is_initialized() + ): + global _COMPILE_PG + if _COMPILE_PG is None: + # , timeout=datetime.timedelta(seconds=2) + _COMPILE_PG = dist.distributed_c10d._new_group_with_tag( + pg_tag="pt2_compile_pg" + ) + return _COMPILE_PG + + return None diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py new file mode 100644 index 0000000000000000000000000000000000000000..c04e4ccb00a97a7508320d1be7e446516eae0421 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py @@ -0,0 +1,1717 @@ +# mypy: allow-untyped-defs +# mypy: disable-error-code="method-assign" + +""" +Functions in this file are responsible for modifying the eval frame +handler at RUNTIME. Therefore, all functions in this file are hot. +Functions that only execute at compile time should be placed +in torch._dynamo.convert_frame. +""" + +from __future__ import annotations + +import contextlib +import functools +import inspect +import logging +import os +import sys +import textwrap +import traceback +import types +import warnings +import weakref +from enum import Enum +from os.path import dirname, join +from typing import ( + Any, + Callable, + Dict, + List, + NamedTuple, + Optional, + Set, + Tuple, + TYPE_CHECKING, + Union, +) +from unittest.mock import patch + +import sympy + +import torch +import torch.fx +import torch.utils._pytree as pytree +import torch.utils.checkpoint +from torch import _guards + +# see discussion at https://github.com/pytorch/pytorch/issues/120699 +from torch._C._dynamo.eval_frame import ( # noqa: F401 + reset_code, + set_guard_error_hook, + skip_code, + unsupported, +) +from torch._dispatch.python import enable_python_dispatcher +from torch._subclasses.fake_tensor import unset_fake_temporarily +from torch._utils_internal import justknobs_check, log_export_usage +from torch.export.dynamic_shapes import _combine_args, _process_dynamic_shapes +from torch.fx import GraphModule +from torch.fx.experimental.proxy_tensor import make_fx +from torch.fx.experimental.symbolic_shapes import ( + ConstraintViolationError, + DimDynamic, + ShapeEnv, + StatelessSymbolicContext, +) +from torch.fx.graph import _PyTreeCodeGen, _PyTreeInfo + +from . import config, convert_frame, external_utils, trace_rules, utils +from .backends.registry import CompilerFn, lookup_backend +from .code_context import code_context +from .exc import CondOpArgsMismatchError, UserError, UserErrorType +from .hooks import Hooks +from .mutation_guard import install_generation_tagging_init +from .utils import common_constant_types, compile_times + + +if TYPE_CHECKING: + from torch._subclasses import fake_tensor + + from .types import CacheEntry, DynamoCallback + + +log = logging.getLogger(__name__) + + +always_optimize_code_objects = utils.ExactWeakKeyDictionary() +null_context = contextlib.nullcontext + + +# See https://github.com/python/typing/pull/240 +class Unset(Enum): + token = 0 + + +cached_backends: Dict[int, CompilerFn] = {} + +unset = Unset.token + + +def _maybe_set_eval_frame(callback: DynamoCallback): + # A wrapper on set_eval_frame that is guarded by a Justknob. + # Users can disable torchDynamo by setting the JK to False. + from torch._C._dynamo.eval_frame import set_eval_frame + + if not justknobs_check("pytorch/compiler:enable_compiler_set_eval_frame"): + torch._dynamo.utils.warn_once( + "Dynamo disabled by Justknob: enable_compiler_set_eval_frame, skipping set_eval_frame" + ) + return callback + else: + return set_eval_frame(callback) + + +def _reset_guarded_backend_cache(): + global cached_backends + for backend in cached_backends.values(): + if hasattr(backend, "reset"): + backend.reset() + cached_backends.clear() + + +DONT_WRAP_FILES = { + # For tracing into fx modules + inspect.getsourcefile(GraphModule), + join(dirname(dirname(__file__)), "onnx/_internal/fx/dynamo_graph_extractor.py"), +} + + +def _debug_get_cache_entry_list( + code: Union[types.CodeType, Callable[..., Any]] +) -> List[CacheEntry]: + """ + Given a code object or a callable object, retrieve the cache entries + stored in this code. + """ + if callable(code): + code = code.__code__ + return torch._C._dynamo.eval_frame._debug_get_cache_entry_list(code) + + +class OptimizedModule(torch.nn.Module): + """ + Wraps the original nn.Module object and later patches its + forward method to optimized self.forward method. + """ + + _torchdynamo_orig_callable: Callable[..., Any] + get_compiler_config: Callable[[], Any] + + _opt_mod_attributes = { + "_orig_mod", + "dynamo_ctx", + "_torchdynamo_orig_callable", + "get_compiler_config", + "forward", + "_forward", + "__dict__", + "named_children_walk", + } + + def __init__(self, mod: torch.nn.Module, dynamo_ctx) -> None: + super().__init__() + # Installs the params/buffer + self._orig_mod = mod + self.dynamo_ctx = dynamo_ctx + self._initialize() + self.training = self._orig_mod.training + + def _initialize(self): + # Do this stuff in constructor to lower overhead slightly + if isinstance(self.dynamo_ctx, DisableContext): + # No need to check trace rules + self.forward = self.dynamo_ctx(self._orig_mod.__call__) + elif isinstance(self._orig_mod.forward, types.MethodType) and ( + trace_rules.check(self._orig_mod.forward) + or getattr(self._orig_mod, "_is_fsdp_managed_module", False) + ): + # This may be a torch.nn.* instance in trace_rules.py which + # won't trigger a frame evaluation workaround to add an extra + # frame we can capture + self.forward = self.dynamo_ctx(external_utils.wrap_inline(self._orig_mod)) + else: + # Invoke hooks outside of dynamo then pickup the inner frame + self.forward = self.dynamo_ctx(self._orig_mod.__call__) + + if hasattr(self._orig_mod, "_initialize_hook"): + self._forward = self.forward + self.forward = self._call_lazy_check + + def __reduce__(self): + return (self.__class__, (self._orig_mod, self.dynamo_ctx)) + + def __getstate__(self): + state = dict(self.__dict__) + state.pop("forward", None) + state.pop("__call__", None) + return state + + def __setstate__(self, state): + self.__dict__ = state + self._initialize() + + @property + def training(self): + return self._orig_mod.training + + @training.setter + def training(self, value): + try: + super().__getattr__("_orig_mod") + self._orig_mod.training = value + except AttributeError: + # still initializing + pass + + def __getattr__(self, name): + if name == "_orig_mod": + return self._modules["_orig_mod"] + return getattr(self._orig_mod, name) + + def __setattr__(self, name, val) -> None: + # Allow patching over class attributes + if hasattr(type(self), name): + return super().__setattr__(name, val) + + if name in OptimizedModule._opt_mod_attributes: + return super().__setattr__(name, val) + return setattr(self._orig_mod, name, val) + + def _call_lazy_check(self, *args, **kwargs): + if hasattr(self._orig_mod, "_initialize_hook"): + # In the case of a lazy module, we want to run + # the pre-hooks which initialize it. + # Afterwards, lazy module deletes its pre-hooks + # to avoid treating it as lazy on subsequent recompile. + self._orig_mod._infer_parameters(self._orig_mod, args, kwargs) + return self._forward(*args, **kwargs) + + def __dir__(self): + orig_mod_attrs = self._orig_mod.__dir__() + return orig_mod_attrs + [ + attr for attr in super().__dir__() if attr not in orig_mod_attrs + ] + + +def remove_from_cache(f): + """ + Make sure f.__code__ is not cached to force a recompile + """ + if isinstance(f, types.CodeType): + reset_code(f) + elif hasattr(f, "__code__"): + reset_code(f.__code__) + elif hasattr(getattr(f, "forward", None), "__code__"): + reset_code(f.forward.__code__) + else: + from . import reset # type: ignore[attr-defined] + + reset() + log.warning("could not determine __code__ for %s", f) + + +def nothing(): + pass + + +def always_false(): + return False + + +def innermost_fn(fn): + """ + In case of nesting of _TorchDynamoContext calls, find the innermost + function. TorchDynamo caches on fn.__code__ object, so its necessary to find + the innermost function to pass on the optimize, run, disable etc. + """ + unaltered_fn = fn + while hasattr(unaltered_fn, "_torchdynamo_orig_callable"): + unaltered_fn = unaltered_fn._torchdynamo_orig_callable + assert callable(unaltered_fn) + return unaltered_fn + + +def make_set_enable_dynamic(enable: bool): + assert isinstance(enable, bool) + if enable: + # Assume everything is dynamic by default + return config._make_closure_patcher(assume_static_by_default=False) + else: + return config._make_closure_patcher( + automatic_dynamic_shapes=False, assume_static_by_default=True + ) + + +class _TorchDynamoContext: + def __init__( + self, + callback: DynamoCallback, + on_enter=nothing, + backend_ctx_ctor=null_context, + patch_fn=nothing, + first_ctx=False, + *, + export=False, + dynamic=None, + compiler_config=None, + ) -> None: + super().__init__() + assert callable(callback) or callback is False or callback is None + self.callback: DynamoCallback = callback + self._backend_ctx_ctor = backend_ctx_ctor + self.prior: Union[Unset, DynamoCallback] = unset + self.first_ctx = first_ctx + self.export = export + self._dynamic = dynamic + self.compiler_config = compiler_config + self.cleanup_fns: List[Callable[[], Any]] = [] + self.enter_exit_hooks = [] + patch_fn() + + # Save the backends so that we can reset them during torch._dynamo.reset + backend = innermost_fn(callback) + cached_backends.setdefault(id(backend), backend) + + if dynamic is not None: + self.enter_exit_hooks.append(make_set_enable_dynamic(dynamic)) + + if on_enter is not nothing: + # this case is not common + def call_on_enter(): + on_enter() + return nothing + + self.enter_exit_hooks.append(call_on_enter) + + if backend_ctx_ctor is not contextlib.nullcontext: + # this case is not common + def call_backend_ctx(): + ctx = backend_ctx_ctor() + ctx.__enter__() + return functools.partial(ctx.__exit__, None, None, None) + + self.enter_exit_hooks.append(call_backend_ctx) + + def __enter__(self): + if config.raise_on_ctx_manager_usage: + raise RuntimeError( + "torch._dynamo.optimize(...) is used with a context manager. " + "Please refer to https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html " + "to use torch._dynamo.optimize(...) as an annotation/decorator. " + ) + self.cleanup_fns = [enter() for enter in self.enter_exit_hooks] + self.prior = _maybe_set_eval_frame(self.callback) + + def __exit__(self, exc_type, exc_val, exc_tb): + assert self.prior is not unset + _maybe_set_eval_frame(self.prior) + self.prior = unset + for cleanup in self.cleanup_fns: + cleanup() + self.cleanup_fns.clear() + + def __call__(self, fn): + # public api for compiler config/options + def get_compiler_config(): + return self.compiler_config + + fn = innermost_fn(fn) + + # add context containing GraphModule to any GraphModule forward functions + if isinstance(fn, GraphModule): + # add context containing GraphModule to any GraphModule forward functions + code_context.get_context(fn.forward.__code__)[ + "orig_graphmodule" + ] = weakref.ref(fn) + + # Optimize the forward method of torch.nn.Module object + if isinstance(fn, torch.nn.Module): + mod = fn + new_mod = OptimizedModule(mod, self) + # Save the function pointer to find the original callable while nesting + # of decorators. + new_mod._torchdynamo_orig_callable = mod.forward + + # when compiling torch.nn.Module, + # provide public api OptimizedModule.get_compiler_config() + assert not hasattr(new_mod, "get_compiler_config") + new_mod.get_compiler_config = get_compiler_config + + return new_mod + + if inspect.isclass(fn): + # User has wrapped the class with compile/disable decorator. Apply + # disable to init/call method. + cls_obj = fn + cls_obj.__call__ = self(cls_obj.__call__) + if issubclass(cls_obj, torch.nn.Module): + # NN module variable tracker directly inlines the _call_impl. + cls_obj._call_impl = self(cls_obj._call_impl) + return cls_obj + + assert callable(fn) + + try: + filename = inspect.getsourcefile(fn) + except TypeError: + filename = None + if ( + (filename is None or trace_rules.check(fn)) + and ( + getattr(fn, "__name__", "") + not in ["_call_impl", "_wrapped_call_impl", "_lazy_forward"] + ) + and filename not in DONT_WRAP_FILES + ): + # call to a builtin without a frame for us to capture + fn = external_utils.wrap_inline(fn) + + def do_nothing(*arg, **kwargs): + pass + + if hasattr(self, "callback"): + callback = self.callback + else: + callback = do_nothing + + is_jit_tracing = torch._C._is_tracing + is_fx_tracing = torch.fx._symbolic_trace.is_fx_tracing + + @functools.wraps(fn) + def _fn(*args, **kwargs): + if is_fx_tracing(): + if config.error_on_nested_fx_trace: + raise RuntimeError( + "Detected that you are using FX to symbolically trace " + "a dynamo-optimized function. This is not supported at the moment." + ) + else: + return fn(*args, **kwargs) + + if is_jit_tracing(): + if config.error_on_nested_jit_trace: + raise RuntimeError( + "Detected that you are using FX to torch.jit.trace " + "a dynamo-optimized function. This is not supported at the moment." + ) + else: + return fn(*args, **kwargs) + + cleanups = [enter() for enter in self.enter_exit_hooks] + prior = _maybe_set_eval_frame(callback) + + # Ensure that if an assertion occurs after graph pushes + # something onto the DynamicLayerStack then we pop it off (the + # constructed graph code isn't guarded with try/finally). + # + # This used to be a context but putting a `with` here is a noticible + # perf regression (#126293) + saved_dynamic_layer_stack_depth = ( + torch._C._functorch.get_dynamic_layer_stack_depth() + ) + + try: + return fn(*args, **kwargs) + finally: + # Restore the dynamic layer stack depth if necessary. + torch._C._functorch.pop_dynamic_layer_stack_and_undo_to_depth( + saved_dynamic_layer_stack_depth + ) + + _maybe_set_eval_frame(prior) + for cleanup in cleanups: + cleanup() + + # hooks to properly handle inlining + _fn._torchdynamo_inline = fn # type: ignore[attr-defined] + + # Save the function pointer to find the original callable while nesting + # of decorators. + _fn._torchdynamo_orig_callable = fn # type: ignore[attr-defined] + + # when compiling user function instead of nn.Module + # provide public api _fn.get_compiler_config() + assert not hasattr(_fn, "get_compiler_config") + _fn.get_compiler_config = get_compiler_config # type: ignore[attr-defined] + + # If the function is called using torch._dynamo.optimize decorator, we + # should prevent any type of skipping. + if callback not in (None, False): + if not hasattr(fn, "__code__"): + raise RuntimeError( + textwrap.dedent( + """ + + torch._dynamo.optimize is called on a non function object. + If this is a callable class, please wrap the relevant code into a function and optimize the + wrapper function. + + >> class CallableClass: + >> def __init__(self) -> None: + >> super().__init__() + >> self.relu = torch.nn.ReLU() + >> + >> def __call__(self, x): + >> return self.relu(torch.sin(x)) + >> + >> def print_hello(self): + >> print("Hello world") + >> + >> mod = CallableClass() + + If you want to optimize the __call__ function and other code, wrap that up in a function + + >> def wrapper_fn(x): + >> y = mod(x) + >> return y.sum() + + and then optimize the wrapper_fn + + >> opt_wrapper_fn = torch._dynamo.optimize(wrapper_fn) + """ + ) + ) + always_optimize_code_objects[fn.__code__] = True + + return _fn + + +class OptimizeContext(_TorchDynamoContext): + def __init__( + self, + callback, + backend_ctx_ctor, + first_ctx=False, + *, + export=False, + dynamic=None, + compiler_config=None, + rebuild_ctx: Optional[ + Callable[[], Union[OptimizeContext, _NullDecorator]] + ] = None, + ) -> None: + def on_enter(): + install_generation_tagging_init() + + super().__init__( + callback=callback, + on_enter=on_enter, + backend_ctx_ctor=backend_ctx_ctor, + patch_fn=TorchPatcher.patch, + first_ctx=first_ctx, + export=export, + dynamic=dynamic, + compiler_config=compiler_config, + ) + + if config.compiled_autograd: + + def call_compiled_autograd(): + assert rebuild_ctx is not None + compiler_fn = rebuild_ctx() + ctx = torch._dynamo.compiled_autograd.enable(compiler_fn) + ctx.__enter__() + return functools.partial(ctx.__exit__, None, None, None) + + self.enter_exit_hooks.append(call_compiled_autograd) + + def __reduce__(self): + return ( + self.__class__, + (self.callback, self._backend_ctx_ctor, self.first_ctx), + { + "export": self.export, + "dynamic": self._dynamic, + "compiler_config": self.compiler_config, + }, + ) + + +class RunOnlyContext(_TorchDynamoContext): + def __init__(self) -> None: + # cudagraph trees relies on generation increment + def on_enter(): + torch._dynamo.mutation_guard.GenerationTracker.generation += 1 + + super().__init__(callback=False, on_enter=on_enter) + + def __reduce__(self): + return (self.__class__, ()) + + +class DisableContext(_TorchDynamoContext): + def __init__(self) -> None: + super().__init__(callback=None) + + def __call__(self, fn): + # Earlier this code was in the base class _TorchDynamoContext. But we + # moved it here to have better code organization. For disable, we just + # want the callback to be None. We don't have to check trace_rules or + # create any wrapper. + fn = innermost_fn(fn) + + if isinstance(fn, torch.nn.Module): + mod = fn + new_mod = OptimizedModule(mod, self) + new_mod._torchdynamo_orig_callable = mod.forward + return new_mod + + if inspect.isclass(fn): + # User has wrapped the class with compile/disable decorator. Apply + # disable to init/call method. + cls_obj = fn + # Disable on init is useful for reconstruction of bytecodes where we + # want to prevent Dynamo from tracing into the init function. Check + # test_reconstruction in test_model_output.py. + cls_obj.__init__ = self(cls_obj.__init__) + cls_obj.__call__ = self(cls_obj.__call__) + if issubclass(cls_obj, torch.nn.Module): + # NN module variable tracker directly inlines the _call_impl. Disable it. + cls_obj._call_impl = self(cls_obj._call_impl) + return cls_obj + + assert callable(fn) + + callback = self.callback + + @functools.wraps(fn) + def _fn(*args, **kwargs): + prior = _maybe_set_eval_frame(callback) + try: + return fn(*args, **kwargs) + finally: + _maybe_set_eval_frame(prior) + + _fn._torchdynamo_disable = True # type: ignore[attr-defined] + + # Save the function pointer to find the original callable while nesting + # of decorators. + _fn._torchdynamo_orig_callable = fn # type: ignore[attr-defined] + + return _fn + + def __reduce__(self): + return (self.__class__, ()) + + +def _optimize_catch_errors( + compile_fn, + hooks: Hooks, + backend_ctx_ctor=null_context, + export=False, + dynamic=None, + compiler_config=None, + rebuild_ctx=None, +): + return OptimizeContext( + convert_frame.catch_errors_wrapper(compile_fn, hooks), + backend_ctx_ctor=backend_ctx_ctor, + first_ctx=True, + export=export, + dynamic=dynamic, + compiler_config=compiler_config, + rebuild_ctx=rebuild_ctx, + ) + + +def get_compiler_fn(compiler_fn): + from .repro.after_dynamo import wrap_backend_debug + + if hasattr(compiler_fn, "compiler_name"): + compiler_str = compiler_fn.compiler_name + elif isinstance(compiler_fn, str): + compiler_str = compiler_fn + else: + compiler_str = None + compiler_fn = lookup_backend(compiler_fn) + return wrap_backend_debug(compiler_fn, compiler_str) + + +class _NullDecorator(contextlib.nullcontext): # type: ignore[type-arg] + def __call__(self, fn): + assert callable(fn) + return fn + + +def check_if_dynamo_supported(): + if sys.version_info >= (3, 13): + raise RuntimeError("Python 3.13+ not yet supported for torch.compile") + + +def is_dynamo_supported(): + try: + check_if_dynamo_supported() + return True + except Exception: + return False + + +def check_if_inductor_supported(): + check_if_dynamo_supported() + + +def is_inductor_supported(): + try: + check_if_inductor_supported() + return True + except Exception: + return False + + +def optimize(*args, **kwargs): + def rebuild_ctx(): + return optimize(*args, **kwargs) + + return _optimize(rebuild_ctx, *args, **kwargs) + + +def _optimize( + rebuild_ctx: Callable[[], Union[OptimizeContext, _NullDecorator]], + backend="inductor", + *, + nopython=False, + guard_export_fn=None, + guard_fail_fn=None, + disable=False, + dynamic=None, +) -> Union[OptimizeContext, _NullDecorator]: + """ + The main entrypoint of TorchDynamo. Do graph capture and call + backend() to optimize extracted graphs. + + Args: + backend: One of the two things: + - Either, a function/callable taking a torch.fx.GraphModule and + example_inputs and returning a python callable that runs the + graph faster. + One can also provide additional context for the backend, like + torch.jit.fuser("fuser2"), by setting the backend_ctx_ctor attribute. + See AOTAutogradMemoryEfficientFusionWithContext for the usage. + - Or, a string backend name in `torch._dynamo.list_backends()` + nopython: If True, graph breaks will be errors and there will + be a single whole-program graph. + disable: If True, turn this decorator into a no-op + dynamic: If True, upfront compile as dynamic a kernel as possible. If False, + disable all dynamic shapes support (always specialize). If None, automatically + detect when sizes vary and generate dynamic kernels upon recompile. + + Example Usage:: + + @torch._dynamo.optimize() + def toy_example(a, b): + ... + """ + check_if_dynamo_supported() + # Note: The hooks object could be global instead of passed around, *however* that would make + # for a confusing API usage and plumbing story wherein we nest multiple .optimize calls. + # There is some prior art around this, w/r/t nesting backend calls are enforced to be the same + # compiler, however, this feels onerous for callback and hooks, and it feels better to give our users an + # easier to understand UX at the cost of a little more plumbing on our end. + hooks = Hooks(guard_export_fn=guard_export_fn, guard_fail_fn=guard_fail_fn) + torch._C._log_api_usage_once("torch._dynamo.optimize") + if ( + disable + or os.environ.get("TORCHDYNAMO_DISABLE", "") == "1" + or (not justknobs_check("pytorch/compiler:enable_dynamo")) + ): + return _NullDecorator() + + backend = get_compiler_fn(backend) + + # Find if backend has any extra context manager + backend_ctx_ctor = getattr(backend, "backend_ctx_ctor", null_context) + + if nopython: + return optimize_assert( + backend, + dynamic=dynamic, + hooks=hooks, + rebuild_ctx=rebuild_ctx, + ) + # The backend function is stashed in the callable returned by + # _optimize_catch_errors in the field _torchdynamo_orig_callable. This can + # be used by eval_frame.c to insert a guard on the backend. + return _optimize_catch_errors( + convert_frame.convert_frame(backend, hooks=hooks), + hooks, + backend_ctx_ctor, + dynamic=dynamic, + compiler_config=backend.get_compiler_config() + if hasattr(backend, "get_compiler_config") + else None, + rebuild_ctx=rebuild_ctx, + ) + + +# TODO(voz): Consider making "explain" output alongside a run / part of a run +@patch("torch._dynamo.symbolic_convert.explain", True) +def explain(f, *extra_args, **extra_kwargs): + def inner(*args, **kwargs): + # TODO(voz): Do we want a decorator for this? + from . import reset # type: ignore[attr-defined] + + reset() + + graphs: List[torch.fx.GraphModule] = [] + break_reasons: List[Any] = [] + op_count: int = 0 + ops_per_graph: List[torch.fx.Node] = [] + out_guards: List[_guards.Guard] = [] + + def dynamo_graph_accumulating_compiler( + gm: torch.fx.GraphModule, example_inputs + ): + from .backends.debugging import _explain_graph_detail + + nonlocal graphs + nonlocal op_count + nonlocal ops_per_graph + nonlocal break_reasons + + gm, graphs, op_count, ops_per_graph, break_reasons = _explain_graph_detail( + gm, graphs, op_count, ops_per_graph, break_reasons + ) + + return gm.forward + + def guard_export_print(guards): + nonlocal out_guards + out_guards.extend(guards) + + opt_f = optimize( + dynamo_graph_accumulating_compiler, + nopython=False, + guard_export_fn=guard_export_print, + )(f) + # TODO(voz): We may have instances of `f` that mutate inputs, we should track sideeffects and reject. + opt_f(*args, **kwargs) + + graph_count = len(graphs) + graph_break_count = graph_count - 1 + compile_time = compile_times(repr="str") + + # TODO(voz): Do we want a decorator for this? + reset() + from .backends.debugging import ExplainOutput + + return ExplainOutput( + graphs, + graph_count, + graph_break_count, + break_reasons, + op_count, + ops_per_graph, + out_guards, + compile_time, + ) + + if extra_args or extra_kwargs: + warnings.warn( + "explain(f, *args, **kwargs) is deprecated, use explain(f)(*args, **kwargs) instead. " + "If you don't migrate, we may break your explain call in the future if your user defined kwargs " + "conflict with future kwargs added to explain(f).", + FutureWarning, + stacklevel=2, + ) + return inner(*extra_args, **extra_kwargs) + else: + return inner + + +class FlattenInputOutputSignature(torch.fx.interpreter.Transformer): + def __init__( + self, + m: torch.fx.GraphModule, + flat_args: Tuple[Any], + matched_input_elements_positions: List[int], + flat_results: List[Any], + matched_output_elements_positions: List[int], + example_fake_inputs: List[torch.Tensor], + flat_args_dynamic_dims: List[Set[int]], + fake_mode: Optional[fake_tensor.FakeTensorMode] = None, + ) -> None: + super().__init__(m) + + assert len(flat_args_dynamic_dims) == len(flat_args) + matched_input_elements_to_fake = { + val: example_fake_inputs[ix] + for ix, val in enumerate(matched_input_elements_positions) + } + + self.new_args = [] + for i in range(0, len(flat_args)): + arg = super().placeholder(f"arg{i}", (), {}) + if i in matched_input_elements_to_fake: + arg.node.meta["val"] = matched_input_elements_to_fake[i] + else: + # Fill node.mata["val"] with faketensor from the input, + # if it's not found in matched_input_elements_positions + if fake_mode is not None and isinstance(flat_args[i], torch.Tensor): + # TODO(zhxchen17) Also preserve all the user constraints here. + arg.node.meta["val"] = fake_mode.from_tensor( + flat_args[i], + symbolic_context=StatelessSymbolicContext( + dynamic_sizes=[ + DimDynamic.DYNAMIC + if d in flat_args_dynamic_dims[i] + else DimDynamic.STATIC + for d in range(len(flat_args[i].shape)) + ], + constraint_sizes=[None] * len(flat_args[i].shape), + ), + ) + self.new_args.append(arg) + self.old_args_gen = (self.new_args[i] for i in matched_input_elements_positions) + self.matched_output_elements_positions = matched_output_elements_positions + self.flat_results = flat_results + + def placeholder(self, target, args, kwargs): + arg = next(self.old_args_gen) + if "val" in self.current_node.meta: + arg.node.meta["val"] = self.current_node.meta["val"] + if "tensor_dict" in self.current_node.meta: + arg.node.meta["tensor_dict"] = self.current_node.meta["tensor_dict"] + if "example_value" in self.current_node.meta: + # NB: intentionally do not use set_example_value + arg.node.meta["example_value"] = self.current_node.meta["example_value"] + if "unbacked_bindings" in self.current_node.meta: + arg.node.meta["unbacked_bindings"] = self.current_node.meta[ + "unbacked_bindings" + ] + return arg + + def output(self, target, args, kwargs): + dynamo_result_flat = args[0] + lookup = [*dynamo_result_flat, *self.new_args] + new_results_flat = [] + for i in range(len(self.flat_results)): + if self.matched_output_elements_positions[i] is not None: + new_results_flat.append( + lookup[self.matched_output_elements_positions[i]] + ) + else: + const_val = self.flat_results[i] + assert isinstance(const_val, tuple(common_constant_types)) + new_results_flat.append(const_val) + return super().output(target, (new_results_flat,), {}) + + def run_node(self, n): + self.current_node = n + result_proxy = super().run_node(n) + if "val" in self.current_node.meta: + result_proxy.node.meta["val"] = self.current_node.meta["val"] + if "example_value" in self.current_node.meta: + # NB: intentionally do not use set_example_value + result_proxy.node.meta["example_value"] = self.current_node.meta[ + "example_value" + ] + if "unbacked_bindings" in self.current_node.meta: + result_proxy.node.meta["unbacked_bindings"] = self.current_node.meta[ + "unbacked_bindings" + ] + if self.current_node.op != "output": + result_proxy.node._rename( + getattr(self.current_node, "name", result_proxy.node.name) + ) + return result_proxy + + def transform(self): + result_gm = super().transform() + if "dynamo_flat_name_to_original_fqn" in self.module.meta: + result_gm.meta["dynamo_flat_name_to_original_fqn"] = self.module.meta[ + "dynamo_flat_name_to_original_fqn" + ] + return result_gm + + +class ExportResult(NamedTuple): + graph_module: torch.fx.GraphModule + guards: _guards.GuardsSet + # NB: Do not add new fields without overriding __iter__; people are + # destructuring so it is BC-breaking + + +def check_signature_rewritable(graph): + input_errors = [] + for node in graph.graph.find_nodes(op="placeholder"): + assert hasattr(node, "_dynamo_source") + source = node._dynamo_source + user_stacks = graph._source_to_user_stacks.get(source) + if user_stacks is None: + continue + assert len(user_stacks) > 0 + # In some cases we may not have a useful stack. Look for a + # useful stack + stack = None + for s in user_stacks: + if len(s) == 0: + continue + stack = s + break + if stack is None: + msg = f"{source.name()}, a closed over free variable" + else: + tb = "".join(traceback.format_list(stack)) + extra = "" + if len(user_stacks) > 1: + extra = f"(elided {len(user_stacks) - 1} more accesses)" + msg = f"{source.name()}, accessed at:\n{tb}{extra}" + # TODO: option to print ALL of the stack traces at once + input_errors.append(msg) + + if input_errors: + raise UserError( + UserErrorType.INVALID_INPUT, + "Cannot export model which references tensors that are neither " + "buffers/parameters/constants nor are direct inputs. For each tensor, if you'd " + "like this tensor to be an explicit input, add it as a dummy argument " + "to the top-level model definition you are exporting; if you would " + "like its value to be embedded as an exported constant, wrap its access " + "in a function marked with @assume_constant_result.\n\n" + + "\n\n".join(input_errors), + ) + + +def rewrite_signature( + f_sig, + graph, + fake_mode, + flat_args, + in_spec, + example_fake_inputs, + graph_captured_input, + graph_captured_output, + dynamo_traced_result, + flat_args_dynamic_dims, +): + orig_args, orig_kwargs = pytree.tree_unflatten(flat_args, in_spec) + + def check_user_input_output(flat_values, error_type): + supported_types = [ + torch.Tensor, + torch.SymInt, + torch.SymFloat, + torch.SymBool, + torch._C.ScriptObject, + ] + list(common_constant_types) + + def is_supported_type(val): + return isinstance(val, tuple(supported_types)) + + value_type = "input" if error_type == UserErrorType.INVALID_INPUT else "output" + # We only check that the outputs are not None. Inputs can be None. + for v in flat_values: + if not is_supported_type(v): + if error_type == UserErrorType.INVALID_INPUT and v is None: + continue + + raise UserError( + error_type, + f"It looks like one of the {value_type}s with type `{type(v)}` " + "is not supported or pytree-flattenable. \n" + f"Exported graphs {value_type}s can only contain the " + f"following supported types: {supported_types}. \n" + "If you are using a custom class object, " + "please register a pytree_flatten/unflatten function " + "using `torch.utils._pytree.register_pytree_node` or " + "`torch.export.register_dataclass`.", + ) + + check_user_input_output(flat_args, UserErrorType.INVALID_INPUT) + flat_results_traced, out_spec_traced = pytree.tree_flatten(dynamo_traced_result) + check_user_input_output(flat_results_traced, UserErrorType.INVALID_OUTPUT) + + def check_optional_input_and_error(f_sig: inspect.Signature): + # Check if function has optional input. + for name, param in f_sig.parameters.items(): + if param.default is not inspect.Parameter.empty: + from torch._dynamo.exc import Unsupported + + log.error( + "Parameter %s is optional with a default value of %s", + name, + param.default, + ) + raise Unsupported( + "Tracing through optional input is not supported yet", + case_name="optional_input", + ) + + def produce_matching(debug_type, sources, candidates): + matched_elements_positions: List[Optional[int]] = [] + dict_of_source_vals = {} + for i, val in enumerate(sources): + dict_of_source_vals[id(val)] = i + + for i, val in enumerate(candidates): + if isinstance(val, tuple(common_constant_types)): + matched_elements_positions.append(None) + elif id(val) not in dict_of_source_vals: + if debug_type == "inputs": + check_optional_input_and_error(f_sig) + raise AssertionError( + f"Unexpectedly found a {type(val)} in the {debug_type}.\n" + 'Please file an issue along with a paste of the logs from TORCH_LOGS="+export"', + ) + else: + matched_elements_positions.append(dict_of_source_vals[id(val)]) + + return matched_elements_positions + + matched_input_elements_positions = produce_matching( + "inputs", flat_args, graph_captured_input + ) + + assert graph_captured_output is not None + matched_output_elements_positions = produce_matching( + "outputs", list(graph_captured_output) + flat_args, flat_results_traced + ) + + new_graph = FlattenInputOutputSignature( + graph, + flat_args, + matched_input_elements_positions, + flat_results_traced, + matched_output_elements_positions, + example_fake_inputs, + flat_args_dynamic_dims, + fake_mode, + ).transform() + + # Make dynamo graph to have same input/output spec as user code + def argument_names(f_sig, args, kwargs) -> List[str]: + def signature_to_fullargspec(sig: inspect.Signature): + # Get a list of Parameter objects from the Signature object + params = list(sig.parameters.values()) + # Separate positional arguments, keyword-only arguments and varargs/varkw + args = [ + p.name + for p in params + if p.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD + ] + kwonlyargs = [ + p.name for p in params if p.kind == inspect.Parameter.KEYWORD_ONLY + ] + varargs = next( + (p.name for p in params if p.kind == inspect.Parameter.VAR_POSITIONAL), + None, + ) + varkw = next( + (p.name for p in params if p.kind == inspect.Parameter.VAR_KEYWORD), + None, + ) + # Get default values for positional arguments and keyword-only arguments + defaults = tuple( + p.default + for p in params + if p.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD + and p.default is not inspect.Parameter.empty + ) + kwonlydefaults = { + p.name: p.default + for p in params + if p.kind == inspect.Parameter.KEYWORD_ONLY + and p.default is not inspect.Parameter.empty + } + # Get annotations for parameters and return value + annotations = {} + if sig.return_annotation: + annotations = {"return": sig.return_annotation} + for parameter in params: + annotations[parameter.name] = parameter.annotation + # Return a FullArgSpec object with the extracted attributes + return inspect.FullArgSpec( + args, varargs, varkw, defaults, kwonlyargs, kwonlydefaults, annotations + ) + + fullargspec = signature_to_fullargspec(f_sig) + + # 1. Map `args` 1-to-1 to positional arguments in original signature. + input_strs = fullargspec.args[: len(args)] + + if len(args) > len(fullargspec.args): + # 2. If there are more arguments left in `args`, they map to varargs in original + # signature. Assign names as {varargs}_0, {varargs}_1, ... + assert fullargspec.varargs is not None, "More arguments than expected" + input_strs += [ + f"{fullargspec.varargs}_{i}" + for i in range(0, len(args) - len(input_strs)) + ] + elif len(args) < len(fullargspec.args): + # 3. If there are fewer arguments in `args` than `fullargspec.args`, + # it implies these are arguments either with default values, or provided in + # `kwargs`. The former can be safely ignored. Because Dynamo.export does not + # export them as part of the function signature. The latter will be handled + # in the next step. + for unprovided_arg in fullargspec.args[ + len(args) : -len(fullargspec.defaults or []) + ]: + assert unprovided_arg in kwargs, f"Missing argument {unprovided_arg}" + + # 4. Keyword arguments provided in `kwargs`. + input_strs += list(kwargs.keys()) + + # 5. Keyword-only arguments with default values if not provided are not exported + # as part of the function signature. + for kwonly_arg in fullargspec.kwonlyargs: + kwonlydefaults = fullargspec.kwonlydefaults or {} + assert ( + kwonly_arg in kwargs or kwonly_arg in kwonlydefaults + ), f"Missing keyword only argument {kwonly_arg}" + + return input_strs + + new_graph.graph._codegen = _PyTreeCodeGen( + _PyTreeInfo( + argument_names(f_sig, orig_args, orig_kwargs), + in_spec, + out_spec_traced, + ) + ) + new_graph.recompile() + return new_graph + + +def export( + f: Callable[..., Any], + *extra_args, + aten_graph: bool = False, + pre_dispatch: bool = False, + decomposition_table: Optional[ + Dict[torch._ops.OpOverload, Callable[..., Any]] + ] = None, + tracing_mode: str = "symbolic", + dynamic_shapes: Optional[Union[Dict[str, Any], Tuple[Any], List[Any]]] = None, + assume_static_by_default: bool = False, + same_signature: bool = True, + disable_constraint_solver: bool = False, + prefer_deferred_runtime_asserts_over_guards: bool = False, + allow_complex_guards_as_runtime_asserts: bool = False, + _log_export_usage: bool = True, + **extra_kwargs, +) -> Callable[..., ExportResult]: + """ + Export an input function f to a format that can be executed outside of PyTorch using the FX graph. + + Args: + f (callable): A PyTorch function to be exported. + + aten_graph (bool): If True, exports a graph with ATen operators. + If False, exports a graph with Python operators. Default is False. + + pre_dispatch (bool): If True, exports a graph with ATen operators, + but before any logic in the PyTorch dispatcher has run. + This can be useful if you want to apply further transformations on a graph before running it + through autograd, autocast, or any other functionalities that are integrated into the dispatcher. + This flag is only valid if aten_graph=True is set. + Default is False. + + decomposition_table (dict): A dictionary that maps operators to their decomposition functions. + Required if aten_graph or tracing_mode is specified. Default is None. + + tracing_mode (str): If "symbolic", turn on dynamic shapes support. Default is "symbolic". + + dynamic_shapes: + An optional argument where the type should either be: + 1) a dict from argument names of ``f`` to their dynamic shape specifications, + 2) a tuple that specifies dynamic shape specifications for each input in original order. + If you are specifying dynamism on keyword args, you will need to pass them in the order that + is defined in the original function signature. + + The dynamic shape of a tensor argument can be specified as either + (1) a dict from dynamic dimension indices to :func:`Dim` types, where it is + not required to include static dimension indices in this dict, but when they are, + they should be mapped to None; or (2) a tuple / list of :func:`Dim` types or None, + where the :func:`Dim` types correspond to dynamic dimensions, and static dimensions + are denoted by None. Arguments that are dicts or tuples / lists of tensors are + recursively specified by using mappings or sequences of contained specifications. + + same_signature (bool): If True, rewrite the returned graph's signature to be the same as f. + + disable_constraint_solver (bool): Whether the dim constraint solver must be disabled. + + Returns: + A function that given args and kwargs, returns a tuple of (graph, guards) + Graph: An FX graph representing the execution of the input PyTorch function with the provided arguments and options. + Guards: The guards we accumulated during tracing f above + + Raises: + AssertionError: If decomposition_table is specified without setting aten_graph=True, + or if graph breaks during tracing in export. + + AssertionError: If Dynamo input and output is not consistent with traced input/output. + + Note - this headerdoc was authored by ChatGPT, with slight modifications by the author. + """ + if _log_export_usage: + log_export_usage(event="export.private_api", flags={"_dynamo"}) + + # Deal with "local variable referenced before assignment" + _f = f + _assume_static_by_default = assume_static_by_default + + def inner(*args, **kwargs): + combined_args = _combine_args(_f, args, kwargs) + constraints = _process_dynamic_shapes(combined_args, dynamic_shapes) + f = _f + assume_static_by_default = _assume_static_by_default + check_if_dynamo_supported() + torch._C._log_api_usage_once("torch._dynamo.export") + if decomposition_table is not None: + assert ( + aten_graph + ), "Specifying a decomposition_table table or tracing mode is illegal without setting aten_graph=True" + if pre_dispatch: + assert aten_graph, "pre_dispatch=True can only be used when aten_graph=True" + f = innermost_fn(f) + call_to_inspect = f.forward if isinstance(f, torch.nn.Module) else f + original_signature = inspect.signature(call_to_inspect) + graph = None + out_guards = None + graph_captured_input = None + graph_captured_result: Optional[Tuple[torch.Tensor, ...]] = None + fake_mode = None + result_traced = None + + def guard_export_print(guards: _guards.GuardsSet): + nonlocal out_guards + assert ( + out_guards is None + ), "whole graph export entails exactly one guard export" + out_guards = guards + + example_inputs = [] + + def dynamo_normalization_capturing_compiler( + gm: torch.fx.GraphModule, inner_example_inputs + ): + nonlocal graph + assert ( + graph is None + ), "Tried to emit a second graph during export. Tracing through 'f' must produce a single graph." + graph = gm + + nonlocal fake_mode, example_inputs + # NB: do NOT pass inner_example_inputs here, we are detecting the + # Dynamo allocated fake mode, which should be DISTINCT from a + # potential outer ambient fake mode which the user provided. + # example_inputs is always the user specified inputs, so they + # would have the wrong fake mode attached to them + fake_mode = _guards.detect_fake_mode() + example_inputs = inner_example_inputs + + def result_capturing_wrapper(*graph_inputs): + nonlocal graph_captured_result + nonlocal graph_captured_input + + graph_captured_input = graph_inputs + assert graph is not None + + named_parameters = dict(graph.named_parameters(remove_duplicate=False)) + named_buffers = dict(graph.named_buffers(remove_duplicate=False)) + + ambient_fake_mode = ( + _guards.detect_fake_mode(graph_inputs) + if _guards.detect_fake_mode(graph_inputs) is not None + else fake_mode + ) + + # We reran fake tensor propagation, but we didn't do + # anything with the resulting unbacked SymInts. Drop them + # from the pending list. + # NB: this is wrong if graph_captured_result has + # data-dependent output size! + ignore_fresh_unbacked = null_context() + if shape_env := ambient_fake_mode.shape_env: + ignore_fresh_unbacked = shape_env.ignore_fresh_unbacked_symbols() + + with ( + ambient_fake_mode + ), enable_python_dispatcher(), ignore_fresh_unbacked: + params_and_buffers = { + **named_parameters, + **named_buffers, + } + fake_params_buffers = {} + + for name, value in params_and_buffers.items(): + fake_params_buffers[name] = ambient_fake_mode.from_tensor( + value, static_shapes=True + ) + + fake_graph_inputs = pytree.tree_map( + ambient_fake_mode.from_tensor, graph_inputs + ) + graph_captured_result = torch.func.functional_call( + graph, fake_params_buffers, fake_graph_inputs + ) + + return graph_captured_result + + return result_capturing_wrapper + + # Note: This is needed by rewrite_signature. We need to put it before + # optimize_assert since user program may mutate the inputs. + flat_args, in_spec = pytree.tree_flatten((args, kwargs)) + + remove_from_cache(f) + constraint_violation_error = None + if tracing_mode != "symbolic": + assume_static_by_default = True + with config.patch( + specialize_int=True, + assume_static_by_default=assume_static_by_default, + automatic_dynamic_shapes=False, + capture_dynamic_output_shape_ops=True, + capture_scalar_outputs=True, + prefer_deferred_runtime_asserts_over_guards=prefer_deferred_runtime_asserts_over_guards, + allow_complex_guards_as_runtime_asserts=allow_complex_guards_as_runtime_asserts, + ): + opt_f = optimize_assert( + dynamo_normalization_capturing_compiler, + hooks=Hooks( + guard_export_fn=guard_export_print, + guard_fail_fn=None, + ), + export=True, + export_constraints=constraints, + )(f) + # TODO(voz): We may have instances of `f` that mutate inputs, we should track sideeffects and reject. + try: + result_traced = opt_f(*args, **kwargs) + except ConstraintViolationError as e: + constraint_violation_error = e + remove_from_cache(f) + + if ( + not disable_constraint_solver + and (shape_env := getattr(fake_mode, "shape_env", None)) is not None + and (dim_constraints := shape_env.dim_constraints) is not None + and not isinstance( + call_to_inspect, (torch._ops.OpOverloadPacket, torch._ops.OpOverload) + ) + and not trace_rules.check(call_to_inspect) + ): + dim_constraints.solve() + forced_specializations = dim_constraints.forced_specializations() + msg = dim_constraints.prettify_results( + original_signature, + dynamic_shapes, + constraint_violation_error, + forced_specializations, + ) + if constraint_violation_error: + constraint_violation_error.args = ( + constraint_violation_error.args[0] + msg, + ) + else: + if forced_specializations: + constraint_violation_error = ConstraintViolationError(msg) + else: + log.info( + "Summary of dimension constraints:%s", + msg, + ) + + # Error if we have any constraints on static values + for k in shape_env.var_to_range.keys(): + if isinstance(k, sympy.Integer): + constraint_violation_error = ConstraintViolationError( + f"{''.join(traceback.format_list(shape_env.var_to_stack[k]))}\n" + "It appears that you're trying to set a constraint on a " + f"value which we evaluated to have a static value of {k}. " + 'Set TORCH_LOGS="+export" for more information.' + ) + if constraint_violation_error: + raise constraint_violation_error + + if graph is None: + assert ( + same_signature + ), "Failed to produce a graph during tracing as no tensor operations were found and same_signature is False." + # If the module does not contain any tensor computation, we would create a graph with inputs and outputs. + # To be consitant with the graph traced by dynano, `graph` will have only tensor inputs as placeholders + # and tensor outputs as output nodes. non-tensor inputs and outputs will be added when rewriting signature. + # We will also construct the `example_inputs`, `graph_captured_input`, and `graph_captured_result` corresponding + # to `graph`. + example_inputs = [] + graph_captured_input = () + graph_captured_result = () + fake_mode = torch._subclasses.FakeTensorMode( + shape_env=ShapeEnv(), export=True + ) + if out_guards is None: + out_guards = _guards.GuardsSet() + assert out_guards is not None # suppress mypy error + parameter_names = list(original_signature.parameters.keys()) + fx_graph = torch.fx.Graph() + for i, name in enumerate(parameter_names): + if torch.is_tensor(flat_args[i]): + node = fx_graph.placeholder(name) + node.meta["val"] = fake_mode.from_tensor( + flat_args[i], static_shapes=True + ) + graph_captured_input = graph_captured_input + (flat_args[i],) + example_inputs.append(flat_args[i]) + fx_graph.output(graph_captured_result) + module = torch.nn.Module() + graph = torch.fx.GraphModule(module, fx_graph) + log.info( + "Failed to capture a graph during tracing as no tensor operations were found.:\n\n%s", + graph.print_readable(print_output=False, colored=True), + ) + else: + assert hasattr(graph, "_source_to_user_stacks") + assert out_guards is not None, "Failed to produce guards during tracing" + assert fake_mode is not None + + log.info( + "Dynamo captured graph:\n\n%s", + graph.print_readable(print_output=False, colored=True), + ) + + # This check need to happened before aten_graph + # because placeholder's _source_node attribute is not preserved by make_fx + if same_signature: + check_signature_rewritable(graph) + + # NB: This is mostly hitting the cache; Dynamo already converted these + example_fake_inputs = [fake_mode.from_tensor(t) for t in example_inputs] + + if aten_graph: + # Running graph with interpreter is needed for propagating the stack_trace + def graph_with_interpreter(*args): + with torch.fx.traceback.preserve_node_meta(): + return torch.fx.Interpreter(graph).run(*args) # type: ignore[arg-type] + + with unset_fake_temporarily(), enable_python_dispatcher(), fake_mode: + try: + graph = make_fx( + graph_with_interpreter, + decomposition_table=decomposition_table, + tracing_mode="real", + _allow_non_fake_inputs=True, + pre_dispatch=pre_dispatch, + _allow_fake_constant=False, + )(*example_fake_inputs) + except CondOpArgsMismatchError as e: + # Wrap the internal error to the user-facing error + raise UserError( # noqa: B904 + UserErrorType.DYNAMIC_CONTROL_FLOW, + str(e), + case_name="cond_operands", + ) + + assert graph is not None + for node in graph.graph.find_nodes(op="get_attr"): + if isinstance(getattr(graph, node.target), torch.Tensor): # type: ignore[arg-type] + node.meta["val"] = fake_mode.from_tensor( + getattr(graph, node.target), static_shapes=True # type: ignore[arg-type] + ) + + if same_signature: + flat_args_dynamic_dims = [ + { + c.dim + for c in (constraints or ()) + if ( + c.t_id == id(x) + and c.constraint_range.vr.lower != c.constraint_range.vr.upper + ) + } + for x in flat_args + ] + graph = rewrite_signature( + original_signature, + graph, + fake_mode, + flat_args, + in_spec, + example_fake_inputs, + graph_captured_input, + graph_captured_result, + result_traced, # type: ignore[possibly-undefined] + flat_args_dynamic_dims, + ) + return ExportResult(graph, out_guards) # type: ignore[arg-type] + + if extra_args or extra_kwargs: + warnings.warn( + "export(f, *args, **kwargs) is deprecated, use export(f)(*args, **kwargs) instead. " + "If you don't migrate, we may break your export call in the future if your user defined kwargs " + "conflict with future kwargs added to export(f).", + FutureWarning, + stacklevel=2, + ) + return inner(*extra_args, **extra_kwargs) + else: + return inner + + +def optimize_assert( + backend, + *, + hooks=Hooks(None, None), + export=False, + export_constraints=None, + dynamic=None, + rebuild_ctx=None, +): + """ + The same as `torch._dynamo.optimize(backend, nopython=True)` + """ + backend = get_compiler_fn(backend) + + # Find if backend has any extra context manager + backend_ctx_ctor = getattr(backend, "backend_ctx_ctor", null_context) + + return _optimize_catch_errors( + convert_frame.convert_frame_assert( + backend, export=export, export_constraints=export_constraints + ), + hooks, + backend_ctx_ctor, + export=export, + dynamic=dynamic, + rebuild_ctx=rebuild_ctx, + ) + + +class TorchPatcher: + @staticmethod + @functools.lru_cache(None) + def patch(): + # A better way to disable the following would be decorate the source + # functions with @torch._disable_dynamo. However, this causes issues + # with torch.deploy internally. + from .decorators import disable + + torch.jit.trace = disable(torch.jit.trace) + torch.jit.trace_module = disable(torch.jit.trace_module) + torch.jit._get_trace_graph = disable(torch.jit._get_trace_graph) + torch.fx._symbolic_trace.Tracer.trace = disable( + torch.fx._symbolic_trace.Tracer.trace + ) + torch.distributions.Distribution.set_default_validate_args(False) + + from torch.optim import ( + adadelta, + adagrad, + adam, + adamax, + adamw, + asgd, + lbfgs, + nadam, + radam, + rmsprop, + rprop, + sgd, + sparse_adam, + ) + + optimizer_modules = { + adadelta, + adagrad, + adam, + adamax, + adamw, + asgd, + lbfgs, + nadam, + radam, + rmsprop, + rprop, + sgd, + sparse_adam, + } + + for opt_mod in optimizer_modules: + opt_name = opt_mod.__name__.split(".")[-1] + fused_fn_name = f"_fused_{opt_name}" + single_tensor_fn_name = f"_single_tensor_{opt_name}" + + if hasattr(opt_mod, fused_fn_name): + setattr( + opt_mod, fused_fn_name, disable(getattr(opt_mod, fused_fn_name)) + ) + + optimizer_classes = [ + opt + for opt in torch.optim.__dict__.values() + if inspect.isclass(opt) and issubclass(opt, torch.optim.Optimizer) + ] + + # Note: we don't support sparsity or tracing through backwards + excluded_optimizer_classes = { + torch.optim.SparseAdam, + torch.optim.LBFGS, + } + + for opt in optimizer_classes: + if opt in excluded_optimizer_classes: + opt.step = disable(opt.step) + + if hasattr(opt, "_init_group"): + opt._init_group = disable(opt._init_group) + + @staticmethod + def suppress_torch_distributed_warnings(fn): + def inner_fn(*args, **kwargs): + warnings.filterwarnings( + "ignore", category=UserWarning, module="torch.distributed" + ) + return fn(*args, **kwargs) + + return inner_fn diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/exc.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/exc.py new file mode 100644 index 0000000000000000000000000000000000000000..0d2108ada9e10e9dc37990e1041027133968b1ff --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/exc.py @@ -0,0 +1,454 @@ +# mypy: allow-untyped-defs +import os +import textwrap +from enum import auto, Enum +from traceback import extract_stack, format_exc, format_list, StackSummary +from typing import Any, cast, NoReturn, Optional, Tuple, TYPE_CHECKING + +import torch._guards + +from . import config +from .utils import counters + + +if TYPE_CHECKING: + from torch._guards import CompileId + + +def exportdb_error_message(case_name): + return ( + "For more information about this error, see: " + + "https://pytorch.org/docs/main/generated/exportdb/index.html#" + + case_name.replace("_", "-") + ) + + +import logging + + +log = logging.getLogger(__name__) +graph_breaks_log = torch._logging.getArtifactLogger(__name__, "graph_breaks") + + +class TorchDynamoException(RuntimeError): + pass + + +class InternalTorchDynamoError(TorchDynamoException): + pass + + +class RestartAnalysis(TorchDynamoException): + restart_reason: str + + def __init__(self, *args, restart_reason=None) -> None: + self.restart_reason = restart_reason + super().__init__(*args) + + +class SpeculationRestartAnalysis(RestartAnalysis): + pass + + +class UnspecializeRestartAnalysis(RestartAnalysis): + pass + + +class CompileCollectiveRestartAnalysis(RestartAnalysis): + pass + + +class SkipFrame(TorchDynamoException): + pass + + +class TorchRuntimeError(TorchDynamoException): + pass + + +class InvalidBackend(TorchDynamoException): + def __init__(self, name) -> None: + super().__init__( + f"Invalid backend: {name!r}, see `torch._dynamo.list_backends()` for available backends." + ) + + +class ResetRequired(TorchDynamoException): + def __init__(self) -> None: + super().__init__( + textwrap.dedent( + """ + Must call `torch._dynamo.reset()` before changing backends. Detected two calls to + `torch.compile()` with a different backend compiler arguments. + """ + ) + ) + + +class BackendCompilerFailed(TorchDynamoException): + def __init__(self, backend_fn, inner_exception) -> None: + self.backend_name = getattr(backend_fn, "__name__", "?") + self.inner_exception = inner_exception + msg = f"backend={self.backend_name!r} raised:\n{type(inner_exception).__name__}: {inner_exception}" + super().__init__(msg) + + +class Unsupported(TorchDynamoException): + def __init__(self, msg, *, case_name=None) -> None: + super().__init__(msg) + self.real_stack = torch._guards.TracingContext.extract_stack() + self.msg = msg + self.category: Optional[str] = None + self.add_to_stats() + self.case_name: Optional[str] = case_name + + def remove_from_stats(self): + assert self.category is not None + counters[self.category][self.msg] -= 1 + if counters[self.category][self.msg] <= 0: + del counters[self.category][self.msg] + + def add_to_stats(self, category="unimplemented"): + self.category = category + counters[category][self.msg] += 1 + + +class RecompileError(TorchDynamoException): + pass + + +class ArgsMismatchError(Unsupported): + def __init__(self, msg) -> None: + super().__init__(msg) + + +class AttributeMutationError(Unsupported): + def __init__(self, msg) -> None: + super().__init__(msg) + + +class CondOpArgsMismatchError(ArgsMismatchError): + """ + Internal error from cond() due to arguments mismatch. + """ + + def __init__(self, msg) -> None: + super().__init__(msg) + + +class UserErrorType(Enum): + DYNAMIC_CONTROL_FLOW = auto() + ANTI_PATTERN = auto() + STANDARD_LIBRARY = auto() + CONSTRAINT_VIOLATION = auto() + DYNAMIC_DIM = auto() + INVALID_INPUT = auto() + INVALID_OUTPUT = auto() + + +class UserError(Unsupported): + def __init__(self, error_type: UserErrorType, msg, case_name=None) -> None: + """ + Type of errors that would be valid in Eager, but not supported in TorchDynamo. + The error message should tell user about next actions. + + error_type: Type of user error + msg: Actionable error message + case_name: (Optional) Unique name (snake case) for the usage example in exportdb. + """ + if case_name is not None: + assert isinstance(case_name, str) + if msg.endswith("."): + msg += " " + else: + msg += "\n" + msg += exportdb_error_message(case_name) + super().__init__(msg) + self.error_type = error_type + self.message = msg + + +class SkipCodeRecursiveException(TorchDynamoException): + pass + + +class CacheLimitExceeded(SkipCodeRecursiveException, Unsupported): + pass + + +class UnsafeScriptObjectError(TorchDynamoException): + pass + + +class UncapturedHigherOrderOpError(TorchDynamoException): + pass + + +class IncorrectUsage(Exception): + pass + + +class ObservedException(TorchDynamoException): + # An exception observed during the tracing. This exception is used by Dynamo to handle exceptions. + pass + + +class ObservedUserStopIteration(ObservedException): + # An UserStopIteraion exception observed during the Dynamo tracing (e.g Dynamo tracing __next__) + value: Optional[Any] + + # Reference `StopIteration_init` in CPython + # https://github.com/python/cpython/blob/3.11/Objects/exceptions.c#L568-L584 + def __init__(self, *args, **kwargs) -> None: + super().__init__("unhandled `raise StopIteration`") + if len(args) > 0: + self.value = args[0] + else: + self.value = None + + +class ObservedKeyError(ObservedException): + # A KeyError exception to be raised from inside Dynamo tracing. This can happen on dict __getitem__ + pass + + +class ObservedAttributeError(ObservedException): + # An AttributeError exception to be raised from inside Dynamo tracing. This can happen on user defined object __getattr__ + pass + + +observed_exception_map = { + StopIteration: ObservedUserStopIteration, + KeyError: ObservedKeyError, + AttributeError: ObservedAttributeError, +} + + +def raise_observed_exception(e, tx, vt): + from .variables import BuiltinVariable + + # CPython here raises an exception. Since there is no python code, we have to manually setup the exception + # stack and raise the exception. + exception_vt = BuiltinVariable(e).call_function(vt, [], {}) + tx.exn_vt_stack.append(exception_vt) + raise observed_exception_map[e] + + +def handle_observed_exception(tx): + # This is essentially exception handling code, equivalent of this pseudo code + # + # try: + # ... somebody raising StopIteration + # except StopIteration + # pass + # + # If this was going through the python code, we would have called exception_handler method, but FOR_ITER + # handles the exception completely in CPython. For example for 3.11, the resulting bytecode is + # + # + # 6 46 LOAD_GLOBAL 2 (StopIteration) + # 58 RAISE_VARARGS 1 + # >> 60 PUSH_EXC_INFO + + # 7 62 LOAD_GLOBAL 2 (StopIteration) + # 74 CHECK_EXC_MATCH + # 76 POP_JUMP_FORWARD_IF_FALSE 3 (to 84) + # 78 POP_TOP + + # 8 80 POP_EXCEPT + # + + # Fortunately this translates to a simple pop from the exn_vt_stack + tx.exn_vt_stack.pop() + + +# These exceptions are ok to fallback to eager/graph_break. +exceptions_allowed_to_be_fallback = ( + torch._subclasses.fake_tensor.DataDependentOutputException, + torch._subclasses.fake_tensor.DynamicOutputShapeException, + torch._subclasses.fake_tensor.UnsupportedOperatorException, + torch._subclasses.fake_tensor.UnsupportedFakeTensorException, +) + + +def unimplemented_with_warning(e: Exception, code, msg: str) -> NoReturn: + # This function calls unimplemented internally and eventually graph breaks + # or falls to eager. unimplemented itself does not print any user warnings, + # i.e., its very silent. This helper function is intended when an error is + # encountered in the torch.compile stack which is worth showing as warning + # to the user. For example, if AOT Autograd backend fails with a fake tensor + # exception, its ok to fallback to eager but not silently. Here, we can use + # this function to log the message and the stack trace. + graph_break_msg = format_error_msg_verbose(e, code) + graph_breaks_log.debug("%s", graph_break_msg) + log.warning(msg) + unimplemented(msg, from_exc=e) + + +_NOTHING = object() + + +def unimplemented( + msg: str, *, from_exc: Any = _NOTHING, case_name: Optional[str] = None +) -> NoReturn: + assert msg != os.environ.get("BREAK", False) + if from_exc is not _NOTHING: + raise Unsupported(msg, case_name=case_name) from from_exc + raise Unsupported(msg, case_name=case_name) + + +def warning(msg: str) -> None: + counters["warnings"][msg] += 1 + assert msg != os.environ.get("BREAK", False) + + +# KeyError has special handling for its args +# see https://github.com/python/cpython/blob/3.11/Objects/exceptions.c#L2534 for details +class KeyErrorMsg: + def __init__(self, value) -> None: + self.value = value + + def __str__(self) -> str: + return str(self.value) + + def __repr__(self) -> str: + return self.__str__() + + +def augment_exc_message(exc: Exception, msg: str = "\n", export: bool = False) -> None: + import traceback + + exc.innermost_user_frame_summary = None # type: ignore[attr-defined] + + real_stack = get_real_stack(exc) + if real_stack is not None and len(real_stack) > 0: + exc.innermost_user_frame_summary = real_stack[-1] # type: ignore[attr-defined] + msg += f"\nfrom user code:\n {''.join(traceback.format_list(real_stack))}" + + if config.replay_record_enabled and hasattr(exc, "record_filename"): + msg += f"\nLast frame execution written to {exc.record_filename}. To run only this frame while debugging, run\ + torch._dynamo.replay('{exc.record_filename}').\n" + + if not config.verbose and hasattr(exc, "real_stack"): + msg += '\nSet TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information\n' + + if hasattr(exc, "inner_exception") and hasattr( + exc.inner_exception, "minifier_path" + ): + if hasattr(exc.inner_exception, "buck_command"): + msg += ( + f"\nMinifier script written to {exc.inner_exception.minifier_path}. Run " + f"this buck command to find the smallest traced graph " + f"which reproduces this error: {exc.inner_exception.buck_command}\n" + ) + else: + msg += ( + f"\nMinifier script written to {exc.inner_exception.minifier_path}. Run " + "this script to find the smallest traced graph which reproduces this error.\n" + ) + + if not config.suppress_errors and not export: + msg += ( + "\n\n" + "You can suppress this exception and fall back to eager by setting:\n" + " import torch._dynamo\n" + " torch._dynamo.config.suppress_errors = True\n" + ) + + old_msg = "" if len(exc.args) == 0 else str(exc.args[0]) + + if isinstance(exc, KeyError): + exc.args = (KeyErrorMsg(old_msg + msg),) + exc.args[1:] + else: + new_msg = old_msg + msg + exc.args = (new_msg,) + exc.args[1:] + + +def get_exc_message( + e: Exception, compile_id: "CompileId" +) -> Tuple[Optional[str], Optional[int]]: + filename = None + lineno = None + if e.innermost_user_frame_summary is not None: # type: ignore[attr-defined] + filename = e.innermost_user_frame_summary.filename # type: ignore[attr-defined] + lineno = e.innermost_user_frame_summary.lineno # type: ignore[attr-defined] + e.compile_id = compile_id # type: ignore[attr-defined] + return filename, lineno + + +def get_real_stack(exc: Exception, frame=None) -> Optional[StackSummary]: + real_stack = getattr(exc, "real_stack", None) + if real_stack is None: + return None + + # NB: it's possible for real_stack to be []; we still attempt to + # report a stack anyway because the stack_above_dynamo may still + # be useful for debugging + + stack_above_dynamo = [] + if frame is not None: + # NB: frame is PyInterpreterFrame on Python 3.11 and later, + # not a TRUE frame object. You can't actually feed it + # to traceback because it doesn't have enough information. + # To solve this problem, we technically should just materialize + # the frame, the same way _PyFrame_GetFrameObject would do + # (but we cannot actually do this, because this populates + # frame_obj field, which default eval frame doesn't like). + # + # Fortunately, in this case, we can hack it: there's no need + # to actually use the truly top frame, we can just extract + # from where we are right now and rely on filter_stack to + # get rid of all the dynamo frames. For ease of testing + # we apply this behavior to ALL Python versions + stack_above_dynamo = filter_stack(extract_stack()) + + return cast(StackSummary, stack_above_dynamo + real_stack) + + +# filter out all frames after entering dynamo +def filter_stack(stack): + user_stack = [] + for frame in stack: + if "convert_frame" in frame.filename: + break + if "eval_frame" in frame.filename or "torch._dynamo.optimize(" in frame.line: + continue + user_stack.append(frame) + + return user_stack + + +def format_error_msg_verbose( + exc: Exception, code, record_filename=None, frame=None +) -> str: + msg = ( + f"WON'T CONVERT {code.co_name} {code.co_filename} line {code.co_firstlineno}\n" + ) + msg += "=" * 10 + " TorchDynamo Stack Trace " + "=" * 10 + "\n" + msg += format_exc() + real_stack = get_real_stack(exc, frame) + if real_stack is not None: + msg += ( + "\n" + + "=" * 10 + + " The above exception occurred while processing the following code " + + "=" * 10 + + "\n\n" + ) + msg += "".join(format_list(real_stack)) + msg += "\n" + msg += "=" * 10 + + return msg + + +def format_error_msg(exc: Exception, code, record_filename=None, frame=None) -> str: + msg = os.linesep * 2 + + if config.verbose: + msg = format_error_msg_verbose(exc, code, record_filename, frame) + else: + msg = f"WON'T CONVERT {code.co_name} {code.co_filename}\ + line {code.co_firstlineno} \ndue to: \n{format_exc()}" + + return msg diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/external_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/external_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..91663b4a99d175c14f8fd02df96d26d6a1d2e013 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/external_utils.py @@ -0,0 +1,144 @@ +# mypy: allow-untyped-defs +# This module contains functions that *will be allowed* by dynamo + +import functools +import warnings +from typing import List + +import torch +import torch.utils._pytree as pytree + + +try: + import numpy as np +except ModuleNotFoundError: + np = None # type: ignore[assignment] + + +def is_compiling() -> bool: + """ + Indicates whether we are tracing/compiling with torch.compile() or torch.export(). + + If need to check specifically that TorchDynamo is used, then use + torch.compiler.is_dynamo_compiling(). + + TODO(khabinov): we should deprecate this function and use one of these two: + * torch.compiler.is_compiling(), + * torch.compiler.is_dynamo_compiling(). + It will depend on the context where to use what. + """ + return torch.compiler.is_compiling() + + +def wrap_inline(fn): + """ + Create an extra frame around fn that is not in skipfiles + """ + + @functools.wraps(fn) + def inner(*args, **kwargs): + return fn(*args, **kwargs) + + return inner + + +def call_hook(hook, *args, **kwargs): + """ + Used by compiled autograd to handle hook returning None + """ + result = hook(*args) + if result is None: + return args[0] + elif kwargs["hook_type"] == "post_acc_grad_hook": + raise RuntimeError("Tensor post accumulate grad hooks should return None.") + return result + + +def wrap_numpy(f): + r"""Decorator that turns a function from ``np.ndarray``s to ``np.ndarray``s into a function + from ``torch.Tensor``s to ``torch.Tensor``s. + """ + if not np: + return f + + @functools.wraps(f) + def wrap(*args, **kwargs): + args, kwargs = pytree.tree_map_only( + torch.Tensor, lambda x: x.numpy(), (args, kwargs) + ) + out = f(*args, **kwargs) + return pytree.tree_map_only(np.ndarray, lambda x: torch.as_tensor(x), out) + + return wrap + + +class FakeBackwardCFunction: + def __init__( + self, + real: torch.autograd.function.BackwardCFunction, + saved_tensors: List[torch.Tensor], + ) -> None: + self.real = real + self.saved_tensors = saved_tensors + + def __getattr__(self, name): + if name == "saved_variables": + warnings.warn( + "'saved_variables' is deprecated; use 'saved_tensors'", + DeprecationWarning, + ) + return self.saved_tensors + + # route any attribute that isn't defined on this obj + return getattr(self.real, name) + + +# This function corresponds to the "eager" implementation of a lifted autograd.Function.backward +def call_backward(backward_c_function, saved_tensors, *args): + fake = FakeBackwardCFunction(backward_c_function, saved_tensors) + grads = fake._forward_cls.backward(fake, *args) # type: ignore[attr-defined] + + # in eager, we wrap in a tuple when there's only one grad output + if type(grads) is not tuple: + grads = (grads,) + + return grads + + +def untyped_storage_size(x: torch.Tensor): + return x.untyped_storage().size() + + +class FakeCompiledAutogradEngine: + @staticmethod + def queue_callback(final_callbacks, cb): + final_callbacks.append(cb) + + @staticmethod + def exec_final_callbacks(final_callbacks): + i = 0 + while i < len(final_callbacks): + cb = final_callbacks[i] + cb() + i += 1 + final_callbacks.clear() + + @staticmethod + def _exec_final_callbacks_stub(): + pass + + +def call_hook_from_backward_state(*args, bw_state, hook_name: str, **kwargs): + return getattr(bw_state, hook_name)(*args, **kwargs) + + +def call_module_hooks_from_backward_state( + _, result, *args, bw_state, hooks_name: str, module_name: str +): + module = getattr(bw_state, module_name) + hooks = getattr(bw_state, hooks_name) + for hook in hooks: + new_result = hook(module, result, *args) + if new_result is not None: + result = new_result + return result diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/funcname_cache.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/funcname_cache.py new file mode 100644 index 0000000000000000000000000000000000000000..fd9e278c871e2247a102dd917e8ba8a588f17ba8 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/funcname_cache.py @@ -0,0 +1,57 @@ +import tokenize +from typing import Dict, List, Optional + + +cache: Dict[str, Dict[int, str]] = {} + + +def clearcache() -> None: + cache.clear() + + +def _add_file(filename: str) -> None: + try: + with tokenize.open(filename) as f: + tokens = list(tokenize.generate_tokens(f.readline)) + except OSError: + cache[filename] = {} + return + + # NOTE: undefined behavior if file is not valid Python source, + # since tokenize will have undefined behavior. + result: Dict[int, str] = {} + # current full funcname, e.g. xxx.yyy.zzz + cur_name = "" + cur_indent = 0 + significant_indents: List[int] = [] + + for i, token in enumerate(tokens): + if token.type == tokenize.INDENT: + cur_indent += 1 + elif token.type == tokenize.DEDENT: + cur_indent -= 1 + # possible end of function or class + if significant_indents and cur_indent == significant_indents[-1]: + significant_indents.pop() + # pop the last name + cur_name = cur_name.rpartition(".")[0] + elif ( + token.type == tokenize.NAME + and i + 1 < len(tokens) + and tokens[i + 1].type == tokenize.NAME + and (token.string == "class" or token.string == "def") + ): + # name of class/function always follows class/def token + significant_indents.append(cur_indent) + if cur_name: + cur_name += "." + cur_name += tokens[i + 1].string + result[token.start[0]] = cur_name + + cache[filename] = result + + +def get_funcname(filename: str, lineno: int) -> Optional[str]: + if filename not in cache: + _add_file(filename) + return cache[filename].get(lineno, None) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/guards.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/guards.py new file mode 100644 index 0000000000000000000000000000000000000000..1923426bf060f8630d3b3b1325f7c8e45f349f93 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/guards.py @@ -0,0 +1,2914 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import ast +import builtins +import collections +import dataclasses +import enum +import functools +import importlib +import inspect +import itertools +import logging +import math +import os +import re +import sys +import textwrap +import types +import weakref +from contextlib import contextmanager +from copy import deepcopy +from inspect import currentframe, getframeinfo +from typing import ( + Any, + Callable, + Dict, + List, + Optional, + Set, + Tuple, + Type, + TYPE_CHECKING, + Union, +) +from weakref import ReferenceType + +import torch +import torch.utils._device +from torch._C._dynamo.guards import ( + check_obj_id, + check_type_id, + dict_version, + DictGuardManager, + install_no_tensor_aliasing_guard, + install_object_aliasing_guard, + RootGuardManager, + TensorGuards, +) +from torch._dynamo.source import ( + is_from_flatten_script_object_source, + is_from_local_source, + is_from_optimizer_source, + TensorProperty, + TensorPropertySource, +) +from torch._guards import ( + CompileContext, + CompileId, + DuplicateInputs, + Guard, + GuardBuilderBase, + GuardEnvExpr, + GuardSource, + Source, +) +from torch._logging import structured +from torch._utils_internal import justknobs_check +from torch.fx.experimental.symbolic_shapes import ( + EqualityConstraint, + is_symbolic, + SYMPY_INTERP, +) +from torch.utils._traceback import format_frame, report_compile_source_on_error +from torch.utils.weak import TensorWeakRef + +from . import config, convert_frame, exc, mutation_guard +from .eval_frame import set_guard_error_hook +from .source import ( + AttrProxySource, + AttrSource, + ChainedSource, + ConstDictKeySource, + DefaultsSource, + FlattenScriptObjectSource, + FSDPNNModuleSource, + GetItemSource, + GlobalSource, + GlobalStateSource, + GlobalWeakRefSource, + GradSource, + LocalSource, + NNModuleSource, + NumpyTensorSource, + ODictGetItemSource, + OptimizerSource, + ScriptObjectQualifiedNameSource, + ShapeEnvSource, + SubclassAttrListSource, + TupleIteratorGetItemSource, + TypeSource, + UnspecializedBuiltinNNModuleSource, + UnspecializedNNModuleSource, + UnspecializedParamBufferSource, + WeakRefCallSource, +) +from .types import CacheEntry, ExtraState, GuardedCode, GuardFail, GuardFn # noqa: F401 +from .utils import ( + common_constant_types, + dict_keys_repr, + get_custom_getattr, + get_torch_function_mode_stack, + guard_failures, + istype, + key_is_id, + key_to_id, + orig_code_map, + tensor_always_has_static_shape, + tuple_iterator_getitem, + tuple_iterator_len, + unpatched_nn_module_getattr, + verify_guard_fn_signature, +) + + +try: + import numpy as np +except ModuleNotFoundError: + np = None # type: ignore[assignment] + + +if TYPE_CHECKING: + from sympy import Symbol + + +log = logging.getLogger(__name__) +guards_log = torch._logging.getArtifactLogger(__name__, "guards") +recompiles_log = torch._logging.getArtifactLogger(__name__, "recompiles") +recompiles_verbose_log = torch._logging.getArtifactLogger( + __name__, "recompiles_verbose" +) +verbose_guards_log = torch._logging.getArtifactLogger(__name__, "verbose_guards") + + +class GuardManager: + """ + A helper class that contains the root guard manager. An instance of this + class is stored in the Dynamo cache entry, so that the cache entry can + access the RootGuardManager stored in the "root" attribute and directly call + the check_nopybind from C++. + """ + + def __init__(self): + self.root = RootGuardManager() + + self.closure_vars = None + self.args = None + self.code_parts = [] + self.verbose_code_parts = None + self.global_scope = None + self.guard_fail_fn = None + self.cache_entry = None + self.extra_state = None + self.id_matched_objs = None + self.no_tensor_aliasing_sources = [] + + self.print_no_tensor_aliasing_guard = True + + @contextmanager + def _preserve_print_no_tensor_aliasing_flag(self): + self.print_no_tensor_aliasing_guard = True + try: + yield + finally: + self.print_no_tensor_aliasing_guard = True + + def get_guard_lines(self, guard): + guard_name = guard.__class__.__name__ + parts = guard.verbose_code_parts() + parts = [guard_name + ": " + part for part in parts] + return parts + + def get_manager_line(self, guard_manager, accessor_str=None): + source = guard_manager.get_source() + t = guard_manager.__class__.__name__ + s = t + ": source=" + source + if accessor_str: + s += ", " + accessor_str + return s + + def construct_dict_manager_string(self, mgr, body): + for idx, (key_mgr, val_mgr) in sorted(mgr.get_key_value_managers().items()): + body.writeline(f"KeyValueManager pair at index={idx}") + with body.indent(): + if key_mgr: + body.writeline(f"KeyManager: {self.get_manager_line(key_mgr)}") + self.construct_manager_string(key_mgr, body) + + if val_mgr: + body.writeline(f"ValueManager: {self.get_manager_line(val_mgr)}") + self.construct_manager_string(val_mgr, body) + + def construct_manager_string(self, mgr, body): + with body.indent(): + for guard in mgr.get_leaf_guards(): + if isinstance(guard, torch._C._dynamo.guards.NO_TENSOR_ALIASING): # type: ignore[attr-defined] + if self.print_no_tensor_aliasing_guard: + self.print_no_tensor_aliasing_guard = False + body.writelines(self.get_guard_lines(guard)) + else: + body.writelines( + [ + guard.__class__.__name__, + ] + ) + else: + body.writelines(self.get_guard_lines(guard)) + + # This works for both DictGuardManager and SubclassedDictGuardManager + if isinstance(mgr, DictGuardManager): + self.construct_dict_manager_string(mgr, body) + + # General case of GuardManager/RootGuardManager + for accessor, child_mgr in zip( + mgr.get_accessors(), mgr.get_child_managers() + ): + body.writeline( + self.get_manager_line(child_mgr, f"accessed_by={accessor.repr()}") + ) + self.construct_manager_string(child_mgr, body) + + def __str__(self): + from torch._inductor.utils import IndentedBuffer + + class IndentedBufferWithPrefix(IndentedBuffer): + def prefix(self): + return "| " * (self._indent * self.tabwidth) + + def writeline(self, line, skip_prefix=False): + if skip_prefix: + super().writeline(line) + else: + super().writeline("+- " + line) + + with self._preserve_print_no_tensor_aliasing_flag(): + body = IndentedBufferWithPrefix() + body.tabwidth = 1 + body.writeline("", skip_prefix=True) + body.writeline("TREE_GUARD_MANAGER:", skip_prefix=True) + body.writeline("RootGuardManager") + self.construct_manager_string(self.root, body) + for guard in self.root.get_epilogue_lambda_guards(): + body.writelines(self.get_guard_lines(guard)) + return body.getvalue() + + def check(self, x): + # Only needed for debugging purposes. + return self.root.check(x) + + def check_verbose(self, x): + # Only needed for debugging purposes. + return self.root.check_verbose(x) + + def populate_code_parts_for_debugging(self): + # This should be called when the guard manager is fully populated + tensor_aliasing_guard_seen = False + + def get_code_parts(leaf_guard): + code_parts = [] + for verbose_code_part in leaf_guard.verbose_code_parts(): + code_part = verbose_code_part.split("#")[0].rstrip() + code_parts.append(code_part) + return code_parts + + def visit(mgr): + nonlocal tensor_aliasing_guard_seen + for guard in mgr.get_leaf_guards(): + if isinstance(guard, torch._C._dynamo.guards.NO_TENSOR_ALIASING): # type: ignore[attr-defined] + if not tensor_aliasing_guard_seen: + self.code_parts.extend(get_code_parts(guard)) + tensor_aliasing_guard_seen = True + else: + self.code_parts.extend(get_code_parts(guard)) + + for child_mgr in mgr.get_child_managers(): + visit(child_mgr) + + visit(self.root) + + +def from_numpy(a): + # If not numpy array, piggy back on e.g. tensor guards to check type + return torch.as_tensor(a) if isinstance(a, (np.generic, np.ndarray)) else a + + +# For user stack printing +@functools.lru_cache(None) +def uninteresting_files(): + import torch._dynamo.external_utils + + mods = [ + torch._dynamo.external_utils, + ] + return {inspect.getfile(m) for m in mods} + + +CLOSURE_VARS = { + "___check_type_id": check_type_id, + "___check_obj_id": check_obj_id, + "___odict_getitem": collections.OrderedDict.__getitem__, + "___key_to_id": key_to_id, + "___dict_version": dict_version, + "___dict_contains": lambda a, b: a in b, + "___tuple_iterator_len": tuple_iterator_len, + "___tuple_iterator_getitem": tuple_iterator_getitem, + "__math_isnan": math.isnan, + "__numpy_isnan": None if np is None else np.isnan, + "inf": float("inf"), + "__load_module": importlib.import_module, + "utils_device": torch.utils._device, + "device": torch.device, + "___from_numpy": from_numpy, + "___as_tensor": torch.as_tensor, + "torch": torch, + "inspect": inspect, +} + +if sys.version_info[:2] <= (3, 8): + # [Note: Python Version <= 3.8] + # This branch should be dropped when we drop support for Python 3.8. + # Reason: 'ast.unparse' function was introduced in Python 3.9. + + try: + import astunparse # type: ignore[import] + + def _ast_unparse(node: ast.AST) -> str: + return astunparse.unparse(node).replace("\n", "") + + HAS_UNPARSE_FUNCTIONS = True + except ImportError: + HAS_UNPARSE_FUNCTIONS = False +else: + HAS_UNPARSE_FUNCTIONS = True + + def _ast_unparse(node: ast.AST) -> str: + return ast.unparse(node).replace("\n", "") + + +def strip_function_call(name): + """ + "___odict_getitem(a, 1)" => "a" + "a.layers[slice(2)][0]._xyz" ==> "a" + "getattr(a.layers[slice(2)][0]._abc, '0')" ==> "a" + "getattr(getattr(a.x[3], '0'), '3')" ==> "a" + "a.layers[slice(None, -1, None)][0]._xyz" ==> "a" + """ + # recursively find valid object name in function + valid_name = re.compile("[A-Za-z_].*") + curr = "" + for char in name: + if char in " (": + curr = "" + elif char in "),[]": + if curr and curr != "None" and valid_name.match(curr): + return strip_function_call(curr) + else: + curr += char + + return strip_getattr_getitem(name) + + +def strip_getattr_getitem(name): + """ + "a[1]" => "a" + "a.foo" => "a" + """ + return re.split(r"[.\[]", name)[0] + + +def get_verbose_code_part(code_part: str, guard: Guard) -> str: + extra = "" + if guard.user_stack: + for fs in reversed(guard.user_stack): + if fs.filename not in uninteresting_files(): + extra = f" # {format_frame(fs, line=True)}" + break + elif guard.stack: + extra = f" # {format_frame(guard.stack.summary()[-1])}" + + return f"{code_part:<60}{extra}" + + +def get_verbose_code_parts( + code_parts: Union[str | List[str]], guard: Guard +) -> List[str]: + if not isinstance(code_parts, list): + code_parts = [code_parts] + return [get_verbose_code_part(code_part, guard) for code_part in code_parts] + + +def convert_to_concrete_values(size_or_stride): + converted: List[Optional[int]] = [] + for dim in size_or_stride: + if not is_symbolic(dim): + converted.append(dim) + else: + assert isinstance(dim, torch.SymInt) + converted.append(dim.node.maybe_as_int()) + return converted + + +def get_tensor_guard_code_part(value, name, sizes, strides): + pytype = type(value) + dispatch_key = ( + torch._C._dispatch_keys(value) | torch._C._dispatch_tls_local_include_set() + ) - torch._C._dispatch_tls_local_exclude_set() + dtype = value.dtype + device_index = value.device.index + requires_grad = value.requires_grad + guard_str = ( + f"check_tensor({name}, {pytype.__qualname__}, {dispatch_key}, {dtype}, " + f"device={device_index}, requires_grad={requires_grad}, size={sizes}, stride={strides})" + ) + return guard_str + + +def get_key_index(dct, key): + return list(dct.keys()).index(key) + + +def get_key_index_source(source, index): + return f"list({source}.keys())[{index}]" + + +@dataclasses.dataclass(frozen=True) +class NNModuleAttrAccessorInfo: + # Represents where is the attr name is present in the nn module attribute + # access + + # Tells that the attribute can be accessed via __dict__ + present_in_generic_dict: bool = False + + # Either the actual name or _parameters/_buffers/_modules + l1_key: Optional[str] = None + + # Actual paramter/buffer/submodule name + l2_key: Optional[str] = None + + +def getitem_on_dict_manager( + source, base_guard_manager, base_example_value, example_value, guard_manager_enum +): + base_source_name = source.base.name() + source_name = source.name() + if isinstance(source.index, ConstDictKeySource): + index = source.index.index + else: + assert isinstance(base_example_value, dict) + index = get_key_index(base_example_value, source.index) + + key_source = get_key_index_source(base_source_name, index) + key_example_value = list(base_example_value.keys())[index] + if isinstance(key_example_value, (int, str)): + value_source = f"{base_source_name}[{key_example_value!r}]" + else: + value_source = f"{base_source_name}[{key_source}]" + if not isinstance(source.index, ConstDictKeySource): + # We have to insert a key manager guard here + # TODO - source debug string is probably wrong here. + base_guard_manager.get_key_manager( + index=index, + source=key_source, + example_value=source.index, + guard_manager_enum=GuardManagerType.GUARD_MANAGER, + ).add_equals_match_guard( + source.index, [f"{key_source} == {key_example_value!r}"] + ) + + return base_guard_manager.get_value_manager( + index=index, + source=value_source, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + + +def match_on_id_for_tensor(guard): + source = guard.originating_source + return source.is_dict_key() and not isinstance(source, GradSource) + + +# The ready to eval generated code (possibly multiple parts) for a guard, plus +# the original guard object that created it for provenance +@dataclasses.dataclass +class GuardCodeList: + code_list: List[str] + guard: Guard + + +class GuardManagerType(enum.Enum): + GUARD_MANAGER = 1 + DICT_GUARD_MANAGER = 2 + DICT_SUBCLASS_GUARD_MANAGER = 3 + + +class GuardBuilder(GuardBuilderBase): + def __init__( + self, + id_ref: Callable[[Any], str], + source_ref: Callable[[Source], str], + lookup_weakrefs: Callable[[object], ReferenceType[object]], + local_scope: Dict[str, object], + global_scope: Dict[str, object], + guard_manager: Optional[GuardManager], + check_fn_manager: CheckFunctionManager, + ): + self.id_ref = id_ref + self.source_ref = source_ref + self.lookup_weakrefs = lookup_weakrefs + self.scope: Dict[str, Dict[str, object]] = {"L": local_scope, "G": global_scope} + self.scope["__builtins__"] = builtins.__dict__.copy() + for ( + name, + package_module, + ) in torch.package.package_importer._package_imported_modules.items(): + name = name.replace(">", "_").replace("<", "_").replace(".", "_dot_") + # Write the package module into the scope so that we can import it + self.scope["__builtins__"][name] = package_module + # Write the demangled name to the scope so that we can use it + self.scope[name] = package_module + self.guard_manager = guard_manager + + self.argnames: List[str] = [] + # Code is python expression strings generated for each guard + self.code: List[GuardCodeList] = [] + # shape_env_code is only used by builder and is used for + # shape env code. This exists only because we need to make sure + # shape env guards get run after tensor match guards (since the + # tensor match guards make sure we actually have tensors) + self.shape_env_code: List[GuardCodeList] = [] + + # [Note - On Eager Tensor Guards] + # Most of the time, we generate Python code in a guard to directly + # check various properties. However, tensors are a bit special; + # it is too slow to check their properties one-by-one in Python. + # Instead, there is a C++ function TensorGuards.check which takes + # all of the tensor arguments and checks them all against compile-time + # examples entirely in C++. Thus, every time we process a + # TENSOR_MATCH guard, we just add another entry to + # tensor_check_names/tensor_check_examples, saying "for this local, + # check it against this example", and it all ends up getting + # swept up into a single call to ___check_tensors. Invariant: + # len(tensor_check_names) == len(tensor_check_examples). + # TODO: something here + self.tensor_check_names: List[str] = [] + self.tensor_check_examples: List[torch.Tensor] = [] + self.tensor_check_guards: List[Guard] = [] + self.tensor_check_guard_managers: List[GuardManager] = [] + + self.check_fn_manager: CheckFunctionManager = check_fn_manager + + # Collect the ids of dicts which need key order guarding. source_name is + # not sufficient because for nn modules, we can have different sources + # to access the same object - self._module["param"] is same as + # self.param. + self.key_order_guarded_dict_ids = set() + for source_name in self.check_fn_manager.output_graph.guard_on_key_order: + self.key_order_guarded_dict_ids.add(id(self.get(source_name))) + + # Keep track of weak references of objects with ID_MATCH guard. This + # info is stored alongside optimized_code and check_fn and is used to + # limit the number of cache entries with same ID_MATCH'd object. + self.id_matched_objs: Dict[str, ReferenceType[object]] = {} + + # Save the guard managers to avoid repeatedly traversing sources. + self._cached_guard_managers: Dict[ + str, torch._C._dynamo.guards.GuardManager + ] = {} + + self._cached_duplicate_input_guards: Set[Tuple[str, str]] = set() + + def guard_on_dict_keys_and_ignore_order(self, example_value, guard): + dict_mgr = self.get_guard_manager(guard) + if isinstance(dict_mgr, DictGuardManager): + raise NotImplementedError( + "Not expecting a DictGuardManager. Seems like Dynamo incorrectly " + f"added the dict to tx.output.guard_on_key_order for {guard.name}" + ) + + # Iterate over the dicts and install a dict_getitem_manager. + dict_source = guard.originating_source.name() + for key in example_value.keys(): + value = example_value[key] + value_source = GetItemSource(guard.originating_source, index=key) + guard_manager_enum = self.get_guard_manager_type( + value_source, example_value + ) + dict_mgr.dict_getitem_manager( + key=key, + source=f"{dict_source}[{key!r}]", + example_value=value, + guard_manager_enum=guard_manager_enum, + ) + + def guard_on_dict_keys_and_order(self, value, guard): + # Add key managers for the DictGuardManager. Then add either an + # ID_MATCH or EQUALS_MATCH guard on the key. + dict_mgr = self.get_guard_manager(guard) + if not isinstance(dict_mgr, DictGuardManager): + raise NotImplementedError( + "Expecting a DictGuardManager. Seems like Dynamo forgot " + f"to set the right guard manager enum for {guard.name}" + ) + assert isinstance(dict_mgr, DictGuardManager) + + for idx, key in enumerate(value.keys()): + key_source = get_key_index_source(guard.name, idx) + key_manager = dict_mgr.get_key_manager( + index=idx, + source=key_source, + example_value=key, + guard_manager_enum=GuardManagerType.GUARD_MANAGER, + ) + if key_is_id(key): + # Install ID_MATCH guard + id_val = self.id_ref(key) + key_manager.add_id_match_guard( + id_val, + get_verbose_code_parts( + f"__check_obj_id({key_source}, {id_val})", guard + ), + ) + else: + # Install EQUALS_MATCH guard + key_manager.add_equals_match_guard( + key, get_verbose_code_parts(f"{key_source} == {key!r}", guard) + ) + + def getattr_on_nn_module( + self, + source, + base_guard_manager, + base_example_value, + example_value, + base_source_name, + source_name, + guard_manager_enum, + ): + """ + This tries to avoid calling the expensive nn module custom getattr method by + checking if the attribute is accessible via __dict__. For attributes that + are not accessible via __dict__ (like descriptors), we fallback to + PyObject_GetAttr. + + There are two cases that we optimize for + 1) attributes present directly in __dict__, e.g training. + 2) parameters/buffers/modules - they can be accessed via _parameters, + _buffers, _modules keys in __dict__. For example, mod.linear can be + accessed as mod.__dict__["_parameters"]["linear"] + + The most common and expensive case for nn module guards is of type + mod.submod1.submod2.submod3.training. We avoid the python getattr of nn + modules by going through the __dict__. + """ + + def getitem_on_dict_mgr( + mgr, key, source_name, base_example_value, example_value, guard_manager_enum + ): + if isinstance(mgr, DictGuardManager): + # Case where the user code relies on key order, e.g., + # named_parameters + index = get_key_index(base_example_value, key) + + # Install the key manager and add equals match guard + key_source = f"list({source_name}.keys())[{index!r}]" + mgr.get_key_manager( + index=index, + source=key_source, + example_value=key, + guard_manager_enum=GuardManagerType.GUARD_MANAGER, + ).add_equals_match_guard(key, [f"{key_source} == {key!r}"]) + + # Install the value manager + return mgr.get_value_manager( + index=index, + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + else: + return mgr.dict_getitem_manager( + key=key, + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + + attr_name = source.member + mod_dict = base_example_value.__dict__ + + all_class_attribute_names: Set[str] = set() + for x in inspect.getmro(base_example_value.__class__): + all_class_attribute_names.update(x.__dict__.keys()) + + accessor_info = NNModuleAttrAccessorInfo(False, None, None) + + if attr_name in mod_dict: + accessor_info = NNModuleAttrAccessorInfo(True, attr_name, None) + elif "_parameters" in mod_dict and attr_name in mod_dict["_parameters"]: + accessor_info = NNModuleAttrAccessorInfo(True, "_parameters", attr_name) + elif "_buffers" in mod_dict and attr_name in mod_dict["_buffers"]: + accessor_info = NNModuleAttrAccessorInfo(True, "_buffers", attr_name) + elif ( + attr_name not in all_class_attribute_names + and "_modules" in mod_dict + and attr_name in mod_dict["_modules"] + ): + # Check test_attr_precedence test - instance attributes always take precedence unless its an nn.Module. + accessor_info = NNModuleAttrAccessorInfo(True, "_modules", attr_name) + + if not accessor_info.present_in_generic_dict: + # The attribute can be accessed by __getattribute__ call, so rely on + # PyObject_GetAttr + return base_guard_manager.getattr_manager( + attr=source.member, + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + else: + assert accessor_info.l1_key + l1_key = accessor_info.l1_key + l2_key = accessor_info.l2_key + + # Set source strings for debug info + mod_dict_source = f"{base_source_name}.__dict__" + l1_source_name = l2_source_name = None + l1_value = l2_value = None + l1_guard_manager_enum = l2_guard_manager_enum = None + if l2_key: + l1_source = AttrSource(source.base, l1_key) + l1_source_name = l1_source.name() + l1_value = mod_dict[l1_key] + # do not guard on key order for _parameters etc unless the user code + # actually needs the key order (e.g. calling named_parameters) + l1_guard_manager_enum = self.get_guard_manager_type(l1_source, l1_value) + + l2_source_name = source_name + l2_value = example_value + l2_guard_manager_enum = self.get_guard_manager_type( + source, example_value + ) + else: + l1_source_name = source_name + l1_value = example_value + l1_guard_manager_enum = self.get_guard_manager_type( + source, example_value + ) + + # Get __dict__ accessor. No need to guard on dict key order, so use base + # Guard Manager + mod_generic_dict_manager = base_guard_manager.get_generic_dict_manager( + source=mod_dict_source, + example_value=mod_dict, + guard_manager_enum=GuardManagerType.GUARD_MANAGER, + ) + + l1_mgr = getitem_on_dict_mgr( + mgr=mod_generic_dict_manager, + key=l1_key, + source_name=l1_source_name, + base_example_value=mod_dict, + example_value=l1_value, + guard_manager_enum=l1_guard_manager_enum, + ) + + if l2_key: + return getitem_on_dict_mgr( + mgr=l1_mgr, + key=l2_key, + source_name=l2_source_name, + base_example_value=l1_value, + example_value=l2_value, + guard_manager_enum=l2_guard_manager_enum, + ) + return l1_mgr + + def requires_key_order_guarding(self, source): + source_name = source.name() + if source_name == "": + return False + obj_id = id(self.get(source_name)) + return obj_id in self.key_order_guarded_dict_ids + + def get_guard_manager_type(self, source, example_value): + guard_manager_enum = GuardManagerType.GUARD_MANAGER + if self.requires_key_order_guarding(source): + assert isinstance(example_value, dict) + # If keys method is not overriden, we can use PyDict_Next to get key + # orderings. Read more in guards.cpp + if type(example_value).keys is type({}).keys: + guard_manager_enum = GuardManagerType.DICT_GUARD_MANAGER + else: + guard_manager_enum = GuardManagerType.DICT_SUBCLASS_GUARD_MANAGER + return guard_manager_enum + + def manager_guards_on_keys(self, mgr_enum): + return ( + mgr_enum == GuardManagerType.DICT_GUARD_MANAGER + or mgr_enum == GuardManagerType.DICT_SUBCLASS_GUARD_MANAGER + ) + + def get_global_guard_manager(self): + assert self.guard_manager # to make mypy happy + return self.guard_manager.root.globals_dict_manager( + f_globals=self.scope["G"], + source="G", + example_value=self.scope["G"], + guard_manager_enum=GuardManagerType.GUARD_MANAGER, + ) + + def get_guard_manager_from_source(self, source): + assert self.guard_manager # to make mypy happy + root_guard_manager = self.guard_manager.root + + example_value = None + source_name = source.name() + + if source_name != "" and source_name in self._cached_guard_managers: + return self._cached_guard_managers[source_name] + + if source_name != "": + example_value = self.get(source_name) + + guard_manager_enum = self.get_guard_manager_type(source, example_value) + + # Get base manager related information + base_source_name = None + base_example_value = None + base_guard_manager = None + base_guard_manager_enum = GuardManagerType.GUARD_MANAGER + if isinstance(source, ChainedSource): + base_source_name = source.base.name() + base_example_value = self.get(base_source_name) + base_guard_manager = self.get_guard_manager_from_source(source.base) + base_guard_manager_enum = self.get_guard_manager_type( + source.base, base_example_value + ) + + # Use istype instead of isinstance to check for exact type of source. + if istype(source, LocalSource): + # RootGuardManager accepts a dict but still its not a + # DictGuardManager because we will eventually move to + # fastlocals. + out = root_guard_manager.dict_getitem_manager( + key=source.local_name, + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + elif istype(source, GlobalSource): + # Global manager accepts a dict but it is not a DictGuardManager + # because globals dict is big and we typically guard on a very + # selected items on globals. + out = self.get_global_guard_manager().dict_getitem_manager( + key=source.global_name, + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + elif istype(source, GlobalWeakRefSource): + out = self.get_global_guard_manager().global_weakref_manager( + global_name=source.global_name, + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + elif istype(source, GlobalStateSource): + # Don't do anything here. We guard on global state completely in + # C++. So just return the root mgr. + return root_guard_manager + elif istype(source, ShapeEnvSource): + return root_guard_manager + elif istype(source, TypeSource): + assert base_guard_manager # to make mypy happy + out = base_guard_manager.type_manager( + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + elif istype( + source, + ( + OptimizerSource, + NNModuleSource, + UnspecializedNNModuleSource, + UnspecializedBuiltinNNModuleSource, + FSDPNNModuleSource, + ), + ): + assert base_guard_manager # to make mypy happy + out = base_guard_manager + elif istype(source, GradSource): + assert base_guard_manager # to make mypy happy + out = base_guard_manager.grad_manager( + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + elif istype(source, (AttrSource, UnspecializedParamBufferSource)): + assert base_guard_manager # to make mypy happy + + if ( + isinstance(base_example_value, torch.nn.Module) + and get_custom_getattr(base_example_value) + is unpatched_nn_module_getattr + ): + out = self.getattr_on_nn_module( + source, + base_guard_manager, + base_example_value, + example_value, + base_source_name, + source_name, + guard_manager_enum, + ) + else: + out = base_guard_manager.getattr_manager( + attr=source.member, + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + elif istype(source, GetItemSource): + assert base_guard_manager # to make mypy happy + if isinstance(base_example_value, (dict, collections.OrderedDict)): + # TODO(anijain2305) - Consider isolating GetItemSource and + # DictGetItemSource (or maybe use ODictGetItemSource for + # dicts) so that GetItemSource is only for non dict objects. + if isinstance(base_guard_manager, DictGuardManager): + assert self.manager_guards_on_keys(base_guard_manager_enum) + out = getitem_on_dict_manager( + source, + base_guard_manager, + base_example_value, + example_value, + guard_manager_enum, + ) + else: + if isinstance(source.index, ConstDictKeySource): + raise RuntimeError( + "Expecting clean index here. Likely Dynamo forgot to mark" + " a dict as guard_on_key_order" + ) + out = base_guard_manager.dict_getitem_manager( + key=source.index, + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + elif isinstance(base_example_value, list) and not source.index_is_slice: + out = base_guard_manager.list_getitem_manager( + key=source.index, + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + elif isinstance(base_example_value, tuple) and not source.index_is_slice: + out = base_guard_manager.tuple_getitem_manager( + key=source.index, + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + else: + index = source.index + if source.index_is_slice: + index = source.unpack_slice() + out = base_guard_manager.getitem_manager( + key=index, + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + elif istype(source, ODictGetItemSource): + if isinstance(base_guard_manager, DictGuardManager): + assert self.manager_guards_on_keys(base_guard_manager_enum) + out = getitem_on_dict_manager( + source, + base_guard_manager, + base_example_value, + example_value, + guard_manager_enum, + ) + else: + assert base_guard_manager # to make mypy happy + out = base_guard_manager.dict_getitem_manager( + key=source.index, + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + elif istype(source, DefaultsSource): + assert base_guard_manager # to make mypy happy + assert callable(base_example_value) + if not source.is_kw: + out = base_guard_manager.func_defaults_manager( + source=base_source_name, + example_value=base_example_value.__defaults__, + guard_manager_enum=GuardManagerType.GUARD_MANAGER, + ).getitem_manager( + key=source.idx_key, + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + else: + # kwdefauts is a dict, so use a DictGuardManager + kwdefaults = base_example_value.__kwdefaults__ + assert base_source_name is not None + kw_source = base_source_name + ".__kwdefaults__" + + # kwdefaults is a dict. No need to guard on dict order. + dict_mgr = base_guard_manager.func_kwdefaults_manager( + source=kw_source, + example_value=kwdefaults, + guard_manager_enum=GuardManagerType.GUARD_MANAGER, + ) + assert not isinstance(dict_mgr, DictGuardManager) + + out = dict_mgr.dict_getitem_manager( + key=source.idx_key, + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + elif istype(source, NumpyTensorSource): + assert base_guard_manager # to make mypy happy + out = base_guard_manager.lambda_manager( + python_lambda=from_numpy, + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + elif istype(source, SubclassAttrListSource): + assert base_guard_manager # to make mypy happy + out = base_guard_manager.lambda_manager( + python_lambda=lambda x: x.__tensor_flatten__()[0], + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + elif istype(source, FlattenScriptObjectSource): + assert base_guard_manager # to make mypy happy + out = base_guard_manager.lambda_manager( + python_lambda=lambda x: x.__obj_flatten__(), + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + elif istype(source, ScriptObjectQualifiedNameSource): + assert base_guard_manager # to make mypy happy + out = base_guard_manager.lambda_manager( + python_lambda=lambda x: x._type().qualified_name(), + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + elif istype(source, AttrProxySource): + assert base_guard_manager # to make mypy happy + out = base_guard_manager.lambda_manager( + python_lambda=lambda x: x.get_base(), + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + elif istype(source, TupleIteratorGetItemSource): + assert base_guard_manager # to make mypy happy + out = base_guard_manager.tuple_iterator_getitem_manager( + index=source.index, + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + elif isinstance(source, ConstDictKeySource): + if not isinstance(base_guard_manager, DictGuardManager): + raise AssertionError( + "ConstDictKeySource can only work on DictGuardManager" + ) + out = base_guard_manager.get_key_manager( + index=source.index, + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + elif isinstance(source, WeakRefCallSource): + assert base_guard_manager # to make mypy happy + out = base_guard_manager.weakref_call_manager( + source=source_name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + else: + raise AssertionError( + f"missing guard manager builder {source} - {source.name()}" + ) + + self._cached_guard_managers[source.name()] = out + return out + + def get_guard_manager(self, guard: Guard): + return self.get_guard_manager_from_source(guard.originating_source) + + def add_python_lambda_leaf_guard_to_root( + self, + code_parts, + verbose_code_parts, + closure_vars=CLOSURE_VARS, + is_epilogue=True, + ): + # Adds a lambda leaf guard to the root guard manager. It wraps the + # code_parts in a function object which is then passed on to the leaf + # guard. + make_guard_fn_args = ", ".join(closure_vars.keys()) + guard_body, pycode = build_guard_function(code_parts, make_guard_fn_args) + out: Dict[str, Any] = {} + globals_for_guard_fn = {"G": self.scope["G"]} + exec(pycode, globals_for_guard_fn, out) + guard_fn = out["___make_guard_fn"](*closure_vars.values()) + assert self.guard_manager # to make mypy happy + if is_epilogue: + # Epilogue guards are run after all the other guards have finished. + # If epilogue guards contain a getattr or getitem access, one of the + # other guards would fail preventing the epilogue guards to run. + self.guard_manager.root.add_epilogue_lambda_guard( + guard_fn, verbose_code_parts + ) + else: + self.guard_manager.root.add_lambda_guard(guard_fn, verbose_code_parts) + + # Warning: use this with care! This lets you access what the current + # value of the value you are guarding on is. You probably don't want + # to actually durably save this value though (because it's specific + # to this frame!) Instead, you should be reading out some property + # (like its type) which is what you permanently install into the + # guard code. + def get(self, name: str) -> Any: + return eval(name, self.scope, CLOSURE_VARS) + + # Registers the usage of the source name referenced by the + # string (or stored in the Guard) as being guarded upon. It's important + # to call this before generating some code that makes use of 'guard', + # because without this call, we won't actually bind the variable + # you reference in the actual guard closure (oops!) + def arg_ref(self, guard: Union[str, Guard]) -> str: + name: str + if isinstance(guard, str): + name = guard + else: + name = guard.name + base = strip_getattr_getitem(strip_function_call(name)) + if base not in self.argnames: + if re.match(r"[a-zA-Z0-9_]+", base): + if re.match(r"^\d+$", base): + log.warning("invalid var name: %s", guard) + self.argnames.append(base) + + return name + + def _guard_on_attribute(self, guard: Guard, attr_name: str, guard_fn): + attr_source = AttrSource(guard.originating_source, attr_name) + # Copy the stack info + new_guard = Guard( + attr_source, guard_fn, stack=guard.stack, user_stack=guard.user_stack + ) + new_guard.create(self) + + # Note: the order of the guards in this file matters since we sort guards on the same object by lineno + def HASATTR(self, guard: Guard): + source = guard.originating_source + if isinstance(source, NNModuleSource): + source = source.base + assert isinstance(source, AttrSource), f"invalid source {guard.name}" + base_source = source.base + base = base_source.name() + attr = source.member + + ref = self.arg_ref(base) + val = hasattr(self.get(base), attr) + code = None + if val: + code = f"hasattr({ref}, {attr!r})" + else: + code = f"not hasattr({ref}, {attr!r})" + self._set_guard_export_info( + guard, [code], provided_guarded_object=self.get(base) + ) + + if config.enable_cpp_guard_manager: + base_manager = self.get_guard_manager_from_source(base_source) + if val: + # Just install a getattr manager. GetAttrGuardAccessor itself + # acts as hasattr guard. + example_value = self.get(source.name()) + base_example_value = self.get(base) + guard_manager_enum = self.get_guard_manager_type(source, example_value) + + # if the base value is nn.Module, check if we can speedup the + # guard by going through __dict__ attrs. + if ( + isinstance(base_example_value, torch.nn.Module) + and get_custom_getattr(base_example_value) + is unpatched_nn_module_getattr + ): + return self.getattr_on_nn_module( + source, + base_manager, + base_example_value, + example_value, + base, + source.name(), + guard_manager_enum, + ) + else: + base_manager.getattr_manager( + attr=attr, + source=guard.name, + example_value=example_value, + guard_manager_enum=guard_manager_enum, + ) + else: + base_manager.add_no_hasattr_guard( + attr, get_verbose_code_parts(code, guard) + ) + else: + self._produce_guard_code(guard, [code]) + + def NOT_PRESENT_IN_GENERIC_DICT(self, guard: Guard, attr=None) -> None: + assert attr is not None + ref = self.arg_ref(guard) + val = self.get(guard.name) + assert isinstance(val, torch.nn.Module) + + base_manager = self.get_guard_manager(guard) + + mod_dict_source = f"{guard.name}.__dict__" + mod_generic_dict_manager = base_manager.get_generic_dict_manager( + source=mod_dict_source, + example_value=val.__dict__, + guard_manager_enum=GuardManagerType.GUARD_MANAGER, + ) + + code = f"not ___dict_contains({attr!r}, {ref}.__dict__)" + mod_generic_dict_manager.add_dict_contains_guard( + False, attr, get_verbose_code_parts(code, guard) + ) + + def TYPE_MATCH(self, guard: Guard) -> None: + # ___check_type_id is same as `id(type(x)) == y` + t = type(self.get(guard.name)) + obj_id = self.id_ref(t) + code = f"___check_type_id({self.arg_ref(guard)}, {obj_id})" + self._set_guard_export_info(guard, [code]) + + if config.enable_cpp_guard_manager: + self.get_guard_manager(guard).add_type_match_guard( + obj_id, get_verbose_code_parts(code, guard) + ) + else: + self._produce_guard_code(guard, [code]) + + def DICT_VERSION(self, guard: Guard): + # ___check_dict_version is same as `dict_version(x) == y` + ref = self.arg_ref(guard) + val = self.get(guard.name) + version = dict_version(self.get(guard.name)) + code = f"___dict_version({ref}) == {version}" + self._set_guard_export_info(guard, [code]) + + if config.enable_cpp_guard_manager: + # TODO(anijain2305) - Delete this when DictGuardManager uses tags + # for dicts. + self.get_guard_manager(guard).add_dict_version_guard( + val, get_verbose_code_parts(code, guard) + ) + else: + self._produce_guard_code(guard, [code]) + + def DICT_CONTAINS(self, guard: Guard, key: str, invert: bool): + dict_ref = self.arg_ref(guard) + + maybe_not = "not " if invert else "" + code = f"{maybe_not}___dict_contains({key!r}, {dict_ref})" + self._set_guard_export_info(guard, [code]) + + if config.enable_cpp_guard_manager: + self.get_guard_manager(guard).add_dict_contains_guard( + not invert, key, get_verbose_code_parts(code, guard) + ) + else: + self._produce_guard_code(guard, [code]) + + def ID_MATCH(self, guard: Guard): + # ___check_obj_id is same as `id(x) == y` + if isinstance(guard.originating_source, TypeSource): + # optional optimization to produce cleaner/faster guard code + return self.TYPE_MATCH( + Guard(guard.originating_source.base, GuardBuilder.TYPE_MATCH) # type: ignore[arg-type] + ) + + ref = self.arg_ref(guard) + val = self.get(guard.name) + id_val = self.id_ref(val) + code = f"___check_obj_id({ref}, {id_val})" + self._set_guard_export_info(guard, [code]) + + if config.enable_cpp_guard_manager: + self.get_guard_manager(guard).add_id_match_guard( + id_val, get_verbose_code_parts(code, guard) + ) + else: + self._produce_guard_code(guard, [code]) + + # Keep track of ID_MATCH'd objects. This will be used to modify the + # cache size logic + if isinstance(guard.originating_source, LocalSource): + # TODO(anijain2305) - This is currently restricted to nn.Module objects + # because many other ID_MATCH'd objects fail - like DeviceMesh. + # Increase the scope of ID_MATCH'd objects. + if isinstance(val, torch.nn.Module): + local_name = guard.originating_source.local_name + weak_id = self.lookup_weakrefs(val) + if weak_id is not None: + self.id_matched_objs[local_name] = weak_id + + def NOT_NONE_MATCH(self, guard: Guard, value=None): + ref = self.arg_ref(guard) + val = self.get(guard.name) + assert isinstance(val, torch.Tensor) + code = f"{ref} is not None" + self._set_guard_export_info(guard, [code]) + + if config.enable_cpp_guard_manager: + self.get_guard_manager(guard).add_not_none_guard( + get_verbose_code_parts(code, guard) + ) + else: + self._produce_guard_code(guard, [code]) + + def NAME_MATCH(self, guard: Guard): + self._guard_on_attribute(guard, "__name__", GuardBuilder.EQUALS_MATCH) + + def DATA_PTR_MATCH(self, guard: Guard): + # Add a type check. C++ guard has the type check internally, so only + # enable it for Python guards. + if not config.enable_cpp_guard_manager: + self.TYPE_MATCH(guard) + + obj = self.get(guard.name) + code = f"{self.arg_ref(guard)}.data_ptr() == {obj.data_ptr()}" + self._set_guard_export_info(guard, [code]) + + if config.enable_cpp_guard_manager: + self.get_guard_manager(guard).add_data_ptr_guard( + obj, get_verbose_code_parts(code, guard) + ) + else: + self._produce_guard_code(guard, [code]) + + def DUAL_LEVEL(self, guard: Guard): + # Invalidate dual level if current dual level is different than the one + # in the fx graph + dual_level = torch.autograd.forward_ad._current_level + code = [f"torch.autograd.forward_ad._current_level == {dual_level}"] + self._set_guard_export_info(guard, [code]) + if config.enable_cpp_guard_manager: + # TODO(anijain2305) - Consider this moving this guard to C++ + forward_ad = torch.autograd.forward_ad + + def fn(x): + return forward_ad._current_level == dual_level + + assert self.guard_manager # to make mypy happy + self.guard_manager.root.add_lambda_guard( + fn, get_verbose_code_parts(code, guard) + ) + else: + self._produce_guard_code(guard, code) + + def FUNCTORCH_STACK_MATCH(self, guard: Guard): + # Invalidate functorch code if current level is different than + # the one when FX graph was generated + cis = torch._functorch.pyfunctorch.retrieve_all_functorch_interpreters() + states = [ci.get_state() for ci in cis] + code = [f"torch._functorch.pyfunctorch.compare_functorch_state({states})"] + self._set_guard_export_info(guard, code) + + if config.enable_cpp_guard_manager: + # TODO(anijain2305) - Consider this moving this guard to C++ + compare_fn = torch._functorch.pyfunctorch.compare_functorch_state + + def fn(x): + return compare_fn(states) + + assert self.guard_manager # to make mypy happy + self.guard_manager.root.add_lambda_guard( + fn, get_verbose_code_parts(code, guard) + ) + else: + self._produce_guard_code(guard, code) + + def TENSOR_SUBCLASS_METADATA_MATCH(self, guard: Guard): + value = self.get(guard.name) + original_metadata = deepcopy(self.get(guard.name).__tensor_flatten__()[1]) + if hasattr(value, "__metadata_guard__"): + verify_guard_fn_signature(value) + + def metadata_checker(x): + return value.__metadata_guard__( + original_metadata, x.__tensor_flatten__()[1] + ) + + else: + + def metadata_checker(x): + return x.__tensor_flatten__()[1] == original_metadata + + global_name = f"___check_metadata_{id(metadata_checker)}_c{CompileContext.current_compile_id()}" + if config.enable_cpp_guard_manager: + self.get_guard_manager(guard).add_lambda_guard( + metadata_checker, get_verbose_code_parts(global_name, guard) + ) + else: + global_scope = self.get("G") + global_scope[global_name] = metadata_checker + code = [f"{global_name}({self.get(guard.name)})"] + self._produce_guard_code(guard, code) + + def EQUALS_MATCH(self, guard: Guard): + ref = self.arg_ref(guard) + val = self.get(guard.name) + t = type(val) + if np: + np_types: Tuple[Type[Any], ...] = ( + np.int8, + np.int16, + np.int32, + np.int64, + np.uint8, + np.uint16, + np.uint32, + np.uint64, + np.float16, + np.float32, + np.float64, + ) + else: + np_types = () + + ok_mutable_types = (list, set) + + ok_types = tuple( + common_constant_types + | { + type, + tuple, + frozenset, + slice, + range, + torch.Size, + *np_types, + *ok_mutable_types, + } + ) + + if torch.distributed.is_available(): + from torch.distributed.device_mesh import DeviceMesh + from torch.distributed.tensor.placement_types import ( + Partial, + Replicate, + Shard, + ) + + ok_types = ok_types + ( + Shard, + Replicate, + Partial, + DeviceMesh, + ) + + if istype(val, dict): + assert all( + istype(x, ok_types) for x in itertools.chain(val.keys(), val.values()) + ) + else: + assert istype( + val, + ok_types, + ), f"Unexpected type {type(val)}, not in {ok_types}" + + # Special case for nan because float("nan") == float("nan") evaluates to False + if istype(val, float) and math.isnan(val): + self.TYPE_MATCH(guard) + code = [] + code.append(f"__math_isnan({ref})") + self._set_guard_export_info(guard, code) + + if config.enable_cpp_guard_manager: + self.get_guard_manager(guard).add_lambda_guard( + CLOSURE_VARS["__math_isnan"], get_verbose_code_parts(code, guard) + ) + else: + self._produce_guard_code(guard, code) + return + + # Python math library doesn't support complex nan, so we need to use numpy + if istype(val, complex) and np.isnan(val): + self.TYPE_MATCH(guard) + code = [] + code.append(f"__numpy_isnan({ref})") + self._set_guard_export_info(guard, code) + + if config.enable_cpp_guard_manager: + self.get_guard_manager(guard).add_lambda_guard( + CLOSURE_VARS["__numpy_isnan"], get_verbose_code_parts(code, guard) + ) + else: + self._produce_guard_code(guard, code) + return + + if config.enable_cpp_guard_manager: + # Construct a debug string to put into the c++ equals match guard. + code = [f"{ref} == {val!r}"] + if istype(val, ok_mutable_types): + # C++ guards perform a pointer equality check to speedup guards, but the assumption is that the object + # is mutable. For a few corner cases like sets and lists, we make a deepcopy to purposefully fail the + # pointer equality check. + val = deepcopy(val) + self.get_guard_manager(guard).add_equals_match_guard( + val, get_verbose_code_parts(code, guard) + ) + self._set_guard_export_info(guard, code) + return + + code = [] + + # If matching equality against list/tuple, we must also check that + # the internal types match. (TODO: what about nested lists?) + if istype(val, (list, tuple)): + # NB: SEQUENCE_LENGTH takes care of the outer __check_type_id test + self.SEQUENCE_LENGTH(guard) + + for idx, elem in enumerate(val): + code.append( + f"___check_type_id({ref}[{idx}], {self.id_ref(type(elem))})" + ) + else: + # Add type check to prevent equality check between tensor and non-tensor. + self.TYPE_MATCH(guard) + + if istype(val, torch.Size): + val = tuple(val) + + # Code object can not be compared against their string representation + # I.e `eval(f"{compile('2+2','','exec')!r}")` raises SyntaxError + assert not istype(val, types.CodeType) + + # TODO: It feels like it would be better to just implement our own + # equality test in C that handles all of the necessary type checking + # and NaN tests + code.append(f"{ref} == {val!r}") + self._produce_guard_code(guard, code) + self._set_guard_export_info(guard, code) + + def CONSTANT_MATCH(self, guard: Guard): + val = self.get(guard.name) + if istype(val, (bool, type(None), types.CodeType)): + self.ID_MATCH(guard) + else: + self.EQUALS_MATCH(guard) + + def NN_MODULE(self, guard: Guard): + self.ID_MATCH(guard) + val = self.get(guard.name) + if hasattr(val, "training"): + assert istype(val.training, bool) + self._guard_on_attribute(guard, "training", GuardBuilder.CONSTANT_MATCH) + else: + exc.unimplemented(f"Guard setup for uninitialized class {type(val)}") + + def FUNCTION_MATCH(self, guard: Guard): + """things like torch.add and user defined functions""" + return self.ID_MATCH(guard) + + def CLOSURE_MATCH(self, guard: Guard): + """matches a closure by __code__ id.""" + val = self.get(guard.name) + # Strictly only want user-defined functions + if type(val) == types.FunctionType and hasattr(val, "__code__"): + self._guard_on_attribute(guard, "__code__", GuardBuilder.HASATTR) + self._guard_on_attribute(guard, "__code__", GuardBuilder.FUNCTION_MATCH) + else: + self.FUNCTION_MATCH(guard) + + def BUILTIN_MATCH(self, guard: Guard): + return self.FUNCTION_MATCH(guard) + + def PYMODULE_MATCH(self, guard: Guard): + return self.FUNCTION_MATCH(guard) + + def SEQUENCE_LENGTH(self, guard): + # This guard is used to check lenght of PySequence objects like list, + # tuple, collections.deque etc + ref = self.arg_ref(guard) + value = self.get(guard.name) + t = type(value) + + if not (config.enable_cpp_guard_manager and isinstance(value, dict)): + # C++ DICT_LENGTH checks for type + self.TYPE_MATCH(guard) + + code = [] + if len(value) == 0: + code.append(f"not {ref}") + else: + code.append(f"len({ref}) == {len(value)}") + + self._set_guard_export_info(guard, code) + if config.enable_cpp_guard_manager: + if isinstance(value, dict): + self.get_guard_manager(guard).add_dict_length_check_guard( + len(value), get_verbose_code_parts(code, guard) + ) + else: + self.get_guard_manager(guard).add_length_check_guard( + len(value), get_verbose_code_parts(code, guard) + ) + else: + self._produce_guard_code(guard, code) + + def TUPLE_ITERATOR_LEN(self, guard): + ref = self.arg_ref(guard) + value = self.get(guard.name) + t = type(value) + + if not config.enable_cpp_guard_manager: + # C++ guard already checks the type + self.TYPE_MATCH(guard) + + code = [] + code.append(f"___tuple_iterator_len({ref}) == {tuple_iterator_len(value)}") + self._set_guard_export_info(guard, code) + + if config.enable_cpp_guard_manager: + t = type(value) + obj_id = self.id_ref(t) + + self.get_guard_manager(guard).add_tuple_iterator_length_guard( + tuple_iterator_len(value), obj_id, get_verbose_code_parts(code, guard) + ) + else: + self._produce_guard_code(guard, code) + + # TODO(voz): Deduplicate w/ AOTAutograd dupe input guards + def DUPLICATE_INPUT(self, guard, source_b): + ref_a = self.arg_ref(guard) + ref_b = self.arg_ref(source_b.name()) + + if is_from_optimizer_source( + guard.originating_source + ) or is_from_optimizer_source(source_b): + return + + code = [f"{ref_b} is {ref_a}"] + self._set_guard_export_info(guard, code) + + if config.enable_cpp_guard_manager: + # Check that the guard has not been inserted already + key = (ref_a, ref_b) + if key in self._cached_duplicate_input_guards: + return + self._cached_duplicate_input_guards.add((ref_a, ref_b)) + self._cached_duplicate_input_guards.add((ref_b, ref_a)) + + install_object_aliasing_guard( + self.get_guard_manager(guard), + self.get_guard_manager_from_source(source_b), + get_verbose_code_parts(code, guard), + ) + else: + self._produce_guard_code(guard, code) + + def DICT_KEYS(self, guard): + # Guard on the keys and their order + ref = self.arg_ref(guard) + value = self.get(guard.name) + t = type(value) + + self.TYPE_MATCH(guard) + code = [] + any_key_is_id = any(key_is_id(k) for k in value.keys()) + const_keys_repr = dict_keys_repr( + key_to_id(value), + local=is_from_local_source(guard.originating_source), + ) + if any_key_is_id: + code.append(f"___key_to_id({ref}) == {const_keys_repr}") + else: + code.append(f"list({ref}.keys()) == {const_keys_repr}") + + self._set_guard_export_info(guard, code) + if config.enable_cpp_guard_manager: + if self.requires_key_order_guarding(guard.originating_source): + self.guard_on_dict_keys_and_order(value, guard) + else: + self.guard_on_dict_keys_and_ignore_order(value, guard) + else: + self._produce_guard_code(guard, code) + + def WEAKREF_ALIVE(self, guard): + code = [f"{self.arg_ref(guard)} is not None"] + + self._set_guard_export_info(guard, code) + if config.enable_cpp_guard_manager: + self.get_guard_manager(guard).add_not_none_guard( + get_verbose_code_parts(code, guard) + ) + else: + self._produce_guard_code(guard, code) + + def DICT_CONST_KEYS(self, guard): + """Constant keys match""" + ref = self.arg_ref(guard) + value = self.get(guard.name) + t = type(value) + + if not config.enable_cpp_guard_manager: + # DictGuardManager supports TYPE_MATCH internally + self.TYPE_MATCH(guard) + + code = [] + code.append(f"list({ref}.keys()) == {list(value.keys())!r}") + self._set_guard_export_info(guard, code) + + if config.enable_cpp_guard_manager: + if self.requires_key_order_guarding(guard.originating_source): + self.guard_on_dict_keys_and_order(value, guard) + else: + self.guard_on_dict_keys_and_ignore_order(value, guard) + else: + self._produce_guard_code(guard, code) + + def EMPTY_NN_MODULE_HOOKS_DICT(self, guard): + """Special guard to skip guards on empty hooks. This is controlled by skip_nnmodule_hook_guards""" + if config.skip_nnmodule_hook_guards: + # This is unsafe if you add/remove a hook on nn module variable + return + self.SEQUENCE_LENGTH(guard) + + def OBJECT_MUTATION(self, guard: Guard): + mutation_guard.watch(self.get(guard.name), self.check_fn_manager) + + def GRAD_MODE(self, guard: Guard): + pass # we always guard on this via GlobalStateGuard() + + def DETERMINISTIC_ALGORITHMS(self, guard: Guard): + pass # we always guard on this via GlobalStateGuard() + + def TORCH_FUNCTION_STATE(self, guard: Guard): + pass # we always guard on this via GlobalStateGuard() + + def FSDP_TRAINING_STATE(self, guard: Guard): + pass # we always guard on this via GlobalStateGuard() + + def DEFAULT_DEVICE(self, guard: Guard): + """Guard on CURRENT_DEVICE per torch.utils._device""" + assert guard.source is GuardSource.GLOBAL + import torch.utils._device as m + + code = [f"utils_device.CURRENT_DEVICE == {m.CURRENT_DEVICE!r}"] + self._set_guard_export_info(guard, code) + + if config.enable_cpp_guard_manager: + self.get_guard_manager(guard).add_default_device_guard( + get_verbose_code_parts(code, guard) + ) + else: + self._produce_guard_code(guard, code) + + def SHAPE_ENV(self, guard: Guard): + # Let's handle ShapeEnv guards. To do this, we will resolve + # shape variables to sources from tracked_fakes. This must happen after + # tensor checks. + assert guard.name == "" + output_graph = self.check_fn_manager.output_graph + # NB: self.output_graph can be None in the debug_nops tests + fs = output_graph.tracked_fakes + input_contexts = [a.symbolic_context for a in fs] + + def get_sources(t_id, dim): + # Looks up base sources mapped to a tensor id and uses them to create + # sources for the corresponding tensor dimension. + return [ + TensorPropertySource(source, TensorProperty.SIZE, dim) + for source in output_graph.tracked_fakes_id_to_source[t_id] + ] + + if output_graph.export_constraints: + names: Dict[str, Tuple[int, int]] = {} + source_pairs: List[Tuple[Source, Source]] = [] + derived_equalities: List[ # type: ignore[type-arg] + Tuple[Source, Union[Source, Symbol], Callable] + ] = [] + phantom_symbols: Dict[str, Symbol] = {} + for constraint in output_graph.export_constraints: + if constraint.t_id in output_graph.tracked_fakes_id_to_source: + torch.export.dynamic_shapes._process_equalities( + constraint, + get_sources, + output_graph.shape_env, + names, + source_pairs, + derived_equalities, + phantom_symbols, + ) + else: + log.warning("Untracked tensor used in export constraints") + equalities_inputs = EqualityConstraint( + source_pairs=source_pairs, + derived_equalities=derived_equalities, + phantom_symbols=list(phantom_symbols.values()), + warn_only=False, + ) + else: + equalities_inputs = None + guards = output_graph.shape_env.produce_guards( + [a.fake for a in fs], + [a.source for a in fs], + input_contexts=input_contexts, + equalities_inputs=equalities_inputs, + source_ref=self.source_ref, + # Export keeps static. + ignore_static=(not self.check_fn_manager.output_graph.export), + ) + # When exporting, we may work with the shape constraints some more in + # postprocessing, so don't freeze yet + if not self.check_fn_manager.output_graph.export: + output_graph.shape_env.freeze() + + for shape_guard in guards: + self._set_guard_export_info(guard, [shape_guard]) + + if config.enable_cpp_guard_manager: + # Install all the symbolic guards in one lambda guard. These are run + # at the very end of the RootGuardManager via epilogue guards. + # TODO(anijain2305,williamwen42) - Consider moving this to C++. + code_parts = guards + self.add_python_lambda_leaf_guard_to_root( + code_parts, + get_verbose_code_parts(code_parts, guard), + closure_vars={**SYMPY_INTERP, **CLOSURE_VARS}, + ) + else: + for shape_guard in guards: + self._produce_guard_code(guard, [shape_guard], shape_env=True) + + def TENSOR_MATCH(self, guard: Guard, value=None): + # For FSDP modules, we can skip guards on nn module tensors because FSDP + # eager assumes that the params are unchanged once the model is wrapped. + if guard.is_fsdp_module(): + return + + # For tensors that are part of the Dynamo extracted Fx graph module, an + # ID_MATCH suffices. Once we turn on inline_inbuilt_nn_modules, these + # will be lifted as inputs and have a TENSOR_MATCH guard. + # For numpy tensors, always use TENSOR_MATCH because __from_numpy leads + # to a new tensor everytime and therefore id differs. + if ( + guard.is_specialized_nn_module() + and not isinstance(guard.originating_source, NumpyTensorSource) + ) or match_on_id_for_tensor(guard): + self.ID_MATCH(guard) + else: + if isinstance(value, TensorWeakRef): + value = value() + + value = value if value is not None else self.get(guard.name) + assert isinstance(value, torch.Tensor) + + tensor_name = self.arg_ref(guard) + # [Note - On Export Tensor Guards] + # + # In eager mode, tensor guards are evaluated through C++, in guards.cpp + # see [Note - On Eager Tensor Guards] for more info. + # + # In export mode, we instead maintain parallel logic between C++ and python + # here, with an exception of checking the dispatch key - with the idea that a dispatch key + # is an entirely runtime notion that would make no sense to keep in an exported graph. + # + # Now, this idea is okay, but to paraphrase @ezyang, this mental model is sufficient for now, although + # not entirely true. + # For example, suppose one of the input tensors had the negative dispatch key. + # You should end up with a graph that is specialized for tensors that have a negative dispatch key. + # If you allow a Tensor that does NOT have this bit set, you will accidentally run it "as if" it were negated. + # Now, negative key only shows up for complex numbers, and most likely, the exported to target doesn't + # support this feature at all, but the point stands that :some: tensor state only shows up on dispatch key. + # TODO(voz): Either populate a dispatch_key check into the guards, or error on users passing in an unsupported + # subset of keys during export. + # + # The list of tensor fields and calls we care about can be found in `terms` below. + # TODO(voz): We are missing storage offset in all our tensor guards? + code: List[str] = [] + if self.check_fn_manager.output_graph.export: + self.TYPE_MATCH(guard) + terms = [ + "dtype", + "device", + "requires_grad", + "ndimension()", + ] + + for term in terms: + real_value = self.get(tensor_name + "." + term) + if istype(real_value, (torch.device, torch.dtype)): + # copy pasted from EQUALS_MATCH + code.append(f"str({tensor_name}.{term}) == {str(real_value)!r}") + else: + code.append(f"{tensor_name}.{term} == {real_value}") + else: + self.tensor_check_examples.append(value) + self.tensor_check_names.append(tensor_name) + self.tensor_check_guards.append(guard) + + if config.enable_cpp_guard_manager: + guard_manager = self.get_guard_manager(guard) + # Keep track of all the tensor guard managers to insert + # NoAliasing check at the end. + self.tensor_check_guard_managers.append(guard_manager) + + output_graph = self.check_fn_manager.output_graph + metadata = output_graph.input_source_to_sizes_strides[ + guard.originating_source + ] + size = convert_to_concrete_values(metadata["size"]) + stride = convert_to_concrete_values(metadata["stride"]) + + verbose_code_parts = get_verbose_code_parts( + get_tensor_guard_code_part(value, tensor_name, size, stride), + guard, + ) + guard_manager.add_tensor_match_guard( + value, + size, + stride, + tensor_name, + verbose_code_parts, + ) + + # A frame is valid for reuse with dynamic dimensions if the new + # (user-requested) dynamic dimensions are a subset of the old + # (already compiled) dynamic dimensions. + # + # It's a little non-obvious why you'd want this: in particular, + # if an already compiled frame matches all of the guards, why + # not just use it, why force a recompile? + # + # We force it for two reasons: + # + # - The user *required* us to compile with a new dynamic dimension, + # we should not ignore that and serve up the old, specialized + # frame. Listen to the user! + # + # - In fact, we are obligated to *raise an error* if we fail to + # make the requested dimension dynamic. If we don't + # recompile, we can't tell if that dimension can actually be + # made dynamic. + # + # If the new dynamic dims are a subset of the old, we already know + # we can make them dynamic (since we made them dynamic in old). + # This is slightly unsound, because maybe your input size is + # [s0, s0, s1] and so you can do it dynamic if you say dynamic + # dims {0, 1, 2} but you can't if you only do {0, 2} (because now + # the second s0 is specialized). But we're not entirely sure if + # this is a good idea anyway lol... (if you want to try removing + # this logic, be my guest! -- ezyang 2024) + # + assert guard.source is not None + static, reason = tensor_always_has_static_shape( + value, is_tensor=True, tensor_source=guard.originating_source + ) + + if not static: + if hasattr(value, "_dynamo_dynamic_indices"): + dynamic_indices = value._dynamo_dynamic_indices + code_part = f"(({tensor_name}._dynamo_dynamic_indices.issubset({dynamic_indices})) if hasattr({tensor_name}, '_dynamo_dynamic_indices') else True)" # noqa: B950 + code.append(code_part) + if config.enable_cpp_guard_manager: + self.get_guard_manager(guard).add_dynamic_indices_guard( + dynamic_indices, get_verbose_code_parts(code_part, guard) + ) + # In the case of us not having any dynamic dimension indices, we compiled the frame with no chance of + # raising for this specific tensor - and any inputs with more dynamic user directives specified must be recompiled. + else: + code_part = ( + f"hasattr({tensor_name}, '_dynamo_dynamic_indices') == False" + ) + code.append(code_part) + if config.enable_cpp_guard_manager: + self.get_guard_manager(guard).add_no_hasattr_guard( + "_dynamo_dynamic_indices", + get_verbose_code_parts(code_part, guard), + ) + if len(code) > 0: + self._set_guard_export_info(guard, code) + if not config.enable_cpp_guard_manager: + self._produce_guard_code(guard, code) + + # A util that appends guarded code + def _produce_guard_code(self, guard, code_list, shape_env=False): + assert not config.enable_cpp_guard_manager + if shape_env: + self.shape_env_code.append(GuardCodeList(code_list, guard)) + else: + self.code.append(GuardCodeList(code_list, guard)) + + # A util that in the case of export, adds data onto guards + def _set_guard_export_info(self, guard, code_list, provided_guarded_object=None): + # WARNING: It is important that cur_frame/caller do NOT stay in + # the current frame, because they will keep things live longer + # than they should. See TestMisc.test_release_module_memory + cur_frame = currentframe() + assert cur_frame is not None + caller = cur_frame.f_back + del cur_frame + assert caller is not None + func_name = getframeinfo(caller)[2] + del caller + # We use func_name for export, so might as well get a nice defensive check out of it + assert func_name in dir( + self.__class__ + ), f"_produce_guard_code must be called from inside GuardedCode. Called from {func_name}" + + # Not all guards have names, some can be installed globally (see asserts on HAS_GRAD) + if provided_guarded_object is None: + name_valid = guard.name is not None and guard.name != "" + + guarded_object = self.get(guard.name) if name_valid else None + else: + guarded_object = provided_guarded_object + + guarded_object_type = ( + weakref.ref(type(guarded_object)) if guarded_object is not None else None + ) + obj_ref = None + # Not necessary to have weakref for Enum type, but there is a bug that + # makes hasattr(guarded_object.__class__, "__weakref__") return True. + if hasattr(guarded_object.__class__, "__weakref__") and not isinstance( + guarded_object, enum.Enum + ): + obj_ref = weakref.ref(guarded_object) + + guard.set_export_info( + func_name, + guarded_object_type, + code_list, + obj_ref, + ) + + +# Common Sub-Expression Elimination for Python expressions. +# +# There are 2 steps to this pass: +# 1. Count the frequency of each sub-expression (i.e. inner +# node in the AST tree) +# +# 2. Replace those that occur more than once by a fresh variable 'v'. +# 'v' will be defined in the 'preface' list (output argument to +# 'NodeTransformer') +# +# NB: the use of 'ast.unparse' while visiting the nodes makes this pass +# quadratic on the depth of the tree. +# +# NB: this pass creates a new variable for each AST node that is repeated +# more than 'USE_THRESHOLD'. e.g. if 'a.b.c.d' is used 10 times, 'a.b.c' +# and 'a.b' are also used 10 times. So, there will be a new variable for +# each of them. +class PyExprCSEPass: + # Maximum number of times a given expression can be used without being + # replaced by a fresh variable. + USE_THRESHOLD = 1 + + # Ad-Hoc: AST nodes this pass focuses on. + ALLOWED_NODE_TYPES = (ast.Attribute, ast.Call, ast.Subscript) + + @dataclasses.dataclass + class Config: + expr_count: Dict[str, int] + expr_to_name: Dict[str, str] + + class ExprCounter(ast.NodeVisitor): + def __init__(self, config: PyExprCSEPass.Config) -> None: + self._config = config + + def visit(self, node: ast.AST) -> Any: + if isinstance(node, PyExprCSEPass.ALLOWED_NODE_TYPES): + self._config.expr_count[_ast_unparse(node)] += 1 + super().visit(node) + + class Replacer(ast.NodeTransformer): + def __init__( + self, + config: PyExprCSEPass.Config, + gen_name: Callable[[], str], + ) -> None: + super().__init__() + self._config = config + self._gen_name = gen_name + self.preface: List[str] = [] + + def visit(self, node: ast.AST) -> Any: + if isinstance(node, PyExprCSEPass.ALLOWED_NODE_TYPES): + expr = _ast_unparse(node) + + # Replacement only occurs if a given expression is used more + # than once. + if self._config.expr_count[expr] > PyExprCSEPass.USE_THRESHOLD: + if expr not in self._config.expr_to_name: + # Parent 'visit' is called so that we CSE the inner expressions first. + # + # The resulting expression is used as right-hand-side of the variable + # assignment. i.e. we are CSE-ing the children before the parents. + # + # Indexing still uses the old 'node', since that's what was counted + # by the 'NodeVisitor'. + node_ = super().visit(node) + expr_ = _ast_unparse(node_) + var_name = self._gen_name() + self.preface.append(f"{var_name} = {expr_}") + self._config.expr_to_name[expr] = var_name + else: + var_name = self._config.expr_to_name[expr] + return ast.Name(var_name, ast.Load()) + + return super().visit(node) + + def __init__(self) -> None: + self._counter = 0 + self._config = self.Config( + expr_count=collections.defaultdict(lambda: 0), expr_to_name={} + ) + + def _new_var(self, prefix: str = "_var") -> str: + name = f"{prefix}{self._counter}" + self._counter += 1 + return name + + def count(self, exprs: List[str]) -> None: + counter = self.ExprCounter(self._config) + for e in exprs: + try: + counter.visit(ast.parse(e)) + except SyntaxError as ex: + log.exception("Failed to visit expr at line %s.\n%s", ex.lineno, e) + raise + + def replace(self, expr: str) -> Tuple[List[str], str]: + replacer = self.Replacer(self._config, self._new_var) + new_node = replacer.visit(ast.parse(expr)) + return replacer.preface, _ast_unparse(new_node) + + +def must_add_nn_module_guards(guard): + # For config.guard_nn_modules=False, we can skip all the guards that + # originate from inside of nn module except for a few categories. + return ( + # Guard for defaults + isinstance(guard.originating_source, DefaultsSource) + # Guard using dict tags if the config flag is set + or ( + config.guard_nn_modules_using_dict_tags + and guard.create_fn is GuardBuilder.NN_MODULE + ) + ) + + +class DeletedGuardFn: + pass + + +# NB: Naively, you'd expect this to only be a function that produces +# the callable that constitutes the guard. However, there is some +# delicate handling for invalidating this check function when the +# locals/globals get invalidated, so there's some extra state +# we have to hold in this manager class. +class CheckFunctionManager: + def __init__( + self, + output_graph=None, + guard_fail_fn: Optional[Callable[[GuardFail], None]] = None, + ): + guards = output_graph.guards if output_graph else None + self._weakrefs: Dict[int, ReferenceType[object]] = {} + self.guard_manager = None + if config.enable_cpp_guard_manager: + self.guard_manager = GuardManager() + self.output_graph = output_graph + w_builder = None + + self.torch_function_mode_stack = ( + output_graph.torch_function_mode_stack if output_graph else None + ) + + def source_ref(source): + guard_source = source.guard_source() + if guard_source is GuardSource.CONSTANT: + # No need to track constants + return source.name() + assert w_builder + r_builder = w_builder() + assert r_builder is not None + return r_builder.arg_ref(source.name()) + + builder = GuardBuilder( + self.id_ref, + source_ref, + self.lookup_weakrefs, + output_graph.local_scope, + output_graph.global_scope, + self.guard_manager, + self, + ) + + # Break retain cycle. See test_release_scope_memory + def cleanup_builder(weak_b): + b = weak_b() + if b: + b.scope = None + + # Break retain cycle. See test_release_input_memory + w_builder = weakref.ref(builder, cleanup_builder) + + guard_on_nn_modules = config.guard_nn_modules and justknobs_check( + "pytorch/compiler:guard_nn_modules" + ) + + if not justknobs_check("pytorch/compiler:guard_nn_modules"): + log.warning("guard_nn_modules is turned off using justknobs killswitch") + + for guard in sorted(guards or [], key=Guard.sort_key): + if ( + not guard_on_nn_modules + and guard.is_specialized_nn_module() + # Default func args must be guarded on. + # TODO: we could make use of 'DefaultsSource' and offer a .guard.is_defaults() API + and "__defaults__" not in guard.name + and "__kwdefaults__" not in guard.name + and (config.skip_nnmodule_hook_guards or "hooks" not in guard.name) + ): + continue + + guard.create(builder) + + self.check_fn = self.compile_check_fn(builder, guards, guard_fail_fn) + + # Keep track of weak references of objects with ID_MATCH guard. This + # info is stored alongside optimized_code and check_fn and is used to + # limit the number of cache entries with same ID_MATCH'd object. + # TODO(anijain2305) - Currently this information is stored as an attr on + # the check_fn itself to avoid changing CacehEntry datastructure in + # eval_frame.c. In future, we should probably replace check_fn with a + # queryable data structure such that this information is already present + # in some form. + self.check_fn.id_matched_objs = builder.id_matched_objs + + if config.enable_cpp_guard_manager: + # TODO: don't do the string rep, do something more structured here + torch._logging.trace_structured( + "dynamo_cpp_guards_str", payload_fn=lambda: str(self.guard_manager) + ) + guards_log.debug("%s", self.guard_manager) + assert self.guard_manager # to make mypy happy + self.guard_manager.id_matched_objs = builder.id_matched_objs + self.check_fn = self.guard_manager + + # Check that the guard returns True. False means that we will always + # recompile. + # TODO(anijain2305, ydwu4) - Skipping export because of following test + # python -s test/dynamo/test_export.py -k test_export_with_symbool_inputs + if not output_graph.export: + if not self.guard_manager.check(output_graph.local_scope): + reasons = get_guard_fail_reason_helper( + self.guard_manager, # type: ignore[arg-type] + output_graph.local_scope, + CompileContext.current_compile_id(), + ) + raise AssertionError(f"Guard check failed: {reasons}") + + # NB - We have to very careful of cleaning up here. Because of the + # invalidate function, we can create a weakref finalizer that keeps + # `self` alive for very long. Sometimes by mistake, we can run + # invalidate for a type/object (check id_ref method) that Python can + # leak by design, preventing us from calling the finalizer. In that + # case, the `self` will be alive even though the cache entry will be + # deleted (check invalidate method), which can cause a memory leak, + # e.g., not setting output_graph = None can keep hold of nn_modules. + self._weakrefs.clear() + self.output_graph = None + + def compile_check_fn(self, builder, guards_out, guard_fail_fn): + # see parallel handling of ".0" / "___implicit0" in _eval_frame.c + largs = builder.argnames + largs += ["**___kwargs_ignored"] + + guards_log.debug("GUARDS:") + + code_parts = [] + verbose_code_parts = [] + structured_guard_fns: list[Callable[[], dict[str, Any]]] = [] + + torch_function_mode_stack_check_fn = make_torch_function_mode_stack_guard( + self.torch_function_mode_stack + ) + + if config.enable_cpp_guard_manager: + from .variables.torch_function import IGNORED_MODES + + # Insert the global_state guard + assert self.guard_manager # to make mypy happy + self.guard_manager.root.add_global_state_guard(["___check_global_state()"]) + + self.guard_manager.root.add_torch_function_mode_stack_guard( + self.torch_function_mode_stack, + list(IGNORED_MODES), + ["___check_torch_function_mode_stack()"], + ) + # Clear references to torch_function modes held in the list + self.torch_function_mode_stack = None + else: + # Don't report this guard, it's always the same, useless! + global_guard = "___check_global_state()" + code_parts.append(global_guard) + verbose_code_parts.append(global_guard) + + tf_mode_stack_guard = "___check_torch_function_mode_stack()" + code_parts.append(tf_mode_stack_guard) + verbose_code_parts.append(tf_mode_stack_guard) + + def add_code_part(code_part, guard, log_only=False): + verbose_code_part = get_verbose_code_part(code_part, guard) + guards_log.debug("%s", verbose_code_part) + + structured_guard_fns.append( + lambda: { + "code": code_part, + "stack": structured.from_traceback(guard.stack.summary()) + if guard.stack + else None, + "user_stack": structured.from_traceback(guard.user_stack) + if guard.user_stack + else None, + } + ) + + if verbose_guards_log.isEnabledFor(logging.DEBUG): + maybe_stack = "" + maybe_user_stack = "" + if guard is not None: + if guard.stack: + maybe_stack = f"\nStack:\n{''.join(guard.stack.format())}" + if guard.user_stack: + maybe_user_stack = ( + f"\nUser stack:\n{''.join(guard.user_stack.format())}" + ) + verbose_guards_log.debug( + "Guard: %s%s%s", + code_part, + maybe_stack, + maybe_user_stack, + ) + + if not log_only: + code_parts.append(code_part) + verbose_code_parts.append(verbose_code_part) + + seen = set() + for gcl in builder.code: + for code in gcl.code_list: + if code not in seen: + # If Cpp guard manager is enabled, we don't need to add to + # code_parts. + add_code_part(code, gcl.guard, config.enable_cpp_guard_manager) + seen.add(code) + + tensor_check_names = builder.tensor_check_names + check_tensors_fn = None + check_tensors_verbose_fn = None + if tensor_check_names and not config.enable_cpp_guard_manager: + tensor_check_guards = builder.tensor_check_guards + assert ( + not self.output_graph.export + ), "Illegal to set tensor_check_names in export." + tensor_check_examples = builder.tensor_check_examples + + dynamic_dims_sizes = [] + dynamic_dims_strides = [] + for t, g in zip(tensor_check_examples, tensor_check_guards): + metadata = self.output_graph.input_source_to_sizes_strides[ + g.originating_source + ] + dynamic_dims_sizes.append(convert_to_concrete_values(metadata["size"])) + dynamic_dims_strides.append( + convert_to_concrete_values(metadata["stride"]) + ) + + tensor_guards = TensorGuards( + *tensor_check_examples, + dynamic_dims_sizes=dynamic_dims_sizes, + dynamic_dims_strides=dynamic_dims_strides, + ) + check_tensors_fn = tensor_guards.check + check_tensors_verbose_fn = tensor_guards.check_verbose + tensor_check_args = ", ".join( + tensor_check_names + ["tensor_check_names=tensor_check_names"] + ) + # Do this manually, to un-stagger the guards in log message + code_parts.append(f"___check_tensors({tensor_check_args})") + verbose_code_parts.append(f"___check_tensors({tensor_check_args})") + + for i, name in enumerate(tensor_check_names): + # This is a copy of what guards.cpp checks against + # Keep this in sync with TensorCheck constructor + t = tensor_check_examples[i] + sizes = dynamic_dims_sizes[i] + strides = dynamic_dims_strides[i] + code_part = get_tensor_guard_code_part(t, name, sizes, strides) + add_code_part(code_part, tensor_check_guards[i], log_only=True) + + if len(tensor_check_names) > 1 and config.enable_cpp_guard_manager: + # Install tensor aliasing guard. TENSOR_MATCH guards are already + # installed for cpp guard manager. + install_no_tensor_aliasing_guard( + builder.tensor_check_guard_managers, + tensor_check_names, + ["check_no_aliasing(" + ", ".join(tensor_check_names) + ")"], + ) + + aotautograd_guards: List[GuardEnvExpr] = ( + self.output_graph.tracing_context.guards_context.aotautograd_guards + if self.output_graph + else [] + ) + + # TODO(anijain2305) - There is a duplicate logic in Dynamo to find + # aliased input tensors. So most probably we don't need this here. + # Revisit. + for guard in aotautograd_guards: + if isinstance(guard, DuplicateInputs): + source_a = guard.input_source_a + source_b = guard.input_source_b + code_part = f"{source_a.name()} is {source_b.name()}" + if config.enable_cpp_guard_manager: + install_object_aliasing_guard( + builder.get_guard_manager_from_source(source_a), + builder.get_guard_manager_from_source(source_b), + [code_part], + ) + add_code_part(code_part, None, config.enable_cpp_guard_manager) + else: + raise RuntimeError(f"Unknown GuardEnvExpr: {guard}") + + # TODO: the "guard" here is actually just the top level SHAPE_ENV + # which is useless. Get ShapeEnv to pass in more provenance. + for gcl in builder.shape_env_code: + for code in gcl.code_list: + # Shape env guards are already added for CPP guard manager in + # SHAPE_ENV implementation. + add_code_part(code, gcl.guard, config.enable_cpp_guard_manager) + + # OK, all done generating guards + if structured_guard_fns: + torch._logging.trace_structured( + "dynamo_guards", payload_fn=lambda: [f() for f in structured_guard_fns] + ) + + global_state = convert_frame.initial_global_state + if global_state is None: + # we should only hit this case in NopTests() + global_state = convert_frame.GlobalStateGuard() + closure_vars = { + "___check_tensors": check_tensors_fn, + "___check_tensors_verbose": check_tensors_verbose_fn, + "___check_global_state": global_state.check, + "___check_torch_function_mode_stack": torch_function_mode_stack_check_fn, + "tensor_check_names": tensor_check_names, + **SYMPY_INTERP, + **CLOSURE_VARS, + } + + globals_for_guard_fn = {"G": builder.scope["G"]} + if config.enable_cpp_guard_manager: + # Guard manager construction is complete + assert self.guard_manager # to make mypy happy + # TODO (anijain2305) - When enable_cpp_guard_manager is ON by + # default, change the guard_fn name to be guard_manager everywhere + # to avoid confusion. + guard_fn = self.guard_manager + # Ensure we did not miss to insert a guard in cpp guard manager. + assert len(code_parts) == 0 + else: + unique_code_parts = list(unique(code_parts)) + make_guard_fn_args = ", ".join(closure_vars.keys()) + guard_body, pycode = build_guard_function( + unique_code_parts, make_guard_fn_args + ) + + if os.environ.get("TORCHDYNAMO_PRINT_GUARDS", None) == "1": + print("GUARDS\n", guard_body) + + out: Dict[str, Any] = {} + + # We don't put builder.scope as the globals in exec call because + # guard_fn.__globals__ becomes equal to builder.scope. This causes + # guard_fn to hold a referece to f_locals sitting in builder.scope["L"] + try: + exec(pycode, globals_for_guard_fn, out) + except SyntaxError as ex: + log.exception("Failed to exec guard at line %s.\n%s", ex.lineno, pycode) + raise + guard_fn = out["___make_guard_fn"](*closure_vars.values()) + + guard_fn.closure_vars = closure_vars + # TODO(whc) maybe '.code_parts' was only kept around for the guard callback? so we don't need both + guard_fn.args = largs + if config.enable_cpp_guard_manager: + guard_fn.populate_code_parts_for_debugging() + else: + guard_fn.code_parts = code_parts + guard_fn.verbose_code_parts = verbose_code_parts + # Grab only G, but preserve "G" because guards access it as "G" + guard_fn.global_scope = globals_for_guard_fn + guard_fn.guard_fail_fn = guard_fail_fn + # will be populated by a non-owning reference to CacheEntry/ExtraState + # when the CacheEntry is constructed + guard_fn.cache_entry = None + guard_fn.extra_state = None + guard_fn.no_tensor_aliasing_sources = tensor_check_names + return guard_fn + + def invalidate(self): + # Some tests reveal that CheckFunctionManager has no attribute + # check_fn, but this case should not be of any concern. + # This case doesn't seem easy to repro. + if ( + hasattr(self, "check_fn") + and self.check_fn is not DeletedGuardFn + and (cache_entry := self.check_fn.cache_entry) is not None + and (extra_state := self.check_fn.extra_state) is not None + ): + assert isinstance(cache_entry, CacheEntry) + assert isinstance(extra_state, ExtraState) + extra_state.invalidate(cache_entry) + self.check_fn.cache_entry = None + self.check_fn.extra_state = None + self.check_fn = DeletedGuardFn + + def id_ref(self, obj): + """add a weakref, return the id""" + try: + if id(obj) not in self._weakrefs: + # We will clear the _weakrefs dict at the end of __init__ + # function, which will delete the callbacks as well. Therefore, + # we are using a finalizer which is kept alive. + self._weakrefs[id(obj)] = weakref.ref(obj) + weakref.finalize(obj, self.invalidate) + except TypeError: + pass # cannot weakref bool object + return id(obj) + + def lookup_weakrefs(self, obj): + """Lookup the _weakrefs created in id_ref function for ID_MATCH'd objects""" + if id(obj) in self._weakrefs: + return self._weakrefs[id(obj)] + return None + + +def build_guard_function(code_parts, closure_args) -> Tuple[str, str]: + from torch._inductor.utils import IndentedBuffer + + if HAS_UNPARSE_FUNCTIONS: + csepass = PyExprCSEPass() + csepass.count(code_parts) + + def replace(expr: str) -> Tuple[List[str], str]: + return csepass.replace(expr) + + else: + + def replace(expr: str) -> Tuple[List[str], str]: + return [], expr + + # Generate the inner body of the guard function. + # i.e. if-chain of the guard expressions. + guard_body = IndentedBuffer() + for expr in code_parts: + preface, expr = replace(expr) + guard_body.writelines(preface) + guard_body.writeline(f"if not ({expr}):") + with guard_body.indent(): + guard_body.writeline("return False") + + # Wrap the inner body into the actual guard function. + guard = IndentedBuffer() + guard.writeline("def guard(L):") + with guard.indent(): + guard.splice(guard_body) + guard.writeline("return True") + + # Wrap the whole guard function into another function + # with the closure variables. + make_guard_fn = IndentedBuffer() + make_guard_fn.writeline(f"def ___make_guard_fn({closure_args}):") + with make_guard_fn.indent(): + make_guard_fn.splice(guard) + make_guard_fn.writeline("return guard") + + return guard_body.getvalue(), make_guard_fn.getvalue() + + +def is_recompiles_enabled(): + return torch._logging._internal.log_state.is_artifact_enabled("recompiles") + + +def is_recompiles_verbose_enabled(): + return torch._logging._internal.log_state.is_artifact_enabled("recompiles_verbose") + + +# this will only be used if cpp guards are disabled +def make_torch_function_mode_stack_guard(intial_stack): + types = [type(x) for x in intial_stack] + from .variables.torch_function import IGNORED_MODES + + def check_torch_function_mode_stack(): + cur_stack = get_torch_function_mode_stack() + if len(cur_stack) != len(types): + return False + + for ty, mode in zip(types, cur_stack): + if ty in IGNORED_MODES: + continue + if ty != type(mode): + return False + + return True + + return check_torch_function_mode_stack + + +def recompilation_reason_for_no_tensor_aliasing_guard(guard_manager, scope): + duplicate_tensors = [] + global_scope = dict(guard_manager.global_scope) + ids_to_source = collections.defaultdict(list) + for tensor_source in guard_manager.no_tensor_aliasing_sources: # type: ignore[attr-defined] + global_scope["__compile_source__"] = tensor_source + tensor_id = id(eval(tensor_source, global_scope, scope)) + ids_to_source[tensor_id].append(tensor_source) + + for key in ids_to_source: + if len(ids_to_source[key]) > 1: + duplicate_tensors.append(f"{ids_to_source[key]}") + + reason = ", ".join(duplicate_tensors) + return [f"Duplicate tensors found: {reason}"] + + +def get_guard_fail_reason_helper( + guard_fn: GuardFn, + f_locals: Dict[str, object], + compile_id: CompileId, +) -> str: + """ + Return the reason why `guard_fn` failed. + Updates `guard_failures` with the generated reason. + Only the first failed check of guard_fn is reported. + """ + scope = {"L": f_locals, "G": guard_fn.global_scope["G"]} + scope.update(guard_fn.closure_vars) + reasons: List[str] = [] + + no_tensor_aliasing_check_failed = False + + verbose_code_parts: List[str] = [] + if config.enable_cpp_guard_manager: + guard_manager = guard_fn + guard_debug_info = guard_manager.check_verbose(f_locals) # type: ignore[attr-defined] + # For test_export_with_map_cond, the check_verbose fail even without the + # C++ guard manager. We need to fix the issue to remove the comment. + # assert not guard_debug_info.result + if not guard_debug_info.result: + verbose_code_parts = guard_debug_info.verbose_code_parts + # verbose_code_parts is either the actual reason (e.g. in case of + # TENSOR_MATCH) or it could be a list of verbose_code_part that we + # passed to the leaf guard at construction time. If its a list, we + # walk through this list and find the guard that failed. This is + # very important for symbolic shape guards which are currently + # installed as a lambda guard and can encompass a long list of code_parts. + + if len(verbose_code_parts) == 1: + if "Duplicate tensor found" in verbose_code_parts[0]: + no_tensor_aliasing_check_failed = True + else: + reasons = verbose_code_parts + verbose_code_parts = [] + else: + verbose_code_parts = guard_fn.verbose_code_parts + # This is not needed for CPP guard because the verbose check is already + # run in C++. + scope["___check_tensors"] = scope["___check_tensors_verbose"] + + if no_tensor_aliasing_check_failed: + reasons = recompilation_reason_for_no_tensor_aliasing_guard(guard_fn, scope) + else: + for part in verbose_code_parts: + global_scope = dict(guard_fn.global_scope) + global_scope["__compile_source__"] = part + with report_compile_source_on_error(): + try: + fail_reason = eval(part, global_scope, scope) + except Exception as e: + if is_recompiles_verbose_enabled(): + continue + else: + raise + # Only ___check_tensors knows how to return a fancy fail reason; + # for everything else we just report the code that failed + + if isinstance(fail_reason, bool) and not fail_reason: + fail_reason = part + if isinstance(fail_reason, str): + reasons.append(fail_reason) + if not is_recompiles_verbose_enabled(): + break + + reason_str = f"{compile_id}: " + "; ".join(reasons) + return reason_str + + +def get_guard_fail_reason( + guard_fn: GuardFn, + code: types.CodeType, + f_locals: Dict[str, object], + compile_id: CompileId, +) -> str: + reason_str = get_guard_fail_reason_helper(guard_fn, f_locals, compile_id) + guard_failures[orig_code_map[code]].append(reason_str) + + try: + if guard_fn.guard_fail_fn is not None: + guard_fn.guard_fail_fn( + GuardFail(reason_str or "unknown reason", orig_code_map[code]) + ) + except Exception as e: + log.exception( + "Failure in guard_fail_fn callback - raising here will cause a NULL Error on guard eval", + ) + + return reason_str + + +def get_and_maybe_log_recompilation_reason( + cache_entry, frame: types.FrameType +) -> List[str]: + """ + Return the list of guard failure reasons using cache_entry. + Logs the recompilation reason if `recompiles` logging is enabled. + Raises a RecompileError if `config.error_on_recompile` is enabled. + """ + reasons = [] + while cache_entry is not None: + reason = get_guard_fail_reason( + cache_entry.check_fn, + cache_entry.code, + frame.f_locals, + cache_entry.compile_id, + ) + if reason: + reasons.append(reason) + cache_entry = cache_entry.next + + code = frame.f_code + + # at least one of "recompiles" or "recompiles_verbose" is enabled + do_recompiles_log = is_recompiles_enabled() or is_recompiles_verbose_enabled() + + if do_recompiles_log or config.error_on_recompile: + if is_recompiles_verbose_enabled(): + failures = "\n\n".join( + f"guard {i} failures:\n" + textwrap.indent(reason, "- ") + for i, reason in enumerate(reasons) + ) + else: + failures = textwrap.indent("\n".join(reasons), "- ") + guard_failure_details = ( + f"triggered by the following guard failure(s):\n{failures}" + ) + message = ( + f"Recompiling function {code.co_name} in {code.co_filename}:{code.co_firstlineno}\n" + f"{textwrap.indent(guard_failure_details, ' ')}" + ) + if do_recompiles_log: + if is_recompiles_verbose_enabled(): + recompiles_verbose_log.debug(message) + else: + recompiles_log.debug(message) + if config.error_on_recompile: + raise exc.RecompileError(message) + + torch._logging.trace_structured( + "artifact", + metadata_fn=lambda: { + "name": "recompile_reasons", + "encoding": "json", + }, + payload_fn=lambda: reasons, + ) + + return reasons + + +def guard_error_hook( + guard_fn: GuardFn, + code: types.CodeType, + f_locals: Dict[str, object], + index: int, + last: bool, +): + print( + f"ERROR RUNNING GUARDS {code.co_name} {code.co_filename}:{code.co_firstlineno}" + ) + print("lambda " + ", ".join(guard_fn.args) + ":") + print(" ", " and\n ".join(guard_fn.code_parts)) + + if config.enable_cpp_guard_manager: + print(guard_fn) + + local_scope = {"L": f_locals, **guard_fn.closure_vars} + for guard in guard_fn.code_parts: + try: + eval(guard, guard_fn.global_scope, local_scope) + except: # noqa: B001,E722 + print(f"Malformed guard:\n{guard}") + + +set_guard_error_hook(guard_error_hook) + + +def unique(seq): + seen = set() + for x in seq: + if x not in seen: + yield x + seen.add(x) + + +def make_dupe_guard(obj_source, dupe_source): + # Note - we may end up in a situation where we invoke something like + # def fn(x, y) + # with fn(x, x) + # Prior to the addition of tracking to all relevant objects, we would handle this just fine by + # eagerly re-entering VB and rewrapping inputs, correctly creating graphargs and placeholders. However, + # with tracking on inputs, duplicate inputs or aliased relationships may end up getting erased here - + # In the fn(x, x) example call above look like a graph with a single input. + # In order to ensure that we do not reuse fn(x, x) for fn(x, y), we create a duplicate input guard. + + # Note - we may not have a source, that is fine, it just means we had an object that is safe to have + # leave unsourced - like a local list created and discharged entirely within a local scope. + if dupe_source and dupe_source != obj_source: + ser_source_is_local = is_from_local_source(dupe_source) + source_is_local = is_from_local_source(obj_source) + if is_from_flatten_script_object_source( + dupe_source + ) or is_from_flatten_script_object_source(obj_source): + raise exc.UnsafeScriptObjectError( + f"{obj_source.name()} is alising {dupe_source.name()}. This is not supported." + f" Please do a clone for corresponding input." + ) + + # Note - both must be local, or global, or we will run afoul of a lack of merging in how we currently + # reconcile guards builder scopes in compile_check_fn. This technically means we miss a guard here, + # so maybe we should do this refactor before we land this... + # TODO(voz): Combine local and global guard builders. + if ser_source_is_local == source_is_local: + # Note - this is a little aggressive - these being duplicate input does not always matter. + # However, this should always be a sound guard to add here. + return functools.partial(GuardBuilder.DUPLICATE_INPUT, source_b=dupe_source) + return None + + +def install_guard(*guards, skip=0): + """ + Add dynamo guards to the current tracing context. + + Args: + guards: guard(s) to add + skip: number of stack frames to ignore for debug stack trace + """ + from torch._guards import TracingContext + + collect_debug_stack = guards_log.isEnabledFor( + logging.DEBUG + ) or verbose_guards_log.isEnabledFor(logging.DEBUG) + add = TracingContext.get().guards_context.dynamo_guards.add + for guard in guards: + assert isinstance(guard, Guard) + add(guard, collect_debug_stack=collect_debug_stack, skip=skip + 1) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/hooks.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/hooks.py new file mode 100644 index 0000000000000000000000000000000000000000..9371dee9d8184c85eb6378a23a8d7a6ae1b47604 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/hooks.py @@ -0,0 +1,12 @@ +import dataclasses +from typing import Callable, Optional + +from torch._guards import GuardsSet + +from .types import GuardFail + + +@dataclasses.dataclass +class Hooks: + guard_export_fn: Optional[Callable[[GuardsSet], None]] = None + guard_fail_fn: Optional[Callable[[GuardFail], None]] = None diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/logging.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/logging.py new file mode 100644 index 0000000000000000000000000000000000000000..55bf1b1d199a518a4d225d65f889bd655caf69c9 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/logging.py @@ -0,0 +1,59 @@ +# mypy: allow-untyped-defs +import itertools +import logging + +from torch.hub import _Faketqdm, tqdm + + +# Disable progress bar by default, not in dynamo config because otherwise get a circular import +disable_progress = True + + +# Return all loggers that torchdynamo/torchinductor is responsible for +def get_loggers(): + return [ + logging.getLogger("torch.fx.experimental.symbolic_shapes"), + logging.getLogger("torch._dynamo"), + logging.getLogger("torch._inductor"), + ] + + +# Creates a logging function that logs a message with a step # prepended. +# get_step_logger should be lazily called (i.e. at runtime, not at module-load time) +# so that step numbers are initialized properly. e.g.: + +# @functools.lru_cache(None) +# def _step_logger(): +# return get_step_logger(logging.getLogger(...)) + +# def fn(): +# _step_logger()(logging.INFO, "msg") + +_step_counter = itertools.count(1) + +# Update num_steps if more phases are added: Dynamo, AOT, Backend +# This is very inductor centric +# _inductor.utils.has_triton() gives a circular import error here + +if not disable_progress: + try: + import triton # noqa: F401 + + num_steps = 3 + except ImportError: + num_steps = 2 + pbar = tqdm(total=num_steps, desc="torch.compile()", delay=0) + + +def get_step_logger(logger): + if not disable_progress: + pbar.update(1) + if not isinstance(pbar, _Faketqdm): + pbar.set_postfix_str(f"{logger.name}") + + step = next(_step_counter) + + def log(level, msg, **kwargs): + logger.log(level, "Step %s: %s", step, msg, **kwargs) + + return log diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/mutation_guard.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/mutation_guard.py new file mode 100644 index 0000000000000000000000000000000000000000..d0e21a1ebd295447c39e4e0d00bad3cad66ff160 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/mutation_guard.py @@ -0,0 +1,150 @@ +# mypy: allow-untyped-defs +# mypy: disable-error-code="method-assign" + +import functools +import weakref + +import torch.nn +from torch.nn import Module + +from . import config +from .utils import ExactWeakKeyDictionary, is_lazy_module, nn_module_has_global_hooks + + +unpatched_nn_module_init = torch.nn.Module.__init__ + + +class MutationTracker: + db = ExactWeakKeyDictionary() + + def __init__(self): + self.mutation_count = 0 + self.watchers = [] + + def on_mutation(self, name): + self.mutation_count += 1 + tmp = self.watchers + self.watchers = [] + for ref in tmp: + guarded = ref() + if guarded is not None: + guarded.invalidate(ref) + + def track(self, guarded_code): + self.watchers.append(weakref.ref(guarded_code)) + + +def watch(obj, guarded_code): + """invalidate guarded_code when obj is mutated""" + ensure_patched(type(obj)) + + if obj not in MutationTracker.db: + MutationTracker.db[obj] = MutationTracker() + tracker = MutationTracker.db[obj] + tracker.track(guarded_code) + + +def ensure_patched(cls): + if getattr(cls, "___needs_mutation_patch", True): + cls.___needs_mutation_patch = False + original_setattr = cls.__setattr__ + + @functools.wraps(original_setattr) + def custom_setattr(self, key, value): + try: + MutationTracker.db[self].on_mutation(key) + except KeyError: + pass + return original_setattr(self, key, value) + + cls.__setattr__ = custom_setattr + + +class GenerationTracker: + generation = 0 + dynamic_classes = ExactWeakKeyDictionary() + generation_values = ExactWeakKeyDictionary() + + @classmethod + def tag(cls, obj): + cls.generation_values[obj] = cls.generation + + @staticmethod + def mark_class_dynamic(cls): + assert issubclass(cls, torch.nn.Module) + GenerationTracker.dynamic_classes[cls] = True + + @classmethod + def get_generation_value(cls, obj): + if obj not in cls.generation_values: + return -1 + return cls.generation_values[obj] + + @classmethod + def check(cls, obj): + return ( + obj in cls.generation_values + and cls.generation_values[obj] == cls.generation + ) + + @classmethod + def clear(cls): + cls.generation = 0 + cls.dynamic_classes = ExactWeakKeyDictionary() + cls.generation_values = ExactWeakKeyDictionary() + + +def is_dynamic_nn_module(obj, is_export): + """Check for nn.Modules() created dynamically or mutated""" + if isinstance(obj, torch.nn.Module) and "forward" in obj.__dict__: + # A monkey patched `.forward` indicates something wacky is going on + return True + if hasattr(obj, "torchdynamo_force_dynamic"): + return obj.torchdynamo_force_dynamic + if is_lazy_module(obj): + return False + # For export, we will have to fix + # 1) Input signature problem because params are lifted as inputs + # 2) nn module stack info changes + # 3) adjust failing tests + if ( + isinstance(obj, torch.nn.Module) + and config.inline_inbuilt_nn_modules + and not is_export + ): + return True + + if isinstance(obj, torch.nn.Module) and nn_module_has_global_hooks(): + return True + dyn = GenerationTracker.dynamic_classes.get(type(obj)) or GenerationTracker.check( + obj + ) + return dyn + + +def install_generation_tagging_init(): + """ + Monkey patch torch.nn.Module.__init__ and torch.nn.Module.__setstate__ + so we can detect nn.Module instances created dynamically inside forward methods. + """ + + if getattr(Module, "___needs_generation_tag_patch", True): + init = Module.__init__ + + def patched_init(self, *args, **kwargs): + init(self, *args, **kwargs) + GenerationTracker.tag(self) + + Module.__init__ = patched_init + + setstate = Module.__setstate__ + + def patched_setstate(self, state): + setstate(self, state) + GenerationTracker.tag(self) + + Module.__setstate__ = patched_setstate + + Module.___needs_generation_tag_patch = False # type: ignore[attr-defined] + + GenerationTracker.generation += 1 diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/output_graph.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/output_graph.py new file mode 100644 index 0000000000000000000000000000000000000000..5684de69601565f783c82f3be4905711a4882ae0 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/output_graph.py @@ -0,0 +1,2190 @@ +# mypy: allow-untyped-defs +import collections +import contextlib +import copy +import dataclasses +import functools +import itertools +import json +import logging +import operator +import re +import sys +import traceback +import weakref +from dataclasses import dataclass +from typing import Any, Callable, Dict, List, Optional, Set, Tuple, TYPE_CHECKING, Union + +import sympy + +import torch._guards +import torch._logging +import torch.distributed as dist +import torch.nn +import torch.utils._pytree as pytree +from torch import fx +from torch._guards import GlobalContextCheckpointState, Source, TracingContext +from torch._utils_internal import signpost_event +from torch.fx._lazy_graph_module import _make_graph_module # type: ignore[attr-defined] +from torch.fx.experimental._backward_state import BackwardState +from torch.fx.experimental.symbolic_shapes import free_symbols, is_symbolic, ShapeEnv +from torch.fx.passes.runtime_assert import insert_deferred_runtime_asserts +from torch.utils._python_dispatch import is_traceable_wrapper_subclass + +from . import config, exc, logging as torchdynamo_logging, variables +from .backends.registry import CompiledFn, CompilerFn +from .bytecode_transformation import ( + create_call_function, + create_instruction, + Instruction, + unique_id, +) +from .code_context import code_context +from .codegen import PyCodegen +from .current_scope_id import enter_new_scope +from .exc import ( + BackendCompilerFailed, + exceptions_allowed_to_be_fallback, + SkipFrame, + unimplemented, + unimplemented_with_warning, +) +from .guards import GuardBuilder, install_guard +from .mutation_guard import is_dynamic_nn_module +from .side_effects import AttributeMutationExisting, SideEffects +from .source import ( + AttrSource, + BackwardStateSource, + ConstantSource, + GetItemSource, + GlobalStateSource, + is_constant_source, + is_from_local_source, + LocalSource, + ParamBufferSource, + ShapeEnvSource, + SyntheticLocalSource, + TensorProperty, + TensorPropertySource, +) +from .utils import ( + _extract_tensor_dict, + checkpoint_params, + CleanupHook, + clone_inputs, + count_calls, + counters, + dynamo_timed, + get_instruction_source_311, + get_locals_to_steal, + get_static_address_type, + get_torch_function_mode_stack, + graph_break_reasons, + increment_op_count, + lazy_format_graph_code, + LazyString, + nn_module_proxy, + same, + set_example_value, +) +from .variables.base import VariableTracker +from .variables.builder import ( + BackwardStateGraphArg, + GraphArg, + TrackedFake, + VariableBuilder, + wrap_fx_proxy, +) +from .variables.lists import BaseListVariable +from .variables.misc import NullVariable +from .variables.nn_module import NNModuleVariable +from .variables.tensor import ( + NumpyNdarrayVariable, + SymNodeVariable, + TensorVariable, + UnspecializedPythonVariable, +) +from .variables.torch_function import TensorWithTFOverrideVariable + + +if TYPE_CHECKING: + from torch._dynamo.symbolic_convert import InstructionTranslatorBase + + +log = logging.getLogger(__name__) +graph_tabular_log = torch._logging.getArtifactLogger(__name__, "graph") +graph_code_log = torch._logging.getArtifactLogger(__name__, "graph_code") +graph_sizes_log = torch._logging.getArtifactLogger(__name__, "graph_sizes") +trace_call_log = torch._logging.getArtifactLogger(__name__, "trace_call") + + +@dataclass(frozen=True) +class VariableTrackerCacheKey: + vt_id: int + # Two different source can point to the same object. However, Dynamo handles + # globals and local source differently when it comes to guards and possibly + # some other parts as well. So, cache also relies on the source. + source: Source + + +class VariableTrackerCache: + def __init__(self): + self.cache = {} + + def lookup(self, value, source): + key = VariableTrackerCacheKey(id(value), source) + if key not in self.cache: + return None + return self.cache[key] + + def add(self, value, source, vt): + key = VariableTrackerCacheKey(id(value), source) + self.cache[key] = vt + + def clone(self): + # Needed for copy and restore graph state + new_cache = VariableTrackerCache() + new_cache.cache.update(self.cache) + return new_cache + + def clear(self): + self.cache.clear() + + +@functools.lru_cache(None) +def _step_logger(): + return torchdynamo_logging.get_step_logger(log) + + +@dataclass +class GraphCompileReason: + """Stores why a given output graph was compiled; i.e. what caused the graph break.""" + + reason: str + user_stack: List[traceback.FrameSummary] + + # Indicates if this was a graph compile reason due to graph break. + graph_break: bool = True + + def __post_init__(self): + if self.graph_break: + graph_break_reasons.append(self) + + +def _get_gen_rand_values_fn(random_calls): + def _gen_rand_values(): + return [fn(*args, **kwargs) for fn, args, kwargs in random_calls] + + return _gen_rand_values + + +class FakeRootModule(torch.nn.Module): + """Trick the constructor of fx.GraphModule""" + + def __init__(self, nn_modules: Dict[str, torch.nn.Module]): + super().__init__() + for k, v in nn_modules.items(): + setattr(self, k, v) + + def __repr__(self): + return "FakeRootModule(...)" + + +class WrapperBackend: + def __init__(self, backend: CompilerFn): + self.backend: CompilerFn = backend + + def __call__(self, gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]): + self.restore = checkpoint_params(gm) + self.gm = gm + copy_gm = copy.deepcopy(self.gm) + self.candidate = self.backend(copy_gm, example_inputs) + + if self.candidate is None or self.candidate is self.gm.forward: + return self.gm.forward + + if not config.verify_correctness: + return self.candidate + + # if verify_correctness=True + try: + correct = self.gm.forward(*clone_inputs(example_inputs)) + result = self.candidate(*clone_inputs(example_inputs)) + + # TODO: replace `same` function with the one in testing + if same(correct, result): + return self.candidate + + raise RuntimeError(f"incorrect results of backend {self}") + return self.gm.forward + + except Exception: + log.exception("error in verify_correctness") + raise + finally: + self.restore() + + +Scope = Dict[str, object] + + +class OutputGraph: + """ + Wrapper class to hold outputs of InstructionTranslator. Mainly the + generated fx.Graph. + + OutputGraph is 1:1 with a frame being processed. Each frame is associated + with some root InstructionTranslator. When user code calls a function, + we construct a InliningInstructionTranslator that continues to write into + the root InstructionTranslator's OutputGraph. + """ + + def __init__( + self, + code_options: Dict[str, Any], + compiler_fn: Optional[CompilerFn], + root_tx, + export: bool, + export_constraints, + frame_state, + local_scope: Scope, + global_scope: Scope, + f_code, + ): + super().__init__() + self.tracers = [SubgraphTracer(self, export_root=export)] + # Map from graph input's `Source` to its `VariableTracker` to + # de-duplicate graph inputs by source and reuse the tracker + self.input_source_to_var: Dict[Source, VariableTracker] = {} + self.export = export + self.export_constraints = export_constraints + self.frame_state = frame_state + # Map from graph input's `Source` to sizes / strides metadata + self.input_source_to_sizes_strides: Dict[Source, Dict[str, Any]] = {} + self.cleanup_hooks: List[Callable[[], Any]] = [] + # compile_id is an id number for the current torch.compile + self.compile_id: int = next(_compile_id_counter) + # Set of globals installed via install_global* APIs + self.installed_globals: Set[str] = set() + + # TODO: maybe should just pass the entire f_code in here? Not + # sure... + self.co_fields = { + "co_name": f_code.co_name, + "co_filename": f_code.co_filename, + "co_firstlineno": f_code.co_firstlineno, + } + + # tracked_fakes says where any tensor that was wrapped to fake came + # from. It is similar to GraphArg, in that all GraphArgs will get + # will get added to TrackedFakes, but TrackedFakes also contains + # GraphArgs that got pruned, and things like Tensor attributes which + # aren't explicit graph inputs. Used by shape guard + self.tracked_fakes: List[TrackedFake] = [] + + # List of symbols for which we have exact bindings in the arguments + # already + self.bound_symbols: Set[sympy.Symbol] = set() + + shape_env = ShapeEnv( + # Reference Cycle! + # Share a reference to the list of TrackedFake. + # + # ShapeEnv needs this in order to be able to reproduce the call + # to produce_guards at an arbitrary time point. That is because + # TrackedFake instances may have its metadata changed throughout + # the program execution. + tracked_fakes=self.tracked_fakes, + allow_scalar_outputs=config.capture_scalar_outputs, + allow_dynamic_output_shape_ops=config.capture_dynamic_output_shape_ops, + prefer_deferred_runtime_asserts_over_guards=config.prefer_deferred_runtime_asserts_over_guards, + allow_complex_guards_as_runtime_asserts=config.allow_complex_guards_as_runtime_asserts, + co_fields=self.co_fields, + ) + + # In export mode, we force the shape_env to strictly disallow any constraining + # of the user marked dynamic dims + import torch._functorch.config as _config + + with _config.patch(fake_tensor_allow_unsafe_data_ptr_access=False): + fake_mode = torch._subclasses.FakeTensorMode( + shape_env=shape_env, + # TODO (tmanlaibaatar) Remove this once we always lift params and buffers + allow_non_fake_inputs=True if self.export else False, + export=self.export, + ) + self.tracing_context: TracingContext = TracingContext(fake_mode) + self.init_ambient_guards() + + # Map each tensor id to a list of sources. This is necessary because + # tensor ids cannot be recovered from tracked fakes (in general). + # We use this map to interpret (i.e., check for violations of) constraints, + # specifically equality constraints, which have shared tensor ids in them. + # This map should also be generally useful, e.g., for (de)serialization. + self.tracked_fakes_id_to_source: Dict[ + int, List[Source] + ] = collections.defaultdict(list) + # Stores the full fqn of a param or buffer to the relevant source. + self.param_name_to_source: Optional[Dict[str, Source]] = {} + self.side_effects = SideEffects() + # Cached variable trackers. This makes symbolic analysis of LOAD_GLOBAL + # and LOAD_ATTR for same python objects free. + self.variable_tracker_cache = VariableTrackerCache() + self.unique_var_id = itertools.count() + self.code_options = dict(code_options) + self.output_instructions: List[Instruction] = [] + # used to track nodes that are added between calls of copy_graphstate + # and restore_graphstate + self.timestamp = 0 + + # A list of register_finalizer_fns to apply to the output graph module + self.register_finalizer_fns: List[Callable[[fx.GraphModule], None]] = [] + + # Not checkpointed + self.compiler_fn: Optional[CompilerFn] = compiler_fn + self.global_scope = global_scope + self.local_scope = local_scope + self.root_tx = root_tx + + # Given a source, what are the user stacks of all locations that + # accessed it? + # + # For efficiency, we only populate this: + # - During export, and + # - If the source could potentially lead to a spurious export input + # + # Feel free to populate this more frequently if other use-cases arise, + # but be aware that we have to generate full stacks for each + # recording! + self.source_to_user_stacks: Dict[Source, List[traceback.StackSummary]] = {} + + self._current_tx: List[InstructionTranslatorBase] = [] + self.cleanups: List[CleanupHook] = [] + self.should_exit = False + self.unspec_variable_map: Dict[str, UnspecializedPythonVariable] = {} + + # Note this returns true iff TF Mode and TF Subclasses are enabled + self.torch_function_enabled = torch._C._is_torch_function_enabled() + # This returns false if TF Overall (both mode and subclass) is disabled OR that TF Mode stack is empty + self.torch_function_mode_enabled = torch._C._is_torch_function_mode_enabled() + # This records the initial torch function mode stack for guarding + self.torch_function_mode_stack = get_torch_function_mode_stack() + + # Tracks if the output graph has a user defined allowed function in the + # graph. This is used later to determine if we should fallback to eager + # for certain exceptions. THe idea is that if the user has applied + # allow_in_graph, they would like to see the error instead of falling + # back for backend errors. + self.has_user_defined_allowed_in_graph = False + + # Tracks a list of called ops that were not tagged with "pt2_compliant_tag". + # This information is useful for logging. + self.non_compliant_ops: Set[torch._ops.OpOverload] = set({}) + + # Tracks a list of called custom ops that were tagged with "pt2_compliant_tag". + # This information is useful for logging. + self.compliant_custom_ops: Set[torch._ops.OpOverload] = set({}) + + # We save the global torch state here to be restored in case of graph + # breaks. The relevant issue is seen here + # https://github.com/pytorch/pytorch/pull/100570#issuecomment-1543427086 + # where inlining of a function changes the global state (because of the + # presence of torch.no_grad) and there is a graph break. + self.save_global_state() + + # Tracks the original FQNs of the constant tensors from the original graph, + # i.e. buffers and parameters. + self.dynamo_flat_name_to_original_fqn: Dict[str, str] = {} + + # All calls to random() are replaced with a single call to __gen_rand_values + # functions that returns a tuple of random values for each original call. + # random_calls tracks calls to random() and random_values_var stores the name of + # the variable that stores __gen_rand_values results. + self.random_calls: List[ + Tuple[Callable[..., object], Tuple[object, ...], Dict[str, object]] + ] = [] + self.random_values_var = None + + # Bytecode to insert right before we call the graph + self.pregraph_bytecode: List[Instruction] = [] + + # Use to pass values to backward hooks when using compiled autograd + self.backward_state: Dict[str, VariableTracker] = {} + self.backward_state_proxy: Optional[torch.fx.Proxy] = None + self.backward_state_var: Optional[str] = None + + self.name_of_builtins_dict_key_in_fglobals: str = ( + self.install_builtins_dict_in_fglobals() + ) + + self.guard_on_key_order: Set[str] = set() + + def install_builtins_dict_in_fglobals(self): + # f_globals["__builtins__"] can be a dict or a module. This is an + # implemenation detail - + # https://docs.python.org/3/library/builtins.html. + + # This makes guarding on any builtin messy because the guard check_fn + # has to check if the __builtins__ is a module or dict, and then access + # by either using getattr or getitem respectively. + + # To solve this problem, we insert a new entry in f_globals which points + # to the builtins __dict__ and then we guard any builtin on this dict. + # To avoid any collision with the pre-existing keys, we use the + # install_global to give us a unique dict key. + + f_builtins = self.global_scope["__builtins__"] + if not isinstance(f_builtins, dict): + f_builtins = f_builtins.__dict__ + return self.install_global("__builtins_dict__", f_builtins) + + def add_backward_state_hook(self, hook: VariableTracker, prefix="hook"): + name = f"{prefix}{len(self.backward_state)}" + assert name not in self.backward_state + self.backward_state[name] = hook + return name, self.get_backward_state_proxy() + + def get_backward_state_proxy(self): + if self.backward_state_proxy is None: + if self.export: + unimplemented("backward_state does not support export") + self.backward_state_proxy = self.root_tracer.create_graph_input( + "dynamo_backward_state", BackwardState, source=BackwardStateSource() + ) + self.backward_state_proxy.node.meta["grapharg"] = BackwardStateGraphArg() + set_example_value(self.backward_state_proxy.node, BackwardState()) + self.backward_state_var = self.new_var() + return self.backward_state_proxy + + # This gets its own helper function so guards DEBUG logs are more informative + def init_ambient_guards(self): + # Register a SHAPE_ENV guard to make sure we setup shape guards + # that show up in ShapeEnv + self.guards.add(ShapeEnvSource().make_guard(GuardBuilder.SHAPE_ENV)) + + self.guards.add( + GlobalStateSource().make_guard(GuardBuilder.DETERMINISTIC_ALGORITHMS) + ) + + self.guards.add(GlobalStateSource().make_guard(GuardBuilder.GRAD_MODE)) + + self.guards.add(GlobalStateSource().make_guard(GuardBuilder.DEFAULT_DEVICE)) + + self.guards.add( + GlobalStateSource().make_guard(GuardBuilder.TORCH_FUNCTION_STATE) + ) + + ci = torch._C._functorch.peek_interpreter_stack() + if ci is not None: + self.guards.add( + GlobalStateSource().make_guard(GuardBuilder.FUNCTORCH_STACK_MATCH) + ) + + def synthetic_graph_input(self, fn, args): + """ + call fn(*args) before the graph runs and turn the result into a fake input. + """ + example_value = fn(*args) + varname = self.new_var() + cg = PyCodegen(self.root_tx) + cg.add_push_null( + lambda: cg.load_import_from( + fn.__module__, + fn.__name__, + ) + ) + cg.foreach(map(variables.ConstantVariable.create, args)) + cg.call_function(len(args), False) + cg.store(varname) + self.pregraph_bytecode.extend(cg.get_instructions()) + source = SyntheticLocalSource(varname) + result = VariableBuilder(self.root_tx, source)(example_value) + TracingContext.get().guards_context.dynamo_guards.remove_guards_with_source( + source + ) + return result + + def add_cleanup_hook(self, fn: Callable[[], Any]): + self.cleanup_hooks.append(fn) + + def call_cleanup_hooks(self): + for hook in reversed(self.cleanup_hooks): + hook() + self.cleanup_hooks.clear() + + @property + def root_tracer(self): + return self.tracers[0] + + @property + def current_tracer(self): + return self.tracers[-1] + + def is_root_tracer(self): + # Helper to tell if we are inside the higher order operator tracing. + return len(self.tracers) == 1 + + @property + def graph(self): + return self.current_tracer.graph + + # TODO(rzou): can delete after we refactor speculate_subgraph to use nested GraphTracer. + @graph.setter + def graph(self, value): + self.current_tracer.graph = value + + @property + def input_name_to_proxy(self): + return self.current_tracer.input_name_to_proxy + + @property + def real_value_cache(self): + return self.current_tracer.real_value_cache + + # If you are here, and you're looking for create_graph_input, + # to avoid ambiguity, please call one of the following: + # - self.current_tracer.create_graph_input + # - self.root_tracer.create_graph_input + # See NOTE [HigherOrderOperator tracing design] for more context. + + def create_proxy(self, *args, **kwargs): + return self.current_tracer.create_proxy(*args, **kwargs) + + def create_node(self, *args, **kwargs): + return self.current_tracer.create_node(*args, **kwargs) + + def remove_node(self, *args, **kwargs): + return self.current_tracer.remove_node(*args, **kwargs) + + @contextlib.contextmanager + def subtracer(self, source_target, prior_tracer): + new_scope_ctx = enter_new_scope() + try: + if prior_tracer: + # Lineage MUST stay preserved + assert prior_tracer.parent is self.current_tracer + new_scope_ctx.__enter__() + tracer = ( + prior_tracer + if prior_tracer + else SubgraphTracer( + self, parent=self.current_tracer, source_target=source_target + ) + ) + self.tracers.append(tracer) + yield tracer + finally: + new_scope_ctx.__exit__(None, None, None) + self.tracers.pop() + + @property + def output(self): + return self + + @property + def fake_mode(self): + return self.tracing_context.fake_mode + + @property + def shape_env(self): + return self.tracing_context.fake_mode.shape_env + + @property + def guards(self) -> torch._guards.GuardsSet: + return self.tracing_context.guards_context.dynamo_guards + + @property + def nn_modules(self) -> Dict[str, Any]: + return self.tracing_context.module_context.nn_modules + + def save_global_state(self, out=None): + """ + Saves to out if it is provided. Else saves to the tracing context's global_state. + """ + global_state = ( + out if out is not None else self.tracing_context.global_context.global_state + ) + + # TODO - Consider having a torch level API for torch_function_state. As + # of now, we create a ref cycle by passing the + # output.set_torch_function_state to + # output.tracing_context.global_context.global_state. In the interim, + # the problem can be solved by manually set + # output.tracing_context.global_context.global_state to None at cleanup. + global_state["torch_function_enabled"] = ( + self.set_torch_function_state, + self.torch_function_enabled, + ) + global_state["grad_enabled"] = (torch.set_grad_enabled, torch.is_grad_enabled()) + + global_state["autocast_enabled"] = ( + functools.partial(torch.set_autocast_enabled, "cuda"), + torch.is_autocast_enabled("cuda"), + ) + global_state["autocast_cpu_enabled"] = ( + functools.partial(torch.set_autocast_enabled, "cpu"), + torch.is_autocast_enabled("cpu"), + ) + global_state["autocast_gpu_dtype"] = ( + functools.partial(torch.set_autocast_dtype, "cuda"), + torch.get_autocast_dtype("cuda"), + ) + global_state["autocast_cpu_dtype"] = ( + functools.partial(torch.set_autocast_dtype, "cpu"), + torch.get_autocast_dtype("cpu"), + ) + global_state["autocast_cache_enabled"] = ( + torch.set_autocast_cache_enabled, + torch.is_autocast_cache_enabled(), + ) + + def push_tx(self, tx): + self._current_tx.append(tx) + + def pop_tx(self): + return self._current_tx.pop() + + @property + def current_tx(self): + return self.root_tx if not self._current_tx else self._current_tx[-1] + + def add_symbol_bindings(self, arg: GraphArg): + # Insert implicit size vars as necessary. With dynamic shapes, we + # maintain the invariant that every sizevar gets a direct SymInt input + # into the graph. This means downstream graph transforms can assume + # every size variable is explicitly bound and accessible, instead of + # having to pull it out implicitly from tensors. + + if self.export: + return + + assert arg.fake_tensor is not None + + def bind_symint(s, prop): + if not (is_symbolic(s) and isinstance(s.node.expr, sympy.Symbol)): + return + s0 = s.node.expr + if s0 in self.bound_symbols: + return + self.bound_symbols.add(s0) + log.debug("bind_symint %s %s", s, prop.name()) + # TODO: don't readd symint if we already have it in graph + # (this is harmless because we do remove the unused ones later) + proxy = self.root_tracer.create_graph_input( + str(s0), + torch.SymInt, + before=True, + source=prop, + ) + set_example_value(proxy.node, s) + proxy.node.meta["grapharg"] = GraphArg( + prop, + s, + pass_arg_as_tensor=False, + fake_tensor=None, + is_tensor=False, + ) + + def handle_tensor(t, src): + for i, s in enumerate(t.size()): + bind_symint(s, TensorPropertySource(src, TensorProperty.SIZE, i)) + if t.layout is torch.strided: + for i, s in enumerate(t.stride()): + bind_symint(s, TensorPropertySource(src, TensorProperty.STRIDE, i)) + bind_symint( + t.storage_offset(), + TensorPropertySource(src, TensorProperty.STORAGE_OFFSET), + ) + elif t.layout is torch.sparse_coo: + handle_tensor(t._indices(), src) + handle_tensor(t._values(), src) + elif t.layout in {torch.sparse_csr, torch.sparse_bsr}: + handle_tensor(t.crow_indices(), src) + handle_tensor(t.col_indices(), src) + elif t.layout in {torch.sparse_csc, torch.sparse_bsc}: + handle_tensor(t.ccol_indices(), src) + handle_tensor(t.row_indices(), src) + if is_traceable_wrapper_subclass(t): + attrs, ctx = t.__tensor_flatten__() + for attr in attrs: + inner_t = getattr(t, attr) + handle_tensor(inner_t, AttrSource(src, attr)) + + handle_tensor(arg.fake_tensor, arg.source) + + def count_calls(self): + return count_calls(self.graph) + + def is_empty_graph(self): + return len(list(self.graph.nodes)) == 0 + + def get_submodule(self, keys): + assert keys + obj: Union[torch.nn.Module, Dict[str, torch.nn.Module]] = self.nn_modules + for k in keys.split("."): + if isinstance(obj, dict): + obj = obj[k] + else: + obj = getattr(obj, k) + return obj + + def new_var(self, name="tmp"): + existing = set(self.code_options["co_varnames"]) + # In common case, this will be O(1) + while True: + var = f"{name}_{next(self.unique_var_id)}" + if var not in existing: + self.code_options["co_varnames"] += (var,) + return var + + def update_co_names(self, name): + """Ensure self.code_options.co_names contains name""" + if name not in self.code_options["co_names"]: + self.code_options["co_names"] += (name,) + + @staticmethod + def module_key_name(*names): + # create a new unique name + name = "_".join(map(str, names)) + # Strip the guard lookup L/G access + name = re.sub(r"^[GL]\['?(.*?)'?\]$", r"\1", name) + # e.g. replace abc.xyz[123].qkv with abc.xyz_123.qkv + name = re.sub(r"\[(\d+)\]", r"_\g<1>", name) + # e.g. replace abc.xyz_123.qkv with abc_xyz_123_qkv + name = re.sub(r"[^a-zA-Z0-9]", "_", name) + + if not name or not name[0].isalpha(): + name = "sub" + name + + return name + + def register_attr_or_module( + self, + target: Union[torch.nn.Module, torch.Tensor, Any], + *names, + **options, + ): + if is_dynamic_nn_module(target, self.root_tx.export): + # Instead of returning UnspecializedNNModuleVariable, call + # VariableBuilder so that it is tracked for mutation. + return VariableBuilder(self.current_tx, **options)(target) + + options = dict(options) + assert "source" in options + source = options["source"] + assert not isinstance(source, ParamBufferSource) + + if isinstance(target, torch.Tensor): + tracer = self.current_tracer + if not self.is_root_tracer(): + # For higher order ops, we don't want to insert the get_attr in + # innermost graph. Instead, we want to raise the params/buffers + # as inputs to the higher-order graph, and register them as + # get_attrs in the root tracer. + + # Note that Dynamo will still call lift_tracked_freevar_to_input + # when these inputs are encountered for the inner graph. The + # only difference is what happens at the root tracer for + # nn.Parameters vs free inputs. The free inputs are registered + # as placeholders in the root graph, whereas the nn.Parameters + # are registered as get_attr nodes in the root graph. + tracer = self.root_tracer + + def wrap_name(module_key): + assert self.param_name_to_source is not None + self.param_name_to_source[module_key] = source + + # Check if the attr has already been registered. This can happen + # when two different sources point to the same tensor. + if target in self.root_tx.output.side_effects: + return self.root_tx.output.side_effects[target] + + if get_static_address_type(target) == "guarded": + install_guard(source.make_guard(GuardBuilder.ID_MATCH)) + elif not is_constant_source(source): + install_guard(source.make_guard(GuardBuilder.TENSOR_MATCH)) + + vt = wrap_fx_proxy( + self.root_tx, + tracer.create_proxy("get_attr", module_key, (), {}), + example_value=target, + **options, + ) + + # Track the object so to avoid duplicate registration in case of + # different sources pointing to the same tensor object. + vt = self.root_tx.output.side_effects.track_object_existing(target, vt) + + assert "tensor_dict" not in vt.proxy.node.meta + vt.proxy.node.meta["tensor_dict"] = _extract_tensor_dict(target) + + return vt + + elif isinstance(target, torch.nn.Module): + assert isinstance(target, torch.nn.Module) + + if source: + install_guard(source.make_guard(GuardBuilder.NN_MODULE)) + + def wrap_name(module_key): + return NNModuleVariable(type(target), module_key, target, **options) + + else: + # This is Dynamo created graph module, e.g., graph module coming + # from higher order ops. NNModuleVariable tracker can't be + # sourceless, so let's return a unspecializedNNModule variable + # tracker. + def wrap_name(module_key): + return variables.UnspecializedNNModuleVariable(target, **options) + + elif isinstance(target, (torch.SymInt, torch.SymFloat)): + # HACKY CODE REGION BEGIN + # WE ARE PIGGYBACKING ON EXISTING INFRA TO REGISTER ATTRS + # This ultimately gets written to self.nn_modules, which is unfortunate + # Attrs that are tenors and symints and such need to be migrated to have their + # own storage + # alas, this is like this for now + + def wrap_name(module_key): + return SymNodeVariable.create( + self, + self.create_proxy("get_attr", module_key, (), {}), + sym_num=target, + **options, + ) + + # HACKY CODE REGION END + else: + + def wrap_name(module_key): + self.output.update_co_names(module_key) + self.global_scope[module_key] = target + return VariableBuilder(self, ConstantSource(source_name=module_key))( + target + ) + + for k, v in self.nn_modules.items(): + if v is target: + # it already exists + return wrap_name(k) + + name = OutputGraph.module_key_name(*names) + + base = name + for i in itertools.count(): + if name not in self.nn_modules: + self.nn_modules[name] = target + if isinstance(target, torch.nn.Module): + + def register_leaf_name(leaf_name): + assert self.param_name_to_source is not None + new_source = ParamBufferSource(source, leaf_name) + new_name = f"{name}.{leaf_name}" + self.param_name_to_source[new_name] = new_source + if isinstance(source, LocalSource): + self.dynamo_flat_name_to_original_fqn[ + OutputGraph.module_key_name(new_source.name()) + ] = leaf_name + + # annoying, but there are cases when we do not have parameters + # see test_nn_moduledict_contains + if hasattr(target, "_parameters"): + for leaf_name, _ in target.named_parameters(): + register_leaf_name(leaf_name) + if hasattr(target, "_buffers"): + for leaf_name, _ in target.named_buffers(): + register_leaf_name(leaf_name) + + return wrap_name(name) + name = f"{base}_{i}" + + raise AssertionError("unreachable") + + def handle_aliases_for_stolen_lists(self, tx): + # If list inputs are stolen, but still needed after the function call, create aliases to keep them alive + maybe_gm = self.local_scope.get("self") + stolen_list_names = get_locals_to_steal(maybe_gm) + if not stolen_list_names: + return [] + + alias_insts = [] + needs_alias: Dict[ + str, List[Union[VariableTracker, AttributeMutationExisting]] + ] = {} + + queue = [ + *tx.stack, + *tx.symbolic_locals.values(), + *self.side_effects.store_attr_mutations.keys(), + ] + + while queue: + x = queue.pop() + if isinstance(x, BaseListVariable): + assert isinstance(x.items, List) + queue += x.items + continue + + if not ( + isinstance(x, (VariableTracker, AttributeMutationExisting)) + and isinstance(x.source, GetItemSource) + and isinstance(x.source.base, LocalSource) + and x.source.base.local_name in stolen_list_names + ): + continue + + stolen_name = x.source.base.local_name + if stolen_name not in needs_alias: + needs_alias[stolen_name] = [] + needs_alias[stolen_name].append(x) + + visited = {} + for arg in self.graphargs: + if not ( + isinstance(arg._example, list) + and isinstance(arg.source, LocalSource) + and arg.source.local_name in needs_alias + ): + continue + + # arg is a list that will be cleared by the compiled function + list_name = arg.source.local_name + assert list_name in self.code_options["co_varnames"] + for x in needs_alias[list_name]: + list_idx = x.source.index + if list_idx not in visited: + alias_name = self.new_var( + f"{list_name}_ref" + ) # self.new_var already adds unique id suffix + + visited[list_idx] = alias_name + # bytecode of `alias_name = list_name[list_idx]` + alias_insts.extend( + [ + create_instruction("LOAD_FAST", argval=list_name), + create_instruction("LOAD_CONST", argval=list_idx), + create_instruction("BINARY_SUBSCR"), + create_instruction("STORE_FAST", argval=alias_name), + ] + ) + + # operate on alias, handled by suffix codegen + x.source = LocalSource(visited[list_idx]) + + return alias_insts + + def compile_subgraph( + self, tx, partial_convert=False, reason: Optional[GraphCompileReason] = None + ): + """ + Generate a subgraph to continue execution on user code. + Automatically restore live variables. + """ + assert reason is not None + + from .decorators import disable + + self.partial_convert = partial_convert + self.compile_subgraph_reason = reason + self.should_exit = True + + log.debug("COMPILING GRAPH due to %s", reason) + + if not all(block.can_restore() for block in tx.block_stack): + unimplemented("compile_subgraph with block_depth != 0") + + prefix_insts: List[Instruction] = [] + if sys.version_info >= (3, 11): + # prefix instructions (Python 3.11+) + for inst in tx.prefix_insts: + if inst.opname == "MAKE_CELL": + prefix_insts.append( + create_instruction("MAKE_CELL", argval=inst.argval) + ) + elif inst.opname == "COPY_FREE_VARS": + prefix_insts.append( + create_instruction( + "COPY_FREE_VARS", arg=len(tx.code_options["co_freevars"]) + ) + ) + else: + prefix_insts.append(copy.copy(inst)) + assert not ( + self.pregraph_bytecode and self.export + ), "export does not support pregraph_bytecode" + prefix_insts.extend(self.pregraph_bytecode) + prefix_insts.extend(self.handle_aliases_for_stolen_lists(tx)) + + def append_prefix_insts(): + self.add_output_instructions(prefix_insts) + prefix_insts.clear() + + for block in reversed(tx.block_stack): + block.exit(tx) + + self.cleanup_graph() + tx.prune_dead_locals() + stack_values = list(tx.stack) + + # realize any unrealized tensor VTs in case they + # need to be added to self.nn_modules as attributes + for value in stack_values: + value.realize() + + # Use nn.Module "proxies" in the constructed GraphModule so that + # the resulting GM does not hold additional strong references to the original modules. + # This prevents a strong ref cycle where Dynamo created code holds on to references + # to modules that also have Dynamo code cache invalidation checks. + # When cache invalidation runs, the generated GM will be invalidated, which also deletes + # the proxies. + nn_modules_proxies = { + name: nn_module_proxy(mod) for name, mod in self.nn_modules.items() + } + root = FakeRootModule(nn_modules_proxies) + # Add all the local vars to the "stack" so restore at the end + restore_vars = [] + val_to_names: Dict[VariableTracker, List[str]] = {} + if stack_values: + val_to_names[stack_values[-1]] = [] + # NB: Typically (i.e., for graph compile from RETURN_VALUE), + # symbolic_locals will be empty at this point, as prune_dead_locals + # will clear out all of symbolic_locals because RETURN_VALUE is the + # last instruction and no more locals are used. The fanciness here + # is only needed for partial graphs. + for k, v in tx.symbolic_locals.items(): + # Note! this explicitly uses .local_name for matching + # Failure to do so will cause spurious registrations in val_to_names. + # This will in turn result in spurious variables showing up in the graph. + # This was very tricky to debug. For an example, dump the graph at call_user_compiler + # while running test_subgraphs.py + if isinstance(v.source, LocalSource) and v.source.local_name == k: + continue # no need to restore initial state + # Do not load variable if it is NULL. + if sys.version_info >= (3, 12): + # Continuation function will load the NULL for v. + if type.__instancecheck__(NullVariable, v): + continue + else: + # A variable should never be NULL in < 3.12 + assert not type.__instancecheck__(NullVariable, v) + if v not in val_to_names: + val_to_names[v] = [] + val_to_names[v].append(k) + for v in val_to_names.keys(): + restore_vars.extend(val_to_names[v]) + stack_values.extend([v] * len(val_to_names[v])) + + # to handle random calls + if len(self.random_calls) > 0: + append_prefix_insts() + random_calls_instructions = [] + self.random_values_var = self.new_var("random_values") + rand_fn = disable(_get_gen_rand_values_fn(self.random_calls)) + rand_fn_name = self.install_global("__gen_rand_values", rand_fn) + codegen = PyCodegen(tx, root) + random_calls_instructions.extend( + codegen.load_function_name(rand_fn_name, True) + ) + random_calls_instructions.extend(create_call_function(0, False)) + random_calls_instructions.append( + codegen.create_store(tx.output.random_values_var), + ) + self.add_output_instructions(random_calls_instructions) + + if ( + stack_values + and all( + not isinstance( + v, + ( + UnspecializedPythonVariable, + NumpyNdarrayVariable, + TensorWithTFOverrideVariable, + ), + ) + and not (isinstance(v, SymNodeVariable) and v.python_type() is float) + for v in stack_values + ) + and all(isinstance(x, TensorVariable) for x in stack_values) + and len(set(stack_values)) == len(stack_values) + and self.side_effects.is_empty() + and not len(tx.debug_locals) != 0 + and not self.backward_state + ): + append_prefix_insts() + # optimization to generate better code in a common case + self.add_output_instructions( + self.compile_and_call_fx_graph(tx, list(reversed(stack_values)), root) + + [create_instruction("UNPACK_SEQUENCE", arg=len(stack_values))] + ) + # restore all the live local vars + self.add_output_instructions( + [PyCodegen(tx).create_store(var) for var in reversed(restore_vars)] + ) + else: + graph_output_var = self.new_var("graph_out") + pass1 = PyCodegen(tx, root, graph_output_var) + self.codegen_suffix(tx, stack_values, pass1) + + # one more time now that we have established tempvars + pass2 = PyCodegen( + tx, + root, + graph_output_var, + tempvars={val: None for val, count in pass1.uses.items() if count > 1}, + ) + self.codegen_suffix(tx, stack_values, pass2) + + stored_graph_output_var = False + output = [] + if count_calls(self.graph) != 0 or len(pass2.graph_outputs) != 0: + output.extend( + self.compile_and_call_fx_graph(tx, pass2.graph_output_vars(), root) + ) + + if len(pass2.graph_outputs) != 0: + output.append(pass2.create_store(graph_output_var)) + stored_graph_output_var = True + else: + output.append(create_instruction("POP_TOP")) + else: + # NB: Important to run compiler collective even when there is + # a graph break + self.run_compiler_collective(tx) + append_prefix_insts() + self.add_output_instructions(output + pass2.get_instructions()) + + # restore all the live local vars + self.add_output_instructions( + [PyCodegen(tx).create_store(var) for var in reversed(restore_vars)] + ) + + if stored_graph_output_var: + self.add_output_instructions( + [PyCodegen(tx).create_delete(graph_output_var)] + ) + + def codegen_suffix(self, tx, stack_values, cg): + if self.backward_state: + assert not self.export + for name, val in self.backward_state.items(): + cg(val) + cg.append_output(cg.create_load(self.backward_state_var)) + cg.store_attr(name) + self.side_effects.codegen_hooks(cg) + self.side_effects.codegen_save_tempvars(cg) + + # Return variables used for logging at the end + for debug_var, args in tx.debug_locals: + cg.add_push_null(lambda: cg(debug_var)) + for arg in args: + cg(arg) + cg.extend_output(create_call_function(len(args), False)) + cg.extend_output([create_instruction("POP_TOP")]) + + cg.restore_stack(stack_values, value_from_source=not tx.export) + self.side_effects.codegen_update_mutated(cg) + + def cleanup_graph(self): + """ + Remove "creation_timestamp" from node meta + + Remove this pattern from the graph: + torch._C._set_grad_enabled(False) + torch._C._set_grad_enabled(True) + """ + assert self.should_exit + nodes = list(self.graph.nodes) + for node in nodes: + node.meta.pop("creation_timestamp", None) + + grad_enabled = torch.is_grad_enabled() + for node1, node2 in zip(nodes, nodes[1:]): + if ( + node1.target is torch._C._set_grad_enabled + and tuple(node1.args) == (not grad_enabled,) + and not node1._erased + ): + grad_enabled = node1.args[0] + if ( + node2.target is torch._C._set_grad_enabled + and tuple(node2.args) == (not grad_enabled,) + and not node2._erased + ): + grad_enabled = node2.args[0] + self.graph.erase_node(node1) + self.graph.erase_node(node2) + + def get_graph_sizes_structured(self): + ret = {} + for node in self.graph.nodes: + example_value = node.meta.get("example_value", None) + if isinstance(example_value, torch._subclasses.FakeTensor): + size = example_value.size() + ret[node.name] = [s if isinstance(s, int) else repr(s) for s in size] + return ret + + def get_graph_sizes(self, name: str): + graph_sizes_str = "TRACED GRAPH TENSOR SIZES\n" + graph_sizes_str += f"===== {name} =====\n" + for node in self.graph.nodes: + example_value = node.meta.get("example_value", None) + if isinstance(example_value, torch._subclasses.FakeTensor): + size = example_value.size() + graph_sizes_str += f"{node.name}: {tuple(size)}\n" + concrete_size = [] + has_symint = False + for sz in size: + if isinstance(sz, int): + concrete_size.append(sz) + elif isinstance(sz, torch.SymInt): + has_symint = True + concrete_size.append(sz.node.hint) + else: + break + else: + if has_symint: + graph_sizes_str += ( + f"{node.name} (concrete): {tuple(concrete_size)}\n" + ) + return graph_sizes_str + + @contextlib.contextmanager + def restore_global_state(self): + """ + Momentarily restores the global state to what it was prior to tracing the current output + """ + prior_global_state = self.tracing_context.global_context.copy_graphstate() + current_global_state: Dict[str, Tuple[Any, bool]] = {} + self.save_global_state(out=current_global_state) + try: + # Set to state prior to tracing the graph + self.tracing_context.global_context.restore_graphstate(prior_global_state) + yield + finally: + # Reset to state at the current time (e.g. before calling the user compiler) + self.tracing_context.global_context.restore_graphstate( + GlobalContextCheckpointState(current_global_state) + ) + + def run_compiler_collective(self, tx): + if (ds := tx.distributed_state) is not None and ds.all_states is None: + compile_pg = ds.compile_pg + log.info("compiler_collective %s", ds.local_state) + torch._logging.trace_structured( + "artifact", + metadata_fn=lambda: { + "name": "compiler_collective", + "encoding": "json", + }, + payload_fn=lambda: json.dumps( + dataclasses.asdict(ds.local_state), + ), + ) + with torch.cuda.device(compile_pg.rank() % torch.cuda.device_count()): + all_states = [None] * compile_pg.size() + dist.all_gather_object(all_states, ds.local_state, group=compile_pg) + ds.all_states = all_states + # Clear speculation log, because are tracing may diverge due to + # this information from the compiler collective + tx.speculation_log.clear() + raise exc.CompileCollectiveRestartAnalysis + + def compile_and_call_fx_graph(self, tx, rv, root): + """ + Generate code from self.graph and return the Instruction()s to + call that generated code. + """ + with torch._guards.TracingContext.clear_frame(): + from .decorators import disable + + assert self.should_exit + + self.run_compiler_collective(tx) + + name = unique_id("__compiled_fn") + + assert isinstance(rv, list) + assert isinstance(root, FakeRootModule) + output_node = self.create_node( + "output", + "output", + (self.current_tracer.create_arg(tuple(x.as_proxy() for x in rv)),), + {}, + ) + tx.output.current_tracer._maybe_preserve_original_meta(tx, output_node) + if not config.do_not_emit_runtime_asserts: + insert_deferred_runtime_asserts( + fx.GraphModule(root, self.graph), + self.shape_env, + name, + ) + # NB: deferred runtime asserts can keep graphargs live, so make sure + # those are inserted before pruning + self.remove_unused_graphargs() + ncalls = count_calls(self.graph) + counters["stats"]["calls_captured"] += ncalls + + # free a bit of memory + self.real_value_cache.clear() + + gm = _make_graph_module(root, self.graph) + for register_finalizer in self.register_finalizer_fns: + register_finalizer(gm) + + gm.compile_subgraph_reason = self.compile_subgraph_reason + gm.meta[ + "dynamo_flat_name_to_original_fqn" + ] = self.dynamo_flat_name_to_original_fqn.copy() + + graph_code_log.debug( + "%s", + lazy_format_graph_code( + name, gm, include_stride=True, include_device=True, colored=True + ), + ) + torch._logging.trace_structured( + "dynamo_output_graph", + lambda: {"sizes": self.get_graph_sizes_structured()}, + payload_fn=lambda: gm.print_readable( + print_output=False, include_stride=True, include_device=True + ), + ) + self.call_cleanup_hooks() + old_fake_mode = self.tracing_context.fake_mode + if not self.export: + import torch._functorch.config as _config + + with _config.patch(fake_tensor_allow_unsafe_data_ptr_access=False): + # TODO(voz): The way export uses gm, and fake tensors, is not supported with us resetting + backend_fake_mode = torch._subclasses.FakeTensorMode( + shape_env=old_fake_mode.shape_env, + ) + # TODO(voz): Ostensibily, this should be scoped and + # restore back to old_fake_mode, but doing so currently violates + # a lot of fake_tensor ownership assumptions and runs afoul of detect_fake_mode + self.tracing_context.fake_mode = backend_fake_mode + + with self.restore_global_state(): + compiled_fn = self.call_user_compiler(gm) + + from torch.fx._lazy_graph_module import _LazyGraphModule + + if isinstance(compiled_fn, _LazyGraphModule) or ( + isinstance(getattr(compiled_fn, "__self__", None), _LazyGraphModule) + and compiled_fn.__name__ == "_lazy_forward" # type: ignore[attr-defined] + ): + # Since dynamo will run the forward method for the GraphModule shortly + # anyways, it does not hurt to do the real recompilation here if + # this is a _LazyGraphModule. This makes it easier for dynamo to + # optimize a _LazyGraphModule. + + lazy_gm = ( + compiled_fn + if isinstance(compiled_fn, _LazyGraphModule) + else compiled_fn.__self__ # type: ignore[attr-defined] + ) + + _LazyGraphModule.force_recompile(lazy_gm) + + if not isinstance(compiled_fn, _LazyGraphModule): + # replace compiled_fn with the real forward method + compiled_fn = lazy_gm.forward + + compiled_fn = disable(compiled_fn) + + counters["stats"]["unique_graphs"] += 1 + # This is safe because we pre-process name to be unique + self.install_global_unsafe(name, compiled_fn) + + cg = PyCodegen(tx) + cg.make_call_generated_code(name) + return cg.get_instructions() + + @property + def placeholders(self) -> List[fx.Node]: + return self.graph.find_nodes(op="placeholder") + + @property + def graphargs(self) -> List[GraphArg]: + return [node.meta["grapharg"] for node in self.placeholders] + + def call_user_compiler(self, gm: fx.GraphModule) -> CompiledFn: + with dynamo_timed( + "OutputGraph.call_user_compiler", phase_name="backend_compile" + ): + return self._call_user_compiler(gm) + + def _call_user_compiler(self, gm: fx.GraphModule) -> CompiledFn: + assert self.compiler_fn is not None + tot = 0 + placeholders = [] + for node in gm.graph.nodes: + if node.op in ("call_function", "call_method", "call_module"): + tot += 1 + if node.op == "placeholder": + placeholders.append(node) + increment_op_count(tot) + for pl in placeholders: + arg = pl.meta["grapharg"] + # TODO: Why isn't this stored in meta :think: + pl._dynamo_source = arg.source + + gm._param_name_to_source = self.param_name_to_source # type: ignore[assignment] + gm._source_to_user_stacks = self.source_to_user_stacks # type: ignore[assignment] + + try: + name = ( + self.compiler_fn.__name__ + if hasattr(self.compiler_fn, "__name__") + else "" + ) + _step_logger()(logging.INFO, f"calling compiler function {name}") + compiler_fn = self.compiler_fn + if config.verify_correctness: + compiler_fn = WrapperBackend(compiler_fn) + compiled_fn = compiler_fn(gm, self.example_inputs()) + _step_logger()(logging.INFO, f"done compiler function {name}") + assert callable(compiled_fn), "compiler_fn did not return callable" + except exceptions_allowed_to_be_fallback as e: + if self.has_user_defined_allowed_in_graph: + raise BackendCompilerFailed(self.compiler_fn, e).with_traceback( + e.__traceback__ + ) from None + msg = ( + "Backend compiler failed with a fake tensor exception at \n" + f"{self.root_tx.format_frame_summary()}" + "Adding a graph break." + ) + unimplemented_with_warning(e, self.root_tx.f_code, msg) + except SkipFrame as e: + # The backend compiler has requested that we skip the frame, instead of + # aborting execution. + raise e + except Exception as e: + raise BackendCompilerFailed(self.compiler_fn, e) from e + + signpost_event( + "dynamo", + "OutputGraph.call_user_compiler", + { + **self.co_fields, + "op_count": tot, + "node_count": len(gm.graph.nodes), + "input_count": len(placeholders), + }, + ) + + return compiled_fn + + def example_inputs(self) -> List[torch.Tensor]: + result = [] + for arg in self.graphargs: + result.append(arg.example) + return result + + def remove_unused_graphargs(self) -> None: + # NB: It's always OK to drop GraphArg for symbols that ended up being + # specialized. You don't even have to make a guard for it, because + # ShapeEnv produce_guards operates on tracked_fakes, which never gets + # pruned. That being said, you'll get marginally better generated + # guard code if you promote the guard into a Dynamo guard (since that + # allows for the guard to be done using C++ guards.) If we get + # ShapeEnv guards to go into C++ guards, this will stop being a thing + # though! + + assert self.should_exit + + # Miniature DCE pass, but only for obviously trivial operations + def is_static_true(b_node: fx.node.Argument): + if b_node is True: + return True + if not isinstance(b_node, fx.Node): + return False + b = b_node.meta.get("example_value") + if b is None: + return False + if b is True: + return True + if ( + isinstance(b, torch.SymBool) + and (r := b.node.maybe_as_bool()) is not None + ): + return r + # TODO: We can also technically remove all cases when the input + # doesn't have unbacked inputs, since it's all in the ShapeEnv + return False + + def is_symnode_arg(a: fx.node.Argument): + from torch.fx.experimental.sym_node import SymTypes + + if isinstance(a, (int, float, bool)): + return True + if isinstance(a, fx.Node): + return isinstance(a.meta.get("example_value"), SymTypes) + return False + + # NB: We assume that you cannot do mutations on int/float/bool, + # because they are immutable types, and therefore is always safe to + # DCE. + def is_symnode_compute_node(node): + from torch.fx.experimental.sym_node import SymTypes + + if node.op != "call_function": + return False + # TODO: I don't think it's possible to have a bare int/float here? + if not isinstance(node.meta.get("example_value"), SymTypes): + return False + # TODO: This will bail here if you ever end up with a more complicated + # computation function, like sum(list_of_ints), even though it + # should be DCE'able + if not all(is_symnode_arg(a) for a in node.args): + return False + if not all(is_symnode_arg(a) for a in node.kwargs.values()): + return False + return True + + from torch.fx.experimental.symbolic_shapes import is_accessor_node + + for node in reversed(list(self.graph.nodes)): + if len(list(node.users)) == 0: + if ( + node.op == "get_attr" + or (node.op == "call_function" and node.target is operator.getitem) + or ( + node.op == "call_function" + and node.target is torch._check + and is_static_true(node.args[0]) + ) + or is_symnode_compute_node(node) + or is_accessor_node(node) + ): + self.remove_node(node) + + def placeholder_binds_symbol(node): + arg = node.meta["grapharg"] + example = arg.example + if isinstance(example, torch.SymInt) and isinstance( + example.node.expr, sympy.Symbol + ): + return example.node.expr + return None + + def remove_unused(node): + log.debug("REMOVE UNUSED GRAPHARG %s", node.meta["grapharg"].source.name()) + # I'm not really sure why you need to delete these from the + # node since the node is going to get removed + del node.meta["grapharg"] + self.remove_node(node) + self.real_value_cache.pop(node, None) + + used_symbols: Set[sympy.Symbol] = set() + + def update_used_symbols(used_symbols, fake: Union[torch.SymInt, torch.Tensor]): + used_symbols |= free_symbols(fake) + + recheck_placeholders = [] + for node in self.placeholders: + binds_symbol = placeholder_binds_symbol(node) is not None + # Don't delete symbol bindings yet + if binds_symbol: + if not node.users: + recheck_placeholders.append(node) + else: + if not node.users and not isinstance( + node.meta["grapharg"], BackwardStateGraphArg + ): + remove_unused(node) + else: + # Register the free symbols as uses + arg = node.meta["grapharg"] + if isinstance(arg, BackwardStateGraphArg): + continue + if isinstance(node.meta["grapharg"].example, torch.ScriptObject): + real_script_obj = node.meta["grapharg"].example + fake_script_obj = node.meta["grapharg"].example_strong_ref + if not torch._library.fake_class_registry.tracing_with_real( + real_script_obj + ): + flat_dict = dict(real_script_obj.__obj_flatten__()) # type: ignore[attr-defined] + for attr in flat_dict.keys(): + fake_attr_val = getattr( + fake_script_obj.wrapped_obj, attr + ) + pytree.tree_map_only( + (torch.SymInt, torch.Tensor), + lambda t: update_used_symbols(used_symbols, t), + fake_attr_val, + ) + continue + fake = ( + arg.fake_tensor if arg.fake_tensor is not None else arg.example + ) + update_used_symbols(used_symbols, fake) + + # After removing unused graphargs, prune unused binds_symbol + for node in recheck_placeholders: + symbol = placeholder_binds_symbol(node) + if symbol is not None: + if symbol not in used_symbols: + remove_unused(node) + else: + # Make sure we delete later occurrences of the same symbol + used_symbols.remove(symbol) + + def add_output_instructions(self, prefix: List[Instruction]) -> None: + """ + We call this on the creation of a new compiled subgraph that is inserted + before user code. + """ + self.output_instructions.extend(prefix) + self.should_exit = True + + def install_global_unsafe(self, name, value) -> None: + """ + WARNING: prefer the safer `install_global_by_id/install_global`. + torch.compile instances should be independent of each other; + one footgun is to have one instance depend on the existence of + a global installed by another instance. This can happen if we mangle + a global the same way across both instances. + """ + assert name not in self.installed_globals + self.installed_globals.add(name) + self.cleanups.append(CleanupHook.create(self.global_scope, name, value)) + + def install_global_by_id(self, prefix, value) -> str: + """ + Installs a global if it hasn't been installed already. + This is determined by (prefix, id(value)) pair. + + Returns the name of the newly installed global. + """ + # NB: need self.compile_id to distinguish this global + # from another global created in a different torch.compile instance + name = f"{prefix}_{id(value)}_c{self.compile_id}" + if name in self.installed_globals: + return name + self.install_global_unsafe(name, value) + return name + + def install_global(self, prefix, value) -> str: + """ + Installs a global, generating a unique name for it. + + Returns the name of the newly installed global. + """ + # NB: unique_id is unique, even across torch.compile instances + name = unique_id(prefix) + self.install_global_unsafe(name, value) + return name + + def cleanup(self) -> None: + # There is a reference cycle between tracer and OutputGraph, causing + # some of the tensor objects to be held alive for longer than necessary. + self.root_tx = None + self.nn_modules.clear() + self.param_name_to_source = None + + for node in self.graph.nodes: + if "grapharg" in node.meta: + del node.meta["grapharg"] + self.real_value_cache.clear() + self.input_name_to_proxy.clear() + self.side_effects.clear() + self.variable_tracker_cache.clear() + self.register_finalizer_fns.clear() + self.dynamo_flat_name_to_original_fqn.clear() + self.tracing_context.clear() + + def set_torch_function_state(self, enabled: bool) -> None: + self.torch_function_enabled = enabled + + def add_graph_finalizer( + self, register_finalizer: Callable[[fx.GraphModule], None] + ) -> None: + self.register_finalizer_fns.append(register_finalizer) + + def example_value_from_input_node(self, node: torch.fx.Node): + """Extract the non-fake example tensor""" + if node.op == "placeholder": + return node.meta["grapharg"].example + assert node.op == "get_attr" + return self.nn_modules[node.target] # type: ignore[index] + + +err_epilogue = ( + "With the current config, we will graph break " + "(and fall back to eager-mode PyTorch) on all ops " + "that have do not have the 'pt2_compliant_tag'. " + "Please see the following doc for how to mark this op as PT2 compliant " + "https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html" +) + + +def check_pt2_compliant_op(output_graph, kind, target, args, kwargs): + if kind != "call_function": + return + + def encountered_compliant_op(target): + if target.namespace in {"prim", "prims", "aten"}: + return + output_graph.compliant_custom_ops.add(target) + + def encountered_non_compliant_op(target, msg): + output_graph.non_compliant_ops.add(target) + if config.only_allow_pt2_compliant_ops: + unimplemented(msg + " " + err_epilogue) + + if isinstance(target, torch._ops.OpOverload): + if torch.Tag.pt2_compliant_tag in target.tags: + encountered_compliant_op(target) + return + encountered_non_compliant_op( + target, + f"Encountered the torch.ops.OpOverload {target} " + f"that is not PT2 compliant.", + ) + return + + if isinstance(target, torch._ops.OpOverloadPacket): + overloads = tuple(target.overloads()) + # Optimization: Overload resolution is expensive. + # If there's only one overload, we know what it will resolve to. + if len(overloads) == 1: + op = getattr(target, overloads[0]) + if torch.Tag.pt2_compliant_tag in op.tags: + encountered_compliant_op(op) + return + encountered_non_compliant_op( + op, + f"Encountered the non-overloaded " + f"torch.ops.OpOverloadPacket {target} " + f"that is not PT2 compliant. ", + ) + return + + args, kwargs = torch._dynamo.utils.get_fake_values_from_nodes( + output_graph.current_tx, (args, kwargs), False + ) + try: + overload = torch._C._jit_resolve_packet( + target._qualified_op_name, *args, **kwargs + ) + except RuntimeError as e: + unimplemented(str(e)) + + op = getattr(target, overload) + if torch.Tag.pt2_compliant_tag in op.tags: + encountered_compliant_op(op) + else: + encountered_non_compliant_op( + op, + f"Encountered the torch.ops.OpOverloadPacket {target} " + f"which resolves to the overload ({overload}) that is " + f"not PT2 compliant.", + ) + + +_compile_id_counter = itertools.count() + + +class SubgraphTracer(fx.Tracer): + """ + Holds an FX graph that is being traced. OutputGraph owns a SubgraphTracer + and the separation of responsibilities is that SubgraphTracer is + responsible for building the graph while OutputGraph is responsible for + compiling and executing the graph. + """ + + def __init__( + self, output_graph, parent=None, export_root=False, source_target=None + ): + super().__init__() + self.output_graph = weakref.proxy(output_graph) + self.graph = torch.fx.Graph() + + # The export is only ever set for the ROOT tracer. It controls + # whether or not certain inputs are allowed to be added or not. + # Look at call sites of create_graph_input to see how it is used. + if export_root: + assert parent is None + self.export_root = export_root + # Map from graph input name to its placeholder proxy object, where the + # map's keys give all current placeholder node names and can be used to + # create unique node names + self.input_name_to_proxy: Dict[str, fx.Proxy] = {} + # Node => computed real value (see utils.get_real_value) + self.real_value_cache: Dict[fx.Node, torch.Tensor] = {} + + # SubgraphTracers can be nested. See NOTE [HigherOrderOperator tracing design] + self.parent = parent + # A dict mapping previously free variables (Proxy objects) + # to new Proxy objects that wrap inputs to this subgraph. + # + # This dict serves two purposes: + # - Proxies are associated with VariableTrackers. If we see + # the same VariableTracker twice (and it is a free variable), + # then we want to use the same Proxy in the current subgraph to + # record the tracing. + # - If we are tracing a HigherOrderOperator's body_fn, then we + # need to keep track of what free variables were lifted so we can + # rewrite the HigherOrderOperator call using the traced body_fn. + # Dicts maintain the order of args for the HigherOrderOperator call. + self.lifted_freevars = {} + self.prev_inst = None + + self._cur_code = None + self._orig_gm_meta = None + self._orig_gm_lineno_map = None + self._orig_gm_firstlineno = None + # Each SubgraphTracer is associated with a source target, which indicates + # which operator this subgraph is attached to. We compute a source_fn_stack + # based on the source target. For the root tracer, it's set to []. + # This is useful for debugging and transforming the exported graph. + if self.parent is None: + self.source_fn_stack = [] + else: + self.source_fn_stack = self.parent.source_fn_stack + [ + (self.graph._target_to_str(source_target), source_target) + ] + + # preserve original meta if it is available + def _maybe_preserve_original_meta(self, tx, node): + if ( + self._orig_gm_meta + and self._orig_gm_lineno_map + and self._orig_gm_firstlineno + ): + lineno = tx.current_instruction.starts_line + node_idx = None + if lineno is not None: + node_idx = self._orig_gm_lineno_map.get( + lineno - self._orig_gm_firstlineno, None + ) + if node_idx is not None: + meta = self._orig_gm_meta[node_idx] + for field in fx.proxy._COPY_META_FIELDS: + if field in meta: + node.meta[field] = meta[field] + if "stack_trace" in meta: + node.meta["stack_trace"] = meta["stack_trace"] + + def create_proxy( + self, + kind, + target, + args, + kwargs, + name=None, + type_expr=None, + proxy_factory_fn=None, + ): + # NOTE: [Nested SubgraphTracer and free_variable handling] + # -------------------------------------------------------- + # Read NOTE [HigherOrderOperator tracing design] first. + # + # Let's say we're in the middle of introspecting the body of a possibly + # nested HigherOrderOperator, and we see a free variable. + # + # There are two cases: + # 1. We see a free variable that is already tracked by Dynamo. + # 2. We see a free variable that has not been tracked by Dynamo + # + # In case 1, we call `maybe_lift_tracked_freevar_to_input` (below) + # which will lift the freevar to be an input of this subgraph + # and also recursively lift it to be an input on the parent(s). + # + # In case 2, before the call to `create_proxy`, the InstructionTranslator + # will see the freevar when it gets loaded by Python bytecode. + # E.g. for Python 3.11 the bytecodes that may do this are LOAD_DEREF or + # LOAD_GLOBAL. + # There, the InstructionTranslator asks Dynamo to begin tracking the + # freevar by building a new Variable. + # Building a new Variable automatically lifts the freevar to be an + # input of the root SubgraphTracer. + # + # The implications for the code below are: + # - We will always be in Case 1 when we get to this code. + # - Any "free variable" we encounter here is guaranteed to already be + # bound, that is, it is either a graph input of the root graph, or + # some local variable of the root graph or a subgraph. + # - The additional work we need to do here is *only* that we need to + # lift this free variable into inputs (recursively) of each nested + # higher-order-op subgraph until we hit the subgraph where the free + # variable is bound + if self.parent is not None: + flat_args, tree_spec = pytree.tree_flatten((args, kwargs)) + new_flat_args = [] + for arg in flat_args: + maybe_new_arg = self.maybe_lift_tracked_freevar_to_input(arg) + new_flat_args.append(maybe_new_arg) + + args, kwargs = pytree.tree_unflatten(new_flat_args, tree_spec) + + rv = super().create_proxy( + kind, target, args, kwargs, name, type_expr, proxy_factory_fn + ) + + # append stack trace to fx node + tx = self.output_graph.current_tx + + # log detailed location of line of code in 3.11 + if sys.version_info >= (3, 11) and kind in ( + "call_function", + "call_method", + "call_module", + ): + cur_inst = tx.current_instruction + if ( + cur_inst is not self.prev_inst + and cur_inst.positions is not None + and cur_inst.positions.lineno is not None + ): + tx_code = tx.f_code + header = tx.get_line_of_code_header(lineno=cur_inst.positions.lineno) + + def get_trace_call_log_str(): + line = get_instruction_source_311(tx_code, cur_inst).rstrip() + return f"TRACE FX call {rv.node.name} from {header}\n{line}" + + trace_call_log.debug("%s", LazyString(get_trace_call_log_str)) + self.prev_inst = cur_inst + + # update reference to original meta if we're tracing a new code object + is_retracing = False + if tx.f_code is not self._cur_code: + orig_graphmodule_maybe = code_context.get_context(tx.f_code).get( + "orig_graphmodule", lambda: None + )() + if isinstance(orig_graphmodule_maybe, torch.fx.GraphModule): + is_retracing = True + self._orig_gm_meta = [ + nd.meta for nd in orig_graphmodule_maybe.graph.nodes + ] + self._orig_gm_lineno_map = orig_graphmodule_maybe._lineno_map + self._orig_gm_firstlineno = ( + orig_graphmodule_maybe.forward.__code__.co_firstlineno + ) + else: + self._orig_gm_meta = None + self._orig_gm_lineno_map = None + self._orig_gm_firstlineno = None + nn_module_stack = tx.nn_module_stack + if nn_module_stack: + rv.node.meta["nn_module_stack"] = nn_module_stack.copy() + + if kind in {"call_function", "call_method"}: + rv.node.meta["source_fn_stack"] = self.source_fn_stack + [ + (rv.node.name, target) + ] + elif kind == "call_module": + if self.parent is not None: + unimplemented("Invoking an nn.Module inside HigherOrderOperator") + # For modules we store the class + rv.node.meta["source_fn_stack"] = self.source_fn_stack + [ + ( + rv.node.name, + rv.node.meta["nn_module_stack"][target][1], + ) + ] + + self._maybe_preserve_original_meta(tx, rv.node) + + if not is_retracing: + if "nn_module_stack" not in rv.node.meta: + nn_module_stack = tx.nn_module_stack + if nn_module_stack: + rv.node.meta["nn_module_stack"] = nn_module_stack.copy() + + if "source_fn_stack" not in rv.node.meta: + if kind in {"call_function", "call_method"}: + rv.node.meta["source_fn_stack"] = self.source_fn_stack + [ + (rv.node.name, target) + ] + elif kind == "call_module": + if self.parent is not None: + unimplemented( + "Invoking an nn.Module inside HigherOrderOperator" + ) + # For modules we store the class + rv.node.meta["source_fn_stack"] = self.source_fn_stack + [ + ( + rv.node.name, + rv.node.meta["nn_module_stack"][target][1], + ) + ] + + if "stack_trace" not in rv.node.meta: + frame_summaries: List[traceback.FrameSummary] = [] + while tx: + # Avoid frame summaries from inside the torch/nn/modules. This ensures that we keep the stack trace of + # the user code. + if not tx.is_co_filename_from_nn_modules(): + frame_summaries.append(tx.frame_summary()) + tx = getattr(tx, "parent", None) + # Reverse the frame_summaries, such that the innermost frame is at the last + frame_summaries.reverse() + + # official from_list stub doesn't have new-style type + msgs = traceback.StackSummary.from_list(frame_summaries).format() + rv.node.stack_trace = "".join(msgs) + + return rv + + def create_node( + self, op, target, args=None, kwargs=None, name=None, type_expr=None + ): + check_pt2_compliant_op(self.output_graph, op, target, args, kwargs) + if self.parent is not None: + flat_args = pytree.arg_tree_leaves(*args, **kwargs) + for arg in flat_args: + if not isinstance(arg, torch.fx.Node): + continue + assert ( + arg.graph == self.graph + ), "create_node using arg not from this SubgraphTracer" + + node = super().create_node(op, target, args, kwargs, name, type_expr) + node.meta["creation_timestamp"] = self.output_graph.timestamp + return node + + # Note: we did not override erase_node since + # we call self.graph.erase_node elsewhere + def remove_node(self, node): + if len(node.users) > 0: + user_graph_nodes: List[torch.fx.Node] = [] + for user in node.users.keys(): + # For the case where user.graph == self.graph, that is a real bug and will raise + # properly. + if user.graph != self.graph: + # This is a nested graph, which needs to be deleted. + # If we do not do this, we will raise on attempting to remove this. + # As we only get here during restoration cleanup, this is sound. + user_graph_nodes.extend(reversed(list(user.graph.nodes))) + for other_graph_node in user_graph_nodes: + other_graph_node.graph.erase_node(other_graph_node) + self.graph.erase_node(node) + self.input_name_to_proxy.pop(node.name, None) + + # when before=True, we will insert this input before the most recent + # inserted proxy. This is a hack to get around an ordering problem, + # where we first insert a tensor argument, and then insert bindings + # for SymInts that may occur in the tensor argument. + # Remove this if https://github.com/pytorch/pytorch/issues/99007 gets + # fixed. + def create_graph_input(self, name, type_expr=None, before=False, source=None): + log.debug( + "create_graph_input %s %s", + name, + source.name() if source is not None else "(none)", + ) + if source is None: + assert ( + self.parent is not None + ), "you are required to provide a source for inputs on the root tracer" + + # In eager, we are generally OK with adding graph inputs whenever we + # want, because we take care of writing the bytecode that knows how + # to source all the inputs. + # + # In export, this is bad, because you want a self-contained export + # object which only depends on the inputs you explicitly passed to it. + # So we are a bit more strict about what sources can become inputs + # in export + if self.export_root: + if not is_from_local_source(source, allow_cell_or_freevar=False): + self.output_graph.source_to_user_stacks.setdefault(source, []).append( + TracingContext.extract_stack() + ) + + # unique + if name in self.input_name_to_proxy: + for i in itertools.count(): + candidate_name = f"{name}_{i}" + if candidate_name not in self.input_name_to_proxy: + name = candidate_name + break + + if self.input_name_to_proxy: + prev_name = next(reversed(self.input_name_to_proxy)) + node = self.input_name_to_proxy[prev_name].node + if before: + ctx = self.graph.inserting_before(node) + else: + ctx = self.graph.inserting_after(node) + else: + ctx = self.graph.inserting_before(None) + with ctx: + proxy = self.create_proxy("placeholder", name, (), {}, type_expr=type_expr) + if self.input_name_to_proxy and before: + k, v = self.input_name_to_proxy.popitem() + self.input_name_to_proxy[name] = proxy + self.input_name_to_proxy[k] = v + else: + self.input_name_to_proxy[name] = proxy + return proxy + + # See NOTE: [Nested SubgraphTracer and free_variable handling] for more details + def lift_tracked_freevar_to_input(self, proxy): + # You're doing something wrong if we are the root SubgraphTracer because + # Dynamo adds tensors to graph inputs before creating a proxy for them. + assert ( + self.parent is not None + ), "lift_tracked_freevar_to_input should not be called on root SubgraphTracer" + # Proxys are associated with VariableTracker. + # It is possible that we've already lifted the Proxy to be an input. + # If that is the case, just return the already lifted Proxy. + if proxy in self.lifted_freevars: + return self.lifted_freevars[proxy] + new_proxy = self.create_graph_input(proxy.node.name) + set_example_value(new_proxy.node, proxy.node.meta["example_value"]) + self.lifted_freevars[proxy] = new_proxy + if self.parent is not None and proxy.tracer != self.parent: + self.parent.lift_tracked_freevar_to_input(proxy) + return new_proxy + + def maybe_lift_tracked_freevar_to_input(self, arg): + """ + If arg is a free variable, then lift it to be an input. + Returns the new lifted arg (if arg was a freevar), else the + original arg. + """ + if not isinstance(arg, torch.fx.Proxy): + return arg + elif arg.tracer == self: + return arg + return self.lift_tracked_freevar_to_input(arg) + + +# NOTE: [HigherOrderOperator tracing design] +# Ignoring HigherOrderOperators for a moment, +# OutputGraph represents the graph being built by Dynamo that may be compiled +# and executed. It holds a root SubgraphTracer where the FX graph is built. +# +# HigherOrderOperators are operators that take functions as their arguments. +# When Dynamo encounters a HigherOrderOperator, then it attempts to introspect +# the function passed to it (call this the "body function"), capture it into a +# GraphModule, and rewrite the call to the HigherOrderOperator to use the +# GraphModule. +# +# The way we handle the capture of body functions is through having +# (possibly nested) SubgraphTracers, one per body function. +# +# Mechanically, we do the introspection by: +# - Creating a new SubgraphTracer via OutputGraph.subtracer +# - Executing the body function. +# This constructs the graph of the body function in the new SubgraphTracer +# while modifying the state of the OutputGraph. For example: +# - the OutputGraph can receive new GraphArgs (if we discover any new +# untracked Tensors) +# - side effects from the body function get accumulated into +# OutputGraph.side_effects +# - guards produced by the body function get accumulated into OutputGraph.guards +# +# The traced function has some special properties that make it easier for us +# to transform later down the line: +# - we lift all free variables to being inputs. +# +# If the introspection fails (due to the existence of graph breaks), then +# we roll back the current OutputGraph state and graph break on the +# HigherOrderOperator. diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/profiler.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/profiler.py new file mode 100644 index 0000000000000000000000000000000000000000..b06fead4c845e790a667d655294b836b87c1160b --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/profiler.py @@ -0,0 +1,156 @@ +# mypy: allow-untyped-defs +import dataclasses +import os +from typing import Any, List + +import torch + +from .utils import print_once + + +@dataclasses.dataclass +class ProfileMetrics: + microseconds: float = 0.0 + operators: int = 0 + fusions: int = 0 + graphs: int = 0 + + def __iadd__(self, other: "ProfileMetrics"): + self.microseconds += other.microseconds + self.operators += other.operators + self.fusions += other.fusions + return self + + def __add__(self, other: "ProfileMetrics"): + assert isinstance(other, ProfileMetrics) + return ProfileMetrics( + self.microseconds + other.microseconds, + self.operators + other.operators, + self.fusions + other.fusions, + ) + + def __truediv__(self, other): + if isinstance(other, int): + other = ProfileMetrics(other, other, other) + return ProfileMetrics( + self.microseconds / max(1, other.microseconds), + self.operators / max(1, other.operators), + self.fusions / max(1, other.fusions), + ) + + def __str__(self) -> str: + return f"{self.operators:4.0%} ops {self.microseconds:4.0%} time" + + def tocsv(self): + return [self.operators, self.microseconds] + + +class ProfileResult: + def __init__(self, captured, total, unique_graphs) -> None: + self.captured: ProfileMetrics = captured or ProfileMetrics() + self.total: ProfileMetrics = total or ProfileMetrics() + self.unique_graphs: int = unique_graphs + + def __iadd__(self, other: "ProfileResult"): + self.captured += other.captured + self.total += other.total + self.unique_graphs += other.unique_graphs + return self + + def percent(self): + return self.captured / self.total + + def __str__(self) -> str: + return ( + f"{self.unique_graphs:2} graphs {self.captured.graphs:2} graph calls " + f"{self.captured.operators:4}/{self.total.operators:4} = " + + str(self.percent()) + ) + + def tocsv(self): + return [ + self.unique_graphs, + self.captured.graphs, + self.captured.operators, + self.total.operators, + ] + self.percent().tocsv() + + +def should_print_missing(): + return os.environ.get("TORCHDYNAMO_PRINT_MISSING") == "1" + + +def print_missing(stack): + if any("/torch/autograd/profiler.py" in x for x in stack): + return + stack = [ + x for x in stack if ("> ".join(stack[-3:])) + + +class Profiler: + unique_graphs = 0 + + def __init__(self) -> None: + self.prof = torch.profiler.profile( + activities=[torch.profiler.ProfilerActivity.CPU], + with_stack=should_print_missing(), + ) + + def results(self): + captured_regions = 0 + captured_ops = 0 + captured_microseconds = 0 + total_ops = 0 + total_microseconds = 0 + + last_op_end_time = -1 + captured_region_end_time = -1 + events = sorted(self.prof.events(), key=lambda x: x.time_range.start) + for e in events: + if e.name == "TORCHDYNAMO": + captured_region_end_time = e.time_range.end + captured_regions += 1 + # ignore `handle = torch.zeros(1)` in record_function.__init__() + total_ops -= 1 + elif e.time_range.start >= last_op_end_time: + last_op_end_time = e.time_range.end + if e.time_range.end <= captured_region_end_time: + captured_ops += 1 + captured_microseconds += e.time_range.elapsed_us() + elif should_print_missing(): + print_missing(e.stack) + total_ops += 1 + total_microseconds += e.time_range.elapsed_us() + else: + pass # ops recursively called from other ops (ignored) + + unique_graphs = Profiler.unique_graphs + Profiler.unique_graphs = 0 + # we counted one extra op that is part of the profiler setup code + total_ops -= 1 + + return ProfileResult( + captured=ProfileMetrics( + microseconds=captured_microseconds, + operators=captured_ops, + fusions=captured_ops - captured_regions, + graphs=captured_regions, + ), + total=ProfileMetrics( + microseconds=total_microseconds, + operators=total_ops, + fusions=total_ops - 1, + ), + unique_graphs=unique_graphs, + ) + + +def fx_insert_profiling(gm: torch.fx.GraphModule, example_inputs: List[Any]): + def _wrapped(*args): + with torch.profiler.record_function("TORCHDYNAMO"): + return gm.forward(*args) + + Profiler.unique_graphs += 1 + return _wrapped diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/replay_record.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/replay_record.py new file mode 100644 index 0000000000000000000000000000000000000000..8a259b6156aa18d3043cf189c9320bce4ebe48fe --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/replay_record.py @@ -0,0 +1,112 @@ +# mypy: allow-untyped-defs +import dataclasses +from dataclasses import field +from types import CodeType, ModuleType +from typing import Any, Dict + +from torch.utils._import_utils import import_dill + + +dill = import_dill() + + +@dataclasses.dataclass +class ModuleRecord: + module: ModuleType + accessed_attrs: Dict[str, Any] = field(default_factory=dict) + + +@dataclasses.dataclass +class DummyModule: + name: str + is_torch: bool = False + + @property + def __name__(self): + return self.name + + +@dataclasses.dataclass +class ExecutionRecord: + code: CodeType + globals: Dict[str, Any] = field(default_factory=dict) + locals: Dict[str, Any] = field(default_factory=dict) + builtins: Dict[str, Any] = field(default_factory=dict) + code_options: Dict[str, Any] = field(default_factory=dict) + + def dump(self, f): + assert dill is not None, "replay_record requires `pip install dill`" + dill.dump(self, f) + + @classmethod + def load(cls, f): + assert dill is not None, "replay_record requires `pip install dill`" + return dill.load(f) + + +@dataclasses.dataclass +class ExecutionRecorder: + LOCAL_MOD_PREFIX = "___local_mod_" + + code: CodeType + globals: Dict[str, Any] = field(default_factory=dict) + locals: Dict[str, Any] = field(default_factory=dict) + builtins: Dict[str, Any] = field(default_factory=dict) + code_options: Dict[str, Any] = field(default_factory=dict) + name_to_modrec: Dict[str, Any] = field(default_factory=dict) + + def add_local_var(self, name, var): + if isinstance(var, ModuleType): + self.locals[name] = self._add_mod(var) + else: + self.locals[name] = var + + def add_global_var(self, name, var): + if isinstance(var, ModuleType): + self.globals[name] = self._add_mod(var) + else: + self.globals[name] = var + + def add_local_mod(self, name, mod): + assert isinstance(mod, ModuleType) + + self.add_global_var(name, mod) + + def record_module_access(self, mod, name, val): + if isinstance(val, ModuleType): + self.name_to_modrec[mod.__name__].accessed_attrs[name] = self._add_mod(val) + return + + if mod.__name__ in self.name_to_modrec: + self.name_to_modrec[mod.__name__].accessed_attrs[name] = val + + def get_record(self): + return ExecutionRecord( + self.code, + ExecutionRecorder._resolve_modules(self.globals), + ExecutionRecorder._resolve_modules(self.locals), + self.builtins.copy(), + self.code_options.copy(), + ) + + def _add_mod(self, mod): + if mod.__name__ not in self.name_to_modrec: + self.name_to_modrec[mod.__name__] = ModuleRecord(mod) + + return self.name_to_modrec[mod.__name__] + + # Convert ModuleRecords -> DummyModule tree + @classmethod + def _resolve_modules(cls, vars): + def resolve_module(var): + if not isinstance(var, ModuleRecord): + return var + + dummy_mod = DummyModule(var.module.__name__) + for attr_name, attr_value in var.accessed_attrs.items(): + attr_value = resolve_module(attr_value) + dummy_mod.__setattr__(attr_name, attr_value) + + return dummy_mod + + return {k: resolve_module(v) for k, v in vars.items()} diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/resume_execution.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/resume_execution.py new file mode 100644 index 0000000000000000000000000000000000000000..132e9e4081bceb4ac53d2ec5627839f8b9b807c9 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/resume_execution.py @@ -0,0 +1,720 @@ +# mypy: allow-untyped-defs +import copy +import dataclasses +import sys +import types +from typing import Any, cast, Dict, List, Optional, Tuple + +from .bytecode_transformation import ( + create_call_function, + create_call_method, + create_dup_top, + create_instruction, + create_jump_absolute, + create_load_method, + Instruction, + InstructionExnTabEntry, + transform_code_object, + unique_id, +) +from .utils import ExactWeakKeyDictionary + + +# taken from code.h in cpython +CO_OPTIMIZED = 0x0001 +CO_NEWLOCALS = 0x0002 +CO_VARARGS = 0x0004 +CO_VARKEYWORDS = 0x0008 +CO_NESTED = 0x0010 +CO_GENERATOR = 0x0020 +CO_NOFREE = 0x0040 +CO_COROUTINE = 0x0080 +CO_ITERABLE_COROUTINE = 0x0100 +CO_ASYNC_GENERATOR = 0x0200 + +# trace_rules.py import this constant for consistency +TORCH_DYNAMO_RESUME_IN_PREFIX = "torch_dynamo_resume_in" + + +def _initial_push_null(insts): + if sys.version_info >= (3, 11): + insts.append(create_instruction("PUSH_NULL")) + if sys.version_info < (3, 13): + insts.append(create_instruction("SWAP", arg=2)) + + +@dataclasses.dataclass(frozen=True) +class ReenterWith: + stack_index: int + target_values: Optional[Tuple[Any, ...]] = None + + # If we do not want to destroy the stack, we can do the same thing as a + # `SETUP_WITH` block, only that we store the context manager in a local_symbol + def try_except(self, code_options, cleanup: List[Instruction]): + """ + Codegen based off of: + load args + enter context + try: + (rest) + finally: + exit context + """ + # NOTE: we assume that TOS is a context manager CLASS! + load_args = [] + if self.target_values: + load_args = [ + create_instruction("LOAD_CONST", argval=val) + for val in self.target_values + ] + ctx_name = unique_id(f"___context_manager_{self.stack_index}") + if ctx_name not in code_options["co_varnames"]: + code_options["co_varnames"] += (ctx_name,) + for name in ["__enter__", "__exit__"]: + if name not in code_options["co_names"]: + code_options["co_names"] += (name,) + + except_jump_target = create_instruction( + "NOP" if sys.version_info < (3, 11) else "PUSH_EXC_INFO" + ) + cleanup_complete_jump_target = create_instruction("NOP") + + setup_finally: List[Instruction] = [] + _initial_push_null(setup_finally) + + # TODO(williamwen42) call method order is wrong for 3.13+ - will fix later + setup_finally.extend( + [ + *load_args, + *create_call_function(len(load_args), False), + create_instruction("STORE_FAST", argval=ctx_name), + create_instruction("LOAD_FAST", argval=ctx_name), + create_load_method("__enter__"), + *create_call_method(0), + create_instruction("POP_TOP"), + ] + ) + + if sys.version_info < (3, 11): + setup_finally.append( + create_instruction("SETUP_FINALLY", target=except_jump_target) + ) + else: + exn_tab_begin = create_instruction("NOP") + exn_tab_end = create_instruction("NOP") + exn_tab_begin.exn_tab_entry = InstructionExnTabEntry( + exn_tab_begin, + exn_tab_end, + except_jump_target, + self.stack_index + 1, + False, + ) + setup_finally.append(exn_tab_begin) + + def create_reset(): + return [ + create_instruction("LOAD_FAST", argval=ctx_name), + create_load_method("__exit__"), + create_instruction("LOAD_CONST", argval=None), + create_dup_top(), + create_dup_top(), + *create_call_method(3), + create_instruction("POP_TOP"), + ] + + if sys.version_info < (3, 9): + epilogue = [ + create_instruction("POP_BLOCK"), + create_instruction("BEGIN_FINALLY"), + except_jump_target, + *create_reset(), + create_instruction("END_FINALLY"), + ] + elif sys.version_info < (3, 11): + epilogue = [ + create_instruction("POP_BLOCK"), + *create_reset(), + create_instruction("JUMP_FORWARD", target=cleanup_complete_jump_target), + except_jump_target, + *create_reset(), + create_instruction("RERAISE"), + cleanup_complete_jump_target, + ] + else: + finally_exn_tab_end = create_instruction("RERAISE", arg=0) + finally_exn_tab_target = create_instruction("COPY", arg=3) + except_jump_target.exn_tab_entry = InstructionExnTabEntry( + except_jump_target, + finally_exn_tab_end, + finally_exn_tab_target, + self.stack_index + 2, + True, + ) + epilogue = [ + exn_tab_end, + *create_reset(), + create_instruction("JUMP_FORWARD", target=cleanup_complete_jump_target), + except_jump_target, # PUSH_EXC_INFO + *create_reset(), + finally_exn_tab_end, # RERAISE 0 + finally_exn_tab_target, # COPY 3 + create_instruction("POP_EXCEPT"), + create_instruction("RERAISE", arg=1), + cleanup_complete_jump_target, + ] + + cleanup[:] = epilogue + cleanup + return setup_finally + + def __call__(self, code_options, cleanup): + """ + Codegen based off of: + with ctx(args): + (rest) + """ + # NOTE: we assume that TOS is a context manager CLASS! + load_args = [] + if self.target_values: + load_args = [ + create_instruction("LOAD_CONST", argval=val) + for val in self.target_values + ] + if sys.version_info < (3, 9): + with_cleanup_start = create_instruction("WITH_CLEANUP_START") + begin_finally = create_instruction("BEGIN_FINALLY") + cleanup[:] = [ + create_instruction("POP_BLOCK"), + begin_finally, + with_cleanup_start, + create_instruction("WITH_CLEANUP_FINISH"), + create_instruction("END_FINALLY"), + ] + cleanup + + return [ + *load_args, + create_instruction("CALL_FUNCTION", arg=len(load_args)), + create_instruction("SETUP_WITH", target=with_cleanup_start), + create_instruction("POP_TOP"), + ], None + elif sys.version_info < (3, 11): + with_except_start = create_instruction("WITH_EXCEPT_START") + pop_top_after_with_except_start = create_instruction("POP_TOP") + + cleanup_complete_jump_target = create_instruction("NOP") + + cleanup[:] = [ + create_instruction("POP_BLOCK"), + create_instruction("LOAD_CONST", argval=None), + create_instruction("DUP_TOP"), + create_instruction("DUP_TOP"), + create_instruction("CALL_FUNCTION", arg=3), + create_instruction("POP_TOP"), + create_instruction("JUMP_FORWARD", target=cleanup_complete_jump_target), + with_except_start, + create_instruction( + "POP_JUMP_IF_TRUE", target=pop_top_after_with_except_start + ), + create_instruction("RERAISE"), + pop_top_after_with_except_start, + create_instruction("POP_TOP"), + create_instruction("POP_TOP"), + create_instruction("POP_EXCEPT"), + create_instruction("POP_TOP"), + cleanup_complete_jump_target, + ] + cleanup + + return [ + *load_args, + create_instruction("CALL_FUNCTION", arg=len(load_args)), + create_instruction("SETUP_WITH", target=with_except_start), + create_instruction("POP_TOP"), + ], None + else: + pop_top_after_with_except_start = create_instruction("POP_TOP") + cleanup_complete_jump_target = create_instruction("NOP") + + def create_load_none(): + return create_instruction("LOAD_CONST", argval=None) + + exn_tab_1_begin = create_instruction("POP_TOP") + exn_tab_1_end = create_instruction("NOP") + exn_tab_1_target = create_instruction("PUSH_EXC_INFO") + exn_tab_2_end = create_instruction("RERAISE", arg=2) + exn_tab_2_target = create_instruction("COPY", arg=3) + + exn_tab_1_begin.exn_tab_entry = InstructionExnTabEntry( + exn_tab_1_begin, + exn_tab_1_end, + exn_tab_1_target, + self.stack_index + 1, + True, + ) + exn_tab_1_target.exn_tab_entry = InstructionExnTabEntry( + exn_tab_1_target, + exn_tab_2_end, + exn_tab_2_target, + self.stack_index + 3, + True, + ) + pop_top_after_with_except_start.exn_tab_entry = InstructionExnTabEntry( + pop_top_after_with_except_start, + pop_top_after_with_except_start, + exn_tab_2_target, + self.stack_index + 3, + True, + ) + + cleanup[:] = [ + exn_tab_1_end, + create_load_none(), + create_load_none(), + create_load_none(), + *create_call_function(2, False), + create_instruction("POP_TOP"), + create_instruction("JUMP_FORWARD", target=cleanup_complete_jump_target), + exn_tab_1_target, # PUSH_EXC_INFO + create_instruction("WITH_EXCEPT_START"), + create_instruction( + "POP_JUMP_FORWARD_IF_TRUE" + if sys.version_info < (3, 12) + else "POP_JUMP_IF_TRUE", + target=pop_top_after_with_except_start, + ), + exn_tab_2_end, # RERAISE 2 + exn_tab_2_target, # COPY 3 + create_instruction("POP_EXCEPT"), + create_instruction("RERAISE", arg=1), + pop_top_after_with_except_start, + create_instruction("POP_EXCEPT"), + create_instruction("POP_TOP"), + create_instruction("POP_TOP"), + cleanup_complete_jump_target, + ] + cleanup + + ret: List[Instruction] = [] + _initial_push_null(ret) + ret.extend( + [ + *load_args, + *create_call_function(len(load_args), False), + create_instruction("BEFORE_WITH"), + exn_tab_1_begin, # POP_TOP + ] + ) + return ret, exn_tab_1_target + + +@dataclasses.dataclass +class ResumeFunctionMetadata: + code: types.CodeType + instructions: List[Instruction] = dataclasses.field(default_factory=list) + # Python 3.11+ fields + # NOTE: Python 3.11 removed blocks, but for our purposes, a "block" consists + # of instructions of all exception table entries that have the same target. + + # map from PUSH_EXC_INFO's in the prefix to original block target offset + prefix_block_target_offset_remap: List[int] = dataclasses.field( + default_factory=list + ) + # map from new block target offsets to original block target offsets + block_target_offset_remap: Optional[Dict[int, int]] = None + + +def _filter_iter(l1, l2, cond): + """ + Two-pointer conditional filter. + e.g. _filter_iter(insts, sorted_offsets, lambda i, o: i.offset == o) + returns the instructions with offsets in sorted_offsets + """ + it = iter(l2) + res: List[Instruction] = [] + try: + cur = next(it) + for val in l1: + if cond(val, cur): + res.append(val) + cur = next(it) + except StopIteration: + pass + return res + + +def _load_tuple_and_call(tup): + insts: List[Instruction] = [] + _initial_push_null(insts) + for val in tup: + insts.append(create_instruction("LOAD_CONST", argval=val)) + insts.extend(create_call_function(len(tup), False)) + return insts + + +class ContinueExecutionCache: + cache = ExactWeakKeyDictionary() + generated_code_metadata = ExactWeakKeyDictionary() + + @classmethod + def lookup(cls, code, lineno, *key): + if code not in cls.cache: + cls.cache[code] = {} + key = tuple(key) + if key not in cls.cache[code]: + cls.cache[code][key] = cls.generate(code, lineno, *key) + return cls.cache[code][key] + + @classmethod + def generate( + cls, + code, + lineno, + offset: int, + setup_fn_target_offsets: Tuple[int], # only used in Python 3.11+ + nstack: int, + argnames: Tuple[str], + argnames_null: Tuple[str], + setup_fns: Tuple[ReenterWith], + stack_ctx_vars: Tuple[int, Tuple[Any]], + argnames_ctx_vars: Tuple[str, Tuple[Any]], + null_idxes: Tuple[int], + ) -> types.CodeType: + assert offset is not None + assert not ( + code.co_flags + & (CO_GENERATOR | CO_COROUTINE | CO_ITERABLE_COROUTINE | CO_ASYNC_GENERATOR) + ) + assert code.co_flags & CO_OPTIMIZED + if code in ContinueExecutionCache.generated_code_metadata: + return cls.generate_based_on_original_code_object( + code, + lineno, + offset, + setup_fn_target_offsets, + nstack, + argnames, + argnames_null, + setup_fns, + stack_ctx_vars, + argnames_ctx_vars, + null_idxes, + ) + + is_py311_plus = sys.version_info >= (3, 11) + meta = ResumeFunctionMetadata(code) + + def update(instructions: List[Instruction], code_options: Dict[str, Any]): + meta.instructions = copy.deepcopy(instructions) + + args = [f"___stack{i}" for i in range(nstack)] + args.extend(v for v in argnames if v not in args) + freevars = tuple(code_options["co_cellvars"] or []) + tuple( + code_options["co_freevars"] or [] + ) + freevars = tuple(sorted(freevars)) + code_options[ + "co_name" + ] = f"{TORCH_DYNAMO_RESUME_IN_PREFIX}_{code_options['co_name']}_at_{lineno}" + if is_py311_plus: + qualified_path = code_options["co_qualname"].rsplit(".", maxsplit=1) + if len(qualified_path) == 1: + code_options["co_qualname"] = code_options["co_name"] + else: + assert len(qualified_path) == 2 + module_name, co_name = qualified_path + code_options[ + "co_qualname" + ] = f"{module_name}.{TORCH_DYNAMO_RESUME_IN_PREFIX}_{co_name}_at_{lineno}" + code_options["co_firstlineno"] = lineno + code_options["co_cellvars"] = () + code_options["co_freevars"] = freevars + code_options["co_argcount"] = len(args) + code_options["co_posonlyargcount"] = 0 + code_options["co_kwonlyargcount"] = 0 + code_options["co_varnames"] = tuple( + args + + [v for v in argnames_null if v not in args] + + [v for v in code_options["co_varnames"] if v not in args] + ) + code_options["co_flags"] = code_options["co_flags"] & ~( + CO_VARARGS | CO_VARKEYWORDS + ) + target = next(i for i in instructions if i.offset == offset) + + prefix = [] + if is_py311_plus: + if freevars: + prefix.append( + create_instruction("COPY_FREE_VARS", arg=len(freevars)) + ) + prefix.append(create_instruction("RESUME", arg=0)) + + cleanup: List[Instruction] = [] + hooks = {fn.stack_index: fn for fn in setup_fns} + hook_target_offsets = { + fn.stack_index: setup_fn_target_offsets[i] + for i, fn in enumerate(setup_fns) + } + offset_to_inst = {inst.offset: inst for inst in instructions} + # map old hook targets to new targets generated by the hook + old_hook_target_remap = {} + null_idxes_i = 0 + stack_ctx_vars_d = dict(stack_ctx_vars) # type: ignore[var-annotated,arg-type] + for i in range(nstack): + while ( + null_idxes_i < len(null_idxes) + and null_idxes[null_idxes_i] == i + null_idxes_i + ): + prefix.append(create_instruction("PUSH_NULL")) + null_idxes_i += 1 + prefix.append(create_instruction("LOAD_FAST", argval=f"___stack{i}")) + if i in hooks: + hook = hooks.pop(i) + hook_insts, exn_target = hook(code_options, cleanup) + prefix.extend(hook_insts) + if is_py311_plus: + hook_target_offset = hook_target_offsets.pop(i) + old_hook_target = offset_to_inst[hook_target_offset] + meta.prefix_block_target_offset_remap.append(hook_target_offset) + old_hook_target_remap[old_hook_target] = exn_target + real_i = i + null_idxes_i + if real_i in stack_ctx_vars_d: + # NOTE: we assume that current stack var is a context manager CLASS! + # Load args for context variable and construct it + prefix.extend(_load_tuple_and_call(stack_ctx_vars_d[real_i])) + + if is_py311_plus: + # reverse the mapping since targets of later/nested contexts are inserted + # into the mapping later, but show up earlier in the prefix. + meta.prefix_block_target_offset_remap = list( + reversed(meta.prefix_block_target_offset_remap) + ) + + assert not hooks + + # NOTE: we assume that local var is a context manager CLASS! + # initialize inactive context vars in argnames + for name, vals in argnames_ctx_vars: + prefix.append(create_instruction("LOAD_FAST", argval=name)) + prefix.extend(_load_tuple_and_call(vals)) + prefix.append(create_instruction("STORE_FAST", argval=name)) + + # 3.12+: store NULL into variables that were NULL + if argnames_null: + assert sys.version_info >= (3, 12) + for v in argnames_null: + assert v not in args + prefix.extend( + [ + create_instruction("PUSH_NULL"), + create_instruction("STORE_FAST", argval=v), + ] + ) + + prefix.append(create_jump_absolute(target)) + + # because the line number table monotonically increases from co_firstlineno + # remove starts_line for any instructions before the graph break instruction + # this will ensure the instructions after the break have the correct line numbers + for inst in instructions: + if inst.offset == target.offset: + break + inst.starts_line = None + if sys.version_info >= (3, 11): + inst.positions = None + + if cleanup: + prefix.extend(cleanup) + prefix.extend(cls.unreachable_codes(code_options)) + + # remap original instructions' exception table entries + if old_hook_target_remap: + assert is_py311_plus + for inst in instructions: + if ( + inst.exn_tab_entry + and inst.exn_tab_entry.target in old_hook_target_remap + ): + inst.exn_tab_entry.target = old_hook_target_remap[ + inst.exn_tab_entry.target + ] + + # TODO(jansel): add dead code elimination here + instructions[:] = prefix + instructions + + new_code = transform_code_object(code, update) + ContinueExecutionCache.generated_code_metadata[new_code] = meta + return new_code + + @staticmethod + def unreachable_codes(code_options) -> List[Instruction]: + """Codegen a `raise None` to make analysis work for unreachable code""" + return [ + create_instruction("LOAD_CONST", argval=None), + create_instruction("RAISE_VARARGS", arg=1), + ] + + @classmethod + def generate_based_on_original_code_object( + cls, code, lineno, offset: int, setup_fn_target_offsets: Tuple[int, ...], *args + ): + """ + This handles the case of generating a resume into code generated + to resume something else. We want to always generate starting + from the original code object so that if control flow paths + converge we only generated 1 resume function (rather than 2^n + resume functions). + """ + + meta: ResumeFunctionMetadata = ContinueExecutionCache.generated_code_metadata[ + code + ] + new_offset = None + + def find_new_offset( + instructions: List[Instruction], code_options: Dict[str, Any] + ): + nonlocal new_offset + (target,) = (i for i in instructions if i.offset == offset) + # match the functions starting at the last instruction as we have added a prefix + (new_target,) = ( + i2 + for i1, i2 in zip(reversed(instructions), reversed(meta.instructions)) + if i1 is target + ) + assert target.opcode == new_target.opcode + new_offset = new_target.offset + + transform_code_object(code, find_new_offset) + + if sys.version_info >= (3, 11): + # setup_fn_target_offsets currently contains the target offset of + # each setup_fn, based on `code`. When we codegen the resume function + # based on the original code object, `meta.code`, the offsets in + # setup_fn_target_offsets must be based on `meta.code` instead. + if not meta.block_target_offset_remap: + block_target_offset_remap = meta.block_target_offset_remap = {} + + def remap_block_offsets( + instructions: List[Instruction], code_options: Dict[str, Any] + ): + # NOTE: each prefix block generates exactly one PUSH_EXC_INFO, + # so we can tell which block a prefix PUSH_EXC_INFO belongs to, + # by counting. Then we can use meta.prefix_block-target_offset_remap + # to determine where in the original code the PUSH_EXC_INFO offset + # replaced. + prefix_blocks: List[Instruction] = [] + for inst in instructions: + if len(prefix_blocks) == len( + meta.prefix_block_target_offset_remap + ): + break + if inst.opname == "PUSH_EXC_INFO": + prefix_blocks.append(inst) + + # offsets into prefix + for inst, o in zip( + prefix_blocks, meta.prefix_block_target_offset_remap + ): + block_target_offset_remap[cast(int, inst.offset)] = o + + # old bytecode targets are after the prefix PUSH_EXC_INFO's + old_start_offset = ( + cast(int, prefix_blocks[-1].offset) if prefix_blocks else -1 + ) + # offsets into old bytecode + old_inst_offsets = sorted( + n for n in setup_fn_target_offsets if n > old_start_offset + ) + targets = _filter_iter( + instructions, old_inst_offsets, lambda inst, o: inst.offset == o + ) + new_targets = _filter_iter( + zip(reversed(instructions), reversed(meta.instructions)), + targets, + lambda v1, v2: v1[0] is v2, + ) + for new, old in zip(new_targets, targets): + block_target_offset_remap[old.offset] = new[1].offset + + transform_code_object(code, remap_block_offsets) + + # if offset is not in setup_fn_target_offsets, it is an error + setup_fn_target_offsets = tuple( + meta.block_target_offset_remap[n] for n in setup_fn_target_offsets + ) + return ContinueExecutionCache.lookup( + meta.code, lineno, new_offset, setup_fn_target_offsets, *args + ) + + +""" +# partially finished support for with statements + +def convert_locals_to_cells( + instructions: List[Instruction], + code_options: Dict[str, Any]): + + code_options["co_cellvars"] = tuple( + var + for var in code_options["co_varnames"] + if var not in code_options["co_freevars"] + and not var.startswith("___stack") + ) + cell_and_free = code_options["co_cellvars"] + code_options["co_freevars"] + for inst in instructions: + if str(inst.argval).startswith("___stack"): + continue + elif inst.opname == "LOAD_FAST": + inst.opname = "LOAD_DEREF" + elif inst.opname == "STORE_FAST": + inst.opname = "STORE_DEREF" + elif inst.opname == "DELETE_FAST": + inst.opname = "DELETE_DEREF" + else: + continue + inst.opcode = dis.opmap[inst.opname] + assert inst.argval in cell_and_free, inst.argval + inst.arg = cell_and_free.index(inst.argval) + +def patch_setup_with( + instructions: List[Instruction], + code_options: Dict[str, Any] +): + nonlocal need_skip + need_skip = True + target_index = next( + idx for idx, i in enumerate(instructions) if i.offset == offset + ) + assert instructions[target_index].opname == "SETUP_WITH" + convert_locals_to_cells(instructions, code_options) + + stack_depth_before = nstack + stack_effect(instructions[target_index].opcode, + instructions[target_index].arg) + + inside_with = [] + inside_with_resume_at = None + stack_depth = stack_depth_before + idx = target_index + 1 + for idx in range(idx, len(instructions)): + inst = instructions[idx] + if inst.opname == "BEGIN_FINALLY": + inside_with_resume_at = inst + break + elif inst.target is not None: + unimplemented("jump from with not supported") + elif inst.opname in ("BEGIN_FINALLY", "WITH_CLEANUP_START", "WITH_CLEANUP_FINISH", "END_FINALLY", + "POP_FINALLY", "POP_EXCEPT", + "POP_BLOCK", "END_ASYNC_FOR"): + unimplemented("block ops not supported") + inside_with.append(inst) + stack_depth += stack_effect(inst.opcode, inst.arg) + assert inside_with_resume_at + + instructions = [ + create_instruction("LOAD_FAST", f"___stack{i}") for i in range(nstack) + ] + [ + create_instruction("SETUP_WITH", target=instructions[target_index].target) + ... call the function ... + unpack_tuple + ] + [ + create_instruction("JUMP_ABSOLUTE", target=inside_with_resume_at) + ] +""" diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/side_effects.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/side_effects.py new file mode 100644 index 0000000000000000000000000000000000000000..fc1bd976ff57df31c87c38b58b60f9f886cac4db --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/side_effects.py @@ -0,0 +1,701 @@ +# mypy: allow-untyped-defs +import functools +import inspect +import warnings +from collections.abc import MutableMapping +from typing import Any, Dict, List, Optional, Type, Union + +import torch.nn + +from . import utils, variables +from .bytecode_transformation import ( + bytecode_from_template, + create_call_function, + create_call_method, + create_instruction, +) +from .codegen import PyCodegen +from .exc import unimplemented +from .source import GlobalSource, LocalSource, Source +from .utils import is_frozen_dataclass, nn_module_new, object_new +from .variables.base import ( + is_side_effect_safe, + MutableLocalBase, + MutableLocalSource, + VariableTracker, +) +from .variables.user_defined import FrozenDataClassVariable + + +class MutableSideEffects(MutableLocalBase): + """ + VariableTracker.mutable_local marker to indicate a list passed as + an input that if we mutate we need to re-apply those mutations after + the graph runs. + """ + + def __init__(self, source: Source, is_modified: bool = False): + super().__init__(MutableLocalSource.Existing) + self.source = source + self.is_modified = is_modified + + +class AttributeMutation(MutableLocalBase): + """ + VariableTracker.mutable_local marker to track changes to attributes + """ + + def __init__(self, typ: MutableLocalSource, source: Optional[Source]): + super().__init__(typ) + self.source = source + + +class AttributeMutationExisting(AttributeMutation): + def __init__(self, source: Source): + super().__init__(MutableLocalSource.Existing, source) + self.source = source + + +class AttributeMutationNew(AttributeMutation): + def __init__(self, source: Optional[Source], cls_source: Optional[Source]): + super().__init__(MutableLocalSource.Local, source) + self.cls_source = cls_source + + +def _manual_update_dict(dict_from, dict_to): + for k, v in dict_from.items(): + dict_to[k] = v + + +class SideEffects: + """ + Track side effects (list mutation, setattr, etc) that need to be + applied after an FX graph is run. + """ + + id_to_variable: Dict[int, VariableTracker] + store_attr_mutations: Dict[MutableLocalBase, Dict[str, VariableTracker]] + keepalive: List[Any] + + def __init__( + self, + id_to_variable=None, + store_attr_mutations=None, + keepalive=None, + save_for_backward=None, + tensor_hooks=None, + ): + super().__init__() + self.id_to_variable = id_to_variable or {} + self.store_attr_mutations = store_attr_mutations or {} + self.keepalive = keepalive or [] + self.save_for_backward = save_for_backward or [] + self.tensor_hooks = tensor_hooks or {} + # Track Compiled Autograd final callbacks that must be called at the end of Compiled Autograd backward graph. + # Only applicable if this graph is created from Dynamo tracing in Compiled Autograd. + self.ca_final_callbacks_var = None + + def __eq__(self, other: object) -> bool: + assert isinstance(other, SideEffects) + # NB: do NOT test keepalive + return ( + self.id_to_variable == other.id_to_variable + and self.store_attr_mutations == other.store_attr_mutations + and self.save_for_backward == other.save_for_backward + and self.tensor_hooks == other.tensor_hooks + ) + + def diff(self, other: "SideEffects") -> Optional[str]: + if self.id_to_variable != other.id_to_variable: + sk_itv = self.id_to_variable.keys() + ok_itv = other.id_to_variable.keys() + if sk_itv != ok_itv: + return f"id_to_variable keys: {sk_itv} != {ok_itv}" + # Feel free to augment this with more fancy diffing logic + # if needed for debugging + return "id_to_variable: unknown diff" + elif self.store_attr_mutations != other.store_attr_mutations: + sk_sam = self.store_attr_mutations.keys() + ok_sam = other.store_attr_mutations.keys() + if sk_sam != ok_sam: + return f"store_attr_mutations keys: {sk_sam} != {ok_sam}" + return "store_attr_mutations: unknown diff" + elif self.save_for_backward != other.save_for_backward: + return "save_for_backward" + elif self.tensor_hooks != other.tensor_hooks: + return "tensor_hooks" + else: + return None + + def clone(self): + """Create a shallow copy""" + return self.__class__( + id_to_variable=dict(self.id_to_variable), + store_attr_mutations={ + k: dict(v) for k, v in self.store_attr_mutations.items() + }, + keepalive=list(self.keepalive), + save_for_backward=self.save_for_backward, + tensor_hooks=self.tensor_hooks, + ) + + def __contains__(self, item): + return id(item) in self.id_to_variable + + def __getitem__(self, item): + return self.id_to_variable[id(item)] + + def check_allowed_side_effect(self, item): + from torch._dynamo.variables.misc import AutogradFunctionContextVariable + + # People do things like self.dim = dim inside autograd.Function. + # These are benign. + if isinstance(item, AutogradFunctionContextVariable): + return True + if not is_side_effect_safe(item.mutable_local): + unimplemented( + "HigherOrderOperator: Mutating a variable not in the current scope (SideEffects)" + ) + + def store_attr(self, item: VariableTracker, name: str, value: VariableTracker): + assert self.is_attribute_mutation(item) + self.check_allowed_side_effect(item) + if item.mutable_local not in self.store_attr_mutations: + self.store_attr_mutations[item.mutable_local] = {} + self.store_attr_mutations[item.mutable_local][name] = value + + def load_attr(self, item, name, deleted_ok=False): + assert self.is_attribute_mutation(item) + result = self.store_attr_mutations[item.mutable_local][name] + if not deleted_ok and isinstance(result, variables.DeletedVariable): + unimplemented("read deleted attribute") + return result + + def store_cell(self, cellvar, value): + assert isinstance(cellvar, variables.NewCellVariable) + assert isinstance(value, variables.VariableTracker) + self.store_attr(cellvar, "cell_contents", value) + + def load_cell(self, cellvar): + assert isinstance(cellvar, variables.NewCellVariable) + return self.load_attr(cellvar, "cell_contents") + + def load_global(self, gvar: VariableTracker, name: str): + assert isinstance(gvar, variables.VariableTracker) + return self.load_attr(gvar, name) + + def store_global(self, gvar: VariableTracker, name: str, value: VariableTracker): + assert isinstance(gvar, variables.VariableTracker) + assert isinstance(value, variables.VariableTracker) + self.store_attr(gvar, name, value) + + @staticmethod + def cls_supports_mutation_side_effects(cls): + return ( + inspect.getattr_static(cls, "__getattribute__", None) + is object.__getattribute__ + ) + + def is_attribute_mutation(self, item): + return isinstance(item.mutable_local, AttributeMutation) + + def has_pending_mutation(self, item): + return self.is_attribute_mutation(item) and bool( + self.store_attr_mutations.get(item.mutable_local) + ) + + def has_pending_mutation_of_attr(self, item, name): + return self.is_attribute_mutation( + item + ) and name in self.store_attr_mutations.get(item.mutable_local, ()) + + def is_modified(self, item): + if isinstance(item.mutable_local, AttributeMutationNew): + return True + if self.is_attribute_mutation(item): + return item.mutable_local in self.store_attr_mutations + return item.mutable_local.is_modified + + def _track_obj( + self, + item: Any, + variable: VariableTracker, + mutable_cls=MutableSideEffects, + ): + """Start tracking a new variable for mutation""" + assert variable.source is not None + + if id(item) in self.id_to_variable: + raise AssertionError( + f"{variable} is already tracked for mutation. This could be " + "because you are not using VariableBuilder to construct " + "the variable tracker. " + f"Source of new object: {variable.source}. " + f"Source of previously tracked object: {self.id_to_variable[id(item)].source}." + ) + + variable.mutable_local = mutable_cls(variable.source) + self.id_to_variable[id(item)] = variable + self.keepalive.append(item) + return variable + + track_mutable = _track_obj + + def track_object_existing( + self, + item: Any, + variable: VariableTracker, + ): + return self._track_obj(item, variable, mutable_cls=AttributeMutationExisting) + + def track_object_new( + self, + cls_source: Source, + user_cls: Any, + variable_cls: Any, + options, + ): + if user_cls is torch.autograd.function.FunctionCtx: + with warnings.catch_warnings(record=True): + obj = torch.autograd.Function() + elif issubclass(user_cls, torch.nn.Module): + obj = nn_module_new(user_cls) + else: + obj = object_new(user_cls) + variable = variable_cls( + obj, + mutable_local=AttributeMutationNew(None, cls_source), + **options, + ) + self.id_to_variable[id(obj)] = variable + self.keepalive.append(obj) + return variable + + def track_object_new_from_user_defined_class( + self, + cls_variable: "variables.UserDefinedClassVariable", + ): + cls_source = cls_variable.source + user_cls = cls_variable.value + + # Find the variable class + variable_cls: Type[ + variables.UserDefinedObjectVariable + ] = variables.UserDefinedObjectVariable + if issubclass(user_cls, torch.nn.Module): + variable_cls = variables.UnspecializedNNModuleVariable + elif issubclass(user_cls, MutableMapping): + variable_cls = variables.MutableMappingVariable + elif is_frozen_dataclass(user_cls): + variable_cls = FrozenDataClassVariable + else: + variable_cls = variables.UserDefinedObjectVariable + + assert issubclass(variable_cls, variables.UserDefinedObjectVariable) + + variable_cls = functools.partial(variable_cls, cls_source=cls_source) + + return self.track_object_new(cls_source, user_cls, variable_cls, {}) + + def track_cell_new( + self, + ): + obj = object() + variable = variables.NewCellVariable( + mutable_local=AttributeMutationNew(None, None), + ) + self.id_to_variable[id(obj)] = variable + self.keepalive.append(obj) + return variable + + def track_cell_existing(self, source: Source, item: Any): + variable = variables.NewCellVariable( + mutable_local=AttributeMutationExisting(source), + ) + self.id_to_variable[id(item)] = variable + self.keepalive.append(item) + return variable + + def track_global_existing(self, source: Source, item: Any): + variable = variables.NewGlobalVariable( + mutable_local=AttributeMutationExisting(source), + ) + self.id_to_variable[id(item)] = variable + self.keepalive.append(item) + return variable + + def track_save_for_backward(self, ctx, args): + assert isinstance(ctx, variables.AutogradFunctionContextVariable) + self.save_for_backward.append((ctx, args)) + + def track_tensor_variables_from_runahead_side_effects(self, other): + # In higher order ops we want to keep track of tensors seen in the + # speculate_subgraph so that we don't lift them again as a new input in + # other speculate_subgraph or in the root tracer. + for other_item in other.keepalive: + other_id = id(other_item) + other_variable = other.id_to_variable[other_id] + if other_id not in self.id_to_variable and isinstance( + other_variable, variables.TensorVariable + ): + self.track_object_existing(other_item, other_variable) + + def prune_dead_object_new(self, tx): + live_new_objects = set() + + # use this to avoid cycles in mutable_local (though I'm not sure if that + # can actually happen). + visited: Any = set({}) + + def visit(var: VariableTracker): + mutable_local = var.mutable_local + if mutable_local is None: + return + if mutable_local in visited: + return + visited.add(mutable_local) + # Object may have been mutated, store this mutation. + if isinstance(mutable_local, AttributeMutationNew): + live_new_objects.add(mutable_local) + # It's possible that we have mutated the value of this variable + # to be another one. The new value is in store_attr_mutations. + # Also recurse through the new value to detect alive AttributeMutationNew. + if var.mutable_local in self.store_attr_mutations: + VariableTracker.visit( + visit, self.store_attr_mutations[var.mutable_local] + ) + + def is_live(var: Union[MutableLocalBase, VariableTracker]): + if isinstance(var, AttributeMutationNew): + return var in live_new_objects + if isinstance(var, VariableTracker): + return is_live(var.mutable_local) + return True + + pre_existing_vars = [ + var + for var in self.id_to_variable.values() + if not isinstance(var.mutable_local, AttributeMutationNew) + ] + + # The only live side effects come from returns (tx.stack), any intermediates + # during a graph break (tx.symbolic_locals), and mutation on pre-existing variables. + # Recursively visit Variables and see if any of them have been mutated. + VariableTracker.visit(visit, (tx.stack, tx.symbolic_locals, pre_existing_vars)) + + # NB: cell variable handling.is tricky. + # cell variables must stay alive if any NestedUserFunctionVariable + # are live. "visit"-ing the NestedUserFunctionVariable visits + # the .closures field, from which we will see if we need to keep + # any mutations to cell variables alive. + + self.id_to_variable = { + k: v for k, v in self.id_to_variable.items() if is_live(v) + } + self.store_attr_mutations = { + k: v for k, v in self.store_attr_mutations.items() if is_live(k) + } + + def mutation(self, var): + self.check_allowed_side_effect(var) + if isinstance(var.mutable_local, MutableSideEffects): + var.mutable_local = MutableSideEffects(var.mutable_local.source, True) + + def _get_modified_vars(self): + return [var for var in self.id_to_variable.values() if self.is_modified(var)] + + def codegen_save_tempvars(self, cg: PyCodegen): + for var in self._get_modified_vars(): + if isinstance( + var.mutable_local, (AttributeMutationExisting, AttributeMutationNew) + ) and isinstance(var, variables.NewCellVariable): + cg.add_push_null( + lambda: cg.load_import_from(utils.__name__, "make_cell") + ) + cg.extend_output(create_call_function(0, False)) + cg.add_cache(var) + if isinstance(var.mutable_local, AttributeMutationNew): + var.mutable_local.source = LocalSource(cg.tempvars[var]) # type: ignore[attr-defined] + elif isinstance(var.mutable_local, AttributeMutationNew): + if isinstance(var, variables.AutogradFunctionContextVariable): + unimplemented("AutogradFunctionContextVariable escaped") + cg.add_push_null( + lambda: cg.load_import_from(utils.__name__, "object_new") + ) + cg(var.mutable_local.cls_source) + cg.extend_output(create_call_function(1, False)) + cg.add_cache(var) + var.mutable_local.source = LocalSource(cg.tempvars[var]) + elif var in cg.tempvars: + assert cg.tempvars.get(var) is None + # subsequent usage should point to the original variable + cg(var.mutable_local.source) + cg.add_cache(var) + + for ctx, args in self.save_for_backward: + cg(ctx.source) + cg.load_method("save_for_backward") + for arg in args: + cg(arg) + cg.extend_output( + [ + *create_call_method(len(args)), + create_instruction("POP_TOP"), + ] + ) + + def register_hook(self, tensor, hook, handle, name): + assert isinstance(tensor, variables.TensorVariable) + assert isinstance(hook, variables.VariableTracker) + assert ( + isinstance(handle, variables.RemovableHandleVariable) + and handle.mutable_local + ) + assert hasattr(torch.Tensor, name) + idx = len(self.tensor_hooks.keys()) + # duplicate index possible because of self.remove_hook() + while idx in self.tensor_hooks: + idx += 1 + self.tensor_hooks[idx] = (tensor, hook, handle, name) + assert not handle.idx + handle.idx = idx + + def remove_hook(self, idx): + del self.tensor_hooks[idx] + + def codegen_hooks(self, cg): + for ( + tensor, + hook, + handle, + name, + ) in self.tensor_hooks.values(): + # Note: [On tensor.register_hook] + # + # register_hook on a tensor, AKA backward hooks, have slightly nuanced differences in how they are implemented + # when it comes to hooks on objects with sources (inputs, params) vs objects without sources (intermediaries). + # + # For tensors with a source, we bypass direct inclusion of register_hook calls in the graph. + # Instead, these are tracked and stashed as a global variable, enabling their association with tensors in + # the residuals. During dynamo's frame creation, these hooks are invoked seamlessly on known reconstructible/fetch-able + # tensors. Because a source indicates knowledge of this object outside the torch compile region, and + # because we are running residuals firmly before .backward() can be run, it is sound to invoke + # `register_hook` on a known tensor. + # + # For tensors without a source, we support a limited subset of hooks. Global functions only, and + # compiled_autograd must be enabled or we will graph break. + # + # Handling the Handle: When a user retains the register_hook result in a handle, we intercept the + # STORE_FAST operation to record the user-designated local variable name. This ensures the reconstructed + # bytecode retains this name. If no handle is defined, we simply pop the generated value to keep the + # stack intact. + # + # Dynamo Tensor Hooks Workflow: + # - Functions passed to register_hook are lifted globally. + # - For tensors with sources: + # - In the "side_effects" phase of codegen, we iterate over tensors with hooks to: + # - Generate the tensor. + # - Issue a register_hook call on the tensor, linking to the globally stored function. + # - Incorporate a handle if one was established in the eager phase. + # - For tensors without sources: + # - We don't generate any instructions for registering a hook. + # - Handles from intermediary hooks are NYI. + # - We produce a call function that utilizes the trace_wrapped higher order op, closing over it. + # - We then manually insert the call function above into the graph. + # - The handle's exact user-specified name, "user_code_variable_name", is discerned and associated during STORE_FAST. + assert tensor.source, "Hooks on non input tensors NYI - should not get here" + + def gen_fn(): + cg(tensor) + cg.extend_output([cg.create_load_attr(name)]) + + cg.add_push_null(gen_fn) + cg(hook) + cg.extend_output(create_call_function(1, False)) + + # Adding the handle to the cache means RemovableHandleVariable().reconstruct() will + # be associated with the return value of register_hook(). This consumes the top of stack. + cg.add_cache(handle) + + def get_ca_final_callbacks_var(self): + from .variables.base import MutableLocal + + if self.ca_final_callbacks_var is None: + self.ca_final_callbacks_var = variables.ListVariable( + [], mutable_local=MutableLocal() + ) + return self.ca_final_callbacks_var + + def codegen_update_mutated(self, cg: PyCodegen): + suffixes = [] + for var in self._get_modified_vars(): + if isinstance(var, variables.ListVariable): + # old[:] = new + cg(var, allow_cache=False) + cg(var.mutable_local.source) # type: ignore[attr-defined] + cg.extend_output( + [ + cg.create_load_const(None), + cg.create_load_const(None), + create_instruction("BUILD_SLICE", arg=2), + ] + ) + suffixes.append([create_instruction("STORE_SUBSCR")]) + elif isinstance(var, variables.CustomizedDictVariable): + # need to update the dict manually since update method may be invalid + varname_map = {} + for name in _manual_update_dict.__code__.co_varnames: + varname_map[name] = cg.tx.output.new_var() + + cg(var.mutable_local.source) # type: ignore[attr-defined] + cg.extend_output( + [create_instruction("STORE_FAST", argval=varname_map["dict_to"])] + ) + + cg(var, allow_cache=False) + cg.extend_output( + [create_instruction("STORE_FAST", argval=varname_map["dict_from"])] + ) + + cg(var.mutable_local.source) # type: ignore[attr-defined] + cg.load_method("clear") + + # unfortunately can't just use DICT_MERGE due to possible custom behaviors + dict_update_insts = bytecode_from_template( + _manual_update_dict, varname_map=varname_map + ) + + suffixes.append( + [ + *create_call_method(0), # clear + create_instruction("POP_TOP"), + *dict_update_insts, + create_instruction("POP_TOP"), + ] + ) + + elif isinstance(var, variables.ConstDictVariable): + cg(var.mutable_local.source) # type: ignore[attr-defined] + cg.load_method("update") + cg(var, allow_cache=False) + + cg(var.mutable_local.source) # type: ignore[attr-defined] + cg.load_method("clear") + + suffixes.append( + [ + *create_call_method(0), # clear + create_instruction("POP_TOP"), + *create_call_method(1), # update + create_instruction("POP_TOP"), + ] + ) + elif isinstance( + var, variables.torch_function.TorchFunctionModeStackVariable + ): + cg.add_push_null( + lambda: cg.load_import_from( + utils.__name__, "set_torch_function_mode_stack" + ) + ) + cg.foreach(var.symbolic_stack) + cg.append_output( + create_instruction("BUILD_LIST", arg=len(var.symbolic_stack)) + ) + cg.call_function(1, False) + cg.append_output(create_instruction("POP_TOP")) + elif self.is_attribute_mutation(var): + # Applying mutations involves two steps: 1) Push all + # reconstructed objects onto the stack. 2) Call STORE_ATTR to + # apply the mutations. + # + # Dynamo must ensure that mutations are applied in the same + # order as in the original program. Therefore, two reverse + # operations occur below. + # + # The first reverse operation concerns `suffixes`. We apply + # suffixes in reverse order due to the way Python handles the + # stack. In Step 1, we push all reconstructed objects onto the + # stack, but the item at the top of the stack refers to the last + # attribute in the mutation order. If not fixed, this will apply + # the mutations of attributes in the reverse order. To account + # for this reversal, we iterate through the mutable attributes + # in reverse order. + for name, value in reversed( + self.store_attr_mutations.get(var.mutable_local, {}).items() + ): + if isinstance(var, variables.NewGlobalVariable): + cg.tx.output.update_co_names(name) + cg(value) + assert isinstance(var.mutable_local.source, GlobalSource) # type: ignore[attr-defined] + suffixes.append( + [create_instruction("STORE_GLOBAL", argval=name)] + ) + elif isinstance(value, variables.DeletedVariable): + if isinstance( + var.mutable_local, AttributeMutationExisting + ) and hasattr(getattr(var, "value", None), name): + cg.tx.output.update_co_names(name) + cg(var.mutable_local.source) + suffixes.append( + [create_instruction("DELETE_ATTR", argval=name)] + ) + elif ( + isinstance(var, variables.UserDefinedObjectVariable) + and var.needs_slow_setattr() + ): + # __setattr__ is defined on this object, so call object.__setattr__ directly + cg.load_import_from("builtins", "object") + cg.load_method("__setattr__") + cg(var.mutable_local.source) # type: ignore[attr-defined] + cg(variables.ConstantVariable(name)) + cg(value) + suffixes.append( + [*create_call_method(3), create_instruction("POP_TOP")] + ) + else: + cg.tx.output.update_co_names(name) + cg(value) + cg(var.mutable_local.source) + suffixes.append([create_instruction("STORE_ATTR", argval=name)]) + elif isinstance(var, variables.TupleIteratorVariable): + for _ in range(var.index): + cg.add_push_null( + lambda: cg.load_import_from(utils.__name__, "iter_next") + ) + cg(var.mutable_local.source) # type: ignore[attr-defined] + cg.call_function(1, False) + cg.pop_top() + elif isinstance(var, variables.RandomVariable): + # set correct random seed state + def gen_fn(): + cg(var.mutable_local.source) # type: ignore[attr-defined] + cg.load_attr("setstate") + + cg.add_push_null(gen_fn) + cg(var.wrap_state(var.random.getstate())) + + suffixes.append( + [ + *create_call_function(1, False), # setstate + create_instruction("POP_TOP"), + ] + ) + else: + raise AssertionError(type(var)) + + # do all the actual mutations at the very end to handle dependencies + for suffix in reversed(suffixes): + cg.extend_output(suffix) + + def is_empty(self): + return not ( + any(map(self.is_modified, self.id_to_variable.values())) + or self.tensor_hooks + or self.save_for_backward + or self.tensor_hooks + ) + + def clear(self): + self.keepalive.clear() + self.id_to_variable.clear() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/source.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/source.py new file mode 100644 index 0000000000000000000000000000000000000000..2d3a4424167da9a75d2f62bd51e2813de0baeadd --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/source.py @@ -0,0 +1,759 @@ +# mypy: allow-untyped-defs +import collections +import dataclasses +import enum +from typing import Any, Optional, Union + +from torch._guards import ChainedSource, GuardSource, Source + +from . import utils +from .bytecode_transformation import create_call_function, create_instruction +from .utils import enum_repr + + +# It shouldn't be supported to construct an NNModuleVariable inside an FSDP module, +# so those cases are omitted intentionally + +# represents nn.Modules tracked with NNModuleVariable (specialized is implicit in the variable name) +_GUARD_SOURCE_SPECIALIZED_NN_MODULE = { + GuardSource.LOCAL: GuardSource.LOCAL_SPECIALIZED_NN_MODULE, + GuardSource.GLOBAL: GuardSource.GLOBAL_SPECIALIZED_NN_MODULE, + GuardSource.LOCAL_SPECIALIZED_NN_MODULE: GuardSource.LOCAL_SPECIALIZED_NN_MODULE, + GuardSource.GLOBAL_SPECIALIZED_NN_MODULE: GuardSource.GLOBAL_SPECIALIZED_NN_MODULE, + # Just to ensure that guard_source() works + GuardSource.LOCAL_UNSPECIALIZED_NN_MODULE: GuardSource.LOCAL_UNSPECIALIZED_NN_MODULE, + GuardSource.GLOBAL_UNSPECIALIZED_NN_MODULE: GuardSource.GLOBAL_UNSPECIALIZED_NN_MODULE, + GuardSource.LOCAL_UNSPECIALIZED_BUILTIN_NN_MODULE: GuardSource.LOCAL_UNSPECIALIZED_BUILTIN_NN_MODULE, + GuardSource.GLOBAL_UNSPECIALIZED_BUILTIN_NN_MODULE: GuardSource.GLOBAL_UNSPECIALIZED_BUILTIN_NN_MODULE, +} + +# represents nn.Modules tracked with UnspecializedNNModuleVariable +_GUARD_SOURCE_UNSPECIALIZED_NN_MODULE = { + GuardSource.LOCAL: GuardSource.LOCAL_UNSPECIALIZED_NN_MODULE, + GuardSource.GLOBAL: GuardSource.GLOBAL_UNSPECIALIZED_NN_MODULE, + GuardSource.LOCAL_UNSPECIALIZED_NN_MODULE: GuardSource.LOCAL_UNSPECIALIZED_NN_MODULE, + GuardSource.GLOBAL_UNSPECIALIZED_NN_MODULE: GuardSource.GLOBAL_UNSPECIALIZED_NN_MODULE, + # this happens for an UnspecializedNNModule submodule on a NNModuleVariable + GuardSource.LOCAL_SPECIALIZED_NN_MODULE: GuardSource.LOCAL_UNSPECIALIZED_NN_MODULE, + GuardSource.GLOBAL_SPECIALIZED_NN_MODULE: GuardSource.GLOBAL_UNSPECIALIZED_NN_MODULE, + # Just to ensure that guard_source() works + GuardSource.LOCAL_UNSPECIALIZED_BUILTIN_NN_MODULE: GuardSource.LOCAL_UNSPECIALIZED_BUILTIN_NN_MODULE, + GuardSource.GLOBAL_UNSPECIALIZED_BUILTIN_NN_MODULE: GuardSource.GLOBAL_UNSPECIALIZED_BUILTIN_NN_MODULE, +} + +# represents nn.Modules tracked with UnspecializedBuiltinNNModuleVariable +_GUARD_SOURCE_UNSPECIALIZED_BUILTIN_NN_MODULE = { + GuardSource.LOCAL: GuardSource.LOCAL_UNSPECIALIZED_BUILTIN_NN_MODULE, + GuardSource.GLOBAL: GuardSource.GLOBAL_UNSPECIALIZED_BUILTIN_NN_MODULE, + GuardSource.LOCAL_UNSPECIALIZED_NN_MODULE: GuardSource.LOCAL_UNSPECIALIZED_BUILTIN_NN_MODULE, + GuardSource.GLOBAL_UNSPECIALIZED_NN_MODULE: GuardSource.GLOBAL_UNSPECIALIZED_BUILTIN_NN_MODULE, + GuardSource.LOCAL_SPECIALIZED_NN_MODULE: GuardSource.LOCAL_UNSPECIALIZED_BUILTIN_NN_MODULE, + GuardSource.GLOBAL_SPECIALIZED_NN_MODULE: GuardSource.GLOBAL_UNSPECIALIZED_BUILTIN_NN_MODULE, + # Just to ensure that guard_source() works + GuardSource.LOCAL_UNSPECIALIZED_BUILTIN_NN_MODULE: GuardSource.LOCAL_UNSPECIALIZED_BUILTIN_NN_MODULE, + GuardSource.GLOBAL_UNSPECIALIZED_BUILTIN_NN_MODULE: GuardSource.GLOBAL_UNSPECIALIZED_BUILTIN_NN_MODULE, +} + +_GUARD_SOURCE_FSDP_MODULE = { + GuardSource.LOCAL: GuardSource.LOCAL_FSDP_MODULE, + GuardSource.GLOBAL: GuardSource.GLOBAL_FSDP_MODULE, + GuardSource.LOCAL_SPECIALIZED_NN_MODULE: GuardSource.LOCAL_FSDP_MODULE, + GuardSource.GLOBAL_SPECIALIZED_NN_MODULE: GuardSource.GLOBAL_FSDP_MODULE, + GuardSource.LOCAL_FSDP_MODULE: GuardSource.LOCAL_FSDP_MODULE, + GuardSource.GLOBAL_FSDP_MODULE: GuardSource.GLOBAL_FSDP_MODULE, + GuardSource.LOCAL_UNSPECIALIZED_NN_MODULE: GuardSource.LOCAL_FSDP_MODULE, + GuardSource.GLOBAL_UNSPECIALIZED_NN_MODULE: GuardSource.GLOBAL_FSDP_MODULE, + GuardSource.LOCAL_UNSPECIALIZED_BUILTIN_NN_MODULE: GuardSource.LOCAL_FSDP_MODULE, + GuardSource.GLOBAL_UNSPECIALIZED_BUILTIN_NN_MODULE: GuardSource.GLOBAL_FSDP_MODULE, +} + + +def is_constant_source(source): + if isinstance(source, ConstantSource): + return True + try: + if source.guard_source() == GuardSource.CONSTANT: + return True + except NotImplementedError: + pass + + return False + + +def reconstruct_getitem( + source: Union["GetItemSource", "ODictGetItemSource"], codegen, index_is_slice +): + source.base.reconstruct(codegen) + if isinstance(source.index, Source): + source.index.reconstruct(codegen) + else: + if index_is_slice: + assert isinstance(source, GetItemSource) + codegen.append_output(codegen.create_load_const(source.unpack_slice())) + else: + codegen.append_output(codegen.create_load_const(source.index)) + + +@dataclasses.dataclass(frozen=True) +class LocalSource(Source): + local_name: str + cell_or_freevar: bool = False + + def reconstruct(self, codegen): + codegen.append_output(codegen.create_load(self.local_name)) + + def guard_source(self): + return GuardSource.LOCAL + + def name(self): + return f"L[{repr(self.local_name)}]" + + +@dataclasses.dataclass(frozen=True) +class SyntheticLocalSource(Source): + local_name: str + + def reconstruct(self, codegen): + codegen.append_output(codegen.create_load(self.local_name)) + + def guard_source(self): + return GuardSource.SYNTHETIC_LOCAL + + def name(self): + return f"SYNTHETIC_LOCAL[{self.local_name!r}]" + + +@dataclasses.dataclass(frozen=True) +class RandomValueSource(Source): + random_call_index: int + + def guard_source(self): + return GuardSource.RANDOM_VALUE + + def reconstruct(self, codegen): + codegen.append_output(codegen.create_load(codegen.tx.output.random_values_var)) + codegen.append_output(codegen.create_load_const(self.random_call_index)) + codegen.append_output(create_instruction("BINARY_SUBSCR")) + + def name(self): + return f"random_value_{self.random_call_index}" + + +@dataclasses.dataclass(frozen=True) +class GlobalSource(Source): + global_name: str + + def reconstruct(self, codegen): + codegen.append_output(codegen.create_load_global(self.global_name, add=True)) + + def guard_source(self): + return GuardSource.GLOBAL + + def name(self): + return f"G[{repr(self.global_name)}]" + + +@dataclasses.dataclass(frozen=True) +class GlobalWeakRefSource(Source): + global_name: str + + def reconstruct(self, codegen): + codegen.add_push_null( + lambda: codegen.append_output( + codegen.create_load_global(self.global_name, add=True) + ) + ) + codegen.extend_output(create_call_function(0, False)) + + def guard_source(self): + return GuardSource.GLOBAL + + def name(self): + return f"G[{repr(self.global_name)}]()" + + +@dataclasses.dataclass(frozen=True) +class WeakRefCallSource(ChainedSource): + def reconstruct(self, codegen): + codegen.add_push_null(lambda: self.base.reconstruct(codegen)) + codegen.extend_output(create_call_function(0, False)) + + def guard_source(self): + return self.base.guard_source() + + def name(self): + return f"{self.base.name()}()" + + +@dataclasses.dataclass(frozen=True) +class AttrSource(ChainedSource): + member: str + + def __post_init__(self): + assert self.base, "Can't construct an AttrSource without a valid base source" + if "." in self.member: + member_parts = self.member.split(".") + object.__setattr__( + self, "base", AttrSource(self.base, ".".join(member_parts[:-1])) + ) + object.__setattr__(self, "member", member_parts[-1]) + + def reconstruct(self, codegen): + self.base.reconstruct(codegen) + codegen.extend_output(codegen.create_load_attrs(self.member)) + + def guard_source(self): + return self.base.guard_source() + + def name(self): + if not self.member.isidentifier(): + return f"getattr({self.base.name()}, {self.member!r})" + return f"{self.base.name()}.{self.member}" + + +# Represents tensor.grad source. It could be represented by AttrSource as well. +# But, we could access grad field on tensor directly in C++ without going +# through the Python bytecodes. Therefore, we use a separate source for grad +# field. +@dataclasses.dataclass(frozen=True) +class GradSource(ChainedSource): + member: str = "grad" + + def reconstruct(self, codegen): + self.base.reconstruct(codegen) + codegen.extend_output(codegen.create_load_attrs(self.member)) + + def guard_source(self): + return self.base.guard_source() + + def name(self): + return f"{self.base.name()}.{self.member}" + + +@dataclasses.dataclass(frozen=True) +class ParamBufferSource(AttrSource): + def guard_source(self): + return _GUARD_SOURCE_SPECIALIZED_NN_MODULE[self.base.guard_source()] + + +# Special AttrSource to differentiate module._buffers or module._parameters +@dataclasses.dataclass(frozen=True) +class UnspecializedParamBufferSource(AttrSource): + pass + + +# This source is intended to be used in places where a source is needed but it is expected +# that the symbol will be simplified out later on. Symbols with ephemeral sources are +# prioritized to be simplified out when e.g. compared against a symbol without an ephemeral +# source. Guarding on this source is an error. +# +# Example: During subclass view fake-ification, any close-over ViewFunc state should be +# symbolicized / fake-ified to avoid invalid specialization during view replay. This source +# is useful for symbols utilized in the middle of the view chain that are not expected to be +# present within the final view shape metadata. +@dataclasses.dataclass(frozen=True) +class EphemeralSource(Source): + desc: Optional[str] = None + + def guard_source(self): + return GuardSource.EPHEMERAL + + def name(self): + return f"" + + def make_guard(self): + raise NotImplementedError + + def is_ephemeral(self): + return True + + +class TensorProperty(enum.Enum): + SIZE = 0 + STRIDE = 1 + STORAGE_OFFSET = 2 + + def method_name(self): + if self is TensorProperty.SIZE: + return "size" + elif self is TensorProperty.STRIDE: + return "stride" + elif self is TensorProperty.STORAGE_OFFSET: + return "storage_offset" + + +@dataclasses.dataclass(frozen=True) +class TensorPropertySource(ChainedSource): + prop: TensorProperty + idx: Optional[int] = None # None for STORAGE_OFFSET + + def __post_init__(self): + assert self.base is not None + if self.prop is TensorProperty.STORAGE_OFFSET: + assert self.idx is None + else: + assert self.idx is not None + + def reconstruct(self, codegen): + def gen_fn(): + self.base.reconstruct(codegen) + codegen.append_output(codegen.create_load_attr(self.prop.method_name())) + + codegen.add_push_null(gen_fn) + if self.idx is not None: + codegen.append_output(codegen.create_load_const(self.idx)) + codegen.extend_output( + create_call_function(1 if self.idx is not None else 0, False) + ) + + def guard_source(self): + return self.base.guard_source() + + def name(self): + if self.prop is TensorProperty.SIZE: + return f"{self.base.name()}.size()[{self.idx}]" + elif self.prop is TensorProperty.STRIDE: + return f"{self.base.name()}.stride()[{self.idx}]" + elif self.prop is TensorProperty.STORAGE_OFFSET: + assert self.idx is None + return f"{self.base.name()}.storage_offset()" + else: + raise AssertionError(f"unhandled {self.prop}") + + +@dataclasses.dataclass(frozen=True) +class NegateSource(ChainedSource): + def __post_init__(self): + assert self.base is not None + + def reconstruct(self, codegen): + raise NotImplementedError + + def guard_source(self): + return self.base.guard_source() + + def name(self): + # NB: use method call so that function stripping regexes work + return f"{self.base.name()}.__neg__()" + + +@dataclasses.dataclass(frozen=True) +class ConvertIntSource(ChainedSource): + def __post_init__(self): + assert self.base is not None + + def reconstruct(self, codegen): + self.base.reconstruct(codegen) + + def guard_source(self): + return self.base.guard_source() + + def name(self): + return f"cast_symbool_to_symint_guardless({self.base.name()})" + + +@dataclasses.dataclass(frozen=True) +class FlattenScriptObjectSource(ChainedSource): + def __post_init__(self): + assert self.base is not None + + def reconstruct(self, codegen): + self.base.reconstruct(codegen) + + def guard_source(self): + return self.base.guard_source() + + def name(self): + return f"{self.base.name()}.__obj_flatten__()" + + +@dataclasses.dataclass(frozen=True) +class ScriptObjectQualifiedNameSource(ChainedSource): + def __post_init__(self): + assert self.base is not None + + def reconstruct(self, codegen): + self.base.reconstruct(codegen) + + def guard_source(self): + return self.base.guard_source() + + def name(self): + return f"{self.base.name()}._type().qualified_name()" + + +class AttrProxySource(ChainedSource): + def reconstruct(self, codegen): + self.base.reconstruct(codegen) + + def guard_source(self): + return self.base.guard_source() + + def name(self): + return f"{self.base.name()}.get_base()" + + +@dataclasses.dataclass(frozen=True) +class DefaultsSource(ChainedSource): + idx_key: Union[int, str] + is_kw: bool = False + field: str = dataclasses.field(init=False, repr=False, compare=False) + _name: str = dataclasses.field(init=False, repr=False, compare=False) + + def __post_init__(self): + assert ( + self.base + ), "Base must be a valid source in order to properly track and guard this Defaults to its origin." + if self.is_kw: + assert isinstance(self.idx_key, str) + object.__setattr__(self, "field", "__kwdefaults__") + object.__setattr__( + self, "_name", f"{self.base.name()}.{self.field}['{self.idx_key}']" + ) + else: + assert isinstance(self.idx_key, int) + object.__setattr__(self, "field", "__defaults__") + object.__setattr__( + self, "_name", f"{self.base.name()}.{self.field}[{self.idx_key}]" + ) + + def reconstruct(self, codegen): + self.base.reconstruct(codegen) + codegen.extend_output(codegen.create_load_attrs(self.field)) + codegen.append_output(codegen.create_load_const(self.idx_key)) + codegen.append_output(create_instruction("BINARY_SUBSCR")) + + def guard_source(self): + return self.base.guard_source() + + def name(self): + return self._name + + +@dataclasses.dataclass(frozen=True) +class GetItemSource(ChainedSource): + index: Any + index_is_slice: bool = False + + def __post_init__(self): + assert self.base is not None + if isinstance(self.index, slice): + # store the hashable version of the slice so the whole GetItemSource is hashable + super().__setattr__("index", self.index.__reduce__()) + super().__setattr__("index_is_slice", True) + + def reconstruct(self, codegen): + reconstruct_getitem(self, codegen, index_is_slice=self.index_is_slice) + codegen.append_output(create_instruction("BINARY_SUBSCR")) + + def guard_source(self): + return self.base.guard_source() + + def unpack_slice(self): + assert self.index_is_slice + slice_class, slice_args = self.index + return slice_class(*slice_args) + + def name(self): + # Index can be of following types + # 1) ConstDictKeySource + # 2) enum.Enum + # 3) index is a slice - example 1:4 + # 4) index is a constant - example string, integer + if isinstance(self.index, Source): + if not isinstance(self.index, ConstDictKeySource): + raise ValueError( + "GetItemSource index must be a constant, enum or ConstDictKeySource" + ) + return f"{self.base.name()}[{self.index.name()}]" + elif self.index_is_slice: + return f"{self.base.name()}[{self.unpack_slice()!r}]" + elif isinstance(self.index, enum.Enum): + return f"{self.base.name()}[{enum_repr(self.index, self.guard_source().is_local())}]" + else: + return f"{self.base.name()}[{self.index!r}]" + + +@dataclasses.dataclass(frozen=True) +class ConstDictKeySource(GetItemSource): + def is_dict_key(self): + return True + + def reconstruct(self, codegen): + codegen.add_push_null( + lambda: codegen.load_import_from(utils.__name__, "dict_keys_getitem") + ) + self.base.reconstruct(codegen) + codegen.append_output(codegen.create_load_const(self.index)) + codegen.extend_output(create_call_function(2, False)) + + def name(self): + # The list creation will be CSE'd by PyExprCSEPass + return f"list({self.base.name()}.keys())[{self.index!r}]" + + +@dataclasses.dataclass(frozen=True) +class TupleIteratorGetItemSource(GetItemSource): + def reconstruct(self, codegen): + codegen.add_push_null( + lambda: codegen.load_import_from(utils.__name__, "tuple_iterator_getitem") + ) + self.base.reconstruct(codegen) + codegen.append_output(codegen.create_load_const(self.index)) + codegen.extend_output(create_call_function(2, False)) + + def name(self): + return f"___tuple_iterator_getitem({self.base.name()}, {self.index!r})" + + +@dataclasses.dataclass(frozen=True) +class TypeSource(ChainedSource): + def __post_init__(self): + assert self.base is not None + + def reconstruct(self, codegen): + codegen.add_push_null(lambda: codegen.load_import_from("builtins", "type")) + self.base.reconstruct(codegen) + codegen.extend_output(create_call_function(1, False)) + + def guard_source(self): + return self.base.guard_source() + + def name(self): + return f"type({self.base.name()})" + + +@dataclasses.dataclass(frozen=True) +class ODictGetItemSource(ChainedSource): + index: Any + + def __post_init__(self): + assert self.base is not None + + def reconstruct(self, codegen): + codegen.add_push_null( + lambda: codegen.append_output( + codegen._create_load_const(collections.OrderedDict.__getitem__) + ) + ) + reconstruct_getitem(self, codegen, index_is_slice=False) + codegen.extend_output(create_call_function(2, False)) + + def guard_source(self): + return self.base.guard_source() + + def name(self): + if isinstance(self.index, type): + rep = f'__load_module("{self.index.__module__}").{self.index.__qualname__}' + return f"___odict_getitem({self.base.name()}, {rep})" + elif isinstance(self.index, Source): + return f"___odict_getitem({self.base.name()}, {self.index.name()})" + else: + return f"___odict_getitem({self.base.name()}, {self.index!r})" + + +@dataclasses.dataclass(frozen=True) +class OptimizerSource(ChainedSource): + def reconstruct(self, codegen): + self.base.reconstruct(codegen) + + def guard_source(self): + return self.base.guard_source() + + def name(self): + return self.base.name() + + +@dataclasses.dataclass(frozen=True) +class NNModuleSource(ChainedSource): + def reconstruct(self, codegen): + self.base.reconstruct(codegen) + + def guard_source(self): + return _GUARD_SOURCE_SPECIALIZED_NN_MODULE[self.base.guard_source()] + + def name(self): + return self.base.name() + + +@dataclasses.dataclass(frozen=True) +class UnspecializedNNModuleSource(NNModuleSource): + def guard_source(self): + return _GUARD_SOURCE_UNSPECIALIZED_NN_MODULE[self.base.guard_source()] + + +@dataclasses.dataclass(frozen=True) +class UnspecializedBuiltinNNModuleSource(UnspecializedNNModuleSource): + def guard_source(self): + return _GUARD_SOURCE_UNSPECIALIZED_BUILTIN_NN_MODULE[self.base.guard_source()] + + +@dataclasses.dataclass(frozen=True) +class FSDPNNModuleSource(NNModuleSource): + def guard_source(self): + return _GUARD_SOURCE_FSDP_MODULE[self.base.guard_source()] + + +@dataclasses.dataclass(frozen=True) +class GlobalStateSource(Source): + def name(self): + return "" + + def guard_source(self): + return GuardSource.GLOBAL + + +@dataclasses.dataclass(frozen=True) +class TorchFunctionModeStackSource(Source): + ind: int + + def name(self): + return "" + + def _get_index(self): + from .variables.torch_function import TorchFunctionModeStackVariable + + return TorchFunctionModeStackVariable.get_mode_index(self.ind) + + def reconstruct(self, codegen): + codegen.add_push_null( + lambda: codegen.load_import_from( + utils.__name__, "get_torch_function_mode_stack_at" + ) + ) + codegen.extend_output([codegen.create_load_const(self._get_index())]) + codegen.extend_output(create_call_function(1, False)) + + def guard_source(self): + return GuardSource.GLOBAL + + +@dataclasses.dataclass(frozen=True) +class ConstantSource(Source): + source_name: str + + def reconstruct(self, codegen): + codegen.append_output(codegen.create_load_global(self.source_name, add=False)) + + def guard_source(self): + return GuardSource.CONSTANT + + def name(self): + return self.source_name + + def make_guard(self, fn): + raise NotImplementedError + + +@dataclasses.dataclass(frozen=True) +class NumpyTensorSource(ChainedSource): + def name(self) -> str: + return f"___from_numpy({self.base.name()})" + + def guard_source(self): + return self.base.guard_source() + + def reconstruct(self, codegen): + codegen.add_push_null(lambda: codegen.load_import_from("torch", "as_tensor")) + self.base.reconstruct(codegen) + codegen.extend_output(create_call_function(1, False)) + + +@dataclasses.dataclass(frozen=True) +class SubclassAttrListSource(ChainedSource): + def name(self) -> str: + return f"{self.base.name()}.__tensor_flatten__()[0]" + + def guard_source(self): + return self.base.guard_source() + + +# NB: We don't expect you to actually ever generate guards against this +# source, it is ephemeral +@dataclasses.dataclass(frozen=True) +class FloatTensorSource(ChainedSource): + def name(self) -> str: + return f"___as_tensor({self.base.name()})" + + def guard_source(self): + return self.base.guard_source() + + +@dataclasses.dataclass(frozen=True) +class CallMethodItemSource(ChainedSource): + def name(self) -> str: + return f"{self.base.name()}.item()" + + def guard_source(self): + return self.base.guard_source() + + +# This is a synthetic source that is associated with the singleton +# shape env guard we always register for all frames. We get the actual +# guard contents from the ambient ShapeEnv +@dataclasses.dataclass(frozen=True) +class ShapeEnvSource(Source): + def name(self): + return "" + + def guard_source(self): + return GuardSource.SHAPE_ENV + + +@dataclasses.dataclass(frozen=True) +class BackwardStateSource(Source): + def name(self): + return "" + + def guard_source(self): + return GuardSource.BACKWARD_STATE + + +def is_from_local_source(source: Source, *, allow_cell_or_freevar=True): + if isinstance(source, ChainedSource): + return is_from_local_source( + source.base, allow_cell_or_freevar=allow_cell_or_freevar + ) + if not isinstance(source, LocalSource): + return False + if not allow_cell_or_freevar and source.cell_or_freevar: + return False + return True + + +def is_from_unspecialized_param_buffer_source(source: Source): + if isinstance(source, UnspecializedParamBufferSource): + return True + if isinstance(source, ChainedSource): + return is_from_unspecialized_param_buffer_source(source.base) + return False + + +def is_from_flatten_script_object_source(source: Source): + if isinstance(source, FlattenScriptObjectSource): + return True + elif isinstance(source, ChainedSource): + return is_from_flatten_script_object_source(source.base) + return False + + +def is_from_optimizer_source(source: Source): + if isinstance(source, OptimizerSource): + return True + if isinstance(source, ChainedSource): + return is_from_optimizer_source(source.base) + return False + + +# TODO: can probably write a generic "test this on everything in the chain" +# helper +def is_from_defaults(source: Source): + if isinstance(source, DefaultsSource): + return True + if isinstance(source, ChainedSource): + return is_from_defaults(source.base) + return False + + +def is_cell_contents(source: Source): + return isinstance(source, AttrSource) and source.member == "cell_contents" diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/symbolic_convert.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/symbolic_convert.py new file mode 100644 index 0000000000000000000000000000000000000000..ab92f82aa0f630dd3ca2ac84bae4b8ad000d421f --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/symbolic_convert.py @@ -0,0 +1,3440 @@ +# mypy: allow-untyped-defs +import collections +import collections.abc +import contextlib +import copy +import dataclasses +import dis +import functools +import importlib +import inspect +import itertools +import linecache +import logging +import operator +import re +import sys +import threading +import traceback +import types +import typing +import weakref +from typing import ( + Any, + Callable, + cast, + Deque, + Dict, + List, + Optional, + Set, + Tuple, + Type, + TYPE_CHECKING, + Union, +) +from unittest.mock import patch + +import torch +import torch._logging +from torch._guards import tracing, TracingContext + +from . import config, exc, logging as torchdynamo_logging, trace_rules, variables +from .bytecode_analysis import ( + get_indexof, + JUMP_OPNAMES, + livevars_analysis, + propagate_line_nums, +) +from .bytecode_transformation import ( + cleaned_instructions, + create_call_function, + create_instruction, + create_jump_absolute, + create_swap, + get_code_keys, + Instruction, + is_generator, + unique_id, +) +from .code_context import code_context +from .codegen import PyCodegen +from .exc import ArgsMismatchError, BackendCompilerFailed, unimplemented, Unsupported +from .funcname_cache import get_funcname +from .guards import GuardBuilder, install_guard +from .output_graph import GraphCompileReason, OutputGraph +from .replay_record import DummyModule, ExecutionRecorder +from .resume_execution import ContinueExecutionCache, ReenterWith +from .source import ( + AttrSource, + GetItemSource, + GlobalSource, + GlobalWeakRefSource, + LocalSource, + Source, + TorchFunctionModeStackSource, +) +from .trace_rules import is_builtin_constant, is_forbidden +from .utils import ( + counters, + get_fake_value, + get_instruction_source_311, + get_torch_function_mode_stack, + graph_break_dup_warning_checker, + istype, + LazyString, + proxy_args_kwargs, +) +from .variables.base import is_side_effect_safe, MutableLocal, typestr, VariableTracker +from .variables.builder import VariableBuilder, wrap_fx_proxy +from .variables.builtin import BuiltinVariable +from .variables.constant import ConstantVariable +from .variables.ctx_manager import ( + ContextWrappingVariable, + GenericContextWrappingVariable, + WithExitFunctionVariable, +) +from .variables.dicts import ConstDictVariable, SetVariable +from .variables.functions import ( + BaseUserFunctionVariable, + NestedUserFunctionVariable, + SkipFunctionVariable, + UserFunctionVariable, + UserMethodVariable, +) +from .variables.iter import MAX_ITERATOR_LIMIT +from .variables.lists import ( + BaseListVariable, + ListIteratorVariable, + ListVariable, + SliceVariable, + TupleVariable, +) +from .variables.misc import ( + ClosureVariable, + GetAttrVariable, + InlinedClosureVariable, + NullVariable, + PythonModuleVariable, + UnknownVariable, +) +from .variables.nn_module import NNModuleVariable, UnspecializedNNModuleVariable +from .variables.tensor import supported_comparison_ops, SymNodeVariable, TensorVariable + + +if TYPE_CHECKING: + from .variables.torch_function import TorchFunctionModeVariable + +from .variables.user_defined import ( + RemovableHandleVariable, + UserDefinedClassVariable, + UserDefinedObjectVariable, +) + + +log = logging.getLogger(__name__) +graph_break_log = torch._logging.getArtifactLogger(__name__, "graph_breaks") +trace_call_log = torch._logging.getArtifactLogger(__name__, "trace_call") +trace_source_log = torch._logging.getArtifactLogger(__name__, "trace_source") +trace_bytecode_log = torch._logging.getArtifactLogger(__name__, "trace_bytecode") +tls = threading.local() +compare_op_handlers: Dict[str, Any] = { + k: BuiltinVariable(v).call_function for k, v in supported_comparison_ops.items() +} +handle_contains = BuiltinVariable(operator.contains).call_function +handle_not = BuiltinVariable(operator.not_).call_function +compare_op_handlers["in"] = lambda tx, args, _: handle_contains( + tx, [*reversed(args)], {} +) +compare_op_handlers["not in"] = lambda tx, args, _: handle_not( + tx, [handle_contains(tx, [*reversed(args)], {})], {} +) + + +PT2_ISSUE_TRACKER_URL = "https://github.com/pytorch/pytorch/issues/new?&labels=oncall%3A+pt2&projects=&template=pt2-bug-report.yml" + + +@dataclasses.dataclass +class SpeculationEntry: + filename: str + lineno: int + instruction_pointer: int + inst: Instruction # for debugging only + failed: bool = False + reason: Optional[GraphCompileReason] = None + + def fail_and_restart_analysis(self): + """ + Start tracing of the current frame over again, and don't take this branch. + """ + self.failed = True + if self.reason is not None: + restart_reason = self.reason.reason + else: + restart_reason = "Unknown fail_and_restart_analysis" + raise exc.SpeculationRestartAnalysis(restart_reason=restart_reason) + + +@dataclasses.dataclass +class SpeculationLog: + """ + SpeculationLog replaces the prior copy_graphstate/restore_graphstate + checkpointing. Rather than saving/restoring state, we restart the + dynamo conversion process over from the beginning -- but when we + hit the start of the speculation that failed, we instead generate + a graph break. + """ + + entries: List[SpeculationEntry] = dataclasses.field(default_factory=list) + index: int = 0 + + def restart(self): + self.index = 0 + + def clear(self): + self.entries.clear() + self.index = 0 + + def next( + self, filename: str, lineno: int, instruction_pointer, inst + ) -> SpeculationEntry: + """ + Lookup or create a SpeculationEntry() that is shared across + RestartAnalysis calls. Args are used only for debug checks. + """ + if len(self.entries) == self.index: + self.entries.append( + SpeculationEntry(filename, lineno, instruction_pointer, inst) + ) + entry = self.entries[self.index] + prev_entry_msg = "" + if self.index != 0: + prev_entry = self.entries[self.index - 1] + prev_entry_msg = ( + f"Previous instruction: {prev_entry.filename}:{prev_entry.lineno}" + f"({prev_entry.inst.opname} @ {prev_entry.instruction_pointer})\n" + ) + assert ( + entry.instruction_pointer == instruction_pointer + and entry.filename == filename + and entry.lineno == lineno + ), f""" +SpeculationLog diverged at index {self.index} (log had {len(self.entries)} entries): +- Expected: {entry.filename}:{entry.lineno} ({entry.inst.opname} at ip={entry.instruction_pointer}) +- Actual: {filename}:{lineno} ({inst.opname} at ip={instruction_pointer}) +{prev_entry_msg} +There are two usual reasons why this may have occured: +- When Dynamo analysis restarted, the second run took a different path than + the first. If this occurred, the previous instruction is the critical instruction that + behaved differently. +- Speculation entries are only added under certain conditions (as seen in + step()), e.g., there must exist operators in the graph; those conditions may + have changed on restart. + +If this divergence was intentional, clear the speculation log before restarting (do NOT +do this for graph breaks, you will infinite loop). + +Otherwise, please submit a bug report, ideally including the contents of TORCH_LOGS=+dynamo +""" + self.index += 1 + return entry + + +@dataclasses.dataclass +class LocalState: + input_sizes: Dict[str, List[int]] = dataclasses.field(default_factory=dict) + input_strides: Dict[str, List[int]] = dataclasses.field(default_factory=dict) + + +# Mutable box that is shared across restarts +@dataclasses.dataclass +class DistributedState: + compile_pg: Any + local_state: LocalState + all_states: Optional[List[LocalState]] = None + + +@functools.lru_cache(None) +def _step_logger(): + return torchdynamo_logging.get_step_logger(log) + + +@dataclasses.dataclass +class BlockStackEntry: + # Current instruction that pushes something to block_stack + inst: Instruction + target: Instruction + stack_index: Optional[int] = None + with_context: Optional[ + Union[ContextWrappingVariable, GenericContextWrappingVariable] + ] = None + + def can_restore(self): + return self.with_context is not None + + def resume_fn(self): + assert self.stack_index is not None + if ( + self.with_context + and hasattr(self.with_context, "target_values") + and self.with_context.target_values + ): + return ReenterWith(self.stack_index, tuple(self.with_context.target_values)) + else: + return ReenterWith(self.stack_index) + + def exit(self, tx): + assert self.with_context is not None + return self.with_context.exit(tx) + + +class ReturnValueOp(Exception): + pass + + +def stack_op(fn: typing.Callable[..., object]): + nargs = len(inspect.signature(fn).parameters) + fn_var = BuiltinVariable(fn) + + @functools.wraps(fn) + def impl(self: "InstructionTranslator", inst: Instruction): + self.push(fn_var.call_function(self, self.popn(nargs), {})) + + return impl + + +def _detect_and_normalize_assert_statement( + self: "InstructionTranslatorBase", + truth_fn: typing.Callable[[object], bool], + push: bool, +): + # Detect if this jump instruction is assert and normalize the assert + # by pushing dummy error message when nothing is given. + # + # Python 3.9 assertion is in following format: + # 18 POP_JUMP_IF_TRUE 28 + # 20 LOAD_ASSERTION_ERROR + # 22 LOAD_CONST 3 ('Assert message') -> optional instruction + # 24 CALL_FUNCTION 1 -> optional instruction + # 26 RAISE_VARARGS + # + # Python 3.8 assertion is in following format: + # 18 POP_JUMP_IF_TRUE 28 + # 20 LOAD_GLOBAL 0 (Assertion type) + # 22 LOAD_CONST 3 ('Assert message') -> optional instruction + # 24 CALL_FUNCTION 1 -> optional instruction + # 26 RAISE_VARARGS 1 + + if (truth_fn is not operator.truth) or push: + return False + + assert isinstance(self.instruction_pointer, int) + current_instruction_pointer = self.instruction_pointer + inst = self.instructions[current_instruction_pointer] + # Detect LOAD_ASSERTION_ERROR or LOAD_GLOBAL 0 + if sys.version_info < (3, 9): + if inst.opname != "LOAD_GLOBAL" or inst.argval != "AssertionError": + return False + else: + if inst.opname != "LOAD_ASSERTION_ERROR": + return False + + current_instruction_pointer += 1 + + # Use dummy error message if its hard to extract + error_msg = "assertion error" + + inst = self.instructions[current_instruction_pointer] + # DETECT RAISE_VARARGS or LOAD CONST + if inst.opname == "LOAD_CONST": + if not isinstance(inst.argval, str): + return False + error_msg = inst.argval + + # if it is LOAD_CONSTANT, it must be followed by CALL_FUNCTION + # (PRECALL for Python 3.11, CALL for Python 3.12+) + current_instruction_pointer += 1 + inst = self.instructions[current_instruction_pointer] + if inst.opname not in ("CALL_FUNCTION", "PRECALL", "CALL"): + return False + + # for Python 3.11, PRECALL should be followed by CALL, then RAISE_VARARGS + # for Python != 3.11, CALL_FUNCTION/CALL should be followed by RAISE_VARARGS + current_instruction_pointer += 1 + if inst.opname == "PRECALL": + current_instruction_pointer += 1 + inst = self.instructions[current_instruction_pointer] + + if inst.opname != "RAISE_VARARGS": + return False + + self.push(ConstantVariable.create(error_msg)) + + return True + + +def generic_jump(truth_fn: typing.Callable[[object], bool], push: bool): + def jump_graph_break(self, inst, value, extra_msg=""): + if not self.should_compile_partial_graph(): + unimplemented("should_compile_partial_graph=False") + # compile a partial subgraph prefix then jump into user code + if self.maybe_has_backedge(): + msg = ( + "Skipping frame because there is a graph break in a for/while loop\n" + f"{self.frame_summary()}" + ) + log.info(msg) + raise exc.SkipFrame(msg) + + self.push(value) + log.debug("generic_jump triggered compile") + self.output.compile_subgraph( + self, + reason=GraphCompileReason( + f"generic_jump {typestr(value)}{extra_msg}", [self.frame_summary()] + ), + ) + self.pop() + + if_next = self.create_call_resume_at(self.next_instruction) + if push: + self.push(value) + if_jump = self.create_call_resume_at(inst.target) + + if sys.version_info >= (3, 13): + # 3.13 requires stack[-1] to be bool type + self.output.add_output_instructions([create_instruction("TO_BOOL")]) + + self.output.add_output_instructions( + [create_instruction(inst.opname, target=if_jump[0])] + if_next + if_jump + ) + + def inner(self: "InstructionTranslatorBase", inst: Instruction): + value: VariableTracker = self.pop() + if ( + config.rewrite_assert_with_torch_assert + and _detect_and_normalize_assert_statement(self, truth_fn, push) + ): + error_msg: VariableTracker = self.pop() + # Skip over things like `assert True` + if value.is_python_constant(): + if bool(value.as_python_constant()): + return self.jump(inst) + else: + jump_graph_break(self, inst, value) + + # TODO maybe should respect DtoH sync intention of users later?? + # Manually insert torch._assert_async instead of python assert and jump over + # assert related instructions as we don't need them anymore. + + # if we see Tensor as assert statement, no need to call scalar_tensor + if isinstance(value, TensorVariable): + self.output.create_proxy( + "call_function", + torch._assert_async, + *proxy_args_kwargs((value, error_msg), {}), + ) + self.jump(inst) + return + + if isinstance(value, SymNodeVariable): + # if the assertion is normal shape expression. + # just install guard and bail out. + sym_expr = value.sym_num + if not isinstance(sym_expr, torch.SymBool): + sym_expr = sym_expr != 0 + + result = torch.fx.experimental.symbolic_shapes.expect_true(sym_expr) + if not result: + unimplemented( + "Assertion failed on symbolic shapes. Did you make sure eager mode succeeds?" + ) + self.jump(inst) + return + + scalar_to_tensor_proxy = self.output.create_proxy( + "call_function", torch.scalar_tensor, *proxy_args_kwargs((value,), {}) + ) + + scalar_to_tensor = wrap_fx_proxy( + self, + scalar_to_tensor_proxy, + example_value=get_fake_value(scalar_to_tensor_proxy.node, self), + ) + + self.output.create_proxy( + "call_function", + torch._assert_async, + *proxy_args_kwargs((scalar_to_tensor, error_msg), {}), + ) + self.jump(inst) + return + + if value.is_python_constant(): + if truth_fn(value.as_python_constant()): + if push: + self.push(value) + self.jump(inst) + elif ( + isinstance(value, (TensorVariable)) and self.should_compile_partial_graph() + ): + jump_graph_break(self, inst, value) + elif isinstance(value, NNModuleVariable): + # Equivalent of "self.nn_module is not None" + mod = self.output.get_submodule(value.module_key) + if truth_fn(mod): + if push: + self.push(value) + self.jump(inst) + elif isinstance(value, UnspecializedNNModuleVariable): + mod = value.value + if truth_fn(mod): + if push: + self.push(value) + self.jump(inst) + elif isinstance(value, UserDefinedObjectVariable): + try: + x = value.var_getattr(self, "__bool__") # type: ignore[arg-type] + except exc.ObservedAttributeError: + exc.handle_observed_exception(self) + # if __bool__ is missing, trying __len__ to infer a truth value. + try: + x = value.var_getattr(self, "__len__") # type: ignore[arg-type] + except exc.ObservedAttributeError: + exc.handle_observed_exception(self) + x = None + + # __bool__ or __len__ is function + if isinstance(x, UserMethodVariable): + result = x.call_function(self, [], {}) # type: ignore[arg-type] + if isinstance(result, ConstantVariable) and isinstance( + result.value, (bool, int) + ): + if truth_fn(result.value): + if push: + self.push(value) + self.jump(inst) + else: + unimplemented( + "generic_jump on UserDefined with __bool__ returning non-constant" + ) + # __bool__ or __len__ is non-function or not existed in the user defined object + else: + if truth_fn(True): + if push: + self.push(value) + self.jump(inst) + elif not isinstance(value, TensorVariable) and value.has_unpack_var_sequence( + self + ): + if truth_fn(len(value.unpack_var_sequence(self))): + if push: + self.push(value) + self.jump(inst) + elif isinstance(value, SymNodeVariable): + try: + eval_result = value.evaluate_expr(self.output) + except exc.UserError as e: + if self.should_compile_partial_graph(): + return jump_graph_break(self, inst, value, extra_msg=f"\n{e}") + raise + if truth_fn(eval_result): + if push: + self.push(value) + self.jump(inst) + elif isinstance(value, variables.BackwardHookVariable): + if truth_fn(True): + if push: + self.push(value) + self.jump(inst) + else: + from .source import is_constant_source + + if value.source is not None and is_constant_source(value.source): + if truth_fn(value.get_real_value()): # type: ignore[attr-defined] + if push: + self.push(value) + self.jump(inst) + else: + # TODO link the torch.cond doc later + raise exc.UserError( + exc.UserErrorType.DYNAMIC_CONTROL_FLOW, + "Dynamic control flow is not supported at the moment. Please use " + "functorch.experimental.control_flow.cond to explicitly capture the control flow.", + case_name="cond_operands", + ) + + return inner + + +explain = False + + +def break_graph_if_unsupported(*, push): + def decorator(inner_fn): + @functools.wraps(inner_fn) + def wrapper(self: "InstructionTranslatorBase", inst: Instruction): + speculation = self.speculate() + if speculation.failed: + assert speculation.reason is not None + return handle_graph_break(self, inst, speculation.reason) + try: + return inner_fn(self, inst) + except Unsupported as excp: + if self.generic_context_manager_depth > 0: + # We don't support graph break under GenericContextWrappingVariable, + # If there is, we roll back to the checkpoint and fall back. + excp.remove_from_stats() + unimplemented("Graph break under GenericContextWrappingVariable") + + if isinstance(excp, exc.UncapturedHigherOrderOpError): + raise + + if not self.should_compile_partial_graph(): + raise + + user_stack = excp.real_stack + # TODO: Also report the traceback from the parent frame + try: + frame_loc = (user_stack[-1].filename, user_stack[-1].lineno) + except IndexError: + # first instruction + code_options = self.code_options + frame_loc = ( + code_options["co_filename"], + code_options["co_firstlineno"], + ) + # torch._dynamo.explain() formats this a little nicer, and presents a slightly + # more actionable user code pointer + if ( + graph_break_log.isEnabledFor(logging.DEBUG) + and not explain + and graph_break_dup_warning_checker.add(frame_loc) + ): + user_stack_formatted = "".join(traceback.format_list(user_stack)) + # This log line is exercised from + # python test/dynamo/test_exc.py -k test_graph_break_log + graph_break_log.debug( + "Graph break: from user code at:\n%s", + user_stack_formatted, + exc_info=True, + ) + else: + # This log line MUST NOT contain the string "Graph break", + # exercised by + # python test/dynamo/test_misc.py -k test_duplicate_graph_break_log + log.debug( + "Unsupported break in user code at %s:%s (details suppressed)", + *frame_loc, + ) + + if self.maybe_has_backedge(): + msg = ( + "Skipping frame because there is a graph break in a for/while loop\n" + f"{self.frame_summary()}" + ) + log.info(msg) + raise exc.SkipFrame(msg) from excp + + excp.remove_from_stats() + excp.add_to_stats("graph_break") + speculation.reason = GraphCompileReason(excp.msg, user_stack) + speculation.fail_and_restart_analysis() + + def handle_graph_break( + self: "InstructionTranslatorBase", + inst: Instruction, + reason: GraphCompileReason, + ): + self.output.compile_subgraph(self, reason=reason) + cg = PyCodegen(self) + cleanup: List[Instruction] = [] + # Reconstruct the context variable CLASS in the block stack + for b in self.block_stack: + assert b.with_context is not None + assert isinstance(b.with_context, ContextWrappingVariable) + b.with_context.reconstruct_type(cg) + cg.extend_output(b.resume_fn().try_except(cg.code_options, cleanup)) + self.output.add_output_instructions(cg.get_instructions()) + del cg + + if sys.version_info >= (3, 11) and inst.opname == "CALL": + kw_names = ( + self.kw_names.as_python_constant() + if self.kw_names is not None + else () + ) + if len(kw_names) > 0: + # KW_NAMES no longer used in 3.13 + assert sys.version_info < (3, 13) + self.output.add_output_instructions( + [create_instruction("KW_NAMES", argval=kw_names)] + ) + self.output.add_output_instructions( + create_call_function(inst.arg, False) + ) + else: + # copy instruction, but without exception table data + assert inst.target is None + inst_copy = copy.copy(inst) + inst_copy.exn_tab_entry = None + self.output.add_output_instructions([inst_copy]) + + self.output.add_output_instructions(cleanup) + + if ( + sys.version_info >= (3, 11) + and sys.version_info < (3, 12) + and inst.opname == "CALL" + ): + # stack effect for PRECALL + CALL is split between the two instructions + stack_effect = dis.stack_effect( + dis.opmap["PRECALL"], inst.arg + ) + dis.stack_effect(dis.opmap["CALL"], inst.arg) + else: + stack_effect = dis.stack_effect(inst.opcode, inst.arg) + self.popn(push - stack_effect) + + for _ in range(push): + self.push(UnknownVariable()) + self.output.add_output_instructions( + self.create_call_resume_at(self.next_instruction) + ) + + return wrapper + + return decorator + + +class BytecodeDistpatchTableMeta(type): + """Installs a `cls.dispatch_table` on every subclass to speed up calls to self.OPCODE()""" + + def __init__(cls, name, bases, dct) -> None: + super().__init__(name, bases, dct) + + def _missing(opname, *args): + unimplemented(f"missing: {opname}") + + dispatch_table = { + op: getattr(cls, opname, functools.partial(_missing, opname)) + for opname, op in dis.opmap.items() + } + cls.dispatch_table = [dispatch_table.get(i) for i in range(2**8)] + + +class InstructionTranslatorBase( + metaclass=BytecodeDistpatchTableMeta, +): + output: OutputGraph + symbolic_locals: Dict[str, VariableTracker] + symbolic_globals: Dict[str, VariableTracker] + symbolic_torch_function_mode_stack: Deque["TorchFunctionModeVariable"] + stack: List[VariableTracker] + instruction_pointer: Optional[int] + current_instruction: Instruction + block_stack: List[BlockStackEntry] + lineno: int + kw_names: Optional[ConstantVariable] + accept_prefix_inst: bool + prefix_insts: List[Instruction] + inline_depth: int + inconsistent_side_effects: bool + current_speculation: Optional[SpeculationEntry] + dispatch_table: List[Any] + exn_vt_stack: List[VariableTracker] + exec_recorder: Optional[ExecutionRecorder] + strict_checks_fn: Optional[Callable[[VariableTracker], bool]] + + def mark_inconsistent_side_effects(self): + """ + InstructionTranslator has encountered instructions which may cause + dynamo to see a different version of history from eager + See: https://github.com/pytorch/pytorch/issues/110765 + """ + self.inconsistent_side_effects = True + + def maybe_has_backedge(self): + # This function employs a heuristic. It does not reliably detect a backedge. + # The heuristic is straightforward: starting from the current instruction and + # continuing to the end, if any jump instruction targets an instruction before + # the current one, there might be a backedge. + + # Python 3.12 introduced changes to bytecode that group common paths in + # blockstacks (with or try...else) and allow for early returns. Consequently, + # there can be multiple RETURN_VALUE instructions. Another heuristic is to + # halt detection upon encountering the first RETURN_VALUE or RETURN_CONST. + + # These heuristics can result in both false positives and negatives, but + # in either case, the Dynamo code remains valid. For false positives + # (where an edge is incorrectly marked as a backedge), Dynamo will + # perform a SkipFrame instead of potentially applying optimizations. For + # false negatives (where an edge that should be marked as a backedge + # isn't), multiple graphs may be generated if there's a break in the + # graph during a for loop. In general, its better to have fewer false + # negatives so that Dynamo does not skip the whole frame. + + cur_offset = self.current_instruction.offset + assert self.instruction_pointer is not None + for inst in self.instructions[self.instruction_pointer :]: + if inst.opname in ("RETURN_VALUE", "RETURN_CONST"): + return False + if inst.opname in JUMP_OPNAMES: + jump_offset = inst.argval + if jump_offset < cur_offset: + return True + return False + + def cell_and_freevars(self): + if not hasattr(self, "_cell_and_freevars"): + self._cell_and_freevars = tuple( + self.code_options["co_cellvars"] or [] + ) + tuple(self.code_options["co_freevars"] or []) + + # An inlined function might depend on the freevar of the parent + # function. So, recursively obtain parent cell and freevars. + if isinstance(self, InliningInstructionTranslator): + self._cell_and_freevars += self.parent.cell_and_freevars() + return self._cell_and_freevars + + def prune_dead_locals(self): + reads = livevars_analysis(self.instructions, self.current_instruction) + # implicit use by super() + # reads = reads | {"__class__"} + # output variables? + reads = reads | set(self.cell_and_freevars()) + self.symbolic_locals = { + k: v for k, v in self.symbolic_locals.items() if k in reads + } + self.output.side_effects.prune_dead_object_new(self) + + def call_function( + self, + fn: VariableTracker, + args: List[VariableTracker], + kwargs: Dict[str, VariableTracker], + ): + assert isinstance(fn, VariableTracker) + assert isinstance(args, list) + assert isinstance(kwargs, dict) + assert all( + isinstance(x, VariableTracker) + for x in itertools.chain(args, kwargs.values()) + ) + inner_fn = None + if hasattr(fn, "value"): + inner_fn = fn.value + if hasattr(fn, "fn"): + inner_fn = fn.fn + if inner_fn and callable(inner_fn) and is_forbidden(inner_fn): + raise AssertionError(f"Attempt to trace forbidden callable {inner_fn}") + self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type] + + def inline_user_function_return(self, fn, args, kwargs): + """ + A call to some user defined function by inlining it. + """ + return InliningInstructionTranslator.inline_call(self, fn, args, kwargs) + + def get_line_of_code_header(self, lineno=None): + if lineno is None: + lineno = self.lineno + inline_depth_str = ( + f" (inline depth: {self.inline_depth})" if self.inline_depth > 0 else "" + ) + funcname = get_funcname(self.f_code.co_filename, lineno) + funcname_str = "" if funcname is None else f" ({funcname})" + return f"{self.f_code.co_filename}:{lineno} in {self.f_code.co_name}{funcname_str}{inline_depth_str}" + + def get_log_starts_line_log_str(self): + log_str = f"TRACE starts_line {self.get_line_of_code_header()}\n" + line = linecache.getline(self.f_code.co_filename, self.lineno).rstrip() + log_str += f" {line}" + return log_str + + def starts_line(self, lineno): + if self.lineno == lineno: + return + self.lineno = lineno + TracingContext.set_current_loc( + self.f_code.co_filename, lineno, self.f_code.co_name + ) + from torch._logging.structured import dump_file + + dump_file(self.f_code.co_filename) + if trace_source_log.isEnabledFor(logging.DEBUG): + trace_source_log.debug("%s", LazyString(self.get_log_starts_line_log_str)) + + def step(self): + """Process exactly one instruction, return False we should exit""" + ip = self.instruction_pointer + if ip is None: + return False + self.current_instruction = inst = self.instructions[ip] + self.instruction_pointer = ip + 1 + + if inst.starts_line: + self.starts_line(inst.starts_line) + + if ( + not self.stack + and self.should_compile_partial_graph() + and self.is_non_empty_graph() + ): + self.current_speculation = self.speculate() + if self.current_speculation.failed: + return self.step_graph_break(inst) + + if trace_bytecode_log.isEnabledFor(logging.DEBUG): + trace_bytecode_log.debug( + "TRACE %s %s %s", inst.opname, inst.argval, self.stack + ) + + self.update_block_stack(inst) + + try: + self.dispatch_table[inst.opcode](self, inst) + return not self.output.should_exit + except exc.ObservedException as e: + self.exception_handler(e) + return True + except ReturnValueOp: + return False + except Unsupported: + if self.current_speculation is None: + log.debug("empty checkpoint") + raise + log.debug("step triggered compile", exc_info=True) + + self.current_speculation.fail_and_restart_analysis() + + if sys.version_info >= (3, 11): + + def update_block_stack(self, inst): + # 3.11+ no longer uses a block stack, but we still keep track of one + # so that we know which contexts are currently active. + # For our purposes, all exception table entries with the same target + # are considered to be part of the same "block". + # NOTE: we only keep track of with blocks that are not contained in try blocks. + # This is because we will not create continuation functions on graph breaks in try blocks, + # but we may for with blocks. We do not push blocks here since + # with blocks are pushed when handling BEFORE_WITH. + entry = inst.exn_tab_entry + if entry: + # Detect when we have exited the top with block. + # The with blocks on the block stack are not enclosed in try + # blocks, so a with block's cleanup code should be in the + # previous with block (if any). + if ( + len(self.block_stack) >= 2 + and entry.target is not self.block_stack[-1].target + and entry.target is self.block_stack[-2].target + ): + # exit the current block + self.block_stack.pop() + else: + # no longer in any block + # It is possible for NOPs to be between two instructions + # in the same block, but the NOPs are not covered by an + # exception table entry. In this case, assume that we + # are still in the same block. + # In 3.12+, JUMP_BACKWARD might also not be covered by + # an exception table entry, so we also assume that we + # are still in the same block. It is probably safe to do + # this in 3.11, even though we haven't encountered this case before. + if self.block_stack and inst.opname not in ("NOP", "JUMP_BACKWARD"): + # If we really escape from a block and the current + # instruction is not in another block, then there + # should be no other nested blocks that we are in. + assert len(self.block_stack) == 1 + self.block_stack.pop() + + else: + + def update_block_stack(self, inst): + pass + + @property + def next_instruction(self): + return self.instructions[self.instruction_pointer] # type: ignore[index] + + def step_graph_break(self, continue_inst): + # generate code from checkpoint + assert not self.output.output_instructions + assert self.current_speculation is not None + self.output.compile_subgraph( + self, + partial_convert=True, + reason=GraphCompileReason("step_unsupported", [self.frame_summary()]), + ) + self.output.add_output_instructions( + [create_jump_absolute(continue_inst)] + self.instructions + ) + + def run_ctx_mgr(self): + # NB: Don't push the top level frame summary; set_current_loc will + # take care of it. However, DO make sure we attach real_stack to + # exceptions + return TracingContext.current_frame(None) + + def run(self): + with self.run_ctx_mgr(): + try: + self.output.push_tx(self) + while self.step(): + pass + except BackendCompilerFailed: + raise + except Exception as e: + if self.exec_recorder: + e.exec_record = self.exec_recorder.get_record() # type: ignore[attr-defined] + raise + finally: + self.output.pop_tx() + # Cleanup the outputGraph to delete the held tensors. We perform the + # cleanup only for InstructionTranslator and not + # InliningInstructionTranslator. The InliningInstructionTranslator + # mutates the output object and is restored to original state if + # there was an exception. + if isinstance(self, InstructionTranslator): + self.output.cleanup() + + def push(self, val: Optional[VariableTracker], name: Any = None): + assert val is None or isinstance( + val, VariableTracker + ), f"push expects VariableTracker, got {typestr(val)}" + self.stack.append(val) # type: ignore[arg-type] + if sys.version_info >= (3, 13): + self.name_stack.append(name) + assert len(self.stack) == len(self.name_stack) + + def push_many(self, vals: List[VariableTracker]): + for val in vals: + self.push(val) + + def pop(self) -> VariableTracker: + if sys.version_info >= (3, 13): + assert len(self.stack) == len(self.name_stack) + self.name_stack.pop() + return self.stack.pop() + + def popn(self, n: int) -> List[VariableTracker]: + return [*reversed([self.pop() for _ in range(n)])] + + def _load_closure(self, name): + return ClosureVariable(name=name) + + def _load_fast(self, name): + if self.exec_recorder and name in self.f_locals: + self.exec_recorder.add_local_var(name, self.f_locals[name]) + + try: + self.push(self.symbolic_locals[name].unwrap(), name=name) + except KeyError: + if sys.version_info >= (3, 13) and name in self.cell_and_freevars(): + # 3.13 merged LOAD_CLOSURE into LOAD_FAST + # If we fail to LOAD_FAST, then we probably should have done LOAD_CLOSURE. + # Closure variable creation is actually done in SET_FUNCTION_ATTRIBUTE, + # but we'll do it again here so that we don't need to push a dummy variable. + # We shouldn't actually be doing anything with this variable anyway. + self.push(self._load_closure(name), name=name) + elif name.startswith("."): + try: + # This happens in dict/list comprehensions + new_name = name.replace(".", "implicit") + self.push(self.symbolic_locals[new_name], name=new_name) + except KeyError: + unimplemented("undefined LOAD_FAST (implicit)") + else: + unimplemented("undefined LOAD_FAST") + + # for continuation functions + if name.startswith("___stack"): + self.symbolic_locals.pop(name) + + def LOAD_FAST(self, inst): + self._load_fast(inst.argval) + + def LOAD_DEREF(self, inst): + assert inst.argval in self.cell_and_freevars() + + if self.exec_recorder and inst.argval in self.f_locals: + self.exec_recorder.add_local_var(inst.argval, self.f_locals[inst.argval]) + + if inst.argval not in self.symbolic_locals: + unimplemented(f"undefined LOAD_DEREF {inst.argval}") + self.push(self.symbolic_locals[inst.argval]) + + def _store_fast(self, name): + loaded_vt = self.pop() + loaded_vt.set_name_hint(name) + self.symbolic_locals[name] = loaded_vt + + def STORE_FAST(self, inst): + self._store_fast(inst.argval) + + def DELETE_FAST(self, inst): + del self.symbolic_locals[inst.argval] + + STORE_DEREF = STORE_FAST + + def LOAD_CLOSURE(self, inst): + self.push(self._load_closure(inst.argval)) + + def _load_const(self, inst): + i = inst.arg + if i is None: + return ConstantVariable.create(value=inst.argval) + val = self._constants_cache[i] + if not val: + self._constants_cache[i] = val = ConstantVariable.create(value=inst.argval) + return val + + def LOAD_CONST(self, inst): + self.push(self._load_const(inst)) + + def _load_global(self, inst): + name = inst.argval + + if self.exec_recorder: + if name in self.f_globals: + self.exec_recorder.add_global_var(name, self.f_globals[name]) + else: + assert name in self.f_builtins + self.exec_recorder.builtins[name] = self.f_builtins[name] + + if name in self.symbolic_globals: + variable = self.output.side_effects[self.symbolic_globals[name]] + self.push(self.output.side_effects.load_global(variable, name)) + return + + try: + value = self.f_globals[name] + except KeyError: + return self.load_builtin(inst) + + source = GlobalSource(name) + self.push(VariableBuilder(self, source)(value)) + + @functools.cached_property + def nn_modules_globals_vt(self): + module_name = "torch.nn.modules.module" + module_source = self.import_source(module_name) + fglobals_value = importlib.import_module(module_name) # type: ignore[assignment] + return VariableBuilder(self, module_source)(fglobals_value) + + def LOAD_GLOBAL(self, inst): + if sys.version_info >= (3, 11) and sys.version_info < (3, 13) and inst.arg % 2: + self.PUSH_NULL(inst) + self._load_global(inst) + if sys.version_info >= (3, 13) and inst.arg % 2: + self.PUSH_NULL(inst) + + def STORE_GLOBAL(self, inst): + value = self.pop() + name = inst.argval + source = GlobalSource(name) + if name not in self.symbolic_globals: + self.symbolic_globals[name] = object() # type: ignore[assignment] # sentinel object + variable = self.output.side_effects.track_global_existing( + source, self.symbolic_globals[name] + ) + if isinstance(value, RemovableHandleVariable): + unimplemented("Storing handles in globals - NYI") + self.output.side_effects.store_global(variable, name, value) + + def import_source(self, module_name): + """Create an alias to a module for use in guards""" + if "torch_package" in module_name: + value = torch.package.package_importer._package_imported_modules[ + module_name + ] + alias = ( + module_name.replace(">", "_").replace("<", "_").replace(".", "_dot_") + ) + else: + value = importlib.import_module(module_name) + alias = f"__import_{module_name.replace('.', '_dot_')}" + f_globals = self.output.global_scope + assert alias not in f_globals or f_globals[alias] is value + f_globals[alias] = value + self.output.update_co_names(alias) + return GlobalSource(alias) + + def resolve_name(self, name, package, level): + """ + Copied from the Cpython implementation of __import__ + Resolve a relative module name to an absolute one. + https://github.com/python/cpython/blob/5a094f0255eea1db58fb2cf14c200971e64ec36e/Lib/importlib/_bootstrap.py#L902 + """ + bits = package.rsplit(".", level - 1) + if len(bits) < level: + raise ImportError("attempted relative import beyond top-level package") + base = bits[0] + return f"{base}.{name}" if name else base + + def calc_package(self): + """ + Copied from the Cpython implementation of __import__ + https://github.com/python/cpython/blob/5a094f0255eea1db58fb2cf14c200971e64ec36e/Lib/importlib/_bootstrap.py#L1090 + """ + package = self.f_globals.get("__package__") + spec = self.f_globals.get("__spec__") + if package is not None: + if spec is not None and package != spec.parent: + log.warning( + "__package__ != __spec__.parent (%r != %r)", + package, + spec.parent, + stacklevel=3, + ) + return package + elif spec is not None: + return spec.parent + else: + log.warning( + "can't resolve package from __spec__ or __package__, " + "falling back on __name__ and __path__", + stacklevel=3, + ) + package = self.f_globals["__name__"] + if "__path__" not in self.f_globals: + package = package.rpartition(".")[0] + return package + + def IMPORT_NAME(self, inst): + level, fromlist = self.popn(2) + level = level.as_python_constant() + fromlist = fromlist.as_python_constant() + module_name = inst.argval + + # Are we replaying? if so, load recorded module + recorded_name = ( + f"{ExecutionRecorder.LOCAL_MOD_PREFIX}_{level}_{fromlist}_{module_name}" + ) + if recorded_name in self.f_globals: + value = self.f_globals[recorded_name] + source = GlobalSource(recorded_name) + else: + try: + value = __import__( + module_name, + fromlist=fromlist, + level=level, + globals=self.f_globals, + ) + except ImportError: + unimplemented("import a module that does not exist") + + if level != 0: + pkg = self.calc_package() + module_name = self.resolve_name(module_name, pkg, level) + + # For __import__, when the name variable is of the form package.module, + # normally, the top-level package (the name up till the first dot) is + # returned, not the module named by module_name. However, when a + # non-empty fromlist argument is given, the module named by name is + # returned. Therefore, we set the source correctly here. + if not fromlist: + top_level_module_name = module_name.partition(".")[0] + source = self.import_source(top_level_module_name) + else: + source = self.import_source(module_name) + + if self.exec_recorder: + self.exec_recorder.add_local_mod(recorded_name, value) + + if istype(value, (types.ModuleType, DummyModule)): + self.push(PythonModuleVariable(value, source=source)) + else: + unimplemented(f"IMPORT_NAME {typestr(value)}") + + def IMPORT_FROM(self, inst): + self.DUP_TOP(inst) + self._load_attr(inst) + + def load_builtin_from_argval(self, argval): + if argval not in self.f_builtins: + raise NameError(f"name '{argval}' is not defined") + val = self.f_builtins[argval] + + if callable(val): + builtins_source = GlobalSource( + self.output.name_of_builtins_dict_key_in_fglobals + ) + var_source = GetItemSource(builtins_source, argval) + self.push(VariableBuilder(self, var_source)(val)) + else: + assert is_builtin_constant(val) + self.push(ConstantVariable.create(value=val)) + + def load_builtin(self, inst): + self.load_builtin_from_argval(inst.argval) + + def jump(self, inst): + self.instruction_pointer = self.indexof[inst.target] + + JUMP_FORWARD = jump + JUMP_ABSOLUTE = jump + + POP_JUMP_IF_FALSE = generic_jump(operator.not_, False) + POP_JUMP_IF_TRUE = generic_jump(operator.truth, False) + JUMP_IF_FALSE_OR_POP = generic_jump(operator.not_, True) + JUMP_IF_TRUE_OR_POP = generic_jump(operator.truth, True) + + def SETUP_LOOP(self, inst): + # only exists in python<=3.7 + self.block_stack.append(BlockStackEntry(inst, inst.target)) + + def SETUP_EXCEPT(self, inst): + # only exists in python<=3.7 + self.block_stack.append(BlockStackEntry(inst, inst.target)) + + def POP_BLOCK(self, inst): + self.block_stack.pop() + + def SETUP_WITH(self, inst): + self.setup_or_before_with(inst) + + def SETUP_FINALLY(self, inst): + self.block_stack.append(BlockStackEntry(inst, inst.target)) + + def BEGIN_FINALLY(self, inst): + self.push(None) + + def WITH_CLEANUP_START(self, inst): + exit, exc = self.popn(2) + assert exc is None + self.push(exc) + self.push(exit.call_function(self, [ConstantVariable.create(None)] * 3, {})) + + def WITH_CLEANUP_FINISH(self, inst): + self.popn(2) + self.push(None) + + def CALL_FINALLY(self, inst): + """ + pushes the address of the next instruction onto the stack and increments + bytecode counter by delta + """ + # Python 3.8 only + addr = self.indexof[self.next_instruction] + self.push(ConstantVariable.create(addr)) + self.jump(inst) + + def END_FINALLY(self, inst): + # Python 3.8 only + # https://docs.python.org/3.8/library/dis.html#opcode-END_FINALLY + tos = self.pop() + if isinstance(tos, ConstantVariable): + self.instruction_pointer = tos.as_python_constant() + else: + pass + + def POP_FINALLY(self, inst): + # Python 3.8 only + preserve_tos = inst.argval + if preserve_tos: + tos = self.pop() + _ = self.pop() + if preserve_tos: + self.push(tos) # type: ignore[possibly-undefined] + + def FOR_ITER(self, inst): + it = self.pop().realize() + try: + val = it.next_variable(self) + self.push(it) + self.push(val) + except (StopIteration, exc.ObservedUserStopIteration) as e: + if isinstance(e, exc.ObservedUserStopIteration): + exc.handle_observed_exception(self) + + # leave iterator upon exhaustion in 3.12 + if sys.version_info >= (3, 12): + # CPython 3.12 actually jumps to the instruction after the END_FOR + # and performs the action of END_FOR as part of FOR_ITER. We jump + # to the END_FOR and run it, so we need to make sure 2 values are + # on the stack for it to pop. + self.push(it) + self.push(ConstantVariable.create(None)) + self.jump(inst) + + def _raise_exception_variable(self, inst): + val = self.pop() + # User can raise exception in 2 ways + # 1) raise exception type - raise NotImplementedError + # 2) raise execption instance - raise NotImplemetedError("foo") + + # 1) when user raises exception type + if isinstance(val, variables.BuiltinVariable): + # Create the instance of the exception type + # https://github.com/python/cpython/blob/3.11/Python/ceval.c#L6547-L6549 + val = val.call_function(self, [], {}) # type: ignore[arg-type] + + # Save the exception in a global data structure + self.exn_vt_stack.append(val) + + # 2) when user raises exception instance + if isinstance(val, variables.ExceptionVariable): + if observed_exception_type := exc.observed_exception_map.get(val.exc_type): + raise observed_exception_type(f"raised exception {val}") + raise exc.ObservedException(f"raised exception {val}") + unimplemented(f"raise {exc}") + + def RAISE_VARARGS(self, inst): + if inst.arg == 0: + unimplemented("re-raise") + elif inst.arg == 1: + self._raise_exception_variable(inst) + else: + # Support raise .. from None ... Dynamo does not track __cause__ and other attributes of exception. So we + # ignore `from None` part. + from_vt = self.pop() + if isinstance(from_vt, ConstantVariable) and from_vt.value is None: + self._raise_exception_variable(inst) + unimplemented("raise ... from ...") + + def RERAISE(self, inst): + if sys.version_info >= (3, 11): + # RERAISE is currently supported in a narrow case of `raise ... from None` + self._raise_exception_variable(inst) + unimplemented("RERAISE") + + def exception_handler(self, raised_exception): + if sys.version_info >= (3, 11): + exn_tab_entry = self.current_instruction.exn_tab_entry + if exn_tab_entry: + # Implementation is based on https://github.com/python/cpython/blob/3.11/Objects/exception_handling_notes.txt + + # 1) pop values from the stack until it matches the stack depth + # for the handler + while len(self.stack) > exn_tab_entry.depth: + self.pop() + + # 2) if 'lasti' is true, then push the offset that the exception was raised at + if exn_tab_entry.lasti: + self.push( + variables.ConstantVariable(self.current_instruction.offset) + ) + + # 3) push the exception to the stack + assert len(self.exn_vt_stack) + self.push(self.exn_vt_stack[-1]) + + # 4) jump to the handler + self.jump(exn_tab_entry) + else: + # No handler found. Bubble the exception to the parent + # instruction translater. We use special exception for this. + self.stack.clear() + if type(self) is InstructionTranslator: + raise Unsupported("Observed exception") + raise raised_exception + else: + if len(self.block_stack): + # base implementation - https://github.com/python/cpython/blob/3.10/Python/ceval.c#L4455 + + assert len(self.exn_vt_stack) + exception_var = self.exn_vt_stack[-1] + + block_stack_entry = self.block_stack.pop() + + while block_stack_entry.inst.opname == "EXCEPT_HANDLER": + # TODO(anijain2305) - This is not tested .. unable to create a testcase + # https://github.com/python/cpython/blob/3.10/Python/ceval.c#L1456 + self.popn(3) + if len(self.block_stack) == 0: + # No handler found in this frame. Bubble the exception to the parent + # instruction translater. + self.stack.clear() + if type(self) is InstructionTranslator: + raise Unsupported("Observed exception") + raise raised_exception + block_stack_entry = self.block_stack.pop() + + if block_stack_entry.inst.opname != "SETUP_FINALLY": + unimplemented( + "exception is raised when top of the block stack " + "is not exception handler (e.g. try .. with .. except). " + f"Current TOS is {block_stack_entry.inst}" + ) + + # Push a dummy block stack entry of EXCEPT_HANDLER + # https://github.com/python/cpython/blob/3.10/Python/ceval.c#L1456 + except_handler_inst = Instruction(1e6, "EXCEPT_HANDLER", None, 0) + self.block_stack.append(BlockStackEntry(except_handler_inst, None)) + + # Push old exception + if len(self.exn_vt_stack) >= 2: + old_exception = self.exn_vt_stack[-2] + + # Push the old exception on to stack - tb, value, type + # Traceback is currently mapped to UnknownVariable + self.push(variables.UnknownVariable()) + self.push(old_exception) + self.push(variables.BuiltinVariable(old_exception.exc_type)) + else: + # Push empty exception tb, value, type + self.push(variables.ConstantVariable(None)) + self.push(variables.ConstantVariable(None)) + self.push(variables.ConstantVariable(None)) + + # Push new exception - tb, val, type + # Traceback is currently mapped to UnknownVariable + self.push(variables.UnknownVariable()) + self.push(exception_var) + self.push(variables.BuiltinVariable(exception_var.exc_type)) + + # Jump to target + self.jump(block_stack_entry) + else: + # No handler found. Bubble the exception to the parent + # instruction translater. We use special exception for this. + self.stack.clear() + if type(self) is InstructionTranslator: + raise Unsupported("Observed exception") + raise raised_exception + + def PUSH_EXC_INFO(self, inst): + val = self.pop() + assert len(self.exn_vt_stack) + self.push(self.exn_vt_stack[-1]) + self.push(val) + + def POP_EXCEPT(self, inst): + if sys.version_info >= (3, 11): + val = self.pop() + assert isinstance(val, variables.ExceptionVariable) + + # This exception is handled and therefore we can clear the error indicator + assert len(self.exn_vt_stack) + self.exn_vt_stack.pop() + else: + assert len(self.block_stack) > 0 + if self.block_stack[-1].inst.opname != "EXCEPT_HANDLER": + raise AssertionError( + "Bug in Dynamo tracing of exception handling." + "Top of the block stack is not EXCEPT_HANDLER." + ) + self.block_stack.pop() + + self.popn(3) + + # This exception is handled and therefore we can clear the error indicator + assert len(self.exn_vt_stack) + self.exn_vt_stack.pop() + + def check_if_exc_matches(self): + assert len(self.stack) >= 2 + expected_exc_types = self.pop() + if sys.version_info >= (3, 11): + # CHECK_EXC_MATCH (which is used from 3.11 onwards) does not pop. + # This is the description from the disassembly doc + # + # Performs exception matching for ``except``. Tests whether the ``STACK[-2]`` + # is an exception matching ``STACK[-1]``. Pops ``STACK[-1]`` and pushes the boolean + # result of the test. + exc_instance = self.stack[-1] + else: + # This is used prior to 3.11 via opcode JUMP_IF_NOT_EXC_MATCH + # There is no documentation but here is the code pointer that does 2 pops + # https://github.com/python/cpython/blob/3.10/Python/ceval.c#L3650-L3665 + exc_instance = self.stack.pop() + + # Users can check exception in 2 ways + # 1) except NotImplementedError --> BuilinVariable + # 2) except (NotImplemetedError, AttributeError) -> TupleVariable + + if not isinstance(expected_exc_types, (BuiltinVariable, TupleVariable)): + unimplemented( + f"except has an unsupported types of objects {expected_exc_types}" + ) + + if sys.version_info >= (3, 11): + if not isinstance(exc_instance, variables.ExceptionVariable): + unimplemented( + f"except expects to recieve an object of exception type but received {exc_instance}" + ) + + if isinstance(expected_exc_types, TupleVariable): + expected_types = expected_exc_types.items + else: + expected_types = [ + expected_exc_types, + ] + + for expected_type in expected_types: + if not isinstance(expected_type, BuiltinVariable): + unimplemented( + f"except has an unsupported types of object {expected_type}" + ) + if isinstance(exc_instance, variables.ExceptionVariable) and issubclass( + exc_instance.exc_type, expected_type.fn + ): + return True + elif isinstance(exc_instance, variables.BuiltinVariable) and issubclass( + exc_instance.fn, expected_type.fn + ): + return True + + return False + + def CHECK_EXC_MATCH(self, inst): + self.push(variables.ConstantVariable(self.check_if_exc_matches())) + + def JUMP_IF_NOT_EXC_MATCH(self, inst): + if not self.check_if_exc_matches(): + self.jump(inst) + + def COMPARE_OP(self, inst): + if inst.argval == "exception match": + self.CHECK_EXC_MATCH(inst) + else: + self.push(compare_op_handlers[inst.argval](self, self.popn(2), {})) + + def GET_ITER(self, inst): + self.call_function(BuiltinVariable(iter), [self.pop()], {}) + + @break_graph_if_unsupported(push=1) + def CALL_FUNCTION(self, inst): + args = self.popn(inst.argval) + fn = self.pop() + self.call_function(fn, args, {}) + + @break_graph_if_unsupported(push=1) + def CALL_FUNCTION_EX(self, inst): + kwargsvars: VariableTracker + if inst.argval == 0: + kwargsvars = ConstDictVariable({}) + argsvars = self.pop() + elif inst.argval == 1: + kwargsvars = self.pop() + argsvars = self.pop() + else: + unimplemented("CALL_FUNCTION_EX") + + if sys.version_info >= (3, 13): + # 3.13 swapped null and callable + null = self.pop() + assert isinstance(null, NullVariable) + + fn = self.pop() + + if sys.version_info >= (3, 11) and sys.version_info < (3, 13): + null = self.pop() + assert isinstance(null, NullVariable) + + if isinstance(fn, GetAttrVariable) and isinstance(fn.obj, TensorVariable): + # realize is requires for Python 3.8 + kwargsvars = kwargsvars.realize() + if fn.name == "view" and isinstance( + argsvars, (ConstantVariable, TensorVariable) + ): + # Hack to handle special case in some bert models. Converts + # x.view(*shape) into x.view(shape), which is correct for view() + # but not generally. See test_transpose_for_scores(). + argsvars = TupleVariable([argsvars]) + elif ( + fn.name == "random_" + and isinstance(argsvars, TupleVariable) + and len(argsvars.items) == 0 + and isinstance(kwargsvars, ConstDictVariable) + and ConstantVariable.create("from") in kwargsvars + ): + # `from`` is python keyword. Adding random_ with `from` in the + # Fx graph causes syntax error. Even if we convert the kwargs to + # args, aot_autograd/inductor while lowering generates + # aten.random.from, again causing syntax errors. Since this + # usecase is uncommon, graph break. + unimplemented("random_ op is called with from keyword") + elif ( + fn.name == "uniform_" + and isinstance(argsvars, TupleVariable) + and len(argsvars.items) == 0 + and isinstance(kwargsvars, ConstDictVariable) + and ConstantVariable.create("from") in kwargsvars + ): + # `from`` is python keyword. Adding uniform_ with `from` in the + # Fx graph causes syntax error. Even if we convert the kwargs to + # args, aot_autograd/inductor while lowering generates + # aten.uniform.from, again causing syntax errors. Since this + # usecase is uncommon, graph break. + unimplemented("uniform_ op is called with from keyword") + + if not isinstance( + argsvars, BaseListVariable + ) and argsvars.has_force_unpack_var_sequence(self): + argsvars = TupleVariable(argsvars.force_unpack_var_sequence(self)) + + # Unpack for cases like fn(**obj) where obj is a map + if isinstance(kwargsvars, UserDefinedObjectVariable): + kwargsvars = BuiltinVariable.call_custom_dict(self, dict, kwargsvars) # type: ignore[arg-type] + + if not isinstance(argsvars, BaseListVariable) or not isinstance( + kwargsvars, ConstDictVariable + ): + unimplemented(f"non-static call {typestr(argsvars)} {typestr(kwargsvars)}") + + # Map to a dictionary of str -> VariableTracker + kwargsvars = kwargsvars.keys_as_python_constant() + self.call_function(fn, argsvars.items, kwargsvars) + + @break_graph_if_unsupported(push=1) + def CALL_FUNCTION_KW(self, inst): + argnames = self.pop() + args = self.popn(inst.argval) + fn = self.pop() + assert isinstance(argnames, TupleVariable) and argnames.is_python_constant() + argnames = argnames.as_python_constant() + args, kwargs_list = args[: -len(argnames)], args[-len(argnames) :] + kwargs = dict(zip(argnames, kwargs_list)) + assert len(kwargs) == len(argnames) + self.call_function(fn, args, kwargs) + + def LOAD_METHOD_SUPER(self, inst): + self.CALL_FUNCTION(dataclasses.replace(inst, argval=2)) + arg = inst.argval[0] + argval = self.code_options["co_names"][arg] + if sys.version_info < (3, 11): + self._load_attr(dataclasses.replace(inst, argval=argval)) + else: + self.LOAD_METHOD(dataclasses.replace(inst, argval=argval)) + + def LOAD_ATTR_SUPER(self, inst): + self.CALL_FUNCTION(dataclasses.replace(inst, argval=2)) + arg = inst.argval[0] + argval = self.code_options["co_names"][arg] + self._load_attr(dataclasses.replace(inst, argval=argval)) + + def LOAD_METHOD(self, inst): + self._load_attr(inst) + obj = self.pop() + if sys.version_info >= (3, 13): + self.push(obj) + self.PUSH_NULL(inst) + elif sys.version_info >= (3, 11): + # always follow the NULL + fn convention, since if obj + # is actually a method, self is already bound to it, so it + # doesn't need to be passed in as an arg. + self.PUSH_NULL(inst) + self.push(obj) + else: + self.push(obj) + self.push(None) + + def CALL_METHOD(self, inst): + args = self.popn(inst.argval) + dummy = self.pop() + assert dummy is None + fn = self.pop() + self.call_function(fn, args, {}) + + def _load_attr(self, inst): + obj = self.pop() + result = BuiltinVariable(getattr).call_function( + self, [obj, ConstantVariable.create(inst.argval)], {} # type: ignore[arg-type] + ) + self.push(result) + + def LOAD_ATTR(self, inst): + if sys.version_info >= (3, 12): + if inst.arg % 2: + self.LOAD_METHOD(inst) + return + self._load_attr(inst) + + def STORE_ATTR(self, inst): + speculation = self.speculate() + if speculation.failed: + return self.store_attr_graph_break(inst) + val, obj = self.popn(2) + + if isinstance(obj, NNModuleVariable) and not isinstance(val, ConstantVariable): + # We don't allow side effects during export on non-constant values + # https://github.com/pytorch/torchdynamo/issues/1475 + assert ( + not self.export + ), f"Mutating module attribute {inst.argval} during export." + + try: + BuiltinVariable(setattr).call_function( + self, [obj, ConstantVariable.create(inst.argval), val], {} # type: ignore[arg-type] + ) + return + except Unsupported as e: + if not self.should_compile_partial_graph(): + raise + log.debug("STORE_ATTR triggered compile", exc_info=True) + e.remove_from_stats() + e.add_to_stats("graph_break") + speculation.fail_and_restart_analysis() + + def store_attr_graph_break(self, inst): + if not self.should_compile_partial_graph(): + unimplemented("should_compile_partial_graph=False") + self.output.compile_subgraph( + self, reason=GraphCompileReason("store_attr", [self.frame_summary()]) + ) + self.output.add_output_instructions([copy.copy(inst)]) + self.popn(2) + self.output.add_output_instructions( + self.create_call_resume_at(self.next_instruction) + ) + + def DELETE_ATTR(self, inst): + obj = self.pop() + BuiltinVariable(delattr).call_function( + self, [obj, ConstantVariable.create(inst.argval)], {} # type: ignore[arg-type] + ) + + def create_call_resume_at(self, offset): + raise AssertionError( + f"create_call_resume_at not overridden by subclass {type(self)}" + ) + + def should_compile_partial_graph(self) -> bool: + raise AssertionError( + f"should_compile_partial_graph not overridden by subclass {type(self)}" + ) + + @break_graph_if_unsupported(push=0) + def STORE_SUBSCR(self, inst): + val, obj, key = self.popn(3) + result = obj.call_method(self, "__setitem__", [key, val], {}) + + def DELETE_SUBSCR(self, inst): + obj, key = self.popn(2) + obj.call_method(self, "__delitem__", [key], {}) + + def BUILD_TUPLE(self, inst): + name_tuple = None + if sys.version_info >= (3, 13): + name_tuple = tuple(self.name_stack[-inst.argval :]) + items = self.popn(inst.argval) + self.push(TupleVariable(items), name=name_tuple) + + def BUILD_SLICE(self, inst): + items = self.popn(inst.argval) + self.push(SliceVariable(items)) + + def BUILD_LIST(self, inst): + items = self.popn(inst.argval) + self.push(ListVariable(items, mutable_local=MutableLocal())) + + def BUILD_SET(self, inst): + if config.inject_BUILD_SET_unimplemented_TESTING_ONLY: + unimplemented("missing: BUILD_SET") + items = self.popn(inst.argval) + new_set = SetVariable(items, mutable_local=MutableLocal()) + self.push(new_set) + + def BUILD_LIST_UNPACK(self, inst, cls=ListVariable): + seqs = self.popn(inst.argval) + items = [] + for seq in seqs: + try: + items.extend(seq.force_unpack_var_sequence(self)) + except NotImplementedError: + unimplemented(f"BUILD_LIST_UNPACK {seq}") + self.push(cls(items, mutable_local=MutableLocal())) + + def BUILD_TUPLE_UNPACK(self, inst): + self.BUILD_LIST_UNPACK(inst, cls=TupleVariable) + + BUILD_TUPLE_UNPACK_WITH_CALL = BUILD_TUPLE_UNPACK + + def BUILD_MAP(self, inst): + items = self.popn(inst.argval * 2) + d = dict(zip(items[::2], items[1::2])) + self.push(ConstDictVariable(d, mutable_local=MutableLocal())) + + def BUILD_MAP_UNPACK(self, inst): + items = self.popn(inst.argval) + # ensure everything is a dict + items = [BuiltinVariable(dict).call_function(self, [x], {}) for x in items] # type: ignore[arg-type] + result = {} + for x in items: + assert isinstance(x, ConstDictVariable) + result.update(x.items) + self.push( + ConstDictVariable( + result, + mutable_local=MutableLocal(), + ) + ) + + BUILD_MAP_UNPACK_WITH_CALL = BUILD_MAP_UNPACK + + def BUILD_CONST_KEY_MAP(self, inst): + keys = self.pop() + values = self.popn(inst.argval) + assert isinstance(keys, TupleVariable) + assert keys.is_python_constant() + + keys = keys.force_unpack_var_sequence(self) + assert len(keys) == len(values) + + self.push( + ConstDictVariable( + dict(zip(keys, values)), + mutable_local=MutableLocal(), + ) + ) + + def MAP_ADD(self, inst): + k, v = self.popn(2) + assert inst.argval > 0 + obj = self.stack[-inst.arg].realize() + assert isinstance(obj, ConstDictVariable) + obj.call_method(self, "__setitem__", (k, v), {}) # type: ignore[arg-type] + + def SET_ADD(self, inst): + v = self.pop() + assert inst.argval > 0 + obj = self.stack[-inst.arg] + assert isinstance(obj, SetVariable) + assert obj.mutable_local + return obj.call_method(self, "add", [v], {}) + + def SET_UPDATE(self, inst): + v = self.pop() + assert inst.argval > 0 + obj = self.stack[-inst.arg] + assert isinstance(obj, SetVariable) + assert obj.mutable_local + obj.call_method(self, "update", [v], {}) + + def LIST_APPEND(self, inst): + v = self.pop() + assert inst.argval > 0 + obj = self.stack[-inst.arg].realize() + assert isinstance(obj, ListVariable) + assert obj.mutable_local + self.output.side_effects.mutation(obj) + obj.items.append(v) + + def MAKE_FUNCTION(self, inst): + flags = inst.arg + old_stack = list(self.stack) + if sys.version_info < (3, 11): + fn_name = self.pop() + code = self.pop() + if sys.version_info >= (3, 11): + # MAKE_FUNCTION behavior actually changed in 3.11, see + # https://github.com/python/cpython/pull/93189/ + assert hasattr(code.value, "co_qualname") # type: ignore[attr-defined] + fn_name = ConstantVariable.create(value=code.value.co_qualname) # type: ignore[attr-defined] + defaults = None + closure = None + annotations = None + kwdefaults = None + + if sys.version_info < (3, 13): + # in 3.13, this is handled in SET_FUNCTION_ATTRIBUTE + if flags & 0x08: + closure = self.pop() + if flags & 0x04: + annotations = self.pop() + if flags & 0x02: + kwdefaults = self.pop() + if flags & 0x01: + defaults = self.pop() + + self.push( + NestedUserFunctionVariable( + fn_name, + code, + self.f_globals, + defaults, + kwdefaults, + annotations, + closure, + closure_scope=self, + ) + ) + + def UNPACK_SEQUENCE(self, inst): + seq = self.pop() + if isinstance(seq, TensorVariable): + val = seq.unpack_var_sequence(self, idxes=range(inst.argval)) # type: ignore[arg-type] + elif isinstance(seq, GetAttrVariable) and isinstance(seq.obj, TensorVariable): + # x, y = a.shape + proxy = getattr(seq.obj.as_proxy(), seq.name) + val = [wrap_fx_proxy(self, proxy[i]) for i in range(inst.argval)] + elif seq.has_force_unpack_var_sequence(self): + val = seq.force_unpack_var_sequence(self) + else: + unimplemented(f"UNPACK_SEQUENCE {seq}") + if len(val) != inst.argval: + unimplemented("UNPACK_SEQUENCE length mismatch") + for i in reversed(val): + self.push(i) + + def UNPACK_EX(self, inst): + assert 0 <= inst.argval <= 0xFFFF + prefix = inst.argval & 0xFF # low byte + suffix = inst.argval >> 8 # high byte + seq = self.pop() + if seq.has_force_unpack_var_sequence(self): + vals = list(seq.force_unpack_var_sequence(self)) + assert len(vals) >= prefix + suffix + vals_prefix = vals[:prefix] + vals_list = vals[prefix : len(vals) - suffix] + vals_suffix = vals[len(vals) - suffix :] + for item in reversed(vals_suffix): + self.push(item) + self.push(TupleVariable(vals_list)) + for item in reversed(vals_prefix): + self.push(item) + else: + unimplemented(f"UNPACK_EX {seq}") + + def NOP(self, inst): + pass + + def POP_TOP(self, inst): + self.pop() + + def ROT_TWO(self, inst): + a = self.pop() + b = self.pop() + self.push(a) + self.push(b) + + def ROT_THREE(self, inst): + a = self.pop() + b = self.pop() + c = self.pop() + self.push(a) + self.push(c) + self.push(b) + + def ROT_FOUR(self, inst): + a = self.pop() + b = self.pop() + c = self.pop() + d = self.pop() + self.push(a) + self.push(d) + self.push(c) + self.push(b) + + def DUP_TOP(self, inst): + a = self.pop() + self.push(a) + self.push(a) + + def DUP_TOP_TWO(self, inst): + a = self.pop() + b = self.pop() + self.push(b) + self.push(a) + self.push(b) + self.push(a) + + def FORMAT_VALUE(self, inst): + flags = inst.arg + if (flags & 0x04) == 0x04: + fmt_spec = self.pop() + else: + fmt_spec = ConstantVariable.create("") + + value = self.pop() + if isinstance(value, SymNodeVariable): + from torch._dynamo.variables.lazy import ( + LazySymNodeFormatString, + LazyVariableTracker, + ) + + value = LazyVariableTracker.create( + LazySymNodeFormatString(value, fmt_spec), source=value.source + ) + self.push(value) + return + if (flags & 0x03) == 0x01: + value = BuiltinVariable(str).call_function(self, [value], {}) # type: ignore[arg-type] + elif (flags & 0x03) == 0x02: + value = BuiltinVariable(repr).call_function(self, [value], {}) # type: ignore[arg-type] + elif (flags & 0x03) == 0x03: + value = BuiltinVariable(ascii).call_function(self, [value], {}) # type: ignore[arg-type] + + fmt_var = ConstantVariable.create("{:" + fmt_spec.as_python_constant() + "}") + + self.call_function(BuiltinVariable(str.format), [fmt_var, value], {}) + + def BUILD_STRING(self, inst): + format_string_parts: List[str] = [] + args: List[VariableTracker] = [] + kwargs: Dict[str, VariableTracker] = {} + for part in self.popn(inst.arg): + if isinstance(part, ConstantVariable): + format_string_parts.append("{}") + args.append(part) + elif isinstance(part, variables.StringFormatVariable): + format_string_parts.append(part.format_string) + args.extend(part.sym_args) + if set(kwargs.keys()) & set(part.sym_kwargs.keys()): + unimplemented( + f"BUILD_STRING key conflict {kwargs} & {part.sym_kwargs}" + ) + kwargs.update(part.sym_kwargs) + else: + unimplemented(f"BUILD_STRING {part}") + self.push( + variables.StringFormatVariable.create( + "".join(format_string_parts), args, kwargs + ) + ) + + def IS_OP(self, inst): + assert inst.argval == 0 or inst.argval == 1 + if inst.argval == 0: + new_argval = "is" + else: + new_argval = "is not" + new_inst = create_instruction("COMPARE_OP", argval=new_argval) + self.COMPARE_OP(new_inst) + + def CONTAINS_OP(self, inst): + assert inst.argval == 0 or inst.argval == 1 + left, right = self.popn(2) + op = inst.argval + self.push(right.call_method(self, "__contains__", [left], {})) + if op == 1: + self.UNARY_NOT(inst) + + def LIST_EXTEND(self, inst): + v = self.pop() + assert inst.argval > 0 + obj = self.stack[-inst.arg] + assert isinstance(obj, ListVariable) + assert obj.mutable_local + obj.call_method(self, "extend", [v], {}) + + def LIST_TO_TUPLE(self, inst): + self.push(BuiltinVariable(tuple).call_function(self, [self.pop()], {})) # type: ignore[arg-type] + + def DICT_MERGE(self, inst): + v = self.pop() + assert inst.argval > 0 + obj = self.stack[-inst.arg].realize() + assert isinstance(obj, ConstDictVariable) + assert obj.mutable_local + obj.call_method(self, "update", [v], {}) + + DICT_UPDATE = DICT_MERGE + + def GEN_START(self, inst): + self.pop() + + def GET_LEN(self, inst): + tos = self.stack[-1] + if tos.is_python_constant(): + self.push(ConstantVariable.create(len(tos.as_python_constant()))) + else: + self.push(tos.call_method(self, "__len__", [], {})) + + def MATCH_MAPPING(self, inst): + tos = self.stack[-1] + assert isinstance(tos, ConstDictVariable) + if isinstance(tos.items, collections.abc.Mapping): + self.push(ConstantVariable.create(True)) + else: + self.push(ConstantVariable.create(False)) + + def MATCH_SEQUENCE(self, inst): + tos = self.stack[-1] + assert tos.is_python_constant() + tos_value = tos.as_python_constant() + if isinstance(tos_value, collections.abc.Sequence) and not isinstance( + tos_value, (str, bytes, bytearray) + ): + self.push(ConstantVariable.create(True)) + else: + self.push(ConstantVariable.create(False)) + + def MATCH_KEYS(self, inst): + tos = self.stack[-1] + tos1 = self.stack[-2] + assert isinstance(tos1, ConstDictVariable) + + if all(k in tos1 for k in tos): # type: ignore[attr-defined] + self.push(TupleVariable([tos1.getitem_const(self, k) for k in tos])) # type: ignore[attr-defined,arg-type] + if sys.version_info < (3, 11): + self.push(ConstantVariable.create(True)) + else: + self.push(ConstantVariable.create(None)) + if sys.version_info < (3, 11): + self.push(ConstantVariable.create(False)) + + def LOAD_ASSERTION_ERROR(self, inst): + self.load_builtin_from_argval("AssertionError") + + UNARY_POSITIVE = stack_op(operator.pos) + UNARY_NEGATIVE = stack_op(operator.neg) + UNARY_NOT = stack_op(operator.not_) + UNARY_INVERT = stack_op(operator.invert) + + BINARY_POWER = stack_op(operator.pow) + BINARY_MULTIPLY = stack_op(operator.mul) + BINARY_MATRIX_MULTIPLY = stack_op(operator.matmul) + BINARY_FLOOR_DIVIDE = stack_op(operator.floordiv) + BINARY_TRUE_DIVIDE = stack_op(operator.truediv) + BINARY_MODULO = stack_op(operator.mod) + BINARY_REMAINDER = stack_op(operator.mod) + BINARY_ADD = stack_op(operator.add) + BINARY_SUBTRACT = stack_op(operator.sub) + BINARY_SUBSCR = break_graph_if_unsupported(push=1)(stack_op(operator.getitem)) + BINARY_LSHIFT = stack_op(operator.lshift) + BINARY_RSHIFT = stack_op(operator.rshift) + BINARY_AND = stack_op(operator.and_) + BINARY_OR = stack_op(operator.or_) + BINARY_XOR = stack_op(operator.xor) + + INPLACE_POWER = stack_op(operator.ipow) + INPLACE_MULTIPLY = stack_op(operator.imul) + INPLACE_MATRIX_MULTIPLY = stack_op(operator.imatmul) + INPLACE_FLOOR_DIVIDE = stack_op(operator.ifloordiv) + INPLACE_TRUE_DIVIDE = stack_op(operator.itruediv) + INPLACE_MODULO = stack_op(operator.imod) + INPLACE_REMAINDER = stack_op(operator.imod) + INPLACE_ADD = stack_op(operator.iadd) + INPLACE_SUBTRACT = stack_op(operator.isub) + INPLACE_LSHIFT = stack_op(operator.ilshift) + INPLACE_RSHIFT = stack_op(operator.irshift) + INPLACE_AND = stack_op(operator.iand) + INPLACE_XOR = stack_op(operator.ixor) + INPLACE_OR = stack_op(operator.ior) + + # 3.11 opcodes + def RESUME(self, inst): + if inst.arg == 0: + self.append_prefix_inst(inst) + self.accept_prefix_inst = False + else: + assert not self.accept_prefix_inst + + if sys.version_info >= (3, 11): + + def BINARY_OP(self, inst): + return _binary_op_lookup[inst.arg](self, inst) + + def PRECALL(self, inst): + pass + + def KW_NAMES(self, inst): + kw_names = self.code_options["co_consts"][inst.arg] + assert isinstance(kw_names, tuple) + for name in kw_names: + assert isinstance(name, str) + assert self.kw_names is None + self.kw_names = ConstantVariable.create(value=kw_names) # type: ignore[assignment] + + def PUSH_NULL(self, inst): + self.push(NullVariable()) + + def _call(self, inst, call_kw=False): + # see https://docs.python.org/3.11/library/dis.html#opcode-CALL + # for convention + if call_kw: + # TOS is kw_names for CALL_KW instruction + assert sys.version_info >= (3, 13) + kw_names = self.pop() + assert isinstance(kw_names, TupleVariable) and kw_names.is_python_constant() + kw_names = kw_names.as_python_constant() + else: + kw_names = self.kw_names.value if self.kw_names else () + + contents = self.popn(inst.arg + 2) + if sys.version_info >= (3, 13): + # NULL and callable swapped + fn = contents[0] + args = [] if isinstance(contents[1], NullVariable) else [contents[1]] + else: + if isinstance(contents[0], NullVariable): + fn = contents[1] + args = [] + else: + fn = contents[0] + args = [contents[1]] + + if kw_names: + args = args + contents[2 : -len(kw_names)] + kwargs_list = contents[-len(kw_names) :] + kwargs = dict(zip(kw_names, kwargs_list)) + assert len(kwargs) == len(kw_names) + else: + args = args + contents[2:] + kwargs = {} + + try: + # if call_function fails, need to set kw_names to None, otherwise + # a subsequent call may have self.kw_names set to an old value + self.call_function(fn, args, kwargs) + finally: + self.kw_names = None + + @break_graph_if_unsupported(push=1) + def CALL(self, inst): + self._call(inst) + + def COPY(self, inst): + self.push(self.stack[-inst.arg]) + + def SWAP(self, inst): + self.stack[-1], self.stack[-inst.arg] = self.stack[-inst.arg], self.stack[-1] + + JUMP_BACKWARD = jump + JUMP_BACKWARD_NO_INTERRUPT = jump + + POP_JUMP_FORWARD_IF_TRUE = generic_jump(operator.truth, False) + POP_JUMP_BACKWARD_IF_TRUE = generic_jump(operator.truth, False) + POP_JUMP_FORWARD_IF_FALSE = generic_jump(operator.not_, False) + POP_JUMP_BACKWARD_IF_FALSE = generic_jump(operator.not_, False) + + def CACHE(self, inst): + pass + + def BEFORE_WITH(self, inst): + self.setup_or_before_with(inst) + + def setup_or_before_with(self, inst): + ctx = self.pop() + if not isinstance( + ctx, (ContextWrappingVariable, GenericContextWrappingVariable) + ): + unimplemented(f"{inst.opname} {ctx}") + + if isinstance(ctx, GenericContextWrappingVariable): + self.generic_context_manager_depth += 1 + + # Need this redundant check for mypy + assert isinstance( + ctx, (ContextWrappingVariable, GenericContextWrappingVariable) + ) + + exit = WithExitFunctionVariable( + ctx, + inst.target, + ) + + if sys.version_info >= (3, 11): + # See create_call_resume_at for block stack details. + # Only push a block if the current instruction's block is a + # with block that is not nested in a try block - that is, the current + # instruction's block target is the same as the top block's target. + if inst.exn_tab_entry and ( + not self.block_stack + or inst.exn_tab_entry.target is not self.block_stack[-1].target + ): + target = None + else: + target = self.next_instruction.exn_tab_entry.target + else: + target = inst.target + + if target: + if isinstance(self, InstructionTranslator): + self.block_stack.append( + BlockStackEntry(inst, target, len(self.stack), ctx) + ) + else: + self.block_stack.append(BlockStackEntry(inst, target)) + + self.push(exit) + self.push(ctx.enter(self)) + + def append_prefix_inst(self, inst): + assert self.accept_prefix_inst + self.prefix_insts.append(inst) + + def MAKE_CELL(self, inst): + if sys.version_info >= (3, 12) and not self.accept_prefix_inst: + # In 3.12+, MAKE_CELL is not longer necessarily a prefix instruction. + # It can be generated by inlined comprehensions. + assert isinstance(self.symbolic_locals[inst.argval], NullVariable) + self.symbolic_locals[ + inst.argval + ] = self.output.side_effects.track_cell_new() + else: + self.append_prefix_inst(inst) + + def COPY_FREE_VARS(self, inst): + self.append_prefix_inst(inst) + + def RETURN_GENERATOR(self, inst): + self.append_prefix_inst(inst) + + # 3.12 opcodes + # BINARY/STORE_SLICE opcodes are broken down into + # BUILD_SLICE 2 and BINARY/STORE_SUBSCR + + def END_FOR(self, inst): + if sys.version_info >= (3, 13): + self.pop() + else: + self.popn(2) + + def LOAD_FAST_CHECK(self, inst): + if isinstance(self.symbolic_locals[inst.argval], NullVariable): + unimplemented("LOAD_FAST_CHECK on uninitialized variable") + self.LOAD_FAST(inst) + + def LOAD_FAST_AND_CLEAR(self, inst): + if inst.argval not in self.symbolic_locals: + self.push(NullVariable()) + else: + self.LOAD_FAST(inst) + self.symbolic_locals[inst.argval] = NullVariable() + + def LOAD_SUPER_ATTR(self, inst): + self.CALL_FUNCTION(dataclasses.replace(inst, argval=2)) + if inst.arg & 1: + self.LOAD_METHOD(inst) + else: + self._load_attr(inst) + + def CALL_INTRINSIC_1(self, inst): + if inst.argval == 5: + # INTRINSIC_UNARY_POSITIVE + self.UNARY_POSITIVE(inst) + elif inst.argval == 6: + # INTRINSIC_LIST_TO_TUPLE + self.push(TupleVariable(self.pop().force_unpack_var_sequence(self))) + else: + unimplemented(f"missing CALL_INTRINSIC_1 operand {inst.argval}") + + def END_SEND(self, inst): + tos = self.pop() + self.pop() + self.push(tos) + + # 3.13 opcodes + # fused instructions LOAD_FAST_LOAD_FAST, STORE_FAST_STORE_FAST, STORE_FAST_LOAD_FAST + # are broken down. + @break_graph_if_unsupported(push=1) + def CALL_KW(self, inst): + self._call(inst, call_kw=True) + + def TO_BOOL(self, inst): + # TO_BOOL only precedes a conditional jump or UNARY_NOT (see compile.c in CPython) + # So we can skip this instruction as long as we remember to codegen a TO_BOOL + # before conditional jumps/UNARY_NOT. + assert self.next_instruction.opname in ( + "POP_JUMP_IF_TRUE", + "POP_JUMP_IF_FALSE", + "UNARY_NOT", + ) + + def SET_FUNCTION_ATTRIBUTE(self, inst): + flags = inst.arg + fn = self.pop() + assert isinstance(fn, NestedUserFunctionVariable) + attr_names = self.name_stack[-1] + attr = self.pop() + + if flags & 0x08: + # 3.13 merged LOAD_CLOSURE into LOAD_FAST, so we won't know if a given LOAD_FAST + # is meant to load a closure variable or not. Our workaround is to maintain a stack + # of LOAD_FAST variable names and tuples (self.name_stack). So if we are indeed + # constructing a closure tuple, we can use self.name_stack to construct the closure + # variables here. + assert isinstance(attr_names, tuple) and all( + isinstance(name, str) for name in attr_names + ) + fn.closure = TupleVariable( + [self._load_closure(name) for name in attr_names] + ) + fn.closure_scope = self + elif flags & 0x04: + fn.annotations = attr + elif flags & 0x02: + fn.kwdefaults = attr + elif flags & 0x01: + fn.defaults = attr + + self.push(fn) + + def _format_value_313(self, fmt_spec): + value = self.pop() + if isinstance(value, SymNodeVariable): + value = ConstantVariable.create(str(value.sym_num)) + + fmt_var = ConstantVariable.create("{:" + fmt_spec.as_python_constant() + "}") + + self.call_function(BuiltinVariable(str.format), [fmt_var, value], {}) + + def FORMAT_SIMPLE(self, inst): + self._format_value_313(ConstantVariable.create("")) + + def FORMAT_WITH_SPEC(self, inst): + self._format_value_313(self.pop()) + + def is_non_empty_graph(self): + if self.output.count_calls() > 1: + # perf optimization only + self.is_non_empty_graph = lambda: True # type: ignore[method-assign] + return True + return False + + def format_frame_summary(self, additional_stack_frames=None): + if additional_stack_frames is None: + additional_stack_frames = [] + return "".join( + traceback.format_list( + [self.frame_summary()] + list(reversed(additional_stack_frames)) + ) + ) + + def frame_summary(self): + return traceback.FrameSummary( + getattr(self.f_code, "co_filename", ""), + self.lineno, + getattr(self.f_code, "co_name", ""), + lookup_line=False, + ) + + def is_co_filename_from_nn_modules(self): + filename = getattr(self.f_code, "co_filename", "") + nn_modules_pattern = re.compile(r".*torch/nn/modules.*") + return nn_modules_pattern.match(filename) is not None + + def store_global_weakref_by_id(self, prefix, value): + global_name = self.output.install_global_by_id(prefix, weakref.ref(value)) + install_guard( + GlobalWeakRefSource(global_name).make_guard(GuardBuilder.WEAKREF_ALIVE) + ) + return global_name + + @property + def fake_mode(self): + return self.output.tracing_context.fake_mode + + def find_symbolic_locals_name(self, tensor_variable): + for key, value in self.symbolic_locals.items(): + if value is tensor_variable: + return key + return None + + @contextlib.contextmanager + def strict_translation_mode(self, check_fn: Callable[[VariableTracker], bool]): + """ + Strict mode is enabled on a per-VariableTracker level depending on the return value of check_fn(node). + """ + prior = self.strict_checks_fn + self.strict_checks_fn = check_fn + try: + yield + finally: + self.strict_checks_fn = prior + + def speculate(self) -> SpeculationEntry: + assert self.instruction_pointer is not None + assert self.instruction_pointer > 0 + return self.speculation_log.next( + self.f_code.co_filename, + self.lineno, + self.instruction_pointer - 1, + self.instructions[self.instruction_pointer - 1], + ) + + def __init__( + self, + output: OutputGraph, + instructions: List[Instruction], + f_locals: Dict[str, Any], + f_globals: Dict[str, Any], + f_builtins: Dict[str, Any], + code_options: Dict[str, Any], + symbolic_locals: Dict[str, VariableTracker], + symbolic_globals: Dict[str, VariableTracker], + symbolic_torch_function_mode_stack: Deque["TorchFunctionModeVariable"], + f_code: types.CodeType, + export: bool, + inline_depth: int, + speculation_log: SpeculationLog, + distributed_state: Optional[DistributedState], + ) -> None: + super().__init__() + self.speculation_log = speculation_log + self.distributed_state = distributed_state + + # Mutable state checkpointed by copy_graphstate() + self.output = output + self.symbolic_locals = symbolic_locals + self.symbolic_globals = symbolic_globals + self.symbolic_torch_function_mode_stack = symbolic_torch_function_mode_stack + self.stack = [] + # stack of variable names for tracking 3.13 closures + self.name_stack: list[Any] = [] + self.instruction_pointer = 0 + self.current_instruction = create_instruction("NOP") + self.block_stack = [] + # states before SETUP_WITH for checkpointing and fallback + self.generic_context_manager_depth = 0 + self.lineno = -1 + self.kw_names = None + self.accept_prefix_inst = True + self.prefix_insts = [] + self.exn_vt_stack = [] + + # Properties of the input/output code + self.instructions: List[Instruction] = instructions + self.indexof: Dict[Instruction, int] = get_indexof(self.instructions) + self.f_locals: Dict[ + str, Any + ] = f_locals # needed for recording accessed locals for replay + self.f_globals: Dict[str, Any] = f_globals + self.f_builtins: Dict[str, Any] = f_builtins + self.code_options: Dict[str, Any] = code_options + self.f_code: types.CodeType = f_code + + # Execution record for replaying errors + if config.replay_record_enabled: + self.exec_recorder = ExecutionRecorder( + code=f_code, code_options=code_options + ) + else: + self.exec_recorder = None + # Stack of module being parsed, current nn.module is at the end of ordered dict. + # The first field of tuple is the fully qualified name of current module + # in original hierarchy. The second field is the type of current nn.module + self.nn_module_stack: Dict[str, Tuple[str, Type[Any]]] = {} + # Flag to indicate whether tracing is used for export. + self.export = export + self.one_graph = False + + self.current_speculation = None + + self.strict_checks_fn = None + + if sys.version_info >= (3, 10): + from .resume_execution import ( + CO_ASYNC_GENERATOR, + CO_COROUTINE, + CO_GENERATOR, + CO_ITERABLE_COROUTINE, + ) + + if f_code.co_flags & ( + CO_GENERATOR | CO_COROUTINE | CO_ITERABLE_COROUTINE | CO_ASYNC_GENERATOR + ): + self.push(BuiltinVariable(None)) + + self.inline_depth = inline_depth + self.inconsistent_side_effects = False + self._constants_cache: List[Optional[VariableTracker]] = [None] * len( + f_code.co_consts + ) + linecache.lazycache(f_code.co_filename, f_globals) + + +class InstructionTranslator(InstructionTranslatorBase): + mutated_closure_cell_contents: Set[str] + + @staticmethod + def current_tx() -> "InstructionTranslator": + return tls.current_tx + + @contextlib.contextmanager + def set_current_tx(self): + prior = getattr(tls, "current_tx", None) + tls.current_tx = self + try: + yield + finally: + tls.current_tx = prior + + def __init__( + self, + instructions: List[Instruction], + f_code, + f_locals, + f_globals, + f_builtins, + code_options, + compiler_fn, + one_graph, + export, + export_constraints, + mutated_closure_cell_contents: Set[str], + frame_state, + speculation_log: SpeculationLog, + distributed_state: Optional[DistributedState], + ) -> None: + _step_logger()( + logging.INFO, + f"torchdynamo start tracing {f_code.co_name} {code_options['co_filename']}:{code_options['co_firstlineno']}", + ) + super().__init__( + output=OutputGraph( + code_options, + compiler_fn, + self, + export, + export_constraints, + frame_state, + local_scope=f_locals, + global_scope=f_globals, + f_code=f_code, + ), + instructions=instructions, + f_locals=f_locals, + f_globals=f_globals, + f_builtins=f_builtins, + code_options=code_options, + symbolic_locals={}, # set below + # A global var is inserted only after a STORE_GLOBAL happens to it + symbolic_globals={}, + symbolic_torch_function_mode_stack=collections.deque(), + f_code=f_code, + export=export, + inline_depth=0, + speculation_log=speculation_log, + distributed_state=distributed_state, + ) + + self._throw_if_in_functorch() + + # as soon as we create the tracing context we should keep it active, so any calls + # into dynamo apis can rely on finding it + with tracing(self.output.tracing_context), self.set_current_tx(): + self.one_graph: bool = one_graph + self.export = export + self.mutated_closure_cell_contents = mutated_closure_cell_contents + if self.export: + assert ( + self.one_graph + ), "Export without one graph - something has gone wrong." + + vars = list(code_options["co_varnames"]) + cells_and_freevars = [x for x in self.cell_and_freevars() if x not in vars] + vars.extend(cells_and_freevars) + cells_and_freevars_set = set(cells_and_freevars) + + self.symbolic_locals = { + k: variables.LazyVariableTracker.create( + f_locals[k], + source=LocalSource(k, cell_or_freevar=k in cells_and_freevars_set), + ) + for k in vars + if k in f_locals + } + + self._init_torch_function_mode_stack() + + self.debug_locals: List[Tuple[VariableTracker, List[VariableTracker]]] = [] + if export: + # export gets confused if we never realize unused inputs + # in export mode just eagerly realize everything + self.symbolic_locals = variables.LazyVariableTracker.realize_all( + self.symbolic_locals + ) + + self._freevars_ids = {} + for name in self.code_options["co_freevars"]: + if name in f_locals: + self._freevars_ids[name] = id(f_locals[name]) + + def _throw_if_in_functorch(self): + # Fallback to eager in case of a graph break inside vmap + eager = torch._dynamo.lookup_backend("eager") + compiler_fn = inspect.getattr_static( + self.output.compiler_fn, "compiler_fn", self.output.compiler_fn + ) + ci = torch._C._functorch.peek_interpreter_stack() + forbidden_keys = ( + torch._C._functorch.TransformType.Vmap, + torch._C._functorch.TransformType.Grad, + torch._C._functorch.TransformType.Jvp, + ) + + if ci is not None and ci.key() in forbidden_keys and compiler_fn is not eager: + name = ci.key().name.lower() + msg = ( + "If you are reaching here, it means dynamo failed for one of the following reasons:\n" + # Calling a torch.compiled function + f"- Calling torch.func.{name}(compiled_fn) function from eager mode is not supported. " + f"Ensure that torch.func.{name} is also wrapped within a torch.compile function. " + "For more information, see PyTorch issue #128711.\n" + # if it reaches here, it means Dynamo failed to inline a functorch function + f"- torch.func.{name}(fn) requires the function to be inlined by dynamo" + ) + unimplemented(msg) + + def _init_torch_function_mode_stack(self): + from .variables.torch_function import TorchFunctionModeStackVariable + + TorchFunctionModeStackVariable.reset() + + self.symbolic_torch_function_mode_stack: Deque[ + TorchFunctionModeVariable + ] = collections.deque() + # We want to retrieve all modes to properly reconstruct the stack if needed + py_stack = get_torch_function_mode_stack(filter_ignored=False) + + if py_stack: + has_device_context = isinstance( + py_stack[0], torch.utils._device.DeviceContext + ) + + for i, val in enumerate(py_stack): + self.symbolic_torch_function_mode_stack.append( + variables.LazyVariableTracker.create( + val, source=TorchFunctionModeStackSource(i) + ) + ) + + def get_example_value(self, source: Source): + if isinstance(source, LocalSource): + return self.f_locals[source.local_name] + if isinstance(source, GlobalSource): + return self.f_globals[source.global_name] + raise KeyError + + def run(self): + super().run() + + def match_nested_cell(self, name, cell): + """Match a cell in this method to one in a function we are inlining""" + try: + value = cell.cell_contents + except ValueError: + return None + # TODO(jansel): check the id of the cell rather than the contents + if id(value) != self._freevars_ids.get(name): + return None + return self.symbolic_locals[name] + + def should_compile_partial_graph(self): + if sys.version_info >= (3, 11): + # Do not compile if current instruction's block is not the top with block + entry = self.current_instruction.exn_tab_entry + if entry and ( + not self.block_stack or entry.target is not self.block_stack[-1].target + ): + return False + return ( + all(b.can_restore() for b in self.block_stack) + and not self.one_graph + and self.generic_context_manager_depth == 0 + ) + + def create_call_resume_at(self, inst): + self.instruction_pointer = None + + if inst.opname == "RETURN_VALUE": + return [create_instruction("RETURN_VALUE")] + elif inst.opname == "RETURN_CONST": + return [create_instruction("RETURN_CONST", argval=inst.argval)] + + reads = livevars_analysis(self.instructions, inst) + all_argnames = tuple( + k + for k in self.symbolic_locals.keys() + if k in reads and k not in self.cell_and_freevars() + ) + # NOTE: do not use isinstance, since it realizes lazy VT's + argnames = tuple( + k + for k in all_argnames + if not type.__instancecheck__(NullVariable, self.symbolic_locals[k]) + ) + argnames_null = tuple( + k + for k in all_argnames + if type.__instancecheck__(NullVariable, self.symbolic_locals[k]) + ) + if sys.version_info < (3, 12): + assert len(argnames_null) == 0, "variables should not be NULL in < 3.12" + + cg = PyCodegen(self) + + # Handle inactive context variables. + # The resume function assumes that context variables are the class, NOT the object. + # e.g. torch.set_grad_enabled(True) will be reconstructed as torch.set_grad_enabled + stack_ctx_vars = [] + for i, var in enumerate(self.stack): + if type.__instancecheck__(ContextWrappingVariable, var): + ctx = cast(ContextWrappingVariable, var) + target_values = ( + () if ctx.target_values is None else tuple(ctx.target_values) + ) + stack_ctx_vars.append((i, target_values)) + # Replace the current stack var with the context class + ctx.reconstruct_type(cg) + cg.extend_output(create_swap(len(self.stack) - i + 1)) + cg.append_output(create_instruction("POP_TOP")) + + argnames_ctx_vars = [] + for name in argnames: + if type.__instancecheck__( + ContextWrappingVariable, var := self.symbolic_locals[name] + ): + ctx = cast(ContextWrappingVariable, var) + target_values = ( + () if ctx.target_values is None else tuple(ctx.target_values) + ) + argnames_ctx_vars.append((name, target_values)) + # Replace the local with the context class + ctx.reconstruct_type(cg) + cg.append_output(create_instruction("STORE_FAST", argval=name)) + + # Python does not allow null to be an arg to a function, so + # we remove nulls from the stack and restore them in the + # prologue of the resume function + + # sorted list of indices of nulls on the stack + null_idxes: List[int] = [] + if sys.version_info >= (3, 11): + # find indices of NullVariables + for i, var in enumerate(self.stack): + if type.__instancecheck__(NullVariable, var): + null_idxes.append(i) + # generate bytecode to pop the nulls + null_cnt = 0 + for i, var in enumerate(reversed(self.stack)): + if type.__instancecheck__(NullVariable, var): + for j in range(2, i + 2 - null_cnt): + cg.append_output(create_instruction("SWAP", arg=j)) + cg.extend_output(cg.pop_null()) + null_cnt += 1 + + # we popped all nulls from the stack at runtime, + # so we should not count NullVariables + stack_len = len(self.stack) - len(null_idxes) + nargs = stack_len + len(argnames) + + name = unique_id(f"__resume_at_{inst.offset}") + + new_code: types.CodeType = ContinueExecutionCache.lookup( + self.f_code, + self.lineno, + inst.offset, + tuple(b.target.offset for b in self.block_stack), + stack_len, + argnames, + argnames_null, + tuple(b.resume_fn() for b in self.block_stack), + tuple(stack_ctx_vars), + tuple(argnames_ctx_vars), + tuple(null_idxes), + ) + + # Add original GraphModule context to the resume function to handle + # the case of a graph break while tracing a GraphModule + orig_graphmodule_maybe = code_context.get_context(self.f_code).get( + "orig_graphmodule", lambda: None + )() + if orig_graphmodule_maybe is not None: + code_context.get_context(new_code)["orig_graphmodule"] = weakref.ref( + orig_graphmodule_maybe + ) + + if new_code.co_freevars: + # expose code object for debugging purposes + self.output.install_global_unsafe(name, new_code) + cg.make_function_with_closure(name, new_code, True, stack_len) + else: + # This is safe: we pre-generate a unique name + self.output.install_global_unsafe( + name, types.FunctionType(new_code, self.f_globals, name) + ) + cg.extend_output(cg.load_function_name(name, True, stack_len)) + + cg.extend_output([cg.create_load(k) for k in argnames]) + cg.extend_output(create_call_function(nargs, False)) + cg.append_output(create_instruction("RETURN_VALUE")) + return cg.get_instructions() + + def symbolic_locals_contain_module_class(self): + for v in self.symbolic_locals.values(): + if isinstance(v, UserDefinedClassVariable) and issubclass( + v.as_python_constant(), torch.nn.Module + ): + return True + return False + + def _return(self, inst): + if ( + self.output.count_calls() == 0 + and not self.inconsistent_side_effects + and not self.symbolic_locals_contain_module_class() + and not self.export + ): + raise exc.SkipFrame("because no content in function call") + self.instruction_pointer = None + _step_logger()( + logging.INFO, + f"torchdynamo done tracing {self.f_code.co_name} ({inst.opname})", + ) + log.debug("%s triggered compile", inst.opname) + self.output.compile_subgraph( + self, + reason=GraphCompileReason( + "return_value", [self.frame_summary()], graph_break=False + ), + ) + return_inst = ( + create_instruction("RETURN_VALUE") + if inst.opname == "RETURN_VALUE" + else create_instruction("RETURN_CONST", argval=inst.argval) + ) + self.output.add_output_instructions([return_inst]) + raise ReturnValueOp + + def RETURN_VALUE(self, inst): + self._return(inst) + + def RETURN_CONST(self, inst): + self._return(inst) + + +if sys.version_info >= (3, 11): + _binary_op_lookup = [ + getattr( + InstructionTranslator, + opname[3:] if "INPLACE" in opname else f"BINARY_{opname[3:]}", + ) + for opname, _ in dis._nb_ops # type: ignore[attr-defined] + ] + + +class InliningInstructionTranslator(InstructionTranslatorBase): + """Trace and inline a called method""" + + symbolic_result: Optional[TensorVariable] + + @classmethod + def inline_call(cls, parent, func, args, kwargs): + with patch.dict(counters, {"unimplemented": counters["inline_call"]}): + return cls.inline_call_(parent, func, args, kwargs) + + @staticmethod + def check_inlineable(func): + if func.has_self(): + unimplemented("inline with __self__") + + result = trace_rules.check_verbose(func, is_inlined_call=True) + if result.skipped: + from torch._dynamo.variables.misc import produce_trampoline_autograd_apply + + # _origin marks this as coming from an internal dynamo known function that is safe to + # trace through. + if hasattr(getattr(func, "fn", None), "_origin") and func.fn._origin in [ + produce_trampoline_autograd_apply, + ]: + # Known sound + return trace_rules.SkipResult( + False, "allowlist in dynamo known function" + ) + fn_qualname = func.fn.__qualname__ if hasattr(func, "fn") else "" + unimplemented( + f"'inline in skipfiles: {fn_qualname} | {func.get_name()} {func.get_filename()}, {result.reason}'" + ) + + if isinstance(func, UserFunctionVariable) and inspect.getattr_static( + func.get_function(), "_torchdynamo_disable", False + ): + unimplemented( + f"call torch._dynamo.disable() wrapped function {func.get_function()}" + ) + else: + return result + + @staticmethod + def inline_call_( + parent, func: VariableTracker, args: List[VariableTracker], kwargs + ): + if isinstance(func, SkipFunctionVariable): + unimplemented("inline with functions in skip files") + assert isinstance( + func, + (UserFunctionVariable, NestedUserFunctionVariable), + ) + result = InliningInstructionTranslator.check_inlineable(func) + assert result.skipped is False + try: + sub_locals, closure_cells = func.bind_args(parent, args, kwargs) + except TypeError as e: + # Wrap the general TypeError during bind_args() to the internal ArgsMismatchError with detailed info + raise ArgsMismatchError( # noqa: B904 + "{reason}.\n func = {func}, args = {args}, kwargs = {kwargs}".format( + reason=str(e), + func=f"'{func.get_name()}' {func.get_filename()}:{func.get_code().co_firstlineno}", + args=[arg.python_type() for arg in args], + kwargs=kwargs, + ), + ) + + for v in itertools.chain(sub_locals.values(), closure_cells.values()): + if not isinstance(v, VariableTracker): + unimplemented(f"unconverted arg {v}") + + code: types.CodeType = func.get_code() + if code.co_name in ("__setitem__", "__setattr__") and not ( + args + and isinstance( + args[0], + (variables.CustomizedDictVariable, variables.UserDefinedObjectVariable), + ) + ): + unimplemented(f"inline {code.co_name}") + + suffix = "" + # TODO: mlazos, add support for enabling multiple artifact logs + # with a single alias + if torch._logging._internal.log_state.is_artifact_enabled("bytecode"): + suffix = f"\n{dis.Bytecode(code).dis()}" + if sys.version_info >= (3, 11): + cur_inst = parent.current_instruction + parent_code = parent.f_code + header = parent.get_line_of_code_header(lineno=cur_inst.positions.lineno) + + def get_trace_call_log_str(): + line = get_instruction_source_311(parent_code, cur_inst).rstrip() + return f"TRACE inlined call {code.co_name} from {header}\n{line}" + + trace_call_log.debug("%s", LazyString(get_trace_call_log_str)) + log.debug("INLINING %s%s, %s", code, suffix, result.reason) + + # Detect inline GraphModule calls in order to propagate node metadata, + # by checking if the first argument (self) is a variable tracking a GraphModule. + if args and isinstance(args[0], NNModuleVariable): + module = parent.output.get_submodule(args[0].module_key) + if isinstance(module, torch.fx.GraphModule): + # The inline call might not actually be a call to `forward`, + # but it is enough to add a context for `forward` in case it is called. + code_context.get_context(module.forward.__code__)[ + "orig_graphmodule" + ] = weakref.ref(module) + + tracer: InliningInstructionTranslator + if is_generator(code): + tracer = InliningGeneratorInstructionTranslator( + parent, + code, + sub_locals, + parent.symbolic_globals, + parent.symbolic_torch_function_mode_stack, + closure_cells, + func, + ) + else: + tracer = InliningInstructionTranslator( + parent, + code, + sub_locals, + parent.symbolic_globals, + parent.symbolic_torch_function_mode_stack, + closure_cells, + func, + ) + + strict_ctx: Any = contextlib.nullcontext() + if parent.strict_checks_fn: + strict_ctx = tracer.strict_translation_mode(parent.strict_checks_fn) + try: + with strict_ctx: + tracer.run() + except exc.ObservedException as e: + msg = f"Observed exception DURING INLING {code} : {e}" + # TODO(anijain2305) - This works but we should probably have a + # global/central data structure for the exception stack. + parent.exn_vt_stack.extend(tracer.exn_vt_stack) + log.debug(msg) + # bubble up the exception to the parent frame. + raise + except exc.SkipFrame as e: + msg = f"SKIPPED INLINING {code}: {e}" + log.debug(msg) + raise Unsupported(msg) from e + except Exception as e: + log.debug("FAILED INLINING %s", code) + raise + assert tracer.symbolic_result is not None + func.export_freevars(parent, tracer) + + if tracer.f_globals is parent.f_globals: + # Merge symbolic_globals back if parent and child are in the same namespace + parent.symbolic_globals.update(tracer.symbolic_globals) + + parent.inconsistent_side_effects |= tracer.inconsistent_side_effects + + log.debug("DONE INLINING %s", code) + + if is_generator(code): + assert isinstance(tracer, InliningGeneratorInstructionTranslator) + assert tracer.symbolic_result.as_python_constant() is None + return ListIteratorVariable( + tracer.generated_items, + mutable_local=MutableLocal(), + ) + else: + return tracer.symbolic_result + + def __init__( + self, + parent: InstructionTranslatorBase, + code: types.CodeType, + symbolic_locals: Dict[str, VariableTracker], + symbolic_globals: Dict[str, VariableTracker], + symbolic_torch_function_mode_stack: Deque["TorchFunctionModeVariable"], + closure_cells: Dict[str, VariableTracker], + funcvar: BaseUserFunctionVariable, + ) -> None: + f_globals = funcvar.get_globals() # type: ignore[attr-defined] + f_builtins = f_globals["__builtins__"] + if not isinstance(f_builtins, dict): + f_builtins = f_builtins.__dict__ + instructions = cleaned_instructions(code) + propagate_line_nums(instructions) + super().__init__( + output=parent.output, + f_locals={}, + f_globals=f_globals, + f_builtins=f_builtins, + symbolic_locals=symbolic_locals, + symbolic_globals=symbolic_globals, + symbolic_torch_function_mode_stack=symbolic_torch_function_mode_stack, + instructions=instructions, + code_options={k: getattr(code, k) for k in get_code_keys()}, + f_code=code, + export=parent.export, + inline_depth=parent.inline_depth + 1, + speculation_log=parent.speculation_log, + distributed_state=parent.distributed_state, + ) + self.parent = parent + self.symbolic_result = None + self.closure_cells = closure_cells + self.nn_module_stack = parent.nn_module_stack.copy() + self.one_graph = parent.one_graph + + @property + def fake_mode(self): + return self.parent.fake_mode + + def run_ctx_mgr(self): + return TracingContext.current_frame(self.parent.frame_summary()) + + def STORE_DEREF(self, inst): + if inst.argval in self.closure_cells: + cell = self.closure_cells[inst.argval] + val = self.pop() + if isinstance(cell, ClosureVariable): + if not self.output.is_root_tracer(): + unimplemented( + "HigherOrderOperator: Mutating a variable not in the current scope (ClosureVariable)" + ) + self.output.root_tx.symbolic_locals[cell.name] = val + else: + self.output.side_effects.store_cell(cell, val) + else: + maybe_cell = self.symbolic_locals.get(inst.argval) + if isinstance( + maybe_cell, + variables.NewCellVariable, + ): + self.output.side_effects.store_cell( + self.symbolic_locals[inst.argval], self.pop() + ) + else: + if ( + maybe_cell is not None + and maybe_cell.source.name() + not in self.output.root_tx.mutated_closure_cell_contents + ): + # Why is the source name here unique? + # mutated_closure_cell_contents is a per-frame + # concept, and sources identify, e.g., particular + # locals from the frame. If you had two locals, + # they'll get different source names, and therefore + # differ here. + self.output.root_tx.mutated_closure_cell_contents.add( + maybe_cell.source.name() + ) + raise exc.UnspecializeRestartAnalysis + unimplemented("write to __closure__ while inlining") + + def LOAD_DEREF(self, inst): + if inst.argval in self.closure_cells: + cell = self.closure_cells[inst.argval] + if isinstance(cell, ClosureVariable): + self.push(self.output.root_tx.symbolic_locals[cell.name]) + else: + self.push(self.output.side_effects.load_cell(cell)) + else: + maybe_sym_local = self.symbolic_locals.get(inst.argval, None) + if isinstance(maybe_sym_local, variables.NewCellVariable): + self.push(self.output.side_effects.load_cell(maybe_sym_local)) + else: + super().LOAD_DEREF(inst) + + def _load_closure(self, name): + assert name in self.cell_and_freevars() + if name in self.closure_cells: + return self.closure_cells[name] + else: + return InlinedClosureVariable(name=name) + + def check_replace_is_safe(self, oldvar): + if not is_side_effect_safe(oldvar.mutable_local): + unimplemented( + "HigherOrderOperator: Mutating a variable not in the current scope (replace_all)" + ) + + def should_compile_partial_graph(self): + return False # inlining functions is all-or-nothing + + def create_call_resume_at(self, offset): + unimplemented("cant resume while inlining") + + def RETURN_VALUE(self, inst): + self.symbolic_result = self.pop() # type: ignore[assignment] + self.instruction_pointer = None + raise ReturnValueOp + + def RETURN_CONST(self, inst): + self.symbolic_result = self._load_const(inst) + self.instruction_pointer = None + raise ReturnValueOp + + def get_globals_source_and_value(self, name): + if "__name__" in self.f_globals: + module_name = self.f_globals["__name__"] + module_source = self.import_source(module_name) + if "torch_package" in module_name: + fglobals_value = torch.package.package_importer._package_imported_modules[module_name] # type: ignore[assignment] + else: + fglobals_value = importlib.import_module(module_name) # type: ignore[assignment] + fglobals_vt = VariableBuilder(self, module_source)(fglobals_value) + global_source = AttrSource(module_source, name) + else: + globals_name = self.output.install_global_by_id( + "___unnamed_scope", self.f_globals + ) + globals_source = GlobalSource(globals_name) + fglobals_value = self.f_globals # type: ignore[assignment] + fglobals_vt = VariableBuilder(self, globals_source)(fglobals_value) + global_source = GetItemSource(globals_source, name) # type: ignore[assignment] + return fglobals_value, fglobals_vt, global_source + + def _load_global(self, inst): + if self.output.global_scope is self.f_globals: + super()._load_global(inst) + else: + name = inst.argval + + _, fglobals_vt, global_source = self.get_globals_source_and_value(name) + if self.output.side_effects.has_pending_mutation_of_attr(fglobals_vt, name): + self.push(self.output.side_effects.load_attr(fglobals_vt, name)) + else: + try: + value = self.f_globals[name] + except KeyError: + return self.load_builtin(inst) + + self.push(VariableBuilder(self, global_source)(value)) + + def STORE_GLOBAL(self, inst): + if self.f_globals is self.parent.f_globals: + super().STORE_GLOBAL(inst) + else: + value = self.pop() + if isinstance(value, RemovableHandleVariable): + unimplemented("Storing handles in globals - NYI") + name = inst.argval + fglobals_value, fglobals_vt, _ = self.get_globals_source_and_value(name) + self.output.side_effects.store_attr(fglobals_vt, name, value) + + +class InliningGeneratorInstructionTranslator(InliningInstructionTranslator): + generated_items: List[VariableTracker] + + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + self.generated_items = [] + + def YIELD_VALUE(self, inst: Instruction): + self.generated_items.append(self.pop()) + if len(self.generated_items) > MAX_ITERATOR_LIMIT: + unimplemented( + "Too many yield values in generator. Maybe you are inlining an infinite generator. " + f"If not, please report a bug at {PT2_ISSUE_TRACKER_URL}", + ) + self.push(ConstantVariable.create(None)) + + def GET_YIELD_FROM_ITER(self, inst): + tos = self.stack[-1] + if not isinstance(tos, ListIteratorVariable): + self.pop() + res = BuiltinVariable(iter).call_function(self, [tos], {}) # type: ignore[arg-type] + self.push(res) + + def YIELD_FROM(self, inst): + assert len(self.stack) >= 2 + val = self.pop() + tos = self.stack[-1] + if not (isinstance(val, ConstantVariable) and val.value is None): + # invoke send + # Unreachable code - if you hit this, you are implementing generator support and have + # lifted the `unimplemented("generator")` in frame conversion. This codepath handles + # subgenerator and lines up with this line in Python 3.10 + # https://github.com/python/cpython/blob/3.10/Python/ceval.c#L2599 + unimplemented("Unreachable sub-generator code") + + try: + val = tos.next_variable(self) + except (StopIteration, exc.ObservedUserStopIteration) as ex: + if isinstance(ex, exc.ObservedUserStopIteration): + exc.handle_observed_exception(self) + + # The iterator is exhausted. Stop the loop and return. + self.pop() + self.push(ConstantVariable.create(ex.value)) + else: + self.push(val) + # Add the value to yield into generated_items and replace the top of the stack with None + self.YIELD_VALUE(inst) + + # Repeat the YIELD_FROM instruction in the next eval loop + assert ( + isinstance(self.instruction_pointer, int) + and self.instruction_pointer > 0 + ) + self.instruction_pointer -= 1 + + def SEND(self, inst): + assert len(self.stack) >= 2 + val = self.pop() + tos = self.stack[-1] + if isinstance(tos, ListIteratorVariable) or ( + isinstance(tos, UserDefinedObjectVariable) + and isinstance(tos.value, collections.abc.Iterator) + ): + if isinstance(val, ConstantVariable) and val.value is None: + try: + val = tos.next_variable(self) + except (StopIteration, exc.ObservedUserStopIteration) as ex: + # To implement SEND, we have to look at the implementation + # when the iterator returns StopIteration. This translates to this code + # 3.11: https://github.com/python/cpython/blob/3.11/Python/ceval.c#L2613-L2619 + # 3.12: https://github.com/python/cpython/blob/3.12/Python/bytecodes.c#L863-L866 + # The implementation is different in 3.11 and 3.12. In 3.12, we rely + # on END_SEND to clean up. In 3.11, SEND does the cleanup as well. + if sys.version_info < (3, 12): + self.pop() # Python 3.12 uses new opcode END_SEND + self.push(ConstantVariable.create(ex.value)) + self.jump(inst) + else: + self.push(val) + else: + # invoke send + # Unreachable code - if you hit this, you are implementing generator support and have + # lifted the `unimplemented("generator")` in frame conversion. This codepath handles + # subgenerator and lines up with this line in Python 3.11 + # https://github.com/python/cpython/blob/3.11/Python/ceval.c#L2597 + unimplemented("Unreachable sub-generator code") + else: + unimplemented(f"SEND {typestr(tos)}") diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/tensor_version_op.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/tensor_version_op.py new file mode 100644 index 0000000000000000000000000000000000000000..889b2450409f48bd0334dbe9e3d0197820a2c820 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/tensor_version_op.py @@ -0,0 +1,59 @@ +# mypy: allow-untyped-defs +import torch +from torch._prims import _make_prim, RETURN_TYPE +from torch._subclasses import FakeTensorMode +from torch._subclasses.functional_tensor import FunctionalTensorMode + + +_tensor_version = _make_prim( + schema="_tensor_version(Tensor self) -> SymInt", + return_type=RETURN_TYPE.NEW, + meta=torch.ops.aten._version.default, + impl_aten=torch.ops.aten._version.default, + doc="Tracable unbacked SymInt version of torch.Tensor._version", +) + + +@_tensor_version.py_impl(FakeTensorMode) +def _tensor_version_fake(fake_mode, self_tensor): + """ + The initial dynamo capture of _tensor_version + _unsafe_set_version_counter turns the + `._version` into an unbacked SymInt so that we don't need to specialize on the `._version` + of input tensors to the graph. + """ + return fake_mode.shape_env.create_unbacked_symint() + + +_unsafe_set_version_counter = _make_prim( + schema="_unsafe_set_version_counter(Tensor self, SymInt version) -> ()", + return_type=RETURN_TYPE.NEW, + meta=lambda self, version: None, + impl_aten=torch._C._autograd._unsafe_set_version_counter, + doc="Tracable+SymInt version of torch._C._autograd._unsafe_set_version_counter", +) +torch.fx.node.has_side_effect(_unsafe_set_version_counter) + + +""" +When we functionalize _tensor_version + _unsafe_set_version_counter, +the ops disappear from the traced graph. We run them eagerly on the +fake tensors used for tracing, in order to get past asserts that would +fail in autograd. + +Why is this ok? +1) Versions on functional tensors don't make any sense since you can't mutate a functional tensor. +2) The whole point of version munging is to trick autograd into doing what we want, and after + AotAtuograd there is no longer any need for these ops. + +Note this is similar to how no_grad is handled. +""" + + +@_tensor_version.py_impl(FunctionalTensorMode) +def _tensor_version_functional(mode, self): + return self._version + + +@_unsafe_set_version_counter.py_impl(FunctionalTensorMode) +def _unsafe_set_version_counter_functional(ctx, self, version): + torch._C._autograd._unsafe_set_version_counter(self, version) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/test_case.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/test_case.py new file mode 100644 index 0000000000000000000000000000000000000000..81c0407833f67cb240f99d1d45ac4eda1b369028 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/test_case.py @@ -0,0 +1,75 @@ +# mypy: allow-untyped-defs +import contextlib +import importlib +import logging + +import torch +import torch.testing +from torch.testing._internal.common_utils import ( # type: ignore[attr-defined] + IS_WINDOWS, + TEST_WITH_CROSSREF, + TEST_WITH_TORCHDYNAMO, + TestCase as TorchTestCase, +) + +from . import config, reset, utils + + +log = logging.getLogger(__name__) + + +def run_tests(needs=()): + from torch.testing._internal.common_utils import run_tests + + if TEST_WITH_TORCHDYNAMO or IS_WINDOWS or TEST_WITH_CROSSREF: + return # skip testing + + if isinstance(needs, str): + needs = (needs,) + for need in needs: + if need == "cuda": + if not torch.cuda.is_available(): + return + else: + try: + importlib.import_module(need) + except ImportError: + return + run_tests() + + +class TestCase(TorchTestCase): + _exit_stack: contextlib.ExitStack + + @classmethod + def tearDownClass(cls): + cls._exit_stack.close() + super().tearDownClass() + + @classmethod + def setUpClass(cls): + super().setUpClass() + cls._exit_stack = contextlib.ExitStack() # type: ignore[attr-defined] + cls._exit_stack.enter_context( # type: ignore[attr-defined] + config.patch( + raise_on_ctx_manager_usage=True, + suppress_errors=False, + log_compilation_metrics=False, + ), + ) + + def setUp(self): + self._prior_is_grad_enabled = torch.is_grad_enabled() + super().setUp() + reset() + utils.counters.clear() + + def tearDown(self): + for k, v in utils.counters.items(): + print(k, v.most_common()) + reset() + utils.counters.clear() + super().tearDown() + if self._prior_is_grad_enabled is not torch.is_grad_enabled(): + log.warning("Running test changed grad mode") + torch.set_grad_enabled(self._prior_is_grad_enabled) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/test_minifier_common.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/test_minifier_common.py new file mode 100644 index 0000000000000000000000000000000000000000..b05542d578f43bd6f4eaadb919b2852233d84e2d --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/test_minifier_common.py @@ -0,0 +1,249 @@ +# mypy: allow-untyped-defs +import dataclasses +import io +import logging +import os +import re +import shutil +import subprocess +import sys +import tempfile +import traceback +from typing import Optional +from unittest.mock import patch + +import torch +import torch._dynamo +import torch._dynamo.test_case +from torch._dynamo.trace_rules import _as_posix_path +from torch.utils._traceback import report_compile_source_on_error + + +@dataclasses.dataclass +class MinifierTestResult: + minifier_code: str + repro_code: str + + def _get_module(self, t): + match = re.search(r"class Repro\(torch\.nn\.Module\):\s+([ ].*\n| *\n)+", t) + assert match is not None, "failed to find module" + r = match.group(0) + r = re.sub(r"\s+$", "\n", r, flags=re.MULTILINE) + r = re.sub(r"\n{3,}", "\n\n", r) + return r.strip() + + def minifier_module(self): + return self._get_module(self.minifier_code) + + def repro_module(self): + return self._get_module(self.repro_code) + + +class MinifierTestBase(torch._dynamo.test_case.TestCase): + DEBUG_DIR = tempfile.mkdtemp() + + @classmethod + def setUpClass(cls): + super().setUpClass() + cls._exit_stack.enter_context( # type: ignore[attr-defined] + torch._dynamo.config.patch(debug_dir_root=cls.DEBUG_DIR) + ) + # These configurations make new process startup slower. Disable them + # for the minification tests to speed them up. + cls._exit_stack.enter_context( # type: ignore[attr-defined] + torch._inductor.config.patch( + { + # https://github.com/pytorch/pytorch/issues/100376 + "pattern_matcher": False, + # multiprocess compilation takes a long time to warmup + "compile_threads": 1, + # https://github.com/pytorch/pytorch/issues/100378 + "cpp.vec_isa_ok": False, + } + ) + ) + + @classmethod + def tearDownClass(cls): + if os.getenv("PYTORCH_KEEP_TMPDIR", "0") != "1": + shutil.rmtree(cls.DEBUG_DIR) + else: + print(f"test_minifier_common tmpdir kept at: {cls.DEBUG_DIR}") + cls._exit_stack.close() # type: ignore[attr-defined] + + def _gen_codegen_fn_patch_code(self, device, bug_type): + assert bug_type in ("compile_error", "runtime_error", "accuracy") + return f"""\ +{torch._dynamo.config.codegen_config()} +{torch._inductor.config.codegen_config()} +torch._inductor.config.{"cpp" if device == "cpu" else "triton"}.inject_relu_bug_TESTING_ONLY = {bug_type!r} +""" + + def _maybe_subprocess_run(self, args, *, isolate, cwd=None): + if not isolate: + assert len(args) >= 2, args + assert args[0] == "python3", args + if args[1] == "-c": + assert len(args) == 3, args + code = args[2] + args = ["-c"] + else: + assert len(args) >= 2, args + with open(args[1]) as f: + code = f.read() + args = args[1:] + + # WARNING: This is not a perfect simulation of running + # the program out of tree. We only interpose on things we KNOW we + # need to handle for tests. If you need more stuff, you will + # need to augment this appropriately. + + # NB: Can't use save_config because that will omit some fields, + # but we must save and reset ALL fields + dynamo_config = torch._dynamo.config.shallow_copy_dict() + inductor_config = torch._inductor.config.shallow_copy_dict() + try: + stderr = io.StringIO() + log_handler = logging.StreamHandler(stderr) + log = logging.getLogger("torch._dynamo") + log.addHandler(log_handler) + try: + prev_cwd = _as_posix_path(os.getcwd()) + if cwd is not None: + cwd = _as_posix_path(cwd) + os.chdir(cwd) + with patch("sys.argv", args), report_compile_source_on_error(): + exec(code, {"__name__": "__main__", "__compile_source__": code}) + rc = 0 + except Exception: + rc = 1 + traceback.print_exc(file=stderr) + finally: + log.removeHandler(log_handler) + if cwd is not None: + os.chdir(prev_cwd) # type: ignore[possibly-undefined] + # Make sure we don't leave buggy compiled frames lying + # around + torch._dynamo.reset() + finally: + torch._dynamo.config.load_config(dynamo_config) + torch._inductor.config.load_config(inductor_config) + + # TODO: return a more appropriate data structure here + return subprocess.CompletedProcess( + args, + rc, + b"", + stderr.getvalue().encode("utf-8"), + ) + else: + if cwd is not None: + cwd = _as_posix_path(cwd) + return subprocess.run(args, capture_output=True, cwd=cwd, check=False) + + # Run `code` in a separate python process. + # Returns the completed process state and the directory containing the + # minifier launcher script, if `code` outputted it. + def _run_test_code(self, code, *, isolate): + proc = self._maybe_subprocess_run( + ["python3", "-c", code], isolate=isolate, cwd=self.DEBUG_DIR + ) + + print("test stdout:", proc.stdout.decode("utf-8")) + print("test stderr:", proc.stderr.decode("utf-8")) + repro_dir_match = re.search( + r"(\S+)minifier_launcher.py", proc.stderr.decode("utf-8") + ) + if repro_dir_match is not None: + return proc, repro_dir_match.group(1) + return proc, None + + # Runs the minifier launcher script in `repro_dir` + def _run_minifier_launcher(self, repro_dir, isolate, *, minifier_args=()): + self.assertIsNotNone(repro_dir) + launch_file = _as_posix_path(os.path.join(repro_dir, "minifier_launcher.py")) + with open(launch_file) as f: + launch_code = f.read() + self.assertTrue(os.path.exists(launch_file)) + + args = ["python3", launch_file, "minify", *minifier_args] + if not isolate: + args.append("--no-isolate") + launch_proc = self._maybe_subprocess_run(args, isolate=isolate, cwd=repro_dir) + print("minifier stdout:", launch_proc.stdout.decode("utf-8")) + stderr = launch_proc.stderr.decode("utf-8") + print("minifier stderr:", stderr) + self.assertNotIn("Input graph did not fail the tester", stderr) + + return launch_proc, launch_code + + # Runs the repro script in `repro_dir` + def _run_repro(self, repro_dir, *, isolate=True): + self.assertIsNotNone(repro_dir) + repro_file = _as_posix_path(os.path.join(repro_dir, "repro.py")) + with open(repro_file) as f: + repro_code = f.read() + self.assertTrue(os.path.exists(repro_file)) + + repro_proc = self._maybe_subprocess_run( + ["python3", repro_file], isolate=isolate, cwd=repro_dir + ) + print("repro stdout:", repro_proc.stdout.decode("utf-8")) + print("repro stderr:", repro_proc.stderr.decode("utf-8")) + return repro_proc, repro_code + + # Template for testing code. + # `run_code` is the code to run for the test case. + # `patch_code` is the code to be patched in every generated file; usually + # just use this to turn on bugs via the config + def _gen_test_code(self, run_code, repro_after, repro_level): + return f"""\ +import torch +import torch._dynamo +{_as_posix_path(torch._dynamo.config.codegen_config())} +{_as_posix_path(torch._inductor.config.codegen_config())} +torch._dynamo.config.repro_after = "{repro_after}" +torch._dynamo.config.repro_level = {repro_level} +torch._dynamo.config.debug_dir_root = "{_as_posix_path(self.DEBUG_DIR)}" +{run_code} +""" + + # Runs a full minifier test. + # Minifier tests generally consist of 3 stages: + # 1. Run the problematic code + # 2. Run the generated minifier launcher script + # 3. Run the generated repro script + # + # If possible, you should run the test with isolate=False; use + # isolate=True only if the bug you're testing would otherwise + # crash the process + def _run_full_test( + self, run_code, repro_after, expected_error, *, isolate, minifier_args=() + ) -> Optional[MinifierTestResult]: + if isolate: + repro_level = 3 + elif expected_error is None or expected_error == "AccuracyError": + repro_level = 4 + else: + repro_level = 2 + test_code = self._gen_test_code(run_code, repro_after, repro_level) + print("running test", file=sys.stderr) + test_proc, repro_dir = self._run_test_code(test_code, isolate=isolate) + if expected_error is None: + # Just check that there was no error + self.assertEqual(test_proc.returncode, 0) + self.assertIsNone(repro_dir) + return None + # NB: Intentionally do not test return code; we only care about + # actually generating the repro, we don't have to crash + self.assertIn(expected_error, test_proc.stderr.decode("utf-8")) + self.assertIsNotNone(repro_dir) + print("running minifier", file=sys.stderr) + minifier_proc, minifier_code = self._run_minifier_launcher( + repro_dir, isolate=isolate, minifier_args=minifier_args + ) + print("running repro", file=sys.stderr) + repro_proc, repro_code = self._run_repro(repro_dir, isolate=isolate) + self.assertIn(expected_error, repro_proc.stderr.decode("utf-8")) + self.assertNotEqual(repro_proc.returncode, 0) + return MinifierTestResult(minifier_code=minifier_code, repro_code=repro_code) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/testing.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/testing.py new file mode 100644 index 0000000000000000000000000000000000000000..4922b521bada34aaf0653702078da7cf369c931b --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/testing.py @@ -0,0 +1,409 @@ +# mypy: allow-untyped-defs +import contextlib +import dis +import functools +import logging +import os.path +import random +import re +import sys +import types +import unittest +from typing import List, Optional, Sequence, Union +from unittest.mock import patch + +import torch +from torch import fx +from torch._dynamo.output_graph import OutputGraph + +from . import config, eval_frame, optimize_assert, reset +from .bytecode_transformation import ( + create_instruction, + debug_checks, + is_generator, + transform_code_object, +) +from .guards import CheckFunctionManager, CompileId, GuardedCode +from .utils import same + + +np: Optional[types.ModuleType] = None +try: + import numpy as np +except ModuleNotFoundError: + np = None + + +unsupported = eval_frame.unsupported +three = 3 + +log = logging.getLogger(__name__) + + +def clone_me(x): + if x is None: + return None + return x.detach().clone().requires_grad_(x.requires_grad) + + +def remove_optimized_module_prefix(name) -> str: + return re.sub(r"^_orig_mod[.]", "", name) + + +def collect_results(model, prediction, loss, example_inputs): + results = [] + results.append(prediction) + results.append(loss) + # if isinstance(loss, torch.Tensor) and loss.item() > 1: + # log.warning( + # f"High loss value alert - {loss:.2f}. Can result in unstable gradients." + # ) + + grads = {} + params = {} + for name, param in model.named_parameters(): + if isinstance(model, eval_frame.OptimizedModule): + name = remove_optimized_module_prefix(name) + param_copy = param + grad = param.grad + # Treat None and zero grad as same + if param.grad is None: + grad = torch.zeros_like(param) + grads[name + ".grad"] = grad + params[name] = param_copy + results.append(grads) + results.append(params) + buffers = {} + for name, buffer in model.named_buffers(): + if isinstance(model, eval_frame.OptimizedModule): + name = remove_optimized_module_prefix(name) + buffers[name] = buffer + results.append(buffers) + for example in example_inputs: + if isinstance(example, (tuple, list)): + for inp in example: + if isinstance(inp, torch.Tensor): + results.append(inp.grad) + else: + if isinstance(example, torch.Tensor): + results.append(example.grad) + return results + + +def requires_bwd_pass(out): + if isinstance(out, torch.Tensor): + return out.requires_grad + elif isinstance(out, (list, tuple)): + return any(requires_bwd_pass(x) for x in out) + elif out is None: + return False + elif isinstance(out, int): + return False + raise NotImplementedError("Don't know how to reduce", type(out)) + + +def reduce_to_scalar_loss(out): + """Reduce the output of a model to get scalar loss""" + if isinstance(out, torch.Tensor): + # Mean does not work on integer tensors + return out.sum() / out.numel() + elif isinstance(out, (list, tuple)): + return sum(reduce_to_scalar_loss(x) for x in out) / len(out) + elif type(out).__name__ in ( + "MaskedLMOutput", + "Seq2SeqLMOutput", + "CausalLMOutputWithCrossAttentions", + ): + return reduce_to_scalar_loss(out.logits) + elif type(out).__name__ == "SquashedNormal": + return out.mean.sum() + elif isinstance(out, dict): + return sum(reduce_to_scalar_loss(value) for value in out.values()) / len( + out.keys() + ) + raise NotImplementedError("Don't know how to reduce", type(out)) + + +def debug_dir() -> str: + path = os.path.join(os.path.dirname(__file__), "../debug") + if not os.path.exists(path): + os.mkdir(path) + return path + + +def debug_dump(name, code: types.CodeType, extra="") -> None: + with open(os.path.join(debug_dir(), name), "w") as fd: + fd.write( + f"{dis.Bytecode(code).info()}\n\n{dis.Bytecode(code).dis()}\n\n{extra}\n" + ) + + +def debug_insert_nops( + frame, cache_size, hooks, _, *, skip: int = 0 +) -> Optional[GuardedCode]: + """used to debug jump updates""" + + def insert_nops(instructions, code_options): + instructions.insert(0, create_instruction("NOP")) + instructions.insert(0, create_instruction("NOP")) + + if is_generator(frame.f_code): + return None + + debug_checks(frame.f_code) + code = transform_code_object(frame.f_code, insert_nops) + graph = OutputGraph( + code_options={}, + compiler_fn=None, + root_tx=None, + export=False, + export_constraints=None, + frame_state={"_id": 0}, + # TODO: shouldn't this be f_locals/f_globals from frame? + local_scope=locals(), + global_scope=globals(), + f_code=frame.f_code, + ) + + return GuardedCode(code, CheckFunctionManager(graph).check_fn, CompileId(0, 0)) + + +class CompileCounter: + def __init__(self): + self.frame_count = 0 + self.op_count = 0 + + def __call__(self, gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]): + self.frame_count += 1 + for node in gm.graph.nodes: + if "call" in node.op: + self.op_count += 1 + return gm.forward + + def clear(self): + self.frame_count = 0 + self.op_count = 0 + + +class CompileCounterWithBackend: + def __init__(self, backend): + self.frame_count = 0 + self.op_count = 0 + self.backend = backend + self.graphs = [] + + def __call__(self, gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]): + from .backends.registry import lookup_backend + + self.frame_count += 1 + for node in gm.graph.nodes: + if "call" in node.op: + self.op_count += 1 + self.graphs.append(gm) + return lookup_backend(self.backend)(gm, example_inputs) + + +# Equivalent to backend="eager", but also records graphs that +# we can assert on +class EagerAndRecordGraphs: + def __init__(self): + self.graphs = [] + + def __call__(self, gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]): + self.graphs.append(gm) + return gm.forward + + +def strip_comment(code) -> str: + code = str(code) + return re.sub(r"(?m)^ *#.*\n?", "", code) + + +def remove_trailing_space(code) -> str: + return "\n".join([line.rstrip() for line in code.split("\n")]) + + +def normalize_gm(gm_str) -> str: + # strip comments as comments have path to files which may differ from + # system to system. + return remove_trailing_space(strip_comment(gm_str)) + + +def empty_line_normalizer(code: str) -> str: + """ + Normalize code: remove empty lines. + """ + normal_code = re.sub(r"[\r\n]+", "\n", code) + return normal_code + + +def standard_test( + self, + fn, + nargs, + expected_ops=None, + expected_ops_dynamic=None, + expected_frame_count=1, +): + if not config.assume_static_by_default and expected_ops_dynamic is not None: + expected_ops = expected_ops_dynamic + + actual = CompileCounter() + + args1 = [torch.randn(10, 10) for _ in range(nargs)] + args2 = [torch.randn(10, 10) for _ in range(nargs)] + correct1 = fn(*args1) + correct2 = fn(*args2) + reset() + opt_fn = optimize_assert(actual)(fn) + val1a = opt_fn(*args1) + val2a = opt_fn(*args2) + val1b = opt_fn(*args1) + val2b = opt_fn(*args2) + reset() + self.assertTrue(same(val1a, correct1)) + self.assertTrue(same(val1b, correct1)) + self.assertTrue(same(val2a, correct2)) + self.assertTrue(same(val2b, correct2)) + self.assertEqual(actual.frame_count, expected_frame_count) + if expected_ops is not None: + self.assertEqual(actual.op_count, expected_ops) + + +def dummy_fx_compile(gm: fx.GraphModule, example_inputs): + return gm.forward + + +def format_speedup(speedup, pvalue, is_correct=True, pvalue_threshold=0.1): + if not is_correct: + return "ERROR" + if pvalue > pvalue_threshold: + return f"{speedup:.3f}x SAME" + return f"{speedup:.3f}x p={pvalue:.2f}" + + +def rand_strided( + size: Sequence[int], + stride: Sequence[int], + dtype: torch.dtype = torch.float32, + device: Union[str, torch.device] = "cpu", + extra_size: int = 0, +): + needed_size = ( + sum((shape - 1) * stride for shape, stride in zip(size, stride)) + + 1 + + extra_size + ) + if dtype.is_floating_point: + if dtype.itemsize == 1: + """ + normal distribution kernel is not implemented for fp8.. + Workaround that by creating a fp16 tensor and then cast. + """ + buffer = torch.randn(needed_size, dtype=torch.float16, device=device).to( + dtype=dtype + ) + else: + buffer = torch.randn(needed_size, dtype=dtype, device=device) + else: + buffer = torch.zeros(size=[needed_size], dtype=dtype, device=device) + return torch.as_strided(buffer, size, stride) + + +def _make_fn_with_patches(fn, *patches): + @functools.wraps(fn) + def _fn(*args, **kwargs): + with contextlib.ExitStack() as stack: + for module, attr, val in patches: + stack.enter_context(patch.object(module, attr, val)) + + return fn(*args, **kwargs) + + return _fn + + +def make_test_cls_with_patches( + cls, cls_prefix, fn_suffix, *patches, xfail_prop=None, decorator=lambda x: x +): + DummyTestClass = type(f"{cls_prefix}{cls.__name__}", cls.__bases__, {}) + DummyTestClass.__qualname__ = DummyTestClass.__name__ + + for name in dir(cls): + if name.startswith("test_"): + fn = getattr(cls, name) + if not callable(fn): + setattr(DummyTestClass, name, getattr(cls, name)) + continue + new_name = f"{name}{fn_suffix}" + new_fn = _make_fn_with_patches(fn, *patches) + new_fn.__name__ = new_name + if xfail_prop is not None and hasattr(fn, xfail_prop): + new_fn = unittest.expectedFailure(new_fn) + setattr(DummyTestClass, new_name, decorator(new_fn)) + # NB: Doesn't handle slots correctly, but whatever + elif not hasattr(DummyTestClass, name): + setattr(DummyTestClass, name, getattr(cls, name)) + + return DummyTestClass + + +# test Python 3.11+ specific features +def skipIfNotPy311(fn): + if sys.version_info >= (3, 11): + return fn + return unittest.skip(fn) + + +def skipIfNotPy312(fn): + if sys.version_info >= (3, 12): + return fn + return unittest.skip(fn) + + +def xfailIfPy312(fn): + if sys.version_info >= (3, 12): + return unittest.expectedFailure(fn) + return fn + + +def skipIfPy312(fn): + if sys.version_info >= (3, 12): + return unittest.skip(fn) + return fn + + +def requiresPy310(fn): + if sys.version_info >= (3, 10): + return fn + else: + unittest.skip(fn) + + +# Controls tests generated in test/inductor/test_torchinductor_dynamic_shapes.py +# and test/dynamo/test_dynamic_shapes.py +def expectedFailureDynamic(fn): + fn._expected_failure_dynamic = True + return fn + + +# Controls tests generated in test/inductor/test_torchinductor_codegen_dynamic_shapes.py +def expectedFailureCodegenDynamic(fn): + fn._expected_failure_codegen_dynamic = True + return fn + + +# Controls test generated in test/inductor/test_cpp_wrapper.py +def expectedFailureDynamicWrapper(fn): + fn._expected_failure_dynamic_wrapper = True + return fn + + +def reset_rng_state(use_xla=False): + torch.manual_seed(1337) + random.seed(1337) + if np: + np.random.seed(1337) + if use_xla: + import torch_xla.core.xla_model as xm + + xm.set_rng_state(1337, str(xm.xla_device())) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/trace_rules.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/trace_rules.py new file mode 100644 index 0000000000000000000000000000000000000000..01cc4dc51be129bf09c6c5235bfacae9583919d9 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/trace_rules.py @@ -0,0 +1,3637 @@ +# mypy: allow-untyped-defs +import _collections_abc +import _weakrefset +import abc +import builtins +import collections +import contextlib +import copy +import copyreg +import dataclasses +import enum +import functools +import importlib +import inspect +import linecache +import logging +import multiprocessing +import operator +import os +import posixpath +import random +import re +import selectors +import signal +import sys +import tempfile +import threading +import tokenize +import traceback +import types +import typing +import unittest +import weakref +from collections import defaultdict +from pathlib import Path +from typing import Any, Callable, cast, Dict, List, Optional, Set, Type, Union + +import torch +import torch._inductor.test_operators +import torch.distributed +import torch.utils._content_store +from torch.utils import _config_module + +from .resume_execution import TORCH_DYNAMO_RESUME_IN_PREFIX +from .utils import getfile, hashable, NP_SUPPORTED_MODULES, unwrap_if_wrapper +from .variables import ( + BuiltinVariable, + FunctionalCallVariable, + FunctorchHigherOrderVariable, + NestedUserFunctionVariable, + PolyfilledFunctionVariable, + SkipFunctionVariable, + TorchInGraphFunctionVariable, + UserFunctionVariable, + UserMethodVariable, +) + + +np: Optional[types.ModuleType] = None +try: + import numpy as np +except ModuleNotFoundError: + pass + + +if typing.TYPE_CHECKING: + from .variables.base import VariableTracker + + +""" +A note on skip/inline rules: + +Dynamo consults this file to determine whether function should be inlined or skipped. + +A skip applies at the frame boundary, meaning dynamo either triggers a graph break +at the beginning of the frame or attempts to trace/inline the whole frame. When skipping +a frame, recursively called frames are still traced by dynamo unless also skipped. + +Skipfiles (skipped at the file level instead of function level) still apply on a +frame-by-frame boundary as dynamo traces, but apply to all functions in that file. + +@skip is a helper decorator that can be applied to your function to cause it to be +included here. + +Dynamo skip/inline rules & priorities are defined as follows: +* Inline is the default behavior and will be used unless explicitly skipped. +* Dynamo has two SKIPLIST: BUILTIN_SKIPLIST and THIRDPARTY_SKIPLIST. + * BUILTIN_SKIPLIST contains builtin python modules, such as abc, collections, etc. + * THIRDPARTY_SKIPLIST contains common third party libraries, such as numpy, pandas, etc. +* Functions in these two SKIPLISTs are always skipped, except: + * They have explicitly defined rule in `manual_torch_name_rule_map`; + * The corresponding python module has been put into MOD_INLINELIST. +* PyTorch(torch) is in the BUILTIN_SKIPLIST by default, but there are many cases + where we want inline the functions under torch namespace. + We should specify inline for the functions in `manual_torch_name_rule_map` or + put the corresponding python module into MOD_INLINELIST to make dynamo inline them. +* If you call functions under skipped modules/files, Dynamo will wrap these functions + as SkipFunctionVariable. There are a few functions(e.g, collections.OrderedDict) that + we have special handling at SkipFunctionVariable.call_function. + +Overall: *_INLINELIST has precedence over *_SKIPLIST has precedence over DEFAULT (inline) + +To figure out what the behavior is, check the following list in order: +* `manual_torch_name_rule_map` (Inline if YES) +* MOD_INLINELIST (Inline if YES) +* BUILTIN_SKIPLIST & THIRDPARTY_SKIPLIST (Skip if YES) +* Inline by default + +In general, if you want to force inline a function or module, please consider adding +the function's python module to MOD_INLINELIST first. +Use the `manual_torch_name_rule_map` only when there are other functions under the same module that +you don't want to inline them. +""" + +""" +Map of function objects to their tracing rules (Dynamo variables). +* TorchInGraphFunctionVariable: The functions should be put into the FX graph or can be constant folded. E.g., + - torch.add: should be put into the FX graph. + - torch.is_floating_point: constant folded. +* SkipFunctionVariable: The objects should be skipped from tracing. +* UserFunctionVariable: The functions should be inlined. + +For developers: If you add/remove a torch level API, it may trigger failures from +test/dynamo/test_trace_rules.py:test_torch_name_rule_map_updated. To fix the failures: +If you are adding a new torch level API or Dynamo implementation: +* Add the name with the corresponding tracing rule to this map + if you are adding a new in graph function or Dynamo implementation for an existing function. +* Remove the object name from test/dynamo/test_trace_rules.ignored_c_binding_in_graph_function_names if it's there. + +If you are removing an existing torch level API: +* Remove the entry represented the API from this map or test/dynamo/test_trace_rules.ignored_c_binding_in_graph_function_names + depends on where it is. + + +""" +manual_torch_name_rule_map = { + "torch.onnx.is_in_onnx_export": TorchInGraphFunctionVariable, + "torch.onnx.operators.shape_as_tensor": TorchInGraphFunctionVariable, + "torch.overrides.is_tensor_like": TorchInGraphFunctionVariable, + "torch.jit.is_scripting": TorchInGraphFunctionVariable, + "torch.jit.is_tracing": TorchInGraphFunctionVariable, + "torch.jit.annotate": TorchInGraphFunctionVariable, + "torch.distributed.is_available": TorchInGraphFunctionVariable, + "torch.distributed.is_initialized": TorchInGraphFunctionVariable, + "torch.distributed.get_rank": TorchInGraphFunctionVariable, + "torch.distributed.get_world_size": TorchInGraphFunctionVariable, + "torch.distributed.tensor._api.DTensor#from_local": TorchInGraphFunctionVariable, + "torch.distributed.distributed_c10d._get_group_size_by_name": TorchInGraphFunctionVariable, + "torch.distributed.distributed_c10d._resolve_group_name_by_ranks_and_tag": TorchInGraphFunctionVariable, + "torch.distributed.distributed_c10d._get_group_tag": TorchInGraphFunctionVariable, + "torch.distributed.distributed_c10d.get_process_group_ranks": TorchInGraphFunctionVariable, + "torch._utils.is_compiling": TorchInGraphFunctionVariable, + "torch.fx._symbolic_trace.is_fx_tracing": TorchInGraphFunctionVariable, + "torch._dynamo.external_utils.is_compiling": TorchInGraphFunctionVariable, + "torch.compiler.is_compiling": TorchInGraphFunctionVariable, + "torch.compiler.is_dynamo_compiling": TorchInGraphFunctionVariable, + "torch.autograd._profiler_enabled": SkipFunctionVariable, + "torch._C._to_dlpack": SkipFunctionVariable, + "torch.to_dlpack": SkipFunctionVariable, + # We graph break on RNG state setters or getters like + # `torch.get_rng_state` or `torch.set_rng_state`. These functions + # are not aten operations and therefore they are completely ignored + # by the AOT dispatcher. As a result, the AOT graph does not have + # these setter or getter functions, producing an incorrect graph + # when it comes to rng states. + "torch.default_generator#get_state": SkipFunctionVariable, + "torch._C.Generator#get_state": SkipFunctionVariable, + "torch.get_rng_state": SkipFunctionVariable, + "torch.cuda.get_rng_state": SkipFunctionVariable, + "torch.default_generator#set_state": SkipFunctionVariable, + "torch._C.Generator#set_state": SkipFunctionVariable, + "torch.set_rng_state": SkipFunctionVariable, + "torch.cuda.set_rng_state": SkipFunctionVariable, + # https://github.com/pytorch/pytorch/issues/107187 + "torch.manual_seed": SkipFunctionVariable, + # https://github.com/pytorch/pytorch/issues/93501 + "torch.nn.utils.rnn.pack_padded_sequence": SkipFunctionVariable, + "torch.nn.Parameter": TorchInGraphFunctionVariable, + "torch.nn.Buffer": TorchInGraphFunctionVariable, + "torch._nested_tensor_from_mask": SkipFunctionVariable, + "torch._nested_from_padded": SkipFunctionVariable, + "torch.nested.nested_tensor_from_jagged": UserFunctionVariable, + # symbol operators implemented in Python + "torch.sym_not": TorchInGraphFunctionVariable, + "torch.sym_float": TorchInGraphFunctionVariable, + "torch.sym_int": TorchInGraphFunctionVariable, + "torch.sym_max": TorchInGraphFunctionVariable, + "torch.sym_min": TorchInGraphFunctionVariable, + "torch.sym_sqrt": TorchInGraphFunctionVariable, + "torch.sym_ite": TorchInGraphFunctionVariable, + "torch.Tensor#_make_wrapper_subclass": SkipFunctionVariable, + "torch.Tensor#__init__": SkipFunctionVariable, + "torch.cuda.set_device": SkipFunctionVariable, + "torch.cuda.current_device": SkipFunctionVariable, + "torch._C.autocast_decrement_nesting": SkipFunctionVariable, + "torch._C.autocast_increment_nesting": SkipFunctionVariable, + "torch.autograd.grad": SkipFunctionVariable, + "torch.autograd.backward": SkipFunctionVariable, + "torch._C.clear_autocast_cache": SkipFunctionVariable, + "torch.distributions.constraints.is_dependent": SkipFunctionVariable, + "torch.jit.isinstance": SkipFunctionVariable, + "torch._C.set_anomaly_enabled": SkipFunctionVariable, + "torch._C.set_autocast_cache_enabled": SkipFunctionVariable, + "torch._C.set_autocast_cpu_dtype": SkipFunctionVariable, + "torch._C.set_autocast_cpu_enabled": SkipFunctionVariable, + "torch._C.set_autocast_enabled": SkipFunctionVariable, + "torch._C.set_autocast_gpu_dtype": SkipFunctionVariable, + "torch._C.set_autocast_ipu_dtype": SkipFunctionVariable, + "torch._C.set_autocast_ipu_enabled": SkipFunctionVariable, + "torch._C.set_autocast_xla_dtype": SkipFunctionVariable, + "torch._C.set_autocast_xla_enabled": SkipFunctionVariable, + "torch.resize_as_": SkipFunctionVariable, + "torch.resize_as_sparse_": SkipFunctionVariable, + "torch.get_default_device": TorchInGraphFunctionVariable, + # functorch/vmap + "torch._functorch.vmap._check_int_or_none": UserFunctionVariable, + "torch._functorch.vmap._check_out_dims_is_int_or_int_pytree": UserFunctionVariable, + "torch._functorch.vmap._check_randomness_arg": UserFunctionVariable, + "torch._functorch.vmap._chunked_vmap": UserFunctionVariable, + "torch._functorch.vmap._concat_chunked_outputs": UserFunctionVariable, + "torch._functorch.vmap._create_batched_inputs": UserFunctionVariable, + "torch._functorch.vmap._flat_vmap": UserFunctionVariable, + "torch._functorch.vmap._flatten_chunks_output": UserFunctionVariable, + "torch._functorch.vmap._get_chunked_inputs": UserFunctionVariable, + "torch._functorch.vmap._get_name": UserFunctionVariable, + "torch._functorch.vmap._maybe_remove_batch_dim": UserFunctionVariable, + "torch._functorch.vmap._num_outputs": UserFunctionVariable, + "torch._functorch.vmap._process_batched_inputs": UserFunctionVariable, + "torch._functorch.vmap._unwrap_batched": UserFunctionVariable, + "torch._functorch.vmap._validate_and_get_batch_size": UserFunctionVariable, + "torch._functorch.vmap.doesnt_support_saved_tensors_hooks": UserFunctionVariable, + "torch._functorch.vmap.get_chunk_sizes": UserFunctionVariable, + # lazy_load_decompositions uses a lock that is not supported yet in dynamo + # "torch._functorch.vmap.lazy_load_decompositions": UserFunctionVariable, + "torch._functorch.vmap.restore_vmap": UserFunctionVariable, + "torch._functorch.apis.vmap": UserFunctionVariable, + "torch._functorch.vmap.unwrap_batched": UserFunctionVariable, + "torch._functorch.vmap.vmap_impl": FunctorchHigherOrderVariable, + "torch._functorch.vmap.wrap_batched": UserFunctionVariable, + # functorch/grad + "torch._functorch.eager_transforms.grad_impl": FunctorchHigherOrderVariable, + "torch._functorch.apis.grad_and_value": UserFunctionVariable, + "torch._functorch.eager_transforms._as_tuple": UserFunctionVariable, + "torch._functorch.eager_transforms._check_unique_non_empty": UserFunctionVariable, + "torch._functorch.eager_transforms._create_differentiable": UserFunctionVariable, + "torch._functorch.eager_transforms._slice_argnums": UserFunctionVariable, + "torch._functorch.eager_transforms._undo_create_differentiable": UserFunctionVariable, + "torch._functorch.eager_transforms._validate_and_wrap_argnum": UserFunctionVariable, + "torch._functorch.eager_transforms._validate_and_wrap_argnums": UserFunctionVariable, + "torch._functorch.eager_transforms._wrap_all_tensors": UserFunctionVariable, + "torch._functorch.eager_transforms._wrap_tensor_for_grad": UserFunctionVariable, + # functorch/jacrev + "torch._functorch.eager_transforms.jacrev": FunctorchHigherOrderVariable, + "torch._functorch.eager_transforms.error_if_complex": UserFunctionVariable, + "torch._functorch.eager_transforms._chunked_standard_basis_for_": UserFunctionVariable, + "torch._functorch.eager_transforms._safe_zero_index": UserFunctionVariable, + # functorch/vjp + "torch._functorch.eager_transforms.vjp": FunctorchHigherOrderVariable, + "torch._functorch.eager_transforms._vjp_with_argnums": UserFunctionVariable, + "torch._functorch.eager_transforms.assert_non_empty_tensor_output": UserFunctionVariable, + # functorch/jvp + "torch._functorch.eager_transforms._jvp_with_argnums": UserFunctionVariable, + "torch._functorch.eager_transforms.jvp": FunctorchHigherOrderVariable, + "torch._functorch.eager_transforms._replace_args": UserFunctionVariable, + "torch._functorch.eager_transforms.safe_unpack_dual": UserFunctionVariable, + "torch._functorch.eager_transforms.assert_non_empty_list_of_tensors": UserFunctionVariable, + "torch._functorch.eager_transforms.assert_output_is_tensor_or_tensors": UserFunctionVariable, + "torch.autograd.forward_ad.enter_dual_level": UserFunctionVariable, + "torch.autograd.forward_ad.exit_dual_level": UserFunctionVariable, + "torch.autograd.forward_ad.make_dual": UserFunctionVariable, + "torch.autograd.forward_ad.unpack_dual": UserFunctionVariable, + # functorch/linearize + "torch._functorch.eager_transforms.linearize": FunctorchHigherOrderVariable, + # functorch/jacfwd + "torch._functorch.eager_transforms.jacfwd": FunctorchHigherOrderVariable, + "torch._functorch.eager_transforms._construct_standard_basis_for": UserFunctionVariable, + "torch._functorch.eager_transforms.safe_unflatten": UserFunctionVariable, + # functorch/hessian + "torch._functorch.eager_transforms.hessian": FunctorchHigherOrderVariable, + # functional_call + "torch._functorch.functional_call.functional_call": FunctionalCallVariable, + "torch.nn.utils.stateless._groupby_tensor": TorchInGraphFunctionVariable, + # functorch/deprecated + "torch._functorch.deprecated.jvp": UserFunctionVariable, + "torch._functorch.deprecated.hessian": UserFunctionVariable, + "torch._functorch.deprecated.jacfwd": UserFunctionVariable, + "torch._functorch.deprecated.jacrev": UserFunctionVariable, + "torch._functorch.deprecated.grad": UserFunctionVariable, + "torch._functorch.deprecated.grad_and_value": UserFunctionVariable, + "torch._functorch.deprecated.vjp": UserFunctionVariable, + # everything else + "torch._constrain_as_size": UserFunctionVariable, + "torch._tensor._convert": UserFunctionVariable, + "torch.jit._unwrap_optional": UserFunctionVariable, + "torch.backends.mha.get_fastpath_enabled": UserFunctionVariable, + "torch._C._functorch._add_batch_dim": TorchInGraphFunctionVariable, + "torch._C._functorch._remove_batch_dim": TorchInGraphFunctionVariable, + "torch._C._functorch._wrap_for_grad": TorchInGraphFunctionVariable, + "torch._C._functorch._unwrap_for_grad": TorchInGraphFunctionVariable, + "torch._C._functorch.maybe_current_level": TorchInGraphFunctionVariable, + "torch._C._functorch.is_batchedtensor": TorchInGraphFunctionVariable, + "torch._dynamo.mark_static": UserFunctionVariable, + "torch.fx.experimental.symbolic_shapes.guard_size_oblivious": TorchInGraphFunctionVariable, + "torch.cuda._get_device_properties": TorchInGraphFunctionVariable, + "torch.utils.hooks.BackwardHook": TorchInGraphFunctionVariable, + "torch.sparse_bsc_tensor": SkipFunctionVariable, + "torch.sparse_bsr_tensor": SkipFunctionVariable, + "torch.sparse_csc_tensor": SkipFunctionVariable, + "torch.sparse_csr_tensor": SkipFunctionVariable, + "torch.sparse_compressed_tensor": SkipFunctionVariable, + "torch._C._autograd._unsafe_set_version_counter": TorchInGraphFunctionVariable, + # avoid skipping user defined modules in distributed unit tests + "torch/testing/_internal/common_fsdp.py#forward": UserFunctionVariable, + f"torch/testing/_internal/common_fsdp.py#{TORCH_DYNAMO_RESUME_IN_PREFIX}": UserFunctionVariable, + "torch/testing/_internal/distributed/_tensor/common_dtensor.py#forward": UserFunctionVariable, + f"torch/testing/_internal/distributed/_tensor/common_dtensor.py#{TORCH_DYNAMO_RESUME_IN_PREFIX}": UserFunctionVariable, + "torch/testing/_internal/common_distributed.py#forward": UserFunctionVariable, + f"torch/testing/_internal/common_distributed.py#{TORCH_DYNAMO_RESUME_IN_PREFIX}": UserFunctionVariable, +} + + +# In graph functions (including constant folding) that are C bindings +torch_c_binding_in_graph_functions = dict.fromkeys( + [ + "math.acos", + "math.acosh", + "math.asin", + "math.asinh", + "math.atan", + "math.atan2", + "math.atanh", + "math.ceil", + "math.comb", + "math.copysign", + "math.cos", + "math.cosh", + "math.degrees", + "math.dist", + "math.erf", + "math.erfc", + "math.exp", + "math.expm1", + "math.fabs", + "math.factorial", + "math.floor", + "math.fmod", + "math.frexp", + "math.fsum", + "math.gamma", + "math.gcd", + "math.hypot", + "math.isclose", + "math.isfinite", + "math.isinf", + "math.isnan", + "math.isqrt", + "math.ldexp", + "math.lgamma", + "math.log", + "math.log10", + "math.log1p", + "math.log2", + "math.modf", + "math.nextafter", + "math.perm", + "math.pow", + "math.prod", + "math.radians", + "math.remainder", + "math.sin", + "math.sinh", + "math.tan", + "math.tanh", + "math.trunc", + "math.ulp", + "torch._adaptive_avg_pool2d", + "torch._adaptive_avg_pool3d", + "torch._add_batch_dim", + "torch._add_relu_", + "torch._add_relu", + "torch._addmm_activation", + "torch._aminmax", + "torch._amp_foreach_non_finite_check_and_unscale_", + "torch._amp_update_scale_", + "torch._assert_async", + "torch._assert_tensor_metadata", + "torch._batch_norm_impl_index", + "torch._C._activate_gpu_trace", + "torch._C._add_cached_tensor", + "torch._C._add_docstr", + "torch._C._are_functorch_transforms_active", + "torch._C._autograd_init", + "torch._C._awaitable_nowait", + "torch._C._awaitable_wait", + "torch._C._awaitable", + "torch._C._backport_for_mobile_from_buffer_to_buffer", + "torch._C._backport_for_mobile_from_buffer", + "torch._C._backport_for_mobile_to_buffer", + "torch._C._backport_for_mobile", + "torch._C._broadcast_coalesced", + "torch._C._broadcast_out", + "torch._C._broadcast", + "torch._C._c10d_init", + "torch._C._calculate_package_version_based_on_upgraders", + "torch._C._can_use_flash_attention", + "torch._C._can_use_mem_efficient_attention", + "torch._C._can_use_cudnn_attention", + "torch._C._check_onnx_proto", + "torch._C._check_sparse_tensor_invariants", + "torch._C._collect_all", + "torch._C._commit_update", + "torch._C._compile_graph_to_code_table", + "torch._C._construct_CUDA_Tensor_From_Storage_And_Metadata", + "torch._C._construct_storage_from_data_pointer", + "torch._C._conv_determine_backend_memory_format", + "torch._C._cpu._is_avx2_supported", + "torch._C._cpu._is_avx512_supported", + "torch._C._cpu._is_avx512_vnni_supported", + "torch._C._cpu._is_avx512_bf16_supported", + "torch._C._cpu._is_amx_tile_supported", + "torch._C._cpu._init_amx", + "torch._C._crash_if_aten_asan", + "torch._C._crash_if_csrc_asan", + "torch._C._crash_if_csrc_ubsan", + "torch._C._crash_if_debug_asserts_fail", + "torch._C._crash_if_vptr_ubsan", + "torch._C._create_function_from_graph", + "torch._C._create_function_from_trace_with_dict", + "torch._C._create_function_from_trace", + "torch._C._create_graph_by_tracing", + "torch._C._create_module_with_type", + "torch._C._create_object_with_type", + "torch._C._cuda_attach_out_of_memory_observer", + "torch._C._cuda_beginAllocateCurrentStreamToPool", + "torch._C._cuda_canDeviceAccessPeer", + "torch._C._cuda_changeCurrentAllocator", + "torch._C._cuda_checkPoolLiveAllocations", + "torch._C._cuda_clearCublasWorkspaces", + "torch._C._cuda_cudaCachingAllocator_raw_alloc", + "torch._C._cuda_cudaCachingAllocator_raw_delete", + "torch._C._cuda_cudaCachingAllocator_set_allocator_settings", + "torch._C._cuda_cudaHostAllocator", + "torch._C._cuda_customAllocator", + "torch._C._cuda_emptyCache", + "torch._C._cuda_endAllocateCurrentStreamToPool", + "torch._C._cuda_exchangeDevice", + "torch._C._cuda_get_conv_benchmark_empty_cache", + "torch._C._cuda_get_cudnn_benchmark_limit", + "torch._C._cuda_get_sync_debug_mode", + "torch._C._cuda_getAllocator", + "torch._C._cuda_getAllocatorBackend", + "torch._C._cuda_getArchFlags", + "torch._C._cuda_getCheckpointState", + "torch._C._cuda_getCompiledVersion", + "torch._C._cuda_getCurrentBlasHandle", + "torch._C._cuda_getCurrentRawStream", + "torch._C._cuda_getCurrentStream", + "torch._C._cuda_getDefaultStream", + "torch._C._cuda_getDevice", + "torch._C._cuda_getDeviceCount", + "torch._C._cuda_hasPrimaryContext", + "torch._C._cuda_init", + "torch._C._cuda_ipc_collect", + "torch._C._cuda_isCurrentStreamCapturing", + "torch._C._cuda_isHistoryEnabled", + "torch._C._cuda_isInBadFork", + "torch._C._cuda_jiterator_compile_and_launch_kernel", + "torch._C._cuda_lock_mutex", + "torch._C._cuda_maybeExchangeDevice", + "torch._C._cuda_memorySnapshot", + "torch._C._cuda_memoryStats", + "torch._C._cuda_record_memory_history_legacy", + "torch._C._cuda_record_memory_history", + "torch._C._cuda_releasePool", + "torch._C._cuda_resetAccumulatedMemoryStats", + "torch._C._cuda_resetPeakMemoryStats", + "torch._C._cuda_set_cudnn_benchmark_limit", + "torch._C._cuda_set_sync_debug_mode", + "torch._C._cuda_setCheckpointPoolState", + "torch._C._cuda_setDevice", + "torch._C._cuda_setMemoryFraction", + "torch._C._cuda_setStream", + "torch._C._cuda_sleep", + "torch._C._cuda_synchronize", + "torch._C._cuda_unlock_mutex", + "torch._C._cudnn_set_conv_benchmark_empty_cache", + "torch._C._cudnn.getCompileVersion", + "torch._C._cudnn.getRuntimeVersion", + "torch._C._cudnn.getVersionInt", + "torch._C._current_autograd_node", + "torch._C._current_graph_task_execution_order", + "torch._C._current_graph_task_id", + "torch._C._cxx_flags", + "torch._C._debug_get_fusion_group_inlining", + "torch._C._debug_only_are_vmap_fallback_warnings_enabled", + "torch._C._debug_only_display_vmap_fallback_warnings", + "torch._C._debug_set_autodiff_subgraph_inlining", + "torch._C._debug_set_fusion_group_inlining", + "torch._C._demangle", + "torch._C._disabled_torch_dispatch_impl", + "torch._C._disabled_torch_function_impl", + "torch._C._dispatch_call_boxed", + "torch._C._dispatch_check_all_invariants", + "torch._C._dispatch_check_invariants", + "torch._C._dispatch_dump_table", + "torch._C._dispatch_dump", + "torch._C._dispatch_find_dangling_impls", + "torch._C._dispatch_find_schema_or_throw", + "torch._C._dispatch_get_all_op_names", + "torch._C._dispatch_get_backend_keyset_from_autograd", + "torch._C._dispatch_get_registrations_for_dispatch_key", + "torch._C._dispatch_has_backend_fallback", + "torch._C._dispatch_has_computed_kernel_for_dispatch_key", + "torch._C._dispatch_has_kernel_for_any_dispatch_key", + "torch._C._dispatch_has_kernel_for_dispatch_key", + "torch._C._dispatch_has_kernel", + "torch._C._dispatch_is_alias_key", + "torch._C._dispatch_is_included_in_alias", + "torch._C._dispatch_is_main_interpreter", + "torch._C._dispatch_isTensorSubclassLike", + "torch._C._dispatch_key_for_device", + "torch._C._dispatch_key_name", + "torch._C._dispatch_key_parse", + "torch._C._dispatch_key_set", + "torch._C._dispatch_keys", + "torch._C._dispatch_keyset_full_after", + "torch._C._dispatch_keyset_full", + "torch._C._dispatch_keyset_to_string", + "torch._C._dispatch_library", + "torch._C._dispatch_num_backends", + "torch._C._dispatch_print_registrations_for_dispatch_key", + "torch._C._dispatch_pystub", + "torch._C._dispatch_set_report_error_callback", + "torch._C._dispatch_tls_is_dispatch_key_excluded", + "torch._C._dispatch_tls_is_dispatch_key_included", + "torch._C._dispatch_tls_local_exclude_set", + "torch._C._dispatch_tls_local_include_set", + "torch._C._dispatch_tls_set_dispatch_key_excluded", + "torch._C._dispatch_tls_set_dispatch_key_included", + "torch._C._dist_autograd_init", + "torch._C._dump_local_tls_set", + "torch._C._dump_upgraders_map", + "torch._C._enable_mobile_interface_call_export", + "torch._C._enter_dual_level", + "torch._C._error_if_any_worker_fails", + "torch._C._exit_dual_level", + "torch._C._export_operator_list", + "torch._C._export_opnames", + "torch._C._faulty_agent_init", + "torch._C._fft.fft_fft", + "torch._C._fft.fft_fft2", + "torch._C._fft.fft_fftfreq", + "torch._C._fft.fft_fftn", + "torch._C._fft.fft_fftshift", + "torch._C._fft.fft_hfft", + "torch._C._fft.fft_hfft2", + "torch._C._fft.fft_hfftn", + "torch._C._fft.fft_ifft", + "torch._C._fft.fft_ifft2", + "torch._C._fft.fft_ifftn", + "torch._C._fft.fft_ifftshift", + "torch._C._fft.fft_ihfft", + "torch._C._fft.fft_ihfft2", + "torch._C._fft.fft_ihfftn", + "torch._C._fft.fft_irfft", + "torch._C._fft.fft_irfft2", + "torch._C._fft.fft_irfftn", + "torch._C._fft.fft_rfft", + "torch._C._fft.fft_rfft2", + "torch._C._fft.fft_rfftfreq", + "torch._C._fft.fft_rfftn", + "torch._C._free_And_Remove_DeleterFn", + "torch._C._freeze_module", + "torch._C._from_dlpack", + "torch._C._functionality_to_backend_keys", + "torch._C._functionalization_reapply_views_tls", + "torch._C._fuse_to_static_module", + "torch._C._gather_out", + "torch._C._gather", + "torch._C._generate_upgraders_graph", + "torch._C._get_autograd_fallback_mode", + "torch._C._get_backcompat_broadcast_warn", + "torch._C._get_backcompat_keepdim_warn", + "torch._C._get_blas_preferred_backend", + "torch._C._get_caught_jit_exception_class_name", + "torch._C._get_caught_jit_exception_original_msg", + "torch._C._get_constant_bool_symnode", + "torch._C._get_cpp_backtrace", + "torch._C._get_cpu_capability", + "torch._C._get_cublas_allow_bf16_reduced_precision_reduction", + "torch._C._get_cublas_allow_fp16_reduced_precision_reduction", + "torch._C._get_cublas_allow_tf32", + "torch._C._get_cudnn_allow_tf32", + "torch._C._get_cudnn_benchmark", + "torch._C._get_cudnn_deterministic", + "torch._C._get_cudnn_enabled", + "torch._C._get_custom_class_python_wrapper", + "torch._C._get_default_device", + "torch._C._get_deterministic_algorithms_warn_only", + "torch._C._get_deterministic_algorithms", + "torch._C._get_deterministic_fill_uninitialized_memory", + "torch._C._get_dispatch_mode", + "torch._C._get_dispatch_stack_at", + "torch._C._get_file_format", + "torch._C._get_flash_sdp_enabled", + "torch._C._get_float32_matmul_precision", + "torch._C._get_function_stack_at", + "torch._C._get_graph_executor_optimize", + "torch._C._get_linalg_preferred_backend", + "torch._C._get_math_sdp_enabled", + "torch._C._get_math_sdp_allow_fp16_bf16_reduction", + "torch._C._get_max_operator_version", + "torch._C._get_mem_efficient_sdp_enabled", + "torch._C._get_mkldnn_enabled", + "torch._C._get_cudnn_sdp_enabled", + "torch._C._set_sdp_use_cudnn", + "torch._C._get_mobile_model_contained_types_from_buffer", + "torch._C._get_mobile_model_contained_types", + "torch._C._get_model_bytecode_version_from_buffer", + "torch._C._get_model_bytecode_version", + "torch._C._get_model_extra_files_from_buffer", + "torch._C._get_model_extra_files", + "torch._C._get_model_ops_and_info_from_buffer", + "torch._C._get_model_ops_and_info", + "torch._C._get_module_info_from_flatbuffer", + "torch._C._get_nnpack_enabled", + "torch._C._get_obj_in_tls", + "torch._C._get_operation_overload", + "torch._C._get_operator_version_map", + "torch._C._get_privateuse1_backend_name", + "torch._C._get_qengine", + "torch._C._get_schema", + "torch._C._get_nested_int", + "torch._C._get_tensor_metadata", + "torch._C._get_tracing_state", + "torch._C._get_upgrader_ranges", + "torch._C._get_upgraders_entry_map", + "torch._C._get_upgraders_map_size", + "torch._C._get_value_trace", + "torch._C._get_version_calculator_flag", + "torch._C._get_warnAlways", + "torch._C._graph_pool_handle", + "torch._C._group_tensors_by_device_and_dtype", + "torch._C._hack_do_not_use_clone_module_with_class", + "torch._C._has_distributed", + "torch._C._has_Standard_Deleter", + "torch._C._has_storage", + "torch._C._has_tensorexpr_cpp_tests", + "torch._C._run_tensorexpr_cpp_tests", + "torch._C._has_torch_function_unary", + "torch._C._has_torch_function_variadic", + "torch._C._has_torch_function", + "torch._C._import_ir_module_from_package", + "torch._C._increment_version", + "torch._C._infer_size", + "torch._C._init_names", + "torch._C._initExtension", + "torch._C._is_alias_of", + "torch._C._is_any_autocast_enabled", + "torch._C._is_cached_tensor", + "torch._C._is_flash_attention_available", + "torch._C._is_fwd_grad_enabled", + "torch._C._is_key_in_tls", + "torch._C._is_multithreading_enabled", + "torch._C._is_torch_function_enabled", + "torch._C._is_torch_function_mode_enabled", + "torch._C._is_tracing", + "torch._C._is_view_replay_enabled", + "torch._C._is_xnnpack_enabled", + "torch._C._itt.is_available", + "torch._C._itt.mark", + "torch._C._itt.rangePop", + "torch._C._itt.rangePush", + "torch._C._ivalue_debug_python_object", + "torch._C._ivalue_tags_match", + "torch._C._jit_assert_is_instance", + "torch._C._jit_can_fuse_on_cpu_legacy", + "torch._C._jit_can_fuse_on_cpu", + "torch._C._jit_can_fuse_on_gpu", + "torch._C._jit_cat_wo_conditionals", + "torch._C._jit_check_alias_annotation", + "torch._C._jit_clear_class_registry", + "torch._C._jit_debug_fuser_num_cached_kernel_specs", + "torch._C._jit_debug_module_iterators", + "torch._C._jit_decay_packed_param_input_types", + "torch._C._jit_decomposition_graph_for_node", + "torch._C._jit_differentiate", + "torch._C._jit_erase_non_input_shape_information", + "torch._C._jit_flatten", + "torch._C._jit_fuser_get_fused_kernel_code", + "torch._C._jit_get_all_schemas", + "torch._C._jit_get_custom_class_schemas", + "torch._C._jit_get_emit_hooks", + "torch._C._jit_get_inline_everything_mode", + "torch._C._jit_get_logging_option", + "torch._C._jit_get_num_profiled_runs", + "torch._C._jit_get_operation", + "torch._C._jit_get_schemas_for_operator", + "torch._C._jit_get_te_cuda_pointwise_block_count", + "torch._C._jit_get_te_cuda_pointwise_block_size", + "torch._C._jit_get_te_cuda_pointwise_loop_levels", + "torch._C._jit_get_te_generate_block_code", + "torch._C._jit_get_te_must_use_llvm_cpu", + "torch._C._jit_get_tracer_state_warn", + "torch._C._jit_has_cpp_tests", + "torch._C._jit_init", + "torch._C._jit_interpret_graph", + "torch._C._jit_is_onnx_log_enabled", + "torch._C._jit_is_script_object", + "torch._C._jit_llga_enabled", + "torch._C._jit_nvfuser_can_be_enabled", + "torch._C._jit_nvfuser_clear_comparison_callback", + "torch._C._jit_nvfuser_enabled", + "torch._C._jit_nvfuser_horizontal_mode", + "torch._C._jit_nvfuser_set_comparison_callback", + "torch._C._jit_nvfuser_single_node_mode", + "torch._C._jit_object_is_non_holding", + "torch._C._jit_onnx_convert_pattern_from_subblock", + "torch._C._jit_onnx_create_full_scope_name", + "torch._C._jit_onnx_list_model_parameters", + "torch._C._jit_onnx_log", + "torch._C._jit_opt_conditionals", + "torch._C._jit_override_can_fuse_on_cpu_legacy", + "torch._C._jit_override_can_fuse_on_cpu", + "torch._C._jit_override_can_fuse_on_gpu", + "torch._C._jit_pass_autocast", + "torch._C._jit_pass_batch_mm", + "torch._C._jit_pass_canonicalize_graph_fuser_ops", + "torch._C._jit_pass_canonicalize", + "torch._C._jit_pass_complete_shape_analysis", + "torch._C._jit_pass_concat_frozen_linear", + "torch._C._jit_pass_constant_loop_unrolling", + "torch._C._jit_pass_constant_pooling", + "torch._C._jit_pass_constant_propagation_immutable_types", + "torch._C._jit_pass_constant_propagation", + "torch._C._jit_pass_convert_frozen_ops_to_mkldnn", + "torch._C._jit_pass_create_autodiff_subgraphs", + "torch._C._jit_pass_create_functional_graphs", + "torch._C._jit_pass_cse", + "torch._C._jit_pass_custom_pattern_based_rewrite_graph", + "torch._C._jit_pass_custom_pattern_based_rewrite", + "torch._C._jit_pass_dbr_quant_remove_redundant_aliases", + "torch._C._jit_pass_dce_allow_deleting_nodes_with_side_effects", + "torch._C._jit_pass_dce", + "torch._C._jit_pass_decompose_ops", + "torch._C._jit_pass_dedup_module_uses", + "torch._C._jit_pass_erase_number_types", + "torch._C._jit_pass_erase_shape_information", + "torch._C._jit_pass_filter_non_tensor_arguments", + "torch._C._jit_pass_fixup_onnx_controlflow_node", + "torch._C._jit_pass_fold_convbn", + "torch._C._jit_pass_fold_frozen_conv_add_or_sub", + "torch._C._jit_pass_fold_frozen_conv_bn", + "torch._C._jit_pass_fold_frozen_conv_mul_or_div", + "torch._C._jit_pass_fold_frozen_linear_bn", + "torch._C._jit_pass_fold_prepacking_ops", + "torch._C._jit_pass_functional_to_inplace_activation", + "torch._C._jit_pass_fuse_add_relu", + "torch._C._jit_pass_fuse_addmm", + "torch._C._jit_pass_fuse_clamp_w_prepacked_linear_conv", + "torch._C._jit_pass_fuse_frozen_conv_add_relu", + "torch._C._jit_pass_fuse_linear", + "torch._C._jit_pass_fuse_quantized_add_relu", + "torch._C._jit_pass_fuse_tensorexprs", + "torch._C._jit_pass_fuse", + "torch._C._jit_pass_inline_fork_wait", + "torch._C._jit_pass_inline_functional_graphs", + "torch._C._jit_pass_inline", + "torch._C._jit_pass_inplace_to_functional_activation", + "torch._C._jit_pass_insert_observer_method_for_ondevice_ptq", + "torch._C._jit_pass_insert_observers", + "torch._C._jit_pass_insert_prepack_unpack", + "torch._C._jit_pass_insert_prepacked_ops", + "torch._C._jit_pass_insert_quant_dequant_for_ondevice_ptq", + "torch._C._jit_pass_insert_quant_dequant", + "torch._C._jit_pass_integer_value_refinement", + "torch._C._jit_pass_lint", + "torch._C._jit_pass_loop_unrolling", + "torch._C._jit_pass_lower_all_tuples", + "torch._C._jit_pass_lower_graph", + "torch._C._jit_pass_metal_fold_prepacking_ops", + "torch._C._jit_pass_metal_fuse_clamp_w_prepacked_conv", + "torch._C._jit_pass_metal_insert_prepacked_ops", + "torch._C._jit_pass_metal_optimize_for_mobile", + "torch._C._jit_pass_onnx_assign_output_shape", + "torch._C._jit_pass_onnx_assign_scoped_names_for_node_and_value", + "torch._C._jit_pass_onnx_autograd_function_process", + "torch._C._jit_pass_onnx_block", + "torch._C._jit_pass_onnx_cast_all_constant_to_floating", + "torch._C._jit_pass_onnx_clear_scope_records", + "torch._C._jit_pass_onnx_constant_fold", + "torch._C._jit_pass_onnx_deduplicate_initializers", + "torch._C._jit_pass_onnx_eliminate_unused_items", + "torch._C._jit_pass_onnx_eval_peephole", + "torch._C._jit_pass_onnx_function_extraction", + "torch._C._jit_pass_onnx_function_substitution", + "torch._C._jit_pass_onnx_graph_shape_type_inference", + "torch._C._jit_pass_onnx_lint", + "torch._C._jit_pass_onnx_node_shape_type_inference", + "torch._C._jit_pass_onnx_peephole", + "torch._C._jit_pass_onnx_preprocess_caffe2", + "torch._C._jit_pass_onnx_preprocess", + "torch._C._jit_pass_onnx_quantization_insert_permutes", + "torch._C._jit_pass_onnx_remove_inplace_ops_for_onnx", + "torch._C._jit_pass_onnx_remove_print", + "torch._C._jit_pass_onnx_scalar_type_analysis", + "torch._C._jit_pass_onnx_set_dynamic_input_shape", + "torch._C._jit_pass_onnx_track_scope_attributes", + "torch._C._jit_pass_onnx_unpack_quantized_weights", + "torch._C._jit_pass_onnx", + "torch._C._jit_pass_optimize_for_inference", + "torch._C._jit_pass_optimize_for_mobile", + "torch._C._jit_pass_optimize_frozen_graph", + "torch._C._jit_pass_pattern_based_rewrite", + "torch._C._jit_pass_peephole_list_idioms", + "torch._C._jit_pass_peephole", + "torch._C._jit_pass_prepare_division_for_onnx", + "torch._C._jit_pass_propagate_device", + "torch._C._jit_pass_propagate_dtype", + "torch._C._jit_pass_propagate_shapes_on_graph_and_build_compute", + "torch._C._jit_pass_propagate_shapes_on_graph", + "torch._C._jit_pass_quant_finalize_for_ondevice_ptq", + "torch._C._jit_pass_quant_finalize", + "torch._C._jit_pass_quant_fusion", + "torch._C._jit_pass_refine_integer_values", + "torch._C._jit_pass_refine_tuple_types", + "torch._C._jit_pass_remove_dropout", + "torch._C._jit_pass_remove_expands", + "torch._C._jit_pass_remove_inplace_ops", + "torch._C._jit_pass_remove_mutation", + "torch._C._jit_pass_replace_old_ops_with_upgraders", + "torch._C._jit_pass_replicate_dequantize", + "torch._C._jit_pass_run_decompositions", + "torch._C._jit_pass_specialize_autogradzero", + "torch._C._jit_pass_swap_functional_linear", + "torch._C._jit_pass_transform_conv1d_to_conv2d", + "torch._C._jit_pass_transpose_frozen_linear", + "torch._C._jit_pass_vulkan_fold_prepacking_ops", + "torch._C._jit_pass_vulkan_fuse_clamp_w_prepacked_conv", + "torch._C._jit_pass_vulkan_insert_prepacked_ops", + "torch._C._jit_pass_vulkan_optimize_for_mobile", + "torch._C._jit_register_decomposition_for_schema", + "torch._C._jit_register_shape_compute_graph_for_node", + "torch._C._jit_resolve_packet", + "torch._C._jit_run_cpp_tests", + "torch._C._jit_script_class_compile", + "torch._C._jit_script_compile_overload", + "torch._C._jit_script_compile", + "torch._C._jit_script_interface_compile", + "torch._C._jit_set_autocast_mode", + "torch._C._jit_set_bailout_depth", + "torch._C._jit_set_emit_hooks", + "torch._C._jit_set_fusion_strategy", + "torch._C._jit_set_inline_everything_mode", + "torch._C._jit_set_llga_enabled", + "torch._C._jit_set_logging_option", + "torch._C._jit_set_logging_stream", + "torch._C._jit_set_num_profiled_runs", + "torch._C._jit_set_nvfuser_enabled", + "torch._C._jit_set_nvfuser_guard_mode", + "torch._C._jit_set_nvfuser_horizontal_mode", + "torch._C._jit_set_nvfuser_single_node_mode", + "torch._C._jit_set_nvfuser_skip_node_kind", + "torch._C._jit_set_onnx_log_enabled", + "torch._C._jit_set_onnx_log_output_stream", + "torch._C._jit_set_profiling_executor", + "torch._C._jit_set_profiling_mode", + "torch._C._jit_set_symbolic_shapes_test_mode", + "torch._C._jit_set_te_cuda_pointwise_block_count", + "torch._C._jit_set_te_cuda_pointwise_block_size", + "torch._C._jit_set_te_cuda_pointwise_loop_levels", + "torch._C._jit_set_te_generate_block_code", + "torch._C._jit_set_te_must_use_llvm_cpu", + "torch._C._jit_set_texpr_dynamic_shape_enabled", + "torch._C._jit_set_texpr_fuser_enabled", + "torch._C._jit_set_texpr_reductions_enabled", + "torch._C._jit_set_tracer_state_warn", + "torch._C._jit_set_utf8_decoding_ignore", + "torch._C._jit_shape_compute_graph_for_node", + "torch._C._jit_symbolic_shapes_test_mode_enabled", + "torch._C._jit_texpr_dynamic_shape_enabled", + "torch._C._jit_texpr_fallback_allowed", + "torch._C._jit_texpr_fuser_enabled", + "torch._C._jit_texpr_reductions_enabled", + "torch._C._jit_texpr_set_fallback_allowed", + "torch._C._jit_to_backend_selective", + "torch._C._jit_to_backend", + "torch._C._jit_to_static_module", + "torch._C._jit_trace_graph", + "torch._C._jit_trace_module", + "torch._C._jit_tree_views.FalseLiteral", + "torch._C._jit_tree_views.NoneLiteral", + "torch._C._jit_tree_views.TrueLiteral", + "torch._C._jit_try_infer_type", + "torch._C._jit_unflatten", + "torch._C._last_executed_optimized_graph", + "torch._C._len_torch_dispatch_stack", + "torch._C._len_torch_function_stack", + "torch._C._linalg._linalg_eigvals", + "torch._C._linalg.linalg_cholesky_ex", + "torch._C._linalg.linalg_cholesky", + "torch._C._linalg.linalg_cond", + "torch._C._linalg.linalg_cross", + "torch._C._linalg.linalg_det", + "torch._C._linalg.linalg_diagonal", + "torch._C._linalg.linalg_eig", + "torch._C._linalg.linalg_eigh", + "torch._C._linalg.linalg_eigvals", + "torch._C._linalg.linalg_eigvalsh", + "torch._C._linalg.linalg_householder_product", + "torch._C._linalg.linalg_inv_ex", + "torch._C._linalg.linalg_inv", + "torch._C._linalg.linalg_ldl_factor_ex", + "torch._C._linalg.linalg_ldl_factor", + "torch._C._linalg.linalg_ldl_solve", + "torch._C._linalg.linalg_lstsq", + "torch._C._linalg.linalg_lu_factor_ex", + "torch._C._linalg.linalg_lu_factor", + "torch._C._linalg.linalg_lu_solve", + "torch._C._linalg.linalg_lu", + "torch._C._linalg.linalg_matmul", + "torch._C._linalg.linalg_matrix_exp", + "torch._C._linalg.linalg_matrix_norm", + "torch._C._linalg.linalg_matrix_power", + "torch._C._linalg.linalg_matrix_rank", + "torch._C._linalg.linalg_multi_dot", + "torch._C._linalg.linalg_norm", + "torch._C._linalg.linalg_pinv", + "torch._C._linalg.linalg_qr", + "torch._C._linalg.linalg_slogdet", + "torch._C._linalg.linalg_solve_ex", + "torch._C._linalg.linalg_solve_triangular", + "torch._C._linalg.linalg_solve", + "torch._C._linalg.linalg_svd", + "torch._C._linalg.linalg_svdvals", + "torch._C._linalg.linalg_tensorinv", + "torch._C._linalg.linalg_tensorsolve", + "torch._C._linalg.linalg_vander", + "torch._C._linalg.linalg_vecdot", + "torch._C._linalg.linalg_vector_norm", + "torch._C._llvm_enabled", + "torch._C._load_for_lite_interpreter_from_buffer", + "torch._C._load_for_lite_interpreter", + "torch._C._load_jit_module_from_bytes", + "torch._C._load_jit_module_from_file", + "torch._C._load_mobile_module_from_bytes", + "torch._C._load_mobile_module_from_file", + "torch._C._log_api_usage_metadata", + "torch._C._log_api_usage_once", + "torch._C._logging_set_logger", + "torch._C._meta_in_tls_dispatch_include", + "torch._C._mps_acquireEvent", + "torch._C._mps_currentAllocatedMemory", + "torch._C._mps_deviceSynchronize", + "torch._C._mps_driverAllocatedMemory", + "torch._C._mps_recommendedMaxMemory", + "torch._C._mps_elapsedTimeOfEvents", + "torch._C._mps_emptyCache", + "torch._C._mps_get_default_generator", + "torch._C._mps_is_available", + "torch._C._mps_is_in_bad_fork", + "torch._C._mps_is_on_macos_13_or_newer", + "torch._C._mps_profilerStartTrace", + "torch._C._mps_profilerStopTrace", + "torch._C._mps_queryEvent", + "torch._C._mps_recordEvent", + "torch._C._mps_releaseEvent", + "torch._C._mps_setMemoryFraction", + "torch._C._mps_synchronizeEvent", + "torch._C._mps_waitForEvent", + "torch._C._multiprocessing_init", + "torch._C._nccl_all_gather", + "torch._C._nccl_all_reduce", + "torch._C._nccl_broadcast", + "torch._C._nccl_init_rank", + "torch._C._nccl_reduce_scatter", + "torch._C._nccl_reduce", + "torch._C._nccl_unique_id", + "torch._C._nccl_version_suffix", + "torch._C._nccl_version", + "torch._C._nested.nested_tensor", + "torch._C._nested.nested_to_padded_tensor", + "torch._C._new_symbolic_shape_symbol", + "torch._C._nn_module_to_mobile", + "torch._C._nn._conv_depthwise2d", + "torch._C._nn._pad_circular", + "torch._C._nn._pad_enum", + "torch._C._nn._parse_to", + "torch._C._nn._test_ambiguous_defaults", + "torch._C._nn._test_optional_filled_intlist", + "torch._C._nn._test_optional_floatlist", + "torch._C._nn._test_optional_intlist", + "torch._C._nn._test_string_default", + "torch._C._nn._test_warn_in_autograd", + "torch._C._nn._upsample_bicubic2d_aa", + "torch._C._nn._upsample_bilinear2d_aa", + "torch._C._nn._upsample_nearest_exact1d", + "torch._C._nn._upsample_nearest_exact2d", + "torch._C._nn._upsample_nearest_exact3d", + "torch._C._nn.adaptive_avg_pool2d", + "torch._C._nn.adaptive_avg_pool3d", + "torch._C._nn.adaptive_max_pool2d", + "torch._C._nn.adaptive_max_pool3d", + "torch._C._nn.avg_pool2d", + "torch._C._nn.avg_pool3d", + "torch._C._nn.binary_cross_entropy", + "torch._C._nn.col2im", + "torch._C._nn.conv_depthwise3d", + "torch._C._nn.cross_entropy_loss", + "torch._C._nn.elu_", + "torch._C._nn.elu", + "torch._C._nn.flatten_dense_tensors", + "torch._C._nn.fractional_max_pool2d", + "torch._C._nn.fractional_max_pool3d", + "torch._C._nn.gelu_", + "torch._C._nn.gelu", + "torch._C._nn.glu", + "torch._C._nn.hardsigmoid_", + "torch._C._nn.hardsigmoid", + "torch._C._nn.hardswish_", + "torch._C._nn.hardswish", + "torch._C._nn.hardtanh_", + "torch._C._nn.hardtanh", + "torch._C._nn.huber_loss", + "torch._C._nn.im2col", + "torch._C._nn.l1_loss", + "torch._C._nn.leaky_relu_", + "torch._C._nn.leaky_relu", + "torch._C._nn.linear", + "torch._C._nn.log_sigmoid", + "torch._C._nn.max_pool2d_with_indices", + "torch._C._nn.max_pool3d_with_indices", + "torch._C._nn.max_unpool2d", + "torch._C._nn.max_unpool3d", + "torch._C._nn.mish_", + "torch._C._nn.mish", + "torch._C._nn.mkldnn_linear", + "torch._C._nn.mkldnn_reorder_conv2d_weight", + "torch._C._nn.mkldnn_reorder_conv3d_weight", + "torch._C._nn.mse_loss", + "torch._C._nn.multi_margin_loss", + "torch._C._nn.multilabel_margin_loss", + "torch._C._nn.nll_loss_nd", + "torch._C._nn.nll_loss", + "torch._C._nn.nll_loss2d", + "torch._C._nn.one_hot", + "torch._C._nn.pad_sequence", + "torch._C._nn.pad", + "torch._C._nn.reflection_pad1d", + "torch._C._nn.reflection_pad2d", + "torch._C._nn.reflection_pad3d", + "torch._C._nn.relu6_", + "torch._C._nn.relu6", + "torch._C._nn.replication_pad1d", + "torch._C._nn.replication_pad2d", + "torch._C._nn.replication_pad3d", + "torch._C._nn.rrelu_with_noise_", + "torch._C._nn.rrelu_with_noise", + "torch._C._nn.scaled_dot_product_attention", + "torch._C._nn.silu_", + "torch._C._nn.silu", + "torch._C._nn.slow_conv_dilated2d", + "torch._C._nn.slow_conv_dilated3d", + "torch._C._nn.slow_conv_transpose2d", + "torch._C._nn.slow_conv_transpose3d", + "torch._C._nn.slow_conv3d", + "torch._C._nn.smooth_l1_loss", + "torch._C._nn.soft_margin_loss", + "torch._C._nn.softplus", + "torch._C._nn.softshrink", + "torch._C._nn.thnn_conv2d", + "torch._C._nn.unflatten_dense_tensors", + "torch._C._nn.upsample_bicubic2d", + "torch._C._nn.upsample_bilinear2d", + "torch._C._nn.upsample_linear1d", + "torch._C._nn.upsample_nearest1d", + "torch._C._nn.upsample_nearest2d", + "torch._C._nn.upsample_nearest3d", + "torch._C._nn.upsample_trilinear3d", + "torch._C._non_sym_sizes", + "torch._C._overlaps", + "torch._C._parallel_info", + "torch._C._parse_dispatch_key", + "torch._C._parse_source_def", + "torch._C._pop_torch_dispatch_stack", + "torch._C._pop_torch_function_stack", + "torch._C._propagate_and_assign_input_shapes", + "torch._C._propagate_shapes", + "torch._C._propagate_xla_data", + "torch._C._push_on_torch_dispatch_stack", + "torch._C._push_on_torch_function_stack", + "torch._C._quantize_ondevice_ptq_dynamic", + "torch._C._register_py_class_for_device", + "torch._C._remove_cached_tensor", + "torch._C._remove_worker_pids", + "torch._C._rename_privateuse1_backend", + "torch._C._replace_", + "torch._C._replace_overloaded_method_decl", + "torch._C._resolve_type_from_object", + "torch._C._resolve_type", + "torch._C._rocm_is_backward_pass", + "torch._C._rpc_init", + "torch._C._run_emit_module_hook", + "torch._C._save_jit_module_to_bytes", + "torch._C._save_jit_module", + "torch._C._save_mobile_module_to_bytes", + "torch._C._save_mobile_module", + "torch._C._save_parameters", + "torch._C._scatter_out", + "torch._C._scatter", + "torch._C._select_conv_backend", + "torch._C._select_batch_norm_backend", + "torch._C._set_autograd_fallback_mode", + "torch._C._set_backcompat_broadcast_warn", + "torch._C._set_backcompat_keepdim_warn", + "torch._C._set_blas_preferred_backend", + "torch._C._set_cached_tensors_enabled", + "torch._C._set_check_sparse_tensor_invariants", + "torch._C._set_conj", + "torch._C._set_cublas_allow_bf16_reduced_precision_reduction", + "torch._C._set_cublas_allow_fp16_reduced_precision_reduction", + "torch._C._set_cublas_allow_tf32", + "torch._C._set_cudnn_allow_tf32", + "torch._C._set_cudnn_benchmark", + "torch._C._set_cudnn_deterministic", + "torch._C._set_cudnn_enabled", + "torch._C._set_default_dtype", + "torch._C._set_default_mobile_cpu_allocator", + "torch._C._set_default_tensor_type", + "torch._C._set_deterministic_algorithms", + "torch._C._set_deterministic_fill_uninitialized_memory", + "torch._C._set_dispatch_mode", + "torch._C._set_float32_matmul_precision", + "torch._C._set_fwd_grad_enabled", + "torch._C._set_grad_enabled", + "torch._C._set_graph_executor_optimize", + "torch._C._set_linalg_preferred_backend", + "torch._C._set_meta_in_tls_dispatch_include", + "torch._C._set_mkldnn_enabled", + "torch._C._set_multithreading_enabled", + "torch._C._set_neg", + "torch._C._set_nnpack_enabled", + "torch._C._set_print_stack_traces_on_fatal_signal", + "torch._C._set_qengine", + "torch._C._set_sdp_use_flash", + "torch._C._set_sdp_use_math", + "torch._C._set_math_sdp_allow_fp16_bf16_reduction", + "torch._C._set_sdp_use_mem_efficient", + "torch._C._set_should_use_format_with_string_table", + "torch._C._set_storage_access_error_msg", + "torch._C._set_tensor_metadata", + "torch._C._set_tracing_state", + "torch._C._set_value_trace", + "torch._C._set_view_replay_enabled", + "torch._C._set_warnAlways", + "torch._C._set_worker_pids", + "torch._C._set_worker_signal_handlers", + "torch._C._should_allow_numbers_as_tensors", + "torch._C._show_config", + "torch._C._sparse._sparse_addmm", + "torch._C._sparse._sparse_log_softmax", + "torch._C._sparse._sparse_mm_reduce_impl", + "torch._C._sparse._sparse_mm", + "torch._C._sparse._sparse_softmax", + "torch._C._sparse._spdiags", + "torch._C._sparse.sparse_sampled_addmm", + "torch._C._special.special_airy_ai", + "torch._C._special.special_bessel_j0", + "torch._C._special.special_bessel_j1", + "torch._C._special.special_bessel_y0", + "torch._C._special.special_bessel_y1", + "torch._C._special.special_chebyshev_polynomial_t", + "torch._C._special.special_chebyshev_polynomial_u", + "torch._C._special.special_chebyshev_polynomial_v", + "torch._C._special.special_chebyshev_polynomial_w", + "torch._C._special.special_digamma", + "torch._C._special.special_entr", + "torch._C._special.special_erf", + "torch._C._special.special_erfc", + "torch._C._special.special_erfcx", + "torch._C._special.special_erfinv", + "torch._C._special.special_exp2", + "torch._C._special.special_expit", + "torch._C._special.special_expm1", + "torch._C._special.special_gammainc", + "torch._C._special.special_gammaincc", + "torch._C._special.special_gammaln", + "torch._C._special.special_hermite_polynomial_h", + "torch._C._special.special_hermite_polynomial_he", + "torch._C._special.special_i0", + "torch._C._special.special_i0e", + "torch._C._special.special_i1", + "torch._C._special.special_i1e", + "torch._C._special.special_laguerre_polynomial_l", + "torch._C._special.special_legendre_polynomial_p", + "torch._C._special.special_log_ndtr", + "torch._C._special.special_log_softmax", + "torch._C._special.special_log1p", + "torch._C._special.special_logit", + "torch._C._special.special_logsumexp", + "torch._C._special.special_modified_bessel_i0", + "torch._C._special.special_modified_bessel_i1", + "torch._C._special.special_modified_bessel_k0", + "torch._C._special.special_modified_bessel_k1", + "torch._C._special.special_multigammaln", + "torch._C._special.special_ndtr", + "torch._C._special.special_ndtri", + "torch._C._special.special_polygamma", + "torch._C._special.special_psi", + "torch._C._special.special_round", + "torch._C._special.special_scaled_modified_bessel_k0", + "torch._C._special.special_scaled_modified_bessel_k1", + "torch._C._special.special_shifted_chebyshev_polynomial_t", + "torch._C._special.special_shifted_chebyshev_polynomial_u", + "torch._C._special.special_shifted_chebyshev_polynomial_v", + "torch._C._special.special_shifted_chebyshev_polynomial_w", + "torch._C._special.special_sinc", + "torch._C._special.special_softmax", + "torch._C._special.special_spherical_bessel_j0", + "torch._C._special.special_xlog1py", + "torch._C._special.special_xlogy", + "torch._C._special.special_zeta", + "torch._C._stash_obj_in_tls", + "torch._C._storage_id", + "torch._C._storage_Use_Count", + "torch._C._supported_qengines", + "torch._C._te.abs", + "torch._C._te.acos", + "torch._C._te.annotate_input_shapes", + "torch._C._te.asin", + "torch._C._te.atan", + "torch._C._te.atan2", + "torch._C._te.ceil", + "torch._C._te.Compute", + "torch._C._te.Compute2", + "torch._C._te.construct_codegen", + "torch._C._te.cos", + "torch._C._te.cosh", + "torch._C._te.erf", + "torch._C._te.erfc", + "torch._C._te.exp", + "torch._C._te.expm1", + "torch._C._te.fixup_missing_shape_info", + "torch._C._te.floor", + "torch._C._te.fmod", + "torch._C._te.frac", + "torch._C._te.ifThenElse", + "torch._C._te.is_graph_compilable", + "torch._C._te.isnan", + "torch._C._te.lgamma", + "torch._C._te.log", + "torch._C._te.log10", + "torch._C._te.log1p", + "torch._C._te.log2", + "torch._C._te.lower", + "torch._C._te.make_shapes_symbolic", + "torch._C._te.pow", + "torch._C._te.Reduce", + "torch._C._te.remainder", + "torch._C._te.remove_graph_output", + "torch._C._te.remove_unused_self_argument", + "torch._C._te.replace_list_output_with_tuple", + "torch._C._te.round", + "torch._C._te.rsqrt", + "torch._C._te.sigmoid", + "torch._C._te.simplify", + "torch._C._te.sin", + "torch._C._te.sinh", + "torch._C._te.sqrt", + "torch._C._te.tan", + "torch._C._te.tanh", + "torch._C._te.trim_graph", + "torch._C._te.trunc", + "torch._C._tensor_impl_raw_handle", + "torch._C._test_only_add_entry_to_op_version_map", + "torch._C._test_only_populate_upgraders", + "torch._C._test_only_remove_entry_to_op_version_map", + "torch._C._test_only_remove_upgraders", + "torch._C._to_functionality_key", + "torch._C._tracer_set_force_outplace", + "torch._C._tracer_set_get_unique_name_fn", + "torch._C._tracer_warn_use_python", + "torch._C._unset_default_mobile_cpu_allocator", + "torch._C._unset_dispatch_mode", + "torch._C._valgrind_supported_platform", + "torch._C._valgrind_toggle_and_dump_stats", + "torch._C._valgrind_toggle", + "torch._C._verbose.mkl_set_verbose", + "torch._C._verbose.mkldnn_set_verbose", + "torch._C._vmapmode_decrement_nesting", + "torch._C._vmapmode_increment_nesting", + "torch._C._warn_deprecation", + "torch._C._warn", + "torch._C._will_engine_execute_node", + "torch._C._wrap_tensor_impl", + "torch._C.fork", + "torch._C.get_autocast_cpu_dtype", + "torch._C.get_autocast_dtype", + "torch._C.get_autocast_gpu_dtype", + "torch._C.get_autocast_ipu_dtype", + "torch._C.get_autocast_xla_dtype", + "torch._C.get_default_dtype", + "torch._C.get_num_interop_threads", + "torch._C.get_num_threads", + "torch._C.import_ir_module_from_buffer", + "torch._C.import_ir_module", + "torch._C.init_num_threads", + "torch._C.is_anomaly_check_nan_enabled", + "torch._C.is_anomaly_enabled", + "torch._C.is_autocast_cache_enabled", + "torch._C.is_autocast_cpu_enabled", + "torch._C.is_autocast_enabled", + "torch._C.is_autocast_ipu_enabled", + "torch._C.is_autocast_xla_enabled", + "torch._C.is_grad_enabled", + "torch._C.is_inference_mode_enabled", + "torch._C.merge_type_from_type_comment", + "torch._C.parse_ir", + "torch._C.parse_schema", + "torch._C.parse_type_comment", + "torch._C.read_vitals", + "torch._C.set_vital", + "torch._C.unify_type_list", + "torch._C.vitals_enabled", + "torch._C.wait", + "torch._cast_Byte", + "torch._cast_Char", + "torch._cast_Double", + "torch._cast_Float", + "torch._cast_Half", + "torch._cast_Int", + "torch._cast_Long", + "torch._cast_Short", + "torch._choose_qparams_per_tensor", + "torch._chunk_cat", + "torch._coalesce", + "torch._compute_linear_combination", + "torch._conj_copy", + "torch._conj_physical", + "torch._conj", + "torch._convert_indices_from_coo_to_csr", + "torch._convert_indices_from_csr_to_coo", + "torch._convert_weight_to_int4pack", + "torch._convolution_mode", + "torch._convolution", + "torch._copy_from_and_resize", + "torch._copy_from", + "torch._cslt_compress", + "torch._cslt_sparse_mm", + "torch._ctc_loss", + "torch._cudnn_ctc_loss", + "torch._cudnn_init_dropout_state", + "torch._cudnn_rnn_flatten_weight", + "torch._cudnn_rnn", + "torch._cufft_clear_plan_cache", + "torch._cufft_get_plan_cache_max_size", + "torch._cufft_get_plan_cache_size", + "torch._cufft_set_plan_cache_max_size", + "torch._cummax_helper", + "torch._cummin_helper", + "torch._debug_has_internal_overlap", + "torch._dim_arange", + "torch._dirichlet_grad", + "torch._disable_functionalization", + "torch._efficientzerotensor", + "torch._embedding_bag_forward_only", + "torch._embedding_bag", + "torch._empty_affine_quantized", + "torch._empty_per_channel_affine_quantized", + "torch._enable_functionalization", + "torch._euclidean_dist", + "torch._fake_quantize_learnable_per_channel_affine", + "torch._fake_quantize_learnable_per_tensor_affine", + "torch._fake_quantize_per_tensor_affine_cachemask_tensor_qparams", + "torch._fft_c2c", + "torch._fft_c2r", + "torch._fft_r2c", + "torch._fill_mem_eff_dropout_mask_", + "torch._foobar", + "torch._foreach_abs_", + "torch._foreach_abs", + "torch._foreach_acos_", + "torch._foreach_acos", + "torch._foreach_add_", + "torch._foreach_add", + "torch._foreach_addcdiv_", + "torch._foreach_addcdiv", + "torch._foreach_addcmul_", + "torch._foreach_addcmul", + "torch._foreach_asin_", + "torch._foreach_asin", + "torch._foreach_atan_", + "torch._foreach_atan", + "torch._foreach_ceil_", + "torch._foreach_ceil", + "torch._foreach_clamp_max_", + "torch._foreach_clamp_max", + "torch._foreach_clamp_min_", + "torch._foreach_clamp_min", + "torch._foreach_copy_", + "torch._foreach_cos_", + "torch._foreach_cos", + "torch._foreach_cosh_", + "torch._foreach_cosh", + "torch._foreach_div_", + "torch._foreach_div", + "torch._foreach_erf_", + "torch._foreach_erf", + "torch._foreach_erfc_", + "torch._foreach_erfc", + "torch._foreach_exp_", + "torch._foreach_exp", + "torch._foreach_expm1_", + "torch._foreach_expm1", + "torch._foreach_floor_", + "torch._foreach_floor", + "torch._foreach_frac_", + "torch._foreach_frac", + "torch._foreach_lerp_", + "torch._foreach_lerp", + "torch._foreach_lgamma_", + "torch._foreach_lgamma", + "torch._foreach_log_", + "torch._foreach_log", + "torch._foreach_log10_", + "torch._foreach_log10", + "torch._foreach_log1p_", + "torch._foreach_log1p", + "torch._foreach_log2_", + "torch._foreach_log2", + "torch._foreach_maximum_", + "torch._foreach_maximum", + "torch._foreach_minimum_", + "torch._foreach_minimum", + "torch._foreach_mul_", + "torch._foreach_mul", + "torch._foreach_neg_", + "torch._foreach_neg", + "torch._foreach_norm", + "torch._foreach_pow_", + "torch._foreach_pow", + "torch._foreach_reciprocal_", + "torch._foreach_reciprocal", + "torch._foreach_round_", + "torch._foreach_round", + "torch._foreach_sigmoid_", + "torch._foreach_sigmoid", + "torch._foreach_sign_", + "torch._foreach_sign", + "torch._foreach_sin_", + "torch._foreach_sin", + "torch._foreach_sinh_", + "torch._foreach_sinh", + "torch._foreach_sqrt_", + "torch._foreach_sqrt", + "torch._foreach_sub_", + "torch._foreach_sub", + "torch._foreach_tan_", + "torch._foreach_tan", + "torch._foreach_tanh_", + "torch._foreach_tanh", + "torch._foreach_trunc_", + "torch._foreach_trunc", + "torch._foreach_zero_", + "torch._freeze_functional_tensor", + "torch._from_functional_tensor", + "torch._functional_assert_async", + "torch._functional_sym_constrain_range_for_size", + "torch._functional_sym_constrain_range", + "torch._functionalize_are_all_mutations_hidden_from_autograd", + "torch._functionalize_commit_update", + "torch._functionalize_enable_reapply_views", + "torch._functionalize_has_data_mutation", + "torch._functionalize_has_metadata_mutation", + "torch._functionalize_is_multi_output_view", + "torch._functionalize_mark_mutation_hidden_from_autograd", + "torch._functionalize_replace", + "torch._functionalize_sync", + "torch._functionalize_was_storage_changed", + "torch._fused_adam_", + "torch._fused_adamw_", + "torch._fused_dropout", + "torch._fused_moving_avg_obs_fq_helper", + "torch._fused_sdp_choice", + "torch._fw_primal_copy", + "torch._grid_sampler_2d_cpu_fallback", + "torch._has_compatible_shallow_copy_type", + "torch._histogramdd_bin_edges", + "torch._histogramdd_from_bin_cts", + "torch._histogramdd_from_bin_tensors", + "torch._index_put_impl_", + "torch._indices_copy", + "torch._int_mm", + "torch._is_all_true", + "torch._is_any_true", + "torch._is_functional_tensor", + "torch._is_zerotensor", + "torch._linalg_check_errors", + "torch._linalg_det", + "torch._linalg_eigh", + "torch._linalg_eigvals", + "torch._linalg_slogdet", + "torch._linalg_solve_ex", + "torch._linalg_svd", + "torch._log_softmax_backward_data", + "torch._log_softmax", + "torch._logcumsumexp", + "torch._lstm_mps", + "torch._lu_with_info", + "torch._make_dep_token", + "torch._make_dual_copy", + "torch._make_dual", + "torch._make_per_channel_quantized_tensor", + "torch._make_per_tensor_quantized_tensor", + "torch._masked_scale", + "torch._masked_softmax", + "torch._mirror_autograd_meta_to", + "torch._mixed_dtypes_linear", + "torch._mkldnn_reshape", + "torch._mkldnn_transpose_", + "torch._mkldnn_transpose", + "torch._mps_convolution_transpose", + "torch._mps_convolution", + "torch._native_batch_norm_legit_no_training", + "torch._native_batch_norm_legit", + "torch._native_multi_head_attention", + "torch._neg_view_copy", + "torch._neg_view", + "torch._nested_from_padded_and_nested_example", + "torch._nested_tensor_from_mask_left_aligned", + "torch._nested_tensor_from_tensor_list", + "torch._nested_tensor_softmax_with_shape", + "torch._nested_view_from_buffer_copy", + "torch._nested_view_from_buffer", + "torch._nnpack_available", + "torch._nnpack_spatial_convolution", + "torch._pack_padded_sequence", + "torch._pad_packed_sequence", + "torch._pin_memory", + "torch._prelu_kernel", + "torch._propagate_xla_data", + "torch._remove_batch_dim", + "torch._reshape_alias_copy", + "torch._reshape_from_tensor", + "torch._resize_output_", + "torch._rowwise_prune", + "torch._sample_dirichlet", + "torch._saturate_weight_to_fp16", + "torch._scaled_dot_product_attention_math", + "torch._scaled_dot_product_efficient_attention", + "torch._scaled_dot_product_flash_attention", + "torch._scaled_dot_product_flash_attention_for_cpu", + "torch._scaled_dot_product_cudnn_attention", + "torch._scaled_mm", + "torch._shape_as_tensor", + "torch._sobol_engine_draw", + "torch._sobol_engine_ff_", + "torch._sobol_engine_initialize_state_", + "torch._sobol_engine_scramble_", + "torch._softmax_backward_data", + "torch._softmax", + "torch._sparse_broadcast_to_copy", + "torch._sparse_broadcast_to", + "torch._sparse_csr_prod", + "torch._sparse_csr_sum", + "torch._sparse_log_softmax_backward_data", + "torch._sparse_semi_structured_addmm", + "torch._sparse_semi_structured_linear", + "torch._sparse_semi_structured_mm", + "torch._sparse_softmax_backward_data", + "torch._sparse_sparse_matmul", + "torch._sparse_sum", + "torch._stack", + "torch._standard_gamma_grad", + "torch._standard_gamma", + "torch._test_autograd_multiple_dispatch_view_copy", + "torch._test_autograd_multiple_dispatch_view", + "torch._test_autograd_multiple_dispatch", + "torch._test_check_tensor", + "torch._test_functorch_fallback", + "torch._test_serialization_subcmul", + "torch._to_cpu", + "torch._to_functional_tensor", + "torch._to_sparse_semi_structured", + "torch._transform_bias_rescale_qkv", + "torch._transformer_encoder_layer_fwd", + "torch._trilinear", + "torch._triton_multi_head_attention", + "torch._triton_scaled_dot_attention", + "torch._unique", + "torch._unique2", + "torch._unpack_dual", + "torch._unsafe_index_put", + "torch._unsafe_index", + "torch._unsafe_masked_index_put_accumulate", + "torch._unsafe_masked_index", + "torch._use_cudnn_ctc_loss", + "torch._use_cudnn_rnn_flatten_weight", + "torch._values_copy", + "torch._weight_int4pack_mm", + "torch._weight_int8pack_mm", + "torch._weight_norm_interface", + "torch._weight_norm", + "torch.abs_", + "torch.abs", + "torch.absolute", + "torch.acos_", + "torch.acos", + "torch.acosh_", + "torch.acosh", + "torch.adaptive_avg_pool1d", + "torch.adaptive_max_pool1d", + "torch.add", + "torch.addbmm", + "torch.addcdiv", + "torch.addcmul", + "torch.addmm", + "torch.addmv_", + "torch.addmv", + "torch.addr", + "torch.adjoint", + "torch.affine_grid_generator", + "torch.alias_copy", + "torch.all", + "torch.allclose", + "torch.alpha_dropout_", + "torch.alpha_dropout", + "torch.amax", + "torch.amin", + "torch.aminmax", + "torch.angle", + "torch.any", + "torch.arange", + "torch.arccos_", + "torch.arccos", + "torch.arccosh_", + "torch.arccosh", + "torch.arcsin_", + "torch.arcsin", + "torch.arcsinh_", + "torch.arcsinh", + "torch.arctan_", + "torch.arctan", + "torch.arctan2", + "torch.arctanh_", + "torch.arctanh", + "torch.argmax", + "torch.argmin", + "torch.argsort", + "torch.argwhere", + "torch.as_strided_", + "torch.as_strided_copy", + "torch.as_strided_scatter", + "torch.as_strided", + "torch.as_tensor", + "torch.asarray", + "torch.asin_", + "torch.asin", + "torch.asinh_", + "torch.asinh", + "torch.atan_", + "torch.atan", + "torch.atan2", + "torch.atanh_", + "torch.atanh", + "torch.avg_pool1d", + "torch.baddbmm", + "torch.bartlett_window", + "torch.batch_norm_backward_elemt", + "torch.batch_norm_backward_reduce", + "torch.batch_norm_elemt", + "torch.batch_norm_gather_stats_with_counts", + "torch.batch_norm_gather_stats", + "torch.batch_norm_stats", + "torch.batch_norm_update_stats", + "torch.batch_norm", + "torch.bernoulli", + "torch.bilinear", + "torch.binary_cross_entropy_with_logits", + "torch.bincount", + "torch.binomial", + "torch.bitwise_and", + "torch.bitwise_left_shift", + "torch.bitwise_not", + "torch.bitwise_or", + "torch.bitwise_right_shift", + "torch.bitwise_xor", + "torch.blackman_window", + "torch.bmm", + "torch.broadcast_to", + "torch.bucketize", + "torch.can_cast", + "torch.cat", + "torch.ccol_indices_copy", + "torch.ceil_", + "torch.ceil", + "torch.celu_", + "torch.celu", + "torch.channel_shuffle", + "torch.cholesky_inverse", + "torch.cholesky_solve", + "torch.cholesky", + "torch.choose_qparams_optimized", + "torch.chunk", + "torch.clamp_", + "torch.clamp_max_", + "torch.clamp_max", + "torch.clamp_min_", + "torch.clamp_min", + "torch.clamp", + "torch.clip_", + "torch.clip", + "torch.clone", + "torch.col_indices_copy", + "torch.column_stack", + "torch.combinations", + "torch.complex", + "torch.concat", + "torch.concatenate", + "torch.conj_physical_", + "torch.conj_physical", + "torch.conj", + "torch.constant_pad_nd", + "torch.conv_tbc", + "torch.conv_transpose1d", + "torch.conv_transpose2d", + "torch.conv_transpose3d", + "torch.conv1d", + "torch.conv2d", + "torch.conv3d", + "torch.convolution", + "torch.copysign", + "torch.corrcoef", + "torch.cos_", + "torch.cos", + "torch.cosh_", + "torch.cosh", + "torch.cosine_embedding_loss", + "torch.cosine_similarity", + "torch.count_nonzero", + "torch.cov", + "torch.cross", + "torch.crow_indices_copy", + "torch.ctc_loss", + "torch.cudnn_affine_grid_generator", + "torch.cudnn_batch_norm", + "torch.cudnn_convolution_add_relu", + "torch.cudnn_convolution_relu", + "torch.cudnn_convolution_transpose", + "torch.cudnn_convolution", + "torch.cudnn_grid_sampler", + "torch.cudnn_is_acceptable", + "torch.cummax", + "torch.cummin", + "torch.cumprod", + "torch.cumsum", + "torch.cumulative_trapezoid", + "torch.deg2rad_", + "torch.deg2rad", + "torch.dequantize", + "torch.det", + "torch.detach_", + "torch.detach_copy", + "torch.detach", + "torch.diag_embed", + "torch.diag", + "torch.diagflat", + "torch.diagonal_copy", + "torch.diagonal_scatter", + "torch.diagonal", + "torch.diff", + "torch.digamma", + "torch.dist", + "torch.div", + "torch.divide", + "torch.dot", + "torch.dropout_", + "torch.dropout", + "torch.dsmm", + "torch.dsplit", + "torch.dstack", + "torch.embedding_bag", + "torch.embedding_renorm_", + "torch.embedding", + "torch.empty_like", + "torch.empty_permuted", + "torch.empty_quantized", + "torch.empty_strided", + "torch.empty", + "torch.eq", + "torch.equal", + "torch.erf_", + "torch.erf", + "torch.erfc_", + "torch.erfc", + "torch.erfinv", + "torch.exp_", + "torch.exp", + "torch.exp2_", + "torch.exp2", + "torch.expand_copy", + "torch.expm1_", + "torch.expm1", + "torch.eye", + "torch.fake_quantize_per_channel_affine", + "torch.fake_quantize_per_tensor_affine", + "torch.fbgemm_linear_fp16_weight_fp32_activation", + "torch.fbgemm_linear_fp16_weight", + "torch.fbgemm_linear_int8_weight_fp32_activation", + "torch.fbgemm_linear_int8_weight", + "torch.fbgemm_linear_quantize_weight", + "torch.fbgemm_pack_gemm_matrix_fp16", + "torch.fbgemm_pack_quantized_matrix", + "torch.feature_alpha_dropout_", + "torch.feature_alpha_dropout", + "torch.feature_dropout_", + "torch.feature_dropout", + "torch.fill_", + "torch.fill", + "torch.fix_", + "torch.fix", + "torch.flatten", + "torch.flip", + "torch.fliplr", + "torch.flipud", + "torch.float_power", + "torch.floor_", + "torch.floor_divide", + "torch.floor", + "torch.fmax", + "torch.fmin", + "torch.fmod", + "torch.frac_", + "torch.frac", + "torch.frexp", + "torch.frobenius_norm", + "torch.from_file", + "torch.from_numpy", + "torch.frombuffer", + "torch.full_like", + "torch.full", + "torch.fused_moving_avg_obs_fake_quant", + "torch.gather", + "torch.gcd_", + "torch.gcd", + "torch.ge", + "torch.geqrf", + "torch.ger", + "torch.get_device", + "torch.gradient", + "torch.greater_equal", + "torch.greater", + "torch.grid_sampler_2d", + "torch.grid_sampler_3d", + "torch.grid_sampler", + "torch.group_norm", + "torch.gru_cell", + "torch.gru", + "torch.gt", + "torch.hamming_window", + "torch.hann_window", + "torch.hardshrink", + "torch.heaviside", + "torch.hinge_embedding_loss", + "torch.histc", + "torch.histogram", + "torch.histogramdd", + "torch.hsmm", + "torch.hsplit", + "torch.hspmm", + "torch.hstack", + "torch.hypot", + "torch.i0_", + "torch.i0", + "torch.igamma", + "torch.igammac", + "torch.imag", + "torch.index_add", + "torch.index_copy", + "torch.index_fill", + "torch.index_put_", + "torch.index_put", + "torch.index_reduce", + "torch.index_select", + "torch.indices_copy", + "torch.inner", + "torch.instance_norm", + "torch.int_repr", + "torch.inverse", + "torch.is_complex", + "torch.is_conj", + "torch.is_distributed", + "torch.is_floating_point", + "torch.is_inference", + "torch.is_neg", + "torch.is_nonzero", + "torch.is_same_size", + "torch.is_signed", + "torch.is_vulkan_available", + "torch.isclose", + "torch.isfinite", + "torch.isin", + "torch.isinf", + "torch.isnan", + "torch.isneginf", + "torch.isposinf", + "torch.isreal", + "torch.istft", + "torch.kaiser_window", + "torch.kl_div", + "torch.kron", + "torch.kthvalue", + "torch.layer_norm", + "torch.lcm_", + "torch.lcm", + "torch.ldexp_", + "torch.ldexp", + "torch.le", + "torch.lerp", + "torch.less_equal", + "torch.less", + "torch.lgamma", + "torch.linspace", + "torch.log_", + "torch.log_softmax", + "torch.log", + "torch.log10_", + "torch.log10", + "torch.log1p_", + "torch.log1p", + "torch.log2_", + "torch.log2", + "torch.logaddexp", + "torch.logaddexp2", + "torch.logcumsumexp", + "torch.logdet", + "torch.logical_and", + "torch.logical_not", + "torch.logical_or", + "torch.logical_xor", + "torch.logit_", + "torch.logit", + "torch.logspace", + "torch.logsumexp", + "torch.lstm_cell", + "torch.lstm", + "torch.lt", + "torch.lu_solve", + "torch.lu_unpack", + "torch.margin_ranking_loss", + "torch.masked_fill", + "torch.masked_scatter", + "torch.masked_select", + "torch.matmul", + "torch.matrix_exp", + "torch.matrix_power", + "torch.max_pool1d_with_indices", + "torch.max_pool1d", + "torch.max_pool2d", + "torch.max_pool3d", + "torch.max", + "torch.maximum", + "torch.mean", + "torch.median", + "torch.min", + "torch.minimum", + "torch.miopen_batch_norm", + "torch.miopen_convolution_add_relu", + "torch.miopen_convolution_relu", + "torch.miopen_convolution_transpose", + "torch.miopen_convolution", + "torch.miopen_depthwise_convolution", + "torch.miopen_rnn", + "torch.mkldnn_adaptive_avg_pool2d", + "torch.mkldnn_convolution", + "torch.mkldnn_linear_backward_weights", + "torch.mkldnn_max_pool2d", + "torch.mkldnn_max_pool3d", + "torch.mkldnn_rnn_layer", + "torch.mm", + "torch.mode", + "torch.moveaxis", + "torch.movedim", + "torch.msort", + "torch.mul", + "torch.multinomial", + "torch.multiply", + "torch.mv", + "torch.mvlgamma", + "torch.nan_to_num_", + "torch.nan_to_num", + "torch.nanmean", + "torch.nanmedian", + "torch.nanquantile", + "torch.nansum", + "torch.narrow_copy", + "torch.narrow", + "torch.native_batch_norm", + "torch.native_channel_shuffle", + "torch.native_dropout", + "torch.native_group_norm", + "torch.native_layer_norm", + "torch.native_norm", + "torch.ne", + "torch.neg_", + "torch.neg", + "torch.negative_", + "torch.negative", + "torch.nextafter", + "torch.nonzero_static", + "torch.nonzero", + "torch.norm_except_dim", + "torch.normal", + "torch.not_equal", + "torch.nuclear_norm", + "torch.numel", + "torch.ones_like", + "torch.ones", + "torch.orgqr", + "torch.ormqr", + "torch.outer", + "torch.pairwise_distance", + "torch.pdist", + "torch.permute_copy", + "torch.permute", + "torch.pinverse", + "torch.pixel_shuffle", + "torch.pixel_unshuffle", + "torch.poisson_nll_loss", + "torch.poisson", + "torch.polar", + "torch.polygamma", + "torch.positive", + "torch.pow", + "torch.prelu", + "torch._print", + "torch.prod", + "torch.promote_types", + "torch.put", + "torch.q_per_channel_axis", + "torch.q_per_channel_scales", + "torch.q_per_channel_zero_points", + "torch.q_scale", + "torch.q_zero_point", + "torch.qr", + "torch.quantile", + "torch.quantize_per_channel", + "torch.quantize_per_tensor_dynamic", + "torch.quantize_per_tensor", + "torch.quantized_batch_norm", + "torch.quantized_gru_cell", + "torch.quantized_lstm_cell", + "torch.quantized_max_pool1d", + "torch.quantized_max_pool2d", + "torch.quantized_max_pool3d", + "torch.quantized_rnn_relu_cell", + "torch.quantized_rnn_tanh_cell", + "torch.rad2deg_", + "torch.rad2deg", + "torch.rand_like", + "torch.rand", + "torch.randint_like", + "torch.randint", + "torch.randn_like", + "torch.randn", + "torch.randperm", + "torch.range", + "torch.ravel", + "torch.real", + "torch.reciprocal_", + "torch.reciprocal", + "torch.relu_", + "torch.relu", + "torch.remainder", + "torch.renorm", + "torch.repeat_interleave", + "torch.reshape", + "torch.resolve_conj", + "torch.resolve_neg", + "torch.result_type", + "torch.rms_norm", + "torch.rnn_relu_cell", + "torch.rnn_relu", + "torch.rnn_tanh_cell", + "torch.rnn_tanh", + "torch.roll", + "torch.rot90", + "torch.round_", + "torch.round", + "torch.row_indices_copy", + "torch.row_stack", + "torch.rrelu_", + "torch.rrelu", + "torch.rsqrt_", + "torch.rsqrt", + "torch.rsub", + "torch.saddmm", + "torch.scalar_tensor", + "torch.scatter_add", + "torch.scatter_reduce", + "torch.scatter", + "torch.searchsorted", + "torch.segment_reduce", + "torch.select_copy", + "torch.select_scatter", + "torch.select", + "torch.selu_", + "torch.selu", + "torch.sgn", + "torch.sigmoid_", + "torch.sigmoid", + "torch.sign", + "torch.signal.windows.windows.sqrt", + "torch.signbit", + "torch.sin_", + "torch.sin", + "torch.sinc_", + "torch.sinc", + "torch.sinh_", + "torch.sinh", + "torch.slice_copy", + "torch.slice_scatter", + "torch.slogdet", + "torch.smm", + "torch.softmax", + "torch.sort", + "torch.split_copy", + "torch.split_with_sizes_copy", + "torch.split_with_sizes", + "torch.spmm", + "torch.sqrt_", + "torch.sqrt", + "torch.square_", + "torch.square", + "torch.squeeze_copy", + "torch.squeeze", + "torch.sspaddmm", + "torch.stack", + "torch.std_mean", + "torch.std", + "torch.sub", + "torch.subtract", + "torch.sum", + "torch.svd", + "torch.swapaxes", + "torch.swapdims", + "torch.sym_constrain_range_for_size", + "torch.sym_constrain_range", + "torch.t_copy", + "torch.t", + "torch.take_along_dim", + "torch.take", + "torch.tan_", + "torch.tan", + "torch.tanh_", + "torch.tanh", + "torch.tensor_split", + "torch.tensor", + "torch.threshold_", + "torch.threshold", + "torch.tile", + "torch.topk", + "torch.trace", + "torch.transpose_copy", + "torch.transpose", + "torch.trapezoid", + "torch.trapz", + "torch.triangular_solve", + "torch.tril_indices", + "torch.tril", + "torch.triplet_margin_loss", + "torch.triu_indices", + "torch.triu", + "torch.true_divide", + "torch.trunc_", + "torch.trunc", + "torch.unbind_copy", + "torch.unbind", + "torch.unflatten", + "torch.unfold_copy", + "torch.unsafe_chunk", + "torch.unsafe_split_with_sizes", + "torch.unsafe_split", + "torch.unsqueeze_copy", + "torch.unsqueeze", + "torch.values_copy", + "torch.vander", + "torch.var_mean", + "torch.var", + "torch.vdot", + "torch.view_as_complex_copy", + "torch.view_as_complex", + "torch.view_as_real_copy", + "torch.view_as_real", + "torch.view_copy", + "torch.vsplit", + "torch.vstack", + "torch.where", + "torch.xlogy_", + "torch.xlogy", + "torch.zero_", + "torch.zeros", + "torch.zeros_like", + "torch._fused_sgd_", + "torch.slice_inverse", + "torch._assert_scalar", + "torch._functional_assert_scalar", + ], + TorchInGraphFunctionVariable, +) + + +if sys.version_info >= (3, 9): + torch_c_binding_in_graph_functions["math.lcm"] = TorchInGraphFunctionVariable +if sys.version_info >= (3, 11): + torch_c_binding_in_graph_functions["math.exp2"] = TorchInGraphFunctionVariable + torch_c_binding_in_graph_functions["math.cbrt"] = TorchInGraphFunctionVariable + + +# In graph functions (including constant folding) that are not C bindings +torch_non_c_binding_in_graph_functions = dict.fromkeys( + [ + "torch.__future__.get_overwrite_module_params_on_conversion", + "torch.__future__.set_overwrite_module_params_on_conversion", + "torch.__getattr__", + "torch._assert", + "torch._check_index", + "torch._check_is_size", + "torch._check_not_implemented", + "torch._check_tensor_all_with", + "torch._check_tensor_all", + "torch._check_type", + "torch._check_value", + "torch._check_with", + "torch._check", + "torch._compile._disable_dynamo", + "torch._functorch.apis.chunk_vmap", + "torch._functorch.autograd_function.custom_function_call_functionalize", + "torch._functorch.autograd_function.custom_function_call_grad", + "torch._functorch.autograd_function.custom_function_call_vmap_generate_rule", + "torch._functorch.autograd_function.custom_function_call_vmap", + "torch._functorch.autograd_function.generate_single_level_function", + "torch._functorch.autograd_function.get_tangents_in_dims", + "torch._functorch.autograd_function.has_overriden_vmap_rule", + "torch._functorch.autograd_function.reductify_leaf", + "torch._functorch.autograd_function.reductify", + "torch._functorch.autograd_function.validate_vmap_returns_tuple_of_two_elements", + "torch._functorch.autograd_function.vmapify_autograd_function", + "torch._functorch.autograd_function.wrap_outputs_maintaining_identity", + "torch._functorch.batch_norm_replacement.batch_norm_without_running_stats", + "torch._functorch.batch_norm_replacement.replace_all_batch_norm_modules_", + "torch._functorch.deprecated.combine_state_for_ensemble", + "torch._functorch.deprecated.functionalize", + "torch._functorch.deprecated.get_warning", + "torch._functorch.deprecated.make_functional_with_buffers", + "torch._functorch.deprecated.make_functional", + "torch._functorch.deprecated.setup_docs", + "torch._functorch.deprecated.warn_deprecated", + "torch._functorch.eager_transforms._any_differentiable", + "torch._functorch.eager_transforms._autograd_grad", + "torch._functorch.eager_transforms._vjp_treespec_compare", + "torch._functorch.eager_transforms._set_tensor_requires_grad", + "torch._functorch.eager_transforms._jvp_treespec_compare", + "torch._functorch.eager_transforms._linearize_treespec_compare", + "torch._functorch.eager_transforms._is_differentiable", + "torch._functorch.eager_transforms._maybe_unwrap_functional_tensor", + "torch._functorch.eager_transforms._maybe_wrap_functional_tensor", + "torch._functorch.eager_transforms._unwrap_all_tensors_from_functional", + "torch._functorch.eager_transforms._wrap_all_tensors_to_functional", + "torch._functorch.eager_transforms.assert_flat_tuple_of_tensors", + "torch._functorch.eager_transforms.functionalize", + "torch._functorch.eager_transforms.lazy_dynamo_disable", + "torch._functorch.eager_transforms.noop", + "torch._functorch.pyfunctorch.coerce_cinterpreter", + "torch._functorch.pyfunctorch.dispatch_functorch", + "torch._functorch.pyfunctorch.nested", + "torch._functorch.pyfunctorch.retrieve_current_functorch_interpreter", + "torch._functorch.pyfunctorch.temporarily_pop_interpreter_stack", + "torch._functorch.utils.enable_single_level_autograd_function", + "torch._functorch.utils.exposed_in", + "torch._functorch.utils.unwrap_dead_wrappers", + "torch._functorch.vmap.lazy_load_decompositions", + "torch._guards.compile_context", + "torch._guards.detect_fake_mode", + "torch._guards.tracing", + "torch._higher_order_ops.map._has_potential_branch_input_alias", + "torch._higher_order_ops.map._has_potential_branch_input_mutation", + "torch._higher_order_ops.map._stack_pytree", + "torch._higher_order_ops.map._unstack_pytree", + "torch._higher_order_ops.map.create_fw_bw_graph", + "torch._higher_order_ops.map.map_autograd", + "torch._higher_order_ops.map.map_dense", + "torch._higher_order_ops.map.map_fake_tensor_mode", + "torch._higher_order_ops.map.map_functionalize", + "torch._higher_order_ops.map.map_proxy_torch_dispatch_mode", + "torch._higher_order_ops.map.map_wrapper", + "torch._higher_order_ops.map.trace_map", + "torch._higher_order_ops.out_dtype.elementwise_dtypes", + "torch._higher_order_ops.out_dtype.is_int_mm", + "torch._higher_order_ops.out_dtype.out_dtype_dense", + "torch._higher_order_ops.out_dtype.out_dtype_fake_tensor_mode", + "torch._higher_order_ops.out_dtype.out_dtype_fallback", + "torch._higher_order_ops.out_dtype.out_dtype_func", + "torch._higher_order_ops.out_dtype.out_dtype_proxy", + "torch._higher_order_ops.out_dtype.trace_out_dtype", + "torch._higher_order_ops.utils.autograd_not_implemented_inner", + "torch._higher_order_ops.utils.autograd_not_implemented", + "torch._linalg_utils._symeig", + "torch._linalg_utils.basis", + "torch._linalg_utils.bform", + "torch._linalg_utils.eig", + "torch._linalg_utils.get_floating_dtype", + "torch._linalg_utils.is_sparse", + "torch._linalg_utils.lstsq", + "torch._linalg_utils.matmul", + "torch._linalg_utils.matrix_rank", + "torch._linalg_utils.qform", + "torch._linalg_utils.solve", + "torch._linalg_utils.symeig", + "torch._load_global_deps", + "torch._lowrank._svd_lowrank", + "torch._lowrank.get_approximate_basis", + "torch._lowrank.pca_lowrank", + "torch._lowrank.svd_lowrank", + "torch._ops._compute_keyset", + "torch._ops._get_tensors", + "torch._ops._to_flat_tuple", + "torch._ops.add_cached_op", + "torch._ops.dl_open_guard", + "torch._ops.get_cached_ops", + "torch._ops.key_extractor", + "torch._ops.reset_cached_ops", + "torch._ops.resolve_key", + "torch._preload_cuda_deps", + "torch._register_device_module", + "torch._running_with_deploy", + "torch._utils._dummy_type", + "torch._weights_only_unpickler._get_allowed_globals", + "torch._weights_only_unpickler.load", + "torch.align_tensors", + "torch.amp.autocast_mode._enter_autocast", + "torch.amp.autocast_mode._exit_autocast", + "torch.amp.autocast_mode.autocast_decorator", + "torch.amp.autocast_mode.custom_bwd", + "torch.amp.autocast_mode.custom_fwd", + "torch.are_deterministic_algorithms_enabled", + "torch.atleast_1d", + "torch.atleast_2d", + "torch.atleast_3d", + "torch.autograd._calculate_shape", + "torch.autograd._is_checkpoint_valid", + "torch.autograd._make_grads", + "torch.autograd._register_py_tensor_class_for_device", + "torch.autograd._tensor_or_tensors_to_tuple", + "torch.autograd.forward_ad._maybe_load_decompositions", + "torch.autograd.function._iter_filter", + "torch.autograd.function._iter_jit_values", + "torch.autograd.function._iter_None_tensors", + "torch.autograd.function._iter_tensors_permissive", + "torch.autograd.function._iter_tensors", + "torch.autograd.function._jit_unwrap_structured", + "torch.autograd.function._map_tensor_data", + "torch.autograd.function._nested_map", + "torch.autograd.function._unflatten", + "torch.autograd.function.once_differentiable", + "torch.autograd.function.traceable", + "torch.autograd.functional._as_tuple_nocheck", + "torch.autograd.functional._as_tuple", + "torch.autograd.functional._autograd_grad", + "torch.autograd.functional._check_requires_grad", + "torch.autograd.functional._construct_standard_basis_for", + "torch.autograd.functional._fill_in_zeros", + "torch.autograd.functional._grad_postprocess", + "torch.autograd.functional._grad_preprocess", + "torch.autograd.functional._jacfwd", + "torch.autograd.functional._tuple_postprocess", + "torch.autograd.functional._validate_v", + "torch.autograd.functional.hessian", + "torch.autograd.functional.hvp", + "torch.autograd.functional.jacobian", + "torch.autograd.functional.jvp", + "torch.autograd.functional.vhp", + "torch.autograd.functional.vjp", + "torch.autograd.grad_mode._enter_inference_mode", + "torch.autograd.grad_mode._exit_inference_mode", + "torch.autograd.graph._get_sid", + "torch.autograd.graph._get_tid", + "torch.autograd.graph.allow_mutation_on_saved_tensors", + "torch.autograd.graph.get_gradient_edge", + "torch.autograd.graph.increment_version", + "torch.autograd.graph.register_multi_grad_hook", + "torch.autograd.variable", + "torch.backends.__allow_nonbracketed_mutation", + "torch.backends.cpu.get_cpu_capability", + "torch.backends.cuda.can_use_efficient_attention", + "torch.backends.cuda.can_use_flash_attention", + "torch.backends.cuda.can_use_cudnn_attention", + "torch.backends.cuda.enable_flash_sdp", + "torch.backends.cuda.enable_math_sdp", + "torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp", + "torch.backends.cuda.enable_mem_efficient_sdp", + "torch.backends.cuda.flash_sdp_enabled", + "torch.backends.cuda.is_built", + "torch.backends.cuda.is_flash_attention_available", + "torch.backends.cuda.math_sdp_enabled", + "torch.backends.cuda.fp16_bf16_reduction_math_sdp_allowed", + "torch.backends.cuda.mem_efficient_sdp_enabled", + "torch.backends.cuda.cudnn_sdp_enabled", + "torch.backends.cuda.enable_cudnn_sdp", + "torch.backends.cuda.preferred_blas_library", + "torch.backends.cuda.preferred_linalg_library", + "torch.backends.cuda.sdp_kernel", + "torch.backends.cudnn._init", + "torch.backends.cudnn.flags", + "torch.backends.cudnn.is_acceptable", + "torch.backends.cudnn.is_available", + "torch.backends.cudnn.set_flags", + "torch.backends.cudnn.version", + "torch.backends.disable_global_flags", + "torch.backends.flags_frozen", + "torch.backends.mkl.is_available", + "torch.backends.mkldnn.flags", + "torch.backends.mkldnn.is_available", + "torch.backends.mkldnn.set_flags", + "torch.backends.mps._init", + "torch.backends.mps.is_available", + "torch.backends.mps.is_built", + "torch.backends.mps.is_macos13_or_newer", + "torch.backends.openmp.is_available", + "torch.backends.quantized._get_qengine_id", + "torch.backends.quantized._get_qengine_str", + "torch.block_diag", + "torch.broadcast_tensors", + "torch.cartesian_prod", + "torch.cdist", + "torch.chain_matmul", + "torch.compile", + "torch.compiled_with_cxx11_abi", + "torch._C._cpu._is_avx2_supported", + "torch._C._cpu._is_avx512_supported", + "torch._C._cpu._is_avx512_vnni_supported", + "torch._C._cpu._is_avx512_bf16_supported", + "torch._C._cpu._is_amx_tile_supported", + "torch.cpu._init_amx", + "torch.cpu.current_device", + "torch.cpu.current_stream", + "torch.cpu.device_count", + "torch.cpu.is_available", + "torch.cpu.set_device", + "torch.cpu.stream", + "torch.cpu.synchronize", + "torch.cuda._check_capability", + "torch.cuda._check_cubins", + "torch.cuda._device_count_amdsmi", + "torch.cuda._device_count_nvml", + "torch.cuda._get_amdsmi_handler", + "torch.cuda._get_amdsmi_device_index", + "torch.cuda._get_device", + "torch.cuda._get_generator", + "torch.cuda._get_nvml_device_index", + "torch.cuda._get_pynvml_handler", + "torch.cuda._get_rng_state_offset", + "torch.cuda._is_compiled", + "torch.cuda._lazy_call", + "torch.cuda._lazy_init", + "torch.cuda._memory_viz._block_extra_legacy", + "torch.cuda._memory_viz._block_extra", + "torch.cuda._memory_viz._format_size", + "torch.cuda._memory_viz._format_viz", + "torch.cuda._memory_viz._frame_filter", + "torch.cuda._memory_viz._frame_fmt", + "torch.cuda._memory_viz._frames_fmt", + "torch.cuda._memory_viz._profile_to_snapshot", + "torch.cuda._memory_viz._report_free", + "torch.cuda._memory_viz._write_blocks", + "torch.cuda._memory_viz.calc_active", + "torch.cuda._memory_viz.compare", + "torch.cuda._memory_viz.format_flamegraph", + "torch.cuda._memory_viz.memory", + "torch.cuda._memory_viz.profile_plot", + "torch.cuda._memory_viz.segment_plot", + "torch.cuda._memory_viz.segments", + "torch.cuda._memory_viz.segsum", + "torch.cuda._memory_viz.trace_plot", + "torch.cuda._memory_viz.trace", + "torch.cuda._nvml_based_avail", + "torch.cuda._parse_visible_devices", + "torch.cuda._raw_device_count_amdsmi", + "torch.cuda._raw_device_count_nvml", + "torch.cuda._raw_device_uuid_amdsmi", + "torch.cuda._raw_device_uuid_nvml", + "torch.cuda._register_triton_kernels", + "torch.cuda._set_rng_state_offset", + "torch.cuda._set_stream_by_id", + "torch.cuda._sleep", + "torch.cuda._transform_uuid_to_ordinals", + "torch.cuda._utils._get_device_index", + "torch.cuda.amp.autocast_mode._cast", + "torch.cuda.amp.autocast_mode.custom_bwd", + "torch.cuda.amp.autocast_mode.custom_fwd", + "torch.cuda.amp.common.amp_definitely_not_available", + "torch.amp.grad_scaler._refresh_per_optimizer_state", + "torch.cuda.can_device_access_peer", + "torch.cuda.check_error", + "torch.cuda.clock_rate", + "torch.cuda.cudart", + "torch.cuda.current_blas_handle", + "torch.cuda.current_stream", + "torch.cuda.default_stream", + "torch.cuda.device_count", + "torch.cuda.get_arch_list", + "torch.cuda.get_device_capability", + "torch.cuda.get_device_name", + "torch.cuda.get_device_properties", + "torch.cuda.get_gencode_flags", + "torch.cuda.get_sync_debug_mode", + "torch.cuda.graphs.graph_pool_handle", + "torch.cuda.graphs.is_current_stream_capturing", + "torch.cuda.graphs.make_graphed_callables", + "torch.cuda.init", + "torch.cuda.ipc_collect", + "torch.cuda.is_available", + "torch.cuda.is_bf16_supported", + "torch.cuda.is_initialized", + "torch.cuda.jiterator._create_jit_fn", + "torch.cuda.jiterator._create_multi_output_jit_fn", + "torch.cuda.memory_usage", + "torch.cuda.memory._dump_snapshot", + "torch.cuda.memory._free_mutex", + "torch.cuda.memory._get_current_allocator", + "torch.cuda.memory._host_allocator", + "torch.cuda.memory._record_memory_history_impl", + "torch.cuda.memory._record_memory_history_legacy", + "torch.cuda.memory._record_memory_history", + "torch.cuda.memory._save_memory_usage", + "torch.cuda.memory._save_segment_usage", + "torch.cuda.memory._set_allocator_settings", + "torch.cuda.memory._snapshot", + "torch.cuda.memory.caching_allocator_alloc", + "torch.cuda.memory.caching_allocator_delete", + "torch.cuda.memory.change_current_allocator", + "torch.cuda.memory.empty_cache", + "torch.cuda.memory.get_allocator_backend", + "torch.cuda.memory.list_gpu_processes", + "torch.cuda.memory.max_memory_allocated", + "torch.cuda.memory.max_memory_cached", + "torch.cuda.memory.max_memory_reserved", + "torch.cuda.memory.mem_get_info", + "torch.cuda.memory.memory_allocated", + "torch.cuda.memory.memory_cached", + "torch.cuda.memory.memory_reserved", + "torch.cuda.memory.memory_snapshot", + "torch.cuda.memory.memory_stats_as_nested_dict", + "torch.cuda.memory.memory_stats", + "torch.cuda.memory.memory_summary", + "torch.cuda.memory.reset_accumulated_memory_stats", + "torch.cuda.memory.reset_max_memory_allocated", + "torch.cuda.memory.reset_max_memory_cached", + "torch.cuda.memory.reset_peak_memory_stats", + "torch.cuda.memory.set_per_process_memory_fraction", + "torch.cuda.nccl._check_sequence_type", + "torch.cuda.nccl.all_gather", + "torch.cuda.nccl.all_reduce", + "torch.cuda.nccl.broadcast", + "torch.cuda.nccl.init_rank", + "torch.cuda.nccl.is_available", + "torch.cuda.nccl.reduce_scatter", + "torch.cuda.nccl.reduce", + "torch.cuda.nccl.unique_id", + "torch.cuda.nccl.version", + "torch.cuda.nvtx.mark", + "torch.cuda.nvtx.range_end", + "torch.cuda.nvtx.range_pop", + "torch.cuda.nvtx.range_push", + "torch.cuda.nvtx.range_start", + "torch.cuda.nvtx.range", + "torch.cuda.power_draw", + "torch.cuda.profiler.init", + "torch.cuda.profiler.profile", + "torch.cuda.profiler.start", + "torch.cuda.profiler.stop", + "torch.cuda.random.get_rng_state_all", + "torch.cuda.random.initial_seed", + "torch.cuda.random.manual_seed_all", + "torch.cuda.random.manual_seed", + "torch.cuda.random.seed_all", + "torch.cuda.random.seed", + "torch.cuda.random.set_rng_state_all", + "torch.cuda.set_stream", + "torch.cuda.set_sync_debug_mode", + "torch.cuda.stream", + "torch.cuda.synchronize", + "torch.cuda.temperature", + "torch.cuda.utilization", + "torch.einsum", + "torch.functional._check_list_size", + "torch.functional._consecutive_return_counts", + "torch.functional._consecutive_return_inverse_false", + "torch.functional._consecutive_return_inverse_true", + "torch.functional._consecutive_return_inverse", + "torch.functional._consecutive_return_output", + "torch.functional._lu_impl", + "torch.functional._lu_no_infos", + "torch.functional._lu_with_infos", + "torch.functional._meshgrid", + "torch.functional._return_counts", + "torch.functional._return_inverse_false", + "torch.functional._return_inverse_true", + "torch.functional._return_inverse", + "torch.functional._return_output", + "torch.functional._unique_consecutive_impl", + "torch.functional._unique_impl", + "torch.functional._unravel_index", + "torch.functional.broadcast_shapes", + "torch.functional.lu", + "torch.functional.unique", + "torch.functional.unravel_index", + "torch.futures.collect_all", + "torch.futures.wait_all", + "torch.fx.experimental.const_fold.split_const_subgraphs", + "torch.fx.experimental.proxy_tensor.make_fx", + "torch.get_deterministic_debug_mode", + "torch.get_float32_matmul_precision", + "torch.is_deterministic_algorithms_warn_only_enabled", + "torch.is_storage", + "torch.is_tensor", + "torch.is_warn_always_enabled", + "torch.masked._ops._any", + "torch.masked._ops._apply_docstring_templates", + "torch.masked._ops._canonical_dim", + "torch.masked._ops._combine_input_and_mask", + "torch.masked._ops._generate_docstring", + "torch.masked._ops._input_mask", + "torch.masked._ops._output_mask", + "torch.masked._ops._reduction_identity", + "torch.masked._ops._sparse_coo_flatten_indices", + "torch.masked._ops._sparse_coo_scatter_reduction_helper", + "torch.masked._ops._sparse_coo_where", + "torch.masked._ops._sparse_csr_segment_reduction_helper", + "torch.masked._ops._sparse_csr_where", + "torch.masked._ops._std_var", + "torch.masked._ops._where", + "torch.masked._ops.amax", + "torch.masked._ops.amin", + "torch.masked._ops.argmax", + "torch.masked._ops.argmin", + "torch.masked._ops.corresponding_real_dtype", + "torch.masked._ops.cumprod", + "torch.masked._ops.cumsum", + "torch.masked._ops.log_softmax", + "torch.masked._ops.logaddexp", + "torch.masked._ops.logsumexp", + "torch.masked._ops.mean", + "torch.masked._ops.median", + "torch.masked._ops.norm", + "torch.masked._ops.normalize", + "torch.masked._ops.prod", + "torch.masked._ops.softmax", + "torch.masked._ops.softmin", + "torch.masked._ops.std", + "torch.masked._ops.sum", + "torch.masked._ops.var", + "torch.meshgrid", + "torch.mps._get_default_mps_generator", + "torch.mps.current_allocated_memory", + "torch.mps.driver_allocated_memory", + "torch.mps.empty_cache", + "torch.mps.get_rng_state", + "torch.mps.manual_seed", + "torch.mps.profiler.profile", + "torch.mps.profiler.start", + "torch.mps.profiler.stop", + "torch.mps.seed", + "torch.mps.set_per_process_memory_fraction", + "torch.mps.set_rng_state", + "torch.mps.synchronize", + "torch.nested._internal.nested_tensor.buffer_from_jagged", + "torch.nested._internal.nested_tensor.get_tensor_symint", + "torch.nested._internal.nested_tensor.is_expandable_to", + "torch.nested._internal.nested_tensor.jagged_from_list", + "torch.nested._internal.nested_tensor.jagged_from_tensor_and_lengths", + "torch.nested._internal.nested_tensor.nested_view_from_values_offsets", + "torch.nested._internal.nested_tensor.nested_view_from_values_offsets_lengths", + "torch.nested.as_nested_tensor", + "torch.nested.narrow", + "torch.nested.nested_tensor", + "torch.nn._reduction.get_enum", + "torch.nn._reduction.legacy_get_enum", + "torch.nn._reduction.legacy_get_string", + "torch.nn.factory_kwargs", + "torch.nn.functional.adaptive_avg_pool2d", + "torch.nn.functional.adaptive_avg_pool3d", + "torch.nn.functional.adaptive_max_pool1d_with_indices", + "torch.nn.functional.adaptive_max_pool1d", + "torch.nn.functional.adaptive_max_pool2d_with_indices", + "torch.nn.functional.adaptive_max_pool2d", + "torch.nn.functional.adaptive_max_pool3d_with_indices", + "torch.nn.functional.adaptive_max_pool3d", + "torch.nn.functional.affine_grid", + "torch.nn.functional.alpha_dropout", + "torch.nn.functional.assert_int_or_pair", + "torch.nn.functional.batch_norm", + "torch.nn.functional.binary_cross_entropy_with_logits", + "torch.nn.functional.binary_cross_entropy", + "torch.nn.functional.celu", + "torch.nn.functional.cosine_embedding_loss", + "torch.nn.functional.cross_entropy", + "torch.nn.functional.ctc_loss", + "torch.nn.functional.dropout", + "torch.nn.functional.dropout1d", + "torch.nn.functional.dropout2d", + "torch.nn.functional.dropout3d", + "torch.nn.functional.elu", + "torch.nn.functional.embedding_bag", + "torch.nn.functional.embedding", + "torch.nn.functional.feature_alpha_dropout", + "torch.nn.functional.fold", + "torch.nn.functional.fractional_max_pool2d_with_indices", + "torch.nn.functional.fractional_max_pool2d", + "torch.nn.functional.fractional_max_pool3d_with_indices", + "torch.nn.functional.fractional_max_pool3d", + "torch.nn.functional.gaussian_nll_loss", + "torch.nn.functional.glu", + "torch.nn.functional.grid_sample", + "torch.nn.functional.group_norm", + "torch.nn.functional.gumbel_softmax", + "torch.nn.functional.hardsigmoid", + "torch.nn.functional.hardswish", + "torch.nn.functional.hardtanh", + "torch.nn.functional.hinge_embedding_loss", + "torch.nn.functional.huber_loss", + "torch.nn.functional.instance_norm", + "torch.nn.functional.interpolate", + "torch.nn.functional.kl_div", + "torch.nn.functional.l1_loss", + "torch.nn.functional.layer_norm", + "torch.nn.functional.leaky_relu", + "torch.nn.functional.local_response_norm", + "torch.nn.functional.log_softmax", + "torch.nn.functional.lp_pool1d", + "torch.nn.functional.lp_pool2d", + "torch.nn.functional.margin_ranking_loss", + "torch.nn.functional.max_pool1d_with_indices", + "torch.nn.functional.max_pool1d", + "torch.nn.functional.max_pool2d_with_indices", + "torch.nn.functional.max_pool2d", + "torch.nn.functional.max_pool3d_with_indices", + "torch.nn.functional.max_pool3d", + "torch.nn.functional.max_unpool1d", + "torch.nn.functional.max_unpool2d", + "torch.nn.functional.max_unpool3d", + "torch.nn.functional.mish", + "torch.nn.functional.mse_loss", + "torch.nn.functional.multi_head_attention_forward", + "torch.nn.functional.multi_margin_loss", + "torch.nn.functional.multilabel_margin_loss", + "torch.nn.functional.multilabel_soft_margin_loss", + "torch.nn.functional.nll_loss", + "torch.nn.functional.normalize", + "torch.nn.functional.poisson_nll_loss", + "torch.nn.functional.relu", + "torch.nn.functional.relu6", + "torch.nn.functional.rrelu", + "torch.nn.functional.selu", + "torch.nn.functional.sigmoid", + "torch.nn.functional.silu", + "torch.nn.functional.smooth_l1_loss", + "torch.nn.functional.soft_margin_loss", + "torch.nn.functional.softmax", + "torch.nn.functional.softmin", + "torch.nn.functional.softsign", + "torch.nn.functional.tanh", + "torch.nn.functional.tanhshrink", + "torch.nn.functional.triplet_margin_loss", + "torch.nn.functional.unfold", + "torch.nn.functional.upsample_bilinear", + "torch.nn.functional.upsample_nearest", + "torch.nn.functional.upsample", + "torch.nn.grad._pair", + "torch.nn.grad._single", + "torch.nn.grad._triple", + "torch.nn.grad.conv1d_input", + "torch.nn.grad.conv1d_weight", + "torch.nn.grad.conv2d_input", + "torch.nn.grad.conv2d_weight", + "torch.nn.grad.conv3d_input", + "torch.nn.grad.conv3d_weight", + "torch.nn.modules.activation._is_make_fx_tracing", + "torch.nn.modules.utils._list_with_default", + "torch.nn.modules.utils._ntuple", + "torch.nn.modules.utils._quadruple", + "torch.nn.modules.utils._reverse_repeat_tuple", + "torch.nn.modules.utils.consume_prefix_in_state_dict_if_present", + "torch.nn.parameter.is_lazy", + "torch.norm", + "torch.quantization.default_eval_fn", + "torch.random._seed_custom_device", + "torch.random.fork_rng", + "torch.random.initial_seed", + "torch.random.seed", + "torch.return_types.pytree_register_structseq", + "torch.set_default_device", + "torch.set_default_dtype", + "torch.set_default_tensor_type", + "torch.set_deterministic_debug_mode", + "torch.set_float32_matmul_precision", + "torch.set_warn_always", + "torch.signal.windows.windows._add_docstr", + "torch.signal.windows.windows._window_function_checks", + "torch.signal.windows.windows.bartlett", + "torch.signal.windows.windows.blackman", + "torch.signal.windows.windows.cosine", + "torch.signal.windows.windows.exponential", + "torch.signal.windows.windows.gaussian", + "torch.signal.windows.windows.general_cosine", + "torch.signal.windows.windows.general_hamming", + "torch.signal.windows.windows.hamming", + "torch.signal.windows.windows.hann", + "torch.signal.windows.windows.kaiser", + "torch.signal.windows.windows.merge_dicts", + "torch.signal.windows.windows.nuttall", + "torch.signal.windows.windows.parse_kwargs", + "torch.sparse.semi_structured.to_sparse_semi_structured", + "torch.sparse.sum", + "torch.split", + "torch.stft", + "torch.sym_float", + "torch.sym_int", + "torch.sym_ite", + "torch.sym_max", + "torch.sym_min", + "torch.sym_not", + "torch.tensordot", + "torch.typename", + "torch.unique_consecutive", + "torch.use_deterministic_algorithms", + ], + TorchInGraphFunctionVariable, +) + + +torch_name_rule_map = [ + manual_torch_name_rule_map, + torch_c_binding_in_graph_functions, + torch_non_c_binding_in_graph_functions, +] + + +""" +Generate the torch object - Dynamo tracing rule (the wrapping variable) map. +""" + + +@functools.lru_cache(None) +def get_torch_obj_rule_map() -> Dict[Any, Type["VariableTracker"]]: + d: Dict[Any, Type[VariableTracker]] = {} + for m in torch_name_rule_map: + for k, v in m.items(): # type: ignore[attr-defined] + if ".py#" not in k: + obj = load_object(k) + else: + obj = _module_dir(torch) + k[len("torch/") :] + if obj is not None: + if obj in d and d[obj] != v: + raise AssertionError( + f"Duplicate torch object {obj} with different rules: {v}, {d[obj]}" + ) + else: + d[obj] = v + return d + + +def _load_obj_from_str(fully_qualified_name): + module, obj_name = fully_qualified_name.rsplit(".", maxsplit=1) + return getattr(importlib.import_module(module), obj_name) + + +""" +Load string represented torch objects. +""" + + +def load_object(name): + try: + x = name.split("#") + if len(x) == 2: + obj = _load_obj_from_str(x[0]) + val = getattr(obj, x[1]) + else: + assert len(x) == 1, f"Invalid obj name {name}" + val = _load_obj_from_str(x[0]) + val = unwrap_if_wrapper(val) + except (AttributeError, ImportError): + val = None + return val + + +""" +Get all torch.Tensor methods which are allowed to be in graph functions. +""" + + +@functools.lru_cache(None) +def get_tensor_method(): + s = set() + for name in dir(torch.Tensor): + method = getattr(torch.Tensor, name) + if isinstance( + method, (types.MethodDescriptorType, types.WrapperDescriptorType) + ): + s.add(method) + return frozenset(s) + + +""" +Return if a torch object is ATen op or torch.Tensor method. +""" + + +def is_aten_op_or_tensor_method(obj): + return obj in get_tensor_method() or isinstance( + obj, + (torch._ops.OpOverloadPacket, torch._ops.OpOverload), + ) + + +class FunctionIdSet: + """ + Track a set of `id()`s of objects which are either allowed or not + allowed to go into the generated FX graph. Use to test for torch.*, + numpy.*, builtins.*, etc. + + Support user modification to permit customization of what can be + added to the graph and what will cause a graph break. + """ + + function_ids: Optional[Set[int]] = None + function_names: Optional[Dict[int, str]] = None + + def __init__( + self, lazy_initializer: Callable[[], Union[Dict[int, str], Set[int]]] + ) -> None: + self.lazy_initializer = lazy_initializer + + def __call__(self) -> Set[int]: + if self.function_ids is None: + value = self.lazy_initializer() + if isinstance(value, dict): + self.function_ids = set(value.keys()) + self.function_names = value + else: + assert isinstance(value, set) + self.function_ids = value + return self.function_ids + + def get_name(self, idx: int, default: str): + self() # lazy init + assert self.function_names is not None + return self.function_names.get(idx, default) + + def add(self, idx: int): + function_ids = self() # lazy init + function_ids.add(idx) + + def remove(self, idx: int): + function_ids = self() + if idx in function_ids: + function_ids.remove(idx) + + def __contains__(self, idx: int) -> bool: + return idx in self() + + +@FunctionIdSet +def _allowed_callable_ids() -> Dict[int, str]: + rv: Dict[int, str] = {} + return rv + + +@FunctionIdSet +def _disallowed_callable_ids() -> Dict[int, str]: + rv: Dict[int, str] = {} + return rv + + +@FunctionIdSet +def _builtin_function_ids() -> Dict[int, str]: + # See also torch/_dynamo/polyfills/loader.py, which removes items in _builtin_function_ids + rv = { + id(v): f"builtins.{k}" + for k, v in builtins.__dict__.items() + if not k.startswith("_") and callable(v) + } + rv.update( + { + id(v): f"operator.{k}" + for k, v in operator.__dict__.items() + if not k.startswith("_") and callable(v) + } + ) + rv.update( + { + id(cast): "typing.cast", + id(functools.reduce): "functools.reduce", + id(copy.deepcopy): "copy.deepcopy", + } + ) + return rv + + +@FunctionIdSet +def _numpy_function_ids() -> Dict[int, str]: + rv = {} + for mod in NP_SUPPORTED_MODULES: + rv.update( + { + id(v): f"{mod.__name__}.{k}" + for k, v in mod.__dict__.items() + if callable(v) + and (getattr(v, "__module__", None) or mod.__name__) == mod.__name__ + } + ) + return rv + + +@FunctionIdSet +def _builtin_constant_ids() -> Dict[int, str]: + """ + Collects constant builtins by eliminating callable items. + """ + rv = { + id(v): f"builtins.{k}" + for k, v in builtins.__dict__.items() + if not k.startswith("_") and not callable(v) + } + return rv + + +_lazy_module_init: Dict[str, List[Callable[[], None]]] = defaultdict(list) + + +def add_module_init_func(name: str, init_func: Callable[[], None]) -> None: + """Register a module without eagerly importing it""" + # If the module is already imported, eagerly run init + assert "." not in name, f"Expected a root module name, but got {name}" + assert name not in _lazy_module_init + _lazy_module_init[name].append(init_func) + + +def _maybe_init_lazy_module(obj: object) -> None: + module = getattr(obj, "__module__", None) + if module is None: + return + + base_module = module.split(".")[0] + init_funcs = _lazy_module_init.pop(base_module, None) + if init_funcs is not None: + for fn in init_funcs: + fn() + + +def is_callable_allowed(obj) -> bool: + _maybe_init_lazy_module(obj) + return id(obj) in _allowed_callable_ids + + +def is_callable_disallowed(obj) -> bool: + _maybe_init_lazy_module(obj) + return id(obj) in _disallowed_callable_ids + + +def is_forbidden(obj) -> bool: + _maybe_init_lazy_module(obj) + return inspect.getattr_static(obj, "_dynamo_forbidden", False) + + +def is_builtin_callable(obj) -> bool: + # See also torch/_dynamo/polyfills/loader.py, which removes items in _builtin_function_ids + return id(obj) in _builtin_function_ids + + +def is_builtin_constant(obj) -> bool: + return id(obj) in _builtin_constant_ids + + +def is_numpy(obj) -> bool: + if np is None: + return False + return isinstance(obj, (np.ndarray, np.generic)) or id(obj) in _numpy_function_ids + + +def is_numpy_dtype(obj) -> bool: + if np is None: + return False + return isinstance(obj, np.dtype) + + +def is_numpy_type_info(obj) -> bool: + if np is None: + return False + return isinstance(obj, (np.finfo, np.iinfo)) + + +BUILTIN_SKIPLIST = ( + abc, + collections, + contextlib, + copy, + copyreg, + dataclasses, + enum, + functools, + importlib, + inspect, + linecache, + logging, + multiprocessing, + operator, + posixpath, + random, + re, + selectors, + signal, + tempfile, + threading, + tokenize, + torch, # torch/* is skipped by default unless specified in FUNC_INLINELIST or MOD_INLINELIST + traceback, + types, + typing, + unittest, + weakref, + _collections_abc, + _weakrefset, +) + +# third party libraries skiplist is defined by str, because users may not use these libraries. +# we should use lazy import & skip in the future. +THIRDPARTY_SKIPLIST = ( + "fx2trt_oss", + "hypothesis", + "networkx", + "numpy", + "omegaconf", + "onnx", + "onnxruntime", + "onnx_tf", + "pandas", + "sklearn", + "tabulate", + "tensorflow", + "tensorrt", + "torch2trt", + "tqdm", + "tree", + "tvm", + "xarray", +) + + +def _as_posix_path(path): + posix_path = Path(os.path.normpath(path)).as_posix() + # os.path.normpath and pathlib.Path remove trailing slash, so we need to add it back + if path.endswith((os.path.sep, "/")): + posix_path += "/" + return posix_path + + +def _strip_init_py(s): + # TODO: Once we require py3.9 use removesuffix instead. + suffix = "__init__.py" + if s.endswith(suffix): + s = s[: -len(suffix)] + return _as_posix_path(s) + + +def _module_dir(m: types.ModuleType): + # Protect against a module not exporting __file__ - this can happen for + # frozen modules, for example. + file = getattr(m, "__file__", None) + return file and _strip_init_py(file) + + +# These are legacy workarounds, don't add new modules to this list. +# Please use the MOD_INLINELIST instead to force inline functions under particular modules. +LEGACY_MOD_INLINELIST = { + "torch._dynamo.external_utils", + "torch._export.db.examples", + "torch._export.wrappers", + "torch._functorch.apis", + "torch._functorch.deprecated", + "torch._higher_order_ops.cond", + "torch._higher_order_ops.while_loop", + "torch._higher_order_ops.associative_scan", + "torch.nn.attention.flex_attention", + "torch.ao.quantization.pt2e.export_utils", + "torch.ao.quantization.pt2e.qat_utils", + "torch.ao.quantization.pt2e.representation.rewrite", + "torch.ao.quantization.pt2e.utils", + "torch.ao.quantization.quantizer.xnnpack_quantizer", + "torch.export.unflatten", + "torch.optim", +} + +if torch.distributed.is_available(): + LEGACY_MOD_INLINELIST |= { + "torch.distributed.tensor._api", + "torch.distributed.tensor.device_mesh", + "torch.distributed.device_mesh", + "torch.distributed.algorithms._checkpoint.checkpoint_wrapper", + "torch.distributed.tensor.parallel._data_parallel_utils", + "torch.distributed.tensor.parallel._utils", + "torch.distributed.tensor.parallel.style", + # we have to add replicate to LEGACY_MOD_INLINELIST to ensure + # the forward_hook won't be ignored. + "torch.distributed._composable.replicate", + } + if not torch._dynamo.config.skip_fsdp_hooks: + LEGACY_MOD_INLINELIST.add("torch.distributed._composable.fsdp") + + +# Force inline functions under these modules, even they are in *_SKIPLIST. +# We are using python module name instead of file or directory object to avoid circular dependency. +# Please keep this sorted alphabetically. +MOD_INLINELIST = [ + "torch._decomp", + "torch._dynamo._trace_wrapped_higher_order_op", + "torch._dynamo.comptime", + "torch._dynamo.polyfills", + "torch._functorch.autograd_function", + "torch._functorch.eager_transforms", + "torch._functorch.functional_call", + "torch._functorch.vmap", + "torch._higher_order_ops.associative_scan", + "torch._higher_order_ops.strict_mode", + "torch._higher_order_ops.while_loop", + "torch._inductor.test_operators", + "torch._library.autograd", + "torch._library.custom_ops", + "torch._prims", + "torch._refs", + "torch._tensor", + "torch.amp.autocast_mode", + "torch.ao.nn", + "torch.autograd.function", + "torch.backends.cuda", + "torch.cuda.amp.autocast_mode", + "torch.distributions", + "torch.export._tree_utils", + "torch.fx._pytree", + "torch.fx._symbolic_trace", + "torch.fx.experimental.proxy_tensor", + "torch.fx.passes.shape_prop", + "torch.nn", + "torch.overrides", + "torch.random", + "torch.sparse", + "torch.testing", + "torch.utils._content_store", + "torch.utils._contextlib", + "torch.utils._foreach_utils", + "torch.utils._python_dispatch", + "torch.utils._pytree", + "torch.utils.hooks", +] +assert sorted(set(MOD_INLINELIST)) == MOD_INLINELIST +MOD_INLINELIST = set(MOD_INLINELIST) + + +if torch.distributed.is_available(): + MOD_INLINELIST.add("torch.distributed") + if not torch._dynamo.config.skip_fsdp_hooks: + MOD_INLINELIST.add("torch.distributed._composable.fsdp") + + +@functools.lru_cache(None) +def get_legacy_mod_inlinelist(): + inlinelist = { + _as_posix_path(_module_dir(torch) + m[len("torch.") :].replace(".", "/")) + for m in LEGACY_MOD_INLINELIST + } + return inlinelist + + +@functools.lru_cache(None) +def get_mod_inlinelist(): + inlinelist = { + _as_posix_path(_module_dir(torch) + m[len("torch.") :].replace(".", "/")) + for m in MOD_INLINELIST + } + return inlinelist + + +# skip some standard python builtin libs +SKIP_DIRS = [ + "", + _as_posix_path(_config_module.__file__), + "triton/backends", +] +SKIP_DIRS.extend(map(_as_posix_path, filter(None, map(_module_dir, BUILTIN_SKIPLIST)))) + +SKIP_DIRS_RE = re.compile(r"match nothing^") + +is_fbcode = importlib.import_module("torch._inductor.config").is_fbcode() +# Skip fbcode paths(including torch.package paths) containing +# one of the following strings. +FBCODE_SKIP_DIRS: Set[str] = set() + +FBCODE_SKIP_DIRS_RE = re.compile(f".*({'|'.join(map(re.escape, FBCODE_SKIP_DIRS))})") + +# Remove this after fbcode is fully migrated to tracing through torchrec. +FBCODE_SKIP_TORCHREC_DIRS = { + "torchrec/distributed", + "trochrec/fb/distributed", + "caffe2/torch/fb/sparsenn/pooled_embeddings_modules.py", +} + +FBCODE_SKIP_TORCHREC_DIRS_RE = re.compile( + f".*({'|'.join(re.escape(_as_posix_path(d)) for d in FBCODE_SKIP_TORCHREC_DIRS)})" +) + +# TODO(yanboliang, anijain2305) - There are a few concerns that we should +# resolve +# 1) Audit if torchrec/distributed is even required in FBCODE_SKIPS_DIR +# 2) To inline just one file but skip others in a directory, we could use +# manual_torch_name_rule_map but this one is hard because FBCODE can add unusual +# names like torch_package. +# So, this is a stop gap solution till then. +FBCODE_INLINE_FILES_IN_SKIPPED_DIRS = { + "torchrec/distributed/types.py", +} +FBCODE_INLINE_FILES_IN_SKIPPED_DIRS_RE = re.compile( + f".*({'|'.join(re.escape(_as_posix_path(d)) for d in FBCODE_INLINE_FILES_IN_SKIPPED_DIRS)})" +) + +# torch.optim is a special case, +# we usually want to inline it, but the directory +# structure does not match the module structure +# and we want to skip the functions in optim/lr_scheduler.py +# this has precedence over all other rules in check_file +FORCE_SKIP_FILES = {f"{_module_dir(torch)}optim/lr_scheduler.py"} + + +def _recompile_re(): + global SKIP_DIRS_RE + SKIP_DIRS_RE = re.compile( + rf"^[^\s<]*({'|'.join(re.escape(_as_posix_path(d)) for d in SKIP_DIRS)})" + ) + + +def add(import_name: str): + if isinstance(import_name, types.ModuleType): + return add(import_name.__name__) + assert isinstance(import_name, str) + from importlib.util import find_spec + + module_spec = find_spec(import_name) + if not module_spec: + return + origin = module_spec.origin + if origin is None: + return + SKIP_DIRS.append(_strip_init_py(origin)) + _recompile_re() + + +@dataclasses.dataclass +class SkipResult: + skipped: bool + reason: Optional[str] + + +def check_file(filename, is_inlined_call=False): + """Should skip this file?""" + if filename is None: + return SkipResult(True, "filename is None") + filename = _as_posix_path(filename) + if filename in FORCE_SKIP_FILES: + return SkipResult(True, "FORCE_SKIP_FILES") + if any(filename.startswith(d) for d in get_legacy_mod_inlinelist()): + return SkipResult( + False, + "LEGACY_MOD_INLINELIST", + ) + if is_inlined_call and is_torch_inline_allowed(filename): + return SkipResult( + False, + "MOD_INLINELIST", + ) + if ( + is_fbcode + and FBCODE_SKIP_DIRS + and bool(FBCODE_SKIP_DIRS_RE.match(filename)) + and not bool(FBCODE_INLINE_FILES_IN_SKIPPED_DIRS_RE.match(filename)) + ): + return SkipResult( + True, + "FBCODE_SKIP_DIRS", + ) + + if ( + is_fbcode + and torch._dynamo.config.skip_torchrec + and FBCODE_SKIP_TORCHREC_DIRS + and bool(FBCODE_SKIP_TORCHREC_DIRS_RE.match(filename)) + and not bool(FBCODE_INLINE_FILES_IN_SKIPPED_DIRS_RE.match(filename)) + ): + return SkipResult(True, "FBCODE_SKIP_TORCHREC_DIRS") + + if bool(SKIP_DIRS_RE.match(filename)): + return SkipResult(True, "SKIP_DIRS") + else: + return SkipResult(False, "inlined by default") + + +@dataclasses.dataclass +class FunctionInfo: + py_obj: Optional[object] + name: Optional[str] + filename: str + code: Optional[types.CodeType] + + +""" +This is the main entry point to determine whether an object (function) should be inlined or skipped. +Let's illustrate the logic with an example: + @torch.compile + def f1(x, y): + ...... + f2(x, y) + ...... + + def f2(x, y): + ...... + f3(x, y) + ...... + + def f3(x, y): + ...... + +There are mainly three call sites of check/check_verbose: +* The compile region entrance (like function f1), the correspoinding code is located at eval_frame.py. +* When tracing the recursively called functions (like function f2 and f3). + * Dynamo decides inline/skip everytime it encounters a new recursively function call, and the call site + is in InliningInstructionTranslator.check_inlineable of symbolic_convert.py. + * If f2 is skipped by Dynamo, when evaluating the frame of f3, Dynamo need the inline/skip check again + and the call site is in catch_errors_wrapper.catch_errors of convert_frame.py. +* For global variables and function arguments, Dynamo needs to decide if they are wrapped as SkipFunctionVariable in builder.py. + +`is_inlined_call` is used to indicate if the current function call is inlined (f2 is inlined call if it passes check) +or not (f3 is not inlined call if f2 is skipped). Inside of the `check_verbose` function, there are more rules +to be checked if this `is_inlined_call`. +The reason to have this flag is that if the upper level function call (e.g, f2) is skipped, +we don't want to inline the lower level function call (e.g, f3) by default. +""" + + +def check_verbose(obj, is_inlined_call=False): + if isinstance( + obj, (UserFunctionVariable, UserMethodVariable, NestedUserFunctionVariable) + ): + try: + py_obj = obj.get_function() + except NotImplementedError: + py_obj = None + fi = FunctionInfo(py_obj, obj.get_name(), obj.get_filename(), obj.get_code()) + elif isinstance(obj, types.CodeType): + fi = FunctionInfo(None, obj.co_name, obj.co_filename, obj) + elif isinstance(obj, (types.FunctionType, types.MethodType)): + fi = FunctionInfo( + obj, obj.__name__, getfile(obj), obj.__code__ # type: ignore[union-attr] # FIXME Add MethodType.__code__ to typeshed + ) + else: + fi = FunctionInfo(obj, None, getfile(obj), None) + + # Consulte the central trace rules defined in torch._dynamo.trace_rules. + reasons: Set[str] = set() + rule = lookup_inner(fi.py_obj, fi.name, fi.filename, is_inlined_call, reasons) + if issubclass(rule, (UserFunctionVariable, PolyfilledFunctionVariable)): + return SkipResult( + False, + f"inlined according trace_rules.lookup {reasons.pop()}", + ) + else: + assert rule == SkipFunctionVariable, rule + return SkipResult( + True, + f"skipped according trace_rules.lookup {reasons.pop()}", + ) + + +def check(obj, is_inlined_call=False): + return check_verbose(obj, is_inlined_call).skipped + + +# skip common third party libs +for _name in THIRDPARTY_SKIPLIST: + add(_name) + +_recompile_re() + + +def is_torch_inline_allowed(filename): + return any(filename.startswith(d) for d in get_mod_inlinelist()) + + +@functools.lru_cache(None) +def dynamo_dir(): + import torch._dynamo + + return _module_dir(torch._dynamo) + + +def is_torch(filename): + if filename.startswith(dynamo_dir()): + return False + return filename.startswith(_module_dir(torch)) + + +""" +Main entry point for looking up the trace rule (the Dynamo variable) for a given callable object. +""" + + +def lookup_callable(obj): + if not hashable(obj): + return None + # Custom allow/disallow in graph takes precedence over the general lookup. + if is_callable_disallowed(obj): + return SkipFunctionVariable + if is_callable_allowed(obj): + return TorchInGraphFunctionVariable + if is_builtin_callable(obj): + return BuiltinVariable + return None + + +""" +Main entry point for looking up the trace rule (the Dynamo variable) for a given function object. +E.g, the lookup result of `torch.sin` is `TorchInGraphFunctionVariable`. +""" + + +def lookup(obj): + return lookup_inner(obj) + + +def lookup_inner( + obj, + name=None, + filename=None, + is_direct_call=True, + reasons: Union[None, Set[str]] = None, +): + # Step 1: lookup obj's tracing rule in `torch_name_rule_map`. + # The rules defined in `torch_name_rule_map` mainly includes two parts: + # - Manually defined rules for any functions. + # - The list of torch in graph functions. + try: + can_hash = hashable(obj) + except Exception: + can_hash = False + if not can_hash: + if reasons is not None: + reasons.add("obj is not hashable") + return None + if obj is not None: + if is_aten_op_or_tensor_method(obj): + return TorchInGraphFunctionVariable + rule = get_torch_obj_rule_map().get(obj, None) + if rule is not None: + if reasons is not None: + reasons.add("get_torch_obj_rule_map") + return rule + elif name is not None and filename is not None and not is_direct_call: + if name.startswith(TORCH_DYNAMO_RESUME_IN_PREFIX): + rule = get_torch_obj_rule_map().get( + filename + "#" + TORCH_DYNAMO_RESUME_IN_PREFIX, None + ) + else: + rule = get_torch_obj_rule_map().get(filename + "#" + name, None) + if rule is not None: + if reasons is not None: + reasons.add("get_torch_obj_rule_map") + return rule + + # Step 2: lookup obj's tracing rule by function name. + if is_direct_call: + if name == "patched_init": + if reasons is not None: + reasons.add("func name is patched_init") + return SkipFunctionVariable + elif name == "__torch_function__": + if reasons is not None: + reasons.add("func name is __torch_function__") + return UserFunctionVariable + + if not is_direct_call: + if name == "__getattr__": + # is_direct_call = False indicates that this is the top-level frame + # being traced (i.e., it is not inlined and not called from + # InliningInstructionTranslator). Tracing __getattr__ at the top + # level is unlikely because we inline it for + # UserDefinedObjectVariable. This scenario occurs only for + # UnspecializedNNModuleVariable, where Dynamo directly calls + # __getattr__ during trace time, generating LOAD_ATTR bytecode + # without going through the underlying __getattr__ data structures. + # When this optimized bytecode is executed, Dynamo is triggered + # again on the __getattr__ call. Therefore, we skip Dynamo tracing + # in this case. + if reasons is not None: + reasons.add( + "Tracing __getattr__ as the top level frame, unsuitable for tracing." + ) + return SkipFunctionVariable + + # Step 3: lookup obj's tracing rule by filename. + if filename is None: + filename = getfile(obj) + + skip_result = check_file(filename, is_direct_call) + if reasons is not None: + reasons.add(skip_result.reason) + if skip_result.skipped: + return SkipFunctionVariable + else: + return UserFunctionVariable + + +def clear_lru_cache(): + torch._dynamo.trace_rules.get_torch_obj_rule_map.cache_clear() + torch._dynamo.trace_rules.get_tensor_method.cache_clear() + torch._dynamo.trace_rules.get_legacy_mod_inlinelist.cache_clear() + torch._dynamo.trace_rules.get_mod_inlinelist.cache_clear() + torch._dynamo.trace_rules.dynamo_dir.cache_clear() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/types.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/types.py new file mode 100644 index 0000000000000000000000000000000000000000..8cab8ed5197fc354970080a072c7d50a2171b906 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/types.py @@ -0,0 +1,96 @@ +import dataclasses +import sys +import types +from typing import Any, Callable, Dict, List, NamedTuple, Optional, Protocol, Union + +# CacheEntry has a `check_fn` field for the guard, and a `code` field for the code object. +from torch._C._dynamo.eval_frame import ( + _CacheEntry as CacheEntry, + _ExtraState as ExtraState, +) +from torch._guards import CompileId + + +if sys.version_info >= (3, 11): + from torch._C._dynamo.eval_frame import _PyInterpreterFrame as DynamoFrameType +else: + from types import FrameType as DynamoFrameType + + +# We use a dict to store additional data per frame. +FrameState = Dict[Any, Any] + + +class GuardFail(NamedTuple): + # A string repr of the piece of failed guard code we eval-ed + reason: str + # A code object where we failed a guard + orig_code: types.CodeType + + +class GuardFn(Protocol): + closure_vars: Dict[str, object] + args: List[str] + code_parts: List[str] + verbose_code_parts: List[str] + global_scope: Dict[str, object] + guard_fail_fn: Optional[Callable[[GuardFail], None]] + cache_entry: Optional[CacheEntry] + extra_state: Optional[ExtraState] + + # maps locals of user function to bool + def __call__(self, f_locals: Dict[str, object]) -> bool: + ... + + +@dataclasses.dataclass +class GuardedCode: + code: types.CodeType + check_fn: GuardFn + compile_id: CompileId + + +class DynamoCallbackFn(Protocol): + def __call__( + self, + frame: DynamoFrameType, + cache_entry: Optional[CacheEntry], + frame_state: FrameState, + ) -> Optional[GuardedCode]: + ... + + +DynamoCallback = Union[DynamoCallbackFn, None, bool] + + +class DynamoGuardHook(Protocol): + def __call__( + self, + guard_fn: GuardFn, + code: types.CodeType, + f_locals: Dict[str, object], + index: int, + last: bool, + ) -> None: + ... + + +class ProfilerStartHook(Protocol): + def __call__( + self, + name: str, + # TODO(whc) how do I annotate a _RecordFunction here? + ) -> Any: + ... + + +class ProfilerEndHook(Protocol): + def __call__(self, record: Any) -> None: + ... + + +class BytecodeHook(Protocol): + def __call__( + self, code: types.CodeType, new_code: types.CodeType + ) -> Optional[types.CodeType]: + ... diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7b0cbbb75d2a192abdadd3c131cefb726a8e63b5 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_dynamo/utils.py @@ -0,0 +1,3181 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import atexit +import collections +import contextlib +import copy +import dataclasses +import datetime +import dis +import enum +import functools +import gc +import importlib +import inspect +import itertools +import linecache +import logging +import math +import operator +import os +import re +import sys +import threading +import time +import types +import typing +import uuid +import warnings +import weakref +from contextlib import contextmanager +from dataclasses import is_dataclass +from functools import lru_cache +from types import MethodWrapperType +from typing import ( + Any, + Callable, + cast, + ClassVar, + Counter, + DefaultDict, + Deque, + Dict, + Iterable, + Iterator, + KeysView, + List, + Optional, + overload, + Set, + Tuple, + Type, + TypeVar, + Union, + ValuesView, +) +from typing_extensions import Literal, TypeGuard + +import torch +import torch._functorch.config +import torch._inductor.config as inductor_config +import torch.fx.experimental.symbolic_shapes +import torch.utils._pytree as pytree +from torch import fx +from torch._C import ( + _get_function_stack_at, + _instruction_counter, + _len_torch_function_stack, + _pop_torch_function_stack, + _push_on_torch_function_stack, +) +from torch._dispatch.python import enable_python_dispatcher +from torch._guards import Source, TracingContext +from torch._subclasses.meta_utils import is_sparse_compressed +from torch._utils_internal import log_chromium_event_internal, log_compilation_event +from torch.fx._utils import _format_graph_code, lazy_format_graph_code +from torch.nn.modules.lazy import LazyModuleMixin +from torch.utils._triton import has_triton, has_triton_package +from torch.utils.hooks import RemovableHandle + + +try: + import numpy as np +except ModuleNotFoundError: + np = None # type: ignore[assignment] + +try: + import torch._logging + import torch._numpy as tnp + from torch._guards import detect_fake_mode # noqa: F401n + from torch._logging import LazyString + + from . import config + + # NOTE: Make sure `NP_SUPPORTED_MODULES` and `NP_TO_TNP_MODULE` are in sync. + if np: + NP_SUPPORTED_MODULES: Tuple[types.ModuleType, ...] = ( + np, + np.fft, + np.linalg, + np.random, + ) + + NP_TO_TNP_MODULE = { + np: tnp, + np.fft: tnp.fft, + np.linalg: tnp.linalg, + np.random: tnp.random, + } + else: + NP_SUPPORTED_MODULES = () + + NP_TO_TNP_MODULE = {} + from torch._subclasses.fake_tensor import FakeTensor, is_fake, maybe_get_fake_mode +except ImportError: + pass + + +T = TypeVar("T") + +unpatched_nn_module_getattr = torch.nn.Module.__getattr__ + +counters: DefaultDict[str, Counter[str]] = collections.defaultdict(collections.Counter) +optimus_scuba_log: Dict[str, Any] = {} +troubleshooting_url = ( + "https://pytorch.org/docs/main/torch.compiler_troubleshooting.html" +) +nnmodule_doc_url = "https://pytorch.org/docs/main/torch.compiler_nn_module.html" +nnmodule_doc_url_msg = f"See {nnmodule_doc_url} for more information and limitations." +log = logging.getLogger(__name__) + +# profiling compilation time by function +compilation_time_metrics: Dict[str, List[float]] = {} + +# profiling compilation time by frame phase +frame_phase_timing: Dict[str, Dict[str, float]] = collections.defaultdict( + lambda: collections.defaultdict(float) +) + +timer_counter = itertools.count() + + +def tabulate( + rows: Union[List[Tuple[str, object]], List[List[object]]], + headers: Union[Tuple[str, ...], List[str]], +) -> str: + try: + import tabulate + + return tabulate.tabulate(rows, headers=headers) + except ImportError: + return "\n".join( + ", ".join(map(str, row)) for row in itertools.chain([headers], rows) + ) + + +curr_frame = 0 + + +# Note: Called for you by dynamo - you almost never ever want to invoke this yourself. +def increment_frame() -> None: + global curr_frame + curr_frame = curr_frame + 1 + + +# Note: Called for you by dynamo - you almost never ever want to invoke this yourself. +def reset_frame_count() -> None: + global curr_frame + frame_phase_timing.clear() + compilation_time_metrics.clear() + curr_frame = 0 + + +op_count = 0 + + +def increment_op_count(cnt: int) -> None: + global op_count + op_count += cnt + + +# Calculate total time spent so far for each phase +# For example, {'entire_frame_compile':8.574629999999999, 'backend_compile':5.26806} +def calculate_time_spent() -> Dict[str, float]: + total_wall_time = 0.0 + total_by_key = {} + for timings in frame_phase_timing.values(): + total_wall_time += timings.get( + "entire_frame_compile", timings.get("inductor_compile", 0) + ) + + for key, timing in timings.items(): + if key not in total_by_key: + total_by_key[key] = timing + else: + total_by_key[key] += timing + + if total_by_key: + total_by_key["total_wall_time"] = total_wall_time + + return total_by_key + + +# Print a report of time spent so far +# Ex: +# TIMING: +# entire_frame_compile:8.574629999999999 +# backend_compile:5.26806 +def print_time_report() -> None: + total_by_key = calculate_time_spent() + + out = "TIMING:" + for key, value in total_by_key.items(): + out = f"{out} {key}:{round(value, 5)}" + + print(out) + + +def _add_time_spent(key: str, phase_name: str, time_spent: float) -> None: + frame_phase_timing[key][phase_name] += time_spent + + +def get_cache_stats() -> Dict[str, Any]: + """Get a bunch of metadata about cache hits and misses to use in chromium events""" + cache_stats = { + "fxgraph_cache_hit": counters["inductor"]["fxgraph_cache_hit"], + "fxgraph_cache_miss": counters["inductor"]["fxgraph_cache_miss"], + "fxgraph_cache_bypass": counters["inductor"]["fxgraph_cache_bypass"], + } + return cache_stats + + +# dynamo_timed is a context manager +# By wrapping a function in dynamo_timed, we can store a record in compilation_time_metrics +# where the key is the functions name. +# For example: +# +# def _foo(...): +# with dynamo_timed("_foo"): +# ... +# +# Would show up as an entry in our timing dict: +# OrderedDict([('_foo', [0.083690, 0.23949, 3.1425e-05])]) +# This is extremely useful for granular debugging. +# +# Although it is tempting to use dynamo_timed as a decorator, please do not. +# In its decorator form it makes cProfile traces less useful as dynamo_timed +# suddenly becomes a bottleneck for lots of function calls (as only one parent +# pointer is recorded). +# +# For a higher-level mode, pass a phase_name into dynamo_timed +# phase_names record an extra record into a separate compilation timing structure, +# one keyed on frame+name rather than function. +# The frame is incremented outside of this function, in def increment_frame() above. +# `fwd_only` is used to identify if this phase or function is only called +# during compiling fwd graphs, e.g, `entire_frame_compile` and `backend_compile`. +# The other phases (`inductor_compile` and `code_gen`) are called for both fwd and bwd graphs. + + +@contextmanager +def dynamo_timed( + key: str, + phase_name: Optional[str] = None, + fwd_only: bool = True, +): + chromium_log: ChromiumEventLogger = get_chromium_event_logger() + if key not in compilation_time_metrics: + compilation_time_metrics[key] = [] + + fail_type: Optional[str] = None + fail_reason: Optional[str] = None + time_spent = float("-inf") + start = time.time_ns() + try: + with torch.profiler.record_function(f"{key} (dynamo_timed)"): + t0 = time.time() + chromium_log.log_event_start(key, start, None) + if phase_name: + chromium_log.log_event_start(phase_name, start) + yield + time_spent = time.time() - t0 + compilation_time_metrics[key].append(time_spent) + except Exception as e: + fail_type = str(type(e)) + fail_reason = str(e) + raise + finally: + # Always log the end event even on exception + if phase_name: + chromium_log.log_event_end( + phase_name, + time.time_ns(), + {"cache_stats": get_cache_stats()}, + start, + ) + chromium_log.log_event_end( + key, time.time_ns(), {"cache_stats": get_cache_stats()}, start + ) + # Only record backward compilation metrics if phase_name is not None! + if phase_name: + frame_key = str(curr_frame) + # fwd only compilation stages: entire_frame_compile, backend_compile. + # use frame_key as time aggregation key. + if fwd_only and fail_type is None: + _add_time_spent(frame_key, phase_name, time_spent) + else: + # fwd + bwd compilation stages: inductor_compile, code_gen. + # use frame_key as time aggregation key for fwd graphs; + # use compile_id as time aggregation key for bwd graphs. + if torch._guards.TracingContext.try_get() is not None: + aot_graph_name = str( + torch._guards.TracingContext.get().aot_graph_name + ) + if ( + "forward" in aot_graph_name or "inference" in aot_graph_name + ) and fail_type is None: + _add_time_spent(frame_key, phase_name, time_spent) + elif "backward" in aot_graph_name: + compile_id = str( + torch._guards.CompileContext.current_compile_id() + ) + if fail_type is None: + _add_time_spent(compile_id, phase_name, time_spent) + + # log backward compilation metrics at the end of `inductor_compile` of bwd graph, + # one record for one bwd graph. + if phase_name == "inductor_compile": + if fail_type is None: + inductor_compile_time = frame_phase_timing[ + compile_id + ].get("inductor_compile", None) + code_gen_time = frame_phase_timing[compile_id].get( + "code_gen", None + ) + else: + inductor_compile_time = None + code_gen_time = None + metrics = BwdCompilationMetrics( + compile_id, + inductor_compile_time, + code_gen_time, + fail_type, + fail_reason, + ) + record_compilation_metrics(metrics) + + +@overload +def compile_times(repr: Literal["str"], aggregate: bool = False) -> str: + ... + + +@overload +def compile_times( + repr: Literal["csv"], aggregate: bool = False +) -> Tuple[List[str], List[object]]: + ... + + +def compile_times(repr="str", aggregate: bool = False): + """ + Get metrics about torchdynamo frontend/backend compilation times. + + Accumulates information from functions tagged with `dynamo_timed`. + + repr='str' returns a printable string for user interaction, and 'csv' + returns headers, rows which can be logged for output + + aggregate causes values from multiple compilations (e.g. split graphs) + to be accumulated into one value. If false, expect more than one value + per metric. + """ + + def fmt_fn(values, item_fn=lambda x: x): + if aggregate: + return item_fn(sum(values)) + return ", ".join(map(item_fn, values)) + + if repr == "str": + rows = [ + (k, fmt_fn(compilation_time_metrics[k], item_fn=lambda x: f"{x:.4f}")) + for k in compilation_time_metrics + ] + out = "TorchDynamo compilation metrics:\n" + out += tabulate(rows, headers=("Function", "Runtimes (s)")) + return out + elif repr == "csv": + values = [ + fmt_fn(v, item_fn=lambda x: f"{x:.6f}") + for v in compilation_time_metrics.values() + ] + headers = list(compilation_time_metrics.keys()) + return headers, values + return None + + +@atexit.register +def dump_compile_times() -> None: + log.info(compile_times(repr="str", aggregate=True)) + + +tensortype_to_dtype = { + torch.FloatTensor: (torch.float32, torch.float), + torch.DoubleTensor: (torch.float64, torch.double), + torch.HalfTensor: (torch.float16, torch.half), + torch.BFloat16Tensor: (torch.bfloat16,), + torch.ByteTensor: (torch.uint8,), + torch.CharTensor: (torch.int8,), + torch.LongTensor: (torch.int64, torch.long), + torch.IntTensor: (torch.int32, torch.int), + torch.ShortTensor: (torch.int16, torch.short), + torch.BoolTensor: (torch.bool,), +} + + +class DuplicateWarningChecker: + def __init__(self, maxsize: int = 4096) -> None: + self.maxsize = maxsize + self.reset() + + def reset(self): + self.set = collections.OrderedDict() + + def add(self, key: Union[str, Tuple[object, object]]) -> bool: + if key in self.set: + self.set.move_to_end(key, last=True) + if not config.verbose: + return False + else: + self.set[key] = None + while len(self.set) > self.maxsize: + self.set.popitem(last=False) + return True + + +graph_break_dup_warning_checker = DuplicateWarningChecker() + + +def setup_compile_debug(): + compile_debug = os.environ.get("TORCH_COMPILE_DEBUG", "0") == "1" + + if compile_debug: + return add_file_handler() + + return contextlib.ExitStack() + + +def reset_graph_break_dup_checker() -> None: + graph_break_dup_warning_checker.reset() + + +def add_file_handler(): + log_path = os.path.join(get_debug_dir(), "torchdynamo") + os.makedirs(log_path, exist_ok=True) + + log_file_handler = logging.FileHandler(os.path.join(log_path, "debug.log")) + logger = logging.getLogger("torch._dynamo") + logger.addHandler(log_file_handler) + + exitstack = contextlib.ExitStack() + exitstack.callback(lambda: logger.removeHandler(log_file_handler)) + return exitstack + + +def setup_log_file(): + exitstack = contextlib.ExitStack() + if config.log_file_name is not None: + log_file_handler = logging.FileHandler(config.log_file_name) + for logger in torch._logging._internal.get_loggers(): + logger.addHandler(log_file_handler) + exitstack.callback(lambda: logger.removeHandler(log_file_handler)) + return exitstack + + return exitstack + + +def gen_record_file_name(exc, code) -> str: + return f"{get_debug_dir()}/error_recordings/\ +{code.co_name}_{type(exc).__name__}_{code.co_firstlineno}.rec" + + +def write_record_to_file(filename: str, exec_record) -> None: + try: + if os.path.exists(filename): + log.warning( + "Unable to write execution record %s; file already exists.", filename + ) + else: + os.makedirs(os.path.dirname(filename), exist_ok=True) + with open(filename, "wb") as f: + exec_record.dump(f) + except Exception: + log.exception("Unable to write execution record %s", filename) + + +def count_calls(g: fx.Graph) -> int: + c = 0 + for n in g.nodes: + if "call" in n.op: + c += 1 + return c + + +def identity(x): + return x + + +def hashable(x): + try: + hash(x) + return True + except TypeError: + return False + # cannot hash writable memoryview object + except ValueError: + return False + + +def nothing(*args, **kwargs): + pass + + +class ExactWeakKeyDictionary: + """Similar to weakref.WeakKeyDictionary, but use `is`/`id` rather than `==` to compare equality""" + + def __init__(self): + self.values = {} + self.refs = {} + + def __getitem__(self, key): + return self.values[id(key)] + + def get(self, key, default=None): + return self.values.get(id(key), default) + + def __contains__(self, key): + return id(key) in self.values + + def __setitem__(self, key, value): + idx = id(key) + if idx not in self.refs: + self.refs[idx] = weakref.ref(key, lambda ref: self._remove_id(idx)) + self.values[idx] = value + + def _remove_id(self, idx): + if idx in self.values: + del self.values[idx] + if idx in self.refs: + del self.refs[idx] + + def clear(self): + self.refs.clear() + self.values.clear() + + +@overload +def istype(obj: object, allowed_types: Type[T]) -> TypeGuard[T]: + ... + + +@overload +def istype( + obj: object, allowed_types: Tuple[Type[List[T]], Type[Tuple[T, ...]]] +) -> TypeGuard[T]: + ... + + +@overload +def istype(obj: object, allowed_types: Iterable[type]) -> bool: + ... + + +def istype(obj, allowed_types): + """isinstance() without subclasses""" + if isinstance(allowed_types, (tuple, list, set)): + return type(obj) in allowed_types + return type(obj) is allowed_types + + +if sys.version_info >= (3, 12): + # Some typing classes moved to C in 3.12, + # which no longer have the _Final mixin. + _builtin_final_typing_classes = ( + typing.ParamSpecArgs, + typing.ParamSpecKwargs, + typing.ParamSpec, + typing.TypeVar, + typing.TypeVarTuple, + typing.TypeAliasType, + ) + + +def is_typing(value): + # _Final catches most of typing classes: + # - Any + # - Callable + # - Union + # ... + # + # NB: we intentionally ignore classes that inherit from Generic, since they + # can be used as both TypingVariable as well as UserDefinedClassVariable. + if sys.version_info >= (3, 12) and isinstance(value, _builtin_final_typing_classes): + return True + return isinstance(value, typing._Final) or value is typing.Generic # type: ignore[attr-defined] + + +def is_numpy_int_type(value): + if not np: + return False + + return istype( + value, + ( + np.int8, + np.int16, + np.int32, + np.int64, + np.uint8, + np.uint16, + np.uint32, + np.uint64, + ), + ) + + +def is_numpy_float_type(value): + if not np: + return False + + return istype( + value, + ( + np.float16, + np.float32, + np.float64, + ), + ) + + +def is_lru_cache_wrapped_function(value): + return isinstance(value, functools._lru_cache_wrapper) and is_function( + inspect.getattr_static(value, "__wrapped__") + ) + + +def is_function_or_wrapper(value): + return is_function(value) or isinstance( + value, (torch._ops.OpOverloadPacket, torch._ops.OpOverload) + ) + + +def is_function(value): + return isinstance( + value, + ( + types.FunctionType, + types.BuiltinFunctionType, + types.MethodDescriptorType, + types.WrapperDescriptorType, + ), + ) + + +def is_wrapper_or_member_descriptor(value): + return isinstance( + value, + ( + # set up by PyGetSetDef + types.GetSetDescriptorType, + # set by PyMethodDef, e.g. list.append + types.MethodDescriptorType, + # slots - list.__add__ + types.WrapperDescriptorType, + # set up by PyMemberDef + types.MemberDescriptorType, + # wrapper over C functions + types.MethodWrapperType, + ), + ) + + +def unwrap_if_wrapper(fn): + return unwrap_with_attr_name_if_wrapper(fn)[0] + + +def unwrap_with_attr_name_if_wrapper(fn): + # TODO(anijain2305) - Investigate if we can get rid of this function + # unpack @torch._dynamo.optimize()(fn) wrapped function + if is_function(fn) and inspect.getattr_static(fn, "_torchdynamo_inline", False): + fn = inspect.getattr_static(fn, "_torchdynamo_inline", fn) + attr_name = "_torchdynamo_inline" + else: + attr_name = None + return fn, attr_name + + +def is_numpy_ndarray(value): + if not np: + return False + + return istype(value, np.ndarray) + + +def istensor(obj): + """Check of obj is a tensor""" + tensor_list: Tuple[type, ...] = ( + torch.Tensor, + torch.nn.Parameter, + *config.traceable_tensor_subclasses, + ) + tensor_list = tensor_list + (torch._subclasses.FakeTensor,) + return istype(obj, tensor_list) + + +def is_lazy_module(mod): + return isinstance(mod, LazyModuleMixin) + + +@functools.lru_cache(4096) +def print_once(*args): + print(*args) + + +def make_cell(val=None): + """Some black magic to create a cell object that usually only exists in a closure""" + x = val + + def f(): + return x + + assert f.__closure__ is not None and len(f.__closure__) == 1 + return f.__closure__[0] + + +def proxy_args_kwargs(args, kwargs): + try: + proxy_args = tuple(arg.as_proxy() for arg in args) + proxy_kwargs = {key: arg.as_proxy() for key, arg in kwargs.items()} + return proxy_args, proxy_kwargs + except NotImplementedError as e: + from .exc import unimplemented + from .variables.base import typestr + + unimplemented( + f"call_function args: {typestr(*args)} {typestr(*list(kwargs.values()))}", + from_exc=e, + ) + + +@dataclasses.dataclass +class CompilationMetrics: + compile_id: str + frame_key: str + co_name: str + co_filename: str + co_firstlineno: int + cache_size: int + accumulated_cache_size: int + guard_count: Optional[int] + shape_env_guard_count: Optional[int] + graph_op_count: Optional[int] + graph_node_count: Optional[int] + graph_input_count: Optional[int] + start_time: float + entire_frame_compile_time_s: Optional[float] + backend_compile_time_s: Optional[float] + inductor_compile_time_s: Optional[float] + code_gen_time_s: Optional[float] + fail_type: Optional[str] + fail_reason: Optional[str] + fail_user_frame_filename: Optional[str] + fail_user_frame_lineno: Optional[int] + non_compliant_ops: Set[str] + compliant_custom_ops: Set[str] + restart_reasons: Set[str] + dynamo_time_before_restart_s: float + # Sometimes, we will finish analyzing a frame but conclude we don't want + # to install any guarded code. True means we actually decided to install + # a compiled frame + has_guarded_code: bool + possibly_missed_reinplacing_opportunities: Optional[int] + + +@dataclasses.dataclass +class BwdCompilationMetrics: + compile_id: str + inductor_compile_time_s: Optional[float] + code_gen_time_s: Optional[float] + fail_type: Optional[str] + fail_reason: Optional[str] + + +DEFAULT_COMPILATION_METRICS_LIMIT = 64 + + +_compilation_metrics: Deque[ + Union[CompilationMetrics, BwdCompilationMetrics] +] = collections.deque(maxlen=DEFAULT_COMPILATION_METRICS_LIMIT) + + +def record_compilation_metrics( + compilation_metrics: Union[CompilationMetrics, BwdCompilationMetrics] +): + global _compilation_metrics + _compilation_metrics.append(compilation_metrics) + if isinstance(compilation_metrics, CompilationMetrics): + name = "compilation_metrics" + else: + name = "bwd_compilation_metrics" + torch._logging.trace_structured( + name, + lambda: { + k: list(v) if isinstance(v, set) else v + for k, v in dataclasses.asdict(compilation_metrics).items() + }, + ) + if config.log_compilation_metrics: + log_compilation_event(compilation_metrics) + + +def set_compilation_metrics_limit(new_size: int) -> None: + global _compilation_metrics + while len(_compilation_metrics) > new_size: + _compilation_metrics.popleft() + new_deque = collections.deque(_compilation_metrics, maxlen=new_size) + _compilation_metrics = new_deque + + +def clear_compilation_metrics() -> None: + global _compilation_metrics + _compilation_metrics.clear() + + +def get_compilation_metrics() -> List[Union[CompilationMetrics, BwdCompilationMetrics]]: + return list(_compilation_metrics) + + +class ChromiumEventLogger: + """Logs chromium events to structured logs. tlparse will concatenate these into a perfetto UI link. + + See https://docs.google.com/document/d/1CvAClvFfyA5R-PhYUmn5OOQtYMH4h6I0nSsKchNAySU/preview#heading=h.yr4qxyxotyw for + a specification of the Chromium Event JSON format. + """ + + def get_stack(self): + if hasattr(self.tls, "stack"): + return self.tls.stack + else: + self.tls.stack = ["__start__"] + return self.tls.stack + + def __init__(self): + self.tls = threading.local() + # Generate a unique id for this logger, which we can use in scuba to filter down + # to a single python run. + self.id_ = str(uuid.uuid4()) + + # TODO: log to init/id tlparse after I add support for it + log.info("ChromiumEventLogger initialized with id %s", self.id_) + + def log_event_start( + self, + event_name: str, + time_ns: int, + metadata: Optional[Dict[str, Any]] = None, + ) -> None: + """ + Logs the start of a single event. + :param str event_name Name of event to appear in trace + :param time_ns Timestamp in nanoseconds + :param metadata: Any extra metadata associated with this event + """ + event = self._log_timed_event( + event_name, + time_ns, + "B", + metadata, + ) + log_chromium_event_internal(event, self.get_stack(), self.id_) + self.get_stack().append(event_name) + + def reset(self) -> None: + # We this on every compile in case a compile crashes or restarts and we haven't + # cleared the stack. + stack = self.get_stack() + stack.clear() + stack.append("__start__") + + def log_event_end( + self, + event_name: str, + time_ns: int, + metadata: Optional[Dict[str, Any]] = None, + start_time_ns: Optional[int] = None, + ) -> None: + """ + Logs the end of a single event. This function should only be + called after log_event_start with the same event_name. + :param event_name: Name of event to appear in trace + :param time_ns: Timestamp in nanoseconds + :param metadata: Any extra metadata associated with this event + """ + # These stack health checks currently never happen, + # but they're written this way to future proof any weird event + # overlaps in the future. + stack = self.get_stack() + if event_name not in stack: + # Something went wrong, we never called start on this event, + # or it was skipped due to overlapping events below + log.warning("ChromiumEventLogger: Start event not in stack, ignoring") + return + + event = self._log_timed_event( + event_name, + time_ns, + "E", + metadata, + ) + + while event_name != stack[-1]: + # If the event isn't the most recent one to end, pop + # off the stack until it is. + # Since event_name in self.stack, this pop is always safe + log.warning( + "ChromiumEventLogger: Detected overlapping events, fixing stack" + ) + stack.pop() + + log_chromium_event_internal(event, stack, self.id_, start_time_ns) + # Finally pop the actual event off the stack + stack.pop() + + def _log_timed_event( + self, + event_name: str, + time_ns: int, + phase: str, + metadata: Optional[Dict[str, Any]] = None, + ) -> Dict[str, Any]: + """ + Logs a timed event in chromium format. See log_event_start, log_event_end, etc. + """ + event = { + "name": event_name, + "ts": time_ns / 1000, # Chromium events are in micro seconds + "args": metadata, + "ph": phase, + # These categories are needed in all chromium traces + "cat": "dynamo_timed", + "tid": 0, + "pid": 0, # pid should be specified on all logs, we don't personally care about the actual process id + } + torch._logging.trace_structured( + "chromium_event", + payload_fn=lambda: event, + suppress_context=False, + expect_trace_id=False, # Not every chromium event will have a trace_id + ) + return event + + def log_instant_event( + self, + event_name: str, + time_ns: int, + metadata: Optional[Dict[str, Any]] = None, + ) -> None: + """ + Log an instant event with no associated duration. + :param str event_name: Name of event to appear in trace + :param int time_ns Timestamp in nanoseconds + :param Optional[Dict[str, Any]] metadata: Any extra metadata associated with this event + :param str cname optional color for the arrow in the trace + """ + event = { + "name": event_name, + "ts": time_ns / 1000, + "args": metadata, + "ph": "i", + # These categories are needed in all chromium traces + "cat": "dynamo_timed", + "tid": 0, + "pid": 0, + "s": "p", # We use "process" level instant events so they all appear on the same row in the trace. + } + torch._logging.trace_structured( + "chromium_event", + payload_fn=lambda: event, + suppress_context=False, + expect_trace_id=True, + ) + # Log an instant event with the same start and end time + log_chromium_event_internal(event, self.get_stack(), self.id_) + + +CHROMIUM_EVENT_LOG: Optional[ChromiumEventLogger] = None + + +def get_chromium_event_logger() -> ChromiumEventLogger: + global CHROMIUM_EVENT_LOG + if CHROMIUM_EVENT_LOG is None: + CHROMIUM_EVENT_LOG = ChromiumEventLogger() + return CHROMIUM_EVENT_LOG + + +@dataclasses.dataclass +class CleanupHook: + """Remove a global variable when hook is called""" + + scope: Dict[str, Any] + name: str + + def __call__(self, *args): + # Make sure we're not shutting down + if CleanupManager is not None: + CleanupManager.count -= 1 + del self.scope[self.name] + + @staticmethod + def create(scope, name, val): + assert name not in scope + CleanupManager.count += 1 + scope[name] = val + return CleanupHook(scope, name) + + +class CleanupManager(ExactWeakKeyDictionary): + count = 0 + instance: ClassVar[CleanupManager] + + def _remove_id(self, idx): + for hook in self.values[idx]: + hook() + super()._remove_id(idx) + + +CleanupManager.instance = CleanupManager() + + +def clone_tensor(x): + """Clone the tensor and its gradient""" + y = x.clone().requires_grad_(x.requires_grad) + if x.is_leaf and x.grad is not None: + y.grad = x.grad.clone() + return y + + +def clone_input(x, *, dtype=None): + """copy while preserving strides""" + # TODO: this is questionable + if is_fake(x): + # this func fails on fake tensors in __torch_dispatch__ + return x + + def torch_clone(x): + y = torch.clone(x) + if x.is_leaf: + y.requires_grad_(x.requires_grad) + if x.is_leaf and x.grad is not None: + y.grad = clone_input(x.grad, dtype=dtype) + if hasattr(x, "_dynamo_dynamic_indices"): + y._dynamo_dynamic_indices = x._dynamo_dynamic_indices.copy() # type: ignore[attr-defined] + return y + + with torch.no_grad(): + if x.device.type == "xla": + # Access data_ptr() for a xla tensor will cause crash + return torch_clone(x) + + # Handle sparse storage (no stride). + if x.layout is torch.sparse_coo: + return torch.sparse_coo_tensor( + torch_clone(x._indices()), + torch_clone(x._values()), + x.shape, + is_coalesced=x.is_coalesced(), + ) + elif is_sparse_compressed(x): + if x.layout in {torch.sparse_csr, torch.sparse_bsr}: + compressed_indices = x.crow_indices() + plain_indices = x.col_indices() + else: + compressed_indices = x.ccol_indices() + plain_indices = x.row_indices() + return torch.sparse_compressed_tensor( + torch_clone(compressed_indices), + torch_clone(plain_indices), + torch_clone(x.values()), + x.shape, + layout=x.layout, + ) + + needed_size = sum( + (shape - 1) * stride for shape, stride in zip(x.size(), x.stride()) + ) + if x.is_quantized: + result = torch.empty_quantized((needed_size + 32,), x) + else: + result = torch.empty( + needed_size + 32, dtype=dtype or x.dtype, device=x.device + ) + cache_line_offset = ( + (x.data_ptr() - result.data_ptr()) % 32 + ) // x.element_size() + result.as_strided_(x.size(), x.stride(), cache_line_offset) + try: + result.copy_(x.clone()) + if x.is_leaf: + result.requires_grad_(x.requires_grad) + if x.is_leaf and x.grad is not None: + result.grad = clone_input(x.grad, dtype=dtype) + except RuntimeError: + # RuntimeError: unsupported operation: more than one element of the written-to + # tensor refers to a single memory location. Please clone() the tensor before + # performing the operation. + return torch_clone(x) + if hasattr(x, "_dynamo_dynamic_indices"): + result._dynamo_dynamic_indices = x._dynamo_dynamic_indices.copy() # type: ignore[attr-defined] + return result + + +def clone_inputs(example_inputs): + res: Union[Dict[Any, Any], List[Any]] + if type(example_inputs) is dict: + res = dict(example_inputs) + for key, value in res.items(): + if isinstance(value, tuple): + res[key] = clone_inputs(value) + else: + assert isinstance(value, torch.Tensor), type(value) + res[key] = clone_input(value) + return res + + res = list(example_inputs) + for i in range(len(res)): + if isinstance(res[i], torch.Tensor): + res[i] = clone_input(res[i]) + return res + + +def skip_frame_if_in_functorch_mode(val: torch.Tensor): + try: + val.data_ptr() # will throw for functorch tensors + except RuntimeError as e: + from .exc import SkipFrame + + # This will be GradTrackingTensor/BatchedTensor/etc + functorch_subclass_name = re.sub(r"\(.*", "", repr(val)) + raise SkipFrame( + f"torch.compile cannot be run in context: {functorch_subclass_name}" + ) from e + + +@contextmanager +def preserve_rng_state(): + disable_functorch = torch._C._DisableFuncTorch + disable_current_modes = torch.utils._python_dispatch._disable_current_modes + with disable_current_modes(), disable_functorch(): + rng_state = torch.clone(torch.random.get_rng_state()) + skip_frame_if_in_functorch_mode(rng_state) + if torch.cuda.is_available(): + cuda_rng_state = torch.clone(torch.cuda.get_rng_state()) + try: + yield + finally: + with torch.utils._python_dispatch._disable_current_modes(): + torch.random.set_rng_state(rng_state) + if torch.cuda.is_available(): + torch.cuda.set_rng_state(cuda_rng_state) # type: ignore[possibly-undefined] + + +def is_jit_model(model0): + return isinstance( + model0, + ( + torch.jit._trace.TopLevelTracedModule, + torch.jit._script.RecursiveScriptModule, + torch.jit.ScriptFunction, + torch.jit.ScriptModule, + ), + ) + + +def torchscript(model, example_inputs, verbose=False): + if is_jit_model(model): + # already done? + return model + + try: + return torch.jit.trace(model, example_inputs) + except Exception: + try: + return torch.jit.script(model) + except Exception: + if verbose: + log.exception("jit error") + else: + log.error("Both torch.jit.trace and torch.jit.script failed") + return None + + +def getfile(obj): + try: + return inspect.getfile(obj) + except (TypeError, OSError): + return None + + +def is_namedtuple(obj): + """Test if an object is a namedtuple or a torch.return_types.* quasi-namedtuple""" + return is_namedtuple_cls(type(obj)) + + +def is_namedtuple_cls(cls): + """Test if an object is a namedtuple or a (torch.return_types|torch.autograd.forward_ad).* quasi-namedtuple""" + try: + if issubclass(cls, tuple): + bases = getattr(cls, "__bases__", []) or [None] + module = getattr(cls, "__module__", None) + return module in ("torch.return_types", "torch.autograd.forward_ad") or ( + bases[0] is tuple and hasattr(cls, "_make") and hasattr(cls, "_fields") + ) + except TypeError: + pass + return False + + +@functools.lru_cache(1) +def namedtuple_fields(cls): + """Get the fields of a namedtuple or a torch.return_types.* quasi-namedtuple""" + if cls is slice: + return ["start", "stop", "step"] + + assert issubclass(cls, tuple) + if hasattr(cls, "_fields"): + # normal namedtuples + return cls._fields + + @dataclasses.dataclass + class Marker: + index: int + + # frustrating ones e.g. torch.return_types.max + assert cls.__module__ == "torch.return_types" + obj = cls(map(Marker, range(cls.n_fields))) + fields: List[Optional[str]] = [None] * cls.n_fields + for name in dir(obj): + if name[0] != "_" and isinstance(getattr(obj, name), Marker): + fields[getattr(obj, name).index] = name + return fields + + +def checkpoint_params(gm): + with torch.no_grad(): + rng_state = torch.clone(torch.random.get_rng_state()) + if torch.cuda.is_available(): + cuda_rng_state = torch.clone(torch.cuda.get_rng_state()) + saved_state = [] + for param in itertools.chain(gm.parameters(), gm.buffers()): + saved_state.append((param, param._version, torch.clone(param))) + + def restore(): + with torch.no_grad(): + torch.random.set_rng_state(rng_state) + if torch.cuda.is_available(): + torch.cuda.set_rng_state(cuda_rng_state) + for param, version, original_value in saved_state: + if param._version != version: + param.copy_(original_value) + + return restore + + +def timed(model, example_inputs, times=1): + if torch.cuda.is_available(): + synchronize = torch.cuda.synchronize + else: + synchronize = nothing + + synchronize() + gc.collect() + torch.manual_seed(1337) + t0 = time.perf_counter() + for _ in range(times): + result = model(*example_inputs) + synchronize() + t1 = time.perf_counter() + return result, t1 - t0 # type: ignore[possibly-undefined] + + +def check_is_cuda(gm, example_inputs): + return all(x.is_cuda for x in itertools.chain(example_inputs, gm.parameters(True))) + + +@lru_cache(32) +def rot_n_helper(n): + assert n > 1 + vars = [f"v{i}" for i in range(n)] + rotated = reversed(vars[-1:] + vars[:-1]) + fn = eval(f"lambda {','.join(vars)}: ({','.join(rotated)})") + fn.__name__ = f"rot_{n}_helper" + return fn + + +common_constant_types: Set[type] = { + int, + float, + complex, + bool, + str, + bytes, + type(None), + Ellipsis.__class__, + types.CodeType, + torch.device, + torch.dtype, + torch.memory_format, + torch.layout, +} + +if has_triton_package(): + import triton + + common_constant_types.add(triton.language.dtype) + +""" + Difference between is_safe_constant and common_constant_types. + * common_constant_types: Constants would be wrapped by VariableBuilder.wrap_literal + as ConstantVariable. + * is_safe_constant: Constants can be loaded by LOAD_CONST bytecode. +""" + + +def is_safe_constant(v): + if istype(v, (tuple, frozenset)): + return all(map(is_safe_constant, v)) + return isinstance(v, (enum.Enum, type, torch.Size)) or istype( + v, + common_constant_types | {slice}, + ) + + +def specialize_symnode(arg): + from .variables import ConstantVariable, SymNodeVariable + + # Guard and specialize + if isinstance(arg, SymNodeVariable): + return ConstantVariable.create(arg.evaluate_expr()) + + return arg + + +def guard_if_dyn(arg): + from .variables import ConstantVariable + + arg = specialize_symnode(arg) + + if isinstance(arg, ConstantVariable): + return arg.as_python_constant() + + return arg + + +def check_constant_args(args, kwargs): + return all(x.is_python_constant() for x in itertools.chain(args, kwargs.values())) + + +def check_unspec_python_args(args, kwargs): + from .variables.constant import ConstantVariable + from .variables.tensor import UnspecializedPythonVariable + + unspec_count = 0 + for x in itertools.chain(args, kwargs.values()): + if isinstance(x, UnspecializedPythonVariable): + unspec_count += 1 + elif not isinstance(x, ConstantVariable): + return False + return unspec_count > 0 + + +def check_unspec_or_constant_args(args, kwargs): + # A fused version of: + # return check_constant_args(args, kwargs) or check_unspec_python_args(args, kwargs) + from .variables.tensor import UnspecializedPythonVariable + + for x in itertools.chain(args, kwargs.values()): + if not (x.is_python_constant() or isinstance(x, UnspecializedPythonVariable)): + return False + return True + + +def check_numpy_ndarray_args(args, kwargs): + from .variables.tensor import NumpyNdarrayVariable + + return any( + isinstance(x, NumpyNdarrayVariable) + for x in itertools.chain(args, kwargs.values()) + ) + + +dict_keys: Type[KeysView[Any]] = type({}.keys()) +dict_values: Type[ValuesView[Any]] = type({}.values()) +odict_values: Type[ValuesView[Any]] = type(collections.OrderedDict().values()) +tuple_iterator: Type[Iterator[Any]] = type(iter(())) +tuple_iterator_len = tuple_iterator.__length_hint__ # type: ignore[attr-defined] +object_new = object.__new__ + + +def nn_module_new(cls): + obj = object_new(cls) + torch.nn.Module.__init__(obj) + return obj + + +def product(it): + return functools.reduce(operator.mul, it, 1) + + +def tuple_iterator_getitem(it, index): + _, (obj,), start = it.__reduce__() + return obj[start + index] + + +iter_next = next + + +def to_subclass(t, cls): + return t.as_subclass(cls) + + +def dict_keys_getitem(d, n): + return next(itertools.islice(iter(d), n, n + 1)) + + +def enum_repr(value, local): + # enum class can override __str__ method. Use __class__ and name attribute + # to extract the class name and key name. + name = value.__class__.__name__ + val = value.name + scope = "L" if local else "G" + local_name = f'{scope}["{name}"].{val}' + return local_name + + +def set_example_value(node, example_value): + # NB: example_value is a bit of a misnomer, because this is always a fake + # tensor of some sort. Furthermore, these example values serve as the + # runtime state of Dynamo tracing, which means if metadata mutation + # occurs, the example_value gets directly updated (so you can't rely on + # this to accurately reflect what the state of the value was at the time + # the program was traced). + node.meta["example_value"] = example_value + shape_env = TracingContext.get().fake_mode.shape_env + if symbol_to_path := torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings( + shape_env, example_value + ): + node.meta["unbacked_bindings"] = symbol_to_path + + +def _get_fake_tensor(vt): + fake_tensor = vt.as_proxy().node.meta.get("example_value") + if not is_fake(fake_tensor): + from .exc import unimplemented + + unimplemented("Cannot check Tensor object identity without its fake value") + return fake_tensor + + +def iter_contains(items, search, tx, check_tensor_identity=False): + from .variables import ( + BuiltinVariable, + ConstantVariable, + TensorVariable, + VariableTracker, + ) + + if search.is_python_constant(): + found_const = any( + x.is_python_constant() + and x.as_python_constant() == search.as_python_constant() + for x in items + ) + return ConstantVariable.create(found_const) + + must_check_tensor_id = False + if check_tensor_identity and isinstance(search, TensorVariable): + must_check_tensor_id = True + # Match of Tensor means match of FakeTensor + search = _get_fake_tensor(search) + + found: Optional[VariableTracker] = None + for x in items: + if must_check_tensor_id: + if isinstance(x, TensorVariable): + if search is _get_fake_tensor(x): # Object equivalence + return ConstantVariable.create(True) + else: + check = BuiltinVariable(operator.eq).call_function(tx, [x, search], {}) + if found is None: + found = check + else: + found = BuiltinVariable(operator.or_).call_function( + tx, [check, found], {} + ) + if found is None: + found = ConstantVariable.create(False) + return found + + +def key_is_id(k): + """Returns whether it indexes dictionaries using its id""" + return isinstance(k, (torch.Tensor, torch.nn.Module, MethodWrapperType)) + + +def key_to_id(value): + return [id(k) if key_is_id(k) else k for k in value.keys()] + + +def const_repr(x, *, local) -> str: + from .trace_rules import is_builtin_callable + + if isinstance(x, (list, tuple)): + elems_repr = ",".join(const_repr(s, local=local) for s in x) + if isinstance(x, list): + return f"[{elems_repr}]" + else: + assert isinstance(x, tuple) + if len(x) == 1: + return f"({elems_repr},)" + else: + return f"({elems_repr})" + elif isinstance(x, enum.Enum): + # To workaround repr(Enum) returning invalid global reference before python 3.11 + # by calling enum_repr and removing quotes to render enum in guard code. + return enum_repr(x, local=local).replace("'", "") + elif is_builtin_callable(x): + return x.__name__ + elif isinstance(x, type): + + def fullname(o): + klass = o.__class__ + module = klass.__module__ + if module == "builtins": + return klass.__qualname__ # avoid outputs like 'builtins.str' + return module + "." + klass.__qualname__ + + return fullname(x) + else: + return f"{x!r}" + + +def dict_keys_repr(const_keys, *, local) -> str: + keys_str = ",".join(const_repr(s, local=local) for s in const_keys) + return "[" + keys_str + "]" + + +GLOBAL_KEY_PREFIX = "__dict_key" + + +from torch._subclasses import UnsupportedFakeTensorException # noqa: F401 + + +def get_safe_global_name(tx, root, obj): + # The global_mangled_class_name should be different for different + # invocations of torch.compile. Otherwise, we can run into a situation + # where multiple torch.compile invocations re-use the same global name, + # but the global's lifetime is tied to the first invocation (and + # may be deleted when the first torch.compile invocation is deleted) + # We mangle it based off of the output_graph's id. + return f"{root}_{id(obj)}_c{tx.output.compile_id}" + + +def wrap_fake_exception(fn): + try: + return fn() + except UnsupportedFakeTensorException as e: + from .exc import unimplemented + + msg = f"Unsupported: {e.reason} with fake tensor propagation." + log.warning(msg) + unimplemented(msg, from_exc=e) + + +def deepcopy_to_fake_tensor(obj, fake_mode): + with torch._subclasses.fake_tensor.FakeCopyMode(fake_mode): + return wrap_fake_exception(lambda: copy.deepcopy(obj)) + + +def rmse(ref, res): + """ + Calculate root mean squared error + """ + return torch.sqrt(torch.mean(torch.square(ref - res))) + + +def same( + ref, + res, + fp64_ref=None, + cos_similarity=False, + tol=1e-4, + equal_nan=False, + exact_dtype=True, + relax_numpy_equality=False, + ignore_non_fp=False, + log_error=log.error, + use_larger_multiplier_for_smaller_tensor=False, +): + """Check correctness to see if ref and res match""" + if fp64_ref is None: + fp64_ref = ref + if isinstance( + ref, (list, tuple, collections.deque, torch.nn.ParameterList, torch.Size) + ): + assert isinstance( + res, (list, tuple, collections.deque) + ), f"type mismatch {type(ref)} {type(res)}" + if len(ref) != len(res): + log_error("Length mismatch") + return False + return len(ref) == len(res) and all( + same( + ai, + bi, + fp64_refi, + cos_similarity, + tol, + equal_nan, + exact_dtype, + relax_numpy_equality, + ignore_non_fp, + log_error=log_error, + use_larger_multiplier_for_smaller_tensor=use_larger_multiplier_for_smaller_tensor, + ) + for ai, bi, fp64_refi in zip(ref, res, fp64_ref) + ) + elif type(ref).__name__ == "QuestionAnsweringModelOutput": + # This skips checking accuracy for start_logits/end_logits. + # Tentatively, start_logits/end_logits appear to be very prone to + # inaccuracies and is somewhat subsumed by checking the loss. + return same( + ref.loss, + res.loss, + fp64_ref.loss, + cos_similarity, + tol, + equal_nan, + exact_dtype, + relax_numpy_equality, + ignore_non_fp, + log_error=log_error, + use_larger_multiplier_for_smaller_tensor=use_larger_multiplier_for_smaller_tensor, + ) + elif isinstance(ref, dict): + assert isinstance(res, dict) + assert set(ref.keys()) == set( + res.keys() + ), f"keys mismatch {set(ref.keys())} == {set(res.keys())}" + for k in sorted(ref.keys()): + if not ( + same( + ref[k], + res[k], + fp64_ref[k], + cos_similarity=cos_similarity, + tol=tol, + equal_nan=equal_nan, + exact_dtype=exact_dtype, + relax_numpy_equality=relax_numpy_equality, + ignore_non_fp=ignore_non_fp, + log_error=log_error, + use_larger_multiplier_for_smaller_tensor=use_larger_multiplier_for_smaller_tensor, + ) + ): + log_error("Accuracy failed for key name %s", k) + return False + return True + elif isinstance(ref, set): + assert isinstance(res, set) + assert set(ref) == set(res), f"elements mismatch {set(ref)} == {set(res)}" + return True + elif isinstance(ref, (torch.Tensor, float)): + assert not isinstance(ref, torch._subclasses.FakeTensor) + assert not isinstance(res, torch._subclasses.FakeTensor) + + def to_tensor(t): + return t if isinstance(t, torch.Tensor) else torch.tensor(t) + + ref, res, fp64_ref = (to_tensor(val) for val in (ref, res, fp64_ref)) + + if ref.is_sparse: + assert res.is_sparse + ref = ref.to_dense() + res = res.to_dense() + assert isinstance(res, torch.Tensor), f"type mismatch {type(ref)} {type(res)}" + if exact_dtype: + if ref.dtype != res.dtype: + log_error("dtype mismatch %s, %s", ref.dtype, res.dtype) + return False + if ref.dtype == torch.bool: + if ignore_non_fp: + return True + # triton stores bool as int8, so add this for more accurate checking + r = torch.allclose( + ref.to(dtype=torch.uint8), + res.to(dtype=torch.uint8), + atol=tol, + rtol=tol, + equal_nan=equal_nan, + ) + if not r: + log_error("Accuracy failed: uint8 tensor did not match") + return r + + if cos_similarity: + ref = ref.flatten().to(torch.float32) + res = res.flatten().to(torch.float32) + if torch.allclose(ref, res, atol=tol, rtol=tol, equal_nan=True): + # early exit that handles zero/nan better + # cosine_similarity(zeros(10), zeros(10), dim=0) is 0 + return True + score = torch.nn.functional.cosine_similarity(ref, res, dim=0, eps=1e-6) + if score < 0.99: + log.warning("Similarity score=%s", score.cpu().detach().item()) + return score >= 0.99 + else: + if not exact_dtype: + ref = ref.to(res.dtype) + + # First try usual allclose + if torch.allclose(ref, res, atol=tol, rtol=tol, equal_nan=equal_nan): + return True + + # Check error from fp64 version + if fp64_ref.dtype == torch.float64: + # Fix a corner case that res and fp64_ref does not contains NaN and match (with loose tolerance) + # while the ref contains NaN. In this case, RMSE should not match any ways. + # But res is 'BETTER' than ref so we count it pass. + # + # This happens for Super_SloMo when loop ordering after fusion is enabled: + # https://gist.github.com/shunting314/11f235c70f7db0d52718d26f4a701cab + loose_tol = 1e-2 * 4 + if ( + not fp64_ref.isnan().any() + and not res.isnan().any() + and ref.isnan().any() + and torch.allclose( + fp64_ref.to(dtype=res.dtype), + res, + atol=loose_tol, + rtol=loose_tol, + equal_nan=equal_nan, + ) + ): + return True + ref_error = rmse(fp64_ref, ref).item() + # ref unable to produce this with stable numerics in this precision, ignore + if math.isnan(ref_error): + log.warning( + "Found nan in reference. Consider running in higher precision." + ) + + res_error = rmse(fp64_ref, res).item() + + # In the case of using AMP (Automatic Mixed Precision), certain models have + # failed the benchmark's correctness check. However, the end-to-end model's + # accuracy when comparing AMP with FP32 is within a difference of less than 0.1%. + # Thus, it's possible that the correctness check failures for these models are + # false alarms. We use multiplier of 3 instead of 2 to avoid these false alarms. + multiplier = ( + 3.0 if res.dtype in (torch.float16, torch.bfloat16) else 2.0 + ) + + if use_larger_multiplier_for_smaller_tensor and ( + fp64_ref.numel() <= 10 and tol >= 4 * 1e-2 + ): + multiplier = 10.0 + elif use_larger_multiplier_for_smaller_tensor and ( + fp64_ref.numel() <= 500 and tol >= 4 * 1e-2 + ): + multiplier = 5.0 + elif ( + fp64_ref.numel() < 1000 + or (ref.ndim == 4 and ref.shape[-1] == ref.shape[-2] == 1) + # large tol means a benchmark has been specified as REQUIRE_HIGHER_TOLERANCE + or tol >= 2 * 1e-2 + ): + # In the presence of noise, noise might dominate our error + # metric for smaller tensors. + # Similary, for 1x1 kernels, there seems to be high noise with amp. + multiplier = 3.0 + + passes_test = res_error <= (multiplier * ref_error + tol / 10.0) + if ( + not passes_test + and equal_nan + and math.isnan(ref_error) + and math.isnan(res_error) + # Some unit test for the accuracy minifier relies on + # returning false in this case. + and not inductor_config.cpp.inject_relu_bug_TESTING_ONLY + ): + passes_test = True + if not passes_test: + log_error( + "RMSE (res-fp64): %.5f, (ref-fp64): %.5f and shape=%s. res.dtype: %s, multiplier: %f, tol: %f" + ", use_larger_multiplier_for_smaller_tensor: %d", + res_error, + ref_error, + res.size(), + res.dtype, + multiplier, + tol, + use_larger_multiplier_for_smaller_tensor, + ) + return passes_test + + if ignore_non_fp: + return True + + log_error("Accuracy failed: allclose not within tol=%s", tol) + return False + elif isinstance(ref, (str, int, type(None), bool, torch.device)): + if ignore_non_fp: + return True + r = ref == res + if not r: + log_error("Accuracy failed (%s): %s != %s", type(ref), ref, res) + return r + elif is_numpy_int_type(ref) or is_numpy_float_type(ref): + if relax_numpy_equality and not ( + is_numpy_int_type(res) or is_numpy_float_type(res) + ): + ref = ref.item() + r = (type(ref) is type(res)) and (ref == res) + if not r: + log_error("Accuracy failed (numpy): %s != %s", ref, res) + return r + elif is_numpy_ndarray(ref): + return (type(ref) is type(res)) and same( + torch.as_tensor(ref), + torch.as_tensor(res), + fp64_ref, + cos_similarity=cos_similarity, + tol=tol, + equal_nan=equal_nan, + exact_dtype=exact_dtype, + relax_numpy_equality=relax_numpy_equality, + ignore_non_fp=ignore_non_fp, + log_error=log_error, + use_larger_multiplier_for_smaller_tensor=use_larger_multiplier_for_smaller_tensor, + ) + elif type(ref).__name__ in ( + "MaskedLMOutput", + "Seq2SeqLMOutput", + "CausalLMOutputWithCrossAttentions", + "LongformerMaskedLMOutput", + "Instances", + "SquashedNormal", + "Boxes", + "Normal", + "TanhTransform", + "Foo", + "Variable", + ): + assert type(ref) is type(res) + return all( + same( + getattr(ref, key), + getattr(res, key), + getattr(fp64_ref, key), + cos_similarity=cos_similarity, + tol=tol, + equal_nan=equal_nan, + exact_dtype=exact_dtype, + relax_numpy_equality=relax_numpy_equality, + ignore_non_fp=ignore_non_fp, + log_error=log_error, + use_larger_multiplier_for_smaller_tensor=use_larger_multiplier_for_smaller_tensor, + ) + for key in ref.__dict__.keys() + ) + else: + raise RuntimeError(f"unsupported type: {type(ref).__name__}") + + +def format_func_info(code): + short_filename = code.co_filename.split("/")[-1] + return f"'{code.co_name}' ({short_filename}:{code.co_firstlineno})" + + +@contextlib.contextmanager +def disable_cache_limit(): + prior = config.cache_size_limit + config.cache_size_limit = sys.maxsize + prior_acc_limit = config.accumulated_cache_size_limit + config.accumulated_cache_size_limit = sys.maxsize + + try: + yield + finally: + config.cache_size_limit = prior + config.accumulated_cache_size_limit = prior_acc_limit + + +# map from transformed code back to original user code +orig_code_map = ExactWeakKeyDictionary() + +# keep a record of code_obj -> list of guard failure reasons for logging +guard_failures: DefaultDict[Any, List[Any]] = collections.defaultdict(list) + +# Keep a record of graph break reasons for logging +graph_break_reasons: List[torch._dynamo.output_graph.GraphCompileReason] = [] + +# keep record of compiled code, if we are in "error if recompile" +# to track code that dynamo has compiled previously +seen_code_map = ExactWeakKeyDictionary() + + +# return same dir unless user changes config between calls +@functools.lru_cache(None) +def _get_debug_dir(root_dir): + dir_name = ( + "run_" + + datetime.datetime.now().strftime("%Y_%m_%d_%H_%M_%S_%f") + # use pid to avoid conflicts among ranks + + "-pid_" + + str(os.getpid()) + ) + return os.path.join(root_dir, dir_name) + + +def get_debug_dir(): + debug_root = config.debug_dir_root + return _get_debug_dir(debug_root) + + +def extract_fake_example_value(node, required=True): + if "example_value" in node.meta and is_fake(node.meta["example_value"]): + return node.meta["example_value"] + elif required: + from torch._dynamo.exc import unimplemented + + unimplemented("`FakeTensor` example value was required but not available") + else: + return None + + +def ensure_graph_fake(e, tx): + assert maybe_get_fake_mode(e) is tx.fake_mode + return e + + +def get_fake_values_from_nodes(tx, nodes, allow_non_graph_fake): + def visit(n: torch.fx.Node): + if n.op == "call_function" and "example_value" not in n.meta: + # fake tensor validity is checked inside get_fake_value using + # ensure_graph_fake + return get_fake_value(n, tx, allow_non_graph_fake) + + out = n.meta["example_value"] + if not allow_non_graph_fake and isinstance(out, torch.Tensor): + return ensure_graph_fake(out, tx) + return out + + return torch.fx.node.map_arg(nodes, visit) + + +def get_fake_value(node, tx, allow_non_graph_fake=False): + """ + Run the computation represented by `node` using fake tensors and return the result. + + allow_non_graph_fake: whether to allow the return result to be: + 1. non-fake or 2. fake that is not created by this instance of Dynamo. + If `True`, you must be prepared to deal with such return values, ideally + by further wrapping them as this graph's fakes. + """ + from torch.utils._sympy.value_ranges import ValueRangeError + + from .exc import ( + TorchRuntimeError, + unimplemented, + Unsupported, + UserError, + UserErrorType, + ) + + op = node.op + + # FX Node should always return the same fake value + if "example_value" in node.meta and is_fake(node.meta["example_value"]): + return node.meta["example_value"] + + args, kwargs = get_fake_values_from_nodes( + tx, (node.args, node.kwargs), allow_non_graph_fake + ) + + nnmodule = None + if op == "call_method" and len(args) > 0 and isinstance(args[0], torch.nn.Module): + # If the first argument is nn.Module, should copy to fake mode. + args = (deepcopy_to_fake_tensor(args[0], tx.fake_mode),) + tuple(args[1:]) + + if op == "call_module": + nnmodule = tx.output.nn_modules[node.target] + + if is_lazy_module(nnmodule) and hasattr(nnmodule, "_initialize_hook"): + # In the case of a lazy module, we want to run + # the pre-hooks which initialize it. + # Afterwards, lazy module deletes its pre-hooks + # to avoid treating it as lazy on subsequent recompile. + nnmodule._infer_parameters(nnmodule, args) + + # no matter it's lazy module or not, we should copy to fake mode. + nnmodule = deepcopy_to_fake_tensor(nnmodule, tx.fake_mode) + + try: + with tx.fake_mode, enable_python_dispatcher(): + ret_val = wrap_fake_exception( + lambda: run_node(tx.output, node, args, kwargs, nnmodule) + ) + except Unsupported: + raise + except RuntimeError as e: + cause: BaseException = e + if e.__cause__ is not None: + cause = e.__cause__ + + if isinstance( + cause, torch._subclasses.fake_tensor.DataDependentOutputException + ): + unimplemented( + f"data dependent operator: {cause.func}; " + "to enable, set torch._dynamo.config.capture_scalar_outputs = True" + ) + elif isinstance( + cause, torch._subclasses.fake_tensor.DynamicOutputShapeException + ): + if not torch._dynamo.config.capture_dynamic_output_shape_ops: + unimplemented( + f"dynamic shape operator: {cause.func}; " + "to enable, set torch._dynamo.config.capture_dynamic_output_shape_ops = True" + ) + else: + unimplemented( + f"dynamic shape operator: {cause.func}; " + "Operator does not have a meta kernel that supports dynamic output shapes, " + "please report an issue to PyTorch" + ) + elif isinstance( + cause, torch._subclasses.fake_tensor.UnsupportedOperatorException + ): + op = cause.func + import_suggestion = "" + if isinstance(op, torch._ops.OpOverload): + maybe_pystub = torch._C._dispatch_pystub( + op._schema.name, op._schema.overload_name + ) + if maybe_pystub is not None: + module, ctx = maybe_pystub + import_suggestion = ( + f"It's possible that the support was implemented in " + f"module `{module}` and you may need to `import {module}`" + f"({ctx}), otherwise " + ) + unimplemented( + f"unsupported operator: {cause.func} ({import_suggestion}see " + "https://docs.google.com/document/d/1GgvOe7C8_NVOMLOCwDaYV1mXXyHMXY7ExoewHqooxrs/edit#heading=h.64r4npvq0w0" + " for how to fix)" + ) + elif isinstance( + cause, torch.fx.experimental.symbolic_shapes.GuardOnDataDependentSymNode + ): + raise UserError( # noqa: B904 + UserErrorType.CONSTRAINT_VIOLATION, + str(cause), + case_name="constrain_as_size_example", + ) + elif isinstance(cause, ValueRangeError): + raise UserError(UserErrorType.CONSTRAINT_VIOLATION, e.args[0]) from e + elif isinstance(cause, TypeError) and "argument" in str(cause): + unimplemented(f"TypeError {node.target}: {cause}") + + raise TorchRuntimeError(str(e)).with_traceback(e.__traceback__) from None + + if not allow_non_graph_fake: + _ = pytree.tree_map_only( + torch.Tensor, functools.partial(ensure_graph_fake, tx=tx), ret_val + ) + return ret_val + + +_current_node = threading.local() + + +def get_current_node(): + return getattr(_current_node, "value", None) + + +@contextmanager +def set_current_node(node): + old = get_current_node() + _current_node.value = node + try: + yield + finally: + _current_node.value = old + + +def run_node(tracer, node, args, kwargs, nnmodule): + """ + Runs a given node, with the given args and kwargs. + + Behavior is dictated by a node's op. + + run_node is useful for extracting real values out of nodes. + See get_real_value for more info on common usage. + + Note: The tracer arg is only used for 'get_attr' ops + Note: The nnmodule arg is only used for 'call_module' ops + + Nodes that are not call_function, call_method, call_module, or get_attr will + raise an AssertionError. + """ + op = node.op + + with set_current_node(node): + + def make_error_message(e): + return f"Failed running {op} {node.target}(*{args}, **{kwargs}):\n" + str(e) + + try: + if op == "call_function": + return node.target(*args, **kwargs) + elif op == "call_method": + return getattr(args[0], node.target)(*args[1:], **kwargs) + elif op == "call_module": + assert nnmodule is not None + return nnmodule(*args, **kwargs) + elif op == "get_attr": + return tracer.output_graph.get_submodule(node.target) + elif op == "placeholder": + assert "example_value" in node.meta + return node.meta["example_value"] + + except (NotImplementedError, UnsupportedFakeTensorException) as e: + # NB: mimic how wrap_fake_exception does it + from .exc import unimplemented + + unimplemented(make_error_message(e), from_exc=e) + except Exception as e: + raise RuntimeError(make_error_message(e)).with_traceback( + e.__traceback__ + ) from e + + raise AssertionError(op) + + +def get_real_value(node, tracer): + """ + Run the actual computation represented by `node` and return the result. + This will execute any dependent nodes in the graph as well. + """ + from .exc import TorchRuntimeError + + cache = tracer.real_value_cache + if node in cache: + return cache[node] + + op = node.op + args, kwargs = torch.fx.node.map_arg( # type: ignore[misc] + (node.args, node.kwargs), + lambda n: get_real_value(n, tracer), + ) + + if op == "placeholder" and "grapharg" in node.meta: + return node.meta["grapharg"].example + + if op == "call_module": + nn_module = tracer.output_graph.nn_modules[node.target] + if not is_lazy_module(nn_module): + nn_module = copy.deepcopy(nn_module) + else: + # In the case of a lazy module, we want to run + # the pre-hooks which initialize it + nn_module(*args, **kwargs) + else: + nn_module = None + + try: + real_value = run_node(tracer, node, args, kwargs, nn_module) + cache[node] = real_value + except RuntimeError as e: + raise TorchRuntimeError(str(e)).with_traceback(e.__traceback__) from None + return real_value + + +def assert_no_fake_params_or_buffers(gm): + from torch._subclasses.fake_tensor import FakeTensorConfig, is_fake + + def stack_or_hint(t): + if FakeTensorConfig.debug: + import traceback + + return f"FAKE TENSOR CREATION TRACEBACK: \n {traceback.format_list(t._debug_trace)}" + else: + return "Enable TORCH_FAKE_TENSOR_DEBUG=1 to get creation stack traces on fake tensors." + + for name, buffer in gm.named_buffers(): + assert not is_fake( + buffer + ), f"Unexpected fake buffer {name} {stack_or_hint(buffer)}" + for name, param in gm.named_parameters(): + assert not is_fake( + param + ), f"Unexpected fake param {name} {stack_or_hint(param)}" + + +def fqn(obj: Any): + """ + Returns the fully qualified name of the object. + """ + return f"{obj.__module__}.{obj.__qualname__}" + + +def ifdynstaticdefault(count1, count2): + if torch._dynamo.config.assume_static_by_default: + return count1 + else: + return count2 + + +def import_submodule(mod: types.ModuleType): + """ + Ensure all the files in a given submodule are imported + """ + for filename in sorted(os.listdir(os.path.dirname(cast(str, mod.__file__)))): + if filename.endswith(".py") and filename[0] != "_": + importlib.import_module(f"{mod.__name__}.{filename[:-3]}") + + +def object_has_getattribute(value: Any): + return class_has_getattribute(type(value)) + + +def class_has_getattribute(cls: type): + try: + if isinstance( + inspect.getattr_static(cls, "__getattribute__"), + types.FunctionType, + ): + return True + except AttributeError: + pass + return False + + +def get_custom_getattr(value: Any, ignore_nn_module_getattr: bool = False): + try: + getattr_fn = inspect.getattr_static(type(value), "__getattr__") + except AttributeError: + getattr_fn = None + if ignore_nn_module_getattr and getattr_fn is torch.nn.Module.__getattr__: + # ignore this case of getattr + getattr_fn = None + return getattr_fn + + +class TensorStaticReason(enum.Enum): + PARAMETER = 2 + NOT_TENSOR = 4 + NN_MODULE_PROPERTY = 5 + + +def tensor_static_reason_to_message(reason: TensorStaticReason): + if reason == TensorStaticReason.PARAMETER: + return "mark_dynamic on parameter, parameters are always static today." + if reason == TensorStaticReason.NOT_TENSOR: + return "mark_dynamic on a non tensor, how did this happen?" + if reason == TensorStaticReason.NN_MODULE_PROPERTY: + return "tensor is static because it is nn module associated." + raise AssertionError(f"Illegal reason {reason}") + + +def tensor_always_has_static_shape( + tensor: Union[torch.Tensor, Any], + is_tensor: bool, + tensor_source: Source, +) -> Tuple[bool, Optional[TensorStaticReason]]: + """ + Given a tensor, source, and is_tensor flag, determine if a shape should be static. + + Args: + tensor - the real tensor to evaluate, parameters force a static shape. + is_tensor - internal dynamo check, essentially "is_tensor": target_cls is TensorVariable, + tensors not in a TensorVariable for whatever reason are forced static. + + Returns a tuple, where the first element is the bool of whether or not this tensor should have a static shape. + The second element is a TensorStaticReason, useful for passing to tensor_static_reason_to_message if needed. + """ + from .source import is_from_unspecialized_param_buffer_source + + if ( + tensor_source.guard_source().is_specialized_nn_module() + or tensor_source.guard_source().is_unspecialized_builtin_nn_module() + ) and config.force_nn_module_property_static_shapes: + return True, TensorStaticReason.NN_MODULE_PROPERTY + + if ( + type(tensor) is torch.nn.Parameter + or is_from_unspecialized_param_buffer_source(tensor_source) + ) and config.force_parameter_static_shapes: + return True, TensorStaticReason.PARAMETER + if not is_tensor: + return True, TensorStaticReason.NOT_TENSOR + return False, None + + +def lazy_format_graph_tabular(fn_name, gm): + def inner(): + try: + from tabulate import tabulate # TODO: Check that this is installed + except ImportError: + return ( + "Tabulate module missing, please install tabulate to log the graph in tabular format, logging code instead:\n" + + str(lazy_format_graph_code(fn_name, gm)) + ) + + node_specs = [ + [n.op, n.name, n.target, n.args, n.kwargs] for n in gm.graph.nodes + ] + graph_str = tabulate( + node_specs, headers=["opcode", "name", "target", "args", "kwargs"] + ) + return _format_graph_code(fn_name, gm.forward.__code__.co_filename, graph_str) + + return LazyString(inner) + + +def format_bytecode(prefix, name, filename, line_no, code): + return f"{prefix} {name} {filename} line {line_no} \n{dis.Bytecode(code).dis()}\n" + + +forward_hook_names = ["_forward_pre_hooks", "_forward_hooks"] +backward_hook_names = ["_backward_pre_hooks", "_backward_hooks"] +state_dict_hook_names = [ + "_state_dict_pre_hooks", + "_state_dict_hooks", + "_load_state_dict_pre_hooks", + "_load_state_dict_post_hooks", +] +all_hook_names = forward_hook_names + backward_hook_names + state_dict_hook_names + + +def nn_module_has_global_hooks(): + # This is limited to backward hooks for now because NNModuleVariable + # supports fwd hooks underneath. + return len(torch.nn.modules.module._global_backward_hooks) or len( + torch.nn.modules.module._global_backward_pre_hooks + ) + + +def nn_module_get_all_hooks( + mod, + check_forward_hooks=False, + check_backward_hooks=False, + check_state_dict_hooks=False, +): + """ + Sometimes its useful to differentiate between types of hooks such as forward/backward/pre + hooks executed during module.__call__, and state_dict hooks which are executed separately. + """ + hook_dicts_to_check = [] + check_all_hooks = ( + not check_forward_hooks + and not check_backward_hooks + and not check_state_dict_hooks + ) + if check_forward_hooks or check_all_hooks: + hook_dicts_to_check.extend(forward_hook_names) + if check_backward_hooks or check_all_hooks: + hook_dicts_to_check.extend(backward_hook_names) + if check_state_dict_hooks: + hook_dicts_to_check.extend(state_dict_hook_names) + + all_hooks = [] + for hook_dict_name in hook_dicts_to_check: + hooks = getattr(mod, hook_dict_name, []) + for hook_name in hooks: + hook = hooks[hook_name] + + all_hooks.append(hook) + return all_hooks + + +def nnmodule_has_hooks( + mod, + check_forward_hooks=False, + check_backward_hooks=False, + check_state_dict_hooks=False, +): + """ + Helper function to check if a module has any hooks attached to it. + """ + hooks = nn_module_get_all_hooks( + mod, + check_forward_hooks=check_forward_hooks, + check_backward_hooks=check_backward_hooks, + check_state_dict_hooks=check_state_dict_hooks, + ) + return bool(hooks) + + +def to_numpy_helper(value): + """Convert tensor and tnp.ndarray to numpy.ndarray.""" + if is_fake(value): + return value + if isinstance(value, tnp.ndarray): + return to_numpy_helper(value.tensor) + elif isinstance(value, torch.Tensor): + return value.numpy(force=True) + elif isinstance(value, (tuple, list)): + return type(value)(to_numpy_helper(obj) for obj in value) + else: + return value + + +def numpy_to_tensor(value): + """Convert tnp.ndarray to tensor, leave other types intact. If a list/tuple, loop through it to convert.""" + assert np is not None + if isinstance(value, np.ndarray): + return torch.as_tensor(value) + if isinstance(value, tnp.ndarray): + return value.tensor + elif isinstance(value, (tuple, list)): + return type(value)(numpy_to_tensor(obj) for obj in value) + else: + return value + + +class numpy_to_tensor_wrapper: + def __init__(self, f): + self.f = f + self.__name__ = "wrapped_" + self.f.__name__ + + def __repr__(self): + return f">" + + def __call__(self, *args, **kwargs): + out = self.f(*args, **kwargs) + return numpy_to_tensor(out) + + +def numpy_attr_wrapper(obj, name): + if isinstance(obj, tnp.ndarray): + out = getattr(obj, name) + return numpy_to_tensor(out) + elif isinstance(obj, torch.Tensor): + out = getattr(tnp.ndarray(obj), name) + return numpy_to_tensor(out) + + +class numpy_method_wrapper: + """Convert obj from torch.Tensor to tnp.ndarray and call method. Then convert result back to torch.Tensor.""" + + def __init__(self, method: str): + self.method = method + self.__name__ = "wrapped_" + self.method + + def __repr__(self): + return f">" + + def __call__(self, *args, **kwargs): + obj = args[0] + if isinstance(obj, torch.Tensor): + obj = tnp.ndarray(obj) + method_callable = getattr(obj, self.method) + out = method_callable(*args[1:], **kwargs) + return numpy_to_tensor(out) + + +class numpy_operator_wrapper: + """Implements dunder methods for tnp.ndarray via functions from the operator library""" + + def __init__(self, op: Callable[..., Any]): + self.op = op + self.__name__ = f"wrapped_{op.__name__}" + + def __repr__(self): + return f">" + + def __call__(self, *args, **kwargs): + assert not kwargs + + args = ( + tnp.ndarray(arg) if isinstance(arg, torch.Tensor) else arg for arg in args + ) + out = self.op(*args) + return numpy_to_tensor(out) + + +def defake(x): + if not isinstance(x, FakeTensor): + return x + size: torch._prims_common.ShapeType + stride: torch._prims_common.StrideType + if x._has_symbolic_sizes_strides: + size = [] + for s in x.size(): + if isinstance(s, torch.SymInt): + size.append(s.node.shape_env.size_hint(s.node.expr)) + else: + size.append(s) + stride = [] + for s in x.stride(): + if isinstance(s, torch.SymInt): + stride.append(s.node.shape_env.size_hint(s.node.expr)) + else: + stride.append(s) + else: + size = x.size() + stride = x.stride() + y = torch.empty_strided( + size, + stride, + dtype=x.dtype, + device=x.device, + requires_grad=x.requires_grad, + ) + y.zero_() + return y + + +def is_utils_checkpoint(obj): + # Lazy import to avoid circular dependencies + import torch.utils.checkpoint + + return obj is torch.utils.checkpoint.checkpoint + + +def build_checkpoint_variable(**options): + import torch._higher_order_ops.wrap as higher_order_ops + + from .variables.higher_order_ops import TorchHigherOrderOperatorVariable + + # TODO - This is a temporary situation where we have two versions of + # checkpointing implementation. We will converge on one and remove the other. + activation_checkpoint_op: torch._ops.HigherOrderOperator = ( + higher_order_ops.tag_activation_checkpoint + ) + if torch._functorch.config.functionalize_rng_ops: + activation_checkpoint_op = higher_order_ops.wrap_activation_checkpoint + + return TorchHigherOrderOperatorVariable.make( + activation_checkpoint_op, + **options, + ) + + +def is_compile_supported(device_type): + from .eval_frame import is_dynamo_supported + + compile_supported = is_dynamo_supported() + if device_type == "cpu": + pass + elif device_type == "cuda" and compile_supported: + compile_supported = has_triton() + else: + compile_supported = False + return compile_supported + + +# The following 3.11 source code functions are adapted from +# https://github.com/python/cpython/blob/v3.11.4/Lib/traceback.py +# in order to output source code corresponding to bytecode in 3.11+. +# We need our own versions since we want to support multiline expressions. +def _fix_offset(str: str, offset: int) -> int: + """ + Convert byte offset `offset` of `str` into character offset. + Byte offset is used for 3.11+ instruction column data. + Takes things like unicode characters into consideration. + + Unchanged from CPython implementation. + """ + as_utf8 = str.encode("utf-8") + return len(as_utf8[:offset].decode("utf-8", errors="replace")) + + +@dataclasses.dataclass +class _Anchors: + # inclusive + left_end_lineno: int + left_end_offset: int + right_start_lineno: int + # exclusive + right_start_offset: int + + +def _extract_anchors_from_expr(segment: str) -> Optional[_Anchors]: + """ + Given source code `segment` corresponding to a bytecode + instruction, determine: + - for binary ops, the location of the binary op + - for indexing, the location of the brackets. + `segment` is expected to be a valid Python expression + """ + assert sys.version_info >= (3, 11) + + import ast + + try: + # Without brackets, `segment` is parsed as a statement. + # We expect an expression, so wrap `segment` in + # brackets to handle multi-line expressions. + tree = ast.parse("(\n" + segment + "\n)") + except SyntaxError: + return None + + if len(tree.body) != 1: + return None + + lines = segment.split("\n") + + # get character index given byte offset + def normalize(lineno, offset): + return _fix_offset(lines[lineno], offset) + + # Gets the next valid character index in `lines`, if + # the current location is not valid. Handles empty lines. + def next_valid_char(lineno, col): + while lineno < len(lines) and col >= len(lines[lineno]): + col = 0 + lineno += 1 + assert lineno < len(lines) and col < len(lines[lineno]) + return lineno, col + + # Get the next valid character index in `lines`. + def increment(lineno, col): + col += 1 + lineno, col = next_valid_char(lineno, col) + assert lineno < len(lines) and col < len(lines[lineno]) + return lineno, col + + # Get the next valid character at least on the next line + def nextline(lineno, col): + col = 0 + lineno += 1 + lineno, col = next_valid_char(lineno, col) + assert lineno < len(lines) and col < len(lines[lineno]) + return lineno, col + + statement = tree.body[0] + if isinstance(statement, ast.Expr): + expr = statement.value + if isinstance(expr, ast.BinOp): + # ast gives locations for BinOp subexpressions, e.g. + # ( left_expr ) + ( right_expr ) + # left^^^^^ right^^^^^ + # -2 since end_lineno is 1-indexed and because we added an extra + # bracket to `segment` when calling ast.parse + cur_lineno = cast(int, expr.left.end_lineno) - 2 + cur_col = normalize(cur_lineno, expr.left.end_col_offset) + cur_lineno, cur_col = next_valid_char(cur_lineno, cur_col) + + # Heuristic to find the operator character. + # The original CPython implementation did not look for ), \, or #, + # leading to incorrect anchor location, e.g. + # (x) + (y) + # ~~^~~~~~~ + while (ch := lines[cur_lineno][cur_col]).isspace() or ch in ")\\#": + if ch in "\\#": + cur_lineno, cur_col = nextline(cur_lineno, cur_col) + else: + cur_lineno, cur_col = increment(cur_lineno, cur_col) + + # binary op is 1 or 2 characters long, on the same line + right_col = cur_col + 1 + if ( + right_col < len(lines[cur_lineno]) + and not (ch := lines[cur_lineno][right_col]).isspace() + and ch not in "\\#" + ): + right_col += 1 + # right_col can be invalid since it is exclusive + + return _Anchors(cur_lineno, cur_col, cur_lineno, right_col) + elif isinstance(expr, ast.Subscript): + # ast gives locations for value and slice subexpressions, e.g. + # ( value_expr ) [ slice_expr ] + # value^^^^^ slice^^^^^ + # subscript^^^^^^^^^^^^^^^^^^^^ + # find left bracket (first '[' after value) + left_lineno = cast(int, expr.value.end_lineno) - 2 + left_col = normalize(left_lineno, expr.value.end_col_offset) + left_lineno, left_col = next_valid_char(left_lineno, left_col) + while lines[left_lineno][left_col] != "[": + left_lineno, left_col = increment(left_lineno, left_col) + # find right bracket (final character of expression) + right_lineno = cast(int, expr.end_lineno) - 2 + right_col = normalize(right_lineno, expr.end_col_offset) + return _Anchors(left_lineno, left_col, right_lineno, right_col) + elif isinstance(expr, ast.Call): + # ( func_expr ) (args, kwargs) + # func^^^^^ + # call^^^^^^^^^^^^^^^^^^^^^^^^ + # find left bracket (first '(' after func) + left_lineno = cast(int, expr.func.end_lineno) - 2 + left_col = normalize(left_lineno, expr.func.end_col_offset) + left_lineno, left_col = next_valid_char(left_lineno, left_col) + while lines[left_lineno][left_col] != "(": + left_lineno, left_col = increment(left_lineno, left_col) + # find right bracket (final character of expression) + right_lineno = cast(int, expr.end_lineno) - 2 + right_col = normalize(right_lineno, expr.end_col_offset) + return _Anchors(left_lineno, left_col, right_lineno, right_col) + + return None + + +def get_instruction_source_311(code: types.CodeType, inst: dis.Instruction) -> str: + """ + Python 3.11+ only. Returns lines of source code (from code object `code`) + corresponding to `inst`'s location data, and underlines relevant code to `inst`. + + Example: CALL on `g`: + f(g( + ^^ + h(x))) + ^^^^^ + + We need our own implementation since `format_frame_summary` in + Python's `traceback` module doesn't handle multi-line expressions + (and their anchor extraction code is not completely correct). + """ + assert inst.positions is not None + if inst.positions.lineno is None: + return "" + # The rstrip + "\n" pattern is used throughout this function to handle + # linecache.getline errors. Error lines are treated as empty strings "", but we want + # to treat them as blank lines "\n". + first_line = linecache.getline(code.co_filename, inst.positions.lineno).rstrip() + if inst.positions.end_lineno is None: + return first_line + if inst.positions.col_offset is None or inst.positions.end_col_offset is None: + return first_line + + # character index of the start of the instruction + start_offset = _fix_offset(first_line, inst.positions.col_offset) + # character index of the end of the instruction + # compute later since end may be a different line + end_offset = None + # expression corresponding to the instruction so we can get anchors + segment = "" + # underline markers to be printed - start with `~` marker and replace with `^` later + markers = [] + + # Compute segment and initial markers + if inst.positions.end_lineno == inst.positions.lineno: + end_offset = _fix_offset(first_line, inst.positions.end_col_offset) + segment = first_line[start_offset:end_offset] + markers.append(" " * start_offset + "~" * (end_offset - start_offset)) + else: + segment = first_line[start_offset:] + "\n" + markers.append(" " * start_offset + "~" * (len(first_line) - start_offset)) + last_line = linecache.getline( + code.co_filename, inst.positions.end_lineno + ).rstrip() + end_offset = _fix_offset(last_line, inst.positions.end_col_offset) + for lineno in range(inst.positions.lineno + 1, inst.positions.end_lineno): + line = linecache.getline(code.co_filename, lineno).rstrip() + segment += line + "\n" + # don't underline leading spaces + num_spaces = len(line) - len(line.lstrip()) + markers.append(" " * num_spaces + "~" * (len(line) - num_spaces)) + segment += last_line[:end_offset] + num_spaces = len(last_line) - len(last_line.lstrip()) + markers.append(" " * num_spaces + "~" * (end_offset - num_spaces)) + + anchors: Optional[_Anchors] = None + try: + anchors = _extract_anchors_from_expr(segment) + except AssertionError: + pass + + # replace `~` markers with `^` where necessary + if anchors is None: + markers = [marker.replace("~", "^") for marker in markers] + else: + # make markers mutable + mutable_markers: List[List[str]] = [list(marker) for marker in markers] + + # anchor positions do not take start_offset into account + if anchors.left_end_lineno == 0: + anchors.left_end_offset += start_offset + if anchors.right_start_lineno == 0: + anchors.right_start_offset += start_offset + + # Turn `~`` markers between anchors to `^` + for lineno in range(len(markers)): + for col in range(len(mutable_markers[lineno])): + if lineno < anchors.left_end_lineno: + continue + if lineno == anchors.left_end_lineno and col < anchors.left_end_offset: + continue + if ( + lineno == anchors.right_start_lineno + and col >= anchors.right_start_offset + ): + continue + if lineno > anchors.right_start_lineno: + continue + if mutable_markers[lineno][col] == "~": + mutable_markers[lineno][col] = "^" + + # make markers into strings again + markers = ["".join(marker) for marker in mutable_markers] + + result = "" + for i in range(len(markers)): + result += ( + linecache.getline(code.co_filename, inst.positions.lineno + i).rstrip() + + "\n" + ) + result += markers[i] + "\n" + return result + + +def get_static_address_type(t): + if isinstance(t, torch.Tensor): + return getattr(t, "_dynamo_static_input_type", None) + + return None + + +def is_rng_state_getter_or_setter(value): + getters = ( + # The following two functions are not identical, so don't remove anyone! + torch._C.Generator.get_state, + torch.default_generator.get_state, + torch.get_rng_state, + torch.cuda.get_rng_state, + ) + setters = ( + torch._C.Generator.set_state, + torch.default_generator.set_state, + torch.set_rng_state, + torch.cuda.set_rng_state, + ) + return value in (*setters, *getters) + + +def is_tensor_base_attr_getter(value): + return ( + isinstance(value, types.MethodWrapperType) + and value.__name__ == "__get__" + and value.__self__.__objclass__ is torch._C._TensorBase # type: ignore[attr-defined] + ) + + +def is_torch_function_object(value): + return hasattr(value, "__torch_function__") + + +def has_torch_function(vt: torch._dynamo.variables.base.VariableTracker) -> bool: + from torch._dynamo.variables import LazyVariableTracker, UserDefinedObjectVariable + from torch._dynamo.variables.torch_function import TensorWithTFOverrideVariable + + if isinstance(vt, TensorWithTFOverrideVariable): + return True + + if isinstance(vt, LazyVariableTracker): + LazyVariableTracker.realize(vt) + + return isinstance(vt, UserDefinedObjectVariable) and hasattr( + vt.value, "__torch_function__" + ) + + +# see note [Tensor Fakification and Symbol Caching] +def to_fake_tensor(t, fake_mode): + symbolic_context = None + source = None + if tracing_context := torch._guards.TracingContext.try_get(): + if t in tracing_context.tensor_to_context: + symbolic_context = tracing_context.tensor_to_context[t] + source = symbolic_context.tensor_source + + return fake_mode.from_tensor( + t, static_shapes=False, symbolic_context=symbolic_context, source=source + ) + + +# NB: this works for both classes and instances +def is_frozen_dataclass(value): + return ( + not object_has_getattribute(value) + and not class_has_getattribute(value) + and is_dataclass(value) + and value.__dataclass_params__.frozen + ) + + +def get_first_attr(obj, *attrs): + """ + Return the first available attribute or throw an exception if none is present. + """ + for attr in attrs: + if hasattr(obj, attr): + return getattr(obj, attr) + + raise AssertionError(f"{obj} does not has any of the attributes: {attrs}") + + +@contextlib.contextmanager +def maybe_enable_compiled_autograd(should_enable, fullgraph=True, dynamic=True): + if not should_enable: + yield + else: + + def compiler_fn(gm): + def inner_compiler(gm_, example_inputs_): + torch._dynamo.utils.counters["compiled_autograd"]["compiles"] += 1 + return torch._inductor.compile(gm_, example_inputs_) + + return torch.compile( + gm, backend=inner_compiler, fullgraph=fullgraph, dynamic=dynamic + ) + + with torch._dynamo.compiled_autograd.enable(compiler_fn) as ctx: + yield ctx + + +def invalid_removeable_handle(): + # need a subclass so weakref works + class Invalid(dict): # type: ignore[type-arg] + pass + + return RemovableHandle(Invalid()) + + +# Returns a "proxy" (new object with the same class and dict) for (non-GraphModule) nn.Module's. +# Attribute changes to the original object/proxy will be reflected in the other. +# This is useful for cases where we want a keep-alive reference to a module without increasing +# its reference count. +def nn_module_proxy(mod): + if not isinstance(mod, torch.nn.Module): + return mod + if isinstance(mod, torch.fx.GraphModule): + # Dynamo-generated GM's shouldn't contain user-created GM's + return mod + proxy = mod.__class__.__new__(mod.__class__) + proxy.__dict__ = mod.__dict__ + return proxy + + +class GmWrapper(torch.nn.Module): + def __init__(self, gm, unflatten_fn): + super().__init__() + self.gm = gm + self.unflatten_fn = unflatten_fn + + def forward(self, *args): + args: List[Any] = list(args) + return self.gm(*self.unflatten_fn(args)) + + +def flatten_graph_inputs(gm: torch.fx.GraphModule, inputs, compile_gm): + """ + Mutate inputs so that they are flat and wrap gm such that it + accepts those inputs. This is needed for graphs that take + bumpy inputs. + """ + inputs_idx_to_clear = [ + i + for i, node in enumerate(gm.graph.nodes) + if node.op == "placeholder" and node.meta.get("steal_arg", False) + ] + + if torch._dynamo.compiled_autograd.in_compiled_autograd_region: + # fast path, avoid pytree overhead + # compiled autograd inputs are always a list of tensors, maybe followed by symints + assert inputs_idx_to_clear == [0] + assert isinstance(inputs[0], list) + boxed_inputs_count = len(inputs[0]) + + def flatten_fn(args): + return args[0] + list(args[1:]) + + def unflatten_fn(flat_args): + return (flat_args[:boxed_inputs_count], *flat_args[boxed_inputs_count:]) + + compiled_fn = compile_gm(GmWrapper(gm, unflatten_fn), flatten_fn(inputs)) + else: + # slow path, don't know inputs structure + flat_inputs, spec = pytree.tree_flatten(inputs) + unflatten_fn = functools.partial(pytree.tree_unflatten, treespec=spec) + compiled_fn = compile_gm(GmWrapper(gm, unflatten_fn), flat_inputs) + # note this doesn't check the spec, assuming it is the same + flatten_fn = pytree.arg_tree_leaves + + def wrapper(*args): + flat_args = flatten_fn(args) + + # flat_args is a new list, so we need to clear references from the old list + for i in inputs_idx_to_clear: + args[i].clear() + + # this call is boxed to avoid increasing refcount until we reach aot_module_simplified forward + return compiled_fn(flat_args) + + return wrapper + + +def get_locals_to_steal(maybe_gm): + if not isinstance(maybe_gm, torch.fx.GraphModule) or not hasattr(maybe_gm, "meta"): + return [] + return maybe_gm.meta.get("locals_to_steal", []) + + +def set_locals_to_steal(gm, locals_to_steal): + gm.meta["locals_to_steal"] = locals_to_steal + + +class Lit: + def __init__(self, s): + self.s = s + + def __repr__(self): + return self.s + + +warn_once_cache: Set[str] = set() + + +def warn_once(msg, stacklevel=1): + # Dynamo causes all warnings.warn (in user code and in Dynamo code) to print all the time. + # https://github.com/pytorch/pytorch/issues/128427. + # warn_once is a workaround: if the msg has been warned on before, then we will not + # warn again. + # NB: it's totally ok to store a cache of all the strings: this is what warnings.warn does as well. + if msg in warn_once_cache: + return + warn_once_cache.add(msg) + warnings.warn(msg, stacklevel=stacklevel + 1) + + +def strip_color_from_string(text): + # This regular expression matches ANSI escape codes + ansi_escape = re.compile(r"\x1B[@-_][0-?]*[ -/]*[@-~]") + return ansi_escape.sub("", text) + + +@contextlib.contextmanager +def _disable_saved_tensors_hooks_during_tracing(): + # See NOTE: [Deferring tensor pack/unpack hooks until runtime] + try: + prior = torch._C._autograd._saved_tensors_hooks_set_tracing(True) + yield + finally: + torch._C._autograd._saved_tensors_hooks_set_tracing(prior) + + +def is_parameter_freezing(): + return torch._inductor.config.freezing and not torch.is_grad_enabled() + + +def get_torch_function_mode_stack(filter_ignored=True): + from .variables.torch_function import IGNORED_MODES + + stack = [_get_function_stack_at(i) for i in range(_len_torch_function_stack())] + if filter_ignored: + stack = [mode for mode in stack if type(mode) not in IGNORED_MODES] + + return stack + + +def get_torch_function_mode_stack_at(ind): + assert ind < _len_torch_function_stack() and ind >= 0 + return torch._C._get_function_stack_at(ind) + + +def set_torch_function_mode_stack(stack): + for i in range(_len_torch_function_stack()): + _pop_torch_function_stack() + + for mode in stack: + _push_on_torch_function_stack(mode) + + +def verify_guard_fn_signature(value): + fn = value.__metadata_guard__ + sig = inspect.signature(fn) + if len(sig.parameters) != 2: + from .exc import InternalTorchDynamoError + + raise InternalTorchDynamoError( + "Tensor subclass method __metadata_guard__ must take exactly two subclass metadata arguments" + ) + if fn.__self__ != value.__class__: + from .exc import InternalTorchDynamoError + + raise InternalTorchDynamoError( + "Tensor subclass method __metadata_guard__ must be a classmethod" + ) + + +def does_not_override_dict_iter_methods(user_cls): + return ( + user_cls.items in (dict.items, collections.OrderedDict.items) + and user_cls.values in (dict.values, collections.OrderedDict.values) + and user_cls.keys in (dict.keys, collections.OrderedDict.keys) + and user_cls.__iter__ in (dict.__iter__, collections.OrderedDict.__iter__) + ) + + +# Helper function to extract relevant parts of a tensor's __dict__ to store in node meta. +# To avoid ref cycles, it's important that no tensors are present here, so leave those out. +def _extract_tensor_dict(t): + KEYS_TO_COPY = [ + "_dynamo_static_input_type", + "tag", + ] + + tensor_dict = { + key: copy.copy(t.__dict__[key]) for key in KEYS_TO_COPY if key in t.__dict__ + } + + return tensor_dict + + +# This is useful for reconstructing within the Dynamo graph the non-graph-input objects +# whose lifetime is governed by the user. +# e.g. torch.cuda.Event is a prime example. +user_obj_id_to_weakref: Dict[int, weakref.ReferenceType[object]] = {} + + +def get_user_object_from_id(obj_id): + obj = user_obj_id_to_weakref[obj_id]() + assert obj is not None, "User object is no longer alive" + return obj + + +def store_user_object_weakref(obj): + obj_id = id(obj) + user_obj_id_to_weakref[obj_id] = weakref.ref(obj) + + +class CompileTimeInstructionCounter: + _counter: int = 0 + _id: int = -1 + _depth = 0 + + @classmethod + def start(cls) -> None: + cls._depth = cls._depth + 1 + if cls._depth == 1: + cls._id = _instruction_counter.start() + + @classmethod + def end(cls) -> None: + cls._depth = cls._depth - 1 + if cls._depth == 0: + cls._counter += _instruction_counter.end(cls._id) + cls._id = -1 + + @classmethod + def clear(cls) -> None: + cls._counter = 0 + + @classmethod + def value(cls) -> int: + return cls._counter + + @classmethod + @contextmanager + def record(cls): + try: + if config.record_compile_time_instruction_count: + cls.start() + yield + finally: + if config.record_compile_time_instruction_count: + cls.end() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..91d893e05cb89c65a9bf2a07b5ed973d20f9a2c6 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/__init__.py @@ -0,0 +1,317 @@ +# mypy: allow-untyped-defs +import copy +import dataclasses +import functools +import io +import json +import logging +import os +import re +import sys +import types +import warnings +import weakref +import zipfile +from collections import OrderedDict +from contextlib import contextmanager +from functools import lru_cache + +from typing import Any, Callable, Dict, List, Optional, Tuple, Union +from unittest.mock import patch + +import torch +import torch.fx +import torch.utils._pytree as pytree + +from torch._dispatch.python import enable_python_dispatcher +from torch._utils_internal import log_export_usage +from torch.export._tree_utils import reorder_kwargs +from torch.export.graph_signature import ( + ArgumentSpec, + ConstantArgument, + ExportGraphSignature, + InputKind, + InputSpec, + OutputKind, + OutputSpec, + SymIntArgument, + TensorArgument, +) +from torch.fx import traceback as fx_traceback +from torch.fx._compatibility import compatibility +from torch.fx.experimental.proxy_tensor import make_fx +from torch._subclasses.fake_tensor import unset_fake_temporarily +from torch.fx.graph import _PyTreeCodeGen, _PyTreeInfo + +from .wrappers import _wrap_submodules + +log = logging.getLogger(__name__) + +@dataclasses.dataclass +class ExportDynamoConfig: + """ + Manage Export-specific configurations of Dynamo. + """ + allow_rnn: bool = True + + +# We only want to print this once to avoid flooding logs in workflows where capture_pre_autograd_graph +# is called multiple times. +@lru_cache +def capture_pre_autograd_graph_warning(): + from torch._inductor import config + + log.warning("+============================+") + log.warning("| !!! WARNING !!! |") + log.warning("+============================+") + log.warning("capture_pre_autograd_graph() is deprecated and doesn't provide any function guarantee moving forward.") + log.warning("Please switch to use torch.export.export_for_training instead.") + if config.is_fbcode(): + log.warning("Unless the unittest is in the blocklist, capture_pre_autograd_graph() will fallback to torch.export.export_for_training.") # noqa: B950 + + +@compatibility(is_backward_compatible=False) +def capture_pre_autograd_graph( + f: torch.nn.Module, + args: Tuple[Any], + kwargs: Optional[Dict[str, Any]] = None, + dynamic_shapes: Optional[Union[Dict[str, Any], Tuple[Any]]] = None, +) -> torch.nn.Module: + """ + A helper function that is intended to trace a module before any pre-autograd + decomposition is run. The produced module will be "non-functional" and + composed of aten operators. Later this API will be deleted in favor of more general + torch.export API. + + Args: + f: nn.Module to be traced + + args: example positional inputs. + + kwargs: optional example keyword inputs. + + dynamic_shapes: Should either be: + 1) a dict from argument names of ``f`` to their dynamic shape specifications, + 2) a tuple that specifies dynamic shape specifications for each input in original order. + If you are specifying dynamism on keyword args, you will need to pass them in the order that + is defined in the original function signature. + + The dynamic shape of a tensor argument can be specified as either + (1) a dict from dynamic dimension indices to :func:`Dim` types, where it is + not required to include static dimension indices in this dict, but when they are, + they should be mapped to None; or (2) a tuple / list of :func:`Dim` types or None, + where the :func:`Dim` types correspond to dynamic dimensions, and static dimensions + are denoted by None. Arguments that are dicts or tuples / lists of tensors are + recursively specified by using mappings or sequences of contained specifications. + + Returns: + An nn.Module containing the traced method. + + """ + from torch.export._trace import _extract_fake_inputs, DEFAULT_EXPORT_DYNAMO_CONFIG, _ignore_backend_decomps + from torch._utils_internal import capture_pre_autograd_graph_using_training_ir + from torch._export.non_strict_utils import make_constraints + from torch._subclasses.functional_tensor import FunctionalTensor + from torch.export._unlift import _create_stateful_graph_module + from torch.export.dynamic_shapes import _combine_args + + capture_pre_autograd_graph_warning() + + if sys.platform == "win32": + raise RuntimeError("capture_pre_autograd_graph not yet supported on Windows") + + assert isinstance(f, torch.nn.Module), "Expected an nn.Module instance." + + if kwargs is None: + kwargs = {} + + if capture_pre_autograd_graph_using_training_ir(): + @lru_cache + def print_export_warning(): + log.warning("Using torch.export.export_for_training(...,strict=True)") + print_export_warning() + module = torch.export.export_for_training(f, args, kwargs, dynamic_shapes=dynamic_shapes, strict=True).module() + else: + log_export_usage(event="export.private_api", flags={"capture_pre_autograd_graph"}) + + # Do not decompose dropout for exported models, because in eval mode the dropout + # op disappears from the graph, which makes it difficult to switch to train mode. + # See https://github.com/pytorch/pytorch/pull/115258#issuecomment-1900755832. + decomp_table = { + op: op.decompose + for op in FunctionalTensor.maybe_aliasing_or_mutating_ops + if op != torch.ops.aten.dropout.default + } + with torch._dynamo.config.patch(dataclasses.asdict(DEFAULT_EXPORT_DYNAMO_CONFIG)), _ignore_backend_decomps(): + m = torch._dynamo.export( + f, + dynamic_shapes=dynamic_shapes, + assume_static_by_default=True, + tracing_mode="symbolic", + decomposition_table=decomp_table, + pre_dispatch=True, + aten_graph=True, + _log_export_usage=False, + )( + *args, + **kwargs, + )[0] + + _, _, fake_mode = _extract_fake_inputs(m, args, kwargs) + + m.meta["inline_constraints"] = { + k: v + for k, v in fake_mode.shape_env.var_to_range.items() + if re.match(r"^[if]\d+$", str(k)) + } + + if isinstance(f, torch.nn.Module): + from torch.export._trace import _restore_state_dict + _restore_state_dict(f, m) + + flat_args, _ = pytree.tree_flatten((args, kwargs or {})) + combined_args = _combine_args(f, args, kwargs) + range_constraints = make_constraints( + fake_mode, + m, + combined_args, + dynamic_shapes, + 0, + ) + + module = _create_stateful_graph_module( + m, + range_constraints=range_constraints, + ) + + error_message = \ + """ + Calling train() or eval() is not supported for exported models. + Alternatively, you may override these methods to do custom user behavior as follows: + + def _my_train(self, mode: bool = True): + ... + + def _my_eval(self): + ... + + model.train = types.MethodType(_my_train, model) + model.eval = types.MethodType(_my_eval, model) + """ + + def _train(self, mode: bool = True): + raise NotImplementedError(error_message) + + def _eval(self, mode: bool = True): + raise NotImplementedError(error_message) + + module.train = types.MethodType(_train, module) # type: ignore[method-assign] + module.eval = types.MethodType(_eval, module) # type: ignore[method-assign] + + # Remove Proxy because they cannot be deepcopied or pickled. + if hasattr(module, "_buffers"): + torch._export.utils.remove_proxy_from_state_dict( + module._buffers, in_place=True + ) + return module + + +def aot_compile( + f: Callable, + args: Tuple[Any], + kwargs: Optional[Dict[str, Any]] = None, + *, + dynamic_shapes: Optional[Dict[str, Any]] = None, + options: Optional[Dict[str, Any]] = None, + remove_runtime_assertions: bool = False, + disable_constraint_solver: bool = False, + same_signature: bool = True, +) -> str: + """ + Note: this function is not stable yet + + Traces either an nn.Module's forward function or just a callable with PyTorch + operations inside, generates executable cpp code from the program, and returns + the path to the generated shared library + + Args: + f: the `nn.Module` or callable to trace. + + args: example positional inputs. + + kwargs: optional example keyword inputs. + + dynamic_shapes: Should either be: + 1) a dict from argument names of ``f`` to their dynamic shape specifications, + 2) a tuple that specifies dynamic shape specifications for each input in original order. + If you are specifying dynamism on keyword args, you will need to pass them in the order that + is defined in the original function signature. + + The dynamic shape of a tensor argument can be specified as either + (1) a dict from dynamic dimension indices to :func:`Dim` types, where it is + not required to include static dimension indices in this dict, but when they are, + they should be mapped to None; or (2) a tuple / list of :func:`Dim` types or None, + where the :func:`Dim` types correspond to dynamic dimensions, and static dimensions + are denoted by None. Arguments that are dicts or tuples / lists of tensors are + recursively specified by using mappings or sequences of contained specifications. + + options: A dictionary of options to control inductor + + disable_constraint_solver: Whether the dim constraint solver must be disabled. + + Returns: + Path to the generated shared library + """ + from torch.export._trace import _export_to_torch_ir + from torch._inductor.decomposition import select_decomp_table + from torch._inductor import config + + if config.is_predispatch: + gm = torch.export._trace._export(f, args, kwargs, dynamic_shapes, pre_dispatch=True).module() + else: + # We want to export to Torch IR here to utilize the pre_grad passes in + # inductor, which run on Torch IR. + gm = _export_to_torch_ir( + f, + args, + kwargs, + dynamic_shapes, + disable_constraint_solver=disable_constraint_solver, + same_signature=same_signature, + # Disabling this flag, because instead we can rely on the mapping + # dynamo_flat_name_to_original_fqn which is coming from Dynamo. + restore_fqn=False, + ) + + with torch.no_grad(): + so_path = torch._inductor.aot_compile(gm, args, kwargs, options=options) # type: ignore[arg-type] + + return so_path + +def aot_load(so_path: str, device: str) -> Callable: + """ + Loads a shared library generated by aot_compile and returns a callable + + Args: + so_path: Path to the shared library + + Returns: + A callable + """ + if device == "cpu": + runner = torch._C._aoti.AOTIModelContainerRunnerCpu(so_path, 1) # type: ignore[call-arg] + elif device == "cuda" or device.startswith("cuda:"): + runner = torch._C._aoti.AOTIModelContainerRunnerCuda(so_path, 1, device) # type: ignore[assignment, call-arg] + else: + raise RuntimeError("Unsupported device " + device) + + def optimized(*args, **kwargs): + call_spec = runner.get_call_spec() # type: ignore[attr-defined] + in_spec = pytree.treespec_loads(call_spec[0]) + out_spec = pytree.treespec_loads(call_spec[1]) + flat_inputs = pytree.tree_flatten((args, 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+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/converter.py @@ -0,0 +1,1584 @@ +# mypy: allow-untyped-defs +import builtins +import logging +import operator +import typing +import warnings +from contextlib import contextmanager +from typing import Any, Dict, List, Optional, Sequence, Set, Tuple, Union + +import torch +import torch.export._trace +from torch import _C +from torch._export.passes.replace_quantized_ops_with_standard_ops_pass import ( + replace_quantized_ops_with_standard_ops, +) +from torch.export.exported_program import ExportedProgram +from torch.export.graph_signature import ( + ConstantArgument, + CustomObjArgument, + InputKind, + InputSpec, + OutputKind, + OutputSpec, + TensorArgument, +) +from torch.fx import subgraph_rewriter + + +log = logging.getLogger(__name__) + + +def _get_param_count_list(method_graph, args_params): + param_count_list = [] + for input_, arg_params_ in zip(method_graph.inputs(), args_params): + if "PackedParams" in str(input_.type()): + in_vars, _ = torch.jit._flatten(arg_params_) + param_count_list.append(len(in_vars)) + else: + param_count_list.append(arg_params_ is not None) + + return param_count_list + + +def _trace_and_get_graph_from_model(model, args): + # A basic sanity check: make sure the state_dict keys are the same + # before and after running the model. Fail fast! + orig_state_dict_keys = torch.jit._unique_state_dict(model).keys() + + # Disable Autocast cache because it replaces kernel's weight and bias + # by (undesired) constants. + # No perf impact for when there are reused weights since https://github.com/pytorch/pytorch/pull/85665 + prev_autocast_cache_enabled = torch.is_autocast_cache_enabled() + torch.set_autocast_cache_enabled(False) + trace_graph, torch_out, inputs_states = torch.jit._get_trace_graph( + model, + args, + strict=False, + _force_outplace=False, + _return_inputs_states=True, + ) + torch.set_autocast_cache_enabled(prev_autocast_cache_enabled) + + if orig_state_dict_keys != torch.jit._unique_state_dict(model).keys(): + raise RuntimeError( + "state_dict changed after running the tracer; " + "something weird is happening in your model!" + ) + + return trace_graph, torch_out + + +def _create_jit_graph( + model: Union[torch.nn.Module, torch.jit.ScriptFunction], args: Sequence[Any] +) -> Tuple[torch.Graph, List["_C.IValue"], Any, Optional[torch.ScriptModule]]: + if isinstance(model, (torch.jit.ScriptFunction, torch.jit.ScriptModule)): + flattened_args = tuple(torch.jit._flatten(tuple(args))[0]) + torch_out = None + + if isinstance(model, torch.jit.ScriptModule): + try: + graph = model.forward.graph # type: ignore[attr-defined] + except AttributeError as e: + raise RuntimeError("'forward' method must be a script method") from e + _C._jit_pass_onnx_function_substitution(graph) + freezed_module = _C._freeze_module( + typing.cast(_C.ScriptModule, model._c), preserveParameters=True + ) + module, params = _C._jit_onnx_list_model_parameters(freezed_module) + method_graph = module._get_method("forward").graph + args_params = tuple(args) + tuple(params) + param_count_list = _get_param_count_list(method_graph, args_params) + in_vars, _ = torch.jit._flatten(args_params) + graph = _C._propagate_and_assign_input_shapes( + method_graph, tuple(in_vars), param_count_list, False, False + ) + return graph, params, torch_out, module + + # torch.jit.ScriptFunction + params = [] + graph = model.graph + _C._jit_pass_onnx_function_substitution(graph) + param_count_list = _get_param_count_list(graph, args) + graph = _C._propagate_and_assign_input_shapes( + graph, flattened_args, param_count_list, False, False + ) + return graph, params, torch_out, None + + graph, torch_out = _trace_and_get_graph_from_model(model, args) + _C._jit_pass_onnx_lint(graph) + state_dict = torch.jit._unique_state_dict(model) + params = list(state_dict.values()) + graph_inputs = list(graph.inputs()) + user_input_num = len(graph_inputs) - len(state_dict) + param_names = list(state_dict.keys()) + for i, inp in enumerate(graph_inputs): + if i >= user_input_num: + inp.setDebugName(param_names[i - user_input_num]) + _C._jit_pass_onnx_function_substitution(graph) + return graph, params, torch_out, None + + +def list_add(a, b): + return a + b + + +def list_append(container, element): + return container + [element] + + +def execute_subgraph_from_prim_loop( + subgraph, iter_idx, len_loop_local_arguments, *args, **kwargs +): + """ + subgraph: GraphModule from sub-block. + iter_idx: The index of interation. + len_loop_local_arguments: The number of loop local arguments in args. + """ + + # Loop local variables. TS graph create those as inputs because their values + # are updated inside the loop. + loop_local_args = args[:len_loop_local_arguments] + # Global variables that are not passed in as inputs to the loop sub-blocks + # but are directly used. Most of time, their values are not updated, but + # the only exception is when there are some operations that perform inplace + # updates. + global_args = args[len_loop_local_arguments:] + return subgraph(*global_args, iter_idx, *loop_local_args, **kwargs) + + +def inplace_optimize_sym_size_div(gm: torch.fx.GraphModule): + def pattern(im, dim, scale): + sym_size_int = torch.ops.aten.sym_size.int(im, dim) + scalar_tensor = torch.ops.aten.scalar_tensor(sym_size_int) + div_scalar_mode = torch.ops.aten.div.Scalar_mode( + scalar_tensor, scale, rounding_mode="trunc" + ) + int_tensor = torch.ops.aten.Int.Tensor(div_scalar_mode) + return int_tensor + + def replacement(im, dim, scale): + sym_size_int = torch.ops.aten.sym_size.int(im, dim) + return sym_size_int // scale + + replaced_patterns = subgraph_rewriter.replace_pattern(gm, pattern, replacement) + + +def is_valid_for_codegen(name): + if len(name) == 0: + raise RuntimeError("Empty argument name for codegen") + if name[0].isdigit(): + return False + return True + + +def normalize_name(name: str, prefix: str = "rename") -> str: + name = name.replace(".", "_") + if is_valid_for_codegen(name): + return name + return f"{prefix}_{name}" + + +def ir_name_to_func_name(name: str) -> str: + """prim::If -> convert_prim_If""" + name_list = name.split("::") + return "convert_" + "_".join(name_list) + + +def get_node_as_placeholder_or_get_attr(fx_graph, name, is_top_level_graph): + if is_top_level_graph: + return fx_graph.get_attr(name) + return fx_graph.placeholder(name) + + +_TORCH_DTYPE_TO_ENUM = { + torch.uint8: 0, + torch.int8: 1, + torch.int16: 2, + torch.int32: 3, + torch.int64: 4, + torch.float16: 5, + torch.float32: 6, + torch.float64: 7, + torch.complex32: 8, + torch.complex64: 9, + torch.complex128: 10, + torch.bool: 11, + torch.qint8: 12, + torch.quint8: 13, + torch.bfloat16: 15, +} + +_TORCH_ENUM_TO_DTYPE = {value: key for key, value in _TORCH_DTYPE_TO_ENUM.items()} + + +def get_dtype_as_int(tensor): + """ + prim::dtype has the signature "Tensor a) -> int", where it gets the dtype of + the tensor and returns the integer corresponding to this dtype based on the + enum in ScalarType.h + """ + dtype = tensor.dtype + if dtype not in _TORCH_DTYPE_TO_ENUM: + raise RuntimeError(f"Unsupported dtype {dtype}") + return _TORCH_DTYPE_TO_ENUM[dtype] + + +# Those operators will be automatically populated to a instance method +# of TS2FXGraphConverter with name convert__(). +# Please check __init__ for method population implementations. +kind_to_standard_operators = { + "prim::max": builtins.max, + "prim::min": builtins.min, + "prim::TupleIndex": operator.getitem, + "aten::__is__": operator.is_, + "aten::__isnot__": operator.is_not, + "aten::__not__": operator.not_, + "aten::__contains__": operator.contains, + "prim::dtype": get_dtype_as_int, + "aten::len": len, + # Mapping from specialized op to its symbolic counterpart. + # They currently do not have any other overrides. + "aten::numel": torch.ops.aten.sym_numel, + "aten::size": torch.ops.aten.sym_size, + "aten::storage_offset": torch.ops.aten.sym_storage_offset, + "aten::stride": torch.ops.aten.sym_stride, +} + + +def get_ir_value_parent_name_and_attr_name(node): + irv_parent_name, irv_name = node.input().debugName(), node.output().debugName() + attr_name = node.s("name") + return irv_name, irv_parent_name, attr_name + + +def construct_fqn(ir, ref_map, name_map): + name_list = [] + while ir in ref_map: + name_list.append(name_map[ir]) + ir = ref_map[ir] + return ".".join(reversed(name_list)) + + +def get_block_to_lifted_attrs(graph: torch._C.Graph) -> Dict[torch._C.Block, Set[str]]: + """ + Perform two passes to get a mapping of blocks to a set of FQNs of its lifted attributes. + When a graph has control flow, the graph will be divided into multiple blocks. We want to convert + each block to a graph which will be passed into torch.cond. A restriction for torch.cond is that model + parameters/buffers are expected to be lifted as inputs to the subgraphs. Before converting the model, + we will run this pass which will: + 1. Figure out which params/buffers are used within blocks through tracing the GetAttr calls. + 2. Process the graph bottom up to find the lifted attributes of each block by taking the union + of the attributes used in the current block, and the lifted attributes of all its child blocks. + + Returns: + A mapping of blocks to a set of FQNs of its lifted attributes. + """ + + # A map from a block to its expected to be lifted arguments. + blocks_to_lifted_attrs: Dict[torch._C.Block, Set[str]] = {} + + # Reference map stores the input (i.e., src) and output (i.e., dest) IR of a + # GetAttr node. By traversing this reference map, we can figure out the + # full IR aliasing pass and figure out the FQN of an attribute. + # E.g., %2 = GetAttr(linear)[%1] --> node_to_parent_map["%2"] = "%1" + node_to_parent_map: Dict[str, str] = {} + + # Used for reconstructing the FQN of an attribute based on the reference map. + # In nutshell, for each GetAttr call, GetAttr(input IR, attribute name) -> output IR + # This name map stores which attribute name is called for a src IR --> dest IR action. + # E.g., %2 = GetAttr(linear)[%1] --> node_to_attr_name["%2"] = "linear" + node_to_attr_name: Dict[str, str] = {} + + def _dfs_get_attr_dependency(entry): + """ + First DFS path to construct reference map and name map. + """ + for node in entry.nodes(): + if node.kind() == "prim::GetAttr": + ( + irv_name, + irv_parent_name, + attr_name, + ) = get_ir_value_parent_name_and_attr_name(node) + node_to_parent_map[irv_name] = irv_parent_name + node_to_attr_name[irv_name] = attr_name + for block in node.blocks(): + _dfs_get_attr_dependency(block) + + def _map_blocks_to_lifted_attrs(entry): + """ + Walk the graph in a bottom-up fashion to build the expected to be + lifted arguments for each block. + """ + arguments: Set[str] = set() + for node in entry.nodes(): + for block in node.blocks(): + # Recursively build. + arguments = arguments.union(_map_blocks_to_lifted_attrs(block)) + if node.kind() == "prim::GetAttr": + irv_name = node.output().debugName() + # Skip for intermediate GetAttr, which will anyway not result a FQN. + # E.g., node_to_parent_name: {"%3": "%2", "%2": "%1"} + # node_to_attr_name: {"%3": "weight", "%2": "linear", "%1": "self"} + # There is only one FQN %3-->%2-->%1: self.linear.weight + # %2-->%1 is not a FQN: self.linear + if irv_name not in set(node_to_parent_map.values()): + arguments.add( + construct_fqn(irv_name, node_to_parent_map, node_to_attr_name) + ) + if not isinstance(entry, torch._C.Graph): # Skip the top level. + blocks_to_lifted_attrs[entry] = arguments + return arguments + + _dfs_get_attr_dependency(graph) + _map_blocks_to_lifted_attrs(graph) + + return blocks_to_lifted_attrs + + +def get_attribute_fqn_from_ts_node( + name_to_attribute_fqn: Dict[str, str], node: torch._C.Node +) -> str: + def get_attr(name: str): + if name in name_to_attribute_fqn: + return name_to_attribute_fqn[name] + else: + raise ValueError(f"Attribute {name} not found") + + if node.kind() == "prim::SetAttr": + input_name = next(node.inputs()).debugName() + elif node.kind() == "prim::GetAttr": + input_name = node.input().debugName() + else: + raise RuntimeError( + f"Unexpected node kind when getting attribute fqn. node: {node} " + ) + + attr_name = node.s("name") + root_attr_name = get_attr(input_name) + attr_fqn = f"{root_attr_name}.{attr_name}" if root_attr_name else attr_name + + return attr_fqn + + +def get_op_overload(node: torch._C.Node): + schema_str = node.schema() + assert schema_str != "(no schema)", f"got empty schema for {node}" + schema: torch._C.FunctionSchema = torch._C.parse_schema(schema_str) + ns, op_name = str(schema.name).split("::") + override = schema.overload_name + + try: + op_overload_mod = getattr(torch.ops, ns) + op_overload_packet = getattr(op_overload_mod, op_name) + if override: + op_overload = getattr(op_overload_packet, override) + else: + op_overload = op_overload_packet.default + except Exception as e: + raise RuntimeError( + f"Unable to find operator {node.kind()} with schema {node.schema()}" + ) from e + + return op_overload + + +class TS2FXGraphConverter: + def __init__( + self, + ts_graph: Union[torch._C.Graph, torch._C.Block], + name_to_param: Dict[str, torch.Tensor], + name_to_buffer: Dict[str, torch.Tensor], + blocks_to_lifted_attrs: Dict[torch._C.Block, Set[str]], + name_to_non_tensor_attribute: Dict[str, Any], + name_to_constant: Dict[str, Any], + ): + self.ts_graph = ts_graph + self.name_to_param = name_to_param + self.name_to_buffer = name_to_buffer + + self.fx_graph: torch.fx.Graph = torch.fx.Graph() + self.input_specs: List[InputSpec] = [] + self.output_specs: List[OutputSpec] = [] + + self.name_to_node: Dict[ + str, Union[torch.fx.Node, List[torch.fx.Node], Dict[Any, torch.fx.Node]] + ] = {} + self.name_to_constant: Dict[str, Any] = name_to_constant + + # Mapping from torchscript node output name to attribute fully qualified name + self.name_to_attribute_fqn: Dict[str, str] = {} + + # Mapping from fully qualified name to real values or a fx graph node + # During convert, this represents the current value of a non-tensor attribute + # One use case is: + # def forward(self, x): + # c1 = self.count + # self.count += 1 + # c2 = self.count + # return x + c1 + c2 + self.name_to_non_tensor_attribute_node: Dict[str, Any] = {} + + # Mapping from fully qualified name to initial real values inputs + # We separate it from self.name_to_non_tensor_attribute_node since + # we need initial real value input when we construct fx.GraphModule + self.name_to_non_tensor_attribute: Dict[str, Any] = name_to_non_tensor_attribute + + self.subgraphs: Dict[str, torch.fx.GraphModule] = {} + + self.blocks_to_lifted_attrs = blocks_to_lifted_attrs + + # Populate methods for the standard operators. + for k in kind_to_standard_operators.keys(): + handler_func_name = ir_name_to_func_name(k) + # Create an indirect function call: + # convert__ --> lambda node: _convert_standard_operator(node) + setattr( + self, + handler_func_name, + lambda node: self._convert_standard_operators(node), + ) + + # This stores a list of return results that do not appear in the original TS + # graph's outputs. The reason we maintain this is because some operations in the sub-block + # might have inplace updates to the variable defined in the parent fx graph. After + # the execution of that sub-block, the variable defined in the parent fx graph also + # needs to be updated. + self.name_update_from_subblock_to_parent: Set[str] = set() + + def _is_get_attr_node(self, fqn): + return ( + fqn in self.name_to_buffer + or fqn in self.name_to_param + or ( + fqn in self.name_to_constant + and isinstance(self.name_to_constant[fqn], torch.ScriptObject) + ) + ) + + def _convert_block_to_subgraph(self, node: torch._C.Node, arguments: List[str]): + subgraph_nodes, subgraph_converters = [], [] + for block in node.blocks(): + subgraph_converter = TS2FXGraphConverter( + block, + self.name_to_param, + self.name_to_buffer, + self.blocks_to_lifted_attrs, + {}, + self.name_to_constant, + ) + subgraph_converter.name_to_attribute_fqn = self.name_to_attribute_fqn + + for block_arg in arguments: + normalized_block_arg_name = normalize_name(block_arg) + placeholder_node = subgraph_converter.fx_graph.placeholder( + normalized_block_arg_name + ) + subgraph_converter.name_to_node[block_arg] = placeholder_node + + subgraph = subgraph_converter.convert() + subgraph_name = self.add_subgraph(subgraph) + subgraph_nodes.append(self.fx_graph.get_attr(subgraph_name)) + subgraph_converters.append(subgraph_converter) + return subgraph_nodes, subgraph_converters + + def _identify_inputs_as_arguments(self, entry): + """ + Identify inputs from the innermost sub-block. This is needed + for nested sub-blocks when the input is hidden in the nested sub-block. + E.g., example IR of input is hidden in the nested sub-block. + Graph[x.1] + %1 = ... + Block[] + Block[x.1] + %2 = x.1 ... + """ + arguments: Set[str] = set() + for block in entry.blocks(): + for block_node in block.nodes(): + for block_node_in in block_node.inputs(): + if ( + block_node_in.debugName() in self.name_to_node + and block_node_in.debugName() not in self.name_to_attribute_fqn + ): + arguments.add(block_node_in.debugName()) + arguments = arguments.union( + self._identify_inputs_as_arguments(block_node) + ) + return arguments + + def is_top_level_graph(self): + return isinstance(self.ts_graph, torch._C.Graph) + + def add_subgraph(self, subgraph) -> str: + name = f"subgraph_{len(self.subgraphs)}" + self.subgraphs[name] = subgraph + return name + + def get_args_kwargs(self, node: torch._C.Node, schema): + args = [] + kwargs = {} + for input, schema_arg in zip(node.inputs(), schema.arguments): + if schema_arg.kwarg_only: + kwargs[schema_arg.name] = self.get_fx_value_by_ir_value(input) + else: + args.append(self.get_fx_value_by_ir_value(input)) + + return tuple(args), kwargs + + def get_fx_value_by_ir_value(self, value: torch._C.Value): + value_name = value.debugName() + + if value_name in self.name_to_node: + input_node = self.name_to_node[value_name] + return input_node + elif value_name in self.name_to_constant: + if isinstance(self.name_to_constant[value_name], torch.ScriptObject): + return self.fx_graph.get_attr(value_name) + return self.name_to_constant[value_name] + else: + raise ValueError(f"Input {value_name} not found") + + def get_fx_value_by_fqn(self, name): + if name in self.name_to_node: + fx_node = self.name_to_node[name] + elif name in self.name_to_constant: + fx_node = self.name_to_constant[name] + elif name in self.name_to_non_tensor_attribute_node: + fx_node = self.name_to_non_tensor_attribute_node[name] + elif name in self.name_to_non_tensor_attribute: + fx_node = self.name_to_non_tensor_attribute[name] + else: + raise ValueError(f"Attribute {name} not found") + return fx_node + + def convert(self) -> torch.fx.GraphModule: + self.convert_graph_inputs() + + for node in self.ts_graph.nodes(): + self.convert_node(node) + + self.convert_graph_outputs() + + # Pass parameter and buffer to the root for lookup. + gm = torch.fx.GraphModule( + { + **self.subgraphs, + **self.name_to_param, + **self.name_to_buffer, + **self.name_to_non_tensor_attribute, + **self.name_to_constant, + }, + self.fx_graph, + ) + + inplace_optimize_sym_size_div(gm) + + gm.graph.lint() + + return gm + + def convert_graph_inputs(self): + for graph_input in self.ts_graph.inputs(): + name = graph_input.debugName() + + if name in self.name_to_param: + normalized_name = normalize_name(name) + self.input_specs.append( + InputSpec( + InputKind.PARAMETER, + arg=TensorArgument(name=normalized_name), + target=name, + ) + ) + fx_node = get_node_as_placeholder_or_get_attr( + self.fx_graph, name, self.is_top_level_graph() + ) + elif name in self.name_to_buffer: + normalized_name = normalize_name(name) + self.input_specs.append( + InputSpec( + InputKind.BUFFER, + arg=TensorArgument(name=normalized_name), + target=name, + persistent=True, + ) + ) + fx_node = get_node_as_placeholder_or_get_attr( + self.fx_graph, name, self.is_top_level_graph() + ) + elif name in self.name_to_constant: + assert isinstance( + self.name_to_constant[name], torch.ScriptObject + ), "Input conversion only handles ScriptObject" + normalized_name = normalize_name(name) + self.input_specs.append( + InputSpec( + InputKind.CUSTOM_OBJ, + arg=CustomObjArgument( + name=normalized_name, class_fqn=normalized_name + ), + target=name, + persistent=False, + ) + ) + fx_node = get_node_as_placeholder_or_get_attr( + self.fx_graph, name, self.is_top_level_graph() + ) + elif isinstance(graph_input.type(), torch.ClassType): + # Directly skip inputs that are ScriptObject but not used in the graph. + continue + else: + normalized_name = normalize_name(name, prefix="input") + self.input_specs.append( + InputSpec( + InputKind.USER_INPUT, + arg=TensorArgument(name=normalized_name), + target=name, + ) + ) + fx_node = self.fx_graph.placeholder(normalized_name) + + self.name_to_node[name] = fx_node + + def convert_aten_Float(self, node: torch._C.Node): + def to_float_tensor(t): + return t.to(dtype=torch.float).item() + + inp_list = [ + self.get_fx_value_by_ir_value(inp) for inp in node.inputs() + ] # noqa: C416 + fx_node = self.fx_graph.call_function( + to_float_tensor, + tuple(inp_list), + ) + self.name_to_node[node.output().debugName()] = fx_node + + def convert_aten_tensor(self, node: torch._C.Node): + """aten::tensor creates a constant tensor ad-hoc --> GetAttr""" + args, kwargs = self.get_args_kwargs(node, torch.ops.aten.tensor.default._schema) + + for k in kwargs: + if k == "requires_grad": + kwargs[k] = bool(kwargs[k]) # 0 -> False, 1 -> True + + to_tensor = ( + torch.tensor + if all(isinstance(a, int) for a in args) + else torch._refs.tensor + ) + + def target(*args, **kwargs): + if "dtype" in kwargs and kwargs["dtype"] is not None: + kwargs["dtype"] = _TORCH_ENUM_TO_DTYPE[kwargs["dtype"]] + return to_tensor(*args, **kwargs) + + # def to_dynamic_tensor(*args, **kwargs): + # if "dtype" in kwargs and kwargs["dtype"] is not None: + # kwargs["dtype"] = _TORCH_ENUM_TO_DTYPE[kwargs["dtype"]] + # return torch._refs.tensor(*args, **kwargs) + + output_name = node.output().debugName() + fx_node = self.fx_graph.call_function(target, args, kwargs) + self.name_to_node[output_name] = fx_node + + def convert_aten_append(self, node: torch._C.Node): + # special handle python list append: "aten::append.t(t[](a!) self, t(c -> *) el) -> t[](a!)" + + # inplace append to the list!! This is kinda crazy, as we are inplace mutating the list + # This makes the converter "non-functional", and the result depends on the order of the nodes being converter + # In a sense, the converter now becomes an stateful interpreter + warnings.warn( + "Converting aten::append.t, which is a inplace mutation of the list. " + "This makes the converter non-functional: the result depends on the order of the append nodes being converter!" + ) + + args = tuple(self.get_fx_value_by_ir_value(inp) for inp in node.inputs()) + fx_node = self.fx_graph.call_function(list_append, args) + self.name_to_node[node.output().debugName()] = fx_node + + # inplace mutate arg[0], which is the python list + self.name_to_node[node.inputsAt(0).debugName()] = fx_node + + # Variables that need to be updated to parent module. + if not self.is_top_level_graph() and args[0].op == "placeholder": + self.name_update_from_subblock_to_parent.add(node.inputsAt(0).debugName()) + + def convert_prim_Constant(self, node: torch._C.Node): + name = node.output().debugName() + + value: Any = None + if node.hasAttribute("value"): + constant_kind = node.kindOf("value") + if constant_kind == "i": + value = node.i("value") + elif constant_kind == "f": + value = node.f("value") + elif constant_kind == "s": + value = node.s("value") + elif constant_kind == "t": + alias_name = ( + f"lifted_tensor_{name}" # Follow naming convention from EP tracing. + ) + fx_node = self.fx_graph.get_attr(alias_name) + self.name_to_node[name] = fx_node + name, value = alias_name, node.t("value") + elif constant_kind == "ival": + value = node.ival("value") + else: + raise ValueError(f"Unsupported constant type: {node.kindOf('value')}") + else: + value = None + + self.name_to_constant[name] = value + + def convert_prim_CallMethod(self, node: torch._C.Node): + inp_list = [ + self.get_fx_value_by_ir_value(inp) for inp in node.inputs() + ] # noqa: C416 + fx_node = self.fx_graph.call_method( + node.s("name"), + tuple(inp_list), + ) + self.name_to_node[node.output().debugName()] = fx_node + + def convert_prim_device(self, node: torch._C.Node): + input_type = node.input().type() + if input_type.isSubtypeOf(torch._C.TensorType.get()): + device = input_type.device() # type: ignore[attr-defined] + output_name = node.output().debugName() + self.name_to_constant[output_name] = device + else: + raise ValueError(f"Unsupported JitType ({input_type}) when get device") + + def convert_prim_GetAttr(self, node: torch._C.Node): + # Build fully qulified name + attr_fqn = get_attribute_fqn_from_ts_node(self.name_to_attribute_fqn, node) + output_name = node.output().debugName() + self.name_to_attribute_fqn[output_name] = attr_fqn + + if self.is_top_level_graph(): + if self._is_get_attr_node(attr_fqn): + # We insert a get_attr node due to two reasons. + # First, ts graph does not lift tensor constants as input nodes. So + # tensor constants may be ignored by in convert_graph_inputs(). + # Second, attr_fqn may have been written to via SetAttr. Two + # GetAttr may give different values. + self.name_to_node[output_name] = self.fx_graph.get_attr(attr_fqn) + else: + if attr_fqn not in self.name_to_non_tensor_attribute_node: + self.name_to_non_tensor_attribute_node[ + attr_fqn + ] = self.name_to_non_tensor_attribute[attr_fqn] + self.name_to_node[output_name] = self.name_to_non_tensor_attribute_node[ + attr_fqn + ] + else: + # Special support for if blocks which do not allow SetAttr TorchScript + # node and get_attr FX Graph Node. + if self._is_get_attr_node(attr_fqn): + self.name_to_node[output_name] = self.name_to_node[attr_fqn] + + def convert_prim_SetAttr(self, node: torch._C.Node): + attr_fqn = get_attribute_fqn_from_ts_node(self.name_to_attribute_fqn, node) + attr_value = tuple(node.inputs())[1] + ts_graph_tensor_input = self.get_fx_value_by_ir_value(attr_value) + if self._is_get_attr_node(attr_fqn): + fx_attr_node = self.fx_graph.get_attr(attr_fqn) + self.fx_graph.call_function( + torch.Tensor.copy_, (fx_attr_node, ts_graph_tensor_input) + ) + else: + self.name_to_non_tensor_attribute_node[attr_fqn] = ts_graph_tensor_input + + def convert_call_function_op(self, node: torch._C.Node): + target = get_op_overload(node) + + args, kwargs = self.get_args_kwargs(node, target._schema) + + fx_node = self.fx_graph.call_function(target, args, kwargs) + + # TODO: covnert sourceRange() into stack_trace + # fx_node.meta["stack_trace"] = node.sourceRange() + + if node.outputsSize() == 1: + output_name = node.output().debugName() + self.name_to_node[output_name] = fx_node + else: + for i, outp in enumerate(node.outputs()): + output_name = outp.debugName() + next_fx_node = self.fx_graph.call_function( + operator.getitem, (fx_node, i) + ) + self.name_to_node[output_name] = next_fx_node + + def convert_prim_TupleConstruct(self, node: torch._C.Node): + self._convert_prim_iterator(node) + + def convert_prim_ListConstruct(self, node: torch._C.Node): + self._convert_prim_iterator(node) + + def _convert_prim_iterator(self, node: torch._C.Node): + output_list = [] + for inp in node.inputs(): + output_list.append(self.get_fx_value_by_ir_value(inp)) + + output_name = node.output().debugName() + self.name_to_node[output_name] = output_list + + def convert_prim_DictConstruct(self, node: torch._C.Node): + output_dict = {} + k, v = None, None + for i, inp in enumerate(node.inputs()): + # We assume key value are stored in pair in the DictConstruct. + # The first element is the key and the following is the value. + if i % 2 == 0: + k = self.get_fx_value_by_ir_value(inp) + else: + v = self.get_fx_value_by_ir_value(inp) + assert ( + k is not None and v is not None + ), "DictConstruct has an empty key value pair." + output_dict[k] = v + k, v = None, None + + assert ( + k is None and v is None + ), "DictConstruct has an odd number of elements (violating our assumption)." + + output_name = node.output().debugName() + self.name_to_node[output_name] = output_dict + + def convert_prim_ListUnpack(self, node: torch._C.Node): + self._convert_prim_unpack_iterator(node) + + def convert_prim_TupleUnpack(self, node: torch._C.Node): + self._convert_prim_unpack_iterator(node) + + def _convert_prim_unpack_iterator(self, node: torch._C.Node): + # Single input and multiple outputs for unpacking. + for i, outp in enumerate(node.outputs()): + outp_name = outp.debugName() + inp = self.get_fx_value_by_ir_value(node.input()) + fx_node = self.fx_graph.call_function(operator.getitem, (inp, i)) + self.name_to_node[outp_name] = fx_node + + def convert_aten_Int(self, node: torch._C.Node): + # converts aten::Int as aten._to_copy + aten::_local_scalar_dense + target = torch.ops.aten._to_copy.default + args = tuple(self.get_fx_value_by_ir_value(input) for input in node.inputs()) + to_copy_node = self.fx_graph.call_function(target, args, {"dtype": torch.int32}) + + fx_node = self.fx_graph.call_function( + torch.ops.aten._local_scalar_dense.default, (to_copy_node,) + ) + + # TODO: covnert sourceRange() into stack_trace + # fx_node.meta["stack_trace"] = node.sourceRange() + + output_name = node.output().debugName() + self.name_to_node[output_name] = fx_node + + def convert_prim_NumToTensor(self, node: torch._C.Node): + # Converts prim::NumToTensor as aten.scalar_tensor. + # prim::NumToTensor IRs are currently triggered by: + # .size() https://github.com/pytorch/pytorch/blob/main/torch/csrc/jit/frontend/tracer.cpp#L950 + # .numel() https://github.com/pytorch/pytorch/blob/main/torch/csrc/jit/frontend/tracer.cpp#L971 + # For both of those APIs, torch.jit.trace implicitly sets the output tensor type + # to be LongTensor. + target = torch.ops.aten.scalar_tensor + args = tuple(self.get_fx_value_by_ir_value(input) for input in node.inputs()) + + fx_node = self.fx_graph.call_function(target, args, {"dtype": torch.long}) + output_name = node.output().debugName() + self.name_to_node[output_name] = fx_node + + def convert_prim_CreateObject(self, node: torch._C.Node): + output_name = node.output().debugName() + self.name_to_attribute_fqn[output_name] = "" + + def convert_aten__convolution(self, node: torch._C.Node): + # converts aten::_convolution as aten.convolution, since aten::_convolution + # doesn't have a meta function + target = torch.ops.aten.convolution.default + args, kwargs = self.get_args_kwargs(node, target._schema) + + fx_node = self.fx_graph.call_function(target, args, kwargs) + + output_name = node.output().debugName() + self.name_to_node[output_name] = fx_node + + def convert_aten_div(self, node: torch._C.Node): + target = get_op_overload(node) + schema = target._schema + + args, kwargs = self.get_args_kwargs(node, schema) + + # converts aten::div.Tensor_mode(x, tensor_constant) + # as aten.div.Scalar_mode(x, tensor_constant.item()) + if schema.overload_name == "Tensor_mode": + arg1_name = args[1].name + if arg1_name in self.name_to_constant and isinstance( + self.name_to_constant[arg1_name], torch.Tensor + ): + tensor_constant = self.name_to_constant[arg1_name] + if tensor_constant.numel() == 1: + updated_args = list(args) + updated_args[1] = self.name_to_constant[arg1_name].item() + + fx_node = self.fx_graph.call_function( + torch.ops.aten.div.Scalar_mode, + tuple(updated_args), + kwargs, + ) + + # TODO: covnert sourceRange() into stack_trace + # fx_node.meta["stack_trace"] = node.sourceRange() + + output_name = node.output().debugName() + self.name_to_node[output_name] = fx_node + return + + self.convert_call_function_op(node) + + def convert_aten___getitem__(self, node: torch._C.Node): + input_container, index = tuple( + self.get_fx_value_by_ir_value(input) for input in node.inputs() + ) + fx_node = self.fx_graph.call_function( + operator.getitem, (input_container, index) + ) + output_name = node.output().debugName() + self.name_to_node[output_name] = fx_node + + def convert_aten_to(self, node: torch._C.Node): + target = get_op_overload(node) + args, kwargs = self.get_args_kwargs(node, target._schema) + + # special handle aten.to.dtype and aten.to.prim_dtype followed by inplace_mutation_op + # coz aten.to + inplace_mutation_op pattern would trigger + # "cannot mutate tensors with frozen storage" functionalization error. + # To work around the issue, we override the copy to be True, so that the output + # is for sure not an alias of input + if target == torch.ops.aten.to.dtype or target == torch.ops.aten.to.prim_dtype: + user_nodes = [use.user for use in node.output().uses()] + user_targets = [ + get_op_overload(user_node) + for user_node in user_nodes + if user_node.schema() != "(no schema)" + ] + has_mutable_target = any( + target._schema.is_mutable for target in user_targets + ) + + if has_mutable_target: + assert len(args) >= 4 + new_args = list(args) + new_args[3] = True # copy, override to True + fx_node = self.fx_graph.call_function( + torch.ops.aten.to.dtype, tuple(new_args) + ) + # temp hack to work around the issue https://github.com/pytorch/pytorch/issues/131679 + # When this issue is fixed, the clone node would be no longer needed + clone_node = self.fx_graph.call_function( + torch.ops.aten.clone.default, (fx_node,) + ) + output_name = node.output().debugName() + self.name_to_node[output_name] = clone_node + return + + self.convert_call_function_op(node) + + def convert_aten_add(self, node: torch._C.Node): + if node.schema() == "(no schema)": + if isinstance(node.inputsAt(0).type(), torch.ListType) and isinstance( + node.inputsAt(1).type(), torch.ListType + ): + target = torch.ops.aten.add.t + else: + raise RuntimeError(f"unable to determind the target for {node}") + else: + target = get_op_overload(node) + + if target == torch.ops.aten.add.t: + # special handle python list/tuple add: "aten::add.t(t[] a, t[] b) -> t[]" for + # RuntimeError: aten::add() Expected a value of type 'List[t]' for argument 'a' but instead found type 'immutable_list'. + args, kwargs = self.get_args_kwargs(node, target._schema) + output_name = node.output().debugName() + self.name_to_node[output_name] = self.fx_graph.call_function(list_add, args) + else: + self.convert_call_function_op(node) + + def _check_prim_loop_support(self, node): + inputs = list(node.inputs()) + + # TODO: (1/N) stage. + if inputs[0].debugName() not in self.name_to_constant: + raise RuntimeError( + "prim::Loop currently cannot run with dynamic value of number of iterations." + ) + + # Make sure the condition is not updated in the subblock. + subblock = next(node.blocks()) + condition_output_name = next(subblock.outputs()).debugName() + for node in subblock.nodes(): + if ( + node.outputsSize() == 1 + and node.output().debugName() == condition_output_name + ): + raise RuntimeError( + "prim::Loop currently cannot run with dynamic value of condition." + ) + if node.outputsSize() >= 2: + for outp in node.outputs(): + if outp.debugName() == condition_output_name: + raise RuntimeError( + "prim::Loop currently cannot run with dynamic value of condition." + ) + + def convert_prim_Loop(self, node: torch._C.Node): + inputs = list(node.inputs()) + self._check_prim_loop_support(node) + + num_iterations = self.get_fx_value_by_ir_value(inputs[0]) + + # Find inputs. + loop_local_arguments = [inp.debugName() for inp in inputs[2:]] + + global_arguments = self._identify_inputs_as_arguments(node) + + # Lift parameters as inputs. + for block in node.blocks(): + global_arguments = global_arguments.union( + self.blocks_to_lifted_attrs[block] + ) + + global_arguments = list(global_arguments) + + subgraph_nodes, subgraph_converters = self._convert_block_to_subgraph( + node, global_arguments + ) + + assert len(subgraph_nodes) == 1 + subgraph_converter = subgraph_converters[0] + if not self.is_top_level_graph(): + self.name_update_from_subblock_to_parent = ( + self.name_update_from_subblock_to_parent.union( + subgraph_converter.name_update_from_subblock_to_parent + ) + ) + + fx_block_args = [ + self.get_fx_value_by_fqn(name) + for name in loop_local_arguments + global_arguments + ] + for iter_idx in range(num_iterations): + loop_node = self.fx_graph.call_function( + execute_subgraph_from_prim_loop, + # Check execute_node function for the expected arguments order. + ( + subgraph_nodes[0], + iter_idx, + len(loop_local_arguments), + *fx_block_args, + ), + {}, + ) + + # Update the value of loop local variables. + if node.outputsSize() >= 1: + for i, outp in enumerate(node.outputs()): + output_name = outp.debugName() + self.name_to_node[output_name] = self.fx_graph.call_function( + operator.getitem, + ( + loop_node, + i + 1, + ), # + 1 because the 0th element is the condition. + ) + fx_block_args[i] = self.name_to_node[output_name] + + # Update the value of global variables, whose values are modified inplace. + for i, name in enumerate( + subgraph_converter.name_update_from_subblock_to_parent + ): + self.name_to_node[name] = self.fx_graph.call_function( + operator.getitem, + ( + loop_node, + i + node.outputsSize() + 1, + ), # + 1 because the 0th element is the condition. + ) + global_argument_index = global_arguments.index(name) + fx_block_args[ + i + node.outputsSize() + global_argument_index + ] = self.name_to_node[name] + + def _check_set_attr_in_if_block(self, if_node: torch._C.Node): + for block in if_node.blocks(): + for node in block.nodes(): + if node.kind() == "prim::SetAttr": + raise RuntimeError( + "During converting prim::If to torch.cond, found prim::SetAttr op" + " which is not supported yet. Please file an issue if you come " + "across this error." + ) + + def convert_prim_If(self, node: torch._C.Node): + self._check_set_attr_in_if_block(node) + + inputs = list(node.inputs()) + assert len(inputs) == 1 + predicate = self.get_fx_value_by_ir_value(inputs[0]) + + # Find inputs. + arguments = self._identify_inputs_as_arguments(node) + + # Lift parameters as inputs. + for block in node.blocks(): + arguments = arguments.union(self.blocks_to_lifted_attrs[block]) + + arguments = list(arguments) + subgraph_nodes, _ = self._convert_block_to_subgraph(node, arguments) + + assert len(subgraph_nodes) == 2 + + fx_block_args = [self.get_fx_value_by_fqn(name) for name in arguments] + + args = ( + predicate, + subgraph_nodes[0], + subgraph_nodes[1], + tuple(fx_block_args), + ) + + cond_node = self.fx_graph.call_function(torch.cond, args, {}) + + # prim::If can also have zero output. + if node.outputsSize() == 1: + output_name = node.output().debugName() + self.name_to_node[output_name] = cond_node + elif node.outputsSize() > 1: + for i, output in enumerate(node.outputs()): + output_name = output.debugName() + getitem = self.fx_graph.call_function(operator.getitem, (cond_node, i)) + self.name_to_node[output_name] = getitem + + def convert_aten_Bool(self, node: torch._C.Node): + self._convert_as_noop(node) + + def convert_prim_Enter(self, node: torch._C.Node): + # export generally treats prim::Enter as noop + # The only context manager export supports is aten::enable_grad. + # Unfortunately, TorchScript does not support aten::enable_grad yet. + # TODO: support aten::enable_grad in both TorchScript and Converter. + return + + def convert_prim_Exit(self, node: torch._C.Node): + # export treats prim::Exit as noop + return + + def _convert_as_noop(self, node: torch._C.Node): + # Converts the node as a no-op by mapping its output node as arg[0] + + target = get_op_overload(node) + schema = target._schema + + args, kwargs = self.get_args_kwargs(node, schema) + + output_name = node.output().debugName() + self.name_to_node[output_name] = args[0] + + def convert_profiler__record_function_exit(self, node: torch._C.Node): + # _record_function_exit has side effect so we keep it in fx.graph + # currently, _record_function_enter_new and _record_function_exit are + # discarded during `retrace_as_exported_program`. + target = torch.ops.profiler._record_function_exit + args = tuple(self.get_fx_value_by_ir_value(input) for input in node.inputs()) + self.fx_graph.call_function(target, args) + + def convert_prim_tolist(self, node: torch._C.Node): + # prim::tolist cannot be supported by `_convert_standard_operators` + # since it requires call_method instead of call_function. + target = "tolist" + args = (self.get_fx_value_by_ir_value(next(node.inputs())),) + fx_node = self.fx_graph.call_method(target, args) + output_name = node.output().debugName() + self.name_to_node[output_name] = fx_node + + def convert_prim_Uninitialized(self, node: torch._C.Node): + # `prim::Uninitialized` is inserted by the compiler when it can prove + # the value will never be used. It can be introduced by exceptions, + # breaks, continues, and returns. + # So we add a dummy constant to the graph. + output_name = node.output().debugName() + self.name_to_constant[output_name] = torch.Tensor() + + def _convert_standard_operators(self, node: torch._C.Node): + target = kind_to_standard_operators[node.kind()] + args = tuple(self.get_fx_value_by_ir_value(input) for input in node.inputs()) + fx_node = self.fx_graph.call_function(target, args) + output_name = node.output().debugName() + self.name_to_node[output_name] = fx_node + + def convert_node(self, node: torch._C.Node): + node_kind = node.kind() + + # Get handler based on namespace and operator name. + # Provide a default node handler as well in case we don't find + # matching converter for that. + handler_func_name = ir_name_to_func_name(node_kind) + handler_func = getattr(self, handler_func_name, self.convert_call_function_op) + + # str calls print function implemented in CPP. To avoid repeating + # the entire logic here, we simply keep first line from node string (getting rid + # of sub-blocks IR prints). + node_str = "".join(str(node).split("\n")[:1]) + log.debug("[%s] converts [%s]", handler_func.__name__, node_str) + try: + handler_func(node) + except Exception as e: + raise RuntimeError(f"TS2EPConverter failed for node {node_kind}") from e + + def convert_graph_outputs(self): + args = [] + outp_name_list = [outp.debugName() for outp in self.ts_graph.outputs()] + list( + self.name_update_from_subblock_to_parent + ) + for output_name in outp_name_list: + if output_name in self.name_to_node: + fx_node = self.name_to_node[output_name] + # TODO: Revisit this later after HigherOrderOp design changes. + # Currently, we cannot directly return input as output. + if ( + not self.is_top_level_graph() + and isinstance(fx_node, torch.fx.Node) + and fx_node.op == "placeholder" + ): + fx_node = self.fx_graph.call_function(torch.clone, (fx_node,)) + args.append(fx_node) + self.output_specs.append( + OutputSpec( + OutputKind.USER_OUTPUT, + arg=TensorArgument(name=output_name), + target=output_name, + ) + ) + elif output_name in self.name_to_constant: + args.append(self.name_to_constant[output_name]) + self.output_specs.append( + OutputSpec( + OutputKind.USER_OUTPUT, + arg=ConstantArgument( + name=output_name, value=self.name_to_constant[output_name] + ), + target=output_name, + ) + ) + else: + raise ValueError(f"Output {output_name} not found") + + if len(args) == 0: + # Sub-block of prim::If can have zero output. + self.fx_graph.output([]) + elif len(args) == 1: + self.fx_graph.output( + args[0] + ) # Get rid of an extra list wrapped around final output. + elif len(args) > 1: + self.fx_graph.output( + args + ) # For prim::Loop and prim::If with multiple outputs. + else: + # Sub-block of prim::Loop can have multiple outputs. + self.fx_graph.output(args) + + +class ExplainTS2FXGraphConverter(TS2FXGraphConverter): + """ + Run TS2FXGraphConverter in an explain mode. It collects all failed operators conversions + and provide that information to users. In order to collect all failed conversions, it + also mocks some internal attributes (e.g., name_to_node). + """ + + class _DictMock(dict): + def __init__(self, dict_data, mock_value): + super().__init__(dict_data) + self.mock_value = mock_value + + def __getitem__(self, key): + # If the original dictionary has the key, return its value. + # Otherwise, return the mock value. + if not super().__contains__(key): + return self.mock_value + return super().__getitem__(key) + + def __contains__(self, key): + return True + + def __init__( + self, + ts_graph: Union[torch._C.Graph, torch._C.Block], + name_to_param: Dict[str, torch.Tensor], + name_to_buffer: Dict[str, torch.Tensor], + blocks_to_lifted_attrs: Dict[torch._C.Block, Set[str]], + name_to_non_tensor_attribute: Dict[str, Any], + name_to_constant: Dict[str, Any], + ): + super().__init__( + ts_graph, + name_to_param, + name_to_buffer, + blocks_to_lifted_attrs, + name_to_non_tensor_attribute, + name_to_constant, + ) + + # Data to keep track of unsupported nodes. + self.unsupported_node_list: List[torch._C.Node] = [] + + # Add mock to needed attributes. + self.name_to_node = ExplainTS2FXGraphConverter._DictMock( + self.name_to_node, + # Dummy node. + torch.fx.Node( + None, # type: ignore[arg-type] + "mock", + "call_function", + lambda: None, + (), + {}, + ), + ) + + def explain(self): + self.convert_graph_inputs() + for node in self.ts_graph.nodes(): + self.convert_node(node) + self.convert_graph_outputs() + + def convert_node(self, node): + try: + super().convert_node(node) + except Exception as e: + self.unsupported_node_list.append(node) + + +@contextmanager +def disable_logging(log): + disabled = log.disabled + log.disabled = True + try: + yield + finally: + log.disabled = disabled + + +class TS2EPConverter: + # TorchScript model to ExportedProgram converter + def __init__( + self, + ts_model: Union[torch.jit.ScriptModule, torch.jit.ScriptFunction], + sample_args: Tuple[Any, ...], + sample_kwargs: Optional[Dict[str, Any]] = None, + ): + self.ts_model = ts_model + self.ts_graph, self.params, _, _ = _create_jit_graph(ts_model, sample_args) + + self.sample_args = sample_args + self.sample_kwargs = sample_kwargs + + self.name_to_param: Dict[str, torch.Tensor] = {} + self.name_to_buffer: Dict[str, torch.Tensor] = {} + param_list = ( + list(self.ts_model.parameters()) + if not isinstance(self.ts_model, torch._C.ScriptFunction) + else [] + ) + if not isinstance(self.ts_model, torch._C.ScriptFunction): + for k, tensor in self.ts_model.state_dict().items(): # type: ignore[union-attr] + # Check if tensor belongs to any parameter. + if any( + (tensor == param).all() + for param in param_list + if tensor.shape == param.shape + ): + self.name_to_param[k] = tensor + else: + self.name_to_buffer[k] = tensor + + self.name_to_non_tensor_attributes: Dict[str, Any] = {} + self.name_to_constant: Dict[str, Any] = {} + + self.lift_get_attr() + + def convert(self) -> ExportedProgram: + log.info( + """ +TS2EPConverter logging starts from here. + +INFO: (TORCH_LOGS="export" ) + * Log TorchScript IR. + +DEBUG: (TORCH_LOGS="+export" ), additionally + * Log conversion IR by IR in a format of [] converts []. + """ + ) + log.info("TorchScript graph\n\n%s\n", self.ts_graph) + + blocks_to_lifted_attrs = get_block_to_lifted_attrs(self.ts_graph) + + graph_converter = TS2FXGraphConverter( + self.ts_graph, + self.name_to_param, + self.name_to_buffer, + blocks_to_lifted_attrs, + self.name_to_non_tensor_attributes, + self.name_to_constant, + ) + gm = graph_converter.convert() + + # Post-proccessing step to deal with quantized operators. + replace_quantized_ops_with_standard_ops(gm) + log.info("GraphModule: %s", gm.print_readable(print_output=False)) + + ep = self.retrace_as_exported_program( + gm, + graph_converter.name_to_constant, + ) + log.info("%s", ep) + + # Post-processing step to ensure ExportedProgram has the same state_dict as + # the original TorchScript model. Throw warnings for additionally populated + # state_dict entries. + if not isinstance(self.ts_model, torch._C.ScriptFunction): + for k, tensor in self.ts_model.state_dict().items(): # type: ignore[union-attr] + if k not in ep.state_dict: + warnings.warn( + f"Manually populate {k} into state_dict ExportedProgram, but it is never used by the ExportedProgram." + ) + ep.state_dict[k] = tensor + + return ep + + @disable_logging(log) + def explain(self, print_output=True): + blocks_to_lifted_attrs = get_block_to_lifted_attrs(self.ts_graph) + + graph_converter = ExplainTS2FXGraphConverter( + self.ts_graph, + self.name_to_param, + self.name_to_buffer, + blocks_to_lifted_attrs, + self.name_to_non_tensor_attributes, + self.name_to_constant, + ) + graph_converter.explain() + if len(graph_converter.unsupported_node_list) > 0: + explain_str = "Unsupported nodes are found in the following list:" + for i, n in enumerate(graph_converter.unsupported_node_list): + node_str = "".join(str(n).split("\n")[:1]) + explain_str += f"\n\n {i}. {n.kind()} [{node_str}]" + else: + explain_str = "Success!" + if print_output: + print(explain_str) + return explain_str + + def retrace_as_exported_program( + self, + gm: torch.fx.GraphModule, + name_to_constant: Dict[str, Any], + ): + # TODO: adjust input orders to match GraphSignature convention + ep = torch.export._trace._export( + gm, + self.sample_args, + strict=False, + pre_dispatch=True, + ) + + # Post-processing to make sure the ExportedProgram states are correct. + # Because during conversion, we set tensor constants as GetAttr, + # retracing cannot recognize them as tensor constants but instead + # treat them as buffers. We need to set them again here. + ep._constants.update( + { + k: v + for k, v in name_to_constant.items() + if isinstance(v, (torch.Tensor, torch.ScriptObject)) + } + ) + for k in name_to_constant: + ep.state_dict.pop(k, None) + + for i, spec in enumerate(ep.graph_signature.input_specs): + # Mark as constant tensors for erroneously traced buffers. + if spec.kind == InputKind.BUFFER and spec.target in name_to_constant: + assert isinstance( + name_to_constant[spec.target], torch.Tensor + ), f"{type(name_to_constant[spec.target])} has been erroneously marked as buffer" + spec.kind = InputKind.CONSTANT_TENSOR + ep.verifier().check(ep) + + return ep + + def lift_get_attr(self): + # This function lifts multiple data types. + + # 1. Tensor constants attributes (e.g., self.data = torch.tensor([2,3])) + # to buffers. Currently, when there are tensor constants, export + # would error and ask users to register tensor constants as buffers. + # Since it is hard to manually do so for TorchScript models + # (e.g., source code is missing), this function automatically + # lifts tensor constants to be buffers. + + # 2. ScriptObbject to constant. It will then be converted to getattr in + # in the fx graph. + # + # This function should happen in TS2EPConverter instead of + # TS2FXGraphConverter since it gets attributes from self.ts_model + # which is not accessable in TS2FXGraphConverter. It is similar to where + # we collect self.name_to_param and self.name_to_buffer. + name_to_attribute_fqn: Dict[str, str] = {} + + def get_attr(fqn: str): + name = fqn.split(".") + v = self.ts_model + for n in name: + v = getattr(v, n) + return v + + def get_fqn(node: torch._C.Node): + attr_name = node.s("name") + input_name = node.input().debugName() + root_attr_name = name_to_attribute_fqn[input_name] + attr_fqn = f"{root_attr_name}.{attr_name}" if root_attr_name else attr_name + return attr_fqn + + def _dfs_get_attr(block): + for node in block.nodes(): + if node.kind() == "prim::CreateObject": + output_name = node.output().debugName() + name_to_attribute_fqn[output_name] = "" + + if node.kind() == "prim::GetAttr": + attr_fqn = get_fqn(node) + value = get_attr(attr_fqn) + output_name = node.output().debugName() + name_to_attribute_fqn[output_name] = attr_fqn + if isinstance(value, torch.Tensor): + if attr_fqn not in self.name_to_buffer: + # Lift tensor constants to be a buffer + self.name_to_buffer[attr_fqn] = value + elif isinstance(value, torch.ScriptObject): + if attr_fqn not in self.name_to_constant: + self.name_to_constant[attr_fqn] = value + else: + self.name_to_non_tensor_attributes[attr_fqn] = value + + for subblock in node.blocks(): + _dfs_get_attr(subblock) + + _dfs_get_attr(self.ts_graph) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..10a55772ab58b21573a6eba0356ddd3080164ac7 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. diff --git 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a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/case.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/case.py new file mode 100644 index 0000000000000000000000000000000000000000..b228f6c2c33773b07e5cb7e82abc1adcad6422d0 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/case.py @@ -0,0 +1,174 @@ +# mypy: allow-untyped-defs +import inspect +import re +import string +from dataclasses import dataclass, field +from enum import Enum +from typing import Any, Dict, List, Optional, Set, Tuple +from types import ModuleType + +import torch + +_TAGS: Dict[str, Dict[str, Any]] = { + "torch": { + "cond": {}, + "dynamic-shape": {}, + "escape-hatch": {}, + "map": {}, + "dynamic-value": {}, + "operator": {}, + "mutation": {}, + }, + "python": { + "assert": {}, + "builtin": {}, + "closure": {}, + "context-manager": {}, + "control-flow": {}, + "data-structure": {}, + "standard-library": {}, + "object-model": {}, + }, +} + + +class SupportLevel(Enum): + """ + Indicates at what stage the feature + used in the example is handled in export. + """ + + SUPPORTED = 1 + NOT_SUPPORTED_YET = 0 + + +ArgsType = Tuple[Any, ...] + + +def check_inputs_type(args, kwargs): + if not isinstance(args, tuple): + raise ValueError( + f"Expecting args type to be a tuple, got: {type(args)}" + ) + if not isinstance(kwargs, dict): + raise ValueError( + f"Expecting kwargs type to be a dict, got: {type(kwargs)}" + ) + for key in kwargs: + if not isinstance(key, str): + raise ValueError( + f"Expecting kwargs keys to be a string, got: {type(key)}" + ) + +def _validate_tag(tag: str): + parts = tag.split(".") + t = _TAGS + for part in parts: + assert set(part) <= set( + string.ascii_lowercase + "-" + ), f"Tag contains invalid characters: {part}" + if part in t: + t = t[part] + else: + raise ValueError(f"Tag {tag} is not found in registered tags.") + + +@dataclass(frozen=True) +class ExportCase: + example_args: ArgsType + description: str # A description of the use case. + model: torch.nn.Module + name: str + example_kwargs: Dict[str, Any] = field(default_factory=dict) + extra_args: Optional[ArgsType] = None # For testing graph generalization. + # Tags associated with the use case. (e.g dynamic-shape, escape-hatch) + tags: Set[str] = field(default_factory=set) + support_level: SupportLevel = SupportLevel.SUPPORTED + dynamic_shapes: Optional[Dict[str, Any]] = None + + def __post_init__(self): + check_inputs_type(self.example_args, self.example_kwargs) + if self.extra_args is not None: + check_inputs_type(self.extra_args, {}) + + for tag in self.tags: + _validate_tag(tag) + + if not isinstance(self.description, str) or len(self.description) == 0: + raise ValueError(f'Invalid description: "{self.description}"') + + +_EXAMPLE_CASES: Dict[str, ExportCase] = {} +_MODULES: Set[ModuleType] = set() +_EXAMPLE_CONFLICT_CASES: Dict[str, List[ExportCase]] = {} +_EXAMPLE_REWRITE_CASES: Dict[str, List[ExportCase]] = {} + + +def register_db_case(case: ExportCase) -> None: + """ + Registers a user provided ExportCase into example bank. + """ + if case.name in _EXAMPLE_CASES: + if case.name not in _EXAMPLE_CONFLICT_CASES: + _EXAMPLE_CONFLICT_CASES[case.name] = [_EXAMPLE_CASES[case.name]] + _EXAMPLE_CONFLICT_CASES[case.name].append(case) + return + + _EXAMPLE_CASES[case.name] = case + + +def to_snake_case(name): + name = re.sub("(.)([A-Z][a-z]+)", r"\1_\2", name) + return re.sub("([a-z0-9])([A-Z])", r"\1_\2", name).lower() + + +def _make_export_case(m, name, configs): + if not isinstance(m, torch.nn.Module): + raise TypeError("Export case class should be a torch.nn.Module.") + + if "description" not in configs: + # Fallback to docstring if description is missing. + assert ( + m.__doc__ is not None + ), f"Could not find description or docstring for export case: {m}" + configs = {**configs, "description": m.__doc__} + return ExportCase(**{**configs, "model": m, "name": name}) + + +def export_case(**kwargs): + """ + Decorator for registering a user provided case into example bank. + """ + + def wrapper(m): + configs = kwargs + module = inspect.getmodule(m) + if module in _MODULES: + raise RuntimeError("export_case should only be used once per example file.") + + assert module is not None + _MODULES.add(module) + module_name = module.__name__.split(".")[-1] + case = _make_export_case(m, module_name, configs) + register_db_case(case) + return case + + return wrapper + + +def export_rewrite_case(**kwargs): + def wrapper(m): + configs = kwargs + + parent = configs.pop("parent") + assert isinstance(parent, ExportCase) + key = parent.name + if key not in _EXAMPLE_REWRITE_CASES: + _EXAMPLE_REWRITE_CASES[key] = [] + + configs["example_args"] = parent.example_args + case = _make_export_case(m, to_snake_case(m.__name__), configs) + _EXAMPLE_REWRITE_CASES[key].append(case) + return case + + return wrapper diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..834dbce32f10bfb339fd2182a2455b43914441c9 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/__init__.py @@ -0,0 +1,61 @@ +# mypy: allow-untyped-defs +import dataclasses +import glob +import inspect +from os.path import basename, dirname, isfile, join + +import torch +from torch._export.db.case import ( + _EXAMPLE_CASES, + _EXAMPLE_CONFLICT_CASES, + _EXAMPLE_REWRITE_CASES, + SupportLevel, + export_case, + ExportCase, +) + + +def _collect_examples(): + case_names = glob.glob(join(dirname(__file__), "*.py")) + case_names = [ + basename(f)[:-3] for f in case_names if isfile(f) and not f.endswith("__init__.py") + ] + + case_fields = {f.name for f in dataclasses.fields(ExportCase)} + for case_name in case_names: + case = __import__(case_name, globals(), locals(), [], 1) + variables = [name for name in dir(case) if name in case_fields] + export_case(**{v: getattr(case, v) for v in variables})(case.model) + +_collect_examples() + +def all_examples(): + return _EXAMPLE_CASES + + +if len(_EXAMPLE_CONFLICT_CASES) > 0: + + def get_name(case): + model = case.model + if isinstance(model, torch.nn.Module): + model = type(model) + return model.__name__ + + msg = "Error on conflict export case name.\n" + for case_name, cases in _EXAMPLE_CONFLICT_CASES.items(): + msg += f"Case name {case_name} is associated with multiple cases:\n " + msg += f"[{','.join(map(get_name, cases))}]\n" + + raise RuntimeError(msg) + + +def filter_examples_by_support_level(support_level: SupportLevel): + return { + key: val + for key, val in all_examples().items() + if val.support_level == support_level + } + + +def get_rewrite_cases(case): + return _EXAMPLE_REWRITE_CASES.get(case.name, []) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 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a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/assume_constant_result.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/assume_constant_result.py new file mode 100644 index 0000000000000000000000000000000000000000..931ce7f7a50fc5a175101ac57c424c88cf31b54c --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/assume_constant_result.py @@ -0,0 +1,20 @@ +# mypy: allow-untyped-defs +import torch +import torch._dynamo as torchdynamo + + +class AssumeConstantResult(torch.nn.Module): + """ + Applying `assume_constant_result` decorator to burn make non-tracable code as constant. + """ + + @torchdynamo.assume_constant_result + def get_item(self, y): + return y.int().item() + + def forward(self, x, y): + return x[: self.get_item(y)] + +example_args = (torch.randn(3, 2), torch.tensor(4)) +tags = {"torch.escape-hatch"} +model = AssumeConstantResult() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/autograd_function.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/autograd_function.py new file mode 100644 index 0000000000000000000000000000000000000000..0458291e2176932b32b0fc2e44d51d5812dcead3 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/autograd_function.py @@ -0,0 +1,23 @@ +# mypy: allow-untyped-defs +import torch + +class MyAutogradFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, x): + return x.clone() + + @staticmethod + def backward(ctx, grad_output): + return grad_output + 1 + +class AutogradFunction(torch.nn.Module): + """ + TorchDynamo does not keep track of backward() on autograd functions. We recommend to + use `allow_in_graph` to mitigate this problem. + """ + + def forward(self, x): + return MyAutogradFunction.apply(x) + +example_args = (torch.randn(3, 2),) +model = AutogradFunction() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/class_method.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/class_method.py new file mode 100644 index 0000000000000000000000000000000000000000..f701f54d4f4ea1cb5816292cd60bb4df3d03c5e8 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/class_method.py @@ -0,0 +1,22 @@ +# mypy: allow-untyped-defs +import torch + +class ClassMethod(torch.nn.Module): + """ + Class methods are inlined during tracing. + """ + + @classmethod + def method(cls, x): + return x + 1 + + def __init__(self) -> None: + super().__init__() + self.linear = torch.nn.Linear(4, 2) + + def forward(self, x): + x = self.linear(x) + return self.method(x) * self.__class__.method(x) * type(self).method(x) + +example_args = (torch.randn(3, 4),) +model = ClassMethod() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/cond_branch_class_method.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/cond_branch_class_method.py new file mode 100644 index 0000000000000000000000000000000000000000..22600cc504348d1d261b0ea2b9ed2d57af76b0a3 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/cond_branch_class_method.py @@ -0,0 +1,44 @@ +# mypy: allow-untyped-defs +import torch + +from functorch.experimental.control_flow import cond + +class MySubModule(torch.nn.Module): + def foo(self, x): + return x.cos() + + def forward(self, x): + return self.foo(x) + +class CondBranchClassMethod(torch.nn.Module): + """ + The branch functions (`true_fn` and `false_fn`) passed to cond() must follow these rules: + - both branches must take the same args, which must also match the branch args passed to cond. + - both branches must return a single tensor + - returned tensor must have the same tensor metadata, e.g. shape and dtype + - branch function can be free function, nested function, lambda, class methods + - branch function can not have closure variables + - no inplace mutations on inputs or global variables + + + This example demonstrates using class method in cond(). + + NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized. + """ + + def __init__(self) -> None: + super().__init__() + self.subm = MySubModule() + + def bar(self, x): + return x.sin() + + def forward(self, x): + return cond(x.shape[0] <= 2, self.subm.forward, self.bar, [x]) + +example_args = (torch.randn(3),) +tags = { + "torch.cond", + "torch.dynamic-shape", +} +model = CondBranchClassMethod() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/cond_branch_nested_function.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/cond_branch_nested_function.py new file mode 100644 index 0000000000000000000000000000000000000000..b28ceeddc7956d136a8cf786c283344731d3e7ac --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/cond_branch_nested_function.py @@ -0,0 +1,41 @@ +# mypy: allow-untyped-defs +import torch + +from functorch.experimental.control_flow import cond + +class CondBranchNestedFunction(torch.nn.Module): + """ + The branch functions (`true_fn` and `false_fn`) passed to cond() must follow these rules: + - both branches must take the same args, which must also match the branch args passed to cond. + - both branches must return a single tensor + - returned tensor must have the same tensor metadata, e.g. shape and dtype + - branch function can be free function, nested function, lambda, class methods + - branch function can not have closure variables + - no inplace mutations on inputs or global variables + + This example demonstrates using nested function in cond(). + + NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized. + """ + + def forward(self, x): + def true_fn(x): + def inner_true_fn(y): + return x + y + + return inner_true_fn(x) + + def false_fn(x): + def inner_false_fn(y): + return x - y + + return inner_false_fn(x) + + return cond(x.shape[0] < 10, true_fn, false_fn, [x]) + +example_args = (torch.randn(3),) +tags = { + "torch.cond", + "torch.dynamic-shape", +} +model = CondBranchNestedFunction() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/cond_branch_nonlocal_variables.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/cond_branch_nonlocal_variables.py new file mode 100644 index 0000000000000000000000000000000000000000..50d0ec87a690d063cb0e841fc057a6ae95c369fb --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/cond_branch_nonlocal_variables.py @@ -0,0 +1,59 @@ +# mypy: allow-untyped-defs +import torch + +from functorch.experimental.control_flow import cond + +class CondBranchNonlocalVariables(torch.nn.Module): + """ + The branch functions (`true_fn` and `false_fn`) passed to cond() must follow these rules: + - both branches must take the same args, which must also match the branch args passed to cond. + - both branches must return a single tensor + - returned tensor must have the same tensor metadata, e.g. shape and dtype + - branch function can be free function, nested function, lambda, class methods + - branch function can not have closure variables + - no inplace mutations on inputs or global variables + + This example demonstrates how to rewrite code to avoid capturing closure variables in branch functions. + + The code below will not work because capturing closure variables is not supported. + ``` + my_tensor_var = x + 100 + my_primitive_var = 3.14 + + def true_fn(y): + nonlocal my_tensor_var, my_primitive_var + return y + my_tensor_var + my_primitive_var + + def false_fn(y): + nonlocal my_tensor_var, my_primitive_var + return y - my_tensor_var - my_primitive_var + + return cond(x.shape[0] > 5, true_fn, false_fn, [x]) + ``` + + NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized. + """ + + def forward(self, x): + my_tensor_var = x + 100 + my_primitive_var = 3.14 + + def true_fn(x, y, z): + return x + y + z + + def false_fn(x, y, z): + return x - y - z + + return cond( + x.shape[0] > 5, + true_fn, + false_fn, + [x, my_tensor_var, torch.tensor(my_primitive_var)], + ) + +example_args = (torch.randn(6),) +tags = { + "torch.cond", + "torch.dynamic-shape", +} +model = CondBranchNonlocalVariables() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/cond_closed_over_variable.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/cond_closed_over_variable.py new file mode 100644 index 0000000000000000000000000000000000000000..183180ab4fc825385170fea2bec6af184374a67e --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/cond_closed_over_variable.py @@ -0,0 +1,22 @@ +# mypy: allow-untyped-defs +import torch + +from functorch.experimental.control_flow import cond + +class CondClosedOverVariable(torch.nn.Module): + """ + torch.cond() supports branches closed over arbitrary variables. + """ + + def forward(self, pred, x): + def true_fn(val): + return x * 2 + + def false_fn(val): + return x - 2 + + return cond(pred, true_fn, false_fn, [x + 1]) + +example_args = (torch.tensor(True), torch.randn(3, 2)) +tags = {"torch.cond", "python.closure"} +model = CondClosedOverVariable() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/cond_operands.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/cond_operands.py new file mode 100644 index 0000000000000000000000000000000000000000..29941d828ae63e45ad81b0f2c7428706df849cf0 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/cond_operands.py @@ -0,0 +1,36 @@ +# mypy: allow-untyped-defs +import torch + +from torch.export import Dim +from functorch.experimental.control_flow import cond + +x = torch.randn(3, 2) +y = torch.randn(2) +dim0_x = Dim("dim0_x") + +class CondOperands(torch.nn.Module): + """ + The operands passed to cond() must be: + - a list of tensors + - match arguments of `true_fn` and `false_fn` + + NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized. + """ + + def forward(self, x, y): + def true_fn(x, y): + return x + y + + def false_fn(x, y): + return x - y + + return cond(x.shape[0] > 2, true_fn, false_fn, [x, y]) + +example_args = (x, y) +tags = { + "torch.cond", + "torch.dynamic-shape", +} +extra_inputs = (torch.randn(2, 2), torch.randn(2)) +dynamic_shapes = {"x": {0: dim0_x}, "y": None} +model = CondOperands() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/cond_predicate.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/cond_predicate.py new file mode 100644 index 0000000000000000000000000000000000000000..68bb8850bba909a0c6546c8f12a1a3fa1bdc70d1 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/cond_predicate.py @@ -0,0 +1,25 @@ +# mypy: allow-untyped-defs +import torch + +from functorch.experimental.control_flow import cond + +class CondPredicate(torch.nn.Module): + """ + The conditional statement (aka predicate) passed to cond() must be one of the following: + - torch.Tensor with a single element + - boolean expression + + NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized. + """ + + def forward(self, x): + pred = x.dim() > 2 and x.shape[2] > 10 + + return cond(pred, lambda x: x.cos(), lambda y: y.sin(), [x]) + +example_args = (torch.randn(6, 4, 3),) +tags = { + "torch.cond", + "torch.dynamic-shape", +} +model = CondPredicate() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/constrain_as_size_example.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/constrain_as_size_example.py new file mode 100644 index 0000000000000000000000000000000000000000..fbed4984ac4d14c0b175874c44e9c7009bfe8ddc --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/constrain_as_size_example.py @@ -0,0 +1,25 @@ +# mypy: allow-untyped-defs +import torch + + +class ConstrainAsSizeExample(torch.nn.Module): + """ + If the value is not known at tracing time, you can provide hint so that we + can trace further. Please look at torch._check and torch._check_is_size APIs. + torch._check_is_size is used for values that NEED to be used for constructing + tensor. + """ + + def forward(self, x): + a = x.item() + torch._check_is_size(a) + torch._check(a <= 5) + return torch.zeros((a, 5)) + + +example_args = (torch.tensor(4),) +tags = { + "torch.dynamic-value", + "torch.escape-hatch", +} +model = ConstrainAsSizeExample() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/constrain_as_value_example.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/constrain_as_value_example.py new file mode 100644 index 0000000000000000000000000000000000000000..c8bfc3d6e36578ee50f1ca2eee80fed9aae14cfd --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/constrain_as_value_example.py @@ -0,0 +1,28 @@ +# mypy: allow-untyped-defs +import torch + + +class ConstrainAsValueExample(torch.nn.Module): + """ + If the value is not known at tracing time, you can provide hint so that we + can trace further. Please look at torch._check and torch._check_is_size APIs. + torch._check is used for values that don't need to be used for constructing + tensor. + """ + + def forward(self, x, y): + a = x.item() + torch._check(a >= 0) + torch._check(a <= 5) + + if a < 6: + return y.sin() + return y.cos() + + +example_args = (torch.tensor(4), torch.randn(5, 5)) +tags = { + "torch.dynamic-value", + "torch.escape-hatch", +} +model = ConstrainAsValueExample() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/decorator.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/decorator.py new file mode 100644 index 0000000000000000000000000000000000000000..7d24cc681a6b62adf40bfd9a2e5283afb3515131 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/decorator.py @@ -0,0 +1,23 @@ +# mypy: allow-untyped-defs +import functools + +import torch + +def test_decorator(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + return func(*args, **kwargs) + 1 + + return wrapper + +class Decorator(torch.nn.Module): + """ + Decorators calls are inlined into the exported function during tracing. + """ + + @test_decorator + def forward(self, x, y): + return x + y + +example_args = (torch.randn(3, 2), torch.randn(3, 2)) +model = Decorator() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dictionary.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dictionary.py new file mode 100644 index 0000000000000000000000000000000000000000..49e688bc0ac1f09567e3b877aaca29a1d02b4121 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dictionary.py @@ -0,0 +1,17 @@ +# mypy: allow-untyped-defs +import torch + +class Dictionary(torch.nn.Module): + """ + Dictionary structures are inlined and flattened along tracing. + """ + + def forward(self, x, y): + elements = {} + elements["x2"] = x * x + y = y * elements["x2"] + return {"y": y} + +example_args = (torch.randn(3, 2), torch.tensor(4)) +tags = {"python.data-structure"} +model = Dictionary() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_assert.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_assert.py new file mode 100644 index 0000000000000000000000000000000000000000..cc822e5553e1ab8bd350a26966c22f1a9a1698cf --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_assert.py @@ -0,0 +1,18 @@ +# mypy: allow-untyped-defs +import torch + +class DynamicShapeAssert(torch.nn.Module): + """ + A basic usage of python assertion. + """ + + def forward(self, x): + # assertion with error message + assert x.shape[0] > 2, f"{x.shape[0]} is greater than 2" + # assertion without error message + assert x.shape[0] > 1 + return x + +example_args = (torch.randn(3, 2),) +tags = {"python.assert"} +model = DynamicShapeAssert() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_constructor.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_constructor.py new file mode 100644 index 0000000000000000000000000000000000000000..157e460274ad58ba71c886b35364ddc0cd4d886a --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_constructor.py @@ -0,0 +1,15 @@ +# mypy: allow-untyped-defs +import torch + +class DynamicShapeConstructor(torch.nn.Module): + """ + Tensor constructors should be captured with dynamic shape inputs rather + than being baked in with static shape. + """ + + def forward(self, x): + return torch.zeros(x.shape[0] * 2) + +example_args = (torch.randn(3, 2),) +tags = {"torch.dynamic-shape"} +model = DynamicShapeConstructor() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_if_guard.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_if_guard.py new file mode 100644 index 0000000000000000000000000000000000000000..21824ef3a0f66eb25f4d8e8c1ba92f53fdd4c275 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_if_guard.py @@ -0,0 +1,19 @@ +# mypy: allow-untyped-defs +import torch + +class DynamicShapeIfGuard(torch.nn.Module): + """ + `if` statement with backed dynamic shape predicate will be specialized into + one particular branch and generate a guard. However, export will fail if the + the dimension is marked as dynamic shape from higher level API. + """ + + def forward(self, x): + if x.shape[0] == 3: + return x.cos() + + return x.sin() + +example_args = (torch.randn(3, 2, 2),) +tags = {"torch.dynamic-shape", "python.control-flow"} +model = DynamicShapeIfGuard() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_map.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_map.py new file mode 100644 index 0000000000000000000000000000000000000000..f8066aed556b9ee588b9744d17ba16c35d8fed6c --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_map.py @@ -0,0 +1,19 @@ +# mypy: allow-untyped-defs +import torch + +from functorch.experimental.control_flow import map + +class DynamicShapeMap(torch.nn.Module): + """ + functorch map() maps a function over the first tensor dimension. + """ + + def forward(self, xs, y): + def body(x, y): + return x + y + + return map(body, xs, y) + +example_args = (torch.randn(3, 2), torch.randn(2)) +tags = {"torch.dynamic-shape", "torch.map"} +model = DynamicShapeMap() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_round.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_round.py new file mode 100644 index 0000000000000000000000000000000000000000..decbf036553cb76544a19e531e5aee98d792ae0b --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_round.py @@ -0,0 +1,21 @@ +# mypy: allow-untyped-defs +import torch + +from torch._export.db.case import SupportLevel +from torch.export import Dim + +class DynamicShapeRound(torch.nn.Module): + """ + Calling round on dynamic shapes is not supported. + """ + + def forward(self, x): + return x[: round(x.shape[0] / 2)] + +x = torch.randn(3, 2) +dim0_x = Dim("dim0_x") +example_args = (x,) +tags = {"torch.dynamic-shape", "python.builtin"} +support_level = SupportLevel.NOT_SUPPORTED_YET +dynamic_shapes = {"x": {0: dim0_x}} +model = DynamicShapeRound() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_slicing.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_slicing.py new file mode 100644 index 0000000000000000000000000000000000000000..360fe15f6f98d9d735366bfa53371d79e0b00209 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_slicing.py @@ -0,0 +1,15 @@ +# mypy: allow-untyped-defs +import torch + +class DynamicShapeSlicing(torch.nn.Module): + """ + Slices with dynamic shape arguments should be captured into the graph + rather than being baked in. + """ + + def forward(self, x): + return x[: x.shape[0] - 2, x.shape[1] - 1 :: 2] + +example_args = (torch.randn(3, 2),) +tags = {"torch.dynamic-shape"} +model = DynamicShapeSlicing() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_view.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_view.py new file mode 100644 index 0000000000000000000000000000000000000000..c45d4aeebb0282a0f56c58a587b4bfe1655f50e3 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/dynamic_shape_view.py @@ -0,0 +1,17 @@ +# mypy: allow-untyped-defs +import torch + +class DynamicShapeView(torch.nn.Module): + """ + Dynamic shapes should be propagated to view arguments instead of being + baked into the exported graph. + """ + + def forward(self, x): + new_x_shape = x.size()[:-1] + (2, 5) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1) + +example_args = (torch.randn(10, 10),) +tags = {"torch.dynamic-shape"} +model = DynamicShapeView() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/fn_with_kwargs.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/fn_with_kwargs.py new file mode 100644 index 0000000000000000000000000000000000000000..46b2637b398c21bf9399d0a3fa2a964354beea3e --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/fn_with_kwargs.py @@ -0,0 +1,30 @@ +# mypy: allow-untyped-defs +import torch + +class FnWithKwargs(torch.nn.Module): + """ + Keyword arguments are not supported at the moment. + """ + + def forward(self, pos0, tuple0, *myargs, mykw0, **mykwargs): + out = pos0 + for arg in tuple0: + out = out * arg + for arg in myargs: + out = out * arg + out = out * mykw0 + out = out * mykwargs["input0"] * mykwargs["input1"] + return out + +example_args = ( + torch.randn(4), + (torch.randn(4), torch.randn(4)), + *[torch.randn(4), torch.randn(4)] +) +example_kwargs = { + "mykw0": torch.randn(4), + "input0": torch.randn(4), + "input1": torch.randn(4), +} +tags = {"python.data-structure"} +model = FnWithKwargs() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/list_contains.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/list_contains.py new file mode 100644 index 0000000000000000000000000000000000000000..35a140f4ee2e5d6f42c3509984333db896f1c081 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/list_contains.py @@ -0,0 +1,17 @@ +# mypy: allow-untyped-defs +import torch + +class ListContains(torch.nn.Module): + """ + List containment relation can be checked on a dynamic shape or constants. + """ + + def forward(self, x): + assert x.size(-1) in [6, 2] + assert x.size(0) not in [4, 5, 6] + assert "monkey" not in ["cow", "pig"] + return x + x + +example_args = (torch.randn(3, 2),) +tags = {"torch.dynamic-shape", "python.data-structure", "python.assert"} +model = ListContains() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/list_unpack.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/list_unpack.py new file mode 100644 index 0000000000000000000000000000000000000000..3e2f8e2469a04b6b9d3358ed3602e9c1a8d6f35b --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/list_unpack.py @@ -0,0 +1,22 @@ +# mypy: allow-untyped-defs +from typing import List + +import torch + +class ListUnpack(torch.nn.Module): + """ + Lists are treated as static construct, therefore unpacking should be + erased after tracing. + """ + + def forward(self, args: List[torch.Tensor]): + """ + Lists are treated as static construct, therefore unpacking should be + erased after tracing. + """ + x, *y = args + return x + y[0] + +example_args = ([torch.randn(3, 2), torch.tensor(4), torch.tensor(5)],) +tags = {"python.control-flow", "python.data-structure"} +model = ListUnpack() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/model_attr_mutation.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/model_attr_mutation.py new file mode 100644 index 0000000000000000000000000000000000000000..4aa623c7dc39efd94fecb8eb32caac3f7420f05d --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/model_attr_mutation.py @@ -0,0 +1,26 @@ +# mypy: allow-untyped-defs +import torch +from torch._export.db.case import SupportLevel + + +class ModelAttrMutation(torch.nn.Module): + """ + Attribute mutation is not supported. + """ + + def __init__(self) -> None: + super().__init__() + self.attr_list = [torch.randn(3, 2), torch.randn(3, 2)] + + def recreate_list(self): + return [torch.zeros(3, 2), torch.zeros(3, 2)] + + def forward(self, x): + self.attr_list = self.recreate_list() + return x.sum() + self.attr_list[0].sum() + + +example_args = (torch.randn(3, 2),) +tags = {"python.object-model"} +support_level = SupportLevel.NOT_SUPPORTED_YET +model = ModelAttrMutation() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/nested_function.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/nested_function.py new file mode 100644 index 0000000000000000000000000000000000000000..e4076ac14dada40b4d78812666a9ec6b5e67045b --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/nested_function.py @@ -0,0 +1,23 @@ +# mypy: allow-untyped-defs +import torch + +class NestedFunction(torch.nn.Module): + """ + Nested functions are traced through. Side effects on global captures + are not supported though. + """ + + def forward(self, a, b): + x = a + b + z = a - b + + def closure(y): + nonlocal x + x += 1 + return x * y + z + + return closure(x) + +example_args = (torch.randn(3, 2), torch.randn(2)) +tags = {"python.closure"} +model = NestedFunction() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/null_context_manager.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/null_context_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..80d09f68097edbe676077be183711dabe5cbc664 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/null_context_manager.py @@ -0,0 +1,21 @@ +# mypy: allow-untyped-defs +import contextlib + +import torch + +class NullContextManager(torch.nn.Module): + """ + Null context manager in Python will be traced out. + """ + + def forward(self, x): + """ + Null context manager in Python will be traced out. + """ + ctx = contextlib.nullcontext() + with ctx: + return x.sin() + x.cos() + +example_args = (torch.randn(3, 2),) +tags = {"python.context-manager"} +model = NullContextManager() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/optional_input.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/optional_input.py new file mode 100644 index 0000000000000000000000000000000000000000..9693aa476f0eb31ec912c840ed3a4426ac832de8 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/optional_input.py @@ -0,0 +1,20 @@ +# mypy: allow-untyped-defs +import torch +from torch._export.db.case import SupportLevel + + +class OptionalInput(torch.nn.Module): + """ + Tracing through optional input is not supported yet + """ + + def forward(self, x, y=torch.randn(2, 3)): + if y is not None: + return x + y + return x + + +example_args = (torch.randn(2, 3),) +tags = {"python.object-model"} +support_level = SupportLevel.NOT_SUPPORTED_YET +model = OptionalInput() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/pytree_flatten.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/pytree_flatten.py new file mode 100644 index 0000000000000000000000000000000000000000..fe401b75e8b94be80247a806839be9c1c89bce25 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/pytree_flatten.py @@ -0,0 +1,16 @@ +# mypy: allow-untyped-defs +import torch + +from torch.utils import _pytree as pytree + +class PytreeFlatten(torch.nn.Module): + """ + Pytree from PyTorch can be captured by TorchDynamo. + """ + + def forward(self, x): + y, spec = pytree.tree_flatten(x) + return y[0] + 1 + +example_args = ({1: torch.randn(3, 2), 2: torch.randn(3, 2)},), +model = PytreeFlatten() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/scalar_output.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/scalar_output.py new file mode 100644 index 0000000000000000000000000000000000000000..86d3b4645330c47c3625736b695d635f4ab58c70 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/scalar_output.py @@ -0,0 +1,23 @@ +# mypy: allow-untyped-defs +import torch + +from torch.export import Dim + +x = torch.randn(3, 2) +dim1_x = Dim("dim1_x") + +class ScalarOutput(torch.nn.Module): + """ + Returning scalar values from the graph is supported, in addition to Tensor + outputs. Symbolic shapes are captured and rank is specialized. + """ + def __init__(self) -> None: + super().__init__() + + def forward(self, x): + return x.shape[1] + 1 + +example_args = (x,) +tags = {"torch.dynamic-shape"} +dynamic_shapes = {"x": {1: dim1_x}} +model = ScalarOutput() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/specialized_attribute.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/specialized_attribute.py new file mode 100644 index 0000000000000000000000000000000000000000..f17092f9afc681b91a982a8a2479ac1dde4f455d --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/specialized_attribute.py @@ -0,0 +1,26 @@ +# mypy: allow-untyped-defs +from enum import Enum + +import torch + +class Animal(Enum): + COW = "moo" + +class SpecializedAttribute(torch.nn.Module): + """ + Model attributes are specialized. + """ + + def __init__(self) -> None: + super().__init__() + self.a = "moo" + self.b = 4 + + def forward(self, x): + if self.a == Animal.COW.value: + return x * x + self.b + else: + raise ValueError("bad") + +example_args = (torch.randn(3, 2),) +model = SpecializedAttribute() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/static_for_loop.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/static_for_loop.py new file mode 100644 index 0000000000000000000000000000000000000000..3924643bd94c299168f8f69155e846ed20dae1ee --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/static_for_loop.py @@ -0,0 +1,17 @@ +# mypy: allow-untyped-defs +import torch + +class StaticForLoop(torch.nn.Module): + """ + A for loop with constant number of iterations should be unrolled in the exported graph. + """ + + def forward(self, x): + ret = [] + for i in range(10): # constant + ret.append(i + x) + return ret + +example_args = (torch.randn(3, 2),) +tags = {"python.control-flow"} +model = StaticForLoop() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/static_if.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/static_if.py new file mode 100644 index 0000000000000000000000000000000000000000..f169380159a45489142ce5ae3523b2e4504c6721 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/static_if.py @@ -0,0 +1,18 @@ +# mypy: allow-untyped-defs +import torch + +class StaticIf(torch.nn.Module): + """ + `if` statement with static predicate value should be traced through with the + taken branch. + """ + + def forward(self, x): + if len(x.shape) == 3: + return x + torch.ones(1, 1, 1) + + return x + +example_args = (torch.randn(3, 2, 2),) +tags = {"python.control-flow"} +model = StaticIf() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/tensor_setattr.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/tensor_setattr.py new file mode 100644 index 0000000000000000000000000000000000000000..8fbc263e7ff2240a3cf8618c56f152e744aa40e8 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/tensor_setattr.py @@ -0,0 +1,15 @@ +# mypy: allow-untyped-defs +import torch + + +class TensorSetattr(torch.nn.Module): + """ + setattr() call onto tensors is not supported. + """ + def forward(self, x, attr): + setattr(x, attr, torch.randn(3, 2)) + return x + 4 + +example_args = (torch.randn(3, 2), "attr") +tags = {"python.builtin"} +model = TensorSetattr() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/type_reflection_method.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/type_reflection_method.py new file mode 100644 index 0000000000000000000000000000000000000000..99ad42a153c512d65aaae1bcac2377ee1e124f25 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/type_reflection_method.py @@ -0,0 +1,22 @@ +# mypy: allow-untyped-defs +import torch + +class A: + @classmethod + def func(cls, x): + return 1 + x + +class TypeReflectionMethod(torch.nn.Module): + """ + type() calls on custom objects followed by attribute accesses are not allowed + due to its overly dynamic nature. + """ + + def forward(self, x): + a = A() + return type(a).func(x) + + +example_args = (torch.randn(3, 4),) +tags = {"python.builtin"} +model = TypeReflectionMethod() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/unsupported_operator.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/unsupported_operator.py new file mode 100644 index 0000000000000000000000000000000000000000..f5a52d80b895b3b2c2d85b878ca4efea511e73ea --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/unsupported_operator.py @@ -0,0 +1,18 @@ +# mypy: allow-untyped-defs +import torch +from torch._export.db.case import SupportLevel + + +class TorchSymMin(torch.nn.Module): + """ + torch.sym_min operator is not supported in export. + """ + + def forward(self, x): + return x.sum() + torch.sym_min(x.size(0), 100) + + +example_args = (torch.randn(3, 2),) +tags = {"torch.operator"} +support_level = SupportLevel.NOT_SUPPORTED_YET +model = TorchSymMin() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/user_input_mutation.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/user_input_mutation.py new file mode 100644 index 0000000000000000000000000000000000000000..3156b3a1bf2ec6f6361395de3dacb098ecf20c3a --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/examples/user_input_mutation.py @@ -0,0 +1,17 @@ +# mypy: allow-untyped-defs +import torch + + +class UserInputMutation(torch.nn.Module): + """ + Directly mutate user input in forward + """ + + def forward(self, x): + x.mul_(2) + return x.cos() + + +example_args = (torch.randn(3, 2),) +tags = {"torch.mutation"} +model = UserInputMutation() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/gen_example.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/gen_example.py new file mode 100644 index 0000000000000000000000000000000000000000..8e44cade322bdde858c5dd05ac116cef47202a33 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/gen_example.py @@ -0,0 +1,21 @@ +import os +import sys + +import torch._export.db.examples as examples + +TEMPLATE = '''import torch + +def {case_name}(x): + """ + """ + + return +''' + +if __name__ == "__main__": + assert len(sys.argv) == 2 + root_dir = examples.__name__.replace(".", "/") + assert os.path.exists(root_dir) + with open(os.path.join(root_dir, sys.argv[1] + ".py"), "w") as f: + print("Writing to", f.name, "...") + f.write(TEMPLATE.format(case_name=sys.argv[1])) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/logging.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/logging.py new file mode 100644 index 0000000000000000000000000000000000000000..2078113fef157e38a465c78156c3b22ff4c235c7 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/db/logging.py @@ -0,0 +1,47 @@ +# mypy: allow-untyped-defs + + +def exportdb_error_message(case_name: str): + from .examples import all_examples + from torch._utils_internal import log_export_usage + + ALL_EXAMPLES = all_examples() + # Detect whether case_name is really registered in exportdb. + if case_name in ALL_EXAMPLES: + url_case_name = case_name.replace("_", "-") + return f"See {case_name} in exportdb for unsupported case. \ + https://pytorch.org/docs/main/generated/exportdb/index.html#{url_case_name}" + else: + log_export_usage( + event="export.error.casenotregistered", + message=case_name, + ) + return f"{case_name} is unsupported." + + +def get_class_if_classified_error(e): + """ + Returns a string case name if the export error e is classified. + Returns None otherwise. + """ + + from torch._dynamo.exc import TorchRuntimeError, Unsupported, UserError + + ALWAYS_CLASSIFIED = "always_classified" + DEFAULT_CLASS_SIGIL = "case_name" + + # add error types that should be classified, along with any attribute name + # whose presence acts like a sigil to further distinguish which errors of + # that type should be classified. If the attribute name is None, then the + # error type is always classified. + _ALLOW_LIST = { + Unsupported: DEFAULT_CLASS_SIGIL, + UserError: DEFAULT_CLASS_SIGIL, + TorchRuntimeError: None, + } + if type(e) in _ALLOW_LIST: + attr_name = _ALLOW_LIST[type(e)] + if attr_name is None: + return ALWAYS_CLASSIFIED + return getattr(e, attr_name, None) + return None diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/error.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/error.py new file mode 100644 index 0000000000000000000000000000000000000000..03b7f52fb9de435b9e58fa4a0bb141cc191e84c5 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/error.py @@ -0,0 +1,56 @@ +from enum import Enum + + +class ExportErrorType(Enum): + # User providing invalid inputs to either tracer, or other public facing APIs + INVALID_INPUT_TYPE = 1 + + # User returning values from their models that we don't support. + INVALID_OUTPUT_TYPE = 2 + + # Generated IR does not conform to Export IR Specification. + VIOLATION_OF_SPEC = 3 + + # User's code contains types and functionalities we don't support. + NOT_SUPPORTED = 4 + + # User's code didn't provide necessary details for us to successfully trace and export. + # For example, we use a lot of decorators and ask users to annotate their model. + MISSING_PROPERTY = 5 + + # User is using an API without proper initialization step. + UNINITIALIZED = 6 + + +def internal_assert(pred: bool, assert_msg: str) -> None: + """ + This is exir's custom assert method. It internally just throws InternalError. + Note that the sole purpose is to throw our own error while maintaining similar syntax + as python assert. + """ + + if not pred: + raise InternalError(assert_msg) + + +class InternalError(Exception): + """ + Raised when an internal invariance is violated in EXIR stack. + Should hint users to report a bug to dev and expose the original + error message. + """ + + def __init__(self, message: str) -> None: + super().__init__(message) + + +class ExportError(Exception): + """ + This type of exception is raised for errors that are directly caused by the user + code. In general, user errors happen during model authoring, tracing, using our public + facing APIs, and writing graph passes. + """ + + def __init__(self, error_code: ExportErrorType, message: str) -> None: + prefix = f"[{error_code}]: " + super().__init__(prefix + message) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/non_strict_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/non_strict_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..5c7331659d26a00dc68e0d169a70328cec251c2b --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/non_strict_utils.py @@ -0,0 +1,523 @@ +# mypy: allow-untyped-defs +import contextlib +import inspect +import logging +from collections import defaultdict +from typing import Any, Callable, Dict, List, Tuple, TYPE_CHECKING, Union + +import torch +import torch.utils._pytree as pytree +from torch._dynamo.source import ( + AttrSource, + GetItemSource, + LocalSource, + TensorProperty, + TensorPropertySource, +) +from torch._dynamo.variables.builder import TrackedFake +from torch._export.passes.add_runtime_assertions_for_constraints_pass import InputDim +from torch._export.passes.lift_constants_pass import ConstantAttrMap +from torch._guards import Source +from torch._library.fake_class_registry import FakeScriptObject +from torch._subclasses.fake_tensor import FakeTensorMode +from torch.export import Constraint +from torch.export.dynamic_shapes import ( + _check_dynamic_shapes, + _combine_args, + _DimHint, + _process_dynamic_shapes, + _transform_shapes_for_default_dynamic, + _tree_map_with_path, +) +from torch.export.graph_signature import CustomObjArgument +from torch.fx.experimental import _config as config +from torch.fx.experimental.symbolic_shapes import ( + _find_user_code_frame, + _suggest_fixes_for_data_dependent_error_non_strict, + ConstraintViolationError, + DimDynamic, + EqualityConstraint, + GuardOnDataDependentSymNode, + ShapeEnv, + StatelessSymbolicContext, + ValueRanges, +) +from torch.utils._pytree import ( + GetAttrKey, + KeyPath, + MappingKey, + SequenceKey, + tree_map_with_path, +) + + +if TYPE_CHECKING: + from sympy import Symbol + + +log = logging.getLogger(__name__) + + +def key_path_to_source(kp: KeyPath) -> Source: + """ + Given a key path, return the source for the key path. + """ + source: Source = LocalSource("args") + for k in kp: + if isinstance(k, SequenceKey): + source = GetItemSource(source, k.idx) + elif isinstance(k, MappingKey): + source = GetItemSource(source, k.key) + elif isinstance(k, GetAttrKey): + source = AttrSource(source, k.name) + else: + raise ValueError(f"Unknown KeyEntry {k}") + + return source + + +def _is_constant_argument(t): + return t is None or isinstance(t, (int, float, bool, str)) + + +def fakify( + mode: FakeTensorMode, + kp: KeyPath, + t: Any, + t_constraints: Dict[int, Dict[int, Constraint]], + sources: Dict[Tuple[int, int], List[Source]], +): + source = key_path_to_source(kp) + if _is_constant_argument(t) or isinstance(t, torch.ScriptObject): + return t + + if not isinstance(t, torch.Tensor): + raise ValueError(f"Unsupported input type {type(t)}") + n_dims = len(t.shape) + symbolic_context = StatelessSymbolicContext( + dynamic_sizes=[DimDynamic.DYNAMIC] * n_dims, + constraint_sizes=[None] * n_dims, + ) + t_id = id(t) + assert mode.shape_env is not None + if t_id in t_constraints: + for i, constraint in t_constraints[t_id].items(): + symbolic_context.constraint_sizes[i] = constraint.constraint_range + src = TensorPropertySource(base=source, prop=TensorProperty.SIZE, idx=i) + sources[(t_id, i)].append(src) + mode.shape_env.source_name_to_debug_name[src.name()] = constraint.name # type: ignore[assignment] + fake = mode.from_tensor(t, source=source, symbolic_context=symbolic_context) + mode.shape_env.tracked_fakes.append(TrackedFake(fake, source, symbolic_context)) # type: ignore[union-attr] + return fake + + +def make_fake_inputs( + nn_module, + args, + kwargs, + dynamic_shapes, + _is_torch_jit_trace=False, + allow_complex_guards_as_runtime_asserts=False, +): + """ + Given an nn module, example inputs, and constraints, return a new fake mode, + fake inputs created in that mode whose dynamic shape dimensions are constrained + by the given ranges, and sources for pairs of dynamic shape dimensions that are + constrained to be equal. + """ + # TODO(avik): refactor Dynamo to avoid duplication of the following code + # between non-strict and strict. + # Specifically, here (non-strict) we do the following pre-tracing steps: + # - Fakify inputs. + # - Process input shape equalities. + # In strict, these steps are spread across multiple files: + # - output_graph.py fakifies inputs. + # - [post-tracing] guards.py processes input shape equalities. + + combined_args = _combine_args(nn_module, args, kwargs) + _check_dynamic_shapes(combined_args, dynamic_shapes) + transformed_dynamic_shapes = _transform_shapes_for_default_dynamic( + combined_args, dynamic_shapes + ) + constraints = _process_dynamic_shapes(combined_args, transformed_dynamic_shapes) + t_constraints: Dict[int, Dict[int, Constraint]] = defaultdict(dict) + for constraint in constraints: + t_constraints[constraint.t_id][constraint.dim] = constraint + + context = torch._guards.TracingContext.try_get() + if context is not None: + # This occurs when we are exporting within dynamo. There already exists + # a toplevel TracingContext with a fake mode, so we do not want to + # create another fake mode. + fake_mode = context.fake_mode + elif not _is_torch_jit_trace: + code = nn_module.forward.__code__ + co_fields = { + "co_name": code.co_name, + "co_filename": code.co_filename, + "co_firstlineno": code.co_firstlineno, + } + fake_mode = FakeTensorMode( + shape_env=ShapeEnv( + tracked_fakes=[], + co_fields=co_fields, + prefer_deferred_runtime_asserts_over_guards=True, + allow_complex_guards_as_runtime_asserts=allow_complex_guards_as_runtime_asserts, + ), + allow_non_fake_inputs=True, + export=True, + ) + else: + fake_mode = FakeTensorMode( + shape_env=ShapeEnv( + tracked_fakes=[], + prefer_deferred_runtime_asserts_over_guards=True, + allow_complex_guards_as_runtime_asserts=allow_complex_guards_as_runtime_asserts, + ), + allow_non_fake_inputs=True, + ) + if fake_mode.shape_env is None or fake_mode.shape_env.tracked_fakes is None: + raise ValueError( + "Detected fake_mode does not have a shape_env with tracked fakes. " + "If you constructed the module under a FakeTensorMode, " + "please initialize it like: FakeTensorMode(shape_env=ShapeEnv(tracked_fakes=[]))" + ) + + with fake_mode: + # FIXME(ycao) ScriptMethod doesn't have signature, I am using an empty one to unblock + if not _is_torch_jit_trace: + original_signature = inspect.signature(nn_module.forward) + else: + original_signature = None + sources: Dict[Tuple[int, int], List[Source]] = defaultdict(list) + fake_args, fake_kwargs = tree_map_with_path( + lambda kp, val: fakify(fake_mode, kp, val, t_constraints, sources), + (args, kwargs), + ) + + names: Dict[str, Tuple[int, int]] = {} + source_pairs: List[Tuple[Source, Source]] = [] + derived_equalities: List[Tuple[Source, Union[Source, Symbol], Callable]] = [] + phantom_symbols: Dict[str, Symbol] = {} + for constraint in constraints: + torch.export.dynamic_shapes._process_equalities( + constraint, + lambda t_id, dim: sources[(t_id, dim)], + fake_mode.shape_env, + names, + source_pairs, + derived_equalities, + phantom_symbols, + ) + + equalities_inputs = EqualityConstraint( + source_pairs=source_pairs, + derived_equalities=derived_equalities, + phantom_symbols=list(phantom_symbols.values()), + warn_only=False, + ) + return ( + fake_mode, + fake_args, + fake_kwargs, + equalities_inputs, + original_signature, + transformed_dynamic_shapes, + ) + + +def _flatten_dynamic_shapes( + combined_args: Dict[str, Any], + dynamic_shapes: Union[Dict[str, Any], Tuple[Any], List[Any]], +) -> List[Any]: + flat_shapes = [] + + def _tree_map_helper(path, t, shape): + nonlocal flat_shapes + flat_shapes.append(shape) + + _tree_map_with_path(_tree_map_helper, combined_args, dynamic_shapes) + return flat_shapes + + +def produce_guards_and_solve_constraints( + fake_mode: FakeTensorMode, + gm: torch.fx.GraphModule, + dynamic_shapes: Union[Dict[str, Any], Tuple[Any], List[Any], None], + equalities_inputs: EqualityConstraint, + original_signature: inspect.Signature, + _is_torch_jit_trace=False, +): + """ + Given a fake mode, sources pairs corresponding to equal dynamic shape dimensions, + and a graph module, produce guards on the fake mode's shape env (raising constraint + violations if any), solve (to suggest simplifications or fixes). + Dynamo already performs this, so this is for non-strict mode. + + Additional inputs: + equalities_inputs: the equality constraints to use for guards + original_signature: the signature of the forward method + """ + shape_env = fake_mode.shape_env + assert shape_env is not None + assert shape_env.tracked_fakes is not None + + placeholders = [tf.fake for tf in shape_env.tracked_fakes] + sources = [tf.source for tf in shape_env.tracked_fakes] + input_contexts = [tf.symbolic_context for tf in shape_env.tracked_fakes] + constraint_violation_error = None + try: + shape_env.produce_guards( + placeholders, + sources, + input_contexts=input_contexts, + equalities_inputs=equalities_inputs, + ignore_static=False, + ) + except ConstraintViolationError as e: + constraint_violation_error = e + + shape_env.frozen = True + dim_constraints = shape_env.dim_constraints + if dim_constraints is None: + # Expected when shape_env.produce_guards throws an early constraint violation error. + # There is nothing to solve for in this case. + # TODO(avik): Maybe record the constraint violation error instead and replay later? + assert constraint_violation_error + raise constraint_violation_error + dim_constraints.solve() + forced_specializations = dim_constraints.forced_specializations() + if not _is_torch_jit_trace: + msg = dim_constraints.prettify_results( + original_signature, + dynamic_shapes, + constraint_violation_error, + forced_specializations, + ) + else: + # FIXME(ycao): This is a hack to get around missing signature from ScriptMethod + msg = "dummy constraint violation message" + if constraint_violation_error: + constraint_violation_error.args = (constraint_violation_error.args[0] + msg,) + elif forced_specializations: + constraint_violation_error = ConstraintViolationError(msg) + if constraint_violation_error: + raise constraint_violation_error + + +def make_constraints( + fake_mode: FakeTensorMode, + gm: torch.fx.GraphModule, + combined_args: Dict[str, Any], + dynamic_shapes: Union[Dict[str, Any], Tuple[Any], List[Any], None], + num_lifted_inputs: int, +): + """ + Given a fake mode's shape env and user-specified dynamic shapes, + return the resulting range constraints and equality constraints. + + Additional args: + num_lifted_inputs: the number of non-user-input placeholder nodes in the graph + (used only to enumerate the user-input nodes) + """ + + shape_env = fake_mode.shape_env + assert shape_env is not None + inline_constraints = gm.meta.get("inline_constraints", []) + range_constraints = { + symbol: inline_constraints[symbol] for symbol in inline_constraints + } + if not dynamic_shapes: + return range_constraints + + # get individual dynamic shapes spec for each input + if not isinstance(dynamic_shapes, dict): + assert isinstance(dynamic_shapes, (tuple, list)) + combined_args = type(dynamic_shapes)(combined_args.values()) # type: ignore[assignment, misc] + flat_dynamic_shapes = _flatten_dynamic_shapes(combined_args, dynamic_shapes) + + # check number of shapes vs. number of inputs + num_placeholders = [node.op == "placeholder" for node in gm.graph.nodes].count(True) + assert len(flat_dynamic_shapes) == num_placeholders - num_lifted_inputs + + input_dims = defaultdict(list) + free_symbols = set() + for input_index, node in enumerate(gm.graph.nodes): + if input_index < num_lifted_inputs or node.op != "placeholder": + continue + if _is_constant_argument(node.meta["val"]) or isinstance( + node.meta["val"], CustomObjArgument + ): + continue + shape_spec = flat_dynamic_shapes[input_index - num_lifted_inputs] + for i, d in enumerate(node.meta["val"].shape): + if isinstance(d, torch.SymInt) and not d.node.expr.is_number: + # Look up the range constraint for the symbol corresponding to this shape dimension + # and store it indexed by the symbolic expression corresponding to it. + # NOTE(avik): Use node._expr instead of node.expr for the lookup here because + # we want the symbol, not its replacement, which could be an expression. Maybe + # there's a better way to do this, e.g., by (re)computing value ranges for expressions? + dim = shape_spec[i] if shape_spec else None + if dim is None or isinstance(dim, _DimHint): + range_constraints[d.node.expr] = shape_env.var_to_range[ + d.node._expr + ] + else: + range_constraints[d.node.expr] = ValueRanges( + lower=dim.min, upper=dim.max + ) + input_dims[d.node.expr].append(InputDim(input_name=node.name, dim=i)) + free_symbols.update(d.node.expr.free_symbols) + + for symbol in free_symbols: + if symbol not in range_constraints: + # Placeholders can have symbolic shapes that are derived expressions. + # The above code will record direct range constraints for them + # so that we can do runtime assertions. In addition, for serde checks + # we want to record range constraints for their root symbols. + range_constraints[symbol] = shape_env.var_to_range[symbol] + + return range_constraints + + +def _gather_constant_attrs(m: torch.nn.Module) -> ConstantAttrMap: + """Search the module hierarchy, gathering up all tensor and ScriptObject constants. + + Returns a dictionary mapping hash(value) to the name of the constant. We + have to abuse `hash` here unfortunately, see: [ScriptObject hash]. + """ + constants = ConstantAttrMap() + buffers_parameters = set(m.buffers()) + buffers_parameters.update(m.parameters()) + + def inner(m: torch.nn.Module, prefix_atoms: List[str], constants): + for k, v in m.__dict__.items(): + if isinstance( + v, + ( + torch.Tensor, + torch.ScriptObject, + FakeScriptObject, + ), + ): + if v in buffers_parameters: + # filter out buffers and parameters, leaving only constants + continue + + fqn = ".".join(prefix_atoms + [k]) + constants.add(v, fqn) + for k, v in m.named_children(): + inner(v, prefix_atoms + [k], constants) + + inner(m, [], constants) + return constants + + +@contextlib.contextmanager +def _fakify_script_objects( + mod: torch.nn.Module, + args: Tuple[Any], + kwargs: Dict[Any, Any], + fake_mode: torch._subclasses.fake_tensor.FakeTensorMode, +): + # This context manager is used to fakify script objects into FakeScriptObject. + # Inputs: + # mod: the module to be exported, it (and its recursive submodules)'s script object attrs haven't been fakified. + # args, kwargs: the args and kwargs inputs for mod, script object inputs haven't been fakified. + # fake_mode: the fake mode to be used for fakifying script objects. It's the same mode that fakify input tensors. + # + # Returns: + # mod: the patched module, its (and its recursive submodules) script object attrs have been fakified. + # fake_args, fake_kwargs: new fakified args and kwargs. + # Script object inputs have been fakified. Don't touch the tensors. + # fake_constant_attrs: a new map from FakeScriptObject to the fqn of the original script object. + # fake_to_real: a mapping between FakeScriptObject and the original script object in order to un-do the patching. + + constant_attrs: ConstantAttrMap = _gather_constant_attrs(mod) + assert not any( + isinstance(obj, FakeScriptObject) for obj in constant_attrs.values() + ), "Mod shouldn't contain any FakeScriptObject." + assert not pytree.tree_any( + lambda obj: isinstance(obj, FakeScriptObject), (args, kwargs) + ), "args and kwargs shouldn't contain any FakeScriptObject." + + patched_attr = {} + fake_constant_attrs = ConstantAttrMap() + fake_to_real = {} + + def _maybe_fakify_obj(obj): + fake_obj = torch._library.fake_class_registry.maybe_to_fake_obj(fake_mode, obj) + fake_to_real[fake_obj] = obj + return fake_obj + + def _leaf_mod_and_attr( + mod: torch.nn.Module, attr_fqn: str + ) -> Tuple[torch.nn.Module, str]: + *prefix_attr, last_attr = attr_fqn.split(".") + cur_mod = mod + for attr in prefix_attr: + cur_mod = getattr(cur_mod, attr) + return cur_mod, last_attr + + try: + for obj, fqns in constant_attrs.items(): + if isinstance(obj, torch.ScriptObject): + fake_script_obj = _maybe_fakify_obj(obj) + for fqn in fqns: + cur_mod, attr = _leaf_mod_and_attr(mod, fqn) + assert obj is getattr(cur_mod, attr) + setattr(cur_mod, attr, fake_script_obj) + fake_constant_attrs.add(fake_script_obj, fqn) + patched_attr[fqn] = obj + else: + for fqn in fqns: + fake_constant_attrs.add(obj, fqn) + + fake_args, fake_kwargs = pytree.tree_map_only( + torch.ScriptObject, _maybe_fakify_obj, (args, kwargs) + ) + yield (mod, fake_args, fake_kwargs, fake_constant_attrs, fake_to_real) + finally: + for fqn, orig_obj in patched_attr.items(): + cur_mod, attr = _leaf_mod_and_attr(mod, fqn) + setattr(cur_mod, attr, orig_obj) + + +class _NonStrictTorchFunctionHandler(torch.overrides.TorchFunctionMode): + """ + 1. Handles data-dependent errors raised by torch function calls in non-strict. + + Any data-dependent error is due to some condition on unbacked symints + that cannot be resolved. A mechanical way of fixing the error is to use + a torch._check() call to assert either that condition or its negation. + The handler suggests these options as code and points to the location + of the torch function call that raised the error as part of the error + message shown to the user, who can then simply select and copy-paste + a suggested fix at that location. + + NOTE: Not all data-dependent errors are raised by torch function calls. + In particular, conditions on unbacked symints can appear outside such + calls, and as such are not handled here. + + 2. Handles line-of-code logging for each torch function call in non-strict. + + Usage: TORCHEXPORT_EXTENDED_DEBUG_CURRENT_LOC=1 TORCH_LOGS="+export" ... + """ + + def __torch_function__(self, func, types, args=(), kwargs=None): + kwargs = kwargs or {} + if log.isEnabledFor(logging.DEBUG) and config.extended_debug_current_loc: + frame = _find_user_code_frame() + if frame is not None: + log.debug( + "%s called at %s:%s in %s", + func.__qualname__, + frame.f_code.co_filename, + frame.f_lineno, + frame.f_code.co_name, + ) + try: + return func(*args, **kwargs) + except GuardOnDataDependentSymNode as e: + _suggest_fixes_for_data_dependent_error_non_strict(e) + raise diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/pass_base.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/pass_base.py new file mode 100644 index 0000000000000000000000000000000000000000..55612c98ce8d51d95999f0f4e124f3479070deb1 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/pass_base.py @@ -0,0 +1,441 @@ +# mypy: allow-untyped-defs +import operator +import traceback +import typing +from contextlib import nullcontext +from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union + +import torch +from functorch.experimental.control_flow import _unstack_pytree +from torch import fx +from torch._dispatch.python import enable_python_dispatcher +from torch._export.pass_infra.node_metadata import NodeMetadata +from torch._export.pass_infra.proxy_value import ProxyValue +from torch._subclasses import FakeTensor, UnsupportedFakeTensorException +from torch._subclasses.fake_tensor import FakeTensorMode +from torch.fx import traceback as fx_traceback +from torch.fx.experimental.proxy_tensor import PythonKeyTracer +from torch.fx.graph import CodeGen +from torch.fx.passes.infra.pass_base import PassBase, PassResult +from torch.fx.passes.shape_prop import _extract_tensor_metadata, TensorMetadata +from torch.utils import _pytree as pytree +from torch.fx.experimental.symbolic_shapes import PropagateUnbackedSymInts, compute_unbacked_bindings + + +__all__ = ["_ExportPassBaseDeprecatedDoNotUse"] + + +Argument = Any +Value = Any +Fn = Callable[..., Any] +PassType = Callable[[torch.fx.GraphModule], Optional[PassResult]] + + +_TORCH_SYM_OPS: Set[Callable] = { + torch.sym_int, + torch.sym_float, + torch.sym_ite, + torch.sym_max, + torch.sym_min, + torch.sym_not, + torch.sym_sqrt, +} + + +class ExportPassBaseError(RuntimeError): + pass + + +class _ExportPassBaseDeprecatedDoNotUse(PassBase): + """ + Interpreter-based pass class to help users maintain the IR spec while writing + transformations. + """ + + @staticmethod + def _create_dummy_node_metadata(): + return NodeMetadata({"stack_trace": "".join(traceback.format_stack(limit=1))}) + + + class ExportTracer(PythonKeyTracer): + def __init__(self, callback: "_ExportPassBaseDeprecatedDoNotUse", codegen: CodeGen) -> None: + super().__init__() + self.callback = callback + self.root = torch.nn.Module() + self.graph = torch.fx.Graph() + self.graph.set_codegen(codegen) + self.tensor_attrs: Dict[str, torch.Tensor] = {} # type: ignore[assignment] + self.fake_tensor_mode: Optional[FakeTensorMode] = None + self.submodules: Dict[torch.nn.Module, str] = {} + + def trace(self) -> None: # type: ignore[override] + raise ExportPassBaseError("ExportTracer doesn't support trace().") + + def create_arg(self, a: Argument) -> torch.fx.Node: + if isinstance(a, torch.nn.Module): + if a not in self.submodules: + name_submodule = f"submodule_{len(self.submodules)}" + self.root.add_module(name_submodule, a) + self.submodules[a] = name_submodule + elif isinstance(a, FakeTensor): + if not hasattr(a, "constant") or a.constant is None: + raise ExportPassBaseError(f"Cannot add {a} to graph.") + a = a.constant + node = super().create_arg(a) + if ( + isinstance(a, torch.Tensor) + and isinstance(node, torch.fx.Node) + and node.op == "get_attr" + ): + self.set_metadata(node, a) + self.callback.on_attr(ProxyValue(a, node)) + return node + + def set_metadata( + self, node: torch.fx.Node, value: Argument, + ) -> None: + # propagate the fake tensor or sym nodes + def make_val( + x: Argument, + ) -> Union[FakeTensor, torch.SymInt, torch.SymFloat, torch.SymBool, int, float, bool, str, None]: + if isinstance(x, FakeTensor): + return x + elif isinstance(x, torch.Tensor): + if x.is_quantized: + # TODO (tmanlaibaatar) properly support Quantized FakeTensor + x = torch.dequantize(x) + + try: + assert self.fake_tensor_mode is not None + # TODO we should allocate static shapes + # for param/buffer values + if isinstance(x, torch.nn.Parameter): + fake_tensor = self.fake_tensor_mode.from_tensor( + x, static_shapes=True + ) + else: + fake_tensor = self.fake_tensor_mode.from_tensor(x) + except UnsupportedFakeTensorException: + # TODO: This is just a workaround to get over the + # x.as_subclass error + print( + "Fakeifying a Tensor subclass is not supported \ + right now. Instead a TensorMetadata is used." + ) + fake_tensor = None + return fake_tensor + elif isinstance(x, (torch.SymInt, torch.SymFloat, torch.SymBool, int, float, bool, str)): + return x + else: + return None + + node.meta["val"] = pytree.tree_map(make_val, value) + + # Set the tensor_metadata for values that do not have a corresponding FakeTensor + def make_tensor_meta(x: Argument) -> Optional[TensorMetadata]: + if not isinstance(x, FakeTensor) and isinstance(x, torch.Tensor): + if x.is_quantized: + # TODO (tmanlaibaatar) properly support Quantized FakeTensor + x = torch.dequantize(x) + + try: + assert self.fake_tensor_mode is not None + _ = self.fake_tensor_mode.from_tensor(x) + tensor_meta = None + except UnsupportedFakeTensorException: + # TODO: This is just a workaround to get over the + # x.as_subclass error + tensor_meta = _extract_tensor_metadata(x) + return tensor_meta + else: + return None + + node.meta["tensor_meta"] = pytree.tree_map(make_tensor_meta, value) + + class ExportInterpreter(fx.Interpreter): + def __init__(self, callback: "_ExportPassBaseDeprecatedDoNotUse", gm: fx.GraphModule) -> None: + super().__init__(gm) + self.callback = callback + self.node: torch.fx.Node = next(iter(gm.graph.nodes)) + + def placeholder( + self, + target: str, # type: ignore[override] + args: Tuple[Argument, ...], + kwargs: Dict[str, Argument], + ) -> ProxyValue: + arg = super().placeholder(target, args, kwargs) + return self.callback.placeholder(target, arg, NodeMetadata(self.node.meta)) + + def output( + self, + target: torch.fx.node.Target, + args: Tuple[Argument, ...], + kwargs: Dict[str, Argument], + ) -> ProxyValue: + return self.callback.output(args[0], NodeMetadata(self.node.meta)).data + + def call_function( + self, + target: torch.fx.node.Target, + args: Tuple[Argument, ...], + kwargs: Dict[str, Argument], + ) -> ProxyValue: + meta = NodeMetadata(self.node.meta) + + if target == operator.getitem: + value, key = args + return self.callback.call_getitem(value, key, meta) + elif getattr(target, "__module__", None) in {"_operator", "math"}: + assert callable(target) + return self.callback.call_sym(target, args, meta) + elif target in _TORCH_SYM_OPS: + assert callable(target) + return self.callback.call_sym(target, args, meta) + elif isinstance(target, (torch._ops.OpOverload, torch._ops.OpOverloadPacket)): + return self.callback.call_operator( + target, + args, + kwargs, + meta, + ) + elif target == torch.ops.higher_order.cond: + pred, true_fn, false_fn, inputs = args + return self.callback.call_cond(pred, true_fn, false_fn, inputs, meta) + elif target == torch.ops.higher_order.map_impl: + f, mapped_args, operands = args # type: ignore[assignment] + return self.callback.call_map(f, mapped_args, operands, meta) + # For other unregistered HigherOrderOps, just interpret them blindly + elif isinstance(target, torch._ops.HigherOrderOperator): + return self.callback._fx( + "call_function", + target, + args, + kwargs, + meta, + ) + else: + raise ExportPassBaseError(f"Unsupported target type: {target}") + + def get_attr( + self, target: str, args: Tuple[Argument, ...], kwargs: Dict[str, Argument] # type: ignore[override] + ) -> Argument: + return super().get_attr(target, args, kwargs) + + def call_module( + self, + target: torch.fx.node.Target, + args: Tuple[Argument, ...], + kwargs: Dict[str, Argument], + ) -> None: + raise ExportPassBaseError("call_module is not supported.") + + def call_method( + self, target: str, args: Tuple[Argument, ...], kwargs: Dict[str, Argument] # type: ignore[override] + ) -> None: + raise ExportPassBaseError("call_method is not supported.") + + def run_node(self, n: torch.fx.Node) -> Argument: + self.node = n + self.callback.node_debug_str = n.format_node() + return super().run_node(n) + + def __init__(self) -> None: + self.interpreter = PropagateUnbackedSymInts( + torch.fx.GraphModule(torch.nn.Module(), torch.fx.Graph()) + ) + self.tracer = self.ExportTracer(self, CodeGen()) + self.fake_tensor_mode: Optional[FakeTensorMode] = None + self._initialized = True + self.node_debug_str: typing.Optional[str] = None + + def _fx( + self, + kind: str, + target: torch.fx.node.Target, + args: Tuple[Argument, ...], + kwargs: Dict[str, Argument], + meta: NodeMetadata, + ) -> ProxyValue: + args_data, kwargs_data = pytree.tree_map_only( + ProxyValue, lambda x: x.data, (args, kwargs) + ) + res_data = getattr(self.interpreter, kind)(target, args_data, kwargs_data) + args_proxy, kwargs_proxy = pytree.tree_map_only( + ProxyValue, lambda x: x.proxy, (args, kwargs) + ) + + name = None + if isinstance(target, torch._ops.OpOverload): + name = self.tracer.graph._target_to_str(target.overloadpacket.__name__) + + res_proxy = self.tracer.create_proxy(kind, target, args_proxy, kwargs_proxy, name=name) + res_proxy.node.meta.update(meta.data) + if self.fake_tensor_mode and (shape_env := self.fake_tensor_mode.shape_env): + if symbol_to_path := compute_unbacked_bindings(shape_env, res_data): + res_proxy.node.meta["unbacked_bindings"] = symbol_to_path + self.tracer.set_metadata(res_proxy.node, res_data) + return ProxyValue(res_data, res_proxy) + + def inputs(self, graph_module: torch.fx.GraphModule) -> List[Argument]: + # TODO(angelayi): Update this with what we decide to do for metadata in + # the exported graph module + if (args := graph_module.meta.get("args", None)) is not None: + return list(args) + + def extract_input(node: torch.fx.Node) -> Optional[FakeTensor]: + if "val" in node.meta: + fake = node.meta["val"] + if hasattr(fake, "constant") and fake.constant is not None: + return fake.constant + return fake + elif tensor_meta := node.meta.get("tensor_meta"): + assert self.fake_tensor_mode is not None + return FakeTensor( + self.fake_tensor_mode, + torch.empty( + tensor_meta.shape, + dtype=tensor_meta.dtype, + device="meta", + requires_grad=tensor_meta.requires_grad, + memory_format=tensor_meta.memory_format, + ), + torch.device("cpu"), + ) + elif len(node.users) == 0: + return None + raise ExportPassBaseError( + f"Cannot construct an input for graph module: {graph_module}.", + ) + + return [ + extract_input(node) + for node in graph_module.graph.nodes + if node.op == "placeholder" + ] + + def on_attr(self, attr: ProxyValue) -> None: + pass + + def placeholder(self, name: str, arg: Argument, meta: NodeMetadata) -> ProxyValue: + arg_proxy = self.tracer.create_proxy("placeholder", name, (), {}) + arg_proxy.node.meta = meta.data + self.tracer.set_metadata(arg_proxy.node, arg) + return ProxyValue(arg, arg_proxy) + + def call_operator( + self, + op, + args: Tuple[Argument, ...], + kwargs: Dict[str, Argument], + meta: NodeMetadata, + ) -> ProxyValue: + return self._fx("call_function", op, args, kwargs, meta) + + def call_sym( + self, + target: Fn, + args: Tuple[Argument, ...], + meta: NodeMetadata, + ) -> ProxyValue: + return self._fx("call_function", target, args, {}, meta) + + def call_cond( + self, + pred: ProxyValue, + true_fn: torch.fx.GraphModule, + false_fn: torch.fx.GraphModule, + inputs: List[Argument], + meta: NodeMetadata, + ) -> ProxyValue: + true_branch = self.call_submodule(true_fn, tuple(inputs)) + false_branch = self.call_submodule(false_fn, tuple(inputs)) + assert true_branch is not None + assert false_branch is not None + return self._fx( + "call_function", + torch.ops.higher_order.cond, + (pred, true_branch.graph_module, false_branch.graph_module, list(inputs)), + {}, + meta, + ) + + def call_map( + self, + f: torch.fx.GraphModule, + mapped_args: List[ProxyValue], + operands: List[ProxyValue], + meta: NodeMetadata, + ) -> ProxyValue: + xs = _unstack_pytree([arg.data for arg in mapped_args])[0] + f_branch = self.call_submodule(f, tuple(xs + [arg.data for arg in operands])) + assert f_branch is not None + return self._fx( + "call_function", + torch.ops.higher_order.map_impl, + (f_branch.graph_module, mapped_args, operands), + {}, + meta, + ) + + def call_getitem( + self, value: ProxyValue, key: int, meta: NodeMetadata + ) -> ProxyValue: + return self._fx("call_function", operator.getitem, (value, key), {}, meta) + + def output(self, results: List[Argument], meta: NodeMetadata) -> ProxyValue: + return self._fx("output", "output", (results,), {}, meta) + + def call_submodule( + self, graph_module: fx.GraphModule, inputs: Tuple[Argument, ...] + ) -> PassResult: + prev_tracer, self.tracer = self.tracer, self.ExportTracer( + self, graph_module.graph._codegen + ) + self.tracer.fake_tensor_mode = prev_tracer.fake_tensor_mode + interpreter = self.ExportInterpreter(self, graph_module) + prev_interpreter, self.interpreter = self.interpreter, torch.fx.Interpreter( # type: ignore[assignment] + torch.fx.GraphModule(torch.nn.Module(), torch.fx.Graph()) + ) + inputs_data = pytree.tree_map_only(ProxyValue, lambda x: x.data, inputs) + with fx_traceback.preserve_node_meta(): + interpreter.run(*inputs_data) + + new_graph_module = torch.fx.GraphModule(self.tracer.root, self.tracer.graph) + + self.tracer = prev_tracer + self.interpreter = prev_interpreter + return PassResult( + new_graph_module, + True, + ) + + def call(self, graph_module: fx.GraphModule) -> PassResult: + if not getattr(self, "_initialized", False): + raise ExportPassBaseError( + "ExportPass is not initialized with __init__().", + ) + + inputs = self.inputs(graph_module) + + fake_tensor_mode = None + for i in inputs: + if isinstance(i, FakeTensor): + assert ( + fake_tensor_mode is None or fake_tensor_mode is i.fake_mode + ), "Multiple fake tensor mode detected." + fake_tensor_mode = i.fake_mode + if fake_tensor_mode is None: + self.tracer.fake_tensor_mode = FakeTensorMode(allow_non_fake_inputs=True) + fake_tensor_mode = nullcontext() # type: ignore[assignment] + dispatcher_mode = nullcontext() # type: ignore[assignment] + else: + fake_tensor_mode.allow_non_fake_inputs = True + self.tracer.fake_tensor_mode = fake_tensor_mode + dispatcher_mode = enable_python_dispatcher() # type: ignore[assignment] + self.fake_tensor_mode = self.tracer.fake_tensor_mode + + with fake_tensor_mode, dispatcher_mode: # type: ignore[assignment, union-attr] + result = self.call_submodule(graph_module, tuple(inputs)) + + return result diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/pass_infra/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/pass_infra/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/pass_infra/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/pass_infra/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..392834cb7fcb8bb2bf8b22e16440e3cabdf3a210 Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/pass_infra/__pycache__/__init__.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/pass_infra/__pycache__/node_metadata.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/pass_infra/__pycache__/node_metadata.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b00114259ae351396d77e32163035bd400466f64 Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/pass_infra/__pycache__/node_metadata.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/pass_infra/__pycache__/proxy_value.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/pass_infra/__pycache__/proxy_value.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e6e5d2708685782261fcfb5d35c33dafbe0644ff Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/pass_infra/__pycache__/proxy_value.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/pass_infra/node_metadata.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/pass_infra/node_metadata.py new file mode 100644 index 0000000000000000000000000000000000000000..4aa9b8093c370dd565dfb7fb44e4b22474446af0 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/pass_infra/node_metadata.py @@ -0,0 +1,32 @@ +from typing import Any, Dict, Set + + +NodeMetadataValue = Any + + +PROTECTED_KEYS: Set[str] = { + "val", + "stack_trace", + "nn_module_stack", + "debug_handle", + "tensor_meta", +} + + +class NodeMetadata: + def __init__(self, data: Dict[str, Any]) -> None: + self.data: Dict[str, Any] = data.copy() + + def __getitem__(self, key: str) -> NodeMetadataValue: + return self.data[key] + + def __setitem__(self, key: str, value: NodeMetadataValue) -> NodeMetadataValue: + if key in PROTECTED_KEYS: + raise RuntimeError(f"Could not override node key: {key}") + self.data[key] = value + + def __contains__(self, key: str) -> bool: + return key in self.data + + def copy(self) -> "NodeMetadata": + return NodeMetadata(self.data.copy()) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/pass_infra/proxy_value.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/pass_infra/proxy_value.py new file mode 100644 index 0000000000000000000000000000000000000000..07d888b306560e8e9a29a4d49748bd28c32720eb --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/pass_infra/proxy_value.py @@ -0,0 +1,42 @@ +# mypy: allow-untyped-defs +# pyre-strict +from typing import Union + +import torch + + +class ProxyValue: + # pyre-ignore + def __init__(self, data, proxy: Union[torch.fx.Proxy, torch.fx.Node]): + # pyre-ignore + self.data = data + self.proxy_or_node = proxy + + @property + def node(self) -> torch.fx.Node: + if isinstance(self.proxy_or_node, torch.fx.Node): + return self.proxy_or_node + assert isinstance(self.proxy_or_node, torch.fx.Proxy) + return self.proxy_or_node.node + + @property + def proxy(self) -> torch.fx.Proxy: + if not isinstance(self.proxy_or_node, torch.fx.Proxy): + raise RuntimeError( + f"ProxyValue doesn't have attached Proxy object. Node: {self.proxy_or_node.format_node()}" + ) + return self.proxy_or_node + + def to_tensor(self) -> torch.Tensor: + assert isinstance(self.data, torch.Tensor) + return self.data + + def is_tensor(self) -> bool: + return isinstance(self.data, torch.Tensor) + + # pyre-ignore + def __iter__(self): + yield from self.data + + def __bool__(self) -> bool: + return bool(self.data) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..aa9ce2ac03c23600c86ff02e38a2a4bfeefef9e2 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/__init__.py @@ -0,0 +1 @@ +from .replace_view_ops_with_view_copy_ops_pass import ReplaceViewOpsWithViewCopyOpsPass diff --git 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0000000000000000000000000000000000000000..3dd87b546da8df08522f1c237bab44e9668b4b47 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/_node_metadata_hook.py @@ -0,0 +1,80 @@ +# mypy: allow-untyped-defs +import contextlib + +import torch +from torch.fx.graph_module import GraphModule + + +_EMPTY_NN_MODULE_STACK_KEY = "_empty_nn_module_stack_from_metadata_hook" + + +def _node_metadata_hook(node: torch.fx.Node, stack_trace: str) -> None: + """ + Hook for adding the appropriate metadata to nodes that are created during a + pass using graph.create_node. An example of how to use it: + + ``` + with _set_node_metadata_hook(gm, + functools.partial(_node_metadata_hook, stack_trace="file") + ): + pass(gm) + ``` + + This hook should not work for all generic cases -- specifically it assumes + that nodes being added are only call_function nodes, and copies over the + first argument node's nn_module_stack. + """ + assert node.op == "call_function" and callable(node.target) + + arg_meta = [arg.meta for arg in node.args if isinstance(arg, torch.fx.Node)] + assert len(arg_meta) >= 1 + arg_meta = arg_meta[0] + + if ( + isinstance(node.target, torch._ops.OpOverload) + and len(node.target._schema.returns) == 0 + ): + node.meta["val"] = None + else: + fake_args = [ + arg.meta["val"] if isinstance(arg, torch.fx.Node) else arg + for arg in node.args + ] + fake_res = node.target(*fake_args) + node.meta["val"] = fake_res + + node.meta["stack_trace"] = stack_trace + node.meta["nn_module_stack"] = arg_meta.get( + "nn_module_stack", + { + _EMPTY_NN_MODULE_STACK_KEY: ( + _EMPTY_NN_MODULE_STACK_KEY, + _EMPTY_NN_MODULE_STACK_KEY, + ) + }, + ) + node.meta["torch_fn"] = ( + f"{node.target.__name__}_0", + f"{node.target.__class__.__name__}.{node.target.__name__}", + ) + + +@contextlib.contextmanager +def _set_node_metadata_hook(gm: torch.fx.GraphModule, f): + """ + Takes a callable which will be called after we create a new node. The + callable takes the newly created node as input and returns None. + """ + assert callable(f), "node_metadata_hook must be a callable." + + # Add the hook to all submodules + for m in gm.modules(): + if isinstance(m, GraphModule): + m._register_create_node_hook(f) + try: + yield + finally: + # Restore hook for all submodules + for m in gm.modules(): + if isinstance(m, GraphModule): + m._unregister_create_node_hook(f) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/add_runtime_assertions_for_constraints_pass.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/add_runtime_assertions_for_constraints_pass.py new file mode 100644 index 0000000000000000000000000000000000000000..e8ed5931a74fc21f71d35a5cf23983846b2be449 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/add_runtime_assertions_for_constraints_pass.py @@ -0,0 +1,227 @@ +# mypy: allow-untyped-defs +import math +import operator +import traceback +from functools import partial +from typing import Callable, Dict, List, NamedTuple, Set + +import sympy + +import torch +import torch.fx +from torch.utils._sympy.value_ranges import ValueRanges +from torch.utils._sympy.numbers import int_oo +from torch.fx.experimental.symbolic_shapes import free_unbacked_symbols +from torch.fx.passes.infra.pass_base import PassBase, PassResult + +__all__ = ["InputDim"] + + +class InputDim(NamedTuple): + input_name: str + dim: int + + +def _convert_to_int(val): + # Convert simple sympy Integers into concrete int + if val in (sympy.oo, int_oo): + return math.inf + if val in (-sympy.oo, -int_oo): + return -math.inf + if isinstance(val, sympy.Integer): + return int(val) + raise RuntimeError( + "Export constraints cannot be non-integer expressions" + ) + + +def _convert_range_to_int(range: ValueRanges): + assert isinstance(range, ValueRanges) + min_val = _convert_to_int(range.lower) + max_val = _convert_to_int(range.upper) + return min_val, max_val + + +class _AddRuntimeAssertionsForInlineConstraintsPass(PassBase): + def __init__( + self, + range_constraints: Dict[sympy.Symbol, ValueRanges], + ): + super().__init__() + self.range_constraints: Dict[sympy.Symbol, ValueRanges] = range_constraints + self._asserts_generated_unbacked_symbols: Set[sympy.Symbol] = set() + self.counter = 0 + + def _assert_range_constraint(self, node, lower, upper, assert_msg): + last_node = node + if lower > -math.inf: + last_node = self._insert_assert_async(last_node, operator.ge, node, lower, assert_msg) + + if upper < math.inf: + last_node = self._insert_assert_async(last_node, operator.le, node, upper, assert_msg) + + def _insert_assert_async(self, last_node, op, lower, upper, assert_msg): + """ + Inserts assert_async call_function nodes in the graph. This function is + called **during** the interpreter-based pass. + """ + self.counter += 1 + graph = last_node.graph + with graph.inserting_after(last_node): + cmp = graph.call_function(op, (lower, upper), {}) + with graph.inserting_after(cmp): + cmp_tensor = graph.call_function(torch.ops.aten.scalar_tensor.default, (cmp,), {}) + with graph.inserting_after(cmp_tensor): + assert_async = graph.call_function( + torch.ops.aten._assert_async.msg, + (cmp_tensor, assert_msg), + {}, + ) + return assert_async + + def call(self, graph_module) -> PassResult: + self.existing_inline_assertions = _get_existing_inline_assertions( + graph_module, self.range_constraints + ) + + for module in graph_module.modules(): + if not isinstance(module, torch.fx.GraphModule): + continue + for node in module.graph.nodes: + if node.op != "call_function": + continue + if "val" not in node.meta: + continue + + val = node.meta["val"] + # In general, we may have to deal the case such as: ret[1].shape[0]. + # We need first find out what symbols require assertion, then we need to follow the path + # from ret to the symbol, construct the proxies along the way and construct the messages + # piece-wise at the same time. + # + # We use post-order traversal to collect all the proxies callbacks needed, construct + # the error message callbacks, and at the top-level traversal tree we execute all the callbacks. + # We need the callbacks because, in order to call the function to create a proxy for shape[0], we + # need the proxy for shape, which further requires the proxy for ret[1], etc. + + def add_assertions(val): + call_backs: List[Callable] = [] + messages: List[str] = [] + if isinstance(val, (torch.SymInt, torch.SymFloat, torch.SymBool)): + symbol = val.node.expr + if symbol in self.existing_inline_assertions: + return call_backs, messages + if isinstance(symbol, sympy.Symbol) and free_unbacked_symbols(symbol): + if symbol in self._asserts_generated_unbacked_symbols: + return call_backs, messages + # We only care about unbacked symints for these inline + # constraints, which are prefixed with 'u' + constraint = self.range_constraints[symbol] + min_val, max_val = _convert_range_to_int(constraint) + assert_msg = f" is outside of inline constraint [{min_val}, {max_val}]." + call_backs.append( + partial(self._assert_range_constraint, lower=min_val, upper=max_val) + ) + messages.append(assert_msg) + self._asserts_generated_unbacked_symbols.add(symbol) + + elif isinstance(val, torch.Tensor): + for i, sym in enumerate(val.shape): + cbs, msgs = add_assertions(sym) + for cb, msg in zip(cbs, msgs): + def sym_size_cb(node, assert_msg, dim): + with node.graph.inserting_after(node): + dim_node = module.graph.call_function( + torch.ops.aten.sym_size.int, + (node, dim), + {}, + ) + cb(node=dim_node, assert_msg=assert_msg) + call_backs.append(partial(sym_size_cb, dim=i)) + messages.append(f".shape[{i}]" + msg) + return call_backs, messages + + callbacks, messages = add_assertions(val) + for cb, msg in zip(callbacks, messages): + cb(node=node, assert_msg=f"{node}" + msg) + + module.recompile() + + # Sometimes this pass would return a wrong graph where we have mismatched + # node names in signature. Before we fix it, let's just skip it. + if self.counter == 0 and type(self) is _AddRuntimeAssertionsForInlineConstraintsPass: + return PassResult(graph_module, False) + + # Populate the stack trace with dummy vals to respect IR + for node in graph_module.graph.nodes: + if not node.meta.get("stack_trace", None) and node.op not in ["placeholder", "output"]: + node.meta["stack_trace"] = "".join(traceback.format_stack(limit=1)) + return PassResult(graph_module, True) + + +def _get_existing_inline_assertions( + graph_module: torch.fx.GraphModule, + range_constraints: Dict[sympy.Symbol, ValueRanges], +) -> Dict[sympy.Symbol, ValueRanges]: + existing_inline_assertions: Dict[sympy.Symbol, ValueRanges] = {} + + for module in graph_module.modules(): + if not isinstance(module, torch.fx.GraphModule): + continue + + # Find all the existing inline assertions. They will look something like: + # %_local_scalar_dense = call_function[target=torch.ops.aten._local_scalar_dense.default](args = (%arg1_1,), kwargs = {}) + # %ge = call_function[target=operator.ge](args = (%_local_scalar_dense, 0), kwargs = {}) + # %_assert_scalar = call_function[target=torch.ops.aten._assert_scalar.default](args = (%scalar_tensor, "..."), kwargs = {}) + for node in module.graph.nodes: + if node.target != torch.ops.aten._assert_scalar.default: + continue + + compare_arg = node.args[0] + if not ( + isinstance(compare_arg, torch.fx.Node) and + compare_arg.op == "call_function" and + compare_arg.target in (operator.le, operator.ge) and + len(compare_arg.args) == 2 + ): + continue + + compare_op = compare_arg.target + lhs, rhs = compare_arg.args + + def maybe_get_symint(x): + if ( + isinstance(x, torch.fx.Node) and + "val" in x.meta and + isinstance(x.meta["val"], torch.SymInt) + ): + return x.meta["val"].node.expr + return x + + lhs = maybe_get_symint(lhs) + rhs = maybe_get_symint(rhs) + + if compare_op == operator.ge: + lhs, rhs = rhs, lhs + + if isinstance(lhs, sympy.Symbol) and isinstance(rhs, int): + symint = lhs + scalar = rhs + elif isinstance(rhs, sympy.Symbol) and isinstance(lhs, int): + symint = rhs + scalar = lhs + else: + continue + + if symint not in range_constraints: + raise RuntimeError(f"Unable to find symint {symint} in {range_constraints}") + + previous_range = existing_inline_assertions.get(symint, ValueRanges(-math.inf, math.inf)) + + if symint is lhs: + bounds = ValueRanges(-math.inf, scalar) + else: + bounds = ValueRanges(scalar, math.inf) + existing_inline_assertions[symint] = previous_range & bounds + + return existing_inline_assertions diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/collect_tracepoints_pass.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/collect_tracepoints_pass.py new file mode 100644 index 0000000000000000000000000000000000000000..c89d2216632fa5dfde608ec5f4b857195bcb19ad --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/collect_tracepoints_pass.py @@ -0,0 +1,102 @@ +# mypy: allow-untyped-defs +import operator + +import torch +from torch.export.exported_program import ConstantArgument, TensorArgument +from torch.fx.passes.infra.pass_base import PassBase, PassResult + + +__all__ = ["CollectTracepointsPass"] + + +class CollectTracepointsPass(PassBase): + """ + Performs constant folding and constant propagation. + """ + + def __init__(self, specs, sig) -> None: + super().__init__() + self.specs = specs + self.sig = sig + + def call(self, gm): + def get_arg_spec(arg): + if isinstance(arg, torch.fx.Node): + if isinstance(arg.meta.get("val"), torch.Tensor): + return TensorArgument(name=arg.name) + else: + raise AssertionError( + "Symint input is not implemented yet for submodule call signature." + ) + else: + return ConstantArgument(name="", value=arg) + + for module in gm.modules(): + if not isinstance(module, torch.fx.GraphModule): + continue + nn_module_stack = None + for node in module.graph.nodes: + if node.op != "call_function": + continue + if node.target == torch.ops.higher_order._export_tracepoint: + kind = node.kwargs["kind"] + if kind == "module_call_outputs": + nn_module_stack = node.meta["nn_module_stack"] + elif kind == "module_call_inputs": + nn_module_stack = None + else: + raise AssertionError(f"Unknown tracepoint kind: {kind}") + elif node.meta["nn_module_stack"] == nn_module_stack: + node.meta["nn_module_stack"].popitem() + else: + nn_module_stack = None + nn_module_stack = None + for node in reversed(module.graph.nodes): + if node.op != "call_function": + continue + if node.target == torch.ops.higher_order._export_tracepoint: + kind = node.kwargs["kind"] + if kind == "module_call_inputs": + nn_module_stack = node.meta["nn_module_stack"] + elif kind == "module_call_outputs": + nn_module_stack = None + else: + raise AssertionError(f"Unknown tracepoint kind: {kind}") + elif node.meta["nn_module_stack"] == nn_module_stack: + node.meta["nn_module_stack"].popitem() + else: + nn_module_stack = None + for module in gm.modules(): + if not isinstance(module, torch.fx.GraphModule): + continue + for node in module.graph.nodes: + if node.op != "call_function": + continue + if node.target == torch.ops.higher_order._export_tracepoint: + for i, arg in enumerate(node.args): + kind = node.kwargs["kind"] + if kind == "module_call_inputs": + self.specs[node.kwargs["path"]].inputs.append( + get_arg_spec(arg) + ) + elif kind == "module_call_outputs": + self.specs[node.kwargs["path"]].outputs.append( + get_arg_spec(arg) + ) + else: + raise AssertionError(f"Unknown tracepoint kind: {kind}") + if isinstance(arg, torch.fx.Node): + for user in node.users: + assert user.op == "call_function" + assert user.target == operator.getitem + assert isinstance(user.args[1], int) + if user.args[1] == i: + user.replace_all_uses_with(arg) + self.sig.replace_all_uses(user.name, arg.name) + break + users = list(node.users) + for user in users: + assert len(user.users) == 0 + gm.graph.erase_node(user) + gm.graph.erase_node(node) + return PassResult(gm, True) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/constant_folding.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/constant_folding.py new file mode 100644 index 0000000000000000000000000000000000000000..b1491ca5d4794647f3cc348dc4bcf4c59134031a --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/constant_folding.py @@ -0,0 +1,299 @@ +# mypy: allow-untyped-defs +import collections +from collections import defaultdict +from typing import Any, Callable, Dict, Optional + +import torch +import torch.utils._pytree as pytree + + +aten = torch.ops.aten + +# We would like to split modules into two subgraphs for runtime weight updates to work correctly. +# The use case and more information could be found at: +# https://docs.google.com/document/d/1inZC-8KarJ6gKB7G9egmYLx1V_dKX_apxon0w4zPC0Q/edit?usp=sharing +META_TAG = "MODULE_TYPE" +MODULE_TAG = "_MAIN_MODULE" +CONST_MODULE_TAG = "_CONST_MODULE" + + +def replace_node_with_constant(gm, node, constant, name=None): + g = gm.graph + + if name: + qualname = name + else: + if not hasattr(gm, "_frozen_param_count"): + gm._frozen_param_count = 0 + i = gm._frozen_param_count + + while True: + qualname = f"_frozen_param{i}" + if not hasattr(gm, qualname): + break + i += 1 + + gm._frozen_param_count = i + 1 + + with g.inserting_before(node): + new_input_node = g.create_node("get_attr", qualname, (), {}) + node.replace_all_uses_with(new_input_node) + new_input_node.meta.update(node.meta) + g.erase_node(node) + + # needed to suppress `does not reference an nn.Module, nn.Parameter, or buffer` warning + gm.register_buffer(qualname, constant) + setattr(gm, qualname, constant) + + +class ConstantFolder(torch.fx.Interpreter): + def __init__( + self, + gm, + skip_constructors=False, + ): + super().__init__(gm) + self.node_replacements: Dict[torch.fx.Node, Any] = {} + self.replaced_uses: Dict[torch.fx.Node, int] = collections.Counter() + self.unknown_value = object() + self.skip_constructors: bool = skip_constructors + + # overwrite this to deallocate env values if their only remaining use + # is the output + self.user_to_last_uses = self.node_to_last_non_output_use() + + def is_impure(self, node: torch.fx.node.Node): + if ( + node.target == torch.ops.prims.convert_element_type.default + and node.args[0].op == "get_attr" # type: ignore[union-attr] + and node.args[0].meta["val"].dtype == torch.int8 # type: ignore[union-attr] + and node.args[1] == torch.bfloat16 + ): + # For int8_weight -> dq -> bf16_weight + return True + if node.target in [ + torch.ops.quantized_decomposed.dequantize_per_channel.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.tensor, + ]: + # For the pattern fp32_weight -> q -> dq + # We only folding fp32_weight -> q + # int8_weight and leave dq in graph to be fused + return True + return False + + def node_to_last_non_output_use(self): + last_non_output_use = collections.defaultdict(list) + seen_uses = set() + output_node = next(iter(reversed(self.module.graph.nodes))) + + for node in reversed(self.module.graph.nodes): + if node.target == "output": + continue + + def add_use(inp): + if inp in seen_uses: + return + + seen_uses.add(inp) + last_non_output_use[node].append(inp) + + # In-place is fine since we don't mutate + pytree.tree_map_only_(torch.fx.Node, add_use, (node.args, node.kwargs)) + + # if this node is only used in output, we want to gc it right away + if len(node.users) == 1 and output_node in node.users: + last_non_output_use[node].append(node) + + return last_non_output_use + + def run_node(self, node): + if node.target == "output": + # because we remove nodes from env on last non output use, + # re-define them now or we'll get error in interpreter + def set_env(arg): + self.env[arg] = self.unknown_value + + # In-place is fine since we don't mutate + pytree.tree_map_only_(torch.fx.Node, set_env, node.args) + return super().run_node(node) + + args, kwargs = self.fetch_args_kwargs_from_env(node) + flattened_inputs = pytree.arg_tree_leaves(*args, **kwargs) + + # We need to do this weird thing because in cases where flattened_inputs + # contains a ScriptObject, equality checking results in a type error if + # the types are different. + if any( + type(self.unknown_value) == type(input_) and self.unknown_value == input_ + for input_ in flattened_inputs + ): + return self.unknown_value + + # TODO - fix errors with this + if ( + node.op == "call_function" + and node.target == aten._efficientzerotensor.default + ): + return self.unknown_value + + # TODO - constant folding triton kernel returns the inputs -- fix this + if ( + node.op == "call_function" + and node.name == "triton_kernel_wrapper_functional_proxy" + ): + return self.unknown_value + + # skip constructors, since inductor generates optimal code for them already + # and turning into tensor would result in an additional global memory read + # TODO - more complicated strategy + if ( + self.skip_constructors + and node.op != "get_attr" + and not any(isinstance(e, torch.Tensor) for e in flattened_inputs) + ): + return self.unknown_value + + # All mutations should either be removed or on inputs which we did not make constant + if ( + isinstance(node.target, torch._ops.OpOverload) + and torch.Tag.nondeterministic_seeded in node.target.tags + ): + return self.unknown_value + + out = super().run_node(node) + + if node.op != "get_attr" and isinstance(out, torch.Tensor): + if out.device.type == "meta": + return out + + if not self.insertable_tensor_check(out): + return out + + if self.is_impure(node): + return self.unknown_value + + self.add_node_replacement(node, out) + + flattened_node_inps = pytree.arg_tree_leaves(*node.args, **node.kwargs) + + for n in flattened_node_inps: + if not isinstance(n, torch.fx.Node): + continue + + self.replaced_uses[n] += 1 + + for to_delete in self.user_to_last_uses.get(node, []): + if self.replaced_uses[to_delete] == len(to_delete.users): + self.node_replacements.pop(to_delete, None) + + return out + + def insertable_tensor_check(self, tensor: torch.Tensor) -> bool: + return True + + def add_node_replacement(self, node: torch.fx.Node, tensor: torch.Tensor) -> None: + self.node_replacements[node] = tensor + + def run(self): + env = {} + for n in self.module.graph.find_nodes(op="placeholder"): + env[n] = self.unknown_value + return super().run(initial_env=env) + + +def constant_fold(gm, constraint_fn: Optional[Callable[[torch.fx.Node], bool]] = None): + with torch.utils._python_dispatch._disable_current_modes(): + cf = ConstantFolder(gm, skip_constructors=True) + cf.run() + + for node, constant in cf.node_replacements.items(): + if constraint_fn is not None and not constraint_fn(node): + continue + replace_node_with_constant(gm, node, constant) + + erased_params = [] + # Get all attr users by looking up the graph instead from node.users, because in this case + # _tensor_constant0 and _tensor_constant0_1 are actually refereing to the same tensor. + + # opcode name target args kwargs + # ------------- ------------------- ---------------- --------------------------- -------- + # placeholder arg0_1 arg0 () {} + # get_attr _tensor_constant0 state () {} + # call_function add aten.add.Tensor (arg0_1, _tensor_constant0) {} + # get_attr _tensor_constant0_1 state () {} + # call_function add_ aten.add_.Tensor (_tensor_constant0_1, 1) {} + # output output output ([add],) {} + + get_attr_node_users = defaultdict(list) + for node in gm.graph.nodes: + if node.op == "get_attr": + get_attr_node_users[node.target].extend(node.users.keys()) + for node in gm.graph.find_nodes(op="get_attr"): + if node.op == "get_attr" and len(get_attr_node_users[node.target]) == 0: + if hasattr(gm, node.target): + delattr(gm, node.target) + erased_params.append(node) + for node in erased_params: + gm.graph.erase_node(node) + + gm.graph.eliminate_dead_code() + gm.graph.lint() + gm.recompile() + + +def constant_graph_tag(gm: torch.fx.GraphModule): + with torch.utils._python_dispatch._disable_current_modes(): + cf = ConstantFolder(gm, skip_constructors=True) + cf.run() + + for node in gm.graph.nodes: + if ( + node.op == "get_attr" + or node in cf.node_replacements + or node in cf.replaced_uses + ): + node.meta[META_TAG] = CONST_MODULE_TAG + else: + node.meta[META_TAG] = MODULE_TAG + + +def run_and_get_constant_graph(gm: torch.fx.GraphModule) -> torch.fx.GraphModule: + """ + Construct a GraphModule which corresponds to the part which could be + constant folded in provided gm. + """ + + constant_graph_tag(gm) + # We rewrite the tags, if it's a constant being directly consumed, without + # any folding opportunity, we keep it in main gm. + for node in gm.graph.find_nodes(op="get_attr"): + used_to_fold = False + for u in node.users: + if u.meta[META_TAG] == CONST_MODULE_TAG: + used_to_fold = True + break + if not used_to_fold: + node.meta[META_TAG] = MODULE_TAG + + new_graph = torch.fx.Graph() + + node_remapping: Dict[torch.fx.Node, torch.fx.Node] = {} + output_nodes = [] + for node in gm.graph.nodes: + if node.meta[META_TAG] == MODULE_TAG: + continue + + new_node = new_graph.node_copy(node, lambda x: node_remapping[x]) + node_remapping[node] = new_node + + for user in node.users: + if user.meta[META_TAG] == MODULE_TAG: + output_nodes.append(new_node) + break + + new_graph.output(tuple(output_nodes)) + new_graph.lint() + new_gm = torch.fx.GraphModule(gm, new_graph) + + return new_gm diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/functionalize_side_effectful_ops_pass.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/functionalize_side_effectful_ops_pass.py new file mode 100644 index 0000000000000000000000000000000000000000..5fcf5adaca5b0b478db87e71633f5136b54969b2 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/functionalize_side_effectful_ops_pass.py @@ -0,0 +1,94 @@ +import copy +from typing import Dict, Optional, Tuple, List + +import torch +from torch._export.pass_base import _ExportPassBaseDeprecatedDoNotUse, PassResult, Argument +from torch._export.pass_infra.node_metadata import NodeMetadata +from torch._export.pass_infra.proxy_value import ProxyValue +from torch._ops import OpOverload + +aten = torch.ops.aten + +_NON_FUNCTIONAL_TO_FUNCTIONAL_SIDE_EFFECTFUL_FUNCS: Dict[OpOverload, OpOverload] = { + aten.sym_constrain_range.default: aten._functional_sym_constrain_range, + aten._assert_async.msg: aten._functional_assert_async.msg, +} + + +class _FunctionalizeSideEffectfulOpsPass(_ExportPassBaseDeprecatedDoNotUse): + """ + Functionalize ops with side effect in graph module by replacing the op with + functional version of it. A new dependency token (`dep_token`) will be + created and propagated through functional ops to output. + For example: + ``` + def f(x): + sym_constrain_range(x.shape[0], min=1, max=3) + return x.add(3) + ``` + Will be transformed to: + ``` + def f(x): + dep_token0 = _make_dep_token() + dep_token1 = _functional_sym_constrain_range( + x.shape[0], min=1, max=3, dep_token=dep_token0 + ) + + return x.add(3), dep_token1 + ``` + """ + + def __init__(self) -> None: + super().__init__() + self._dep_token: Optional[ProxyValue] = None + self._next_dep_token_index: Optional[int] = None + + def call(self, graph_module: torch.fx.GraphModule) -> PassResult: + # Early return if no non-functional assertions. + if not any( + n.target in _NON_FUNCTIONAL_TO_FUNCTIONAL_SIDE_EFFECTFUL_FUNCS + for n in graph_module.graph.nodes + ): + return PassResult(graph_module=graph_module, modified=False) + + gm = copy.deepcopy(graph_module) + self._dep_token = None + self._next_dep_token_index = None + return super().call(gm) + + def call_operator( + self, + op: OpOverload, + args: Tuple[Argument, ...], + kwargs: Dict[str, Argument], + meta: NodeMetadata, + ) -> ProxyValue: + if op not in _NON_FUNCTIONAL_TO_FUNCTIONAL_SIDE_EFFECTFUL_FUNCS: + return super().call_operator(op, args, kwargs, meta) + + if self._dep_token is None: + self._dep_token = super().call_operator( + aten._make_dep_token, + args=(), + kwargs={}, + meta=self._create_dummy_node_metadata(), + ) + self._dep_token.node.name = "dep_token0" + self._next_dep_token_index = 1 + + self._dep_token = super().call_operator( + _NON_FUNCTIONAL_TO_FUNCTIONAL_SIDE_EFFECTFUL_FUNCS[op], + args=args, + kwargs={**kwargs, "dep_token": self._dep_token}, + meta=meta, + ) + assert self._next_dep_token_index is not None + self._dep_token.node.name = f"dep_token{self._next_dep_token_index}" + self._next_dep_token_index += 1 + + return self._dep_token + + def output(self, results: List[Argument], meta: NodeMetadata) -> ProxyValue: + assert self._dep_token is not None + + return super().output(results=(*results, self._dep_token), meta=meta) # type: ignore[arg-type] diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/lift_constants_pass.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/lift_constants_pass.py new file mode 100644 index 0000000000000000000000000000000000000000..3d1ce32cda31776015a448b59bdadfd6fdd363a2 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/lift_constants_pass.py @@ -0,0 +1,318 @@ +# mypy: allow-untyped-defs +import collections +import warnings +from typing import Any, Dict, List, Union + +import torch +from torch._export.verifier import SpecViolationError +from torch._guards import detect_fake_mode +from torch._library.fake_class_registry import FakeScriptObject +from torch._subclasses.fake_tensor import unset_fake_temporarily +from torch.export.exported_program import ( + ArgumentSpec, + CustomObjArgument, + ExportGraphSignature, + InputKind, + InputSpec, + TensorArgument, +) + + +class ConstantAttrMap(collections.abc.MutableMapping): + """A mapping class that understands how to use module constants (tensors, + ScriptObjects, FakeScriptObjects) as keys. We store tensors and FakeScriptObjects normally, + but ScriptObjects are stored by hash, because different torch.ScriptObjects can point to + the same underlying value (but we guarantee that they will `hash()` to the same value + if that's the case). + """ + + def __init__(self) -> None: + # Underlying dict that we use to implement this mapping. + self._constant_attrs: Dict[ + Union[int, torch.Tensor, FakeScriptObject], List[Any] + ] = {} + # Map from the hash(ScriptObject) to the ScriptObject itself. Used for + # APIs like `__iter__` that should look like they're returning the + # original ScriptObjects. + self._script_object_map: Dict[int, torch.ScriptObject] = {} + + def __getitem__( + self, key: Union[torch.Tensor, torch.ScriptObject, FakeScriptObject] + ) -> Any: + real_key = hash(key) if isinstance(key, torch.ScriptObject) else key + assert isinstance(real_key, (int, torch.Tensor, FakeScriptObject)) + return self._constant_attrs[real_key] + + def __setitem__(self, key: Union[torch.Tensor, torch.ScriptObject], value): + # we shouldn't actually call this, should go to add() instead to handle aliasing + raise NotImplementedError( + """Directly setting values for ConstantAttrMap is not supported, please use add(key, value) instead. +The same key can be mapped to multiple values, for handling constant aliasing.""" + ) + + def add( + self, key: Union[torch.Tensor, torch.ScriptObject, FakeScriptObject], value: Any + ) -> None: + if isinstance(key, torch.ScriptObject): + if hash(key) not in self._constant_attrs: + self._constant_attrs[hash(key)] = [] + self._constant_attrs[hash(key)].append(value) + self._script_object_map[hash(key)] = key + elif isinstance(key, (torch.Tensor, FakeScriptObject)): + if key not in self._constant_attrs: + self._constant_attrs[key] = [] + self._constant_attrs[key].append(value) + else: + raise TypeError( + f"Expected key to be a tensor or ScriptObject, got {type(key)}" + ) + + def __delitem__(self, key): + real_key = hash(key) if isinstance(key, torch.ScriptObject) else key + + del self._constant_attrs[real_key] + + def __iter__(self): + for key in self._constant_attrs: + if isinstance(key, int): + yield self._script_object_map[key] + else: + yield key + + def __len__(self): + return len(self._constant_attrs) + + def __contains__(self, key: object) -> bool: + real_key = hash(key) if isinstance(key, torch.ScriptObject) else key + return real_key in self._constant_attrs + + +def get_constant_fqn(node: torch.fx.Node, constant_name: str) -> str: + # The FQN of the constant tensor in the state dict should + # correspond to the module where the constant tensor was + # originally used. + if len(node.meta["nn_module_stack"]) == 0: + return constant_name + parent_fqn = list(node.meta["nn_module_stack"].values())[-1][0] + if len(parent_fqn) > 0: + return f"{parent_fqn}.{constant_name}" + else: + return constant_name + + +def _get_first_fqn( + const_attrs: ConstantAttrMap, + key: Union[torch.Tensor, torch.ScriptObject, FakeScriptObject], +) -> Any: + fqns = const_attrs.get(key) + return fqns[0] if fqns else None + + +def lift_constants_pass( + gm: torch.fx.GraphModule, + graph_signature: ExportGraphSignature, + constant_attrs: ConstantAttrMap, +) -> Dict[str, Union[torch.Tensor, torch.ScriptObject, FakeScriptObject]]: + """ + Takes a graph module, graph signature, and modifies them implace to lift any + constants (tensors or custom classes) as inputs to the graph. Returns a + dictionary of names to constants. + + Arguments: + gm (torch.fx.GraphModule): The graph module containing the graph and constants to lift. + graph_signature (ExportGraphSignature): This graph signature will be + mutated to add additional CONSTANT_TENSOR and CUSTOM_OBJ inputs. + constant_attrs (ConstantAttr): A mapping from a constant value to its + fully-qualified path in `gm`. This is used to maintain consistent + location of constants between the original module and the exported + version. + + Returns: + A dictionary of fqn => constant value. + """ + all_constants: Dict[ + str, Union[torch.Tensor, torch.ScriptObject, FakeScriptObject] + ] = {} + + inputs = graph_signature.input_specs + num_custom_obj = sum( + input_specs.kind == InputKind.CUSTOM_OBJ for input_specs in inputs + ) + num_tensor_constants = sum( + input_specs.kind == InputKind.CONSTANT_TENSOR for input_specs in inputs + ) + + fake_mode = detect_fake_mode( + tuple(node.meta["val"] for node in gm.graph.nodes if node.op == "placeholder") + ) + + first_user_input_loc, first_user_input = 0, None + for node in gm.graph.nodes: + if node.op == "placeholder" and node.name in graph_signature.user_inputs: + first_user_input = node + break + first_user_input_loc += 1 + + lifted_objs = ConstantAttrMap() + for node in gm.graph.nodes: + if node.op == "get_attr": + constant_val = getattr(gm, node.target) + if constant_val in lifted_objs: + # We already lifted this constant elsewhere. Just rewrite uses + # of this get_attr to point to the already-existing placeholder + # node. + const_placeholder_node = _get_first_fqn(lifted_objs, constant_val) + node.replace_all_uses_with(const_placeholder_node) + gm.graph.erase_node(node) + continue + + # For ScriptObject, Tensor and FakeScriptObject constants: + # First check if the constant was an attribute on some module by + # consulting `constant_attrs` map. If it is, use the fqn that keeps + # its location consistent with the eager module. + # + # If it's not in the `constant_attrs` map, that means it's an inline + # constant (e.g. x + torch.tensor(0)), and thus did not have a + # specific location in the eager module. In that case, just generate + # some name and attach it to the module in which it was used. + if isinstance(constant_val, (torch.ScriptObject, FakeScriptObject)): + constant_kind = InputKind.CUSTOM_OBJ + constant_fqn = _get_first_fqn(constant_attrs, constant_val) + if constant_fqn is not None: + constant_name = constant_fqn.replace(".", "_") + else: + constant_name = f"lifted_custom_{num_custom_obj}" + constant_fqn = get_constant_fqn(node, constant_name) + num_custom_obj += 1 + elif isinstance(constant_val, torch.Tensor): + # Remove the parameterness of constant_val + if isinstance(constant_val, torch.nn.Parameter): + warnings.warn( + f"{node.target} created when tracing {node.meta['stack_trace']} is a parameter. But" + f"it's not registered with register_parameter(). export will treat it as a constant tensor" + ) + # We get the real data out of the parameter by disabling the surrounding fake mode. + with unset_fake_temporarily(): + constant_val = constant_val.data + constant_kind = InputKind.CONSTANT_TENSOR + constant_fqn = _get_first_fqn(constant_attrs, constant_val) + if constant_fqn is not None: + constant_name = constant_fqn.replace(".", "_") + else: + constant_name = f"lifted_tensor_{num_tensor_constants}" + constant_fqn = get_constant_fqn(node, constant_name) + num_tensor_constants += 1 + elif isinstance(constant_val, torch.fx.GraphModule): + continue + elif "LoweredBackendModule" in type(constant_val).__name__: + continue + else: + raise SpecViolationError( + f"getattr node {node} referencing unsupported type {type(constant_val)}" + ) + + with gm.graph.inserting_before(first_user_input): + # Insert the constant node before the first user input + const_placeholder_node = gm.graph.placeholder(constant_name) + # match target name with its node name in case there is name collision + # and suffix is added to node name in fx + const_placeholder_node.target = const_placeholder_node.name + + for k, v in node.meta.items(): + const_placeholder_node.meta[k] = v + + # Once the FQN has been used, remove nn_module_stack, stack_trace + const_placeholder_node.meta.pop("nn_module_stack") + const_placeholder_node.meta.pop("stack_trace", None) + + input_spec_arg: ArgumentSpec + if isinstance(constant_val, torch.Tensor): + if fake_mode is not None: + const_placeholder_node.meta["val"] = fake_mode.from_tensor( + constant_val, static_shapes=True + ) + const_placeholder_node.meta["val"].constant = constant_val + else: + const_placeholder_node.meta["val"] = constant_val + input_spec_arg = TensorArgument(name=const_placeholder_node.name) + elif isinstance(constant_val, torch._C.ScriptObject): + class_fqn = constant_val._type().qualified_name() # type: ignore[attr-defined] + const_placeholder_node.meta["val"] = CustomObjArgument( + constant_fqn, class_fqn + ) + input_spec_arg = CustomObjArgument( + name=const_placeholder_node.name, class_fqn=class_fqn + ) + elif isinstance(constant_val, FakeScriptObject): + class_fqn = constant_val.script_class_name + const_placeholder_node.meta["val"] = CustomObjArgument( + constant_fqn, class_fqn, constant_val + ) + input_spec_arg = CustomObjArgument( + name=const_placeholder_node.name, + class_fqn=class_fqn, + fake_val=constant_val, + ) + else: + raise SpecViolationError( + f"tried to lift unsupported type {type(constant_val)} from node {node.format_node()}" + ) + + lifted_objs.add(constant_val, const_placeholder_node) + node.replace_all_uses_with(const_placeholder_node) + gm.graph.erase_node(node) + + # Add the constant as a buffer to the graph signature + graph_signature.input_specs.insert( + first_user_input_loc, + InputSpec( + kind=constant_kind, + arg=input_spec_arg, + target=constant_fqn, + ), + ) + if constant_val in constant_attrs: + for fqn in constant_attrs[constant_val]: + all_constants[fqn] = constant_val + else: + all_constants[constant_fqn] = constant_val + first_user_input_loc += 1 + + return all_constants + + +def rewrite_script_object_meta( + gm: torch.fx.GraphModule, +) -> Dict[str, Union[torch.Tensor, torch.ScriptObject, FakeScriptObject],]: + """When tracing, we produce a graph with FakeScriptObject in the + meta["val"]. + + For now, we rewrie meta["val"] to be a placeholder CustomObjArgument + """ + constants: Dict[ + str, + Union[ + torch.Tensor, + torch.ScriptObject, + FakeScriptObject, + ], + ] = {} + for node in gm.graph.nodes: + if "val" not in node.meta: + continue + + old_meta = node.meta["val"] + + if isinstance(old_meta, torch.ScriptObject): + class_fqn = old_meta._type().qualified_name() # type: ignore[attr-defined] + new_meta = CustomObjArgument(node.name, class_fqn) + constants[node.name] = old_meta + node.meta["val"] = new_meta + + elif isinstance(old_meta, FakeScriptObject): + class_fqn = old_meta.script_class_name # type: ignore[attr-defined] + new_meta = CustomObjArgument(node.name, class_fqn, old_meta) + constants[node.name] = old_meta + node.meta["val"] = new_meta + + return constants diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/remove_runtime_assertions.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/remove_runtime_assertions.py new file mode 100644 index 0000000000000000000000000000000000000000..a80b62d2765a87b0e20dc7614c6a353c86225d81 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/remove_runtime_assertions.py @@ -0,0 +1,27 @@ +# mypy: allow-untyped-defs +import torch +from torch.fx.passes.infra.pass_base import PassBase, PassResult + + +class _RemoveRuntimeAssertionsPass(PassBase): + """ + Remove runtime assertions inserted by the + _AddRuntimeAssertionsForInlineConstraintsPass. + """ + + def call(self, graph_module) -> PassResult: + modified = False + for module in graph_module.modules(): + if not isinstance(module, torch.fx.GraphModule): + continue + for node in module.graph.nodes: + if node.target == torch.ops.aten._assert_async.msg: + assert_async_node = node + if len(assert_async_node.users) > 0: + continue + module.graph.erase_node(assert_async_node) + # the upstream scalar_tensor <- {le, ge} <- sym_size + # linear chain of nodes of nodes is removed by the + # downstream dead code elimination + modified = True + return PassResult(graph_module, modified) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/replace_autocast_with_hop_pass.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/replace_autocast_with_hop_pass.py new file mode 100644 index 0000000000000000000000000000000000000000..a15a3ef9c3b3f7465f4dc9cf8f6a2f32ea1b0884 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/replace_autocast_with_hop_pass.py @@ -0,0 +1,179 @@ +# mypy: allow-untyped-defs +from typing import List + +import torch +from torch._higher_order_ops.wrap import wrap_with_autocast + +from ..utils import node_inline_, nodes_filter, nodes_first, sequential_split +from .replace_with_hop_pass_util import ( + _replace_with_hop_helper, + _replace_with_hop_pass_helper, + _sequential_split_and_maybe_inline_subgraphs_helper, +) + + +def _is_autocast_node(node: torch.fx.Node): + return ( + node + and node.op == "call_function" + and node.target + in [ + torch.amp.autocast_mode._enter_autocast, + torch.amp.autocast_mode._exit_autocast, + ] + ) + + +def _is_enter_autocast_node(node: torch.fx.Node): + return ( + node + and node.op == "call_function" + and node.target == torch.amp.autocast_mode._enter_autocast + ) + + +def _is_exit_autocast_node(node: torch.fx.Node): + return ( + node + and node.op == "call_function" + and node.target == torch.amp.autocast_mode._exit_autocast + ) + + +def _is_autocast_sub_mod(node: torch.fx.Node): + """ + Check if the first non-placeholder node is `torch.amp.autocast_mode._enter_autocast`. + """ + if node.op == "call_module": + assert isinstance(node.target, str) + subgm = getattr(node.graph.owning_module, node.target) + first_non_ph = nodes_first( + subgm.graph.nodes, lambda node: node.op != "placeholder" + ) + if ( + first_non_ph + and first_non_ph.op == "call_function" + and first_non_ph.target == torch.amp.autocast_mode._enter_autocast + ): + # TODO: check if current auto-cast type is the same as the args of + # _enter_autocast. If so, return False, i.e. do not create a submodule. + return True + return False + + +def _check_valid_autocast_block(enter_autocast_node, exit_autocast_node): + assert _is_enter_autocast_node(enter_autocast_node) + assert _is_exit_autocast_node(exit_autocast_node) + assert exit_autocast_node.args[0] == enter_autocast_node + + +def _replace_with_hop(node: torch.fx.Node): + assert node.op == "call_module" + graph: torch.fx.Graph = node.graph + gm: torch.fx.GraphModule = graph.owning_module + assert isinstance(node.target, str) + sub_gm = getattr(gm, node.target) + sub_graph = sub_gm.graph + autocast_nodes = nodes_filter(sub_graph.nodes, _is_autocast_node) + if len(autocast_nodes) > 0: + assert len(autocast_nodes) > 1 # need at least an enter node and an exist node + enter_autocast_node = autocast_nodes[0] + exit_autocast_node = autocast_nodes[-1] + _check_valid_autocast_block(enter_autocast_node, exit_autocast_node) + + _replace_with_hop_helper( + node, enter_autocast_node, _is_autocast_node, wrap_with_autocast + ) + sub_graph.erase_node(exit_autocast_node) + sub_graph.erase_node(enter_autocast_node) + + +def _split_autocast(gm: torch.fx.GraphModule) -> torch.fx.GraphModule: + """ + split_autocast creates a new graph module that splits the input graph module into multiple submodules + based on the `_enter_autocast` and `_exit_autocast` nodes. It doesn't mutate the input graph module. + + Nodes between the **outer-most** `_enter_autocast` and `_exit_autocast(_enter_autocast)` are splitted + into a submodule. Nested autocast regions are not splitted. + `_enter_autocast` and `_exit_autocast(_enter_autocast)` nodes are in the submodule as well. + + Below is an example of splitting. A, B, C, D, E are blocks of non-autocast nodes in the original graph + module. Nodes marked with the same number are grouped into the same submodule. + A # 0 + enter_autocast # 1 + B # 1 + exit_autocast # 1 + C # 2 + enter_autocast # 3 + D # 3 + exit_autocast # 3 + E # 4 + """ + enter_autocast_node_stack: List[torch.fx.Node] = [] + first_node_after_outer_most_exit: bool = False + + def node_call_back(node: torch.fx.Node): + nonlocal enter_autocast_node_stack, first_node_after_outer_most_exit + if first_node_after_outer_most_exit or ( + len(enter_autocast_node_stack) == 0 and _is_enter_autocast_node(node) + ): + assert len(enter_autocast_node_stack) == 0 + first_node_after_outer_most_exit = False + if _is_enter_autocast_node(node): + enter_autocast_node_stack.append(node) + return True + if _is_exit_autocast_node(node): + assert len(enter_autocast_node_stack) > 0 + last_enter_autocast_node = enter_autocast_node_stack.pop() + assert node.args[0] == last_enter_autocast_node + if len(enter_autocast_node_stack) == 0: + # next node should be in the next submodule since + # autocast block ends + first_node_after_outer_most_exit = True + return False + + return sequential_split(gm, node_call_back) + + +def _sequential_split_and_maybe_inline_subgraphs( + gm: torch.fx.GraphModule, graph_signature +): + """ + Helper function for replace_autocast_with_hop_pass(). + Split the graph module into multiple subgraphs based on the autocast nodes. + For each subgraph, decides whether to construct a HOO subgraph, or inline the calls + back into the parent graph module. + Nodes between `_enter_autocast` and `_exit_autocast(_enter_autocast)` are considered + as a subgraph. + """ + need_replacing = any(_is_autocast_node(node) for node in gm.graph.nodes) + if not need_replacing: + return gm, graph_signature + + # split_autocast returns a new graph module that could have different output + # args names. We need to fix the graph signature in `_sequential_split_and_maybe_inline_subgraphs_helper`. + new_gm = _split_autocast(gm) + + def _maybe_inline_or_replace_with_hop(node: torch.fx.Node): + if _is_autocast_sub_mod(node): + _replace_with_hop(node) + else: + assert node.op == "call_module" + assert isinstance(node.target, str) + node_inline_(node) + + return _sequential_split_and_maybe_inline_subgraphs_helper( + new_gm, graph_signature, _maybe_inline_or_replace_with_hop + ) + + +def replace_autocast_with_hop_pass(gm: torch.fx.GraphModule, graph_signature): + """ + Split gm into sub-graph-modules using `sequential_split_and_maybe_inline_subgraphs`, and + then recursively call itself on each of the submodules. + """ + return _replace_with_hop_pass_helper( + gm, + graph_signature, + _sequential_split_and_maybe_inline_subgraphs, + ) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/replace_quantized_ops_with_standard_ops_pass.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/replace_quantized_ops_with_standard_ops_pass.py new file mode 100644 index 0000000000000000000000000000000000000000..47a93a035aa5389ce347a49d99ca6230484d3b16 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/replace_quantized_ops_with_standard_ops_pass.py @@ -0,0 +1,673 @@ +# mypy: allow-untyped-defs +import logging +import operator +from typing import List, Optional, Tuple, Union + +import torch +import torch.export._trace +from torch._ops import OpOverload +from torch.ao.quantization.fx._decomposed import ( + dequantize_per_channel, + dequantize_per_tensor, + quantize_per_tensor, +) +from torch.ao.quantization.utils import calculate_qmin_qmax +from torch.fx.graph_module import _assign_attr + + +log = logging.getLogger(__name__) + +# Those values will need to be carried over multiple operators. +_INPUT_Q_DTYPE: Optional[Union[torch.dtype, torch.fx.Node]] = None +_SCALE: Optional[Union[float, torch.fx.Node]] = None +_ZERO_POINT: Optional[Union[float, torch.fx.Node]] = None + + +def int_to_valid_dtype(val: int) -> torch.dtype: + from torch._export.converter import _TORCH_ENUM_TO_DTYPE # No circular import. + + if isinstance(val, torch.dtype): + return val + dtype = _TORCH_ENUM_TO_DTYPE[val] + if dtype == torch.quint8: + return torch.uint8 + elif dtype == torch.qint8: + return torch.int8 + return dtype + + +def fx_enum_to_dtype(gm: torch.fx.GraphModule, val: int) -> torch.fx.Node: + return gm.graph.call_function(int_to_valid_dtype, (val,)) + + +def insert_quantized_node( + gm: torch.fx.GraphModule, + val_node: torch.fx.Node, + scale_node: Union[float, torch.fx.Node], + zero_point_node: Union[float, torch.fx.Node], + qmin_node: Union[float, int, torch.fx.Node], + qmax_node: Union[float, int, torch.fx.Node], + dtype_node: Union[torch.dtype, torch.fx.Node], + qscheme: Optional[torch.qscheme], +) -> torch.fx.Node: + return gm.graph.call_function( + quantize_per_tensor, + ( + val_node, + scale_node, + zero_point_node, + qmin_node, + qmax_node, + dtype_node, + ), + ) + + +def get_dequantized( + val: torch.Tensor, + scale: Union[float, torch.Tensor], + zero_point: Union[float, torch.Tensor], + qmin: Union[float, int], + qmax: Union[float, int], + dtype: torch.dtype, + axis: Optional[int], + qscheme: Optional[torch.qscheme], +) -> torch.Tensor: + if qscheme is torch.per_tensor_affine: + return dequantize_per_tensor( + val, + scale, + zero_point, + qmin, + qmax, + dtype, + ) + elif qscheme is torch.per_channel_affine: + return dequantize_per_channel( + val, + scale, + zero_point, + axis, + qmin, + qmax, + dtype, + ) + else: + raise RuntimeError(f"Unsupported dequantization scheme: {qscheme}") + + +def insert_dequantized_node( + gm: torch.fx.GraphModule, + val_node: torch.fx.Node, + scale_node: Union[float, torch.fx.Node], + zero_point_node: Union[float, torch.fx.Node], + qmin_node: Union[float, int, torch.fx.Node], + qmax_node: Union[float, int, torch.fx.Node], + dtype_node: Union[torch.dtype, torch.fx.Node], + axis_node: Optional[Union[int, torch.fx.Node]], + qscheme: Optional[torch.qscheme], +) -> torch.fx.Node: + if qscheme is torch.per_tensor_affine: + return gm.graph.call_function( + dequantize_per_tensor, + ( + val_node, + scale_node, + zero_point_node, + qmin_node, + qmax_node, + dtype_node, + ), + ) + elif qscheme is torch.per_channel_affine: + return gm.graph.call_function( + dequantize_per_channel, + ( + val_node, + scale_node, + zero_point_node, + axis_node, + qmin_node, + qmax_node, + dtype_node, + ), + ) + else: + raise RuntimeError(f"Unsupported dequantization scheme: {qscheme}") + + +def get_qmin_qmax(dtype: torch.dtype) -> Tuple[Union[int, float], Union[int, float]]: + return calculate_qmin_qmax(None, None, False, dtype, False) # type: ignore[arg-type] + + +def insert_qmin_qmax_node( + gm: torch.fx.GraphModule, dtype_node: Union[torch.dtype, torch.fx.Node] +) -> Tuple[torch.fx.Node, torch.fx.Node]: + q_min_max_node = gm.graph.call_function( + calculate_qmin_qmax, (None, None, False, dtype_node, False) + ) + qmin_node = gm.graph.call_function(operator.getitem, (q_min_max_node, 0)) + qmax_node = gm.graph.call_function(operator.getitem, (q_min_max_node, 1)) + return qmin_node, qmax_node + + +def get_script_object( + gm: torch.nn.Module, node: torch.fx.Node +) -> torch._C.ScriptObject: + assert isinstance(node, torch.fx.Node) + assert node.op == "get_attr" + attr_name = node.target + assert isinstance(attr_name, str) + + mod = gm + for attr in attr_name.split("."): + mod = getattr(mod, attr) + assert isinstance(mod, torch._C.ScriptObject) + return mod + + +def insert_weight_and_bias_get_attr_node_from_get_attr_to_scriptobject( + gm: torch.fx.GraphModule, + param_node: torch.fx.Node, +) -> Tuple[torch.fx.Node, Optional[torch.fx.Node]]: + """Directly inline tensor from a get_attr fx node.""" + mod = get_script_object(gm, param_node) + w_qtensor, b_qtensor = mod.unpack() # type: ignore[attr-defined] + w_attr_name, b_attr_name = ( + f"dequantized_{param_node.target}_w", + f"dequantized_{param_node.target}_b", + ) + return insert_weight_and_bias_get_attr_node( + gm, w_qtensor, b_qtensor, w_attr_name, b_attr_name + ) + + +def insert_weight_and_bias_get_attr_node_from_get_attr_to_qtensor( + gm: torch.fx.GraphModule, + get_attr_to_weight_node: torch.fx.Node, + get_attr_to_bias_node: Optional[torch.fx.Node], +) -> Tuple[torch.fx.Node, Optional[torch.fx.Node]]: + assert isinstance(get_attr_to_weight_node.target, str) + w_qtensor = getattr(gm, get_attr_to_weight_node.target) + w_attr_name = f"dequantized_{get_attr_to_weight_node.target}_w" + + if get_attr_to_bias_node is not None: + assert isinstance(get_attr_to_bias_node.target, str) + b_qtensor = getattr(gm, get_attr_to_bias_node.target) + b_attr_name = f"dequantized_{get_attr_to_bias_node.target}_b" + else: + b_qtensor, b_attr_name = None, "" + + return insert_weight_and_bias_get_attr_node( + gm, w_qtensor, b_qtensor, w_attr_name, b_attr_name + ) + + +def insert_weight_and_bias_get_attr_node( + gm: torch.fx.GraphModule, + w_qtensor: torch.Tensor, + b_qtensor: Optional[torch.Tensor], + w_attr_name: str, + b_attr_name: str, +) -> Tuple[torch.fx.Node, Optional[torch.fx.Node]]: + w_tensor = get_tensor_from_qtensor(w_qtensor) + _assign_attr(w_tensor, gm, w_attr_name) + w_tensor_attr = gm.graph.get_attr(w_attr_name) + + if b_qtensor is not None: + b_tensor = get_tensor_from_qtensor(b_qtensor, dequant=False) + _assign_attr(b_tensor, gm, b_attr_name) + b_tensor_attr = gm.graph.get_attr(b_attr_name) + else: + b_tensor_attr = None + + return w_tensor_attr, b_tensor_attr + + +def get_tensor_from_qtensor( + qtensor: torch.Tensor, dequant: bool = True +) -> torch.Tensor: + # Manual conversion because qint8 is not used anymore. + if qtensor.dtype in [torch.qint8, torch.quint8]: + tensor = qtensor.int_repr() + else: + tensor = qtensor + + # Weights need dequantization with scaling and zero_point adjustment, but + # bias does not need that. + if dequant: + qscheme = qtensor.qscheme() + if qscheme == torch.per_channel_affine: + scale, zero_point, axis = ( + qtensor.q_per_channel_scales(), + qtensor.q_per_channel_zero_points(), + qtensor.q_per_channel_axis(), + ) + else: + scale, zero_point, axis = ( + qtensor.q_scale(), # type: ignore[assignment] + qtensor.q_zero_point(), # type: ignore[assignment] + None, + ) + dtype = tensor.dtype + qmin, qmax = get_qmin_qmax(dtype) + return get_dequantized( + tensor, scale, zero_point, qmin, qmax, dtype, axis, qscheme + ) + return tensor + + +def insert_fused_activation_node( + gm: torch.fx.GraphModule, opname: str, fx_node: torch.fx.Node +) -> torch.fx.Node: + if opname in ["conv1d_relu", "conv2d_relu", "linear_relu", "add_relu", "mul_relu"]: + fx_node = gm.graph.call_function(torch.ops.aten.relu, (fx_node,)) + return fx_node + + +def _conv1d_op_with_squeeze( + inp: torch.Tensor, + weight: torch.Tensor, + bias: Optional[torch.Tensor], + stride: List[int], + padding: List[int], + dilation: List[int], + groups: int, +) -> torch.Tensor: + # In quantized version, conv1d is emulated using conv2d with squeeze and unsqueeze + # operations before and after the conv2d operation to match the dimension of weights. + # Reference: https://github.com/pytorch/pytorch/blob/eca0cb0fbe84bb0a34fa94afe261bceecd52c436/aten/src/ATen/native/quantized/cpu/qconv.cpp#L1827 # noqa: B950 + s_inp = torch.ops.aten.unsqueeze(inp, 2) + conv1d_res = torch.ops.aten.conv2d( + s_inp, + weight, + bias, + stride, + padding, + dilation, + groups, + ) + uns_conv1d_res = torch.ops.aten.squeeze(conv1d_res, 2) + return uns_conv1d_res + + +def _transform_conv_with_packedparam(gm: torch.fx.GraphModule, node: torch.fx.Node): + """Conv specfic transformation function.""" + assert isinstance(node.target, torch._ops.OpOverload) + opname = node.target._opname + scale_node, zero_point_node = node.args[2], node.args[3] + + op_f = ( + torch.ops.aten.conv2d + if opname in ["conv2d", "conv2d_relu"] + else _conv1d_op_with_squeeze + ) + + inp_node, param_node = node.args[0], node.args[1] + assert isinstance(inp_node, torch.fx.Node) + assert isinstance(param_node, torch.fx.Node) + + if param_node.op == "call_function": + # Using Conv2dPrepackParam from conv_prepack. + # We directly skip the packing call and inline weights and bias. + w_node, b_node = param_node.args[0], param_node.args[1] + assert isinstance(w_node, torch.fx.Node) + assert b_node is None or isinstance(b_node, torch.fx.Node) + ( + param_0, + param_1, + ) = insert_weight_and_bias_get_attr_node_from_get_attr_to_qtensor( + gm, w_node, b_node + ) + op_res_node = gm.graph.call_function( + op_f, (inp_node, param_0, param_1, *param_node.args[2:]) + ) + else: + # Using ConvPrepackedParam. + param = get_script_object(gm, param_node) + ( + param_0, + param_1, + ) = insert_weight_and_bias_get_attr_node_from_get_attr_to_scriptobject( + gm, param_node + ) # type: ignore[assignment] + op_res_node = gm.graph.call_function( + op_f, + ( + inp_node, + param_0, + param_1, + param.stride(), # type: ignore[attr-defined] + param.padding(), # type: ignore[attr-defined] + param.dilation(), # type: ignore[attr-defined] + param.groups(), # type: ignore[attr-defined] + ), + ) + return op_res_node, scale_node, zero_point_node + + +def _transform_linear_with_packedparam(gm: torch.fx.GraphModule, node: torch.fx.Node): + """Linear specfic transformation function.""" + scale_node, zero_point_node = node.args[2], node.args[3] + + inp_node, param_node = node.args[0], node.args[1] + assert isinstance(inp_node, torch.fx.Node) + assert isinstance(param_node, torch.fx.Node) + + if param_node.op == "call_function": + # Using LinearPrepackParam from linear_prepack. + # We directly skip the packing call and inline weights and bias. + w_node, b_node = param_node.args[0], param_node.args[1] + assert isinstance(w_node, torch.fx.Node) + assert b_node is None or isinstance(b_node, torch.fx.Node) + ( + param_0, + param_1, + ) = insert_weight_and_bias_get_attr_node_from_get_attr_to_qtensor( + gm, w_node, b_node + ) + op_res_node = gm.graph.call_function( + torch.ops.aten.linear, (inp_node, param_0, param_1, *param_node.args[2:]) + ) + else: + # Using LinearPackedParams. + ( + param_0, + param_1, + ) = insert_weight_and_bias_get_attr_node_from_get_attr_to_scriptobject( + gm, param_node + ) # type: ignore[assignment] + op_res_node = gm.graph.call_function( + torch.ops.aten.linear, (inp_node, param_0, param_1) + ) + return op_res_node, scale_node, zero_point_node + + +def _transform_op_where_last_two_arguments_are_scale_and_zero_point( + gm: torch.fx.GraphModule, node: torch.fx.Node +): + """ + This transformation function can be used for function where the last two + parameters are scale and zero point. Additionally, the function's parameters + do not need any unpacking. + """ + to_standard_op = { + "mul": torch.ops.aten.mul, + "mul_relu": torch.ops.aten.mul, + "add": torch.ops.aten.add, + "add_relu": torch.ops.aten.add, + "softmax": torch.ops.aten.softmax, + "cat": torch.ops.aten.cat, + "hardswish": torch.ops.aten.hardswish, + } + + assert isinstance(node.target, torch._ops.OpOverload) + opname, args = node.target._opname, node.args + scale_node, zero_point_node = args[-2], args[-1] + op_res_node = gm.graph.call_function(to_standard_op[opname], tuple(args[:-2])) + return op_res_node, scale_node, zero_point_node + + +def _transform_scalar_arithmetic(gm: torch.fx.GraphModule, node: torch.fx.Node): + """Transform scalar overload for basic arithmetic.""" + to_standard_op = { + "mul": torch.ops.aten.mul.Scalar, + "add": torch.ops.aten.add.Scalar, + } + assert isinstance(node.target, torch._ops.OpOverload) + opname, args = node.target._opname, node.args + op_res_node = gm.graph.call_function(to_standard_op[opname], args) + return op_res_node, _SCALE, _ZERO_POINT + + +def _transform_prepacked_op(gm: torch.fx.GraphModule, node: torch.fx.Node): + """ + Transformation for functions under prepacked namespace, where they share + the same handling logic that [...]OpContext contains all parameters. + """ + assert isinstance(node.target, torch._ops.OpOverload) + opname, args = node.target._opname, node.args + op_f = None + if opname == "conv2d_clamp_run": + op_f = torch.ops.aten.conv2d + elif opname == "linear_clamp_run": + op_f = torch.ops.aten.linear + else: + raise RuntimeError(f"Invalid operator {opname}") + + assert isinstance(args[1], torch.fx.Node) + so = get_script_object(gm, args[1]) + + func_args = [] + func_args += [args[0]] + func_args += so.unpack()[:2] # type: ignore[attr-defined] + if opname == "conv2d_clamp_run": + func_args += torch.ops.prepacked.unpack_prepacked_sizes_conv2d(so)[2:] + + op_res_node = gm.graph.call_function(op_f, tuple(func_args)) + return op_res_node + + +def _transform_batch_norm(gm: torch.fx.GraphModule, node: torch.fx.Node): + args = node.args + scale_node, zero_point_node = args[-2], args[-1] + op_res_node = gm.graph.call_function( + torch.ops.aten.native_batch_norm, (*args[:-3], False, 0.1, args[-3]) + ) + op_res_node = gm.graph.call_function(operator.getitem, (op_res_node, 0)) + return op_res_node, scale_node, zero_point_node + + +def fx_transform_quantized_op_to_standard_op( + gm: torch.fx.GraphModule, node: torch.fx.Node +) -> torch.fx.Node: + global _SCALE, _ZERO_POINT, _INPUT_Q_DTYPE + + assert isinstance(node.target, torch._ops.OpOverload) + opname, overload = node.target._opname, node.target._overloadname + + key = f"{opname}.{overload}" + opname_to_transform_f = { + "conv1d.new": _transform_conv_with_packedparam, + "conv1d_relu.new": _transform_conv_with_packedparam, + "conv1d.default": _transform_conv_with_packedparam, + "conv1d_relu.default": _transform_conv_with_packedparam, + "conv2d.new": _transform_conv_with_packedparam, + "conv2d_relu.new": _transform_conv_with_packedparam, + "conv2d.default": _transform_conv_with_packedparam, + "conv2d_relu.default": _transform_conv_with_packedparam, + "linear.default": _transform_linear_with_packedparam, + "linear_relu.default": _transform_linear_with_packedparam, + "add.default": _transform_op_where_last_two_arguments_are_scale_and_zero_point, + "add_relu.default": _transform_op_where_last_two_arguments_are_scale_and_zero_point, + "mul.default": _transform_op_where_last_two_arguments_are_scale_and_zero_point, + "mul_relu.default": _transform_op_where_last_two_arguments_are_scale_and_zero_point, + "softmax.default": _transform_op_where_last_two_arguments_are_scale_and_zero_point, + "cat.default": _transform_op_where_last_two_arguments_are_scale_and_zero_point, + "hardswish.default": _transform_op_where_last_two_arguments_are_scale_and_zero_point, + "batch_norm2d.default": _transform_batch_norm, + "mul.Scalar": _transform_scalar_arithmetic, + "add.Scalar": _transform_scalar_arithmetic, + } + + if f"{key}" not in opname_to_transform_f: + raise RuntimeError(f"Unsupported quantized op during transformation: {key}") + + op_res_node, scale_node, zero_point_node = opname_to_transform_f[f"{key}"](gm, node) + + # Add fused activation layer. + op_res_node = insert_fused_activation_node(gm, opname, op_res_node) + _SCALE, _ZERO_POINT = scale_node, zero_point_node + + assert _INPUT_Q_DTYPE is not None + qmin_node, qmax_node = insert_qmin_qmax_node(gm, _INPUT_Q_DTYPE) + q_fx_node = insert_quantized_node( + gm, + op_res_node, + scale_node, + zero_point_node, + qmin_node, + qmax_node, + _INPUT_Q_DTYPE, + torch.per_tensor_affine, + ) + dq_fx_node = insert_dequantized_node( + gm, + q_fx_node, + scale_node, + zero_point_node, + qmin_node, + qmax_node, + _INPUT_Q_DTYPE, + None, + torch.per_tensor_affine, + ) + return dq_fx_node + + +def replace_quantized_ops_with_standard_ops(gm: torch.fx.GraphModule): + """ + Replace legacy quantized ops (aten.quantize_per_tensor, quantized.conv) with + PT2 ops (quantize_decomposed.quantize_per_tensor, aten.conv). + + Before: x || -> aten.q || -> quantized.conv2d || -> quantized.linear || -> aten.dq || -> y + + After: x || -> qd.q -> qd.dq || -> aten.conv2d -> qd.q -> qd.dq || aten.linear -> qd.q -> qd.dq || -> y + + (qd == quantized_decomposed library, q = quantize, dq = dequantize) + ^ + | + getattr(w), getattr(b) from Conv2dParamPrepack + + During each iteration, the transformation spits out the transformed operator, its quantized output, + and its dequantized value together. We did this because dequantization need to use the + scale and zero point parameters from the quantization to recover the approximate original value. After each + iteration, the new dequantization node will be used as the input to the next node (e.g., dq2 -> linear). + + For operators like conv2d and linear, their weights and bias are packed in a quantized format in the ScriptObject. + During the transformation, we unpack those objects, get their dequantized tensor, populate those + as attributes to the module, and use getattr to access them. + + One exception in the transformation is conv_prepack and linear_prepack. Those calls pack + weight and bias constant tensors into ScriptObject, which are then used by subsequent conv2d or linear calls. + During transformation, we directly skip transforming conv_prepack or linear_prepack. We check whether ScriptObject to the + quantized::conv2d or linear is from conv_prepack or linear_prepack. If it is, we then inline those parameters + to the operator by converting them to a getattr fx.node. + + For prepacked::conv2d_clamp_run and prepacked::linear_clamp_run, we directly convert them to aten.conv2d and aten.linear + without the need of doing de/quantization. + + Three global variables defined are _INPUT_Q_DTYPE, _SCALE, _ZERO_POINT. _INPUT_Q_DTYPE determines the de/quantization + data type, which is the same across the entire program, but it only shows up in the very first quantization + call. _SCALE and _ZERO_POINT are used only when operators do not have those specified. E.g., mul.Scalar. + """ + + global _INPUT_Q_DTYPE + + quantized = False + + last_quantized_node = None + for node in gm.graph.nodes: + if isinstance(node.target, OpOverload): + with gm.graph.inserting_before(node): + namespace, opname = node.target.namespace, node.target._opname + if namespace == "quantized" and opname not in [ + "conv_prepack", + "linear_prepack", + ]: + quantized = True + fx_node = fx_transform_quantized_op_to_standard_op(gm, node) + node.replace_all_uses_with(fx_node) + last_quantized_node = fx_node + elif namespace == "prepacked": + quantized = True + fx_node = _transform_prepacked_op(gm, node) + node.replace_all_uses_with(fx_node) + last_quantized_node = fx_node + elif namespace == "aten" and opname == "quantize_per_tensor": + inp_node, scale_node, zero_point_node, dtype_node = node.args + dtype_node = fx_enum_to_dtype(gm, dtype_node) + _INPUT_Q_DTYPE = dtype_node + qmin_node, qmax_node = insert_qmin_qmax_node(gm, dtype_node) + q_fx_node = insert_quantized_node( + gm, + inp_node, + scale_node, + zero_point_node, + qmin_node, + qmax_node, + dtype_node, + torch.per_tensor_affine, + ) + dq_fx_node = insert_dequantized_node( + gm, + q_fx_node, + scale_node, + zero_point_node, + qmin_node, + qmax_node, + dtype_node, + None, + torch.per_tensor_affine, + ) + node.replace_all_uses_with(dq_fx_node) + last_quantized_node = dq_fx_node + elif namespace == "aten" and opname == "dequantize": + assert last_quantized_node is not None + node.replace_all_uses_with(last_quantized_node) + else: + last_quantized_node = node + + # Post-processing again to remove legacy ScriptObjects and quantizated tensors + # stored as attributes or in the buffer. This is used to clean up the GraphModule + # to not trigger tracing errors like missing __obj_flatten__ functions. + def _clean_attr(mod: torch.nn.Module): + for submod in mod.modules(): + attr_names_to_clean = set() + for k, v in submod.__dict__.items(): + if isinstance(v, torch.ScriptObject): + attr_names_to_clean.add(k) + if k == "_buffers": + buffer_name_to_clean = set() + for b_name, b_value in v.items(): + if isinstance(b_value, torch.Tensor) and b_value.dtype in [ + torch.qint8, + torch.quint8, + ]: + buffer_name_to_clean.add(b_name) + for b_name in buffer_name_to_clean: + v.pop(b_name, None) + for attr_name in attr_names_to_clean: + delattr(submod, attr_name) + + if quantized: + """ + TODO: SetAttr + quantized ops will result incorrect program. This flag is used to temporarily + bypass test cases. + + The deadcode elimination pass is needed to remove legacy quantized ops. Otherwise, retracing + will throw errors. However, the current way of SetAttr does inplace update to attributes, so + this pass regard them as dead code and remove them. Below is an example of GraphModule before + and after the dead code elimination pass. + + class GraphModule(torch.nn.Module): + def forward(self, x_1): + # No stacktrace found for following nodes + data = self.data; data = None + data_1 = self.data + add_tensor = torch.ops.aten.add.Tensor(data_1, x_1, alpha = 1); data_1 = None + data_2 = self.data + copy_ = torch_Tensor_copy_(data_2, add_tensor); data_2 = add_tensor = copy_ = None + data_3 = self.data + add_tensor_1 = torch.ops.aten.add.Tensor(x_1, data_3, alpha = 1); x_1 = data_3 = None + return add_tensor_1 + + class GraphModule(torch.nn.Module): + def forward(self, x_1): + # No stacktrace found for following nodes + data_3 = self.data + add_tensor_1 = torch.ops.aten.add.Tensor(x_1, data_3, alpha = 1); x_1 = data_3 = None + return add_tensor_1 + """ + gm.graph.eliminate_dead_code() + _clean_attr(gm) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/replace_set_grad_with_hop_pass.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/replace_set_grad_with_hop_pass.py new file mode 100644 index 0000000000000000000000000000000000000000..96104a83ce8b67b62d5aac1fca11cea395ccf2d5 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/replace_set_grad_with_hop_pass.py @@ -0,0 +1,110 @@ +# mypy: allow-untyped-defs + +import torch +from torch._higher_order_ops.wrap import wrap_with_set_grad_enabled + +from ..utils import node_inline_, nodes_filter, nodes_first, nodes_map, sequential_split +from .replace_with_hop_pass_util import ( + _replace_with_hop_helper, + _replace_with_hop_pass_helper, + _sequential_split_and_maybe_inline_subgraphs_helper, +) + + +def _is_set_grad_enabled_node(node: torch.fx.Node): + return ( + node + and node.op == "call_function" + and node.target == torch._C._set_grad_enabled + ) + + +def _is_set_grad_enabled_sub_mod(node: torch.fx.Node, omit_if_same_with_ambient=False): + if node.op == "call_module": + assert isinstance(node.target, str) + subgm = getattr(node.graph.owning_module, node.target) + first_non_ph = nodes_first( + subgm.graph.nodes, lambda node: node.op != "placeholder" + ) + if ( + first_non_ph + and first_non_ph.op == "call_function" + and first_non_ph.target == torch._C._set_grad_enabled + ): + return ( + first_non_ph.args[0] != torch.is_grad_enabled() + if omit_if_same_with_ambient + else True + ) + return False + + +def _replace_with_hop(node: torch.fx.Node): + assert node.op == "call_module" + graph: torch.fx.Graph = node.graph + gm: torch.fx.GraphModule = graph.owning_module + assert isinstance(node.target, str) + sub_gm = getattr(gm, node.target) + sub_graph = sub_gm.graph + set_grad_nodes = nodes_filter(sub_graph.nodes, _is_set_grad_enabled_node) + if len(set_grad_nodes) > 0: + assert len(set_grad_nodes) == 1 + set_grad_node = set_grad_nodes[0] + _replace_with_hop_helper( + node, set_grad_node, _is_set_grad_enabled_node, wrap_with_set_grad_enabled + ) + sub_graph.erase_node(set_grad_node) + + +def _remove_set_grad_and_inline(node: torch.fx.Node): + assert node.op == "call_module" + graph: torch.fx.Graph = node.graph + gm: torch.fx.GraphModule = graph.owning_module + assert isinstance(node.target, str) + sub_gm = getattr(gm, node.target) + sub_graph = sub_gm.graph + nodes_map( + sub_graph.nodes, + lambda n: sub_graph.erase_node(n) if _is_set_grad_enabled_node(n) else n, + ) + node_inline_(node) + + +def _sequential_split_and_maybe_inline_subgraphs( + gm: torch.fx.GraphModule, graph_signature +): + """ + Helper function for replace_set_grad_with_hop_pass(). + Split the graph module into multiple subgraphs based on the set_grad_enabled nodes. + For each subgraph, decides whether to construct a HOO subgraph, or inline the calls + back into the parent graph module. + """ + need_replacing = any(_is_set_grad_enabled_node(node) for node in gm.graph.nodes) + if not need_replacing: + return gm, graph_signature + + # sequential_split returns a new graph module that could have different output + # args names. We need to fix the graph signature. + new_gm = sequential_split(gm, _is_set_grad_enabled_node) + + def _maybe_inline_or_replace_with_hop(node: torch.fx.Node): + if _is_set_grad_enabled_sub_mod(node, omit_if_same_with_ambient=True): + _replace_with_hop(node) + else: + _remove_set_grad_and_inline(node) + + return _sequential_split_and_maybe_inline_subgraphs_helper( + new_gm, graph_signature, _maybe_inline_or_replace_with_hop + ) + + +def replace_set_grad_with_hop_pass(gm: torch.fx.GraphModule, graph_signature): + """ + Split gm into sub-graph-modules using `sequential_split_and_maybe_inline_subgraphs`, and + then recursively call itself on each of the submodules. + """ + return _replace_with_hop_pass_helper( + gm, + graph_signature, + _sequential_split_and_maybe_inline_subgraphs, + ) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/replace_view_ops_with_view_copy_ops_pass.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/replace_view_ops_with_view_copy_ops_pass.py new file mode 100644 index 0000000000000000000000000000000000000000..6723ac5f86a6cbf703e886318ee44d5ebfc2e13f --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/replace_view_ops_with_view_copy_ops_pass.py @@ -0,0 +1,65 @@ +# mypy: allow-untyped-defs +from typing import Dict, Optional +import torch +from torch._ops import OpOverload, HigherOrderOperator +from torch._export.error import InternalError +from torch._export.pass_base import _ExportPassBaseDeprecatedDoNotUse + + +__all__ = ["ReplaceViewOpsWithViewCopyOpsPass"] + + +_NON_FUNCTIONAL_OPS_TO_FUNCTIONAL_OPS: Dict[OpOverload, OpOverload] = { + torch.ops.aten._unsafe_view.default: torch.ops.aten.view_copy.default, +} + + +def is_view_op(schema: torch._C.FunctionSchema) -> bool: + if len(schema.arguments) == 0: + return False + alias_info = schema.arguments[0].alias_info + return (alias_info is not None) and (not alias_info.is_write) + + +def get_view_copy_of_view_op(schema: torch._C.FunctionSchema) -> Optional[OpOverload]: + if is_view_op(schema) and schema.name.startswith("aten::"): + view_op_name = schema.name.split("::")[1] + view_op_overload = ( + schema.overload_name + if schema.overload_name != "" + else "default" + ) + view_copy_op_name = view_op_name + "_copy" + if not hasattr(torch.ops.aten, view_copy_op_name): + raise InternalError(f"{schema.name} is missing a view_copy variant") + + view_copy_op_overload_packet = getattr(torch.ops.aten, view_copy_op_name) + + if not hasattr(view_copy_op_overload_packet, view_op_overload): + raise InternalError(f"{schema.name} is missing a view_copy variant") + + return getattr(view_copy_op_overload_packet, view_op_overload) + + return None + + +class ReplaceViewOpsWithViewCopyOpsPass(_ExportPassBaseDeprecatedDoNotUse): + """ + Our backend expects pure functional operators. For efficiency + purposes, we keep view ops around while functionalizing the exported + program. This pass replaces view ops with view copy ops for backends that + need AOT memory planning. + """ + def call_operator(self, op, args, kwargs, meta): + if op in _NON_FUNCTIONAL_OPS_TO_FUNCTIONAL_OPS: + return super().call_operator( + (_NON_FUNCTIONAL_OPS_TO_FUNCTIONAL_OPS[op]), args, kwargs, meta + ) + + if isinstance(op, HigherOrderOperator): + return super().call_operator(op, args, kwargs, meta) + + if view_copy_op := get_view_copy_of_view_op(op._schema): + return super().call_operator(view_copy_op, args, kwargs, meta) + + return super().call_operator(op, args, kwargs, meta) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/replace_with_hop_pass_util.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/replace_with_hop_pass_util.py new file mode 100644 index 0000000000000000000000000000000000000000..b2ca55025bd0a8e0abbe59b28012131bddc1e51f --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/passes/replace_with_hop_pass_util.py @@ -0,0 +1,178 @@ +# mypy: allow-untyped-defs + +import contextlib +import copy +import operator +from typing import Callable + +import torch +from torch._ops import HigherOrderOperator + +from ..utils import node_replace_, nodes_map + + +def _replace_with_hop_helper( + node: torch.fx.Node, + enter_block_node: torch.fx.Node, + node_filter: Callable, + wrap_hoo: HigherOrderOperator, +): + graph: torch.fx.Graph = node.graph + gm: torch.fx.GraphModule = graph.owning_module + assert isinstance(node.target, str) + sub_gm = getattr(gm, node.target) + + def set_hoo_node_meta(call_func_node): + call_func_node.meta["nn_module_stack"] = copy.copy( + enter_block_node.meta.get("nn_module_stack", {}) + ) + call_func_node.meta["torch_fn"] = ( + f"{wrap_hoo.__name__}", + f"{wrap_hoo.__class__.__name__}.{wrap_hoo.__name__}", + ) + if isinstance(output_args, (tuple, list)): + call_func_node.meta["val"] = tuple(arg.meta["val"] for arg in output_args) + elif isinstance(output_args, torch.fx.Node): + call_func_node.meta["val"] = (output_args.meta["val"],) + + with graph.inserting_before(node): + get_attr_node = graph.get_attr(node.target) + get_attr_node.meta["nn_module_stack"] = copy.copy( + enter_block_node.meta.get("nn_module_stack", {}) + ) + output_node = next(iter(reversed(sub_gm.graph.nodes)), None) + # Split_module pass intentially doesn't add output node + # if the graph doesn't return anything. + # TODO (tmanlaibaatar) Figure out if this is right behaviour + # for split_module + if isinstance(output_node, torch.fx.Node) and output_node.op != "output": + output_node = None + if output_node is not None: + assert len(output_node.args) == 1 + output_args = output_node.args[0] + enter_block_node_args = enter_block_node.args + if isinstance(output_args, (tuple, list)): + call_func_node = graph.call_function( + wrap_hoo, + (*enter_block_node_args, get_attr_node, *node.args), + {}, + ) + # Create the metadata + set_hoo_node_meta(call_func_node) + node_replace_(node, call_func_node) + + # Rename the name of getitem nodes to the actual name of its contents + # for passing verifier and better readability, also propagate metadata + for get_item_node in call_func_node.users.keys(): + idx: int = get_item_node.args[1] # type: ignore[assignment] + output_node = output_args[idx] + get_item_node._rename(output_node.name) + get_item_node.meta = output_node.meta + + elif isinstance(output_args, torch.fx.Node): + call_func_node = graph.create_node( + "call_function", + wrap_hoo, + (*enter_block_node_args, get_attr_node, *node.args), + {}, + output_args.name, + ) + # Modify the subgraph to output a singleton list. + output_node.args = ((output_args,),) + # Add in an extra `getitem(wrap_hoo, 0)` node to the toplevel graph. + get_item_node = graph.create_node( + "call_function", + operator.getitem, + (call_func_node, 0), + {}, + ) + # Create the metadata + get_item_node.meta = output_args.meta + set_hoo_node_meta(call_func_node) + node_replace_(node, get_item_node) + else: + raise NotImplementedError( + f"repalce_with_hop_pass doesnt' support output type {type(output_args)}" + ) + else: + # TODO (shangdiy): remove this line, since the export graph can be non-functional + node.graph.erase_node(node) + + +def _sequential_split_and_maybe_inline_subgraphs_helper( + new_gm: torch.fx.GraphModule, + graph_signature, + maybe_inline_or_replace_with_hop: Callable[[torch.fx.Node], None], +): + """ + Helper function for replacing graph nodse with higher order nodes. + For each subgraph in `new_gm`, decides whether to construct a HOO subgraph, or inline the calls + back into the parent graph module, depending on `maybe_inline_or_replace_with_hop`. + """ + # new_gm is a new graph module that could have different output args names. + # We need to fix the graph signature. + replace_ctx = contextlib.nullcontext() + new_signature = None + if graph_signature is not None: + # Cannot deep copy a real ScriptObject, which is referenced + # in the FakeScriptObject. Copy should be good enough to guard + # against accidental mutation to original graph_signature. + new_signature = copy.copy(graph_signature) + new_gm_out_node = next(reversed(new_gm.graph.find_nodes(op="output"))) + assert new_gm_out_node.op == "output" and len(new_gm_out_node.args[0]) == len( + new_signature.output_specs + ) + for arg_node, out_spec in zip( + new_gm_out_node.args[0], new_signature.output_specs + ): + if arg_node is None: + assert out_spec.arg.value is None + elif ( + isinstance(arg_node, torch.fx.Node) + and out_spec.arg.name != arg_node.name + ): + out_spec.arg.name = arg_node.name + + replace_ctx = new_gm._set_replace_hook(new_signature.get_replace_hook()) # type: ignore[assignment] + + with replace_ctx: + nodes_map( + list(new_gm.graph.nodes), + lambda node: ( + maybe_inline_or_replace_with_hop(node) + if node.op == "call_module" + else node + ), + ) + new_gm.recompile() + return new_gm, new_signature + + +def _replace_with_hop_pass_helper( + gm: torch.fx.GraphModule, + graph_signature, + sequential_split_and_maybe_inline_subgraphs: Callable, +): + """ + Split gm into sub-graph-modules using `sequential_split_and_maybe_inline_subgraphs`, and + then recursively call itself on each of the submodules. + """ + new_gm, new_signature = sequential_split_and_maybe_inline_subgraphs( + gm, graph_signature + ) + # recursively call + for node in new_gm.graph.nodes: + if node.op == "get_attr": + subgm = getattr(new_gm, node.target) + if not isinstance(subgm, torch.fx.GraphModule): + continue + new_subgm, _ = _replace_with_hop_pass_helper( + subgm, + None, + sequential_split_and_maybe_inline_subgraphs, + ) + setattr(new_gm, node.target, new_subgm) + + new_gm.recompile() + new_gm.graph.lint() + return new_gm, new_signature diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/serde/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/serde/__init__.py new file mode 100644 index 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mode 100644 index 0000000000000000000000000000000000000000..f24822d9b07d68cecfcb207b6b0c1b6829c3d3d8 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/serde/dynamic_shapes.py @@ -0,0 +1,321 @@ +import dataclasses +from typing import Any, Dict, List, Optional, Tuple, Union + +import torch +from torch._dynamo.exc import UserError, UserErrorType +from torch.export.dynamic_shapes import ( + _check_dynamic_shapes, + _DerivedDim, + _Dim, + _DimHint, + _tree_map_with_path, + Dim, +) +from torch.utils._pytree import tree_map + +from .serialize import _dataclass_to_dict + + +@dataclasses.dataclass +class RootDim: + """ + This represents a _Dim object. + """ + + min: int + max: Union[int, None] + derived: List[str] + + +@dataclasses.dataclass +class DynamicShapesSpec: + """ + This stores a dynamic_shapes spec for de/serialization. + """ + + dynamic_shapes: Union[Dict[str, Any], Tuple[Any], List[Any], None] + dims: Dict[str, RootDim] + + +def _postprocess_serialized_shapes( + dynamic_shapes: Union[Dict[str, Any], Tuple[Any], List[Any], None], + dims: Dict[str, Dict[str, Union[int, List[str], None]]], + to_dict: Optional[bool] = False, +) -> Union[DynamicShapesSpec, Dict[str, Any]]: + """ + Sorts dims and dumps to dictionary format. + """ + from torch.utils._sympy.numbers import int_oo + + dims = { + k: RootDim( + min=v["min"], # type: ignore[arg-type] + max=None if v["max"] is int_oo else v["max"], # type: ignore[arg-type] + derived=sorted(v["derived"]), # type: ignore[arg-type] + ) + for k, v in sorted(dims.items()) + } + spec = DynamicShapesSpec(dynamic_shapes=dynamic_shapes, dims=dims) + if to_dict: + return _dataclass_to_dict(spec) + else: + return spec + + +def _dump_dynamic_shapes( + dynamic_shapes: Union[Dict[str, Any], Tuple[Any], List[Any], None], + args: Tuple[Any], + kwargs: Optional[Dict[str, Any]] = None, + to_dict: Optional[bool] = False, +) -> Union[DynamicShapesSpec, Dict[str, Any]]: + """ + Utility function for dynamic shapes serialization, serializing a dynamic_shapes spec. + Returns a DynamicShapesSpec dataclass containing 2 fields, "dynamic_shapes" and "dims". + Uses args & kwargs to distinguish between tensor-level and dim-level specs (only for Nones). + + dynamic_shapes: A pytree structure mirroring the dynamic_shapes input to export(): + - Each tensor input is represented with a list of values, non-tensor inputs with None. + - dynamic dimensions (i.e. symbols) in tensors and Dim enums are represented with strings. + - static dimensions are represented with ints. + + dims: A dictionary mapping each symbol name to the min/max range and derived dim names. + + For example: + ``` + dx = Dim("dx", min=4, max=16) + dy = dx + 1 + + inputs = ( + [ + torch.randn(4, 4), + torch.randn(5, 4), + ], + torch.randn(4), + torch.randn(4, 4), + "hello", + ) + dynamic_shapes = { + "a": [ + (dx, 4), + (dy, 4), + ], + "b": (Dim.STATIC,), + "c": None, + "d": None, + } + out = _dump_dynamic_shapes(dynamic_shapes, inputs, to_dict=True) + ``` + would generate the following output: + ``` + { + 'dynamic_shapes': ( + [ + ['dx', 4], + ['dx + 1', 4], + ], + ['_DimHint.STATIC'], + ['_DimHint.STATIC', '_DimHint.STATIC'], + None, + ), + 'dims': { + 'dx': { + 'min': 4, + 'max': 16, + 'derived': ['dx + 1'], + }, + }, + } + ``` + """ + dims: Dict[str, Dict[str, Any]] = {} + + def _standardize_shapes(path, tensor, shape): # type: ignore[no-untyped-def] + """ + Helps standardize the dynamic_shapes tree structure we serialize, + returning lists for each tensor shape, handling tensor-level Nones. + """ + if not isinstance(tensor, torch.Tensor): + return None + if shape is None: + return [Dim.STATIC] * len(tensor.shape) # type: ignore[attr-defined] + + out = [] + if isinstance(shape, dict): + for i, s in enumerate(tensor.shape): + out.append(s if shape.get(i) is None else shape.get(i)) + else: + assert isinstance(shape, (tuple, list)) + for i, s in enumerate(tensor.shape): + out.append(s if shape[i] is None else shape[i]) + return out + + def _track_dim_from_dims( + val: Union[None, int, _DimHint, _Dim] + ) -> Union[None, int, str]: + """ + Tracks dims, ranges, derived dims from the standardized dynamic_shapes spec. + """ + if val is None or isinstance(val, int): # non-tensor input or static + return val + if isinstance(val, _DimHint): # store enum as string + return val.__class__.__name__ + "." + val.name + + assert isinstance(val, _Dim) + + # track root dim + root = val.root if isinstance(val, _DerivedDim) else val # type: ignore[attr-defined] + if root.__name__ not in dims: + dims[root.__name__] = { + "min": root.min, + "max": root.max, + "derived": set(), + } + + # track derived dims + if isinstance(val, _DerivedDim): + dims[root.__name__]["derived"].add(val.__name__) + + return val.__name__ + + if dynamic_shapes is None: + return {"dynamic_shapes": None, "dims": {}} + + # convert to tuple of specs, for each arg/kwarg + kwargs = kwargs or {} + if isinstance(dynamic_shapes, dict): + dynamic_shapes = dynamic_shapes.values() # type: ignore[assignment] + dynamic_shapes = tuple(dynamic_shapes) + combined_args = tuple(args) + tuple(kwargs.values()) + + # run same check when we're processing shapes for export - is this too lazy? + _check_dynamic_shapes(dict(enumerate(combined_args)), dynamic_shapes) # type: ignore[arg-type] + + tree_shapes = _tree_map_with_path( + _standardize_shapes, combined_args, dynamic_shapes, tree_name="inputs" + ) + serialized_shapes = tree_map(_track_dim_from_dims, tree_shapes) + return _postprocess_serialized_shapes(serialized_shapes, dims, to_dict=to_dict) + + +def _load_dynamic_shapes( + spec: Union[DynamicShapesSpec, Dict[str, Any]], + from_dict: Optional[bool] = False, +) -> Union[Dict[str, Any], Tuple[Any], List[Any], None]: + """ + Utility function for dynamic shapes serialization. + Deserializes a DynamicShapesSpec or corresponding dictionary into a dynamic_shapes input to export(). + """ + import sympy + + from torch.fx.experimental.symbolic_shapes import _is_supported_equivalence + + if from_dict: + if not isinstance(spec, dict): + raise UserError( + UserErrorType.INVALID_INPUT, + f"With from_dict=True, expected `spec` to be a dict, got {type(spec)}", + ) + if sorted(spec.keys()) != ["dims", "dynamic_shapes"]: + raise UserError( + UserErrorType.INVALID_INPUT, + "With from_dict=True, expected `spec` to have keys `dims` and `dynamic_shapes`, " + f"instead found {spec.keys()}", + ) + dims = {} + for k, v in spec["dims"].items(): + if not isinstance(k, str): + raise UserError( + UserErrorType.INVALID_INPUT, + f"Expected `spec['dims']` keys to be strings for symbols, got key {type(k)}", + ) + if sorted(v.keys()) != ["derived", "max", "min"]: + raise UserError( + UserErrorType.INVALID_INPUT, + f"Expected `spec['dims']` values to have keys `derived`, `max`, and `min`, " + f"instead found {v.keys()}", + ) + if not isinstance(v["min"], int): + raise UserError( + UserErrorType.INVALID_INPUT, + f"Expected dims in `spec['dims']` to map `min` to an int, got {k}: {v['min']}", + ) + if not isinstance(v["max"], int) or v["max"] is None: + raise UserError( + UserErrorType.INVALID_INPUT, + f"Expected dims in `spec['dims']` to map `max` to an int or None, got {k}: {v['max']}", + ) + if not isinstance(v["derived"], list) or any( + not isinstance(d, str) for d in v["derived"] + ): + raise UserError( + UserErrorType.INVALID_INPUT, + "Expected dims in `spec['dims']` to map `derived` to a list of derived expressions, " + f"got {k}: {v['derived']}", + ) + dims[k] = RootDim(**v) + dynamic_shapes = spec["dynamic_shapes"] + else: + if not isinstance(spec, DynamicShapesSpec): + raise UserError( + UserErrorType.INVALID_INPUT, + f"Expected `spec` to be a DynamicShapesSpec, got {type(spec)}", + ) + dims = spec.dims + dynamic_shapes = spec.dynamic_shapes + + if dynamic_shapes is None: + return None + + dim_cache = {} + for name, info in dims.items(): + symbol = sympy.sympify(name) + if not isinstance(symbol, sympy.Symbol): + raise UserError( + UserErrorType.INVALID_INPUT, + f"Expected `spec['dims']` keys to be symbols, got {name}", + ) + dim_cache[name] = Dim(name, min=info.min, max=info.max) # cache root dim + for _expr in info.derived: + expr = sympy.sympify(_expr) + if len(expr.free_symbols) != 1 or symbol not in expr.free_symbols: + raise UserError( + UserErrorType.INVALID_INPUT, + f"Expected derived expressions in to have {name} as the only free symbol, got {expr}", + ) + if not _is_supported_equivalence(expr): + raise UserError( + UserErrorType.INVALID_INPUT, + f"Expected derived expressions to be linear expressions, got {expr}", + ) + modulus, remainder = sympy.polys.polytools.div(expr, symbol) + ddim = dim_cache[name] + if modulus != 1: + ddim = int(modulus) * ddim + if remainder != 0: + ddim = ddim + int(remainder) + dim_cache[_expr] = ddim # cache derived dims + + def deserialize_shape( + val: Union[None, int, str] + ) -> Union[None, int, _Dim, _DimHint]: + if val is None or isinstance(val, int): + return val + elif val == "_DimHint.AUTO": + return _DimHint.AUTO + elif val == "_DimHint.STATIC": + return _DimHint.STATIC + if not isinstance(val, str): + raise UserError( + UserErrorType.INVALID_INPUT, + "Expected leaves in `spec['dynamic_shapes']` to be ints, None, Dim.AUTO/STATIC, symbols, " + f" or derived expressions, got {val}", + ) + if val not in dim_cache: + raise UserError( + UserErrorType.INVALID_INPUT, + "Expected dims in `spec['dynamic_shapes']` to be tracked in `spec['dims']`, " + f"got {val} which is not in {dims.keys()}", + ) + return dim_cache[val] + + return tree_map(deserialize_shape, dynamic_shapes) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/serde/schema.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/serde/schema.py new file mode 100644 index 0000000000000000000000000000000000000000..ce102b39367ad0178fe46236a428d1cf924dfe3c --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/serde/schema.py @@ -0,0 +1,381 @@ +# NOTE: This is a placeholder for iterating on export serialization schema design. +# Anything is subject to change and no guarantee is provided at this point. + +from dataclasses import dataclass, field +from enum import IntEnum +from typing import Dict, List, Optional, Tuple + +from torch._export.serde.union import _Union + +# NOTE: Please update this value if any modifications are made to the schema +SCHEMA_VERSION = (7, 3) +TREESPEC_VERSION = 1 + + +class ScalarType(IntEnum): + UNKNOWN = 0 + BYTE = 1 + CHAR = 2 + SHORT = 3 + INT = 4 + LONG = 5 + HALF = 6 + FLOAT = 7 + DOUBLE = 8 + COMPLEXHALF = 9 + COMPLEXFLOAT = 10 + COMPLEXDOUBLE = 11 + BOOL = 12 + BFLOAT16 = 13 + + +class Layout(IntEnum): + Unknown = 0 + SparseCoo = 1 + SparseCsr = 2 + SparseCsc = 3 + SparseBsr = 4 + SparseBsc = 5 + _mkldnn = 6 + Strided = 7 + + +class MemoryFormat(IntEnum): + Unknown = 0 + ContiguousFormat = 1 + ChannelsLast = 2 + ChannelsLast3d = 3 + PreserveFormat = 4 + + +@dataclass +class Device: + type: str + index: Optional[int] = None + + +@dataclass(repr=False) +class SymExprHint(_Union): + as_int: int + as_float: float + as_bool: bool + + +# This is for storing the symbolic expressions behind symints/symfloats/symbools +# For example, we can get something like +# SymExpr(expr_str="s0 + s1", hint=SymExprHint(as_int=4) +# if we also have the hint that s0 and s1 are both 2. +@dataclass +class SymExpr: + expr_str: str + hint: Optional[SymExprHint] = None + + +@dataclass(repr=False) +class SymInt(_Union): + as_expr: SymExpr + as_int: int + + +@dataclass(repr=False) +class SymBool(_Union): + as_expr: SymExpr + as_bool: bool + + +@dataclass +class TensorMeta: + dtype: ScalarType + sizes: List[SymInt] + requires_grad: bool + device: Device + strides: List[SymInt] + storage_offset: SymInt + layout: Layout + + +# In most cases we will use the "as_name" field to store arguments which are +# SymInts. +# The "as_int" field is used in the case where we have a list containing a mix +# of SymInt and ints (ex. [1, s0, ...]). We will serialize this type of list to +# be List[SymIntArgument] and map the SymInts to the "as_name" field, and ints +# to the "as_int" field. +@dataclass(repr=False) +class SymIntArgument(_Union): + as_name: str + as_int: int + + +# In most cases we will use the "as_name" field to store arguments which are +# SymBools. +# The "as_bool" field is used in the case where we have a list containing a mix +# of SymBool and bools (ex. [True, i0, ...]). We will serialize this type of list to +# be List[SymboolArgument] and map the SymBools to the "as_name" field, and bools +# to the "as_bool" field. +@dataclass(repr=False) +class SymBoolArgument(_Union): + as_name: str + as_bool: bool + + +@dataclass +class TensorArgument: + name: str + + +@dataclass +class TokenArgument: + name: str + + +# This is use for storing the contents of a list which contain optional tensors +# (Tensor?[], ex. [Tensor, None, ...]), where the list will be serialized to the +# type List[OptionalTensorArgument], with tensor values seiralized to the +# "as_tensor" field, and None values serialized to the "as_none" field. +@dataclass(repr=False) +class OptionalTensorArgument(_Union): + as_tensor: TensorArgument + as_none: Tuple[()] + + +@dataclass +class GraphArgument: + name: str + graph: 'Graph' + + +@dataclass +class CustomObjArgument: + name: str + class_fqn: str + + +# This is actually a union type +@dataclass(repr=False) +class Argument(_Union): + as_none: Tuple[()] + as_tensor: TensorArgument + as_tensors: List[TensorArgument] + as_int: int + as_ints: List[int] + as_float: float + as_floats: List[float] + as_string: str + as_strings: List[str] + as_sym_int: SymIntArgument + as_sym_ints: List[SymIntArgument] + as_scalar_type: ScalarType + as_memory_format: MemoryFormat + as_layout: Layout + as_device: Device + as_bool: bool + as_bools: List[bool] + as_sym_bool: SymBoolArgument + as_sym_bools: List[SymBoolArgument] + as_graph: GraphArgument + as_optional_tensors: List[OptionalTensorArgument] + as_custom_obj: CustomObjArgument + as_operator: str + + +@dataclass +class NamedArgument: + # Argument name from the operator schema + name: str + arg: Argument + + +@dataclass +class Node: + target: str + inputs: List[NamedArgument] + outputs: List[Argument] + metadata: Dict[str, str] + + +@dataclass +class Graph: + inputs: List[Argument] + outputs: List[Argument] + nodes: List[Node] + tensor_values: Dict[str, TensorMeta] + sym_int_values: Dict[str, SymInt] + sym_bool_values: Dict[str, SymBool] + # This is for deserializing the submodule graphs from higher order ops + # (ex. cond, map) where single tensor returns will just return a single + # tensor, rather than following export schema and returning a singleton + # list. + is_single_tensor_return: bool = False + custom_obj_values: Dict[str, CustomObjArgument] = field(default_factory=dict) + + +@dataclass +class UserInputSpec: + # Actually, only tensors and SymInts are allowed here + arg: Argument + + +@dataclass(repr=False) +class ConstantValue(_Union): + as_none: Tuple[()] + as_int: int + as_float: float + as_string: str + as_bool: bool + + +@dataclass +class ConstantInputSpec: + name: str + value: ConstantValue + + +@dataclass +class InputToParameterSpec: + arg: TensorArgument + parameter_name: str + + +@dataclass +class InputToBufferSpec: + arg: TensorArgument + buffer_name: str + persistent: bool + + + +@dataclass +class InputToTensorConstantSpec: + arg: TensorArgument + tensor_constant_name: str + + +@dataclass +class InputToCustomObjSpec: + arg: CustomObjArgument + custom_obj_name: str + + +@dataclass +class InputTokenSpec: + arg: TokenArgument + + +@dataclass(repr=False) +class InputSpec(_Union): + user_input: UserInputSpec + parameter: InputToParameterSpec + buffer: InputToBufferSpec + tensor_constant: InputToTensorConstantSpec + custom_obj: InputToCustomObjSpec + token: InputTokenSpec + constant_input: ConstantInputSpec + + +@dataclass +class UserOutputSpec: + arg: Argument + + +@dataclass +class LossOutputSpec: + arg: TensorArgument + + +@dataclass +class BufferMutationSpec: + arg: TensorArgument + buffer_name: str + + +@dataclass +class GradientToParameterSpec: + arg: TensorArgument + parameter_name: str + + +@dataclass +class GradientToUserInputSpec: + arg: TensorArgument + user_input_name: str + + +@dataclass +class UserInputMutationSpec: + arg: TensorArgument + user_input_name: str + + +@dataclass +class OutputTokenSpec: + arg: TokenArgument + + +@dataclass(repr=False) +class OutputSpec(_Union): + user_output: UserOutputSpec + loss_output: LossOutputSpec + buffer_mutation: BufferMutationSpec + gradient_to_parameter: GradientToParameterSpec + gradient_to_user_input: GradientToUserInputSpec + user_input_mutation: UserInputMutationSpec + token: OutputTokenSpec + + +@dataclass +class GraphSignature: + input_specs: List[InputSpec] + output_specs: List[OutputSpec] + + +@dataclass +class RangeConstraint: + min_val: int + max_val: int + + +@dataclass +class ModuleCallSignature: + inputs: List[Argument] + outputs: List[Argument] + + # These are serialized by calling pytree.treespec_loads + # And deserialized by calling pytree.treespec_dumps + in_spec: str + out_spec: str + + +@dataclass +class ModuleCallEntry: + fqn: str + signature: Optional[ModuleCallSignature] = None + + +@dataclass +class GraphModule: + graph: Graph + signature: GraphSignature + # This is used for unflattening, by tracking the calling structure of all of + # the modules in order to unflatten the modules back to the eager calling + # conventions. + module_call_graph: List[ModuleCallEntry] + metadata: Dict[str, str] = field(default_factory=dict) + + +# Invariant: Every time a change is made to the schema, one of the versions +# should be upadted. +@dataclass +class SchemaVersion: + major: int # Major version number is bumped every time a breaking change is made. + minor: int # Minor version number is bumped when a compatible change is made. + + +@dataclass +class ExportedProgram: + graph_module: GraphModule + # Key is the opset namespace (ex. aten), and value is the version number + opset_version: Dict[str, int] + range_constraints: Dict[str, RangeConstraint] + schema_version: SchemaVersion + verifiers: List[str] = field(default_factory=list) + torch_version: str = "<=2.4" diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/serde/schema.yaml b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/serde/schema.yaml new file mode 100644 index 0000000000000000000000000000000000000000..25a9a295ad0b97967885cbb715cdfbb553dfb4eb --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/serde/schema.yaml @@ -0,0 +1,437 @@ +# @generated by update_schema.py +# checksum<<923abf371a1f8802cacb037d409d28273867777a98f6542fba28616c2b92b639>> +Argument: + kind: union + fields: + as_none: + type: Tuple[()] + as_tensor: + type: TensorArgument + as_tensors: + type: List[TensorArgument] + as_int: + type: int + as_ints: + type: List[int] + as_float: + type: float + as_floats: + type: List[float] + as_string: + type: str + as_strings: + type: List[str] + as_sym_int: + type: SymIntArgument + as_sym_ints: + type: List[SymIntArgument] + as_scalar_type: + type: ScalarType + as_memory_format: + type: MemoryFormat + as_layout: + type: Layout + as_device: + type: Device + as_bool: + type: bool + as_bools: + type: List[bool] + as_sym_bool: + type: SymBoolArgument + as_sym_bools: + type: List[SymBoolArgument] + as_graph: + type: GraphArgument + as_optional_tensors: + type: List[OptionalTensorArgument] + as_custom_obj: + type: CustomObjArgument + as_operator: + type: str +BufferMutationSpec: + kind: struct + fields: + arg: + type: TensorArgument + buffer_name: + type: str +ConstantInputSpec: + kind: struct + fields: + name: + type: str + value: + type: ConstantValue +ConstantValue: + kind: union + fields: + as_none: + type: Tuple[()] + as_int: + type: int + as_float: + type: float + as_string: + type: str + as_bool: + type: bool +CustomObjArgument: + kind: struct + fields: + name: + type: str + class_fqn: + type: str +Device: + kind: struct + fields: + type: + type: str + index: + type: Optional[int] + default: None +ExportedProgram: + kind: struct + fields: + graph_module: + type: GraphModule + opset_version: + type: Dict[str, int] + range_constraints: + type: Dict[str, RangeConstraint] + schema_version: + type: SchemaVersion + verifiers: + type: List[str] + default: '[]' + torch_version: + type: str + default: <=2.4 +GradientToParameterSpec: + kind: struct + fields: + arg: + type: TensorArgument + parameter_name: + type: str +GradientToUserInputSpec: + kind: struct + fields: + arg: + type: TensorArgument + user_input_name: + type: str +Graph: + kind: struct + fields: + inputs: + type: List[Argument] + outputs: + type: List[Argument] + nodes: + type: List[Node] + tensor_values: + type: Dict[str, TensorMeta] + sym_int_values: + type: Dict[str, SymInt] + sym_bool_values: + type: Dict[str, SymBool] + is_single_tensor_return: + type: bool + default: 'False' + custom_obj_values: + type: Dict[str, CustomObjArgument] + default: '{}' +GraphArgument: + kind: struct + fields: + name: + type: str + graph: + type: Graph +GraphModule: + kind: struct + fields: + graph: + type: Graph + signature: + type: GraphSignature + module_call_graph: + type: List[ModuleCallEntry] + metadata: + type: Dict[str, str] + default: '{}' +GraphSignature: + kind: struct + fields: + input_specs: + type: List[InputSpec] + output_specs: + type: List[OutputSpec] +InputSpec: + kind: union + fields: + user_input: + type: UserInputSpec + parameter: + type: InputToParameterSpec + buffer: + type: InputToBufferSpec + tensor_constant: + type: InputToTensorConstantSpec + custom_obj: + type: InputToCustomObjSpec + token: + type: InputTokenSpec + constant_input: + type: ConstantInputSpec +InputToBufferSpec: + kind: struct + fields: + arg: + type: TensorArgument + buffer_name: + type: str + persistent: + type: bool +InputToCustomObjSpec: + kind: struct + fields: + arg: + type: CustomObjArgument + custom_obj_name: + type: str +InputToParameterSpec: + kind: struct + fields: + arg: + type: TensorArgument + parameter_name: + type: str +InputToTensorConstantSpec: + kind: struct + fields: + arg: + type: TensorArgument + tensor_constant_name: + type: str +InputTokenSpec: + kind: struct + fields: + arg: + type: TokenArgument +Layout: + kind: enum + fields: + Unknown: 0 + SparseCoo: 1 + SparseCsr: 2 + SparseCsc: 3 + SparseBsr: 4 + SparseBsc: 5 + _mkldnn: 6 + Strided: 7 +LossOutputSpec: + kind: struct + fields: + arg: + type: TensorArgument +MemoryFormat: + kind: enum + fields: + Unknown: 0 + ContiguousFormat: 1 + ChannelsLast: 2 + ChannelsLast3d: 3 + PreserveFormat: 4 +ModuleCallEntry: + kind: struct + fields: + fqn: + type: str + signature: + type: Optional[ModuleCallSignature] + default: None +ModuleCallSignature: + kind: struct + fields: + inputs: + type: List[Argument] + outputs: + type: List[Argument] + in_spec: + type: str + out_spec: + type: str +NamedArgument: + kind: struct + fields: + name: + type: str + arg: + type: Argument +Node: + kind: struct + fields: + target: + type: str + inputs: + type: List[NamedArgument] + outputs: + type: List[Argument] + metadata: + type: Dict[str, str] +OptionalTensorArgument: + kind: union + fields: + as_tensor: + type: TensorArgument + as_none: + type: Tuple[()] +OutputSpec: + kind: union + fields: + user_output: + type: UserOutputSpec + loss_output: + type: LossOutputSpec + buffer_mutation: + type: BufferMutationSpec + gradient_to_parameter: + type: GradientToParameterSpec + gradient_to_user_input: + type: GradientToUserInputSpec + user_input_mutation: + type: UserInputMutationSpec + token: + type: OutputTokenSpec +OutputTokenSpec: + kind: struct + fields: + arg: + type: TokenArgument +RangeConstraint: + kind: struct + fields: + min_val: + type: int + max_val: + type: int +ScalarType: + kind: enum + fields: + UNKNOWN: 0 + BYTE: 1 + CHAR: 2 + SHORT: 3 + INT: 4 + LONG: 5 + HALF: 6 + FLOAT: 7 + DOUBLE: 8 + COMPLEXHALF: 9 + COMPLEXFLOAT: 10 + COMPLEXDOUBLE: 11 + BOOL: 12 + BFLOAT16: 13 +SchemaVersion: + kind: struct + fields: + major: + type: int + minor: + type: int +SymBool: + kind: union + fields: + as_expr: + type: SymExpr + as_bool: + type: bool +SymBoolArgument: + kind: union + fields: + as_name: + type: str + as_bool: + type: bool +SymExpr: + kind: struct + fields: + expr_str: + type: str + hint: + type: Optional[SymExprHint] + default: None +SymExprHint: + kind: union + fields: + as_int: + type: int + as_float: + type: float + as_bool: + type: bool +SymInt: + kind: union + fields: + as_expr: + type: SymExpr + as_int: + type: int +SymIntArgument: + kind: union + fields: + as_name: + type: str + as_int: + type: int +TensorArgument: + kind: struct + fields: + name: + type: str +TensorMeta: + kind: struct + fields: + dtype: + type: ScalarType + sizes: + type: List[SymInt] + requires_grad: + type: bool + device: + type: Device + strides: + type: List[SymInt] + storage_offset: + type: SymInt + layout: + type: Layout +TokenArgument: + kind: struct + fields: + name: + type: str +UserInputMutationSpec: + kind: struct + fields: + arg: + type: TensorArgument + user_input_name: + type: str +UserInputSpec: + kind: struct + fields: + arg: + type: Argument +UserOutputSpec: + kind: struct + fields: + arg: + type: Argument +SCHEMA_VERSION: +- 7 +- 3 +TREESPEC_VERSION: 1 diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/serde/schema_check.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/serde/schema_check.py new file mode 100644 index 0000000000000000000000000000000000000000..b22b9778819e73635aa6d37a254aa4b643abd5f5 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/serde/schema_check.py @@ -0,0 +1,286 @@ +# mypy: allow-untyped-defs +import dataclasses +import hashlib +import re +import typing +from enum import IntEnum +from typing import Any, Dict, Optional, Union + +from torch._export.serde import schema +from torch._export.serde.union import _Union + + +class SchemaUpdateError(Exception): + pass + + +def _check(x, msg): + if not x: + raise SchemaUpdateError(msg) + + +def _staged_schema(): + ret: Dict[str, Any] = {} + defs = {} + + def _handle_aggregate(ty): + def dump_type(t): + if isinstance(t, type): + return t.__name__ + elif isinstance(t, str): + assert t in defs + return t + elif o := typing.get_origin(t): + # Lemme know if there's a better way to do this. + if o == list: + head = "List" + elif o == dict: + head = "Dict" + elif o == tuple: + if typing.get_args(t) == (): + return "Tuple[()]" + head = "Tuple" + elif o == Union: + args = typing.get_args(t) + assert len(args) == 2 and args[1] == type(None) + return f"Optional[{dump_type(args[0])}]" + else: + raise AssertionError(f"Type {t} is not supported in export schema.") + return ( + f"{head}[{', '.join([dump_type(x) for x in typing.get_args(t)])}]" + ) + elif t == (): + return "()" + else: + raise AssertionError(f"Type {t} is not supported in export schema.") + + def dump_field(f): + t = dump_type(f.type) + ret = {"type": t} + + value = dataclasses.MISSING + if f.default is not dataclasses.MISSING: + value = f.default + elif f.default_factory is not dataclasses.MISSING: + value = f.default_factory() + + if t.startswith("Optional[") and value is not None: + raise AssertionError( + f"Optional field {ty.__name__}.{f.name} must have default value to be None." + ) + + if value is not dataclasses.MISSING: + default = str(value) + ret["default"] = default + return ret + + return {f.name: dump_field(f) for f in dataclasses.fields(ty)} + + def _handle_int_enum(name, ty): + ret[name] = {"kind": "enum", "fields": {x.name: x.value for x in ty}} + + def _handle_struct(name, ty): + ret[name] = {"kind": "struct", "fields": _handle_aggregate(ty)} + + def _handle_union(name, ty): + ret[name] = {"kind": "union", "fields": _handle_aggregate(ty)} + + for name in dir(schema): + if name.startswith("_"): + continue + + value = getattr(schema, name) + + if hasattr(value, "__module__") and value.__module__ != schema.__name__: + continue + + defs[name] = value + + for name, value in defs.items(): + if isinstance(value, type): + if issubclass(value, IntEnum): + _handle_int_enum(name, value) + elif dataclasses.is_dataclass(value): + if issubclass(value, _Union): + _handle_union(name, value) + else: + _handle_struct(name, value) + else: + raise AssertionError(f"Unknown schema type {name}: {value}") + elif isinstance(value, (int, tuple)): + assert name in ("SCHEMA_VERSION", "TREESPEC_VERSION") + else: + raise AssertionError(f"Unknown variable {name}: {value}") + + ret["SCHEMA_VERSION"] = list(defs["SCHEMA_VERSION"]) + assert all(x > 0 for x in ret["SCHEMA_VERSION"]) + ret["TREESPEC_VERSION"] = defs["TREESPEC_VERSION"] + assert ret["TREESPEC_VERSION"] > 0 + return ret + + +def _diff_schema(dst, src): + additions = {key: src[key] for key in src.keys() - dst.keys()} + subtractions = {key: dst[key] for key in dst.keys() - src.keys()} + + common_keys = src.keys() & dst.keys() + + versions = {"SCHEMA_VERSION", "TREESPEC_VERSION"} + common_keys -= versions + + for key in common_keys: + src_kind = src[key]["kind"] + src_fields = src[key]["fields"] + dst_kind = dst[key]["kind"] + dst_fields = dst[key]["fields"] + _check( + src_kind == dst_kind, + f"Type {key} changed kind from {dst_kind} to {src_kind}", + ) + assert isinstance(src_fields, dict) and isinstance(dst_fields, dict) + added_fields = { + key: src_fields[key] for key in src_fields.keys() - dst_fields.keys() + } + subtracted_fields = { + key: dst_fields[key] for key in dst_fields.keys() - src_fields.keys() + } + common_fields = src_fields.keys() & dst_fields.keys() + + for field in common_fields: + src_field = src_fields[field] + dst_field = dst_fields[field] + if src_kind == "struct": + _check( + src_field["type"] == dst_field["type"], + f"Type of the field {key}.{field} changed from {dst_field['type']} to {src_field['type']}", + ) + if "default" in src_field and "default" not in dst_field: + added_fields[field] = {} + added_fields[field]["default"] = src_field["default"] + if "default" not in src_field and "default" in dst_field: + subtracted_fields[field] = {} + subtracted_fields[field]["default"] = dst_field["default"] + elif src_kind == "enum": + _check( + src_field == dst_field, + f"Value of the enum field {key}.{field} changed from {dst_field} to {src_field}", + ) + elif src_kind == "union": + _check( + src_field["type"] == dst_field["type"], + f"Type of the field {key}.{field} changed from {dst_field['type']} to {src_field['type']}", + ) + else: + raise AssertionError(f"Unknown kind {src_kind}: {key}") + if len(added_fields) > 0: + assert key not in additions + additions[key] = {} + additions[key]["fields"] = added_fields + if len(subtracted_fields) > 0: + assert key not in subtractions + subtractions[key] = {} + subtractions[key]["fields"] = subtracted_fields + + return additions, subtractions + + +def _hash_schema(s): + return hashlib.sha256(repr(s).encode("utf-8")).hexdigest() + + +@dataclasses.dataclass +class _Commit: + result: Dict[str, Any] + checksum_result: str + path: str + additions: Dict[str, Any] + subtractions: Dict[str, Any] + base: Dict[str, Any] + checksum_base: Optional[str] + + +def update_schema(): + import importlib.resources + + if importlib.resources.is_resource(__package__, "schema.yaml"): + content = importlib.resources.read_text(__package__, "schema.yaml") + match = re.search("checksum<<([A-Fa-f0-9]{64})>>", content) + _check(match is not None, "checksum not found in schema.yaml") + assert match is not None + checksum_base = match.group(1) + from yaml import load, Loader + + dst = load(content, Loader=Loader) + assert isinstance(dst, dict) + else: + checksum_base = None + dst = {"SCHEMA_VERSION": None, "TREESPEC_VERSION": None} + + src = _staged_schema() + additions, subtractions = _diff_schema(dst, src) + return _Commit( + result=src, + checksum_result=_hash_schema(src), + path=__package__.replace(".", "/") + "/schema.yaml", + additions=additions, + subtractions=subtractions, + base=dst, + checksum_base=checksum_base, + ) + + +def check(commit: _Commit, force_unsafe: bool = False): + next_version = None + reason = "" + # Step 1: Detect major schema updates. + if len(commit.additions) > 0: + for k, v in commit.additions.items(): + if k not in commit.base: + continue + kind = commit.result[k]["kind"] + fields = v["fields"] + for f, d in fields.items(): + if "default" not in d and kind == "struct": + reason += ( + f"Field {k}.{f} is added to schema.py without a default value as an incomparible change " + + "which requires major version bump.\n" + ) + next_version = [commit.base["SCHEMA_VERSION"][0] + 1, 1] + + if len(commit.subtractions) > 0: + for k, v in commit.subtractions.items(): + if k not in commit.result: + continue + for f in v["fields"]: + reason = f"Field {k}.{f} is removed from schema.py as an incompatible change which requires major version bump.\n" + next_version = [commit.base["SCHEMA_VERSION"][0] + 1, 1] + + if force_unsafe: + reason += "--force-unsafe is used." + next_version = commit.result["SCHEMA_VERSION"] + else: + # Step 2: Detect minor schema updates. + if next_version is None and len(commit.additions) > 0: + for k, v in commit.additions.items(): + for f in v["fields"]: + reason += ( + f"Field {k}.{f} is added to schema.py as an compatible change " + + "which still requires minor version bump.\n" + ) + next_version = [ + commit.base["SCHEMA_VERSION"][0], + commit.base["SCHEMA_VERSION"][1] + 1, + ] + if next_version is None and len(commit.subtractions) > 0: + for k, v in commit.subtractions.items(): + for f in v["fields"]: + reason += ( + f"Field {k}.{f} is removed from schema.py as an compatible change " + + "which still requires minor version bump.\n" + ) + next_version = [ + commit.base["SCHEMA_VERSION"][0], + commit.base["SCHEMA_VERSION"][1] + 1, + ] + + return next_version, reason diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/serde/serialize.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/serde/serialize.py new file mode 100644 index 0000000000000000000000000000000000000000..44153ccc78eb42155a155caf742da91ab4c93cc0 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/serde/serialize.py @@ -0,0 +1,2960 @@ +# mypy: allow-untyped-defs +import base64 +import copy +import copyreg +import dataclasses +import heapq +import inspect +import io +import json +import logging +import math +import operator +import re +import typing +import traceback + +from contextlib import contextmanager +from dataclasses import dataclass, field +from enum import Enum +from typing import ( + Any, + Callable, + cast, + Dict, + final, + Iterator, + List, + Optional, + Set, + Tuple, + Type, + Union, +) + +import sympy + +import torch +import torch.export.exported_program as ep +from torch._export.serde.schema import SchemaVersion +from torch._export.verifier import load_verifier +from torch._library.fake_class_registry import FakeScriptObject +from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode +from torch.fx.experimental import symbolic_shapes +from torch.utils import _pytree as pytree +from torch.utils._pytree import treespec_dumps, treespec_loads +from torch.utils._sympy.numbers import int_oo +from torch.utils._sympy.value_ranges import ValueRanges + +from .schema import ( # type: ignore[attr-defined] + Argument, + BufferMutationSpec, + ConstantInputSpec, + ConstantValue, + CustomObjArgument, + Device, + ExportedProgram, + GradientToParameterSpec, + GradientToUserInputSpec, + Graph, + GraphArgument, + GraphModule, + GraphSignature, + InputSpec, + InputToBufferSpec, + InputToCustomObjSpec, + InputTokenSpec, + InputToParameterSpec, + InputToTensorConstantSpec, + Layout, + LossOutputSpec, + MemoryFormat, + ModuleCallEntry, + ModuleCallSignature, + NamedArgument, + Node, + OptionalTensorArgument, + OutputSpec, + OutputTokenSpec, + RangeConstraint, + ScalarType, + SCHEMA_VERSION, + SymBool, + SymBoolArgument, + SymExpr, + SymExprHint, + SymInt, + SymIntArgument, + TensorArgument, + TensorMeta, + TokenArgument, + TREESPEC_VERSION, + UserInputMutationSpec, + UserInputSpec, + UserOutputSpec, +) +from .union import _Union +from ..utils import remove_proxy_from_state_dict + +__all__ = [ + "serialize", + "GraphModuleSerializer", + "ExportedProgramSerializer", + "GraphModuleDeserializer", + "ExportedProgramDeserializer", +] + +log = logging.getLogger(__name__) + + +class SerializeError(RuntimeError): + pass + + +def _reverse_map(d: Dict[Any, Enum]): + return {v.value: k for k, v in d.items()} + + +MetaType = Union[ + FakeTensor, int, torch.SymInt, bool, torch.SymBool, ep.CustomObjArgument +] + + +ST_DELIMITER = ";" + +_TORCH_TO_SERIALIZE_DTYPE = { + torch.uint8: ScalarType.BYTE, + torch.int8: ScalarType.CHAR, + torch.int16: ScalarType.SHORT, + torch.int32: ScalarType.INT, + torch.int64: ScalarType.LONG, + torch.float16: ScalarType.HALF, + torch.float32: ScalarType.FLOAT, + torch.float64: ScalarType.DOUBLE, + torch.complex32: ScalarType.COMPLEXHALF, + torch.complex64: ScalarType.COMPLEXFLOAT, + torch.complex128: ScalarType.COMPLEXDOUBLE, + torch.bool: ScalarType.BOOL, + torch.bfloat16: ScalarType.BFLOAT16, +} + + +_SERIALIZE_TO_TORCH_DTYPE = _reverse_map(_TORCH_TO_SERIALIZE_DTYPE) # type: ignore[arg-type] + + +_TORCH_TO_SERIALIZE_LAYOUT = { + torch.sparse_coo: Layout.SparseCoo, + torch.sparse_csr: Layout.SparseCsr, + torch.sparse_csc: Layout.SparseCsc, + torch.sparse_bsr: Layout.SparseBsr, + torch.sparse_bsc: Layout.SparseBsc, + torch._mkldnn: Layout._mkldnn, # type: ignore[attr-defined] + torch.strided: Layout.Strided, +} + + +_SERIALIZE_TO_TORCH_LAYOUT = _reverse_map(_TORCH_TO_SERIALIZE_LAYOUT) # type: ignore[arg-type] + + +_TORCH_TO_SERIALIZE_MEMORY_FORMAT = { + torch.contiguous_format: MemoryFormat.ContiguousFormat, + torch.channels_last: MemoryFormat.ChannelsLast, + torch.channels_last_3d: MemoryFormat.ChannelsLast3d, + torch.preserve_format: MemoryFormat.PreserveFormat, +} + + +_SERIALIZE_TO_TORCH_MEMORY_FORMAT = _reverse_map(_TORCH_TO_SERIALIZE_MEMORY_FORMAT) # type: ignore[arg-type] + + +_SYM_INT_OPS = { + operator.mul, + operator.add, + operator.sub, + operator.floordiv, + operator.mod, + operator.pow, + torch.sym_int, + torch.sym_float, + torch.sym_ite, + torch.sym_max, + torch.sym_min, + torch.sym_sqrt, +} + + +_SYM_BOOL_OPS = { + operator.eq, + operator.ne, + operator.le, + operator.ge, + operator.lt, + operator.gt, + torch.sym_not, +} + + +assert not any(isinstance(op, torch._ops.OpOverload) for op in _SYM_INT_OPS) +assert not any(isinstance(op, torch._ops.OpOverload) for op in _SYM_BOOL_OPS) + + +@dataclass +class SerializedArtifact: + exported_program: bytes + state_dict: bytes + constants: bytes + example_inputs: bytes + + +@dataclass +class _SerializedProgram: + exported_program: ExportedProgram + state_dict: bytes + constants: bytes + example_inputs: bytes + + +def deserialize_device(d: Device) -> torch.device: + if d.index is None: + return torch.device(type=d.type) # type: ignore[call-overload] + return torch.device(type=d.type, index=d.index) + + +def serialize_sym_int(s: Union[int, torch.SymInt]) -> SymInt: + if isinstance(s, (torch.SymInt, sympy.Symbol, int)): + if symbolic_shapes.is_concrete_int(s): + return SymInt.create(as_int=int(s)) + else: + assert isinstance(s, (torch.SymInt, sympy.Symbol)) + if s.node.hint is None: + return SymInt.create(as_expr=SymExpr(str(s))) + else: + return SymInt.create( + as_expr=SymExpr(str(s), hint=SymExprHint.create(as_int=s.node.hint)) + ) + else: + raise SerializeError( + f"SymInt should be either symbol or int, got `{s}` of type `{type(s)}`" + ) + + +def serialize_sym_bool(s: Union[bool, torch.SymBool]) -> SymBool: + if isinstance(s, (torch.SymBool, bool)): + if symbolic_shapes.is_concrete_bool(s): + return SymBool.create(as_bool=bool(s)) + else: + return SymBool.create(as_expr=SymExpr(expr_str=str(s))) + else: + raise SerializeError( + f"SymBool should be either symbol or bool, got `{s}` of type `{type(s)}`" + ) + + +def serialize_tensor_meta(t: torch.Tensor) -> TensorMeta: + """ + Extract a TensorMeta describing `t`. + """ + return TensorMeta( + dtype=_TORCH_TO_SERIALIZE_DTYPE[t.dtype], + sizes=[serialize_sym_int(s) for s in t.shape], + requires_grad=t.requires_grad, + device=Device(type=t.device.type, index=t.device.index), + strides=[serialize_sym_int(s) for s in t.stride()], + storage_offset=serialize_sym_int(0), # TODO needs to be fixed. + layout=_TORCH_TO_SERIALIZE_LAYOUT[t.layout], + ) + + +_CURRENT_DESERIALIZER: Optional["GraphModuleDeserializer"] = None + + +def _reduce_fake_tensor(fake_tensor: FakeTensor): + is_parameter = isinstance(fake_tensor, torch.nn.Parameter) + tensor_meta = serialize_tensor_meta(fake_tensor) + tensor_meta_bytes = json.dumps( + _dataclass_to_dict(tensor_meta), cls=EnumEncoder + ).encode("utf-8") + return _reconstruct_fake_tensor, (tensor_meta_bytes, is_parameter) + + +def _reconstruct_fake_tensor( + serialized_tensor_meta: bytes, is_parameter: bool +) -> FakeTensor: + # Deserialize the bytes into a TensorMeta + json_tensor_meta = json.loads(serialized_tensor_meta.decode("utf-8")) + tensor_meta = _dict_to_dataclass(TensorMeta, json_tensor_meta) + # Find the current fake mode + assert ( + _CURRENT_DESERIALIZER is not None + ), "Need access to current deserializer state" + fake_tensor = _CURRENT_DESERIALIZER.deserialize_tensor_meta(tensor_meta) + if is_parameter: + fake_tensor = torch.nn.Parameter(fake_tensor) # type: ignore[assignment] + return fake_tensor + + +def serialize_torch_artifact(artifact: Optional[Any]) -> bytes: + if artifact is None: + return b"" + + assert ( + FakeTensor not in copyreg.dispatch_table + ), "Refusing to stomp on existing FakeTensor reducer" + try: + copyreg.pickle(FakeTensor, _reduce_fake_tensor) + buffer = io.BytesIO() + # This is a workaround for backend's tensor deserialization problem: + # unpickleTensor() always create a tensor on the device where it was originally saved + # This behavior is bad for multi-gpu training, as we wish to directly load the tensor + # on the designated device. + # For now, we simply move the tensor to cpu before saving. + # TODO: this should be fixed by deserialization instead. + torch.save(artifact, buffer) + return buffer.getvalue() + finally: + del copyreg.dispatch_table[FakeTensor] + + +def deserialize_torch_artifact(serialized: Union[Dict[str, Any], Tuple[Any, ...], bytes]): + if isinstance(serialized, (dict, tuple)): + return serialized + if len(serialized) == 0: + return {} + buffer = io.BytesIO(serialized) + buffer.seek(0) + # weights_only=False as we want to load custom objects here (e.g. ScriptObject) + artifact = torch.load(buffer, weights_only=False) + assert isinstance(artifact, (tuple, dict)) + return artifact + + +def _sympy_int_to_int(val: sympy.Expr, adjust: str): + # Convert simple sympy Integers into concrete int + if val in (sympy.oo, int_oo): + return math.inf + if val in (-sympy.oo, -int_oo): + return -math.inf + if isinstance(val, sympy.Integer): + return int(val) + + # TODO: Remove this adjustment when Ed gets rid of fractional ranges + log.warning( + "Export constraints cannot be non-integer expressions. Found " + "type %s, and value %s. We will attempt to %s " + "this value.", type(val), val, adjust + ) + + if adjust == "floor": + return math.floor(val) + elif adjust == "ceil": + return math.ceil(val) + else: + raise RuntimeError(f"Got invalid adjustment {adjust}") + + +def _int_to_sympy_int(val) -> sympy.Expr: + # Convert concrete int into simple sympy Integers + if val == math.inf: + return int_oo + if val == -math.inf: + return -int_oo + return sympy.Integer(val) + + +def serialize_range_constraints( + range_constraints: Dict[sympy.Symbol, ValueRanges] +) -> Dict[str, RangeConstraint]: + return { + str(k): RangeConstraint( + _sympy_int_to_int(v.lower, "ceil"), # type: ignore[arg-type] + _sympy_int_to_int(v.upper, "floor"), # type: ignore[arg-type] + ) + for k, v in range_constraints.items() + } + + +def _get_schema_from_target(target): + if isinstance(target, torch._ops.OpOverload): + return target._schema + elif type(target) in _serialization_registry: + return _serialization_registry[type(target)].op_schema(target) + raise RuntimeError(f"Cannot find schema for {type(target)}") + + +def _is_single_tensor_return(target) -> bool: + schema = _get_schema_from_target(target) + returns = schema.returns + return len(returns) == 1 and isinstance(returns[0].real_type, torch.TensorType) + + +def _is_single_tensor_list_return(target: Any) -> bool: + schema = _get_schema_from_target(target) + returns = schema.returns + + if len(returns) != 1: + return False + return_type = returns[0].real_type + return isinstance(return_type, torch.ListType) and isinstance( + return_type.getElementType(), torch.TensorType + ) + + +@dataclass +class GraphState: + inputs: List[Argument] = field(default_factory=list) + outputs: List[Argument] = field(default_factory=list) + nodes: List[Node] = field(default_factory=list) + tensor_values: Dict[str, TensorMeta] = field(default_factory=dict) + sym_int_values: Dict[str, SymInt] = field(default_factory=dict) + sym_bool_values: Dict[str, SymBool] = field(default_factory=dict) + is_single_tensor_return: bool = False + custom_obj_values: Dict[str, CustomObjArgument] = field(default_factory=dict) + + +class Final(type): + def __new__(metacls, name, bases, classdict): + for b in bases: + if isinstance(b, Final): + raise TypeError(f"type '{b.__name__}' is not an acceptable base type") + return type.__new__(metacls, name, bases, dict(classdict)) + + +@final +class GraphModuleSerializer(metaclass=Final): + def __init__( + self, + graph_signature: ep.ExportGraphSignature, + module_call_graph: List[ep.ModuleCallEntry], + ): + self.graph_state = GraphState() + self.graph_signature = graph_signature + self.module_call_graph = module_call_graph + self.custom_objs: Dict[str, torch._C.ScriptObject] = {} + self.duplicate_getitem_nodes: Dict[str, str] = {} + + @contextmanager + def save_graph_state(self): + saved = self.graph_state + self.graph_state = GraphState() + try: + yield + finally: + self.graph_state = saved + + def handle_placeholder(self, node: torch.fx.Node): + assert node.op == "placeholder" + if isinstance(node.meta["val"], torch.Tensor): + graph_input = Argument.create(as_tensor=TensorArgument(name=node.name)) + self.graph_state.tensor_values[node.name] = serialize_tensor_meta( + node.meta["val"] + ) + elif isinstance(node.meta["val"], torch.SymInt): + raise AssertionError("SymInt graph input is not implemented yet.") + elif isinstance(node.meta["val"], (int, bool, str, float, type(None))): + graph_input = self.serialize_input(node.meta["val"]) + elif isinstance(node.meta["val"], ep.CustomObjArgument): + class_fqn = node.meta["val"].class_fqn + graph_input = Argument.create( + as_custom_obj=CustomObjArgument(name=node.name, class_fqn=class_fqn) + ) + self.graph_state.custom_obj_values[node.name] = ( + self.serialize_script_obj_meta(node.meta["val"]) + ) + else: + raise AssertionError(f"Unimplemented graph input type: {node.meta['val']}") + self.graph_state.inputs.append(graph_input) + + def handle_output(self, node: torch.fx.Node): + assert node.op == "output" + assert len(node.args) == 1, "FX.Node's args should have one arg" + node_args = node.args[0] + if isinstance(node_args, torch.fx.Node): + # For singleton tensor returns + self.graph_state.is_single_tensor_return = True + self.graph_state.outputs = [self.serialize_input(node_args)] + else: + assert isinstance(node_args, (tuple, list)) + self.graph_state.outputs = [self.serialize_input(arg) for arg in node_args] + + def serialize_operator(self, target) -> str: + if isinstance(target, str): + return target + elif target.__module__.startswith("torch._ops"): + # TODO(zhxchen17) Maybe provide a function name helper in FX. + # From torch.fx.node._get_qualified_name + module = target.__module__.replace("torch._ops", "torch.ops") + return f"{module}.{target.__name__}" + else: # TODO(zhxchen17) Don't catch all here. + return f"{target.__module__}.{target.__name__}" + + def handle_call_function(self, node: torch.fx.Node): + assert node.op == "call_function" + + # getitem has been handled in the producer node, skip it here + if node.target is operator.getitem: + return + + meta_val = node.meta.get("val") + if ( + node.target in _SYM_INT_OPS + or node.target in _SYM_BOOL_OPS + or (meta_val is not None and isinstance(meta_val, (torch.SymInt, torch.SymBool))) + ): + assert len(node.kwargs) == 0 + ex_node = Node( + target=self.serialize_operator(node.target), + inputs=self.serialize_sym_op_inputs(node.target, node.args), + outputs=[ + Argument.create( + as_sym_int=self.serialize_sym_int_output(node.name, meta_val) + ) + if (node.target in _SYM_INT_OPS or isinstance(meta_val, torch.SymInt)) + else Argument.create( + as_sym_bool=self.serialize_sym_bool_output(node.name, meta_val) + ) + ], + metadata=self.serialize_metadata(node), + ) + elif isinstance(node.target, torch._ops.OpOverload): + ex_node = Node( + target=self.serialize_operator(node.target), + inputs=self.serialize_inputs(node.target, node.args, node.kwargs), + outputs=self.serialize_outputs(node), + # TODO: create a new tensor_values here, meta might have faketensor info + metadata=self.serialize_metadata(node), + ) + elif isinstance(node.target, torch._ops.HigherOrderOperator): + ex_node = Node( + target=self.serialize_operator(node.target), + inputs=self.serialize_hoo_inputs(node.args, node.kwargs), + outputs=self.serialize_hoo_outputs(node), + metadata=self.serialize_metadata(node), + ) + elif type(node.target) in _serialization_registry: + # Sanity check for unhandled serialization. + assert type(node.target) in _serialization_registry, f"{type(node.target)} is not supported in export serialization." + + handler = _serialization_registry[type(node.target)] + namespace = handler.namespace() + op_name = handler.to_op_name(node.target) + assert isinstance(namespace, str) and isinstance(op_name, str) + assert ":" not in namespace and ":" not in op_name + ex_node = Node( + target=f"#{namespace}:{op_name}", + inputs=self.serialize_inputs(node.target, node.args, node.kwargs), + outputs=self.serialize_outputs(node), + metadata=self.serialize_metadata(node), + ) + else: + raise SerializeError(f"Serializing {node.target} is not supported") + + self.graph_state.nodes.append(ex_node) + + def handle_get_attr(self, node): + pass + + def _output_node_at_index(self, node, index) -> Optional[torch.fx.Node]: + user_node = None + for user in node.users: + assert user.target is operator.getitem, f"{user} is not a getitem node" + if index == user.args[1]: + if user_node is None: + user_node = user + else: + # We want to deduplicate getitem nodes that are trying to + # index to the same index + self.duplicate_getitem_nodes[user.name] = user_node.name + return user_node + + def _output_node_name_at_index(self, node, index) -> str: + user_node = self._output_node_at_index(node, index) + if user_node is None: + return f"{node.name}_unused_{index}" + else: + return user_node.name + + def serialize_metadata(self, node: torch.fx.Node) -> Dict[str, str]: + ret = {} + if stack_trace := node.meta.get("stack_trace"): + ret["stack_trace"] = stack_trace + + if nn_module_stack := node.meta.get("nn_module_stack"): + + def export_nn_module_stack(val): + assert isinstance(val, tuple) and len(val) == 2 + path, ty = val + + assert isinstance(path, str) + assert isinstance(ty, str) + + return path + "," + ty + + # Serialize to "key,orig_path,type_str" + nn_module_list = [ + f"{k},{export_nn_module_stack(v)}" for k, v in nn_module_stack.items() + ] + ret["nn_module_stack"] = ST_DELIMITER.join(nn_module_list) + + if source_fn_st := node.meta.get("source_fn_stack"): + source_fn_list = [ + f"{source_fn[0]},{self.serialize_operator(source_fn[1])}" + for source_fn in source_fn_st + ] + ret["source_fn_stack"] = ST_DELIMITER.join(source_fn_list) + + if torch_fn := node.meta.get("torch_fn"): + ret["torch_fn"] = ST_DELIMITER.join(list(torch_fn)) + + if custom := node.meta.get("custom"): + try: + ret["custom"] = json.dumps(custom) + except Exception as e: + raise SerializeError( + f"Failed to serialize custom metadata for node {node.name} with error {e}" + ) from e + + return ret + + def serialize_script_obj_meta( + self, script_obj_meta: ep.CustomObjArgument + ) -> CustomObjArgument: + return CustomObjArgument( + name=script_obj_meta.name, + class_fqn=script_obj_meta.class_fqn, + ) + + def serialize_sym_op_inputs(self, op, args) -> List[NamedArgument]: + if isinstance(op, torch._ops.OpOverload): + args_names = [arg.name for arg in op._schema.arguments] + else: + assert op in _SYM_INT_OPS or op in _SYM_BOOL_OPS + args_names = list(inspect.signature(op).parameters.keys()) + serialized_args = [] + for args_name, arg in zip(args_names, args): + serialized_args.append( + NamedArgument(name=args_name, arg=self.serialize_input(arg)) + ) + return serialized_args + + def serialize_inputs( + self, + target: Any, # torch._ops.OpOverload and other custom operator types. + args, + kwargs=None + ) -> List[NamedArgument]: + assert isinstance(target, (torch._ops.OpOverload, *_registered_extension_types())) + kwargs = kwargs or {} + serialized_args = [] + + schema = _get_schema_from_target(target) + + for i, schema_arg in enumerate(schema.arguments): + if schema_arg.name in kwargs: + serialized_args.append( + NamedArgument( + name=schema_arg.name, + arg=self.serialize_input(kwargs[schema_arg.name], schema_arg.type), + ) + ) + elif not schema_arg.kwarg_only and i < len(args): + serialized_args.append( + NamedArgument( + name=schema_arg.name, + arg=self.serialize_input(args[i], schema_arg.type), + ) + ) + else: + # We intentionally don't serialize the missing arguments + # with default values + pass + + return serialized_args + + def serialize_hoo_inputs(self, args, kwargs) -> List[NamedArgument]: + """ + For serializing HOO inputs since HOOs do not have a schema. + """ + inputs = [ + NamedArgument( + name="", + arg=self.serialize_input(a), + ) + for a in args + ] + inputs.extend( + [ + NamedArgument(name=name, arg=self.serialize_input(a)) + for name, a in kwargs.items() + ] + ) + return inputs + + def is_sym_int_arg(self, arg) -> bool: + return isinstance(arg, int) or ( + isinstance(arg, torch.fx.Node) + and arg.name in self.graph_state.sym_int_values + ) + + def is_sym_bool_arg(self, arg) -> bool: + return isinstance(arg, bool) or ( + isinstance(arg, torch.fx.Node) + and arg.name in self.graph_state.sym_bool_values + ) + + def serialize_input( + self, arg, arg_type: Optional[torch._C.Argument] = None + ) -> Argument: + import torch._inductor.ir as inductor_ir + + inductor_tensor_buffers = ( + inductor_ir.Buffer, + inductor_ir.ReinterpretView, + ) + + if isinstance(arg, torch.fx.Node): + if arg.op == "get_attr": + assert isinstance(arg.target, str) + attr = getattr(arg.graph.owning_module, arg.target) + + if isinstance(attr, torch.Tensor): + raise SerializeError( + "getattr nodes containing tensors should not appear in the graph" + ) + elif isinstance(attr, torch.fx.GraphModule): + with self.save_graph_state(): + graph = self.serialize_graph(attr) + return Argument.create( + as_graph=GraphArgument(name=arg.target, graph=graph) + ) + else: + raise SerializeError( + f"Unsupported getattr attribute {arg.target} with type: {type(attr)}" + ) + elif self.is_sym_int_arg(arg): + return Argument.create( + as_sym_int=SymIntArgument.create(as_name=arg.name) + ) + elif self.is_sym_bool_arg(arg): + return Argument.create( + as_sym_bool=SymBoolArgument.create(as_name=arg.name) + ) + elif isinstance(arg.meta["val"], ep.CustomObjArgument): + return Argument.create( + as_custom_obj=CustomObjArgument( + name=arg.name, class_fqn=arg.meta["val"].class_fqn + ) + ) + elif arg.name in self.duplicate_getitem_nodes: + dedup_name = self.duplicate_getitem_nodes[arg.name] + return Argument.create(as_tensor=TensorArgument(name=dedup_name)) + else: + return Argument.create(as_tensor=TensorArgument(name=arg.name)) + elif isinstance(arg, inductor_tensor_buffers): + # Other branches are for arguments in fx node. + # This is a special branch for handling buffers (representing tensor arguments) + # for inductor's ExternalFallbackNode + # export_extern_kernel_node() is using this function to serialize arguments + arg_name = arg.get_name() + assert arg_name is not None, "Buffer must have valid name" + return Argument.create(as_tensor=TensorArgument(name=arg_name)) + elif isinstance(arg, torch.SymInt): + # This is a special branch for handling SymInt args in inductor's + # ExternalFallbackNode. + # For regular FX graph, SymInt arg should be a fx.Node with + # self.is_sym_int_arg(arg) being true + return Argument.create(as_sym_int=SymIntArgument.create(as_name=str(arg))) + elif isinstance(arg, bool): + return Argument.create(as_bool=arg) + elif isinstance(arg, str): + return Argument.create(as_string=arg) + elif isinstance(arg, int): + return Argument.create(as_int=arg) + elif isinstance(arg, float): + return Argument.create(as_float=arg) + elif arg is None: + return Argument.create(as_none=()) + elif isinstance(arg, (list, tuple)): + if len(arg) == 0: + if arg_type is not None: + if isinstance(arg_type, torch.OptionalType): + arg_type = arg_type.getElementType() # type: ignore[assignment] + assert isinstance(arg_type, torch.ListType) + elem_type = arg_type.getElementType() + if isinstance(elem_type, torch.OptionalType): + elem_type = elem_type.getElementType() + + if isinstance(elem_type, torch.BoolType): + return Argument.create(as_bools=[]) + elif isinstance(elem_type, torch.IntType): + return Argument.create(as_ints=[]) + elif isinstance(elem_type, torch.FloatType): + return Argument.create(as_floats=[]) + elif isinstance(elem_type, torch.StringType): + return Argument.create(as_strings=[]) + elif isinstance(elem_type, torch.TensorType): + return Argument.create(as_tensors=[]) + else: + # I believe empty symint lists default to ints, but + # please file an issue if this is not the case + raise SerializeError(f"Empty list with type {elem_type} nyi.") + else: + # We could serialize this by default to a tensor list. This + # is needed in the HOO case + log.warning( + "Unsure how to serialize the given empty list, " + "as we don't know what is the type of this argument. " + "Serializing it as a tensor list by default." + ) + return Argument.create(as_tensors=[]) + + # Must check bool first, as bool is also treated as int + if all(isinstance(a, bool) for a in arg): + return Argument.create(as_bools=list(arg)) + elif all(isinstance(a, int) for a in arg): + return Argument.create(as_ints=list(arg)) + elif all(isinstance(a, float) for a in arg): + return Argument.create(as_floats=list(arg)) + elif all(isinstance(a, str) for a in arg): + return Argument.create(as_strings=list(arg)) + elif all(isinstance(a, torch.SymInt) for a in arg): + # This is a special branch for handling SymInt args in inductor's + # ExternalFallbackNode. + # For regular FX graph, SymInt arg should be a fx.Node with + # self.is_sym_int_arg(arg) being true + return Argument.create( + as_sym_ints=[SymIntArgument.create(as_name=str(a)) for a in arg] + ) + elif all(self.is_sym_int_arg(a) for a in arg): + # list of sym_ints + values = [] + for a in arg: + if isinstance(a, torch.fx.Node): + values.append(SymIntArgument.create(as_name=a.name)) + elif isinstance(a, int): + values.append(SymIntArgument.create(as_int=a)) + return Argument.create(as_sym_ints=values) + elif all(self.is_sym_bool_arg(a) for a in arg): + # list of sym_bools + values = [] + for a in arg: + if isinstance(a, torch.fx.Node): + values.append(SymBoolArgument.create(as_name=a.name)) + elif isinstance(a, bool): + values.append(SymBoolArgument.create(as_bool=a)) + return Argument.create(as_sym_bools=values) + elif all(isinstance(a, torch.fx.Node) for a in arg): + # list of tensors + arguments = [] + for a in arg: + if a.op == "get_attr": + raise SerializeError( + "getattr nodes containing tensors should not appear in the graph" + ) + arguments.append(TensorArgument(name=a.name)) + return Argument.create(as_tensors=arguments) + elif all(isinstance(a, (torch.fx.Node, type(None))) for a in arg): + # list of optional tensors + def serialize_optional_tensor_args(a): + if a is None: + return OptionalTensorArgument.create(as_none=()) + elif isinstance(a, torch.fx.Node): + return OptionalTensorArgument.create( + as_tensor=TensorArgument(name=a.name) + ) + else: + raise SerializeError(f"Unsupported list/tuple argument: {a}") + + return Argument.create( + as_optional_tensors=list(map(serialize_optional_tensor_args, arg)) + ) + elif all(isinstance(a, inductor_tensor_buffers) for a in arg): + # list of inductor buffers + return Argument.create( + as_tensors=[TensorArgument(name=a.get_name()) for a in arg], + ) + elif all( + isinstance(a, (*inductor_tensor_buffers, type(None))) for a in arg + ): + # list of inductor buffers as optional tensors + def serialize_optional_tensor_args(a): + if a is None: + return OptionalTensorArgument.create(as_none=()) + elif isinstance(a, inductor_tensor_buffers): + return OptionalTensorArgument.create( + as_tensor=TensorArgument(name=a.get_name()) + ) + else: + raise SerializeError(f"Unsupported list/tuple argument: {a}") + + return Argument.create( + as_optional_tensors=list(map(serialize_optional_tensor_args, arg)) + ) + else: + raise SerializeError( + f"Unsupported list/tuple argument type: {[type(a) for a in arg]}" + ) + elif isinstance(arg, torch.dtype): + return Argument.create(as_scalar_type=_TORCH_TO_SERIALIZE_DTYPE[arg]) + elif isinstance(arg, torch.device): + return Argument.create(as_device=Device(type=arg.type, index=arg.index)) + elif isinstance(arg, torch.memory_format): + return Argument.create( + as_memory_format=_TORCH_TO_SERIALIZE_MEMORY_FORMAT[arg] + ) + elif isinstance(arg, torch.layout): + return Argument.create(as_layout=_TORCH_TO_SERIALIZE_LAYOUT[arg]) + elif isinstance(arg, torch._C.ScriptObject): + if not ( + arg._has_method("__getstate__") # type: ignore[attr-defined] + and arg._has_method("__setstate__") # type: ignore[attr-defined] + ): + raise SerializeError( + f"Unable to serialize custom class {arg}. Please define " + "serialization methods via def_pickle()." + ) + # Custom objects through torchind are serializable with pickle, + # through implementing the .def_pickle function. This should result + # in the object containing a __getstate__ and __setstate__ + # serialize/deserialize function. + custom_obj_name = f"_custom_obj_{len(self.custom_objs)}" + self.custom_objs[custom_obj_name] = arg + class_fqn = arg._type().qualified_name() # type: ignore[attr-defined] + return Argument.create( + as_custom_obj=CustomObjArgument(custom_obj_name, class_fqn) + ) + elif isinstance(arg, (torch._ops.OpOverload, torch._ops.HigherOrderOperator)): + return Argument.create(as_operator=self.serialize_operator(arg)) + else: + raise SerializeError(f"Unsupported argument type: {type(arg)} with schema arg_type {arg_type}") + + def serialize_tensor_output(self, name, meta_val) -> TensorArgument: + assert name not in self.graph_state.tensor_values + self.graph_state.tensor_values[name] = serialize_tensor_meta(meta_val) + return TensorArgument(name=name) + + def serialize_sym_int_output(self, name, meta_val) -> SymIntArgument: + assert name not in self.graph_state.sym_int_values + self.graph_state.sym_int_values[name] = serialize_sym_int(meta_val) + return SymIntArgument.create(as_name=name) + + def serialize_sym_bool_output(self, name, meta_val) -> SymIntArgument: + assert name not in self.graph_state.sym_bool_values + self.graph_state.sym_bool_values[name] = serialize_sym_bool(meta_val) + return SymBoolArgument.create(as_name=name) + + def serialize_input_spec(self, spec: ep.InputSpec) -> InputSpec: + if spec.kind == ep.InputKind.USER_INPUT: + if isinstance(spec.arg, ep.ConstantArgument): + if isinstance(spec.arg.value, int): + constant_spec = ConstantValue.create(as_int=spec.arg.value) + elif isinstance(spec.arg.value, bool): + constant_spec = ConstantValue.create(as_bool=spec.arg.value) + elif isinstance(spec.arg.value, str): + constant_spec = ConstantValue.create(as_string=spec.arg.value) + elif isinstance(spec.arg.value, float): + constant_spec = ConstantValue.create(as_float=spec.arg.value) + elif spec.arg.value is None: + constant_spec = ConstantValue.create(as_none=()) + else: + raise SerializeError(f"Unhandled constant input {spec.arg.value} to serialize") + return InputSpec.create( + constant_input=ConstantInputSpec( + name=spec.arg.name, value=constant_spec + ) + ) + else: + return InputSpec.create( + user_input=UserInputSpec( + arg=self.serialize_argument_spec(spec.arg) + ) + ) + elif spec.kind == ep.InputKind.PARAMETER: + assert spec.target is not None + assert isinstance(spec.arg, ep.TensorArgument) + return InputSpec.create( + parameter=InputToParameterSpec( + arg=TensorArgument(name=spec.arg.name), + parameter_name=spec.target, + ) + ) + elif spec.kind == ep.InputKind.BUFFER: + assert spec.target is not None + assert isinstance(spec.arg, ep.TensorArgument) + assert spec.persistent is not None + return InputSpec.create( + buffer=InputToBufferSpec( + arg=TensorArgument(name=spec.arg.name), + buffer_name=spec.target, + persistent=spec.persistent, + ) + ) + elif spec.kind == ep.InputKind.CONSTANT_TENSOR: + assert spec.target is not None + assert isinstance(spec.arg, ep.TensorArgument) + return InputSpec.create( + tensor_constant=InputToTensorConstantSpec( + arg=TensorArgument(name=spec.arg.name), + tensor_constant_name=spec.target, + ) + ) + elif spec.kind == ep.InputKind.CUSTOM_OBJ: + assert spec.target is not None + assert isinstance(spec.arg, ep.CustomObjArgument) + return InputSpec.create( + custom_obj=InputToCustomObjSpec( + arg=CustomObjArgument( + name=spec.arg.name, class_fqn=spec.arg.class_fqn + ), + custom_obj_name=spec.target, + ) + ) + elif spec.kind == ep.InputKind.TOKEN: + assert isinstance(spec.arg, ep.TokenArgument) + return InputSpec.create( + token=InputTokenSpec( + arg=TokenArgument(name=spec.arg.name), + ) + ) + else: + raise AssertionError(f"Unknown argument kind: {spec}") + + def serialize_output_spec(self, spec: ep.OutputSpec) -> OutputSpec: + if spec.kind == ep.OutputKind.USER_OUTPUT: + return OutputSpec.create( + user_output=UserOutputSpec(arg=self.serialize_argument_spec(spec.arg)) + ) + elif spec.kind == ep.OutputKind.LOSS_OUTPUT: + assert isinstance(spec.arg, ep.TensorArgument) + return OutputSpec.create( + loss_output=LossOutputSpec(arg=TensorArgument(name=spec.arg.name)) + ) + elif spec.kind == ep.OutputKind.BUFFER_MUTATION: + assert spec.target is not None + assert isinstance(spec.arg, ep.TensorArgument) + return OutputSpec.create( + buffer_mutation=BufferMutationSpec( + arg=TensorArgument(name=spec.arg.name), + buffer_name=spec.target, + ) + ) + elif spec.kind == ep.OutputKind.GRADIENT_TO_PARAMETER: + assert spec.target is not None + assert isinstance(spec.arg, ep.TensorArgument) + return OutputSpec.create( + gradient_to_parameter=GradientToParameterSpec( + arg=TensorArgument(name=spec.arg.name), + parameter_name=spec.target, + ) + ) + elif spec.kind == ep.OutputKind.GRADIENT_TO_USER_INPUT: + assert spec.target is not None + assert isinstance(spec.arg, ep.TensorArgument) + return OutputSpec.create( + gradient_to_user_input=GradientToUserInputSpec( + arg=TensorArgument(name=spec.arg.name), + user_input_name=spec.target, + ) + ) + elif spec.kind == ep.OutputKind.USER_INPUT_MUTATION: + assert spec.target is not None + assert isinstance(spec.arg, ep.TensorArgument) + return OutputSpec.create( + user_input_mutation=UserInputMutationSpec( + arg=TensorArgument(name=spec.arg.name), + user_input_name=spec.target, + ) + ) + elif spec.kind == ep.OutputKind.TOKEN: + assert isinstance(spec.arg, ep.TokenArgument) + return OutputSpec.create( + token=OutputTokenSpec( + arg=TokenArgument(name=spec.arg.name), + ) + ) + else: + raise AssertionError(f"Unknown argument kind: {spec}") + + def serialize_signature(self, sig: ep.ExportGraphSignature) -> GraphSignature: + return GraphSignature( + input_specs=[self.serialize_input_spec(s) for s in sig.input_specs], + output_specs=[self.serialize_output_spec(s) for s in sig.output_specs], + ) + + def serialize_argument_spec(self, x: ep.ArgumentSpec) -> Argument: + if isinstance(x, ep.TensorArgument): + return Argument.create(as_tensor=TensorArgument(name=x.name)) + elif isinstance(x, ep.SymIntArgument): + return Argument.create(as_sym_int=SymIntArgument.create(as_name=x.name)) + elif isinstance(x, ep.ConstantArgument): + return self.serialize_input(x.value) + elif isinstance(x, ep.CustomObjArgument): + return Argument.create( + as_custom_obj=CustomObjArgument(name=x.name, class_fqn=x.class_fqn) + ) + else: + raise AssertionError("TODO") + + def serialize_module_call_signature( + self, module_call_signature: ep.ModuleCallSignature + ) -> ModuleCallSignature: + return ModuleCallSignature( + inputs=[ + self.serialize_argument_spec(x) for x in module_call_signature.inputs + ], + outputs=[ + self.serialize_argument_spec(x) for x in module_call_signature.outputs + ], + in_spec=treespec_dumps(module_call_signature.in_spec, TREESPEC_VERSION), + out_spec=treespec_dumps(module_call_signature.out_spec, TREESPEC_VERSION), + ) + + def serialize_module_call_graph( + self, module_call_graph: List[ep.ModuleCallEntry] + ) -> List[ModuleCallEntry]: + return [ + ModuleCallEntry( + fqn=entry.fqn, + signature=( + self.serialize_module_call_signature(entry.signature) + if entry.signature + else None + ), + ) + for entry in module_call_graph + ] + + def serialize_outputs(self, node: torch.fx.Node) -> List[Argument]: + """For a given node, return the dataclass representing its output values. + + [NOTE: Multiple outputs] We handle aggregates differently than FX. For + FX, it looks like: + + x = call_function("multiple_return", ...) + element0 = call_function(getitem, x, 0) + foo = call_function("use_output", element0) + + We do not want the intermediate `getitem` call, so our serialized thing looks like: + + element0, element1, element2 = call_function("multiple_return", ...) + foo = call_function("use_output", element0) + + We want names to be consistent across these two schemes, so that we can + mostly reuse the names coming from FX. This function computes a mapping from + the FX representation to our representation, preserving the names. + """ + assert node.op == "call_function" and isinstance(node.target, (torch._ops.OpOverload, *_registered_extension_types())) + + schema = _get_schema_from_target(node.target) + returns = schema.returns + + if len(returns) == 0: + return [] + + meta_val = node.meta["val"] + + # Check single value return + if _is_single_tensor_list_return(node.target): + # e.g "-> Tensor[]" + tensor_args = [] + for idx, meta in enumerate(meta_val): + name = self._output_node_name_at_index(node, idx) + tensor_args.append(self.serialize_tensor_output(name, meta)) + return [Argument.create(as_tensors=tensor_args)] + elif len(returns) == 1: + return [self.serialize_output(node.name, meta_val)] + + # There are a two possibilities at this point: + # - This operator returns a tuple of Tensors, e.g. "-> (Tensor, Tensor)" + # - This operator returns a tuple of mixed of Tensor and Tensors, e.g. "-> (Tensor, Tensor[])" + # + # Either way, start by gathering a list of TensorArguments with the correct names. + # For consistent naming with FX, consult the downstream `getitem` node and + # make sure our outputs have the same name. + + output_arguments = [] + for idx, (meta, return_schema) in enumerate(zip(meta_val, returns)): + if meta is None: + assert isinstance( + return_schema.real_type, (torch.OptionalType, torch.TensorType) + ) + # When the return type is annoated as Tensor type, the op can also return an + # undefined Tensor which will be implicitly converted to None in Python. + output_arguments.append(Argument.create(as_none=())) + elif isinstance(meta, FakeTensor): + assert isinstance(return_schema.real_type, (torch.OptionalType, torch.TensorType)) + name = self._output_node_name_at_index(node, idx) + output_arguments.append(self.serialize_output(name, meta)) + elif isinstance(meta, list): + # for List[Tensor] return type + assert isinstance( + return_schema.real_type, torch.ListType + ) and isinstance( + return_schema.real_type.getElementType(), torch.TensorType + ) + user_node = self._output_node_at_index(node, idx) + assert user_node is not None + + args = [] + for i, m in enumerate(meta): + if m is None: + continue + sub_user_node_name = self._output_node_name_at_index(user_node, i) + args.append(self.serialize_tensor_output(sub_user_node_name, m)) + output_arguments.append(Argument.create(as_tensors=args)) + elif isinstance(meta, (int, SymInt)): + user_node_name = self._output_node_name_at_index(node, idx) + output_arguments.append(self.serialize_output(user_node_name, meta)) + else: + raise ValueError( + f"Unhandled output type {type(meta)} from node {node.format_node()}" + ) + + return output_arguments + + def serialize_hoo_outputs(self, node: torch.fx.Node) -> List[Argument]: + """ + For serializing HOO outputs since HOOs do not have a schema. + """ + meta_val = node.meta["val"] + + if isinstance(meta_val, tuple): + # Note: Since we don't have a schema, we just serialize all tuple + # outputs to be a list of values. Even if the output is supposed to + # be a tensor list (Tensor[]), we will serialize it to be a list of + # tensors (Tensor, Tensor, Tensor). An exception is that if there's + # a singleton tensor, we will serialize this to be a singleton + # tensor list so that the deserializer knows to insert getitem nodes. + + if len(meta_val) == 1: + assert isinstance(meta_val[0], torch.Tensor) + name = self._output_node_name_at_index(node, 0) + return [Argument.create(as_tensors=[self.serialize_tensor_output(name, meta_val[0])])] + + outputs = [] + for i, element_meta_val in enumerate(meta_val): + user_node = self._output_node_at_index(node, i) + if isinstance(element_meta_val, list): + # e.g "-> Tensor[]" + assert user_node is not None + + tensors = [] + for j, m in enumerate(element_meta_val): + if not isinstance(m, torch.Tensor): + raise SerializeError(f"Serialize list output with type {type(m)} nyi") + + name = self._output_node_name_at_index(user_node, j) + tensors.append(self.serialize_tensor_output(name, m)) + outputs.append(Argument.create(as_tensors=tensors)) + + else: + name = ( + user_node.name + if user_node is not None + else f"{node.name}_unused_{i}" + ) + + outputs.append(self.serialize_output(name, element_meta_val)) + + return outputs + else: + return [self.serialize_output(node.name, meta_val)] + + def serialize_output(self, name: str, meta_val: Any) -> Argument: + # Check single value return + if meta_val is None: + return Argument.create(as_none=()) + if isinstance(meta_val, torch.Tensor): + # e.g "-> Tensor" + return Argument.create( + as_tensor=self.serialize_tensor_output(name, meta_val) + ) + elif isinstance(meta_val, (int, torch.SymInt)): + # e.g "-> SymInt" + return Argument.create( + as_sym_int=self.serialize_sym_int_output(name, meta_val) + ) + elif isinstance(meta_val, torch.SymBool): + # e.g "-> SymBool" + return Argument.create( + as_sym_bool=self.serialize_sym_bool_output(name, meta_val) + ) + + # list outputs should've been handled earlier + raise SerializeError(f"Unable to serialize output {meta_val}") + + def _handle_getitem_users(self, node: torch.fx.Node) -> List[TensorArgument]: + meta_val = node.meta["val"] + + idx_to_name = {} + for user in node.users: + assert ( + user.target is operator.getitem + ), f"User node {user} of {node} is incorrect" + idx_to_name[user.args[1]] = user.name + + for idx, _ in enumerate(meta_val): + # FX does not emit a getitem node for any outputs that are unused. + # However, we need a name for them so that the number of outputs will + # correctly match the schema. Just assign a dummy name. + if idx not in idx_to_name: + idx_to_name[idx] = f"{node.name}_unused_{idx}" + + arg_list = [] + for i, element_meta_val in enumerate(meta_val): + arg_list.append( + self.serialize_tensor_output(idx_to_name[i], element_meta_val) + ) + + return arg_list + + def serialize_graph(self, graph_module: torch.fx.GraphModule) -> Graph: + assert isinstance(graph_module, torch.fx.GraphModule) + for node in graph_module.graph.nodes: + try: + getattr(self, f"handle_{node.op}")(node) + except Exception as e: + raise SerializeError( + f"Failed serializing node {node} in graph: {node.format_node()}\n Original exception {traceback.format_exc()}" + ) from e + + return Graph( + inputs=self.graph_state.inputs, + nodes=self.graph_state.nodes, + tensor_values=self.graph_state.tensor_values, + sym_int_values=self.graph_state.sym_int_values, + sym_bool_values=self.graph_state.sym_bool_values, + custom_obj_values=self.graph_state.custom_obj_values, + outputs=self.graph_state.outputs, + is_single_tensor_return=self.graph_state.is_single_tensor_return, + ) + + def serialize_graph_module_metadata(self, meta: Dict[str, Any]): + ret = {} + if custom := meta.get("custom"): + try: + ret["custom"] = json.dumps(custom) + except Exception as e: + raise SerializeError( + f"Failed to serialize custom metadata for graph with error {e}" + ) from e + + return ret + + def serialize(self, graph_module: torch.fx.GraphModule) -> GraphModule: + graph = self.serialize_graph(graph_module) + + return GraphModule( + graph=graph, + signature=self.serialize_signature(self.graph_signature), + module_call_graph=self.serialize_module_call_graph(self.module_call_graph), + metadata=self.serialize_graph_module_metadata(graph_module.meta) + ) + + +@final +class ExportedProgramSerializer(metaclass=Final): + def __init__(self, opset_version: Optional[Dict[str, int]] = None): + self.opset_version: Dict[str, int] = {} + if opset_version: + self.opset_version.update(opset_version) + if "aten" not in self.opset_version: + self.opset_version["aten"] = torch._C._get_max_operator_version() + + def serialize(self, exported_program: ep.ExportedProgram) -> _SerializedProgram: + """ + Args: + exported_program: Exported Program to serialize + """ + exported_program.validate() + + gm_serializer = GraphModuleSerializer( + exported_program.graph_signature, exported_program.module_call_graph + ) + serialized_graph_module = gm_serializer.serialize(exported_program.graph_module) + serialized_range_constraints = serialize_range_constraints( + exported_program.range_constraints + ) + + # TODO: Directly serialize exported_program.constants once + # CustomClassHolders get stored in the ExportedProgram rather than in + # the graph + constants = {} + for n, c in gm_serializer.custom_objs.items(): + constants[n] = c + for n, t in exported_program.constants.items(): + assert n not in constants + constants[n] = t + + serialized_ep = ExportedProgram( + graph_module=serialized_graph_module, + opset_version=self.opset_version, + range_constraints=serialized_range_constraints, + schema_version=SchemaVersion( + major=SCHEMA_VERSION[0], + minor=SCHEMA_VERSION[1], + ), + verifiers=[v.dialect for v in exported_program.verifiers], + torch_version=torch.__version__, + ) + + # Test canonical form is well defined. + canonicalize(serialized_ep) + + # Proxy cannot be dumped, so we remove them. + new_state_dict = remove_proxy_from_state_dict( + exported_program.state_dict, in_place=False + ) + return _SerializedProgram( + serialized_ep, + serialize_torch_artifact(new_state_dict), + serialize_torch_artifact(constants), + serialize_torch_artifact(exported_program.example_inputs), + ) + + +@final +class GraphModuleDeserializer(metaclass=Final): + @dataclasses.dataclass + class Result: + graph_module: torch.fx.GraphModule + signature: ep.ExportGraphSignature + module_call_graph: List[ep.ModuleCallEntry] + names_to_symbols: Dict[str, sympy.Symbol] + state_dict: Dict[str, Union[torch.Tensor, torch.nn.Parameter]] + constants: Dict[str, Union[torch.Tensor, FakeScriptObject, torch.ScriptObject]] + example_inputs: Optional[Tuple[Tuple[torch.Tensor, ...], Dict[str, Any]]] + + def __init__(self) -> None: + self.serialized_name_to_node: Dict[str, torch.fx.Node] = {} + self.serialized_name_to_meta: Dict[str, MetaType] = {} + self.graph = torch.fx.Graph() + self.module = torch.nn.Module() + + @contextmanager + def save_graph_module(self) -> Iterator[None]: + saved = ( + self.graph, + self.module, + self.serialized_name_to_node, + self.serialized_name_to_meta, + ) + self.graph = torch.fx.Graph() + self.module = torch.nn.Module() + self.serialized_name_to_node = {} + self.serialized_name_to_meta = {} + try: + yield + finally: + ( + self.graph, + self.module, + self.serialized_name_to_node, + self.serialized_name_to_meta, + ) = saved + + def deserialize_extension_operator(self, serialized_target: str): + namespace, op_name = serialized_target.split(":") + namespace = namespace[1:] # starting with # + handler = _deserialization_registry[namespace] + return handler.from_op_name(op_name) + + def deserialize_operator(self, serialized_target: str): + if serialized_target.startswith( + "_operator" + ): # TODO(zhxchen17) Follow up on this. + module = operator + serialized_target_names = serialized_target.split(".")[1:] + elif serialized_target.startswith("torch"): + module = torch # type: ignore[misc] + serialized_target_names = serialized_target.split(".")[1:] + elif serialized_target.startswith("#"): + return self.deserialize_extension_operator(serialized_target) + else: # TODO(zhxchen17) Don't catch all here. + return serialized_target + + target = module + for name in serialized_target_names: + if not hasattr(target, name): + return serialized_target + else: + target = getattr(target, name) + return target + + def deserialize_sym_int(self, s: SymInt) -> Union[int, torch.SymInt]: + val = s.value + if s.type == "as_expr": + if val.hint is None: + hint = None + else: + assert val.hint.type == "as_int" + hint = val.hint.value + + if val.expr_str in self.symbol_name_to_symbol: + sym = self.symbol_name_to_symbol[val.expr_str] + else: + sym = sympy.sympify( + val.expr_str, + locals={**self.sympy_functions, **self.symbol_name_to_symbol}, + ) + # NOTE(avik): Assumptions on symbols are not explicitly serialized. + # This seems dangerous: it might cause unknown differences in shape env behavior + # on deserialization? Probably deserves a follow-up. + + # Here we force symbols corresponding to SymInts to be at least integers. + # Otherwise some expressions that the shape env would otherwise evaluate to False, + # e.g., 2*s = 9, can have rational solutions, e.g., 9/2. + # TODO: This is HIGHLY SUSPICIOUS ezyang(May 2024) + sym = sym.subs( + {s: sympy.Symbol(s.name, integer=True) for s in sym.free_symbols} + ) + # We need to check if the symbol has already been allocated, + # self.symbol_name_to_symbol is not enough because the + # integer-ification of symbols can induce simplification; + # e.g., (2**s0 + 1) // 2 --> s0 when we know s0 is integral + if isinstance(sym, sympy.Symbol) and sym not in self.shape_env.var_to_val: + self.symbol_name_to_symbol[val.expr_str] = sym + if hint is not None: + self.shape_env.add_var_to_val(sym, hint) + + if vr := self.symbol_name_to_range.get(val.expr_str): + self.shape_env.constrain_symbol_range( + sym, + compiler_min=vr.lower, # type: ignore[arg-type] + compiler_max=vr.upper, # type: ignore[arg-type] + ) + else: + # Placeholders, in particular, can have shapes as symbolic expressions. + # We need to populate the shape env with the range constraints of their + # free symbols, otherwise evaluating such expressions will error. + self.symbol_name_to_symbol[val.expr_str] = sym + free_symbols = sym.free_symbols + for s in free_symbols: + if s.name not in self.symbol_name_to_symbol: + self.symbol_name_to_symbol[s.name] = s # type: ignore[assignment] + if vr := self.symbol_name_to_range.get(s.name): + self.shape_env.constrain_symbol_range( + s, + compiler_min=vr.lower, # type: ignore[arg-type] + compiler_max=vr.upper, # type: ignore[arg-type] + ) + + return self.shape_env.create_symintnode(sym, hint=hint) + elif s.type == "as_int": + assert isinstance(val, int) + return val + else: + raise SerializeError( + f"SymInt has invalid field type {s.type} with value {s.value}" + ) + + def deserialize_sym_bool(self, s: SymBool) -> Union[bool, torch.SymBool]: + val = s.value + if s.type == "as_expr": + # first we sympify this just to access any untracked symbols + expr = sympy.sympify(val.expr_str) + for sym in expr.free_symbols: + if ( + not isinstance(sym, sympy.Number) + and str(sym) not in self.symbol_name_to_symbol + ): + self.deserialize_sym_int(SymInt.create(as_expr=SymExpr(str(sym)))) + # then we sympify again using locals to correctly reify with the constructed symbols + expr = sympy.sympify(val.expr_str, locals=self.symbol_name_to_symbol) + return self.shape_env.create_symboolnode(expr) + elif s.type == "as_bool": + assert isinstance(val, bool) + return val + else: + raise SerializeError( + f"SymBool has invalid field type {s.type} with value {s.value}" + ) + + def deserialize_tensor_meta( + self, + tensor_meta: TensorMeta, + ) -> FakeTensor: + with self.fake_tensor_mode: + return cast( + FakeTensor, + torch.empty_strided( + tuple(self.deserialize_sym_int(val) for val in tensor_meta.sizes), # type: ignore[misc] + tuple(self.deserialize_sym_int(val) for val in tensor_meta.strides), # type: ignore[misc] + device=deserialize_device(tensor_meta.device), + dtype=_SERIALIZE_TO_TORCH_DTYPE[tensor_meta.dtype], + ), + ) + + def deserialize_script_obj_meta( + self, script_obj_meta: CustomObjArgument + ) -> ep.CustomObjArgument: + return ep.CustomObjArgument( + name=script_obj_meta.name, + class_fqn=script_obj_meta.class_fqn, + ) + + def deserialize_graph_output(self, output) -> Optional[Union[torch.fx.Node, int]]: + if output.type == "as_tensor": + return self.serialized_name_to_node[output.as_tensor.name] + elif output.type == "as_sym_int": + return self.serialized_name_to_node[output.as_sym_int.as_name] + elif output.type == "as_sym_bool": + return self.serialized_name_to_node[output.as_sym_bool.as_name] + elif output.type == "as_int": + return output.as_int + elif output.type == "as_none": + return None + else: + raise SerializeError(f"Unable to deserialize output node {output}") + + def deserialize_graph(self, serialized_graph: Graph) -> torch.fx.Graph: + # Handle the tensor metas. + for name, tensor_value in serialized_graph.tensor_values.items(): + meta_val = self.deserialize_tensor_meta(tensor_value) + self.serialized_name_to_meta[name] = meta_val + + for name, sym_int_value in serialized_graph.sym_int_values.items(): + self.serialized_name_to_meta[name] = self.deserialize_sym_int(sym_int_value) + + for name, sym_bool_value in serialized_graph.sym_bool_values.items(): + self.serialized_name_to_meta[name] = self.deserialize_sym_bool( + sym_bool_value + ) + + for name, script_obj_meta in serialized_graph.custom_obj_values.items(): + self.serialized_name_to_meta[name] = self.deserialize_script_obj_meta( + script_obj_meta + ) + + # Inputs: convert to placeholder nodes in FX. + for i, input_ in enumerate(serialized_graph.inputs): + if input_.type in ("as_tensor", "as_sym_int", "as_custom_obj"): + node_name = input_.value.name + placeholder_node = self.graph.placeholder(node_name) + # FX might declare a name illegal (e.g. some nn.Modules use "input" as forward() arguments) + # we will overwrite it + placeholder_node.name = node_name + self.sync_fx_node(node_name, placeholder_node) + elif input_.type in ( + "as_int", + "as_float", + "as_bool", + "as_none", + "as_string", + ): + node_name = self.signature.input_specs[i].arg.name + placeholder_node = self.graph.placeholder(node_name) + placeholder_node.meta["val"] = self.deserialize_input(input_) + else: + raise SerializeError(f"Invalid input type {input_}") + + # Nodes: convert to call_function nodes. + for serialized_node in serialized_graph.nodes: + try: + target = self.deserialize_operator(serialized_node.target) + self.deserialize_node(serialized_node, target) + + except Exception as e: + raise SerializeError( + f"Failed deserializing node {serialized_node}\n Original exception {traceback.format_exc()}" + ) from e + + # Outputs: convert to a single `output` node. + outputs = [] + for output in serialized_graph.outputs: + outputs.append(self.deserialize_graph_output(output)) + + if serialized_graph.is_single_tensor_return: + assert len(outputs) == 1 + outputs = outputs[0] # type: ignore[assignment] + else: + outputs = tuple(outputs) # type: ignore[assignment] + + output_node = self.graph.output(outputs) + + if serialized_graph.is_single_tensor_return: + output_node.meta["val"] = output_node.args[0].meta["val"] + else: + output_node.meta["val"] = tuple( + arg.meta["val"] if isinstance(arg, torch.fx.Node) else arg + for arg in output_node.args[0] + ) + + return self.graph + + def deserialize_node(self, serialized_node: Node, target: Callable) -> None: + if ( + target in _SYM_BOOL_OPS + or target in _SYM_INT_OPS + or target == torch.ops.aten.item.default # this can produce either SymInt or SymBool + ): + name = serialized_node.outputs[0].value.as_name + args = self.deserialize_sym_op_inputs(serialized_node.inputs) + + fx_node = self.graph.create_node("call_function", target, args, {}, name) + self.deserialize_sym_op_outputs(serialized_node, fx_node) + + elif isinstance(target, torch._ops.HigherOrderOperator): + args, kwargs = self.deserialize_hoo_inputs(serialized_node.inputs) + # If HOP returns a single tensor, name the + # newly-created node after it. This ensures that these tensor values + # have names that are consistent with serialized. + # + # HOPs don't have schema yet, just check the output lengths and as_tensor attribute + name = ( + serialized_node.outputs[0].as_tensor.name + if len(serialized_node.outputs) == 1 + and hasattr(serialized_node.outputs[0], "as_tensor") + else None + ) + fx_node = self.graph.create_node( + "call_function", target, args, kwargs, name + ) + self.deserialize_outputs(serialized_node, fx_node) + fx_node.meta.update(self.deserialize_metadata(serialized_node.metadata)) + + elif isinstance(target, (torch._ops.OpOverload, *_registered_extension_types())): + # For convenience: if this node returns a single tensor, name the + # newly-created node after it. This ensures that these tensor values + # have names that are consistent with serialized. + name = ( + serialized_node.outputs[0].as_tensor.name + if _is_single_tensor_return(target) + else None # FX will generate a name for us. + ) + args, kwargs = self.deserialize_inputs(target, serialized_node) + fx_node = self.graph.create_node( + "call_function", target, args, kwargs, name + ) + self.deserialize_outputs(serialized_node, fx_node) + else: + raise SerializeError( + f"Unsupported target type for node {serialized_node}: {type(target)}" + ) + + fx_node.meta.update(self.deserialize_metadata(serialized_node.metadata)) + if fx_node.op not in ["placeholder", "output"] and "nn_module_stack" not in fx_node.meta: + fx_node.meta["nn_module_stack"] = {} # serialization throws away empty dicts + + def deserialize_input_spec(self, i: InputSpec) -> ep.InputSpec: + if i.type == "user_input": + return ep.InputSpec( + kind=ep.InputKind.USER_INPUT, + arg=self.deserialize_argument_spec(i.user_input.arg), + target=None, + ) + elif i.type == "parameter": + return ep.InputSpec( + kind=ep.InputKind.PARAMETER, + arg=ep.TensorArgument(name=i.parameter.arg.name), + target=i.parameter.parameter_name, + ) + elif i.type == "buffer": + return ep.InputSpec( + kind=ep.InputKind.BUFFER, + arg=ep.TensorArgument(name=i.buffer.arg.name), + target=i.buffer.buffer_name, + persistent=i.buffer.persistent, + ) + elif i.type == "tensor_constant": + return ep.InputSpec( + kind=ep.InputKind.CONSTANT_TENSOR, + arg=ep.TensorArgument(name=i.tensor_constant.arg.name), + target=i.tensor_constant.tensor_constant_name, + ) + elif i.type == "custom_obj": + return ep.InputSpec( + kind=ep.InputKind.CUSTOM_OBJ, + arg=ep.CustomObjArgument( + name=i.custom_obj.arg.name, class_fqn=i.custom_obj.arg.class_fqn + ), + target=i.custom_obj.custom_obj_name, + ) + elif i.type == "token": + return ep.InputSpec( + kind=ep.InputKind.TOKEN, + arg=ep.TokenArgument(name=i.token.arg.name), + target=None + ) + elif i.type == "constant_input": + return ep.InputSpec( + kind=ep.InputKind.USER_INPUT, + arg=ep.ConstantArgument( + name=i.constant_input.name, + value=self.deserialize_constant_input(i.constant_input.value) + ), + target=None, + ) + else: + raise AssertionError(f"Unknown input spec {i}") + + def deserialize_output_spec(self, o: OutputSpec) -> ep.OutputSpec: + if o.type == "user_output": + return ep.OutputSpec( + kind=ep.OutputKind.USER_OUTPUT, + arg=self.deserialize_argument_spec(o.user_output.arg), + target=None, + ) + elif o.type == "loss_output": + return ep.OutputSpec( + kind=ep.OutputKind.LOSS_OUTPUT, + arg=ep.TensorArgument(name=o.loss_output.arg.name), + target=None, + ) + elif o.type == "buffer_mutation": + return ep.OutputSpec( + kind=ep.OutputKind.BUFFER_MUTATION, + arg=ep.TensorArgument(name=o.buffer_mutation.arg.name), + target=o.buffer_mutation.buffer_name, + ) + elif o.type == "gradient_to_parameter": + return ep.OutputSpec( + kind=ep.OutputKind.GRADIENT_TO_PARAMETER, + arg=ep.TensorArgument(name=o.gradient_to_parameter.arg.name), + target=o.gradient_to_parameter.parameter_name, + ) + elif o.type == "gradient_to_user_input": + return ep.OutputSpec( + kind=ep.OutputKind.GRADIENT_TO_USER_INPUT, + arg=ep.TensorArgument(name=o.gradient_to_user_input.arg.name), + target=o.gradient_to_user_input.user_input_name, + ) + elif o.type == "user_input_mutation": + return ep.OutputSpec( + kind=ep.OutputKind.USER_INPUT_MUTATION, + arg=ep.TensorArgument(name=o.user_input_mutation.arg.name), + target=o.user_input_mutation.user_input_name, + ) + elif o.type == "token": + return ep.OutputSpec( + kind=ep.OutputKind.TOKEN, + arg=ep.TokenArgument(name=o.token.arg.name), + target=None + ) + else: + raise AssertionError(f"Unknown output spec {o}") + + def deserialize_signature(self, sig: GraphSignature) -> ep.ExportGraphSignature: + return ep.ExportGraphSignature( + input_specs=[self.deserialize_input_spec(i) for i in sig.input_specs], + output_specs=[self.deserialize_output_spec(o) for o in sig.output_specs], + ) + + def deserialize( + self, + serialized_graph_module: GraphModule, + serialized_state_dict: Union[Dict[str, torch.Tensor], bytes], + constants: Union[Dict[str, Any], bytes], + example_inputs: Optional[Union[Tuple[Tuple[torch.Tensor, ...], Dict[str, Any]], bytes]] = None, + symbol_name_to_range: Optional[Dict[str, symbolic_shapes.ValueRanges]] = None, + ) -> Result: + global _CURRENT_DESERIALIZER + assert _CURRENT_DESERIALIZER is None + _CURRENT_DESERIALIZER = self + try: + self.shape_env = symbolic_shapes.ShapeEnv(assume_static_by_default=True) + self.fake_tensor_mode = FakeTensorMode( + allow_fallback_kernels=False, + allow_non_fake_inputs=True, + shape_env=self.shape_env, + ) + self.sympy_functions = { + # all torch.utils._sympy.functions should go here + # TODO(avik): find a better way to keep this collection in sync; + # e.g.., `exec('from torch.utils._sympy.functions import *', ...)` + # would work as long as the public API of that module is complete + "FloorDiv": torch.utils._sympy.functions.FloorDiv, + "ModularIndexing": torch.utils._sympy.functions.ModularIndexing, + "Where": torch.utils._sympy.functions.Where, + "PythonMod": torch.utils._sympy.functions.PythonMod, + "Mod": torch.utils._sympy.functions.Mod, + "CleanDiv": torch.utils._sympy.functions.CleanDiv, + "CeilToInt": torch.utils._sympy.functions.CeilToInt, + "FloorToInt": torch.utils._sympy.functions.FloorToInt, + "CeilDiv": torch.utils._sympy.functions.CeilDiv, + "LShift": torch.utils._sympy.functions.LShift, + "RShift": torch.utils._sympy.functions.RShift, + "PowByNatural": torch.utils._sympy.functions.PowByNatural, + "FloatPow": torch.utils._sympy.functions.FloatPow, + "FloatTrueDiv": torch.utils._sympy.functions.FloatTrueDiv, + "IntTrueDiv": torch.utils._sympy.functions.IntTrueDiv, + "IsNonOverlappingAndDenseIndicator": torch.utils._sympy.functions.IsNonOverlappingAndDenseIndicator, + "TruncToFloat": torch.utils._sympy.functions.TruncToFloat, + "TruncToInt": torch.utils._sympy.functions.TruncToInt, + "RoundToInt": torch.utils._sympy.functions.RoundToInt, + "RoundDecimal": torch.utils._sympy.functions.RoundDecimal, + "ToFloat": torch.utils._sympy.functions.ToFloat, + "Identity": torch.utils._sympy.functions.Identity, + } + self.symbol_name_to_symbol: Dict[str, sympy.Symbol] = {} + self.constants = deserialize_torch_artifact(constants) + self.signature = self.deserialize_signature(serialized_graph_module.signature) + + # deserialization does analysis with checks on 0/1, so we create fake range constraints and + # restore the original range constraints afterwards + self.symbol_name_to_range = {} + if symbol_name_to_range: + for k, vr in symbol_name_to_range.items(): + lower = vr.lower + if vr.upper >= 2: # max is >= 2, not sym bool range + lower = max(2, lower) + self.symbol_name_to_range[k] = symbolic_shapes.ValueRanges(_int_to_sympy_int(lower), vr.upper) + + if example_inputs is not None and len(example_inputs) > 0: + self.example_inputs = deserialize_torch_artifact(example_inputs) + else: + self.example_inputs = None + self.deserialize_graph(serialized_graph_module.graph) + + module_call_graph = self.deserialize_module_call_graph( + serialized_graph_module.module_call_graph + ) + graph_module = ep._create_graph_module_for_export( + self.module, self.graph + ) + meta = {} + if custom := serialized_graph_module.metadata.get("custom"): + meta["custom"] = json.loads(custom) + graph_module.meta = meta + return GraphModuleDeserializer.Result( + graph_module=graph_module, + signature=self.signature, + module_call_graph=module_call_graph, + names_to_symbols=self.symbol_name_to_symbol, + state_dict=deserialize_torch_artifact(serialized_state_dict), + constants=self.constants, + example_inputs=self.example_inputs, + ) + finally: + _CURRENT_DESERIALIZER = None + + def sync_fx_node(self, name: str, fx_node: torch.fx.Node): + if name in self.serialized_name_to_node: + raise SerializeError(f"Node {name} has already been deserialized before.") + # overwrite name + fx_node.name = name + self.serialized_name_to_node[name] = fx_node + assert "val" not in fx_node.meta + fx_node.meta["val"] = self.serialized_name_to_meta[name] + + def deserialize_sym_op_inputs(self, inputs): + return tuple(self.deserialize_input(input.arg) for input in inputs) + + def deserialize_inputs(self, target, serialized_node: Node): + schema_args = _get_schema_from_target(target).arguments + actual_args = { + input.name: self.deserialize_input(input.arg) + for input in serialized_node.inputs + } + args = [] + kwargs = {} + for schema_arg in schema_args: + is_positional = ( + not schema_arg.has_default_value() and not schema_arg.kwarg_only + ) + if is_positional: + args.append(actual_args[schema_arg.name]) + else: + if schema_arg.name in actual_args: + kwargs[schema_arg.name] = actual_args[schema_arg.name] + return tuple(args), kwargs + + def deserialize_hoo_inputs(self, inputs: List[NamedArgument]): + """ + For deserializing HOO inputs since HOOs do not have a schema. + """ + args = [] + kwargs = {} + for input_ in inputs: + if input_.name != "": + kwargs[input_.name] = self.deserialize_input(input_.arg) + else: + args.append(self.deserialize_input(input_.arg)) + return (tuple(args), kwargs) + + def deserialize_input(self, inp: Argument) -> Any: + value = inp.value + typ_ = inp.type + if typ_ == "as_none": + # None should converted as None, but is encoded as bool in serialized + # Convert serialized object to torch equivalent + return None + elif typ_ == "as_tensor": + return self.serialized_name_to_node[inp.as_tensor.name] + elif typ_ == "as_scalar_type": + return _SERIALIZE_TO_TORCH_DTYPE[inp.as_scalar_type] + elif typ_ == "as_memory_format": + return _SERIALIZE_TO_TORCH_MEMORY_FORMAT[inp.as_memory_format] + elif typ_ == "as_layout": + return _SERIALIZE_TO_TORCH_LAYOUT[inp.as_layout] + elif typ_ == "as_graph": + assert isinstance(value, GraphArgument) + with self.save_graph_module(): + self.deserialize_graph(value.graph) + submodule = ep._create_graph_module_for_export(self.module, self.graph) + self.module.register_module(value.name, submodule) + return self.graph.create_node( + "get_attr", + value.name, + name=value.name, + ) + elif typ_ == "as_device": + return deserialize_device(inp.as_device) + elif typ_ == "as_int": + return inp.as_int + elif typ_ == "as_float": + return inp.as_float + elif typ_ == "as_bool": + return inp.as_bool + elif typ_ == "as_string": + return inp.as_string + elif typ_ == "as_sym_int": + return self.deserialize_sym_argument(inp.as_sym_int) + elif typ_ == "as_sym_bool": + return self.deserialize_sym_argument(inp.as_sym_bool) + elif isinstance(value, list): + if len(value) == 0: + return [] + elif typ_ == "as_tensors": + result = [] + for arg in value: + result.append(self.serialized_name_to_node[arg.name]) + return result + elif typ_ in ("as_ints", "as_floats", "as_bools", "as_strings"): + # convert from serialized.python.types.List to python list + return list(value) + elif typ_ in ("as_sym_ints", "as_sym_bools"): + return [self.deserialize_sym_argument(arg) for arg in value] + elif typ_ == "as_optional_tensors": + + def deserialize_optional_tensor_args(a): + if a.type == "as_none": + return None + elif a.type == "as_tensor": + return self.serialized_name_to_node[a.value.name] + else: + raise SerializeError(f"Unhandled argument {inp}") + + return list(map(deserialize_optional_tensor_args, value)) + else: + raise SerializeError(f"Unhandled argument {inp}") + elif typ_ == "as_custom_obj": + if inp.as_custom_obj.name in self.serialized_name_to_node: + # Custom object has been lifted as an input + return self.serialized_name_to_node[inp.as_custom_obj.name] + return self.constants[inp.as_custom_obj.name] + elif typ_ == "as_operator": + return self.deserialize_operator(inp.as_operator) + else: + raise SerializeError(f"Unhandled argument {inp}") + + def deserialize_constant_input(self, inp: ConstantValue) -> Any: + if inp.type == "as_int": + return int(inp.as_int) + elif inp.type == "as_float": + return float(inp.as_float) + elif inp.type == "as_string": + return str(inp.as_string) + elif inp.type == "as_bool": + return bool(inp.as_bool) + elif inp.type == "as_none": + return None + else: + raise SerializeError(f"Unhandled constant argument {inp} to deserialize") + + def deserialize_sym_argument(self, sym_arg): + if isinstance(sym_arg, SymIntArgument): + if sym_arg.type == "as_int": + return sym_arg.as_int + elif sym_arg.type == "as_name": + return self.serialized_name_to_node[sym_arg.as_name] + elif isinstance(sym_arg, SymBoolArgument): + if sym_arg.type == "as_bool": + return sym_arg.as_bool + elif sym_arg.type == "as_name": + return self.serialized_name_to_node[sym_arg.as_name] + raise SerializeError(f"Unknown symbolic argument type: {sym_arg}") + + def deserialize_sym_op_outputs(self, serialized_node: Node, fx_node: torch.fx.Node): + self.sync_fx_node(serialized_node.outputs[0].value.as_name, fx_node) + + def deserialize_outputs(self, serialized_node: Node, fx_node: torch.fx.Node): + # Check single value return + if len(serialized_node.outputs) == 0: + return + if ( + len(serialized_node.outputs) == 1 + and serialized_node.outputs[0].type == "as_tensor" + ): + self.sync_fx_node(serialized_node.outputs[0].as_tensor.name, fx_node) + return + elif len(serialized_node.outputs) == 1 and isinstance( + serialized_node.outputs[0].value, (SymIntArgument, SymBoolArgument) + ): + self.sync_fx_node(serialized_node.outputs[0].value.as_name, fx_node) + return + + self.deserialize_multiple_outputs(serialized_node, fx_node) + + def deserialize_multiple_outputs( + self, serialized_node: Node, fx_node: torch.fx.Node + ) -> None: + deserialized_metadata = self.deserialize_metadata(serialized_node.metadata) + + def generate_getitem( + meta_val, + fx_node: torch.fx.Node, + arg: Union[TensorArgument, SymIntArgument], + idx: int, + ): + if isinstance(arg, TensorArgument): + name = arg.name + elif isinstance(arg, SymIntArgument): + name = arg.as_name + else: + raise AssertionError( + f"generate_getitem got unknown argument type {type(arg)}" + ) + individual_output = self.graph.create_node( + "call_function", + operator.getitem, + (fx_node, idx), + name=name, + ) + self.sync_fx_node(name, individual_output) + meta_val.append(self.serialized_name_to_meta[name]) + # The derived `getitem` nodes should have the same stacktrace as the + # original `fx_node` + individual_output.meta.update(deserialized_metadata) + + def generate_getitems(meta_val, fx_node: torch.fx.Node, args): + for idx, arg in enumerate(args): + if isinstance(arg, Argument): + arg = arg.value + if isinstance(arg, (TensorArgument, SymIntArgument)): + generate_getitem(meta_val, fx_node, arg, idx) + elif isinstance(arg, (list, tuple)): + list_output = self.graph.create_node( + "call_function", + operator.getitem, + (fx_node, idx), + ) + meta_val.append([]) + generate_getitems(meta_val[-1], list_output, arg) + list_output.meta.update(deserialized_metadata) + list_output.meta["val"] = meta_val[-1] + else: + raise NotImplementedError(f"Unimplemented node output type: {arg}") + + # Convert multiple return types to FX format. + # In FX, each node only returns one value. So in order to represent + # multiple return values, we have to emit a `getitem` node for each + # return value. + # This performs the inverse mapping of the `serialize_outputs` call in + # serialization, see [NOTE: Multiple outputs] + meta_val: List[Any] = [] + if len(serialized_node.outputs) == 1: + assert isinstance(serialized_node.outputs[0].value, list) + assert isinstance(serialized_node.outputs[0].value[0], TensorArgument) + generate_getitems(meta_val, fx_node, serialized_node.outputs[0].as_tensors) + else: + generate_getitems(meta_val, fx_node, serialized_node.outputs) + + # also update the metaval for `fx_node` to be a list(meta) + fx_node.meta["val"] = tuple(meta_val) + self.serialized_name_to_node[fx_node.name] = fx_node + + def deserialize_metadata(self, metadata: Dict[str, str]) -> Dict[str, Any]: + ret: Dict[str, Any] = {} + if stack_trace := metadata.get("stack_trace"): + ret["stack_trace"] = stack_trace + + def deserialize_meta_func(serialized_target: str): + module = None + if serialized_target.startswith("torch.nn"): + module = torch.nn + serialized_target_names = serialized_target.split(".")[2:] + elif serialized_target.startswith("torch"): + module = torch + serialized_target_names = serialized_target.split(".")[1:] + else: + return self.deserialize_operator(serialized_target) + + target = module + for name in serialized_target_names: + if not hasattr(target, name): + return serialized_target + else: + target = getattr(target, name) + return target + + if nn_module_stack_str := metadata.get("nn_module_stack"): + # Originally serialized to "key,orig_path,type_str" + def import_nn_module_stack(key, path, ty): + return key, (path, ty) + + # Helper function that splits strings by commas except for those + # encapsulated by parens, which are valid traces. + # TODO: Currently this is needed due to indexing Sequential + # layers introducing names in the form "layer.slice(1, None, None)". + # If that naming is improved, this fancier splitting can probably be + # reverted to a simple split by comma. + def metadata_split(metadata): + # Remove the parentheses and commas inside them + metadata = re.sub(r'\(.*?\)', '', metadata) + # Split the string by comma, except for those inside parentheses + return re.split(r'(? ep.ArgumentSpec: + if x.type == "as_tensor": + return ep.TensorArgument(name=x.as_tensor.name) + elif x.type == "as_sym_int": + return ep.SymIntArgument(name=x.as_sym_int.as_name) + elif x.type == "as_custom_obj": + return ep.ConstantArgument(name=x.as_custom_obj.name, value=self.deserialize_input(x)) + else: + return ep.ConstantArgument(name="", value=self.deserialize_input(x)) + + def deserialize_module_call_signature( + self, module_call_signature: ModuleCallSignature + ) -> ep.ModuleCallSignature: + return ep.ModuleCallSignature( + inputs=[ + self.deserialize_argument_spec(x) for x in module_call_signature.inputs + ], + outputs=[ + self.deserialize_argument_spec(x) for x in module_call_signature.outputs + ], + in_spec=treespec_loads(module_call_signature.in_spec), + out_spec=treespec_loads(module_call_signature.out_spec), + ) + + def deserialize_module_call_graph( + self, module_call_graph: List[ModuleCallEntry] + ) -> List[ep.ModuleCallEntry]: + return [ + ep.ModuleCallEntry( + fqn=entry.fqn, + signature=( + self.deserialize_module_call_signature(entry.signature) + if entry.signature + else None + ), + ) + for entry in module_call_graph + ] + + +@final +class ExportedProgramDeserializer(metaclass=Final): + def __init__(self, expected_opset_version: Optional[Dict[str, int]] = None): + self.expected_opset_version: Dict[str, int] = {} + if expected_opset_version: + self.expected_opset_version.update(expected_opset_version) + if "aten" not in self.expected_opset_version: + self.expected_opset_version["aten"] = torch._C._get_max_operator_version() + + def deserialize_range_constraints( + self, + symbol_name_to_range: Dict[str, symbolic_shapes.ValueRanges], + symbol_name_to_symbol: Dict[str, sympy.Symbol], + ) -> Dict[sympy.Symbol, ValueRanges]: + range_constraints = {} + for k, v in symbol_name_to_range.items(): + if symbol := symbol_name_to_symbol.get(k): + range_constraints[symbol] = v # type: ignore[arg-type] + else: + log.warning(f"Symbol {k} did not appear in the graph that was deserialized") # noqa: G004 + return range_constraints + + def deserialize( + self, + exported_program: ExportedProgram, + state_dict: Union[Dict[str, torch.Tensor], bytes], + constants: Union[Dict[str, torch.Tensor], bytes], + example_inputs: Optional[Union[Tuple[Tuple[torch.Tensor, ...], Dict[str, Any]], bytes]] = None, + ) -> ep.ExportedProgram: + assert isinstance(exported_program, ExportedProgram) + version = exported_program.schema_version + + # TODO(zhxchen17) blocked on thrift schema refactor + if version.major != SCHEMA_VERSION[0] and not (version.major == 0 and version.minor == 0): + raise SerializeError( + f"Serialized schema version {exported_program.schema_version} " + f"does not match our current schema version {SCHEMA_VERSION}." + ) + + symbol_name_to_range = { + k: symbolic_shapes.ValueRanges( + _int_to_sympy_int(v.min_val), _int_to_sympy_int(v.max_val) + ) + for k, v in exported_program.range_constraints.items() + } + res = ( + GraphModuleDeserializer() + .deserialize( + exported_program.graph_module, + state_dict, + constants, + example_inputs, + symbol_name_to_range, + ) + ) + range_constraints = self.deserialize_range_constraints( + symbol_name_to_range, + res.names_to_symbols, + ) + + return ep.ExportedProgram( + root=res.graph_module, + graph=res.graph_module.graph, + graph_signature=res.signature, + state_dict=res.state_dict, # type: ignore[arg-type] + range_constraints=range_constraints, + module_call_graph=res.module_call_graph, + example_inputs=res.example_inputs, + constants=res.constants, + verifiers=[load_verifier(v) for v in exported_program.verifiers], + ) + + +class EnumEncoder(json.JSONEncoder): + def default(self, obj): + if isinstance(obj, Enum): + return obj.value + if isinstance(obj, bytes): + return base64.b64encode(obj).decode("utf-8") + return super().default(obj) + + +def _dataclass_to_dict(obj): + if isinstance(obj, _Union): + return {obj.type: _dataclass_to_dict(obj.value)} + elif dataclasses.is_dataclass(obj): + return { + f.name: _dataclass_to_dict(getattr(obj, f.name)) + for f in dataclasses.fields(obj) + if not (f.default is None and getattr(obj, f.name) is None) + } + elif isinstance(obj, list): + return [_dataclass_to_dict(x) for x in obj] + elif isinstance(obj, tuple): + return tuple(_dataclass_to_dict(x) for x in obj) + elif isinstance(obj, dict): + return {k: _dataclass_to_dict(v) for k, v in obj.items()} + else: + return obj + + +def serialize( + exported_program: ep.ExportedProgram, + opset_version: Optional[Dict[str, int]] = None, +) -> SerializedArtifact: + serialized_program = ExportedProgramSerializer(opset_version).serialize( + exported_program + ) + assert isinstance(serialized_program.exported_program, ExportedProgram) + + json_program = json.dumps( + _dataclass_to_dict(serialized_program.exported_program), cls=EnumEncoder + ) + json_bytes = json_program.encode("utf-8") + artifact = SerializedArtifact( + json_bytes, + serialized_program.state_dict, + serialized_program.constants, + serialized_program.example_inputs + ) + return artifact + + +def _dict_to_dataclass(cls, data): + assert not isinstance(cls, str), f"Unresolved class type: '{cls}'." + if typing.get_origin(cls) == typing.Union and type(None) in typing.get_args(cls): + if data is None: + return None + ty_args = typing.get_args(cls) + assert len(ty_args) == 2 + return _dict_to_dataclass(ty_args[0], data) + elif isinstance(cls, type) and issubclass(cls, _Union): + assert isinstance(data, dict) + assert len(data) == 1 + _type = next(iter(data.keys())) + _value = next(iter(data.values())) + assert isinstance(_type, str) + field_type = cls.__annotations__[_type] + return cls.create(**{_type: _dict_to_dataclass(field_type, _value)}) + elif dataclasses.is_dataclass(cls): + obj = cls(**data) # type: ignore[assignment] + type_hints = typing.get_type_hints(cls) + for f in dataclasses.fields(cls): + name = f.name + new_field_obj = _dict_to_dataclass(type_hints[name], getattr(obj, name)) + setattr(obj, name, new_field_obj) + return obj + elif isinstance(data, list): + if len(data) == 0: + return data + d_type = typing.get_args(cls)[0] + return [_dict_to_dataclass(d_type, d) for d in data] + elif isinstance(data, dict): + v_type = typing.get_args(cls)[1] + return {k: _dict_to_dataclass(v_type, v) for k, v in data.items()} + return data + + +def deserialize( + artifact: SerializedArtifact, + expected_opset_version: Optional[Dict[str, int]] = None, +) -> ep.ExportedProgram: + assert isinstance(artifact.exported_program, bytes) + exported_program_str = artifact.exported_program.decode("utf-8") + exported_program_dict = json.loads(exported_program_str) + serialized_exported_program = _dict_to_dataclass(ExportedProgram, exported_program_dict) + return ( + ExportedProgramDeserializer(expected_opset_version) + .deserialize( + serialized_exported_program, + artifact.state_dict, + artifact.constants, + artifact.example_inputs, + ) + ) + + +def _canonicalize_graph( + sorted_inputs, sorted_outputs, graph +) -> Tuple[Graph, Dict[str, str]]: + def _get_argument(a: Argument): + if a.type == "as_none": + return None + elif a.type == "as_tensor": + return a.as_tensor + elif a.type == "as_tensors": + return a.as_tensors + elif a.type == "as_int": + return None + elif a.type == "as_ints": + return None + elif a.type == "as_float": + return None + elif a.type == "as_floats": + return None + elif a.type == "as_string": + return None + elif a.type == "as_strings": + return None + elif a.type == "as_sym_int": + return a.as_sym_int + elif a.type == "as_sym_ints": + return a.as_sym_ints + elif a.type == "as_scalar_type": + return None + elif a.type == "as_memory_format": + return None + elif a.type == "as_layout": + return None + elif a.type == "as_device": + return None + elif a.type == "as_bool": + return None + elif a.type == "as_bools": + return None + elif a.type == "as_sym_bool": + return a.as_sym_bool + elif a.type == "as_sym_bools": + return a.as_sym_bools + elif a.type == "as_graph": + return None + elif a.type == "as_optional_tensors": + return a.as_optional_tensors + elif a.type == "as_custom_obj": + return None + elif a.type == "as_operator": + return None + else: + raise AssertionError(f"Unknown input type to the ExportedProgram: {a}") + + # Stage 1: Reorder named items. + def for_args(f, a): + assert isinstance(a, Argument) + pytree.tree_map(f, _get_argument(a)) + + def sort_nodes(nodes): + @dataclass + class Edges: + outs: List[int] + ins: int + + graph_inputs: Set[str] = set() + def_table: Dict[str, int] = {} + edges: Dict[int, Edges] = {} + candidates: List[Tuple[str, List[Tuple[str, List[int]]], int]] = [] + rank: Dict[str, int] = {} + ret: List[Node] = [] + + def get_name(a) -> Optional[str]: + if a is None: + return None + if isinstance(a, TensorArgument): + return a.name + elif isinstance(a, (SymIntArgument, SymBoolArgument)): + if a.type == "as_name": + return a.as_name + elif a.type in ("as_int", "as_bool"): + return None + else: + raise AssertionError(f"Unknown argument type: {a}") + elif isinstance(a, OptionalTensorArgument): + if a.type == "as_tensor": + return a.as_tensor.name + elif a.type == "as_none": + return None + else: + raise AssertionError(f"Unknown optional tensor type: {a}") + else: + raise AssertionError(f"Unknown argument type: {a}") + + for i in sorted_inputs: + + def add_input(a): + if s := get_name(a): + graph_inputs.add(s) + + for_args(add_input, i) + + for idx, node in enumerate(nodes): + + def add_def(a): + if s := get_name(a): + assert s not in def_table + def_table[s] = idx + + for o in node.outputs: + for_args(add_def, o) + + edges[idx] = Edges([], 0) + + for idx, user in enumerate(nodes): + + def add_edge(a): + if s := get_name(a): + if s not in def_table: + assert s in graph_inputs + return + src = def_table[s] + edges[src].outs.append(idx) + edges[idx].ins += 1 + + for i in user.inputs: + for_args(add_edge, i.arg) + + def add_rank(a): + if s := get_name(a): + assert s not in rank + rank[s] = len(rank) + + def get_rank(a): + if s := get_name(a): + return rank[s] + else: + return -1 + + for i in sorted_inputs: + for_args(add_rank, i) + + def add_candidate(idx: int): + def get_ranks(i): + ranks = [] + for_args(lambda x: ranks.append(get_rank(x)), i) + return ranks + + node = nodes[idx] + args_rank = [(a.name, get_ranks(a.arg)) for a in node.inputs] + heapq.heappush(candidates, (node.target, args_rank, idx)) + + for idx, e in edges.items(): + if e.ins == 0: + add_candidate(idx) + + while len(candidates) > 0: + _, _, idx = heapq.heappop(candidates) + node = nodes[idx] + for o in node.outputs: + for_args(add_rank, o) + ret.append(node) + assert idx in edges + for user in edges[idx].outs: + e = edges[user] + assert e.ins > 0 + e.ins -= 1 + if e.ins == 0: + add_candidate(user) + edges[idx].outs.clear() + + return ret + + sorted_nodes = sort_nodes(graph.nodes) + assert len(sorted_nodes) == len(graph.nodes) + + # Stage 2: Rename nodes. + name_table: Dict[str, str] = {} + + def rename_def(a): + def _rename(arg_name, values): + new_name = f"_{len(name_table)}" + assert arg_name not in name_table + name_table[arg_name] = new_name + assert arg_name in values + values[new_name] = values.pop(arg_name) + return new_name + + if a is None: + return + if isinstance(a, TensorArgument): + a.name = _rename(a.name, graph.tensor_values) + elif isinstance(a, SymIntArgument): + if a.type == "as_name": + a.as_name = _rename(a.as_name, graph.sym_int_values) + elif isinstance(a, SymBoolArgument): + if a.type == "as_name": + a.as_name = _rename(a.as_name, graph.sym_bool_values) + else: + raise AssertionError(f"Unknown argument type: {a}") + + def replace_use(a): + if a is None: + return + if isinstance(a, TensorArgument): + a.name = name_table.get(a.name, a.name) + elif isinstance(a, SymIntArgument): + if a.type == "as_name": + a.as_name = name_table.get(a.as_name, a.as_name) + elif isinstance(a, SymBoolArgument): + if a.type == "as_name": + a.as_name = name_table.get(a.as_name, a.as_name) + elif isinstance(a, OptionalTensorArgument): + if a.type == "as_tensor": + a.as_tensor.name = name_table.get(a.as_tensor.name, a.as_tensor.name) + else: + raise AssertionError(f"Unknown argument type: {a}") + + for i in sorted_inputs: + for_args(rename_def, i) + + for n in sorted_nodes: + for o in n.outputs: + for_args(rename_def, o) + + for n in sorted_nodes: + for i in n.inputs: + for_args(replace_use, i.arg) + + for o in sorted_outputs: + for_args(replace_use, o) + + # Stage 3: Remove unstable fields. + for n in sorted_nodes: + n.metadata.clear() + + # Stage 4: Aggregate values. + sorted_tensor_values = dict(sorted(graph.tensor_values.items(), key=operator.itemgetter(0))) + sorted_sym_int_values = dict( + sorted(graph.sym_int_values.items(), key=operator.itemgetter(0)) + ) + sorted_sym_bool_values = dict( + sorted(graph.sym_bool_values.items(), key=operator.itemgetter(0)) + ) + + # Stage 5: Recurse in subgraphs. + counter = 0 + for node in sorted_nodes: + for i in node.inputs: + a = i.arg + if a.type == "as_graph": + a.as_graph.graph, _ = _canonicalize_graph( + a.as_graph.graph.inputs, a.as_graph.graph.outputs, a.as_graph.graph + ) + a.as_graph.name = f"_g{counter}" + counter += 1 + + graph = Graph( + inputs=sorted_inputs, + outputs=sorted_outputs, + nodes=sorted_nodes, + tensor_values=sorted_tensor_values, + sym_int_values=sorted_sym_int_values, + sym_bool_values=sorted_sym_bool_values, + is_single_tensor_return=graph.is_single_tensor_return, + ) + return graph, name_table + + +def canonicalize(ep: ExportedProgram) -> ExportedProgram: + """ + Normalize a serialized ExportedProgram, so that different eager program which + shares the same semantics can get a single representation on disk. + + This function canonicalizes an ExportedProgram by: + + 1. Sorting nodes in topological order. + 2. Rename nodes to have unique names. + 3. Remove unstable fields. + 4. Aggregate the above program fields. + 5. Recurse in subgraphs. + + Args: + ep (ExportedProgram): The ExportedProgram to canonicalize. + + Returns: + ExportedProgram: The canonicalized exported program. + """ + ep = copy.deepcopy(ep) + + opset_version = dict(sorted(ep.opset_version.items(), key=operator.itemgetter(0))) + range_constraints = dict(sorted(ep.range_constraints.items(), key=operator.itemgetter(0))) + module_call_graph = sorted(ep.graph_module.module_call_graph, key=lambda x: x.fqn) + signature = ep.graph_module.signature + graph = ep.graph_module.graph + + assert len(graph.inputs) == len(signature.input_specs) + assert len(graph.outputs) == len(signature.output_specs) + + def rank_input(inp) -> Tuple[int, Optional[str], int]: + idx, (arg, spec) = inp + assert isinstance(spec, InputSpec) + if spec.type == "user_input": + return 5, None, idx + elif spec.type == "parameter": + return 1, spec.parameter.parameter_name, idx + elif spec.type == "buffer": + return 2, spec.buffer.buffer_name, idx + elif spec.type == "tensor_constant": + return 3, spec.tensor_constant.tensor_constant_name, idx + elif spec.type == "custom_obj": + return 4, spec.custom_obj.custom_obj_name, idx + elif spec.type == "token": + return 0, None, idx + elif spec.type == "constant_input": + return 6, spec.constant_input.name, idx + else: + raise AssertionError(f"Unknown input type: {spec}") + + def rank_output(out) -> Tuple[int, Optional[str], int]: + idx, (arg, spec) = out + assert isinstance(spec, OutputSpec) + if spec.type == "user_output": + return 3, None, idx + elif spec.type == "loss_output": + return 3, None, idx + elif spec.type == "buffer_mutation": + return 1, spec.buffer_mutation.buffer_name, idx + elif spec.type == "gradient_to_parameter": + return 4, spec.gradient_to_parameter.parameter_name, idx + elif spec.type == "gradient_to_user_input": + return 5, None, idx + elif spec.type == "user_input_mutation": + return 2, None, idx + elif spec.type == "token": + return 0, None, idx + else: + raise AssertionError(f"Unknown output type: {spec}") + + sorted_ins = sorted( + enumerate(zip(graph.inputs, signature.input_specs)), key=rank_input + ) + + if len(sorted_ins) > 0: + sorted_inputs, input_specs = zip(*(i for idx, i in sorted_ins)) # type: ignore[assignment] + else: + sorted_inputs = () + input_specs = () + + sorted_outs = sorted( + enumerate(zip(graph.outputs, signature.output_specs)), key=rank_output + ) + sorted_outputs, output_specs = zip(*(i for idx, i in sorted_outs)) # type: ignore[assignment] + + sorted_graph, replace_table = _canonicalize_graph( + sorted_inputs, sorted_outputs, graph + ) + + def replace_input(inp): + assert isinstance(spec, InputSpec) + if spec.type == "user_input": + arg = spec.user_input.arg + if arg.type == "as_tensor": + t = arg.as_tensor + t.name = replace_table[t.name] + elif arg.type == "as_sym_int": + s = arg.as_sym_int + if s.type == "as_name": + s.as_name = replace_table[s.as_name] + elif s.type == "as_int": + pass + else: + raise AssertionError(f"Unknown sym_int type: {s}") + elif arg.type in ( + "as_none", + "as_bool", + "as_int", + "as_float", + "as_string", + "as_custom_obj", + ): + return + else: + raise AssertionError(f"Unknown input type: {arg}") + elif spec.type == "parameter": + t = spec.parameter.arg + t.name = replace_table[t.name] + elif spec.type == "buffer": + t = spec.buffer.arg + t.name = replace_table[t.name] + elif spec.type == "tensor_constant": + t = spec.tensor_constant.arg + t.name = replace_table[t.name] + elif spec.type == "custom_obj": + return + elif spec.type == "token": + tok = spec.token.arg + tok.name = replace_table[tok.name] + elif spec.type == "constant_input": + return + else: + raise AssertionError(f"Unknown input type: {spec}") + + def replace_output(out): + assert isinstance(spec, OutputSpec) + if spec.type == "user_output": + arg = spec.user_output.arg + if arg.type == "as_tensor": + t = arg.as_tensor + t.name = replace_table[t.name] + elif arg.type == "as_sym_int": + s = arg.as_sym_int + if s.type == "as_name": + s.as_name = replace_table[s.as_name] + elif s.type == "as_int": + pass + else: + raise AssertionError(f"Unknown sym_int type: {s}") + elif arg.type in ("as_none", "as_int", "as_float", "as_string"): + return + else: + raise AssertionError(f"Unknown input type: {arg}") + elif spec.type == "loss_output": + t = spec.loss_output.arg + t.name = replace_table[t.name] + elif spec.type == "buffer_mutation": + t = spec.buffer_mutation.arg + t.name = replace_table[t.name] + elif spec.type == "gradient_to_parameter": + t = spec.gradient_to_parameter.arg + t.name = replace_table[t.name] + elif spec.type == "gradient_to_user_input": + g = spec.gradient_to_user_input + g.arg.name = replace_table[g.arg.name] + g.user_input_name = replace_table[g.user_input_name] + elif spec.type == "user_input_mutation": + u = spec.user_input_mutation + u.arg.name = replace_table[u.arg.name] + u.user_input_name = replace_table[u.user_input_name] + elif spec.type == "token": + tok = spec.token.arg + tok.name = replace_table[tok.name] + else: + raise AssertionError(f"Unknown output type: {spec}") + + for spec in input_specs: + replace_input(spec) + + for spec in output_specs: + replace_output(spec) + + return ExportedProgram( + graph_module=GraphModule( + graph=sorted_graph, + signature=GraphSignature( + input_specs=list(input_specs), + output_specs=list(output_specs), + ), + module_call_graph=module_call_graph, + ), + opset_version=opset_version, + range_constraints=range_constraints, + schema_version=ep.schema_version, + verifiers=ep.verifiers, + torch_version=ep.torch_version, + ) + + +class ExtensionHandler: + """ + Base class for handling extension operators. + """ + @classmethod + def namespace(cls) -> str: + raise NotImplementedError(f"{cls.__class__} namespace() must be implemented") + + @classmethod + def to_op_name(cls, op) -> str: + raise NotImplementedError(f"{cls.__class__} op_name() must be implemented") + + @classmethod + def from_op_name(cls, name: str): + raise NotImplementedError(f"{cls.__class__} op_name() must be implemented") + + @classmethod + def op_schema(cls, op) -> torch.FunctionSchema: + raise NotImplementedError(f"{cls.__class__} op_schema() must be implemented") + + +def register_extension( + op_type: Type[Any], + extension_handler: Type[ExtensionHandler], +): + """Register custom de/serialization method for a node with non-standard type.""" + assert issubclass(extension_handler, ExtensionHandler), f"Expected ExtensionHandler, got {extension_handler}." + assert op_type not in _serialization_registry, f"{op_type} is already registered." + assert isinstance(op_type, type) # Maybe a good idea to enforce this first. + assert not (op_type.__module__.startswith("torch") or op_type.__module__.startswith("builtins")) + assert extension_handler.namespace() not in _deserialization_registry + _serialization_registry[op_type] = extension_handler + _deserialization_registry[extension_handler.namespace()] = extension_handler + + +def _registered_extension_types(): + return tuple( + _serialization_registry.keys() + ) + + +# Registry to store all custom serialization implementations. +# The registry maps a operation to its serialization function (a callable), in their own +# namespace to avoid conflicts. +# Serialization: Op type --> custom handler. +# De-serialization: Namespace --> custom handler. +_serialization_registry: Dict[Type[Any], Type[ExtensionHandler]] = {} +_deserialization_registry: Dict[str, Type[ExtensionHandler]] = {} diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/serde/union.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/serde/union.py new file mode 100644 index 0000000000000000000000000000000000000000..b129e8dd9a89ef4870d7ef3dc724aca8ccae3a43 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/serde/union.py @@ -0,0 +1,70 @@ +# mypy: allow-untyped-defs +import functools +from dataclasses import fields +from typing import Hashable, Set + + +class _UnionTag(str): + _cls: Hashable + + @staticmethod + def create(t, cls): + tag = _UnionTag(t) + assert not hasattr(tag, "_cls") + tag._cls = cls + return tag + + def __eq__(self, cmp) -> bool: + assert isinstance(cmp, str) + other = str(cmp) + assert other in _get_field_names( + self._cls + ), f"{other} is not a valid tag for {self._cls}. Available tags: {_get_field_names(self._cls)}" + return str(self) == other + + def __hash__(self): + return hash(str(self)) + + +@functools.lru_cache(maxsize=None) +def _get_field_names(cls) -> Set[str]: + return {f.name for f in fields(cls)} + + +class _Union: + _type: _UnionTag + + @classmethod + def create(cls, **kwargs): + assert len(kwargs) == 1 + obj = cls(**{**{f.name: None for f in fields(cls)}, **kwargs}) # type: ignore[arg-type] + obj._type = _UnionTag.create(next(iter(kwargs.keys())), cls) + return obj + + def __post_init__(self): + assert not any(f.name in ("type", "_type", "create", "value") for f in fields(self)) # type: ignore[arg-type, misc] + + @property + def type(self) -> str: + try: + return self._type + except AttributeError as e: + raise RuntimeError( + f"Please use {type(self).__name__}.create to instantiate the union type." + ) from e + + @property + def value(self): + return getattr(self, self.type) + + def __getattribute__(self, name): + attr = super().__getattribute__(name) + if attr is None and name in _get_field_names(type(self)) and name != self.type: # type: ignore[arg-type] + raise AttributeError(f"Field {name} is not set.") + return attr + + def __str__(self): + return self.__repr__() + + def __repr__(self): + return f"{type(self).__name__}({self.type}={getattr(self, self.type)})" diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/tools.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/tools.py new file mode 100644 index 0000000000000000000000000000000000000000..a4b96f909d1642f888546d1068d31d1a5f4ee9f1 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/tools.py @@ -0,0 +1,146 @@ +# mypy: allow-untyped-defs +import logging +import warnings +from typing import Any, Dict, Iterable, Optional, Tuple + +import torch +import torch.export +import torch.export._trace +from torch._utils_internal import log_export_usage + + +log = logging.getLogger(__name__) + +__all__ = ["report_exportability"] + + +def _generate_inputs_for_submodules( + model: torch.nn.Module, + target_submodules: Iterable[str], + args: Tuple[Any, ...], + kwargs: Optional[Dict[str, Any]] = None, +) -> Dict[str, Tuple[Any, Any]]: + """ + Generate inputs for targeting submdoules in the given model. Note that if two submodules refer to the same obj, this + function doesn't work. + + Args: + model: root model. + inputs: inputs to the root model. + target_submodules: submodules that we want to generate inputs for. + + Returns: + A dict that maps from submodule name to its inputs. + """ + kwargs = kwargs or {} + + handles = [] + results = {} + submodule_to_names = {mod: name for name, mod in model.named_modules()} + + def pre_forward(module, module_args, module_kwargs): + results[submodule_to_names[module]] = (module_args, module_kwargs) + + try: + for name, mod in model.named_modules(): + if name in target_submodules: + handles.append( + mod.register_forward_pre_hook(pre_forward, with_kwargs=True) + ) + model(*args, **kwargs) + except Exception as e: + warnings.warn( + f"Failed to generate submodule inputs because of the following error:\n{e}" + ) + finally: + for h in handles: + h.remove() + return results + + +def report_exportability( + mod: torch.nn.Module, + args: Tuple[Any, ...], + kwargs: Optional[Dict[str, Any]] = None, + *, + strict: bool = True, + pre_dispatch: bool = False, +) -> Dict[str, Optional[Exception]]: + """ + Report exportability issues for a module in one-shot. + + Args: + mod: root module. + args: args to the root module. + kwargs: kwargs to the root module. + Returns: + A dict that maps from submodule name to the exception that was raised when trying to export it. + `None` means the module is exportable without issue. + Sample output: + { + '': UnsupportedOperatorException(func=), + 'submod_1': UnsupportedOperatorException(func=), + 'submod_2': None + } + """ + + log_export_usage(event="export.report_exportability") + + kwargs = kwargs or {} + + all_submod_names = [name for name, _ in mod.named_modules() if name != ""] + submod_inputs = _generate_inputs_for_submodules(mod, all_submod_names, args, kwargs) + + tried_module_types = set() + report: Dict[str, Optional[Exception]] = {} + + def try_export(module, module_name, args, kwargs): + nonlocal submod_inputs, report, strict, pre_dispatch, tried_module_types + + if type(module) in tried_module_types: + return + tried_module_types.add(type(module)) + + if args is not None or kwargs is not None: + try: + torch.export._trace._export( + module, + args, + kwargs, + strict=strict, + pre_dispatch=pre_dispatch, + ) + report[module_name] = None + log.info("Successfully exported `%s`", module_name) + return + except Exception as e: + short_msg = repr(e).split("\n")[0] + log.warning( + "Failed exporting `%s` with exception: %s", module_name, short_msg + ) + report[module_name] = e + + for name, submod in module.named_children(): + sub_module_name = name if module_name == "" else f"{module_name}.{name}" + + submod_args, submod_kwargs = submod_inputs.get( + sub_module_name, (None, None) + ) + + try_export(submod, sub_module_name, submod_args, submod_kwargs) + + return + + try_export(mod, "", args, kwargs) + + unique_issues = set() + for exception in report.values(): + if exception is not None: + key = repr(exception).split("\\n")[0] + unique_issues.add(key) + + log.warning("Found %d export issues:", len(unique_issues)) + for issue in unique_issues: + log.warning(issue) + + return report diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e085e18b68a20753efa38d6f85e6eadf78f8895a --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/utils.py @@ -0,0 +1,893 @@ +# mypy: allow-untyped-defs +import ast +import dataclasses +import inspect +import math +import operator +import re +from inspect import Parameter +from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, TYPE_CHECKING + +import torch +from torch._guards import detect_fake_mode +from torch._subclasses.fake_tensor import FakeTensor + + +if TYPE_CHECKING: + from torch._export.passes.lift_constants_pass import ConstantAttrMap + from torch.export import ExportedProgram + from torch.export.graph_signature import ExportGraphSignature + +from torch.export.graph_signature import InputKind, OutputKind +from torch.utils._pytree import ( + _register_pytree_node, + Context, + FlattenFunc, + FromDumpableContextFn, + GetAttrKey, + KeyPath, + keystr, + MappingKey, + SequenceKey, + ToDumpableContextFn, + tree_flatten_with_path, + UnflattenFunc, +) + + +placeholder_prefixes = { + InputKind.USER_INPUT: "", + InputKind.PARAMETER: "p_", + InputKind.BUFFER: "b_", + InputKind.CONSTANT_TENSOR: "c_", + InputKind.CUSTOM_OBJ: "obj_", + InputKind.TOKEN: "token", +} + + +def _collect_and_set_constant_attrs( + graph_signature, constants, mod +) -> "ConstantAttrMap": + # the exported module will store constants & non-persistent buffers such that + # retracing treats them as persistent buffers, so we inform the constants lifting pass + # and overwrite the new graph signature using the previous program. This is intended to only be used + # in run_decompositions where we still have access to original EP. + from torch._export.passes.lift_constants_pass import ConstantAttrMap + + constant_attrs = ConstantAttrMap() + non_persistent_buffers = { + spec.target + for spec in graph_signature.input_specs + if spec.kind == InputKind.BUFFER and not spec.persistent + } + for name, value in constants.items(): + if name in non_persistent_buffers: + continue + # recursive getattr + _mod = mod + *atoms, attr = name.split(".") + for atom in atoms: + _mod = getattr(_mod, atom) + # remove as buffer, reassign as constant/non-persistent buffer + _mod._buffers.pop(attr, None) + setattr(_mod, attr, value) + constant_attrs.add(value, name) + return constant_attrs + + +def _overwrite_signature_for_non_persistent_buffers( + old_sig: "ExportGraphSignature", new_sig: "ExportGraphSignature" +): + # overwrite signature for non-persistent buffers + non_persistent_buffers = { + spec.target + for spec in old_sig.input_specs + if spec.kind == InputKind.BUFFER and not spec.persistent + } + + for spec in new_sig.input_specs: + if spec.kind == InputKind.BUFFER and spec.target in non_persistent_buffers: + spec.persistent = False + return new_sig + + +def _collect_param_buffer_metadata(mod: torch.fx.GraphModule) -> Dict[str, Any]: + """ + Param/buffer metadata needs to be saved before lowering to aten IR + because aten IR lifts them, as a result, automatic preservation doesn't work. + This is intended to be called on the strict mode tracing right before lowering to + aten IR OR run_decomposition pass. + """ + params_buffers_to_node_meta = {} + + def _getattr(model: torch.fx.GraphModule, attr_name: str): + *prefix, field = attr_name.split(".") + t = model + for item in prefix: + t = getattr(t, item, None) # type: ignore[assignment] + assert t is not None + + return getattr(t, field) + + for node in mod.graph.nodes: + target = node.target + meta = node.meta + if node.op == "call_module": + submodule = _getattr(mod, target) + if isinstance(submodule, torch.nn.Module): + for name, _ in submodule.named_parameters( + recurse=True, remove_duplicate=False + ): + params_buffers_to_node_meta[target + "." + name] = meta + + for name, _ in submodule.named_buffers( + recurse=True, remove_duplicate=False + ): + params_buffers_to_node_meta[target + "." + name] = meta + + if node.op == "get_attr": + submodule = _getattr(mod, target) + if not isinstance(submodule, torch.fx.GraphModule): + params_buffers_to_node_meta[target] = meta + + # If the call_function uses param as input, we also need to update params' meta + # with this call_function node's meta. + # This is basically the same flow as torch.fx.traceback.preserve_meta() + if node.op == "call_function" and not isinstance( + node.target, torch._ops.HigherOrderOperator + ): + for arg in node._input_nodes: + if arg.op == "get_attr": + for entry in torch.fx.proxy._COPY_META_FIELDS: + if entry in meta: + params_buffers_to_node_meta[arg.target][entry] = meta[entry] + + return params_buffers_to_node_meta + + +def _populate_param_buffer_metadata_to_new_gm( + params_buffers_to_node_meta: Dict[str, Any], + gm: torch.fx.GraphModule, + new_sig: "ExportGraphSignature", +) -> None: + """ + Given that we collected param'buffer metadata before, we put them back in + newly traced graph module + """ + # Don't copy over nn_module_stack, stack_trace metadata for params/buffers nodes + for metadata in params_buffers_to_node_meta.values(): + metadata.pop("nn_module_stack", None) + metadata.pop("stack_trace", None) + + for node in gm.graph.nodes: + if node.op == "placeholder": + if node.target in new_sig.inputs_to_parameters: + param_name = new_sig.inputs_to_parameters[node.target] + if param_name in params_buffers_to_node_meta: + for k, v in params_buffers_to_node_meta[param_name].items(): + node.meta[k] = v + if node.target in new_sig.inputs_to_buffers: + buffer_name = new_sig.inputs_to_buffers[node.target] + if buffer_name in params_buffers_to_node_meta: + for k, v in params_buffers_to_node_meta[buffer_name].items(): + node.meta[k] = v + + +def _get_shape_env_from_gm(gm: torch.fx.GraphModule): + vals = [ + node.meta["val"] + for node in gm.graph.nodes + if node.meta.get("val", None) is not None + ] + + fake_mode = _detect_fake_mode_from_gm(gm) + if fake_mode is not None: + return fake_mode.shape_env + for v in vals: + if isinstance(v, torch.SymInt): + return v.node.shape_env + + +def _rename_without_collisions( + name_map: Dict[str, str], + orig_name: str, + name: str, + is_placeholder: bool = False, +): + """ + Renames nodes to avoid name collisions, with suffixing. + name_map: map from original name to new name + orig_name: mapping key + name: candidate name (potentially suffixed, e.g. mul_2) + is_placeholder: if the node is a placeholder, avoid detecting suffix + """ + if name in name_map.values(): + # non-placeholder nodes may be suffixed with the count + # instead of adding another suffix, we will try to increment it + match = re.match(r"(.*)_(\d+)", name) + if match and not is_placeholder: + name, n = match.group(1), int(match.group(2)) + else: + n = 0 + while (dup_name := f"{name}_{n + 1}") in name_map.values(): + n += 1 + name_map[orig_name] = dup_name + else: + name_map[orig_name] = name + return name_map[orig_name] + + +def _check_input_constraints_for_graph( + input_placeholders: List[torch.fx.Node], flat_args_with_path, range_constraints +): + def get_keystr(key_path: KeyPath) -> str: + """For a given index into the flat_args, return a human readable string + describing how to access it, e.g. "*args["foo"][0].bar" + """ + # Prefix the keypath with "*args" or "**kwargs" to make it clearer where + # the arguments come from. Ultimately we ought to serialize the + # original arg names for the best error message here. + args_kwargs_key_path = key_path[0] + assert isinstance(args_kwargs_key_path, SequenceKey) + if args_kwargs_key_path.idx == 0: + return f"*args{keystr(key_path[1:])}" + else: + kwarg_key = key_path[1] + assert isinstance(kwarg_key, MappingKey) + name = str(kwarg_key)[1:-1] # get rid of the enclosed [] + return f"{name}{keystr(key_path[2:])}" + + import sympy + + from torch._export.passes.add_runtime_assertions_for_constraints_pass import ( + _convert_range_to_int, + ) + from torch.utils._sympy.solve import try_solve + + if len(flat_args_with_path) != len(input_placeholders): + raise RuntimeError( + "Unexpected number of inputs " + f"(expected {len(input_placeholders)}, got {len(flat_args_with_path)})" + ) + # NOTE: export already guarantees that the same symbol is used in metadata + # for all InputDims related by equality constraints, so we can just unify + # symbols with given input dimension values to check equality constraints. + unification_map: Dict[sympy.Symbol, Any] = {} + for (key_path, arg), node in zip(flat_args_with_path, input_placeholders): + node_val = node.meta.get("val") + if isinstance(node_val, FakeTensor): + if not isinstance(arg, torch.Tensor): + raise RuntimeError( + f"Expected input at {get_keystr(key_path)} to be a tensor, but got {type(arg)}", + ) + + if len(node_val.shape) != len(arg.shape): + raise RuntimeError( + f"Unexpected number of dimensions in input at {get_keystr(key_path)}.shape " + f"(expected {node_val.shape}, got {arg.shape})" + ) + + for j, (arg_dim, node_dim) in enumerate(zip(arg.shape, node_val.shape)): + # TODO(avik): Assert the following property in the IR verifier: + # node_dim is either an int or a SymInt containing an int or a unary sympy.Expr + if ( + isinstance(node_dim, torch.SymInt) + and len(node_dim.node.expr.free_symbols) == 1 + ): + symbol = next(iter(node_dim.node.expr.free_symbols)) + if symbol in unification_map: + existing_dim = node_dim.node.expr.subs(unification_map) + if arg_dim != existing_dim: + raise RuntimeError( + f"Expected input at {get_keystr(key_path)}.shape[{j}] to be equal to " + f"{existing_dim}, but got {arg_dim}", + ) + else: + if ( + isinstance(arg_dim, torch.SymInt) + and not arg_dim.node.expr.is_number + ): + # This can happen when, say, arg is a fake tensor. + # We do not run checks on symbolic shapes of fake inputs as + # such checks can affect the shape env. + pass + else: + if isinstance(node_dim.node.expr, sympy.Symbol): + # Short cut for try_solve below. Also useful in cases where + # sympy.Eq(node_dim.node.expr, arg_dim) would evaluate to False + # purely because symbol is constrained to be size-like, + # e.g., when node_dim.node.expr = symbol and arg_dim = 0. + unification_map[symbol] = int(arg_dim) + else: + solution = try_solve( + sympy.Eq(node_dim.node.expr, arg_dim), symbol + ) + if solution is None: + raise RuntimeError( # noqa: B904 + f"Expected input {node.name}.shape[{j}] = {arg_dim} to be " + f"of the form {node_dim.node.expr}, where {symbol} is an integer" + ) + else: + unification_map[symbol] = int(solution[1]) + + if node_dim.node.expr in range_constraints: + min_val, max_val = _convert_range_to_int( + range_constraints[node_dim.node.expr] + ) + # NOTE: we allow dimensions to be 0/1 at runtime + if min_val > 2: + if arg_dim < min_val: + raise RuntimeError( + f"Expected input at {get_keystr(key_path)}.shape[{j}] to be >= " + f"{min_val}, but got {arg_dim}", + ) + if max_val < math.inf: + if arg_dim > max_val: + raise RuntimeError( + f"Expected input at {get_keystr(key_path)}.shape[{j}] to be <= " + f"{max_val}, but got {arg_dim}", + ) + else: + if arg_dim != node_dim: + if ( + isinstance(node_dim, torch.SymInt) + and not node_dim.node.expr.is_number + ): + # this means we deferred a guard from export analysis to runtime, let this pass + # we'll add a runtime assert checking equality to this replacement expression + continue + raise RuntimeError( + f"Expected input at {get_keystr(key_path)}.shape[{j}] to be equal to " + f"{node_dim}, but got {arg_dim}", + ) + elif isinstance(node_val, (int, float, str)): + if type(arg) != type(node_val) or arg != node_val: + raise RuntimeError( + f"Expected input at {get_keystr(key_path)} to be equal to {node_val}, but got {arg}", + ) + + +def register_dataclass_as_pytree_node( + cls: Type[Any], + flatten_fn: Optional[FlattenFunc] = None, + unflatten_fn: Optional[UnflattenFunc] = None, + *, + serialized_type_name: Optional[str] = None, + to_dumpable_context: Optional[ToDumpableContextFn] = None, + from_dumpable_context: Optional[FromDumpableContextFn] = None, + return_none_fields: bool = False, +) -> None: + assert dataclasses.is_dataclass( + cls + ), f"Only dataclasses can be registered with this function: {cls}" + + def default_flatten_fn(obj: Any) -> Tuple[List[Any], Context]: + flattened = [] + flat_names = [] + none_names = [] + for f in dataclasses.fields(obj): + name, val = f.name, getattr(obj, f.name) + if val is not None or return_none_fields: + flattened.append(val) + flat_names.append(name) + else: + none_names.append(name) + return flattened, [flat_names, none_names] + + def default_unflatten_fn(values: Iterable[Any], context: Context) -> Any: + flat_names, none_names = context + return cls(**dict(zip(flat_names, values)), **dict.fromkeys(none_names)) + + def default_flatten_fn_with_keys(obj: Any) -> Tuple[List[Any], Context]: + flattened, (flat_names, none_names) = flatten_fn(obj) # type: ignore[misc] + return [(MappingKey(k), v) for k, v in zip(flat_names, flattened)], flat_names + + flatten_fn = flatten_fn if flatten_fn is not None else default_flatten_fn + unflatten_fn = unflatten_fn if unflatten_fn is not None else default_unflatten_fn + + if (to_dumpable_context is None) ^ (from_dumpable_context is None): + raise ValueError( + f"Both to_dumpable_context and from_dumpable_context for {cls} must " + "be None or registered." + ) + + _register_pytree_node( + cls, + flatten_fn, + unflatten_fn, + serialized_type_name=serialized_type_name, + flatten_with_keys_fn=default_flatten_fn_with_keys, + to_dumpable_context=to_dumpable_context, + from_dumpable_context=from_dumpable_context, + ) + + +def is_param(program: "ExportedProgram", node: torch.fx.Node) -> bool: + """ + Checks if the given node is a parameter within the exported program + """ + + return node.name in program.graph_signature.inputs_to_parameters + + +def get_param( + program: "ExportedProgram", + node: torch.fx.Node, +) -> Optional[torch.nn.Parameter]: + """ + Returns the parameter associated with the given node in the exported program. + Returns None if the node is not a parameter within the exported program + """ + + if is_param(program, node): + parameter_name = program.graph_signature.inputs_to_parameters[node.name] + return program.state_dict[parameter_name] + + return None + + +def is_buffer(program: "ExportedProgram", node: torch.fx.Node) -> bool: + """ + Checks if the given node is a buffer within the exported program + """ + + return node.name in program.graph_signature.inputs_to_buffers + + +def get_buffer( + program: "ExportedProgram", + node: torch.fx.Node, +) -> Optional[torch.Tensor]: + """ + Returns the buffer associated with the given node in the exported program. + Returns None if the node is not a buffer within the exported program + """ + + if is_buffer(program, node): + buffer_name = program.graph_signature.inputs_to_buffers[node.name] + if buffer_name in program.graph_signature.non_persistent_buffers: + return program.constants[buffer_name] + else: + return program.state_dict[buffer_name] + + return None + + +def is_lifted_tensor_constant( + program: "ExportedProgram", + node: torch.fx.Node, +) -> bool: + """ + Checks if the given node is a lifted tensor constant within the exported program + """ + + return node.name in program.graph_signature.inputs_to_lifted_tensor_constants + + +def get_lifted_tensor_constant( + program: "ExportedProgram", + node: torch.fx.Node, +) -> Optional[torch.Tensor]: + """ + Returns the lifted tensor constant associated with the given node in the exported program. + Returns None if the node is not a lifted tensor constant within the exported program + """ + + if is_lifted_tensor_constant(program, node): + lifted_tensor_name = program.graph_signature.inputs_to_lifted_tensor_constants[ + node.name + ] + return program.constants[lifted_tensor_name] + + return None + + +def sequential_split(gm: torch.fx.GraphModule, node_call_back) -> torch.fx.GraphModule: + """ + sequential_split creates a new graph module that splits the input graph module into multiple submodules + based on the node_call_back. It doesn't mutate the input graph module. The node_call_back should return + True if the node is a delimiter. Delimiter will be the first node in the next submodule. + """ + from torch.fx.passes.split_module import split_module + + split_map = {} + split_id = 0 + for node in gm.graph.nodes: + if node_call_back(node): + split_id += 1 + split_map[node] = split_id + + new_gm = split_module( + gm, + gm, + lambda node: split_map[node], + keep_original_order=True, + keep_original_node_name=True, + ) + # Keep the codegen from original graph module to preserve e.g. pytree info. + new_gm.graph._codegen = gm.graph._codegen + new_gm.recompile() + return new_gm + + +def nodes_filter(nodes: List[torch.fx.Node], node_call_back) -> List[torch.fx.Node]: + """Returns the nodes that match the node_call_back as a list.""" + return [node for node in nodes if node_call_back(node)] + + +def nodes_first( + nodes: List[torch.fx.Node], node_call_back=None +) -> Optional[torch.fx.Node]: + """ + Returns the first node that matches the node_call_back. If no node matches, returns None. + When node_call_back is None, returns the first node in the node list. + """ + ret = nodes_filter(nodes, node_call_back if node_call_back else lambda node: True) + if len(ret) > 0: + return ret[0] + return None + + +def nodes_count(nodes: List[torch.fx.Node], node_call_back) -> int: + """Returns the number of nodes that match the node_call_back.""" + return len(nodes_filter(nodes, node_call_back)) + + +def nodes_map(nodes: List[torch.fx.Node], node_call_back) -> List[torch.fx.Node]: + """ + Sequentially visit the nodes list and invoke node_call_back on each element. + Returns the nodes list after the node_call_back is invoked on each element. + """ + for node in nodes: + node_call_back(node) + return nodes + + +def node_replace_(old_node: torch.fx.Node, new_node: torch.fx.Node) -> None: + """ + Replace all uses of old_node with new_node. + """ + old_node.replace_all_uses_with(new_node) + old_node.users.clear() + old_node.graph.erase_node(old_node) + + +def node_inline_(call_mod_node: torch.fx.Node) -> None: + """ + Inline the submodule of the given node into the parent module. + Note: we only support the case where submodule takes tensors inputs. + """ + assert call_mod_node.op == "call_module" + gm = call_mod_node.graph.owning_module + + assert isinstance(call_mod_node.target, str) + sub_gm = getattr(gm, call_mod_node.target) + + phs = (node for node in sub_gm.graph.nodes if node.op == "placeholder") + body = ( + node for node in sub_gm.graph.nodes if node.op not in ("placeholder", "output") + ) + output = [node for node in sub_gm.graph.nodes if node.op == "output"] + + for ph, arg in zip(phs, call_mod_node.args): + assert isinstance(arg, torch.fx.Node) + node_replace_(ph, arg) + + with gm.graph.inserting_before(call_mod_node): + for node in body: + new_node = gm.graph.node_copy(node) + node_replace_(node, new_node) + + if len(output) > 0: + assert len(output) == 1 and len(output[0].args) == 1 + new_output = output[0].args[0] + + if isinstance(new_output, torch.fx.Node): + # Clear the users of the output node and set + # the users to be the users of original call_module node. + new_output.users.clear() + node_replace_(call_mod_node, new_output) + elif isinstance(new_output, (list, tuple)): + # Pop subgraph output node from users. + for node in new_output: + node.users.pop(output[0]) + + # Inline the get_item calls for the output node. + get_item_users = nodes_filter( + list(call_mod_node.users.keys()), + lambda node: node.op == "call_function" + and node.target == operator.getitem, + ) + # get_item_node.args[1] is the idx referring to new_output[idx] + nodes_map( + get_item_users, + lambda get_item_node: node_replace_( + get_item_node, + new_output[get_item_node.args[1]], + ), + ) + call_mod_node.graph.erase_node(call_mod_node) + else: + raise NotImplementedError( + f"Unsupported output type {type(new_output)}. Expect it to be a Node or a list/tuple of Nodes." + ) + else: + call_mod_node.graph.erase_node(call_mod_node) + + gm.delete_all_unused_submodules() + gm.recompile() + return gm + + +def _get_torch_jit_trace_forward_signature(mod: torch.nn.Module): + """ + Get source code and parse argument names using AST. The function returns + a signature of the forward() function. + + # TODO: Directly provide inspect.signature compatible TS-d module. + """ + ast_mod = ast.parse(mod.code) + ast_func_def: ast.FunctionDef = ast_mod.body[0] # type: ignore[assignment] + + # FIXME(jiashenc): TorchScript should only allow positional or keywords arguments. + arg_type_map = {"args": Parameter.POSITIONAL_OR_KEYWORD} + + # Traverse all argument types in AST tree and create associated parameters. + param_list = [] + for arg_type, param_type in arg_type_map.items(): + arg_name_list = [a.arg for a in getattr(ast_func_def.args, arg_type)] + for arg_name in arg_name_list: + if arg_name == "self": + continue # Skip self argument. + param_list.append(inspect.Parameter(arg_name, param_type)) + + return inspect.Signature(parameters=param_list) + + +def _bind_signature_to_inputs(mod, fake_args, fake_kwargs): + if isinstance(mod, (torch.jit.ScriptModule, torch.jit.TracedModule)): + sig = _get_torch_jit_trace_forward_signature(mod) + + # Sanity check for placeholder names coming from TorchScript. + assert len(sig.parameters) == len(fake_args) + len(fake_kwargs), ( + "Arguments other than POSITIONAL_OR_KEYWORD kinds in forward() " + "are not supported in _get_torch_jit_trace_forward_signature" + ) + else: + sig = inspect.signature(mod.forward) + + return sig.bind(*fake_args, **fake_kwargs).arguments + + +def _name_hoo_subgraph_placeholders(gm: torch.fx.GraphModule) -> None: + """ + Propagate placeholder names from the top-level graph into HigherOrderOp subgraphs, + and handle collisions with non-placeholders by count suffixing. + Different HOO subgraph types have different input schemas, so we first enumerate them + and gather the top-level named placeholder nodes. + """ + # gather all HOO subgraphs and their top-level named placeholder nodes + subgraph_ph_tuples: List[Tuple[torch.fx.GraphModule, List[torch.fx.Node]]] = [] + for node in gm.graph.nodes: + if node.op == "call_function" and isinstance( + node.target, torch._ops.HigherOrderOperator + ): + # HOO subgraphs have varying input schemas, so we enumerate them there + if node.target._name == "cond": + _, true_graph, false_graph, cond_args = node._args + subgraph_ph_tuples.append((getattr(gm, true_graph.target), cond_args)) + subgraph_ph_tuples.append((getattr(gm, false_graph.target), cond_args)) + elif node.target._name == "wrap_with_set_grad_enabled": + subgraph, phs = node._args[1], node._args[2:] + subgraph_ph_tuples.append((getattr(gm, subgraph.target), phs)) + elif node.target._name == "map_impl": + body_graph, array, args = node._args + subgraph_ph_tuples.append( + (getattr(gm, body_graph.target), array + args) + ) + + # propagate names + for subgraph, hoo_phs in subgraph_ph_tuples: + name_map: Dict[str, str] = {} + for i, node in enumerate(subgraph.graph.nodes): + if i < len(hoo_phs): # placeholder, retain name + name_map[node.name] = hoo_phs[i].name + node.name = node.target = hoo_phs[i].name + else: # non-placeholder, check for collisions + node.name = _rename_without_collisions(name_map, node.name, node.name) + + # recurse and recompile + _name_hoo_subgraph_placeholders(subgraph) + subgraph.recompile() + + +def placeholder_naming_pass( + gm: torch.fx.GraphModule, + export_graph_signature: "ExportGraphSignature", + mod: torch.nn.Module, + fake_args, + fake_kwargs, + fake_params_buffers, + constants: Dict[str, Any], +) -> None: + """ + This pass is run at the end of _export_non_strict() to assign better placeholder node names: + - User inputs: + These follow the signature of mod.forward(), e.g. forward(x, y) produces nodes x, y. + For nested inputs from dictionaries, lists, tuples, or dataclasses, + the names are a concatenation of the path to the tensor. + e.g. x = { + 'a': torch.randn(), + 'b': [torch.randn(), torch.randn()] + } + produces nodes x_a, x_b_0, x_b_1. + - Parameters/buffers/constants/custom objects: + These follow the FQN of the object, prefixed by "p", "b", "c", "obj" respectively. + e.g. self.bar.l0.weight produces "p_bar_l0_weight". + - Effect tokens: + These are named token, token_1, ... + """ + + def _strip_name(x): + if x.startswith("L__self___"): + x = x[len("L__self___") :] + elif x.startswith("self_"): + x = x[len("self_") :] + x = re.sub(r"[^a-zA-Z0-9]", "_", x) + return x + + def _extract_pytree_key(x): + if isinstance(x, MappingKey): + x = re.sub(r"[^a-zA-Z0-9]", "_", str(x.key)) + return x + elif isinstance(x, SequenceKey): + return str(x.idx) + elif isinstance(x, GetAttrKey): + return x.name + else: + raise RuntimeError(f"Pytree key of type {type(x)} not handled for {x}") + + name_map: Dict[str, str] = {} + + # map user input names with mod.forward() signature + combined_args = _bind_signature_to_inputs(mod, fake_args, fake_kwargs) + + flat_args_with_path, _ = tree_flatten_with_path(combined_args) + user_input_names = [ + spec.arg.name + for spec in export_graph_signature.input_specs + if spec.kind == InputKind.USER_INPUT + ] + + # use pytree path to name nested user inputs + for (arg_path, arg), user_input_name in zip(flat_args_with_path, user_input_names): + if user_input_name: + _rename_without_collisions( + name_map, + user_input_name, + placeholder_prefixes[InputKind.USER_INPUT] + + "_".join(_extract_pytree_key(x).lower() for x in arg_path), + is_placeholder=True, + ) + + # use graph signature input specs to map param/buffer/constant names + # name effect tokens as token, token_1, ... (these aren't visible to user) + for spec in export_graph_signature.input_specs: + if spec.kind == InputKind.USER_INPUT: + continue + if spec.kind == InputKind.TOKEN: + base_name = "" + else: + base_name = _strip_name(spec.target).lower() + base_name = re.sub(r"[^a-zA-Z0-9]", "_", base_name) + + _rename_without_collisions( + name_map, + spec.arg.name, + placeholder_prefixes[spec.kind] + base_name, + is_placeholder=True, + ) + + # handle naming collisions with call_function/get_attr inputs. + # here, we want to prioritize user input names over call_function names + # e.g. not have forward(self, mul): lead to a placeholder node called mul_13, + # so we increment the suffix of call_function nodes as needed + for node in gm.graph.nodes: + if node.op == "placeholder": + continue + _rename_without_collisions(name_map, node.name, node.name) + + # assign new node names + for node in gm.graph.nodes: + if node.op == "placeholder": + assert node.name in name_map + node.name = node.target = name_map[node.name] + elif node.name in name_map: + node.name = name_map[node.name] + + # propagate names to higher order op subgraphs + _name_hoo_subgraph_placeholders(gm) + + # re-generate graph module code + gm.recompile() + + # modify graph signature (input specs, output specs, user input mutations) + for spec in export_graph_signature.input_specs: + assert spec.arg.name in name_map + spec.arg.name = name_map[spec.arg.name] + if ( # handle targets for custom objects + spec.kind == InputKind.CUSTOM_OBJ and spec.target in name_map + ): + spec.target = name_map[spec.target][4:] # strip obj_ prefix + + for spec in export_graph_signature.output_specs: + if spec.arg.name in name_map: + spec.arg.name = name_map[spec.arg.name] + if spec.kind == OutputKind.USER_INPUT_MUTATION and spec.target in name_map: + spec.target = name_map[spec.target] + + # rename keys in constants dict for custom objects + for name in list(constants.keys()): + constant = constants[name] + if name in name_map and not isinstance( + constant, torch.Tensor + ): # rename custom objects with generic names + new_name = name_map[name] + if ( + new_name != name + and re.match(r"arg(\d+)_1", name) + and new_name != placeholder_prefixes[InputKind.CUSTOM_OBJ] + name + ): + constants[new_name] = constant + del constants[name] + + +def remove_proxy_from_state_dict(state_dict: Dict, in_place: bool) -> Dict: + """ + If `in_place` is false, return a new copy of `state_dict` with "proxy" removed from `v.__dict__`. + `v` is the values in the dictionary. + If `in_place` is true, modify `state_dict` in place. + """ + if in_place: + for k, v in state_dict.items(): + if hasattr(v, "proxy"): + delattr(state_dict[k], "proxy") + return state_dict + else: + new_state_dict = {} + for k, v in state_dict.items(): + if hasattr(v, "proxy"): + new_state_dict[k] = v.clone().detach() + else: + new_state_dict[k] = v + return new_state_dict + + +def _detect_fake_mode_from_gm( + gm: torch.fx.GraphModule, +) -> torch._subclasses.fake_tensor.FakeTensorMode: + """ + For a given graph module, we look at the "val" of placeholder nodes to find the fake inputs. + Additionally, if gm doesn't have placeholders, we further look at the "example_value" or "val" of other nodes. + If no fake mode is found, we return None for fake_mode. + """ + + fake_inps: List[torch.Tensor] = [] + fake_vals: List[torch.Tensor] = [] + for node in gm.graph.nodes: + if node.op == "placeholder" and "val" in node.meta: + fake_val = node.meta["val"] + if fake_val is not None and isinstance(fake_val, torch.Tensor): + fake_inps.append(fake_val) + elif len(fake_inps) == 0 and ( + "example_value" in node.meta or "val" in node.meta + ): + fake_val = None + if "example_value" in node.meta: + fake_val = node.meta["example_value"] + elif "val" in node.meta: + fake_val = node.meta["val"] + if fake_val is not None and isinstance(fake_val, torch.Tensor): + fake_vals.append(fake_val) + + return detect_fake_mode(fake_inps + fake_vals) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/verifier.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/verifier.py new file mode 100644 index 0000000000000000000000000000000000000000..68c5bcaae39af69f5527e8dcf0e08ed49bad4563 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/verifier.py @@ -0,0 +1,456 @@ +# mypy: allow-untyped-defs +import inspect +import math +import operator +from collections.abc import Iterable +from typing import Any, Dict, final, List, Tuple, Type, TYPE_CHECKING + +import torch +from torch._ops import HigherOrderOperator, OpOverload +from torch._subclasses.fake_tensor import FakeTensor +from torch.export.graph_signature import ( + CustomObjArgument, + InputKind, + SymIntArgument, + TensorArgument, + TokenArgument, +) +from torch.fx import GraphModule + +if TYPE_CHECKING: + from torch.export.exported_program import ExportedProgram + +class SpecViolationError(Exception): + pass + + +def is_functional(op: OpOverload) -> bool: + return not op._schema.is_mutable + + +def _check_has_fake_tensor(node: torch.fx.Node) -> None: + # TODO(angelayi): remove this in favor of _check_val + return _check_val(node) + + +def _check_val(node: torch.fx.Node) -> None: + from torch.fx.experimental.symbolic_shapes import SymBool, SymFloat, SymInt + + def _check_correct_val(val): + if val is None: + return True + elif isinstance(val, (int, bool, str, float)): + return True + elif isinstance(val, (torch.memory_format, torch.dtype, torch.device, torch.layout)): + return True + elif isinstance(val, (FakeTensor, torch.Tensor)): # TODO(zhxchen17) Remove Tensor. + return True + elif isinstance(val, (SymInt, SymFloat, SymBool)): + return True + elif isinstance(val, CustomObjArgument): + return True + elif isinstance(val, Iterable): + return all(_check_correct_val(x) for x in val) + return False + + def _no_returns(op): + if not isinstance(op, OpOverload): + return False + return len(op._schema.returns) == 0 + + if "val" not in node.meta: + if node.op == "call_function" and _no_returns(node.target): + return + raise SpecViolationError(f"Node.meta {node.name} is missing val field.") + + val = node.meta["val"] + if not _check_correct_val(val): + raise SpecViolationError(f"Node.meta {node.name} has invalid val field {val}") + + +def _check_torch_fn(node: torch.fx.Node) -> None: + torch_fn = node.meta.get("torch_fn") + if torch_fn is None: + raise SpecViolationError(f"Unable to find torch_fn metadata for node {node.name}") + if ( + not isinstance(torch_fn, tuple) and + isinstance(torch_fn[0], str) and + isinstance(torch_fn[1], str) + ): + raise SpecViolationError(f"Node.meta {node.name} has invalid torch_fn field {torch_fn}") + +class _VerifierMeta(type): + _registry: Dict[str, Type['Verifier']] = {} + + def __new__(metacls, name, bases, attrs): + if bases: + if "check" in attrs or "_check_graph_module" in attrs: + raise SyntaxError("Overriding method check is not allowed.") + assert "dialect" in attrs and attrs["dialect"] != "ATEN" + else: + assert "check" in attrs + assert "_check_graph_module" in attrs + assert attrs["dialect"] == "ATEN" + + assert isinstance(attrs["dialect"], str) + ret = type.__new__(metacls, name, bases, attrs) + metacls._registry[attrs["dialect"]] = ret # type: ignore[assignment] + return ret + +def getattr_recursive(obj: Any, target: str) -> Any: + target_atoms = target.split('.') + attr_itr = obj + for i, atom in enumerate(target_atoms): + if not hasattr(attr_itr, atom): + raise RuntimeError(f"Node referenced nonexistent target {'.'.join(target_atoms[:i])}") + attr_itr = getattr(attr_itr, atom) + return attr_itr + + +class Verifier(metaclass=_VerifierMeta): + dialect = "ATEN" + + def allowed_builtin_ops(self) -> List: + return [ + operator.getitem, + operator.add, + operator.mul, + operator.sub, + operator.truediv, + operator.ge, + operator.le, + operator.gt, + operator.lt, + operator.eq, + operator.ne, + operator.floordiv, + operator.mod, + operator.and_, + operator.or_, + operator.not_, + operator.pow, + operator.neg, + operator.abs, + math.ceil, + math.floor, + math.trunc, + ] + + def allowed_op_types(self) -> Tuple[Type[Any], ...]: + return (OpOverload, HigherOrderOperator) + + def allowed_getattr_types(self) -> Tuple[Type[Any], ...]: + return (torch.fx.GraphModule,) + + def check_valid_op(self, op): + pass + + def check_additional(self, gm: GraphModule) -> None: + """ + Additional checks that are specific to some dialects. + """ + + @final + def check(self, ep: "ExportedProgram") -> None: + self._check_graph_module(ep.graph_module) + _verify_exported_program_module_call_graph(ep) + _verify_exported_program_signature(ep) + + @final + def _check_graph_module(self, gm: torch.fx.GraphModule) -> None: + def _allowed_getattr_types() -> Tuple[Type[Any], ...]: + ret = self.allowed_getattr_types() + assert not any(t is object for t in ret) + return ret + + def _check_valid_op(op) -> None: + def _allowed_builtin_ops() -> List: + ret = self.allowed_builtin_ops() + assert all(inspect.isbuiltin(op) for op in ret) + return ret + + def _allowed_op_types() -> Tuple[Type[Any], ...]: + ret = self.allowed_op_types() + assert not any(t is object for t in ret) + return ret + + # TODO Remove this allowlist. + _allowed_torch_functions = ( + torch.autograd.grad_mode.set_grad_enabled, + torch.sym_int, + torch.sym_float, + torch.sym_ite, + torch.sym_max, + torch.sym_min, + torch.sym_not, + torch.sym_sqrt, + # TODO (tmanlaibaatar) + # Predispatch export is able to contain autograd ops. + # These will be modeled as HOO later + torch._C._set_grad_enabled, + ) + + if not isinstance(op, _allowed_op_types()): + if op not in _allowed_builtin_ops() and op not in _allowed_torch_functions: + raise SpecViolationError( + f"Operator '{op}' is not an allowed operator type: {_allowed_op_types()}\n" + f"Valid builtin ops: {_allowed_builtin_ops()}" + f"Valid torch functions: {_allowed_torch_functions}" + ) + + if isinstance(op, OpOverload): + # All ops functional + # TODO (tmanlaibaatar) more proper way is needed here + if self.dialect != "TRAINING" and not is_functional(op): + raise SpecViolationError( + f"operator '{op}' is not functional" + ) + self.check_valid_op(op) + + for mod in gm.modules(): + if not isinstance(mod, torch.fx.GraphModule): + continue + + mod.graph.lint() + for node in mod.graph.nodes: + # TODO(T140410192): should have fake tensor for all dialects + if node.op in {"call_module", "call_method"}: + raise SpecViolationError( + f"call_module is not valid: got a class '{node.target}' ", + ) + + elif node.op == "call_function": + _check_val(node) + + _check_valid_op(node.target) + + elif node.op == "get_attr": + if not isinstance(node.target, str): + raise SpecViolationError( + f"Expected get_attr target to be string, but got {type(node.target)}" + ) + + attr = getattr_recursive(mod, node.target) + if isinstance(attr, torch.nn.Module): + def _is_type(name, ty): + return isinstance(getattr(attr, name, None), ty) + if type(attr).__name__ == "LoweredBackendModule": + if _is_type("backend_id", str) \ + and _is_type("processed_bytes", bytes) \ + and _is_type("compile_specs", list) \ + and hasattr(attr, "original_module"): + continue + else: + backend_id = getattr(attr, "backend_id", None) + processed_bytes = getattr(attr, "processed_bytes", None) + compile_specs = getattr(attr, "compile_specs", None) + raise SpecViolationError( + f"Invalid get_attr type {type(attr)}. \n" + f"LoweredBackendModule fields: " + f"backend_id(str) : {type(backend_id)}, " + f"processed_bytes(bytes) : {type(processed_bytes)}, " + f"compile_specs(list) : {type(compile_specs)}" + ) + + if not isinstance(attr, _allowed_getattr_types()): + raise SpecViolationError( + f"Invalid get_attr type {type(attr)}. \n" + f"Valid get_attr types: {_allowed_getattr_types()}" + ) + + + elif node.op == "placeholder": + _check_val(node) + # TODO(zhxchen17) + # elif node.op == "output": + # _check_flattened_outputs() + + self.check_additional(gm) + + +class TrainingIRVerifier(Verifier): + dialect = "TRAINING" + + +def _verify_exported_program_module_call_graph(exported_program) -> None: + module_call_graph = exported_program.module_call_graph + nodes = { + node.name for node in exported_program.graph.nodes + } + for entry in module_call_graph: + if entry.signature is not None: + for arg in entry.signature.inputs: + if arg.name and arg.name not in nodes: + raise SpecViolationError( + f"Input {arg.name} does not exist in the graph." + ) + for arg in entry.signature.outputs: + if arg.name and arg.name not in nodes: + raise SpecViolationError( + f"Output {arg.name} does not exist in the graph." + ) + + +def _verify_exported_program_signature(exported_program) -> None: + # Check ExportedProgram signature matches + gs = exported_program.graph_signature + + # Check every node in the signature exists in the graph + input_node_names = [node.name for node in exported_program.graph.nodes if node.op == "placeholder"] + + if len(input_node_names) != len(gs.input_specs): + raise SpecViolationError( + f"Number of graph inputs ({len(input_node_names)}) " + f"does not match number of inputs in the graph signature ({len(gs.input_specs)})" + ) + + for input_spec, node in zip(gs.input_specs, input_node_names): + if isinstance(input_spec.arg, (TensorArgument, SymIntArgument)): + if input_spec.arg.name != node: + raise SpecViolationError( + f"Input spec name {input_spec.arg.name} does not match node name {node}" + ) + + if input_spec.kind == InputKind.USER_INPUT: + continue + + elif input_spec.kind == InputKind.PARAMETER: + if not isinstance(input_spec.arg, TensorArgument): + raise SpecViolationError( + f"Parameter {input_spec.name} is not a tensor argument. Found {input_spec.arg} instead." + ) + if input_spec.target is None: + raise SpecViolationError( + f"InputSpec for {input_spec.name} has no target." + ) + + param = input_spec.target + if param not in exported_program.state_dict: + raise SpecViolationError( + f"Parameter {param} is not in the state dict." + ) + + if not isinstance(exported_program.state_dict[param], torch.nn.Parameter): + raise SpecViolationError( + f"State dict entry for parameter {param} is not an instance of torch.nn.Parameter." + ) + + elif input_spec.kind == InputKind.BUFFER: + if not isinstance(input_spec.arg, TensorArgument): + raise SpecViolationError( + f"Buffer {input_spec.name} is not a tensor argument. Found {input_spec.arg} instead." + ) + if input_spec.target is None: + raise SpecViolationError( + f"InputSpec for {input_spec.name} has no target." + ) + + buffer = input_spec.target + if input_spec.persistent is None: + raise SpecViolationError( + f"Buffer {buffer} is missing a persistence flag" + ) + + if input_spec.persistent is True and buffer not in exported_program.state_dict: + raise SpecViolationError( + f"Buffer {buffer} is not in the state dict." + ) + + if input_spec.persistent is False and buffer in exported_program.state_dict: + raise SpecViolationError( + f"Non-persistent buffer {buffer} is in the state dict, it should not be." + ) + elif input_spec.kind == InputKind.CONSTANT_TENSOR: + if not isinstance(input_spec.arg, TensorArgument): + raise SpecViolationError( + f"Constant tensor {input_spec.name} is not a tensor argument. Found {input_spec.arg} instead." + ) + if input_spec.target is None: + raise SpecViolationError( + f"InputSpec for {input_spec.name} has no target." + ) + + tensor_const = input_spec.target + if tensor_const not in exported_program.constants: + raise SpecViolationError( + f"Constant tensor {tensor_const} is not in the constants dictionary." + ) + elif input_spec.kind == InputKind.CUSTOM_OBJ: + if not isinstance(input_spec.arg, CustomObjArgument): + raise SpecViolationError( + f"Custom object {input_spec.name} is not a custom object argument. Found {input_spec.arg} instead." + ) + if input_spec.target is None: + raise SpecViolationError( + f"InputSpec for {input_spec.name} has no target." + ) + + custom_obj = input_spec.target + if custom_obj not in exported_program.constants: + raise SpecViolationError( + f"Custom object {custom_obj} is not in the constants dictionary." + ) + elif input_spec.kind == InputKind.TOKEN: + if not isinstance(input_spec.arg, TokenArgument): + raise SpecViolationError( + f"Constant tensor {input_spec.name} is not a tensor argument. Found {input_spec.arg} instead." + ) + else: + raise SpecViolationError( + f"Unknown InputKind {input_spec.kind}." + ) + + # Check outputs + output_node = list(exported_program.graph.nodes)[-1] + assert output_node.op == "output" + output_nodes = [ + arg.name if isinstance(arg, torch.fx.Node) else arg + for arg in output_node.args[0] + ] + + if len(output_nodes) != len(gs.output_specs): + raise SpecViolationError( + f"Number of output nodes {len(output_nodes)} is different " + "Than the number of outputs specified by the graph signature: \n" + f"Number of mutated buffers: {len(gs.buffers_to_mutate)}. \n" + f"Number of user outputs: {len(gs.user_outputs)}. \n" + ) + + num_tokens = len(gs.output_tokens) + end = len(gs.buffers_to_mutate) + len(gs.user_inputs_to_mutate) + num_tokens + mutate_nodes: List[str] = output_nodes[num_tokens:end] + user_output_nodes = output_nodes[end:end + len(gs.user_outputs)] + + for mutation_node in mutate_nodes: + if mutation_node in gs.buffers_to_mutate: + if gs.buffers_to_mutate[mutation_node] not in gs.buffers: + raise SpecViolationError( + f"Buffer output {mutation_node} does not point to a buffer that exists. \n" + f"Dict of buffers that are mutated, in order: {gs.buffers_to_mutate} \n" + f"Buffer nodes available: {gs.buffers} \n" + ) + elif mutation_node in gs.user_inputs_to_mutate: + if gs.user_inputs_to_mutate[mutation_node] not in gs.user_inputs: + raise SpecViolationError( + f"User input output {mutation_node} does not point to a user input that exists. \n" + f"Dict of user inputs that are mutated, in order: {gs.user_inputs_to_mutate} \n" + f"User input nodes available: {gs.user_inputs} \n") + else: + raise SpecViolationError( + f"Mutation node {mutation_node} is neither a buffer nor a user input. " + f"Buffers to mutate: {gs.buffers_to_mutate}, User inputs to mutate: {gs.user_inputs_to_mutate}" + ) + + for user_output_node, user_output_name in zip(user_output_nodes, gs.user_outputs): + if user_output_node != user_output_name: + raise SpecViolationError( + f"User output {user_output_node} is not in the correct " + "order or is not found in the " + f"exported program's user_output list: {gs.user_outputs}. " + ) + + +def load_verifier(dialect: str) -> Type[Verifier]: + if dialect == "ATEN" or dialect == "": + return _VerifierMeta._registry.get(dialect, Verifier) + return _VerifierMeta._registry[dialect] diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/wrappers.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/wrappers.py new file mode 100644 index 0000000000000000000000000000000000000000..d57ff46de41c8f5961a859d3d1e2871984929b8d --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_export/wrappers.py @@ -0,0 +1,121 @@ +# mypy: allow-untyped-defs +from contextlib import contextmanager + +import torch +import torch._custom_ops +from torch._C import DispatchKey +from torch._higher_order_ops.strict_mode import strict_mode +from torch._higher_order_ops.utils import autograd_not_implemented +from torch._ops import HigherOrderOperator +from torch._subclasses.fake_tensor import FakeTensorMode +from torch.fx.experimental.proxy_tensor import ProxyTorchDispatchMode, track_tensor_tree +from torch.utils import _pytree as pytree + + +class ExportTracepoint(HigherOrderOperator): + def __init__(self): + super().__init__("_export_tracepoint") + + def __call__(self, *args, **kwargs): + return super().__call__(*args, **kwargs) + + +_export_tracepoint = ExportTracepoint() + + +@_export_tracepoint.py_impl(ProxyTorchDispatchMode) +def export_tracepoint_dispatch_mode(mode, *args, **kwargs): + p_args, p_kwargs = pytree.tree_map(mode.tracer.unwrap_proxy, (args, kwargs)) + proxy = mode.tracer.create_proxy( + "call_function", _export_tracepoint, p_args, p_kwargs + ) + return track_tensor_tree(args, proxy, constant=None, tracer=mode.tracer) + + +@_export_tracepoint.py_impl(FakeTensorMode) +def export_tracepoint_fake_tensor_mode(mode, *args, **kwargs): + with mode: + return args + + +@_export_tracepoint.py_functionalize_impl +def export_tracepoint_functional(ctx, *args, **kwargs): + unwrapped_args = ctx.unwrap_tensors(args) + unwrapped_kwargs = ctx.unwrap_tensors(kwargs) + + with ctx.redispatch_to_next(): + out = _export_tracepoint(*unwrapped_args, **unwrapped_kwargs) + return ctx.wrap_tensors(out) + + +_export_tracepoint.py_impl(DispatchKey.Autograd)( + autograd_not_implemented(_export_tracepoint, deferred_error=True) +) + + +@_export_tracepoint.py_impl(DispatchKey.CPU) +def export_tracepoint_cpu(*args, **kwargs): + return args + + +def _wrap_submodule(mod, path, module_call_specs): + assert isinstance(mod, torch.nn.Module) + assert path != "" + submodule = mod + for name in path.split("."): + if not hasattr(submodule, name): + raise RuntimeError(f"Couldn't find submodule at path {path}") + submodule = getattr(submodule, name) + + def update_module_call_signatures(path, in_spec, out_spec): + if path in module_call_specs: + assert module_call_specs[path]["in_spec"] == in_spec + assert module_call_specs[path]["out_spec"] == out_spec + module_call_specs[path] = {"in_spec": in_spec, "out_spec": out_spec} + + def check_flattened(flat_args): + for a in flat_args: + if not (isinstance(a, (torch.Tensor, str, int, float, bool)) or a is None): + raise AssertionError( + f"Only Tensors or scalars are supported as pytree flattened inputs, got: {a}" + ) + + def pre_hook(module, args, kwargs): + flat_args, in_spec = pytree.tree_flatten((args, kwargs)) + check_flattened(flat_args) + flat_args = _export_tracepoint(*flat_args, kind="module_call_inputs", path=path) + args, kwargs = pytree.tree_unflatten(flat_args, in_spec) + return args, kwargs + + def post_hook(module, args, kwargs, res): + _, in_spec = pytree.tree_flatten((args, kwargs)) + flat_res, out_spec = pytree.tree_flatten(res) + check_flattened(flat_res) + flat_res = _export_tracepoint(*flat_res, kind="module_call_outputs", path=path) + update_module_call_signatures(path, in_spec, out_spec) + return pytree.tree_unflatten(flat_res, out_spec) + + pre_handle = submodule.register_forward_pre_hook(pre_hook, with_kwargs=True) + post_handle = submodule.register_forward_hook(post_hook, with_kwargs=True) + return pre_handle, post_handle + + +@contextmanager +def _wrap_submodules(f, preserve_signature, module_call_signatures): + handles = [] + + try: + for path in preserve_signature: + handles.extend(_wrap_submodule(f, path, module_call_signatures)) + yield + finally: + for handle in handles: + handle.remove() + + +def _mark_strict_experimental(cls): + def call(self, *args): + return strict_mode(self, args) + + cls.__call__ = call + return cls diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..10a55772ab58b21573a6eba0356ddd3080164ac7 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py new file mode 100644 index 0000000000000000000000000000000000000000..f78ebb31c6cb4e6cab3d5519c3f1545ff82bd5c3 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py @@ -0,0 +1,1557 @@ +# mypy: ignore-errors + +import itertools +from contextlib import contextmanager, nullcontext +from functools import partial, wraps +from typing import Any, Callable, Dict, List, NewType, Optional, Tuple +from unittest.mock import patch + +import torch +import torch._dynamo.logging +import torch.nn as nn +import torch.utils._pytree as pytree +import torch.utils.dlpack +from torch import Tensor +from torch._decomp.decompositions_for_rng import PhiloxStateTracker, rng_decompositions +from torch._dispatch.python import enable_python_dispatcher +from torch._dynamo import compiled_autograd +from torch._dynamo.utils import dynamo_timed, preserve_rng_state +from torch._guards import detect_fake_mode +from torch._inductor.utils import BoxedBool +from torch._subclasses import FakeTensor, FakeTensorMode +from torch.fx.experimental.proxy_tensor import make_fx +from torch.fx.experimental.symbolic_shapes import ShapeEnv +from torch.utils._python_dispatch import is_traceable_wrapper_subclass + + +static_inputs_log = torch._logging.getArtifactLogger( + __name__, "cudagraph_static_inputs" +) + +from . import config +from ._aot_autograd.autograd_cache import ( # noqa: F401 + AOTAutogradCache, + autograd_cache_key, +) +from ._aot_autograd.collect_metadata_analysis import ( # noqa: F401 + run_functionalized_fw_and_collect_metadata, +) +from ._aot_autograd.functional_utils import ( # noqa: F401 + _check_if_mutation_can_be_in_graph, + are_all_mutations_hidden_from_autograd, + are_all_mutations_under_no_grad_or_inference_mode, + assert_functional_graph, + from_fun, + gen_alias_from_base, + has_data_mutation, + has_metadata_mutation, + is_fun, + sync_functional_tensor, + to_fun, +) +from ._aot_autograd.input_output_analysis import ( # noqa: F401 + _tensors_definitely_do_not_overlap, + compute_overlapping_inputs, + create_graph_signature, + create_synthetic_base_metadata, + remove_dupe_metadata, +) +from ._aot_autograd.jit_compile_runtime_wrappers import ( # noqa: F401 + aot_dispatch_autograd, + aot_dispatch_base, + aot_dispatch_export, +) +from ._aot_autograd.logging_utils import ( # noqa: F401 + callback_set, + describe_input, + format_guard_bug_msg, + get_aot_compilation_context, + get_aot_graph_name, + get_graph_being_compiled, + graph_being_compiled, + model_name, + nth_graph, + set_model_name, + setup_stacktrace_preservation_hooks, + track_graph_compiling, +) +from ._aot_autograd.runtime_wrappers import ( # noqa: F401 + AOTDedupeWrapper, + AOTSyntheticBaseWrapper, +) +from ._aot_autograd.schemas import ( # noqa: F401 + AOTConfig, + BackwardSignature, + FQN, + GraphInputName, + GraphOutputName, + GraphSignature, + InputAliasInfo, + MutationType, + OutputAliasInfo, + OutputType, + SubclassCreationMeta, + SubclassMeta, + TensorAlias, + ViewAndMutationMeta, +) +from ._aot_autograd.subclass_utils import ( # noqa: F401 + create_metadata_for_subclass, + requires_subclass_dispatch, + unwrap_tensor_subclasses, + wrap_tensor_subclasses, + wrap_tensor_subclasses_maybe_joint, +) +from ._aot_autograd.traced_function_transforms import ( # noqa: F401 + aot_dispatch_subclass, + create_functional_call, + create_functionalized_fn, + create_functionalized_rng_ops_wrapper, + create_joint, + fn_input_mutations_to_outputs, + fn_prepped_for_autograd, +) +from ._aot_autograd.utils import ( # noqa: F401 + _get_autocast_states, + _get_symint_hints, + call_func_at_runtime_with_args, + create_tree_flattened_fn, + KNOWN_TYPES, + make_boxed_compiler, + make_boxed_func, + maybe_to_fresh_input, + normalize_as_list, + partial_flatten_asdict, + root_module_when_exporting_non_strict, + strict_zip, +) +from .partitioners import default_partition + + +zip = strict_zip + +# This global counter increments every time we compile a graph with +# AOTAutograd. You can use this to correlate runtime error messages +# with compile time (e.g., if you get an error at runtime saying +# compiled graph 3 failed, you can set a breakpoint at compile time +# for this graph number to investigate further at compile time.) +# +# NB: this is different from get_aot_compilation_context, which tracks +# each underlying graph that is compiled. In contrast, AOT_COUNTER +# corresponds to top-level invocations of aot_module/aot_function; +# one counter is allocated per entire compiled block (but this block +# may involve compiling multiple subgraphs; e.g., for forwards/backwards) +AOT_COUNTER = itertools.count() + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# AOT Autograd contains a pretty non-trivial amount of logic to handle edge cases around aliasing and mutation +# that are external to the graph (they show up as side effects in some way when you run the graph). +# +# Take a look at `test_aotdispatch.py TestAOTAutograd.test_input_mutation*` tests for some examples functions +# and what they're compiled graphs looks like. +# Below is a very long comment detailing several edge cases, and showing how AOT Autograd handles them. +# +# Note [AOT Autograd: input data mutations] +# +# If we compile a function that mutates inputs, then those input mutations are real side effects +# that a user expects to see after running the compiled graph. +# However, the graph that we want to send to a backend needs to be *entirely* functional. +# The way we reconcile this difference is that we remove the mutations completely from the graph that we compile +# but we update the graph to return (updated_inputs, user_outputs). +# In the epilogue that runs after the compiled graph is executed, we copy the updated inputs back to the originals. +# +# Example: original user code: +# def f(x): +# x.mul_(2) +# out = x.mul(3) +# return out +# +# After AOT Autograd compiles, we end up with a: +# (a) compiled graph +# (b) autograd.Function.forward() method, that executes the compiled graph +# (c) wrapper function, that calls the autograd.Function.forward() and performs the epilogue +# +# The output of (a, b, c) are all written below. +# +# def compiled_forward_graph(x): +# x_updated = x.mul(2) +# out = x_updated.mul(3) +# return x_updated, out +# +# # x_updated gets a gradient in the compiled backward +# def compiled_backward_graph(grad_x_updated, grad_out): +# grad_x = ... +# return grad_x +# +# def autograd.Function.forward(x): +# x_updated, out = compiled_forward_graph(x) +# return x_updated, out +# +# def compiled_wrapper(x): +# x_updated, out = autograd.Function.apply(x) +# x.copy_(x_updated) +# return out +# +# Another important thing to note is that updated inputs (due to data mutations) *do* participate +# in the compiled backward graph! Since the compiled forward graph gets N extra outputs +# (due to updated inputs showing up as graph outputs), +# The compiled backward gets an additional N inputs. +# That way, during the x.copy_(x_updated) bit in the epilogue, gradients will flow from the updated input +# back to the original input. + + +# Note [AOT Autograd: input metadata mutations] +# +# For the same reason as input mutations, we also don't put input metadata mutations in the graph. +# Instead, we return the updated version of the input (a view), and mutate the input's metadata outside of the graph +# +# Example: original user code: +# def f(x): +# x.t_() +# out = x.mul(3) +# return out +# +# AOT Autograd output (compiled graph, autograd.Function.forward(), wrapper function): +# def compiled_forward_graph(x): +# x_updated = x.t() +# out = x_updated.mul(3) +# return x_updated, out +# +# # x_updated does *not* get a gradient in the compiled backward +# def compiled_backward_graph(grad_out): +# grad_x = ... +# return grad_x +# +# def autograd.Function.forward(x): +# x_updated, out = compiled_forward_graph(x) +# return x_updated, out +# +# def compiled_wrapper(x): +# x_updated, out = autograd.Function.apply(x) +# x.as_strided_(x_updated) +# return out + + +# Note [AOT Autograd: outputs aliasing inputs or intermediates!] +# +# AOT Autograd needs special handling for outputs that alias graph inputs or intermediates! +# Why? +# (1) autograd.Function.forward() has a limitation, where views that returned in the forward cannot later be mutated. +# (2) views don't need to be compiled in the graph anyway - it's cheap to generate them outside of the compiled graph, +# in an epilogue. +# For outputs that alias inputs, we do the following: +# (a) *still* return the aliased output as a graph output +# (b) In the AOT Autograd wrapper/epilogue, we don't return that aliased output. Instead, we use it to regenerate the output. +# +# For outputs that alias *intermediates*, we do the following: +# (a) Return the output in the compiled forward, **and** return it's ._base (a graph intermediates) as an output in the forward +# (b) Use (output, graph_intermediate) to regenerate the alias, and return that to the user (instead of the compiled fw output). +# You might wonder why we return the aliased output directly in the graph (and making the graph compute it), +# only to not return it and instead generate a fresh alias off of the intermediate, +# instead of (say) just storing metadata about the size/stride of the output somewhere to generate the alias. There are two reasons: +# (1) Getting the actual alias tensor allows us to use view-replay to generate the alias, instead of an as_strided() call +# (2) Inductor (and other backends) are free to change the memory format of graph outputs, if it results in better performance. +# This can result in problems if a user later tries to .view() that output expecting it to have one set of strides, +# when it has a different set of strides. +# By including the view op directly in the graph, inductor takes that into account when deciding what memory format +# the graph intermediate should be. +# +# Another important thing to note is how our traced backward() graph handles aliases. +# (this applies to outputs aliasing inputs, outputs aliasing intermediates, +# *and* updated inputs returned in the compiled forward due to metadata-only mutations). +# Any outputs that alias (either inputs or intermediates) do NOT participate in the compiled backward graph +# It would be wasteful to include them in the compiled backward(), because we regenerate them eagerly +# at the end of the forward. +# +# Example: original user code: +# def f(x): +# out1 = x.t() +# intermediate = x.mul(2) +# out2 = intermediate.view(-1) +# return out1, out2 +# +# AOT Autograd output (compiled graph, autograd.Function.forward(), wrapper function): +# def compiled_forward_graph(x): +# out1 = x.t() +# intermediate = x.mul(2) +# out2 = intermediate.view(-1) +# # the compiled graph also returns the intermediate +# return out1, out2, intermediate +# +# # intermediate gets a gradient in the compiled backward. +# # both output aliases (out1 and out2) do not. +# def compiled_backward_graph(grad_intermediate): +# grad_x = ... +# return grad_x +# +# def autograd.Function.forward(x): +# out1, out2, intermediate = compiled_forward_graph(x) +# return out1, out2, intermediate +# +# def compiled_wrapper(x): +# out1, out2, intermediate = autograd.Function.apply(x) +# # regenerate out1 from the input +# out1_regenerated = out1._view_func(x) +# # regenerate out1 from the intermediate +# out2_regenerated = out2._view_func(intermediate) +# return out1_regenerated, out2_regenerated + + +# Note [AOT Autograd: mutations to inputs that alias other inputs] +# +# Another edge case that is (only partially) handled today is when an input is mutated, but itself aliases another input. +# AOT Autograd needs to **ensure** that functionalization knows that the two inputs are aliased to each other. +# That way, when the aliased input is accessed later in the graph, functionalization knows to "update" the alias +# given the mutation that occurred. +# +# This is handled by updating the calling convention: we create a "synthetic base" that becomes a new input +# in the compiled function, and we regenerate the original (aliased) inputs directly off of the base +# inside of the compiled function. +# +# This logic is fully encapsulated in aot_wrapper_synthetic_base() +# +# Example: original user code: +# def f(x, x_view): +# x.mul_(2) +# out = x * x_view +# return out +# f(x, x.view(-1)) +# +# AOT Autograd output (compiled graph, autograd.Function.forward(), wrapper function): +# def compiled_forward_graph(base) +# x = generate_x(base) +# x_view = generate_x_view(base) +# x_updated = x.mul(2) +# x_view_updated = x_updated.view(-1) +# out = x_updated * x_view_updated +# return x_updated, out +# +# # The calling convention change from (aliases) -> (base) happens +# # *outside* of the autograd.Function.forward(). +# # That means the forward() only has 1 input (base), +# # and the backward() only has 1 output (grad_base) +# def compiled_backward_graph(grad_out): +# grad_base = ... +# return grad_base +# +# def autograd.Function.forward(base): +# x_updated, out = compiled_forward_graph(base) +# return x_updated, out +# +# # The compiled wrapper is where we create synthetic bases. +# # The info on which inputs are mutated is also tracked *before* synthetic base creation. +# def compiled_wrapper(x, x_view): +# base = merge_view_inputs(x, x_view) +# x_updated, out = autograd.Function.apply(base) +# # x and x_view are aliased in eager mode, so this mutation to x will automatically affect x_view. +# x.copy_(x_updated) +# return out + + +# Note [AOT Autograd: Views to avoid tangents aliasing inputs] +# +# We view every forward output when creating out tangent tensors to handle the problematic +# case in which a subclass does extra aliasing between graph outputs/inputs in a way that +# is not visible above the sublass. +# +# Ordinarily, when constructing the joint function that we want to trace in AOTAutograd, +# we're guaranteed that the tangent tensors that we pass +# into the joint are distinct tensors from the primals. This is because when +# decide which forward outputs to create tangents for, we only create tangents +# for forward outputs that are not aliases of inputs (See Note +# [AOT Autograd: outputs aliasing inputs or intermediates!]). +# +# However, when wrapper tensor subclasses enter the picture, it is possible +# to have an output of the forward that is a subclass that is not an +# input / alias of an input, but one of its inner tensors is an alias! +# NestedTensor is an example: Performing an out-of-place pointwise op on a +# NestedTensor constructs a fresh NestedTensor that holds onto the input's +# offsets tensor directly. +# +# Having tangent tensors that are the same as the (primal) forward inputs, +# can cause problems during tracing as make_fx() will specialize on our +# duplicate inputs: If we passed in the same tensor for primals_1 and +# tangents_1 during tracing, make_fx() will happily sub out all usages of +# tangents_1 with primals_1 in the graph, which is not what we want. +# +# To work around this, we view every forward output when creating out tangent +# tensors so that tangents can never be the same as forward inputs even if +# forward inputs alias forward outputs. + +# Note [Side-Effectful Tokens in AOTAutograd] +# +# We allow some some side-effectful operators in +# the post-AOTAutograd (functional) graph, such as prints and torchbind operations. +# To ensure that these side-effects are compatible to future graph passes that +# assume that the graph is functional, we will thread "effect tokens" to show +# data dependence between these side-effectful operators. Practically speaking, +# effect tokens are just dummy values (torch.tensor([])). The graph would look +# like the following: +# +# def gm(self, token0, reader): +# token1, frame = with_token(ordered_effect_op, (reader,), token0) +# frame = frame * 2 +# token2, frame2 = with_token(ordered_effect_op, (reader,), token1) +# frame2 = frame2 * 2 +# return token2, frame, frame2 +# +# We will pass the token as an input to the graph, thread it through +# side-effectful operators using the `with_effects` high order operator, and then +# return the updated token as an output. +# So the signature of the graph input would look something like +# (*tokens, *params_buffers, *user_inputs), and the signature of the graph +# output would look something like (*tokens, *outputs). +# +# However, Inductor does not want the concept of tokens in the final generated +# code's input and output. Since changing the graph signature inside of inductor +# is difficult, after generating the forward graph, we will run a pass to +# remove the tokens from the inputgenerate the following graph for Inductor, where +# the tokens are created and sunk within the graph, rather than as inputs and +# outputs: +# +# def gm(self, reader): +# token0 = torch.ops.prims._make_token() +# token1, frame = with_token(ordered_effect_op, (reader,), token0) +# frame = frame * 2 +# token2, frame2 = with_token(ordered_effect_op, (reader,), token1) +# frame2 = frame2 * 2 +# sink_token = torch.ops.prims._sink_tokens([token2]) +# return frame, frame2 + +# +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +aot_autograd_decompositions = {} + +FakifiedFlatArgs = NewType("FakifiedFlatArgs", List[Any]) + + +def process_inputs( + flat_args: List[Any], + aot_config: AOTConfig, + fake_mode: FakeTensorMode, + shape_env: Optional[ShapeEnv], +) -> FakifiedFlatArgs: + with fake_mode: + + def convert(idx, x): + if shape_env is not None: + from torch._dynamo.source import ConstantSource + + if isinstance(x, int): + # We always specialize on scalar values in export. + if aot_config.is_export: + return x + source = ConstantSource(f"sym_{idx}") + return shape_env.create_symintnode( + shape_env.create_symbol(x, source), hint=x, source=source + ) + if isinstance(x, torch.ScriptObject): + return torch._library.fake_class_registry.maybe_to_fake_obj( + fake_mode, x + ) + if not isinstance(x, torch.Tensor): + return x + if isinstance(x, FakeTensor): + assert x.fake_mode is fake_mode + return x + if is_traceable_wrapper_subclass(x): + attrs, _ = x.__tensor_flatten__() + if all(isinstance(getattr(x, attr), FakeTensor) for attr in attrs): + assert all( + getattr(x, attr).fake_mode is fake_mode for attr in attrs + ) + return x + + # see note [Tensor Fakification and Symbol Caching] + symbolic_context = None + source = None + trace = True + if tracing_context := torch._guards.TracingContext.try_get(): + if x in tracing_context.tensor_to_context: + symbolic_context = tracing_context.tensor_to_context[x] + source = symbolic_context.tensor_source + # We already fakeified this tensor in Dynamo, don't + # dump the trace for it again + trace = False + if ( + idx < aot_config.num_params_buffers + and config.static_weight_shapes + and not symbolic_context + ): + # TODO: Ensure that this codepath is never exercised from + # Dynamo + return fake_mode.from_tensor(x, static_shapes=True) + + return fake_mode.from_tensor( + x, + static_shapes=False, + symbolic_context=symbolic_context, + source=source, + trace=trace, + ) + + return FakifiedFlatArgs([convert(idx, x) for idx, x in enumerate(flat_args)]) + + +def construct_fake_mode( + flat_args: List[Any], aot_config: AOTConfig +) -> Tuple[FakeTensorMode, Optional[ShapeEnv]]: + fake_mode = detect_fake_mode(flat_args) + if fake_mode is None: + shape_env = ShapeEnv() if aot_config.dynamic_shapes else None + fake_mode = FakeTensorMode(shape_env=shape_env) + else: + shape_env = fake_mode.shape_env + return (fake_mode, shape_env) + + +def create_aot_dispatcher_function( + flat_fn, + fake_flat_args: FakifiedFlatArgs, + aot_config: AOTConfig, + fake_mode: FakeTensorMode, + shape_env: Optional[ShapeEnv], +) -> Tuple[Callable, ViewAndMutationMeta]: + with dynamo_timed("create_aot_dispatcher_function"): + return _create_aot_dispatcher_function( + flat_fn, fake_flat_args, aot_config, fake_mode, shape_env + ) + + +def _create_aot_dispatcher_function( + flat_fn, + fake_flat_args: FakifiedFlatArgs, + aot_config: AOTConfig, + fake_mode: FakeTensorMode, + shape_env: Optional[ShapeEnv], +) -> Tuple[Callable, ViewAndMutationMeta]: + """ + Traces the forward and backward graphs of the attr:`flat_fn` to generate a + joint graph. The joint graph is an Fx graph with Aten ops. Please refer to + the tracing mechanism to understand the graph capturing details. + + The joint graph is then passed through attr:`partition_fn` to isolate the + forward and backward portions, which are then respectively compiled via the + provided attr:`fw_compiler` and attr:`bw_compiler`. + + The resulting compiled forward and backward graphs are then wrapped up in a + ``torch.autograd.Function`` object. + + The calling convention here is that the first aot_config.num_params_buffers + inputs in flat_args are parameters and buffers, and the rest are inputs. + + We use this to assume that parameters/buffer's shapes don't change. + + Note: this function is used both by aot_function and aot_export (controlled by aot_config.is_export) + When aot_config.is_export is True, we return an FX graph + metadata + When aot_config.is_export is False, we return an ordinary runtime function + """ + + # This is the main entry point. + # TODO: Chillee argues that dynamo itself should pass in fake tensors to + # the list of arguments when compiling; at the moment we do not do this + + if aot_config.decompositions is None: + aot_config.decompositions = {} + + aot_config.decompositions = { + **aot_autograd_decompositions, + **aot_config.decompositions, + } + + if config.functionalize_rng_ops: + # Update the decompositions with functionalized random decompositions + aot_config.decompositions = { + **rng_decompositions, + **aot_config.decompositions, + } + + # Check flat_args to see if they're already fake. If so, use that fake + # mode instead. + + python_dispatcher_mode = ( + enable_python_dispatcher() if shape_env is not None else nullcontext() + ) + + # See NOTE: [Deferring tensor pack/unpack hooks until runtime] + # If any saved tensor hooks are active, we **don't** want to trace them. + # Instead, we'll let them run at runtime, around the custom autograd.Function + # that we generate in torch.compile. + with torch.autograd.set_multithreading_enabled( + False + ), preserve_rng_state(), ( + fake_mode + ), ( + python_dispatcher_mode + ), PhiloxStateTracker(), torch._dynamo.utils._disable_saved_tensors_hooks_during_tracing(): + from torch._library.fake_class_registry import ( + FakeScriptObject, + maybe_to_fake_obj, + ) + + # Tracing may mutate the states the fake script object, + # so we need to duplicate the fake script objects so that subsequent tracing + # won't be affected. + def _dup_fake_script_obj(fake_flat_args): + return [ + maybe_to_fake_obj(detect_fake_mode(fake_flat_args), arg.real_obj) + if isinstance(arg, FakeScriptObject) + else arg + for arg in fake_flat_args + ] + + needs_autograd = any( + x.requires_grad for x in fake_flat_args if isinstance(x, Tensor) + ) + + with enable_python_dispatcher(): + # Patch set_rng_state as set_rng_state with fake tensors is + # nonsensical. This does not affect the collection of metadata. + with patch("torch.cuda.set_rng_state", lambda *args: None): + mod = root_module_when_exporting_non_strict(flat_fn) + if mod is not None: + ctx = _detect_attribute_assignment(mod) + else: + ctx = nullcontext() + with ctx: + fw_metadata = run_functionalized_fw_and_collect_metadata( + flat_fn, + static_input_indices=aot_config.static_input_indices, + keep_input_mutations=aot_config.keep_inference_input_mutations, + is_train=needs_autograd, + pre_dispatch=aot_config.pre_dispatch, + )(*_dup_fake_script_obj(fake_flat_args)) + + req_subclass_dispatch = requires_subclass_dispatch( + fake_flat_args, fw_metadata + ) + + output_and_mutation_safe = not any( + x.requires_grad + # view-type operations preserve requires_grad even in no_grad. + # Do not count aliases of inputs with requires_grad as reason to make a training graph, + # as AOTAutograd will perform view-replay to regenerate the view outputs at runtime, + # setting their grad_fn properly. + and not ( + x.output_type + in (OutputType.alias_of_input, OutputType.is_input) + and fw_metadata.input_info[x.base_idx].requires_grad + ) + for x in fw_metadata.output_info + ) and not any( + x.requires_grad + and x.mutates_data + and not x.mutations_under_no_grad_or_inference_mode + and not x.mutations_hidden_from_autograd + for x in fw_metadata.input_info + ) + + if needs_autograd and output_and_mutation_safe: + # We realized that none of the outputs require grad, + # and none of the inputs that require grad are mutated. + # so we actually have an inference graph. + needs_autograd = False + # A bit silly: right now in the subclass codepath, our ViewAndMutationMeta + # changes depending on whether we pass in is_train / keep_input_mutations, + # so we're forced to recompute the metadata. + # TODO: refactor the subclass path of run_functionalized_fw_and_collect_metadata + # so that this is unnecessary. + if req_subclass_dispatch: + fw_metadata = run_functionalized_fw_and_collect_metadata( + flat_fn, + keep_input_mutations=aot_config.keep_inference_input_mutations, + is_train=False, + pre_dispatch=aot_config.pre_dispatch, + static_input_indices=aot_config.static_input_indices, + )(*fake_flat_args) + else: + fw_metadata = ViewAndMutationMeta( + input_info=fw_metadata.input_info, + output_info=fw_metadata.output_info, + num_intermediate_bases=fw_metadata.num_intermediate_bases, + keep_input_mutations=aot_config.keep_inference_input_mutations, + traced_tangents=fw_metadata.traced_tangents, + subclass_inp_meta=fw_metadata.subclass_inp_meta, + subclass_fw_graph_out_meta=fw_metadata.subclass_fw_graph_out_meta, + subclass_tangent_meta=fw_metadata.subclass_tangent_meta, + is_train=False, + tokens=fw_metadata.tokens, + static_input_indices=fw_metadata.static_input_indices, + ) + + if fw_metadata.num_intermediate_bases > 0: + assert not req_subclass_dispatch, f"""\ +torch.compile is currently being used with tensor subclass inputs: +{','.join([str(type(x)) for x in fake_flat_args])}. We are attempting to a compile a graph with two graph outputs +that alias one another, which is currently unsupported in the subclass use case. If you run into this, +please file a github issue""" + + if aot_config.is_export: + # aot_export: ban input metadata mutations for now to keep shared code paths simpler. + # Keeping .resize_() in the graph will require some work + # Allowing it but keeping the graph functional will require some calling convention changes. + if len([x for x in fw_metadata.input_info if x.mutates_metadata]) != 0: + raise RuntimeError( + f"""\ +Found an input that received a metadata mutation, through e.g. a call to `.resize_()` or `.transpose_()`. +This is currently banned in the aot_export workflow. If you need this functionality, please file a github issue. + +fw_metadata={str(fw_metadata)}""" + ) + # In export, banning data mutations on inputs that require grad for now. + # This should be rare, and is tricky to get right. When we trace the backward, + # we currently trace with autograd.grad instead of .backward(), which makes it difficult + # to ensure that we run autograd all the way through the input **before** it saw the mutation. + if ( + len( + [ + x + for x in fw_metadata.input_info + if x.requires_grad and x.mutates_data + ] + ) + != 0 + ): + raise RuntimeError( + f"""\ +Found a graph input that requires gradients, and received a mutation. +This is currently banned in the aot_export workflow. If you need this functionality, please file a github issue. + +fw_metadata={str(fw_metadata)}""" + ) + if req_subclass_dispatch: + raise RuntimeError( + """\ +aot_export is not currently supported with traceable tensor subclass. +If you need this feature, please comment on """ + ) + + # Need to decide on a strategy for functionalized RNG: toggling via global config seems bad, + # and turning it on will require a non-trivial calling convention change for any export runtime. + if config.functionalize_rng_ops: + raise RuntimeError( + """\ +Functionalized RNG is not currently supported in the aot_export workflow. Please file a github issue, +or otherwise set torch._functorch.config.functionalize_rng_ops = False.""" + ) + + def choose_dispatcher(needs_autograd, aot_config): + """ + Pick a dispatcher based on the config rules. + """ + if aot_config.is_export: + # export uses just the "graph bits", whereas the other + # two dispatchers include some extra work around handling a runtime epilogue + return partial(aot_dispatch_export, needs_autograd=needs_autograd) + elif needs_autograd and not aot_config.pre_dispatch: + return aot_dispatch_autograd + else: + return aot_dispatch_base + + compiler_fn = choose_dispatcher(needs_autograd, aot_config) + + compiled_fn, fw_metadata = compiler_fn( + flat_fn, + _dup_fake_script_obj(fake_flat_args), + aot_config, + fw_metadata=fw_metadata, + ) + return compiled_fn, fw_metadata + + +def aot_function( + fn: Callable, + fw_compiler: Callable, + bw_compiler: Optional[Callable] = None, + partition_fn: Callable = default_partition, + decompositions: Optional[Dict] = None, + num_params_buffers: int = 0, + keep_inference_input_mutations: bool = False, + inference_compiler: Optional[Callable] = None, + *, + # Whether or not to trace with dynamic shapes + dynamic=False, + enable_log=True, +) -> Callable: + """ + Traces the forward and backward graph of :attr:`fn` using torch dispatch + mechanism, and then compiles the generated forward and backward graphs + through :attr:`fw_compiler` and :attr:`bw_compiler`. + + :func:`aot_function` traces the forward and backward graph ahead of time, + and generates a joint forward and backward graph. :attr:`partition_fn` is + then used to separate out forward and backward graphs. The partitioner + function can be used to perform optimizations such as recomputation. One can + set `decompositions` dictionary to decompose the operators into a sequence + of core or simpler operators supported by the backend compilers. + + .. warning:: + This API is experimental and likely to change. + + Args: + fn (Callable): A Python function that takes one ore more arguments. Must + return one or more Tensors. + fw_compiler (Callable): A Python function that accepts an Fx graph with + Aten ops and input args, and returns a Callable that semantically is + equivalent to the input Fx graph. + bw_compiler (Optional[Callable]): A Python function that accepts an + Fx graph with Aten ops and input args, and returns a Callable that + semantically is equivalent to the input Fx graph. Default: None + (when None, it defaults to the :attr:`fw_compiler`) + partition_fn (Callable): A Python function that takes a joint forward + and backward graph, and partitions it into separate forward and + backward graphs. + decompositions (Dict): A dictionary to define the decomposition of + larger Aten ops into simpler or core Aten ops. + inference_compiler (Optional[Callable]): A Python function that accepts an + Fx graph with Aten ops and input args, and returns a Callable that + semantically is equivalent to the input Fx graph. inference_compiler is invoked + if no autograd is needed. Default: None + (when None, it defaults to the :attr:`fw_compiler`) + Returns: + Returns a ``Callable`` that retains the eager behavior of the original + :attr:`fn`, but with forward and backward graph compiled via + :attr:`fw_compile` and :attr:`bw_compile`. + + A simple example usage of :func:`aot_function` is as follows. This example + will print the forward and backward graphs of the function ``fn`` + + >>> fn = lambda x : x.sin().cos() + >>> def print_compile_fn(fx_module, args): + >>> print(fx_module) + >>> return fx_module + >>> aot_fn = aot_function(fn, print_compile_fn) + >>> x = torch.randn(4, 5, requires_grad=True) + >>> aot_fn(x) + """ + + if bw_compiler is None: + bw_compiler = fw_compiler + if inference_compiler is None: + inference_compiler = fw_compiler + aot_config = AOTConfig( + fw_compiler=fw_compiler, + bw_compiler=bw_compiler, + inference_compiler=inference_compiler, + partition_fn=partition_fn, + decompositions=decompositions, + num_params_buffers=num_params_buffers, + aot_id=next(AOT_COUNTER), + keep_inference_input_mutations=keep_inference_input_mutations, + dynamic_shapes=dynamic, + aot_autograd_arg_pos_to_source=None, + is_export=False, + no_tangents=False, + enable_log=enable_log, + ) + cached_res = None + + @wraps(fn) + def returned_function(*args, **kwargs): + nonlocal cached_res + # Now flatten the tensor args + flat_args = pytree.arg_tree_leaves(*args, **kwargs) + + # Compile the function and save it in the cache + if cached_res is None: + flat_fn, out_spec = create_tree_flattened_fn(fn, args, kwargs) + (fake_mode, shape_env) = construct_fake_mode(flat_args, aot_config) + fake_flat_args: FakifiedFlatArgs = process_inputs( + flat_args, aot_config, fake_mode, shape_env + ) + compiled_fn, _ = create_aot_dispatcher_function( + flat_fn, + fake_flat_args, + aot_config, + fake_mode, + shape_env, + ) + cached_res = (compiled_fn, out_spec) + + cached_fn, out_spec = cached_res + out = cached_fn(flat_args) + return out_spec.unflatten(out) + + return returned_function + + +def aot_module(mod: nn.Module, *args, **kwargs) -> nn.Module: + """ + Traces the forward and backward graph of :attr:`mod` using torch dispatch + tracing mechanism. It is wrapper function, that underneath uses + :func:`aot_function` to perform tracing and compilation. + + :func:`aot_module` lifts the parameters and buffers of ``nn.Module`` as inputs + to a new callable which is then compiled through :func:`aot_function`. + + .. warning:: + This API is experimental and likely to change. + + Args: + mod (Callable): A ``nn.Module`` module. + args : args to be passed to :func:`aot_function` + kwargs : kwargs to be passed to :func:`aot_function` + + Returns: + Returns a ``nn.Module`` that retains the eager behavior of the original + :attr:`mod`, but with forward and backward graph compiled. + + """ + # See Note: [Fake Modules and AOTAutograd] + torch._dynamo.utils.assert_no_fake_params_or_buffers(mod) + + def functional_call(named_params, named_buffers, *args, **kwargs): + params_and_buffers = {**named_params, **named_buffers} + return torch.func.functional_call(mod, params_and_buffers, args, kwargs) + + named_params = dict(mod.named_parameters(remove_duplicate=False)) + named_buffers = dict(mod.named_buffers(remove_duplicate=False)) + num_params_buffers = len(named_params) + len(named_buffers) + compiled_f = aot_function( + functional_call, *args, num_params_buffers=num_params_buffers, **kwargs + ) + + class AOTModule(nn.Module): + def __init__(self) -> None: + super().__init__() + self.orig_module = mod + + def forward(self, *args, **kwargs): + return compiled_f( + named_params, + named_buffers, + *args, + **kwargs, + ) + + return AOTModule() + + +def aot_module_simplified( + mod: nn.Module, + args, + fw_compiler: Callable, + bw_compiler: Optional[Callable] = None, + partition_fn: Callable = default_partition, + decompositions: Optional[Dict] = None, + keep_inference_input_mutations=False, + inference_compiler: Optional[Callable] = None, + cudagraphs: Optional[BoxedBool] = None, +) -> nn.Module: + """ + This is the simplified or low overhead version of aot_module. For frontends + like TorchDynamo, the input functions/modules to AOT are static and have + unpacked inputs/outputs. This gives us an opportunity to remove the + (1) pytree overhead to parse inputs/outputs, + (2) AOT Autograd cache, + (3) Reading of params/buffers in every forward call + + :func:`aot_module_simplified` removes these overheads. + """ + params = { + **dict(mod.named_parameters(remove_duplicate=False)), + **dict(mod.named_buffers(remove_duplicate=False)), + } + params_flat, params_spec = pytree.tree_flatten(params) + params_flat = list(params_flat) + params_len = len(params_flat) + + if cudagraphs is None: + cudagraphs = BoxedBool(torch._inductor.config.triton.cudagraphs) + + if bw_compiler is None: + bw_compiler = fw_compiler + if inference_compiler is None: + inference_compiler = fw_compiler + + seen_sources = set() + + full_args = [] + # First, the params + full_args.extend(params_flat) + + if tracing_context := torch._guards.TracingContext.try_get(): + tracing_context.params_flat = params_flat + + aot_autograd_arg_pos_to_source = None + # Then, the params 1:1 mapped sources, if relevant. + if hasattr(mod, "_param_name_to_source"): + aot_autograd_arg_pos_to_source = [] + # We now know this came from dynamo, and (1) we care about guards, + # so setting up aot_autograd_arg_pos_to_source for downstream dedup guards + # can now be done safely. (2) Dynamo logic protects the 1:1 sizing below. + for name in params.keys(): + assert name in mod._param_name_to_source, f"{name} not found." + source = mod._param_name_to_source[name] + assert source not in seen_sources, source + seen_sources.add(source) + aot_autograd_arg_pos_to_source.append(source) + + # Next, the input args + full_args.extend(args) + + static_input_indices = [] + if hasattr(mod, "graph"): + # Non dynamo entrypoints can get to here... + for pos, node in enumerate(mod.graph.find_nodes(op="placeholder")): + if hasattr(node, "_dynamo_source"): + # ... but not here! + if aot_autograd_arg_pos_to_source is None: + aot_autograd_arg_pos_to_source = [] + source = node._dynamo_source + assert source not in seen_sources, source + seen_sources.add(source) + aot_autograd_arg_pos_to_source.append(source) + source_name = source.name() if source else str(source) + + if "tensor_dict" in node.meta and node.meta["tensor_dict"].get( + "_dynamo_static_input_type", None + ): + static_inputs_log.debug( + "Adding static input pos %s for source %s", pos, source_name + ) + static_input_indices.append(pos) + else: + static_inputs_log.debug( + "Non-static input pos %s for source %s", pos, source_name + ) + + if aot_autograd_arg_pos_to_source is not None: + assert len(full_args) == len(aot_autograd_arg_pos_to_source) + + dynamic_shapes = False + for x in full_args: + if isinstance(x, FakeTensor): + dynamic_shapes = x.fake_mode.shape_env is not None + break + + aot_config = AOTConfig( + fw_compiler=fw_compiler, + bw_compiler=bw_compiler, + inference_compiler=inference_compiler, + partition_fn=partition_fn, + decompositions=decompositions, + num_params_buffers=params_len, + aot_id=next(AOT_COUNTER), + keep_inference_input_mutations=keep_inference_input_mutations, + dynamic_shapes=dynamic_shapes, + aot_autograd_arg_pos_to_source=aot_autograd_arg_pos_to_source, + static_input_indices=static_input_indices, + is_export=False, + no_tangents=False, + cache_key=None, + ) + fake_mode, shape_env = construct_fake_mode(full_args, aot_config) + fake_flat_args = process_inputs(full_args, aot_config, fake_mode, shape_env) + + def dispatch_and_compile(): + functional_call = create_functional_call(mod, params_spec, params_len) + with compiled_autograd.disable(): + compiled_fn, _ = create_aot_dispatcher_function( + functional_call, + fake_flat_args, + aot_config, + fake_mode, + shape_env, + ) + return compiled_fn + + # Autograd cache stuff + if config.enable_autograd_cache: + compiled_fn = AOTAutogradCache.load( + dispatch_and_compile, mod, fake_flat_args, aot_config, cudagraphs + ) + else: + compiled_fn = dispatch_and_compile() + + if isinstance(mod, torch._dynamo.utils.GmWrapper): + # This function is called by the flatten_graph_inputs wrapper, which boxes + # the inputs so that they can be freed before the end of this scope. + # For overhead reasons, this is not the default wrapper, see comment: + # https://github.com/pytorch/pytorch/pull/122535/files#r1560096481 + def boxed_forward(runtime_args: List[Any]): + flat_args = [] + flat_args.extend(params_flat) + flat_args.extend(runtime_args) + runtime_args.clear() + return compiled_fn(flat_args) + + # Just for convenience + boxed_forward.zero_grad = mod.zero_grad + boxed_forward.named_parameters = mod.named_parameters + boxed_forward.named_buffers = mod.named_buffers + return boxed_forward + + # TODO: There is something deeply wrong here; compiled_fn running with + # the boxed calling convention, but aot_module_simplified somehow + # historically returned a function that was not the boxed calling + # convention. This should get fixed... + # NB: GraphModule/nn.Module rely on the non-boxed calling convention here + def forward(*runtime_args: Tuple[Any]): + full_args = [] + full_args.extend(params_flat) + full_args.extend(runtime_args) + return compiled_fn(full_args) + + # Just for convenience + forward.zero_grad = mod.zero_grad + forward.named_parameters = mod.named_parameters + forward.named_buffers = mod.named_buffers + + return forward + + +def aot_export_module( + mod: nn.Module, + args, + *, + decompositions: Optional[Dict] = None, + # If true, we'll return a joint forward-backward graph, + # As well as metadata on the loss + gradients in the backward. + trace_joint: bool, + # If trace_joint is True, we expect your module to return a scalar loss. + # Your module can return multiple outputs, so you must specify which output the loss is. + output_loss_index: Optional[int] = None, + pre_dispatch: bool = False, + # If None, will be infered from inputs and mod.graph.nodes if mod is a graph module, but the inferred result might be wrong. + dynamic_shapes: Optional[bool] = None, + kwargs=None, +) -> Tuple[torch.fx.GraphModule, GraphSignature]: + """ + This function takes in a module, and returns: + (1) an FX graph that can be exported + (2) some metadata about the graph + + If `trace_joint=True` we will return a joint graph of the forward + backward. + + The traced FX graph will have the following properties compared to the original module: + (1) Inputs and outputs to the module will be pytree-flattened + (2) Parameters and buffers on the module will be lifted into graph inputs, + graph_inputs = (*parameters, *buffers, *user_inputs) + (3) The graph will be fully functionalized + (4) Any input mutations will be converted into additional outputs in the graph, + meaning whoever calls this graph is responsible for applying the mutations + back to the original inputs. + (5) If is_joint is provided the graph will return parameter gradients in addition to user outputs. + The graph output will look like: + graph_outputs = (*updated_inputs, *user_outputs, *param_gradients) + + There are also several restrictions on what modules can use this API. In particular: + (1) If trace_joint is specified, we expect the loss function to be **fused** + into the module forward. One of the outputs to the forward must be a scalar loss, + which is specified with `output_loss_index`. + All other outputs to the forward are presumed to not require gradients. + (2) This API cannot capture optimizers (although in theory we could build an API for this). + (3) Metadata mutations on params/buffers/inputs are banned. + (4) Data mutations on anything that requires gradients are banned (parameters) + (5) If an input is mutated, it is not allowed to alias any other inputs. + (6) Parameters must not be duplicated. + """ + if pre_dispatch and trace_joint: + raise RuntimeError("pre_dispatch is not supported when trace_joint is True.") + named_parameters = dict(mod.named_parameters(remove_duplicate=False)) + named_buffers = dict(mod.named_buffers(remove_duplicate=False)) + + params_and_buffers = { + **dict(named_parameters), + **dict(named_buffers), + } + params_and_buffers_flat, params_spec = pytree.tree_flatten(params_and_buffers) + params_and_buffers_flat = tuple(params_and_buffers_flat) + params_len = len(params_and_buffers_flat) + + kwargs = kwargs or {} + + functional_call = create_functional_call( + mod, params_spec, params_len, store_orig_mod=True + ) + + num_fw_outs = None + + if trace_joint: + # This helper effectively just adds some extra asserts about what the backward will look like: + # Outputs must include a scalar loss, that we compute gradients w.r.t. + # We don't compute gradients w.r.t. anything else: so just in case we detach() + # and other output tensors. + def fn_to_trace(*args): + nonlocal num_fw_outs + out = functional_call(*args) + if output_loss_index is None: + raise RuntimeError( + """\ +If trace_joint=Trueit is required that one of your forward outputs must be a scalar loss. +You must specify the which (index) output is the loss with output_loss_index.""" + ) + if isinstance(out, (torch.Tensor)): + out = (out,) + if not isinstance(out, (tuple, list)): + raise RuntimeError( + f"Expected forward output to be either a tensor or a list/tuple of tensors. found {type(out)}" + ) + + for i, o in enumerate(out): + # We only want to create a backward graph w.r.t. the loss that the user passed in. + # This implies that every other output should not require gradients. + # Instead of making this an error (and forcing the user to detach all other outputs + # of their forward), + # we'll automatically detach them here. + if o.requires_grad and i != output_loss_index: + raise RuntimeError( + f"""\ +Found an output of the forward that requires gradients, that was not the scalar loss. +We require all outputs to the forward that are not the scalar loss to not require gradient, +because we will only compute a backward graph against the scalar loss. +You can fix this by calling .detach() on each of your forward outputs that is not the loss. +You specified that output index {output_loss_index} is the loss, but we found that +the output at index {i} requires gradients.""" + ) + out_loss = out[output_loss_index] + num_fw_outs = len(out) + if not out_loss.requires_grad: + raise RuntimeError( + f"""\ +The output at index {output_loss_index} was marked as the loss, but it does not require gradients""" + ) + if out_loss.numel() != 1: + raise RuntimeError( + f"""\ +We require the output marked as the loss (at index {output_loss_index}) to be a scalar, but it has shape {out_loss.shape}""" + ) + return out + + ctx = nullcontext + else: + # Run under no_grad, so our tracing machinery only traces an inference graph. + # However if pre_dispatch=True, we want to correctly trace set_grad_enabled calls for training. + ctx = nullcontext if pre_dispatch else torch.no_grad + fn_to_trace = functional_call + + full_args = [] + # First, the params + # NB: It is REQUIRED that parameters come first, Inductor infers "fixed" + # parameters by looking at the difference in parameter count outside + # and inside AOTAutograd, and assumes the prefix of arguments are fixed + # arguments + full_args.extend(params_and_buffers_flat) + # Next, the input args + full_args.extend(args) + + with ctx(): + fx_g, metadata, in_spec, out_spec = _aot_export_function( + fn_to_trace, + full_args, + decompositions=decompositions, + num_params_buffers=params_len, + no_tangents=True, + pre_dispatch=pre_dispatch, + dynamic_shapes=dynamic_shapes, + kwargs=kwargs, + ) + if trace_joint: + + def flattened_joint(*args): + # The idea here is that the joint graph that AOTAutograd creates has some strict properties: + # (1) It accepts two arguments (primals, tangents), and pytree_flattens them + # (2) It returns a tuple of (fw_outs, gradients) + # This is a very useful convention for anyone who wants to partition the joint graph + # into a separate forward and backward graph. + # However, + # (1) for people exporting a single joint graph, it would be preferable not to have + # any pytrees in the graph. + # (2) We are guaranteed in the aot_export_module case that the forward outputs a loss, + # and there are therefore no tangents that are needed to run the joint graph. + # (3) AOTAutograd creates a grad_input for every input in the forward, + # including None's for inputs that are not grad-requiring tensors. + # we don't want these in our export graph. + # and there are therefore no tangents that are needed to run the joint graph. + # This function "fixes" both of the above by removing any tangent inputs, + # and removing pytrees from the original FX graph. + fake_tangents = [ + None + for _ in range( + metadata.num_outputs + metadata.num_mutated_inp_runtime_indices + ) + ] + fw_outs, gradients = fx_g(args, fake_tangents) + assert len(gradients) == len(args) + output_gradients = [] + for i, (a, grad) in enumerate(zip(args, gradients)): + if isinstance(a, torch.Tensor) and a.requires_grad: + assert ( + grad is not None + ), """\ +Found a parameter that did not receive a gradient. +"This is most likely a bug, but if this needs to be supported please comment on this Github issue: +https://github.com/pytorch/pytorch/issues/101192 +""" + output_gradients.append(grad) + else: + assert grad is None + return *fw_outs, *output_gradients + + fx_g = make_fx(flattened_joint)(*full_args) + + user_args_flat = pytree.arg_tree_leaves(*args, **kwargs) + return fx_g, create_graph_signature( + fx_g, + metadata, + in_spec, + out_spec, + user_args_flat=user_args_flat, + params_and_buffers_flat=params_and_buffers_flat, + param_names=list(named_parameters.keys()), + buffer_names=list(named_buffers.keys()), + trace_joint=trace_joint, + num_user_fw_outs=num_fw_outs, + loss_index=output_loss_index, + ) + + +def aot_export_joint_simple( + func: Callable, + args, + *, + trace_joint: bool, + # It looks like the main consequence of this API is that for dynamic shapes, + # it will assume that parms/buffers are static. + # With the new inferred dynamic shapes API, maybe this doesn't matter? + num_params_buffers: int = 0, + decompositions: Optional[Dict] = None, +) -> torch.fx.GraphModule: + """ + A simplified version of export. Used by higher order operators. + + This function makes a high-level "no calling convention changes" guarantee: + - If no inputs require grad (so we export an inference graph), + there are *no* calling convention change between the exported graph, and "func". + - If at least one input requires grad (so we trace out and export a joint fw-bw graph), + Then if you were partition the graph into a separate forward and backward graph, + The forward graph will have no calling convention changes compared to "func". + + The above also relies on some strong restrictions around which functions this API accepts: + (1) `args` cannot contain any pytrees (they must have been pytree_flattened already) + (2) `func` cannot mutate any inputs + (3) The outputs of `func` cannot alias any inputs. + + Note: this function is only lightly tested today. It will probably be tested more heavily by higher order ops. + """ + if trace_joint: + ctx = nullcontext + else: + # Run under no_grad, so our tracing machinery only traces an inference graph. + ctx = torch.no_grad + + with ctx(): + fx_g, metadata, in_spec, out_spec = _aot_export_function( + func, + args, + decompositions=decompositions, + ) + in_spec, _kw_in_spec = in_spec.children_specs + # At this point, we can just directly return the (joint or inference graph) that we traced. + # First though: a bunch of assertions to make sure that our graph doesn't require + # any calling convention changes compared to the original function. + # These restrictions are *in addition to* the general restrictions on export. + + # No input mutations + if ( + len([x for x in metadata.input_info if x.mutates_data or x.mutates_metadata]) + != 0 + ): + raise RuntimeError( + f"aot_export_joint_simple does not support input mutations. {str(metadata)}" + ) + # No output aliasing + if ( + len([x for x in metadata.output_info if x.output_type != OutputType.non_alias]) + != 0 + ): + raise RuntimeError( + f"aot_export_joint_simple does not support outputs that alias inputs. {str(metadata)}" + ) + # No pytrees + if in_spec.is_leaf(): + raise RuntimeError( + f"aot_export_joint_simple requires inputs to be a single list/tuple. in_spec={str(in_spec)}" + ) + if not all(child.is_leaf() for child in in_spec.children_specs): + raise RuntimeError( + f"aot_export_joint_simple requires individual inputs not to be pytrees. in_spec={str(in_spec)}" + ) + if out_spec.is_leaf(): + raise RuntimeError( + f"aot_export_joint_simple requires outputs to be a single list/tuple. out_spec={str(out_spec)}" + ) + if not all(child.is_leaf() for child in out_spec.children_specs): + raise RuntimeError( + f"aot_export_joint_simple requires individual outputs not to be pytrees. out_spec={str(out_spec)}" + ) + # TODO: we might have to temporarily patch config.functionalize_rng + # so that it doesn't run when we're exporting a higher order op. + + if config.debug_assert: + # Smoke test that after partitioning, we can run the forward without any calling convention changes. + fw_module, bw_module = aot_config.default_partition( # noqa: F821 + fx_g, args, num_fwd_outputs=len(fw_metadata.output_infos) # noqa: F821 + ) + # Attempt to run the fw_module with the original user inputs + fake_mode = detect_fake_mode(args) + if fake_mode is None: + fake_mode = FakeTensorMode() + with fake_mode: + fw_module(*args) + return fx_g + + +# Private for now because we aren't providing a contract on what to return +# for joint graphs (we could when there's a clearer use case) +# In the future, we may need to add more export API's that provide their own strong guarantees. +# This is meant as a general helper function for handling various export-y use cases. +def _aot_export_function( + func: Callable, + args, + *, + num_params_buffers: int = 0, + decompositions: Optional[Dict] = None, + # If we're exporting a joint graph and we don't want any tangent inputs in the graph + # (because we are backpropping through a scalar 1 loss), + # we need to explicitly specify not to include tangents in the graph. + # It's not enough just to check that our tangent is a scalar, since we also + # need to know if it is a 1 (no need to make it a graph input), or something else + # (requiring it to be a graph input). + # We don't know this info at trace time though, so we need to make it an explicit config. + no_tangents: bool = False, + pre_dispatch: bool = False, + # If None, `dynamic_shapes` will be infered from inputs, but the inferred result might be wrong. + dynamic_shapes: Optional[bool] = None, + kwargs=None, +) -> Tuple[torch.fx.GraphModule, ViewAndMutationMeta, pytree.TreeSpec, pytree.TreeSpec]: + kwargs = kwargs or {} + + flat_fn, out_spec = create_tree_flattened_fn(func, args, kwargs) + flat_args, in_spec = pytree.tree_flatten((args, kwargs)) + + if dynamic_shapes is None: + # Try to infer `dynamic_shapes from inputs and graph nodes + fake_mode = detect_fake_mode(flat_args) + if ( + fake_mode is None + and hasattr(func, "_orig_mod") + and isinstance(func._orig_mod, torch.fx.GraphModule) + ): + vals = [ + node.meta["val"] + for node in func._orig_mod.graph.nodes + if "val" in node.meta + ] + fake_mode = detect_fake_mode(vals) + dynamic_shapes = fake_mode is not None and fake_mode.shape_env is not None + + # The export use case doesn't care about several bits of AOTConfig + # (1) compilers (we just export the graph) + # (2) partitioners (export is only full graph, user can partition themselves) + aot_config = AOTConfig( + fw_compiler=None, + bw_compiler=None, + inference_compiler=None, + partition_fn=None, + decompositions=decompositions, + num_params_buffers=num_params_buffers, + aot_id=next(AOT_COUNTER), + # For now there's no use case involving keeping input mutations in the graph + # (which we can only do in the inference case anyway). + # We can add this later if we need to. + keep_inference_input_mutations=False, + dynamic_shapes=dynamic_shapes, + aot_autograd_arg_pos_to_source=None, + is_export=True, + no_tangents=no_tangents, + pre_dispatch=pre_dispatch, + ) + fake_mode, shape_env = construct_fake_mode(flat_args, aot_config) + fake_flat_args = process_inputs(flat_args, aot_config, fake_mode, shape_env) + + fx_g, meta = create_aot_dispatcher_function( + flat_fn, + fake_flat_args, + aot_config, + fake_mode, + shape_env, + ) + return fx_g, meta, in_spec, out_spec.spec + + +@contextmanager +def _detect_attribute_assignment(mod: torch.nn.Module): + # Do not allow assignment of tensor attributes during export unless + # the attribute is registered as a buffer. + + STD_ATTRS = { + "_backward_hooks", + "_backward_pre_hooks", + "_buffers", + "_forward_hooks", + "_forward_hooks_always_called", + "_forward_hooks_with_kwargs", + "_forward_pre_hooks", + "_forward_pre_hooks_with_kwargs", + "_is_full_backward_hook", + "_load_state_dict_post_hooks", + "_load_state_dict_pre_hooks", + "_modules", + "_non_persistent_buffers_set", + "_parameters", + "_state_dict_hooks", + "_state_dict_pre_hooks", + "training", + } + + def _get_attributes(mod): + # return any attributes of a module that are not standard attributes + return {k: v for k, v in mod.__dict__.items() if k not in STD_ATTRS} + + # save state of attributes before enter + snapshot = pytree.tree_map(lambda x: x, _get_attributes(mod)) + try: + yield + finally: + # after exit, compare state of attributes with snapshot + # to detect which tensor attributes were assigned + assigned_tensor_attributes = [] + + def _collect_assigned_tensor_attributes(kp, v, _v): + if _v is not v: + attr, *rest = kp + if isinstance(v, torch.Tensor): + assigned_tensor_attributes.append( + f"self.{attr.key}{pytree.keystr(rest)}" + ) + # TODO(avik): Assigning all other types are allowed right now. + # Maybe in the future we want to limit this to primitive types? + + pytree.tree_map_with_path( + _collect_assigned_tensor_attributes, snapshot, _get_attributes(mod) + ) + # restore state of all attributes (including, e.g., of primitive types) + mod.__dict__.update(snapshot) + + if assigned_tensor_attributes: + if len(assigned_tensor_attributes) > 1: + noun, verb = "attributes", "were" + else: + noun, verb = "attribute", "was" + raise ValueError( + f"The tensor {noun} {', '.join(assigned_tensor_attributes)} {verb} assigned during export. " + "Such attributes must be registered as buffers using the `register_buffer` API " + "(https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.register_buffer)." + ) + + +compiled_function = aot_function +compiled_module = aot_module diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/apis.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/apis.py new file mode 100644 index 0000000000000000000000000000000000000000..d906f3c906c9893bfda830dbc6f9220106d0fe72 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/apis.py @@ -0,0 +1,449 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +# NOTE: We allow Dynamo to see this file (via torch/_dynamo/trace_rules.py) so that it can +# trace through functorch transforms. +# Currently, we can't allow Dynamo to see `eager_transforms.py`/`vmap.py` as that break a lot of thing +# and there isn't a mechanism to selectively expose only some functions (eg. grad) from a file +# to Dynamo. +import functools + +from torch._functorch.utils import argnums_t, exposed_in +from torch._functorch.vmap import ( + _check_out_dims_is_int_or_int_pytree, + _check_randomness_arg, + _chunked_vmap, + _process_batched_inputs, + Callable, + in_dims_t, + out_dims_t, + vmap_impl, +) + + +# vmap(func)(inputs) wraps all Tensor inputs to be batched in BatchedTensors, +# sends those into func, and then unwraps the output BatchedTensors. Operations +# on BatchedTensors perform the batched operations that the user is asking for. +# +# vmap's randomness behavior differs from JAX's, which would require a PRNG key +# to be passed everywhere. + + +@exposed_in("torch.func") +def vmap( + func: Callable, + in_dims: in_dims_t = 0, + out_dims: out_dims_t = 0, + randomness: str = "error", + *, + chunk_size=None, +) -> Callable: + """ + vmap is the vectorizing map; ``vmap(func)`` returns a new function that + maps ``func`` over some dimension of the inputs. Semantically, vmap + pushes the map into PyTorch operations called by ``func``, effectively + vectorizing those operations. + + vmap is useful for handling batch dimensions: one can write a function + ``func`` that runs on examples and then lift it to a function that can + take batches of examples with ``vmap(func)``. vmap can also be used to + compute batched gradients when composed with autograd. + + .. note:: + :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for + convenience. Use whichever one you'd like. + + Args: + func (function): A Python function that takes one or more arguments. + Must return one or more Tensors. + in_dims (int or nested structure): Specifies which dimension of the + inputs should be mapped over. ``in_dims`` should have a + structure like the inputs. If the ``in_dim`` for a particular + input is None, then that indicates there is no map dimension. + Default: 0. + out_dims (int or Tuple[int]): Specifies where the mapped dimension + should appear in the outputs. If ``out_dims`` is a Tuple, then + it should have one element per output. Default: 0. + randomness (str): Specifies whether the randomness in this + vmap should be the same or different across batches. If 'different', + the randomness for each batch will be different. If 'same', the + randomness will be the same across batches. If 'error', any calls to + random functions will error. Default: 'error'. WARNING: this flag + only applies to random PyTorch operations and does not apply to + Python's random module or numpy randomness. + chunk_size (None or int): If None (default), apply a single vmap over inputs. + If not None, then compute the vmap :attr:`chunk_size` samples at a time. + Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. + If you run into memory issues computing the vmap, please try a non-None chunk_size. + + Returns: + Returns a new "batched" function. It takes the same inputs as + ``func``, except each input has an extra dimension at the index + specified by ``in_dims``. It takes returns the same outputs as + ``func``, except each output has an extra dimension at the index + specified by ``out_dims``. + + .. warning: + :func:`vmap` works best with functional-style code. Please do not + perform any side-effects in ``func``, with the exception of + in-place PyTorch operations. Examples of side-effects include mutating + Python data structures and assigning values to variables not captured + in ``func``. + + One example of using :func:`vmap` is to compute batched dot products. PyTorch + doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully + rummaging through docs, use :func:`vmap` to construct a new function. + + >>> torch.dot # [D], [D] -> [] + >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] + >>> x, y = torch.randn(2, 5), torch.randn(2, 5) + >>> batched_dot(x, y) + + :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler + model authoring experience. + + >>> batch_size, feature_size = 3, 5 + >>> weights = torch.randn(feature_size, requires_grad=True) + >>> + >>> def model(feature_vec): + >>> # Very simple linear model with activation + >>> return feature_vec.dot(weights).relu() + >>> + >>> examples = torch.randn(batch_size, feature_size) + >>> result = torch.vmap(model)(examples) + + :func:`vmap` can also help vectorize computations that were previously difficult + or impossible to batch. One example is higher-order gradient computation. + The PyTorch autograd engine computes vjps (vector-Jacobian products). + Computing a full Jacobian matrix for some function f: R^N -> R^N usually + requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, + we can vectorize the whole computation, computing the Jacobian in a single + call to ``autograd.grad``. + + >>> # Setup + >>> N = 5 + >>> f = lambda x: x ** 2 + >>> x = torch.randn(N, requires_grad=True) + >>> y = f(x) + >>> I_N = torch.eye(N) + >>> + >>> # Sequential approach + >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] + >>> for v in I_N.unbind()] + >>> jacobian = torch.stack(jacobian_rows) + >>> + >>> # vectorized gradient computation + >>> def get_vjp(v): + >>> return torch.autograd.grad(y, x, v) + >>> jacobian = torch.vmap(get_vjp)(I_N) + + :func:`vmap` can also be nested, producing an output with multiple batched dimensions + + >>> torch.dot # [D], [D] -> [] + >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] + >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) + >>> batched_dot(x, y) # tensor of size [2, 3] + + If the inputs are not batched along the first dimension, ``in_dims`` specifies + the dimension that each inputs are batched along as + + >>> torch.dot # [N], [N] -> [] + >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] + >>> x, y = torch.randn(2, 5), torch.randn(2, 5) + >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension + + If there are multiple inputs each of which is batched along different dimensions, + ``in_dims`` must be a tuple with the batch dimension for each input as + + >>> torch.dot # [D], [D] -> [] + >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] + >>> x, y = torch.randn(2, 5), torch.randn(5) + >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None + + If the input is a Python struct, ``in_dims`` must be a tuple containing a struct + matching the shape of the input: + + >>> f = lambda dict: torch.dot(dict['x'], dict['y']) + >>> x, y = torch.randn(2, 5), torch.randn(5) + >>> input = {'x': x, 'y': y} + >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) + >>> batched_dot(input) + + By default, the output is batched along the first dimension. However, it can be batched + along any dimension by using ``out_dims`` + + >>> f = lambda x: x ** 2 + >>> x = torch.randn(2, 5) + >>> batched_pow = torch.vmap(f, out_dims=1) + >>> batched_pow(x) # [5, 2] + + For any function that uses kwargs, the returned function will not batch the kwargs but will + accept kwargs + + >>> x = torch.randn([2, 5]) + >>> def fn(x, scale=4.): + >>> return x * scale + >>> + >>> batched_pow = torch.vmap(fn) + >>> assert torch.allclose(batched_pow(x), x * 4) + >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] + + .. note:: + vmap does not provide general autobatching or handle variable-length + sequences out of the box. + """ + from torch._dynamo import is_compiling + + _check_randomness_arg(randomness) + if not (chunk_size is None or chunk_size > 0): + raise ValueError( + f"vmap: chunk_size should be None or greater than 0. (got {chunk_size})" + ) + + def wrapped(*args, **kwargs): + return vmap_impl( + func, in_dims, out_dims, randomness, chunk_size, *args, **kwargs + ) + + if not is_compiling(): + wrapped = functools.wraps(func)(wrapped) + + return wrapped + + +def chunk_vmap( + func: Callable, + in_dims: in_dims_t = 0, + out_dims: out_dims_t = 0, + randomness: str = "error", + chunks=2, +) -> Callable: + """ + chunk_vmap is the vectorizing map (vmap) using chunks of input data. It is a mix of vmap (which vectorizes + everything) and map (which executes things sequentially). ``chunk_vmap`` vectorizes the input with number of + chunks at a time. For more details about vectorizing map, see :func:`vmap`. + + .. note:: + Please use :func:`vmap` with ``chunk_size`` argument instead of this API. + + Args: + func (function): A Python function that takes one or more arguments. + Must return one or more Tensors. + in_dims (int or nested structure): Specifies which dimension of the + inputs should be mapped over. ``in_dims`` should have a + structure like the inputs. If the ``in_dim`` for a particular + input is None, then that indicates there is no map dimension. + Default: 0. + out_dims (int or Tuple[int]): Specifies where the mapped dimension + should appear in the outputs. If ``out_dims`` is a Tuple, then + it should have one element per output. Default: 0. + randomness (str): Specifies whether the randomness in this + vmap should be the same or different across batches. If 'different', + the randomness for each batch will be different. If 'same', the + randomness will be the same across batches. If 'error', any calls to + random functions will error. Default: 'error'. WARNING: this flag + only applies to random PyTorch operations and does not apply to + Python's random module or numpy randomness. + chunks (int): Number of chunks to use to split the input data. Default is 2. + If equals to 1 then :func:`vmap` is called. + + Returns: + Returns a new "batched" function. It takes the same inputs as + ``func``, except each input has an extra dimension at the index + specified by ``in_dims``. It takes returns the same outputs as + ``func``, except each output has an extra dimension at the index + specified by ``out_dims``. + """ + _check_randomness_arg(randomness) + + if chunks == 1: + return vmap(func, in_dims=in_dims, out_dims=out_dims, randomness=randomness) + + def _get_chunk_flat_args(flat_args_, flat_in_dims_, chunks_): + flat_args_chunks = tuple( + t.chunk(chunks_, dim=in_dim) + if in_dim is not None + else [ + t, + ] + * chunks_ + for t, in_dim in zip(flat_args_, flat_in_dims_) + ) + # transpose chunk dim and flatten structure + # chunks_flat_args is a list of flatten args + chunks_flat_args = zip(*flat_args_chunks) + return chunks_flat_args + + @functools.wraps(func) + def wrapped_with_chunks(*args, **kwargs): + _check_out_dims_is_int_or_int_pytree(out_dims, func) + _, flat_in_dims, flat_args, args_spec = _process_batched_inputs( + in_dims, args, func + ) + # Chunk flat arguments + chunks_flat_args = _get_chunk_flat_args(flat_args, flat_in_dims, chunks) + + # Apply vmap on chunks + return _chunked_vmap( + func, + flat_in_dims, + chunks_flat_args, + args_spec, + out_dims, + randomness, + **kwargs, + ) + + return wrapped_with_chunks + + +@exposed_in("torch.func") +def grad(func: Callable, argnums: argnums_t = 0, has_aux: bool = False) -> Callable: + """``grad`` operator helps computing gradients of ``func`` with respect to the + input(s) specified by ``argnums``. This operator can be nested to + compute higher-order gradients. + + Args: + func (Callable): A Python function that takes one or more arguments. + Must return a single-element Tensor. If specified ``has_aux`` equals ``True``, + function can return a tuple of single-element Tensor and other auxiliary objects: + ``(output, aux)``. + argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to. + ``argnums`` can be single integer or tuple of integers. Default: 0. + has_aux (bool): Flag indicating that ``func`` returns a tensor and other + auxiliary objects: ``(output, aux)``. Default: False. + + Returns: + Function to compute gradients with respect to its inputs. By default, the output of + the function is the gradient tensor(s) with respect to the first argument. + If specified ``has_aux`` equals ``True``, tuple of gradients and output auxiliary objects + is returned. If ``argnums`` is a tuple of integers, a tuple of output gradients with + respect to each ``argnums`` value is returned. + + Example of using ``grad``: + + >>> # xdoctest: +SKIP + >>> from torch.func import grad + >>> x = torch.randn([]) + >>> cos_x = grad(lambda x: torch.sin(x))(x) + >>> assert torch.allclose(cos_x, x.cos()) + >>> + >>> # Second-order gradients + >>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x) + >>> assert torch.allclose(neg_sin_x, -x.sin()) + + When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients: + + >>> # xdoctest: +SKIP + >>> from torch.func import grad, vmap + >>> batch_size, feature_size = 3, 5 + >>> + >>> def model(weights, feature_vec): + >>> # Very simple linear model with activation + >>> assert feature_vec.dim() == 1 + >>> return feature_vec.dot(weights).relu() + >>> + >>> def compute_loss(weights, example, target): + >>> y = model(weights, example) + >>> return ((y - target) ** 2).mean() # MSELoss + >>> + >>> weights = torch.randn(feature_size, requires_grad=True) + >>> examples = torch.randn(batch_size, feature_size) + >>> targets = torch.randn(batch_size) + >>> inputs = (weights, examples, targets) + >>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))(*inputs) + + Example of using ``grad`` with ``has_aux`` and ``argnums``: + + >>> # xdoctest: +SKIP + >>> from torch.func import grad + >>> def my_loss_func(y, y_pred): + >>> loss_per_sample = (0.5 * y_pred - y) ** 2 + >>> loss = loss_per_sample.mean() + >>> return loss, (y_pred, loss_per_sample) + >>> + >>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True) + >>> y_true = torch.rand(4) + >>> y_preds = torch.rand(4, requires_grad=True) + >>> out = fn(y_true, y_preds) + >>> # > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample)) + + .. note:: + Using PyTorch ``torch.no_grad`` together with ``grad``. + + Case 1: Using ``torch.no_grad`` inside a function: + + >>> # xdoctest: +SKIP + >>> def f(x): + >>> with torch.no_grad(): + >>> c = x ** 2 + >>> return x - c + + In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``. + + Case 2: Using ``grad`` inside ``torch.no_grad`` context manager: + + >>> # xdoctest: +SKIP + >>> with torch.no_grad(): + >>> grad(f)(x) + + In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the + outer one. This is because ``grad`` is a "function transform": its result + should not depend on the result of a context manager outside of ``f``. + + """ + # To avoid cyclical dependency. + import torch._functorch.eager_transforms as eager_transforms + from torch._dynamo import is_compiling + + def wrapper(*args, **kwargs): + return eager_transforms.grad_impl(func, argnums, has_aux, args, kwargs) + + if not is_compiling(): + wrapper = functools.wraps(func)(wrapper) + + return wrapper + + +@exposed_in("torch.func") +def grad_and_value( + func: Callable, argnums: argnums_t = 0, has_aux: bool = False +) -> Callable: + """ + Returns a function to compute a tuple of the gradient and primal, or + forward, computation. + + Args: + func (Callable): A Python function that takes one or more arguments. + Must return a single-element Tensor. If specified ``has_aux`` + equals ``True``, function can return a tuple of single-element + Tensor and other auxiliary objects: ``(output, aux)``. + argnums (int or Tuple[int]): Specifies arguments to compute gradients + with respect to. ``argnums`` can be single integer or tuple of + integers. Default: 0. + has_aux (bool): Flag indicating that ``func`` returns a tensor and + other auxiliary objects: ``(output, aux)``. Default: False. + + Returns: + Function to compute a tuple of gradients with respect to its inputs + and the forward computation. By default, the output of the function is + a tuple of the gradient tensor(s) with respect to the first argument + and the primal computation. If specified ``has_aux`` equals + ``True``, tuple of gradients and tuple of the forward computation with + output auxiliary objects is returned. If ``argnums`` is a tuple of + integers, a tuple of a tuple of the output gradients with respect to + each ``argnums`` value and the forward computation is returned. + + See :func:`grad` for examples + """ + from torch._dynamo import is_compiling + from torch._functorch import eager_transforms + + def wrapper(*args, **kwargs): + return eager_transforms.grad_and_value_impl( + func, argnums, has_aux, args, kwargs + ) + + if not is_compiling(): + wrapper = functools.wraps(func)(wrapper) + + return wrapper diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/autograd_function.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/autograd_function.py new file mode 100644 index 0000000000000000000000000000000000000000..cb501e2c924213c5d31baa5f7e256920ce3351c2 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/autograd_function.py @@ -0,0 +1,752 @@ +# mypy: allow-untyped-defs +from typing import Any, NamedTuple, Tuple + +import torch +import torch.utils._pytree as pytree +from torch._C._functorch import ( + _unwrap_for_grad, + _wrap_for_grad, + current_level, + TransformType, +) +from torch._functorch.apis import vmap +from torch._functorch.utils import enable_single_level_autograd_function +from torch._functorch.vmap import ( + _add_batch_dim, + _broadcast_to_and_flatten, + restore_vmap, + unwrap_batched, + wrap_batched, +) +from torch._ops import HigherOrderOperator +from torch.autograd.forward_ad import _set_fwd_grad_enabled + + +# autograd.Function technically runs before the regular PyTorch dispatcher. +# This is how features like autocast and torch_dispatch (e.g. PythonTLSSnapshot) +# work with it. One day we might decide to change this, but until then, +# we need to give the illusion that autograd.Function runs before those things. +# +# We do this by using creating a custom HigherOrderOperator that only functorch +# dispatches specially. +class CustomFunctionHigherOrderOperator(HigherOrderOperator): + def __init__(self) -> None: + super().__init__("custom_function_call") + + def __call__(self, autograd_function, *args, **kwargs): + # When custom_function_call is done dispatching through functorch, + # it should just invoke the autograd.Function. This is consistent + # with the autograd.Function behavior of being invoked before the + # PyTorch dispatcher. + # + # This will lead us into trouble later down the line, but this is + # pre-existing. There is an invariant that a function traced by + # make_fx should have the same behavior when provided the same + # Tensor. However, make_fx sees autograd.Function as a composite + # (because autograd.Function happens before the Python dispatch key) + # and only traces the forward pass. + if torch._C._are_functorch_transforms_active(): + return super().__call__(autograd_function, *args, **kwargs) + return autograd_function.apply(*args, **kwargs) + + +# "custom_function_call" +# This is the mechanism for an autograd.Function that works with functorch transforms. +# It wraps an autograd.Function; interactions with functorch transforms are defined +# via PyDispatcher and HigherOrderOperator rather than through the traditional PyTorch +# dispatcher. +custom_function_call = CustomFunctionHigherOrderOperator() + + +# The grad rule for custom_function_call is to construct a new _SingleLevelFunction +# (autograd.Function that only works with a single layer (level) of functorch) that: +# - unwraps the inputs +# - redispatches to custom_function_call +# - wraps the outputs +# and whose backward pass calls the original autograd.Function's backward. +# +# Why do we need to redispatch to custom_function_call? +# ----------------------------------------------------- +# This is consistent with how ATen operators work with functorch's grad transform: +# they always redispatch to the original operator. +# Consider torch.sin, and let's say we do grad0(grad1(torch.sin))(x) +# +# grad1 will: +# - set up the autograd graph +# - unwrap the inputs +# - redispatch to at::sin (*) +# - rewrap the outputs on the return +# +# On the redispatch in (*), grad0 will: +# - set up the autograd graph +# - unwrap the inputs +# - redispatch to at::sin +# - rewrap the outputs on the return +# +# To "set up the autograd graph", we generate a _SingleLevelFunction +# and apply it. +@custom_function_call.py_impl(TransformType.Grad) +@custom_function_call.py_impl(TransformType.Jvp) +def custom_function_call_grad(interpreter, autograd_function, *operands): + Generated = generate_single_level_function(interpreter, autograd_function) + with enable_single_level_autograd_function(): + flat_out = Generated.apply(*operands) + return flat_out + + +def generate_single_level_function(interpreter, autograd_function): + level = interpreter.level() + + def forward(*operands): + unwrapped_operands = pytree.tree_map_only( + torch.Tensor, lambda x: _unwrap_for_grad(x, level), operands + ) + # Both enable_grad() and _set_fwd_grad_enabled() are necessary no matter + # the transform. _SingleLevelFunction will turn off both fwd and bwd + # gradient computation and we need to turn it back on here. + with torch.enable_grad(), _set_fwd_grad_enabled(True), interpreter.lower(): + unwrapped_output = custom_function_call( + autograd_function, *unwrapped_operands + ) + + # See NOTE [mark_dirty object identity check] + def wrap_fn(output): + return _wrap_for_grad(output, level) + + return wrap_outputs_maintaining_identity( + unwrapped_output, unwrapped_operands, operands, wrap_fn + ) + + def setup_context(ctx, inputs, output): + return autograd_function.setup_context(ctx, inputs, output) + + # backward is only used if the transform is TransformType.Grad + def backward(ctx, *grads): + result = autograd_function.backward(ctx, *grads) + return result + + # jvp is only used if the transform is TransformType.Jvp + def jvp(ctx, *tangents): + result = autograd_function.jvp(ctx, *tangents) + return result + + # This is the sequence of magic words to dynamically generate a Subclass with + # a given name. A Tensor's .grad_fn field has a class name that is the original + # autograd.Function's name + Backward, so we do this to generate some + # meaningful name. + name = f"{autograd_function.__name__}Generated" + Generated = type( + name, + (torch.autograd.function._SingleLevelFunction,), + { + "forward": staticmethod(forward), + "backward": staticmethod(backward), + "jvp": staticmethod(jvp), + "setup_context": staticmethod(setup_context), + }, + ) + return Generated + + +# wrap_outputs_maintaining_identity handles outputs from the vmap, +# backward (vjp), and jvp staticmethod. The way it distinguishes +# between the vmap case and the {backward, jvp} case is if the out_dims +# are specified or not. +# +# NB: we cannot use out_dims=None as the deciding factor. This because +# out_dims=None can still happen in the vmap staticmethod! What the +# user is saying in that case is that their output does not have a +# dimension that is being vmapped over, which is valid. +NO_OUT_DIMS = "not specified" + + +# NOTE [mark_dirty object identity check] +# autograd.Function's ctx.mark_dirty expect a returned input +# to have the same object identity as the input. +# Mode-only functorch will greatly simplify this logic. +def wrap_outputs_maintaining_identity( + outputs, unwrapped_inputs, orig_inputs, wrap_fn, out_dims=NO_OUT_DIMS +): + flat_unwrapped_inputs = pytree.arg_tree_leaves(*unwrapped_inputs) + flat_orig_inputs = pytree.arg_tree_leaves(*orig_inputs) + + unwrapped_input_to_orig_input = { + id(unwrapped): orig + for unwrapped, orig in zip(flat_unwrapped_inputs, flat_orig_inputs) + } + + flat_outputs, spec = pytree.tree_flatten(outputs) + result = [] + + out_dims_specified = out_dims != NO_OUT_DIMS + + if out_dims_specified: + flat_out_dims = _broadcast_to_and_flatten(out_dims, spec) + # _broadcast_to_and_flatten returns None if it is unable to broadcast. + # TODO: update following link from master to stable once that's out + if flat_out_dims is None: + raise RuntimeError( + f"The autograd.Function's vmap staticmethod returned an " + f"incompatible (output, out_dims) tuple. " + f"Expected out_dims={out_dims} " + f"to be compatible with the structure of `output`. " + f"out_dims has structure {pytree.tree_flatten(out_dims)[1]} " + f"but output has structure {spec}. " + f"For more details, please see " + f"https://pytorch.org/docs/main/notes/extending.func.html" + ) + + for i, output in enumerate(flat_outputs): + if not isinstance(output, torch.Tensor): + result.append(output) + continue + if id(output) in unwrapped_input_to_orig_input: + result.append(unwrapped_input_to_orig_input[id(output)]) + continue + if out_dims_specified: + result.append(wrap_fn(output, flat_out_dims[i])) # type: ignore[possibly-undefined, index] + else: + result.append(wrap_fn(output)) + + return pytree.tree_unflatten(result, spec) + + +# NOTE: [functorch vjp and autograd interaction] +# There's an edge case with the functorch vjp and autograd interaction +# that will eventually be fixed by mode-only functorch. +# The TL;DR is that there's no way to unwrap a dead GradTensorWrapper, +# so we (the framework) need to do it manually. Regular PyTorch operators +# automatically do so this is consistent. +# +# class MyExp(torch.autograd.Function): +# @staticmethod +# def forward(x): +# return x.exp() +# +# @staticmethod +# def setup_context(ctx, inputs, output): +# y = output +# ctx.save_for_backward(y) +# +# @staticmethod +# def backward(gy): +# y, = ctx.saved_tensors() +# return MyMul.apply(gy, y) +# +# x = torch.randn([], requires_grad=True) +# gy = torch.randn([], requires_grad=True) +# _, vjp_fn = vjp(MySin.apply, x) +# result = vjp_fn(gy) +# +# MyMul is an autograd.Function that is not shown here. +# It saves a `y` for backward (since gy requires grad). +# +# in vjp_fn(gy), we get: +# > MyMul.apply(gy, GradTensorWrapper(y, level=dead)) +# Because the y that is saved for backward by MyExp is a GradTensorWrapper +# but is now dead since we are outside the vjp context. +# +# PyTorch dispatcher operations, upon seeing a dead GradTensorWrapper, +# will automatically unwrap the GradTensorWrapper when applied. +# But since autograd.Function technically sits above the regular PyTorch +# dispatcher, it doesn't get this treatment. So we manually do +# the unwrapping to be consistent with regular PyTorch dispatcher operations. + + +class VmapInfo(NamedTuple): + batch_size: int + randomness: str + + +def has_overriden_vmap_rule(autograd_function): + return autograd_function.vmap is not torch.autograd.Function.vmap + + +def validate_vmap_returns_tuple_of_two_elements(result): + base_error_msg = ( + "Expected the vmap staticmethod to have two returns, an output " + "and out_dims with pytree structure compatible with the output. " + ) + if not isinstance(result, tuple): + raise RuntimeError(base_error_msg + f"Got a {type(result)} instead") + if not len(result) == 2: + raise RuntimeError(base_error_msg + f"Got {len(result)} returns instead") + + +@custom_function_call.py_impl(TransformType.Vmap) +def custom_function_call_vmap(interpreter, autograd_function, *operands, **kwargs): + if any( + isinstance(val, torch.Tensor) + for val in torch.utils._pytree.tree_flatten(kwargs)[0] + ): + raise NotImplementedError( + f"Run vmap on autograd.Function with kwarg-only Tensor args. " + f"Please do not pass kwarg-only Tensors to autograd.Function. " + f"Got: {kwargs}" + ) + + if autograd_function.generate_vmap_rule: + if has_overriden_vmap_rule(autograd_function): + # TODO: Update link to stable once that's out + # https://github.com/pytorch/pytorch/issues/92029 + raise RuntimeError( + f"You tried to vmap over {autograd_function.__name__}, but " + f"it has both generate_vmap_rule=True and an overriden vmap " + f"staticmethod. Please set generate_vmap_rule=False or delete " + f"the overriden vmap staticmethod to avoid ambiguity. " + f"For more details, please see " + f"https://pytorch.org/docs/main/notes/extending.func.html" + ) + return custom_function_call_vmap_generate_rule( + interpreter, autograd_function, *operands + ) + + if not has_overriden_vmap_rule(autograd_function): + # TODO: Update link to stable once that's out + # https://github.com/pytorch/pytorch/issues/92029 + raise RuntimeError( + f"You tried to vmap over {autograd_function.__name__}, but " + f"it does not have vmap support. Please override and implement the " + f"vmap staticmethod or set generate_vmap_rule=True. " + f"For more details, please see " + f"https://pytorch.org/docs/main/notes/extending.func.html" + ) + + return custom_function_call_vmap_helper( + interpreter, autograd_function.vmap, autograd_function, *operands, **kwargs + ) + + +def custom_function_call_vmap_helper( + interpreter, vmap_function, op, *operands, **kwargs +): + current_level = interpreter.level() + info = VmapInfo( + batch_size=interpreter.batch_size(), + randomness=interpreter.randomness(), + ) + unwrapped_operands, in_dims = unwrap_batched(operands, current_level) + # If none of the tensors are batched at the current level, then we skip the + # current level. This saves the user from needing to handle this case in + # their vmap staticmethod (and is consistent with our C++ batching rule API) + if pytree.tree_all(lambda dim: dim is None, in_dims): + with interpreter.lower(): + if isinstance(op, torch.autograd.function.FunctionMeta): + return custom_function_call(op, *operands) + else: + return op(*operands, **kwargs) + + with interpreter.lower(): + result = vmap_function(info, in_dims, *unwrapped_operands, **kwargs) + validate_vmap_returns_tuple_of_two_elements(result) + unwrapped_output, out_dims = result + + # See NOTE [mark_dirty object identity check] + def wrap_fn(output, out_dim): + return ( + output + if out_dim is None + else _add_batch_dim(output, out_dim, current_level) + ) + + return wrap_outputs_maintaining_identity( + unwrapped_output, unwrapped_operands, operands, wrap_fn, out_dims=out_dims + ) + + +def custom_function_call_vmap_generate_rule(interpreter, autograd_function, *operands): + unwrapped_operands, in_dims = unwrap_batched(operands, interpreter.level()) + vmapped_function, get_out_dims = vmapify_autograd_function( + autograd_function, in_dims, interpreter.batch_size(), interpreter.randomness() + ) + + with interpreter.lower(): + output = custom_function_call(vmapped_function, *unwrapped_operands) + + out_dims = get_out_dims() + return wrap_batched(output, out_dims, interpreter.level()) + + +@custom_function_call.py_impl(TransformType.Functionalize) +def custom_function_call_functionalize( + interpreter, autograd_function, generate_vmap_rule, *operands +): + raise RuntimeError("NYI: Functionalize rule for custom_function_call") + + +def vmapify_autograd_function(autograd_function, in_dims, batch_size, randomness): + # The following values are saved from the forward() and setup_context() + # and used in backward(). + # Why do we save the values out here instead of on the ctx object? + # - out_dims: There's no way to retrieve this from forward() + # - input_shapes, saved_tensors_bdims: I'm a bit scared of nesting + # vmap(vmap( but not completely sure if it is a problem. If we + # assigned those fields to the ctx object, the worry is that they + # get overwritten. + init_val = "not populated" + out_dims = init_val + input_shapes: Any = init_val + saved_tensors_bdims: Any = init_val + + def forward(*operands): + nonlocal out_dims + outputs, out_dims = restore_vmap( + autograd_function.forward, in_dims, batch_size, randomness + )(*operands) + return outputs + + def setup_context(ctx, inputs, outputs): + input_shapes_ = None + saved_tensors_bdims_ = None + + def inner(inputs, outputs): + # wrapped_ctx.save_for_backward will: + # - unwrap batchedtensors into (tensor, bdim) + # - save_for_backward(*unwrapped_tensors) + # - assign the bdims to wrapped_ctx._pt_saved_tensors_bdims + wrapped_ctx = CtxCustomSave(ctx, current_level()) + autograd_function.setup_context(wrapped_ctx, inputs, outputs) + + # input_shapes are used for reductify later to reduce expanded gradients + # to the correct shape. + # See NOTE: [Why can't we rely on autograd to reduce expanded gradients?] + # for more details + nonlocal input_shapes_ + input_shapes_ = tuple( + inp.shape if isinstance(inp, torch.Tensor) else None for inp in inputs + ) + nonlocal saved_tensors_bdims_ + saved_tensors_bdims_ = wrapped_ctx._pt_saved_tensors_bdims + + # See NOTE: [Why do we need to run setup_context under a vmap?] + restore_vmap( + inner, + (in_dims, out_dims), + batch_size, + randomness, + )(inputs, outputs) + + nonlocal input_shapes + input_shapes = input_shapes_ + nonlocal saved_tensors_bdims + saved_tensors_bdims = saved_tensors_bdims_ + + def jvp(ctx, *tangents): + assert out_dims != init_val + assert saved_tensors_bdims != init_val + + def jvp_no_context(saved_tensors, tangents): + wrapped_ctx = CtxWithSavedTensors(ctx, saved_tensors) + return autograd_function.jvp(wrapped_ctx, *tangents) + + tangent_in_dims = get_tangents_in_dims(in_dims, tangents) + out_tangents, out_tangents_dims = restore_vmap( + jvp_no_context, + (saved_tensors_bdims, tangent_in_dims), + batch_size, + randomness, + )(ctx.saved_tensors, tangents) + + result = reductify(out_tangents, out_tangents_dims, out_dims, batch_size) + return result + + def backward(ctx, *grad_outputs): + assert out_dims != init_val + assert input_shapes != init_val + assert saved_tensors_bdims != init_val + + def backward_no_context(inputs): + saved_tensors, grad_outputs = inputs + wrapped_ctx = CtxWithSavedTensors(ctx, saved_tensors) + return autograd_function.backward(wrapped_ctx, *grad_outputs) + + grad_ins, grad_ins_dims = restore_vmap( + backward_no_context, + ((saved_tensors_bdims, out_dims),), + batch_size, + randomness, + )((ctx.saved_tensors, grad_outputs)) + result = reductify(grad_ins, grad_ins_dims, in_dims, batch_size, input_shapes) + return result + + name = f"Vmapped{autograd_function.__name__}" + Generated = type( + name, + (torch.autograd.Function,), + { + "forward": staticmethod(forward), + "backward": staticmethod(backward), + "jvp": staticmethod(jvp), + "setup_context": staticmethod(setup_context), + "generate_vmap_rule": True, + }, + ) + + def get_out_dims(): + assert out_dims != init_val + return out_dims + + return Generated, get_out_dims + + +# tangents might be None, so we need to replace +# the corresponding in_dims with None. +def get_tangents_in_dims(input_dims, tangents): + flat_in_dims, spec = pytree.tree_flatten(input_dims) + flat_tangents = pytree.arg_tree_leaves(*tangents) + result = [ + None if tangent is None else in_dim + for in_dim, tangent in zip(flat_in_dims, flat_tangents) + ] + return pytree.tree_unflatten(result, spec) + + +# NOTE: [Why do we need to run setup_context under a vmap?] +# Consider the following autograd.Function +# +# class Sum(torch.autograd.Function): +# @staticmethod +# def forward(x): +# return x.sum() +# @staticmethod +# def setup_context(ctx, inputs, outputs): +# ctx.x_shape = inputs[0] +# @staticmethod +# def backward(ctx, gy): +# return gy.expand(ctx.x_shape) +# +# x = torch.randn(B, 4) +# in_dims = 0 +# vmap(Sum.apply, in_dims)(x) +# +# Let's assume for a moment that we didn't vmap setup_context in VmappedSum: +# +# class VmappedSum(torch.autograd.Function): +# @staticmethod +# def forward(x): +# return vmap(Sum.forward, in_dims)(x) +# +# @staticmethod +# def setup_context(ctx, inputs, outputs): +# Sum.setup_context(ctx, inputs, outputs) +# +# @staticmethod +# def backward(ctx, gy): +# def backward_no_context(gy): +# return gy.expand(ctx.x_shape) +# +# dims = (0,) +# gx = vmap(backward_no_context, dims)(gy) +# return gx +# +# We end up saving [B, 4] as x_shape. In the backward, gy has shape [B], +# and we're doing: +# +# def backward_no_context(gy): +# return gy.expand([B, 4]) +# +# gx = vmap(backward_no_context, dims)(gy: "Tensor[B]") +# +# This gives us the wrong result (gx has shape [B, B, 4], but it should +# have shape [4]). Performing vmap over setup_context means the shape +# saved has shape [4] and leads to a correct result shape for gx. + + +# Wraps a ctx object. Forwards all attr accesses to the underlying object +# except for the attrs in _pt_attrs +class WrappedCtx: + _pt_reserved_attrs: Tuple[str, ...] = ("_pt_reserved_attrs", "_pt_inner_ctx") + + def __init__(self, ctx): + if not isinstance(ctx, WrappedCtx): + reserved_attrs = type(self)._pt_reserved_attrs + for name in reserved_attrs: + if not hasattr(ctx, name): + continue + raise RuntimeError( + f"PyTorch reserves the {reserved_attrs} field on ctx. " + "Please name your fields on ctx something else to avoid name " + "collision." + ) + self._pt_inner_ctx = ctx + + def __getattr__(self, name): + return getattr(self._pt_inner_ctx, name) + + def __setattr__(self, name, value): + if name in type(self)._pt_reserved_attrs: + self.__dict__[name] = value + return + return setattr(self._pt_inner_ctx, name, value) + + +# Wraps ctx to create a new ctx object that overrides saved_tensors. +class CtxWithSavedTensors(WrappedCtx): + _pt_reserved_attrs = ("_pt_new_saved_tensors", *WrappedCtx._pt_reserved_attrs) + + def __init__(self, ctx, new_saved_tensors): + super().__init__(ctx) + self._pt_new_saved_tensors = new_saved_tensors + + @property + def saved_tensors(self): + return self._pt_new_saved_tensors + + +class CtxCustomSave(WrappedCtx): + _pt_reserved_attrs = ( + "_pt_saved_tensors_bdims", + "_pt_current_level", + *WrappedCtx._pt_reserved_attrs, + ) + + def __init__(self, ctx, current_level): + super().__init__(ctx) + self._pt_saved_tensors_bdims = () + self._pt_current_level = current_level + + def save_for_backward(self, *tensors): + unwrapped_tensors, bdims = unwrap_batched(tensors, self._pt_current_level) + self._pt_inner_ctx.save_for_backward(*unwrapped_tensors) + self._pt_saved_tensors_bdims = bdims + + def save_for_forward(self, *tensors): + unwrapped_tensors, bdims = unwrap_batched(tensors, self._pt_current_level) + self._pt_inner_ctx.save_for_forward(*unwrapped_tensors) + self._pt_saved_tensors_bdims = bdims + + +def reductify( + grad_input, + grad_input_bdim, + input_bdim, + batch_size, + target_shape_without_bdim_to_reduce_to=None, +): + if not isinstance(grad_input, tuple): + grad_input = (grad_input,) + if not isinstance(grad_input_bdim, tuple): + grad_input_bdim = (grad_input_bdim,) + if not isinstance(input_bdim, tuple): + input_bdim = (input_bdim,) + + if target_shape_without_bdim_to_reduce_to is None: + target_shape_without_bdim_to_reduce_to = len(grad_input) * (None,) + result = tuple( + reductify_leaf(gi, gi_bdim, i_bdim, batch_size, maybe_ishape) + for gi, gi_bdim, i_bdim, maybe_ishape in zip( + grad_input, + grad_input_bdim, + input_bdim, + target_shape_without_bdim_to_reduce_to, + ) + ) + return result + + +def reductify_leaf( + grad_input, + grad_input_bdim, + input_bdim, + batch_size, + target_shape_without_bdim_to_reduce_to=None, +): + if grad_input is None: + return None + + if grad_input_bdim is None and input_bdim is None: + return grad_input + + if grad_input_bdim is not None and input_bdim is None: + return grad_input.sum(grad_input_bdim) + + # NOTE: [Why can't we rely on autograd to reduce expanded gradients?] + # For reverse-mode AD, + # given a grad_input and input, it is valid for the user to return a + # grad_input that has a broadcasted shape when compared to the input. + # In this situation, autograd automatically reduces the grad_input to + # the shape of the input. + # + # However, when input_bdim is not None, we have problems. + # + # [example 1] + # grad_input: Tensor[3, 4], input: Tensor[B, 4] + # We can expand grad_input to Tensor[B, 3, 4], but that isn't broadcastable + # from [B, 4]. + # + # [example 2] + # grad_input: Tensor[3, B, 4], input: Tensor[B, 4] + # We can swizzle grad_input to Tensor[B, 3, 4], but that isn't broadcastable + # from [B, 4]. + # + # This means that we need to also reduce the grad_input to the shape of the + # input. This behavior is controlled by the `target_shape_without_bdim_to_reduce_to` flag; + # if not-None then we do the reducing manually, otherwise, we do not do a reduction. + assert input_bdim is not None + + if grad_input_bdim is None: + grad_input = grad_input.unsqueeze(input_bdim) + new_shape = list(grad_input.shape) + new_shape[input_bdim] = batch_size + grad_input = grad_input.expand(new_shape) + grad_input_bdim = input_bdim + + if target_shape_without_bdim_to_reduce_to is not None: + return vmap( + torch.Tensor.sum_to_size, + in_dims=(grad_input_bdim, None), + out_dims=input_bdim, + )(grad_input, target_shape_without_bdim_to_reduce_to) + + if input_bdim != grad_input_bdim: + grad_input = grad_input.movedim(grad_input_bdim, input_bdim) + return grad_input + + +def autograd_function_forward_rewritten(original_forward, original_setup_context): + def new_forward(ctx, *args, **kwargs): + output = original_forward(*args, **kwargs) + original_setup_context(ctx, args, output) + return output + + return new_forward + + +class AutogradFunctionApply(HigherOrderOperator): + def __init__(self) -> None: + super().__init__("autograd_function_apply") + + def __call__(self, fwd, bwd, *fwd_args, **fwd_kwargs): + saved_values = None + args_tensor_mask = fwd_kwargs["args_tensor_mask"] + non_differentiable_idx = fwd_kwargs["non_differentiable_idx"] + length_of_tensor_args = sum(args_tensor_mask) + # Filter out the original tensor args from fwd_args, + # lifted freevars should not be args of ApplyTemplate.apply + # since we don't need to calculate the gradients of them. + new_fwd_args = fwd_args[:length_of_tensor_args] + + class ApplyTemplate(torch.autograd.Function): + @staticmethod + def forward(ctx, *args): + nonlocal saved_values + output, saved_values = fwd(None, *fwd_args) + + # If users call ctx.mark_non_differentiable() in the original fwd function. + if len(non_differentiable_idx) > 0: + non_differentiable_output = [] + for i, x in enumerate(output): + if i in non_differentiable_idx: + non_differentiable_output.append(x) + ctx.mark_non_differentiable(*non_differentiable_output) + + return output + + @staticmethod + def backward(ctx, *grad): + return bwd(None, *grad, *saved_values) + + return ApplyTemplate.apply(*new_fwd_args) + + +autograd_function_apply = AutogradFunctionApply() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/batch_norm_replacement.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/batch_norm_replacement.py new file mode 100644 index 0000000000000000000000000000000000000000..90e4fec99b554a4f3f267caf7c2a0873618fb44d --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/batch_norm_replacement.py @@ -0,0 +1,29 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import torch.nn as nn +from torch._functorch.utils import exposed_in + + +def batch_norm_without_running_stats(module: nn.Module): + if ( + isinstance(module, nn.modules.batchnorm._BatchNorm) + and module.track_running_stats + ): + module.running_mean = None + module.running_var = None + module.num_batches_tracked = None + module.track_running_stats = False + + +@exposed_in("torch.func") +def replace_all_batch_norm_modules_(root: nn.Module) -> nn.Module: + """ + In place updates :attr:`root` by setting the ``running_mean`` and ``running_var`` to be None and + setting track_running_stats to be False for any nn.BatchNorm module in :attr:`root` + """ + # base case + batch_norm_without_running_stats(root) + + for obj in root.modules(): + batch_norm_without_running_stats(obj) + return root diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/benchmark_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/benchmark_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e0bcae4c836e9331f01fda7b27ec475f10d8a00b --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/benchmark_utils.py @@ -0,0 +1,231 @@ +# mypy: ignore-errors + +import contextlib +import json +import operator +import os +import time + +import torch +from torch.profiler import profile, ProfilerActivity + + +def synchronize(): + pass + + +def dump_chrome_trace( + f, + input, + trace_filename, + optimize_ctx, + activities, + num_runs=1, + devices=None, + kwargs_for_f=None, + kwargs_for_profiler=None, +): + """ + Output the chrome trace of running f(input, **kwargs_for_f) with [optimize_ctx] + [num_runs] times to [trace_filename]. + + [activities] are the activities that the profiler will record, e.g. ProfilerActivity.CUDA. + Return total runtime without the profiler + + Outputs to trace_filename + """ + + if devices is None: + devices = ["cuda"] + + global synchronize + if devices != ["cpu"] and torch.cuda.is_available(): + synchronize = torch.cuda.synchronize + + if kwargs_for_f is None: + kwargs_for_f = {} + if kwargs_for_profiler is None: + kwargs_for_profiler = {} + + with optimize_ctx: + torch.manual_seed(1337) + for _ in range(5): # warmup runs + f(input, **kwargs_for_f) + synchronize() + torch.manual_seed(1337) + t0 = time.perf_counter() + for _ in range(num_runs): + f(input, **kwargs_for_f) + synchronize() + t1 = time.perf_counter() + timing = t1 - t0 + + with profile(activities=activities, **kwargs_for_profiler) as prof: + with optimize_ctx: + synchronize() + torch.manual_seed(1337) + for _ in range(num_runs): + f(input, **kwargs_for_f) + synchronize() + prof.export_chrome_trace(trace_filename) + + return timing + + +def get_chrome_trace_events(filename): + f = open(filename) + data = json.load(f) + events = data["traceEvents"] + return events + + +def is_gpu_compute_event(event): + global gpu_pids + return ( + "pid" in event + and event["pid"] in gpu_pids + and "ph" in event + and event["ph"] == "X" + ) + + +def get_sorted_gpu_events(events): + sorted_gpu_events = [] + for event in events: + if not is_gpu_compute_event(event): + continue + sorted_gpu_events.append(event) + return sorted(sorted_gpu_events, key=operator.itemgetter("ts")) + + +def get_duration(sorted_gpu_events): + if len(sorted_gpu_events) == 0: + return 0 + event = sorted_gpu_events[0] + current_end_time = event["ts"] + event["dur"] + total_duration = event["dur"] + for event in sorted_gpu_events[1:]: + start_time = max(event["ts"], current_end_time) + end_time = event["ts"] + event["dur"] + total_duration = total_duration + max(end_time - start_time, 0) + current_end_time = max(current_end_time, end_time) + return total_duration + + +def get_sorted_gpu_mm_conv_events(events): + def is_mm_conv_event(event): + return "name" in event and ( + "gemm" in event["name"] + or "conv" in event["name"] + or "cutlass" in event["name"] + or "wgrad" in event["name"] + ) + + gpu_events = get_sorted_gpu_events(events) + sorted_events = [] + for event in gpu_events: + if not is_mm_conv_event(event): + continue + sorted_events.append(event) + return sorted_events + + +gpu_pids = [] + + +def compute_utilization(filename: str, total_length: float): + """ + Process the chrome traces outputs by the pytorch profiler to compute GPU Utilization + and percent of times spent on matmul and convolution + + Args: + filename(str): Name of chrome traces file produced by pytorch profiler + + total_length(float): total length of the process without profiler in second + + Return: + tuple: (GPU Utilization, percent of time spent on matmul and convolution) + """ + events = get_chrome_trace_events(filename) + + # get pids of GPU events + global gpu_pids + gpu_pids = [] + for event in events: + if "name" not in event: + continue + if event["name"] == "process_labels" and "GPU" in event["args"]["labels"]: + gpu_pids.append(event["pid"]) + + total_length = total_length * 1e6 + sorted_gpu_events = get_sorted_gpu_events(events) + utilization = get_duration(sorted_gpu_events) / total_length + + sorted_gpu_mm_conv_events = get_sorted_gpu_mm_conv_events(events) + mm_conv_utilization = get_duration(sorted_gpu_mm_conv_events) / total_length + + return utilization, mm_conv_utilization + + +def benchmark_utilization( + f, + input, + trace_folder, + optimize_ctx=None, + trace_file_name="tmp_chrome_trace", + num_runs=1, +): + """ + Benchmark the GPU Utilization and percent of time spent on matmul and convolution operations of + running f(input, **kwargs_for_f) with [optimize_ctx] [num_runs] times. + It will produce a chrome trace file in trace_folder/trace_file_name.json + + Example: + + ``` + def f(a): + return a.sum() + a = torch.rand(2**20, device="cuda") + utilization, mm_conv_utilization = benchmark_utilization(f, a, "tmp", trace_file_name = "tmp_chrome_trace") + ``` + + Args: + f: function to benchmark + + input: input to :attr:`f` + + trace_folder: name of the folder to store the chrome trace + + optimize_ctx: the context in which f will run + + trace_file_name: name of the dumped chrome trace file, default to "tmp_chrome_trace" + + num_runs: number of times to run f, excluding the warm-up runs, default to 1. + + Return: + tuple: (GPU Utilization, percent of time spent on matmul and convolution) + + """ + isExist = os.path.exists(trace_folder) + if not isExist: + os.makedirs(trace_folder) + print("create folder " + trace_folder) + + if optimize_ctx is None: + optimize_ctx = contextlib.nullcontext() + + chrome_trace_file_name = os.path.join(trace_folder, trace_file_name + ".json") + total_length = dump_chrome_trace( + f, + input, + chrome_trace_file_name, + optimize_ctx, + [ProfilerActivity.CUDA], + num_runs=num_runs, + devices="cuda", + ) + utilization, mm_conv_utilization = compute_utilization( + chrome_trace_file_name, total_length + ) + + return utilization, mm_conv_utilization diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/compile_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/compile_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..3bf61f1af3bf3c11250911720873197cae78a8c1 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/compile_utils.py @@ -0,0 +1,176 @@ +# mypy: ignore-errors + + +from typing import Callable + +import torch +import torch.fx as fx +from torch.multiprocessing.reductions import StorageWeakRef +from torch.utils import _pytree as pytree +from torch.utils._pytree import tree_flatten + + +aten = torch.ops.aten + + +def get_aten_target(node: fx.Node) -> Callable: + if hasattr(node.target, "overloadpacket"): + return node.target.overloadpacket + return node.target + + +rand_ops = [ + aten.dropout, + aten._fused_dropout, + aten._standard_gamma, + aten.bernoulli, + aten.multinomial, + aten.native_dropout, + aten.normal, + aten.poisson, + aten.binomial, + aten.rrelu, + aten.rand_like, + aten.rand, + aten.randint, + aten.randn, + aten.randperm, +] + + +# return a new copy of torch.fx.graph.Graph with CSE applied to the input graph +def fx_graph_cse(fx_g: torch.fx.graph.Graph): + new_graph = fx.Graph() + env = {} # map from node in the old graph to node in the new graph + hash_env = {} # map from hash to a node in the new graph + token_map = {} # map from hash to token + + from torch._inductor.pattern_matcher import ( + compute_mutation_region_ids, + same_mutation_regions, + ) + + compute_mutation_region_ids(fx_g) # type: ignore[arg-type] + + # Make a set of separate storages returned from the output, which will be preserved + # when pruning. This prevents us from deduplicating returned tensors which have + # experienced identical operations, but are separate data structures in eager mode. + output_node: fx.Node = list(fx_g.nodes)[-1] + assert output_node.op == "output" + + def checkable_node(node: fx.Node) -> bool: + """We can evaluate only nodes that represent tensors with defined storage.""" + if "val" not in node.meta or not isinstance(node.meta["val"], torch.Tensor): + return False + + try: + node.meta["val"].untyped_storage() + except NotImplementedError: + return False + + return True + + output_storages = { + StorageWeakRef(n.meta["val"].untyped_storage()) + for n in output_node.all_input_nodes + if checkable_node(n) + } + nodes_that_alias_outputs = { + n + for n in fx_g.nodes + if checkable_node(n) + and StorageWeakRef(n.meta["val"].untyped_storage()) in output_storages + } + + for n in fx_g.nodes: + # The placeholder, output, and get_attr nodes are copied to the new graph without change + # do not CSE away random operations + if ( + n.op == "placeholder" + or n.op == "output" + or n.op == "get_attr" + or get_aten_target(n) in rand_ops + # aten.empty is non-deterministic, so don't CSE it. + # Also, aten.empty is almost always fusible into its consumer, + # so it's not worth CSEing. + or get_aten_target(n) is aten.empty + or n in nodes_that_alias_outputs + ): + new_node = new_graph.node_copy(n, lambda x: env[x]) + env[n] = new_node + else: # n.op == 'call_function', should never see n.op == 'call_module' or 'call_method' + # substitute args and kwargs members to their mapping in env if exists + # specs can be used to reconstruct nested list/dictionaries + def substitute(arg_list): + arg_list, spec = tree_flatten(arg_list) + for i in range(len(arg_list)): + v = arg_list[i] + if isinstance(v, torch.fx.node.Node) and v in env: + arg_list[i] = env[v] + if isinstance(v, (torch.SymBool, torch.SymInt, torch.SymFloat)): + arg_list[i] = v.node + return tuple(arg_list), spec + + args, args_spec = substitute(n.args) + kwargs, kwargs_spec = substitute(n.kwargs) + + # each token corresponds to a unique node + # nodes with the same token can be substituted + token = { + "target": n.target, + "args": args, + "args_spec": args_spec, + "kwargs": kwargs, + "kwargs_spec": kwargs_spec, + } + + # hash substituted args to a number, do not hash specs because specs are not hashable + # We need to add type into hash to avoid situations like: + # hash((primals_2, 1.0)) == hash((primals_2, 1)) + hash_arg = hash( + (tuple((a, type(a)) for a in args), tuple((a, type(a)) for a in kwargs)) + ) + hash_val = (n.target, hash_arg) + + # check if a node has a substitute and can be eliminated + hash_val_in_hash_env = hash_val in hash_env + overwrite_due_to_mutation = False + if hash_val_in_hash_env and token_map[hash_val] == token: + duplicate_n_prev = hash_env[hash_val] + if same_mutation_regions(n, duplicate_n_prev): + env[n] = duplicate_n_prev + continue + else: + # any futures duplicates should replace with n, not duplicate_n_prev + overwrite_due_to_mutation = True + + new_node = new_graph.node_copy(n, lambda x: env[x]) + env[n] = new_node + if overwrite_due_to_mutation or not hash_val_in_hash_env: + hash_env[hash_val] = new_node + token_map[hash_val] = token + + return new_graph + + +def strip_overloads(gm): + """ + Modifies the target of graph nodes in :attr:`gm` to strip overloads. + + Args: + gm(fx.GraphModule): The input Fx graph module to be modified + """ + for node in gm.graph.nodes: + if isinstance(node.target, torch._ops.OpOverload): + node.target = node.target.overloadpacket + gm.recompile() + + +def get_placeholders(graph): + return graph.find_nodes(op="placeholder") + + +def get_outputs(graph): + for node in graph.find_nodes(op="output"): + return pytree.tree_leaves(node.args[0]) + raise AssertionError("No output node found") diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/compilers.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/compilers.py new file mode 100644 index 0000000000000000000000000000000000000000..b420daca5ac347f9c1bc5354d495e91bb53177ec --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/compilers.py @@ -0,0 +1,445 @@ +# mypy: ignore-errors + +import copy +import logging +import os +import pickle +import random +from contextlib import contextmanager +from functools import partial +from typing import Callable, Union + +import sympy + +import torch +import torch.fx as fx +import torch.nn as nn +import torch.utils._pytree as pytree +from torch import SymInt +from torch._decomp import get_decompositions +from torch.fx.experimental.symbolic_shapes import bind_symbols + +from .aot_autograd import aot_function, aot_module, make_boxed_compiler +from .compile_utils import strip_overloads +from .partitioners import ( + default_partition, + draw_graph, + min_cut_rematerialization_partition, +) + + +log = logging.getLogger(__name__) + + +# These canonicalizations are needed here (and not decompositions), as the ops +# we're trying to canonicalize to CompositeImplicitAutograd. +def _canonicalize(fx_g): + for node in fx_g.graph.find_nodes( + op="call_function", target=torch.ops.aten._to_copy + ): + node.target = torch.ops.aten.to + fx_g.recompile() + return fx_g + + +@contextmanager +def _disable_jit_autocast(): + old_jit_autocast_flag = torch._C._jit_set_autocast_mode(False) + try: + yield + finally: + torch._C._jit_set_autocast_mode(old_jit_autocast_flag) + + +@make_boxed_compiler +def ts_compile(fx_g: fx.GraphModule, inps) -> Callable: + """ + Compiles the :attr:`fx_g` with Torchscript compiler. + + .. warning:: + This API is experimental and likely to change. + + Args: + fx_g(fx.GraphModule): The input Fx graph module to be compiled. + + Returns: + Torch scripted model. + """ + + with _disable_jit_autocast(): + strip_overloads(fx_g) + + for node in fx_g.graph.find_nodes( + op="call_function", target=torch.ops.aten._to_copy + ): + if len(node.args) == 1 and len(node.kwargs) == 1 and "dtype" in node.kwargs: + node.target = torch.ops.aten.to + + for node in fx_g.graph.nodes: + new_kwargs = {} + for k, v in node.kwargs.items(): + if isinstance(v, torch.device): + v = v.type + new_kwargs[k] = v + node.kwargs = new_kwargs + + fx_g.graph.lint() + + fx_g.recompile() + + f = torch.jit.script(fx_g) + + torch._C._jit_pass_remove_mutation(f.graph) + + f = torch.jit.freeze(f.eval()) + f = torch.jit.optimize_for_inference(f) + if not any(isinstance(t, torch._subclasses.FakeTensor) for t in inps): + f(*inps) + return f + + +def _draw_graph_compile(fx_g, _, name, clear_meta=True): + print(fx_g.code) + draw_graph(fx_g, name, clear_meta=clear_meta) + return fx_g + + +def draw_graph_compile(name): + return make_boxed_compiler(partial(_draw_graph_compile, name=name)) + + +@make_boxed_compiler +def nop(fx_g: fx.GraphModule, _) -> Callable: + """ + Returns the :attr:`fx_g` Fx graph module as it is. This is a no-op compiler + and can be used to check accuracy. + + .. warning:: + This API is experimental and likely to change. + + """ + return fx_g + + +class DebugInterpreter(fx.Interpreter): + def run(self, *args): + self.symbol_mapping = bind_symbols(self.module, *args) + super().run(*args) + + def run_node(self, n): + def subst_symint(ni): + if not isinstance(ni, SymInt): + return ni + r = sympy.expand(ni.node.expr.xreplace(self.symbol_mapping)) + assert r.is_number, r + return int(r) + + def subst_symint_tuple(nis): + return tuple(subst_symint(ni) for ni in nis) + + def check_significant_strides(a, b): + if subst_symint(a.numel()) > 0: + for idx in range(a.ndim): + if ( + subst_symint(a.stride(idx)) != b.stride(idx) + and subst_symint(a.size(idx)) > 1 + ): + return False + return True + + def check(nv, rv, desc): + assert callable(desc) + assert nv.dtype == rv.dtype, f"{desc()}: {nv.dtype} != {rv.dtype}" + assert ( + subst_symint_tuple(nv.size()) == rv.size() + ), f"{desc()}: {nv.size()} aka {subst_symint_tuple(nv.size())} != {rv.size()}" + same_strides = check_significant_strides(nv, rv) + assert ( + same_strides + ), f"{desc()}: {nv.stride()} aka {subst_symint_tuple(nv.stride())} != {rv.stride()}" + + r = super().run_node(n) + if "val" in n.meta: + n_vals, n_spec = pytree.tree_flatten(n.meta["val"]) + r_vals, r_spec = pytree.tree_flatten(r) + # TODO: There is some sort of problem where we record that an + # operator returned a tuple/list, and then later it turns out the + # real version of the operator returned a list/tuple. Need to + # figure out what's actually going on here, the error itself is + # harmless enough as we only getitem out the outputs. + # assert n_spec == r_spec, f"{n_spec} != {r_spec}" + assert len(n_vals) == len(r_vals), f"{len(n_vals)} != {len(r_vals)}" + for i, nv, rv in zip(range(len(n_vals)), n_vals, r_vals): + if not isinstance(rv, torch.Tensor): + continue + check(nv, rv, lambda: f"output {i} where {self.symbol_mapping}") + return r + + +@make_boxed_compiler +def debug_nop(fx_g: fx.GraphModule, _) -> Callable: + """ + Returns a (slow) interpreter over the FX graph module that also checks + various debugging properties (e.g., that tracing strides matched real + strides.) + """ + return DebugInterpreter(fx_g).run + + +@make_boxed_compiler +def simple_ts_compile(fx_g, _): + strip_overloads(fx_g) + f = torch.jit.script(fx_g) + f = torch.jit.freeze(f.eval()) + return f + + +def nnc_jit(f): + return aot_function(f, simple_ts_compile) + + +aten = torch.ops.aten +default_decompositions = { + aten.detach, + aten.gelu_backward, + aten.leaky_relu_backward, + aten.sigmoid_backward, + aten.threshold_backward, + aten.hardtanh_backward, + aten.hardsigmoid_backward, + aten.hardswish_backward, + aten.tanh_backward, + aten.silu_backward, + aten.elu_backward, + aten.cudnn_batch_norm, + aten.cudnn_batch_norm_backward, + aten.masked_fill.Scalar, + aten.masked_fill.Tensor, + aten.elu, + aten.leaky_relu, + aten.hardtanh, + aten.hardswish, + aten.hardsigmoid, + aten.conj_physical, + aten.is_same_size, +} + +default_decompositions = get_decompositions(default_decompositions) + + +@make_boxed_compiler +def print_compile(fx_g, _): + print(fx_g.code) + return fx_g + + +def memory_efficient_fusion( + fn: Union[Callable, nn.Module], + **kwargs, +): + """ + Wrapper function over :func:`aot_function` and :func:`aot_module` to perform + memory efficient fusion. It uses the + :func:`min_cut_rematerialization_partition` partitioner to perform efficient + recomputation. It uses NVFuser to compile the generated forward and backward + graphs. + + .. warning:: + This API is experimental and likely to change. + + Args: + fn (Union[Callable, nn.Module]): A Python function or a ``nn.Module`` + that takes one ore more arguments. Must return one or more Tensors. + **kwargs: Any other overrides you want to make to the settings + + Returns: + Returns a ``Callable`` or ``nn.Module`` that retains the eager behavior + of the original :attr:`fn`, but whose forward and backward graphs have + gone through recomputation optimizations, and the graphs have been + compiled with nvfuser. + + """ + config = { + "fw_compiler": ts_compile, + "bw_compiler": ts_compile, + "partition_fn": min_cut_rematerialization_partition, + "decompositions": default_decompositions, + } + config.update(kwargs) + if isinstance(fn, torch.nn.Module): + return aot_module(fn, **config) + else: + return aot_function(fn, **config) + + +def debug_compile(fx_g, inps): + fx_g.to_folder("foo") + print( + f""" +############################################################## +# To minimize FX graph, copy and paste the below and run it # +############################################################## + +import torch +import torch.fx as fx +from functorch.compile import minifier, check_nvfuser_subprocess, check_nvfuser_correctness_subprocess + +inps = {[(i.shape, i.dtype) for i in inps]} +inps = [torch.ones(shape, dtype=dtype, device='cuda') for (shape, dtype) in inps] +from foo import FxModule +mod = FxModule().cuda() + +with torch.jit.fuser("fuser2"): + # check_nvfuser_subprocess can be replaced with check_nvfuser_correctness_subprocess + minifier(fx.symbolic_trace(mod), inps, check_nvfuser_subprocess) +""" + ) + from foo import FxModule + + FxModule().cuda()(*inps) + + return ts_compile(fx_g, inps) + + +graph_index = 0 + + +def get_inputs(input_data_path): + """ + Return a random input for the given inputs meta generated from _save_fx_default. + """ + inputs = [] + with open(input_data_path, "rb") as f: + inputs_meta = pickle.load(f) + inputs = [] + for meta in inputs_meta: + if len(meta) == 1: + type = meta + input = type(random.rand()) + else: + type, shape, stride, dtype, device = meta + if dtype in { + torch.int, + torch.int32, + torch.int64, + torch.bool, + torch.int, + torch.uint8, + int, + float, + }: + input = torch.randint(0, 1, shape, dtype=dtype, device=device) + else: + input = torch.rand(shape, dtype=dtype, device=device) + inputs.append(input) + return inputs + + +def _save_fx_default(current_name, folder_name, dump_example_input, gm, example_inputs): + """ + The forward, backward, and joint computation graph will be stored in + {folder_name}/{current_name}/{current_name}_forward_{graph_index}, + {folder_name}/{current_name}/{current_name}_backward_{graph_index}, and + {folder_name}/{current_name}/{current_name}_joint_{graph_index} respectively. + The input shape of the graphs will be stored in the .input files. + These files can be loaded with pickle, + and is a list of format (type, shape, stride, dtype, device). + In the case of type = int or float, it is just (type,). + For joint graph input, it is a nested list [[],[]] + where the two inner lists have the same format. + If dump_example_input is True, example_inputs will be stored in .pt file. + Since each function might produce multiple graphs, + the graph_index is used to distinguish difference graphs + """ + from functorch.compile import aot_module_simplified + + def get_input_meta(args): + input_meta = [] + if len(args) > 0 and isinstance(args[0], tuple): # joint input + input_meta += get_input_meta(args[0]) + input_meta += get_input_meta(args[1]) + return input_meta + for arg in args: + if type(arg) == int or type(arg) == float: + input_meta.append((type(arg),)) + else: + input_meta.append( + (type(arg), arg.shape, arg.stride(), arg.dtype, arg.device) + ) + return input_meta + + def graph_saver_helper(gm_to_save, args, type_name): + global graph_index + if len(gm_to_save.graph.nodes) == 0: + log.log( + logging.WARNING, + "No nodes in graph {%s}_{%s}_{%s}.", + current_name, + type_name, + graph_index, + ) + return + + gm = copy.deepcopy(gm_to_save) + gm.graph.set_codegen(torch.fx.graph.CodeGen()) # remove codegen + gm.recompile() + + input_meta = get_input_meta(args) + + os.makedirs(f"{folder_name}/{current_name}", exist_ok=True) + gm.to_folder( + f"{folder_name}/{current_name}/{current_name}_{type_name}_{graph_index}" + ) + pickle.dump( + input_meta, + open( + f"{folder_name}/{current_name}/{current_name}_{type_name}_{graph_index}/{current_name}_{type_name}_{graph_index}.input", # noqa: B950 + "wb", + ), + ) # noqa: E501 + if dump_example_input: + torch.save( + args, + f"{folder_name}/{current_name}/{current_name}_{type_name}_{graph_index}/{current_name}_{type_name}_{graph_index}.pt", # noqa: B950 + ) # noqa: E501 + + def graph_saver_forward(gm, fw_args): + graph_saver_helper(gm, fw_args, "forward") + return gm + + def graph_saver_backward(gm, bw_args): + graph_saver_helper(gm, bw_args, "backward") + global graph_index + graph_index += 1 + return gm + + def graph_saver_joint(gm, joint_args): + graph_saver_helper(gm, joint_args, "joint") + return default_partition(gm, joint_args) + + return aot_module_simplified( + gm, + example_inputs, + fw_compiler=graph_saver_forward, + bw_compiler=graph_saver_backward, + partition_fn=graph_saver_joint, + decompositions=default_decompositions, + ) + + +# WARNING: This isn't tested anywhere!! +def graph_dumper_aot(current_name, folder_name, dump_example_input=False): + """ + Dump the forward, backward, and joint computation graph. + Example Usage: + save_fx_func = graph_dumper_aot(current_name, folder_name, dump_example_input = False) + optimize_ctx = torchdynamo.optimize( + save_fx_func + ) + with torch.enable_grad(): + with optimize_ctx: + result = forward_and_backward_pass(model, example_inputs) + """ + global graph_index + graph_index = 0 + return partial(_save_fx_default, current_name, folder_name, dump_example_input) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/config.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/config.py new file mode 100644 index 0000000000000000000000000000000000000000..04976c06965c0047e3352846477ef6544275921a --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/config.py @@ -0,0 +1,203 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +Global flags for aot autograd +""" +import os +import sys +from typing import TYPE_CHECKING + + +# Converts torch rng ops to their functional philox rng equivalents. Note that +# we functionalize only CUDA rng ops today. +functionalize_rng_ops = False + +# can be useful for debugging if we are incorrectly creating meta fake tensors +fake_tensor_allow_meta = os.environ.get("FAKE_ALLOW_META", "1") != "0" + +# Enables optional asserts in hotpath code to check for errors. If +# you are seeing weird accuracy problems, try turning this on. +# This is currently off by default as it will harm tracing time, +# but it is on by default for aot_eager. +debug_assert = False + +debug_partitioner = os.environ.get("AOT_PARTITIONER_DEBUG", "0") != "0" + +# Today, if you are in a situation where there is "false aliasing" +# (e.g. you have a bunch of model parameters that all alias the same underlying buffer), +# our checks for this situation are very slow if these inputs have dynamic shapes. +# This config is set to ensure that there aren't too many aliased inputs in this situation, +# so that we error loudly instead of compiling forever. +# Eventually, we should make these checks faster. +# For now, however, you can simply turn off dynamic shapes by marking your inputs static +# when you run into this situation. +_max_aliased_inputs_with_dynamic_shapes_enabled = 5 + +static_weight_shapes = True + +# Applies CSE to the graph before partitioning +cse = True + + +enable_autograd_cache = os.environ.get("ENABLE_AOT_AUTOGRAD_CACHE", "0") == "1" + +# When AOTAutograd regenerates aliased graph outputs, +# attempt to use functionalization's view-replay logic +# before falling back to the autograd engine's view replay or as_strided. +# This can have some perf implications +# (although for many models this will not matter). +# (1) If you have many view ops chained together, replaying all of them +# at runtime can have more overhead compared to a single as_strided call +# (2) If you are doing training, AsStridedBackward is quite slow, +# and the individual view op backward formulas will likely be faster. +# (3) Some backends like XLA do not support as_strided + +# Temporary hack: disable this flag for internal +# (needed to fix an internal issue while avoiding bumping XLA pin) +# eventually: either default this config to false completely +# once XLA pin update works, +# or default config to true and fix relevant bugs +from torch._inductor.config import is_fbcode + + +# View replay is currently not compatible with AOTAutogradCache, since +# FunctionalTensors are not serializable. We'll need to make them +# serializable before enabling warm cache with this config turned on. +view_replay_for_aliased_outputs = (not is_fbcode()) and (not enable_autograd_cache) + +# Restricts the amount of computation AOTAutograd can do. +# NB: We have essentially disabled this heuristic now. However, this is kept +# here for now in case it's useful. Setting it low can artificially reduce the +# amount of recomputation AOTAutograd performs, although not in any kind of +# principled way. +max_dist_from_bw = 1000 + + +# Bans recomputation of nodes that are reading from nodes that is far before +# the current node +ban_recompute_used_far_apart = True +# Breaks up long chain of fusible ops, as otherwise we can have an arbitrarily +# long chain of recomputation in the backwards pass. +ban_recompute_long_fusible_chains = True +# Bans recomputation of nodes that must be materialized in the backwards pass +# (used by a non-fusible node) +ban_recompute_materialized_backward = True +# Chooses to ban recomputation of nodes based off an allowlist. Setting it to +# False changes it to use a denylist. Main change is on operators like +# sort/pool/stuff that isn't cheap enough to be fusible for free but also isn't +# that expensive +ban_recompute_not_in_allowlist = True +# Chooses to ban recomputation of reductions. This is generally a good idea, as +# the result of reductions is generally very small but recomputing reductions in +# a fusion can be expensive. +ban_recompute_reductions = True +# Prevents the partitioner from ever saving views (i.e. always recompute them). +# Generally a good idea since views are free to recompute. +recompute_views = False + +# By default, the partitioner is purely trying to optimize for runtime (although +# it should always use less memory than eager) +# This knob controls the partitioner to make that tradeoff for you, choosing the +# fastest option that saves less activations than the memory budget. +# Specifically, 0.0 corresponds to the activation memory from applying +# activation checkpointing to the full compiled region, and 1.0 corresponds to +# the activation memory from the default runtime-optimized strategy. So, 0.4 +# would result in a strategy that saves 40% of the activations compared to the +# default strategy. +# It solves a 0-1 knapsack to find the minimum recompute necessary to stay below +# the activation memory budget. +# NOTE: This *cannot* be treated as +activation_memory_budget = 1.0 + +# This controls how we estimate the runtime when deciding what the cheapest +# operators to recompute are. The 3 options are +# "flops": Bases it off of the flop count provided by torch.utils.flop_counter +# "profile": Benchmarks each operator to come up with a runtime +# "testing": Returns 1 for everything +activation_memory_budget_runtime_estimator = "flops" + +# This controls the solver used for the 0-1 knapsack. By default we use a +# quantized DP solution ("dp"). The other approaches are a "greedy" and a "ilp" +# (which has a scipy dependency). +activation_memory_budget_solver = "dp" + +# This dumps out a png visualization of the expected runtime vs. activation +# memory tradeoffs for all memory budget values from 0 to 1 in increments of +# 0.5. See an example here: +# https://github.com/pytorch/pytorch/pull/126320#discussion_r1625104015 +visualize_memory_budget_pareto = ( + os.environ.get("PARTITIONER_MEMORY_BUDGET_PARETO", "0") == "1" +) + +# Sets all of the ban_recompute heuristics to False except ban_recompute_reductions +# Generally, this will probably result in some memory improvement, but at the +# cost of some performance +aggressive_recomputation = False + +# If FakeTensor.data_ptr() should error. +# This option is independent of AOTAutograd and torch.compile, but our policy +# is to turn it off during torch.compile. +fake_tensor_allow_unsafe_data_ptr_access = True + +# Unlifts effect tokens from the inputs/outputs in the traced graph and instead +# inserts make_token/sink_token calls in the graph to create tokens and then +# sink them at the end. Note that this means the graph is no longer functional +# which may lead to silent errors unless the backend knows how to handle the +# tokens. +unlift_effect_tokens = False + +# This mode specifies that we should also keep track of the real +# tensor along with the fake tensor, and do real compute. While +# seemingly this eliminates the whole point of fake tensors, there are +# two obvious use cases for it: +# +# 1. When users call item()/other data dependent operations, +# if we propagate_real_tensors we are able to determine what +# the true value is and keep going. +# +# 2. It can be useful for testing, when you want to see if the fake +# and real tensors agree with each other. (Note that there are +# currently known inaccuracies in how we clone real tensors, that +# would have to be tightened up for this to be useful in this +# case.) +# +# Note that fake tensors are typically understood to be cheap to store +# indefinitely, so we tend to hold on to them longer than we would +# hold onto the real tensors. So we also support you explicitly +# deallocating the real tensor associated with a fake tensor, at which +# point we will stop propagating real tensors. +# +# One more thing: when you provide a real tensor to fakeify, we will +# clone it, so that we can safely perform mutations on it if necessary. +# This will increase live memory usage. This could potentially be +# optimized by using COW. We also currently do not faithfully +# maintain autograd metadata on the real tensor; this is fine because +# AOTAutograd will only use the fake tensor to determine leafness/etc +# of tensors in question. +fake_tensor_propagate_real_tensors = False + +# This controls whether we collect donated buffer. This flag must be set +# False if a user wants to retain_graph=True for backward. +donated_buffer = False + +# Controls the default graph output format used by draw_graph +# Supported formats are defined here https://graphviz.org/docs/outputs/ +torch_compile_graph_format = os.environ.get("TORCH_COMPILE_GRAPH_FORMAT", "svg") + + +# Error on BypassAOTAutogradCache instead of just a warning +# Used for tests +strict_autograd_cache = False + +if TYPE_CHECKING: + from torch.utils._config_typing import * # noqa: F401, F403 + +from torch.utils._config_module import install_config_module + + +# adds patch, save_config, invalid config checks, etc +install_config_module(sys.modules[__name__]) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/deprecated.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/deprecated.py new file mode 100644 index 0000000000000000000000000000000000000000..ebb930e8ecb742d23a6cfeefb76ffe8587cea952 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/deprecated.py @@ -0,0 +1,172 @@ +# mypy: allow-untyped-defs +""" +The APIs in this file are exposed as `functorch.*`. They are thin wrappers +around the torch.func.* APIs that have deprecation warnings -- we're trying +to move people to the torch.func.* equivalents. + +NB: We don't use *args, **kwargs in the signatures because that changes the +documentation. +""" + +import textwrap +import warnings +from typing import Any, Callable, Optional, Tuple, Union + +import torch._functorch.apis as apis +import torch._functorch.eager_transforms as _impl +import torch._functorch.make_functional as _nn_impl +import torch.nn as nn +from torch._functorch.eager_transforms import argnums_t +from torch._functorch.vmap import in_dims_t, out_dims_t + + +def get_warning(api, new_api=None, replace_newlines=False): + if new_api is None: + new_api = f"torch.func.{api}" + warning = ( + f"We've integrated functorch into PyTorch. As the final step of the \n" + f"integration, `functorch.{api}` is deprecated as of PyTorch \n" + f"2.0 and will be deleted in a future version of PyTorch >= 2.3. \n" + f"Please use `{new_api}` instead; see the PyTorch 2.0 release notes \n" + f"and/or the `torch.func` migration guide for more details \n" + f"https://pytorch.org/docs/main/func.migrating.html" + ) + if replace_newlines: + warning = warning.replace("\n", "") + return warning + + +def warn_deprecated(api, new_api=None): + warning = get_warning(api, new_api, replace_newlines=True) + warnings.warn(warning, FutureWarning, stacklevel=3) + + +def setup_docs(functorch_api, torch_func_api=None, new_api_name=None): + api_name = functorch_api.__name__ + if torch_func_api is None: + torch_func_api = getattr(_impl, api_name) + # See https://docs.python.org/3/using/cmdline.html#cmdoption-OO + if torch_func_api.__doc__ is None: + return + + warning = get_warning(api_name, new_api_name) + warning_note = "\n.. warning::\n\n" + textwrap.indent(warning, " ") + warning_note = textwrap.indent(warning_note, " ") + functorch_api.__doc__ = torch_func_api.__doc__ + warning_note + + +def vmap( + func: Callable, + in_dims: in_dims_t = 0, + out_dims: out_dims_t = 0, + randomness: str = "error", + *, + chunk_size=None, +) -> Callable: + warn_deprecated("vmap", "torch.vmap") + return apis.vmap(func, in_dims, out_dims, randomness, chunk_size=chunk_size) + + +def grad(func: Callable, argnums: argnums_t = 0, has_aux: bool = False) -> Callable: + warn_deprecated("grad") + return apis.grad(func, argnums, has_aux) + + +def grad_and_value( + func: Callable, argnums: argnums_t = 0, has_aux: bool = False +) -> Callable: + warn_deprecated("grad_and_value") + return apis.grad_and_value(func, argnums, has_aux) + + +def vjp(func: Callable, *primals, has_aux: bool = False): + warn_deprecated("vjp") + return _impl.vjp(func, *primals, has_aux=has_aux) + + +def jvp( + func: Callable, + primals: Any, + tangents: Any, + *, + strict: bool = False, + has_aux: bool = False, +): + warn_deprecated("jvp") + return _impl.jvp(func, primals, tangents, strict=strict, has_aux=has_aux) + + +def jacrev( + func: Callable, + argnums: Union[int, Tuple[int]] = 0, + *, + has_aux=False, + chunk_size: Optional[int] = None, + _preallocate_and_copy=False, +): + warn_deprecated("jacrev") + return _impl.jacrev( + func, + argnums, + has_aux=has_aux, + chunk_size=chunk_size, + _preallocate_and_copy=_preallocate_and_copy, + ) + + +def jacfwd( + func: Callable, + argnums: argnums_t = 0, + has_aux: bool = False, + *, + randomness: str = "error", +): + warn_deprecated("jacfwd") + return _impl.jacfwd(func, argnums, has_aux, randomness=randomness) + + +def hessian(func, argnums=0): + warn_deprecated("hessian") + return _impl.hessian(func, argnums=argnums) + + +def functionalize(func: Callable, *, remove: str = "mutations") -> Callable: + warn_deprecated("functionalize") + return _impl.functionalize(func, remove=remove) + + +def make_functional(model: nn.Module, disable_autograd_tracking: bool = False): + warn_deprecated("make_functional", "torch.func.functional_call") + return _nn_impl.make_functional(model, disable_autograd_tracking) + + +def make_functional_with_buffers( + model: nn.Module, disable_autograd_tracking: bool = False +): + warn_deprecated("make_functional_with_buffers", "torch.func.functional_call") + return _nn_impl.make_functional_with_buffers(model, disable_autograd_tracking) + + +def combine_state_for_ensemble(models): + warn_deprecated("combine_state_for_ensemble", "torch.func.stack_module_state") + return _nn_impl.combine_state_for_ensemble(models) + + +setup_docs(vmap, apis.vmap, "torch.vmap") +setup_docs(grad, apis.grad) +setup_docs(grad_and_value, apis.grad_and_value) +setup_docs(vjp) +setup_docs(jvp) +setup_docs(jacrev) +setup_docs(jacfwd) +setup_docs(hessian) +setup_docs(functionalize) +setup_docs(make_functional, _nn_impl.make_functional, "torch.func.functional_call") +setup_docs( + make_functional_with_buffers, _nn_impl.make_functional, "torch.func.functional_call" +) +setup_docs( + combine_state_for_ensemble, + _nn_impl.combine_state_for_ensemble, + "torch.func.stack_module_state", +) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/eager_transforms.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/eager_transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..d389c7fda789497025a98ed84fdc0e9108685c67 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/eager_transforms.py @@ -0,0 +1,1839 @@ +# mypy: ignore-errors + +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +from functools import partial, wraps +from typing import Any, Callable, List, Optional, Tuple, Union + +import torch +import torch.autograd.forward_ad as fwAD +from torch._C._functorch import ( + _assert_wrapped_functional, + _func_decrement_nesting, + _func_increment_nesting, + _grad_decrement_nesting, + _grad_increment_nesting, + _jvp_decrement_nesting, + _jvp_increment_nesting, + _propagate_functional_input_mutation, + _unwrap_for_grad, + _unwrap_functional_tensor, + _wrap_for_grad, + _wrap_functional_tensor, + get_inplace_requires_grad_allowed, + set_inplace_requires_grad_allowed, +) +from torch._functorch.utils import argnums_t, exposed_in +from torch._subclasses.functional_tensor import FunctionalTensor +from torch.fx.experimental import const_fold +from torch.fx.experimental.proxy_tensor import make_fx +from torch.utils import _pytree as pytree +from torch.utils._pytree import ( + tree_flatten, + tree_map, + tree_map_, + tree_map_only, + tree_unflatten, + treespec_pprint, +) + +from .apis import vmap +from .vmap import doesnt_support_saved_tensors_hooks, get_chunk_sizes + + +def lazy_dynamo_disallow(func): + import torch._dynamo + + return torch._dynamo.disallow_in_graph(func) + + +@contextlib.contextmanager +def enable_inplace_requires_grad(enabled): + prev_state = get_inplace_requires_grad_allowed() + set_inplace_requires_grad_allowed(enabled) + try: + yield + finally: + set_inplace_requires_grad_allowed(prev_state) + + +def _vjp_treespec_compare(primals_out, cotangents): + # Revert this once #116264 gets fixed + _, primals_out_spec = tree_flatten(primals_out) + _, cotangents_spec = tree_flatten(cotangents) + # Dynamo fails to trace operator.ne below. To bypass this limitation, this + # function is not inlined. + if primals_out_spec != cotangents_spec: + raise RuntimeError( + f"Expected pytree structure of cotangents to be the same " + f"as pytree structure of outputs to the function. " + f"cotangents: {treespec_pprint(cotangents_spec)}, " + f"primal output: {treespec_pprint(primals_out_spec)}" + ) + + +def _jvp_treespec_compare(primals, tangents): + # Revert this once #116264 gets fixed + _, primals_spec = tree_flatten(primals) + _, tangents_spec = tree_flatten(tangents) + if primals_spec != tangents_spec: + raise RuntimeError( + f"{jvp_str}: Expected primals and tangents to have the same python " + f"structure. For example, if primals is a tuple of 3 tensors, " + f"tangents also must be. Got primals with structure {primals_spec} " + f"and tangents with structure {tangents_spec}" + ) + + +def _linearize_treespec_compare(primals, tangents): + # Revert this once #116264 gets fixed + _, primals_argspec = tree_flatten(primals) + _, tangent_argspec = tree_flatten(tangents) + if tangent_argspec != primals_argspec: + raise RuntimeError( + f"Expected the tangents {tangent_argspec} to have " + f"the same argspec as the primals {primals_argspec}" + ) + + +def _set_tensor_requires_grad(x): + # avoid graph-break on x.requires_grad_() + # https://github.com/pytorch/pytorch/pull/110053 + return x.requires_grad_() + + +def _create_differentiable(inps, level=None): + def create_differentiable(x): + if isinstance(x, torch.Tensor): + with enable_inplace_requires_grad(True): + return _set_tensor_requires_grad(x) + raise ValueError( + f"Thing passed to transform API must be Tensor, " f"got {type(x)}" + ) + + return tree_map(create_differentiable, inps) + + +def _undo_create_differentiable(inps, level=None): + def unwrap_tensors(x): + if isinstance(x, torch.Tensor): + return _unwrap_for_grad(x, level) + # TODO: Remove the following hack for namedtuples + if isinstance(x, tuple): + return tree_map(unwrap_tensors, tuple(x)) + + raise RuntimeError(f"Expected tensors, got unsupported type {type(x)}") + + return tree_map(unwrap_tensors, inps) + + +def _is_differentiable(maybe_tensor): + if not isinstance(maybe_tensor, torch.Tensor): + return False + return maybe_tensor.requires_grad + + +def _any_differentiable(tensor_or_tuple_of_tensors): + flat_args, _ = tree_unflatten(tensor_or_tuple_of_tensors) + return any(tuple(map(_is_differentiable, flat_args))) + + +def _wrap_tensor_for_grad(maybe_tensor, level): + if not isinstance(maybe_tensor, torch.Tensor): + return maybe_tensor + return _wrap_for_grad(maybe_tensor, level) + + +def _wrap_all_tensors(tensor_pytree, level): + return tree_map(partial(_wrap_tensor_for_grad, level=level), tensor_pytree) + + +def _as_tuple(val): + if isinstance(val, tuple): + return val + return (val,) + + +# Version of autograd.grad that handles outputs that don't depend on inputs + + +def _autograd_grad( + outputs, inputs, grad_outputs=None, retain_graph=False, create_graph=True +): + if grad_outputs is None: + diff_outputs = tuple(out for out in outputs if out.requires_grad) + else: + result = tuple( + (out, go) for out, go in zip(outputs, grad_outputs) if out.requires_grad + ) + if len(result) == 0: + diff_outputs, grad_outputs = (), () + else: + diff_outputs, grad_outputs = zip(*result) + if len(diff_outputs) == 0: + return tuple(torch.zeros_like(inp) for inp in inputs) + grad_inputs = torch.autograd.grad( + diff_outputs, + inputs, + grad_outputs, + retain_graph=retain_graph, + create_graph=create_graph, + allow_unused=True, + ) + grad_inputs = tuple( + torch.zeros_like(inp) if gi is None else gi + for gi, inp in zip(grad_inputs, inputs) + ) + return grad_inputs + + +# NOTE [grad and vjp interaction with no_grad] +# +# def f(x): +# with torch.no_grad(): +# c = x ** 2 +# return x - c +# +# The thing to consider is if enable_grad is on/off before grad gets called. +# +# Case 1: enable_grad is on. +# grad(f)(x) +# In this case, `grad` should respect the inner torch.no_grad. +# +# Case 2: enable_grad is off +# with torch.no_grad(): +# grad(f)(x) +# In this case, `grad` should respect the inner torch.no_grad, but not the +# outer one. This is because `grad` is a "function transform": its result +# should not depend on the result of a context manager outside of `f`. +# +# This gives us the following desired behavior: +# - (nested) grad transforms must obey torch.no_grad inside them +# - (nested) grad transforms should not obey torch.no_grad outside them +# +# To achieve this behavior, upon entering grad/vjp: +# - we save the current ("previous") is_grad_enabled (*) +# - we unconditionally enable grad. +# +# Inside DynamicLayerBackFallback, when we're temporarily popping `grad` layer +# off the stack: +# - if grad_mode is disabled, then we do nothing. (there is a torch.no_grad +# active, all subsequent grad transforms must obey it). +# - if grad_mode is enabled, and the previous is_grad_enabled (*) is False, +# then we temporarily restore the previous `is_grad_enabled`. This is +# because we're crossing the boundary from a `grad` outside the +# no_grad to a `grad` inside the no_grad. +# +# NB: vjp has some interesting behavior because the vjp's callable can be called +# under a different grad_mode than the forward computation... +# +# NB: forward-mode AD: forward-mode AD doesn't respect torch.no_grad, but +# it respects c10::AutoFwGradMode. We've implemented the same logic for +# our jvp transform (it will have special handling if FwGradMode is disabled). + + +# How do we increment and decrement the nesting? I don't think we can. +@exposed_in("torch.func") +def vjp(func: Callable, *primals, has_aux: bool = False): + """ + Standing for the vector-Jacobian product, returns a tuple containing the + results of ``func`` applied to ``primals`` and a function that, when + given ``cotangents``, computes the reverse-mode Jacobian of ``func`` with + respect to ``primals`` times ``cotangents``. + + Args: + func (Callable): A Python function that takes one or more arguments. Must + return one or more Tensors. + primals (Tensors): Positional arguments to ``func`` that must all be + Tensors. The returned function will also be computing the + derivative with respect to these arguments + has_aux (bool): Flag indicating that ``func`` returns a + ``(output, aux)`` tuple where the first element is the output of + the function to be differentiated and the second element is + other auxiliary objects that will not be differentiated. + Default: False. + + Returns: + Returns a ``(output, vjp_fn)`` tuple containing the output of ``func`` + applied to ``primals`` and a function that computes the vjp of + ``func`` with respect to all ``primals`` using the cotangents passed + to the returned function. If ``has_aux is True``, then instead returns a + ``(output, vjp_fn, aux)`` tuple. + The returned ``vjp_fn`` function will return a tuple of each VJP. + + When used in simple cases, :func:`vjp` behaves the same as :func:`grad` + + >>> x = torch.randn([5]) + >>> f = lambda x: x.sin().sum() + >>> (_, vjpfunc) = torch.func.vjp(f, x) + >>> grad = vjpfunc(torch.tensor(1.))[0] + >>> assert torch.allclose(grad, torch.func.grad(f)(x)) + + However, :func:`vjp` can support functions with multiple outputs by + passing in the cotangents for each of the outputs + + >>> x = torch.randn([5]) + >>> f = lambda x: (x.sin(), x.cos()) + >>> (_, vjpfunc) = torch.func.vjp(f, x) + >>> vjps = vjpfunc((torch.ones([5]), torch.ones([5]))) + >>> assert torch.allclose(vjps[0], x.cos() + -x.sin()) + + :func:`vjp` can even support outputs being Python structs + + >>> x = torch.randn([5]) + >>> f = lambda x: {'first': x.sin(), 'second': x.cos()} + >>> (_, vjpfunc) = torch.func.vjp(f, x) + >>> cotangents = {'first': torch.ones([5]), 'second': torch.ones([5])} + >>> vjps = vjpfunc(cotangents) + >>> assert torch.allclose(vjps[0], x.cos() + -x.sin()) + + The function returned by :func:`vjp` will compute the partials with + respect to each of the ``primals`` + + >>> x, y = torch.randn([5, 4]), torch.randn([4, 5]) + >>> (_, vjpfunc) = torch.func.vjp(torch.matmul, x, y) + >>> cotangents = torch.randn([5, 5]) + >>> vjps = vjpfunc(cotangents) + >>> assert len(vjps) == 2 + >>> assert torch.allclose(vjps[0], torch.matmul(cotangents, y.transpose(0, 1))) + >>> assert torch.allclose(vjps[1], torch.matmul(x.transpose(0, 1), cotangents)) + + ``primals`` are the positional arguments for ``f``. All kwargs use their + default value + + >>> x = torch.randn([5]) + >>> def f(x, scale=4.): + >>> return x * scale + >>> + >>> (_, vjpfunc) = torch.func.vjp(f, x) + >>> vjps = vjpfunc(torch.ones_like(x)) + >>> assert torch.allclose(vjps[0], torch.full(x.shape, 4.)) + + .. note:: + Using PyTorch ``torch.no_grad`` together with ``vjp``. + Case 1: Using ``torch.no_grad`` inside a function: + + >>> def f(x): + >>> with torch.no_grad(): + >>> c = x ** 2 + >>> return x - c + + In this case, ``vjp(f)(x)`` will respect the inner ``torch.no_grad``. + + Case 2: Using ``vjp`` inside ``torch.no_grad`` context manager: + + >>> # xdoctest: +SKIP(failing) + >>> with torch.no_grad(): + >>> vjp(f)(x) + + In this case, ``vjp`` will respect the inner ``torch.no_grad``, but not the + outer one. This is because ``vjp`` is a "function transform": its result + should not depend on the result of a context manager outside of ``f``. + """ + return _vjp_with_argnums(func, *primals, has_aux=has_aux) + + +@contextlib.contextmanager +def grad_increment_nesting(): + try: + grad_level = _grad_increment_nesting() + yield grad_level + finally: + _grad_decrement_nesting() + + +def enter_jvp_nesting(): + global JVP_NESTING + jvp_level = _jvp_increment_nesting() + JVP_NESTING += 1 + return jvp_level + + +def exit_jvp_nesting(): + global JVP_NESTING + _jvp_decrement_nesting() + JVP_NESTING -= 1 + + +@contextlib.contextmanager +def jvp_increment_nesting(): + try: + yield enter_jvp_nesting() + finally: + exit_jvp_nesting() + + +@doesnt_support_saved_tensors_hooks +def _vjp_with_argnums( + func: Callable, *primals, argnums: Optional[argnums_t] = None, has_aux: bool = False +): + # This is the same function as vjp but also accepts an argnums argument + # All args are the same as vjp except for the added argument + # argnums (Optional[int or tuple[int]]): Optional, specifies the argument(s) to compute gradients with respect to. + # If None, computes the gradients with respect to all inputs (used for vjp). Default: None + # + # WARN: Users should NOT call this function directly and should just be calling vjp. + # It is only separated so that inputs passed to jacrev but not differentiated get the correct wrappers. + # + # NOTE: All error messages are produced as if vjp was being called, even if this was called by jacrev + # + # Returns the same two elements as :func:`vjp` but the function returned, vjp_fn, returns a tuple of VJPs + # for only the primal elements given by argnums. + with grad_increment_nesting() as level: + # See NOTE [grad and vjp interaction with no_grad] + with torch.enable_grad(): + primals = _wrap_all_tensors(primals, level) + # Note for the reviewer: This is extremely odd but it passes the + # assertion "len(self.block_stack) == 1" on symbolic_convert.py + # The equivalent "if argnums is None" fails for some reason + if not isinstance(argnums, int) and not argnums: + diff_primals = _create_differentiable(primals, level) + else: + diff_primals = _slice_argnums(primals, argnums, as_tuple=False) + tree_map_(partial(_create_differentiable, level=level), diff_primals) + primals_out = func(*primals) + + if has_aux: + if not (isinstance(primals_out, tuple) and len(primals_out) == 2): + raise RuntimeError( + "vjp(f, *primals): output of function f should be a tuple: (output, aux) " + "if has_aux is True" + ) + primals_out, aux = primals_out + aux = _undo_create_differentiable(aux, level) + + flat_primals_out, primals_out_spec = tree_flatten(primals_out) + assert_non_empty_tensor_output(flat_primals_out, "vjp(f, *primals)") + flat_diff_primals, primals_spec = tree_flatten(diff_primals) + results = _undo_create_differentiable(primals_out, level) + + for primal_out in flat_primals_out: + assert isinstance(primal_out, torch.Tensor) + if primal_out.is_floating_point() or primal_out.is_complex(): + continue + raise RuntimeError( + "vjp(f, ...): All outputs of f must be " + "floating-point or complex Tensors, got Tensor " + f"with dtype {primal_out.dtype}" + ) + + def wrapper(cotangents, retain_graph=True, create_graph=None): + if create_graph is None: + create_graph = torch.is_grad_enabled() + flat_cotangents, cotangents_spec = tree_flatten(cotangents) + _vjp_treespec_compare(primals_out, cotangents) + result = _autograd_grad( + flat_primals_out, + flat_diff_primals, + flat_cotangents, + retain_graph=retain_graph, + create_graph=create_graph, + ) + return tree_unflatten(result, primals_spec) + + if has_aux: + return results, wrapper, aux + else: + return results, wrapper + + +def _safe_zero_index(x): + assert len(x) == 1 + return x[0] + + +# jacrev and jacfwd don't support complex functions +# Helper function to throw appropriate error. +def error_if_complex(func_name, args, is_input): + flat_args = pytree.tree_leaves(args) + for idx, arg in enumerate(flat_args): + if isinstance(arg, torch.Tensor) and arg.dtype.is_complex: + input_or_output = "inputs" if is_input else "outputs" + err_msg = ( + f"{func_name}: Expected all {input_or_output} " + f"to be real but received complex tensor at flattened input idx: {idx}" + ) + raise RuntimeError(err_msg) + + +@exposed_in("torch.func") +def jacrev( + func: Callable, + argnums: Union[int, Tuple[int]] = 0, + *, + has_aux=False, + chunk_size: Optional[int] = None, + _preallocate_and_copy=False, +): + """ + Computes the Jacobian of ``func`` with respect to the arg(s) at index + ``argnum`` using reverse mode autodiff + + .. note:: + Using :attr:`chunk_size=1` is equivalent to computing the jacobian + row-by-row with a for-loop i.e. the constraints of :func:`vmap` are + not applicable. + + Args: + func (function): A Python function that takes one or more arguments, + one of which must be a Tensor, and returns one or more Tensors + argnums (int or Tuple[int]): Optional, integer or tuple of integers, + saying which arguments to get the Jacobian with respect to. + Default: 0. + has_aux (bool): Flag indicating that ``func`` returns a + ``(output, aux)`` tuple where the first element is the output of + the function to be differentiated and the second element is + auxiliary objects that will not be differentiated. + Default: False. + chunk_size (None or int): If None (default), use the maximum chunk size + (equivalent to doing a single vmap over vjp to compute the jacobian). + If 1, then compute the jacobian row-by-row with a for-loop. + If not None, then compute the jacobian :attr:`chunk_size` rows at a time + (equivalent to doing multiple vmap over vjp). If you run into memory issues computing + the jacobian, please try to specify a non-None chunk_size. + + Returns: + Returns a function that takes in the same inputs as ``func`` and + returns the Jacobian of ``func`` with respect to the arg(s) at + ``argnums``. If ``has_aux is True``, then the returned function + instead returns a ``(jacobian, aux)`` tuple where ``jacobian`` + is the Jacobian and ``aux`` is auxiliary objects returned by ``func``. + + A basic usage with a pointwise, unary operation will give a diagonal array + as the Jacobian + + >>> from torch.func import jacrev + >>> x = torch.randn(5) + >>> jacobian = jacrev(torch.sin)(x) + >>> expected = torch.diag(torch.cos(x)) + >>> assert torch.allclose(jacobian, expected) + + If you would like to compute the output of the function as well as the + jacobian of the function, use the ``has_aux`` flag to return the output + as an auxiliary object: + + >>> from torch.func import jacrev + >>> x = torch.randn(5) + >>> + >>> def f(x): + >>> return x.sin() + >>> + >>> def g(x): + >>> result = f(x) + >>> return result, result + >>> + >>> jacobian_f, f_x = jacrev(g, has_aux=True)(x) + >>> assert torch.allclose(f_x, f(x)) + + :func:`jacrev` can be composed with vmap to produce batched + Jacobians: + + >>> from torch.func import jacrev, vmap + >>> x = torch.randn(64, 5) + >>> jacobian = vmap(jacrev(torch.sin))(x) + >>> assert jacobian.shape == (64, 5, 5) + + Additionally, :func:`jacrev` can be composed with itself to produce + Hessians + + >>> from torch.func import jacrev + >>> def f(x): + >>> return x.sin().sum() + >>> + >>> x = torch.randn(5) + >>> hessian = jacrev(jacrev(f))(x) + >>> assert torch.allclose(hessian, torch.diag(-x.sin())) + + By default, :func:`jacrev` computes the Jacobian with respect to the first + input. However, it can compute the Jacboian with respect to a different + argument by using ``argnums``: + + >>> from torch.func import jacrev + >>> def f(x, y): + >>> return x + y ** 2 + >>> + >>> x, y = torch.randn(5), torch.randn(5) + >>> jacobian = jacrev(f, argnums=1)(x, y) + >>> expected = torch.diag(2 * y) + >>> assert torch.allclose(jacobian, expected) + + Additionally, passing a tuple to ``argnums`` will compute the Jacobian + with respect to multiple arguments + + >>> from torch.func import jacrev + >>> def f(x, y): + >>> return x + y ** 2 + >>> + >>> x, y = torch.randn(5), torch.randn(5) + >>> jacobian = jacrev(f, argnums=(0, 1))(x, y) + >>> expectedX = torch.diag(torch.ones_like(x)) + >>> expectedY = torch.diag(2 * y) + >>> assert torch.allclose(jacobian[0], expectedX) + >>> assert torch.allclose(jacobian[1], expectedY) + + .. note:: + Using PyTorch ``torch.no_grad`` together with ``jacrev``. + Case 1: Using ``torch.no_grad`` inside a function: + + >>> def f(x): + >>> with torch.no_grad(): + >>> c = x ** 2 + >>> return x - c + + In this case, ``jacrev(f)(x)`` will respect the inner ``torch.no_grad``. + + Case 2: Using ``jacrev`` inside ``torch.no_grad`` context manager: + + >>> with torch.no_grad(): + >>> jacrev(f)(x) + + In this case, ``jacrev`` will respect the inner ``torch.no_grad``, but not the + outer one. This is because ``jacrev`` is a "function transform": its result + should not depend on the result of a context manager outside of ``f``. + """ + if not (chunk_size is None or chunk_size > 0): + raise ValueError("jacrev: `chunk_size` should be greater than 0.") + + def wrapper_fn(*args): + error_if_complex("jacrev", args, is_input=True) + vjp_out = _vjp_with_argnums(func, *args, argnums=argnums, has_aux=has_aux) + if has_aux: + output, vjp_fn, aux = vjp_out + else: + output, vjp_fn = vjp_out + + # See NOTE: [Computing jacobian with vmap and vjp for multiple outputs] + flat_output, output_spec = tree_flatten(output) + + error_if_complex("jacrev", flat_output, is_input=False) + + # NB: vjp already checks that all outputs are tensors + # Step 1: Construct grad_outputs by splitting the standard basis + flat_output_numels = tuple(out.numel() for out in flat_output) + + primals = _slice_argnums(args, argnums) + flat_primals, primals_spec = tree_flatten(primals) + + def compute_jacobian_stacked(): + # Helper function to compute chunked Jacobian + # The intermediate chunked calculation are only + # scoped at this function level. + chunked_results = [] + for flat_basis_chunk in _chunked_standard_basis_for_( + flat_output, flat_output_numels, chunk_size=chunk_size + ): + if chunk_size == 1: + # sanity check. + for t in flat_basis_chunk: + assert t.size(0) == 1 + + flat_basis_chunk = tree_map( + lambda t: torch.squeeze(t, 0), flat_basis_chunk + ) + + basis = tree_unflatten(flat_basis_chunk, output_spec) + + if chunk_size == 1: + # Behaviour with `chunk_size=1` is same as `for-loop` + # i.e. user shouldn't deal with the limitations of vmap. + chunked_result = vjp_fn(basis) + else: # chunk_size is None or chunk_size != 1 + chunked_result = vmap(vjp_fn)(basis) + + flat_results = pytree.tree_leaves(chunked_result) + + if chunk_size == 1: + flat_results = tree_map( + lambda t: torch.unsqueeze(t, 0), flat_results + ) + + chunked_results.append(flat_results) + + if len(chunked_results) == 1: + # Short-circuit if we used a single chunk + return chunked_results[0] + + # Concatenate chunks. + flat_results = [] + # Iterate and concat the jacobians of different + # inputs. + for idx in range(len(flat_primals)): + r = tuple(r_[idx] for r_ in chunked_results) + flat_results.append(torch.cat(r, 0)) + + return flat_results + + def compute_jacobian_preallocate_and_copy(): + # Helper function to compute chunked Jacobian + # The intermediate chunked calculation are only + # scoped at this function level. + out_vec_size = sum(flat_output_numels) + + # Don't pre-allocate if we have a single chunk. + if not (chunk_size is None or chunk_size >= out_vec_size): + stacked_results = [ + primal.new_zeros(out_vec_size, *primal.shape) + for primal in flat_primals + ] + + for idx, flat_basis_chunk in enumerate( + _chunked_standard_basis_for_( + flat_output, flat_output_numels, chunk_size=chunk_size + ) + ): + if chunk_size == 1: + # sanity check. + for t in flat_basis_chunk: + assert t.size(0) == 1 + + flat_basis_chunk = [torch.squeeze(t, 0) for t in flat_basis_chunk] + + basis = tree_unflatten(flat_basis_chunk, output_spec) + + if chunk_size == 1: + # Behaviour with `chunk_size=1` is same as `for-loop` + # i.e. user shouldn't deal with the limitations of vmap. + chunked_result = vjp_fn(basis) + else: # chunk_size is None or chunk_size != 1 + chunked_result = vmap(vjp_fn)(basis) + + flat_results = pytree.tree_leaves(chunked_result) + + # Short-circuit if we have a single chunk. + if chunk_size is None or chunk_size >= out_vec_size: + if chunk_size == 1: # and out_vec_size == 1 + # Since we squeezed the output dim + flat_results = tree_map( + lambda t: torch.unsqueeze(t, 0), flat_results + ) + return flat_results + + for r, sr in zip(flat_results, stacked_results): + sr[idx * chunk_size : (idx + 1) * chunk_size].copy_(r) + + return stacked_results + + if _preallocate_and_copy: + flat_jacobians_per_input = compute_jacobian_preallocate_and_copy() + else: + flat_jacobians_per_input = compute_jacobian_stacked() + + # Step 2: The returned jacobian is one big tensor per input. In this step, + # we split each Tensor by output. + flat_jacobians_per_input = [ + result.split(flat_output_numels, dim=0) + for result in flat_jacobians_per_input + ] + flat_input_flat_output = [ + tuple( + split.view(out.shape + primal.shape) + for split, out in zip(splits, flat_output) + ) + for splits, primal in zip(flat_jacobians_per_input, flat_primals) + ] + + # Step 3: Right now, `jacobian` is a List[List[Tensor]]. + # The outer List corresponds to the number of primals, + # the inner List corresponds to the number of outputs. + # We need to: + # a. Exchange the order of the outer List and inner List + # b. tree_unflatten the inner Lists (which correspond to the primals) + # c. handle the argnums=int case + # d. tree_unflatten the outer List (which corresponds to the outputs) + flat_output_flat_input = tuple(zip(*flat_input_flat_output)) + + flat_output_input = tuple( + tree_unflatten(flat_input, primals_spec) + for flat_input in flat_output_flat_input + ) + + if isinstance(argnums, int): + flat_output_input = tuple( + _safe_zero_index(flat_input) for flat_input in flat_output_input + ) + output_input = tree_unflatten(flat_output_input, output_spec) + if has_aux: + return output_input, aux + return output_input + + # Dynamo does not support HOP composition if their inner function is + # annotated with @functools.wraps(...). We circumvent this issue by applying + # wraps only if we're not tracing with dynamo. + if not torch._dynamo.is_compiling(): + wrapper_fn = wraps(func)(wrapper_fn) + + return wrapper_fn + + +# NOTE: [Computing jacobian with vmap and vjp for multiple outputs] +# +# Let's consider f(x) = (x**2, x.sum()) and let x = torch.randn(3). +# It turns out we can compute the jacobian of this function with a single +# call to autograd.grad by using vmap over the correct grad_outputs. +# +# Firstly, one way to compute the jacobian is to stack x**2 and x.sum() +# into a 4D vector. E.g., use g(x) = torch.stack([x**2, x.sum()]) +# +# To get the first row of the jacobian, we call +# >>> autograd.grad(g(x), x, grad_outputs=torch.tensor([1, 0, 0, 0])) +# To get the 2nd row of the jacobian, we call +# >>> autograd.grad(g(x), x, grad_outputs=torch.tensor([0, 1, 0, 0])) +# and so on. +# +# Using vmap, we can vectorize all 4 of these computations into one by +# passing the standard basis for R^4 as the grad_output. +# vmap(partial(autograd.grad, g(x), x))(torch.eye(4)). +# +# Now, how do we compute the jacobian *without stacking the output*? +# We can just split the standard basis across the outputs. So to +# compute the jacobian of f(x), we'd use +# >>> autograd.grad(f(x), x, grad_outputs=_construct_standard_basis_for(...)) +# The grad_outputs looks like the following: +# ( torch.tensor([[1, 0, 0], +# [0, 1, 0], +# [0, 0, 1], +# [0, 0, 0]]), +# torch.tensor([[0], +# [0], +# [0], +# [1]]) ) +# +# But we're not done yet! +# >>> vmap(partial(autograd.grad(f(x), x, grad_outputs=...))) +# returns a Tensor of shape [4, 3]. We have to remember to split the +# jacobian of shape [4, 3] into two: +# - one of shape [3, 3] for the first output +# - one of shape [ 3] for the second output + + +def _chunked_standard_basis_for_(tensors, tensor_numels, chunk_size=None): + # This function: + # - constructs a N=sum(tensor_numels) standard basis. i.e. an NxN identity matrix. + # - Splits the identity matrix into chunks with each chunk size determined by `tensor_numels`. + # - Each chunk corresponds to one tensor. The chunk has the same dtype and + # device as the tensor + # + # For example, with tensor_numels = [1, 2, 1], this function returns: + # ( tensor([[1], tensor([[0, 0], tensor([[0], + # [0], [1, 0], [0], + # [0], [0, 1], [0], + # [0]]) , [0, 0]]) , [1]]) ) + # + # Precondition: tensor_numels == tuple(tensor.numel() for tensor in tensors) + # Precondition: tensors always has at least one element. + # + # See NOTE: [Computing jacobian with vmap and grad for multiple tensors] + # for context behind this function. + # NOTE: Argument `chunk_size` is used to generate chunked basis instead of + # one huge basis matrix. `chunk_size` dictates the maximum size of the + # basis matrix along dim=0. + assert len(tensors) == len(tensor_numels) + assert len(tensors) > 0 + assert chunk_size is None or chunk_size > 0 + total_numel = sum(tensor_numels) + if chunk_size and chunk_size < total_numel: + chunk_numels = get_chunk_sizes(total_numel, chunk_size) + else: # chunk_size is None or chunk_size >= total_numel + chunk_size = total_numel + chunk_numels = [total_numel] + + diag_start_indices = ( + 0, + *torch.tensor(tensor_numels).cumsum(dim=0)[:-1].neg().unbind(), + ) + + for chunk_idx, total_numel in enumerate(chunk_numels): + chunks = tuple( + tensor.new_zeros(total_numel, tensor_numel) + for tensor, tensor_numel in zip(tensors, tensor_numels) + ) + + for chunk, diag_start_idx in zip(chunks, diag_start_indices): + chunk.diagonal(diag_start_idx + chunk_idx * chunk_size).fill_(1) + chunks = tuple( + chunk.view(total_numel, *tensor.shape) + for chunk, tensor in zip(chunks, tensors) + ) + yield chunks + + +def _construct_standard_basis_for(tensors, tensor_numels): + for basis in _chunked_standard_basis_for_(tensors, tensor_numels, chunk_size=None): + return basis + + +def _validate_and_wrap_argnum(argnum, num_args): + if not isinstance(argnum, int): + raise RuntimeError(f"argnum must be int, got: {type(argnum)}") + if argnum >= 0 and argnum < num_args: + return argnum + if argnum < 0 and argnum >= -num_args: + return argnum + num_args + raise RuntimeError(f"Got argnum={argnum}, but only {num_args} positional inputs") + + +def _check_unique_non_empty(argnums): + if isinstance(argnums, tuple): + if len(argnums) == 0: + raise RuntimeError("argnums must be non-empty") + if len(set(argnums)) != len(argnums): + raise RuntimeError(f"argnums elements must be unique, got {argnums}") + + +def _replace_args(old_args, new_args, argnums): + if isinstance(argnums, int): + if len(new_args) != 1: + raise RuntimeError( + f"new_args should be of size 1, was of size {len(new_args)}" + ) + return tuple( + new_args[0] if i == argnums else old_args[i] for i in range(len(old_args)) + ) + if isinstance(argnums, tuple): + if len(new_args) != len(argnums): + raise RuntimeError( + "new_args should have the same size as argnums. " + f"Argnums size {len(argnums)}, new_args size {len(new_args)}" + ) + + def get_right_elem(i): + return new_args[argnums.index(i)] if i in argnums else old_args[i] + + return tuple(get_right_elem(i) for i in range(len(old_args))) + raise RuntimeError(f"argnums must be int or Tuple[int, ...], got: {type(argnums)}") + + +def _validate_and_wrap_argnums(argnums, num_args): + if isinstance(argnums, int): + return _validate_and_wrap_argnum(argnums, num_args) + if isinstance(argnums, tuple): + return tuple(_validate_and_wrap_argnum(argnum, num_args) for argnum in argnums) + raise AssertionError("Should never get here") + + +def _slice_argnums(args, argnums, as_tuple=True): + if not isinstance(argnums, int) and not isinstance(argnums, tuple): + raise RuntimeError( + f"argnums must be int or Tuple[int, ...], got: {type(argnums)}" + ) + argnums = _validate_and_wrap_argnums(argnums, len(args)) + _check_unique_non_empty(argnums) + if isinstance(argnums, int): + if as_tuple: + return (args[argnums],) + else: + return args[argnums] + return tuple(args[i] for i in argnums) + + +JVP_NESTING = 0 + + +def assert_flat_tuple_of_tensors(elts: Any, api: str, argname: str) -> None: + if not isinstance(elts, tuple): + raise RuntimeError( + f"{api}: Expected {argname} to be a tuple of Tensors, got {type(elts)}" + ) + for elt in elts: + if isinstance(elt, torch.Tensor): + continue + raise RuntimeError( + f"{api}: Expected {argname} to be a tuple of Tensors, got " + f"a tuple with an element of type {type(elt)}" + ) + if len(elts) == 0: + raise RuntimeError( + f"{api}: Expected {argname} to be a non-empty tuple of Tensors." + ) + + +def assert_non_empty_tensor_output(output: List[Any], api: str) -> None: + if (len(output) == 1 and output[0] is None) or len(output) < 1: + raise RuntimeError( + f"{api}: Expected f to be a function that has non-empty output (got output = {output})" + ) + for o in output: + if not isinstance(o, torch.Tensor): + raise RuntimeError( + f"{api}: expected f(*primals) to return only tensors" + f", got unsupported type {type(o)}" + ) + + +def assert_output_is_tensor_or_tensors(output: Any, api: str) -> None: + if isinstance(output, torch.Tensor): + return + if not isinstance(output, tuple): + raise RuntimeError( + f"{api}: Expected output of f to be a Tensor or Tensors, got " + f"{type(output)}" + ) + if len(output) == 0: + raise RuntimeError( + f"{api}: Expected output of f to be a non-empty tuple of Tensors." + ) + for out in output: + if isinstance(out, torch.Tensor): + continue + raise RuntimeError( + f"{api}: Expected output of f to be a Tensor or Tensors, got " + f"{type(out)} as an output" + ) + + +def assert_non_empty_list_of_tensors( + output: List[torch.Tensor], api: str, argname: str +) -> None: + if len(output) == 0: + raise RuntimeError(f"{api}: Expected {argname} to contain at least one Tensor.") + for out in output: + if isinstance(out, torch.Tensor): + continue + raise RuntimeError( + f"{api}: Expected {argname} to only contain Tensors, got " f"{type(out)}" + ) + + +jvp_str = "jvp(f, primals, tangents)" + + +def safe_unpack_dual(dual, strict): + if not isinstance(dual, torch.Tensor): + raise RuntimeError( + f"{jvp_str}: expected f(*args) to return only tensors" + f", got unsupported type {type(dual)}" + ) + + primal, tangent = fwAD.unpack_dual(dual) + if tangent is None: + if strict: + raise RuntimeError( + "jvp(f, primals, tangents, strict=True): " + "The output of f is independent of " + "the inputs. This is not allowed with strict=True." + ) + tangent = torch.zeros_like(primal) + return primal, tangent + + +@exposed_in("torch.func") +def jvp( + func: Callable, + primals: Any, + tangents: Any, + *, + strict: bool = False, + has_aux: bool = False, +): + """ + Standing for the Jacobian-vector product, returns a tuple containing + the output of `func(*primals)` and the "Jacobian of ``func`` evaluated at + ``primals``" times ``tangents``. This is also known as forward-mode autodiff. + + Args: + func (function): A Python function that takes one or more arguments, + one of which must be a Tensor, and returns one or more Tensors + primals (Tensors): Positional arguments to ``func`` that must all be + Tensors. The returned function will also be computing the + derivative with respect to these arguments + tangents (Tensors): The "vector" for which Jacobian-vector-product is + computed. Must be the same structure and sizes as the inputs to + ``func``. + has_aux (bool): Flag indicating that ``func`` returns a + ``(output, aux)`` tuple where the first element is the output of + the function to be differentiated and the second element is + other auxiliary objects that will not be differentiated. + Default: False. + + Returns: + Returns a ``(output, jvp_out)`` tuple containing the output of ``func`` + evaluated at ``primals`` and the Jacobian-vector product. + If ``has_aux is True``, then instead returns a ``(output, jvp_out, aux)`` tuple. + + .. note:: + You may see this API error out with "forward-mode AD not implemented + for operator X". If so, please file a bug report and we will prioritize it. + + jvp is useful when you wish to compute gradients of a function R^1 -> R^N + + >>> from torch.func import jvp + >>> x = torch.randn([]) + >>> f = lambda x: x * torch.tensor([1., 2., 3]) + >>> value, grad = jvp(f, (x,), (torch.tensor(1.),)) + >>> assert torch.allclose(value, f(x)) + >>> assert torch.allclose(grad, torch.tensor([1., 2, 3])) + + :func:`jvp` can support functions with multiple inputs by passing in the + tangents for each of the inputs + + >>> from torch.func import jvp + >>> x = torch.randn(5) + >>> y = torch.randn(5) + >>> f = lambda x, y: (x * y) + >>> _, output = jvp(f, (x, y), (torch.ones(5), torch.ones(5))) + >>> assert torch.allclose(output, x + y) + + """ + + return _jvp_with_argnums( + func, primals, tangents, argnums=None, strict=strict, has_aux=has_aux + ) + + +def _jvp_with_argnums( + func: Callable, + primals: Any, + tangents: Any, + argnums: Optional[argnums_t], + *, + strict: bool = False, + has_aux: bool, +): + # This is the same function as jvp but also accepts an argnums argument + # Most args are the same as jvp except for the added argument + # argnums (Optional[int or tuple[int]]): Optional, specifies the argument(s) to compute gradients with respect to. + # If None, computes the gradients with respect to all inputs (used for jvp). Default: None + # Because of this, tangents must be of length argnums and matches up to the corresponding primal whose index is + # given by argnums + # + # WARN: Users should NOT call this function directly and should just be calling jvp. + # It is only separated so that inputs passed to jacfwd but not differentiated get the correct wrappers. + # + # NOTE: All error messages are produced as if jvp was being called, even if this was called by jacfwd + # + # Returns the same two elements as :func:`jvp` but the returned tuple, ``jvp_out``, only has JVPs with respect to + # the primals given by argnums + if not isinstance(primals, tuple): + raise RuntimeError( + f"{jvp_str}: Expected primals to be a tuple. " + f"E.g. it should be valid to call f(*primals)." + ) + diff_args = primals if argnums is None else _slice_argnums(primals, argnums) + flat_primals, primals_spec = tree_flatten(diff_args) + flat_tangents, tangents_spec = tree_flatten(tangents) + _jvp_treespec_compare(diff_args, tangents) + assert_non_empty_list_of_tensors(flat_primals, jvp_str, "primals") + assert_non_empty_list_of_tensors(flat_tangents, jvp_str, "tangents") + + global JVP_NESTING + + with jvp_increment_nesting() as level: + with fwAD._set_fwd_grad_enabled(True): + ctx = fwAD.dual_level if JVP_NESTING == 1 else contextlib.nullcontext + with ctx(): + flat_duals = tuple( + fwAD.make_dual(p, t) for p, t in zip(flat_primals, flat_tangents) + ) + duals = tree_unflatten(flat_duals, primals_spec) + # Note for the reviewer: This is extremely odd but it passes the + # assertion "len(self.block_stack) == 1" on symbolic_convert.py + # The equivalent "if argnums is not None" fails for some reason + if isinstance(argnums, (int, tuple)): + primals = _wrap_all_tensors(primals, level) + duals = _replace_args(primals, duals, argnums) + result_duals = func(*duals) + if has_aux: + if not (isinstance(result_duals, tuple) and len(result_duals) == 2): + raise RuntimeError( + f"{jvp_str}: output of function f should be a tuple: (output, aux) " + "if has_aux is True" + ) + result_duals, aux = result_duals + aux = _undo_create_differentiable(aux, level) + + result_duals, spec = tree_flatten(result_duals) + assert_non_empty_tensor_output(result_duals, jvp_str) + + primals_out, tangents_out = zip( + *[safe_unpack_dual(dual, strict) for dual in result_duals] + ) + primals_out = tree_map( + partial(_undo_create_differentiable, level=level), primals_out + ) + tangents_out = tree_map( + partial(_undo_create_differentiable, level=level), tangents_out + ) + + primals_out_unflatten = tree_unflatten(primals_out, spec) + tangents_out_unflatten = tree_unflatten(tangents_out, spec) + if has_aux: + return primals_out_unflatten, tangents_out_unflatten, aux + + return primals_out_unflatten, tangents_out_unflatten + + +def safe_unflatten(tensor, dim, shape): + if len(shape) == 0: + assert tensor.shape[dim] == 1 + return tensor.squeeze(dim) + return tensor.unflatten(dim, shape) + + +@exposed_in("torch.func") +def jacfwd( + func: Callable, + argnums: argnums_t = 0, + has_aux: bool = False, + *, + randomness: str = "error", +): + """ + Computes the Jacobian of ``func`` with respect to the arg(s) at index + ``argnum`` using forward-mode autodiff + + Args: + func (function): A Python function that takes one or more arguments, + one of which must be a Tensor, and returns one or more Tensors + argnums (int or Tuple[int]): Optional, integer or tuple of integers, + saying which arguments to get the Jacobian with respect to. + Default: 0. + has_aux (bool): Flag indicating that ``func`` returns a + ``(output, aux)`` tuple where the first element is the output of + the function to be differentiated and the second element is + auxiliary objects that will not be differentiated. + Default: False. + randomness(str): Flag indicating what type of randomness to use. + See :func:`vmap` for more detail. Allowed: "different", "same", "error". + Default: "error" + + Returns: + Returns a function that takes in the same inputs as ``func`` and + returns the Jacobian of ``func`` with respect to the arg(s) at + ``argnums``. If ``has_aux is True``, then the returned function + instead returns a ``(jacobian, aux)`` tuple where ``jacobian`` + is the Jacobian and ``aux`` is auxiliary objects returned by ``func``. + + .. note:: + You may see this API error out with "forward-mode AD not implemented + for operator X". If so, please file a bug report and we will prioritize it. + An alternative is to use :func:`jacrev`, which has better operator coverage. + + A basic usage with a pointwise, unary operation will give a diagonal array + as the Jacobian + + >>> from torch.func import jacfwd + >>> x = torch.randn(5) + >>> jacobian = jacfwd(torch.sin)(x) + >>> expected = torch.diag(torch.cos(x)) + >>> assert torch.allclose(jacobian, expected) + + :func:`jacfwd` can be composed with vmap to produce batched + Jacobians: + + >>> from torch.func import jacfwd, vmap + >>> x = torch.randn(64, 5) + >>> jacobian = vmap(jacfwd(torch.sin))(x) + >>> assert jacobian.shape == (64, 5, 5) + + If you would like to compute the output of the function as well as the + jacobian of the function, use the ``has_aux`` flag to return the output + as an auxiliary object: + + >>> from torch.func import jacfwd + >>> x = torch.randn(5) + >>> + >>> def f(x): + >>> return x.sin() + >>> + >>> def g(x): + >>> result = f(x) + >>> return result, result + >>> + >>> jacobian_f, f_x = jacfwd(g, has_aux=True)(x) + >>> assert torch.allclose(f_x, f(x)) + + Additionally, :func:`jacrev` can be composed with itself or :func:`jacrev` + to produce Hessians + + >>> from torch.func import jacfwd, jacrev + >>> def f(x): + >>> return x.sin().sum() + >>> + >>> x = torch.randn(5) + >>> hessian = jacfwd(jacrev(f))(x) + >>> assert torch.allclose(hessian, torch.diag(-x.sin())) + + By default, :func:`jacfwd` computes the Jacobian with respect to the first + input. However, it can compute the Jacboian with respect to a different + argument by using ``argnums``: + + >>> from torch.func import jacfwd + >>> def f(x, y): + >>> return x + y ** 2 + >>> + >>> x, y = torch.randn(5), torch.randn(5) + >>> jacobian = jacfwd(f, argnums=1)(x, y) + >>> expected = torch.diag(2 * y) + >>> assert torch.allclose(jacobian, expected) + + Additionally, passing a tuple to ``argnums`` will compute the Jacobian + with respect to multiple arguments + + >>> from torch.func import jacfwd + >>> def f(x, y): + >>> return x + y ** 2 + >>> + >>> x, y = torch.randn(5), torch.randn(5) + >>> jacobian = jacfwd(f, argnums=(0, 1))(x, y) + >>> expectedX = torch.diag(torch.ones_like(x)) + >>> expectedY = torch.diag(2 * y) + >>> assert torch.allclose(jacobian[0], expectedX) + >>> assert torch.allclose(jacobian[1], expectedY) + + """ + + def wrapper_fn(*args): + error_if_complex("jacfwd", args, is_input=True) + primals = args if argnums is None else _slice_argnums(args, argnums) + flat_primals, primals_spec = tree_flatten(primals) + flat_primals_numels = tuple(p.numel() for p in flat_primals) + flat_basis = _construct_standard_basis_for(flat_primals, flat_primals_numels) + basis = tree_unflatten(flat_basis, primals_spec) + + def push_jvp(basis): + output = _jvp_with_argnums( + func, args, basis, argnums=argnums, has_aux=has_aux + ) + # output[0] is the output of `func(*args)` + error_if_complex("jacfwd", output[0], is_input=False) + if has_aux: + _, jvp_out, aux = output + return jvp_out, aux + _, jvp_out = output + return jvp_out + + results = vmap(push_jvp, randomness=randomness)(basis) + if has_aux: + results, aux = results + # aux is in the standard basis format, e.g. NxN matrix + # We need to fetch the first element as original `func` output + flat_aux, aux_spec = tree_flatten(aux) + flat_aux = [value[0] for value in flat_aux] + aux = tree_unflatten(flat_aux, aux_spec) + + jac_outs, spec = tree_flatten(results) + # Most probably below output check can never raise an error + # as jvp should test the output before + # assert_non_empty_output(jac_outs, 'jacfwd(f, ...)(*args)') + + jac_outs_ins = tuple( + tuple( + safe_unflatten(jac_out_in, -1, primal.shape) + for primal, jac_out_in in zip( + flat_primals, + jac_out.movedim(0, -1).split(flat_primals_numels, dim=-1), + ) + ) + for jac_out in jac_outs + ) + jac_outs_ins = tuple( + tree_unflatten(jac_ins, primals_spec) for jac_ins in jac_outs_ins + ) + + if isinstance(argnums, int): + jac_outs_ins = tuple(jac_ins[0] for jac_ins in jac_outs_ins) + if has_aux: + return tree_unflatten(jac_outs_ins, spec), aux + return tree_unflatten(jac_outs_ins, spec) + + # Dynamo does not support HOP composition if their inner function is + # annotated with @functools.wraps(...). We circumvent this issue by applying + # wraps only if we're not tracing with dynamo. + if not torch._dynamo.is_compiling(): + wrapper_fn = wraps(func)(wrapper_fn) + + return wrapper_fn + + +@exposed_in("torch.func") +def hessian(func, argnums=0): + """ + Computes the Hessian of ``func`` with respect to the arg(s) at index + ``argnum`` via a forward-over-reverse strategy. + + The forward-over-reverse strategy (composing ``jacfwd(jacrev(func))``) is + a good default for good performance. It is possible to compute Hessians + through other compositions of :func:`jacfwd` and :func:`jacrev` like + ``jacfwd(jacfwd(func))`` or ``jacrev(jacrev(func))``. + + Args: + func (function): A Python function that takes one or more arguments, + one of which must be a Tensor, and returns one or more Tensors + argnums (int or Tuple[int]): Optional, integer or tuple of integers, + saying which arguments to get the Hessian with respect to. + Default: 0. + + Returns: + Returns a function that takes in the same inputs as ``func`` and + returns the Hessian of ``func`` with respect to the arg(s) at + ``argnums``. + + .. note:: + You may see this API error out with "forward-mode AD not implemented + for operator X". If so, please file a bug report and we will prioritize it. + An alternative is to use ``jacrev(jacrev(func))``, which has better + operator coverage. + + A basic usage with a R^N -> R^1 function gives a N x N Hessian: + + >>> from torch.func import hessian + >>> def f(x): + >>> return x.sin().sum() + >>> + >>> x = torch.randn(5) + >>> hess = hessian(f)(x) # equivalent to jacfwd(jacrev(f))(x) + >>> assert torch.allclose(hess, torch.diag(-x.sin())) + + """ + return jacfwd(jacrev(func, argnums), argnums) + + +@doesnt_support_saved_tensors_hooks +def grad_and_value_impl(func, argnums, has_aux, args, kwargs) -> Callable: + with grad_increment_nesting() as level: + output, aux, grad_input = None, None, None + # See NOTE [grad and vjp interaction with no_grad] + with torch.enable_grad(): + args = _wrap_all_tensors(args, level) + kwargs = _wrap_all_tensors(kwargs, level) + diff_args = _slice_argnums(args, argnums, as_tuple=False) + tree_map_(partial(_create_differentiable, level=level), diff_args) + + output = func(*args, **kwargs) + if has_aux: + if not (isinstance(output, tuple) and len(output) == 2): + raise RuntimeError( + "grad_and_value(f)(*args): output of function f should be a tuple: (output, aux) " + "if has_aux is True" + ) + output, aux = output + + if not isinstance(output, torch.Tensor): + raise RuntimeError( + "grad_and_value(f)(*args): Expected f(*args) " + f"to return a Tensor, got {type(output)}" + ) + if output.dim() != 0: + raise RuntimeError( + "grad_and_value(f)(*args): Expected f(*args) " + "to return a scalar Tensor, got tensor with " + f"{output.dim()} dims. Maybe you wanted to " + "use the vjp or jacrev APIs instead?" + ) + + flat_diff_args, spec = tree_flatten(diff_args) + + # NB: need create_graph so that backward pass isn't run in no_grad mode + flat_outputs = _as_tuple(output) + flat_grad_input = _autograd_grad( + flat_outputs, flat_diff_args, create_graph=True + ) + grad_input = tree_unflatten(flat_grad_input, spec) + + grad_input = _undo_create_differentiable(grad_input, level) + output = _undo_create_differentiable(output, level) + if has_aux: + aux = _undo_create_differentiable(aux, level) + + if has_aux: + return grad_input, (output, aux) + return grad_input, output + + +def grad_impl(func: Callable, argnums: argnums_t, has_aux: bool, args, kwargs): + results = grad_and_value_impl(func, argnums, has_aux, args, kwargs) + if has_aux: + grad, (_, aux) = results + return grad, aux + grad, _ = results + return grad + + +def _maybe_wrap_functional_tensor( + maybe_tensor, level, *, _python_functionalize: bool = False +): + if not isinstance(maybe_tensor, torch.Tensor): + return maybe_tensor + wrapped = _wrap_functional_tensor(maybe_tensor, level) + _assert_wrapped_functional(maybe_tensor, wrapped) + if _python_functionalize: + out = FunctionalTensor(wrapped) + torch._mirror_autograd_meta_to(maybe_tensor, out) + return out + return wrapped + + +def _wrap_all_tensors_to_functional( + tensor_pytree, level, *, _python_functionalize: bool = False +): + return tree_map( + partial( + lambda x: _maybe_wrap_functional_tensor( + x, level, _python_functionalize=_python_functionalize + ) + ), + tensor_pytree, + ) + + +def _maybe_unwrap_functional_tensor(maybe_tensor, *, reapply_views: bool): + if not isinstance(maybe_tensor, torch.Tensor): + return maybe_tensor + if isinstance(maybe_tensor, FunctionalTensor): + maybe_tensor = maybe_tensor.elem + + if not torch._is_functional_tensor(maybe_tensor): + # If it's not a functional tensor, just return it. + # This can happen if we functionalize a fn that returns a global, + # which was never wrapped properly. + return maybe_tensor + # Sync any pending updates on the output tensor + torch._sync(maybe_tensor) + return _unwrap_functional_tensor(maybe_tensor, reapply_views) + + +def _unwrap_all_tensors_from_functional(tensor_pytree, *, reapply_views: bool): + return tree_map( + lambda t: _maybe_unwrap_functional_tensor(t, reapply_views=reapply_views), + tensor_pytree, + ) + + +@exposed_in("torch.func") +def functionalize(func: Callable, *, remove: str = "mutations") -> Callable: + """ + functionalize is a transform that can be used to remove (intermediate) + mutations and aliasing from a function, while preserving the function's + semantics. + + ``functionalize(func)`` returns a new function with the same semantics + as ``func``, but with all intermediate mutations removed. + Every inplace operation performed on an intermediate tensor: + ``intermediate.foo_()`` + gets replaced by its out-of-place equivalent: + ``intermediate_updated = intermediate.foo()``. + + functionalize is useful for shipping a pytorch program off to + backends or compilers that aren't able to easily represent + mutations or aliasing operators. + + Args: + func (Callable): A Python function that takes one or more arguments. + remove (str): An optional string argument, that takes on either + the value 'mutations' or 'mutations_and_views'. + If 'mutations' is passed in then all mutating operators + will be replaced with their non-mutating equivalents. + If 'mutations_and_views' is passed in, then additionally, all aliasing + operators will be replaced with their non-aliasing equivalents. + Default: 'mutations'. + + Returns: + Returns a new "functionalized" function. It takes the same inputs as + ``func``, and has the same behavior, but any mutations + (and optionally aliasing) performed on intermediate tensors + in the function will be removed. + + functionalize will also remove mutations (and views) that were performed on function inputs. + However to preserve semantics, functionalize will "fix up" the mutations after + the transform has finished running, by detecting if any tensor inputs "should have" + been mutated, and copying the new data back to the inputs if necessary. + + + Example:: + + >>> # xdoctest: +SKIP + >>> import torch + >>> from torch.fx.experimental.proxy_tensor import make_fx + >>> from torch.func import functionalize + >>> + >>> # A function that uses mutations and views, but only on intermediate tensors. + >>> def f(a): + ... b = a + 1 + ... c = b.view(-1) + ... c.add_(1) + ... return b + ... + >>> inpt = torch.randn(2) + >>> + >>> out1 = f(inpt) + >>> out2 = functionalize(f)(inpt) + >>> + >>> # semantics are the same (outputs are equivalent) + >>> print(torch.allclose(out1, out2)) + True + >>> + >>> f_traced = make_fx(f)(inpt) + >>> f_no_mutations_traced = make_fx(functionalize(f))(inpt) + >>> f_no_mutations_and_views_traced = make_fx(functionalize(f, remove='mutations_and_views'))(inpt) + >>> + >>> print(f_traced.code) + + + + def forward(self, a_1): + add = torch.ops.aten.add(a_1, 1); a_1 = None + view = torch.ops.aten.view(add, [-1]) + add_ = torch.ops.aten.add_(view, 1); view = None + return add + + >>> print(f_no_mutations_traced.code) + + + + def forward(self, a_1): + add = torch.ops.aten.add(a_1, 1); a_1 = None + view = torch.ops.aten.view(add, [-1]); add = None + add_1 = torch.ops.aten.add(view, 1); view = None + view_1 = torch.ops.aten.view(add_1, [2]); add_1 = None + return view_1 + + >>> print(f_no_mutations_and_views_traced.code) + + + + def forward(self, a_1): + add = torch.ops.aten.add(a_1, 1); a_1 = None + view_copy = torch.ops.aten.view_copy(add, [-1]); add = None + add_1 = torch.ops.aten.add(view_copy, 1); view_copy = None + view_copy_1 = torch.ops.aten.view_copy(add_1, [2]); add_1 = None + return view_copy_1 + + + >>> # A function that mutates its input tensor + >>> def f(a): + ... b = a.view(-1) + ... b.add_(1) + ... return a + ... + >>> f_no_mutations_and_views_traced = make_fx(functionalize(f, remove='mutations_and_views'))(inpt) + >>> # + >>> # All mutations and views have been removed, + >>> # but there is an extra copy_ in the graph to correctly apply the mutation to the input + >>> # after the function has completed. + >>> print(f_no_mutations_and_views_traced.code) + + + + def forward(self, a_1): + view_copy = torch.ops.aten.view_copy(a_1, [-1]) + add = torch.ops.aten.add(view_copy, 1); view_copy = None + view_copy_1 = torch.ops.aten.view_copy(add, [2]); add = None + copy_ = torch.ops.aten.copy_(a_1, view_copy_1); a_1 = None + return view_copy_1 + + + There are a few "failure modes" for functionalize that are worth calling out: + (1) Like other torch.func transforms, `functionalize()` doesn't work with functions + that directly use `.backward()`. The same is true for torch.autograd.grad. + If you want to use autograd, you can compute gradients directly + with `functionalize(grad(f))`. + (2) Like other torch.func transforms, `functionalize()` doesn't work with global state. + If you call `functionalize(f)` on a function that takes views / mutations of + non-local state, functionalization will simply no-op and pass the view/mutation + calls directly to the backend. + One way to work around this is is to ensure that any non-local state creation + is wrapped into a larger function, which you then call functionalize on. + (3) `resize_()` has some limitations: functionalize will only work on programs + that use resize_()` as long as the tensor being resized is not a view. + (4) `as_strided()` has some limitations: functionalize will not work on + `as_strided()` calls that result in tensors with overlapping memory. + + + Finally, a helpful mental model for understanding functionalization is that + most user pytorch programs are writing with the public torch API. + When executed, torch operators are generally decomposed into + our internal C++ "ATen" API. + The logic for functionalization happens entirely at the level of ATen. + Functionalization knows how to take every aliasing operator in ATen, + and map it to its non-aliasing equivalent + (e.g. ``tensor.view({-1})`` -> ``at::view_copy(tensor, {-1})``), + and how to take every mutating operator in ATen, + and map it to its non-mutating equivalent + (e.g. ``tensor.add_(1)`` -> ``at::add(tensor, -1)``), + while tracking aliases and mutations out-of-line to know when to fix things up. + Information about which ATen operators are aliasing or mutating all comes from + https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml. + """ + if remove == "mutations": + reapply_views = True + elif remove == "mutations_and_views": + reapply_views = False + else: + raise RuntimeError( + f"functionalize(f, remove='mutations'): received invalid argument for remove={remove}." + " Valid options are:\n" + " remove='mutations': all inplace and out= operators will be removed from the program, and replaced" + " with their out-of-place equivalents.\n" + " remove='mutations_and_views': In addition to the above, all aliasing operators {view} will be" + " replaced with their non-aliasing counterparts, {view}_copy.\n" + ) + + @wraps(func) + def wrapped(*args, **kwargs): + try: + func_level = _func_increment_nesting(reapply_views) + func_args = _wrap_all_tensors_to_functional(args, func_level) + func_kwargs = _wrap_all_tensors_to_functional(kwargs, func_level) + + flattened_unwrapped_args = pytree.arg_tree_leaves(*args) + flattened_wrapped_args = pytree.arg_tree_leaves(*func_args) + flattened_unwrapped_kwargs = pytree.arg_tree_leaves(**kwargs) + flattened_wrapped_kwargs = pytree.arg_tree_leaves(**func_kwargs) + + func_outputs = func(*func_args, **func_kwargs) + outputs = _unwrap_all_tensors_from_functional( + func_outputs, reapply_views=reapply_views + ) + flat_outputs, func_out_spec = tree_flatten(outputs) + + for a in flattened_wrapped_args + flattened_wrapped_kwargs: + if isinstance(a, torch.Tensor): + # Call sync_() on the inputs, to ensure that any pending mutations have been applied. + torch._sync(a) + + # And if any mutations were applied to the inputs, we need to propagate them back to the user. + for unwrapped, wrapped in zip( + flattened_unwrapped_args, flattened_wrapped_args + ): + if isinstance(unwrapped, torch.Tensor) and isinstance( + wrapped, torch.Tensor + ): + _propagate_functional_input_mutation(unwrapped, wrapped) + for unwrapped, wrapped in zip( + flattened_unwrapped_kwargs, flattened_wrapped_kwargs + ): + if isinstance(unwrapped, torch.Tensor) and isinstance( + wrapped, torch.Tensor + ): + _propagate_functional_input_mutation(unwrapped, wrapped) + + return outputs + finally: + _func_decrement_nesting() + + return wrapped + + +@exposed_in("torch.func") +def linearize(func: Callable, *primals) -> Tuple[Any, Callable]: + """ + Returns the value of ``func`` at ``primals`` and linear approximation + at ``primals``. + + Args: + func (Callable): A Python function that takes one or more arguments. + primals (Tensors): Positional arguments to ``func`` that must all be + Tensors. These are the values at which the function is linearly approximated. + + Returns: + Returns a ``(output, jvp_fn)`` tuple containing the output of ``func`` + applied to ``primals`` and a function that computes the jvp of + ``func`` evaluated at ``primals``. + + linearize is useful if jvp is to be computed multiple times at ``primals``. However, + to achieve this, linearize saves intermediate computation and has higher memory requirements + than directly applying `jvp`. So, if all the ``tangents`` are known, it maybe more efficient + to compute vmap(jvp) instead of using linearize. + + .. note:: + linearize evaluates ``func`` twice. Please file an issue for an implementation + with a single evaluation. + + Example:: + >>> import torch + >>> from torch.func import linearize + >>> def fn(x): + ... return x.sin() + ... + >>> output, jvp_fn = linearize(fn, torch.zeros(3, 3)) + >>> jvp_fn(torch.ones(3, 3)) + tensor([[1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.]]) + >>> + + """ + # Note: We evaluate `fn` twice. + # Once for returning the output and other while + # tracing the graph. + # If this becomes a bottle-neck, we should update + # make_fx such that it also returns the output. + + output = func(*primals) + _, output_spec = tree_flatten(output) + + flat_primals, primals_argspec = tree_flatten(primals) + + # tangents for tracing + flat_tangents = tuple(p.new_empty(()).expand_as(p) for p in flat_primals) + + # function to trace + def trace_fn(flat_tangents): + with fwAD.dual_level(): + flat_duals = tuple( + fwAD.make_dual(p, t) for p, t in zip(flat_primals, flat_tangents) + ) + duals = tree_unflatten(flat_duals, primals_argspec) + output = func(*duals) + tangents = tree_map_only( + torch.Tensor, lambda dual: safe_unpack_dual(dual, False)[1], output + ) + + return tangents + + jvp_graph = lazy_dynamo_disallow(make_fx)(trace_fn)(flat_tangents) + const_folded_jvp_graph = lazy_dynamo_disallow(const_fold.split_const_subgraphs)( + jvp_graph + ) + + # Hold only the meta-data regarding the primals. + flat_primals_shape = tuple(p.shape for p in flat_primals) + flat_primals_device = tuple(p.device for p in flat_primals) + flat_primals_dtype = tuple(p.dtype for p in flat_primals) + + def forward_ad_checks(flat_tangents): + for idx, t in enumerate(flat_tangents): + if t.shape != flat_primals_shape[idx]: + msg = ( + f"tangent:{idx} with shape {t.shape} in flattened " + f"pytree doesn't match the shape {flat_primals_shape[idx]} " + "of the corresponding primal." + ) + raise RuntimeError(msg) + + if t.device != flat_primals_device[idx]: + msg = ( + f"tangent:{idx} with device {t.device} in flattened " + f"pytree doesn't match the device {flat_primals_device[idx]} " + "of the corresponding primal." + ) + raise RuntimeError(msg) + + if t.dtype != flat_primals_dtype[idx]: + msg = ( + f"tangent:{idx} with dtype {t.dtype} in flattened " + f"pytree doesn't match the dtype {flat_primals_dtype[idx]} " + "of the corresponding primal." + ) + raise RuntimeError(msg) + + # jvp_fn : callable to return + # It takes care of checking the argspec of tangents, + # calling the folded fx graph and unflattening fx graph output + def jvp_fn(*tangents): + flat_tangents, tangent_argspec = tree_flatten(tangents) + _linearize_treespec_compare(primals, tangents) + + forward_ad_checks(flat_tangents) + + flat_output = const_folded_jvp_graph(*flat_tangents) + # const folded graph can return flat output, + # so transform output. + return tree_unflatten(flat_output, output_spec) + + return output, jvp_fn diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/functional_call.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/functional_call.py new file mode 100644 index 0000000000000000000000000000000000000000..86c63be17fc9d0390b1e1172bb8e249c7b2a45b5 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/functional_call.py @@ -0,0 +1,253 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +from typing import Any, Dict, List, Optional, Sequence, Tuple, Union + +import torch +import torch.nn as nn +from torch import Tensor +from torch._functorch.utils import exposed_in + + +@exposed_in("torch.func") +def functional_call( + module: "torch.nn.Module", + parameter_and_buffer_dicts: Union[Dict[str, Tensor], Sequence[Dict[str, Tensor]]], + args: Union[Any, Tuple], + kwargs: Optional[Dict[str, Any]] = None, + *, + tie_weights: bool = True, + strict: bool = False, +): + r"""Performs a functional call on the module by replacing the module parameters + and buffers with the provided ones. + + .. note:: If the module has active parametrizations, passing a value in the + :attr:`parameter_and_buffer_dicts` argument with the name set to the regular parameter + name will completely disable the parametrization. + If you want to apply the parametrization function to the value passed + please set the key as ``{submodule_name}.parametrizations.{parameter_name}.original``. + + .. note:: If the module performs in-place operations on parameters/buffers, these will be reflected + in the ``parameter_and_buffer_dicts`` input. + + + Example:: + + >>> a = {'foo': torch.zeros(())} + >>> # xdoctest: +SKIP + >>> mod = Foo() # does self.foo = self.foo + 1 + >>> print(mod.foo) # tensor(0.) + >>> functional_call(mod, a, torch.ones(())) + >>> print(mod.foo) # tensor(0.) + >>> print(a['foo']) # tensor(1.) + + .. note:: If the module has tied weights, whether or not functional_call respects the tying is determined by the + tie_weights flag. + + Example:: + + >>> a = {'foo': torch.zeros(())} + >>> # xdoctest: +SKIP + >>> mod = Foo() # has both self.foo and self.foo_tied which are tied. Returns x + self.foo + self.foo_tied + >>> print(mod.foo) # tensor(1.) + >>> mod(torch.zeros(())) # tensor(2.) + >>> functional_call(mod, a, torch.zeros(())) # tensor(0.) since it will change self.foo_tied too + >>> functional_call(mod, a, torch.zeros(()), tie_weights=False) # tensor(1.)--self.foo_tied is not updated + >>> new_a = {'foo': torch.zeros(()), 'foo_tied': torch.zeros(())} + >>> functional_call(mod, new_a, torch.zeros()) # tensor(0.) + + An example of passing multiple dictionaries + + .. code-block:: python + + a = ({'weight': torch.ones(1, 1)}, {'buffer': torch.zeros(1)}) # two separate dictionaries + mod = nn.Bar(1, 1) # return self.weight @ x + self.buffer + print(mod.weight) # tensor(...) + print(mod.buffer) # tensor(...) + x = torch.randn((1, 1)) + print(x) + functional_call(mod, a, x) # same as x + print(mod.weight) # same as before functional_call + + + And here is an example of applying the grad transform over the parameters + of a model. + + .. code-block:: python + + import torch + import torch.nn as nn + from torch.func import functional_call, grad + + x = torch.randn(4, 3) + t = torch.randn(4, 3) + model = nn.Linear(3, 3) + + def compute_loss(params, x, t): + y = functional_call(model, params, x) + return nn.functional.mse_loss(y, t) + + grad_weights = grad(compute_loss)(dict(model.named_parameters()), x, t) + + .. note:: If the user does not need grad tracking outside of grad transforms, they can detach all of the + parameters for better performance and memory usage + + Example:: + + >>> detached_params = {k: v.detach() for k, v in model.named_parameters()} + >>> grad_weights = grad(compute_loss)(detached_params, x, t) + >>> grad_weights.grad_fn # None--it's not tracking gradients outside of grad + + This means that the user cannot call ``grad_weight.backward()``. However, if they don't need autograd tracking + outside of the transforms, this will result in less memory usage and faster speeds. + + Args: + module (torch.nn.Module): the module to call + parameters_and_buffer_dicts (Dict[str, Tensor] or tuple of Dict[str, Tensor]): the parameters that will be used in + the module call. If given a tuple of dictionaries, they must have distinct keys so that all dictionaries can + be used together + args (Any or tuple): arguments to be passed to the module call. If not a tuple, considered a single argument. + kwargs (dict): keyword arguments to be passed to the module call + tie_weights (bool, optional): If True, then parameters and buffers tied in the original model will be treated as + tied in the reparameterized version. Therefore, if True and different values are passed for the tied + parameters and buffers, it will error. If False, it will not respect the originally tied parameters and + buffers unless the values passed for both weights are the same. Default: True. + strict (bool, optional): If True, then the parameters and buffers passed in must match the parameters and + buffers in the original module. Therefore, if True and there are any missing or unexpected keys, it will + error. Default: False. + + Returns: + Any: the result of calling ``module``. + """ + if isinstance(parameter_and_buffer_dicts, dict): + parameters_and_buffers = parameter_and_buffer_dicts + elif isinstance(parameter_and_buffer_dicts, Sequence): + if not all(isinstance(d, dict) for d in parameter_and_buffer_dicts): + raise ValueError( + "Expected all elements of parameter_and_buffer_dicts to be dictionaries" + ) + all_keys = [k for d in parameter_and_buffer_dicts for k in d.keys()] + all_keys_counter: Dict[str, int] = {} + for k in all_keys: + v = all_keys_counter.get(k, 0) + all_keys_counter[k] = v + 1 + repeated_keys = [key for key, n in all_keys_counter.items() if n > 1] + if len(repeated_keys) > 0: + raise ValueError( + f"{repeated_keys} appeared in multiple dictionaries; behavior of functional call is ambiguous" + ) + parameters_and_buffers = { + k: v for d in parameter_and_buffer_dicts for k, v in d.items() + } + else: + raise ValueError( + f"Expected parameter_and_buffer_dicts to be a dict, or a list/tuple of dicts, " + f"but got {type(parameter_and_buffer_dicts)}" + ) + + return nn.utils.stateless._functional_call( + module, + parameters_and_buffers, + args, + kwargs, + tie_weights=tie_weights, + strict=strict, + ) + + +@exposed_in("torch.func") +def stack_module_state( + models: List[nn.Module], +) -> Tuple[Dict[str, Any], Dict[str, Any]]: + """stack_module_state(models) -> params, buffers + + Prepares a list of torch.nn.Modules for ensembling with :func:`vmap`. + + Given a list of ``M`` ``nn.Modules`` of the same class, returns two dictionaries + that stack all of their parameters and buffers together, indexed by name. + The stacked parameters are optimizable (i.e. they are new leaf nodes in the + autograd history that are unrelated to the original parameters and can be + passed directly to an optimizer). + + Here's an example of how to ensemble over a very simple model: + + .. code-block:: python + + num_models = 5 + batch_size = 64 + in_features, out_features = 3, 3 + models = [torch.nn.Linear(in_features, out_features) for i in range(num_models)] + data = torch.randn(batch_size, 3) + + def wrapper(params, buffers, data): + return torch.func.functional_call(models[0], (params, buffers), data) + + params, buffers = stack_module_state(models) + output = vmap(wrapper, (0, 0, None))(params, buffers, data) + + assert output.shape == (num_models, batch_size, out_features) + + When there's submodules, this follows state dict naming conventions + + .. code-block:: python + + import torch.nn as nn + class Foo(nn.Module): + def __init__(self, in_features, out_features): + super().__init__() + hidden = 4 + self.l1 = nn.Linear(in_features, hidden) + self.l2 = nn.Linear(hidden, out_features) + + def forward(self, x): + return self.l2(self.l1(x)) + + num_models = 5 + in_features, out_features = 3, 3 + models = [Foo(in_features, out_features) for i in range(num_models)] + params, buffers = stack_module_state(models) + print(list(params.keys())) # "l1.weight", "l1.bias", "l2.weight", "l2.bias" + + .. warning:: + All of the modules being stacked together must be the same (except for + the values of their parameters/buffers). For example, they should be in the + same mode (training vs eval). + """ + if len(models) == 0: + raise RuntimeError("stack_module_state: Expected at least one model, got 0.") + if not (all(m.training for m in models) or all(not m.training for m in models)): + raise RuntimeError( + "stack_module_state: Expected all models to have the same training/eval mode." + ) + model0_typ = type(models[0]) + if not all(type(m) == model0_typ for m in models): + raise RuntimeError( + "stack_module_state: Expected all models to be of the same class." + ) + all_params = [dict(model.named_parameters()) for model in models] + params = { + k: construct_stacked_leaf(tuple(params[k] for params in all_params), k) + for k in all_params[0] + } + all_buffers = [dict(model.named_buffers()) for model in models] + buffers = { + k: construct_stacked_leaf(tuple(buffers[k] for buffers in all_buffers), k) + for k in all_buffers[0] + } + + return params, buffers + + +def construct_stacked_leaf( + tensors: Union[Tuple[Tensor, ...], List[Tensor]], name: str +) -> Tensor: + all_requires_grad = all(t.requires_grad for t in tensors) + none_requires_grad = all(not t.requires_grad for t in tensors) + if not all_requires_grad and not none_requires_grad: + raise RuntimeError( + f"Expected {name} from each model to have the same .requires_grad" + ) + result = torch.stack(tensors) + if all_requires_grad: + result = result.detach().requires_grad_() + return result diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/fx_minifier.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/fx_minifier.py new file mode 100644 index 0000000000000000000000000000000000000000..d908a666d25350ce6af095779eb4f5efc6975962 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/fx_minifier.py @@ -0,0 +1,501 @@ +# mypy: ignore-errors + +import copy +import math +import os +import sys +from dataclasses import dataclass +from functools import partial, wraps +from typing import Callable, List + +import torch +import torch.fx as fx +from torch.hub import tqdm +from torch.multiprocessing.reductions import StorageWeakRef +from torch.utils._content_store import ContentStoreWriter + +from .compile_utils import get_outputs, get_placeholders + + +is_tuple = object() + + +@dataclass +class LoadTensorMeta: + size: List[int] + stride: List[int] + dtype: torch.dtype + device: torch.device + + +class ConcreteProp(torch.fx.Interpreter): + def __init__(self, mod, *, writer=None, skip_offload=False): + super().__init__(mod) + self.writer = writer + self.skip_offload = skip_offload + self.seen_storages = set() + + def run_node(self, n): + self.pbar.update(1) + r = super().run_node(n) + name = n.name + + if isinstance(r, torch.Tensor): + if self.writer is None: + n.meta["concrete_value"] = r + else: + if StorageWeakRef(r.untyped_storage()) in self.seen_storages: + # Refuse to offload tensors which alias other live + # tensors, because this will violate operator contracts + n.meta["concrete_value"] = None + else: + if not self.skip_offload: + self.writer.write_tensor(os.path.join("eager", name), r) + n.meta["concrete_value"] = LoadTensorMeta( + r.size(), r.stride(), r.dtype, r.device + ) + self.seen_storages.add(StorageWeakRef(r.untyped_storage())) + else: + n.meta["concrete_value"] = is_tuple + + return r + + def propagate(self, *args): + with tqdm( + desc="Saving intermediates for delta debugging", + total=len(self.module.graph.nodes), + disable=self.writer is None, + ) as pbar: + self.pbar = pbar + r = super().run(*args) + if not self.skip_offload: + pbar.set_description( + "Saved! To skip next time, run with --skip-saving-eager-intermediates" + ) + return r + + +def is_load_tensor_node(node): + return ( + node.op == "call_function" + and node.target is torch.ops.debugprims.load_tensor.default + ) + + +# inplace modifies node/inps +def _convert_node_to_placeholder(graph, node, inps): + if node.op == "output" or node.op == "placeholder": + return False + + if is_load_tensor_node(node): + return False + + concrete_val = node.meta.get("concrete_value", None) + + if isinstance(concrete_val, torch.Tensor): + node.op = "placeholder" + node.target = node.name + node.args = () + node.kwargs = {} + + inps.append(concrete_val) + return True + + elif concrete_val is None: + return False + + elif concrete_val is is_tuple: + r = False + for tuple_user in list(node.users): + r = _convert_node_to_placeholder(graph, tuple_user, inps) or r + # NB: We must not erase the node at this point, because + # we are iterating over the nodes and this would change + # the iteration order + # graph.erase_node(node) + return r + + elif isinstance(concrete_val, LoadTensorMeta): + node.op = "call_function" + node.target = torch.ops.debugprims.load_tensor.default + node.args = ( + os.path.join("eager", node.name), + concrete_val.size, + concrete_val.stride, + ) + node.kwargs = { + "device": concrete_val.device, + "dtype": concrete_val.dtype, + } + return True + + return False + + +def create_minified_hlo_graph(minified_fx_graph, inputs): + """ + Takes minified FX graph as primary input, and ports it to HLO via StableHLO + Provides minified HLO graph as output, and archive them to local directory + """ + hlo_dir = f"{os.getcwd()}/hlo_files" + os.makedirs(hlo_dir, exists_ok=True) + + from torch_xla.stablehlo import save_torch_model_as_stablehlo + + save_torch_model_as_stablehlo(minified_fx_graph, inputs, hlo_dir) + + +def dump_state(fx_g, inps): + print( + f""" +# Working Repro with {len(fx_g.graph.nodes)} nodes +inps = {[(i.shape, i.dtype, i.device.type) for i in inps]} +inps = [torch.zeros(())] + [torch.ones(shape, dtype=dtype, device=device) for (shape, dtype, device) in inps] +{fx_g.code} +""" + ) + + +def is_power_of_two(n): + if n == 0: + return False + return (n & (n - 1)) == 0 + + +@dataclass +class ReproState: + graph: fx.Graph + inps: List[torch.Tensor] + + def __post_init__(self): + ph_nodes = get_placeholders(self.graph) + assert len(ph_nodes) == len(self.inps) + + +def minifier( + fail_f: fx.GraphModule, + inps, + module_fails, + dump_state: Callable = dump_state, + *, + save_dir=None, + offload_to_disk=False, + skip_offload=False, + skip_sanity=False, + max_granularity=None, +): + """ + Minimizes a FX graph with given inputs, such that the resulting FX graph still returns True for module_fails. + + Does 2 main strategies: + 1. Truncates suffix: Removes some suffix from the graph and sets a new output. + 2. Delta Debugging: Tries replacing half of the graph with inputs. If fails, + tries replacing quarter of the graph, etc. + + >>> # xdoctest: +SKIP(failing) + >>> failing_function = fx.symbolic_trace(f) + >>> minimize(failing_function, [torch.randn(5)], lambda fx_g, inps: fx_g(*inps)) + + note: module_fails returns True if it fails. + """ + assert isinstance(inps, (tuple, list)) + + failing_graph = fail_f.graph + cur_size = len(failing_graph.nodes) + + if max_granularity is not None and not is_power_of_two(max_granularity): + raise RuntimeError(f"max_granularity {max_granularity} not power of two") + + num_queries = 0 + + def deepcopy_fx_graph(fx_graph): + return fx.GraphModule(fail_f, copy.deepcopy(fx_graph)).graph + + def graph_fails(graph, inps): + nonlocal num_queries + graph = copy.deepcopy(graph) + num_queries += 1 + mod = fx.GraphModule(fail_f, graph) + mod.graph.lint() + return module_fails(mod, inps) + + writer = None + if offload_to_disk: + writer = ContentStoreWriter(save_dir) + + ConcreteProp(fail_f, writer=writer, skip_offload=skip_offload).propagate(*inps) + if not skip_sanity and not graph_fails(failing_graph, inps): + raise RuntimeError("Input graph did not fail the tester") + print(f"Started off with {cur_size} nodes", file=sys.stderr) + + def _register_strategy(strategy: Callable, name: str): + @wraps(strategy) + def new_func(old_state: ReproState, granularity=1): + print(file=sys.stderr) + print( + f"Strategy: {name} (G: {granularity}) " + f"({len(old_state.graph.nodes)} nodes, {len(old_state.inps)} inputs)", + file=sys.stderr, + ) + new_state = strategy( + deepcopy_fx_graph(old_state.graph), list(old_state.inps), granularity + ) + if new_state is not None: + new_nodes = len(new_state.graph.nodes) + old_nodes = len(old_state.graph.nodes) + new_inps = len(new_state.inps) + old_inps = len(old_state.inps) + new_outs = len(get_outputs(new_state.graph)) + old_outs = len(get_outputs(old_state.graph)) + progress_made = False + if new_nodes < old_nodes: + progress_made = True + print( + f"SUCCESS: Went from {old_nodes} to {new_nodes} nodes", + file=sys.stderr, + ) + if new_inps > old_inps: + progress_made = True + print( + f"SUCCESS: Went from {old_inps} to {new_inps} inputs", + file=sys.stderr, + ) + if new_outs < old_outs: + progress_made = True + print( + f"SUCCESS: Went from {old_outs} to {new_outs} outputs", + file=sys.stderr, + ) + + if not progress_made: + raise RuntimeError("Success raised but no progress made?") + + if not graph_fails(new_state.graph, new_state.inps): + print( + "WARNING: Something went wrong, not applying this minification", + file=sys.stderr, + ) + return None + return new_state + else: + print(f"FAIL: {name}", file=sys.stderr) + return None + + return new_func + + def register_strategy(name: str): + return partial(_register_strategy, name=name) + + @register_strategy("Truncate suffix") + def remove_suffix(cur_graph, cur_inps, granularity): + tested = set() + new_graph = fx.Graph() + env = {} + for idx, node in enumerate(cur_graph.nodes): + new_node = new_graph.node_copy(node, lambda x: env[x]) + if node.op not in ["placeholder", "output"]: + # If idx is divisible by (granularity * 2), it would have been checked already. + if ( + idx % granularity == 0 + and (idx % (granularity * 2) != 0) + and idx not in tested + ): + output_node = new_graph.output((new_node,)) + if len(new_graph.nodes) < len(cur_graph.nodes) and graph_fails( + new_graph, cur_inps + ): + return ReproState(new_graph, cur_inps) + else: + tested.add(idx) + new_graph.erase_node(output_node) + env[node] = new_node + return None + + @register_strategy("Remove outputs") + def remove_outputs(cur_graph, cur_inps, granularity): + granularity = max(1, granularity // 2) + for idx, node in enumerate(cur_graph.nodes): + node.idx = idx + if node.op == "output": + output = node + break + + if isinstance(output.args[0], fx.Node): + return None + + output_args = sorted( + output.args[0], key=lambda x: x.idx if isinstance(x, fx.Node) else int(1e9) + ) + if len(output_args) == 1: + return None + + for idx in range(0, len(output_args), granularity): + output.args = (output_args[:idx] + output_args[idx + granularity :],) + if graph_fails(cur_graph, cur_inps): + return ReproState(cur_graph, cur_inps) + return None + + def remove_unused_inputs_unchecked(cur_state: ReproState): + cur_graph = cur_state.graph + cur_inps = cur_state.inps + ph_nodes = get_placeholders(cur_graph) + assert len(ph_nodes) == len(cur_inps) + + new_inps = [] + for idx in range(len(ph_nodes)): + if len(ph_nodes[idx].users) == 0: + cur_graph.erase_node(ph_nodes[idx]) + else: + new_inps.append(cur_inps[idx]) + if len(new_inps) < len(cur_inps): + return ReproState(cur_graph, new_inps) + return None + + def remove_unused_inputs_checked(cur_state: ReproState): + new_state = remove_unused_inputs_unchecked(cur_state) + if new_state is not None and graph_fails(new_state.graph, new_state.inps): + return new_state + return None + + def _remove_unused_wrapper(cur_graph, cur_inps, granularity): + return remove_unused_inputs_checked(ReproState(cur_graph, cur_inps)) + + remove_unused_inputs = register_strategy("Remove unused inputs")( + _remove_unused_wrapper + ) + + @register_strategy("Eliminate dead code") + def eliminate_dead_code(cur_graph, cur_inps, granularity): + if cur_graph.eliminate_dead_code() and graph_fails(cur_graph, cur_inps): + return ReproState(cur_graph, cur_inps) + return None + + def _consolidate_placeholders(cur_graph, inps): + new_graph = fx.Graph() + env = {} + seen_non_placeholder = False + + # Move all placeholders to the front; also, if any load_tensor + # is at the front, convert it into an input (because it can be live + # all the time) + for node in cur_graph.nodes: + if node.op == "placeholder": + new_node = new_graph.node_copy(node, lambda x: env[x]) + env[node] = new_node + elif not seen_non_placeholder and is_load_tensor_node(node): + new_node = new_graph.placeholder(node.name) + env[node] = new_node + inps.append( + torch.ops.debugprims.load_tensor.default(*node.args, **node.kwargs) + ) + else: + seen_non_placeholder = True + + # Move everyone else + for node in cur_graph.nodes: + if node not in env: + new_node = new_graph.node_copy(node, lambda x: env[x]) + env[node] = new_node + return new_graph + + @register_strategy("Delta Debugging") + def delta_debugging(cur_graph: fx.Graph, cur_inps, granularity): + num_nodes = len(cur_graph.nodes) + for start_range in range(0, num_nodes, granularity): + is_removing = False + new_graph = deepcopy_fx_graph(cur_graph) + new_inps = cur_inps[:] + end_range = min(num_nodes, start_range + granularity) + for idx in range(start_range, end_range): + new_node = list(new_graph.nodes)[idx] + if _convert_node_to_placeholder(new_graph, new_node, new_inps): + is_removing = True + if not is_removing: + continue + new_graph.eliminate_dead_code() + new_graph = _consolidate_placeholders(new_graph, new_inps) + new_state = remove_unused_inputs_unchecked(ReproState(new_graph, new_inps)) + if new_state is None: + new_state = ReproState(new_graph, new_inps) + if graph_fails(new_state.graph, new_state.inps): + return ReproState(new_state.graph, new_state.inps) + + return None + + @register_strategy("Consolidate Inputs") + def consolidate_inputs(cur_graph, cur_inps, granularity): + old_len = len(cur_inps) + cur_graph = _consolidate_placeholders(cur_graph, cur_inps) + if len(cur_inps) > old_len and graph_fails(cur_graph, cur_inps): + return ReproState(cur_graph, cur_inps) + return None + + failing_state = ReproState(failing_graph, inps) + + def try_granularity(failing_state, granularity, use_non_granular): + print(f"Trying granularity {granularity}", file=sys.stderr) + + strategies = [] + num_nodes = len(failing_state.graph.nodes) + num_outputs = len(get_outputs(failing_state.graph)) + if num_outputs > num_nodes // 2: + strategies += [remove_outputs] + + if use_non_granular: + strategies += [ + eliminate_dead_code, + remove_unused_inputs, + consolidate_inputs, + ] + + strategies += [remove_suffix, delta_debugging] + + for strategy in strategies: + new_state = strategy(failing_state, granularity) + if new_state is not None: + return new_state + return None + + while True: + dump_state(fx.GraphModule(fail_f, failing_state.graph), failing_state.inps) + granularity = int(2 ** (math.floor(math.log2(len(failing_state.graph.nodes))))) + if max_granularity is not None: + granularity = min(max_granularity, granularity) + new_state = try_granularity(failing_state, granularity, use_non_granular=True) + if new_state is not None: + failing_state = new_state + continue + + granularity //= 2 + has_progress = False + while granularity >= 1: + new_state = try_granularity( + failing_state, granularity, use_non_granular=False + ) + if new_state is not None: + failing_state = new_state + has_progress = True + break + granularity //= 2 + if has_progress: + continue + + new_state = remove_outputs(failing_state, 1) + if new_state is not None: + failing_state = new_state + continue + + break + + if not graph_fails(failing_state.graph, failing_state.inps): + raise RuntimeError("Uh oh, something went wrong :( Final graph is not failing") + + print(f"Made {num_queries} queries", file=sys.stderr) + failing_fx = fx.GraphModule(fail_f, failing_state.graph) + + # If XLA debugging environment is enabled, create minified HLO graph as well + if "XLA_HLO_DEBUG" in os.environ: + create_minified_hlo_graph(failing_fx, failing_state.inps) + + dump_state(failing_fx, failing_state.inps) + print("Wrote minimal repro out to repro.py", file=sys.stderr) + return failing_fx, failing_state.inps diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/make_functional.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/make_functional.py new file mode 100644 index 0000000000000000000000000000000000000000..98a064c46ae1f1d1e695ef32e017c6d78322611a --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/make_functional.py @@ -0,0 +1,617 @@ +# mypy: allow-untyped-defs +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +import copy +from typing import ( + Any, + Callable, + Dict, + Iterable, + List, + NoReturn, + Sequence, + Tuple, + Type, + Union, +) + +import torch +import torch.nn as nn +from torch import Tensor +from torch.nn.utils._named_member_accessor import NamedMemberAccessor + + +# Utilities to make nn.Module "functional" +# In particular the goal is to be able to provide a function that takes as input +# the parameters and evaluate the nn.Module using fixed inputs. + + +def raise_parameter_tying_error() -> NoReturn: + raise RuntimeError( + "make_functional(module): we don't yet support models that " + "do parameter tying (also sometimes known as weight sharing). " + "Please try to rewrite your model by replacing all instances of the " + "tied parameter with another and/or comment your support in " + "https://github.com/pytorch/functorch/issues/446" + ) + + +def create_names_map( + named_params: Union[Dict[str, Tensor], Iterable[Tuple[str, Tensor]]], + tied_named_params: Union[Dict[str, Tensor], Iterable[Tuple[str, Tensor]]], +) -> Dict[str, List[str]]: + """ + named_params is a dictionary of tensors: {'A': A, 'B': B} + tied_named_params is another dictionary of tensors {'A': A, 'B': B, 'B_tied': B} + with potentially tied (or 'duplicated') tensors + + This function creates a mapping from the names in named_params to the + names in tied_named_params: {'A': ['A'], 'B': ['B', 'B_tied']}. + """ + named_params = dict(named_params) + tied_named_params = dict(tied_named_params) + + tensors_dict_keys = set(named_params.keys()) + tied_tensors_dict_keys = set(tied_named_params.keys()) + assert tensors_dict_keys.issubset(tied_tensors_dict_keys) + + tensor_to_mapping: Dict[Tensor, Tuple[str, List[str]]] = {} + for key, tensor in named_params.items(): + tensor_to_mapping[tensor] = (key, []) + for key, tensor in tied_named_params.items(): + assert tensor in tensor_to_mapping + tensor_to_mapping[tensor][1].append(key) + return dict(tensor_to_mapping.values()) + + +def _extract_members( + mod: nn.Module, + named_members: Callable[..., Iterable[Tuple[str, Tensor]]], + subclass: Callable[[Tensor], Tensor], +) -> Tuple[Tuple[Tensor, ...], Tuple[str, ...], Dict[str, List[str]]]: + all_named_members = tuple(named_members(remove_duplicate=False)) + unique_named_members = tuple(named_members(remove_duplicate=True)) + names_map = create_names_map(unique_named_members, all_named_members) + + # Remove all the members in the model + memo = {} + accessor = NamedMemberAccessor(mod) + for name, p in all_named_members: + if p not in memo: + memo[p] = subclass(torch.empty_like(p, device="meta")) + replacement = memo[p] + accessor.set_tensor(name, replacement) + + if len(unique_named_members) == 0: + names, params = (), () + else: + names, params = zip(*unique_named_members) # type: ignore[assignment] + return params, names, names_map + + +def extract_weights( + mod: nn.Module, +) -> Tuple[Tuple[Tensor, ...], Tuple[str, ...], Dict[str, List[str]]]: + """ + This function removes all the Parameters from the model and + return them as a tuple as well as their original attribute names. + The weights must be re-loaded with `load_weights` before the model + can be used again. + Note that this function modifies the model in place and after this + call, mod.parameters() will be empty. + """ + return _extract_members(mod, mod.named_parameters, nn.Parameter) + + +def extract_buffers( + mod: nn.Module, +) -> Tuple[Tuple[Tensor, ...], Tuple[str, ...], Dict[str, List[str]]]: + return _extract_members(mod, mod.named_buffers, lambda x: x) + + +def load_weights( + mod: nn.Module, + names: Sequence[str], + params: Sequence[Tensor], + as_params: bool = False, +) -> None: + """ + Reload a set of weights so that `mod` can be used again to perform a forward pass. + Note that the `params` are regular Tensors (that can have history) and so are left + as Tensors. This means that mod.parameters() will still be empty after this call. + """ + accessor = NamedMemberAccessor(mod) + if as_params: + params = [nn.Parameter(p) for p in params] + accessor.set_tensors(names, params) + + +def _swap_state( + mod: nn.Module, names_map: Dict[str, List[str]], elems: Iterable[Tensor] +) -> List[Tensor]: + result: List[Tensor] = [] + accessor = NamedMemberAccessor(mod) + for (_, attr_names), elem in zip(names_map.items(), elems): + for i, attr_name in enumerate(attr_names): + if i == 0: + result.append(accessor.swap_tensor(attr_name, elem)) + else: + accessor.set_tensor(attr_name, elem) + return result + + +def load_buffers( + mod: nn.Module, + names: Sequence[str], + buffers: Sequence[Tensor], + as_params: bool = False, +) -> None: + accessor = NamedMemberAccessor(mod) + accessor.set_tensors(names, buffers) + + +def load_state( + model: nn.Module, + weights: Sequence[Tensor], + weight_names: Sequence[str], + buffers: Sequence[Tensor] = (), + buffer_names: Sequence[str] = (), +) -> nn.Module: + """load_state(model, weights, weight_names, buffers=(), buffer_names=()) -> model + + load_state takes `weights` and `buffers` and assigns them to the model. + This is the inverse operation of `make_functional_deprecated_v1`. + """ + assert len(weight_names) == len(weights) + load_weights(model, weight_names, weights) + if len(buffers) > 0: + assert len(buffer_names) == len(buffers) + load_buffers(model, buffer_names, buffers) + return model + + +def make_functional_deprecated_v1(model: nn.Module): + """make_functional_deprecated_v1(model) -> weights, func, weight_names + + Given an nn.Module, make_functional_deprecated_v1 extracts the state (weights) + and returns a functional version of the model, `func`. This makes + it so that it is possible use transforms over the parameters of + `model`. + + `func` can be invoked as follows: + ``` + x = torch.randn(4, 3) + model = nn.Linear(3, 3) + weights, func, _ = make_functional_deprecated_v1(model) + func(weights, (x,)) + ``` + + And here is an example of applying the grad transform: + ``` + x = torch.randn(4, 3) + model = nn.Linear(3, 3) + weights, _, func = make_functional_deprecated_v1(model) + grad_weights = grad(func)(weights, (x,)) + ``` + + To put the state back into a model, use `load_state`. + """ + buffers = list(model.buffers()) + if len(buffers) > 0: + raise RuntimeError( + "make_functional_deprecated_v1(model): `model` has buffers. Please use " + "make_functional_with_buffers_deprecated_v1(model) instead." + ) + weights, descriptors, _ = extract_weights(model) + + def fun(weights, data): + mutable_model = copy.deepcopy(model) + load_weights(mutable_model, descriptors, weights) + return mutable_model(*data) + + return weights, fun, descriptors + + +def make_functional_with_buffers_deprecated_v1(model: nn.Module): + """make_functional_with_buffers_deprecated_v1(model) -> weights, buffers, func, weight_names, buffer_names + + Given an nn.Module, make_functional_with_buffers_deprecated_v1 extracts the state (weights and buffers) + and returns a functional version of the model, `func`. + + `func` can be invoked as follows: + ``` + x = torch.randn(4, 3) + model = nn.Linear(3, 3) + weights, buffers, func, _, _ = make_functional_with_buffers_deprecated_v1(model) + func(weights, buffers, (x,)) + ``` + + And here is an example of applying the grad transform: + ``` + x = torch.randn(4, 3) + model = nn.Linear(3, 3) + weights, buffers, func, _, _ = make_functional_with_buffers_deprecated_v1(model) + func(weights, buffers, (x,)) + grad_weights = grad(func)(weights, buffers, (x,)) + ``` + + To put the state back into a model, use `load_state`. + """ + weights, weight_descriptors, _ = extract_weights(model) + buffers, buf_descriptors, _ = extract_buffers(model) + + def fun(weights, buffers, data): + mutable_model = copy.deepcopy(model) + load_weights(mutable_model, weight_descriptors, weights) + load_buffers(mutable_model, buf_descriptors, buffers) + return mutable_model(*data) + + return weights, buffers, fun, weight_descriptors, buf_descriptors + + +class FunctionalModuleWithBuffers(nn.Module): + """ + This is the callable object returned by :func:`make_functional_with_buffers`. + """ + + def __init__( + self, + stateless_model: nn.Module, + param_names: Tuple[str, ...], + buffer_names: Tuple[str, ...], + param_names_map: Dict[str, List[str]], + buffer_names_map: Dict[str, List[str]], + ) -> None: + super().__init__() + self.stateless_model = stateless_model + self.param_names = param_names + self.buffer_names = buffer_names + + self.all_names_map = dict(param_names_map) + self.all_names_map.update(buffer_names_map) + + @staticmethod + def _create_from( + model: nn.Module, disable_autograd_tracking: bool = False + ) -> Tuple["FunctionalModuleWithBuffers", Tuple[Tensor, ...], Tuple[Tensor, ...]]: + # TODO: We don't need to copy the model to create a stateless copy + model_copy = copy.deepcopy(model) + params, param_names, param_names_map = extract_weights(model_copy) + buffers, buffer_names, buffer_names_map = extract_buffers(model_copy) + if disable_autograd_tracking: + for param in params: + param.requires_grad_(False) + return ( + FunctionalModuleWithBuffers( + model_copy, param_names, buffer_names, param_names_map, buffer_names_map + ), + params, + buffers, + ) + + def forward( + self, params: Iterable[Tensor], buffers: Iterable[Tensor], *args, **kwargs + ) -> Any: + # Temporarily load the state back onto self.stateless_model + old_state = _swap_state( + self.stateless_model, + self.all_names_map, + tuple(params) + tuple(buffers), + ) + try: + return self.stateless_model(*args, **kwargs) + finally: + # Remove the loaded state on self.stateless_model + _swap_state(self.stateless_model, self.all_names_map, old_state) + + +class FunctionalModule(nn.Module): + """ + This is the callable object returned by :func:`make_functional`. + """ + + def __init__( + self, + stateless_model: nn.Module, + param_names: Tuple[str, ...], + names_map: Dict[str, List[str]], + ) -> None: + super().__init__() + self.stateless_model = stateless_model + self.param_names = param_names + self.names_map = names_map + + @staticmethod + def _create_from( + model: nn.Module, disable_autograd_tracking: bool = False + ) -> Tuple["FunctionalModule", Tuple[Tensor, ...]]: + # TODO: We don't need to copy the model to create a stateless copy + model_copy = copy.deepcopy(model) + params, param_names, names_map = extract_weights(model_copy) + if disable_autograd_tracking: + for param in params: + param.requires_grad_(False) + return FunctionalModule(model_copy, param_names, names_map), params + + def forward(self, params: Iterable[Tensor], *args, **kwargs) -> Any: + # Temporarily load the state back onto self.stateless_model + old_state = _swap_state(self.stateless_model, self.names_map, params) + try: + return self.stateless_model(*args, **kwargs) + finally: + # Remove the loaded state on self.stateless_model + _swap_state(self.stateless_model, self.names_map, old_state) + + +def make_functional( + model: nn.Module, disable_autograd_tracking: bool = False +) -> Tuple[FunctionalModule, Tuple[Tensor, ...]]: + """make_functional(model, disable_autograd_tracking=False) -> func, params + + Given a ``torch.nn.Module``, :func:`make_functional` extracts the state + (params) and returns a functional version of the model, ``func``. This + makes it so that it is possible use transforms over the parameters of + ``model``. + + ``func`` can be invoked as follows: + + .. code-block:: python + + import torch + import torch.nn as nn + from functorch import make_functional + + x = torch.randn(4, 3) + model = nn.Linear(3, 3) + func, params = make_functional(model) + func(params, x) + + And here is an example of applying the grad transform over the parameters + of a model. + + .. code-block:: python + + import torch + import torch.nn as nn + from functorch import make_functional, grad + + x = torch.randn(4, 3) + t = torch.randn(4, 3) + model = nn.Linear(3, 3) + func, params = make_functional(model) + + def compute_loss(params, x, t): + y = func(params, x) + return nn.functional.mse_loss(y, t) + + grad_weights = grad(compute_loss)(params, x, t) + + If the model has any buffers, please use :func:`make_functional_with_buffers` instead. + + Args: + model (torch.nn.Module): Input model. + disable_autograd_tracking (bool): Flag to disable gradients tracking for output parameters. + The returned params are unrelated to the set of params from the original model. If False (default), + the params will have ``requires_grad=True`` on them (aka they will be trackable with regular + PyTorch autograd), matching the requires_grad-ness of the params from the original model. + Otherwise, the returned params will have ``requires_grad=False``. Default, False. + If you plan on using regular PyTorch autograd (e.g., if you want to call ``.backward()`` or + ``torch.autograd.grad()``, then set ``disable_autograd_tracking=False``. + Otherwise, if you're only planning on using functorch's gradient transforms, + then please set ``disable_autograd_tracking=True`` to avoid unnecessarily tracking + history with PyTorch autograd. + + """ + buffers = list(model.buffers()) + if len(buffers) > 0: + raise RuntimeError( + "make_functional(model): `model` has buffers. Please use " + "make_functional_with_buffers(model) instead." + ) + return FunctionalModule._create_from( + model, disable_autograd_tracking=disable_autograd_tracking + ) + + +def make_functional_with_buffers( + model: nn.Module, disable_autograd_tracking: bool = False +) -> Tuple[FunctionalModuleWithBuffers, Tuple[Tensor, ...], Tuple[Tensor, ...]]: + """make_functional_with_buffers(model, disable_autograd_tracking=False) -> func, params, buffers + + Given a ``torch.nn.Module``, make_functional_with_buffers extracts the + state (params and buffers) and returns a functional version of the model + ``func`` that can be invoked like a function. + + ``func`` can be invoked as follows: + + .. code-block:: python + + import torch + import torch.nn as nn + from functorch import make_functional_with_buffers + + x = torch.randn(4, 3) + model = nn.Linear(3, 3) + func, params, buffers = make_functional_with_buffers(model) + func(params, buffers, x) + + And here is an example of applying the grad transform over the parameters + of a model: + + .. code-block:: python + + import torch + import torch.nn as nn + from functorch import make_functional_with_buffers, grad + + x = torch.randn(4, 3) + t = torch.randn(4, 3) + model = nn.Linear(3, 3) + func, params, buffers = make_functional_with_buffers(model) + + def compute_loss(params, buffers, x, t): + y = func(params, buffers, x) + return nn.functional.mse_loss(y, t) + + grad_weights = grad(compute_loss)(params, buffers, x, t) + + Args: + model (torch.nn.Module): Input model. + disable_autograd_tracking (bool): Flag to disable gradients tracking for output parameters. + The returned params are unrelated to the set of params from the original model. If False (default), + the params will have ``requires_grad=True`` on them (aka they will be trackable with regular + PyTorch autograd), matching the requires_grad-ness of the params from the original model. + Otherwise, the returned params will have ``requires_grad=False``. Default, False. + If you plan on using regular PyTorch autograd (e.g., if you want to call ``.backward()`` or + ``torch.autograd.grad()``, then set ``disable_autograd_tracking=False``. + Otherwise, if you're only planning on using functorch's gradient transforms, + then please set ``disable_autograd_tracking=True`` to avoid unnecessarily tracking + history with PyTorch autograd. + + """ + return FunctionalModuleWithBuffers._create_from( + model, disable_autograd_tracking=disable_autograd_tracking + ) + + +def transpose_stack( + tuple_of_tuple_of_tensors: Tuple[Tuple[Tensor, ...], ...] +) -> Tuple[Tensor, ...]: + tuple_of_tuple_of_tensors = tuple(zip(*tuple_of_tuple_of_tensors)) + results = tuple( + torch.stack(shards).detach() for shards in tuple_of_tuple_of_tensors + ) + return results + + +def combine_state_for_ensemble( + models: Sequence[nn.Module], +) -> Tuple[FunctionalModuleWithBuffers, Tuple[Tensor, ...], Tuple[Tensor, ...]]: + """combine_state_for_ensemble(models) -> func, params, buffers + + Prepares a list of torch.nn.Modules for ensembling with :func:`vmap`. + + Given a list of ``M`` ``nn.Modules`` of the same class, stacks all of their + parameters and buffers together to make ``params`` and ``buffers``. + Each parameter and buffer in the result will have an additional dimension + of size ``M``. + + :func:`combine_state_for_ensemble` also returns ``func``, a functional + version of one of the models in :attr:`models`. One cannot directly run + ``func(params, buffers, *args, **kwargs)`` directly, you probably want to + use ``vmap(func, ...)(params, buffers, *args, **kwargs)`` + + Here's an example of how to ensemble over a very simple model: + + .. code-block:: python + + num_models = 5 + batch_size = 64 + in_features, out_features = 3, 3 + models = [torch.nn.Linear(in_features, out_features) for i in range(num_models)] + data = torch.randn(batch_size, 3) + + fmodel, params, buffers = combine_state_for_ensemble(models) + output = vmap(fmodel, (0, 0, None))(params, buffers, data) + + assert output.shape == (num_models, batch_size, out_features) + + .. warning:: + All of the modules being stacked together must be the same (except for + the values of their parameters/buffers). For example, they should be in the + same mode (training vs eval). + + This API is subject to change -- we're investigating better ways to + create ensembles and would love your feedback how to improve this. + """ + if len(models) == 0: + raise RuntimeError( + "combine_state_for_ensemble: Expected at least one model, got 0." + ) + if not (all(m.training for m in models) or all(not m.training for m in models)): + raise RuntimeError( + "combine_state_for_ensemble: Expected all models to " + "have the same training/eval mode." + ) + model0_typ = type(models[0]) + if not all(type(m) == model0_typ for m in models): + raise RuntimeError( + "combine_state_for_ensemble: Expected all models to be of the same class." + ) + funcs, params, buffers = zip( + *[make_functional_with_buffers(model) for model in models] + ) + params = transpose_stack(params) + buffers = transpose_stack(buffers) + return funcs[0], params, buffers + + +def functional_init( + model_class: Type[nn.Module], + ensemble_shape: Union[Tuple[()], Tuple[int]] = (), + device: torch.types.Device = "cpu", +): + def wrapped(*args, **kwargs): + if len(ensemble_shape) >= 2: + raise ValueError("NYI: ensemble_shape with more than 1 element") + if len(ensemble_shape) == 0: + model = model_class(*args, **kwargs).to(device) + return make_functional_deprecated_v1(model) + num_models = ensemble_shape[0] # type: ignore[misc] + if num_models <= 0: + raise ValueError(f"num_models {num_models} should be > 0") + # NB: Not very efficient, more of a POC + models = tuple( + model_class(*args, **kwargs).to(device) for _ in range(num_models) + ) + _, fn, names = make_functional_deprecated_v1(model_class(*args, **kwargs)) + weights = tuple(make_functional_deprecated_v1(model)[0] for model in models) + weights = tuple(zip(*weights)) + weights = tuple(torch.stack(shards).detach() for shards in weights) + return weights, fn, names + + return wrapped + + +def functional_init_with_buffers( + model_class: Type[nn.Module], + ensemble_shape: Union[Tuple[()], Tuple[int]] = (), + device: torch.types.Device = "cpu", +): + def wrapped(*args, **kwargs): + if len(ensemble_shape) >= 2: + raise ValueError("NYI: ensemble_shape with more than 1 element") + if len(ensemble_shape) == 0: + model = model_class(*args, **kwargs).to(device) + return make_functional_deprecated_v1(model) + num_models = ensemble_shape[0] # type: ignore[misc] + if num_models <= 0: + raise ValueError(f"num_models {num_models} should be > 0") + # NB: Not very efficient, more of a POC + models = tuple( + model_class(*args, **kwargs).to(device) for _ in range(num_models) + ) + ( + _, + _, + fn, + weight_names, + buffer_names, + ) = make_functional_with_buffers_deprecated_v1(model_class(*args, **kwargs)) + weights, buffers = zip( + *tuple( + make_functional_with_buffers_deprecated_v1(model)[:2] + for model in models + ) + ) + weights = tuple(zip(*weights)) + weights = tuple(torch.stack(shards).detach() for shards in weights) + buffers = tuple(zip(*buffers)) + buffers = tuple(torch.stack(shards).detach() for shards in buffers) + return weights, buffers, fn, weight_names, buffer_names + + return wrapped diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/partitioners.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/partitioners.py new file mode 100644 index 0000000000000000000000000000000000000000..b0f609dc75615cffbebf1e8191f9b85917e1b3ce --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/partitioners.py @@ -0,0 +1,1933 @@ +# mypy: allow-untyped-defs +import copy +import functools +import heapq +import itertools +import logging +import math +import operator +import os +from collections import defaultdict +from dataclasses import dataclass, replace +from typing import Callable, Dict, List, Optional, Set, Tuple, TYPE_CHECKING, Union + +import torch +import torch._inductor.inductor_prims +import torch.fx as fx +import torch.utils._pytree as pytree +from torch.fx.experimental._backward_state import BackwardState +from torch.fx.experimental.proxy_tensor import is_sym_node, py_sym_types +from torch.fx.experimental.sym_node import magic_methods, method_to_operator +from torch.fx.experimental.symbolic_shapes import ( + find_symbol_binding_fx_nodes, + free_symbols, + hint_int, + is_symbol_binding_fx_node, +) +from torch.fx.passes import graph_drawer +from torch.utils.checkpoint import CheckpointPolicy + +from . import config +from ._aot_autograd.logging_utils import get_aot_graph_name +from ._aot_autograd.utils import is_with_effects +from .compile_utils import fx_graph_cse, get_aten_target + + +if TYPE_CHECKING: + import sympy + + +AOT_PARTITIONER_DEBUG = config.debug_partitioner +log = logging.getLogger(__name__) + +aten = torch.ops.aten +prims = torch.ops.prims + + +@dataclass +class OpTypes: + """Class for keeping track of different operator categories""" + + fusible_ops: Set[Callable] + compute_intensive_ops: Set[Callable] + random_ops: Set[Callable] + view_ops: Set[Callable] + recomputable_ops: Set[Callable] + + def is_fusible(self, node: fx.Node): + return get_aten_target(node) in self.fusible_ops + + def is_compute_intensive(self, node: fx.Node): + return get_aten_target(node) in self.compute_intensive_ops + + def is_random(self, node: fx.Node): + return get_aten_target(node) in self.random_ops + + def is_view(self, node: fx.Node): + return get_aten_target(node) in self.view_ops + + def is_recomputable(self, node: fx.Node): + return get_aten_target(node) in self.recomputable_ops + + +@dataclass +class NodeInfo: + # Be careful about iterating over these explicitly, as their order may not + # be deterministic + inputs: List[fx.Node] + _required_fw_nodes: Set[fx.Node] + required_bw_nodes: Set[fx.Node] + unclaimed_nodes: Set[fx.Node] + fw_order: Dict[fx.Node, int] + + @functools.cached_property + def required_fw_nodes(self) -> List[fx.Node]: + return sorted( + (n for n in self._required_fw_nodes), key=lambda n: self.fw_order[n] + ) + + def is_required_fw(self, n: fx.Node) -> bool: + return n in self._required_fw_nodes + + def is_required_bw(self, n: fx.Node) -> bool: + return n in self.required_bw_nodes + + def is_unclaimed(self, n: fx.Node) -> bool: + return n in self.unclaimed_nodes + + def get_fw_order(self, n: fx.Node) -> int: + assert n in self._required_fw_nodes, f"Node {n} not in fw nodes!" + return self.fw_order[n] + + +@dataclass +class MinCutOptions: + ban_if_used_far_apart: bool + ban_if_long_fusible_chains: bool + ban_if_materialized_backward: bool + ban_if_not_in_allowlist: bool + ban_if_reduction: bool + + +def must_recompute(node: fx.Node) -> bool: + return node.meta.get("recompute", None) in [ + CheckpointPolicy.MUST_RECOMPUTE, + CheckpointPolicy.PREFER_RECOMPUTE, + ] + + +def has_recomputable_ops(fx_g: fx.GraphModule) -> bool: + found = False + for node in fx_g.graph.nodes: + if must_recompute(node): + return True + return False + + +def has_recomputable_rng_ops(fx_g: fx.GraphModule) -> bool: + for node in fx_g.graph.nodes: + if ( + must_recompute(node) + and hasattr(node.target, "tags") + and torch.Tag.nondeterministic_seeded in node.target.tags + ): + return True + return False + + +def sym_node_size(node: fx.Node) -> int: + if isinstance(node.meta["val"], (torch.SymInt, torch.SymBool)): + return 1 + assert isinstance(node.meta["val"], torch.SymFloat) + return 4 + + +class InvalidNodeBase: + def __repr__(self): + return "Invalid Node" + + +InvalidNode = InvalidNodeBase() + + +def _extract_graph_with_inputs_outputs( + joint_graph: fx.Graph, + inputs: List[fx.Node], + outputs: List[fx.Node], + subgraph: Optional[str] = None, +) -> fx.Graph: + """ + Given a graph, extracts out a subgraph that takes the specified nodes as + inputs and returns the specified outputs. + + This includes specifying non-placeholder nodes as inputs. + + The general strategy is to initialize all inputs with proxies as we + encounter them, and trace through the graph, only keeping values which take + in valid proxies. Then, all dead code is eliminated. + """ + new_graph = fx.Graph() + env = {} + + # Add new placeholder nodes in the order specified by the inputs + for node in inputs: + new_node = new_graph.placeholder(node.name) + # Can't use node_copy here as we may be turning previous call_function into placeholders + new_node.meta = node.meta + env[node] = new_node + + for node in joint_graph.nodes: + if _must_be_in_backward(node) and subgraph != "backward": + env[node] = InvalidNode # type: ignore[assignment] + continue + + if node in env: + # Node must be one of our inputs. (Any member of env which wasn't an + # input to start must have been created by this loop and won't be in + # joint_graph.nodes). + continue + elif node.op == "placeholder": + env[node] = InvalidNode # type: ignore[assignment] + elif node.op == "call_function": + all_args = pytree.arg_tree_leaves(*node.args, **node.kwargs) + all_args = [ + isinstance(env[x], InvalidNodeBase) + for x in all_args + if isinstance(x, fx.Node) + ] + if any(all_args): + env[node] = InvalidNode # type: ignore[assignment] + continue + env[node] = new_graph.node_copy(node, lambda x: env[x]) + elif node.op == "get_attr": + env[node] = new_graph.node_copy(node, lambda x: env[x]) + elif node.op == "output": + pass + output_values = [] + for x in outputs: + if isinstance(x, fx.Node): + if x not in env: + raise RuntimeError(f"Node {x} couldn't be found in env") + assert not isinstance( + env[x], InvalidNodeBase + ), f"Node {x} was invalid, but is output" + output_values.append(env[x]) + else: + output_values.append(x) + new_graph.output(tuple(output_values)) + + new_graph.eliminate_dead_code() + new_graph.lint() + return new_graph + + +def _is_primal(node: fx.Node) -> bool: + return ( + node.op == "placeholder" + and "tangents" not in str(node.target) + and not _is_bwd_seed_offset(node) + and not _is_fwd_seed_offset(node) + ) + + +def _is_tangent(node: fx.Node) -> bool: + return node.op == "placeholder" and "tangents" in str(node.target) + + +def _is_bwd_seed_offset(node: fx.Node) -> bool: + return node.op == "placeholder" and ( + "bwd_seed" in str(node.target) or "bwd_base_offset" in str(node.target) + ) + + +def _is_fwd_seed_offset(node: fx.Node) -> bool: + return node.op == "placeholder" and ( + "fwd_seed" in str(node.target) or "fwd_base_offset" in str(node.target) + ) + + +def _is_backward_state(node: fx.Node) -> bool: + return node.op == "placeholder" and isinstance(node.meta.get("val"), BackwardState) + + +def _has_tag_is_backward(node: fx.Node) -> bool: + return node.meta.get("partitioner_tag", None) == "is_backward" + + +def _has_tag_must_be_in_backward(node: fx.Node) -> bool: + return node.meta.get("partitioner_tag", None) == "must_be_in_backward" + + +def _must_be_in_backward(node: fx.Node) -> bool: + return _has_tag_must_be_in_backward(node) or ( + _has_tag_is_backward(node) and is_with_effects(node) + ) + + +def _extract_fwd_bwd_outputs( + joint_module: fx.GraphModule, *, num_fwd_outputs +) -> Tuple[List[fx.Node], List[fx.Node]]: + outputs = pytree.arg_tree_leaves( + *(node.args for node in joint_module.graph.find_nodes(op="output")) + ) + fwd_outputs = outputs[:num_fwd_outputs] + bwd_outputs = outputs[num_fwd_outputs:] + return fwd_outputs, bwd_outputs + + +def _remove_by_name(saved_values: List[fx.Node], name: str): + for saved_value in saved_values: + if saved_value.name == name: + saved_values.remove(saved_value) + break + + +def _extract_fwd_bwd_modules( + joint_module: fx.GraphModule, + saved_values: List[fx.Node], + saved_sym_nodes: List[fx.Node], + *, + num_fwd_outputs: int, +) -> Tuple[fx.GraphModule, fx.GraphModule]: + fwd_outputs, bwd_outputs = _extract_fwd_bwd_outputs( + joint_module, num_fwd_outputs=num_fwd_outputs + ) + placeholders = joint_module.graph.find_nodes(op="placeholder") + primal_inputs = [*filter(_is_primal, placeholders)] + tangent_inputs = [*filter(_is_tangent, placeholders)] + fwd_seed_offset_inputs = [*filter(_is_fwd_seed_offset, placeholders)] + bwd_seed_offset_inputs = [*filter(_is_bwd_seed_offset, placeholders)] + backward_state_inputs = [*filter(_is_backward_state, placeholders)] + + bwd_graph = _extract_graph_with_inputs_outputs( + joint_module.graph, + saved_sym_nodes + saved_values + tangent_inputs + bwd_seed_offset_inputs, + bwd_outputs, + "backward", + ) + + for node in bwd_graph.find_nodes(op="placeholder"): + # This is to filter out saved values that don't actually end up being used by the backwards pass + if not node.users: + _remove_by_name(saved_values, node.name) + _remove_by_name(saved_sym_nodes, node.name) + elif _is_backward_state(node): + # BackwardState is saved directly + _remove_by_name(saved_values, node.name) + assert backward_state_inputs + + # Now that we have the finalized list of saved values, we need to ensure + # we propagate all symbols which are referenced by backwards inputs. + # These are not directly used in the graph but are required for downstream + # sizevar assignment + saved_symbols: Set[sympy.Symbol] = set() + saved_sym_nodes_binding = [] + saved_sym_nodes_derived = [] + + # Some symbols may already be bound in the directly saved_sym_nodes, + # keep track of them so we don't re-bind them + for node in saved_sym_nodes: + symbol = is_symbol_binding_fx_node(node) + if symbol: + saved_symbols.add(symbol) + saved_sym_nodes_binding.append(node) + else: + saved_sym_nodes_derived.append(node) + + # Now go through all of the prospective backward inputs and track any + # other symbols we need to bind + symbol_bindings = find_symbol_binding_fx_nodes(joint_module.graph) + for node in itertools.chain(saved_sym_nodes_derived, saved_values, tangent_inputs): + if "val" not in node.meta: + continue + new_symbols = free_symbols(node.meta["val"]) - saved_symbols + # NB: Deterministic order please! + for s in sorted(new_symbols, key=lambda s: s.name): + # NB: For well formed graphs, the symbol should always be present, + # but we also have ways to produce ill-formed graphs, e.g., direct + # make_fx usages, so don't choke in this case + if s not in symbol_bindings: + continue + saved_sym_nodes_binding.append(symbol_bindings[s]) + saved_symbols |= new_symbols + + # Update saved_sym_nodes that are now reordered to have all bindings at + # front. This can also be used later on to figure out the position of saved + # sym nodes in the output of fwd graph. + saved_sym_nodes.clear() + saved_sym_nodes.extend(saved_sym_nodes_binding + saved_sym_nodes_derived) + + # Now, we re-generate the fwd/bwd graphs. + # NB: This might increase compilation time, but I doubt it matters + fwd_graph = _extract_graph_with_inputs_outputs( + joint_module.graph, + primal_inputs + fwd_seed_offset_inputs, + fwd_outputs + saved_values + saved_sym_nodes, + "forward", + ) + bwd_graph = _extract_graph_with_inputs_outputs( + joint_module.graph, + saved_sym_nodes + + saved_values + + tangent_inputs + + bwd_seed_offset_inputs + + backward_state_inputs, + bwd_outputs, + "backward", + ) + + fwd_module = fx._lazy_graph_module._make_graph_module(joint_module, fwd_graph) + bwd_module = fx._lazy_graph_module._make_graph_module(joint_module, bwd_graph) + return fwd_module, bwd_module + + +def default_partition( + joint_module: fx.GraphModule, _joint_inputs, *, num_fwd_outputs +) -> Tuple[fx.GraphModule, fx.GraphModule]: + """ + Partitions the :attr:`joint_module` in a manner that closely resembles the + behavior observed in the original ``.forward()`` and ``.backward()`` of the + callable, i.e., the resulting forward graph contains those operators that + are executed in the original ``.forward()`` callable passed to + :func:`aot_function`. + + The default partitioner collects the operators that are between the forward + inputs and the forward outputs. This helps in finding the tensors which have + to be stashed for the backward pass. These stashed tensors become the output + of the generated forward graph. The remaining operators are then placed in + the backward graph. + + .. warning:: + This API is experimental and likely to change. + + Args: + joint_module(fx.GraphModule): The joint forward and backward graph. This + is the result of AOT Autograd tracing. + + Returns: + Returns the generated forward and backward Fx graph modules. + """ + if has_recomputable_ops(joint_module): + return min_cut_rematerialization_partition( + joint_module, _joint_inputs, num_fwd_outputs=num_fwd_outputs + ) + primal_inputs = list(filter(_is_primal, joint_module.graph.nodes)) + fwd_seed_offset_inputs = list(filter(_is_fwd_seed_offset, joint_module.graph.nodes)) + inputs = primal_inputs + fwd_seed_offset_inputs + fwd_outputs, bwd_outputs = _extract_fwd_bwd_outputs( + joint_module, num_fwd_outputs=num_fwd_outputs + ) + forward_only_graph = _extract_graph_with_inputs_outputs( + joint_module.graph, inputs, fwd_outputs, "forward" + ) + forward_node_names = { + node.name for node in forward_only_graph.nodes if node.op != "output" + } + saved_values = [] + saved_sym_nodes = [] + + for node in joint_module.graph.nodes: + if node.name not in forward_node_names: + continue + if is_sym_node(node): + # Symints must be kept separate from tensors so that PythonFunction only calls + # save_for_backward on tensors and stashes symints in autograd .ctx + saved_sym_nodes.append(node) + elif "tensor_meta" not in node.meta and node.op == "call_function": + # Since we can't save tuple of tensor values, we need to flatten out what we're saving + users = node.users + assert all(user.target == operator.getitem for user in users) + saved_values.extend(users) + else: + backward_usages = [ + n for n in node.users if n.name not in forward_node_names + ] + if "tensor_meta" in node.meta and all( + is_sym_node(n) for n in backward_usages + ): + # If we have a tensor in the forward, where only its sizes/strides are needed in the backward, + # and not the actual tensor data, + # then it will be a lot cheaper to save only the sizes/strides, and not the actual tensor. + # + # Note that saving the tensor could also cause compilation problems: + # If the user mutated an input in the forward and uses its sizes/strides in the backward, + # then we would be obligated to clone the input before saving it to appease autograd. + # (This is how we originally found this bug). + saved_sym_nodes.extend(backward_usages) + else: + saved_values.append(node) + saved_values = list(dict.fromkeys(saved_values).keys()) + saved_sym_nodes = list(dict.fromkeys(saved_sym_nodes).keys()) + + return _extract_fwd_bwd_modules( + joint_module, + saved_values, + saved_sym_nodes=saved_sym_nodes, + num_fwd_outputs=num_fwd_outputs, + ) + + +INT_INF = int(1e6) + + +def _tensor_nbytes(numel: int, dtype) -> int: + return numel * dtype.itemsize + + +def _size_of(node: fx.Node) -> int: + def object_nbytes(x) -> int: + if not isinstance(x, torch.Tensor): + return 0 + return _tensor_nbytes(hint_int(x.numel(), fallback=4096), x.dtype) + + if "val" in node.meta: + val = node.meta["val"] + if isinstance(val, py_sym_types): + return 1 + # NB: The fallback values here are meaningless, maybe we should respect + # torch._inductor.config.unbacked_symint_fallback (but this is a + # layering violation) + elif isinstance(val, (list, tuple)): + return sum(object_nbytes(n) for n in val) + elif isinstance(val, dict): + return sum(object_nbytes(n) for _, n in val.items()) + elif isinstance(val, torch.Tensor): + return object_nbytes(val) + + raise RuntimeError(f"Unknown metadata type {type(val)} on node {node}") + if node.op == "get_attr": + return 0 + raise RuntimeError( + f"Node {node} didn't have `val` metadata; we should always have `val` metadata on the nodes." + ) + + +# Used for some investigative purposes +def _count_ops(graph: fx.Graph): + from collections import defaultdict + + cnt: Dict[str, int] = defaultdict(int) + for node in graph.nodes: + if node.op == "call_function": + cnt[node.target.__name__] += 1 + print(sorted(cnt.items(), key=lambda x: x[1], reverse=True)) + + +@functools.lru_cache(None) +def pointwise_ops(): + ops = [] + for attr_name in dir(torch.ops.aten): + opoverloadpacket = getattr(torch.ops.aten, attr_name) + if not isinstance(opoverloadpacket, torch._ops.OpOverloadPacket): + continue + + for overload in opoverloadpacket.overloads(): + op_overload = getattr(opoverloadpacket, overload) + if torch.Tag.pointwise in op_overload.tags: + # currently aot autograd uses packet not overload + ops.append(opoverloadpacket) + break + + return ops + + +def sort_depths(args, depth_map: Dict[fx.Node, int]) -> List[Tuple[fx.Node, int]]: + arg_depths = { + arg: depth_map[arg] for arg in args if isinstance(arg, torch.fx.node.Node) + } + return sorted(arg_depths.items(), key=lambda x: x[1], reverse=True) + + +def reordering_to_mimic_autograd_engine(gm: fx.GraphModule) -> fx.GraphModule: + """ + This pass finds the first bwd node in the graph (by looking at users of + tangents) and then reorders the graph by walking from this node to all the + way to the end of the graph. At each op in this traveral, we insert this op + in a new graph and try to bring only the relevant subgraph from the other + non-bwd edges relevant for this op. This closely mimics the behavior of + autograd engine. + + Why is this pass required in the first place? + + This is an artifact of how partitioners work today. The starting point of + partitioner is a joint graph, which is fwd and then bwd graph. In the case + of checkpointing, we keep portions of fwd graph in their original place in + the joint graph, while obtaining a bwd graph. As a result, the resulting bwd + graph has copies of recomputed fwd subgraphs followed by the original bwd + graph. If we run this naively, this leads to bad memory footprint, because + the fwd subgraphs are live for way longer duration than necessary. This pass + reorders the operations such that we prioritize the ops for the original bwd + graph while only realizing those ops from the fwd graph that are necessary + at any given point in the graph. + """ + + new_graph = fx.Graph() + env: Dict[fx.Node, fx.Node] = {} + + # Add new placeholder nodes in the order specified by the inputs + for node in gm.graph.find_nodes(op="placeholder"): + env[node] = new_graph.node_copy(node, lambda x: env[x]) + + order = {} + for idx, node in enumerate(gm.graph.nodes): + order[node] = idx + + def insert_node_in_graph(node): + cur_nodes = [node] + insertable_nodes = set() + while len(cur_nodes) > 0: + node = cur_nodes.pop() + if node in insertable_nodes or node in env: + continue + insertable_nodes.add(node) + + # Bias traversal towards the nodes that have higher depth - prioritizes + # critical path first. + cur_nodes += node.all_input_nodes + + insertable_nodes = sorted(insertable_nodes, key=lambda n: order[n]) + for node in insertable_nodes: + env[node] = new_graph.node_copy(node, lambda x: env[x]) + + # Find first bwd node in the graph + tangent_inputs = list(filter(_is_tangent, gm.graph.nodes)) + first_node_in_bwd = None + minimum_order = math.inf + for tangent in tangent_inputs: + for user in tangent.users: + if order[user] < minimum_order: + minimum_order = order[user] + first_node_in_bwd = user + + # If gradInp does not depend upon gradOut, we may not find any nodes in the "backwards pass" + if first_node_in_bwd is None: + return gm + + # Build the graph op-by-op by starting from the node all the way to the end + for node in list(gm.graph.nodes)[order[first_node_in_bwd] :]: + insert_node_in_graph(node) + + # The output node is already built by the traversal. + new_gm = torch.fx.GraphModule(gm, new_graph) + return new_gm + + +def functionalize_rng_ops( + joint_module: fx.GraphModule, + fw_module: fx.GraphModule, + bw_module: fx.GraphModule, + num_sym_nodes: int, +) -> Tuple[fx.GraphModule, fx.GraphModule]: + # During user-driven activation checkpointing, we have to ensure that a rng + # op in fwd yields the same output as the recomputed rng op in the bwd. To + # do this, we use functionalize wrappers to wrap the random ops and share + # rng state between the fwd and bwd graphs. + + # There are 3 main steps to do this + # Step 1 - Construct a mapping of rng node between the fwd and its counterpart in bwd. + # Step 2 - Modify the fwd pass such that + # 1) Replace rand with run_and_save_rng_state wrapper + # 2) Replace the users of the original op with the output[1] of this op. + # 3) Collect all the rng_state - output[0] of each op, and make them + # output nodes. Special care needs to be taken here because fwd outputs + # has symints at the very end. + # Step 3 - Modify the bwd pass such that + # 1) Add the input nodes just before the tangents for the stashed rng states + # 2) Replace rand with run_with_save_rng_state wrappers + # 3) Use the stashed states as inputs to these ops + + # Unique id to generate name + uid = itertools.count() + + def get_rng_ops(gmod): + random_nodes = {} + for node in gmod.graph.nodes: + if ( + node.op == "call_function" + and hasattr(node.target, "tags") + and torch.Tag.nondeterministic_seeded in node.target.tags + ): + random_nodes[node.name] = node + return random_nodes + + def get_device(node): + """ + Check the example value of the node outputs to find the device type. + """ + if "val" not in node.meta: + return None + + candidates = node.meta["val"] + if not isinstance(candidates, tuple): + candidates = (candidates,) + + for candidate in candidates: + if isinstance(candidate, torch.Tensor): + if candidate.device.type == "cuda": + return "cuda" + + return "cpu" + + def get_sample_rng_state(device): + if device == "cuda": + return torch.cuda.get_rng_state() + return torch.get_rng_state() + + # Step 1 - Construct a mapping of rng node between the fwd and its counterpart in bwd. + joint_graph_rng_ops = get_rng_ops(joint_module) + fw_graph_rng_ops = get_rng_ops(fw_module) + bw_graph_rng_ops = get_rng_ops(bw_module) + recomputable_rng_ops_map = {} + for node in joint_module.graph.nodes: + if ( + must_recompute(node) + and hasattr(node.target, "tags") + and torch.Tag.nondeterministic_seeded in node.target.tags + ): + base_node = joint_graph_rng_ops[node.name] + fw_node = fw_graph_rng_ops[node.name] + bw_node = bw_graph_rng_ops[node.name] + recomputable_rng_ops_map[base_node] = {"fwd": fw_node, "bwd": bw_node} + + run_and_save_rng = torch._prims.rng_prims.run_and_save_rng_state + run_with_rng_state = torch._prims.rng_prims.run_with_rng_state + bw_tangent_start_node = None + for node in bw_module.graph.find_nodes(op="placeholder"): + if "tangent" in node.name: + bw_tangent_start_node = node + break + if bw_tangent_start_node is None: + raise RuntimeError( + "Couldn't find tangent node in graph inputs. This is unexpected, please file a bug if you see this" + ) + + fw_rng_state_outputs = [] + for base_node, node_pair in recomputable_rng_ops_map.items(): + # Step 2 - Modify the fwd pass such that + fw_node = node_pair["fwd"] + bw_node = node_pair["bwd"] + fw_graph = fw_module.graph + with fw_graph.inserting_before(fw_node): + functional_fw_node = fw_graph.create_node( + "call_function", + run_and_save_rng, + args=(fw_node.target, *fw_node.args), + kwargs=fw_node.kwargs, + ) + state = fw_graph.create_node( + "call_function", + operator.getitem, + args=(functional_fw_node, 0), + kwargs={}, + ) + rng_output = fw_graph.create_node( + "call_function", + operator.getitem, + args=( + functional_fw_node, + 1, + ), + kwargs={}, + ) + fw_node.replace_all_uses_with(rng_output) + fw_graph.erase_node(fw_node) + fw_rng_state_outputs.append(state) + + # Step 3 - Modify the bwd pass such that + bw_graph = bw_module.graph + with bw_graph.inserting_before(bw_tangent_start_node): + state_name = f"rng_state_output_{next(uid)}" + bw_rng_state_node = bw_graph.placeholder(state_name) + bw_rng_state_node.meta["val"] = get_sample_rng_state(get_device(fw_node)) + + with bw_graph.inserting_before(bw_node): + rng_output = bw_graph.create_node( + "call_function", + run_with_rng_state, + args=(bw_rng_state_node, bw_node.target, *bw_node.args), + kwargs=bw_node.kwargs, + ) + + bw_node.replace_all_uses_with(rng_output) + bw_graph.erase_node(bw_node) + + # Add the rng states in the output of the fwd graph. AOT Autograd assumes + # that symints are at the end of forward graph outputs. So, insert the new + # rng states accordingly. + fw_output_node = next(iter(fw_module.graph.find_nodes(op="output"))) + fw_outputs = fw_output_node.args[0] + sym_node_start_idx = len(fw_outputs) - num_sym_nodes + outputs = ( + fw_outputs[:sym_node_start_idx] + + tuple(fw_rng_state_outputs) + + fw_outputs[sym_node_start_idx:] + ) + fw_module.graph.output(outputs) + fw_module.graph.erase_node(fw_output_node) + fw_module.recompile() + bw_module.recompile() + return fw_module, bw_module + + +def cleanup_recompute_tags(joint_module: fx.GraphModule) -> fx.GraphModule: + """ + If there are two consecutive checkpointed blocks with no operator in + between, we would still want to stash the tensor at the boundary of + checkpointed blocks. The following pass makes the last output node + non-recomputable to allow for that. + """ + for node in joint_module.graph.nodes: + if must_recompute(node): + for user in node.users: + if ( + must_recompute(user) + and user.meta["ac_graph_id"] > node.meta["ac_graph_id"] + ): + node.meta["recompute"] = CheckpointPolicy.MUST_SAVE + return joint_module + + +def solve_min_cut( + joint_graph: fx.Graph, + node_info: NodeInfo, + min_cut_options: MinCutOptions, + dont_ban=None, +): + if dont_ban is None: + dont_ban = set() + op_types = get_default_op_list() + + if AOT_PARTITIONER_DEBUG: + joint_module_ops = { + str(node.target._overloadpacket) + for node in joint_graph.nodes + if node.op == "call_function" and hasattr(node.target, "_overloadpacket") + } + ops_ignored = joint_module_ops - {str(i) for i in op_types.recomputable_ops} + print("Ops banned from re-materialization: ", ops_ignored) + print() + + def can_fuse_into_auto_functionalized(a, b): + if b.target != torch.ops.higher_order.auto_functionalized: + return False + mutable_op = b.args[0] + ( + mutable_arg_names, + _, + ) = torch._higher_order_ops.auto_functionalize.get_mutable_args(mutable_op) + for name in mutable_arg_names: + arg = b.kwargs[name] + if a is arg: + return True + if isinstance(arg, list): + if a in arg: + return True + return False + + def can_fuse_into_triton_kernel_wrapper_functional(a, b): + if b.target != torch.ops.higher_order.triton_kernel_wrapper_functional: + return False + mutable_arg_names = b.kwargs["tensors_to_clone"] + for name in mutable_arg_names: + arg = b.kwargs["kwargs"][name] + if a is arg: + return True + return False + + def is_fusible(a, b): + # We can perform "memory fusion" into a cat, but cat cannot be a + # producer to a fusion + if get_aten_target(b) == aten.cat: + return True + if can_fuse_into_auto_functionalized(a, b): + return True + if can_fuse_into_triton_kernel_wrapper_functional(a, b): + return True + return op_types.is_fusible(a) and op_types.is_fusible(b) + + try: + import networkx as nx + except ImportError as e: + raise RuntimeError( + "Need networkx installed to perform smart recomputation " "heuristics" + ) from e + + def is_materialized_backwards(node): + if op_types.is_view(node): + return False + cur_nodes = {node} + while len(cur_nodes) > 0: + cur = cur_nodes.pop() + for user in cur.users: + if not node_info.is_required_fw(user) and not is_fusible(cur, user): + return True + if op_types.is_view(user): + cur_nodes.add(user) + + return False + + def should_ban_recomputation(node): + if node.op != "call_function": + return False + if node.target == operator.getitem: + return False + if node.meta.get("recompute", None) == CheckpointPolicy.MUST_SAVE: + return True + if config.recompute_views and op_types.is_view(node): + return False + if node.target in [aten.lift_fresh_copy.default, aten.lift_fresh.default]: + return False + + if min_cut_options.ban_if_not_in_allowlist: + if not op_types.is_recomputable(node): + return True + else: + if op_types.is_random(node) or op_types.is_compute_intensive(node): + return True + + # If a node *must* be materialized in the backwards pass, then we + # should never recompute it. This is a pretty subtle point. In + # general, the assumption we make is that recomputing a node in the + # backwards pass is "free". However, if a node must be materialized + # in the backwards pass, then recomputing it is never free. + if min_cut_options.ban_if_materialized_backward and is_materialized_backwards( + node + ): + log.info("materialized backwards: %s %s", node, tuple(node.users)) + return True + + # Arbitrary hack that sometimes seems to help things. The above + # modification appears to have made this heuristic a lot less critical + # for performance. + # NB: As of PR #121692, this hack no longer seems necessary. + if node.dist_from_bw < 1000 and node.dist_from_bw > config.max_dist_from_bw: + return True + + # If the output of an op is 4x smaller (arbitrary choice), + # then we don't allow recomputation. The idea here is that for + # things like reductions, saving the output of the reduction is very + # cheap/small, and it makes sure we don't do things like recompute + # normalizations in the backwards. + if min_cut_options.ban_if_reduction: + input_tensors_size = sum( + _size_of(i) for i in node.args if isinstance(i, fx.Node) + ) + output_size = _size_of(node) + return output_size * 4 < input_tensors_size + return False + + def is_materialized(node): + if node.op == "placeholder": + return True + + return not all(is_fusible(node, user) for user in node.users) + + def get_node_weight(node) -> float: + mem_sz = _size_of(node) + if config.recompute_views and op_types.is_view(node): + # If `config.recompute_views=True`, we don't save views. This is generally + # a good idea since views are free to recompute, and it makes it a bit simpler + # to analyze. + # NB: If they're not free to recompute (e.g. nested tensors)... I + # think we should modify checks for view_ops to `is_view` and check + # that. Basically, with nested tensors, `aten.view` is not a "view + # op". + return math.inf + + if isinstance(node.meta["val"], py_sym_types): + # We never want to save symfloats + if not isinstance(node.meta["val"], torch.SymInt): + return INT_INF + + # Heuristic to bias towards nodes closer to the backwards pass + # Complete guess about current value + mem_sz = int(mem_sz * (1.1 ** max(min(node.dist_from_bw, 100), 1))) + if is_materialized(node): + return mem_sz + else: + return mem_sz * 2 + + nx_graph = nx.DiGraph() + banned_nodes = set() + + def ban_recomputation_if_allowed(node): + if op_types.is_view(node): + return False + if node in dont_ban: + return False + # This bans recomputation of the node unless we've been forced not to by + # user annotation + if must_recompute(node): + return False + + if "val" in node.meta and isinstance(node.meta["val"], torch.SymFloat): + return False + + banned_nodes.add(node) + # A node will only ever be recomputed if there is a path from an + # ancestor of this node to the backwards path through this node that + # doesn't go through any saved value. If this node is saved, then that + # condition is not possible. + nx_graph.add_edge("source", node.name + "_in", capacity=math.inf) + return True + + for node in joint_graph.nodes: + if node.op == "output": + continue + + if node in node_info.required_bw_nodes: + if node not in node_info.inputs: + nx_graph.add_edge(node.name + "_in", "sink", capacity=math.inf) + continue + # If someone saves a input for backward as-is and backward + # returns that tensor as-is as a grad input, then the node x would + # be both a required_bw_node and an input. In this case we + # (1) connect x_in to to the source, (2) x_out to the sink, and + # (3) assign the proper weight to the x_in-x_out edge, so that + # x would be part of cut nodes. A case where this happens is if + # NestedTensor saves a offset tensor as part of the singleton int + # in sizes. + nx_graph.add_edge(node.name + "_out", "sink", capacity=math.inf) + + if must_recompute(node): + # If user explicitly says they want to recompute a node, we honor it + # by adding an inf-capacity edge from X_in to the sink. + # This way, X_in node is guaranteed to be part of the subgraph that contains "sink" + # after the cut, thus guaranteeing that X op will be recomputed. + nx_graph.add_edge(node.name + "_in", "sink", capacity=math.inf) + continue + + if _is_primal(node) or _is_fwd_seed_offset(node): + ban_recomputation_if_allowed(node) + + # If a node can't be recomputed (too expensive or involves randomness), + # we prevent it from being recomputed by adding an inf edge to the source + # We only need to ban nodes in the fw pass, as those are the only ones that would be recomputed. + if node_info.is_required_fw(node) and should_ban_recomputation(node): + ban_recomputation_if_allowed(node) + + # Checks if a node is actually a tuple. Can be simplified to just an isinstance check if we always use faketensors. + is_non_tensor_node = ( + "val" not in node.meta and "tensor_meta" not in node.meta + ) or ("val" in node.meta and not isinstance(node.meta["val"], torch.Tensor)) + + if is_sym_node(node): + weight = float(sym_node_size(node)) + elif is_non_tensor_node: + weight = ( + 0.0 if isinstance(node.meta.get("val"), BackwardState) else math.inf + ) + else: + weight = get_node_weight(node) + # Creates the weights on the "node" edge + nx_graph.add_edge(node.name + "_in", node.name + "_out", capacity=weight) + for user in node.users: + nx_graph.add_edge(node.name + "_out", user.name + "_in", capacity=math.inf) + + # todo(chilli): This is the most questionable of the 3 heuristics for banning recompute. + # Some example models to look at where this helps perf: poolformer_m36, + # mixer_b16_224, cait_m36_384 + + # The "rough" idea here is that if you have some node that is used by both a + # node nearby downstream as well as a node far downstream, if we recompute + # both of the downstream nodes, we're unlikely to be able to fuse both + # downstream nodes together. + + # Thus, we shouldn't aim to recompute far downstream nodes that depend on + # this node. That intuition of "far downstream" is captured by whether + # there's an unfusible op along the chain somewhere + + # It could probably be improved by properly analyzing what's going on in the + # backwards pass instead of only relying on whether it's unfusible in the + # forwards. + + def find_first_unfusible(start_nodes: List[fx.Node], max_range: int) -> int: + """ + Finds the first unfusible node in the chain of nodes starting from + `start_nodes` and returns its position. + """ + sorted_nodes: List[Tuple[int, fx.Node, bool]] = [] + for n in start_nodes: + heapq.heappush(sorted_nodes, (node_info.get_fw_order(n), n, True)) + + while len(sorted_nodes) > 0: + _, node, node_is_fusible = heapq.heappop(sorted_nodes) + if not node_is_fusible: + return node_info.get_fw_order(node) + for user in node.users: + if node_info.is_required_fw(user): + if node_info.get_fw_order(user) > max_range: + continue + heapq.heappush( + sorted_nodes, + (node_info.get_fw_order(user), user, is_fusible(node, user)), + ) + return max_range + + if min_cut_options.ban_if_used_far_apart: + for used_node in node_info.required_fw_nodes: + orders = [ + node_info.get_fw_order(user) + for user in used_node.users + if node_info.is_required_fw(user) + ] + fw_users = [ + user for user in used_node.users if node_info.is_required_fw(user) + ] + if len(orders) > 0: + first_unfusible_use = find_first_unfusible(fw_users, max(orders)) + for user in tuple(used_node.users): + if ( + node_info.is_required_fw(user) + and node_info.get_fw_order(user) > first_unfusible_use + and is_fusible(used_node, user) + ): + if user in banned_nodes: + continue + log.info( + "used above/below fusible %s:(%s) -> %s -> %s:(%s)", + used_node, + node_info.get_fw_order(used_node), + first_unfusible_use, + user, + node_info.get_fw_order(user), + ) + ban_recomputation_if_allowed(user) + + # This heuristic is fairly straightforward. The idea is that although it is + # cheap to recompute bandwidth-bound ops, we don't want to end up in a situation + # where we have a long chain of pointwise ops from the beginning to the end + # of the model (like say, residual connections) + + # todo: I'm not totally sure why this heuristic matters. It's possible that this is + # working around Inductor fusion decisions, or that it's a patch over + # suboptimal partitioning decisions + + # Some models it improves perf on are cait_m36_384, mixer_b16_224, poolformer_m36 + + if min_cut_options.ban_if_long_fusible_chains: + visited = set() + for start_node in joint_graph.nodes: + if not node_info.is_required_fw(start_node): + continue + fusible = [(node_info.get_fw_order(start_node), start_node)] + start_order = node_info.get_fw_order(start_node) + while len(fusible) > 0: + _, cur = heapq.heappop(fusible) + if cur in visited: + continue + visited.add(cur) + # 100 is arbitrary choice to try and prevent degenerate cases + if ( + node_info.get_fw_order(cur) > start_order + 100 + and len(fusible) == 0 + ): + log.info( + "too long %s %s %s %s", + cur, + start_node, + node_info.get_fw_order(cur), + node_info.get_fw_order(start_node), + ) + ban_recomputation_if_allowed(cur) + break + + for user in cur.users: + if ( + node_info.is_required_fw(user) + and is_fusible(cur, user) + and user not in banned_nodes + ): + heapq.heappush(fusible, (node_info.get_fw_order(user), user)) + + try: + cut_value, partition = nx.minimum_cut(nx_graph, "source", "sink") + except Exception: + print("Failed to compute min-cut on following graph:") + print("\n".join(nx.readwrite.edgelist.generate_edgelist(nx_graph))) + visualize_min_cut_graph(nx_graph) + raise + + reachable, non_reachable = partition + cutset: Set[Tuple[str, str]] = set() + for u, nbrs in ((n, nx_graph[n]) for n in reachable): + cutset.update((u, v) for v in nbrs if v in non_reachable) + + cut_nodes = set() + for node_in, node_out in cutset: + assert node_in[:-3] == node_out[:-4] + node_name = node_in[:-3] + cut_nodes.add(node_name) + + name_to_node = get_name_to_node(joint_graph) + # To make this stuff deterministic + node_idx = {node: idx for idx, node in enumerate(joint_graph.nodes)} + saved_values = sorted( + (name_to_node[node] for node in cut_nodes), key=lambda x: node_idx[x] + ) + return saved_values, banned_nodes + + +def visualize_min_cut_graph(nx_graph): + import networkx as nx + import pydot + + dot_format = nx.nx_pydot.to_pydot(nx_graph).to_string() + dot_graph = pydot.graph_from_dot_data(dot_format)[0] + for edge in dot_graph.get_edges(): + weight = nx_graph[edge.get_source()][edge.get_destination()]["capacity"] + # Set edge label to weight + edge.set_label(str(weight)) + # Color edges with weight 'inf' as red + if weight == float("inf"): + edge.set_color("red") + print("Visualizing the failed graph to min_cut_failed.svg") + dot_graph.write_svg("min_cut_failed.svg") + + +def get_default_op_list() -> OpTypes: + default_recomputable_ops: List[Callable] = [ + aten.add, + aten.sub, + aten.div, + aten.atan2, + aten.mul, + aten.max, + aten.min, + aten.pow, + aten.remainder, + aten.fmod, + aten.__and__, + aten.__or__, + aten.__xor__, + aten.__lshift__, + aten.__rshift__, + aten.eq, + aten.ne, + aten.ge, + aten.gt, + aten.le, + aten.lt, + aten.abs, + aten.bitwise_not, + aten.ceil, + aten.floor, + aten.frac, + aten.neg, + aten.relu, + aten.round, + aten.silu, + aten.trunc, + aten.log, + aten.log10, + aten.log1p, + aten.log2, + aten.lgamma, + aten.exp, + aten.expm1, + aten.erf, + aten.erfc, + aten.cos, + aten.acos, + aten.cosh, + aten.sin, + aten.asin, + aten.sinh, + aten.tan, + aten.atan, + aten.tanh, + aten.atanh, + aten.sqrt, + aten.rsqrt, + aten.reciprocal, + aten.sigmoid, + aten.softplus, + aten.threshold, + aten.threshold_backward, + aten.clamp, + aten.where, + aten.lerp, + aten.addcmul, + aten.gelu, + aten.gelu_backward, + aten.sum, + aten.mean, + aten._grad_sum_to_size, + aten.sum_to_size, + aten.amax, + aten.to, + aten.type_as, + operator.getitem, + aten.squeeze, + aten.unsqueeze, + aten.rsub, + aten._to_copy, + ] # noqa: E501,B950 + recomputable_view_ops = [aten.squeeze, aten.unsqueeze, aten.alias] + recomputable_view_ops += [ + aten.view, + aten.slice, + aten.t, + prims.broadcast_in_dim, + aten.expand, + aten.as_strided, + aten.permute, + ] + view_ops = recomputable_view_ops + default_recomputable_ops += [ + prims.div, + prims.convert_element_type, + aten.clone, + aten._to_copy, + aten.full_like, + prims.var, + prims.sum, + aten.var, + aten.std, + prims.broadcast_in_dim, + aten.select, + aten._unsafe_view, + aten.view, + aten.expand, + aten.slice, + aten.reshape, + aten.broadcast_tensors, + aten.scalar_tensor, + aten.ones, + aten.new_zeros, + aten.lift_fresh_copy, + aten.arange, + aten.triu, + aten.var_mean, + aten.isinf, + aten.any, + aten.full, + aten.as_strided, + aten.zeros, + aten.empty, + aten.empty_like, + aten.argmax, + aten.maximum, + prims.iota, + prims._low_memory_max_pool2d_offsets_to_indices, + ] # noqa: E501,B950 + # Natalia said that we should allow recomputing indexing :) + default_recomputable_ops += [aten.index, aten.gather] + default_recomputable_ops += view_ops + + default_recomputable_ops += pointwise_ops() + + default_recomputable_ops += [ + aten.zeros_like, + ] + + default_recomputable_ops += [method_to_operator(m) for m in magic_methods] + recomputable_ops = set(default_recomputable_ops) + + random_ops = [aten.native_dropout, aten.rand_like, aten.randn_like] + compute_intensive_ops = [ + aten.mm, + aten.convolution, + aten.convolution_backward, + aten.bmm, + aten.addmm, + aten._scaled_dot_product_flash_attention, + aten._scaled_dot_product_efficient_attention, + aten._flash_attention_forward, + aten._efficient_attention_forward, + aten.upsample_bilinear2d, + aten._scaled_mm, + ] # noqa: E501,B950 + + fusible_ops = recomputable_ops | set(random_ops) + return OpTypes( + set(fusible_ops), + set(compute_intensive_ops), + set(random_ops), + set(view_ops), + set(recomputable_ops), + ) + + +def get_name_to_node(graph: fx.Graph): + name_to_node = {} + for node in graph.nodes: + name_to_node[node.name] = node + return name_to_node + + +def greedy_knapsack( + memory: List[float], runtimes: List[float], max_memory: float +) -> Tuple[float, List[int], List[int]]: + n = len(runtimes) + items = list(range(n)) + + # Sort items based on the ratio of runtime to memory in descending order + items = sorted(items, key=lambda i: runtimes[i] / memory[i], reverse=True) + + total_memory = 0.0 + total_runtime = 0.0 + items_to_save = [] + items_to_allow_recomputing = [] + + for i in items: + if total_memory + memory[i] <= max_memory: + total_memory += memory[i] + total_runtime += runtimes[i] + items_to_save.append(i) + else: + items_to_allow_recomputing.append(i) + return total_runtime, items_to_save, items_to_allow_recomputing + + +def ilp_knapsack( + memory: List[float], runtimes: List[float], max_memory: float +) -> Tuple[float, List[int], List[int]]: + import numpy as np + + try: + from scipy.optimize import Bounds, LinearConstraint, milp + except ImportError: + raise RuntimeError( + "To use the ILP for memory budget checkpointing you need to install scipy" + ) from None + + np_memory = np.array(memory) + np_runtimes = np.array(runtimes) + c = -np_runtimes # type: ignore[operator] + + memory_constraint = LinearConstraint(A=np_memory, ub=np.array(max_memory)) + constraints = [memory_constraint] + + integrality = np.ones_like(c) + res = milp( + c=c, constraints=constraints, integrality=integrality, bounds=Bounds(0, 1) + ) + if not res.success: + raise RuntimeError("Somehow scipy solving failed") + + items_to_save = [] + items_to_allow_recomputing = [] + for idx, i in enumerate(res.x): + if i == 1: + items_to_save.append(idx) + else: + items_to_allow_recomputing.append(idx) + return -res.fun, items_to_save, items_to_allow_recomputing + + +def dp_knapsack( + memory: List[float], runtimes: List[float], max_memory: float +) -> Tuple[float, List[int], List[int]]: + # Scaling factor to convert floating point weights to integers + S = 10000 + + # Quantize the memory weights + quantized_memory = torch.tensor( + [int(round(m * S)) for m in memory], dtype=torch.long, device="cpu" + ) + runtimes = torch.tensor(runtimes, dtype=torch.float32, device="cpu") + + # Quantized pseudopolynomial DP for 0-1 Knapsack + quantized_max_memory = int(round(max_memory * S)) + + n = len(memory) + + # Initialize the DP table + # TODO(chilli): I think if needed, this memory can be optimized with sliding + # window trick + Hirschberg trick: + # https://codeforces.com/blog/entry/47247?#comment-316200 + dp = torch.zeros( + (n + 1, quantized_max_memory + 1), dtype=torch.float32, device="cpu" + ) + + for i in range(1, n + 1): + current_memory = quantized_memory[i - 1] + current_runtime = runtimes[i - 1] + + # Copy the previous row + dp[i, :] = dp[i - 1, :] + + # Update dp[i, j] for all j >= current_memory + if current_memory == 0: + dp[i, :] = dp[i - 1, :] + current_runtime + else: + dp[i, current_memory:] = torch.maximum( + dp[i - 1, current_memory:], + dp[i - 1, :-current_memory] + current_runtime, + ) + + # Backtrack to find the items included in the knapsack + saved_items = [] + recomputable_items = [] + j: int = quantized_max_memory + for i in range(n, 0, -1): + if dp[i][j] != dp[i - 1][j]: + saved_items.append(i - 1) # Include this item (indexing from 0) + j -= int(quantized_memory[i - 1].item()) + else: + recomputable_items.append(i - 1) + + saved_items.reverse() # To get items in the order they were added + + # The maximum runtime that can be achieved within the max_memory constraint + max_runtime = dp[n][quantized_max_memory].item() + + return max_runtime, saved_items, recomputable_items + + +def _optimize_runtime_with_given_memory( + memory: List[float], + runtimes: List[float], + max_memory: float, +) -> Tuple[float, List[int], List[int]]: + SOLVER = config.activation_memory_budget_solver + if SOLVER == "greedy": + return greedy_knapsack(memory, runtimes, max_memory) + elif SOLVER == "ilp": + return ilp_knapsack(memory, runtimes, max_memory) + elif SOLVER == "dp": + return dp_knapsack(memory, runtimes, max_memory) + else: + raise RuntimeError(f"Not aware of memory budget knapsack solver: {SOLVER}") + + +from torch.utils._mode_utils import no_dispatch + + +def estimate_runtime(node): + RUNTIME_MODE = config.activation_memory_budget_runtime_estimator + + def materialize_arg(x): + if isinstance(x, fx.Node) and isinstance(x.meta["val"], torch.Tensor): + shape = list(x.meta["val"].shape) + + def realize_symbol(d): + return hint_int(d, fallback=4096) + + shape = [realize_symbol(s) for s in shape] + return x.meta["val"].new_empty_strided( + shape, stride=x.meta["tensor_meta"].stride + ) + elif isinstance(x, fx.Node) and isinstance(x.meta["val"], torch.SymInt): + return hint_int(x.meta["val"], fallback=4096) + elif isinstance(x, fx.Node) and isinstance(x.meta["val"], torch.SymFloat): + return 1.0 + elif isinstance(x, fx.Node) and isinstance(x.meta["val"], torch.SymBool): + return True + else: + return x + + if RUNTIME_MODE == "testing": + return 1 + + elif RUNTIME_MODE == "profile": + with no_dispatch(): + from torch._inductor.runtime.benchmarking import benchmarker + + args, kwargs = pytree.tree_map(materialize_arg, (node.args, node.kwargs)) + ms = benchmarker.benchmark_gpu(lambda: node.target(*args, **kwargs)) + return ms + + elif RUNTIME_MODE == "flops": + # todo(chilli): Normalize this to also return ms + from torch.utils.flop_counter import FlopCounterMode + + args, kwargs = pytree.tree_map(materialize_arg, (node.args, node.kwargs)) + with FlopCounterMode(display=False) as mode: + node.target(*args, **kwargs) + counted_flops = mode.get_total_flops() + return max(counted_flops, 1) + else: + raise RuntimeError(f"Not aware of runtime estimator: {RUNTIME_MODE}") + + +def choose_saved_values_set( + joint_graph: fx.Graph, node_info: NodeInfo, memory_budget=1 +) -> List[fx.Node]: + if memory_budget > 1 or memory_budget < 0: + raise RuntimeError( + f"The valid ranges for memory budget are 0 <= m <= 1. The provided value is {memory_budget}" + ) + min_cut_options = MinCutOptions( + ban_if_used_far_apart=config.ban_recompute_used_far_apart, + ban_if_long_fusible_chains=config.ban_recompute_long_fusible_chains, + ban_if_materialized_backward=config.ban_recompute_materialized_backward, + ban_if_not_in_allowlist=config.ban_recompute_not_in_allowlist, + ban_if_reduction=config.ban_recompute_reductions, + ) + + if config.aggressive_recomputation: + min_cut_options = replace( + min_cut_options, + ban_if_used_far_apart=False, + ban_if_long_fusible_chains=False, + ban_if_materialized_backward=False, + ban_if_not_in_allowlist=False, + ) + if memory_budget == 0: + return node_info.inputs + + runtime_optimized_saved_values, _ = solve_min_cut( + joint_graph, + node_info, + min_cut_options, + ) + # return runtime_optimized_saved_values + if memory_budget == 1: + return runtime_optimized_saved_values + + def estimate_activations_size(saved_values: List[fx.Node]) -> float: + return sum(map(_size_of, saved_values)) / 1e9 + + min_act_size = estimate_activations_size(node_info.inputs) + max_act_size = estimate_activations_size(runtime_optimized_saved_values) + # The optimized choice is smaller than the inputs anyways + if max_act_size <= min_act_size: + return runtime_optimized_saved_values + + def get_normalized_size(sz): + return (sz / 1e9) / (max_act_size - min_act_size) + + def get_mem_ratio(activations: List[fx.Node]): + return (estimate_activations_size(activations) - min_act_size) / ( + max_act_size - min_act_size + ) + + more_aggressive_options = replace( + min_cut_options, + ban_if_used_far_apart=False, + ban_if_long_fusible_chains=False, + ban_if_materialized_backward=False, + ) + more_aggressive_saved_values, _ = solve_min_cut( + joint_graph, node_info, more_aggressive_options + ) + if get_mem_ratio(more_aggressive_saved_values) < memory_budget: + return more_aggressive_saved_values + + aggressive_options = replace( + more_aggressive_options, + ban_if_not_in_allowlist=False, + ) + aggressive_recomputation_saved_values, banned_nodes = solve_min_cut( + joint_graph, node_info, aggressive_options + ) + + if get_mem_ratio(aggressive_recomputation_saved_values) < memory_budget: + return aggressive_recomputation_saved_values + + from torch._inductor.fx_utils import get_node_storage + + input_storages = {get_node_storage(node) for node in node_info.inputs} + + def get_recomputable_banned_nodes(banned_nodes: List[fx.Node]) -> List[fx.Node]: + return [ + i + for i in banned_nodes + if ( + # Only allow recomputing nodes that are actually required for BW + i.dist_from_bw < int(1e9) # type: ignore[attr-defined] + and get_node_storage(i) not in input_storages + ) + ] + + recomputable_banned_nodes = get_recomputable_banned_nodes(banned_nodes) + + # default: runtime_optimized_saved_values + # more aggressive: more_aggressive_saved_values + # full aggressive: aggressive_recomputation_saved_values + + all_recomputable_banned_nodes = sorted( + recomputable_banned_nodes, key=_size_of, reverse=True + ) + if len(all_recomputable_banned_nodes) == 0: + return node_info.inputs + memories_banned_nodes = [ + get_normalized_size(_size_of(i)) for i in all_recomputable_banned_nodes + ] + runtimes_banned_nodes = [ + estimate_runtime(node) for node in all_recomputable_banned_nodes + ] + from torch.utils._mode_utils import no_dispatch + + def get_saved_values_knapsack(memory_budget): + with no_dispatch(): + ( + expected_runtime, + saved_node_idxs, + recomputable_node_idxs, + ) = _optimize_runtime_with_given_memory( + memories_banned_nodes, runtimes_banned_nodes, max(memory_budget, 0) + ) + dont_ban = set() + for idx in recomputable_node_idxs: + dont_ban.add(all_recomputable_banned_nodes[idx]) + assert dont_ban.issubset(all_recomputable_banned_nodes) + + saved_values, _ = solve_min_cut( + joint_graph, + node_info, + aggressive_options, + dont_ban, + ) + return saved_values, expected_runtime + + if config.visualize_memory_budget_pareto: + options = [] + for sweep_memory_budget in range(100, -1, -5): + saved_values, expected_runtime = get_saved_values_knapsack( + sweep_memory_budget / 100 + ) + options.append( + ( + sweep_memory_budget, + sum(runtimes_banned_nodes) - expected_runtime, + get_mem_ratio(saved_values), + ) + ) + + import matplotlib.pyplot as plt + + x_values = [item[2] for item in options] + y_values = [item[1] for item in options] + + # Plotting the values with updated axis labels and chart title + plt.figure(figsize=(10, 6)) + plt.plot(x_values, y_values, marker="o") + + # Adding labels for each point + for i, txt in enumerate(x_values): + plt.annotate( + f"{txt:.2f}", + (txt, y_values[i]), + textcoords="offset points", + xytext=(0, 10), + ha="center", + ) + + plt.xlabel("Memory Budget") + plt.ylabel("Runtime of Recomputed Components") + plt.title("Pareto Frontier of Memory Budget vs. Recomputation Runtime") + plt.grid(True) + fig = plt.gcf() + plt.show() + fig_name = f"memory_budget_pareto_{get_aot_graph_name()}.png" + fig.savefig(fig_name) + log.warning("Generated Pareto frontier curve at %s", fig_name) + + # todo(chilli): Estimated doesn't align exactly with actual - actual is + # usually less memory than estimated. i'm guessing (actually quite + # unsure about this) that's because estimated is just only including + # tensors we actually banned from recompute, but there may be other + # tensors that we choose to save. + + return get_saved_values_knapsack(memory_budget=memory_budget)[0] + + +def min_cut_rematerialization_partition( + joint_module: fx.GraphModule, + _joint_inputs, + compiler="inductor", + *, + num_fwd_outputs, +) -> Tuple[fx.GraphModule, fx.GraphModule]: + """ + Partitions the joint graph such that the backward recomputes the forward. + Recomputing helps in trading off memory bandwidth with computation. + + To create the fwd and bwd graph, we copy the joint graph, manually set the + outputs to just original forward or backward outputs. And then we run the + resulting graphs through dead code elimination. + + .. warning:: + This API is experimental and likely to change. + + Args: + joint_module(fx.GraphModule): The joint forward and backward graph. This + is the result of AOT Autograd tracing. + _joint_inputs: The inputs to the joint graph. This is unused. + compiler: This option determines the default set of recomputable ops. + Currently, there are two options: ``nvfuser`` and ``inductor``. + recomputable_ops: This is an optional set of recomputable ops. If this + is not None, then this set of ops will be used instead of the + default set of ops. + num_fwd_outputs: The number of outputs from the forward graph. + + Returns: + Returns the generated forward and backward Fx graph modules. + """ + + joint_module.graph.eliminate_dead_code() + joint_module.recompile() + + fx_g = joint_module.graph + + # add the CSE pass + if config.cse: + cse_graph = fx_graph_cse(fx_g) + joint_module.graph = cse_graph + joint_graph = joint_module.graph + + graph_has_recomputable_ops = has_recomputable_ops(joint_module) + graph_has_recomputable_rng_ops = has_recomputable_rng_ops(joint_module) + if graph_has_recomputable_ops: + joint_module = cleanup_recompute_tags(joint_module) + + def classify_nodes(joint_module): + name_to_node = get_name_to_node(joint_module.graph) + required_bw_nodes = set() + for node in joint_module.graph.nodes: + if node.op == "placeholder" and "tangents" in node.target: + required_bw_nodes.add(node) + elif _must_be_in_backward(node): + required_bw_nodes.add(node) + + if node in required_bw_nodes: + for user in node.users: + required_bw_nodes.add(user) + + primal_inputs = list(filter(_is_primal, joint_module.graph.nodes)) + fwd_seed_offset_inputs = list( + filter(_is_fwd_seed_offset, joint_module.graph.nodes) + ) + inputs = primal_inputs + fwd_seed_offset_inputs + fwd_outputs, bwd_outputs = _extract_fwd_bwd_outputs( + joint_module, num_fwd_outputs=num_fwd_outputs + ) + required_bw_nodes.update( + o for o in bwd_outputs if o is not None and o.op != "output" + ) + forward_only_graph = _extract_graph_with_inputs_outputs( + joint_module.graph, inputs, fwd_outputs, "forward" + ) + required_fw_nodes: Set[fx.Node] = { + name_to_node[node.name] + for node in forward_only_graph.nodes + if node.op != "output" + } + unclaimed_nodes = { + node + for node in joint_module.graph.nodes + if node not in required_fw_nodes and node not in required_bw_nodes + } + fw_cnt = 0 + fw_order = {} + for node in joint_module.graph.nodes: + if node in required_fw_nodes: + fw_order[node] = fw_cnt + fw_cnt += 1 + return NodeInfo( + inputs, required_fw_nodes, required_bw_nodes, unclaimed_nodes, fw_order + ) + + node_info = classify_nodes(joint_module) + + # networkx blows up on graphs with no required backward nodes + # Since there's nothing to partition anyway, and the default partitioner can "handle" + # this case, send our graph over to the default partitioner. + if len(node_info.required_bw_nodes) == 0: + return default_partition( + joint_module, _joint_inputs, num_fwd_outputs=num_fwd_outputs + ) + + for node in reversed(joint_module.graph.nodes): + if node.op == "output": + node.dist_from_bw = int(1e9) + elif not node_info.is_required_fw(node): + node.dist_from_bw = 0 + else: + node.dist_from_bw = int(1e9) + for user in node.users: + node.dist_from_bw = min(node.dist_from_bw, user.dist_from_bw + 1) + + memory_budget = config.activation_memory_budget + for node in joint_graph.nodes: + if isinstance(node.meta.get("memory_budget", None), float): + memory_budget = node.meta["memory_budget"] + break + # print("Memory Budget: ", memory_budget) + saved_values = choose_saved_values_set( + joint_graph, node_info, memory_budget=memory_budget + ) + # save_for_backward on tensors and stashes symints in autograd .ctx + saved_sym_nodes = list(filter(is_sym_node, saved_values)) + saved_values = list(filter(lambda n: not is_sym_node(n), saved_values)) + + # NB: saved_sym_nodes will be mutated to reflect the actual saved symbols + fw_module, bw_module = _extract_fwd_bwd_modules( + joint_module, + saved_values, + saved_sym_nodes=saved_sym_nodes, + num_fwd_outputs=num_fwd_outputs, + ) + + if graph_has_recomputable_ops: + if graph_has_recomputable_rng_ops: + fw_module, bw_module = functionalize_rng_ops( + joint_module, fw_module, bw_module, len(saved_sym_nodes) + ) + bw_module = reordering_to_mimic_autograd_engine(bw_module) + + if AOT_PARTITIONER_DEBUG: + from torch._inductor.fx_utils import get_node_storage + + storages = {get_node_storage(node) for node in saved_values} + print( + "Theoretical Activations Stored: ", + sum(_size_of(i) for i in saved_values) / 1e9, + ) + sorted_sizes = sorted([(_size_of(i), str(i)) for i in saved_values]) + fw_module_nodes = { + node.name for node in fw_module.graph.nodes if node.op == "call_function" + } + bw_module_nodes = { + node.name for node in bw_module.graph.nodes if node.op == "call_function" + } + remat_nodes = fw_module_nodes & bw_module_nodes + + counts: Dict[str, int] = defaultdict(int) + for node in fw_module.graph.nodes: + if node.name in remat_nodes and hasattr(node.target, "_overloadpacket"): + counts[str(node.target._overloadpacket)] += 1 + print( + f"# remat/fw/bw: {len(remat_nodes)}/{len(fw_module_nodes)}/{len(bw_module_nodes)}" + ) + print( + "Count of Ops Rematerialized: ", + sorted(counts.items(), key=lambda x: x[1], reverse=True), + ) + return fw_module, bw_module + + +def draw_graph( + traced: torch.fx.GraphModule, + fname: str, + figname: str = "fx_graph", + clear_meta: bool = True, + prog: Optional[Union[str, List[str]]] = None, + parse_stack_trace: bool = False, + dot_graph_shape: Optional[str] = None, +) -> None: + if clear_meta: + new_graph = copy.deepcopy(traced.graph) + traced = fx.GraphModule(traced, new_graph) + for node in traced.graph.nodes: + node.meta = {} + base, ext = os.path.splitext(fname) + if not ext: + ext = "." + config.torch_compile_graph_format + print(f"Writing FX graph to file: {base}{ext}") + g = graph_drawer.FxGraphDrawer( + traced, + figname, + parse_stack_trace=parse_stack_trace, + dot_graph_shape=dot_graph_shape, + ) + x = g.get_main_dot_graph() + write_method = getattr(x, "write_" + ext.lstrip(".")) + fname = f"{base}{ext}" + if prog is None: + write_method(fname) + else: + write_method(fname, prog=prog) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/pyfunctorch.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/pyfunctorch.py new file mode 100644 index 0000000000000000000000000000000000000000..b2dfaa116f72924c68351aadcb9e007886343faa --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/pyfunctorch.py @@ -0,0 +1,294 @@ +# mypy: allow-untyped-defs +import contextlib +from abc import ABC, abstractmethod +from typing import Any, List, Tuple + +import torch +import torch.utils._pytree as pytree +from torch._C._functorch import ( + CFunctionalizeInterpreterPtr, + CGradInterpreterPtr, + CInterpreter, + CJvpInterpreterPtr, + CVmapInterpreterPtr, + pop_dynamic_layer_stack, + push_dynamic_layer_stack, + RandomnessType, + TransformType, +) +from torch.autograd.forward_ad import _set_fwd_grad_enabled + + +""" +This file contains the functorch integration with PyDispatcher. + +PyDispatcher does not understand functorch's DynamicLayerStack dispatching +logic because it is entirely implemented in C++ in the fallbacks for two +dispatch keys, FuncTorchDynamicLayer{Front, Back}Mode (PyDispatcher is unable +to directly reuse C++ boxed fallbacks). + +Instead of trying to hammer PyDispatcher into understanding those fallbacks, +we re-implement the logic of peeking the top of the stack for an interpreter, +selecting the interpreter to dispatch on, etc, in Python. This leads to a +simpler design. + +The main difference between C++ functorch and PyDispatcher's functorch logic +is that: +- C++ functorch needs to manually tweak dispatch keys to ping-pong between + DynamicLayerFrontMode and DynamicLayerBackMode. +- PyDispatcher's functorch logic pops an Interpreter from the top of the stack + and asks it to execute the rule associated with the Interpreter. + +In C++ we do the ping-pong because e.g. vmap rules are associated with the +batched DispatchKey, but in PyDispatcher we are able to avoid this by asking +the user to register a batching rule directly to a transform that an +interpreter then invokes. +""" + + +# FuncTorchInterpreter is the Python version of Interpreter (recall that +# the DynamicLayerStack is a stack of interpreters). +# It is a wrapper around the actual C++ Interpreter object. +# +# Keep the methods in sync with aten/src/ATen/functorch/Interpreter.h +class FuncTorchInterpreter(ABC): + def __init__(self, cptr: Any): + self._cptr = cptr + + # Process an operation. eg for vmap, this is invoking a batching rule. + # Conceptually this is analogous to Interpreter::process in C++ + @abstractmethod + def process(self, op, args, kwargs): + pass + + # lower an operation from this Interpreter to the next Interpreter on the stack. + # Concretely, this involves temporarily popping the current Interpreter. + # Conceptually this is analogous to Interpreter::sendToNextInterpreter in C++ + def lower(self): + return temporarily_pop_interpreter_stack() + + def level(self): + return self._cptr.level() + + def key(self): + return self._cptr.key() + + def get_state(self): + raise NotImplementedError + + def check_state(self, state): + return state == self.get_state() + + +@contextlib.contextmanager +def temporarily_pop_interpreter_stack(): + try: + saved = pop_dynamic_layer_stack() + yield + finally: + push_dynamic_layer_stack(saved) + + +@contextlib.contextmanager +def temporarily_clear_interpreter_stack(): + stack = [] + try: + while torch._C._functorch.peek_interpreter_stack() is not None: + stack.append(pop_dynamic_layer_stack()) + yield list(stack) + finally: + while stack: + push_dynamic_layer_stack(stack.pop()) + + +@contextlib.contextmanager +def temporarily_restore_interpreter_stack(stack): + pushed = [] + try: + for s in reversed(stack): + push_dynamic_layer_stack(s) + pushed.append(s) + yield + finally: + for s in reversed(pushed): + # TODO: would be nice to assert that the layers are the same, but + # Python object identity is not preserved + pop_dynamic_layer_stack() + + +class VmapInterpreter(FuncTorchInterpreter): + def __init__(self, cdata: CInterpreter): + assert cdata.key() == TransformType.Vmap + # NOTE: [Interpreter cdata vs cptr] + # cdata is a generic CInterpreter. We wrap it in a CVmapInterpreterPtr + # so that we can access methods specific to the vmap interpreter + self._cdata = cdata + self._cptr = CVmapInterpreterPtr(cdata) + + def process(self, op, args, kwargs): + kernel = op.functorch_table[TransformType.Vmap] + return kernel(self, *args, **kwargs) + + def batch_size(self): + return self._cptr.batchSize() + + def randomness(self): + typ = self._cptr.randomness() + if typ == RandomnessType.Error: + return "error" + elif typ == RandomnessType.Same: + return "same" + elif typ == RandomnessType.Different: + return "different" + raise RuntimeError(f"Unknown RandomnessType: {typ}") + + def get_state(self): + return (self.key().name, self.level(), self.randomness()) + + +@contextlib.contextmanager +def nested(*contexts): + with contextlib.ExitStack() as stack: + for ctx in contexts: + stack.enter_context(ctx) + yield contexts + + +class GradInterpreter(FuncTorchInterpreter): + def __init__(self, cdata: CInterpreter): + assert cdata.key() == TransformType.Grad + # See NOTE: [Interpreter cdata vs cptr] + self._cdata = cdata + self._cptr = CGradInterpreterPtr(cdata) + + def lift(self, args, kwargs): + args, kwargs = pytree.tree_map_only( + torch.Tensor, self._cptr.lift, [args, kwargs] + ) + return args, kwargs + + def process(self, op, args, kwargs): + kernel = op.functorch_table[TransformType.Grad] + args, kwargs = self.lift(args, kwargs) + return kernel(self, *args, **kwargs) + + # GradInterpreter has custom lower because of the no_grad interaction + # See NOTE [grad and vjp interaction with no_grad] + # This logic is mirrored from C++ GradInterpreterPtr::sendToNextInterpreter + def lower(self): + prev_grad_mode = self.prev_grad_mode() + if not prev_grad_mode: + return nested(torch.no_grad(), super().lower()) + return super().lower() + + def prev_grad_mode(self): + return self._cptr.prevGradMode() + + def get_state(self): + return (self.key().name, self.level(), self.prev_grad_mode()) + + +class JvpInterpreter(FuncTorchInterpreter): + def __init__(self, cdata: CInterpreter): + assert cdata.key() == TransformType.Jvp + # See NOTE: [Interpreter cdata vs cptr] + self._cdata = cdata + self._cptr = CJvpInterpreterPtr(cdata) + + def lift(self, args, kwargs): + args, kwargs = pytree.tree_map_only( + torch.Tensor, self._cptr.lift, [args, kwargs] + ) + return args, kwargs + + def process(self, op, args, kwargs): + kernel = op.functorch_table[TransformType.Jvp] + args, kwargs = self.lift(args, kwargs) + return kernel(self, *args, **kwargs) + + # Jvp has custom lower because of the no_fwd_grad interaction + # See NOTE [grad and vjp interaction with no_grad] for related info. + # This logic is mirrored from C++ JvpInterpreterPtr::sendToNextInterpreter + def lower(self): + prev_fwd_grad_mode = self.prev_fwd_grad_mode() + if not prev_fwd_grad_mode: + return nested(_set_fwd_grad_enabled(False), super().lower()) + return super().lower() + + def prev_fwd_grad_mode(self): + return self._cptr.prevFwdGradMode() + + def get_state(self): + return (self.key().name, self.level(), self.prev_fwd_grad_mode()) + + +class FunctionalizeInterpreter(FuncTorchInterpreter): + def __init__(self, cdata: CInterpreter): + assert cdata.key() == TransformType.Functionalize + self._cdata = cdata + self._cptr = CFunctionalizeInterpreterPtr(cdata) + + def process(self, op, args, kwargs): + kernel = op.functorch_table[TransformType.Functionalize] + return kernel(self, *args, **kwargs) + + def functionalize_add_back_views(self): + return self._cptr.functionalizeAddBackViews() + + def get_state(self): + return (self.key().name, self.level()) + + +def coerce_cinterpreter(cinterpreter: CInterpreter) -> FuncTorchInterpreter: + key = cinterpreter.key() + if key == TransformType.Grad: + return GradInterpreter(cinterpreter) + if key == TransformType.Vmap: + return VmapInterpreter(cinterpreter) + if key == TransformType.Jvp: + return JvpInterpreter(cinterpreter) + if key == TransformType.Functionalize: + return FunctionalizeInterpreter(cinterpreter) + raise RuntimeError(f"NYI: PyDispatcher has not implemented support for {key}") + + +def retrieve_current_functorch_interpreter() -> FuncTorchInterpreter: + interpreter = torch._C._functorch.peek_interpreter_stack() + assert interpreter is not None + return coerce_cinterpreter(interpreter) + + +def retrieve_all_functorch_interpreters() -> List[FuncTorchInterpreter]: + cis = torch._C._functorch.get_interpreter_stack() + if cis is None: + return [] + return [coerce_cinterpreter(ci) for ci in cis] + + +def compare_functorch_state(states: List[Tuple[Any, ...]]) -> bool: + # There are four possible cases covered here: + # 1. Current stack empty AND stack when generated not empty -> Invalidate + # 2. Current stack not empty AND stack when generated empty -> Invalidate + # 3. Current stack and generated stack empty -> Valid FX graph + # 4. Current stack and generated stack not empty -> Valid if both states match + peek = torch._C._functorch.peek_interpreter_stack() + if (peek is None and len(states) != 0) or (peek is not None and len(states) == 0): + return False + + cis = retrieve_all_functorch_interpreters() + return len(cis) == len(states) and all( + ci.check_state(state) for ci, state in zip(cis, states) + ) + + +def dispatch_functorch(op, args, kwargs): + interpreter = retrieve_current_functorch_interpreter() + # In traditional PyTorch operators, DispatchKey::FuncTorchTensorWrapper's + # unwrap_dead_tensors fallback handles unwrapping dead tensor wrappers. + # PyDispatcher sidesteps the PyTorch dispatcher when dealing with functorch + # transforms, so we manually unwrap the dead tensors here. + # This logic won't need to exist when we have mode-only functorch. + args, kwargs = pytree.tree_map_only( + torch.Tensor, torch._C._functorch.unwrap_if_dead, (args, kwargs) + ) + return interpreter.process(op, args, kwargs) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/python_key.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/python_key.py new file mode 100644 index 0000000000000000000000000000000000000000..557334f68928a057a6a9e036c904c8c0bd3231c1 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/python_key.py @@ -0,0 +1,15 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. +__all__ = ["make_fx", "dispatch_trace", "PythonKeyTracer", "pythonkey_decompose"] +from torch.fx.experimental.proxy_tensor import ( + decompose, + dispatch_trace, + make_fx, + PythonKeyTracer, +) + + +pythonkey_decompose = decompose diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/pytree_hacks.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/pytree_hacks.py new file mode 100644 index 0000000000000000000000000000000000000000..96dea7ad100705ae53139aa5ae729fd2206182af --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/pytree_hacks.py @@ -0,0 +1,23 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +import warnings + +# TODO: remove this file when the migration of the pytree utility is done +from torch.utils._pytree import tree_map_, treespec_pprint + + +__all__ = ["tree_map_", "treespec_pprint"] + + +with warnings.catch_warnings(): + warnings.simplefilter("always") + warnings.warn( + "`torch._functorch.pytree_hacks` is deprecated and will be removed in a future release. " + "Please `use torch.utils._pytree` instead.", + DeprecationWarning, + stacklevel=2, + ) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/top_operators_github_usage.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/top_operators_github_usage.py new file mode 100644 index 0000000000000000000000000000000000000000..1fcdbe0b41ac00c21198e3a82d58c5a446d19324 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/top_operators_github_usage.py @@ -0,0 +1,632 @@ +# mypy: ignore-errors + +""" +From https://docs.google.com/spreadsheets/d/12R3nCOLskxPYjjiNkdqy4OdQ65eQp_htebXGODsjSeA/edit#gid=0 +Try to keep this list in sync with that. +""" +import operator + + +top_torch = [ + ("t", 6837449), + ("tensor", 585786), + ("mode", 462182), + ("cat", 394818), + ("max", 368038), + ("zeros", 329495), + ("load", 327756), + ("no_grad", 294694), + ("save", 265130), + ("from_numpy", 243063), + ("manual_seed", 165044), + ("ones", 153696), + ("randn", 150796), + ("stack", 133358), + ("sum", 130772), + ("arange", 98087), + ("rand", 94715), + ("mean", 88546), + ("exp", 73883), + ("zeros_like", 72831), + ("min", 72248), + ("sigmoid", 66798), + ("log", 62135), + ("matmul", 47811), + ("clamp", 45304), + ("sqrt", 44911), + ("abs", 43535), + ("tanh", 42793), + ("empty", 40311), + ("argmax", 38435), + ("bmm", 33984), + ("pow", 33571), + ("norm", 31125), + ("mm", 30995), + ("is_tensor", 29546), + ("ones_like", 29512), + ("nonzero", 28681), + ("full", 28373), + ("unsqueeze", 27911), + ("where", 26585), + ("randperm", 26450), + ("eye", 24342), + ("mul", 23236), + ("topk", 22537), + ("as_tensor", 21967), + ("sort", 21412), + ("squeeze", 20863), + ("randint", 20771), + ("linspace", 20041), + ("add", 19201), + ("transpose", 18663), + ("split", 18325), + ("gather", 17904), + ("set_grad_enabled", 16013), + ("sin", 15669), + ("cos", 15562), + ("div", 15513), + ("index_select", 14866), + ("multinomial", 14331), + ("flatten", 14267), + ("isnan", 14170), + ("randn_like", 13096), + ("eq", 12680), + ("einsum", 12480), + ("round", 12367), + ("floor", 11628), + ("allclose", 11000), + ("reshape", 10605), + ("diag", 10167), + ("chunk", 9581), + ("std", 9379), + ("set_default_tensor_type", 9281), + ("triu", 8559), + ("meshgrid", 8292), + ("set_num_threads", 8126), + ("unique", 7964), + ("full_like", 7780), + ("tril", 7538), + ("dot", 7275), + ("sign", 6943), + ("equal", 6916), + ("normal", 6750), + ("cumsum", 6556), + ("dist", 6058), + ("isfinite", 6030), + ("gt", 5935), + ("set_printoptions", 5888), + ("range", 5491), + ("empty_like", 5351), + ("flip", 5342), + ("masked_select", 5341), + ("bernoulli", 5262), + ("atan", 5253), + ("var", 5247), + ("prod", 5200), + ("erf", 5088), + ("inverse", 5072), + ("addmm", 4854), + ("logsumexp", 4582), + ("fft", 4436), + ("lt", 4421), + ("log2", 4316), + ("enable_grad", 4238), + ("rand_like", 4187), + ("argsort", 3972), + ("seed", 3932), + ("mv", 3547), + ("ger", 3309), + ("ge", 3248), + ("atan2", 3210), + ("ceil", 3202), + ("ne", 3075), + ("bincount", 3063), + ("acos", 3055), + ("rsqrt", 3031), + ("svd", 3029), + ("numel", 3003), + ("log1p", 2840), + ("unbind", 2808), + ("le", 2714), + ("isinf", 2707), + ("cross", 2646), + ("set_default_dtype", 2536), + ("argmin", 2535), + ("sparse_coo_tensor", 2489), + ("log10", 2304), + ("kthvalue", 2192), + ("set_rng_state", 2158), + ("get_rng_state", 1996), + ("get_default_dtype", 1879), + ("det", 1868), + ("qr", 1864), + ("histc", 1852), + ("symeig", 1832), + ("trace", 1801), + ("median", 1795), + ("addcmul", 1751), + ("remainder", 1717), + ("baddbmm", 1693), + ("lgamma", 1665), + ("repeat_interleave", 1598), + ("fmod", 1576), + ("reciprocal", 1575), + ("tan", 1560), + ("initial_seed", 1532), + ("take", 1529), + ("stft", 1487), + ("get_num_threads", 1477), + ("real", 1459), + ("cholesky", 1406), + ("quantize_per_tensor", 1392), + ("diag_embed", 1364), + ("lerp", 1363), + ("asin", 1345), + ("eig", 1333), + ("trunc", 1290), + ("diagonal", 1287), + ("cosh", 1279), + ("rfft", 1269), + ("cumprod", 1260), + ("addr", 1211), + ("roll", 1198), + ("narrow", 1188), + ("digamma", 1172), + ("square", 1163), + ("sinh", 1131), + ("logspace", 1084), + ("broadcast_tensors", 1070), + ("irfft", 1013), + ("frac", 997), + ("hann_window", 994), + ("solve", 989), + ("logdet", 977), + ("expm1", 968), + ("cdist", 946), + ("addmv", 903), + ("randint_like", 888), + ("tensordot", 888), + ("ifft", 877), + ("true_divide", 854), + ("erfinv", 830), + ("addcdiv", 819), + ("addbmm", 813), + ("renorm", 781), + ("pinverse", 753), + ("isclose", 740), + ("erfc", 729), + ("is_storage", 725), + ("triangular_solve", 723), + ("rot90", 709), + ("logical_not", 686), + ("geqrf", 681), + ("slogdet", 677), + ("lu", 665), + ("hamming_window", 659), + ("orgqr", 651), + ("ormqr", 622), + ("is_floating_point", 602), + ("diagflat", 562), + ("cholesky_solve", 559), + ("tril_indices", 552), + ("chain_matmul", 551), + ("triu_indices", 548), + ("angle", 522), + ("poisson", 505), + ("matrix_power", 485), + ("unique_consecutive", 471), + ("quantize_per_channel", 465), + ("std_mean", 458), + ("bartlett_window", 447), + ("var_mean", 428), + ("lstsq", 421), + ("logical_and", 419), + ("mvlgamma", 411), + ("blackman_window", 400), + ("bitwise_not", 395), + ("cholesky_inverse", 388), + ("as_strided", 384), + ("floor_divide", 353), + ("cartesian_prod", 321), + ("lu_solve", 317), + ("set_flush_denormal", 310), + ("empty_strided", 283), + ("logical_xor", 282), + ("polygamma", 282), + ("logical_or", 280), + ("set_num_interop_threads", 278), + ("combinations", 274), + ("trapz", 270), + ("matrix_rank", 260), + ("lu_unpack", 255), + ("result_type", 244), + ("conj", 231), + ("cummax", 230), + ("lobpcg", 229), + ("bitwise_xor", 217), + ("promote_types", 213), + ("get_num_interop_threads", 211), + ("cummin", 205), + ("bitwise_and", 198), + ("dequantize", 192), + ("bitwise_or", 191), + ("imag", 191), + ("can_cast", 184), + ("istft", 180), + ("compiled_with_cxx11_abi", 159), + ("is_complex", 151), + ("block_diag", 136), + ("pca_lowrank", 124), + ("absolute", 122), + ("svd_lowrank", 108), + ("neg", 2), +] + +top_nn_functional = [ + ("nn.functional.softmax", 10522), + ("nn.functional.relu", 8572), + ("nn.functional.interpolate", 7277), + ("nn.functional.pad", 5207), + ("nn.functional.log_softmax", 4699), + ("nn.functional.normalize", 2338), + ("nn.functional.cross_entropy", 2083), + ("nn.functional.grid_sample", 1970), + ("nn.functional.one_hot", 1967), + ("nn.functional.mse_loss", 1920), + ("nn.functional.conv2d", 1593), + ("nn.functional.dropout", 1516), + ("nn.functional.softplus", 1385), + ("nn.functional.sigmoid", 1128), + ("nn.functional.linear", 1036), + ("nn.functional.gelu", 930), + ("nn.functional.avg_pool2d", 899), + ("nn.functional.max_pool2d", 876), + ("nn.functional.nll_loss", 863), + ("nn.functional.embedding", 737), + ("nn.functional.tanh", 664), + ("nn.functional.leaky_relu", 640), + ("nn.functional.adaptive_avg_pool2d", 633), + ("nn.functional.cosine_similarity", 627), + ("nn.functional.unfold", 609), + ("nn.functional.conv1d", 596), + ("nn.functional.binary_cross_entropy_with_logits", 591), + ("nn.functional.l1_loss", 571), + ("nn.functional.binary_cross_entropy", 492), + ("nn.functional.elu", 416), + ("nn.functional.batch_norm", 413), + ("nn.functional.upsample", 413), + ("nn.functional.fold", 305), + ("nn.functional.affine_grid", 298), + ("nn.functional.max_pool1d", 297), + ("nn.functional.torch", 294), + ("nn.functional.threshold", 263), + ("nn.functional.smooth_l1_loss", 262), + ("nn.functional.pairwise_distance", 253), + ("nn.functional.logsigmoid", 243), + ("nn.functional.adaptive_max_pool2d", 235), + ("nn.functional.relu6", 213), + ("nn.functional.pixel_shuffle", 209), + ("nn.functional.avg_pool3d", 203), + ("nn.functional.bilinear", 203), + ("nn.functional.conv_transpose2d", 201), + ("nn.functional.gumbel_softmax", 197), + ("nn.functional.max_unpool2d", 196), + ("nn.functional.kl_div", 191), + ("nn.functional.hardtanh", 189), + ("nn.functional.ctc_loss", 185), + ("nn.functional.layer_norm", 178), + ("nn.functional.conv3d", 172), + ("nn.functional.max_unpool3d", 167), + ("nn.functional.hardshrink", 165), + ("nn.functional.hardswish", 156), + ("nn.functional.selu", 156), + ("nn.functional.glu", 155), + ("nn.functional.assert_int_or_pair", 150), + ("nn.functional.hardsigmoid", 146), + ("nn.functional.upsample_bilinear", 146), + ("nn.functional.max_pool3d", 140), + ("nn.functional.adaptive_avg_pool3d", 139), + ("nn.functional.instance_norm", 124), + ("nn.functional.embedding_bag", 122), + ("nn.functional.upsample_nearest", 110), + ("nn.functional.avg_pool1d", 105), + ("nn.functional.prelu", 102), + ("nn.functional.celu", 92), + ("nn.functional.dropout2d", 86), + ("nn.functional.hinge_embedding_loss", 82), + ("nn.functional.softsign", 81), + ("nn.functional.max_unpool1d", 74), + ("nn.functional.silu", 74), + ("nn.functional.softshrink", 70), + ("nn.functional.leaky_relu_", 68), + ("nn.functional.softmin", 67), + ("nn.functional.channel_shuffle", 66), + ("nn.functional.multilabel_margin_loss", 66), + ("nn.functional.dropout3d", 65), + ("nn.functional.multi_margin_loss", 65), + ("nn.functional.lp_pool2d", 64), + ("nn.functional.conv_transpose1d", 62), + ("nn.functional.triplet_margin_loss", 62), + ("nn.functional.tanhshrink", 61), + ("nn.functional.adaptive_max_pool1d", 59), + ("nn.functional.cosine_embedding_loss", 58), + ("nn.functional.multi_head_attention_forward", 58), + ("nn.functional.max_pool1d_with_indices", 53), + ("nn.functional.poisson_nll_loss", 53), + ("nn.functional.margin_ranking_loss", 52), + ("nn.functional.soft_margin_loss", 52), + ("nn.functional.adaptive_max_pool3d", 51), + ("nn.functional.group_norm", 51), + ("nn.functional.local_response_norm", 51), + ("nn.functional.multilabel_soft_margin_loss", 51), + ("nn.functional.relu_", 50), + ("nn.functional.alpha_dropout", 49), + ("nn.functional.feature_alpha_dropout", 49), + ("nn.functional.lp_pool1d", 49), + ("nn.functional.adaptive_max_pool1d_with_indices", 48), + ("nn.functional.adaptive_max_pool2d_with_indices", 48), + ("nn.functional.adaptive_max_pool3d_with_indices", 48), + ("nn.functional.fractional_max_pool2d", 48), + ("nn.functional.fractional_max_pool2d_with_indices", 48), + ("nn.functional.fractional_max_pool3d", 48), + ("nn.functional.fractional_max_pool3d_with_indices", 48), + ("nn.functional.max_pool2d_with_indices", 48), + ("nn.functional.max_pool3d_with_indices", 48), + ("nn.functional.handle_torch_function", 47), + ("nn.functional.has_torch_function", 47), + ("nn.functional.adaptive_avg_pool1d", 43), + ("nn.functional.pdist", 43), + ("nn.functional.rrelu_", 37), + ("nn.functional.elu_", 34), + ("nn.functional.boolean_dispatch", 33), + ("nn.functional.hardtanh_", 26), + ("nn.functional.triplet_margin_with_distance_loss", 23), + ("nn.functional.selu_", 20), + ("nn.functional.pixel_unshuffle", 19), + ("nn.functional.conv_transpose3d", 18), + ("nn.functional.gaussian_nll_loss", 15), + ("nn.functional.has_torch_function_unary", 15), + ("nn.functional.has_torch_function_variadic", 15), + ("nn.functional.celu_", 13), + ("nn.functional.huber_loss", 7), + ("nn.functional.mish", 4), + ("nn.functional.threshold_", 3), + ("nn.functional.grad", 2), + ("nn.functional.conv_tbc", 1), + ("nn.functional.math", 1), +] + +top_nn_module = [ + ("nn.Module", 927129, None), + ("nn.Linear", 530688, "nn.functional.linear"), + ("nn.Sequential", 384968, None), + ("nn.Conv2d", 383320, "nn.functional.conv2d"), + ("nn.ReLU", 318877, "nn.functional.relu"), + ("nn.BatchNorm2d", 233265, "nn.functional.batch_norm"), + ("nn.Dropout", 179268, "nn.functional.dropout"), + ("nn.ModuleList", 171225, None), + ("nn.Parameter", 153291, None), + ("nn.CrossEntropyLoss", 152696, "nn.functional.cross_entropy"), + ("nn.MaxPool2d", 138619, "nn.functional.max_pool2d"), + ("nn.Embedding", 111844, "nn.functional.embedding"), + ("nn.DataParallel", 104238, None), + ("nn.MSELoss", 82954, "nn.functional.mse_loss"), + ("nn.Sigmoid", 75810, "nn.functional.sigmoid"), + ("nn.LeakyReLU", 65632, "nn.functional.leaky_relu"), + ("nn.BatchNorm1d", 65374, "nn.functional.batch_norm"), + ("nn.Softmax", 65114, "nn.functional.softmax"), + ("nn.Tanh", 59445, "nn.functional.tanh"), + ("nn.AdaptiveAvgPool2d", 59071, "nn.functional.adaptive_avg_pool2d"), + ("nn.AvgPool2d", 58377, "nn.functional.avg_pool2d"), + ("nn.ConvTranspose2d", 57524, "nn.functional.conv_transpose2d"), + ("nn.LSTM", 57411, None), + ("nn.Conv1d", 41108, "nn.functional.conv1d"), + ("nn.LayerNorm", 36089, "nn.functional.layer_norm"), + ("nn.BCELoss", 34005, "nn.functional.binary_cross_entropy"), + ("nn.Upsample", 32527, "nn.functional.interpolate"), + ("nn.BCEWithLogitsLoss", 29944, "nn.functional.binary_cross_entropy_with_logits"), + ("nn.GRU", 25421, None), + ("nn.Dropout2d", 23512, "nn.functional.dropout2d"), + ("nn.LogSoftmax", 22897, "nn.functional.log_softmax"), + ("nn.L1Loss", 22778, "nn.functional.l1_loss"), + ("nn.GroupNorm", 22183, "nn.functional.group_norm"), + ("nn.NLLLoss", 21751, "nn.functional.nll_loss"), + ("nn.Conv3d", 20874, "nn.functional.conv3d"), + ("nn.Identity", 17911, None), + ("nn.InstanceNorm2d", 16426, "nn.functional.instance_norm"), + ("nn.BatchNorm3d", 16378, "nn.functional.batch_norm"), + ("nn.PReLU", 13472, "nn.functional.prelu"), + ("nn.ReLU6", 12622, "nn.functional.relu6"), + ("nn.ELU", 12508, "nn.functional.elu"), + ("nn.LSTMCell", 10885, None), + ("nn.Flatten", 10384, "torch.flatten"), + ("nn.ModuleDict", 10255, None), + ("nn.ReflectionPad2d", 9954, "nn.functional.pad"), + ("nn.MaxPool3d", 9526, "nn.functional.max_pool3d"), + ("nn.MaxPool1d", 9154, "nn.functional.max_pool1d"), + ("nn.RNN", 9154, None), + ("nn.ZeroPad2d", 8847, "nn.functional.pad"), + ("nn.ParameterList", 7702, None), + ("nn.SyncBatchNorm", 6814, None), + ("nn.PixelShuffle", 6571, "nn.functional.pixel_shuffle"), + ("nn.SmoothL1Loss", 6517, "nn.functional.smooth_l1_loss"), + ("nn.Hardswish", 6458, "nn.functional.hardswish"), + ("nn.AdaptiveMaxPool2d", 6071, "nn.functional.adaptive_max_pool2d"), + ("nn.SELU", 6043, "nn.functional.selu"), + ("nn.ConvTranspose3d", 6039, "nn.functional.conv_transpose3d"), + ("nn.GRUCell", 5840, None), + ("nn.ReplicationPad2d", 5600, "nn.functional.pad"), + ("nn.KLDivLoss", 5541, "nn.functional.kl_div"), + ("nn.ConvTranspose1d", 5183, "nn.functional.conv_transpose1d"), + ("nn.Softplus", 5120, "nn.functional.softplus"), + ("nn.SiLU", 4895, "nn.functional.silu"), + ("nn.AvgPool3d", 4523, "nn.functional.avg_pool3d"), + ("nn.CosineSimilarity", 4058, "nn.functional.cosine_similarity"), + ("nn.GELU", 3932, "nn.functional.gelu"), + ("nn.UpsamplingBilinear2d", 3673, "nn.functional.interpolate"), + ("nn.InstanceNorm1d", 3658, "nn.functional.instance_norm"), + ("nn.Transformer", 3604, None), + ("nn.MultiheadAttention", 3435, "nn.functional.multi_head_attention_forward"), + ("nn.AvgPool1d", 3195, "nn.functional.avg_pool1d"), + ("nn.Dropout3d", 2964, "nn.functional.dropout3d"), + ("nn.AdaptiveAvgPool3d", 2915, "nn.functional.adaptive_avg_pool3d"), + ("nn.InstanceNorm3d", 2893, "nn.functional.instance_norm"), + ("nn.Hardtanh", 2613, "nn.functional.hardtanh"), + ("nn.MarginRankingLoss", 2568, "nn.functional.margin_ranking_loss"), + ("nn.GLU", 2526, "nn.functional.glu"), + ("nn.AdaptiveAvgPool1d", 2481, "nn.functional.adaptive_avg_pool1d"), + ("nn.EmbeddingBag", 2344, "nn.functional.embedding_bag"), + ("nn.TransformerEncoderLayer", 2292, None), + ("nn.TransformerEncoder", 2091, None), + ("nn.MaxUnpool2d", 2031, "nn.functional.max_unpool2d"), + ("nn.UpsamplingNearest2d", 2004, "nn.functional.interpolate"), + ("nn.ConstantPad1d", 1904, "nn.functional.pad"), + ("nn.ConstantPad2d", 1791, "nn.functional.pad"), + ("nn.CTCLoss", 1789, "nn.functional.ctc_loss"), + ("nn.AdaptiveMaxPool1d", 1713, "nn.functional.adaptive_max_pool1d"), + ("nn.AdaptiveLogSoftmaxWithLoss", 1665, None), + ("nn.Bilinear", 1664, "nn.functional.bilinear"), + ("nn.RNNCell", 1653, None), + ("nn.MultiLabelSoftMarginLoss", 1624, "nn.functional.multilabel_soft_margin_loss"), + ("nn.Unfold", 1452, "nn.functional.unfold"), + ("nn.RReLU", 1431, "nn.functional.rrelu"), + ("nn.CosineEmbeddingLoss", 1357, "nn.functional.cosine_embedding_loss"), + ("nn.LocalResponseNorm", 1331, "nn.functional.local_response_norm"), + ("nn.Softmax2d", 1300, "nn.functional.softmax"), + ("nn.PairwiseDistance", 1241, "nn.functional.pairwise_distance"), + ("nn.LogSigmoid", 1235, "nn.functional.logsigmoid"), + ("nn.TripletMarginLoss", 1230, "nn.functional.triplet_margin_loss"), + ("nn.RNNBase", 1133, None), + ("nn.Threshold", 1043, "nn.functional.threshold"), + ("nn.AdaptiveMaxPool3d", 1025, "nn.functional.adaptive_max_pool3d"), + ("nn.CELU", 1018, "nn.functional.celu"), + ("nn.NLLLoss2d", 966, "nn.functional.nll_loss"), + ("nn.Softsign", 877, "nn.functional.softsign"), + ("nn.ReplicationPad1d", 862, "nn.functional.pad"), + ("nn.SoftMarginLoss", 856, "nn.functional.soft_margin_loss"), + ("nn.ParameterDict", 742, None), + ("nn.ReflectionPad1d", 731, "nn.functional.pad"), + ("nn.Softshrink", 713, "nn.functional.softshrink"), + ("nn.AlphaDropout", 710, "nn.functional.alpha_dropout"), + ("nn.Tanhshrink", 681, "nn.functional.tanhshrink"), + ("nn.PoissonNLLLoss", 676, "nn.functional.poisson_nll_loss"), + ("nn.MaxUnpool3d", 660, "nn.functional.max_unpool3d"), + ("nn.Fold", 630, "nn.functional.fold"), + ("nn.MultiMarginLoss", 622, "nn.functional.multi_margin_loss"), + ("nn.TransformerDecoderLayer", 614, None), + ("nn.TransformerDecoder", 607, None), + ("nn.Hardshrink", 592, "nn.functional.hardshrink"), + ("nn.ConstantPad3d", 582, "nn.functional.pad"), + ("nn.MultiLabelMarginLoss", 580, "nn.functional.multilabel_margin_loss"), + ("nn.LPPool2d", 550, "nn.functional.lp_pool2d"), + ("nn.Softmin", 537, "nn.functional.softmin"), + ("nn.MaxUnpool1d", 518, "nn.functional.max_unpool1d"), + ("nn.FractionalMaxPool2d", 484, "nn.functional.fractional_max_pool2d"), + ("nn.Hardsigmoid", 477, "nn.functional.hardsigmoid"), + ("nn.ReplicationPad3d", 470, "nn.functional.pad"), + ("nn.HingeEmbeddingLoss", 442, "nn.functional.hinge_embedding_loss"), + ("nn.LPPool1d", 386, "nn.functional.lp_pool1d"), + ("nn.FractionalMaxPool3d", 252, "nn.functional.fractional_max_pool3d"), + ("nn.Container", 217, None), + ("nn.Unflatten", 206, "nn.functional.unflatten"), + ("nn.FeatureAlphaDropout", 136, "nn.functional.feature_alpha_dropout"), + ( + "nn.TripletMarginWithDistanceLoss", + 107, + "nn.functional.triplet_margin_with_distance_loss", + ), + ("nn.ChannelShuffle", 90, "nn.functional.channel_shuffle"), + ("nn.RNNCellBase", 88, None), + ("nn.LazyLinear", 81, "nn.functional.linear"), + ("nn.UninitializedParameter", 60, None), + ("nn.CrossMapLRN2d", 59, None), + ("nn.GaussianNLLLoss", 55, "nn.functional.gaussian_nll_loss"), + ("nn.PixelUnshuffle", 45, "nn.functional.pixel_unshuffle"), + ("nn.Mish", 31, "nn.functional.mish"), + ("nn.ReflectionPad3d", 22, "nn.functional.pad"), + ("nn.HuberLoss", 18, "nn.functional.huber_loss"), + ("nn.LazyConv2d", 15, None), + ("nn.LazyConv1d", 9, None), + ("nn.LazyConv3d", 8, None), + ("nn.LazyConvTranspose1d", 8, None), + ("nn.LazyConvTranspose2d", 8, None), + ("nn.LazyConvTranspose3d", 8, None), + ("nn.LazyBatchNorm1d", 3, None), + ("nn.LazyBatchNorm2d", 3, None), + ("nn.LazyBatchNorm3d", 3, None), + ("nn.UninitializedBuffer", 3, None), +] + +# No rankings because these are a little hard to get rankings for +method_only_ops = [ + "bfloat16", + "bool", + "byte", + "char", + "contiguous", + "cpu", + "cuda", + "detach", + "double", + "expand", + "expand_as", + "float", + "get_device", + "half", + "hardshrink", + "index_add", + "index_copy", + "index_fill", + "index_put", + "int", + "is_contiguous", + "is_pinned", + "is_set_to", + "is_shared", + "is_signed", + "item", + "long", + "masked_scatter", + "masked_fill", + "narrow_copy", + "numpy", + "pin_memory", + "repeat", + "reshape_as", + "select", + "short", + "storage_offset", + "sum_to_size", + "to", + "to_mkldnn", + "tolist", + "type", + "type_as", + "unfold", + "view", + "view_as", +] + + +def get_nn_functional_top_list(): + top_nn_functional_ = dict(top_nn_functional) + for _, count, functional_name in top_nn_module: + if functional_name is None: + continue + if functional_name == "torch.flatten": + continue + if functional_name not in top_nn_functional_: + top_nn_functional_[functional_name] = count + else: + top_nn_functional_[functional_name] += count + + top_nn_functional_ = list(top_nn_functional_.items()) + top_nn_functional_.sort(key=operator.itemgetter(1), reverse=True) + return top_nn_functional_ + + +usage_count = {} +for k, v in get_nn_functional_top_list(): + usage_count[k] = v +for k, v in top_torch: + usage_count[k] = v diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..514b2f4e25586d6a432fe257197571dd4b95f11b --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/utils.py @@ -0,0 +1,40 @@ +# mypy: allow-untyped-defs +import contextlib +from typing import Tuple, Union + +import torch +from torch._C._functorch import ( + get_single_level_autograd_function_allowed, + set_single_level_autograd_function_allowed, + unwrap_if_dead, +) +from torch.utils._exposed_in import exposed_in + + +__all__ = [ + "exposed_in", + "argnums_t", + "enable_single_level_autograd_function", + "unwrap_dead_wrappers", +] + + +@contextlib.contextmanager +def enable_single_level_autograd_function(): + try: + prev_state = get_single_level_autograd_function_allowed() + set_single_level_autograd_function_allowed(True) + yield + finally: + set_single_level_autograd_function_allowed(prev_state) + + +def unwrap_dead_wrappers(args): + # NB: doesn't use tree_map_only for performance reasons + result = tuple( + unwrap_if_dead(arg) if isinstance(arg, torch.Tensor) else arg for arg in args + ) + return result + + +argnums_t = Union[int, Tuple[int, ...]] diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/vmap.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/vmap.py new file mode 100644 index 0000000000000000000000000000000000000000..fcb96ad06d24ec39a09576f912aab064c007e7a7 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_functorch/vmap.py @@ -0,0 +1,532 @@ +# mypy: ignore-errors + +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +import functools +import itertools +import os +import threading +from functools import partial +from typing import Any, Callable, List, Optional, Tuple, Union + +import torch +from torch import Tensor +from torch._C._functorch import ( + _add_batch_dim, + _remove_batch_dim, + _vmap_decrement_nesting, + _vmap_increment_nesting, + is_batchedtensor, +) +from torch.utils._pytree import ( + _broadcast_to_and_flatten, + tree_flatten, + tree_map_, + tree_unflatten, + TreeSpec, +) + + +in_dims_t = Union[int, Tuple] +out_dims_t = Union[int, Tuple[int, ...]] + + +def doesnt_support_saved_tensors_hooks(f): + message = ( + "torch.func.{grad, vjp, jacrev, hessian} don't yet support saved tensor hooks. " + "Please open an issue with your use case." + ) + + @functools.wraps(f) + def fn(*args, **kwargs): + with torch.autograd.graph.disable_saved_tensors_hooks(message): + return f(*args, **kwargs) + + return fn + + +# Checks that all args-to-be-batched have the same batch dim size +def _validate_and_get_batch_size( + flat_in_dims: List[Optional[int]], flat_args: List +) -> int: + batch_sizes = [ + arg.size(in_dim) + for in_dim, arg in zip(flat_in_dims, flat_args) + if in_dim is not None + ] + if len(batch_sizes) == 0: + raise ValueError("vmap: Expected at least one Tensor to vmap over") + if batch_sizes and any(size != batch_sizes[0] for size in batch_sizes): + raise ValueError( + f"vmap: Expected all tensors to have the same size in the mapped " + f"dimension, got sizes {batch_sizes} for the mapped dimension" + ) + return batch_sizes[0] + + +def _num_outputs(batched_outputs: Union[Tensor, Tuple[Tensor, ...]]) -> int: + if isinstance(batched_outputs, tuple): + return len(batched_outputs) + return 1 + + +# If value is a tuple, check it has length `num_elements`. +# If value is not a tuple, make a tuple with `value` repeated `num_elements` times + + +def _as_tuple( + value: Any, num_elements: int, error_message_lambda: Callable[[], str] +) -> Tuple: + if not isinstance(value, tuple): + return (value,) * num_elements + if len(value) != num_elements: + raise ValueError(error_message_lambda()) + return value + + +def _process_batched_inputs( + in_dims: in_dims_t, args: Tuple, func: Callable +) -> Tuple[int, List[Any], List[Any], TreeSpec]: + if not isinstance(in_dims, int) and not isinstance(in_dims, tuple): + raise ValueError( + f"vmap({_get_name(func)}, in_dims={in_dims}, ...)(): " + f"expected `in_dims` to be int or a (potentially nested) tuple " + f"matching the structure of inputs, got: {type(in_dims)}." + ) + if len(args) == 0: + raise ValueError( + f"vmap({_get_name(func)})(): got no inputs. Maybe you forgot to add " + f"inputs, or you are trying to vmap over a function with no inputs. " + f"The latter is unsupported." + ) + + flat_args, args_spec = tree_flatten(args) + flat_in_dims = _broadcast_to_and_flatten(in_dims, args_spec) + if flat_in_dims is None: + raise ValueError( + f"vmap({_get_name(func)}, in_dims={in_dims}, ...)(): " + f"in_dims is not compatible with the structure of `inputs`. " + f"in_dims has structure {tree_flatten(in_dims)[1]} but inputs " + f"has structure {args_spec}." + ) + + for i, (arg, in_dim) in enumerate(zip(flat_args, flat_in_dims)): + if not isinstance(in_dim, int) and in_dim is not None: + raise ValueError( + f"vmap({_get_name(func)}, in_dims={in_dims}, ...)(): " + f"Got in_dim={in_dim} for an input but in_dim must be either " + f"an integer dimension or None." + ) + if isinstance(in_dim, int) and not isinstance(arg, Tensor): + raise ValueError( + f"vmap({_get_name(func)}, in_dims={in_dims}, ...)(): " + f"Got in_dim={in_dim} for an input but the input is of type " + f"{type(arg)}. We cannot vmap over non-Tensor arguments, " + f"please use None as the respective in_dim" + ) + if in_dim is not None and (in_dim < -arg.dim() or in_dim >= arg.dim()): + raise ValueError( + f"vmap({_get_name(func)}, in_dims={in_dims}, ...)(): " + f"Got in_dim={in_dim} for some input, but that input is a Tensor " + f"of dimensionality {arg.dim()} so expected in_dim to satisfy " + f"-{arg.dim()} <= in_dim < {arg.dim()}." + ) + if in_dim is not None and in_dim < 0: + flat_in_dims[i] = in_dim % arg.dim() + + return ( + _validate_and_get_batch_size(flat_in_dims, flat_args), + flat_in_dims, + flat_args, + args_spec, + ) + + +# Creates BatchedTensors for every Tensor in arg that should be batched. +# Returns the (potentially) batched arguments and the batch_size. + + +def _create_batched_inputs( + flat_in_dims: List[Any], flat_args: List[Any], vmap_level: int, args_spec +) -> Tuple: + # See NOTE [Ignored _remove_batch_dim, _add_batch_dim] + batched_inputs = [ + arg if in_dim is None else _add_batch_dim(arg, in_dim, vmap_level) + for in_dim, arg in zip(flat_in_dims, flat_args) + ] + return tree_unflatten(batched_inputs, args_spec) + + +def _maybe_remove_batch_dim(name, batched_output, vmap_level, batch_size, out_dim): + if out_dim is None: + if isinstance(batched_output, torch.Tensor) and is_batchedtensor( + batched_output + ): + raise ValueError( + f"vmap({name}, ...): `{name}` can not return a " + f"BatchedTensor when out_dim is None" + ) + return batched_output + + # out_dim is non None + if not isinstance(batched_output, torch.Tensor): + raise ValueError( + f"vmap({name}, ...): `{name}` must only return " + f"Tensors, got type {type(batched_output)}. " + "Did you mean to set out_dims= to None for output?" + ) + + return _remove_batch_dim(batched_output, vmap_level, batch_size, out_dim) + + +# Undos the batching (and any batch dimensions) associated with the `vmap_level`. +def _unwrap_batched( + batched_outputs: Union[Tensor, Tuple[Tensor, ...]], + out_dims: out_dims_t, + vmap_level: int, + batch_size: int, + func: Callable, +) -> Tuple: + flat_batched_outputs, output_spec = tree_flatten(batched_outputs) + + def incompatible_error(): + raise ValueError( + f"vmap({_get_name(func)}, ..., out_dims={out_dims})(): " + f"out_dims is not compatible with the structure of `outputs`. " + f"out_dims has structure {tree_flatten(out_dims)[1]} but outputs " + f"has structure {output_spec}." + ) + + if isinstance(batched_outputs, torch.Tensor): + # Some weird edge case requires us to spell out the following + # see test_out_dims_edge_case + if isinstance(out_dims, int): + flat_out_dims = [out_dims] + elif isinstance(out_dims, tuple) and len(out_dims) == 1: + flat_out_dims = out_dims + elif out_dims is None: + flat_out_dims = [out_dims] + else: + incompatible_error() + else: + flat_out_dims = _broadcast_to_and_flatten(out_dims, output_spec) + if flat_out_dims is None: + incompatible_error() + + flat_outputs = [ + _maybe_remove_batch_dim( + _get_name(func), batched_output, vmap_level, batch_size, out_dim + ) + for batched_output, out_dim in zip(flat_batched_outputs, flat_out_dims) + ] + return tree_unflatten(flat_outputs, output_spec) + + +def _check_int_or_none(x, func, out_dims): + if isinstance(x, int): + return + if x is None: + return + raise ValueError( + f"vmap({_get_name(func)}, ..., out_dims={out_dims}): `out_dims` must be " + f"an int, None or a python collection of ints representing where in the outputs the " + f"vmapped dimension should appear." + ) + + +def _check_out_dims_is_int_or_int_pytree(out_dims: out_dims_t, func: Callable) -> None: + if isinstance(out_dims, int): + return + tree_map_(partial(_check_int_or_none, func=func, out_dims=out_dims), out_dims) + + +def _get_name(func: Callable): + if hasattr(func, "__name__"): + return func.__name__ + + # Not all callables have __name__, in fact, only static functions/methods do. + # A callable created via functools.partial or an nn.Module, to name some + # examples, don't have a __name__. + return repr(func) + + +DECOMPOSITIONS_LOADED = False +DECOMPOSITIONS_LOCK = threading.Lock() +VMAP_DECOMPOSITIONS_LIB = None + + +# torch.package, Python 3.11, and torch.jit-less environments are unhappy with +# decompositions. Only load them when needed if possible. +def lazy_load_decompositions(): + global DECOMPOSITIONS_LOADED + if DECOMPOSITIONS_LOADED: + return + + with DECOMPOSITIONS_LOCK: + if DECOMPOSITIONS_LOADED: + return + + if not (os.environ.get("PYTORCH_JIT", "1") == "1" and __debug__): + DECOMPOSITIONS_LOADED = True + return + + # use an alternate way to register an operator into the decomposition table + # _register_jit_decomposition doesn't work for some operators, e.g. addr, + # because the Tensor types generated cannot be unioned by torchscript + # decomp should be type OpOverload + global VMAP_DECOMPOSITIONS_LIB + VMAP_DECOMPOSITIONS_LIB = torch.library.Library( + "aten", "IMPL", "FuncTorchBatched" + ) + + from torch._decomp import decomposition_table + + def _register_python_decomposition_vmap(decomp): + if decomp in decomposition_table: + VMAP_DECOMPOSITIONS_LIB.impl(decomp, decomposition_table[decomp]) + else: + raise RuntimeError(f"could not find decomposition for {decomp}") + + _register_python_decomposition_vmap(torch.ops.aten.mse_loss_backward.default) + _register_python_decomposition_vmap( + torch.ops.aten.smooth_l1_loss_backward.default + ) + _register_python_decomposition_vmap(torch.ops.aten.huber_loss_backward.default) + _register_python_decomposition_vmap(torch.ops.aten.nll_loss_forward.default) + _register_python_decomposition_vmap(torch.ops.aten.nll_loss2d_forward.default) + _register_python_decomposition_vmap(torch.ops.aten.nll_loss_backward.default) + _register_python_decomposition_vmap(torch.ops.aten.nll_loss2d_backward.default) + _register_python_decomposition_vmap(torch.ops.aten.addr.default) + + DECOMPOSITIONS_LOADED = True + + +def vmap_impl(func, in_dims, out_dims, randomness, chunk_size, *args, **kwargs): + lazy_load_decompositions() + _check_out_dims_is_int_or_int_pytree(out_dims, func) + batch_size, flat_in_dims, flat_args, args_spec = _process_batched_inputs( + in_dims, args, func + ) + + if chunk_size is not None: + chunks_flat_args = _get_chunked_inputs( + flat_args, flat_in_dims, batch_size, chunk_size + ) + return _chunked_vmap( + func, + flat_in_dims, + chunks_flat_args, + args_spec, + out_dims, + randomness, + **kwargs, + ) + + # If chunk_size is not specified. + return _flat_vmap( + func, + batch_size, + flat_in_dims, + flat_args, + args_spec, + out_dims, + randomness, + **kwargs, + ) + + +def get_chunk_sizes(total_elems, chunk_size): + n_chunks = n_chunks = total_elems // chunk_size + chunk_sizes = [chunk_size] * n_chunks + # remainder chunk + remainder = total_elems % chunk_size + if remainder != 0: + chunk_sizes.append(remainder) + return chunk_sizes + + +def _get_chunked_inputs(flat_args, flat_in_dims, batch_size, chunk_size): + split_idxs = (batch_size,) + if chunk_size is not None: + chunk_sizes = get_chunk_sizes(batch_size, chunk_size) + split_idxs = tuple(itertools.accumulate(chunk_sizes)) + + flat_args_chunks = tuple( + t.tensor_split(split_idxs, dim=in_dim) + if in_dim is not None + else [ + t, + ] + * len(split_idxs) + for t, in_dim in zip(flat_args, flat_in_dims) + ) + + # transpose chunk dim and flatten structure + # chunks_flat_args is a list of flatten args + chunks_flat_args = zip(*flat_args_chunks) + return chunks_flat_args + + +def _flatten_chunks_output(chunks_output_): + # chunks_output is a list of chunked outputs + # flatten chunked outputs: + flat_chunks_output = [] + arg_spec = None + for output in chunks_output_: + flat_output, arg_specs = tree_flatten(output) + flat_chunks_output.append(flat_output) + if arg_spec is None: + arg_spec = arg_specs + + # transpose chunk dim and flatten structure + # flat_output_chunks is flat list of chunks + flat_output_chunks = list(zip(*flat_chunks_output)) + return flat_output_chunks, arg_spec + + +def _concat_chunked_outputs(out_dims, arg_spec, flat_output_chunks): + # concat chunks on out_dim + flat_out_dims = _broadcast_to_and_flatten(out_dims, arg_spec) + assert len(flat_out_dims) == len(flat_output_chunks) + flat_output = [] + for idx, out_dim in enumerate(flat_out_dims): + flat_output.append(torch.cat(flat_output_chunks[idx], dim=out_dim)) + # release tensors + flat_output_chunks[idx] = None + + return flat_output + + +# Applies vmap on chunked_input and returns concatenated output over the chunks. +def _chunked_vmap( + func, flat_in_dims, chunks_flat_args, args_spec, out_dims, randomness, **kwargs +): + chunks_output = [] + rs = torch.get_rng_state() if randomness == "same" else None + for flat_args in chunks_flat_args: + batch_size = _validate_and_get_batch_size(flat_in_dims, flat_args) + + # The way we compute split the input in `_get_chunked_inputs`, + # we may get a tensor with `0` batch-size. We skip any computation + # in that case. + # Eg. + # >>> chunk_size = 1 + # >>> batch_size = 6 + # >>> t = torch.zeros(batch_size, 1) + # >>> t.tensor_split([1, 2, 3, 4, 5, 6]) + # (tensor([[0.]]), tensor([[0.]]), tensor([[0.]]), tensor([[0.]]), + # tensor([[0.]]), tensor([[0.]]), tensor([], size=(0, 1))) + if batch_size == 0: + continue + + if rs is not None: + torch.set_rng_state(rs) + chunks_output.append( + _flat_vmap( + func, + batch_size, + flat_in_dims, + flat_args, + args_spec, + out_dims, + randomness, + **kwargs, + ) + ) + + flat_output_chunks, arg_spec = _flatten_chunks_output(chunks_output) + + # chunked output tensors are held by both `flat_output_chunks` and `chunks_output`. + # eagerly remove the reference from `chunks_output`. + del chunks_output + + # concat chunks on out_dim + flat_output = _concat_chunked_outputs(out_dims, arg_spec, flat_output_chunks) + + # finally unflatten the output + return tree_unflatten(flat_output, arg_spec) + + +# Vmap refactored helper functions: +def _check_randomness_arg(randomness): + if randomness not in ["error", "different", "same"]: + raise RuntimeError( + f"Only allowed values for randomness are 'error', 'different', or 'same'. Got {randomness}" + ) + + +@contextlib.contextmanager +def vmap_increment_nesting(batch_size, randomness): + try: + vmap_level = _vmap_increment_nesting(batch_size, randomness) + yield vmap_level + finally: + _vmap_decrement_nesting() + + +def _flat_vmap( + func, batch_size, flat_in_dims, flat_args, args_spec, out_dims, randomness, **kwargs +): + with vmap_increment_nesting(batch_size, randomness) as vmap_level: + batched_inputs = _create_batched_inputs( + flat_in_dims, flat_args, vmap_level, args_spec + ) + batched_outputs = func(*batched_inputs, **kwargs) + return _unwrap_batched(batched_outputs, out_dims, vmap_level, batch_size, func) + + +# `restore_vmap` is a private helper function. It is vmap but has the following +# differences: +# - instead of returning outputs, it returns an (outputs, out_dims) tuple. +# out_dims is a pytree of same shape as outputs and contains Optional[int] +# specifying where the vmapped dimension, if it exists, is in the corresponding output. +# - does no validation on in_dims or inputs (vmap expects at least one Tensor to be vmapped). +# restore_vmap allows for no inputs to have the vmap dimension +# - does no validation on outputs (vmap expects only Tensor outputs) +# restore_vmap allows for return of arbitrary outputs (not just Tensors) +# +# The TL;DR is that restore_vmap is more general than vmap and has a slightly +# different API. The relaxations are so that we can "pause" vmap in the middle +# of its execution and then "restore" it later (this is what we do in +# the generate_vmap_rule=True implementation of autograd.Function). +# +# restore_vmap can be technically used in the implementation of vmap, but doing +# that refactor is a bit technically challenging because: +# - vmap couples the tensor-wrapping code with error checking +# - vmap's tensor unwrapping code is in C++; we would need to rewrite part of it +# in python because it overlaps with unwrap_batched +def restore_vmap(func, in_dims, batch_size, randomness): + def inner(*args, **kwargs): + with vmap_increment_nesting(batch_size, randomness) as vmap_level: + batched_inputs = wrap_batched(args, in_dims, vmap_level) + batched_outputs = func(*batched_inputs, **kwargs) + return unwrap_batched(batched_outputs, vmap_level) + + return inner + + +def wrap_batched(args, bdims, level): + flat_args, spec = tree_flatten(args) + flat_bdims = _broadcast_to_and_flatten(bdims, spec) + assert flat_bdims is not None + result = _create_batched_inputs(flat_bdims, flat_args, level, spec) + return result + + +def unwrap_batched(args, level): + flat_args, spec = tree_flatten(args) + if len(flat_args) == 0: + return args, () + result = [ + torch._C._functorch._unwrap_batched(arg, level) + if isinstance(arg, torch.Tensor) + else (arg, None) + for arg in flat_args + ] + output, bdims = zip(*result) + return tree_unflatten(output, spec), tree_unflatten(bdims, spec) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..72800cae7fc98ca67e94b7bc420a79c2799791d5 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/__init__.py @@ -0,0 +1,16 @@ +from torch._higher_order_ops.cond import cond +from torch._higher_order_ops.flex_attention import ( + flex_attention, + flex_attention_backward, +) +from torch._higher_order_ops.hints_wrap import hints_wrapper +from torch._higher_order_ops.while_loop import while_loop + + +__all__ = [ + "cond", + "while_loop", + "flex_attention", + "flex_attention_backward", + "hints_wrapper", +] diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..753c8da83a2dca1481d98dc575d089d4d4228e4a Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/__pycache__/__init__.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/__pycache__/associative_scan.cpython-311.pyc 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+import itertools +from typing import Callable, List + +import torch +import torch._prims_common as utils +import torch._subclasses.functional_tensor +import torch.utils._pytree as pytree +from torch._C import DispatchKey +from torch._higher_order_ops.utils import ( + _maybe_run_with_interpreter, + _set_compilation_env, + autograd_not_implemented, + reenter_make_fx, + unique_graph_id, +) +from torch._inductor.utils import is_pointwise_use +from torch._ops import HigherOrderOperator +from torch._subclasses.fake_tensor import FakeTensorMode +from torch.fx.experimental.proxy_tensor import ( + disable_proxy_modes_tracing, + ProxyTorchDispatchMode, + track_tensor_tree, +) + + +aten = torch._ops.ops.aten + + +def wrap_combine_fn_flat(*args, combine_fn, spec, num_leaves): + assert len(args) == 2 * num_leaves + lhs = pytree.tree_unflatten(args[:num_leaves], spec) + rhs = pytree.tree_unflatten(args[num_leaves:], spec) + combined = combine_fn(lhs, rhs) + combined_leaves = pytree.tree_leaves(combined) + assert num_leaves == len(combined_leaves) + return combined_leaves + + +def _interleave(a, b, dim): + # https://stackoverflow.com/questions/60869537/how-can-i-interleave-5-pytorch-tensors + if b_trunc := (a.shape[dim] == b.shape[dim] + 1): + pad = ( + [0] * ((b.ndim - dim - 1) * 2 + 1) + + [1] + + [0] * (b.ndim * 2 - ((b.ndim - dim - 1) * 2 + 2)) + ) + b = torch.nn.functional.pad(b, pad) + + stacked = torch.stack([a, b], dim=dim + 1) + interleaved = torch.flatten(stacked, start_dim=dim, end_dim=dim + 1) + if b_trunc: + # TODO: find torch alternative for slice_along dim for torch.jit.script to work + interleaved = aten.slice(interleaved, dim, 0, b.shape[dim] + a.shape[dim] - 1) + return interleaved + + +def safe_map(f, *args): + args = list(map(list, args)) + n = len(args[0]) + for arg in args[1:]: + if len(arg) != n: + raise ValueError("length mismatch: {list(map(len, args))}") + + def nf(a): + return f(*a) + + return list(map(nf, zip(*args))) + + +class AssociativeScanOp(HigherOrderOperator): + def __init__(self): + super().__init__("associative_scan") + + def __call__(self, combine_fn, input, dim): + return super().__call__(combine_fn, input, dim) + + +associative_scan_op = AssociativeScanOp() + + +def associative_scan( + combine_fn: Callable[[pytree.PyTree, pytree.PyTree], pytree.PyTree], + input: pytree.PyTree, + dim: int, + reverse: bool = False, + combine_mode: str = "pointwise", +) -> torch.Tensor: + r""" + Performs an inclusive scan with an associative pointwise combine function. + + .. warning:: + `torch.associative_scan` is a prototype feature in PyTorch. It currently + does not support autograd and you may run into miscompiles. + Read more about feature classification at: + https://pytorch.org/blog/pytorch-feature-classification-changes/#prototype + + This operator requires runtime code generation and so requires support for + ``torch.compile``. Further, only CUDA device codegen is supported at the moment. + + Args: + combine_fn (Callable): A binary callable with type ``(Tensor, Tensor) -> Tensor``, + or if input is a pytree ``(pytree, pytree) -> pytree``. + This function must be pure, pointwise, and satisfy the associative property. + input (torch.Tensor): The input tensor, or nested pytree of tensors. + All inputs are expected to have the same shape. + dim (int): the dimension to scan over + reverse (bool): A boolean stating if the scan should be reversed with respect to the dimension. + combine_mode (str): A string indicating whether the ``combine_fn`` is ``pointwise`` or ``generic``. + If ``combine_mode=pointwise``, ``combine_fn`` must be pure, may only contain pointwise operations + and ``input`` must be CUDA tensors. + In all other cases ``combine_mode=generic`` should be used. + Note: ``combine_mode=pointwise`` is more efficient than ``combine_mode=generic``. + + + Example:: + + def add(x: torch.Tensor, y: torch.Tensor): + return x + y + + cumsum = associative_scan(add, x, dim) + + """ + assert callable(combine_fn), "combine_fn must be a callable, but got {combine_fn}" + assert isinstance(dim, int), "dim must be an int, but got {type(dim)}" + assert combine_mode in ["pointwise", "generic"] + + if not torch._dynamo.is_compiling(): + with _set_compilation_env(), torch._dynamo.utils.disable_cache_limit(): + return torch.compile(associative_scan, fullgraph=True)( + combine_fn, input, dim, reverse=reverse, combine_mode=combine_mode + ) + + leaves, spec = pytree.tree_flatten(input) + + if combine_mode == "pointwise" and not all(l.device.type == "cuda" for l in leaves): + raise ValueError( + "For combine_mode='pointwise', all input tensors need to be on CUDA" + ) + + assert len(leaves) >= 1, "expected at least 1 input leaf" + assert all( + isinstance(x, torch.Tensor) for x in leaves + ), "input leaves must be a Tensor" + + if reverse: + leaves = [torch.flip(elem, [dim]) for elem in leaves] + + shape = leaves[0].shape + ndim = len(shape) + dim = utils.canonicalize_dim(ndim, dim) + + for x in leaves[1:]: + assert x.shape == shape, "All input tensors must have the same shape" + + out = combine_fn( + pytree.tree_unflatten(leaves, spec), + pytree.tree_unflatten(leaves, spec), + ) + out_leaves, tree_out = pytree.tree_flatten(out) + assert len(leaves) == len( + out_leaves + ), "The pytree of the output of the operator needs to match the input pytree" + for x in out_leaves: + assert ( + x.shape == shape + ), "The pytree of the output of the operator needs to match the input pytree" + + combine_fn = functools.partial( + wrap_combine_fn_flat, combine_fn=combine_fn, spec=spec, num_leaves=len(leaves) + ) + + if combine_mode == "generic": + result_flat = generic_associative_scan(combine_fn, leaves, dim) + else: + result_flat = associative_scan_op(combine_fn, leaves, dim) + + if reverse: + result_flat = [torch.flip(elem, [dim]) for elem in result_flat] + + return pytree.tree_unflatten(result_flat, spec) + + +def generic_associative_scan(operator, elems_flat, dim=0): + r""" + This function performs the associative_scan operation. + The algorithm works by recursively collecting neighbours of ``elems_flat`` and subsequently + applying the ``operator`` on all pairs in parallel along ``dim``. + The results of the recursive calls are later combined. + + Args: + operator (Callable): A binary callable with type ``(Tensor, Tensor) -> Tensor``, + or if input is a pytree ``(pytree, pytree) -> pytree``. + This function must be pure, pointwise, and satisfy the associative property. + elems_flat (torch.Tensor): A list of torch.Tensors converted from the pytree of + ``input`` provided to ``associative_scan``. + All inputs are expected to have the same shape. + dim (int): the dimension to scan over + + + Example:: + + def add(x: torch.Tensor, y: torch.Tensor): + return x + y + + elems_flat = torch.tensor([0.0, 1.0, 2.0, 3.0]) + + First iteration of _scan -> + # odd_elems -> apply operator on all neighbours + # odd_elems = operator([torch.tensor([0.0, 2.0])], + # [torch.tensor([1.0, 3.0])]) + odd_elems = torch.tensor([1.0, 5.0]) + Second iteration of _scan -> + # odd_elems = operator([torch.tensor([1.0])], + # [torch.tensor([5.0])]) + odd_elems = torch.tensor([6.0]) + # even_elems -> apply operator on all odd_elems and + # every second element of ``elems``, starting from the second element. + # even_elems is expanded with the first element of ``elems`` + even_elems = [1.0] + # Merges odd_elems and even_elems + res = torch.tensor([1.0, 6.0]) + # even_elems -> apply operator on all odd_elems and + # every second element of ``elems``, starting from the second element. + # even_elems is expanded with the first element of ``elems`` + even_elems = [0.0, 3.0] + # Merges odd_elems and even_elems + res = torch.tensor([0.0, 1.0, 3.0, 6.0]) + + """ + + def _scan(elems): + """Perform the actual recursive scan on ``elems``.""" + num_elems = elems[0].shape[dim] + + if num_elems < 2: + return elems + + reduced_elems = operator( + *[aten.slice(elem, dim, 0, -1, 2) for elem in elems], + *[aten.slice(elem, dim, 1, None, 2) for elem in elems], + ) + + # Recursively compute scan for partially reduced tensors. + odd_elems = _scan(reduced_elems) + + if num_elems % 2 == 0: + even_elems = operator( + *[aten.slice(e, dim, 0, -1) for e in odd_elems], + *[aten.slice(e, dim, 2, None, 2) for e in elems], + ) + else: + even_elems = operator( + *odd_elems, + *[aten.slice(e, dim, 2, None, 2) for e in elems], + ) + + # The first element of a scan is the same as the first element + # of the original `elems`. + even_elems = [ + torch.cat([aten.slice(elem, dim, 0, 1), result], dim=dim) + if result.shape.numel() > 0 and elem.shape[dim] > 0 + else result + if result.shape.numel() > 0 + else aten.slice( + elem, dim, 0, 1 + ) # Jax allows/ignores concat with 0-dim, Pytorch does not + for (elem, result) in zip(elems, even_elems) + ] + + return list( + safe_map(functools.partial(_interleave, dim=dim), even_elems, odd_elems) + ) + + scans = _scan(elems_flat) + + return scans + + +def trace_associative_scan( + proxy_mode, func_overload, combine_fn: Callable, input: List[torch.Tensor], dim: int +): + with disable_proxy_modes_tracing(): + sample_inputs = [ + torch.empty_like( + x, + dtype=x.dtype, + device=x.device, + requires_grad=x.requires_grad, + ) + for x in itertools.chain(input, input) + ] + combine_graph = reenter_make_fx(combine_fn)(*sample_inputs) + + outputs = None + for node in combine_graph.graph.nodes: + if node.op == "output": + assert outputs is None + assert len(node.args) == 1 + outputs = node.args[0] + + if not all(is_pointwise_use(use) or use.op == "output" for use in node.users): + raise ValueError( + "For combine_mode='pointwise', the combine_fn needs to be pointwise" + ) + + assert outputs is not None + assert len(outputs) == len( + input + ), f"expected combine_fn to return {len(input)} results but got {len(outputs)}" + + for i, o in zip(input, outputs): + o_meta = o.meta["tensor_meta"] + assert o_meta.dtype == i.dtype, ( + f"combine_fn output type mismatch, expected {i.dtype} " + + f"but got {o_meta.dtype}" + ) + + _, combine_graph_name = unique_graph_id(proxy_mode, prefix="scan_combine_graph") + + proxy_mode.tracer.root.register_module(combine_graph_name, combine_graph) + + args = (combine_graph, input, dim) + proxy_args = pytree.tree_map(proxy_mode.tracer.unwrap_proxy, args) + out_proxy = proxy_mode.tracer.create_proxy( + "call_function", func_overload, proxy_args, {}, name="associative_scan" + ) + + with disable_proxy_modes_tracing(): + out = [aten.clone(x) for x in input] + + return track_tensor_tree(out, out_proxy, constant=None, tracer=proxy_mode.tracer) + + +@associative_scan_op.py_impl(DispatchKey.CompositeExplicitAutograd) +def associative_scan_op_dense(combine_fn, input, dim): + raise NotImplementedError("associative_scan is not implemented for eager") + + +associative_scan_op.py_impl(DispatchKey.Autograd)( + autograd_not_implemented(associative_scan_op, deferred_error=True) +) + + +@associative_scan_op.py_impl(ProxyTorchDispatchMode) +def associative_scan_proxy_mode(mode, combine_fn, input, dim): + return trace_associative_scan(mode, associative_scan_op, combine_fn, input, dim) + + +@associative_scan_op.py_impl(FakeTensorMode) +def assoiciative_scan_fake_tensor_mode(mode, combine_fn, input, dim): + with mode: + return [x.clone() for x in input] + + +@associative_scan_op.py_functionalize_impl +def associative_scan_functionalize(ctx, combine_fn, input, dim): + unwrapped_input = ctx.unwrap_tensors(input) + with ctx.redispatch_to_next() as m: + functional_combine_fn = ctx.functionalize( + _maybe_run_with_interpreter(combine_fn) + ) + ret = associative_scan_op(functional_combine_fn, unwrapped_input, dim) + return ctx.wrap_tensors(ret) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/auto_functionalize.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/auto_functionalize.py new file mode 100644 index 0000000000000000000000000000000000000000..232981f1f0192eb8f50b35586298aa645304be30 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/auto_functionalize.py @@ -0,0 +1,713 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import warnings +from dataclasses import dataclass +from typing import Any, Dict, List, Optional, Sequence, Tuple, Union + +import torch +import torch.utils._pytree as pytree +from torch import Tensor +from torch._C import DispatchKey +from torch._ops import HigherOrderOperator, OperatorBase, OpOverload +from torch._prims_common import clone_preserve_strides +from torch._subclasses.fake_tensor import FakeTensorMode +from torch.fx.experimental.proxy_tensor import ( + disable_proxy_modes_tracing, + ProxyTorchDispatchMode, + track_tensor_tree, +) + + +def get_base(tensor): + if torch.is_inference_mode_enabled(): + return tensor._inference_mode_base + else: + return tensor._base + + +@dataclass +class ViewInfo: + base_index: int + size: Optional[Sequence[Union[int, torch.SymInt]]] = None + stride: Optional[Sequence[Union[int, torch.SymInt]]] = None + storage_offset: Optional[int] = None + # When is_view is false, the tensor is the base, and + # size, stride and storage_offset are all None. + is_view: bool = True + + def regenerate_view(self, bases_list: List[Tensor]): + if not self.is_view: + return bases_list[self.base_index] + + assert self.stride is not None + assert self.size is not None + assert self.storage_offset is not None + + return torch.as_strided( + bases_list[self.base_index], + self.size, + self.stride, + self.storage_offset, + ) + + +def write_view_information_to_args( + mutable_arg_names: List[str], + mutable_arg_types: List[torch.Type], + kwargs: Dict[str, Any], + arg_to_base_index: Dict[str, Any], +): + """ + This function writes the view information into kwargs. It reads mutable_args from kwargs. + and uses arg_to_base_index and tensor information to write ViewInfo into kwargs. + mutable_arg_names: mutable custom operator arg names. + mutable_arg_types: mutable custom operator arg types. + kwargs: the original custom operator args. + arg_to_base_index: maps mutable_arg_name to int | [int] that refers to the base tensor that + corresponds to the input tensor + """ + + def write_single_view(prefix: str, tensor: Tensor, base_index: int): + assert f"{prefix}_base_index" not in kwargs + assert f"{prefix}_size" not in kwargs + assert f"{prefix}_stride" not in kwargs + assert f"{prefix}_storage_offset" not in kwargs + + if tensor is None: + kwargs[f"{prefix}_base_index"] = None + elif get_base(tensor) is None: + # if the tensor is the base (not view), for simplicity we do not serialize view meta. + kwargs[f"{prefix}_base_index"] = base_index + else: + kwargs[f"{prefix}_base_index"] = base_index + kwargs[f"{prefix}_size"] = tensor.size() + kwargs[f"{prefix}_stride"] = tensor.stride() + kwargs[f"{prefix}_storage_offset"] = tensor.storage_offset() + + for arg_name, arg_type in zip(mutable_arg_names, mutable_arg_types): + arg = kwargs[arg_name] + if isinstance(arg_type, torch.ListType): + if arg is None: + kwargs[f"_{arg_name}_length"] = None + + kwargs[f"_{arg_name}_length"] = len(arg) + for i, elem in enumerate(arg): + write_single_view( + f"_{arg_name}_{i}", elem, arg_to_base_index[arg_name][i] + ) + + elif isinstance(arg_type, (torch.TensorType, torch.OptionalType)): + write_single_view( + f"_{arg_name}", + kwargs[arg_name], + arg_to_base_index.get(arg_name, None), + ) + else: + raise RuntimeError(f"Unsupported type {arg_type}") + + +# Returns a dict of arg_name -> ViewInfo | [ViewInfo] +def read_view_information_from_args( + mutable_arg_names: List[str], + mutable_arg_types: List[torch.Type], + kwargs: Dict[str, Any], + all_bases: List[Tensor], +): + """ + This reads the view information added by `write_view_information_to_args` from kwargs, pop them, + and returns a dict arg_name -> ViewInfo | [ViewInfo](if the input is list). that maps each mutable arg + to its view information. + mutable_arg_names: mutable custom operator arg names. + mutable_arg_types: mutable custom operator arg types. + kwargs : args of auto_functionalize(custom_op, kwargs) + """ + + def get_arg(name): + return kwargs.pop(name) + + def read_single_view(prefix): + base_index = get_arg(f"{prefix}_base_index") + if base_index is None: + return None + elif f"{prefix}_size" not in kwargs: + assert f"{prefix}_stride" not in kwargs + assert f"{prefix}_storage_offset" not in kwargs + + # This means that the argument is the base tensor + return ViewInfo(base_index, all_bases[base_index], is_view=False) + + else: + size = get_arg(f"{prefix}_size") + stride = get_arg(f"{prefix}_stride") + storage_offset = get_arg(f"{prefix}_storage_offset") + return ViewInfo(base_index, size, stride, storage_offset, is_view=True) + + args_view_info: Dict[str, Any] = {} + for arg_name, arg_type in zip(mutable_arg_names, mutable_arg_types): + if isinstance(arg_type, torch.ListType): + length = get_arg(f"_{arg_name}_length") + if length is None: + # The whole list is None. + args_view_info[arg_name] = None + else: + args_view_info[arg_name] = [ + read_single_view(f"_{arg_name}_{i}") for i in range(length) + ] + + elif isinstance(arg_type, (torch.TensorType, torch.OptionalType)): + args_view_info[arg_name] = read_single_view(f"_{arg_name}") + else: + raise RuntimeError(f"Unsupported type {arg_type}") + return args_view_info + + +# NOTE: [auto-functionalizing custom ops] +# Users may wish to torch.compile custom ops that mutate their inputs. +# torch.compile will automatically support this op without anyone needing +# to provide a functionalization kernel for it. Here's how. +# +# Let's say we have a hypothetical mylib::sin_(Tensor(a!) x) -> () +# op. First, when FakeTensor sees this op: +# - If the schema says it returns nothing, we can generate a trivial +# FakeTensor rule for it (that returns nothing). +# - Otherwise, the user needs to provide a FakeTensor impl (fake impl) +# +# Next, when Python FunctionalTensor sees the op, it will functionalize +# it by emitting a call to an auto_functionalize(op, ["x"], {"x": ...}) +# HOP and replacing the mutated inputs with corresponding outputs of this HOP. +# This HOP effectively runs the functional version of the op when +# called: it clones inputs that will be mutated, runs the op, and +# then returns (output, Tensors with the new values) +# +# auto_functionalize_v2 is an improved version of auto_functionalize that better handle +# re-inplacing views. + + +class AutoFunctionalized(HigherOrderOperator): + """auto_functionalized(_mutable_op, **kwargs) + + This HOP runs a "functional" version of _mutable_op. + + Concretely, it looks at all the arguments that are mutable through + _mutable_op's operator schema, clones those kwargs, runs + `out = _mutable_op(**kwargs)` with the cloned values, and then returns the + operator output concatenated with the cloned values that were mutated. + + We have some restrictions on `_mutable_op`. + See `can_auto_functionalize` for the restrictions. We can likely lift + many of these if users request it. + + The reason why _mutable_op is prefixed with an + underscore is to prevent collisions with kwarg names in **kwargs. + """ + + def __init__(self) -> None: + super().__init__("auto_functionalized") + + def __call__( + self, + /, + _mutable_op: OpOverload, + **kwargs: Any, + ) -> Tuple[Any, Tuple[Tensor, ...]]: + assert can_auto_functionalize(_mutable_op) + assert isinstance(kwargs, dict) + return super().__call__(_mutable_op, **kwargs) + + +auto_functionalized = AutoFunctionalized() +auto_functionalized.__module__ = "torch.ops.higher_order" + +auto_functionalized.fallthrough(DispatchKey.AutogradCPU) +auto_functionalized.fallthrough(DispatchKey.AutogradCUDA) + + +class AutoFunctionalizedV2(HigherOrderOperator): + """auto_functionalized_v2(_mutable_op, **kwargs) + + This HOP runs a "functional" version of _mutable_op. + Unlike AutoFunctionalized, this version is improved to better handle + view tensors. This version is only used in non export mode. + """ + + def __init__(self) -> None: + super().__init__("auto_functionalized_v2") + + def __call__( + self, + /, + _mutable_op: OpOverload, + **kwargs: Any, + ) -> Tuple[Any, Tuple[Tensor, ...]]: + assert can_auto_functionalize(_mutable_op) + assert isinstance(kwargs, dict) + return super().__call__(_mutable_op, **kwargs) + + +auto_functionalized_v2 = AutoFunctionalizedV2() +auto_functionalized_v2.__module__ = "torch.ops.higher_order" + +auto_functionalized_v2.fallthrough(DispatchKey.AutogradCPU) +auto_functionalized_v2.fallthrough(DispatchKey.AutogradCUDA) + + +def can_auto_functionalize(op: OperatorBase) -> bool: + if not isinstance(op, OpOverload): + return False + + if torch._library.utils.is_builtin(op): + # We control the built-ins. These may (in rare cases) + # do input metadata mutation (which we have banned on custom ops) + return False + schema = op._schema + if not schema.is_mutable: + return False + schema = op._schema + + for arg in schema.arguments: + if arg.alias_info is None: + continue + if not arg.alias_info.is_write: + continue + if type(arg.type) is torch.TensorType: + continue + if ( + type(arg.type) is torch.OptionalType + and type(arg.type.getElementType()) is torch.TensorType + ): + continue + if ( + type(arg.type) is torch.ListType + and type(arg.type.getElementType()) is torch.TensorType + ): + continue + # Not yet supported: other Tensor types. This includes things like + # Tensor?[], Tensor[]?. + return False + + if len(schema.returns) == 1 and isinstance(schema.returns[0].type, torch.NoneType): + # Skip schema returns -> None + return True + # The returns must not alias anything + for ret in schema.returns: + if ret.alias_info is None and type(ret.type) is torch.TensorType: + continue + # Not yet supported: List[Tensor] return. + return False + if torch._C._dispatch_has_kernel_for_dispatch_key(op.name(), "Functionalize"): + return False + return True + + +def get_mutable_args(op: OpOverload) -> Tuple[List[str], List[torch.Type]]: + """ + Returns the list of argument names that get mutated according to the + schema and their types. + """ + mutable_args_names = [ + arg.name + for arg in op._schema.arguments + if arg.alias_info is not None and arg.alias_info.is_write + ] + + mutable_args_types = [ + arg.type + for arg in op._schema.arguments + if arg.alias_info is not None and arg.alias_info.is_write + ] + return mutable_args_names, mutable_args_types + + +def do_auto_functionalize( + op: OpOverload, + args: Tuple[Any, ...], + kwargs: Dict[str, Any], +) -> Any: + """Functionalizes a call to op(*args, **kwargs) by emitting a call to + `outs = auto_functionalized(op, normalized_kwargs)` + and replacing the mutated (args, kwargs) with the corresponding outputs. + + The normalized_kwargs are just the (args, kwargs), but all in kwarg form. + This makes handling easier for the auto_functionalized HOP. + """ + from torch._subclasses.functional_tensor import PythonFunctionalizeAPI + + ctx = PythonFunctionalizeAPI() + + # All of the (args, kwargs), but all as kwargs. The names for the + # args come from the schema. This makes it easier for us to work with them. + normalized_kwargs = {} + schema = op._schema + for idx, arg in enumerate(schema.arguments): + # NB: torch_dispatch kwargs are the args defined as kwarg-only in the schema + if arg.name in kwargs: + normalized_kwargs[arg.name] = kwargs[arg.name] + elif idx < len(args): + # if its out of bounds we don't need to do anything + # as it means the the optional arg was passed with its default + # value + normalized_kwargs[arg.name] = args[idx] + else: + normalized_kwargs[arg.name] = arg.default_value + + unwrapped_kwargs = ctx.unwrap_tensors(normalized_kwargs) # type: ignore[arg-type] + if "self" in unwrapped_kwargs or "self_" in unwrapped_kwargs: + warnings.warn( + "Using `self` or `self_` as an argument in the definition of custom ops may lead to ambiguous parsing. " + "Please consider using a different name for this argument to avoid potential issues." + ) + with ctx.redispatch_to_next(): + unwrapped_outs = auto_functionalized( + op, **unwrapped_kwargs # type: ignore[arg-type] + ) + + # List of the name of args that get mutated (according to the schema) + mutable_args_names, _ = get_mutable_args(op) + + unwrapped_actual_out: Union[Any, Tuple[Any]] = unwrapped_outs[ + : -len(mutable_args_names) + ] + unwrapped_mutable_out = unwrapped_outs[-len(mutable_args_names) :] + + if len(op._schema.returns) == 0: + assert unwrapped_actual_out[0] is None + unwrapped_actual_out = None + elif len(op._schema.returns) == 1: + assert len(unwrapped_actual_out) == 1 + unwrapped_actual_out = unwrapped_actual_out[0] + else: + assert len(unwrapped_actual_out) == len(op._schema.returns) + + for name, unwrapped_out in zip(mutable_args_names, unwrapped_mutable_out): + # Can be None if input was `Tensor(a!)?` + if unwrapped_out is None: + continue + + # We only handle Tensor or List[Tensor] here for now. + def sync_update(o, orig_arg): + ctx.replace(orig_arg, o) + ctx.commit_update(orig_arg) + ctx.sync(orig_arg) + + orig_arg = normalized_kwargs[name] + + if isinstance(unwrapped_out, torch.Tensor): + sync_update(unwrapped_out, orig_arg) + elif isinstance(unwrapped_out, list) and all( + isinstance(o, torch.Tensor) for o in unwrapped_out + ): + assert len(orig_arg) == len(unwrapped_out) + for orig_a, o in zip(orig_arg, unwrapped_out): + sync_update(o, orig_a) + else: + raise RuntimeError( + f"unsupported type for auto-functionalization: {unwrapped_out}" + ) + + return ctx.wrap_tensors(unwrapped_actual_out) # type: ignore[arg-type] + + +def do_auto_functionalize_v2( + op: OpOverload, + args: Tuple[Any, ...], + kwargs: Dict[str, Any], +) -> Any: + from torch._subclasses.functional_tensor import PythonFunctionalizeAPI + + ctx = PythonFunctionalizeAPI() + + # All of the (args, kwargs), but all as kwargs. The names for the + # args come from the schema. This makes it easier for us to work with them. + normalized_kwargs = {} + + schema = op._schema + for idx, arg in enumerate(schema.arguments): + # NB: torch_dispatch kwargs are the args defined as kwarg-only in the schema + if arg.name in kwargs: + normalized_kwargs[arg.name] = kwargs[arg.name] + elif idx < len(args): + # if its out of bounds we don't need to do anything + # as it means the the optional arg was passed with its default + # value + normalized_kwargs[arg.name] = args[idx] + else: + normalized_kwargs[arg.name] = arg.default_value + + # List of the name of args that get mutated (according to the schema) + mutable_args_names, mutable_args_types = get_mutable_args(op) + + # A list of all bases of mutable args without duplication + all_bases = [] + all_bases_addresses: list[int] = [] + + # Map arg_name to the index of its base in all_bases. + arg_to_base_index: Dict[str, Any] = {} + + def update_dict(tensor, arg_name, index=None): + base = tensor if get_base(tensor) is None else get_base(tensor) + + def set_result(base_index): + if index is None: + arg_to_base_index[arg_name] = base_index + else: + arg_to_base_index[arg_name][index] = base_index + + if not all_bases_addresses.__contains__(base._cdata): + all_bases_addresses.append(base._cdata) + all_bases.append(base) + set_result(len(all_bases) - 1) + else: + set_result(all_bases_addresses.index(base._cdata)) + + for arg_name in mutable_args_names: + arg = normalized_kwargs[arg_name] + if arg is None: + continue + + if isinstance(arg, list): + arg_to_base_index[arg_name] = {} + for i, tensor in enumerate(arg): + if tensor is None: + arg_to_base_index[arg_name].append(None) + continue + + update_dict(tensor, arg_name, i) + + else: + update_dict(arg, arg_name) + + # add view_meta for each args into unwrapped_kwargs. + write_view_information_to_args( + mutable_args_names, + mutable_args_types, + normalized_kwargs, + arg_to_base_index, + ) + + # remove mutated args from the kwargs (its a function of _all_bases now) + for arg_name in mutable_args_names: + del normalized_kwargs[arg_name] # type: ignore[arg-type] + + unwrapped_kwargs = ctx.unwrap_tensors(normalized_kwargs) # type: ignore[arg-type] + if "self" in unwrapped_kwargs or "self_" in unwrapped_kwargs: + warnings.warn( + "Using `self` or `self_` as an argument in the definition of custom ops may lead to ambiguous parsing. " + "Please consider using a different name for this argument to avoid potential issues." + ) + all_basis_unwrapped = ctx.unwrap_tensors(all_bases) + + with ctx.redispatch_to_next(): + unwrapped_outs = auto_functionalized_v2( + op, **dict(unwrapped_kwargs, _all_bases=all_basis_unwrapped) # type: ignore[arg-type] + ) + + unwrapped_actual_out: Union[Any, Tuple[Any]] = ( + unwrapped_outs if len(all_bases) == 0 else unwrapped_outs[: -len(all_bases)] + ) + + unwrapped_mutable_out = ( + [] if len(all_bases) == 0 else unwrapped_outs[-len(all_bases) :] + ) + + if len(op._schema.returns) == 0: + assert unwrapped_actual_out[0] is None + unwrapped_actual_out = None + elif len(op._schema.returns) == 1: + assert len(unwrapped_actual_out) == 1 + unwrapped_actual_out = unwrapped_actual_out[0] + else: + assert len(unwrapped_actual_out) == len(op._schema.returns) + + for orig_arg, unwrapped_out in zip(all_bases, unwrapped_mutable_out): + # Can be None if input was `Tensor(a!)?` + if unwrapped_out is None: + continue + + # We only handle Tensor or List[Tensor] here for now. + def sync_update(o, orig_arg): + ctx.replace(orig_arg, o) + ctx.commit_update(orig_arg) + ctx.sync(orig_arg) + + if isinstance(unwrapped_out, torch.Tensor): + sync_update(unwrapped_out, orig_arg) + elif isinstance(unwrapped_out, list) and all( + isinstance(o, torch.Tensor) for o in unwrapped_out + ): + assert len(orig_arg) == len(unwrapped_out) + for orig_a, o in zip(orig_arg, unwrapped_out): + sync_update(o, orig_a) + else: + raise RuntimeError( + f"unsupported type for auto-functionalization: {unwrapped_out}" + ) + + return ctx.wrap_tensors(unwrapped_actual_out) # type: ignore[arg-type] + + +# auto_functionalize functions +@auto_functionalized.py_impl(DispatchKey.CompositeExplicitAutograd) +def auto_functionalized_dense( + _mutable_op: OpOverload, + _only_clone_these_tensors: Optional[Tuple[str, ...]] = None, + **kwargs: Any, +) -> Tuple[Any, Tuple[Tensor, ...]]: + new_kwargs = dict(**kwargs) + result = [] + + _mutable_args_names, _ = get_mutable_args(_mutable_op) + for name in _mutable_args_names: + if ( + _only_clone_these_tensors is not None + and name not in _only_clone_these_tensors + ): + new_kwargs[name] = kwargs[name] + else: + new_kwargs[name] = ( + [clone_preserve_strides(x) for x in kwargs[name]] + if kwargs[name] is not None and isinstance(kwargs[name], list) + else clone_preserve_strides(kwargs[name]) + if kwargs[name] is not None + else None + ) + result.append(new_kwargs[name]) + out = _mutable_op(**new_kwargs) + + if isinstance(out, tuple): + return (*out, *result) # type: ignore[return-value] + else: + return (out, *result) # type: ignore[return-value] + + +@auto_functionalized.py_impl(FakeTensorMode) +def auto_functionalized_fake( + mode, + _mutable_op: OpOverload, + **kwargs: Any, +) -> Tuple[Any, Tuple[Tensor, ...]]: + with mode: + result = auto_functionalized_dense(_mutable_op, **kwargs) + return result + + +@auto_functionalized.py_impl(ProxyTorchDispatchMode) +def auto_functionalized_proxy( + mode, + _mutable_op: OpOverload, + **kwargs: Any, +) -> Tuple[Any, Tuple[Tensor, ...]]: + with disable_proxy_modes_tracing(): + out = auto_functionalized(_mutable_op, **kwargs) + + proxy_kwargs = pytree.tree_map(mode.tracer.unwrap_proxy, kwargs) + out_proxy = mode.tracer.create_proxy( + "call_function", + auto_functionalized, + (_mutable_op,), + proxy_kwargs, + ) + result = track_tensor_tree(out, out_proxy, constant=None, tracer=mode.tracer) + return result + + +@auto_functionalized.py_functionalize_impl +def auto_functionalized_func(ctx, _mutable_op, **kwargs): + unwrapped_kwargs = ctx.unwrap_tensors(kwargs) + with ctx.redispatch_to_next(): + result = auto_functionalized(_mutable_op, **unwrapped_kwargs) + return ctx.wrap_tensors(result) + + +# auto_functionalized_v2 functions +@auto_functionalized_v2.py_impl(DispatchKey.CompositeExplicitAutograd) +def auto_functionalized_v2_dense( + _mutable_op: OpOverload, + _only_clone_these_bases: Optional[Tuple[int, ...]] = None, + **kwargs: Any, +) -> Tuple[Any, Tuple[Tensor, ...]]: + all_bases: List[Tensor] = kwargs.pop("_all_bases", []) + mutable_args_names, mutable_args_types = get_mutable_args(_mutable_op) + args_view_info = read_view_information_from_args( + mutable_args_names, mutable_args_types, kwargs, all_bases + ) + + if _only_clone_these_bases is None: + _only_clone_these_bases = tuple(range(len(all_bases))) + + def maybe_copy(i, t): + if t is None: + return None + if i in _only_clone_these_bases: + return clone_preserve_strides(t) + else: + return t + + all_bases_new = [maybe_copy(i, t) for i, t in enumerate(all_bases)] + + # create new args + new_kwargs = dict(**kwargs) + + # re-generate all inputs from all_bases_new using args_view_info and add them to new_kwargs. + for arg_name in mutable_args_names: + if args_view_info[arg_name] is None: + new_kwargs[arg_name] = None + elif isinstance(args_view_info[arg_name], list): + new_kwargs[arg_name] = [] + for i, elem in enumerate(args_view_info[arg_name]): + if elem is None: + new_kwargs[arg_name].append(None) + else: + view_info = args_view_info[arg_name][i] + new_kwargs[arg_name].append( + view_info.regenerate_view(all_bases_new) + ) + else: + new_kwargs[arg_name] = args_view_info[arg_name].regenerate_view( + all_bases_new + ) + + out = _mutable_op(**new_kwargs) + + if isinstance(out, tuple): + return (*out, *all_bases_new) # type: ignore[return-value] + else: + return (out, *all_bases_new) # type: ignore[return-value] + + +@auto_functionalized_v2.py_impl(FakeTensorMode) +def auto_functionalized_v2_fake( + mode, + _mutable_op: OpOverload, + **kwargs: Dict[str, Any], +) -> Tuple[Any, Tuple[Tensor, ...]]: + with mode: + result = auto_functionalized_v2_dense(_mutable_op, **kwargs) + return result + + +@auto_functionalized_v2.py_impl(ProxyTorchDispatchMode) +def auto_functionalized_v2_proxy( + mode, + _mutable_op: OpOverload, + **kwargs: Dict[str, Any], +) -> Tuple[Any, Tuple[Tensor, ...]]: + with disable_proxy_modes_tracing(): + out = auto_functionalized_v2(_mutable_op, **kwargs) + + proxy_kwargs = pytree.tree_map(mode.tracer.unwrap_proxy, kwargs) + out_proxy = mode.tracer.create_proxy( + "call_function", + auto_functionalized_v2, + (_mutable_op,), + proxy_kwargs, + ) + result = track_tensor_tree(out, out_proxy, constant=None, tracer=mode.tracer) + return result + + +@auto_functionalized_v2.py_functionalize_impl +def auto_functionalized_v2_func(ctx, _mutable_op, **kwargs): + unwrapped_kwargs = ctx.unwrap_tensors(kwargs) + with ctx.redispatch_to_next(): + result = auto_functionalized_v2(_mutable_op, **unwrapped_kwargs) + return ctx.wrap_tensors(result) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/cond.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/cond.py new file mode 100644 index 0000000000000000000000000000000000000000..dee400d76f5964fc330acfb16459700cba6679e7 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/cond.py @@ -0,0 +1,521 @@ +# mypy: allow-untyped-defs +import contextlib +import logging + +import torch +import torch._subclasses.functional_tensor +import torch.utils._pytree as pytree +from torch._C import DispatchKey +from torch._C._functorch import ( + _add_batch_dim, + get_unwrapped, + is_batchedtensor, + maybe_get_bdim, +) +from torch._dispatch.python import suspend_functionalization +from torch._functorch.utils import exposed_in +from torch._guards import detect_fake_mode +from torch._higher_order_ops.utils import ( + _has_potential_branch_input_alias, + _has_potential_branch_input_mutation, + _maybe_run_with_interpreter, + _set_compilation_env, + reenter_make_fx, + unique_graph_id, + UnsupportedAliasMutationException, +) +from torch._ops import HigherOrderOperator +from torch._subclasses.fake_tensor import FakeTensorMode +from torch._subclasses.functional_tensor import disable_functional_mode +from torch.fx.experimental.proxy_tensor import ( + _temp_remove_pre_dispatch_torch_function_mode, + disable_proxy_modes_tracing, + ProxyTorchDispatchMode, + track_tensor_tree, +) +from torch.fx.passes.shape_prop import _extract_tensor_metadata +from torch.utils._python_dispatch import _get_current_dispatch_mode + +from .utils import _from_fun, create_fw_bw_graph + + +log = logging.getLogger(__name__) + +""" +We're going to define a `cond_op` operation. +In order to do this, we need implementations for each of the dispatch keys. +""" + + +class CondOp(HigherOrderOperator): + def __init__(self): + super().__init__("cond") + + def __call__(self, pred, true_fn, false_fn, operands): + return super().__call__(pred, true_fn, false_fn, operands) + + +cond_op = CondOp() + + +@exposed_in("torch") +def cond(pred, true_fn, false_fn, operands): + r""" + Conditionally applies `true_fn` or `false_fn`. + + .. warning:: + `torch.cond` is a prototype feature in PyTorch. It has limited support for input and output types and + doesn't support training currently. Please look forward to a more stable implementation in a future version of PyTorch. + Read more about feature classification at: https://pytorch.org/blog/pytorch-feature-classification-changes/#prototype + + `cond` is structured control flow operator. That is, it is like a Python if-statement, + but has restrictions on `true_fn`, `false_fn`, and `operands` that enable it to be + capturable using torch.compile and torch.export. + + Assuming the constraints on `cond`'s arguments are met, `cond` is equivalent to the following:: + + def cond(pred, true_branch, false_branch, operands): + if pred: + return true_branch(*operands) + else: + return false_branch(*operands) + + Args: + pred (Union[bool, torch.Tensor]): A boolean expression or a tensor with one element, + indicating which branch function to apply. + + true_fn (Callable): A callable function (a -> b) that is within the + scope that is being traced. + + false_fn (Callable): A callable function (a -> b) that is within the + scope that is being traced. The true branch and false branch must + have consistent input and outputs, meaning the inputs have to be + the same, and the outputs have to be the same type and shape. + + operands (Tuple of possibly nested dict/list/tuple of torch.Tensor): A tuple of inputs to the true/false functions. + + Example:: + + def true_fn(x: torch.Tensor): + return x.cos() + def false_fn(x: torch.Tensor): + return x.sin() + return cond(x.shape[0] > 4, true_fn, false_fn, (x,)) + + Restrictions: + - The conditional statement (aka `pred`) must meet one of the following constraints: + + - It's a `torch.Tensor` with only one element, and torch.bool dtype + + - It's a boolean expression, e.g. `x.shape[0] > 10` or `x.dim() > 1 and x.shape[1] > 10` + + - The branch function (aka `true_fn`/`false_fn`) must meet all of the following constraints: + + - The function signature must match with operands. + + - The function must return a tensor with the same metadata, e.g. shape, + dtype, etc. + + - The function cannot have in-place mutations on inputs or global variables. + (Note: in-place tensor operations such as `add_` for intermediate results + are allowed in a branch) + + .. warning:: + Temporal Limitations: + + - The **output** of branches must be a **single Tensor**. Pytree of tensors will be supported in the future. + + """ + if torch.compiler.is_dynamo_compiling(): + return cond_op(pred, true_fn, false_fn, operands) + + if isinstance(pred, (bool, int, float)): + log.warning( + "Pred is a Python constant. When used with torch.cond, it executes only one of the branches." + " If you want torch.cond to perserve two branches, please make the predicate a boolean tensor or a SymBool." + ) + if pred: + return true_fn(*operands) + else: + return false_fn(*operands) + + def _validate_input(pred, true_fn, false_fn, operands): + if not isinstance(pred, (bool, torch.Tensor, torch.SymBool)): + raise RuntimeError(f"Expected pred to be bool or tensor, but got {pred}.") + + if isinstance(pred, torch.Tensor) and pred.numel() != 1: + raise RuntimeError( + f"Expected pred to be bool or single-element tensor, but got {pred}." + ) + + if not callable(true_fn) or not callable(false_fn): + raise RuntimeError("Expect both branches to be callbale.") + + if not isinstance(operands, (tuple, list)) or pytree.tree_any( + lambda t: not isinstance(t, torch.Tensor), operands + ): + raise RuntimeError( + "Expect operands to be a tuple of possibly nested dict/list/tuple that only" + f"consists of tensor leaves, but got {operands}." + ) + + _validate_input(pred, true_fn, false_fn, operands) + + if not torch._dynamo.is_dynamo_supported(): + raise RuntimeError("torch.cond requires dynamo support.") + + # Dynamo is expecting a callable with "__code__" attribute. + # We cannot directly pass cond_op to it. So we wrap it in a dummy function. + def _cond_op_wrapper(*args, **kwargs): + return cond_op(*args, **kwargs) + + with _set_compilation_env(): + with torch._dynamo.utils.disable_cache_limit(): + with _temp_remove_pre_dispatch_torch_function_mode(): + return torch.compile(_cond_op_wrapper, backend="eager", fullgraph=True)( + pred, true_fn, false_fn, operands + ) + + +def create_fw_bw_graph_branches(true_fn, false_fn, *operands): + # See Note [HOP create fw_bw graph] in create_fw_bw_graph in utils.py + + with suspend_functionalization(), disable_functional_mode(): + with disable_proxy_modes_tracing(): + fw_inputs = pytree.tree_map(_from_fun, operands) + + fw_outputs_true = pytree.tree_map(_from_fun, true_fn(*fw_inputs)) + if any( + not isinstance(out, torch.Tensor) + for out in fw_outputs_true + if out is not None + ): + raise RuntimeError( + "Expect outputs of true_fn to only contains tensors or None. " + f"Got types {[type(out) for out in fw_outputs_true]}." + ) + fw_outputs_false = pytree.tree_map(_from_fun, false_fn(*fw_inputs)) + if any( + not isinstance(out, torch.Tensor) + for out in fw_outputs_false + if out is not None + ): + raise RuntimeError( + "Expect outputs of false_fn to only contains tensors or None. " + f"Got types {[type(out) for out in fw_outputs_false]}." + ) + + # TODO: There is a major issue that the create_fw_bw in the higher_order_op is invoked twice: + # Once in the forward path (as it should) and once in the backward path, where it shouldn't be called + # If we can get rid of the second invokation, it would simplify this function + fw_true_graph, joint_true_graph = create_fw_bw_graph( + true_fn, False, fw_inputs, fw_outputs_true + ) + fw_false_graph, joint_false_graph = create_fw_bw_graph( + false_fn, False, fw_inputs, fw_outputs_false + ) + + return fw_true_graph, fw_false_graph, joint_true_graph, joint_false_graph + + +def trace_cond(proxy_mode, func_overload, pred, true_fn, false_fn, operands): + assert isinstance( + operands, (list, tuple) + ), "Cond operands must be a list or tuple of tensors" + assert all( + isinstance(o, torch.Tensor) for o in operands + ), "Cond operands must be a list of tensors" + + true_graph = reenter_make_fx(true_fn)(*operands) + false_graph = reenter_make_fx(false_fn)(*operands) + + true_outs = [] + false_outs = [] + for node in true_graph.graph.nodes: + if node.op == "output": + true_outs.extend(node.args) + + for node in false_graph.graph.nodes: + if node.op == "output": + false_outs.extend(node.args) + + flat_true_outs = pytree.arg_tree_leaves(*true_outs) + flat_false_outs = pytree.arg_tree_leaves(*false_outs) + if len(flat_true_outs) != len(flat_false_outs): + raise torch._dynamo.exc.CondOpArgsMismatchError( + f"Expected to return same number of outputs but got:" + f"\n true branch returns {len(flat_true_outs)} item(s)" + f"\n false branch returns {len(flat_false_outs)} item(s)" + ) + + for i in range(0, len(flat_true_outs)): + true_out = flat_true_outs[i] + false_out = flat_false_outs[i] + + # Note that we need skip the check for requires_grad because we're after + # after autograd key during tracing, so the rquires_grad attribute of the tensors + # are no longer. See Note [invariants for node meta 'val'] + def _same_meta_except_requires_grad(true_out, false_out): + if true_out is None and false_out is None: + return True + elif true_out is None or false_out is None: + # Consider the following case: + # def true_fn(x, y): + # return x * y + # + # def false_fn(x, y): + # return x.sin() + # + # We'll get the following graphs for backward: + # def backward_true_fn(x, y, grad_out): + # return grad_out * y, grad_out * x + # + # def backward_false_fn(x, y, grad_out): + # retrun grad_out, None + # + # This suggests that when we make_fx into the backward graph, + # the output graph would produce outputs with metadata, this is undesirable. + # + # Ideally, we should provide an optional type to indicate that one of the branches might + # return None. But we'll just let it pass for now and let downstream/runtime handle. + # + # Note that this corner case should **only** happen when user want to trace backward graph because + # if it's foward, dynamo will error. + return True + true_meta = true_out.meta.get("tensor_meta", None) + false_meta = false_out.meta.get("tensor_meta", None) + return ( + true_meta.shape == false_meta.shape + and true_meta.dtype == false_meta.dtype + and true_meta.stride == false_meta.stride + ) + + if not _same_meta_except_requires_grad(true_out, false_out): + raise torch._dynamo.exc.CondOpArgsMismatchError( + f"Expected each tensor to have same metadata but got:" + f"\n {true_fn.__name__} returns {true_out.meta['tensor_meta']}" + f"\n {false_fn.__name__} returns {false_out.meta['tensor_meta']}" + ) + + i, true_name = unique_graph_id(proxy_mode, prefix="true_graph") + + false_name = f"false_graph_{i}" + assert not hasattr(proxy_mode.tracer.root, false_name) + + proxy_mode.tracer.root.register_module(true_name, true_graph) + proxy_mode.tracer.root.register_module(false_name, false_graph) + + args = (pred, true_graph, false_graph, operands) + + proxy_args = pytree.tree_map(proxy_mode.tracer.unwrap_proxy, args) + + out_proxy = proxy_mode.tracer.create_proxy( + "call_function", func_overload, proxy_args, {} + ) + + # At this point, we're *guaranteed* that whether an output came from the + # true or false branch is indistinguishable. So, as this is just for tracing + # purposes, choose the true branch. + + # TODO: the unbacked symbol allocations MUST NOT leak out, if you want to + # support this we need to arrange for the reenter_make_fx unbacked SymInts + # to be used, AND we need to arrange for some sort of unification between + # the two branches (but not really unification; e.g., if one branch + # returns [u0] and the other returns [5] this is OK but you MUST NOT + # conclude the result is 5. Also if one branch returns [3] and another + # branch returns [5] you can make it work by immediately allocating a new + # unbacked SymInt here). + ignore_fresh_unbacked = contextlib.nullcontext() + if (fake_mode := detect_fake_mode()) and fake_mode.shape_env: + ignore_fresh_unbacked = fake_mode.shape_env.ignore_fresh_unbacked_symbols() + + # TODO: Uhh.... it shouldn't matter, but changing this to true_fn results in + # a FakeTensorMode error : + # `Current active mode not registered` + # TODO Sometimes the operands are not completely FakeTensor, something seems went wrong in + # dynamo? Because of that it runs real computation sometimes and re-triggering downstream dispatch keys. + with ignore_fresh_unbacked: + out = false_fn(*operands) + + return track_tensor_tree(out, out_proxy, constant=None, tracer=proxy_mode.tracer) + + +@cond_op.py_impl(DispatchKey.CompositeExplicitAutograd) +def cond_op_dense(pred, true_fn, false_fn, operands): + mode = _get_current_dispatch_mode() + assert mode is None, "Mode should never be enabled for CPU/CUDA key" + if pred: + return true_fn(*operands) + else: + return false_fn(*operands) + + +class CondAutogradOp(torch.autograd.Function): + @staticmethod + def forward( + ctx, + pred, + fw_true_graph, + fw_false_graph, + joint_true_graph, + joint_false_graph, + *operands, + ): + ctx._pred = pred + ctx._joint_true_graph = joint_true_graph + ctx._joint_false_graph = joint_false_graph + ctx.save_for_backward(*operands) + + with torch._C._AutoDispatchBelowAutograd(): + return cond_op(pred, fw_true_graph, fw_false_graph, operands) + + @staticmethod + def backward(ctx, *flat_grads): + operands = ctx.saved_tensors + + grads = cond_op( + ctx._pred, + ctx._joint_true_graph, + ctx._joint_false_graph, + flat_grads + operands, + ) + return None, None, None, None, None, *grads + + +@cond_op.py_impl(DispatchKey.Autograd) +def cond_autograd(pred, true_fn, false_fn, operands): + # A shortcut for the case where all inputs don't require gradient, + # we skip tracing the forward and backward graph. + if pytree.tree_all_only( + torch.Tensor, + lambda t: not t.requires_grad, # type: ignore[union-attr] + (pred, operands), + ): + with torch._C._AutoDispatchBelowAutograd(): + return cond_op(pred, true_fn, false_fn, operands) + + ( + fw_true_graph, + fw_false_graph, + joint_true_graph, + joint_false_graph, + ) = create_fw_bw_graph_branches(true_fn, false_fn, *operands) + flat_out = CondAutogradOp.apply( + pred, + fw_true_graph, + fw_false_graph, + joint_true_graph, + joint_false_graph, + *operands, + ) + return flat_out + + +@cond_op.py_impl(ProxyTorchDispatchMode) +def inner(mode, pred, true_fn, false_fn, operands): + return trace_cond(mode, cond_op, pred, true_fn, false_fn, operands) + + +@cond_op.py_impl(FakeTensorMode) +def cond_fake_tensor_mode(mode, pred, true_fn, false_fn, operands): + # Ignore here, because if you've gotten here but you're not manually + # tracing the inner graphs, that means that you intend to reuse the graph + # directly. Which means the old unbacked symbol bindings are appropriate. + # This strategy will not work if unbacked symbols can escape. + ignore_fresh_unbacked = contextlib.nullcontext() + if mode.shape_env: + ignore_fresh_unbacked = mode.shape_env.ignore_fresh_unbacked_symbols() + + with mode, ignore_fresh_unbacked: + true_outs = true_fn(*operands) + flat_true_outs = pytree.tree_leaves(true_outs) + flat_false_outs = pytree.tree_leaves(false_fn(*operands)) + if len(flat_true_outs) != len(flat_false_outs): + raise RuntimeError("Unmatched number of outputs from cond() branches.") + + for true_out, false_out in zip(flat_true_outs, flat_false_outs): + true_meta = _extract_tensor_metadata(true_out) + false_meta = _extract_tensor_metadata(false_out) + if true_meta != false_meta: + raise torch._dynamo.exc.CondOpArgsMismatchError( + f"Expected each tensor to have same metadata but got:" + f"\n {true_fn.__name__} returns {true_meta}" + f"\n {false_fn.__name__} returns {false_meta}" + ) + return true_outs + + +@cond_op.py_functionalize_impl +def cond_func(ctx, pred, true_fn, false_fn, inputs): + unwrapped_inputs = ctx.unwrap_tensors(inputs) + unwrapped_pred = ctx.unwrap_tensors(pred) + with ctx.redispatch_to_next() as m: + functional_true = ctx.functionalize(_maybe_run_with_interpreter(true_fn)) + functional_false = ctx.functionalize(_maybe_run_with_interpreter(false_fn)) + pre_dispatch = hasattr(ctx, "mode") and ctx.mode.pre_dispatch + for branch in [functional_true, functional_false]: + if _has_potential_branch_input_mutation( + branch, unwrapped_inputs, pre_dispatch=pre_dispatch + ): + raise UnsupportedAliasMutationException( + "One of torch.cond branch might be modifying the input!" + ) + for branch in [true_fn, false_fn]: + if _has_potential_branch_input_alias( + branch, unwrapped_inputs, pre_dispatch=pre_dispatch + ): + raise UnsupportedAliasMutationException( + "One of torch.cond branch might be aliasing the input!" + ) + + cond_return = cond_op( + unwrapped_pred, functional_true, functional_false, unwrapped_inputs + ) + return ctx.wrap_tensors(cond_return) + + +@cond_op.py_impl(torch._C._functorch.TransformType.Vmap) +def cond_batch_rule(interpreter, pred, true_fn, false_fn, inputs): + assert isinstance( + inputs, (list, tuple) + ), "Cond inputs must be a list or tuple of tensors" + assert all( + isinstance(i, torch.Tensor) for i in inputs + ), "Cond inputs must be a list of tensors" + + pred_ = get_unwrapped(pred) if is_batchedtensor(pred) else pred + + # unbatched tensors are not vmapped + tensors, in_dims = zip( + *[ + (get_unwrapped(t), maybe_get_bdim(t)) if is_batchedtensor(t) else (t, None) + for t in inputs + ] + ) + + if is_batchedtensor(pred): + # prepend "pred" and vmap everything + tensors = (pred_,) + tensors + in_dims = (0,) + in_dims + + def fn(p, *args): + t = true_fn(*args) + f = false_fn(*args) + return torch.where(p, t[0], f[0]) + + with interpreter.lower(): + result = torch.vmap(fn, in_dims=in_dims)(*tensors) + + else: + # predicate is known at this stage and it is a boolean expression or a + # tensor with one element. + true_fn = torch.vmap(true_fn, in_dims=in_dims) + false_fn = torch.vmap(false_fn, in_dims=in_dims) + + with interpreter.lower(): + result = cond_op(pred, true_fn, false_fn, tensors) + + if not isinstance(result, tuple): + result = (result,) + lvl = interpreter.level() + return tuple([_add_batch_dim(r, 0, lvl) for r in result]) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/effects.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/effects.py new file mode 100644 index 0000000000000000000000000000000000000000..bb3d93bc69eb6b89ae2a3694bd28e24dee38ecbe --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/effects.py @@ -0,0 +1,289 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +from enum import Enum +from typing import Any, Dict, Optional, Tuple, Union +from weakref import WeakKeyDictionary + +import torch +import torch.utils._pytree as pytree +from torch._C import DispatchKey +from torch._higher_order_ops.torchbind import call_torchbind +from torch._ops import HigherOrderOperator +from torch._subclasses.fake_tensor import FakeTensorMode +from torch.fx.experimental.proxy_tensor import ( + disable_proxy_modes_tracing, + ProxyTorchDispatchMode, + track_tensor_tree, +) + + +class _EffectType(Enum): + ORDERED = "Ordered" + + +OpType = Union[torch._ops.HigherOrderOperator, torch._ops.OpOverload] + + +SIDE_EFFECTS: "WeakKeyDictionary[OpType, _EffectType]" = WeakKeyDictionary( + { + torch.ops.aten._print.default: _EffectType.ORDERED, + call_torchbind: _EffectType.ORDERED, + } +) + + +def _register_effectful_op(op: OpType, effect: _EffectType): + assert isinstance( + op, (torch._ops.OpOverload, torch._ops.HigherOrderOperator) + ) and not has_aliasing(op) + if op in SIDE_EFFECTS and SIDE_EFFECTS[op] != effect: + raise RuntimeError( + f"Already registered effect type {SIDE_EFFECTS[op]} to op {op}, " + f"trying to register a different effect type {effect}." + ) + SIDE_EFFECTS[op] = effect + + +def _deregister_effectful_op(op: OpType): + if op not in SIDE_EFFECTS: + raise RuntimeError(f"Op {op} is not registered as effectful") + + del SIDE_EFFECTS[op] + + +class WithEffects(HigherOrderOperator): + """ + with_effects(token, op, args, kwargs) -> (new_token, op_results) + + This HOP helps ensure ordering between side effectful ops like prints or ops + using torchbind objects. This is needed to ensure a traced graph from + AOTAutograd is functional so that future optimization passes do not reorder + these operators. This is done through threading "effect tokens" through the + graph to enforce data dependence between side effectful ops. + + The tokens are basically dummy values (torch.tensor([])). We create a token + per "effect type", which are enumerated in the _EffectType enum. + """ + + def __init__(self) -> None: + super().__init__("with_effects") + + def __call__( + self, + token, + op: OpType, + *args: Tuple[Any, ...], + **kwargs: Dict[str, Any], + ) -> Tuple[Any, ...]: + assert isinstance(op, (torch._ops.HigherOrderOperator, torch._ops.OpOverload)) + assert not has_aliasing(op), "Ops with aliasing is not supported" + assert has_effects(op, args, kwargs) + assert isinstance(kwargs, dict) + return super().__call__(token, op, *args, **kwargs) + + +with_effects = WithEffects() + + +def has_aliasing(op: OpType): + # NOT FOR PUBLIC USE + if isinstance(op, torch._ops.HigherOrderOperator): + return op not in SIDE_EFFECTS + + for arg in op._schema.arguments: + if arg.alias_info is not None: + return True + for arg in op._schema.returns: + if arg.alias_info is not None: + return True + return False + + +def has_effects(op, args, kwargs) -> bool: + # Skip over the profiler's RecordFunction as they should not show up in the graph + _skip_ops = {torch.ops.profiler._record_function_exit._RecordFunction} + if op in _skip_ops: + return False + + return ( + isinstance(op, (torch._ops.HigherOrderOperator, torch._ops.OpOverload)) + and not has_aliasing(op) + and get_effect_key(op, args, kwargs) is not None + ) + + +def get_effect_key(op, args, kwargs) -> Optional[_EffectType]: + if op in SIDE_EFFECTS: + return SIDE_EFFECTS[op] + + for arg in args: + if isinstance(arg, torch.ScriptObject): + # Add it to the table so that next time we see the same op we don't + # have to parse through the args again + SIDE_EFFECTS[op] = _EffectType.ORDERED + return _EffectType.ORDERED + + return None + + +def new_token_tensor() -> torch.Tensor: + # Use dtype bool to not affect Inductor dtype promotions + return torch.tensor([], dtype=torch.bool) + + +@with_effects.py_impl(DispatchKey.CompositeExplicitAutograd) +def with_effects_dense( + token: torch.Tensor, + op: torch._ops.OpOverload, + *args: Tuple[Any, ...], + **kwargs: Dict[str, Any], +) -> Tuple[torch.Tensor, ...]: + out = op(*args, **kwargs) + new_token = new_token_tensor() + if isinstance(out, tuple): + return (new_token, *out) + return (new_token, out) + + +@with_effects.py_impl(FakeTensorMode) +def with_effects_fake( + mode, + token: torch.Tensor, + op: torch._ops.OpOverload, + *args: Tuple[Any, ...], + **kwargs: Dict[str, Any], +) -> Tuple[torch.Tensor, ...]: + with mode: + result = with_effects_dense(token, op, *args, **kwargs) + return result + + +@with_effects.py_impl(ProxyTorchDispatchMode) +def with_effects_proxy( + mode, + token: torch.Tensor, + op: torch._ops.OpOverload, + *args: Tuple[Any, ...], + **kwargs: Dict[str, Any], +) -> Tuple[torch.Tensor, ...]: + with disable_proxy_modes_tracing(): + out = with_effects(token, op, *args, **kwargs) + + proxy_token = mode.tracer.unwrap_proxy(token) + proxy_args = pytree.tree_map(mode.tracer.unwrap_proxy, args) + proxy_kwargs = pytree.tree_map(mode.tracer.unwrap_proxy, kwargs) + + from torch.fx.node import has_side_effect + + # To avoid the being DCEed by graph.eliminate_dead_code if they. + # don't have output or their outputs are not used. + has_side_effect(op) + + out_proxy = mode.tracer.create_proxy( + "call_function", + with_effects, + (proxy_token, op, *proxy_args), + proxy_kwargs, + ) + result = track_tensor_tree(out, out_proxy, constant=None, tracer=mode.tracer) + return result + + +with_effects.fallthrough(DispatchKey.AutogradCPU) +with_effects.fallthrough(DispatchKey.AutogradCUDA) + + +def _get_schema(op, args) -> torch.FunctionSchema: + if isinstance(op, torch._ops.OpOverload): + return op._schema + elif op == call_torchbind: + return getattr(args[0], args[1]).schema + else: + raise RuntimeError(f"Unable to get schema for op {op}") + + +def handle_effects( + allow_token_discovery: bool, + tokens: Dict[_EffectType, torch.Tensor], + op: OpType, + args: Tuple[Any, ...], + kwargs: Dict[str, Any], +) -> Any: + """ + Args: + allow_token_discovery: Whether or not we are discovering tokens. If this + is true, we will create a token for every side effect type seen that + does not have a token assigned yet. If this is false, the tokens + should've all been created ahead of time, so we will error if there is + no token mapping to every effect type. + + tokens: Map of effect type to tokens. This is to chain operators of the + same effects together so that they do not get reordered in later + optimization passes. + """ + + # Get a token. We can't do `tokens.get(op, torch.tensor([]))` because + # this will create an empty tensor during proxy mode tracing if the token + # doesn't exist. But the tokens should always exist during proxy mode tracing. + key = get_effect_key(op, args, kwargs) + assert key is not None + if key not in tokens: + assert ( + allow_token_discovery + ), f"Could not find a token for effect {key} which came from the function {op}" + proxy_tensor_mode = torch._C._get_dispatch_mode( + torch._C._TorchDispatchModeKey.PROXY + ) + if proxy_tensor_mode is not None: + # If we discovered a new token during tracing, we are in backward. + # Then we patch the graph, adding additional tangents_token as input to the joint graph. + tracer = proxy_tensor_mode.tracer + + from torch.fx.experimental.proxy_tensor import ( + disable_proxy_modes_tracing, + track_tensor_tree, + ) + + with disable_proxy_modes_tracing(): + token_tensor = new_token_tensor() + + token_proxy = proxy_tensor_mode.tracer.create_proxy( + "placeholder", "tangents_token", (), {}, name="tangents_token" + ) + track_tensor_tree(token_tensor, token_proxy, constant=None, tracer=tracer) + + tokens[key] = token_tensor + else: + tokens[key] = new_token_tensor() + + token = tokens[key] + + from torch._subclasses.functional_tensor import PythonFunctionalizeAPI + + ctx = PythonFunctionalizeAPI() + + unwrapped_token = ctx.unwrap_tensors([token])[0] # type: ignore[arg-type] + unwrapped_args = ctx.unwrap_tensors(args) # type: ignore[arg-type] + unwrapped_kwargs = ctx.unwrap_tensors(kwargs) # type: ignore[arg-type] + with ctx.redispatch_to_next(): + (new_token, *unwrapped_outs) = with_effects( + unwrapped_token, op, *unwrapped_args, **unwrapped_kwargs # type: ignore[arg-type] + ) + + schema = _get_schema(op, unwrapped_args) + if len(schema.returns) == 0: + assert unwrapped_outs[0] is None + unwrapped_outs = None # type: ignore[assignment] + elif len(schema.returns) == 1: + assert len(unwrapped_outs) == 1 + unwrapped_outs = unwrapped_outs[0] + else: + assert len(unwrapped_outs) == len(schema.returns) + + # Add the newly created token into the tokens map for a following call to + # use this token. + wrapped_token = ctx.wrap_tensors(new_token) + assert isinstance(wrapped_token, torch.Tensor) + tokens[key] = wrapped_token + + return ctx.wrap_tensors(unwrapped_outs) # type: ignore[arg-type] diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/executorch_call_delegate.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/executorch_call_delegate.py new file mode 100644 index 0000000000000000000000000000000000000000..a6ee5205ff4ebbde1240929727ec9e47b0a2433a --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/executorch_call_delegate.py @@ -0,0 +1,175 @@ +# mypy: allow-untyped-defs + +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +# pyre-strict + +from __future__ import annotations + +from typing import Any, cast + +import torch +import torch.utils._pytree as pytree +from torch._ops import HigherOrderOperator +from torch._subclasses.fake_tensor import FakeTensorMode +from torch.fx.experimental.proxy_tensor import ( + disable_proxy_modes_tracing, + get_proxy_slot, + ProxyTorchDispatchMode, + track_tensor_tree, +) +from torch.utils._pytree import tree_flatten + + +class ExecutorchCallDelegate(HigherOrderOperator): + def __init__(self): + super().__init__("executorch_call_delegate") + + def __call__(self, lowered_module, *args): + return super().__call__(lowered_module, *args) + + +executorch_call_delegate = ExecutorchCallDelegate() +executorch_call_delegate.fallthrough(torch._C.DispatchKey.PythonDispatcher) +executorch_call_delegate.fallthrough(torch._C.DispatchKey.PythonTLSSnapshot) +executorch_call_delegate.fallthrough(torch._C.DispatchKey.ADInplaceOrView) +executorch_call_delegate.fallthrough(torch._C.DispatchKey.AutocastCPU) + +LOWERED_BACKEND_MODULE_TYPE = "LoweredBackendModule" + + +# pyre-ignore +def trace_call_delegate(proxy_mode, func_overload, lowered_module, *args): + # pyre-ignore + def _unwrap_proxy(e): + if not isinstance(e, (torch.Tensor, torch.SymInt, torch.SymFloat)): + return e + return get_proxy_slot( + cast(torch.Tensor, e), proxy_mode.tracer, e, lambda e: e.proxy # type: ignore[attr-defined] + ) + + if not is_lowered_module(lowered_module): + raise ValueError( + "executorch_call_delegate()'s first argument must be a LoweredBackendModule" + ) + + with disable_proxy_modes_tracing(): + out = call_delegate_cpu(lowered_module, *args) + + get_lowered_module_name(proxy_mode.tracer.root, lowered_module) + + node_args = (lowered_module, *args) + proxy_args = pytree.tree_map(_unwrap_proxy, node_args) + out_proxy = proxy_mode.tracer.create_proxy( + "call_function", func_overload, proxy_args, {}, name="executorch_call_delegate" + ) + return track_tensor_tree(out, out_proxy, constant=None, tracer=proxy_mode.tracer) + + +@executorch_call_delegate.py_impl(torch._C.DispatchKey.CompositeExplicitAutograd) +# pyre-ignore +def call_delegate_cpu(lowered_module, *args): + # FX creates this immutable_dict/list concept. Get rid of this. + map_types = { + torch.fx.immutable_collections.immutable_dict: dict, + torch.fx.immutable_collections.immutable_list: list, + } + new_args = pytree.tree_map_only( + tuple(map_types.keys()), + lambda a: map_types[type(a)](a), + args, + lambda a: isinstance(a, tuple(map_types.keys())), + ) + return lowered_module.original_module.module()(*new_args) + + +@executorch_call_delegate.py_impl(torch._C.DispatchKey.Autograd) +# pyre-ignore +def call_delegate_autograd(lowered_module, *args): + # TODO: support autograd + flat_operands, _ = tree_flatten([lowered_module, *args]) + requires_grad = any( + f.requires_grad for f in flat_operands if isinstance(f, torch.Tensor) + ) + + with torch._C._ExcludeDispatchKeyGuard( + torch._C.DispatchKeySet(torch._C.DispatchKey.AutogradCPU) + ): + res = executorch_call_delegate(lowered_module, *args) + + if requires_grad: + # Create aliases of the output that has requires_grad=True. We need + # at least one of the inputs to err_fn to require grad so that the + # output will have a grad_fn. + + # pyre-ignore + def fake_requires_grad(var): + if var is not None: + var = var.detach() + if torch.is_floating_point(var) or torch.is_complex(var): + var.requires_grad = True + return var + + return pytree.tree_map_only(torch.Tensor, fake_requires_grad, res) + + return res + + +@executorch_call_delegate.py_impl(ProxyTorchDispatchMode) +# pyre-ignore +def call_delegate_proxy_torch_dispatch_mode(mode, lowered_module, *args): + res = trace_call_delegate(mode, executorch_call_delegate, lowered_module, *args) + return res + + +@executorch_call_delegate.py_impl(FakeTensorMode) +# pyre-ignore +def call_delegate_fake_tensor_mode(mode, lowered_module, *args): + with mode: + return call_delegate_cpu(lowered_module, *args) + + +@executorch_call_delegate.py_functionalize_impl +# pyre-ignore +def call_delegate_functionalize(ctx, lowered_module, *args): + unwrapped_args = tuple(ctx.unwrap_tensors(arg) for arg in args) + with ctx.redispatch_to_next(): + res = executorch_call_delegate(lowered_module, *unwrapped_args) + return ctx.wrap_tensors(res) + + +# pyre-ignore: Missing parameter annotation [2]: Parameter `obj` must have a type other than `Any`.Pyre +def is_lowered_module(obj: Any) -> bool: + """ + This function is added to avoid using isinstance(obj, + LoweredBackendModule) as it will import LoweredBackendModule, which may + cause a circular import. + """ + return type(obj).__name__ == LOWERED_BACKEND_MODULE_TYPE + + +def get_lowered_module_name( + root: torch.nn.Module, + # pyre-ignore: Undefined or invalid type [11]: Annotation `LoweredBackendModule` is not defined as a type. + lowered_module: LOWERED_BACKEND_MODULE_TYPE, # type: ignore[valid-type] +) -> str: + """ + Adds the given lowered_module into the given root module and returns the + name of the module added. + """ + # Find a qualifying name for the lowered submodule + qualname = None + i = 0 + while True: + qualname = f"lowered_module_{i}" + if not hasattr(root, qualname): + break + i += 1 + assert qualname is not None + + root.add_module(qualname, lowered_module) + return qualname diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/flex_attention.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/flex_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..2fc62594c50941f8fdf2edef58b059d127e1ad97 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/flex_attention.py @@ -0,0 +1,1054 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import math +from typing import Any, Callable, Dict, Sequence, Tuple, Union + +import torch +import torch.utils._pytree as pytree +from torch._C import DispatchKey +from torch._higher_order_ops.utils import ( + _has_potential_branch_input_mutation, + autograd_not_implemented, + reenter_make_fx, + UnsupportedAliasMutationException, +) +from torch._ops import HigherOrderOperator +from torch._subclasses import FakeTensorMode +from torch.fx.experimental.proxy_tensor import ( + make_fx, + ProxyTorchDispatchMode, + track_tensor_tree, +) +from torch.fx.graph_module import GraphModule +from torch.overrides import TorchFunctionMode + + +# Duplicate of _inductor/kernel/flex_attention.py to avoid circular import +def _construct_strides( + sizes: Sequence[int], + fill_order: Sequence[int], +) -> Sequence[int]: + """From a list of sizes and a fill order, construct the strides of the permuted tensor.""" + # Initialize strides + assert len(sizes) == len( + fill_order + ), "Length of sizes must match the length of the fill order" + strides = [0] * len(sizes) + + # Start with stride 1 for the innermost dimension + current_stride = 1 + + # Iterate through the fill order populating strides + for dim in fill_order: + strides[dim] = current_stride + current_stride *= sizes[dim] + + return strides + + +def _permute_strides(out: torch.Tensor, query_strides: Tuple[int, ...]) -> torch.Tensor: + """ + Create a new tensor with the same data and shape as the input, + but with strides permuted based on the input tensor's stride order. + + Args: + out (torch.Tensor): The output tensor of attention. + query_strides (List[int]): The stride order of the input query tensor + + Returns: + torch.Tensor: A new tensor with same shape and data as the input, + but with strides permuted based on the query tensor's stride order. + """ + from torch._inductor.ir import get_stride_order, stride_order2fill_order + + stride_order = get_stride_order(query_strides) + fill_order = stride_order2fill_order(stride_order) + assert out.storage_offset() == 0, "Only support storage_offset == 0" + out_strides = _construct_strides(out.shape, fill_order) + new_out = out.new_empty(out.shape).as_strided(out.shape, out_strides) + new_out.copy_(out) + return new_out + + +class TransformGetItemToIndex(TorchFunctionMode): + # This is needed since we want to support calling + # A[q_idx], where q_idx is a scalar tensor in score_mod. + # Today, when q_idx is a scalar tensor, we implicitly convert it to a python + # scalar and create a view. We do not want that behavior in this case, so we + # use this torchfunctionmode to override that behavior for score_mod + # wherever we're running it. + def __torch_function__(self, func, types, args, kwargs=None): + if func == torch.Tensor.__getitem__: + index_args = pytree.tree_leaves(args[1]) + if all(isinstance(x, torch.Tensor) for x in index_args): + return torch.ops.aten.index(args[0], index_args) + return func(*args, **(kwargs or {})) + + +class FlexAttentionHOP(HigherOrderOperator): + def __init__(self) -> None: + super().__init__("flex_attention") + + def __call__( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + score_mod: Callable, + block_mask: Tuple, + scale: float, + kernel_options: Dict[str, Any], + score_mod_other_buffers: Tuple = (), + mask_mod_other_buffers: Tuple = (), + ) -> Tuple[torch.Tensor, torch.Tensor]: + if not all( + isinstance(buf, torch.Tensor) + for buf in score_mod_other_buffers + mask_mod_other_buffers + ): + raise RuntimeError("Other buffers must be tensors.") + return super().__call__( + query, + key, + value, + score_mod, + block_mask, + scale, + kernel_options, + score_mod_other_buffers, + mask_mod_other_buffers, + ) + + +flex_attention = FlexAttentionHOP() + + +class FlexAttentionBackwardHOP(HigherOrderOperator): + def __init__(self) -> None: + super().__init__("flex_attention_backward") + + def __call__( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + out: torch.Tensor, + logsumexp: torch.Tensor, + grad_out: torch.Tensor, + grad_logsumexp: torch.Tensor, + fw_graph: Union[Callable, GraphModule], + joint_graph: GraphModule, + block_mask: Tuple, + scale: float, + kernel_options: Dict[str, Any], + score_mod_other_buffers: Tuple = (), + mask_mod_other_buffers: Tuple = (), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + if not all( + isinstance(buf, torch.Tensor) + for buf in score_mod_other_buffers + mask_mod_other_buffers + ): + raise RuntimeError("Other buffers must be tensors.") + return super().__call__( + query, + key, + value, + out, + logsumexp, + grad_out, + grad_logsumexp, + fw_graph, + joint_graph, + block_mask, + scale, + kernel_options, + score_mod_other_buffers, + mask_mod_other_buffers, + ) + + +flex_attention_backward = FlexAttentionBackwardHOP() + + +def _math_attention_inner( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + score_mod: Callable, + block_mask: Tuple, + scale: float, + kernel_options: Dict[str, Any], + score_mod_other_buffers: Tuple = (), + mask_mod_other_buffers: Tuple = (), +) -> Tuple[torch.Tensor, torch.Tensor]: + working_precision = torch.float64 if query.dtype == torch.float64 else torch.float32 + + scores = (query @ key.transpose(-2, -1)).to(dtype=working_precision) + + b = torch.arange(0, scores.size(0), device=scores.device) + h = torch.arange(0, scores.size(1), device=scores.device) + m = torch.arange(0, scores.size(2), device=scores.device) + n = torch.arange(0, scores.size(3), device=scores.device) + + captured_buffers_in_dim = (None,) * len(score_mod_other_buffers) + from torch.nn.attention.flex_attention import _vmap_for_bhqkv + + # first input is score + score_mod = _vmap_for_bhqkv(score_mod, prefix=(0,), suffix=captured_buffers_in_dim) + + mask_mod = block_mask[-1] + mask_mod_in_dim_buffers = (None,) * len(mask_mod_other_buffers) + mask_mod = _vmap_for_bhqkv(mask_mod, prefix=(), suffix=mask_mod_in_dim_buffers) + + with TransformGetItemToIndex(): + scores = (scores * scale).to(working_precision) + post_mod_scores = torch.where( + mask_mod(b, h, m, n, *mask_mod_other_buffers), + score_mod(scores, b, h, m, n, *score_mod_other_buffers), + torch.tensor(-float("inf"), dtype=working_precision, device=scores.device), + ) + + return scores, post_mod_scores + + +def math_attention( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + score_mod: Callable, + block_mask: Tuple, + scale: float, + kernel_options: Dict[str, Any], + score_mod_other_buffers: Tuple = (), + mask_mod_other_buffers: Tuple = (), +) -> Tuple[torch.Tensor, torch.Tensor]: + """Eager implementation + + This implementation uses vmap to vectorize the score_mod function over the batch, head, m, and n dimensions. + We then apply the vectorized score_mod function to the scores matrix. Each wrap of vmap applies one of the + batch, head, m, or n dimensions. We need to apply vmap 4 times to vectorized over all 4 dimensions. + + Args: + query: The query tensor + key: The key tensor + value: The value tensor + score_mod: The score_mod function + other_buffers: Other buffers that are passed to the score_mod function + """ + # broadcast query & key along head dim for GQA + G = query.size(1) // key.size(1) + value = torch.repeat_interleave(value, G, dim=1) + key = torch.repeat_interleave(key, G, dim=1) + + _, post_mod_scores = _math_attention_inner( + query, + key, + value, + score_mod, + block_mask, + scale, + kernel_options, + score_mod_other_buffers, + mask_mod_other_buffers, + ) + + # Set fully masked rows' sumexp to 0.0 + logsumexp = post_mod_scores.logsumexp(dim=-1) + masked_rows = torch.all(post_mod_scores == -float("inf"), dim=-1) + logsumexp = torch.where(masked_rows, -float("inf"), logsumexp) + + post_mod_scores = torch._safe_softmax(post_mod_scores, dim=-1) + + return post_mod_scores.to(query.dtype) @ value, logsumexp / math.log(2) + + +@flex_attention.py_impl(DispatchKey.CompositeExplicitAutograd) +def sdpa_dense( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + score_mod: Callable, + block_mask: Tuple, + scale: float, + kernel_options: Dict[str, Any], + score_mod_other_buffers: Tuple = (), + mask_mod_other_buffers: Tuple = (), +) -> Tuple[torch.Tensor, torch.Tensor]: + out, lse = math_attention( + query, + key, + value, + score_mod, + block_mask, + scale, + kernel_options, + score_mod_other_buffers, + mask_mod_other_buffers, + ) + out = _permute_strides(out, query.stride()) + return out, lse + + +def trace_flex_attention( + proxy_mode: ProxyTorchDispatchMode, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + score_mod: Callable, + block_mask: Tuple, + scale: float, + kernel_options: Dict[str, Any], + score_mod_other_buffers: Tuple = (), + mask_mod_other_buffers: Tuple = (), +) -> Tuple[torch.Tensor, torch.Tensor]: + """Traces the flex_attention operator with the given score_mod function and other_buffers. + + Trace SDPA will call make_fx with "fake" example vals and then trace the score_mod function + This will produce a GraphModule that will be stored on the root tracer as "sdpa_score". We + access this graph module in inductor to inline the score_mod function to the triton template. + """ + example_out = flex_attention( + query, + key, + value, + score_mod, + block_mask, + scale, + kernel_options, + score_mod_other_buffers, + mask_mod_other_buffers, + ) + example_vals = [ + torch.zeros((), dtype=query.dtype, requires_grad=query.requires_grad) + ] + [torch.zeros((), dtype=torch.int) for _ in range(4)] + mask_example_vals = [torch.zeros((), dtype=torch.int) for _ in range(4)] + mask_mod = block_mask[-1] + with TransformGetItemToIndex(): + score_graph = reenter_make_fx(score_mod)( + *example_vals, *score_mod_other_buffers + ) + mask_graph = reenter_make_fx(mask_mod)( + *mask_example_vals, *mask_mod_other_buffers + ) + assert isinstance(proxy_mode.tracer, torch.fx.Tracer) + block_mask = block_mask[:-1] + (mask_graph,) + qualname = proxy_mode.tracer.get_fresh_qualname("sdpa_score") + proxy_mode.tracer.root.register_module(qualname, score_graph) + mask_qualname = proxy_mode.tracer.get_fresh_qualname("sdpa_mask") + proxy_mode.tracer.root.register_module(mask_qualname, mask_graph) + node_args = ( + query, + key, + value, + score_graph, + block_mask, + scale, + kernel_options, + score_mod_other_buffers, + mask_mod_other_buffers, + ) + proxy_args = pytree.tree_map(proxy_mode.tracer.unwrap_proxy, node_args) + out_proxy = proxy_mode.tracer.create_proxy( + "call_function", flex_attention, proxy_args, {} + ) + return track_tensor_tree( + example_out, out_proxy, constant=None, tracer=proxy_mode.tracer + ) + + +@flex_attention.py_impl(ProxyTorchDispatchMode) +def flex_attention_proxy_torch_dispatch_mode( + mode: ProxyTorchDispatchMode, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + score_mod: Callable, + block_mask: Tuple, + scale: float, + kernel_options: Dict[str, Any], + score_mod_other_buffers: Tuple = (), + mask_mod_other_buffers: Tuple = (), +) -> Tuple[torch.Tensor, torch.Tensor]: + assert mode is not None, "Mode should always be enabled for python fallback key" + return trace_flex_attention( + mode, + query, + key, + value, + score_mod, + block_mask, + scale, + kernel_options, + score_mod_other_buffers, + mask_mod_other_buffers, + ) + + +@flex_attention.py_functionalize_impl +def flex_attention_functionalize( + ctx: torch._subclasses.functional_tensor.BaseFunctionalizeAPI, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + score_mod: Callable, + block_mask: Tuple, + scale: float, + kernel_options: Dict[str, Any], + score_mod_other_buffers: Tuple = (), + mask_mod_other_buffers: Tuple = (), +) -> Tuple[torch.Tensor, torch.Tensor]: + """Defines the functionalization rules for the flex_attention operator. + + Write now we are unwrapping each tensor and then redispatching to the next, however we want to + guard against any mutations in the score_mod function, to the other_buffers since those + are free variables. + """ + query_unwrapped = ctx.unwrap_tensors(query) + key_unwrapped = ctx.unwrap_tensors(key) + value_unwrapped = ctx.unwrap_tensors(value) + block_mask_unwrapped = ctx.unwrap_tensors(block_mask) + score_mod_other_buffers_unwrapped = ctx.unwrap_tensors(score_mod_other_buffers) + mask_mod_other_buffers_unwrapped = ctx.unwrap_tensors(mask_mod_other_buffers) + + # Appease the mypy overlords + assert isinstance(query_unwrapped, torch.Tensor) + assert isinstance(key_unwrapped, torch.Tensor) + assert isinstance(value_unwrapped, torch.Tensor) + assert isinstance(block_mask_unwrapped, tuple) + assert isinstance(score_mod_other_buffers_unwrapped, tuple) + assert isinstance(mask_mod_other_buffers_unwrapped, tuple) + assert all( + isinstance(item, torch.Tensor) + for item in score_mod_other_buffers_unwrapped + mask_mod_other_buffers_unwrapped + ) + + example_vals = ( + [torch.zeros((), dtype=query.dtype)] + + [torch.zeros((), dtype=torch.int) for _ in range(4)] + + list(score_mod_other_buffers_unwrapped) + ) + with ctx.redispatch_to_next() as m: + functional_score_mod = ctx.functionalize(score_mod) + pre_dispatch = hasattr(ctx, "mode") and ctx.mode.pre_dispatch + with TransformGetItemToIndex(): + mutates = _has_potential_branch_input_mutation( + functional_score_mod, example_vals, pre_dispatch + ) + # The only care about mutations of existing buffers since we can't replay these. + # However, we can just error if anything is detected + if mutates: + raise UnsupportedAliasMutationException("Mutations detected in score_mod") + + out = flex_attention( + query_unwrapped, + key_unwrapped, + value_unwrapped, + functional_score_mod, + block_mask_unwrapped, + scale, + kernel_options, + score_mod_other_buffers_unwrapped, + mask_mod_other_buffers_unwrapped, + ) + return ctx.wrap_tensors(out) # type: ignore[return-value, arg-type] + + +@flex_attention.py_impl(FakeTensorMode) +def flex_attention_fake_tensor_mode( + mode: FakeTensorMode, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + score_mod: Callable, + block_mask: Tuple, + scale: float, + kernel_options: Dict[str, Any], + score_mod_other_buffers: Tuple = (), + mask_mod_other_buffers: Tuple = (), +) -> Tuple[torch.Tensor, torch.Tensor]: + with mode: + v_head_dim = value.size(-1) + batch_size, num_heads, seq_len_q, q_head_dim = query.shape + logsumexp = query.new_empty( + batch_size, num_heads, seq_len_q, dtype=torch.float32 + ) + out_shape = (batch_size, num_heads, seq_len_q, v_head_dim) + out = query.new_empty(out_shape) + out = _permute_strides(out, query.stride()) + return out, logsumexp + + +# ---------------------------- Autograd Implementation ---------------------------- +def create_fw_bw_graph(score_mod, index_values, other_buffers): + # See Note:[HOP create fw_bw graph] + + # All of these imports need to be here in order to avoid circular dependencies + from torch._dispatch.python import suspend_functionalization + from torch._functorch.aot_autograd import AOTConfig, create_joint + from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode + from torch._subclasses.functional_tensor import disable_functional_mode + from torch.fx.experimental.proxy_tensor import disable_proxy_modes_tracing + + dummy_aot_config = AOTConfig( + fw_compiler=None, # type: ignore[arg-type] + bw_compiler=None, # type: ignore[arg-type] + partition_fn=None, # type: ignore[arg-type] + decompositions={}, + num_params_buffers=0, + aot_id=0, + keep_inference_input_mutations=False, + ) + + with suspend_functionalization(), disable_functional_mode(): + with disable_proxy_modes_tracing(): + + def _from_fun(t): + return torch.empty_strided( + t.size(), + t.stride(), + device=t.device, + dtype=t.dtype, + requires_grad=t.requires_grad, + ) + + # If someone runs this hop under the default compiler backend ("eager") + # Then this path will be run with the actual user inputs. We convert them + # to fake tensors in order to not perform any actual compute. + from torch._guards import detect_fake_mode + + fake_mode = detect_fake_mode(index_values) + if fake_mode is None: + fake_mode = FakeTensorMode(allow_non_fake_inputs=True) + + with fake_mode: + unwrapped_score_mod_indexes = pytree.tree_map(_from_fun, index_values) + unwrapped_other_buffers = pytree.tree_map(_from_fun, other_buffers) + + assert all(isinstance(t, FakeTensor) for t in unwrapped_score_mod_indexes) + assert all(isinstance(t, FakeTensor) for t in unwrapped_other_buffers) + + example_flat_out = pytree.tree_map( + _from_fun, + score_mod(*unwrapped_score_mod_indexes, *unwrapped_other_buffers), + ) + if not isinstance(example_flat_out, torch.Tensor): + raise RuntimeError( + "Expected output of score_mod to be a tensor." + f"Got type {type(example_flat_out)}." + ) + example_grad = _from_fun(example_flat_out) + + def joint_f(score, b, h, m, n, example_grad, *other_buffers): + def fw_with_masks(*args): + fw_out = score_mod(*args) + out_requires_grad = fw_out.requires_grad + return ((fw_out,), (out_requires_grad,)) + + joint = create_joint(fw_with_masks, aot_config=dummy_aot_config) + args = [score, b, h, m, n] + list(other_buffers) + optional_grad = [example_grad] if example_grad.requires_grad else [] + _, grads = joint(args, optional_grad) + + return grads + + joint_graph = make_fx(joint_f)( + *unwrapped_score_mod_indexes, example_grad, *unwrapped_other_buffers + ) + return score_mod, joint_graph + + +class FlexAttentionAutogradOp(torch.autograd.Function): + @staticmethod + def forward( + ctx, + query, + key, + value, + fw_graph, + joint_graph, + block_mask, + scale, + kernel_options, + score_mod_other_buffers, + mask_mod_other_buffers, + ) -> Tuple[torch.Tensor, torch.Tensor]: + any_buffer_requires_grad = any( + buffer.requires_grad + for buffer in score_mod_other_buffers + mask_mod_other_buffers + ) + assert ( + not any_buffer_requires_grad + ), "Captured buffers that require grad are not yet supported." + ctx._fw_graph = fw_graph + ctx._joint_graph = joint_graph + ctx._mask_graph = block_mask[-1] + # KV_BLOCK_SIZE and Q_BLOCK_SIZE are integers, so can't use ctx.save_for_backward + ctx._KV_BLOCK_SIZE = block_mask[8] + ctx._Q_BLOCK_SIZE = block_mask[9] + ctx.scale = scale + ctx.kernel_options = kernel_options + ctx._score_mod_other_buffers_len = len(score_mod_other_buffers) + with torch._C._AutoDispatchBelowAutograd(): + out, logsumexp = flex_attention( + query, + key, + value, + fw_graph, + block_mask, + scale, + kernel_options, + score_mod_other_buffers, + mask_mod_other_buffers, + ) + + ctx.save_for_backward( + query, + key, + value, + out, + logsumexp, + *block_mask[:8], + *score_mod_other_buffers, + *mask_mod_other_buffers, + ) + return out, logsumexp + + @staticmethod + def backward(ctx, grad_out, grad_logsumexp): + fw_args = ctx.saved_tensors + ( + query, + key, + value, + out, + logsumexp, + kv_num_blocks, + kv_indices, + full_kv_num_blocks, + full_kv_indices, + q_num_blocks, + q_indices, + full_q_num_blocks, + full_q_indices, + *other_buffers, + ) = fw_args + fw_graph = ctx._fw_graph + joint_graph = ctx._joint_graph + mask_graph = ctx._mask_graph + KV_BLOCK_SIZE = ctx._KV_BLOCK_SIZE + Q_BLOCK_SIZE = ctx._Q_BLOCK_SIZE + scale = ctx.scale + kernel_options = ctx.kernel_options + score_mod_other_buffers = tuple( + other_buffers[: ctx._score_mod_other_buffers_len] + ) + mask_mod_other_buffers = tuple( + other_buffers[ctx._score_mod_other_buffers_len :] + ) + # We have asserted that other_buffers do not require grad in the forward + none_grads = [None] * 7 + grad_query, grad_key, grad_value = flex_attention_backward( + query, + key, + value, + out, + logsumexp, + grad_out, + grad_logsumexp, + fw_graph, + joint_graph, + ( + kv_num_blocks, + kv_indices, + full_kv_num_blocks, + full_kv_indices, + q_num_blocks, + q_indices, + full_q_num_blocks, + full_q_indices, + KV_BLOCK_SIZE, + Q_BLOCK_SIZE, + mask_graph, + ), + scale, + kernel_options, + score_mod_other_buffers, + mask_mod_other_buffers, + ) + return grad_query, grad_key, grad_value, *none_grads + + +@flex_attention.py_impl(DispatchKey.Autograd) +def flex_attention_autograd( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + score_mod: Callable, + block_mask: Tuple, + scale: float, + kernel_options: Dict[str, Any], + score_mod_other_buffers: Tuple = (), + mask_mod_other_buffers: Tuple = (), +) -> Tuple[torch.Tensor, torch.Tensor]: + with TransformGetItemToIndex(): + input_requires_grad = any(t.requires_grad for t in (query, key, value)) + if torch.is_grad_enabled() and input_requires_grad: + example_vals = [ + torch.zeros((), dtype=query.dtype, requires_grad=input_requires_grad) + ] + [torch.zeros((), dtype=torch.int) for _ in range(4)] + fw_graph, bw_graph = create_fw_bw_graph( + score_mod, example_vals, score_mod_other_buffers + ) + else: + fw_graph, bw_graph = score_mod, None + out, logsumexp = FlexAttentionAutogradOp.apply( + query, + key, + value, + fw_graph, + bw_graph, + block_mask, + scale, + kernel_options, + score_mod_other_buffers, + mask_mod_other_buffers, + ) + return out, logsumexp + + +# ---------------------------- Backward HOP Implementation ---------------------------- + + +@flex_attention_backward.py_impl(DispatchKey.CompositeExplicitAutograd) +def sdpa_dense_backward( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + out: torch.Tensor, + logsumexp: torch.Tensor, + grad_out: torch.Tensor, + grad_logsumexp: torch.Tensor, + fw_graph: Callable, # GraphModule type hint? + joint_graph: Callable, + block_mask: Tuple, + scale: float, + kernel_options: Dict[str, Any], + score_mod_other_buffers: Tuple, + mask_mod_other_buffers: Tuple, +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + # Get outputs before calling repeat interleave + actual_grad_query = torch.empty_like(query) + actual_grad_key = torch.empty_like(key) + actual_grad_value = torch.empty_like(value) + + G = query.size(1) // key.size(1) + key = torch.repeat_interleave(key, G, dim=1) + value = torch.repeat_interleave(value, G, dim=1) + + # We're undoing the log -> log2 change of base in the forwards + logsumexp = logsumexp * math.log(2) + # The backwards formula for the log -> log2 change of base in the forwards + grad_logsumexp = grad_logsumexp / math.log(2) + scores, post_mod_scores = _math_attention_inner( + query, + key, + value, + fw_graph, + block_mask, + scale, + kernel_options, + score_mod_other_buffers, + mask_mod_other_buffers, + ) + masked_out_rows = logsumexp == -float("inf") + softmax_scores = torch.exp(post_mod_scores - logsumexp.unsqueeze(-1)) + softmax_scores = torch.where(masked_out_rows.unsqueeze(-1), 0, softmax_scores) + + grad_value = softmax_scores.to(query.dtype).transpose(-2, -1) @ grad_out + + grad_softmax_scores = grad_out @ value.transpose(-2, -1) + + sum_scores = torch.sum(out * grad_out, -1, keepdim=True) + grad_score_mod = softmax_scores * ( + grad_softmax_scores - sum_scores + grad_logsumexp.unsqueeze(-1) + ) + + b = torch.arange(0, scores.size(0), device=scores.device) + h = torch.arange(0, scores.size(1), device=scores.device) + m = torch.arange(0, scores.size(2), device=scores.device) + n = torch.arange(0, scores.size(3), device=scores.device) + + mask_graph = block_mask[-1] + # Gradient of the inline score_mod function, with respect to the scores + captured_buffers_in_dim = (None,) * len(score_mod_other_buffers) + out_dims = [0, None, None, None, None] + [None] * len(score_mod_other_buffers) + from torch.nn.attention.flex_attention import _vmap_for_bhqkv + + # inputs are [score, b, h, q_idx, kv_idx, gradOut, ...] + # score and gradOut are "fully" batched + joint_score_mod = _vmap_for_bhqkv( + joint_graph, + prefix=(0,), + suffix=(0,) + captured_buffers_in_dim, + out_dims=out_dims, + ) + with TransformGetItemToIndex(): + grad_scores, *_ = joint_score_mod( + scores, b, h, m, n, grad_score_mod, *score_mod_other_buffers + ) + grad_scores = grad_scores * scale + grad_scores = grad_scores.to(query.dtype) + + mask_mod = _vmap_for_bhqkv( + mask_graph, prefix=(), suffix=(None,) * len(mask_mod_other_buffers) + ) + with TransformGetItemToIndex(): + mask_scores = mask_mod(b, h, m, n, *mask_mod_other_buffers) + grad_scores = torch.where( + mask_scores, grad_scores, torch.tensor(0, dtype=query.dtype) + ) + + grad_query = grad_scores @ key + grad_key = grad_scores.transpose(-2, -1) @ query + + # Reduce DK, DV along broadcasted heads. + grad_key = grad_key.view( + grad_key.size(0), -1, G, grad_key.size(-2), grad_key.size(-1) + ) + grad_value = grad_value.view( + grad_value.size(0), -1, G, grad_value.size(-2), grad_value.size(-1) + ) + + grad_key = torch.sum(grad_key, 2, keepdim=False) + grad_value = torch.sum(grad_value, 2, keepdim=False) + + actual_grad_query.copy_(grad_query) + actual_grad_key.copy_(grad_key) + actual_grad_value.copy_(grad_value) + + return actual_grad_query, actual_grad_key, actual_grad_value + + +def trace_flex_attention_backward( + proxy_mode: ProxyTorchDispatchMode, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + out: torch.Tensor, + logsumexp: torch.Tensor, + grad_out: torch.Tensor, + grad_logsumexp: torch.Tensor, + fw_graph: Union[Callable, GraphModule], + joint_graph: GraphModule, + block_mask: Tuple, + scale: float, + kernel_options: Dict[str, Any], + score_mod_other_buffers: Tuple = (), + mask_mod_other_buffers: Tuple = (), +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """We already have the forward graph and joint graph from the forward pass, so we create a proxy attach both graphs""" + example_out = flex_attention_backward( + query, + key, + value, + out, + logsumexp, + grad_out, + grad_logsumexp, + fw_graph, + joint_graph, + block_mask, + scale, + kernel_options, + score_mod_other_buffers, + mask_mod_other_buffers, + ) + + fw_example_vals = [ + torch.zeros((), dtype=query.dtype, requires_grad=query.requires_grad) + ] + [torch.zeros((), dtype=torch.int) for _ in range(4)] + bw_example_vals = fw_example_vals + [torch.zeros((), dtype=query.dtype)] + mask_example_vals = [torch.zeros((), dtype=torch.int) for _ in range(4)] + mask_graph = block_mask[-1] + with TransformGetItemToIndex(): + fw_graph = reenter_make_fx(fw_graph)(*fw_example_vals, *score_mod_other_buffers) + joint_graph = reenter_make_fx(joint_graph)( + *bw_example_vals, *score_mod_other_buffers + ) + mask_graph = reenter_make_fx(mask_graph)( + *mask_example_vals, *mask_mod_other_buffers + ) + assert isinstance(proxy_mode.tracer, torch.fx.Tracer) + block_mask = block_mask[:-1] + (mask_graph,) + proxy_mode.tracer.root.register_module("fw_graph", fw_graph) # type: ignore[arg-type] + proxy_mode.tracer.root.register_module("joint_graph", joint_graph) + proxy_mode.tracer.root.register_module("mask_graph", mask_graph) + node_args = ( + query, + key, + value, + out, + logsumexp, + grad_out, + grad_logsumexp, + fw_graph, + joint_graph, + block_mask, + scale, + kernel_options, + score_mod_other_buffers, + mask_mod_other_buffers, + ) + proxy_args = pytree.tree_map(proxy_mode.tracer.unwrap_proxy, node_args) + out_proxy = proxy_mode.tracer.create_proxy( + "call_function", + flex_attention_backward, + proxy_args, + {}, + name="flex_attention_backward", + ) + return track_tensor_tree( + example_out, out_proxy, constant=None, tracer=proxy_mode.tracer + ) + + +@flex_attention_backward.py_impl(ProxyTorchDispatchMode) +def flex_attention_backward_proxy_torch_dispatch_mode( + mode: ProxyTorchDispatchMode, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + out: torch.Tensor, + logsumexp: torch.Tensor, + grad_out: torch.Tensor, + grad_logsumexp: torch.Tensor, + fw_graph: Union[Callable, GraphModule], + joint_graph: GraphModule, + block_mask: Tuple, + scale: float, + kernel_options: Dict[str, Any], + score_mod_other_buffers: Tuple = (), + mask_mod_other_buffers: Tuple = (), +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + assert mode is not None, "Mode should always be enabled for python fallback key" + return trace_flex_attention_backward( + mode, + query, + key, + value, + out, + logsumexp, + grad_out, + grad_logsumexp, + fw_graph, + joint_graph, + block_mask, + scale, + kernel_options, + score_mod_other_buffers, + mask_mod_other_buffers, + ) + + +@flex_attention_backward.py_functionalize_impl +def flex_attention_backward_functionalize( + ctx: torch._subclasses.functional_tensor.BaseFunctionalizeAPI, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + out: torch.Tensor, + logsumexp: torch.Tensor, + grad_out: torch.Tensor, + grad_logsumexp: torch.Tensor, + fw_graph: Union[Callable, GraphModule], + joint_graph: GraphModule, + block_mask: Tuple, + scale: float, + kernel_options: Dict[str, Any], + score_mod_other_buffers: Tuple = (), + mask_mod_other_buffers: Tuple = (), +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Defines the functionalization rules for the flex_attention operator. + + Write now we are unwrapping each tensor and then redispatching to the next, + since we know that the forward score mod function is assured to be free of mutations + to the other_buffers, we skip that mutate check and go straight to redispatching. + """ + query_unwrapped = ctx.unwrap_tensors(query) + key_unwrapped = ctx.unwrap_tensors(key) + value_unwrapped = ctx.unwrap_tensors(value) + out_unwrapped = ctx.unwrap_tensors(out) + logsumexp_unwrapped = ctx.unwrap_tensors(logsumexp) + grad_out_unwrapped = ctx.unwrap_tensors(grad_out) + grad_logsumexp_unwrapped = ctx.unwrap_tensors(grad_logsumexp) + block_mask_unwrapped = ctx.unwrap_tensors(block_mask) + score_mod_other_buffers_unwrapped = ctx.unwrap_tensors(score_mod_other_buffers) + mask_mod_other_buffers_unwrapped = ctx.unwrap_tensors(mask_mod_other_buffers) + + # Appease the mypy overlords + assert isinstance(query_unwrapped, torch.Tensor) + assert isinstance(key_unwrapped, torch.Tensor) + assert isinstance(value_unwrapped, torch.Tensor) + assert isinstance(out_unwrapped, torch.Tensor) + assert isinstance(logsumexp_unwrapped, torch.Tensor) + assert isinstance(grad_out_unwrapped, torch.Tensor) + assert isinstance(grad_logsumexp_unwrapped, torch.Tensor) + assert isinstance(block_mask_unwrapped, tuple) + assert isinstance(score_mod_other_buffers_unwrapped, tuple) + assert isinstance(mask_mod_other_buffers_unwrapped, tuple) + assert all( + isinstance(item, torch.Tensor) + for item in score_mod_other_buffers_unwrapped + mask_mod_other_buffers_unwrapped + ) + + with ctx.redispatch_to_next() as m: + functional_fw_graph = ctx.functionalize(fw_graph) + functional_joint_graph = ctx.functionalize(joint_graph) + + grad_query, grad_key, grad_value = flex_attention_backward( + query_unwrapped, + key_unwrapped, + value_unwrapped, + out_unwrapped, + logsumexp_unwrapped, + grad_out_unwrapped, + grad_logsumexp_unwrapped, + functional_fw_graph, # type: ignore[arg-type] + functional_joint_graph, # type: ignore[arg-type] + block_mask_unwrapped, + scale, + kernel_options, + score_mod_other_buffers_unwrapped, + mask_mod_other_buffers_unwrapped, + ) + + return ctx.wrap_tensors((grad_query, grad_key, grad_value)) # type: ignore[return-value,arg-type] + + +@flex_attention_backward.py_impl(FakeTensorMode) +def flex_attention_backward_fake_tensor_mode( + mode: FakeTensorMode, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + out: torch.Tensor, + logsumexp: torch.Tensor, + grad_out: torch.Tensor, + grad_logsumexp: torch.Tensor, + fw_graph: Union[Callable, GraphModule], + joint_graph: GraphModule, + block_mask: Tuple, + scale: float, + kernel_options: Dict[str, Any], + score_mod_other_buffers: Tuple = (), + mask_mod_other_buffers: Tuple = (), +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + with mode: + grad_query = torch.empty_like(query) + grad_key = torch.empty_like(key) + grad_value = torch.empty_like(value) + return grad_query, grad_key, grad_value + + +flex_attention_backward.py_impl(DispatchKey.Autograd)( + autograd_not_implemented(flex_attention_backward, deferred_error=True) +) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/hints_wrap.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/hints_wrap.py new file mode 100644 index 0000000000000000000000000000000000000000..c211d405614636085575369e4a3738d3ced3fe02 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/hints_wrap.py @@ -0,0 +1,151 @@ +# mypy: allow-untyped-defs +import torch +import torch.utils._pytree as pytree +from torch._C import DispatchKey +from torch._higher_order_ops.utils import ( + _has_potential_branch_input_alias, + _has_potential_branch_input_mutation, + autograd_not_implemented, + reenter_make_fx, + unique_graph_id, + UnsupportedAliasMutationException, +) +from torch._ops import HigherOrderOperator +from torch._subclasses.fake_tensor import FakeTensorMode +from torch.fx.experimental.proxy_tensor import ProxyTorchDispatchMode, track_tensor_tree + + +# used for wrapping a function/op with context hints +class HintsWrapper(HigherOrderOperator): + def __init__(self): + super().__init__("hints_wrapper") + + def __call__(self, body_fn, args, kwargs, hints): + r""" + Call implementation of hints_wrapper + + Args: + body_fn (Callable): A callable function that is within the scope + that is being traced. + + args (Tuple of torch.Tensor/int/float/bool): A tuple of inputs to + body_fn. + + kwargs (dict): Keyword argument to the body_fn. + + hints (dict): A dict of context hints which could be passed to + backend compiler. + """ + if not isinstance(args, tuple): + raise RuntimeError(f"args must be a tuple, got {type(args)}") + + if not all(isinstance(t, (torch.Tensor, int, float, bool)) for t in args): + raise RuntimeError( + "args must be a tuple of tensors, ints, floats, or bools, got " + f"{args}" + ) + + if not isinstance(kwargs, dict): + raise RuntimeError(f"kwargs must be a dict, got {type(kwargs)}") + + if len(kwargs) > 0: + raise RuntimeError( + f"kwargs except for hints are not supported, got {kwargs}" + ) + + if not isinstance(hints, dict): + raise RuntimeError(f"hints must be a dict, got {type(hints)}") + + for k, v in hints.items(): + if not isinstance(k, str): + raise RuntimeError(f"hints key must be a str, got {k}.") + + if not isinstance(v, (int, float, bool, str)): + raise RuntimeError( + "hints must be a dict containing int, float, bool or str " + f"value, got value {v} for key {k}." + ) + + return super().__call__(body_fn, args, kwargs, hints) + + +hints_wrapper = HintsWrapper() + + +@hints_wrapper.py_impl(DispatchKey.CompositeExplicitAutograd) +def hints_wrapper_dense(body_fn, args, kwargs, hints): + return body_fn(*args, **kwargs) + + +hints_wrapper.py_impl(DispatchKey.Autograd)( + autograd_not_implemented(hints_wrapper, deferred_error=True) +) + + +@hints_wrapper.py_impl(FakeTensorMode) +def hints_wrapper_fake_tensor_mode(mode, body_func, args, kwargs, hints): + flat_args = pytree.tree_leaves(args) + with mode: + return body_func(*flat_args, **kwargs) + + +@hints_wrapper.py_functionalize_impl +def hints_wrapper_functionalize(ctx, body_fn, args, kwargs, hints): + unwrapped_args = ctx.unwrap_tensors(args) + unwrapped_kwargs = ctx.unwrap_tensors(kwargs) + unwrapped_hints = ctx.unwrap_tensors(hints) + with ctx.redispatch_to_next(): + functional_body_fn = ctx.functionalize(body_fn) + pre_dispatch = hasattr(ctx, "mode") and ctx.mode.pre_dispatch + if _has_potential_branch_input_mutation( + functional_body_fn, unwrapped_args, pre_dispatch=pre_dispatch + ): + raise UnsupportedAliasMutationException( + "body_fn of hints_wrapper might be modifying the input!" + ) + if _has_potential_branch_input_alias( + functional_body_fn, unwrapped_args, pre_dispatch=pre_dispatch + ): + raise UnsupportedAliasMutationException( + "body_fn of hints_wrapper might be aliasing the input!" + ) + outputs = hints_wrapper( + functional_body_fn, + unwrapped_args, + unwrapped_kwargs, + unwrapped_hints, + ) + return ctx.wrap_tensors(outputs) + + +def trace_hints_wrapper(proxy_mode, hints_wrapper, body_fn, args, kwargs, hints): + flat_args = tuple(pytree.tree_leaves(args)) + body_graph = reenter_make_fx(body_fn)(*flat_args, **kwargs) + + _, body_graph_name = unique_graph_id(proxy_mode, prefix="hints_wrapper_body_graph") + proxy_mode.tracer.root.register_module(body_graph_name, body_graph) + + new_args: tuple = (body_graph, flat_args, {}) + # merge hints into kwargs + new_kwargs = {} + new_kwargs["hints"] = hints + + proxy_args = pytree.tree_map(proxy_mode.tracer.unwrap_proxy, new_args) + proxy_kwargs = pytree.tree_map(proxy_mode.tracer.unwrap_proxy, new_kwargs) + + out_proxy = proxy_mode.tracer.create_proxy( + "call_function", hints_wrapper, proxy_args, proxy_kwargs, name="hints_wrapper" + ) + + out = body_fn(*flat_args, **kwargs) + return track_tensor_tree(out, out_proxy, constant=None, tracer=proxy_mode.tracer) + + +@hints_wrapper.py_impl(ProxyTorchDispatchMode) +def inner(proxy_mode, body_fn, args, kwargs, hints): + if proxy_mode.enable_tracing: + return trace_hints_wrapper( + proxy_mode, hints_wrapper, body_fn, args, kwargs, hints + ) + else: + return hints_wrapper(body_fn, args, kwargs, hints) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/map.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/map.py new file mode 100644 index 0000000000000000000000000000000000000000..d57d68d5e473f722f47e2112ff9a210dd2dc1bc9 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/map.py @@ -0,0 +1,264 @@ +# mypy: allow-untyped-defs +import torch +import torch.utils._pytree as pytree +from torch._C import DispatchKey +from torch._dispatch.python import suspend_functionalization +from torch._functorch.aot_autograd import AOTConfig, create_joint +from torch._higher_order_ops.utils import ( + _has_potential_branch_input_alias, + _has_potential_branch_input_mutation, + _maybe_run_with_interpreter, + reenter_make_fx, + UnsupportedAliasMutationException, +) +from torch._ops import HigherOrderOperator +from torch._subclasses.fake_tensor import FakeTensorMode +from torch._subclasses.functional_tensor import disable_functional_mode +from torch.fx.experimental.proxy_tensor import ( + disable_proxy_modes_tracing, + make_fx, + ProxyTorchDispatchMode, + track_tensor_tree, +) + +from .utils import ( + _from_fun, + _stack_pytree, + _unstack_pytree, + clone_outputs_aliasing_inputs, + prepare_fw_with_masks, +) + + +# TODO: We add this to prevent dymamo from tracing into map_wrapper, +# remove the wrapper call when it's ready. +class MapWrapper(HigherOrderOperator): + def __init__(self): + super().__init__("map") + + def __call__(self, xs, *args): + return map_wrapper(xs, *args) + + +class MapImpl(HigherOrderOperator): + def __init__(self): + super().__init__("map_impl") + + def __call__(self, *args, **kwargs): + return super().__call__(*args, **kwargs) + + +map = MapWrapper() + +map_impl = MapImpl() + +dummy_aot_config = AOTConfig( + fw_compiler=None, # type: ignore[arg-type] + bw_compiler=None, # type: ignore[arg-type] + partition_fn=None, # type: ignore[arg-type] + decompositions={}, + num_params_buffers=0, + aot_id=0, + keep_inference_input_mutations=False, +) + + +def create_fw_bw_graph(f, num_mapped_args, *args): + mapped_xs = args[:num_mapped_args] + pos_args = args[num_mapped_args:] + + # See Note [HOP create fw_bw graph] in create_fw_bw_graph in utils.py + + with suspend_functionalization(), disable_functional_mode(): + with disable_proxy_modes_tracing(): + unwrapped_mapped_xs = pytree.tree_map(_from_fun, mapped_xs) + example_xs = _unstack_pytree(unwrapped_mapped_xs)[0] + + example_pos_args = [ + _from_fun(arg) if isinstance(arg, torch.Tensor) else arg + for arg in pos_args + ] + example_flat_out = pytree.tree_map( + _from_fun, f(*example_xs, *example_pos_args) + ) + if any( + not isinstance(out, torch.Tensor) + for out in example_flat_out + if out is not None + ): + raise RuntimeError( + "Expect outputs of map only contains tensors or None. " + f"Got types {[type(out) for out in example_flat_out]}." + ) + example_grad = [_from_fun(out) for out in example_flat_out] + + fw_graph = make_fx(f)(*example_xs, *example_pos_args) + + def joint_f(*example_args): + joint_mapped_args = example_args[:joint_num_mapped] + args = example_args[joint_num_mapped:] + + mapped_input = joint_mapped_args[:num_mapped_args] + mapped_grads = joint_mapped_args[num_mapped_args:] + + joint = create_joint(prepare_fw_with_masks(f), aot_config=dummy_aot_config) + _, grads = joint( + list(mapped_input) + list(args), + [ + grad + for grad in mapped_grads + if grad is not None and grad.requires_grad + ], + ) + + # In order to keep map functional for backward graph, + # we clone outputs that are aliasing inputs + maybe_clone = clone_outputs_aliasing_inputs(example_args) + + return pytree.tree_map(maybe_clone, grads) + + joint_num_mapped = len(example_grad) + len(example_xs) + joint_graph = make_fx(joint_f)(*example_xs, *example_grad, *example_pos_args) + return fw_graph, joint_graph + + +def map_wrapper(f, xs, *args): + flat_xs, xs_spec = pytree.tree_flatten(xs) + if not all(isinstance(t, torch.Tensor) for t in flat_xs): + raise RuntimeError(f"Mapped xs can only consist of tensors. Got xs {flat_xs}.") + + num_mapped_args = len(flat_xs) + shapes = [xs.shape for xs in flat_xs] + leading_dim_size = shapes[0][0] + if leading_dim_size == 0: + raise RuntimeError("Leading dimensions of mapped xs cannot be 0.") + + if any(cur_shape[0] != leading_dim_size for cur_shape in shapes): + raise RuntimeError( + f"Leading dimensions of mapped xs must be consistent. Got shapes {shapes}." + ) + + out_spec = None + + def flat_fn(*flat_args): + xs = pytree.tree_unflatten(list(flat_args[:num_mapped_args]), xs_spec) + unflattened_out = f(xs, *flat_args[num_mapped_args:]) + flat_out, tmp_out_spec = pytree.tree_flatten(unflattened_out) + + nonlocal out_spec + out_spec = tmp_out_spec + return flat_out + + return pytree.tree_unflatten( + map_impl(flat_fn, flat_xs, args), out_spec # type: ignore[arg-type] + ) + + +class MapAutogradOp(torch.autograd.Function): + @staticmethod + def forward(ctx, fw_graph, joint_graph, num_mapped_args, *flat_args): + ctx.save_for_backward(*flat_args) + ctx._joint_graph = joint_graph + ctx._num_mapped_args = num_mapped_args + with torch._C._AutoDispatchBelowAutograd(): + return ( + *map_impl( + fw_graph, flat_args[:num_mapped_args], flat_args[num_mapped_args:] + ), + ) + + @staticmethod + def backward(ctx, *flat_grads): + fw_args = ctx.saved_tensors + fw_mapped_args = fw_args[: ctx._num_mapped_args] + pos_args = fw_args[ctx._num_mapped_args :] + + grads = map_impl( + ctx._joint_graph, + fw_mapped_args + flat_grads, + pos_args, + ) + return None, None, None, *grads + + +def trace_map(proxy_mode, func_overload, f, xs, pos_args): + leading_dim_size = xs[0].shape[0] + + example_input = _unstack_pytree(xs)[0] + body_graph = f + + body_graph = reenter_make_fx(body_graph)(*example_input, *pos_args) + + next_name = proxy_mode.tracer.get_fresh_qualname("body_graph_") + + proxy_mode.tracer.root.register_module(next_name, body_graph) + + with disable_proxy_modes_tracing(): + example_outs = body_graph(*example_input, *pos_args) + + def expand_tensor(t): + if isinstance(t, torch.Tensor): + return t.expand(leading_dim_size, *t.shape) + return t + + expanded_outs = pytree.tree_map(expand_tensor, example_outs) + + node_args = (body_graph, list(xs), list(pos_args)) + proxy_args = pytree.tree_map(proxy_mode.tracer.unwrap_proxy, node_args) + out_proxy = proxy_mode.tracer.create_proxy( + "call_function", func_overload, proxy_args, {}, name="map_impl" + ) + return track_tensor_tree( + expanded_outs, out_proxy, constant=None, tracer=proxy_mode.tracer + ) + + +@map_impl.py_impl(DispatchKey.CompositeExplicitAutograd) +def map_dense(f, xs, pos_args): + pytrees = [] + for inp in _unstack_pytree(xs): + pytrees.append(f(*inp, *pos_args)) + return _stack_pytree(pytrees) + + +@map_impl.py_impl(DispatchKey.Autograd) +def map_autograd(f, xs, pos_args): + num_mapped_args = len(xs) + fw_graph, bw_graph = create_fw_bw_graph(f, num_mapped_args, *xs, *pos_args) + flat_out = MapAutogradOp.apply(fw_graph, bw_graph, num_mapped_args, *xs, *pos_args) + return flat_out + + +@map_impl.py_impl(ProxyTorchDispatchMode) +def map_proxy_torch_dispatch_mode(mode, f, xs, args): + return trace_map(mode, map_impl, f, xs, args) + + +@map_impl.py_impl(FakeTensorMode) +def map_fake_tensor_mode(mode, f, xs, args): + with mode: + return map_dense(f, xs, args) + + +@map_impl.py_functionalize_impl +def map_functionalize(ctx, f, xs, pos_args): + unwrapped_xs = ctx.unwrap_tensors(xs) + unwrapped_args = ctx.unwrap_tensors(pos_args) + wrapped_fn = ctx.functionalize(_maybe_run_with_interpreter(f)) + + with ctx.redispatch_to_next(): + with disable_proxy_modes_tracing(): + example_inputs = (*_unstack_pytree(unwrapped_xs)[0], *unwrapped_args) + pre_dispatch = hasattr(ctx, "mode") and ctx.mode.pre_dispatch + if _has_potential_branch_input_mutation( + f, example_inputs, pre_dispatch=pre_dispatch + ): + raise UnsupportedAliasMutationException("torch.map is mutating the input!") + + if _has_potential_branch_input_alias( + f, example_inputs, pre_dispatch=pre_dispatch + ): + raise UnsupportedAliasMutationException("torch.map is aliasing the input!") + + map_return = map_impl(wrapped_fn, unwrapped_xs, unwrapped_args) + return ctx.wrap_tensors(map_return) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/out_dtype.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/out_dtype.py new file mode 100644 index 0000000000000000000000000000000000000000..3a2dfbe8ae2fb5f394d507fce632fe9802261136 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/out_dtype.py @@ -0,0 +1,166 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs + +import torch +import torch.utils._pytree as pytree +from torch._C import DispatchKey +from torch._higher_order_ops.utils import autograd_not_implemented +from torch._ops import HigherOrderOperator +from torch._prims_common import elementwise_dtypes, ELEMENTWISE_TYPE_PROMOTION_KIND +from torch._subclasses.fake_tensor import FakeTensorMode +from torch.fx.experimental.proxy_tensor import ( + disable_proxy_modes_tracing, + maybe_handle_decomp, + ProxyTorchDispatchMode, + track_tensor_tree, +) + + +# TODO to figure out a more generic approach +ALLOWABLE_OPS = [ + torch.ops.aten.linear.default, + torch.ops.aten.mm.default, + torch.ops.aten.conv2d.default, + torch.ops.aten.convolution.default, + torch.ops.aten.mul.Tensor, + torch.ops.aten.mul.Scalar, + torch.ops.aten.div.Tensor, + torch.ops.aten.div.Scalar, +] + + +class OutDtypeOperator(HigherOrderOperator): + """ + The out_dtype operator takes an existing ATen functional operator, an + `out_dtype` argument, and arguments to the original operator, and executes + the original operator and returns a Tensor with the `out_dtype` precision. + This operator does not mandate a compute precision so it allows the + representation to not be opinionated about the exact implementation. + + The general implementation for all operators will be the following: + 1. Promote inputs dtypes based on default PyTorch dtype promotion rules, + using the dtypes of all input Tensors/Scalars and the `out_dtype` + arugument. + 2. Execute the operator + 3. Cast the output to `out_dtype` + """ + + def __init__(self) -> None: + super().__init__("out_dtype") + + def __call__(self, op, output_dtype, *args): + if not isinstance(op, torch._ops.OpOverload): + raise ValueError("out_dtype's first argument must be an OpOverload") + if op._schema.is_mutable: + raise ValueError( + "out_dtype's first argument needs to be a functional operator" + ) + if not ( + len(op._schema.returns) == 1 + and isinstance(op._schema.returns[0].type, torch.TensorType) + ): + raise ValueError( + "out_dtype's can only apply to ops that return a single tensor" + f"Instead got {[r.type for r in op._schema.returns]}" + ) + + if op not in ALLOWABLE_OPS: + raise ValueError( + f"out_dtype only allows the following operators: {ALLOWABLE_OPS}." + ) + + res = super().__call__(op, output_dtype, *args) + + return res + + +out_dtype = OutDtypeOperator() + + +def trace_out_dtype(proxy_mode, func_overload, op, output_dtype, *args): + # NB: Long-term we should put the decomposition logic into + # ProxyTorchDispatchMode so that people do not need to call maybe_handle_decomp + # in all HigherOrderOp proxy implementations. + r = maybe_handle_decomp(proxy_mode, func_overload, (op, output_dtype, *args), {}) + if r is not NotImplemented: + return r + + with disable_proxy_modes_tracing(): + # This is a simplified implementation of this operator just for tracing. + # Actual implementation may also first promote the arguments + out = op(*args).to(dtype=output_dtype) + + node_args = (op, output_dtype, *args) + proxy_args = pytree.tree_map(proxy_mode.tracer.unwrap_proxy, node_args) + out_proxy = proxy_mode.tracer.create_proxy( + "call_function", func_overload, proxy_args, {}, name="out_dtype" + ) + return track_tensor_tree(out, out_proxy, constant=None, tracer=proxy_mode.tracer) + + +@out_dtype.py_impl(DispatchKey.CompositeExplicitAutograd) +def out_dtype_dense(op: torch._ops.OpOverload, output_dtype: torch.dtype, *args): + if is_int_mm(op, output_dtype, args): + return torch._int_mm(*args) + return out_dtype_fallback(op, output_dtype, *args) + + +def is_int_mm(op, output_dtype, args): + return ( + op == torch.ops.aten.mm.default + and output_dtype == torch.int32 + and len(args) == 2 + and args[0].dtype == torch.int8 + and args[1].dtype == torch.int8 + and args[0].is_cuda + and args[1].is_cuda + ) + + +def out_dtype_fallback(op, output_dtype, *args): + flat_inputs = pytree.arg_tree_leaves(*args) + [torch.ones(1, dtype=output_dtype)] + promote_dtype: torch.dtype = elementwise_dtypes( + *flat_inputs, + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, + )[0] + + casted_args = pytree.tree_map_only( + torch.Tensor, lambda arg: arg.to(dtype=promote_dtype), args + ) + res = op(*casted_args).to(dtype=output_dtype) + return res + + +out_dtype.py_impl(DispatchKey.Autograd)( + autograd_not_implemented(out_dtype, deferred_error=True) +) + + +@out_dtype.py_impl(ProxyTorchDispatchMode) +def out_dtype_proxy( + mode: ProxyTorchDispatchMode, + op: torch._ops.OpOverload, + output_dtype: torch.dtype, + *args, +): + return trace_out_dtype(mode, out_dtype, op, output_dtype, *args) + + +@out_dtype.py_impl(FakeTensorMode) +def out_dtype_fake_tensor_mode( + mode: FakeTensorMode, + op: torch._ops.OpOverload, + output_dtype: torch.dtype, + *args, +): + with mode: + return out_dtype_dense(op, output_dtype, *args) + + +@out_dtype.py_functionalize_impl +def out_dtype_func(ctx, op, output_dtype, *args): + unwrapped_args = tuple(ctx.unwrap_tensors(arg) for arg in args) + + with ctx.redispatch_to_next(): + res = out_dtype(op, output_dtype, *unwrapped_args) + return ctx.wrap_tensors(res) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/run_const_graph.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/run_const_graph.py new file mode 100644 index 0000000000000000000000000000000000000000..1f49ee28394a1c464612d2c5ee8a59abd6be1b49 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/run_const_graph.py @@ -0,0 +1,60 @@ +# mypy: allow-untyped-defs +import torch +from torch._C import DispatchKey +from torch._higher_order_ops.utils import autograd_not_implemented +from torch._ops import HigherOrderOperator +from torch._subclasses.fake_tensor import FakeTensorMode +from torch.fx.experimental.proxy_tensor import ProxyTorchDispatchMode, track_tensor_tree +from torch.utils import _pytree as pytree + + +class RunConstGraph(HigherOrderOperator): + def __init__(self): + super().__init__("run_const_graph") + + def __call__(self, *args): + return super().__call__(*args) + + +run_const_graph = RunConstGraph() + + +@run_const_graph.py_impl(ProxyTorchDispatchMode) +def run_const_graph_dispatch_mode(mode, *args): + const_gm, weights = args + p_args = pytree.tree_map(mode.tracer.unwrap_proxy, args) + assert isinstance(const_gm, torch.fx.GraphModule) + assert not hasattr(mode.tracer.root, "_const_graph") + mode.tracer.root.register_module("_const_graph", const_gm) + + proxy = mode.tracer.create_proxy("call_function", run_const_graph, p_args, {}) + + out = const_gm(*weights) + return track_tensor_tree(out, proxy, constant=None, tracer=mode.tracer) + + +@run_const_graph.py_functionalize_impl +def run_const_graph_functional(ctx, *args): + unwrapped_args = ctx.unwrap_tensors(args) + + with ctx.redispatch_to_next(): + out = run_const_graph(*unwrapped_args) + return ctx.wrap_tensors(out) + + +run_const_graph.py_impl(DispatchKey.Autograd)( + autograd_not_implemented(run_const_graph, deferred_error=True) +) + + +@run_const_graph.py_impl(FakeTensorMode) +def run_const_graph_fake_tensor_mode(mode, graph, args): + assert isinstance(graph, torch.fx.GraphModule) + with mode: + return graph(*args) + + +@run_const_graph.py_impl(DispatchKey.CPU) +def run_const_graph_cpu(graph, args): + assert isinstance(graph, torch.fx.GraphModule) + return graph(*args) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/strict_mode.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/strict_mode.py new file mode 100644 index 0000000000000000000000000000000000000000..7324e20dcd4cd7bb91fe0a94c50028df9a47cc78 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/strict_mode.py @@ -0,0 +1,94 @@ +# mypy: allow-untyped-defs +import torch +import torch._subclasses.functional_tensor +import torch.utils._pytree as pytree +from torch._C import DispatchKey +from torch._functorch.utils import exposed_in +from torch._higher_order_ops.utils import _set_compilation_env, autograd_not_implemented +from torch._ops import HigherOrderOperator +from torch._subclasses.fake_tensor import FakeTensorMode +from torch.fx.experimental.proxy_tensor import ( + disable_proxy_modes_tracing, + make_fx, + ProxyTorchDispatchMode, + track_tensor_tree, +) +from torch.utils._python_dispatch import _get_current_dispatch_mode + + +@exposed_in("torch") +def strict_mode(callable, operands): + if torch.compiler.is_dynamo_compiling(): + return strict_mode_op(callable, operands) + + with _set_compilation_env(): + with torch._dynamo.utils.disable_cache_limit(): + return torch.compile(strict_mode_op, backend="eager", fullgraph=True)( + callable, operands + ) + + +class StrictMode(HigherOrderOperator): + def __init__(self): + super().__init__("strict_mode") + + def __call__(self, callable, operands): + return super().__call__(callable, operands) + + +strict_mode_op = StrictMode() + + +@strict_mode_op.py_impl(DispatchKey.CompositeExplicitAutograd) +def strict_mode_op_dense(callable, operands): + mode = _get_current_dispatch_mode() + assert mode is None, "Mode should never be enabled for CPU/CUDA key" + return callable(*operands) + + +strict_mode_op.py_impl(DispatchKey.Autograd)( + autograd_not_implemented(strict_mode_op, deferred_error=True) +) + + +@strict_mode_op.py_impl(ProxyTorchDispatchMode) +def inner(mode, callable, operands): + return trace_strict_mode(mode, strict_mode_op, callable, operands) + + +def trace_strict_mode(mode, strict_mode_op, callable, operands): + pre_dispatch = getattr(mode, "pre_dispatch", False) + + with disable_proxy_modes_tracing(): + graph = make_fx(callable, pre_dispatch=pre_dispatch)(*operands) + + graph_name = mode.tracer.get_fresh_qualname("strict_graph_") + mode.tracer.root.register_module(graph_name, graph) + + args = (graph, operands) + + proxy_args = pytree.tree_map(mode.tracer.unwrap_proxy, args) + + out_proxy = mode.tracer.create_proxy( + "call_function", strict_mode_op, proxy_args, {}, name="strict_mode" + ) + + out = graph(*operands) + return track_tensor_tree(out, out_proxy, constant=None, tracer=mode.tracer) + + +@strict_mode_op.py_impl(FakeTensorMode) +def strict_mode_fake_tensor_mode(mode, callable, operands): + with mode: + true_outs = callable(*operands) + return true_outs + + +@strict_mode_op.py_functionalize_impl +def strict_mode_func(ctx, callable, inputs): + unwrapped_inputs = ctx.unwrap_tensors(inputs) + with ctx.redispatch_to_next(): + functional_callable = ctx.functionalize(callable) + + cond_return = strict_mode_op(functional_callable, unwrapped_inputs) + return ctx.wrap_tensors(cond_return) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/torchbind.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/torchbind.py new file mode 100644 index 0000000000000000000000000000000000000000..b35b8d5b296d118045347c35940454d88da07133 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/torchbind.py @@ -0,0 +1,142 @@ +# mypy: allow-untyped-defs +import logging +from contextlib import contextmanager + +import torch +from torch._C import DispatchKey # @manual +from torch._functorch._aot_autograd.utils import KNOWN_TYPES +from torch._higher_order_ops.utils import autograd_not_implemented +from torch._library.fake_class_registry import _ns_and_class_name, FakeScriptObject +from torch._ops import HigherOrderOperator +from torch._subclasses.fake_tensor import FakeTensorMode +from torch.fx.experimental.proxy_tensor import ProxyTorchDispatchMode, track_tensor_tree +from torch.fx.node import has_side_effect +from torch.utils import _pytree as pytree + + +log = logging.getLogger(__name__) + + +# The call_torchbind operator represents a method invocation on a torchbind +# object. The calling convention is: +# call_torchbind(self: ScriptObject, method_name: str, *method_args, **method_kwargs) +# We do not expect users to write this operator directly. Instead it will be +# emitted by Dynamo when tracing encounters a torchbind object. +class CallTorchBind(HigherOrderOperator): + def __init__(self): + super().__init__("call_torchbind") + + def __call__(self, obj, method, *args, **kwargs): + return super().__call__(obj, method, *args, **kwargs) + + +call_torchbind = CallTorchBind() + +# Register this operator as side-effectful with FX. +# TODO: this is not really sufficient. While passes (hopefully) check +# Node.is_impure() and make good decisions, we also assume we can execute the +# graph as many times as we want without changing behavior, which is NOT true of +# ops that mutate torchbind object state. +has_side_effect(call_torchbind) + +_orig_scriptmethod_call = torch.ScriptMethod.__call__ + + +def torchbind_method_redispatch(self, *args, **kwargs): + if isinstance(self.raw_owner, torch.ScriptObject): + return call_torchbind(self.raw_owner, self.name, *args, **kwargs) + return _orig_scriptmethod_call(self, *args, **kwargs) + + +@contextmanager +def enable_torchbind_tracing(): + """Context manager that acts as a feature flag to enable torchbind tracing + behavior. Once torchbind tracing has been stabilized, we can remove this and + turn it always on. + """ + try: + KNOWN_TYPES.append(torch.ScriptObject) + torch.ScriptMethod.__call__ = torchbind_method_redispatch # type: ignore[method-assign] + yield + finally: + assert ( + KNOWN_TYPES.pop() is torch.ScriptObject + ), "Someone else messed with KNOWN_TYPES during tracing, exploding." + torch.ScriptMethod.__call__ = _orig_scriptmethod_call # type: ignore[method-assign] + + +@call_torchbind.py_impl(DispatchKey.CompositeExplicitAutograd) +def call_torchbind_impl(obj, method, *args, **kwargs): + if isinstance(obj, torch.ScriptObject): + return _orig_scriptmethod_call(getattr(obj, method), *args, **kwargs) + elif isinstance(obj, FakeScriptObject): + return getattr(obj.wrapped_obj, method)(*args, **kwargs) + else: + raise RuntimeError(f"Unsupported first arg type {type(obj)} for call_torchbind") + + +@call_torchbind.py_impl(ProxyTorchDispatchMode) +def inner(mode, *args, **kwargs): + proxy_args = pytree.tree_map(mode.tracer.unwrap_proxy, args) + proxy_kwargs = pytree.tree_map(mode.tracer.unwrap_proxy, kwargs) + + out_proxy = mode.tracer.create_proxy( + "call_function", + call_torchbind, + proxy_args, + proxy_kwargs, + ) + out = call_torchbind(*args, **kwargs) + + obj, method, *rest_args = args + if isinstance(obj, torch.ScriptObject): + ns, class_name = _ns_and_class_name( + obj._type().qualified_name() # type: ignore[attr-defined] + ) + log.warning( + "Tracing torchbind method %s.%s with real ScriptObject. This may" + " cause the original object being mutated. If this is not intended," + ' You can register a fake class with torch._library.register_fake_class("%s::%s").', + class_name, + method, + ns, + class_name, + ) + + ret = track_tensor_tree(out, out_proxy, constant=None, tracer=mode.tracer) + if "val" not in out_proxy.node.meta: + assert out is None or isinstance( + out, (int, float, bool) + ), "Currently, only these constant dtypes are supported to be returned from torchbind methods." + out_proxy.node.meta["val"] = out + return ret + + +# When tracing with fake script object, the call_torchbind op will return a fake tensor +# When tracing with real script object, the call_torchbind op may return a real tensor, +# we need to convert it to fake tensor mannually. Dynamic shape is surpported. +@call_torchbind.py_impl(FakeTensorMode) +def call_torchbind_fake(mode, *args, **kwargs): + with mode: + out = call_torchbind_impl(*args, **kwargs) + return pytree.tree_map_only( + torch.Tensor, + lambda x: mode.from_tensor(x, static_shapes=True) + if not isinstance(x, torch._subclasses.fake_tensor.FakeTensor) + else x, + out, + ) + + +call_torchbind.py_impl(DispatchKey.Autograd)( + autograd_not_implemented(call_torchbind, deferred_error=True) +) + + +@call_torchbind.py_functionalize_impl +def call_torchbind_func(ctx, *args, **kwargs): + from torch._higher_order_ops.effects import handle_effects + + return handle_effects( + ctx.mode._allow_token_discovery, ctx.mode._tokens, call_torchbind, args, kwargs + ) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/triton_kernel_wrap.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/triton_kernel_wrap.py new file mode 100644 index 0000000000000000000000000000000000000000..5a1ad4405c5ec3d8d7cfd0967cc9a19a11e3c1ce --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/triton_kernel_wrap.py @@ -0,0 +1,1044 @@ +# mypy: allow-untyped-defs +import collections +import copy +import dataclasses +import inspect +import logging +import threading +from collections import defaultdict +from typing import Any, Dict, List, Optional, Union + +import torch +import torch.fx as fx +import torch.utils._pytree as pytree +from torch import Tensor +from torch._C import DispatchKey +from torch._ops import HigherOrderOperator +from torch._prims_common import clone_preserve_strides +from torch._subclasses.fake_tensor import FakeTensorMode +from torch.fx.experimental.proxy_tensor import ( + disable_proxy_modes_tracing, + ProxyTorchDispatchMode, + track_tensor_tree, +) + + +log = logging.getLogger("torch._dynamo") + + +############################################################################### +# Kernel Side Table + + +# We cannot put Triton Kernels into the FX graph as the graph nodes +# do not support arbitrary functions. +# Use a side table. +# We use two dicts so that fetching both the kernel and id are O(1) +class KernelSideTable: + id_to_kernel: Dict[int, Any] = {} + kernel_to_id: Dict[Any, int] = {} + constant_args: Dict[int, Any] = {} + lock = threading.Lock() + + # Returns index on the table + def add_kernel(self, kernel) -> int: + with self.lock: + if kernel in self.kernel_to_id: + return self.kernel_to_id[kernel] + + idx = len(self.id_to_kernel) + self.id_to_kernel[idx] = kernel + self.kernel_to_id[kernel] = idx + return idx + + # Returns the triton kernel at the given index + def get_kernel(self, idx: int): + # No need to lock here as fetching from dict is atomic + assert idx in self.id_to_kernel + return self.id_to_kernel[idx] + + # Not every constant arg can be added to the graph. Use this side table + # for constant args. + def add_constant_args(self, args) -> int: + with self.lock: + idx = len(self.constant_args) + self.constant_args[idx] = args + return idx + + # Returns the constant args + def get_constant_args(self, idx: int): + # No need to lock here as fetching from dict is atomic + assert idx in self.constant_args + return self.constant_args[idx] + + # Resets the table (only meant to be used in unit tests) + # This is only safe assuming single threaded execution + def reset_table(self) -> None: + self.id_to_kernel = {} + self.kernel_to_id = {} + self.constant_args = {} + + +kernel_side_table = KernelSideTable() + + +############################################################################### +# Mutation Tracker + + +@dataclasses.dataclass(frozen=True) +class Param: + idx: int + + +@dataclasses.dataclass(frozen=True) +class Intermediate: + idx: int + + def fake(self): + return self.idx < 0 + + +@dataclasses.dataclass(frozen=True) +class Op: + name: str + fn_call_name: Optional[str] + args: List[Union[Param, Intermediate]] + ret: Intermediate = dataclasses.field(repr=False) + + def __post_init__(self): + if self.name == "tt.call": + assert self.fn_call_name is not None + else: + assert self.fn_call_name is None + + +def generate_ttir(kernel, kwargs): + """ + Uses Triton's internal code generation to create TTIR + """ + import sympy + import triton + from triton.compiler.compiler import ASTSource + from triton.runtime.autotuner import Autotuner + from triton.runtime.jit import JITFunction + + import torch + import torch._inductor.ir + from torch._subclasses.fake_tensor import FakeTensor + + if isinstance(kernel, Autotuner): + if len(kernel.configs) > 0: + # If we are autotuning, then it doesn't matter which version gets + # picked for tracing purposes, so lets pick the first one + kwargs = {**kwargs, **kernel.configs[0].kwargs} + kernel = kernel.fn + + assert isinstance(kernel, JITFunction) + + if len(kwargs) != len(kernel.arg_names): + raise ValueError("Incorrect number of arguments passed to kernel") + + # Replace all SymExprs with a regular value for TTIR generation + # Replace all FakeTensor/TensorBox with real tensors + # These replacements are needed for triton's type, key and config functions + ordered_args: Dict[str, Any] = {} + for name in kernel.arg_names: + a = kwargs[name] + if isinstance(a, (torch.SymInt, torch.SymFloat, torch.SymBool, sympy.Expr)): + ordered_args[name] = 2 + elif isinstance(a, (FakeTensor, torch._inductor.ir.TensorBox)): + with torch._C._DisableTorchDispatch(): + ordered_args[name] = torch.empty(2, dtype=a.dtype) + else: + ordered_args[name] = a + + ordered_tensor_names = [ + name for name, arg in ordered_args.items() if isinstance(arg, Tensor) + ] + specialization = kernel._get_config(*ordered_args.values()) + constants = { + name: arg for name, arg in ordered_args.items() if not isinstance(arg, Tensor) + } + + # Build kernel signature -- doesn't include constexpr arguments. + signature = { + name: kernel._type_of(kernel._key_of(arg)) + for i, (name, arg) in enumerate(ordered_args.items()) + if i not in kernel.constexprs + } + + context = triton._C.libtriton.ir.context() + target = triton.runtime.driver.active.get_current_target() + backend = triton.compiler.compiler.make_backend(target) + options = backend.parse_options({}) + triton._C.libtriton.ir.load_dialects(context) + backend.load_dialects(context) + + src = ASTSource(kernel, signature, constants, specialization) + + # Triton changes ASTSource.make_ir to take 3/4 arguments. Handle + # backward compatibility here. + make_ir_sig_params = len(inspect.signature(src.make_ir).parameters) + if make_ir_sig_params == 2: + ttir_module = src.make_ir(options, context) + elif make_ir_sig_params == 3: + codegen_fns = backend.get_codegen_implementation() + ttir_module = src.make_ir(options, codegen_fns, context) + else: + codegen_fns = backend.get_codegen_implementation() + module_map = backend.get_module_map() + ttir_module = src.make_ir(options, codegen_fns, module_map, context) + if not ttir_module.verify(): + raise RuntimeError("Verification for TTIR module has failed") + + return ttir_module, ordered_tensor_names + + +def ttir_to_functions(ttir_module) -> Dict[str, Dict[Intermediate, List[Op]]]: + """ + Walk the `ttir_module` bottom up to mine the `functions` from + the structured MLIR entities representing the Triton kernel + (mlir::Operation, mlir::Block, mlir::Region). + """ + functions: Dict[str, Dict[Intermediate, List[Op]]] = {} + + # block id --> op result (Intermediate) --> one or more ops + op_stack: Dict[int, Dict[Intermediate, List[Op]]] = defaultdict( + lambda: defaultdict(list) + ) + region_id_to_block_ids: Dict[int, List[int]] = defaultdict(list) + block_id_to_block_arg_ids: Dict[int, List[int]] = {} + replacements: Dict[int, Union[Intermediate, Param]] = {} + reindex_map: Dict[int, int] = {} + next_fake_intermediate = 0 + + def reindex(idx): + if idx not in reindex_map: + reindex_map[idx] = len(reindex_map) + return reindex_map[idx] + + def mlir_to_functions(op) -> None: + name: str = op.get_name() + if name == "builtin.module": + # this wraps all tt.func ops + return + + operand_ids: List[int] = [ + reindex(op.get_operand(i).id()) for i in range(op.get_num_operands()) + ] + result_ids: List[int] = [ + reindex(op.get_result(i).id()) for i in range(op.get_num_results()) + ] + + child_block_ids: List[int] = [] + for i in [op.get_region(i).id() for i in range(op.get_num_regions())]: + # as the walk is bottom-up, the region_id_to_block_ids[i] + # must be populated by the time we process the enclosing op + child_block_ids.extend(region_id_to_block_ids[i]) + + parent_block_id = -1 + parent_block = op.get_block() + if parent_block is not None: + parent_block_id = parent_block.id() + if parent_block_id not in block_id_to_block_arg_ids: + block_id_to_block_arg_ids[parent_block_id] = [] + for i in range(parent_block.get_num_arguments()): + block_id_to_block_arg_ids[parent_block_id].append( + reindex(parent_block.get_argument(i).id()), + ) + # the region info is collected via ops' parent blocks to be + # used later when the region's encloding op is traversed + parent_region = parent_block.get_parent() + if parent_region is not None: + region_id_to_block_ids[parent_region.id()].append(parent_block_id) + + nonlocal next_fake_intermediate + + if name == "tt.func": + # for function ops: gather and inline + # the ops from all child blocks + fn_ops = defaultdict(list) + for child_block_id in child_block_ids: + for result, block_fn_ops in op_stack.pop(child_block_id).items(): + for block_fn_op in block_fn_ops: + fn_ops[result].append(block_fn_op) + + # replace the corresponding Intermediates in the + # child op args with the function args (Params) + for i, idx in enumerate(block_id_to_block_arg_ids[child_block_ids[0]]): + replacements[idx] = Param(i) + + for fn_op_list in fn_ops.values(): + for fn_op in fn_op_list: + for i in range(len(fn_op.args)): + arg = fn_op.args[i] + seen = set() # to break cycles + # there can be transitive replacements, but likely + # no cycles (we keep the `seen` set just in case) + while ( + isinstance(arg, Intermediate) + and arg.idx in replacements + and arg.idx not in seen + ): + seen.add(arg.idx) + arg = fn_op.args[i] = replacements[arg.idx] + + # next function capture starts + # with empty replacements + replacements.clear() + + fn_name = op.get_str_attr("sym_name") + functions[fn_name] = fn_ops + elif child_block_ids: + if name in {"scf.if", "scf.for", "scf.while", "tt.reduce", "tt.scan"}: + # for blocked ops: inline the enclosed ops into + # the parent block + rewire the last op in each + # child block to return the block result + return_ops = [] + for block_id in child_block_ids: + if name == "scf.for": + # example: + # %result = scf.for %iv = %lb to %ub step %step iter_args(%arg = %init) -> (i32) ... + # block args: 2 (%iv, %arg) + # op operands: 4 (%lb, %ub, %step, %init) + # `%arg` is mapping to `%init` + for i, idx in enumerate(block_id_to_block_arg_ids[block_id]): + if i == 0: + next_fake_intermediate -= 1 + replacements[idx] = Intermediate(next_fake_intermediate) + else: + replacements[idx] = Intermediate(operand_ids[i + 2]) + elif name == "scf.while": + # example: + # %3:3 = scf.while (%arg2 = %1, %arg3 = %2, %arg4 = %c0_i32_8) ... + # block args: 3 (%arg2, %arg3, %arg4) + # op operands: 3 (%1, %2, %c0_i32_8) + # `%arg2` is mapping to `%1`, `%arg3` is mapping to `%2`, ... + for i, idx in enumerate(block_id_to_block_arg_ids[block_id]): + replacements[idx] = Intermediate(operand_ids[i]) + elif name == "scf.if": + # the scf block args are ignored by the pass. but, as they + # may be used as operands of the ops inside the block + # (and nested blocks inlined in the current block by now), + # they are replaced by new fake Intermediates to avoid "this + # operand is not returned by any other op in the fn" error + # in the downstream analysis + for idx in block_id_to_block_arg_ids[block_id]: + next_fake_intermediate -= 1 + replacements[idx] = Intermediate(next_fake_intermediate) + else: + assert name in ("tt.reduce", "tt.scan") + # wire the block arguments to the op arguments + num_operands = len(operand_ids) + block_arg_ids = block_id_to_block_arg_ids[block_id] + assert len(block_arg_ids) == 2 * num_operands, ( + f"{name} is expected to have twice as " + "many block arguments as op arguments: " + f"{operand_ids=}, {block_arg_ids=}." + ) + for i, idx in enumerate(block_arg_ids): + # for a tt.reduce/tt.scan op with N arguments, the block + # arguments comprise N reduced values followed by + # N current values corresponding to the N op args + replacements[idx] = Intermediate( + operand_ids[i % num_operands] + ) + + if block_id in op_stack: + block_ops = op_stack.pop(block_id) + if not block_ops: + continue + last_ret, last_ops = block_ops.popitem() + if all( + op.name + in ("scf.yield", "tt.reduce.return", "tt.scan.return") + for op in last_ops + ): + # if last_ops are all return ops, treat them separately + return_ops.extend(last_ops) + else: + # otherwise, return last_ops to the block + block_ops[last_ret] = last_ops + for op_result, child_ops in block_ops.items(): + op_stack[parent_block_id][op_result].extend(child_ops) + + scf_results = [Intermediate(idx) for idx in result_ids] + for scf_result in scf_results: + for return_op in return_ops: + op_stack[parent_block_id][scf_result].append(return_op) + else: + raise RuntimeError( + f"Unknown blocked function: {name}. Can't capture the TTIR." + ) + else: + callee = None + if name == "tt.call": + callee = op.get_flat_symbol_ref_attr("callee") + args: List[Union[Param, Intermediate]] = [ + Intermediate(operand) for operand in operand_ids + ] + block_ops = op_stack[parent_block_id] + if result_ids: + for result_id in result_ids: + res = Intermediate(result_id) + block_ops[res].append(Op(name, callee, args, res)) + else: + next_fake_intermediate -= 1 + fake_res = Intermediate(next_fake_intermediate) + block_ops[fake_res].append(Op(name, callee, args, fake_res)) + + ttir_module.walk(mlir_to_functions) + + return functions + + +class MemoizeWithCycleCheck: + def __init__(self, fn): + self.fn = fn + self.reset() + + def __call__(self, functions, fn_name, num_args): + key = (fn_name, num_args) + if key not in self.cache: + self.cache[key] = None + self.cache[key] = self.fn(functions, fn_name, num_args) + if self.cache[key] is None: + raise RuntimeError("Recursion is not supported") + return self.cache[key] + + def reset(self): + self.cache = {} + + +@MemoizeWithCycleCheck +def analyze_kernel_mutations(functions, fn_name, num_args): + """ + Analyzes the graph to detect all sinks from a predefined list of sinks + by using triton's MemWrite trait list. NOTE: What if triton exposed this? + From each sink, it traverses the CFG backwards to identify all the input + pointers that are mutated. + """ + # Name of mutation op to mutated parameter indices + # List from Triton Github include/triton/Dialect/Triton/IR/TritonOps.td + # All the OPs that have MemWrite trait. + # What if Triton exposed this? + MUTATION_OPS = {"tt.store": [0], "tt.atomic_cas": [0], "tt.atomic_rmw": [0]} + # Ops that we want to bail out on + UNKNOWN_OPS = {"tt.elementwise_inline_asm"} + + stack: List[Union[Param, Intermediate]] = [] + visited = set() + ops = functions[fn_name] + for op_list in ops.values(): + for op in op_list: + if op.name in UNKNOWN_OPS: + raise RuntimeError( + f"ttir analysis hit an op we do not know how to analyze: {op.name}" + ) + + if op.name == "tt.call": + assert op.fn_call_name in functions + mutations = analyze_kernel_mutations( + functions, op.fn_call_name, len(op.args) + ) + stack.extend(arg for arg, mutated in zip(op.args, mutations) if mutated) + else: + for idx in MUTATION_OPS.get(op.name, []): + stack.append(op.args[idx]) + + # The following is an iterative DFS algorithm + mutated = [False] * num_args + while stack: + arg = stack.pop() + if arg in visited: + continue + + visited.add(arg) + + if isinstance(arg, Param): + if arg.idx >= num_args: + # This is an argument defined in the kernel, not passed in + continue + mutated[arg.idx] = True + elif isinstance(arg, Intermediate) and not arg.fake(): + for op in ops[arg]: + # Skip arguments to load + if op.name != "tt.load": + stack.extend(op.args) + return mutated + + +def identify_mutated_tensors(kernel, kwargs): + """ + Given a triton kernel and the arguments for this kernel, this function + 1) Retrieves the TTIR converted version of the kernel from Triton's API. + 2) Parses the TTIR and creates a control flow graph + 3) Analyzes the graph to detect all input tensor mutations + """ + + ttir_module = None + functions = None + try: + ttir_module, ordered_tensor_names = generate_ttir(kernel, kwargs) + + # extract functions from TTIR using MLIR bindings exposed by Triton code + functions = ttir_to_functions(ttir_module) + + assert functions is not None + kernel_name = next(iter(functions.keys())) + # Triton codegen modifies the name + assert kernel.fn.__name__ in kernel_name + # Reset the cache between top level invocations + # The cache for analyze kernel mutations is mainly used for cycle + # detection, so each top level invocation needs a clean cache + analyze_kernel_mutations.reset() + mutations = analyze_kernel_mutations( + functions, kernel_name, len(ordered_tensor_names) + ) + + return [ + ordered_tensor_names[i] for i, mutated in enumerate(mutations) if mutated + ] + except Exception as e: + log.warning( + "Encountered an exception in identify_mutated_tensors, assuming every input is mutated", + exc_info=True, + ) + if ttir_module is not None: + log.debug("TTIR:\n%s", str(ttir_module)) + if functions is not None: + log.debug("functions:") + for name, fn in functions.items(): + log.debug("===\t%s\t===", name) + for ret, ops in fn.items(): + log.debug("%s\t=>\t%s", ret, ops) + return [key for key, value in kwargs.items() if isinstance(value, Tensor)] + + +############################################################################### +# Triton Kernel Wrappers + + +# Used for wrapping a Triton Kernel +class TritonKernelWrapperMutation(HigherOrderOperator): + def __init__(self) -> None: + super().__init__("triton_kernel_wrapper_mutation") + + def __call__(self, kernel_idx, constant_args_idx, grid, kwargs): + return super().__call__( + kernel_idx=kernel_idx, + constant_args_idx=constant_args_idx, + grid=grid, + kwargs=kwargs, + ) + + +triton_kernel_wrapper_mutation = TritonKernelWrapperMutation() + + +# Used for wrapping a Triton Kernel in a functional manner +class TritonKernelWrapperFunctional(HigherOrderOperator): + def __init__(self) -> None: + super().__init__("triton_kernel_wrapper_functional") + + def __call__(self, kernel_idx, constant_args_idx, grid, kwargs, tensors_to_clone): + return super().__call__( + kernel_idx=kernel_idx, + constant_args_idx=constant_args_idx, + grid=grid, + kwargs=kwargs, + tensors_to_clone=tensors_to_clone, + ) + + +triton_kernel_wrapper_functional = TritonKernelWrapperFunctional() + + +@triton_kernel_wrapper_mutation.py_impl(DispatchKey.CompositeExplicitAutograd) +def triton_kernel_wrapper_mutation_dense( + *, kernel_idx, constant_args_idx, grid, kwargs +): + from torch._inductor.codegen.wrapper import user_defined_kernel_grid_fn_code + + kernel = kernel_side_table.get_kernel(kernel_idx) + constant_args = kernel_side_table.get_constant_args(constant_args_idx) + + if len(grid) == 1: + grid_fn = grid[0] + else: + fn_name, code = user_defined_kernel_grid_fn_code( + kernel.fn.__name__, kernel.configs, grid + ) + namespace: Dict[str, Any] = {} + exec(code, namespace) + grid_fn = namespace[fn_name] + + kernel[grid_fn](**kwargs, **constant_args) + + +@triton_kernel_wrapper_mutation.py_impl(FakeTensorMode) +def triton_kernel_wrapper_mutation_fake_tensor_mode( + mode, *, kernel_idx, constant_args_idx, grid, kwargs +): + with mode: + return None + + +@triton_kernel_wrapper_mutation.py_impl(DispatchKey.Meta) +def _(*, kernel_idx, constant_args_idx, grid, kwargs): + return None + + +def trace_triton_kernel_wrapper(proxy_mode, func_overload, node_args): + with disable_proxy_modes_tracing(): + out = func_overload(**node_args) + + proxy_args = pytree.tree_map(proxy_mode.tracer.unwrap_proxy, node_args) + out_proxy = proxy_mode.tracer.create_proxy( + "call_function", + func_overload, + (), + proxy_args, + name=func_overload.__name__ + "_proxy", + ) + ret = track_tensor_tree(out, out_proxy, constant=None, tracer=proxy_mode.tracer) + return ret + + +@triton_kernel_wrapper_mutation.py_impl(ProxyTorchDispatchMode) +def triton_kernel_wrapper_mutation_proxy_torch_dispatch_mode( + mode, *, kernel_idx, constant_args_idx, grid, kwargs +): + trace_triton_kernel_wrapper( + mode, + triton_kernel_wrapper_mutation, + { + "kernel_idx": kernel_idx, + "constant_args_idx": constant_args_idx, + "grid": grid, + "kwargs": kwargs, + }, + ) + + return None + + +@triton_kernel_wrapper_mutation.py_functionalize_impl +def triton_kernel_wrapper_mutation_functionalize( + ctx, kernel_idx, constant_args_idx, grid, kwargs +): + unwrapped_kwargs = ctx.unwrap_tensors(kwargs) + kernel = kernel_side_table.get_kernel(kernel_idx) + constant_args = kernel_side_table.get_constant_args(constant_args_idx) + # TODO(oulgen): Preexisting bug, if two kernel inputs are views of each + # other, and one gets mutated in kernel, and later another gets mutated, + # they are no longer equal. Fix this by graph breaking on this condition + # earlier in dynamo. + tensors_to_clone = identify_mutated_tensors( + kernel, {**unwrapped_kwargs, **constant_args} + ) + with ctx.redispatch_to_next(): + unwrapped_outputs = triton_kernel_wrapper_functional( + kernel_idx=kernel_idx, + constant_args_idx=constant_args_idx, + grid=grid, + kwargs=unwrapped_kwargs, + tensors_to_clone=tensors_to_clone, + ) + + assert set(unwrapped_outputs.keys()).issubset(set(kwargs.keys())) + for key, output_arg in unwrapped_outputs.items(): + if not isinstance(output_arg, Tensor): + continue + input_arg = kwargs[key] + assert isinstance(input_arg, Tensor) + + ctx.replace(input_arg, output_arg) + # indicate that above replace is hidden from autograd + ctx.mark_mutation_hidden_from_autograd(input_arg) + ctx.commit_update(input_arg) + ctx.sync(input_arg) + return None + + +@triton_kernel_wrapper_functional.py_impl(DispatchKey.CompositeExplicitAutograd) +def triton_kernel_wrapper_functional_dense( + *, kernel_idx, constant_args_idx, grid, kwargs, tensors_to_clone +): + # TODO(oulgen): For performance reasons, we want to ensure that these + # `clone_preserve_strides` calls are never executed at runtime + # (inductor should always optimize them away). + # Requires https://github.com/pytorch/pytorch/issues/109240 + kwargs = { + key: (clone_preserve_strides(val) if key in tensors_to_clone else val) + for key, val in kwargs.items() + } + triton_kernel_wrapper_mutation( + kernel_idx=kernel_idx, + constant_args_idx=constant_args_idx, + grid=grid, + kwargs=kwargs, + ) + return {key: val for key, val in kwargs.items() if key in tensors_to_clone} + + +@triton_kernel_wrapper_functional.py_impl(FakeTensorMode) +def triton_kernel_wrapper_functional_fake_tensor_mode( + mode, *, kernel_idx, constant_args_idx, grid, kwargs, tensors_to_clone +): + # TODO(oulgen): For performance reasons, we want to ensure that these + # `clone_preserve_strides` calls are never executed at runtime + # (inductor should always optimize them away). + # Requires https://github.com/pytorch/pytorch/issues/109240 + with mode: + return { + key: clone_preserve_strides(val) + for key, val in kwargs.items() + if key in tensors_to_clone + } + + +@triton_kernel_wrapper_functional.py_impl(ProxyTorchDispatchMode) +def triton_kernel_wrapper_functional_proxy_torch_dispatch_mode( + mode, *, kernel_idx, constant_args_idx, grid, kwargs, tensors_to_clone +): + return trace_triton_kernel_wrapper( + mode, + triton_kernel_wrapper_functional, + { + "kernel_idx": kernel_idx, + "constant_args_idx": constant_args_idx, + "grid": grid, + "kwargs": kwargs, + "tensors_to_clone": tensors_to_clone, + }, + ) + + +@triton_kernel_wrapper_functional.py_functionalize_impl +def triton_kernel_wrapper_functional_functionalize( + ctx, kernel_idx, constant_args_idx, grid, kwargs, tensors_to_clone +): + unwrapped_kwargs = ctx.unwrap_tensors(kwargs) + with ctx.redispatch_to_next(): + outputs = triton_kernel_wrapper_functional( + kernel_idx=kernel_idx, + constant_args_idx=constant_args_idx, + grid=grid, + kwargs=unwrapped_kwargs, + tensors_to_clone=tensors_to_clone, + ) + return ctx.wrap_tensors(outputs) + + +triton_kernel_wrapper_mutation.fallthrough(DispatchKey.PythonDispatcher) # type: ignore[attr-defined] +triton_kernel_wrapper_mutation.fallthrough(DispatchKey.PythonTLSSnapshot) # type: ignore[attr-defined] +triton_kernel_wrapper_mutation.fallthrough(DispatchKey.ADInplaceOrView) +triton_kernel_wrapper_mutation.fallthrough(DispatchKey.BackendSelect) +triton_kernel_wrapper_mutation.fallthrough(DispatchKey.AutocastCPU) # type: ignore[attr-defined] +triton_kernel_wrapper_mutation.fallthrough(DispatchKey.AutocastCUDA) # type: ignore[attr-defined] +triton_kernel_wrapper_mutation.fallthrough(DispatchKey.AutogradCUDA) +triton_kernel_wrapper_mutation.fallthrough(DispatchKey.AutogradCPU) + +triton_kernel_wrapper_functional.fallthrough(DispatchKey.PythonDispatcher) # type: ignore[attr-defined] +triton_kernel_wrapper_functional.fallthrough(DispatchKey.PythonTLSSnapshot) # type: ignore[attr-defined] +triton_kernel_wrapper_functional.fallthrough(DispatchKey.ADInplaceOrView) +triton_kernel_wrapper_functional.fallthrough(DispatchKey.BackendSelect) +triton_kernel_wrapper_functional.fallthrough(DispatchKey.AutocastCPU) # type: ignore[attr-defined] +triton_kernel_wrapper_functional.fallthrough(DispatchKey.AutocastCUDA) # type: ignore[attr-defined] +triton_kernel_wrapper_functional.fallthrough(DispatchKey.AutogradCUDA) +triton_kernel_wrapper_functional.fallthrough(DispatchKey.AutogradCUDA) +triton_kernel_wrapper_functional.fallthrough(DispatchKey.AutogradCPU) + + +############################################################################### +# The "TritonHOPifier": a class that transforms a call to a triton kernel into +# a call to the triton_kernel_wrapper_mutation HOP. + + +class TritonHOPifier: + """Orchestrator for converting a user-defined triton kernel into a call + to the triton_kernel_wrapper_mutation HOP. + + It has two main use cases. + + 1. When Dynamo sees a triton kernel, it wraps it into a TritonKernelVariable + and uses the TritonHOPifier to convert calls to the TritonKernelVariable + into a call to the HOP. + + 2. In order to capture a user-defined triton kernel while performing + tracing (via make_fx or non-strict export), a user must annotate their + triton kernel with the `capture_triton` decorator. The decorator uses + TritonHOPifier to convert calls to the triton kernel into a call + to the HOP (which can then be traced). + + Because Dynamo has its own calling conventions for e.g. invoking a user-defined function + TritonHOPifier is an abstract class that can be overriden by its subclasses. + """ + + def raise_unsupported(self, msg): + raise NotImplementedError("abstract method") + + def is_callable(self, maybe_callable): + raise NotImplementedError("abstract method") + + def get_value(self, val): + raise NotImplementedError("abstract method") + + def call_grid(self, grid, meta, tx): + raise NotImplementedError("abstract method") + + def call_HOP(self, variable, grids, combined_args, tx): + raise NotImplementedError("abstract method") + + def check_grid(self, grid): + raise NotImplementedError("abstract method") + + def init_variable(self, variable, kernel, kernel_idx, grid): + from triton.runtime.autotuner import Autotuner + + assert kernel is not None + + variable.kernel = kernel + variable.kernel_idx = kernel_side_table.add_kernel(kernel) + + assert kernel_idx is None or variable.kernel_idx == kernel_idx + + variable.grid = grid + + if isinstance(kernel, Autotuner): + import torch + import torch._dynamo + + # We only support configs and keys arguments of triton.autotune + # Make sure other arguments are defaulted + defaults = inspect.signature(Autotuner.__init__).parameters + + # Newer version of triton change attribute name from warmup to num_warmup and rep to num_rep. + # The call to get_first_attr is to maintain backward-compatibility. + if ( + not torch._inductor.config.unsafe_ignore_unsupported_triton_autotune_args + and ( + ( + "warmup" in defaults + and defaults["warmup"].default + != torch._dynamo.utils.get_first_attr( + kernel, "num_warmups", "warmup" + ) + ) + or ( + "rep" in defaults + and defaults["rep"].default + != torch._dynamo.utils.get_first_attr(kernel, "num_reps", "rep") + ) + or ( + "prune_configs_by" in defaults + and defaults["prune_configs_by"].default + != kernel.early_config_prune + ) + # Set via reset_to_zero argument + or len(kernel.reset_idx) != 0 + or len(kernel.restore_idx) != 0 + or ( + "use_cuda_graph" in defaults + and defaults["use_cuda_graph"].default != kernel.use_cuda_graph + ) + ) + ): + self.raise_unsupported( + "Only configs and keys are supported for triton.autotune" + ) + + def call_getitem(self, variable, args): + # __getitem__ should only be called if we don't already have a grid + # Only grid needs to be passed + if variable.grid is not None or len(args) != 1: + self.raise_unsupported( + "Triton kernels should be called with only a single grid" + ) + + return type(variable)( + kernel=variable.kernel, + kernel_idx=variable.kernel_idx, + grid=args[0], + ) + + def call_run(self, variable, args, kwargs, tx): + if "grid" not in kwargs: + self.raise_unsupported("Triton kernel requires to be called with a grid") + grid = kwargs.pop("grid") + kwargs.pop("warmup", None) + # rewrite kernel.run(*args, grid=grid) to kernel[grid](*args) + return self.call_triton_kernel( + type(variable)( + kernel=variable.kernel, kernel_idx=variable.kernel_idx, grid=grid + ), + args, + kwargs, + tx, + ) + + def call_triton_kernel(self, variable, args, kwargs, tx): + from triton.runtime.autotuner import autotune, Autotuner, Config + + if "num_ctas" in kwargs: + self.raise_unsupported( + "Passing num_ctas directly to the Triton kernel is not supported. " + "Please use a Config in @triton.autotune instead." + ) + + special_kwargs = {} + for name in ("num_warps", "num_stages"): + if name in kwargs: + # remove special kwargs from `kwargs` + val = kwargs.pop(name) + special_kwargs[name] = self.get_value(val) + + if special_kwargs: + if isinstance(variable.kernel, Autotuner): + # if there is Autotuner already, set + # special kwargs to each of its configs + new_configs = copy.deepcopy(variable.kernel.configs) + for config in new_configs: + config.__dict__.update(special_kwargs) + new_kernel = autotune(configs=new_configs, key=[])(variable.kernel.fn) + else: + # if there is no Autotuner, wrap the kernel into a + # new one with a single config with special kwargs + new_config = Config(kwargs={}, **special_kwargs) + new_kernel = autotune(configs=[new_config], key=[])(variable.kernel) + + # create a new variable to contain the new (wrapped) kernel; + # skip kernel_idx to get a new record in the kernel side table + new_var = type(variable)(new_kernel, None, variable.grid) + return self.call_triton_kernel(new_var, args, kwargs, tx) + + if variable.grid is None: + self.raise_unsupported("Triton kernels should always be called with a grid") + + # Both for grid's meta as well as for the kernel, we need combined + # args and kwargs combined and normalized + combined_args_raw = {**dict(zip(variable.kernel.arg_names, args)), **kwargs} + + configs = ( + [config.kwargs for config in variable.kernel.configs] + if isinstance(variable.kernel, Autotuner) + else [{}] + ) + grids = [] + for config_args in configs: + # If the grid is a function, then lets execute it and convert it to + # a list + grid = variable.grid + if self.is_callable(grid): + # Populate the special "meta" argument to call the grid function + meta = {**combined_args_raw, **config_args} + grid = self.call_grid(grid, meta, tx) + grids.append(self.check_grid(grid)) + + for i in range(len(grids)): + if not isinstance(grids[i], tuple): + self.raise_unsupported("Only tuple grids are supported") + # inductor expects all grids to be 3-tuple so lets make it + if len(grids[i]) == 1: + grids[i] = (grids[i][0], 1, 1) + elif len(grids[i]) == 2: + grids[i] = (grids[i][0], grids[i][1], 1) + elif len(grids[i]) > 3: + self.raise_unsupported("Grid can have at most rank 3") + + assert len(grids) != 0 + + def intify(x): + if isinstance(x, torch.SymInt): + return int(x) + else: + return x + + if len(set(pytree.tree_map(intify, grids))) == 1: + # If there's only one unique grid, lets simplify + grids = [grids[0]] + + return self.call_HOP(variable, grids, combined_args_raw, tx) + + +############################################################################### +# Helpers for capture_triton API that makes a user-defined triton kernel traceable into +# a graph via make_fx or non-strict export (coming soon) + + +class TracingTritonHOPifier(TritonHOPifier): + def raise_unsupported(self, msg): + raise RuntimeError(msg) + + def is_callable(self, maybe_callable): + return callable(maybe_callable) + + def get_value(self, val): + return val + + def call_grid(self, grid, meta, tx): + assert tx is None + return grid(meta) + + def check_grid(self, grid): + if not isinstance(grid, collections.abc.Sequence): + raise RuntimeError( + "capture_triton can only handle grids that resolve to Sequence[int]." + ) + # normalize to tuple + return tuple(grid) + + def call_HOP(self, variable, grids, combined_args, tx): + assert tx is None + + def is_graphable(val): + return isinstance(val, fx.node.base_types) + + non_graphable_args = { + k: v for k, v in combined_args.items() if not is_graphable(v) + } + graphable_args = {k: v for k, v in combined_args.items() if is_graphable(v)} + + constant_args_idx = kernel_side_table.add_constant_args(non_graphable_args) + return triton_kernel_wrapper_mutation( + kernel_idx=variable.kernel_idx, + constant_args_idx=constant_args_idx, + grid=grids, + kwargs=graphable_args, + ) + + +tracing_triton_hopifier_singleton = TracingTritonHOPifier() + + +class TraceableTritonKernelWrapper: + def __init__(self, kernel, kernel_idx, grid): + self.kernel = None + self.grid = None + tracing_triton_hopifier_singleton.init_variable(self, kernel, kernel_idx, grid) + assert self.kernel is not None + + def __getitem__(self, *args): + return tracing_triton_hopifier_singleton.call_getitem(self, args) + + def run(self, *args, **kwargs): + from torch._library.triton import is_capture_triton_enabled + + if is_capture_triton_enabled(): + return tracing_triton_hopifier_singleton.call_run(self, args, kwargs, None) + else: + assert self.kernel is not None + return self.kernel.run(*args, **kwargs) + + def __call__(self, *args, **kwargs): + from torch._library.triton import is_capture_triton_enabled + + if is_capture_triton_enabled(): + return tracing_triton_hopifier_singleton.call_triton_kernel( + self, args, kwargs, None + ) + else: + assert self.kernel is not None + return self.kernel[self.grid](*args, **kwargs) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..3b88d5f7dbac8ae0c4cc55d162804fb3fcb9113a --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/utils.py @@ -0,0 +1,379 @@ +# mypy: allow-untyped-defs +import functools +from contextlib import contextmanager +from dataclasses import dataclass +from typing import Any, Callable + +import torch +import torch.fx.traceback as fx_traceback +import torch.utils._pytree as pytree +from torch._ops import OperatorBase +from torch.fx.experimental.proxy_tensor import make_fx +from torch.multiprocessing.reductions import StorageWeakRef + + +@dataclass +class UnsupportedAliasMutationException(RuntimeError): + reason: str + + +def autograd_not_implemented_inner( + operator: OperatorBase, delayed_error: bool, *args: Any, **kwargs: Any +) -> Any: + """If autograd is enabled and any of the arguments require grad this will either + raise an error or return a DelayedError depending on the value of delayed. + + Args: + operator: The Operator to call with the *args and **kwargs with + op_name: The name of the Operator + delayed_error: If True, return a DelayedError instead of raising an error + args: The flattened operands to the Operator + kwargs: The keyword arguments to the Operator + + Raises: + RuntimeError: If autograd is enabled and any of the arguments to the Operator + """ + with torch._C._AutoDispatchBelowAutograd(): + result = operator(*args, **kwargs) + flat_operands = pytree.arg_tree_leaves(*args) + if torch.is_grad_enabled() and any( + f.requires_grad for f in flat_operands if isinstance(f, torch.Tensor) + ): + if delayed_error: + err_fn = torch._C._functions.DelayedError( + f"Autograd not implemented for {str(operator)}", + 1, + ) + + def fake_requires_grad(tensor): + if torch.is_floating_point(tensor) or torch.is_complex(tensor): + tensor = tensor.detach() + tensor.requires_grad = True + return tensor + + return pytree.tree_map_only( + torch.Tensor, lambda x: err_fn(fake_requires_grad(x)), result + ) + else: + raise RuntimeError(f"Autograd not implemented for {str(operator)}") + return result + + +def autograd_not_implemented(op: OperatorBase, deferred_error: bool) -> Callable: + def inner(*args, **kwargs): + return autograd_not_implemented_inner(op, deferred_error, *args, **kwargs) + + return inner + + +def _maybe_run_with_interpreter(fn): + maybe_interpreted_fn = fn + if isinstance(fn, torch.fx.GraphModule) and fx_traceback.has_preserved_node_meta(): + # Running graph with interpreter is needed for propagating the stack_trace + def graph_with_interpreter(*args): + with fx_traceback.preserve_node_meta(): + return torch.fx.Interpreter(fn).run(*args) + + maybe_interpreted_fn = graph_with_interpreter + return maybe_interpreted_fn + + +def reenter_make_fx(fn): + from torch.fx.experimental.proxy_tensor import _CURRENT_MAKE_FX_TRACER + + @functools.wraps(fn) + def wrapped(*args): + assert ( + _CURRENT_MAKE_FX_TRACER is not None + ), "Cannot reenter make_fx when we're not under a make_fx tracing session" + return _CURRENT_MAKE_FX_TRACER.trace_subgraph( + _maybe_run_with_interpreter(fn), *args + ) + + return wrapped + + +def _maybe_reenter_make_fx(fn): + from torch.fx.experimental.proxy_tensor import _CURRENT_MAKE_FX_TRACER + + if _CURRENT_MAKE_FX_TRACER is not None: + return reenter_make_fx(fn) + else: + return make_fx(fn) + + +@contextmanager +def _set_compilation_env(): + _old_is_tracing = torch.fx._symbolic_trace._is_fx_tracing_flag + try: + # We need to turn off the is_fx_tracing_flag. Remove this flag check from dyanmo + # once we are confident fx tracing works with dynamo. + torch.fx._symbolic_trace._is_fx_tracing_flag = False + yield + finally: + torch.fx._symbolic_trace._is_fx_tracing_flag = _old_is_tracing + + +def _has_potential_branch_input_mutation(branch, inputs, pre_dispatch=False): + """ + Dispatch-trace the branch with inputs and check if + producing graph has mutable op on the input. This is + bit restrictive as the branch must be traceable. + """ + try: + gm = make_fx(branch, pre_dispatch=pre_dispatch)(*inputs) + except UnsupportedAliasMutationException: + # this can happen when nested cond_op is + # functionalized + return True + except Exception as e: + raise e + + def _detect_input_mutation(gm): + input_nodes = set() + for node in gm.graph.nodes: + if node.op == "placeholder": + input_nodes.add(node) + if node.op == "call_function": + target = node.target + if ( + isinstance(target, torch._ops.OpOverload) + and target._schema.is_mutable + ): + for arg in node.args: + if arg in input_nodes: + return True + + for _, module in gm.named_children(): + if isinstance(module, torch.fx.GraphModule): + if _detect_input_mutation(module): + return True + + return False + + return _detect_input_mutation(gm) + + +def _has_potential_branch_input_alias(branch, inputs, pre_dispatch=False): + """ + Dispatch-trace the branch with inputs and check if + producing graph has output aliasing the branch input. This is + bit restrictive as the branch must be traceable. + """ + try: + gm = make_fx(branch, pre_dispatch=pre_dispatch)(*inputs) + except UnsupportedAliasMutationException: + # this can happen when nested cond_op is + # functionalized + return True + except Exception as e: + raise e + + def _detect_input_alias(gm): + input_storages = set() + for node in gm.graph.nodes: + # We need to check existence of "val" because we reuse the logic here + # for map operator, where num_mapped_args is a scalar + # and doesn't have a "val" meta. + if node.op == "placeholder" and "val" in node.meta: + input_storages.add(StorageWeakRef(node.meta["val"]._typed_storage())) + if node.op == "output": + + def check_alias(out): + if out is not None and "val" in out.meta: + out_storage = StorageWeakRef(out.meta["val"]._typed_storage()) + return out_storage in input_storages + return False + + if any(pytree.tree_leaves(pytree.tree_map(check_alias, node.args))): + return True + + for _, module in gm.named_children(): + if isinstance(module, torch.fx.GraphModule) and _detect_input_alias(module): + return True + + return False + + return _detect_input_alias(gm) + + +def unique_graph_id(proxy_mode, prefix): + """Returns a unique name and id for a graph to be added to a proxy_mode tracer""" + # There are probably better ways - I know that create_arg has some self incrementing name + # magic to it, but since we explicitly have to get the name for register_module, + # I was not sure how to do that. This kinda simulates it. + next_name = None + i = 0 + while not next_name: + candidate = f"{prefix}_{i}" + if hasattr(proxy_mode.tracer.root, candidate): + i += 1 + else: + next_name = candidate + return i, next_name + + +def _from_fun(t): + from torch._functorch.aot_autograd import from_fun + from torch._subclasses.functional_tensor import FunctionalTensor + + if isinstance(t, torch.Tensor): + if t.dtype != torch.bool: + return torch.empty_strided( + t.size(), + t.stride(), + dtype=t.dtype, + requires_grad=t.requires_grad, + ) + else: + # clone of a functional tensor produces a functional tensor + # but we want to avoid it so we clone a non-functional version + maybe_unfunc_t = t + if isinstance(t, FunctionalTensor): + torch._sync(t) + maybe_unfunc_t = from_fun(t) + elif torch._is_functional_tensor(t): + # need to handle both types of functionalization here: + # these are the tensors that came from the user, + # which could be either FunctionalTensorWrapper or FunctionalTensor + torch._sync(t) + maybe_unfunc_t = torch._from_functional_tensor(t) + return maybe_unfunc_t.clone() + return t + + +def clone_outputs_aliasing_inputs(args): + input_storage = { + StorageWeakRef(arg._typed_storage()) + for arg in args + if isinstance(arg, torch.Tensor) + } + + def maybe_clone(t): + if ( + isinstance(t, torch.Tensor) + and StorageWeakRef(t._typed_storage()) in input_storage + ): + return t.clone() + return t + + return maybe_clone + + +def prepare_fw_with_masks(fn): + def fw_with_masks(*args): + fw_out = fn(*args) + return fw_out, [ + True if isinstance(ret, torch.Tensor) and ret.requires_grad else False + for ret in fw_out + ] + + return fw_with_masks + + +# TODO: The parameter use_output_and_grad_bw is required because some operations +# that utilize this function, such as the while_loop, may require (grad, fwd_outputs) +def create_fw_bw_graph(fn, use_output_and_grad_bw, fw_inputs, fw_outputs): + from torch._functorch.aot_autograd import AOTConfig, create_joint + + # Note:[HOP create fw_bw graph] We create "clean" environments for make_fx by suspending all dispatch keys + # between Autograd and Python key. Currently, we only suspend functionalization but more can be + # added when required. Will encounter two problems if we don't suspend functionalization: + # + # 1. make_fx fails to capture operations on input: the inputs are wrapped as _to_functional_tensor_wrapper, + # but they will be unwrapped before entering ProxyTorchDispatchMode as part of the dispatching. + # However, it's the outside wrapper that tracer creates proxies for. This casuses tracer fail to + # fetch the proxy for the inputs and fail to capture any operations on them. + # + # 2. make_fx fails to capture output: the outputs after ProxyTorchDispatchMode are further + # wrapped as FunctionalTensorWrapper in Functionalize key after return. However, the tracer + # only associates the inner tensor with proxy in ProxyTorchDispatchMode. Therefore, + # when creating the output node, it fails to associate the wrapped tensor with its proxy. + # Instead, it will create _tensor_constant as output. + + dummy_aot_config = AOTConfig( + fw_compiler=None, # type: ignore[arg-type] + bw_compiler=None, # type: ignore[arg-type] + partition_fn=None, # type: ignore[arg-type] + decompositions={}, + num_params_buffers=0, + aot_id=0, + keep_inference_input_mutations=False, + ) + + example_grad = [_from_fun(out) for out in fw_outputs] + num_grads = len(example_grad) + fw_graph = _maybe_reenter_make_fx(fn)(*fw_inputs) + + def joint_fn(*joint_operands_grads): + if use_output_and_grad_bw: + grads = joint_operands_grads[0] + inputs = joint_operands_grads[1][-1:] + else: + grads = joint_operands_grads[:num_grads] + inputs = joint_operands_grads[num_grads:] + + joint = create_joint(prepare_fw_with_masks(fn), aot_config=dummy_aot_config) + _, grads = joint( + list(inputs), + [grad for grad in grads if grad is not None and grad.requires_grad], + ) + + # In order to keep map functional for backward graph, + # we clone outputs that are aliasing inputs + maybe_clone = clone_outputs_aliasing_inputs(joint_operands_grads) + + return pytree.tree_map(maybe_clone, grads) + + if use_output_and_grad_bw: + example_xs_out = list(fw_inputs) + list(fw_outputs) + joint_graph = _maybe_reenter_make_fx(joint_fn)( + (list(example_grad), list(example_xs_out)) + ) + else: + example_xs_out = list(fw_inputs) + joint_graph = _maybe_reenter_make_fx(joint_fn)( + *(list(example_grad) + list(example_xs_out)) + ) + + return fw_graph, joint_graph + + +def _unstack_pytree(xs): + flat_xs, inspec = pytree.tree_flatten(xs) + if not all(isinstance(xs, torch.Tensor) for xs in flat_xs): + raise RuntimeError(f"Leaves of xs must be Tensor {flat_xs}") + + if not all(xs.shape[0] == flat_xs[0].shape[0] for xs in flat_xs): + raise RuntimeError( + f"Leaves of xs must have same leading dimension size {[xs.shape for xs in flat_xs]}" + ) + + a = zip(*flat_xs) + + pytrees = [] + for tuple in a: + pytrees.append(pytree.tree_unflatten(tuple, inspec)) + return pytrees + + +def _stack_pytree(pytrees): + flat_out = [] + out_spec = None + for pt in pytrees: + flat_pt, out_spec = pytree.tree_flatten(pt) + flat_out.append(flat_pt) + assert out_spec is not None + b = zip(*flat_out) + stacked_out = [] + for leaves in b: + if all(isinstance(leaf, torch.Tensor) for leaf in leaves): + stacked_out.append(torch.stack(leaves)) + elif all(leaf is None for leaf in leaves): + # Backward graph can return None output when forward inputs doesn't require grad. + # When we eagerly execute backward graph, we need to call _stack_pytree on its output, + # therefore we need to deal with None output. + stacked_out.append(None) # type: ignore[arg-type] + else: + raise RuntimeError(f"Cannot stack {leaves}.") + return pytree.tree_unflatten(stacked_out, out_spec) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/while_loop.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/while_loop.py new file mode 100644 index 0000000000000000000000000000000000000000..d64f512399d9d5101227876477a9f338309204a1 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/while_loop.py @@ -0,0 +1,268 @@ +# mypy: allow-untyped-defs +from typing import Callable, Tuple, Union + +import torch +import torch.utils._pytree as pytree +from torch._C import DispatchKey +from torch._higher_order_ops.utils import ( + _has_potential_branch_input_alias, + _has_potential_branch_input_mutation, + _maybe_run_with_interpreter, + _set_compilation_env, + autograd_not_implemented, + reenter_make_fx, + UnsupportedAliasMutationException, +) +from torch._ops import HigherOrderOperator +from torch._subclasses.fake_tensor import FakeTensorMode +from torch.fx.experimental.proxy_tensor import ProxyTorchDispatchMode, track_tensor_tree + + +class WhileLoopOp(HigherOrderOperator): + def __init__(self) -> None: + super().__init__("while_loop") + + def __call__( + self, + cond_fn: Callable, + body_fn: Callable, + carried_inputs: Tuple[Union[torch.Tensor, int, float, bool]], + additional_inputs: Tuple[Union[torch.Tensor, int, float, bool]], + /, + ): + if not isinstance(carried_inputs, tuple): + raise RuntimeError( + f"carried_inputs must be a tuple, got {type(carried_inputs)}" + ) + if not isinstance(additional_inputs, tuple): + raise RuntimeError( + f"additional_inputs must be a tuple, got {type(additional_inputs)}" + ) + if not all( + isinstance(t, (torch.Tensor, int, float, bool)) for t in carried_inputs + ): + raise RuntimeError( + "carried_inputs must be a tuple of tensors, ints, floats, or bools, got " + f"{carried_inputs}" + ) + + if not all( + isinstance(t, (torch.Tensor, int, float, bool)) for t in additional_inputs + ): + raise RuntimeError( + "additional_inputs must be a tuple of tensors, ints, floats, or bools, got " + f"{additional_inputs}" + ) + return super().__call__(cond_fn, body_fn, carried_inputs, additional_inputs) + + +while_loop_op = WhileLoopOp() + + +def while_loop(cond_fn, body_fn, carried_inputs): + r""" + Run body_fn(*carried_inputs) while cond_fn(*carried_inputs) returns a True scalar tensor. Returns the output of body_fn or + initial carried_inputs. + + .. warning:: + `torch.while_loop` is a prototype feature in PyTorch. It has limited support for input and output types and + doesn't support training currently. Please look forward to a more stable implementation in a future version of PyTorch. + Read more about feature classification at: https://pytorch.org/blog/pytorch-feature-classification-changes/#prototype + + `while_loop` is a structured control flow operator. It preserves the loop semantic across the torch.compile and torch.export. + + `while_loop` is equivalent to the following: + + def while_loop(cond_fn, body_fn, carried_inputs): + val = carried_inputs + while cond_fn(*val): + val = body_fn(*val) + return val + + Args: + cond_fn (Callable): A callable function that returns a boolean Scalar tensor. + + body_fn (Callable): A callable function that takes the same inputs as `cond_fn` and returns a tuple of tensors + + carried_inputs (Tuple of possibly nested dict/list/tuple of tensors): A tuple of inputs to cond_fn and body_fn. It's also + the initial value of states that are carried across iterations. + + Example: + + def cond_fn(iter, x): + return iter.sum() < 10 + + def body_fn(iter, x): + return iter + 1, x.sin() + + while_loop(cond_fn, body_fn, (torch.zeros(1), torch.randn(3, 4))) + + Restrictions: + + - body_fn must return tensors with the same metadata (e.g.shape, dtype) as inputs. + + - body_fn and cond_fn must not in-place mutate the carried_inputs. A clone before the mutation is required. + + - body_fn and cond_fn must not mutate python varialbles (e.g. list/dict) created outside of the body_fn. + + - body_fn and cond_fn's output cannot aliase any of the inputs. A clone is required. + + .. warning:: + Temporal Limitations: + + - 'while_loop' only supports **inference** right now. Autograd will be supported in the future. + + """ + + # Currently, additional_inputs is not a user-facing input. It will be automatically set in dynamo. + # parameters and buffers accessed in cond_fn or body_fn or tensor closures will become additional_inputs. + additional_inputs: Tuple = () + if torch.compiler.is_dynamo_compiling(): + return while_loop_op(cond_fn, body_fn, carried_inputs, additional_inputs) + + def _validate_input(cond_fn, body_fn, carried_inputs): + if not callable(cond_fn) or not callable(body_fn): + raise RuntimeError("Expect cond_fn and body_fn to be callbale.") + + if not isinstance(carried_inputs, (tuple, list)) or pytree.tree_any( + lambda t: not isinstance(t, torch.Tensor), carried_inputs + ): + raise RuntimeError( + "Expect carried_inputs to be a tuple of possibly nested dict/list/tuple that only" + f"consists of tensor leaves, but got {carried_inputs}." + ) + + _validate_input(cond_fn, body_fn, carried_inputs) + + # Dynamo is expecting a callable with "__code__" attribute. + # We cannot directly pass cond_op to it. So we wrap it in a dummy function. + def _while_loop_op_wrapper(*args, **kwargs): + return while_loop_op(*args, **kwargs) + + with _set_compilation_env(), torch._dynamo.utils.disable_cache_limit(): + return torch.compile(_while_loop_op_wrapper, backend="eager", fullgraph=True)( + cond_fn, body_fn, carried_inputs, additional_inputs + ) + + +@while_loop_op.py_impl(DispatchKey.CompositeExplicitAutograd) +def while_loop_dense(cond_fn, body_fn, carried_inputs, additional_inputs): + carried_vals = carried_inputs + + def _is_boolean_scalar_tensor(pred): + return ( + isinstance(pred, torch.Tensor) + and pred.size() == torch.Size([]) + and pred.dtype == torch.bool + ) + + if not isinstance(carried_inputs, tuple): + raise RuntimeError( + f"carried_inputs must be a tuple but got {type(carried_inputs)}" + ) + + while pred := cond_fn(*carried_vals, *additional_inputs): + if not _is_boolean_scalar_tensor(pred): + raise RuntimeError( + f"cond_fn must return a boolean scalar tensor but got {pred}" + ) + out = body_fn(*carried_vals, *additional_inputs) + assert isinstance( + out, tuple + ), f"body_fn should return a tuple but got {type(out)}" + assert len(out) == len( + carried_inputs + ), "body_fn should return the same number of elements as carried_inputs" + carried_vals = out + return carried_vals + + +while_loop_op.py_impl(DispatchKey.Autograd)( + autograd_not_implemented(while_loop_op, deferred_error=True) +) + + +@while_loop_op.py_impl(ProxyTorchDispatchMode) +def while_loop_tracing(mode, cond_fn, body_fn, carried_inputs, additional_inputs): + def _trace_while_loop( + proxy_mode, while_loop_op, cond_fn, body_fn, carried_inputs, additional_inputs + ): + cond_graph = reenter_make_fx(cond_fn)(*carried_inputs, *additional_inputs) + body_graph = reenter_make_fx(body_fn)(*carried_inputs, *additional_inputs) + + next_name = None + i = 0 + while not next_name: + candidate = f"while_loop_cond_graph_{i}" + if hasattr(proxy_mode.tracer.root, candidate): + i += 1 + else: + next_name = candidate + cond_graph_name = next_name + body_graph_name = f"while_loop_body_graph_{i}" + assert not hasattr(proxy_mode.tracer.root, body_graph_name) + + proxy_mode.tracer.root.register_module(cond_graph_name, cond_graph) + proxy_mode.tracer.root.register_module(body_graph_name, body_graph) + + args = (cond_graph, body_graph, carried_inputs, additional_inputs) + + proxy_args = pytree.tree_map(proxy_mode.tracer.unwrap_proxy, args) + + out_proxy = proxy_mode.tracer.create_proxy( + "call_function", while_loop_op, proxy_args, {}, name="while_loop" + ) + + # body_fn return output with the same pytree and tensor meta data as carried_inputs + # so we could just return the output after one iteration. + out = body_fn(*carried_inputs, *additional_inputs) + return track_tensor_tree( + out, out_proxy, constant=None, tracer=proxy_mode.tracer + ) + + return _trace_while_loop( + mode, while_loop_op, cond_fn, body_fn, carried_inputs, additional_inputs + ) + + +@while_loop_op.py_impl(FakeTensorMode) +def while_loop_fake_tensor_mode( + mode, cond_fn, body_fn, carried_inputs, additional_inputs +): + with mode: + return body_fn(*carried_inputs, *additional_inputs) + + +@while_loop_op.py_functionalize_impl +def while_loop_func(ctx, cond_fn, body_fn, carried_inputs, additional_inputs): + unwrapped_carried_inputs = ctx.unwrap_tensors(carried_inputs) + unwrapped_additional_inputs = ctx.unwrap_tensors(additional_inputs) + unwrapped_inputs = unwrapped_carried_inputs + unwrapped_additional_inputs + with ctx.redispatch_to_next() as m: + functional_cond_fn = ctx.functionalize(_maybe_run_with_interpreter(cond_fn)) + functional_body_fn = ctx.functionalize(_maybe_run_with_interpreter(body_fn)) + pre_dispatch = hasattr(ctx, "mode") and ctx.mode.pre_dispatch + for fn, fn_name in [ + (functional_cond_fn, "cond_fn"), + (functional_body_fn, "body_fn"), + ]: + if _has_potential_branch_input_mutation( + fn, unwrapped_inputs, pre_dispatch=pre_dispatch + ): + raise UnsupportedAliasMutationException( + f"torch.while_loop's {fn_name} might be modifying the input!" + ) + + if _has_potential_branch_input_alias( + fn, unwrapped_inputs, pre_dispatch=pre_dispatch + ): + raise UnsupportedAliasMutationException( + f"torch.while_loop's {fn_name} might be aliasing the input!" + ) + ret = while_loop_op( + functional_cond_fn, + functional_body_fn, + unwrapped_carried_inputs, + unwrapped_additional_inputs, + ) + return ctx.wrap_tensors(ret) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/wrap.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/wrap.py new file mode 100644 index 0000000000000000000000000000000000000000..7327b3114a1d158418f8f6539bf1a52055ecbd0e --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_higher_order_ops/wrap.py @@ -0,0 +1,240 @@ +# mypy: allow-untyped-defs +import inspect +import itertools +import logging +from typing import Optional + +from torch._logging import warning_once +from torch._ops import HigherOrderOperator +from torch.types import _dtype +from torch.utils.checkpoint import checkpoint, CheckpointPolicy + + +log = logging.getLogger(__name__) + +uid = itertools.count(1) + + +# Used for testing the HigherOrderOperator mechanism +class Wrap(HigherOrderOperator): + def __init__(self) -> None: + super().__init__("wrap") + + def __call__(self, func, *args, **kwargs): + # Dynamo already traces the body of HigherOrderOp beforehand when it + # so no need to trace into it. + import torch._dynamo # noqa: F401 + from torch._dynamo import disable + + @disable + def wrapper(): + result = func(*args, **kwargs) + return result + + return wrapper() + + +wrap = Wrap() + + +class WrapWithSetGradEnabled(HigherOrderOperator): + def __init__(self) -> None: + super().__init__("wrap_with_set_grad_enabled") + + def __call__(self, enable_grad, wrapped_func, *args, **kwargs): + # Dynamo already traces the body of HigherOrderOp beforehand when it + # so no need to trace into it. + import torch._dynamo # noqa: F401 + from torch._dynamo import disable + + @disable + def wrapper(): + with torch.set_grad_enabled(enable_grad): + return wrapped_func(*args, **kwargs) + + return wrapper() + + +wrap_with_set_grad_enabled = WrapWithSetGradEnabled() + + +class WrapWithAutocast(HigherOrderOperator): + def __init__(self): + super().__init__("wrap_with_autocast") + + def __call__( + self, + device_type: str, + dtype: Optional[_dtype], + enabled: bool, + cache_enabled: Optional[bool], + wrapped_func, + *args, + **kwargs, + ): + # Dynamo already traces the body of HigherOrderOp beforehand when it + # so no need to trace into it. + import torch._dynamo # noqa: F401 + from torch._dynamo import disable + + @disable + def wrapper(): + with torch.autocast(device_type, dtype, enabled, cache_enabled): + return wrapped_func(*args, **kwargs) + + return wrapper() + + +wrap_with_autocast = WrapWithAutocast() + + +class WrapActivationCheckpoint(HigherOrderOperator): + """ + This operator is used to wrap torch.utils.checkpoint. This avoids + TorchDynamo to look into saved tensor hooks and directly passes the control + to AOT Autograd, which is ok with tracing saved tensor hooks. As a result of + AOT tracing torch.utils.checkpoint code, we have a backward graph with + recomputed forward nodes. + + However, we might deprecate this operator soon. The difficulty arises in the + functionalization of rng ops. Today, there are two different + functionalization of rng ops - one at AOT autograd and other at Inductor. + And they are difficult to map to each other. The rng states also complicate + pattern matching in Inductor. Due to the ease of implementation, we are + currently inclined towards functionalization at Inductor level, which means + that duplication/recomputation is done as a compiler pass in the + partitioners. See TagActivationCheckpoint for more information. + """ + + def __init__(self) -> None: + super().__init__("wrap_activation_checkpoint") + + def __call__(self, function, *args, **kwargs): + # use_reentrant is set to False because this op is going to be traced. + # And we ensure that AOT Autograd traces through the non reentrant + # version of checkpointing. + import torch.fx.traceback as fx_traceback + from torch.fx import Interpreter + + kwargs["use_reentrant"] = False + kwargs["preserve_rng_state"] = False + # Using interpreter allows preservation of metadata through torch.compile stack. + with fx_traceback.preserve_node_meta(): + return checkpoint(Interpreter(function).run, *args, **kwargs) + + +wrap_activation_checkpoint = WrapActivationCheckpoint() + + +class TagActivationCheckpoint(HigherOrderOperator): + """ + This operator is supposed to be used only with torch.compile stack. This + accepts a Fx graph module which needs to be checkpointed. This operator adds + "recomputable" tag to the nodes of the Fx graph that should be recomputed. + + The goal is to: + 1. Avoid using Dynamo to trace through saved tensor hooks. + 2. For selective checkpointing case, let AOTAutograd trace through + saved tensor hooks but has special logic with TorchDispatchMode to override + the usual saved_tensor_hooks fn logic in order to tag the nodes. + 3. Rely on the partitioners to actually duplicate the nodes. + This sits well in the torch.compile stack, because by the time graph + reaches partitioner, inductor has already run its functionalization of rng + ops (by setting fixed seed for each random op, see `replace_random_passes`). + Therefore, the duplication of nodes, by design, respects the rng states in + the forward and recomputed forward in backward. + """ + + def __init__(self) -> None: + super().__init__("tag_activation_checkpoint") + + @staticmethod + def divide_kwargs(kwargs): + """ + checkpoint fn can have mixed kwargs between checkpointed fn and + checkpoint fn itself. For example + >> def gn(x, y, z=None): + >> a = torch.matmul(x, y) + >> if z is not None: + >> return torch.matmul(a, z) + >> return a + >> def fn(x, y, z): + >> return torch.cos(checkpoint(gn, x, y, use_reentrant=False, z=z)) + In the above case, z belongs to checkpointed function gn, but + use_reentrant belongs to the checkpoint function. This function splits + the kwargs into checkpoint_kwargs and gmod_kwargs (or + checkpointed_fn_kwargs). + We do sorting to ensure same graph from run to run for better + debuggability. It is not required for correctness. + """ + ckpt_signature = inspect.signature(checkpoint) + checkpoint_keys = set() + for name in ckpt_signature.parameters: + if name in ("function", "args", "kwargs"): + continue + checkpoint_keys.add(name) + + # `preserve_rng_state` is not a regular kwarg + checkpoint_keys.add("preserve_rng_state") + + checkpoint_kwargs = { + name: kwargs[name] for name in kwargs.keys() if name in checkpoint_keys + } + gmod_kwargs = { + name: kwargs[name] for name in kwargs.keys() if name not in checkpoint_keys + } + return checkpoint_kwargs, gmod_kwargs + + def tag_nodes(self, gmod, is_sac): + unique_graph_id = next(uid) + for node in gmod.graph.nodes: + if node.op in ("call_function", "call_method", "call_module"): + node.meta["ac_graph_id"] = unique_graph_id + if is_sac: + # For selective checkpointing, we will populate this tag later in _CachingTorchDispatchMode. + node.meta["recompute"] = None + else: + # Under vanilla activation checkpointing, all nodes should be recomputed. + node.meta["recompute"] = CheckpointPolicy.PREFER_RECOMPUTE + return gmod + + def __call__(self, gmod, *args, **kwargs): + import torch.fx.traceback as fx_traceback + from torch.fx import Interpreter + + if "_checkpoint_context_fn" in gmod.meta: + warning_once( + log, + """ +Detected that context_fn is passed to torch.utils.checkpoint under torch.compile. +Please make sure the checkpointed region does not contain in-place ops (e.g. torch.relu_). +""", + ) + # use_reentrant is set to False because this op is going to be traced. + # And we ensure that AOT Autograd traces through the non reentrant + # version of checkpointing. + kwargs["use_reentrant"] = False + # preserve_rng_state is set to False because we want to prevent AOTAutograd from tracing through + # `torch.random.fork_rng` op (which is not supported yet under CUDA). + # This doesn't mean that we don't preserve RNG state. Instead, we will always preserve RNG state + # regardless of this flag (by doing RNG functionalization via `replace_random_passes` in Inductor + # instead of in AOTAutograd). + kwargs["preserve_rng_state"] = False + kwargs["context_fn"] = gmod.meta["_checkpoint_context_fn"] + # We first tag all nodes as "recompute" in this graph, and then we undo the "recompute" tag + # for specific nodes in _CachingTorchDispatchMode in torch/utils/checkpoint.py. + gmod = self.tag_nodes(gmod, is_sac=True) + # Using interpreter allows preservation of metadata through torch.compile stack. + with fx_traceback.preserve_node_meta(): + return checkpoint(Interpreter(gmod).run, *args, **kwargs) + else: + gmod = self.tag_nodes(gmod, is_sac=False) + # Using interpreter allows preservation of metadata through torch.compile stack. + # TODO: We want to use the same `checkpoint(Interpreter(gmod).run, *args, **kwargs)` here + # as the `context_fn != None` case, but that depends on in-place op support in TorchDispatchMode + torch.compile. + # (for details on in-place op issue, run `test_compile_selective_checkpoint_inplace_op` unit test) + with fx_traceback.preserve_node_meta(): + return Interpreter(gmod).run(*args) + + +tag_activation_checkpoint = TagActivationCheckpoint() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f95e7caaf71e95c675d4ea9e467c99d76ebdb842 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/__init__.py @@ -0,0 +1,179 @@ +# mypy: allow-untyped-defs +from typing import Any, Dict, List, Optional, Tuple + +import torch.fx +import torch.utils._pytree as pytree + + +__all__ = ["compile", "list_mode_options", "list_options", "cudagraph_mark_step_begin"] + + +def compile( + gm: torch.fx.GraphModule, + example_inputs: List[torch.Tensor], + options: Optional[Dict[str, Any]] = None, +): + """ + Compile a given FX graph with TorchInductor. This allows compiling + FX graphs captured without using TorchDynamo. + + Args: + gm: The FX graph to compile. + example_inputs: List of tensor inputs. + options: Optional dict of config options. See `torch._inductor.config`. + + Returns: + Callable with same behavior as gm but faster. + """ + from .compile_fx import compile_fx + + return compile_fx(gm, example_inputs, config_patches=options) + + +def aot_compile( + gm: torch.fx.GraphModule, + args: Tuple[Any], + kwargs: Optional[Dict[str, Any]] = None, + *, + options: Optional[Dict[str, Any]] = None, +) -> str: + """ + Ahead-of-time compile a given FX graph with TorchInductor into a shared library. + + Args: + gm: The FX graph to compile. + args: Example arguments + kwargs: Example keyword arguments + options: Optional dict of config options. See `torch._inductor.config`. + + Returns: + Path to the generated shared library + """ + from .compile_fx import compile_fx_aot, graph_returns_tuple + + assert graph_returns_tuple(gm), ( + "Graph output must be a tuple(). This is so that we can avoid " + "pytree processing of the outputs. Please change the module to " + "have tuple outputs." + ) + + # We will serialize the pytree info into the .so as constant strings + in_spec = None + out_spec = None + if isinstance(gm.graph._codegen, torch.fx.graph._PyTreeCodeGen): + codegen = gm.graph._codegen + gm.graph._codegen = torch.fx.graph.CodeGen() + gm.recompile() + + if codegen.pytree_info.in_spec is not None: + in_spec = codegen.pytree_info.in_spec + if codegen.pytree_info.out_spec is not None: + out_spec = codegen.pytree_info.out_spec + + else: + if hasattr(gm, "_in_spec"): + in_spec = gm._in_spec + if hasattr(gm, "_out_spec"): + out_spec = gm._out_spec + + serialized_in_spec = pytree.treespec_dumps(in_spec) if in_spec is not None else "" + serialized_out_spec = ( + pytree.treespec_dumps(out_spec) if out_spec is not None else "" + ) + + flat_args_with_path, received_spec = pytree.tree_flatten_with_path( + (args, kwargs or {}) + ) + + # Replace non-tensor (constant) inputs with Nones, since these are not being + # used anyways by the graph + flat_example_inputs = [ + x[1] if isinstance(x[1], torch.Tensor) else None for x in flat_args_with_path + ] + + if in_spec is not None and received_spec != in_spec: + raise ValueError( # noqa: B904 + "Trying to flatten user inputs with exported input tree spec: \n" + f"{in_spec}\n" + "but actually got inputs with tree spec of: \n" + f"{received_spec}" + ) + + options = ( + { + "aot_inductor.serialized_in_spec": serialized_in_spec, + "aot_inductor.serialized_out_spec": serialized_out_spec, + } + if options is None + else { + **options, + "aot_inductor.serialized_in_spec": serialized_in_spec, + "aot_inductor.serialized_out_spec": serialized_out_spec, + } + ) + + return compile_fx_aot( + gm, + flat_example_inputs, # type: ignore[arg-type] + config_patches=options, + ) + + +def list_mode_options( + mode: Optional[str] = None, dynamic: Optional[bool] = None +) -> Dict[str, Any]: + r"""Returns a dictionary describing the optimizations that each of the available + modes passed to `torch.compile()` performs. + + Args: + mode (str, optional): The mode to return the optimizations for. + If None, returns optimizations for all modes + dynamic (bool, optional): Whether dynamic shape is enabled. + + Example:: + >>> torch._inductor.list_mode_options() + """ + + mode_options: Dict[str, Dict[str, bool]] = { + "default": {}, + # enable cudagraphs + "reduce-overhead": { + "triton.cudagraphs": True, + }, + # enable max-autotune + "max-autotune-no-cudagraphs": { + "max_autotune": True, + }, + # enable max-autotune + # enable cudagraphs + "max-autotune": { + "max_autotune": True, + "triton.cudagraphs": True, + }, + } + return mode_options[mode] if mode else mode_options # type: ignore[return-value] + + +def list_options() -> List[str]: + r"""Returns a dictionary describing the optimizations and debug configurations + that are available to `torch.compile()`. + + The options are documented in `torch._inductor.config`. + + Example:: + + >>> 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a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/aoti_eager.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/aoti_eager.py new file mode 100644 index 0000000000000000000000000000000000000000..f733ce4fbd5a1748c663892c10cf349166bc9461 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/aoti_eager.py @@ -0,0 +1,298 @@ +import json +import logging +import os +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional, Tuple +from unittest import mock + +import torch +import torch._export +from torch._inductor.utils import is_cpu_device + +from .runtime.runtime_utils import cache_dir + + +log = logging.getLogger(__name__) + + +def aoti_eager_cache_dir(namespace: str, device: str) -> Path: + return Path(cache_dir()) / "aoti_eager" / namespace / device + + +def aoti_eager_op_conf_lock(op_func_name_with_overload: str) -> Any: + from filelock import FileLock + + # Avoid circular import + from torch._inductor.codecache import get_lock_dir, LOCK_TIMEOUT + + op_conf_lock_file = f"{op_func_name_with_overload}.lock" + lock_dir = get_lock_dir() + return FileLock(os.path.join(lock_dir, op_conf_lock_file), timeout=LOCK_TIMEOUT) + + +def load_aoti_eager_cache( + ns: str, op_func_name_with_overload: str, device_type: str +) -> List[Optional[Dict[str, Any]]]: + device_kernel_cache = aoti_eager_cache_dir(ns, device_type) + op_conf = device_kernel_cache / f"{op_func_name_with_overload}.json" + if not op_conf.exists(): + return [] + + try: + with aoti_eager_op_conf_lock(op_func_name_with_overload): + with open(op_conf) as f: + json_data = json.load(f) + for item in json_data: + # Get absolution path for kernel library + kernel_lib_abs_path = device_kernel_cache / item["kernel_path"] + item["kernel_path"] = kernel_lib_abs_path.as_posix() + + # Check if the kernel library exists + if not kernel_lib_abs_path.exists(): + return [] + + for metadata in item["meta_info"]: + if metadata.get("is_dynamic"): + raise NotImplementedError( + "Only support static shape for now" + ) + if ( + "device_type" in metadata + and metadata["device_type"] == "cpu" + ): + metadata["device_index"] = -1 + for dtype_key in ["dtype", "dtype_value"]: + if dtype_key in metadata: + metadata[dtype_key] = getattr( + torch, metadata[dtype_key].split(".")[-1] + ) + if "layout_value" in metadata: + metadata["layout_value"] = getattr( + torch, metadata["layout_value"].split(".")[-1] + ) + if "memory_format_value" in metadata: + metadata["memory_format_value"] = getattr( + torch, metadata["memory_format_value"].split(".")[-1] + ) + + return json_data + except Exception as e: + err_msg = f"Failed to load aoti eager cache: {e}" + log.exception(err_msg) + return [] + + +def supported_builtin_dtype_torch_dtype() -> Dict[type, torch.dtype]: + return {int: torch.int32, float: torch.float, bool: torch.bool} + + +def supported_scalar_types() -> Tuple[type, ...]: + type_to_torch_dtype = supported_builtin_dtype_torch_dtype() + return tuple(type_to_torch_dtype.keys()) + + +def extract_tensor_metadata(dynamic: bool, input: torch.Tensor) -> Dict[str, Any]: + metadata: Dict[str, Any] = {} + metadata["is_dynamic"] = dynamic + + assert isinstance(input, torch.Tensor) + metadata["device_type"] = f"{input.device.type}" + if is_cpu_device([input]): + metadata["device_index"] = -1 + else: + metadata["device_index"] = input.device.index + metadata["dtype"] = f"{input.dtype}" + metadata["sizes"] = list(input.size()) + metadata["strides"] = list(input.stride()) + metadata["requires_grad"] = input.requires_grad + metadata["dispatch_key_set"] = torch._C._dispatch_keys(input).raw_repr() + return metadata + + +def extract_tensor_list_metadata( + dynamic: bool, + input: List[torch.Tensor], +) -> Dict[str, Any]: + metadata_list = [] + for item in input: + assert isinstance(item, torch.Tensor) + metadata_list.append(extract_tensor_metadata(dynamic, item)) + + metadata: Dict[str, Any] = {} + metadata["tensor_list"] = metadata_list + return metadata + + +def extract_scalar_metadata(device_type: str, input: Any) -> Dict[str, Any]: + assert isinstance(input, supported_scalar_types()) + metadata: Dict[str, Any] = {} + metadata["is_dynamic"] = False + # Scalar tensor + metadata["device_type"] = device_type + metadata["device_index"] = -1 if device_type == "cpu" else 0 + type_to_torch_dtype = supported_builtin_dtype_torch_dtype() + metadata["dtype"] = f"{type_to_torch_dtype[type(input)]}" + metadata["scalar_value"] = input + return metadata + + +def extract_string_metadata(input: str) -> Dict[str, Any]: + assert isinstance(input, str) + metadata: Dict[str, Any] = {} + metadata["string_value"] = input + return metadata + + +def extract_dtype_metadata(input: torch.dtype) -> Dict[str, Any]: + assert isinstance(input, torch.dtype) + metadata: Dict[str, Any] = {} + metadata["dtype_value"] = f"{input}" + return metadata + + +def extract_device_metadata(input: torch.device) -> Dict[str, Any]: + assert isinstance(input, torch.device) + metadata: Dict[str, Any] = {} + metadata["device_type_value"] = f"{input.type}" + metadata["device_index_value"] = input.index + return metadata + + +def extract_layout_metadata(input: torch.layout) -> Dict[str, Any]: + assert isinstance(input, torch.layout) + metadata: Dict[str, Any] = {} + metadata["layout_value"] = f"{input}" + return metadata + + +def aoti_compile_with_persistent_cache( + ns: str, + op_func_name_with_overload: str, + device_type: str, + dynamic: bool, + f: Callable[..., Any], + args: Tuple[Any], + kwargs: Dict[str, Any], + *, + dynamic_shapes: Optional[Dict[str, Any]] = None, + options: Optional[Dict[str, Any]] = None, + remove_runtime_assertions: bool = False, + disable_constraint_solver: bool = False, +) -> str: + """ + Compile the given function with persistent cache for AOTI eager mode. + """ + assert not dynamic, "Only support static shape for now" + flattened_inputs = list(args) + list(kwargs.values()) + if not all( + isinstance( + input, + ( + supported_scalar_types(), + torch.Tensor, + list, + str, + torch.dtype, + torch.device, + torch.layout, + ), + ) + for input in flattened_inputs + ): + err_msg = f"Unsupported input types: {flattened_inputs}" + log.exception(err_msg) + raise NotImplementedError(err_msg) + + for input in flattened_inputs: + if isinstance(input, list) and not all( + isinstance(item, torch.Tensor) for item in input + ): + err_msg = f"_impl_with_aoti_compile encounters unsupported input types: {flattened_inputs}" + log.exception(err_msg) + raise NotImplementedError(err_msg) + + persistent_cache = aoti_eager_cache_dir(ns, device_type) + if not persistent_cache.exists(): + persistent_cache.mkdir(parents=True) + + persistent_cache_lib = persistent_cache / "lib" + if not persistent_cache_lib.exists(): + persistent_cache_lib.mkdir() + + with mock.patch.dict( + os.environ, + {"TORCHINDUCTOR_CACHE_DIR": persistent_cache_lib.absolute().as_posix()}, + ): + try: + kernel_lib_path = torch._export.aot_compile( + f, + args, + kwargs, + dynamic_shapes=dynamic_shapes, + remove_runtime_assertions=remove_runtime_assertions, + disable_constraint_solver=disable_constraint_solver, + # Some operations may have non-Tensor parameters like int, float, bool. These + # non-Tensor parameters will not be the input of the graph. Therefore, we do + # need to keep the same signature. + same_signature=False, + ) + + kernel_metadata_items = [] + + for idx, input in enumerate(flattened_inputs): + if isinstance(input, torch.Tensor): + metadata = extract_tensor_metadata(dynamic, input) + elif isinstance(input, list): + assert all(isinstance(item, torch.Tensor) for item in input) + metadata = extract_tensor_list_metadata(dynamic, input) + elif isinstance(input, supported_scalar_types()): + metadata = extract_scalar_metadata(device_type, input) + elif isinstance(input, str): + metadata = extract_string_metadata(input) + elif isinstance(input, torch.dtype): + metadata = extract_dtype_metadata(input) + elif isinstance(input, torch.device): + metadata = extract_device_metadata(input) + elif isinstance(input, torch.layout): + metadata = extract_layout_metadata(input) + else: + raise NotImplementedError(f"Unsupported input type: {type(input)}") + + metadata["arg_order"] = idx + kernel_metadata_items.append(metadata) + + kernel_meta_info: Dict[str, Any] = {} + kernel_meta_info["meta_info"] = kernel_metadata_items + kernel_meta_info["kernel_path"] = ( + Path(kernel_lib_path).relative_to(persistent_cache).as_posix() + ) + + json_data = [] + update_json = True + op_conf = persistent_cache / f"{op_func_name_with_overload}.json" + mode = "r" if op_conf.exists() else "w" + with aoti_eager_op_conf_lock(op_func_name_with_overload): + with open(op_conf, mode) as op_conf_file: + try: + json_data = json.load(op_conf_file) + except Exception as e: + json_data = [] + + assert isinstance(json_data, list) + for item in json_data: + assert isinstance(item, dict) + # Same kernel meta info already exists in the json file + if item["meta_info"] == kernel_metadata_items: + update_json = False + break + + if update_json: + json_data.append(kernel_meta_info) + with open(op_conf, "w") as op_conf_file: + json.dump(json_data, op_conf_file, indent=4) + + return kernel_lib_path + except Exception as e: + err_msg = f"Failed to compile {op_func_name_with_overload}: {e}" + log.exception(err_msg) + return "" diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/async_compile.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/async_compile.py new file mode 100644 index 0000000000000000000000000000000000000000..0794ceb3eed5719b5037de6ceb1c89c30a52e1cd --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/async_compile.py @@ -0,0 +1,297 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import functools +import logging +import multiprocessing +import os +import sys +from concurrent.futures import Future, ProcessPoolExecutor, ThreadPoolExecutor +from concurrent.futures.process import BrokenProcessPool +from functools import partial +from time import time +from typing import Any, Callable, Dict, List, Optional, Set, TYPE_CHECKING + +import torch +from torch._dynamo.device_interface import get_registered_device_interfaces +from torch._inductor import config +from torch._inductor.codecache import ( + CodeCacheFuture, + CppCodeCache, + CppPythonBindingsCodeCache, + CUDACodeCache, + HalideCodeCache, + LambdaFuture, + ROCmCodeCache, + TritonCodeCache, + TritonFuture, +) +from torch._inductor.compile_worker.subproc_pool import ( + _warm_process_pool, + AnyPool, + SubprocPool, +) +from torch._inductor.compile_worker.watchdog import _async_compile_initializer +from torch._inductor.runtime.compile_tasks import ( + _set_triton_ptxas_path, + _worker_compile_triton, +) +from torch.hub import _Faketqdm, tqdm +from torch.utils._triton import has_triton_package + + +if TYPE_CHECKING: + from torch._inductor.runtime.hints import HalideMeta + +# timing metrics for time spent in the compilation +_cumulative_compile_time = 0.0 +_t0: Optional[float] = None + +kernel_code_log = torch._logging.getArtifactLogger(__name__, "kernel_code") + + +def pre_fork_setup(): + """ + Setup that must be done prior to forking with a process pool. + """ + # ensure properties have been calculated before processes + # are forked + caching_device_properties() + + # Computing the triton key can be slow. If we call it before fork, + # it will be cached for the forked subprocesses. + try: + from triton.compiler.compiler import triton_key + + triton_key() + except ImportError: + # Triton might not be installed or might be an old version. + pass + + +def caching_device_properties(): + for _, device_interface in get_registered_device_interfaces(): + if device_interface.is_available(): + device_interface.Worker.get_device_properties() + + +def _compile_start() -> None: + global _t0 + if _t0 is None: + _t0 = time() + + +def _compile_end() -> None: + global _cumulative_compile_time, _t0 + if _t0 is not None: + t1 = time() + _cumulative_compile_time += t1 - _t0 + _t0 = None + # print("CUMULATIVE COMPILE TIME", _cumulative_compile_time) + + +_IS_WINDOWS = sys.platform == "win32" + +log = logging.getLogger(__name__) + + +# Used to keep track of all process pools invoked so far. +_pool_set: Set[AnyPool] = set() + + +def shutdown_compile_workers() -> None: + """Shut down all outstanding compile-worker pools.""" + for pool in _pool_set: + pool.shutdown() + after_fork() + + +def after_fork(): + """Reset pools to initial state without shutting them down""" + _pool_set.clear() + AsyncCompile.process_pool.cache_clear() + + +try: + os.register_at_fork(after_in_child=after_fork) +except AttributeError: + pass # register_at_fork does not exists on windows + + +class AsyncCompile: + def __init__(self) -> None: + pass + + @staticmethod + @functools.lru_cache(1) + def pool() -> ThreadPoolExecutor: + assert config.compile_threads > 1 + return ThreadPoolExecutor(config.compile_threads) + + @staticmethod + def _get_ready(): + """No-op function to help mark when the subprocess pool is ready.""" + return "ready" + + @staticmethod + @functools.lru_cache(1) + def process_pool() -> AnyPool: + assert config.compile_threads > 1 + pool: AnyPool + if config.worker_start_method == "subprocess": + # Wrapper around ProcessPoolExecutor forks in a new process we control + pool = SubprocPool(config.compile_threads) + else: + pre_fork_setup() + ctx = multiprocessing.get_context(config.worker_start_method) + pool = ProcessPoolExecutor( + config.compile_threads, + mp_context=ctx, + initializer=partial(_async_compile_initializer, os.getpid()), + ) + # when this pool is created in a subprocess object, the normal exit handler + # doesn't run, and we need to register our own handler. + # exitpriority has to be high, because another one of the finalizers will + # kill the worker thread that sends the shutdown message to the workers... + multiprocessing.util.Finalize(None, pool.shutdown, exitpriority=sys.maxsize) + + # Set an attribute we can check to see if the pool is ready. + pool.ready_future = pool.submit(AsyncCompile._get_ready) # type: ignore[union-attr] + _pool_set.add(pool) + return pool + + @classmethod + def warm_pool(cls) -> None: + if config.compile_threads <= 1: + return + _compile_start() + _warm_process_pool(cls.process_pool(), config.compile_threads) + _compile_end() + + @classmethod + def submit(cls, task: Callable[..., Any]) -> Any: + if config.compile_threads <= 1: + return task() + return cls.pool().submit(task) + + def _use_process_pool(self): + return ( + config.compile_threads > 1 + and self.process_pool().ready_future.done() # type: ignore[union-attr] + ) + + def triton(self, kernel_name: str, source_code: str, device_str: str = "cuda"): + kernel_code_log.info("Triton Kernel:\n%s", source_code) + _compile_start() + _set_triton_ptxas_path() + + kernel = TritonCodeCache.load(kernel_name, source_code) + if self._use_process_pool(): + # We want to support changing these env vars after (and while) the + # process pool is running, so pass them to the subprocess to reset. + env_vars = ["TORCHINDUCTOR_CACHE_DIR", "TRITON_CACHE_DIR"] + extra_env = {v: os.environ[v] for v in env_vars if v in os.environ} + return TritonFuture( + kernel, + self.process_pool().submit( + _worker_compile_triton, + kernel._reload_in_subproc, + extra_env, + ), + ) + else: + kernel.precompile() + return kernel + + def multi_kernel(self, *args, **kwargs) -> Any: + from torch._inductor.codegen.multi_kernel import MultiKernelCall + + # no need to call this in parallel since the sub-kernels are already parallel tasks + return MultiKernelCall(*args, **kwargs) + + def cpp(self, source_code: str): + kernel_code_log.info("CPP Kernel:\n%s", source_code) + if config.compile_threads <= 1: + return CppCodeCache.load(source_code).kernel + else: + get_result = CppCodeCache.load_async(source_code, submit_fn=self.submit) + return LambdaFuture(lambda: get_result().kernel) + + def cpp_pybinding(self, argtypes: List[str], source_code: str): + kernel_code_log.info("CPP+Bindings Kernel:\n%s", source_code) + if config.compile_threads <= 1: + return CppPythonBindingsCodeCache.load_pybinding(argtypes, source_code) + else: + get_result = CppPythonBindingsCodeCache.load_pybinding_async( + argtypes, source_code, submit_fn=self.submit + ) + return LambdaFuture(get_result) + + def cuda(self, source_code, dst_file_ext): + kernel_code_log.info("CUDA Kernel:\n%s", source_code) + + def task(): + return CUDACodeCache.load(source_code, dst_file_ext)[0] + + return self.submit(task) + + def rocm(self, source_code, dst_file_ext): + kernel_code_log.info("ROCm Kernel:\n%s", source_code) + + def task(): + return ROCmCodeCache.load(source_code, dst_file_ext)[0] + + return self.submit(task) + + def halide(self, meta: HalideMeta, source_code: str): + kernel_code_log.info("Halide Kernel:\n%r\n%s", meta, source_code) + if config.compile_threads <= 1: + return HalideCodeCache.generate_halide(meta, source_code) + else: + get_result = HalideCodeCache.generate_halide_async( + meta, source_code, submit_fn=self.submit + ) + return LambdaFuture(get_result) + + def wait(self, scope: Dict[str, Any]) -> None: + num_kernels = len( + [ + value + for key, value in scope.items() + if isinstance(value, (Future, CodeCacheFuture)) + ] + ) + pbar = tqdm( + total=num_kernels, + desc="Inductor Compilation", + disable=config.disable_progress, + delay=0, + ) + if config.compile_threads > 1: + for key, result in scope.items(): + if config.verbose_progress and not isinstance(pbar, _Faketqdm): + pbar.set_postfix_str(key) + if isinstance(result, (Future, CodeCacheFuture)): + try: + scope[key] = result.result() + except BrokenProcessPool as e: + raise RuntimeError( + "A compilation subprocess exited unexpectedly. This " + "is likely due to a crash. To facilitate debugging, " + "you can re-run with TORCHINDUCTOR_COMPILE_THREADS=1 " + "to cause compilation to occur in the main process." + ) from e + pbar.update(1) + + _compile_end() + + +if ( + os.environ.get("TORCH_TNT_IN_USE", "0") == "1" + or os.environ.get("TORCH_WARM_POOL", "1") != "1" + # The subprocess pool is only used for the Triton backend + or not has_triton_package() +): + pass +else: + AsyncCompile.warm_pool() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/autoheuristic/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/autoheuristic/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/autoheuristic/autoheuristic.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/autoheuristic/autoheuristic.py new file mode 100644 index 0000000000000000000000000000000000000000..c2dc04cc20f9d38dfaf962b70095984e8b1cf49f --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/autoheuristic/autoheuristic.py @@ -0,0 +1,315 @@ +import json +import os +from functools import partial +from typing import Any, Callable, Dict, List, Optional + +import torch +from torch._inductor.autoheuristic.autoheuristic_utils import ( + AHContext, + AHMetadata, + AHOperation, + Choice, + CHOICE_COL, + Feedback, + FEEDBACK_COL, + get_metadata_str_from_log, +) +from torch._inductor.autoheuristic.learned_heuristic_controller import ( + LearnedHeuristicController, +) +from torch._inductor.ir import ChoiceCaller +from torch._inductor.runtime.runtime_utils import cache_dir +from torch._inductor.utils import get_gpu_shared_memory + + +class LocalFeedback: + """ + To be able to collect data for a choice, a function providing feedback given a choice has to be provided. + LocalFeedback can be used when AutoHeuristic should immediately run the function to collect feedback for each choice + (see pad_mm.py, where the autotuning happens locally, for an example). + """ + + def __init__(self, feedback_fn: Callable[[Choice], Feedback]) -> None: + self.feedback_fn = feedback_fn + + def __call__(self, choice: Choice) -> Feedback: + return self.feedback_fn(choice) + + +class InconsistentMetadata(Exception): + """ + Exception that is thrown when AutoHeuristic tries to log data to a file where the metadata stored in the file does + not match the metadata it would store if the file didn't exist. + """ + + +class AutoHeuristic: + """ + AutoHeuristic is a framework that allows one to collect data, learn a heuristic (i.e. a regression tree) and + generate the heuristic to code. This class allows one to collect data. The collected data can then be used to train + a heuristic (see torchgen/autoheuristic/). + """ + + collected_feedback: Dict[Choice, Feedback] + + def __init__( + self, + fallback: Callable[[], Choice], + choices: List[Choice], + feedback: Optional[LocalFeedback], + context: AHContext, + name: str, + augment_context: Optional[List[AHOperation]] = None, + precondition: Optional[Callable[[AHMetadata, AHContext], bool]] = None, + ) -> None: + """ + Initializes an instance of the AutoHeuristic class. + + Args: + fallback: A callable that returns a Choice when the heuristic is unsure which choice to make, or + AutoHeuristic is in data collection mode. + choices: A list of possible choices the heuristic can make. + feedback: An instance of LocalFeedback that provides feedback for a given choice. + context: Context to store with each choice and feedback. + name: A string that identifies the heuristic. + augment_context: An optional list of AHOperation instances that augment the context. + precondition: A callable that returns a boolean indicating whether AutoHeuristic should run. + """ + self.fallback = fallback + self.choices = choices + self.feedback = feedback + self.context = context + self.name = name + self.collected_feedback = {} + self.augment_context = augment_context + self.metadata = AHMetadata( + get_gpu_shared_memory(), + torch.cuda.get_device_capability(), + self.choices, + self.name, + ) + self.precondition = precondition + + if not self.satisfies_precondition(): + return + + if torch._inductor.config.autoheuristic_log_path == "DEFAULT": + self.log_path = self.get_default_log_path() + else: + self.log_path = torch._inductor.config.autoheuristic_log_path + + if torch._inductor.config.collect_autoheuristic(self.name): + if self.feedback is not None: + for choice in self.choices: + feedback_val = self.feedback(choice) + self.save_data(choice, feedback_val) + + def satisfies_precondition(self) -> bool: + return self.precondition is None or self.precondition( + self.metadata, self.context + ) + + def get_choice(self) -> Choice: + """ + Returns the chosen option based on the value of autoheuristic_use. + If self.name is one of the comma separated strings in autoheuristic_use, + it queries a learned heuristic to make a decision. Otherwise, it returns the fallback option. + """ + + if not self.satisfies_precondition(): + return self.fallback() + + if torch._inductor.config.use_autoheuristic(self.name): + if self.augment_context is not None: + self.context.apply_operations(self.augment_context) + controller = LearnedHeuristicController( + self.metadata, + self.context, + ) + decision = controller.get_decision() + if decision not in self.choices: + # TODO(AlnisM): We might want to allow this in the future + return self.fallback() + if decision is not None: + return decision + return self.fallback() + + def get_top_k_choices( + self, top_k: int, always_included: Optional[List[str]] = None + ) -> Optional[List[Choice]]: + if not self.satisfies_precondition(): + return None + if torch._inductor.config.use_autoheuristic(self.name): + if self.augment_context is not None: + self.context.apply_operations(self.augment_context) + controller = LearnedHeuristicController( + self.metadata, + self.context, + ) + choices = controller.get_decisions_ranked(top_k) + if choices is None: + return None + if always_included is not None: + for choice in always_included: + if choice not in choices: + choices.append(choice) + return choices + return None + + def get_collected_feedback(self, choice: Choice) -> Any: + return self.collected_feedback.get(choice, None) + + @staticmethod + def get_device_identifier() -> str: + # a heuristic might work well for one GPU, but not for another + # we store the collected data per GPU model and learn a heuristic per GPU model + + # TODO(AlnisM): just using the device name for now, but the same GPU model can have different names + device_name = torch.cuda.get_device_name().replace(" ", "_") + return device_name + + def get_default_log_path(self) -> str: + device_name = self.get_device_identifier() + path = f"{cache_dir()}/autoheuristic/{device_name}/" + os.makedirs(path, exist_ok=True) + path += f"{self.name}.txt" + return path + + def serialize_metadata(self) -> str: + metadata_dict = self.metadata.to_dict() + ( + num_features, + cat_features, + ) = self.context.get_numerical_and_categorical_features() + metadata_dict["numerical_features"] = num_features + metadata_dict["categorical_features"] = cat_features + return json.dumps(metadata_dict) + + def save_data(self, choice: Choice, feedback_val: Feedback) -> None: + self.collected_feedback[choice] = feedback_val + log_path = self.log_path + + lines = [] + log_exists = os.path.exists(log_path) + if log_exists: + # if log already exists, make sure it is consistent + metadata = self.serialize_metadata() + existing_metadata = get_metadata_str_from_log(self.log_path) + if existing_metadata != metadata: + raise InconsistentMetadata( + "Given metadata does not match existing metadata" + ) + else: + lines.append(self.serialize_metadata()) + feature_header = self.context.get_feature_names_csv() + header = feature_header + "," + CHOICE_COL + "," + FEEDBACK_COL + lines.append(header) + + line = "" + feature_values = self.context.get_feature_values_csv() + line += feature_values + "," + choice + "," + str(feedback_val) + lines.append(line) + + with open(log_path, "a") as f: + f.write("\n".join(lines) + "\n") + + +class AutoHeuristicSelectAlgorithm(AutoHeuristic): + """ + AutoHeuristicSelectAlgorithm is a subclass of AutoHeuristic that allows one to collect data and learn a heuristic + when one wants to use AutoHeuristic for kernel choice selection. + """ + + def __init__( + self, + fallback: Callable[[], Optional[ChoiceCaller]], + choices: List[ChoiceCaller], + input_nodes: List[Any], + context: AHContext, + name: str, + augment_context: Optional[List[AHOperation]] = None, + precondition: Optional[Callable[[AHMetadata, AHContext], bool]] = None, + ) -> None: + """ + The arguments choices, input_nodes and name have to match the ones used in the call to + autotune_select_algorithm(), e.g. if the following call is made + autotune_select_algorithm(name, choices, input_nodes, layout), the same name, choices and input_nodes + have to be used here. + """ + self.input_nodes = input_nodes + self.choicestr2choice: Dict[str, ChoiceCaller] = {} + for choice in choices: + self.choicestr2choice[choice.autoheuristic_id()] = choice + choices_str = list(self.choicestr2choice.keys()) + + def fallback_str() -> str: + fallback_choice = fallback() + if fallback_choice is None: + # TODO: Find a nicer way to handle this + return "unsure" + return fallback_choice.autoheuristic_id() + + super().__init__( + fallback_str, + choices_str, + None, + context, + name, + augment_context, + precondition, + ) + + if ( + torch._inductor.config.collect_autoheuristic(self.name) + and self.satisfies_precondition() + ): + self.register_global_feedback(input_nodes, choices) + + def register_global_feedback( + self, input_nodes: List[Any], choices: List[ChoiceCaller] + ) -> None: + """ + Registers a callback in select_algorithm, which is called with the timing of each choice. + """ + + from torch._inductor.select_algorithm import ( + add_feedback_saver, + create_inputs_key, + create_precompile_key, + ) + + def store_global_feedback( + ah_inputs_key: str, + ah_precompile_key: str, + timings: Dict[ChoiceCaller, float], + name: str, + input_nodes: List[Any], + choices: List[ChoiceCaller], + ) -> None: + current_inputs_key = create_inputs_key(input_nodes) + if current_inputs_key != ah_inputs_key: + return + current_precompile_key = create_precompile_key( + name, current_inputs_key, choices + ) + if current_precompile_key != ah_precompile_key: + return + for choice, time in timings.items(): + self.save_data(choice.autoheuristic_id(), time) + + inputs_key = create_inputs_key(input_nodes) + precompile_key = create_precompile_key(self.name, inputs_key, choices) + feedback_saver = partial(store_global_feedback, inputs_key, precompile_key) + add_feedback_saver(feedback_saver) + + def get_choice_caller(self) -> Optional[ChoiceCaller]: + choice = self.get_choice() + return self.choicestr2choice.get(choice, None) + + def get_top_k_choices_caller( + self, top_k: int, always_included: Optional[List[str]] = None + ) -> Optional[List[ChoiceCaller]]: + choices = self.get_top_k_choices(top_k, always_included) + if choices is None: + return None + return [self.choicestr2choice[choice] for choice in choices] diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/autoheuristic/autoheuristic_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/autoheuristic/autoheuristic_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4db054aa23f1d8b5153758fa8127d58c880764c9 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/autoheuristic/autoheuristic_utils.py @@ -0,0 +1,339 @@ +import functools +from typing import Any, Callable, Dict, List, Tuple + +import torch + + +Feedback = float +Choice = str +Value = Any + +CHOICE_COL = "choice" +FEEDBACK_COL = "feedback" + + +class AHFeature: + """ + The context, that AutoHeuristic stores, is a list of features. AutoHeuristic needs to know whether a feature is + categorical (i.e., not a continuous variable) to learn a machine learning model. + """ + + def __init__(self, name: str, value: Value, is_categorical: bool = False) -> None: + self.name = name + self.value = value + self.is_categorical = is_categorical + + +class AHOperation: + """ + AHOperation can be used to augment the data collected by AutoHeuristic. + One might for example store features like m, k, n, but also want to use + features like m*n, or k*n, to learn a heuristic. Instead of storing features + that can be created from the collected data, one can use AHOperation to + create new features from the collected data. + """ + + def __init__( + self, name: str, func: Callable[[Any], Value], is_categorical: bool = False + ) -> None: + self.name = name + self.func = func + self.is_categorical = is_categorical + + def apply_operation(self, data: Any) -> None: + data[self.name] = self.func(data) + + +class AHContext: + """ + This class is used to specify which information AutoHeuristic should store. For each choice, AutoHeursitic will + store the context and the collected feedback. The context could be something like the shape of a tensor, i.e., + information that will help to learn a heuristic. + """ + + features: List[AHFeature] + context_dict: Dict[str, Value] + + def __init__(self) -> None: + self.features = [] + self.context_dict = {} + + def add_feature( + self, name: str, value: Value, is_categorical: bool = False + ) -> None: + self.features.append(AHFeature(name, value, is_categorical=is_categorical)) + self.context_dict[name] = value + + def get_numerical_and_categorical_features(self) -> Tuple[List[str], List[str]]: + numerical_features = [] + categorical_features = [] + for feature in self.features: + if feature.is_categorical: + categorical_features.append(feature.name) + else: + numerical_features.append(feature.name) + + return numerical_features, categorical_features + + def get_feature_names_csv(self) -> str: + return ",".join(feature.name for feature in self.features) + + def get_feature_values_csv(self) -> str: + return ",".join(str(feature.value) for feature in self.features) + + def get_value(self, name: str) -> Value: + return self.context_dict[name] + + def apply_operations(self, operations: List[AHOperation]) -> None: + for op in operations: + op.apply_operation(self.context_dict) + + +class AHMetadata: + def __init__( + self, + shared_memory: Any, + device_capa: Tuple[int, int], + choices: List[Choice], + name: str, + ) -> None: + # use amount of shared_memory and device_capability to identify GPU + # TODO(AlnisM): there might be a better way to do this + self.shared_memory = shared_memory + self.device_capa = device_capa + self.choices = choices + self.name = name + + def to_dict(self) -> Dict[str, Value]: + return { + "shared_memory": self.shared_memory, + "device_capa": self.device_capa, + "name": self.name, + } + + +def get_metadata_str_from_log(log_path: str) -> str: + with open(log_path, newline="") as file: + json_string = file.readline().strip() + return json_string + + +def check_minsize(context: AHContext, minsize: int) -> bool: + return ( + context.get_value("m") >= minsize + and context.get_value("k") >= minsize + and context.get_value("n") >= minsize + ) + + +def pad_mm_precondition(metadata: AHMetadata, context: AHContext) -> bool: + if metadata.shared_memory == 166912 and metadata.device_capa == (8, 0): + # A100 precondition + return check_minsize(context, 512) + elif metadata.shared_memory == 232448 and metadata.device_capa == (9, 0): + # H100 precondition + return check_minsize(context, 768) + return True + + +def get_mixedmm_precondition(metadata: AHMetadata, context: AHContext) -> bool: + m = context.get_value("m") + k = context.get_value("k") + n = context.get_value("n") + if m > 128 or k < 1024 or n < 1024: + return False + mat1_iscontig = context.get_value("mat1_iscontig") + mat2_iscontig = context.get_value("mat2_iscontig") + return mat1_iscontig and not mat2_iscontig + + +def get_mult_dims_ops() -> List[AHOperation]: + m_times_k_op = AHOperation("m*k", lambda data: data["m"] * data["k"]) + m_times_n_op = AHOperation("m*n", lambda data: data["m"] * data["n"]) + k_times_n_op = AHOperation("k*n", lambda data: data["k"] * data["n"]) + return [m_times_k_op, m_times_n_op, k_times_n_op] + + +def get_arith_intensity(data: Any) -> float: + m = data["m"] + k = data["k"] + n = data["n"] + if m == 0 or k == 0 or n == 0: + return 0.0 + return m * k * n / (m * k + k * n + m * n) + + +def pad_mm_operations() -> List[AHOperation]: + mult_dims_ops = get_mult_dims_ops() + k_div_m_times_n_op = AHOperation( + "k/(m*n)", lambda data: data["k"] / (data["m"] * data["n"]) + ) + + def bfloat_perf_hit(data: Any) -> bool: + m = data["m"] + k = data["k"] + n = data["n"] + is_bfloat = str(data["mat1_dtype"]) == "torch.bfloat16" + return k > (m * 1024) and k > (n * 1024) and is_bfloat + + bfloat_perf_hit_op = AHOperation( + "bfloat_perf_hit", bfloat_perf_hit, is_categorical=True + ) + + arith_intensity_op = AHOperation("arith_intensity", get_arith_intensity) + dims_need_padding_ops = get_dims_need_padding_ops() + dims_multiple_ops = get_dims_multiple_ops() + is_contig_ops = get_is_contig_ops() + + ah_operations = mult_dims_ops + [ + k_div_m_times_n_op, + bfloat_perf_hit_op, + arith_intensity_op, + ] + ah_operations.extend(dims_need_padding_ops) + ah_operations.extend(dims_multiple_ops) + ah_operations.extend(is_contig_ops) + return ah_operations + + +def between_op(data: Any, dim: str, lower: int, upper: int) -> bool: + return data[dim] >= lower and data[dim] <= upper + + +def between_ops() -> List[AHOperation]: + dims = ["m", "k", "n"] + limits = [(1, 16), (17, 32), (33, 64), (65, 128), (129, 256)] + ah_operations = [] + for dim in dims: + for lower, upper in limits: + between_op_fn = functools.partial( + between_op, dim=dim, lower=lower, upper=upper + ) + # using 'LEQ' instead of '<=' because '<=' cannot be exported to dot + between_op_name = f"{lower}LEQ{dim}LEQ{upper}" + ah_operations.append( + AHOperation(between_op_name, between_op_fn, is_categorical=True) + ) + return ah_operations + + +def pow2_op(data: Any, dim: str, exponent: int) -> bool: + return data[dim] == 2**exponent + + +def mm_operations() -> List[AHOperation]: + mult_dims_ops = get_mult_dims_ops() + arith_intensity_op = AHOperation("arith_intensity", get_arith_intensity) + return mult_dims_ops + [arith_intensity_op] + + +def mixed_mm_operations() -> List[AHOperation]: + return mm_operations() + between_ops() + + +def is_multiple(data: Any, dim: str, mult: int) -> bool: + return data[dim] % mult == 0 + + +def get_dims_multiple_ops() -> List[AHOperation]: + multiples = [2, 4, 8, 16, 32] + dims = ["m", "k", "n"] + dims_multiple_ops = [] + for dim in dims: + for mult in multiples: + is_multiple_fn = functools.partial(is_multiple, dim=dim, mult=mult) + dims_multiple_op = AHOperation( + f"{dim}_multiple_{mult}", is_multiple_fn, is_categorical=True + ) + dims_multiple_ops.append(dims_multiple_op) + return dims_multiple_ops + + +def get_dims_need_padding_ops() -> List[AHOperation]: + def mat1_innermost_needs_padding_fn(data: Any) -> bool: + mat1_stride_0 = data["mat1_stride_0"] + mat1_stride_1 = data["mat1_stride_1"] + m_padded_length = data["m_padded_length"] + k_padded_length = data["k_padded_length"] + mat1_innermost_needs_padding = False + if mat1_stride_0 == 1 and m_padded_length != 0: + mat1_innermost_needs_padding = True + if mat1_stride_1 == 1 and k_padded_length != 0: + mat1_innermost_needs_padding = True + return mat1_innermost_needs_padding + + mat1_innermost_op = AHOperation( + "mat1_innermost_needs_padding", + mat1_innermost_needs_padding_fn, + is_categorical=True, + ) + + def mat2_innermost_needs_padding_fn(data: Any) -> bool: + mat2_stride_0 = data["mat2_stride_0"] + mat2_stride_1 = data["mat2_stride_1"] + k_padded_length = data["k_padded_length"] + n_padded_length = data["n_padded_length"] + mat2_innermost_needs_padding = False + if mat2_stride_0 == 1 and k_padded_length != 0: + mat2_innermost_needs_padding = True + if mat2_stride_1 == 1 and n_padded_length != 0: + mat2_innermost_needs_padding = True + return mat2_innermost_needs_padding + + mat2_innermost_op = AHOperation( + "mat2_innermost_needs_padding", + mat2_innermost_needs_padding_fn, + is_categorical=True, + ) + + def num_dims_needs_padding_fn(data: Any) -> int: + m_padded_length = data["m_padded_length"] + k_padded_length = data["k_padded_length"] + n_padded_length = data["n_padded_length"] + num_dims_needs_padding = 0 + if m_padded_length != 0: + num_dims_needs_padding += 1 + if k_padded_length != 0: + num_dims_needs_padding += 1 + if n_padded_length != 0: + num_dims_needs_padding += 1 + return num_dims_needs_padding + + num_dims_op = AHOperation("num_dims_needs_padding", num_dims_needs_padding_fn) + return [mat1_innermost_op, mat2_innermost_op, num_dims_op] + + +def get_is_contig_ops() -> List[AHOperation]: + def mat1_is_contig_fn(data: Any) -> bool: + stride_0 = data["mat1_stride_0"] + stride_1 = data["mat1_stride_1"] + k = data["k"] + return stride_0 == k and stride_1 == 1 + + mat1_is_contig_op = AHOperation( + "mat1_iscontig", mat1_is_contig_fn, is_categorical=True + ) + + def mat2_is_contig_fn(data: Any) -> bool: + stride_0 = data["mat2_stride_0"] + stride_1 = data["mat2_stride_1"] + n = data["n"] + return stride_0 == n and stride_1 == 1 + + mat2_is_contig_op = AHOperation( + "mat2_iscontig", mat2_is_contig_fn, is_categorical=True + ) + + return [mat1_is_contig_op, mat2_is_contig_op] + + +def context_add_strides(context: AHContext, name: str, stride: Tuple[int, ...]) -> None: + for i, s in enumerate(stride): + context.add_feature(f"{name}_stride_{i}", s) + + +def context_add_using_tf32(context: AHContext, dtype: torch.dtype) -> None: + using_tf32 = "not_float_32" + if dtype == torch.float32: + using_tf32 = torch.backends.cuda.matmul.allow_tf32 + context.add_feature("using_tf32", using_tf32, is_categorical=True) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/autoheuristic/learned_heuristic_controller.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/autoheuristic/learned_heuristic_controller.py new file mode 100644 index 0000000000000000000000000000000000000000..23663c148df192b85178aa62d685ac0b332f9256 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/autoheuristic/learned_heuristic_controller.py @@ -0,0 +1,119 @@ +import importlib +import inspect +import pkgutil +from collections import defaultdict +from typing import Any, Dict, List, Optional + +from torch._inductor.autoheuristic.autoheuristic_utils import ( + AHContext, + AHMetadata, + Choice, +) +from torch._inductor.autoheuristic.learnedheuristic_interface import LearnedHeuristic + + +def find_and_instantiate_subclasses( + package_name: str, base_class: Any +) -> List[LearnedHeuristic]: + instances = [] + + package = importlib.import_module(package_name) + for _, module_name, _ in pkgutil.walk_packages( + package.__path__, package.__name__ + "." + ): + try: + module_basename = module_name.split(".")[-1] + if not module_basename.startswith("_"): + # learned heuristics start with an underscore + continue + module = importlib.import_module(module_name) + + # look for classes that are subclasses of base_class + for name, obj in inspect.getmembers(module): + if ( + inspect.isclass(obj) + and issubclass(obj, base_class) + and obj != base_class + ): + instance = obj() + instances.append(instance) + except Exception as e: + print(f"Error processing module {module_name}: {e}") + + return instances + + +class LearnedHeuristicController: + """ + Class that finds and instantiates all learned heuristics. It also provides + a way to get the decision of a learned heuristic. + """ + + existing_heuristics: Dict[str, List[LearnedHeuristic]] = defaultdict(list) + """ + A dictionary that stores all the learned heuristics for each optimization. + The key is the optimization name, and the value is a list of LearnedHeuristic objects. + """ + + heuristics_initialized: bool = False + """ + A flag that indicates whether the learned heuristics have been initialized. + Set to true when the get_decision() function is called for the first time. + """ + + def __init__( + self, + metadata: AHMetadata, + context: AHContext, + ) -> None: + self.metadata = metadata + self.context = context + + def get_heuristics(self, name: str) -> List[LearnedHeuristic]: + """ + Returns a list of learned heuristics for the given optimization name. + """ + + if not LearnedHeuristicController.heuristics_initialized: + # learned heuristics are generated into the following package + learned_heuristics_package = "torch._inductor.autoheuristic.artifacts" + + # learned heuristics have to be of type LearnedHeuristic + base_class = LearnedHeuristic + found_heuristics = find_and_instantiate_subclasses( + learned_heuristics_package, base_class + ) + + for learned_heuristic in found_heuristics: + opt_name = learned_heuristic.get_name() + LearnedHeuristicController.existing_heuristics[opt_name].append( + learned_heuristic + ) + LearnedHeuristicController.heuristics_initialized = True + + return LearnedHeuristicController.existing_heuristics[name] + + def get_decision(self) -> Optional[Choice]: + """ + Returns the decision made by the learned heuristic or None if no heuristic was found or the heuristic is unsure + which choice to make. + """ + + heuristics = self.get_heuristics(self.metadata.name) + for heuristic in heuristics: + if heuristic.check_precondition(self.metadata, self.context): + return heuristic.get_decision(self.context, self.metadata.choices) + return None + + def get_decisions_ranked(self, top_k: int) -> Optional[List[Choice]]: + heuristics = self.get_heuristics(self.metadata.name) + for heuristic in heuristics: + if heuristic.check_precondition(self.metadata, self.context): + choices = heuristic.get_decisions_ranked(self.context) + if choices is None: + return None + avail_choices = [ + choice for choice in choices if choice in self.metadata.choices + ] + return avail_choices[:top_k] + return None diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/autoheuristic/learnedheuristic_interface.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/autoheuristic/learnedheuristic_interface.py new file mode 100644 index 0000000000000000000000000000000000000000..dfa7671f0223e1bea0b2e34fbdb50053c0db6371 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/autoheuristic/learnedheuristic_interface.py @@ -0,0 +1,92 @@ +from typing import List, Optional, Tuple + +from torch._inductor.autoheuristic.autoheuristic_utils import ( + AHContext, + AHMetadata, + Choice, +) + + +class LearnedHeuristic: + """ + LearnedHeuristic is a base class for all learned heuristics. + """ + + def __init__(self) -> None: + pass + + def check_precondition( + self, + metadata: AHMetadata, + context: AHContext, + ) -> bool: + return True + + def get_decision( + self, context: AHContext, choices: List[Choice] + ) -> Optional[Choice]: + return None + + def get_confidence_threshold(self) -> float: + return 1.0 + + def get_name(self) -> str: + return "" + + def get_decisions_ranked(self, context: AHContext) -> Optional[List[str]]: + return None + + +class LearnedHeuristicRegression(LearnedHeuristic): + def __init__(self) -> None: + super().__init__() + + def get_feedback(self, context: AHContext, choice: Choice) -> float: + return 1.0 + + def get_decision( + self, context: AHContext, choices: List[Choice] + ) -> Optional[Choice]: + choice2feedback = {} + for choice in choices: + predicted_feedback = self.get_feedback(context, choice) + choice2feedback[choice] = predicted_feedback + sorted_choices_feedback = sorted(choice2feedback.items(), key=lambda t: t[1]) + highest_feedback = sorted_choices_feedback[-1][1] + second_highest_feedback = sorted_choices_feedback[-2][1] + if highest_feedback / second_highest_feedback > self.get_confidence_threshold(): + return sorted_choices_feedback[-1][0] + # We are not sure which choice is the best one + return None + + +class LearnedHeuristicDecision(LearnedHeuristic): + def __init__(self) -> None: + super().__init__() + + def get_choice(self, idx: int) -> Optional[str]: + return None + + def get_decision( + self, context: AHContext, choices: List[Choice] + ) -> Optional[Choice]: + best_choices = self.get_best_choices(context) + if not best_choices: + return None + (best_choice_proba, best_choice_idx) = best_choices[0] + if best_choice_proba <= self.get_confidence_threshold(): + return None + return self.get_choice(best_choice_idx) + + def get_decisions_ranked(self, context: AHContext) -> Optional[List[str]]: + feedback_idx_list = self.get_best_choices(context) + if feedback_idx_list is None: + return None + choices = [ + self.get_choice(feedback_idx[1]) for feedback_idx in feedback_idx_list + ] + choices = [choice for choice in choices if choice is not None] + return choices + + def get_best_choices(self, context: AHContext) -> Optional[List[Tuple[float, int]]]: + return [] diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/autotune_process.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/autotune_process.py new file mode 100644 index 0000000000000000000000000000000000000000..eea4f8d6573d82c5d629382893920c8256953c6a --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/autotune_process.py @@ -0,0 +1,876 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import contextlib +import ctypes +import dataclasses +import functools +import logging +import os +import queue +import time +import warnings +from concurrent.futures import ThreadPoolExecutor +from ctypes import byref, c_size_t, c_void_p, CDLL +from typing import ( + Any, + Callable, + Dict, + Iterable, + List, + Optional, + Sequence, + TYPE_CHECKING, + Union, +) + +import torch +import torch._inductor.async_compile # noqa: F401 required to warm up AsyncCompile pools +from torch import multiprocessing +from torch._dynamo.testing import rand_strided +from torch._inductor import ir +from torch._inductor.codecache import ( + CppCodeCache, + CUDACodeCache, + DLLWrapper, + get_hash, + PyCodeCache, +) + + +if TYPE_CHECKING: + from multiprocessing.process import BaseProcess + from multiprocessing.queues import Queue + from types import ModuleType + + from torch._inductor.select_algorithm import TritonTemplateCaller + +from . import config +from .runtime.benchmarking import benchmarker +from .virtualized import V + + +CUDA_VISIBLE_DEVICES = "CUDA_VISIBLE_DEVICES" +EXIT_HANDLER_REGISTERED = False + +log = logging.getLogger(__name__) + + +# Used to synchronize between parent and child processes +class Ping: + pass + + +class Pong: + pass + + +class NonzeroWorkspaceNotSupportedError(Exception): + pass + + +@contextlib.contextmanager +def set_cuda_visible_device(device: Optional[int]): + """ + Context manager to set the CUDA_VISIBLE_DEVICES environment variable to the + specified single device. If device is None, don't manipulate the environment. + """ + if device is None: + yield + return + + current = os.environ.get(CUDA_VISIBLE_DEVICES) + os.environ[CUDA_VISIBLE_DEVICES] = str(device) + try: + yield + finally: + if current is None: + del os.environ[CUDA_VISIBLE_DEVICES] + else: + os.environ[CUDA_VISIBLE_DEVICES] = current + + +@dataclasses.dataclass +class TuningProcess: + """ + Abstraction for launching a helper process to benchmark kernels. Spawns + the parent process and uses multiprocessing queues to send benchmark + requests and return results. + """ + + device: Optional[int] = None + process: Optional[BaseProcess] = None + request_queue: Optional[Queue[Any]] = None + response_queue: Optional[Queue[Any]] = None + + @staticmethod + def process_main( + request_queue: Queue[Any], + response_queue: Queue[Any], + ) -> None: + """ + Entry point for the child process. + """ + log.debug( + "Entering TuningProcess child. Visible devices = %s", + os.environ.get(CUDA_VISIBLE_DEVICES), + ) + try: + TuningProcess.workloop(request_queue, response_queue) + except Exception as ex: + log.exception("Exception in TuningProcess") + + @staticmethod + def workloop(request_queue: Queue[Any], response_queue: Queue[Any]) -> None: + """ + Work loop for the benchmarking subprocess. + """ + while True: + obj = request_queue.get() + + if obj is None: + break # None is a sentinel for the child to terminate + elif isinstance(obj, Ping): + response_queue.put(Pong()) + elif isinstance(obj, BenchmarkRequest): + response_queue.put(obj.benchmark()) + else: + raise RuntimeError(f"Invalid request type {type(obj)}") + + def valid(self) -> bool: + """ + True if the sub-process has been initialized. + """ + return ( + self.process is not None + and self.request_queue is not None + and self.response_queue is not None + ) + + def clear(self) -> None: + """ + Reset to an uninitialized state. + """ + self.process = self.request_queue = self.response_queue = None + + def initialize(self) -> None: + """ + Create child process, request/response queues, and do the warm up. + Set the environment to make only the provided GPU device visible + to the process. + """ + if self.valid(): + return + + # cuda runtime does not work with "fork", use "spawn" to start processes. + ctx = multiprocessing.get_context("spawn") + self.request_queue = ctx.Queue() + self.response_queue = ctx.Queue() + + self.process = ctx.Process( + target=self.process_main, + args=( + self.request_queue, + self.response_queue, + ), + ) + assert self.process is not None + with set_cuda_visible_device(self.device): + self.process.start() + + def put(self, obj: Any) -> None: + """ + Push a work item to the child process. + """ + # In case of a prior crash, ensure the subprocess is running + self.initialize() + assert self.request_queue is not None + self.request_queue.put(obj) + + def get( + self, result_timeout=120.0, graceful_timeout=3.0, terminate_timeout=1.0 + ) -> Any: + """ + Get a response from the child process. Raises queue.Empty on timeout + or if the process dies. + + This method is (so far) only used by TuningProcessPool, where torch._inductor.config entries are being used + to populate the timeouts: + + Arguments: + + @param result_timeout: Timeout in seconds, defaults to 120.0 or to + config.max_autotune_subproc_result_timeout_seconds when called by TuningProcessPool + @param graceful_timeout: Timeout in seconds to allow graceful shutdown (SIGTERM is sent after this time). + Defaults to 3.0 or to config.max_autotune_subproc_graceful_timeout_seconds + @param terminate_timeout: Timeout in seconds after SIGTERM, until we send SIGKILL if the process + remains alive. Defaults to 1.0 or to + config.max_autotune_subproc_terminate_timeout_seconds. + Returns: + A response from the child process (Any type) + """ + assert self.process is not None + assert self.response_queue is not None + while True: + try: + remaining_timeout = result_timeout + res = None + while remaining_timeout is not None and remaining_timeout >= 1.0: + remaining_timeout -= 0.5 + try: + res = self.response_queue.get(timeout=0.5) + break + except queue.Empty: + if not self.process.is_alive(): + raise # is being caught a few lines below + if res is None: + res = self.response_queue.get(timeout=remaining_timeout) + return res + except queue.Empty: + status = self.process.exitcode + if status is None: + self.kill( + graceful_timeout=graceful_timeout, + terminate_timeout=terminate_timeout, + ) + else: + # child process crashed + self.clear() + raise + + def terminate(self) -> None: + """ + Signal the child process to terminate. + """ + if self.valid(): + assert self.process is not None + assert self.request_queue is not None + self.request_queue.put(None) + + def wait(self) -> None: + """ + Wait for the child process to exit. + """ + if self.process is not None: + self.process.join() + self.clear() + + def kill(self, graceful_timeout=5.0, terminate_timeout=1.0) -> None: + # Tries to kill the process, using a graceful_timeout in which the process + # is allowed to exit gracefully. If the process is still alive, + # it will be terminated. If that is not sufficient to end it + # within terminate_timeout seconds, it will be killed. + if self.process is not None: + self.terminate() + self.process.join(timeout=graceful_timeout) + if self.process.is_alive(): + log.warning( + "Sending SIGTERM to process with PID %d", + self.process.pid, + ) + self.process.terminate() + self.process.join(timeout=terminate_timeout) + if self.process.is_alive(): + log.error( + "Sending SIGKILL to process with PID %d", + self.process.pid, + ) + self.process.kill() # This should definitely end the process + self.clear() + + +@dataclasses.dataclass +class TuningProcessPool: + """ + Maintains a pool of TuningProcesses to benchmark kernels in parallel + across devices. By default, we create one TuningProcess per device and + set the sub-process environment to make only that device visible. + """ + + processes: Optional[queue.Queue[TuningProcess]] = None + executor: Optional[ThreadPoolExecutor] = None + + def initialize(self) -> None: + """ + Start the child processes. + """ + assert (self.processes is None) == (self.executor is None) + if self.processes is not None: + return + + devices = self.get_device_list() + log.debug("Sub-process autotune device list: %s", devices) + + # Launch the child processes and push a msg to "warm up" + self.processes = queue.Queue() + for device in devices: + p = TuningProcess(device=device) + p.initialize() + p.put(Ping()) + self.processes.put(p) + + # Wait for the initialization to finish + for p in self.processes.queue: + assert isinstance(p.get(result_timeout=None), Pong) + + # Use a thread pool to manage distributing work to the subprocesses. + # Threads block on an available process, so it makes sense to match + # the number of threads with the number of devices. + self.executor = ThreadPoolExecutor(max_workers=len(devices)) + + # Register the exit handler for the parent process so it will terminate + # the child processes. + global EXIT_HANDLER_REGISTERED + if not EXIT_HANDLER_REGISTERED: + EXIT_HANDLER_REGISTERED = True + import atexit + + atexit.register(self.terminate) + + def get_device_list(self) -> Sequence[Optional[int]]: + """ + Gather the list of devices to be used in the pool. + """ + if not config.autotune_multi_device: + # Don't use multiple devices + return [None] + + count = torch.cuda.device_count() + + # If the user specified the visible devices in the env, use those. + if CUDA_VISIBLE_DEVICES in os.environ: + devices = [int(d) for d in os.environ[CUDA_VISIBLE_DEVICES].split(",")] + assert len(devices) <= count + return devices + + return list(range(count)) + + def terminate(self) -> None: + """ + Signal all child processes to terminate. + """ + if self.executor is not None: + self.executor.shutdown() + self.executor = None + + if self.processes is not None: + for p in self.processes.queue: + p.terminate() + for p in self.processes.queue: + p.wait() + self.processes = None + + def target(self, choice: TritonTemplateCaller) -> float: + """ + Entry point for the thread-pool helper threads: Wait for an open TuningProcess, + remove it from the queue, execute the benchmark in that subprocess, and return + the TuningProcess to the queue. + """ + assert choice.bmreq is not None + assert self.processes is not None + + process = self.processes.get() + process.put(choice.bmreq) + try: + return process.get( + config.max_autotune_subproc_result_timeout_seconds, + config.max_autotune_subproc_graceful_timeout_seconds, + config.max_autotune_subproc_terminate_timeout_seconds, + ) + except queue.Empty: + warnings.warn( + f"Failed to benchmark choice '{choice}'. It will be ignored. " + "Please debug the root cause in case the choice can bring perf gains." + ) + # set to INF so this choice will be ignored + return float("inf") + finally: + self.processes.put(process) + + def benchmark( + self, + choices: List[TritonTemplateCaller], + ) -> Dict[TritonTemplateCaller, float]: + """ + Benchmark each choice in a separate process. + """ + assert self.processes is not None, "Tuning process pool is not initialized" + assert self.executor is not None + + results = {} + + # Use a ThreadExecutorPool to spread the work across the subprocesses and + # to grab subprocesses as soon as they're free. + for choice, result in zip(choices, self.executor.map(self.target, choices)): + results[choice] = result + + return results + + +tuning_pool = TuningProcessPool() + + +LayoutOrBuffer = Union[ir.Layout, ir.Buffer] + + +@dataclasses.dataclass +class TensorMeta: + device: torch.device + dtype: torch.dtype + sizes: torch._prims_common.ShapeType + strides: torch._prims_common.StrideType + offset: int + name: Optional[str] = None + + @classmethod + def from_irnodes( + cls, irnodes: Union[LayoutOrBuffer, Sequence[LayoutOrBuffer]] + ) -> Union[TensorMeta, List[TensorMeta]]: + if isinstance(irnodes, Sequence): + result: List[Any] = [cls.from_irnodes(x) for x in irnodes] + assert all(isinstance(x, TensorMeta) for x in result) + return result + + node = irnodes + if isinstance(node, ir.Layout): + node = ir.Buffer("fake", node) + + dtype = node.get_dtype() + assert dtype is not None + + return TensorMeta( + device=node.get_device(), + dtype=dtype, + sizes=V.graph.sizevars.size_hints( + node.get_size(), + fallback=config.unbacked_symint_fallback, + ), + strides=V.graph.sizevars.size_hints( + node.get_stride(), + fallback=config.unbacked_symint_fallback, + ), + offset=V.graph.sizevars.size_hint( + node.get_layout().offset, + fallback=config.unbacked_symint_fallback, + ), + name=node.get_name(), + ) + + def to_tensor(self) -> torch.Tensor: + return rand_strided( + self.sizes, + self.strides, + device=self.device, + dtype=self.dtype, + extra_size=self.offset, + ) + + +@dataclasses.dataclass +class BenchmarkRequest: + """ + Only handle triton template benchmark for now. The extern kernel benchmark + can be done inside the same process since they usually don't cause crash. + + Important: Instances of this class and subclasses have to be serializable + across process boundaries. Do not put CUDA Tensors in here! + """ + + def __init__( + self, + kernel_name: str, + input_tensor_meta: Union[TensorMeta, List[TensorMeta]], + output_tensor_meta: Union[TensorMeta, List[TensorMeta]], + extra_args: Iterable[Any], + ) -> None: + # the kernel name defined in the module + self.kernel_name = kernel_name + + if isinstance(input_tensor_meta, TensorMeta): + input_tensor_meta = [input_tensor_meta] + self.input_tensor_meta = input_tensor_meta + + if isinstance(output_tensor_meta, (tuple, list)): + assert len(output_tensor_meta) == 1 + output_tensor_meta = output_tensor_meta[0] + self.output_tensor_meta = output_tensor_meta + + self.extra_args = extra_args + + def make_run_fn( + self, *input_tensors: torch.Tensor, output_tensor: torch.Tensor + ) -> Callable[[], None]: + raise NotImplementedError + + def cleanup_run_fn(self) -> None: + pass + + def do_bench( + self, + fn, + *input_tensors: torch.Tensor, + output_tensor: Optional[torch.Tensor] = None, + ) -> float: + raise NotImplementedError + + def benchmark( + self, + *input_tensors: torch.Tensor, + output_tensor: Optional[torch.Tensor] = None, + ) -> float: + debug = log.isEnabledFor(logging.DEBUG) + if debug: + start_ts = time.time() + + # create args and out tensor + if output_tensor is None: + assert len(input_tensors) == 0 + input_tensors = tuple(x.to_tensor() for x in self.input_tensor_meta) + output_tensor = self.output_tensor_meta.to_tensor() + + if debug: + create_tensor_elapse = time.time() - start_ts # type: ignore[possibly-undefined] + start_ts = time.time() + try: + fn = self.make_run_fn(*input_tensors, output_tensor=output_tensor) + except NonzeroWorkspaceNotSupportedError: + # Skipping all ops with nonzero workspace requirements + log.info("Skipping op due to nonzero workspace requirement") + return float("inf") + + if debug: + load_elapse = time.time() - start_ts # type: ignore[possibly-undefined] + start_ts = time.time() + + out = self.do_bench(fn, *input_tensors, output_tensor) + + if debug: + bench_elapse = time.time() - start_ts # type: ignore[possibly-undefined] + log.debug( + "InChildProcess %s: load %f, create tensor %f, bench %f", + str(self), + load_elapse, # type: ignore[possibly-undefined] + create_tensor_elapse, # type: ignore[possibly-undefined] + bench_elapse, + ) + self.cleanup_run_fn() + return out + + +class TestBenchmarkRequest(BenchmarkRequest): + """ + Supports unit testing. Defined in this file so that the TuningProcess + sub-process knows how to unpickle these objects. + """ + + def __init__(self, value: Optional[float] = None) -> None: + self.value = value + + def benchmark( + self, *input_tensors: torch.Tensor, output_tensor: Optional[torch.Tensor] = None + ) -> float: + if self.value is None: + raise Exception("Failed to run") # noqa: TRY002 + return self.value + + +class GPUDeviceBenchmarkRequest(BenchmarkRequest): + def do_bench( + self, + fn, + *input_tensors: torch.Tensor, + output_tensor: Optional[torch.Tensor] = None, + ) -> float: + device_idx_set = { + tensor.device.index + for tensor in [*input_tensors, output_tensor] + if isinstance(tensor, torch.Tensor) + and tensor.is_cuda + and tensor.device.index is not None + } + assert len(device_idx_set) <= 1, f"Can not mix devices {device_idx_set}" + if len(device_idx_set) == 1: + device_idx = next(iter(device_idx_set)) + else: + device_idx = torch.cuda.current_device() + + with torch.cuda.device(device_idx): + out = benchmarker.benchmark_gpu(fn) + torch.cuda.synchronize() # shake out any CUDA errors + + return out + + +class TritonBenchmarkRequest(GPUDeviceBenchmarkRequest): + # Important: Instances of this class have to be serializable + # across process boundaries. Do not put CUDA Tensors in here! + def __init__( + self, + kernel_name: str, + input_tensor_meta: Union[TensorMeta, List[TensorMeta]], + output_tensor_meta: Union[TensorMeta, List[TensorMeta]], + extra_args: Iterable[Any], + module_path: str, # the path of the module defining the triton kernel + module_cache_key: str, + grid: List[int], + num_stages: int, + num_warps: int, + matrix_instr_nonkdim: int = 0, # only used for hip to choose the shape of mfma instruction. + ) -> None: + super().__init__(kernel_name, input_tensor_meta, output_tensor_meta, extra_args) + self.module_path = module_path + self.module_cache_key = module_cache_key + self.grid = grid + self.num_stages = num_stages + self.num_warps = num_warps + self.matrix_instr_nonkdim = matrix_instr_nonkdim + + def make_run_fn( + self, *input_tensors: torch.Tensor, output_tensor: torch.Tensor + ) -> Callable[[], None]: + mod = PyCodeCache.load_by_key_path(self.module_cache_key, self.module_path) + log.debug( + "benchmark module key: %s, path: %s", + self.module_cache_key, + self.module_path, + ) + + run_method = getattr(mod, self.kernel_name).run + extra_args = list(self.extra_args) + + # Newer version of triton add warmup argument to JITFunction.run. + # This code handles backward-compatibility. + warmup_arg = {} + import inspect + + if "warmup" in inspect.signature(run_method).parameters: + warmup_arg["warmup"] = False + + from torch._C import _cuda_getCurrentRawStream as get_raw_stream + + if torch.version.hip and self.matrix_instr_nonkdim != 0: + return functools.partial( + run_method, + *input_tensors, + output_tensor, + *self.extra_args, + grid=self.grid, + **warmup_arg, + stream=get_raw_stream(self.output_tensor_meta.device.index), + ) + else: + return functools.partial( + run_method, + *input_tensors, + output_tensor, + *self.extra_args, + grid=self.grid, + **warmup_arg, + stream=get_raw_stream(self.output_tensor_meta.device.index), + ) + + def precompile(self): + mod = PyCodeCache.load_by_key_path(self.module_cache_key, self.module_path) + getattr(mod, self.kernel_name).precompile() + + def __str__(self) -> str: + return f"{self.kernel_name=}, {self.module_path=}, {self.module_cache_key=}" + + +class CUDABenchmarkRequest(GPUDeviceBenchmarkRequest): + # Important: Instances of this class have to be serializable + # across process boundaries. Do not put CUDA Tensors in here! + + def __init__( + self, + kernel_name: str, + input_tensor_meta: Union[TensorMeta, List[TensorMeta]], + output_tensor_meta: Union[TensorMeta, List[TensorMeta]], + extra_args: Iterable[Any], + source_code: str, + ) -> None: + super().__init__(kernel_name, input_tensor_meta, output_tensor_meta, extra_args) + self.source_code = source_code + self.workspace_size: int = 0 + self.workspace: Optional[torch.Tensor] = None + self.DLL: Optional[DLLWrapper] = None + self._workspace_size_updated = False + self.hash_key: str = "" + self.source_file: str = "" + self.hash_key, self.source_file = CUDACodeCache.write(self.source_code, "so") + + def precompile(self): + # Prepopulate CUDACodeCache + # may happen in separate Threadpool + log.debug("Precompiling %s", self) + CUDACodeCache.compile(self.source_code, "so") + log.debug("Done precompiling %s", self) + + def make_run_fn( + self, *input_tensors: torch.Tensor, output_tensor: torch.Tensor + ) -> Callable[[], None]: + self.ensure_dll_loaded() + self.update_workspace_size() + args = [ + c_void_p(tensor.data_ptr()) + for tensor in list(input_tensors) + [output_tensor] + ] + log.debug( + "make_run_fn: self.kernel_name=%s, self.source_file=%s, self.hash_key=%s, self.DLL=%s, args=%s, self.extra_args=%s", + self.kernel_name, + self.source_file, + self.hash_key, + self.DLL, + args, + self.extra_args, + ) + stream_ptr = c_void_p(torch.cuda.current_stream().cuda_stream) + run_method = getattr(self.DLL, self.kernel_name) + workspace_ptr = c_void_p(0) + if self.workspace_size > 0: + self.workspace = torch.zeros( + (self.workspace_size + 7) // 8, + dtype=torch.float64, + device=output_tensor.device, + ) + workspace_ptr = c_void_p(self.workspace.data_ptr()) + + # Generate partial function. + return functools.partial( + run_method, + *args, + *self.extra_args, + None, # null workspace size ptr + workspace_ptr, # set workspace ptr, + stream_ptr, + ) + + def update_workspace_size(self) -> None: + if self._workspace_size_updated: + return + self.ensure_dll_loaded() + unique_input_count = len({meta.name for meta in self.input_tensor_meta}) + args = [c_void_p(None) for _ in range(unique_input_count + 1)] + stream_ptr = c_void_p(torch.cuda.current_stream().cuda_stream) + + run_method = getattr(self.DLL, self.kernel_name) + # Retrieve workspace_size and initialize workspace. + c_workspace_size = c_size_t() + run_method( + *args, # input ptrs and output ptrs + *self.extra_args, + byref( + c_workspace_size + ), # set workspace size ptr to retrieve workspace size + None, # null workspace ptr + stream_ptr, + ) + torch.cuda.synchronize() # shake out any CUDA errors + self.workspace_size = c_workspace_size.value + log.debug( + "update_workspace_size called: new workspace size=%d, self.kernel_name=%s, self.source_file=%s, self.hash_key=%s, self.DLL=%s, args=%s, self.extra_args=%s", # noqa: B950 + self.workspace_size, + self.kernel_name, + self.source_file, + self.hash_key, + self.DLL, + args, + self.extra_args, + ) + self._workspace_size_updated = True + + def ensure_dll_loaded(self): + if self.DLL is None: + self.DLL, self.hash_key, self.source_file = CUDACodeCache.load( + self.source_code, "so" + ) + + def cleanup_run_fn(self) -> None: + if self.DLL is not None: + self.DLL.close() + self.workspace = None + + def __str__(self) -> str: + return f"{self.kernel_name=}, {self.source_file=}, {self.hash_key=}" + + +class CPUDeviceBenchmarkRequest(BenchmarkRequest): + def do_bench( + self, + fn, + *input_tensors: torch.Tensor, + output_tensor: Optional[torch.Tensor] = None, + ) -> float: + return benchmarker.benchmark_cpu(fn) + + +class CppBenchmarkRequest(CPUDeviceBenchmarkRequest): + # Important: Instances of this class have to be serializable + # across process boundaries. Do not put Tensors in here! + + def __init__( + self, + kernel_name: str, + input_tensor_meta: Union[TensorMeta, List[TensorMeta]], + output_tensor_meta: Union[TensorMeta, List[TensorMeta]], + extra_args: Iterable[Any], + source_code: str, + ) -> None: + super().__init__(kernel_name, input_tensor_meta, output_tensor_meta, extra_args) + self.source_code = source_code + self.hash_key = get_hash(source_code) + self.DLL: Optional[Union[CDLL, ModuleType]] = None + + def precompile(self): + # Prepopulate CppCodeCache + # may happen in separate Threadpool + log.debug("Precompiling %s", self) + CppCodeCache.load(self.source_code, cuda=False) + log.debug("Done precompiling %s", self) + + def make_run_fn( + self, *input_tensors: torch.Tensor, output_tensor: torch.Tensor + ) -> Callable[[], None]: + # TODO(jgong5): use CppPythonBindingsCodeCache for better binding perf + self.DLL = CppCodeCache.load(self.source_code, cuda=False) + args = [tensor.data_ptr() for tensor in list(input_tensors) + [output_tensor]] + log.debug( + "make_run_fn: self.kernel_name=%s, self.DLL=%s, args=%s, self.extra_args=%s", + self.kernel_name, + self.DLL, + args, + self.extra_args, + ) + run_method = getattr(self.DLL, self.kernel_name) + # Assume only size with type ctypes.c_ulonglong in extra_args + assert all(isinstance(arg, ctypes.c_ulonglong) for arg in self.extra_args) + run_method.argtypes = [ctypes.c_ulonglong] * ( + len(args) + len(list(self.extra_args)) + ) + + # Generate partial function. + return functools.partial( + run_method, + *args, + *self.extra_args, + ) + + def cleanup_run_fn(self) -> None: + if self.DLL is not None: + """ + Check close attr due to it crash on Windows. + """ + if hasattr(self.DLL, "close"): + self.DLL.close() + + def __str__(self) -> str: + return f"{self.kernel_name=}" + + +def benchmark_in_sub_process( + choices: List[TritonTemplateCaller], +) -> Dict[TritonTemplateCaller, float]: + """ + Do benchmarking in a subprocess and return the perf number (latency). + """ + return tuning_pool.benchmark(choices) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/bounds.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/bounds.py new file mode 100644 index 0000000000000000000000000000000000000000..7452f2bb1b62b6862a63bcb8d508032d5e33c7e9 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/bounds.py @@ -0,0 +1,140 @@ +# mypy: allow-untyped-defs +import logging +import operator +from functools import partial +from typing import Any, Callable, Dict + +from sympy import Expr + +import torch +from torch.utils._sympy.value_ranges import bound_sympy, ValueRangeAnalysis, ValueRanges + +from .loop_body import InterpreterShim, LoopBody, LoopBodyBlock +from .utils import cache_on_self, dominated_nodes +from .virtualized import V + + +log = logging.getLogger(__name__) + + +class BoundVars: + """ + Performs Value Range Analysis on LoopBody's fx graph by calling BoundVars.run() + It exposes the ranges of the nodes in the `bounds` variable + + Note. A current limitation of this analysis is that it just works on a per-loop basis. + We should be able to propagate the bounds between across the whole graph. This may benefit + the case a bounded variable is returned by a kernel and fed into another. + """ + + def __init__(self, loop_body: LoopBody) -> None: + def upper_bound(v): + return bound_sympy(v).upper if isinstance(v, Expr) else v + + self.loop_body = loop_body + self.replacement_vals = { + k: ValueRanges[Expr](0, upper_bound(v) - 1) + for k, v in loop_body.var_ranges.items() + } + # avoid computing these values, pessimistically assume that they are unbounded + self.unbounded_vars = dominated_nodes( + node + for node in self.loop_body.get_nodes() + if node.target in ["load", "reduction", operator.getitem] + or "masked_subblock" in node.target + ) + # To access this variable call `get_bounds()` + self._bounds: Dict[torch.fx.Node, ValueRanges[Expr]] = {} + + def __repr__(self) -> str: + return ( + f"{self.__class__.__name__}(" + f"loop_body={self.loop_body},\n " + f"replacement_vals={self.replacement_vals}, \n" + f"unbounded_vars={self.unbounded_vars}, \n" + f"_bounds={self._bounds})" + ) + + @cache_on_self + def get_bounds(self) -> Dict[torch.fx.Node, ValueRanges[Expr]]: + submodules = self.swap_submodules(self.loop_body.submodules) + + # Initialize the environment with the unbounded variables + for node in self.unbounded_vars: + # we need to evaluate masked_subblock to recurse, and we need to set indirect values + if not isinstance(node.target, str) or ( + "masked_subblock" not in node.target + and "set_indirect" not in node.target + ): + self._bounds[node] = ValueRanges[Expr].unknown() + + with V.set_ops_handler(ValueRangeAnalysis()): + interpreter = InterpreterShim(self.loop_body.root_block.graph, submodules) + log.debug("get_bounds:\n%s", self.loop_body.root_block.graph) + interpreter.run(V.get_ops_handler(), initial_env=self._bounds) + return self._bounds + + def swap_submodules( + self, submodules: Dict[str, Callable[..., Any]] + ) -> Dict[str, Callable[..., ValueRanges[Expr]]]: + result: Dict[str, Callable[..., ValueRanges[Expr]]] = {} + for key in submodules.keys(): + if key == "get_index": + result[key] = self.get_index + elif "masked_subblock" in key: + subblock = self.loop_body.subblocks[key] + # The result within the lambda will reference to the final + # set of modules at the end of the for-loop as it stores a reference to it + + # bind subblock in a function because python lambdas close over by reference + # moving the lambda out of make_fn would close over the reference to subblock, + # so all lambdas would have the same subblock reference that is the final + # subblock in the loop + def make_fn(subblock): + return lambda mask, value: self.masked_subblock( + subblock, self._bounds, mask, value, result + ) + + result[key] = make_fn(subblock) + elif "set_indirect" in key: + idx = int(key[len("set_indirect") :]) + var = self.loop_body.indirect_vars[idx] + indirect = partial(self.set_indirect, var) + result[key] = indirect + else: + assert "scan" in key + result[key] = submodules[key] + + return result + + def masked_subblock( + self, + subblock: LoopBodyBlock, + env: Dict[torch.fx.Node, ValueRanges[Expr]], + mask: Any, + value: Any, + submodules: Dict[str, Callable[..., Any]], + ) -> ValueRanges[Expr]: + interp = InterpreterShim(subblock.graph, submodules) + interp.run(V.get_ops_handler(), initial_env=env) + output = [node for node in subblock.graph.nodes if node.target == "output"] + assert len(output) == 1 + # dont bother unioning with value since the load from buffer will be + # pessimistically assumed to be inf anyway + return interp.env[output[0]] + + def set_indirect(self, old: Expr, new: ValueRanges[Expr]) -> ValueRanges[Expr]: + assert isinstance(new, ValueRanges) + self.replacement_vals[old] = new + return new + + def get_index(self, name: Expr) -> ValueRanges[Expr]: + expr = self.loop_body.indexing_exprs[name] + bound = self.replacement_vals.get(expr) + if bound is None: + bound = bound_sympy(expr, self.replacement_vals) + # The following assertion is true at the time of this writing + # We don't assert is as to not execute bound_sympy when bound is not None + # assert bound is None or bound == bound_sympy(expr, self.replacement_vals) + self.replacement_vals[name] = bound + return bound diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codecache.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codecache.py new file mode 100644 index 0000000000000000000000000000000000000000..59cc47ac06c94e492bed109e60c28bb6b7e61ba0 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codecache.py @@ -0,0 +1,3353 @@ +from __future__ import annotations + +import base64 +import copyreg +import dataclasses +import functools +import hashlib +import importlib +import io +import json +import logging +import os +import pickle +import pkgutil +import re +import shlex +import shutil +import struct +import subprocess +import sys +import sysconfig +import tempfile +import textwrap +import threading +import warnings +from bisect import bisect_right +from copy import copy +from ctypes import c_void_p, CDLL, cdll +from datetime import timedelta +from functools import partial +from pathlib import Path +from time import time, time_ns +from types import ModuleType +from typing import ( + Any, + Callable, + cast, + Counter, + Dict, + Generator, + List, + NoReturn, + Optional, + Sequence, + Set, + Tuple, + TYPE_CHECKING, + TypeVar, + Union, +) +from typing_extensions import TypeAlias + +import torch +import torch.distributed as dist +from torch import SymInt, Tensor +from torch._dynamo.utils import counters, dynamo_timed, get_chromium_event_logger +from torch._inductor import config, exc, metrics +from torch._inductor.codegen.cuda import cuda_env +from torch._inductor.codegen.rocm.compile_command import ( + rocm_compile_command, + rocm_compiler, +) +from torch._utils_internal import log_cache_bypass + +from .utils import _align + + +T = TypeVar("T") + + +if TYPE_CHECKING: + from collections.abc import KeysView + + from .remote_cache import JsonDataTy, RemoteCache + + +""" +codecache.py, cpp_builder.py and cpu_vec_isa.py import rule: +https://github.com/pytorch/pytorch/issues/124245#issuecomment-2197778902 +""" +from torch._inductor.cpp_builder import ( + _set_gpu_runtime_env, + _transform_cuda_paths, + CppBuilder, + CppOptions, + CppTorchCudaOptions, + get_compiler_version_info, + get_cpp_compiler, + get_name_and_dir_from_output_file_path, + normalize_path_separator, +) +from torch._inductor.cpu_vec_isa import pick_vec_isa +from torch._inductor.cudagraph_utils import ( + BoxedDeviceIndex, + CudagraphCachedInfo, + log_cudagraph_skip_and_bump_counter, +) +from torch._inductor.runtime.compile_tasks import ( + _module_to_triton_kernel, + _reload_python_module, + _reload_python_module_in_subproc, +) +from torch._inductor.runtime.runtime_utils import cache_dir, default_cache_dir +from torch._inductor.utils import ( + ALIGN_BYTES, + align_inputs_from_check_idxs, + BoxedBool, + clear_on_fresh_inductor_cache, + is_linux, + is_windows, + set_tracing_context_output_strides, +) +from torch._logging import trace_structured +from torch._subclasses.fake_tensor import ( + extract_tensor_metadata, + FakeTensor, + TensorMetadata, +) +from torch.fx.experimental.symbolic_shapes import has_hint, hint_int, ShapeEnv + + +if TYPE_CHECKING: + from concurrent.futures import Future + + from torch._inductor.graph import GraphLowering + from torch._inductor.ir import ChoiceCaller + from torch._inductor.runtime.hints import HalideInputSpec, HalideMeta + + +_HERE = os.path.abspath(__file__) +_TORCH_PATH = os.path.dirname(os.path.dirname(_HERE)) +_LINKER_SCRIPT = os.path.join(_TORCH_PATH, "_inductor/script.ld") + +_IS_WINDOWS = sys.platform == "win32" + +if config.is_fbcode(): + from triton.fb import build_paths + from triton.fb.build import _run_build_command + + from torch._inductor.fb.utils import ( + log_global_cache_errors, + log_global_cache_stats, + log_global_cache_vals, + use_global_cache, + ) +else: + + def log_global_cache_errors(*args: Any, **kwargs: Any) -> None: # type: ignore[misc] + pass + + def log_global_cache_stats(*args: Any, **kwargs: Any) -> None: # type: ignore[misc] + pass + + def log_global_cache_vals(*args: Any, **kwargs: Any) -> None: # type: ignore[misc] + pass + + def use_global_cache() -> bool: # type: ignore[misc] + return False + + +output_code_log = torch._logging.getArtifactLogger(__name__, "output_code") + +LOCK_TIMEOUT = 600 + +_IS_WINDOWS = sys.platform == "win32" + + +log = logging.getLogger(__name__) + + +def cpp_wrapper_cache_dir(name: str) -> str: + cu_str = ( + "cpu" + if torch.version.cuda is None + else f'cu{torch.version.cuda.replace(".", "")}' + ) + python_version = f"py{sys.version_info.major}{sys.version_info.minor}" + build_folder = f"{python_version}_{cu_str}" + + cpp_wrapper_dir = os.path.join(cache_dir(), build_folder) + cpp_wrapper_build_directory = os.path.join(cpp_wrapper_dir, name) + os.makedirs(cpp_wrapper_build_directory, exist_ok=True) + return cpp_wrapper_build_directory + + +def get_cpp_wrapper_cubin_path_name() -> str: + return "cubin_path" if torch.version.hip is None else "hsaco_path" + + +class CacheBase: + @staticmethod + @functools.lru_cache(None) + def get_system() -> Dict[str, Any]: + try: + from triton.compiler.compiler import triton_key + + # Use triton_key instead of triton.__version__ as the version + # is not updated with each code change + triton_version = triton_key() + except ModuleNotFoundError: + triton_version = None + + try: + system: Dict[str, Any] = { + "device": {"name": None}, + "version": { + "triton": triton_version, + }, + } + device_properties = torch.cuda.get_device_properties( + torch.cuda.current_device() + ) + if torch.version.cuda is not None: + system["device"]["name"] = device_properties.name + system["version"]["cuda"] = torch.version.cuda + else: + system["device"]["name"] = device_properties.gcnArchName + system["version"]["hip"] = torch.version.hip + except (AssertionError, RuntimeError): + # If cuda is not installed, none of the above config is relevant. + system = {} + + system["hash"] = hashlib.sha256( + json.dumps(system, sort_keys=True).encode("utf-8") + ).hexdigest() + + return system + + @staticmethod + @clear_on_fresh_inductor_cache + @functools.lru_cache(None) + def get_local_cache_path() -> Path: + return Path(os.path.join(cache_dir(), "cache", CacheBase.get_system()["hash"])) + + @staticmethod + @functools.lru_cache(None) + def get_global_cache_path() -> Optional[Path]: + return ( + Path(os.path.join(config.global_cache_dir, CacheBase.get_system()["hash"])) + if config.global_cache_dir is not None + else None + ) + + def __init__(self) -> None: + self.system = CacheBase.get_system() + + def get_local_cache(self) -> Dict[str, Any]: + local_cache_path = self.get_local_cache_path() + if not local_cache_path.is_file(): + return {} + with open(local_cache_path) as local_cache_fp: + local_cache = json.load(local_cache_fp) + return local_cache["cache"] + + def update_local_cache(self, local_cache: Dict[str, Any]) -> None: + local_cache_path = self.get_local_cache_path() + write_atomic( + str(local_cache_path), + json.dumps({"system": self.system, "cache": local_cache}, indent=4), + make_dirs=True, + ) + + +class LocalCache(CacheBase): + def lookup(self, *keys: str) -> Optional[Dict[str, Any]]: + cache = self.get_local_cache() + + sub_cache = cache + for key in keys: + if key in cache: + sub_cache = cache[key] + else: + return None + + return sub_cache + + def set_value(self, *keys: str, value: Any) -> None: + cache = self.get_local_cache() + + sub_cache = cache + for key in keys[0:-1]: + sub_cache.setdefault(key, {}) + sub_cache = sub_cache[key] + sub_cache[keys[-1]] = value + + self.update_local_cache(cache) + + +class PersistentCache(CacheBase): + @functools.lru_cache(None) # noqa: B019 + def get_global_cache(self) -> Dict[str, Any]: + global_cache_path = self.get_global_cache_path() + if global_cache_path is None or not global_cache_path.is_file(): + return {} + with open(global_cache_path) as global_cache_fp: + global_cache = json.load(global_cache_fp) + return global_cache["cache"] + + def lookup( + self, + choices: List[ChoiceCaller], + op: str, + inputs: str, + benchmark: Optional[Callable[[Any], Dict[ChoiceCaller, float]]], + ) -> Dict[ChoiceCaller, float]: + """ + Check to see if we have benchmarked the given choice callers. For each + choice caller: + + 1. Check global_cache[op][inputs][choice][precision], return benchmark if cached. + 2. Check local_cache[op][inputs][choice][precision], return benchmark if cached. + 3. If benchmark is not None: + a. `max_autotune_gemm=True`: benchmark the choice, update + local_cache[op][inputs][choice], and return the benchmark. + b. `max_autotune_gemm=False`: don't benchmark the choice, return nothing. + """ + precision = torch.get_float32_matmul_precision() + + log_stats = partial(log_global_cache_stats, self.system, op, inputs, precision) + log_vals = partial(log_global_cache_vals, self.system, op, inputs, precision) + log_errors = partial( + log_global_cache_errors, self.system, op, inputs, precision + ) + timings = {} + + def check_cache(cache: Dict[str, Any], callback: Any = None) -> bool: + """Check if `cache` contains data for all the choices""" + hit = True + for choice in choices: + choice_hash = choice.hash_key() + if choice_hash in cache.get(op, {}).get(inputs, {}).get(precision, {}): + # cache hit + timings[choice] = cache[op][inputs][precision][choice_hash] + else: + # cache miss + hit = False + break + if callback: + callback(cached=hit) + return hit + + if config.max_autotune or config.max_autotune_gemm: + local_cache = self.get_local_cache() if config.autotune_local_cache else {} + # check local cache first since it is data specific to the current machine + if ( + not check_cache(local_cache) + and not ( + use_global_cache() + and check_cache(self.get_global_cache(), callback=log_stats) + ) + and benchmark is not None + ): + try: + # re-benchmark everything to try to get consistent numbers from the same machine + timings = benchmark(choices) + assert all(choice in timings for choice in choices) + local_cache.setdefault(op, {}) + local_cache[op].setdefault(inputs, {}).setdefault(precision, {}) + for choice, timing in timings.items(): + local_cache[op][inputs][precision][choice.hash_key()] = timing + except RuntimeError as e: + # catch and log autotuning failures + log_errors(e) + raise e + + self.update_local_cache(local_cache) + + timings_to_log = { + choice.hash_key(): timings[choice] for choice in choices + } + log_vals(timings_to_log) + elif use_global_cache(): + # only check global cache, not local one + check_cache(self.get_global_cache(), callback=log_stats) + # may have a partial cache hit, where not everything is benchmarked + + return timings + + +def get_lock_dir() -> str: + lock_dir = os.path.join(cache_dir(), "locks") + if not os.path.exists(lock_dir): + os.makedirs(lock_dir, exist_ok=True) + return lock_dir + + +def sha256_hash(data: bytes) -> str: + # [:51] to strip off the "Q====" suffix common to every hash value. + return base64.b32encode(hashlib.sha256(data).digest())[:51].decode("utf-8").lower() + + +def code_hash(code: Union[str, bytes], extra: str = "") -> str: + hashing_str = code if isinstance(code, bytes) else code.encode("utf-8") + if extra != "": + hashing_str = hashing_str + b"||" + extra.encode("utf-8") + return "c" + sha256_hash(hashing_str) + + +def get_path( + basename: str, extension: str, specified_dir: str = "" +) -> Tuple[str, str, str]: + if specified_dir: + if os.path.isabs(specified_dir): + subdir = specified_dir + else: + subdir = os.path.join(cache_dir(), specified_dir) + else: + subdir = os.path.join(cache_dir(), basename[1:3]) + path = os.path.join(subdir, f"{basename}.{extension}") + return basename, subdir, path + + +def get_hash( + content: Union[str, bytes], extra: str = "", hash_type: str = "code" +) -> str: + if hash_type == "code": + return code_hash(content, extra) + if hash_type in ["cubin", "hsaco", "spv"]: + return code_hash(repr(content)) + raise AssertionError(f"Unknown hash type {hash_type}") + + +def write( + content: Union[str, bytes], + extension: str, + extra: str = "", + hash_type: str = "code", + specified_dir: str = "", +) -> Tuple[str, str]: + # use striped content to compute hash so we don't end up with different + # hashes just because the content begins/ends with different number of + # spaces. + key: str = get_hash(content.strip(), extra, hash_type) + basename, subdir, path = get_path(key, extension, specified_dir) + encode_utf_8: bool = hash_type == "code" + if not os.path.exists(path): + write_atomic(path, content, make_dirs=True) + return basename, path + + +def write_text(text: str) -> str: + """ + Write the `text` to a file and return the path computed based on the hash. + """ + return write(text, "txt")[1] + + +def write_atomic( + path_: str, + content: Union[str, bytes], + make_dirs: bool = False, + encode_utf_8: bool = False, +) -> None: + # Write into temporary file first to avoid conflicts between threads + # Avoid using a named temporary file, as those have restricted permissions + assert isinstance( + content, (str, bytes) + ), "Only strings and byte arrays can be saved in the cache" + path = Path(path_) + if make_dirs: + path.parent.mkdir(parents=True, exist_ok=True) + tmp_path = path.parent / f".{os.getpid()}.{threading.get_ident()}.tmp" + write_mode = "w" if isinstance(content, str) else "wb" + with tmp_path.open(write_mode, encoding="utf-8" if encode_utf_8 else None) as f: + f.write(content) + tmp_path.rename(path) + + +@dataclasses.dataclass +class TensorMetadataAndValues: + """ + TensorMetadata plus the elements as a list of raw values. + Used for hashing inlined constants. + """ + + tensor_metadata: TensorMetadata + values: List[Any] + + +def _ident(x: T) -> T: + return x + + +def extract_tensor_metadata_for_cache_key( + device_map: Dict[torch.device, torch.device], t: Tensor +) -> TensorMetadata: + """ + Extracts the tensor metadata and removes fields of the TensorMetadata + that are not needed for caching + """ + meta = extract_tensor_metadata(t) + if not hasattr(t, "_is_inductor_static"): + meta = dataclasses.replace(meta, storage_offset=0, storage_bytes=None) + + # The pickle implementation avoids serializing the same object more than once. + # That behavior means the byte stream we create to hash will vary if, for example, + # we see two tensor objects with the same device, but the torch.device object is + # actually the same object vs. merely equivalent. We want to produce the same hash + # value in either situation, so we memoize the device objects and always reference + # the same object for a given device. It's possible other metadata fields deserve + # the same treatment, but so far we've only observed this issue with the device. + if meta.device not in device_map: + device_map[meta.device] = meta.device + meta = dataclasses.replace(meta, device=device_map[meta.device]) + + return meta + + +def _reduce_fake_tensor( + device_map: Dict[torch.device, torch.device], t: Tensor +) -> Tuple[Callable[[T], T], Tuple[TensorMetadata]]: + """ + See FxGraphCachePickler. Custom reducer to pickle FakeTensors. + """ + metadata = extract_tensor_metadata_for_cache_key(device_map, t) + return (_ident, (metadata,)) + + +def _reduce_tensor( + device_map: Dict[torch.device, torch.device], t: Tensor +) -> Tuple[Callable[[T], T], Tuple[TensorMetadataAndValues]]: + """ + See FxGraphCachePickler. Custom reducer to pickle Tensors. + If we see tensors, we know they're constants stored as attributes on + the GraphModule. Include the values in the key calculation. Small + tensors will be inlined, so we can't serve the same cache entry for + different values anyway. Large constants are treated as parameters, + so we could conceivably reuse a cache entry. To do that, however, + PyCodeCache would need more complexity to create a new module from its + cache, but with the right constants attached as attributes. + """ + if t.is_mkldnn: + # TODO: These tensors don't currently pickle, so we can't cache a + # compiled graph containing them. Just fail now. If mkldnn tensors + # get pickling support, we can remove this. + raise BypassFxGraphCache("mkldnn tensors unpickleable.") + + # Very large tensors could be expensive to copy to cpu and hash. Let's + # at least report if we find slowness. + start = time() + values = t.tolist() + elapsed = time() - start + if elapsed > 1.0: + warnings.warn( + f"FX graph cache handling of a large constant took {elapsed:.1}s. Please file an issue." + ) + + metadata = extract_tensor_metadata_for_cache_key(device_map, t) + return (_ident, (TensorMetadataAndValues(metadata, values),)) + + +def _reduce_symint(s: SymInt) -> Tuple[Callable[[T], T], Tuple[str]]: + """ + See FxGraphCachePickler. Custom reducer to pickle SymInts. + """ + # For hashing purposes, we only care about the name of the symbol and + # not the backed value. We evaluate guards stored with a cached graph + # to ensure a cached entity with SymInt args is safe to reuse. + return (_ident, (str(s),)) + + +def _reduce_unsupported(s: Any) -> NoReturn: + """ + See FxGraphCachePickler. Custom reducer to handle any objects that we don't + support and therefore raise to bypass caching. + """ + raise BypassFxGraphCache("Reduce unsupported.") + + +class FxGraphCachePickler(pickle.Pickler): + """ + Custom pickler to customize the pickling of some objects (Tensors), only for the + purpose of computing a hash for keying into the FxGraphCache. Tensors contain + objects that don't pickle and/or vary between runs, and we want to capture the + data that allow us to compute a stable, but safe hash. + """ + + # See extract_tensor_metadata_for_cache_key. Whenever we extract metadata during + # pickling, we make sure devices always reference the same torch.device object. + _device_map: Dict[torch.device, torch.device] = {} + + dispatch_table = copyreg.dispatch_table.copy() + dispatch_table[FakeTensor] = functools.partial(_reduce_fake_tensor, _device_map) + dispatch_table[torch.Tensor] = functools.partial(_reduce_tensor, _device_map) + dispatch_table[torch.SymInt] = _reduce_symint + dispatch_table[ + torch.fx.experimental._backward_state.BackwardState + ] = _reduce_unsupported + + @classmethod + def dumps(cls, obj: Any) -> bytes: + """ + Pickle an object using the FxGraphCachePickler. + """ + with io.BytesIO() as stream: + pickler = cls(stream) + # TODO: pickler.fast is technically deprecated. Will this work on new python versions? + pickler.fast = True # Run with pickler.fast so it doesn't intern strings, making the hash result more predictable + try: + pickler.dump(obj) + except (TypeError, AttributeError) as e: + # Some configs options are callables, e.g., post_grad_custom_pre_pass, + # and may not pickle. + log.warning("Can't pickle", exc_info=True) + raise BypassFxGraphCache("Config options may be unpickleable.") from e + return stream.getvalue() + + @classmethod + def get_hash(cls, obj: Any) -> str: + """ + Serialize an object using the FxGraphCachePickler and return a hash + of the pickled object. + """ + serialized_data = cls.dumps(obj) + return sha256_hash(serialized_data) + + @classmethod + def debug_lines(cls, inp: FxGraphHashDetails) -> List[str]: + """ + Get a printable string describing in more detail all the attributes + comprising an object. Useful for debugging when one graph hashes + to a different value than another. + """ + + def get_str(obj: Any) -> str: + if isinstance(obj, torch.Tensor): + return str(extract_tensor_metadata_for_cache_key(cls._device_map, obj)) + elif isinstance(obj, bytes): + return "" + elif type(obj) in cls.dispatch_table: + # Run the reducer on the object + return str(cls.dispatch_table[type(obj)](obj)[1]) + else: + return str(obj) + + lines = [] + for attr, obj in vars(inp).items(): + if isinstance(obj, list): + for ii in range(len(obj)): + h = cls.get_hash(obj[ii]) + lines.append(f"[{h}] {attr}[{ii}]: {get_str(obj[ii])}") + elif isinstance(obj, dict): + for k, v in obj.items(): + h = cls.get_hash(v) + lines.append(f"[{h}] {attr}[{k}]: {get_str(v)}") + else: + h = cls.get_hash(obj) + lines.append(f"[{h}] {attr}: {get_str(obj)}") + return lines + + +def build_code_hash( + roots: List[str] | None, prefix: str, hasher: hashlib._Hash +) -> None: + for lib in sorted(pkgutil.iter_modules(roots, prefix), key=lambda x: x.name): + spec = lib.module_finder.find_spec(lib.name, None) + assert spec is not None + module = spec.origin + assert module is not None + with open(module, "rb") as f: + hasher.update(spec.name.encode("utf-8")) + hasher.update(f.read()) + if lib.ispkg: + # need to also hash submodules + build_code_hash(spec.submodule_search_locations, f"{spec.name}.", hasher) + + +@functools.lru_cache(None) +def torch_key() -> bytes: + """ + Compute a key that contains relevant information about torch source files + """ + if not config.is_fbcode(): + + def get_code_hash(root: str) -> bytes: + # This function isn't meant to be used outside of torch_key, just a + # helper for clarity. Instead, use torch_key() directly when you need + # a hash representing the state of the source code. + extra_files = ( + "codegen/aoti_runtime/interface.cpp", + "codegen/aoti_runtime/implementation.cpp", + "codegen/cpp_prefix.h", + "script.ld", + ) + inductor_root = os.path.dirname(__file__) + extra_files = [os.path.join(inductor_root, x) for x in extra_files] + hasher = hashlib.sha256() + hasher.update(torch.__version__.encode("utf-8")) + build_code_hash([root], "", hasher) + for path in extra_files: + if os.path.exists(path): + with open(path, "rb") as f: + hasher.update(f.read()) + return hasher.digest() + + return get_code_hash(_TORCH_PATH) + + from libfb.py import parutil + + return parutil.get_file_contents("torch/src_hash.txt").rstrip().encode("ascii") + + +def get_inductor_root() -> str: + return os.path.dirname(__file__) + + +@dataclasses.dataclass +class OrderedSetHolder: + """ + See FxGraphHashDetails. Holds a sorted list to support stable hashing + of set kwargs. + """ + + items: List[Any] + + +class BypassFxGraphCache(Exception): + """ + Exception to indicate that the FxGraphCache should be bypassed. + """ + + +class FxGraphHashDetails: + """ + Object to capture all the details for a compiled FX graph relevant to computing + a safe and stable cache key. + """ + + # Excluded kwargs param that are not stable between runs + EXCLUDED_KWARGS = ["graph_id"] + + def __init__( + self, + gm: torch.fx.GraphModule, + example_inputs: List[torch.Tensor], + fx_kwargs: Dict[str, Any], + inputs_to_check: Sequence[int], + ) -> None: + self.gm = gm + self.example_inputs = example_inputs + + # Order kwargs so hashing is stable to changes in kwarg order. + self.fx_kwargs = {} + for k in sorted(fx_kwargs): + if k not in self.EXCLUDED_KWARGS: + if type(fx_kwargs[k]) is set: + # Special case to handle set params. Python sets can't be + # ordered, so sort the elements and store them in a proxy. + self.fx_kwargs[k] = OrderedSetHolder(sorted(fx_kwargs[k])) + else: + self.fx_kwargs[k] = fx_kwargs[k] + + # Alignment checks + self.inputs_to_check = inputs_to_check + + # 'Deterministic algorithms' can affect codegen via lowering to cuda kernels. + self.deterministic_algorithms_settings = ( + torch.are_deterministic_algorithms_enabled(), + torch.is_deterministic_algorithms_warn_only_enabled(), + torch.utils.deterministic.fill_uninitialized_memory, # type: ignore[attr-defined] + ) + + # Global settings affecting matmul codegen. + self.cuda_matmul_settings = ( + torch.backends.cuda.matmul.allow_tf32, + torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction, + torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction, + ) + + # Also hash on various system info (including the triton compiler version). + self.torch_version = torch_key() + self.system_info = CacheBase.get_system() + self.inductor_config = config.save_config_portable() + + def debug_lines(self) -> List[str]: + """ + Get a printable string describing in more detail all the attributes + comprising this object. Useful for debugging when one graph hashes + to a different value than another. + """ + return FxGraphCachePickler.debug_lines(self) + + +def compiled_fx_graph_hash( + gm: torch.fx.GraphModule, + example_inputs: List[torch.Tensor], + fx_kwargs: Dict[str, Any], + inputs_to_check: Sequence[int], +) -> Tuple[str, List[str]]: + """ + Generate a unique hash of the FX graph for caching. + """ + details = FxGraphHashDetails(gm, example_inputs, fx_kwargs, inputs_to_check) + # The prefix distinguishes among the other kinds of objects we + # cache in this module. + key = "f" + FxGraphCachePickler.get_hash(details) + debug_lines = details.debug_lines() + debug_str = "\n".join(debug_lines) + log.debug(f"FX graph cache hash details for key {key}:\n{debug_str}") # noqa: G004 + return key, debug_lines + + +def cudagraph_post_compile( + example_inputs: List[Any], + compiled_graph: CompiledFxGraph, + cudagraphs: BoxedBool, +) -> None: + """ + Checks for any reasons not to run cudagraphs and then + runs it on compiled_graph. + Mutates the `compiled_graph.current_callable` and `cudagraphs` + """ + assert compiled_graph.current_callable is not None + assert compiled_graph.cudagraph_info is not None + cached_info = compiled_graph.cudagraph_info + cudagraph_fail_reasons = cached_info.cudagraph_fail_reasons + inputs_to_check = compiled_graph.inputs_to_check + boxed_forward_device_index = compiled_graph.boxed_forward_device_index + is_inference = compiled_graph.fx_kwargs["is_inference"] + is_backward = compiled_graph.fx_kwargs["is_backward"] + + if not cudagraph_fail_reasons: + fx_kwargs = compiled_graph.fx_kwargs + static_input_idxs = fx_kwargs["static_input_idxs"] + + placeholders = cached_info.placeholders + stack_traces = cached_info.stack_traces + if not config.triton.cudagraph_trees: + # Force specialize all inputs so that CUDA graphs will work + for t in example_inputs: + if isinstance(t, torch.SymInt): + int(t) # guard + + if ( + boxed_forward_device_index is not None + and not is_inference + and not is_backward + ): + boxed_forward_device_index.set(next(iter(compiled_graph.device_idxs))) + + from .compile_fx import cudagraphify + + current_callable = compiled_graph.current_callable + assert current_callable is not None + compiled_graph.current_callable = cudagraphify( + current_callable, + static_input_idxs=static_input_idxs, + device_index=next(iter(compiled_graph.device_idxs)), + stack_traces=stack_traces, + is_backward=is_backward, + is_inference=is_inference, + constants=tuple(compiled_graph.constants.values()), + placeholders=placeholders, + mutated_input_idxs=tuple(compiled_graph.mutated_input_idxs), + ) + + else: + BoxedBool.disable(cudagraphs) + + # See [Backward Generation Handling] + # if cudagraph'd the forward and set the device, we need to let the cudagraph manager + # know we are we running the backward even if we will not run it in cudagraphs + if is_backward and config.triton.cudagraph_trees: + assert boxed_forward_device_index is not None + assert boxed_forward_device_index.value is not None + compiled_graph_callable = compiled_graph.current_callable + + manager = torch._inductor.cudagraph_trees.get_manager( + boxed_forward_device_index.value, create_if_none_exists=False + ) + # should already exist from forward + assert manager is not None + + def compiled_artifact(new_inputs: List[Any]) -> Callable[..., Any]: + manager.set_to_running_backward() # type: ignore[union-attr] + return compiled_graph_callable(new_inputs) + + compiled_graph.current_callable = compiled_artifact + + if "cuda" in compiled_graph.device_types: + # prefer better disable_cudagraphs_reason bc stack trace + # TODO: migrate all disable reasons to stack trace, refactor + if compiled_graph.disabled_cudagraphs_reason: + log_cudagraph_skip_and_bump_counter( + compiled_graph.disabled_cudagraphs_reason + ) + else: + log_cudagraph_skip_and_bump_counter( + f"skipping cudagraphs due to {cudagraph_fail_reasons}" + ) + + +def maybe_realign_inputs( + ran_cudagraphs: BoxedBool, + compiled_graph: CompiledFxGraph, + inputs_to_check: Sequence[int], +) -> None: + """ + Realigns input strides from inputs_to_check if + we didn't end up running cudagraphs. Mutates + `compiled_graph.current_callable` if cudagraphs + was run. Otherwise, does nothing. + """ + if not ran_cudagraphs: + assert compiled_graph.current_callable is not None + new_callable = align_inputs_from_check_idxs( + compiled_graph.current_callable, inputs_to_check + ) + if new_callable is not compiled_graph.current_callable: + compiled_graph.current_callable = new_callable + + +def add_ephemeral_timeout_increase_for_distributed(time_saved_ns: int) -> int: + """ + Ephemerally increases the NCCL timeout when compiling for a distributed job + Returns amount of seconds increased + """ + if not torch.distributed.is_available() or not torch.distributed.is_initialized(): + return 0 + + increased_timeout_sec = int(time_saved_ns // 1e9) # convert to seconds + + if config.is_fbcode(): + fudge_factor = torch._utils_internal.justknobs_getval_int( + "pytorch/remote_cache:ephemeral_timeout_fudge_factor_percentage" + ) + log.info( + "Ephemeral NCCL timeout increase fudge factor %d and original increase value %d", + fudge_factor, + increased_timeout_sec, + ) + increased_timeout_sec += int(increased_timeout_sec * fudge_factor / 100) + + log.info("Increasing NCCL timeout by %d", increased_timeout_sec) + dist.distributed_c10d._add_ephemeral_timeout_for_all_pgs( + timedelta(seconds=increased_timeout_sec) + ) + return increased_timeout_sec + + +class FxGraphCache: + """ + Supports caching and reusing compiled Fx graphs. + + The overall strategy is as follows: + - This cache stores entries on disk. When saving an entry, we can't + serialize callables (that could be C++, Triton, etc.), so we serialize + their own disk cache location. We then recreate the compiled artifact + after fetching from disk. + - For indexing the cache, we gather the fields relevant to identifying an + FxGraph (the graph module, graph inputs, system settings etc.) into an + FxGraphCacheDetails object, pickle it, and compute a hash for the key. + See FxGraphCachePickler. + - Among the metadata we store, we also include a guards expression that's + appropriate for validating any symbols for Tensor arguments that have + symbolic bounds. On cache lookup then, we evaluate those guards in the + current context to validate that a cached entry can be served. + - A given graph could have multiple compiled versions, corresponding to + different sets of guards. Therefore, we store cache entries in the form: + // + - On lookup, we compute the key from the graph details, iterate over all + leaf files in the corresponding subdirectory, deserialize the entry, and + evaluate its guards expression. If the evaluation succeeds, we have a + cache hit. If it fails, we compile the graph and store a new entry. + - Finally, on a cache hit, we need to make sure any guards that would + have been created during compilation are added to the current context. + """ + + # TODO(masnesral): Investigate whether it's beneficial to store compiled graphs + # in an in-memory cache after loading from disk. + @staticmethod + def _get_tmp_dir() -> str: + """ + Get the toplevel temporary directory for storing compiled graphs. + """ + return os.path.join(cache_dir(), "fxgraph") + + @staticmethod + def _get_tmp_dir_for_key(key: str) -> str: + """ + Return the disk location for a given cache key. + """ + return os.path.join(FxGraphCache._get_tmp_dir(), key[1:3], key) + + @staticmethod + def _filter_backed_symints(inputs: List[Any]) -> List[torch.SymInt]: + """ + Get the backed SymInt objects from the input list. Note that we can never + have guards that depend on unbacked symint. + """ + return [s for s in inputs if isinstance(s, torch.SymInt) and has_hint(s)] + + @staticmethod + def _get_shape_env() -> Optional[ShapeEnv]: + """ + Helper to get the shape env from the tracing context. + """ + ctx = torch._guards.TracingContext.try_get() + if not ctx: + return None + return ctx.fake_mode.shape_env + + @staticmethod + def _lookup_graph( + key: str, + example_inputs: List[torch.Tensor], + local: bool, + remote_cache: Optional[RemoteCache[JsonDataTy]], + ) -> Optional[CompiledFxGraph]: + """ + Lookup a compiled graph in the cache by key. On a hit, return the + deserialized CompiledFxGraph object. On a miss, return None. + """ + shape_env = FxGraphCache._get_shape_env() + assert shape_env is not None + + symints = FxGraphCache._filter_backed_symints(example_inputs) + hints = [hint_int(s) for s in symints] + + def iterate_over_candidates() -> Generator[CompiledFxGraph, None, None]: + if local: + subdir = FxGraphCache._get_tmp_dir_for_key(key) + if os.path.exists(subdir): + for path in sorted(os.listdir(subdir)): + try: + with open(os.path.join(subdir, path), "rb") as f: + yield pickle.load(f) + except Exception: + log.warning( + "fx graph cache unable to load compiled graph", + exc_info=True, + ) + + if remote_cache: + try: + if (cache_data := remote_cache.get(key)) is not None: + assert isinstance(cache_data, dict) + data = cache_data["data"] + assert isinstance(data, (str, bytes)) + content = base64.b64decode(data) + yield pickle.loads(content) + except Exception: + log.warning( + "fx graph cache unable to load compiled graph", exc_info=True + ) + + # Iterate over any entries in the subdir for this key and evaluate + # their guards to determine whether there's a hit. + graph = None + + for candidate in iterate_over_candidates(): + if not candidate.guards_expr: + # No guards to evaluate, so this is a hit. + graph = candidate + break + + # Evaluate the guard expression in the current context. + # If there's not a cache hit, we don't want the evaluation to + # affect the current env, e.g., cause the creation of new guards, + # so we evaluate with the hints instead of the symbols. + hit = bool( + shape_env.evaluate_guards_expression(candidate.guards_expr, hints) + ) + log.debug( + "fx graph cache key %s evaluating guards [%s] with values %s => hit=%s", + key, + candidate.guards_expr, + hints, + hit, + ) + if hit: + graph = candidate + break + + if graph is None: + return None + + # See _save_graph(); we don't store the callable in the cache entry so + # recreate it here from the PyCodeCache disk cache. + artifact_path = get_path(graph.cache_key, "py")[2] + code = graph.source_code + if not os.path.exists(artifact_path): + counters["inductor"]["fxgraph_lookup_write_file"] += 1 + Path(os.path.dirname(artifact_path)).mkdir(parents=True, exist_ok=True) + cpp_pp = cpp_prefix_path() + if os.path.basename(cpp_pp) in code: + if cpp_pp in code: + # Great the name is correct + pass + else: + # Old dir name is included, replace it + pattern = rf'#include\s*"[^"]+{os.path.basename(cpp_pp)}"' + code = re.sub(pattern, f'#include "{cpp_pp}"', code) + + write_atomic(artifact_path, code, make_dirs=True) + + try: + graph.current_callable = PyCodeCache.load_by_key_path( + graph.cache_key, + artifact_path, + graph.cache_linemap, + graph.constants, + ).call + except OSError: + # Not expected, but in case the PyCodeCache entry is removed from + # underneath us, treat it as a cache miss and recompile. + log.error("Failed to load cached artifact: %s", artifact_path) + return None + + # Now re-evaluate with the symints to add any guards to the current env. + if graph.guards_expr: + check = bool( + shape_env.evaluate_guards_expression(graph.guards_expr, symints) + ) + assert check is True + log.debug( + "fx graph cache key %s post-load guards: %s", key, shape_env.guards + ) + + # Increment the cached metrics/counters by the amounts recorded when the FX + # graph was compiled for this cache entry. Pretending these counters + # were incremented normally is useful for testing with the cache enabled. + metrics.CachedMetricsHelper.apply_deltas(graph.metrics_deltas) + counters["inductor"] += graph.counter_deltas + + from .graph import GraphLowering + + GraphLowering.save_output_code(code) + output_code_log.debug("Output code written to: %s", artifact_path) + output_code_log.debug("Output code: \n%s", code) + # On cache hit, use artifact path as filename + trace_structured( + "inductor_output_code", + lambda: {"filename": artifact_path}, + payload_fn=lambda: code, + ) + return graph + + @staticmethod + def post_compile( + compiled_graph: CompiledFxGraph, + example_inputs: List[torch.Tensor], + cudagraphs: BoxedBool, + ) -> CompiledFxGraph: + """ + Run a set of post processing steps after loading from the cache. These involve: + - Setting the tracing context output strides + - Running cudagraphs if enabled + - Realigning inputs + + This runs whether or not we have a cache hit, and always runs directly after we get a CompiledFxGraph. + The results of this function are *not* saved in the cache itself. + """ + set_tracing_context_output_strides(example_inputs, compiled_graph) + + if cudagraphs: + # It's possible that cudagraphs is enabled, but was disabled + # during a previous compilation we're loading from the cache. + # If so, we need to disable it on this new process too. + if compiled_graph.disabled_cudagraphs_reason: + if "cuda" in compiled_graph.device_types: + log_cudagraph_skip_and_bump_counter( + f"skipping cudagraphs due to {compiled_graph.disabled_cudagraphs_reason}" + ) + else: + counters["inductor"]["cudagraph_skips"] += 1 + BoxedBool.disable(cudagraphs) + else: + cudagraph_post_compile( + example_inputs, + compiled_graph, + cudagraphs, + ) + inputs_to_check = compiled_graph.inputs_to_check + # cudagraphs could have been disabled from the earlier conditions + # so we still need to realign inputs if that happens + maybe_realign_inputs( + cudagraphs, + compiled_graph, + inputs_to_check, + ) + + return compiled_graph + + @staticmethod + def _save_graph( + key: str, + compiled_graph: CompiledFxGraph, + example_inputs: List[torch.Tensor], + local: bool, + remote_cache: Optional[RemoteCache[JsonDataTy]], + ) -> None: + """ + Store a serialized CompiledFxGraph on disk. + """ + disk_compiled_graph = copy(compiled_graph) + # We can't really serialize callables that may be C++/Triton/etc., + # so we serialize their PyCodeCache disk cache location instead. + # TODO: This could be better if we're ever able to serialize compiled + # models to disk. + disk_compiled_graph.current_callable = None + + # Before serializing, compute the guard expression that will be used to + # ensure that a CompiledFxGraph is valid when loaded from the cache. It's + # sufficient to consider only the SymInt args to the fx graph since the + # Tensor shapes are already captured in the hash for the cache key. Any + # Tensor arg with a symbolic shape will have a SymInt arg for the graph. + shape_env = FxGraphCache._get_shape_env() + assert shape_env is not None + symints = FxGraphCache._filter_backed_symints(example_inputs) + guards = shape_env.get_pruned_guards(symints) + disk_compiled_graph.guards_expr = shape_env.produce_guards_expression( + placeholders=symints, guards=guards + ) + + try: + content = pickle.dumps(disk_compiled_graph) + except Exception: + log.warning( + "fx graph cache unable to serialize compiled graph", exc_info=True + ) + counters["inductor"]["fxgraph_cache_pickle_error"] += 1 + return + + try: + if local: + subdir = FxGraphCache._get_tmp_dir_for_key(key) + if not os.path.exists(subdir): + os.makedirs(subdir, exist_ok=True) + + # Use a hash of the serialized CompiledFxGraph to get a unique file + # name. The specific name doesn't matter since a lookup involves + # iterating over all entries in the parent subdir. + path = os.path.join(subdir, sha256_hash(content)) + write_atomic(path, content, make_dirs=True) + + if remote_cache: + time_taken_ms = int((disk_compiled_graph._time_taken_ns or 0) // 1e6) + cache_data: JsonDataTy = { + "data": base64.b64encode(content).decode("ascii"), + "time_taken_ms": time_taken_ms, + } + remote_cache.put(key, cache_data) + except Exception: + log.warning("fx graph unable to write to cache", exc_info=True) + counters["inductor"]["fxgraph_cache_write_error"] += 1 + + @staticmethod + def _check_can_cache(gm: torch.fx.GraphModule) -> None: + """ + Check some conditions that would preclude caching and raise BypassFxGraphCache + to bypass in case caching is not possible. + """ + # Freezing can embed constants that wouldn't be static across runs. + if config.freezing or config.aot_inductor.use_runtime_constant_folding: + raise BypassFxGraphCache( + "Freezing may introduce constants that aren't static across runs." + ) + + # The treatment of guards in the caching implementation requires that + # we have a shape env. + if FxGraphCache._get_shape_env() is None: + log.debug("fx graph cache no shape env") + raise BypassFxGraphCache("No shape env.") + + # HigherOrderOperators should be handled on a case-by-case basis. + # Currently, we just skip caching if we have any. + # We also skip if there are any torchbind objects. + for node in gm.graph.nodes: + if isinstance(node.target, torch._ops.HigherOrderOperator): + raise BypassFxGraphCache("Can't cache HigherOrderOperators.") + if node.op == "getattr" and isinstance( + getattr(gm, node.target), torch._C.ScriptObject + ): + raise BypassFxGraphCache("Can't cache torchbind objects.") + + @staticmethod + def load( # type: ignore[no-untyped-def] + compile_fx_fn: Callable[..., Any], + gm: torch.fx.GraphModule, + example_inputs: List[torch.Tensor], + fx_kwargs: Dict[str, Any], + inputs_to_check: Sequence[int], + local: bool, + remote: bool, + ): + """ + Load a compiled graph from the cache. If a cached entry does not exist, + compile the graph and save it to the cache. + """ + assert local or remote, "at least one of them needs to be enabled" + compiled_graph = None + cache_state = None + cache_event_time = None + cache_info: Dict[str, Any] = {} + try: + FxGraphCache._check_can_cache(gm) + key, debug_lines = compiled_fx_graph_hash( + gm, example_inputs, fx_kwargs, inputs_to_check + ) + cache_info["key"] = key + cache_info["components"] = debug_lines + + remote_cache: Optional[RemoteCache[JsonDataTy]] = None + if remote: + cache_id = "fx-graph-v1" + try: + if config.is_fbcode(): + from torch._inductor.fb.remote_cache import FbRemoteFxGraphCache + + remote_cache = FbRemoteFxGraphCache(cache_id) + else: + from torch._inductor.remote_cache import RemoteFxGraphCache + + remote_cache = RemoteFxGraphCache(cache_id) + except ModuleNotFoundError as e: + # No need for a stack trace on this error + remote_cache = None + log.warning("Unable to create a remote cache: %s", e) + except Exception: + remote_cache = None + log.warning("Unable to create a remote cache", exc_info=True) + + compiled_graph = FxGraphCache._lookup_graph( + key, example_inputs, local, remote_cache + ) + + if compiled_graph is None: + log.debug("fx graph cache miss for key %s", key) + counters["inductor"]["fxgraph_cache_miss"] += 1 + cache_state = "miss" + start_time = time_ns() + cache_event_time = start_time + compiled_graph = compile_fx_fn( + gm, example_inputs, inputs_to_check, fx_kwargs + ) + compiled_graph._time_taken_ns = time_ns() - start_time + cache_info["time_taken_ns"] = compiled_graph._time_taken_ns + FxGraphCache._save_graph( + key, + compiled_graph, + example_inputs, + local, + remote_cache, + ) + else: + log.debug("fx graph cache hit for key %s", key) + counters["inductor"]["fxgraph_cache_hit"] += 1 + cache_state = "hit" + cache_event_time = time_ns() + if (time_saved_ns := compiled_graph._time_taken_ns) is not None: + cache_info["time_saved_ns"] = time_saved_ns + if ( + ephemeral_increase := add_ephemeral_timeout_increase_for_distributed( + time_saved_ns + ) + ) != 0: + cache_info["ephemeral_timeout_increase"] = ephemeral_increase + compiled_graph._fx_graph_cache_key = key + except BypassFxGraphCache as e: + counters["inductor"]["fxgraph_cache_bypass"] += 1 + cache_state = "bypass" + log.info("Bypassing FX Graph Cache because '%s'", e) + cache_info["cache_bypass_reason"] = str(e) + if remote: + log_cache_bypass("bypass_fx_graph", str(e)) + cache_event_time = time_ns() + + if not compiled_graph: + compiled_graph = compile_fx_fn( + gm, example_inputs, inputs_to_check, fx_kwargs + ) + assert compiled_graph is not None + cache_info["cache_state"] = cache_state + chromium_log = get_chromium_event_logger() + chromium_log.log_instant_event( + f"fx_graph_cache_{cache_state}", cache_event_time, metadata=cache_info + ) + torch._logging.trace_structured( + "artifact", + metadata_fn=lambda: { + "name": "fx_graph_cache_hash", + "encoding": "json", + }, + payload_fn=lambda: json.dumps(cache_info), + ) + # Use the passed in cudagraphs so that we mutate the BoxedBool correctly + FxGraphCache.post_compile( + compiled_graph, example_inputs, fx_kwargs["cudagraphs"] + ) + return compiled_graph + + @staticmethod + def clear() -> None: + """ + Clear out the on-disk cache. + """ + try: + shutil.rmtree(FxGraphCache._get_tmp_dir()) + except FileNotFoundError: + pass + + +_StrideExprStr: TypeAlias = str + + +@dataclasses.dataclass +class CompiledFxGraph: + """ + Class holding a compiled FX graph. This is the object serialized on disk + to support FxGraph caching. + """ + + current_callable: Optional[Callable[..., Any]] + cache_key: str + source_code: str = dataclasses.field(repr=False) # Do not display source_code + cache_linemap: Optional[List[Tuple[int, str]]] + device_types: Set[str] + device_idxs: Set[int] + mutated_inputs: Set[str] + mutated_input_idxs: Set[int] + constants: Dict[str, torch.Tensor] + torchbind_constants: Dict[str, torch._C.ScriptObject] + output_strides: Optional[List[Optional[Tuple[_StrideExprStr, ...]]]] + disabled_cudagraphs_reason: Optional[str] + metrics_deltas: metrics.CachedMetricsDeltas + counter_deltas: Counter[str] + # This is a string representation of an expression we serialize + # with the object so the guards can be evaluated in a different + # context in order to verify the validity of serving a cached + # fx graph. The expression must be generated by: + # ShapeEnv.produce_guards_expression() + guards_expr: Optional[str] + + cudagraph_info: Optional[CudagraphCachedInfo] + fx_kwargs: Dict[str, Any] + inputs_to_check: Sequence[int] + boxed_forward_device_index: Optional[BoxedDeviceIndex] + + _time_taken_ns: Optional[int] = None + _boxed_call: Optional[bool] = None + _fx_graph_cache_key: Optional[str] = None + + def __init__( + self, + current_callable: Optional[Callable[..., Any]], + graph: GraphLowering, + output_strides: List[Optional[Tuple[_StrideExprStr, ...]]], + disabled_cudagraphs_reason: Optional[str], + metrics_deltas: metrics.CachedMetricsDeltas, + counter_deltas: Counter[str], + ) -> None: + self.current_callable = current_callable + self.cache_key = graph.cache_key + if graph.cache_path: + with open(graph.cache_path) as f: + self.source_code = f.read() + self.cache_linemap = graph.cache_linemap + # TODO - ordered set + self.device_types = set(graph.device_types) + self.device_idxs = set(graph.device_idxs) + self.mutated_inputs = set(graph.mutated_inputs) + self.mutated_input_idxs = set(graph.mutated_input_idxs) + self.constants = graph.constants + self.torchbind_constants = graph.torchbind_constants + self.output_strides = output_strides + self.disabled_cudagraphs_reason = disabled_cudagraphs_reason + self.metrics_deltas = metrics_deltas + self.counter_deltas = counter_deltas + self.guards_expr = None + self.cudagraph_info = None + self.fx_kwargs = {} + self.inputs_to_check = () + self.boxed_forward_device_index = None + + def __call__(self, inputs: List[Any]) -> Any: + assert self.current_callable is not None + return self.current_callable(inputs) + + +def run_command_and_check(cmd_: str) -> None: + cmd = shlex.split(cmd_) + try: + subprocess.check_call(cmd) + except subprocess.CalledProcessError as e: + raise exc.CppCompileError(cmd, e.output) from e + + +@functools.lru_cache(None) +def split_aot_inductor_output_path(path: str) -> Tuple[str, str]: + """Returns the path where the AOT Inductor compiled kernels are stored.""" + if path.endswith(".so"): + return os.path.split(path) + elif path.endswith(".pt2"): + return os.path.split(path) + else: + return path, "" + + +@clear_on_fresh_inductor_cache +class CudaKernelParamCache: + cache: Dict[str, Dict[str, str]] = {} + cache_clear = staticmethod(cache.clear) + + @classmethod + def set(cls, key: str, params: Dict[str, str], cubin: str, bin_type: str) -> None: + _, path = write( + cubin, + bin_type, + hash_type=bin_type, + specified_dir=split_aot_inductor_output_path( + config.aot_inductor.output_path + )[0], + ) + + params[get_cpp_wrapper_cubin_path_name()] = path + + cls.cache[key] = params + + @classmethod + def get(cls, key: str) -> Optional[Dict[str, str]]: + return cls.cache.get(key, None) + + @classmethod + def get_keys(cls) -> KeysView[str]: + return cls.cache.keys() + + +class AotCodeCompiler: + @classmethod + def compile( + cls, + graph: GraphLowering, + source_code: str, + serialized_extern_kernel_nodes: Optional[str], + cuda: bool, + ) -> str: + if sys.platform == "win32": + raise RuntimeError("AotCodeCompiler not yet supported for inductor") + + _set_gpu_runtime_env() # cpp_extension consults the env + + picked_vec_isa = pick_vec_isa() + vec_isa_cmd_gen = CppBuilder( + name="o", + sources="i", + BuildOption=CppTorchCudaOptions( + vec_isa=picked_vec_isa, + cuda=cuda, + aot_mode=graph.aot_mode, + ), + ) + # write function will calc source_code hash, the same source code with different + # ISA level should be generate different hash. + # So we need get a command_line which contains isa related parameter as a part of hash key. + # And then pass the command_line to below write function as extra parameter to + # guarantee the source code hash contains ISA difference. + cpp_command = repr(vec_isa_cmd_gen.get_command_line()) + + fbcode_aot_cpu_re = False + use_absolute_path = False + if config.is_fbcode(): + ld_command = build_paths.ld() + if not cuda and graph.aot_mode: # Meta internal AOTInductor CPU + objcopy_command = build_paths.objcopy_fallback() + fbcode_aot_cpu_re = True + use_absolute_path = True + else: + objcopy_command = build_paths.objcopy() + else: + ld_command = "ld" + objcopy_command = "objcopy" + + ( + specified_output_path, + specified_so_name, + ) = split_aot_inductor_output_path(config.aot_inductor.output_path) + key, input_path = write( + source_code, + "cpp", + extra=cpp_command, + specified_dir=specified_output_path, + ) + output_code_log.info("Output code written to: %s", input_path) + trace_structured( + "graph_dump", + lambda: { + "name": "inductor_aot_code", + "type": "cpp", + "filename": input_path, + }, + payload_fn=lambda: source_code, + ) + + # We use a file lock below to protect FS operations. The lock file + # is scoped to the 'key', so make sure the consts_path is protected + # by the same lock: + consts_specified_dir = os.path.join(os.path.split(input_path)[0], key) + + def _compile_consts_linux(consts: bytes) -> str: + _, consts_path = write( + consts, + "bin", + specified_dir=consts_specified_dir, + ) + + consts_o = os.path.splitext(consts_path)[0] + ".o" + if fbcode_aot_cpu_re: + cmd = f"{ld_command} -r -b binary -o {os.path.basename(consts_o)} {os.path.basename(consts_path)}" + compile_file(consts_path, consts_o, cmd.split()) + os.chmod(consts_o, 0o644) + else: + cmd = f"{ld_command} -r -b binary -o {consts_o} {consts_path}" + run_command_and_check(cmd) + log.debug("aot constant binary command: %s", cmd) + + if graph.mutated_buffers & set(graph.constants.keys()): + # .data section is between .text and .bss. When the size of .data is large, + # during the linking, the relocation of .text against .bss may overflow. + # Rename it to .ldata so that it won't be in between the .text and .bss section + if len(consts) > 2_000_000_000: + raise ValueError( + "Models with buffer mutation included doesn't support constants greater than 2GB!" + ) + rename_data = " .data=.ldata" + else: + # if no buffer mutation is needed, we could instead set the data region + # as read-only (i.e. .lrodata) which could accomodate larger size of data + # to be linked. + rename_data = " .data=.lrodata,alloc,load,readonly,data,contents" + + assert ( + ALIGN_BYTES & (ALIGN_BYTES - 1) + ) == 0 and ALIGN_BYTES >= 64, "must be power of 2 and >= 64" + cmd = ( + f"{objcopy_command} --rename-section" + f"{rename_data}" + f" --set-section-alignment .data={ALIGN_BYTES}" # following the gAlignment of CPU in c10/core/alignment.h + f" {consts_o} {consts_o}" + ) + log.debug("aot constant rename section command: %s", cmd) + run_command_and_check(cmd) + + cmd = f"rm {consts_path}" + log.debug("aot constant bin removal command: %s", cmd) + run_command_and_check(cmd) + + if fbcode_aot_cpu_re: + body = re.sub(r"[\W]", "_", os.path.basename(consts_path)) + else: + body = re.sub(r"[\W]", "_", consts_path) + + symbol_list = [] + symbol_list.append( + f"{objcopy_command} --redefine-sym _binary_{body}_start=_binary_constants_bin_start {consts_o}" + ) + symbol_list.append( + f"{objcopy_command} --redefine-sym _binary_{body}_size=_binary_constants_bin_size {consts_o}" + ) + symbol_list.append( + f"{objcopy_command} --redefine-sym _binary_{body}_end=_binary_constants_bin_end {consts_o}" + ) + log.debug("aot constant binary redefine symbol: %s", " ".join(symbol_list)) + for cmd in symbol_list: + run_command_and_check(cmd) + return consts_o + + def _compile_consts_darwin(consts: bytes) -> str: + if config.aot_inductor.debug_dump_consts_bin: + _, _binary_constants_path = write( + consts, + "bin", + specified_dir=consts_specified_dir, + ) + log.debug("binary constants path: %s", _binary_constants_path) + + is_large_consts = len(consts) > 1024 + consts_asm = "\t.section\t__DATA,__data\n" + consts_asm += "\t.globl\t__binary_constants_bin_start\n" + consts_asm += "__binary_constants_bin_start:\n" + if not is_large_consts: + for c in consts: + consts_asm += f"\t.byte {c}\n" + # Add one element even if constants are empty + # Otherwise assembler will not put them in data section + if not consts: + consts_asm += "\t.space 1\n" + else: + consts_asm += "\t.quad 0x1234567899abcdef\n" + consts_asm += f"\t.space {len(consts) - 8}\n" + consts_asm += ".globl\t__binary_constants_bin_end\n" + consts_asm += "__binary_constants_bin_end:\n" + _, consts_path = write( + consts_asm, + "S", + specified_dir=consts_specified_dir, + ) + consts_o = os.path.splitext(consts_path)[0] + ".o" + cmd = f"{get_cpp_compiler()} -c -o {consts_o} {consts_path}" + run_command_and_check(cmd) + if is_large_consts: + with open(consts_o, "r+b") as f: + f.seek(0) + hdr = f.read(1024) + # Search for magic number and write the actual data over it + start_idx = hdr.find(b"\xef\xcd\xab\x99\x78\x56\x34\x12") + assert start_idx != -1 + f.seek(start_idx) + pos = 0 + while pos < len(consts): + rc = f.write(consts[pos:]) + pos += rc + return consts_o + + from filelock import FileLock + + lock_dir = get_lock_dir() + lock = FileLock(os.path.join(lock_dir, key + ".lock"), timeout=LOCK_TIMEOUT) + with lock: + # Currently, this only support serializing extern nodes in fbcode + # Eventually, we should also have a serializer for OSS. + if serialized_extern_kernel_nodes: + output_json = os.path.splitext(input_path)[0] + ".json" + with open(output_json, "w") as f: + f.write(serialized_extern_kernel_nodes) + + output_so = ( + config.aot_inductor.output_path + if specified_so_name + else os.path.splitext(input_path)[0] + ".so" + ) + + output_o = os.path.splitext(input_path)[0] + ".o" + + all_cuda = all( + graph.get_original_value_of_constant(name).is_cuda + for name in graph.constants.keys() + if name not in graph.folded_constants + ) + + def get_nbytes_of_tensor(tensor: torch.Tensor, all_cuda: bool) -> int: + n_bytes = ( + torch.ops.mkldnn._nbytes(tensor) + if tensor.is_mkldnn + else tensor.untyped_storage().nbytes() + ) + return n_bytes if all_cuda else _align(n_bytes) + + consts_size = sum( + get_nbytes_of_tensor(tensor, all_cuda) + for (name, tensor) in graph.constants.items() + if name not in graph.folded_constants + ) + # TODO: Fix mmap weights with cuda + use_mmap_weights = not config.is_fbcode() and consts_size > 2_000_000_000 + if config.aot_inductor.force_mmap_weights: + use_mmap_weights = True + + if config.aot_inductor.package: + ( + object_output_name, + object_output_dir, + ) = get_name_and_dir_from_output_file_path(input_path) + object_build_options = CppTorchCudaOptions( + vec_isa=picked_vec_isa, + cuda=cuda, + aot_mode=graph.aot_mode, + compile_only=True, + use_absolute_path=use_absolute_path, + use_mmap_weights=use_mmap_weights, + ) + object_builder = CppBuilder( + name=object_output_name, + sources=input_path, + output_dir=object_output_dir, + BuildOption=object_build_options, + ) + compile_cmd = object_builder.get_command_line() + output_o = object_builder.get_target_file_path() + + compile_flags = os.path.splitext(input_path)[0] + "_compile_flags.json" + object_build_options.save_flags_to_file(compile_flags) + + else: + ( + object_output_name, + object_output_dir, + ) = get_name_and_dir_from_output_file_path(input_path) + object_build_options = CppTorchCudaOptions( + vec_isa=picked_vec_isa, + cuda=cuda, + aot_mode=graph.aot_mode, + compile_only=True, + use_absolute_path=use_absolute_path, + use_mmap_weights=use_mmap_weights, + ) + object_builder = CppBuilder( + name=object_output_name, + sources=input_path, + output_dir=object_output_dir, + BuildOption=object_build_options, + ) + compile_cmd = object_builder.get_command_line() + output_o = object_builder.get_target_file_path() + + log.debug("aot compilation command: %s", compile_cmd) + if fbcode_aot_cpu_re: + output_o = os.path.splitext(input_path)[0] + ".o" + compile_file(input_path, output_o, compile_cmd.split()) + os.chmod(output_o, 0o644) + else: + run_command_and_check(compile_cmd) + + def _to_bytes(t: torch.Tensor, all_cuda: bool) -> bytes: + def _pad_to_alignment(raw_bytes: bytes) -> bytes: + padded_bytes = raw_bytes.ljust( + (len(raw_bytes) + ALIGN_BYTES - 1) // ALIGN_BYTES * ALIGN_BYTES, + b"\x00", + ) + return padded_bytes + + # This serializes the tensor's untyped_storage to bytes by accessing + # the raw data of the underlying structure. + import ctypes + + if t.numel() == 0: + return b"" + + if t.is_mkldnn: + data_ptr = torch.ops.mkldnn.data_ptr(t) + nbytes = torch.ops.mkldnn._nbytes(t) + else: + t_cpu = t.untyped_storage().cpu() + data_ptr = t_cpu.data_ptr() + nbytes = t_cpu.nbytes() + + raw_array = ctypes.cast( + data_ptr, + ctypes.POINTER(ctypes.c_ubyte * nbytes), + ) + raw_bytes = bytes(raw_array.contents) + return raw_bytes if all_cuda else _pad_to_alignment(raw_bytes) + + serialized_weights = b"".join( + _to_bytes(graph.get_original_value_of_constant(name), all_cuda) + for name in graph.constants.keys() + if name not in graph.folded_constants + ) + if not use_mmap_weights: + aot_constants = serialized_weights + magic_number = 0 + else: + magic_number = cast( + int, torch.randint(0, torch.iinfo(torch.int64).max, (1,)).item() + ) + aot_constants = struct.pack("qq", consts_size + 8, magic_number) + + consts_o = { + "linux": _compile_consts_linux, + "darwin": _compile_consts_darwin, + }[sys.platform](aot_constants) + + if config.aot_inductor.package: + output_name, output_dir = get_name_and_dir_from_output_file_path( + output_so + ) + so_build_options = CppTorchCudaOptions( + vec_isa=picked_vec_isa, + cuda=cuda, + aot_mode=graph.aot_mode, + use_absolute_path=use_absolute_path, + ) + so_builder = CppBuilder( + name=output_name, + sources=[output_o, consts_o], + output_dir=output_dir, + BuildOption=so_build_options, + ) + link_cmd = so_builder.get_command_line() + output_so = so_builder.get_target_file_path() + + linker_flags = os.path.splitext(input_path)[0] + "_linker_flags.json" + so_build_options.save_flags_to_file(linker_flags) + + from torch._inductor.package import package_aoti + + if use_mmap_weights: + weight_file = ( + os.path.splitext(input_path)[0] + "_serialized_weights.bin" + ) + with open(weight_file, "wb") as f_weights: + f_weights.write(serialized_weights) + f_weights.write(struct.pack("q", magic_number)) + + archive_path = package_aoti(os.path.split(input_path)[0]) + return archive_path + else: + output_name, output_dir = get_name_and_dir_from_output_file_path( + output_so + ) + so_build_options = CppTorchCudaOptions( + vec_isa=picked_vec_isa, + cuda=cuda, + aot_mode=graph.aot_mode, + use_absolute_path=use_absolute_path, + ) + so_builder = CppBuilder( + name=output_name, + sources=[output_o, consts_o], + output_dir=output_dir, + BuildOption=so_build_options, + ) + link_cmd = so_builder.get_command_line() + output_so = so_builder.get_target_file_path() + + log.debug("aot linkage command: %s", link_cmd) + if fbcode_aot_cpu_re: + output_so = ( + config.aot_inductor.output_path + if specified_so_name + else os.path.splitext(input_path)[0] + ".so" + ) + compile_file([output_o, consts_o], output_so, link_cmd.split()) + os.chmod(output_so, 0o755) + else: + run_command_and_check(link_cmd) + + if use_mmap_weights: + import resource + + page_size_ = resource.getpagesize() + page_size = max(16384, page_size_) + + with open(output_so, "a+b") as f_so: + so_size = f_so.tell() + # Page align the weights + f_so.write(b" " * (page_size - so_size % page_size)) + f_so.write(serialized_weights) + f_so.write(struct.pack("q", magic_number)) + + # Append cmds to the end of codegen-ed wrapper file + with open(input_path, "a") as f: + f.write("\n") + f.write(f"// Compile cmd\n// {compile_cmd}\n") + f.write(f"// Link cmd\n// {link_cmd}\n") + + return output_so + + +# Putting this fn in cpp.py (unfortunately) causes a deadlock, which is why it's in codecache.py. +# Why? importing from cpp.py invokes codecache.pick_vec_isa(), which takes out a lock. +# Cycle goes: +# - CppCodeCache.load() +# - pick_vec_isa() +# - valid_vec_isa_list() +# - VecISA.__bool__() <-- takes out a lock +# - compile_file() <-- imports cpp_prefix_path from cpp, which causes us to try to take out the same lock. +@clear_on_fresh_inductor_cache +@functools.lru_cache +def cpp_prefix_path() -> str: + path = Path(__file__).parent / "codegen/cpp_prefix.h" + with path.open() as f: + content = f.read() + _, filename = write( + content, + "h", + ) + return normalize_path_separator(filename) + + +def cpp_prefix() -> str: + filename = cpp_prefix_path() + if config.is_fbcode(): + # We need relative paths, since we bundle up + # everything that we compile into a folder for remote compilation. + return f'#include "{os.path.basename(filename)}"' + else: + return f'#include "{filename}"' + + +# Given a path to an input cpp file and an output path, +# Attempts to compile the file, storing the output in "output_path" +def compile_file( + input_path: Union[str, List[str]], output_path: str, cmd: List[str] +) -> None: + with dynamo_timed("compile_file"): + return _compile_file(input_path, output_path, cmd) + + +def _compile_file( + input_path: Union[str, List[str]], output_path: str, cmd: List[str] +) -> None: + input_paths = [input_path] if isinstance(input_path, str) else input_path + input_files = [ + os.path.basename(ip) if config.is_fbcode() else ip for ip in input_paths + ] + try: + if config.is_fbcode(): + # Need to copy our header into the same folder as the sourcecode. + header_path = cpp_prefix_path() + header_name = os.path.basename(header_path) + output_name = os.path.basename(output_path) + # When we build remotely, we need to make sure to carefully copy any files + # that are required during the compilation process into our build directly. + # This is where all of the ATen/c10/Torch includes come from. + torch_includes_path = os.path.join(_TORCH_PATH, "include") + with tempfile.TemporaryDirectory() as tmp_dir: + # Copy everything to tmp compilation folder + shutil.copy(header_path, os.path.join(tmp_dir, header_name)) + shutil.copy(_LINKER_SCRIPT, os.path.join(tmp_dir, "script.ld")) + for p, f in zip(input_paths, input_files): + shutil.copy(p, os.path.join(tmp_dir, f)) + dest_include_path = os.path.join(tmp_dir, "include") + shutil.copytree(torch_includes_path, dest_include_path) + # Run the build + output_file_path = _run_build_command(cmd, tmp_dir, output_name) + # Copy output from the build + if os.path.exists(output_path): + os.remove(output_path) + shutil.copy(output_file_path, output_path) + else: + subprocess.check_output(cmd, stderr=subprocess.STDOUT) + except subprocess.CalledProcessError as e: + output = e.output.decode("utf-8") + openmp_problem = "'omp.h' file not found" in output or "libomp" in output + if openmp_problem and sys.platform == "darwin": + instruction = ( + "\n\nOpenMP support not found. Please try one of the following solutions:\n" + "(1) Set the `CXX` environment variable to a compiler other than Apple clang++/g++ " + "that has builtin OpenMP support;\n" + "(2) install OpenMP via conda: `conda install llvm-openmp`;\n" + "(3) install libomp via brew: `brew install libomp`;\n" + "(4) manually setup OpenMP and set the `OMP_PREFIX` environment variable to point to a path" + " with `include/omp.h` under it." + ) + output += instruction + raise exc.CppCompileError(cmd, output) from e + + +_libgomp: Optional[CDLL] = None + + +def custom_op_wrapper(op: str, *args: Any) -> Union[list[c_void_p], c_void_p]: + # This function will be called from generated cpp wrapper code in the JIT mode. + # Because tensors will be passed in as AtenTensorHandle, we need to explicitly convert them. + def convert_arg(arg: Any) -> Any: + if str(type(arg)) == "": + # No easy way to do isinstance check on PyCapsule + return torch._C._aoti.alloc_tensor_by_stealing_from_void_ptr(arg) + elif isinstance(arg, (list, tuple)): + return type(arg)(convert_arg(a) for a in arg) + else: + return arg + + converted_args = [convert_arg(arg) for arg in args] + + assert op.startswith("torch.ops."), ( + op + " can not be called through custom_op_wrapper" + ) + func = None + for i, s in enumerate(op.split(".")): + if i == 0: + func = importlib.import_module(s) + func = getattr(func, s) + + assert callable(func), op + " can not be loaded through custom_op_wrapper" + result = func(*converted_args) + if isinstance(result, (list, tuple)): + for r in result: + assert isinstance(r, torch.Tensor), op + " returns a list of non-tensors" + return torch._C._aoti.unsafe_alloc_void_ptrs_from_tensors(result) # type: ignore[arg-type] + else: + assert isinstance(result, torch.Tensor), op + " returns a non-tensor" + return torch._C._aoti.unsafe_alloc_void_ptr_from_tensor(result) + + +@clear_on_fresh_inductor_cache +class CppCodeCache: + cache: Dict[str, Callable[[], Union[CDLL, ModuleType]]] = {} + cache_clear = staticmethod(cache.clear) + cpp_compile_command_flags: Dict[str, Any] = {} + + @staticmethod + def _load_library_inner(path: str, key: str) -> Union[CDLL, ModuleType]: + return cdll.LoadLibrary(path) + + @classmethod + def _load_library(cls, path: str, key: str) -> Union[CDLL, ModuleType]: + try: + result = cls._load_library_inner(path, key) + result.key = key # type: ignore[union-attr] + return result + except (ImportError, OSError) as e: + if "gomp" in str(e) and os.path.exists("/usr/lib64/libgomp.so.1"): + # hacky workaround for fbcode/buck + global _libgomp + _libgomp = cdll.LoadLibrary("/usr/lib64/libgomp.so.1") + result = cls._load_library_inner(path, key) + result.key = key # type: ignore[union-attr] + return result + if "failed to map segment from shared object" in str(e): + raise OSError( + f"{e}. The most common reason this may occur is if the {tempfile.gettempdir()} folder " + "is mounted with noexec (e.g., by default Docker mounts tmp file systems " + f"as noexec). Please remount {tempfile.gettempdir()} with exec enabled, or set another " + "temporary directory with TORCHINDUCTOR_CACHE_DIR environment variable." + ) from e + raise + + @classmethod + def load_async( + cls, + source_code: str, + cuda: bool = False, + submit_fn: Any = None, + extra_flags: Sequence[str] = (), + ) -> Any: + compile_command = { + **cls.cpp_compile_command_flags, + "cuda": cuda, + "vec_isa": pick_vec_isa(), + "extra_flags": extra_flags, + } + + _set_gpu_runtime_env() # cpp_extension consults the env + + command_gen = CppBuilder( + name="o", sources="i", BuildOption=CppTorchCudaOptions(**compile_command) + ) + # write function will calc source_code hash, the same source code with different + # ISA level should be generate different hash. + # So we need get a command_line which contains isa related parameter as a part of hash key. + # And then pass the command_line to below write function as extra parameter to + # guarantee the source code hash contains ISA difference. + vec_isa_cmd = repr(command_gen.get_command_line()) + key, input_path = write(source_code, "cpp", extra=vec_isa_cmd) + + if key not in cls.cache: + from filelock import FileLock + + lock_path = os.path.join(get_lock_dir(), key + ".lock") + output_name, output_dir = get_name_and_dir_from_output_file_path(input_path) + """ + If `fb_code` env, it need to be dispatched to original `compile_file` function. + So, we still need to prepare parameters for the function: `input_path` and `fb_output_path`. + """ + fb_output_path = input_path[:-3] + "so" + future: Optional[Future[Any]] = None + lib = None + + cpp_build_option = CppTorchCudaOptions(**compile_command) + cpp_builder = CppBuilder( + name=output_name, + sources=input_path, + output_dir=output_dir, + BuildOption=cpp_build_option, + ) + + worker_fn = functools.partial( + _worker_compile_cpp, + lock_path, + cpp_builder, + input_path, + fb_output_path, + ) + + binary_path = normalize_path_separator( + fb_output_path + if config.is_fbcode() + else cpp_builder.get_target_file_path() + ) + + def load_fn() -> Any: + nonlocal lib + if lib is None: + if future is not None: + future.result() + result = worker_fn() + assert result is None + lib = cls._load_library(binary_path, key) + assert lib is not None + return lib + + if submit_fn is not None: + with FileLock(lock_path, timeout=LOCK_TIMEOUT): + if not os.path.exists(binary_path): + future = submit_fn(worker_fn) + + cls.cache[key] = load_fn + + return cls.cache[key] + + @classmethod + def load(cls, source_code: str, cuda: bool = False) -> Any: + return cls.load_async(source_code, cuda)() + + +def _worker_compile_cpp( + lock_path: str, + cpp_builder: CppBuilder, + fb_input_path: str, + fb_output_path: str, +) -> None: + from filelock import FileLock + + with FileLock(lock_path, timeout=LOCK_TIMEOUT): + binary_path = ( + fb_output_path if config.is_fbcode() else cpp_builder.get_target_file_path() + ) + if not os.path.exists(binary_path): + if config.is_fbcode(): + compile_file( + fb_input_path, + fb_output_path, + shlex.split(cpp_builder.get_command_line()), + ) + else: + cpp_builder.build() + + +# Customized Python binding for cpp kernels +@clear_on_fresh_inductor_cache +class CppPythonBindingsCodeCache(CppCodeCache): + cache: Dict[str, Callable[[], Union[CDLL, ModuleType]]] = {} + cache_clear = staticmethod(cache.clear) + cpp_compile_command_flags = { + # kernels have no dependency on libtorch + "include_pytorch": False, + "shared": True, + } + entry_function = "kernel" + call_entry_function = "kernel(%s);Py_RETURN_NONE;" + extra_parse_arg = "" + suffix_template = textwrap.dedent( + """ + // Python bindings to call %s(): + #define PY_SSIZE_T_CLEAN + #include + #include + #include + + #ifndef _MSC_VER + #if __cplusplus < 202002L + // C++20 (earlier) code + // https://en.cppreference.com/w/cpp/language/attributes/likely + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) + #endif + #else + #define likely(x) (x) + #define unlikely(x) (x) + #endif + + // This is defined in guards.cpp so we don't need to import PyTorch headers that are slooow. + // We manually link it below to workaround issues with fbcode build. + static void* (*_torchinductor_pyobject_tensor_data_ptr)(PyObject* obj); + + template static inline T parse_arg(PyObject* args, size_t n) { + static_assert(std::is_pointer::value, "arg type must be pointer or long"); + return static_cast(_torchinductor_pyobject_tensor_data_ptr(PyTuple_GET_ITEM(args, n))); + } + template <> inline int64_t parse_arg(PyObject* args, size_t n) { + auto result = PyLong_AsSsize_t(PyTuple_GET_ITEM(args, n)); + if(unlikely(result == -1 && PyErr_Occurred())) + throw std::runtime_error("expected int arg"); + return result; + } + template <> inline uintptr_t parse_arg(PyObject* args, size_t n) { + auto result = PyLong_AsVoidPtr(PyTuple_GET_ITEM(args, n)); + if(unlikely(result == reinterpret_cast(-1) && PyErr_Occurred())) + throw std::runtime_error("expected int arg"); + return reinterpret_cast(result); + } + + %s + + static PyObject* %s_py(PyObject* self, PyObject* args) { + try { + if(unlikely(!PyTuple_CheckExact(args))) + throw std::runtime_error("tuple args required"); + if(unlikely(PyTuple_GET_SIZE(args) != %s)) + throw std::runtime_error("requires %s args"); + %s + } catch(std::exception const& e) { + PyErr_SetString(PyExc_RuntimeError, e.what()); + return nullptr; + } catch(...) { + PyErr_SetString(PyExc_RuntimeError, "unhandled error"); + return nullptr; + } + } + + static PyMethodDef py_methods[] = { + {"%s", %s_py, METH_VARARGS, ""}, + {NULL, NULL, 0, NULL}}; + + static struct PyModuleDef py_module = + {PyModuleDef_HEAD_INIT, "%s", NULL, -1, py_methods}; + + PyMODINIT_FUNC PyInit_%s(void) { + const char* str_addr = std::getenv("_TORCHINDUCTOR_PYOBJECT_TENSOR_DATA_PTR"); + if(!str_addr) { + PyErr_SetString(PyExc_RuntimeError, "_TORCHINDUCTOR_PYOBJECT_TENSOR_DATA_PTR must be set"); + return nullptr; + } + std::istringstream iss(str_addr); + uintptr_t addr = 0; + iss >> addr; + _torchinductor_pyobject_tensor_data_ptr = + reinterpret_cast(addr); + return PyModule_Create(&py_module); + } + """ + ) + + @classmethod + def _load_library_inner(cls, path: str, key: str) -> ModuleType: + os.environ["_TORCHINDUCTOR_PYOBJECT_TENSOR_DATA_PTR"] = str( + torch._C._dynamo.guards._torchinductor_pyobject_tensor_data_ptr # type: ignore[attr-defined] + ) + module_name = f"{key}.{cls.entry_function}" + try: + return sys.modules[module_name] + except KeyError: + pass + spec = importlib.util.spec_from_file_location(module_name, path) + assert spec is not None + module = importlib.util.module_from_spec(spec) + sys.modules[module_name] = module + spec.loader.exec_module(module) # type: ignore[union-attr] + return module + + @classmethod + def load_pybinding_async( + cls, + argtypes: List[str], + source_code: str, + cuda: bool = False, + num_outputs: int = -1, + submit_fn: Any = None, + extra_flags: Sequence[str] = (), + ) -> Any: + """ + Wrap a C++ function in fast Python bindings. + + Args: + argtypes: The types of args to ENTRY_FUNCTION(), e.g. ["float*", "long"] + source_code: C++ source code containing a ENTRY_FUNCTION() function + + Returns: + A python version of ENTRY_FUNCTION() + """ + parseargs = ", ".join( + f"parse_arg<{argtype.replace('const ', '')}>(args, {n})" + for n, argtype in enumerate(argtypes) + ) + suffix = cls.suffix_template % ( + cls.entry_function, + cls.extra_parse_arg % num_outputs if cls.extra_parse_arg else "", + cls.entry_function, + len(argtypes), + len(argtypes), + cls.call_entry_function % parseargs, + cls.entry_function, + cls.entry_function, + cls.entry_function, + cls.entry_function, + ) + get_result = cls.load_async( + source_code + suffix, cuda, submit_fn=submit_fn, extra_flags=extra_flags + ) + result = None + + def future() -> Any: + nonlocal result + if result is None: + result = get_result() + assert isinstance(result, ModuleType) + return getattr(result, cls.entry_function) + + return future + + @classmethod + def load_pybinding(cls, *args: Any, **kwargs: Any) -> Any: + return cls.load_pybinding_async(*args, **kwargs)() + + +@clear_on_fresh_inductor_cache +class CppWrapperCodeCache(CppPythonBindingsCodeCache): + cache: Dict[str, Callable[[], Union[CDLL, ModuleType]]] = {} + cache_clear = staticmethod(cache.clear) + cpp_compile_command_flags = { + "include_pytorch": True, + "shared": True, + } + entry_function = "inductor_entry_cpp" + call_entry_function = "return inductor_entry_cpp(%s);" + extra_parse_arg = textwrap.dedent( + """ + #include + + static inline std::vector unpack_tensor_handle_list(PyObject* pyvec) { + std::vector result; + size_t result_len = PyList_GET_SIZE(pyvec); + result.reserve(result_len); + for (size_t i = 0; i < result_len; i++) { + // AtenTensorHandle is essentially a pointer + void* elem = PyCapsule_GetPointer(PyList_GET_ITEM(pyvec, i), NULL); + result.push_back(reinterpret_cast(elem)); + } + return result; + } + + static inline PyObject* pack_tensor_handle_list(const std::vector& cppvec) { + size_t result_len = cppvec.size(); + PyObject* result = PyList_New(static_cast(result_len)); + for (size_t i = 0; i < result_len; i++) { + PyObject *elem = + cppvec[i] == nullptr + ? Py_None + // Store AtenTensorHandle as PyCapsulate + : PyCapsule_New(reinterpret_cast(cppvec[i]), NULL, NULL); + PyList_SET_ITEM(result, i, elem); + } + return result; + } + + template <> inline std::vector parse_arg>(PyObject* args, size_t n) { + return unpack_tensor_handle_list(PyTuple_GET_ITEM(args, n)); + } + + PyObject* inductor_entry_cpp(std::vector&& input_handles) { + // For outputs, we only allocate a vector to hold returned tensor handles, + // not allocating the actual output tensor storage here + std::vector output_handles(%s); + try { + inductor_entry_impl(input_handles.data(), output_handles.data()); + return pack_tensor_handle_list(output_handles); + } catch(std::exception const& e) { + PyErr_SetString(PyExc_RuntimeError, e.what()); + return {}; + } catch(...) { + PyErr_SetString(PyExc_RuntimeError, "unhandled error"); + return {}; + } + } + """ + ) + + +@clear_on_fresh_inductor_cache +class HalideCodeCache(CppPythonBindingsCodeCache): + cache: Dict[str, Callable[[], Union[ModuleType, CDLL]]] = {} + cache_clear = staticmethod(cache.clear) + _standalone_runtime_path: Optional[str] = None + prefix = textwrap.dedent( + """ + #include "{halideruntime_h}" + #include "{headerfile}" + #include + #include + + namespace c10 {{ + inline long div_floor_integer(long a, long b) {{ + if ((a<0) != (b<0)) {{ + const auto quot = a / b; + const auto rem = a % b; + return rem ? quot - 1 : quot; + }} + return a / b; + }} + }} + """ + ) + glue_template_cpp = prefix + textwrap.dedent( + """ + void kernel({argdefs}) {{ + {buffers} + int err = halide_kernel({buffer_names}); + if(err != 0) throw std::runtime_error("halide_kernel failed"); + }} + """ + ) + glue_template_cuda = prefix + textwrap.dedent( + """ + #include + static const halide_device_interface_t* cuda_interface = halide_cuda_device_interface(); + + void kernel({argdefs}, uintptr_t stream) {{ + {buffers} + int err = halide_kernel(reinterpret_cast(stream), {buffer_names}); + if(err != 0) throw std::runtime_error("halide_kernel failed"); + }} + """ + ) + standalone_runtime_cuda_init = textwrap.dedent( + """ + #include "{}" + #include + + static int acquire_context(void* user_context, + void** cuda_context_out, + bool create) {{ + return cuCtxGetCurrent(reinterpret_cast(cuda_context_out)); + }} + + static int release_context(void* user_context) {{ + return 0; + }} + + static int get_stream(void* user_context, + void* cuda_context, + void** stream_out) {{ + *stream_out = user_context; + return 0; + }} + + static int register_halide_hooks() {{ + halide_set_cuda_acquire_context(&acquire_context); + halide_set_cuda_release_context(&release_context); + halide_set_cuda_get_stream(&get_stream); + return 0; + }} + + int inductor_register_halide_hooks_result = register_halide_hooks(); + """ + ) + + @classmethod + def _codegen_buffer(cls, name: str, arg: HalideInputSpec, cuda: bool) -> List[str]: + assert arg.shape is not None + assert arg.stride is not None and len(arg.shape) == len(arg.stride) + assert arg.offset is not None + data_ptr = f"{arg.alias_of or arg.name} + {arg.offset}" + if cuda: + device = f"reinterpret_cast({data_ptr})" + device_interface = "cuda_interface" + host = "nullptr" + flags = "halide_buffer_flag_device_dirty" + else: + device = "0" + device_interface = "nullptr" + host = f"reinterpret_cast({data_ptr})" + flags = "halide_buffer_flag_host_dirty" + + dims = [] + for size, stride in zip(arg.shape, arg.stride): + dims.append(f"halide_dimension_t(0, {size}, {stride})") + + return [ + f"halide_buffer_t {name};", + f"halide_dimension_t {name}_dims[] = {{{', '.join(dims)}}};", + f"{name}.device = {device};", + f"{name}.device_interface = {device_interface};", + f"{name}.host = {host};", + f"{name}.flags = {flags};", + f"{name}.type = {arg.halide_type()};", + f"{name}.dimensions = {len(dims)};", + f"{name}.dim = {name}_dims;", + f"{name}.padding = nullptr;", + ] + + @classmethod + def _codegen_glue(cls, meta: HalideMeta, headerfile: object) -> str: + is_cuda = meta.is_cuda() + assert is_cuda is ("user_context" in meta.target) + assert "no_runtime" in meta.target + buffers = [] + buffer_names = [] + for i, arg in enumerate(meta.argtypes): + if arg.is_buffer(): + buffer_names.append(f"&hl_buf_{i}") + buffers.extend(cls._codegen_buffer(f"hl_buf_{i}", arg, is_cuda)) + else: + assert "*" not in arg.ctype + buffer_names.append(arg.name) + buffers = "\n".join([f" {line}" for line in buffers]).lstrip() + + glue_template = cls.glue_template_cuda if is_cuda else cls.glue_template_cpp + glue_code = glue_template.format( + halideruntime_h=cls.find_header( + "HalideRuntimeCuda.h" if is_cuda else "HalideRuntime.h" + ), + headerfile=headerfile, + argdefs=", ".join( + f"{a.bindings_type()} {a.name}" + for a in meta.argtypes + if a.alias_of is None + ), + buffers=buffers, + buffer_names=", ".join(buffer_names), + ) + return glue_code + + @classmethod + @functools.lru_cache(None) + def config_hash(cls) -> str: + command_gen = CppBuilder( + name="O", + sources="I", + BuildOption=CppOptions(), + ) + command_line = command_gen.get_command_line() + return sha256_hash( + "\n".join( + [ + cls.glue_template_cpp, + cls.glue_template_cuda, + cls.standalone_runtime_cuda_init, + command_line, + ] + ).encode("utf-8") + ) + + @staticmethod + def _search_for_file(suffix: str, errmsg: str) -> str: + spec = importlib.machinery.PathFinder.find_spec("halide") + if spec is None or not spec.submodule_search_locations: + raise RuntimeError("halide python bindings not installed") + try: + search = spec.submodule_search_locations[0] + for file in os.listdir(search): + if file.endswith(".so"): + try: + out = subprocess.check_output( + ["ldd", os.path.join(search, file)] + ) + except subprocess.SubprocessError: + continue + m = re.search(r"(/.*)/libHalide.so", out.decode("utf-8")) + if m: + path = os.path.join(os.path.abspath(m.group(1)), suffix) + if os.path.exists(path): + return os.path.abspath(path) + except Exception as e: + raise RuntimeError(errmsg) from e + raise RuntimeError(errmsg) + + @staticmethod + @functools.lru_cache(None) + def find_libautoschedule(name: str) -> str: + sofile = f"libautoschedule_{name.lower()}.so" + if "HALIDE_LIB" in os.environ: + path = os.path.join(os.environ["HALIDE_LIB"], sofile) + if os.path.exists(path): + return path + errmsg = ( + f"Can't find {sofile}, set env HALIDE_LIB to the directory containing it" + ) + return HalideCodeCache._search_for_file(sofile, errmsg) + + @staticmethod + @functools.lru_cache(None) + def find_header(name: str) -> str: + if "HALIDE_INCLUDE" in os.environ: + path = os.path.join(os.environ["HALIDE_INCLUDE"], name) + if os.path.exists(path): + return path + if "HALIDE_LIB" in os.environ: + path = os.path.abspath( + os.path.join(os.environ["HALIDE_LIB"], f"../include/{name}") + ) + if os.path.exists(path): + return path + errmsg = ( + f"Can't find {name}, set env HALIDE_INCLUDE to the directory containing it" + ) + return HalideCodeCache._search_for_file(f"../include/{name}", errmsg) + + @classmethod + def generate_halide_async( + cls, meta: HalideMeta, source_code: str, submit_fn: Any = None + ) -> Callable[[], Any]: + dirpath = Path( + get_path( + code_hash( + source_code, + extra=repr((cls.config_hash(), meta)), + ), + "halide", + )[2] + ) + os.makedirs(dirpath, exist_ok=True) + wait_for_compile = None + genfile = str(dirpath / "generate_kernel.py") + libfile = str(dirpath / "halide_kernel.a") + headerfile = str(dirpath / "halide_kernel.h") + donefile = str(dirpath / "done") + lockfile = str(dirpath / "lock") + need_compile = not os.path.exists(donefile) + jobs = [] + if need_compile: + write_atomic(genfile, source_code) + cmd = [ + sys.executable, + genfile, + "-g", + "kernel", + "-o", + f"{dirpath}", + "-f", + "halide_kernel", + "-e", + "static_library,h,schedule", + ] + if meta.scheduler: + cmd.extend(["-p", cls.find_libautoschedule(meta.scheduler)]) + cmd.extend(meta.args()) + jobs.append(functools.partial(subprocess.check_call, cmd)) + + binding_types = [ + arg.bindings_type() for arg in meta.argtypes if arg.alias_of is None + ] + if meta.is_cuda(): + binding_types.append("uintptr_t") # stream + bindings_future = cls.load_pybinding_async( + binding_types, + cls._codegen_glue(meta, headerfile), + extra_flags=(libfile, cls.build_standalone_runtime()), + submit_fn=jobs.append if need_compile else None, + cuda=meta.is_cuda(), + ) + + if need_compile: + jobs.append(functools.partial(touch, donefile)) + task = functools.partial(_worker_task_halide, lockfile, jobs) + if submit_fn: + wait_for_compile = submit_fn(task).result + else: + task() + + def load() -> Callable[[], Any]: + if wait_for_compile: + wait_for_compile() + return bindings_future() + + return load + + @classmethod + def generate_halide(cls, *args: Any, **kwargs: Any) -> Callable[[], Any]: + return cls.generate_halide_async(*args, **kwargs)() + + @classmethod + def build_standalone_runtime(cls) -> str: + if cls._standalone_runtime_path and os.path.exists( + cls._standalone_runtime_path + ): + return cls._standalone_runtime_path + is_cuda = torch.cuda.is_available() + libname = "libStandaloneHalideRuntime.so" + target = "host-cuda" if is_cuda else "host" + if cls._standalone_runtime_path: + assert not os.path.exists(cls._standalone_runtime_path) + # We hit this case in unittests when we run with fresh_inductor_cache() + # Generating a fresh runtime over and over causes errors because we initialize + # cuda hundreds of times in the same process and run out of file descriptors. + # Workaround by jail breaking the current fresh_inductor_cache(). + base = default_cache_dir() + else: + base = cache_dir() + dirpath = Path(base) / f"halide-runtime-{target}-{cls.config_hash()}" + os.makedirs(dirpath, exist_ok=True) + donefile = str(dirpath / "done") + lockfile = str(dirpath / "lock") + hookfile = str(dirpath / "hooks.cpp") + afile = str(dirpath / "standalone_halide_runtime.a") + sofile = str(dirpath / libname) + if not os.path.exists(donefile): + import filelock + import halide as hl # type: ignore[import-untyped,import-not-found] + + with filelock.FileLock(lockfile, LOCK_TIMEOUT): + if not os.path.exists(donefile): + with open(hookfile, "w") as f: + if is_cuda: + f.write( + cls.standalone_runtime_cuda_init.format( + cls.find_header("HalideRuntimeCuda.h") + ) + ) + hl.compile_standalone_runtime(afile, hl.Target(target)) + + name, output_dir = get_name_and_dir_from_output_file_path(sofile) + halide_cmd_gen = CppBuilder( + name=name, + sources=[hookfile, afile], + output_dir=output_dir, + BuildOption=CppTorchCudaOptions( + cuda=is_cuda, + ), + ) + + subprocess.check_call( + shlex.split(halide_cmd_gen.get_command_line()) + ) + touch(donefile) + assert os.path.exists(sofile) + cls._standalone_runtime_path = sofile + return sofile + + +def _worker_task_halide(lockfile: str, jobs: List[partial[Any]]) -> None: + from filelock import FileLock + + try: + with FileLock(lockfile, LOCK_TIMEOUT): + for job in jobs: + job() + except subprocess.SubprocessError as e: + if os.environ.get("HALIDE_REPRO") == "1": + python, script, *cmd = getattr(e, "cmd", ("", "", "")) + if os.path.basename(python).startswith("python"): + code = open(script).read() + main = " hl.main()" + assert code.count(main) == 1 + + class Out: + def __repr__(self) -> str: + return "out" + + cmd[cmd.index("-o") + 1] = Out() # type: ignore[call-overload] + repl = textwrap.indent( + textwrap.dedent( + f"""\ + import sys, tempfile + with tempfile.TemporaryDirectory() as out: + sys.argv = {["repro.py", *cmd]!r} + hl.main() + """ + ), + " ", + ) + code = code.replace(main, repl) + with open("repro.py", "w") as fd: + fd.write(code.lstrip()) + raise RuntimeError(f"wrote repro.py: {e}") from e + raise + + +def touch(filename: str): # type: ignore[no-untyped-def] + open(filename, "a").close() + + +@clear_on_fresh_inductor_cache +class PyCodeCache: + cache: Dict[str, ModuleType] = {} + linemaps: Dict[str, List[Tuple[Any, ...]]] = {} + cache_clear = staticmethod(cache.clear) + + @classmethod + def write(cls, source_code: str, extra: str = "") -> Tuple[str, str]: + return write(source_code, "py", extra=extra) + + @classmethod + def load( + cls, + source_code: str, + extra: str = "", + linemap: Optional[List[Tuple[int, str]]] = None, + attrs: Optional[Dict[str, Any]] = None, + ) -> ModuleType: + key, path = write(source_code, "py", extra=extra) + return cls.load_by_key_path(key, path, linemap, attrs) + + @classmethod + def load_by_key_path( + cls, + key: str, + path: str, + linemap: Optional[List[Tuple[int, str]]] = None, + attrs: Optional[Dict[str, Any]] = None, + ) -> ModuleType: + if linemap is None: + linemap = [] + if key not in cls.cache: + mod = _reload_python_module(key, path) + + # another thread might set this first + cls.cache.setdefault(key, mod) + # unzip into separate lines/nodes lists + cls.linemaps[path] = list(zip(*linemap)) + + if attrs is not None: + for k, v in attrs.items(): + setattr(mod, k, v) + + if not (linemap or attrs): + mod._reload_in_subproc = functools.partial( # type: ignore[attr-defined] + _reload_python_module_in_subproc, key, path + ) + + return cls.cache[key] + + @classmethod + @functools.lru_cache(None) + def stack_frames_for_code( + cls, path: str, lineno: int + ) -> Optional[List[Dict[str, Any]]]: + if path not in cls.linemaps: + return None + # [(starting_line, ), ...] + lines, nodes = cls.linemaps[path] + p = bisect_right(lines, lineno) + if p == 0: + return None + entry = nodes[p - 1] + if not entry: + return None + + def parse_stack_trace(stack_trace: str) -> List[Dict[str, Any]]: + # ideally fx stores stack traces as data rather than a string + # but this is not along a performance critical path + regex = r'File "(.+)", line (\d+), in (.+)\n' + matches = re.findall(regex, stack_trace) + return [ + {"filename": f, "line": int(l), "name": n} + for f, l, n in reversed(matches) + ] + + return parse_stack_trace(entry) + + +class TritonCodeCache: + @classmethod + def load(cls, kernel_name: str, source_code: str) -> ModuleType: + return _module_to_triton_kernel(PyCodeCache.load(source_code), kernel_name) + + +def _cuda_compiler() -> Optional[str]: + if cuda_env.nvcc_exist(config.cuda.cuda_cxx): + return config.cuda.cuda_cxx + if config.is_fbcode(): + return os.path.join(build_paths.cuda(), "bin", "nvcc") + if cuda_env.nvcc_exist(os.getenv("CUDACXX")): + return os.getenv("CUDACXX", "") + if cuda_env.nvcc_exist(os.getenv("CUDA_HOME")): + return os.path.realpath(os.path.join(os.getenv("CUDA_HOME", ""), "bin/nvcc")) + return "nvcc" + + +def _cutlass_include_paths() -> List[str]: + if config.is_fbcode(): + from libfb.py import parutil + + cutlass_path = parutil.get_dir_path("cutlass-3-headers") + else: + cutlass_path = config.cuda.cutlass_dir + return [ + # Use realpath to get canonical absolute paths, in order not to mess up cache keys + os.path.realpath(os.path.join(cutlass_path, "include")), + os.path.realpath(os.path.join(cutlass_path, "tools/library/include")), + os.path.realpath(os.path.join(cutlass_path, "tools/library/src")), + os.path.realpath(os.path.join(cutlass_path, "tools/util/include")), + ] + + +def _cuda_lib_options() -> List[str]: + _set_gpu_runtime_env() # cpp_extension consults the env + from torch.utils import cpp_extension + + lpaths = cpp_extension.library_paths(cuda=True) + [ + sysconfig.get_config_var("LIBDIR") + ] + extra_ldflags: List[str] = [] + if is_linux(): + _transform_cuda_paths(lpaths) + for path in lpaths: + # -rpath ensures the DLL can find its dependencies when loaded, even + # if the library path is non-standard. + extra_ldflags.extend([f"-L{path}", "-Xlinker", f"-rpath={path}"]) + extra_ldflags.append("-lcuda") + extra_ldflags.append("-lcudart") + else: + raise NotImplementedError( + "Unsupported env, failed to find cuda libs! Currently only Linux is supported." + ) + return extra_ldflags + + +def _nvcc_host_compiler_options() -> List[str]: + return [ + "-fPIC", + "-fno-strict-aliasing", + "-fvisibility=hidden", + "-Wconversion", + ] + + +def _nvcc_compiler_options() -> List[str]: + arch = cuda_env.get_cuda_arch() + if arch == "90": + # Required by cutlass compilation. + arch = "90a" + code = [f"sm_{arch}", f"compute_{arch}"] + if config.cuda.enable_cuda_lto: + code += [f"lto_{arch}"] + options = [ + "-t=0", + "-DCUTLASS_ENABLE_TENSOR_CORE_MMA=1", + "-w", + f"-gencode=arch=compute_{arch},code=[{','.join(code)}]", + config.cuda.compile_opt_level, + "-std=c++17", + "--expt-relaxed-constexpr", + "-DNDEBUG", + ] + if config.is_fbcode(): + options.extend(["-ccbin", os.path.dirname(build_paths.gcc())]) + if config.cuda.enable_debug_info: + options.extend(["-lineinfo", "-g", "-DCUTLASS_DEBUG_TRACE_LEVEL=1"]) + if config.cuda.enable_ptxas_info: + options.extend( + [ + "--keep", # Keep the intermediate files for debugging (including ptx, sass, cubin etc.) + "--ptxas-options=--warn-on-local-memory-usage", # warn us if local memory is used in CUDA Kernels + "--ptxas-options=--warn-on-spills", # warn us if register spilling happens in CUDA Kernels + "--resource-usage", # Report on CUDA resource usage (shared mem, registers etc.) + "--source-in-ptx", + ] + ) # Annotate the ptx file with source information + if config.cuda.use_fast_math: + options.extend( + [ + "--use_fast_math", + "-DCUTLASS_USE_TANH_FOR_SIGMOID=1", + ] + ) + return options + + +def cuda_compile_command( + src_files: List[str], + dst_file: str, + dst_file_ext: str, + extra_args: Optional[List[str]] = None, +) -> str: + if extra_args is None: + extra_args = [] + include_paths = _cutlass_include_paths() + cuda_lib_options = _cuda_lib_options() + nvcc_host_compiler_options = _nvcc_host_compiler_options() + nvcc_compiler_options = _nvcc_compiler_options() + options = ( + nvcc_compiler_options + + extra_args + + [ + f"-Xcompiler {opt}" if "=" in opt else f"-Xcompiler={opt}" + for opt in nvcc_host_compiler_options + ] + + ["-I" + path for path in include_paths] + + cuda_lib_options + ) + src_file = " ".join(src_files) + res = "" + if dst_file_ext == "o": + res = f"{_cuda_compiler()} {' '.join(options)} -c -o {dst_file} {src_file}" + elif dst_file_ext == "so": + options.append("-shared") + res = f"{_cuda_compiler()} {' '.join(options)} -o {dst_file} {src_file}" + elif dst_file_ext == "exe": + res = f"{_cuda_compiler()} {' '.join(options)} -o {dst_file} {src_file}" + else: + raise NotImplementedError(f"Unsupported output file suffix {dst_file_ext}!") + log.debug("CUDA command: %s", res) + return res + + +class DLLWrapper: + """A wrapper for a dynamic library.""" + + def __init__( + self, + lib_path: str, + ) -> None: + self.lib_path = lib_path + self.is_open = False + self.DLL = cdll.LoadLibrary(lib_path) + self.is_open = True + + def close(self) -> None: + if self.is_open: + self._dlclose() + self.is_open = False + + def _dlclose(self) -> None: + f_dlclose = None + + if is_linux(): + syms = CDLL(None) + if not hasattr(syms, "dlclose"): + # Apline Linux + syms = CDLL("libc.so") + + if hasattr(syms, "dlclose"): + f_dlclose = syms.dlclose + elif is_windows(): + import ctypes + + kernel32 = ctypes.CDLL("kernel32", use_last_error=True) + + f_dlclose = kernel32.FreeLibrary + else: + raise NotImplementedError("Unsupported env, failed to do dlclose!") + + if f_dlclose is not None: + if is_linux(): + f_dlclose.argtypes = [c_void_p] + f_dlclose(self.DLL._handle) + elif is_windows(): + import ctypes + from ctypes import wintypes + + f_dlclose.argtypes = [wintypes.HMODULE] + f_dlclose(self.DLL._handle) + else: + log.warning( + "dll unloading function was not found, library may not be unloaded properly!" + ) + + def __getattr__(self, name: str) -> Callable[..., None]: + if not self.is_open: + raise RuntimeError(f"Cannot use closed DLL library: {self.lib_path}") + + method = getattr(self.DLL, name) + + def _wrapped_func(*args: Any) -> None: + err = method(*args) + if err: + raise RuntimeError(f"Error in function: {method.__name__}") + + return _wrapped_func + + def __enter__(self) -> DLLWrapper: # noqa: PYI034 + return self + + def __exit__(self, *args: Any) -> None: + self.close() + + def __del__(self) -> None: + self.close() + + +@clear_on_fresh_inductor_cache +class CUDACodeCache: + @dataclasses.dataclass + class CacheEntry: + input_path: str + output_path: str + + cache: Dict[str, CacheEntry] = {} + cache_clear = staticmethod(cache.clear) + _SOURCE_CODE_SUFFIX = "cu" + + @classmethod + def write(cls, source_code: str, dst_file_ext: str) -> Tuple[str, str]: + """ + Writes source code into a file with dst_file_ext as the file extension. + Returns the hash key of source code, and the path to the file. + """ + + cuda_command = repr( + cuda_compile_command(["dummy_input"], "dummy_output", dst_file_ext) + ) + key, input_path = write( + source_code, cls._SOURCE_CODE_SUFFIX, extra=cuda_command + ) + return key, input_path + + @classmethod + def compile( + cls, source_code: str, dst_file_ext: str, extra_args: Optional[List[str]] = None + ) -> Tuple[str, str, str]: + """ + Compiles CUDA source_code into a file with dst_file_ext extension. + Returns a tuple of dst_file_path, hash_key, source_code_path + """ + key, input_path = cls.write(source_code, dst_file_ext) + if key not in cls.cache: + from filelock import FileLock + + lock_dir = get_lock_dir() + lock = FileLock(os.path.join(lock_dir, key + ".lock"), timeout=LOCK_TIMEOUT) + with lock: + output_path = input_path[: -len(cls._SOURCE_CODE_SUFFIX)] + dst_file_ext + if not os.path.exists(output_path): + cmd = cuda_compile_command( + [input_path], output_path, dst_file_ext, extra_args + ) + start_time = time() + log.debug("CUDA Compilation: %s", cmd) + cmd_parts = cmd.split(" ") + try: + subprocess.check_output( + cmd_parts, stderr=subprocess.STDOUT, env=os.environ + ) + except subprocess.CalledProcessError as error: + raise exc.CUDACompileError(cmd_parts, error.output) from error + end_time = time() + log_duration_msg = f"CUDA Compilation took {end_time - start_time} seconds. Compile command: {cmd}" + log.info(log_duration_msg) + else: + log.debug( + "CUDA Compilation skipped: %s since output already exists", + input_path, + ) + cls.cache[key] = CUDACodeCache.CacheEntry(input_path, output_path) + + return (cls.cache[key].output_path, key, input_path) + + @classmethod + def load(cls, source_code: str, dst_file_ext: str) -> Tuple[DLLWrapper, str, str]: + """ + Compiles source code and loads the generated .so file. + Returns a tuple of DLLWrapper, hash_key, source_code_path + """ + + if dst_file_ext != "so": + raise RuntimeError( + f"Only support loading a .so file for now. " + f"Requested file extension: {dst_file_ext}. Source code: {source_code}" + ) + dst_file_path, hash_key, source_code_path = cls.compile( + source_code, dst_file_ext + ) + return (DLLWrapper(dst_file_path), hash_key, source_code_path) + + +@clear_on_fresh_inductor_cache +class ROCmCodeCache: + @dataclasses.dataclass + class CacheEntry: + input_path: str + output_path: str + + cache: Dict[str, CacheEntry] = {} + cache_clear = staticmethod(cache.clear) + _SOURCE_CODE_SUFFIX = "cpp" + _logged_compiler_version = False + + @classmethod + def write(cls, source_code: str, dst_file_ext: str) -> Tuple[str, str]: + """ + Writes source code into a file with dst_file_ext as the file extension. + Returns the hash key of source code, and the path to the file. + """ + + cuda_command = repr( + rocm_compile_command(["dummy_input"], "dummy_output", dst_file_ext) + ) + key, input_path = write( + source_code, cls._SOURCE_CODE_SUFFIX, extra=cuda_command + ) + return key, input_path + + @classmethod + def compile( + cls, source_code: str, dst_file_ext: str, extra_args: Optional[List[str]] = None + ) -> Tuple[str, str, str]: + """ + Compiles source_code into a file with dst_file_ext extension, + using the compile command specific for the ROCm platform. + Returns a tuple of dst_file_path, hash_key, source_code_path + """ + if not cls._logged_compiler_version: + cls._logged_compiler_version = True + log.debug(get_compiler_version_info(str(rocm_compiler()))) + + key, input_path = cls.write(source_code, dst_file_ext) + if key not in cls.cache: + from filelock import FileLock + + lock_dir = get_lock_dir() + lock = FileLock(os.path.join(lock_dir, key + ".lock"), timeout=LOCK_TIMEOUT) + with lock: + output_path = input_path[: -len(cls._SOURCE_CODE_SUFFIX)] + dst_file_ext + if not os.path.exists(output_path): + cmd = rocm_compile_command( + [input_path], output_path, dst_file_ext, extra_args + ) + start_time = time() + cmd_parts = cmd.split(" ") + try: + output = subprocess.check_output( + cmd_parts, + stderr=subprocess.STDOUT, + text=True, + env=os.environ, + ) + log.debug("Compilation output: %s", output) + except subprocess.CalledProcessError as error: + raise exc.CUDACompileError(cmd_parts, error.output) from error + end_time = time() + log_duration_msg = f"Compilation took {end_time - start_time} seconds. Compile command: {cmd}" + log.info(log_duration_msg) + else: + log.debug( + "Compilation skipped: %s since output already exists", + input_path, + ) + cls.cache[key] = ROCmCodeCache.CacheEntry(input_path, output_path) + + return (cls.cache[key].output_path, key, input_path) + + @classmethod + def load(cls, source_code: str, dst_file_ext: str) -> Tuple[DLLWrapper, str, str]: + """ + Compiles source code and loads the generated .so file. + Returns a tuple of DLLWrapper, hash_key, source_code_path + """ + + if dst_file_ext != "so": + raise RuntimeError( + f"Only support loading a .so file for now. " + f"Requested file extension: {dst_file_ext}. Source code: {source_code}" + ) + dst_file_path, hash_key, source_code_path = cls.compile( + source_code, dst_file_ext + ) + return (DLLWrapper(dst_file_path), hash_key, source_code_path) + + +class CodeCacheFuture: + def result(self) -> None: + raise NotImplementedError + + +class TritonFuture(CodeCacheFuture): + kernel: ModuleType + + def __init__( + self, + kernel: Any, + future: Optional[Future[Any]], + ) -> None: + self.kernel = kernel + self.future = future + + def result(self) -> ModuleType: # type: ignore[override] + if self.future is not None: + # If the worker failed this will throw an exception. + result = self.future.result() + assert result is None + self.future = None + self.kernel.precompile() + return self.kernel + + +class LambdaFuture(CodeCacheFuture): + def __init__(self, result_fn: Callable[..., Any]) -> None: + self.result_fn = result_fn + + def result(self) -> Callable[..., Any]: # type: ignore[override] + return self.result_fn() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/aoti_hipify_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/aoti_hipify_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..80085aa6d18f2d59c0ddb00b77b607ce08441d9c --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/aoti_hipify_utils.py @@ -0,0 +1,32 @@ +# mypy: allow-untyped-defs +import re + +import torch +from torch.utils.hipify.hipify_python import PYTORCH_MAP, PYTORCH_TRIE + + +# It is not a good idea to directly apply hipify_torch to codegen, which will be vulnerable to cases like: +# "... +# from ..codecache import CudaKernelParamCache +# ..." +# In such cases, we do not need to hipify_torch the orignial class/file name in codegen/codecache + + +def maybe_hipify_code_wrapper(source_codes: str, force_hipify: bool = False) -> str: + if torch.version.hip is None and not force_hipify: + return source_codes + + def c2_repl(m): + return PYTORCH_MAP[m.group(0)] + + # We need to redefine RE_PYTORCH_PREPROCESSOR here since in hipify_torch, + # it will apply positive lookbehind (?<=\W) to the pattern to avoid matching + # keyword at the beginning of code line. However, this can happen in codegen, + # which will cause the pattern to not match. + + # Note that lookahead (?=\W) is still needed to keep hipification idomponent, for example + # we need to skip replacing "getStreamFromExternal" in "getStreamFromExternalMasqueradingAsCUDA" + RE_PYTORCH_PREPROCESSOR = re.compile(rf"({PYTORCH_TRIE.export_to_regex()})(?=\W)") + + source_codes = RE_PYTORCH_PREPROCESSOR.sub(c2_repl, source_codes) + return source_codes diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/codegen_device_driver.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/codegen_device_driver.py new file mode 100644 index 0000000000000000000000000000000000000000..c31017fe6471cb5dca9a41feac23c15d1a71b8aa --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/codegen_device_driver.py @@ -0,0 +1,91 @@ +import torch + + +# Provide aoti module launch hip/cuda drivers. This file is also used for unit testing purpose + + +def cuda_kernel_driver() -> str: + source_codes = """ + #define CUDA_DRIVER_CHECK(EXPR) \\ + do { \\ + CUresult code = EXPR; \\ + const char *msg; \\ + cuGetErrorString(code, &msg); \\ + if (code != CUDA_SUCCESS) { \\ + throw std::runtime_error( \\ + std::string("CUDA driver error: ") + \\ + std::string(msg)); \\ + } \\ + } while (0); + + namespace { + + struct Grid { + Grid(uint32_t x, uint32_t y, uint32_t z) + : grid_x(x), grid_y(y), grid_z(z) {} + uint32_t grid_x; + uint32_t grid_y; + uint32_t grid_z; + + bool is_non_zero() { + return grid_x > 0 && grid_y > 0 && grid_z > 0; + } + }; + + } // anonymous namespace + + static inline CUfunction loadKernel( + std::string filePath, + const std::string &funcName, + uint32_t sharedMemBytes, + const std::optional &cubinDir = std::nullopt) { + if (cubinDir) { + std::filesystem::path p1{*cubinDir}; + std::filesystem::path p2{filePath}; + filePath = (p1 / p2.filename()).string(); + } + + CUmodule mod; + CUfunction func; + CUDA_DRIVER_CHECK(cuModuleLoad(&mod, filePath.c_str())); + CUDA_DRIVER_CHECK(cuModuleGetFunction(&func, mod, funcName.c_str())); + if (sharedMemBytes > 0) { + CUDA_DRIVER_CHECK(cuFuncSetAttribute( + func, + CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, + sharedMemBytes + )) + } + return func; + } + + static inline void launchKernel( + CUfunction func, + uint32_t gridX, + uint32_t gridY, + uint32_t gridZ, + uint32_t numWarps, + uint32_t sharedMemBytes, + void* args[], + cudaStream_t stream) { + CUDA_DRIVER_CHECK(cuLaunchKernel( + func, gridX, gridY, gridZ, 32*numWarps, 1, 1, sharedMemBytes, stream, args, nullptr + )); + } + """ + if torch.version.hip is not None: + # Adjusting the warp size to GPU supported wavefront size on AMD GPU + prop = torch.cuda.get_device_properties(torch.cuda.current_device()) + source_codes = source_codes.replace( + "32*numWarps", str(prop.warp_size) + "*numWarps" + ) + return source_codes + + +def cuda_kernel_header() -> str: + source_codes = """ + #include + #include + #include + """ + return source_codes diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/common.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/common.py new file mode 100644 index 0000000000000000000000000000000000000000..f80d1f71e380d08c20e6358a17addc8d009d4efe --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/common.py @@ -0,0 +1,2167 @@ +# mypy: allow-untyped-defs +import contextlib +import dataclasses +import functools +import itertools +import logging +import math +import operator +import re +from enum import auto, Enum +from itertools import chain +from typing import ( + Any, + Callable, + ClassVar, + Dict, + List, + NamedTuple, + Optional, + Tuple, + Union, +) + +import sympy +from sympy.printing.printer import Printer + +import torch +import torch.fx +from torch._prims_common import ELEMENTWISE_TYPE_PROMOTION_KIND +from torch.utils import _pytree as pytree +from torch.utils._ordered_set import OrderedSet +from torch.utils._sympy.numbers import int_oo +from torch.utils._sympy.symbol import free_symbol_is_type, symbol_is_type, SymT +from torch.utils._sympy.value_ranges import bound_sympy, ValueRangeAnalysis, ValueRanges + +from .. import config, metrics +from ..utils import ( + DeferredLineBase, + generate_assert, + IndentedBuffer, + sympy_dot, + sympy_subs, + unique, +) +from ..virtualized import ops, OpsHandler, OpsValue, ReductionType, StoreMode, V + + +schedule_log = torch._logging.getArtifactLogger(__name__, "schedule") + + +def data_type_logger(msg): + if schedule_log.isEnabledFor(logging.DEBUG): + schedule_log.debug("Data type propagation: %s", msg) + + +@dataclasses.dataclass +class WorkspaceArg: + """A temporary buffer used for a single kernel, then discarded. + + Not registered as a traditional buffer since there are no users, + so it would be dead code eliminated. + """ + + nbytes: sympy.Expr + zero_fill: bool + + +@dataclasses.dataclass +class TensorArg: + name: str + buffer: str + dtype: torch.dtype + offset: sympy.Expr = sympy.Integer(0) # c++ only + alias_of: Optional[str] = None # halide only + + +@dataclasses.dataclass +class SizeArg: + name: str + expr: sympy.Expr + + @property + def alias_of(self): + return None + + +@dataclasses.dataclass +class DeviceCodegen: + scheduling: Any + wrapper_codegen: type + cpp_wrapper_codegen: type = type(None) + + +KernelArgType = Union[WorkspaceArg, TensorArg, SizeArg] + +device_codegens: Dict[str, DeviceCodegen] = {} + + +class DeviceOpOverrides: + def import_get_raw_stream_as(self, name): + raise NotImplementedError + + def set_device(self, device_idx): + raise NotImplementedError + + def synchronize(self): + raise NotImplementedError + + def device_guard(self, device_idx): + raise NotImplementedError + + +device_op_overrides_dict: Dict[str, DeviceOpOverrides] = {} + + +# The code generated by Inductor consists of two main parts: kernel code and wrapper code. +# For any new backend looking to integrate with Inductor, customization of these two main +# parts are necessary to generate its specific code. +# +# Kernel code generation is determined by different Scheduling. Consequently, a new +# backend needs to provide a custom Scheduling for its unique kernel code generation. Currently, +# CppScheduling and TritonScheduling serve the C++/OpenMP and Triton backends, respectively. +# +# For the Wrapper, Inductor provides a WrapperCodeGen class to generate the Python wrapper code +# that bridges kernels. This allows out-of-tree backends to inherit from WrapperCodeGen, +# and override specific member functions to create backend-specific Python wrapper code. +# +# Other classes, such as CppKernel and TritonKernel, used for code generation, typically form part +# of the logic for either Scheduling or WrapperCodeGen. So the Scheduling and WrapperCodeGen interfaces +# provide flexibility to the backend. A backend can choose to implement these classes from scratch, +# or reuse them by extending and overriding as necessary. And Inductor provides the registration API, +# register_backend_for_device, to equip a new backend at runtime. +# +# Intel has developed a new backend on top of Triton to support Intel GPUs, leveraging these interfaces. +# This backend can be used as a reference: +# https://github.com/intel/intel-extension-for-pytorch/blob/5dcc9d57e5422cf295e1a1ee97896d6b6a554a85/intel_extension_for_pytorch/_inductor/__init__.py#L9 +def register_backend_for_device( + device: str, + device_scheduling: Any, + device_wrapper_codegen: type, + device_cpp_wrapper_codegen: type = type(None), +): + device_codegens[device] = DeviceCodegen( + device_scheduling, device_wrapper_codegen, device_cpp_wrapper_codegen + ) + + +class BackendFeature(Enum): + FOREACH = auto() + BUCKETIZE = auto() + INPLACE_BUFFERS = auto() + MASKED_SCATTER_WITH_INDEX = auto() + SCAN = auto() + SORT = auto() + TUPLE_REDUCTION = auto() + PREFER_STORE_LOOP_ORDER = auto() + TRITON_TEMPLATES = auto() + REDUCE_TO_SINGLE_ELEMENT = auto() + + +def get_backend_features(device: Union[torch.device, str]): + init_backend_registration() + if isinstance(device, torch.device): + device_type = device.type + else: + assert isinstance(device, str) + device_type = device + device = torch.device(device_type) + scheduling = get_scheduling_for_device(device_type) + return scheduling(None).get_backend_features(device) + + +def has_backend_feature(device, feature): + """See also V.graph.has_feature""" + assert isinstance(feature, BackendFeature) + return feature in get_backend_features(device) + + +def get_scheduling_for_device(device: str): + return device_codegens[device].scheduling if device in device_codegens else None + + +def get_wrapper_codegen_for_device(device: str, cpp_wrapper: bool = False): + if device in device_codegens: + wrapper_codegen_obj: DeviceCodegen = device_codegens[device] + return ( + wrapper_codegen_obj.cpp_wrapper_codegen + if cpp_wrapper + else wrapper_codegen_obj.wrapper_codegen + ) + else: + return None + + +@functools.lru_cache(None) +def init_backend_registration(): + from .cpp import CppScheduling + from .cpp_wrapper_cpu import CppWrapperCpu + from .cpp_wrapper_cuda import CppWrapperCuda + from .cuda_combined_scheduling import CUDACombinedScheduling + from .halide import HalideScheduling + from .triton import TritonScheduling + from .wrapper import WrapperCodeGen + + if get_scheduling_for_device("cpu") is None: + cpu_backends = {"cpp": CppScheduling, "halide": HalideScheduling} + register_backend_for_device( + "cpu", + lambda *args, **kwargs: cpu_backends[config.cpu_backend](*args, **kwargs), + WrapperCodeGen, + CppWrapperCpu, + ) + + if get_scheduling_for_device("cuda") is None: + # CUDACombinedScheduling combines Triton and CUDA C++ scheduling for CUDA devices via delegation + cuda_backends = {"triton": CUDACombinedScheduling, "halide": HalideScheduling} + register_backend_for_device( + "cuda", + lambda *args, **kwargs: cuda_backends[config.cuda_backend](*args, **kwargs), + WrapperCodeGen, + CppWrapperCuda, + ) + + if get_scheduling_for_device("xpu") is None: + register_backend_for_device("xpu", TritonScheduling, WrapperCodeGen) + + private_backend = torch._C._get_privateuse1_backend_name() + if ( + private_backend != "privateuseone" + and get_scheduling_for_device(private_backend) is None + ): + from torch.utils.backend_registration import _get_custom_mod_func + + try: + device_scheduling = _get_custom_mod_func("Scheduling") + wrapper_codegen = _get_custom_mod_func("WrapperCodeGen") + cpp_wrapper_codegen = _get_custom_mod_func("CppWrapperCodeGen") + if device_scheduling and wrapper_codegen and cpp_wrapper_codegen: + register_backend_for_device( + private_backend, + device_scheduling, + wrapper_codegen, + cpp_wrapper_codegen, + ) + except RuntimeError: + pass + + +def index_prevent_reordering(index: List[sympy.Expr], index_vars, sizes): + from ..ir import FlexibleLayout + + # added contiguous index prevents reordering + return [*index, sympy_dot(index_vars, FlexibleLayout.contiguous_strides(sizes))] + + +def register_device_op_overrides(device: str, device_op_overrides: DeviceOpOverrides): + device_op_overrides_dict[device] = device_op_overrides + + +def get_device_op_overrides(device: str): + assert isinstance(device, str) + + if not device_op_overrides_dict.keys(): + from .cuda import device_op_overrides # noqa: F401 + from .xpu import device_op_overrides as xpu_op_overrides # noqa: F401 + + if device in device_op_overrides_dict.keys(): + return device_op_overrides_dict[device] + + +@functools.lru_cache(None) +def boolean_ops(): + return ( + "isinf", + "isnan", + "logical_not", + "signbit", + "le", + "lt", + "ge", + "gt", + "eq", + "ne", + ) + + +DTYPE_TO_COMPUTATION_DTYPE = { + torch.bfloat16: torch.float, + torch.float16: torch.float, + **{ + dtype: dtype + for dtype in [ + torch.bool, + torch.float32, + torch.float64, + torch.int8, + torch.int16, + torch.int32, + torch.int64, + torch.uint8, + torch.uint16, + torch.uint32, + torch.uint64, + ] + }, +} + + +def deduce_output_dtype_by_name( + op_name: str, + *args, + **kwargs, +) -> Optional[torch.dtype]: + """ + Given op name and a list of input dtypes, deduce the output dtype + """ + if op_name in boolean_ops(): + return torch.bool + elif op_name in ( + "to_dtype", + "index_expr", + ): + return kwargs["dtype"] if "dtype" in kwargs else args[-1] + elif op_name in ( + "rand", + "randn", + ): + return torch.float + elif op_name in ( + "get_index", + "randint64", + "load_seed", + ): + return torch.int64 + elif op_name == "reduction": + return kwargs["dtype"] if "dtype" in kwargs else args[1] + elif op_name == "constant": + dtype = kwargs["dtype"] if "dtype" in kwargs else args[-1] + return DTYPE_TO_COMPUTATION_DTYPE[dtype] # type: ignore[index] + elif op_name in ( + "load", + "store", + "store_reduction", + ): + buf_name = args[1] + return V.graph.get_dtype(buf_name) # type: ignore[arg-type] + elif op_name == "to_dtype_bitcast": + return kwargs["dtype"] if "dtype" in kwargs else args[-2] + return None + + +class DataTypePropagation: + def __init__(self, body) -> None: + self.body = body + self.graphs: Dict[Union[Callable[..., Any], str], Any] = { + "root": body.root_block.graph + } + for k, v in body.subblocks.items(): + self.graphs[k] = v.graph + + def deduce_node_dtype_by_inputs(self, node: torch.fx.Node): + inputs = node.all_input_nodes + input_nodes = [ + n for n in inputs if isinstance(n, torch.fx.Node) and n.op != "placeholder" + ] + if len(input_nodes) == 0: + return None + + all_input_nodes_propagated = all( + OptimizationContext.key in n.meta + and n.meta[OptimizationContext.key].dtype is not None + for n in input_nodes + ) + if not all_input_nodes_propagated: + return None + + return functools.reduce( + torch.promote_types, + [n.meta[OptimizationContext.key].dtype for n in input_nodes], + ) + + def deduce_node_dtype_by_subgraph(self, node: torch.fx.Node): + sub_graph = self.graphs[node.target] + dtype = self.propagate_graph(sub_graph) + assert dtype + return dtype + + def deduce_node_dtype(self, node: torch.fx.Node): + if node.op == "placeholder": + return None + + if node.target == "output" and len(node.args) != 1: + # we can infer output node if it only have 1 arg + return None + + if node.target == operator.getitem: + return self.deduce_node_dtype(node.args[0]) # type: ignore[arg-type] + + assert isinstance(node.target, str) + + if node.target.startswith("masked_subblock"): + return self.deduce_node_dtype_by_subgraph(node) + + if ( + output_dtype := deduce_output_dtype_by_name( + node.target, + *node.args, + **node.kwargs, + ) + ) is not None: + return output_dtype + + return self.deduce_node_dtype_by_inputs(node) + + def propagate_graph(self, graph: torch.fx.Graph): + assert graph.nodes + graph_dtype = None + # For masked_subblock, we use output's dtype to represent + # the dtype of this subgraph. For other cases, graph_dtype + # might be None + for node in graph.nodes: + if OptimizationContext.key in node.meta: + opt_ctx = node.meta[OptimizationContext.key] + else: + opt_ctx = OptimizationContext() + + opt_ctx.dtype = self.deduce_node_dtype(node) + node.meta[OptimizationContext.key] = opt_ctx + if node.target == "output": + graph_dtype = opt_ctx.dtype + return graph_dtype + + def propagate(self): + self.propagate_graph(self.graphs["root"]) + + @classmethod + def propagate_loopbody(cls, body): + return cls(body).propagate() + + @classmethod + def propagate_scheduler_node(cls, node): + from ..loop_body import LoopBody + from ..scheduler import SchedulerNode + + assert isinstance(node, SchedulerNode) + assert isinstance(node._body, LoopBody) + DataTypePropagation.propagate_loopbody(node._body) + + +# This printer contains rules that are supposed to be generic for both C/C++ and +# Python +class ExprPrinter(Printer): + @staticmethod + def paren(string): + def all_in_parens(string): + if string[0] != "(" or len(string) < 2: + return False + count = 1 + for i, char in enumerate(string[1:]): + if char == "(": + count += 1 + elif char == ")": + count -= 1 + if count == 0 and i != len(string) - 2: + return False + assert count == 0 + return True + + if ( + isinstance(string, CSEVariable) + or re.match(r"^[a-z0-9_.]+$", string, re.IGNORECASE) + or re.match(r"^\([^)]*\)$", string, re.IGNORECASE) + or string == "" + ): + return string + # don't put extra parens for strings that are already wrapped in parens + if all_in_parens(string): + return string + return f"({string})" + + def _print_Relational(self, expr): + return f" {expr.rel_op} ".join(map(self.paren, map(self._print, expr.args))) + + def _print_Mul(self, expr): + return "*".join(map(self.paren, map(self._print, expr.args))) + + def _print_Add(self, expr): + return " + ".join(map(self.paren, map(self._print, expr.args))) + + # NB: this is OK to put here, because Mod is only defined for positive + # numbers, and so across C/Python its behavior is consistent + def _print_Mod(self, expr): + return " % ".join(map(self.paren, map(self._print, expr.args))) + + def _print_FloatTrueDiv(self, expr): + lhs, rhs = expr.args + return f"{self.paren(self._print(lhs))} / {self.paren(self._print(rhs))}" + + def _print_CleanDiv(self, expr): + return self._print_FloorDiv(expr) + + def _print_Identity(self, expr): + return self._print(expr.args[0]) + + def _print_GreaterThan(self, expr): + # GreaterThan: >= + # StrictlyGreaterThan: > + # Go figure... + return " >= ".join(map(self.paren, map(self._print, expr.args))) + + # NB: The C implementation is injected into codegen at + # torch/_inductor/codegen/wrapper.py + def _print_align(self, expr): + assert len(expr.args) == 1 + return f"align({self._print(expr.args[0])})" + + # This must be implemented because sympy will collect x * x into Pow(x, 2), without + # any explicit intervention. We print it just like x * x, notably, we + # never generate sympy.Pow with floats. + # + # NB: this pow by natural, you should never have used builtin sympy.pow + # for FloatPow, and a symbolic exponent should be PowByNatural. These + # means exp is guaranteed to be integer. + def _print_Pow(self, expr): + base, exp = expr.args + base = self._print(base) + assert exp == int(exp), exp + exp = int(exp) + assert exp >= 0 + if exp > 0: + return "*".join([self.paren(base)] * exp) + else: # exp == 0 + return "1" + + # Explicit NotImplemented functions are to prevent default sympy printing + # behavior, which will just barf out ToFloat(...) to your IR. The error + # message is better here because it tells you which printer class it needs + # to go in. + + def _print_ToFloat(self, expr): + raise NotImplementedError(f"_print_ToFloat not implemented for {type(self)}") + + def _print_Infinity(self, expr): + raise NotImplementedError(f"_print_Infinity not implemented for {type(self)}") + + def _print_NegativeInfinity(self, expr): + raise NotImplementedError( + f"_print_NegativeInfinity not implemented for {type(self)}" + ) + + def _print_FloorDiv(self, expr): + raise NotImplementedError(f"_print_FloorDiv not implemented for {type(self)}") + + def _print_PythonMod(self, expr): + raise NotImplementedError(f"_print_PythonMod not implemented for {type(self)}") + + def _print_IntTrueDiv(self, expr): + raise NotImplementedError(f"_print_IntTrueDiv not implemented for {type(self)}") + + def _print_PowByNatural(self, expr): + raise NotImplementedError( + f"_print_PowByNatural not implemented for {type(self)}" + ) + + def _print_FloatPow(self, expr): + raise NotImplementedError(f"_print_FloatPow not implemented for {type(self)}") + + def _print_TruncToInt(self, expr): + raise NotImplementedError(f"_print_TruncToInt not implemented for {type(self)}") + + def _print_RoundToInt(self, expr): + raise NotImplementedError(f"_print_RoundToInt not implemented for {type(self)}") + + def _print_RoundDecimal(self, expr): + raise NotImplementedError( + f"_print_RoundDecimal not implemented for {type(self)}" + ) + + # NB: Some float operations are INTENTIONALLY not implemented for + # printers. You can implement them as a quick unblock, but it is better + # to ask yourself why we haven't done this computation in the Tensor + # universe instead + + def _print_TruncToFloat(self, expr): + raise NotImplementedError( + f"_print_TruncToFloat not implemented for {type(self)}" + ) + + def doprint(self, expr, *, simplify: bool = True): + # TODO: why are people passing strings to the printer here :think: + if simplify and isinstance(expr, sympy.Expr) and hasattr(V.graph, "sizevars"): + expr = V.graph.sizevars.simplify(expr) + return super().doprint(expr) + + +class PythonPrinter(ExprPrinter): + def _print_ToFloat(self, expr): + assert len(expr.args) == 1 + return f"float({self._print(expr.args[0])})" + + def _print_ModularIndexing(self, expr): + x, div, mod = expr.args + x = self.paren(self.doprint(x)) + div = self.paren(self.doprint(div)) + mod = self.paren(self.doprint(mod)) + if div != "1": + x = f"({x} // {div})" + return f"{x} % {mod}" + + def _print_Infinity(self, expr): + return "math.inf" + + def _print_NegativeInfinity(self, expr): + return "-math.inf" + + # WARNING: this is dangerous for Triton, which has C-style modulus + def _print_PythonMod(self, expr): + return " % ".join(map(self.paren, map(self._print, expr.args))) + + # WARNING: this is dangerous for Triton, which has C-style modulus + def _print_FloorDiv(self, expr): + x, div = expr.args + x = self.paren(self.doprint(x)) + div = self.paren(self.doprint(div)) + return f"({x} // {div})" + + # WARNING: this is dangerous for Triton, when lhs, rhs > 2**53, Python + # does a special algorithm + def _print_IntTrueDiv(self, expr): + lhs, rhs = expr.args + return f"{self.paren(self._print(lhs))} / {self.paren(self._print(rhs))}" + + def _helper_sqrt(self, expr): + return f"math.sqrt({self._print(expr)})" + + def _print_OpaqueUnaryFn_sqrt(self, expr): + return self._helper_sqrt(expr.args[0]) + + def _print_FloatPow(self, expr): + base, exp = expr.args + return f"{self.paren(self._print(base))} ** {self.paren(self._print(exp))}" + + # TODO: Not sure this works with Triton, even when base/exp are integral + def _print_PowByNatural(self, expr): + base, exp = expr.args + return f"{self.paren(self._print(base))} ** {self.paren(self._print(exp))}" + + def _print_floor(self, expr): + assert len(expr.args) == 1 + return f"math.floor({self._print(expr.args[0])})" + + def _print_FloorToInt(self, expr): + assert len(expr.args) == 1 + return f"math.floor({self._print(expr.args[0])})" + + def _print_TruncToInt(self, expr): + assert len(expr.args) == 1 + # This also could have been int(), they'll do the same thing for float + return f"math.trunc({self._print(expr.args[0])})" + + def _print_ceiling(self, expr): + assert len(expr.args) == 1 + return f"math.ceil({self._print(expr.args[0])})" + + def _print_CeilToInt(self, expr): + assert len(expr.args) == 1 + return f"math.ceil({self._print(expr.args[0])})" + + def _print_Abs(self, expr): + assert len(expr.args) == 1 + return f"abs({self._print(expr.args[0])})" + + # NB: It's expected that we've made explicit any promotion in the sympy + # expression, so it doesn't matter that Python max/min doesn't perform + # promotion + def _print_Max(self, expr): + assert len(expr.args) >= 2 + return f"max({', '.join(map(self._print, expr.args))})" + + def _print_Min(self, expr): + assert len(expr.args) >= 2 + return f"min({', '.join(map(self._print, expr.args))})" + + def _print_OpaqueUnaryFn_cos(self, expr): + assert len(expr.args) == 1 + return f"math.cos({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_cosh(self, expr): + assert len(expr.args) == 1 + return f"math.cosh({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_acos(self, expr): + assert len(expr.args) == 1 + return f"math.acos({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_sin(self, expr): + assert len(expr.args) == 1 + return f"math.sin({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_sinh(self, expr): + assert len(expr.args) == 1 + return f"math.sinh({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_asin(self, expr): + assert len(expr.args) == 1 + return f"math.asin({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_tan(self, expr): + assert len(expr.args) == 1 + return f"math.tan({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_tanh(self, expr): + assert len(expr.args) == 1 + return f"math.tanh({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_atan(self, expr): + assert len(expr.args) == 1 + return f"math.atan({self._print(expr.args[0])})" + + def _print_RoundToInt(self, expr): + assert len(expr.args) == 1 + return f"round({self._print(expr.args[0])})" + + def _print_RoundDecimal(self, expr): + assert len(expr.args) == 2 + number, ndigits = expr.args + assert isinstance(ndigits, sympy.Integer) + return f"round({self._print(number)}, {ndigits})" + + +class OpOverrides: + def __init__(self, parent): + super().__init__() + self._parent = parent + + def __getattr__(self, item): + return getattr(self._parent, item) + + @staticmethod + def identity(value): + # used to trigger cse + return value + + @staticmethod + def constant(value, dtype): + return repr(value) + + @staticmethod + def reciprocal(x): + return ops.truediv(ops.constant(1, torch.int32), x) + + @staticmethod + def square(x): + return ops.mul(x, x) + + @staticmethod + def erfc(x): + return ops.sub(ops.constant(1, torch.float32), ops.erf(x)) + + @staticmethod + def erfcx(x): + return ops.mul(ops.exp(ops.square(x)), ops.erfc(x)) + + @staticmethod + def expm1(x): + return ops.sub(ops.exp(x), ops.constant(1, torch.float32)) + + @staticmethod + def log10(x): + return ops.mul(ops.log(x), ops.constant(1 / math.log(10), torch.float32)) + + @staticmethod + def log2(x): + return ops.mul(ops.log(x), ops.constant(1 / math.log(2), torch.float32)) + + @staticmethod + def exp2(x): + return ops.exp(ops.mul(x, ops.constant(math.log(2), torch.float32))) + + @staticmethod + def log1p(x): + return ops.log(ops.add(x, ops.constant(1, torch.int32))) + + @staticmethod + def sigmoid(x): + one = ops.constant(1, torch.int32) + return ops.truediv(one, ops.add(one, ops.exp(ops.neg(x)))) + + @staticmethod + def libdevice_sigmoid(x): + one = ops.constant(1, torch.int32) + return ops.truediv(one, ops.add(one, ops.libdevice_exp(ops.neg(x)))) + + @staticmethod + def relu(x): + return ops.maximum(x, ops.constant(0, torch.int32)) + + @staticmethod + def libdevice_abs(x): + return ops.abs(x) + + @staticmethod + def libdevice_sqrt(x): + return ops.sqrt(x) + + @staticmethod + def libdevice_cos(x): + return ops.cos(x) + + @staticmethod + def libdevice_sin(x): + return ops.sin(x) + + @staticmethod + def libdevice_log(x): + return ops.log(x) + + @staticmethod + def libdevice_exp(x): + return ops.exp(x) + + @staticmethod + def bitwise_not(x): + return f"~{ExprPrinter.paren(x)}" + + @staticmethod + def logical_not(a): + return f"{ExprPrinter.paren(a)} == 0" + + @staticmethod + def bitwise_and(x, y): + return f"{ExprPrinter.paren(x)} & {ExprPrinter.paren(y)}" + + @staticmethod + def bitwise_or(x, y): + return f"{ExprPrinter.paren(x)} | {ExprPrinter.paren(y)}" + + @staticmethod + def bitwise_xor(x, y): + return f"{ExprPrinter.paren(x)} ^ {ExprPrinter.paren(y)}" + + @staticmethod + def bitwise_left_shift(x, y): + return f"{ExprPrinter.paren(x)} << {ExprPrinter.paren(y)}" + + @staticmethod + def bitwise_right_shift(x, y): + return f"{ExprPrinter.paren(x)} >> {ExprPrinter.paren(y)}" + + @staticmethod + def remainder(a, b): + r = ops.mod(a, b) + cond = ops.and_( + ops.ne(r, ops.constant(0, torch.int32)), + ops.ne(ops.signbit(r), ops.signbit(b)), + ) + return ops.where(cond, ops.add(r, b), r) + + @staticmethod + def trunc_to_int(a, dtype): + return ops.to_dtype(ops.trunc(a), dtype) + + @staticmethod + def floor_to_int(a, dtype): + return ops.to_dtype(ops.floor(a), dtype) + + @staticmethod + def ceil_to_int(a, dtype): + return ops.to_dtype(ops.ceil(a), dtype) + + @staticmethod + def round_to_int(a, dtype): + return ops.to_dtype(ops.round(a), dtype) + + @staticmethod + def int_truediv(a, b): + # TODO: this is wrong + # TODO: an easy bandaid is to generate runtime asserts that it's + # <= 2**53, which is when this equation is correct + return ops.truediv(a, b) + + @staticmethod + def load_seed(name, offset): + return ops.load(name, sympy.Integer(offset)) + + @classmethod + def _initialize_pointwise_overrides(cls, target): + assert target in {"triton", "cpp", "cppvec"}, target + + for funcname, data in pointwise_overrides_data.items(): + impl = getattr(data, target) + if impl is None: + continue + setattr(cls, funcname, staticmethod(impl)) + + +@dataclasses.dataclass +class OverridesData: + name: str + cpp: Callable[..., str] + # None when not impl in libdevice/triton + triton: Optional[Callable[..., str]] = None + # None when not impl in aten/.../vec + cppvec: Optional[Callable[..., str]] = None + type_promotion_kind: ELEMENTWISE_TYPE_PROMOTION_KIND = ( + ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT + ) + + +# NB: if you add a new special function, don't forget to update +# torch._inductor.ops_handler too +pointwise_overrides_data: Dict[str, OverridesData] = dict( + airy_ai=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"airy_ai_forward({x})", + name="special_airy_ai", + ), + bessel_j0=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"bessel_j0_forward({x})", + triton=lambda x: f"libdevice.j0({x})", + name="special_bessel_j0", + ), + bessel_j1=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"bessel_j1_forward({x})", + triton=lambda x: f"libdevice.j1({x})", + name="special_bessel_j1", + ), + bessel_y0=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"bessel_y0_forward({x})", + triton=lambda x: f"libdevice.y0({x})", + name="special_bessel_y0", + ), + bessel_y1=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"bessel_y1_forward({x})", + triton=lambda x: f"libdevice.y1({x})", + name="special_bessel_y1", + ), + digamma=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"calc_digamma({x})", + cppvec=lambda x: f"{x}.digamma()", + name="digamma", + ), + # no cpp nor triton implementation for entr, it is defined as decomposition + # erf, erfc + erfcx=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"calc_erfcx({x})", + triton=lambda x: f"libdevice.erfcx({x})", + name="special_erfcx", + ), + fma=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x, y, z: f"std::fma({x}, {y}, {z})", + cppvec=lambda x, y, z: f"fmadd({x}, {y}, {z})", + triton=lambda x, y, z: f"libdevice.fma({x}, {y}, {z})", + name="fma", + ), + # erfinv, exp2, expit, gammaln + igamma=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x, y: f"calc_igamma({x}, {y})", + name="igamma", + ), + igammac=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x, y: f"calc_igammac({x}, {y})", + name="igammac", + ), + gammainc=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x, y: f"calc_igamma({x}, {y})", + name="special_gammainc", + ), + gammaincc=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x, y: f"calc_igammac({x}, {y})", + name="special_gammaincc", + ), + i0=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"calc_i0({x})", + triton=lambda x: f"libdevice.cyl_bessel_i0({x})", + cppvec=lambda x: f"{x}.i0()", + name="i0", + ), + i0e=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"calc_i0e({x})", + cppvec=lambda x: f"{x}.i0e()", + name="special_i0e", + ), + i1=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"calc_i1({x})", + triton=lambda x: f"libdevice.cyl_bessel_i1({x})", + name="special_i1", + ), + i1e=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"calc_i1e({x})", + name="special_i1e", + ), + log_ndtr=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"calc_log_ndtr({x})", + name="special_log_ndtr", + ), + # logit + modified_bessel_i0=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"modified_bessel_i0_forward({x})", + triton=lambda x: f"libdevice.cyl_bessel_i0({x})", + name="special_modified_bessel_i0", + ), + modified_bessel_i1=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"modified_bessel_i1_forward({x})", + triton=lambda x: f"libdevice.cyl_bessel_i1({x})", + name="special_modified_bessel_i1", + ), + modified_bessel_k0=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"modified_bessel_k0_forward({x})", + name="special_modified_bessel_k0", + ), + modified_bessel_k1=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"modified_bessel_k1_forward({x})", + name="special_modified_bessel_k1", + ), + # multigamma + ndtr=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"calc_ndtr({x})", + name="special_ndtr", + ), + ndtri=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"calc_ndtri({x})", + name="special_ndtri", + ), + polygamma=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x, y: f"calc_polygamma({y}, {x})", + name="polygamma", + ), + # psi - alias to digamma + # round + scaled_modified_bessel_k0=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"scaled_modified_bessel_k0_forward({x})", + name="special_scaled_modified_bessel_k0", + ), + scaled_modified_bessel_k1=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"scaled_modified_bessel_k1_forward({x})", + name="special_scaled_modified_bessel_k1", + ), + # sinc + spherical_bessel_j0=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x: f"spherical_bessel_j0_forward({x})", + name="special_spherical_bessel_j0", + ), + zeta=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x, y: f"zeta({x}, {y})", + name="special_zeta", + ), + chebyshev_polynomial_t=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x, y: f"chebyshev_polynomial_t_forward({x}, {y})", + name="special_chebyshev_polynomial_t", + ), + chebyshev_polynomial_u=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x, y: f"chebyshev_polynomial_u_forward({x}, {y})", + name="special_chebyshev_polynomial_u", + ), + chebyshev_polynomial_v=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x, y: f"chebyshev_polynomial_v_forward({x}, {y})", + name="special_chebyshev_polynomial_v", + ), + chebyshev_polynomial_w=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x, y: f"chebyshev_polynomial_w_forward({x}, {y})", + name="special_chebyshev_polynomial_w", + ), + legendre_polynomial_p=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x, y: f"legendre_polynomial_p_forward({x}, {y})", + name="special_legendre_polynomial_p", + ), + shifted_chebyshev_polynomial_t=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x, y: f"shifted_chebyshev_polynomial_t_forward({x}, {y})", + name="special_shifted_chebyshev_polynomial_t", + ), + shifted_chebyshev_polynomial_u=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x, y: f"shifted_chebyshev_polynomial_u_forward({x}, {y})", + name="special_shifted_chebyshev_polynomial_u", + ), + shifted_chebyshev_polynomial_v=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x, y: f"shifted_chebyshev_polynomial_v_forward({x}, {y})", + name="special_shifted_chebyshev_polynomial_v", + ), + shifted_chebyshev_polynomial_w=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x, y: f"shifted_chebyshev_polynomial_w_forward({x}, {y})", + name="special_shifted_chebyshev_polynomial_w", + ), + hermite_polynomial_h=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x, y: f"hermite_polynomial_h_forward({x}, {y})", + name="special_hermite_polynomial_h", + ), + hermite_polynomial_he=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x, y: f"hermite_polynomial_he_forward({x}, {y})", + name="special_hermite_polynomial_he", + ), + laguerre_polynomial_l=OverridesData( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + cpp=lambda x, y: f"laguerre_polynomial_l_forward({x}, {y})", + name="special_laguerre_polynomial_l", + ), +) + + +# Use mypy to check protocol implemented correctly +def _typecheck_OpOverrides(h: OpOverrides) -> OpsHandler[str]: + return h + + +class DeferredLine(DeferredLineBase): + """A line that can be 'unwritten' by adding name to V.graph.removed_buffers""" + + def __init__(self, name, line): + super().__init__(line) + self.name = name + assert not isinstance(line, DeferredLineBase) + + def __call__(self): + if all( + self.name not in x + for x in ( + V.graph.removed_buffers, + V.kernel.removed_buffers, + V.graph.inplaced_to_remove, + V.kernel.inplaced_to_remove, + ) + ): + return self.line + return None + + def _new_line(self, line): + return DeferredLine(self.name, line) + + +class BracesBuffer(IndentedBuffer): + def indent(self, offset=1): + @contextlib.contextmanager + def ctx(): + for _ in range(offset): + self.writeline("{") + self._indent += 1 + for _ in range(-offset): + self._indent -= 1 + self.writeline("}") + yield + for _ in range(-offset): + self.writeline("{") + self._indent += 1 + for _ in range(offset): + self._indent -= 1 + self.writeline("}") + + return ctx() + + +class InplacedBuffer(NamedTuple): + inner_name: str + other_names: List[str] + + +class KernelArgs: + @staticmethod + def _lookup(prefix, odict, name): + assert isinstance(name, (str, sympy.Symbol)) + if name not in odict: + odict[name] = f"{prefix}{len(odict)}" + return odict[name] + + def __init__(self, sizevars=None): + self.input_buffers = {} + self.output_buffers = {} + self.inplace_buffers = {} + self.sizevars = sizevars or {} + self.workspace_arg = None + + def __repr__(self): + return "KernelArgs({})".format( + ", ".join( + map( + repr, + [ + self.input_buffers, + self.output_buffers, + self.inplace_buffers, + self.sizevars, + ], + ) + ) + ) + + def _buffer_is_marked_removed(self, name): + return isinstance(name, str) and name.startswith("REMOVED") + + def input(self, name): + if V.graph.scheduler: + name = V.graph.scheduler.mutation_real_name.get(name, name) + assert name not in V.graph.removed_buffers, name + if name in self.output_buffers: + return self.output_buffers[name] + if name in self.inplace_buffers: + return self.inplace_buffers[name].inner_name + if name.startswith("seed"): + return self._lookup("seed", self.input_buffers, name) + return self._lookup("in_ptr", self.input_buffers, name) + + def output(self, name): + if V.graph.scheduler: + name = V.graph.scheduler.mutation_real_name.get(name, name) + assert name not in V.graph.removed_buffers, name + if name in self.inplace_buffers: + return self.inplace_buffers[name].inner_name + return self._lookup("out_ptr", self.output_buffers, name) + + def make_inplace(self, input_name, output_name): + assert output_name not in self.inplace_buffers + if input_name in self.inplace_buffers: + buf = self.inplace_buffers[input_name] + buf.other_names.append(output_name) + self.inplace_buffers[output_name] = buf + else: + buf = InplacedBuffer( + f"in_out_ptr{len(unique(self.inplace_buffers.values()))}", + [input_name, output_name], + ) + self.inplace_buffers[input_name] = buf + self.inplace_buffers[output_name] = buf + + def workspace(self, nbytes: sympy.Expr, zero_fill: bool): + if self.workspace_arg is None: + self.workspace_arg = WorkspaceArg(nbytes, zero_fill) + return "ws_ptr", 0 + + offset = self.workspace_arg.nbytes + zero_fill = zero_fill or self.workspace_arg.zero_fill + self.workspace_arg = WorkspaceArg(offset + nbytes, zero_fill) + return "ws_ptr", offset + + def seed_offset(self, name, value): + if value in self.sizevars: + return self.sizevars[value] + if name in self.sizevars.values(): + name = ( + f"{name}{sum(1 for v in self.sizevars.values() if v.startswith(name))}" + ) + self.sizevars[value] = name + return name + + def size(self, name): + if str(name) == "seed": + self.sizevars["seed"] = "seed" + return "seed" + return self._lookup("ks", self.sizevars, name) + + def call_names(self): + return chain( + self.input_buffers.keys(), self.output_buffers.keys(), self.sizevars.keys() + ) + + def wrap_ptr_arg(self, buf, dtype): + return buf + + def wrap_size_arg(self, size): + return str(size) + + def cpp_argdefs(self): + from .cpp_utils import DTYPE_TO_CPP, INDEX_TYPE + + call_args = [] + arg_defs = [] + arg_types = [] + for inplaced in unique(self.inplace_buffers.values()): + if self._buffer_is_marked_removed(inplaced): + continue + outer = inplaced.other_names[-1] + inner = inplaced.inner_name + dtype = V.graph.get_dtype(outer) + cpp_dtype = DTYPE_TO_CPP[dtype] + arg_defs.append(f"{cpp_dtype}* {inner}") + call_args.append(self.wrap_ptr_arg(outer, dtype)) + arg_types.append(f"{cpp_dtype}*") + for outer, inner in self.input_buffers.items(): + if outer in self.inplace_buffers: + continue + dtype = V.graph.get_dtype(outer) + cpp_dtype = DTYPE_TO_CPP[dtype] + arg_defs.append(f"const {cpp_dtype}* {inner}") + call_args.append(self.wrap_ptr_arg(outer, dtype)) + arg_types.append(f"const {cpp_dtype}*") + for outer, inner in self.output_buffers.items(): + if outer in self.inplace_buffers or self._buffer_is_marked_removed(inner): + continue + dtype = V.graph.get_dtype(outer) + cpp_dtype = DTYPE_TO_CPP[dtype] + arg_defs.append(f"{cpp_dtype}* {inner}") + call_args.append(self.wrap_ptr_arg(outer, dtype)) + arg_types.append(f"{cpp_dtype}*") + for outer, inner in self.sizevars.items(): + arg_defs.append(f"const {INDEX_TYPE} {inner}") + call_args.append(self.wrap_size_arg(outer)) + arg_types.append(f"const {INDEX_TYPE}") + if V.graph.wrapper_code: + V.graph.wrapper_code.ensure_size_computed(outer) + assert self.workspace_arg is None, "Workspace not supported on CPU " + return arg_defs, call_args, arg_types + + def python_argdefs(self): + arg_defs: List[str] = [] + call_args: List[str] = [] + arg_types: List[torch.dtype] = [] + precompile_args: List[Union[TensorArg, SizeArg, WorkspaceArg]] = [] + for inplaced in unique(self.inplace_buffers.values()): + if self._buffer_is_marked_removed(inplaced): + continue + arg_defs.append(inplaced.inner_name) + call_args.append(inplaced.other_names[-1]) + arg_types.append(V.graph.get_dtype(inplaced.other_names[-1])) + precompile_args.append( + TensorArg( + name=inplaced.inner_name, + buffer=inplaced.other_names[-1], + dtype=V.graph.get_dtype(inplaced.other_names[-1]), + ) + ) + for outer, inner in chain( + self.input_buffers.items(), self.output_buffers.items() + ): + if outer in self.inplace_buffers or self._buffer_is_marked_removed(inner): + continue + arg_defs.append(inner) + call_args.append(outer) + arg_types.append(V.graph.get_dtype(outer)) + precompile_args.append( + TensorArg( + name=inner, + buffer=outer, + dtype=V.graph.get_dtype(outer), + ) + ) + for outer, inner in self.sizevars.items(): + arg_defs.append(inner) + call_args.append(outer) + arg_types.append(type(outer)) # type: ignore[arg-type] + precompile_args.append(SizeArg(inner, outer)) + if V.graph.wrapper_code: + V.graph.wrapper_code.ensure_size_computed(outer) + if self.workspace_arg is not None: + arg_defs.append("ws_ptr") + call_args.append("workspace") + precompile_args.append(self.workspace_arg) + return arg_defs, call_args, precompile_args, arg_types + + def aliases(self): + for inplaced in unique(self.inplace_buffers.values()): + if self._buffer_is_marked_removed(inplaced): + continue + for other in inplaced.other_names: + if ( + other in V.graph.inplaced_to_remove + or other in V.kernel.inplaced_to_remove + ): + continue + if other in self.input_buffers: + yield self.input_buffers[other], inplaced.inner_name + if other in self.output_buffers: + yield self.output_buffers[other], inplaced.inner_name + + def is_removed(self, name): + def _is_removed(name, buffers): + return name not in buffers or self._buffer_is_marked_removed(buffers[name]) + + return _is_removed(name, self.output_buffers) and _is_removed( + name, self.inplace_buffers + ) + + # Includes inplace buffers, excludes removed buffers. Essentially, + # after you do a call into this kernel, which buffers actually contain + # updated data? Modeled off of python_argdefs. + def live_output_buffers(self): + live_outs = OrderedSet() # type: ignore[var-annotated] + for inplaced in unique(self.inplace_buffers.values()): + if self._buffer_is_marked_removed(inplaced): + continue + live_outs.add(inplaced.other_names[-1]) + for outer, inner in self.output_buffers.items(): + if outer in self.inplace_buffers or self._buffer_is_marked_removed(inner): + continue + live_outs.add(outer) + return live_outs + + +class CSEVariable: + """A CSEVariable is just a name for an expression but it is useful to be able to annotate them on a backend dependent basis. + To do so, the backends can simply overload `Kernel.create_cse_var` + The "CSEVariable.update_on_args" method gives you a hook for annotations + See example of TritonCSEVariable in triton.py + """ + + def __init__(self, name, bounds: ValueRanges[Any]): + assert isinstance(bounds, ValueRanges) + self.name = name + self.bounds = bounds + self.use_count = 1 # track how many tims this expression is used + + def __str__(self): + return self.name + + def __hash__(self) -> int: + return hash(self.name) + + def __eq__(self, other) -> bool: + return type(other) == type(self) and other.name == self.name + + def update_on_args(self, name, args, kwargs): + pass + + def __repr__(self): + return f"{self.__class__.__name__}({self.name!r})" + + +class CppWrapperKernelArgs(KernelArgs): + def wrap_ptr_arg(self, buf, dtype): + from .cpp_utils import DTYPE_TO_CPP + + if config.abi_compatible: + # In the abi_compatible model, we just return the buf here. + # We will form correct call args later in wrapper.generate_kernel_all. + return buf + else: + return f"({DTYPE_TO_CPP[dtype]}*)({buf}.data_ptr())" + + def wrap_size_arg(self, size): + return f"{size}" + + +class CSE: + """Common subexpression elimination""" + + def __init__( + self, + prefix="", + suffix="", + name_prefix="tmp", + iter_buffers=None, + store_cache=None, + reduction_cache=None, + varname_map=None, + ): + self.prefix = prefix + self.suffix = suffix + self.cache = {} + self.name_prefix = name_prefix + self.store_cache = store_cache or {} + self.reduction_cache = reduction_cache or {} + self.iter_buffer_ids = iter_buffers or itertools.count() + self.invalidated_stores = OrderedSet() # type: ignore[var-annotated] + self.varname_map = varname_map or {} + + def invalidate(self, keep_vars: OrderedSet[str]): + for name, tmp in list(self.store_cache.items()): + if tmp not in keep_vars: + del self.store_cache[name] + self.invalidated_stores.add(name) + self.cache = {k: v for k, v in self.cache.items() if v in keep_vars} + + def clone(self): + # Note(fdrocha): reduction_cache is not being cloned, not sure if this is intentional + return CSE( + prefix=self.prefix, + suffix=self.suffix, + name_prefix=self.name_prefix, + iter_buffers=self.iter_buffer_ids, + store_cache=self.store_cache, + varname_map=self.varname_map, + ) + + def generate( + self, + buffer: IndentedBuffer, + expr: Union[str, CSEVariable, OpsValue, IndentedBuffer], + *, + bounds: ValueRanges[Any] = ValueRanges.unknown(), + write=True, + assignment=True, + ) -> CSEVariable: + if isinstance(expr, OpsValue): + expr = expr.value + + assert isinstance(expr, (str, CSEVariable, IndentedBuffer)), type(expr) + assert write or assignment + if isinstance(expr, CSEVariable): + # If the expressions were always created with all the information, we could + # assert expr.bounds == bounds, but sometimes the expression is created + # with the loose ValueRanges.unknown(), so we need to tighten the bounds + expr.bounds = expr.bounds.tighten(bounds) + expr.use_count += 1 + return expr + cache_key = expr.getvalue() if isinstance(expr, IndentedBuffer) else expr + var = self.cache.get(cache_key, None) + if not var: + var = self.newvar(bounds) + self.cache[cache_key] = var + if write: + if V.kernel.current_node: + V.kernel.current_node.codegen_originating_info( + buffer, only_once=True + ) + if isinstance(expr, IndentedBuffer): + if assignment: + buffer.writeline(f"{self.prefix}{var} =") + buffer.splice(expr) + buffer.writeline(self.suffix) + else: + if assignment: + line = f"{self.prefix}{var} = {expr}{self.suffix}" + else: + line = f"{expr}{self.suffix}" + buffer.writeline(line) + else: + var.bounds = var.bounds.tighten(bounds) + var.use_count += 1 + + return var + + def newvar(self, bounds: ValueRanges[Any] = ValueRanges.unknown()) -> CSEVariable: + var_name = f"{self.name_prefix}{next(self.iter_buffer_ids)}" + var = V.kernel.create_cse_var(var_name, bounds) + self.varname_map[var_name] = var + return var + + +class CodeGen: + def __init__(self) -> None: + super().__init__() + self.exit_stack = contextlib.ExitStack() + + def __enter__(self): + self.exit_stack.__enter__() + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + self.exit_stack.__exit__(exc_type, exc_val, exc_tb) + + +class ScopedDict: + def __init__(self, original_dict): + self.original_dict = original_dict + self.new_items = {} + + def __getitem__(self, key): + if key in self.new_items: + return self.new_items[key] + return self.original_dict[key] + + def __setitem__(self, key, value): + self.new_items[key] = value + + def __contains__(self, key): + return key in self.new_items or key in self.original_dict + + def get(self, key, default=None): + if key in self.new_items: + return self.new_items[key] + return self.original_dict.get(key, default) + + +class Kernel(CodeGen): + newvar_prefix = "" + suffix = "" + overrides: Optional[Callable[[OpsHandler[Any]], OpsHandler[Any]]] = None + # TODO: these look dead, but with all the getattr it's hard to tell... + load_format: None = None + store_format: None = None + + def __init__(self, args=None, increase_kernel_count=True): + super().__init__() + if increase_kernel_count: + metrics.generated_kernel_count += 1 + self.args = args or KernelArgs() + self.loads = IndentedBuffer() + self.compute = IndentedBuffer() + self.stores = IndentedBuffer() + + self.num_load = 0 + self.num_reduction = 0 + + self.cse: CSE = CSE(self.newvar_prefix, self.suffix) + self.must_keep_buffers = OrderedSet() # type: ignore[var-annotated] + self.store_buffer_names = OrderedSet() # type: ignore[var-annotated] + self._load_mask = None + self._load_other = None + # OrderedSet in set_current_node + self.current_node = None + self.node_to_bounds: Optional[Dict[torch.fx.Node, ValueRanges[Any]]] = None + + self.removed_buffers = OrderedSet() # type: ignore[var-annotated] + self.inplaced_to_remove = OrderedSet() # type: ignore[var-annotated] + + # key: the buffer to write + # value: the buffer to read and whose memory can be reused for + # the buffer specified by key + self.inplace_update_buffers = {} + # Set minimum number of elements processed per thread. + self.min_elem_per_thread = 1 + self.kernel_name = None + + @contextlib.contextmanager + def set_current_node(self, node): + prior = self.current_node + self.current_node = node + self.node_to_bounds = node._body.bounds().get_bounds() + try: + yield + finally: + self.current_node = prior + + @contextlib.contextmanager + def swap_buffers(self, lb, cb=None, sb=None): + def scope_cse(cse): + new_cse = cse.clone() + new_cse.cache = ScopedDict(cse.cache) + new_cse.reduction_cache = ScopedDict(cse.reduction_cache) + new_cse.store_cache = ScopedDict(cse.store_cache) + return new_cse + + if cb is None: + cb = lb + loads = self.loads + compute = self.compute + stores = self.stores + cse = self.cse + self.loads = lb + self.compute = cb + self.stores = sb + self.cse = scope_cse(cse) + try: + yield + finally: + self.loads = loads + self.compute = compute + self.stores = stores + self.cse = cse + + def load(self, name: str, index: sympy.Expr) -> CSEVariable: + raise NotImplementedError + + def indirect_load(self, name: str, index: sympy.Expr): + """A load the depends on an index we have read""" + prior = self.loads + try: + # put the load in the compute section as it might have deps + self.loads = self.compute + return self.load(name, index) + finally: + self.loads = prior + + def store_reduction(self, name: str, index: sympy.Expr, value: CSEVariable): + raise NotImplementedError + + def store( + self, name: str, index: sympy.Expr, value: CSEVariable, mode: StoreMode = None + ) -> None: + raise NotImplementedError + + def reduction( + self, + dtype: torch.dtype, + src_dtype: torch.dtype, + reduction_type: ReductionType, + value: Union[CSEVariable, Tuple[CSEVariable, ...]], + ) -> Union[CSEVariable, Tuple[CSEVariable, ...]]: + raise NotImplementedError + + def scan( + self, + dtypes: Tuple[torch.dtype, ...], + combine_fn: Callable[ + [Tuple[CSEVariable, ...], Tuple[CSEVariable, ...]], Tuple[CSEVariable, ...] + ], + values: Tuple[CSEVariable, ...], + ) -> Tuple[CSEVariable, ...]: + raise NotImplementedError + + def sort( + self, + dtypes: Tuple[torch.dtype, ...], + values: Tuple[CSEVariable, ...], + stable: bool, + descending: bool, + ) -> Tuple[CSEVariable, ...]: + raise NotImplementedError + + def var_ranges(self): + raise NotImplementedError + + def bucketize( + self, + values: CSEVariable, + offsets_name: str, + offsets_size: sympy.Expr, + indexing_dtype: torch.dtype, + right: bool, + ) -> CSEVariable: + """ + See [Note: Inductor bucketize op] + """ + raise NotImplementedError + + @property + def assert_function(self) -> str: + raise NotImplementedError + + def indirect_assert( + self, + var: Union[CSEVariable, str], + lower: Optional[str], + upper: Optional[str], + mask: Optional[Union[CSEVariable, str]] = None, + ) -> str: + if isinstance(var, CSEVariable): + var = str(var) + assert isinstance(var, str) + assert lower is None or isinstance(lower, str) + assert upper is None or isinstance(upper, str) + if lower and upper: + # The conditions need to be in parens because of Python's operator precedence. + # It'd be less error-prone to use and/or/not, which is suported by triton + cond = f"({lower} <= {var}) & ({var} < {upper})" + cond_print = f"{lower} <= {var} < {upper}" + elif lower: + cond = f"{lower} <= {var}" + cond_print = cond + else: + assert upper + cond = f"{var} < {upper}" + cond_print = cond + + if mask: + cond = f"({cond}) | ~({mask})" + + return f'{self.assert_function}({cond}, "index out of bounds: {cond_print}")' + + def check_bounds( + self, expr: sympy.Expr, size: sympy.Expr, lower: bool, upper: bool + ): + raise NotImplementedError + + def index_to_str(self, index: sympy.Expr) -> str: + raise NotImplementedError + + def __enter__(self): + # TODO: hoist this to top level + class CSEProxy: + self.name = "CSEProxy" + vr_analysis = ValueRangeAnalysis() + + @staticmethod + def __getattr__(name: str) -> Callable[..., CSEVariable]: # type: ignore[misc] + def inner(*args, **kwargs): + bounds = CSEProxy._bound_variable(name, *args, **kwargs) + + value = getattr(parent_handler, name)(*args, **kwargs) # type: ignore[has-type] + + def do_cse(v): + csevar = V.kernel.cse.generate( + V.kernel.compute, v, bounds=bounds + ) + csevar.update_on_args(name, args, kwargs) + return csevar + + return pytree.tree_map(do_cse, value) + + return inner + + @staticmethod + def _bound_variable(name, *args, **kwargs): + """ + If the variable comes from an FX node, we forward the bound we have already computed + Else, if the variable when codegen'ing another op, we try to compute its bounds + """ + from ..select_algorithm import TritonTemplateKernel + + if isinstance(V.kernel, TritonTemplateKernel): + return ValueRanges.unknown() + + fx_node = V.interpreter.current_node + if fx_node.target == name and self.node_to_bounds is not None: + assert isinstance(self.node_to_bounds, dict) + return self.node_to_bounds.get(fx_node, ValueRanges.unknown()) + elif config.compute_all_bounds and hasattr(ValueRangeAnalysis, name): + # These create lots of inner strings. We would need to compute the bounds at the ops + # We will also likely not get much from computing VRs on these nodes + if any( + s in fx_node.target + for s in ("set_indirect", "reduction", "scan") + ): + return ValueRanges.unknown() + + # We assume that the inputs come from `ops.` and are not strings. If you want to generate + # intermediary strings, wrap them in CSE variables with properly initialised bounds. + + # If there is no FX bound but we know how to compute one we do so + assert not kwargs + + def arg_to_bound(x): + if isinstance(x, CSEVariable): + return x.bounds + elif isinstance(x, sympy.Expr): + return bound_sympy(x) + else: + return x + + arg_bounds = list(map(arg_to_bound, args)) + return getattr(CSEProxy.vr_analysis, name)(*arg_bounds) + else: + return ValueRanges.unknown() + + @staticmethod + def indirect_indexing( + var: CSEVariable, + size: Union[sympy.Expr, int], + check: bool = True, + wrap_neg=True, + ): + if isinstance(size, int): + size = sympy.Integer(size) + assert isinstance(size, sympy.Expr), size + # Skip CSE since this doesn't return an expression + + if var.bounds.lower < 0: # type: ignore[operator] + if wrap_neg: + stm = ops.add(var, ops.index_expr(size, torch.long)) + # Mixed negative and non-negative + if var.bounds.upper >= 0: # type: ignore[operator] + lt = ops.lt(var, 0) + stm = ops.where(lt, stm, var) + else: + stm = var + + # Propagate bounds as we know how to compute them properly + new_bounds = ValueRanges.unknown() + if var.bounds != ValueRanges.unknown() and isinstance( + size, sympy.Number + ): + # Take the negative part of the bound and add size to it + # Then take union of that and the positive part + # This is a tighter bound than that of a generic ops.where, as we have info on the cond + neg_bounds = var.bounds & ValueRanges(-int_oo, -1) + new_bounds = ValueRanges( + neg_bounds.lower + size, neg_bounds.upper + size + ) + # We don't have a good way of representing the empty range + if var.bounds.upper >= 0: # type: ignore[operator] + pos = var.bounds & ValueRanges(0, int_oo) + new_bounds = new_bounds | pos + + var = self.cse.generate(self.compute, stm, bounds=new_bounds) + + sympy_var = parent_handler.indirect_indexing(var, size, check) + if generate_assert(check): + assert_lower = not (var.bounds.lower >= 0) + # value ranges cannot x < s when x and s are symbols + assert_upper = not isinstance(size, sympy.Number) or not ( + var.bounds.upper < size + ) + self.check_bounds(sympy_var, size, assert_lower, assert_upper) + return sympy_var + + @staticmethod + def check_bounds( + expr: sympy.Expr, size: sympy.Expr, lower: bool, upper: bool + ): + return self.check_bounds(expr, size, lower, upper) + + @staticmethod + def load(name: str, index: sympy.Expr) -> CSEVariable: + if name in self.cse.invalidated_stores: + # A load from an invalidated store requires us to + # keep the actual buffer around + V.kernel.must_keep_buffers.add(name) + if free_symbol_is_type(index, SymT.TMP): + return self.indirect_load(name, index) + store_cache = self.cse.store_cache + if name in store_cache: + return store_cache[name] + out = self.load(name, index) + # count load that is not in the store_cache, and also not in the + # cse cache. + if out.use_count == 1: + self.num_load += 1 + return out + + @staticmethod + def _update_store_cache(name: str, value: CSEVariable): + self.cse.store_cache[name] = value + if self.current_node and name in V.graph.name_to_buffer: + buf = self.current_node.get_output(name) + for other_name in buf.get_mutations(): + self.cse.store_cache[other_name] = value + + @staticmethod + def store( + name: str, index: sympy.Expr, value: CSEVariable, mode: StoreMode = None + ) -> None: + self.store_buffer_names.add(name) + if mode is None: + CSEProxy._update_store_cache(name, value) + if name not in V.graph.removed_buffers: + return self.store(name, index, value, mode=mode) + else: + return None # type: ignore[return-value] + + @staticmethod + def store_reduction(name: str, index: sympy.Expr, value: CSEVariable): + self.store_buffer_names.add(name) + CSEProxy._update_store_cache(name, value) + + if name not in V.graph.removed_buffers: + return self.store_reduction(name, index, value) + + @staticmethod + def reduction( + dtype: torch.dtype, + src_dtype: torch.dtype, + reduction_type: ReductionType, + value: Union[CSEVariable, Tuple[CSEVariable, ...]], + ) -> Union[CSEVariable, Tuple[CSEVariable, ...]]: + self.num_reduction += 1 + return self.reduction(dtype, src_dtype, reduction_type, value) + + @staticmethod + def scan( + dtypes: Tuple[torch.dtype, ...], + combine_fn: Callable[ + [Tuple[CSEVariable, ...], Tuple[CSEVariable, ...]], + Tuple[CSEVariable, ...], + ], + values: Tuple[CSEVariable, ...], + ) -> Tuple[CSEVariable, ...]: + return self.scan(dtypes, combine_fn, values) + + @staticmethod + def sort( + dtypes: Tuple[torch.dtype, ...], + values: Tuple[CSEVariable, ...], + stable: bool, + descending: bool, + ) -> Tuple[CSEVariable, ...]: + return self.sort(dtypes, values, stable, descending) + + @staticmethod + def bucketize( + values: CSEVariable, + offsets_name: str, + offsets_size: sympy.Expr, + indexing_dtype: torch.dtype, + right: bool, + ) -> CSEVariable: + """ + [Note: Inductor bucketize op] + + Given values (tensor) and offsets_name (reference to the name of a 1D + tensor), calculate the bucket that each value belongs to. + + e.g. for values [-1, 0, 1, 2, 3, 4, 5, 9], offsets [0, 4, 4, 8], right=True + return = [ 0, 1, 1, 1, 1, 3, 3, 4]. + + When right == False, bucket i refers to range (offsets[i], offsets[i+1]]. + When right == True, bucket i refers to range [offsets[i], offsets[i+1]). + + Offsets must be non-decreasing or the result is undefined. + """ + return self.bucketize( + values, offsets_name, offsets_size, indexing_dtype, right + ) + + # Use mypy to check protocol implemented correctly + def _typecheck_CSEProxy(h: CSEProxy) -> OpsHandler[CSEVariable]: + return h + + super().__enter__() + assert self.overrides + parent_handler = self.overrides(V.get_ops_handler()) + self.exit_stack.enter_context(V.set_ops_handler(CSEProxy())) + self.exit_stack.enter_context(V.set_kernel_handler(self)) + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + """ + Note that V.graph.scheduler can be None when codegening triton template + kernels. + """ + if V.graph.scheduler: + V.graph.scheduler.remove_kernel_local_buffers() + super().__exit__(exc_type, exc_val, exc_tb) + + def rename_indexing(self, index) -> sympy.Expr: + # adds the necessary kernel args for index expressions + # and renames variables in index expressions to kernel arg names + if isinstance(index, (list, tuple)): + return [self.rename_indexing(x) for x in index] # type: ignore[return-value] + index = V.graph.sizevars.simplify(index) + sorted_symbols = sorted(index.free_symbols, key=lambda s: s.name) + replacements = { + x: self.args.size(x) + for x in sorted_symbols + if symbol_is_type( + x, + ( + SymT.UNBACKED_INT, + SymT.SIZE, + SymT.PRECOMPUTED_SIZE, + ), + ) + } + return sympy_subs(index, replacements) + + def create_cse_var(self, *args, **kwargs): + return CSEVariable(*args, **kwargs) + + +@dataclasses.dataclass +class OptimizationContext: + key: ClassVar[str] = "opt_ctx" + + dtype: Optional[torch.dtype] = None + ops_name: str = "" + + +@functools.lru_cache(None) +def jinja2_env(): + try: + import jinja2 + + return jinja2.Environment( + undefined=jinja2.StrictUndefined, + ) + except ImportError: + return None + + +class KernelTemplate: + """ + Base class for defining kernel templates. + + Children classes: TritonTemplate, CUDATemplate + """ + + @staticmethod + def indent_except_first(source: str, num_indents: int, indents_spacing=4): + lines = source.splitlines(True) + if len(lines) > 1: + lines[1:] = [ + (" " * indents_spacing * num_indents) + line for line in lines[1:] + ] + return "".join(lines) + + @staticmethod + def _template_from_string(source): + env = jinja2_env() + if env is not None: + env.filters["indent_except_first"] = KernelTemplate.indent_except_first + from jinja2 import TemplateSyntaxError + + class DetailedTemplateSyntaxError(TemplateSyntaxError): + def __init__(self, original_error): + super().__init__( + original_error.message, + original_error.lineno, + original_error.name, + original_error.filename, + ) + self.original_error = original_error + + def __str__(self): + error_info = f"Error in template at line {self.lineno}\n" + error_info += f"Error message: {self.message}\n" + if hasattr(self.original_error, "source"): + lines = self.original_error.source.split("\n") + error_info += "Context:\n" + start = max(0, self.lineno - 2) + end = min(len(lines), self.lineno + 2) + for i in range(start, end): + if i == self.lineno - 1: + error_info += f"{i+1}: --> {lines[i]}\n" + if hasattr(self.original_error, "column"): + error_info += ( + " " + + " " * (self.original_error.column - 1) + + "^\n" + ) + else: + error_info += f"{i+1}: {lines[i]}\n" + return error_info + + try: + return env.from_string(source) + except TemplateSyntaxError as e: + raise DetailedTemplateSyntaxError(e) from e + + return None + + @staticmethod + def _fake_get_dtype(fake_out): + _get_dtype_real = V.graph.get_dtype + + def get_dtype(name): + if name == fake_out.get_name(): + return fake_out.get_dtype() + return _get_dtype_real(name) + + return get_dtype + + def __init__(self, name: str): + self.name = name + + def maybe_append_choice(self, choices, **kwargs): + """ + Maybe generates a new ChoiceCaller and appends it into existing choices. + + choices: A list of ChoiceCallers. + kwargs: Additional kwargs to be passed to self.generate() to generate a new ChoiceCaller. + """ + + try: + choices.append(self.generate(**kwargs)) + except NotImplementedError as e: + pass + + def generate(self, **kwargs) -> "torch._inductor.ir.ChoiceCaller": + """ + Generates a ChoiceCaller instance from the given arguments. + """ + + raise NotImplementedError diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp.py new file mode 100644 index 0000000000000000000000000000000000000000..e1d54762d9e8fd5e9914d4706ba88e4d8818ab36 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp.py @@ -0,0 +1,4978 @@ +# mypy: allow-untyped-defs +import contextlib +import dataclasses +import functools +import itertools +import math +import re +import sys +import warnings +from copy import copy, deepcopy +from enum import Enum +from typing import cast, Dict, List, Optional, Sequence, Set, Tuple, Union + +import sympy + +import torch +import torch.fx +from torch._inductor import dependencies +from torch._prims_common import is_float_dtype, is_integer_dtype +from torch.utils._sympy.functions import CeilDiv, FloorDiv, ModularIndexing +from torch.utils._sympy.symbol import free_symbol_is_type, symbol_is_type, SymT + +from ..._dynamo.utils import counters +from .. import codecache, config, cpp_builder, cpu_vec_isa, ir, metrics +from ..loop_body import LoopBody +from ..scheduler import ( + BaseSchedulerNode, + BaseScheduling, + ForeachKernelSchedulerNode, + FusedSchedulerNode, + Scheduler, + SchedulerNode, +) +from ..utils import ( + cache_on_self, + get_bounds_index_expr, + get_fused_kernel_name, + has_free_symbols, + is_welford_reduction, + parallel_num_threads, + Placeholder, + sympy_index_symbol, + sympy_index_symbol_with_prefix, + sympy_product, + sympy_subs, +) +from ..virtualized import NullKernelHandler, ops, OpsValue, V +from .common import ( + BackendFeature, + BracesBuffer, + CppWrapperKernelArgs, + CSE, + CSEVariable, + DataTypePropagation, + DeferredLine, + DTYPE_TO_COMPUTATION_DTYPE, + IndentedBuffer, + Kernel, + KernelArgs, + OpOverrides, + OptimizationContext, +) +from .cpp_utils import ( + _get_dtype_from_loopbodies, + _get_loop_body, + cexpr, + cexpr_index, + codegen_rand, + CppCSEVariable, + DTYPE_TO_CPP, + INDEX_TYPE, + LocalBufferContext, + promote_args, + unify_mask_base_type, + value_to_cpp, +) + + +_IS_WINDOWS = sys.platform == "win32" + + +def get_export_declaration(): + return "__declspec(dllexport)" if _IS_WINDOWS else "" + + +schedule_log = torch._logging.getArtifactLogger(__name__, "schedule") + +NATIVE_OMP_RTYPES = {"+", "*", "^", "||", "min", "max"} +RTYPE_TO_CPP = { + "sum": "+", + "prod": "*", + "xor_sum": "^", + "min": "min", + "max": "max", + "argmin": "argmin", + "argmax": "argmax", + "any": "||", + "welford_reduce": "welford", + "welford_combine": "welford", +} +VECTORIZABLE_RTYPES = { + "max", + "min", + "sum", + "prod", + "xor_sum", + "welford_reduce", + "welford_combine", + "argmin", + "argmax", + "any", +} + +PYTHON_TO_CPP = { + "Tensor": "at::Tensor", + "int": "long", + "float": "double", + "bool": "bool", + "str": "std::string", + "ScalarType": "c10::ScalarType", + "MemoryFormat": "at::MemoryFormat", + "Layout": "at::Layout", + "Device": "at::Device", + "number": "at::Scalar", +} + +CONTAINER_PYTHON_TO_CPP = { + "List": "std::vector", + "Optional": "std::optional", +} + +DTYPE_LOWP_FP = [ + torch.bfloat16, + torch.float16, +] + +VECTORIZABLE_DTYPES: List[torch.dtype] = [ + torch.float64, + torch.float, + torch.bfloat16, + torch.float16, + torch.bool, + torch.uint8, + torch.int8, + torch.int32, + torch.int64, +] + +MASKED_VECTORIZABLE_DTYPES: List[torch.dtype] = [ + torch.float, + torch.bfloat16, + torch.float16, + torch.uint8, + torch.int8, +] + + +def reduction_init(reduction_type, dtype): + if dtype in DTYPE_LOWP_FP: + # Since load promotes all half-precision inputs to float, the initial + # constant for reduction must be promoted as well + dtype = torch.float32 + if reduction_type in ("xor_sum", "sum", "any"): + return 0 + if reduction_type == "prod": + return 1 + if reduction_type in ("max", "argmax", "min", "argmin"): + cdtype = DTYPE_TO_CPP[dtype] + min_var = ( + f"-std::numeric_limits<{cdtype}>::infinity()" + if is_float_dtype(dtype) + else f"std::numeric_limits<{cdtype}>::min()" + ) + max_var = ( + f"std::numeric_limits<{cdtype}>::infinity()" + if is_float_dtype(dtype) + else f"std::numeric_limits<{cdtype}>::max()" + ) + init_var = min_var if reduction_type in ("max", "argmax") else max_var + return ( + init_var + if reduction_type in ("max", "min") + else f"IndexValue<{cdtype}>{{0, {init_var}}}" + ) + if is_welford_reduction(reduction_type): + return f"Welford<{DTYPE_TO_CPP[dtype]}>()" + raise AssertionError(reduction_type) + + +def reduction_acc_type(reduction_type, dtype): + scalar_type = DTYPE_TO_CPP[DTYPE_TO_COMPUTATION_DTYPE[dtype]] + if is_welford_reduction(reduction_type): + return f"Welford<{scalar_type}>" + if reduction_type in {"argmin", "argmax"}: + return f"IndexValue<{scalar_type}>" + return scalar_type + + +def reduction_combine( + reduction_type, + var, + next_value, + index: Optional[sympy.Symbol] = None, + src_dtype=None, +): + is_bool = src_dtype == torch.bool + if reduction_type == "sum": + conjunction = "|" if is_bool else "+" + return f"{var} {conjunction} {next_value}" + if reduction_type == "prod": + return f"{var} * {next_value}" + if reduction_type == "xor_sum": + return f"{var} ^ {next_value}" + if reduction_type == "any": + return f"{var} || {next_value}" + if reduction_type in ("min", "max"): + return f"{reduction_type}_propagate_nan({var}, {next_value})" + if reduction_type == "welford_reduce": + return f"welford_combine({var}, {next_value})" + if reduction_type == "welford_combine": + if isinstance(next_value, tuple): + mean, m2, weight = next_value + else: + mean, m2, weight = reduction_project(reduction_type, next_value) + return f"welford_combine({var}, {{{mean}, {m2}, {weight}}})" + if reduction_type in ("argmin", "argmax"): + if index is not None: + return f"{reduction_type}_combine({var}, {next_value}, {index})" + else: + return f"{reduction_type}_combine({var}, {next_value})" + raise AssertionError(reduction_type) + + +def reduction_project(reduction_type, acc): + if is_welford_reduction(reduction_type): + return f"{acc}.mean", f"{acc}.m2", f"{acc}.weight" + elif reduction_type in {"argmin", "argmax"}: + return f"{acc}.index" + return acc + + +@functools.lru_cache +def stride_at(index: sympy.Expr, var: sympy.Symbol): + if not index.has(var): + # see test_torchinductor_dynamic_shapes.py::test_full_boolean_dynamic_shapes_cpu + # which has tmp0 = ops.index_expr(s0 >= 1024, torch.bool) and fails below calculation. + # in this case, there is no dependencies between index and var. + return sympy.Integer(0) + replacement = {var: var + 1} + new_index = sympy_subs(index, replacement) # type: ignore[arg-type] + return sympy.simplify(new_index - index) + + +@functools.lru_cache +def simplify_index_in_vec_range(index: sympy.Expr, var: sympy.Expr, vec_length: int): + """ + Simplifies the index expression within the range of a vectorized loop. + Given a vectorized loop variable `var` in the range of a loop with `vec_length`, + this function transforms the `index` into an equivalent form. It handles + simplifications for cases where `var` can be expressed as `vec_length * a + b`, + where `b` ranges from 0 to `vec_length - 1`. The function reduces occurrences + of `FloorDiv` and `ModularIndexing` in the `index` with best-effort optimizations. + + NOTE: + The simplified index expression is intended for analysis purposes only, not + for code generation. It replaces `FloorDiv` and `ModularIndexing` with free variables + which are not dependent on the loop variable `var` in the vectorized range. Check + https://github.com/pytorch/pytorch/pull/117221#discussion_r1449746217 for more details. + + Examples: + 1. If `var` is `x3` and `vec_length` is 16, and `x3 = 16*a + b`, then + `FloorDiv(x3, div)` or `ModularIndexing(x3, div, mod)` becomes a free variable + when `div` is divisible by 16. + 2. `ModularIndexing(x3, 1, mod)` can be simplified to `x3 + c` where `c` is a free + variable when `mod` is divisible by 16. + """ + + div_freevar_id = 0 + mod_freevar_id = 0 + + def visit_indexing_div(divisor): + nonlocal div_freevar_id + result = FloorDiv(var, divisor) + if sympy.gcd(divisor, vec_length) == vec_length: + result = sympy.Symbol(f"{var}_div_c{div_freevar_id}") + div_freevar_id += 1 + return result + + def visit_modular_indexing(divisor, modulus): + nonlocal mod_freevar_id + result = ModularIndexing(var, divisor, modulus) + if sympy.gcd(divisor, vec_length) == vec_length: + result = sympy.Symbol(f"{var}_mod_c{mod_freevar_id}") + mod_freevar_id += 1 + elif divisor == 1 and sympy.gcd(modulus, vec_length) == vec_length: + result = var + sympy.Symbol(f"{var}_mod_c{mod_freevar_id}") + mod_freevar_id += 1 + return result + + original_index = index + + div = sympy.Wild("divisor", integer=True) + if index.has(FloorDiv): + index = index.replace(FloorDiv(var, div), visit_indexing_div) + + mod = sympy.Wild("modulus", integer=True) + if index.has(ModularIndexing): + index = index.replace(ModularIndexing(var, div, mod), visit_modular_indexing) + + index = sympy.simplify(index) + if index != original_index: + return simplify_index_in_vec_range(index, var, vec_length) + + return index + + +@functools.lru_cache +def stride_at_vec_range( + index: sympy.Expr, var: sympy.Symbol, vec_length: Optional[int] = None +): + if vec_length: + index = simplify_index_in_vec_range(index, var, vec_length) + return stride_at(index, var) + + +class OuterLoopFusedSchedulerNode(FusedSchedulerNode): + @classmethod + def fuse( # type: ignore[override] + cls, node1: BaseSchedulerNode, node2: BaseSchedulerNode, outer_loop_fusion_depth + ): + assert node1.scheduler is node2.scheduler + assert all( + type(node) + in ( + OuterLoopFusedSchedulerNode, + SchedulerNode, + FusedSchedulerNode, + ) + for node in (node1, node2) + ) + if any(type(node) is OuterLoopFusedSchedulerNode for node in (node1, node2)): + return cls( + node1.scheduler, + ( + list(node1.get_outer_nodes()) + if type(node1) is OuterLoopFusedSchedulerNode + else [ + node1, + ] + ) + + ( + list(node2.get_outer_nodes()) + if type(node2) is OuterLoopFusedSchedulerNode + else [ + node2, + ] + ), + outer_loop_fusion_depth, + ) + else: + return cls(node1.scheduler, [node1, node2], outer_loop_fusion_depth) # type: ignore[list-item] + + def __init__( + self, + scheduler: "Scheduler", + outer_fused_nodes: List[Union[FusedSchedulerNode, SchedulerNode]], + outer_loop_fusion_depth, + ): + self.outer_fused_nodes: List[ + Union[FusedSchedulerNode, SchedulerNode] + ] = outer_fused_nodes + self.outer_loop_fusion_depth = outer_loop_fusion_depth + flatten_snodes = [] + for _node in self.outer_fused_nodes: + assert isinstance(_node, (SchedulerNode, FusedSchedulerNode)) + flatten_snodes.extend(list(_node.get_nodes())) + super().__init__(scheduler, flatten_snodes) # type: ignore[arg-type] + + def get_outer_nodes(self): + return self.outer_fused_nodes + + def check_outer_fusion_loop_level_attr( + self, cpp_kernel_proxy_list, outer_loop_fusion_depth + ): + # This function ensures that the same tiling split is applied at each loop level within the outer loop fusion depth. + # In the fusion stage, we only examine nodes with same vars and reduce. + # However, for nodes with same vars and reduce, the loops may still have different tile splits. + # For example (test_expr_vec_non_contiguous in test_cpu_repro.py): + # * buf0 tiling along the 2nd loop level, buf1 tiling along the 3rd loop level. + # If the check failed, we should fall back to standard loop codegen. + def _inner( + left_loop_level: LoopLevel, + right_loop_level: LoopLevel, + loop_fusion_depth: int, + ) -> bool: + # Check if same loop level attr + outer_loops_attr_compare_list = [ + "var", + "size", + "offset", + "steps", + ] + if not ( + all( + getattr(left_loop_level, attr_compare) + == getattr(right_loop_level, attr_compare) + for attr_compare in outer_loops_attr_compare_list + ) + ): + return False + + assert loop_fusion_depth >= 1 + if (loop_fusion_depth := loop_fusion_depth - 1) > 0: + # If the next loop level is expected to undergo outer loop fusion, + # there should be no kernel present at the current loop level. + assert ( + left_loop_level.kernel is None and right_loop_level.kernel is None + ) + # Check next loop level attr + if any( + # Assume no main/tail loop split at any outer loop fusion depth + # Given no clear performance benefit for this complex case + len(loop_level.inner) != 1 + for loop_level in [left_loop_level, right_loop_level] + ) or not _inner( + left_loop_level.inner[0], + right_loop_level.inner[0], + loop_fusion_depth, + ): + return False + + return True + + for idx in range(len(cpp_kernel_proxy_list) - 1): + left_loop_nest = cpp_kernel_proxy_list[idx].loop_nest + right_loop_nest = cpp_kernel_proxy_list[idx + 1].loop_nest + if any( + # Assume no main/tail loop split at any outer loop fusion depth + len(loop_nest.root) != 1 + for loop_nest in [left_loop_nest, right_loop_nest] + ) or not _inner( + left_loop_nest.root[0], right_loop_nest.root[0], outer_loop_fusion_depth + ): + return False + + return True + + def merge_outer_fusion_kernels( + self, + cpp_kernel_proxy_list, + ): + loop_nest_list: List[LoopNestWithSplit] = [ + kernel.loop_nest for kernel in cpp_kernel_proxy_list + ] + kernel_group = cpp_kernel_proxy_list[0].kernel_group + + def _merge_outer_fusion_loop_levels( + loop_level_nested_list: List[List["LoopLevel"]], + outer_loop_fusion_depth, + ): + assert outer_loop_fusion_depth >= 1 + # Assume no main/tail loop split at any outer loop fusion depth + assert all( + len(loop_level_list) == 1 for loop_level_list in loop_level_nested_list + ) + if (outer_loop_fusion_depth := outer_loop_fusion_depth - 1) >= 1: + # Further merge the next loop level + next_loop_level_nested_list = [ + loop_level_list[0].inner + for loop_level_list in loop_level_nested_list + ] + _merge_outer_fusion_loop_levels( + next_loop_level_nested_list, + outer_loop_fusion_depth, + ) + else: + outer_loop_fused_kernel = OuterLoopFusedKernel(kernel_group) + loop_level_of_first_kernel = loop_level_nested_list[0][0] + for kernel_idx in range(len(loop_level_nested_list)): + outer_loop_fused_kernel.inner.append( + deepcopy(loop_level_nested_list[kernel_idx][0]), + ) + loop_level_of_first_kernel.inner = [] + loop_level_of_first_kernel.kernel = outer_loop_fused_kernel + + # Merge the List[LoopNestWithSplit] from cpp_kernel_proxy_list + # into cpp_kernel_proxy_list[0].loop_nest + _merge_outer_fusion_loop_levels( + [_loop_nest.root for _loop_nest in loop_nest_list], # type: ignore[misc] + self.outer_loop_fusion_depth, + ) + return cpp_kernel_proxy_list[0] + + +class RecordOptimizationContext: + def __init__(self, func_name: str = ""): + self.func_name = func_name + self.current_node: Optional[torch.fx.Node] = None + self.opt_ctx: Optional[OptimizationContext] = None + + def __enter__(self): + assert V.interpreter + assert V.interpreter.current_node + + self.current_node = V.interpreter.current_node + assert self.current_node is not None + if OptimizationContext.key in self.current_node.meta: + self.opt_ctx = self.current_node.meta[OptimizationContext.key] + else: + self.opt_ctx = OptimizationContext() + assert self.opt_ctx is not None + self.opt_ctx.ops_name = self.func_name + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + assert self.current_node + assert self.opt_ctx + self.current_node.meta[OptimizationContext.key] = self.opt_ctx + + def get_opt_ctx(self): + return self.opt_ctx + + def get_fx_node(self): + assert self.current_node + return self.current_node + + +class CppOverrides(OpOverrides): + """Map element-wise ops to C++""" + + @staticmethod + def add(a, b): + return f"decltype({a})({a} + {b})" + + @staticmethod + def sub(a, b): + return f"decltype({a})({a} - {b})" + + @staticmethod + def mul(a, b): + return f"decltype({a})({a} * {b})" + + @staticmethod + def to_dtype(x, dtype, src_dtype=None, use_compute_types=True): + assert isinstance(x, CppCSEVariable) + if src_dtype is None: + src_dtype = x.dtype + expr = V.kernel.get_to_dtype_expr(x, dtype, src_dtype) + csevar = V.kernel.cse.generate(V.kernel.compute, expr) + csevar.update_on_args("to_dtype", (x, dtype), {"src_dtype": src_dtype}) + if dtype in [torch.bfloat16, torch.float16] and src_dtype == torch.float: + """ + https://github.com/pytorch/pytorch/issues/115260 + For FusedSchedulerNode[node1, node2], the node2 loads what node1 stores and the buffer is + in low-precision floating point data type. When the output of node1 also serves as the output of the + kernel, the result of nodes would be different from the case when output of node1 is not the output + of the kernel (where we don't need to insert `to_dtype` for legalization). To address the problem, on + storing the lowp node1 output, we also add the inverse dtype conversion to high precision data type + to the cse cache. + + Example (pseudo code): + node1_output = ... + node1_output_lowp = to_dtype(node1_output, dtype=torch.bfloat16) + store(buf, node1_output_lowp) + node2_input_lowp = load(buf) + node2_input = to_dtype(node2_input_lowp, dtype=torch.float) + + Without cse cache trick: + node1_output = ... + node1_output_lowp = to_dtype(node1_output, dtype=torch.bfloat16) + store(buf, node1_output_lowp) + node2_input_lowp = node_output_lowp # hit store cache + node2_input = to_dtype(node2_input_lowp, dtype=torch.float) + + With cse cache trick: + node1_output = ... + node1_output_lowp = to_dtype(node1_output, dtype=torch.bfloat16) + # also add `to_dtype(node1_input_lowp, dtype=torch.float)` -> `node1_output` to cse cache + store(buf, node1_output_lowp) + node2_input_lowp = node_output_lowp # hit store cache + node2_input = node1_output # hit cse cache + """ + V.kernel.cache_dtype_convert(x, src_dtype, csevar, dtype) + return csevar + + @staticmethod + def to_dtype_bitcast(x, dtype, src_dtype): + assert dtype in DTYPE_TO_CPP, f"{dtype} missing from {__name__}.DTYPE_TO_CPP" + if src_dtype in (torch.float16, torch.bfloat16): + # c10::bit_cast requires the source and target have the bitwidth. + # Because the input tensor's dtype could be promoted, e.g. from float16 to + # float, we have to cast the tensor to its original source dtype before + # invoking bit_cast. We also need to convert the bit-casted tensor + # back to float to make sure we keep using higher precision values + # for the rest of the computation. + cast_x = f"c10::convert<{DTYPE_TO_CPP[src_dtype]}>({x})" + cast_x = f"c10::bit_cast<{DTYPE_TO_CPP[dtype]}>({cast_x})" + return f"c10::convert<{DTYPE_TO_CPP[torch.float32]}>({cast_x})" + else: + return f"c10::bit_cast<{DTYPE_TO_CPP[dtype]}>({x})" + + @staticmethod + def abs(x): + return f"std::abs({x})" + + @staticmethod + def sin(x): + return f"std::sin({x})" + + @staticmethod + def cos(x): + return f"std::cos({x})" + + @staticmethod + def neg(x): + return f"decltype({x})(-{x})" + + @staticmethod + def exp(x): + # return f"Sleef_expf_u10({x})" + return f"std::exp({x})" + + @staticmethod + def exp2(x): + return f"std::exp2({x})" + + @staticmethod + def expm1(x): + return f"std::expm1({x})" + + @staticmethod + def erf(x): + return f"std::erf({x})" + + @staticmethod + def erfc(x): + return f"std::erfc({x})" + + @staticmethod + def erfinv(x): + return f"calc_erfinv({x})" + + @staticmethod + def sqrt(x): + return f"std::sqrt({x})" + + @staticmethod + def rsqrt(x): + return f"1 / std::sqrt({x})" + + @staticmethod + def log1p(x): + bug = config.cpp.inject_log1p_bug_TESTING_ONLY + if bug == "accuracy": + return f"{x} + decltype({x})(1)" + elif bug is None: + return f"std::log1p({x})" + else: + raise AssertionError( + f"unrecognized config cpp.inject_log1p_bug_TESTING_ONLY = {bug!r}" + ) + + @staticmethod + def tan(x): + return f"std::tan({x})" + + @staticmethod + def tanh(x): + return f"std::tanh({x})" + + @staticmethod + def signbit(x): + """ + On windows std::signbit only support float type. + Ref: https://learn.microsoft.com/en-us/cpp/c-runtime-library/reference/signbit?view=msvc-170 + """ + return ( + f"std::signbit(static_cast({x}))" + if _IS_WINDOWS + else f"std::signbit({x})" + ) + + @staticmethod + def pow(a, b): + return f"std::pow({a}, {b})" + + @staticmethod + def log(x): + return f"std::log({x})" + + @staticmethod + def round(x): + return f"std::nearbyint({x})" + + @staticmethod + def floor(x): + return f"std::floor({x})" + + @staticmethod + def floordiv(a, b): + # a and b are integer type + quot = f"{a} / {b}" + rem = f"{a} % {b}" + return f"(({a} < 0) != ({b} < 0) ? ({rem} != 0 ? {quot} - 1 : {quot}) : {quot})" + + @staticmethod + def ceil(x): + return f"std::ceil({x})" + + @staticmethod + def trunc(x): + return f"std::trunc({x})" + + @staticmethod + def truncdiv(a, b): + # a and b are integer type + return f"{a} / {b}" + + @staticmethod + def fmod(a, b): + return f"std::fmod({a}, {b})" + + @staticmethod + def isinf(x): + return f"std::isinf({x})" + + @staticmethod + def isnan(x): + return f"std::isnan({x})" + + @staticmethod + def lgamma(x): + return f"std::lgamma({x})" + + @staticmethod + def acos(x): + return f"std::acos({x})" + + @staticmethod + def acosh(x): + return f"std::acosh({x})" + + @staticmethod + def cosh(x): + return f"std::cosh({x})" + + @staticmethod + def sinh(x): + return f"std::sinh({x})" + + @staticmethod + def asin(x): + return f"std::asin({x})" + + @staticmethod + def asinh(x): + return f"std::asinh({x})" + + @staticmethod + def atan2(x, y): + return f"std::atan2({x}, {y})" + + @staticmethod + def atan(x): + return f"std::atan({x})" + + @staticmethod + def atanh(x): + return f"std::atanh({x})" + + @staticmethod + def copysign(x, y): + return f"std::copysign({x}, {y})" + + @staticmethod + def frexp(x): + cache_keys = f"frexp({x})[0]", f"frexp({x})[1]" + if all(cache_key in V.kernel.cse.cache for cache_key in cache_keys): + return tuple(V.kernel.cse.cache[cache_key] for cache_key in cache_keys) + + code = BracesBuffer() + exponent = V.kernel.cse.newvar() + mantissa = V.kernel.cse.newvar() + code.writeline(f"int32_t {exponent};") + code.writeline(f"auto {mantissa} = std::frexp({x}, &{exponent});") + V.kernel.compute.splice(code) + cse_vars = (mantissa, exponent) + for cache_key, cse_var in zip(cache_keys, cse_vars): + V.kernel.cse.cache[cache_key] = cse_var + return mantissa, exponent + + @staticmethod + def hypot(x, y): + return f"std::hypot({x}, {y})" + + @staticmethod + def log10(x): + return f"std::log10({x})" + + @staticmethod + def log2(x): + return f"std::log2({x})" + + @staticmethod + def nextafter(x, y): + return f"std::nextafter({x}, {y})" + + @staticmethod + def relu(x): + bug = config.cpp.inject_relu_bug_TESTING_ONLY + if bug == "compile_error": + return "compile error!" + elif bug == "runtime_error": + return f"{x}; throw 1" + elif bug == "accuracy": + return f"{x} + decltype({x})(1)" + elif bug is None: + return f"std::max({x}, decltype({x})(0))" + else: + raise AssertionError( + f"unrecognized config cpp.inject_relu_bug_TESTING_ONLY = {bug!r}" + ) + + @staticmethod + def minimum(a, b): + return f"min_propagate_nan({a}, {b})" + + @staticmethod + def maximum(a, b): + return f"max_propagate_nan({a}, {b})" + + @staticmethod + def where(a, b, c): + return f"{a} ? {b} : {c}" + + @staticmethod + def mod(a, b): + return f"mod({a}, {b})" + + @staticmethod + def constant(val, dtype): + if dtype in DTYPE_LOWP_FP: + # Since load promotes all half-precision inputs to float, constants + # must be promoted as well + dtype = torch.float32 + return value_to_cpp(val, DTYPE_TO_CPP[dtype]) + + @staticmethod + def index_expr(expr, dtype): + idx_str = cexpr(V.kernel.rename_indexing(expr)) + var = V.kernel.cse.generate( + V.kernel.compute, idx_str, bounds=get_bounds_index_expr(expr) + ) + return ops.to_dtype(var, dtype) + + @staticmethod + def masked(mask, body, other): + code = BracesBuffer() + + # Write masked operation into a lambda + body_var = V.kernel.cse.newvar() + code.writeline(f"auto {body_var} = [&]") + with V.kernel.swap_buffers(code), code.indent(): + result = body() + code.writeline(f"return {result};") + code.writeline(";") + V.kernel.compute.splice(code) + + # Use the lambda's return type as the type of other + other_code = value_to_cpp(other, f"decltype({body_var}())") + return f"{mask} ? {body_var}() : {other_code}" + + @staticmethod + def logical_and(a, b): + return f"{a} && {b}" + + @staticmethod + def logical_not(a): + return f"!{a}" + + @staticmethod + def logical_or(a, b): + return f"{a} || {b}" + + @staticmethod + def logical_xor(a, b): + return f"{a} != {b}" + + @staticmethod + def bitwise_and(a, b): + return f"decltype({a})({a} & {b})" + + @staticmethod + def bitwise_not(a): + return f"decltype({a})(~{a})" + + @staticmethod + def bitwise_or(a, b): + return f"decltype({a})({a} | {b})" + + @staticmethod + def bitwise_xor(a, b): + return f"decltype({a})({a} ^ {b})" + + @staticmethod + def bitwise_left_shift(a, b): + return f"decltype({a})({a} << {b})" + + @staticmethod + def bitwise_right_shift(a, b): + return f"decltype({a})({a} >> {b})" + + @staticmethod + def rand(seed: sympy.Expr, offset: sympy.Expr): + return f"normalized_rand_cpu({seed}, {offset})" + + @staticmethod + def randn(seed: sympy.Expr, offset: sympy.Expr): + return f"randn_cpu({seed}, {offset})" + + @staticmethod + def randint64(seed: sympy.Expr, offset: sympy.Expr, low, high): + return f"randint64_cpu({seed}, {offset}, {low}, {high})" + + @staticmethod + def sigmoid(x): + return f"decltype({x})(1) / (decltype({x})(1) + std::exp(-{x}))" + + @staticmethod + def sign(x): + code = BracesBuffer() + scalar_zero = f"decltype({x})(0)" + scalar_one = f"decltype({x})(1)" + code.writeline("[&]()") + with code.indent(): + code.writeline(f"auto left = {x} > 0 ? {scalar_one} : {scalar_zero};") + code.writeline(f"auto right = {x} < 0 ? {scalar_one} : {scalar_zero};") + code.writeline("return left - right;") + code.writeline("()") + return code + + +CppOverrides._initialize_pointwise_overrides("cpp") + + +class CppVecOverrides(CppOverrides): + """Map element-wise ops to aten vectorization C++""" + + def __new__(cls, *args, **kargs): + self = super().__new__(cls) + + def wrap(func): + # `CppVecKernel` generates both scalar ops and vector ops according to + # whether the inputs are scalars or vectors while all ops in `CppVecOverrides` + # (except for some ops explained below) assume the inputs are vectors. We wrap the ops in + # `CppVecOverrides` to broadcast scalar inputs to vectors if needed or fallback to + # `CppOverrides` when all inputs are scalars. + # + # Notes on ops handled separately in their own functions: + # `ops.masked`: + # needs recursive handling of masked body. + # `ops.index_expr`: + # needs to further analyze the dependency of the index expression on + # the tiling itervar. + def wrapper(*args, **kwargs): + scalars = [ + arg + for arg in args + if isinstance(arg, (int, sympy.Expr)) + or (isinstance(arg, CppCSEVariable) and not arg.is_vec) + ] + vectors = [ + arg + for arg in args + if isinstance(arg, CppCSEVariable) and arg.is_vec + ] + new_args = list(args) + if scalars and vectors: + new_args = [] + for arg in args: + if isinstance(arg, (int, sympy.Expr)): + if isinstance(arg, sympy.Expr) and not arg.is_number: + arg = ops.index_expr(arg, torch.int64) + else: + arg = ops.constant(arg, torch.int64) + arg = arg.value if isinstance(arg, OpsValue) else arg + new_args.append(arg) + + # DType Promotion + if vectors: + # We have saw several data type mismatch issues related with index_expr in + # the lowering phase of torch.int8. torch.int32, torch.int64. + # 1. int32 and int64 in test_torchinductor.py::test_max_pool2d_with_indices_backward3_cpu + # 2. int8 and int32 in test_torchinductor.py::test_max_pool2d5_cpu + # 3. int32 and fp32 in test_torchinductor_dynamic_shapes.py::test_avg_pool2d8_dynamic_shapes_cpu + if len(new_args) == 2: + new_args = promote_args(new_args) + elif func == CppVecOverrides.where: + new_args[1:] = promote_args(new_args[1:]) + + # Broadcast scalar args to vector + if scalars and vectors: + assert isinstance(V.kernel, CppVecKernel) + new_args = [ + V.kernel.broadcast(new_arg) + if ( + isinstance(new_arg, CppCSEVariable) + and not new_arg.is_vec + and func + not in [ + CppVecOverrides.rand, + CppVecOverrides.randn, + CppVecOverrides.randint64, + ] + ) + else new_arg + for new_arg in new_args + ] + + if vectors: + return func(*new_args, **kwargs) + else: + # fallback to scalar ops + scalar_ops = super(CppVecOverrides, self) + scalar_func = getattr( + scalar_ops, func.__name__, scalar_ops.__getattr__(func.__name__) # type: ignore[attr-defined] + ) + assert scalar_func is not None + return scalar_func(*args, **kwargs) + + return wrapper + + for name, method in vars(CppVecOverrides).items(): + if getattr(method, "__class__", None) == staticmethod and name not in [ + "masked", + "index_expr", + ]: + setattr(self, name, wrap(method.__func__)) + + return self + + @staticmethod + def add(a, b): + return f"{a} + {b}" + + @staticmethod + def sub(a, b): + return f"{a} - {b}" + + @staticmethod + def mul(a, b): + return f"{a} * {b}" + + @staticmethod + def truediv(a, b): + return f"{a} / {b}" + + @staticmethod + def abs(x): + return f"{x}.abs()" + + @staticmethod + def sin(x): + return f"{x}.sin()" + + @staticmethod + def cos(x): + return f"{x}.cos()" + + @staticmethod + def exp(x): + return f"{x}.exp()" + + @staticmethod + def exp2(x): + return f"{x}.exp2()" + + @staticmethod + def expm1(x): + # decompose for a better performance + vec_one = f"decltype({x})(1)" + return f"{x}.exp() - {vec_one}" + + @staticmethod + def erf(x): + return f"{x}.erf()" + + @staticmethod + def erfc(x): + return f"{x}.erfc()" + + @staticmethod + def erfinv(x): + return f"{x}.erfinv()" + + @staticmethod + def sqrt(x): + return f"{x}.sqrt()" + + @staticmethod + def eq(x, y): + assert isinstance(V.kernel, CppVecKernel) + assert isinstance(x, CppCSEVariable) + assert x.dtype is not None + return f"{V.kernel._get_mask_type(x.dtype)}({x} == {y})" + + @staticmethod + def ne(x, y): + assert isinstance(V.kernel, CppVecKernel) + assert isinstance(x, CppCSEVariable) + if x.dtype == torch.bool: + assert y.dtype == torch.bool + x_cast, y_cast = unify_mask_base_type(V.kernel.compute, (x, y)) + return f"{x_cast} != {y_cast}" + else: + assert x.dtype is not None + return f"{V.kernel._get_mask_type(x.dtype)}({x} != {y})" + + @staticmethod + def lt(x, y): + assert isinstance(V.kernel, CppVecKernel) + assert isinstance(x, CppCSEVariable) + assert x.dtype is not None + return f"{V.kernel._get_mask_type(x.dtype)}({x} < {y})" + + @staticmethod + def gt(x, y): + assert isinstance(V.kernel, CppVecKernel) + assert isinstance(x, CppCSEVariable) + assert x.dtype is not None + return f"{V.kernel._get_mask_type(x.dtype)}({x} > {y})" + + @staticmethod + def le(x, y): + assert isinstance(V.kernel, CppVecKernel) + assert isinstance(x, CppCSEVariable) + assert x.dtype is not None + return f"{V.kernel._get_mask_type(x.dtype)}({x} <= {y})" + + @staticmethod + def ge(x, y): + assert isinstance(V.kernel, CppVecKernel) + assert isinstance(x, CppCSEVariable) + assert x.dtype is not None + return f"{V.kernel._get_mask_type(x.dtype)}({x} >= {y})" + + @staticmethod + def and_(x, y): + return f"{x} & {y}" + + @staticmethod + def rsqrt(x): + return f"{x}.rsqrt()" + + @staticmethod + def pow(a, b): + return f"{a}.pow({b})" + + @staticmethod + def log(x): + return f"{x}.log()" + + @staticmethod + def round(x): + return f"{x}.round()" + + @staticmethod + def floor(x): + return f"{x}.floor()" + + @staticmethod + def ceil(x): + return f"{x}.ceil()" + + @staticmethod + def trunc(x): + return f"{x}.trunc()" + + @staticmethod + def fmod(a, b): + return f"{a}.fmod({b})" + + @staticmethod + def lgamma(x): + return f"{x}.lgamma()" + + @staticmethod + def logical_and(a, b): + return f"{a} & {b}" + + @staticmethod + def logical_not(a): + return f"~{a}" + + @staticmethod + def logical_or(a, b): + return f"{a} | {b}" + + @staticmethod + def logical_xor(a, b): + return f"{a} ^ {b}" + + @staticmethod + def bitwise_and(a, b): + return f"{a} & {b}" + + @staticmethod + def bitwise_not(a): + return f"~{a}" + + @staticmethod + def bitwise_or(a, b): + return f"{a} | {b}" + + @staticmethod + def bitwise_xor(a, b): + return f"{a} ^ {b}" + + @staticmethod + def bitwise_left_shift(a, b): + return f"{a} << {b}" + + @staticmethod + def bitwise_right_shift(a, b): + return f"{a} >> {b}" + + @staticmethod + def load_seed(name, offset): + assert isinstance(V.kernel, CppVecKernel) + return f"{V.kernel.load(name, offset)}" + + @staticmethod + def rand(seed, offset): + assert isinstance(V.kernel, CppVecKernel) + code = BracesBuffer() + rand_function = ( + f"result[offset_idx] = normalized_rand_cpu({seed}, offset[offset_idx]);" + ) + return codegen_rand(offset, code, rand_function) + + @staticmethod + def randn(seed, offset): + assert isinstance(V.kernel, CppVecKernel) + code = BracesBuffer() + rand_function = f"result[offset_idx] = randn_cpu({seed}, offset[offset_idx]);" + return codegen_rand(offset, code, rand_function) + + @staticmethod + def randint64(seed, offset, low, high): + assert isinstance(V.kernel, CppVecKernel) + code = BracesBuffer() + rand_function = f"result[offset_idx] = randint64_cpu({seed}, offset[offset_idx], {low}, {high});" + return codegen_rand(offset, code, rand_function, torch.int64) + + @staticmethod + def remainder(a, b): + assert ( + a.dtype == b.dtype + ), "remainder vec implementation expect the same inputs' dtype." + return f"{a} - ({CppVecOverrides.floordiv(a, b)}) * {b}" + + @staticmethod + def tan(a): + return f"{a}.tan()" + + @staticmethod + def tanh(a): + vec_one = f"decltype({a})(1)" + vec_two = f"decltype({a})(2)" + vec_minus_two = f"decltype({a})(-2)" + return f"{vec_two} / ({vec_one} + ({vec_minus_two} * {a}).exp()) - {vec_one}" + + @staticmethod + def reciprocal(a): + return f"{a}.reciprocal()" + + @staticmethod + def atan(x): + return f"{x}.atan()" + + @staticmethod + def acos(x): + return f"{x}.acos()" + + @staticmethod + def asin(x): + return f"{x}.asin()" + + @staticmethod + def cosh(x): + return f"{x}.cosh()" + + @staticmethod + def sinh(x): + return f"{x}.sinh()" + + @staticmethod + def log10(x): + return f"{x}.log10()" + + @staticmethod + def log2(x): + return f"{x}.log2()" + + @staticmethod + def nextafter(x, y): + return f"{x}.nextafter({y})" + + @staticmethod + def copysign(a, b): + return f"{a}.copysign({b})" + + @staticmethod + def atan2(a, b): + return f"{a}.atan2({b})" + + @staticmethod + def hypot(a, b): + return f"{a}.hypot({b})" + + @staticmethod + def atanh(x): + # For real x, atanh(x) = 1/2 * log((1+x)/(1-x)) + vec_one = f"decltype({x})(1)" + vec_one_half = f"decltype({x})(0.5)" + return f"{vec_one_half} * (({vec_one} + {x})/({vec_one} - {x})).log()" + + @staticmethod + def asinh(x): + # For real x, asinh(x) = log(x + sqrt(1 + x**2)) + vec_one = f"decltype({x})(1)" + return f"({x} + ({vec_one} + {x}*{x}).sqrt()).log()" + + @staticmethod + def acosh(x): + return f"{x}.acosh()" + + @staticmethod + def relu(x): + bug = config.cpp.inject_relu_bug_TESTING_ONLY + if bug == "compile_error": + return "compile error!" + elif bug == "runtime_error": + return f"{x}; throw 1" + elif bug == "accuracy": + return f"{x} + decltype({x})(1)" + elif bug is None: + return f"at::vec::clamp_min({x}, decltype({x})(0))" + else: + raise AssertionError( + f"unrecognized config cpp.inject_relu_bug_TESTING_ONLY = {bug!r}" + ) + + # TODO: this seems to be dead + @staticmethod + def sigmoid(x): + return f"decltype({x})(1)/(decltype({x})(1) + {x}.neg().exp())" + + @staticmethod + def neg(x): + return f"{x}.neg()" + + @staticmethod + def floordiv(a, b): + if is_float_dtype(a.dtype): + assert ( + a.dtype == b.dtype + ), "div_floor_floating_vec implementation expect the same inputs' dtype." + return f"div_floor_floating_vec({a}, {b})" + else: + assert all(is_integer_dtype(item.dtype) for item in [a, b]) + # a and b are integer type + _t = f"decltype({a})" + if V.kernel._get_raw_num_vectors(b.dtype) < 1: + # Doing blend to set the remaining bits of b to non-zero + b = f"{_t}::blend<{(1 << V.kernel.tiling_factor) - 1}>({_t}(1), {b})" + quot = f"{a} / {b}" + has_rem = f"({a} % {b} != {_t}(0))" + is_neg = f"(({a} < {_t}(0)) != ({b} < {_t}(0)))" + return f"{_t}::blendv({quot}, {quot} - {_t}(1), {has_rem} & {is_neg})" + + @staticmethod + def truncdiv(a, b): + # a and b are integer type + if V.kernel._get_raw_num_vectors(b.dtype) < 1: + # Doing blend to set the remaining bits of b to non-zero + _t = f"decltype({b})" + b = f"{_t}::blend<{(1 << V.kernel.tiling_factor) - 1}>({_t}(1), {b})" + return f"{a} / {b}" + + @staticmethod + def minimum(a, b): + if a.dtype == torch.bool: + assert b.dtype == torch.bool + a_cast, b_cast = unify_mask_base_type(V.kernel.compute, (a, b)) + return f"{a_cast} & {b_cast}" + else: + return f"at::vec::minimum({a}, {b})" + + @staticmethod + def maximum(a, b): + if a.dtype == torch.bool: + assert b.dtype == torch.bool + a_cast, b_cast = unify_mask_base_type(V.kernel.compute, (a, b)) + return f"{a_cast} | {b_cast}" + else: + return f"at::vec::maximum({a}, {b})" + + @staticmethod + def square(a): + return f"{a} * {a}" + + @staticmethod + def where(a, b, c): + assert isinstance(V.kernel, CppVecKernel) + if b.dtype == torch.bool: + assert c.dtype == torch.bool + blendv_a, blendv_b, blendv_c = unify_mask_base_type( + V.kernel.compute, (a, b, c) + ) + return f"decltype({blendv_b})::blendv({blendv_c}, {blendv_b}, {blendv_a})" + else: + return f"decltype({b})::blendv({c}, {b}, {V.kernel._get_mask_cast(a, b.dtype)})" + + @staticmethod + def sign(x): + code = BracesBuffer() + vec_zero = f"decltype({x})(0)" + vec_one = f"decltype({x})(1)" + blendv_l = f"decltype({x})::blendv({vec_zero}, {vec_one}, {vec_zero} < {x})" + blendv_r = f"decltype({x})::blendv({vec_zero}, {vec_one}, {x} < {vec_zero})" + code.writeline("[&]()") + with code.indent(): + code.writeline(f"auto left = {blendv_l};") + code.writeline(f"auto right = {blendv_r};") + code.writeline("return left - right;") + code.writeline("()") + return code + + @staticmethod + def to_dtype(x, dtype, src_dtype=None, use_compute_dtypes=True): + assert dtype in [ + torch.bool, + torch.float64, + torch.float, + torch.bfloat16, + torch.float16, + torch.uint8, + torch.int8, + torch.int32, + torch.int64, + ], f"{__name__} does not support {dtype}" + assert isinstance(x, CppCSEVariable) + src_dtype = x.dtype + expr = V.kernel.get_to_dtype_expr(x, dtype, src_dtype) + csevar = V.kernel.cse.generate(V.kernel.compute, expr) + csevar.update_on_args("to_dtype", (x, dtype), {"src_dtype": src_dtype}) + if dtype in [torch.bfloat16, torch.float16] and src_dtype == torch.float: + V.kernel.cache_dtype_convert(x, src_dtype, csevar, dtype) + return csevar + + @staticmethod + def log1p(x): + bug = config.cpp.inject_log1p_bug_TESTING_ONLY + if bug == "accuracy": + return f"{x} + decltype({x})(1)" + elif bug is None: + return f"{x}.log1p()" + else: + raise AssertionError( + f"unrecognized config cpp.inject_log1p_bug_TESTING_ONLY = {bug!r}" + ) + + @staticmethod + def masked(mask, body, other): + assert isinstance(V.kernel, CppVecKernel) + code = BracesBuffer() + var = V.kernel.cse.newvar() + with V.kernel.masked(mask) as new_mask: + code.writeline(f"auto {var} = [&]") + with V.kernel.swap_buffers(code), code.indent(): + result = body() + code.writeline(f"return {result};") + code.writeline(";") + V.kernel.compute.splice(code) + + dtype = result.dtype + body_code = f"{var}()" + body_code_vec = ( + body_code + if result.is_vec + else f"{V.kernel._get_vec_type(dtype)}({body_code})" + ) + other_code = value_to_cpp(other, DTYPE_TO_CPP[dtype]) + # loading bool as VecMask + other_code_vec = ( + f"{V.kernel._get_mask_type()}::from({other_code})" + if dtype == torch.bool + else f"{V.kernel._get_vec_type(dtype)}({other_code})" + ) + assert isinstance(new_mask, CppCSEVariable), new_mask + if new_mask.is_vec: + code = BracesBuffer() + code.writeline("[&]") + with V.kernel.swap_buffers(code), code.indent(): + code.writeline(f"if ({new_mask}.all_zero())") + with code.indent(): + code.writeline(f"return {other_code_vec};") + code.writeline("else") + with code.indent(): + # Create cse variable to reuse kernel.overrides.where + body_vec_var = V.kernel.cse.generate( + V.kernel.compute, + body_code_vec, + ) + other_vec_var = V.kernel.cse.generate( + V.kernel.compute, + other_code_vec, + ) + assert isinstance(body_vec_var, CppCSEVariable), body_vec_var + assert isinstance(other_vec_var, CppCSEVariable), other_vec_var + body_vec_var.dtype = dtype + other_vec_var.dtype = dtype + code.writeline( + f"return {V.kernel.overrides.where(new_mask, body_vec_var, other_vec_var)};" + ) + code.writeline("()") + csevar = V.kernel.cse.generate( + V.kernel.compute, + code, + ) + elif result.is_vec: + csevar = V.kernel.cse.generate( + V.kernel.compute, f"{mask} ? {body_code_vec} : {other_code_vec}" + ) + else: + csevar = V.kernel.cse.generate( + V.kernel.compute, f"{mask} ? {body_code} : {other_code}" + ) + # `result` is explicitly added to the args for correct propagation + # of relevant itervars and vectorization status. + csevar.update_on_args("masked", (mask, body, other, result), {}) + return csevar + + @staticmethod + def index_expr(expr, dtype): + assert isinstance(V.kernel, CppVecKernel) + index = V.kernel.rename_indexing(expr) + tiling_var = V.kernel.itervars[V.kernel.tiling_idx] + stride = V.kernel._try_get_const_stride(index, tiling_var) + if stride == 0: + return CppOverrides.index_expr(expr, dtype) + elif stride is not None: + idx = V.kernel.cse.generate( + V.kernel.compute, cexpr(index), bounds=get_bounds_index_expr(expr) + ) + value = ops.to_dtype(idx, dtype) + if isinstance(value, OpsValue): + value = value.value + csevar = V.kernel.arange(value, stride) + else: + csevar = V.kernel._load_or_store_non_contiguous( # type: ignore[assignment] + None, index, dtype, V.kernel.compute + ) + csevar.update_on_args("index_expr", (expr, dtype), {}) + return csevar + + @staticmethod + def frexp(x): + cache_keys = f"frexp({x})[0]", f"frexp({x})[1]" + if all(cache_key in V.kernel.cse.cache for cache_key in cache_keys): + return tuple(V.kernel.cse.cache[cache_key] for cache_key in cache_keys) + + cdtype = DTYPE_TO_CPP[x.dtype] + size = V.kernel.tail_size if V.kernel.tail_size else V.kernel.tiling_factor + code = BracesBuffer() + exponent = V.kernel.cse.newvar() + mantissa = V.kernel.cse.newvar() + exponent.update_on_args("frexp", (x,), kwargs={}) + mantissa.update_on_args("frexp", (x,), kwargs={}) + n_vec = V.kernel._get_num_vectors(x.dtype) + mantissa_t = ( + f"at::vec::Vectorized<{cdtype}>" + if n_vec == 1 + else f"at::vec::VectorizedN<{cdtype}, {n_vec}>" + ) + code.writeline( + f"at::vec::Vectorized {exponent};" + if n_vec == 1 + else f"at::vec::VectorizedN {exponent};" + ) + code.writeline(f"{mantissa_t} {mantissa};") + code.writeline("[&]()") + with code.indent(): + code.writeline( + f"__at_align__ std::array<{cdtype}, {V.kernel.tiling_factor}> tmpbuf;" + ) + code.writeline(f"{x}.store(tmpbuf.data(), {cexpr_index(size)});") + code.writeline( + f"__at_align__ std::array tmpbuf_exponent;" + ) + code.writeline( + f"__at_align__ std::array<{cdtype}, {V.kernel.tiling_factor}> tmpbuf_mantissa;" + ) + code.writeline(f"for (int i = 0; i < {cexpr_index(size)}; i++)") + with code.indent(): + code.writeline( + "tmpbuf_mantissa[i] = std::frexp(tmpbuf[i], &tmpbuf_exponent[i]);" + ) + code.writeline( + f"{exponent} = at::vec::Vectorized::loadu(tmpbuf_exponent.data(), {cexpr_index(size)});" + if n_vec == 1 + else f"{exponent} = at::vec::VectorizedN::loadu(tmpbuf_exponent.data(), {cexpr_index(size)});" + ) + code.writeline( + f"{mantissa} = {mantissa_t}::loadu(tmpbuf_mantissa.data(), {cexpr_index(size)});" + ) + code.writeline("();") + V.kernel.compute.splice(code) + cse_vars = (mantissa, exponent) + for cache_key, cse_var in zip(cache_keys, cse_vars): + V.kernel.cse.cache[cache_key] = cse_var + return mantissa, exponent + + @classmethod + def scalarize(cls, scalar_func): + def inner(*args, **kwargs): + assert not kwargs + kernel = V.kernel + assert isinstance(kernel, CppVecKernel) + code = BracesBuffer() + code.writeline("[&]()") + vec_dtype = args[0].dtype + n_vec = kernel._get_num_vectors(vec_dtype) + size = kernel.tail_size if kernel.tail_size else kernel.tiling_factor + scalar_args = [] + cdtype = DTYPE_TO_CPP[vec_dtype] + output_mask = scalar_func.__name__ in ( + "isinf", + "isnan", + "signbit", + ) + octype = "bool" if output_mask else cdtype + octype = ( + DTYPE_TO_CPP[args[-2]] + if (scalar_func.__name__ == "to_dtype_bitcast") + else octype + ) + with code.indent(): + for argidx, arg in enumerate(args): + if isinstance(arg, CppCSEVariable): + assert arg.is_vec + assert arg.dtype == vec_dtype + code.writeline( + f"__at_align__ std::array<{cdtype}, {kernel.tiling_factor}> tmpbuf{argidx};" + ) + code.writeline( + f"{arg}.store(tmpbuf{argidx}.data(), {cexpr_index(size)});" + ) + scalar_args.append(f"tmpbuf{argidx}[i]") + else: + scalar_args.append(arg) + code.writeline( + f"__at_align__ std::array<{octype}, {kernel.tiling_factor}> tmpbuf_out;" + ) + res = scalar_func(*scalar_args) + code.writeline(f"for (int i = 0; i < {cexpr_index(size)}; i++)") + with code.indent(): + code.writeline(f"tmpbuf_out[i] = {res};") + if output_mask: + assert not kernel.tail_size + load_args = "tmpbuf_out.data()" + load_fn = f"at::vec::VecMask<{cdtype},{n_vec}>::from" + else: + load_args = f"tmpbuf_out.data(), {cexpr_index(size)}" + if n_vec == 1: + load_fn = f"at::vec::Vectorized<{octype}>::loadu" + else: + load_fn = f" at::vec::VectorizedN<{octype}, {n_vec}>::loadu" + code.writeline(f"return {load_fn}({load_args});") + code.writeline("()") + return code + + return inner + + @classmethod + def _initialize_scalarize(cls): + for name, method in vars(CppOverrides).items(): + if getattr(method, "__class__", None) == staticmethod and name not in vars( + CppVecOverrides + ): + func = cls.scalarize(method.__func__) + func.__name__ = name + setattr(cls, name, staticmethod(func)) + + +CppVecOverrides._initialize_pointwise_overrides("cppvec") +CppVecOverrides._initialize_scalarize() + + +class CppTile2DOverrides(CppVecOverrides): + @staticmethod + def index_expr(expr, dtype): + assert isinstance(V.kernel, CppTile2DKernel) + expr = V.kernel.transform_indexing(expr) + return CppVecOverrides.index_expr(expr, dtype) + + +class CppKernel(Kernel): + overrides = CppOverrides # type: ignore[assignment] + sexpr = cexpr + newvar_prefix = "auto " + suffix = ";" + + def __init__(self, args, num_threads): + super().__init__(args) + self.call_ranges: Optional[Tuple[sympy.Expr, ...]] = None + self.ranges: List[sympy.Expr] = [] + self.itervars: List[sympy.Symbol] = [] + self.reduction_depth = None + self.reduction_prefix = IndentedBuffer() + self.reduction_suffix = IndentedBuffer() + self.parallel_reduction_prefix = IndentedBuffer() + self.parallel_reduction_suffix = IndentedBuffer() + self.local_reduction_init = IndentedBuffer() + self.local_reduction_stores = IndentedBuffer() + self.is_reduction = False + self.non_parallel_reduction_prefix = IndentedBuffer() + self.reduction_cse = CSE(self.newvar_prefix, self.suffix, name_prefix="tmp_acc") + self.weight_recps_cse = CSE( + self.newvar_prefix, self.suffix, name_prefix="wrecps" + ) + self.preloads = IndentedBuffer() + self.poststores = IndentedBuffer() + self.num_threads = num_threads # num_threads the kernel specialized for + self.reduction_omp_dec: Dict[Tuple[str, str], str] = {} + + def _gen_parallel_reduction_buffers( + self, + acc, + acc_type, + reduction_type, + dtype, + reduction_combine_fn=reduction_combine, + reduction_init_fn=reduction_init, + welford_weight_reciprocal_vec_fn=None, + ): + if config.cpp.dynamic_threads and not self.parallel_reduction_prefix: + self.parallel_reduction_prefix.writeline( + "int max_threads = omp_get_max_threads();" + ) + acc_local = f"{acc}_local" + num_threads = ( + "max_threads" if config.cpp.dynamic_threads else parallel_num_threads() + ) + acc_per_thread_var_name = f"{acc}_arr" + acc_per_thread = f"{acc_per_thread_var_name}[{num_threads}]" + """ + MSVC don't support dynamic array(VLA). Please use std::unique_ptr to instead of it. + Ref: https://stackoverflow.com/questions/56555406/creating-dynamic-sized-array-using-msvc-c-compiler + MSVC is the only one compiler, which not support VLA. And MSVC can't get good inductor performance. + So, we can use unique_ptr make it works on MSVC. + For other compilers, we continue to use VLA to get best performence. + """ + acc_per_thread_unique_ptr_decl = f"auto {acc_per_thread_var_name} = std::make_unique<{acc_type}[]>({num_threads})" + acc_per_thread_vla_decl = f"{acc_per_thread_var_name}[{num_threads}]" + acc_local_in_array = acc_per_thread.replace(f"[{num_threads}]", "[tid]") + self.local_reduction_init.writeline( + f"{acc_type} {acc_local} = {reduction_init_fn(reduction_type, dtype)};" + ) + self.parallel_reduction_prefix.writeline( + f"{acc_per_thread_unique_ptr_decl};" + if cpp_builder.is_msvc_cl() + else f"{acc_type} {acc_per_thread_vla_decl};" + ) + self.parallel_reduction_prefix.writelines( + [ + f"for (int tid = 0; tid < {num_threads}; tid++)", + "{", + f" {acc_local_in_array} = {reduction_init_fn(reduction_type, dtype)};", + "}", + ], + ) + self.local_reduction_stores.writelines( + [ + f"{acc_local_in_array} = {acc_local};", + ] + ) + self.parallel_reduction_suffix.writelines( + [ + f"for (int tid = 0; tid < {num_threads}; tid++)", + "{", + f" {acc} = {reduction_combine_fn(reduction_type, acc, acc_local_in_array, src_dtype=dtype)};", + "}", + ], + ) + + def get_reduction_var_pattern(self, line: str): + return re.search("tmp_acc[0-9]+", line) + + def update_stores_with_parallel_reduction(self): + for i, line in enumerate(self.stores._lines): + if isinstance(line, str): + m = self.get_reduction_var_pattern(line) + if m: + var_name = m.group(0) + self.stores._lines[i] = line.replace(var_name, f"{var_name}_local") + + @contextlib.contextmanager + def masked(self, mask): + """Context manager to add an additional mask to loads and stores.""" + prior = self._load_mask + if prior: + mask = ops.and_(mask, prior) + if isinstance(mask, OpsValue): + mask = mask.value + assert isinstance(mask, CppCSEVariable) + # see NOTE [dtype of CppCSEVariable] + # mask's dtype should be bool + mask.dtype = torch.bool + + self._load_mask = mask + try: + yield mask + finally: + self._load_mask = prior + + def scale_index_with_offset( + self, index: sympy.Expr, scale=1, itervar_idx=-1, offset=0 + ): + var = self.itervars[itervar_idx] + replacement = {var: var * scale + offset} + new_index = sympy_subs(index, replacement) + return new_index + + def index_to_str(self, index: sympy.Expr) -> str: + """ + Convert an index expr to a string that can be used in cpp code. + e.g. a sympy expression "s2" may actually appear as "ks1" in the cpp kernel. + """ + return cexpr(self.rename_indexing(index)) + + def index_indirect_depends_on(self, index: sympy.Expr, itervar: sympy.Symbol): + """ + Check if an index has free symbol CppCSEVariable that depends on `itervar`. + """ + return any( + self.cse.varname_map[s.name].depends_on(itervar) # type: ignore[attr-defined] + for s in index.free_symbols + if s.name in self.cse.varname_map # type: ignore[attr-defined] + and isinstance(self.cse.varname_map[s.name], CppCSEVariable) # type: ignore[attr-defined] + ) + + def index_depends_on(self, index: sympy.Expr, itervar: sympy.Symbol): + return itervar in index.free_symbols or self.index_indirect_depends_on( + index, itervar + ) + + def var_ranges(self): + return dict(zip(self.itervars, self.ranges)) + + def check_bounds( + self, + expr: sympy.Expr, + size: sympy.Expr, + lower: bool, + upper: bool, + ): + if not (lower or upper): + return + + indirect = free_symbol_is_type(expr, SymT.TMP) + if indirect: + # indexing in compute + csevar = ops.index_expr(expr, torch.int64).value + buffer = V.kernel.compute + else: + # indexing in loads + prior_compute = V.kernel.compute + try: + V.kernel.compute = self.loads + csevar = ops.index_expr(expr, torch.int64).value + finally: + V.kernel.compute = prior_compute + buffer = self.loads + + size_str = V.kernel.sexpr(self.rename_indexing(size)) if upper else None + + line = self.indirect_assert( + csevar, "0" if lower else None, size_str, self._load_mask + ) + self.cse.generate(buffer, line, assignment=False) + + def load(self, name: str, index: sympy.Expr): + var = self.args.input(name) + index = self.rename_indexing(index) + line = f"{var}[{cexpr_index(index)}]" + csevar = self.cse.generate(self.loads, line) + csevar.update_on_args("load", (self, name, index), {}) + return csevar + + def store(self, name, index, value, mode=None): + assert "buf" in name + var = self.args.output(name) + index = self.rename_indexing(index) + if mode is None: + line = f"{var}[{cexpr_index(index)}] = {value};" + elif mode == "atomic_add": + if not config.cpp.dynamic_threads and self.num_threads == 1: + line = f"{var}[{cexpr_index(index)}] += {value};" + else: + dtype = V.graph.get_dtype(name) + # mirroring static_cast(...) in load: + value = f"static_cast<{DTYPE_TO_CPP[dtype]}>({value})" + line = f"atomic_add(&{var}[{cexpr_index(index)}], {value});" + else: + raise NotImplementedError(f"store mode={mode}") + self.stores.writeline(DeferredLine(name, line)) + + def reduction(self, dtype, src_dtype, reduction_type, value): + argmax_or_argmin = reduction_type in {"argmax", "argmin"} + reduction_key = src_dtype, reduction_type, value + if reduction_key in self.reduction_cse.reduction_cache: + return self.reduction_cse.reduction_cache[reduction_key] + + acc = self.reduction_cse.generate( + self.loads, f"reduction {reduction_key}", write=False + ) + self.is_reduction = True + init_dtype = src_dtype if argmax_or_argmin else dtype + acc_type = reduction_acc_type(reduction_type, init_dtype) + self.reduction_prefix.writeline( + f"{acc_type} {acc} = {reduction_init(reduction_type, init_dtype)};" + ) + assert self.reduction_depth is not None + index = self.itervars[self.reduction_depth] + for i in range(self.reduction_depth + 1, len(self.itervars)): + index = index * self.ranges[i] + self.itervars[i] + self.stores.writeline( + f"{acc} = {reduction_combine(reduction_type, acc, value, index)};" + ) + self._gen_parallel_reduction_buffers(acc, acc_type, reduction_type, init_dtype) + result = reduction_project(reduction_type, acc) + self.reduction_cse.reduction_cache[reduction_key] = result + return result + + def store_reduction(self, name, index, value): + index = self.rename_indexing(index) + var = self.args.output(name) + self.reduction_suffix.writeline( + DeferredLine(name, f"{var}[{cexpr_index(index)}] = {value};") + ) + + def set_ranges(self, lengths, reduction_lengths): + if self.call_ranges: + assert self.call_ranges == tuple(lengths) + tuple( + reduction_lengths + ), f"{self.call_ranges} == {tuple(lengths)} + {tuple(reduction_lengths)}" + assert self.reduction_depth == len(lengths) + else: + self.call_ranges = tuple(lengths) + tuple(reduction_lengths) + self.ranges = [self.rename_indexing(x) for x in self.call_ranges] + self.itervars = [ + sympy_index_symbol_with_prefix(SymT.XBLOCK, n) + for n in range(len(self.ranges)) + ] + self.reduction_depth = len(lengths) + return ( + self.itervars[: self.reduction_depth], + self.itervars[self.reduction_depth :], + ) + + def size_hint(self): + return V.graph.sizevars.size_hint( + sympy_product(self.call_ranges), fallback=8192 + ) + + def codegen_loops_impl(self, loop_nest, code, worksharing): + threads = parallel_num_threads() + assert self.call_ranges is not None + kernels = loop_nest.get_kernels() + has_outer_loop_kernel = any( + isinstance(kernel, OuterLoopFusedKernel) for kernel in kernels + ) + if has_outer_loop_kernel: + assert len(kernels) == 1 + assert isinstance(kernels[0], OuterLoopFusedKernel) + par_depth = kernels[0].decide_parallel_depth( + loop_nest.max_parallel_depth(), threads + ) + else: + par_depth = self.decide_parallel_depth( + loop_nest.max_parallel_depth(), threads + ) + + with contextlib.ExitStack() as stack: + if par_depth: + if loop_nest.is_reduction_only(): + # need to close the worksharing scope to define reduction vars outside it + worksharing.close() + else: + worksharing.parallel(threads) + loop_nest.mark_parallel(par_depth) + elif threads > 1: + if worksharing.single(): + stack.enter_context(code.indent()) + + def gen_loop_kernel(loop: LoopLevel): + def is_parallel_reduction(loop): + root = loop.get_root() + return root.is_reduction and root.parallel + + kernels = loop.get_kernels() + assert len(kernels) == 1 + if not isinstance( + kernels[0], OuterLoopFusedKernel + ) and is_parallel_reduction(loop): + kernels[0].update_stores_with_parallel_reduction() + gen_kernel(kernels[0]) + + def gen_kernel(kernel): + if isinstance(kernel, OuterLoopFusedKernel): + for loop in kernel.inner: + if loop.inner: + gen_loops(loop.inner, loop.is_reduction) + else: + with contextlib.ExitStack() as stack: + # If there is any kernel existing at the final outer loop fusion level, + # the kernel code should be placed within its respective indent to prevent + # the duplication of variable definitions. + stack.enter_context(code.indent()) + gen_loop_kernel(loop) + else: + with contextlib.ExitStack() as stack: + assert kernel + if hasattr(kernel, "codegen_inner_loops"): + code.splice(kernel.preloads) + kernel.codegen_inner_loops(code) + stack.enter_context(code.indent()) + code.splice(kernel.loads) + code.splice(kernel.compute) + code.splice(kernel.stores) + if hasattr(kernel, "codegen_inner_loops"): + code.splice(kernel.poststores) + + def get_reduction_code_buffer(loops, buffer="prefix"): + assert buffer in ("prefix", "suffix", "local") + for loop in loops: + for kernel in loop.get_kernels(): + if buffer == "local": + return ( + kernel.local_reduction_init, + kernel.local_reduction_stores, + ) + elif buffer == "suffix": + suffix = kernel.reduction_suffix + if loop.parallel: + suffix = kernel.parallel_reduction_suffix + suffix + return suffix + else: + prefix = kernel.reduction_prefix + if loop.parallel: + prefix = prefix + kernel.parallel_reduction_prefix + else: + prefix = prefix + kernel.non_parallel_reduction_prefix + return prefix + + def gen_loops(loops: List[LoopLevel], in_reduction=False): + with contextlib.ExitStack() as stack_outer: + local_reduction_init = local_reduction_stores = None + if loops: + loop = loops[0] + if loop.is_reduction and not in_reduction: + reduction_prefix = get_reduction_code_buffer(loops) + if reduction_prefix: + stack_outer.enter_context(code.indent()) + code.splice(reduction_prefix) + if loop_nest.is_reduction_only() and loop.parallel: + ( + local_reduction_init, + local_reduction_stores, + ) = get_reduction_code_buffer(loops, "local") + worksharing.parallel(threads) + if local_reduction_init: + assert local_reduction_stores + code.splice(local_reduction_init) + + for loop in loops: + gen_loop(loop) + + if loops: + loop = loops[0] + if loop_nest.is_reduction_only() and loop.parallel: + if local_reduction_stores: + code.splice(local_reduction_stores) + worksharing.close() + if loop.is_reduction and not in_reduction: + code.splice(get_reduction_code_buffer(loops, "suffix")) + + def gen_loop(loop: LoopLevel): + with contextlib.ExitStack() as stack: + loop_lines = loop.lines() + if loop_lines is None: + return + code.writelines(loop_lines) + stack.enter_context(code.indent()) + # generate inner loops or loop body + if loop.inner: + gen_loops(loop.inner, loop.is_reduction) + else: + gen_loop_kernel(loop) + + stack.enter_context(code.indent()) + if loop_nest.root: + if ( + has_outer_loop_kernel + and isinstance(V.local_buffer_context, LocalBufferContext) + and V.local_buffer_context.local_buffers + ): + # Allocate local buffer + local_buffers = V.local_buffer_context.local_buffers + for local_buffer in local_buffers.values(): + # For dynamic size, rename s to ks + local_buf_size = sympy_product( + [ + self.rename_indexing(size_val) + for size_val in local_buffer.get_layout().size + ] + ) + local_buf_dtype = DTYPE_TO_CPP[local_buffer.get_layout().dtype] + allocate = f"std::make_unique<{local_buf_dtype} []>({cexpr(local_buf_size)})" + local_buffer_name = local_buffer.get_name() + code.splice( + f"std::unique_ptr<{local_buf_dtype} []> buf_{local_buffer_name} = {allocate};" + ) + code.splice( + f"{local_buf_dtype}* {local_buffer_name} = buf_{local_buffer_name}.get();" + ) + gen_loops(loop_nest.root) + else: + gen_kernel(loop_nest.kernel) + + def codegen_loops(self, code, worksharing): + loop_nest = LoopNestWithSplit.build(self) + self.codegen_loops_impl(loop_nest, code, worksharing) + + @property + def assert_function(self) -> str: + if V.graph.aot_mode: + # TODO: Using AOTI_TORCH_CHECK is causing performance drop for some models + # compared with JIT Inductor which uses TORCH_CHECK + return "AOTI_TORCH_CHECK" + else: + return "TORCH_CHECK" + + def decide_parallel_depth(self, max_parallel_depth, threads): + assert self.call_ranges is not None + ranges = self.call_ranges[:max_parallel_depth] + seq = self.size_hint() + par = 1 + depth = 0 + for expr in ranges: + hint = V.graph.sizevars.size_hint(expr, fallback=8192) + if par >= 2 * threads or par == threads: + break + if seq // threads < config.cpp.min_chunk_size: + # not enough work + break + depth += 1 + par *= hint + seq /= hint + # if we assume thread number is dynamic, make sure we + # have at least one parallel scope and let OMP runtime + # to manage the serial vs. parallel. + if config.cpp.dynamic_threads and depth == 0 and len(ranges) > 0: + depth = 1 + return depth + + @contextlib.contextmanager + def write_to_suffix(self): + prior = (self.loads, self.compute, self.stores, self.cse) + self.loads = IndentedBuffer() + self.compute = IndentedBuffer() + self.stores = IndentedBuffer() + self.cse = self.cse.clone() + yield + self.reduction_suffix.splice(self.loads) + self.reduction_suffix.splice(self.compute) + self.reduction_suffix.splice(self.stores) + (self.loads, self.compute, self.stores, self.cse) = prior + + def create_cse_var(self, *args, **kwargs): + return CppCSEVariable(*args, **kwargs) + + def get_to_dtype_expr(self, src, dtype, src_dtype): + return f"c10::convert<{DTYPE_TO_CPP[dtype]}>({src})" + + def cache_dtype_convert(self, dst, dst_dtype, src, src_dtype): + expr = self.get_to_dtype_expr(src, dst_dtype, src_dtype) + self.cse.cache[expr] = dst + + +class CppVecKernel(CppKernel): + overrides = CppVecOverrides # type: ignore[assignment] + + def __init__( + self, + args, + num_threads, + tiling_factor, + tiling_idx, + tail_size=None, + ): + super().__init__(args, num_threads) + self.vec_isa = cpu_vec_isa.pick_vec_isa() + assert self.vec_isa + assert tiling_factor > 0, "Expect pass in Non-Zero tiling_factor explicitly" + self.tiling_factor = tiling_factor + self.tiling_idx = tiling_idx + self.tail_size = tail_size + self.num_elems = tail_size if tail_size else tiling_factor + + def _try_get_const_stride(self, index: sympy.Expr, itervar: sympy.Symbol): + if self.index_indirect_depends_on(index, itervar): + return None + for indirect_var in ( + self.cse.varname_map[s.name] # type: ignore[attr-defined] + for s in index.free_symbols + if symbol_is_type(s, SymT.TMP) + ): + assert isinstance(indirect_var, CppCSEVariable) + if indirect_var.is_vec: + return None + stride = stride_at_vec_range(index, itervar, self.tiling_factor) + return stride if stride.is_number else None + + def _get_num_vectors(self, dtype: torch.dtype) -> int: + num_vectors = math.ceil( + self.tiling_factor * dtype.itemsize * 8 / self.vec_isa.bit_width() + ) + assert num_vectors >= 1 + return num_vectors + + def _get_raw_num_vectors(self, dtype: torch.dtype) -> float: + # This utility function is used to check if the vector lanes has been + # fully utilized. For example, uint8 will only use 1/4 of the vector lanes. + return self.tiling_factor * dtype.itemsize * 8 / self.vec_isa.bit_width() + + def _get_vec_type(self, dtype: torch.dtype) -> str: + num_vectors = self._get_num_vectors(dtype) + if num_vectors == 1: + return f"at::vec::Vectorized<{DTYPE_TO_CPP[dtype]}>" + else: + return f"at::vec::VectorizedN<{DTYPE_TO_CPP[dtype]},{num_vectors}>" + + def _get_mask_type(self, dtype: torch.dtype = torch.float) -> str: + if dtype == torch.bool: + return "" + num_vectors = self._get_num_vectors(dtype) + return f"at::vec::VecMask<{DTYPE_TO_CPP[dtype]},{num_vectors}>" + + def _get_mask_cast(self, mask: CppCSEVariable, dtype: torch.dtype) -> str: + assert mask.dtype == torch.bool, repr(mask) + num_vectors = self._get_num_vectors(dtype) + return f"{mask}.template cast<{DTYPE_TO_CPP[dtype]},{num_vectors}>()" + + def get_reduction_var_pattern(self, line: str): + return re.search("tmp_acc[0-9]+_vec", line) + + def _get_vec_load_line( + self, + var: str, + index: sympy.Expr, + dtype: torch.dtype, + load_mask: Optional[CppCSEVariable] = None, + ): + """ + Get a load line str that loads a vector from `var` at `index` of type `dtype`. + If `load_mask` is not None, we do a masked load accordingly. + Notes on the `dtype`: + 1. We always load `self.tiling_factor` number of elements regardless of the `dtype`. + It means we load half of the vector lanes for 16-bit data types and quarter of the + vector lanes for 8-bit data types. + 2. `torch.bool` and `torch.uint8` could mean masks and we load them as float mask vectors. + """ + cpp_type = DTYPE_TO_CPP[dtype] + num_vectors = self._get_num_vectors(dtype) + load_mask_str = None + if load_mask: + if not load_mask.is_vec: + # TODO: avoid hard-code torch.float + load_mask_str = f"{self._get_mask_type(torch.float)}::from({load_mask})" + else: + load_mask_str = f"{self._get_mask_cast(load_mask, torch.float)}" + loadbuf = f"{var} + {cexpr_index(index)}" if index != 0 else var + if dtype == torch.bool: + # TODO: should we consider load mask here? + line = f"{self._get_mask_type()}::from({loadbuf})" + else: + line = ( + f"{load_mask_str}.template loadu<{cpp_type},{num_vectors}>({loadbuf})" + if load_mask_str + else f"{self._get_vec_type(dtype)}::loadu({loadbuf}, {cexpr_index(self.num_elems)})" + ) + return line + + def _load_or_store_non_contiguous( + self, + var: Optional[str], + index: sympy.Expr, + dtype: torch.dtype, + buffer: Optional[IndentedBuffer] = None, + store_value: Optional[Union[str, CppCSEVariable]] = None, + accu_store: bool = False, + ) -> Optional[CppCSEVariable]: + """ + Load or store a vector in a non-contiguous way. The vector is initialized from an array that is + filled in an inner loop over the tiling factor. + :param var: buffer to load from or store to, i.e. `var[transformed(index)]`. If None, we load the index + as index expression, i.e. `transformed(index)`. + :param index: index into the `var` or the index expression by its own if `var` is None. + The `index` could contain indirect indexing or the tiling itervar. When used in + the inner loop, the index is transformed as follows: + 1. the index is linearized along the tiling dim. + 2. the indirect indexing vector variables are transformed into arrays over the tiling dim. + :param dtype: data type of `var` or `index` if `var` is None. + :param buffer: the code buffer to write the generated code to. If None, we write to `self.loads`. + :param store_value: the value to store. If None, we load the vector. + :param accu_store: whether accumulate the store_value to store_ptr. If True, a store_value should be provided + :return: a CppCSEVariable that represents the loaded vector or None if it is a store. + """ + assert not store_value or var is not None, "store var must be provided" + if accu_store: + assert store_value + if buffer is None: + buffer = self.loads + + def get_result_size(dtype: torch.dtype) -> int: + if dtype.itemsize < 4: + return self.num_elems * (4 // dtype.itemsize) + else: + return self.num_elems + + def get_tiling_size(dtype: torch.dtype) -> int: + if dtype.itemsize < 4: + return self.tiling_factor * (4 // dtype.itemsize) + else: + return self.tiling_factor + + def vec_to_array(vec_var: CppCSEVariable) -> CppCSEVariable: + assert vec_var.is_vec + code = BracesBuffer() + code.writeline("[&]") + with code.indent(): + vec_dtype = vec_var.dtype + assert vec_dtype is not None + if vec_dtype == torch.bool: + vec_dtype = torch.float + result_size = get_result_size(vec_dtype) + tiling_size = get_tiling_size(vec_dtype) + code.writeline( + f"__at_align__ std::array<{DTYPE_TO_CPP[vec_dtype]}, {tiling_size}> tmpbuf;" + ) + line = f"{vec_var}.store(tmpbuf.data(), {cexpr_index(result_size)});" + code.writeline(line) + code.writeline("return tmpbuf;") + code.writeline("()") + csevar = self.cse.generate(buffer, code) + assert isinstance(csevar, CppCSEVariable) + return csevar + + code = BracesBuffer() + code.writeline("[&]") + with code.indent(): + result_size = get_result_size(dtype) + tiling_size = get_tiling_size(dtype) + result_declare = ( + f"__at_align__ std::array<{DTYPE_TO_CPP[dtype]}, {tiling_size}> tmpbuf;" + ) + code.writeline(result_declare) + if store_value: + code.writeline( + f"{store_value}.store(tmpbuf.data(), {cexpr_index(result_size)});" + ) + itervar_inner = sympy_index_symbol( + f"{self.itervars[self.tiling_idx]}_inner" + ) + replacements = {} + for indirect_var in ( + self.cse.varname_map[s.name] # type: ignore[attr-defined] + for s in index.free_symbols + if symbol_is_type(s, SymT.TMP) + ): + assert isinstance(indirect_var, CppCSEVariable) + if indirect_var.is_vec: + array_var = vec_to_array(indirect_var) + replacements[indirect_var] = f"{array_var}[{itervar_inner}]" + index = self.scale_index_with_offset( + index, itervar_idx=self.tiling_idx, offset=itervar_inner + ) + load_mask = None + if self._load_mask is not None: + assert not store_value, "unexpected store with load mask" + assert isinstance(self._load_mask, CppCSEVariable), self._load_mask + if self._load_mask.is_vec: + load_mask = f"{self._load_mask}.is_masked({itervar_inner})" + else: + load_mask = f"{self._load_mask} != 0" + if cpp_builder.is_gcc(): + code.writeline(f"#pragma GCC unroll {self.tiling_factor}") + else: + code.writeline(f"#pragma unroll {self.tiling_factor}") + code.writeline( + f"for (long {itervar_inner} = 0; " + + f"{itervar_inner} < {cexpr_index(self.num_elems)}; " + + f"{itervar_inner}++)" + ) + with code.indent(), contextlib.ExitStack() as stack: + index_c = cexpr_index(index) + for indirect_var in replacements: + index_c = re.sub( + r"\b" + f"{indirect_var}" + r"\b", + replacements[indirect_var], + index_c, + ) + rhs = f"{var}[{index_c}]" if var is not None else f"{index_c}" + if load_mask: + code.writeline(f"if ({load_mask})") + stack.enter_context(code.indent()) + if store_value: + conjunction = "+=" if accu_store else "=" + code.writeline(f"{rhs} {conjunction} tmpbuf[{itervar_inner}];") + else: + code.writeline(f"tmpbuf[{itervar_inner}] = {rhs};") + if not store_value: + load_line = self._get_vec_load_line("tmpbuf.data()", 0, dtype) # type: ignore[arg-type] + code.writeline(f"return {load_line};") + code.writeline("()") + if store_value: + code.writeline(";") + buffer.splice(code) + return None + else: + csevar = self.cse.generate(buffer, code) + assert isinstance(csevar, CppCSEVariable) + csevar.is_vec = True + return csevar + + def load(self, name: str, index: sympy.Expr): + var = self.args.input(name) + index = self.rename_indexing(index) + dtype = V.graph.get_dtype(name) + tiling_var = self.itervars[self.tiling_idx] + stride = self._try_get_const_stride(index, tiling_var) + if stride == 0: + # load scalar and lazily broadcast it on demand + return super().load(name, index) + elif stride == 1: + # load contiguously + line = self._get_vec_load_line(var, index, dtype, self._load_mask) + csevar = self.cse.generate(self.loads, line) # type: ignore[assignment] + else: + csevar = self._load_or_store_non_contiguous(var, index, dtype) # type: ignore[assignment] + assert isinstance(csevar, CppCSEVariable) + csevar.update_on_args("load", (self, name, index), {}) + csevar.is_vec = True + return csevar + + def _get_store_line( + self, + value: Union[str, CppCSEVariable], + var: str, + index: sympy.Expr, + dtype: torch.dtype, + accu_store: bool = False, + ): + """ + Get a store line buffer that stores `value` into `var` at `index` of `dtype`. It handles + both contiguous and non-contiguous store cases. + :param value: Vectorized type templaterized on `dtype`. + :param var: buffer to store into. + :index: index into the `var`. + """ + # when value's type is str (e.g., welford reduction), caller should make sure + # it is a vector + assert isinstance(value, str) or ( + isinstance(value, CppCSEVariable) and value.is_vec + ), value + tiling_var = self.itervars[self.tiling_idx] + var_expr = f"{var} + {cexpr_index(index)}" + stride = self._try_get_const_stride(index, tiling_var) + code = IndentedBuffer() + if stride == 1: + if dtype == torch.float and self.tail_size is None: + code.writeline(f"{value}.store({var_expr});") + else: + code.writeline( + f"{value}.store({var_expr}, {cexpr_index(self.num_elems)});" + ) + else: + self._load_or_store_non_contiguous( + var, index, dtype, buffer=code, store_value=value, accu_store=accu_store + ) + return code + + def store(self, name, index, value, mode=None): + assert "buf" in name + assert isinstance(value, CppCSEVariable), value + if not value.is_vec: + # this happens when we store a scalar into a vectorized buffer like "fill" + value = self.broadcast(value) + var = self.args.output(name) + index = self.rename_indexing(index) + dtype = V.graph.get_dtype(name) + if mode is None: + code = self._get_store_line(value, var, index, dtype) + self.stores.splice(code.map(lambda x: DeferredLine(name, x))) + elif mode == "atomic_add": + if not config.cpp.dynamic_threads and self.num_threads == 1: + code = self._get_store_line( + f"{value}", + var, + index, + dtype, + accu_store=True, + ) + self.stores.splice(code.map(lambda x: DeferredLine(name, x))) + else: + n_src = self._get_num_vectors(dtype) + n_idx = self._get_num_vectors(torch.int64) + cdtype = DTYPE_TO_CPP[dtype] + index = ops.index_expr(index, torch.int64).value + assert index.is_vec + line = f"atomic_add_vec<{cdtype}, {n_idx}, {n_src}>({var}, {index}, {value});" + self.stores.writeline(DeferredLine(name, line)) + else: + raise NotImplementedError(f"store mode={mode}") + + def reduction(self, dtype, src_dtype, reduction_type, value): + assert reduction_type in VECTORIZABLE_RTYPES + argmax_or_argmin = reduction_type in {"argmax", "argmin"} + horizontal_reduction = self.tiling_idx >= self.reduction_depth + init_dtype = src_dtype if argmax_or_argmin else dtype + assert isinstance(value, CppCSEVariable), value + + if not value.is_vec: + value = self.broadcast(value) + + reduction_key = src_dtype, reduction_type, value + if reduction_key in self.reduction_cse.reduction_cache: + return self.reduction_cse.reduction_cache[reduction_key] + + vec_ns = "at::vec" + vec = f"{vec_ns}::Vectorized<{DTYPE_TO_CPP[dtype]}>" + acc_type = reduction_acc_type(reduction_type, init_dtype) + acc_type_vec = self.reduction_acc_type_vec(reduction_type, init_dtype) + + acc = self.reduction_cse.generate( + self.loads, f"reduction {reduction_key}", write=False + ) + acc_vec = f"{acc}_vec" + self.is_reduction = True + self.reduction_prefix.writeline( + f"{acc_type} {acc} = {reduction_init(reduction_type, init_dtype)};" + ) + self.reduction_prefix.writeline( + f"{acc_type_vec} {acc_vec} = {self.reduction_init_vec(reduction_type, init_dtype)};" + ) + if reduction_type == "welford_reduce": + # save the reciprocal of weights for welford reduce + assert self.reduction_depth is not None + # use masked acc_vec for tail vec kernel + self.reduction_prefix.writeline( + f"{acc_type_vec} masked_{acc_vec} = {self.reduction_init_vec(reduction_type, dtype)};" + ) + reduction_size = functools.reduce( + lambda x, y: x * y, self.ranges[self.reduction_depth :] + ) + reduction_factor = ( + self.tiling_factor if self.tiling_idx >= self.reduction_depth else 1 + ) + self.weight_recp_vec_range = FloorDiv(reduction_size, reduction_factor) + if self.weight_recp_vec_range not in self.weight_recps_cse.reduction_cache: + self.weight_recps_val = self.weight_recps_cse.generate( + self.compute, f"reduction {self.weight_recp_vec_range}", write=False + ) + self.weight_recps_cse.reduction_cache[ + self.weight_recp_vec_range + ] = self.weight_recps_val + self.non_parallel_reduction_prefix.writeline( + self.welford_weight_reciprocal_vec(dtype) + ) + # generate weight_recps for parallel reduction + num_threads = ( + "max_threads" + if config.cpp.dynamic_threads + else parallel_num_threads() + ) + self.local_reduction_init.writeline( + self.welford_weight_reciprocal_vec(dtype, num_threads) + ) + else: + self.weight_recps_val = self.weight_recps_cse.reduction_cache[ + self.weight_recp_vec_range + ] + # use masked acc_vec for tail vec kernel + acc_vec_ = f"masked_{acc_vec}" if self.tail_size else acc_vec + self.stores.writeline( + f"{acc_vec_} = {self.reduction_combine_vec(reduction_type, acc_vec_, value, True)};" + ) + else: + assert self.reduction_depth is not None + index = self.itervars[self.reduction_depth] + for i in range(self.reduction_depth + 1, len(self.itervars)): + index = index * self.ranges[i] + self.itervars[i] + combine = self.reduction_combine_vec( + reduction_type, + acc_vec, + value, + index=index, + horizontal_reduction=horizontal_reduction, + src_dtype=src_dtype, + ) + self.stores.writeline(f"{acc_vec} = {combine};") + self._gen_parallel_reduction_buffers( + acc, + acc_type, + reduction_type, + init_dtype, + ) + self._gen_parallel_reduction_buffers( + acc_vec, + acc_type_vec, + reduction_type, + init_dtype, + reduction_combine_fn=self.reduction_combine_vec, + reduction_init_fn=self.reduction_init_vec, + ) + if reduction_type == "welford_reduce": + # use masked acc_vec for tail vec kernel + self._gen_parallel_reduction_buffers( + f"masked_{acc_vec}", + acc_type_vec, + reduction_type, + dtype, + reduction_combine_fn=self.reduction_combine_vec, + reduction_init_fn=self.reduction_init_vec, + ) + tmpvar: Union[str, CSEVariable] + is_bool = dtype == torch.bool + if horizontal_reduction: + # Horizontal reduction + if is_welford_reduction(reduction_type): + assert self._get_num_vectors(dtype) in [ + 1, + 2, + ], "Welford reduction does not support VectorizedN (N>2)" + next_value = f"welford_vec_reduce_all({acc_vec})" + masked_next_value = f"welford_vec_reduce_all(masked_{acc_vec})" + self.reduction_suffix.writeline( + f"{acc} = {reduction_combine(reduction_type, acc, masked_next_value)};" + ) + elif argmax_or_argmin: + next_value = f"{reduction_type}_vec_reduce_all({acc_vec})" + elif is_bool: + if reduction_type in ( + "any", + "sum", + "max", + ): + next_value = f"!{acc_vec}.all_zero()" + else: + assert reduction_type == "min" + next_value = f"{acc_vec}.all_masked()" + else: + reduce_all_body = ( + "{ return " + + self.reduction_combine_vec(reduction_type, "x", "y") + + "; }" + ) + is_bool = dtype == torch.bool + # we are using at::vec::VecMask for bool + vec_dtype = torch.float if is_bool else dtype + vec = f"at::vec::Vectorized<{DTYPE_TO_CPP[vec_dtype]}>" + vec_reduce_all_func = f"at::vec::vec_reduce_all<{DTYPE_TO_CPP[vec_dtype]}, {self._get_num_vectors(vec_dtype)}>" + next_value = f"{vec_reduce_all_func}([]({vec}& x, {vec}& y) {reduce_all_body}, {acc_vec})" + + self.reduction_suffix.writeline( + f"{acc} = {reduction_combine(reduction_type, acc, next_value, src_dtype=src_dtype)};" + ) + tmpvar = acc + else: + tmpvar = acc_vec + if is_welford_reduction(reduction_type): + masked_tmpvar = f"masked_{tmpvar}" + self.reduction_suffix.writeline( + f"{tmpvar} = {reduction_combine(reduction_type, tmpvar, masked_tmpvar)};" + ) + + result = reduction_project(reduction_type, tmpvar) + self.reduction_cse.reduction_cache[reduction_key] = result + return result + + def store_reduction(self, name, index, value): + index = self.rename_indexing(index) + var = self.args.output(name) + out_dtype = V.graph.get_dtype(name) + dtype = ( + (out_dtype if out_dtype == torch.double else torch.float) + if out_dtype.is_floating_point + else torch.int64 + ) + out_num_vectors = V.kernel._get_num_vectors(out_dtype) + src_num_vectors = V.kernel._get_num_vectors(dtype) + code = IndentedBuffer() + if self.tiling_idx >= self.reduction_depth: + # Horizontal reduction + code.writeline( + f"{var}[{cexpr_index(index)}] = static_cast<{DTYPE_TO_CPP[out_dtype]}>({value});" + ) + else: + # Vertical reduction + if out_dtype != dtype: + converted_value = f"{DTYPE_TO_CPP[out_dtype]}_{value}" + if out_dtype == torch.bool: + convert = f"{value}.template cast()" + else: + if src_num_vectors == out_num_vectors == 1: + convert = ( + f"at::vec::convert<{DTYPE_TO_CPP[out_dtype]}>({value})" + ) + else: + convert = ( + f"at::vec::convert<{DTYPE_TO_CPP[out_dtype]}," + f"{out_num_vectors},{DTYPE_TO_CPP[dtype]},{src_num_vectors}>({value})" + ) + code.writeline(f"auto {converted_value} = {convert};") + value = converted_value + code.splice(self._get_store_line(value, var, index, out_dtype)) + self.reduction_suffix.splice(code.map(lambda x: DeferredLine(name, x))) + + def broadcast(self, scalar_var: CppCSEVariable) -> CppCSEVariable: + assert not scalar_var.is_vec + if scalar_var.dtype == torch.bool: + vec_var = self.cse.generate( + self.compute, f"{self._get_mask_type()}::from({scalar_var.name})" + ) + else: + assert scalar_var.dtype is not None + vec_var = self.cse.generate( + self.compute, + f"{self._get_vec_type(scalar_var.dtype)}({scalar_var.name})", + ) + assert isinstance(vec_var, CppCSEVariable) + vec_var.dtype = scalar_var.dtype + vec_var.dependent_itervars = scalar_var.dependent_itervars + vec_var.is_vec = True + return vec_var + + def arange(self, index: CppCSEVariable, stride: sympy.Symbol) -> CppCSEVariable: + assert not index.is_vec + assert index.dtype is not None + csevar = self.cse.generate( + self.compute, + f"{self._get_vec_type(index.dtype)}::arange({index}, {stride})", + ) + assert isinstance(csevar, CppCSEVariable) + csevar.dtype = index.dtype + csevar.is_vec = True + return csevar + + def reduction_init_vec(self, reduction_type, dtype): + scalar_type = DTYPE_TO_COMPUTATION_DTYPE[dtype] + vec_type = self._get_vec_type(scalar_type) + + if is_welford_reduction(reduction_type): + return f"Welford<{vec_type}>()" + + if reduction_type in {"argmin", "argmax"}: + cdtype = DTYPE_TO_CPP[scalar_type] + acc_type = self.reduction_acc_type_vec(reduction_type, dtype) + if reduction_type == "argmin": + val = ( + f"std::numeric_limits<{cdtype}>::infinity()" + if is_float_dtype(dtype) + else f"std::numeric_limits<{cdtype}>::max()" + ) + else: + val = ( + f"-std::numeric_limits<{cdtype}>::infinity()" + if is_float_dtype(dtype) + else f"std::numeric_limits<{cdtype}>::min()" + ) + return f"{acc_type}({val})" + + if reduction_type == "any": + return f"{self._get_mask_type()}::from(0)" + + scalar_init = reduction_init(reduction_type, dtype) + vec_init = f"{vec_type}({scalar_init})" + if dtype == torch.bool: + assert reduction_type in ("min", "max", "sum") + return f"{self._get_mask_type()}::from({scalar_init})" + return vec_init + + def reduction_acc_type_vec(self, reduction_type, dtype): + scalar_type = DTYPE_TO_COMPUTATION_DTYPE[dtype] + vec_type = self._get_vec_type(scalar_type) + if is_welford_reduction(reduction_type): + return f"Welford<{vec_type}>" + if reduction_type in {"argmin", "argmax"}: + n_src = self._get_num_vectors(scalar_type) + n_idx = self._get_num_vectors(torch.int64) + return f"IndexValueVec<{DTYPE_TO_CPP[scalar_type]}, {n_src}, {n_idx}>" + if dtype == torch.bool: + assert reduction_type in ("min", "max", "any", "sum") + return f"{self._get_mask_type()}" + return vec_type + + def welford_weight_reciprocal_vec(self, dtype, num_threads=None): + vec_num_range_thread = ( + CeilDiv(self.weight_recp_vec_range, num_threads) + if num_threads + else self.weight_recp_vec_range + ) + vec_num_range_thread_expr = cexpr_index(vec_num_range_thread) + return ( + f"static WeightRecp<{self._get_vec_type(dtype)}> {self.weight_recps_val}" + f"(" + f"{vec_num_range_thread_expr}" + f");" + ) + + def reduction_combine_vec( + self, + reduction_type, + var, + next_value, + use_weight_recps=False, + index: Optional[sympy.Symbol] = None, + horizontal_reduction: Optional[bool] = None, + src_dtype: Optional[torch.dtype] = torch.float32, + ): + is_bool = src_dtype == torch.bool + if reduction_type == "max": + if self.tail_size: + return f"max_masked_reduce({var}, {next_value}, {cexpr_index(self.tail_size)})" + else: + return ( + f"{var} | {next_value}" + if is_bool + else f"at::vec::maximum({var}, {next_value})" + ) + elif reduction_type == "min": + if self.tail_size: + return f"min_masked_reduce({var}, {next_value}, {cexpr_index(self.tail_size)})" + else: + return ( + f"{var} & {next_value}" + if is_bool + else f"at::vec::minimum({var}, {next_value})" + ) + elif reduction_type == "sum": + if self.tail_size: + return f"sum_masked_reduce({var}, {next_value}, {cexpr_index(self.tail_size)})" + else: + conjunction = "|" if is_bool else "+" + return f"{var} {conjunction} {next_value}" + elif reduction_type == "prod": + if self.tail_size: + return f"prod_masked_reduce({var}, {next_value}, {cexpr_index(self.tail_size)})" + else: + return f"{var} * {next_value}" + elif reduction_type == "xor_sum": + if self.tail_size: + return f"xor_sum_masked_reduce({var}, {next_value}, {cexpr_index(self.tail_size)})" + else: + return f"{var} ^ {next_value}" + elif reduction_type == "welford_reduce": + if use_weight_recps: + if self.tail_size: + return f"welford_combine({var}, {next_value}, {cexpr_index(self.tail_size)}, &{self.weight_recps_val})" + else: + return f"welford_combine({var}, {next_value}, &{self.weight_recps_val})" + else: + if self.tail_size: + return f"welford_combine({var}, {next_value}, {cexpr_index(self.tail_size)})" + else: + return f"welford_combine({var}, {next_value})" + elif reduction_type == "welford_combine": + if isinstance(next_value, tuple): + # When reading a value from Inductor IR we have a tuple of variable names + mean, m2, weight = next_value + else: + # When combining intermediate accumulators we have a Welford struct + mean, m2, weight = reduction_project(reduction_type, next_value) + if self.tail_size: + return f"welford_combine({var}, {{{mean}, {m2}, {weight}}}, {cexpr_index(self.tail_size)})" + else: + return f"welford_combine({var}, {{{mean}, {m2}, {weight}}})" + elif reduction_type in ("argmin", "argmax"): + assert src_dtype is not None + cdtype = DTYPE_TO_CPP[src_dtype] + n_src = self._get_num_vectors(src_dtype) + n_idx = self._get_num_vectors(torch.int64) + t_extra = "" + arg_extra = "" + if index is not None: + assert horizontal_reduction is not None + t_extra = f", {str(horizontal_reduction).lower()}" + arg_extra = f", {index}" + if self.tail_size: + return ( + f"{reduction_type}_combine_vec<{cdtype}, {n_src}, {n_idx}{t_extra}>" + f"({var}, {next_value}{arg_extra}, {cexpr_index(self.tail_size)})" + ) + else: + return f"{reduction_type}_combine_vec<{cdtype}, {n_src}, {n_idx}{t_extra}>({var}, {next_value}{arg_extra})" + elif reduction_type == "any": + return f"{var} | {next_value}" + else: + raise NotImplementedError + + def indirect_assert(self, var, lower, upper, mask=None): + assert isinstance(var, CppCSEVariable) + assert var.dtype is not None + if not var.is_vec: + if isinstance(mask, CppCSEVariable) and mask.is_vec: + mask = f"({mask}).all_masked()" + return super().indirect_assert(var, lower, upper, mask) + lower_scalar = lower + upper_scalar = upper + if lower: + lower = f"{self._get_vec_type(var.dtype)}({lower})" + if upper: + upper = f"{self._get_vec_type(var.dtype)}({upper})" + if lower and upper: + cond = f"({lower} <= {var}) & ({var} < {upper})" + cond_print = f"{lower_scalar} <= {var} < {upper_scalar}" + elif lower: + cond = f"{lower} <= {var}" + cond_print = f"{lower_scalar} <= {var}" + else: + assert upper + cond = f"{var} < {upper}" + cond_print = f"{var} < {upper_scalar}" + cond = f"{self._get_mask_type(var.dtype)}({cond})" + if mask: + if not mask.is_vec: + mask = f"{self._get_mask_type(var.dtype)}({mask})" + # We need not check when the mask is False + cond = f"({cond}) | ~({mask})" + if self.tail_size: + cond = ( + f"{self._get_mask_type(var.dtype)}::set({self._get_mask_type(var.dtype)}::from(1)" + f", ({cond}), {cexpr_index(self.tail_size)})" + ) + cond = f"({cond}).all_masked()" + return f'{self.assert_function}({cond}, "index out of bounds: {cond_print}")' + + def get_to_dtype_expr(self, src, dtype, src_dtype): + assert isinstance(src, CppCSEVariable) + if not src.is_vec: + return super().get_to_dtype_expr(src, dtype, src_dtype) + src_cpp_type = DTYPE_TO_CPP[src_dtype] + src_num_vectors = self._get_num_vectors(src_dtype) + dst_cpp_type = DTYPE_TO_CPP[dtype] + dst_num_vectors = self._get_num_vectors(dtype) + expr = f"({src})" + if src_dtype != torch.bool and dtype == torch.bool: + expr = f"{self._get_mask_type(src_dtype)}::from<{src_cpp_type},{src_num_vectors}>({src})" + elif src_dtype == torch.bool and dtype != torch.bool: + expr = f"{src}.to<{dst_cpp_type},{dst_num_vectors}>()" + elif src_dtype != dtype: + if src_num_vectors == dst_num_vectors == 1: + expr = f"at::vec::convert<{dst_cpp_type}>({src})" + else: + expr = f"at::vec::convert<{dst_cpp_type},{dst_num_vectors},{src_cpp_type},{src_num_vectors}>({src})" + return expr + + +class CppTile2DKernel(CppVecKernel): + """ + A vector kernel that handles the 2d tiles with the tile size defined in `tiling_factor` on + the inner-most loop level and one of the outer loop level (`outer_tiling_idx`). When the data + tile is accessed in a contiguous way from the outer loop axis, a transposition is applied on the + tile to make the access contiguous from the inner-most loop axis. Then, the same vectorization + logic from its parent `CppVecKernel` is leveraged for load/store/compute. The transposed tile load + and store are generated into kernel.preloads and kernel.poststores buffers. + + The loop structure looks like below: + for ... + for i_outer ... + for ... + for inner_most ... + // generated by CppTile2DKernel + float tmp0[16*16]; at::vec::transpose_mxn<...>(tmp0, in_ptr0 + ..., ...); // into kernel.preloads + float tmp1[16*16]; // into kernel.preloads + for i_inner ... { // the kernel inner loop + vectorized loads/compute/stores (e.g., load tmp0, store tmp1) // into kernel.loads/compute/stores + } + at::vec::transpose_mxn(out_ptr0 + ..., tmp1, ...) // into kernel.poststores + for inner_most ... (tail) + // generated by CppVecKernel + ... + for i_outer ... (tail) + for ... + for ... + // generated by CppKernel + ... + """ + + overrides = CppTile2DOverrides # type: ignore[assignment] + + def __init__( + self, + args, + num_threads, + tiling_factor, + tiling_indices, + inner_tail_size=None, + outer_tail_size=None, + ): + super().__init__( + args, + num_threads, + tiling_factor, + tiling_indices[1], + inner_tail_size, + ) + self.tiling_indices = tiling_indices + self.inner_tail_size = inner_tail_size + self.outer_tail_size = outer_tail_size + self.inner_num_elems = inner_tail_size if inner_tail_size else tiling_factor + self.outer_num_elems = outer_tail_size if outer_tail_size else tiling_factor + self.inner_is_tiling_idx = True + + def inner_itervar(self): + return sympy_index_symbol(f"{self.itervars[self.outer_idx]}_inner") + + def need_vec_transpose(self, index): + outer_var = self.itervars[self.outer_idx] + inner_var = self.itervars[self.tiling_idx] + outer_stride = stride_at_vec_range(index, outer_var, self.tiling_factor) + inner_stride = stride_at_vec_range(index, inner_var, self.tiling_factor) + return ( + self._load_mask is None # TODO: support transposition with mask + and outer_stride == 1 + and index.has(inner_var) + and not inner_stride.has(inner_var) + and not inner_stride.has(outer_var) + ) + + def gen_transposed_tile_load_store(self, name, var, index, is_store): + # transposed tile load/store outside the kernel inner loop + dtype = V.graph.get_dtype(name) + factor = self.tiling_factor + src = f"{var} + {cexpr_index(index)}" + dst = "__place_holder__" + ld_src = f"{cexpr_index(stride_at_vec_range(index, self.itervars[self.tiling_idx], self.tiling_factor))}" + ld_dst = f"{cexpr_index(self.num_elems)}" + if is_store: + src, dst = dst, src + ld_src, ld_dst = ld_dst, ld_src + + need_define = True + if self.inner_is_tiling_idx ^ is_store: + M, N = self.inner_num_elems, self.outer_num_elems + else: + M, N = ( + self.outer_num_elems, + self.inner_num_elems, + ) + if (isinstance(M, sympy.Expr) and not M.is_number) or ( + isinstance(N, sympy.Expr) and not N.is_number + ): + load_or_store = ( + f"at::vec::transpose_mxn<{DTYPE_TO_CPP[dtype]}>" + f"({src}, {ld_src}, {dst}, {ld_dst}, {cexpr_index(M)}, {cexpr_index(N)});" + ) + else: + load_or_store = ( + f"at::vec::transpose_mxn<{DTYPE_TO_CPP[dtype]},{cexpr_index(M)},{cexpr_index(N)}>" + f"({src}, {ld_src}, {dst}, {ld_dst});" + ) + if is_store: + tile_var = self.cse.newvar() + elif load_or_store not in self.cse.cache: + tile_var = self.cse.generate(self.preloads, load_or_store, write=False) + else: + need_define = False + tile_var = self.cse.cache[load_or_store] + + if need_define: + define_line = f"alignas({factor}) {DTYPE_TO_CPP[dtype]} {tile_var}[{factor}*{factor}];" + self.preloads.writeline(define_line) + + load_or_store = load_or_store.replace("__place_holder__", str(tile_var)) + if is_store: + self.poststores.writeline(DeferredLine(name, load_or_store)) + else: + self.preloads.writeline(load_or_store) + + return tile_var + + def load(self, name: str, index: sympy.Expr): + var = self.args.input(name) + index = self.rename_indexing(index) + + inner = self.inner_itervar() + if self.need_vec_transpose(index): + tile_var = self.gen_transposed_tile_load_store( + name, var, index, is_store=False + ) + # vector load inside the kernel inner loop + loadbuf = f"{tile_var} + {cexpr_index(inner * self.num_elems)}" + dtype = V.graph.get_dtype(name) + line = self._get_vec_load_line(loadbuf, 0, dtype) # type: ignore[arg-type] + csevar = self.cse.generate(self.loads, line) + csevar.update_on_args("load", (self, name, index), {}) + assert isinstance(csevar, CppCSEVariable) + csevar.is_vec = True + return csevar + else: + new_index = self.transform_indexing(index) + return super().load(name, new_index) + + def store(self, name, index, value, mode=None): + assert "buf" in name + var = self.args.output(name) + + inner = self.inner_itervar() + index = self.rename_indexing(index) + assert mode is None + if self.need_vec_transpose(index): + tile_var = self.gen_transposed_tile_load_store( + name, var, index, is_store=True + ) + # vector store inside the kernel inner loop + storebuf = f"{tile_var} + {cexpr_index(inner * self.num_elems)}" + if self.tail_size or V.graph.get_dtype(name) in DTYPE_LOWP_FP + [ + torch.uint8, + torch.int8, + ]: + line = f"{value}.store({storebuf}, {cexpr_index(self.num_elems)});" + else: + line = f"{value}.store({storebuf});" + self.stores.writeline(DeferredLine(name, line)) + else: + new_index = self.transform_indexing(index) + super().store(name, new_index, value, mode) + + def codegen_inner_loops(self, code): + inner = self.inner_itervar() + if self.inner_is_tiling_idx: + code.writeline( + f"for (long {inner} = 0; {inner} < {cexpr_index(self.outer_num_elems)}; {inner}++)" + ) + else: + code.writeline( + f"for (long {inner} = 0; {inner} < {cexpr_index(self.inner_num_elems)}; {inner}++)" + ) + + def set_ranges(self, group, reduction_group): + vars = super().set_ranges(group, reduction_group) + # do vertical reduction as the tail loop + self.outer_idx, self.tiling_idx = ( + self.tiling_indices + if self.tiling_indices[1] < self.reduction_depth + else reversed(self.tiling_indices) + ) + if self.tiling_idx == self.tiling_indices[0]: + self.tail_size = self.outer_tail_size + self.num_elems = self.outer_num_elems + self.inner_is_tiling_idx = False + else: + self.tail_size = self.inner_tail_size + self.num_elems = self.inner_num_elems + self.inner_is_tiling_idx = True + return vars + + def transform_indexing(self, index: sympy.Expr) -> sympy.Expr: + return self.scale_index_with_offset( + index, + itervar_idx=self.outer_idx, + offset=self.inner_itervar(), + ) + + +def get_loop_body_lowp_fp(_body: LoopBody) -> Tuple[Optional[torch.dtype], bool]: + """ + Returns the low precision data type (torch.float16/torch.bfloat16) contained in the nodes + and if all the nodes can codegen with this data type without converting to float. + Otherwise returns None and True. + """ + sub_blocks = [_body.root_block] + list(_body.subblocks.values()) + + _lowp_fp_type: Optional[torch.dtype] = None + _use_fp32 = False + for sub_block in sub_blocks: + for _node in sub_block.graph.nodes: + if _node.op == "placeholder" or _node.target in ( + "get_index", + "index_expr", + ): + continue + + # Fast path if all operations can support bf16/fp16 without converting to fp32 + if _node.target not in [ + "load", + "store", + "abs", + "neg", + "output", + ]: + _use_fp32 = True + + if hasattr(_node, "meta") and _node.meta: + assert OptimizationContext.key in _node.meta + opt_ctx: OptimizationContext = _node.meta[OptimizationContext.key] + if not opt_ctx.dtype or opt_ctx.dtype not in DTYPE_LOWP_FP: + _use_fp32 = True + elif _lowp_fp_type is not None: + if _lowp_fp_type != opt_ctx.dtype: + warnings.warn("bf16 and fp16 are mixed in the scheduler node.") + else: + _lowp_fp_type = opt_ctx.dtype + else: + _use_fp32 = True + + return _lowp_fp_type, _use_fp32 + + +class TilingSelect: + """ + Implement the heuristic to select the tiling factors and tiling indices. + In the future, we can implement advanced heuristic in a subclass. + """ + + def __init__(self): + super().__init__() + + def select_tiling( + self, + fn_list, + var_sizes_list, + ) -> Tuple[List[int], List[int]]: + # TODO(jgong5): support alternative tiling factors and data types + loop_bodies = _get_loop_body(fn_list) + all_dtypes = _get_dtype_from_loopbodies(loop_bodies) + assert all_dtypes + if any(dtype not in VECTORIZABLE_DTYPES for dtype in all_dtypes): + return [], [] + dtype = torch.float + _lowp_fp_dtype = get_loop_body_lowp_fp(loop_bodies[0])[0] + if _lowp_fp_dtype and all( + (get_loop_body_lowp_fp(loop_body)[0] == _lowp_fp_dtype) + for loop_body in loop_bodies[1:] + ): + dtype = _lowp_fp_dtype + + tiling_factor = cpu_vec_isa.pick_vec_isa().nelements(dtype=dtype) + tiling_indices = self._select_tiling_indices( + fn_list, var_sizes_list, tiling_factor + ) + + if tiling_indices: + group, reduction_group = max( + var_sizes_list, key=lambda sizes: len(sizes[1]) + ) + call_ranges = tuple(group) + tuple(reduction_group) + + if config.cpp.enable_tiling_heuristics: + + def _try_get_stride( + index, + itervars, + tiling_factor, + tiling_indices, + ): + itervar = itervars[tiling_indices[0]] + stride = stride_at_vec_range(index, itervar, tiling_factor) + return stride if stride.is_number else None + + def _update_negative_op_count( + node_name, non_contig_indexing_op_counter + ): + if node_name not in non_contig_indexing_op_counter: + non_contig_indexing_op_counter[node_name] = 1 + else: + non_contig_indexing_op_counter[node_name] += 1 + + def _is_valid_indices( + itervars, + tiling_indices, + ): + return ( + len(tiling_indices) == 1 + and len(itervars) > 0 + and ( + tiling_indices[0] + if tiling_indices[0] >= 0 + else tiling_indices[0] + len(itervars) + ) + < len(itervars) + ) + + itervars = [ + sympy_index_symbol_with_prefix(SymT.XBLOCK, n) + for n in range(len(call_ranges)) + ] + reduction_depth = len(group) + vars, reduction_vars = ( + itervars[:reduction_depth], + itervars[reduction_depth:], + ) + op_counter: Dict[str, int] = {} + # ops may cause overhead with vectorization, like non-contiguous + # index_expr, load, store + non_contig_indexing_op_counter: Dict[str, int] = {} + for _body in loop_bodies: + sub_blocks = [_body.root_block] + list(_body.subblocks.values()) + for sub_block in sub_blocks: + for _node in sub_block.graph.nodes: + if _node.target in ["index_expr", "load", "store"]: + # get the index and replace prefix from z to x + arg_idx = 1 if _node.target == "index_expr" else 2 + index = sub_block.body.indexing_from_args( + (vars, reduction_vars) + )[_node.args[arg_idx].args[0]] + if _is_valid_indices(itervars, tiling_indices): + stride = _try_get_stride( + index, itervars, tiling_factor, tiling_indices + ) + if ( + stride is None + if _node.target == "index_expr" + else stride not in [0, 1] + ): + _update_negative_op_count( + _node.target, non_contig_indexing_op_counter + ) + if isinstance(_node.target, str) and not ( + _node.target.startswith("masked_subblock") + or _node.target + in ["ops", "output", "constant", "get_index"] + ): + if _node.target not in op_counter: + op_counter[_node.target] = 1 + else: + op_counter[_node.target] += 1 + + op_num = sum(op_counter.values()) + non_contig_indexing_op_num = sum( + non_contig_indexing_op_counter.values() + ) + threshold = 0.08 + if op_num > 0 and non_contig_indexing_op_num / op_num >= threshold: + # Too many non-contiguous load/store/index_expr which hurts the + # vectorization performance. Disable vectorization when exceeding + # the threshold. + return [], [] + + if ( + not reduction_group + and group + and len(tiling_indices) == 1 + and not has_free_symbols( + [ + group[tiling_indices[0]], + ] + ) + and group[tiling_indices[0]] < tiling_factor / 2 + ): + # For case of Multi Thread AMP Static shape of pyhpc_isoneutral_mixing, + # the inner loop range doesn't have enough elements to do vectorization + # explicitly and found that `#pragma GCC ivdep` has better performance than + # `#pragma omp simd simdlen(8)`. Disable vectorization for this case. + # Leslie: maybe we can always disable vectorization when loop range is less + # than tiling factor and enable `#pragma omp simd simdlen(8)` for scalar kernel + # when needed. + return [], [] + + if dtype in DTYPE_LOWP_FP: + # For lower precision data type, if the call_range is not long enough, + # use tiling_factor // 2 for better performance + factor_lowp = cpu_vec_isa.pick_vec_isa().nelements(dtype=dtype) + for tiling_indice in tiling_indices: + if tiling_indice < 0: + tiling_indice = tiling_indice + len(call_ranges) + if tiling_indice < 0 or tiling_indice >= len(call_ranges): + continue + if has_free_symbols(call_ranges): + call_range = V.graph.sizevars.size_hint( + call_ranges[tiling_indice], fallback=0 + ) + if call_range < factor_lowp: + V.graph.sizevars.guard_lt(call_range, factor_lowp) + tiling_factor = factor_lowp // 2 + break + elif call_ranges[tiling_indice] < factor_lowp: + tiling_factor = factor_lowp // 2 + break + + if len(tiling_indices) == 1: + return [tiling_factor], tiling_indices + if len(tiling_indices) == 2: + return [tiling_factor, tiling_factor], tiling_indices + return [], [] + + def _select_tiling_indices( + self, + fn_list, + var_sizes_list, + tiling_factor, + ): + all_index = [] + for fn, var_sizes in zip(fn_list, var_sizes_list): + rw = dependencies.extract_read_writes(fn, *var_sizes) + all_index += [dep.index for dep in itertools.chain(rw.reads, rw.writes)] + contig_vars = set() + contig_vars_list = [] + non_contig_stride_const = set() + non_contig_stride_other = set() + for index in all_index: + for var in index.free_symbols: + if not re.search(r"^d\d+$", var.name): + continue + stride = stride_at_vec_range(index, var, tiling_factor) + if stride == 0: + continue + elif stride == 1: + contig_vars.add(int(var.name[1:])) + contig_vars_list.append(int(var.name[1:])) + elif all(symbol_is_type(s, SymT.SIZE) for s in stride.free_symbols): + non_contig_stride_const.add(int(var.name[1:])) + else: + non_contig_stride_other.add(int(var.name[1:])) + contig_only = contig_vars - non_contig_stride_const - non_contig_stride_other + group, reduction_group = max(var_sizes_list, key=lambda sizes: len(sizes[1])) + num_itervars = len(group) + len(reduction_group) + if len(contig_vars) == 0: + # no contiguous vars + return [num_itervars - 1] + if contig_only: + return sorted(contig_only)[-1:] + contig_and_const_stride = ( + contig_vars & non_contig_stride_const + ) - non_contig_stride_other + contig_vars_sorted = sorted(contig_vars) + if ( + len(contig_vars_sorted) == 2 + and contig_vars_sorted[-1] in contig_and_const_stride + and contig_vars_sorted[-1] == num_itervars - 1 + ): + return contig_vars_sorted + return sorted(contig_vars_sorted, key=contig_vars_list.count)[-1:] + + +class CppKernelProxy(CppKernel): + def __init__(self, kernel_group): + super().__init__(kernel_group.args, kernel_group.ws.num_threads) + self.kernel_group = kernel_group + self.loop_nest = None + self.call_ranges = None + self.picked_vec_isa: cpu_vec_isa.VecISA = cpu_vec_isa.pick_vec_isa() + + def data_type_propagation(self, nodes): + for _node in nodes: + assert isinstance(_node, SchedulerNode) + DataTypePropagation.propagate_scheduler_node(_node) + + # Check if all the nodes of a given fx graph can support BF16/FP16 + def is_lowp_fp_scheduler(self, scheduler_node: SchedulerNode): + if not isinstance(scheduler_node._body, LoopBody): + return True + # Propagate the dtype to check if all the fx node is bf16/fp16 + DataTypePropagation.propagate_scheduler_node(scheduler_node) + return ( + get_loop_body_lowp_fp(scheduler_node._body)[0] is not None + and not get_loop_body_lowp_fp(scheduler_node._body)[1] + ) + + def legalize_lowp_fp_dtype_loopbody(self, loop_body: LoopBody): + def add_to_dtype(sub_graph: torch.fx.Graph): + def is_lowp_fp_load(node: torch.fx.Node): + if node.target not in ["load"]: + return False + assert len(node.args) == 3 + load_dtype = V.graph.get_dtype(node.args[1]) # type: ignore[arg-type] + return load_dtype in DTYPE_LOWP_FP + + def is_lowp_fp_store(node: torch.fx.Node): + if node.target != "store": + return False + _, store_var, _, _, _ = node.args + store_dtype = V.graph.get_dtype(store_var) # type: ignore[arg-type] + return store_dtype in DTYPE_LOWP_FP + + sub_graph_nodes = list(sub_graph.nodes) + to_lowp_fp_legalized_nodes = [] + for _node in sub_graph_nodes: + if is_lowp_fp_load(_node): + # No need to promote to float if all users are direct stores + if all(user.target == "store" for user in _node.users): + continue + ops = _node.args[0] + with sub_graph.inserting_after(_node): + to_type_node = sub_graph.call_method( + "to_dtype", args=(ops, _node, torch.float) + ) + to_type_node_args = to_type_node.args + _node.replace_all_uses_with(to_type_node) + to_type_node.args = to_type_node_args + metrics.cpp_to_dtype_count += 1 + elif is_lowp_fp_store(_node): + ops, name, _, value_var, _ = _node.args + # No need to promote to float if it is a user of a load which are all directly stored + if value_var.target == "load" and all( + user.target == "store" for user in value_var.users + ): + continue + dtype = V.graph.get_dtype(name) + with sub_graph.inserting_before(_node): + to_type_node = sub_graph.call_method( + "to_dtype", args=(ops, value_var, dtype) + ) + _node.replace_input_with(value_var, to_type_node) + metrics.cpp_to_dtype_count += 1 + elif _node.target == "reduction": + ( + ops, + dtype, + src_dtype, + reduction_type, + value, + ) = _node.args + if src_dtype in DTYPE_LOWP_FP: + # Since we always convert the load/store value to float if the tensor is bfloat16/float16. + # Therefore, the reduction should never work with bfloat16/float16 value. Hence, we update + # the bfloat16/float16 reduction by + # 1) updating the src_dtype to float + # and 2) updating the dtype to float if it is bfloat16/float16. + assert dtype in [ + torch.float, + torch.bfloat16, + torch.float16, + torch.int64, + ] + _node.args = ( + ops, + torch.float if dtype in DTYPE_LOWP_FP else dtype, + torch.float, + reduction_type, + value, + ) + elif _node.target == "to_dtype" and _node.args[-1] in DTYPE_LOWP_FP: + (ops, x, _) = _node.args + # The legalization always loads the BF16/FP16 tensor as FP32 for computation + # and converts back to BF16/FP16 after the computation. + # Hence, there should be no computation w/ BF16/FP16. + # Therefore, we update the to_dtype by replacing the bf16/fp16 dtype with fp32. + # Save the legalized to_dtype node for the elimination(eliminate_to_dtype step): + # 1) Eliminate the redundant to_dtype node if we have a pattern as follows: + # graph(): + # %lowp_fp_legalized = call_method[target=to_dtype](args = (%ops, %input, torch.float)) + # %to_dtype2 = call_method[target=to_dtype](args = (%ops, %lowp_fp_legalized, torch.bfloat16/float16)) + # Regarding the first to_dtype, it is redundant because + # the second to_type also converts to the torch.bfloat16/torch.float16. + # Hence, we remove the first to_type. + to_lowp_fp_legalized_nodes.append(_node) + _node.args = (ops, x, torch.float) + else: + pass + + def eliminate_to_dtype(sub_graph: torch.fx.Graph): + def _eliminate_duplicate_to_node(sub_graph: torch.fx.Graph): + # Eliminate the redundant to_dtype node. Let's consider a pattern as follows: + # graph(): + # %to_dtype1 = call_method[target=to_dtype](args = (%ops, %input, torch.float), kwargs = {}) + # %to_dtype2 = call_method[target=to_dtype](args = (%ops, %to_dtype1, torch.float), kwargs = {}) + # Regarding the first to_dtype, it is redundant because the second to_type also converts to the + # torch.float. Hence, we remove the first to_type + def _used_by_to(to_node: torch.fx.Node): + return all(usr.target == "to_dtype" for usr in to_node.users) + + all_to_nodes = [ + node for node in sub_graph.nodes if node.target == "to_dtype" + ] + all_to_nodes_and_users = [ + {node: node.users} for node in all_to_nodes if _used_by_to(node) + ] + for node_users in all_to_nodes_and_users: + for node, users in node_users.items(): + if node in sub_graph.nodes and ( + all(usr.args[-1] == node.args[-1] for usr in users) + or ( + node in to_lowp_fp_legalized_nodes + and all( + usr.args[-1] in DTYPE_LOWP_FP for usr in users + ) + ) + ): + val_node = node.all_input_nodes[-1] + node.replace_all_uses_with(val_node) + sub_graph.erase_node(node) + + # For debug mode, the graph of LoopBody will attach a new GraphModule as + # owning_module for debugging while the release mode will not. The lint will + # check whether the graph has owning_module to decide if it needs to check + # call_module. LoopBody might contain get_index as a module call. But it + # is just a function. Hence, it cannot pass the lint check for debug mode. + # We bypass the check if the owning_module is None. Eventually, we should call + # get_index via call_function but not call_module. + if sub_graph.owning_module is None: + sub_graph.lint() + + _eliminate_duplicate_to_node(sub_graph) + + eliminate_to_dtype(sub_graph) + + sub_blocks = [loop_body.root_block] + list(loop_body.subblocks.values()) + for sub_block in sub_blocks: + add_to_dtype(sub_block.graph) + + def legalize_lowp_fp_dtype(self, nodes): + if all( + isinstance(_node, SchedulerNode) and self.is_lowp_fp_scheduler(_node) + for _node in nodes + ): + # Mark the load node to load bf16/fp16 + for _node in nodes: + sub_blocks = [_node._body.root_block] + list( + _node._body.subblocks.values() + ) + for sub_block in sub_blocks: + for fx_node in sub_block.graph.nodes: + if fx_node.target in ["load", "store"]: + assert fx_node.meta + assert OptimizationContext.key in fx_node.meta + opt_ctx: OptimizationContext = fx_node.meta[ + OptimizationContext.key + ] + assert opt_ctx.dtype in DTYPE_LOWP_FP + + # Bypass the legalization as the kernel can run with bf16/fp16 directly + return + + for _node in nodes: + assert isinstance(_node, SchedulerNode) + assert isinstance(_node._body, LoopBody) + body: LoopBody = _node._body + if not body.is_memory_copy(): + self.legalize_lowp_fp_dtype_loopbody(body) + + def codegen_functions(self, fn_list, var_sizes_list): + assert len(fn_list) == len(var_sizes_list) + kernel_group = self.kernel_group + group, reduction_group = max(var_sizes_list, key=lambda sizes: len(sizes[1])) + + self.set_ranges(group, reduction_group) + + def codegen_kernel(cls, *args): + with kernel_group.new_kernel(cls, *args) as kernel: + # Ugly hack to maintain the metrics kernel count since + # we only count in CppKernelProxy, not those contained in it + metrics.generated_kernel_count -= 1 + + run(kernel) + return kernel + + def run(kernel): + vars, reduction_vars = kernel.set_ranges(group, reduction_group) + in_suffix = False + for fn, var_sizes in zip(fn_list, var_sizes_list): + if var_sizes in [ + (group, reduction_group), + (tuple(itertools.chain(group, reduction_group)), ()), + ]: + assert not in_suffix + fn(vars, reduction_vars) + else: + in_suffix = True + assert var_sizes == ( + group, + (), + ), f"unexpected group: {var_sizes} != {group}, {reduction_group}" + # we can fuse in some extra pointwise into the suffix + with kernel.write_to_suffix(): + fn(vars, ()) + + scalar_kernel = codegen_kernel(CppKernel) + V.graph.removed_buffers |= scalar_kernel.removed_buffers + V.graph.inplaced_to_remove |= scalar_kernel.inplaced_to_remove + self.loop_nest = LoopNestWithSplit.build(scalar_kernel) + + if not self.picked_vec_isa: + return + + if not self.itervars: + # not a loop + return + + # Kernels share the same global contexts like V.graph.wrapper_code, V.kernel.args. + # But the generated scalar kernel has updated these global contexts. Hence, the other kernels + # should not do this again to avoid context conflict. By now, we only control the + # config.inplace_buffers. In the future, we could maintain more contexts. + with torch._inductor.config.patch(inplace_buffers=False): + tiling_select = TilingSelect() + tiling_factors, tiling_indices = tiling_select.select_tiling( + fn_list, var_sizes_list + ) + assert len(tiling_factors) == len(tiling_indices) + # This should be removed after full support for vectorization is implemented. + could_masked_vec = True + all_dtypes = _get_dtype_from_loopbodies(_get_loop_body(fn_list)) + if any(dtype not in MASKED_VECTORIZABLE_DTYPES for dtype in all_dtypes): + # can be removed after masked vectorizable dtype are same with vectorizable dtype + could_masked_vec = False + + if len(tiling_indices) == 1: + vec_kernel = codegen_kernel( + CppVecKernel, tiling_factors[0], tiling_indices[0] + ) + metrics.generated_cpp_vec_kernel_count += 1 + main_loop, tail_loop = self.loop_nest.split_with_tiling( + tiling_indices[0], factor=tiling_factors[0] + ) + main_loop.set_kernel(vec_kernel) + main_loop.simd_vec = True + if config.cpp.enable_loop_tail_vec and could_masked_vec: + tail_loop.steps = tail_loop.size - tail_loop.offset + masked_vec_kernel = codegen_kernel( + CppVecKernel, + tiling_factors[0], + tiling_indices[0], + tail_loop.steps, + ) + tail_loop.set_kernel(masked_vec_kernel) + tail_loop.simd_vec = True + else: + tail_loop.set_kernel(scalar_kernel) + tail_loop.simd_omp = True + # We chop the loop into two cubes by the nelements - main loop and tail loop. + # Regarding the main loop, it is straightforward that it could be vectorized with + # nelements. But for the tail loop, it still could be vectorized. For example, + # if the nelements is 8(256bits), then the tail loop still could be vectorized + # as 4(128bits). + tail_loop.simd_nelements = tiling_factors[0] // 2 + elif len(tiling_indices) == 2: + assert ( + tiling_indices[1] == len(self.itervars) - 1 + and tiling_factors[0] == tiling_factors[1] + ) + + metrics.generated_cpp_vec_kernel_count += 2 + outer_main_loop, outer_tail_loop = self.loop_nest.split_with_tiling( + tiling_indices[0], factor=tiling_factors[0] + ) + ( + inner_main_loop, + inner_tail_loop, + ) = outer_main_loop.split_with_tiling( + tiling_indices[1] - tiling_indices[0], factor=tiling_factors[0] + ) + tile2d_kernel = codegen_kernel( + CppTile2DKernel, tiling_factors[0], tiling_indices + ) + inner_main_loop.set_kernel(tile2d_kernel) + + if config.cpp.enable_loop_tail_vec and could_masked_vec: + ( + inner_main_loop_of_outer_tail_loop, + inner_tail_loop_of_outer_tail_loop, + ) = outer_tail_loop.split_with_tiling( + tiling_indices[1] - tiling_indices[0], factor=tiling_factors[0] + ) + + for tail_loop in ( + inner_tail_loop, + outer_tail_loop, + inner_tail_loop_of_outer_tail_loop, + ): + tail_loop.steps = tail_loop.size - tail_loop.offset + + for tail_loop, inner_tail_size, outer_tail_size in ( + (inner_tail_loop, inner_tail_loop.steps, None), + ( + inner_main_loop_of_outer_tail_loop, + None, + outer_tail_loop.steps, + ), + ( + inner_tail_loop_of_outer_tail_loop, + inner_tail_loop_of_outer_tail_loop.steps, + outer_tail_loop.steps, + ), + ): + masked_tile2d_kernel = codegen_kernel( + CppTile2DKernel, + tiling_factors[0], + tiling_indices, + inner_tail_size, + outer_tail_size, + ) + tail_loop.set_kernel(masked_tile2d_kernel) + else: + vec_kernel = codegen_kernel( + CppVecKernel, tiling_factors[0], tiling_indices[0] + ) + inner_tail_loop.set_kernel(vec_kernel) + + outer_tail_loop.set_kernel(scalar_kernel) + + def codegen_loop_bodies(self, loop_bodies, var_sizes_list): + for body in loop_bodies: + self.legalize_lowp_fp_dtype_loopbody(body) + DataTypePropagation.propagate_loopbody(body) + self.codegen_functions(loop_bodies, var_sizes_list) + + def codegen_nodes(self, nodes: List[SchedulerNode]): + # Legalize BF16 node by adding to_dtype explicitly + self.legalize_lowp_fp_dtype(nodes) + self.data_type_propagation(nodes) + assert len(nodes) >= 1 + + def fn(node, *index_vars): + node.decide_inplace_update() + node.mark_run() + if isinstance(V.kernel, NullKernelHandler): + return node._body(*index_vars) + else: + return node.codegen(index_vars) + + fn_list = [functools.partial(fn, node) for node in nodes] + + if ( + isinstance(V.local_buffer_context, LocalBufferContext) + and V.local_buffer_context.local_buffers + ): + + def wrap_fn(fn): + wrapped_fn = V.local_buffer_context.localize_function( + fn, + ) + wrapped_fn.original_fn = fn + return wrapped_fn + + fn_list = [wrap_fn(fn) for fn in fn_list] + + var_sizes_list = [node.group[1] for node in nodes] + self.codegen_functions(fn_list, var_sizes_list) + + def codegen_loops(self, code, worksharing): + self.codegen_loops_impl(self.loop_nest, code, worksharing) + + +class OuterLoopFusedKernel(CppKernel): + def __init__(self, kernel_group): + super().__init__(kernel_group.args, kernel_group.ws.num_threads) + self.inner: List[LoopLevel] = [] + + def decide_parallel_depth(self, max_parallel_depth, threads) -> int: + kernels_parallel_depth = [] + nested_kernels: List[List[CppKernel]] = [ + loop.get_kernels() for loop in self.inner + ] + for kernels in nested_kernels: + # For any ScalarKernel, VecKernel, or Tile2DKernel, + # they should all have the same call_ranges + call_ranges = kernels[0].call_ranges + assert call_ranges is not None + assert all(kernel.call_ranges == call_ranges for kernel in kernels) + kernels_parallel_depth.append( + kernels[0].decide_parallel_depth(len(call_ranges), threads) + ) + return min( + max_parallel_depth, + max(kernels_parallel_depth), + ) + + +class ReasonFusedNodes(Enum): + SAME_VARS_REDUCE = "same_vars_reduce" + COMPATIBLE_REDUCTION = "compatible_reduction" + COMPATIBLE_RANGES_NO_REDUCTION = "compatible_ranges_no_reduction" + + +class CppScheduling(BaseScheduling): + # ctypes limits the number of args to 1024, refer to: + # https://github.com/python/cpython/commit/a285af7e626d1b81cf09f8b2bf7656f100bc1237 + # We set a conservative threshold here. + MAX_FUSED_KERNEL_ARGS_NUM = 500 + backend_features = dict.fromkeys( + [ + BackendFeature.INPLACE_BUFFERS, + BackendFeature.REDUCE_TO_SINGLE_ELEMENT, + ] + ) + + @classmethod + def get_backend_features(cls, device: torch.device): + return cls.backend_features + + def __init__(self, scheduler): + super().__init__() + self.scheduler = scheduler + if scheduler: + self.reset_kernel_group() + self._ready_to_flush = False + + def _set_flush_status(self, status: bool): + self._ready_to_flush = status + + def group_fn(self, sizes): + return tuple(tuple(map(V.graph.sizevars.simplify, s)) for s in sizes) + + def reset_kernel_group(self): + from .cpp_wrapper_cpu import CppWrapperCpu + + self.kernel_group: Union[CppWrapperKernelGroup, KernelGroup] + if isinstance(V.graph.wrapper_code, CppWrapperCpu): + self.kernel_group = CppWrapperKernelGroup() + else: + self.kernel_group = KernelGroup() + + def fuse(self, node1, node2): + if node1.is_foreach() or node2.is_foreach(): + return ForeachKernelSchedulerNode.fuse(node1, node2) + elif node1.is_template(): + assert not node2.is_template() + return FusedSchedulerNode.fuse(node1, node2) + else: + if ( + self._why_fuse_nodes(node1, node2) + == ReasonFusedNodes.COMPATIBLE_RANGES_NO_REDUCTION + ): + assert isinstance(node1, (SchedulerNode, FusedSchedulerNode)) + assert isinstance(node2, (SchedulerNode, FusedSchedulerNode)) + + _, (vars1, reduce1) = node1.group + _, (vars2, reduce2) = node2.group + assert reduce1 == () and reduce2 == (), (reduce1, reduce2) + + def get_indexing_ranges_exprs(node): + if isinstance(node, FusedSchedulerNode): + assert len(node.snodes) > 0, node.snodes + var_ranges = None + indexing_exprs = set() + for snode in node.snodes: + v, exprs = get_indexing_ranges_exprs(snode) + if var_ranges is None: + var_ranges = v + assert var_ranges == v, (var_ranges, v, node.snodes) + indexing_exprs.update(exprs) + return var_ranges, list(indexing_exprs) + else: + assert isinstance(node, SchedulerNode) + comp_buffer = node.node + assert isinstance(comp_buffer, ir.ComputedBuffer) + _, body, _ = comp_buffer.get_default_sizes_body() + return body.var_ranges, list(body.indexing_exprs.values()) + + node_to_recomp = node1 if len(vars1) < len(vars2) else node2 + assert isinstance(node_to_recomp, SchedulerNode) + + ref_node = node2 if len(vars1) < len(vars2) else node1 + + extra_indexing_constraints = get_indexing_ranges_exprs(ref_node) + + node_to_recomp.recompute_size_and_body( + extra_indexing_constraints=extra_indexing_constraints + ) + + _, (vars1, _) = node1.group + _, (vars2, _) = node2.group + assert vars1 == vars2, (vars1, vars2) + return FusedSchedulerNode.fuse(node1, node2) + elif self.can_fuse_vertical_outer_loop(node1, node2): + return OuterLoopFusedSchedulerNode.fuse( + node1, node2, self._get_outer_loop_fusion_depth(node1, node2) + ) + else: + return FusedSchedulerNode.fuse(node1, node2) + + def _why_fuse_nodes(self, node1, node2) -> Optional[ReasonFusedNodes]: + _, (vars1, reduce1) = node1.group + _, (vars2, reduce2) = node2.group + + if vars1 == vars2 and reduce1 == reduce2: + return ReasonFusedNodes.SAME_VARS_REDUCE + if reduce1 == () and vars1 == vars2 + reduce2: + return ReasonFusedNodes.COMPATIBLE_REDUCTION + if self._can_fuse_nodes_with_compatible_ranges(node1, node2): + return ReasonFusedNodes.COMPATIBLE_RANGES_NO_REDUCTION + # TODO(jansel): allow fusion pointwise (vars1, ()) suffix? + return None + + def _can_fuse_nodes_with_compatible_ranges(self, node1, node2): + # Here we try to fuse SchedulerNode/FusedSchedulerNode with compatible ranges + # e.g. (s0, s1, s2) and (s0 * s1 * s2) + _, (vars1, reduce1) = node1.group + _, (vars2, reduce2) = node2.group + + c1 = reduce1 == () and reduce2 == () + c2 = math.prod(vars1) == math.prod(vars2) + c3 = len(vars1) == 1 or len(vars2) == 1 + if not (c1 and c2 and c3): + return False + + node_to_recomp = node1 if len(vars1) < len(vars2) else node2 + ref_node = node2 if len(vars1) < len(vars2) else node1 + + # We can not recompute sizes and body for nodes other than SchedulerNode + # TODO: we can extend fusion support with compatible ranges for FusedSchedulerNode + if isinstance(node_to_recomp, FusedSchedulerNode): + return False + + # It may happen that node1 and node2 compatible number of elements + # but different original ranges, for example: + # {d0: s0, d1: s1, d2: s2} vs {d0: s0*s1*s2} + # See https://github.com/pytorch/pytorch/pull/120077/files#r1500427848 for more details + # TODO: we can fix if it allows us to CSE at least one of the variables + + assert isinstance(node_to_recomp, SchedulerNode) + if isinstance(node_to_recomp.node, ir.TemplateBuffer): + return False + assert isinstance(node_to_recomp.node, ir.ComputedBuffer) + # node.data.get_size() is a cheaper version of node.get_read_writes().var_ranges + # but without variable name + ranges2 = node_to_recomp.node.data.get_size() + ranges1 = None + if isinstance(ref_node, FusedSchedulerNode): + ranges_set = set() + for snode in ref_node.snodes: + if isinstance(snode.node, ir.TemplateBuffer): + break + assert isinstance(snode.node, ir.ComputedBuffer) + ranges_set.add(tuple(snode.node.data.get_size())) + + if len(ranges_set) != 1: + return False + + ranges1 = list(next(iter(ranges_set))) + else: + assert isinstance(ref_node, SchedulerNode) + assert isinstance(ref_node.node, ir.ComputedBuffer) + ranges1 = ref_node.node.data.get_size() + + if ranges1 != ranges2: + return False + + return True + + def _can_fuse_horizontal_impl(self, node1, node2): + assert isinstance(node1, (FusedSchedulerNode, SchedulerNode)) + assert isinstance(node2, (FusedSchedulerNode, SchedulerNode)) + if any( + isinstance(node, OuterLoopFusedSchedulerNode) for node in (node1, node2) + ): + return False + return self._why_fuse_nodes(node1, node2) is not None + + def can_fuse_horizontal(self, node1, node2): + if node1.is_template() or node2.is_template(): + return False + if ( + len(node1.get_nodes()) + len(node2.get_nodes()) + > config.cpp.max_horizontal_fusion_size + ): + return False + + return self._can_fuse_horizontal_impl(node1, node2) + + def _get_outer_loop_fusion_depth(self, node1, node2): + DISABLE_OUTER_LOOP_FUSION = 0 + if not all( + type(node) + in (OuterLoopFusedSchedulerNode, FusedSchedulerNode, SchedulerNode) + for node in (node1, node2) + ): + return DISABLE_OUTER_LOOP_FUSION + + _node1 = ( + node1.get_outer_nodes()[-1] + if isinstance(node1, OuterLoopFusedSchedulerNode) + else node1 + ) + assert isinstance(_node1, (FusedSchedulerNode, SchedulerNode)) + _node2 = ( + node2.get_outer_nodes()[0] + if isinstance(node2, OuterLoopFusedSchedulerNode) + else node2 + ) + assert isinstance(_node2, (FusedSchedulerNode, SchedulerNode)) + + _, (vars1, reduce1) = _node1.group + _, (vars2, reduce2) = _node2.group + if vars1 == () and vars2 == () and reduce1 != () and reduce2 != (): + # Reduction only + return DISABLE_OUTER_LOOP_FUSION + if all(type(node) is OuterLoopFusedSchedulerNode for node in (node1, node2)): + return ( + node1.outer_loop_fusion_depth + if node1.outer_loop_fusion_depth == node2.outer_loop_fusion_depth + else DISABLE_OUTER_LOOP_FUSION + ) + outer_loop_fusion_depth = min(len(vars1), len(vars2)) + if ( + outer_loop_fusion_depth >= 1 + and vars1[:outer_loop_fusion_depth] == vars2[:outer_loop_fusion_depth] + ): + if any( + type(node) is OuterLoopFusedSchedulerNode for node in (node1, node2) + ): + _compare_node = ( + node1 if type(node1) is OuterLoopFusedSchedulerNode else node2 + ) + if _compare_node.outer_loop_fusion_depth == outer_loop_fusion_depth: + # Same outer loop fusion depth as prev nodes in OuterLoopFusedSchedulerNode + return outer_loop_fusion_depth + else: + return DISABLE_OUTER_LOOP_FUSION + else: + # First 2 nodes to generate OuterLoopFusedSchedulerNode + return outer_loop_fusion_depth + return DISABLE_OUTER_LOOP_FUSION + + def can_fuse_vertical_outer_loop(self, node1, node2): + return ( + not node1.is_template() + and not node2.is_template() + and node1.get_operation_names() & node2.ancestors + and not ( + self._can_fuse_horizontal_impl(node1, node2) + and not node1.is_reduction() + ) + and self._get_outer_loop_fusion_depth(node1, node2) >= 1 + ) + + def get_fusion_pair_priority(self, node1, node2): + if self.can_fuse_vertical_outer_loop(node1, node2): + # Outer loop fusion with lower priority + return 1 + else: + return 0 + + def can_fuse_vertical(self, node1, node2): + if node2.is_template(): + # TODO(jgong5): support pre-op fusion with template + return False + if node1.is_template(): + return not node2.is_reduction() + return ( + self._can_fuse_horizontal_impl(node1, node2) and not node1.is_reduction() + ) or self.can_fuse_vertical_outer_loop(node1, node2) + + def try_loop_split(self, nodes: List[SchedulerNode]): + """ + Apply loop split optimization. + When one of the indexing_exprs contains a division, we eliminate the division by splitting the loop + to avoid non-contiguous loads, subject to the following conditions: + 1. No reduction and no mudular index for all nodes. + 2. Only one node's one indexing_exprs contains a division, according to this indexing_exprs, + we can get the dimension that needs to be split, and the split dimension is contiguous + in all other indexing_exprs. + + For example, if the node's var_ranges: {z0: 2, z1: 9216, z2: 960} and indexing_exprs: + {'index0': 8847360*z0 + 960*z1 + z2, 'index1': 32*z0 + (z2//30), 'index2': z2}, + we will split z2 -> 30*z2 + z3, then the node's var_ranges will be changed to + {z0: 2, z1: 9216, z2: 32, z3: 30} and indexing_exprs will be changed to + {'index0': 8847360*z0 + 960*z1 + 30*z2 + z3, 'index1': 32*z0 + z2, 'index2': 30*z2 + z3}. + """ + + # No reduction and no mudular + if any( + len(node.group[1][1]) != 0 + or any( + expr.has(ModularIndexing) for expr in node._body.indexing_exprs.values() + ) + for node in nodes + ): + return nodes + + split_var = None + split_number = None + divide_index_name = None + num_div = 0 + match_div = False + matched_node = None + + for node in nodes: + assert isinstance(node.node, ir.ComputedBuffer) + _, original_body, _ = node.node.get_default_sizes_body() + for name, expr in original_body.indexing_exprs.items(): + num_div += expr.count(FloorDiv) + if num_div > 1: + return nodes + if expr.count(FloorDiv) == 1: + div_expr = expr.find(FloorDiv).pop() + split_var = div_expr.args[0] + split_number = div_expr.args[1] + divide_index_name = name + if ( + isinstance(split_number, sympy.core.numbers.Integer) + and isinstance(split_var, sympy.core.symbol.Symbol) + and split_var in original_body.iter_vars + and divide_index_name is not None + and all( + stride_at_vec_range(expr, split_var) == 1 + for name, expr in original_body.indexing_exprs.items() + if name != divide_index_name + ) + ): + match_div = True + matched_node = node + + # Only one node contains a division, and the split dimension is contiguous in all other indexing_exprs. + if not match_div: + return nodes + + extra_indexing_constraints = None + + def loop_split(sizes, body, vars): + index_size, reduce_size = sizes + index_vars, reduce_vars = vars + split_idx = index_vars.index(split_var) + new_index_size = index_size.copy() + new_index_size[split_idx] = index_size[split_idx] // split_number + new_index_size.insert(split_idx + 1, split_number) + (new_index_vars, _), var_ranges = dependencies.index_vars_no_squeeze( + new_index_size, reduce_size, prefix="y" + ) + iter_vars = new_index_vars.copy() + divisor_var = iter_vars.pop(split_idx + 1) + iter_vars[split_idx] = split_number * iter_vars[split_idx] + divisor_var + body = ir.LoopBody( + body, [iter_vars, reduce_vars], var_ranges, new_index_vars, reduce_vars + ) + nonlocal extra_indexing_constraints + if not extra_indexing_constraints: + extra_indexing_constraints = ( + body.var_ranges, + list(body.indexing_exprs.values()), + ) + return ( + (new_index_size, reduce_size), + body, + (new_index_vars, reduce_vars), + ) + + # Here decide the final loop order + for node in nodes: + if node == matched_node: + node.recompute_size_and_body(recompute_sizes_body_func=loop_split) + for node in nodes: + if node != matched_node: + node.recompute_size_and_body( + extra_indexing_constraints=extra_indexing_constraints, + recompute_sizes_body_func=loop_split, + ) + + return nodes + + def codegen_outer_loop_node( + self, + node: OuterLoopFusedSchedulerNode, + ): + """ + Generate the code for the outer loop fused scheduler node. + 1. Codegen with fused outer loop: depends on the analysis of + the outer loop fused scheduler node, with or without the local buffer. + 2. If failed, fallback to standard codegen. + """ + kernel_group = self.kernel_group + generated_cpp_vec_kernel_count = metrics.generated_cpp_vec_kernel_count + cpp_kernel_proxy_list: List[CppKernelProxy] = [] + nodes_list: List[List[SchedulerNode]] = [] + assert isinstance(node, OuterLoopFusedSchedulerNode) + + def try_outer_loop_fusion_with_local_buf(node: OuterLoopFusedSchedulerNode): + """ + Codegen code with fused outer loop and local Buffer. + """ + assert isinstance(node, OuterLoopFusedSchedulerNode) + cpp_kernel_proxy_list.clear() + nodes_list.clear() + + def get_call_ranges(node: BaseSchedulerNode): + assert isinstance(node, (SchedulerNode, FusedSchedulerNode)) + nodes: List[SchedulerNode] = node.get_nodes() # type: ignore[assignment] + _, (group, reduction_group) = max( + nodes, key=lambda x: int(x.is_reduction()) + ).group + call_ranges = tuple(group) + tuple(reduction_group) + return call_ranges + + local_buffers: List[ir.Buffer] = [] + # Map local buffer name to a list of global buffers + local_to_global_buffers: Dict[str, List[ir.Buffer]] = {} + if all( + len(get_call_ranges(_node)) == node.outer_loop_fusion_depth + 1 + for _node in node.get_outer_nodes() + ): + # Ref to the typical case of local buffer + # in https://github.com/pytorch/pytorch/blob/ + # 1115a25c36340554442f28f9570abd42f0aface2/aten/src/ATen/native/cpu/SoftMaxKernel.cpp#L159 + # where the buffer is with size of last dim and contiguous. + # Only support this typical case at first. + visited_scheduler_nodes: Set[str] = set() + for scheduler_node in node.get_nodes(): + # all users inside same OuterLoopFusedSchedulerNode + assert isinstance(scheduler_node, SchedulerNode) + visited_scheduler_nodes.add(scheduler_node.get_name()) + if ( + scheduler_node.is_reduction() + or len(scheduler_node.get_outputs()) != 1 + ): + continue + + scheduler_buffer = scheduler_node.get_outputs()[0] + if all( + user.node in node.get_nodes() for user in scheduler_buffer.users + ): + global_buffer = scheduler_buffer.node + assert isinstance(global_buffer, ir.ComputedBuffer) + global_buffer_layout = global_buffer.get_layout() + size_offset = node.outer_loop_fusion_depth - len( + get_call_ranges(scheduler_node) + ) + + def is_all_write_read_contiguous(): + contiguous_index_expr = 0 + stride = 1 + for var, range in reversed( + scheduler_node._body.var_ranges.items() + ): + contiguous_index_expr += stride * var + stride *= range + write_index_expr = scheduler_node._body.get_write_expr( + scheduler_buffer.get_name() + ) + + def is_contiguous_index(x): + return x == contiguous_index_expr + + return is_contiguous_index(write_index_expr) and all( + isinstance(user.node, SchedulerNode) + and is_contiguous_index( + user.node._body.get_read_expr( + scheduler_buffer.get_name() + ), + ) + for user in scheduler_buffer.users + ) + + if not ( + global_buffer_layout.is_contiguous() + and is_all_write_read_contiguous() + ): + continue + # Local Buffer is a view of global buffer + local_buffer_layout = ir.FixedLayout( + global_buffer_layout.device, + global_buffer_layout.dtype, + global_buffer_layout.size[size_offset:], + global_buffer_layout.stride[size_offset:], + ) + + def try_share_local_buffer(local_buffer_layout, local_buffers): + for local_buf in local_buffers: + if local_buffer_layout == local_buf.layout and all( + all( + user.node.get_name() in visited_scheduler_nodes + for user in V.graph.scheduler.name_to_buf[ + global_buffer.name + ].users + ) + for global_buffer in local_to_global_buffers[ + local_buf.name + ] + if global_buffer.name is not None + ): + return local_buf + return None + + local_buf_prefix = "local_buffer_data" + # Share existing local buffer + local_buffer_used = try_share_local_buffer( + local_buffer_layout, local_buffers + ) + if not local_buffer_used: + # Create new local buffer + local_buffer_used = ir.Buffer( + f"{local_buf_prefix}_{len(local_buffers)}", + local_buffer_layout, + ) + local_buffers.append(local_buffer_used) + local_to_global_buffers[local_buffer_used.name] = [] + local_to_global_buffers[local_buffer_used.name].append( + global_buffer, + ) + + with LocalBufferContext(kernel_group.args) as scope: + if len(local_buffers) > 0: + for local_buffer in local_buffers: + assert local_buffer.name is not None + scope.add_local_buffer( + local_buffer, local_to_global_buffers[local_buffer.name] + ) + for _node in node.get_outer_nodes(): + assert isinstance(_node, (FusedSchedulerNode, SchedulerNode)) + cpp_kernel_proxy = CppKernelProxy(kernel_group) + cpp_kernel_proxy.codegen_nodes(_node.get_nodes()) # type: ignore[arg-type] + cpp_kernel_proxy_list.append(cpp_kernel_proxy) + nodes_list.append(_node.get_nodes()) # type: ignore[arg-type] + + if not node.check_outer_fusion_loop_level_attr( + cpp_kernel_proxy_list, node.outer_loop_fusion_depth + ): + return False + metrics.cpp_outer_loop_fused_inner_counts.append( + metrics.CppOuterLoopFusedCount( + len(cpp_kernel_proxy_list), + local_buffer_number=len(scope.local_buffers), + ) + ) + outer_fusion_cpp_kernel_proxy = node.merge_outer_fusion_kernels( + cpp_kernel_proxy_list, + ) + kernel_group.finalize_kernel( + outer_fusion_cpp_kernel_proxy, + [_node for _nodes in nodes_list for _node in _nodes], + ) + + return True + + if not try_outer_loop_fusion_with_local_buf(node): + # Reset generated_cpp_vec_kernel_count to codegen again + metrics.generated_cpp_vec_kernel_count = generated_cpp_vec_kernel_count + cpp_kernel_proxy_list.clear() + nodes_list.clear() + # Similar as comment in + # https://github.com/pytorch/pytorch/blob/469383755fe416eb1c41fa724762ad3eaecdff07/torch/_inductor/codegen/cpp.py#L3269-L3272 + # Kernels share the same global contexts like V.graph.wrapper_code, V.kernel.args. + with torch._inductor.config.patch(inplace_buffers=False): + for _node in node.get_outer_nodes(): + assert isinstance(_node, (FusedSchedulerNode, SchedulerNode)) + _nodes: List[SchedulerNode] = _node.get_nodes() # type: ignore[assignment] + cpp_kernel_proxy = CppKernelProxy(kernel_group) + cpp_kernel_proxy.codegen_nodes(_nodes) + kernel_group.finalize_kernel(cpp_kernel_proxy, _nodes) + + def codegen_node( + self, + node: Union[OuterLoopFusedSchedulerNode, FusedSchedulerNode, SchedulerNode], + ): + """ + Turn an set of pre-fused nodes into a C++ kernel. + """ + kernel_group = self.kernel_group + + if isinstance(node, OuterLoopFusedSchedulerNode): + self.codegen_outer_loop_node(node) + else: + nodes: List[SchedulerNode] = node.get_nodes() # type: ignore[assignment] + nodes = self.try_loop_split(nodes) + cpp_kernel_proxy = CppKernelProxy(kernel_group) + cpp_kernel_proxy.codegen_nodes(nodes) + kernel_group.finalize_kernel(cpp_kernel_proxy, nodes) + + args_num = self._get_scheduled_num_args() + if args_num > CppScheduling.MAX_FUSED_KERNEL_ARGS_NUM: + self._set_flush_status(True) + + def is_cpp_template(self, node: BaseSchedulerNode) -> bool: + return isinstance(node, SchedulerNode) and isinstance( + node.node, ir.CppTemplateBuffer + ) + + def codegen_template( + self, + template_node: BaseSchedulerNode, + epilogue_nodes: Sequence[BaseSchedulerNode], + ): + """ + Codegen a CPP template, possibly with fused epilogues + """ + counters["inductor"]["cpp_epilogue_fusion_counter"] += len(epilogue_nodes) + assert self.is_cpp_template( + template_node + ), "Template node passed to CppScheduler.codegen_template must be a SchedulerNode that wraps a CppTemplateBuffer" + template_node = cast(SchedulerNode, template_node) + _, (_, rnumel) = template_node.group + assert rnumel == () + ctb: ir.CppTemplateBuffer = cast(ir.CppTemplateBuffer, template_node.node) + epilogue_ir_nodes: List[Optional[ir.Operation]] = [ + n.node for n in epilogue_nodes + ] + assert all( + isinstance(n, ir.ComputedBuffer) for n in epilogue_ir_nodes + ), "Epilogue nodes must all be instances of ir.ComputedBuffer" + + def template_buffer_has_other_users( + template_buffer, outputs_by_name, epilogue_nodes + ): + assert template_buffer.get_name() in outputs_by_name + users = outputs_by_name[template_buffer.get_name()].users + return not all( + isinstance(user.node, BaseSchedulerNode) + and user.node.node in epilogue_nodes + for user in users + ) + + flag_template_buffer_has_other_users = template_buffer_has_other_users( + ctb, template_node.outputs_by_name, epilogue_ir_nodes + ) + kernel, render = ctb.make_kernel_render( + ctb, + flag_template_buffer_has_other_users=flag_template_buffer_has_other_users, + epilogue_nodes=epilogue_ir_nodes, + ) + with kernel: + for node in [template_node, *epilogue_nodes]: + node.mark_run() # type: ignore[attr-defined] + src_code = render() + + with V.set_kernel_handler(kernel): + node_schedule = [template_node, *epilogue_nodes] + kernel_name = self.define_kernel(src_code, node_schedule, kernel.args) + kernel.call_kernel(kernel_name, ctb) + V.graph.removed_buffers |= kernel.removed_buffers + self.scheduler.free_buffers() + + def _get_scheduled_num_args(self): + return self.kernel_group.get_num_args() + + def ready_to_flush(self): + return self._ready_to_flush + + def codegen_sync(self): + pass + + def define_kernel(self, src_code, nodes, kernel_args=None): + wrapper = V.graph.wrapper_code + fused_name = ( + get_fused_kernel_name(nodes, config.cpp.descriptive_names) + if config.cpp.descriptive_names + else "" + ) + kernel_name = "_".join(["cpp", fused_name, wrapper.next_kernel_suffix()]) + kernel_decl_name = kernel_name if V.graph.cpp_wrapper else "kernel" + src_code = src_code.replace(str(Placeholder.KERNEL_NAME), kernel_decl_name) + src_code = src_code.replace(str(Placeholder.DESCRIPTIVE_NAME), kernel_name) + # TODO(voz): Ostensibly, we should not need this. But there are cases where C++ codegen does + # not use BracesBuffer, so we have no good indicator of a C++ buffer atm. + src_code = src_code.replace("#pragma CMT", "//") + + compile_wrapper = IndentedBuffer() + args = self.kernel_group.args if kernel_args is None else kernel_args + _, _, arg_types = args.cpp_argdefs() + if not V.graph.cpp_wrapper: + compile_wrapper.writeline(f"async_compile.cpp_pybinding({arg_types!r}, '''") + compile_wrapper.splice(src_code, strip=True) + if not V.graph.cpp_wrapper: + compile_wrapper.writeline("''')") + wrapper.define_kernel(kernel_name, compile_wrapper.getvalue(), cuda=False) + return kernel_name + + def flush(self): + src_code = self.kernel_group.codegen_group() + if src_code: + kernel_name = self.define_kernel( + src_code, self.kernel_group.scheduled_nodes + ) + self.kernel_group.call_kernel(V.graph.wrapper_code, kernel_name) + self.reset_kernel_group() + self._set_flush_status(False) + + +class KernelGroup: + def __init__(self): + super().__init__() + self.args = KernelArgs() + self.loops_code = BracesBuffer() + self.ws = WorkSharing(self.loops_code) + self.stack = contextlib.ExitStack() + self.stack.enter_context(self.ws) + self.scheduled_nodes = [] + + def new_kernel(self, cls, *args): + return cls(self.args, parallel_num_threads(), *args) + + def finalize_kernel(self, new_kernel, nodes): + self.scheduled_nodes += nodes + code = self.loops_code + ws = self.ws + new_kernel.codegen_loops(code, ws) + + def get_num_args(self): + arg_defs, call_args, arg_types = self.args.cpp_argdefs() + args_num = len(arg_defs) + return args_num + + def codegen_group(self, name=None) -> str: + self.stack.close() + if not self.scheduled_nodes: + return "" + code = BracesBuffer() + # 1. Include header files + # TODO: support kernel profile on other platforms + enable_kernel_profile = config.cpp.enable_kernel_profile and sys.platform in [ + "linux", + "win32", + ] + if enable_kernel_profile: + code.writelines(["#include "]) + code.writeline(codecache.cpp_prefix()) + + # 2. Function definition + kernel_decl_name = str(Placeholder.KERNEL_NAME) if name is None else name + kernel_name = str(Placeholder.DESCRIPTIVE_NAME) if name is None else name + arg_defs, _, _ = self.args.cpp_argdefs() + arg_defs = ",\n".ljust(25).join(arg_defs) + func_export_decl = get_export_declaration() + code.writeline( + f'extern "C" {func_export_decl} void {kernel_decl_name}({arg_defs})' + ) + + # 3. Function body + with code.indent(): + if enable_kernel_profile: + graph_id = V.graph.graph_id + prefix = "graph_" + str(graph_id) + "_" if graph_id is not None else "" + code.writelines( + [ + f'RECORD_FUNCTION("{prefix + kernel_name}", c10::ArrayRef({{}}));' + ] + ) + for old, new in self.args.aliases(): + code.writeline(f"auto {old} = {new};") + code.splice(self.loops_code) + return code.getvalue() + + def call_kernel(self, wrapper, kernel_name): + _, call_args, arg_types = self.args.cpp_argdefs() + wrapper.generate_kernel_call( + kernel_name, call_args, cuda=False, arg_types=arg_types + ) + + +class CppWrapperKernelGroup(KernelGroup): + def __init__(self): + super().__init__() + self.args = CppWrapperKernelArgs() + + +class WorkSharing: + def __init__(self, code): + self.code = code + self.in_parallel = False + self.num_threads = None + self.stack = contextlib.ExitStack() + + def parallel(self, threads): + if self.in_parallel and threads != self.num_threads: + # wrong number of threads + self.close() + if not self.in_parallel: + self.num_threads = threads + self.in_parallel = True + if config.cpp.dynamic_threads: + self.code.writeline("#pragma omp parallel") + else: + self.code.writeline(f"#pragma omp parallel num_threads({threads})") + self.stack.enter_context(self.code.indent()) + self.code.writeline( + "int tid = omp_get_thread_num();", + ) + + def single(self): + if self.in_parallel: + self.code.writeline("#pragma omp single") + return self.in_parallel + + def close(self): + self.stack.close() + self.in_parallel = False + + def __enter__(self): + self.stack.__enter__() + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + self.stack.__exit__(exc_type, exc_val, exc_tb) + + +@dataclasses.dataclass +class LoopLevel: + var: Optional[sympy.Expr] = None + size: Optional[sympy.Expr] = None + offset: sympy.Expr = sympy.Integer(0) + steps: sympy.Expr = sympy.Integer(1) + parallel: int = 0 + simd_omp: bool = False + simd_vec: bool = False + collapsed: bool = False + is_reduction: bool = False + parent: Optional["LoopLevel"] = None + # the next inner level of the loop, empty if it is inner-most + # contains >1 LoopLevel if the inner level of loop is split + inner: List["LoopLevel"] = dataclasses.field(default_factory=list) + # kernel assigned to this loop level, only valid when it is a leaf + kernel: Optional[CppKernel] = None + + def __post_init__(self): + # Regarding the C++/OpenMP backend, `cpu_vec_isa.pick_vec_isa()` to check + # vectorization ISA is a time-consuming and one-shot operation. It leads + # to taking a longer time to import `codegen.cpp` package because the + # `LoopLevel` of the package is decorated by `@dataclasses.dataclass` while + # the decorator will invoke `cpu_vec_isa.pick_vec_isa()` to initialize the + # `simd_nelements` of the `LoopLevel`. It might introduce additional compilation + # overhead to the Triton backend. Therefore, we moved the `simd_nelements` to + # `__post_init__` + picked_vec_isa: cpu_vec_isa.VecISA = cpu_vec_isa.pick_vec_isa() + self.simd_nelements: int = picked_vec_isa.nelements() if picked_vec_isa else 0 + + def get_kernels(self) -> List[CppKernel]: + """Get all kernel objects under this loop level""" + if self.kernel: + return [self.kernel] + kernels = [] + for loop in self.inner: + kernels += loop.get_kernels() + return kernels + + def get_root(self): + """Get all kernel objects under this loop level""" + root = self + while root.parent: + root = root.parent + return root + + def set_kernel(self, kernel: CppKernel): + """ + Set the kernel under this loop level. No split is allowed under + this loop level. + """ + if not self.inner: + self.kernel = kernel + loop: Optional[LoopLevel] = self + assert loop is not None + return + assert len(self.inner) == 1 + self.inner[0].set_kernel(kernel) + + def get_loops_at(self, depth) -> List["LoopLevel"]: + if depth == 0: + return [self] + else: + loops = [] + for loop in self.inner: + loops += loop.get_loops_at(depth - 1) + return loops + + def split_with_tiling(self, depth, factor): + def clone_inner(): + inner = [] + if self.inner: + for loop in self.inner: + inner.append(loop.clone()) + return inner + + def do_split_with_tiling(): + sympy_factor = sympy.Integer(factor) + + offset = FloorDiv(self.size, sympy_factor) * sympy_factor + main_loop = LoopLevel(self.var, offset) + main_loop.steps = sympy_factor + main_loop.parallel = self.parallel + main_loop.collapsed = False + main_loop.is_reduction = self.is_reduction + main_loop.inner = clone_inner() + if main_loop.inner: + for loop in main_loop.inner: + loop.parent = main_loop + + tail_loop = LoopLevel(self.var, self.size) + tail_loop.offset = offset + tail_loop.parallel = self.parallel + tail_loop.collapsed = False + tail_loop.is_reduction = self.is_reduction + tail_loop.inner = clone_inner() + if tail_loop.inner: + for loop in tail_loop.inner: + loop.parent = tail_loop + + return main_loop, tail_loop + + if depth == 0: + main_loop, tail_loop = do_split_with_tiling() + parent = self.parent + if parent: + parent.inner = [main_loop, tail_loop] + main_loop.parent = parent + tail_loop.parent = parent + return main_loop, tail_loop + else: + assert len(self.inner) == 1 + return self.inner[0].split_with_tiling(depth - 1, factor) + + def clone(self): + loop = copy(self) + loop.inner = [] + if self.inner: + for inner_loop in self.inner: + inner_loop_clone = inner_loop.clone() + inner_loop_clone.parent = loop + loop.inner.append(inner_loop_clone) + loop.kernel = deepcopy(self.kernel) + return loop + + def lines(self): + offset_expr = cexpr_index(self.offset) + size_expr = cexpr_index(self.size) + if config.cpp.no_redundant_loops and offset_expr == size_expr: + return None + simd = ( + f"simd simdlen({self.simd_nelements}) " + if self.simd_omp and self.simd_nelements > 1 + else "" + ) + if self.parallel: + # TODO(jansel): look into chunk size and other schedules + line1 = "#pragma omp for" + if self.parallel > 1: + line1 += f" collapse({self.parallel})" + if self.simd_omp: + line1 = line1.replace(" for ", f" for {simd}") + elif self.simd_vec: + line1 = "" + elif self.simd_omp: + line1 = f"#pragma omp {simd}" + elif not self.is_reduction and cpp_builder.is_gcc(): + line1 = "#pragma GCC ivdep" + else: + line1 = "" + offset_str = f"{INDEX_TYPE} {self.var}={offset_expr}" + size_str = f"{self.var}<{size_expr}" + if self.steps.is_number: + steps_str = f"{self.var}+={cexpr_index(self.steps)}" + else: + # If the step size is 0, change it to 1 because a step size of 0 + # will cause floating point exception (core dump) during parallelization. + steps_str = ( + f"{self.var}+=({cexpr_index(self.steps)} == 0 ? " + f"1 : {cexpr_index(self.steps)})" + ) + line2 = f"for({offset_str}; {size_str}; {steps_str})" + if self.collapsed or not line1: + return [line2] + return [line1, line2] + + +@dataclasses.dataclass +class LoopNestWithSplit: + """ + A loop-nest like structure but with some loop level split along + the loop range into the main tiling loop and the tail. It is built + with the `build` method as a loop nest and then split with + `split_with_tiling` at some depth. + + A typical case is for vectorization where we typically split at the inner-most + loop level. A more complicated case is 2D tiling where we split at + both inner-most and outer levels. + """ + + root: Optional[List[LoopLevel]] = None + kernel: Optional[CppKernel] = None + + @staticmethod + def build(kernel: CppKernel): + """Build a LoopNest with the given `kernel` as the leaf""" + itervars = kernel.itervars + ranges = kernel.ranges + reduction_depth = kernel.reduction_depth + assert reduction_depth is not None + + root: List[LoopLevel] = [] + levels: List[LoopLevel] = root + loop: Optional[LoopLevel] = None + for loop_idx, (var, size) in enumerate(zip(itervars, ranges)): + loop = LoopLevel(var, size, parent=loop) + if loop_idx >= reduction_depth: + loop.is_reduction = kernel.is_reduction + levels.append(loop) + levels = loop.inner + loop_nest = LoopNestWithSplit(root) + if loop: + loop.kernel = kernel + else: + loop_nest.kernel = kernel + return loop_nest + + def __bool__(self): + return bool(self.root) + + def get_loops_at(self, depth) -> List[LoopLevel]: + """Get all the loop levels at the given `depth` (most outer loop has depth 0)""" + loops: List[LoopLevel] = [] + assert self.root is not None + for loop in self.root: + loops += loop.get_loops_at(depth) + return loops + + @cache_on_self + def max_parallel_depth(self): + """ + Maximal allowed depth for parallelism: + 1) Levels without splitting and + 2) All reduction or non-reduction levels + When the loop is split at the top level, the max depth is 1. + """ + max_depth = 0 + assert self.root is not None + loops = self.root + if len(loops) > 1: + return 1 + is_reduction = loops[0].is_reduction if loops else False + while len(loops) == 1 and loops[0].is_reduction == is_reduction: + max_depth += 1 + loops = loops[0].inner + return max_depth + + def is_reduction_only(self): + """ + Whether all the loops are for reduction. Reduction loops + are always the inner most ones. + """ + return ( + self.root is not None and len(self.root) > 0 and self.root[0].is_reduction + ) + + def mark_parallel(self, par_depth): + assert ( + par_depth <= self.max_parallel_depth() + ), "Parallel depth cannot exceed the maximal allowed parallel depth" + assert self.root is not None + loops = self.root + for loop in loops: + loop.parallel = par_depth + for i in range(1, par_depth): + loops = loops[0].inner + loops[0].collapsed = True + + def split_with_tiling(self, depth, factor): + """ + Split the loop into main and tail loops at given `depth` so that the range + of the main loop has range `floor_div(range, factor) * factor` and + the tail loop handles the remainder. The main loop is tiled + according to the `factor`. + """ + loops = self.get_loops_at(depth) + assert len(loops) == 1 + split_loops = loops[0].split_with_tiling(0, factor) + if depth == 0: + self.root = split_loops + return split_loops + + def get_kernels(self) -> List[CppKernel]: + """Get all kernel objects under this loop nest""" + if self.kernel: + return [self.kernel] + kernels: List[CppKernel] = [] + assert self.root is not None + for loop in self.root: + kernels += loop.get_kernels() + return kernels diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_gemm_template.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_gemm_template.py new file mode 100644 index 0000000000000000000000000000000000000000..b2a2b1c41576d0d68435dcab31e11b94cf436c15 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_gemm_template.py @@ -0,0 +1,1043 @@ +# mypy: allow-untyped-defs +import contextlib +import logging +import math +from functools import lru_cache +from typing import Any, Callable, cast, List, Optional, Set, Union +from unittest.mock import patch + +import torch +import torch.utils + +from ..._dynamo.utils import counters +from .. import config, ir, lowering as L +from ..kernel.mm_common import mm_args +from ..select_algorithm import DataProcessorTemplateWrapper +from ..utils import cache_on_self, has_free_symbols, parallel_num_threads +from ..virtualized import ops, V +from .cpp import get_export_declaration +from .cpp_micro_gemm import CppMicroGemmAMX, create_micro_gemm, LayoutType +from .cpp_template import CppTemplate +from .cpp_template_kernel import CppTemplateKernel +from .cpp_utils import ( + create_epilogue_with_attr, + DTYPE_TO_CPP, + GemmBlocking, + get_gemm_template_output_and_compute_dtype, +) + + +log = logging.getLogger(__name__) + +GEMM_TEMPLATE = r""" +{{template.header().getvalue()}} + +{{micro_gemm.codegen_define(kernel)}} + +{%- if x_scale is not none %} + {%- set kernel_args = {"X": X, "W": W, "inp": inp, "x_scale": x_scale, "x_zp": x_zp, "w_scale": w_scale, "w_zp": w_zp,} %} +{%- else %} + {%- set kernel_args = {"X": X, "W": W, "inp": inp} %} +{%- endif %} + +extern "C" {{export_declaration}} +{{kernel.def_kernel(inputs=kernel_args, outputs={"Y": Y}, aliases=aliases)}} +{ + {{kernel.maybe_codegen_profile()}} + constexpr int64_t num_threads = {{num_threads}}; + constexpr int64_t N = {{N}}; + constexpr int64_t K = {{K}}; + constexpr int64_t Mr = {{micro_gemm.register_blocking.block_m}}; + constexpr int64_t Nr = {{micro_gemm.register_blocking.block_n}}; + constexpr int64_t Kr = {{micro_gemm.register_blocking.block_k}}; + constexpr int64_t Nr_blocks = (N + Nr - 1) / Nr; + constexpr int64_t Kr_blocks = (K + Kr - 1) / Kr; + +{%- if is_dynamic_M %} + const int64_t M = {{kernel.size(GemmOut, 0)}}; + const int64_t Mr_blocks = (M + Mr - 1) / Mr; + {%- if num_threads > 1 %} + int64_t Mt_blocks, Nt_blocks, Kt_blocks; + mm_get_thread_blocking(num_threads, {{config.cpp.gemm_max_k_slices}}, M, N, K, Mr, Nr, Kr, Mt_blocks, Nt_blocks, Kt_blocks); + {%- else %} + const auto Mt_blocks = Mr_blocks; + const auto Nt_blocks = Nr_blocks; + const auto Kt_blocks = Kr_blocks; + {%- endif %} + int64_t Mc_blocks, Nc_blocks, Kc_blocks; + uint32_t L1_cache_size = {{L1_cache_size}}; + uint32_t L2_cache_size = {{L2_cache_size}}; + mm_get_cache_blocking<{{kernel.dtype(X)}}, {{kernel.dtype(W)}}>( + num_threads, + M, + N, + K, + Mr, + Nr, + Kr, + Mt_blocks, + Nt_blocks, + Kt_blocks, + Mc_blocks, + Nc_blocks, + Kc_blocks, + L1_cache_size, + L2_cache_size + ); + const int64_t num_Mc_blocks = (Mr_blocks + Mc_blocks - 1) / Mc_blocks; + const int64_t num_Nc_blocks = (Nr_blocks + Nc_blocks - 1) / Nc_blocks; + const int64_t num_k_slices = (Kr_blocks + Kt_blocks - 1) / Kt_blocks; +{%- else %} + constexpr int64_t M = {{kernel.size(GemmOut, 0)}}; + constexpr int64_t Mr_blocks = (M + Mr - 1) / Mr; + constexpr int64_t Mt_blocks = {{template.thread_blocking().block_m}}; + constexpr int64_t Nt_blocks = {{template.thread_blocking().block_n}}; + constexpr int64_t Kt_blocks = {{template.thread_blocking().block_k}}; + constexpr int64_t Mc_blocks = {{template.cache_blocking().block_m}}; + constexpr int64_t Nc_blocks = {{template.cache_blocking().block_n}}; + constexpr int64_t Kc_blocks = {{template.cache_blocking().block_k}}; + constexpr int64_t num_Mc_blocks = (Mr_blocks + Mc_blocks - 1) / Mc_blocks; + constexpr int64_t num_Nc_blocks = (Nr_blocks + Nc_blocks - 1) / Nc_blocks; + constexpr int64_t num_k_slices = (Kr_blocks + Kt_blocks - 1) / Kt_blocks; +{%- endif %} + + // make sure all partitions are assigned + {{kernel.assert_function}}( + Mt_blocks * Nt_blocks * Kt_blocks * {{num_threads}} >= Mr_blocks * Nr_blocks * Kr_blocks, + "Not all partitions are assigned." + ); + +{%- if maybe_k_slicing %} + std::unique_ptr[]> local_buf_ptrs; + if (num_k_slices > 1) { + local_buf_ptrs.reset(new std::unique_ptr<{{DTYPE_TO_CPP[acc_buf_dtype]}}[]>[num_Mc_blocks * num_Nc_blocks * num_k_slices]); + } +{%- endif %} + +{%- if num_threads > 1 %} + #pragma omp parallel num_threads({{num_threads}}) + { + const int tid = omp_get_thread_num(); + int64_t m_block_start, m_block_end, n_block_start, n_block_end, k_block_start, k_block_end; + mm_get_thread_blocks( + tid, Mr_blocks, Nr_blocks, Kr_blocks, Mt_blocks, Nt_blocks, Kt_blocks, + m_block_start, m_block_end, n_block_start, n_block_end, k_block_start, k_block_end); + {%- if maybe_k_slicing %} + const int64_t k_group_id = tid / num_k_slices; + const int64_t k_slice_id = tid % num_k_slices; + {%- endif %} +{%- else %} + { + const int tid = 0; + const int64_t m_block_start = 0; + const int64_t m_block_end = Mr_blocks; + const int64_t n_block_start = 0; + const int64_t n_block_end = Nr_blocks; + const int64_t k_block_start = 0; + const int64_t k_block_end = Kr_blocks; +{%- endif %} + {{ micro_gemm.codegen_init(kernel) }} +{%- if use_local_acc %} + {%- set acc_buf_name = "local_acc_buf" %} + {{ kernel.define_buffer(acc_buf_name, ["Mc_blocks*Mr", "Nc_blocks*Nr"], acc_buf_dtype) }} +{%- endif %} + for (int64_t mc = m_block_start; mc < m_block_end; mc += Mc_blocks) { + const int64_t m_start = mc * Mr; + const int64_t m_end = std::min(std::min(mc + Mc_blocks, m_block_end) * Mr, M); + const int64_t m_size = m_end - m_start; + for (int64_t nc = n_block_start; nc < n_block_end; nc += Nc_blocks) { + const int64_t n_start = nc * Nr; + const int64_t n_end = std::min(std::min(nc + Nc_blocks, n_block_end) * Nr, N); + const int64_t n_size = n_end - n_start; + // NB: assume we pad N, nc_block_end won't exceed padded N here. + const int64_t nc_block_end = std::min(nc + Nc_blocks, n_block_end); +{%- if use_local_acc %} + {%- set acc = kernel.local_buffers[acc_buf_name] %} + {{ kernel.reinit_buffer_if_null(acc_buf_name) }} +{%- else %} + {%- set acc = kernel.slice_nd(GemmOut, [("m_start", "m_end"), ("n_start", "n_end")]) %} +{%- endif %} + for (int64_t kc = k_block_start; kc < k_block_end; kc += Kc_blocks) { + int64_t k_start = kc * Kr; + int64_t k_end = std::min(std::min(kc + Kc_blocks, k_block_end) * Kr, K); +{%- set tile_X = kernel.slice_nd(X, [("m_start", "m_end"), ("k_start", "k_end")]) %} + for (int64_t nci = nc; nci < nc_block_end; nci++) { +{%- set acc_slice = kernel.slice_nd(acc, [("0", "m_end - m_start"), ("(nci - nc)*Nr", "(nci - nc + 1)*Nr")]) %} +{%- set tile_W_3d = kernel.slice_nd(W, [("nci", "nci + 1"), ("k_start", "k_end"), ()]) %} +{%- set tile_W = kernel.view(tile_W_3d, ["k_end - k_start", micro_gemm.register_blocking.block_n]) %} + if (kc == k_block_start) { + {{ micro_gemm.codegen_call(kernel, tile_X, tile_W, acc_slice, accum=False)|indent(28, false) }} + } else { + {{ micro_gemm.codegen_call(kernel, tile_X, tile_W, acc_slice, accum=True)|indent(28, false) }} + } + } + } +{%- if maybe_k_slicing %} + if (num_k_slices > 1) { + const int64_t mxn_cache_block_id = (mc / Mc_blocks) * num_Nc_blocks + nc; + local_buf_ptrs[mxn_cache_block_id * num_k_slices + k_slice_id].reset({{ kernel.release_buffer(acc_buf_name) }}); + } else +{%- endif %} + { +{%- set tile_Y = kernel.slice_nd(Y_2d, [("m_start", "m_end"), ("n_start", "n_end")]) %} +{%- set tile_acc = kernel.slice_nd(acc, [("0", "m_end - m_start"), ("0", "n_end - n_start")]) %} + {{ kernel.store_output( + tile_Y, tile_acc, GemmOut, epilogue_nodes, offsets=("m_start", "n_start"), reindexers=reindexers + )|indent(20, false) + }} + } + } + } +{%- if maybe_k_slicing %} + if (num_k_slices > 1) { + #pragma omp barrier + for (int64_t mc = m_block_start; mc < m_block_end; mc += Mc_blocks) { + // We slice M-dim and each thread in the k-slicing group works on a slice + const int64_t m_start_unsliced = mc * Mr; + const int64_t m_end_unsliced = std::min(std::min(mc + Mc_blocks, m_block_end) * Mr, M); + const int64_t m_size_unsliced = m_end_unsliced - m_start_unsliced; + const int64_t m_slice_size = (m_size_unsliced + num_k_slices - 1) / num_k_slices; + const int64_t m_start = std::min(m_start_unsliced + m_slice_size * k_slice_id, m_end_unsliced); + const int64_t m_end = std::min(m_start_unsliced + m_slice_size * (k_slice_id + 1), m_end_unsliced); + const int64_t m_size = m_end - m_start; + const int64_t m_offset = m_start - m_start_unsliced; + for (int64_t nc = n_block_start; nc < n_block_end; nc += Nc_blocks) { + const int64_t n_start = nc * Nr; + const int64_t n_end = std::min(std::min(nc + Nc_blocks, n_block_end) * Nr, N); + const int64_t n_size = n_end - n_start; + const int64_t mxn_cache_block_id = (mc / Mc_blocks) * num_Nc_blocks + nc; + auto {{acc_buf_name}} = local_buf_ptrs[mxn_cache_block_id * num_k_slices].get(); + for (int64_t other_slice = 1; other_slice < num_k_slices; other_slice++) { + auto other_acc = local_buf_ptrs[mxn_cache_block_id * num_k_slices + other_slice].get(); + for (int64_t m = m_offset; m < m_offset + m_size; m++) { + #pragma omp simd + for (int64_t n = 0; n < n_size; n++) { + {{acc_buf_name}}[m*Nr + n] += other_acc[m*Nr + n]; + } + } + } + {%- set tile_acc_m_slice = kernel.slice_nd(tile_acc, [("m_offset", "m_offset + m_end - m_start"), ()]) %} + {{ kernel.store_output( + tile_Y, tile_acc_m_slice, GemmOut, epilogue_nodes, offsets=("m_start", "n_start"), reindexers=reindexers + )|indent(20, false) + }} + } + } + } +{%- endif %} + {{ micro_gemm.codegen_finalize(kernel) }} + } +} +""" + + +def get_padded_n(n, block_n): + return (n + block_n - 1) // block_n * block_n + + +class CppPackedGemmTemplate(CppTemplate): + def __init__( + self, + input_nodes, + layout: ir.Layout, + num_threads: int, + register_blocking: GemmBlocking, + beta=1, + alpha=1, + has_bias=False, + epilogue_creator: Optional[Callable[[ir.Buffer], ir.Pointwise]] = None, + ) -> None: + assert layout.dtype in [torch.float, torch.bfloat16, torch.half, torch.uint8] + super().__init__( + "packed_gemm", + input_nodes, + layout, + num_threads, + epilogue_creator=epilogue_creator, + ) + self.beta = beta + self.alpha = alpha + self.has_bias = has_bias + self.register_blocking = register_blocking + m, n = layout.size + _, k = input_nodes[0].get_size() + self.m, self.n, self.k = m, n, k + self.padded_n = get_padded_n(n, self.register_blocking.block_n) + self.is_dynamic_M = has_free_symbols((m,)) + + @cache_on_self + def thread_blocking(self) -> GemmBlocking: + """ + NOTE [Thread blocking in Cpp GEMM] + We use simple heuristics to decide the thread blocking: + 1. Make sure all threads are occupied as much as possible. + 2. For (m, n) blocks, favor more square-sized thread blocks for better data reuse. + 3. If (m, n) blocks cannot occupy all the threads, we consider k-slicing. + TODO(jgong5): allow tuning various blocking options + """ + + @lru_cache(maxsize=100) + def get_factors(number): + factors = [] + for i in range(int(number**0.5), 0, -1): + if number % i == 0: + factors.append(number // i) + factors.append(i) + return factors + + def get_blocking(m_factor, n_factor, k_factor, m_blocks, n_blocks, k_blocks): + thread_block_k = math.ceil(k_blocks / k_factor) + thread_block_n = math.ceil(n_blocks / n_factor) + thread_block_m = math.ceil(m_blocks / m_factor) + return GemmBlocking(thread_block_m, thread_block_n, thread_block_k) + + assert ( + not self.is_dynamic_M + ), "Unable to determine thread blocking for dynamic M." + register_blocking = self.register_blocking + m_blocks = math.ceil(self.m / register_blocking.block_m) + n_blocks = math.ceil(self.n / register_blocking.block_n) + k_blocks = math.ceil(self.k / register_blocking.block_k) + factors = get_factors(self.num_threads) + assert len(factors) > 0 + + if config.cpp.gemm_thread_factors is not None: + factors = [int(i) for i in config.cpp.gemm_thread_factors.split(",")] + assert len(factors) == 3 + assert math.prod(factors) == self.num_threads + return get_blocking( + factors[0], factors[1], factors[2], m_blocks, n_blocks, k_blocks + ) + + # we favor square-sized thread blocks for good data reuse + def get_better_blocking(blocking, best_blocking): + if best_blocking is None: + best_blocking = blocking + else: + block_m_size = blocking.block_m * register_blocking.block_m + block_n_size = blocking.block_n * register_blocking.block_n + best_block_m_size = best_blocking.block_m * register_blocking.block_m + best_block_n_size = best_blocking.block_n * register_blocking.block_n + if blocking.block_k > best_blocking.block_k: + best_blocking = blocking + elif ( + blocking.block_k == best_blocking.block_k + and block_m_size + block_n_size + < best_block_m_size + best_block_n_size + ): + best_blocking = blocking + return best_blocking + + best_blocking = None + # check if we can have a thread-blocking to occupy all threads without k-slicing + for n_factor in factors: + m_factor = self.num_threads // n_factor + if n_blocks >= n_factor and m_blocks >= m_factor: + blocking = get_blocking( + m_factor, n_factor, 1, m_blocks, n_blocks, k_blocks + ) + best_blocking = get_better_blocking(blocking, best_blocking) + + if best_blocking is None: + for k_factor in factors: + if k_blocks >= k_factor and ( + config.cpp.gemm_max_k_slices == 0 + or k_factor <= config.cpp.gemm_max_k_slices + ): + n_factors = get_factors(self.num_threads // k_factor) + for n_factor in n_factors: + m_factor = (self.num_threads // k_factor) // n_factor + if n_blocks >= n_factor and m_blocks >= m_factor: + blocking = get_blocking( + m_factor, + n_factor, + k_factor, + m_blocks, + n_blocks, + k_blocks, + ) + best_blocking = get_better_blocking(blocking, best_blocking) + + if best_blocking is None: + for n_factor in factors: + m_factor = self.num_threads // n_factor + if n_blocks >= n_factor or m_blocks >= m_factor: + blocking = get_blocking( + m_factor, n_factor, 1, m_blocks, n_blocks, k_blocks + ) + best_blocking = get_better_blocking(blocking, best_blocking) + + assert best_blocking is not None + return best_blocking + + @cache_on_self + def cache_blocking(self) -> GemmBlocking: + def get_cache_blocking(register_blocking, thread_blocking): + Mr = register_blocking.block_m + Nr = register_blocking.block_n + Kr = register_blocking.block_k + + Mt_blocks = thread_blocking.block_m + Nt_blocks = thread_blocking.block_n + Kt_blocks = thread_blocking.block_k + + if config.cpp.gemm_cache_blocking is not None: + blockings = [int(i) for i in config.cpp.gemm_cache_blocking.split(",")] + assert len(blockings) == 3 + Mc_blocks, Nc_blocks, Kc_blocks = blockings + return ( + min(Mc_blocks, Mt_blocks), + min(Nc_blocks, Nt_blocks), + min(Kc_blocks, Kt_blocks), + ) + + # The ratios below are empirically determined to decide + # the effective sizes of L1 and L2. + # TODO: tune the factor here + L1_limit_factor = 0.8 + L2_limit_factor = 0.5 + + L1_cache_size = ( + torch._C._cpu._L1d_cache_size() + ) # per core cache size in Bytes + assert ( + L1_cache_size > 0 + ), f"Expect L1_cache_size > 0 but got {L1_cache_size}" + L1 = L1_cache_size * L1_limit_factor + + L2_cache_size = ( + torch._C._cpu._L2_cache_size() + ) # per core cache size in Bytes + assert ( + L2_cache_size > 0 + ), f"Expect L2_cache_size > 0 but got {L2_cache_size}" + L2 = L2_cache_size * L2_limit_factor + + def get_num_byte(dtype): + return torch.tensor([], dtype=dtype).element_size() + + num_byte_A = get_num_byte(self.input_nodes[0].get_dtype()) + num_byte_B = get_num_byte(self.input_nodes[1].get_dtype()) + + # NOTE [CPP GEMM Cache Blocking Algorithm] + # Our overall strategy is to + # 1) Make cache blocks of B L1-reside and reused by multiple rows of A, i.e. Mc. + # Here, B is Kc x Nr where Nr is a single register block. We use L1 size to + # decide Kc. We want to make Mc large enough to better reuse B. + # 2) Make cache blocks of A L2-reside, which would limit Mc. We want to reuse A + # along N, where we have two sub-strategies (see notes below) to decide Mc and Nc. + + # Step 1: Decide Kc assuming B block is L1-reside. + size_cache_B = Kr * Kt_blocks * Nr * num_byte_B + Kc_blocks = Kt_blocks + if size_cache_B > L1: + Kc_blocks = math.floor(L1 / (Kr * Nr * num_byte_B)) + + # Step 2: Decide Mc assuming A block is L2-reside. + min_Mc_ratio = 2 # TODO(jgong5): something to tune? + min_Mc_blocks = math.ceil(min_Mc_ratio * Mr / Nr) + assert min_Mc_blocks >= 1 + Kt_bytes = Kt_blocks * Kr * num_byte_A + if min_Mc_blocks * Mr * Kt_bytes < L2: + # Strategy 1: A (Mc x Kt) resides in L2 and reused by all Nt + # when Nc_blocks is kept 1. Mc should be large enough (>= min_Mc_blocks) + # to reuse B (Kc x Nr) in L1. This makes C (Mc x Nr) small enough to reside + # in L1. + Mc_blocks = min(Mt_blocks, math.floor(L2 / (Mr * Kt_bytes))) + Nc_blocks = 1 + else: + # Strategy 2: Kt is too large to hold A (Mc x Kt) in L2, we reuse + # A (Mc x Kc) in L2 by B (Kc x Nc). C (Mc x Nc) resides in L2. + Mc_blocks = Mt_blocks + Nc_blocks = min(math.ceil(Mc_blocks * Mr / Nr), Nt_blocks) + Nc_bytes = Nc_blocks * Nr * 4 # assume C or acc is float32/int32 + Kc_bytes = Kc_blocks * Kr * num_byte_A + if Mc_blocks * Mr * (Kc_bytes + Nc_bytes) > L2: + # The following is the solution for 4*Mc*Nc + Mc*Kc_bytes = L2, + # assuming Mc == Nc for good data reuse. + M_max = (math.sqrt(Kc_bytes * Kc_bytes + 16 * L2) - Kc_bytes) / 8 + if M_max < Mc_blocks * Mr: + Mc_blocks = math.floor(M_max / Mr) + Nc_blocks = min(math.ceil(Mc_blocks * Mr / Nr), Nt_blocks) + + return Mc_blocks, Nc_blocks, Kc_blocks + + assert ( + not self.is_dynamic_M + ), "Unable to determine cache blocking for dynamic M." + register_blocking = self.register_blocking + thread_blocking = self.thread_blocking() + + return GemmBlocking(*get_cache_blocking(register_blocking, thread_blocking)) + + def log_blockings(self): + log.debug(f"Register blocking: {self.register_blocking}") # noqa: G004 + if self.is_dynamic_M: + # thread and cache blockings are determined at runtime for dynamic shapes + return + log.debug(f"Cache blocking: {self.cache_blocking()}") # noqa: G004 + thread_blocking = self.thread_blocking() + log.debug(f"Thread blocking: {thread_blocking}") # noqa: G004 + + def get_occupancy(): + m_blocks = math.ceil(self.m / self.register_blocking.block_m) + n_blocks = math.ceil(self.n / self.register_blocking.block_n) + k_blocks = math.ceil(self.k / self.register_blocking.block_k) + m = math.ceil(m_blocks / thread_blocking.block_m) + n = math.ceil(n_blocks / thread_blocking.block_n) + k = math.ceil(k_blocks / thread_blocking.block_k) + return (m, n, k) + + log.debug( + f"Number of threads: {self.num_threads}, occupancy: {get_occupancy()}" # noqa: G004 + ) + + def maybe_k_slicing(self): + if self.num_threads == 1: + return False + if self.is_dynamic_M: + # TODO(jgong5): perhaps use size hint to decide? + return True + register_blocking = self.register_blocking + k_blocks = math.ceil(self.k / register_blocking.block_k) + thread_blocking = self.thread_blocking() + return k_blocks > thread_blocking.block_k + + @staticmethod + def add_choices( + choices, + layout, + input_nodes, + beta=1, + alpha=1, + has_bias=False, + trans_w=False, + input_indices=None, + epilogue_creator: Optional[Callable[[ir.Buffer], ir.Pointwise]] = None, + ): + if input_indices is None: + input_indices = list(range(len(input_nodes))) + + def reorder_and_filter(inputs, layout_or_out): + if has_bias: + assert len(input_indices) >= 3 + # Assume the input order is [inp, x, w] and we reorder it to [x, w, inp] + inp_idx = input_indices[0] + x_idx = input_indices[1] + w_idx = input_indices[2] + return [ + inputs[x_idx], + inputs[w_idx], + inputs[inp_idx], + *[inputs[idx] for idx in input_indices[3:]], + ], layout_or_out + else: + assert len(input_indices) >= 2 + return [inputs[idx] for idx in input_indices], layout_or_out + + def maybe_to_dense(inputs, layout_or_out): + new_inputs = list(inputs) + if isinstance(inputs[1], torch.Tensor): + W = inputs[1] + new_inputs[1] = W.to_dense() if W.is_mkldnn else W + return new_inputs, layout_or_out + + def normalize_shapes(inputs, layout_or_out): + if not trans_w: + return inputs, layout_or_out + new_inputs = list(inputs) + X = inputs[0] + W = inputs[1] + B = inputs[2] if has_bias else None + if isinstance(W, ir.IRNode): + if trans_w: + if not isinstance(W, ir.TensorBox): + W = ir.TensorBox(W) + W = L.permute(W, [1, 0]) + else: + if trans_w: + assert isinstance(W, torch.Tensor) + W = W.transpose(0, 1) + if B is not None: + if isinstance(B, ir.IRNode): + if not isinstance(B, ir.TensorBox): + B = ir.TensorBox(B) + B = L.expand(B, (X.get_size()[0], B.get_size()[-1])) + else: + assert isinstance(B, torch.Tensor) + B = B.expand(X.shape[0], B.shape[-1]) + new_inputs[1] = W + if B is not None: + new_inputs[2] = B + return new_inputs, layout_or_out + + # TODO(jgong5): decide proper number of threads per problem size + num_threads = parallel_num_threads() + new_inputs, _ = normalize_shapes( + *maybe_to_dense(*reorder_and_filter(input_nodes, layout)) + ) + m, n, k, *_ = mm_args(new_inputs[0], new_inputs[1]) + output_dtype, compute_dtype = get_gemm_template_output_and_compute_dtype( + new_inputs[0].get_dtype() + ) + micro_gemm = create_micro_gemm( + "micro_gemm", + m, + n, + k, + input_dtype=new_inputs[0].get_dtype(), + input2_dtype=new_inputs[1].get_dtype(), + output_dtype=output_dtype, + compute_dtype=compute_dtype, + alpha=alpha, + num_threads=num_threads, + ) + assert micro_gemm is not None + _, block_n, _ = micro_gemm.register_blocking + padded_n = get_padded_n(n, block_n) + + def pack_weight(inputs, layout_or_out): + W = inputs[1] + new_inputs = list(inputs) + blocked_w: Union[ir.IRNode, torch.Tensor] = W + if isinstance(W, ir.IRNode): + new_size = [padded_n // block_n, k, block_n] + blocked_w = ir.Buffer( + W.get_name(), # Borrow the registered buffer name + ir.FixedLayout( + W.get_device(), + W.get_dtype(), + new_size, + ir.FlexibleLayout.contiguous_strides(new_size), + 0, + ), + ) + else: + blocked_w = ( + torch.nn.functional.pad(W, (0, padded_n - n)) + .reshape(k, padded_n // block_n, block_n) + .transpose(0, 1) + .contiguous() + ) + if micro_gemm.get_b_layout() != LayoutType.NORMAL: + layout_str = ( + "VNNI4" + if micro_gemm.get_b_layout() == LayoutType.VNNI4 + else "VNNI2" + ) + assert micro_gemm.get_b_layout() in [ + LayoutType.VNNI2, + LayoutType.VNNI4, + ], f"We only support {layout_str} for now" + vnni_size = ( + 4 if micro_gemm.get_b_layout() == LayoutType.VNNI4 else 2 + ) + assert ( + k % vnni_size == 0 + ), f"k should be divisible by vnni_size for {layout_str} layout" + blocked_w = ( + blocked_w.view( + padded_n // block_n, k // vnni_size, vnni_size, block_n + ) + .transpose(-1, -2) + .contiguous() + .view(padded_n // block_n, k, block_n) + ) + # normalize stride to be "contiguous_strides" per size + # this avoids the problems in L.view during template codegen + new_stride = [1] + for sz in reversed(blocked_w.shape[1:]): + new_stride.insert(0, new_stride[0] * sz) + blocked_w = blocked_w.as_strided(blocked_w.shape, new_stride) + new_inputs[1] = blocked_w + + def _is_int8_gemm(inputs): + return ( + isinstance(inputs[0], ir.IRNode) + and inputs[0].get_dtype() == torch.uint8 + ) or ( + isinstance(inputs[0], torch.Tensor) + and inputs[0].dtype == torch.uint8 + ) + + if _is_int8_gemm(new_inputs): + BCompensate = None + if isinstance(W, ir.IRNode): + BCompensate = V.graph.add_tensor_constant( + V.graph.constants[W.get_name() + "_BMatrixCompens"], + W.get_name() + "_BMatrixCompens", + ) + else: + BCompensate = torch.sum(W.to_dense().to(torch.float), dim=0) # type: ignore[assignment] + new_inputs.append(BCompensate) + return new_inputs, layout_or_out + + def preprocessor(inputs, layout): + return pack_weight( + *normalize_shapes(*maybe_to_dense(*reorder_and_filter(inputs, layout))) + ) + + def postprocessor(output): + if isinstance(output, ir.TensorBox): + # prepack the weight as input to the template buffer + template_buffer = ir.InputsKernel.unwrap_storage_for_input(output) + assert isinstance(template_buffer, ir.CppTemplateBuffer) + new_input_nodes, _ = reorder_and_filter(input_nodes, layout) + + W_node = new_input_nodes[1] + assert W_node.get_name() in V.graph.constants + W = V.graph.constants[W_node.get_name()] + new_input_nodes[1] = W + new_input_nodes, _ = pack_weight( + *normalize_shapes(*maybe_to_dense(new_input_nodes, layout)) + ) + + # By using the new packed weight for the GEMM template, we can prune the + # old weight if it has no other users. This saves memory but makes the FX graph + # non-retraceable. To support retracing, we can add a repack node to the + # FX graph. For example: + # mkldnn._linear_pointwise <- repack_linear_wgt <- packed_wgt_for_template + W_tensor_users = 0 + for node in reversed(V.graph.graph.nodes): + # Case may happen when the wgt tensor is used by more than 1 get_attr node + # https://github.com/pytorch/pytorch/issues/134998 + if node.op == "get_attr" and hasattr( + V.graph.module, node.name + ): # wgt might already be deleted + comp_tensor = getattr(V.graph.module, node.name) + if ( + W.is_mkldnn == comp_tensor.is_mkldnn + and W.dtype == comp_tensor.dtype + and W.device == comp_tensor.device + and ( + ( + not W.is_mkldnn + and ( + W.untyped_storage().data_ptr() + == comp_tensor.untyped_storage().data_ptr() + ) + ) + or ( + W.is_mkldnn + and ( + torch.ops.mkldnn.data_ptr(W) + == torch.ops.mkldnn.data_ptr(comp_tensor) + ) + ) + ) + ): + W_tensor_users += 1 + + for node in reversed(V.graph.graph.nodes): + # The wgt tensor has been used by only 1 get_attr node + # The get_attr node has only 1 user fx node + if ( + node.name == W_node.get_name() + and len(node.users) == 1 + and W_tensor_users == 1 + ): + del V.graph.constants[node.name] + delattr(V.graph.module, node.name) + delattr(V.graph.graph.owning_module, node.name) + + W_packed = new_input_nodes[1] + W_packed_constant = V.graph.add_tensor_constant(W_packed) + template_buffer.inputs[1] = ir.InputsKernel.unwrap_storage_for_input( + W_packed_constant + ) + return output + + template = DataProcessorTemplateWrapper( + CppPackedGemmTemplate, + preprocessor, + postprocessor, + input_nodes=input_nodes, + layout=layout, + num_threads=num_threads, + register_blocking=micro_gemm.register_blocking, + beta=beta, + alpha=alpha, + has_bias=has_bias, + epilogue_creator=epilogue_creator, + ) + template.maybe_append_choice(choices) + return template + + def render( # type: ignore[override,return] + self, + kernel: CppTemplateKernel, + template_buffer_node: Optional[ir.CppTemplateBuffer] = None, + flag_template_buffer_has_other_users: Optional[bool] = None, + epilogue_nodes: Optional[List[ir.IRNode]] = None, + **kwargs, + ) -> str: + assert len(self.input_nodes) >= 2 + + int8_gemm = self.input_nodes[0].get_dtype() == torch.uint8 + x_scale = None + x_zp = None + w_scale = None + w_zp = None + if int8_gemm: + X, W = self.input_nodes[0], self.input_nodes[1] + bias_idx = 2 if self.has_bias else 1 + inp = self.input_nodes[bias_idx] if self.has_bias else None + x_scale = self.input_nodes[bias_idx + 1] + x_zp = self.input_nodes[bias_idx + 2] + w_scale = self.input_nodes[bias_idx + 3] + w_zp = self.input_nodes[bias_idx + 4] + Y = self.output_node + else: + X, W = self.input_nodes[0], self.input_nodes[1] + Y = self.output_node + inp = self.input_nodes[2] if self.has_bias else None + + template_buffer_has_other_users = None + + if template_buffer_node is not None: + # Use the updated prepacked weight buffer + W = template_buffer_node.inputs[1] + Y = template_buffer_node + + assert flag_template_buffer_has_other_users is not None + template_buffer_has_other_users = flag_template_buffer_has_other_users + + template_buffer = Y + gemm_output_buffer = template_buffer + + epilogues: List[ir.IRNode] = [] + reindexers: List[Optional[Callable[[List[Any]], List[Any]]]] = [] + epilogue_creators: List[Callable[[ir.Buffer], ir.Pointwise]] = [] + fake_buffers: List[ir.Buffer] = [] + Y_aliases: Set[str] = set() + + use_local_acc = ( + self.layout.dtype != torch.float + or template_buffer_has_other_users + or int8_gemm + or self.padded_n != self.n + or self.maybe_k_slicing() + ) + + # TODO(jgong5): for int8 gemm, bias-add is handled outside of gemm template, + # but we'd better move it here to align with fp. + if inp is not None and self.beta != 0 and not int8_gemm: + # add an epilogue for bias add + def _bias_add_epilogue(buf): + return create_epilogue_with_attr( + buf, "bias_add", other=inp, beta=self.beta, dtype=self.layout.dtype + ) + + epilogue_creators.append(_bias_add_epilogue) + + if self.epilogue_creator is not None: + epilogue_creators.append(self.epilogue_creator) + + # When the GEMM output buffer is localized but it has users other than the epilogue nodes, + # we need to copy the value in the GEMM output local buffer to a global buffer. + def need_copy_from_local_to_global_buffer_epilogue( + use_local_acc, template_buffer_has_other_users, epilogue_creators + ): + # The GEMM output buffer is a global buffer, thus copy is not needed. + if not use_local_acc: + return False + + # The possible value of template_buffer_has_other_users is (None, False, True) + # It is None when generating the gemm template during autotune and it will have value during scheduler codegen. + # extra copy_from_local_to_global_buffer_epilogue is not needed in either of the below two cases: + # 1. template_buffer_has_other_users is None (i.e. when doing the codegen during autotune) + # 2. template_buffer_has_other_users is False, which means it's safe to keep the value in the + # GEMM output buffer in local buffer only (no users outside of the epilogues will use its value). + if not template_buffer_has_other_users: + return False + + # When bias is not None or self.epilogue_creator is not None, + # there will be epilogue_creators after the GEMM. + # The GEMM output buffer is localized while + # the output buffer of the epilogue_creators is a global buffer. + if epilogue_creators: + return False + + return True + + if need_copy_from_local_to_global_buffer_epilogue( + use_local_acc, template_buffer_has_other_users, epilogue_creators + ): + + def copy_from_local_to_global_buffer_epilogue(input_buffer: ir.Buffer): + dtype = self.layout.dtype + input_loader = input_buffer.make_loader() + + def copy_inner(index): + input = input_loader(index) + result = ops.to_dtype(input, dtype) + return result + + return ir.Pointwise( + device=input_buffer.get_device(), + dtype=self.layout.dtype, + inner_fn=copy_inner, + ranges=input_buffer.get_size(), + ) + + epilogue_creators.append(copy_from_local_to_global_buffer_epilogue) + + # NOTE [How CPP GEMM template epilogues are organized] + # gemm_output_buffer + # --> zero or more in-template epilogues (created by `epilogue_creators`) --> + # template_buffer + # --> zero or more out-of-template epilogues (`epilogue_nodes`) --> + # Y + if epilogue_creators: + gemm_output_name = "buf_GemmOut" + gemm_output_buffer = ir.Buffer(gemm_output_name, template_buffer.layout) + current_input_buffer = gemm_output_buffer + for i, creator in enumerate(epilogue_creators): + if i == len(epilogue_creators) - 1: + buffer_name = template_buffer.get_name() + else: + buffer_name = f"buf_GemmOut_epilogue_{i}" + epilogues.append( + ir.ComputedBuffer( + name=buffer_name, + layout=template_buffer.layout, + data=creator(current_input_buffer), + ) + ) + fake_buffers.append(current_input_buffer) + Y_aliases.add(current_input_buffer.get_name()) + reindexers.append(None) + if i < len(epilogue_creators) - 1: + current_input_buffer = ir.Buffer( + buffer_name, template_buffer.layout + ) + + Y_2d: Union[ir.Buffer, ir.ReinterpretView] = Y + + if epilogue_nodes: + epilogues.extend(epilogue_nodes) + assert Y.get_numel() == epilogues[-1].get_numel() + Y = cast(ir.Buffer, epilogues[-1]) + + if not template_buffer_has_other_users: + Y_aliases.add(template_buffer.get_name()) + + if ( + Y.get_size() == template_buffer.get_size() + and Y.get_stride() == template_buffer.get_stride() + ): + reindexers.extend([None] * len(epilogue_nodes)) + Y_2d = Y + else: + + def get_reindexer(epilogue_node): + # From template_buffer to epilogue_node_ordered (ordered by stride decreasingly, in dense format), for example: + # template_buffer: + # size (324, 512), stride (512, 1) + # epilogue_node_ordered (ordered by stride decreasingly, in dense format): + # size (1, 18, 18, 512), stride (165888, 9216, 512, 1) + stride_order = list( + ir.get_stride_order( + V.graph.sizevars.size_hints(epilogue_node.get_stride()) + ) + ) + fill_order = ir.stride_order2fill_order(stride_order) + reversed_fill_order = list(reversed(fill_order)) + size_with_stride_ordered_decreasingly = [ + epilogue_node.get_size()[i] for i in reversed_fill_order + ] + reshape_reindex = ir.View.dynamic_reshape_indexer( + size_with_stride_ordered_decreasingly, + template_buffer.get_size(), + ) + + # From epilogue_node_ordered (ordered by stride decreasingly, in dense format) to epilogue_node, for example: + # epilogue_node_ordered (ordered by stride decreasingly, in dense format): + # size (1, 18, 18, 512), stride (165888, 9216, 512, 1) + # epilogue_node: + # size (1, 18, 18, 512), stride (165888, 1, 9216, 512) + from_stride_ordered_decreasingly_to_epilogue_node_order = [ + (len(stride_order) - 1) - stride_order[i] + for i in range(len(stride_order)) + ] + stride_reindex = ir.same_reorder( + from_stride_ordered_decreasingly_to_epilogue_node_order + ) + + reindexer = ir.fuse_reindexing(stride_reindex, reshape_reindex) + return reindexer + + reindexers.extend([get_reindexer(epilogue_node) for epilogue_node in epilogue_nodes]) # type: ignore[list-item] + if isinstance(Y, ir.BaseView): + storage = ir.StorageBox(Y.unwrap_view()) + else: + assert isinstance(Y, ir.Buffer) + storage = ir.StorageBox(Y) + Y_2d = ir.ReinterpretView(storage, template_buffer.get_layout()) + + output_dtype, compute_dtype = get_gemm_template_output_and_compute_dtype( + X.get_dtype() + ) + micro_gemm = create_micro_gemm( + f"{kernel.kernel_name}_micro_gemm", + self.m, + self.n, + self.k, + input_dtype=X.get_dtype(), + input2_dtype=W.get_dtype(), + output_dtype=output_dtype, + compute_dtype=compute_dtype, + alpha=self.alpha, + num_threads=self.num_threads, + ) + assert micro_gemm is not None + assert self.register_blocking == micro_gemm.register_blocking + self.log_blockings() + if isinstance(micro_gemm, CppMicroGemmAMX): + counters["inductor"]["cpp_micro_gemm_amx_counter"] += 1 + + L1_cache_size = torch._C._cpu._L1d_cache_size() # per core cache size in Bytes + assert L1_cache_size > 0, f"Expect L1_cache_size > 0 but got {L1_cache_size}" + + L2_cache_size = torch._C._cpu._L2_cache_size() # per core cache size in Bytes + assert L2_cache_size > 0, f"Expect L2_cache_size > 0 but got {L2_cache_size}" + + options = dict( + X=X, + W=W, + inp=inp, + Y=Y, + N=self.n, + K=self.k, + PADDED_N=self.padded_n, + GemmOut=gemm_output_buffer, + aliases={alias: Y.get_name() for alias in Y_aliases}, + beta=self.beta, + alpha=self.alpha, + num_threads=self.num_threads, + micro_gemm=micro_gemm, + is_dynamic_M=self.is_dynamic_M, + template=self, + kernel=kernel, + export_declaration=get_export_declaration(), + epilogue_nodes=epilogues, + reindexers=reindexers, + Y_2d=Y_2d, + use_local_acc=use_local_acc, + maybe_k_slicing=self.maybe_k_slicing(), + x_scale=x_scale, + x_zp=x_zp, + w_scale=w_scale, + w_zp=w_zp, + acc_buf_dtype=torch.int32 if int8_gemm else torch.float, + DTYPE_TO_CPP=DTYPE_TO_CPP, + L1_cache_size=L1_cache_size, + L2_cache_size=L2_cache_size, + config=config, + ) + with contextlib.ExitStack() as stack: + for buf in fake_buffers: + stack.enter_context( + patch.object(V.graph, "get_dtype", self._fake_get_dtype(buf)) + ) + return self._template_from_string(GEMM_TEMPLATE).render(**options) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_micro_gemm.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_micro_gemm.py new file mode 100644 index 0000000000000000000000000000000000000000..cb26c48fce53c66f05e57c7a36d68063e0f3240b --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_micro_gemm.py @@ -0,0 +1,850 @@ +# mypy: allow-untyped-defs +import dataclasses +import sys +from enum import Enum +from typing import Callable, Dict, List, Optional, Type + +import sympy + +import torch + +from .. import ir +from ..cpu_vec_isa import pick_vec_isa, VecAMX, VecAVX2, VecAVX512, VecISA +from ..utils import IndentedBuffer, parallel_num_threads +from ..virtualized import V +from .common import KernelTemplate +from .cpp_template_kernel import CppTemplateKernel +from .cpp_utils import DTYPE_TO_CPP, GemmBlocking, value_to_cpp + + +class LayoutType(Enum): + NORMAL = 0 + VNNI2 = 1 + VNNI4 = 2 + + +_IS_WINDOWS = sys.platform == "win32" + + +def get_restrict_keyword() -> str: + if _IS_WINDOWS: + # https://learn.microsoft.com/en-us/cpp/cpp/extension-restrict?view=msvc-170 + return "__restrict" + else: + return "__restrict__" + + +class CppMicroGemm: + """ + A class that codegens a kernel that computes small-sized matrix multiplication. + + A micro GEMM kernel is responsible for register blocking, instruction selection, + and other CPU architecture-specific optimizations. + + The subclasses need to override `codegen_define` to define the kernel function + that is called by the code generated by `codegen_call`. + """ + + # TODO(jgong5): support constant shapes and lds as template args. + DECLARE_KERNEL = r""" +template +inline void {{kernel_name}}( +{%- if kernel_extra_args_declare %} + {{kernel_extra_args_declare}} +{%- endif %} + const {{input_t}}* {{restrict_keyword}} A, + const {{input2_t}}* {{restrict_keyword}} B, + {{output_t}}* {{restrict_keyword}} C, + int64_t M, + int64_t N, + int64_t K, + int64_t lda, + int64_t ldb, + int64_t ldc +) +""" + + def __init__( + self, + name, + input_dtype, + input2_dtype, + output_dtype, + compute_dtype, + register_blocking, + alpha=1, + ) -> None: + self.name = name + self.input_dtype = input_dtype + assert input2_dtype is not None + self.input2_dtype = input2_dtype + self.output_dtype = output_dtype + self.compute_dtype = compute_dtype + self.register_blocking = register_blocking + self.alpha = alpha + + def get_common_options(self): + if self.input_dtype == torch.uint8: + assert self.compute_dtype == torch.int32 + assert self.output_dtype == torch.int32 + assert self.input2_dtype == torch.int8 + return { + "torch": torch, + "kernel_name": self.name, + "input_dtype": self.input_dtype, + "input2_dtype": self.input2_dtype, + "output_dtype": self.output_dtype, + "compute_dtype": self.compute_dtype, + "input_t": DTYPE_TO_CPP[self.input_dtype], + "input2_t": DTYPE_TO_CPP[self.input2_dtype], + "output_t": DTYPE_TO_CPP[self.output_dtype], + "compute_t": DTYPE_TO_CPP[self.compute_dtype], + "alpha": self.alpha, + "kernel_extra_args_declare": self.get_kernel_extra_args_declare(), + "int8_gemm": self.input_dtype == torch.uint8, + "vnni_size": 4 if self.input_dtype == torch.uint8 else 2, + "restrict_keyword": get_restrict_keyword(), + } + + def get_kernel_declaration(self): + options = self.get_common_options() + return KernelTemplate._template_from_string(self.DECLARE_KERNEL).render(options) + + def get_kernel_extra_args_declare(self) -> str: + return "" + + def get_kernel_extra_args(self) -> str: + return "" + + def codegen_define(self, kernel: CppTemplateKernel) -> str: + raise NotImplementedError + + def codegen_call( + self, + kernel: CppTemplateKernel, + A: ir.Buffer, + B: ir.Buffer, + C: ir.Buffer, + accum: bool, + ) -> str: + """ + Generate the code for calling the templated kernel that computes + `C += alpha * A @ B` if `accum` is True, or `C = alpha * A @ B` otherwise. + """ + A_ptr = f"&({kernel.index(A, [0, 0])})" + B_ptr = f"&({kernel.index(B, [0, 0])})" + C_ptr = f"&({kernel.index(C, [0, 0])})" + M = kernel.size(C, 0) + N = kernel.size(C, 1) + K = kernel.size(A, 1) + lda = kernel.stride(A, 0) + ldb = kernel.stride(B, 0) + ldc = kernel.stride(C, 0) + res = IndentedBuffer() + res.writeline(f"{self.name}<{value_to_cpp(accum, 'bool')}>(") + with res.indent(): + extra_args = self.get_kernel_extra_args() + if extra_args: + res.writeline(extra_args) + res.writeline(f"{A_ptr},") + res.writeline(f"{B_ptr},") + res.writeline(f"{C_ptr},") + res.writeline(f"{M},") + res.writeline(f"{N},") + res.writeline(f"{K},") + res.writeline(f"{lda},") + res.writeline(f"{ldb},") + res.writeline(f"{ldc}") + res.writeline(");") + return res.getvalue() + + def codegen_init( + self, + kernel: CppTemplateKernel, + ) -> str: + return "" + + def codegen_finalize( + self, + kernel: CppTemplateKernel, + ) -> str: + return "" + + def get_b_layout(self) -> LayoutType: + return LayoutType.NORMAL + + +@dataclasses.dataclass +class CppMicroGemmConfig: + input_dtype: torch.dtype + input2_dtype: torch.dtype + output_dtype: torch.dtype + compute_dtype: torch.dtype + vec_isa_cls: Type[VecISA] + register_blocking: GemmBlocking + extra_check: Optional[Callable[..., bool]] = None + + +micro_gemm_configs: Dict[Type[CppMicroGemm], List[CppMicroGemmConfig]] = {} + + +def register_micro_gemm(*configs): + def inner(cls): + assert ( + cls not in micro_gemm_configs + ), f"Duplicate micro_gemm registration for {cls}" + assert len(configs) > 0, f"No micro_gemm configs provided for {cls}" + micro_gemm_configs[cls] = list(configs) + return cls + + return inner + + +def generate_gemm_config( + vec_isa_cls, + register_blockings, + input_dtype=torch.float, + input2_dtype=None, + output_dtype=None, + compute_dtype=None, + extra_check=None, +): + if output_dtype is None: + output_dtype = input_dtype + if compute_dtype is None: + compute_dtype = output_dtype + if input2_dtype is None: + input2_dtype = input_dtype + return [ + CppMicroGemmConfig( + input_dtype, + input2_dtype, + output_dtype, + compute_dtype, + vec_isa_cls, + GemmBlocking(*blocking), + extra_check, + ) + for blocking in register_blockings + ] + + +class CppMicroGemmRef(CppMicroGemm): + """ + A reference implementation of the CppMicroGemm class with naive C++ code. + It is used for correctness debugging. + """ + + TEMPLATE_ENTRY = r""" +{{declare_kernel}} { + for (int64_t m = 0; m < M; ++m) { + for (int64_t n = 0; n < N; ++n) { + {{compute_t}} result = accum ? C[m * ldc + n] : 0; + for (int64_t k = 0; k < K; ++k) { + result += ({{compute_t}})A[m * lda + k] * ({{compute_t}})B[k * ldb + n] * {{alpha}}; + } + C[m * ldc + n] = result; + } + } +} +""" + + def __init__( + self, name, input_dtype, input2_dtype, output_dtype, compute_dtype, alpha + ) -> None: + super().__init__( + name, + input_dtype, + input2_dtype, + output_dtype, + compute_dtype, + GemmBlocking(1, 1, 1), + alpha, + ) + + def codegen_define(self, kernel: CppTemplateKernel) -> str: + options = { + "declare_kernel": self.get_kernel_declaration(), + **self.get_common_options(), + } + return KernelTemplate._template_from_string(self.TEMPLATE_ENTRY).render(options) + + +@register_micro_gemm( + *generate_gemm_config( + VecAVX512, + [(8, 48, 1), (8, 32, 1), (16, 16, 1)], + input_dtype=torch.float, + ), + *generate_gemm_config( + VecAVX512, + [(8, 48, 1), (8, 32, 1), (16, 16, 1)], + input_dtype=torch.bfloat16, + output_dtype=torch.float, + ), + *generate_gemm_config( + VecAVX512, + [(8, 48, 1), (8, 32, 1), (16, 16, 1)], + input_dtype=torch.half, + output_dtype=torch.float, + ), + *generate_gemm_config( + VecAVX512, + [(8, 48, 1), (8, 32, 1), (16, 16, 1)], + input_dtype=torch.bfloat16, + input2_dtype=torch.int8, + output_dtype=torch.float, + compute_dtype=torch.float, + ), + *generate_gemm_config( + VecAVX2, + [(4, 24, 1), (4, 16, 1), (8, 8, 1)], + input_dtype=torch.float, + ), + *generate_gemm_config( + VecAVX2, + [(4, 24, 1), (4, 16, 1), (8, 8, 1)], + input_dtype=torch.bfloat16, + output_dtype=torch.float, + ), + *generate_gemm_config( + VecAVX2, + [(4, 24, 1), (4, 16, 1), (8, 8, 1)], + input_dtype=torch.half, + output_dtype=torch.float, + ), + *generate_gemm_config( + VecAVX2, + [(4, 24, 1), (4, 16, 1), (8, 8, 1)], + input_dtype=torch.bfloat16, + input2_dtype=torch.int8, + output_dtype=torch.float, + compute_dtype=torch.float, + ), +) +class CppMicroGemmFP32Vec(CppMicroGemm): + """ + This class generates the code for micro gemm using fp32 vec instructions for compute. + It supports input types of torch.float, torch.bfloat16, and torch.half with fp32 output. + The output of the microkernel is in FP32, but it would be converted to BF16/FP16 in the template, + if the desired output is BF16/FP16. + """ + + TEMPLATE_ENTRY = r""" +{{declare_kernel}} { + TORCH_CHECK(N % {{block_n}} == 0, "N dimension must be multiple of {{block_n}}"); + TORCH_CHECK(K % {{block_k}} == 0, "K dimension must be multiple of {{block_k}}"); + // TODO(jgong5): loop unroll for M and N + for (int64_t m = 0; m < M; m += {{block_m}}) { + int64_t block_m = std::min(M - m, {{block_m}}); + for (int64_t n = 0; n < N; n += {{block_n}}) { + if (block_m == {{block_m}}) { + {{kernel_name}}_kernel<{{block_m}}, {{block_n}}, accum>( + A + m * lda, + B + n, + C + m * ldc + n, + K, + lda, + ldb, + ldc + ); + } else { + switch (block_m) { +{%- for b in range(block_m - 1, 0, -1) %} + case {{b}}: + {{kernel_name}}_kernel<{{b}}, {{block_n}}, accum>( + A + m * lda, + B + n, + C + m * ldc + n, + K, + lda, + ldb, + ldc + ); + break; +{%- endfor %} + default: + {{kernel.assert_function}}(false, "Unsupported block_m: ", block_m); + } + } + } + } +} +""" + + TEMPLATE_KERNEL = r""" +template +inline void {{kernel_name}}_kernel( + const {{input_t}}* {{restrict_keyword}} A, + const {{input2_t}}* {{restrict_keyword}} B, + {{output_t}}* {{restrict_keyword}} C, + int64_t K, + int64_t lda, + int64_t ldb, + int64_t ldc +) { + using Vectorized = at::vec::Vectorized<{{compute_t}}>; + using VectorizedIn = at::vec::Vectorized<{{input_t}}>; + constexpr auto VLEN = Vectorized::size(); + constexpr auto ROWS = BLOCK_M; + constexpr auto COLS = BLOCK_N / VLEN; + + Vectorized va; + at::vec::VectorizedN<{{compute_t}}, COLS> vb; + at::vec::VectorizedN<{{compute_t}}, ROWS*COLS> vc; + + auto loadc = [&](auto i) { + if constexpr (accum) { + constexpr int row = i / COLS; + constexpr int col = i % COLS; + vc[i] = Vectorized::loadu(C + row * ldc + col * VLEN); + } else { + vc[i] = Vectorized(0.0f); + } + }; + c10::ForcedUnroll{}(loadc); + + auto compute = [&, COLS](auto i, int k) { + constexpr int row = i / COLS; + constexpr int col = i % COLS; + + if constexpr (col == 0) { +{%- if alpha != 1 %} + va = Vectorized(static_cast<{{compute_t}}>(A[row * lda + k]) * {{alpha}}); +{%- else %} + va = Vectorized(static_cast<{{compute_t}}>(A[row * lda + k])); +{%- endif %} + } + + if constexpr (row == 0) { +{%- if input2_dtype in [torch.bfloat16, torch.float16] %} + auto b = VectorizedIn::loadu(B + k * ldb + col * VLEN, VLEN); + vb[col] = at::vec::convert<{{compute_t}}>(b); +{%- elif input2_dtype == torch.int8 %} + // Convert VLEN int8 elements to int32, and then fp32 + auto b32 = at::vec::convert_to_int32(B + k * ldb + col * VLEN); + vb[col] = at::vec::convert(b32); +{%- else %} + vb[col] = Vectorized::loadu(B + k * ldb + col * VLEN); +{%- endif %} + } + + constexpr int idx = row * COLS + col; + vc[idx] = at::vec::fmadd(va, vb[col], vc[idx]); + }; + + for (int k = 0; k < K; ++k) { + c10::ForcedUnroll{}(compute, k); + } + + // store to C + auto storec = [&](auto i) { + constexpr int row = i / COLS; + constexpr int col = i % COLS; + vc[i].store(C + row * ldc + col * VLEN); + }; + c10::ForcedUnroll{}(storec); +} +""" + + def codegen_define(self, kernel: CppTemplateKernel) -> str: + options = { + "declare_kernel": self.get_kernel_declaration(), + "kernel": kernel, + "block_m": self.register_blocking.block_m, + "block_n": self.register_blocking.block_n, + "block_k": self.register_blocking.block_k, + "restrict_keyword": get_restrict_keyword(), + **self.get_common_options(), + } + result = KernelTemplate._template_from_string(self.TEMPLATE_KERNEL).render( + options + ) + result += KernelTemplate._template_from_string(self.TEMPLATE_ENTRY).render( + options + ) + return result + + +# extra check for CppMicroGemmAMX +def check_amx_extra(config, m, n, k, alpha, num_threads): + vnni_size = 4 if config.input_dtype == torch.uint8 else 2 + return k % vnni_size == 0 and alpha == 1 + + +@register_micro_gemm( + *generate_gemm_config( + VecAMX, + [(32, 32, 32), (48, 16, 32), (16, 48, 32)], + input_dtype=torch.bfloat16, + input2_dtype=torch.int8, + output_dtype=torch.float, + compute_dtype=torch.float, + extra_check=check_amx_extra, + ), + *generate_gemm_config( + VecAMX, + [(32, 32, 32), (48, 16, 32), (16, 48, 32)], + input_dtype=torch.bfloat16, + output_dtype=torch.float, + extra_check=check_amx_extra, + ), + *generate_gemm_config( + VecAMX, + [(32, 32, 64), (48, 16, 64)], + input_dtype=torch.uint8, + input2_dtype=torch.int8, + output_dtype=torch.int32, + compute_dtype=torch.int32, + extra_check=check_amx_extra, + ), +) +class CppMicroGemmAMX(CppMicroGemm): + """ + This class generates the code for micro gemm using Advanced Matrix eXtention (AMX) + instructions available in 4th generation Intel Xeon for compute. + It supports input types of torch.bfloat16 with fp32 output. + TODO(jgong5): support int8 data type. + """ + + TEMPLATE_ENTRY = r""" +{{declare_kernel}} { + TORCH_CHECK(N % {{block_n}} == 0, "N dimension must be multiple of {{block_n}}"); + TORCH_CHECK(K % 2 == 0, "K dimension must be multiple of 2"); + // TODO(jgong5): loop unroll for M and N + for (int64_t m = 0; m < M; m += {{block_m}}) { + int64_t block_m = std::min(M - m, {{block_m}}); + int64_t m_tail = m; + for (int64_t n = 0; n < N; n += {{block_n}}) { +{%- for num_rows in range(block_m, 0, -16) %} + {%- if num_rows != block_m %} + else + {%- endif %} + if (block_m >= {{num_rows}}) { + {{kernel_name}}_amx_kernel_{{num_rows}}_{{num_columns}}( + amx_state, + A + m * lda, + B + n, + C + m * ldc + n, + K, + lda, + ldb, + ldc, + 16 + ); + block_m -= {{num_rows}}; + m_tail += {{num_rows}}; + } +{%- endfor %} + if (block_m > 0) { + {{kernel_name}}_amx_kernel_16_{{num_columns}}( + amx_state, + A + m_tail * lda, + B + n, + C + m_tail * ldc + n, + K, + lda, + ldb, + ldc, + block_m + ); + } + } + } +} +""" + + TEMPLATE_KERNEL = r""" +template +inline void {{kernel_name}}_amx_kernel_{{num_rows}}_{{num_columns}}( + AMXState& amx_state, + const {{input_t}}* {{restrict_keyword}} A, + const {{input2_t}}* {{restrict_keyword}} B, + {{output_t}}* {{restrict_keyword}} C, + int64_t K, + int64_t lda, + int64_t ldb, + int64_t ldc, + uint8_t tilecfg_rows +) { + // TODO(jgong5): add prefetch hint for A, B, C + auto loadconfig = [](const amx_tilecfg& cfg) { + _tile_loadconfig(&cfg); + }; + const auto last_k_offset = K / {{block_k}} * {{block_k}}; + const auto tail_k_size = K - last_k_offset; + if C10_LIKELY (last_k_offset > 0) { + amx_state.configure(tilecfg_rows, 64, {{num_rows}} / 16, {{num_columns}}, loadconfig); + } else { + amx_state.configure(tilecfg_rows, tail_k_size * sizeof({{input_t}}), {{num_rows}} / 16, {{num_columns}}, loadconfig); + } + auto load_c = [&]() { +{%- for tile_row in range(num_rows // 16) %} + {%- for tile_col in range(num_columns) %} + {%- set tile_idx = tile_row * num_columns + tile_col %} + _tile_loadd({{tile_idx}}, C + {{tile_row * 16}} * ldc + {{tile_col * 16}}, ldc * sizeof({{output_t}})); + {%- endfor %} +{%- endfor %} + }; + auto zero_c = [&]() { +{%- for tile_row in range(num_rows // 16) %} + {%- for tile_col in range(num_columns) %} + {%- set tile_idx = tile_row * num_columns + tile_col %} + _tile_zero({{tile_idx}}); + {%- endfor %} +{%- endfor %} + }; + + if constexpr (accum) { + load_c(); + } else { + zero_c(); + } + +{%- if input_dtype == torch.bfloat16 and input2_dtype == torch.int8 %} + // create a buffer for tiles of B. + alignas(64) {{input_t}} bf16_weights_buf[512]; + + int num_b_rows = (last_k_offset > 0) ? 16 : (tail_k_size * sizeof({{input_t}})) / 4; + int b_tile_ptr_stride = ldb * {{vnni_size}}; + + auto load_B_row = [&]({{input2_t}}* src, {{input_t}}* dst) { + {{kernel.unroll_pragma(2)}} + for (int i = 0; i < 2; i++) { + // int8 -> int32 -> fp32 -> bf16 + auto b32 = at::vec::convert_to_int32(src + i * 16); + auto b_bf16 = at::vec::convert<{{input_t}}>(b32); + b_bf16.store(dst + i * 16); + } + }; + + auto load_B_in_buf = [&]({{input2_t}}* B_ptr) { + {{kernel.unroll_pragma(8)}} + for (int i = 0; i < num_b_rows; i++) { + load_B_row( + B_ptr + i * b_tile_ptr_stride, + bf16_weights_buf + i * 32 + ); + } + }; +{%- endif %} + + auto compute = [&](int k) { +{%- set tile_offset_a = num_rows // 16 * num_columns %} +{%- set tile_offset_b = tile_offset_a + num_rows // 16 %} +{%- for tile_row in range(num_rows // 16) %} + {%- for tile_col in range(num_columns) %} + {%- set tile_idx_a = tile_offset_a + tile_row %} + {%- set tile_idx_b = tile_offset_b + tile_col %} + {%- set tile_idx_c = tile_row * num_columns + tile_col %} + {%- if tile_col == 0 %} + _tile_stream_loadd({{tile_idx_a}}, A + {{tile_row * 16}} * lda + k, lda * sizeof({{input_t}})); + {%- endif %} + {%- if tile_row == 0 %} + {%- if input_dtype == torch.bfloat16 and input2_dtype == torch.int8 %} + load_B_in_buf(const_cast<{{input2_t}}*>(B) + k * ldb + {{tile_col * 16 * vnni_size}}); + _tile_loadd({{tile_idx_b}}, bf16_weights_buf, 64); + {%- else %} + _tile_loadd({{tile_idx_b}}, B + k * ldb + {{tile_col * 16 * vnni_size}}, ldb * {{vnni_size}} * sizeof({{input_t}})); + {%- endif %} + {%- endif %} + {%- if int8_gemm %} + _tile_dpbusd({{tile_idx_c}}, {{tile_idx_a}}, {{tile_idx_b}}); + {%- else %} + _tile_dpbf16ps({{tile_idx_c}}, {{tile_idx_a}}, {{tile_idx_b}}); + {%- endif %} + {%- endfor %} +{%- endfor %} + }; + + {{kernel.unroll_pragma(4)}} + for (int k = 0; k < last_k_offset; k += {{block_k}}) { + compute(k); + } + + auto store_c = [&]() { + // store to C +{%- for tile_row in range(num_rows // 16) %} + {%- for tile_col in range(num_columns) %} + {%- set tile_idx = tile_row * num_columns + tile_col %} + _tile_stored({{tile_idx}}, C + {{tile_row * 16}} * ldc + {{tile_col * 16}}, ldc * sizeof({{output_t}})); + {%- endfor %} +{%- endfor %} + }; + + // TODO(jgong5): move tail k computation to separate loopnest to save tile configuration overhead + if C10_UNLIKELY (tail_k_size > 0) { + if C10_LIKELY (last_k_offset > 0) { + store_c(); + amx_state.configure(tilecfg_rows, tail_k_size * sizeof({{input_t}}), {{num_rows}} / 16, {{num_columns}}, loadconfig); + load_c(); + } + compute(last_k_offset); + } + + store_c(); +} +""" + + def codegen_define(self, kernel: CppTemplateKernel) -> str: + block_m, block_n, block_k = self.register_blocking + assert block_m % 16 == 0, "Only support block_m % 16 == 0 for AMX" + assert block_n % 16 == 0, "Only support block_n % 16 == 0 for AMX" + if self.input_dtype == torch.uint8: + assert block_k == 64, "Only support block_k = 64 for AMX INT8" + else: + assert block_k == 32, "Only support block_k = 32 for AMX Bfloat16/Float16" + num_columns = block_n // 16 + options = { + "declare_kernel": self.get_kernel_declaration(), + "kernel": kernel, + "block_m": block_m, + "block_n": block_n, + "block_k": block_k, + "num_columns": num_columns, + "restrict_keyword": get_restrict_keyword(), + **self.get_common_options(), + } + result = "" + for num_rows in range(block_m, 0, -16): + amx_kernel_options = {**options, "num_rows": num_rows} + result += KernelTemplate._template_from_string(self.TEMPLATE_KERNEL).render( + amx_kernel_options + ) + result += KernelTemplate._template_from_string(self.TEMPLATE_ENTRY).render( + options + ) + return result + + def codegen_init( + self, + kernel: CppTemplateKernel, + ) -> str: + return "AMXState amx_state;" + + def codegen_finalize( + self, + kernel: CppTemplateKernel, + ) -> str: + return "amx_state.release([]() { _tile_release(); });" + + def get_kernel_extra_args_declare(self) -> str: + return "AMXState& amx_state," + + def get_kernel_extra_args(self) -> str: + return "amx_state," + + def get_b_layout(self): + if self.input_dtype == torch.uint8: + return LayoutType.VNNI4 + else: + return LayoutType.VNNI2 + + +def create_micro_gemm( + name, + m, + n, + k, + input_dtype, + input2_dtype, + output_dtype=None, + compute_dtype=None, + alpha=1, + num_threads=-1, + use_ref=True, +) -> Optional[CppMicroGemm]: + def create_from_config(cls, config: CppMicroGemmConfig): + return cls( + name, + config.input_dtype, + config.input2_dtype, + config.output_dtype, + config.compute_dtype, + config.register_blocking, + alpha, + ) + + assert isinstance(n, int) or n.is_number, n + assert isinstance(k, int) or k.is_number, k + m = V.graph.sizevars.size_hint(m, fallback=1) if isinstance(m, sympy.Expr) else m + assert isinstance(m, int), m + if output_dtype is None: + output_dtype = input_dtype + if compute_dtype is None: + compute_dtype = output_dtype + if num_threads < 0: + num_threads = parallel_num_threads() + vec_isa = pick_vec_isa() + matched_configs = [] + for cls, configs in micro_gemm_configs.items(): + for config in configs: + if not issubclass(vec_isa.__class__, config.vec_isa_cls): + continue + if ( + config.input_dtype == input_dtype + and config.compute_dtype == compute_dtype + and config.input2_dtype == input2_dtype + and config.output_dtype == output_dtype + # The output_dtype here is the output dtype of the micro-kernel. + # In some cases, the actual output dtype of the op for which the micro-kernel + # is being created would be same as that of the activation, but the micro-kernels + # compute output in Float/int32, which is converted in the GEMM template. This is + # subject to change in the future. + ): + if config.extra_check is not None and not config.extra_check( + config, m, n, k, alpha, num_threads + ): + continue + block_m, block_n, block_k = config.register_blocking + if ( + config.vec_isa_cls == VecAMX + and m < block_m + and input_dtype == torch.bfloat16 + and input2_dtype == torch.int8 + ): + # For int8 WoQ GEMM, AMX micro-kernel may not perform well if m < block_m + continue + # Criteria on the ranking of configurations + # 1. ISA: AMX > VEC + # 2. Dividable by block sizes (block_m, block_n, block_k) + # 3. Number of mxn blocks is large enough to occupy all the threads + # 4. Register blocks are larger + isa_score = 0 + if config.vec_isa_cls == VecAMX: + isa_score += 1 + dividable_score = 0 + if m % block_m == 0: + dividable_score += 1 + if n % block_n == 0: + dividable_score += 1 + if k % block_k == 0: + dividable_score += 1 + occupancy_score = 0 + n_blocks = (n + block_n - 1) // block_n + total_mxn_blocks = n_blocks * ((m + block_m - 1) // block_m) + if n_blocks >= num_threads: + occupancy_score += 1 + if total_mxn_blocks >= num_threads: + occupancy_score += 1 + register_bytes = ( + block_m * block_n * config.compute_dtype.itemsize + + (block_m * block_k + block_k * block_n) + * config.input_dtype.itemsize + ) + matched_configs.append( + ( + (isa_score, dividable_score, occupancy_score, register_bytes), + cls, + config, + ) + ) + if len(matched_configs) == 0: + if use_ref: + return CppMicroGemmRef( + name, input_dtype, input2_dtype, output_dtype, compute_dtype, alpha + ) + else: + return None + # TODO(jgong5): allow autotuning on choices of configs + return create_from_config(*max(matched_configs, key=lambda x: x[0])[1:]) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_prefix.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_prefix.h new file mode 100644 index 0000000000000000000000000000000000000000..b46e772cd6ce11287f5cd04e79c7f06357fd4169 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_prefix.h @@ -0,0 +1,973 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +// WARNING: be extra careful when including more ATen/c10 header files here! +// Because AOTInductor generated code will copy-paste this cpp_prefix.h for +// the CPU backend, we have to make sure the used headers are implemented +// in a header-only way, i.e. all the function and class definitions are +// in .h files instead of .cpp files, to avoid ABI backward-compatiblity breakage. + +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +#if defined(CPU_CAPABILITY_AVX512) || defined(CPU_CAPABILITY_AVX2) || defined(CPU_CAPABILITY_ZVECTOR) || defined(CPU_CAPABILITY_NEON) || defined(CPU_CAPABILITY_VSX) +#define INDUCTOR_USE_VECTOR_TYPES() 1 +#else +#define INDUCTOR_USE_VECTOR_TYPES() 0 +#endif + +#if INDUCTOR_USE_VECTOR_TYPES() +#include +#include +#else +// For calc_erfinv +#include +#endif + +typedef at::Half half; +typedef at::BFloat16 bfloat16; + +typedef at::Float8_e4m3fn float8_e4m3fn; +typedef at::Float8_e5m2 float8_e5m2; + +template +struct Welford { + T mean = T(0); + T m2 = T(0); + // Use weight for tail cases since the index of each element in the vec may be + // different. A single index can not express masked welford reduction. + T weight = T(0); + uint64_t index = 0; +}; + + +template +struct IsVecType: std::false_type {}; + +#if INDUCTOR_USE_VECTOR_TYPES() +template +struct IsVecType>: std::true_type {}; +#endif + +template +struct WeightRecp { + using scalar_t = typename T::value_type; + std::vector weight_recps; + WeightRecp(uint64_t N) { + weight_recps.reserve(N); + for (const auto i : c10::irange(N)) { + weight_recps.push_back( + scalar_t(static_cast(1) / static_cast(i + 1))); + } + } +}; + +template +Welford welford_combine(const Welford& a, const Welford& b, bool use_index=false) { + if (a.index == 0) { + return b; + } + if (b.index == 0) { + return a; + } + auto delta = b.mean - a.mean; + auto a_weight = use_index ? T(a.index) : a.weight; + auto b_weight = use_index ? T(b.index) : b.weight; + auto new_weight = a_weight + b_weight; + auto new_index = a.index + b.index; + auto wb_over_w = b_weight / new_weight; + if constexpr (IsVecType::value) { + // Guard against division by zero + wb_over_w = T::blendv(wb_over_w, T(0), new_weight == T(0)); + } + auto result = Welford{ + a.mean + delta * wb_over_w, + a.m2 + b.m2 + delta * delta * a_weight * wb_over_w, + new_weight, + new_index + }; + return result; +} + +template +Welford welford_combine(const Welford& acc, const T& data, const WeightRecp* w=nullptr) { + // Add a single data point + uint64_t new_index = acc.index + 1; + auto new_weight = acc.weight + T(1); + auto delta = data - acc.mean; + T new_mean; + if constexpr (!IsVecType::value) { + new_mean = acc.mean + delta / new_weight; + } else { + // use new_index to fecth 1 / new_weight to avoid divisions + new_mean = acc.mean + + ((w == nullptr || acc.index >= w->weight_recps.size()) + ? delta / new_weight + : delta * T(w->weight_recps[acc.index])); + } + auto new_delta = data - new_mean; + auto result = Welford{ + new_mean, + acc.m2 + delta * new_delta, + new_weight, + new_index + }; + return result; +} + +template +struct IndexValue { + int64_t index; + T value; + IndexValue(int64_t idx, T val) :index(idx), value(val) {}; + IndexValue() {}; +}; + +#if INDUCTOR_USE_VECTOR_TYPES() +template +Welford welford_combine(const Welford& acc, const T& data, const int64_t tail_size, const WeightRecp* w=nullptr) { + auto out = welford_combine(acc, data, w); + return Welford{ + T::set(acc.mean, out.mean, tail_size), + T::set(acc.m2, out.m2, tail_size), + T::set(acc.weight, out.weight, tail_size), + out.index + }; +} + +template +T max_masked_reduce(const T& a, const T& b, const int64_t tail_size) { + auto out = at::vec::maximum(a, b); + return T::set(a, out, tail_size); +} + +template +T min_masked_reduce(const T& a, const T& b, const int64_t tail_size) { + auto out = at::vec::minimum(a, b); + return T::set(a, out, tail_size); +} + +template +T sum_masked_reduce(const T& a, const T& b, const int64_t tail_size) { + auto out = a + b; + return T::set(a, out, tail_size); +} + +template +T prod_masked_reduce(const T& a, const T& b, const int64_t tail_size) { + auto out = a * b; + return T::set(a, out, tail_size); +} + +template +T xor_sum_masked_reduce(const T& a, const T& b, const int64_t tail_size) { + auto out = a ^ b; + return T::set(a, out, tail_size); +} +#endif + +// Refer to https://github.com/pytorch/pytorch/blob/b5b36cf0c4e1958f1ff25120f5d4beeef3288187/ +// aten/src/ATen/native/SharedReduceOps.h#L419-L445 +template +inline bool greater_or_nan(scalar_t a, scalar_t b, int64_t idx_a, int64_t idx_b) { + // If (a == b), then choose the one with lower idx, else max(a, b) + if (at::_isnan(a)) { + if (at::_isnan(b)) { + return idx_a < idx_b; + } + return true; + } + return (a == b) ? idx_a < idx_b : (a > b); +} + +template +inline bool less_or_nan(scalar_t a, scalar_t b, int64_t idx_a, int64_t idx_b) { + // If (a == b), then choose the one with lower idx, else min(a, b) + if (at::_isnan(a)) { + if (at::_isnan(b)) { + return idx_a < idx_b; + } + return true; + } + return (a == b) ? idx_a < idx_b : (a < b); +} + +template +inline IndexValue& argmin_combine(IndexValue& a, T next_value, int64_t next_index){ + if(!(less_or_nan(a.value, next_value, a.index, next_index))){ + a.value = next_value; + a.index = next_index; + } + return a; +} +template +inline IndexValue& argmax_combine(IndexValue& a, T next_value, int64_t next_index){ + if(!(greater_or_nan(a.value, next_value, a.index, next_index))){ + a.value = next_value; + a.index = next_index; + } + return a; +} +template +inline IndexValue& argmin_combine(IndexValue& a, const IndexValue& next){ + return argmin_combine(a, next.value, next.index); +} +template +inline IndexValue& argmax_combine(IndexValue& a, const IndexValue& next){ + return argmax_combine(a, next.value, next.index); +} + +#if INDUCTOR_USE_VECTOR_TYPES() + +template +inline at::vec::Vectorized div_floor_floating_vec( + const at::vec::Vectorized& a, + const at::vec::Vectorized& b) { + using vec_t = at::vec::Vectorized; + const auto basic_div = a / b; + vec_t inf(std::numeric_limits::infinity()); + auto mod = a.fmod(b); + // Fixup for a case that isn't properly handled by Sleef_fmod + auto floor = vec_t::blendv(a - mod, a, (basic_div.abs() == inf) & (a.abs() != inf)); + auto div = floor / b; + const auto zero = vec_t(0); + auto mask = (mod != zero) & ((b < zero) ^ (mod < zero)); + const auto one = vec_t(1); + div = vec_t::blendv(div, div - one, mask); + auto floordiv = div.floor(); + mask = (div - floordiv) > vec_t(0.5); + floordiv = vec_t::blendv(floordiv, floordiv + one, mask); + floordiv = vec_t::blendv(floordiv, zero.copysign(basic_div), div == zero); + floordiv = vec_t::blendv(floordiv, basic_div, b == zero); + return floordiv; +}; + +template +inline at::vec::VectorizedN div_floor_floating_vec( + const at::vec::VectorizedN& a, + const at::vec::VectorizedN& b) { + at::vec::VectorizedN result; +#ifndef _MSC_VER +#pragma unroll +#endif + for (int i = 0; i < N; ++i) { + result[i] = div_floor_floating_vec(a[i], b[i]); + } + return result; +} + +template +struct IndexValueVec { + at::vec::VectorizedN value; + at::vec::VectorizedN index; + + IndexValueVec(const T _value) { + value = at::vec::VectorizedN(_value); + index = at::vec::VectorizedN(0); + }; + + IndexValueVec() {}; +}; + + +template , int> = 0> +at::vec::VecMask inline get_mask_for_argmin_argmax( + const at::vec::VecMask& vmask, + const IndexValueVec& a, + const at::vec::VectorizedN& value, + const at::vec::VectorizedN& index +){ + /* + vec impl for less_or_nan and greater_or_nan + example for argmin: + a.value = [NaN, NaN, 0, 2, 1, 0] + value = [NaN, 0, 0, 1, 2, NaN] + vmask = [false, false, false, false, true, false] + all_nan_or_equal = [true, false, true, false, false, false] + imask = [a.index[0] < index[0], ..., a.index[-1] < index[-1]] + iv_mask = blendv (vmask, imask, all_nan_or_equal) + [a.index[0] < index[0], false, a.index[2] < index[2], false, true, false] + a_nan_b_not: [false, false, false, false, false, true] + mask = iv_mask | a_nan_b_not + [a.index[0] < index[0], false, a.index[2] < index[2], false, true, true] + */ + using v_t = at::vec::VecMask; + using i_t = at::vec::VecMask; + i_t vmask_itype = vmask.template cast(); + // use itype here since there is vec impl for operator~ for itype + // while there may not vec impl for vtype + v_t isnan_a = a.value.isnan(); + i_t isnan_a_itype = isnan_a.template cast(); + v_t isnan_b = value.isnan(); + i_t isnan_b_type = isnan_b.template cast(); + i_t all_nan_mask = isnan_a_itype & isnan_b_type; + v_t equal_mask = (a.value == value); + i_t equal_mask_itype = equal_mask.template cast(); + i_t all_nan_or_equal = all_nan_mask | equal_mask_itype; + i_t imask(a.index < index); + i_t iv_mask = i_t::blendv(vmask_itype, imask, all_nan_or_equal); + i_t isnan_a_notnan_b = isnan_a_itype & (~isnan_b_type); + return iv_mask | isnan_a_notnan_b; +} + +template , int> = 0> +at::vec::VecMask inline get_mask_for_argmin_argmax( + const at::vec::VecMask& vmask, + const IndexValueVec& a, + const at::vec::VectorizedN& value, + const at::vec::VectorizedN& index +){ + using v_t = at::vec::VecMask; + using i_t = at::vec::VecMask; + i_t vmask_itype = vmask.template cast(); + v_t equal_mask = (a.value == value); + i_t equal_mask_itype = equal_mask.template cast(); + i_t imask(a.index < index); + return i_t::blendv(vmask_itype, imask, equal_mask_itype); +} + + +template +inline IndexValueVec& argmin_vec_impl(IndexValueVec& a, at::vec::VectorizedN value, at::vec::VectorizedN index, std::optional tail_size){ + at::vec::VecMask vmask(a.value < value); + at::vec::VecMask final_mask = get_mask_for_argmin_argmax(vmask, a, value, index); + if (tail_size.has_value()) { + a.value = at::vec::VectorizedN::set(a.value, at::vec::minimum(a.value, value), tail_size.value()); + a.index = at::vec::VectorizedN::set(a.index, at::vec::VecMask::blendv(index, a.index, final_mask), tail_size.value()); + } else { + a.value = at::vec::minimum(a.value, value); + a.index = at::vec::VecMask::blendv(index, a.index, final_mask); + } + return a; +} + +template +inline IndexValueVec& argmax_vec_impl(IndexValueVec& a, at::vec::VectorizedN value, at::vec::VectorizedN index, std::optional tail_size){ + at::vec::VecMask vmask(a.value > value); + at::vec::VecMask final_mask = get_mask_for_argmin_argmax(vmask, a, value, index); + if (tail_size.has_value()) { + a.value = at::vec::VectorizedN::set(a.value, at::vec::maximum(a.value, value), tail_size.value()); + a.index = at::vec::VectorizedN::set(a.index, at::vec::VecMask::blendv(index, a.index, final_mask), tail_size.value()); + } else { + a.value = at::vec::maximum(a.value, value); + a.index = at::vec::VecMask::blendv(index, a.index, final_mask); + } + return a; +} + +template +inline at::vec::VectorizedN create_index(int64_t next_index){ + at::vec::VectorizedN next_idx; + if constexpr (horizontal) { + next_idx = at::vec::VectorizedN::arange(next_index, 1); + } else { + next_idx = at::vec::VectorizedN(next_index); + } + return next_idx; +} + +template +inline IndexValueVec& argmin_combine_vec(IndexValueVec& a, at::vec::VectorizedN next_value, int64_t next_index, std::optional tail_size = std::nullopt){ + auto next_idx = create_index(next_index); + return argmin_vec_impl(a, next_value, next_idx, tail_size); +} + +template +inline IndexValueVec& argmax_combine_vec(IndexValueVec& a, at::vec::VectorizedN next_value, int64_t next_index, std::optional tail_size = std::nullopt){ + auto next_idx = create_index(next_index); + return argmax_vec_impl(a, next_value, next_idx, tail_size); +} + +template +inline IndexValue argmin_vec_reduce_all(const IndexValueVec& vec){ + constexpr int len = at::vec::VectorizedN::size(); + __at_align__ T tmpval[len]; + __at_align__ int64_t tmpidx[len]; + vec.value.store(tmpval); + vec.index.store(tmpidx); + IndexValue res = IndexValue(tmpidx[0], tmpval[0]); + for (int i = 1; i < len; i++){ + res = argmin_combine(res, tmpval[i], tmpidx[i]); + } + return res; +} + +template +inline IndexValue argmax_vec_reduce_all(const IndexValueVec& vec){ + constexpr int len = at::vec::VectorizedN::size(); + __at_align__ T tmpval[len]; + __at_align__ int64_t tmpidx[len]; + vec.value.store(tmpval); + vec.index.store(tmpidx); + IndexValue res = IndexValue(tmpidx[0], tmpval[0]); + for (int i = 1; i < len; i++){ + res = argmax_combine(res, tmpval[i], tmpidx[i]); + } + return res; +} + +template +inline IndexValueVec& argmin_combine_vec(IndexValueVec& vec_a, const IndexValueVec& vec_b, std::optional tail_size = std::nullopt){ + return argmin_vec_impl(vec_a, vec_b.value, vec_b.index, tail_size); +} + +template +inline IndexValueVec& argmax_combine_vec(IndexValueVec& vec_a, const IndexValueVec& vec_b, std::optional tail_size = std::nullopt){ + return argmax_vec_impl(vec_a, vec_b.value, vec_b.index, tail_size); +} + +template +inline at::vec::Vectorized vec_shuffle_down(at::vec::Vectorized x, size_t n) { + using Vec = at::vec::Vectorized; + alignas(alignof(Vec)) scalar_t array[Vec::size()]; + x.store(array); + for (size_t i = 0; i + n < Vec::size(); i += 2 * n) { + array[i] = array[i + n]; + } + return Vec::loadu(array); +} + +#ifdef CPU_CAPABILITY_AVX2 +inline at::vec::Vectorized vec_shuffle_down(at::vec::Vectorized x, size_t n) { + using vec_t = at::vec::Vectorized; +#define SHUFFLE_MASK(z, y, x, w) ((z << 6) | (y << 4) | (x << 2) | w) + switch (n) { + case 1: + return vec_t(_mm256_permute_ps(x, SHUFFLE_MASK(1, 1, 3, 3))); + case 2: + return vec_t(_mm256_permute_ps(x, SHUFFLE_MASK(2, 2, 2, 2))); + case 4: + return vec_t(_mm256_permute2f128_ps(x, x, SHUFFLE_MASK(1, 1, 1, 1))); + } + TORCH_CHECK(false, "Unhandled vec_shuffle_down value ", n); +} +#endif + +#ifdef CPU_CAPABILITY_AVX512 +inline at::vec::Vectorized vec_shuffle_down(at::vec::Vectorized x, size_t n) { + using vec_t = at::vec::Vectorized; +#define SHUFFLE_MASK(z, y, x, w) ((z << 6) | (y << 4) | (x << 2) | w) + switch (n) { + case 1: + return vec_t(_mm512_permute_ps(x, SHUFFLE_MASK(1, 1, 3, 3))); + case 2: + return vec_t(_mm512_permute_ps(x, SHUFFLE_MASK(2, 2, 2, 2))); + case 4: + return vec_t(_mm512_permutexvar_ps( + _mm512_set_epi32( + 12, 12, 12, 12, 12, 12, 12, 12, 4, 4, 4, 4, 4, 4, 4, 4), + x)); + case 8: + return vec_t(_mm512_permutexvar_ps( + _mm512_set_epi32(8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8), x)); + } + TORCH_CHECK(false, "Unhandled vec_shuffle_down value ", n); +} +#endif + +template +Welford welford_vec_reduce_all(Welford> acc) { + using Vec = at::vec::Vectorized; + Welford result; + if (acc.index == 0) { + return result; + } + // if all values of acc.weight are same as index, + // use index to reduce to save the overhead of vec_shuffle_down for acc.weight + bool use_index = (acc.weight - Vec(acc.index)).zero_mask() == static_cast((1 << Vec::size()) - 1); + for (size_t n = 1; n < Vec::size(); n *= 2) { + auto shuffled = Welford{ + vec_shuffle_down(acc.mean, n), + vec_shuffle_down(acc.m2, n), + use_index ? Vec(0) : vec_shuffle_down(acc.weight, n), + acc.index}; + acc = welford_combine(acc, shuffled, use_index); + } + + alignas(alignof(Vec)) scalar_t array[Vec::size()]; + acc.mean.store(array); + result.mean = array[0]; + + acc.m2.store(array); + result.m2 = array[0]; + + acc.weight.store(array); + result.weight = array[0]; + result.index = result.weight; + + return result; +} + +template +Welford welford_vec_reduce_all(Welford> acc) { + auto Welford0 = Welford>{ + acc.mean[0], + acc.m2[0], + acc.weight[0], + acc.index + }; + auto Welford1 = Welford>{ + acc.mean[1], + acc.m2[1], + acc.weight[1], + acc.index + }; + return welford_vec_reduce_all(welford_combine(Welford0, Welford1)); +} +#endif + + +template inline typename std::common_type::type mod(T a, U b) { return a % b; } +template <> inline float mod(float a, float b) { return std::fmod(a, b); } +template <> inline double mod(double a, double b) { return std::fmod(a, b); } + +template +inline scalar_t max_propagate_nan(scalar_t a, scalar_t b) { + if (at::_isnan(a)) { + return a; + } + return a > b ? a : b; +} + +template +inline scalar_t min_propagate_nan(scalar_t a, scalar_t b) { + if (at::_isnan(a)) { + return a; + } + return a < b ? a : b; +} + +constexpr float uint32_to_uniform_float(uint32_t value) { + // maximum value such that `MAX_INT * scale < 1.0` (with float rounding) + constexpr float scale = 4.6566127342e-10; + return static_cast(value & 0x7FFFFFFF) * scale; +} + +float normalized_rand_cpu(uint32_t seed, uint32_t offset) { + return uint32_to_uniform_float(at::Philox4_32(seed, 0, offset)()); +} + +float randn_cpu(uint32_t seed, uint32_t offset) { + at::Philox4_32 engine(seed, 0, offset); + return engine.randn(10); +} + +int64_t randint64_cpu(uint32_t seed, uint32_t offset, int64_t low, int64_t high) { + auto gen = at::Philox4_32(seed, 0, offset); + uint64_t r0 = gen(); + uint64_t r1 = gen(); + uint64_t result = r0 | (r1 << 32); + return static_cast(result % (high - low)) + low; +} + +template struct AsIntegerType { typedef T type; }; +template <> struct AsIntegerType { typedef uint32_t type; }; +template <> struct AsIntegerType { typedef uint64_t type; }; +template <> struct AsIntegerType { typedef uint16_t type; }; + +template +typename std::enable_if_t, T> +inline fetch_value(volatile T *addr) { + return *addr; +} + +template +typename std::enable_if_t, T> +inline fetch_value(volatile T *addr) { + return T(addr->x, T::from_bits()); +} + +template +typename std::enable_if_t> +atomic_add(volatile T *addr, T offset) { + typedef typename AsIntegerType::type alt_type; + + static_assert(sizeof(std::atomic) == sizeof(T), + "std::atomic issue"); + + alt_type expected; + + alt_type desired; + + std::atomic *atomic_addr = (std::atomic *)addr; + do { + T val = fetch_value(addr); + reinterpret_cast(&expected)[0] = val; + reinterpret_cast(&desired)[0] = val + offset; + } while (!atomic_addr->compare_exchange_weak(expected, desired, + std::memory_order_relaxed)); +} + +// Since C++20 float is supported by fetch_add, but the performance may not +// better than compare_exchange_weak, which can be checked by microbenchmark +// inductor_cpu_atomic.py +template +typename std::enable_if_t> +atomic_add(volatile T *addr, T offset) { + static_assert(sizeof(std::atomic) == sizeof(T), + "std::atomic issue"); + std::atomic *atomic_addr = (std::atomic *)addr; + atomic_addr->fetch_add(offset, std::memory_order_relaxed); +} + +#if INDUCTOR_USE_VECTOR_TYPES() +template +void atomic_add_vec(T *addr, at::vec::VectorizedN index, at::vec::VectorizedN offset) { + constexpr int len = at::vec::VectorizedN::size(); + static_assert(len <= at::vec::VectorizedN::size()); + __at_align__ std::array tmpbuf; + __at_align__ std::array tmpidx; + offset.store(tmpbuf.data()); + index.store(tmpidx.data()); + for (int i = 0; i < len; i++){ + atomic_add(addr + tmpidx[i], tmpbuf[i]); + } +} +#endif + +std::tuple, int> _get_factors(int64_t number) { + int count = 0; + for (int64_t i = std::sqrt(number); i > 0; --i) { + if (number % i == 0) { + count += 2; + } + } + auto factors = std::shared_ptr(new int64_t[count]); + int index = 0; + for (int64_t i = std::sqrt(number); i > 0; --i) { + if (number % i == 0) { + factors[index++] = number / i; + factors[index++] = i; + } + } + return std::make_tuple(factors, count); +} + +std::tuple, int> get_factors(int64_t number) { + thread_local std::map, int>> cache; + auto it = cache.find(number); + if (it != cache.end()) { + return it->second; + } else { + auto factors = _get_factors(number); + cache[number] = factors; + return factors; + } +} + +void _mm_get_thread_blocking( + int num_threads, + int max_k_slices, + int64_t M, + int64_t N, + int64_t K, + int64_t Mr, + int64_t Nr, + int64_t Kr, + int64_t& Mt, + int64_t& Nt, + int64_t& Kt) { + // see NOTE [Thread blocking in Cpp GEMM] for heuristics + Mt = Nt = Kt = 0; + + auto get_blocking = [](int64_t m_factor, + int64_t n_factor, + int64_t k_factor, + int64_t m_blocks, + int64_t n_blocks, + int64_t k_blocks) { + int64_t thread_block_k = (k_blocks + k_factor - 1) / k_factor; + int64_t thread_block_n = (n_blocks + n_factor - 1) / n_factor; + int64_t thread_block_m = (m_blocks + m_factor - 1) / m_factor; + return std::make_tuple(thread_block_m, thread_block_n, thread_block_k); + }; + + auto is_better_blocking = [=](int64_t Mt_, + int64_t Nt_, + int64_t Kt_, + int64_t Mt, + int64_t Nt, + int64_t Kt) { + return Mt == 0 || Kt_ < Kt || Mt_ * Mr + Nt_ * Nr < Mt * Mr + Nt * Nr; + }; + + int64_t m_blocks = (M + Mr - 1) / Mr; + int64_t n_blocks = (N + Nr - 1) / Nr; + int64_t k_blocks = (K + Kr - 1) / Kr; + + auto [factors, count] = get_factors(num_threads); + assert(count > 0); + + for (int i = 0; i < count; ++i) { + int64_t n_factor = factors[i]; + int64_t m_factor = num_threads / n_factor; + if (n_blocks >= n_factor && m_blocks >= m_factor) { + auto [Mt_, Nt_, Kt_] = get_blocking( + m_factor, n_factor, 1, m_blocks, n_blocks, k_blocks); + if (is_better_blocking(Mt_, Nt_, Kt_, Mt, Nt, Kt)) { + std::tie(Mt, Nt, Kt) = std::make_tuple(Mt_, Nt_, Kt_); + } + } + } + + if (Mt != 0) { + return; + } + + for (int i = 0; i < count; ++i) { + int64_t k_factor = factors[i]; + if (k_blocks >= k_factor && (max_k_slices == 0 || k_factor <= max_k_slices)) { + auto [mxn_factors, mxn_count] = get_factors(num_threads / k_factor); + for (int j = 0; j < mxn_count; ++j) { + int64_t n_factor = mxn_factors[j]; + int64_t m_factor = num_threads / (k_factor * n_factor); + if (n_blocks >= n_factor && m_blocks >= m_factor) { + auto [Mt_, Nt_, Kt_] = get_blocking( + m_factor, n_factor, k_factor, m_blocks, n_blocks, k_blocks); + if (is_better_blocking(Mt_, Nt_, Kt_, Mt, Nt, Kt)) { + std::tie(Mt, Nt, Kt) = std::make_tuple(Mt_, Nt_, Kt_); + } + } + } + } + } + + if (Mt != 0) { + return; + } + + for (int i = 0; i < count; ++i) { + int64_t n_factor = factors[i]; + int64_t m_factor = num_threads / n_factor; + if (n_blocks >= n_factor || m_blocks >= m_factor) { + auto [Mt_, Nt_, Kt_] = get_blocking( + m_factor, n_factor, 1, m_blocks, n_blocks, k_blocks); + if (is_better_blocking(Mt_, Nt_, Kt_, Mt, Nt, Kt)) { + std::tie(Mt, Nt, Kt) = std::make_tuple(Mt_, Nt_, Kt_); + } + } + } + + assert(Mt != 0); +} + +void mm_get_thread_blocking( + int num_threads, + int max_k_slices, + int64_t M, + int64_t N, + int64_t K, + int64_t Mr, + int64_t Nr, + int64_t Kr, + int64_t& Mt, + int64_t& Nt, + int64_t& Kt) { + thread_local std::map< + std::tuple, + std::tuple> cache; + auto key = std::make_tuple(num_threads, max_k_slices, M, N, K, Mr, Nr, Kr); + auto it = cache.find(key); + if (it != cache.end()) { + std::tie(Mt, Nt, Kt) = it->second; + return; + } else { + _mm_get_thread_blocking(num_threads, max_k_slices, M, N, K, Mr, Nr, Kr, Mt, Nt, Kt); + cache[key] = std::make_tuple(Mt, Nt, Kt); + } +} + +template +void _mm_get_cache_blocking( + int num_threads, + int64_t M, + int64_t N, + int64_t K, + int64_t Mr, + int64_t Nr, + int64_t Kr, + int64_t Mt_blocks, + int64_t Nt_blocks, + int64_t Kt_blocks, + int64_t& Mc_blocks, + int64_t& Nc_blocks, + int64_t& Kc_blocks, + uint32_t L1_cache_size, + uint32_t L2_cache_size) { + // See NOTE [CPP GEMM Cache Blocking Algorithm] for the cache blocking algorithm. + // TODO(jgong5): cache cache blocking results + // TODO: tune the factor here + float L1_limit_factor = 0.8; + float L2_limit_factor = 0.5; + + auto L1 = L1_cache_size * L1_limit_factor; + auto L2 = L2_cache_size * L2_limit_factor; + + constexpr size_t num_byte_A = sizeof(X_t); + constexpr size_t num_byte_B = sizeof(W_t); + + int64_t size_cache_B = Kr * Kt_blocks * Nr * num_byte_B; + Kc_blocks = Kt_blocks; + if (size_cache_B > L1) { + Kc_blocks = (int64_t)std::floor(L1 / (Kr * Nr * num_byte_B)); + } + + float min_Mc_ratio = 2; + int64_t min_Mc_blocks = std::ceil(min_Mc_ratio * Mr / Nr); + auto Kt_bytes = Kt_blocks * Kr * num_byte_A; + if (min_Mc_blocks * Mr * Kt_bytes < L2) { + Mc_blocks = std::min(Mt_blocks, (int64_t)std::floor(L2 / (Mr * Kt_bytes))); + Nc_blocks = 1; + } else { + Mc_blocks = Mt_blocks; + Nc_blocks = std::min((int64_t)std::ceil((float)Mc_blocks * Mr / Nr), Nt_blocks); + auto Nc_bytes = Nc_blocks * Nr * 4; + auto Kc_bytes = Kc_blocks * Kr * num_byte_A; + if (Mc_blocks * Mr * (Kc_bytes + Nc_bytes) > L2) { + auto M_max = (std::sqrt(Kc_bytes * Kc_bytes + 16 * L2) - Kc_bytes) / 8; + if (M_max < Mc_blocks * Mr) { + Mc_blocks = (int64_t)std::floor(M_max / Mr); + Nc_blocks = std::min((int64_t)std::ceil((float)Mc_blocks * Mr / Nr), Nt_blocks); + } + } + } +} + +template +void mm_get_cache_blocking( + int num_threads, + int64_t M, + int64_t N, + int64_t K, + int64_t Mr, + int64_t Nr, + int64_t Kr, + int64_t Mt_blocks, + int64_t Nt_blocks, + int64_t Kt_blocks, + int64_t& Mc_blocks, + int64_t& Nc_blocks, + int64_t& Kc_blocks, + uint32_t L1_cache_size, + uint32_t L2_cache_size) { + thread_local std::map< + std::tuple, + std::tuple> cache; + auto key = std::make_tuple(num_threads, M, N, K, Mr, Nr, Kr, Mt_blocks, Nt_blocks, Kt_blocks, L1_cache_size, L2_cache_size); + auto it = cache.find(key); + if (it != cache.end()) { + std::tie(Mc_blocks, Nc_blocks, Kc_blocks) = it->second; + return; + } else { + _mm_get_cache_blocking( + num_threads, M, N, K, Mr, Nr, Kr, Mt_blocks, Nt_blocks, Kt_blocks, Mc_blocks, Nc_blocks, Kc_blocks, L1_cache_size, L2_cache_size); + cache[key] = std::make_tuple(Mc_blocks, Nc_blocks, Kc_blocks); + } +} + +inline void mm_get_thread_blocks( + int thread_id, + int64_t M_blocks, + int64_t N_blocks, + int64_t K_blocks, + int64_t Mt_blocks, + int64_t Nt_blocks, + int64_t Kt_blocks, + int64_t& m_block_start, + int64_t& m_block_end, + int64_t& n_block_start, + int64_t& n_block_end, + int64_t& k_block_start, + int64_t& k_block_end) { + int64_t num_Kt = (K_blocks + Kt_blocks - 1) / Kt_blocks; + k_block_start = (thread_id % num_Kt) * Kt_blocks; + k_block_end = std::min(k_block_start + Kt_blocks, K_blocks); + thread_id /= num_Kt; + int64_t num_Nt = (N_blocks + Nt_blocks - 1) / Nt_blocks; + n_block_start = (thread_id % num_Nt) * Nt_blocks; + n_block_end = std::min(n_block_start + Nt_blocks, N_blocks); + thread_id /= num_Nt; + m_block_start = std::min(thread_id * Mt_blocks, M_blocks); + m_block_end = std::min(m_block_start + Mt_blocks, M_blocks); +} + +struct amx_tilecfg { + uint8_t palette_id; + uint8_t start_row; + uint8_t reserved_0[14]; + uint16_t colsb[16]; + uint8_t rows[16]; +}; + +class AMXState { + private: + amx_tilecfg tilecfg_; + uint8_t rows_; + uint16_t colsb_; + uint8_t num_tile_rows_; + uint8_t num_tile_columns_; + + public: + AMXState() : rows_(0), colsb_(0), num_tile_rows_(0), num_tile_columns_(0) { + memset(&tilecfg_, 0, sizeof(tilecfg_)); + } + + inline void configure( + uint8_t rows, + uint16_t colsb, + uint8_t num_tile_rows, + uint8_t num_tile_columns, + void (*loadconfig)(const amx_tilecfg&)) { + if (tilecfg_.palette_id == 1 && rows_ == rows && colsb_ == colsb && + num_tile_rows_ == num_tile_rows && + num_tile_columns_ == num_tile_columns) { + return; + } + tilecfg_.palette_id = 1; + rows_ = rows; + colsb_ = colsb; + num_tile_rows_ = num_tile_rows; + num_tile_columns_ = num_tile_columns; + const auto num_c_tiles = num_tile_rows * num_tile_columns; + // For C + for (int i = 0; i < num_c_tiles; i++) { + tilecfg_.rows[i] = rows; + tilecfg_.colsb[i] = 64; + } + // For A + for (int i = 0; i < num_tile_rows; i++) { + tilecfg_.rows[i + num_c_tiles] = rows; + tilecfg_.colsb[i + num_c_tiles] = colsb; + } + // For B + for (int i = 0; i < num_tile_columns; i++) { + tilecfg_.rows[i + num_c_tiles + num_tile_rows] = colsb / 4; + tilecfg_.colsb[i + num_c_tiles + num_tile_rows] = 64; + } + loadconfig(tilecfg_); + } + + inline void release(void (*tile_release)()) { + tilecfg_.palette_id = 0; + tile_release(); + } +}; diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_template.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_template.py new file mode 100644 index 0000000000000000000000000000000000000000..2bce16b2ad1538624be9c4e32893e32a412e7f46 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_template.py @@ -0,0 +1,128 @@ +# mypy: allow-untyped-defs +import ctypes +import functools +import itertools +import logging +import sys +from typing import Callable, List, Optional +from unittest.mock import patch + +import sympy + +from .. import codecache, config, ir +from ..autotune_process import CppBenchmarkRequest, TensorMeta +from ..utils import IndentedBuffer, Placeholder, unique +from ..virtualized import V +from .common import KernelTemplate +from .cpp_template_kernel import CppTemplateCaller, CppTemplateKernel + + +log = logging.getLogger(__name__) + + +class CppTemplate(KernelTemplate): + index_counter = itertools.count() + + def __init__( + self, + name: str, + input_nodes, + layout: ir.Layout, + num_threads: int, + epilogue_creator: Optional[Callable[[ir.Buffer], ir.Pointwise]] = None, + ) -> None: + super().__init__(name) + self.input_nodes = input_nodes + self.output_node: ir.Buffer = ir.Buffer("buf_out", layout) + self.layout = layout + self.num_threads = num_threads + self.epilogue_creator = epilogue_creator + + def generate(self, **kwargs): + kernel_name = f"cpp_{self.name}" + with patch.object( + V.graph, "get_dtype", self._fake_get_dtype(self.output_node) + ), patch.object(ir.FlexibleLayout, "allow_indexing", True), CppTemplateKernel( + kernel_name=kernel_name, num_threads=self.num_threads + ) as kernel: + code = kernel.render(self, **kwargs) + _, call_args, _, _ = kernel.args.python_argdefs() + log.debug("Generated Code:\n%s", code) + log.debug( + "Args: cpp_argdefs: %s, python_argdefs: %s", + kernel.args.cpp_argdefs(), + kernel.args.python_argdefs(), + ) + + expected_args = list( + unique(input_node.get_name() for input_node in self.input_nodes) + ) + expected_args.extend([self.output_node.get_name()]) + assert list(call_args)[: len(expected_args)] == expected_args, ( + call_args, + expected_args, + ) + extra_args = V.graph.sizevars.size_hints( + map(sympy.expand, call_args[len(expected_args) :]) + ) + # Cast the size hint from int to ctypes.c_ulonglong explicitly + # since in cpp kernel, we bind it to C long + extra_args = tuple(ctypes.c_ulonglong(x) for x in extra_args) + + kernel_hash_name = f"cpp_{self.name}_{next(self.index_counter)}" + + # Create the BenchmarkRequest for CPP + bmreq = CppBenchmarkRequest( + kernel_name=kernel_name, + input_tensor_meta=TensorMeta.from_irnodes(self.input_nodes), + output_tensor_meta=TensorMeta.from_irnodes(self.output_node), + extra_args=extra_args, + source_code=code, + ) + + def make_kernel_render( + template_node: ir.CppTemplateBuffer, + flag_template_buffer_has_other_users: bool, + epilogue_nodes: Optional[List[ir.IRNode]] = None, + ): + kernel = CppTemplateKernel( + kernel_name=str(Placeholder.KERNEL_NAME), num_threads=self.num_threads + ) + render = functools.partial( + kernel.render, + self, + template_buffer_node=template_node, + flag_template_buffer_has_other_users=flag_template_buffer_has_other_users, + epilogue_nodes=epilogue_nodes, + **kwargs, + ) + return kernel, render + + return CppTemplateCaller( + kernel_hash_name, + self.name, + self.input_nodes, + self.output_node.get_layout(), + make_kernel_render, + bmreq, + self, + ) + + def header(self) -> IndentedBuffer: + res = IndentedBuffer() + res.writeline(codecache.cpp_prefix()) + res.splice( + """ + #include "c10/util/Unroll.h" + """ + ) + enable_kernel_profile = config.cpp.enable_kernel_profile and sys.platform in [ + "linux", + "win32", + ] + if enable_kernel_profile: + res.writelines(["#include "]) + return res + + def render(self, **kwargs) -> str: + raise NotImplementedError diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_template_kernel.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_template_kernel.py new file mode 100644 index 0000000000000000000000000000000000000000..0333720bbdc689c8748c518ed71d2782f8c75602 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_template_kernel.py @@ -0,0 +1,384 @@ +# mypy: allow-untyped-defs +import itertools +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import sympy +from sympy.parsing.sympy_parser import parse_expr + +import torch +from torch.utils._sympy.symbol import SymT + +from .. import config, cpp_builder, ir, lowering as L +from ..autotune_process import CppBenchmarkRequest +from ..loop_body import LoopBody +from ..select_algorithm import PartialRender +from ..utils import sympy_index_symbol, sympy_index_symbol_with_prefix +from ..virtualized import V +from .common import CppWrapperKernelArgs +from .cpp import CppKernel, CppKernelProxy, KernelGroup +from .cpp_utils import cexpr_index, DTYPE_TO_CPP, LocalBufferContext +from .cpp_wrapper_cpu import CppWrapperCpu + + +def parse_expr_with_index_symbols(expr): + if isinstance(expr, sympy.Expr): + return expr + elif isinstance(expr, (list, tuple)): + return [parse_expr_with_index_symbols(e) for e in expr] + else: + expr = parse_expr(str(expr)) + int_symbols = {sym: sympy_index_symbol(sym.name) for sym in expr.free_symbols} + return expr.subs(int_symbols) + + +def wrap_with_tensorbox(node) -> ir.TensorBox: + return ( + ir.TensorBox.create(node) if isinstance(node, ir.Buffer) else ir.TensorBox(node) + ) + + +class CppTemplateKernel(CppKernel): + def __init__(self, kernel_name, num_threads): + super().__init__(None, num_threads) + self.kernel_name = kernel_name + self.render_hooks = {} + self.local_buffers = {} + if isinstance(V.graph.wrapper_code, CppWrapperCpu): + self.args = CppWrapperKernelArgs() + + def render(self, template, **kwargs): + return PartialRender( + template.render(kernel=self, **kwargs), self.render_hooks + ).finalize_all() + + def def_kernel( + self, + inputs: Dict[str, ir.Buffer], + outputs: Dict[str, ir.Buffer], + aliases: Optional[Dict[str, str]] = None, + ) -> str: + for name, inp in inputs.items(): + if inp is not None: + self.args.input_buffers[inp.get_name()] = name + for name, out in outputs.items(): + self.args.output_buffers[out.get_name()] = name + if aliases is not None: + for alias, orig in aliases.items(): + if orig in self.args.input_buffers: + self.args.input_buffers[alias] = self.args.input_buffers[orig] + if orig in self.args.output_buffers: + self.args.output_buffers[alias] = self.args.output_buffers[orig] + + unique_sizevars = { + s + for input in inputs.values() + if input is not None + for sym in itertools.chain(input.get_size(), input.get_stride()) + if isinstance(sym, sympy.Expr) + for s in sym.free_symbols + } + unique_sizevars |= { + s + for output in outputs.values() + for sym in itertools.chain(output.get_size(), output.get_stride()) + if isinstance(sym, sympy.Expr) + for s in sym.free_symbols + } + sizevars = sorted(unique_sizevars, key=str) + for sizevar in sizevars: + self.args.sizevars[sizevar] = f"k{sizevar}" + + def hook(): + # remove all aliases before generate function definition + if aliases is not None: + for alias in aliases: + if alias in self.args.input_buffers: + self.args.input_buffers[alias] = "REMOVED" + if alias in self.args.output_buffers: + self.args.output_buffers[alias] = "REMOVED" + cpp_argdefs, _, _ = self.args.cpp_argdefs() + return f"void {self.kernel_name}({', '.join(cpp_argdefs)})" + + placeholder = "" + assert placeholder not in self.render_hooks + self.render_hooks[placeholder] = hook + return placeholder + + def call_kernel(self, name: str, node: ir.CppTemplateBuffer): + wrapper = V.graph.wrapper_code + _, call_args, arg_types = self.args.cpp_argdefs() + wrapper.generate_kernel_call(name, call_args, cuda=False, arg_types=arg_types) + + def dtype(self, node: ir.Buffer) -> str: + return DTYPE_TO_CPP[node.get_dtype()] + + def acc_dtype(self, node: ir.Buffer) -> str: + if node.get_dtype() in [torch.float32, torch.bfloat16, torch.half]: + return "float" + else: + raise NotImplementedError(f"Unsupported dtype: {node.get_dtype()}") + + def size(self, node: ir.Buffer, dim: int) -> str: + return cexpr_index(self.rename_indexing(node.get_size()[dim])) + + def stride(self, node: ir.Buffer, dim: int) -> str: + return cexpr_index(self.rename_indexing(node.get_stride()[dim])) + + def index(self, node: ir.Buffer, indices: List[Any]) -> str: + indexer = node.layout.as_fixed().make_indexer() + index = indexer(parse_expr_with_index_symbols(indices)) + index = self.rename_indexing(index) + outer_name = node.get_name() + inner_name = ( + outer_name + if outer_name in self.local_buffers + else self.args.input(node.get_name()) + ) + return f"{inner_name}[{cexpr_index(index)}]" + + def slice_nd(self, node, ranges: List[Tuple[Any, Any]]) -> ir.ReinterpretView: + """ + Slice the given node with a list of ranges (start and end) corresponding to its dims. + The dim is not sliced if the corresponding range is empty. + """ + assert len(ranges) == len(node.get_size()), f"{ranges=}, {node=}" + sliced = wrap_with_tensorbox(node) + for dim, _range in enumerate(ranges): + if len(_range) == 0: + continue + assert len(_range) == 2 + start, end = parse_expr_with_index_symbols(_range) + sliced = L.slice_(sliced, dim, start, end, clamp=False) + assert isinstance(sliced.data, ir.ReinterpretView), sliced.data + return sliced.data + + def view(self, node, sizes: List[Any]) -> ir.View: + node = wrap_with_tensorbox(node) + sizes = parse_expr_with_index_symbols(sizes) + return L.view(node, sizes).data + + def permute(self, node, dims): + node = wrap_with_tensorbox(node) + permuted = L.permute(node, dims).data + assert isinstance(permuted, ir.ReinterpretView) + return permuted + + def maybe_codegen_profile(self) -> str: + if config.cpp.enable_kernel_profile: + graph_id = V.graph.graph_id + prefix = "graph_" + str(graph_id) + "_" if graph_id is not None else "" + return f'RECORD_FUNCTION("{prefix}{self.kernel_name}", c10::ArrayRef({{}}));' + else: + return "" + + def unroll_pragma(self, unroll): + if cpp_builder.is_gcc(): + return f"#pragma GCC unroll {unroll}" + else: + return f"#pragma unroll {unroll}" + + def define_buffer(self, name, sizes: List[Any], dtype=torch.float) -> str: + """Define kernel local buffer""" + sizes = parse_expr_with_index_symbols(sizes) + buf = ir.Buffer(name, ir.FixedLayout(torch.device("cpu"), dtype, sizes)) + self.local_buffers[name] = buf + ctype = f"{DTYPE_TO_CPP[dtype]}" + numel = f"{cexpr_index(buf.get_numel())}" + return f"auto _{name} = std::make_unique<{ctype}[]>({numel}); auto {name} = _{name}.get();" + + def reinit_buffer_if_null(self, name): + """Reinit the previously defined local buffer if it is null""" + assert name in self.local_buffers + buf = self.local_buffers[name] + ctype = f"{DTYPE_TO_CPP[buf.layout.dtype]}" + numel = f"{cexpr_index(buf.get_numel())}" + return f"if (_{name} == nullptr) {{ _{name} = std::make_unique<{ctype}[]>({numel}); {name} = _{name}.get(); }}" + + def release_buffer(self, name): + """Codegen the code to release the ownership of a local buffer to others""" + assert name in self.local_buffers + return f"_{name}.release()" + + def store_pointwise_nodes( + self, + dst: ir.Buffer, + nodes: List[ir.IRNode], + offsets: Optional[List[sympy.Expr]] = None, + reindexers: Optional[List[Optional[Callable[[List[Any]], List[Any]]]]] = None, + ) -> str: + var_sizes = (tuple(dst.get_size()), ()) + var_ranges = { + sympy_index_symbol_with_prefix(SymT.INDEX, i): sz + for i, sz in enumerate(var_sizes[0]) + } + if not offsets: + offsets = [sympy.Integer(0)] * len(var_sizes[0]) + if not reindexers: + reindexers = [None] * len(nodes) + assert len(offsets) == len(var_sizes[0]) + output_index = dst.get_layout().make_indexer()(var_ranges.keys()) + kernel_group = KernelGroup() + kernel_group.args = self.args + cpp_kernel_proxy = CppKernelProxy(kernel_group) + bodies = [] + var_sizes_list = [] + for i, node in enumerate(nodes): + output_name = node.get_name() if i < len(nodes) - 1 else dst.get_name() + node = node.data if isinstance(node, ir.ComputedBuffer) else node + assert isinstance(node, ir.Pointwise), node + + def fn(*args): + assert len(args) == 2 + assert len(args[0]) == len(var_sizes[0]) + assert len(args[1]) == 0 + new_args = [arg + offset for arg, offset in zip(args[0], offsets)] # type: ignore[arg-type] + if reindexers[i] is not None: + new_args = reindexers[i](new_args) # type: ignore[misc] + V.ops.store( + output_name, + output_index, + node.make_loader()(new_args).value, + ) + + body = LoopBody( + fn, + (list(var_ranges.keys()), ()), + var_ranges, + list(var_ranges.keys()), + tuple(), + ) + bodies.append(body) + var_sizes_list.append(var_sizes) + + cpp_kernel_proxy.codegen_loop_bodies(bodies, var_sizes_list) + kernel_group.finalize_kernel(cpp_kernel_proxy, []) + return kernel_group.loops_code.getvalue() + + def store_output( + self, + dst: ir.Buffer, + src: ir.Buffer, + orig_src: Optional[ir.Buffer] = None, + epilogue_nodes: Optional[List[ir.IRNode]] = None, + offsets: Optional[List[Any]] = None, + reindexers: Optional[List[Optional[Callable[[List[Any]], List[Any]]]]] = None, + ): + """ + Store the `src` buffer to the `dst` buffer. The size of `src` and `dst` should match. + If `epilogue_nodes` is provided, the `src` buffer is firstly computed with the epilogues + before stored to `dst`. The `epilogues_nodes` are all pointwise. + + Notes: + 1. `src` and `dst` buffer could be the same buffer in which case we are doing in-place compute + and stores. In case `epilogue_nodes` are not provided, we do nothing. + 2. The `epilogue_nodes`, if exist, have computations on `src` before storing to `dst` but since + they come form the original Inductor IR, they might need to be adjusted before working with + `src` and `dst` as outlined below: + a) `src` or `dst` buffer could be a sub-slice of the ranges the `epilogue_nodes`work on. + In this case, the `offsets` could be provided to adjust the indices passed to + `epilogue_nodes` during codegen and the data ranges are also configured according to + the sizes of `src` and `dst`. + b) `dst` might be indexed in a different way as the `epilogue_nodes`, hence a `reindexer` is + needed on the indices to `epilogue_nodes` to match the indexing of `dst`. + c) If `src` is local, we need to add a local buffer for it and localize the `orig_src` buffer + in `epilogue_nodes` with `src`. + """ + assert dst.get_size() == src.get_size(), f"{dst=}, {src=}" + if offsets: + offsets = parse_expr_with_index_symbols(offsets) + if epilogue_nodes: + with LocalBufferContext(self.args) as scope: + assert orig_src is not None + if orig_src.get_name() != src.get_name(): + scope.add_local_buffer( + src, + [ + orig_src, + ], + ) + epilogue_nodes = scope.localize_nodes(epilogue_nodes) + return self.store_pointwise_nodes( + dst, epilogue_nodes, offsets, reindexers # type: ignore[arg-type] + ) + else: + if dst.get_name() != src.get_name(): + # src is local + copy = L.copy(dst, src).data.data + with LocalBufferContext(self.args) as scope: + scope.add_local_buffer(src) + return self.store_pointwise_nodes(dst, [copy]) + else: + assert dst.layout == src.layout, f"{dst=}, {src=}" + return "" + + +class CppTemplateCaller(ir.ChoiceCaller): + """ + CppTemplateCaller + + This class represents a caller for CPP template kernels. It is a subclass of ir.ChoiceCaller. + Attributes: + name (str): The name of the caller. + category (str): The category of the caller. + bmreq (CppBenchmarkRequest): The benchmark request for the caller. + template_buffer (ir.CppTemplateBuffer): The template buffer for the caller. + """ + + def __init__( + self, + name: str, + category: str, + input_nodes: List[ir.Buffer], + layout: ir.Layout, + make_kernel_render: Callable[ + [ + ir.CppTemplateBuffer, + bool, + Optional[List[ir.IRNode]], + ], + str, + ], + bmreq: CppBenchmarkRequest, + template: "CppTemplate", # type: ignore[name-defined] # noqa: F821 + info_kwargs: Optional[ + Dict[str, Union[ir.PrimitiveInfoType, List[ir.PrimitiveInfoType]]] + ] = None, + ): + super().__init__(name, input_nodes, layout) + self.category = category + self.make_kernel_render = make_kernel_render + self.bmreq = bmreq + self.template = template + self.info_kwargs = info_kwargs + + def precompile(self) -> None: + assert self.bmreq is not None + self.bmreq.precompile() + + def benchmark(self, *args, out) -> float: + assert self.bmreq is not None + return self.bmreq.benchmark(*args, output_tensor=out) + + def hash_key(self) -> str: + return "-".join( + [ + self.category, + self.bmreq.hash_key, + ] + ) + + def info_dict( + self, + ) -> Dict[str, Union[ir.PrimitiveInfoType, List[ir.PrimitiveInfoType]]]: + return {"backend": "CPP", "op_type": "unknown"} + + def output_node(self) -> ir.TensorBox: + return ir.TensorBox.create( + ir.CppTemplateBuffer( + layout=self.layout, + inputs=self.input_nodes, + make_kernel_render=self.make_kernel_render, + template=self.template, + choice=self, + ) + ) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e69bb3637f724936da1255c777fb85c96500f226 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_utils.py @@ -0,0 +1,916 @@ +# mypy: allow-untyped-defs +import contextlib +import copy +import functools +import math +import sys +from collections import namedtuple +from typing import Any, Callable, Dict, List, Optional, Set, Tuple +from unittest.mock import patch + +import sympy + +import torch +from torch._prims_common import is_integer_dtype +from torch.utils._sympy.symbol import symbol_is_type, SymT +from torch.utils._sympy.value_ranges import ValueRanges + +from .. import ir +from ..loop_body import LoopBody +from ..utils import IndentedBuffer, sympy_index_symbol_with_prefix, sympy_subs +from ..virtualized import ops, OpsValue, V +from .common import ( + CSEVariable, + deduce_output_dtype_by_name, + ExprPrinter, + Kernel, + KernelArgs, + OptimizationContext, +) + + +DTYPE_TO_CPP = { + torch.float32: "float", + torch.float64: "double", + torch.float16: "half", + torch.int64: "int64_t", + torch.int32: "int32_t", + torch.int16: "int16_t", + torch.int8: "int8_t", + torch.uint64: "uint64_t", + torch.uint32: "uint32_t", + torch.uint16: "uint16_t", + torch.uint8: "uint8_t", + torch.bool: "bool", + torch.bfloat16: "bfloat16", + torch.complex64: "c10::complex", + torch.float8_e4m3fn: "float8_e4m3fn", + torch.float8_e5m2: "float8_e5m2", +} + +DTYPE_TO_ATEN = { + torch.float32: "at::kFloat", + torch.float64: "at::kDouble", + torch.float16: "at::kHalf", + torch.int64: "at::kLong", + torch.int32: "at::kInt", + torch.int16: "at::kShort", + torch.int8: "at::kChar", + torch.uint64: "at::kUInt64", + torch.uint32: "at::kUInt32", + torch.uint16: "at::kUInt16", + torch.uint8: "at::kByte", + torch.uint32: "at::kUInt32", + torch.uint64: "at::kUInt64", + torch.bool: "at::kBool", + torch.bfloat16: "at::kBFloat16", + torch.complex32: "at::kComplexHalf", + torch.complex64: "at::kComplexFloat", + torch.complex128: "at::kComplexDouble", + torch.float8_e4m3fn: "at::kFloat8_e4m3fn", + torch.float8_e5m2: "at::kFloat8_e5m2", + torch.float8_e4m3fnuz: "at::kFloat8_e4m3fnuz", + torch.float8_e5m2fnuz: "at::kFloat8_e5m2fnuz", +} + +DEVICE_TO_ATEN = { + "cpu": "at::kCPU", + "cuda": "at::kCUDA", +} + +LAYOUT_TO_ATEN = { + torch.strided: "at::kStrided", + torch._mkldnn: "at::kMkldnn", # type: ignore[attr-defined] +} + +_IS_WINDOWS = sys.platform == "win32" + +INDEX_TYPE = "int64_t" + +GemmBlocking = namedtuple("GemmBlocking", ["block_m", "block_n", "block_k"]) + + +def get_promote_dtype(args): + return ( + functools.reduce( + torch.promote_types, # type: ignore[arg-type] + [n.dtype for n in args if isinstance(n, CppCSEVariable)], + ) + if all(n.dtype is not None for n in args if isinstance(n, CppCSEVariable)) + else None # not enough info to calculate the promote dtype + ) + + +def promote_args(new_args): + def promote_arg(arg, promote_type): + if ( + isinstance(arg, CppCSEVariable) + and arg.dtype + and promote_type + and arg.dtype != promote_type + ): + arg = ops.to_dtype(arg, promote_type) + arg = arg.value if isinstance(arg, OpsValue) else arg + arg.dtype = promote_type + return arg + + promote_type = get_promote_dtype(new_args) + promote_fn = functools.partial( + promote_arg, + promote_type=promote_type, + ) + if ( + all( + new_arg.dtype is not None + for new_arg in new_args + if isinstance(new_arg, CppCSEVariable) + ) + and promote_type + ): + new_args = list(map(promote_fn, new_args)) + return new_args + + +def get_opt_ctx(node: torch.fx.Node) -> OptimizationContext: + return node.meta.get(OptimizationContext.key, None) + + +def get_current_node_opt_ctx() -> OptimizationContext: + assert V.interpreter.current_node + return get_opt_ctx(V.interpreter.current_node) + + +def deduce_dtype_for_cpp_cse_variable(name, *args, **kwargs): + if ( + output_dtype := deduce_output_dtype_by_name( + name, + *args, + **kwargs, + ) + ) is not None: + return output_dtype + elif name == "masked": + # Leslie: perhaps we can also deduce the masked dtype by + # inputs' CppCseVariable like other. Let's check it if any + # unexpected failures. + assert ( + hasattr(V.interpreter, "current_node") + and V.interpreter.current_node.target.startswith("masked_subblock") + and get_current_node_opt_ctx() is not None + ) + return get_current_node_opt_ctx().dtype + else: + # deduce output dtype by inputs' dtype + assert all( + arg.dtype is not None for arg in args if isinstance(arg, CppCSEVariable) + ) + return functools.reduce( + torch.promote_types, # type: ignore[arg-type] + [arg.dtype for arg in args if isinstance(arg, CppCSEVariable)], + ) + + +class CppCSEVariable(CSEVariable): + def __init__(self, name, bounds: ValueRanges[Any]) -> None: + super().__init__(name, bounds) + self.is_vec = False + self.dtype: Optional[torch.dtype] = None + self.dependent_itervars: Set[sympy.Symbol] = set() + + def __repr__(self) -> str: + return ( + f"CppCSEVariable(name: {self.name}, bounds: {self.bounds}, is_vec: {self.is_vec}, dtype: {self.dtype}, " + f"dependent_itervars: {self.dependent_itervars})" + ) + + def update_on_args(self, name, args, kwargs): + if name == "load": + # args[2] is index + self._set_dependent_itervars(args[2]) + else: + # propagate relevant itervars and is_vec from args + self.dependent_itervars.update( + *[ + arg.dependent_itervars + for arg in args + if isinstance(arg, CppCSEVariable) + ] + ) + if name == "index_expr": + self._set_dependent_itervars(args[0]) + if any(arg.is_vec for arg in args if isinstance(arg, CppCSEVariable)): + self.is_vec = True + # NOTE [Deduce dtype of CppCSEVariable at runtime] + self.dtype = deduce_dtype_for_cpp_cse_variable(name, *args, **kwargs) + assert self.dtype is not None + + def _set_dependent_itervars(self, index: sympy.Expr): + """ + Set the relevant itervars for this variable based on the `index` expression. + This includes the itervars directly used in the `index` as well as relevant itervars + of other cse variables used in the `index`. + """ + for s in index.free_symbols: + if s in V.kernel.itervars: + self.dependent_itervars.add(s) # type: ignore[arg-type] + elif s.name in V.kernel.cse.varname_map: # type: ignore[attr-defined] + self.dependent_itervars.update( + V.kernel.cse.varname_map[s.name].dependent_itervars # type: ignore[attr-defined] + ) + + def depends_on(self, itervar: sympy.Symbol): + return itervar in self.dependent_itervars + + +class CppPrinter(ExprPrinter): + def _print_Integer(self, expr): + return ( + f"{int(expr)}LL" if sys.platform in ["darwin", "win32"] else f"{int(expr)}L" + ) + + def _print_Where(self, expr): + c = self.paren(self.doprint(expr.args[0])) + p = self.paren(self.doprint(expr.args[1])) + q = self.paren(self.doprint(expr.args[2])) + return f"{c} ? {p} : {q}" + + def _print_ModularIndexing(self, expr): + x, div, mod = expr.args + x = self.paren(self.doprint(x)) + if div != 1: + div = self.paren(self.doprint(div)) + if expr.is_integer: + x = f"c10::div_floor_integer(static_cast({x}), static_cast({div}))" + else: + x = f"c10::div_floor_floating(static_cast({x}), static_cast({div}))" + mod = self.paren(self.doprint(mod)) + return f"static_cast<{INDEX_TYPE}>({x}) % static_cast<{INDEX_TYPE}>({mod})" + + def _print_FloorDiv(self, expr): + x, div = expr.args + x = self.paren(self.doprint(x)) + div = self.paren(self.doprint(div)) + if expr.is_integer: + return f"c10::div_floor_integer(static_cast({x}), static_cast({div}))" + return f"c10::div_floor_floating(static_cast({x}), static_cast({div}))" + + def _print_floor(self, expr): + assert len(expr.args) == 1 + r = f"std::floor({self._print(expr.args[0])})" + return f"static_cast<{INDEX_TYPE}>({r})" if expr.is_integer else r + + def _print_FloorToInt(self, expr): + assert len(expr.args) == 1 + r = f"std::floor({self._print(expr.args[0])})" + return f"static_cast<{INDEX_TYPE}>({r})" if expr.is_integer else r + + def _print_TruncToInt(self, expr): + assert len(expr.args) == 1 + r = f"std::trunc({self._print(expr.args[0])})" + return f"static_cast<{INDEX_TYPE}>({r})" + + def _print_TruncToFloat(self, expr): + assert len(expr.args) == 1 + return f"std::trunc({self._print(expr.args[0])})" + + def _print_ToFloat(self, expr): + assert len(expr.args) == 1 + return f"static_cast({self._print(expr.args[0])})" + + # TODO: This is wrong if one of the inputs is negative. This is hard to + # tickle though, as the inputs are typically positive (and if we can prove + # they are positive, we will have used Mod instead, for which this codegen + # is right). + def _print_PythonMod(self, expr): + return " % ".join(map(self.paren, map(self._print, expr.args))) + + def _print_CMod(self, expr): + return " % ".join(map(self.paren, map(self._print, expr.args))) + + def _print_IntTrueDiv(self, expr): + lhs, rhs = expr.args + # TODO: This is only accurate up to 2**53 + return f"static_cast({self._print(lhs)}) / static_cast({self._print(rhs)})" + + # TODO: PowByNatural: we need to implement our own int-int pow. Do NOT + # use std::pow, that operates on floats + def _print_PowByNatural(self, expr): + raise NotImplementedError( + f"_print_PowByNatural not implemented for {type(self)}" + ) + + def _print_FloatTrueDiv(self, expr): + lhs, rhs = expr.args + return f"{self.paren(self._print(lhs))} / {self.paren(self._print(rhs))}" + + def _print_FloatPow(self, expr): + base, exp = expr.args + return f"std::pow({self._print(base)}, {self._print(exp)})" + + def _print_Pow(self, expr): + # Uses float constants to perform FP div + base, exp = expr.args + base = self._print(base) + + if exp == 0.5 or exp == -0.5: + return f"std::sqrt({base})" if exp == 0.5 else f"1.0/std::sqrt({base})" + if exp.is_integer: + exp = int(exp) + if exp > 0: + r = "*".join([self.paren(base)] * exp) + elif exp < 0: + r = "1.0/" + self.paren("*".join([self.paren(base)] * abs(exp))) + else: # exp == 0 + r = "1.0" + + return f"static_cast<{INDEX_TYPE}>({r})" if expr.is_integer else r + else: + # TODO: float vs double + return f"std::pow({base}, {float(exp)})" + + def _print_Rational(self, expr): + # Uses float constants to perform FP div + if expr.q == 1: + r = f"{expr.p}" + else: + r = f"{expr.p}.0/{expr.q}.0" + return f"static_cast<{INDEX_TYPE}>({r})" if expr.is_integer else r + + def _print_ceiling(self, expr): + assert len(expr.args) == 1 + r = f"std::ceil({self._print(expr.args[0])})" + return f"static_cast<{INDEX_TYPE}>({r})" if expr.is_integer else r + + def _print_CeilToInt(self, expr): + assert len(expr.args) == 1 + r = f"std::ceil({self._print(expr.args[0])})" + return f"static_cast<{INDEX_TYPE}>({r})" if expr.is_integer else r + + def _print_Min(self, expr): + args = [self._print(a) for a in expr.args] + if len(args) == 2: + return f"std::min(static_cast<{INDEX_TYPE}>({args[0]}), static_cast<{INDEX_TYPE}>({args[1]}))" + else: + # Initializer list overload + il = "{" + ", ".join(args) + "}" + return f"std::min({il})" + + def _print_Max(self, expr): + args = [self._print(a) for a in expr.args] + if len(args) == 2: + return f"std::max(static_cast<{INDEX_TYPE}>({args[0]}), static_cast<{INDEX_TYPE}>({args[1]}))" + else: + # Initializer list overload + il = "{" + ", ".join(args) + "}" + return f"std::max({il})" + + def _print_Abs(self, expr): + assert len(expr.args) == 1 + return f"std::abs({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_cos(self, expr): + assert len(expr.args) == 1 + return f"std::cos({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_cosh(self, expr): + assert len(expr.args) == 1 + return f"std::cosh({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_acos(self, expr): + assert len(expr.args) == 1 + return f"std::acos({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_sin(self, expr): + assert len(expr.args) == 1 + return f"std::sin({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_sinh(self, expr): + assert len(expr.args) == 1 + return f"std::sinh({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_asin(self, expr): + assert len(expr.args) == 1 + return f"std::asin({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_tan(self, expr): + assert len(expr.args) == 1 + return f"std::tan({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_tanh(self, expr): + assert len(expr.args) == 1 + return f"std::tanh({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_atan(self, expr): + assert len(expr.args) == 1 + return f"std::atan({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_sqrt(self, expr): + return f"std::sqrt({self._print(expr.args[0])})" + + def _print_RoundToInt(self, expr): + assert len(expr.args) == 1 + # TODO: dispatch to llrint depending on index type + return f"std::lrint({self._print(expr.args[0])})" + + def _print_RoundDecimal(self, expr): + assert len(expr.args) == 2 + number, ndigits = expr.args + if number.is_integer: + # ndigits < 0 should have been filtered by the sympy function + assert ndigits < 0 + raise ValueError( + f"For integer inputs, only non-negative ndigits are currently supported, but got {ndigits}." + ) + return f"static_cast(std::nearbyint(1e{ndigits} * {self.paren(self._print(number))}) * 1e{-ndigits})" + + def _print_BooleanTrue(self, expr): + return "true" + + def _print_BooleanFalse(self, expr): + return "false" + + +# A function to print, useful for printing sympy symbols. +cexpr = CppPrinter().doprint + + +def cexpr_index(index): + return f"static_cast<{INDEX_TYPE}>({cexpr(index)})" + + +def value_to_cpp(value, cpp_type): + if value == float("-inf"): + return f"-std::numeric_limits<{cpp_type}>::infinity()" + elif value == float("inf"): + return f"std::numeric_limits<{cpp_type}>::infinity()" + elif isinstance(value, bool): + return f"static_cast<{cpp_type}>({str(value).lower()})" + elif math.isnan(value): + return f"std::numeric_limits<{cpp_type}>::quiet_NaN()" + else: + return f"static_cast<{cpp_type}>({repr(value)})" + + +def rewrite_index_for_function( + localize_buffer_handler: "LocalizeBufferHandler", + index: sympy.Expr, + global_buf_name: str, +): + # Local buffer at the inner dimensions + snode = V.graph.scheduler.name_to_buf[global_buf_name].defining_op + local_buf = localize_buffer_handler.global_to_local[global_buf_name] + scheduler_nodes = snode.get_nodes() + _, (group, reduction_group) = max( + scheduler_nodes, key=lambda x: int(x.is_reduction()) + ).group + call_ranges = tuple(group) + tuple(reduction_group) + indices_to_keep = [ + f"x{len(call_ranges) - (idx + 1)}" + for idx in range(len(local_buf.get_layout().size)) + ] + sorted_symbols = sorted(index.free_symbols, key=lambda s: s.name) # type: ignore[attr-defined] + replacements = {} + for x in sorted_symbols: + if x.name.startswith("x") and x.name not in indices_to_keep: # type: ignore[attr-defined] + # Only keep index used by local buffer + replacements[x] = sympy.core.numbers.Zero() + index = sympy_subs(index, replacements) # type: ignore[arg-type] + return index + + +def rewrite_index_for_nodes( + localize_buffer_handler: "LocalizeBufferHandler", + index: sympy.Expr, + global_buf_name: str, +): + used_vars = {s for s in index.free_symbols if symbol_is_type(s, SymT.INDEX)} + index_vars = [] + local_buf = localize_buffer_handler.global_to_local[global_buf_name] + for i in range(len(local_buf.get_size())): + var = sympy_index_symbol_with_prefix(SymT.INDEX, i) + index_vars.append(var if var in used_vars else 0) + index = local_buf.layout.make_indexer()(index_vars) + return index + + +class LocalizeBufferHandler(V.WrapperHandler): # type: ignore[name-defined] + def __init__( + self, + inner, + global_to_local: Dict[str, ir.Buffer], + rewrite_index: Callable[["LocalizeBufferHandler", sympy.Expr, str], sympy.Expr], + ) -> None: + super().__init__(inner) + self.global_to_local = global_to_local + self.rewrite_index = rewrite_index + + def localize(self, name: str, index: sympy.Expr): + if self.global_to_local and name in self.global_to_local: + assert self.rewrite_index is not None + index = self.rewrite_index(self, index, name) + name = self.global_to_local[name].get_name() + return name, index + + def load(self, name: str, index: sympy.Expr): + return self._inner.load(*self.localize(name, index)) + + def store(self, name, index, value, mode=None): + local_buffer_name, local_buffer_index = self.localize(name, index) + res = self._inner.store(local_buffer_name, local_buffer_index, value, mode) + if ( + self.global_to_local + and name in self.global_to_local + and isinstance(V.kernel, Kernel) + ): + # Remove name of local buffer from Kernel.store_buffer_names + # local_buffer_name is added to Kernel.store_buffer_names in Kernel.CSEProxy.store. + V.kernel.store_buffer_names.discard(local_buffer_name) + return res + + def store_reduction(self, name, index, value): + return self._inner.store_reduction(*self.localize(name, index), value) + + +class LocalBufferContext: + """ + This class creates a context that helps to generate code involving Inductor IR with + function local buffers. These buffers are constructed during the codegen process and + are used to store intermediate results such as local accumulators. We do not want to + add them to `V.graph` since they are not global and we do not want to add them as + function arguments either. So we patch the codegen processes under this scope to support + these buffers without exposure to the outside world. + """ + + def __init__(self, kernel_args: KernelArgs) -> None: + self.kernel_args = kernel_args + self.exit_stack = contextlib.ExitStack() + # map local buffer name to local buffer + self.local_buffers: Dict[str, ir.Buffer] = {} + # map global buffer name to global buffer + self.global_buffers: Dict[str, ir.Buffer] = {} + # map global buffer name to local buffer + self.global_to_local: Dict[str, ir.Buffer] = {} + + def __enter__(self): + self.exit_stack.__enter__() + original_get_dtype = V.graph.get_dtype + + def get_dtype(name): + if name in self.local_buffers: + return self.local_buffers[name].get_dtype() + return original_get_dtype(name) + + self.exit_stack.enter_context(patch.object(V.graph, "get_dtype", get_dtype)) + + original_input = self.kernel_args.input + + def input(name): + if name in self.local_buffers: + return name + return original_input(name) + + self.exit_stack.enter_context(patch.object(self.kernel_args, "input", input)) + + original_output = self.kernel_args.output + + def output(name): + if name in self.local_buffers: + return name + return original_output(name) + + self.exit_stack.enter_context(patch.object(self.kernel_args, "output", output)) + + # Set current LocalBufferContext into V + self.exit_stack.enter_context(V.set_local_buffer_context(self)) + + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + self.local_buffers.clear() + self.exit_stack.__exit__(exc_type, exc_val, exc_tb) + + def add_local_buffer( + self, local_buffer: ir.Buffer, global_buffers: Optional[List[ir.Buffer]] = None + ): + assert local_buffer.get_name() not in self.local_buffers + self.local_buffers[local_buffer.get_name()] = local_buffer + if global_buffers: + for global_buffer in global_buffers: + global_buffer_name = global_buffer.get_name() + assert ( + global_buffer_name not in self.global_buffers + and global_buffer_name not in self.global_to_local + ) + self.global_buffers[global_buffer_name] = global_buffer + self.global_to_local[global_buffer_name] = local_buffer + V.graph.removed_buffers.add(global_buffer_name) + + def localize_function( + self, + fn: Callable[..., Any], + rewrite_index: Callable[ + ["LocalizeBufferHandler", sympy.Expr, str], sympy.Expr + ] = rewrite_index_for_function, + ): + def inner(*args, **kwargs): + with V.set_ops_handler( + LocalizeBufferHandler( + V.get_ops_handler(), + global_to_local=self.global_to_local, + rewrite_index=rewrite_index, + ) + ): + return fn(*args, **kwargs) + + return inner + + def localize_nodes( + self, + nodes: List[ir.IRNode], + rewrite_index: Callable[ + ["LocalizeBufferHandler", sympy.Expr, str], sympy.Expr + ] = rewrite_index_for_nodes, + ) -> List[ir.IRNode]: + """ + Given `local_buf` and `global_buf` registered in current `LocalBufferContext` + though the method of `add_local_buffer`, localizes the `global_buf` to `local_buf` + for the given `nodes` and returns a new list of IR nodes that work on `local_buf` + instead of `global_buf`, i.e., all the loads and stores are redirected to + `local_buf`. This helps the fused loops to work on smaller-sized local buffers + for better data locality. + + The the data access of `local_buf` is assumed to be contiguous with the + same order as the `global_buf`. + """ + assert len(nodes) > 0 + + def wrap_inner_fn_for_node(node: ir.IRNode): + loops = node.data if isinstance(node, ir.ComputedBuffer) else node + assert isinstance(loops, ir.Loops) + new_loops = copy.copy(loops) + if isinstance(node, ir.ComputedBuffer): + new_node = ir.ComputedBuffer( + node.get_name(), node.get_layout(), new_loops + ) + else: + new_node = new_loops # type: ignore[assignment] + + new_loops.inner_fn = self.localize_function( + new_loops.inner_fn, + rewrite_index, + ) + return new_node + + return [wrap_inner_fn_for_node(node) for node in nodes] + + +def unify_mask_base_type( + buffer: IndentedBuffer, + vars: Tuple[CSEVariable, ...], + dtype=torch.float, +): + """ + Given list of cse variables, + Cast each to new mask base dtype and return casted cse variable. + """ + new_vars = ( + V.kernel.cse.generate( + buffer, + f"{V.kernel._get_mask_cast(var, dtype)}", + ) + for var in vars + ) + return new_vars + + +def codegen_rand(offset, code, rand_function, dst_dtype=torch.float32): + assert is_integer_dtype(offset.dtype) + code.writeline("[&]()") + with code.indent(): + code.writeline( + f"{DTYPE_TO_CPP[offset.dtype]} offset[{V.kernel.tiling_factor}];" + ) + code.writeline(f"{DTYPE_TO_CPP[dst_dtype]} result[{V.kernel.tiling_factor}];") + code.writeline(f"{offset}.store(offset);") + code.writeline( + f"for( {DTYPE_TO_CPP[offset.dtype]} offset_idx = 0; offset_idx < {V.kernel.tiling_factor}; offset_idx++ )" + ) + with code.indent(): + code.writeline(rand_function) + num_vectors = V.kernel._get_num_vectors(dtype=dst_dtype) + if num_vectors == 1: + code.writeline( + f"return at::vec::Vectorized<{DTYPE_TO_CPP[dst_dtype]}>::loadu(result);" + ) + else: + code.writeline( + f"return at::vec::VectorizedN<{DTYPE_TO_CPP[dst_dtype]}, {num_vectors}>::loadu(result);" + ) + code.writeline("()") + return code + + +def get_gemm_template_output_and_compute_dtype(input_dtype): + if input_dtype == torch.uint8: + return (torch.int32, torch.int32) + else: + return (torch.float32, torch.float32) + + +def create_epilogue_with_attr(input_buffer, attr, **kwargs): + input_loader = input_buffer.make_loader() + dtype = input_buffer.get_dtype() + if attr == "relu": + + def inner_fn(index): + input = input_loader(index) + zero = ops.constant(0, dtype) + return ops.maximum(input, zero) + + elif attr == "gelu": + assert "algorithm" in kwargs + if kwargs["algorithm"] == "none": + + def inner_fn(index): + input = input_loader(index) + if dtype != torch.float: + input = ops.to_dtype(input, torch.float) + half = ops.constant(0.5, torch.float) + one = ops.constant(1.0, torch.float) + const = ops.constant(0.7071067811865476, torch.float) + result = input * half * (ops.erf(input * const) + one) + if dtype != torch.float: + result = ops.to_dtype(result, dtype) + return result + + else: + assert kwargs["algorithm"] == "tanh" + + def inner_fn(index): + input = input_loader(index) + if dtype != torch.float: + input = ops.to_dtype(input, torch.float) + half = ops.constant(0.5, torch.float) + one = ops.constant(1.0, torch.float) + const1 = ops.constant(0.7978845608028654, torch.float) + const2 = ops.constant(0.044715, torch.float) + result = ( + half + * input + * ( + one + + ops.tanh(const1 * (input + const2 * input * input * input)) + ) + ) + if dtype != torch.float: + result = ops.to_dtype(result, dtype) + return result + + elif attr == "swish": + + def inner_fn(index): + input = input_loader(index) + result = input * ops.sigmoid(input) + return result + + elif attr == "sigmoid": + + def inner_fn(index): + return ops.sigmoid(input_loader(index)) + + elif attr == "tanh": + + def inner_fn(index): + return ops.tanh(input_loader(index)) + + elif attr == "hardswish" or attr == "hardsigmoid": + + def hardsigmoid_float(input): + zero = ops.constant(0, torch.float) + six = ops.constant(6, torch.float) + three = ops.constant(3, torch.float) + one_over_six = ops.constant(0.16666666666666666, torch.float) + max = ops.maximum(input + three, zero) + min = ops.minimum(max, six) + return min * one_over_six + + def inner_fn(index): + input = input_loader(index) + if dtype != torch.float: + input = ops.to_dtype(input, torch.float) + result = hardsigmoid_float(input) + if attr == "hardswish": + result = input * result + if dtype != torch.float: + result = ops.to_dtype(result, dtype) + return result + + elif attr == "leaky_relu": + assert "scalars" in kwargs + assert len(kwargs["scalars"]) == 1 + negative_slope = kwargs["scalars"][0] + + def inner_fn(index): + input = input_loader(index) + if dtype != torch.float: + input = ops.to_dtype(input, torch.float) + zero = ops.constant(0, torch.float) + result = ops.where( + input > zero, input, input * ops.constant(negative_slope, torch.float) + ) + if dtype != torch.float: + result = ops.to_dtype(result, dtype) + return result + + elif attr == "hardtanh": + assert "scalars" in kwargs + assert len(kwargs["scalars"]) == 2 + min_value = kwargs["scalars"][0] + max_value = kwargs["scalars"][1] + + def inner_fn(index): + input = input_loader(index) + if dtype != torch.float: + input = ops.to_dtype(input, torch.float) + result = ops.minimum( + ops.maximum(input, ops.constant(min_value, torch.float)), + ops.constant(max_value, torch.float), + ) + if dtype != torch.float: + result = ops.to_dtype(result, dtype) + return result + + elif attr in ["add", "sub", "mul"]: + assert "other" in kwargs + other = kwargs["other"] + num_input_dims = len(input_buffer.get_size()) + num_other_dims = len(other.get_size()) + dims_diff = num_input_dims - num_other_dims + other_loader = other.make_loader() + + def inner_fn(index): + op = getattr(ops, attr) + if dims_diff != 0: + return op(input_loader(index), other_loader(index[dims_diff:])) + else: + return op(input_loader(index), other_loader(index)) + + elif attr == "bias_add": + assert "other" in kwargs + assert "beta" in kwargs + assert "dtype" in kwargs + beta = kwargs["beta"] + other = kwargs["other"] + dtype = kwargs["dtype"] + bias_loader = other.make_loader() + + def inner_fn(index): + bias = bias_loader(index) + input = input_loader(index) + if beta != 1: + result = ops.constant(beta, torch.float) * bias + input + else: + result = bias + input + return result + + else: + raise ValueError(f"Unsupported epilogue attribute: {attr}") + return ir.Pointwise( + device=input_buffer.get_device(), + dtype=dtype, + inner_fn=inner_fn, + ranges=input_buffer.get_size(), + ) + + +def _get_loop_body(fn_list): + if all(isinstance(fn, LoopBody) for fn in fn_list): + loop_bodies = fn_list + else: + if hasattr(fn_list[0], "original_fn"): + # For the case of local buffer, we wrap the fn with localize_function + assert all(hasattr(fn, "original_fn") for fn in fn_list) + assert all( + isinstance(fn.original_fn.args[0]._body, LoopBody) for fn in fn_list + ) + loop_bodies = [fn.original_fn.args[0]._body for fn in fn_list] + else: + assert all(isinstance(fn, functools.partial) for fn in fn_list) + assert all(isinstance(fn.args[0]._body, LoopBody) for fn in fn_list) + loop_bodies = [fn.args[0]._body for fn in fn_list] + assert loop_bodies is not None + return loop_bodies + + +def _get_dtype_from_loopbodies(loop_bodies): + dtypes = set() + for loop_body in loop_bodies: + graphs = [loop_body.root_block.graph] + [ + body.graph for body in list(loop_body.subblocks.values()) + ] + for graph in graphs: + for node in graph.nodes: + if node.op != "call_method": + continue + dtypes.add(node.meta[OptimizationContext.key].dtype) + return dtypes diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_wrapper_cpu.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_wrapper_cpu.py new file mode 100644 index 0000000000000000000000000000000000000000..290787b99b9930c88fb9bd6d8316c5fcf60ea9d4 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_wrapper_cpu.py @@ -0,0 +1,2595 @@ +# mypy: allow-untyped-defs +import functools +import math +import os +import sys +from itertools import count +from typing import Dict, List, Optional, Tuple + +import sympy +from sympy import Expr + +import torch +import torch._inductor.async_compile # noqa: F401 required to warm up AsyncCompile pools +import torch._ops +from torch._inductor.codegen.debug_utils import IntermediateValueDebuggingLevel +from torch.fx.experimental.symbolic_shapes import ConvertIntKey, DivideByKey, SymTypes + +from .. import config, ir +from ..utils import _align, ALIGN_BYTES, cache_on_self, sympy_product +from ..virtualized import V +from .aoti_hipify_utils import maybe_hipify_code_wrapper +from .common import IndentedBuffer +from .cpp_utils import ( + cexpr, + DEVICE_TO_ATEN, + DTYPE_TO_ATEN, + DTYPE_TO_CPP, + LAYOUT_TO_ATEN, +) +from .wrapper import EnterSubgraphLine, ExitSubgraphLine, WrapperCodeGen + + +class CppWrapperCpu(WrapperCodeGen): + """ + Generates cpp wrapper for running on CPU and calls cpp kernels + """ + + def __init__(self): + if not hasattr(self, "device"): + self.device = "cpu" + super().__init__() + self.declare = "auto " + self.declare_maybe_reference = "decltype(auto) " + self.ending = ";" + self.open_bracket = "{" + self.closed_bracket = "}" + self.comment = "//" + self.namespace = "at::" + self.none_str = "nullptr" if config.abi_compatible else "at::Tensor()" + self.extern_call_ops = set() + self.size = "sizes()" + self.stride = "strides()" + self.cuda = False + self.supports_intermediate_hooks = False + self.outputs_need_copy = set() + self.kernel_callsite_id = count() + self.var_array_id = ( + count() + ) # for different types of local array variable declarations + self.declared_var_array_vars = set() + self.int_array_id = count() # for int array local variable declarations + self.declared_int_array_vars = set() + self.tmp_tensor_id = count() # for tmp tensor local variable declarations + self.arg_var_id = count() + self.used_cached_devices = set() + self.used_cached_dtypes = set() + self.used_cached_layouts = set() + self.cached_output_id = count() + self.scalar_to_tensor_id = count() + self.custom_op_wrapper_loaded = False + self.expr_printer = cexpr + + def generate_kernel_call( + self, + kernel_name: str, + call_args, + grid=None, + device_index=None, + cuda=True, + triton=True, + arg_types=None, + raw_args=None, + grid_fn: str = "grid", + triton_meta=None, + autotune_configs=None, + grid_extra_kwargs="", + ): + """ + Generates kernel call code. + + cuda: Defines whether the backend is GPU. Otherwise the backend is CPU. + + triton: Defines whether the GPU backend uses Triton for codegen. + Otherwise it uses the CUDA language for codegen. + Only valid when cuda == True. + """ + if cuda: + return super().generate_kernel_call( + kernel_name, + call_args, + grid, + device_index, + cuda, + triton, + arg_types, + raw_args, + grid_fn, + triton_meta, + autotune_configs, + grid_extra_kwargs, + ) + else: + if config.abi_compatible: + assert arg_types is not None and len(call_args) == len( + arg_types + ), "Mismatch call_args and arg_types in generate_kernel_call" + new_args = [] + for idx, arg in enumerate(call_args): + if "*" in arg_types[idx]: + var_name = f"var_{next(self.arg_var_id)}" + self.writeline( + f"auto* {var_name} = get_data_ptr_wrapper({arg});" + ) + new_args.append(f"({arg_types[idx]})({var_name})") + else: + # arg is a scalar + new_args.append(arg) + self.writeline(self.wrap_kernel_call(kernel_name, new_args)) + else: + self.writeline(self.wrap_kernel_call(kernel_name, call_args)) + + def write_constant(self, name, hashed): + # include a hash so our code cache gives different constants different files + self.header.writeline(f"// {name} {hashed}") + + def write_header(self): + if V.graph.is_const_graph: + # We do not write header for constant graph, it will be written by main module. + return + + if V.graph.aot_mode: + for header_cpp_file in ("interface.cpp", "implementation.cpp"): + with open( + os.path.join( + os.path.dirname(__file__), "aoti_runtime", header_cpp_file + ) + ) as f: + self.header.splice(f.read()) + else: + self.header.splice( + """ + import torch + from torch._inductor.codecache import CppWrapperCodeCache + + cpp_wrapper_src = ( + ''' + """ + ) + + if config.abi_compatible: + self.header.splice( + f"#include " + ) + self.header.splice( + """ + #include + #include + #include + """ + ) + if V.graph.aot_mode: + self.header.splice( + """ + #include + """ + ) + else: + self.header.splice( + """ + #include + #include + #include + #include + #include + #include + #include + #include + #include + + #define reinterpret_tensor torch::inductor::_reinterpret_tensor + #define alloc_from_pool torch::inductor::_alloc_from_pool + """ + ) + enable_kernel_profile = config.cpp.enable_kernel_profile and sys.platform in [ + "linux", + "win32", + ] + if config.profiler_mark_wrapper_call or enable_kernel_profile: + self.header.splice("#include ") + + self.header.splice("typedef at::Half half;") + self.header.splice("typedef at::BFloat16 bfloat16;") + self.header.splice("#include ") + + if not V.graph.aot_mode: + self.header.splice( + """ + #include + + namespace py = pybind11; + using namespace torch::aot_inductor; + + class RAIIPyObject { + public: + RAIIPyObject() : obj_(nullptr) {} + RAIIPyObject(PyObject* obj) : obj_(obj) {} + ~RAIIPyObject() { + Py_XDECREF(obj_); + } + RAIIPyObject& operator=(const RAIIPyObject& other) { + if (this != &other) { + Py_XDECREF(obj_); + obj_ = other.obj_; + Py_XINCREF(obj_); + } + return *this; + } + operator PyObject*() { + return obj_; + } + PyObject* get() { + return obj_; + } + private: + PyObject* obj_; + }; + """ + ) + + # Round up to the nearest multiple of ALIGN_BYTES + # ALIGN_BYTES must be a power of 2 + self.header.splice( + f""" + [[maybe_unused]] static int64_t align(int64_t nbytes) {{ + return (nbytes + {ALIGN_BYTES} - 1) & -{ALIGN_BYTES}; + }} + """ + ) + + @functools.lru_cache(None) # noqa: B019 + def include_extra_header(self, header: str): + # This is needed for cpp to python dtype conversion + self.header.splice(f"#include <{header}>") + + def mark_output_type(self): + # mark output type to unwrap tensor back to python scalar + from ..ir import ShapeAsConstantBuffer + + output_is_tensor = {} + for idx, x in enumerate(V.graph.graph_outputs): + if isinstance(x, ShapeAsConstantBuffer): + output_is_tensor[idx] = False + else: + output_is_tensor[idx] = True + + self.output_is_tensor = output_is_tensor + + def write_prefix(self): + if V.graph.is_const_graph: + # We do not write prefix for constant graph, it will be written by main module. + return + + if V.graph.aot_mode: + self.prefix.writeline("namespace torch {") + self.prefix.writeline("namespace aot_inductor {") + + def write_input_output_info( + self, + info_kind: str, + idx: int, + name: str, + ): + self.prefix.writeline(f"""{info_kind}[{idx}].name = "{name}";""") + + @staticmethod + def get_input_cpp_type(input): + assert config.use_minimal_arrayref_interface + + if isinstance(input, sympy.Expr): + from ..graph import may_get_constant_buffer_dtype + + dtype = may_get_constant_buffer_dtype(input) + assert dtype is not None, f"Failed to get the dtype of sympy.Expr: {input}" + return DTYPE_TO_CPP[dtype] + return f"ArrayRefTensor<{DTYPE_TO_CPP[input.get_dtype()]}>" + + def generate_input_output_runtime_checks(self): + # In debug_compile mode, we generate checks to ensure the dtype/shape/stride of each + # real input/output tensor match ones provided at compile time via sample + # input/output. + def gen_check(handle_kind, idx, name, tensor): + self.prefix.writeline(f"auto {name} = {handle_kind}[{idx}];") + self.codegen_tensor_dtype_var_decl(self.prefix, name) + expected_dtype_name = DTYPE_TO_ATEN[tensor.dtype] + dtype_str = str(tensor.dtype).split(".")[-1] + self.prefix.splice( + f""" + int32_t {name}_expected_dtype = aoti_torch_dtype_{dtype_str}(); + if ({name}_expected_dtype != {name}_dtype) {{ + std::stringstream ss; + ss << "{handle_kind}[{idx}]: unmatched dtype, " + << "expected: " << {name}_expected_dtype << "({expected_dtype_name}), " + << "but got: " << {name}_dtype << "\\n"; + throw std::runtime_error(ss.str()); + }} + """ + ) + self.codegen_input_size_var_decl(self.prefix, name) + for dim_idx, d in enumerate(tensor.get_size()): + if isinstance(d, (int, sympy.Integer)): + self.prefix.splice( + f""" + if ({d} != {name}_size[{dim_idx}]) {{ + std::stringstream ss; + ss << "{handle_kind}[{idx}]: unmatched dim value at {dim_idx}, " + << "expected: {d}, " << "but got: " << {name}_size[{dim_idx}] + << "\\n"; + throw std::runtime_error(ss.str()); + }} + """ + ) + else: + from torch.utils._sympy.value_ranges import bound_sympy + + sym_range = bound_sympy(d, V.graph.sizevars.shape_env.var_to_range) + if not math.isinf(sym_range.lower): + self.prefix.splice( + f""" + if ({name}_size[{dim_idx}] < {sym_range.lower}) {{ + std::stringstream ss; + ss << "{handle_kind}[{idx}]: dim value is too small at {dim_idx}, " + << "expected it to be >= {sym_range.lower}, " << "but got: " + << {name}_size[{dim_idx}] << "\\n"; + throw std::runtime_error(ss.str()); + }} + """ + ) + if not math.isinf(sym_range.upper): + self.prefix.splice( + f""" + if ({name}_size[{dim_idx}] > {sym_range.upper}) {{ + std::stringstream ss; + ss << "{handle_kind}[{idx}]: dim value is too large at {dim_idx}, " + << "expected to be <= {sym_range.upper}, " << "but got: " + << {name}_size[{dim_idx}] << "\\n"; + throw std::runtime_error(ss.str()); + }} + """ + ) + + self.codegen_input_stride_var_decl(self.prefix, name) + for stride_idx, s in enumerate(tensor.get_stride()): + if not isinstance(s, (int, sympy.Integer)): + continue + self.prefix.splice( + f""" + if ({s} != {name}_stride[{stride_idx}]) {{ + std::stringstream ss; + ss << "{handle_kind}[{idx}]: unmatched stride value at {stride_idx}, " + << "expected: {s}, " << "but got: " << {name}_stride[{stride_idx}] + << "\\n"; + throw std::runtime_error(ss.str()); + }} + """ + ) + + # force noinline to avoid any potential compilation slowdown due to aggressive + # inline done by the host compiler + self.prefix.splice( + """ + AOTI_NOINLINE static void __check_inputs_outputs( + AtenTensorHandle* input_handles, + AtenTensorHandle* output_handles) { + """ + ) + with self.prefix.indent(): + for idx, (name, tensor) in enumerate(V.graph.graph_inputs.items()): + gen_check("input_handles", idx, name, tensor) + self.prefix.writeline("}") + + def write_wrapper_decl(self): + inputs_len = len(V.graph.graph_inputs.keys()) + if V.graph.aot_mode: + if config.use_minimal_arrayref_interface and not V.graph.is_const_graph: + input_cpp_types = ", ".join( + f"{CppWrapperCpu.get_input_cpp_type(x)}" + for x in V.graph.graph_inputs.values() + ) + output_arrayref_types = ", ".join( + f"ArrayRefTensor<{DTYPE_TO_CPP[x.get_dtype()]}>" + for x in V.graph.graph_outputs + ) + + self.prefix.splice( + f""" + using AOTInductorModelInputs = std::tuple<{input_cpp_types}>; + using AOTInductorModelOutputs = std::tuple<{output_arrayref_types}>; + """ + ) + + if V.graph.const_module: + self.header.splice(V.graph.const_module.wrapper_code.header) + self.prefix.splice(V.graph.const_code) + + if V.graph.is_const_graph: + self.prefix.splice( + """ + void AOTInductorModel::_const_run_impl( + std::vector& output_handles, + DeviceStreamType stream, + AOTIProxyExecutorHandle proxy_executor + ) { + """ + ) + else: + if not config.aot_inductor.use_runtime_constant_folding: + # If we do not split the constant graph, we'll just create + # an empty implementation when wrapping the main module. + self.prefix.splice( + """ + void AOTInductorModel::_const_run_impl( + std::vector& output_handles, + DeviceStreamType stream, + AOTIProxyExecutorHandle proxy_executor + ) {} + + """ + ) + + run_impl_proto = """ + void AOTInductorModel::run_impl( + AtenTensorHandle* + input_handles, // array of input AtenTensorHandle; handles + // are stolen; the array itself is borrowed + AtenTensorHandle* + output_handles, // array for writing output AtenTensorHandle; handles + // will be stolen by the caller; the array itself is + // borrowed + DeviceStreamType stream, + AOTIProxyExecutorHandle proxy_executor + ) { + """ + # Since we are removing non-abi-compatible mode, let's generate + # runtime checks only for abi_compatible mode to avoid extra branches. + if config.aot_inductor.debug_compile and config.abi_compatible: + self.generate_input_output_runtime_checks() + run_impl_proto += """ + __check_inputs_outputs(input_handles, output_handles); + """ + if config.use_minimal_arrayref_interface: + self.prefix.splice( + """ + template <> + AOTInductorModelOutputs AOTInductorModel::run_impl_minimal_arrayref_interface< + AOTInductorModelInputs, AOTInductorModelOutputs>( + const AOTInductorModelInputs& inputs, + DeviceStreamType stream, + AOTIProxyExecutorHandle proxy_executor + ) { + """ + ) + self.suffix.splice(run_impl_proto) + self.suffix.splice( + """ + AOTInductorModelInputs inputs; + convert_handles_to_inputs(input_handles, inputs); + auto outputs = run_impl_minimal_arrayref_interface( + inputs, stream, proxy_executor); + // NOTE: outputs is full of ArrayRef to thread_local storage. If in the future we need this + // interface to perform well for a DSO using the minimal arrayref interface, all we need + // to do is provide ThreadLocalCachedTensor for each one! + convert_outputs_to_handles(outputs, output_handles); + } + """ + ) + + self.suffix.splice( + """ + extern "C" AOTIRuntimeError AOTInductorModelRunMinimalArrayrefInterface( + AOTInductorModelHandle model_handle, + const AOTInductorModelInputs& inputs, + AOTInductorModelOutputs& outputs) { + auto model = reinterpret_cast(model_handle); + CONVERT_EXCEPTION_TO_ERROR_CODE({ + outputs = model->run_impl_minimal_arrayref_interface( + inputs, + (torch::aot_inductor::DeviceStreamType)nullptr, + nullptr); + }) + } + """ + ) + else: + self.prefix.splice(run_impl_proto) + else: + # cpp entry function for JIT with cpp wrapper + self.prefix.splice( + """ + void inductor_entry_impl( + AtenTensorHandle* + input_handles, // array of input AtenTensorHandle; handles + // are stolen; the array itself is borrowed + AtenTensorHandle* + output_handles // array for writing output AtenTensorHandle; handles + // will be stolen by the caller; the array itself is + // borrowed) + ) { + """ + ) + with self.prefix.indent(): + # assign inputs and outputs in both cases so the later codegen can be simplified + if not config.use_minimal_arrayref_interface: + if not V.graph.is_const_graph: + if V.graph.aot_mode: + num_args = len(V.graph.graph_inputs) + else: + # Weights are promoted in the JIT mode + num_args = len(V.graph.graph_inputs) + len(V.graph.constants) + # release GIL to support multiple instances inference (in different threads of the same process) + self.prefix.splice("py::gil_scoped_release release;") + + if config.abi_compatible: + self.prefix.splice( + f""" + auto inputs = steal_from_raw_handles_to_raii_handles(input_handles, {num_args}); + """ + ) + else: + # This looks dumb, but can avoid creating two versions of code in the AOTInductor runtime. + self.prefix.splice( + f""" + auto inputs = alloc_tensors_by_stealing_from_handles(input_handles, {num_args}); + """ + ) + + if inputs_len != 0: + for idx, input_key in enumerate(V.graph.graph_inputs.keys()): + if config.use_minimal_arrayref_interface: + self.prefix.writeline( + f"auto {input_key} = std::get<{idx}>(inputs);" + ) + continue + # unwrap input tensor back to scalar + if isinstance(V.graph.graph_inputs[input_key], sympy.Expr): + from ..graph import may_get_constant_buffer_dtype + + dtype = may_get_constant_buffer_dtype( + V.graph.graph_inputs[input_key] # type: ignore[arg-type] + ) + assert ( + dtype is not None + ), "Fails to get the dtype of the sympy.Expr" + cpp_dtype = DTYPE_TO_CPP[dtype] + if config.abi_compatible: + self.codegen_tensor_item( + dtype, f"inputs[{idx}]", input_key, self.prefix + ) + else: + self.prefix.writeline( + f"{cpp_dtype} {input_key} = inputs[{idx}].item<{cpp_dtype}>();" + ) + else: + self.prefix.writeline( + f"auto {input_key} = std::move(inputs[{idx}]);" + ) + + assert all( + isinstance(v, torch.Tensor) for v in list(V.graph.constants.values()) + ), "Expect all constants to be Tensor" + for idx, constants_key in enumerate(V.graph.constants.keys()): + if V.graph.aot_mode: + # Weights are stored in constants_ and owned by RAIIAtenTensorHandle there. + # Don't call std::move here because it will cause constants_ to lose the ownership. + if config.abi_compatible: + self.prefix.writeline( + f"""auto {constants_key} = constants_->at({idx});""" + ) + else: + self.prefix.writeline( + f"auto {constants_key} = *tensor_handle_to_tensor_pointer(" + + f"""constants_->at({idx}));""" + ) + else: + # Append constants as inputs to the graph + constants_idx = inputs_len + idx + if config.abi_compatible: + self.prefix.writeline( + f"auto {constants_key} = std::move(inputs[{constants_idx}]);" + ) + else: + self.prefix.writeline( + f"auto {constants_key} = inputs[{constants_idx}];" + ) + + self.codegen_inputs(self.prefix, V.graph.graph_inputs) + + if V.graph.aot_mode: + if not V.graph.is_const_graph: + if config.use_minimal_arrayref_interface: + # TODO: input shape checking for regular tensor interface as well? + self.codegen_input_numel_asserts() + else: + self.prefix.writeline("inputs.clear();") + self.prefix.writeline( + "auto& kernels = static_cast(*this->kernels_.get());" + ) + + def codegen_input_numel_asserts(self): + for name, buf in V.graph.graph_inputs.items(): + if isinstance(buf, sympy.Expr): + continue + + # comparing strides for 0 size tensor is tricky. Ignore them for now. + if sympy_product(buf.get_size()) == 0: + continue + numel = buf.get_numel() + self.prefix.writeline(f"assert_numel({name}, {numel});") + + def codegen_tensor_dtype_var_decl(self, code: IndentedBuffer, name): + if config.abi_compatible: + code.writeline(f"int32_t {name}_dtype;") + code.writeline( + "AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_get_dtype" + f"({name}, &{name}_dtype));" + ) + else: + # Note that we don't have a corresponding class method from + # the WrapperCodeGen since this method is used for asserting AOTI + # cpp wrapper code. + code.writeline(f"auto {name}_dtype = {name}.dtype();") + + def codegen_input_size_var_decl(self, code: IndentedBuffer, name): + if config.abi_compatible: + code.writeline(f"int64_t* {name}_size;") + code.writeline( + f"AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_get_sizes({name}, &{name}_size));" + ) + else: + super().codegen_input_size_var_decl(code, name) + + def codegen_input_stride_var_decl(self, code: IndentedBuffer, name): + if config.abi_compatible: + code.writeline(f"int64_t* {name}_stride;") + code.writeline( + f"AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_get_strides({name}, &{name}_stride));" + ) + else: + super().codegen_input_stride_var_decl(code, name) + + def codegen_model_kernels(self): + self.prefix.writeline("namespace {") + self.prefix.writeline( + "class AOTInductorModelKernels : public AOTInductorModelKernelsBase {" + ) + self.prefix.writeline(" public:") + declare_kernel = set(self.src_to_kernel.values()) + declare_kernel.update( + entry[0] for entry in self.user_defined_kernel_cache.values() + ) + if V.graph.const_module: + declare_kernel.update( + V.graph.const_module.wrapper_code.src_to_kernel.values() + ) + for kernel in sorted(declare_kernel): + self.prefix.writeline( + maybe_hipify_code_wrapper(f" CUfunction {kernel}{{nullptr}};") + ) + self.prefix.writeline("};") + self.prefix.writeline("} // namespace") + + def codegen_model_constructor(self): + """ + // Generated code example + AOTInductorModel::AOTInductorModel() + : AOTInductorModelBase(4, 1) { + inputs_info_[0].name = "input0"; + inputs_info_[0].dtype = "torch.float16"; + ... + constants_info_[0].name = "L__self___weight"; + constants_info_[0].dtype = at::kFloat; + constants_info_[0].offset = 0; + constants_info_[0].data_size = 8192; + constants_info_[0].shape = {64, 32}; + constants_info_[0].stride = {32, 1}; + ... + outputs_info_[0].name = "output0"; + outputs_info_[0].dtype = "torch.float16"; + } + """ + + num_inputs = len(V.graph.graph_inputs) + num_outputs = len(V.graph.graph_outputs) + num_constants = len(V.graph.constants) + self.prefix.splice( + f""" + AOTInductorModel::AOTInductorModel(std::shared_ptr constants_map, + std::shared_ptr> constants_array, + const std::string& device_str, + std::optional cubin_dir) + : AOTInductorModelBase({num_inputs}, {num_outputs}, {num_constants}, device_str, cubin_dir) {{ + """ + ) + + with self.prefix.indent(): + for idx, (name, inp) in enumerate(V.graph.graph_inputs.items()): + assert not isinstance( + inp, sympy.Expr + ), f"input {name=} cannot be symbolic" + self.write_input_output_info("inputs_info_", idx, name) + + all_cuda = all( + V.graph.get_original_value_of_constant(name).is_cuda + for name in V.graph.constants.keys() + if name not in V.graph.folded_constants + ) + for idx, name in enumerate(V.graph.constants.keys()): + tensor = V.graph.get_original_value_of_constant(name) + assert isinstance(tensor, torch.Tensor) + self.prefix.writeline(f"""constants_info_[{idx}].name = "{name}";""") + self.prefix.writeline( + f"constants_info_[{idx}].dtype = static_cast({self.codegen_dtype(tensor.dtype)});" + ) + self.prefix.writeline( + f"constants_info_[{idx}].offset = {tensor.storage_offset()};" + ) + + # If constants to serialize contain cpu tensors, we always align data_size it to 64. + # When loading the constants, the valid data will depends on the size + # not the data_size so there won't be correctness issue. + data_size = ( + torch.ops.mkldnn._nbytes(tensor) + if tensor.is_mkldnn + else tensor.untyped_storage().nbytes() + ) + self.prefix.writeline( + f"constants_info_[{idx}].data_size = {data_size if all_cuda else _align(data_size)};" + ) + + from_folded = "true" if name in V.graph.folded_constants else "false" + self.prefix.writeline( + f"constants_info_[{idx}].from_folded = {from_folded};" + ) + + size_str = ", ".join([str(s) for s in tensor.size()]) + self.prefix.writeline(f"constants_info_[{idx}].shape = {{{size_str}}};") + + stride_str = ", ".join([str(s) for s in tensor.stride()]) + self.prefix.writeline( + f"constants_info_[{idx}].stride = {{{stride_str}}};" + ) + self.prefix.writeline( + f"constants_info_[{idx}].layout = static_cast({self.codegen_layout(tensor.layout)});" + ) + + if tensor.is_mkldnn: + opaque_metadata_tensor = torch.ops.mkldnn._get_mkldnn_serialized_md( + tensor + ) + assert ( + opaque_metadata_tensor.dim() == 1 + ), "Expect opaque_metadata_tensor to be 1-D" + + opaque_metadata_list = opaque_metadata_tensor.tolist() + opaque_metadata_str = self.codegen_shape_tuple(opaque_metadata_list) + self.prefix.writeline( + f"constants_info_[{idx}].opaque_metadata = {opaque_metadata_str};" + ) + if name in V.graph.dynamo_flat_name_to_original_fqn: + original_fqn = V.graph.dynamo_flat_name_to_original_fqn.get( + name, name + ) + elif name in V.graph.allocated_constant_name: + original_fqn = V.graph.allocated_constant_name[name] + else: + raise AssertionError("original_fqn must be set for constant") + self.prefix.writeline( + f"""constants_info_[{idx}].original_fqn = "{original_fqn}";""" + ) + self.prefix.writeline("update_constants_map(std::move(constants_map));") + self.prefix.writeline("update_constants_array(std::move(constants_array));") + + def escape_string(x): + return ( + x.replace("\\", "\\\\") + .replace('"', '\\"') + .replace("\n", "\\n") + .replace("\t", "\\t") + ) + + self.prefix.writeline( + f'in_spec_ = "{escape_string(config.aot_inductor.serialized_in_spec)}";' + ) + self.prefix.writeline( + f'out_spec_ = "{escape_string(config.aot_inductor.serialized_out_spec)}";' + ) + + for idx, output in enumerate(V.graph.graph_outputs): + assert not isinstance( + output, sympy.Expr + ), f"output {name=} cannot be symbolic" + name = f"output{idx}" + self.write_input_output_info("outputs_info_", idx, name) + + self.prefix.writeline( + "this->kernels_ = std::make_unique();" + ) + + self.prefix.writeline("}") + + def codegen_const_run_driver(self): + """ + // Generated code example + std::unordered_map AOTInductorModel::const_run_impl( + DeviceStreamType stream, + AOTIProxyExecutorHandle proxy_executor, + bool initialization + ) { + std::unordered_map folded_constants_map; + std::vector output_handles; + // build up output_handles over here. + _const_run_impl(output_handles, stream, proxy_executor); + // build up folded_constants_map + return folded_constants_map; + } + """ + + self.prefix.splice( + """ + std::unordered_map AOTInductorModel::const_run_impl( + DeviceStreamType stream, + AOTIProxyExecutorHandle proxy_executor, + bool initialization + ) { + """ + ) + if not config.aot_inductor.use_runtime_constant_folding: + self.prefix.splice( + """ + if (!initialization) { + std::cerr << "[WARNING] Calling constant_folding in model, but compiled with config: " + << "aot_inductor.use_runtime_constant_folding=False\\n"; + } + return {}; + } + """ + ) + return + + with self.prefix.indent(): + # This is a mapping to the index of constant folding graph's output + const_index_mapping: List[Optional[Tuple[int, str]]] = [None] * len( + V.graph.const_output_index + ) + for idx, (name, _) in enumerate(V.graph.constants.items()): + if name in V.graph.const_output_index: + const_index_mapping[V.graph.const_output_index[name]] = (idx, name) # type: ignore[call-overload] + assert ( + None not in const_index_mapping + ), "Not all constant gets mapped for constant folding graph." + + self.prefix.writeline( + f""" + std::unordered_map folded_constants_map; + folded_constants_map.reserve({len(const_index_mapping)}); + std::vector output_handles({len(const_index_mapping)}); + """ + ) + + self.prefix.splice( + """ + // The below assignment of output_handles to constants is not used directly. + // It's only used to memo the correspondence of handle and constants. + """ + ) + + for output_idx, (const_idx, _) in enumerate(const_index_mapping): # type: ignore[misc] + self.prefix.writeline( + f"output_handles[{output_idx}] = constants_->at({const_idx});" + ) + + self.prefix.writeline( + "_const_run_impl(output_handles, stream, proxy_executor);" + ) + + for output_idx, (_, const_name) in enumerate(const_index_mapping): # type: ignore[misc] + self.prefix.writeline( + f'folded_constants_map["{const_name}"] = output_handles[{output_idx}];' + ) + self.prefix.writeline("return folded_constants_map;") + + self.prefix.writeline("}") + + def generate(self, is_inference): + if V.graph.aot_mode and not V.graph.is_const_graph: + self.codegen_model_kernels() + self.codegen_model_constructor() + self.codegen_const_run_driver() + self.write_wrapper_decl() + return super().generate(is_inference) + + def finalize_prefix(self): + cached_dtypes_buffer = IndentedBuffer() + if config.abi_compatible: + for dtype in self.used_cached_dtypes: + cached_dtypes_buffer.writeline(f"CACHE_TORCH_DTYPE({dtype});") + for device in self.used_cached_devices: + cached_dtypes_buffer.writeline(f"CACHE_TORCH_DEVICE({device});") + for layout in self.used_cached_layouts: + cached_dtypes_buffer.writeline(f"CACHE_TORCH_LAYOUT({layout});") + cached_dtypes_buffer.splice(self.prefix) + self.prefix = cached_dtypes_buffer + + def define_kernel( + self, name: str, kernel: str, metadata: Optional[str] = None, cuda=False + ): + self.header.splice(f"\n{kernel}\n") + + def codegen_scalar_to_tensor(self, output: str): + name = f"scalar_to_tensor_{next(self.scalar_to_tensor_id)}" + self.wrapper_call.writeline( + f"RAIIAtenTensorHandle {name} = scalar_to_tensor_handle({output});" + ) + return name + + def codegen_tensor_item( + self, dtype: torch.dtype, tensor: str, scalar: str, indented_buffer=None + ): + assert ( + config.abi_compatible + ), "codegen_tensor_item is only used for the ABI-compatible mode" + dtype_str = str(dtype).split(".")[-1] + writer = indented_buffer or self + + if dtype == torch.float16 or dtype == torch.bfloat16: + scalar_tmp = f"{scalar}_tmp" + writer.writeline(f"{DTYPE_TO_CPP[dtype]} {scalar_tmp};") + + # need convert_arrayref_tensor_to_tensor for ArrayRefTensors + tensor = f"convert_arrayref_tensor_to_tensor({tensor})" + + writer.writeline( + f"AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_item_{dtype_str}({tensor}, &{scalar_tmp}));" + ) + writer.writeline(f"float {scalar} = float({scalar_tmp});") + else: + writer.writeline(f"{DTYPE_TO_CPP[dtype]} {scalar};") + + # need convert_arrayref_tensor_to_tensor for ArrayRefTensors + tensor = f"convert_arrayref_tensor_to_tensor({tensor})" + + writer.writeline( + f"AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_item_{dtype_str}({tensor}, &{scalar}));" + ) + + @cache_on_self + def get_output_refs(self): + return [ + f"torch::tensor({x.codegen_reference(self.wrapper_call)})" + if isinstance(x, ir.ShapeAsConstantBuffer) and not config.abi_compatible + else x.codegen_reference(self.wrapper_call) + for x in V.graph.graph_outputs + ] + + def generate_return(self, output_refs: List[str]): + cst_names = V.graph.constants.keys() + arr_iface = ( + not V.graph.is_const_graph and config.use_minimal_arrayref_interface + ) # For brevity. + + def use_thread_local_cached_output_tensor(idx, output): + cached_output_name = f"cached_output_{next(self.cached_output_id)}" + cache_type = "Array" if arr_iface else "Tensor" + self.wrapper_call.writeline( + f"thread_local ThreadLocalCachedOutput{cache_type}> " + f"{cached_output_name}({output});" + ) + if arr_iface: + self.wrapper_call.writeline( + f"{cached_output_name}.copy_data_from({output});" + ) + output_entry = f"std::get<{idx}>(output_arrayref_tensors)" + element_type = f"std::decay_t" + self.wrapper_call.writeline( + f"{output_entry} = {cached_output_name}.arrayref_tensor<{element_type}>();" + ) + else: + self.wrapper_call.writeline( + f"{cached_output_name}.copy_data_from({output});" + ) + self.wrapper_call.writeline( + f"AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_new_uninitialized_tensor(&output_handles[{idx}]));" + ) + self.wrapper_call.writeline( + f"AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_assign_tensors({cached_output_name}.tensor(), " + f"output_handles[{idx}]));" + ) + + if arr_iface: + self.wrapper_call.writeline( + "AOTInductorModelOutputs output_arrayref_tensors;" + ) + + output2idx: Dict[str, int] = {} + for idx, output in enumerate(output_refs): + if output == self.none_str: + continue + + is_constant_buffer = output in cst_names + output_buffer = V.graph.graph_outputs[idx] + if isinstance(output_buffer, ir.BaseView): + output_storage = output_buffer.unwrap_view() + if isinstance(output_storage.data, ir.ConstantBuffer): + is_constant_buffer = True + + if config.abi_compatible: + if isinstance(output_buffer, ir.ShapeAsConstantBuffer): + # Need to wrap scalar into tensor as the main function returns a vector of tensors + output_tensor = self.codegen_scalar_to_tensor(output) + self.wrapper_call.writeline( + f"output_handles[{idx}] = {output_tensor}.release();" + ) + continue + + output_is_tensor_handle_expr = ( + f"std::is_same_v," + "RAIIAtenTensorHandle> || " + f"std::is_same_v," + "AtenTensorHandle> || " + f"std::is_same_v," + "ConstantHandle>" + ) + self.wrapper_call.writeline( + f"if constexpr ({output_is_tensor_handle_expr}) {{" + ) + with self.wrapper_call.indent(): + if arr_iface: + cached_output_name = ( + f"cached_output_{next(self.cached_output_id)}" + ) + output_value_type = f"std::decay_t(output_arrayref_tensors).data()[0])>" + self.wrapper_call.writeline( + f"thread_local RAIIAtenTensorHandle {cached_output_name};" + ) + if is_constant_buffer: + # NOTE(return_constant): In some rare cases where we return + # a constant, we have to return a copy of this constant, + # because (1) constants are not owned by the Model instance + # (2) constants remain the same cross inference runs, + # assuming they are not updated at runtime Basically, we + # cannot release or transfer the ownership of any original + # constant to the user. + self.wrapper_call.writeline( + f"AtenTensorHandle {cached_output_name}_tmp;" + ) + self.wrapper_call.writeline( + f"aoti_torch_clone({output}, &{cached_output_name}_tmp);" + ) + self.wrapper_call.writeline( + f"{cached_output_name} = {cached_output_name}_tmp;" + ) + else: + self.wrapper_call.writeline( + f"{cached_output_name} = {output}.release();" + ) + self.wrapper_call.writeline( + f"convert_handle_to_arrayref_tensor({cached_output_name}, " + f"std::get<{idx}>(output_arrayref_tensors));" + ) + else: + if is_constant_buffer: + # See NOTE(return_constant) above. + self.wrapper_call.writeline( + f"aoti_torch_clone({output}, &output_handles[{idx}]);" + ) + else: + if output in output2idx: + src_idx = output2idx[output] + self.wrapper_call.writeline( + f"output_handles[{idx}] = output_handles[{src_idx}];" + ) + else: + self.wrapper_call.writeline( + f"output_handles[{idx}] = {output}.release();" + ) + self.wrapper_call.writeline("} else {") + with self.wrapper_call.indent(): + use_thread_local_cached_output_tensor(idx, output) + self.wrapper_call.writeline("}") + + else: + assert ( + not arr_iface + ), "minimal ArrayRef interface is only supported in ABI-compatible mode" + if is_constant_buffer: + output_expr = f"{output}.clone()" + # See NOTE(return_constant) above. + else: + output_expr = output + self.wrapper_call.writeline( + f"output_handles[{idx}] = reinterpret_cast(" + + f"new at::Tensor({output_expr}));" + ) + + if output not in output2idx: + output2idx[output] = idx + if arr_iface: + self.wrapper_call.writeline("return output_arrayref_tensors;") + + def generate_before_suffix(self, result): + if not V.graph.is_const_graph: + if V.graph.aot_mode: + result.writeline("} // AOTInductorModel::run_impl") + else: + result.writeline("} // inductor_entry_impl") + + def generate_end(self, result): + if V.graph.aot_mode: + if V.graph.is_const_graph: + result.writeline("} // AOTInductorModel::_const_run_impl") + else: + result.writeline("} // namespace aot_inductor") + result.writeline("} // namespace torch") + return + + # cpp entry function for JIT with cpp wrapper + result.writeline("'''\n)") + result.splice( + f""" + inductor_entry = CppWrapperCodeCache.load_pybinding( + ["std::vector"], cpp_wrapper_src, {self.cuda}, {len(V.graph.graph_outputs)}) + """ + ) + + wrapper_body = "input_tensors = [arg if isinstance(arg, torch.Tensor) else torch.tensor(arg) for arg in args]" + if V.graph.constants: + # Append constants to the input args for cpp wrapper. + # Python wrapper directly gets the value inside the wrapper call + # as a global variable passed when calling exec(code, mod.__dict__, mod.__dict__). + # For cpp wrapper, we need to pass this python value to the inductor_entry_impl function explicitly. + assert all( + isinstance(v, torch.Tensor) for v in list(V.graph.constants.values()) + ), "Expect all constants to be Tensor" + constants_str = f"[{', '.join(V.graph.constants.keys())}]" + wrapper_body += f""" + constants_tensor = {constants_str} + input_tensors.extend(constants_tensor) + """ + # Convert vector of at::Tensor to vector of AtenTensorHandle. + # If we pass at::Tensor, the compilation will be too slow. + wrapper_body += """ + input_handles = torch._C._aoti.unsafe_alloc_void_ptrs_from_tensors(input_tensors) + """ + # Release the inputs for memory reuse. + wrapper_body += """ + args.clear() + """ + + # unwrap output tensor back to python scalar + if all(x for x in self.output_is_tensor.values()): + # If no ShapeAsConstantBuffer in the output, directly return the output as tensors + outputs_str = "output_tensors" + else: + outputs = [ + f"output_tensors[{i}]" + if self.output_is_tensor[i] + else f"output_tensors[{i}].item()" + for i in range(len(V.graph.graph_outputs)) + ] + outputs_str = f"[{', '.join(outputs)}]" + wrapper_body += f""" + output_handles = f(input_handles) + output_tensors = torch._C._aoti.alloc_tensors_by_stealing_from_void_ptrs(output_handles) + return {outputs_str} + """ + + # Wrap the func to support setting result._boxed_call = True + result.splice( + f""" + def _wrap_func(f): + def g(args): + {wrapper_body} + return g + + call = _wrap_func(inductor_entry) + """ + ) + + def get_c_shim_func_name(self, kernel): + if not config.abi_compatible or kernel.startswith("aoti_torch_"): + return kernel + + assert "::" in kernel, "Cpp kernel name: " + kernel + " does not contain '::'" + kernel_tokens = kernel.split("::") + kernel_suffix = kernel_tokens[-1] + if kernel_suffix == "call": + kernel_suffix = kernel_tokens[-2] + + shim_fn = f"aoti_torch_{self.device}_{kernel_suffix}" + return shim_fn + + def generate_c_shim_extern_kernel_call(self, kernel, args): + # In the abi_compatible mode, we call fallback aten ops through a C shim layer + # Setting self.allow_stack_allocation to False because the exchange between + # ArrayRefTensor and at::Tensor is still fragile. + self.allow_stack_allocation = False + + wrapped_args = [] + + args_to_print_or_save = None + debug_printer_manager = V.graph.wrapper_code.debug_printer + if ( + debug_printer_manager.debug_printer_level + != IntermediateValueDebuggingLevel.OFF + ): + args_to_print_or_save = [] + + for x in args: + pieces = x.split(", ") + for piece in pieces: + # We only really *need* convert_arrayref_tensor_to_tensor for + # ArrayRefTensors. The code flowing into here uses `0` for nullptr, + # which convert_arrayref_tensor_to_tensor would blindly coerce to int, + # so just avoid wrapping integers. + # Name matching is to find tensor is hacky, but fixing all the + # ArrayRefTensor issues is not a priority for now. + if isinstance(piece, str) and piece.startswith( + ("buf", "arg", "wrap_with_raii_handle_if_needed") + ): + # TODO: The current way to find a 'tensor' type arg is hacky also as mentioned above + # Find a more reliable way to detect tensor kernel args for extern kernel calls + if ( + debug_printer_manager.debug_printer_level + != IntermediateValueDebuggingLevel.OFF + ): + if piece.startswith(("buf", "arg")): + args_to_print_or_save.append(piece) + piece = f"convert_arrayref_tensor_to_tensor({piece})" + wrapped_args.append(piece) + + debug_printer_manager.set_printer_args( + args_to_print_or_save, kernel, None, None + ) + with debug_printer_manager: + shim_fn = self.get_c_shim_func_name(kernel) + self.writeline( + f"AOTI_TORCH_ERROR_CODE_CHECK({shim_fn}({', '.join(wrapped_args)}));" + ) + + def generate_c_shim_extern_kernel_alloc(self, extern_kernel, args): + # registered output buffer name + name = extern_kernel.name + output_handle_name = f"{name}_handle" + self.writeline(f"AtenTensorHandle {output_handle_name};") + output_arg = f"&{output_handle_name}" + self.generate_c_shim_extern_kernel_call( + extern_kernel.get_kernel_name(), args + [output_arg] + ) + self.writeline(f"RAIIAtenTensorHandle {name}({output_handle_name});") + + def generate_extern_kernel_alloc(self, extern_kernel, args): + if config.abi_compatible: + self.generate_c_shim_extern_kernel_alloc(extern_kernel, args) + else: + super().generate_extern_kernel_alloc(extern_kernel, args) + + def generate_c_shim_fallback_kernel(self, fallback_kernel, args): + output_args = [] + output_raii_handles = [] + output_name_base = fallback_kernel.get_name() + for idx, output in enumerate(fallback_kernel.outputs): + if isinstance(output, ir.MultiOutput): + # TODO: handle integer output (e.g., as in attention) + name = f"{output.get_name()}" + output_handle_name = f"{name}_handle" + if output.indices: + assert ( + output.indices[0][1] == idx + ), f"expected {output.indices[0][1]=} == {idx=} for {output_name_base=}" + self.writeline(f"AtenTensorHandle {output_handle_name};") + output_args.append(f"&{output_handle_name}") + output_raii_handles.append( + f"RAIIAtenTensorHandle {name}({output_handle_name});" + ) + elif isinstance(output, int): + output_name = f"{output_name_base}_{idx}" + self.writeline(f"int64_t {output_name} = {output};") + output_args.append(f"&{output_name}") + elif isinstance(output, sympy.Symbol): + output_name = f"{output_name_base}_{idx}" + self.writeline(f"auto {output_name} = {output};") + output_args.append(f"&{output_name}") + elif output is None: + output_args.append("nullptr") + else: + raise NotImplementedError(f"unsupported type of {output=}") + args = args + output_args + self.generate_c_shim_extern_kernel_call(fallback_kernel.cpp_kernel_name, args) + for raii_handle in output_raii_handles: + self.writeline(raii_handle) + + def generate_fallback_kernel(self, fallback_kernel, args): + if config.abi_compatible: + self.generate_c_shim_fallback_kernel(fallback_kernel, args) + else: + super().generate_fallback_kernel(fallback_kernel, args) + + def generate_extern_kernel_out( + self, kernel: str, out: str, out_view: Optional[str], args: List[str] + ): + if out_view: + out_name = f"{out}_as_strided" + self.writeline(f"auto {out_name} = {out_view};") + args.insert(0, out_name) + else: + args.insert(0, out) + + if config.abi_compatible: + self.generate_c_shim_extern_kernel_call(kernel, args) + else: + # TODO: add debug printing info for non-abi compatible mode extern kernel call + self.writeline(self.wrap_kernel_call(kernel, args)) + + def generate_scatter_fallback( + self, + output, + inputs, + cpp_kernel_name, + python_kernel_name, + src_is_tensor, + reduce, + kwargs, + ): + # No stack allocation when there is a fallback op + self.allow_stack_allocation = False + + if config.abi_compatible: + # call the ABI shim function instead of the ATen one + cpp_kernel_name = self.get_c_shim_func_name(cpp_kernel_name) + # TODO: consider remove "_out" and add missing inplace variants to fallback_ops.py + cpp_kernel_name = cpp_kernel_name.replace("__", "_") + "_out" + inputs_wrapped = [ + f"convert_arrayref_tensor_to_tensor({x})" + if isinstance(x, str) + else str(x) + for x in inputs + ] + line = f"{cpp_kernel_name}(convert_arrayref_tensor_to_tensor({output}), {','.join(inputs_wrapped)}" + else: + line = f"{cpp_kernel_name}({','.join(map(str, inputs))}" + + if python_kernel_name.startswith("aten.scatter_reduce"): + line += f", {','.join(kwargs)}" + else: + if src_is_tensor: + if reduce: + line += f", {V.graph.wrapper_code.val_to_arg_str(reduce)}" + else: + assert ( + reduce is None + ), "Expect reduce to be None for aten.scatter_ with scalar src" + line += ");" + self.writeline(line) + + def generate_index_put_fallback(self, kernel, x, indices, values, accumulate): + # No stack allocation when there is a fallback op + self.allow_stack_allocation = False + + # TODO: update aoti_torch_index_put_out in ir.py to use autogen out version + if config.abi_compatible: + # See the comment in codegen_reinterpret_view about why having something like + # RAIIAtenTensorHandle(tmp_tensor_handle_2) in a tmp array can cause the correponding + # tensor prematurely deallocated, thus this std::vector().data() trick here. + indices_str = ( + "std::vector{" + + ( + ", ".join( + [f"convert_arrayref_tensor_to_tensor({ind})" for ind in indices] + ) + ) + + "}.data()" + ) + args = [ + f"convert_arrayref_tensor_to_tensor({x})", + indices_str, + str(len(indices)), + f"convert_arrayref_tensor_to_tensor({values})", + accumulate, + ] + args.insert( + 0, f"convert_arrayref_tensor_to_tensor({x})" + ) # set x as the output tensor, this fallback mutates x. + else: + indices_str = ( + f"{self.open_bracket}{', '.join(indices)}{self.closed_bracket}" + ) + args = [x, indices_str, values, accumulate] + args.insert(0, x) # set x as the output tensor, this fallback mutates + + self.writeline(self.wrap_kernel_call(kernel, args)) + + def add_benchmark_harness(self, output): + if V.graph.aot_mode: + return + super().add_benchmark_harness(output) + + def codegen_sizevar(self, x: Expr) -> str: + return self.expr_printer(V.graph.sizevars.simplify(x)) + + def codegen_tuple_access(self, basename: str, name: str, index: str) -> str: + if config.abi_compatible: + # in the abi_compatible mode, outputs are returned via arguments + return name + else: + return f"std::get<{index}>({basename})" + + def codegen_shape_tuple(self, shape: Tuple[Expr, ...]) -> str: + parts = list(map(self.codegen_sizevar, shape)) + if len(parts) == 0: + return "{}" + if len(parts) == 1: + return f"{{{parts[0]}, }}" + return f"{{{', '.join(parts)}}}" + + def codegen_dynamic_scalar(self, node): + (data,) = (t.codegen_reference() for t in node.inputs) + if config.abi_compatible: + self.codegen_tensor_item( + node.inputs[0].get_dtype(), data, f"{node.sym}_raw" + ) + else: + convert_type = DTYPE_TO_ATEN[node.inputs[0].get_dtype()].replace( + "at::k", "to" + ) + self.writeline(f"auto {node.sym}_raw = {data}.item().{convert_type}();") + + if len(node.keypath) == 0: + self.writeline(f"auto {node.sym} = {node.sym}_raw;") + elif len(node.keypath == 1) and isinstance(node.keypath[0], ConvertIntKey): + self.writeline(f"int64_t {node.sym} = {node.sym}_raw ? 1 : 0;") + elif len(node.keypath == 1) and isinstance(node.keypath[0], DivideByKey): + # TODO: assert divisibility here + self.writeline( + f"int64_t {node.sym} = {node.sym}_raw / {node.keypath[0].divisor};" + ) + else: + raise AssertionError(f"unrecognized keypath {node.keypath}") + + # record in unbacked_symbol_decls so we won't generate a declaration of the symbol again + self.unbacked_symbol_decls.add(str(node.sym)) + + def can_stack_allocate_buffer(self, buffer): + return ( + self.allow_stack_allocation + and buffer.get_device().type == "cpu" + and self.can_prove_buffer_has_static_shape(buffer) + and ir.is_contiguous_strides_for_shape( + buffer.get_stride(), buffer.get_size() + ) + ) + + def make_buffer_free(self, buffer): + return ( + "" + if isinstance(buffer.get_layout(), ir.MultiOutputLayout) + or (V.graph.aot_mode and buffer.get_name() in self.stack_allocated_buffers) + or ( + config.use_minimal_arrayref_interface + and V.graph.aot_mode + and buffer.get_name() in V.graph.graph_inputs + ) + else f"{buffer.get_name()}.reset();" + ) + + def make_free_by_names(self, names_to_del: List[str]): + return " ".join(f"{name}.reset();" for name in names_to_del) + + def codegen_exact_buffer_reuse(self, old_name: str, new_name: str, del_line: str): + if config.abi_compatible: + return f"auto {new_name} = std::move({old_name}); // reuse" + else: + return super().codegen_exact_buffer_reuse(old_name, new_name, del_line) + + def generate_profiler_mark_wrapper_call(self, stack): + self.wrapper_call.writeline( + 'RECORD_FUNCTION("inductor_wrapper_call", c10::ArrayRef());' + ) + + def write_triton_header_once(self): + pass + + def generate_start_graph(self): + pass + + def generate_end_graph(self): + pass + + def generate_inf_and_nan_checker(self, nodes): + for buf in nodes.get_names(): + # TODO: Add buf name directly into check_inf_and_nan. + self.writeline( + f"AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_check_inf_and_nan({buf}));" + ) + + def codegen_device(self, device): + if config.abi_compatible: + self.used_cached_devices.add(device.type) + return f"cached_torch_device_type_{device.type}, {device.index if device.index else 0}" + else: + return ( + f"c10::Device({DEVICE_TO_ATEN[device.type]}, {device.index})" + if device.index is not None + else f"{DEVICE_TO_ATEN[device.type]}" + ) + + def codegen_dtype(self, dtype): + if config.abi_compatible: + dtype_str = str(dtype).split(".")[-1] + self.used_cached_dtypes.add(dtype_str) + return f"cached_torch_dtype_{dtype_str}" + else: + return DTYPE_TO_ATEN[dtype] + + def codegen_layout(self, layout): + if config.abi_compatible: + layout_str = str(layout).split(".")[-1] + self.used_cached_layouts.add(layout_str) + return f"cached_torch_layout_{layout_str}" + else: + return LAYOUT_TO_ATEN[layout] + + @functools.lru_cache(None) # noqa: B019 + def codegen_int_array_var( + self, + int_array: str, + writer=None, + known_statically=False, + graph=None, # for per-graph caching + ): + # This is used for size/stride declaration + # Because the memory planning is done in two passes (see the implementation + # of self.generate), the writeline behavior is different in the two passes. + # As a result, the emitted int array declarations may appear in a later + # position of the generated code, so the second pass codegen should not + # reuse int array declarations generated in the first pass + if writer is None: + # The first pass codegen uses `self` as the writer + writer = self + + var = f"int_array_{next(self.int_array_id)}" + ctype = "int64_t" + if var not in self.declared_int_array_vars: + self.declared_int_array_vars.add(var) + if known_statically: + writer.writeline(f"static constexpr {ctype} {var}[] = {int_array};") + else: + writer.writeline(f"const {ctype} {var}[] = {int_array};") + return var + + def make_buffer_allocation(self, buffer): + return self.make_allocation( + buffer.get_name(), + buffer.get_device(), + buffer.get_dtype(), + buffer.get_size(), + buffer.get_stride(), + buffer if self.can_stack_allocate_buffer(buffer) else None, + ) + + def make_allocation( + self, name, device, dtype, shape, stride, buffer_if_can_stack_allocate=None + ): + orig_stride = stride + device_str = self.codegen_device(device) + dtype_code = self.codegen_dtype(dtype) + size = self.codegen_shape_tuple(shape) + stride = self.codegen_shape_tuple(orig_stride) + if config.abi_compatible: + size_array_var = self.codegen_int_array_var( + size, + self.wrapper_call, + known_statically=self.is_statically_known_list_of_ints(shape), + graph=self.get_codegened_graph(), + ) + stride_array_var = self.codegen_int_array_var( + stride, + self.wrapper_call, + known_statically=self.is_statically_known_list_of_ints(orig_stride), + graph=self.get_codegened_graph(), + ) + device_type, device_id = device_str.split(",") + device_idx = "this->device_idx_" if V.graph.aot_mode else device_id + if buffer_if_can_stack_allocate is not None: + self.stack_allocated_buffers[name] = buffer_if_can_stack_allocate + cpp_type = DTYPE_TO_CPP[dtype] + numel = buffer_if_can_stack_allocate.get_numel() + # Note: we don't zero storage because empty_strided doesn't zero either. + self.wrapper_call.writeline(f"{cpp_type} {name}_storage[{numel}];") + args = [ + f"{name}_storage", + size_array_var, + stride_array_var, + device_type, + device_idx, + ] + return f"ArrayRefTensor<{cpp_type}> {name}({', '.join(args)});" + + args = [ + str(len(shape)), + size_array_var, + stride_array_var, + dtype_code, + device_type, + device_idx, + f"&{name}_handle", + ] + + self.wrapper_call.writeline(f"AtenTensorHandle {name}_handle;") + self.wrapper_call.writeline( + f"AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_empty_strided({', '.join(args)}));" + ) + + return f"RAIIAtenTensorHandle {name}({name}_handle);" + + if V.graph.aot_mode and device_str.startswith("c10::Device("): + tensor_device = f"{device_str.split(',')[0]}, this->device_idx_)" + else: + tensor_device = device_str + + if device.type == "cpu": + return f"at::Tensor {name} = at::detail::empty_strided_cpu({size}, {stride}, {dtype_code});" + if device.type == "cuda": + return ( + f"at::Tensor {name} = at::detail::empty_strided_cuda(" + f"{size}, {stride}, {dtype_code}, c10::DeviceType::CUDA);" + ) + return ( + f"{self.declare}{name} = {self.namespace}empty_strided(" + f"{size}, {stride}, at::TensorOptions({tensor_device}).dtype({dtype_code})){self.ending}" + ) + + def codegen_alloc_from_pool(self, name, offset, dtype, shape, stride) -> str: + if config.abi_compatible: + size = self.codegen_shape_tuple(shape) + stride = self.codegen_shape_tuple(stride) + tmp_name = f"tmp_tensor_handle_{next(self.tmp_tensor_id)}" + args = [ + name, + self.expr_printer(offset), # bytes not numel + self.codegen_dtype(dtype), + str(len(shape)), + self.codegen_int_array_var( + size, self.wrapper_call, graph=self.get_codegened_graph() + ), + self.codegen_int_array_var( + stride, self.wrapper_call, graph=self.get_codegened_graph() + ), + f"&{tmp_name}", + ] + self.wrapper_call.writeline(f"AtenTensorHandle {tmp_name};") + self.wrapper_call.writeline( + f"AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch__alloc_from_pool({', '.join(args)}));" + ) + return f"RAIIAtenTensorHandle({tmp_name})" + + return "alloc_from_pool({})".format( + ", ".join( + [ + name, + self.expr_printer(offset), # bytes not numel + self.codegen_dtype(dtype), + self.codegen_shape_tuple(shape), + self.codegen_shape_tuple(stride), + ] + ) + ) + + def codegen_reinterpret_view( + self, data, size_list, stride_list, offset, writer, dtype=None + ) -> str: + dim = str(len(size_list)) + original_offset = offset + size = self.codegen_shape_tuple(size_list) + stride = self.codegen_shape_tuple(stride_list) + offset = self.codegen_sizevar(offset) + call_strs = [] + if config.abi_compatible: + final_tmp_name = None + final_tmp_name_is_RAIIAtenTensorHandle = False + + def create_reinterpret_call(): + tmp_name = f"tmp_tensor_handle_{next(self.tmp_tensor_id)}" + args = [ + f"{data.get_name()}", + dim, + self.codegen_int_array_var( + size, + writer, + known_statically=self.is_statically_known_list_of_ints( + size_list + ), + graph=self.get_codegened_graph(), + ), + self.codegen_int_array_var( + stride, + writer, + known_statically=self.is_statically_known_list_of_ints( + stride_list + ), + graph=self.get_codegened_graph(), + ), + offset, + ] + call_str = ( + f"auto {tmp_name} = reinterpret_tensor_wrapper({', '.join(args)});" + ) + return tmp_name, call_str + + def create_dtypeview_call(reinterpret_call): + tmp_AtenTensorHandle = ( + f"tmp_{data.get_name()}_{next(self.tmp_tensor_id)}" + ) + call_strs = [f"AtenTensorHandle {tmp_AtenTensorHandle};"] + dtype_name = str(dtype).split(".")[-1] + device_name = "cuda" if data.layout.device.type == "cuda" else "cpu" + get_dtype_function = f"aoti_torch_dtype_{dtype_name}" + dtypeview_function = f"aoti_torch_{device_name}_view_dtype" + call_strs.append( + f"AOTI_TORCH_ERROR_CODE_CHECK({dtypeview_function}" + f"({reinterpret_call}, {get_dtype_function}(), &{tmp_AtenTensorHandle}));" + ) + tmp_RAIIAtenTensorHandle = ( + f"tmp_{data.get_name()}_{next(self.tmp_tensor_id)}_handle" + ) + call_strs.append( + f"RAIIAtenTensorHandle {tmp_RAIIAtenTensorHandle}({tmp_AtenTensorHandle});" + ) + return tmp_RAIIAtenTensorHandle, call_strs + + if ( + size_list == data.layout.size + and stride_list == data.layout.stride + and original_offset == data.layout.offset + ): + # pure dtypeview + if dtype is not None and dtype != data.dtype: + tmp_output_name, tmp_call_strs = create_dtypeview_call( + data.get_name() + ) + call_strs.extend(tmp_call_strs) + final_tmp_name = tmp_output_name + final_tmp_name_is_RAIIAtenTensorHandle = True + else: + return f"{data.get_name()}" + else: + # firstly create reinterpretview + final_tmp_name, reinterpret_call = create_reinterpret_call() + call_strs.append(reinterpret_call) + + if dtype is not None and dtype != data.dtype: + # wrap it with dtypeview + final_tmp_name, tmp_call_strs = create_dtypeview_call( + reinterpret_call + ) + call_strs.extend(tmp_call_strs) + # Because the memory planning is done in two passes (see the implementation + # of self.generate), the writeline behavior is different in the two passes. + if writer is None: + writer = self + writer.writelines(call_strs) + if ( + self.can_stack_allocate_buffer(data) + and self.is_statically_known_list_of_ints(size_list) + and self.is_statically_known_list_of_ints(stride_list) + and ir.is_contiguous_strides_for_shape(stride_list, size_list) + ): + return final_tmp_name + + # NB, the return handle here represents a temporary tensor, which will be automatically + # released. + # Here's a sample usage in the cpp wrapper code: + # ``` + # aoti_torch_addmm_out( + # buf1, + # arg1_1, + # RAIIAtenTensorHandle(tmp_tensor_handle_0), + # buf0, + # 1L, + # 1L)); + # ``` + # RAIIAtenTensorHandle(tmp_tensor_handle_0) will be released after the call to addmm_out. + # This could be problematic when it's used in a different pattern, for example: + # ```` + # AtenTensorHandle tensor_args[] = {RAIIAtenTensorHandle(tmp_tensor_handle_2), buf5, buf6}; + # aoti_torch_proxy_executor_call_function(..., tensor_args); + # ```` + # RAIIAtenTensorHandle(tmp_tensor_handle_2) will be invalid when it's used in the latter + # kernel call. + # + # This is solved by updating the proxy_executor invocation to + # ``` + # aoti_torch_proxy_executor_call_function(..., + # std::vector{ + # RAIIAtenTensorHandle(tmp_tensor_handle_2), buf5, buf6 + # }.data() + # ); + # ``` + if not final_tmp_name_is_RAIIAtenTensorHandle: + return f"wrap_with_raii_handle_if_needed({final_tmp_name})" + else: + return final_tmp_name + else: + args = [data.get_name(), size, stride, offset] + return f"reinterpret_tensor({', '.join(args)})" + + def codegen_device_copy(self, src, dst): + if config.abi_compatible: + # aoti_torch_tensor_copy_ takes AtenTensorHandle as input, + # while stack-allocation results in ArrayRefTensor + # so disable stack allocation here + self.allow_stack_allocation = False + self.writeline( + f"AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_tensor_copy_(expensive_copy_to_tensor_if_needed({src}), {dst}));" + ) + else: + self.writeline(f"{dst}.copy_({src});") + + def codegen_multi_output(self, name, value): + # in the abi_compatible mode, outputs are retrieved by passing + # output pointers, so we skip its codegen here. + if not config.abi_compatible: + super().codegen_multi_output(name, value) + + def codegen_subgraph_prefix(self, subgraph, outer_inputs, outer_outputs): + for inner_input, outer_input in zip(subgraph.graph.graph_inputs, outer_inputs): + if config.abi_compatible: + # in ABI-compatible mode, we copy the underlying at::Tensor of the conditional + # input (outer_input) into another at::Tensor to be used as a subgraph input + # (inner_input) in the nested scope. we can't std::move here, as the codegened + # outer input may be an expression / rvalue (e.g., reinterpret_view(x)), so we + # can't necessarily std::move it back to the origin (x). + self.writeline(f"AtenTensorHandle {inner_input}_handle;") + self.writeline( + f"AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_assign_tensors_out({outer_input}, &{inner_input}_handle));" + ) + self.writeline( + f"RAIIAtenTensorHandle {inner_input}({inner_input}_handle);" + ) + else: + self.writeline( + f"{self.declare}{inner_input} = {outer_input}{self.ending}" + ) + + def codegen_subgraph_suffix(self, subgraph, outer_inputs, outer_outputs): + for inner_output, outer_output in zip( + subgraph.graph.graph_outputs, outer_outputs + ): + src = inner_output.codegen_reference() + if config.abi_compatible: + # in ABI-compatible mode, we need to std::move subgraph output (inner_output) + # to the conditional output (outer_output), as RAIIAtenTensorHandle's copy + # constructor is deleted. + src = f"std::move({src})" + # in case the outer_output carried a value + # before (e.g., in the while_loop codegen) + self.writeline(f"{outer_output}.reset();") + self.writeline(f"{outer_output} = {src}{self.ending}") + + def codegen_conditional(self, conditional): + name = conditional.get_name() + outer_inputs = [f"{buf.codegen_reference()}" for buf in conditional.operands] + if config.abi_compatible: + outer_outputs = [] + for out in conditional.outputs: + # in ABI-compatible mode, ir.MultiOutput is not codegened, + # hence pre-declare output variables directly and separately + self.writeline(f"RAIIAtenTensorHandle {out.get_name()};") + outer_outputs.append(out.get_name()) + + if not isinstance(conditional.predicate, ir.ShapeAsConstantBuffer): + # in ABI-compatible mode, we need to use the ABI shim function + # to extract a C++ bool from the unrelying scalar bool Tensor + predicate = f"{conditional.predicate.get_name()}_scalar" + self.codegen_tensor_item( + torch.bool, + conditional.predicate.codegen_reference(), + predicate, + ) + else: + # the predicate is not a Tensor: SymBool or Python bool + predicate = conditional.predicate.codegen_reference() + else: + # in non-ABI-compatible mode, we can codegen the conditional outputs + # as array of at::Tensor instances, as the ir.MultiOutput is codegened + outer_outputs = [f"{name}[{i}]" for i in range(len(conditional.outputs))] + self.writeline(f"at::Tensor {name}[{len(conditional.outputs)}];") + predicate = f"{conditional.predicate.codegen_reference()}" + if not isinstance(conditional.predicate, ir.ShapeAsConstantBuffer): + # move the Tensor predicate to host + predicate = f"{predicate}.item()" + + self.writeline(f"if ({predicate}) {{") + self.writeline(EnterSubgraphLine(self, conditional.true_subgraph.graph)) + self.codegen_subgraph(conditional.true_subgraph, outer_inputs, outer_outputs) + self.writeline(ExitSubgraphLine(self)) + self.writeline("} else {") + self.writeline(EnterSubgraphLine(self, conditional.false_subgraph.graph)) + self.codegen_subgraph(conditional.false_subgraph, outer_inputs, outer_outputs) + self.writeline(ExitSubgraphLine(self)) + self.writeline("}") + + def codegen_while_loop(self, while_loop): + name = while_loop.get_name() + outer_carried_inputs = [ + buf.codegen_reference() for buf in while_loop.carried_inputs + ] + outer_additional_inputs = [ + buf.codegen_reference() for buf in while_loop.additional_inputs + ] + cond_result_name = f"{name}_cond_result" + + if config.abi_compatible: + self.writeline(f"RAIIAtenTensorHandle {cond_result_name};") + + cond_outer_inputs = [] + for inp, out in zip(outer_carried_inputs, while_loop.outputs): + # in ABI-compatible mode, the carried inputs are codegened + # as buffers outside the while loop and set to the initial + # values. at the end of each while_loop iteration, they + # will be assined the carried values. + out_name = out.get_name() + self.writeline(f"AtenTensorHandle {out_name}_handle;") + self.writeline( + f"AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_assign_tensors_out({inp}, &{out_name}_handle));" + ) + self.writeline(f"RAIIAtenTensorHandle {out_name}({out_name}_handle);") + cond_outer_inputs.append(out_name) + + # additional inputs will be assinged within the while_loop + # iteration directly from the corresponding outer graph buffers + cond_outer_inputs.extend(outer_additional_inputs) + else: + self.writeline(f"at::Tensor {cond_result_name};") + self.writeline(f"at::Tensor {name}[{len(outer_carried_inputs)}];") + for i, inp in enumerate(outer_carried_inputs): + # set the initial state before the loop + self.writeline(f"{name}[{i}] = {inp};") + + cond_outer_inputs = [ + *[f"{name}[{i}]" for i in range(len(outer_carried_inputs))], + *outer_additional_inputs, + ] + + cond_outer_outputs = [cond_result_name] + body_outer_inputs = list(cond_outer_inputs) + body_outer_outputs = body_outer_inputs[: len(outer_carried_inputs)] + + self.writeline("while (1) {") + self.writeline(EnterSubgraphLine(self, while_loop.cond_subgraph.graph)) + self.codegen_subgraph( + while_loop.cond_subgraph, cond_outer_inputs, cond_outer_outputs + ) + + if config.abi_compatible: + cond_result = f"{cond_result_name}_scalar" + self.codegen_tensor_item(torch.bool, cond_result_name, cond_result) + else: + cond_result = f"{cond_result_name}.item()" + self.writeline(f"if (!{cond_result}) break;") + + self.writeline(ExitSubgraphLine(self)) + self.writeline(EnterSubgraphLine(self, while_loop.body_subgraph.graph)) + self.codegen_subgraph( + while_loop.body_subgraph, body_outer_inputs, body_outer_outputs + ) + self.writeline(ExitSubgraphLine(self)) + self.writeline("}") + + def generate_extern_kernel_args_decl_if_needed( + self, op_overload, raw_args, output_args + ): + arg_types = [x.real_type for x in op_overload._schema.arguments] + return_types = [x.type for x in op_overload._schema.returns] + + new_tensor_args = [] + new_int_args = [] + + def fill_args(arg, arg_type): + static_arg_types = ( + torch.FloatType, + torch.BoolType, + torch.StringType, + torch.Type, + torch.DeviceObjType, + ) + inductor_tensor_buffers = ( + ir.Buffer, + ir.ReinterpretView, + ) + + if isinstance(arg_type, torch.TensorType): + assert isinstance(arg, inductor_tensor_buffers), f"got {type(arg)}" + new_tensor_args.append(f"{arg.codegen_reference()}") + elif isinstance(arg_type, torch.IntType): + # int + new_int_args.append(str(arg)) + elif isinstance(arg_type, torch.SymIntType): + # SymInt + expr = arg.node.expr if isinstance(arg, torch.SymInt) else arg + new_int_args.append(self.expr_printer(expr)) + elif isinstance(arg_type, torch.NumberType): + # Scalar of type int + assert isinstance(arg, (int, float, bool)) + # Only treat int Scalar as dynamic + if isinstance(arg, int): + new_int_args.append(str(arg)) + elif isinstance(arg_type, torch.ListType): + assert isinstance(arg, (list, tuple)) + + # List[Tensor] + if isinstance(arg_type.getElementType(), torch.TensorType): + new_tensor_args.extend([f"{a.codegen_reference()}" for a in arg]) + # List[Optional[Tensor]] + elif isinstance( + arg_type.getElementType(), torch.OptionalType + ) and isinstance( + arg_type.getElementType().getElementType(), torch.TensorType + ): + new_tensor_args.extend( + [f"{a.codegen_reference()}" for a in arg if a is not None] + ) + # List[int] + elif isinstance(arg_type.getElementType(), torch.IntType): + new_int_args.extend([str(a) for a in arg]) + # List[SymInt] + elif isinstance(arg_type.getElementType(), torch.SymIntType): + expressions = [ + a.node.expr if isinstance(a, torch.SymInt) else a for a in arg + ] + new_int_args.extend( + [self.expr_printer(expr) for expr in expressions] + ) + # List[Scalar] + elif isinstance(arg_type.getElementType(), torch.NumberType): + # Only treat int Scalar as dynamic + is_int_type = [isinstance(a, int) for a in arg] + if any(is_int_type): + assert all( + is_int_type + ), "AOTInductor only supports int scalars of the same type" + new_int_args.extend([str(a) for a in arg]) + else: + assert isinstance( + arg_type.getElementType(), static_arg_types # type: ignore[arg-type] + ), f"Fall through arguments must be one of static_arg_types, got {type(arg_type)}" + else: + assert isinstance( + arg_type, static_arg_types # type: ignore[arg-type] + ), f"Fall through arguments must be one of static_arg_types, got {type(arg_type)}" + + for arg, arg_type in zip(raw_args, arg_types): + if arg is not None: + if isinstance(arg_type, torch.OptionalType): + fill_args(arg, arg_type.getElementType()) + else: + fill_args(arg, arg_type) + + def fill_output_arg(arg, return_type): + if isinstance(return_type, torch.TensorType): + self.writeline(f"AtenTensorHandle {arg}_handle; // output buffer") + self.writeline( + f"AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_new_uninitialized_tensor(&{arg}_handle));" + ) + self.writeline(f"RAIIAtenTensorHandle {arg}({arg}_handle);") + new_tensor_args.append(f"{arg}") + elif isinstance(return_type, torch.SymIntType): + raise NotImplementedError("NYI support for return type: SymInt") + elif isinstance(return_type, torch.ListType) and isinstance( + return_type.getElementType(), torch.SymIntType + ): + raise NotImplementedError("NYI support for return type: List[SymInt]") + else: + raise AssertionError(f"Unsupported return type found: {return_type}") + + # TODO: Only support tensor(s) returns for now, SymInt is not implemented yet + for return_type in return_types: + if isinstance(return_type, (torch.TensorType)): + pass + elif isinstance(return_type, torch.OptionalType): + assert isinstance(return_type.getElementType(), torch.TensorType) + elif isinstance(return_type, torch.ListType): + assert isinstance(return_type.getElementType(), torch.TensorType) + else: + raise NotImplementedError( + f"return type {return_type} is not yet supported." + ) + + for output_arg in output_args: + assert output_arg is not None, "Optional return types are not yet supported" + if isinstance(output_arg, (list, tuple)): + for out in output_arg: + fill_output_arg(out, torch.TensorType.get()) + else: + fill_output_arg(output_arg, torch.TensorType.get()) + + return new_tensor_args, new_int_args + + def generate_extern_kernel_alloc_and_find_schema_if_needed( + self, + buf_name: str, + python_kernel_name: str, + cpp_kernel_name: str, + codegen_args: List[str], + cpp_op_schema: str, + cpp_kernel_key: str, + cpp_kernel_overload_name: str = "", + op_overload: Optional[torch._ops.OpOverload] = None, + raw_args=None, + outputs=None, + ): + # No stack allocation when there is a fallback op + self.allow_stack_allocation = False + + def extract_output_name(out): + if out is None: + # Because out is not a MultiOutput, we assume the kernel returns a single output + return [buf_name] + elif isinstance(out, (ir.MultiOutput, ir._CollectiveKernel)): + return out.get_name() + elif isinstance(out, (list, tuple)): + return type(out)(extract_output_name(o) for o in out) + else: + raise AssertionError(f"Unexpected output: {type(out)}") + + # output_args has the same pytree structure as outputs + output_args = None + if config.abi_compatible: + output_args = extract_output_name(outputs) + if isinstance(output_args, str): + output_args = [output_args] + + if V.graph.aot_mode and config.abi_compatible: + assert op_overload is not None + assert raw_args is not None + assert outputs is not None + + return self.generate_extern_kernel_alloc_and_find_schema_if_needed_with_proxy_executor( + cpp_kernel_key, + op_overload, + raw_args, + output_args, + ) + else: + return self.generate_extern_kernel_alloc_and_find_schema_if_needed_jit( + buf_name, + python_kernel_name, + cpp_kernel_name, + codegen_args, + cpp_op_schema, + cpp_kernel_key, + cpp_kernel_overload_name, + op_overload, + raw_args, + output_args, + ) + + def generate_scoped_gil_acquire(self, declarations_before_scope, lines_in_scope): + scoped_lines = IndentedBuffer() + for declaration in declarations_before_scope: + scoped_lines.writeline(declaration) + + scoped_lines.writeline("{") + with scoped_lines.indent(): + scoped_lines.writeline("py::gil_scoped_acquire acquire;") + scoped_lines.writelines(lines_in_scope.split("\n")) + scoped_lines.writelines("}") + return scoped_lines._lines + + def load_custom_op_wrapper(self): + # TODO: need to support control flow + if self.custom_op_wrapper_loaded: + return + + lines = """ +RAIIPyObject codecache_module(PyImport_ImportModule("torch._inductor.codecache")); +if (codecache_module.get() == NULL) { + throw std::runtime_error("Failed to load torch._inductor.codecache"); +} +custom_op_wrapper = PyObject_GetAttrString(codecache_module, "custom_op_wrapper"); +if (custom_op_wrapper.get() == NULL) { + throw std::runtime_error("Failed to load torch._inductor.codecache.custom_op_wrapper"); +}""" + + declarations_before_scope = ["RAIIPyObject custom_op_wrapper;"] + scope_gil_acquire = self.generate_scoped_gil_acquire( + declarations_before_scope, lines + ) + self.writelines(scope_gil_acquire) + + self.custom_op_wrapper_loaded = True + + def generate_py_arg(self, py_args_var, idx, raw_arg, arg_type): + def generate_py_arg_inner(lines, raw_arg, arg_type): + if raw_arg is None: + # Py_None is a singleton, so we have to explicitly incref it here + lines.append("Py_INCREF(Py_None);\n") + return "Py_None" + elif isinstance(arg_type, torch.TensorType): + # Store AtenTensorHandle as void* + base_handle = raw_arg.codegen_reference() + ( + tmp_raii_handle_var, + tmp_raii_handle_var_decl, + ) = self.create_tmp_raii_handle_var(base_handle) + if tmp_raii_handle_var: + lines.append(tmp_raii_handle_var_decl) + base_handle = tmp_raii_handle_var + return f"PyCapsule_New(reinterpret_cast({base_handle}.get()), NULL, NULL)" + elif isinstance(arg_type, torch.OptionalType): + return generate_py_arg_inner(lines, raw_arg, arg_type.getElementType()) + elif isinstance(arg_type, torch.IntType): + # int + return f"PyLong_FromLongLong({raw_arg})" + elif isinstance(arg_type, torch.SymIntType): + # SymInt + expr = ( + raw_arg.node.expr if isinstance(raw_arg, torch.SymInt) else raw_arg + ) + return f"PyLong_FromLongLong({self.expr_printer(expr)})" + elif isinstance(arg_type, torch.FloatType): + return f"PyFloat_FromDouble({raw_arg})" + elif isinstance(arg_type, torch.BoolType): + return f"PyBool_FromLong({1 if raw_arg else 0})" + elif isinstance(arg_type, torch.StringType): + return f'PyUnicode_FromString("{raw_arg}")' + elif isinstance(arg_type, torch.NumberType): + # Union[bool, int, float, complex] + # torch/_prims_common/__init__.py + if isinstance(raw_arg, int): + return f"PyLong_FromLongLong({raw_arg})" + elif isinstance(raw_arg, float): + return f"PyFloat_FromDouble({raw_arg})" + elif isinstance(raw_arg, bool): + return f"PyBool_FromLong({1 if raw_arg else 0})" + elif isinstance(raw_arg, complex): + return f"PyComplex_FromDoubles({raw_arg.real, raw_arg.imag})" + elif isinstance(raw_arg, torch.SymInt): + expr = raw_arg.node.expr + return f"PyLong_FromLongLong({self.expr_printer(expr)})" + else: + raise NotImplementedError( + f"arg type {arg_type} with raw_arg {raw_arg}, {type(raw_arg)} is not yet supported by custom_op_wrapper" + ) + elif isinstance(raw_arg, torch.dtype): + # dtype + self.include_extra_header("torch/csrc/DynamicTypes.h") + return f"Py_NewRef(torch::getTHPDtype(static_cast({self.codegen_dtype(raw_arg)})))" + else: + raise NotImplementedError( + f"arg type {arg_type} is not yet supported by custom_op_wrapper" + ) + + lines = [] + if isinstance(arg_type, torch.ListType): + assert isinstance(raw_arg, (list, tuple)), str(raw_arg) + " is not a list" + lines.append( + f"PyObject* {py_args_var}_{idx} = PyList_New({len(raw_arg)});\n" + ) + for i, elem in enumerate(raw_arg): + lines.append( + f"PyList_SetItem({py_args_var}_{idx}, {i}, {generate_py_arg_inner(lines, elem, arg_type.getElementType())});\n" + ) + lines.append( + f"PyTuple_SetItem({py_args_var}, {idx}, {py_args_var}_{idx});\n" + ) + else: + lines.append( + f"PyTuple_SetItem({py_args_var}, {idx}, {generate_py_arg_inner(lines, raw_arg, arg_type)});\n" + ) + return "".join(lines) + + def generate_extern_kernel_alloc_and_find_schema_if_needed_jit( + self, + buf_name: str, + python_kernel_name: str, + cpp_kernel_name: str, + codegen_args: List[str], + cpp_op_schema: str, + cpp_kernel_key: str, + cpp_kernel_overload_name: str = "", + op_overload: Optional[torch._ops.OpOverload] = None, + raw_args=None, + output_args: Optional[List[str]] = None, + ): + if not config.abi_compatible: + # Will update this to use an OSS version ProxyExecutor + if cpp_kernel_key not in self.extern_call_ops: + self.writeline( + f"static auto op_{cpp_kernel_key} = c10::Dispatcher::singleton()" + ) + self.writeline( + f'\t.findSchemaOrThrow("{cpp_kernel_name}", "{cpp_kernel_overload_name}")' + ) + self.writeline(f"\t.typed<{cpp_op_schema}>();") + self.extern_call_ops.add(cpp_kernel_key) + + self.writeline( + f"auto {buf_name} = op_{cpp_kernel_key}.call({', '.join(codegen_args)});" + ) + else: + # In the JIT mode, because of the ABI-compatible requirement, we can't directly call + # c10::Dispatcher to find the custom op and call it. Instead, we go back to Python + # to invoke this custom op. + self.load_custom_op_wrapper() + + assert output_args is not None, "output_args should not be None" + num_args = len(raw_args) + py_args_var = f"py_args_{next(self.arg_var_id)}" + # First arg is always the python op name + lines = f""" +RAIIPyObject {py_args_var}(PyTuple_New({num_args+1})); +if ({py_args_var}.get() == NULL) {{ + throw std::runtime_error("PyTuple_New {py_args_var} failed"); +}} +PyTuple_SetItem({py_args_var}, 0, PyUnicode_FromString("{python_kernel_name}")); +""" + + assert op_overload is not None, "op_overload should not be None" + + for idx, (raw_arg, schema_arg) in enumerate( + zip(raw_args, op_overload._schema.arguments) + ): + lines += self.generate_py_arg( + py_args_var, idx + 1, raw_arg, schema_arg.real_type + ) + + lines += f""" +// Call the custom op in Python +RAIIPyObject py_{buf_name}(PyObject_CallObject(custom_op_wrapper, {py_args_var})); +if (py_{buf_name}.get() == NULL) {{ + throw std::runtime_error("PyObject_CallObject {python_kernel_name} failed"); +}}""" + + if len(output_args) == 1: + # result is a single tensor + lines += f""" +{output_args[0]} = reinterpret_cast(PyCapsule_GetPointer(py_{buf_name}.get(), NULL));""" + else: + # result is a tuple of tensors + for idx, output_arg in enumerate(output_args): + lines += f""" +{output_arg} = + reinterpret_cast(PyCapsule_GetPointer(PyList_GET_ITEM(py_{buf_name}.get(), {idx}), NULL));""" + + declarations_before_scope = [ + f"RAIIAtenTensorHandle {output_arg};" + for idx, output_arg in enumerate(output_args) + ] + scope_gil_acquire = self.generate_scoped_gil_acquire( + declarations_before_scope, lines + ) + self.writelines(scope_gil_acquire) + + def generate_extern_kernel_alloc_and_find_schema_if_needed_with_proxy_executor( + self, + cpp_kernel_key, + op_overload, + raw_args, # contains both args and flatten kwargs + output_args: Optional[List[str]] = None, + ): + ( + tensor_call_args, + int_call_args, + ) = self.generate_extern_kernel_args_decl_if_needed( + op_overload, raw_args, output_args + ) + + tensor_call_args_str = ", ".join(tensor_call_args) + int_call_args_str = ", ".join(int_call_args) + + extern_kernel_node_index = len(V.graph.extern_kernel_nodes) - 1 + + self.writeline( + f"aoti_torch_proxy_executor_call_function(proxy_executor, " + f"{extern_kernel_node_index}, " + f"{len(int_call_args)}, " + f"std::vector{{{int_call_args_str}}}.data(), " + f"{len(tensor_call_args)}, " + f"std::vector{{{tensor_call_args_str}}}.data());" + ) + + self.extern_call_ops.add(cpp_kernel_key) + + def generate_reset_kernel_saved_flags(self): + pass + + def generate_save_uncompiled_kernels(self): + pass + + def c_type_for_prim_type(self, val, type_) -> str: + assert ( + config.abi_compatible + ), "c_type_for_prim_type is only used in ABI compatible mode" + if isinstance(type_, torch.OptionalType): + return f"{self.c_type_for_prim_type(val, type_.getElementType())}*" + elif isinstance(type_, torch.TensorType): + return "AtenTensorHandle" + elif isinstance(type_, (torch.IntType, torch.SymIntType)): + return "int64_t" + elif isinstance( + type_, (torch.BoolType, torch.SymBoolType, torch.EnumType) + ) or repr(type_) in ("ScalarType", "Layout"): + return "int32_t" + elif isinstance(type_, torch.FloatType): + return "double" + elif isinstance(type_, torch.NumberType): + if isinstance(val, bool): + return "int32_t" + elif isinstance(val, int): + return "int64_t" + elif isinstance(val, float): + return "double" + elif val is None: + # This could happen when val is an optional value + return "double" + else: + raise AssertionError( + f"Unexpected type in c_type_for_prim_type: {type_=}" + ) + elif isinstance(type_, torch.StringType): + return "const char*" + else: + raise AssertionError(f"Unexpected type in c_type_for_prim_type: {type_=}") + + def val_to_arg_str_for_prim_type(self, val, type_) -> str: + # TODO: not using type_ as the first step of refactoring. Will update this later. + if isinstance(val, bool): + if config.abi_compatible: + return "1" if val else "0" + else: + return "true" if val else "false" + elif isinstance(val, int): + # uint64_t is long on Linux, but long long on MacOS and Windows + return f"{val}LL" if sys.platform in ["darwin", "win32"] else f"{val}L" + elif isinstance(val, str): + return f'"{val}"' + elif isinstance( + val, (ir.Buffer, ir.ReinterpretView, ir.StorageBox, ir.TensorBox) + ): + return val.codegen_reference() + elif isinstance(val, torch.device): + return self.codegen_device(val) + elif isinstance(val, torch.dtype): + return self.codegen_dtype(val) + elif isinstance(val, float) and val in [float("inf"), float("-inf")]: + if val == float("inf"): + return "std::numeric_limits::infinity()" + else: + return "-std::numeric_limits::infinity()" + elif isinstance(val, (list, tuple)): + # FIXME: This happens because type_ is not always properly set to torch.ListType + return f"{{{', '.join(self.val_to_arg_str(x, None) for x in val)}}}" + elif isinstance(val, SymTypes): + return self.expr_printer(val.node.expr) + elif isinstance(val, sympy.Expr): + return self.expr_printer(val) + else: + return repr(val) + + def val_to_arg_str(self, val, type_=None) -> str: + if val is None: + # None needs special care. It either represent nullopt or an empty tensor + if config.abi_compatible: + if type_ is None or isinstance(type_, torch.OptionalType): + if type_ is not None and isinstance( + type_.getElementType(), + ( + torch.ListType, + torch.TupleType, + torch.DeviceObjType, + ), + ): + return "0, 0" + else: + return "0" # nullptr is not available in C + elif isinstance(type_, torch.TensorType): + # create an empty tensor, the equivalent of at::Tensor() + var_name = f"var_{next(self.arg_var_id)}" + self.writeline(f"AtenTensorHandle {var_name}_handle;") + self.writeline( + f"AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_new_uninitialized_tensor(&{var_name}_handle));" + ) + self.writeline( + f"RAIIAtenTensorHandle {var_name}({var_name}_handle);" + ) + return var_name + else: + raise AssertionError("Can not map None to a known data type") + else: + return "std::nullopt" + + if isinstance(type_, torch.OptionalType): + element_type = type_.getElementType() + if config.abi_compatible: + if not isinstance(element_type, torch.TensorType): + var_name = f"var_{next(self.arg_var_id)}" + if isinstance( + element_type, + (torch.ListType, torch.TupleType, torch.DeviceObjType), + ): + # type_ is something like Optional[List] or Optional[Device] + arg_str = self.val_to_arg_str(val, element_type) + # For datatypes with auxiliary info, we need to hoist out the extra arguments. + # NOTE: This only works if there is one additional argument, though it can easily be generalized. + main_value, aux = arg_str.rsplit(", ") + self.writeline(f"auto {var_name} = {main_value};") + return f"&{var_name}, {aux}" + else: + self.writeline( + f"{self.c_type_for_prim_type(val, element_type)} {var_name} = {self.val_to_arg_str(val, element_type)};" + ) + return f"&{var_name}" + else: + # type_ is Optional[Tensor] + # Similar to other data type, use pointer to denote optional tensor arg in v2 C shim + base_handle = self.val_to_arg_str(val, element_type) + if config.use_minimal_arrayref_interface: + base_handle = ( + f"convert_arrayref_tensor_to_tensor({base_handle})" + ) + ( + tmp_raii_handle_var, + tmp_raii_handle_var_decl, + ) = self.create_tmp_raii_handle_var(base_handle) + if tmp_raii_handle_var: + self.writeline(tmp_raii_handle_var_decl) + base_handle = tmp_raii_handle_var + var_name = f"var_{next(self.arg_var_id)}" + self.writeline( + f"AtenTensorHandle {var_name} = {base_handle}.get();" + ) + return f"&{var_name}" + else: + return self.val_to_arg_str(val, element_type) + + elif isinstance(type_, torch.ListType): + assert isinstance( + val, (list, tuple) + ), f"{val} does not match with arg type {type_}" + element_type = type_.getElementType() + if config.abi_compatible: + var_name = f"var_array_{next(self.var_array_id)}" + if len(val) == 0: + # Zero-size array is not supported in the C or C++ standard, so + # we declare a null pointer for it. + self.writeline( + f"const {self.c_type_for_prim_type(None, element_type)}* {var_name} = nullptr;" + ) + else: + result = f"{{{', '.join(self.val_to_arg_str(x, element_type) for x in val)}}}" + self.writeline( + f"const {self.c_type_for_prim_type(val[0], element_type)} {var_name}[] = {result};" + ) + # Need to pass the array length because we can't use std::vector + return f"{var_name}, {len(val)}" + else: + return f"{{{', '.join(self.val_to_arg_str(x, element_type) for x in val)}}}" + + return self.val_to_arg_str_for_prim_type(val, type_) + + def create_tmp_raii_handle_var(self, base_handle): + if base_handle.startswith( + ( + "convert_arrayref_tensor_to_tensor", + "wrap_with_raii_handle_if_needed", + ) + ): + # wrap_with_raii_handle_if_needed creates a temp RAIIAtenTensorHandle, so we need to + # explicitly store it. Otherwise, it will be destroyed before the fallback kernel call. + tmp_var_name = f"var_{next(self.arg_var_id)}" + return ( + tmp_var_name, + f"RAIIAtenTensorHandle {tmp_var_name} = {base_handle};\n", + ) + else: + return "", "" diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_wrapper_cuda.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_wrapper_cuda.py new file mode 100644 index 0000000000000000000000000000000000000000..b82a707961df9e940c5849734db64d2dc6784ad1 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cpp_wrapper_cuda.py @@ -0,0 +1,432 @@ +# mypy: allow-untyped-defs +import functools +import os +from itertools import chain, count +from typing import Any, Callable, List, Optional, Tuple, TYPE_CHECKING, Union + +import sympy + +from torch import dtype as torch_dtype +from torch._inductor.codecache import get_cpp_wrapper_cubin_path_name +from torch._inductor.runtime.triton_heuristics import grid as default_grid + +from .. import config +from ..codecache import CudaKernelParamCache +from ..utils import DeferredLineBase +from ..virtualized import V +from .aoti_hipify_utils import maybe_hipify_code_wrapper +from .codegen_device_driver import cuda_kernel_driver, cuda_kernel_header +from .cpp_utils import cexpr, DTYPE_TO_CPP +from .cpp_wrapper_cpu import CppWrapperCpu +from .wrapper import SymbolicCallArg + + +if TYPE_CHECKING: + from ..graph import GraphLowering + + +class DeferredCudaKernelLine(DeferredLineBase): + """ + When using cpp wrapper, CUDA kernel load and launch needs to wait for Triton kernels + to be tuned and stored as cubin files, so use a deferred line to backfill those information + """ + + def __init__( + self, + kernel_name: str, + line_template: str, + keys: Tuple[str, ...], + ): + super().__init__(line_template) + assert not isinstance(line_template, DeferredLineBase) + self.kernel_name = kernel_name + self.line_template = line_template + self.keys = keys + + def __call__(self): + params = CudaKernelParamCache.get(self.kernel_name) + assert ( + params is not None + ), f"{self.kernel_name} not found in CudaKernelParamCache" + for key in self.keys: + assert ( + key in params + ), f"{key} not found in CudaKernelParamCache[{self.kernel_name}]" + if key == get_cpp_wrapper_cubin_path_name(): + assert os.path.exists(params[key]), f"{params[key]} does not exist" + + return self.line_template % tuple(params[key] for key in self.keys) + + def _new_line(self, line): + return DeferredCudaKernelLine(self.kernel_name, line, self.keys) + + +class DeferredCudaDefaultGrid: + """ + A container for the default grid, which may be used by DeferredCudaGridLine + """ + + def __init__( + self, + kernel_name: str, + grid, + grid_callable: Optional[Callable[..., Any]] = None, + **grid_extra_kwargs, + ): + self.kernel_name = kernel_name + self.grid = grid + self.grid_callable = grid_callable + self.grid_extra_kwargs = grid_extra_kwargs + + def _process_grid(self, grid: Union[List[Any], Tuple[Any, ...]]): + if isinstance(grid, (list, tuple)): + return [self._process_grid(e) for e in grid] + else: + return grid.inner_expr if isinstance(grid, SymbolicCallArg) else grid + + def __call__(self): + grid = self.grid + assert isinstance(grid, (list, tuple)), f"expected {grid=} to be a list" + grid = self._process_grid(grid) + grid_callable = self.grid_callable or default_grid + if not self.grid_extra_kwargs: + grid_fn = grid_callable(*grid) + else: + grid_fn = grid_callable(*grid, **self.grid_extra_kwargs) + + params = CudaKernelParamCache.get(self.kernel_name) + assert ( + params is not None + ), f"{self.kernel_name} not found in CudaKernelParamCache" + block_cfg = { + "XBLOCK": params["x_block"], + "YBLOCK": params["y_block"], + "ZBLOCK": params["z_block"], + } + return grid_fn(block_cfg) + + +class DeferredCudaGridLine(DeferredLineBase): + """ + When using cpp wrapper, CUDA kernel load and launch needs to wait for Triton kernels + to be tuned and stored as cubin files, so use a deferred line to backfill those information + """ + + def __init__( + self, + kernel_name: str, + grid_var: str, + grid, + autotune_configs, + ): + super().__init__("") + self.kernel_name = kernel_name + self.grid_var = grid_var + self.grid = grid + self.autotune_configs = autotune_configs + + def __call__(self): + params = CudaKernelParamCache.get(self.kernel_name) + assert ( + params is not None + ), f"{self.kernel_name} not found in CudaKernelParamCache" + + if self.autotune_configs is not None: + # This indicates the Triton kernel is a user-defined one. + grid = None + if len(self.grid) == 1: + grid = self.grid[0] + else: + for i, c in enumerate(self.autotune_configs): + if all(arg == params["meta"][key] for key, arg in c.kwargs.items()): + grid = self.grid[i] + break + assert grid is not None + elif isinstance(self.grid, DeferredCudaDefaultGrid): + grid = self.grid() + else: + grid = self.grid + + assert len(grid) != 0, "Grid can't be empty" + grid_args_str = ", ".join( + [cexpr(V.graph.sizevars.simplify(item)) for item in grid] + ) + return f" Grid {self.grid_var} = Grid({grid_args_str});" + + def _new_line(self, line): + return DeferredCudaGridLine( + self.kernel_name, self.grid_var, self.grid, self.autotune_configs + ) + + +class CppWrapperCuda(CppWrapperCpu): + """ + Generates cpp wrapper for running on GPU and calls CUDA kernels + """ + + def __init__(self) -> None: + self.device = "cuda" + super().__init__() + self.grid_id = count() + self.cuda = True + + def write_header(self): + if V.graph.is_const_graph: + # We do not write header for constant graph, it will be written by main module. + return + + super().write_header() + + self.header.splice("#include ") + if config.abi_compatible: + self.header.splice( + "#include " + ) + else: + self.header.splice(maybe_hipify_code_wrapper(cuda_kernel_header())) + self.header.splice(maybe_hipify_code_wrapper(cuda_kernel_driver())) + + def write_get_raw_stream(self, index, graph=None): + name = f"stream{index}" + self.writeline(maybe_hipify_code_wrapper(f"cudaStream_t {name};")) + self.writeline( + f"AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_get_current_cuda_stream({index}, (void**)&{name}));" + ) + return name + + def define_kernel( + self, name: str, kernel: str, metadata: Optional[str] = None, cuda=True + ): + if not cuda: + return super().define_kernel(name, kernel, metadata, cuda) + + def generate(self, is_inference): + self.prefix.writeline("\n") + if not V.graph.aot_mode: + for kernel in chain( + sorted(self.src_to_kernel.values()), + sorted([entry[0] for entry in self.user_defined_kernel_cache.values()]), + ): + self.prefix.writeline( + maybe_hipify_code_wrapper(f"static CUfunction {kernel} = nullptr;") + ) + self.prefix.writeline("\n") + return super().generate(is_inference) + + def generate_user_defined_triton_kernel( + self, + kernel_name: str, + raw_args: List[Any], + grid: List[Any], + configs, + triton_meta, + constexprs, + ): + # in C++ wrapper, we don't pass constexpr args, as they don't + # get added as parameters to the PTX code compiled from the + # user-defined Triton kernel (only non-constexpr args do) + raw_args = [ + raw_arg for i, raw_arg in enumerate(raw_args) if i not in constexprs + ] + args = [self.val_to_arg_str(v) for v in raw_args] + arg_types = [ + arg.get_dtype() if hasattr(arg, "get_dtype") else type(arg) + for arg in raw_args + ] + self.generate_kernel_call( + kernel_name, + args, + arg_types=arg_types, + raw_args=raw_args, + grid=grid, + cuda=True, + triton=True, + triton_meta=triton_meta, + autotune_configs=configs, + ) + + @functools.lru_cache(None) # noqa: B019 + def generate_load_kernel_once( + self, + kernel_name: str, + graph: "GraphLowering", # for per-graph caching + ): + keys = (get_cpp_wrapper_cubin_path_name(), "mangled_name", "shared_mem") + kernel_var_name = f"kernels.{kernel_name}" if V.graph.aot_mode else kernel_name + self.writeline(f"if ({kernel_var_name} == nullptr) {{") + self.writeline( + DeferredCudaKernelLine( + kernel_name, + """ """ + + kernel_var_name + + """ = loadKernel("%s", "%s", %s, this->cubin_dir_);""" + if V.graph.aot_mode + else """ """ + + kernel_var_name + + """ = loadKernel("%s", "%s", %s);""", + keys, + ) + ) + self.writeline("}") + return kernel_var_name + + def generate_args_decl(self, call_args, arg_types): + new_args = [] + for arg, arg_type in zip(call_args, arg_types): + var_name = f"var_{next(self.arg_var_id)}" + if isinstance(arg_type, torch_dtype): + if arg.endswith(".item()"): + # Need to declare a scalar in this case + ctype = DTYPE_TO_CPP[arg_type] + arg = arg[:-7] + if config.abi_compatible: + self.codegen_tensor_item( + arg_type, + arg, + var_name, + ) + else: + from torch import bfloat16, float16 + + if arg_type in (float16, bfloat16): + var_name_tmp = f"{var_name}_tmp" + self.writeline( + f"{ctype} {var_name_tmp} = {arg}.item<{ctype}>();" + ) + self.writeline(f"float {var_name} = float({var_name_tmp});") + else: + self.writeline( + f"{ctype} {var_name} = {arg}.item<{ctype}>();" + ) + else: + if config.abi_compatible: + self.writeline( + maybe_hipify_code_wrapper(f"CUdeviceptr {var_name};") + ) + self.writeline( + f"AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_get_data_ptr({arg}, reinterpret_cast(&{var_name})));" + ) + else: + self.writeline( + maybe_hipify_code_wrapper( + f"CUdeviceptr {var_name} = reinterpret_cast({arg}.data_ptr());" + ) + ) + elif arg_type in (sympy.Integer, int): + self.writeline(f"int {var_name} = {self.expr_printer(arg)};") + elif arg_type in (sympy.Float, float): + self.writeline(f"float {var_name} = {self.expr_printer(arg)};") + else: + self.writeline(f"auto {var_name} = {self.expr_printer(arg)};") + new_args.append(f"&{var_name}") + + return ", ".join(new_args) + + def generate_default_grid( + self, + kernel_name: str, + grid: List[Any], + cuda: bool = True, + grid_callable: Optional[Callable[..., Any]] = None, + **grid_extra_kwargs, + ): + """ + Generate grid configs for launching a CUDA kernel using the grid + function from triton_heuristics. Because its computation needs + to read kernel config after autotune, it is done in a deferred way + using DeferredCudaDefaultGrid. + """ + if not cuda: + return grid + return DeferredCudaDefaultGrid( + kernel_name, grid, grid_callable, **grid_extra_kwargs + ) + + def generate_kernel_call( + self, + kernel_name: str, + call_args, + grid=None, + device_index=None, + cuda=True, + triton=True, + arg_types=None, + raw_args=None, + grid_fn: str = "grid", + triton_meta=None, + autotune_configs=None, + grid_extra_kwargs="", + ): + assert arg_types is not None and len(call_args) == len( + arg_types + ), "call_args and arg_types do not match" + + if not cuda: + # Even in CppWrapperCuda, we may see cpp kernels + return super().generate_kernel_call( + kernel_name, + call_args, + grid, + device_index, + cuda, + triton, + arg_types, + raw_args, + grid_fn, + triton_meta, + autotune_configs, + grid_extra_kwargs, + ) + + device_index, call_args = self.prepare_triton_kernel_call( + device_index, call_args + ) + kernel_var_name = self.generate_load_kernel_once(kernel_name, V.graph) + + # args with value 1 are added into equal_to_1 and constants + # in triton_meta (in the Python codegen) which makes them + # inlined in the PTX and compiled CUBIN + if ( + triton_meta is not None + and "configs" in triton_meta + and triton_meta["configs"] + ): + equal_to_1 = triton_meta["configs"][0].equal_to_1 + call_args = [arg for i, arg in enumerate(call_args) if i not in equal_to_1] + arg_types = [t for i, t in enumerate(arg_types) if i not in equal_to_1] + + call_args_str = self.generate_args_decl(call_args, arg_types) + kernel_args_var = f"kernel_args_var_{next(self.kernel_callsite_id)}" + self.writeline(f"void* {kernel_args_var}[] = {{{call_args_str}}};") + stream = ( + "stream" + if V.graph.aot_mode + else self.write_get_raw_stream(device_index, V.graph) + ) + + grid_var = f"{kernel_name}_grid_{next(self.grid_id)}" + self.writeline( + DeferredCudaGridLine(kernel_name, grid_var, grid, autotune_configs) + ) + + kernel_var_name = f"kernels.{kernel_name}" if V.graph.aot_mode else kernel_name + # add debug printer code for all triton kernel related calls + debug_printer_manager = V.graph.wrapper_code.debug_printer + debug_printer_manager.set_printer_args(call_args, kernel_name, arg_types, None) + with debug_printer_manager: + self.writeline(f"if ({grid_var}.is_non_zero()) {{") + self.writeline( + DeferredCudaKernelLine( + kernel_name, + r" launchKernel({}, {}, {}, {}, %s, %s, {}, {});".format( + kernel_var_name, + f"{grid_var}.grid_x", + f"{grid_var}.grid_y", + f"{grid_var}.grid_z", + kernel_args_var, + stream, + ), + ("num_warps", "shared_mem"), + ), + ) + self.writeline("}") diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cuda_combined_scheduling.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cuda_combined_scheduling.py new file mode 100644 index 0000000000000000000000000000000000000000..909710453796563636d55194024a69001a7038bb --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/cuda_combined_scheduling.py @@ -0,0 +1,100 @@ +# mypy: allow-untyped-defs +from typing import Sequence, Union + +from ..scheduler import ( + BaseSchedulerNode, + BaseScheduling, + FusedSchedulerNode, + Scheduler, + SchedulerNode, +) +from .cuda.cuda_cpp_scheduling import CUDACPPScheduling +from .rocm.rocm_cpp_scheduling import ROCmCPPScheduling +from .triton import TritonScheduling + + +class CUDACombinedScheduling(BaseScheduling): + """ + Scheduler for CUDA Kernels, which delegates calls as appropriate + to the CUDA-C++ and Triton Schedulers, which both work for CUDA devices + and use a unified-wrapper for codegen. + + If Scheduling code needs to be specialized for the case of mixed Triton / CUDA C++ code, + this would also be the place to do it. + """ + + def __init__(self, scheduler: Scheduler) -> None: + super().__init__() + self._scheduler = scheduler + self._triton_scheduling = TritonScheduling(scheduler) + self._cuda_cpp_scheduling = CUDACPPScheduling(scheduler) + self._rocm_cpp_scheduling = ROCmCPPScheduling(scheduler) + + def get_backend_features(self, device): + return self._triton_scheduling.get_backend_features(device) + + def choose_node_backend(self, node: BaseSchedulerNode) -> BaseScheduling: + if self._cuda_cpp_scheduling.is_cuda_cpp_template(node): + return self._cuda_cpp_scheduling + if self._rocm_cpp_scheduling.is_rocm_cpp_template(node): + return self._rocm_cpp_scheduling + return self._triton_scheduling + + def can_fuse_vertical(self, node1: BaseSchedulerNode, node2: BaseSchedulerNode): + if self._cuda_cpp_scheduling.can_fuse_vertical(node1, node2): + return True + return self._triton_scheduling.can_fuse_vertical(node1, node2) + + def can_fuse_horizontal(self, node1: BaseSchedulerNode, node2: BaseSchedulerNode): + for node in (node1, node2): + if self._cuda_cpp_scheduling.is_cuda_cpp_template(node): + return self._cuda_cpp_scheduling.can_fuse_horizontal( + node1, node2 + ) # always False at the moment + return self._triton_scheduling.can_fuse_horizontal(node1, node2) + + def group_fn(self, sizes): + return self._triton_scheduling.group_fn(sizes) + + def codegen_template( + self, + template_node: BaseSchedulerNode, + epilogue_nodes: Sequence[BaseSchedulerNode], + ): + if self._cuda_cpp_scheduling.is_cuda_cpp_template(template_node): + assert epilogue_nodes is None or len(epilogue_nodes) == 0 + return self._cuda_cpp_scheduling.codegen_template( + template_node, epilogue_nodes + ) + elif self._rocm_cpp_scheduling.is_rocm_cpp_template(template_node): + assert epilogue_nodes is None or len(epilogue_nodes) == 0 + return self._rocm_cpp_scheduling.codegen_template( + template_node, epilogue_nodes + ) + else: + return self._triton_scheduling.codegen_template( + template_node, epilogue_nodes + ) + + def codegen_node(self, node: Union[FusedSchedulerNode, SchedulerNode]): + return self._triton_scheduling.codegen_node(node) + + def codegen_sync(self): + return self._triton_scheduling.codegen_sync() + + def flush(self): + return self._triton_scheduling.flush() + + def codegen_combo_kernel(self, *args, **kwargs): + return self._triton_scheduling.codegen_combo_kernel(*args, **kwargs) + + def benchmark_fused_nodes(self, nodes): + return self._triton_scheduling.benchmark_fused_nodes(nodes) + + def generate_kernel_code_from_nodes(self, nodes, benchmark_kernel=False): + return self._triton_scheduling.generate_kernel_code_from_nodes( + nodes, benchmark_kernel + ) + + def benchmark_combo_kernel(self, node_list): + return self._triton_scheduling.benchmark_combo_kernel(node_list) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/debug_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/debug_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7b3dcd8eea2cf5ab5c7f688891297a05c812de99 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/debug_utils.py @@ -0,0 +1,175 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import functools +import logging +from enum import Enum +from typing import List, Optional + +from torch import dtype as torch_dtype + +from .. import config +from ..virtualized import V +from .multi_kernel import MultiKernel + + +log = logging.getLogger(__name__) + + +# AOTI debug printing related configs +class IntermediateValueDebuggingLevel(Enum): + # OFF: No intermediate tensor value debug info will be printed or saved. + OFF = "0" + # LEVEL 1: Save all intermediate tensor values to individual `.pt` files. No debug printing will be displayed. + SAVE_ONLY = "1" + # LEVEL 2: Print all intermediate tensor values by default to the console. No debug saving will be performed. + PRINT_ONLY = "2" + + +class DebugPrinterManager: + def __init__( + self, + debug_printer_level, + args_to_print_or_save: Optional[List[str]] = None, + kernel_name: str = "", + kernel=None, + arg_signatures: Optional[List[type]] = None, + ): + self.debug_printer_level = IntermediateValueDebuggingLevel(debug_printer_level) + if args_to_print_or_save is None: + args_to_print_or_save = [] + self.args_to_print_or_save = args_to_print_or_save + self.kernel_name = kernel_name + self.arg_signatures: Optional[List[type]] = None + self.kernel = kernel + self.filtered_kernel_names_to_print = self._get_debug_filtered_kernel_names() + + def __enter__(self): + self._perform_debug_print_or_save_helper( + self.args_to_print_or_save, + self.kernel_name, + before_launch=True, + arg_signatures=self.arg_signatures, + ) + + def __exit__(self, args_to_print_or_save, kernel_name, arg_signatures): + self._perform_debug_print_or_save_helper( + args_to_print_or_save, + kernel_name, + before_launch=False, + arg_signatures=arg_signatures, + ) + + def _perform_debug_print_or_save_helper( + self, + args_to_print_or_save, + kernel_name, + before_launch, + arg_signatures: Optional[List[type]] = None, + ): + if self.debug_printer_level == IntermediateValueDebuggingLevel.OFF: + return + if self.debug_printer_level == IntermediateValueDebuggingLevel.SAVE_ONLY: + # by default save all the tensor values before launch + self.codegen_intermediate_tensor_value_save( + self.args_to_print_or_save, + self.kernel_name, + before_launch, + arg_signatures=self.arg_signatures, + ) + if self.debug_printer_level == IntermediateValueDebuggingLevel.PRINT_ONLY: + # by default print all the tensor values before launch + self.codegen_intermediate_tensor_value_print( + self.args_to_print_or_save, + self.kernel_name, + before_launch, + arg_signatures=self.arg_signatures, + ) + + @functools.lru_cache # noqa: B019 + def _get_debug_filtered_kernel_names(self) -> List[str]: + if config.aot_inductor.filtered_kernel_names is None: + return [] + return [ + x.strip() + for x in config.aot_inductor.filtered_kernel_names.lower().split(",") + ] + + def set_printer_args( + self, + args_to_print_or_save: List[str], + kernel_name: str, + arg_signatures: Optional[List[type]], + kernel, + ): + # Note: MultiKernel debug printing is not supported for now + if isinstance(kernel, MultiKernel): + log.info( + "MultiKernel type is not supported in AOTI debug printer tool yet." + ) + self.debug_printer_level = IntermediateValueDebuggingLevel.OFF + self.args_to_print_or_save = args_to_print_or_save + self.kernel_name = kernel_name + self.arg_signatures = arg_signatures + self.kernel = kernel + + def codegen_intermediate_tensor_value_save( + self, + args_to_save, + kernel_name, + before_launch=True, + arg_signatures: Optional[List[type]] = None, + ) -> None: + for i, arg in enumerate(args_to_save): + if arg_signatures is not None and not isinstance( + arg_signatures[i], torch_dtype + ): + # infer from the arg data type (has torch.dtype) to see if it is a tensor type + continue + launch_prefix = "before_launch" if before_launch else "after_launch" + if V.graph.cpp_wrapper: + if config.abi_compatible: + V.graph.wrapper_code.writeline( + f'aoti_torch_save_tensor_handle({arg}, "{arg}", "{launch_prefix}", "{kernel_name}");' + ) + else: + # TODO: add non-abi compatible mode debug printing info + pass + else: + # currently, not cpp wrapper codegen mode not supported. + pass + + def codegen_intermediate_tensor_value_print( + self, + args_to_print, + kernel_name, + before_launch=True, + arg_signatures: Optional[List[type]] = None, + ) -> None: + for i, arg in enumerate(args_to_print): + if arg_signatures is not None and not isinstance( + arg_signatures[i], torch_dtype + ): + # infer from the arg data type (has torch.dtype) to see if it is a tensor type + continue + if self.debug_printer_level == IntermediateValueDebuggingLevel.PRINT_ONLY: + # when debug printing is enabled i.e. IntermediateValueDebuggingLevel.PRINT_ONLY, + # check if filtered kernel name list is provided + if ( + len(self.filtered_kernel_names_to_print) > 0 + and kernel_name not in self.filtered_kernel_names_to_print + ): + continue + + launch_prefix = "before_launch" if before_launch else "after_launch" + if V.graph.cpp_wrapper: + if config.abi_compatible: + V.graph.wrapper_code.writeline( + f'aoti_torch_print_tensor_handle({arg}, "{launch_prefix} - {kernel_name} - {arg}");' + ) + else: + # TODO: add non-abi compatible mode debug printing info + pass + else: + line = f"print('{launch_prefix} - {kernel_name} - {arg}', {arg})" + V.graph.wrapper_code.writeline(line) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/halide.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/halide.py new file mode 100644 index 0000000000000000000000000000000000000000..20968a57a4444f48548d48f27684484150086b58 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/halide.py @@ -0,0 +1,1699 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import dataclasses +import functools +import itertools +import logging +import re +from collections import defaultdict +from math import inf +from typing import ( + Any, + Callable, + Dict, + List, + Optional, + Sequence, + Tuple, + TYPE_CHECKING, + Union, +) + +import sympy + +import torch +import torch._logging + +from ..._prims_common import is_integer_dtype +from ...utils._sympy.functions import FloorDiv, ModularIndexing +from ...utils._sympy.symbol import symbol_is_type, SymT +from ...utils._sympy.value_ranges import ValueRanges +from .. import config, ir +from ..codecache import HalideCodeCache +from ..ir import get_reduction_combine_fn +from ..metrics import is_metric_table_enabled, log_kernel_metadata +from ..ops_handler import AddParenHandler, MockHandler +from ..runtime.hints import HalideInputSpec, HalideMeta, ReductionHint +from ..utils import ( + get_bounds_index_expr, + get_kernel_metadata, + parallel_num_threads, + sympy_index_symbol, + sympy_subs, +) +from ..virtualized import _ops as ops, OpsHandler, V +from .common import ( + BackendFeature, + CSEVariable, + DeferredLine, + IndentedBuffer, + OpOverrides, + PythonPrinter, + SizeArg, + TensorArg, +) +from .cpp import DTYPE_TO_CPP +from .cpp_utils import cexpr +from .simd import constant_repr, SIMDKernel, SIMDScheduling + + +if TYPE_CHECKING: + from torch.utils._ordered_set import OrderedSet + + from ..ops_handler import ReductionType, StoreMode + +log = logging.getLogger(__name__) + + +def halide_constant(val): + if isinstance(val, int) and not (-2147483648 <= val <= 2147483647): + info = torch.iinfo(torch.int64) + if val == info.min: + return "hl.Int(64).min()" + if val == info.max: + return "hl.Int(64).max()" + return f"hl.i64({val!r})" + if isinstance(val, float): + return f"hl.f64({constant_repr(val)})" + return repr(val) + + +class Unsupported(RuntimeError): + def __init__(self, thing) -> None: + super().__init__(f"halide backend does not support: {thing}") + + +class HalidePrinter(PythonPrinter): + @staticmethod + def cast_index(expr): + return f"hl.cast({V.kernel.index_dtype}, {expr})" + + @staticmethod + def cast_float(expr): + return f"hl.cast(hl.Float(32), {expr})" + + def _print_Float(self, expr): + return f"hl.f32({expr})" + + def _print_ToFloat(self, expr): + assert len(expr.args) == 1 + return f"hl.f32({self._print(expr.args[0])})" + + def _print_floor(self, expr): + assert len(expr.args) == 1 + return self.cast_index(f"hl.floor({self._print(expr.args[0])})") + + def _print_Trunc(self, expr): + assert len(expr.args) == 1 + return self.cast_index(f"hl.trunc({self._print(expr.args[0])})") + + _print_TruncToInt = _print_Trunc + + def _print_ceiling(self, expr): + assert len(expr.args) == 1 + return self.cast_index(f"hl.ceil({self._print(expr.args[0])})") + + def _helper_sqrt(self, expr): + return f"hl.sqrt({self.cast_float(self._print(expr))})" + + def _print_Where(self, expr): + c = self.doprint(expr.args[0]) + p = self.doprint(expr.args[1]) + q = self.doprint(expr.args[2]) + return f"hl.select({c}, {p}, {q})" + + def _print_Min(self, expr): + if len(expr.args) == 1: + return self._print(expr.args[0]) + + mid = len(expr.args) // 2 + a = self._print(sympy.Min(*expr.args[:mid])) + b = self._print(sympy.Min(*expr.args[mid:])) + return f"hl.min({a}, {b})" + + def _print_Max(self, expr): + if len(expr.args) == 1: + return self._print(expr.args[0]) + + mid = len(expr.args) // 2 + a = self._print(sympy.Max(*expr.args[:mid])) + b = self._print(sympy.Max(*expr.args[mid:])) + + return f"hl.max({a}, {b})" + + def _print_Abs(self, expr): + assert len(expr.args) == 1 + return self.cast_index(f"hl.abs({self._print(expr.args[0])})") + + def _print_OpaqueUnaryFn_cos(self, expr): + assert len(expr.args) == 1 + return f"hl.cos(({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_cosh(self, expr): + assert len(expr.args) == 1 + return f"hl.cosh(({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_acos(self, expr): + assert len(expr.args) == 1 + return f"hl.acos(({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_sin(self, expr): + assert len(expr.args) == 1 + return f"hl.sin(({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_sinh(self, expr): + assert len(expr.args) == 1 + return f"hl.sinh(({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_asin(self, expr): + assert len(expr.args) == 1 + return f"hl.asin(({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_tan(self, expr): + assert len(expr.args) == 1 + return f"hl.tan(({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_tanh(self, expr): + assert len(expr.args) == 1 + return f"hl.tanh(({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_atan(self, expr): + assert len(expr.args) == 1 + return f"hl.atan(({self._print(expr.args[0])})" + + def _print_FloorDiv(self, expr): + if expr.is_integer: + return super()._print_FloorDiv(expr) + + x, div = expr.args + x = self.cast_float(self.paren(self.doprint(x))) + div = self.cast_float(self.paren(self.doprint(div))) + return self.cast_index(f"hl.floor({x} / {div})") + + def _print_Round(self, expr): + assert len(expr.args) == 1 + return self.cast_index(f"hl.round({self._print(expr.args[0])})") + + _print_RoundToInt = _print_Round + + def _print_IntTrueDiv(self, expr): + a, b = expr.args + # force a cast to float + return f"({a}) / ({b}+hl.f32(0))" + + def _print_RoundDecimal(self, expr): + val, n = expr.args + val = self._print(val) + n = int(n) + return f"hl.f32({10.**(-n)!r})*hl.round(({val})*hl.f32({10.**n!r}))" + + +texpr = HalidePrinter().doprint +pexpr = PythonPrinter().doprint + + +_halide_type = { + torch.bool: "hl.Bool()", + torch.bfloat16: "hl.BFloat(16)", + torch.float16: "hl.Float(16)", + torch.float32: "hl.Float(32)", + torch.float64: "hl.Float(64)", + torch.int8: "hl.Int(8)", + torch.int16: "hl.Int(16)", + torch.int32: "hl.Int(32)", + torch.int64: "hl.Int(64)", + torch.uint8: "hl.UInt(8)", + torch.uint16: "hl.UInt(16)", + torch.uint32: "hl.UInt(32)", + torch.uint64: "hl.UInt(64)", +} + + +def halide_type(dtype): + return _halide_type[dtype] + + +def halide_acc_type(dtype): + if is_integer_dtype(dtype) and dtype.is_signed and dtype != torch.int64: + dtype = torch.int32 + if dtype in (torch.float16, torch.bfloat16): + dtype = torch.float32 + return halide_type(dtype) + + +class HalideOverrides(OpOverrides): + @staticmethod + def to_dtype( + x, + dtype: torch.dtype, + src_dtype: Optional[torch.dtype] = None, + use_compute_types=True, + ): + if dtype == torch.bool: + return f"({x} != 0)" + return f"hl.cast({halide_type(dtype)}, {x})" + + @staticmethod + def to_dtype_bitcast(x, dtype: torch.dtype, src_dtype: torch.dtype): + if src_dtype in (torch.float16, torch.bfloat16): + x = f"hl.cast({halide_type(src_dtype)}, {x})" # body compute is upcast to fp32 + line = f"hl.reinterpret({halide_type(dtype)}, {x})" + if dtype in (torch.float16, torch.bfloat16): + line = f"hl.cast(hl.Float(32), {line})" + return line + + @classmethod + def constant(cls, value, dtype): + return cls.to_dtype(halide_constant(value), dtype) + + @staticmethod + def abs(x): + return f"hl.abs({x})" + + @staticmethod + def exp(x): + if not hasattr(x, "name"): + return f"hl.exp({x})" + return f"hl.fast_exp(hl.cast(hl.Float(32), {x})) if {x.name}.type().bits() <= 32 else hl.exp({x})" + + @staticmethod + def libdevice_exp(x): + return f"hl.exp({x})" # higher precision that ops.exp + + @staticmethod + def sqrt(x): + return f"hl.sqrt({x})" + + @staticmethod + def minimum(a, b): + # return f"hl.min({a}, {b})" <== handles nan wrong + if not hasattr(a, "name"): + return f"hl.min({a}, {b})" + b = f"hl.cast({a.name}.type(), {b})" + return f"hl.select(({a}<{b})|hl.is_nan({a}), {a}, {b}) if {a.name}.type().is_float() else hl.min({a}, {b})" + + @staticmethod + def maximum(a, b): + # return f"hl.max({a}, {b})" <== handles nan wrong + if not hasattr(a, "name"): + return f"hl.max({a}, {b})" + b = f"hl.cast({a.name}.type(), {b})" + return f"hl.select(({a}>{b})|hl.is_nan({a}), {a}, {b}) if {a.name}.type().is_float() else hl.max({a}, {b})" + + @staticmethod + def where(a, b, c): + if hasattr(b, "name"): + c = f"hl.cast({b.name}.type(), {c})" + return f"hl.select({a}, {b}, {c})" + + @staticmethod + def cos(x): + return f"hl.cos({x})" + + @staticmethod + def sin(x): + return f"hl.sin({x})" + + @staticmethod + def lgamma(x): + raise Unsupported("lgamma") + + @staticmethod + def erf(x): + return f"hl.erf({x})" + + @staticmethod + def cosh(x): + return f"hl.cosh({x})" + + @staticmethod + def sinh(x): + return f"hl.sinh({x})" + + @staticmethod + def acos(x): + return f"hl.acos({x})" + + @staticmethod + def acosh(x): + return f"hl.acosh({x})" + + @staticmethod + def asin(x): + return f"hl.asin({x})" + + @staticmethod + def asinh(x): + return f"hl.asinh({x})" + + @staticmethod + def atan2(x, y): + return f"hl.atan2({x}, {y})" + + @staticmethod + def atan(x): + return f"hl.atan({x})" + + @staticmethod + def atanh(x): + return f"hl.atanh({x})" + + @staticmethod + def copysign(x, y): + raise Unsupported("copysign") + + @staticmethod + def erfinv(x): + raise Unsupported("erfinv") + + @staticmethod + def hypot(x, y): + return f"hl.hypot({x}, {y})" + + @staticmethod + def nextafter(x, y): + raise Unsupported("nextafter") + + @staticmethod + def logical_and(a, b): + return f"{a} & {b}" + + @staticmethod + def logical_not(a): + return f"{a} == 0" + + @staticmethod + def logical_or(a, b): + return f"{a} | {b}" + + @staticmethod + def logical_xor(a, b): + return f"({a} ^ {b})" + + @staticmethod + def bitwise_and(a, b): + return f"{a} & {b}" + + @staticmethod + def bitwise_not(a): + return f"~{a}" + + @staticmethod + def bitwise_or(a, b): + return f"{a} | {b}" + + @staticmethod + def bitwise_xor(a, b): + return f"{a} ^ {b}" + + @staticmethod + def bitwise_left_shift(a, b): + return f"{a} << {b}" + + @staticmethod + def bitwise_right_shift(a, b): + return f"{a} >> {b}" + + @staticmethod + def rand(seed, offset): + return f"halide_helpers.rand({seed}, {offset})" + + @staticmethod + def randn(seed, offset): + return f"halide_helpers.randn({seed}, {offset})" + + @staticmethod + def randint64(seed, offset, low, high): + return f"halide_helpers.randint64({seed}, {offset}, {low}, {high})" + + @staticmethod + def load_seed(name, offset): + return f"{ops.load(name, 0)} + {V.kernel.args.seed_offset('load_seed_offset', offset)}" + + @staticmethod + def rsqrt(x): + # return f"hl.fast_inverse_sqrt({x})" <== accuracy issues + return f"1./hl.sqrt({x})" + + @staticmethod + def tan(x): + return f"hl.tan({x})" + + @staticmethod + def tanh(x): + return f"hl.tanh({x})" + + @staticmethod + def signbit(x): + return f"(hl.reinterpret(hl.UInt(32), hl.cast(hl.Float(32), {x})) >> 31) != 0" + + @staticmethod + def fmod(a, b): + # TODO(jansel): find a better way to do this, builtin % has wrong sign + return f"{a} - hl.trunc({a}/{b})*{b}" + + @staticmethod + def pow(a, b): + return f"hl.pow({a}, {b})" # hl.fast_pow fails accuracy + + @staticmethod + def log(x): + return f"hl.log({x})" # hl.fast_log fails accuracy + + @staticmethod + def isinf(x): + # workaround https://github.com/halide/Halide/issues/8309 + return f"hl.is_inf(hl.cast(hl.Float(32), {x}))" + + @staticmethod + def isnan(x): + # workaround https://github.com/halide/Halide/issues/8309 + return f"hl.is_nan(hl.cast(hl.Float(32), {x}))" + + @staticmethod + def round(x): + return f"hl.round({x})" + + @staticmethod + def floor(x): + return f"hl.floor({x})" + + @staticmethod + def int_truediv(a, b): + return f"({a}) / ({b} + hl.f32(0))" + + @staticmethod + def floordiv(a, b): + # TODO(jansel): find a better ways to do this, the select-based trick from triton.py didn't work + return ( + f"hl.floor(hl.cast(hl.Float(max(32, {a.name}.type().bits())), {a}) / {b})" + ) + + @classmethod + def sign(cls, x): + left = ops.to_dtype(ops.lt("0", x), torch.int8) + right = ops.to_dtype(ops.lt(x, "0"), torch.int8) + sub = ops.sub(left, right) + return f"hl.cast({x.name}.type(), {sub})" + + @staticmethod + def trunc(x): + return f"hl.trunc({x})" + + @staticmethod + def truncdiv(a, b): + # this causes crashes with floating point exception, see test_div_zero_dim_cpu + # return f"hl.div_round_to_zero({a}, {b})" + return ( + f"hl.trunc(hl.cast(hl.Float(max(32, {a.name}.type().bits())), {a}) / {b})" + ) + + @staticmethod + def ceil(x): + return f"hl.ceil({x})" + + @staticmethod + def relu(x): + return f"hl.max({x}, 0)" + + @classmethod + def index_expr(cls, expr, dtype): + index = V.kernel.prepare_indexing(expr) + var = V.kernel.genfunc( + V.kernel.index_to_str(index), + V.kernel.used_dims_from_index(index), + bounds=get_bounds_index_expr(expr), + ) + if dtype not in {torch.int32, torch.int64}: + return ops.to_dtype(var, dtype) + return var + + @classmethod + def indirect_indexing(cls, index_var, size, check=True, wrap_neg=True): + # TODO(jansel): Halide only supports 32-bit indexing, we should error on overflow + index_var = ops.to_dtype(index_var, torch.int32) + index_var = ops.halide_clamp(index_var, size, check) + index_var.indirect_indexing_size = size + return sympy_index_symbol(str(index_var)) + + @classmethod + def halide_clamp(cls, value, size, check): + end = V.kernel.kexpr(V.kernel.rename_indexing(size) - 1) + if not isinstance(size, (int, sympy.Integer)): + end = f"hl.cast({value.name}.type(), {end})" + # Skip unsafe_promise_clamped to workaround: https://github.com/halide/Halide/issues/8261#issuecomment-2148835692 + # return f"hl.unsafe_promise_clamped({value}, 0, {end})" + return f"hl.clamp({value}, 0, {end})" + + @staticmethod + def masked(mask, body, other): + with V.kernel.mask_loads(mask, other) as new_mask: + result = body() + + if result.bounds.is_bool: + other = bool(other) + + # Take dtype from result to prevent accidental promotion + other = V.kernel.genfunc( + f"hl.cast({result.name}.type(), {halide_constant(other)})", + [], + bounds=ValueRanges.wrap(other), + ) + # TODO(jansel): look into removing the where in the same places triton does + return ops.where(new_mask, result, other) + + +# Use mypy to check protocol implemented correctly +def _typecheck_HalideOverrides(h: HalideOverrides) -> OpsHandler[str]: + return h + + +class HalideCSEVariable(CSEVariable): + undefined_re = re.compile(r"\b(tmp\d+)\[\?\]") + + def __init__(self, name, bounds: ValueRanges[Any]) -> None: + super().__init__(name, bounds) + self.used_dims: Optional[List[sympy.Symbol]] = None + + def update_on_args(self, name, args, kwargs): + used = set(self.used_dims or ()) + for arg in itertools.chain(args, kwargs.values()): + if isinstance(arg, HalideCSEVariable): + assert arg.used_dims is not None, (name, arg, args) + used.update(arg.used_dims) + self.used_dims = V.kernel.sort_used_dims(used) + + def index_str(self, dims): + if len(dims) == 0: + return f"{self.name}[()]" + # Reversed since Halide is column major + return f"{self.name}[{', '.join(map(str, dims))}]" + + def __str__(self) -> str: + if self.used_dims is None: + # This will get recomputed and replaced in codegen_kernel() + return f"{self.name}[?]" + return self.index_str(self.used_dims) + + def subs_str(self, replacements): + assert self.used_dims is not None and all( + isinstance(x, sympy.Expr) for x in self.used_dims + ) + return self.index_str([replacements.get(n, n) for n in self.used_dims]) + + +@dataclasses.dataclass +class DimensionInfo: + expr: Optional[sympy.Expr] + size: sympy.Expr + stride: sympy.Expr + + def __init__(self, expr, size, stride) -> None: + super().__init__() + if V.graph.sizevars.statically_known_lt(stride, 0): + stride = -stride + expr = -expr + self.expr = expr + self.size = size + self.stride = stride + + def index_str(self, replacements=None, zero_vars=False): + assert self.expr is not None + expr = self.expr + if zero_vars and expr == 0: + return "hl.Var()" + if replacements: + replacements = {**replacements} + for sym in expr.free_symbols: + if symbol_is_type(sym, SymT.TMP): + assert isinstance(sym, sympy.Symbol) + var = V.kernel.lookup_cse_var(sym.name) + assert isinstance(var, HalideCSEVariable) + replacements[sym] = sympy_index_symbol(var.subs_str(replacements)) + expr = sympy_subs(expr, replacements) + return V.kernel.index_to_str(expr) + + +def eq(left, right): + if V.graph.sizevars.statically_known_equals(left, right): + return True + try: + a = V.graph.sizevars.size_hint(left) + b = V.graph.sizevars.size_hint(right) + except TypeError: # unbacked symints + return False + if a == b: + V.graph.sizevars.guard_equals(left, right) + return a == b + + +def lt(left, right): + if V.graph.sizevars.statically_known_lt(left, right): + return True + try: + a = V.graph.sizevars.size_hint(left) + b = V.graph.sizevars.size_hint(right) + except TypeError: # unbacked symints + gcd = sympy.gcd(left, right) + if gcd == left: + return left != right + return False + if a < b: + V.graph.sizevars.guard_lt(left, right) + return a < b + + +class HalideKernel(SIMDKernel): + overrides = HalideOverrides # type: ignore[assignment] + kexpr: Callable[[sympy.Expr], str] = texpr + + def __init__( + self, + *groups, + index_dtype: str, + mutations: Optional[OrderedSet[str]] = None, + pid_cache=None, + reduction_hint=ReductionHint.DEFAULT, + override_persistent_reduction=None, + ) -> None: + super().__init__( + *groups, + index_dtype=index_dtype, + mutations=mutations, + reduction_hint=reduction_hint, + pid_cache=pid_cache, + override_persistent_reduction=override_persistent_reduction, + ) + # For halide, we just write directly to the body + self.compute = self.body + self.loads = self.body + self.stores = self.body + self.indexing_code_dom = IndentedBuffer() + self.needs_dom_indexing = self.inside_reduction + self.has_reduction = self.inside_reduction + self.buffer_dimensions: Dict[str, List[DimensionInfo]] = {} + self.buffer_offsets: Dict[str, sympy.Expr] = {} + # {h0: size1, h1: size2, ...} + self.halide_vars: Dict[sympy.Symbol, sympy.Expr] = {} + # {x0: h0, x1: h1+10*h2, ...} + self.index_replacements: Dict[sympy.Expr, sympy.Expr] = {} + # {h1: hr1, ...} + self.reduction_renames: Dict[sympy.Symbol, sympy.Symbol] = {} + # {"i": {h0: hi0}, "o": ...} + self.dom_renames: Dict[str, Dict[sympy.Symbol, sympy.Symbol]] = {} + # {"in_ptr0": ["in_ptr0_view0"], ...} + self.buffer_aliases: Dict[str, List[str]] = defaultdict(list) + self.has_indirect_indexing = False + + def create_cse_var(self, name, bounds=None): + self.body.writeline(f"{name} = hl.Func({name!r})") + return HalideCSEVariable(name, bounds) + + def finalize_indexing(self, indices: Sequence[sympy.Expr]): + """ + Hook called right before codegen with every index that will be + used in the fused kernel. + + This populates self.halide_vars/index_replacements/reduction_renames which is an alternate indexing + scheme that avoids using divide and modulus. Instead of xindex/yindex/rindex + we base indexing on a larger number of vars whose product combines to those. + + This function populates self.halide_vars, self.index_replacements, and self.reduction_renames + """ + assert not ( + self.index_replacements or self.halide_vars or self.reduction_renames + ) + size_hint = functools.partial(V.graph.sizevars.size_hint, fallback=inf) # type: ignore[arg-type] + indices = dict.fromkeys(map(super().prepare_indexing, indices)) + all_used_symbols = set() + sym_to_node = { + n.symbol(): n + for n in itertools.chain.from_iterable( + [tree.nodes.values() for tree in self.range_trees] + ) + } + + def simplify(expr): + return sympy.simplify( + V.graph.sizevars.remove_precomputed_replacements(expr) + ) + + def visit_modular_indexing(base, divisor, modulus): + if base in sym_to_node: + node = sym_to_node[base] + all_used_symbols.add( + node.root.lookup( + node.divisor * divisor, + V.graph.sizevars.evaluate_min( + modulus, FloorDiv(node.length, divisor) + ), + ).symbol() + ) + + def visit_floor_div(base, divisor): + if base in sym_to_node: + node = sym_to_node[base] + all_used_symbols.add( + node.root.lookup( + node.divisor * divisor, + FloorDiv(node.length, divisor), + ).symbol() + ) + + # first figure out all_used_symbols to do dead symbol elimination + for index in indices: + if index.has(ModularIndexing): + index.replace( + ModularIndexing( + sympy.Wild("base"), + sympy.Wild("divisor"), + sympy.Wild("modulus"), + ), + visit_modular_indexing, + ) + if index.has(FloorDiv): + index.replace( + FloorDiv( + sympy.Wild("base"), + sympy.Wild("divisor"), + ), + visit_floor_div, + ) + all_used_symbols.update(super().prepare_indexing(index).free_symbols) + + self.has_indirect_indexing = any( + symbol_is_type(sym, SymT.INDIRECT) for sym in all_used_symbols + ) + + had_fallback = False + for tree in reversed(self.range_trees): + nodes = [n for n in tree.nodes.values() if n.symbol() in all_used_symbols] + nodes.sort(key=lambda n: size_hint(n.divisor)) + if not nodes: + nodes.append(tree.lookup(1, tree.numel)) + handled_count = 0 + divisor = sympy.Integer(1) + added_sym_size = [] + # decide on a minimal set of symbols and put them in self.halide_vars + while handled_count < len(nodes) and not eq(tree.numel, divisor): + sizes_to_add = [ + simplify(n.length) for n in nodes if eq(n.divisor, divisor) + ] + handled_count += len(sizes_to_add) + assert sizes_to_add, nodes + end = divisor * functools.reduce( + V.graph.sizevars.evaluate_max, sizes_to_add + ) + sizes_to_add.extend( + [ + simplify(n.divisor / divisor) + for n in nodes + if lt(divisor, n.divisor) and lt(n.divisor, end) + ] + ) + while sizes_to_add: + next_size = functools.reduce(sympy.gcd, sizes_to_add) + if eq(next_size, 1): + # sizes share no common factors, e.g [2, 21, 42, 441, 889056] + # TODO(jansel): we should just prevent fusion in cases that hit this + next_size = simplify(tree.numel / divisor) + assert not eq(next_size, 1) + sizes_to_add = [] + handled_count = len(nodes) + had_fallback = True + sym = sympy_index_symbol(f"h{len(self.halide_vars)}") + if tree.prefix == "r": + self.reduction_renames[sym] = sympy_index_symbol( + f"hr{len(self.halide_vars)}" + ) + self.halide_vars[sym] = next_size + added_sym_size.append((sym, next_size)) + divisor *= next_size + new_sizes = [n.length for n in nodes if eq(n.divisor, divisor)] + handled_count += len(new_sizes) + prior_len = len(sizes_to_add) + sizes_to_add = [ + sympy.simplify(s / next_size) + for s in sizes_to_add + if not eq(s, next_size) + ] + assert len(sizes_to_add) < prior_len or prior_len == 0 + sizes_to_add.extend(new_sizes) + + # create a mapping to the new set of symbols in self.index_replacements + for node in nodes: + try: + idx = 0 + divisor = 1 + while not eq(node.divisor, divisor): + sym, size = added_sym_size[idx] + idx += 1 + divisor *= size + length = 1 + expr = sympy.Integer(0) + while not eq(node.length, length): + sym, size = added_sym_size[idx] + idx += 1 + expr += length * sym + length *= size + self.index_replacements[node.symbol()] = expr + except IndexError: + assert had_fallback + full_index = sympy.Integer(0) + stride = sympy.Integer(1) + for sym, size in added_sym_size: + full_index += stride * sym + stride *= size + self.index_replacements[ + node.symbol() + ] = V.graph.sizevars.simplify_with_ranges( + ModularIndexing(full_index, node.divisor, node.length), + self.halide_vars, # type: ignore[arg-type] + ) + + # codegen the variable definitions + for sym in self.halide_vars: + self.indexing_code.writeline(f"{sym} = hl.Var({sym.name!r})") + if self.reduction_renames: + self.codegen_rdom( + "rdom", + {rv: self.halide_vars[v] for v, rv in self.reduction_renames.items()}, + ) + + def setup_dom_indexing(self): + """RDom based indexing uses explicit iteration ranges for Func updates""" + prefix = "i" if self.inside_reduction else "o" + if prefix in self.dom_renames: + return self.dom_renames[prefix] + + renames = {} + for var in self.halide_vars.keys(): + if not self.inside_reduction and var in self.reduction_renames: + continue + m = re.match(r"^h(\d+)$", var.name) + assert m + renames[var] = sympy_index_symbol(f"h{prefix}{m.group(1)}") + + self.codegen_rdom( + f"{prefix}dom", {rv: self.halide_vars[v] for v, rv in renames.items()} + ) + + self.dom_renames[prefix] = renames + return renames + + def codegen_rdom(self, name, vars): + rsizes = [ + f"hl.Range(0, {self.kexpr(self.rename_indexing(size))})" + for size in vars.values() + ] + self.indexing_code.writeline(f"{name} = hl.RDom([{', '.join(rsizes)}])") + for i, rsym in enumerate(vars.keys()): + self.indexing_code.writeline(f"{rsym} = {name}[{i}]") + + def prepare_indexing( + self, + index: sympy.Expr, + ): + index = super().prepare_indexing(index) + index = sympy_subs(index, self.index_replacements) + return V.graph.sizevars.simplify_with_ranges(index, self.halide_vars) # type: ignore[arg-type] + + def sym_size(self, sym): + """The size of an index symbol""" + if symbol_is_type(sym, SymT.TMP): + return self.lookup_cse_var(sym.name).indirect_indexing_size + return self.halide_vars[sym] + + def indexing_to_dimensions(self, var: str, index: sympy.Expr, is_store: bool): + """Convert address-based indexing into dimensions using self.halide_vars""" + symbols = [] + for sym in sorted(index.free_symbols, key=lambda x: x.name): # type: ignore[attr-defined] + if symbol_is_type(sym, (SymT.HALIDE, SymT.TMP)): + symbols.append(sym) + else: + assert symbol_is_type( + sym, + ( + SymT.UNBACKED_INT, + SymT.SIZE, + SymT.PRECOMPUTED_SIZE, + ), + ), sym + + # group the expression by variables used + offset = sympy.Integer(0) + split_expr = {s: sympy.Integer(0) for s in symbols} + split_failed: List[Tuple[List[sympy.Symbol], sympy.Expr]] = [] + index = sympy.expand(self.rename_indexing(index)) + for part in index.args if isinstance(index, sympy.Add) else [index]: + part_vars = [v for v in part.free_symbols if v in split_expr] + if len(part_vars) == 0: + offset += part + elif len(part_vars) == 1: + split_expr[part_vars[0]] += part + else: + new_split_failed = [] + for i in range(len(split_failed)): + assert split_failed[i] is not None + other_vars, other_part = split_failed[i] + if set(other_vars) & set(part_vars): + part_vars.extend([v for v in other_vars if v not in part_vars]) + part += other_part + else: + new_split_failed.append((other_vars, other_part)) + split_failed = [*new_split_failed, (part_vars, part)] + + def expr_to_dimension(expr, syms): + expr = sympy.factor(expr) + if len(syms) == 1: + stride_wild = sympy.Wild("wild", exclude=symbols) + m = expr.match(stride_wild * syms[0]) + if m: + return DimensionInfo( + syms[0], self.sym_size(syms[0]), m[stride_wild] + ) + assert not is_store, expr + length = sympy.simplify( + sympy_subs(expr, {sym: self.sym_size(sym) - 1 for sym in syms}) + 1 + ) + stride = sympy.Integer(1) + if isinstance(expr, sympy.Mul): + for term in expr.args: + if isinstance(term, sympy.Integer): + stride *= term + expr = sympy.simplify(expr / term) + length = sympy.simplify(sympy.ceiling(length / term)) + return DimensionInfo(expr, length, stride) + + # try to turn each group into a strided access + dims = [] + for syms, expr in split_failed: + for v in syms: + expr += split_expr.pop(v) + dims.append(expr_to_dimension(expr, syms)) + for sym, expr in split_expr.items(): + dims.append(expr_to_dimension(expr, [sym])) + dims.sort(key=lambda d: V.graph.sizevars.size_hint(d.stride, fallback=inf)) # type: ignore[arg-type] + + if not dims: # scalar load/store + if self.has_indirect_indexing: + # workaround https://github.com/halide/Halide/issues/8338 + dims.append(DimensionInfo(sympy.Integer(0), 1, 1)) + elif not V.graph.sizevars.statically_known_equals(dims[0].stride, 1): + # Halide assumes dimension 0 is stride == 1, so add a dummy dimension + dims.insert( + 0, DimensionInfo(sympy.Integer(0), 1 if is_store else dims[0].stride, 1) + ) + + if dims and not is_store: + if var in self.buffer_offsets and V.graph.sizevars.statically_known_geq( + offset, self.buffer_offsets[var] + ): + # reuse the existing offset to avoid needing an input alias + self.apply_offset_to_dimension(dims, offset - self.buffer_offsets[var]) + offset = self.buffer_offsets[var] + elif V.graph.sizevars.statically_known_gt( + offset, 0 + ): # TODO(jansel): negative offsets + # roll the offset into the dimensions for cleaner indexing + self.apply_offset_to_dimension(dims, offset) + offset = 0 + + orig_var = var + for i in itertools.count(): + if self.install_dims(var, dims, offset, is_store): + return var, dims + assert not is_store + var = f"{orig_var}_view{i}" + if var not in self.buffer_aliases[orig_var]: + self.buffer_aliases[orig_var].append(var) + + def install_dims(self, var, dims, offset, is_store): + """Try to set self.buffer_dimensions[var], return True on success""" + if var not in self.buffer_dimensions: + self.buffer_dimensions[var] = dims + self.buffer_offsets[var] = offset + return True + if self.buffer_offsets[var] != offset or len( + self.buffer_dimensions[var] + ) != len(dims): + return False + if is_store: + return self.buffer_dimensions[var] == dims + for old, new in zip(self.buffer_dimensions[var], dims): + if old.stride != new.stride: + return False + if old.size != new.size or old.expr != new.expr: + old.size = V.graph.sizevars.evaluate_max(old.size, new.size) + old.expr = None + return True + + def apply_offset_to_dimension(self, dims, offset): + if offset == 0: + return + for i in reversed(range(len(dims))): + if dims[i].stride == 1 or V.graph.sizevars.statically_known_geq( + offset, dims[i].stride + ): + part = FloorDiv(offset, dims[i].stride) + offset -= part * dims[i].stride + dims[i].expr += part + assert offset == 0 + + def used_dims_from_index(self, index: sympy.Expr): + """Detect which range trees are used to populate HalideCSEVariable.used_dims""" + used_dims = set() + for sym in index.free_symbols: + assert isinstance(sym, sympy.Symbol) + if symbol_is_type(sym, SymT.TMP): + # indirect indexing + cse_var = self.lookup_cse_var(sym.name) + assert ( + isinstance(cse_var, HalideCSEVariable) + and cse_var.used_dims is not None + ) + used_dims.update(cse_var.used_dims) + elif symbol_is_type(sym, SymT.HALIDE): + used_dims.add(sym) + elif symbol_is_type( + sym, (SymT.UNBACKED_INT, SymT.SIZE, SymT.PRECOMPUTED_SIZE, SymT.INDEX) + ): + pass + else: + raise NotImplementedError(f"unhandled symbol {sym}") + return self.sort_used_dims(used_dims) + + def sort_used_dims(self, used_dims): + assert all(isinstance(x, sympy.Expr) for x in used_dims) + ordered = [ + sym + for sym in itertools.chain( + self.halide_vars, self.reduction_renames.values() + ) + if sym in used_dims + ] + assert len(ordered) == len(used_dims) + return ordered + + def make_index_str(self, dims, replacements=None, zero_vars=False): + index_str = ", ".join(d.index_str(replacements, zero_vars) for d in dims) + if len(dims) == 0: + index_str = "()" + elif len(dims) == 1: + # workaround for https://github.com/halide/Halide/issues/8299 + index_str = f"{index_str}," + return index_str + + def load(self, name: str, index: sympy.Expr): + """Codegen a load from an InputBuffer""" + var = self.args.input(name) + index = self.prepare_indexing(index) + var, dims = self.indexing_to_dimensions(var, index, False) + line = f"{var}[{self.make_index_str(dims)}]" + dtype = V.graph.get_dtype(name) + if dtype in (torch.float16, torch.bfloat16): + dtype = torch.float32 + line = f"hl.cast(hl.Float(32), {line})" + + if self._load_mask: + assert ( + isinstance(self._load_mask, HalideCSEVariable) + and self._load_mask.used_dims is not None + ) + used_dims = {*self.used_dims_from_index(index), *self._load_mask.used_dims} + result = self.newfunc(self.sort_used_dims(used_dims)) + if result.used_dims: + self.body.writeline(f"{result.name}_mask = hl.RDom([hl.Range(0, 1)])") + self.body.writeline(f"{result.name}_mask.where({self._load_mask})") + other = self.kexpr(self._load_other or 0) # type: ignore[arg-type] + self.body.writeline( + f"{result} = hl.cast({halide_type(dtype)}, {other})" + ) + self.body.writeline( + f"{result} = {line} + hl.cast({halide_type(dtype)}, {result.name}_mask)" + ) + else: + # scalar case + self.body.writeline( + f"{result} = hl.select({self._load_mask}, {line}, hl.cast({halide_type(dtype)}, 0))" + ) + return result + else: + return self.genfunc(line, self.used_dims_from_index(index)) + + def lookup_cse_var(self, name: str): + return self.cse.varname_map[re.sub(r"\[.*", "", name)] + + def store( + self, name: str, index: sympy.Expr, value: CSEVariable, mode: StoreMode = None + ) -> None: + """Codegen a store to an OutputBuffer""" + assert isinstance(value, HalideCSEVariable) + var = self.args.output(name) + index = self.prepare_indexing(index) + var, dims = self.indexing_to_dimensions(var, index, True) + if self.is_indirect_indexing(index) or mode is not None: + replacements = self.setup_dom_indexing() + index_str = self.make_index_str(dims, replacements) + value_str = value.subs_str(replacements) + undef_dims = (", ".join(["hl.Var()"] * len(dims))) or "()" + self.body.writeline( + DeferredLine(name, f"{var}[{undef_dims}] = hl.undef({var}.type())") + ) + else: + index_str = self.make_index_str(dims, zero_vars=True) + value_str = str(value) + + dtype = V.graph.get_dtype(name) + if mode is None: + line = f"{var}[{index_str}] = hl.cast({halide_type(dtype)}, {value_str})" + elif mode == "atomic_add": + line = f"{var}[{index_str}] += hl.cast({halide_type(dtype)}, {value_str})" + else: + raise NotImplementedError(f"store mode={mode}") + self.body.writeline(DeferredLine(name, line)) + + def reduction( + self, + dtype: torch.dtype, + src_dtype: torch.dtype, + reduction_type: ReductionType, + value: Union[CSEVariable, Tuple[CSEVariable, ...]], + ) -> Union[CSEVariable, Tuple[CSEVariable, ...]]: + """Codegen a reduction operation""" + assert self.inside_reduction + assert not self._load_mask + cache_key = (src_dtype, reduction_type, value) + if cache_key in self.cse.reduction_cache: + return self.cse.reduction_cache[cache_key] + + if isinstance(value, tuple): + assert reduction_type == "welford_combine" + self.cse.reduction_cache[ + cache_key + ] = result_tuple = self.welford_combine_impl(*value) + return result_tuple + + assert isinstance(value, HalideCSEVariable) and value.used_dims is not None + reduction_vars = {*self.reduction_renames} + result_var = self.newfunc( + [v for v in value.used_dims if v not in reduction_vars] + ) + if reduction_vars - {*value.used_dims}: + value = self.genfunc( + f"{value}", self.sort_used_dims({*value.used_dims, *reduction_vars}) + ) + value_str = value.subs_str(self.reduction_renames) + default = ir.Reduction.default_accumulator(reduction_type, src_dtype) + acc_type = halide_acc_type(dtype) + + if reduction_type in ("argmax", "argmin"): + index = f"{result_var.name}_{reduction_type}" + self.body.writeline(f"{index} = hl.{reduction_type}(rdom, {value_str})") + # turn the N-D argmax index into a 1-D one + parts = [] + stride = 1 + for i, sym in enumerate(self.reduction_renames): + parts.append(f"{index}[{i}]") + if stride != 1: + parts[-1] += f"*{stride}" + stride *= self.halide_vars[sym] + self.body.writeline(f"{result_var} = {' + '.join(parts)}") + elif reduction_type == "welford_reduce": + # TODO(jansel): implement welford_reduce without fallback + result_var = self.welford_reduce_fallback(dtype, value) + else: + combine_fn = get_reduction_combine_fn(reduction_type, acc_type) + with V.set_ops_handler(AddParenHandler(HalideOverrides(MockHandler()))): + combine_str = combine_fn(result_var, value_str) # type: ignore[arg-type] + default_str = f"hl.cast({acc_type}, {halide_constant(default)})" + self.body.writeline(f"{result_var} = {default_str}") + self.body.writeline(f"{result_var} = {combine_str}") + + self.cse.reduction_cache[cache_key] = result_var + return result_var + + def welford_combine_impl(self, mean, m2, weight): + assert isinstance(mean, HalideCSEVariable) and mean.used_dims is not None + assert isinstance(m2, HalideCSEVariable) and m2.used_dims is not None + assert isinstance(weight, HalideCSEVariable) and weight.used_dims is not None + used_dims = {*mean.used_dims, *m2.used_dims, *weight.used_dims} or { + *self.halide_vars + } + used_dims -= {*self.reduction_renames} + result_var = self.newfunc(self.sort_used_dims(used_dims)) + default = [f"hl.cast({x.name}.type(), 0)" for x in (mean, m2, weight)] + pfx = result_var.name + self.body.writeline(f"{result_var} = hl.Tuple([{', '.join(default)}])") + self.body.writeline(f"{pfx}_mean_1 = {result_var}[0]") + self.body.writeline(f"{pfx}_m2_1 = {result_var}[1]") + self.body.writeline(f"{pfx}_weight_1 = {result_var}[2]") + self.body.writeline(f"{pfx}_mean_2 = {mean.subs_str(self.reduction_renames)}") + self.body.writeline(f"{pfx}_m2_2 = {m2.subs_str(self.reduction_renames)}") + self.body.writeline( + f"{pfx}_weight_2 = {weight.subs_str(self.reduction_renames)}" + ) + self.body.writeline(f"{pfx}_delta = {pfx}_mean_2 - {pfx}_mean_1") + self.body.writeline(f"{pfx}_new_weight = {pfx}_weight_1 + {pfx}_weight_2") + self.body.writeline( + f"{pfx}_w2_over_w = hl.select({pfx}_new_weight == 0.0, 0.0, {pfx}_weight_2 / {pfx}_new_weight)" + ) + update = [ + f"{pfx}_mean_1 + {pfx}_delta * {pfx}_w2_over_w", + f"{pfx}_m2_1 + {pfx}_m2_2 + {pfx}_delta * {pfx}_delta * {pfx}_weight_1 * {pfx}_w2_over_w", + f"{pfx}_new_weight", + ] + self.body.writeline(f"{result_var} = hl.Tuple([{', '.join(update)}])") + + unpacked = [] + for i in range(3): + unpacked.append(self.newfunc(result_var.used_dims)) + self.body.writeline(f"{unpacked[-1]} = {result_var}[{i}]") + return tuple(unpacked) + + def scan( + self, + dtypes: Tuple[torch.dtype, ...], + combine_fn: Callable[ + [Tuple[CSEVariable, ...], Tuple[CSEVariable, ...]], Tuple[CSEVariable, ...] + ], + values_orig: Tuple[CSEVariable, ...], + ) -> Tuple[CSEVariable, ...]: + assert self.inside_reduction + assert len(dtypes) == len(values_orig) + values: List[HalideCSEVariable] = [] + all_used_dims = set() + for value in values_orig: + assert isinstance(value, HalideCSEVariable) and value.used_dims is not None + if set(value.used_dims) & set(self.reduction_renames): + values.append(value) + else: + values.append( + self.genfunc( + f"{value}", [*value.used_dims, [*self.reduction_renames][:1]] + ) + ) + all_used_dims.update(value.used_dims) + result_var = self.newfunc(self.sort_used_dims(all_used_dims)) + assert result_var.used_dims and set(result_var.used_dims) & set( + self.reduction_renames + ) + initial = [ + f"hl.cast({halide_acc_type(dtype)}, {value})" + for dtype, value in zip(dtypes, values) + ] + + length = self.kexpr(self.rename_indexing(self.range_trees[-1].numel)) + scan_dom = f"{result_var.name}_rdom" + scan = f"{scan_dom}.x" + self.body.writeline(f"{scan_dom} = hl.RDom([hl.Range(1, {length})])") + + assert ( + len(self.reduction_renames) == 1 + ), "multi-dimensional scan not implemented" + (scan_var,) = [*self.reduction_renames] # type: ignore[misc] + scan_renames_cur = {scan_var: sympy_index_symbol(scan)} + scan_renames_pri = {scan_var: sympy_index_symbol(scan) - 1} + + if len(values) == 1: + + def maybe_tuple(x): + return x[0] + + read_left = [result_var.subs_str(scan_renames_pri)] + read_right = [result_var.subs_str(scan_renames_cur)] + else: + + def maybe_tuple(x): + return f"hl.Tuple([{', '.join(x)}])" + + read_left = [ + result_var.subs_str(scan_renames_pri) + f"[{i}]" + for i in range(len(values)) + ] + read_right = [ + result_var.subs_str(scan_renames_cur) + f"[{i}]" + for i in range(len(values)) + ] + + self.body.writeline(f"{result_var} = {maybe_tuple(initial)}") + + # Disable CSE for update fn + with V.set_ops_handler(AddParenHandler(HalideOverrides(MockHandler()))): + combine_str = combine_fn(read_left, read_right) # type: ignore[arg-type] + self.body.writeline( + f"{result_var.subs_str(scan_renames_cur)} = {maybe_tuple(combine_str)}" + ) + + if len(values) == 1: + return (result_var,) + + unpack_vars = [self.newfunc(self.sort_used_dims(all_used_dims)) for _ in values] + for i, v in enumerate(unpack_vars): + self.body.writeline(f"{v} = {result_var}[{i}]") + return tuple(unpack_vars) + + def genfunc( + self, line, used_dims, *, bounds=ValueRanges.unknown() + ) -> HalideCSEVariable: + var = self.cse.generate(self.body, line, bounds=bounds) + assert isinstance(var, HalideCSEVariable) + var.used_dims = used_dims + return var + + def newfunc(self, used_dims) -> HalideCSEVariable: + var = self.cse.newvar() + assert isinstance(var, HalideCSEVariable) + var.used_dims = used_dims + return var + + def halide_buffer_numel(self, name: str): + """ + We map all tensors to 1D buffers in Halide since Halide has trouble representing some strides that PyTorch + supports. If there are gaps in the underlying layout the numel we pass to Halide includes the gaps while + PyTorch's numel excludes them. + """ + return V.graph.get_buffer(name).get_layout().storage_size() + + def halide_argdefs(self): + """ + Halide requires scalar inputs before outputs, so need to reorder args. + """ + + def arg_order(arg_tuple): + call_str, arg = arg_tuple + if isinstance(arg, SizeArg): + return 1 # this would normally be at the end, move it to middle + elif "out_ptr" in arg.name: + return 2 + else: + assert "in_ptr" in arg.name + return 0 + + result = [] + _, a, b, _ = self.args.python_argdefs() + for call_str, arg in sorted(zip(a, b), key=arg_order): + result.append((call_str, arg)) + if isinstance(arg, TensorArg): + assert arg.offset == 0 and arg.alias_of is None + for alias in self.buffer_aliases.get(arg.name, ()): + result.append( + ( + None, + TensorArg( + alias, + arg.buffer, + arg.dtype, + arg.offset, + alias_of=arg.name, + ), + ) + ) + return result + + def halide_kernel_meta(self) -> HalideMeta: + """Compute metadata required by codecache.py""" + argtypes = [] + for _, arg in self.halide_argdefs(): + if isinstance(arg, SizeArg): + shape = None + stride = None + offset = None + dtype = "long" + else: + shape = [ + cexpr(self.rename_indexing(x.size)) + for x in self.buffer_dimensions[arg.name] + ] + stride = [ + cexpr(self.rename_indexing(x.stride)) + for x in self.buffer_dimensions[arg.name] + ] + assert len(shape) == len(stride) + offset = cexpr(self.buffer_offsets[arg.name]) + dtype = f"{DTYPE_TO_CPP[arg.dtype]}*" + argtypes.append( + HalideInputSpec( + dtype, + arg.name, + shape=shape, + stride=stride, + offset=offset, + alias_of=arg.alias_of, + ) + ) + + current_device = V.graph.scheduler.get_current_device_or_throw() + if current_device.type == "cpu": + target = [config.halide.cpu_target] + schduler = config.halide.scheduler_cpu + scheduler_flags = { + "parallelism": parallel_num_threads(), + } + cuda_device = None + else: + assert current_device.type == "cuda", "only cpu/cuda supported" + assert current_device.index <= 0, "only default device supported" + target = [config.halide.gpu_target] + schduler = config.halide.scheduler_cuda + capability = torch.cuda.get_device_properties(current_device) + if "cuda_capability" not in target[0]: + for major, minor in [(8, 6), (8, 0), (7, 5), (7, 0), (6, 1)]: + if capability.major >= major and capability.minor >= minor: + target.append(f"cuda_capability_{major}{minor}") + break + target.append("user_context") + scheduler_flags = { + "parallelism": capability.multi_processor_count, + # TODO(jansel): explore other flags, see: + # grep parser.parse ~/Halide/src/autoschedulers/anderson2021/AutoSchedule.cpp + } + cuda_device = max(0, current_device.index) + + # strict_float is requires for correctness + target.append("strict_float") + + # without this we will initialize cuda once per kernel and hit errors + target.append("no_runtime") + + if not config.halide.asserts: + target.append("no_asserts") + + if config.halide.debug: + target.append("debug") + + if "64" in self.index_dtype: + # TODO(jansel): it is unclear if this does anything, since input sizes are still int32 + target.append("large_buffers") + + return HalideMeta( + argtypes, + target="-".join(target), + scheduler=schduler, + scheduler_flags=scheduler_flags, + cuda_device=cuda_device, + ) + + def codegen_kernel(self, name=None): + """Called at the end to generate a final kernel string""" + if self.args.inplace_buffers: + raise Unsupported("inplace_buffers") + meta = self.halide_kernel_meta() # ensure needed args are added early + code = IndentedBuffer() + code.splice( + """ + import halide as hl + from torch._inductor.runtime import halide_helpers + from math import inf, nan + + @hl.generator(name="kernel") + class Kernel: + """, + strip=True, + ) + code.do_indent() + for _, arg in self.halide_argdefs(): + if isinstance(arg, SizeArg): + code.writeline(f"{arg.name} = hl.InputScalar({self.index_dtype})") + else: + assert arg.buffer, arg + argcls = "hl.OutputBuffer" if "out" in arg.name else "hl.InputBuffer" + argtype = halide_type(arg.dtype) + ndim = len(self.buffer_dimensions[arg.name]) + code.writeline(f"{arg.name} = {argcls}({argtype}, {ndim})") + code.splice( + """ + def generate(g): + """ + ) + code.do_indent() + for _, arg in self.halide_argdefs(): + code.writeline(f"{arg.name} = g.{arg.name}") + for old, new in self.args.aliases(): + code.writeline(f"{old} = {new}") + code.splice(self.indexing_code) + + def update_index(m): + var = self.cse.varname_map[m.group(1)] + assert var.used_dims is not None, var + return str(var) + + for line in self.body._lines: + if isinstance(line, str): + # fill in missing indices + line = HalideCSEVariable.undefined_re.sub(update_index, line) + code.writeline(line) + code.writeline("") + code.writeline("assert g.using_autoscheduler()") + + for _, arg in self.halide_argdefs(): + # fallback=1 below because halide requires buffers to be at least as large as the estimates + # This causes crashes if our estimate is greater than the vector length + # https://github.com/halide/Halide/issues/3103 + if isinstance(arg, SizeArg): + hint = V.graph.sizevars.size_hint(arg.expr, fallback=1) + code.writeline(f"{arg.name}.set_estimate({hint})") + else: + dims = self.buffer_dimensions[arg.name] + range_hints = [] + for i, dim in enumerate(dims): + hint = self._autoscheduler_workarounds( + V.graph.sizevars.size_hint(dim.size, fallback=1), dims + ) + range_hints.append(f"hl.Range(0, {hint})") + if "out" not in arg.name: + code.writeline(f"{arg.name}.dim({i}).set_min(0)") + try: + code.writeline( + f"{arg.name}.dim({i}).set_stride({int(dim.stride)})" + ) + except TypeError: + pass # not integer + try: + code.writeline( + f"{arg.name}.dim({i}).set_extent({int(dim.size)})" + ) + except TypeError: + pass # not integer + code.writeline(f"{arg.name}.set_estimates([{', '.join(range_hints)}])") + + code.do_unindent(2) + code.splice( + """ + if __name__ == "__main__": + hl.main() + """.rstrip(), + ) + if meta.scheduler: + code.splice( + f""" + else: + hl.load_plugin({HalideCodeCache.find_libautoschedule(meta.scheduler)!r}) + target = hl.Target({meta.target!r}) + autoscheduler = hl.AutoschedulerParams({meta.scheduler!r}, {meta.scheduler_flags!r}) + with hl.GeneratorContext(target, autoscheduler): + gen = Kernel() + pipeline = gen._build_pipeline() + # gen.compile_to_callable() does not run the autoscheduler + pipeline.apply_autoscheduler(target, autoscheduler) + kernel = pipeline.compile_to_callable([ + gen._get_input_parameter(a.name)._to_argument() + for a in gen._get_arginfos() + if a.dir == hl.ArgInfoDirection.Input + ], target) + """, + strip=True, + ) + else: + code.splice( + f""" + else: + with hl.GeneratorContext(hl.Target({meta.target!r})): + kernel = Kernel().compile_to_callable() + """, + strip=True, + ) + return code.getvalue() + + @staticmethod + def _autoscheduler_workarounds(n, dims): + if ( + len(dims) == 1 + and config.halide.scheduler_cuda == "Anderson2021" + and V.graph.scheduler.get_current_device_or_throw().type == "cuda" + ): + # workaround https://github.com/halide/Halide/issues/8246 + n = max(2, n) + return n + + def call_kernel(self, name: str, node=None): + """Codegen a call to this kernel""" + wrapper = V.graph.wrapper_code + call_args = [f"{n}" for n, arg in self.halide_argdefs() if arg.alias_of is None] + current_device = V.graph.scheduler.get_current_device_or_throw() + if current_device.type == "cuda": + stream_name = wrapper.write_get_raw_stream(current_device.index, V.graph) + call_args.append(stream_name) + wrapper.generate_kernel_call( + name, + call_args, + cuda=False, # grid/stream is handled internally in halide + ) + + def generate_assert(self, check): + return False # TODO(jansel): support asserts + + def check_bounds( + self, expr: sympy.Expr, size: sympy.Expr, lower: bool, upper: bool + ): + pass # TODO(jansel): support asserts + + +class HalideScheduling(SIMDScheduling): + int32_type = "hl.Int(32)" + # TODO(jansel): Halide doesn't actually support 64 bit indexing... + int64_type = "hl.Int(64)" + kernel_type = HalideKernel # type: ignore[arg-type] + + @classmethod + def get_backend_features(cls, device: torch.device): + result = dict.fromkeys( + [ + BackendFeature.TUPLE_REDUCTION, + BackendFeature.PREFER_STORE_LOOP_ORDER, + BackendFeature.REDUCE_TO_SINGLE_ELEMENT, + ] + ) + if config.halide.scan_kernels: + result[BackendFeature.SCAN] = None + return result + + def define_kernel(self, src_code, node_schedule, kernel): + """Codegen kernel definition to go in output wrapper code""" + wrapper = V.graph.wrapper_code + if src_code in wrapper.src_to_kernel: + kernel_name = wrapper.src_to_kernel[src_code] + else: + kernel_name = f"halide_kernel_{wrapper.next_kernel_suffix()}" + wrapper.src_to_kernel[src_code] = kernel_name + wrapper.add_import_once( + "from torch._inductor.runtime.hints import HalideMeta, HalideInputSpec" + ) + + compile_wrapper = IndentedBuffer() + compile_wrapper.writeline( + f"async_compile.halide({kernel.halide_kernel_meta()!r}, '''" + ) + compile_wrapper.splice(src_code, strip=True) + compile_wrapper.writeline("''')") + + origins, detailed_origins = get_kernel_metadata(node_schedule, wrapper) + metadata_comment = f"{origins}\n{detailed_origins}" + wrapper.define_kernel( + kernel_name, compile_wrapper.getvalue(), metadata_comment + ) + if is_metric_table_enabled("kernel_metadata"): + log_kernel_metadata(kernel_name, "", src_code) + + return kernel_name diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/memory_planning.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/memory_planning.py new file mode 100644 index 0000000000000000000000000000000000000000..60360597ec1cb1cc6d82d138d6807aeda1d42ac3 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/memory_planning.py @@ -0,0 +1,770 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import collections +import dataclasses +import itertools +import pprint +from typing import Any, Dict, Iterable, List, Optional, Protocol + +import sympy + +import torch + +from .. import config, ir +from ..utils import _align, align, cache_on_self, CachedMethod, IndentedBuffer +from ..virtualized import V +from .wrapper import ( + AllocateLine, + FreeIfNotReusedLine, + MemoryPlanningLine, + NullLine, + ReuseLine, +) + + +@dataclasses.dataclass +class LiveRange: + """ + A range where a given tensor is live. Begin and end are both counters + representing points in the program of grouped memory operations. + Begin is inclusive, end is exclusive. + + Invariant: begin <= end + """ + + begin: float # int | +/-inf + end: float # int | +/-inf + + def contains(self, other: LiveRange): + """Is other entirely within self""" + return self.begin <= other.begin and other.end <= self.end + + def join(self, other: LiveRange): + """Combine two ranges using a union operation""" + return LiveRange(min(self.begin, other.begin), max(self.end, other.end)) + + def __len__(self): + return self.end - self.begin + + +class LiveRanges: + """ + A collection of LiveRange regions, allowing for non-contiguous + live regions. + + Invariant: LiveRanges.ranges is in sorted order and non-overlapping + """ + + def __init__(self, ranges: Iterable[LiveRange]): + ranges = [*sorted(ranges, key=lambda x: x.begin)] + self.ranges = ranges[:1] + for r in ranges[1:]: + assert self.ranges[-1].begin <= r.begin + if self.ranges[-1].end >= r.begin: + self.ranges[-1] = LiveRange.join(self.ranges[-1], r) + else: + self.ranges.append(r) + + def overlaps(self, other: LiveRanges): + """Check if any pair of ranges in self and other overlap""" + left = collections.deque(self.ranges) + right = collections.deque(other.ranges) + while left and right: + if left[0].begin > right[0].begin: + left, right = right, left + assert left[0].begin <= right[0].begin + if left[0].end > right[0].begin: + return True + left.popleft() + return False + + @property + def begin(self): + return self.ranges[0].begin + + @property + def end(self): + return self.ranges[-1].end + + def __repr__(self): + return f"{self.__class__.__name__}([{', '.join(map(repr, self.ranges))}])" + + +class AllocationTreeNode: + """ + Abstract base class for nodes in allocation pool. + """ + + def allocate(self, block: Allocation, is_last: bool) -> bool: + """ + Try to assign block to a memory location in this bool. Return True if + an assignment was made. + """ + return False + + def get_live_ranges(self) -> LiveRanges: + """Aggregate LiveRanges for all objects below this in tree""" + raise NotImplementedError + + def get_size_hint(self) -> int: + """Number of bytes used for example inputs""" + raise NotImplementedError + + def get_symbolic_size(self) -> sympy.Expr: + """Number of bytes needed at runtime""" + raise NotImplementedError + + def finalize(self, pool, offset) -> AllocationTreeNode: + """Called after all allocations have been made""" + return self + + def is_empty(self): + return False + + +@dataclasses.dataclass +class Allocation(AllocationTreeNode): + """ + Represents memory allocated to a given node in the allocation pool. + """ + + node: ir.Buffer + live_range: LiveRange + size_hint: int + symbolic_size: sympy.Expr + allocated: bool = False + pool: Optional[AllocationPool] = None + offset: Optional[sympy.Expr] = None + + @property + def device(self): + return self.node.get_device() + + def get_live_ranges(self): + return LiveRanges([self.live_range]) + + def get_size_hint(self): + return self.size_hint + + def get_symbolic_size(self): + return self.symbolic_size + + def mark_allocated(self): + assert not self.allocated + self.allocated = True + + def finalize(self, pool, offset): + assert self.pool is None and self.offset is None + self.pool = pool + self.offset = offset + return self + + def codegen_alloc_from_pool(self, wrapper): + assert self.pool + node = self.node + shape = tuple(node.get_size()) + stride = tuple(node.get_stride()) + return wrapper.codegen_alloc_from_pool( + self.pool.name, self.offset, node.get_dtype(), shape, stride + ) + + def __repr__(self): + return ( + f"{self.__class__.__name__}(" + f"node={self.node.get_name()}, " + f"live_range={self.live_range}, " + f"size_hint={self.size_hint}, " + f"symbolic_size={self.symbolic_size}, " + f"pool={self.pool.name if self.pool else None}, " + f"offset={self.offset})" + ) + + +@dataclasses.dataclass +class Empty(AllocationTreeNode): + """ + Placeholder to represent empty space in the allocation pool. + Only exists to get the size_hint correct in parent nodes. + """ + + size_hint: int + + def get_live_ranges(self): + return LiveRanges([]) + + def get_size_hint(self): + return self.size_hint + + def get_symbolic_size(self): + return 0 + + def is_empty(self): + return True + + +class MemorySplitProtocol(Protocol): + get_live_ranges: CachedMethod[[], LiveRanges] + get_size_hint: CachedMethod[[], int] + get_symbolic_size: CachedMethod[[], sympy.Expr] + + def _allocate(self, block: Allocation, is_last: bool) -> bool: + ... + + +class ClearCacheOnAllocateMixin(MemorySplitProtocol): + """ + Helper to assist in caching get_live_ranges, get_size_hint, and + get_symbolic_size. + """ + + def allocate(self, block: Allocation, is_last: bool): + is_allocated = self._allocate(block, is_last) + if is_allocated: + self.clear_cache() + return is_allocated + + def clear_cache(self): + self.get_live_ranges.clear_cache(self) + self.get_size_hint.clear_cache(self) + self.get_symbolic_size.clear_cache(self) + + +@dataclasses.dataclass +class TemporalSplit(ClearCacheOnAllocateMixin, AllocationTreeNode): + """ + Contains a list of allocations not overlapping in LiveRanges. + + Invariant: no pair (a,b) in self.allocations will have: + a.get_live_ranges().overlaps(b.get_live_ranges()) + """ + + allocations: List[AllocationTreeNode] + + def _allocate(self, block: Allocation, is_last: bool): + slot_size = self.get_size_hint() + block_size = block.get_size_hint() + if not is_last and block_size > slot_size: + return False # doesn't fit + + block_live = block.get_live_ranges() + overlapping = [ + s for s in self.allocations if s.get_live_ranges().overlaps(block_live) + ] + if len(overlapping) > 1: + # TODO(jansel): we could try harder here by merging overlapping in space + return False + elif len(overlapping) == 1: + return overlapping[0].allocate(block, is_last) + else: + block.mark_allocated() + + if len(self.allocations) == 1 and isinstance(self.allocations[-1], Empty): + self.allocations.pop() + + if slot_size == block_size: + # perfect fit + self.allocations.append(block) + elif slot_size > block_size: + self.allocations.append( + SpatialSplit.create(block, slot_size - block_size) + ) + else: # grow this allocation + assert is_last + self.allocations = [ + *( + SpatialSplit.create(a, block_size - slot_size) + for a in self.allocations + ), + block, + ] + return True + + @cache_on_self + def get_live_ranges(self) -> LiveRanges: + return LiveRanges( + itertools.chain.from_iterable( + x.get_live_ranges().ranges for x in self.allocations + ) + ) + + @cache_on_self + def get_size_hint(self) -> int: + if not self.allocations: + return 0 + return max(x.get_size_hint() for x in self.allocations) + + @cache_on_self + def get_symbolic_size(self) -> sympy.Expr: + if not self.allocations: + return 0 # type: ignore[return-value] + return sympy.Max(*[x.get_symbolic_size() for x in self.allocations]) + + def is_empty(self): + return len(self.allocations) == 1 and self.allocations[0].is_empty() + + def finalize(self, pool, offset): + self.allocations = [block.finalize(pool, offset) for block in self.allocations] + self.clear_cache() + if len(self.allocations) == 1: + return self.allocations[0] + return self + + +@dataclasses.dataclass +class SpatialSplit(ClearCacheOnAllocateMixin, AllocationTreeNode): + """ + Contains two allocations, left and right, that do not overlap in space. + Right will be allocated immediately after left in memory. + """ + + left: TemporalSplit + right: TemporalSplit + + @staticmethod + def create(left, extra_space): + assert isinstance(left, AllocationTreeNode) + assert isinstance(extra_space, int) and extra_space >= 1 + return SpatialSplit(TemporalSplit([left]), TemporalSplit([Empty(extra_space)])) + + def _allocate(self, block: Allocation, is_last: bool): + return self.left.allocate(block, False) or self.right.allocate(block, is_last) + + @cache_on_self + def get_live_ranges(self): + return LiveRanges( + itertools.chain( + self.left.get_live_ranges().ranges, self.right.get_live_ranges().ranges + ) + ) + + @cache_on_self + def get_size_hint(self) -> int: + return _align(self.left.get_size_hint()) + self.right.get_size_hint() + + @cache_on_self + def get_symbolic_size(self) -> sympy.Expr: + return align(self.left.get_symbolic_size()) + self.right.get_symbolic_size() + + def finalize(self, pool, offset): + self.left = self.left.finalize(pool, offset) + self.right = self.right.finalize( + pool, offset + align(self.left.get_symbolic_size()) + ) + self.clear_cache() + if self.right.is_empty(): + return self.left + return self + + +@dataclasses.dataclass +class AllocationPool: + """ + Represents a pool of allocations that will be generated by a single + call to torch.empty. + """ + + device: torch.device + root: TemporalSplit + can_expand: bool = True + restrict_live_range: Optional[LiveRange] = None + name: Optional[str] = None + names_to_del: List[str] = dataclasses.field(default_factory=list) + creation_cache: Dict[str, str] = dataclasses.field(default_factory=dict) + + def allocate(self, block: Allocation, is_last: bool): + if self.restrict_live_range and not self.restrict_live_range.contains( + block.live_range + ): + return False + + is_last = self.can_expand and is_last + if self.root.allocate(block, is_last): + return True + + if is_last: + return self.allocate_at_end(block) + + return False + + def allocate_at_end(self, block): + block.mark_allocated() + self.root = TemporalSplit([SpatialSplit(self.root, TemporalSplit([block]))]) + return True + + def finalize(self, name): + assert not self.name + self.name = name + self.names_to_del.append(name) + self.root.finalize(self, 0) + + def codegen_create(self, wrapper, code: IndentedBuffer): + assert self.name + nbytes = self.root.get_symbolic_size() + for block in self.root.allocations: + if isinstance(block, Allocation) and nbytes == block.get_symbolic_size(): + # optimization: fuse first allocation and pool creation + node = block.node + code.writeline( + wrapper.make_allocation( + self.name, + device=self.device, + dtype=node.get_dtype(), + shape=tuple(node.get_size()), + stride=tuple(node.get_stride()), + ) + ) + self.creation_cache[block.codegen_alloc_from_pool(wrapper)] = self.name + return + else: + code.writeline( + wrapper.make_allocation( + self.name, + device=self.device, + dtype=torch.uint8, + shape=(nbytes,), + stride=(1,), + ) + ) + + def codegen_destroy(self, wrapper, code: IndentedBuffer): + code.writeline(wrapper.make_free_by_names(self.names_to_del)) + + def __eq__(self, other): + return self is other + + def __hash__(self): + return id(self) + + +@dataclasses.dataclass +class AllocationPools: + """ + Collection of many AllocationPool objects grouped by device. + """ + + device_to_pools: Dict[torch.device, List[AllocationPool]] = dataclasses.field( + default_factory=dict + ) + + def get_pools(self, block): + if block.device not in self.device_to_pools: + self.device_to_pools[block.device] = [] + return self.device_to_pools[block.device] + + def allocate(self, block: Allocation): + pools = self.get_pools(block) + + for pool in pools: + if pool.allocate(block, is_last=pool is pools[-1]): + return + + # everything is full, make a new pool + pools.append( + AllocationPool( + block.device, + TemporalSplit([block]), + can_expand=config.memory_pool != "none", + ) + ) + block.mark_allocated() + + def allocate_output(self, block: Allocation): + """Outputs get different pools so memory gets freed properly""" + pools = self.get_pools(block) + if pools and config.memory_pool in ("outputs", "combined"): + pools[-1].allocate_at_end(block) + else: + # create a new pool + block.mark_allocated() + pools.append( + AllocationPool( + block.device, + TemporalSplit([block]), + can_expand=config.memory_pool == "combined", + ) + ) + + def finalize(self): + """Called at the end of allocation process""" + for i, pool in enumerate( + itertools.chain.from_iterable(self.device_to_pools.values()) + ): + pool.finalize(f"pool{i}") + + def pprint(self): + for pool in itertools.chain.from_iterable(self.device_to_pools.values()): + print() + print(pool.name) + print(pool.root.get_live_ranges()) + pprint.pprint(pool.root) + + +class BufferGroup: + """ + Due to inplace reuse an allocated buffer can have many names. + This tracks these collections of buffers sharing underlying memory. + """ + + def __init__(self, node: ir.Buffer): + self.node = node + self.names = [node.get_name()] + self.is_output = False + self.allocation: Optional[Allocation] = None + self.live_range = LiveRange(float("inf"), -float("inf")) + + def update_usage(self, timestep: int): + """Expand self.live_range to include timestep""" + self.live_range = LiveRange( + min(timestep, self.live_range.begin), + max(timestep, self.live_range.end), + ) + + def sym_nbytes(self): + return self.node.get_layout().storage_size() * self.node.get_dtype().itemsize + + def make_allocation(self): + assert not self.allocation, "multiple allocations" + assert isinstance(self.live_range.begin, int), "live ranges not computed" + nbytes = self.sym_nbytes() + # For now, fallback value will be used if we encounter an unbacked SymInt. The longer-term plan is to have + # size_hint() use better heuristics for unbackeds, at which point the fallback value will be ignored. + size_hint = V.graph.sizevars.size_hint(nbytes, fallback=64) + self.allocation = Allocation( + self.node, + self.live_range, + size_hint=size_hint, + symbolic_size=nbytes, + ) + + def __repr__(self): + return ( + f"{self.__class__.__name__}({self.names!r}, is_output={self.is_output}, " + f"live_range={self.live_range}" + ) + + +@dataclasses.dataclass +class PoolMemoryPlanningLine(MemoryPlanningLine): + """Abstract base class for {Alloc,Dealloc}FromPoolLine""" + + group: BufferGroup + timestep: Optional[int] = None + + @property + def node(self): + return self.group.node + + +@dataclasses.dataclass +class AllocFromPoolLine(PoolMemoryPlanningLine): + """Similar to AllocationLine, but takes memory from a pool""" + + is_first_pool_usage: bool = False + + def codegen(self, code: IndentedBuffer): + allocation = self.group.allocation + assert allocation and allocation.pool + pool = allocation.pool + name = self.node.get_name() + + if self.is_first_pool_usage: + pool.codegen_create(self.wrapper, code) + + pool.names_to_del.extend(self.group.names) + alloc_from_pool = allocation.codegen_alloc_from_pool(self.wrapper) + if alloc_from_pool in pool.creation_cache: + code.writeline( + self.wrapper.make_tensor_alias( + name, pool.creation_cache[alloc_from_pool], "alloc" + ) + ) + else: + pool.creation_cache[alloc_from_pool] = name + code.writeline( + f"{self.wrapper.declare}{name} = {alloc_from_pool}{self.wrapper.ending}" + ) + + +@dataclasses.dataclass +class DeallocFromPoolLine(PoolMemoryPlanningLine): + """Similar to FreeIfNotReusedLine, but takes memory from a pool""" + + is_last_pool_usage: bool = False + + def codegen(self, code: IndentedBuffer): + if self.is_last_pool_usage: + assert self.group.allocation and self.group.allocation.pool + self.group.allocation.pool.codegen_destroy(self.wrapper, code) + + +@dataclasses.dataclass +class MemoryPlanner: + """ + Coordination object to run memory planning passes during wrapper + codegen. + """ + + wrapper: Any + pools: AllocationPools = dataclasses.field(default_factory=AllocationPools) + buffer_groups: Optional[List[BufferGroup]] = None + + def plan(self, lines: List[Any]) -> List[Any]: + """Call all the memory planning passes in sequence""" + lines = [*lines] + self.drop_removed_buffers(lines) + self.convert_to_pool_lines(lines) + self.compute_live_ranges(lines) + self.allocate_groups() + self.mark_first_last_usage(lines) + return lines + + def drop_removed_buffers(self, lines): + """ + Replace any memory planning lines in V.graph.removed_buffers with NullLine + """ + # drop any removed buffers + for i, line in enumerate(lines): + if isinstance(line, (AllocateLine, FreeIfNotReusedLine, ReuseLine)): + if line.node.get_name() in V.graph.removed_buffers: + lines[i] = NullLine(self.wrapper) + + def compute_buffer_groups(self, lines): + """ + Populates self.buffer_groups with BufferGroup objects that join + allocations with common storage (due to inplace reuse) into a + single object. + """ + name_to_group = {} + for line in lines: + if isinstance(line, AllocateLine): + name = line.node.get_name() + assert name not in name_to_group + name_to_group[name] = BufferGroup(line.node) + elif isinstance(line, ReuseLine): + old_name = line.node.get_name() + new_name = line.reused_as.get_name() + assert new_name not in name_to_group + # TODO(jansel): we should support reusing buffers created via ExternKernelAlloc + if old_name in name_to_group: + name_to_group[old_name].names.append(new_name) + name_to_group[new_name] = name_to_group[old_name] + + outputs = set(V.graph.get_output_names()) + unique_groups = [*{id(g): g for g in name_to_group.values()}.values()] + for group in unique_groups: + group.is_output = any(x in outputs for x in group.names) + + assert self.buffer_groups is None + self.buffer_groups = unique_groups + return name_to_group + + def convert_to_pool_lines(self, lines): + """ + Convert AllocateLine/FreeIfNotReusedLine/ReuseLine into their + pool-based counterparts. + """ + name_to_group = self.compute_buffer_groups(lines) + for i, line in enumerate(lines): + if isinstance(line, AllocateLine): + if line.node.get_name() in name_to_group: + lines[i] = AllocFromPoolLine( + self.wrapper, name_to_group[line.node.get_name()] + ) + elif isinstance(line, FreeIfNotReusedLine): + assert not line.is_reused + if line.node.get_name() in name_to_group: + lines[i] = DeallocFromPoolLine( + self.wrapper, name_to_group[line.node.get_name()] + ) + elif isinstance(line, ReuseLine): + if line.node.get_name() in name_to_group: + line.delete_old = False + + def compute_live_ranges(self, lines): + """Populate every BufferGroup.live_ranges field based on first/last usage""" + timestep = 0 + worklist = collections.deque(lines) + while worklist: + if isinstance(worklist[0], MemoryPlanningLine): + timestep += 1 + while worklist and isinstance(worklist[0], MemoryPlanningLine): + line = worklist.popleft() + if isinstance(line, PoolMemoryPlanningLine): + line.group.update_usage(timestep) + line.timestep = timestep + else: + worklist.popleft() + + timestep += 1 + assert self.buffer_groups is not None + for group in self.buffer_groups: + if group.is_output: + group.update_usage(timestep) + + def allocate_groups(self): + """ + Assign every allocation to a specific location in a specific AllocationPool. + """ + assert config.memory_pool in ("none", "intermediates", "outputs", "combined") + assert self.buffer_groups is not None + + for group in self.buffer_groups: + group.make_allocation() + + outputs: List[Allocation] = [] + intermediates: List[Allocation] = [] + for group in self.buffer_groups: + assert group.allocation + if group.is_output and config.memory_pool != "combined": + outputs.append(group.allocation) + else: + intermediates.append(group.allocation) + + for block in sorted( + outputs, + key=lambda x: ( + x.size_hint, + -len(x.live_range), + ), + ): + self.pools.allocate_output(block) + + for block in sorted( + intermediates, + key=lambda x: ( + -x.size_hint, + -len(x.live_range), + ), + ): + self.pools.allocate(block) + + self.pools.finalize() + + def mark_first_last_usage(self, lines): + """ + Populate the AllocFromPoolLine.is_first_pool_usage and + DeallocFromPoolLine.is_last_pool_usage fields so that pools + are created/destroyed. + """ + seen = set() + for line in lines: + if isinstance(line, AllocFromPoolLine): + assert line.group.allocation + pool = line.group.allocation.pool + assert pool is not None + if pool not in seen: + line.is_first_pool_usage = True + seen.add(pool) + + seen = set() + for line in reversed(lines): + if isinstance(line, DeallocFromPoolLine): + assert line.group.allocation + pool = line.group.allocation.pool + assert pool is not None + if pool not in seen: + line.is_last_pool_usage = ( + pool.root.get_live_ranges().end <= line.timestep + ) + seen.add(pool) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/multi_kernel.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/multi_kernel.py new file mode 100644 index 0000000000000000000000000000000000000000..020c38dcc77e4ef9d185cb1fd900dab6c768d36a --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/multi_kernel.py @@ -0,0 +1,385 @@ +# mypy: allow-untyped-defs +import logging +import os +import pathlib +from typing import Any, List + +from torch._inductor.metrics import get_metric_table, is_metric_table_enabled +from torch.utils._ordered_set import OrderedSet + +from .. import config +from ..codecache import get_path, TritonFuture +from ..runtime.benchmarking import benchmarker +from ..utils import cache_on_self, IndentedBuffer +from ..virtualized import V +from .common import TensorArg + + +log = logging.getLogger(__name__) + + +def get_kernel_argdefs(kernel): + arg_defs, _, _, _ = kernel.args.python_argdefs() + return arg_defs + + +def _get_all_args(args_list, arg_types_list=None): + all_args = max(args_list, key=len)[:] + arg_types = max(arg_types_list, key=len)[:] if arg_types_list is not None else None + for args in args_list: + assert set(args).issubset(set(all_args)), f"{args} v.s. {all_args}" + + return all_args, arg_types + + +def get_all_kernel_argdefs(kernels): + """ + The logic here must match with `get_all_call_args`, except no need to get arg_types here + """ + argdefs_list = [get_kernel_argdefs(kernel) for kernel in kernels] + + return _get_all_args(argdefs_list)[0] + + +def get_all_call_args(call_args_list, arg_types_list): + """ + Passed in the call_args for each subkernel and return the call_args for the + combined multi-kernel. + + Note an algorithm as follows does not always work: + ``` + all_call_args: Dict[ + Any, None + ] = {} # use a dict rather than set to maintain insertion order + for call_args in call_args_list: + all_call_args.update({arg: None for arg in call_args}) + + all_call_args = list(all_call_args.keys()) + ``` + It will fail if any kernel has the same argument passed in multiple times. + Check test_pass_same_arg_multi_times in test_multi_kernel.py + + Instead, we pick the longest call args and assert that other call args are + a subset of it. + """ + return _get_all_args(call_args_list, arg_types_list) + + +def get_numel_argdefs(kernel): + numel_argdefs = [] + for tree in kernel.range_trees: + if tree.prefix != "r" or kernel.inside_reduction: + numel_argdefs.append(f"{tree.prefix}numel") + + return numel_argdefs + + +class MultiKernelState: + """ + Maintain state of multi-kernel compilation so we don't define duplicated + multi-kernel for the same set of sub-kernels. + + V.graph.wrapper_code has a reference to MultiKernelState instance. + """ + + def __init__(self): + self.subkernel_to_kernel_name = {} + + def define_kernel(self, kernels): + """ + Previously we name the multi kernel as "multi_kernel_{kernel_names[0]}". + This has some minor issue. + + E.g. for persistent reduction https://gist.github.com/shunting314/39e7c00ff8bb2055942ed5a3255d61ca , + there are 2 flavors of non-persistent reduction: + https://gist.github.com/shunting314/056d43d35907e87efb883970b35c17d4 + and + https://gist.github.com/shunting314/02ee753b65c513c54e695626afe682bd + + The only different is cache eviction policy. + + We should name the multi-kernel differently in these 2 cases. + """ + kernel_names = tuple(k.kernel_name for k in kernels) + if kernel_names in self.subkernel_to_kernel_name: + return self.subkernel_to_kernel_name[kernel_names] + + # name the multi kernel based on the first kernel + multi_kernel_name = f"multi_kernel_{len(self.subkernel_to_kernel_name)}" + self.subkernel_to_kernel_name[kernel_names] = multi_kernel_name + + if V.graph.cpp_wrapper: + # we should not generate any python code for multi-kernel during + # the second pass of cpp-wrapper. + return multi_kernel_name + + buf = IndentedBuffer() + buf.writeline( + f"{multi_kernel_name} = async_compile.multi_kernel({multi_kernel_name!r}, [" + ) + with buf.indent(): + for name in kernel_names: + buf.writeline(f"{name},") + buf.writeline("])") + + wrapper = V.graph.wrapper_code + wrapper.header.splice(buf) + if config.triton.autotune_at_compile_time: + wrapper.kernel_autotune_defs.splice(buf) + + return multi_kernel_name + + +class MultiKernel: + """ + This class maintains the compile time state for multi kernels. + + Assume we do codegen for a MultiKernel encapsulating kernel1 and kernel2. + The generated definition for the multi-kernel will looks like: + ``` + multi_kernel_kernel1 = MultiKernelCall([kernel1, kernel2], multi_kernel_definition_code) + ``` + + Here is an concrete example: https://gist.github.com/shunting314/d9f3fb6bc6cee3dbae005825ca196d39 + """ + + def __init__(self, kernels): + assert len(kernels) >= 2 + + self.kernels = kernels + self.kernel_name = V.graph.wrapper_code.multi_kernel_state.define_kernel( + kernels + ) + + # need this since some code in inductor check if the kernel object has an args + # attribute to decide if it's a non-null kernel. + self.args = object() + + def call_kernel(self, kernel_name): + """ + Collect the union of arguments from all subkernels as the arguments + for the multi-kernel. + """ + assert kernel_name == self.kernel_name + V.graph.wrapper_code.write_triton_header_once() + _, call_args, _, arg_types = self.kernels[0].args.python_argdefs() + for kernel in self.kernels[1:]: + _, other_call_args, _, other_arg_types = kernel.args.python_argdefs() + assert call_args == other_call_args + assert arg_types == other_arg_types + + grid: List[Any] = [] + + if V.graph.cpp_wrapper: + # for the second pass of cpp-wrapper codegen, we should call + # the fast kernel directly + picked_kernel = MultiKernelCall.lookup_choice(kernel_name) + kernel_name = self.kernels[picked_kernel].kernel_name + + # numels for all subkernels should be the same. Use kernels[0] here + self.kernels[0].add_numel_to_call_args_and_grid( + kernel_name, call_args, arg_types, grid + ) + + grid = V.graph.wrapper_code.generate_default_grid(kernel_name, grid) + V.graph.wrapper_code.generate_kernel_call( + kernel_name, + call_args, + grid, + arg_types=arg_types, + ) + + def codegen_nan_check(self): + wrapper = V.graph.wrapper_code + seen = set() + for k in self.kernels: + _, call_args, precompile_args, _ = k.args.python_argdefs() + for arg, precompile_arg in zip(call_args, precompile_args): + if arg in seen: + continue + seen.add(arg) + if isinstance(precompile_arg, TensorArg): + line = f"assert not {arg}.isnan().any().item()" + wrapper.writeline(line) + line = f"assert not {arg}.isinf().any().item()" + wrapper.writeline(line) + + @property + def removed_buffers(self): + return OrderedSet.intersection(*[k.removed_buffers for k in self.kernels]) + + @property + def inplaced_to_remove(self): + return OrderedSet.intersection(*[k.inplaced_to_remove for k in self.kernels]) + + @property + @cache_on_self + def inplace_update_buffers(self): + """ + Make sure all kernels have the same inplace update mappings. + """ + for k in self.kernels[1:]: + assert k.inplace_update_buffers == self.kernels[0].inplace_update_buffers + return self.kernels[0].inplace_update_buffers + + def warn_mix_layout(self, kernel_name: str): + pass + + +class MultiKernelCall: + """ + This class is called at run time to actually run the kernel + """ + + def __init__(self, multi_kernel_name, kernels): + assert len(kernels) >= 2 + self._kernels = kernels + self.multi_kernel_name = multi_kernel_name + + self.disable_cache = os.environ.get( + "TORCHINDUCTOR_DISABLE_MULTI_KERNEL_CACHE" + ) == "1" or is_metric_table_enabled("persistent_red_perf") + + self.picked_kernel = None + if config.triton.multi_kernel > 1: + # manually force a subkernel to ease perf testing + picked_by_config = config.triton.multi_kernel - 2 + assert picked_by_config < len(self._kernels) + self.picked_kernel = picked_by_config + elif not self.disable_cache: + self.load_cache() + + self._recorded = False + + def cache_file_path(self): + _, _, path = get_path(self.kernels[0].fn.cache_key, "picked_kernel") + return pathlib.Path(path) + + def load_cache(self): + assert self.picked_kernel is None + path = self.cache_file_path() + if path.exists(): + with path.open() as fd: + self.picked_kernel = int(fd.read()) + assert self.picked_kernel >= 0 and self.picked_kernel < len( + self._kernels + ) + log.debug( + "Load picked kernel %d from cache file %s", self.picked_kernel, path + ) + + def store_cache(self): + assert self.picked_kernel is not None + path = self.cache_file_path() + path.parent.mkdir(parents=True, exist_ok=True) + + with path.open("w") as fd: + fd.write(str(self.picked_kernel)) + log.debug("Store picked kernel %d to cache file %s", self.picked_kernel, path) + + @property + def kernels(self): + """ + Read results from future. + + This should be called after parallel compilation is done. + In case you call this before compilation is done, + it may slow down the parallel compilation. + """ + for i, kernel in enumerate(self._kernels): + if isinstance(kernel, TritonFuture): + self._kernels[i] = kernel.result() + + return self._kernels + + def benchmark_sub_kernels(self, *args, **kwargs): + """ + Benchmark all the sub kernels and return the execution time + (in milliseconds) for each of time. + + Unit test may mock this method to force a specific kernel to + be picked. + """ + + def wrap_fn(kernel): + def inner(): + args_clone, kwargs_clone = kernel.clone_args(*args, **kwargs) + return kernel.run(*args_clone, **kwargs_clone) + + return inner + + return [ + benchmarker.benchmark_gpu(wrap_fn(kernel), rep=40, fast_flush=True) + for kernel in self.kernels + ] + + # record_choice and lookup_choice are helper functions for cpp-wrapper + # codegen. The first pass use record_choice to keep the choice and + # the second pass do lookup by calling lookup_choice. + # + # An alternative that reused the multi-kernel cache does not work well + # since during codegen of the second pass, it's very hard to know the + # path for the cache file. Also reading the cache file need do some IO + # which can be slower. + @staticmethod + def record_choice(multi_kernel_name, choice): + """ + Record the multi-kernel choice for cpp-wrapper first pass codegen + for the second pass. + + We should do nothing if this function is not called during codegen. + """ + from torch._inductor.graph import GraphLowering + + if not isinstance(V.graph, GraphLowering): + return + + if not V.graph.record_multi_kernel_choice: + return + + V.graph.multi_kernel_to_choice[multi_kernel_name] = choice + + @staticmethod + def lookup_choice(multi_kernel_name): + # this should always been done during cpp-wrapper codegen + assert V.graph.record_multi_kernel_choice + # there should be no miss + return V.graph.multi_kernel_to_choice[multi_kernel_name] + + def run(self, *args, **kwargs): + if self.picked_kernel is None: + timings = self.benchmark_sub_kernels(*args, **kwargs) + self.picked_kernel = timings.index(min(timings)) + k0 = self.kernels[0] + log.debug( + "pick %dth sub-kernel in %s. Size hints %s. Reduction hint %s. Timings %s", + self.picked_kernel, + [k.inductor_meta.get("kernel_name") for k in self.kernels], + k0.size_hints, + k0.inductor_meta.get("reduction_hint"), + timings, + ) + + def get_kernel_path(k): + return k.fn.fn.__code__.co_filename + + get_metric_table("persistent_red_perf").add_row( + lambda: { + "kernel1_name": get_kernel_path(self.kernels[0]), + "kernel2_name": get_kernel_path(self.kernels[1]), + "kernel1_latency": timings[0], + "kernel2_latency": timings[1], + "size_hints": k0.size_hints, + "reduction_hint": k0.inductor_meta.get("reduction_hint"), + "speedup": timings[1] / timings[0], + } + ) + + if not self.disable_cache: + self.store_cache() + + if not self._recorded: + self._recorded = True + self.record_choice(self.multi_kernel_name, self.picked_kernel) + self.run = self.kernels[self.picked_kernel].run # type: ignore[method-assign] + self.run(*args, **kwargs) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/simd.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/simd.py new file mode 100644 index 0000000000000000000000000000000000000000..59c126b993e3d4ddf95de04b9bc6de6eaf9b4d25 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/simd.py @@ -0,0 +1,1869 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import collections +import contextlib +import dataclasses +import functools +import itertools +import logging +import math +import operator +from typing import ( + Any, + Callable, + Counter, + DefaultDict, + Dict, + Iterable, + List, + Optional, + Sequence, + Tuple, + Union, +) + +import sympy + +import torch +import torch._logging +from torch.utils._ordered_set import OrderedSet +from torch.utils._sympy.functions import FloorDiv, Identity, ModularIndexing +from torch.utils._sympy.symbol import free_symbol_is_type, symbol_is_type, SymT + +from ..._dynamo.utils import counters +from .. import config, ir, scheduler +from ..codecache import code_hash +from ..dependencies import Dep, MemoryDep, StarDep, WeakDep +from ..ir import IRNode, TritonTemplateBuffer +from ..optimize_indexing import indexing_dtype_strength_reduction +from ..runtime.hints import ReductionHint +from ..runtime.runtime_utils import green_text, yellow_text +from ..scheduler import BaseSchedulerNode, BaseScheduling, WhyNoFuse +from ..utils import ( + get_dtype_size, + IndentedBuffer, + Placeholder, + sympy_index_symbol, + sympy_product, + sympy_subs, + unique, +) +from ..virtualized import ops, OpsWrapper, V +from .common import CSEVariable, index_prevent_reordering, Kernel, PythonPrinter +from .multi_kernel import MultiKernel + + +log = logging.getLogger(__name__) +perf_hint_log = torch._logging.getArtifactLogger(__name__, "perf_hints") +schedule_log = torch._logging.getArtifactLogger(__name__, "schedule") +fusion_log = torch._logging.getArtifactLogger(__name__, "fusion") + + +pexpr = PythonPrinter().doprint + + +@dataclasses.dataclass +class IterationRanges: + """ + Each range tree represents multiple sets of iteration indexing + in a single tiled dimension in the output kernel. + + If you have two loops ranges one (4, 3, 2) and another (4, 6), + then the range tree will be: + 4 (i0) + 3 (i1) 6 (i3) + 2 (i2) + Where i0 is shared between both loops, but then the split into + different indexing vars. All loop ranges must iterate over + the same number of elements. + """ + + def __init__( + self, + name: str, + var_list: List[sympy.Symbol], + var_ranges: Dict[sympy.Symbol, sympy.Expr], + numel: sympy.Expr, + prefix: str, + *, + kernel: SIMDKernel, + divisor=sympy.Integer(1), + length=sympy.Integer(1), + root: IterationRangesRoot, + ) -> None: + super().__init__() + self.name = name + self.var_list = var_list + self.var_ranges = var_ranges + self.numel = numel + self.prefix = prefix + self.divisor = divisor + self.length = length + self.kernel = kernel + self.root = root + + def symbol(self): + return sympy_index_symbol(self.name) + + +class IterationRangesRoot(IterationRanges): + def __init__( + self, + name: str, + numel: sympy.Expr, + # TODO: this is probably SymTy.INDEX and SymTy.RINDEX + prefix: str, + index: int, + kernel: SIMDKernel, + pid_cache=None, + *, + is_loop: bool, + tensor_dim: Optional[int], + grid_dim: Optional[int], + has_zdim: bool, + ) -> None: + if pid_cache is None: + pid_cache = {} + super().__init__( + name=name, + var_list=[], + var_ranges={}, + numel=numel, + prefix=prefix, + kernel=kernel, + root=self, + ) + self.index = index + # Store all the nodes in one flat list + self.nodes: Dict[sympy.Expr, IterationRangesEntry] = {} + # This is for re-ordering program ID in triton mm template + # pid_cache["tl.program_id(0)"] = pid_m + self.pid_cache: Dict[str, str] = pid_cache + + # True if the dimension is implemented as a single program looping over + # the full dimension (currently only used for non-persistent reduction) + assert not is_loop or (prefix == "r" and grid_dim is None) + self.is_loop = is_loop + # Index of corresponding dimension on triton tensors + self.tensor_dim = tensor_dim + # Index of corresponding dimension in the triton grid + self.grid_dim = grid_dim + self.has_zdim = has_zdim + + def __repr__(self) -> str: + return f"IterationRangesRoot({self.name!r}, {self.numel}, ...)" + + def cache_clear(self): + for node in self.nodes.values(): + node.cache_clear() + + def index_sym(self): + return sympy_index_symbol(f"{self.prefix}index") + + def lookup(self, divisor, length): + """ + Lookup a given RangeTreeEntry, creating it if needed + """ + if V.graph.sizevars.statically_known_equals(divisor * length, self.numel): + expr = FloorDiv(self.index_sym(), divisor) + else: + expr = ModularIndexing(self.index_sym(), divisor, length) + + if expr not in self.nodes: + node = IterationRangesEntry( + f"{self.prefix}{next(V.kernel.iter_vars_count)}", + divisor, + length, + expr, + self, + ) + V.kernel.range_tree_nodes[node.symbol()] = node + self.var_list.append(node.symbol()) + self.var_ranges[node.symbol()] = length + self.nodes[expr] = node + return self.nodes[expr] + + def construct_entries(self, lengths: List[sympy.Expr]): + divisor = sympy.Integer(1) + itervars = [] + for length in reversed(lengths): + itervars.append(self.lookup(divisor, length)) + divisor = divisor * length + return list(reversed(itervars)) + + def construct(self, lengths: List[sympy.Expr]): + return [e.symbol() for e in self.construct_entries(lengths)] + + def vars_and_sizes(self, index: sympy.Expr): + """Figure out vars from this tree used in index""" + nodes = [V.kernel.range_tree_nodes.get(s) for s in index.free_symbols] + nodes = [n for n in nodes if n and n.prefix == self.prefix] + nodes.sort( + key=lambda x: V.graph.sizevars.size_hint( + x.divisor, fallback=config.unbacked_symint_fallback + ) + ) + divisor = sympy.Integer(1) + index_vars = [] + sizes = [] + + def add(node): + nonlocal divisor + index_vars.append(node.symbol()) + sizes.append(node.length) + divisor = divisor * node.length + + for node in nodes: + if not V.graph.sizevars.statically_known_equals(node.divisor, divisor): + # fill in unused index var + add(self.lookup(divisor, FloorDiv(node.divisor, divisor))) + divisor = node.divisor + add(node) + if not V.graph.sizevars.statically_known_equals(self.numel, divisor): + # fill in unused index var + add(self.lookup(divisor, FloorDiv(self.numel, divisor))) + + return list(reversed(index_vars)), list(reversed(sizes)) + + +class IterationRangesEntry(IterationRanges): + def __init__( + self, + name: str, + divisor: sympy.Expr, + length: sympy.Expr, + expr: sympy.Expr, + parent: IterationRanges, + ) -> None: + super().__init__( + name=name, + numel=parent.numel / length, + var_list=parent.var_list, + var_ranges=parent.var_ranges, + prefix=parent.prefix, + divisor=divisor, + length=length, + kernel=parent.kernel, + root=parent.root, + ) + self.parent = parent + self.codegen = functools.lru_cache(None)(self._codegen) + self.expr = expr + + def __repr__(self) -> str: + return f"IterationRangesEntry({self.name}, {self.divisor}, {self.length}, {self.expr}, {self.var_ranges})" + + def set_name(self, name): + self.codegen = lambda: name # type: ignore[assignment] + self.codegen.cache_clear = lambda: None # type: ignore[method-assign] + self.name = name + + def cache_clear(self): + self.codegen.cache_clear() + + def _codegen(self): + V.kernel.codegen_iteration_ranges_entry(self) + return self.name + + def precomputed_args(self): + # for dynamic shapes, find parts of indexing expressions that have to be precomputed + precomputed_args: List[sympy.Expr] = [] + if isinstance(self.expr, sympy.Symbol): + return precomputed_args + assert isinstance(self.expr, (FloorDiv, ModularIndexing)), type(self.expr) + for arg in self.expr.args[1:]: + if not isinstance(arg, (sympy.Integer, sympy.Symbol)): + symbols = arg.free_symbols + if len(symbols) > 0 and all( + symbol_is_type(s, SymT.SIZE) for s in symbols + ): + precomputed_args.append(arg) + return precomputed_args + + def __hash__(self): + return hash(self.name) + + def __eq__(self, other): + return self.name == other.name + + +def constant_repr(value): + if value == float("inf"): + return 'float("inf")' + elif value == float("-inf"): + return 'float("-inf")' + elif math.isnan(value): + return 'float("nan")' + return repr(value) + + +class SIMDKernel(Kernel): + """ + Common base class for Triton/Halide codegen which both use flattened indexing rather than loop nests. + """ + + sexpr = pexpr + kexpr: Callable[[sympy.Expr], str] + allow_block_ptr = False + + def __init__( + self, + *groups, + index_dtype: str, + mutations: Optional[OrderedSet[str]] = None, + pid_cache=None, + reduction_hint=ReductionHint.DEFAULT, + override_persistent_reduction=None, + ) -> None: + if pid_cache is None: + pid_cache = {} + super().__init__() + self.body = IndentedBuffer() + self.indexing_code = IndentedBuffer() + self.numels = [V.graph.sizevars.simplify(s) for s in groups] + self.mutations: OrderedSet[str] = ( + mutations if mutations is not None else OrderedSet() + ) + self.range_trees: List[IterationRangesRoot] = [] + self.range_tree_nodes: Dict[sympy.Symbol, IterationRangesEntry] = {} + self.iter_vars_count = itertools.count() + self.inside_reduction = self.numels[-1] != 1 + self.reduction_hint = reduction_hint + self.index_dtype: str = index_dtype + self.last_usage: OrderedSet[str] = OrderedSet() + self.buf_accesses: DefaultDict[str, List[Dep]] = collections.defaultdict(list) + self.persistent_reduction: bool = ( + override_persistent_reduction + if override_persistent_reduction is not None + else self.should_use_persistent_reduction() + ) + self.no_x_dim = self.want_no_x_dim() + self.code_hash: Union[str, None] = None + + # define this in a closure to make cache local to object + @functools.lru_cache(None) + def simplify_indexing(index: sympy.Expr): + index = V.graph.sizevars.simplify_with_ranges(index, self.var_ranges()) + for tree in self.range_trees: + index = self.combine_contiguous_dims(index, tree) + + return self.combine_modular_indexing_pairs(index) + + self.simplify_indexing = simplify_indexing + self.initialize_range_tree(pid_cache) + + def want_no_x_dim(self): + return False + + def initialize_range_tree(self, pid_cache): + no_r_dim = not self.inside_reduction or self.numels[-1] == 1 + + prefixes = "zyxr" + active_prefixes = prefixes[-len(self.numels) :] + + grid_dims = "xyz" + if self.no_x_dim: + tensor_dims = "r" + elif no_r_dim: + tensor_dims = "xyz" + else: + tensor_dims = "xyzr" + + tensor_dims = "".join(p for p in tensor_dims if p in active_prefixes) + + for i, prefix in enumerate(active_prefixes): + is_reduction = prefix == "r" + tensor_dim = tensor_dims.find(prefix) if prefix in tensor_dims else None + grid_dim = None if is_reduction else grid_dims.find(prefix) + index = i if grid_dim is None else grid_dim + self.range_trees.append( + IterationRangesRoot( + f"{prefix}index", + self.numels[i], + prefix, + index, + self, + pid_cache=pid_cache, + is_loop=is_reduction and not self.persistent_reduction, + tensor_dim=tensor_dim, + grid_dim=grid_dim, + has_zdim="z" in active_prefixes, + ) + ) + + def finalize_indexing(self, indices: Sequence[sympy.Expr]): + """ + Hook called right before codegen with every index that will be + used in the fused kernel. + """ + + def store_reduction(self, name: str, index: sympy.Expr, value: CSEVariable): + prior = self.inside_reduction + self.inside_reduction = False + try: + return self.store(name, index, value) + finally: + self.inside_reduction = prior + + def should_use_persistent_reduction(self) -> bool: + return False # defined in subclass + + def var_ranges(self): + return dict( + itertools.chain.from_iterable( + tree.var_ranges.items() for tree in self.range_trees + ) + ) + + def triton_tensor_ndim(self): + return sum(int(tree.tensor_dim is not None) for tree in self.range_trees) + + def indexing_size_str(self, i): + sizes = ["None"] * self.triton_tensor_ndim() + sizes[i] = ":" + return f"[{', '.join(sizes)}]" + + def dense_size_list(self) -> List[str]: + sizes = ["1"] * self.triton_tensor_ndim() + for tree in self.range_trees: + if tree.tensor_dim is None: + continue + + if tree.prefix != "r" or self.inside_reduction: + sizes[tree.tensor_dim] = f"{tree.prefix.upper()}BLOCK" + return sizes + + def dense_size_str(self): + sizes = self.dense_size_list() + return f"[{', '.join(sizes)}]" + + def combine_modular_indexing_pairs(self, index): + if not isinstance(index, ModularIndexing): + return index + x = index.args[0] + if (tree_node := self.range_tree_nodes.get(x)) is None: + return index + new_index = sympy_subs(index, {x: tree_node.expr}) + new_index = V.graph.sizevars.combine_modular_indexing_pairs(new_index) + # the index now contains xindex/etc, which is nonstandard, fix it up + return sympy_subs( + new_index, + { + tree_node.root.index_sym(): tree_node.root.lookup( + sympy.Integer(1), tree_node.root.numel + ).symbol() + }, + ) + + def combine_contiguous_dims(self, index: sympy.Expr, tree: IterationRangesRoot): + if expand_res := V.graph.sizevars.expand_floor_div(index): + new_index, denominator = expand_res # type: ignore[misc] + return FloorDiv(self._combine_contiguous_dims(new_index, tree), denominator) + else: + return self._combine_contiguous_dims(index, tree) + + def _combine_contiguous_dims(self, index: sympy.Expr, tree: IterationRangesRoot): + """ + More aggressive simplification to merge contiguous dims + """ + if isinstance(index, (sympy.Integer, sympy.Symbol)): + return index + index_vars, sizes = tree.vars_and_sizes(index) + if len(sizes) <= 1: + return index + new_sizes, reindex, prune = V.graph.sizevars._simplify_loops( + index_vars, sizes, index_prevent_reordering([index], index_vars, sizes) + ) + if new_sizes == sizes: + return index + new_index_vars = tree.construct(new_sizes) + new_index = sympy_subs(index, dict(zip(index_vars, reindex(new_index_vars)))) + return new_index + + def set_last_usage(self, nodes): + if not self.inside_reduction or self.persistent_reduction: + return + self.last_usage = OrderedSet( + itertools.chain.from_iterable( + n.last_usage for n in nodes if n is not EnableReduction + ) + ) + + def disable_reduction(self): + should_flush = self.range_trees[-1].is_loop + + @contextlib.contextmanager + def ctx(): + if self.numels[-1] == 1: + assert not self.inside_reduction + yield + return + if should_flush: + # calling codegen_body() will flush all the pending buffers + # and write out a reduction loop + self.codegen_body() + self.inside_reduction = False + try: + yield + if should_flush: + # flush out any code before opening the next loop + self.codegen_body() + finally: + self.inside_reduction = True + + return ctx() + + def set_ranges(self, *lengths): + assert len(lengths) == len(self.range_trees) + return [ + ranges.construct(length) + for length, ranges in zip(lengths, self.range_trees) + ] + + @staticmethod + def _split_iteration_ranges( + groups: Iterable[sympy.Expr], lengths: Sequence[Sequence[sympy.Expr]] + ): + sv = V.graph.sizevars + new_ranges: List[List[sympy.Expr]] = [[] for _ in groups] + remaining = [sv.simplify(g) for g in groups] + var_count = itertools.count() + + def add_range(i, expr): + expr = sv.simplify(expr) + if not sv.statically_known_multiple_of(remaining[i], expr): + raise CantSplit + # guard on the last item out + remaining[i] = FloorDiv(remaining[i], expr) + new_ranges[i].append(expr) + return next(var_count) + + def make_combined(size, idx1, idx2): + def getter(flat_vars): + return size * flat_vars[idx1] + flat_vars[idx2] + + return getter + + return_getters_groups = [] + current_group = 0 + for length_group in lengths: + return_getters = [] + for size in length_group: + if sv.statically_known_equals(size, 1): # type: ignore[arg-type] + return_getters.append(lambda _: sympy.Integer(0)) + continue + + while current_group < len(remaining) and sv.statically_known_equals( + remaining[current_group], 1 # type: ignore[arg-type] + ): + # scroll to next group with remaining elements + current_group += 1 + + if current_group + 1 < len(remaining) and sv.statically_known_gt( + size, remaining[current_group] + ): + # need to break size in two + if not sv.statically_known_multiple_of( + size, remaining[current_group] + ): + raise CantSplit + size1 = remaining[current_group] + size2 = FloorDiv(size, remaining[current_group]) + return_getters.append( + make_combined( + size2, + add_range(current_group, size1), + add_range(current_group + 1, size2), + ) + ) + else: + return_getters.append( + operator.itemgetter(add_range(current_group, size)) + ) + return_getters_groups.append(return_getters) + + assert all( + V.graph.sizevars.size_hint(s) == 1 for s in remaining + ), f"failed to set ranges {remaining} {lengths}" + + return new_ranges, return_getters_groups + + @classmethod + def is_compatible( + cls, groups: Iterable[sympy.Expr], lengths: Sequence[Sequence[sympy.Expr]] + ): + try: + cls._split_iteration_ranges(groups, lengths) + return True + except CantSplit: + return False + + def split_and_set_ranges(self, lengths: List[List[sympy.Expr]]): + """ + We may want to fuse `for i0 in s0*s1` into a tiled kernel with groups (s0, s1). + + To do this we need to split up the iteration space of i0 into something like: + for i1 in s0: + for i2 in s1: + i0 = i1*s1 + i2 + .... + + This function matches and resplits lengths to the groups of + this kernel to enable tiled + non-tiled fusions. + """ + groups = [rt.numel for rt in self.range_trees] + if not self.inside_reduction: + groups[-1] = sympy.Integer(1) + + if len(lengths) == len(self.range_trees) and all( + V.graph.sizevars.simplify(sympy_product(x) - g) == 0 + for x, g in zip(lengths, groups) + ): + return self.set_ranges(*lengths) + + new_ranges, return_getters_groups = self._split_iteration_ranges( + groups, lengths + ) + itervars = list(itertools.chain.from_iterable(self.set_ranges(*new_ranges))) + return [[fn(itervars) for fn in fns] for fns in return_getters_groups] + + def is_indirect_indexing(self, index: sympy.Expr): + # tmpX means indirect indexing + return free_symbol_is_type(index, SymT.TMP) + + def is_broadcasted(self, index: sympy.Expr): + # Note. This may not be correct when there is indirect indexing + if self.is_indirect_indexing(index): + return False + + index_numels = [1] * len(self.numels) + for symbol in index.free_symbols: + if symbol not in self.range_tree_nodes: + # Non-iterated variables, e.g. strides + continue + entry = self.range_tree_nodes[symbol] # type: ignore[index] + assert isinstance(entry.parent, IterationRangesRoot) + index_numels[entry.parent.index] *= entry.length + + # If the index variables only iterate over a subset of the kernel + # numels, then it must be broadcasted. + simplify = V.graph.sizevars.simplify + return any( + simplify(idx_range) != simplify(iter_range) # type: ignore[arg-type] + for idx_range, iter_range in zip(index_numels, self.numels) + ) + + def index_to_str(self, index: sympy.Expr) -> str: + """ + Convert an index expr to a string that can be used in output code. + e.g. a sympy expression "s2" may actually appear as "ks1" in the generated kernel. + + Index expressions often need to be passed in as arguments to the triton kernel. + Rename_indexing and codegen_indexing keep track of the needed indices and add + new parameters to the function signature. + """ + if isinstance(index, list): + return f"[{', '.join(map(self.index_to_str, index))}]" + return self.kexpr(self.rename_indexing(index)) # type: ignore[call-arg] + + def prepare_indexing( + self, + index: sympy.Expr, + ): + index = self.simplify_indexing(index) + index = sympy_subs(index, V.graph.sizevars.precomputed_replacements) + # if simple replacements didn't get rid of floor/ceil, try full subs + if len(index.atoms(sympy.floor)) or len(index.atoms(sympy.ceiling)): + index = index.subs(V.graph.sizevars.precomputed_replacements) + # last resort, if no range vars are in the expr, hoist it + # TODO instead of trying to blindly find complicated exprs, we should hoist the + # inputs/outputs sizes and strides, but at the time indexing is generated + # kernel inputs and outputs are not set yet, we'd need a deeper refactor + # to do it this way + + if len(index.atoms(sympy.ceiling)): + for a in index.atoms(sympy.ceiling): + # for nested exprs, atoms yields top level first (?) + # so if everything goes fine, lower level replacements will come up empty + symbols = a.free_symbols + if len(symbols) > 0 and all( + symbol_is_type(s, (SymT.SIZE, SymT.PRECOMPUTED_SIZE)) + for s in symbols + ): + replacements = {a: V.graph.sizevars.lookup_precomputed_size(a)} + index = sympy_subs(index, replacements) + + simp_index = self.simplify_indexing(index) + + # Now that we are done simplifying we can unwrap Identity so that downstream handling + # for its contained expression will work. previously, tl.full wrapping of sympy.Integer + # would not occur + simp_index = ( + simp_index if not isinstance(simp_index, Identity) else simp_index.args[0] + ) + + return self.codegen_indexing(simp_index) + + def active_range_trees(self, reorder=False): + trees = [ + t for t in self.range_trees if t.prefix != "r" or self.inside_reduction + ] + if reorder and len(trees) > 1: + count = sum(t.prefix in "xyz" for t in trees) + assert "".join(t.prefix for t in trees[:count]) == "zyx"[-count:], [ + t.prefix for t in trees[:count] + ] + trees[:count] = reversed(trees[:count]) + return trees + + def codegen_indexing(self, expr: sympy.Expr): + expr = V.graph.sizevars.simplify_with_ranges(expr, self.var_ranges()) + for sym in sorted(expr.free_symbols, key=str): + if sym in self.range_tree_nodes: + # if indexing expression is complicated, we precompute it on the host side + # and send the result as a kernel argument + replacements = {} + for ps in self.range_tree_nodes[sym].precomputed_args(): # type: ignore[index] + replacements[ps] = V.graph.sizevars.lookup_precomputed_size(ps) + if len(replacements) > 0: + self.range_tree_nodes[sym].expr = sympy_subs( # type: ignore[index] + self.range_tree_nodes[sym].expr, replacements # type: ignore[index] + ) + self.range_tree_nodes[sym].codegen() # type: ignore[index] + return expr + + def codegen_nan_check(self) -> None: + raise NotImplementedError("NYI: codegen_nan_check") + + def call_kernel(self, name: str, node: Optional[IRNode] = None) -> None: + raise NotImplementedError("NYI: call_kernel") + + @contextlib.contextmanager + def mask_loads(self, mask, value): + """Context manager to add an additional mask to tl.load/store""" + prior = self._load_mask + prior_val = self._load_other + if prior: + mask = ops.logical_and(mask, prior) + + mask = OpsWrapper._unwrap(mask) + self._load_mask = mask + self._load_other = value + try: + # TODO(jansel): do we need a reshape here? + yield mask + finally: + self._load_mask = prior + self._load_other = prior_val + + def get_strides_of_load(self, index: sympy.Expr): + """ + This gets the stride of the index for each of the tiling variables + (technically, it does it at index 0) + + For example, if + xindex = x0 + 512*x1 + 1024*r0 + x0 = (xindex//512) + x1 = (xindex % 512) + r0 = rindex // 1024 + + this function would return + {xindex: 512, rindex: 1024} + """ + index_to_tile_indexes = {k: v.expr for k, v in self.range_tree_nodes.items()} + index_in_tile_vars = sympy_subs(index, index_to_tile_indexes) # type: ignore[arg-type] + strides = {} + for range_tree in self.range_trees: + s = sympy_index_symbol(range_tree.name) + strides[s] = sympy_subs(index_in_tile_vars, {s: 1}) - sympy_subs( + index_in_tile_vars, {s: 0} + ) + return strides + + @staticmethod + def _map_tuple_or_scalar(fn, value): + if isinstance(value, tuple): + return tuple(map(fn, value)) + return fn(value) + + def estimate_kernel_num_bytes(self): + """ + Try the best to estimate the total size (in bytes) of the + kernel's inputs and outputs, which is used for estimating the memory + throughput of this kernel. This information is used for checking how + far we are from the peak memory bandwidth. It's important that + we want to avoid overestimating the sizes of the inputs and outputs, + because it can wrongfully give us a very large memory traffic value, + which may be even larger than the theoretical bandwidth and thus + become very misleading. This is particularly problematic for cases + where we slice some inputs. In those cases, we should only count + the size of the "slices" instead of the original inputs, because + only the slices contribute to the real memory traffic. + """ + nbytes = [] + ninplace_args = len(unique(self.args.inplace_buffers.values())) + _, call_args, _, _ = self.args.python_argdefs() + + # For pointwise and reduction kernels, this is the upper-bound numels + # for the output buffer. + # FIXME: This is not exactly right for cases like below: + # def foo(tensor0, tensor1): + # x0 = narrow(tensor0) + # return cat(x0, tensor1) + # For this example, we will end up overestimate the size for the + # slice s0. Potentially, we could have precise inputs information + # if we maintained the original inputs of the Pointwise kernel created + # for the "cat". However, I think it might be a bit overwhelming that + # we add such complexity only for handling some particular cases for + # benchmarking. + out_numel = V.graph.sizevars.size_hint(sympy_product(self.numels)) + for i, arg in enumerate(call_args): + # "buf" may be narrowed. In this case, the number of memory accesses + # should be estimated based on the reinterpreted layout. + # On the other hand, buf may be broadcasted. In this case, + # counting the size of the underline storage would give us + # a better estimation in terms of memory accesses. + if arg not in self.buf_accesses: + nbytes.append(0) + continue + arg_numel = V.graph.get_numel(arg) + buf_size = V.graph.sizevars.size_hint(arg_numel) + if buf_size > out_numel: + # This arg points to a buf that has been sliced. + # We need to count each individual slice to have + # a better estimation. + indices: OrderedSet[Any] = OrderedSet() + no_index_dep_count = 0 + for dep in self.buf_accesses[arg]: + if isinstance(dep, (StarDep, WeakDep)): + indices.add(f"no_index_dep_{no_index_dep_count}") + no_index_dep_count += 1 + else: + indices.add(dep.index) + numel = len(indices) * out_numel + else: + numel = buf_size + dtype = V.graph.get_dtype(arg) + dtype_size = get_dtype_size(dtype) + nbytes.append(numel * dtype_size * (1 + int(i < ninplace_args))) + return sum(nbytes) + + def warn_mix_layout(self, kernel_name): + """ + Print message if the kernel have mixed layout inputs. + Only care about 4D tensor for now. + """ + if ( + len(self.args.input_buffers) == 1 + and len(self.args.output_buffers) == 1 + and len(self.args.inplace_buffers) == 0 + ): + # even if input buffer and output buffer have different layout, + # this can be a layout conversion kernel. No need to warn for + # the mix layouts. + return + + argdefs, call_args, signature, _ = self.args.python_argdefs() + uniform_stride_order = None + for arg_name in call_args: + buf = V.graph.try_get_buffer(arg_name) + if buf and len(buf.layout.size) == 4: + # ignore the tensor if only 1 dimension is non-zero + if len([x for x in buf.layout.size if x == 1]) == 3: + continue + stride_order = ir.get_stride_order(buf.layout.stride) + if uniform_stride_order is None: + uniform_stride_order = stride_order + elif uniform_stride_order != stride_order: + msg = yellow_text( + f"Expected stride order {uniform_stride_order}, but found stride order" + + f" {stride_order} for kernel {kernel_name}" + ) + log.warning(msg) + + stride_order_list = [ + ir.get_stride_order(V.graph.get_buffer(name).layout.stride) + if V.graph.try_get_buffer(name) + else None + for name in call_args + ] + size_list = [ + V.graph.get_buffer(name).layout.size + if V.graph.try_get_buffer(name) + else None + for name in call_args + ] + source_list = [ + "GraphInput" + if name in V.graph.graph_inputs + else "IntermediateBuffer" + if name in V.graph.name_to_buffer + else None + for name in call_args + ] + + msg = yellow_text( + f" param names {argdefs}\n buf names {call_args}\n strides {stride_order_list}" + + f"\n sizes {size_list}\n sources {source_list}\n" + ) + log.warning(msg) + return + msg = green_text( + f"All the inputs for the triton kernel {kernel_name} have uniform layout" + ) + log.warning(msg) + + def welford_reduce_fallback(self, dtype, value): + sum_ = ops.reduction(dtype, dtype, "sum", value) + self.inside_reduction = False + rnumel = ops.index_expr(self.numels[-1], dtype) + mean = ops.truediv(sum_, rnumel) + + self.inside_reduction = True + dx = ops.sub(value, mean) + dx2 = ops.mul(dx, dx) + m2 = ops.reduction(dtype, dtype, "sum", dx2) + return OpsWrapper._unwrap((mean, m2, rnumel)) + + def codegen_kernel(self): + raise NotImplementedError + + def codegen_body(self): + pass + + def codegen_iteration_ranges_entry(self, entry: IterationRangesEntry): + pass + + +class SIMDScheduling(BaseScheduling): + kernel_type = SIMDKernel # override in subclass + int32_type = "torch.int32" + int64_type = "torch.int64" + + def __init__(self, scheduler) -> None: + super().__init__() + self.scheduler = scheduler + + def group_fn(self, sizes): + return tuple(V.graph.sizevars.simplify(sympy_product(s)) for s in sizes) + + def can_fuse(self, node1, node2): + """ + Hook called by Scheduler to determine if the Triton backend + can fuse node1 and node2. These nodes might already be + FusedSchedulerNodes. + """ + if isinstance(node1, scheduler.ForeachKernelSchedulerNode) or isinstance( + node2, scheduler.ForeachKernelSchedulerNode + ): + return scheduler.ForeachKernelSchedulerNode.can_fuse(node1, node2) + + _, (numel1, rnumel1) = node1.group + _, (numel2, rnumel2) = node2.group + why = WhyNoFuse(node1, node2) + + if node1.is_split_scan() and not node2.is_split_scan(): + if node2.is_reduction(): + why("Split scan cannot fuse with reductions") + elif node2.is_split_scan() and not node1.is_split_scan(): + if node1.is_reduction(): + why("Split scan cannot fuse with reductions") + + if node1.is_reduction() and node2.is_reduction(): + reduction_can_fuse = numel1 == numel2 and rnumel1 == rnumel2 + if not reduction_can_fuse: + why( + "numel/rnumel mismatch (reduce) (%s, %s), (%s, %s)", + numel1, + numel2, + rnumel1, + rnumel2, + ) + return reduction_can_fuse + + if not node1.is_reduction() and not node2.is_reduction(): + if not (numel1 == numel2 and rnumel1 == rnumel2): + why( + "numel/rnumel mismatch (non-reduce) (%s, %s), (%s, %s)", + numel1, + numel2, + rnumel1, + rnumel2, + ) + return False + + if node1.is_template(): + # Only allow fusion for TritonTemplates for now. + # Fusion for CUDATemplates are not supported. + is_triton_template = isinstance(node1.node, TritonTemplateBuffer) + if not is_triton_template: + why("node1 is not TritonTemplateBuffer") + return is_triton_template + + # check for a bad combined tiling + tiling1 = self.select_tiling(node1.get_nodes(), numel1, rnumel1) + tiling2 = self.select_tiling(node2.get_nodes(), numel1, rnumel1) + tiling3 = self.select_tiling( + node1.get_nodes() + node2.get_nodes(), numel1, rnumel1 + ) + if config.triton.tiling_prevents_pointwise_fusion: + cond = True + if len(tiling1) > 2: + if len(tiling2) > 2: + cond = tiling1 == tiling2 == tiling3 + else: + cond = tiling1 == tiling3 + elif len(tiling2) > 2: + cond = tiling2 == tiling3 + if not cond: + why( + "tiling mismatch (%s, %s, %s)", + tiling1, + tiling2, + tiling3, + ) + return False + + return True + + if not node1.is_reduction() and node2.is_reduction(): + assert rnumel1 == 1 and rnumel2 != 1 + if numel1 == numel2 * rnumel2: + if not all( + SIMDKernel.is_compatible((numel2, rnumel2), n.get_ranges()) + for n in node1.get_nodes() + ): + why("nodes numel/rnumel incompatibility") + return False + if ( + config.triton.tiling_prevents_reduction_fusion + and not node1.is_template() + ): + is_reduction_tiling_valid = self.select_tiling( + node1.get_nodes(), numel1 + ) in ( + (numel1, 1), + (numel2, rnumel2, 1), + ) + if not is_reduction_tiling_valid: + why("invalid tiling for reduction") + return is_reduction_tiling_valid + return True + + if numel1 != numel2: + why("nodes numel incompatibility") + return numel1 == numel2 + + assert node1.is_reduction() and not node2.is_reduction() + # swap args to hit the case above + return self.can_fuse_horizontal(node2, node1) + + can_fuse_vertical = can_fuse + can_fuse_horizontal = can_fuse + + def generate_node_schedule(self, nodes, numel, rnumel): + node_schedule: List[Any] = [] + done: OrderedSet[scheduler.BaseSchedulerNode] = OrderedSet() + # Writes with a reduced shape, meaning they are only present once the + # reduction loop has ended + not_ready_yet_nodes: OrderedSet[str] = OrderedSet() + + def fits_in_main_body(n): + _, (node_numel, node_rnumel) = n.group + return (node_numel == numel and node_rnumel == rnumel) or ( + node_numel == numel * rnumel and node_rnumel == 1 + ) + + def fits_outside_reduction(n): + _, (node_numel, node_rnumel) = n.group + return node_numel == numel and node_rnumel == 1 and rnumel != 1 + + def schedule_node_in_loop(n): + done.add(n) + node_schedule.append(n) + # A scan is modelled as a reduction in the scheduler but has a + # full sized output that can be used inside the loop body + if ( + n.is_reduction() + and isinstance(n, scheduler.SchedulerNode) + and isinstance(n.node, ir.ComputedBuffer) + and not isinstance(n.node.data, ir.Scan) + ): + not_ready_yet_nodes.add(n.get_name()) + + @contextlib.contextmanager + def end_current_reduction_loop(): + if node_schedule and node_schedule[-1] is EnableReduction: + node_schedule.pop() + else: + node_schedule.append(DisableReduction) + yield + node_schedule.append(EnableReduction) + not_ready_yet_nodes.clear() + + def requires_closing_previous_reduction(node, node_schedule): + if rnumel == 1: + return False + if not not_ready_yet_nodes & node.ancestors: + return False + assert node_schedule and not isinstance( + node_schedule[-1], (EnableReduction, DisableReduction) + ) + return bool(not_ready_yet_nodes) + + for index, node in enumerate(nodes): + if node in done: + continue + done.add(node) + + if fits_in_main_body(node): + if requires_closing_previous_reduction(node, node_schedule): + with end_current_reduction_loop(): + pass # need to start a new reduction loop + + schedule_node_in_loop(node) + elif fits_outside_reduction(node): + with end_current_reduction_loop(): + node_schedule.append(node) + else: + raise NotImplementedError( + f"unexpected group: ({numel}, {rnumel}) != {node.group[1]}" + ) + + return node_schedule + + def codegen_node( + self, node: Union[scheduler.FusedSchedulerNode, scheduler.SchedulerNode] + ): + """ + Given a set of pre-fused nodes, generate a Triton kernel. + """ + + nodes: List[scheduler.SchedulerNode] = node.get_nodes() # type: ignore[assignment] + + _, (numel, rnumel) = max(nodes, key=lambda x: int(x.is_reduction())).group + + node_schedule = self.generate_node_schedule(nodes, numel, rnumel) + buf_accesses = collections.defaultdict(list) + for node in nodes: + for access in node.read_writes.reads | node.read_writes.writes: + buf_accesses[access.name].append(access) + + schedule_log.debug("Schedule:\n %s", node_schedule) + + return self.codegen_node_schedule(node_schedule, buf_accesses, numel, rnumel) + + @staticmethod + def reduction_hint(node): + assert node.is_reduction() + if all( + dep.is_contiguous() + for dep in itertools.chain(node.read_writes.reads, node.read_writes.writes) + ): + return ReductionHint.INNER + else: + return node.node.data.reduction_hint + + @staticmethod + def can_use_32bit_indexing( + numel: sympy.Expr, buffers: Iterable[Union[ir.Buffer, ir.TensorBox]] + ) -> bool: + int_max = torch.iinfo(torch.int32).max + size_hint = V.graph.sizevars.size_hint + has_hint = V.graph.sizevars.shape_env.has_hint + + def within_32bit(e): + # Allow for unhinted e as long as we can still statically prove + # (e.g., via ValueRanges) that it is still in bounds + if V.graph.sizevars.is_expr_static_and_true(e <= int_max): + return True + # Otherwise, the hint MUST exist and be in range + return has_hint(e) and size_hint(e) <= int_max + + if not within_32bit(numel): + return False + + # Any use of a MultiOutputLayout will create a buffer with a + # Layout whose sizes are accounted for + buf_sizes = [ + buf.get_layout().storage_size() + for buf in buffers + if not isinstance(buf.get_layout(), ir.MultiOutputLayout) + ] + + if not all(within_32bit(size) for size in buf_sizes): + return False + + # Only install guards for 32-bit indexing as there is no correctness + # issue with using 64-bit for everything + V.graph.sizevars.guard_leq(numel, int_max) # type: ignore[arg-type] + for size in buf_sizes: + V.graph.sizevars.guard_leq(size, int_max) # type: ignore[arg-type] + return True + + @classmethod + def select_index_dtype(cls, node_schedule, numel, reduction_numel): + # Gather all used buffer names + buffer_names: OrderedSet[str] = OrderedSet() + for node in node_schedule: + if not isinstance(node, scheduler.BaseSchedulerNode): + continue + + buffer_names.update(node.get_buffer_names()) + buffer_names.update(node.used_buffer_names()) + + # Get buffers objects + + def _get_buffer(name: str) -> Union[ir.Buffer, ir.TensorBox]: + buf = V.graph.get_buffer(name) + if buf is None: + raise RuntimeError(f"Failed to find buffer matching name {name}") + return buf + + buffers = [V.graph.get_buffer(name) for name in buffer_names] + + # In theory we can separately check xnumel and rnumel are <= int_max + # but some indexers do use the full linear index so we need to be + # conservative here. + total_numel = numel * reduction_numel + + if SIMDScheduling.can_use_32bit_indexing(total_numel, buffers): + return cls.int32_type + return cls.int64_type + + def has_non_contiguous_pw_in_reduction_kernel(self, node_schedule, numel, rnumel): + pointwise_nodes = list( + filter( + lambda n: n not in (EnableReduction, DisableReduction) + and not n.is_reduction() + and n.group[1][0] == numel * rnumel, + node_schedule, + ) + ) + for node in pointwise_nodes: + # An index can be an integer when loading a random seed. + if not all( + not isinstance(dep, MemoryDep) + or dep.is_contiguous() + or isinstance(dep.index, (sympy.Integer, int)) + or dep.stride1_for_last_dim() + for dep in itertools.chain( + node.read_writes.reads, node.read_writes.writes + ) + ): + return True + return False + + def get_kernel_args(self, node_schedule, numel, reduction_numel): + reductions = list( + filter( + lambda n: n not in (EnableReduction, DisableReduction) + and n.is_reduction(), + node_schedule, + ) + ) + if len(reductions) > 0: + hints = [self.reduction_hint(n) for n in reductions] + if hints.count(hints[0]) == len(hints): + reduction_hint_val = hints[0] + else: + reduction_hint_val = ReductionHint.DEFAULT + + if ( + reduction_hint_val == ReductionHint.INNER + and self.has_non_contiguous_pw_in_reduction_kernel( + node_schedule, numel, reduction_numel + ) + ): + reduction_hint_val = ReductionHint.DEFAULT + else: + reduction_hint_val = ReductionHint.DEFAULT + + mutations: OrderedSet[str] = OrderedSet() + for node in node_schedule: + if node in (DisableReduction, EnableReduction): + continue + + for buf in node.get_outputs(): + mutations.update(buf.get_mutations()) + + index_dtype = self.select_index_dtype(node_schedule, numel, reduction_numel) + + return reduction_hint_val, mutations, index_dtype + + def codegen_node_schedule( + self, node_schedule, buf_accesses, numel, reduction_numel + ): + from torch._inductor.codegen.triton_split_scan import TritonSplitScanKernel + + tiled_groups = self.select_tiling(node_schedule, numel, reduction_numel) + ( + reduction_hint_val, + mutations, + index_dtype, + ) = self.get_kernel_args(node_schedule, numel, reduction_numel) + + is_split_scan = any( + isinstance(node, BaseSchedulerNode) and node.is_split_scan() + for node in node_schedule + ) + kernel_type: type = self.kernel_type + if is_split_scan and issubclass(TritonSplitScanKernel, kernel_type): + kernel_type = TritonSplitScanKernel + + kernel_args = tiled_groups + kernel_kwargs = dict( + reduction_hint=reduction_hint_val, + mutations=mutations, + index_dtype=index_dtype, + ) + + def _node_has_sort(node): + if node in (EnableReduction, DisableReduction): + return False + + sort_nodes = node._body.root_block.graph.find_nodes( + op="call_method", target="sort" + ) + return bool(sort_nodes) + + # ops.sort only works with persistent reduction, and is not bandwidth bound anyway + # so taking the hit of non-coalesced loads is okay + has_sort = any(_node_has_sort(node) for node in node_schedule) + if has_sort: + kernel_kwargs["override_persistent_reduction"] = True + + kernel = kernel_type( + *kernel_args, + **kernel_kwargs, + ) + kernel.buf_accesses = buf_accesses + + kernel2: Optional[SIMDKernel] = None + if kernel.persistent_reduction and config.triton.multi_kernel and not has_sort: + kernel2 = self.kernel_type( + *kernel_args, + **kernel_kwargs, + override_persistent_reduction=False, + ) + self.codegen_node_schedule_with_kernel(node_schedule, kernel2) + with V.set_kernel_handler(kernel2): + src_code2 = kernel2.codegen_kernel() + kernel_name2 = self.define_kernel(src_code2, node_schedule, kernel) + kernel2.kernel_name = kernel_name2 + kernel2.code_hash = code_hash(src_code2) + + # Keep buffers needed by the non-persistent reduction so both + # kernels have the same arguments + kernel.must_keep_buffers = set(kernel2.must_keep_buffers) + + self.codegen_node_schedule_with_kernel(node_schedule, kernel) + + with V.set_kernel_handler(kernel): + src_code = kernel.codegen_kernel() + + kernel_name = self.define_kernel(src_code, node_schedule, kernel) + log.debug("Generating kernel code with kernel_name: %s", kernel_name) + kernel.kernel_name = kernel_name + kernel.code_hash = code_hash(src_code) + + final_kernel = MultiKernel([kernel, kernel2]) if kernel2 is not None else kernel + + with V.set_kernel_handler(final_kernel): + for node in node_schedule: + if node not in (EnableReduction, DisableReduction): + node.mark_run() + + self.codegen_comment(node_schedule) + final_kernel.call_kernel(final_kernel.kernel_name) + + if config.nan_asserts: + final_kernel.codegen_nan_check() + if config.warn_mix_layout: + final_kernel.warn_mix_layout(kernel_name) + + V.graph.removed_buffers |= final_kernel.removed_buffers + V.graph.inplaced_to_remove |= final_kernel.inplaced_to_remove + + if ( + V.graph.wrapper_code.supports_intermediate_hooks + and config.generate_intermediate_hooks + ): + # Not every node in the schedule will actually be live on output; + # we can't check dead buffers. + live_outs = kernel.args.live_output_buffers() + for node in node_schedule: + if not isinstance(node, scheduler.BaseSchedulerNode): + continue + name = node.get_name() + if name not in live_outs: + continue + assert node.node is not None + origin_node = node.node.get_origin_node() + if origin_node is not None: + counters["inductor"]["intermediate_hooks"] += 1 + V.graph.wrapper_code.writeline( + f"run_intermediate_hooks({origin_node.name!r}, {name})" + ) + + self.scheduler.free_buffers() + + def codegen_node_schedule_with_kernel(self, node_schedule, kernel): + def current_reduction_nodes(nodes): + return itertools.takewhile(lambda n: n is not DisableReduction, nodes) + + with kernel: + stack = contextlib.ExitStack() + kernel.set_last_usage(current_reduction_nodes(node_schedule)) + all_indexing = {} + + # First pass to collect indexing and decide inplace updates + for node in node_schedule: + if node is DisableReduction: + stack.enter_context(kernel.disable_reduction()) + elif node is EnableReduction: + stack.close() + else: + node.decide_inplace_update() + index_vars = kernel.split_and_set_ranges(node.get_ranges()) + all_indexing.update( + dict.fromkeys( + node._body.indexing_from_args(index_vars).values() + ) + ) + + kernel.finalize_indexing(all_indexing.keys()) + + # Second pass to do codegen + for i, node in enumerate(node_schedule): + if node is DisableReduction: + stack.enter_context(kernel.disable_reduction()) + elif node is EnableReduction: + stack.close() + kernel.set_last_usage(current_reduction_nodes(node_schedule[i:])) + else: + # TODO - use split ranges ? + indexing_dtype_strength_reduction(node._body) + index_vars = kernel.split_and_set_ranges(node.get_ranges()) + node.codegen(index_vars) + + def codegen_template( + self, template_node, epilogue_nodes, only_gen_src_code=False + ) -> Optional[str]: + """ + Codegen a triton template + + If `only_gen_src_code` the src code will be returned instead of codegen'd into the wrapper + """ + _, (numel, rnumel) = template_node.group + assert rnumel == 1 + kernel, render = template_node.node.make_kernel_render(template_node.node) + with kernel: + if not only_gen_src_code: + for node in [template_node, *epilogue_nodes]: + node.mark_run() + partial_code = render() + with kernel.set_subgraph_body(""): + for node in epilogue_nodes: + node.codegen(kernel.split_and_set_ranges(node.get_ranges())) + + if not isinstance(partial_code, str): + partial_code.finalize_hook("") + partial_code.finalize_hook("", strict=False) + # finalize must be called after adding epilogue above + with V.set_kernel_handler(kernel): + # TODO: Maybe unify CUDATemplateKernel to also use PartialRender for flexible epilogue fusion. + with kernel.set_subgraph_body(""): + if isinstance(partial_code, str): + src_code = partial_code + else: + partial_code.finalize_hook("") + src_code = partial_code.code + node_schedule = [template_node, *epilogue_nodes] + + if config.benchmark_kernel: + num_gb = kernel.estimate_kernel_num_bytes() / 1e9 + grid_args = V.graph.sizevars.size_hints(kernel.call_sizes) + assert kernel.meta is not None, "meta is None" + grid = kernel.grid_fn(*grid_args, kernel.meta) + src_code = ( + f"{kernel.imports_for_benchmark_kernel()}\n" + f"{src_code}\n" + f"{kernel.codegen_kernel_benchmark(num_gb, grid).getvalue()}" + ) + + if only_gen_src_code: + return src_code + + kernel_name = self.define_kernel(src_code, node_schedule, kernel) + + self.codegen_comment(node_schedule) + kernel.call_kernel(kernel_name, template_node.node) + + V.graph.removed_buffers |= kernel.removed_buffers + V.graph.inplaced_to_remove |= kernel.inplaced_to_remove + self.scheduler.free_buffers() + return None + + def codegen_sync(self): + V.graph.wrapper_code.writeline(V.graph.device_ops.synchronize()) + + def generate_combo_kernel_code( + self, + subkernel_nodes: List[BaseSchedulerNode], + custom_part_algorithm: bool, + enable_autotune: bool, + mixed_sizes: bool, + only_gen_src_code: bool = False, + ) -> List[Tuple[str, Any, Any]]: + from .triton_combo_kernel import ComboKernel + + fused_node_lists = [node.get_nodes() for node in subkernel_nodes] + subkernel_map, node_schedule_map = {}, {} + for pn, nodes in zip(subkernel_nodes, fused_node_lists): + _, (numel, rnumel) = max(nodes, key=lambda x: int(x.is_reduction())).group + node_schedule = self.generate_node_schedule(nodes, numel, rnumel) + tiled_groups = self.select_tiling(node_schedule, numel, rnumel) + node_schedule_map[pn] = node_schedule, tiled_groups, numel, rnumel + ( + reduction_hint_val, + mutations, + index_dtype, + ) = self.get_kernel_args(node_schedule, numel, rnumel) + subkernel_map[pn] = ComboKernel.create_triton_kernel( + *tiled_groups, + reduction_hint=reduction_hint_val, + mutations=mutations, + index_dtype=index_dtype, + optimize_mask=not mixed_sizes, + ) + + partitions = ComboKernel.horizontal_partition( + nodes=subkernel_nodes, + triton_scheduling=self, + custom_algorithm=custom_part_algorithm, + kernel_map=subkernel_map, + node_info_map=node_schedule_map, + ) + log.debug( + "ComboKernels: %d nodes partitioned into %s groups", + len(subkernel_nodes), + [len(p) for p in partitions], + ) + kernel_code_list = [] + for node_group in partitions: + fused_node_lists = [node.get_nodes() for node in node_group] + kernel = ComboKernel( + enable_autotune=enable_autotune, + mixed_sizes=mixed_sizes, + ) + + for pn, nodes in zip(node_group, fused_node_lists): + if only_gen_src_code: + # empty last_usage. May cause more aggressive 'evict_last'. Should be fine. + for n in nodes: + n.last_usage = OrderedSet() + self.codegen_node_schedule_with_kernel( + node_schedule_map[pn][0], + kernel.create_sub_kernel(subkernel_map[pn]), + ) + subkernel = subkernel_map[pn] + node_schedule = node_schedule_map[pn][0] + if not only_gen_src_code: + with V.set_kernel_handler(subkernel): # type: ignore[call-arg] + for node in node_schedule: + if node not in (EnableReduction, DisableReduction): + node.mark_run() + V.graph.removed_buffers |= subkernel.removed_buffers + V.graph.inplaced_to_remove |= subkernel.inplaced_to_remove + + src_code = kernel.codegen_kernel() + kernel_code_list.append((src_code, kernel, node_group)) + return kernel_code_list + + def codegen_combo_kernel(self, combo_kernel_node): + subkernel_nodes = combo_kernel_node.get_subkernel_nodes() + custom_part_algorithm = combo_kernel_node.use_custom_partition_algo + enable_autotune = combo_kernel_node.enable_autotune + mixed_sizes = config.combo_kernel_allow_mixed_sizes > 1 or ( + config.combo_kernel_allow_mixed_sizes == 1 and custom_part_algorithm + ) + + kernel_code_list = self.generate_combo_kernel_code( + subkernel_nodes, custom_part_algorithm, enable_autotune, mixed_sizes + ) + + for src_code, kernel, _ in kernel_code_list: + kernel_name = self.define_kernel(src_code, [combo_kernel_node], kernel) + self.codegen_comment([combo_kernel_node]) + log.debug("ComboKernels: generated kernel %s.", kernel_name) + kernel.call_kernel(V.graph.wrapper_code, kernel_name) + + self.scheduler.free_buffers() + + @staticmethod + @functools.lru_cache(32) + def candidate_tilings(node): + ranges, reduction_ranges = node.get_ranges() + if len(ranges) <= 1: + return () + + rw = node.pointwise_read_writes() + assert len(rw.range_vars) == len(ranges) + + # isinstance(dep, MemoryDep): this filters out StarDeps. StarDeps refer to reads + # that need to access the entire tensor; they don't contribute read indexing + # information (and practically, they don't have dep.index so they can't be used + # for stride_hints below + dep_sources = [rw.reads, rw.writes] + assert all( + isinstance(dep, (MemoryDep, StarDep)) + for dep in itertools.chain.from_iterable(dep_sources) + ) + deps = [ + dep + for dep in itertools.chain.from_iterable(dep_sources) + if dep.name not in V.graph.removed_buffers and isinstance(dep, MemoryDep) + ] + write_names = {dep.name for dep in rw.writes} + + tilings: List[CandidateTiling] = [] + + for dep in deps: + strides = V.graph.sizevars.stride_hints(dep.index, rw.range_vars) + assert len(strides) == len(ranges) + try: + split = strides.index(1) + 1 + if split == len(ranges): + continue + if all(s == 0 for s in strides[split:]): + # if this is a broadcasted tensor and all dimensions after split are broadcast, + # this is not a real split + continue + + except ValueError: + continue + tiled_groups = ( + V.graph.sizevars.simplify(sympy_product(ranges[:split])), + V.graph.sizevars.simplify(sympy_product(ranges[split:])), + ) + # score by number of elements + score = V.graph.sizevars.size_hint( + sympy_product( + size for size, stride in zip(ranges, strides) if stride != 0 + ) + ) + if dep.name in write_names: + # ngimel said contiguous writes is more important than reads + score *= 2 + if CandidateTiling.is_good_size(tiled_groups[0]): + score *= 2 + if CandidateTiling.is_good_size(tiled_groups[1]): + score *= 2 + + if ( + V.graph.sizevars.size_hint( + score - sympy_product(itertools.chain(ranges, reduction_ranges)) + ) + >= 0 + ): + tilings.append(CandidateTiling(tiled_groups, score, dep.name)) + return tilings + + @classmethod + def select_tiling(cls, node_schedule, numel, reduction_numel=sympy.Integer(1)): + """ + Heuristics to decide how to tile kernels. + Currently, we tile based on stride-1 dimensions. + + Returns: + `(tile1, tile2, reduction_numel)` s.t. `tile1 * tile2 == numel` + + """ + if reduction_numel != 1 or config.triton.max_tiles <= 1: + # TODO(jansel): should we tile reductions? + # do perf hint here if stride-1 dim is not being reduced + if perf_hint_log.level <= logging.WARNING: + for node in EnableReduction.filter(node_schedule): + if len(cls.candidate_tilings(node)) > 0: + perf_hint_log.info("reduction over non-contiguous dims") + break + return (numel, reduction_numel) + + seen_names: OrderedSet[str] = OrderedSet() + candidate_tiles: Counter[Any] = collections.Counter() + for node in EnableReduction.filter(node_schedule): + for tiling in cls.candidate_tilings(node): + if tiling.name in seen_names: + continue + seen_names.add(tiling.name) + candidate_tiles[tiling.tiling] += tiling.score + + ranked_tilings = [tiling for tiling, score in candidate_tiles.most_common()] + + if config.triton.max_tiles >= 3: + # Consider adding a third dimension of tiling, but only + # when a1 is a multiple of b1; otherwise, you have a lot + # of stragglers which is annoying to generate code for. + # + # NB: More than three max tiles is not enabled by default. + + # Add one 3D tiling choice + for i in range(1, len(ranked_tilings)): + a0, a1 = ranked_tilings[0] + b0, b1 = ranked_tilings[i] + if V.graph.sizevars.size_hint(a1 - b1) == 0: + continue + if V.graph.sizevars.size_hint(a1 - b1) < 0: + # swap so a0 is bigger + a0, a1 = ranked_tilings[i] + b0, b1 = ranked_tilings[0] + assert V.graph.sizevars.size_hint(a1 - b1) > 0 + if V.graph.sizevars.statically_known_multiple_of(a1, b1): + tiling = (a0, FloorDiv(a1, b1), b1) + ranked_tilings = [tiling] + ranked_tilings + break # only 1 choice for now + + if len(ranked_tilings) > 1: + perf_hint_log.info("possibly bad tiling: %s", ranked_tilings) + + # Optionally, prefer tiling into as many dimensions as possible. + if config.triton.prefer_nd_tiling: + # Get candidate tilings from the node ranges. + node_ranges = [ + node.get_ranges()[0] + for node in EnableReduction.filter(node_schedule) + if isinstance(node, scheduler.SchedulerNode) + ] + new_tilings: OrderedSet[Tuple[sympy.Expr]] = OrderedSet() + for node_range in node_ranges: + # Collapse leading dims, to fit in the maximum dimensionality. + num_leading_dims = max(0, len(node_range) - config.triton.max_tiles) + first_trailing_dim = num_leading_dims + 1 + collapsed_leading_dim = sympy_product(node_range[:first_trailing_dim]) + tiling = [collapsed_leading_dim] + list(node_range[first_trailing_dim:]) + new_tilings.add(tuple(tiling)) + + # Rank tilings by the number of dimensions. E.g., prefer 2D to 1D. + # Since this is a stable sort, ties are broken by schedule order. + ranked_new_tilings = sorted(new_tilings, key=len, reverse=True) + ranked_tilings = ranked_new_tilings + ranked_tilings + + for tiled_groups in ranked_tilings: + new_groups = (*tiled_groups, reduction_numel) + if all( + SIMDKernel.is_compatible(new_groups, node.get_ranges()) + for node in node_schedule + if isinstance(node, scheduler.SchedulerNode) + ): + return new_groups + + return (numel, reduction_numel) + + def flush(self): + pass + + def ready_to_flush(self) -> bool: + return False + + def generate_kernel_code_from_nodes(self, nodes, benchmark_kernel=False): + @dataclasses.dataclass + class LastUsageHolder: + n: Any + last_usage: Any + + def __del__(self) -> None: + self.n.last_usage = self.last_usage + + last_usage_holders = [LastUsageHolder(n, n.last_usage) for n in nodes] + + # empty last_usage. May cause more aggressive 'evict_last'. Should be fine. + for n in nodes: + n.last_usage = OrderedSet() + + if not nodes[0].is_template(): + _, (numel, rnumel) = max(nodes, key=lambda x: int(x.is_reduction())).group + node_schedule = self.generate_node_schedule(nodes, numel, rnumel) + + tiled_groups = self.select_tiling(node_schedule, numel, rnumel) + reduction_hint_val, mutations, index_dtype = self.get_kernel_args( + node_schedule, numel, rnumel + ) + + kernel = self.kernel_type( + *tiled_groups, + reduction_hint=reduction_hint_val, + mutations=mutations, + index_dtype=index_dtype, + ) + + self.codegen_node_schedule_with_kernel(node_schedule, kernel) + with config.patch( + "benchmark_kernel", benchmark_kernel + ), V.set_kernel_handler(kernel): + src_code = kernel.codegen_kernel() + else: + template_node = nodes[0] + epilogue_nodes = nodes[1:] + + with config.patch("benchmark_kernel", benchmark_kernel): + src_code = self.codegen_template( + template_node, epilogue_nodes, only_gen_src_code=True + ) + + src_code = src_code.replace(str(Placeholder.KERNEL_NAME), "triton_") + return src_code + + def codegen_comment(self, node_schedule): + pass + + def define_kernel(self, src_code, node_schedule, kernel): + raise NotImplementedError + + +@dataclasses.dataclass +class CandidateTiling: + tiling: Tuple[sympy.Expr, sympy.Expr] + score: int # higher is better + name: Optional[str] = None + + @staticmethod + def is_good_size(s): + """Somewhat arbitrary heuristic used to boost scores for some sizes""" + s = V.graph.sizevars.size_hint(s) + return s >= 32 and (s % 32 == 0) + + +class DisableReduction: + """ + Marker to invoke `kernel.disable_reduction()`. This closes a + reduction loop and allows for pointwise ops to occur on the output + of a reduction. + """ + + +class EnableReduction: + """ + Marker to end a DisableReduction block. + """ + + @staticmethod + def filter(node_schedule): + """ + Get the nodes from node_schedule skipping those in a + DisableReduction block. + """ + disabled = False + for node in node_schedule: + if node in (EnableReduction, DisableReduction): + # Don't tile stuff outside the main reduction loop + disabled = node is DisableReduction + elif disabled: + pass + else: + yield node + + +class CantSplit(Exception): + pass diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/triton.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/triton.py new file mode 100644 index 0000000000000000000000000000000000000000..2596f3d59793e90fd7a90dc0bd81ec5bc2426552 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/triton.py @@ -0,0 +1,3225 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import dataclasses +import functools +import itertools +import logging +import os +import textwrap +from functools import lru_cache +from typing import ( + Any, + Callable, + cast, + Dict, + Iterable, + List, + Optional, + Tuple, + TYPE_CHECKING, + Union, +) + +import sympy + +import torch +import torch._logging +from torch._dynamo.utils import preserve_rng_state +from torch._inductor.runtime.hints import AutotuneHint, DeviceProperties +from torch._prims_common import is_integer_dtype +from torch.utils._ordered_set import OrderedSet +from torch.utils._sympy.functions import CeilDiv, FloorDiv, ModularIndexing +from torch.utils._triton import has_triton_package + +from ...utils._sympy.symbol import free_symbol_is_type, prefix_str, symbol_is_type, SymT +from ...utils._sympy.value_ranges import ValueRanges +from .. import config, ir +from ..codecache import code_hash, get_path, PyCodeCache +from ..metrics import is_metric_table_enabled, log_kernel_metadata +from ..runtime.benchmarking import benchmarker +from ..runtime.hints import ReductionHint, TRITON_MAX_BLOCK +from ..runtime.runtime_utils import get_max_y_grid, next_power_of_2 +from ..utils import ( + cache_on_self, + get_bounds_index_expr, + get_fused_kernel_name, + get_kernel_metadata, + is_welford_reduction, + Placeholder, + sympy_dot, + sympy_subs, +) +from ..virtualized import _ops as ops, OpsHandler, ReductionType, StoreMode, V +from ..wrapper_benchmark import get_kernel_category_by_source_code +from .common import ( + BackendFeature, + CSE, + CSEVariable, + DeferredLine, + IndentedBuffer, + OpOverrides, + PythonPrinter, + SizeArg, + TensorArg, + WorkspaceArg, +) +from .simd import ( + constant_repr, + IterationRangesEntry, + IterationRangesRoot, + pexpr, + SIMDKernel, + SIMDScheduling, +) +from .triton_utils import ( + config_of, + should_unwrap_unspec_arg, + signature_of, + signature_to_meta, +) + + +if TYPE_CHECKING: + from ..ir import IRNode + +log = logging.getLogger(__name__) +perf_hint_log = torch._logging.getArtifactLogger(__name__, "perf_hints") +schedule_log = torch._logging.getArtifactLogger(__name__, "schedule") +fusion_log = torch._logging.getArtifactLogger(__name__, "fusion") + + +@lru_cache(None) +def gen_attr_descriptor_import(): + """ + import AttrsDescriptor if the triton version is new enough to have this + class defined. + """ + if not has_triton_package(): + return "" + + import triton.compiler.compiler + + if hasattr(triton.compiler.compiler, "AttrsDescriptor"): + return "from triton.compiler.compiler import AttrsDescriptor" + else: + return "" + + +@lru_cache(None) +def gen_common_triton_imports(): + imports = IndentedBuffer() + imports.splice( + """ + import triton + import triton.language as tl + """ + ) + if attr_desc := gen_attr_descriptor_import(): + imports.writeline(attr_desc) + + imports.splice( + """ + from torch._inductor.runtime import triton_helpers, triton_heuristics + from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math + from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties + """ + ) + return imports.getvalue() + + +block_offsets = { + symt: sympy.Symbol(f"{prefix_str[symt]}offset", integer=True, nonnegative=True) + for symt in [SymT.XBLOCK, SymT.YBLOCK, SymT.RINDEX] +} + +block_sizes = { + symt: sympy.Symbol(f"{prefix_str[symt].upper()}BLOCK", integer=True, positive=True) + for symt in [SymT.XBLOCK, SymT.YBLOCK, SymT.RINDEX] +} + + +@dataclasses.dataclass +class IndexingOptions: + index_str: str + mask_vars: OrderedSet[str] + mask_str: str + expand_str: Optional[str] + _has_rindex: bool + index: sympy.Expr + + def has_mask(self): + return bool(self.mask_vars) + + def has_indirect(self): + return free_symbol_is_type(self.index, SymT.TMP) + + def has_rindex(self): + return self._has_rindex + + def has_tmpmask(self): + return "tmp" in self.mask_str + + def has_rmask(self): + return "rmask" in self.mask_str + + +@dataclasses.dataclass +class BlockPtrOptions: + params: BlockParameters + constant_offset: sympy.Expr + order: List[int] + mask_vars: OrderedSet[str] + reshape_suffix: List[str] + + @property + def shape(self) -> List[sympy.Expr]: + return self.params.shape + + @property + def block_shape(self) -> List[sympy.Expr]: + return self.params.block_shape + + @property + def strides(self) -> List[sympy.Expr]: + return self.params.strides + + @property + def offsets(self) -> List[sympy.Expr]: + return self.params.offsets + + @staticmethod + def create( + *, + params: BlockParameters, + constant_offset: sympy.Expr, + range_trees: List[IterationRangesEntry], + mask_vars: OrderedSet[str], + ) -> BlockPtrOptions: + """Helper to create a BlockPtrOptions instance""" + reshape_suffix = [f"{t.prefix.upper()}BLOCK" for t in range_trees] + + # Only drop broadcast dims if the output has the same + # rank as the block. Otherwise, we will get shape errors. + drop_broadcasts = len(reshape_suffix) == len(params.strides) + + broadcasting_dim = [s == 0 for s in params.strides] + for i, is_broadcasting in enumerate(broadcasting_dim): + if is_broadcasting and drop_broadcasts: + # drop any stride==0 dimensions for performance + reshape_suffix[i] = "1" + + if V.kernel.no_x_dim: + assert range_trees[0].prefix == "x" + reshape_suffix.pop(0) + + if ( + not V.kernel.inside_reduction + and len(params.strides) == len(V.kernel.numels) - 1 + and V.kernel.numels[-1] != 1 + ): + # Need to expand rank by 1 to match rank when self.inside_reduction=True + reshape_suffix.append("1") + + def filter(it): + """Removes any broadcasting dims from a given sequence""" + assert len(it) == len(broadcasting_dim) + return [ + item + for item, is_broadcasting in zip(it, broadcasting_dim) + if not is_broadcasting or not drop_broadcasts + ] + + # Drop broadcasting dimensions from the input. + params = BlockParameters( + **{key: filter(val) for key, val in dataclasses.asdict(params).items()} + ) + + def lookup_size(exprs: Iterable[sympy.Expr]) -> List[sympy.Expr]: + return [V.graph.sizevars.lookup_precomputed_size(expr) for expr in exprs] + + # Look up precomputed sizes + params.shape = lookup_size(params.shape) + params.strides = lookup_size(params.strides) + + return BlockPtrOptions( + params=params, + constant_offset=V.graph.sizevars.lookup_precomputed_size(constant_offset), + order=list(reversed(range(len(params.shape)))), + mask_vars=mask_vars, + reshape_suffix=reshape_suffix, + ) + + def replace_roffset(self, expr: sympy.Expr, replacement: sympy.Expr) -> sympy.Expr: + """ + Replaces instances of roffset with the new expression. + """ + roffset = block_offsets[SymT.RINDEX] + return sympy_subs(expr, {roffset: replacement}) + + def format(self, name: str, roffset=True) -> str: + """ + Codegen a call to tl.make_block_ptr() + + Args: + name: variable name for pointer + roffset: should roffset be included in offsets=..., for use with tl.advance() + + Returns: + "tl.make_block_ptr(...)" + """ + f = V.kernel.index_to_str + offsets = [*self.offsets] + if not roffset: + offsets = [ + self.replace_roffset(offset, sympy.Integer(0)) for offset in offsets + ] + args = [ + f"{name} + ({f(self.constant_offset)})" + if self.constant_offset != 0 + else name, + f"shape={f(self.shape)}", + f"strides={f(self.strides)}", + f"block_shape={f(self.block_shape)}", + f"order={f(self.order)}", + f"offsets={f(offsets)}", + ] + return f"tl.make_block_ptr({', '.join(args)})" + + @cache_on_self + def boundary_check(self) -> List[int]: + """List of indices to pass to tl.load(boundary_check=...)""" + sizevars = V.graph.sizevars + + # Substitute maximum block sizes in shape expressions. + # This works in multiple_of checks because block sizes are powers of 2. + block_to_max: Dict[sympy.Expr, Any] = { + block_size: TRITON_MAX_BLOCK[prefix_str[symt].upper()] + for symt, block_size in block_sizes.items() + } + + return [ + idx + for idx in range(len(self.shape)) + if ( + not sizevars.statically_known_equals( + self.strides[idx], sympy.Integer(0) + ) + and not sizevars.statically_known_multiple_of( + self.shape[idx], self.block_shape[idx] + ) + and not sizevars.statically_known_multiple_of( + self.shape[idx], sympy_subs(self.block_shape[idx], block_to_max) + ) + and not ( + V.kernel.no_x_dim + and self.block_shape[idx] == block_sizes[SymT.XBLOCK] + ) + ) + ] + + def advance_roffset(self): + """ + Codegen string to pass to tl.advance(name, ...). + + Advance is the difference between offsets in each loop iteration. + To compute it, we replace roffset with multiples of RBLOCK. + Since we expect roffset to vary in range(0, rnumel, RBLOCK), the first + iteration has roffset=0, while the second has roffset=RBLOCK. + """ + rblock = block_sizes[SymT.RINDEX] + advance = [ + ( + self.replace_roffset(offset, rblock) + - self.replace_roffset(offset, sympy.Integer(0)) + ) + for offset in self.offsets + ] + return V.kernel.index_to_str(advance) + + def has_indirect(self): + return False # block_ptr can't do indirect indexing + + def has_rindex(self) -> bool: + return any(free_symbol_is_type(expr, SymT.RINDEX) for expr in self.block_shape) + + def has_rmask(self): + return self.has_rindex() + + def has_tmpmask(self): + return False # block_ptr can't do indirect indexing + + def has_mask(self): + return bool(self.boundary_check()) + + +def triton_reshape(value: str, old_shape: List[str], new_shape: List[str]): + """Workaround https://github.com/openai/triton/issues/2836""" + assert isinstance(old_shape, list) and isinstance(new_shape, list) + if old_shape == new_shape: + return value + if [s for s in new_shape if s != "1"] != old_shape: + return f"tl.reshape({value}, [{', '.join(new_shape)}])" + # rewrite to [:, None] syntax, which is less buggy + idx = 0 + expand = [] + for size in new_shape: + if idx < len(old_shape) and size == old_shape[idx]: + expand.append(":") + idx += 1 + else: + assert size == "1" + expand.append("None") + assert idx == len(old_shape) + return f"{value}[{', '.join(expand)}]" + + +# NB: Inheriting from PythonPrinter is somewhat dangerous, because there are a +# number of operators which Triton "implements", but in a way that is +# inconsistent with Python semantics (and consistent with C semantics). We +# must override all of these, or it is potential silent correctness problem +class TritonPrinter(PythonPrinter): + def _print_TruncToInt(self, expr): + assert len(expr.args) == 1 + return ( + f"libdevice.trunc({self._print(expr.args[0])}).to({V.kernel.index_dtype})" + ) + + def _print_ToFloat(self, expr): + assert len(expr.args) == 1 + return f"{self.paren(self._print(expr.args[0]))}.to(tl.float64)" + + def _print_PythonMod(self, expr): + quot, div = expr.args + quot_s = self._print(quot) + div_s = self._print(div) + if quot.is_nonnegative and div.is_nonnegative: + return f"{self.paren(quot_s)} % {self.paren(div_s)}" + return f"triton_helpers.remainder_integer({quot_s}, {div_s})" + + def _print_FloorDiv(self, expr): + assert expr.is_integer + quot, div = expr.args + quot_s = self._print(quot) + div_s = self._print(div) + if quot.is_nonnegative and div.is_nonnegative: + return f"({self.paren(quot_s)} // {self.paren(div_s)})" + return f"triton_helpers.div_floor_integer({quot_s}, {div_s})" + + # TODO: This is wrong, when lhs, rhs > 2**53, Python does a higher + # precision algorithm, which we would need to replicate here + def _print_IntTrueDiv(self, expr): + lhs, rhs = expr.args + return f"{self.paren(self._print(lhs))} / {self.paren(self._print(rhs))}" + + # NB: sympy.floor/ceiling produce integers, so we have to do the + # conversion to index dtype + def _print_floor(self, expr): + assert len(expr.args) == 1 + return ( + f"libdevice.floor({self._print(expr.args[0])}).to({V.kernel.index_dtype})" + ) + + def _print_FloorToInt(self, expr): + assert len(expr.args) == 1 + return ( + f"libdevice.floor({self._print(expr.args[0])}).to({V.kernel.index_dtype})" + ) + + def _print_ceiling(self, expr): + assert len(expr.args) == 1 + return f"libdevice.ceil({self._print(expr.args[0])}).to({V.kernel.index_dtype})" + + def _print_CeilToInt(self, expr): + assert len(expr.args) == 1 + return f"libdevice.ceil({self._print(expr.args[0])}).to({V.kernel.index_dtype})" + + def _helper_sqrt(self, expr): + return f"libdevice.sqrt({self._print(expr)}.to(tl.float32))" + + def _print_FloatPow(self, expr): + return ( + f"libdevice.pow({self._print(expr.args[0])}, {self._print(expr.args[1])})" + ) + + _print_PowByNatural = _print_FloatPow + + def _print_Where(self, expr): + c = self.doprint(expr.args[0]) + p = self.doprint(expr.args[1]) + q = self.doprint(expr.args[2]) + return f"tl.where({c}, {p}, {q})" + + def _print_min_max_helper(self, expr: sympy.Expr, cmp: str) -> str: + """ + Helper for max/min code genereration. + cmp: > or < + """ + nargs = len(expr.args) + if len(expr.args) == 1: + return self._print(expr.args[0]) + + mid = len(expr.args) // 2 + cls = type(expr) + a = self._print(cls(*expr.args[:mid])) + b = self._print(cls(*expr.args[mid:])) + + # Use a macro so we can propagate constexprs. + # https://github.com/triton-lang/triton/issues/3815 + a, b = tuple(f"({x})" for x in (a, b)) + assert cmp in (">", "<"), f"Unexpected comparator: '{cmp}'" + return f"({a} * ({a} {cmp}= {b}) + {b} * ({b} {cmp} {a}))" + + def _print_Min(self, expr): + return self._print_min_max_helper(expr, "<") + + def _print_Max(self, expr): + return self._print_min_max_helper(expr, ">") + + def _print_Abs(self, expr): + assert len(expr.args) == 1 + return f"tl_math.abs({self._print(expr.args[0])})" + + def _print_OpaqueUnaryFn_cos(self, expr): + assert len(expr.args) == 1 + return f"libdevice.cos(({self._print(expr.args[0])}).to(tl.float32))" + + def _print_OpaqueUnaryFn_cosh(self, expr): + assert len(expr.args) == 1 + return f"libdevice.cosh(({self._print(expr.args[0])}).to(tl.float32))" + + def _print_OpaqueUnaryFn_acos(self, expr): + assert len(expr.args) == 1 + return f"libdevice.acos(({self._print(expr.args[0])}).to(tl.float32))" + + def _print_OpaqueUnaryFn_sin(self, expr): + assert len(expr.args) == 1 + return f"libdevice.sin(({self._print(expr.args[0])}).to(tl.float32))" + + def _print_OpaqueUnaryFn_sinh(self, expr): + assert len(expr.args) == 1 + return f"libdevice.sinh(({self._print(expr.args[0])}).to(tl.float32))" + + def _print_OpaqueUnaryFn_asin(self, expr): + assert len(expr.args) == 1 + return f"libdevice.asin(({self._print(expr.args[0])}).to(tl.float32))" + + def _print_OpaqueUnaryFn_tan(self, expr): + assert len(expr.args) == 1 + return f"libdevice.tan(({self._print(expr.args[0])}).to(tl.float32))" + + def _print_OpaqueUnaryFn_tanh(self, expr): + assert len(expr.args) == 1 + return f"libdevice.tanh(({self._print(expr.args[0])}).to(tl.float32))" + + def _print_OpaqueUnaryFn_atan(self, expr): + assert len(expr.args) == 1 + return f"libdevice.atan(({self._print(expr.args[0])}).to(tl.float32))" + + def _print_RoundToInt(self, expr): + assert len(expr.args) == 1 + return f"libdevice.llrint({self._print(expr.args[0])})" + + def _print_RoundDecimal(self, expr): + assert len(expr.args) == 2 + number, ndigits = expr.args + if number.is_integer: + # ndigits < 0 should have been filtered by the sympy function + assert ndigits < 0 + raise ValueError( + f"For integer inputs, only non-negative ndigits are currently supported, but got {ndigits}." + ) + return f"libdevice.nearbyint(1e{ndigits} * {self.paren(self._print(number))}) * 1e{-ndigits}" + + +texpr = TritonPrinter().doprint + + +def triton_compute_type(dtype): + triton_type_name = str(dtype).split(".")[-1] + if triton_type_name == "bool": + triton_type_name = "int1" + elif ( + triton_type_name in ("float16", "bfloat16") + and config.triton.codegen_upcast_to_fp32 + ): + # float16 math is done in float32 inside the kernel + triton_type_name = "float32" + elif triton_type_name == "float8_e4m3fn": + triton_type_name = "float8e4nv" + elif triton_type_name == "float8_e5m2": + triton_type_name = "float8e5" + elif triton_type_name == "float8_e4m3fnuz": + triton_type_name = "float8e4b8" + elif triton_type_name == "float8_e5m2fnuz": + triton_type_name = "float8e5b16" + return f"tl.{triton_type_name}" + + +def _get_primitive_bitwidth(dtype): + if hasattr(dtype, "is_floating_point"): + if dtype.is_floating_point: + # triton_compute_type changes the bitwidth + if ( + dtype in [torch.bfloat16, torch.float16] + and config.triton.codegen_upcast_to_fp32 + ): + return 32 + return torch.finfo(dtype).bits + else: + return torch.iinfo(dtype).bits + else: + return -1 + + +def triton_store_type(dtype): + triton_type_name = str(dtype).split(".")[-1] + if triton_type_name == "bool": + triton_type_name = "int8" + elif triton_type_name == "float8_e4m3fn": + triton_type_name = "float8e4nv" + elif triton_type_name == "float8_e5m2": + triton_type_name = "float8e5" + return f"tl.{triton_type_name}" + + +def triton_acc_type(dtype): + if is_integer_dtype(dtype) and dtype.is_signed: + nbits = 64 if dtype == torch.int64 else 32 + return f"tl.int{nbits}" + return triton_compute_type(dtype) + + +class TritonCSEVariable(CSEVariable): + def __init__(self, name, bounds: ValueRanges[Any]) -> None: + super().__init__(name, bounds) + # We'll use this to track which masks the variable needs when used for indirect indexing + self.mask_vars: OrderedSet[str] = OrderedSet() + + def update_on_args(self, name, args, kwargs): + for arg in args: + if isinstance(arg, TritonCSEVariable): + self.mask_vars.update(arg.mask_vars) + elif isinstance(arg, sympy.Symbol) and arg.name[0] in "xyr": + # most of the time index vars don't need masks associated with them + # however, when index vars are used to compute indices for indirect reads + # those reads should subsequently be masked, + self.mask_vars.update({f"{arg.name[0]}mask"}) + + +class TritonOverrides(OpOverrides): + """Map element-wise ops to Triton""" + + @staticmethod + def to_dtype( + x, + dtype: torch.dtype, + src_dtype: Optional[torch.dtype] = None, + use_compute_types=True, + ): + def _get_min_elements_per_thread( + src_dtype: torch.dtype, dst_dtype: torch.dtype + ) -> int: + if src_dtype == dst_dtype: + # No data type conversion is needed. No requirements on min_elem_per_thread. + return 0 + + # fp8 data type conversions has min_elem_per_thread requirements. + # Refer to Triton implementations here: + # https://github.com/openai/triton/blob/10f59d8ce04052521c1bc0cb3a3f8b98918fc7e3/lib/Conversion/TritonGPUToLLVM/ElementwiseOpToLLVM.cpp#L10. + fp8_dtypes = ( + torch.float8_e4m3fn, + torch.float8_e5m2, + ) + # Triton doesn't support type conversions between fp8_e4m3 and fp8_e5m2. + assert not ( + src_dtype in fp8_dtypes + and dst_dtype in fp8_dtypes + and src_dtype != dst_dtype + ), "Conversions between float8_e5m2 and float8_e4m3fn is not supported!" + if src_dtype == torch.float8_e5m2 or dst_dtype == torch.float8_e5m2: + return 4 + if src_dtype == torch.float8_e4m3fn or dst_dtype == torch.float8_e4m3fn: + return 2 + # No requirements on min_elem_per_thread. + return 0 + + if src_dtype is not None: + # Both dtype and src_dtype are set. This is used by torch to(dtype=dtype). + # It takes the maximum min_elem_per_thread if there are multiple fp8 conversions + # in the same kernel. + V.kernel.min_elem_per_thread = max( + _get_min_elements_per_thread(src_dtype, dtype), + V.kernel.min_elem_per_thread, + ) + + if dtype == torch.bool: + return f"({x} != 0)" + elif dtype == torch.uint8: + # to work around llvm uint conversion semantics + # that produces 0's for negative values + return f"{x}.to(tl.int8).to(tl.uint8)" + + if use_compute_types: + out_dtype = triton_compute_type(dtype) + else: + out_dtype = triton_store_type(dtype) + + return f"{x}.to({out_dtype})" + + @staticmethod + def to_dtype_bitcast(x, dtype: torch.dtype, src_dtype: torch.dtype): + triton_dtype = triton_compute_type(dtype) + # We may promote float16 or bfloat16 to float32 and cause the + # bitwidth of dtype to be different from the input tensor (i.e. float32). + # In such as case, we will have to convert the input tensor to + # its src_type, perform bitcast, and then convert the bit-casted + # tensor back to float to ensure we use values with the right precision. + if ( + src_dtype in (torch.float16, torch.bfloat16) + and config.triton.codegen_upcast_to_fp32 + ): + triton_src_dtype = str(src_dtype).split(".")[-1] + cast_x = f"{x}.to(tl.{triton_src_dtype})" + if dtype in (torch.float16, torch.bfloat16): + triton_type_name = str(dtype).split(".")[-1] + triton_dtype = f"tl.{triton_type_name}" + cast_x = f"{cast_x}.to({triton_dtype}, bitcast=True)" + return f"{cast_x}.to(tl.float32)" + else: + src_dtype_bitwidth = _get_primitive_bitwidth(src_dtype) + target_dtype_bitwidth = _get_primitive_bitwidth(dtype) + bitcast = "True" if src_dtype_bitwidth == target_dtype_bitwidth else "False" + return f"{x}.to({triton_dtype}, bitcast={bitcast})" + + @staticmethod + def _shaped_constant(value, dtype, shape): + type_ = torch._prims_common.dtype_to_type(dtype) + triton_val = constant_repr(type_(value)) + triton_type = triton_compute_type(dtype) + + if triton_type == "tl.float32": + # Float constants are always f32 in triton + return triton_val + + # NOTE: We use a tensor here in order to get the expected type. + # Otherwise, e.g. float64 constants would be trunctated to float32. + return f"tl.full({shape}, {triton_val}, {triton_type})" + + @classmethod + def constant(cls, value, dtype): + return cls._shaped_constant(value, dtype, shape=[]) + + @staticmethod + def abs(x): + return f"tl_math.abs({x})" + + @staticmethod + def libdevice_abs(x): + return f"libdevice.abs({x})" + + @staticmethod + def exp(x): + return f"tl_math.exp({x})" + + @staticmethod + def libdevice_exp(x): + return f"libdevice.exp({x})" + + @staticmethod + def exp2(x): + return f"libdevice.exp2({x})" + + @staticmethod + def expm1(x): + return f"libdevice.expm1({x})" + + @staticmethod + def sqrt(x): + return f"libdevice.sqrt({x})" + + @staticmethod + def libdevice_sqrt(x): + return f"libdevice.sqrt({x})" + + @staticmethod + def relu(x): + bug = config.triton.inject_relu_bug_TESTING_ONLY + if bug == "compile_error": + return "compile error!" + elif bug == "runtime_error": + # NB: this only triggers runtime error as long as input + # is not all zero + return f'triton_helpers.device_assert_then({x} == 0, "injected assert fail", {x})' + elif bug == "accuracy": + return f"{x} + 1" + elif bug is None: + return ops.maximum(ops.constant(0, torch.int32), x) + else: + raise AssertionError( + f"unrecognized config triton.inject_relu_bug_TESTING_ONLY = {bug!r}" + ) + + @staticmethod + def minimum(a, b): + return f"triton_helpers.minimum({a}, {b})" + + @staticmethod + def maximum(a, b): + return f"triton_helpers.maximum({a}, {b})" + + @staticmethod + def where(a, b, c): + return f"tl.where({a}, {b}, {c})" + + @staticmethod + def inline_asm_elementwise( + *inputs, asm, constraints=None, dtype=torch.float32, is_pure=True, pack=1 + ): + triton_type = triton_compute_type(dtype) + input_refs = ", ".join([str(i) for i in inputs]) + if constraints is None: + constraints = ", ".join(["=r"] + ["r" for _ in inputs]) + return f"tl.inline_asm_elementwise('{asm}', '{constraints}', [{input_refs}], dtype={triton_type}, is_pure={is_pure}, pack={pack})" # noqa: B950 + + @staticmethod + def cos(x): + return f"tl_math.cos({x})" + + @staticmethod + def libdevice_cos(x): + return f"libdevice.cos({x})" + + @staticmethod + def sin(x): + return f"tl_math.sin({x})" + + @staticmethod + def libdevice_sin(x): + return f"libdevice.sin({x})" + + @classmethod + def index_expr(cls, expr, dtype): + raise NotImplementedError("ops.index_expr not implemented outside a kernel") + + @staticmethod + def masked(mask, body, other): + raise NotImplementedError("ops.masked not implemented outside a kernel") + + @staticmethod + def lgamma(x): + return f"libdevice.lgamma({x})" + + @staticmethod + def erf(x): + return f"libdevice.erf({x})" + + @staticmethod + def cosh(x): + return f"libdevice.cosh({x})" + + @staticmethod + def sinh(x): + return f"libdevice.sinh({x})" + + @staticmethod + def acos(x): + return f"libdevice.acos({x})" + + @staticmethod + def acosh(x): + return f"libdevice.acosh({x})" + + @staticmethod + def asin(x): + return f"libdevice.asin({x})" + + @staticmethod + def asinh(x): + return f"libdevice.asinh({x})" + + @staticmethod + def atan2(x, y): + return f"libdevice.atan2({x}, {y})" + + @staticmethod + def atan(x): + return f"libdevice.atan({x})" + + @staticmethod + def atanh(x): + return f"libdevice.atanh({x})" + + @staticmethod + def copysign(x, y): + return f"libdevice.copysign({x}, {y})" + + @staticmethod + def erfc(x): + return f"libdevice.erfc({x})" + + @staticmethod + def erfinv(x): + return f"libdevice.erfinv({x})" + + @staticmethod + def hypot(x, y): + return f"libdevice.hypot({x}, {y})" + + @staticmethod + def log10(x): + return f"libdevice.log10({x})" + + @staticmethod + def log2(x): + return f"libdevice.log2({x})" + + @staticmethod + def nextafter(x, y): + return f"libdevice.nextafter({x}, {y})" + + @staticmethod + def logical_and(a, b): + return f"{a} & {b}" + + @staticmethod + def logical_not(a): + return f"{a} == 0" + + @staticmethod + def logical_or(a, b): + return f"{a} | {b}" + + @staticmethod + def logical_xor(a, b): + return f"({a} ^ {b})" + + @staticmethod + def bitwise_and(a, b): + return f"{a} & {b}" + + @staticmethod + def bitwise_not(a): + return f"~{a}" + + @staticmethod + def bitwise_or(a, b): + return f"{a} | {b}" + + @staticmethod + def bitwise_xor(a, b): + return f"{a} ^ {b}" + + @staticmethod + def bitwise_left_shift(a, b): + return f"{a} << {b}" + + @staticmethod + def bitwise_right_shift(a, b): + return f"{a} >> {b}" + + @staticmethod + def rand(seed, offset): + offset = f"({offset}).to(tl.uint32)" + return f"tl.rand({seed}, {offset})" + + @staticmethod + def randn(seed, offset): + offset = f"({offset}).to(tl.uint32)" + return f"tl.randn({seed}, {offset})" + + @staticmethod + def randint64(seed, offset, low, high): + offset = f"({offset}).to(tl.uint32)" + return f"triton_helpers.randint64({seed}, {offset}, {low}, {high})" + + @staticmethod + def load_seed(name, offset): + raise NotImplementedError("ops.load_seed not implemented outside a kernel") + + @staticmethod + def rsqrt(x): + return f"libdevice.rsqrt({x})" + + @staticmethod + def log1p(x): + return f"libdevice.log1p({x})" + + @staticmethod + def tan(x): + return f"libdevice.tan({x})" + + @staticmethod + def tanh(x): + return f"libdevice.tanh({x})" + + @staticmethod + def sigmoid(x): + return f"tl.sigmoid({x})" + + @staticmethod + def signbit(x): + # XX: This is wrong for the value -0.0 in floating point + return f"libdevice.signbit({x}) if ({x}).dtype is tl.float32 else {x} < 0" + + @staticmethod + def fmod(a, b): + return f"libdevice.fmod({a}, {b})" + + @staticmethod + def pow(a, b): + return f"libdevice.pow({a}, {b})" + + @staticmethod + def log(x): + return f"tl_math.log({x})" + + @staticmethod + def libdevice_log(x): + return f"libdevice.log({x})" + + @staticmethod + def isinf(x): + return f"libdevice.isinf({x}).to(tl.int1)" + + @staticmethod + def isnan(x): + return f"libdevice.isnan({x}).to(tl.int1)" + + @staticmethod + def round(x): + return f"libdevice.nearbyint({x})" + + @staticmethod + def floor(x): + return f"libdevice.floor({x})" + + @staticmethod + def floordiv(a, b): + # See the comment in lowering.div_mode. a and b are integer type. + # Similar to div_floor_kernel_cuda in pytorch core. + # Notice that // in triton behaves as truncdiv instead of floordiv + quot = f"{a} // {b}" + rem = f"{a} % {b}" + return f"tl.where(({a} < 0) != ({b} < 0), tl.where({rem} != 0, {quot} - 1, {quot}), {quot})" + + @staticmethod + def sign(x): + z = ops.constant(0, torch.int32) + left = ops.to_dtype((ops.lt(z, x)), torch.int8) + right = ops.to_dtype((ops.lt(x, z)), torch.int8) + sub = ops.sub(left, right) + return f"{sub}.to({x}.dtype)" + + @staticmethod + def trunc(x): + return f"libdevice.trunc({x})" + + @staticmethod + def truncdiv(a, b): + # See the comment in lowering.div_mode. a and b are integer type. + # Notice that // in triton behaves as truncdiv instead of floordiv + return f"{a} // {b}" + + @staticmethod + def ceil(x): + return f"libdevice.ceil({x})" + + +TritonOverrides._initialize_pointwise_overrides("triton") + + +# Use mypy to check protocol implemented correctly +def _typecheck_TritonOverrides(h: TritonOverrides) -> OpsHandler[str]: + return h + + +class TritonKernelOverrides(TritonOverrides): + """Map element-wise ops to Triton within a TritonKernel + + Unlike TritonOverrides, these assume the code is going to be inserted into + the body of the main triton kernel and so it may use indexing and mask + variables which are assumed to already be defined in the current scope. + """ + + @classmethod + def constant(cls, value, dtype): + # NOTE: Cannot use shape=[] as it's not supported by triton-rocm + # We could use shape=[1] instead but starting with the correct + # ndim avoids extra `tt.expand_dim` ops appearing in the triton IR. + ndim = V.kernel.triton_tensor_ndim() + shape = [1] * ndim + return cls._shaped_constant(value, dtype, shape=shape) + + @classmethod + def index_expr(cls, expr, dtype): + indexing = V.kernel.indexing(expr, block_ptr=False) + assert isinstance(indexing, IndexingOptions) + var = V.kernel.cse.generate( + V.kernel.compute, indexing.index_str, bounds=get_bounds_index_expr(expr) + ) + + if dtype not in (torch.int32, torch.int64): + var = V.kernel.cse.generate(V.kernel.compute, cls.to_dtype(var, dtype)) + var.mask_vars = indexing.mask_vars + return var + + @staticmethod + def masked(mask, body, other): + if mask is not None and torch.version.hip is not None: + mask = V.kernel.cse.generate( + V.kernel.compute, + f"{mask}.to(tl.int1)", + ) + + nodes = body.graph.find_nodes(op="output") + assert nodes, "graph for body does not contain an output" + + need_where = False + for node in nodes: + for arg in node.args: + if arg.target != "load" or should_unwrap_unspec_arg(arg.args[0]): + need_where = True + + value = None if need_where else other + with V.kernel.mask_loads(mask, value=value) as new_mask: + result = body() + + if need_where: + # Remove once CSEVariables track the dtype + if result.bounds.is_bool: + other = bool(other) + # Take dtype from result to prevent accidental promotion + other = V.kernel.cse.generate( + V.kernel.compute, + f"tl.full({result}.shape, {constant_repr(other)}, {result}.dtype)", + bounds=ValueRanges.wrap(other), + ) + ret = ops.where(new_mask, result, other) + else: + ret = result + + ret.mask_vars.discard(new_mask) + return ret + + @staticmethod + def load_seed(name, offset): + var = V.kernel.args.input(name) + return ( + f"tl.load({var} + {V.kernel.args.seed_offset('load_seed_offset', offset)})" + ) + + @staticmethod + def frexp(x): + cache_key = f"frexp({x})" + if cache_key in V.kernel.cse.cache: + return V.kernel.cse.cache[cache_key] + + mantissa = V.kernel.cse.newvar() + exponent = V.kernel.cse.newvar() + V.kernel.compute.writeline( + f"{mantissa}, {exponent} = triton_helpers.frexp({x})" + ) + V.kernel.cse.cache[cache_key] = (mantissa, exponent) + return (mantissa, exponent) + + +# Use mypy to check protocol implemented correctly +def _typecheck_TritonKernelOverrides(h: TritonKernelOverrides) -> OpsHandler[str]: + return h + + +class HelperFunctions: + """An ordered set of helper functions.""" + + _templates_seen: Dict[str, str] # Template code to function name + finalized_helpers: List[str] + + def __init__(self) -> None: + self._templates_seen = {} + self.finalized_helpers = [] + + def add(self, template_code: str, *, base_name="_triton_helper_fn") -> str: + """This accepts a function definition with the function name + left as a format specifier e.g. + + @triton.jit + def {name}(arg0, arg1): + return arg0 + arg1 + + We add the templated code to the function set and return the name + assigned to that function. + + """ + existing_name = self._templates_seen.get(template_code) + if existing_name is not None: + # Don't duplicate existing helpers + return existing_name + + name = f"{base_name}{len(self.finalized_helpers)}" + self._templates_seen[template_code] = name + self.finalized_helpers.append(template_code.format(name=name)) + return name + + def __iter__(self): + return iter(self.finalized_helpers) + + def __getitem__(self, idx): + return self.finalized_helpers[idx] + + +@dataclasses.dataclass +class BlockParameters: + """ + Class representing ND block dimensions, for block pointer analysis. + """ + + shape: List[sympy.Expr] = dataclasses.field(default_factory=list) + block_shape: List[sympy.Expr] = dataclasses.field(default_factory=list) + strides: List[sympy.Expr] = dataclasses.field(default_factory=list) + offsets: List[sympy.Expr] = dataclasses.field(default_factory=list) + + def __add__(self, other: BlockParameters) -> BlockParameters: + """ + Concatenates block parameters. + """ + cls = type(self) + a, b = tuple(dataclasses.asdict(x) for x in (self, other)) + return cls(**{key: a[key] + b[key] for key in a}) + + +class TritonKernel(SIMDKernel): + overrides = TritonKernelOverrides # type: ignore[assignment] + helper_functions: HelperFunctions + kexpr: Callable[[sympy.Expr], str] = texpr + allow_block_ptr = True + + def __init__( + self, + *groups, + index_dtype: str, + mutations: Optional[OrderedSet[str]] = None, + pid_cache=None, + reduction_hint=ReductionHint.DEFAULT, + min_elem_per_thread=0, + override_persistent_reduction=None, + optimize_mask=True, + ) -> None: + self.optimize_mask: bool = optimize_mask + super().__init__( + *groups, + index_dtype=index_dtype, + mutations=mutations, + reduction_hint=reduction_hint, + pid_cache=pid_cache, + override_persistent_reduction=override_persistent_reduction, + ) + self.suffix: IndentedBuffer = IndentedBuffer() # type: ignore[assignment] + self.outside_loop_vars: OrderedSet[Any] = OrderedSet() + self.min_elem_per_thread = min_elem_per_thread + self.block_ptr_id = itertools.count() + self.helper_functions = HelperFunctions() + + # A set of autotuning hints to pass as part of triton_meta + self.autotune_hints: OrderedSet[AutotuneHint] = OrderedSet() + self.triton_meta: Optional[Dict[str, object]] = None + + self.codegen_range_tree() + + def _get_symt(self, tree: IterationRangesEntry) -> SymT: + prefix_to_symt = {prefix: symt for symt, prefix in prefix_str.items()} + return prefix_to_symt[tree.prefix] + + def _get_block_size(self, tree: IterationRangesEntry) -> sympy.Symbol: + return block_sizes[self._get_symt(tree)] + + def _get_block_offset(self, tree: IterationRangesEntry) -> sympy.Symbol: + return block_offsets[self._get_symt(tree)] + + def _max_block_size(self, tree: IterationRangesEntry) -> int: + return TRITON_MAX_BLOCK[tree.prefix.upper()] + + def codegen_range_tree(self): + for tree in self.range_trees: + # reduction indexing goes inside a loop + if not tree.is_loop: + self.iteration_ranges_codegen_header(tree, self.body) + if self.inside_reduction and self.range_trees[-1].is_loop: + # workaround for this issue: + # https://gist.github.com/jansel/6527126f781559095c5531f98a4235a7 + self.body.writeline( + f"rbase = {self.iteration_ranges_ranges_code(self.range_trees[-1])}" + ) + + def need_numel_args(self): + r""" + Indicate whether we need provide numel as arguments for the generated + kernel calls in the benchmark. + + Should be true for pointwise/reduction kernels but false for triton + matmul kernels. + """ + return True + + def should_use_persistent_reduction(self) -> bool: + """ + Heuristic to set self.persistent_reduction and add guards + if needed. + """ + if not (self.inside_reduction and config.triton.persistent_reductions): + return False + threshold = { + ReductionHint.INNER: 1024, + }.get(self.reduction_hint, 64) + + # If multi_kernel is enabled, we do more aggressive persistent reduction. + # This may result in some persistent reductions slower than the + # corresponding non-persistent reductions. MultiKernel will do benchmarking + # to pick the faster one. + if config.triton.multi_kernel: + threshold *= 16 + last_numel = self.numels[-1] + return V.graph.sizevars.statically_known_leq(last_numel, threshold) # type: ignore[arg-types] + + def want_no_x_dim(self): + return ( + self.reduction_hint == ReductionHint.INNER + and self.persistent_reduction + and len(self.numels) == 2 + and V.graph.sizevars.statically_known_geq(self.numels[-1], 256) # type: ignore[arg-types] + ) + + @property + def assert_function(self) -> str: + return "tl.device_assert" + + def indexing( + self, + index: sympy.Expr, + *, + copy_shape=None, + dense_indexing=False, + override_mask=None, + block_ptr=False, + ): + """ + Compute the index and mask to pass to tl.load() or tl.store() + """ + index = self.prepare_indexing(index) + index_vars = index.free_symbols + has_rindex = False + + mask_vars: OrderedSet[str] = OrderedSet() + for var in index_vars: + assert isinstance(var, sympy.Symbol) + has_rindex = has_rindex or symbol_is_type(var, SymT.RINDEX) + if override_mask: + pass + elif symbol_is_type(var, SymT.TMP): + # indirect indexing + cse_var = self.cse.varname_map[var.name] + mask_vars.update(cse_var.mask_vars) + elif symbol_is_type( + var, + ( + SymT.UNBACKED_INT, + SymT.SIZE, + SymT.PRECOMPUTED_SIZE, + SymT.INDEX, + SymT.FLOAT, + SymT.UNBACKED_FLOAT, + ), + ): + pass + else: + # var is one of xN, yN or rN + assert symbol_is_type( + var, (SymT.RINDEX, SymT.XBLOCK, SymT.YBLOCK) + ), var.name + mask_vars.add(f"{var.name[0]}mask") + + need_dense = ( + config.triton.dense_indexing + or dense_indexing + or self._load_mask is not None + ) and index != 0 + + have_dense = True + have_loop_vars = False + dense_mask_vars: OrderedSet[str] = OrderedSet() + + for tree in self.active_range_trees(): + if index_vars.intersection(tree.var_list): + have_loop_vars = True + else: + have_dense = False + dense_mask_vars.add(f"{tree.prefix}mask") + + if ( + block_ptr + and self.allow_block_ptr + and config.triton.use_block_ptr + and not override_mask + and not self._load_mask + and len(mask_vars - dense_mask_vars) == 0 + and not self.is_indirect_indexing(index) + and have_loop_vars + # workaround https://github.com/openai/triton/issues/2821 + and self.index_dtype == "tl.int32" + ): + + def match_strided_block( + index: sympy.Expr, range_tree: IterationRangesEntry + ) -> Optional[BlockParameters]: + """ + Matches expressions of the form: + idx = s * xindex + + This implies stride (s,), and shape (XBLOCK,). + """ + symbol = range_tree.symbol() + stride = sympy.Wild("stride", exclude=[symbol]) + m = index.match(symbol * stride) + if m is None: + return None + + return BlockParameters( + shape=[range_tree.numel], + block_shape=[self._get_block_size(range_tree)], + strides=[m[stride]], + offsets=[self._get_block_offset(range_tree)], + ) + + def match_mod_div_block( + index: sympy.Expr, range_tree: IterationRangesEntry + ) -> Optional[BlockParameters]: + """ + Matches higher-dimensional blocks coming from FloorDiv and ModularIndexing. + + Example expression to match: + sN * ((rindex//(d1 * ... * d(N-1)))) + + s1 * ModularIndexing(rindex, 1, d1) + + ... + + s(N-1) * ModularIndexing(rindex, d1 * ... * d(N-2), d(N-1)) + + This iterates over a block of shape (dN, ..., d1) and stride + (sN, ..., s1). (d1,...,d(N-1)) and (s1,...,sN) are + wildcards that we match. + + Note that dN does not appear in the expression, but we solve for it + using range tree numels and the other dims. + """ + # Bound the possible number of dims. We use the following heuristics: + # - At least one dim for each range tree node. + # - At least one dim for every FloorDiv or ModularIndexing op. + # - At least 2 dims to pattern match. + num_dims = max( + 2, + len(self.range_tree_nodes), + (index.count(FloorDiv) + index.count(ModularIndexing)), + ) + + # Pattern match to find the strides and offset. + index_var = range_tree.symbol() + wild = functools.partial(sympy.Wild, exclude=[index_var]) + dims: List[sympy.Expr] = [ + wild(f"dim_mod{idx}") for idx in range(num_dims) + ] + strides: List[sympy.Expr] = [ + wild(f"stride_mod{idx}") for idx in range(num_dims) + ] + + def get_slice_numels(dims: List[Any]) -> List[Any]: + """ + Compute the cumulative size of each dimension's slice. + This proceeds from the last dim up to the second. + """ + numels = [sympy.Integer(1)] + for dim in dims[:0:-1]: + numel = dim * numels[0] + numels.insert(0, numel) + return numels + + # The first dimension's index is computed by division. + # The remaining are computed by modulo. + slice_numels = get_slice_numels(dims[:num_dims]) + block_index_exprs = [FloorDiv(index_var, slice_numels[0])] + [ + ModularIndexing(index_var, numel, dim) + for dim, numel in zip(dims[1:], slice_numels[1:]) + ] + + # Calculate a linear index from block indices. + match_expr = sympy_dot(strides, block_index_exprs) + + # Pattern match. + match = index.match(match_expr) + if match is None: + return None + + # Provide default values for unmatched dims and strides. + for dim in dims[1:]: + if dim not in match: + match[dim] = sympy.Integer(1) + for stride in strides[1:]: + if stride not in match: + match[stride] = sympy.Integer(0) + + sizevars = V.graph.sizevars + + def get_match(expr: sympy.Expr) -> sympy.Expr: + return sizevars.lookup_precomputed_size(match[expr]) + + # Replace wildcards with matched expressions. + dims = [dims[0]] + [get_match(dim) for dim in dims[1:]] + strides = [get_match(stride) for stride in strides] + slice_numels = get_slice_numels(dims) + block_index_exprs = [ + sympy_subs(expr, match) for expr in block_index_exprs + ] + + # The leading dimension is not directly matched in our expression. + # We solve for it by dividing the range tree numel by the product of + # all other dimensions. We quit if they are not known to be divisible. + assert ( + dims[0] not in match + ), "Expected not to match the leading dimension!" + if not sizevars.statically_known_multiple_of( + range_tree.numel, slice_numels[0] + ): + return None + dims[0] = range_tree.numel / slice_numels[0] + + # Check for applicable iteration range sizes. + # When mapping a 1D block into an ND one, we need to know that + # the number of elements is not changed. This means the slice numels of + # the ND iteration range must evenly divide the length of the 1D block. + # There are two cases where we can guarantee this: + # 1. Numels are powers of 2. If numel == 2 ** n, and we know XBLOCK == 2 ** m, + # with n and m integers, then either numel is a multiple of XBLOCK, or numel + # is less than XBLOCK. (If numel is less than XBLOCK, we round up to 1 below.) + # 2. Numels are multiples of the maximum possible block size. + max_block = self._max_block_size(range_tree) + if any( + not sizevars.statically_known_multiple_of(numel, max_block) + and not sizevars.statically_known_power_of_2(numel) + for numel in slice_numels + ): + return None + + def identity(expr: sympy.Expr) -> sympy.Expr: + return expr + + # Compute the ND block shape from the linear block size. + # Use CielDiv to round leading dimensions up to 1. + # Non-leading dimensions are clamped to the size of the iteration range, + # while the leading dimension can exceed this to accomodate a larger + # block size. + linear_block_size = self._get_block_size(range_tree) + block_shape: List[sympy.Expr] = [ + CeilDiv(linear_block_size, slice_numels[0]) + ] + [ + sympy.Min(CeilDiv(linear_block_size, numel), dim) + for numel, dim in zip(slice_numels[1:], dims[1:]) + ] + + # Compute block offsets from {xyzr}offset and the matched expressions. + block_offsets: List[sympy.Expr] = [ + sympy_subs(expr, {index_var: self._get_block_offset(range_tree)}) + for expr in block_index_exprs + ] + + return BlockParameters( + shape=dims, + block_shape=block_shape, + strides=strides, + offsets=block_offsets, + ) + + def match_block_pointer_subexpr( + expr: sympy.Expr, range_tree: IterationRangesEntry + ) -> Optional[BlockParameters]: + """ + Match a block indexing subexpression involving a single range tree. + """ + for match_func in ( + match_strided_block, + match_mod_div_block, + ): + match = match_func(expr, range_tree) + if match is not None: + return match + + return None + + def match_block_pointer() -> Optional[BlockPtrOptions]: + index_relative_to_xyr_index = sympy_subs( + index, {v: t.expr for v, t in self.range_tree_nodes.items()} + ) + range_trees = self.active_range_trees(reorder=True) + + # Match each range tree separately. + range_symbols = {tree.symbol() for tree in range_trees} + index_terms = sympy.Add.make_args(index_relative_to_xyr_index) + block_params = BlockParameters() + for tree in range_trees: + # Partition the index into subexpressions pertaining to each range tree. + # For example xindex * 5 + rindex * 3 is partitioned to + # (xindex * 5, rindex * 3). + symbol = tree.symbol() + subexpr = sympy.Integer(0) + sum( + expr for expr in index_terms if symbol in expr.free_symbols + ) + + # Reject mixed terms, e.g. xindex * rindex. + # NB: the zero expression is allowed, for broadcasting. + if len(range_symbols.intersection(subexpr.free_symbols)) > 1: + return None + + # Match the subexpression for this range tree. + params = match_block_pointer_subexpr(subexpr, tree) + if params is None: + return None + block_params += params + + # Collect leftover terms as a constant offset. + offset = sum( + expr + for expr in index_terms + if not range_symbols.intersection(expr.free_symbols) + ) + + # Form the block pointer. + self.filter_masks(mask_vars) + return BlockPtrOptions.create( + params=block_params, + constant_offset=offset, + range_trees=range_trees, + mask_vars=mask_vars, + ) + + # Return a block pointer, if indexing matches the pattern. + options = match_block_pointer() + if options is not None: + return options + + expand_str = None + index_str = self.index_to_str(index) + if isinstance(index, sympy.Integer): + expand_str = f"{copy_shape}.shape" if copy_shape else self.dense_size_str() + index_str = f"tl.full({expand_str}, {index_str}, tl.int32)" + return IndexingOptions( + index_str, OrderedSet(), "None", expand_str, has_rindex, index + ) + + if need_dense and not have_dense: + expand_str = f"{copy_shape}.shape" if copy_shape else self.dense_size_str() + index_str = f"tl.broadcast_to({index_str}, {expand_str})" + mask_vars = dense_mask_vars + elif not have_loop_vars and copy_shape: + index_str = f"tl.broadcast_to({index_str}, {copy_shape}.shape)" + mask_vars = dense_mask_vars + + if override_mask: + mask_vars = OrderedSet([override_mask]) + + if self._load_mask: + mask_vars.add(self._load_mask) + + self.filter_masks(mask_vars) + + mask_str = " & ".join(sorted(map(str, mask_vars))) if mask_vars else "None" + return IndexingOptions(index_str, mask_vars, mask_str, expand_str, has_rindex, index) # type: ignore[arg-type] + + def codegen_block_ptr( + self, name: str, var: str, indexing: BlockPtrOptions, other="" + ) -> Tuple[str, Optional[DeferredLine], str]: + advance_block_ptr = None + check = indexing.boundary_check() + if not check: + # workaround https://github.com/openai/triton/issues/2813 + other = "" + elif other: + assert other == ", other=0.0" + other = f", boundary_check={check!r}, padding_option='zero'" + else: + other = f", boundary_check={check!r}" + if ( + self.inside_reduction + and self.range_trees[-1].is_loop + and indexing.has_rindex() + ): + block_ptr = f"block_ptr{next(self.block_ptr_id)}" + self.body.writeline( + DeferredLine( + name, f"{block_ptr} = {indexing.format(var, roffset=False)}" + ) + ) + advance_block_ptr = DeferredLine( + name, + f"{block_ptr} = tl.advance({block_ptr}, {indexing.advance_roffset()})", + ) + else: + block_ptr = indexing.format(var) + return block_ptr, advance_block_ptr, other + + def codegen_block_ptr_store_line(self, name, indexing, block_ptr, value, other=""): + # broadcasting is not implicit for block_ptrs + value = ( + f"tl.broadcast_to({value}, {self.index_to_str(indexing.reshape_suffix)})" + ) + # drop any extra size=1 dimensions + block_shape = [V.kernel.index_to_str(expr) for expr in indexing.block_shape] + value = triton_reshape(value, indexing.reshape_suffix, block_shape) + # workaround https://github.com/openai/triton/issues/2814 + value = f"{value}.to({triton_store_type(V.graph.get_dtype(name))})" + return f"tl.store({block_ptr}, {value}{other})" + + def check_bounds( + self, + expr: sympy.Expr, + size: sympy.Expr, + lower: bool, + upper: bool, + ): + if not (lower or upper): + return + + assert isinstance(expr, sympy.Expr) + indexing = self.indexing(expr, block_ptr=False) + assert isinstance(indexing, IndexingOptions) + + index_str = indexing.index_str + mask_str = indexing.mask_str if indexing.has_mask() else None + size_str = texpr(self.rename_indexing(size)) if upper else None + + # expr is already wrapped + line = self.indirect_assert( + index_str, "0" if lower else None, size_str, mask_str + ) + + indirect = self.is_indirect_indexing(expr) or any( + isinstance(m, TritonCSEVariable) for m in indexing.mask_vars + ) + buffer = self.get_load_buffer(indexing) + self.cse.generate(buffer, line, assignment=False) + + def get_load_buffer(self, indexing): + if indexing.has_indirect() or indexing.has_tmpmask(): + # Masked loads must come after the mask is computed + return self.compute + elif ( + self.inside_reduction + and self.range_trees[-1].is_loop + and not indexing.has_rindex() + ): + # can lift a common load outside of reduction loop + # One exception is when this is an indirect_load. + return self.body + else: + return self.loads + + def load(self, name: str, index: sympy.Expr): + var = self.args.input(name) + indirect_indexing = self.is_indirect_indexing(index) + original_index = index + indexing = self.indexing(index, block_ptr=True) + has_rindex = indexing.has_rindex() + has_tmpmask = indexing.has_tmpmask() + + # Keep the variable in cache if were going to reuse it. Equiv., if any of the following hold + # 1) We are doing broadcasting + # 2) It is a non-coalesced load. The intuition is that if it's + # non-coalesced, we will likely load each element multiple times in + # practice. + # 3) It will be used later and it won't be CSE'd. Equiv., if all the following hold + # 3.1) We are in a reduction loop + # 3.2) Its not its last use + # 3.3) This load will not be lifted to the body + # + is_coalesced = any( + i == 1 for i in self.get_strides_of_load(original_index).values() + ) + if self.is_broadcasted(original_index): + ep = ", eviction_policy='evict_last'" + elif not is_coalesced: + ep = ", eviction_policy='evict_last'" + elif self.inside_reduction and self.range_trees[-1].is_loop: + if name in self.args.inplace_buffers: + names: OrderedSet[str] = OrderedSet( + self.args.inplace_buffers[name].other_names + ) + else: + names = OrderedSet([name]) + last_use = len(names & self.last_usage) > 0 + evict_last = not last_use and (has_rindex or indirect_indexing) + if evict_last: + ep = ", eviction_policy='evict_last'" + else: + ep = ", eviction_policy='evict_first'" + else: + ep = "" + + if (has_tmpmask or has_rindex) and indexing.has_mask(): + if self._load_other: + other = f", other={constant_repr(self._load_other)}" + else: + other = ", other=0.0" + else: + other = "" + + advance_block_ptr = None + append_broadcast = None + if should_unwrap_unspec_arg(name): + line = var + else: + if isinstance(indexing, BlockPtrOptions): + block_ptr, advance_block_ptr, other = self.codegen_block_ptr( + name, var, indexing, other + ) + line = f"tl.load({block_ptr}{other}{ep})" + # add needed size=1 dimensions + block_shape = [str(dim) for dim in indexing.block_shape] + line = triton_reshape(line, block_shape, indexing.reshape_suffix) + elif isinstance(original_index, sympy.Integer): + line = f"tl.load({var} + ({original_index}))" + append_broadcast = indexing.expand_str + else: + line = f"tl.load({var} + ({indexing.index_str}), {indexing.mask_str}{ep}{other})" + + dtype = V.graph.get_dtype(name) + if ( + dtype in (torch.float16, torch.bfloat16) + and config.triton.codegen_upcast_to_fp32 + ): + line += ".to(tl.float32)" + if dtype == torch.bool and torch.version.hip is None: + # Workaround for https://github.com/openai/triton/issues/2151 + # tl.load returns int8 when loading from pointer to int1 + # NOTE: Currently causes hangs on bool UTs for ROCm + line += ".to(tl.int1)" + + load_buffer = self.get_load_buffer(indexing) + result_var = self.cse.generate(load_buffer, line) + assert isinstance(result_var, TritonCSEVariable) + result_var.mask_vars = indexing.mask_vars # type: ignore[assignment] + + if append_broadcast: + line = f"tl.broadcast_to({result_var}, {append_broadcast})" + result_var = self.cse.generate(load_buffer, line) + + if advance_block_ptr: + load_buffer.writeline(advance_block_ptr) + + if not self.inside_reduction or (not indexing.has_rmask() and not has_rindex): + self.outside_loop_vars.add(result_var) + + return result_var + + def store( + self, name: str, index: sympy.Expr, value: CSEVariable, mode: StoreMode = None + ) -> None: + var = self.args.output(name) + original_index = index + indexing = self.indexing(index, dense_indexing=True, block_ptr=mode is None) + + # Guard against write-after-read corruption in triton. + # See # https://github.com/openai/triton/issues/1615 + # This triton bug means that a load which is broadcasted over multiple + # warps may see the result of a store that happens later in the triton + # program. The workaround is to add a barrier before storing, which + # enforces that all warps have already read the data. + is_inplace = name in self.args.inplace_buffers + is_broadcasted = self.is_broadcasted(original_index) + if is_inplace and is_broadcasted: + self.stores.writeline(DeferredLine(name, "tl.debug_barrier()")) + + advance_block_ptr = None + if isinstance(indexing, BlockPtrOptions): + block_ptr, advance_block_ptr, other = self.codegen_block_ptr( + name, var, indexing + ) + # block_ptr stores don't do implicit casting + line = self.codegen_block_ptr_store_line( + name, indexing, block_ptr, value, other + ) + elif mode is None: + line = f"tl.store({var} + ({indexing.index_str}), {value}, {indexing.mask_str})" + elif mode == "atomic_add": + line = f"tl.atomic_add({var} + ({indexing.index_str}), {value}, {indexing.mask_str}, sem='relaxed')" + else: + raise NotImplementedError(f"store mode={mode}") + self.stores.writeline(DeferredLine(name, line)) + if advance_block_ptr: + self.stores.writeline(advance_block_ptr) + + if not self.inside_reduction: + self.outside_loop_vars.add(value) + + def bucketize( + self, + values: CSEVariable, + offsets_name: str, + offsets_size: sympy.Expr, + indexing_dtype: torch.dtype, + right: bool, + ) -> CSEVariable: + """ + See [Note: Inductor bucketize op] + """ + + # Triton performance for bucketize_binary_search is much better when the number + # of threads equals the number of elements. + # If we're trying to use a bucketize kernel, we should make sure that an + # autotuning config with num_elements_per_warp=32 exists. + self.autotune_hints.add(AutotuneHint.ELEMENTS_PER_WARP_32) + + offsets_ptr = self.args.input(offsets_name) + block_size = self.dense_size_str() + offsets_size_str = self.index_to_str(offsets_size) + + if indexing_dtype == torch.int32: + triton_dtype = "tl.int32" + elif indexing_dtype == torch.int64: + triton_dtype = "tl.int64" + else: + raise NotImplementedError( + "Bucketize only supports indexing with int32 and int64" + ) + + result = self.cse.generate( + self.compute, + f"triton_helpers.bucketize_binary_search({values}, {offsets_ptr}, {triton_dtype}, {right}, {offsets_size_str}, {block_size})", # noqa: B950 line too long + ) + + return result + + def reduction_resize(self, value): + ndims = self.triton_tensor_ndim() + if ndims == 1: + return f"triton_helpers.promote_to_tensor({value})" + + sizes = [":"] * ndims + sizes[-1] = "None" + return f"{value}[{', '.join(sizes)}]" + + def reduction( + self, + dtype: torch.dtype, + src_dtype: torch.dtype, + reduction_type: ReductionType, + value: Union[CSEVariable, Tuple[CSEVariable, ...]], + ) -> Union[CSEVariable, Tuple[CSEVariable, ...]]: + assert self.inside_reduction + masks = OrderedSet(f"{tree.prefix}mask" for tree in self.range_trees) + self.filter_masks(masks) + masks = sorted(masks) + if self._load_mask: + masks.append(self._load_mask) + reduction_range_prefix = self.range_trees[-1].prefix + + # Say we have + # tmp0 = ops.constant(1, torch.int64) + # tmp1 = ops.reduction(torch.int64, torch.int64, "sum", tmp0) + # tmp0 in the triton code is either a scalar, or single-element tensor + # so if we emit tl.sum directly, it will only give 1 instead of RBLOCK * 1 + # To avoid this, we broadcast to the expected shape first. + dense_size_str = self.dense_size_str() + value = self._map_tuple_or_scalar( + lambda v: self.cse.generate( + self.compute, f"tl.broadcast_to({v}, {dense_size_str})" + ), + value, + ) + + dim: int + root_op: str + + def final_reduction(value): + use_helper = reduction_type in {"any", "max", "min", "prod"} + module = "triton_helpers" if use_helper else "tl" + if reduction_type in {"max", "min"}: + return self.reduction_resize( + f"{module}.{reduction_type}2({value}, {dim})" + ) + return self.reduction_resize(f"{module}.{reduction_type}({value}, {dim})") + + def final_argreduce(buffer, result_var, value, index): + buffer.splice( + f"""\ + _, {result_var}_tmp = triton_helpers.{root_op}_with_index({value}, {index}, {dim}) + {result_var} = {self.reduction_resize(f'{result_var}_tmp')} + """ + ) + + cache_key = (src_dtype, reduction_type, value) + if cache_key in self.cse.reduction_cache: + return self.cse.reduction_cache[cache_key] + + dim = self.triton_tensor_ndim() - 1 + acc_type = triton_acc_type(src_dtype) + result_var: Any = self.cse.newvar() + result_var.mask_vars = OrderedSet(var for var in masks if var[0] != "r") + cond = " & ".join(masks) + + def where_cond(tval, fval): + if not cond: + return tval + return TritonKernelOverrides.where(cond, tval, fval) + + if self.persistent_reduction: + default = ir.Reduction.default_value(reduction_type, src_dtype) + default = self._map_tuple_or_scalar(constant_repr, default) + + def _mask_value(value, default): + return self.cse.generate(self.compute, where_cond(value, default)) + + if isinstance(value, tuple): + masked_value = [_mask_value(v, d) for v, d in zip(value, default)] + else: + masked_value = _mask_value(value, default) + + if reduction_type in {"argmax", "argmin"}: + accumulator_index = str( + self.cse.generate( + self.compute, + f"tl.broadcast_to({reduction_range_prefix}index, {masked_value}.shape)", + ) + ) + root_op = {"argmax": "max", "argmin": "min"}[reduction_type] + final_argreduce( + self.compute, result_var, masked_value, accumulator_index + ) + elif reduction_type == "welford_reduce": + # For persistent reductions, don't bother with + # welford's algorithm since it uses more registers, and + # taking two reductions doesn't increase memory usage. + result_var = self.welford_reduce_fallback(dtype, value) + elif reduction_type == "welford_combine": + mean, m2, weight = masked_value + welford = f"triton_helpers.welford({mean}, {m2}, {weight}, {dim})" + mean, m2, weight = (self.cse.newvar() for _ in range(3)) + self.compute.writeline(f"{mean}, {m2}, {weight} = {welford}") + + result_var = tuple( + self.cse.generate(self.compute, self.reduction_resize(var_name)) + for var_name in (mean, m2, weight) + ) + else: + result_var = self.cse.generate( + self.compute, final_reduction(masked_value) + ) + else: + accumulator = f"_{result_var}" + default = ir.Reduction.default_accumulator(reduction_type, src_dtype) + default = self._map_tuple_or_scalar(constant_repr, default) + if not isinstance(default, tuple): + self.body.writeline( + f"{accumulator} = tl.full({self.dense_size_str()}, {default}, {acc_type})" + ) + + if reduction_type in {"argmax", "argmin"}: + accumulator_index = f"_{result_var}_index" + long_max = torch.iinfo(torch.int64).max + self.body.writeline( + f"{accumulator_index} = tl.full({self.dense_size_str()}, {long_max}, tl.int64)" + ) + root_op = {"argmax": "max", "argmin": "min"}[reduction_type] + + self.compute.splice( + f"""\ + {accumulator}_next, {accumulator_index}_next = triton_helpers.{root_op}imum_with_index( + {accumulator}, {accumulator_index}, {value}, {reduction_range_prefix}index + ) + {accumulator} = {where_cond(f'{accumulator}_next', accumulator)} + {accumulator_index} = {where_cond(f'{accumulator_index}_next', accumulator_index)} + """ + ) + final_argreduce(self.suffix, result_var, accumulator, accumulator_index) + elif is_welford_reduction(reduction_type): + accumulator = f"{result_var}_mean" + accumulator_m2 = f"{result_var}_m2" + accumulator_weight = f"{result_var}_weight" + self.body.writeline( + f"{accumulator} = tl.zeros({self.dense_size_str()}, {acc_type})" + ) + self.body.writeline( + f"{accumulator_m2} = tl.zeros({self.dense_size_str()}, {acc_type})" + ) + self.body.writeline( + f"{accumulator_weight} = tl.zeros({self.dense_size_str()}, {acc_type})" + ) + + if reduction_type == "welford_combine": + mean, m2, weight = value + self.compute.splice( + f"""\ + {accumulator}_next, {accumulator_m2}_next, {accumulator_weight}_next = triton_helpers.welford_combine( + {accumulator}, {accumulator_m2}, {accumulator_weight}, + {mean}, {m2}, {weight} + ) + """ + ) + else: + assert reduction_type == "welford_reduce" + self.compute.splice( + f"""\ + {accumulator}_next, {accumulator_m2}_next, {accumulator_weight}_next = triton_helpers.welford_reduce( + {value}, {accumulator}, {accumulator_m2}, {accumulator_weight}, roffset == 0 + ) + """ + ) + + self.compute.splice( + f"""\ + {accumulator} = {where_cond(f'{accumulator}_next', accumulator)} + {accumulator_m2} = {where_cond(f'{accumulator_m2}_next', accumulator_m2)} + {accumulator_weight} = {where_cond(f'{accumulator_weight}_next', accumulator_weight)} + """ + ) + + result_mean = result_var + result_m2 = self.cse.newvar() + result_weight = self.cse.newvar() + self.suffix.splice( + f"""\ + {result_mean}_tmp, {result_m2}_tmp, {result_weight}_tmp = triton_helpers.welford( + {accumulator}, {accumulator_m2}, {accumulator_weight}, {dim} + ) + {result_mean} = {self.reduction_resize(f'{result_mean}_tmp')} + {result_m2} = {self.reduction_resize(f'{result_m2}_tmp')} + {result_weight} = {self.reduction_resize(f'{result_weight}_tmp')} + """ + ) + result_var = result_mean, result_m2, result_weight + else: + combine_fn = ir.get_reduction_combine_fn(reduction_type, src_dtype) + updated = combine_fn(accumulator, value) + self.compute.writeline( + f"{accumulator} = {where_cond(updated, accumulator)}" + ) + + if src_dtype == torch.bool: + # This is only really used for aten.any. It changes the + # final reduction of a non-persistent reduction from + # tmp5 = triton_helpers.max(_tmp5, 1)[:, None] + # to + # tmp5 = triton_helpers.max(_tmp5.to(tl.int8), 1)[:, None].to(tl.int1) + # which is needed because tl.reduce doesn't support tl.int1 + accumulator = f"{accumulator}.to(tl.int8)" + result_type = triton_compute_type(dtype) + self.suffix.writeline( + f"{result_var} = {final_reduction(accumulator)}.to({result_type})" + ) + else: + self.suffix.writeline( + f"{result_var} = {final_reduction(accumulator)}" + ) + + self.cse.reduction_cache[cache_key] = result_var + + if isinstance(result_var, tuple): + assert all(isinstance(x, TritonCSEVariable) for x in result_var) + self.outside_loop_vars |= OrderedSet(result_var) + else: + assert isinstance(result_var, TritonCSEVariable) + self.outside_loop_vars.add(result_var) + + return result_var + + def store_reduction(self, name: str, index: sympy.Expr, value: CSEVariable): + assert self.inside_reduction + self.inside_reduction = False + indexing = self.indexing(index, block_ptr=True) + self.inside_reduction = True + var = self.args.output(name) + + if isinstance(indexing, BlockPtrOptions): + self.suffix.writeline( + DeferredLine( + name, + self.codegen_block_ptr_store_line( + name, + indexing, + indexing.format(var), + value, + f", boundary_check={indexing.boundary_check()!r}", + ), + ) + ) + else: + assert isinstance(indexing, IndexingOptions) + self.suffix.writeline( + DeferredLine( + name, + f"tl.store({var} + ({indexing.index_str}), {value}, {indexing.mask_str})", + ) + ) + + def _lift_helper(self, fn, num_args) -> str: + # Lift IR function for scan operations into a triton function + # in the global namespace + helper = IndentedBuffer() + helper.writeline("@triton.jit") + args = [tuple(f"arg{i}_{n}" for n in range(num_args)) for i in range(2)] + signature = ", ".join(itertools.chain.from_iterable(args)) + helper.writeline(f"def {{name}}({signature}):") + + cse = CSE(prefix="", suffix="") + overrides = TritonOverrides(V.MockHandler()) + + # Build a name that changes depending on fn to workaround a triton bug + # where the combine_fn to reduce and scan is not hashed, and so different + # scan ops may collide in the triton cache. + # This is fixed with the latest triton pin, but not the triton-rocm pin. + helper_name = "_triton_helper_fn" + + class CSEProxy: + def __getattr__(self, name: str) -> Callable[..., CSEVariable]: + def inner(*args, **kwargs): + nonlocal helper_name + helper_name += f"_{name}" + return cse.generate( + helper, + getattr(overrides, name)(*args, **kwargs), + ) + + return inner + + with helper.indent(), V.set_ops_handler(CSEProxy()): + outputs = fn(*args) + outputs = ", ".join(str(output) for output in outputs) + helper.writeline(f"return {outputs}") + + return self.helper_functions.add(helper.getvalue(), base_name=helper_name) + + def scan( + self, + dtypes: Tuple[torch.dtype, ...], + combine_fn: Callable[ + [Tuple[CSEVariable, ...], Tuple[CSEVariable, ...]], Tuple[CSEVariable, ...] + ], + values: Tuple[CSEVariable, ...], + ) -> Tuple[CSEVariable, ...]: + assert self.inside_reduction + masks = OrderedSet(f"{tree.prefix}mask" for tree in self.range_trees) + self.filter_masks(masks) + masks = sorted(masks) + assert not self._load_mask, "ops.scan not supported inside ops.masked" + reduction_range_prefix = self.range_trees[-1].prefix + + broadcasted_values = [] + accumulators = [] + + cse_compute = functools.partial(self.cse.generate, self.compute) + combine_helper_fn = self._lift_helper(combine_fn, len(values)) + dim = self.triton_tensor_ndim() - 1 + + for value, dtype in zip(values, dtypes): + acc_type = triton_acc_type(dtype) + cond = " & ".join(masks) + + value_dtype = self.cse.generate( + self.compute, + f"{value}.to({triton_compute_type(dtype)})", + ) + value = self.cse.generate( + self.compute, + f"tl.broadcast_to({value_dtype}, {self.dense_size_str()})", + ) + broadcasted_values.append(value) + + acc_type = triton_acc_type(dtype) + cond = " & ".join(masks) + + if not self.persistent_reduction: + accumulator = self.cse.newvar() + reduced_size = self.dense_size_list() + reduced_size[-1] = "1" + reduced_size = f"[{', '.join(reduced_size)}]" + + default = "float('nan')" if dtype.is_floating_point else "-1" + self.body.writeline( + f"{accumulator} = tl.full({reduced_size}, {default}, {acc_type})" + ) + + accumulators.append(accumulator) + + def csv(values): + return " ".join(f"{value}," for value in values) + + def cse_multiple(line, n, masks): + cache_keys = [f"{line}, {i}, {masks}" for i in range(n)] + if all(cache_key in self.cse.cache for cache_key in cache_keys): + return [self.cse.cache[cache_key] for cache_key in cache_keys] + result_vars = [self.cse.newvar() for _ in range(n)] + self.compute.writeline( + f"{csv(result_vars)} = {line}", + ) + for result_var, cache_key in zip(result_vars, cache_keys): + if masks: + result_var.mask_vars = masks # type: ignore[attr-defined] + self.cse.cache[cache_key] = result_var + return tuple(result_vars) + + partial_scan_vars = cse_multiple( + f"tl.associative_scan(({csv(broadcasted_values)}), {dim}, {combine_helper_fn})", + len(values), + masks, + ) + + if not self.persistent_reduction: + # tl.reduce doesn't work for non-commutative operators, so instead + # of repeating the scan op as a reduction, we use sum to select the + # last scan value + partial_reduce_vars = [ + cse_compute( + f"triton_helpers.select_one(({partial_scan_var}), rbase == (RBLOCK - 1), dim=-1, keep_dims=True)" + ) + for partial_scan_var in partial_scan_vars + ] + accs_next = combine_fn(tuple(accumulators), tuple(partial_reduce_vars)) + full_scan_vars = combine_fn(tuple(accumulators), partial_scan_vars) + result_vars = [ + cse_compute(f"tl.where(roffset > 0, {full_scan}, {partial_scan})") + for full_scan, partial_scan in zip(full_scan_vars, partial_scan_vars) + ] + for acc_next, accumulator, partial_reduce in zip( + accs_next, accumulators, partial_reduce_vars + ): + self.compute.writeline( + f"{accumulator} = tl.where(roffset > 0, {acc_next}, {partial_reduce})" + ) + else: + result_vars = partial_scan_vars + + for result_var in result_vars: + result_var.mask_vars = masks # type: ignore[attr-defined] + + return tuple(result_vars) + + def sort( + self, + dtypes: Tuple[torch.dtype, ...], + values: Tuple[CSEVariable, ...], + stable: bool, + descending: bool, + ) -> Tuple[CSEVariable, ...]: + assert self.inside_reduction + masks = OrderedSet(f"{tree.prefix}mask" for tree in self.range_trees) + self.filter_masks(masks) + masks = sorted(masks) + assert not self._load_mask, "ops.sort not supported inside ops.masked" + assert ( + self.persistent_reduction + ), "ops.sort is only supported in persistent reductions" + reduction_range_prefix = self.range_trees[-1].prefix + + cse_compute = functools.partial(self.cse.generate, self.compute) + dim = self.triton_tensor_ndim() - 1 + + broadcasted_values = [ + cse_compute(f"tl.broadcast_to({value}, {self.dense_size_str()})") + for value in values + ] + + def csv(values): + return " ".join(f"{value}," for value in values) + + def cse_multiple(line, n, masks): + cache_keys = [f"{line}, {i}, {masks}" for i in range(n)] + if all(cache_key in self.cse.cache for cache_key in cache_keys): + return [self.cse.cache[cache_key] for cache_key in cache_keys] + result_vars = [self.cse.newvar() for _ in range(n)] + self.compute.writeline( + f"{csv(result_vars)} = {line}", + ) + for result_var, cache_key in zip(result_vars, cache_keys): + if masks: + result_var.mask_vars = masks # type: ignore[attr-defined] + self.cse.cache[cache_key] = result_var + return tuple(result_vars) + + assert self.range_trees[-1].prefix == "r" + rnumel = "None" if self._has_constant_mask(self.range_trees[-1]) else "rnumel" + + if len(values) == 2: + line = ( + f"triton_helpers.sort_with_index({broadcasted_values[0]}, {broadcasted_values[1]}," + f" {rnumel}, {dim}, stable={stable}, descending={descending})" + ) + result_vars = cse_multiple(line, len(values), masks) + else: + raise AssertionError("Unhandled sort") + + for result_var, input_var in zip(result_vars, values): + result_var.mask_vars = masks # type: ignore[attr-defined] + result_var.bounds = input_var.bounds + + return tuple(result_vars) + + def codegen_body(self): + """ + Concat output code from index_code, loads, compute, stores, + suffix into self.body. + + For pointwise kernels, this is called just once at the end. + + For reduction kernels, this generates a loop over the reduction + axis. + """ + if not ( + self.indexing_code + or self.loads + or self.stores + or self.compute + or self.suffix + ): + return + + if self.inside_reduction and self.range_trees[-1].is_loop: + self.body.writeline("for roffset in range(0, rnumel, RBLOCK):") + with self.body.indent(): + # last range tree is always reduction + self.iteration_ranges_codegen_header(self.range_trees[-1], self.body) + self.body.splice(self.indexing_code) + self.body.splice(self.loads) + self.body.splice(self.compute) + self.body.splice(self.stores) + + # invalidate any caches that came from inside the reduction loop + self.cse.invalidate(self.outside_loop_vars) + self.range_trees[-1].cache_clear() + else: + self.body.splice(self.indexing_code) + self.body.splice(self.loads) + self.body.splice(self.compute) + self.body.splice(self.stores) + self.body.splice(self.suffix) + self.indexing_code.clear() + self.loads.clear() + self.compute.clear() + self.stores.clear() + self.suffix.clear() + + def codegen_kernel_benchmark(self, num_gb, grid=None): + result = IndentedBuffer() + argdefs, call_args, signature, _ = self.args.python_argdefs() + + result.writelines(["", "", "def get_args():"]) + with result.indent(): + name_cnt = itertools.count() + var_names = [] + for arg_name, arg_sig in zip(call_args, signature): + var_name = f"arg_{next(name_cnt)}" + buf = V.graph.try_get_buffer(arg_name) + if buf: + result.writeline( + f"{var_name} = rand_strided({V.graph.sizevars.size_hints(buf.get_size())}, {V.graph.sizevars.size_hints(buf.get_stride())}, device='{buf.get_device()}', dtype={buf.get_dtype()})" # noqa: B950 line too long + ) + elif arg_name in V.graph.constants: + # note that random seed is put in V.graph.constants + const_tensor = V.graph.constants[arg_name] + result.writeline( + f"{var_name} = rand_strided({V.graph.sizevars.size_hints(const_tensor.size())}, {V.graph.sizevars.size_hints(const_tensor.stride())}, device='{const_tensor.device}', dtype={const_tensor.dtype})" # type: ignore[arg-type] # noqa: B950 line too long + ) + elif isinstance(arg_sig, SizeArg): + symval_hint = V.graph.sizevars.size_hint(arg_sig.expr) + + # Force the seed_offset to be 0 so calls to the same kernel + # using different seed offset will have the same benchmark harness. + # We can dedup kernel definitions in this case. + if "seed_offset" in arg_sig.name: + symval_hint = 0 + result.writeline(f"{var_name} = {symval_hint}") + elif isinstance(arg_sig, WorkspaceArg): + device = V.graph.scheduler.get_current_device_or_throw() + nbytes = V.graph.sizevars.size_hint(arg_sig.nbytes) + result.writeline( + f"{var_name} = torch.zeros({nbytes}, device='{device}', dtype=torch.uint8)" + ) + else: + raise KeyError( + f"Don't find the buffer or const tensor for {arg_name}" + ) + var_names.append(var_name) + result.writeline(f"return {', '.join(var_names)},") + + result.writelines(["\n", "\n", "def call(args):"]) + if grid is None: + grid = [] + extra_args = [] + extra_args_str = None + for tree in self.active_range_trees(): + expr = pexpr(V.graph.sizevars.size_hint(tree.numel)) + extra_args.append(expr) + if tree.prefix != "r": + grid.append(expr) + if self.need_numel_args(): + extra_args_str = ", ".join(map(str, extra_args)) + ", " + else: + extra_args_str = "" + grid_arg = f"{extra_args_str}grid=grid({', '.join(grid)})" + else: + grid_arg = f"grid={grid}" + current_device = V.graph.scheduler.get_current_device_or_throw() + index = current_device.index + with result.indent(): + result.writeline(f"with {V.graph.device_ops.device_guard(index)}:") + with result.indent(): + result.writeline( + V.graph.device_ops.set_device(index) + ) # no-op to ensure context + stream_name = f"stream{index}" + result.writeline(f"{stream_name} = get_raw_stream({index})") + result.writeline( + f"{str(Placeholder.KERNEL_NAME)}.run(*args, {grid_arg}, stream={stream_name})" + ) + + # benchmark all configs + result.writelines(["\n", "\n", "def benchmark_all_configs(args):"]) + with result.indent(): + result.writeline(f"with {V.graph.device_ops.device_guard(index)}:") + with result.indent(): + result.writeline( + V.graph.device_ops.set_device(index) + ) # no-op to ensure context + result.writeline( + f"return {str(Placeholder.KERNEL_NAME)}.benchmark_all_configs(*args, {grid_arg})" + ) + + result.writelines(["\n", "\n", "if __name__ == '__main__':"]) + with result.indent(): + result.writeline( + "from torch._inductor.runtime.benchmarking import benchmarker" + ) + result.writeline("") + + result.writeline("args = get_args()") + result.writeline( + "ms = benchmarker.benchmark_gpu(lambda: call(args), rep=40, fast_flush=True)" + ) + result.writeline(f"num_gb = {num_gb}") + result.writeline("gb_per_s = num_gb / (ms / 1e3)") + result.writeline( + 'print(f"{ms:.3f}ms {num_gb:.3f}GB {gb_per_s:.2f}GB/s")' + ) + + return result + + def imports_for_benchmark_kernel(self): + return textwrap.dedent( + """ + from torch._dynamo.testing import rand_strided + {} + import torch + from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid + """.format( + V.graph.device_ops.import_get_raw_stream_as("get_raw_stream") + ) + ) + + def _get_heuristic(self): + if self.persistent_reduction: + assert self.inside_reduction + return "persistent_reduction" + elif self.inside_reduction: + return "reduction" + return "pointwise" + + @staticmethod + def inductor_meta_common(): + inductor_meta = { + "backend_hash": torch.utils._triton.triton_hash_with_backend(), + "are_deterministic_algorithms_enabled": torch.are_deterministic_algorithms_enabled(), + "assert_indirect_indexing": config.assert_indirect_indexing, + "autotune_local_cache": config.autotune_local_cache, + "autotune_pointwise": config.triton.autotune_pointwise, + "autotune_remote_cache": config.autotune_remote_cache, + "force_disable_caches": config.force_disable_caches, + "dynamic_scale_rblock": config.dynamic_scale_rblock, + "max_autotune": config.max_autotune, + "max_autotune_pointwise": config.max_autotune_pointwise, + "min_split_scan_rblock": config.triton.min_split_scan_rblock, + "spill_threshold": config.triton.spill_threshold, + "store_cubin": config.triton.store_cubin, + } + if torch.version.hip is not None: + inductor_meta["is_hip"] = True + if config.is_fbcode(): + inductor_meta["is_fbcode"] = True + if config.profile_bandwidth: + inductor_meta["profile_bandwidth"] = config.profile_bandwidth + inductor_meta["profile_bandwidth_regex"] = config.profile_bandwidth_regex + inductor_meta["profile_bandwidth_output"] = config.profile_bandwidth_output + inductor_meta[ + "profile_bandwidth_with_do_bench_using_profiling" + ] = config.profile_bandwidth_with_do_bench_using_profiling + if config.coordinate_descent_tuning: + inductor_meta[ + "coordinate_descent_tuning" + ] = config.coordinate_descent_tuning + inductor_meta[ + "coordinate_descent_search_radius" + ] = config.coordinate_descent_search_radius + inductor_meta[ + "coordinate_descent_check_all_directions" + ] = config.coordinate_descent_check_all_directions + return inductor_meta + + def codegen_kernel(self, name=None): + code = IndentedBuffer() + + size_hints = [] + for numel in self.numels: + numel_hint = V.graph.sizevars.symbolic_hint(numel) + if not isinstance(numel_hint, (int, sympy.Integer)): + # This default heuristic hint was picked carefully: it is + # large, to ensure that we don't shrink the block size (since + # if you don't have many elements, it'd be wasteful to pick a + # large block size). Since we don't know how many elements we + # might have, we should be OK with some inefficiency to make + # sure we handle the large case well. 8192 is the largest + # block size we support, so we pick that. + # + # If we have a better hint for unbacked SymInts (e.g., because + # a user told us, or we are tracking upper bounds) we could + # use that here. + size_hint = 8192 + else: + size_hint = next_power_of_2(int(numel_hint)) + size_hints.append(size_hint) + + if not self.inside_reduction: + size_hints.pop() + + heuristics = self._get_heuristic() + + if name is None: + code.splice(gen_common_triton_imports()) + + if config.benchmark_kernel: + code.splice(self.imports_for_benchmark_kernel()) + + argdefs, _, signature, _ = self.args.python_argdefs() + # maps actual expression to SizeArg if it is in sizevars replacements + for i, arg in enumerate(signature): + if isinstance(arg, SizeArg): + # mypy is unhappy about the sympy.Expr + # type for the key of the dict below + symbol = cast(sympy.Symbol, arg.expr) + if symbol in V.graph.sizevars.inv_precomputed_replacements: + signature[i] = SizeArg( + arg.name, V.graph.sizevars.inv_precomputed_replacements[symbol] + ) + + mutated_args: OrderedSet[str] = OrderedSet() + for mutation in self.mutations: + if mutation in self.args.input_buffers: + mutated_args.add(self.args.input_buffers[mutation]) + if ( + mutation in self.args.inplace_buffers + and mutation not in V.graph.removed_buffers + and mutation not in self.removed_buffers + ): + mutated_args.add(self.args.inplace_buffers[mutation].inner_name) + if mutation in self.args.output_buffers: + mutated_args.add(self.args.output_buffers[mutation]) + + # workspace arguments are mutated, but are not marked as mutations in self.mutations + # because their buffers are added during codegen, and aren't tracked during + # lowering/scheduling. So we add them as mutated_args explicitly below. + # + # In the logic below, we only mark the workspaces a mutated if they are marked with + # zero_fill: that's because, if we don't expect the buffer to be pre-filled with + # zeros, then, although we still mutate the data, we don't care about those + # mutations because we don't make any assumptions about the contents of the + # workspace buffer. + for argname, arg in zip(argdefs, signature): + if isinstance(arg, WorkspaceArg) and arg.zero_fill: + mutated_args.add(argname) + + mutated_args = sorted(mutated_args) + + triton_meta_signature = signature_to_meta( + signature, size_dtype=self.index_dtype + ) + triton_meta = { + "signature": triton_meta_signature, + "device": DeviceProperties.create( + V.graph.scheduler.get_current_device_or_throw() + ), + "constants": {}, + } + + inductor_meta = { + "autotune_hints": set(self.autotune_hints), + "kernel_name": str(Placeholder.DESCRIPTIVE_NAME), + "mutated_arg_names": mutated_args, + "no_x_dim": self.no_x_dim, + "num_load": self.num_load, + "num_reduction": self.num_reduction, + **self.inductor_meta_common(), + } + + num_gb = None + if config.benchmark_kernel or config.profile_bandwidth: + num_gb = self.estimate_kernel_num_bytes() / 1e9 + inductor_meta["kernel_num_gb"] = num_gb + + for tree in self.active_range_trees(): + sizearg = SizeArg(f"{tree.prefix}numel", tree.numel) + signature.append(sizearg) + triton_meta_signature[len(argdefs)] = signature_of( + sizearg, size_dtype=self.index_dtype + ) + argdefs.append(f"{tree.prefix}numel") + # constexpr version causes issues, see + # https://github.com/pytorch/torchdynamo/pull/1362 + # triton_meta["constants"][len(argdefs)] = V.graph.sizevars.size_hint( + # tree.numel + # ) + # argdefs.append(f"{tree.prefix}numel: tl.constexpr") + triton_meta["configs"] = [config_of(signature)] + + # Triton compiler includes equal_to_1 args into constants even + # when they are not constexpr. otherwise there may be a segfault + # during launching the Inductor-compiled Triton kernel. + # https://github.com/pytorch/pytorch/issues/120478#issuecomment-1962822307 + # https://github.com/openai/triton/blob/231efe9ed2d200be0f69a07c298e4342b08efe3d/python/triton/runtime/jit.py#L384 + for arg_num in triton_meta["configs"][0].equal_to_1: # type: ignore[index] + triton_meta["constants"][arg_num] = 1 # type: ignore[index] + + self.triton_meta = triton_meta + + for tree in self.range_trees: + if tree.prefix == "r" and self.persistent_reduction: + # RBLOCK for persistent_reduction is defined in codegen_static_numels + continue + if tree.tensor_dim is None: + continue + argdefs.append(f"{tree.prefix.upper()}BLOCK : tl.constexpr") + + self.codegen_body() + + for helper in self.helper_functions: + code.writeline("") + code.splice(helper) + + if self.inside_reduction: + reduction_hint = self.reduction_hint + heuristics_line = f""" + @triton_heuristics.{heuristics}( + size_hints={size_hints!r}, + reduction_hint={reduction_hint}, + filename=__file__, + triton_meta={triton_meta!r}, + inductor_meta={inductor_meta!r} + ) + @triton.jit + """ + else: + tile_hint = "" + if len(size_hints) == 2: + if len(signature) == 4: # input, output and 2 args + tile_hint = "tile_hint=TileHint.SQUARE," + else: + tile_hint = "tile_hint=TileHint.DEFAULT," + heuristics_line = f""" + @triton_heuristics.{heuristics}( + size_hints={size_hints!r}, {tile_hint} + filename=__file__, + triton_meta={triton_meta!r}, + inductor_meta={inductor_meta!r}, + min_elem_per_thread={self.min_elem_per_thread} + ) + @triton.jit + """ + code.splice(heuristics_line) + code.writeline( + f"def {name or str(Placeholder.KERNEL_NAME)}({', '.join(argdefs)}):" + ) + with code.indent(): + self.codegen_static_numels(code) + for old, new in self.args.aliases(): + code.writeline(f"{old} = {new}") + code.splice(self.body) + + if config.benchmark_kernel: + code.splice(self.codegen_kernel_benchmark(num_gb)) + + return code.getvalue() + + def _get_persistent_RBLOCK(self, rnumel): + rnumel = V.graph.sizevars.simplify(rnumel) + if isinstance(rnumel, (sympy.Integer, int)): + val = int(rnumel) + val = next_power_of_2(val) + else: + val = 128 + while not V.graph.sizevars.statically_known_leq(rnumel, val): + assert val <= 16 * 1024, f"Failed to find static RBLOCK for {rnumel}" + val *= 2 + return val + + def codegen_static_numels(self, code): + """ + We get a small speedup from hard coding numels if they are static. + + This code stomps on the passed-in values by writing an constant to the top of the kernel. + + In a kernel like: + def KERNEL_NAME(in_ptr0, in_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr): + + We would add + xnumel = 4096 + rnumel = 768 + + After the signature, before the kernel code, if we decided to make these static. As its hardcoded, it becomes + a better signal to triton on how to unroll and do some static indexing. So, it's not so much that downstream + knows that its a static numel, as that you just plop a constant into the kernel. + """ + for tree in self.range_trees: + if tree.prefix != "r" or self.inside_reduction: + simplified_tree_numel = V.graph.sizevars.simplify(tree.numel) + if isinstance(simplified_tree_numel, (sympy.Integer, int)): + code.writeline(f"{tree.prefix}numel = {int(simplified_tree_numel)}") + + if tree.prefix == "r" and self.persistent_reduction: + val = self._get_persistent_RBLOCK(tree.numel) + code.writeline(f"RBLOCK: tl.constexpr = {val}") + + if tree.prefix == "x" and self.no_x_dim: + code.writeline("XBLOCK: tl.constexpr = 1") + + def _get_grid_fn(self): + return "grid" + + def add_numel_to_call_args_and_grid(self, name, call_args, arg_types, grid): + # TODO(jansel): if there are constants, we shouldn't bother passing them as args + for tree in self.range_trees: + if isinstance(tree.numel, (sympy.Integer, sympy.Symbol)): + expr = tree.numel + else: + expr = V.graph.wrapper_code.generate_numel_expr(name, tree) + + if tree.prefix != "r" or self.inside_reduction: + call_args.append(expr) + arg_types.append(type(expr)) + if tree.grid_dim is not None: + grid.append(expr) + + def call_kernel(self, name: str, node: Optional[IRNode] = None): + wrapper = V.graph.wrapper_code + wrapper.write_triton_header_once() + _, call_args, _, arg_types = self.args.python_argdefs() + grid: List[Any] = [] + self.add_numel_to_call_args_and_grid(name, call_args, arg_types, grid) + current_device = V.graph.scheduler.get_current_device_or_throw() + + if self.args.workspace_arg is not None: + ws = self.args.workspace_arg + wrapper.generate_workspace_allocation( + ws.nbytes, current_device, ws.zero_fill + ) + + grid = wrapper.generate_default_grid(name, grid) + wrapper.generate_kernel_call( + name, + call_args, + grid, + current_device.index, + cuda=True, + triton=True, + arg_types=arg_types, + grid_fn=self._get_grid_fn(), + triton_meta=self.triton_meta, + ) + + if self.args.workspace_arg is not None: + wrapper.writeline(wrapper.make_free_by_names(["workspace"])) + + def codegen_nan_check(self): + wrapper = V.graph.wrapper_code + _, call_args, arg_signatures, _ = self.args.python_argdefs() + for arg, arg_signature in zip(call_args, arg_signatures): + if isinstance(arg_signature, TensorArg): + if V.graph.cpp_wrapper: + if config.abi_compatible: + wrapper.writeline( + f'AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_check_inf_and_nan("{arg}", {arg}));' + ) + else: + wrapper.writeline(f'assert_inf_and_nan("{arg}", {arg});') + else: + line = f"assert not {arg}.isnan().any().item()" + wrapper.writeline(line) + line = f"assert not {arg}.isinf().any().item()" + wrapper.writeline(line) + + def create_cse_var(self, *args, **kwargs): + return TritonCSEVariable(*args, **kwargs) + + def codegen_iteration_ranges_entry(self, entry: IterationRangesEntry): + line = f"{entry.name} = {self.kexpr(self.rename_indexing(entry.expr))}" + if entry.root.is_loop: + self.indexing_code.writeline(line) + else: + # lift non-reduction stores outside loop + self.body.writeline(line) + + def iteration_ranges_ranges_code(self, entry): + assert entry.tensor_dim is not None + size = self.indexing_size_str(entry.tensor_dim) + index_dtype = self.index_dtype + convert = f".to({index_dtype})" if index_dtype != "tl.int32" else "" + return f"tl.arange(0, {entry.prefix.upper()}BLOCK){size}{convert}" + + def iteration_ranges_scalar_code(self, entry, value): + index_dtype = self.index_dtype + ndim = self.triton_tensor_ndim() + size = [1] * ndim + return f"tl.full({size}, {value}, {index_dtype})" + + def iteration_ranges_get_pid(self, entry): + assert entry.grid_dim is not None + key = f"tl.program_id({entry.grid_dim})" + # y_grid has a limit, so express it in terms of y and z in case of overflow. + # z grid is only exercised when max_tiles == 3 (off by default). + if ( + entry.grid_dim == 1 + and not entry.has_zdim + and not V.graph.sizevars.statically_known_leq(entry.numel, get_max_y_grid()) + ): + # For ynumel larger than max_ygrid, we need to use zdim. + # For each z dimension, there are tl.num_programs(1) yblocks which is passed by grad(x,y,z). + # So, we need to add tl.program_id(z) * tl.num_programs(y) *YBLOCK to get the correct yoffset. + key = f"({key} + tl.program_id({entry.grid_dim + 1}) * tl.num_programs({entry.grid_dim}))" + pid = entry.pid_cache.get(key, key) + if self.index_dtype != "tl.int32": + return f"{pid}.to({self.index_dtype})" + return pid + + def _has_constant_mask(self, tree: IterationRangesRoot): + if not self.optimize_mask: + return False + if V.graph.sizevars.statically_known_equals(tree.numel, 1): # type: ignore[arg-type] + return True + # Masks are superfluous if numel is a multiple of BLOCK + # (We use the fact that BLOCK is required by triton to be a power of 2) + if tree.prefix == "r" and self.persistent_reduction: + max_block = self._get_persistent_RBLOCK(tree.numel) + elif tree.prefix == "x" and self.no_x_dim: + max_block = 1 + else: + if tree.prefix.upper() not in TRITON_MAX_BLOCK: + return False + max_block = TRITON_MAX_BLOCK[tree.prefix.upper()] + + # Optional optimization: if block divides numel exactly, we will + # never need to do a masked load to handle stragglers at the end. + # It's faster to avoid masking at all. But it is sound to always + # mask. + return V.graph.sizevars.statically_known_multiple_of(tree.numel, max_block) + + def filter_masks(self, mask_vars): + for tree in self.range_trees: + if self._has_constant_mask(tree): + mask_vars.discard(f"{tree.prefix}mask") + + def iteration_ranges_codegen_header(self, entry, code): + x = entry.prefix + if entry.is_loop: + code.writeline(f"{entry.name} = {x}offset + {x}base") + elif entry.grid_dim is None: + # no need to "{x}offset = " + code.writeline(f"{entry.name} = {self.iteration_ranges_ranges_code(entry)}") + code.writeline(f"{x}offset = 0") + else: + if entry.tensor_dim is not None: + line = f"{x}offset + {self.iteration_ranges_ranges_code(entry)}" + else: + line = self.iteration_ranges_scalar_code(entry, f"{x}offset") + code.writelines( + [ + f"{x}offset = {self.iteration_ranges_get_pid(entry)} * {x.upper()}BLOCK", + f"{entry.name} = {line}", + ] + ) + + if self._has_constant_mask(entry): + sizes = self.dense_size_str() + code.writeline(f"{x}mask = tl.full({sizes}, True, tl.int1)") + else: + code.writeline(f"{x}mask = {entry.name} < {x}numel") + + +class TritonScheduling(SIMDScheduling): + int32_type = "tl.int32" + int64_type = "tl.int64" + kernel_type = TritonKernel + backend_features = dict.fromkeys( # dict for deterministic order + [ + BackendFeature.FOREACH, + BackendFeature.BUCKETIZE, + BackendFeature.INPLACE_BUFFERS, + BackendFeature.MASKED_SCATTER_WITH_INDEX, + BackendFeature.SCAN, + BackendFeature.TRITON_TEMPLATES, + ] + ) + if torch.version.hip is None: + backend_features.update( + dict.fromkeys( + [ + # TODO: Move this above when ROCm triton adds support for multiple inputs + BackendFeature.TUPLE_REDUCTION, + BackendFeature.SORT, + ] + ) + ) + + @classmethod + def get_backend_features(cls, device: torch.device): + return cls.backend_features + + def codegen_comment(self, node_schedule): + wrapper = V.graph.wrapper_code + origins, detailed_origins = get_kernel_metadata(node_schedule, wrapper) + if origins: + wrapper.writeline(origins) + + if config.debug_fusion: + from torch._inductor.scheduler import ( + BaseSchedulerNode, + ForeachKernelSchedulerNode, + ) + + if not any( + isinstance(n, ForeachKernelSchedulerNode) for n in node_schedule + ): + # We probably should look what are the nodes inside a foreach + # schedule node + node_names = [ + n.get_name() + for n in node_schedule + if isinstance(n, BaseSchedulerNode) + ] + wrapper.writeline( + f"{wrapper.comment} Fused node name list: {', '.join(node_names)}" + ) + + def define_kernel(self, src_code, node_schedule, kernel): + wrapper = V.graph.wrapper_code + if src_code in wrapper.src_to_kernel: + kernel_name = wrapper.src_to_kernel[src_code] + else: + fused_name = ( + get_fused_kernel_name(node_schedule, config.triton.descriptive_names) + if config.triton.descriptive_names + else "" + ) + kernel_category = get_kernel_category_by_source_code(src_code)[:3] + kernel_name = "_".join( + ["triton", kernel_category, fused_name, wrapper.next_kernel_suffix()] + ) + # use the original src_code as the key + wrapper.src_to_kernel[src_code] = kernel_name + subs_name = kernel_name if config.triton.unique_kernel_names else "triton_" + + # DESCRIPTIVE_NAME is used for profiling purposes; it shows the full kernel name + # even when unique_kernel_names is turned off. Meanwhile, KERNEL_NAME is sometimes set + # to "triton_" to maximize caching opportunities (when unique_kernel_names = False). + src_code = src_code.replace(str(Placeholder.DESCRIPTIVE_NAME), kernel_name) + src_code = src_code.replace(str(Placeholder.KERNEL_NAME), subs_name) + + # TODO(voz): Ostensibly, we should not need this. But there are cases where C++ codegen does + # not use BracesBuffer, so we have no good indicator of a C++ buffer atm. + src_code = src_code.replace("#pragma CMT", "#") + + basename, _, kernel_path = get_path(code_hash(src_code.strip()), "py") + + compile_wrapper = IndentedBuffer() + compile_wrapper.writeline(f"async_compile.triton({subs_name!r}, '''") + compile_wrapper.splice(src_code, strip=True) + current_device = V.graph.scheduler.get_current_device_or_throw() + compile_wrapper.writeline(f"''', device_str='{current_device.type}')") + + metadata_comment = f"# kernel path: {kernel_path}" + origins, detailed_origins = get_kernel_metadata(node_schedule, wrapper) + metadata_comment += "\n" + origins + "\n" + detailed_origins + wrapper.define_kernel( + kernel_name, compile_wrapper.getvalue(), metadata_comment + ) + + # log kernel metadata for offline analysis. + # E.g. one can find all unaligned inner reduction and check if + # padding helps with the perf kernel by kernel. + if is_metric_table_enabled("kernel_metadata"): + log_kernel_metadata(kernel_name, kernel_path, src_code) + + return kernel_name + + def benchmark_fused_nodes(self, nodes): + with preserve_rng_state(): + src_code = self.generate_kernel_code_from_nodes( + nodes, benchmark_kernel=True + ) + mod = PyCodeCache.load(src_code) + + def cache_file_path(): + assert mod.__file__ is not None + return os.path.splitext(mod.__file__)[0] + ".kernel_perf" + + def load_cache(): + path = cache_file_path() + if os.path.exists(path): + with open(path) as fd: + return float(fd.read()) + return None + + def store_cache(): + path = cache_file_path() + with open(path, "w") as fd: + fd.write(str(ms)) + + log.debug( + "kernel src code for %s written to: %s", + {n.get_name() for n in nodes}, + mod.__file__, + ) + ms = load_cache() + if ms is not None: + return ms, mod.__file__ + + args = mod.get_args() + call = mod.call + wrapped_jit_function = mod.triton_ + + # call once to trigger the compilation + try: + call(wrapped_jit_function.clone_args(*args)[0]) + except Exception as e: + log.debug( + "Exception (%s) in compiling fused nodes %s", + e, + {n.get_name() for n in nodes}, + ) + ms = float("inf") + store_cache() + return ms, mod.__file__ + + launchers = wrapped_jit_function.launchers + assert len(launchers) == 1 + if launchers[0].n_spills > 0: + # skip benchmarking the kernel if there are register spills + ms = float("inf") + else: + # We have to clone the inplace updated arguments to avoid earlier calls + # generating out of range indices for later calls. + ms = benchmarker.benchmark_gpu( + lambda: call(wrapped_jit_function.clone_args(*args)[0]) + ) + + # overhead of cloning args gives bias for fusing the kernel + # in the case of mutating/in-placeable second fusion + # TODO - would be better as a hook in triton do_bench that reset + # the input values between benchmarking + ms = ms - benchmarker.benchmark_gpu( + lambda: wrapped_jit_function.clone_args(*args) + ) + + log.debug( + "The fused kernel for %s took %.3f ms to run", + {n.get_name() for n in nodes}, + ms, + ) + store_cache() + return ms, mod.__file__ + + def benchmark_combo_kernel(self, node_list): + def cache_file_path(): + assert mod.__file__ is not None + return os.path.splitext(mod.__file__)[0] + ".kernel_perf" + + def load_cache(): + path = cache_file_path() + if os.path.exists(path): + with open(path) as fd: + return tuple(float(e) for e in fd.read().split()) + return (None, None) + + def store_cache(): + path = cache_file_path() + with open(path, "w") as fd: + fd.write(str(ms) + " " + str(ms_clone)) + + total_ms, file_list = 0, [] + total_clone_ms = 0 + removed_buffers_orig = V.graph.removed_buffers + V.graph.removed_buffers = OrderedSet(removed_buffers_orig) + inplaced_to_remove_orig = V.graph.inplaced_to_remove + V.graph.inplaced_to_remove = OrderedSet(inplaced_to_remove_orig) + enable_autotune = config.combo_kernels_autotune > 0 + mixed_sizes = config.combo_kernel_allow_mixed_sizes > 0 + kernel_code_list = self.generate_combo_kernel_code( + subkernel_nodes=node_list, + custom_part_algorithm=True, + enable_autotune=enable_autotune, + mixed_sizes=mixed_sizes, + only_gen_src_code=True, + ) + + for src_code, _, node_group in kernel_code_list: + fused_node_lists = [node.get_nodes() for node in node_group] + names = [n.get_name() for nodes in fused_node_lists for n in nodes] + + src_code = src_code.replace(str(Placeholder.KERNEL_NAME), "triton_") + mod = PyCodeCache.load(src_code) + + log.debug( + "kernel src code for %s written to: %s", + names, + mod.__file__, + ) + ms, ms_clone = load_cache() + if ms is not None: + total_ms += ms + total_clone_ms += ms_clone + file_list.append(mod.__file__) + continue + + args = mod.get_args() + call = mod.call + wrapped_jit_function = mod.triton_ + + # call once to trigger the compilation + call(wrapped_jit_function.clone_args(*args)[0]) + + launchers = wrapped_jit_function.launchers + assert len(launchers) == 1 + if launchers[0].n_spills > 0: + # skip benchmarking the kernel if there are register spills + ms = ms_clone = float("inf") + else: + # We have to clone the inplace updated arguments to avoid earlier calls + # generating out of range indices for later calls. + ms = benchmarker.benchmark_gpu( + lambda: call(wrapped_jit_function.clone_args(*args)[0]) + ) + ms_clone = benchmarker.benchmark_gpu( + lambda: wrapped_jit_function.clone_args(*args)[0] + ) + + log.debug( + "The fused kernel for %s took %.3f ms to run, %.3f ms to clone inputs", + {n.get_name() for n in node_group}, + ms, + ms_clone, + ) + store_cache() + total_ms += ms + total_clone_ms += ms_clone + file_list.append(mod.__file__) + V.graph.removed_buffers = removed_buffers_orig + V.graph.inplaced_to_remove = inplaced_to_remove_orig + return total_ms, total_clone_ms, file_list diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/triton_combo_kernel.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/triton_combo_kernel.py new file mode 100644 index 0000000000000000000000000000000000000000..abfd1dc76c698e044cc1e417b23ad47253328829 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/triton_combo_kernel.py @@ -0,0 +1,1123 @@ +import itertools +import logging +import textwrap +from collections import defaultdict +from dataclasses import dataclass +from typing import ( + Any, + Callable, + cast, + Dict, + Iterable, + List, + Optional, + Tuple, + Type, + Union, +) + +from sympy import Integer, Symbol + +from torch.utils._ordered_set import OrderedSet + +from .. import config, metrics +from ..runtime.hints import DeviceProperties, ReductionHint +from ..runtime.runtime_utils import next_power_of_2 +from ..runtime.triton_heuristics import grid_combo_kernels +from ..scheduler import BaseSchedulerNode +from ..utils import Placeholder +from ..virtualized import V +from .common import ( + DeferredLine, + IndentedBuffer, + Kernel, + PythonPrinter, + SizeArg, + WorkspaceArg, +) +from .simd import SIMDScheduling +from .triton import gen_common_triton_imports, TritonKernel +from .triton_utils import config_of, signature_to_meta + + +log = logging.getLogger(__name__) +pexpr = PythonPrinter().doprint +LARGE_NUMELS = 512e5 +BLOCK_UTILIZATION = 0.8 + + +def _default_custom_combo_kernel_horizontal_partition( + nodes: List[BaseSchedulerNode], + triton_scheduling: SIMDScheduling, + kernel_map: Dict[BaseSchedulerNode, TritonKernel], + node_info_map: Dict[BaseSchedulerNode, Tuple[Any, Any, Any, Any]], +) -> List[List[BaseSchedulerNode]]: + """Horizontally partition the given list of nodes into a list of list of nodes where each sublist + represents a partion. Nodes in different partitions are implemented in different combo kernels. + Nodes in the same partition are likely to be implemented + in the same combo kernel, but subject to subsequent restrictions like CUDA limits for number of args. + + Input arguments: + nodes: a list of fused scheduler nodes to partition. + triton_scheduling: TritonScheduling instance. + kernel_map: a map from node to its kernel. + node_info_map: a map from node to (node_schedule, tiled_groups, numel, rnumel). + Output: + a list of list of nodes with each sublist representing a partition. + + The default algorithm is to partition nodes based on the following rules: + 1) nodes with the same number of block dimensions are grouped together. + 2) large pointwise nodes (numels greater than LARGE_NUMELS) are separated from other nodes. + 3) large reduce nodes are separated from other nodes. + """ + + assert len(nodes) >= 1 + + # first partition nodes based on number of block dimensions + tilings = [node_info_map[n][1] for n in nodes] + + max_dims = max(len(t) for t in tilings) + nodes_per_ndim = [] + for i in range(2, max_dims + 1): + group_per_dim = [n for n, t in zip(nodes, tilings) if len(t) == i] + reduction = [ + n + for n in group_per_dim + if kernel_map[n].inside_reduction + and not (kernel_map[n].persistent_reduction and kernel_map[n].no_x_dim) + ] + not_reduction = [n for n in group_per_dim if n not in reduction] + # rnumel > 2048 usually has long execution time + # BaseSchedulerNode.group[-1][-1] is rnumel for reduction nodes + long_reduction = [ + n for n in reduction if V.graph.sizevars.size_hint(n.group[-1][-1]) > 2048 + ] + short_reduction = [n for n in reduction if n not in long_reduction] + if long_reduction: + log.warning( + "ComboKernels: %d long reduction nodes are separated", + len(long_reduction), + ) + large_pointwise = [ + n + for n in not_reduction + if not kernel_map[n].inside_reduction + and len(kernel_map[n].numels) == 2 + and V.graph.sizevars.size_hint(kernel_map[n].numels[0]) > LARGE_NUMELS + ] + if large_pointwise: + # TODO benchmark the performance when large pointwise nodes combining with others + log.warning( + "ComboKernels: %d large pointwise nodes are separated", + len(large_pointwise), + ) + not_reduction = [n for n in not_reduction if n not in large_pointwise] + for node in large_pointwise: + nodes_per_ndim.append([node]) + + for g in (not_reduction, short_reduction, long_reduction): + if g: + nodes_per_ndim.append(g) + + assert sum(len(p) for p in nodes_per_ndim) == len(nodes) + return nodes_per_ndim + + +_custom_combo_kernel_horizontal_partition_algorithm: Callable[ + [ + List[BaseSchedulerNode], + SIMDScheduling, + Dict[BaseSchedulerNode, TritonKernel], + Dict[BaseSchedulerNode, Tuple[Any, Any, Any, Any]], + ], + List[List[BaseSchedulerNode]], +] = _default_custom_combo_kernel_horizontal_partition + + +def set_custom_combo_kernel_horizontal_partition( + algorithm: Callable[ + [ + List[BaseSchedulerNode], + SIMDScheduling, + Dict[BaseSchedulerNode, TritonKernel], + Dict[BaseSchedulerNode, Tuple[Any, Any, Any, Any]], + ], + List[List[BaseSchedulerNode]], + ] +) -> None: + """Sets the algorithm used to partition nodes into horizontal partitions. Nodes in different partitions + are implemented in different combo kernels. Nodes in the same partition are likely to be implemented + in the same combo kernel, but subject to subsequent restricts like CUDA limits for number of args. + + The algorithm should take a list of nodes and return a list of list of nodes. + + The default algorithm is to partition nodes based on number of block dimensions. + """ + global _custom_combo_kernel_horizontal_partition_algorithm + _custom_combo_kernel_horizontal_partition_algorithm = algorithm + + +@dataclass +class PartitionState: + partitions: List[List[BaseSchedulerNode]] + cur_partition: List[BaseSchedulerNode] + cur_count: int + + def finalize(self) -> None: + if self.cur_partition: + self.partitions.append(self.cur_partition) + + +class ComboKernel(Kernel): + MAX_NUM_ARGS = 250 # number where I would no longer get triton errors + + @staticmethod + def _update_partition( + partition_state: PartitionState, + node_rw_count: int, + node_info: BaseSchedulerNode, + ) -> None: + if partition_state.cur_count + node_rw_count > ComboKernel.MAX_NUM_ARGS: + partition_state.partitions.append(partition_state.cur_partition) + partition_state.cur_partition = [node_info] + partition_state.cur_count = node_rw_count + else: + partition_state.cur_count += node_rw_count + partition_state.cur_partition.append(node_info) + + @staticmethod + def _base_horizontal_partition( + subkernel_nodes: List[BaseSchedulerNode], + triton_scheduling: SIMDScheduling, + node_info_map: Dict[BaseSchedulerNode, Tuple[Any, Any, Any, Any]], + custom_algorithm: bool, + ) -> List[List[BaseSchedulerNode]]: + """Generates a list of lists of node info tuples which consist of (fused_nodes, tiling, numel, rnumel) + for each subkernel node where each sublist is guaranteed to not exceed CUDA limits for number of args + (read/writes) and to have the same 2D or 1D blocking strategy.""" + # TODO support combination of kernels with different block dimensions + assert len(subkernel_nodes) >= 1 + mixed_sizes = config.combo_kernel_allow_mixed_sizes > 1 or ( + config.combo_kernel_allow_mixed_sizes == 1 and custom_algorithm + ) + + ndim_to_partition_state: Dict[int, PartitionState] = defaultdict( + lambda: PartitionState([], [], 0) + ) + yelem_to_partition_state: Dict[int, PartitionState] = defaultdict( + lambda: PartitionState([], [], 0) + ) + + for node in subkernel_nodes: + node_schedule, tiled_groups, numel, rnumel = node_info_map[node] + node_info = node + + read_writes = node.read_writes + read_write_count = len(read_writes.reads) + len(read_writes.writes) + + ndim = len(tiled_groups) + assert ndim >= 2, f"Combokernel not support tile {tiled_groups}" + if not mixed_sizes and ndim == 3: + y_elem = tiled_groups[0] + partition_state = yelem_to_partition_state[y_elem] + ComboKernel._update_partition( + partition_state, read_write_count, node_info + ) + else: + assert mixed_sizes or ndim <= 3, f"No mixed sizes: tile {tiled_groups}" + partition_state = ndim_to_partition_state[ndim] + ComboKernel._update_partition( + partition_state, read_write_count, node_info + ) + + all_partitions = [] + for partition_state in ndim_to_partition_state.values(): + partition_state.finalize() + all_partitions.extend(partition_state.partitions) + for partition_state in yelem_to_partition_state.values(): + partition_state.finalize() + all_partitions.extend(partition_state.partitions) + + return all_partitions + + @staticmethod + def horizontal_partition( + nodes: List[BaseSchedulerNode], + triton_scheduling: SIMDScheduling, + kernel_map: Dict[BaseSchedulerNode, TritonKernel], + node_info_map: Dict[BaseSchedulerNode, Tuple[Any, Any, Any, Any]], + custom_algorithm: bool = False, + ) -> List[List[BaseSchedulerNode]]: + """Generates a list of lists of node info tuples which consist of (fused_nodes, tiling, numel, rnum) + for each subkernel node where each sublist forms a ComboKernel. It horizontally partitions nodes into + sublists in the following way: + 1) call _custom_combo_kernel_horizontal_partition_algorithm() if custom_algorithm is True + 2) then, call _base_horizontal_partition() to partition nodes into sublists, each sublist is + guaranteed to not exceed CUDA limits for number of args (read/writes) and to have the same + 2D or 1D blocking strategy. + """ + if custom_algorithm: + raw_partitions = _custom_combo_kernel_horizontal_partition_algorithm( + nodes, triton_scheduling, kernel_map, node_info_map + ) + else: + raw_partitions = [nodes] + + """Generates a list of lists of node info tuples which consist of (fused_nodes, tiling, numel, rnumel) + for each subkernel node where each sublist is guaranteed to not exceed CUDA limits for number of args + (read/writes) and to have the same 2D or 1D blocking strategy.""" + all_partitions = [] + for raw_partition in raw_partitions: + all_partitions.extend( + ComboKernel._base_horizontal_partition( + raw_partition, triton_scheduling, node_info_map, custom_algorithm + ) + ) + return all_partitions + + class SequentialDispatch: + """ + The dispatcher which dispatches the subkernels in a sequential manner: + the blocks are first dispatched to the 1st subkernel (until it is filled), + then to the 2nd subkernel, and so on. + The class defines the methods specific to the dispatch algorithm. + Methods: + codegen_pid_range(...): codegen the pid range for each subkernel. + grid(...): codegen the grid size for launching the combo kernel. + """ + + @classmethod + def codegen_pid_range( + cls, kernel: "ComboKernel", num: int, code: IndentedBuffer + ) -> None: + if num == 0: + cls._calculate_xblocks(kernel, code) + code.splice(f"if pid < num_xblocks_{num}:") + with code.indent(): + code.splice("pid_offset = pid") + else: + code.splice(f"elif pid < num_xblocks_{num}:") + with code.indent(): + code.splice(f"pid_offset = pid - num_xblocks_{num-1}") + + @classmethod + def _calculate_xblocks( + cls, kernel: "ComboKernel", code: IndentedBuffer + ) -> None: + x_numels_list = kernel.x_numels_list + for i in range(len(x_numels_list)): + xnumels, no_x_dim = ( + (x_numels_list[i], False) + if isinstance(x_numels_list[i], str) + and cast(str, x_numels_list[i])[0] != "-" + or ( + isinstance(x_numels_list[i], int) + and cast(int, x_numels_list[i]) > 0 + ) + else (kernel.min_x_blocks_list[i], True) + ) + xblock_str = ( + f"tl.cdiv({xnumels}, XBLOCK)" if not no_x_dim else f"{xnumels}" + ) + if i == 0: + code.splice(f"num_xblocks_{i} = {xblock_str}") + else: + code.splice(f"num_xblocks_{i} = num_xblocks_{i-1} + {xblock_str}") + + @classmethod + def grid( + cls, + sub_kernel_numels: List[List[int]], + x_blocks_list: List[Union[str, int]], + dynamic_shape: bool, + ) -> Tuple[Any, ...]: + xnumel = list(x_blocks_list) + ynumel: Any = [e[-2] if len(e) > 1 else None for e in sub_kernel_numels] + znumel: Any = [e[-3] if len(e) > 2 else None for e in sub_kernel_numels] + + if dynamic_shape: + ynumel = None if None in ynumel else ynumel + znumel = None if None in znumel else znumel + else: + # TODO: improve 1d/2d mixed cases + ynumel = ( + None + if any(e is None for e in cast(List[Any], ynumel)) + else max(cast(Iterable[int], ynumel)) + ) + znumel = ( + None + if any(e is None for e in cast(List[Any], znumel)) + else max(cast(Iterable[int], znumel)) + ) + + numels = ( + (xnumel,) + if not ynumel + else (ynumel, xnumel) + if not znumel + else (znumel, ynumel, xnumel) + ) + return numels + + class RoundRobinDispatch: + """ + The dispatcher which dispatches the subkernels in a round robin manner: + the blocks are interleavedly dispatched to each subkernel to execute them + in parallel. + The class defines the methods specific to the dispatch algorithm. + Methods: + codegen_pid_range(...): codegen the pid range for each subkernel. + grid(...): codegen the grid size for launching the combo kernel. + """ + + @classmethod + def codegen_pid_range( + cls, kernel: "ComboKernel", num: int, code: IndentedBuffer + ) -> None: + num_kernels = len(kernel.sub_kernels) + if num == 0: + cond = "if" + else: + cond = "elif" + code.splice(f"{cond} pid % {num_kernels} == {num}:") + with code.indent(): + code.splice(f"pid_offset = pid // {num_kernels}") + + @classmethod + def grid( + cls, + sub_kernel_numels: List[List[int]], + x_blocks_list: List[Union[str, int]], + dynamic_shape: bool, + ) -> Tuple[Any, ...]: + xnumel = x_blocks_list + # set no_x_dim xnumels to 0 + xnumel_x_dim = [max(e, 0) for e in xnumel] + ynumel = [e[-2] if len(e) > 1 else None for e in sub_kernel_numels] + znumel = [e[-3] if len(e) > 2 else None for e in sub_kernel_numels] + + # TODO: support 1d/2d mixed cases + xnumel = ( + None + if any(e is None for e in xnumel) + else xnumel + if dynamic_shape + else max(xnumel_x_dim) # type: ignore[type-var, arg-type] + ) + ynumel = ( + None + if any(e is None for e in ynumel) + else ynumel + if dynamic_shape + else max(ynumel) # type: ignore[type-var, arg-type] + ) + znumel = ( + None + if any(e is None for e in znumel) + else znumel + if dynamic_shape + else max(znumel) # type: ignore[type-var, arg-type] + ) + + numels = ( + (xnumel,) + if not ynumel + else (ynumel, xnumel) + if not znumel + else (znumel, ynumel, xnumel) + ) + return numels + + def __init__( + self, enable_autotune: bool = False, mixed_sizes: bool = False + ) -> None: + super().__init__() + self.sub_kernels: List[TritonKernel] = [] + self.iter_vars_count = itertools.count() + self.grids: List[List[int]] = [] + self.min_x_blocks_list: List[Union[int, str]] = [] + self.x_numels_list: List[Union[int, str]] = [] + self.enable_autotune = enable_autotune + self.mixed_sizes = mixed_sizes + self.dispatch_class: Optional[ + Union[ + Type[ComboKernel.SequentialDispatch], + Type[ComboKernel.RoundRobinDispatch], + ] + ] = None + self.block_args: List[str] = [] + # there following are used when autotuning is disabled + self.block_size_1d = 1024 # Try tuning this value + self.block_size_2d = 32 + self.num_warps = 8 + self.block_size_reduce = 256 + self.dynamic_shape_args: List[str] = [] + + def create_sub_kernel(self, triton_kernel: TritonKernel) -> TritonKernel: + sub_kernel = triton_kernel + metrics.generated_kernel_count -= 1 + sub_kernel.args = self.args + sub_kernel.iter_vars_count = self.iter_vars_count + sub_kernel.cse.iter_buffer_ids = self.cse.iter_buffer_ids + self.sub_kernels.append(sub_kernel) + return sub_kernel + + @staticmethod + def create_triton_kernel( + *groups: Any, + index_dtype: str, + mutations: OrderedSet[str], + reduction_hint: ReductionHint, + optimize_mask: bool, + ) -> TritonKernel: + """ + Only allow optimize_mask=True when 1) sequential dispatch is used, + 2) numels except x dimension are the same for each sub kernel. + """ + return TritonKernel( + *groups, + index_dtype=index_dtype, + mutations=mutations, + pid_cache={"tl.program_id(0)": "pid_offset"}, + reduction_hint=reduction_hint, + optimize_mask=optimize_mask, + ) + + def codegen_static_numels_sub_kernel( + self, code: IndentedBuffer, sub_kernel: TritonKernel, num: int + ) -> List[str]: + """ + We get a small speedup from hard coding numels if they are static. + + This code stomps on the passed-in values by writing an constant to the top of the kernel. + + In a kernel like: + def KERNEL_NAME(in_ptr0, in_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr): + + We would add + xnumel = 4096 + rnumel = 768 + + After the signature, before the kernel code, if we decided to make these static. As its hardcoded, it becomes + a better signal to triton on how to unroll and do some static indexing. So, it's not so much that downstream + knows that its a static numel, as that you just plop a constant into the kernel. + """ + grid = [] + uniquify_block_sizes = [] + for tree in sub_kernel.range_trees: + simplified_tree_numel = V.graph.sizevars.simplify(tree.numel) + if isinstance(simplified_tree_numel, (Integer, int)): + code.writeline(f"{tree.prefix}numel = {int(simplified_tree_numel)}") + else: + assert f"{tree.prefix}numel_{num}" in self.dynamic_shape_args + uniquify_block_sizes.append(f"{tree.prefix}numel") + + if tree.prefix != "r": + if isinstance(simplified_tree_numel, (Integer, int)): + grid.append(int(simplified_tree_numel)) + else: + grid.append(f"{tree.prefix}numel_{num}") + + if tree.prefix == "r" and sub_kernel.persistent_reduction: + if isinstance(simplified_tree_numel, (Integer, int)): + val = int(simplified_tree_numel) + else: + raise RuntimeError( + "Dynamic shape on reduction dimension is not supported" + ) + val = next_power_of_2(val) + code.writeline(f"RBLOCK_{num}: tl.constexpr = {val}") + uniquify_block_sizes.append("RBLOCK") + + if tree.prefix == "x" and sub_kernel.no_x_dim: + code.writeline(f"XBLOCK_{num}: tl.constexpr = 1") + uniquify_block_sizes.append("XBLOCK") + self.grids.append(grid) + return uniquify_block_sizes + + def min_x_blocks_sub_kernel(self, sub_kernel: TritonKernel, num: int) -> None: + """ + Kernels with no_x_dim being true has no tunable XBLOCK. They have a fixed number of X blocks. + Grid calculation needs to make sure that they are assigned with enough number of blocks. + """ + min_x_blocks: Union[int, str] = 0 + x_numels: Union[int, str] = 0 + for tree in sub_kernel.range_trees: + simplified_tree_numel = V.graph.sizevars.simplify(tree.numel) + if tree.prefix == "x": + if isinstance(simplified_tree_numel, (Integer, int)): + x_numels = int(simplified_tree_numel) + else: + x_numels = f"{tree.prefix}numel_{num}" + if sub_kernel.no_x_dim: + min_x_blocks = x_numels + x_numels = ( + -min_x_blocks + if isinstance(x_numels, int) + else "-" + cast(str, x_numels) + ) + else: + if isinstance(simplified_tree_numel, (Integer, int)): + x_numels = int(simplified_tree_numel) + else: + x_numels = f"{tree.prefix}numel_{num}" + self.min_x_blocks_list.append(min_x_blocks) + self.x_numels_list.append(x_numels) + + def select_heuristics(self, sub_kernel: TritonKernel) -> Tuple[str, List[int]]: + size_hints = [ + next_power_of_2(V.graph.sizevars.size_hint(numel)) + for numel in sub_kernel.numels + ] + if sub_kernel.persistent_reduction: + assert sub_kernel.inside_reduction + heuristics = "persistent_reduction" + elif sub_kernel.inside_reduction: + heuristics = "reduction" + else: + size_hints.pop() + heuristics = "pointwise" + return heuristics, size_hints + + def select_combo_heuristics( + self, heuristics_list: List[str], size_hints_list: List[List[int]] + ) -> Tuple[str, List[int], TritonKernel]: + if not self.enable_autotune: + return "foreach", size_hints_list[0], self.sub_kernels[0] + if "reduction" in heuristics_list: + i, _ = max( + enumerate(size_hints_list), + key=lambda x: x[1][0] if heuristics_list[x[0]] == "reduction" else 0, + ) + return heuristics_list[i], size_hints_list[i], self.sub_kernels[i] + elif "pointwise" in heuristics_list: + i, _ = max( + enumerate(size_hints_list), + key=lambda x: x[1][0] if heuristics_list[x[0]] == "pointwise" else 0, + ) + # modify size_hint to avoid oom check fail (may be a false alarm) + num_pointwise = len([e for e in heuristics_list if e == "pointwise"]) + num_reduction = len([e for e in heuristics_list if e == "reduction"]) + num_persistent_reduction = len( + [e for e in heuristics_list if e == "persistent_reduction"] + ) + assert ( + num_reduction == 0 + ), "combining pointwise and reduction are not supported yet." + heuristics = ( + "pointwise_with_reduction" + if num_persistent_reduction > 0 + else "pointwise" + ) + if len(heuristics_list) - num_pointwise >= 4: + size_hints = size_hints_list[i] + size_hints[0] = min(128, size_hints[0]) + return heuristics, size_hints_list[i], self.sub_kernels[i] + else: + return heuristics_list[0], size_hints_list[0], self.sub_kernels[0] + + def get_mutated_args_sub_kernels(self) -> List[str]: + mutated_args = set() + for sub_kernel in self.sub_kernels: + for mutation in sub_kernel.mutations: + if mutation in sub_kernel.args.input_buffers: + mutated_args.add(sub_kernel.args.input_buffers[mutation]) + if ( + mutation in sub_kernel.args.inplace_buffers + and mutation not in V.graph.removed_buffers + and mutation not in sub_kernel.removed_buffers + ): + mutated_args.add( + sub_kernel.args.inplace_buffers[mutation].inner_name + ) + if mutation in sub_kernel.args.output_buffers: + mutated_args.add(sub_kernel.args.output_buffers[mutation]) + return sorted(mutated_args) + + def select_dispatch_strategy(self) -> None: + if self.dispatch_class is not None: + return + # mixed_sizes is used for optimize_mask, so it only allows sequential dispatch + # Not mixed sizes on y dim technically is ok to use round robin as wells. + if not self.mixed_sizes or any(isinstance(e, str) for e in self.x_numels_list): + # str in min_x_blocks_list means a dynamic shape + self.dispatch_class = ComboKernel.SequentialDispatch + return + # A negative x_blocks_list element means the kernel is not tunable, + # i.e., no_x_dim = True + x_numels_list = [abs(cast(int, e)) for e in self.x_numels_list] + total = max(x_numels_list) * len(x_numels_list) + needed = sum(x_numels_list) + if needed / total > BLOCK_UTILIZATION: + # Introduced overhead (masked blocks) is less than 20% + self.dispatch_class = ComboKernel.RoundRobinDispatch + else: + self.dispatch_class = ComboKernel.SequentialDispatch + + def jit_line( + self, + heuristics: str, + size_hints: List[int], + selected_kernel: TritonKernel, + pointwise_with_reduce: bool = False, + signature: Optional[List[Any]] = None, + ) -> str: + can_use_32bit = all(k.index_dtype == "tl.int32" for k in self.sub_kernels) + size_dtype = "tl.int32" if can_use_32bit else "tl.int64" + if signature is None: + _, _, signature, _ = self.args.python_argdefs() + for i, sub in enumerate(self.sub_kernels): + self.min_x_blocks_sub_kernel(sub, i) + self.select_dispatch_strategy() + triton_meta = { + "signature": signature_to_meta(signature, size_dtype=size_dtype), + "device": DeviceProperties.create( + V.graph.scheduler.get_current_device_or_throw() + ), + "constants": {}, + } + triton_meta["configs"] = [config_of(signature)] + mutated_args = self.get_mutated_args_sub_kernels() + inductor_meta = { + "kernel_name": str(Placeholder.DESCRIPTIVE_NAME), + "mutated_arg_names": mutated_args, + **TritonKernel.inductor_meta_common(), + } + + sub_kernel = selected_kernel + if heuristics == "foreach": + heuristics_line = f""" + @triton_heuristics.foreach( + num_warps={self.num_warps}, + triton_meta={triton_meta!r}, + inductor_meta={inductor_meta!r}, + ) + @triton.jit + """ + elif sub_kernel.inside_reduction: + reduction_hint = sub_kernel.reduction_hint + heuristics_line = f""" + @triton_heuristics.{heuristics}( + size_hints={size_hints!r}, + reduction_hint={reduction_hint}, + filename=__file__, + triton_meta={triton_meta!r}, + inductor_meta={inductor_meta!r} + ) + @triton.jit + """ + else: + tile_hint = "" + if len(size_hints) == 2: + tile_hint = "tile_hint=TileHint.SQUARE," + else: + tile_hint = "tile_hint=TileHint.DEFAULT," + heuristics_line = f""" + @triton_heuristics.{heuristics}( + size_hints={size_hints!r}, {tile_hint} + filename=__file__, + triton_meta={triton_meta!r}, + inductor_meta={inductor_meta!r} + ) + @triton.jit + """ + + return heuristics_line + + def codegen_blocks(self, code: IndentedBuffer) -> None: + for block in self.block_args: + assert block in [ + "XBLOCK", + "YBLOCK", + "RBLOCK", + ], f"{block} is not supported without autotuning" + if "YBLOCK" in self.block_args: + code.splice(f"XBLOCK: tl.constexpr = {self.block_size_2d}") + code.splice(f"YBLOCK: tl.constexpr = {self.block_size_2d}") + else: + code.splice(f"XBLOCK: tl.constexpr = {self.block_size_1d}") + if "RBLOCK" in self.block_args: + code.splice(f"RBLOCK: tl.constexpr = {self.block_size_reduce}") + + def add_blockd_to_args(self, argdefs: List[str]) -> List[str]: + block_args = {} + block_names = {} + for num, sub_kernel in enumerate(self.sub_kernels): + # TODO: we assume all sub_kernels have the same block size + for tree in sub_kernel.range_trees: + if tree.prefix == "r" and ( + not sub_kernel.inside_reduction or sub_kernel.persistent_reduction + ): + continue + if tree.prefix == "x" and sub_kernel.no_x_dim: + continue + block_args[f"{tree.prefix.upper()}BLOCK : tl.constexpr"] = tree.prefix + block_names[f"{tree.prefix.upper()}BLOCK"] = tree.prefix + if self.enable_autotune: + argdefs.extend(block_args) + self.block_args = list(block_names.keys()) + return argdefs + + def add_numel_to_args(self, argdefs: List[str], signature: List[Any]) -> List[str]: + for num, sub_kernel in enumerate(self.sub_kernels): + for tree in sub_kernel.active_range_trees(): + if not isinstance(tree.numel, (Integer, int)): + # only if it is a dynamic shape + sizearg = SizeArg(f"{tree.prefix}numel_{num}", tree.numel) + signature.append(sizearg) + argdefs.append(f"{tree.prefix}numel_{num}") + self.dynamic_shape_args.append(f"{tree.prefix}numel_{num}") + return argdefs + + def add_numel_to_call_args_and_grid( + self, name: str, call_args: List[Any], arg_types: List[Any], grid: List[Any] + ) -> None: + for num, sub_kernel in enumerate(self.sub_kernels): + for i, tree in enumerate(sub_kernel.range_trees): + numel_name = f"{tree.prefix}numel_{num}" + if numel_name not in self.dynamic_shape_args: + continue + if isinstance(tree.numel, (Integer, Symbol)): + expr = tree.numel + else: + expr = V.graph.wrapper_code.generate_numel_expr( + name, tree, suffix=str(num) + ) + if tree.prefix != "r": + assert isinstance( + grid[i][num], str + ), f"Grid {grid[i][num]} should be a dynamic shape." + numel_sign = grid[i][num][0] if grid[i][num][0] == "-" else "" + assert ( + grid[i][num] == numel_sign + numel_name + ), f"numel args mismatch: {grid[i][num]} vs {numel_name}" + grid[i][num] = -expr if numel_sign == "-" else expr + + if tree.prefix != "r" or sub_kernel.inside_reduction: + call_args.append(expr) + arg_types.append(type(expr)) + + def add_numel_to_call_args_and_grid_benchmark( + self, extra_args: List[Any], grid: Union[List[Any], Tuple[Any, ...]] + ) -> None: + for num, sub_kernel in enumerate(self.sub_kernels): + for i, tree in enumerate(sub_kernel.range_trees): + numel_name = f"{tree.prefix}numel_{num}" + if numel_name not in self.dynamic_shape_args: + continue + expr = V.graph.sizevars.size_hint(tree.numel) + if tree.prefix != "r": + assert isinstance( + grid[i][num], str + ), f"Grid {grid[i][num]} should be a dynamic shape." + numel_sign = grid[i][num][0] if grid[i][num][0] == "-" else "" + assert ( + grid[i][num] == numel_sign + numel_name + ), f"grid mismatch: {grid[i][num]} vs {numel_name}" + grid[i][num] = -expr if numel_sign == "-" else expr + if tree.prefix != "r" or sub_kernel.inside_reduction: + extra_args.append(expr) + + def codegen_kernel(self, name: Optional[str] = None) -> str: + # TODO: is it correct to use the first sub kernel's heuristics? + heuristics_list, size_hints_list = [], [] + for subkernel in self.sub_kernels: + h, s = self.select_heuristics(subkernel) + heuristics_list.append(h) + size_hints_list.append(s) + heuristics, size_hints, selected_kernel = self.select_combo_heuristics( + heuristics_list, size_hints_list + ) + pointwise_with_reduction, heuristics = ( + (True, "pointwise") + if heuristics == "pointwise_with_reduction" + else (False, heuristics) + ) + code = IndentedBuffer() + + code.splice(gen_common_triton_imports()) + if config.benchmark_combo_kernel: + code.splice(self.imports_for_benchmark_kernel()) + + argdefs, _, signature, _ = self.args.python_argdefs() + argdefs = self.add_numel_to_args(argdefs, signature) + argdefs = self.add_blockd_to_args(argdefs) + code.splice( + self.jit_line( + heuristics, + size_hints, + selected_kernel, + pointwise_with_reduce=pointwise_with_reduction, + signature=signature, + ) + ) + code.writeline( + f"def {name or str(Placeholder.KERNEL_NAME)}({', '.join(argdefs)}):" + ) + + with code.indent(): + code.splice("pid = tl.program_id(0)") + if not self.enable_autotune: + self.codegen_blocks(code) + + for num, sub_kernel in enumerate(self.sub_kernels): + assert self.dispatch_class is not None + self.dispatch_class.codegen_pid_range(self, num, code) + with code.indent(): + uniquify = self.codegen_static_numels_sub_kernel( + code, sub_kernel, num + ) + sub_kernel.codegen_body() + uniquified_body = self.uniquify_block_sizes( + sub_kernel.body, num, uniquify + ) + code.splice(uniquified_body) + + code.splice("else:") + with code.indent(): + code.splice("pass") + + if config.benchmark_combo_kernel: + code.splice(self.codegen_kernel_benchmark(num_gb=0)) + + return code.getvalue() + + def codegen_kernel_benchmark( + self, num_gb: float, grid: Optional[List[Any]] = None + ) -> IndentedBuffer: + result = IndentedBuffer() + argdefs, call_args, signature, _ = self.args.python_argdefs() + + result.writelines(["", "", "def get_args():"]) + with result.indent(): + name_cnt = itertools.count() + var_names = [] + for arg_name, arg_sig in zip(call_args, signature): + var_name = f"arg_{next(name_cnt)}" + buf = V.graph.try_get_buffer(arg_name) + if buf: + result.writeline( + f"{var_name} = rand_strided({V.graph.sizevars.size_hints(buf.get_size())}, {V.graph.sizevars.size_hints(buf.get_stride())}, device='{buf.get_device()}', dtype={buf.get_dtype()})" # noqa: B950 line too long + ) + elif arg_name in V.graph.constants: + # note that random seed is put in V.graph.constants + const_tensor = V.graph.constants[arg_name] + result.writeline( + f"{var_name} = rand_strided({V.graph.sizevars.size_hints(const_tensor.size())}, {V.graph.sizevars.size_hints(const_tensor.stride())}, device='{const_tensor.device}', dtype={const_tensor.dtype})" # type: ignore[arg-type] # noqa: B950 line too long + ) + elif isinstance(arg_sig, SizeArg): + symval_hint = V.graph.sizevars.size_hint(arg_sig.expr) + + # Force the seed_offset to be 0 so calls to the same kernel + # using different seed offset will have the same benchmark harness. + # We can dedup kernel definitions in this case. + if "seed_offset" in arg_sig.name: + symval_hint = 0 + result.writeline(f"{var_name} = {symval_hint}") + elif isinstance(arg_sig, WorkspaceArg): + device = V.graph.scheduler.get_current_device_or_throw() + nbytes = V.graph.sizevars.size_hint(arg_sig.nbytes) + result.writeline( + f"{var_name} = torch.zeros({nbytes}, device='{device}', dtype=torch.uint8)" + ) + else: + raise KeyError( + f"Don't find the buffer or const tensor for {arg_name}" + ) + var_names.append(var_name) + result.writeline(f"return {', '.join(var_names)},") + + result.writelines(["\n", "\n", "def call(args):"]) + if grid is None: + assert self.dispatch_class is not None + dynamic_shape = self.dynamic_shape_args != [] + grid_tuple = self.dispatch_class.grid( + self.grids, self.x_numels_list, dynamic_shape + ) + extra_args_str = "" + extra_args: List[Any] = [] + if dynamic_shape: + self.add_numel_to_call_args_and_grid_benchmark(extra_args, grid_tuple) + # convert nested list to list of str + grid_tuple = tuple( + "[" + ", ".join(pexpr(item) for item in e) + ",]" + for e in grid_tuple + ) + extra_args_str = ", ".join(map(str, extra_args)) + ", " + min_blocks = None + else: + min_blocks = max(self.min_x_blocks_list) * len(self.sub_kernels) + grid_str = ", ".join(pexpr(item) for item in grid_tuple) + grid_extra_kwargs = ( + f"num_kernels={len(self.sub_kernels)}, " + f"min_blocks={min_blocks}, " + f"is_sequential={self.dispatch_class is self.SequentialDispatch}" + ) + grid_str = f"{grid_str}, {grid_extra_kwargs}" + grid_arg = f"{extra_args_str}grid=grid_combo_kernels({grid_str})" + else: + grid_arg = f"grid={grid}" + index = V.graph.scheduler.get_current_device_or_throw().index + with result.indent(): + result.writeline(f"with {V.graph.device_ops.device_guard(index)}:") + with result.indent(): + result.writeline( + V.graph.device_ops.set_device(index) + ) # no-op to ensure context + stream_name = f"stream{index}" + result.writeline(f"{stream_name} = get_raw_stream({index})") + result.writeline( + f"{str(Placeholder.KERNEL_NAME)}.run(*args, {grid_arg}, stream={stream_name})" + ) + + # benchmark all configs + result.writelines(["\n", "\n", "def benchmark_all_configs(args):"]) + with result.indent(): + result.writeline(f"with {V.graph.device_ops.device_guard(index)}:") + with result.indent(): + result.writeline( + V.graph.device_ops.set_device(index) + ) # no-op to ensure context + result.writeline( + f"return {str(Placeholder.KERNEL_NAME)}.benchmark_all_configs(*args, {grid_arg})" + ) + + result.writelines(["\n", "\n", "if __name__ == '__main__':"]) + with result.indent(): + result.writeline( + "from torch._inductor.runtime.benchmarking import benchmarker" + ) + result.writeline("") + + result.writeline("args = get_args()") + result.writeline( + "ms = benchmarker.benchmark_gpu(lambda: call(args), rep=40, fast_flush=True)" + ) + result.writeline(f"num_gb = {num_gb}") + result.writeline("gb_per_s = num_gb / (ms / 1e3)") + result.writeline( + 'print(f"{ms:.3f}ms {num_gb:.3f}GB {gb_per_s:.2f}GB/s")' + ) + + return result + + def imports_for_benchmark_kernel(self) -> str: + return textwrap.dedent( + """ + from torch._dynamo.testing import rand_strided + {} + import torch + from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels + """.format( + V.graph.device_ops.import_get_raw_stream_as("get_raw_stream") + ) + ) + + def uniquify_block_sizes( + self, code: IndentedBuffer, num_kernel: int, uniquify: List[str] + ) -> IndentedBuffer: + if not uniquify: + return code + modified = IndentedBuffer(initial_indent=code._indent) + for line in code._lines: + if isinstance(line, str) and (blocks := [e for e in uniquify if e in line]): + modified_line = line + for block in blocks: + modified_line = modified_line.replace( + block, f"{block}_{num_kernel}" + ) + modified.writeline(modified_line) + elif isinstance(line, DeferredLine) and ( + blocks := [e for e in uniquify if e in line.line] + ): + modified_line = line.line + for block in blocks: + modified_line = modified_line.replace( + block, f"{block}_{num_kernel}" + ) + new_line = DeferredLine(line.name, modified_line) + modified.writeline(new_line) + else: + modified.writeline(line) + return modified + + def call_kernel(self, code: IndentedBuffer, name: str) -> None: + _, call_args, _, arg_types = self.args.python_argdefs() + + wrapper = V.graph.wrapper_code + assert self.dispatch_class is not None + dynamic_shape = self.dynamic_shape_args != [] + grid = list( + self.dispatch_class.grid(self.grids, self.x_numels_list, dynamic_shape) + ) + num_kernels = len(self.sub_kernels) + min_blocks = ( + max(self.min_x_blocks_list) * num_kernels if not dynamic_shape else None + ) + is_sequential = self.dispatch_class is self.SequentialDispatch + if dynamic_shape: + self.add_numel_to_call_args_and_grid(name, call_args, arg_types, grid) + # convert nested list to list of str + # grid = tuple("["+", ".join(pexpr(item) for item in e)+",]" for e in grid) + if not self.enable_autotune and not dynamic_shape: + launch_grid = self.grid_no_autotune( + grid, num_kernels, cast(int, min_blocks), is_sequential + ) + V.graph.wrapper_code.generate_kernel_call( + name, + call_args, + grid=launch_grid, + arg_types=arg_types, + grid_fn="", + ) + return + # autotuning is enabled + grid = wrapper.generate_default_grid( + name, + list(grid), + grid_callable=grid_combo_kernels, + num_kernels=num_kernels, + min_blocks=min_blocks, + is_sequential=is_sequential, + default_meta=None if self.enable_autotune else self.get_default_meta(), + ) + wrapper.generate_kernel_call( + name, + call_args, + grid, + V.graph.scheduler.get_current_device_or_throw().index, + cuda=True, + triton=True, + arg_types=arg_types, + grid_fn="grid_combo_kernels", + grid_extra_kwargs=( + f"num_kernels={num_kernels}, " + f"min_blocks={min_blocks}, " + f"is_sequential={is_sequential}, " + f"default_meta={None if self.enable_autotune else self.get_default_meta()}" + ), + ) + + def grid_no_autotune( + self, + grid: Union[Tuple[Any], List[Any]], + num_kernels: int, + min_blocks: int, + is_sequential: bool, + ) -> List[int]: + meta = self.get_default_meta() + grid_func = grid_combo_kernels( + *grid, + num_kernels=num_kernels, + min_blocks=min_blocks, + is_sequential=is_sequential, + ) + return grid_func(meta) + + def get_default_meta(self) -> Dict[str, int]: + if "YBLOCK" in self.block_args: + meta = {"XBLOCK": self.block_size_2d, "YBLOCK": self.block_size_2d} + else: + meta = {"XBLOCK": self.block_size_1d} + return meta diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/triton_split_scan.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/triton_split_scan.py new file mode 100644 index 0000000000000000000000000000000000000000..9eea00fbb8d6b6127e2aa117f8da8be36b556e5b --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/triton_split_scan.py @@ -0,0 +1,174 @@ +# mypy: allow-untyped-defs +import functools +from typing import Optional + +import torch._inductor.runtime.hints +from torch._inductor import config +from torch._inductor.codegen.simd import IterationRangesRoot +from torch._inductor.codegen.triton import triton_compute_type, TritonKernel +from torch._prims_common import prod +from torch.utils._ordered_set import OrderedSet +from torch.utils._sympy.functions import CeilDiv + + +class TritonSplitScanKernel(TritonKernel): + """Generates a triton kernel that supports ops.scan calls while also splitting + the reduction dimension over multiple triton programs. + + For this kernel, loop numels will always take the form ``(xdim, rdim)`` + and the grid has the shape ``(CeilDiv(rdim, RBLOCK), xdim)``. Communication + between blocks occurs within a global memory workspace buffer, which + must be zero-filled before launching the kernel. + + Note that generation for ``ops.reduction`` is not supported. + + For details of the communication strategy, see + https://research.nvidia.com/publication/2016-03_single-pass-parallel-prefix-scan-decoupled-look-back + + """ + + def __init__( + self, + *groups, + index_dtype: str, + mutations: Optional[OrderedSet[str]] = None, + reduction_hint=torch._inductor.runtime.hints.ReductionHint.DEFAULT, + min_elem_per_thread=0, + ) -> None: + super().__init__( + *groups, + index_dtype=index_dtype, + mutations=mutations, + pid_cache=None, + reduction_hint=reduction_hint, + min_elem_per_thread=min_elem_per_thread, + ) + self.no_x_dim = True + + def should_use_persistent_reduction(self) -> bool: + return False + + def initialize_range_tree(self, pid_cache): + prefixes = "yxr" + assert len(self.numels) <= len( + prefixes + ), "z dimension not supported for split scan" + active_prefixes = prefixes[len(prefixes) - len(self.numels) :] + + grid_dims = "rxy" + for numel, prefix in zip(self.numels, active_prefixes): + is_reduction = prefix == "r" + tensor_dim = 0 if is_reduction else None + grid_dim = grid_dims.find(prefix) + self.range_trees.append( + IterationRangesRoot( + f"{prefix}index", + numel, + prefix, + grid_dim, + self, + pid_cache=pid_cache, + is_loop=False, + tensor_dim=tensor_dim, + grid_dim=grid_dim, + has_zdim=False, + ) + ) + + def reduction(self, dtype, src_dtype, reduction_type, value): + raise NotImplementedError("NYI TritonSplitDimKernel reductions") + + def scan(self, dtypes, combine_fn, values): + import triton.language as tl + + (dtype,) = dtypes + (value,) = values + + compute_type = triton_compute_type(dtype) + compute_type_triton = getattr(tl, compute_type[3:]) + + element_nbits = compute_type_triton.primitive_bitwidth + + scratch_type = "tl.uint32" if element_nbits <= 16 else "tl.uint64" + scratch_type_triton = getattr(tl, scratch_type[3:]) + scratch_elems_per_block = 3 if element_nbits == 64 else 1 + scratch_nbytes_per_block = scratch_elems_per_block * ( + scratch_type_triton.primitive_bitwidth // 8 + ) + + cse_load = functools.partial(self.cse.generate, self.loads) + cse_compute = functools.partial(self.cse.generate, self.compute) + + assert len(self.numels) == 2, "Unexpected tiling" + min_rblock = config.triton.min_split_scan_rblock + max_blocks = prod(self.numels[:-1]) * CeilDiv(self.numels[-1], min_rblock) + nbytes = scratch_nbytes_per_block * max_blocks + scratch_base, offset = self.args.workspace(nbytes=nbytes, zero_fill=True) + if offset != 0: + scratch_base = cse_load(f"{scratch_base} + {self.index_to_str(offset)}") + runtime_rblocks = cse_load(f"tl.num_programs({self.range_trees[-1].index})") + scratch_base = cse_load( + f"{scratch_base}.to(tl.pointer_type({scratch_type})) + xoffset * " + f"{scratch_elems_per_block} * {runtime_rblocks}" + ) + + masks = {f"{tree.prefix}mask" for tree in self.range_trees} + self.filter_masks(masks) + masks = sorted(masks) + assert not self._load_mask, "ops.scan not supported inside ops.masked" + + value = cse_compute(f"{value}.to({compute_type})") + value = cse_compute(f"tl.broadcast_to({value}, {self.dense_size_str()})") + + combine_helper_fn = self._lift_helper(combine_fn, 1) + dim = self.triton_tensor_ndim() - 1 + assert dim == 0, "" + + block_sum = cse_compute(f"tl.reduce({value}, {dim}, {combine_helper_fn})") + exclusive_prefix = self.cse.newvar() + if element_nbits == 64: + self.compute.splice( + f""" + {exclusive_prefix} = triton_helpers.exclusive_scan_decoupled_lookback_64( + {scratch_base}, + {block_sum}, + {self.iteration_ranges_get_pid(self.range_trees[-1])}, + {combine_helper_fn}, + ) + """, + strip=True, + ) + + else: + assert element_nbits <= 32 + value_as_uint_dtype = f"tl.uint{element_nbits}" + + self.compute.splice( + f""" + {exclusive_prefix} = triton_helpers.exclusive_scan_decoupled_lookback( + {scratch_base}, + {block_sum}, + {self.iteration_ranges_get_pid(self.range_trees[-1])}, + {combine_helper_fn}, + DTYPE_VALUE_AS_UINT={value_as_uint_dtype}, + DTYPE_PACK={scratch_type}, + ) + """, + strip=True, + ) + # Compute final cumsum + block_scan = cse_compute( + f"tl.associative_scan({value}, {dim}, {combine_helper_fn})" + ) + combined_result = cse_compute( + f"{combine_helper_fn}({exclusive_prefix}, {block_scan})" + ) + return ( + cse_compute(f"tl.where(roffset == 0, {block_scan}, {combined_result})"), + ) + + def _get_heuristic(self): + return "split_scan" + + def _get_grid_fn(self): + return "split_scan_grid" diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/triton_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/triton_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c8dbd516f13595449e5c7803b6f4a78174f88d52 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/triton_utils.py @@ -0,0 +1,170 @@ +# mypy: allow-untyped-defs +from typing import Any, Dict, List, Optional + +import sympy + +import torch + +from .. import config +from ..runtime.hints import instance_descriptor +from ..utils import _type_of +from ..virtualized import V +from .common import KernelArgType, SizeArg, TensorArg, WorkspaceArg + + +def should_unwrap_unspec_arg(name: str): + if V.graph.is_unspec_arg(name): + # Unwrap on all devices except CPU + if V.graph.scheduler.get_current_device_or_throw().type != "cpu": + return True + # Only unwrap on CPU if the input is not used as an output + if name not in V.graph.mutated_buffers: + return True + return False + + +def signature_of(arg: KernelArgType, *, size_dtype: str) -> str: + if isinstance(arg, TensorArg): + # TODO: Remove fp8 special handling when Triton supports PyTorch fp8 dtypes. + # Related PR: https://github.com/openai/triton/pull/2279/ + if arg.dtype == torch.float8_e4m3fn: + tye = "*fp8e4nv" + elif arg.dtype == torch.float8_e5m2: + tye = "*fp8e5" + elif arg.dtype == torch.float8_e4m3fnuz: + tye = "*fp8e4b8" + elif arg.dtype == torch.float8_e5m2fnuz: + tye = "*fp8e5b16" + else: + tye = _type_of(arg.dtype) + if should_unwrap_unspec_arg(arg.buffer): + # had unwrapped 0d tensor as scalar + new_tye = tye.lstrip("*") + if new_tye in ["fp16", "bf16"]: + return "fp32" + else: + return new_tye + else: + return tye + if isinstance(arg, SizeArg): + if arg.expr is None: + # From triton/runtime/jit.py + # `None` is nullptr. Implicitly convert to *i8. + return "*i8" + elif isinstance(arg.expr, (float, sympy.Float)): + return "fp32" + if size_dtype == "tl.int32": + return "i32" + elif size_dtype == "tl.int64": + return "i64" + else: + raise NotImplementedError(f"unhandled size_dtype {size_dtype}") + if isinstance(arg, WorkspaceArg): + return "*i8" + raise NotImplementedError(f"unhandled {type(arg)}: {arg}") + + +def signature_to_meta( + signature: List[KernelArgType], + *, + size_dtype: str, + indices: Optional[List[int]] = None, +) -> Dict[int, str]: + if indices is None: + indices = list(range(len(signature))) + return { + i: signature_of(arg, size_dtype=size_dtype) + for i, arg in zip(indices, signature) + } + + +def is_unaligned_buffer(arg: TensorArg): + buf_name = arg.buffer + if buf_name in V.graph.graph_inputs: + # See Note: [Input Alignment handling in Inductor] + return buf_name not in V.graph.aligned_inputs + + if buf_name in V.graph.constants: + # all constants are assumed to be aligned + return False + + if V.graph.scheduler: + layout = V.graph.scheduler.get_buffer_layout(buf_name) + else: + buffer = V.graph.try_get_buffer(buf_name) + # output arg + if not buffer: + assert buf_name == V.kernel.output_node.name + layout = V.kernel.output_node.layout + else: + layout = buffer.get_layout() + + if isinstance(layout, torch._inductor.ir.NonOwningLayout): + return not layout.maybe_guard_aligned() + else: + return False + + +def config_of( + args: List[KernelArgType], + *, + indices: Optional[List[int]] = None, +) -> Any: + if indices is None: + indices = list(range(len(args))) + + def is_aligned(x: KernelArgType, alignment: int, include_tensor: bool) -> bool: + """ + Roughly follow triton code here: + https://github.com/openai/triton/blob/5282ed890d453e10b9ee30076ef89115dd197761/python/triton/runtime/jit.py#L208-L222 + """ + if isinstance(x, TensorArg): + if include_tensor: + offset_aligned = V.graph.sizevars.statically_known_multiple_of( + x.offset * x.dtype.itemsize, alignment # type: ignore[arg-type] + ) + return offset_aligned and not is_unaligned_buffer(x) + else: + return False + if isinstance(x, SizeArg): + # TODO(voz): These are kinda redundant, if we can solve out statically_known_multiple_of with + # _maybe_evaluate_static... + if x.name.startswith("load_seed_offset"): + return False + if x.expr is None: + return False + if isinstance(x.expr, float): + return False + return V.graph.sizevars.statically_known_multiple_of(x.expr, alignment) # type: ignore[arg-type] + if isinstance(x, WorkspaceArg): + return V.graph.sizevars.statically_known_multiple_of(x.nbytes, alignment) # type: ignore[arg-type] + raise NotImplementedError(f"unhandled {type(x)}: {x}") + + if config.triton.divisible_by_16: + divisible_by_16 = tuple( + i + for i, arg in zip(indices, args) + if is_aligned(arg, alignment=16, include_tensor=True) + ) + else: + divisible_by_16 = () + divisible_by_8 = tuple( + i + for i, arg in zip(indices, args) + if is_aligned(arg, alignment=8, include_tensor=False) + ) + + equal_to_1 = tuple( + i + for i, arg in zip(indices, args) + if isinstance(arg, SizeArg) + and isinstance(arg.expr, (int, sympy.Integer)) + and V.graph.sizevars.statically_known_equals(arg.expr, 1) # type: ignore[arg-type] + ) + # ids_of_folded_args is set from equal_to_1 + # and None args by the Triton compiler + ids_of_folded_args = tuple(equal_to_1) + + return instance_descriptor( + divisible_by_16, equal_to_1, ids_of_folded_args, divisible_by_8 + ) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/wrapper.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..ddc422f4c294cb719e1036e72cfc68d32e58ced8 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/codegen/wrapper.py @@ -0,0 +1,2057 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import collections +import contextlib +import dataclasses +import dis +import functools +import inspect +import logging +import operator +import re +import tempfile +from itertools import count +from typing import ( + Any, + Callable, + Dict, + Iterator, + List, + Optional, + Set, + Tuple, + TYPE_CHECKING, + Union, +) + +import sympy +from sympy import Expr + +import torch +import torch._ops +from torch import dtype as torch_dtype +from torch._dynamo.utils import counters, dynamo_timed +from torch._inductor.codegen.debug_utils import DebugPrinterManager +from torch._inductor.codegen.multi_kernel import MultiKernelState +from torch._inductor.runtime.runtime_utils import cache_dir +from torch.fx.experimental.symbolic_shapes import ConvertIntKey, DivideByKey, SymTypes +from torch.fx.node import _get_qualified_name +from torch.utils._sympy.singleton_int import SingletonInt +from torch.utils._sympy.symbol import symbol_is_type, SymT + +from .. import async_compile, config, ir +from ..codecache import output_code_log +from ..ir import ReinterpretView +from ..runtime import triton_heuristics +from ..runtime.hints import DeviceProperties +from ..utils import ( + cache_on_self, + get_benchmark_name, + LineContext, + sympy_product, + sympy_str, +) +from ..virtualized import V +from .aoti_hipify_utils import maybe_hipify_code_wrapper +from .common import CodeGen, DeferredLine, IndentedBuffer, PythonPrinter +from .triton_utils import config_of, should_unwrap_unspec_arg, signature_to_meta + + +if TYPE_CHECKING: + import triton + + from ..graph import GraphLowering + + +pexpr = PythonPrinter().doprint + + +ReuseKey = Tuple[torch.device, torch.dtype, str] + + +def buffer_reuse_key(node: ir.Buffer) -> ReuseKey: + return ( + node.get_device(), + node.get_dtype(), + # NB: this is symbolic so that we don't try to reuse a buffer + # for s0 for s1, just because they happen to share the same + # size hint + sympy_str(V.graph.sizevars.simplify(node.layout.storage_size())), + ) + + +def convert_arg_type(arg: torch.Argument) -> str: + from .cpp import CONTAINER_PYTHON_TO_CPP, PYTHON_TO_CPP + + # use x.real_type instead of x.type so that we get ScalarType instead of int + python_type = repr(arg.real_type) # type: ignore[attr-defined] + + if python_type == "Tensor": + # Conversions rules follow https://github.com/pytorch/pytorch/tree/main/aten/src/ATen/native#func + if arg.alias_info is not None and arg.alias_info.is_write: + return f"at::{python_type}&" + else: + return f"at::{python_type} const&" + + if python_type in PYTHON_TO_CPP: + cpp_type = PYTHON_TO_CPP[python_type] + return cpp_type + + # Convert args of container types e.g. Optional[*] + for py_container, cpp_container in CONTAINER_PYTHON_TO_CPP.items(): + container_match = re.findall(py_container + r"\[([a-zA-Z_]+)]", python_type) + if len(container_match) == 1: + contained_type = container_match[0] + assert ( + contained_type in PYTHON_TO_CPP + ), f"unsupported {py_container} type in convert_arg_type: {contained_type}" + cpp_contained_type = PYTHON_TO_CPP[contained_type] + return f"{cpp_container}<{cpp_contained_type}>" + + raise AssertionError(f"unsupport python_type: {python_type}") + + +def convert_return_type(ret: torch.Argument) -> str: + # use x.real_type instead of x.type so that we get ScalarType instead of int + python_type = repr(ret.real_type) # type: ignore[attr-defined] + python_to_cpp = { + "Tensor": "at::Tensor", + "List[Tensor]": "std::vector", + } + + cpp_type = python_to_cpp.get(python_type, None) + assert cpp_type is not None, f"NYI return type: {python_type}" + # An output aliasing an input is returned by reference only when it's a + # Tensor, not when it's a Tensor[]. For example, aten.split.Tensor's output + # aliases the input tensor, but the op returns a vector by value. + if python_type == "Tensor" and ret.alias_info is not None: + cpp_type += "&" + return cpp_type + + +def get_cpp_op_schema(kernel: torch._ops.OpOverload) -> str: + args = kernel._schema.arguments + returns = kernel._schema.returns + + num_returns = len(returns) + assert num_returns > 0, "must have at least one return value" + + if num_returns == 1: + cpp_return_value = convert_return_type(returns[0]) + elif num_returns > 1: + tuple_returns = ", ".join([convert_return_type(r) for r in returns]) + cpp_return_value = f"std::tuple<{tuple_returns}>" + + cpp_arg_type = [f"{convert_arg_type(arg)} {arg.name}" for arg in args] + return f"{cpp_return_value}({', '.join(cpp_arg_type)})" # type: ignore[possibly-undefined] + + +# TODO: Move to a well known place +TritonMetaParams = Dict[str, int] +TritonGrid = Union[ + Tuple[Union[int, sympy.Expr], ...], Callable[[TritonMetaParams], Tuple[int, ...]] +] + + +def user_defined_kernel_grid_fn_code( + name: str, + configs: List[triton.Config], # type: ignore[name-defined] + grids: List[TritonGrid], + wrapper: Optional[WrapperCodeGen] = None, +) -> Tuple[str, str]: + output = IndentedBuffer() + + def _convert_to_sympy_expr(item: Union[int, sympy.Expr]) -> sympy.Expr: + return item if isinstance(item, sympy.Expr) else sympy.Integer(item) + + def determine_grid( + grid: TritonGrid, + ): + """ + This function return a tuple of two values: the first one is for the real grid + which is used in the generated code; the second one is an example grid with + concreate values which is used in the autotune block to run the generated + kernels at compile time. + """ + if wrapper is None or callable(grid): + # return as-is when used in eager mode or when grid is callable + return grid, grid + # Grid contains ints/Expr, so utilize wrapper's expr printer for codegen + sympy_grid = tuple(_convert_to_sympy_expr(g) for g in grid) + return ( + wrapper.codegen_shape_tuple(sympy_grid), + wrapper.codegen_shape_tuple( + tuple( + wrapper.generate_example_arg_value(g, type(g)) for g in sympy_grid + ) + ) + if config.triton.autotune_at_compile_time + else None, + ) + + def writeline(line: str, example_grid: Optional[str] = None): + output.writeline(line) + if ( + wrapper + and config.triton.autotune_at_compile_time + and name not in wrapper.kernel_autotune_names + ): + wrapper.kernel_autotune_calls.writeline(example_grid or line) + + fn_name = f"grid_wrapper_for_{name}" + writeline(f"def {fn_name}(meta):") + kernel_autotune_calls_indent = ( + wrapper.kernel_autotune_calls.indent() + if wrapper and config.triton.autotune_at_compile_time + else contextlib.nullcontext() + ) + with output.indent(), kernel_autotune_calls_indent: + if len(grids) == 1: + grid, example_grid = determine_grid(grids[0]) + writeline(f"return {grid}", f"return {example_grid}") + else: + assert len(grids) > 1 + assert len(grids) == len(configs) + seen = set() + for grid, c in zip(grids, configs): + guards = [f"meta['{name}'] == {val}" for name, val in c.kwargs.items()] + guards = " and ".join(guards) + grid, example_grid = determine_grid(grid) + statement = f"if {guards}: return {grid}" + if statement in seen: + continue + seen.add(statement) + writeline(statement, f"if {guards}: return {example_grid}") + + return fn_name, output.getvalue() + + +@dataclasses.dataclass +class SymbolicCallArg: + inner: str + # the original symbolic expression represented by inner + inner_expr: sympy.Expr + + def __str__(self): + return str(self.inner) + + +# Default thread stack sizes vary by platform: +# - Linux: 8 MB +# - macOS: 512 KB +# - Windows: 1 MB +# Just pick something comfortably smaller than the smallest for now. +MAX_STACK_ALLOCATION_SIZE = 1024 * 100 + + +class MemoryPlanningState: + def __init__(self): + super().__init__() + self.reuse_pool: Dict[ + ReuseKey, List[FreeIfNotReusedLine] + ] = collections.defaultdict(list) + self.total_allocated_buffer_size: int = 0 + + def __contains__(self, key: ReuseKey) -> bool: + return bool(self.reuse_pool.get(key, None)) + + def pop(self, key: ReuseKey) -> FreeIfNotReusedLine: + item = self.reuse_pool[key].pop() + assert not item.is_reused + return item + + def push(self, key: ReuseKey, item: FreeIfNotReusedLine) -> None: + assert not item.is_reused + self.reuse_pool[key].append(item) + + +class WrapperLine: + pass + + +@dataclasses.dataclass +class EnterSubgraphLine(WrapperLine): + wrapper: WrapperCodeGen + graph: GraphLowering + + def __post_init__(self) -> None: + self.wrapper.push_computed_sizes(self.wrapper.computed_sizes) + + def codegen(self, code: IndentedBuffer) -> None: + self.wrapper.push_codegened_graph(self.graph) + code.do_indent() + + +@dataclasses.dataclass +class ExitSubgraphLine(WrapperLine): + wrapper: WrapperCodeGen + + def __post_init__(self) -> None: + self.wrapper.computed_sizes = self.wrapper.pop_computed_sizes() + + def codegen(self, code: IndentedBuffer) -> None: + self.wrapper.pop_codegened_graph() + code.do_unindent() + + +@dataclasses.dataclass +class EnterDeviceContextManagerLine(WrapperLine): + device_idx: int + last_seen_device_guard_index: Optional[int] + + def codegen(self, code: IndentedBuffer) -> None: + if V.graph.cpp_wrapper: + code.writeline("\n") + if V.graph.aot_mode: + # In AOT mode, we have a stream provided as a param. A stream is + # associated with a device, so we never expect the device to change. + # CUDAStreamGuard sets the stream and the device. + if self.last_seen_device_guard_index is None: + if config.abi_compatible: + code.writeline( + "AOTICudaStreamGuard stream_guard(stream, this->device_idx_);" + ) + else: + code.writeline( + maybe_hipify_code_wrapper( + "at::cuda::CUDAStreamGuard stream_guard(" + + "at::cuda::getStreamFromExternal(stream, this->device_idx_));" + ) + ) + else: + assert ( + self.last_seen_device_guard_index == self.device_idx + ), "AOTInductor only supports running on one CUDA device" + else: + if self.last_seen_device_guard_index is None: + code.writeline( + f"AOTICudaGuard device_guard({self.device_idx});" + if config.abi_compatible + else maybe_hipify_code_wrapper( + f"at::cuda::CUDAGuard device_guard({self.device_idx});" + ) + ) + else: + code.writeline(f"device_guard.set_index({self.device_idx});") + else: + # Note _DeviceGuard has less overhead than device, but only accepts + # integers + code.writeline(f"with {V.graph.device_ops.device_guard(self.device_idx)}:") + code.do_indent() + code.writeline(V.graph.device_ops.set_device(self.device_idx)) + + +class ExitDeviceContextManagerLine(WrapperLine): + def codegen(self, code: IndentedBuffer) -> None: + if not V.graph.cpp_wrapper: + code.do_unindent() + + +@dataclasses.dataclass +class MemoryPlanningLine(WrapperLine): + wrapper: WrapperCodeGen + + def plan(self, state: MemoryPlanningState) -> MemoryPlanningLine: + """First pass to find reuse""" + return self + + def codegen(self, code: IndentedBuffer) -> None: + """Second pass to output code""" + + def __str__(self) -> str: + """ + Emits a string representation that fits on one line. + """ + args: List[str] = [] + for field in dataclasses.fields(self): + if field.name == "wrapper": + continue + val = getattr(self, field.name) + args.append( + f"{field.name}={val.get_name() if field.type is ir.Buffer else val}" + ) + return f"{type(self).__name__}({', '.join(args)})" + + +@dataclasses.dataclass +class AllocateLine(MemoryPlanningLine): + node: ir.Buffer + + def plan(self, state: MemoryPlanningState) -> MemoryPlanningLine: + if self.node.get_name() in V.graph.removed_buffers: + return NullLine(self.wrapper) + + # try to reuse a recently freed buffer + key = buffer_reuse_key(self.node) + if config.allow_buffer_reuse and key in state: + free_line = state.pop(key) + free_line.is_reused = True + return ReuseLine(self.wrapper, free_line.node, self.node) + + if self.node.get_device().type == "cpu": + static_shape = self.wrapper.static_shape_for_buffer_or_none(self.node) + if static_shape is not None: + state.total_allocated_buffer_size += int( + functools.reduce(operator.mul, static_shape, 1) + ) + + return self + + def codegen(self, code: IndentedBuffer) -> None: + assert self.node.get_name() not in V.graph.removed_buffers + line = self.wrapper.make_buffer_allocation(self.node) + code.writeline(line) + + +@dataclasses.dataclass +class FreeIfNotReusedLine(MemoryPlanningLine): + node: ir.Buffer + is_reused: bool = False + + def plan(self, state: MemoryPlanningState) -> MemoryPlanningLine: + if len(self.node.get_inputs_that_alias_output()) > 0: + return self + if isinstance(self.node.layout, ir.MultiOutputLayout): + return self + assert not self.is_reused + if self.node.get_name() in V.graph.removed_buffers: + return NullLine(self.wrapper) + if config.allow_buffer_reuse: + state.push(buffer_reuse_key(self.node), self) + return self + + def codegen(self, code: IndentedBuffer) -> None: + assert self.node.get_name() not in V.graph.removed_buffers + if not self.is_reused: + code.writeline(self.wrapper.make_buffer_free(self.node)) + + +@dataclasses.dataclass +class ReuseLine(MemoryPlanningLine): + node: ir.Buffer + reused_as: ir.Buffer + delete_old: bool = True + + def plan(self, state: MemoryPlanningState) -> MemoryPlanningLine: + if self.node.get_name() in V.graph.removed_buffers: + assert self.reused_as.get_name() in V.graph.removed_buffers + return NullLine(self.wrapper) + assert self.reused_as.get_name() not in V.graph.removed_buffers + return self + + def codegen(self, code: IndentedBuffer) -> None: + assert self.node.get_name() not in V.graph.removed_buffers + assert self.reused_as.get_name() not in V.graph.removed_buffers + code.writeline( + self.wrapper.make_buffer_reuse(self.node, self.reused_as, self.delete_old) + ) + + +class NullLine(MemoryPlanningLine): + pass + + +BufferName = str + + +class WrapperCodeGen(CodeGen): + """ + Generate outer wrapper in Python that calls the kernels. + """ + + def __init__(self): + super().__init__() + self._names_iter: Iterator[int] = count() + self.imports = IndentedBuffer() + self.header = IndentedBuffer() + self.prefix = IndentedBuffer() + self.suffix = IndentedBuffer() + self.wrapper_call = IndentedBuffer() + self.kernel_autotune_defs = IndentedBuffer() + self.kernel_autotune_calls = IndentedBuffer() + self.kernel_autotune_names: Set[str] = set() + # If the generated source code is exactly the same, reuse the + # pre-existing kernel for it + self.src_to_kernel: Dict[str, str] = {} + self.kernel_numel_expr: Set[Tuple[str, GraphLowering]] = set() + self.lines: List[Union[MemoryPlanningLine, LineContext]] = [] + self.declare = "" + self.declare_maybe_reference = "" + self.ending = "" + self.open_bracket = "[" + self.closed_bracket = "]" + self.comment = "#" + self.namespace = "" + self.none_str = "None" + self.size = "size()" + self.stride = "stride()" + self.last_seen_device_guard_index: Optional[int] = None + self.supports_intermediate_hooks = True + self.expr_printer: Callable[[Any], str] = pexpr + self.user_defined_kernel_cache: Dict[Tuple[Any, ...], Tuple[str, Any]] = {} + self.unbacked_symbol_decls: Set[str] = set() # str of sympy.Symbol + self.allow_stack_allocation: Optional[bool] = None + self.stack_allocated_buffers: Dict[BufferName, ir.Buffer] = {} + self.computed_sizes: Set[sympy.Symbol] = set() + + # this is used for tracking which GraphLowering instance---parent graph + # or (nested) subgraph---is currently codegened; the primary use case is + # including the graph instance into a cache key to avoid cross-graph + # caching during lowering of nested subgraphs + self.codegened_graph_stack = [] + self.computed_sizes_stack = [] + + self.write_header() + self.write_prefix() + self.write_kernel_autotune_defs_header() + + if not V.graph.aot_mode: + for name, hashed in V.graph.constant_reprs.items(): + # include a hash so our code cache puts different constants into different files + self.write_constant(name, hashed) + + self.allocated: Set[BufferName] = set() + self.freed: Set[BufferName] = set() + + # maps from reusing buffer to reused buffer + self.reuses: Dict[BufferName, BufferName] = {} + + self.write_get_raw_stream = functools.lru_cache(None)( # type: ignore[assignment] + self.write_get_raw_stream + ) + + @functools.lru_cache(None) + def add_import_once(line: str) -> None: + self.imports.writeline(line) + if config.triton.autotune_at_compile_time: + self.kernel_autotune_calls.writeline(line) + + self.add_import_once = add_import_once + self._metas: Dict[str, str] = {} + self._meta_vars: Set[str] = set() + self.multi_kernel_state = MultiKernelState() + + # intermediate tensor value printing utility + self.debug_printer = DebugPrinterManager( + debug_printer_level=config.aot_inductor.debug_intermediate_value_printer + ) + + def write_constant(self, name: str, hashed: str) -> None: + self.header.writeline(f"{name} = None # {hashed}") + + def write_header(self) -> None: + context = torch._guards.TracingContext.try_get() + aot_config_comment = "" + if context is not None and context.aot_graph_name is not None: + aot_config_comment = f"# AOT ID: {context.aot_graph_name}" + self.imports.splice( + f""" + {aot_config_comment} + from ctypes import c_void_p, c_long, c_int + import torch + import math + import random + import os + import tempfile + from math import inf, nan + from torch._inductor.hooks import run_intermediate_hooks + from torch._inductor.utils import maybe_profile + from torch._inductor.codegen.memory_planning import _align as align + from torch import device, empty_strided + from {async_compile.__name__} import AsyncCompile + from torch._inductor.select_algorithm import extern_kernels + from torch._inductor.codegen.multi_kernel import MultiKernelCall + """, + strip=True, + ) + self.header.splice( + """ + aten = torch.ops.aten + inductor_ops = torch.ops.inductor + _quantized = torch.ops._quantized + assert_size_stride = torch._C._dynamo.guards.assert_size_stride + empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu + empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda + empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu + reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor + alloc_from_pool = torch.ops.inductor._alloc_from_pool + async_compile = AsyncCompile() + """, + strip=True, + ) + + def write_kernel_autotune_defs_header(self) -> None: + self.kernel_autotune_defs.splice( + f""" + import torch + from torch._dynamo.testing import rand_strided + from torch._dynamo.utils import preserve_rng_state + from torch._inductor.select_algorithm import AlgorithmSelectorCache + from {async_compile.__name__} import AsyncCompile + + async_compile = AsyncCompile() + generate_example_value = AlgorithmSelectorCache.generate_example_value + """ + ) + + @cache_on_self + def write_triton_header_once(self) -> None: + import_str = f""" + import triton + import triton.language as tl + from {triton_heuristics.__name__} import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph + """ + self.imports.splice(import_str, strip=True) + if config.triton.autotune_at_compile_time: + self.kernel_autotune_calls.splice(import_str) + self.write_get_raw_stream_header_once() + + @cache_on_self + def write_get_raw_stream_header_once(self) -> None: + self.imports.writeline( + V.graph.device_ops.import_get_raw_stream_as("get_raw_stream") + ) + if config.triton.autotune_at_compile_time: + self.kernel_autotune_calls.writeline( + V.graph.device_ops.import_get_raw_stream_as("get_raw_stream") + ) + + def add_meta_once(self, meta: TritonMetaParams) -> str: + meta = repr(meta) + if meta not in self._metas: + var = f"meta{len(self._metas)}" + self._metas[meta] = var + self.header.writeline(f"{var} = {meta}") + if config.triton.autotune_at_compile_time: + self.kernel_autotune_calls.writeline(f"{var} = {meta}") + self._meta_vars.add(var) + return self._metas[meta] + + @cache_on_self + def get_output_refs(self) -> List[str]: + return [x.codegen_reference(self.wrapper_call) for x in V.graph.graph_outputs] + + def mark_output_type(self) -> None: + return + + def codegen_input_size_asserts(self) -> None: + for name, buf in V.graph.graph_inputs.items(): + if isinstance(buf, sympy.Expr): + continue + + # comparing strides for 0 size tensor is tricky. Ignore them for now. + if sympy_product(buf.get_size()) == 0: + continue + size = self.codegen_shape_tuple(buf.get_size()) + stride = self.codegen_shape_tuple(buf.get_stride()) + self.prefix.writeline(f"assert_size_stride({name}, {size}, {stride})") + + def codegen_input_nan_asserts(self) -> None: + self.prefix.writeline("# make sure graph inputs are not nan/inf") + for name, buf in V.graph.graph_inputs.items(): + if isinstance(buf, sympy.Expr): + continue + + line = f"assert not {name}.isnan().any().item()" + self.prefix.writeline(line) + line = f"assert not {name}.isinf().any().item()" + self.prefix.writeline(line) + + def write_prefix(self) -> None: + self.prefix.splice( + """ + + async_compile.wait(globals()) + del async_compile + + def call(args): + """ + ) + with self.prefix.indent(): + if config.triton.debug_sync_graph: + self.prefix.writeline(V.graph.device_ops.synchronize()) + if V.graph.graph_inputs: + lhs = ", ".join(V.graph.graph_input_names) + if len(V.graph.graph_input_names) == 1: + lhs += "," + self.prefix.writeline(f"{lhs} = args") + self.prefix.writeline("args.clear()") + + self.codegen_inputs(self.prefix, V.graph.graph_inputs) + if config.size_asserts: + self.codegen_input_size_asserts() + if config.nan_asserts: + self.codegen_input_nan_asserts() + + # this function (and below) takes a graph as input so + # that stream caching happens per graph instance. this + # is important for nested subgraph codegening. + def write_get_raw_stream(self, device_idx: int, graph=None) -> str: + self.write_get_raw_stream_header_once() + name = f"stream{device_idx}" + self.writeline(f"{name} = get_raw_stream({device_idx})") + return name + + def get_codegened_graph(self): + return self.codegened_graph_stack[-1] + + def push_codegened_graph(self, graph): + self.codegened_graph_stack.append(graph) + + def pop_codegened_graph(self): + return self.codegened_graph_stack.pop() + + def push_computed_sizes(self, computed_sizes): + from copy import deepcopy + + return self.computed_sizes_stack.append(deepcopy(computed_sizes)) + + def pop_computed_sizes(self): + return self.computed_sizes_stack.pop() + + def next_kernel_suffix(self) -> str: + return f"{next(self._names_iter)}" + + def codegen_device_guard_enter(self, device_idx: int) -> None: + self.writeline( + EnterDeviceContextManagerLine(device_idx, self.last_seen_device_guard_index) + ) + if config.triton.autotune_at_compile_time: + # mimic logic of EnterDeviceContextManagerLine.codegen for the autotune code block + self.write_triton_header_once() + self.kernel_autotune_calls.writeline( + f"with {V.graph.device_ops.device_guard(device_idx)}:" + ) + self.kernel_autotune_calls.do_indent() + self.kernel_autotune_calls.writeline( + V.graph.device_ops.set_device(device_idx) + ) + self.kernel_autotune_calls.writeline( + f"stream{device_idx} = get_raw_stream({device_idx})" + ) + self.last_seen_device_guard_index = device_idx + + def codegen_device_guard_exit(self) -> None: + self.writeline(ExitDeviceContextManagerLine()) + if config.triton.autotune_at_compile_time: + self.kernel_autotune_calls.do_unindent() + + def generate_return(self, output_refs: List[str]) -> None: + if output_refs: + self.wrapper_call.writeline("return (" + ", ".join(output_refs) + ", )") + else: + self.wrapper_call.writeline("return ()") + + def generate_before_suffix(self, result: IndentedBuffer) -> None: + return + + def generate_end(self, result: IndentedBuffer) -> None: + return + + def generate_fallback_kernel(self, fallback_kernel, args): + self.generate_extern_kernel_alloc(fallback_kernel, args) + + def generate_extern_kernel_alloc(self, extern_kernel, args): + # If it's a NoneLayout then the extern_kernel should essentially be + # treated as if it doesn't return anything + no_return = isinstance(extern_kernel.layout, ir.NoneLayout) + output_name = extern_kernel.get_name() + origin_node = extern_kernel.get_origin_node() + kernel_name = extern_kernel.get_kernel_name() + ending = self.ending + if config.memory_planning and "view_as_complex" in kernel_name: + # view operation fallbacks cause issues since inductor + # doesn't know the memory is still needed and might reuse it. + ending = f".clone(){ending}" + + if no_return: + self.writeline(f"{self.declare}{kernel_name}({', '.join(args)}){ending}") + else: + self.writeline( + f"{self.declare}{output_name} = {kernel_name}({', '.join(args)}){ending}" + ) + if ( + self.supports_intermediate_hooks + and config.generate_intermediate_hooks + and origin_node is not None + ): + counters["inductor"]["intermediate_hooks"] += 1 + self.writeline( + f"run_intermediate_hooks({origin_node.name!r}, {output_name})" + ) + + def generate_extern_kernel_out( + self, kernel: str, out: str, out_view: Optional[str], args: List[str] + ): + args.append(f"out={out_view if out_view else out}") + self.writeline(f"{kernel}({', '.join(args)})") + + def generate_user_defined_triton_kernel( + self, + kernel_name: str, + raw_args: List[Any], + grid: List[Any], + configs, + triton_meta, + constexprs, + ): + grid_fn, code = user_defined_kernel_grid_fn_code( + kernel_name, configs, grid, wrapper=self + ) + # Must happen after free symbols are already codegened + # Emit the grid wrapper function right before the call + for line in code.split("\n"): + self.writeline(line) + + args = [self.val_to_arg_str(v) for v in raw_args] + arg_types = [ + arg.get_dtype() if hasattr(arg, "get_dtype") else type(arg) + for arg in raw_args + ] + self.generate_kernel_call( + kernel_name, args, grid_fn=grid_fn, arg_types=arg_types, raw_args=raw_args + ) + + def generate_scatter_fallback( + self, + output, + inputs, + cpp_kernel_name, + python_kernel_name, + src_is_tensor, + reduce, + kwargs, + ): + line = f"{python_kernel_name}({','.join(map(str, inputs))}" + if python_kernel_name.startswith("aten.scatter_reduce"): + line += ", ".join([""] + kwargs) + else: + if reduce: + line += f", reduce={repr(reduce)}" + line += ")" + self.writeline(line) + + def generate_index_put_fallback(self, kernel, x, indices, values, accumulate): + indices_str = f"{self.open_bracket}{', '.join(indices)}{self.closed_bracket}" + args = [x, indices_str, values, accumulate] + self.writeline(self.wrap_kernel_call(kernel, args)) + + def generate_extern_kernel_alloc_and_find_schema_if_needed( + self, + buf_name: str, + python_kernel_name: str, + cpp_kernel_name: str, + codegen_args: List[str], + cpp_op_schema: str, + cpp_kernel_key: str, + cpp_kernel_overload_name: str = "", + op_overload: Optional[torch._ops.OpOverload] = None, + raw_args=None, + outputs=None, + ): + self.writeline(f"{buf_name} = {python_kernel_name}({', '.join(codegen_args)})") + + def generate(self, is_inference): + with dynamo_timed("WrapperCodeGen.generate"): + return self._generate(is_inference) + + def _generate(self, is_inference): + if config.profile_bandwidth: + self.write_triton_header_once() + result = IndentedBuffer() + result.splice(self.imports) + result.writeline("") + result.splice(self.header) + # We do not want the cpp header for intermediate const graph. Headers would be + # rendered by the main module instead. + if V.graph.aot_mode and V.graph.cpp_wrapper and V.graph.is_const_graph: + result = IndentedBuffer() + + with contextlib.ExitStack() as stack: + stack.enter_context(self.wrapper_call.indent()) + if config.profiler_mark_wrapper_call: + self.generate_profiler_mark_wrapper_call(stack) + if config.profile_bandwidth: + self.generate_start_graph() + + # We disable planning during training because it presently increases peak memory consumption. + if is_inference and config.memory_planning: + self.memory_plan() + # TODO: integrate memory planning & stack allocation? + self.allow_stack_allocation = False + else: + self.memory_plan_reuse() + + if config.triton.store_cubin: + self.generate_reset_kernel_saved_flags() + + for line in self.lines: + if isinstance(line, WrapperLine): + line.codegen(self.wrapper_call) + else: + self.wrapper_call.writeline(line) + + output_refs = self.get_output_refs() + self.mark_output_type() + if config.triton.debug_sync_graph: + self.wrapper_call.writeline(V.graph.device_ops.synchronize()) + + if config.profile_bandwidth: + self.generate_end_graph() + + if config.triton.store_cubin: + self.generate_save_uncompiled_kernels() + + if config.triton.autotune_at_compile_time: + self.generate_and_run_autotune_block() + + self.generate_return(output_refs) + + self.finalize_prefix() + result.splice(self.prefix) + + with result.indent(): + result.splice(self.wrapper_call) + + self.generate_before_suffix(result) + result.splice(self.suffix) + + self.generate_end(result) + + self.add_benchmark_harness(result) + + return result.getvaluewithlinemap() + + def generate_and_run_autotune_block(self): + """ + Compose self.kernel_autotune_defs and self.kernel_autotune_calls into a single block of + code and execute it to trigger Triton kernel compilation and auto-tuning + """ + self.kernel_autotune_defs.splice( + """ + async_compile.wait(globals()) + del async_compile + """ + ) + scope = {} # type: ignore[var-annotated] + tuning_code = ( + self.kernel_autotune_defs.getvalue() + self.kernel_autotune_calls.getvalue() + ) + if output_code_log.level == logging.DEBUG: + # Save the autotuning code block into a file + # Create a temporary file + with tempfile.NamedTemporaryFile( + dir=cache_dir(), suffix=".py", delete=False + ) as f: + f.write(tuning_code.encode("utf-8")) + file_path = f.name + output_code_log.debug( + "\nCompile-time auto-tuning code: \n%s\nAuto-tuning code written to %s", + tuning_code, + file_path, + ) + # Execute the code to autotune kernels + exec(tuning_code, scope) + + def memory_plan(self): + from .memory_planning import MemoryPlanner + + self.lines = MemoryPlanner(self).plan(self.lines) + + def memory_plan_reuse(self): + out_names = V.graph.get_output_names() + + while ( + self.lines + and isinstance(self.lines[-1], MemoryPlanningLine) + # TODO: this seems legit, NullLine has no node + and self.lines[-1].node.name not in out_names # type: ignore[attr-defined] + ): + # these lines will be pointless + self.lines.pop() + + # codegen allocations in two passes + planning_states = [MemoryPlanningState()] + past_planning_states = [] + for i in range(len(self.lines)): + line = self.lines[i] + if isinstance(line, MemoryPlanningLine): + self.lines[i] = line.plan(planning_states[-1]) + elif isinstance(line, EnterSubgraphLine): + planning_states.append(MemoryPlanningState()) + elif isinstance(line, ExitSubgraphLine): + past_planning_states.append(planning_states.pop()) + past_planning_states.append(planning_states.pop()) + assert len(planning_states) == 0 + + # conservatively use the sum of all allocated buffer sizes + # in potentially nested scopes as the total allocated size + total_allocated_buffer_size = sum( + s.total_allocated_buffer_size for s in past_planning_states + ) + + self.allow_stack_allocation = ( + self.allow_stack_allocation is not False + and config.allow_stack_allocation + and total_allocated_buffer_size <= MAX_STACK_ALLOCATION_SIZE + ) + + def codegen_input_size_var_decl(self, code: IndentedBuffer, name): + code.writeline(f"{self.declare}{name}_size = {name}.{self.size}{self.ending}") + + def codegen_input_stride_var_decl(self, code: IndentedBuffer, name): + code.writeline( + f"{self.declare}{name}_stride = {name}.{self.stride}{self.ending}" + ) + + def codegen_inputs( + self, code: IndentedBuffer, graph_inputs: Dict[str, ir.TensorBox] + ): + """Assign all symbolic shapes to locals""" + + @functools.lru_cache(None) + def sizeof(name): + self.codegen_input_size_var_decl(code, name) + return f"{name}_size" + + @functools.lru_cache(None) + def strideof(name): + self.codegen_input_stride_var_decl(code, name) + return f"{name}_stride" + + # Assign all symbolic shapes needed to local variables + bound_vars: Set[sympy.Symbol] = set() + + def is_expr(x): + return isinstance(x[1], sympy.Expr) + + graph_inputs_expr = list(filter(is_expr, graph_inputs.items())) + graph_inputs_tensors = list( + filter(lambda x: not is_expr(x), graph_inputs.items()) + ) + + for name, shape in graph_inputs_expr: + if isinstance(shape, sympy.Symbol) and shape not in bound_vars: + code.writeline(f"{self.declare}{shape} = {name}{self.ending}") + bound_vars.add(shape) + + for name, value in graph_inputs_tensors: + shapes = value.get_size() + for dim, shape in enumerate(shapes): + if isinstance(shape, sympy.Symbol) and shape not in bound_vars: + code.writeline( + f"{self.declare}{shape} = {sizeof(name)}[{dim}]{self.ending}" + ) + bound_vars.add(shape) + + for name, value in graph_inputs_tensors: + shapes = value.get_stride() + for dim, shape in enumerate(shapes): + if isinstance(shape, sympy.Symbol) and shape not in bound_vars: + code.writeline( + f"{self.declare}{shape} = {strideof(name)}[{dim}]{self.ending}" + ) + bound_vars.add(shape) + + def ensure_size_computed(self, sym: sympy.Symbol): + if isinstance(sym, sympy.Symbol) and symbol_is_type(sym, SymT.PRECOMPUTED_SIZE): + if sym in self.computed_sizes: + return + self.computed_sizes.add(sym) + expr = V.graph.sizevars.inv_precomputed_replacements[sym] + self.writeline( + f"{self.declare}{sym} = {self.expr_printer(expr)}{self.ending}" + ) + + def finalize_prefix(self): + pass + + def codegen_python_sizevar(self, x: Expr, *, simplify: bool = True) -> str: + return pexpr(x, simplify=simplify) + + def codegen_sizevar(self, x: Expr) -> str: + return self.codegen_python_sizevar(x) + + def codegen_tuple_access(self, basename: str, name: str, index: str) -> str: + return f"{basename}[{index}]" + + def codegen_python_shape_tuple(self, shape: Tuple[Expr, ...]) -> str: + parts = list(map(self.codegen_python_sizevar, shape)) + if len(parts) == 0: + return "()" + if len(parts) == 1: + return f"({parts[0]}, )" + return f"({', '.join(parts)})" + + def codegen_shape_tuple(self, shape: Tuple[Expr, ...]) -> str: + return self.codegen_python_shape_tuple(shape) + + def codegen_alloc_from_pool(self, name, offset, dtype, shape, stride) -> str: + return "alloc_from_pool({})".format( + ", ".join( + [ + name, + pexpr(offset), # bytes not numel + str(dtype), + self.codegen_shape_tuple(shape), + self.codegen_shape_tuple(stride), + ] + ) + ) + + def codegen_reinterpret_view( + self, data, size, stride, offset, writer, dtype=None + ) -> str: + if ( + size == data.layout.size + and stride == data.layout.stride + and offset == data.layout.offset + ): + if dtype is not None and dtype != data.dtype: + return f"aten.view.dtype({data.get_name()}, {dtype})" + else: + return f"{data.get_name()}" + else: + size = self.codegen_shape_tuple(size) + stride = self.codegen_shape_tuple(stride) + offset = self.codegen_sizevar(offset) + if dtype is not None and dtype != data.dtype: + return f"aten.view.dtype(reinterpret_tensor({data.get_name()}, {size}, {stride}, {offset}), {dtype})" + else: + return ( + f"reinterpret_tensor({data.get_name()}, {size}, {stride}, {offset})" + ) + + def codegen_device_copy(self, src, dst): + self.writeline(f"{dst}.copy_({src})") + + def codegen_multi_output(self, name, value): + self.writeline(f"{self.declare}{name} = {value}{self.ending}") + + def codegen_dynamic_scalar(self, node): + (data,) = (t.codegen_reference() for t in node.inputs) + if len(node.keypath) == 0: + self.writeline(f"{node.sym} = {data}.item()") + elif len(node.keypath) == 1 and isinstance(node.keypath[0], ConvertIntKey): + self.writeline(f"{node.sym} = 1 if {data}.item() else 0") + elif len(node.keypath) == 1 and isinstance(node.keypath[0], DivideByKey): + self.writeline(f"{node.sym}_undivided = {data}.item()") + self.writeline( + f"assert {node.sym}_undivided % {node.keypath[0].divisor} == 0, " + f"f'{{{node.sym}_undivided}} not divisible by {node.keypath[0].divisor}'" + ) + self.writeline( + f"{node.sym} = {node.sym}_undivided // {node.keypath[0].divisor}" + ) + else: + raise AssertionError(f"unrecognized keypath {node.keypath}") + # No one should ever use this buffer, but for uniformity + # define the variable and assign it None + self.writeline(f"{node.get_name()} = None") + + def benchmark_compiled_module(self, output): + def add_fake_input(name, shape, stride, device, dtype): + output.writeline( + f"{name} = rand_strided(" + f"{self.codegen_python_shape_tuple(shape)}, " + f"{self.codegen_python_shape_tuple(stride)}, " + f"device='{device}', dtype={dtype})" + ) + + def add_expr_input(name, val): + output.writeline(f"{name} = {val}") + + def add_torchbind_input(name, value): + import pickle + + output.writeline(f"{name} = pickle.loads({pickle.dumps(value)!r})") + + output.writelines( + ["", "", "def benchmark_compiled_module(times=10, repeat=10):"] + ) + with output.indent(): + output.splice( + """ + from torch._dynamo.testing import rand_strided + from torch._inductor.utils import print_performance + """, + strip=True, + ) + + for name, value in V.graph.constants.items(): + # all the constants are global variables, that's why we need + # these 'global var_name' lines + output.writeline(f"global {name}") + add_fake_input( + name, value.size(), value.stride(), value.device, value.dtype + ) + + if len(V.graph.torchbind_constants) > 0: + output.writeline("import pickle") + for name, torchbind_obj in V.graph.torchbind_constants.items(): + # all the constants are global variables, that's why we need + # these 'global var_name' lines + output.writeline(f"global {name}") + add_torchbind_input(name, torchbind_obj) + + for name, value in V.graph.graph_inputs.items(): + if isinstance(value, sympy.Symbol) and isinstance( + V.graph.sizevars.var_to_val.get(value, None), SingletonInt + ): + # Inductor should only work with dense -> dense graph, and + # SingletonInts belong to metadata that should only live on + # the subclass. + continue + if isinstance(value, sympy.Expr): # Don't need to add symbolic + # TODO: this fallback and those below actually will generate possibly + # invalid benchmark code, because it's not guaranteed 42 + # is actually a valid value for the kernel in question. + # See https://github.com/pytorch/pytorch/issues/124686 + add_expr_input(name, V.graph.sizevars.size_hint(value, fallback=42)) + else: + shape = [ + V.graph.sizevars.size_hint(x, fallback=42) + for x in value.get_size() + ] + stride = [ + V.graph.sizevars.size_hint(x, fallback=42) + for x in value.get_stride() + ] + add_fake_input( + name, + shape, + stride, + value.get_device(), + value.get_dtype(), + ) + + call_str = f"call([{', '.join(V.graph.graph_inputs.keys())}])" + output.writeline(f"fn = lambda: {call_str}") + output.writeline("return print_performance(fn, times=times, repeat=repeat)") + + def add_benchmark_harness(self, output): + """ + Append a benchmark harness to generated code for debugging + """ + if not config.benchmark_harness: + return + + self.benchmark_compiled_module(output) + + output.writelines(["", "", 'if __name__ == "__main__":']) + with output.indent(): + output.writelines( + [ + "from torch._inductor.wrapper_benchmark import compiled_module_main", + f"compiled_module_main('{get_benchmark_name()}', benchmark_compiled_module)", + ] + ) + + def define_kernel( + self, name: str, kernel: str, metadata: Optional[str] = None, cuda=True + ): + metadata_comment = f"{metadata}\n" if metadata else "" + body = f"\n\n{metadata_comment}{name} = {kernel}" + self.header.splice(body) + if config.triton.autotune_at_compile_time: + self.kernel_autotune_defs.splice(body) + + def define_user_defined_triton_kernel(self, kernel, configs, kwargs): + from torch.utils._triton import patch_triton_dtype_repr + + patch_triton_dtype_repr() + + original_name = kernel.__name__ + + from .common import KernelArgType, SizeArg, TensorArg + + signature: List[KernelArgType] = [] + constants: Dict[int, Any] = {} + non_constant_indices = [] + equal_to_1_arg_idx: List[int] = [] + for idx, key in enumerate(kernel.arg_names): + if key not in kwargs: + continue + arg = kwargs[key] + if idx in kernel.constexprs: + constants[idx] = arg + else: + non_constant_indices.append(idx) + if isinstance(arg, ir.Buffer): + signature.append( + TensorArg( + name=key, + buffer=arg.get_name(), + dtype=arg.get_dtype(), + ) + ) + elif isinstance(arg, ir.ReinterpretView): + # for ReinterpretView we use the underlying + # buffer name and note the (possibly non-zero) + # offset relative to the underlying buffer + signature.append( + TensorArg( + name=key, + buffer=arg.data.get_name(), + dtype=arg.get_dtype(), + offset=arg.layout.offset, + ) + ) + else: + signature.append(SizeArg(key, arg)) + if isinstance( + arg, (int, sympy.Integer) + ) and V.graph.sizevars.statically_known_equals( + arg, 1 # type: ignore[arg-type] + ): + equal_to_1_arg_idx.append(idx) + index_dtype = "tl.int32" + triton_meta = { + "signature": signature_to_meta( + signature, + size_dtype=index_dtype, + indices=non_constant_indices, + ), + "device": DeviceProperties.create( + V.graph.scheduler.get_current_device_or_throw() + ), + # Triton compiler includes equal_to_1 args into constants even + # when they are not constexpr. otherwise there may be a segfault + # during launching the Inductor-compiled Triton kernel. + # TODO(aakhundov): add None args to constants, too. currently, this + # causes CUDA errors in test_aot_inductor.test_triton_kernel_with_none_input. + # https://github.com/pytorch/pytorch/issues/120478#issuecomment-1962822307 + # https://github.com/openai/triton/blob/231efe9ed2d200be0f69a07c298e4342b08efe3d/python/triton/runtime/jit.py#L384 + "constants": { + **constants, + **dict.fromkeys(equal_to_1_arg_idx, 1), + }, + "configs": [ + config_of( + signature, + indices=non_constant_indices, + ) + ], + } + + # Distinguish between different functions using function id + cache_key: List[Any] = [id(kernel.fn)] + if len(configs) > 0: + for arg in kwargs.values(): + # We need to key on non tensor arg only in autotune mode + if not isinstance(arg, (ir.Buffer, ir.ReinterpretView)): + cache_key.append(arg) + cache_key.append(str(triton_meta)) + cache_key = tuple(cache_key) + + if cache_key in self.user_defined_kernel_cache: + return self.user_defined_kernel_cache[cache_key] + + name = f"{original_name}_{len(self.user_defined_kernel_cache)}" + # Add to the cache for the next use + self.user_defined_kernel_cache[cache_key] = (name, triton_meta) + + compile_wrapper = IndentedBuffer() + compile_wrapper.writeline(f"async_compile.triton({original_name!r}, '''") + + from .triton import gen_common_triton_imports, TritonKernel + + compile_wrapper.splice(gen_common_triton_imports()) + + inductor_meta = { + "kernel_name": name, + **TritonKernel.inductor_meta_common(), + } + + configs = [ + { + "kwargs": config.kwargs, + "num_warps": config.num_warps, + "num_stages": config.num_stages, + } + for config in configs + ] + + compile_wrapper.splice( + f""" + @triton_heuristics.user_autotune( + configs={configs!r}, + inductor_meta={inductor_meta!r}, + triton_meta={triton_meta!r}, + filename=__file__, + custom_kernel=True, + ) + @triton.jit + """ + ) + compile_wrapper.splice(kernel.src, strip=True) + + # Also include any possible kernel being called indirectly + from triton import JITFunction # type: ignore[name-defined, attr-defined] + from triton.language import constexpr # type: ignore[name-defined] + + # global constexpr vars handled above + symbols_included = {original_name} + + def traverse(cur_kernel): + # here we extract the unqualified names (i.e., not attributes and + # without prepended module name) loaded in the kernel code, which + # are matched with the co_names and __globals__ below to codegen + # the respective imports necessary for the kernel compilation + unqualified_loads = { + inst.argval + for inst in dis.Bytecode(cur_kernel.fn) + if inst.opname == "LOAD_GLOBAL" + } + global_annotations = cur_kernel.fn.__globals__.get("__annotations__", {}) + for symbol_name in cur_kernel.fn.__code__.co_names: + if symbol_name in symbols_included: + continue + if symbol_name in cur_kernel.fn.__globals__: + symbol = cur_kernel.fn.__globals__[symbol_name] + if isinstance(symbol, JITFunction): + compile_wrapper.newline() + compile_wrapper.writeline("@triton.jit") + compile_wrapper.splice(symbol.src, strip=True) + symbols_included.add(symbol_name) + traverse(symbol) + elif isinstance(symbol, (int, str, bool, constexpr)): + compile_wrapper.newline() + if isinstance(symbol, constexpr): + symbol_str = f"tl.constexpr({symbol.value!r})" + else: + symbol_str = f"{symbol!r}" + if annotation := global_annotations.get(symbol_name): + annotion_code = "" + if isinstance(annotation, type): + annotation_code = ( + f": {annotation.__module__}.{annotation.__name__}" + ) + else: + annotation_code = f": {annotation!r}" + compile_wrapper.writeline( + f"{symbol_name}{annotation_code} = {symbol_str}" + ) + else: + compile_wrapper.writeline(f"{symbol_name} = {symbol!r}") + symbols_included.add(symbol_name) + elif ( + symbol_name in unqualified_loads + and symbol_name != "tl" # already imported + and hasattr(symbol, "__module__") + # only codegen imports from triton; JITFunctions + # imported from other modules will be codegened + # in the separate branch above + and symbol.__module__.startswith("triton") + ): + # a global symbol imported from triton is referenced + # without module qualification (i.e., `store` instead + # of `tl.store`): need to codegen an import + compile_wrapper.writeline( + f"from {symbol.__module__} import {symbol.__name__} as {symbol_name}" + ) + symbols_included.add(symbol_name) + + traverse(kernel) + + current_device = V.graph.scheduler.get_current_device_or_throw() + compile_wrapper.writeline(f"''', device_str='{current_device.type}')") + _, lineno = inspect.getsourcelines(kernel.fn) + srcfile = inspect.getsourcefile(kernel.fn) + metadata = f"# Original path: {srcfile}:{lineno}" + self.define_kernel( + name, + compile_wrapper.getvalue(), + metadata, + ) + return name, triton_meta + + def generate_numel_expr(self, kernel_name: str, tree, suffix: Optional[str] = None): + expr = f"{kernel_name}_{tree.prefix}numel" + if suffix is not None: + expr += f"_{suffix}" + if (expr, V.graph) not in self.kernel_numel_expr: + # declare expr once in each graph (scope) + self.kernel_numel_expr.add((expr, V.graph)) + self.writeline( + f"{self.declare}{expr} = {self.expr_printer(tree.numel)}{self.ending}" + ) + else: + self.writeline(f"{expr} = {self.expr_printer(tree.numel)}{self.ending}") + # We can get symbolic expressions here, like s0*64 + # It is fine to have them here, but we need to handle them correctly as their own type + # This is tricky to do, so we wrap in a custom type, distinct from scalars, but also from sympy* + # scalars as well. + # This is handled in `generate_args_decl` which has a correct comment of: TODO: only works for + # constant now, need type info. I agree, this needs type info, and while this is not true type info + # it suffices as a type hint for the purposes of producing the correct code for this type. + return SymbolicCallArg(expr, tree.numel) + + def generate_workspace_allocation(self, nbytes, device, zero_fill): + line = self.make_allocation( + "workspace", device, torch.uint8, shape=(nbytes,), stride=(1,) + ) + self.writeline(line) + if zero_fill: + self.writeline(f"workspace.zero_(){self.ending}") + + def wrap_kernel_call(self, name, call_args): + return f"{name}({', '.join(call_args)}){self.ending}" + + def generate_profiler_mark_wrapper_call(self, stack): + self.wrapper_call.writeline("from torch.profiler import record_function") + self.wrapper_call.writeline( + f"with record_function('graph_{V.graph.graph_id}_inductor_wrapper_call'):" + ) + stack.enter_context(self.wrapper_call.indent()) + + def generate_start_graph(self): + self.wrapper_call.writeline("start_graph()") + + def generate_end_graph(self): + self.wrapper_call.writeline(f"end_graph({config.profile_bandwidth_output!r})") + + def generate_reset_kernel_saved_flags(self): + self.wrapper_call.splice( + f""" + for kernel in globals().values(): + if isinstance(kernel, {triton_heuristics.__name__}.CachingAutotuner): + kernel.cuda_kernel_saved = False + """ + ) + + def generate_save_uncompiled_kernels(self): + """ + Precompile and save the CUBINs of the Triton kernels that haven't + been precompiled and saved as a side effect of running the generated + JIT model (Python wrapper). This can happen when the model contains + control flow: only one pass through the control flow operators covers + the kernels that are saved, the remaining kernels are not launched, + hence not saved. The main purpose of this codegen is to compile and + save the Triton kernels outside the active control flow path for + subsequent AOTInductor code generation and compilation. + """ + self.wrapper_call.splice( + f""" + for kernel in globals().values(): + if isinstance(kernel, {triton_heuristics.__name__}.CachingAutotuner): + if not kernel.cuda_kernel_saved: + if len(kernel.launchers) == 0: + kernel.precompile() + kernel.save_gpu_kernel( + grid=(0, 0, 0), # use dummy grid + stream="stream", # use dummy stream + launcher=kernel.launchers[0], + ) + """ + ) + + def generate_default_grid( + self, + kernel_name: str, + grid: List[Any], + cuda: bool = True, + grid_callable: Optional[Callable[..., Any]] = None, + **grid_extra_kwags, + ): + return grid + + def prepare_triton_kernel_call(self, device_index, call_args): + def wrap_arg(arg): + if isinstance(arg, str): + # dynamo wraps unspec variable as 0d CPU tensor, need convert to scalar + return arg + ".item()" if should_unwrap_unspec_arg(arg) else arg + elif isinstance(arg, (int, float, bool, SymbolicCallArg)): + return str(arg) + else: + return self.expr_printer(V.graph.sizevars.simplify(arg)) + + call_args = [wrap_arg(arg) for arg in call_args] + + if device_index is None: + current_device = V.graph.scheduler.get_current_device_or_throw() + device_index = current_device.index + + return device_index, call_args + + def generate_example_arg_value(self, arg, arg_type, raw_arg=None, index=None): + if isinstance(arg_type, torch_dtype): + if V.graph.try_get_buffer(arg) is not None: + buf_name = arg + buf = V.graph.get_buffer(arg) + else: + assert ( + raw_arg is not None + ), "V.graph.get_buffer(arg) and raw_arg can't be None at the same time" + buf_name = f"tmp_arg_{index}" + buf = raw_arg + + size = V.graph.sizevars.size_hints( + buf.get_size(), + fallback=config.unbacked_symint_fallback, + ) + stride = V.graph.sizevars.size_hints( + buf.get_stride(), + fallback=config.unbacked_symint_fallback, + ) + device = buf.get_device() + dtype = buf.get_dtype() + offset = V.graph.sizevars.size_hint( + buf.layout.offset, + fallback=config.unbacked_symint_fallback, + ) + value = f"generate_example_value({size}, {stride}, '{device}', {dtype}, {offset})" + self.kernel_autotune_calls.writeline(f"{buf_name} = {value}") + return buf_name + elif issubclass(arg_type, sympy.Basic) or isinstance(arg, SymbolicCallArg): + # arg is a symbol or symbolic expression + if isinstance(arg, str): + if arg in self._meta_vars: + return arg + if raw_arg is None: + return "None" + arg = raw_arg + if isinstance(arg, SymbolicCallArg): + arg = arg.inner_expr + if arg in V.graph.sizevars.inv_precomputed_replacements: + arg = V.graph.sizevars.inv_precomputed_replacements[arg] + return str( + V.graph.sizevars.size_hint( + arg, + fallback=config.unbacked_symint_fallback, + ) + ) + elif isinstance(arg, (str, int, float, bool)): + return str(arg) + elif isinstance(arg, list): + return f"[{', '.join(self.generate_example_arg_value(a, type(a)) for a in arg)}]" + else: + raise NotImplementedError(f"Unsupported type {type(arg)}") + + def _grid_dim_str(self, grid_per_dim): + if isinstance(grid_per_dim, list): + return ( + "[" + ", ".join(self._grid_dim_str(item) for item in grid_per_dim) + "]" + ) + else: + return pexpr(grid_per_dim) + + def generate_kernel_call( + self, + kernel_name, + call_args, + grid=None, + device_index=None, + cuda=True, + triton=True, + arg_types=None, + raw_args=None, + grid_fn: str = "grid", + triton_meta=None, + autotune_configs=None, + grid_extra_kwargs="", + ): + """ + Generates kernel call code. + + cuda: Defines whether the backend is GPU. Otherwise the backend is CPU. + + triton: Defines whether the GPU backend uses Triton for codegen. + Otherwise it uses the CUDA language for codegen. + Only valid when cuda == True. + """ + if cuda: + device_index, call_args_str = self.prepare_triton_kernel_call( + device_index, call_args + ) + call_args_str = ", ".join(call_args_str) + stream_name = self.write_get_raw_stream(device_index, V.graph) + if triton: + self.write_triton_header_once() + if grid is None: + grid_str = grid_fn + else: + grid_str = ", ".join(self._grid_dim_str(item) for item in grid) + if grid_extra_kwargs: + grid_str = f"{grid_str}, {grid_extra_kwargs}" + grid_str = f"{grid_fn}({grid_str})" + self.writeline( + f"{kernel_name}.run({call_args_str}, grid={grid_str}, stream={stream_name})" + ) + if ( + config.triton.autotune_at_compile_time + and kernel_name not in self.kernel_autotune_names + ): + # Create example args for autotune in a separate epilogue + assert arg_types is not None and len(call_args) == len( + arg_types + ), "call_args and arg_types do not match" + + tensor_args = {} + all_args = [] + if raw_args is None: + # create a dummy raw_args for uniform behavior in the following loop + raw_args = [None] * len(call_args) + else: + assert len(raw_args) == len( + call_args + ), "call_args and raw_args do not match" + + for i, (arg, arg_type, raw_arg) in enumerate( + zip(call_args, arg_types, raw_args) + ): + key = None + if isinstance(arg, str) and "=" in str(arg): + # arg may be passed in a kwarg style, and then we need to extract its value + key, arg = arg.split("=") + + if isinstance(arg_type, torch_dtype): + if arg not in tensor_args: + arg_str = self.generate_example_arg_value( + arg, arg_type, raw_arg, i + ) + tensor_args[arg] = arg_str + else: + arg_str = tensor_args[arg] + else: + arg_str = self.generate_example_arg_value( + arg, arg_type, raw_arg, i + ) + all_args.append(arg_str if key is None else f"{key}={arg_str}") + + if grid is None: + grid_str = grid_fn + else: + grid_str = ", ".join( + self.generate_example_arg_value(g, type(g)) for g in grid + ) + if grid_extra_kwargs: + grid_str = f"{grid_str}, {grid_extra_kwargs}" + grid_str = f"{grid_fn}({grid_str})" + + self.kernel_autotune_calls.writeline( + f"{kernel_name}.run({', '.join(all_args)}, grid={grid_str}, stream={stream_name})" + ) + self.kernel_autotune_calls.writeline( + f"del {', '.join(arg for arg in tensor_args.values())}\n", + ) + self.kernel_autotune_names.add(kernel_name) + else: + stream_ptr = f"c_void_p({stream_name})" + self.writeline( + f"{kernel_name}.{kernel_name}({call_args_str}, {stream_ptr})" + ) + else: + self.writeline(self.wrap_kernel_call(kernel_name, call_args)) + + def writeline(self, line): + self.lines.append(line) + + def writelines(self, lines): + for line in lines: + self.writeline(line) + + def enter_context(self, ctx): + self.lines.append(LineContext(ctx)) + + def val_to_arg_str(self, s, type_=None): + from torch.utils._triton import dtype_to_string, has_triton_package + + if has_triton_package(): + import triton + + if isinstance(s, SymTypes): + return pexpr(s.node.expr) + elif isinstance(s, sympy.Expr): + return pexpr(s) + elif isinstance(s, (tuple, list)): + + @dataclasses.dataclass + class Shim: + ref: Any + + def __repr__(self): + return self.ref + + return repr(type(s)(Shim(self.val_to_arg_str(a)) for a in s)) + elif isinstance(s, torch._ops.OpOverload): + return _get_qualified_name(s) + elif isinstance(s, (ir.Buffer, ReinterpretView)): + return s.codegen_reference() + elif has_triton_package() and isinstance(s, triton.language.dtype): # type: ignore[possibly-undefined] + return dtype_to_string(s) + else: + return repr(s) + + # The following methods are for memory management + def make_buffer_allocation(self, buffer): + device = buffer.get_device() + dtype = buffer.get_dtype() + shape = tuple(buffer.get_size()) + stride = tuple(buffer.get_stride()) + return self.make_allocation(buffer.get_name(), device, dtype, shape, stride) + + def make_allocation(self, name, device, dtype, shape, stride): + if device.type in ("cpu", "cuda", "xpu"): + # optimized path for faster allocations, saving ~2us versus the stuff below + return ( + f"{name} = empty_strided_{device.type}(" + f"{self.codegen_shape_tuple(shape)}, " + f"{self.codegen_shape_tuple(stride)}, " + f"{dtype})" + ) + # all other devices: + return ( + f"{name} = empty_strided(" + f"{self.codegen_shape_tuple(shape)}, " + f"{self.codegen_shape_tuple(stride)}, " + f"device='{device.type}', dtype={dtype})" + ) + + def make_tensor_alias(self, new_name, old_name, comment=""): + return f"{self.declare}{new_name} = {old_name}{self.ending} {self.comment} {comment}" + + def make_buffer_free(self, buffer): + return f"del {buffer.get_name()}" + + def make_free_by_names(self, names_to_del: List[str]): + return f"del {', '.join(name for name in names_to_del)}" + + def codegen_exact_buffer_reuse(self, old_name: str, new_name: str, del_line: str): + return f"{self.declare_maybe_reference}{new_name} = {old_name}{del_line}{self.ending} {self.comment} reuse" + + def make_buffer_reuse(self, old: ir.Buffer, new: ir.Buffer, delete_old: bool): + assert old.get_dtype() == new.get_dtype() + old_name = old.get_name() + new_name = new.get_name() + del_line = ";" + if old_name not in V.graph.get_output_names() and delete_old: + del_line = f"; {self.make_buffer_free(old)}" + + if old.get_size() == new.get_size() and old.get_stride() == new.get_stride(): + if old_name in self.stack_allocated_buffers: + self.stack_allocated_buffers[new_name] = new + return self.codegen_exact_buffer_reuse(old_name, new_name, del_line) + + reinterpret_view = self.codegen_reinterpret_view( + old, new.get_size(), new.get_stride(), 0, self.wrapper_call + ) + if reinterpret_view in self.stack_allocated_buffers: + self.stack_allocated_buffers[new_name] = new + return f"{self.declare_maybe_reference}{new_name} = {reinterpret_view}{del_line} {self.comment} reuse" + + def codegen_deferred_allocation(self, name, layout): + self.writeline( + DeferredLine( + name, + f"{self.declare_maybe_reference}{name} = {layout.view.codegen_reference()}{self.ending} " + f"{self.comment} alias", + ) + ) + + def codegen_allocation(self, buffer: ir.Buffer): + name = buffer.get_name() + + if name in V.graph.removed_buffers or name in self.allocated: + return + self.allocated.add(name) + if isinstance( + buffer.get_defining_op(), + (ir.ExternKernelAlloc, ir.MultiOutput), + ): + return + + layout = buffer.get_layout() + if isinstance(layout, ir.MutationLayoutSHOULDREMOVE): + return + if isinstance(layout, ir.NoneLayout): + return + if isinstance(layout, ir.NonOwningLayout): + assert isinstance( + layout.view, ir.ReinterpretView + ), f"unexpected {type(layout.view)}: {layout.view}" + assert isinstance(layout.view.data, ir.StorageBox), type(layout.view.data) + assert isinstance(layout.view.data.data, ir.Buffer), type(layout.view.data) + self.codegen_allocation(layout.view.data.data) + self.codegen_deferred_allocation(name, layout) + return + + self.writeline(AllocateLine(self, buffer)) + + def codegen_free(self, buffer): + name = buffer.get_name() + + # can be freed but not reused + if isinstance(buffer, ir.InputBuffer): + self.writeline(self.make_buffer_free(buffer)) + return + + if not self.can_reuse(buffer): + return + self.freed.add(name) + + self.writeline(FreeIfNotReusedLine(self, buffer)) + + def can_reuse(self, input_buffer, output_buffer=None): + name = input_buffer.get_name() + return not ( + name in V.graph.removed_buffers + or name in V.graph.graph_inputs + or name in V.graph.constants + or name in V.graph.torchbind_constants + or name in V.graph.never_reuse_buffers + or name in self.freed + ) + + def did_reuse(self, buffer, reused_buffer): + # Check whether a given buffer was reused by a possible reuser in the wrapper codegen + # Can be consulted from inside ir codegen, e.g. to determine whether a copy is needed + return ( + buffer.get_name() in self.reuses + and self.reuses[buffer.get_name()] == reused_buffer.get_name() + ) + + def codegen_inplace_reuse(self, input_buffer: ir.Buffer, output_buffer: ir.Buffer): + assert buffer_reuse_key(input_buffer) == buffer_reuse_key(output_buffer) + self.codegen_allocation(input_buffer) + self.freed.add(input_buffer.get_name()) + self.allocated.add(output_buffer.get_name()) + self.reuses[output_buffer.get_name()] = input_buffer.get_name() + self.writeline(ReuseLine(self, input_buffer, output_buffer)) + + def codegen_unbacked_symbol_decl(self, symbol): + name = str(symbol) + if name in self.unbacked_symbol_decls: + return name + else: + # When in CppWrapperCpu, we should only generate the declaration once + self.unbacked_symbol_decls.add(name) + return self.declare + name + + def codegen_subgraph_prefix(self, subgraph, outer_inputs, outer_outputs): + for inner_input, outer_input in zip(subgraph.graph.graph_inputs, outer_inputs): + self.writeline(f"{self.declare}{inner_input} = {outer_input}{self.ending}") + + def codegen_subgraph_suffix(self, subgraph, outer_inputs, outer_outputs): + for inner_output, outer_output in zip( + subgraph.graph.graph_outputs, outer_outputs + ): + self.writeline( + f"{outer_output} = {inner_output.codegen_reference()}{self.ending}" + ) + + def codegen_subgraph(self, subgraph, outer_inputs, outer_outputs): + try: + self.push_codegened_graph(subgraph.graph) + self.writeline(f"{self.comment} subgraph: {subgraph.name}") + self.codegen_subgraph_prefix(subgraph, outer_inputs, outer_outputs) + parent_graph = V.graph + with V.set_graph_handler(subgraph.graph): + subgraph.graph.codegen_subgraph( + parent_graph=parent_graph, + ) + self.codegen_subgraph_suffix(subgraph, outer_inputs, outer_outputs) + finally: + self.pop_codegened_graph() + + def codegen_conditional(self, conditional): + name = conditional.get_name() + + self.writeline(f"{name} = [None] * {len(conditional.outputs)}") + + outer_inputs = [buf.codegen_reference() for buf in conditional.operands] + outer_outputs = [f"{name}[{i}]" for i in range(len(conditional.outputs))] + + predicate = conditional.predicate.codegen_reference() + if not isinstance(conditional.predicate, ir.ShapeAsConstantBuffer): + # move the Tensor predicate to host + predicate = f"{predicate}.item()" + + self.writeline(f"{name} = [None] * {len(conditional.outputs)}") + self.writeline(f"if {predicate}:") + self.writeline(EnterSubgraphLine(self, conditional.true_subgraph.graph)) + self.codegen_subgraph(conditional.true_subgraph, outer_inputs, outer_outputs) + self.writeline(ExitSubgraphLine(self)) + self.writeline("else:") + self.writeline(EnterSubgraphLine(self, conditional.false_subgraph.graph)) + self.codegen_subgraph(conditional.false_subgraph, outer_inputs, outer_outputs) + self.writeline(ExitSubgraphLine(self)) + + def codegen_while_loop(self, while_loop): + name = while_loop.get_name() + outer_carried_inputs = [ + buf.codegen_reference() for buf in while_loop.carried_inputs + ] + outer_additional_inputs = [ + buf.codegen_reference() for buf in while_loop.additional_inputs + ] + + self.writeline(f"{name} = [None] * {len(outer_carried_inputs)}") + for i, inp in enumerate(outer_carried_inputs): + # set the initial state before the loop + self.writeline(f"{name}[{i}] = {inp}") + + cond_outer_inputs = [ + *[f"{name}[{i}]" for i in range(len(outer_carried_inputs))], + *outer_additional_inputs, + ] + cond_outer_outputs = [f"{name}_cond_result"] + body_outer_inputs = list( + cond_outer_inputs + ) # same inputs for cond_fn and body_fn + # Carry over the state from body_fn. Note: We only carry over + # the carried_inputs part of the inputs, the additional ones + # are passed in as they're before. + body_outer_outputs = body_outer_inputs[: len(outer_carried_inputs)] + + self.writeline("while True:") + self.writeline(EnterSubgraphLine(self, while_loop.cond_subgraph.graph)) + self.codegen_subgraph( + while_loop.cond_subgraph, cond_outer_inputs, cond_outer_outputs + ) + self.writeline( + f"if not {cond_outer_outputs[0]}.item(): break" + ) # condition doesn't hold + self.writeline(ExitSubgraphLine(self)) + self.writeline(EnterSubgraphLine(self, while_loop.body_subgraph.graph)) + self.codegen_subgraph( + while_loop.body_subgraph, body_outer_inputs, body_outer_outputs + ) + self.writeline(ExitSubgraphLine(self)) + + @staticmethod + def statically_known_int_or_none(x): + try: + if getattr(x, "free_symbols", None): + # _maybe_evaluate_static will return (s0 // (2 // s0)) as 2, but + # the actual codegen will still generate the full expression here. + return None + if isinstance(x, int): + return x + val = V.graph._shape_env._maybe_evaluate_static(x) + return int(val) + except Exception: + return None + + @staticmethod + def statically_known_list_of_ints_or_none(lst): + result = [] + for x in lst: + num = WrapperCodeGen.statically_known_int_or_none(x) + if num is None: + return None + result.append(num) + return result + + @staticmethod + def is_statically_known_list_of_ints(lst): + return WrapperCodeGen.statically_known_list_of_ints_or_none(lst) is not None + + @staticmethod + def static_shape_for_buffer_or_none(buffer): + return WrapperCodeGen.statically_known_list_of_ints_or_none(buffer.get_size()) + + @staticmethod + def can_prove_buffer_has_static_shape(buffer): + return WrapperCodeGen.static_shape_for_buffer_or_none(buffer) is not None diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/comm_analysis.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/comm_analysis.py new file mode 100644 index 0000000000000000000000000000000000000000..f8a233a3b9e21eb33d79b6ad60cd3ba4f276ddae --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/comm_analysis.py @@ -0,0 +1,264 @@ +import functools +import math +from enum import IntEnum + +import sympy + +import torch + +from . import ir +from .utils import get_dtype_size, sympy_product +from .virtualized import V + + +class NCCL_COLL(IntEnum): + ALL_REDUCE = 0 + ALL_GATHER = 1 + REDUCE_SCATTER = 2 + + +class NVIDIA_GPU_TYPE(IntEnum): + VOLTA = 0 + AMPERE = 1 + HOPPER = 2 + + +@functools.lru_cache +def get_gpu_type() -> NVIDIA_GPU_TYPE: + gpu_info = torch.utils.collect_env.get_gpu_info(torch.utils.collect_env.run) or "" + if "V100" in gpu_info: + return NVIDIA_GPU_TYPE.VOLTA + elif "A100" in gpu_info: + return NVIDIA_GPU_TYPE.AMPERE + elif "H100" in gpu_info: + return NVIDIA_GPU_TYPE.HOPPER + else: + # for other gpu types, assume Ampere + return NVIDIA_GPU_TYPE.AMPERE + + +def get_collective_type(node: ir.IRNode) -> NCCL_COLL: + if not isinstance(node, ir._CollectiveKernel): + raise ValueError(f"node is not a collective kernel: {node}") + + kernel_name = node.python_kernel_name + assert kernel_name is not None + if "all_reduce" in kernel_name: + return NCCL_COLL.ALL_REDUCE + elif "all_gather" in kernel_name: + return NCCL_COLL.ALL_GATHER + elif "reduce_scatter" in kernel_name: + return NCCL_COLL.REDUCE_SCATTER + else: + raise ValueError(f"Unsupported collective kernel: {kernel_name}") + + +def get_collective_input_size_bytes(node: ir.IRNode) -> int: + sz_bytes = 0 + for inp in node.inputs: # type: ignore[attr-defined] + numel = sympy_product(inp.layout.size) + if isinstance(numel, sympy.Integer): + # For ease of testing + numel = int(numel) + else: + numel = V.graph.sizevars.size_hint(numel, fallback=0) + sz_bytes += numel * get_dtype_size(inp.layout.dtype) + return sz_bytes + + +def get_collective_group_size(node: ir.IRNode) -> int: + if type(node) == ir._CollectiveKernel: + from torch.distributed.distributed_c10d import _get_group_size_by_name + + return _get_group_size_by_name(node.constant_args[-1]) + else: + raise TypeError(f"Unsupported collective type: {node}") + + +#################################################################################################################### +# The following code and constants are adapted from https://github.com/NVIDIA/nccl/blob/master/src/graph/tuning.cc # +#################################################################################################################### + + +class NCCL_HW(IntEnum): + NVLINK = 0 + PCI = 1 + NET = 2 + + +class NCCL_ALGO(IntEnum): + TREE = 0 + RING = 1 + + +class NCCL_PROTO(IntEnum): + # The ordering and enum values here matches original in + # https://github.com/NVIDIA/nccl/blob/0b083e52096c387bad7a5c5c65b26a9dca54de8c/src/include/devcomm.h#L28 + # For difference between these protocols, see https://github.com/NVIDIA/nccl/issues/281#issuecomment-571816990 + LL = 0 # Low-latency + # LL128 = 1 # Low-latency 128-byte + # SIMPLE = 2 + + +# Latencies in us +# len(NCCL_ALGO) x len(NCCL_PROTO) +# NOTE: use array instead of tensor to prevent incompatibility with fake mode +baseLat = [ + # Tree + [ + 6.8, # LL + ], + # Ring + [ + 6.6, # LL + ], +] + +# Latencies in us +# len(NCCL_HW) x len(NCCL_ALGO) x len(NCCL_PROTO) +hwLat = [ + # NVLINK + [ + [0.6], # Tree (LL) + [0.6], # Ring (LL) + ], + # PCI + [ + [1.0], # Tree (LL) + [1.0], # Ring (LL) + ], + # NET + [ + [5.0], # Tree (LL) + [2.7], # Ring (LL) + ], +] + + +# LL128 max BW per channel +llMaxBws = [ + # Volta-N1/Intel-N2/Intel-N4 + [ + 39.0, + 39.0, + 20.4, + ], + # Ampere-N1/AMD-N2/AMD-N4 + [ + 87.7, + 22.5, # avg of ring & tree + 19.0, + ], + # Hopper-N1/AMD-N2/AMD-N4 + [ + 87.7, + 22.5, # avg of ring & tree + 19.0, + ], +] + + +def estimate_nccl_collective_runtime(node: ir.IRNode) -> float: + """ + Returns estimated NCCL collective runtime in nanoseconds (ns). + + The following heuristics are copied from https://github.com/NVIDIA/nccl/blob/master/src/graph/tuning.cc. + We aim to estimate the runtime as accurately as possible. + + Assumptions: + - only ring algorithm (NCCL_ALGO_RING) is used + - only Low-Latency protocol (NCCL_PROTO_LL) is used, i.e. Simple or LL128 is not used + - 8 gpus per node # TODO: Need to find a way to get accurate "gpus per node" and "# nodes" info. + - collective is one of: allreduce, reducescatter, allgather + """ + tensor_storage_size_bytes = get_collective_input_size_bytes(node) + # Convert bytes to GB + tensor_storage_size_GB = tensor_storage_size_bytes / 1024 / 1024 / 1024 + + # Currently assumes each node has 8 gpus. And when >1 node is used, assumes each node uses all 8 gpus. + # TODO: Need to find a way to get accurate "gpus per node" and "# nodes" info. + num_gpus_per_node = 8 + group_size = get_collective_group_size(node) + nNodes = math.ceil(group_size / num_gpus_per_node) + nRanks = group_size # this is total # of gpus globally that participate in this collective op + + if nRanks <= 1: + return 0 + + # Assumes ring algorithm + nccl_algo = NCCL_ALGO.RING + nccl_proto = NCCL_PROTO.LL + coll = get_collective_type(node) + + # =============== bandwidth computation =============== + # First compute bandwidth in GB/s; then at the end, convert it to GB/ns + + bwIntra = torch._inductor.config.intra_node_bw + bwInter = torch._inductor.config.inter_node_bw + + compCapIndex = get_gpu_type() + index2 = nNodes - 1 if nNodes <= 2 else 2 + # LL: for single node, we look at GPU type; for multi-node, we look at CPU type + index1 = compCapIndex if nNodes == 1 else 0 + llMaxBw = llMaxBws[index1][index2] + + # NOTE: each step of ring algorithm is synchronized, + # and is bottlenecked by the slowest link which is the inter-node interconnect. + # hence when nNodes >= 2, bw is inter-node bandwidth. + # NOTE: the original code in https://github.com/NVIDIA/nccl/blob/master/src/graph/tuning.cc + # have this as `if nNodes <= 2` which seems wrong. Corrected it here. + bw = bwIntra if nNodes == 1 else bwInter + nChannels = 2 # Assume # channels is 2 + busBw = nChannels * bw + + # Various model refinements + busBw = min( + llMaxBw, + busBw + * (1.0 / 4.0 if (nNodes > 1 or coll == NCCL_COLL.ALL_REDUCE) else 1.0 / 3.0), + ) + + if coll == NCCL_COLL.ALL_REDUCE: + nsteps = 2 * (nRanks - 1) + elif coll in (NCCL_COLL.REDUCE_SCATTER, NCCL_COLL.ALL_GATHER): + nsteps = nRanks - 1 + + # Convert bus BW to algorithm BW (tensor bytes / algoBW = actual execution time) + ratio = (1.0 * nRanks) / nsteps # type: ignore[possibly-undefined] + bandwidth = busBw * ratio + # Convert GB/s to GB/ns + bandwidth_GB_per_ns = bandwidth / 1e9 + + # =============== latency computation =============== + intraHw = NCCL_HW.NVLINK + + if coll == NCCL_COLL.ALL_REDUCE: + if nNodes > 1: + nInterSteps = 2 * nNodes + else: + nInterSteps = 0 + elif coll in (NCCL_COLL.REDUCE_SCATTER, NCCL_COLL.ALL_GATHER): + nInterSteps = nNodes - 1 + + # First compute latency in us; then at the end, convert it to ns + latency = baseLat[nccl_algo][nccl_proto] + intraLat = hwLat[intraHw][nccl_algo][nccl_proto] + interLat = hwLat[NCCL_HW.NET][nccl_algo][nccl_proto] + + # Inter-node rings still have to launch nsteps * net overhead. + netOverhead = 0.0 + if nNodes > 1: + netOverhead = 1.0 # getNetOverhead(comm); + intraLat = max(intraLat, netOverhead) + latency += (nsteps - nInterSteps) * intraLat + nInterSteps * interLat # type: ignore[possibly-undefined] + # Convert us to ns + latency_ns = latency * 1e3 + + # =============== final result =============== + transport_ns = tensor_storage_size_GB / bandwidth_GB_per_ns + return transport_ns + latency_ns + + +################################################################################################################ +# The above code and constants are adapted from https://github.com/NVIDIA/nccl/blob/master/src/graph/tuning.cc # +################################################################################################################ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/comms.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/comms.py new file mode 100644 index 0000000000000000000000000000000000000000..dcad1e1bf67c9841937792f06e1eac6cda52ce62 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/comms.py @@ -0,0 +1,640 @@ +# mypy: allow-untyped-defs +# pyre-strict +from __future__ import annotations + +import heapq +import operator +import sys +from collections import defaultdict +from typing import Dict, List, Set, TYPE_CHECKING + +import torch + +from . import config, ir +from .dependencies import WeakDep +from .utils import ( + contains_collective, + contains_wait, + find_recursive_deps_of_node, + find_recursive_users_of_node, + is_collective, + is_fallback_op, + is_wait, +) + + +overlap_log = torch._logging.getArtifactLogger(__name__, "overlap") + +if TYPE_CHECKING: + from .scheduler import BaseSchedulerNode + + +def sink_waits(snodes: List[BaseSchedulerNode]) -> List[BaseSchedulerNode]: + """ + Greedily schedules waits as late as possible. + """ + return _schedule_for_comm( + snodes, raise_comms=False, sink_waits=True, reorder_for_overlap=False + ) + + +def raise_comms(snodes: List[BaseSchedulerNode]) -> List[BaseSchedulerNode]: + """ + Greedily schedules comms as early as possible. + """ + return _schedule_for_comm( + snodes, raise_comms=True, sink_waits=False, reorder_for_overlap=False + ) + + +def reorder_compute_for_overlap( + snodes: List[BaseSchedulerNode], +) -> List[BaseSchedulerNode]: + """ + This achieves the following overall scheduling procedure: + Step 1: Given that we've currently scheduled comm N, we now schedule all compute nodes + that are required for comm N + 1 but do not depend on comm N, to run at the same time with comm N. + Step 2: If all those compute nodes are sufficient to overlap comm N, we're done. + Otherwise, we now need to look elsewhere to find compute that overlaps with comm N. + We prioritize compute nodes that are needed sooner. + Step 3: We schedule the compute nodes dependent on comm N and required for comm N + 1. + Step 4: We schedule comm N + 1. + Repeat this for subsequent comm nodes. + """ + return _schedule_for_comm( + snodes, raise_comms=True, sink_waits=True, reorder_for_overlap=True + ) + + +def _schedule_for_comm( + snodes: List[BaseSchedulerNode], + raise_comms: bool, + sink_waits: bool, + reorder_for_overlap: bool, +) -> List[BaseSchedulerNode]: + """ + Schedule `snodes` for various comm optimization objectives. + + Args: + snodes: the nodes to be scheduled. + raise_comms: whether to greedily schedule collectives as early as possible + sink_wait: whether to greedily schedule waits as late as possible + reorder_compute_for_overlap: whether to reorder compute nodes to + optimize for compute/communication overlapping. + + Returns: + The new schedule order. + + Some notes on the synergy between different options: + - `raise_comms` provides more overlapping oppurtunies for `reorder_compute_for_overlap`. + - When both `raise_comms` and `sink_waits` is `True`, `raise_comms` is prioritized. + """ + # We assign each node a tuple of scores (score_0, score_1, score_2), + # decreasing in importance, with a lower value indicating a higher ranking: + # + # - score_0: the lowest comm_idx among the comm nodes that the node blocks. + # If a node doesn't block any comm nodes, its score_0 is set to + # sys.maxsize. This score ensures that comm nodes get scheduled as early as + # possible. + # - score_1: 1 if the node is a wait node, 0 otherwise. This score ensures + # that wait nodes are deferred as late as possible. + # - score_2: the index of the node in the original topological order. This + # score provides stability in case of ties. + # + # When only raise_comms is True, only score_0 and score_2 are considered. + # When only sink_waits is True, only score_1 and score_2 are considered. + # When neither is True, the original order is yielded. + buf_name_to_snode = {} + name_to_fused_node = {} + scores_0, scores_1, scores_2 = {}, {}, {} + for idx, snode in enumerate(snodes): + for buf_name in snode.get_buffer_names(): + buf_name_to_snode[buf_name] = snode + + for op_name in snode.get_operation_names(): + name_to_fused_node[op_name] = snode + name_to_fused_node[snode.get_name()] = snode + + node_name = snode.get_name() + scores_0[node_name] = sys.maxsize + scores_1[node_name] = 0 + scores_2[node_name] = idx + + comm_idx = 0 + for snode in snodes: + if raise_comms and contains_collective(snode): + scores_0[snode.get_name()] = comm_idx + for anc in snode.ancestors: + anc_fused_name = name_to_fused_node[anc].get_name() + scores_0[anc_fused_name] = min(scores_0[anc_fused_name], comm_idx) + comm_idx += 1 + elif sink_waits and contains_wait(snode): + scores_1[snode.get_name()] = 1 + + class Runnable: + def __init__(self, snode) -> None: + self.snode = snode + name = next(iter(snode.get_operation_names())) + fused_name = name_to_fused_node[name].get_name() + self.score = ( + scores_0[fused_name], + scores_1[fused_name], + scores_2[fused_name], + ) + + def __lt__(self, other): + return self.score < other.score + + unmet_deps: Dict[BaseSchedulerNode, Set[str]] = { + snode: {dep.name for dep in snode.unmet_dependencies} for snode in snodes + } + + ready: List[Runnable] = [] + buffer_users: Dict[str, Set[BaseSchedulerNode]] = defaultdict(set) + snode_to_cost = {snode: estimate_op_runtime(snode) for snode in snodes} + + for snode, deps in unmet_deps.items(): + if len(deps) == 0: + heapq.heappush(ready, Runnable(snode)) + for dep in deps: + buffer_users[dep].add(snode) + + scheduled = [] + + def schedule(snode): + """ + Schedules `snode` and put all unblocked nodes onto the ready queue. + """ + scheduled.append(snode) + for buf_name in snode.get_buffer_names(): + for snode in buffer_users[buf_name]: + unmet_deps[snode].remove(buf_name) + if len(unmet_deps[snode]) == 0: + heapq.heappush(ready, Runnable(snode)) + + def get_overlapping_candidate(): + """ + Return the next node in the ready queue that's neither a collective or + a wait. + """ + candidates = [ + x + for x in ready + if not contains_collective(x.snode) and not contains_wait(x.snode) + ] + if len(candidates) == 0: + return None + return min(candidates, key=lambda x: x.score) + + def schedule_collective_for_overlap(snode): + """ + Schedules collective node `snode`, along with one or more compute nodes + to overlap with it. The strategy is described in the comment of + `reorder_compute_for_overlap`. + """ + assert contains_collective(snode) + schedule(snode) + + collective_cost = snode_to_cost[snode] + while ( + collective_cost > 0 + and (candidate := get_overlapping_candidate()) is not None + ): + ready.remove(candidate) + schedule(candidate.snode) + collective_cost -= snode_to_cost[candidate.snode] + heapq.heapify(ready) + + while len(ready): + snode = heapq.heappop(ready).snode + if reorder_for_overlap and contains_collective(snode): + schedule_collective_for_overlap(snode) + else: + schedule(snode) + + for snode, deps in unmet_deps.items(): + assert len(deps) == 0, ( + "Detected unscheduled nodes. " + f"Nodes with unmet dependencies: {unmet_deps}" + ) + return scheduled + + +def decide_global_ordering_of_comms( + nodes: List[BaseSchedulerNode], name_to_buf, name_to_fused_node +) -> List[BaseSchedulerNode]: + """ + Decide global ordering of comms, by just enforcing the ordering that's in the input graph + (might not be the same ordering as the eager mode program). + TODO: Come up with a better approach + """ + # If FSDP2 is used, we apply FSDP-specific passes. + if any( + is_fallback_op( + x.node, + { + torch.ops.fsdp.all_gather_copy_in.default, + torch.ops.fsdp.chunk_cat.default, + }, + ) + for x in nodes + ): + nodes = enforce_comm_ordering_for_fsdp(nodes, name_to_buf, name_to_fused_node) + + comm_nodes = [n for n in nodes if contains_collective(n)] + + for i in range(1, len(comm_nodes)): + # Enforce ordering by making previous comm a `WeakDep` dependency of the next comm + mutating_buf = next(iter(comm_nodes[i].get_buffer_names())) + for buf in comm_nodes[i - 1].get_buffer_names(): + comm_nodes[i].add_fake_dep(WeakDep(buf, mutating_buf=mutating_buf)) + + return nodes + + +def estimate_op_runtime(snode: BaseSchedulerNode) -> float: + """ + Returns estimated op runtime in nanoseconds (ns) + """ + if config.estimate_op_runtime == "default": + runtime = snode.get_estimated_runtime() + else: + assert callable(config.estimate_op_runtime) + runtime = config.estimate_op_runtime(snode) + return runtime + + +def node_summary(snode): + detail = "" + if isinstance(snode.node, ir.ExternKernelOut): + detail = f" ({snode.node.python_kernel_name})" + out_tensor_info = "" + if ( + hasattr(snode.node, "layout") + and hasattr(snode.node.layout, "size") + and hasattr(snode.node.layout, "stride") + ): + out_tensor_info = ( + f" (size={snode.node.layout.size}, stride={snode.node.layout.stride})" + ) + node_name = "" + if hasattr(snode.node, "name"): + node_name = snode.node.name + return f"{snode.node.__class__.__name__}{detail}{out_tensor_info} ({node_name})" + + +def visualize_overlap(order): + total_est_runtime: float = 0.0 + cur_comm_node = None + for snode in order: + if cur_comm_node is None: + if contains_collective(snode): + total_est_runtime += estimate_op_runtime(snode) + cur_comm_node = snode.node + elif is_wait(snode.node): + raise AssertionError( + "Wait is not expected when there is no collective running" + ) + else: # exposed compute op + total_est_runtime += estimate_op_runtime(snode) + overlap_log.debug(f"{node_summary(snode)}") # noqa: G004 + else: # cur_comm_node is not None + if contains_collective(snode): + raise AssertionError( + "Found two collectives running at the same time. " + "`visualize_overlap` needs to be updated to handle this case" + ) + elif is_wait(snode.node): # end of this comm op + overlap_log.debug(f"{node_summary(snode)}") # noqa: G004 + cur_comm_node = None + else: # overlapped compute op + overlap_log.debug(f"| {node_summary(snode)}") # noqa: G004 + overlap_log.debug( + f"Est. runtime (ms): {total_est_runtime / 1000 / 1000}" # noqa: G004 + ) + + +def reorder_compute_and_comm_for_overlap( + snodes: List[BaseSchedulerNode], +) -> List[BaseSchedulerNode]: + order = snodes + + for p in config.reorder_for_compute_comm_overlap_passes: + if isinstance(p, str) and p in globals(): + p = globals()[p] # it is a builtin pass + if torch.distributed.get_rank() == 0: + overlap_log.debug( + f"==== Visualize overlap before reordering pass {p} ====" # noqa: G004 + ) + try: + visualize_overlap(order) + except Exception as e: + overlap_log.debug(str(e)) + order = p(order) # type: ignore[operator] + if torch.distributed.get_rank() == 0: + overlap_log.debug( + f"==== Visualize overlap after reordering pass {p} ====" # noqa: G004 + ) + try: + visualize_overlap(order) + except Exception as e: + overlap_log.debug(str(e)) + return order + + +def reinplace_fsdp_all_gather(graph: torch.fx.Graph) -> None: + try: + import torch.distributed._composable.fsdp._fsdp_collectives + + assert torch.distributed.is_available() + # Assert existence of these ops + assert ( + torch.ops._c10d_functional.all_gather_into_tensor + and torch.ops._c10d_functional.all_gather_into_tensor_out + ) + except (ImportError, AttributeError, AssertionError): + return + + from .pattern_matcher import ( + CallFunction, + KeywordArg, + Match, + PatternMatcherPass, + register_graph_pattern, + ) + + """ + all_gather_copy_in = torch.ops.fsdp.all_gather_copy_in.default(...); + getitem = all_gather_copy_in[0]; + (getitem_1 = all_gather_copy_in[1];) # optional + + all_gather_into_tensor = torch.ops._c10d_functional.all_gather_into_tensor.default(getitem, ...); + + -> + + all_gather_copy_in = torch.ops.fsdp.all_gather_copy_in.default(...); + getitem = all_gather_copy_in[0]; + getitem_1 = all_gather_copy_in[1]; + + all_gather_into_tensor = torch.ops._c10d_functional.all_gather_into_tensor_out.default(getitem, ..., out=getitem_1); + """ + + def remove_unused_getitem(g): + # Remove `getitem_X = all_gather_copy_in[1]` which is never used. + node_list = list(g.nodes) + for n in node_list: + if ( + n.target == operator.getitem + and n.args[0].target is torch.ops.fsdp.all_gather_copy_in.default + and n.args[1] == 1 + ): + g.erase_node(n) + + graph_pass = PatternMatcherPass() + + @register_graph_pattern( + CallFunction( + torch.ops._c10d_functional.all_gather_into_tensor.default, + CallFunction( + operator.getitem, + CallFunction( + torch.ops.fsdp.all_gather_copy_in.default, + KeywordArg("all_gather_inputs"), + KeywordArg("inp_split_sizes"), + KeywordArg("all_gather_input_numel"), + KeywordArg("world_size"), + KeywordArg("rank"), + KeywordArg("dtype"), + KeywordArg("device"), + ), + KeywordArg("item_idx"), + ), + KeywordArg("group_size"), + KeywordArg("group_name"), + ), + pass_dict=graph_pass, + extra_check=lambda match: match.kwargs["item_idx"] == 0, + ) + def reinplace_all_gather(match: Match, *args, **kwargs): + def repl( + *args, + ): + copy_in_args = args[:-2] + group_size = args[-2] + group_name = args[-1] + all_gather_copy_in = torch.ops.fsdp.all_gather_copy_in.default( + *copy_in_args + ) + getitem = all_gather_copy_in[0] + getitem_1 = all_gather_copy_in[1] + all_gather_into_tensor = ( + torch.ops._c10d_functional.all_gather_into_tensor_out.default( + getitem, group_size, group_name, out=getitem_1 + ) + ) + return all_gather_into_tensor + + match.replace_by_example( + repl, + [ + kwargs["all_gather_inputs"], + kwargs["inp_split_sizes"], + kwargs["all_gather_input_numel"], + kwargs["world_size"], + kwargs["rank"], + kwargs["dtype"], + kwargs["device"], + kwargs["group_size"], + kwargs["group_name"], + ], + ) + + remove_unused_getitem(graph) + graph_pass.apply(graph) # type: ignore[arg-type] + + +def get_op_idx(snode): + assert not isinstance( + snode, + ( + torch._inductor.scheduler.FusedSchedulerNode, + torch._inductor.scheduler.GroupedSchedulerNode, + ), + ) + return int(snode.get_name()[2:]) + + +def enforce_comm_ordering_for_fsdp( + snodes: List[torch._inductor.scheduler.BaseSchedulerNode], + name_to_buf: Dict[str, torch._inductor.scheduler.SchedulerBuffer], + name_to_fused_node: Dict[str, BaseSchedulerNode], +) -> List[torch._inductor.scheduler.BaseSchedulerNode]: + from . import scheduler + + new_order: list[BaseSchedulerNode] = [] + scheduled = set() + ag_exists = False + rs_exists = False + ag_grouped_node_to_wait_grouped_node = {} + rs_grouped_node_to_wait_grouped_node = {} + snode_name_to_final_snode = {} + + def _create_group_node(snodes_to_group): + group_node = scheduler.GroupedSchedulerNode.create(snodes_to_group) + for snode in snodes_to_group: + snode_name_to_final_snode[snode.get_name()] = group_node + snode_name_to_final_snode[group_node.get_name()] = group_node + return group_node + + # Create grouped nodes for specific sets of ops + for snode in snodes: + # Case 1: Handle AllGather + if is_collective( + snode.node, op=torch.ops._c10d_functional.all_gather_into_tensor_out.default + ) and any( + is_fallback_op( + name_to_fused_node[x].node, torch.ops.fsdp.all_gather_copy_in.default + ) + for x in snode.ancestors + ): + ag_exists = True + ag_snode = snode + ag_related_snode_set: set[scheduler.BaseSchedulerNode] = set() + + # Find the "cast + copy_in + getitem + all_gather" code block + find_recursive_deps_of_node( + ag_snode, + ag_related_snode_set, + name_to_buf, + name_to_fused_node, + ) + + # Find the "all_gather + all_gather_wait_tensor + copy_out + set_" code block + allowed_ops = { + torch.ops._c10d_functional.all_gather_into_tensor_out.default, + torch.ops._c10d_functional.wait_tensor.default, + torch.ops.fsdp.split_with_sizes_copy.default, + torch.ops.aten.set_.source_Tensor, + } + find_recursive_users_of_node( + ag_snode, + ag_related_snode_set, + name_to_buf, + name_to_fused_node, + criteria_cb=lambda x: not ( + isinstance(x, scheduler.NopKernelSchedulerNode) + or ( + isinstance(x, scheduler.ExternKernelSchedulerNode) + and x.node.op_overload in allowed_ops # type: ignore[union-attr] + ) + ), + ) + + # sort nodes by original operation order + ag_related_snodes = sorted( + ag_related_snode_set, key=lambda x: get_op_idx(x) + ) + + # In the "reuse layer" case, some ops in the 2nd all-gather code block could also + # depend on ops in the 1st all-gather code block, and we don't want to group them together. + end_idx_of_current_ag_block = len(ag_related_snodes) + copy_out_count = 0 + for i in range(len(ag_related_snodes)): + cur_snode = ag_related_snodes[i] + if is_fallback_op( + cur_snode.node, torch.ops.fsdp.split_with_sizes_copy.default + ): + copy_out_count += 1 + if copy_out_count > 1: + end_idx_of_current_ag_block = i + break + + ag_related_snodes = ag_related_snodes[:end_idx_of_current_ag_block] + + # Group "cast + copy_in + getitem + all_gather" into one GroupedSchedulerNode + wait_node_idx = None + for i in range(len(ag_related_snodes) - 1): + if isinstance(ag_related_snodes[i + 1].node, ir._WaitKernel): + wait_node_idx = i + 1 + break + assert wait_node_idx is not None + ag_group_node = _create_group_node(ag_related_snodes[:wait_node_idx]) + + # Group "all_gather_wait_tensor + copy_out + set_" into one GroupedSchedulerNode + ag_wait_group_node = _create_group_node(ag_related_snodes[wait_node_idx:]) + + ag_grouped_node_to_wait_grouped_node[ag_group_node] = ag_wait_group_node + + # Case 2: Handle ReduceScatter + elif is_fallback_op(snode.node, torch.ops.fsdp.chunk_cat.default): + rs_exists = True + rs_snode = snode + + # Find the "reduce_scatter copy-in + reduce_scatter comm + reduce_scatter wait" code block + rs_related_snode_set: set[scheduler.BaseSchedulerNode] = set() + find_recursive_users_of_node( + rs_snode, + rs_related_snode_set, + name_to_buf, + name_to_fused_node, + ) + + # sort nodes by original operation order + rs_related_snodes = sorted( + rs_related_snode_set, key=lambda x: get_op_idx(x) + ) + + # Group "reduce_scatter copy-in + reduce_scatter comm" into one GroupedSchedulerNode + wait_node_idx = None + for i in range(len(rs_related_snodes) - 1): + if isinstance(rs_related_snodes[i + 1].node, ir._WaitKernel): + wait_node_idx = i + 1 + break + assert wait_node_idx is not None + rs_group_node = _create_group_node(rs_related_snodes[:wait_node_idx]) + + # Group "reduce_scatter wait + related output nodes" into one GroupedSchedulerNode + rs_wait_group_node = _create_group_node(rs_related_snodes[wait_node_idx:]) + + rs_grouped_node_to_wait_grouped_node[rs_group_node] = rs_wait_group_node + + assert len(snode_name_to_final_snode) > 0 + if ag_exists: + assert len(ag_grouped_node_to_wait_grouped_node) > 0 + if rs_exists: + assert len(rs_grouped_node_to_wait_grouped_node) > 0 + + # Build the new node schedule, taking GroupedSchedulerNode into account + for snode in snodes: + if snode.get_name() in snode_name_to_final_snode: + snode = snode_name_to_final_snode[snode.get_name()] + if snode in scheduled: + continue + new_order.append(snode) + scheduled.add(snode) + + # Enforce AllGather ordering: previous AllGather's "wait then copy_out" group node must run + # before next AllGather's "copy_in then AG" group node + prev_ag_wait = None + for ag_group_node, wait_group_node in ag_grouped_node_to_wait_grouped_node.items(): + if prev_ag_wait is not None: + mutating_buf = next(iter(ag_group_node.get_buffer_names())) + for o in prev_ag_wait.get_outputs(): + ag_group_node.add_fake_dep( + WeakDep(o.get_name(), mutating_buf=mutating_buf) + ) + prev_ag_wait = wait_group_node + + # Enforce ReduceScatter ordering: previous ReduceScatter's "wait" group node must run + # before next ReduceScatter's "copy_in then RS" group node + prev_rs_wait = None + for rs_group_node, wait_group_node in rs_grouped_node_to_wait_grouped_node.items(): + if prev_rs_wait is not None: + mutating_buf = next(iter(rs_group_node.get_buffer_names())) + for o in prev_rs_wait.get_outputs(): + rs_group_node.add_fake_dep( + WeakDep(o.get_name(), mutating_buf=mutating_buf) + ) + prev_rs_wait = wait_group_node + + return new_order # type: ignore[return-value] diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/compile_fx.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/compile_fx.py new file mode 100644 index 0000000000000000000000000000000000000000..db4c1e0eddfeceb3b65a7551c9e3615eabebc257 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/compile_fx.py @@ -0,0 +1,1629 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import contextlib +import functools +import io +import itertools +import logging +import os +import sys +import time +import warnings +from itertools import count +from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union +from unittest import mock + +import torch._inductor.async_compile # noqa: F401 required to warm up AsyncCompile pools +import torch.fx +import torch.utils._pytree as pytree +from functorch.compile import min_cut_rematerialization_partition +from torch._dynamo import ( + compiled_autograd, + config as dynamo_config, + logging as dynamo_logging, + utils as dynamo_utils, +) +from torch._dynamo.device_interface import get_interface_for_device +from torch._dynamo.repro.after_aot import wrap_compiler_debug +from torch._dynamo.utils import ( + counters, + detect_fake_mode, + flatten_graph_inputs, + lazy_format_graph_code, +) +from torch._functorch import config as functorch_config +from torch._functorch.aot_autograd import aot_export_module, make_boxed_func +from torch._inductor.codecache import ( + _StrideExprStr, + code_hash, + CompiledFxGraph, + FxGraphCache, +) +from torch._inductor.cudagraph_utils import ( + BoxedDeviceIndex, + CudagraphCachedInfo, + get_placeholder_info, + log_cudagraph_skip_and_bump_counter, + PlaceholderInfo, +) +from torch._inductor.debug import save_args_for_compile_fx_inner +from torch._inductor.runtime.runtime_utils import cache_dir +from torch._inductor.utils import ( + BoxedBool, + count_tangents, + fresh_inductor_cache, + InputType, + is_gpu, + should_assume_input_aligned, + tensor_is_aligned, +) +from torch._logging import trace_structured +from torch._ops import OpOverload +from torch.fx.experimental.symbolic_shapes import free_unbacked_symbols, SymExprPrinter +from torch.fx.passes.fake_tensor_prop import FakeTensorProp +from torch.monitor import _WaitCounter + +from .._dynamo.backends.common import aot_autograd +from ..fx._lazy_graph_module import _use_lazy_graph_module # type: ignore[attr-defined] +from ..fx.graph import _PyTreeCodeGen +from . import config, metrics +from .debug import DebugContext +from .decomposition import select_decomp_table +from .fx_passes.joint_graph import joint_graph_passes +from .fx_passes.post_grad import post_grad_passes, view_to_reshape +from .fx_passes.pre_grad import pre_grad_passes +from .graph import GraphLowering +from .ir import ExternKernelNode +from .utils import ( + align_inputs_from_check_idxs, + clone_preserve_strides, + copy_misaligned_inputs, + get_cloned_parameter_buffer_name, + has_incompatible_cudagraph_ops, + maybe_get_suppress_shape_guards_ctx, + output_node, + remove_unaligned_input_idxs, + shape_env_from_inputs, +) +from .virtualized import V + + +if config.is_fbcode(): + from torch._inductor.fb.utils import log_optimus_to_scuba, time_and_log +else: + # no-op decorator + def time_and_log(attr: str): + return dynamo_utils.identity + + +log = logging.getLogger(__name__) +perf_hint_log = torch._logging.getArtifactLogger(__name__, "perf_hints") +post_grad_graphs_log = torch._logging.getArtifactLogger(__name__, "post_grad_graphs") +static_inputs_log = torch._logging.getArtifactLogger( + __name__, "cudagraph_static_inputs" +) + + +# copy_ fails when trying to write to tensors with memory overlap, +# for expanded dimensions (a dimension which used to have size 1 -> ?) +# we can select one element from that dimension and write to it +# to achieve writing to all values of that dimension of the input tensor +def get_expanded_dims(t): + if not isinstance(t, torch.Tensor): + return None + return [i for i in range(t.ndim) if t.stride(i) == 0 and t.size(i) != 1] + + +def index_expanded_dims(t: torch.Tensor, expanded_dims: List[int]) -> torch.Tensor: + for expanded_dim in expanded_dims: + t = torch.ops.aten.slice(t, expanded_dim, 0, 1) + return t + + +def complex_memory_overlap(t: torch.Tensor) -> bool: + # if torch._debug_has_internal_overlap thinks this tensor potentially has + # memory overlap internally, let's dig deeper to find out whether it's true. + # + # Call squeeze() so that dimension with size 1 does not cause false positive. + t = index_expanded_dims(t, get_expanded_dims(t)).squeeze() + if torch._debug_has_internal_overlap(t) != 0: + strides = t.stride() + sizes = t.shape + indices = list(range(len(strides))) + indices = [x for _, x in sorted(zip(strides, indices))] + for i in range(len(strides)): + prev_stride = 1 if i == 0 else strides[indices[i - 1]] + prev_size = 1 if i == 0 else sizes[indices[i - 1]] + if strides[indices[i]] < prev_stride * prev_size: + return True + return False + + +def get_static_input_idxs(num_fixed): + # If we are inlining NNModules, we treat all torch.nn.Parameters as static for the purposes + # of cudagraphs. Rather than copying these into cudagraph-owned memory + # like we do for normal inputs on each run, we will re-record a cudagraph if these + # parameter locations change. + context = torch._guards.TracingContext.try_get() + fixed = list(range(num_fixed)) + if not context or not context.fw_metadata: + return fixed + + return fixed + context.fw_metadata.static_input_indices + + +@functools.lru_cache(None) +def _step_logger(): + return dynamo_logging.get_step_logger(log) + + +@functools.lru_cache(None) +def _warn_tf32_disabled(): + if ( + torch.cuda.is_available() + and not torch.backends.cuda.matmul.allow_tf32 + and torch.cuda.get_device_capability() >= (8, 0) + ): + warnings.warn( + "TensorFloat32 tensor cores for float32 matrix multiplication available but not enabled. " + "Consider setting `torch.set_float32_matmul_precision('high')` for better performance." + ) + + +def _unlift_graph(mod, gm, graph_signature): + from torch.export.unflatten import _assign_attr, _AttrKind + + state_dict = {} + for name, param in mod.named_parameters(remove_duplicate=False): + state_dict[name] = param + _assign_attr( + param, + gm, + name, + attr_kind=_AttrKind.PARAMETER, + ) + for name, buffer in mod.named_buffers(remove_duplicate=False): + state_dict[name] = buffer + _assign_attr( + buffer, + gm, + name, + attr_kind=_AttrKind.BUFFER, + ) + + placeholder_nodes = gm.graph.find_nodes(op="placeholder") + lifted_inputs = [] + + # In AOTI, module parameters and buffers are not lifted as graph inputs. + # As a result, mutation to buffers has side effect which makes their initial + # values different from Eager. So we clone them here as a copy. + # We are not cloning for parameters, although it will be needed if we want to + # support training. + for node in placeholder_nodes: + node_name = node.name + if node_name in graph_signature.inputs_to_parameters: + parameter_name = graph_signature.inputs_to_parameters[node_name] + lifted_inputs.append(parameter_name) + elif node_name in graph_signature.inputs_to_buffers: + buffer_name = graph_signature.inputs_to_buffers[node_name] + lifted_inputs.append(buffer_name) + gm.meta[ + get_cloned_parameter_buffer_name(buffer_name) + ] = clone_preserve_strides(state_dict[buffer_name]) + else: + assert node_name in graph_signature.user_inputs + lifted_inputs.append(None) + + from torch.export._unlift import _unlift + + outputs = list(gm.graph.nodes)[-1].args[0] + mutated_outputs = [] + buffer_mutations = graph_signature.buffers_to_mutate + user_input_mutations = graph_signature.user_inputs_to_mutate + output_tokens = graph_signature.output_tokens + for idx, out in enumerate(outputs): + value = None + + if idx < len(buffer_mutations) + len(user_input_mutations) + len(output_tokens): + if out.name in buffer_mutations: + value = buffer_mutations[out.name] + elif out.name in user_input_mutations: + value = user_input_mutations[out.name] + + mutated_outputs.append(value) + + unlifted_gm = _unlift( + gm, + lifted_inputs, + mutated_outputs, + pytree.LeafSpec(), + None, + state_dict, + {}, + ) + return unlifted_gm + + +def _get_subgraph_names(gm): + for node in sorted( + itertools.chain( + gm.graph.find_nodes(op="call_function", target=torch.ops.higher_order.cond), + gm.graph.find_nodes( + op="call_function", target=torch.ops.higher_order.while_loop + ), + ) + ): + if node.target == torch.ops.higher_order.cond: + true_subgraph_name = node.args[1].name + false_subgraph_name = node.args[2].name + yield true_subgraph_name + yield false_subgraph_name + elif node.target == torch.ops.higher_order.while_loop: + cond_subgraph_name = node.args[0].name + body_subgraph_name = node.args[1].name + yield cond_subgraph_name + yield body_subgraph_name + + +def _recursive_pre_grad_passes(gm, example_inputs): + for subgraph_name in _get_subgraph_names(gm): + subgraph = getattr(gm, subgraph_name) + # as we don't have recursive example inputs, passing None here + new_subgraph = _recursive_pre_grad_passes(subgraph, example_inputs=None) + setattr(gm, subgraph_name, new_subgraph) + return pre_grad_passes(gm, example_inputs) + + +def _recursive_joint_graph_passes(gm): + for subgraph_name in _get_subgraph_names(gm): + subgraph = getattr(gm, subgraph_name) + _recursive_joint_graph_passes(subgraph) + joint_graph_passes(gm) + + +def _recursive_post_grad_passes(gm, is_inference: bool = False): + for subgraph_name in _get_subgraph_names(gm): + subgraph = getattr(gm, subgraph_name) + _recursive_post_grad_passes(subgraph, is_inference) + post_grad_passes(gm, is_inference) + + +def split_const_gm( + gm: torch.fx.GraphModule, + lifted_constants: Optional[Dict[str, Any]] = None, + skip_folding_node_fn: Optional[Callable[[torch.fx.Node], bool]] = None, +) -> Tuple[torch.fx.GraphModule, Dict[str, int]]: + """ + This function takes an GraphModule input "gm". + The gm will be split into 2 components, + 1) const_gm, which consists the subgraph of gm that can be constant folded. + 2) gm (being inplace modified,) which returns the graph after constant folding. + + If an additional "lifted_constants" argument is passed in, we will assume the gm has + been lifted and run the transformation accordingly. + + When a "skip_folding_node_fn" callback is passed, we will skip constant folding on + the nodes for which the callback returns True. + + const_output_index is a mapping of corresponding node name from gm to the + output index of const_gm. + Returns (const_gm, const_output_index) + """ + from torch._inductor.constant_folding import ( + CONST_MODULE_TAG, + META_TAG, + MODULE_TAG, + replace_node_with_constant, + run_and_get_constant_graph, + ) + + const_gm, const_result = run_and_get_constant_graph( + gm, lifted_constants, skip_folding_node_fn + ) + + const_outputs = { + x.name: idx for idx, x in enumerate(tuple(const_gm.graph.nodes)[-1].args[0]) + } + + to_erase_node = [] + to_replace_node = [] + const_output_index = {} + for node in gm.graph.nodes: + if node.name in const_outputs: + to_replace_node.append(node) + elif node.meta[META_TAG] == CONST_MODULE_TAG and node.op != "placeholder": + to_erase_node.append(node) + + for node in to_replace_node: + new_const_name = "_FOLDED_CONST_" + node.name + replace_node_with_constant( + gm, + node, + const_result[const_outputs[node.name]], + new_const_name, + ) + const_output_index[new_const_name] = const_outputs[node.name] + for node in to_erase_node[::-1]: + if node.users: + for n in node.users: + assert n.meta[META_TAG] == MODULE_TAG, f"node: {node} user not empty." + else: + gm.graph.erase_node(node) + gm.recompile() + + return const_gm, const_output_index + + +def is_tf32_warning_applicable(gm: torch.fx.GraphModule): + aten = torch.ops.aten + tf32_ops = { + aten.mm.default, + aten.addmm.default, + aten.bmm.default, + aten.baddbmm.default, + } + for target in tf32_ops: + for node in gm.graph.find_nodes(op="call_function", target=target): + if ( + isinstance(node.meta.get("val", None), torch.Tensor) + and node.meta["val"].dtype == torch.float32 + and node.meta["val"].device.type == "cuda" + ): + return True + return False + + +def maybe_disable_comprehensive_padding(example_inputs: List[torch.Tensor]): + """ + For CPU backend, enable comprehensive padding causes some unit tests + fail due to changing number of generated kernels. Skip for now. + """ + has_gpu = any( + is_gpu(t.device.type) for t in example_inputs if isinstance(t, torch.Tensor) + ) + + if config.disable_padding_cpu and config.comprehensive_padding and not has_gpu: + perf_hint_log.info("Skip comprehensive padding on CPU") + return config.patch(comprehensive_padding=False) + else: + return contextlib.nullcontext() + + +def fake_tensor_prop( + gm: torch.fx.GraphModule, + example_inputs: List[torch.Tensor], + force_allow_non_fake_inputs: bool = False, +): + """ + If we can not detect fake mode from the context of inputs, create one. + + The created fake mode will be returned. + """ + fake_mode = detect_fake_mode(example_inputs) + if not fake_mode: + fake_mode = torch._subclasses.FakeTensorMode(allow_non_fake_inputs=True) + FakeTensorProp(gm, mode=fake_mode).propagate(*example_inputs) + else: + ctx = ( + contextlib.nullcontext() + if not force_allow_non_fake_inputs + else mock.patch.object(fake_mode, "allow_non_fake_inputs", True) + ) + with ctx: # type: ignore[attr-defined] + FakeTensorProp(gm, mode=fake_mode).propagate_dont_convert_inputs( + *example_inputs + ) + + return fake_mode + + +def should_use_remote_fx_graph_cache(): + if config.fx_graph_remote_cache is not None: + return config.fx_graph_remote_cache + if not config.is_fbcode(): + return False + + if torch._utils_internal.is_fb_unit_test(): + return False + + try: + from torch._inductor.fb.remote_cache import REMOTE_CACHE_VERSION + except ModuleNotFoundError: + return False + + jk_name = "pytorch/remote_cache:fx_graph_memcache_version" + if torch.version.hip is not None: + jk_name = "pytorch/remote_cache:fx_graph_memcache_version_amd" + + return REMOTE_CACHE_VERSION >= torch._utils_internal.justknobs_getval_int(jk_name) + + +# pass config dict back to user +def get_patched_config_dict(config_patches=None) -> Dict[str, Any]: + with config.patch(config_patches): + return config.get_config_copy() + + +@contextlib.contextmanager +def with_fresh_cache_if_config(): + if config.force_disable_caches: + # Don't delete the cache dir because it has to survive beyond the + # compile_fx call. Let's put the temp dirs under the default cache + # dir so they're easier to locate. + with fresh_inductor_cache(dir=cache_dir(), delete=False): + yield + else: + yield + + +def compile_fx_inner(*args, **kwargs): + # Need with_fresh_cache_if_config for compile_fx_inner even if we already have one for + # compile_fx. The reason is the compilation for backward graph may happen after + # compile_fx return and we may want to use the _LazyGraphModule for compiling + # the backward graph as well. + with contextlib.ExitStack() as stack: + stack.enter_context(torch.utils._python_dispatch._disable_current_modes()) + stack.enter_context(_use_lazy_graph_module(dynamo_config.use_lazy_graph_module)) + stack.enter_context( + dynamo_utils.dynamo_timed( + "compile_fx_inner", phase_name="inductor_compile", fwd_only=False + ) + ) + stack.enter_context(with_fresh_cache_if_config()) + stack.enter_context(DebugContext()) + + return wrap_compiler_debug(_compile_fx_inner, compiler_name="inductor")( + *args, **kwargs + ) + + +@time_and_log(attr="compilation time (in seconds)") +def _compile_fx_inner( + gm: torch.fx.GraphModule, + example_inputs: List[torch.Tensor], + cudagraphs: Optional[BoxedBool] = None, + static_input_idxs: Optional[List[int]] = None, + is_backward: bool = False, + graph_id: Optional[int] = None, + cpp_wrapper: bool = False, + aot_mode: bool = False, + is_inference: bool = False, + boxed_forward_device_index: Optional[BoxedDeviceIndex] = None, + user_visible_outputs: Optional[Dict[str, None]] = None, + layout_opt: Optional[bool] = None, + extern_node_serializer: Optional[Callable[[List[ExternKernelNode]], Any]] = None, +) -> Union[CompiledFxGraph, str]: + """ + Inductor API that compiles a single graph. + + If you change the argument list for this function, make sure you + also update the call to save_args_for_compile_fx_inner below accordingly. + """ + if dynamo_utils.count_calls(gm.graph) == 0 and not aot_mode: + # trigger the real recompilation for _LazyGraphModule before returning + # the forward method. + from torch.fx._lazy_graph_module import _LazyGraphModule + + _LazyGraphModule.force_recompile(gm) + return make_boxed_func(gm.forward) + + if static_input_idxs is None: + static_input_idxs = [] + + static_inputs_log.debug("static input idxs compile_fx_inner: %s", static_input_idxs) + + assert isinstance( + next(iter(reversed(gm.graph.nodes))).args[0], (tuple, list) + ), f"inductor can only compile FX graphs which return a tuple/list, but got {gm.graph}" + + if config.save_args: + save_args_for_compile_fx_inner( + gm, + example_inputs, + cudagraphs=cudagraphs, + static_input_idxs=static_input_idxs, + is_backward=is_backward, + graph_id=graph_id, + cpp_wrapper=cpp_wrapper, + aot_mode=aot_mode, + is_inference=is_inference, + boxed_forward_device_index=boxed_forward_device_index, + user_visible_outputs=user_visible_outputs, + layout_opt=layout_opt, + ) + + if cudagraphs is None: + cudagraphs = BoxedBool(config.triton.cudagraphs) + + # Inputs to fx_codegen_and_compile + # Anything that affects codegen should go here, so if the signature + # of fx_codegen_and_compile changes, the dict should be updated accordingly + graph_kwargs = { + "cudagraphs": cudagraphs, + "static_input_idxs": static_input_idxs, + "is_backward": is_backward, + "graph_id": graph_id, + "cpp_wrapper": cpp_wrapper, + "aot_mode": aot_mode, + "is_inference": is_inference, + "user_visible_outputs": user_visible_outputs, + "layout_opt": layout_opt, + "extern_node_serializer": extern_node_serializer, + } + + start = time.time() + + fx_graph_remote_cache = should_use_remote_fx_graph_cache() + + inputs_to_check = get_input_idxs_to_check(example_inputs, static_input_idxs) # type: ignore[arg-type] + + def codegen_and_compile( + gm, + example_inputs, + inputs_to_check, + fx_kwargs, + ): + """ + This function calls fx_codegen_and_compile and also adds some extra metadata to the resulting + compiled fx graph. The metadata is saved to FXGraphCache. + """ + compiled_graph = fx_codegen_and_compile(gm, example_inputs, **fx_kwargs) + if isinstance(compiled_graph, str): + # We only return a string in aot mode, in which case we don't + # need to do any post-compilation steps: we just return the string, + # which is the filename of the compiled code. + return compiled_graph + cudagraph_info = None + if cudagraphs: + # check cudagraph disabling reasons from inductor lowering + if compiled_graph.disabled_cudagraphs_reason: + if "cuda" in compiled_graph.device_types: + log_cudagraph_skip_and_bump_counter( + f"skipping cudagraphs due to {compiled_graph.disabled_cudagraphs_reason}" + ) + else: + counters["inductor"]["cudagraph_skips"] += 1 + BoxedBool.disable(cudagraphs) + else: + complex_memory_overlap_inputs = any( + complex_memory_overlap(t) + for t in example_inputs + if isinstance(t, torch.Tensor) + ) + + if not config.triton.cudagraph_support_input_mutation: + # Skip supports for cudagraph-managed tensors + from torch._inductor.cudagraph_utils import ( + check_for_mutation_ignore_cuda_graph_managed_tensor, + ) + + has_mutation_str = ( + check_for_mutation_ignore_cuda_graph_managed_tensor( + gm, + compiled_graph, + static_input_idxs, # type:ignore[arg-type] + ) + ) + has_mutation = has_mutation_str is not None + + if has_mutation: + compiled_graph.disabled_cudagraphs_reason = has_mutation_str + else: + # Check mutation later to support cudagraph-managed tensors + has_mutation = None + + cudagraph_tests = [ + (not has_mutation, "mutated inputs"), + (not has_incompatible_cudagraph_ops(gm), "incompatible ops"), + (not complex_memory_overlap_inputs, "complex memory overlap"), + ( + all( + isinstance(t, (torch.Tensor, torch.SymInt)) + for t in example_inputs + ), + "non-Tensor inputs", + ), + ] + output = output_node(gm) + # output args are tuple of first argument + assert len(output.args) == 1 + stack_traces = [ + (arg.stack_trace if isinstance(arg, torch.fx.node.Node) else None) + for arg in output.args[0] + ] + cudagraph_fail_reasons = [s for b, s in cudagraph_tests if not b] + placeholders = tuple(get_placeholder_info(gm.graph)) + cudagraph_info = CudagraphCachedInfo( + placeholders, stack_traces, cudagraph_fail_reasons + ) + + compiled_graph.cudagraph_info = cudagraph_info + compiled_graph.inputs_to_check = inputs_to_check + compiled_graph.fx_kwargs = fx_kwargs + # TODO: should this be part of fx_kwargs + compiled_graph.boxed_forward_device_index = boxed_forward_device_index + return compiled_graph + + with _WaitCounter("pytorch.wait_counter.fx_codegen_and_compile").guard() as _: + if ( + not config.force_disable_caches + and (config.fx_graph_cache or fx_graph_remote_cache) + and not aot_mode + ): + for i, input in enumerate(example_inputs): + if ( + isinstance(input, torch.Tensor) + and input.device.type == "cuda" + and i in static_input_idxs + ): + input._is_inductor_static = True # type: ignore[attr-defined] + + compiled_graph = FxGraphCache.load( + codegen_and_compile, + gm, + example_inputs, + graph_kwargs, + inputs_to_check, + local=config.fx_graph_cache, + remote=fx_graph_remote_cache, + ) + else: + compiled_graph = codegen_and_compile( + gm, example_inputs, inputs_to_check, graph_kwargs # type: ignore[arg-type] + ) + if aot_mode: + # AOT mode is special because codegen_and_compile returns a string. + # In that case, we don't need to run all post compilation steps, we just need + # to return the string directly. + return compiled_graph + compiled_graph = FxGraphCache.post_compile( + compiled_graph, example_inputs, cudagraphs + ) + + log.debug("FX codegen and compilation took %.3fs", time.time() - start) + + _step_logger()( + logging.INFO, + "torchinductor done compiling " + f"{'BACKWARDS' if is_backward else 'FORWARDS'} " + f"graph {graph_id}", + ) + # aot autograd needs to know to pass in inputs as a list + compiled_graph._boxed_call = True + return compiled_graph + + +def fx_codegen_and_compile( + gm: torch.fx.GraphModule, + example_inputs: List[torch.Tensor], + cudagraphs: Optional[BoxedBool] = None, + static_input_idxs: Optional[List[int]] = None, + is_backward: bool = False, + graph_id: Optional[int] = None, + cpp_wrapper: bool = False, + aot_mode: bool = False, + is_inference: bool = False, + # Use a dict with None value rather than a set for deterministic + # iteration order just in case. + user_visible_outputs: Optional[Dict[str, None]] = None, + layout_opt: Optional[bool] = None, + extern_node_serializer: Optional[Callable[[List[ExternKernelNode]], Any]] = None, +) -> Union[CompiledFxGraph, str]: + if (sleep_sec := config.sleep_sec_TESTING_ONLY) is not None: + import time + + log.warning("Sleeping for %s since sleep_sec_TESTING_ONLY is set", sleep_sec) + time.sleep(sleep_sec) + + with dynamo_utils.preserve_rng_state(): + if is_tf32_warning_applicable(gm): + _warn_tf32_disabled() + + inductor_counters = counters["inductor"].copy() + + # lift the maximum depth of the Python interpreter stack + # to adapt large/deep models + sys.setrecursionlimit(max(sys.getrecursionlimit(), 2000)) + + _step_logger()( + logging.INFO, + "torchinductor compiling " + f"{'BACKWARDS' if is_backward else 'FORWARDS'} " + f"graph {graph_id}", + ) + + def log_graph_runnable(): + fd = io.StringIO() + torch._dynamo.repro.after_aot.save_graph_repro( + fd, gm, example_inputs, "inductor", save_dir=None + ) + return fd.getvalue() + + torch._logging.trace_structured( + "artifact", + metadata_fn=lambda: { + "name": "fx_graph_runnable", + "encoding": "string", + }, + payload_fn=lambda: log_graph_runnable(), + ) + + V.debug.fx_graph(gm, example_inputs) + # TODO: Should we actually dump this? It should be redundant with the aot + # structured logs... + # trace_structured("inductor_input_graph", payload_fn=lambda: gm.print_readable(print_output=False)) + + shape_env = shape_env_from_inputs(example_inputs) + + # Convert view to reshape in the graph. This is necessary primarily for + # layout optimization. Do it unconditionally for uniformity. + # + # It's needed because when we do layout optimization, an contiguous tensor + # in eager mode may becomes a channels last tensor. A view op previously + # can be applied to the contiguous tensor may not be able to be applied + # on the channels tensor any more. An error like + # RuntimeError: view size is not compatible with input tensor's size and stride + # (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead. + # will be printed. + # + # Replace view op to reshape op in this case. + # As an example, timm_resnest/botnet26t_256/convnext_base etc. will fail if we don't do this. + # + # Also this has to be done before FakeTensorProp below to avoid the failed + # .view() call. + view_to_reshape(gm) + + # It is safe to run FakeTensorProp under no_grad because by the time + # we're in inductor, we assume that AOTAutograd has already "taken care" + # of autograd, so there should be no more autograd-related API's in the + # graph. + with torch.no_grad(): + fake_mode = fake_tensor_prop(gm, example_inputs) + + # pattern matcher passes might not preserve striding information + # on node.meta["val"]. if in the future we rely on these being + # correct we will need to fix. + + with V.set_fake_mode(fake_mode): + # has some issues with memory in training + _recursive_post_grad_passes(gm, is_inference=is_inference) + V.debug.fx_graph_transformed(gm, example_inputs) + post_grad_graphs_log.debug( + "%s", + lazy_format_graph_code( + "AFTER POST GRAD", + gm, + include_stride=True, + include_device=True, + colored=True, + ), + ) + trace_structured( + "inductor_post_grad_graph", + payload_fn=lambda: gm.print_readable( + print_output=False, include_stride=True, include_device=True + ), + ) + if config.is_fbcode(): + log_optimus_to_scuba( + extra_logging={"pt2_configs": str(get_patched_config_dict())} + ) + + with V.set_fake_mode(fake_mode), maybe_disable_comprehensive_padding( + example_inputs + ): + const_output_index = None + const_graph = None + const_code = None + + if aot_mode and config.aot_inductor.use_runtime_constant_folding: + const_gm, const_output_index = split_const_gm(gm) + + const_graph = GraphLowering( + const_gm, + example_inputs=[], + shape_env=shape_env, + graph_id=graph_id, + cpp_wrapper=cpp_wrapper, + aot_mode=aot_mode, + user_visible_outputs=user_visible_outputs, + extern_node_serializer=extern_node_serializer, + is_inference=is_inference, + is_const_graph=True, + ) + with V.set_graph_handler(const_graph): + assert cpp_wrapper, "AOT mode only supports C++ wrapper" + const_graph.run() + + const_code, _ = const_graph.codegen_with_cpp_wrapper() + + graph = GraphLowering( + gm, + # example_inputs will be used by AOTInductor to dry-run the generated code for Triton kernel tuning. + # For the forward pass, we have the real inputs to be used as example_inputs. For the backward pass, + # we currently use fake tensors and defake them later. + example_inputs=example_inputs, + shape_env=shape_env, + graph_id=graph_id, + cpp_wrapper=cpp_wrapper, + aot_mode=aot_mode, + user_visible_outputs=user_visible_outputs, + extern_node_serializer=extern_node_serializer, + is_inference=is_inference, + const_output_index=const_output_index, + const_code=const_code, + const_module=const_graph, + ) + metrics_helper = metrics.CachedMetricsHelper() + with V.set_graph_handler(graph): + graph.run(*example_inputs) + output_strides: List[Optional[Tuple[_StrideExprStr, ...]]] = [] + if graph.graph_outputs is not None: + # We'll put the output strides in the compiled graph so we + # can later return them to the caller via TracingContext + p = SymExprPrinter() + for out in graph.graph_outputs: + if ( + hasattr(out, "layout") + and len(free_unbacked_symbols(out.layout.stride)) == 0 + ): + # Convert to string for eval on the load path + output_strides.append( + tuple(p.doprint(s) for s in out.layout.stride) + ) + else: + output_strides.append(None) + + _check_triton_bf16_support(graph) + compiled_fn = graph.compile_to_fn() + num_bytes, nodes_num_elem, node_runtimes = graph.count_bytes() + metrics.num_bytes_accessed += num_bytes + metrics.node_runtimes += node_runtimes + metrics.nodes_num_elem += nodes_num_elem + + if ( + cudagraphs + and config.triton.cudagraph_skip_dynamic_graphs + and not V.graph.disable_cudagraphs_reason + and torch._inductor.utils.any_is_symbolic(*example_inputs) + ): + stack_trace = None + for node in gm.graph.nodes: + meta_val = node.meta.get("val", None) + if ( + node.op == "placeholder" + or not isinstance(meta_val, torch.Tensor) + or not torch._inductor.utils.any_is_symbolic(meta_val) + ): + continue + + if stack_trace := node.meta.get("stack_trace", None): + break + disable = "graph with symbolic shapes inputs and config.triton.cudagraph_skip_dynamic_graphs=True." + if stack_trace: + disable = f"{disable} Found from {stack_trace}\n" + else: + disable = f"{disable}\n" + V.graph.disable_cudagraphs_reason = disable + + if V.aot_compilation is True: + return compiled_fn + + if cudagraphs and not V.graph.disable_cudagraphs_reason: + from torch._inductor.cudagraph_utils import ( + check_lowering_disable_cudagraph, + ) + + V.graph.disable_cudagraphs_reason = ( + check_lowering_disable_cudagraph(V.graph.device_node_mapping) + ) + + compiled_graph = CompiledFxGraph( + compiled_fn, + graph, + output_strides, + V.graph.disable_cudagraphs_reason, + metrics_helper.get_deltas(), + counters["inductor"] - inductor_counters, + ) + + return compiled_graph + + +def get_input_idxs_to_check( + inputs: List[InputType], + static_input_idxs: Sequence[int], +) -> Sequence[int]: + """ + This function runs at compile time, and generates a list of indices for which we + might need to do a copy to preserve alignment requirements. + """ + ids_to_check = [] + + for i, input in enumerate(inputs): + if not isinstance(input, torch.Tensor): + # non-tensors don't need alignment + continue + if not is_gpu(input.device.type): + # right now we only care for gpu tensors + continue + with maybe_get_suppress_shape_guards_ctx(): + # suppress guards so that tensor_is_aligned and should_assume_input_aligned + # do not add guards on input's storage offset + if i in static_input_idxs and tensor_is_aligned(input): + continue + if not should_assume_input_aligned(input): + continue + + # if we get here, then + # (a) our triton code assumes that the input is aligned + # (b) we can't be sure ahead of time that the input will actually be aligned. + # therefore, at runtime, we'll need to check that the input is aligned + # (and if not, clone it to make it aligned.) + ids_to_check.append(i) + + return ids_to_check + + +def cudagraphify( + model: Callable[..., Any], + static_input_idxs: Sequence[int] = (), + *, + device_index: int, + stack_traces: List[Optional[str]], + is_backward: bool, + is_inference: bool, + constants: Tuple[torch.Tensor, ...] = (), + placeholders: Sequence[PlaceholderInfo] = (), + mutated_input_idxs: Tuple[int, ...] = (), +) -> Callable[..., Any]: + from torch._inductor.cudagraph_trees import ( + cudagraphify_impl as new_cudagraphify_impl, + ) + + cudagraphify_fn: Callable[..., Any] + if config.triton.cudagraph_trees: + cudagraphify_fn = functools.partial( + new_cudagraphify_impl, + device_index=device_index, + stack_traces=stack_traces, + is_backward=is_backward, + is_inference=is_inference, + constants=constants, + placeholders=placeholders, + mutated_input_idxs=mutated_input_idxs, + ) + else: + cudagraphify_fn = cudagraphify_impl + + compiled_fn = None + + def run(new_inputs): + nonlocal compiled_fn + if compiled_fn is None: + with dynamo_utils.dynamo_timed( + "cudagraphify" + ), dynamo_utils.preserve_rng_state(): + compiled_fn = cudagraphify_fn(model, new_inputs, static_input_idxs) + return compiled_fn(new_inputs) + + return run + + +def static_input(x: torch.Tensor) -> torch.Tensor: + """ + Copy and input while preserving strides + """ + return torch.empty_strided(x.size(), x.stride(), dtype=x.dtype, device=x.device) + + +def index_expanded_dims_and_copy_( + dst: torch.Tensor, + src: torch.Tensor, + expanded_dims: List[int], +): + "Index into expanded dimensions of both dst and src then copy_" + dst = index_expanded_dims(dst, expanded_dims) + src = index_expanded_dims(src, expanded_dims) + dst.copy_(src) + + +def cudagraphify_impl( + model: Callable[..., Any], + inputs: List[torch.Tensor], + static_input_idxs: Sequence[int] = (), +): + """ + Assumes inputs[static_input_idxs[i]] are always the same memory address + """ + check_input_idxs = get_input_idxs_to_check(inputs, static_input_idxs) # type: ignore[arg-type] + static_input_idxs = remove_unaligned_input_idxs(inputs, static_input_idxs) # type: ignore[arg-type] + copy_misaligned_inputs(inputs, check_input_idxs) # type: ignore[arg-type] + + assert isinstance(inputs, list) + + inps_expanded_dims = [ + get_expanded_dims(x) if idx not in static_input_idxs else [] + for idx, x in enumerate(inputs) + ] + + # allocate static tensor inputs + static_inputs = [ + x + if not isinstance(x, torch.Tensor) + else static_input(x) + if idx not in static_input_idxs + else x.detach() + for idx, x in enumerate(inputs) + ] + + # copy over input values for fresh allocations + for idx, (x, expanded_dims) in enumerate(zip(inputs, inps_expanded_dims)): + if isinstance(x, torch.Tensor) and idx not in static_input_idxs: + index_expanded_dims_and_copy_(static_inputs[idx], x, expanded_dims) + + # warmup + torch.cuda.synchronize() + stream = torch.cuda.Stream() + stream.wait_stream(torch.cuda.current_stream()) + # copy static_inputs because it will be cleared in model + with torch.cuda.stream(stream): + model(list(static_inputs)) + stream.synchronize() + torch.cuda.current_stream().wait_stream(stream) + torch.cuda.synchronize() + + # record + graph = torch.cuda.CUDAGraph() + with torch.cuda.graph(graph, stream=stream, capture_error_mode="thread_local"): + static_outputs = model(list(static_inputs)) + if not isinstance(static_outputs, (list, tuple)): + static_outputs = (static_outputs,) + + if config.size_asserts: + + def run(new_inputs): + assert len(static_inputs) == len(new_inputs) + for idx, (dst, src, expanded_dims) in enumerate( + zip(static_inputs, new_inputs, inps_expanded_dims) + ): + if not isinstance(dst, torch.Tensor): + pass + elif idx in static_input_idxs: + assert dst.data_ptr() == src.data_ptr() + else: + # TODO - could make one single op of multiple slices + # and avoid dispatch. + # Could also pre-index the `dst` tensors + index_expanded_dims_and_copy_(dst, src, expanded_dims) + new_inputs.clear() + graph.replay() + return static_outputs + + else: + copy_indices = [ + idx for idx in range(len(static_inputs)) if idx not in static_input_idxs + ] + + def run(new_inputs): + for idx in copy_indices: + expanded_dims = inps_expanded_dims[idx] + index_expanded_dims_and_copy_( + static_inputs[idx], new_inputs[idx], expanded_dims + ) + new_inputs.clear() + graph.replay() + return static_outputs + + return align_inputs_from_check_idxs(run, check_input_idxs) + + +def compile_fx_aot( + model_: torch.fx.GraphModule, + example_inputs_: List[torch.Tensor], + inner_compile: Callable[..., Any] = compile_fx_inner, + config_patches: Optional[Dict[str, Any]] = None, +): + config_patches: Dict[str, Any] = ( + {"cpp_wrapper": True} + if config_patches is None + else {**config_patches, "cpp_wrapper": True} + ) + if ( + "aot_inductor.output_path" not in config_patches + and not config.aot_inductor.output_path + ): + config_patches = { + **config_patches, + "aot_inductor.output_path": code_hash(model_.code), + } + + extern_node_serializer = config_patches.pop("extern_node_serializer", None) + with V.set_aot_compilation(True): + compiled_lib_path = compile_fx( + model_, + example_inputs_, + inner_compile=functools.partial( + inner_compile, + aot_mode=True, + extern_node_serializer=extern_node_serializer, + ), + config_patches=config_patches, + ) + assert os.path.exists( + compiled_lib_path + ), f"AOTInductor compiled library does not exist at {compiled_lib_path}" + return compiled_lib_path + + +_graph_counter = count(0) + + +def fw_compiler_freezing( + aot_autograd_model: torch.fx.GraphModule, + aot_example_inputs: List[torch.Tensor], + dynamo_model: torch.fx.GraphModule, + num_example_inputs: int, + inner_compile: Callable[..., Any], + cudagraphs: BoxedBool, + graph_id: int, + forward_device: BoxedDeviceIndex, +): + from torch._inductor.freezing import convert_conv_weights_to_channels_last, freeze + + # partition_fn won't be called + _recursive_joint_graph_passes(aot_autograd_model) + + layout_opt = GraphLowering.decide_layout_opt(aot_autograd_model, is_inference=True) + if layout_opt: + # make sure meta['val'] is properly setup + fake_tensor_prop(aot_autograd_model, aot_example_inputs, True) + convert_conv_weights_to_channels_last(aot_autograd_model) + + opt_model, preserved_arg_indices = freeze( + dynamo_model, + aot_autograd_model, + aot_example_inputs, # type: ignore[arg-type] + ) + + aot_example_inputs = [aot_example_inputs[ind] for ind in preserved_arg_indices] + num_fixed = len(preserved_arg_indices) - num_example_inputs + + fake_mode = detect_fake_mode(aot_example_inputs) + + # for freezing, all graph outputs should be user visible + *_, model_outputs_node = opt_model.graph.nodes + model_outputs = model_outputs_node.args[0] + user_visible_outputs = dict.fromkeys( + n.name for n in model_outputs if isinstance(n, torch.fx.Node) + ) + + static_input_idxs = list(range(num_fixed)) + # constant params will be real tensors, not fake + tracing_context = torch._guards.TracingContext.try_get() + if tracing_context is not None: + params_flat = tracing_context.params_flat + assert params_flat is not None + for i in range(len(params_flat)): + if i not in preserved_arg_indices: + params_flat[i] = None + + if tracing_context.fw_metadata: + static_input_idxs += tracing_context.fw_metadata.static_input_indices + + with mock.patch.object(fake_mode, "allow_non_fake_inputs", True): + optimized_function = inner_compile( + opt_model, + aot_example_inputs, + static_input_idxs=static_input_idxs, + cudagraphs=cudagraphs, + graph_id=graph_id, + is_inference=True, + boxed_forward_device_index=forward_device, + layout_opt=layout_opt, + user_visible_outputs=user_visible_outputs, + ) + + # aot_inductor codegens a call that takes in just the inputs, so we don't return a wrapper + # that drops constant-ified params + if V.aot_compilation is True: + return optimized_function + + def wrapper(args): + args_new = [args[i] for i in preserved_arg_indices] + args.clear() + return optimized_function(args_new) + + wrapper._boxed_call = True # type: ignore[attr-defined] + + return wrapper + + +def compile_fx( + model_: torch.fx.GraphModule, + example_inputs_: List[torch.Tensor], + inner_compile: Callable[..., Any] = compile_fx_inner, + config_patches: Optional[Dict[str, Any]] = None, + decompositions: Optional[Dict[OpOverload, Callable[..., Any]]] = None, +): + with _use_lazy_graph_module(dynamo_config.use_lazy_graph_module): + """Main entrypoint to a compile given FX graph""" + if config_patches: + with config.patch(config_patches): + return compile_fx( + model_, + example_inputs_, + # need extra layer of patching as backwards is compiled out of scope + inner_compile=config.patch(config_patches)(inner_compile), + decompositions=decompositions, + ) + + if config.cpp_wrapper: + with config.patch( + { + "cpp_wrapper": False, + # For triton.autotune_at_compile_time, disable by default for + # FBCode, but enabled by default for OSS. + "triton.autotune_at_compile_time": config.triton.autotune_at_compile_time + if config.is_fbcode() + else os.environ.get( + "TORCHINDUCTOR_TRITON_AUTOTUNE_AT_COMPILE_TIME", "1" + ) + == "1", + "triton.autotune_cublasLt": False, + "triton.cudagraphs": False, + "triton.store_cubin": True, + } + ), V.set_real_inputs(example_inputs_): + inputs_ = example_inputs_ + if isinstance(model_, torch.fx.GraphModule): + fake_inputs = [ + node.meta.get("val") + for node in model_.graph.nodes + if node.op == "placeholder" + ] + if all(v is not None for v in fake_inputs): + # Validate devices before switching to fake tensors. + for idx, fi, i in zip(count(), fake_inputs, inputs_): + if fi.device != i.device: + raise ValueError( + f"Device mismatch between fake input and example input at position #{idx}: " + f"{fi.device} vs {i.device}. If the model was exported via torch.export(), " + "make sure torch.export() and torch.aot_compile() run on the same device." + ) + inputs_ = fake_inputs + return compile_fx( + model_, + inputs_, + inner_compile=functools.partial(inner_compile, cpp_wrapper=True), + decompositions=decompositions, + ) + + recursive_compile_fx = functools.partial( + compile_fx, + inner_compile=inner_compile, + decompositions=decompositions, + ) + + if not graph_returns_tuple(model_): + return make_graph_return_tuple( + model_, + example_inputs_, + recursive_compile_fx, + ) + + if isinstance(model_, torch.fx.GraphModule): + if isinstance(model_.graph._codegen, _PyTreeCodeGen): + # this graph is the result of dynamo.export() + return handle_dynamo_export_graph( + model_, + example_inputs_, + recursive_compile_fx, + ) + + model_ = _recursive_pre_grad_passes(model_, example_inputs_) + + if any(isinstance(x, (list, tuple, dict)) for x in example_inputs_): + return flatten_graph_inputs( + model_, + example_inputs_, + recursive_compile_fx, + ) + + assert not config._raise_error_for_testing + num_example_inputs = len(example_inputs_) + cudagraphs = BoxedBool(config.triton.cudagraphs) + forward_device = BoxedDeviceIndex(None) + + graph_id = next(_graph_counter) + + decompositions = ( + decompositions if decompositions is not None else select_decomp_table() + ) + + def fw_compiler_base( + model: torch.fx.GraphModule, + example_inputs: List[torch.Tensor], + is_inference: bool, + ): + with dynamo_utils.dynamo_timed("compile_fx..fw_compiler_base"): + return _fw_compiler_base(model, example_inputs, is_inference) + + def _fw_compiler_base( + model: torch.fx.GraphModule, + example_inputs: List[torch.Tensor], + is_inference: bool, + ): + if is_inference: + # partition_fn won't be called + _recursive_joint_graph_passes(model) + + fixed = torch._inductor.utils.num_fw_fixed_arguments( + num_example_inputs, len(example_inputs) + ) + + user_visible_outputs = {} + + if config.keep_output_stride: + model_outputs_node = output_node(model) + model_outputs = pytree.arg_tree_leaves(*model_outputs_node.args) + num_model_outputs = len(model_outputs) + + context = torch._guards.TracingContext.try_get() + # See Note [User Outputs in the inductor graph] + if context is not None and context.fw_metadata and not is_inference: + original_output_start_index = ( + context.fw_metadata.num_mutated_inp_runtime_indices + ) + else: + original_output_start_index = 0 + + if isinstance(model_, torch.fx.GraphModule): + *_, orig_model_outputs_node = model_.graph.nodes + assert orig_model_outputs_node.op == "output" + orig_model_outputs, _ = pytree.tree_flatten( + orig_model_outputs_node.args + ) + num_orig_model_outputs = len(orig_model_outputs) + else: + num_orig_model_outputs = num_model_outputs + + assert num_orig_model_outputs <= num_model_outputs + + # Note [User Outputs in the inductor graph] + # We makes the following assumption + # For inference + # len(orig_model_outputs) == len(model_outputs) + # For training + # len(orig_model_outputs) <= len(model_outputs) + # During training, most of the time the model_outputs starts with + # original module's outputs followed by saved activations. + # But this can be not true if the model have inplace updated tensors. + # AOTAutograd will make those tensors being returned before the original + # module's output. + # To make things safe, we'll use original_output_start_index field + # set by AOTAutograd to decide where the original module outputs start. + orig_output_end_idx = ( + original_output_start_index + num_orig_model_outputs + ) + # Sanity chec: we are about to splice out the "user" outputs from the full set + # of "graph" outputs. Make sure we're within bounds. + assert orig_output_end_idx <= num_model_outputs + + user_visible_outputs = dict.fromkeys( + n.name + for n in model_outputs[ + original_output_start_index:orig_output_end_idx + ] + if isinstance(n, torch.fx.Node) + ) + + return inner_compile( + model, + example_inputs, + static_input_idxs=get_static_input_idxs(fixed), + cudagraphs=cudagraphs, + graph_id=graph_id, + is_inference=is_inference, + boxed_forward_device_index=forward_device, + user_visible_outputs=user_visible_outputs, + ) + + fw_compiler = functools.partial(fw_compiler_base, is_inference=False) + + if config.freezing and not torch.is_grad_enabled(): + inference_compiler = functools.partial( + fw_compiler_freezing, + dynamo_model=model_, + num_example_inputs=num_example_inputs, + inner_compile=inner_compile, + cudagraphs=cudagraphs, + graph_id=graph_id, + forward_device=forward_device, + ) + else: + inference_compiler = functools.partial(fw_compiler_base, is_inference=True) + + def partition_fn(graph, joint_inputs, **kwargs): + _recursive_joint_graph_passes(graph) + return min_cut_rematerialization_partition( + graph, joint_inputs, **kwargs, compiler="inductor" + ) + + def bw_compiler( + model: torch.fx.GraphModule, example_inputs: List[torch.Tensor] + ): + with dynamo_utils.dynamo_timed("compile_fx..bw_compiler"): + user_visible_outputs = {} + + if config.bw_outputs_user_visible: + model_outputs_node = output_node(model) + model_outputs = pytree.arg_tree_leaves(*model_outputs_node.args) + user_visible_outputs = dict.fromkeys( + n.name for n in model_outputs if isinstance(n, torch.fx.Node) + ) + fixed = count_tangents(model) + return inner_compile( + model, + example_inputs, + static_input_idxs=list(range(fixed)), + cudagraphs=cudagraphs, + is_backward=True, + graph_id=graph_id, + boxed_forward_device_index=forward_device, + user_visible_outputs=user_visible_outputs, + ) + + # TODO: can add logging before/after the call to create_aot_dispatcher_function + # in torch._functorch/aot_autograd.py::aot_module_simplified::aot_function_simplified::new_func + # once torchdynamo is merged into pytorch + + fake_mode = detect_fake_mode( + example_inputs_ + ) or torch._subclasses.FakeTensorMode(allow_non_fake_inputs=True) + tracing_context = ( + torch._guards.TracingContext.try_get() + or torch._guards.TracingContext(fake_mode) + ) + + if V.aot_compilation is True: + with functorch_config.patch(unlift_effect_tokens=True): + gm, graph_signature = aot_export_module( + model_, + example_inputs_, + trace_joint=False, + decompositions=decompositions, + ) + unlifted_gm = _unlift_graph(model_, gm, graph_signature) + if "dynamo_flat_name_to_original_fqn" in model_.meta: + unlifted_gm.meta["dynamo_flat_name_to_original_fqn"] = model_.meta[ + "dynamo_flat_name_to_original_fqn" + ] + + # Disable amp as in aot_dispatch_autograd (https://github.com/pytorch/pytorch/pull/86515) + # In inference_compiler (fw_compiler_base), _recursive_joint_graph_passes will call into + # _sfdp_init() to register patterns. + # When fallback_random is set to True, the sdpa patterns will be traced during runtime. + # If amp is turned on, the traced FP32 patterns will have prims.convert_element_type which + # will be the same as the generated FP16 patterns. + disable_amp = torch._C._is_any_autocast_enabled() + context = ( + torch._C._DisableAutocast if disable_amp else contextlib.nullcontext + ) + with V.set_fake_mode(fake_mode), compiled_autograd.disable(), context(): + return inference_compiler(unlifted_gm, example_inputs_) + + with V.set_fake_mode(fake_mode), torch._guards.tracing( + tracing_context + ), compiled_autograd.disable(), functorch_config.patch( + unlift_effect_tokens=True + ): + return aot_autograd( + fw_compiler=fw_compiler, + bw_compiler=bw_compiler, + inference_compiler=inference_compiler, + decompositions=decompositions, + partition_fn=partition_fn, + keep_inference_input_mutations=True, + cudagraphs=cudagraphs, + )(model_, example_inputs_) + + +def graph_returns_tuple(gm: torch.fx.GraphModule): + """True if a FX graph returns a tuple""" + if not isinstance(gm, torch.fx.GraphModule): + return True # can't check this, assume true + (rv,) = output_node(gm).args + if isinstance(rv, (list, tuple)): + return True + if ( + isinstance(rv, torch.fx.node.Node) + and hasattr(rv.target, "_schema") + and len(rv.target._schema.returns) > 1 + and all(str(ret.type) == "Tensor" for ret in rv.target._schema.returns) + ): + # for graphs whose result is one node with multiple outputs + return True + return False + + +def make_graph_return_tuple( + gm: torch.fx.GraphModule, + inputs: List[torch.Tensor], + compile_gm: Callable[..., Any], +): + """ + Mutate gm so it returns a tuple. This is only needed for graphs + not created by torchdynamo that return non-tuples. + """ + node = output_node(gm) + (rv,) = node.args + rv, spec = pytree.tree_flatten(rv) + with gm.graph.inserting_before(node): + gm.graph.output(rv) + gm.graph.erase_node(node) + assert graph_returns_tuple(gm) + + compiled_fn = compile_gm(gm, inputs) + + @functools.wraps(compiled_fn) + def wrapper(*args, **kwargs): + return pytree.tree_unflatten(compiled_fn(*args, **kwargs), spec) + + return wrapper + + +def handle_dynamo_export_graph( + gm: torch.fx.GraphModule, + inputs: List[torch.Tensor], + compile_gm: Callable[..., Any], +): + """ + `torch._dynamo.export` embeds pytrees in the FX graph codegen object, + convert that to a normal FX graph so inductor can compile it. + """ + codegen = gm.graph._codegen + gm.graph._codegen = torch.fx.graph.CodeGen() + gm.recompile() + + compiled_fn = compile_gm(gm, codegen.process_inputs(*inputs)) + + @functools.wraps(compiled_fn) + def wrapper(*args): + return codegen.process_outputs(compiled_fn(*codegen.process_inputs(*args))) + + return wrapper + + +def _check_triton_bf16_support(graph: GraphLowering) -> None: + def warn_and_skip(device) -> None: + from torch._dynamo.exc import SkipFrame + + device_interface = get_interface_for_device(device.type) + device_props = device_interface.get_device_properties(device) + warnings.warn( + f"{device_props.name} does not support bfloat16 compilation natively, skipping" + ) + raise SkipFrame("BF16 is not supported") + + for inp in graph.graph_inputs.values(): + device = getattr(inp, "get_device", lambda: torch.device("meta"))() + if (not is_gpu(device.type)) or inp.get_dtype() != torch.bfloat16: + continue + # Print warning and skip frame if attempting to compile for bfloat16 + # on device without hardware support for dtype + device_interface = get_interface_for_device(device.type) + if device_interface.is_bf16_supported(including_emulation=False): + return + warn_and_skip(device) + + for out in graph.graph_outputs: + device = getattr(out, "get_device", lambda: torch.device("meta"))() + if (not is_gpu(device.type)) or out.get_dtype() != torch.bfloat16: + continue + # Print warning and skip frame if attempting to compile for bfloat16 + # on device without hardware support for dtype + device_interface = get_interface_for_device(device.type) + if device_interface.is_bf16_supported(including_emulation=False): + return + warn_and_skip(device) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/compile_worker/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/compile_worker/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/compile_worker/__main__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/compile_worker/__main__.py new file mode 100644 index 0000000000000000000000000000000000000000..0f6503b7901a5a1b2fb76f03c9550b562b623a88 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/compile_worker/__main__.py @@ -0,0 +1,47 @@ +# mypy: allow-untyped-defs +import argparse +import logging +import os +import sys + +from torch._inductor.async_compile import pre_fork_setup +from torch._inductor.compile_worker.subproc_pool import SubprocMain +from torch._inductor.compile_worker.watchdog import _async_compile_initializer +from torch._inductor.runtime.compile_tasks import _set_triton_ptxas_path + + +log = logging.getLogger(__name__) + +_set_triton_ptxas_path() + +try: + import triton + + assert triton is not None # preload in parent +except ImportError: + pass + + +def main(): + try: + parser = argparse.ArgumentParser() + parser.add_argument("--workers", type=int) + parser.add_argument("--parent", type=int) + parser.add_argument("--read-fd", type=int) + parser.add_argument("--write-fd", type=int) + args = parser.parse_args() + if os.getppid() != args.parent: + sys.exit(0) + read_fd = os.fdopen(args.read_fd, "rb") + write_fd = os.fdopen(args.write_fd, "wb") + + pre_fork_setup() + + _async_compile_initializer(args.parent) + SubprocMain(args.workers, read_fd, write_fd).main() + except Exception: + log.exception("Uncaught exception in compile_worker subprocess") + + +if __name__ == "__main__": + main() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/compile_worker/subproc_pool.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/compile_worker/subproc_pool.py new file mode 100644 index 0000000000000000000000000000000000000000..77938dc2e44ddd4fa70034daf520e00a659b57b5 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/compile_worker/subproc_pool.py @@ -0,0 +1,314 @@ +# mypy: allow-untyped-defs +import functools +import itertools +import logging +import multiprocessing +import os +import pickle +import struct +import subprocess +import sys +import threading +import traceback +import typing +from concurrent.futures import Future, ProcessPoolExecutor +from concurrent.futures.process import BrokenProcessPool +from typing import Any, Callable, Dict + +from torch._inductor import config +from torch._inductor.compile_worker.watchdog import _async_compile_initializer + + +log = logging.getLogger(__name__) + + +def _pack_msg(job_id, length): + return struct.pack("nn", job_id, length) + + +def _unpack_msg(data): + if not data: + return -1, -1 + return struct.unpack("nn", data) + + +msg_bytes = len(_pack_msg(0, 0)) + + +def _send_msg(write_pipe, job_id, job_data=b""): + length = len(job_data) + write_pipe.write(_pack_msg(job_id, length)) + if length > 0: + write_pipe.write(job_data) + write_pipe.flush() + + +def _recv_msg(read_pipe): + job_id, length = _unpack_msg(read_pipe.read(msg_bytes)) + data = read_pipe.read(length) if length > 0 else b"" + return job_id, data + + +def _get_ld_library_path(): + path = os.environ.get("LD_LIBRARY_PATH", "") + if config.is_fbcode(): + from libfb.py.parutil import get_runtime_path + + runtime_path = get_runtime_path() + if runtime_path: + lib_path = os.path.join(runtime_path, "runtime", "lib") + path = os.pathsep.join([lib_path, path]) if path else lib_path + + return path + + +class _SubprocExceptionInfo: + """ + Carries exception info from subprocesses across the wire. traceback + objects are not pickleable, so we store the trace as a string and + use it for the message in the exception thrown in the main process. + """ + + def __init__(self, details) -> None: + self.details = details + + +class SubprocException(Exception): + """ + Thrown when a job in a subprocess raises an Exception. + """ + + def __init__(self, details) -> None: + super().__init__(f"An exception occurred in a subprocess:\n\n{details}") + + +class SubprocPool: + """ + Mimic a concurrent.futures.ProcessPoolExecutor, but wrap it in + a subprocess.Popen() to try to avoid issues with forking/spawning + """ + + def __init__(self, nprocs: int) -> None: + entry = os.path.join(os.path.dirname(__file__), "__main__.py") + + subproc_read_fd, write_fd = os.pipe() + read_fd, subproc_write_fd = os.pipe() + self.write_pipe = os.fdopen(write_fd, "wb") + self.read_pipe = os.fdopen(read_fd, "rb") + + cmd = [ + sys.executable, + entry, + f"--workers={nprocs}", + f"--parent={os.getpid()}", + f"--read-fd={str(subproc_read_fd)}", + f"--write-fd={str(subproc_write_fd)}", + ] + self.process = subprocess.Popen( + cmd, + env={ + **os.environ, + # We need to set the PYTHONPATH so the subprocess can find torch. + "PYTHONPATH": os.pathsep.join(sys.path), + # We don't want to re-warm the pool when the subprocess imports + # torch._inductor.codecache since the warming process is what + # creates the SubprocPool in the first place. + "TORCH_WARM_POOL": "0", + # Some internal usages need a modified LD_LIBRARY_PATH. + "LD_LIBRARY_PATH": _get_ld_library_path(), + }, + pass_fds=(subproc_read_fd, subproc_write_fd), + ) + self.write_lock = threading.Lock() + self.read_thread = threading.Thread(target=self._read_thread, daemon=True) + + self.futures_lock = threading.Lock() + self.pending_futures: Dict[int, Future[Any]] = {} + self.job_id_count = itertools.count() + + self.running = True + + # Start thread last to ensure all member variables are initialized + # before any access. + self.read_thread.start() + + def submit(self, job_fn: Callable[..., Any], *args): + if args: + job_fn = functools.partial(job_fn, *args) + job_data = pickle.dumps(job_fn, pickle.HIGHEST_PROTOCOL) + future: Future[Any] + with self.futures_lock: + job_id = next(self.job_id_count) + self.pending_futures[job_id] = future = Future() + future.set_running_or_notify_cancel() + with self.write_lock: + if not self.running: + raise RuntimeError("submit() on closed pool") + _send_msg(self.write_pipe, job_id, job_data) + return future + + def _read_thread(self): + try: + while True: + job_id, data = _recv_msg(self.read_pipe) + if job_id < 0: + if self.running: + log.warning("SubprocPool unclean exit") + self.read_pipe.close() + return + result = pickle.loads(data) + with self.futures_lock: + if not self.running: + return + if isinstance(result, _SubprocExceptionInfo): + # An exception occurred in the submitted job + self.pending_futures[job_id].set_exception( + SubprocException(result.details) + ) + elif isinstance(result, Exception): + # An exception occurred in some of our subprocess machinery. + self.pending_futures[job_id].set_exception(result) + else: + self.pending_futures[job_id].set_result(result) + del self.pending_futures[job_id] + except Exception: + log.exception("failure in SubprocPool._read_thread") + + def shutdown(self): + try: + with self.write_lock: + if not self.running: + return + self.running = False + _send_msg(self.write_pipe, -1) + self.write_pipe.close() + self.process.wait(10) + except OSError as e: + log.warning("Ignored OSError in pool shutdown: %s", e) + finally: + with self.futures_lock: + for future in self.pending_futures.values(): + if not future.cancel(): + future.set_exception(RuntimeError("SubprocPool closed")) + self.pending_futures.clear() + + +class SubprocMain: + """Communicates with a SubprocPool in the parent process, called by __main__.py""" + + def __init__(self, nprocs, read_pipe, write_pipe) -> None: + self.read_pipe = read_pipe + self.write_pipe = write_pipe + self.write_lock = threading.Lock() + self.nprocs = nprocs + self.pool = self._new_pool(nprocs, True) + self.running = True + + def _new_pool(self, nprocs, warm): + pool = ProcessPoolExecutor( + nprocs, + mp_context=multiprocessing.get_context("fork"), + initializer=functools.partial(_async_compile_initializer, os.getpid()), + ) + multiprocessing.util.Finalize(None, pool.shutdown, exitpriority=sys.maxsize) + if warm: + _warm_process_pool(pool, nprocs) + return pool + + def main(self): + while True: + job_id, data = _recv_msg(self.read_pipe) + if job_id < 0: + return self._shutdown() + self.submit(job_id, data) + + def _shutdown(self): + with self.write_lock: + self.running = False + try: + _send_msg(self.write_pipe, -1) + self.write_pipe.close() + except BrokenPipeError: + pass # parent process already shutdown + self.read_pipe.close() + self.pool.shutdown() + + def submit(self, job_id, data): + while self.running: + try: + self._submit_inner(job_id, data) + return + except BrokenProcessPool: + # If any subprocess in the pool crashes, we get a BrokenProcessPool + # exception and the whole pool becomes unusable. Handle crashes by + # recreating the pool and resubmitting. + self.pool = self._new_pool(self.nprocs, False) + + def _submit_inner(self, job_id, data): + future = self.pool.submit(functools.partial(SubprocMain.do_job, data)) + + def callback(_): + if not self.running: + return + try: + result = future.result() + except Exception as e: + log.exception("Error in subprocess") + result = pickle.dumps(e, pickle.HIGHEST_PROTOCOL) + assert isinstance(result, bytes) + with self.write_lock: + if self.running: + _send_msg(self.write_pipe, job_id, result) + + future.add_done_callback(callback) + + @staticmethod + def do_job(data): + # do the pickle/unpickle in the sub-subproc + job = pickle.loads(data) + try: + result = job() + except Exception as e: + result = _SubprocExceptionInfo(traceback.format_exc()) + return pickle.dumps(result, pickle.HIGHEST_PROTOCOL) + + +AnyPool = typing.Union[ProcessPoolExecutor, SubprocPool] + + +def _warm_process_pool(pool: AnyPool, n: int): + if isinstance(pool, SubprocPool): + return # no need + assert isinstance(pool, ProcessPoolExecutor) + + # We have to fork processes for compiler workers, but the more memory and other resources that are loaded, the + # slower the os.fork time is, quite drastically. It also holds the GIL so we can't put it on another thread. + + # Examples: + # A simple x + x + x script: 10ms seconds in the middle of the program, 2ms at startup + # tf_efficientnet_b0 benchmark: 50ms! in the middle of the program , 3ms at startup + + # So we want to start the workers early when it is still cheap, and also to allow the workers to get + # ready before we have work for them. + + # ProcessPoolExecutor also does not launch the workers until it finds a point when all the workers are idle. + # But if we waited until then fork time will be long and we will be waiting for the processes to initialize. + + # We force them to start here with some YOLOing of the internal methods. + + # TODO(masnesral): Are these still relevant? + if hasattr(pool, "_start_queue_management_thread"): + pool._start_queue_management_thread() + else: + for _ in range(n): + pool._adjust_process_count() + if hasattr(pool, "_start_executor_manager_thread"): + pool._start_executor_manager_thread() + + +class TestException(RuntimeError): + pass + + +def raise_testexc(): + raise TestException diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/compile_worker/watchdog.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/compile_worker/watchdog.py new file mode 100644 index 0000000000000000000000000000000000000000..f3956e1272e9b6997a10d8cd8f354094701df3a0 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/compile_worker/watchdog.py @@ -0,0 +1,38 @@ +# mypy: allow-untyped-defs +import os +import signal +from threading import Thread +from time import sleep +from typing import Optional + + +# If this process dies abnormally (e.g. segfault) +# it will not shut down the workers. Instead, +# the workers will have their parent reassigned to the +# init process. This launches a separate thread to +# watch for the worker getting reassigned, +# and cleans it up in this case. +# +# This function cannot be an inner function since otherwise mp_context="spawn" would +# not work for ProcessPoolExecutor since inner functions cannot be pickled. +def _async_compile_initializer(orig_ppid) -> None: + def run() -> None: + while True: + sleep(1) + if orig_ppid != os.getppid(): + os.kill(os.getpid(), signal.SIGKILL) + + global _watchdog_thread, _original_parent + _original_parent = orig_ppid + _watchdog_thread = Thread(target=run, daemon=True) + _watchdog_thread.start() + # Ignore Ctrl-C (i.e. SIGINT) sent to pool workers to avoid meaningless log spam. + signal.signal(signal.SIGINT, signal.SIG_IGN) + + +_watchdog_thread: Optional[Thread] = None +_original_parent: Optional[int] = None + + +def has_parent_changed() -> bool: + return _original_parent != os.getppid() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/config.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/config.py new file mode 100644 index 0000000000000000000000000000000000000000..80b65fbc31af9173e89c7005c67718caa3f51c9c --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/config.py @@ -0,0 +1,1241 @@ +import os # noqa: C101 +import sys +from typing import Any, Callable, Dict, List, Optional, TYPE_CHECKING, Union + +import torch + + +def is_fbcode() -> bool: + return not hasattr(torch.version, "git_version") + + +def fx_graph_remote_cache_default() -> Optional[bool]: + if os.environ.get("TORCHINDUCTOR_FX_GRAPH_REMOTE_CACHE") == "1": + return True + if os.environ.get("TORCHINDUCTOR_FX_GRAPH_REMOTE_CACHE") == "0": + return False + return None + + +def autotune_remote_cache_default() -> Optional[bool]: + if os.environ.get("TORCHINDUCTOR_AUTOTUNE_REMOTE_CACHE") == "1": + return True + if os.environ.get("TORCHINDUCTOR_AUTOTUNE_REMOTE_CACHE") == "0": + return False + return None + + +# Enable auto_functionalized_v2 (enabled by default) +enable_auto_functionalized_v2 = ( + os.environ.get("TORCHDYNAMO_AUTO_FUNCTIONALIZED_V2", "0") == "1" +) + +# add some debug printouts +debug = False + +# Whether to disable a progress bar for autotuning +disable_progress = True + +# Whether to enable printing the source code for each future +verbose_progress = False + +# use fx aot graph codegen cache +fx_graph_cache = ( + os.environ.get("TORCHINDUCTOR_FX_GRAPH_CACHE", "0" if is_fbcode() else "1") == "1" +) + +# use remote fx aot graph codegen cache +# False: Disables the cache +# True: Enables the cache +# None: Not set -- Off for OSS, JustKnobs based for internal +fx_graph_remote_cache: Optional[bool] = fx_graph_remote_cache_default() + +# enable autotune local cache +autotune_local_cache = True + +# enable autotune remote cache +# False: Disables the cache +# True: Enables the cache +# None: Not set -- Off for OSS, JustKnobs based for internal +autotune_remote_cache: Optional[bool] = autotune_remote_cache_default() + +# Force disabled all inductor level caching -- This will override any other caching flag +force_disable_caches = os.environ.get("TORCHINDUCTOR_FORCE_DISABLE_CACHES") == "1" + +# sleep in inductor for testing +sleep_sec_TESTING_ONLY: Optional[int] = None + +# The default layout constraint for custom operators. +# This must be the name of one of the layout constraint tags +# (that is, one of {"needs_fixed_stride_order", "flexible_layout"}), +# If the custom op does not have a layout constraint tag already +# then we assume the following applies. +custom_op_default_layout_constraint = "flexible_layout" + +# use cpp wrapper instead of python wrapper +cpp_wrapper = os.environ.get("TORCHINDUCTOR_CPP_WRAPPER", "0") == "1" + +# codegen cpp wrapper code in an ABI compatible mode +abi_compatible = ( + os.environ.get("TORCHINDUCTOR_ABI_COMPATIBLE", "1" if is_fbcode() else "0") == "1" +) + +c_shim_version = os.environ.get("TORCHINDUCTOR_C_SHIM_VERSION", "2") + +# dead code elimination +dce = False + +# assume weight tensors are fixed size +static_weight_shapes = True + +# put correctness assertions in generated code +size_asserts = os.environ.get("TORCHINDUCTOR_SIZE_ASSERTS", "1") == "1" +nan_asserts = os.environ.get("TORCHINDUCTOR_NAN_ASSERTS") == "1" + +# enable loop reordering based on input orders +pick_loop_orders = True + +# reuse a kernel input as the output +inplace_buffers = True + +# reuse a buffer for an unrelated purpose +allow_buffer_reuse = True + +# Enable pooled allocations for non-output tensors +memory_planning = os.environ.get("TORCHINDUCTOR_MEMORY_PLANNING", "0") == "1" + +# How to organize memory under memory_planning=True: +# - "none": do not try to pool storage, just reuse +# - "intermediates": all non-outputs share storage, outputs each get unique storage +# - "outputs": two pools, one for intermediates (freed on return) and one for outputs +# - "combined": a single pool for both intermediates and outputs +memory_pool = os.environ.get("TORCHINDUCTOR_MEMORY_POOL", "intermediates") + +# codegen benchmark harness +benchmark_harness = True + +# fuse pointwise into templates +epilogue_fusion = True + +# do epilogue fusions before other fusions +epilogue_fusion_first = False + +# enable pattern match+replace optimizations +pattern_matcher = True + +# set to True to enable the back-to-back GEMM pass +b2b_gemm_pass = False + +# register custom graph optimization pass hook. so far, pre/post passes are +# only applied before/after pattern_matcher in post_grad_passes. +# +# def my_custom_pre_pass(graph: torch.fx.graph.Graph): +# # my custom graph optimization pass +# ... +# +# def my_custom_post_pass(graph: torch.fx.graph.Graph): +# # my custom graph optimization pass +# ... +# +# torch._inductor.config.post_grad_custom_pre_pass = my_custom_pre_pass +# torch._inductor.config.post_grad_custom_post_pass = my_custom_post_pass +post_grad_custom_pre_pass: Optional[Callable[[torch.fx.graph.Graph], None]] = None +post_grad_custom_post_pass: Optional[Callable[[torch.fx.graph.Graph], None]] = None + +# Registers a custom joint graph pass. +joint_custom_pre_pass: Optional[Callable[[torch.fx.Graph], None]] = None +joint_custom_post_pass: Optional[Callable[[torch.fx.Graph], None]] = None + +# Registers a custom pregrad pass. Note that the pre-grad IR is 1. +# non-functional, 2. non-normalized, and 3. prone to change. Ideally we should +# use post-grad passes. +pre_grad_custom_pass: Optional[Callable[[torch.fx.graph.Graph], None]] = None + +# Registers a custom pass to be run right before fusion in Inductor scheduler. +# WARNING: Inductor scheduler IR is at prototype stage and subject to change, +# hence custom IR passes built on top of it might break in the future. +_pre_fusion_custom_pass: Optional[ + Callable[ + [List["torch._inductor.scheduler.BaseSchedulerNode"]], + List["torch._inductor.scheduler.BaseSchedulerNode"], + ] +] = None + +# Deprecated +split_cat_fx_passes = True + +# Optimize conv-batchnorm if batchnorm is in eval mode. Slightly reduces numerical stability. +efficient_conv_bn_eval_fx_passes = False + +# Enable predispatch aten IR for export +is_predispatch = False + +# Deprecated +group_fusion = False + +# Deprecated +batch_fusion = True + +# Pre grad fusion and options in order, set to empty dict to disable fusion. +# Call `torch._inductor.fx_passes.group_batch_fusion.list_group_batch_fusions()` to see available fusions. +# batch fusion options: +# batch_linear +# batch_linear_lhs +# batch_layernorm +# batch_tanh +# batch_relu +# batch_sigmoid + +# split cat fusion options: +# normalization_pass +# remove_split_with_size_one_pass +# merge_getitem_cat_pass +# merge_stack_tahn_unbind +# merge_splits_pass +# mutate_cat_pass +# split_cat_pass +pre_grad_fusion_options: Dict[str, Dict[str, Any]] = { + "batch_linear": {}, + "batch_linear_lhs": {}, + "batch_layernorm": {}, + "batch_tanh": {}, + "batch_relu": {}, + "batch_sigmoid": {}, +} + +# Post grad fusion and options, set to empty dict to disable fusion. +# Call `torch._inductor.fx_passes.group_batch_fusion.list_group_batch_fusions(False)` to see available fusions. +post_grad_fusion_options: Dict[str, Dict[str, Any]] = {} + +# enable reordering pass for improving memory locality +reorder_for_locality = True + +# Scale down RBLOCK for better occupancy +dynamic_scale_rblock = os.environ.get("TORCHINDUCTOR_DYNAMIC_SCALE_RBLOCK", "1") == "1" + +# this forces fusion for int_mm with mul. Needed when you want to avoid realizing the int32 +# but the mul gets fused with other pointwise ops instead. +force_fuse_int_mm_with_mul = False + +# for pattern torch.mm(a, b.to(dtype)) with cuda tensors, +# enable torch._inductor.kernel.mm.tuned_mixed_mm fused kernel. +# Autotune will compare perf with normal cast->then->mm option +use_mixed_mm = True + +# enable runtime numeric check for pre/post grad fx passes +# floating point provides limited accuracy (about 7 decimal digits for single precision +# floating point numbers,about 16 decimal digits for double precision floating point numbers) +# according to PyTorch documentation. +# https://pytorch.org/docs/stable/notes/numerical_accuracy.html#batched-computations-or-slice-computations +fx_passes_numeric_check: Dict[str, Any] = { + "pre_grad": False, + "precision": 1e-4, + "num_iterations": 1, + "requires_optimizer": True, +} + +# mixed_mm_choice can be used to control the behaviour for pattern torch.mm(a, b.to(dtype)) with cuda tensors. +# The fallback aten implementation is normal cast->then->mm option. +# If mixed_mm_choice is "default": this flag will be ignored. +# If mixed_mm_choice is "triton": +# - Always use torch._inductor.kernel.mm.tuned_mixed_mm's fused kernel. +# - Autotune will not compare with fallback. +# If mixed_mm_choice is "aten": always use the fallback aten implementation. +# If mixed_mm_choice is "heuristic": +# - Enables the heuristic. +# - If the heuristic decides to add a config, it will add the config as the first choice. +# - If autotune is disabled, this config will always be chosen. +# - If autotune is enabled, it will also compare with fallback aten implementation and fused kernel. +# The use_mixed_mm flag will be ignored if mixed_mm_choice != "default". +mixed_mm_choice = "heuristic" + +# enable reordering pass for increasing overlap between compute and communication +reorder_for_compute_comm_overlap = False + +# passes (in execution order) for increasing overlap between compute and communication +# for built-in passes, use string name; for user-defined passes, pass in the function handle +# WARNING: Inductor scheduler IR is at prototype stage and subject to change, +# hence custom IR passes built on top of it might break in the future. +reorder_for_compute_comm_overlap_passes = [ + "reorder_compute_for_overlap", + "sink_waits", + "raise_comms", +] + +# runtime estimation function for ops +# for built-in estimation function, pass in "default"; for user-defined estimation function, pass in the function handle +estimate_op_runtime = "default" + +# unit: GB/s, uni-directional P2P bandwidth per card +# default value is NVLink +intra_node_bw = 300 + +# unit: GB/s, uni-directional P2P bandwidth per node +# default value is InfiniBand +inter_node_bw = 25 + +# enable slow autotuning passes to select algorithms +max_autotune = os.environ.get("TORCHINDUCTOR_MAX_AUTOTUNE") == "1" + +# enable slow autotuning passes to select pointwise/reductions algorithms +max_autotune_pointwise = os.environ.get("TORCHINDUCTOR_MAX_AUTOTUNE_POINTWISE") == "1" + +# enable slow autotuning passes to select gemm algorithms +max_autotune_gemm = os.environ.get("TORCHINDUCTOR_MAX_AUTOTUNE_GEMM") == "1" + +# force cublas and triton to use the same precision; cublas supports TF32 for matmul operations +# when m, n, k are multiples of 16, 16, 8, whereas triton supports TF32 for matmul operations +# for any combinations of m, n, k, regardless of their alignment. setting this flag will ensure +# that triton does not use TF32 wherever cublas would not use TF32 +force_same_precision = ( + True if is_fbcode() else os.environ.get("TORCHINDUCTOR_FORCE_SAME_PRECISION") == "1" +) + +# Specify candidate backends for gemm autotune. +# Possible choices are combinations of: ATen, Triton, CUTLASS, CK, CPP. +# ATen: default Pytorch ATen kernels. +# Triton: Triton templates defined in torch inductor (AMD and NVidia GPUs). +# CUTLASS: Cutlass templates and kernels (NVidia GPUs only). +# CK: Composable Kernel templates and kernels (AMD Instinct GPUs only). +# CPP: CPP templates and kernels for CPU. +max_autotune_gemm_backends = os.environ.get( + "TORCHINDUCTOR_MAX_AUTOTUNE_GEMM_BACKENDS", "ATEN,TRITON,CPP" +).upper() + +# As above, specify candidate backends for conv autotune. +# NB: in some cases for 1x1 convs we emit as matmul, +# which will use the backends of `max_autotune_gemm_backends` +max_autotune_conv_backends = os.environ.get( + "TORCHINDUCTOR_MAX_AUTOTUNE_CONV_BACKENDS", "ATEN,TRITON" +).upper() + + +# Specify the size of the search space for GEMM autotuning. +# DEFAULT - balance between compile time overhead and performance +# EXHAUSTIVE - maximize performance +max_autotune_gemm_search_space = os.environ.get( + "TORCHINDUCTOR_MAX_AUTOTUNE_GEMM_SEARCH_SPACE", "DEFAULT" +).upper() + +# Whether we fall back to ATen or hard error when no matches are found during autotuning +autotune_fallback_to_aten = ( + os.environ.get("TORCHINDUCTOR_AUTOTUNE_FALLBACK_TO_ATEN", "1") == "1" +) + +# the value used as a fallback for the unbacked SymInts +# that can appear in the input shapes (e.g., in autotuning) +unbacked_symint_fallback = 8192 + +# DEPRECATED, DO NOT USE +search_autotune_cache = False + +save_args = os.environ.get("TORCHINDUCTOR_SAVE_ARGS") == "1" + +# We will disable creating subprocess for autotuning if this is False +autotune_in_subproc = os.environ.get("TORCHINDUCTOR_AUTOTUNE_IN_SUBPROC") == "1" + +# The following three timeouts are applicable if autotune_in_subproc is True: + +# Max time that a a valid benchmark result may take during autotuning +max_autotune_subproc_result_timeout_seconds = 60.0 +# Additional time we allow subprocesses to terminate gracefully after the timeout until we send a SIGTERM +max_autotune_subproc_graceful_timeout_seconds = 1.0 +# Additional time that we grant after a SIGTERM until we do a hard SIGKILL of subprocesses +max_autotune_subproc_terminate_timeout_seconds = 2.0 + +# If autotuning in subprocess, whether to use multiple devices +autotune_multi_device = os.environ.get("TORCHINDUCTOR_AUTOTUNE_MULTI_DEVICE") == "1" + +coordinate_descent_tuning = ( + os.environ.get("TORCHINDUCTOR_COORDINATE_DESCENT_TUNING") == "1" +) +coordinate_descent_check_all_directions = ( + os.environ.get("TORCHINDUCTOR_COORDINATE_DESCENT_CHECK_ALL_DIRECTIONS") == "1" +) +coordinate_descent_search_radius = int( + os.environ.get("TORCHINDUCTOR_COORDINATE_DESCENT_RADIUS", "1") +) + +# AutoHeuristic is a framework that allows one to collect data from autotuning, use the data to learn a heuristic, and +# generate the learned heursitic to code which is shipped with the compiler +# Specify a list of comma separated optimizations to collect data for +autoheuristic_collect = os.environ.get("TORCHINDUCTOR_AUTOHEURISTIC_COLLECT", "") +# Specify a list of comma separated optimizations to use learned heuristics for +autoheuristic_use = os.environ.get("TORCHINDUCTOR_AUTOHEURISTIC_USE", "mixed_mm") + + +def run_autoheuristic(name: str) -> bool: + return collect_autoheuristic(name) or use_autoheuristic(name) + + +def collect_autoheuristic(name: str) -> bool: + return name in torch._inductor.config.autoheuristic_collect.split(",") + + +def use_autoheuristic(name: str) -> bool: + return name in torch._inductor.config.autoheuristic_use.split(",") + + +# If set to "DEFAULT", this will use the default log path specified in autoheuristic.py. +# If set to another path, autoheuristic will instead log results to the given path. +autoheuristic_log_path = os.environ.get( + "TORCHINDUCTOR_AUTOHEURISTIC_LOG_PATH", "DEFAULT" +) + +# Disabled by default on ROCm, opt-in if model utilises NHWC convolutions +layout_opt_default = "1" if not torch.version.hip else "0" +layout_optimization = ( + os.environ.get("TORCHINDUCTOR_LAYOUT_OPTIMIZATION", layout_opt_default) == "1" +) + +force_layout_optimization = os.environ.get("TORCHINDUCTOR_FORCE_LAYOUT_OPT", "0") == "1" + + +# Whether to keep the output strides the same as eager after layout optimization. +keep_output_stride = os.environ.get("TORCHINDUCTOR_KEEP_OUTPUT_STRIDE", "1") == "1" + +# Enabling this will let compiler print warning messages if a generated triton +# kernel has inputs with mixed layouts. This is helpful for perf debugging +# since kernel with mixed layout inputs may run much slower then one whose inputs +# have uniform layouts. +warn_mix_layout = os.environ.get("TORCHINDUCTOR_WARN_MIX_LAYOUT") == "1" + +# control store vs recompute heuristic +# For fanouts, rematerialization can lead to exponential blowup. So, have +# smaller threshold +realize_reads_threshold = 4 +realize_opcount_threshold = 30 + +# Threshold to prevent excessive accumulation of ops in one buffer during lowering +realize_acc_reads_threshold = 8 + +# fallback to eager for random/dropout, this is slow but useful for debugging +fallback_random = False + +# automatically create fallbacks when encountering an unhandled op +implicit_fallbacks = True + +# fuse even in cases without common reads +aggressive_fusion = False + +# For each fused kernel in the wrapper, comment with the nodes that get fused. +# Useful for debugging fusion. +debug_fusion = os.environ.get("TORCHINDUCTOR_DEBUG_FUSION") == "1" +benchmark_fusion = os.environ.get("TORCHINDUCTOR_BENCHMARK_FUSION") == "1" +enabled_metric_tables = os.environ.get("TORCHINDUCTOR_ENABLED_METRIC_TABLES", "") +loop_ordering_after_fusion = ( + os.environ.get("TORCHINDUCTOR_LOOP_ORDERING_AFTER_FUSION", "0") == "1" +) + +# For Triton Templates, select fastest of best template + epilogue vs best template + separate epilogue kernel +benchmark_epilogue_fusion = ( + os.environ.get("TORCHINDUCTOR_BENCHMARK_EPILOGUE_FUSION", "1") == "1" +) + +# Take how many of the top triton kernels to benchmark epilogue +max_epilogue_benchmarked_choices = 1 + +# how many nodes to allow into a single fusion +max_fusion_size = 64 + +# max number of inputs to generate cat as a pointwise op with masked laods +max_pointwise_cat_inputs = 8 + +# replace small reductions with pointwise, disable with `= 1` +unroll_reductions_threshold = 8 + +# Add extra comments to output code (causes compile cache misses) +comment_origin = False + +# Convert 1x1 convs into matmuls +conv_1x1_as_mm = False + +# Enable split reductions for better utilization when the dimension +# being reduced over is large (by splitting it) +split_reductions = True + +benchmark_kernel = os.environ.get("TORCHINDUCTOR_BENCHMARK_KERNEL", "0") == "1" + +# Enable constant and index_expr folding +constant_and_index_propagation = True + +# we always add constants into graph.constants without +# performing any constant-inlining optimization +always_keep_tensor_constants = False + +# assert that indirect indexing does not read / write out of bounds +assert_indirect_indexing = True + +# compute CSE bounds on variables that do not appear in the FX graph +compute_all_bounds = False + +# enable the combo kernel that combines data-independent kernels (additional +# to foreach kernels) into a single one (Experimental) +combo_kernels = False +# benchmark combo kernels and only allow ones with perf gains +benchmark_combo_kernel = False +# combo_kernel autotuning options: 0 - disable, 1 - enable except for foreach, +# 2 - enable for all +combo_kernels_autotune = 1 +# Enable masking for combining kernels of mixed sizes: 0 - disable, 1 - enable +# for all except for foreach, 2 - enable for all +combo_kernel_allow_mixed_sizes = 1 +# Enable dynamic shapes for foreach kernels +combo_kernel_foreach_dynamic_shapes = False + +# constant folding on the joint graph +joint_graph_constant_folding = True + +# Enable indirect_indexing asserts for decompositions and lowerings +debug_index_asserts = False + +# Mode to emulate pytorch eager numerics for lower precision (fp16, bf16) +# Pytorch eager computes bf16/fp16 by upcasting inputs to fp32 and downcasting after +# For multiple, fused pointwise nodes, inductor will elide the intermediary upcasts and downcasts +# Typically this should be closer to fp64 ref numerics. However, it can be useful for debugging +# to emulate the eager numerics. +emulate_precision_casts = False + +# warnings intended for PyTorch developers, disable for point releases +is_nightly_or_source = "dev" in torch.__version__ or "git" in torch.__version__ +developer_warnings = is_fbcode() or is_nightly_or_source + +# This pattern matches a special usage of scatter +# 1. It's applied to a constant tensor +# 2. The index tensor has size 1 in the scatter dimension +# Such pattern generates a sparse matrix when the const tensor is all-zero. +# We can lower this pattern to a pointwise kernel for more fusion opportunities +# and saving memory footprint. +optimize_scatter_upon_const_tensor = ( + os.environ.get("TORCHINDUCTOR_OPTIMIZE_SCATTER_UPON_CONST_TENSOR", "1") == "1" +) + + +# The multiprocessing start method to use for inductor workers in the codecache. +# Can be "subprocess" or "fork". +def decide_worker_start_method() -> str: + start_method = os.environ.get( + "TORCHINDUCTOR_WORKER_START", "fork" if is_fbcode() else "subprocess" + ) + assert start_method in ( + "subprocess", + "fork", + ), f"Invalid start method: {start_method}" + return start_method + + +worker_start_method = decide_worker_start_method() + +# Flags to turn on all_reduce fusion. These 2 flags should be automaticaly turned +# on by DDP and should not be set by the users. +_fuse_ddp_communication = False +_fuse_ddp_bucket_size = 25 + +# Flag to control which fusion passes to apply. Functions in the list will +# be applied in order. There are two different different fusion passes +# --"fuse_ddp_with_concat_op" and "fuse_ddp_with_coalesced_op". The default +# one is "fuse_ddp_with_concat_op". Users can also change this to a customized +# fusion function. +# +# The fusion currently does not support multiple DDP with different PG or +# data type. This feature will be added in the future PRs. +# +# "schedule_comm_wait" is used to delay the wait ops to maximize comm/comp +# overlapping. At this moment, this pass performs better than +# reorder_for_compute_comm_overlap_passes but we will add the logic of +# "schedule_comm_wait" in the future and remove the one here. +_fuse_ddp_communication_passes: List[Union[Callable[..., None], str]] = [ + "fuse_ddp_with_concat_op", + "schedule_comm_wait", +] + +_micro_pipeline_tp: bool = False + + +def decide_compile_threads() -> int: + """ + Here are the precedence to decide compile_threads + 1. User can override it by TORCHINDUCTOR_COMPILE_THREADS. One may want to disable async compiling by + setting this to 1 to make pdb happy. + 2. Set to 1 if it's win32 platform + 3. decide by the number of CPU cores + """ + if "TORCHINDUCTOR_COMPILE_THREADS" in os.environ: + return int(os.environ["TORCHINDUCTOR_COMPILE_THREADS"]) + elif sys.platform == "win32": + return 1 + elif is_fbcode(): + return 1 + else: + cpu_count = ( + len(os.sched_getaffinity(0)) + if hasattr(os, "sched_getaffinity") + else os.cpu_count() + ) + assert cpu_count + return min(32, cpu_count) + + +compile_threads = decide_compile_threads() + +# gemm autotuning global cache dir +if is_fbcode(): + try: + from libfb.py import parutil + + if __package__: + global_cache_dir = parutil.get_dir_path( + os.path.join(__package__.replace(".", os.sep), "fb/cache") + ) + else: + global_cache_dir = parutil.get_dir_path("fb/cache") + except (ValueError, ModuleNotFoundError): + global_cache_dir = None + +else: + global_cache_dir = None + +# If kernel is fused, the name is generated from the origin node op names +# for larger kernels limit this +kernel_name_max_ops = 10 + +# Pad input tensors of matmul/bmm/addmm to leverage Tensor Cores in NVIDIA GPUs +shape_padding = os.environ.get("TORCHINDUCTOR_SHAPE_PADDING", "1") == "1" + +# Control if we will do padding for pointwise/reductions +comprehensive_padding = ( + os.environ.get("TORCHINDUCTOR_COMPREHENSIVE_PADDING", "1") == "1" +) +pad_channels_last = False + +# Disable comprehensive padding on the CPU +disable_padding_cpu = True + +# The width of comprehensive padding, in bytes. +# CUDA max memory transaction size is 128 bytes for a warp. +padding_alignment_bytes = 128 + +# Threshold on the minimum stride that will be padded. +# +# Don't align a too small stride since that causes too much memory increase. +# Pad too small stride may also cause perf loss. We may result in many tiny data blocks +# with gaps in between. That causes less coalesced GPU memory access! +# +# Initially we pick 320 as the threshold since for alignement=16, +# that results in at most 5% memory cost. +# +# But later on we raise the threshold to 1024 to avoid interfere with persistent reduction. +# Let's say an inner reduction has a row size 513. Inductor will generate +# persistent reduction code. +# If we do padding, the strides are not contiguous any more. Inductor +# uses a much smaller threshold for persistent reduction in this case and +# generates potentially worse non-persistent reduction code. +# +# This change turns HF AllenaiLongformerBase amp training from a loss of 1.09x to a win of 1.05x. +# (baseline: 71.09ms, padding w/o this change: 77.38ms, padding with this change: 67.77ms) +padding_stride_threshold = 1024 + +# Enable padding outputs, even if they would not be padded in eager mode. +# By default, we use the same strides as eager mode. +pad_outputs = False + +# Whether to treat output of the backward graph as user visible. +# For user visible outputs, inductor will make sure the stride matches with eager. +bw_outputs_user_visible = True + +# Whether to always use shape padding if it is enabled and possible +force_shape_pad: bool = False + +# Fx-based linear/matmul/bmm + permute/transpose vertical fusion +permute_fusion = os.environ.get("TORCHINDUCTOR_PERMUTE_FUSION", "0") == "1" + +# Mark the wrapper call in PyTorch profiler +profiler_mark_wrapper_call = False + +# Generate hook calls to torch._inductor.hooks.run_intermediate_hooks for +# every intermediate for which we can correlate it with an intermediate +# from the original FX graph +generate_intermediate_hooks = False + +# Populate traceback field on IRNode; good for debugging why origin_node is +# not populated, or finding out where an IRNode was constructed +debug_ir_traceback = False + +# used for debugging to make sure config is properly set +_raise_error_for_testing = False + +_profile_var = os.environ.get("TORCHINDUCTOR_PROFILE", "") +profile_bandwidth = _profile_var != "" +profile_bandwidth_regex = "" if _profile_var == "1" else _profile_var +# Specify a file where we print out the profiling results. +# None means we do not dump results to a file. +profile_bandwidth_output = os.environ.get("TORCHINDUCTOR_PROFILE_OUTPUT", None) +# Switch to do_bench_using_profiling to exclude the CPU overheads +profile_bandwidth_with_do_bench_using_profiling = ( + os.environ.get("TORCHINDUCTOR_PROFILE_WITH_DO_BENCH_USING_PROFILING") == "1" +) + + +# TODO: remove later +disable_cpp_codegen = False + + +# Freezing will attempt to inline weights as constants in optimization +# and run constant folding and other optimizations on them. After freezing, weights +# can no longer be updated. +freezing: bool = os.environ.get("TORCHINDUCTOR_FREEZING", "0") == "1" + +# Make freezing invalidate the eager Parameters of nn modules, to avoid memory overhead +# of potentially keeping multiple copies of weights. +freezing_discard_parameters: bool = False + +# Kill switch for allowing temporary tensors to be allocated as stack arrays. Tests +# should be run with this flag both on and off to make sure we have coverage. +allow_stack_allocation: bool = ( + os.environ.get("TORCHINDUCTOR_STACK_ALLOCATION", "1" if is_fbcode() else "0") == "1" +) + +# Enables an alternate DSO interface (the "minimal ArrayRef interface") intended +# to maximize performance for use cases that it can accommodate at the expense of +# generality. In brief: +# - inputs and outputs are ArrayRefTensor (note that strides are required, but the +# tensor must be contiguous) +# - constant handling is unchanged because it is not a per-inference-iteration bottleneck +# +# When the DSO is generated in this mode, the usual interface will also be supported, +# but performance for that interface may be degraded. +use_minimal_arrayref_interface: bool = False + +# decompose some memory bound matmul/bmm to mul +decompose_mem_bound_mm: bool = False + +# assume_aligned_inputs means that we assume that inputs will be aligned; we generate +# code using this assumption, and clone tensors before use if they aren't aligned. +# In the common case, most inputs will be aligned. +assume_aligned_inputs: bool = False + +# For the user-written Triton kernels compiled with the model, ignore the unsupported +# arguments passed to the @triton.autotune in the user's code; this is unsafe, as +# ignoring the unsupported args may lead to unexpected autotuning behavior: don't +# set unless you know what you're doing. +unsafe_ignore_unsupported_triton_autotune_args: bool = False + +# When True, we will check in scheduler.py _codegen that there are no "loops" +# in the call stack; that is to say, the same frame multiple times. This +# ensures that a cProfile trace to this frame will be a straight line without +# any cycles. +check_stack_no_cycles_TESTING_ONLY: bool = False + + +# config specific to codegen/cpp.py +class cpp: + # set to torch.get_num_threads() + threads = -1 + + # Do not generate loops when the condition doesn't hold, like: + # for(long i0=4096; i0<4096; i0+=1) + no_redundant_loops = ( + os.environ.get("TORCHINDUCTOR_CPP_NO_REDUNDANT_LOOPS", "1") == "1" + ) + + # Assume number of threads is dynamic, don't specialize thread number. + # Kernels don't recompile on thread number changes with this flag on. + # For single-threaded workload, turning it on would incur a slight + # performance degradation. + dynamic_threads = os.environ.get("TORCHINDUCTOR_CPP_DYNAMIC_THREADS", "0") == "1" + + simdlen: Optional[int] = None + min_chunk_size = int(os.environ.get("TORCHINDUCTOR_CPP_MIN_CHUNK_SIZE", "4096")) + cxx = ( + None, # download gcc12 from conda-forge if conda is installed + # "g++-12", + # "g++-11", + # "g++-10", + # "clang++", + os.environ.get("CXX", "clang++" if sys.platform == "darwin" else "g++"), + # "g++.par", + ) + # Allow kernel performance profiling via PyTorch profiler + enable_kernel_profile = ( + os.environ.get("TORCHINDUCTOR_CPP_ENABLE_KERNEL_PROFILE", "0") == "1" + ) + + # enable weight prepacking to get a better performance; may lead to large memory footprint + weight_prepack = os.environ.get("TORCHINDUCTOR_CPP_WEIGHT_PREPACK", "1") == "1" + + # Inject a bug into our relu implementation; useful for testing our repro + # extraction and minification functionality. + # Valid values: "compile_error", "runtime_error", "accuracy" + inject_relu_bug_TESTING_ONLY: Optional[str] = None + inject_log1p_bug_TESTING_ONLY: Optional[str] = None + + # If None, autodetect whether or not AVX512/AVX2 can be used. Otherwise, + # force usage as specified, without testing. + vec_isa_ok: Optional[bool] = None + + # similar to config.triton.descriptive_names + descriptive_names = "original_aten" + + # how many nodes to allow into a single horizontal fusion + max_horizontal_fusion_size = int( + os.environ.get("TORCHINDUCTOR_CPP_MAX_HORIZONTAL_FUSION_SIZE", "16") + ) + + # Make scatter_reduce fallback when reduce is sum to avoid performance regression + # using atomic_add. + fallback_scatter_reduce_sum = ( + os.environ.get("TORCHINDUCTOR_CPP_FALLBACK_SCATTER_REDUCE_SUM", "1") == "1" + ) + + # Use funsafe-math-optimizations when compiling + enable_unsafe_math_opt_flag = ( + os.environ.get("TORCHINDUCTOR_CPP_ENABLE_UNSAFE_MATH_OPT_FLAG", "0") == "1" + ) + + # Use ffp-contract when compiling + enable_floating_point_contract_flag = ( + os.environ.get("TORCHINDUCTOR_CPP_ENABLE_FLOATING_POINT_CONTRACT_FLAG", "0") + == "1" + ) + + # Disable the tiling select heuristic + enable_tiling_heuristics = ( + os.environ.get("TORCHINDUCTOR_CPP_ENABLE_TILING_HEURISTIC", "1") == "1" + ) + + # Maximal allowed number of slices on K-dim for a GEMM kernel. This controls + # the maximal parallelism of K-slicing. Since K-slicing requires extra thread + # synchronization and buffers, the maximal number of slices is limited to + # mitigate the sync overhead and memory usage. + # When set to 0, the number of slices is unlimited. + gemm_max_k_slices = int(os.environ.get("TORCHINDUCTOR_CPP_GEMM_MAX_K_SLICES", "1")) + + # For perf tuning and debugging purpose, configure the pre-defined cache blocking for + # MxNxK dims respectively. The blockings are separated by comma and the unit is + # the number of register blocks. + # For example, "4,1,10" means 4 register blocks on M, 1 on N and 10 on K respectively. + gemm_cache_blocking = os.environ.get("TORCHINDUCTOR_CPP_GEMM_CACHE_BLOCKING", None) + + # For perf tuning and debugging purpose, configure the pre-defined thread blocking factors for + # MxNxK dims respectively. The factors are separated by comma and their product + # should be the same as the total number of threads. + # For example, if the total number of threads is 56, "7,4,2" means the work is + # decomposed into 7x4x2 thread blocks along MxNxK of a GEMM. + gemm_thread_factors = os.environ.get("TORCHINDUCTOR_CPP_GEMM_THREAD_FACTORS", None) + + # Whether to enable masked vectorization for the tail_loop. + enable_loop_tail_vec = True + + +# config specific to codegen/triton.py +class triton: + # Use cudagraphs on output code + cudagraphs = os.environ.get("TORCHINDUCTOR_CUDAGRAPHS") == "1" + + # Use cudagraph trees for memory pooling if `cudagraphs` is True + cudagraph_trees = True + + # Should we skip cudagraphing graphs with dynamic shape inputs + # If False, we will re-record a graph for each unique set of shape inputs + cudagraph_skip_dynamic_graphs = False + + # assertions not on the fast path, steady state + slow_path_cudagraph_asserts = True + + # TODO - need to debug why this prevents cleanup + cudagraph_trees_history_recording = False + + # Enable cudagraph support for mutated inputs from prior cudagraph pool + cudagraph_support_input_mutation = False if is_fbcode() else True + + # Maximal number of allowed cudagraph re-record for a function and + # a cudagraph node due to static input tensor address changes or + # cudagraph managed tensor data pointer changed. + # i.e., allow num_recording <= cudagraph_unexpected_rerecord_limit + # note: we are conservative here and choose a large limit. + cudagraph_unexpected_rerecord_limit = 128 + + # Warn loudly when the number of cudagraphs due to dynamic shape + # exceeds this limit + cudagraph_dynamic_shape_warn_limit: Optional[int] = 50 + + # synchronize after cudagraph invocation + force_cudagraph_sync = False + + # always run cudagraphs in the eager warmup stage + # instead of recording and executing cudagraphs + force_cudagraphs_warmup = False + + # assertions on the fast path + fast_path_cudagraph_asserts = False + + # skip warmup for cudagraph trees + skip_cudagraph_warmup = False + + # Synchronize before and after every compiled graph. + debug_sync_graph = False + + # Synchronize after every kernel launch, to help pinpoint bugs + debug_sync_kernel = False + + # Always load full blocks (rather than broadcasting inside the block) + dense_indexing = False + + # limit tiling dimensions + max_tiles = 2 + + # Prefer higher dimensional tilings. This simplifies indexing expressions, making + # it easier to identify block pointers. + prefer_nd_tiling: bool = False + + # use triton.autotune for pointwise ops with complex layouts + # this should only be disabled for debugging/testing + autotune_pointwise = True + + # max autotune gemm with cublasLt + autotune_cublasLt = True + + # Tune the generated Triton kernels at compile time instead of first time they run + autotune_at_compile_time = False + + # should we stop a fusion to allow better tiling? + tiling_prevents_pointwise_fusion = True + tiling_prevents_reduction_fusion = True + + # should we give different names to kernels + # Note: This is orthogonal to descriptive_names - this is deciding whether + # our triton kernel names should all be `triton_` (to maximize caching) or + # whether they should be unique. + unique_kernel_names = os.environ.get("TORCHINDUCTOR_UNIQUE_KERNEL_NAMES") == "1" + + # should we put op names in kernel names + # False: No special names (just triton__1, triton__2, etc.) + # "torch": Maps to the fx op in the Dynamo graph (module name, method name, etc.) + # "original_aten": Maps to the highest-level aten op (i.e. pre-decompositions) + # "inductor_node": Maps to the node name in the FX graph passed to Inductor + descriptive_names = "original_aten" + + # use alternate codegen for smaller reductions + persistent_reductions = ( + os.environ.get("TORCHINDUCTOR_PERSISTENT_REDUCTIONS", "1") == "1" + ) + + # 0/False: disable + # 1/True: enable, use tuning to pick between different subkernels + # 2: enable, force using persistent reduction (for debugging) + # 3: enable, force using non-persistent reduction (for debugging) + multi_kernel = int(os.environ.get("TORCHINDUCTOR_MULTI_KERNEL", "0")) + + # hint to Triton when arguments are divisible by 16 + divisible_by_16 = True + + # Minimum RBLOCK to be used for a TritonSplitScanKernel + # NOTE: This also indirectly controls the size of workspace buffer required + min_split_scan_rblock = 256 + + # Store the generated cubin files for cpp wrapper code to load + store_cubin = False + + # the max number of spills we allow for the configs we benchmark. + # Setting this to 0 means we skip a config if it spills even a single + # register. + # Setting it to a larger value allows a config spilling a small amount + # of registers being benchmarked. + # + # NOTE: triton will always report >0 register spills for kernels using sin/cos. + # (check this issue https://github.com/openai/triton/issues/1756 ) + # So far we see a fixed 8 spilled registers for kernels using sin/cos. + # Raise the threshold to 16 to be safe. + # We should revisit this once we understand more of the source of register spills. + spill_threshold: int = 16 + + # Generate code containing the newer tl.make_block_ptr() API for loads/store + use_block_ptr = False + + # Inject a bug into our relu implementation; useful for testing our repro + # extraction and minification functionality. + # Valid values: "compile_error", "runtime_error", "accuracy" + inject_relu_bug_TESTING_ONLY: Optional[str] = None + + # Whether to upcast float16 / bfloat16 to float32 in triton codegen (Experimental) + codegen_upcast_to_fp32 = True + + +class aot_inductor: + # AOTInductor output path + # If an absolute path is specified, the generated lib files will be stored under the directory; + # If a relative path is specified, it will be used as a subdirectory under the default caching path; + # If not specified, a temp directory will be created under the default caching path. + # If the specified path contains something like "model.so", the sub-string will be used + # to name the generated library. + output_path = "" + + debug_compile = os.environ.get("AOT_INDUCTOR_DEBUG_COMPILE", "0") == "1" + + debug_dump_consts_bin: bool = ( + os.environ.get("AOT_INDUCTOR_DEBUG_DUMP_CONSTS_BIN", "0") == "1" + ) + + # option for debug printing/saving for intermediate tensor values for aot inductor + # 0: disable debug dumping + # 1: enable saving intermediate tensor values + # 2: enable printing intermediate tensor values + debug_intermediate_value_printer = os.environ.get( + "AOT_INDUCTOR_DEBUG_INTERMEDIATE_VALUE_PRINTER", "0" + ) + + # filtered nodes to be printed for debug values. Specify this option when debug_intermediate_value_printer is set to 2 + filtered_kernel_names = os.environ.get( + "AOT_INDUCTOR_FILTERED_KERNELS_TO_PRINT", None + ) + + # Serialized tree spec for flattening inputs + serialized_in_spec = "" + + # Serialized tree spec for flattening outputs + serialized_out_spec = "" + + # flag to decide whether to create a submodule for constant graph. + use_runtime_constant_folding: bool = False + + # flag to force weight to be appened to the shared library and mmaped by the runtime + # rather than embedded into the data section. Needed to support 1B+ parameter models + force_mmap_weights: bool = False + + package: bool = False + + +class cuda: + # CUDA arch to use for CUDA template kernel compilation. + # e.g. "70", "75", "80", "90", etc. + # When arch is None, Inductor uses torch.cuda.get_device_capability(0). + arch: Optional[str] = None + + # CUDA version to use for CUDA template kernel compilation. + # e.g. "11.4", "12.1", etc. + # When version is None, Inductor uses torch.version.cuda. + version: Optional[str] = None + + # Optimization level for the host compiler. + compile_opt_level = "-O1" + + # Whether to enable device LTO (link-time-optimization). + enable_cuda_lto = False + + # Whether to keep intermediate files dring compilation. + enable_ptxas_info = False + + # Whether to enable debug info, e.g. line number, cutlass debug info. + enable_debug_info = False + + # Whether to use fast math. + use_fast_math = False + + # Path to the CUTLASS repo root directory. + # The default path only works under PyTorch local development environment. + cutlass_dir = os.environ.get( + "TORCHINDUCTOR_CUTLASS_DIR", + os.path.abspath( + os.path.join(os.path.dirname(torch.__file__), "../third_party/cutlass/") + ), + ) + + # Configures the maximum number of CUTLASS configs to profile in max_autotune. + # By default it's None, so that all CUTLASS configs are tuned. + # This is mainly used to reduce test time in CI. + cutlass_max_profiling_configs: Optional[int] = None + + # Path to CUDA NVCC. + # NVCC search order: + # 1) cuda_cxx set in this config + # 2) CUDACXX environment variable + # 3) CUDA_HOME environment variable + # 4) default system search PATH. + cuda_cxx: Optional[str] = None + + # Minimum value of M*N*K to consider the CUTLASS backend for GEMM ops. + cutlass_backend_min_gemm_size: int = 1 + + # enable generation of inline standalone runner in CUDA CPP generated code + # which allows to compile the generated code into a standalone executable. + generate_test_runner: bool = ( + os.environ.get("INDUCTOR_CUDA_BACKEND_GENERATE_TEST_RUNNER_CODE", "1") == "1" + ) + + # Keep only Cutlass op configs which contain this regular expression pattern + # Set this to "warpspecialized_cooperative_epi_tma" to enable only SM90 TMA Cutlass Kernels for large GEMMs + cutlass_op_allowlist_regex: Optional[str] = None + + # Note: Names of Cutlass ops names can be obtained by calling + # op.configuration_name() on a Cutlass op instance, for example those + # returned from cutlass_utils.gen_ops() or the op argument passed to + # CUTLASSGemmTemplate.render(...) + + # Filter Cutlass configs which contain this regular expression pattern + # Set this to "pingpong" to avoid numerical issues + # caused by the op ordering of the "pingpong" memory access + # pattern used by some Cutlass Kernels. + cutlass_op_denylist_regex: Optional[str] = "pingpong" + + +class rocm: + # Offload arch list for device code compilation, e.g. ["gfx941", "gfx942"]. + # If empty, the `native` arch is used + arch: List[str] = [] + + # Enable the CK backend for CDNA2 and CDNA3 only (for now) + # Processor name reference: https://llvm.org/docs/AMDGPUUsage.html#processors + ck_supported_arch: List[str] = ["gfx90a", "gfx940", "gfx941", "gfx942"] + + # Optimization level, use to balance compilation speed and runtime performance + compile_opt_level = "-O2" + + # Flag to keep debug information in compiled objects + is_debug = False + + # Flag to keep intermediate files (assembly listings, preprocessed sources, etc.) + save_temps = False + + # Flag to add `-ffast-math`` to compile flags + use_fast_math = True + + # Flag to add `-fgpu-flush-denormals-to-zero` to compile flags + flush_denormals = True + + # Flag to print register and LDS usage during compilation + print_kernel_resource_usage = False + + # Path to ROCm installation, if None, use env variable ROCM_HOME + rocm_home: Optional[str] = None + + # Path to Composable Kernel library. + # Install with `pip install git+https://github.com/rocm/composable_kernel@develop`. + ck_dir = os.environ.get("TORCHINDUCTOR_CK_DIR") + + # Number of op instance choices to trade off between runtime perf and compilation time + n_max_profiling_configs: Optional[int] = None + + # Flag to use a short list of CK instances which perform well across a variety of shapes. + # Currently RCR and F16 only + use_preselected_instances: bool = False + + +# Backend to use for CPU codegen either "cpp" or "halide" (experimental) +cpu_backend = "cpp" + +# Backend to use for CUDA codegen either "triton" or "halide" (experimental) +cuda_backend = "triton" + + +class halide: + # Base halide target to use for CPU devices + cpu_target = "host" + + # Base halide target to use for CUDA devices + gpu_target = "host-cuda" + + # Halide autoscheduler to use, choices are: + # "Anderson2021" (gpu-only), "Li2018", "Adams2019" (cpu-only), or "Mullapudi2016" (cpu-only) + scheduler_cuda = "Anderson2021" + scheduler_cpu = "Adams2019" + + # Controls `no_asserts` flag passed to Halide target (warning: can false positive) + asserts = False + + # Controls `debug` flag passed to Halide target + debug = False + + # Enable (or fallback on) scan kernels such as cumsum + # Halide autoschedulers struggle with these kernels + scan_kernels = False + + +# create a directory containing lots of debug information +class trace: + # master switch for all debugging flags below + enabled = os.environ.get("TORCH_COMPILE_DEBUG", "0") == "1" + + # Save debug information to a temporary directory + # If not specified, a temp directory will be created by system + debug_dir: Optional[str] = None + + # Save python logger call >=logging.DEBUG + debug_log = False + + # Save python logger call >=logging.INFO + info_log = False + + # Save input FX graph (post decomps, pre optimization) + fx_graph = True + + # Save FX graph after transformations + fx_graph_transformed = True + + # Save TorchInductor IR before fusion pass + ir_pre_fusion = True + + # Save TorchInductor IR after fusion pass + ir_post_fusion = True + + # Copy generated code to trace dir + output_code = True + + # SVG figure showing post-fusion graph + graph_diagram = os.environ.get("INDUCTOR_POST_FUSION_SVG", "0") == "1" + + # SVG figure showing fx with fusion + draw_orig_fx_graph = os.environ.get("INDUCTOR_ORIG_FX_SVG", "0") == "1" + + # We draw our fx graphs with the "record" shape attribute by default. + # Sometimes, when the graph is very complex, we may hit dot errors like below: + # "flat edge between adjacent nodes one of which has a record shape - + # replace records with HTML-like labels" + # and thus fail to generate a graph. So, let's give the user an option + # to specify the shape attribute for the dot graph. For example, passing + # INDUCTOR_DOT_GRAPH_SHAPE_SVG = "none" would let us generate HTML-like lables + # to workaround the above failure. + dot_graph_shape = os.environ.get("INDUCTOR_DOT_GRAPH_SHAPE_SVG", None) + + # If not None, this is the URL that saves the SVG files of the input/output + # graph of each pass that changed the graph + # The nodes that are being transformed in each pass will be colored in yellow + # URL only supports local directory for now + log_url_for_graph_xform = os.environ.get("INDUCTOR_LOG_URL_FOR_GRAPH_XFORM", None) + + # Store cProfile (see snakeviz to view) + compile_profile = False + + # Upload the .tar.gz file + # Needs to be overriden based on specific environment needs + upload_tar: Optional[Callable[[str], None]] = None + + log_autotuning_results: bool = False + + +_save_config_ignore = [ + # workaround: "Can't pickle " + "trace.upload_tar", + "post_grad_custom_post_pass", + "post_grad_custom_pre_pass", + "joint_custom_pre_pass", + "joint_custom_post_pass", + "pre_grad_custom_pass", +] + +_cache_config_ignore_prefix = [ + # trace functions are not relevant to config caching + "trace", + # uses absolute path + "cuda.cutlass_dir", + # not relevant + "compile_threads", +] + +if TYPE_CHECKING: + from torch.utils._config_typing import * # noqa: F401, F403 + +from torch.utils._config_module import install_config_module + + +# adds patch, save_config, etc +install_config_module(sys.modules[__name__]) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/constant_folding.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/constant_folding.py new file mode 100644 index 0000000000000000000000000000000000000000..72f34d32475f39c94a30308455edbcfcb8e03d08 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/constant_folding.py @@ -0,0 +1,348 @@ +import collections +from typing import Any, Callable, Dict, List, Optional, Tuple + +import torch +import torch.utils._pytree as pytree + + +aten = torch.ops.aten + +# We would like to split modules into two subgraphs for runtime weight updates to work correctly. +# The use case and more information could be found at: +# https://docs.google.com/document/d/1inZC-8KarJ6gKB7G9egmYLx1V_dKX_apxon0w4zPC0Q/edit?usp=sharing +META_TAG = "MODULE_TYPE" +MODULE_TAG = "_MAIN_MODULE" +CONST_MODULE_TAG = "_CONST_MODULE" + + +def replace_node_with_constant( + gm: torch.fx.GraphModule, + node: torch.fx.Node, + constant: torch.Tensor, + name: Optional[str] = None, +) -> None: + g = gm.graph + + if name: + qualname = name + else: + if not hasattr(gm, "_frozen_param_count"): + gm._frozen_param_count = 0 # type: ignore[assignment] + i = gm._frozen_param_count + + while True: + qualname = f"_frozen_param{i}" + if not hasattr(gm, qualname): + break + i += 1 + + gm._frozen_param_count = i + 1 + + with g.inserting_before(node): + new_input_node = g.create_node("get_attr", qualname, (), {}) + node.replace_all_uses_with(new_input_node) + new_input_node.meta.update(node.meta) + g.erase_node(node) + + # needed to suppress `does not reference an nn.Module, nn.Parameter, or buffer` warning + gm.register_buffer(qualname, constant) + setattr(gm, qualname, constant) + + +def is_const_source( + node: torch.fx.Node, lifted_constants: Optional[Dict[str, Any]] +) -> bool: + return node.op == "get_attr" or ( + node.op == "placeholder" + and lifted_constants is not None + and node.name in lifted_constants + ) + + +class ConstantFolder(torch.fx.Interpreter): + def __init__( + self, + gm: torch.fx.GraphModule, + skip_constructors: bool = False, + lifted_constants: Optional[Dict[str, torch.Tensor]] = None, + skip_folding_node_fn: Optional[Callable[[torch.fx.Node], bool]] = None, + ) -> None: + super().__init__(gm) + self.node_replacements: Dict[torch.fx.Node, Any] = {} + self.replaced_uses: Dict[torch.fx.Node, int] = collections.Counter() + self.unknown_value = object() + self.skip_constructors: bool = skip_constructors + + # overwrite this to deallocate env values if their only remaining use + # is the output + self.user_to_last_uses = self.node_to_last_non_output_use() + self.lifted_constants = lifted_constants + + def _support_dynamic_shape(self) -> bool: + # ConstantFolder not support dynamic shape now + return False + + def _deduce_value(self, node: torch.fx.Node) -> Any: + return super().run_node(node) + + def is_impure(self, node: torch.fx.node.Node) -> bool: + if ( + node.target == torch.ops.prims.convert_element_type.default + and is_const_source(node.args[0], self.lifted_constants) # type: ignore[arg-type] + and node.args[0].meta["val"].dtype == torch.int8 # type: ignore[union-attr] + and node.args[1] == torch.bfloat16 + ): + # For int8_weight -> dq -> bf16_weight + return True + if node.target in [ + torch.ops.quantized_decomposed.dequantize_per_channel.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.tensor, + ]: + # For the pattern fp32_weight -> q -> dq + # We only folding fp32_weight -> q + # int8_weight and leave dq in graph to be fused + return True + return False + + def node_to_last_non_output_use(self) -> Dict[torch.fx.Node, List[torch.fx.Node]]: + last_non_output_use = collections.defaultdict(list) + seen_uses = set() + output_node = next(iter(reversed(self.module.graph.nodes))) + + for node in reversed(self.module.graph.nodes): + if node.target == "output": + continue + + def add_use(inp: torch.fx.Node) -> None: + if inp in seen_uses: + return + + seen_uses.add(inp) + last_non_output_use[node].append(inp) + + # In-place is fine since we don't mutate + pytree.tree_map_only_(torch.fx.Node, add_use, (node.args, node.kwargs)) + + # if this node is only used in output, we want to gc it right away + if len(node.users) == 1 and output_node in node.users: + last_non_output_use[node].append(node) + + return last_non_output_use + + def run_node(self, node: torch.fx.Node) -> Any: + if node.target == "output": + # because we remove nodes from env on last non output use, + # re-define them now or we'll get error in interpreter + def set_env(arg: torch.fx.Node) -> None: + self.env[arg] = self.unknown_value + + # In-place is fine since we don't mutate + pytree.tree_map_only_(torch.fx.Node, set_env, node.args) + return super().run_node(node) + + args, kwargs = self.fetch_args_kwargs_from_env(node) + flattened_inputs = pytree.arg_tree_leaves(*args, **kwargs) + + # We need to do this weird thing because in cases where flattened_inputs + # contains a ScriptObject, equality checking results in a type error if + # the types are different. + if any( + type(self.unknown_value) == type(input_) and self.unknown_value == input_ + for input_ in flattened_inputs + ): + return self.unknown_value + + # TODO - fix errors with this + if ( + node.op == "call_function" + and node.target == aten._efficientzerotensor.default + ): + return self.unknown_value + + # TODO - constant folding triton kernel returns the inputs -- fix this + if ( + node.op == "call_function" + and node.name == "triton_kernel_wrapper_functional_proxy" + ): + return self.unknown_value + + # skip constructors, since inductor generates optimal code for them already + # and turning into tensor would result in an additional global memory read + # TODO - more complicated strategy + if ( + self.skip_constructors + and not is_const_source(node, self.lifted_constants) + and not any(isinstance(e, torch.Tensor) for e in flattened_inputs) + ): + return self.unknown_value + + # All mutations should either be removed or on inputs which we did not make constant + if ( + isinstance(node.target, torch._ops.OpOverload) + and torch.Tag.nondeterministic_seeded in node.target.tags + ): + return self.unknown_value + + out = self._deduce_value(node) + if out == self.unknown_value: + return self.unknown_value + + if not is_const_source(node, self.lifted_constants) and isinstance( + out, torch.Tensor + ): + if out.device.type == "meta": + return out + + if not self.insertable_tensor_check(out): + return out + + if self.is_impure(node): + return self.unknown_value + + self.add_node_replacement(node, out) + + flattened_node_inps = pytree.arg_tree_leaves(*node.args, **node.kwargs) + + for n in flattened_node_inps: + if not isinstance(n, torch.fx.Node): + continue + + self.replaced_uses[n] += 1 + + for to_delete in self.user_to_last_uses.get(node, []): + if self.replaced_uses[to_delete] == len(to_delete.users): + self.node_replacements.pop(to_delete, None) + + return out + + def insertable_tensor_check(self, tensor: torch.Tensor) -> bool: + return True + + def add_node_replacement(self, node: torch.fx.Node, tensor: torch.Tensor) -> None: + self.node_replacements[node] = tensor + + def run(self) -> Any: # type: ignore[override] + env: Dict[torch.fx.Node, Any] = {} + self.insert_placerholder_values(env) + return super().run(initial_env=env) + + def insert_placerholder_values(self, env: Dict[torch.fx.Node, Any]) -> None: + for n in self.module.graph.find_nodes(op="placeholder"): + if self.lifted_constants is not None and n.name in self.lifted_constants: + env[n] = self.lifted_constants[n.name] + else: + env[n] = self.unknown_value # type: ignore[assignment] + + +def constant_fold( + gm: torch.fx.GraphModule, + constraint_fn: Optional[Callable[[torch.fx.Node], bool]] = None, +) -> None: + with torch.utils._python_dispatch._disable_current_modes(): + cf = ConstantFolder(gm, skip_constructors=True) + cf.run() + + for node, constant in cf.node_replacements.items(): + if constraint_fn is not None and not constraint_fn(node): + continue + replace_node_with_constant(gm, node, constant) + + erased_params = [] + for node in gm.graph.find_nodes(op="get_attr"): + if len(node.users) == 0: + if hasattr(gm, node.target): + delattr(gm, node.target) + erased_params.append(node) + + for node in erased_params: + gm.graph.erase_node(node) + + gm.graph.eliminate_dead_code() + gm.graph.lint() + gm.recompile() + + +def constant_graph_tag( + gm: torch.fx.GraphModule, + lifted_constants: Optional[Dict[str, Any]], + skip_folding_node_fn: Optional[Callable[[torch.fx.Node], bool]], +) -> None: + with torch.utils._python_dispatch._disable_current_modes(): + cf = ConstantFolder( + gm, skip_constructors=True, lifted_constants=lifted_constants + ) + cf.run() + + for node in gm.graph.nodes: + if skip_folding_node_fn is not None and skip_folding_node_fn(node): + node.meta[META_TAG] = MODULE_TAG + continue + if ( + is_const_source(node, lifted_constants) + or node in cf.node_replacements + or node in cf.replaced_uses + ): + node.meta[META_TAG] = CONST_MODULE_TAG + else: + node.meta[META_TAG] = MODULE_TAG + + +def run_and_get_constant_graph( + gm: torch.fx.GraphModule, + lifted_constants: Optional[Dict[str, Any]], + skip_folding_node_fn: Optional[Callable[[torch.fx.Node], bool]], +) -> Tuple[torch.fx.GraphModule, Tuple[torch.Tensor, ...]]: + """ + Construct a GraphModule which corresponds to the part which could be + constant folded in provided gm. + """ + + constant_graph_tag(gm, lifted_constants, skip_folding_node_fn) + + def untag(node: torch.fx.Node) -> bool: + used_to_fold = False + for u in node.users: + if u.meta[META_TAG] == CONST_MODULE_TAG: + used_to_fold = True + break + if not used_to_fold: + node.meta[META_TAG] = MODULE_TAG + return used_to_fold + + const_args = [] + if lifted_constants is not None: + placeholders = list(gm.graph.find_nodes(op="placeholder")) + for node in placeholders: + if node.meta[META_TAG] == MODULE_TAG: + continue + if untag(node): + const_args.append(lifted_constants[node.name]) + + # We rewrite the tags, if it's a constant being directly consumed, without + # any folding opportunity, we keep it in main gm. + for node in gm.graph.find_nodes(op="get_attr"): + untag(node) + + new_graph = torch.fx.Graph() + + node_remapping: Dict[torch.fx.Node, torch.fx.Node] = {} + output_nodes = [] + for node in gm.graph.nodes: + if node.meta[META_TAG] == MODULE_TAG: + continue + + new_node = new_graph.node_copy(node, lambda x: node_remapping[x]) + node_remapping[node] = new_node + + for user in node.users: + if user.meta[META_TAG] == MODULE_TAG: + output_nodes.append(new_node) + break + + new_graph.output(tuple(output_nodes)) + new_graph.lint() + new_gm = torch.fx.GraphModule(gm, new_graph) + + const_result = new_gm(*const_args) + return new_gm, const_result diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/cpp_builder.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/cpp_builder.py new file mode 100644 index 0000000000000000000000000000000000000000..95a0bff86fd8a4f1963d279d096b82dd33934ab2 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/cpp_builder.py @@ -0,0 +1,1511 @@ +# This CPP builder is designed to support both Windows and Linux OS. +# The design document please check this RFC: https://github.com/pytorch/pytorch/issues/124245 + +import copy +import errno +import functools +import json +import logging +import os +import platform +import re +import shlex +import shutil +import subprocess +import sys +import sysconfig +import warnings +from ctypes import cdll +from pathlib import Path +from typing import Any, List, Optional, Sequence, Tuple, Union + +import torch +from torch._dynamo.utils import dynamo_timed +from torch._inductor import config, exc +from torch._inductor.cpu_vec_isa import invalid_vec_isa, VecISA +from torch._inductor.runtime.runtime_utils import cache_dir +from torch.torch_version import TorchVersion + + +if config.is_fbcode(): + from triton.fb import build_paths # noqa: F401 + + from torch._inductor.fb.utils import ( + log_global_cache_errors, + log_global_cache_stats, + log_global_cache_vals, + use_global_cache, + ) +else: + + def log_global_cache_errors(*args: Any, **kwargs: Any) -> None: + pass + + def log_global_cache_stats(*args: Any, **kwargs: Any) -> None: + pass + + def log_global_cache_vals(*args: Any, **kwargs: Any) -> None: + pass + + def use_global_cache() -> bool: + return False + + +# Windows need setup a temp dir to store .obj files. +_BUILD_TEMP_DIR = "CxxBuild" + +# initialize variables for compilation +_IS_LINUX = sys.platform.startswith("linux") +_IS_MACOS = sys.platform.startswith("darwin") +_IS_WINDOWS = sys.platform == "win32" + +SUBPROCESS_DECODE_ARGS = ("utf-8",) if _IS_WINDOWS else () + +log = logging.getLogger(__name__) + + +# =============================== toolchain =============================== +@functools.lru_cache(1) +def cpp_compiler_search(search: str) -> str: + from torch._inductor.codecache import get_lock_dir, LOCK_TIMEOUT + + for cxx in search: + try: + if cxx is None: + # gxx package is only available for Linux + # according to https://anaconda.org/conda-forge/gxx/ + if sys.platform != "linux": + continue + # Do not install GXX by default + if not os.getenv("TORCH_INDUCTOR_INSTALL_GXX"): + continue + from filelock import FileLock + + lock_dir = get_lock_dir() + lock = FileLock( + os.path.join(lock_dir, "g++.lock"), timeout=LOCK_TIMEOUT + ) + with lock: + cxx = install_gcc_via_conda() + subprocess.check_output([cxx, "--version"]) + return cxx + except (subprocess.SubprocessError, FileNotFoundError, ImportError): + continue + raise exc.InvalidCxxCompiler + + +def install_gcc_via_conda() -> str: + """On older systems, this is a quick way to get a modern compiler""" + prefix = os.path.join(cache_dir(), "gcc") + cxx_path = os.path.join(prefix, "bin", "g++") + if not os.path.exists(cxx_path): + log.info("Downloading GCC via conda") + conda = os.environ.get("CONDA_EXE", "conda") + if conda is None: + conda = shutil.which("conda") + if conda is not None: + subprocess.check_call( + [ + conda, + "create", + f"--prefix={prefix}", + "--channel=conda-forge", + "--quiet", + "-y", + "python=3.8", + "gxx", + ], + stdout=subprocess.PIPE, + ) + return cxx_path + + +@functools.lru_cache(None) +def check_compiler_exist_windows(compiler: str) -> None: + """ + Check if compiler is ready, in case end user not activate MSVC environment. + """ + try: + output_msg = ( + subprocess.check_output([compiler, "/help"], stderr=subprocess.STDOUT) + .strip() + .decode(*SUBPROCESS_DECODE_ARGS) + ) + except FileNotFoundError as exc: + raise RuntimeError(f"Compiler: {compiler} is not found.") from exc + except subprocess.SubprocessError: + # Expected that some compiler(clang, clang++) is exist, but they not support `/help` args. + pass + + +def get_cpp_compiler() -> str: + if _IS_WINDOWS: + compiler = os.environ.get("CXX", "cl") + check_compiler_exist_windows(compiler) + else: + if config.is_fbcode(): + return ( + build_paths.cc() if torch.version.hip is None else build_paths.clang() + ) + if isinstance(config.cpp.cxx, (list, tuple)): + search = tuple(config.cpp.cxx) + else: + search = (config.cpp.cxx,) + compiler = cpp_compiler_search(search) + return compiler + + +@functools.lru_cache(None) +def _is_apple_clang(cpp_compiler: str) -> bool: + version_string = subprocess.check_output([cpp_compiler, "--version"]).decode("utf8") + return "Apple" in version_string.splitlines()[0] + + +def _is_clang(cpp_compiler: str) -> bool: + # Mac OS apple clang maybe named as gcc, need check compiler info. + if sys.platform == "darwin": + return _is_apple_clang(cpp_compiler) + elif _IS_WINDOWS: + # clang suite have many compilers, and only clang-cl is supported. + if re.search(r"((clang$)|(clang\+\+$))", cpp_compiler): + raise RuntimeError( + "Please use clang-cl, due to torch.compile only support MSVC-like CLI (compiler flags syntax)." + ) + return bool(re.search(r"(clang-cl)", cpp_compiler)) + return bool(re.search(r"(clang|clang\+\+)", cpp_compiler)) + + +def _is_gcc(cpp_compiler: str) -> bool: + if sys.platform == "darwin" and _is_apple_clang(cpp_compiler): + return False + return bool(re.search(r"(gcc|g\+\+)", cpp_compiler)) + + +@functools.lru_cache(None) +def _is_msvc_cl(cpp_compiler: str) -> bool: + if not _IS_WINDOWS: + return False + + try: + output_msg = ( + subprocess.check_output([cpp_compiler, "/help"], stderr=subprocess.STDOUT) + .strip() + .decode(*SUBPROCESS_DECODE_ARGS) + ) + return "Microsoft" in output_msg.splitlines()[0] + except FileNotFoundError as exc: + return False + + return False + + +@functools.lru_cache(None) +def _is_intel_compiler(cpp_compiler: str) -> bool: + def _check_minimal_version(compiler_version: TorchVersion) -> None: + """ + On Windows: early version icx has `-print-file-name` issue, and can't preload correctly for inductor. + """ + min_version = "2024.2.1" if _IS_WINDOWS else "0.0.0" + if compiler_version < TorchVersion(min_version): + raise RuntimeError( + f"Intel Compiler error: less than minimal version {min_version}." + ) + + try: + output_msg = ( + subprocess.check_output( + [cpp_compiler, "--version"], stderr=subprocess.DEVNULL + ) + .strip() + .decode(*SUBPROCESS_DECODE_ARGS) + ) + is_intel_compiler = "Intel" in output_msg.splitlines()[0] + if is_intel_compiler: + if _IS_WINDOWS: + if re.search(r"((icx$)|(icx-cc$))", cpp_compiler): + raise RuntimeError( + "Please use icx-cl, due to torch.compile only support MSVC-like CLI (compiler flags syntax)." + ) + + # Version check + icx_ver_search = re.search(r"(\d+[.]\d+[.]\d+[.]\d+)", output_msg) + if icx_ver_search is not None: + icx_ver = icx_ver_search.group(1) + _check_minimal_version(TorchVersion(icx_ver)) + + return is_intel_compiler + except FileNotFoundError as exc: + return False + except subprocess.SubprocessError: + # --version args not support. + return False + + return False + + +@functools.lru_cache(None) +def is_gcc() -> bool: + return _is_gcc(get_cpp_compiler()) + + +@functools.lru_cache(None) +def is_clang() -> bool: + return _is_clang(get_cpp_compiler()) + + +@functools.lru_cache(None) +def is_intel_compiler() -> bool: + return _is_intel_compiler(get_cpp_compiler()) + + +@functools.lru_cache(None) +def is_apple_clang() -> bool: + return _is_apple_clang(get_cpp_compiler()) + + +@functools.lru_cache(None) +def is_msvc_cl() -> bool: + return _is_msvc_cl(get_cpp_compiler()) + + +def get_compiler_version_info(compiler: str) -> str: + env = os.environ.copy() + env["LC_ALL"] = "C" # Don't localize output + try: + version_string = subprocess.check_output( + [compiler, "-v"], stderr=subprocess.STDOUT, env=env + ).decode(*SUBPROCESS_DECODE_ARGS) + except Exception as e: + try: + version_string = subprocess.check_output( + [compiler, "--version"], stderr=subprocess.STDOUT, env=env + ).decode(*SUBPROCESS_DECODE_ARGS) + except Exception as e: + return "" + # Mutiple lines to one line string. + version_string = version_string.replace("\r", "_") + version_string = version_string.replace("\n", "_") + return version_string + + +# =============================== cpp builder =============================== +def _append_list(dest_list: List[str], src_list: List[str]) -> None: + for item in src_list: + dest_list.append(copy.deepcopy(item)) + + +def _remove_duplication_in_list(orig_list: List[str]) -> List[str]: + new_list: List[str] = [] + for item in orig_list: + if item not in new_list: + new_list.append(item) + return new_list + + +def _create_if_dir_not_exist(path_dir: str) -> None: + if not os.path.exists(path_dir): + try: + Path(path_dir).mkdir(parents=True, exist_ok=True) + except OSError as exc: # Guard against race condition + if exc.errno != errno.EEXIST: + raise RuntimeError( # noqa: TRY200 (Use `raise from`) + f"Fail to create path {path_dir}" + ) + + +def _remove_dir(path_dir: str) -> None: + if os.path.exists(path_dir): + for root, dirs, files in os.walk(path_dir, topdown=False): + for name in files: + file_path = os.path.join(root, name) + os.remove(file_path) + for name in dirs: + dir_path = os.path.join(root, name) + os.rmdir(dir_path) + os.rmdir(path_dir) + + +def _run_compile_cmd(cmd_line: str, cwd: str) -> bytes: + cmd = shlex.split(cmd_line) + try: + status = subprocess.check_output(args=cmd, cwd=cwd, stderr=subprocess.STDOUT) + except subprocess.CalledProcessError as e: + output = e.output.decode("utf-8") + openmp_problem = "'omp.h' file not found" in output or "libomp" in output + if openmp_problem and sys.platform == "darwin": + instruction = ( + "\n\nOpenMP support not found. Please try one of the following solutions:\n" + "(1) Set the `CXX` environment variable to a compiler other than Apple clang++/g++ " + "that has builtin OpenMP support;\n" + "(2) install OpenMP via conda: `conda install llvm-openmp`;\n" + "(3) install libomp via brew: `brew install libomp`;\n" + "(4) manually setup OpenMP and set the `OMP_PREFIX` environment variable to point to a path" + " with `include/omp.h` under it." + ) + output += instruction + raise exc.CppCompileError(cmd, output) from e + return status + + +def run_compile_cmd(cmd_line: str, cwd: str) -> bytes: + with dynamo_timed("compile_file"): + return _run_compile_cmd(cmd_line, cwd) + + +def normalize_path_separator(orig_path: str) -> str: + if _IS_WINDOWS: + return orig_path.replace(os.sep, "/") + return orig_path + + +class BuildOptionsBase: + """ + This is the Base class for store cxx build options, as a template. + Acturally, to build a cxx shared library. We just need to select a compiler + and maintains the suitable args. + """ + + def __init__( + self, + compiler: str = "", + definitions: Optional[List[str]] = None, + include_dirs: Optional[List[str]] = None, + cflags: Optional[List[str]] = None, + ldflags: Optional[List[str]] = None, + libraries_dirs: Optional[List[str]] = None, + libraries: Optional[List[str]] = None, + passthrough_args: Optional[List[str]] = None, + aot_mode: bool = False, + use_absolute_path: bool = False, + compile_only: bool = False, + ) -> None: + self._compiler = compiler + self._definations: List[str] = definitions or [] + self._include_dirs: List[str] = include_dirs or [] + self._cflags: List[str] = cflags or [] + self._ldflags: List[str] = ldflags or [] + self._libraries_dirs: List[str] = libraries_dirs or [] + self._libraries: List[str] = libraries or [] + # Some args is hard to abstract to OS compatable, passthough it directly. + self._passthough_args: List[str] = passthrough_args or [] + + self._aot_mode: bool = aot_mode + self._use_absolute_path: bool = use_absolute_path + self._compile_only: bool = compile_only + + def _process_compile_only_options(self) -> None: + if self._compile_only: + self._libraries_dirs = [] + self._libraries = [] + + def _remove_duplicate_options(self) -> None: + self._definations = _remove_duplication_in_list(self._definations) + self._include_dirs = _remove_duplication_in_list(self._include_dirs) + self._cflags = _remove_duplication_in_list(self._cflags) + self._ldflags = _remove_duplication_in_list(self._ldflags) + self._libraries_dirs = _remove_duplication_in_list(self._libraries_dirs) + self._libraries = _remove_duplication_in_list(self._libraries) + self._passthough_args = _remove_duplication_in_list(self._passthough_args) + + def _finalize_options(self) -> None: + self._process_compile_only_options + self._remove_duplicate_options + + def get_compiler(self) -> str: + return self._compiler + + def get_definations(self) -> List[str]: + return self._definations + + def get_include_dirs(self) -> List[str]: + return self._include_dirs + + def get_cflags(self) -> List[str]: + return self._cflags + + def get_ldflags(self) -> List[str]: + return self._ldflags + + def get_libraries_dirs(self) -> List[str]: + return self._libraries_dirs + + def get_libraries(self) -> List[str]: + return self._libraries + + def get_passthough_args(self) -> List[str]: + return self._passthough_args + + def get_aot_mode(self) -> bool: + return self._aot_mode + + def get_use_absolute_path(self) -> bool: + return self._use_absolute_path + + def get_compile_only(self) -> bool: + return self._compile_only + + def save_flags_to_file(self, file: str) -> None: + attrs = { + "compiler": self.get_compiler(), + "definitions": self.get_definations(), + "include_dirs": self.get_include_dirs(), + "cflags": self.get_cflags(), + "ldflags": self.get_ldflags(), + "libraries_dirs": self.get_libraries_dirs(), + "libraries": self.get_libraries(), + "passthrough_args": self.get_passthough_args(), + "aot_mode": self.get_aot_mode(), + "use_absolute_path": self.get_use_absolute_path(), + "compile_only": self.get_compile_only(), + } + + with open(file, "w") as f: + json.dump(attrs, f) + + +def _get_warning_all_cflag(warning_all: bool = True) -> List[str]: + if not _IS_WINDOWS: + return ["Wall"] if warning_all else [] + else: + return [] + + +def _get_cpp_std_cflag(std_num: str = "c++17") -> List[str]: + if _IS_WINDOWS: + """ + On Windows, only c++20 can support `std::enable_if_t`. + Ref: https://learn.microsoft.com/en-us/cpp/overview/cpp-conformance-improvements-2019?view=msvc-170#checking-for-abstract-class-types # noqa: B950 + Note: + Only setup c++20 for Windows inductor. I tried to upgrade all project to c++20, but it is failed: + https://github.com/pytorch/pytorch/pull/131504 + """ + std_num = "c++20" + return [f"std:{std_num}"] + else: + return [f"std={std_num}"] + + +def _get_os_related_cpp_cflags(cpp_compiler: str) -> List[str]: + if _IS_WINDOWS: + cflags = [ + "wd4819", + "wd4251", + "wd4244", + "wd4267", + "wd4275", + "wd4018", + "wd4190", + "wd4624", + "wd4067", + "wd4068", + "EHsc", + ] + else: + cflags = ["Wno-unused-variable", "Wno-unknown-pragmas"] + if _is_clang(cpp_compiler): + cflags.append("Werror=ignored-optimization-argument") + return cflags + + +def _get_optimization_cflags() -> List[str]: + if _IS_WINDOWS: + return ["O2"] + else: + cflags = ["O0", "g"] if config.aot_inductor.debug_compile else ["O3", "DNDEBUG"] + cflags.append("ffast-math") + cflags.append("fno-finite-math-only") + + if not config.cpp.enable_unsafe_math_opt_flag: + cflags.append("fno-unsafe-math-optimizations") + if not config.cpp.enable_floating_point_contract_flag: + cflags.append("ffp-contract=off") + + if sys.platform != "darwin": + # https://stackoverflow.com/questions/65966969/why-does-march-native-not-work-on-apple-m1 + # `-march=native` is unrecognized option on M1 + if not config.is_fbcode(): + if platform.machine() == "ppc64le": + cflags.append("mcpu=native") + else: + cflags.append("march=native") + + return cflags + + +def _get_shared_cflag(compile_only: bool) -> List[str]: + if _IS_WINDOWS: + """ + MSVC `/MD` using python `ucrtbase.dll` lib as runtime. + https://learn.microsoft.com/en-us/cpp/c-runtime-library/crt-library-features?view=msvc-170 + """ + SHARED_FLAG = ["DLL", "MD"] + else: + if compile_only: + return ["fPIC"] + if platform.system() == "Darwin" and "clang" in get_cpp_compiler(): + # This causes undefined symbols to behave the same as linux + return ["shared", "fPIC", "undefined dynamic_lookup"] + else: + return ["shared", "fPIC"] + + return SHARED_FLAG + + +def get_cpp_options( + cpp_compiler: str, + compile_only: bool, + warning_all: bool = True, + extra_flags: Sequence[str] = (), +) -> Tuple[List[str], List[str], List[str], List[str], List[str], List[str], List[str]]: + definations: List[str] = [] + include_dirs: List[str] = [] + cflags: List[str] = [] + ldflags: List[str] = [] + libraries_dirs: List[str] = [] + libraries: List[str] = [] + passthough_args: List[str] = [] + + cflags = ( + _get_shared_cflag(compile_only) + + _get_optimization_cflags() + + _get_warning_all_cflag(warning_all) + + _get_cpp_std_cflag() + + _get_os_related_cpp_cflags(cpp_compiler) + ) + + passthough_args.append(" ".join(extra_flags)) + + return ( + definations, + include_dirs, + cflags, + ldflags, + libraries_dirs, + libraries, + passthough_args, + ) + + +class CppOptions(BuildOptionsBase): + """ + This class is inherited from BuildOptionsBase, and as cxx build options. + This option need contains basic cxx build option, which contains: + 1. OS related args. + 2. Toolchains related args. + 3. Cxx standard related args. + Note: + 1. This Options is good for assist modules build, such as x86_isa_help. + """ + + def __init__( + self, + compile_only: bool = False, + warning_all: bool = True, + extra_flags: Sequence[str] = (), + use_absolute_path: bool = False, + ) -> None: + super().__init__() + self._compiler = get_cpp_compiler() + self._use_absolute_path = use_absolute_path + self._compile_only = compile_only + + ( + definations, + include_dirs, + cflags, + ldflags, + libraries_dirs, + libraries, + passthough_args, + ) = get_cpp_options( + cpp_compiler=self._compiler, + compile_only=compile_only, + extra_flags=extra_flags, + warning_all=warning_all, + ) + + _append_list(self._definations, definations) + _append_list(self._include_dirs, include_dirs) + _append_list(self._cflags, cflags) + _append_list(self._ldflags, ldflags) + _append_list(self._libraries_dirs, libraries_dirs) + _append_list(self._libraries, libraries) + _append_list(self._passthough_args, passthough_args) + self._finalize_options() + + +def _get_glibcxx_abi_build_flags() -> List[str]: + if not _IS_WINDOWS: + return ["-D_GLIBCXX_USE_CXX11_ABI=" + str(int(torch._C._GLIBCXX_USE_CXX11_ABI))] + else: + return [] + + +def _get_torch_cpp_wrapper_defination() -> List[str]: + return ["TORCH_INDUCTOR_CPP_WRAPPER"] + + +def _use_custom_generated_macros() -> List[str]: + return [" C10_USING_CUSTOM_GENERATED_MACROS"] + + +def _use_fb_internal_macros() -> List[str]: + if not _IS_WINDOWS: + if config.is_fbcode(): + fb_internal_macros = [ + "C10_USE_GLOG", + "C10_USE_MINIMAL_GLOG", + "C10_DISABLE_TENSORIMPL_EXTENSIBILITY", + ] + # TODO: this is to avoid FC breakage for fbcode. When using newly + # generated model.so on an older verion of PyTorch, need to use + # the v1 version for aoti_torch_create_tensor_from_blob + create_tensor_from_blob_v1 = "AOTI_USE_CREATE_TENSOR_FROM_BLOB_V1" + + fb_internal_macros.append(create_tensor_from_blob_v1) + return fb_internal_macros + else: + return [] + else: + return [] + + +def _setup_standard_sys_libs( + cpp_compiler: str, + aot_mode: bool, + use_absolute_path: bool, +) -> Tuple[List[str], List[str], List[str]]: + from torch._inductor.codecache import _LINKER_SCRIPT + + cflags: List[str] = [] + include_dirs: List[str] = [] + passthough_args: List[str] = [] + if _IS_WINDOWS: + return cflags, include_dirs, passthough_args + + if config.is_fbcode(): + cflags.append("nostdinc") + # Note that the order of include paths do matter, as a result + # we need to have several branches interleaved here + if torch.version.hip is None: + include_dirs.append(build_paths.sleef()) + include_dirs.append(build_paths.openmp()) + include_dirs.append(build_paths.python()) + if torch.version.hip is not None: + include_dirs.append(build_paths.clang_include()) + include_dirs.append(build_paths.gcc_include()) + include_dirs.append(build_paths.gcc_install_tools_include()) + else: + include_dirs.append(build_paths.cc_include()) + include_dirs.append(build_paths.libgcc()) + include_dirs.append(build_paths.libgcc_arch()) + include_dirs.append(build_paths.libgcc_backward()) + include_dirs.append(build_paths.glibc()) + include_dirs.append(build_paths.linux_kernel()) + include_dirs.append("include") + + if aot_mode and not use_absolute_path: + linker_script = _LINKER_SCRIPT + else: + linker_script = os.path.basename(_LINKER_SCRIPT) + + if _is_clang(cpp_compiler): + passthough_args.append(" --rtlib=compiler-rt") + passthough_args.append(" -fuse-ld=lld") + passthough_args.append(f" -Wl,--script={linker_script}") + passthough_args.append(" -B" + build_paths.glibc_lib()) + passthough_args.append(" -L" + build_paths.glibc_lib()) + + return cflags, include_dirs, passthough_args + + +def _get_build_args_of_chosen_isa(vec_isa: VecISA) -> Tuple[List[str], List[str]]: + macros = [] + build_flags = [] + if vec_isa != invalid_vec_isa: + # Add Windows support later. + for x in vec_isa.build_macro(): + macros.append(copy.deepcopy(x)) + + build_flags = [vec_isa.build_arch_flags()] + + if config.is_fbcode(): + cap = str(vec_isa).upper() + macros = [ + f"CPU_CAPABILITY={cap}", + f"CPU_CAPABILITY_{cap}", + f"HAVE_{cap}_CPU_DEFINITION", + ] + + return macros, build_flags + + +def _get_torch_related_args( + include_pytorch: bool, aot_mode: bool +) -> Tuple[List[str], List[str], List[str]]: + from torch.utils.cpp_extension import _TORCH_PATH, TORCH_LIB_PATH + + include_dirs = [ + os.path.join(_TORCH_PATH, "include"), + os.path.join(_TORCH_PATH, "include", "torch", "csrc", "api", "include"), + # Some internal (old) Torch headers don't properly prefix their includes, + # so we need to pass -Itorch/lib/include/TH as well. + os.path.join(_TORCH_PATH, "include", "TH"), + os.path.join(_TORCH_PATH, "include", "THC"), + ] + libraries_dirs = [TORCH_LIB_PATH] + libraries = [] + if sys.platform != "darwin" and not config.is_fbcode(): + libraries = ["torch", "torch_cpu"] + if not aot_mode: + libraries.append("torch_python") + + if _IS_WINDOWS: + libraries.append("sleef") + + # Unconditionally import c10 for non-abi-compatible mode to use TORCH_CHECK - See PyTorch #108690 + if not config.abi_compatible: + libraries.append("c10") + libraries_dirs.append(TORCH_LIB_PATH) + + return include_dirs, libraries_dirs, libraries + + +def _get_python_include_dirs() -> List[str]: + include_dir = Path(sysconfig.get_path("include")) + # On Darwin Python executable from a framework can return + # non-existing /Library/Python/... include path, in which case + # one should use Headers folder from the framework + if not include_dir.exists() and platform.system() == "Darwin": + std_lib = Path(sysconfig.get_path("stdlib")) + include_dir = (std_lib.parent.parent / "Headers").absolute() + if not (include_dir / "Python.h").exists(): + warnings.warn(f"Can't find Python.h in {str(include_dir)}") + return [str(include_dir)] + + +def _get_python_related_args() -> Tuple[List[str], List[str]]: + python_include_dirs = _get_python_include_dirs() + python_include_path = sysconfig.get_path( + "include", scheme="nt" if _IS_WINDOWS else "posix_prefix" + ) + if python_include_path is not None: + python_include_dirs.append(python_include_path) + + if _IS_WINDOWS: + python_path = os.path.dirname(sys.executable) + python_lib_path = [os.path.join(python_path, "libs")] + else: + python_lib_path = [sysconfig.get_config_var("LIBDIR")] + + if config.is_fbcode(): + python_include_dirs.append(build_paths.python()) + + return python_include_dirs, python_lib_path + + +@functools.lru_cache(None) +def is_conda_llvm_openmp_installed() -> bool: + try: + command = "conda list llvm-openmp --json" + output = subprocess.check_output(command.split()).decode("utf8") + return len(json.loads(output)) > 0 + except subprocess.SubprocessError: + return False + + +@functools.lru_cache(None) +def homebrew_libomp() -> Tuple[bool, str]: + try: + # check if `brew` is installed + subprocess.check_output(["which", "brew"]) + # get the location of `libomp` if it is installed + # this is the location that `libomp` **would** be installed + # see https://github.com/Homebrew/brew/issues/10261#issuecomment-756563567 for details + libomp_path = ( + subprocess.check_output(["brew", "--prefix", "libomp"]) + .decode("utf8") + .strip() + ) + # check if `libomp` is installed + omp_available = os.path.exists(libomp_path) + return omp_available, libomp_path + except subprocess.SubprocessError: + return False, "" + + +@functools.lru_cache(None) +def perload_clang_libomp_win(cpp_compiler: str, omp_name: str) -> None: + try: + output = subprocess.check_output([cpp_compiler, "-print-file-name=bin"]).decode( + "utf8" + ) + omp_path = os.path.join(output.rstrip(), omp_name) + if os.path.isfile(omp_path): + os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" + omp_module = cdll.LoadLibrary(omp_path) + except subprocess.SubprocessError: + pass + + +@functools.lru_cache(None) +def perload_icx_libomp_win(cpp_compiler: str) -> None: + def _load_icx_built_in_lib_by_name(cpp_compiler: str, lib_name: str) -> bool: + try: + output = subprocess.check_output( + [cpp_compiler, f"-print-file-name={lib_name}"], + stderr=subprocess.DEVNULL, + ).decode(*SUBPROCESS_DECODE_ARGS) + omp_path = output.rstrip() + if os.path.isfile(omp_path): + os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" + omp_module = cdll.LoadLibrary(omp_path) + return True + except subprocess.SubprocessError: + pass + return False + + """ + Intel Compiler implenmented more math libraries than clang, for performance proposal. + We need preload them like openmp library. + """ + preload_list = [ + "libiomp5md.dll", # openmp + "svml_dispmd.dll", # svml library + "libmmd.dll", # libm + ] + + for lib_name in preload_list: + _load_icx_built_in_lib_by_name(cpp_compiler, lib_name) + + +def _get_openmp_args( + cpp_compiler: str, +) -> Tuple[List[str], List[str], List[str], List[str], List[str], List[str]]: + cflags: List[str] = [] + ldflags: List[str] = [] + include_dir_paths: List[str] = [] + lib_dir_paths: List[str] = [] + libs: List[str] = [] + passthough_args: List[str] = [] + if _IS_MACOS: + # Per https://mac.r-project.org/openmp/ right way to pass `openmp` flags to MacOS is via `-Xclang` + cflags.append("Xclang") + cflags.append("fopenmp") + + # only Apple builtin compilers (Apple Clang++) require openmp + omp_available = not _is_apple_clang(cpp_compiler) + + # check the `OMP_PREFIX` environment first + omp_prefix = os.getenv("OMP_PREFIX") + if omp_prefix is not None: + header_path = os.path.join(omp_prefix, "include", "omp.h") + valid_env = os.path.exists(header_path) + if valid_env: + include_dir_paths.append(os.path.join(omp_prefix, "include")) + lib_dir_paths.append(os.path.join(omp_prefix, "lib")) + else: + warnings.warn("environment variable `OMP_PREFIX` is invalid.") + omp_available = omp_available or valid_env + + if not omp_available: + libs.append("omp") + + # prefer to use openmp from `conda install llvm-openmp` + conda_prefix = os.getenv("CONDA_PREFIX") + if not omp_available and conda_prefix is not None: + omp_available = is_conda_llvm_openmp_installed() + if omp_available: + conda_lib_path = os.path.join(conda_prefix, "lib") + include_dir_paths.append(os.path.join(conda_prefix, "include")) + lib_dir_paths.append(conda_lib_path) + # Prefer Intel OpenMP on x86 machine + if os.uname().machine == "x86_64" and os.path.exists( + os.path.join(conda_lib_path, "libiomp5.dylib") + ): + libs.append("iomp5") + + # next, try to use openmp from `brew install libomp` + if not omp_available: + omp_available, libomp_path = homebrew_libomp() + if omp_available: + include_dir_paths.append(os.path.join(libomp_path, "include")) + lib_dir_paths.append(os.path.join(libomp_path, "lib")) + + # if openmp is still not available, we let the compiler to have a try, + # and raise error together with instructions at compilation error later + elif _IS_WINDOWS: + """ + On Windows, `clang` and `icx` have their specific openmp implenmention. + And the openmp lib is in compiler's some sub-directory. + For dynamic library(DLL) load, the Windows native APIs are `LoadLibraryA` and `LoadLibraryExA`, and their search + dependencies have some rules: + https://learn.microsoft.com/en-us/windows/win32/api/libloaderapi/nf-libloaderapi-loadlibraryexa#searching-for-dlls-and-dependencies + In some case, the rules may not include compiler's sub-directories. + So, it can't search and load compiler's openmp library correctly. + And then, the whole application would be broken. + + To avoid the openmp load failed, we can automatic locate the openmp binary and preload it. + 1. For clang, the function is `perload_clang_libomp_win`. + 2. For icx, the function is `perload_icx_libomp_win`. + """ + if _is_clang(cpp_compiler): + cflags.append("openmp") + libs.append("libomp") + perload_clang_libomp_win(cpp_compiler, "libomp.dll") + elif _is_intel_compiler(cpp_compiler): + cflags.append("Qiopenmp") + libs.append("libiomp5md") + perload_icx_libomp_win(cpp_compiler) + else: + # /openmp, /openmp:llvm + # llvm on Windows, new openmp: https://devblogs.microsoft.com/cppblog/msvc-openmp-update/ + # msvc openmp: https://learn.microsoft.com/zh-cn/cpp/build/reference/openmp-enable-openmp-2-0-support?view=msvc-170 + cflags.append("openmp") + cflags.append("openmp:experimental") # MSVC CL + else: + if config.is_fbcode(): + include_dir_paths.append(build_paths.openmp()) + + openmp_lib = build_paths.openmp_lib() + fb_openmp_extra_flags = f"-Wp,-fopenmp {openmp_lib}" + passthough_args.append(fb_openmp_extra_flags) + + libs.append("omp") + else: + if _is_clang(cpp_compiler): + # TODO: fix issue, can't find omp.h + cflags.append("fopenmp") + libs.append("gomp") + elif _is_intel_compiler(cpp_compiler): + cflags.append("fiopenmp") + else: + cflags.append("fopenmp") + libs.append("gomp") + + return cflags, ldflags, include_dir_paths, lib_dir_paths, libs, passthough_args + + +def get_mmap_self_macro(use_mmap_weights: bool) -> List[str]: + macros = [] + if use_mmap_weights: + macros.append(" USE_MMAP_SELF") + return macros + + +def get_cpp_torch_options( + cpp_compiler: str, + vec_isa: VecISA, + include_pytorch: bool, + aot_mode: bool, + compile_only: bool, + use_absolute_path: bool, + use_mmap_weights: bool, +) -> Tuple[List[str], List[str], List[str], List[str], List[str], List[str], List[str]]: + definations: List[str] = [] + include_dirs: List[str] = [] + cflags: List[str] = [] + ldflags: List[str] = [] + libraries_dirs: List[str] = [] + libraries: List[str] = [] + passthough_args: List[str] = [] + + torch_cpp_wrapper_definations = _get_torch_cpp_wrapper_defination() + use_custom_generated_macros_definations = _use_custom_generated_macros() + + ( + sys_libs_cflags, + sys_libs_include_dirs, + sys_libs_passthough_args, + ) = _setup_standard_sys_libs(cpp_compiler, aot_mode, use_absolute_path) + + isa_macros, isa_ps_args_build_flags = _get_build_args_of_chosen_isa(vec_isa) + + ( + torch_include_dirs, + torch_libraries_dirs, + torch_libraries, + ) = _get_torch_related_args(include_pytorch=include_pytorch, aot_mode=aot_mode) + + python_include_dirs, python_libraries_dirs = _get_python_related_args() + + ( + omp_cflags, + omp_ldflags, + omp_include_dir_paths, + omp_lib_dir_paths, + omp_lib, + omp_passthough_args, + ) = _get_openmp_args(cpp_compiler) + + cxx_abi_passthough_args = _get_glibcxx_abi_build_flags() + fb_macro_passthough_args = _use_fb_internal_macros() + + mmap_self_macros = get_mmap_self_macro(use_mmap_weights) + + definations = ( + torch_cpp_wrapper_definations + + use_custom_generated_macros_definations + + isa_macros + + fb_macro_passthough_args + + mmap_self_macros + ) + include_dirs = ( + sys_libs_include_dirs + + python_include_dirs + + torch_include_dirs + + omp_include_dir_paths + ) + cflags = sys_libs_cflags + omp_cflags + ldflags = omp_ldflags + libraries_dirs = python_libraries_dirs + torch_libraries_dirs + omp_lib_dir_paths + libraries = torch_libraries + omp_lib + passthough_args = ( + sys_libs_passthough_args + + isa_ps_args_build_flags + + cxx_abi_passthough_args + + omp_passthough_args + ) + + return ( + definations, + include_dirs, + cflags, + ldflags, + libraries_dirs, + libraries, + passthough_args, + ) + + +class CppTorchOptions(CppOptions): + """ + This class is inherited from CppTorchOptions, which automatic contains + base cxx build options. And then it will maintains torch related build + args. + 1. Torch include_directories, libraries, libraries_directories. + 2. Python include_directories, libraries, libraries_directories. + 3. OpenMP related. + 4. Torch MACROs. + 5. MISC + """ + + def __init__( + self, + vec_isa: VecISA = invalid_vec_isa, + include_pytorch: bool = False, + warning_all: bool = True, + aot_mode: bool = False, + compile_only: bool = False, + use_absolute_path: bool = False, + use_mmap_weights: bool = False, + shared: bool = True, + extra_flags: Sequence[str] = (), + ) -> None: + super().__init__( + compile_only=compile_only, + warning_all=warning_all, + extra_flags=extra_flags, + use_absolute_path=use_absolute_path, + ) + + self._aot_mode = aot_mode + + ( + torch_definations, + torch_include_dirs, + torch_cflags, + torch_ldflags, + torch_libraries_dirs, + torch_libraries, + torch_passthough_args, + ) = get_cpp_torch_options( + cpp_compiler=self._compiler, + vec_isa=vec_isa, + include_pytorch=include_pytorch, + aot_mode=aot_mode, + compile_only=compile_only, + use_absolute_path=use_absolute_path, + use_mmap_weights=use_mmap_weights, + ) + + _append_list(self._definations, torch_definations) + _append_list(self._include_dirs, torch_include_dirs) + _append_list(self._cflags, torch_cflags) + _append_list(self._ldflags, torch_ldflags) + _append_list(self._libraries_dirs, torch_libraries_dirs) + _append_list(self._libraries, torch_libraries) + _append_list(self._passthough_args, torch_passthough_args) + self._finalize_options() + + +def _set_gpu_runtime_env() -> None: + if ( + config.is_fbcode() + and torch.version.hip is None + and "CUDA_HOME" not in os.environ + and "CUDA_PATH" not in os.environ + ): + os.environ["CUDA_HOME"] = build_paths.cuda() + + +def _transform_cuda_paths(lpaths: List[str]) -> None: + # This handles two cases: + # 1. Meta internal cuda-12 where libs are in lib/cuda-12 and lib/cuda-12/stubs + # 2. Linux machines may have CUDA installed under either lib64/ or lib/ + for i, path in enumerate(lpaths): + if ( + "CUDA_HOME" in os.environ + and path.startswith(os.environ["CUDA_HOME"]) + and not os.path.exists(f"{path}/libcudart_static.a") + ): + for root, dirs, files in os.walk(path): + if "libcudart_static.a" in files: + lpaths[i] = os.path.join(path, root) + lpaths.append(os.path.join(lpaths[i], "stubs")) + break + + +def get_cpp_torch_cuda_options( + cuda: bool, + aot_mode: bool = False, + compile_only: bool = False, +) -> Tuple[List[str], List[str], List[str], List[str], List[str], List[str], List[str]]: + definations: List[str] = [] + include_dirs: List[str] = [] + cflags: List[str] = [] + ldflags: List[str] = [] + libraries_dirs: List[str] = [] + libraries: List[str] = [] + passthough_args: List[str] = [] + if ( + config.is_fbcode() + and "CUDA_HOME" not in os.environ + and "CUDA_PATH" not in os.environ + ): + os.environ["CUDA_HOME"] = ( + build_paths.rocm() if torch.version.hip else build_paths.cuda() + ) + + _set_gpu_runtime_env() + from torch.utils import cpp_extension + + include_dirs = cpp_extension.include_paths(cuda) + libraries_dirs = cpp_extension.library_paths(cuda) + + if cuda: + definations.append(" USE_ROCM" if torch.version.hip else " USE_CUDA") + + if torch.version.hip is not None: + if config.is_fbcode(): + libraries += ["amdhip64"] + else: + libraries += ["c10_hip", "torch_hip"] + definations.append(" __HIP_PLATFORM_AMD__") + else: + if config.is_fbcode(): + libraries += ["cuda"] + else: + libraries += ["c10_cuda", "cuda", "torch_cuda"] + + if aot_mode: + if config.is_fbcode(): + from torch._inductor.codecache import cpp_prefix_path + + cpp_prefix_include_dir = [f"{os.path.dirname(cpp_prefix_path())}"] + include_dirs += cpp_prefix_include_dir + + if cuda and torch.version.hip is None: + _transform_cuda_paths(libraries_dirs) + + if config.is_fbcode(): + if torch.version.hip is not None: + include_dirs.append(os.path.join(build_paths.rocm(), "include")) + else: + include_dirs.append(os.path.join(build_paths.cuda(), "include")) + + if aot_mode and cuda: + if torch.version.hip is None: + if not compile_only: + # Only add link args, when compile_only is false. + passthough_args = ["-Wl,-Bstatic -lcudart_static -Wl,-Bdynamic"] + + return ( + definations, + include_dirs, + cflags, + ldflags, + libraries_dirs, + libraries, + passthough_args, + ) + + +class CppTorchCudaOptions(CppTorchOptions): + """ + This class is inherited from CppTorchOptions, which automatic contains + base cxx build options and torch common build options. And then it will + maintains cuda device related build args. + """ + + def __init__( + self, + vec_isa: VecISA = invalid_vec_isa, + include_pytorch: bool = False, + cuda: bool = True, + aot_mode: bool = False, + compile_only: bool = False, + use_absolute_path: bool = False, + use_mmap_weights: bool = False, + shared: bool = True, + extra_flags: Sequence[str] = (), + ) -> None: + super().__init__( + vec_isa=vec_isa, + include_pytorch=include_pytorch, + aot_mode=aot_mode, + compile_only=compile_only, + use_absolute_path=use_absolute_path, + use_mmap_weights=use_mmap_weights, + extra_flags=extra_flags, + ) + + cuda_definations: List[str] = [] + cuda_include_dirs: List[str] = [] + cuda_cflags: List[str] = [] + cuda_ldflags: List[str] = [] + cuda_libraries_dirs: List[str] = [] + cuda_libraries: List[str] = [] + cuda_passthough_args: List[str] = [] + + ( + cuda_definations, + cuda_include_dirs, + cuda_cflags, + cuda_ldflags, + cuda_libraries_dirs, + cuda_libraries, + cuda_passthough_args, + ) = get_cpp_torch_cuda_options( + cuda=cuda, aot_mode=aot_mode, compile_only=compile_only + ) + _append_list(self._definations, cuda_definations) + _append_list(self._include_dirs, cuda_include_dirs) + _append_list(self._cflags, cuda_cflags) + _append_list(self._ldflags, cuda_ldflags) + _append_list(self._libraries_dirs, cuda_libraries_dirs) + _append_list(self._libraries, cuda_libraries) + _append_list(self._passthough_args, cuda_passthough_args) + self._finalize_options() + + +def get_name_and_dir_from_output_file_path( + file_path: str, +) -> Tuple[str, str]: + """ + This function help prepare parameters to new cpp_builder. + Example: + input_code: /tmp/tmpof1n5g7t/5c/c5crkkcdvhdxpktrmjxbqkqyq5hmxpqsfza4pxcf3mwk42lphygc.cpp + name, dir = get_name_and_dir_from_output_file_path(input_code) + Run result: + name = c5crkkcdvhdxpktrmjxbqkqyq5hmxpqsfza4pxcf3mwk42lphygc + dir = /tmp/tmpof1n5g7t/5c/ + + put 'name' and 'dir' to CppBuilder's 'name' and 'output_dir'. + CppBuilder --> get_target_file_path will format output path accoding OS: + Linux: /tmp/tmppu87g3mm/zh/czhwiz4z7ca7ep3qkxenxerfjxy42kehw6h5cjk6ven4qu4hql4i.so + Windows: [Windows temp path]/tmppu87g3mm/zh/czhwiz4z7ca7ep3qkxenxerfjxy42kehw6h5cjk6ven4qu4hql4i.dll + """ + name_and_ext = os.path.basename(file_path) + name, ext = os.path.splitext(name_and_ext) + dir = os.path.dirname(file_path) + + return name, dir + + +class CppBuilder: + """ + CppBuilder is a cpp jit builder, and it supports both Windows, Linux and MacOS. + Args: + name: + 1. Build target name, the final target file will append extension type automatically. + 2. Due to the CppBuilder is supports mutliple OS, it will maintains ext for OS difference. + sources: + Source code file list to be built. + BuildOption: + Build options to the builder. + output_dir: + 1. The output_dir the taget file will output to. + 2. The default value is empty string, and then the use current dir as output dir. + 3. Final target file: output_dir/name.ext + """ + + def __get_python_module_ext(self) -> str: + SHARED_LIB_EXT = ".pyd" if _IS_WINDOWS else ".so" + return SHARED_LIB_EXT + + def __get_object_ext(self) -> str: + EXT = ".obj" if _IS_WINDOWS else ".o" + return EXT + + def __init__( + self, + name: str, + sources: Union[str, List[str]], + BuildOption: BuildOptionsBase, + output_dir: str = "", + ) -> None: + self._compiler = "" + self._cflags_args = "" + self._definations_args = "" + self._include_dirs_args = "" + self._ldflags_args = "" + self._libraries_dirs_args = "" + self._libraries_args = "" + self._passthough_parameters_args = "" + + self._output_dir = "" + self._target_file = "" + + self._use_absolute_path: bool = False + self._aot_mode: bool = False + + self._name = name + + # Code start here, initial self internal veriables firstly. + self._compiler = BuildOption.get_compiler() + self._use_absolute_path = BuildOption.get_use_absolute_path() + self._aot_mode = BuildOption.get_aot_mode() + + self._output_dir = output_dir + + self._compile_only = BuildOption.get_compile_only() + file_ext = ( + self.__get_object_ext() + if self._compile_only + else self.__get_python_module_ext() + ) + self._target_file = os.path.join(self._output_dir, f"{self._name}{file_ext}") + + if isinstance(sources, str): + sources = [sources] + + if config.is_fbcode(): + if self._aot_mode and not self._use_absolute_path: + inp_name = sources + # output process @ get_name_and_dir_from_output_file_path + else: + # We need to copy any absolute-path torch includes + inp_name = [os.path.basename(i) for i in sources] + self._target_file = os.path.basename(self._target_file) + + self._sources_args = " ".join(inp_name) + else: + self._sources_args = " ".join(sources) + + for cflag in BuildOption.get_cflags(): + if _IS_WINDOWS: + self._cflags_args += f"/{cflag} " + else: + self._cflags_args += f"-{cflag} " + + for defination in BuildOption.get_definations(): + if _IS_WINDOWS: + self._definations_args += f"/D {defination} " + else: + self._definations_args += f"-D {defination} " + + for inc_dir in BuildOption.get_include_dirs(): + if _IS_WINDOWS: + self._include_dirs_args += f"/I {inc_dir} " + else: + self._include_dirs_args += f"-I{inc_dir} " + + for ldflag in BuildOption.get_ldflags(): + if _IS_WINDOWS: + self._ldflags_args += f"/{ldflag} " + else: + self._ldflags_args += f"-{ldflag} " + + for lib_dir in BuildOption.get_libraries_dirs(): + if _IS_WINDOWS: + self._libraries_dirs_args += f'/LIBPATH:"{lib_dir}" ' + else: + self._libraries_dirs_args += f"-L{lib_dir} " + + for lib in BuildOption.get_libraries(): + if _IS_WINDOWS: + self._libraries_args += f'"{lib}.lib" ' + else: + self._libraries_args += f"-l{lib} " + + for passthough_arg in BuildOption.get_passthough_args(): + self._passthough_parameters_args += f"{passthough_arg} " + + def get_command_line(self) -> str: + def format_build_command( + compiler: str, + sources: str, + include_dirs_args: str, + definations_args: str, + cflags_args: str, + ldflags_args: str, + libraries_args: str, + libraries_dirs_args: str, + passthougn_args: str, + target_file: str, + ) -> str: + if _IS_WINDOWS: + # https://learn.microsoft.com/en-us/cpp/build/walkthrough-compile-a-c-program-on-the-command-line?view=msvc-1704 + # https://stackoverflow.com/a/31566153 + cmd = ( + f"{compiler} {include_dirs_args} {definations_args} {cflags_args} {sources} " + f"{passthougn_args} /LD /Fe{target_file} /link {libraries_dirs_args} {libraries_args} {ldflags_args} " + ) + cmd = normalize_path_separator(cmd) + else: + compile_only_arg = "-c" if self._compile_only else "" + cmd = re.sub( + r"[ \n]+", + " ", + f""" + {compiler} {sources} {definations_args} {cflags_args} {include_dirs_args} + {passthougn_args} {ldflags_args} {libraries_args} {libraries_dirs_args} {compile_only_arg} -o {target_file} + """, + ).strip() + return cmd + + command_line = format_build_command( + compiler=self._compiler, + sources=self._sources_args, + include_dirs_args=self._include_dirs_args, + definations_args=self._definations_args, + cflags_args=self._cflags_args, + ldflags_args=self._ldflags_args, + libraries_args=self._libraries_args, + libraries_dirs_args=self._libraries_dirs_args, + passthougn_args=self._passthough_parameters_args, + target_file=self._target_file, + ) + return command_line + + def get_target_file_path(self) -> str: + return normalize_path_separator(self._target_file) + + def build(self) -> Tuple[bytes, str]: + """ + It is must need a temperary directory to store object files in Windows. + After build completed, delete the temperary directory to save disk space. + """ + _create_if_dir_not_exist(self._output_dir) + _build_tmp_dir = os.path.join( + self._output_dir, f"{self._name}_{_BUILD_TEMP_DIR}" + ) + _create_if_dir_not_exist(_build_tmp_dir) + + build_cmd = self.get_command_line() + + status = run_compile_cmd(build_cmd, cwd=_build_tmp_dir) + + _remove_dir(_build_tmp_dir) + return status, self._target_file diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/cpu_vec_isa.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/cpu_vec_isa.py new file mode 100644 index 0000000000000000000000000000000000000000..bc4838e5f168550656e4e0024d8fb2003ccf06a9 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/cpu_vec_isa.py @@ -0,0 +1,373 @@ +# mypy: allow-untyped-defs +import dataclasses +import functools +import os +import platform +import re +import subprocess +import sys +from typing import Any, Callable, Dict, List + +import torch +from torch._inductor import config + + +_IS_WINDOWS = sys.platform == "win32" + + +def _get_isa_dry_compile_fingerprint(isa_flags: str) -> str: + # ISA dry compile will cost about 1 sec time each startup time. + # Please check the issue: https://github.com/pytorch/pytorch/issues/100378 + # Actually, dry compile is checking compile capability for ISA. + # We just record the compiler version, isa options and pytorch version info, + # and generated them to output binary hash path. + # It would optimize and skip compile existing binary. + from torch._inductor.cpp_builder import get_compiler_version_info, get_cpp_compiler + + compiler_info = get_compiler_version_info(get_cpp_compiler()) + torch_version = torch.__version__ + fingerprint = f"{compiler_info}={isa_flags}={torch_version}" + return fingerprint + + +class VecISA: + _bit_width: int + _macro: List[str] + _arch_flags: str + _dtype_nelements: Dict[torch.dtype, int] + + # Note [Checking for Vectorized Support in Inductor] + # TorchInductor CPU vectorization reuses PyTorch vectorization utility functions + # Hence, TorchInductor would depend on Sleef* to accelerate mathematical functions + # like exp, pow, sin, cos and etc. + # But PyTorch and TorchInductor might use different compilers to build code. If + # PyTorch uses gcc-7/g++-7 to build the release package, the libtorch_cpu.so + # will not expose the Sleef* AVX512 symbols since gcc-7/g++-7 cannot pass + # avx512 check in CMake - FindAVX.cmake. But TorchInductor install the latest + # gcc/g++ compiler by default while it could support the AVX512 compilation. + # Therefore, there would be a conflict sleef version between PyTorch and + # TorchInductor. Hence, we dry-compile the following code to check whether current + # HW platform and PyTorch both could support AVX512 or AVX2. And suppose ARM + # also needs the logic + # In fbcode however, we are using the same compiler for pytorch and for inductor codegen, + # making the runtime check unnecessary. + _avx_code = """ +#if defined(CPU_CAPABILITY_AVX512) || defined(CPU_CAPABILITY_AVX2) || defined(CPU_CAPABILITY_ZVECTOR) || defined(CPU_CAPABILITY_NEON) || defined(CPU_CAPABILITY_VSX) +#include +#include +#endif + +alignas(64) float in_out_ptr0[16] = {0.0}; + +extern "C" void __avx_chk_kernel() { + auto tmp0 = at::vec::Vectorized(1); + auto tmp1 = tmp0.exp(); + tmp1.store(in_out_ptr0); +} +""" # noqa: B950 + + _avx_py_load = """ +import torch +from ctypes import cdll +cdll.LoadLibrary("__lib_path__") +""" + + def bit_width(self) -> int: + return self._bit_width + + def nelements(self, dtype: torch.dtype = torch.float) -> int: + return self._dtype_nelements[dtype] + + def build_macro(self) -> List[str]: + return self._macro + + def build_arch_flags(self) -> str: + return self._arch_flags + + def __hash__(self) -> int: + return hash(str(self)) + + def check_build(self, code: str) -> bool: + from torch._inductor.codecache import get_lock_dir, LOCK_TIMEOUT, write + from torch._inductor.cpp_builder import ( + CppBuilder, + CppTorchOptions, + normalize_path_separator, + ) + + key, input_path = write( + code, + "cpp", + extra=_get_isa_dry_compile_fingerprint(self._arch_flags), + ) + from filelock import FileLock + + lock_dir = get_lock_dir() + lock = FileLock(os.path.join(lock_dir, key + ".lock"), timeout=LOCK_TIMEOUT) + with lock: + output_dir = os.path.dirname(input_path) + buid_options = CppTorchOptions(vec_isa=self, warning_all=False) + x86_isa_help_builder = CppBuilder( + key, + [input_path], + buid_options, + output_dir, + ) + try: + # Check if the output file exist, and compile when not. + output_path = normalize_path_separator( + x86_isa_help_builder.get_target_file_path() + ) + if not os.path.isfile(output_path): + status, target_file = x86_isa_help_builder.build() + + # Check build result + subprocess.check_call( + [ + sys.executable, + "-c", + VecISA._avx_py_load.replace("__lib_path__", output_path), + ], + cwd=output_dir, + stderr=subprocess.DEVNULL, + env={**os.environ, "PYTHONPATH": ":".join(sys.path)}, + ) + except Exception as e: + return False + + return True + + @functools.lru_cache(None) # noqa: B019 + def __bool__(self) -> bool: + if config.cpp.vec_isa_ok is not None: + return config.cpp.vec_isa_ok + + if config.is_fbcode(): + return True + + return self.check_build(VecISA._avx_code) + + +@dataclasses.dataclass +class VecNEON(VecISA): + _bit_width = 256 # This is required to leverage the compute implemented in aten/src/ATen/cpu/vec/vec256/vec256_float_neon.h + _macro = ["CPU_CAPABILITY_NEON"] + if sys.platform == "darwin" and platform.processor() == "arm": + _macro.append("AT_BUILD_ARM_VEC256_WITH_SLEEF") + _arch_flags = "" # Unused + _dtype_nelements = {torch.float: 8, torch.bfloat16: 16, torch.float16: 16} + + def __str__(self) -> str: + return "asimd" # detects the presence of advanced SIMD on armv8-a kernels + + __hash__: Callable[[VecISA], Any] = VecISA.__hash__ + + +@dataclasses.dataclass +class VecAVX512(VecISA): + _bit_width = 512 + _macro = ["CPU_CAPABILITY_AVX512"] + _arch_flags = ( + "-mavx512f -mavx512dq -mavx512vl -mavx512bw -mfma" + if not _IS_WINDOWS + else "/arch:AVX512" + ) # TODO: use cflags + _dtype_nelements = {torch.float: 16, torch.bfloat16: 32, torch.float16: 32} + + def __str__(self) -> str: + return "avx512" + + __hash__: Callable[[VecISA], Any] = VecISA.__hash__ + + +@dataclasses.dataclass +class VecAMX(VecAVX512): + _arch_flags = VecAVX512._arch_flags + " -mamx-tile -mamx-bf16 -mamx-int8" + + def __str__(self) -> str: + return super().__str__() + " amx_tile" + + __hash__: Callable[[VecISA], Any] = VecISA.__hash__ + + _amx_code = """ +#include +#include + +struct amx_tilecfg { + uint8_t palette_id; + uint8_t start_row; + uint8_t reserved_0[14]; + uint16_t colsb[16]; + uint8_t rows[16]; +}; + +extern "C" void __amx_chk_kernel() { + amx_tilecfg cfg = {0}; + _tile_loadconfig(&cfg); + _tile_zero(0); + _tile_dpbf16ps(0, 1, 2); + _tile_dpbusd(0, 1, 2); +} +""" + + @functools.lru_cache(None) # noqa: B019 + def __bool__(self) -> bool: + if super().__bool__(): + if config.is_fbcode(): + return False + if self.check_build(VecAMX._amx_code) and torch.cpu._init_amx(): + return True + return False + + +@dataclasses.dataclass +class VecAVX2(VecISA): + _bit_width = 256 + _macro = ["CPU_CAPABILITY_AVX2"] + _arch_flags = ( + "-mavx2 -mfma -mf16c" if not _IS_WINDOWS else "/arch:AVX2" + ) # TODO: use cflags + _dtype_nelements = {torch.float: 8, torch.bfloat16: 16, torch.float16: 16} + + def __str__(self) -> str: + return "avx2" + + __hash__: Callable[[VecISA], Any] = VecISA.__hash__ + + +@dataclasses.dataclass +class VecZVECTOR(VecISA): + _bit_width = 256 + _macro = [ + "CPU_CAPABILITY_ZVECTOR", + "CPU_CAPABILITY=ZVECTOR", + "HAVE_ZVECTOR_CPU_DEFINITION", + ] + _arch_flags = "-mvx -mzvector" + _dtype_nelements = {torch.float: 8, torch.bfloat16: 16, torch.float16: 16} + + def __str__(self) -> str: + return "zvector" + + __hash__: Callable[[VecISA], Any] = VecISA.__hash__ + + +@dataclasses.dataclass +class VecVSX(VecISA): + _bit_width = 256 # VSX simd supports 128 bit_width, but aten is emulating it as 256 + _macro = ["CPU_CAPABILITY_VSX"] + _arch_flags = "-mvsx" + _dtype_nelements = {torch.float: 8, torch.bfloat16: 16, torch.float16: 16} + + def __str__(self) -> str: + return "vsx" + + __hash__: Callable[[VecISA], Any] = VecISA.__hash__ + + +class InvalidVecISA(VecISA): + _bit_width = 0 + _macro = [""] + _arch_flags = "" + _dtype_nelements = {} + + def __str__(self) -> str: + return "INVALID_VEC_ISA" + + def __bool__(self) -> bool: # type: ignore[override] + return False + + __hash__: Callable[[VecISA], Any] = VecISA.__hash__ + + +def x86_isa_checker() -> List[str]: + supported_isa: List[str] = [] + + def _check_and_append_supported_isa( + dest: List[str], isa_supported: bool, isa_name: str + ) -> None: + if isa_supported: + dest.append(isa_name) + + Arch = platform.machine() + """ + Arch value is x86_64 on Linux, and the value is AMD64 on Windows. + """ + if Arch != "x86_64" and Arch != "AMD64": + return supported_isa + + avx2 = torch.cpu._is_avx2_supported() + avx512 = torch.cpu._is_avx512_supported() + amx_tile = torch.cpu._is_amx_tile_supported() + + _check_and_append_supported_isa(supported_isa, avx2, "avx2") + _check_and_append_supported_isa(supported_isa, avx512, "avx512") + _check_and_append_supported_isa(supported_isa, amx_tile, "amx_tile") + + return supported_isa + + +invalid_vec_isa = InvalidVecISA() +supported_vec_isa_list = [VecAMX(), VecAVX512(), VecAVX2(), VecNEON()] + + +# Cache the cpuinfo to avoid I/O overhead. Meanwhile, the cpuinfo content +# might have too much redundant content that is useless for ISA check. Hence, +# we only cache some key isa information. +@functools.lru_cache(None) +def valid_vec_isa_list() -> List[VecISA]: + isa_list: List[VecISA] = [] + if sys.platform == "darwin" and platform.processor() == "arm": + isa_list.append(VecNEON()) + + if sys.platform not in ["linux", "win32"]: + return isa_list + + arch = platform.machine() + if arch == "s390x": + with open("/proc/cpuinfo") as _cpu_info: + while True: + line = _cpu_info.readline() + if not line: + break + # process line + featuresmatch = re.match(r"^features\s*:\s*(.*)$", line) + if featuresmatch: + for group in featuresmatch.groups(): + if re.search(r"[\^ ]+vxe[\$ ]+", group): + isa_list.append(VecZVECTOR()) + break + elif arch == "ppc64le": + isa_list.append(VecVSX()) + elif arch == "aarch64": + isa_list.append(VecNEON()) + elif arch in ["x86_64", "AMD64"]: + """ + arch value is x86_64 on Linux, and the value is AMD64 on Windows. + """ + _cpu_supported_x86_isa = x86_isa_checker() + for isa in supported_vec_isa_list: + if all(flag in _cpu_supported_x86_isa for flag in str(isa).split()) and isa: + isa_list.append(isa) + + return isa_list + + +def pick_vec_isa() -> VecISA: + if config.is_fbcode() and (platform.machine() in ["x86_64", "AMD64"]): + return VecAVX2() + + _valid_vec_isa_list: List[VecISA] = valid_vec_isa_list() + if not _valid_vec_isa_list: + return invalid_vec_isa + + # If the simdlen is None, it indicates determine the vectorization length automatically + if config.cpp.simdlen is None: + assert _valid_vec_isa_list + return _valid_vec_isa_list[0] + + for isa in _valid_vec_isa_list: + if config.cpp.simdlen == isa.bit_width(): + return isa + + return invalid_vec_isa diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/cudagraph_trees.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/cudagraph_trees.py new file mode 100644 index 0000000000000000000000000000000000000000..5a33de0e3668998ac6f6d71ab16c696938ab1265 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/cudagraph_trees.py @@ -0,0 +1,2441 @@ +""" +CUDA graph trees are a safety abstraction over CUDAGraphs, similar to make_graph_callables, +which share the same memory pool. Sharing a memory pool is an extremely +important optimization when chaining multiple CUDA graphs together, as it +prevents you from needing to copy intermediate tensors from one graph to the +next, and reduces overall memory usage by allowing dead memory from the first +pool to be reused in the second. + +The standard graph/make_graph_callables support sharing memory pool, but +with a lot of caveats. CUDA graph trees remove these restrictions: + +* Previously, if you recorded graphs A, B, you had to replay A, B in that + order. With CUDA graph trees, after replaying A, you can change your + mind and record/replay a different graph B'; we will support efficient + execution of both A, B and A, B', using only max(mem(A, B), mem(A, B')). In + other words: we support arbitrary trees of CUDA graph operations, not just + sequences (this is why this feature is called CUDA graph trees.) + +* Previously, if you executed graph A, some non-CUDA graph code, and then + graph B, after executing graph B, it was not safe to retain any references + to intermediates produced by A. With CUDA graph trees, we track if any +outputs of graph A are still live by the time graph B is run, and make + sure graph B doesn't clobber there memory when reusing the CUDA graphs + pool. You'll get a separate recording of B depending on what tensors + stay live or dead. + +CUDA graph trees are flexible enough to be used in Dynamo across graph breaks, +which is their primary use case. + +The ability to switch from replay to record is fairly nontrivial: remember that +when you replay a CUDA graph, you only replay CUDA operations; no CPU side state +is updated. In particular, the CPU-side book-keeping for the allocator is not +reconstructed. However, to record a new child CUDA graph, we must restore this +book-keeping. This is what checkpoint pool state is used for. +""" + +from __future__ import annotations + +import contextlib +import dataclasses +import functools +import gc +import itertools +import operator +import sys +import threading +import traceback +import warnings +import weakref +from collections import defaultdict +from enum import auto, Enum +from typing import ( + Any, + Callable, + cast, + ContextManager, + Dict, + Generator, + Iterator, + List, + Optional, + Sequence, + Set, + Tuple, + Type, + TYPE_CHECKING, + TypeVar, + Union, +) + +import torch.fx +from torch import Tensor +from torch._dynamo.mutation_guard import GenerationTracker +from torch._dynamo.utils import counters, preserve_rng_state +from torch._inductor.compile_fx import ( + align_inputs_from_check_idxs, + copy_misaligned_inputs, + get_expanded_dims, + get_input_idxs_to_check, + index_expanded_dims, + remove_unaligned_input_idxs, + static_input, +) +from torch._inductor.cudagraph_utils import ( + check_for_mutation, + CheckInvariantStatus, + FunctionID, + log_cudagraph_skip_and_bump_counter, + log_data_ptr_mismatch, + maybe_warning_due_to_dynamic_shape, + ModelType, + OutputType, + PlaceholderInfo, + WrappedFunction, +) +from torch.multiprocessing.reductions import StorageWeakRef +from torch.storage import UntypedStorage +from torch.utils import _pytree as pytree +from torch.utils.weak import TensorWeakRef + + +if TYPE_CHECKING: + from torch._inductor.utils import InputType + from torch.types import _bool + +StorageWeakRefPointer = int +StorageDataPtr = int +NBytes = int +S = TypeVar("S", bound="StorageWeakRefWrapper") + + +if torch.backends.cuda.is_built(): + from torch._C import ( + _cuda_CUDAAllocator_AllocatorState as AllocatorState, + _set_cached_tensors_enabled as _set_cached_tensors_enabled, + ) +else: + + class AllocatorState: # type: ignore[no-redef] + pass + + def _set_cached_tensors_enabled(enabled: _bool) -> None: + pass + + +log = torch._logging.getArtifactLogger(__name__, "cudagraphs") + + +from . import config + + +@dataclasses.dataclass(frozen=True) +class GraphID: + "Unique counter of a cuda graph recording" + id: int + + +def clear_cublass_cache() -> None: + """ + Cublas keeps a persistent workspace allocation for running matmuls. This poses a problem for + doing warmup within a CUDAGraph private pool because we do not want persistent allocations from + one one run to the next. When we begin a new run of a cudagraphs path (generation), all tensors + from the previous generation are freed. This frees them the memory pool, but not elsewhere. + A tensor in the cublas workspace would continue to be in use the workspace but would also get allocated + in the next run. The memory would be in use in two places. + + To solve this, we clear cublas caches before and after warming up or recording. If a workspace is required + it will be allocated to the cudagraph private pool and accounted for in the allocator for the duration of the + program. There is no overhead to this on replay since cudagraphs removes allocation overhead. + """ + torch._C._cuda_clearCublasWorkspaces() + + +@contextlib.contextmanager +def clear_cublas_manager() -> Generator[None, None, None]: + "Context manager around clearing cublas caches that will clear on enter and exit" + clear_cublass_cache() + try: + yield + finally: + clear_cublass_cache() + + +@contextlib.contextmanager +def disable_conv_cache_emptying() -> Generator[None, None, None]: + prev = torch._C._cuda_get_conv_benchmark_empty_cache() + torch._C._cudnn_set_conv_benchmark_empty_cache(False) + try: + yield + finally: + torch._C._cudnn_set_conv_benchmark_empty_cache(prev) + + +@contextlib.contextmanager +def enable_history_recording() -> Generator[None, None, None]: + "Turns on history recording in the CUDA Caching Allocator" + enabled = torch._C._cuda_isHistoryEnabled() + try: + if not enabled: + torch.cuda.memory._record_memory_history() + yield + finally: + if not enabled: + torch.cuda.memory._record_memory_history(None) + + +def get_history_recording() -> ContextManager[None]: + # TODO - remove, prevents cleanup + if not config.triton.cudagraph_trees_history_recording: + return contextlib.nullcontext() + return enable_history_recording() + + +class TreeManagerContainer: + """ + Manages the lifetime of the tree manager. Like `PrivatePool` in cuda caching allocator, + the tree and its corresponding memory pool should be kept alive as long as any outstanding + graph or tensor which is an output of a graph remains alive. + + There is a single tree manager container per device. + + The lifecycle of a tree_manager is: + - Is constructed, no graph, no fns, no tensors + - Tree manager is fetched, resulting in tree manager being allocated + - We generate a bunch of functions, calling add_strong_reference + - These functions die, calling finalize_reference + - When all the functions die, we finalize_tree_manager. + + TODO: in the future, we would like to do the following once storage weak refs land + - We look for all the live storages and add references to THOSE + - We count as storages die + - All the storages are dead, we deallocate the tree manager + """ + + def __init__(self, device_index: int) -> None: + # This class keeps a strong reference to tree_manager, + # but upon all other strong references to the tree_manager will reset it to None. + # We need a strong reference so that we can still access its attributes upon cleanup. + self.tree_manager: Optional[CUDAGraphTreeManager] = None + + # Number of outstanding references to the current tree manager + self.live_cudagraphify_fns = 0 + + self.device_index = device_index + + # Following two objects are only set in the case that Tensor outputs outlive + # the cudagraphify_fns. Reference to the Graph is needed to keep the private pool from + # deallocation. + self.live_storages_count = 0 + self.graph: Optional[torch.cuda.CUDAGraph] = None + + self.lock = threading.Lock() + + def _finalize_tensor(self) -> None: + with self.lock: + self.live_storages_count -= 1 + if self.live_storages_count == 0: + self.graph = None + + # manager was used again after existing cleanup, + # we shouldnt set it to None + if self.live_cudagraphify_fns == 0: + self.tree_manager = None + + def finalize_cudagraphify_fn(self) -> None: + with self.lock: + self.live_cudagraphify_fns -= 1 + if self.live_cudagraphify_fns == 0: + self._finalize_tree_manager() + + def _finalize_tree_manager(self) -> None: + assert self.lock.locked() + self.tree_manager = None + + # TODO - when issue #91395 is landed, we can set a weakref on + # storages and trigger a deallocation when all outputs of the + # cudagraph are dead. + + # live_storages = list( + # tree_manager.live_cudagraph_pool_storages_in_curr_execution() + # ) + + # # Maintain reference to graph to keep tensors alive + # assert len(tree_manager.roots) > 0, "expected at least one use" + # root = next(tree_manager.get_roots()) + # self.graph = root.graph + # seen_storages = set() + # for stor in live_storages: + # if stor in seen_storages: + # continue + # seen_storages.add(stor) + # self.live_storages_count += 1 + # . weakref.finalize(stor, self._finalize_tensor) + + def add_strong_reference(self, fn: Callable[..., Any]) -> None: + with self.lock: + self.live_cudagraphify_fns += 1 + + weakref.finalize(fn, self.finalize_cudagraphify_fn) + + def get_tree_manager(self) -> CUDAGraphTreeManager: + with self.lock: + if self.tree_manager is None: + self.tree_manager = CUDAGraphTreeManager(self.device_index) + return self.tree_manager + + +local = threading.local() + +# one tree manager per device +local.tree_manager_containers = {} +local.tree_manager_locks = defaultdict(threading.Lock) + + +# only incremented by user call of mark_step_begin +class MarkStepBox: + mark_step_counter = 0 + + +# We need to register this as an object that will be copied over as TLS when new +# threads are created in autograd +torch._C._stash_obj_in_tls("tree_manager_containers", local.tree_manager_containers) +torch._C._stash_obj_in_tls("tree_manager_locks", local.tree_manager_locks) + + +def mark_step_begin() -> None: + "Indicates that a new iteration of inference or training is about to begin." + + # iterate down to distinguish from GenerationTracking counter + MarkStepBox.mark_step_counter -= 1 + + +def reset_cudagraph_trees() -> None: + "Clear all cudagraph trees" + # see shutdown below for why this is necessary + container_dict = get_obj(local, "tree_manager_containers") + locks_dict = get_obj(local, "tree_manager_locks") + for device, lock in locks_dict.items(): + with lock: + container = container_dict.get(device) + if not container or not container.tree_manager: + continue + + container.tree_manager.shutdown() + + _set_cached_tensors_enabled(False) + container_dict.clear() + + MarkStepBox.mark_step_counter = 0 + + +def get_obj(local: Any, attr_name: str) -> Any: + if hasattr(local, attr_name): + return getattr(local, attr_name) + else: + assert torch._C._is_key_in_tls(attr_name) + return torch._C._get_obj_in_tls(attr_name) + + +def get_container(device_index: int) -> TreeManagerContainer: + container_dict = get_obj(local, "tree_manager_containers") + lock = get_obj(local, "tree_manager_locks")[device_index] + + with lock: + if device_index not in container_dict: + container_dict[device_index] = TreeManagerContainer(device_index) + + return container_dict[device_index] + + +def get_manager( + device_index: int, create_if_none_exists: bool = True +) -> Optional[CUDAGraphTreeManager]: + if create_if_none_exists: + return get_container(device_index).get_tree_manager() + return get_container(device_index).tree_manager + + +def cudagraphify_impl( + model: ModelType, + inputs: List[InputType], + static_input_idxs: Sequence[int], + *args: Any, + **kwargs: Any, +) -> ModelType: + fn_cache: Dict[Tuple[int, ...], Callable[..., Any]] = {} + + # Detect int inputs: we need to index on these + int_key = [i for i, v in enumerate(inputs) if isinstance(v, int)] + get_ints: Any = operator.itemgetter(*int_key) if int_key else lambda _: None + + has_warn = False + + del inputs + + def deferred_cudagraphify(inputs: List[InputType]) -> OutputType: + nonlocal has_warn + + int_key = get_ints(inputs) + fn = fn_cache.get(int_key) + if fn is not None: + return fn(inputs) + + if int_key is None: + log.info("recording cudagraph tree for graph without symints") + else: + log.info("recording cudagraph tree for symint key %s", int_key) + + if not has_warn: + has_warn = maybe_warning_due_to_dynamic_shape(fn_cache, int_key) + + # first get indices we need to check to align, then update our static inputs, + # and finally copy + check_input_idxs = get_input_idxs_to_check(inputs, static_input_idxs) + new_static_input_idxs = remove_unaligned_input_idxs(inputs, static_input_idxs) + copy_misaligned_inputs(inputs, check_input_idxs) + + fn, out = cudagraphify(model, inputs, new_static_input_idxs, *args, **kwargs) + fn = align_inputs_from_check_idxs(fn, inputs_to_check=check_input_idxs) + fn_cache[int_key] = fn + + return out + + return deferred_cudagraphify + + +def cudagraphify( + model: ModelType, + inputs: List[InputType], + static_input_idxs: Sequence[int] = (), + *, + device_index: int, + is_backward: bool, + is_inference: bool, + stack_traces: Optional[StackTraces] = None, + constants: Tuple[torch.Tensor, ...] = (), + placeholders: Tuple[PlaceholderInfo, ...] = (), + mutated_input_idxs: Tuple[int, ...] = (), +) -> Tuple[ModelType, OutputType]: + manager = get_container(device_index).get_tree_manager() + assert not (is_backward and is_inference) + mode = ( + CompilationMode.BACKWARD + if is_backward + else (CompilationMode.INFERENCE if is_inference else CompilationMode.FORWARD) + ) + + return manager.add_function( + model, + inputs, + static_input_idxs, + stack_traces, + mode, + constants, + placeholders, + mutated_input_idxs, + ) + + +class StorageWeakRefWrapper: + """ + Wrapper around a storage weak ref. Will deallocate it upon expiration if invoked. + """ + + __slots__ = ["ref", "_data_ptr", "extra_ref_check"] + + storage_ref: Optional[StorageWeakRef] + + def __init__( + self, + inp: Union[Tensor, UntypedStorage], + extra_ref_check: Optional[Callable[[], bool]] = None, + ) -> None: + """ + extra_ref_check is an additional check we need to run to check if the + weak ref has expired. in checking storage use count we assume extra_ref_check + will hold an additional reference to the storage. + """ + if isinstance(inp, Tensor): + stor = inp.untyped_storage() + else: + assert isinstance(inp, UntypedStorage) + stor = inp + self.ref = StorageWeakRef(stor) + self._data_ptr = stor.data_ptr() + self.extra_ref_check = extra_ref_check + + @classmethod + def from_weakref_and_data_ptr( + cls: Type[S], + cdata: Any, + data_ptr: int, + extra_ref_check: Optional[Callable[[], bool]] = None, + ) -> StorageWeakRefWrapper: + instance = cls.__new__(cls) + instance._data_ptr = data_ptr + instance.ref = StorageWeakRef.from_weakref(cdata) + instance.extra_ref_check = extra_ref_check + return instance + + def __call__(self) -> Optional[StorageWeakRefPointer]: + if self.expired(): + return None + + return self.ref.cdata + + def swap_weakref(self, cdata: Any) -> None: + self.ref.__del__() + self.ref.cdata = cdata + + def data_ptr(self) -> int: + "NB: returns the data ptr even if the storage has expired" + return self._data_ptr + + def remove_extra_reference(self) -> None: + self.extra_ref_check = None + + def expired(self) -> bool: + if self.extra_ref_check is not None and not self.extra_ref_check(): + return False + + # if extra_ref_check is not None we expect an additional reference + stor_count = torch._C._storage_Use_Count(self.ref.cdata) + return (stor_count - (self.extra_ref_check is not None)) == 0 + + def __repr__(self) -> str: + if self.ref is None or self.ref.expired(): + return f"StorageWeakRefWrapper to {self.data_ptr()}; dead" + else: + return f"StorageWeakRefWrapper to {self.data_ptr()}; alive" + + +def is_live(weak_ref: Optional[StorageWeakRefWrapper]) -> bool: + return maybe_deref(weak_ref) is not None + + +def maybe_deref( + weak_ref: Optional[StorageWeakRefWrapper], +) -> Optional[Tuple[StorageWeakRefPointer, int]]: + if weak_ref is None: + return None + r = weak_ref() + if r is None: + return None + # NB: r.data_ptr() does not necessarily equal weak_ref.data_ptr() + return r, weak_ref.data_ptr() + + +@contextlib.contextmanager +def _use_cuda_memory_pool_manager( + device: int, mem_pool: Tuple[int, int], stream: torch.cuda.Stream +) -> Generator[None, None, None]: + """ + Context manager to use cuda graph pool for new allocations. If you use this manager + all cudagraph tensors in use should be reflected in the allocator or they will be overwritten. + existing_graph should already have been used in a capture, and the mem_pool must already exist, + because this manager will not preserve a reference to the pool which keeps it alive. + """ + torch.cuda.synchronize() + stream.wait_stream(torch.cuda.current_stream()) + + with torch.cuda.stream(stream), torch.device(device): + torch._C._cuda_beginAllocateCurrentStreamToPool(device, mem_pool) + try: + yield + finally: + torch._C._cuda_endAllocateCurrentStreamToPool(device, mem_pool) + torch._C._cuda_releasePool(device, mem_pool) + + torch.cuda.current_stream().wait_stream(stream) + + +def map_to_ref(t: Optional[Tensor]) -> Optional[StorageWeakRefWrapper]: + if not isinstance(t, torch.Tensor): + assert t is None + return None + return StorageWeakRefWrapper(t) + + +# A path index of (depth, offset) indices into a graph that is `depth`` number of nodes from the root +# at graph output offset +PathOutputIndex = Tuple[int, int] + +# For each node in the path, for each output, is the output alive +PathLiveness = List[List[bool]] + +StackTraces = List[Optional[str]] + + +class CUDAWarmupNode: + """ + Simplified Wrapper around A CUDA Model that wraps outputs in storage refs and exposes + apis to get the live storages in the current chain of warmup. + + A CUDAWarmupNode may have either CUDAGraphNode or CUDAWarmupNode as a parent, but may only have + CUDAWarmupNode as children, because we cannot record or execute with tensors which do not have stable + memory addresses. + + CUDAWarmupNode and CUDAGraphNode have a number of differences that make it easier to use separate classes. + - Much of the CUDAGraphNode logic & initialization is based on the tensor properties of first recording. In the + first instance of warmup, these are not finalized yet. + - All Inputs to the RecordedFunction must be copied over to the cuda graph memory pool, this is unnecessary in warmup. + - CUDAWarmup is only used once and so does not need to optimize as much bookkeeping. It is much simpler. + + NB: this class and CUDAGraphNode need to expose `path_live_weakrefs`, `all_outputs_are_dead`, and + `self.outputs_weakrefs`, `stack_traces`, and `tensor_weakrefs` for compatibility. + """ + + def __init__( + self, + wrapped_function: WrappedFunction, + parent: Optional[Union[CUDAGraphNode, CUDAWarmupNode]], + cuda_graphs_pool: Tuple[int, int], + existing_cuda_graph: Optional[torch.cuda.CUDAGraph], + device_index: int, + stack_traces: Optional[StackTraces], + stream: torch.cuda.Stream, + already_warm: bool, + id: GraphID, + ) -> None: + self.wrapped_function = wrapped_function + self.parent: Optional[Union[CUDAGraphNode, CUDAWarmupNode]] = parent + self.cuda_graphs_pool = cuda_graphs_pool + self.outputs_weakrefs: List[Optional[StorageWeakRefWrapper]] = [] + self.tensor_weakrefs: List[Optional[TensorWeakRef]] = [] + self.existing_cuda_graph = existing_cuda_graph + self.has_run = False + self.device_index = device_index + self.stack_traces = stack_traces + self.stream = stream + self.already_warm = already_warm + self.id = id + + def run(self, new_inputs: Any) -> OutputType: + assert not self.has_run, "Wrapped function should never be run twice" + + # See: output_is_alias_of_persistent_static_inputs below. We should only be returning freshly created + # storages in path_live_weakrefs. + existing_path_data_ptrs = { + t.data_ptr() for t in self.path_live_weakrefs() if t() + } + + def get_non_cudagraph_inps() -> List[weakref.ReferenceType[UntypedStorage]]: + non_cudagraph_inps = [] + for t in itertools.chain(new_inputs, self.wrapped_function.constants): + if ( + isinstance(t, torch.Tensor) + and t.untyped_storage().data_ptr() not in existing_path_data_ptrs + ): + non_cudagraph_inps.append(weakref.ref(t.untyped_storage())) + return non_cudagraph_inps + + non_cudagraph_inps_storages = get_non_cudagraph_inps() + + if config.triton.slow_path_cudagraph_asserts and not self.already_warm: + refs = list(self.path_live_weakrefs()) + check_memory_pool(self.device_index, self.cuda_graphs_pool, refs) + + with torch.cuda.device( + self.device_index + ), disable_conv_cache_emptying(), clear_cublas_manager(), _use_cuda_memory_pool_manager( + self.device_index, self.cuda_graphs_pool, self.stream + ), get_history_recording(): + out = self.wrapped_function.model(new_inputs) + + # We need to know which outputs are allocated within the cudagraph pool + # so that we can deallocate them at the beginning of the next cudagraph step, + # and set their access to error. + # We use a weakref to the inputs storage, in case a block which was previously + # allocated to the general caching allocator pool gets reallocated to a private pool. + + non_cudagraph_inps_storage_ptrs = set() + for storage in non_cudagraph_inps_storages: + s = storage() + if s is not None: + non_cudagraph_inps_storage_ptrs.add(s._cdata) + + assert len(new_inputs) == 0 + + # sdpa returns cpu tensors when not recording cuda graph + def add_ref(o: Any) -> bool: + return ( + isinstance(o, torch.Tensor) + and o.is_cuda + and o.untyped_storage()._cdata not in non_cudagraph_inps_storage_ptrs + and o.untyped_storage().data_ptr() != 0 + ) + + self.outputs_weakrefs.extend( + [map_to_ref(o) if add_ref(o) else None for o in out] + ) + self.tensor_weakrefs.extend( + [TensorWeakRef(o) if add_ref(o) else None for o in out] + ) + + if config.triton.slow_path_cudagraph_asserts and not self.already_warm: + out_refs = list(self.path_live_weakrefs()) + check_memory_pool(self.device_index, self.cuda_graphs_pool, out_refs) + + return out + + @property + def _path_from_root( + self, + ) -> Generator[Union[CUDAGraphNode, CUDAWarmupNode], None, None]: + nodes = [] + node: Union[CUDAGraphNode, CUDAWarmupNode] = self + while node: + nodes.append(node) + node = node.parent # type: ignore[assignment] + + yield from reversed(nodes) + + def path_live_weakrefs(self) -> Iterator[StorageWeakRefWrapper]: + "Returns all live storages weakrefs that created by nodes in this path" + for node in self._path_from_root: + for output in node.outputs_weakrefs: + if is_live(output): + yield output # type: ignore[misc] + + def all_outputs_are_dead(self) -> bool: + return not list(self.path_live_weakrefs()) + + def _is_cuda_graph_recorded_tensor(self, t: torch.Tensor) -> bool: + for storage_weak_ref in self.path_live_weakrefs(): + if t.untyped_storage().data_ptr() == storage_weak_ref.data_ptr(): + return True + return False + + +# Aliases for List that say what the indices denote +InputList = List # input indexes +OutputList = List # output indexes +LevelList = List # levels (distance from root of tree) + + +class OutputAliasInfo: + pass + + +class _UnaliasedStorage(OutputAliasInfo): + "Singleton to mark that the graph output constructs a new alias or is None" + + +UnaliasedStorage = _UnaliasedStorage() + + +class AliasesPriorGraphOutput(OutputAliasInfo): + "Marks that the graph output aliases an output of a prior graph" + __slots__ = ["index"] + + index: PathOutputIndex + + def __init__(self, index: PathOutputIndex) -> None: + assert isinstance(index, tuple) + self.index = index + + +class AliasesNewOutput(OutputAliasInfo): + "Marks that the graph output aliases an index in the new, returned outputs" + + __slots__ = ["index"] + + index: int + + def __init__(self, index: int) -> None: + assert isinstance(index, int) + self.index = index + + +class CUDAGraphNode: + """ + A single recording of a function into a CUDA Graph. Recordings of CUDA Graphs share a single memory pool + and are structured into a tree, where there is a single recording that can precede it (parent) and multiple + subsequent recordings that may follow (children). A node will have no parent if it is the first recording + in a tree; i.e., when it is first recorded, there are no live tensors from a previous recording which + would force a dependency. + + On first recording, all of the live tensors in the current CUDA Graph Node path will be + reflected in the corresponding private pool. On subsequent executions, the caching allocator + is unaffected when the graph is replayed. + + In order to support recording a subsequent cuda graph recording after execution of this graph, + we checkpoint the state of the memory pool so that it may later be resumed. + + WrappedFunction should have already been warmed up prior to invocation. + + See [setCheckpointPoolState] for further explanation, as well as + https://user-images.githubusercontent.com/13564/222815509-374f3400-f83d-4f7d-8fa6-4a092b3250bb.png + """ + + def __init__( + self, + wrapped_function: WrappedFunction, + id: GraphID, + parent: Optional[CUDAGraphNode], + inputs: List[InputType], + cuda_graphs_pool: Tuple[int, int], + device_index: int, + stack_traces: Optional[StackTraces], + stream: torch.cuda.Stream, + ) -> None: + assert isinstance(inputs, (list, tuple)) + + self.wrapped_function = wrapped_function + self.id = id + self.device = device_index + self.stack_traces = stack_traces + self.stream = stream + + # Enable re-record a cudagraph when static tensor address changed. + # if not we should error when it changed. + self.rerecord_if_static_inputs_change = ( + torch._dynamo.config.inline_inbuilt_nn_modules + or torch._inductor.config.triton.cudagraph_support_input_mutation + ) + + # if this is a root parent will be None. use weakref to prevent reference cycle + self._parent = weakref.ref(parent) if parent is not None else None + # reference to the shared memory pool for the entire cuda graphs tree + self.cuda_graphs_pool = cuda_graphs_pool + + # A single wrapped function may be recorded multiple times if memory patterns or + # invariants change from one execution to the next + self.children: Dict[FunctionID, List[CUDAGraphNode]] = defaultdict(list) + + # StorageWeakRef maintains whether the Storage C++ object remains allocated, + # not whether the corresponding memory has been deallocated. In order + # to use them to track memory deallocations we must maintain a single StorageWeakRef + # for all Storages that reference that memory (even if we are constructing Storages + # that do not have a deallocator function). We maintain one single storage_cache + # as we execute any tree path. When we retrieve a storage from the cache we + # check that it is still alive, and we hash based on observed recording data ptr + # and storage cdata. + + # we preserve a single reference to executed outputs that is then referenced + # in children to avoid children having to chase parent pointers in the hot path + # DO NOT reassign output_weakrefs, only call `clear()` + # Path is a series of nodes from root to the current node + self.outputs_weakrefs: OutputList[Optional[StorageWeakRefWrapper]] = [] + self.path_weakrefs: LevelList[OutputList[Optional[StorageWeakRefWrapper]]] = [ + node.outputs_weakrefs for node in self._path_from_root + ] + self.path_stacktraces: LevelList[Optional[StackTraces]] = [ + node.stack_traces for node in self._path_from_root + ] + self.tensor_weakrefs: OutputList[Optional[TensorWeakRef]] = [] + + # tensors which are outputs of previous graphs in the tree + self.cudagraph_managed_idxs: List[int] = [ + idx + for idx, t in enumerate(inputs) + if isinstance(t, torch.Tensor) and self._is_cuda_graph_recorded_tensor(t) + ] + + self.static_input_idxs: List[int] = list( + set(wrapped_function.static_input_idxs) | set(self.cudagraph_managed_idxs) + ) + + self.non_static_input_idx: LevelList[int] = [ + i for i in range(len(inputs)) if i not in self.static_input_idxs + ] + + counters["inductor"]["cudagraph_recorded_non_static_inputs"] += len( + self.non_static_input_idx + ) + + self.non_managed_static_input_idxs: LevelList[int] = [ + i + for i in wrapped_function.static_input_idxs + if i not in self.cudagraph_managed_idxs + ] + + def maybe_get_static_data_ptr( + idx: int, + inputs: List[Union[torch.Tensor, int]], + static_input_idxs: List[int], + ) -> Optional[int]: + inp = inputs[idx] + if isinstance(inp, torch.Tensor) and idx in static_input_idxs: + return inp.data_ptr() + return None + + self.static_input_data_ptrs: InputList[Optional[int]] = [ + maybe_get_static_data_ptr(i, inputs, self.static_input_idxs) + for i in range(len(inputs)) + ] + + # When we checkpoint, and free generations, we will be manually freeing the outputs + # of CUDAGraphNodes. We should not be freeing parameters, not do we need to account for + # their liveness (they are static), so we need to compute which outputs are aliases of + # parameters. Some static inputs are saved tensors from the forward that die in the backward. + # Their locations are static but lifetimes are not. We only include the persistent static + # data ptrs below because the non persistent data ptrs may be outputs of this record and + # fresh allocations. + + # precompute expanded dims to avoid computing in the hot path + self.expanded_dims: List[List[int]] = [ + get_expanded_dims(x) + if isinstance(x, torch.Tensor) and idx not in self.static_input_idxs + else [] + for idx, x in enumerate(inputs) + ] + + # For each node in path, which outputs were observed to be live + # before invoking graph recording, and after graph recording + self.recorded_liveness_before_graph: LevelList[OutputList[bool]] = [] + self.recorded_liveness_after_graph: LevelList[OutputList[bool]] = [] + + # List of Tuples of (depth, output_index) that index into node at depth + # number of nodes from root and output_index of outputs. Will index into + # path_weakrefs. + self.expected_dead_indices_before_graph: List[PathOutputIndex] = [] + self.expected_dead_indices_after_graph: List[PathOutputIndex] = [] + + # all live indices after graph recording + self.live_indices_after_graph: List[PathOutputIndex] = [] + + if self.parent is not None: + previous_liveness = self.parent.recorded_liveness_after_graph + curr_liveness = self._get_liveness(self.path_weakrefs) + + different_indices = self._get_different_indices( + previous_liveness, curr_liveness + ) + + self.recorded_liveness_before_graph = curr_liveness + self.expected_dead_indices_before_graph = different_indices + + recording_inputs = self._allocate_and_copy_recording_inputs(inputs) + # recording inputs will copy over memory, so we can free non recording inputs + inputs.clear() + del inputs + + # graph used for recording model invocation + self.graph: Optional[torch.cuda.CUDAGraph] = torch.cuda.CUDAGraph() + + # we allocate non-static inputs within the same memory pool as the CUDAGraph + # which we will record the model with. For memory efficiency, it is important + # to reclaim the input memory when the inputs are no longer live. To accomplish this, + # we reconstruct tensors at the correct data pointers of our inputs which are + # non owning and do not prevent deallocation. On subsequent executions, input values + # will be copied over to these tensors. + self.reconstructed_inputs: List[InputType] = [ + self._reconstruct_from_tensor_metadata(self._tensor_metadata(x)) + if isinstance(x, torch.Tensor) + else x + for x in recording_inputs + ] + + # DO THE RECORDING!!! + # We record the CUDA graph in the constructor of CUDAGraphNode, which + # gives you what the CPU side compute of the function would do. We + # don't throw the recording outputs away: their memory is + # correctly accounted for in the CUDAGraphs caching allocator. This + # means on the very FIRST run of the CUDA graph node, we can directly + # do more recording, because we have a valid caching allocator state. + # NB: This relies on run() being called immediately after the + # constructor, otherwise this optimization would not be valid. + + # initialized below in _record + + self.checkpointed_caching_state: Optional[AllocatorState] = None + + # Output Storage Alias information, can be: + # - A new, unaliased storage, or the output is None + # - An alias of an output of a prior graph + # - An alias of an output already created in the reconstructed outputs + # This is None if the output in question is an int + self.output_storage_alias: OutputList[Optional[OutputAliasInfo]] = [] + + # is the output Storage unaliased in subsequent outputs, of all subsequent paths + # if it is, we cached the output tensor and adjust storage liveness tracking to also + # check if the output tensor does not have an additional python reference. + # If a descendent node discovers it has an alias of a prior output, then the output + # will no longer be cached in the ancestor. + # The large majority of tensors are unaliased, and preserving aliased output tensors would add + # significant additional complexity with marginal gains + # The cached tensor outputs are added on the first execution, and cleared whenever we need + # to do subsequent recording + self.unaliased_in_all_paths: OutputList[bool] = [] + self.cached_tensor_outputs: OutputList[Optional[Tensor]] = [] + + # if an output aliases a static, persistent input then the corresponding Tensor will + # be set here. These are different than cached tensors, because they are tensors that + # are aliases of parameters that are always live. + self.static_output_tensors: OutputList[Optional[Tensor]] = [] + + # Cleared after recording + self.recording_outputs: Optional[OutputType] = self._record( + wrapped_function.model, recording_inputs + ) + self.outputs_metadata: OutputList[Union[Dict[str, Any], int, None]] = [] + + # As with inputs, we do not want to keep the outputs permanently alive because that would prevent + # their memory being reclaimed in subsequent cuda graph recordings. We record the tensor metadata + # needed to reconstruct instead. + assert self.recording_outputs is not None + for out in self.recording_outputs: + if isinstance(out, torch.Tensor): + self.outputs_metadata.append( + self._tensor_metadata(out, ignore_storage_offset=False) + ) + else: + assert isinstance(out, (int, type(None))), type(out) + self.outputs_metadata.append(out) + + self.graph.replay() + + def _copy_inputs_and_remove_from_src( + self, dsts: List[InputType], srcs: List[InputType] + ) -> None: + dst_tensors = [] + src_tensors = [] + for idx in self.non_static_input_idx: + if not isinstance(srcs[idx], torch.Tensor): + continue + expanded_dims = self.expanded_dims[idx] + dst_tensors.append(index_expanded_dims(dsts[idx], expanded_dims)) # type: ignore[arg-type] + src_tensors.append(index_expanded_dims(srcs[idx], expanded_dims)) # type: ignore[arg-type] + srcs[idx] = None # type: ignore[call-overload] + # Fails on empty lists + if dst_tensors: + torch._foreach_copy_(dst_tensors, src_tensors) + + def check_static_inputs_are_stable(self, new_inputs: List[InputType]) -> None: + # avoid checking managed tensor static points since we already checked those in check_invariants + if ( + not self.rerecord_if_static_inputs_change + and not torch._C._tensors_data_ptrs_at_indices_equal( + new_inputs, # type: ignore[arg-type] + self.static_input_data_ptrs, + self.non_managed_static_input_idxs, + ) + ): + # this should error + error_msg = log_data_ptr_mismatch( + self.wrapped_function.placeholders, + new_inputs, + self.static_input_data_ptrs, + self.non_managed_static_input_idxs, + CheckInvariantStatus.StaticInputIdxMismatch, + ) + torch._check(False, lambda: error_msg) + + def run_first_inputs(self, new_inputs: List[InputType]) -> OutputType: + if config.triton.fast_path_cudagraph_asserts: + self.debug_check_invariants_before_invocation() + + # graph is already invoked in the __init__ + # inputs are copied over in _allocate_recording_inputs and subsequently cleared + assert len(new_inputs) == 0 + outputs = self.recording_outputs + self.recording_outputs = None + assert outputs is not None + return outputs + + def run(self, new_inputs: List[InputType]) -> OutputType: + self.check_static_inputs_are_stable(new_inputs) + + self._copy_inputs_and_remove_from_src(self.reconstructed_inputs, new_inputs) + new_inputs.clear() + + self.run_graph() + + outputs = self.reconstruct_outputs() + + if config.triton.fast_path_cudagraph_asserts: + self.debug_check_invariants_after_invocation() + + if config.triton.force_cudagraph_sync: + torch.cuda.synchronize() + + # Reset this to run the check in the future + self.static_inputs_stable = False + + return outputs + + def reconstruct_outputs(self) -> OutputType: + "Reconstruct output tensors according to their saved metadata and alias information" + + # Cached tensors will not yet be set on the first execution + # They are also cleared in checkpointing, so if we checkpoint this node + # and then execute it again we will need to repopulate cached tensors + if not self.cached_tensor_outputs: + self._initialize_cached_tensors() + + outputs: OutputType = [] + + for i, (storage_info, metadata) in enumerate( + zip(self.output_storage_alias, self.outputs_metadata) + ): + if not isinstance(metadata, dict): # tensor metadata + assert isinstance(metadata, (int, type(None))) + outputs.append(metadata) + continue + + cached_t = self.cached_tensor_outputs[i] + if cached_t is not None: + # this output represents a fresh allocated tensor. + # We return the same TensorImpl from run to run to avoid overhead. + # autograd.Function will reset the Autograd meta of output tensors + # as part of aot_autograd, but _backward_hooks are stored on tensors separately, + # so we need to manually reset hooks. + if cached_t._backward_hooks is not None: + cached_t._backward_hooks = None + + # No need to update weakrefs, already correctly initialized + outputs.append(cached_t) + continue + + static_t = self.static_output_tensors[i] + if static_t is not None: + assert self.outputs_weakrefs[i] is None + outputs.append(static_t) + continue + + storage = self.prepare_alias_info_for_tensor_construction( + storage_info, metadata + ) + + if isinstance(storage, UntypedStorage) or storage is None: + out = self._reconstruct_from_tensor_metadata(metadata, storage) + else: + assert isinstance(storage, int) + out = self._reconstruct_from_tensor_metadata( + metadata, cast(torch.Tensor, outputs[storage]).untyped_storage() + ) + + outputs.append(out) + w = self.outputs_weakrefs[i] + assert w is not None + w.swap_weakref(out.untyped_storage()._weak_ref()) + + return outputs + + def prepare_alias_info_for_tensor_construction( + self, + out_alias_info: Optional[OutputAliasInfo], + metadata: Union[Dict[str, Any], int, None], + ) -> Union[UntypedStorage, None, int]: + if ( + isinstance(metadata, (int, type(None))) + or out_alias_info is UnaliasedStorage + ): + return None + + if isinstance(out_alias_info, AliasesPriorGraphOutput): + depth, existing_output_index = out_alias_info.index + ref = self.path_weakrefs[depth][existing_output_index] + assert ref is not None + return torch.UntypedStorage._new_with_weak_ptr(ref()) + + assert isinstance(out_alias_info, AliasesNewOutput) + return out_alias_info.index + + def prepare_storages_for_construction( + self, + ) -> List[Union[UntypedStorage, None, int]]: + output_storages = [] + for output_storage_alias, metadata in zip( + self.output_storage_alias, self.outputs_metadata + ): + output_storages.append( + self.prepare_alias_info_for_tensor_construction( + output_storage_alias, metadata + ) + ) + + return output_storages + + def run_graph(self) -> None: + assert self.graph is not None + self.graph.replay() + + def all_outputs_are_dead(self) -> bool: + "All outputs of the path from this node to its root are dead" + for depth, output_index in self.live_indices_after_graph: + if is_live(self.path_weakrefs[depth][output_index]): + return False + return True + + def _record(self, model: ModelType, inputs: List[InputType]) -> OutputType: + "Record the model" + + def static_input_iter() -> Generator[torch.Tensor, None, None]: + for i in self.wrapped_function.static_input_idxs: + _inp = inputs[i] + if isinstance( + _inp, torch.Tensor + ) and not self._is_cuda_graph_recorded_tensor(_inp): + yield _inp + + # see: output_is_alias_of_persistent_static_inputs above + static_input_persistent_storage_ptrs: Dict[int, StorageWeakRefWrapper] = { + inp.untyped_storage().data_ptr(): StorageWeakRefWrapper(inp) + for inp in itertools.chain( + static_input_iter(), self.wrapped_function.constants + ) + } + + if config.triton.slow_path_cudagraph_asserts: + # need to use parent live weakrefs because live_indices isnt set yet + memory = ( + [] if self.parent is None else list(self.parent.path_live_weakrefs()) + ) + memory += [ + StorageWeakRefWrapper(elem) + for i, elem in enumerate(inputs) + if isinstance(elem, torch.Tensor) + and i not in self.wrapped_function.static_input_idxs + and elem.untyped_storage().data_ptr() != 0 + ] + check_memory_pool(self.device, self.cuda_graphs_pool, memory) + + with preserve_rng_state(), torch.cuda.device( + self.device + ), clear_cublas_manager(), torch.cuda.graph( + self.graph, + stream=self.stream, + pool=self.cuda_graphs_pool, + capture_error_mode="thread_local", + ), get_history_recording(): + static_outputs = model(inputs) + + # running model should reclaim memory + assert len(inputs) == 0 + + if not isinstance(static_outputs, (list, tuple)): + static_outputs = (static_outputs,) + + self._add_first_outputs(static_outputs, static_input_persistent_storage_ptrs) + + return static_outputs + + def _add_first_outputs( + self, + outputs: OutputType, + static_input_persistent_storage_ptrs: Dict[int, StorageWeakRefWrapper], + ) -> None: + "Add the outputs from the first invocation of the node and set up metadata" + + # getting liveness before we have added the outputs to path, so the length + # of the two lists is equal + prev_liveness = self.recorded_liveness_before_graph + curr_liveness = self._get_liveness(self.path_weakrefs) + + delta = self._get_different_indices(prev_liveness, curr_liveness) + self.expected_dead_indices_after_graph = delta + + assert len(self.outputs_weakrefs) == 0 + # index from data pointer to index in outputs + output_new_storages_index: Dict[StorageDataPtr, int] = {} + + self.unaliased_in_all_paths = [False for _ in range(len(outputs))] + self.static_output_tensors = [None for _ in range(len(outputs))] + + for i, o in enumerate(outputs): + if o is None or not isinstance(o, torch.Tensor): + self.output_storage_alias.append(UnaliasedStorage) + continue + + torch._check( + o.is_cuda or o.untyped_storage().data_ptr() == 0, + lambda: ( + "Expected all cuda outputs in cuda graph recording. Non cuda output " + f"from {self.stack_traces[i] if self.stack_traces else '(unknown)'}" + ), + ), + + ref = static_input_persistent_storage_ptrs.get( + o.untyped_storage().data_ptr(), None + ) + # also treat empty storages as static outputs because we do not need to manage their lifetime + # and they should not participate in checkpointing + is_empty_storage = o.untyped_storage().data_ptr() == 0 + if (ref and ref() is not None) or is_empty_storage: + self.output_storage_alias.append(None) + self.static_output_tensors[i] = o + continue + + path_ref = self._is_alias_of_live_recorded_tensor(o) + if path_ref is not None: + self._mark_prior_graph_output_as_aliased(path_ref) + self.output_storage_alias.append(AliasesPriorGraphOutput(path_ref)) + continue + + if o.untyped_storage().data_ptr() in output_new_storages_index: + index = output_new_storages_index[o.untyped_storage().data_ptr()] + self.unaliased_in_all_paths[index] = False + self.output_storage_alias.append(AliasesNewOutput(index)) + continue + + output_new_storages_index[o.untyped_storage().data_ptr()] = i + self.output_storage_alias.append(UnaliasedStorage) + self.unaliased_in_all_paths[i] = True + + if self.stack_traces is None: + self.stack_traces = [None for _ in range(len(outputs))] + else: + assert len(self.stack_traces) == len( + outputs + ), "Wrong number of stack traces passed in" + + assert not self.outputs_weakrefs + for out, static_output_tensor in zip(outputs, self.static_output_tensors): + if not isinstance(out, torch.Tensor) or static_output_tensor is not None: + self.outputs_weakrefs.append(None) + self.tensor_weakrefs.append(None) + else: + self.outputs_weakrefs.append(StorageWeakRefWrapper(out)) + self.tensor_weakrefs.append(TensorWeakRef(out)) + + self.recorded_liveness_after_graph = self._get_liveness(self.path_weakrefs) + self.checkpointed_caching_state = torch._C._cuda_getCheckpointState( + self.device, self.cuda_graphs_pool + ) + + # now, get liveness with outputs added + for depth in range(len(self.path_weakrefs)): + for output_index in range(len(self.path_weakrefs[depth])): + if is_live(self.path_weakrefs[depth][output_index]): + self.live_indices_after_graph.append((depth, output_index)) + + self.debug_check_invariants_after_invocation() + if config.triton.slow_path_cudagraph_asserts: + check_memory_pool( + self.device, self.cuda_graphs_pool, list(self.path_live_weakrefs()) + ) + + def _mark_prior_graph_output_as_aliased(self, index: PathOutputIndex) -> None: + "Remove a graph output from the unaliased, cached tensors in an ancestor node" + depth, output_index = index + node = list(self._path_from_root)[depth] + node.unaliased_in_all_paths[output_index] = False + x = self.path_weakrefs[depth][output_index] + assert x is not None + x.remove_extra_reference() + + def _initialize_cached_tensors(self) -> None: + # we should not be clearing output_weakrefs, and they should be set in the first + # record run + assert len(self.outputs_weakrefs) == len(self.outputs_metadata) + + for i, (storage_info, metadata, make_cached) in enumerate( + zip( + self.output_storage_alias, + self.outputs_metadata, + self.unaliased_in_all_paths, + ) + ): + if not make_cached: + self.cached_tensor_outputs.append(None) + continue + + assert storage_info is UnaliasedStorage + assert isinstance(metadata, dict) + s = self.create_storage(metadata) + out = self._reconstruct_from_tensor_metadata(metadata, storage=s) # type: ignore[arg-type] + + # XXX: let autograd know that there will be an additional reference to the tensor + # that can be ignored when deciding whether to do gradient buffer inplacing. + # Otherwise, inplacing could differ between tracing and subsequent execution. + # For some models we tested this led to inputs no longer being in cudagraph pools, + # leading to spurious re-recordings. + # It also tells AMP cache that even though the tensor impls cannot be cached + # in dtype conversions. + + torch._C._add_cached_tensor(out) + + self_ref = weakref.ref(self) + + # one reference in our array, and calling sys.getrefcount bumps the refcount by one + def check_refcount(i: int) -> bool: + self_loc = self_ref() + if self_loc is None: + return False + return self_loc.get_output_refcount(i) == 2 + + check = functools.partial(check_refcount, i=i) + + self.outputs_weakrefs[i] = StorageWeakRefWrapper(out, extra_ref_check=check) + self.cached_tensor_outputs.append(out) + + def get_output_refcount(self, index: int) -> int: + return sys.getrefcount(self.cached_tensor_outputs[index]) + + @property + def parent(self) -> Optional[CUDAGraphNode]: + "unwraps the weakref to _parent" + return self._parent() if self._parent is not None else None + + @property + def _path_to_root(self) -> Generator[CUDAGraphNode, None, None]: + "Returns all nodes in the path starting at self and ending at root" + node = self + while node: + yield node + node = node.parent # type: ignore[assignment] + + @property + def _path_from_root(self) -> Generator[CUDAGraphNode, None, None]: + "Returns all nodes in the path starting at the root and ending at self" + nodes = reversed(list(self._path_to_root)) + yield from nodes + + def _is_cuda_graph_recorded_tensor(self, t: torch.Tensor) -> bool: + "Is this tensor an output of a node in this path" + for output_refs in self.path_weakrefs: + for storage_weak_ref in output_refs: + if storage_weak_ref is None: + continue + # don't need to check liveness of storage since the cuda graph managed + # memory is never released. + data_ptr = storage_weak_ref.data_ptr() + if t.untyped_storage().data_ptr() == data_ptr: + return True + + return False + + def _is_alias_of_live_recorded_tensor( + self, t: torch.Tensor + ) -> Optional[PathOutputIndex]: + for depth, output_refs in enumerate(self.path_weakrefs): + for output_index, storage_ref in enumerate(output_refs): + if (storage_and_ptr := maybe_deref(storage_ref)) is not None: + storage, ptr = storage_and_ptr + if ptr == t.untyped_storage().data_ptr(): + return (depth, output_index) + + return None + + @staticmethod + def _check_liveness( + indices: List[PathOutputIndex], + output_refs: List[List[Optional[StorageWeakRefWrapper]]], + ) -> bool: + "Check that all of the indices specified are dead references" + for depth, output_index in indices: + w = output_refs[depth][output_index] + assert w is not None + if w() is not None: + return False + return True + + def add_child(self, function_id: FunctionID, node: CUDAGraphNode) -> None: + "Adds node as a a child of self" + self.children[function_id].append(node) + + @staticmethod + def _get_different_indices( + prev: List[List[bool]], curr: List[List[bool]] + ) -> List[PathOutputIndex]: + "Find indices where the two lists differ." + dead_indices = [] + assert len(prev) <= len(curr) + for i, (outputs1, outputs2) in enumerate(zip(prev, curr)): + assert len(outputs1) == len(outputs2) + for j, (output1, output2) in enumerate(zip(outputs1, outputs2)): + if output1 != output2: + dead_indices.append((i, j)) + + return dead_indices + + @staticmethod + def _get_liveness( + weakrefs: List[List[Optional[StorageWeakRefWrapper]]], + ) -> List[List[bool]]: + "Maps weakrefs to true if the reference is alive and false otherwise" + if len(weakrefs) == 0: + return [] + + return [pytree.tree_map(is_live, outputs) for outputs in weakrefs] + + def debug_assert_invariants( + self, expected_liveness: List[List[bool]], newly_dead: List[PathOutputIndex] + ) -> None: + if not config.triton.fast_path_cudagraph_asserts: + return + + for i, node in enumerate(self._path_from_root): + assert self.path_weakrefs[i] is node.outputs_weakrefs + + nodes = list(self._path_from_root) + + live_blocks = get_block_addrs(self.cuda_graphs_pool) + + live_storage_data_ptrs = set() + live_storage_weak_ptrs = set() + + for depth, outputs_liveness in enumerate(expected_liveness): + for output_idx, output_liveness in enumerate(outputs_liveness): + # tensor can die early, but it can't be alive when it should be dead + w = self.path_weakrefs[depth][output_idx] + if (stor_weak_ptr_and_data_ptr := maybe_deref(w)) is not None: + assert output_liveness + stor_weak_ptr, stor_data_ptr = stor_weak_ptr_and_data_ptr + assert (stor_data_ptr in live_storage_data_ptrs) == ( + stor_weak_ptr in live_storage_weak_ptrs + ) + live_storage_data_ptrs.add(stor_data_ptr) + live_storage_weak_ptrs.add(stor_weak_ptr) + + is_persistent_alias = ( + nodes[depth].static_output_tensors[output_idx] is not None + ) + + if is_persistent_alias: + assert stor_data_ptr not in live_blocks + + for depth, output_index in newly_dead: + assert not is_live(self.path_weakrefs[depth][output_index]) + + def debug_check_invariants_before_invocation(self) -> None: + self.debug_assert_invariants( + self.recorded_liveness_before_graph, self.expected_dead_indices_before_graph + ) + + def debug_check_invariants_after_invocation(self) -> None: + self.debug_assert_invariants( + self.recorded_liveness_before_graph, self.expected_dead_indices_after_graph + ) + + def data_ptrs_dead_since_invocation(self) -> List[int]: + """ + Since this node was invoked, return data ptrs of all tensor outputs that have died + in the current executing tree path. + """ + curr_liveness = self._get_liveness(self.path_weakrefs) + _get_different_indices = self._get_different_indices( + self.recorded_liveness_after_graph, curr_liveness + ) + + path = list(self._path_from_root) + ptrs_to_deallocate = [] + for depth, output_index in _get_different_indices: + ptrs_to_deallocate.append( + path[depth].outputs_metadata[output_index]["data_ptr"] # type: ignore[index] + ) + + return ptrs_to_deallocate + + def path_live_weakrefs(self) -> Iterator[StorageWeakRefWrapper]: + for i, j in self.live_indices_after_graph: + out = self.path_weakrefs[i][j] + if out is not None and is_live(out): + yield out + + def remove_node_cached_tensors(self) -> None: + for t in self.cached_tensor_outputs: + if t is not None: + torch._C._remove_cached_tensor(t) + self.cached_tensor_outputs.clear() + + for i, unaliased in enumerate(self.unaliased_in_all_paths): + if unaliased: + n = self.outputs_weakrefs[i] + assert n is not None + n.remove_extra_reference() + + def remove_path_cached_tensors(self) -> None: + for node in self._path_from_root: + node.remove_node_cached_tensors() + + def clear_path_state(self) -> None: + "Clear the path state in this current executing node" + # this doesnt actually do anything right now, leaving it as placeholder + + @staticmethod + def _tensor_metadata( + x: torch.Tensor, ignore_storage_offset: bool = True + ) -> Dict[str, Any]: + assert isinstance(x, torch.Tensor) + # We ignore the storage offset for inputs, but not for outputs + # TODO: - should we make the storage resizable ? + return { + "nbytes": x.untyped_storage().nbytes(), + "data_ptr": x.untyped_storage().data_ptr(), + "size": x.shape, + "stride": x.stride(), + "dtype": x.dtype, + "device": x.device, + "storage_offset": x.storage_offset() if not ignore_storage_offset else 0, + } + + def _reconstruct_from_tensor_metadata( + self, metadata: Dict[str, Any], storage: Optional[UntypedStorage] = None + ) -> Tensor: + s = self.create_storage(metadata) if storage is None else storage + return torch._C._construct_CUDA_Tensor_From_Storage_And_Metadata(metadata, s) # type: ignore[arg-type] + + def create_storage(self, metadata: Dict[str, Any]) -> torch.types.Storage: + return torch._C._construct_storage_from_data_pointer( + metadata["data_ptr"], metadata["device"], metadata["nbytes"] + ) + + def _allocate_and_copy_recording_inputs( + self, inputs: List[InputType] + ) -> List[Union[torch.Tensor, int]]: + """ + Allocate inputs for non static, non cudagraph managed tensors in the memory pool + and copy over the tensor values. + """ + + torch.cuda.synchronize() + self.stream.wait_stream(torch.cuda.current_stream()) + recording_inputs: List[InputType] = [] + + with warnings.catch_warnings(record=True), torch.cuda.device( + self.device + ), _use_cuda_memory_pool_manager( + self.device, + mem_pool=self.cuda_graphs_pool, + stream=self.stream, + ): + for i, inp in enumerate(inputs): + if not isinstance(inp, torch.Tensor): + assert isinstance(inp, int) + recording_inputs.append(inp) + elif i not in self.static_input_idxs: + # static_input does an allocation! + recording_inputs.append(static_input(inp)) + else: + recording_inputs.append(inp) + + self._copy_inputs_and_remove_from_src(recording_inputs, inputs) + + return recording_inputs + + def check_invariants( + self, inputs: List[InputType] + ) -> Tuple[CheckInvariantStatus, Callable[..., str]]: + """ + Checks if this node can be run. The same pattern of tensor liveness, static inputs, + and tensors managed in the cudagraph private pool must remain stable. + """ + + _logger = functools.partial( + log_data_ptr_mismatch, + self.wrapped_function.placeholders, + inputs, + self.static_input_data_ptrs, + ) + + # previously managed data pointers remain stable + # this is on the hot path so moved to C++. equivalent to: + # return all(t.data_ptr() == data_ptr for (t, data_ptr) in zip(tensors, data_ptrs)) + if not torch._C._tensors_data_ptrs_at_indices_equal( + inputs, # type: ignore[arg-type] + self.static_input_data_ptrs, + self.cudagraph_managed_idxs, + ): + status = CheckInvariantStatus.CudagraphManagedIdxMismatch + _logger = functools.partial( + _logger, + self.cudagraph_managed_idxs, + status, + ) + return status, _logger + + if not self._check_liveness( + self.expected_dead_indices_before_graph, self.path_weakrefs + ): + status = CheckInvariantStatus.ExpectedDeadIndicesBeforeGraphMismatch + return status, lambda: f"{status}" + + # static input data pointers should remain stable + # if we are inlining builtin nn modules we re-record in this case + # if we are not inlining builtin nn modules, we check this in check_static_inputs_are_stable + # and error if they are not stable + if ( + self.rerecord_if_static_inputs_change + and not torch._C._tensors_data_ptrs_at_indices_equal( + inputs, # type: ignore[arg-type] + self.static_input_data_ptrs, + self.static_input_idxs, + ) + ): + status = CheckInvariantStatus.StaticInputIdxMismatch + _logger = functools.partial( + _logger, + self.static_input_idxs, + status, + ) + return status, _logger + + # the cudagraph managed tensors which died upon recording must also die upon + # this invocation. it is too late to check after we've replayed the graph, + # because we would have already written over their memory. + for idx in self.cudagraph_managed_idxs: + inputs[idx] = None # type: ignore[call-overload] + + torch._check( + self._check_liveness( + self.expected_dead_indices_after_graph, self.path_weakrefs + ), + lambda: "TODO: graph recording observed an input tensor deallocate during graph " + " recording that did not occur during replay. Please file an issue.", + ) + return CheckInvariantStatus.SUCCESS, lambda: f"{CheckInvariantStatus.SUCCESS}" + + def num_descendants(self) -> int: + "Total number of descendents of this node" + num_desc = 0 + for children in self.children.values(): + for child in children: + num_desc += 1 + num_desc += child.num_descendants() + return num_desc + + +def get_cudagraph_segments(pool_id: Tuple[int, int]) -> Any: + segments = torch.cuda.memory_snapshot() + return [segment for segment in segments if segment["segment_pool_id"] == pool_id] + + +def get_block_addrs(pool_id: Tuple[int, int], live_only: bool = True) -> List[int]: + blocks = [] + + for segment in get_cudagraph_segments(pool_id): + addr = segment["address"] + for block in segment["blocks"]: + if block["state"] == "active_allocated" or not live_only: + blocks.append(addr) + + addr += block["size"] + + return blocks + + +def format_tb(frames: List[Any]) -> str: + formatted_traceback = [] + + for entry in frames: + formatted_traceback.append( + traceback.FrameSummary(entry["filename"], entry["line"], entry["name"]) + ) + + return "".join(traceback.format_list(formatted_traceback)) + + +def check_memory_pool( + device: int, + pool_id: Tuple[int, int], + live_storages_ptrs: List[StorageWeakRefWrapper], +) -> None: + assert all( + isinstance(elem, StorageWeakRefWrapper) for elem in live_storages_ptrs + ) # noqa: C419 + unique_storages = {stor.data_ptr() for stor in live_storages_ptrs if stor()} + + # check if there is a divergence first, then do the expensive snapshot call after + # we know it will error + if torch._C._cuda_checkPoolLiveAllocations(device, pool_id, unique_storages): + return + + # at this point we are past the fast-path. we have seen rare cases where a dead tensor is dead, + # but hasn't been gc'd yet, and gives false positive for allocated_not_in_live_storages + gc.collect() + + segments = get_cudagraph_segments(pool_id) + + allocated_not_in_live_storages = {} + + for segment in segments: + addr = segment["address"] + for block in segment["blocks"]: + if block["state"] == "active_allocated": + if addr not in unique_storages: + allocated_not_in_live_storages[addr] = block + else: + unique_storages.remove(addr) + + addr += block["size"] + + torch._check( + len(unique_storages) == 0, + lambda: f"These storage data ptrs are not allocated in pool {pool_id} but should be {unique_storages}", + ) + + if len(allocated_not_in_live_storages) != 0: + formatted = [] + for dp, block in allocated_not_in_live_storages.items(): + trace = format_tb(block.get("frames", [])) + formatted.append(f"Data Pointer: {dp}, history: \n{trace}") + formatted_s = "\n".join(formatted) + msg = ( + f"These live storage data ptrs are in the cudagraph pool but not " + f"accounted for as an output of cudagraph trees: \n\n{formatted_s}" + ) + raise RuntimeError(msg) + + +class ExecutionState(Enum): + """ + Represents the state of the CUDAGraph Tree. Will be None if there is no live current memory allocated + in the cuda graph pool. Otherwise will reflect the state of the most recently executed node. + """ + + NONE = auto() + WARMUP = auto() + RECORDING = auto() + EXECUTION = auto() + + +class CompilationMode(Enum): + FORWARD = auto() + BACKWARD = auto() + INFERENCE = auto() + + +class CUDAGraphTreeManager: + """ + Groups individual recordings or executions of cuda graphs into a tree of recordings, + and checks required invariants, and manages warmups of graphs. + + When graphs are recorded in the same tree, it enforces subsequent execution + to follow the same order and have the same output tensor livespans. To remove + unnecessary coupling of cuda graphs (and additional imposed invariants), + the tree manager will end a currently recording tree whenever it is valid - when + the memory pool no longer has any live allocations. + + We ignore outputs from a previous generation that correspond to prior model outputs. + Currently this is hardcoded `GenerationTracker.generation` tracked in torch dynamo. + # TODO: make generation increment configurable, warn on overwrite. + + We run graph warmups in the cudagraph memory pool and return the result on the first invocation + of a function. For many models it is important to reclaim activations as you run the backward. + If we were to warm up the model and keep an extra copy of the inputs around to subsequently + use for recording, we would incur a memory penalty. Additionally, if we are part way through training + your model and need to recompile, memory will be allocated to the cuda graph pool, so we run this + warmup run in the cuda graph memory pool. As for recording, warm up needs the state of live tensors + to be accurately reflected so we checkpoint the allocator state if we need to warm up following graph + replay. + """ + + def __init__(self, device_index: int) -> None: + # roots are functions which have no dependencies on an other node. I.e., + # when they are first invoked, none of their inputs are outputs are outputs + # of another node, nor are there any live outputs of another node whose + # liveness would create a dependency. + self.roots: Dict[FunctionID, List[CUDAGraphNode]] = defaultdict(list) + + # mapping from function id to wrapped function + self.ids_to_funcs: Dict[FunctionID, WrappedFunction] = {} + + self.ids_to_stack_traces: Dict[FunctionID, Optional[StackTraces]] = {} + + self.warmed_up_functions: Set[FunctionID] = set() + # if we fail to increment generation, and are stuck warming up, + # only warn on each function once + self.warned_functions: Set[FunctionID] = set() + torch._C._set_cached_tensors_enabled(True) + + # warn only once if a function mutates inputs + self.warned_mutation: Set[FunctionID] = set() + + # NB: cuda caching allocator will remember the stream a segment is allocated to + # and only allocate that segment to the same stream. we need to use a single stream + # for all allocations to the memory pool, otherwise the allocations to separate streams + # will not be reused; separate recordings would have use the same memory pool, but not + # the same memory. + + with torch.cuda.device(device_index): + torch.cuda.synchronize() + self.stream = torch.cuda.Stream() + self.stream.wait_stream(torch.cuda.current_stream()) + + # Keeps Memory Pool Alive + self.graph: Optional[torch.cuda.CUDAGraph] = torch.cuda.CUDAGraph() + self.cuda_graphs_thread_pool = torch.cuda.graph_pool_handle() + + with warnings.catch_warnings(record=True), torch.cuda.graph( + self.graph, + pool=self.cuda_graphs_thread_pool, + stream=self.stream, + capture_error_mode="thread_local", + ): + pass + + self.graph_counter = itertools.count(0) + self.func_counter = itertools.count(0) + + # mapping from graph_id to (function id to mutation type hint) since we are + # specializing on a particular combination of Parent Node -> Function ID. + self.non_cudagraph_managed_mutation_hint: Dict[ + Optional[GraphID], Dict[FunctionID, bool] + ] = defaultdict(dict) + self.warmup_node_counter = itertools.count(start=-1, step=-1) + + # mapping from graph_id to (function id to re-record count). We fall back to + # eager function if a function is re-recorded frequently on a node. + self.num_rerecord: Dict[Optional[GraphID], Dict[FunctionID, int]] = defaultdict( + lambda: defaultdict(lambda: 0) + ) + + # whether we the current node is in a state of warmup, recording, execution. If + # there is no current node the state will be ExecutionState.None. + self.path_state = ExecutionState.NONE + self.device_index = device_index + + # the most recently invoked cudagraph wrapping of a function. Will be None + # when there is no output from a previous recording or execution whose memory + # we need to respect in the cuda caching allocation. If you incremented generation, + # this will also be none, as ignore those allocations. + self.current_node: Optional[Union[CUDAGraphNode, CUDAWarmupNode]] = None + + # current generation of cudagraph invocations. when torch.compile is run + # we increment the current generation. are willing to ignore live outputs + # of a previous generation in checking liveness. + self.current_gen: int = -1 + + # number of instances we are in execution and failed to match to an + # existing child + self.debug_fail_counter = 0 + # number of instances we had to checkpoint the function + self.debug_checkpointing_counter = 0 + + self.id_to_mode: Dict[FunctionID, CompilationMode] = {} + + # Note: [Backward Generation Handling] + # We generally perform a sequence of forward executions followed by backward executions. + # If multiple torch.compile wrapped forwards are executed with their backwards pending, + # we should not disregard the outputs from a prior torch.compile since the entire training + # loop hasn't completed. Occasionally, a backward pass corresponding to a forward pass may + # not be executed, so we cannot wait for all pending forward pass backward completions, so + # we cannot wait for all backwards to have been invoked. Instead we wait for a single backward + # invocation. Triggering a backward pass typically doesn't lead to another torch.compile + # invocation, making it less likely for the generation to increase between multiple + # backward calls. The following use case is covered by this approach: + # mod1 = torch.compile(...) + # mod2 = torch.compile(...) + # mod2(mod1(x)).sum().backward() + + self.running_forwards_with_pending_backwards = False + + def run(self, new_inputs: List[InputType], function_id: FunctionID) -> OutputType: + assert self.graph is not None, "Running CUDAGraph after shutdown" + out = self._run(new_inputs, function_id) + + # The forwards are only pending following invocation, not before + mode = self.id_to_mode[function_id] + if mode == CompilationMode.FORWARD: + self.running_forwards_with_pending_backwards = True + elif mode == CompilationMode.BACKWARD: + self.running_forwards_with_pending_backwards = False + + return out + + def set_to_running_backward(self) -> None: + self.running_forwards_with_pending_backwards = False + + def _get_cuda_graph_recorded_tensor_checker(self) -> Callable[[Tensor], bool]: + return ( + self.current_node._is_cuda_graph_recorded_tensor + if isinstance(self.current_node, (CUDAGraphNode, CUDAWarmupNode)) + else lambda _: False + ) + + def new_warmup_node_id(self) -> GraphID: + return GraphID(next(self.warmup_node_counter)) + + def _update_non_cudagraph_managed_mutation( + self, function_id: FunctionID, inputs: List[InputType] + ) -> None: + node_id = self._get_node_id() + if maybe_mutation_str := check_for_mutation( + self.ids_to_funcs[function_id], + inputs, + self._get_cuda_graph_recorded_tensor_checker(), + ): + self.non_cudagraph_managed_mutation_hint[node_id][function_id] = True + # warn once per function_id + if function_id in self.warned_mutation: + return + self.warned_mutation.add(function_id) + log_cudagraph_skip_and_bump_counter(maybe_mutation_str) + else: + self.non_cudagraph_managed_mutation_hint[node_id][function_id] = False + + def _get_node_id(self) -> Optional[GraphID]: + if self.current_node is None: + return None + elif isinstance(self.current_node, (CUDAGraphNode, CUDAWarmupNode)): + return self.current_node.id + else: + raise RuntimeError(f"Unknown node type {type(self.current_node)}") + + def exceed_rerecord_limit( + self, node_id: Optional[GraphID], function_id: FunctionID + ) -> bool: + if torch._dynamo.config.inline_inbuilt_nn_modules: + return False + + return ( + self.num_rerecord[node_id][function_id] + > torch._inductor.config.triton.cudagraph_unexpected_rerecord_limit + ) + + def _run(self, new_inputs: List[InputType], function_id: FunctionID) -> OutputType: + # we will try to end the current execution lazily, since + # we dont want to do unnecessary checking of the existing outputs + # on the hot path, but both recording and warmup only happen once + # so we check up front + if self.in_recording: + self.try_end_curr_recording(function_id) + + if self.in_warmup: + self.try_end_curr_warmup(function_id) + + node_id = self._get_node_id() + if function_id not in self.non_cudagraph_managed_mutation_hint[node_id]: + self._update_non_cudagraph_managed_mutation(function_id, new_inputs) + + # Early exit if the function mutates inputs which are neither parameters/buffers nor + # cudagraph recorded tensors. This check should happen after `try_end_curr_recording` + # and `try_end_curr_warmup` which may change self.current_node. + if self.non_cudagraph_managed_mutation_hint[node_id][ + function_id + ] or self.exceed_rerecord_limit(node_id, function_id): + return self.ids_to_funcs[function_id].model(new_inputs) + + # warming up a function and subsequentally recording may use different memory addresses + # because both depend on the state of the caching allocator. if we warm up graph A, + # then warm up graph B and make more allocations, the subsequent recording of A will not + # necessarily use the same addresses as in the warm up. Thus any warm up of a node can only + # be followed by warm up runs. + if ( + ( + not ( + function_id in self.warmed_up_functions + or config.triton.skip_cudagraph_warmup + ) + ) + or self.in_warmup + or config.triton.force_cudagraphs_warmup + ): + # If we are in the middle of executing cuda graphs, then we need to checkpoint memory state. + # Both Recording and Warmup will be reflected in the allocator and dont need changes + if self.path_state == ExecutionState.EXECUTION: + self.apply_checkpoint_execution_state_in_allocator() + + return self.run_eager(new_inputs, function_id) + + assert not isinstance(self.current_node, CUDAWarmupNode) + child_nodes = ( + self.roots if self.current_node is None else self.current_node.children + ) + + if not self.in_recording: + unexpected_rerecord, unexpected_rerecord_reason = False, lambda: "" + for child in child_nodes[function_id]: + # here we are checking memory consistency between recording and execution, + # as well as things like stability of tensor locations, etc + # and other + status, status_logger = child.check_invariants(new_inputs) + if status == CheckInvariantStatus.SUCCESS: + return self.execute_node(child, new_inputs) + + if ( + status == CheckInvariantStatus.StaticInputIdxMismatch + or status == CheckInvariantStatus.CudagraphManagedIdxMismatch + ): + unexpected_rerecord = True + unexpected_rerecord_reason = status_logger + + # now that we know the new function can't be run as a child of the + # current node, if it is a root, try to end the current execution. + # as noted above, we want to do this lazily to avoid having to + # check all existing outputs + if self.current_node is not None and function_id in self.roots: + self.try_end_curr_execution() + + # run again to hit the root matching case which must succeed + if self.current_node is None: + return self.run(new_inputs, function_id) + + if len(self.ids_to_funcs[function_id].mutated_input_idxs) > 0: + self._update_non_cudagraph_managed_mutation(function_id, new_inputs) + if self.non_cudagraph_managed_mutation_hint[self._get_node_id()][ + function_id + ]: + return self.ids_to_funcs[function_id].model(new_inputs) + + # nb: run before checkpointing because checkpointing is slow, and we will + # be using the eager caching allocator pool which does not require live + # accounting of tensors in cudagraph allocator + if unexpected_rerecord: + curr_node_id = self._get_node_id() + self.num_rerecord[curr_node_id][function_id] += 1 + if self.exceed_rerecord_limit(curr_node_id, function_id): + _id = curr_node_id.id if curr_node_id else None + log_cudagraph_skip_and_bump_counter( + f"skipping cudagraph due to function {function_id.id} exceeding max " + f"re-recording limit " + f"(={torch._inductor.config.triton.cudagraph_unexpected_rerecord_limit}) " + f"on cudagraph node {_id} due to {unexpected_rerecord_reason()}." + ) + return self.ids_to_funcs[function_id].model(new_inputs) + + # at this point, we necessarily will do a new recording + self.debug_fail_counter += 1 + + self.try_end_curr_execution() + if self.current_node is not None: + self.apply_checkpoint_execution_state_in_allocator() + + # now, we are in a recording state ! + return self.record_function(new_inputs, function_id) + + def shutdown(self) -> None: + """ + Remove all cached tensors in all nodes. Because cached tensors can hold gradients which in turn + might reference a backward which invokes a CUDA Graph Node, we have to manually clear them on shutdown + to avoid a reference cycle. + """ + nodes = [] + for roots in self.roots.values(): + nodes.extend(roots) + + while nodes: + node = nodes.pop() + for children in node.children.values(): + nodes.extend(children) + node.remove_node_cached_tensors() + node.graph = None + + self.graph = None + self.roots = None # type: ignore[assignment] + self.current_node = None + + def record_function( + self, new_inputs: List[InputType], function_id: FunctionID + ) -> OutputType: + assert not isinstance(self.current_node, CUDAWarmupNode) + graph_id = self.new_graph_id() + log.debug( + "Recording function %d of graph recording id %d", + function_id.id, + graph_id.id, + ) + torch.cuda.synchronize() + node = CUDAGraphNode( + self.ids_to_funcs[function_id], + graph_id, + self.current_node, + new_inputs, + self.cuda_graphs_thread_pool, + self.device_index, + self.ids_to_stack_traces[function_id], + self.stream, + ) + if self.current_node is None: + self.roots[function_id].append(node) + else: + self.current_node.add_child(function_id, node) + self.current_node = node + self.path_state = ExecutionState.RECORDING + self.update_generation() + torch.cuda.synchronize() + return node.run_first_inputs(new_inputs) + + def execute_node( + self, node: CUDAGraphNode, new_inputs: List[InputType] + ) -> OutputType: + self.current_node = node + self.path_state = ExecutionState.EXECUTION + self.update_generation() + return node.run(new_inputs) + + def run_eager( + self, new_inputs: List[InputType], function_id: FunctionID + ) -> OutputType: + # this is only stored on current node, because when we start a new path, + # we will deallocate it + already_warm = function_id in self.warmed_up_functions + if not already_warm: + log.debug("Running warmup of function %d", function_id.id) + else: + log.debug( + "Running eager of function %d because ancestor needed to warm up", + function_id.id, + ) + self.warmed_up_functions.add(function_id) + node = CUDAWarmupNode( + self.ids_to_funcs[function_id], + self.current_node, + self.cuda_graphs_thread_pool, + self.graph, + self.device_index, + self.ids_to_stack_traces[function_id], + self.stream, + already_warm, + self.new_warmup_node_id(), + ) + self.current_node = node + self.path_state = ExecutionState.WARMUP + self.update_generation() + return node.run(new_inputs) + + def new_graph_id(self) -> GraphID: + return GraphID(next(self.graph_counter)) + + def new_func_id(self) -> FunctionID: + return FunctionID(next(self.func_counter)) + + def add_function( + self, + model: ModelType, + inputs: List[InputType], + static_input_idxs: Sequence[int], + stack_traces: Optional[StackTraces], + mode: CompilationMode, + constants: Tuple[torch.Tensor, ...], + placeholders: Tuple[PlaceholderInfo, ...], + mutated_input_idxs: Tuple[int, ...], + ) -> Tuple[ModelType, OutputType,]: + id = self.new_func_id() + self.ids_to_stack_traces[id] = stack_traces + self.ids_to_funcs[id] = WrappedFunction( + model, + list(static_input_idxs), + id, + tuple(t for t in constants if isinstance(t, torch.Tensor) and t.is_cuda), + placeholders, + mutated_input_idxs, + ) + self.id_to_mode[id] = mode + fn = functools.partial(self.run, function_id=id) + + # container needs to set clean up when fn dies + get_container(self.device_index).add_strong_reference(fn) + return fn, fn(inputs) + + @property + def in_recording(self) -> bool: + return self.path_state == ExecutionState.RECORDING + + @property + def in_warmup(self) -> bool: + return self.path_state == ExecutionState.WARMUP + + def get_roots(self) -> Iterator[CUDAGraphNode]: + for nodes in self.roots.values(): + yield from nodes + + @property + def current_node(self) -> Optional[Union[CUDAGraphNode, CUDAWarmupNode]]: + return self._current_node + + @current_node.setter + def current_node( + self, value: Optional[Union[CUDAGraphNode, CUDAWarmupNode]] + ) -> None: + self._current_node = value + if value is None: + self.path_state = ExecutionState.NONE + + def update_generation(self) -> None: + self.current_gen = self.get_curr_generation() + + @staticmethod + def get_curr_generation() -> int: + if MarkStepBox.mark_step_counter != 0: + return MarkStepBox.mark_step_counter + + return GenerationTracker.generation + + @staticmethod + def user_invoked_mark_step() -> bool: + return MarkStepBox.mark_step_counter != 0 + + def can_start_new_generation(self) -> bool: + if not self.in_new_torch_compile_invocation(): + return False + + if self.user_invoked_mark_step(): + return True + + return not self.running_forwards_with_pending_backwards + + def in_new_torch_compile_invocation(self) -> bool: + return self.current_gen != self.get_curr_generation() + + def try_end_curr_recording(self, function_id: FunctionID) -> None: + """ + Check if the current recording can be terminated, either because all outputs of the + previously recorded node are dead or because it was executed in a different + generation. Will set current_node to None and in_recording to False if successful. + """ + assert self.in_recording + assert self.current_node is not None + + # multiple invocations, allow overwriting the previous generation + if self.can_start_new_generation(): + self.dealloc_current_path_weakrefs() + self.clear_current_path_state_and_set_to_none() + return + + if self.current_node.all_outputs_are_dead(): + self.clear_current_path_state_and_set_to_none() + return + + self.check_warn_on_unable_to_start_executing(function_id) + + def try_end_curr_execution(self) -> None: + """ + Check if the current executing node can be terminated, either because all outputs of the + previously executed node are dead or because it was executed in a different generation. + Will set current_node to None if successful. + """ + + assert not self.in_recording + if self.current_node is None: + return + + if self.can_start_new_generation(): + self.clear_current_path_state_and_set_to_none() + return + + if self.current_node.all_outputs_are_dead(): + self.clear_current_path_state_and_set_to_none() + + def try_end_curr_warmup(self, function_id: FunctionID) -> None: + if self.can_start_new_generation(): + self.dealloc_current_path_weakrefs() + self.current_node = None + return + + assert self.current_node is not None + if self.current_node.all_outputs_are_dead(): + self.current_node = None + return + + self.check_warn_on_unable_to_start_executing(function_id) + + def check_warn_on_unable_to_start_executing(self, function_id: FunctionID) -> None: + "Warn if we in a potential loop where we are unable to hit fast path" + if ( + function_id in self.warned_functions + or not self.in_new_torch_compile_invocation() + ): + return + + assert self.current_node is not None + existing_nodes = [ + node + for node in self.current_node._path_from_root + if node.wrapped_function.id == function_id + ] + + if len(existing_nodes) <= 1: + return + + # repeated same pattern + parents = { + n.parent.wrapped_function.id + for n in itertools.chain(existing_nodes, (self.current_node,)) + if n.parent is not None + } + if len(parents) == len(existing_nodes): + return + + self.warned_functions.add(function_id) + warnings.warn( + "Unable to hit fast path of CUDAGraphs because of pending, uninvoked backwards. " + "Consider running with torch.no_grad() or using torch.compiler.cudagraph_mark_step_begin() " + "before each model invocation" + ) + + def dealloc_current_path_weakrefs(self) -> None: + assert self.current_node is not None + # TODO: we could also allow the these weak refs to continue to be allocated, + # but that adds some complications. + for node in self.current_node._path_from_root: + assert node.stack_traces is not None + assert len(node.tensor_weakrefs) == len(node.stack_traces) + for t, stack_trace in zip(node.tensor_weakrefs, node.stack_traces): + ten = None if t is None else t() + if ten is None: + continue + + stack_trace = ( + stack_trace.strip() + if stack_trace + else "[Could not find stack trace]" + ) + msg = ( + "Error: accessing tensor output of CUDAGraphs that has been overwritten by a subsequent run. " + f"Stack trace: {stack_trace}. " + "To prevent overwriting, clone the tensor outside of torch.compile() " + "or call torch.compiler.cudagraph_mark_step_begin() before each model invocation." + ) + torch._C._set_storage_access_error_msg(ten, msg) + + deleted = set() + for storage_ref in self.current_node.path_live_weakrefs(): + _storage_deref = storage_ref() + if _storage_deref and storage_ref.data_ptr() not in deleted: + deleted.add(storage_ref.data_ptr()) + torch._C._free_And_Remove_DeleterFn(_storage_deref) + + def clear_current_path_state_and_set_to_none(self) -> None: + assert isinstance(self.current_node, CUDAGraphNode) + self.current_node.clear_path_state() + self.current_node = None + + def apply_checkpoint_execution_state_in_allocator(self) -> None: + """ + Checkpoint the current execution state in the caching allocator so that + additional cudagraph recordings can be made respecting existent live storages. + """ + assert isinstance(self.current_node, CUDAGraphNode) + self.debug_checkpointing_counter += 1 + log.debug( + "Checkpointing cuda caching allocator state. Number of checkpoints %d", + self.debug_checkpointing_counter, + ) + + state = self.current_node.checkpointed_caching_state + device = self.current_node.device + assert state is not None and device is not None + + # currently we deallocate on instead of allowing stale recordings + stale_storages: List[int] = [] + + # remove cached tensors, otherwise they would prevent memory from being + # reclaimed in subsequent recordings + self.current_node.remove_path_cached_tensors() + live_storages_wrappers = list(self.current_node.path_live_weakrefs()) + + # path_live_weakrefs guarantees that t() will not be None + live_storages_weak_refs: list[int] = [t() for t in live_storages_wrappers] # type: ignore[misc] + ptrs_to_deallocate = self.current_node.data_ptrs_dead_since_invocation() + torch._C._cuda_setCheckpointPoolState( + device, state, stale_storages, live_storages_weak_refs + ) + + # NB: deduplicate aliased outputs + for ptr in set(ptrs_to_deallocate): + torch._C._cuda_cudaCachingAllocator_raw_delete(ptr) + + # Now the live blocks should be exactly equal to the live storages in private pool + if config.triton.slow_path_cudagraph_asserts: + check_memory_pool( + self.device_index, self.cuda_graphs_thread_pool, live_storages_wrappers + ) + for wrapper in live_storages_wrappers: + storage_ptr = wrapper() + assert storage_ptr is not None + assert torch._C._has_Standard_Deleter(storage_ptr) + assert wrapper.data_ptr() not in ptrs_to_deallocate + + def live_cudagraph_pool_storages_in_curr_execution( + self, + ) -> List[StorageWeakRefPointer]: + if self.current_node is None: + return [] + # explicitly ignoring previous recorded outputs from past path + # path_live_weakrefs() guarantees that t() will not be None + return [t() for t in self.current_node.path_live_weakrefs()] # type: ignore[misc] diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/cudagraph_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/cudagraph_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4f97e1daf60f623f3baea38f8b60f0466af81385 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/cudagraph_utils.py @@ -0,0 +1,330 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import dataclasses +from enum import Enum +from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union + +import torch +from torch._dynamo.utils import counters +from torch._inductor.utils import InputType + + +perf_hint_log = torch._logging.getArtifactLogger(__name__, "perf_hints") +static_inputs_log = torch._logging.getArtifactLogger( + __name__, "cudagraph_static_inputs" +) + + +OutputType = List[Optional[Union[int, torch.Tensor]]] +ModelType = Callable[[List[InputType]], OutputType] + + +@dataclasses.dataclass(frozen=True) +class FunctionID: + "Unique counter of a function wrapped in cudagraphify_impl" + id: int + + +@dataclasses.dataclass(frozen=True) +class PlaceholderInfo: + """ + A serializable version of torch.fx.Node that contains information + pertinent to placeholder stack traces. We use these in logging and error messages + related to cudagraphs, and will cache these results. + """ + + name: str + stack_trace: Optional[str] + # This field is recursive, but never cyclic (since a node never uses itself) + users: List[PlaceholderInfo] + mutating_use_stack_trace: Optional[str] + + +@dataclasses.dataclass(frozen=True) +class WrappedFunction: + """ + Represents a function that you want to record for CUDA graph replay, + with a little more metadata so we can identify if we have an applicable + CUDA graph in our CUDA graph tree for it. + """ + + model: Callable[..., Any] + static_input_idxs: Sequence[int] + id: FunctionID + constants: Tuple[torch.Tensor, ...] + placeholders: Sequence[PlaceholderInfo] + mutated_input_idxs: Sequence[int] + + +def get_mutating_use_stack_trace_from_node( + placeholder_node: torch.fx.Node, +) -> Optional[str]: + # reinplaced uses might have a single, non-copy_ use + if len(placeholder_node.users) == 1: + return next(iter(placeholder_node.users)).meta.get("stack_trace", None) + + for use in placeholder_node.users: + if use.target == torch.ops.aten.copy_.default: + if stack_trace := use.meta.get("stack_trace", None): + return stack_trace + + return None + + +def get_mutating_use_stack_trace(placeholder_info: PlaceholderInfo) -> Optional[str]: + return placeholder_info.mutating_use_stack_trace + + +def to_placeholder_info(placeholder_node: torch.fx.Node) -> PlaceholderInfo: + name = placeholder_node.name + stack_trace = placeholder_node.meta.get("stack_trace", None) + users = [] + mutating_use_stack_trace = None + # Only recurse to users once, since we only care about user's stack traces + if placeholder_node.op == "placeholder": + users = [to_placeholder_info(i) for i in placeholder_node.users] + mutating_use_stack_trace = get_mutating_use_stack_trace_from_node( + placeholder_node + ) + + return PlaceholderInfo(name, stack_trace, users, mutating_use_stack_trace) + + +def get_placeholder_info(graph: torch.fx.Graph) -> List[PlaceholderInfo]: + return [ + to_placeholder_info(node) for node in graph.nodes if node.op == "placeholder" + ] + + +def format_default_skip_message(reason: str) -> str: + return f"skipping cudagraphs due to {reason}" + + +def get_mutation_stack_trace( + placeholders: Sequence[PlaceholderInfo], mutation_indices: Sequence[int] +) -> str: + stack_trace: Optional[str] = "" + + for idx in mutation_indices: + placeholder = placeholders[idx] + if stack_trace := get_mutating_use_stack_trace(placeholder): + break + + msg = format_default_skip_message( + f"mutated inputs ({len(mutation_indices)} instances)" + ) + if stack_trace: + return f"{msg}. Found from : \n {stack_trace}" + + return msg + + +def check_for_mutation( + func: WrappedFunction, + inputs: List[InputType], + is_cuda_graph_recorded_tensor: Callable[[torch.Tensor], bool], +) -> Optional[str]: + # doesnt work for non-trees because the warmup run would apply mutation twice + if torch._inductor.config.triton.cudagraph_trees: + # checking if mutation is only on parameters/static inputs + mutation_indices: Sequence[int] = [ + idx + for idx in func.mutated_input_idxs + if not ( + idx in func.static_input_idxs + or is_cuda_graph_recorded_tensor(inputs[idx]) # type: ignore[arg-type] + ) + ] + else: + mutation_indices = func.mutated_input_idxs + + static_inputs_log.debug( + "check mutation static input indices: %s", func.static_input_idxs + ) + static_inputs_log.debug("check mutation mutation indices: %s", mutation_indices) + + return ( + get_mutation_stack_trace(func.placeholders, mutation_indices) + if mutation_indices + else None + ) + + +def _get_use_stack_trace(node) -> Optional[str]: + for use in node.users: + if stack_trace := use.meta.get("stack_trace", None): + return stack_trace + return None + + +def check_multiple_devices_or_any_cpu_nodes( + device_node_mapping: Dict[torch.device, torch.fx.Node] +) -> Optional[str]: + if cpu_node := device_node_mapping.get(torch.device("cpu")): + msg = f"cpu device ({cpu_node.name})" + if stack_trace := _get_use_stack_trace(cpu_node): + return format_default_skip_message(f"{msg}. Found from : \n {stack_trace}") + + return format_default_skip_message(msg) + + if ( + len(device_node_mapping) == 1 + and next(iter(device_node_mapping.keys())).type == "cuda" + ): + return None + + keys_repr = (repr(key) for key in device_node_mapping.keys()) + return format_default_skip_message(f"multiple devices: {', '.join(keys_repr)}") + + +def check_lowering_disable_cudagraph( + device_node_mapping: Dict[torch.device, torch.fx.Node] +): + return check_multiple_devices_or_any_cpu_nodes(device_node_mapping) + + +def log_cudagraph_skip_and_bump_counter(msg): + perf_hint_log.warning(msg) + counters["inductor"]["cudagraph_skips"] += 1 + + +@dataclasses.dataclass +class BoxedDeviceIndex: + value: Optional[int] + + def set(self, device_idx: Optional[int]): + assert device_idx is None or isinstance(device_idx, int) + self.value = device_idx + + +def check_for_mutation_ignore_cuda_graph_managed_tensor( + gm: torch.fx.GraphModule, compiled_graph, static_input_idxs: Sequence[int] +) -> Optional[str]: + default_msg = format_default_skip_message("mutated inputs") + + # doesnt work for non-trees because the warmup run would apply mutation twice + if torch._inductor.config.triton.cudagraph_trees: + unique_idxs = set(static_input_idxs) + # checking if mutation is only on parameters/static inputs + mutation_indices = [ + idx for idx in compiled_graph.mutated_input_idxs if idx not in unique_idxs + ] + has_mutation = len(mutation_indices) != 0 + if not has_mutation: + return None + placeholders = get_placeholder_info(gm.graph) + return get_mutation_stack_trace(placeholders, mutation_indices) + + else: + has_mutation = len(compiled_graph.mutated_inputs) != 0 + return None if not has_mutation else default_msg + + +def get_placeholder_stack_trace(placeholder: PlaceholderInfo) -> Optional[str]: + """ + Gets the first non-empty stack trace of a placeholder or its users. + """ + if placeholder.stack_trace: + return placeholder.stack_trace + + for user in placeholder.users: + if user.stack_trace: + return user.stack_trace + + return None + + +class CheckInvariantStatus(Enum): + # Check invariant succeeded + SUCCESS = 1 + + # Previously managed data pointers are not stable + CudagraphManagedIdxMismatch = 2 + + # Static tensor input addresses are not stable + StaticInputIdxMismatch = 3 + + # Expected dead indices before graph are live + ExpectedDeadIndicesBeforeGraphMismatch = 4 + + def __str__(self) -> str: + if self.name == "CudagraphManagedIdxMismatch": + return "cudagraph managed tensor data pointer changed" + elif self.name == "StaticInputIdxMismatch": + return "static input data pointer changed" + elif self.name == "ExpectedDeadIndicesBeforeGraphMismatch": + return "expected dead indices before graph are live" + else: + return f"{self.name}: {self.value}" + + +def log_data_ptr_mismatch( + placeholders: Sequence[PlaceholderInfo], + inputs: List[InputType], + recorded_data_ptr: Sequence[Optional[int]], + target_idxs: Sequence[int], + mismatch: CheckInvariantStatus, +) -> str: + """ + Logs the mismatch between input data pointers and recorded data pointers. + This checks only idxs in target_idxs. + """ + assert len(inputs) == len(recorded_data_ptr) and len(inputs) == len( + placeholders + ), "length mismatch between inputs, recorded_data_ptr, and placeholders" + + t_tensors = [inputs[i] for i in target_idxs] + t_data_ptrs = [recorded_data_ptr[i] for i in target_idxs] + error_msg = f"{mismatch}.\n" + for i, (tensor, data_ptr) in enumerate(zip(t_tensors, t_data_ptrs)): + assert isinstance(tensor, torch.Tensor) + index = target_idxs[i] + if tensor.data_ptr() != data_ptr: + placeholder = placeholders[index] + error_msg = ( + f"{error_msg}input name: {placeholder.name}. " + f"data pointer changed from {data_ptr} to {tensor.data_ptr()}. " + f"input stack trace: {get_placeholder_stack_trace(placeholder)}\n" + ) + return error_msg + + +def maybe_warning_due_to_dynamic_shape( + fn_cache: Dict[Tuple[int, ...], Callable[..., Any]], + new_int_key: Any, +) -> bool: + num_cudagraphs = len(fn_cache.keys()) + 1 + + def warn_msg(): + return ( + "CUDAGraph supports dynamic shapes by recording a new graph for each " + "distinct input size. Recording too many CUDAGraphs may lead to " + f"extra overhead. We have observed {num_cudagraphs} distinct sizes. " + "Please consider the following options for better performance: " + "a) padding inputs to a few fixed number of shapes; or b) set " + "torch._inductor.config.triton.cudagraph_skip_dynamic_graphs=True. " + "Set torch._inductor.config.triton.cudagraph_dynamic_shape_warn_limit=None " + "to silence this warning." + ) + + if ( + torch._inductor.config.triton.cudagraph_dynamic_shape_warn_limit + and num_cudagraphs + > torch._inductor.config.triton.cudagraph_dynamic_shape_warn_limit + ): + perf_hint_log.warning(warn_msg()) + return True + + return False + + +@dataclasses.dataclass(frozen=True) +class CudagraphCachedInfo: + """ + Info needed to realign inputs + """ + + placeholders: Sequence[PlaceholderInfo] + stack_traces: List[Optional[str]] + cudagraph_fail_reasons: List[str] diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/debug.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/debug.py new file mode 100644 index 0000000000000000000000000000000000000000..868833a425be4b13d76dbca5318498ee3b7e5a46 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/debug.py @@ -0,0 +1,693 @@ +import collections +import contextlib +import dataclasses +import functools +import itertools +import logging +import os +import os.path +import pickle +import pstats +import shutil +import subprocess +from typing import Any, Callable, Dict, IO, Iterator, List, Optional, Type, Union +from unittest.mock import patch + +import torch +from functorch.compile import draw_graph, get_aot_graph_name, get_graph_being_compiled +from torch import fx as fx +from torch._dynamo.repro.after_aot import save_graph_repro +from torch._dynamo.utils import get_debug_dir +from torch.fx.graph_module import GraphModule +from torch.fx.passes.shape_prop import _extract_tensor_metadata, TensorMetadata +from torch.fx.passes.tools_common import legalize_graph +from torch.utils._pytree import tree_map + +from . import config, ir # noqa: F811, this is needed +from .scheduler import ( + BaseSchedulerNode, + FusedSchedulerNode, + NopKernelSchedulerNode, + OutputNode, + SchedulerNode, +) +from .virtualized import V + + +log = logging.getLogger(__name__) + +SchedulerNodeList = List[Any] +BufMeta = collections.namedtuple("BufMeta", ["name", "n_origin"]) +GRAPHVIZ_COMMAND_SCALABLE = ["dot", "-Gnslimit=2", "-Gnslimit1=2", "-Gmaxiter=5000"] + + +@functools.lru_cache(None) +def has_dot() -> bool: + try: + subprocess.check_output(["which", "dot"], stderr=subprocess.PIPE) + return True + except subprocess.SubprocessError: + return False + + +def draw_buffers( + nodes: List[BaseSchedulerNode], + print_graph: bool = False, + fname: Optional[str] = None, +) -> None: + """ + Draw a graph in fname.svg. + """ + if not has_dot(): + log.warning("draw_buffers() requires `graphviz` package") + return + + if fname is None: + fname = get_graph_being_compiled() + + graph = create_fx_from_snodes(nodes) + + for node in graph.nodes: + if "fusion_meta" not in node.meta: + continue + group = node.meta["fusion_meta"].group + if isinstance(group, tuple): + if isinstance(group[1], int): + group = (group[1],) + else: + group = group[1] + + # gather meta data + dtype = None + if isinstance(node, ir.ComputedBuffer): + dtype = node.data.dtype + + metadata = TensorMetadata(group, dtype, None, None, None, None, None) # type: ignore[arg-type] + node.meta["tensor_meta"] = metadata + + if print_graph: + print(graph) + + gm = GraphModule({}, graph) + legalize_graph(gm) + gm.graph.lint() + draw_graph( + gm, fname, clear_meta=False, dot_graph_shape=config.trace.dot_graph_shape + ) + + +def create_fx_from_snodes(snodes: List[BaseSchedulerNode]) -> fx.Graph: + """ + Creates a FX Graph from a list of SchedulerNode objects. + """ + + def get_fake_func(name: str) -> Callable[..., int]: + def func1(*args: Any) -> int: + return 0 + + func1.__name__ = name + return func1 + + FusionMeta = collections.namedtuple("FusionMeta", ["group", "snode", "type"]) + + buf_to_fx_node = {} + node_to_fx_node = {} + graph = torch.fx.Graph() + first_node = None + + outputs = [] + group: Any = None + # create call_function node for each Buffer and Kernel + for snode in snodes: + if snode.is_extern(): + node_type = "extern" + group = node_type + elif snode.is_template(): + node_type = "template" + group = node_type + elif isinstance(snode, NopKernelSchedulerNode): + node_type = "nop" + group = node_type + elif isinstance(snode, SchedulerNode): + node_type = "compute" + group = snode.group + elif isinstance(snode, FusedSchedulerNode): + node_type = "fused" + group = snode.group + else: + raise RuntimeError("Unknown node type") + + fused_name = torch._inductor.utils.get_fused_kernel_name( + snode.get_nodes(), "original_aten" + ) + func_name = f"{node_type}: {fused_name}" + node_func = get_fake_func(func_name) + kwargs = {} + if hasattr(snode, "get_device"): + kwargs = {"device": snode.get_device()} + fx_node = graph.call_function(node_func, args=(), kwargs=kwargs) # type: ignore[arg-type] + + def in_output(snode: Union[BaseSchedulerNode, FusedSchedulerNode]) -> bool: + if isinstance(snode, FusedSchedulerNode): + return any(in_output(x) for x in snode.snodes) + return any( + isinstance(user.node, OutputNode) + for buf in snode.get_outputs() + for user in buf.users + ) + + if in_output(snode): + outputs.append(fx_node) + name = snode.get_name() + fx_node.name = name + + fx_node.meta["fusion_meta"] = FusionMeta(group, snode, node_type) + + node_to_fx_node[name] = fx_node + for buf in snode.get_outputs(): + buf_to_fx_node[buf.get_name()] = fx_node + + if first_node is None: + first_node = fx_node + + # create edges between nodes + for snode in snodes: + name = snode.get_name() + deps = snode.read_writes.reads + + fx_node = node_to_fx_node[name] + new_args = [] + for dep in deps: + if dep.name in buf_to_fx_node: + dep_node = buf_to_fx_node[dep.name] + else: + with graph.inserting_before(first_node): + dep_node = graph.placeholder(dep.name) + buf_to_fx_node[dep.name] = dep_node + if dep_node == fx_node: # to avoid cycles + continue + new_args.append(dep_node) + + fx_node.args = tuple(new_args) + + graph.output(outputs[0] if len(outputs) == 1 else tuple(outputs)) + return graph + + +def update_orig_fx_node_name_to_buf_name( + nodes: Optional[SchedulerNodeList], + node_name_to_buf_name: Dict[str, str], + parent_buf_name: Optional[str] = None, + n_origins: int = 0, +) -> None: + if nodes is None: + return + for node in nodes: + # for FusedSchedulerNode, traverse recursively into get_nodes() + buf_name = node.get_name() + children_nodes = node.get_nodes() + if children_nodes is not None and len(children_nodes) > 1: + update_orig_fx_node_name_to_buf_name( + children_nodes, + node_name_to_buf_name, + buf_name if parent_buf_name is None else parent_buf_name, + ) + continue + else: + assert len(children_nodes) == 1 and children_nodes[0] == node + + ir_node = node.node + if ir_node is None or ir_node.origins is None: + continue + for origin in ir_node.origins: + node_name = origin.name + # when buf1 and buf2 both have origin=node1 + # we draw node1 according to buf1 + if node_name not in node_name_to_buf_name: + node_name_to_buf_name[node_name] = ( + buf_name if parent_buf_name is None else parent_buf_name + ) + + +def get_node_name_to_buf_meta( + node_name_to_buf_name: Dict[str, str] +) -> Dict[str, BufMeta]: + buf_name_to_n_node = {} + for node_name, buf_name in node_name_to_buf_name.items(): + if buf_name not in buf_name_to_n_node: + buf_name_to_n_node[buf_name] = {node_name} + else: + buf_name_to_n_node[buf_name].add(node_name) + + node_name_to_buf_meta = {} + for node_name, buf_name in node_name_to_buf_name.items(): + n_node = len(buf_name_to_n_node[buf_name]) + node_name_to_buf_meta[node_name] = BufMeta(buf_name, n_node) + return node_name_to_buf_meta + + +def annotate_orig_fx_with_snodes( + gm: torch.fx.GraphModule, + snodes: SchedulerNodeList, +) -> None: + """ + Creates a FX Graph from a list of SchedulerNode objects. + """ + node_name_to_buf_name: Dict[str, str] = {} + update_orig_fx_node_name_to_buf_name(snodes, node_name_to_buf_name) + if node_name_to_buf_name is None: + return + node_name_to_buf_meta = get_node_name_to_buf_meta(node_name_to_buf_name) + for node in gm.graph.nodes: + if node.name in node_name_to_buf_meta: + node.meta["buf_meta"] = node_name_to_buf_meta.get(node.name) + + +@contextlib.contextmanager +def enable_aot_logging() -> Iterator[None]: + compile_debug = os.environ.get("TORCH_COMPILE_DEBUG", "0") == "1" + + import torch._functorch.aot_autograd + + log = logging.getLogger(torch._functorch.aot_autograd.__name__) + + stack = contextlib.ExitStack() + if not compile_debug: + try: + yield + finally: + stack.close() + return + + # Enable all graphs to be logged to a file by setting the flags to True + # and the log level of the file logger to DEBUG + stack.enter_context(patch("functorch.compile.config.debug_partitioner", True)) + + path = os.path.join(get_debug_dir(), "torchinductor") + os.makedirs(path, exist_ok=True) + + fh = logging.FileHandler( + os.path.join( + path, + f"aot_{get_aot_graph_name()}_debug.log", + ) + ) + fh.setLevel(logging.DEBUG) + fh.setFormatter( + logging.Formatter("[%(filename)s:%(lineno)d %(levelname)s] %(message)s") + ) + log.addHandler(fh) + try: + yield + finally: + log.removeHandler(fh) + stack.close() + + +class DebugContext: + _counter = itertools.count() + + @staticmethod + def create_debug_dir(folder_name: str) -> Optional[str]: + debug_dir = config.trace.debug_dir or get_debug_dir() + for n in DebugContext._counter: + dirname = os.path.join( + debug_dir, + "torchinductor", + f"{folder_name}.{n}", + ) + if not os.path.exists(dirname): + os.makedirs(dirname) + return dirname + return None + + def __init__(self) -> None: + self._prof = None + self._path = None + self._stack = contextlib.ExitStack() + + def copy(self, new_path: str) -> None: + if not self._path: + return + assert new_path.endswith(".debug"), new_path + from filelock import FileLock + + try: + with FileLock(f"{new_path}.lock"): + if os.path.exists(new_path): + shutil.rmtree(new_path) + shutil.copytree(self._path, new_path) + except OSError: + log.warning( + "Failed to copy debug files from %s to %s", self._path, new_path + ) + + def fopen( + self, + filename: str, + write_mode: str = "w", + *args: Any, + **kwargs: Any, + ) -> IO[Any]: + assert self._path + return open(os.path.join(self._path, filename), write_mode, *args, **kwargs) + + @contextlib.contextmanager + def fopen_context( + self, + filename: str, + write_mode: str = "w", + *args: Any, + **kwargs: Any, + ) -> Iterator[IO[Any]]: + assert self._path + with open(os.path.join(self._path, filename), write_mode, *args, **kwargs) as f: + yield f + + def filename(self, suffix: str) -> str: + assert self._path + return os.path.join(self._path, suffix) + + def upload_tar(self) -> None: + if config.trace.upload_tar is not None: + import tarfile + + assert self._path + tar_file = os.path.join( + self._path, f"{os.path.basename(self._path)}.tar.gz" + ) + with tarfile.open(tar_file, "w:gz") as tar: + tar.add(self._path, arcname=os.path.basename(self._path)) + config.trace.upload_tar(tar_file) + + def __enter__(self) -> None: + if config.debug: + log = logging.getLogger("torch._dynamo") + prev_level = log.level + log.setLevel(logging.DEBUG) + + def reset_log_level(level: Any) -> None: + log.setLevel(level) + + self._stack.callback(reset_log_level, prev_level) + + self._stack.enter_context(V.set_debug_handler(self)) + + if not config.trace.enabled: + return + + self._path = self.create_debug_dir(get_aot_graph_name()) # type: ignore[assignment] + + if config.trace.debug_log: + self._setup_log_capture("debug.log", logging.DEBUG) + if config.trace.info_log: + self._setup_log_capture("info.log", logging.INFO) + + def _setup_log_capture( + self, + filename: str, + level: int, + ) -> None: + log = logging.getLogger("torch._inductor") + fd = self._stack.enter_context(self.fopen(filename)) + ch = logging.StreamHandler(fd) + ch.setLevel(level) + ch.setFormatter( + logging.Formatter("[%(filename)s:%(lineno)d %(levelname)s] %(message)s") + ) + log.addHandler(ch) + log.setLevel(min(log.level, level)) + self._stack.callback(log.removeHandler, ch) + + def __exit__( + self, + exc_type: Optional[Type[BaseException]], + exc_val: Optional[BaseException], + exc_tb: Optional[Any], + ) -> None: + if self._prof: + self._prof.disable() + self._save_profile_data() + + if self._path: + self.upload_tar() + log.warning("%s debug trace: %s", get_graph_being_compiled(), self._path) + self._stack.close() + + def _save_profile_data(self) -> None: + assert self._prof + self._prof.dump_stats(self.filename("compile.prof")) + with self.fopen("compile.stats") as fd: + stats = pstats.Stats(self._prof, stream=fd) + stats.strip_dirs() + stats.sort_stats("cumtime") + stats.print_stats(100) + stats.sort_stats("tottime") + stats.print_stats(100) + + def __getattr__(self, name: str) -> Optional[Callable[..., None]]: + if config.trace.enabled and getattr(config.trace, name): + try: + return getattr(DebugFormatter(self), name) + except Exception: + log.warning("Ignoring exception in debug code", exc_info=True) + return None + else: + + def ignored(*args: Any, **kwargs: Any) -> None: + pass + + return ignored + + +class DebugFormatter: + def __init__(self, handler: DebugContext) -> None: + self.fopen = handler.fopen + self.fopen_context = handler.fopen_context + self.filename = handler.filename + self.handler = handler + + def fx_graph( + self, + gm: torch.fx.GraphModule, + inputs: List[torch.Tensor], + ) -> None: + with self.fopen("fx_graph_runnable.py") as fd: + save_graph_repro(fd, gm, inputs, "inductor") + + with self.fopen("fx_graph_readable.py") as fd: + fd.write(gm.print_readable(print_output=False)) + + def fx_graph_transformed( + self, + gm: torch.fx.GraphModule, + inputs: List[torch.Tensor], + ) -> None: + with self.fopen("fx_graph_transformed.py") as fd: + fd.write(gm.print_readable(print_output=False)) + + def ir_pre_fusion(self, nodes: SchedulerNodeList) -> None: + self._write_ir("ir_pre_fusion.txt", nodes) + + def ir_post_fusion(self, nodes: SchedulerNodeList) -> None: + self._write_ir("ir_post_fusion.txt", nodes) + + def _write_ir( + self, + filename: str, + nodes: SchedulerNodeList, + ) -> None: + with self.fopen(filename) as fd: + log.info("Writing debug ir to %s", fd.name) + for node in nodes: + fd.write(node.debug_str()) + fd.write("\n\n\n") + + def graph_diagram(self, nodes: SchedulerNodeList) -> None: + draw_buffers(nodes, fname=self.filename("graph_diagram.svg")) + + def draw_orig_fx_graph( + self, + gm: torch.fx.GraphModule, + nodes: SchedulerNodeList, + ) -> None: + annotate_orig_fx_with_snodes(gm, nodes) + draw_graph( + gm, + fname=self.filename("orig_fx_graph_diagram.svg"), + clear_meta=False, + prog=GRAPHVIZ_COMMAND_SCALABLE, + parse_stack_trace=True, + dot_graph_shape=config.trace.dot_graph_shape, + ) + + def output_code(self, filename: str) -> None: + shutil.copy(filename, self.filename("output_code.py")) + + def log_autotuning_results( + self, + name: str, + input_nodes: List[ir.IRNode], + timings: Dict["ChoiceCaller", float], # type: ignore[name-defined] # noqa: F821 + elapse: float, + precompile_elapse: float, + ) -> None: + import json + + from .ir import FixedLayout + + def build_node_info(node: ir.IRNode) -> Dict[str, str]: + if hasattr(node, "name"): + node_name = node.name + else: + node_name = "" + node_info = { + "name": node_name, + "type": type(node).__name__, + } + try: + layout = node.get_layout() + if isinstance(layout, FixedLayout): + offset = 0 + try: + offset = int(layout.offset) + except Exception: + try: + offset = V.graph.sizevars.size_hint( + layout.offset, fallback=0 + ) + except Exception: + pass + static_layout = FixedLayout( + layout.device, + dtype=layout.dtype, + size=list(V.graph.sizevars.size_hints(layout.size)), + stride=list(V.graph.sizevars.size_hints(layout.stride)), + offset=offset, + ) + node_info["layout"] = str(static_layout) + else: + node_info["layout"] = str(node.get_layout()) + except Exception as e: + pass + try: + node_info["dtype"] = str(node.get_dtype()) + except Exception as e: + pass + try: + node_info["device"] = str(node.get_device()) + except Exception as e: + pass + try: + node_info["stride"] = str( + V.graph.sizevars.size_hints(node.get_stride()) + ) + except Exception as e: + pass + try: + node_info["size"] = str(V.graph.sizevars.size_hints(node.get_size())) + except Exception as e: + pass + try: + node_info["numel"] = str(V.graph.sizevars.size_hint(node.get_numel())) + except Exception as e: + pass + if hasattr(node, "data") and isinstance(node.data, ir.IRNode): + node_info["data"] = build_node_info(node.data) + return node_info + + general_properties = { + "op_name": name, + "cuda_device_name": torch.cuda.get_device_name(), + "cuda_device_count": torch.cuda.device_count(), + "input_nodes": [build_node_info(node) for node in input_nodes], + "autotuning_time": elapse, + "precompile_time": precompile_elapse, + } + with self.fopen_context( + "autotuning_result_json_list.txt", "at", encoding="utf-8" + ) as fd: + for caller, time in timings.items(): + info_dict = dict(caller.info_dict()) + info_dict.update(general_properties) + info_dict["benchmark_result"] = time + json.dump(info_dict, fd) + fd.write("\n") + + +@dataclasses.dataclass +class TensorMetadataHolder: + tensor_metadata: TensorMetadata + device: torch.device + + +save_args_cnt = itertools.count() + + +def save_args_for_compile_fx_inner(*args: Any, **kwargs: Any) -> None: + """ + This function is used to save arguments for a compile_fx_inner function call + to the file system. Later on one can replay the compile_fx_inner call + with the saved arguments using load_args_and_run_compile_fx_inner. + """ + + folder = "/tmp/inductor_saved_args" + if not os.path.exists(folder): + os.mkdir(folder) + + def handle_tensor(x: Any) -> Any: + """ + Pickle FakeTensor will result in error: + AttributeError: Can't pickle local object 'WeakValueDictionary.__init__..remove' + + Convert all Tensor to metadata. This may also makes pickle faster. + """ + if isinstance(x, torch.Tensor): + return TensorMetadataHolder(_extract_tensor_metadata(x), x.device) + else: + return x + + args_to_save, kwargs_to_save = tree_map(handle_tensor, (args, kwargs)) + + fn_name = "compile_fx_inner" + path = f"{folder}/{fn_name}_{next(save_args_cnt)}.pkl" + with open(path, "wb") as f: + pickle.dump((args_to_save, kwargs_to_save), f) + + if log.isEnabledFor(logging.DEBUG): + message = f""" +Arguments for a compile_fx_inner call is saved to {path}. To replay the call, +run the following: + +from torch._inductor.debug import load_args_and_run_compile_fx_inner +load_args_and_run_compile_fx_inner({path!r}) + """ + # call print rather than log.debug. log.debug will print message + # prefix for each line which makes the code snippet harder to be + # copied. + # Not a big deal since the code is already been guarded by checking + # the log level. + print(message) + + +def load_args_and_run_compile_fx_inner(path: str) -> Any: + from torch._inductor.compile_fx import compile_fx_inner + + with open(path, "rb") as f: + args, kwargs = pickle.load(f) + + def handle_tensor(x: Any) -> Any: + if isinstance(x, TensorMetadataHolder): + return torch._dynamo.testing.rand_strided( + x.tensor_metadata.shape, + x.tensor_metadata.stride, + x.tensor_metadata.dtype, + x.device, + ) + else: + return x + + fake_mode = torch._subclasses.FakeTensorMode(allow_non_fake_inputs=True) + with fake_mode, config.patch("save_args", False): + args, kwargs = tree_map(handle_tensor, (args, kwargs)) + return compile_fx_inner(*args, **kwargs) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/decomposition.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/decomposition.py new file mode 100644 index 0000000000000000000000000000000000000000..0e067395f807121789f2a51fe4919f122f03f170 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/decomposition.py @@ -0,0 +1,980 @@ +# mypy: allow-untyped-decorators +import functools +import logging +import math +import sys +import typing +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import torch +import torch._decomp as decomp +import torch._prims_common as utils +import torch.ao.quantization.fx._decomposed +from torch._decomp import ( + core_aten_decompositions, + get_decompositions, + remove_decompositions, +) +from torch._decomp.decompositions import ( + _grid_sampler_2d as decomp_grid_sampler_2d, + pw_cast_for_opmath, +) +from torch._decomp.decompositions_for_rng import extra_random_decomps +from torch._dynamo.utils import counters +from torch._higher_order_ops.out_dtype import out_dtype +from torch._inductor.utils import pad_listlike +from torch._prims_common import ( + elementwise_dtypes, + ELEMENTWISE_TYPE_PROMOTION_KIND, + type_to_dtype, +) +from torch.fx.experimental.symbolic_shapes import definitely_true, guard_size_oblivious + +from . import config, inductor_prims +from .utils import ( + is_gpu, + needs_fallback_due_to_atomic_add_limitations, + use_scatter_fallback, +) + + +log = logging.getLogger(__name__) +aten = torch.ops.aten +prims = torch.ops.prims +quantized = torch.ops.quantized +_quantized = torch.ops._quantized +quantized_decomposed = torch.ops.quantized_decomposed + +inductor_decompositions = get_decompositions( + [ + aten._adaptive_avg_pool2d_backward, + aten.addmv, + aten.arange, + aten.bitwise_and_, + aten.bitwise_or_, + aten.clamp_min_, + aten.dist, + aten.empty_like, + aten.flip, + aten.gelu, + aten.hardtanh, + aten.index_select, + aten.lcm, + aten.leaky_relu, + aten.linalg_vector_norm, + aten._log_softmax, + aten.max_pool2d_with_indices_backward, + aten._native_batch_norm_legit, + aten._native_batch_norm_legit_functional, + aten._native_batch_norm_legit_no_training, + aten._batch_norm_with_update, + aten._batch_norm_with_update_functional, + aten._batch_norm_no_update, + aten.batch_norm_backward, + aten.native_batch_norm, + aten.native_group_norm, + aten.native_layer_norm, + aten.nll_loss2d_backward, + aten._softmax, + aten.sin_, + aten.sqrt_, + out_dtype, + aten._to_copy, + aten.tril_indices, + aten.triu_indices, + aten.upsample_bilinear2d.vec, + quantized.linear_dynamic_fp16_unpacked_weight, + _quantized.wrapped_quantized_linear, + ] +) +decompositions = {**core_aten_decompositions(), **inductor_decompositions} + +# Remove unwanted decompositions included via the core ATen decompositions from +# the Inductor decomp table. +decomps_to_exclude = [ + aten._unsafe_index, + aten._unsafe_masked_index, + aten._unsafe_masked_index_put_accumulate, + aten._scaled_dot_product_flash_attention_for_cpu.default, # See comments in torch/_decomp/decompositions.py + aten._softmax_backward_data, + aten.clamp_max, + aten.clamp_min, + aten.glu, # inductor lowers this directly + aten.select_scatter, # need to be in the ATen graph in order for it to work with the re-inplacing pass + aten.slice_scatter, # need to be in the ATen graph in order for it to work with the re-inplacing pass + aten.split.Tensor, # inductor lowers this directly + aten.squeeze, # inductor lowers this directly + aten.sum, # inductor lowers this directly + aten.unbind, # inductor lowers this directly +] + +remove_decompositions(decompositions, decomps_to_exclude) + + +def register_decomposition( + ops: List[Union[torch._ops.OperatorBase, torch._ops.OpOverloadPacket]] +) -> Callable[..., Any]: + for op in [ops] if callable(ops) else ops: # type: ignore[attr-defined] + if op in decompositions: + log.warning("duplicate decomp: %s", ops) + return decomp.register_decomposition(ops, decompositions) + + +# TODO: for now, inductor doesn't handle asserts +# because the condition is symbol -> tensor in the graph. +@register_decomposition([aten._assert_async.msg]) +def assert_async_msg_decomp(tensor: torch.Tensor, msg: str) -> None: + return + + +# Following `assert_async_msg_decomp` and implement as non-op. +@register_decomposition([aten._functional_assert_async.msg]) +def functional_assert_async_msg_decomp(tensor: torch.Tensor, msg: str) -> None: + return + + +@register_decomposition([aten.sym_constrain_range_for_size.default]) +def sym_constrain_range_for_size( + symbol: torch.SymInt, + *, + min: Optional[torch.types.Number] = None, + max: Optional[torch.types.Number] = None, +) -> None: + return + + +@register_decomposition([aten.clamp]) +@pw_cast_for_opmath +def clamp( + x: torch.Tensor, + min: Optional[torch.types.Number] = None, + max: Optional[torch.types.Number] = None, +) -> torch.Tensor: + if min is not None: + x = x.clamp_min(min) + if max is not None: + x = x.clamp_max(max) + return x + + +@register_decomposition([aten.full]) +def full( + size: List[Union[int, torch.SymInt]], + fill_value: torch.types.Number, + **kwargs: Any, +) -> torch.Tensor: + dtype = kwargs.get("dtype") + if dtype is None: + kwargs["dtype"] = type_to_dtype(type(fill_value)) + return torch.full(size, fill_value, **kwargs) + return NotImplemented + + +# Not really sure how to put this into the main library. PrimTorch wants +# empty_permuted to go to the prim, and typically users don't really want +# to decompose to empty_strided (but inductor is OK with it, because we are +# cool with strides and everything goes to empty_strided) +@register_decomposition([aten.empty_permuted.default]) +def empty_permuted( + size: List[Union[int, torch.SymInt]], + physical_layout: List[int], + **kwargs: Any, +) -> torch.Tensor: + perm = [0] * len(size) + for p, l in enumerate(physical_layout): + perm[l] = p + return torch.empty([size[l] for l in physical_layout], **kwargs).permute(perm) + + +@register_decomposition([aten.convolution_backward]) +def convolution_backward( + grad_output: torch.Tensor, + input: torch.Tensor, + weight: torch.Tensor, + bias_sizes: List[int], + stride: Union[int, List[int]], + padding: Union[int, List[int]], + dilation: Union[int, List[int]], + transposed: bool, + output_padding: List[int], + groups: int, + output_mask: List[bool], +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + if not output_mask[2] or not is_gpu(grad_output.device.type): + return NotImplemented + grad_bias = aten.sum(grad_output, [0] + list(range(2, grad_output.dim()))) + grad_inp, grad_weight, _ = aten.convolution_backward( + grad_output, + input, + weight, + bias_sizes, + stride, + padding, + dilation, + transposed, + output_padding, + groups, + [output_mask[0], output_mask[1], False], + ) + return (grad_inp, grad_weight, grad_bias) + + +@register_decomposition([aten.round.decimals]) +def round_dec(x: torch.Tensor, decimals: int = 0) -> torch.Tensor: + ten_pow_decimals = 10.0**decimals + return aten.round(x * ten_pow_decimals) * (1.0 / ten_pow_decimals) + + +@register_decomposition([aten.bmm]) +@pw_cast_for_opmath +def bmm( + self: torch.Tensor, + batch2: torch.Tensor, +) -> torch.Tensor: + if config.coordinate_descent_tuning: + if guard_size_oblivious(self.shape[1] == 1) or guard_size_oblivious( + batch2.shape[2] == 1 + ): + out = (self.unsqueeze(-1) * batch2.unsqueeze(1)).sum(dim=2) + return out + if self.device.type == "cpu": + if guard_size_oblivious(self.size(1) == 1) and guard_size_oblivious( + batch2.size(-1) == 1 + ): + counters["inductor"]["decompose_bmm"] += 1 + return torch.sum( + self.squeeze(1) * batch2.squeeze(-1), dim=1, keepdim=True + ).unsqueeze(1) + return NotImplemented + + +@register_decomposition([aten.addmm]) +@pw_cast_for_opmath +def addmm( + self: torch.Tensor, + mat1: torch.Tensor, + mat2: torch.Tensor, + beta: torch.types.Number = 1, + alpha: torch.types.Number = 1, +) -> torch.Tensor: + if self.device.type == "cpu": + if guard_size_oblivious(mat1.size(0) == 1) and guard_size_oblivious( + mat2.size(-1) == 1 + ): + counters["inductor"]["decompose_addmm"] += 1 + out = torch.sum( + mat1.squeeze(0) * mat2.squeeze(-1), dim=0, keepdim=True + ).unsqueeze(0) + return alpha * out + beta * self + if ( + guard_size_oblivious(mat1.size(0) == 1) + and definitely_true(mat2.size(0) <= 16) + and definitely_true(mat2.size(1) <= 16) + ): + counters["inductor"]["decompose_addmm"] += 1 + out = (mat1.T * mat2).sum(dim=0, keepdim=True) + return alpha * out + beta * self + return NotImplemented + + +@register_decomposition([aten.mm]) +@pw_cast_for_opmath +def mm( + self: torch.Tensor, + input2: torch.Tensor, +) -> torch.Tensor: + # Our matrix vector multiplies only achieve peak bandwidth with coordinate descent tuning. + # todo: Look into why and fix it (hopefully) + if config.coordinate_descent_tuning: + if guard_size_oblivious(self.shape[0] == 1) or guard_size_oblivious( + input2.shape[1] == 1 + ): + return (self.unsqueeze(2) * input2.unsqueeze(0)).sum(dim=1) + if self.device.type == "cpu": + if ( + guard_size_oblivious(self.size(-1) == 1) + and guard_size_oblivious(self.size(0) > 0) + and guard_size_oblivious(input2.size(0) == 1) + and (self.dtype == input2.dtype) + and definitely_true((torch.numel(self) + torch.numel(input2)) <= 32) + ): + counters["inductor"]["decompose_mm"] += 1 + return torch.cat([self[i, :] * input2 for i in range(self.size(0))]) + if guard_size_oblivious(self.size(0) == 1) and guard_size_oblivious( + input2.size(-1) == 1 + ): + counters["inductor"]["decompose_mm"] += 1 + return torch.sum( + self.squeeze(0) * input2.squeeze(-1), dim=0, keepdim=True + ).unsqueeze(0) + return NotImplemented + + +# This pass does two things: +# - Eliminate cat when there is only one tensor input +# - Normalize cat calls, so that legacy empty 1-D tensors are removed (NB: we +# don't remove ALL empty tensors, only the naughty ones) +@register_decomposition([aten.cat.default]) +def cat( + tensors: List[torch.Tensor], + dim: int = 0, +) -> torch.Tensor: + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + def non_empty_tensor(x: torch.Tensor) -> bool: + # For better or worse, this is a valid cat: + # + # torch.cat([torch.randn(2, 2, 4), torch.randn(0), torch.randn(3, 2, 4)]) + # + # We'd like to eliminate naughtiness like this for downstream passes + # like split_cat. The easiest way is to just drop such inputs + # (guarding that they are non-zero). + # + # Is it permissible for this filtering to be size-oblivious? A case + # where this could matter is cat([(2, 2), (u0,)], dim=0); if u0 + # happened to be zero, we would have liked to have filtered it out. + # But actually, the ONLY way this could have passed is if u0 == 0, + # so by the time we get here we have already installed a deferred + # runtime assert forcing u0 to be zero. So if this hasn't happened, + # we know that the unbacked SymInt has appropriate size and there are + # no problems. + if len(x.shape) == 1 and guard_size_oblivious(x.shape[0] == 0): + return False + + if dim < len(x.shape) and guard_size_oblivious(x.shape[dim] == 0): + return False + + return True + + filtered_tensors = list(filter(non_empty_tensor, tensors)) + + if len(filtered_tensors) == 1: + return filtered_tensors[0].clone() + elif 1 < len(filtered_tensors) < len(tensors): + # on the first call, when we remove empty tensors, we redispatch recursively + return aten.cat.default(filtered_tensors, dim) + + # optimization, avoid concat for single, repeated input + if len(filtered_tensors) > 1 and all( + t is filtered_tensors[0] for t in filtered_tensors + ): + inp = filtered_tensors[0] + shape = list(inp.shape) + dim = dim + len(inp.shape) if dim < 0 else dim + shape.insert(dim, len(filtered_tensors)) + return inp.unsqueeze(dim).expand(*shape).flatten(dim, dim + 1).clone() + + # when no 'filtering' has occurred, we raise to prevent infinite recursion (no more decomposition needed) + return NotImplemented + + +@register_decomposition([aten.angle]) +def angle(x: torch.Tensor) -> torch.Tensor: + if x.is_complex(): + return torch.where( + torch.isnan(x.real), float("nan"), torch.atan2(x.imag, x.real) + ) + + # when x is real number + # if x >= 0, return 0 + # if x < 0, return pi + # if x is nan, return nan + _, dtype = elementwise_dtypes( + x, + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + ) + pi = torch.scalar_tensor(math.pi, dtype=dtype, device=x.device) + ret = torch.where(x < 0, pi, 0.0) + return torch.where(torch.isnan(x), float("nan"), ret) + + +@register_decomposition([aten.add]) +def add( + x: torch.Tensor, + y: torch.Tensor, + *, + alpha: Optional[torch.types.Number] = None, +) -> torch.Tensor: + # Require both x and y to be complex tensors. + x_is_complex_tensor = torch.is_tensor(x) and x.is_complex() + y_is_complex_tensor = torch.is_tensor(y) and y.is_complex() + if not x_is_complex_tensor or not y_is_complex_tensor: + return NotImplemented + z = y + if alpha is not None: + z = alpha * y + complex_type = torch.promote_types(x.dtype, y.dtype) + + # For complex typed `x`, `x.view(x.real.dtype)` doubles the last dimension and can cause problem + # when broadcasting the add. + def reshape_tensor_complex(tensor: torch.Tensor) -> torch.Tensor: + """Reshape tensor from [*initial_dims, last_dim] to *initial_dims, last_dim/2, 2]""" + # Get the current shape of the tensor + *initial_dims, last_dim = tensor.shape + + # Check if the last dimension is even. We should never reach here since `x.view(x.real.dtype)` + # doubles the last dimension for complex numbers. + if last_dim % 2 != 0: + raise AssertionError( + "The size of the last dimension must be even to reshape it to [..., last_dim/2, 2]" + ) + + # Reshape the tensor + new_shape = (*initial_dims, last_dim // 2, 2) + reshaped_tensor = tensor.view(new_shape) + return reshaped_tensor + + x_reshaped = reshape_tensor_complex(x.view(x.real.dtype)) + z_reshaped = reshape_tensor_complex(z.view(y.real.dtype)) + result = torch.flatten(x_reshaped + z_reshaped, start_dim=-2).view(complex_type) + return result + + +@register_decomposition([aten.conj_physical]) +def conj_physical(self: torch.Tensor) -> torch.Tensor: + assert not self.is_complex(), "TODO: implement this" + return self + + +@register_decomposition([aten.lift, aten.detach_]) +def lift(self: torch.Tensor) -> torch.Tensor: + return self + + +@register_decomposition([aten.bernoulli.default]) +def bernoulli( + self: torch.Tensor, + *, + generator: Optional[torch.Generator] = None, +) -> torch.Tensor: + assert generator is None + return (torch.rand_like(self, dtype=torch.float32) < self).to(self.dtype) + + +@register_decomposition([aten.fmin, prims.fmin]) +def fmin(self: torch.Tensor, other: torch.Tensor) -> torch.Tensor: + return torch.where(torch.isnan(other) | (other > self), self, other) + + +@register_decomposition([aten.fmax, prims.fmax]) +def fmax(self: torch.Tensor, other: torch.Tensor) -> torch.Tensor: + return torch.where(torch.isnan(other) | (other < self), self, other) + + +@register_decomposition(aten.amax) +def amax( + self: torch.Tensor, + dim: Optional[int] = None, + keepdim: bool = False, +) -> torch.Tensor: + if self.dtype == torch.bool: + return torch.any(self, dim=dim, keepdim=keepdim) + return NotImplemented + + +@register_decomposition(aten.amin) +def amin( + self: torch.Tensor, + dim: Optional[int] = None, + keepdim: bool = False, +) -> torch.Tensor: + if self.dtype == torch.bool: + return torch.all(self, dim=dim, keepdim=keepdim) + return NotImplemented + + +@register_decomposition([aten.narrow_copy]) +def narrow_copy( + self: torch.Tensor, + dim: int, + start: int, + length: int, +) -> torch.Tensor: + return torch.narrow(self, dim, start, length).clone() + + +@register_decomposition([aten.view_copy.default]) +def view_copy_default( + self: torch.Tensor, + size: List[Union[int, torch.SymInt]], +) -> torch.Tensor: + return aten.view(self, size).clone() + + +@register_decomposition([aten.view_copy.dtype]) +def view_copy_dtype( + self: torch.Tensor, + dtype: torch.dtype, +) -> torch.Tensor: + return self.to(dtype).clone() + + +def get_like_layout( + tensor: torch.Tensor, + memory_format: Optional[torch.memory_format] = None, +) -> torch.memory_format: + # TODO: _to_copy tensor to stride permutation + if memory_format is torch.preserve_format or memory_format is None: + return utils.suggest_memory_format(tensor) + else: + return memory_format + + +@register_decomposition(aten.rand_like) +def rand_like( + self: torch.Tensor, + *, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + memory_format: Optional[torch.memory_format] = None, + **kwargs: Any, +) -> torch.Tensor: + return torch.rand( + [*self.size()], + dtype=dtype or self.dtype, + device=device or self.device, + **kwargs, + ).to(memory_format=get_like_layout(self, memory_format)) + + +@register_decomposition(aten.randn_like) +def randn_like( + self: torch.Tensor, + *, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + memory_format: Optional[torch.memory_format] = None, + **kwargs: Any, +) -> torch.Tensor: + return torch.randn( + [*self.size()], + dtype=dtype or self.dtype, + device=device or self.device, + **kwargs, + ).to(memory_format=get_like_layout(self, memory_format)) + + +@register_decomposition(aten.full_like) +def full_like( + self: torch.Tensor, + fill_value: Union[int, float], + *, + dtype: Optional[torch.dtype] = None, + layout: Optional[torch.layout] = None, + device: Optional[torch.device] = None, + pin_memory: bool = False, + requires_grad: bool = False, + memory_format: torch.memory_format = torch.preserve_format, +) -> torch.Tensor: + return torch.full( + [*self.size()], + fill_value, + dtype=dtype or self.dtype, + layout=layout or self.layout, + device=device or self.device, + requires_grad=requires_grad, + ).to(memory_format=get_like_layout(self, memory_format)) + + +@register_decomposition(aten.randint_like.default) +def randint_like( + self: torch.Tensor, + high: int, + *, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + memory_format: Optional[torch.memory_format] = None, + **kwargs: Any, +) -> torch.Tensor: + return aten.randint.low( + 0, + high, + [*self.size()], + dtype=dtype or self.dtype, + device=device or self.device, + **kwargs, + ).to(memory_format=get_like_layout(self, memory_format)) + + +@register_decomposition(aten.randint_like.low_dtype) +def randint_like_low( + self: torch.Tensor, + low: int, + high: int, + *, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + memory_format: Optional[torch.memory_format] = None, + **kwargs: Any, +) -> torch.Tensor: + return aten.randint.low( + low, + high, + [*self.size()], + dtype=dtype or self.dtype, + device=device or self.device, + **kwargs, + ).to(memory_format=get_like_layout(self, memory_format)) + + +@register_decomposition(aten.randint.default) +def randint( + high: int, + size: List[Union[int, torch.SymInt]], + **kwargs: Any, +) -> torch.Tensor: + return aten.randint.low(0, high, size, **kwargs) + + +@register_decomposition(quantized.linear_dynamic_fp16_unpacked_weight.default) +def linear_dynamic_fp16_unpacked_weight( + input: torch.Tensor, + weight: torch.Tensor, + bias: torch.Tensor, +) -> torch.Tensor: + packed_weight = torch.ops._quantized.wrapped_fbgemm_pack_gemm_matrix_fp16(weight) + return torch.ops._quantized.wrapped_fbgemm_linear_fp16_weight( + input, packed_weight, bias, weight.size()[0] + ) + + +@register_decomposition(_quantized.wrapped_quantized_linear.default) +def wrapped_quantized_linear( + input: torch.Tensor, + input_scale: torch.Tensor, + input_zero_point: torch.Tensor, + weight: torch.Tensor, + weight_scale: torch.Tensor, + weight_zero_point: torch.Tensor, + bias: torch.Tensor, + out_scale: torch.Tensor, + out_zero_point: torch.Tensor, + out_channel: int, +) -> torch.Tensor: + packed_weight = torch.ops._quantized._wrapped_linear_prepack( + weight, weight_scale, weight_zero_point, bias + ) + return torch.ops._quantized._wrapped_quantized_linear_prepacked( + input, + input_scale, + input_zero_point, + packed_weight, + out_scale, + out_zero_point, + out_channel, + ) + + +@register_decomposition(torch.ops.quantized.embedding_bag_byte_unpack) +def q_embedding_bag_byte_unpack_decomp(packed: torch.Tensor) -> torch.Tensor: + def bitcast_u8_to_f32(u8: torch.Tensor) -> torch.Tensor: + x, y, z, w = (u8[..., n].to(torch.int32) for n in (0, 1, 2, 3)) + if sys.byteorder == "little": + return (x + (y << 8) + (z << 16) + (w << 24)).view(torch.float32)[..., None] + else: + return ((x << 24) + (y << 16) + (z << 8) + w).view(torch.float32)[..., None] + + scales = bitcast_u8_to_f32(packed[..., -8:-4]) + offsets = bitcast_u8_to_f32(packed[..., -4:]) + return packed[..., :-8].to(torch.float32) * scales + offsets + + +@register_decomposition([aten.grid_sampler_2d]) +@pw_cast_for_opmath +def grid_sampler_2d( + a: torch.Tensor, + grid: torch.Tensor, + interpolation_mode: int = 0, + padding_mode: int = 0, + align_corners: bool = False, +) -> torch.Tensor: + # We do not expand the grid (_expand_grid=False) on cpu for performance reasons + # Experimenting locally it was found that compiled CUDA code is accelerated by ~5x + # and CPU code by ~2x on bicubic mode, if we expand the grid from (N, H, W, 2) into (N, C, H, W, 2) + # However, this leads to a slowdown around ~0.8x on CPU bilinear mode, channels first. + # Thus we apply this hack to not expand the grid for this case. + _expand_grid = not ( + a.device == torch.device("cpu") + and interpolation_mode == 0 + and a.is_contiguous(memory_format=torch.contiguous_format) + ) + + output = decomp_grid_sampler_2d( + a, + grid=grid, + interpolation_mode=interpolation_mode, + padding_mode=padding_mode, + align_corners=align_corners, + _expand_grid=_expand_grid, + ) + return output + + +@register_decomposition(aten._foreach_addcmul.Scalar) +def _foreach_addcmul_scalar( + self: List[torch.Tensor], + left_tensors: List[torch.Tensor], + right_tensors: List[torch.Tensor], + scalar: float = 1, +) -> List[torch.Tensor]: + return aten._foreach_add.List( + self, aten._foreach_mul.List(left_tensors, right_tensors), alpha=scalar + ) + + +@register_decomposition(aten._foreach_addcdiv.Scalar) +def _foreach_addcdiv_scalar( + self: List[torch.Tensor], + left_tensors: List[torch.Tensor], + right_tensors: List[torch.Tensor], + scalar: float = 1, +) -> List[torch.Tensor]: + return aten._foreach_add.List( + self, aten._foreach_div.List(left_tensors, right_tensors), alpha=scalar + ) + + +@register_decomposition(aten._foreach_lerp.Scalar) +def _foreach_lerp_scalar( + start_tensors: List[torch.Tensor], + end_tensors: List[torch.Tensor], + weight: torch.types.Number, +) -> List[torch.Tensor]: + return aten._foreach_add.List( + start_tensors, + aten._foreach_mul.Scalar( + aten._foreach_sub.List(end_tensors, start_tensors), weight + ), + ) + + +@aten.miopen_batch_norm.default.py_impl(torch._C.DispatchKey.Autograd) +@register_decomposition(aten.miopen_batch_norm) +def miopen_batch_norm( + input: torch.Tensor, + weight: torch.Tensor, + bias: typing.Optional[torch.Tensor], + running_mean: typing.Optional[torch.Tensor], + running_var: typing.Optional[torch.Tensor], + training: bool, + exponential_average_factor: float, + epsilon: float, +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + a, b, c = aten.native_batch_norm( + input, + weight, + bias, + running_mean, + running_var, + training, + exponential_average_factor, + epsilon, + ) + + if training: + return (a, b, c) + return ( + a, + weight.new_zeros((0,)), + weight.new_zeros((0,)), + ) + + +@functools.lru_cache(None) +def fast_random_decomps() -> Dict[Any, Callable[..., Any]]: + return {**decompositions, **extra_random_decomps} + + +# TODO(aakhundov): replace this (and the above) Any by more +# specific type and fix all the cascading mypy errors +def select_decomp_table() -> Dict[Any, Callable[..., Any]]: + """decomps can change based on config""" + if config.fallback_random: + return decompositions + return fast_random_decomps() + + +@register_decomposition(aten.masked_scatter) +def masked_scatter( + self: torch.Tensor, + mask: torch.Tensor, + source: torch.Tensor, +) -> torch.Tensor: + from .codegen.common import BackendFeature, has_backend_feature + + if has_backend_feature(self.device, BackendFeature.MASKED_SCATTER_WITH_INDEX): + # This two-step algorithm is the same as eager CUDA, for eager CPU we + # use a 1-shot serial iteration. + self, mask = aten.broadcast_tensors([self, mask]) + source_idx = mask.reshape(-1).cumsum(0) - 1 + self_flat, mask_flat, source_flat = (x.flatten() for x in (self, mask, source)) + result = aten._unsafe_masked_index(source_flat, mask_flat, [source_idx], 0) + return torch.where(mask_flat, result, self_flat).view(self.shape) + return NotImplemented + + +@register_decomposition(quantized_decomposed.choose_qparams.tensor) +def choose_qparams_tensor( + input: torch.Tensor, + quant_min: int, + quant_max: int, + eps: float, + dtype: torch.dtype, +) -> Tuple[torch.Tensor, torch.Tensor]: + min_val, max_val = torch.aminmax(input) + scale = (max_val - min_val) / float(quant_max - quant_min) + scale = torch.max(scale, torch.Tensor([eps])) + zero_point = quant_min - torch.round(min_val / scale).to(torch.int) + zero_point = torch.clamp(zero_point, quant_min, quant_max) + return scale.to(torch.float64), zero_point.to(torch.int64) + + +@register_decomposition(aten.put) +def put( + self: torch.Tensor, + index: torch.Tensor, + source: torch.Tensor, + accumulate: bool = False, +) -> torch.Tensor: + flattened = self.flatten() + flattened = torch.index_put( + flattened, [index], source.reshape(index.shape), accumulate + ) + return flattened.reshape(self.shape) + + +@register_decomposition(aten.put_) +def put_( + self: torch.Tensor, + index: torch.Tensor, + source: torch.Tensor, + accumulate: bool = False, +) -> torch.Tensor: + out = aten.put(self, index, source, accumulate=accumulate) + return self.copy_(out) + + +@register_decomposition(aten._softmax_backward_data.default) +@pw_cast_for_opmath +def _softmax_backward_data( + grad_output: torch.Tensor, + output: torch.Tensor, + dim: int, + input_dtype: torch.dtype, +) -> torch.Tensor: + new_grad_output = grad_output * output + sum_new_grad = torch.sum(new_grad_output, dim=dim, keepdim=True) + # grad_input = new_grad_output - output * sum_new_grad + grad_input = inductor_prims.fma(-output, sum_new_grad, new_grad_output) + + # CPU kernel doesn't respect input_dtype, but following check doesn't work for meta tensor + # if grad_output.device == torch.device("cpu"): + # return grad_input.contiguous() + + if grad_output.dtype != input_dtype: + grad_input = grad_input.to(input_dtype) + return grad_input.contiguous() + + +@register_decomposition(aten.index_reduce) +def index_reduce( + self: torch.Tensor, + dim: int, + index: torch.Tensor, + src: torch.Tensor, + reduction_type: str, + *, + include_self: bool = True, +) -> torch.Tensor: + if reduction_type == "mean" and not needs_fallback_due_to_atomic_add_limitations( + self.dtype + ): + true_division = self.dtype.is_floating_point or self.dtype.is_complex + ones = torch.ones_like(src) + if include_self: + out = self + counts = torch.ones_like(self).index_add(dim, index, ones) + else: + out = self.index_fill(dim, index, 0) + counts = torch.zeros_like(self).index_add(dim, index, ones) + counts = counts.masked_fill(counts < 1, 1) + out = out.index_add(dim, index, src) + return out / counts if true_division else out // counts + + if use_scatter_fallback( + aten.scatter_reduce_.two, + reduction_type, + self.dtype, + src.dtype, + src.device.type, + True, + ): + return NotImplemented + + repeats = self.shape[dim + 1 :].numel() * self.shape[:dim].numel() + index_shape = (index.numel(), *self.shape[dim + 1 :], *self.shape[:dim]) + perm = (*range(self.ndim - dim, self.ndim), 0, *range(1, self.ndim - dim)) + scatter_index = ( + index.to(torch.int64) + .repeat_interleave(repeats) + .reshape(index_shape) + .permute(perm) + ) + return self.scatter_reduce( + dim, + scatter_index, + src, + reduction_type, + include_self=include_self, + ) + + +@register_decomposition(aten.max_pool2d_with_indices) +def max_pool2d_with_indices( + x: torch.Tensor, + kernel_size: List[int], + stride: Optional[Union[int, List[int]]] = None, + padding: Union[int, List[int]] = 0, + dilation: Union[int, List[int]] = 1, + ceil_mode: bool = False, +) -> Tuple[torch.Tensor, torch.Tensor]: + if dilation == 1: + dilation = [1, 1] + + if padding == 0: + padding = [0, 0] + + if not stride: + stride = kernel_size + + kernel_size = pad_listlike(kernel_size, 2) + dilation = pad_listlike(dilation, 2) + padding = pad_listlike(padding, 2) + stride = pad_listlike(stride, 2) + + window_size = kernel_size[0] * kernel_size[1] + # We fallback when using non-default dilation or when the window size is too large + if ( + torch._inductor.lowering.should_fallback_max_pool2d_with_indices( + kernel_size, dilation + ) + or window_size > torch.iinfo(torch.int8).max + ): + return NotImplemented + + vals, offsets = prims._low_memory_max_pool2d_with_offsets( + x, + kernel_size, + stride, + padding, + dilation, + ceil_mode, + ) + indices = prims._low_memory_max_pool2d_offsets_to_indices( + offsets, + kernel_size[1], + x.size(-1), + stride, + padding, + ) + return vals, indices diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/dependencies.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/dependencies.py new file mode 100644 index 0000000000000000000000000000000000000000..95a48281977c87002e9ee486ae80e0b58b706d33 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/dependencies.py @@ -0,0 +1,745 @@ +# mypy: allow-untyped-defs +import abc +import dataclasses +import itertools +import logging +import re +import typing +from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union +from unittest.mock import patch + +import sympy + +import torch +from torch.fx.experimental.symbolic_shapes import free_unbacked_symbols +from torch.utils._ordered_set import OrderedSet + +from .codegen.common import index_prevent_reordering +from .utils import ( + get_dtype_size, + reduction_num_outputs, + sympy_index_symbol, + sympy_str, + sympy_subs, + VarRanges, +) +from .virtualized import OpsHandler, ReductionType, V + + +log = logging.getLogger(__name__) +is_indirect = re.compile(r"indirect|tmp").search + + +class Dep(abc.ABC): + name: str + index: sympy.Expr + + @abc.abstractmethod + def rename(self, renames: Dict[str, str]) -> "Dep": + pass + + @abc.abstractmethod + def get_numel(self) -> sympy.Expr: + pass + + @abc.abstractmethod + def numbytes_hint(self): + pass + + @abc.abstractmethod + def has_unbacked_symbols(self) -> bool: + pass + + @abc.abstractmethod + def is_contiguous(self) -> bool: + pass + + def normalize_with_stride_order(self, prefix="t"): + return self + + +@dataclasses.dataclass(frozen=True) +class MemoryDep(Dep): + name: str + index: sympy.Expr + var_names: Tuple[sympy.Symbol, ...] + size: Tuple[sympy.Expr, ...] + mode: Optional[str] = None + + def __repr__(self) -> str: + return f"MemoryDep({self.name!r}, {self.index}, {self.ranges}, {self.mode})" + + @property + def num_vars(self): + return len(self.var_names) + + def decide_loop_order_to_match(self, other): + """ + Can return None if not able to decide loop orders. + """ + assert self.num_vars == other.num_vars + + # ignore broadcast for now since broadcast causes extra 0 strides + # which makes it hard to decide the correct loop orders. + if self.num_vars != len(self.index.free_symbols): + return None + if other.num_vars != len(other.index.free_symbols): + return None + + # bail out if any size is 0 or 1 + # For size == 0, it's an empty tensor, any strides for that dimension + # are equivalent. Skip for simplicity and it may not matter that much. + # + # For size == 1, it cause cause tie for strides of different dimensions. + # Also when we first time create LoopBody in ComputedBuffer.simplify_and_reorder + # we can dependencies.index_vars_squeeze which should already sqeeuze + # the size == 1 dimensions. + if any(s == 0 or s == 1 for s in itertools.chain(self.size, other.size)): + return None + + # Extract strides for both expression + self_strides = V.graph.sizevars.stride_hints(self.index, self.var_names) + other_strides = V.graph.sizevars.stride_hints(other.index, other.var_names) + + # Even if the shape contains no 0/1, some complex index expression may + # still have duplicate stride values. Here is an example: + # https://gist.github.com/shunting314/511a7e1ec88aa2e1a8ec85d8445ab129 + # We don't reorder the loop for these cases for now, but in theory + # we could improve the algorithm to detect the correct loop orders. + if len(set(self_strides)) != len(self_strides) or len( + set(other_strides) + ) != len(other_strides): + log.debug( + "unable to decide loop order. self_dep=%s v.s. other_dep=%s, self_strides=%s v.s. other_strides=%s", + self, + other, + self_strides, + other_strides, + ) + return None + + # May hanppen if self and other are as follows + # MemoryDep('addmm_6', 393216*d0 + 768*d1 + d2, {d0: 16, d1: 512, d2: 768}, None) + # MemoryDep('addmm_6', 98304*d0 + d1 + 768*d2, {d0: 64, d1: 768, d2: 128}, None) + if set(self_strides) != set(other_strides): + return None + + stride_to_index = {s: i for i, s in enumerate(self_strides)} + order = [] + for s in other_strides: + order.append(stride_to_index[s]) + + assert set(order) == set(range(0, self.num_vars)) + return order + + def get_offset(self): + """ + Return the offset by setting every variable to be 0. + """ + return sympy_subs(self.index, dict.fromkeys(self.var_names, 0)) + + def normalize(self) -> "MemoryDep": + """ + Normalize by merging loops. The different to normalize_with_stride_order is, + this method does not reorder loops while normalize_with_stride_order reorder + loops based on stride order. + """ + return MemoryDep( + self.name, + *_RecordLoadStoreInner._normalize(self.index, self.ranges), # type: ignore[arg-type] + self.mode, + ) + + def normalize_with_stride_order(self, prefix="t"): + r""" + Used to decide if two MemoryDep does not equal due to different loop orders. + More specifically, when dep1 and dep2 are not equal, we can normalize + both and check if they are equal after that. If yes, then the mismatch is + caused by different loop orders. + """ + # import here to avoid circular import + from torch._inductor import ir + + strides = V.graph.sizevars.stride_hints(self.index, self.var_names) + + # pick a loop order with stride ordered decreasingly + order = sorted(range(len(strides)), key=strides.__getitem__, reverse=True) + stride_reorder = ir.same_reorder(order) + sizes = self.size + var_names = self.var_names + + new_reordered_sizes = stride_reorder(sizes) + new_reordered_var_names = stride_reorder(var_names) + + new_simplified_sizes, reindex, prune = V.graph.sizevars._simplify_loops( + new_reordered_var_names, + new_reordered_sizes, + index_prevent_reordering( + [self.index], new_reordered_var_names, new_reordered_sizes + ), + ) + + # now let's create new symbols with the passed in prefix + var_ranges, add_var = var_builder(prefix) + replacement = dict( + zip( + new_reordered_var_names, + reindex([add_var(x) for x in new_simplified_sizes]), + ) + ) + new_index = sympy_subs(sympy.expand(self.index), replacement) # type: ignore[arg-type] # next PR + + out = MemoryDep(self.name, new_index, tuple(var_ranges.keys()), tuple(var_ranges.values())) # type: ignore[arg-type] + return out + + @property + def ranges(self) -> Dict[sympy.Symbol, sympy.Expr]: + """{c0: 128, c1: 512, ...}""" + return dict(zip(self.var_names, self.size)) + + def get_numel(self) -> sympy.Expr: + if self.is_indirect(): + numel = V.graph.get_numel(self.name) + else: + vars: OrderedSet[sympy.Basic] = OrderedSet(self.index.free_symbols) + numel = sympy.Integer(1) + for var, size in zip(self.var_names, self.size): + if var in vars: + numel = numel * size + return numel # type: ignore[return-value] + + def rename(self, renames: Dict[str, str]) -> "MemoryDep": + if self.name in renames: + return MemoryDep( + renames[self.name], + self.index, + var_names=self.var_names, + size=self.size, + mode=self.mode, + ) + return self + + def numbytes_hint(self): + return V.graph.sizevars.size_hint(self.get_numel()) * get_dtype_size( + V.graph.get_dtype(self.name) + ) + + def has_unbacked_symbols(self): + return len(free_unbacked_symbols(self.get_numel())) > 0 + + def is_contiguous(self) -> bool: + return isinstance(self.index, sympy.Symbol) and self.index in self.var_names + + def stride1_for_last_dim(self, result_for_complex_expression=True) -> bool: + """ + Whether the stride for the last dimension is 1. + """ + # python test/inductor/test_torchinductor_opinfo.py -k test_comprehensive_masked_scatter_cuda_float16 + # will exercise thru this corner case. + if len(self.var_names) == 0: + return True + + terms = self.index.args if isinstance(self.index, sympy.Add) else [self.index] + + last_sym = self.var_names[-1] + for term in terms: + if term is last_sym: + return True + + # Having a >1 stride for the last dimension is bad for perf + # return False. + if ( + isinstance(term, sympy.Mul) + and len(term.args) == 2 + and term.args[1] is last_sym + and isinstance(term.args[0], (int, sympy.Integer)) + and term.args[0] > 1 + ): + return False + + return result_for_complex_expression + + def is_scalar(self) -> bool: + if isinstance(self.index, sympy.Symbol): + return self.index not in self.var_names and not self.is_indirect() + return isinstance(self.index, (int, sympy.Integer)) + + def is_indirect(self) -> bool: + return any(is_indirect(v.name) for v in self.index.free_symbols) # type: ignore[attr-defined] + + +@dataclasses.dataclass(frozen=True) +class StarDep(Dep): + name: str + mode: Optional[str] = None + + # depends on the entire buffer + @property + def index(self): + raise NotImplementedError("StarDep does not have an index") + + def get_numel(self) -> sympy.Expr: + return V.graph.get_numel(self.name) # type: ignore[return-value] + + def rename(self, renames: Dict[str, str]) -> "StarDep": + if self.name in renames: + return StarDep(renames[self.name], self.mode) + return self + + def numbytes_hint(self): + return V.graph.sizevars.size_hint(self.get_numel()) * get_dtype_size( + V.graph.get_dtype(self.name) + ) + + def has_unbacked_symbols(self): + return len(free_unbacked_symbols(self.get_numel())) > 0 + + def is_contiguous(self) -> bool: + return False + + def is_scalar(self) -> bool: + return False + + def is_indirect(self) -> bool: + return False + + +# Used for tracking mutation ordering +# if A reads a buffer and B mutates it +# B must be ordered after A +# +# This is useful for a variety of reasons. +# For example, if A's read is never actually used, we can eliminate it. +# Another case is if A's buffer ends up being fused away, we never need to +# materialize that buffer +@dataclasses.dataclass(frozen=True) +class WeakDep(Dep): + # Fake dependency on unused buffer + name: str + # Buffer that is doing the mutation + mutating_buf: str + + @property + def index(self): + raise NotImplementedError("WeakDep does not have an index") + + def get_numel(self) -> sympy.Expr: + return sympy.Integer(1) + + def rename(self, renames: Dict[str, str]) -> "WeakDep": + if self.name in renames: + return WeakDep(renames[self.name], self.mutating_buf) + return self + + def numbytes_hint(self): + return 1 # Purely inserted for ordering, not an actual dep + + def has_unbacked_symbols(self): + return False + + def is_contiguous(self) -> bool: + return False + + +@dataclasses.dataclass(frozen=True) +class IndexExprDep: + index: sympy.Expr # type: ignore[assignment] + var_names: Tuple[sympy.Symbol, ...] + size: Tuple[sympy.Expr, ...] + + +@dataclasses.dataclass +class ReadWrites: + reads: OrderedSet[Dep] + writes: OrderedSet[Dep] + index_exprs: OrderedSet[IndexExprDep] + range_vars: Optional[List[sympy.Expr]] = None + var_ranges: Optional[VarRanges] = None + + def rename(self, renames: typing.Dict[str, str]) -> "ReadWrites": + return ReadWrites( + OrderedSet(dep.rename(renames) for dep in self.reads), + OrderedSet(dep.rename(renames) for dep in self.writes), + self.index_exprs, + self.range_vars, + self.var_ranges, + ) + + def with_read(self, dep: Union[Dep, Set[Dep]]) -> "ReadWrites": + assert isinstance(dep, (WeakDep, StarDep, set)) + if not isinstance(dep, set): + dep = {dep} + return ReadWrites( + OrderedSet.union(self.reads, dep), + self.writes, + self.index_exprs, + self.range_vars, + self.var_ranges, + ) + + def merge(self, other: "ReadWrites"): + reads = OrderedSet.union(self.reads, other.reads) + writes = OrderedSet.union(self.writes, other.writes) + index_exprs = OrderedSet.union(self.index_exprs, other.index_exprs) + return ReadWrites(reads - writes, writes, index_exprs) + + @staticmethod + def merge_list(read_writes: List["ReadWrites"]): + all_writes = OrderedSet.union(*[rw.writes for rw in read_writes]) + all_reads = OrderedSet.union(*[rw.reads for rw in read_writes]) - all_writes + all_index_exprs = OrderedSet.union(*[rw.index_exprs for rw in read_writes]) + return ReadWrites(all_reads, all_writes, all_index_exprs) + + def remove_reads(self, rem_reads): + return ReadWrites( + self.reads - rem_reads, + self.writes, + self.index_exprs, + self.range_vars, + self.var_ranges, + ) + + def reads_and_writes(self): + return itertools.chain(self.reads, self.writes) + + def buffer_names(self, ignore_integer_index=True): + """ + Integer index is used for load_seed. + """ + names: OrderedSet[str] = OrderedSet() + for dep in self.reads_and_writes(): + if not isinstance(dep, MemoryDep): + continue + if not ignore_integer_index or not isinstance( + dep.index, (int, sympy.Integer) + ): + names.add(dep.name) + return names + + +class _RecordLoadStoreInner(V.MockHandler): # type: ignore[name-defined] + def __init__(self, var_ranges: VarRanges, normalize: bool) -> None: + super().__init__() + self._reads: OrderedSet[Dep] = OrderedSet() + self._writes: OrderedSet[MemoryDep] = OrderedSet() + self._index_exprs: OrderedSet[IndexExprDep] = OrderedSet() + self._var_ranges: VarRanges = var_ranges + self._should_normalize: bool = normalize + + @staticmethod + def drop_unused_symbols(index, var_names, sizes): + """ + Reduction has last (reduced) dim in its sizes, but + downstream users won't. Normalize this away. + """ + if not isinstance(index, sympy.Expr): + # index can be an int + return + free_symbols = index.free_symbols + while var_names and var_names[-1] not in free_symbols: + var_names.pop() + sizes.pop() + + @classmethod + def _normalize( + cls, index: sympy.Expr, var_ranges: VarRanges + ) -> Tuple[sympy.Expr, Tuple[sympy.Symbol, ...], Tuple[sympy.Expr, ...]]: + # Try to further simplify the indexes even if simplify_loops didn't + # convert it to the simplest form because of the interference from + # different indexing formulas. + index_vars = [*var_ranges.keys()] + sizes = tuple(var_ranges.values()) # type: ignore[assignment] + new_sizes, reindex, prune = V.graph.sizevars._simplify_loops( + index_vars, + sizes, + index_prevent_reordering([index], index_vars, sizes), + ) + + # assign new variables each dimension to deal with numbering mismatches + # d0, d1, d2 could become d0, d2 -- which won't match d0, d1 + new_vars, add_var = var_builder(canonicalization_prefix()) + replacement = dict(zip(index_vars, reindex([add_var(x) for x in new_sizes]))) + index = sympy_subs(sympy.expand(index), replacement) + + new_vars = [*new_vars.keys()] + new_sizes = [*new_sizes] + cls.drop_unused_symbols(index, new_vars, new_sizes) + return index, tuple(new_vars), tuple(new_sizes) # type: ignore[arg-type] + + def canonicalize( + self, index: sympy.Expr + ) -> Tuple[sympy.Expr, Tuple[sympy.Symbol, ...], Tuple[sympy.Expr, ...]]: + if not self._should_normalize: + sizes = [V.graph.sizevars.simplify(x) for x in self._var_ranges.values()] + var_names = [k for k, v in zip(self._var_ranges.keys(), sizes) if v != 1] + sizes = [v for v in sizes if v != 1] + + self.drop_unused_symbols(index, var_names, sizes) + + return index, tuple(var_names), tuple(sizes) # type: ignore[return-value, arg-type] + var_ranges = { + k: V.graph.sizevars.simplify(v) + for k, v in self._var_ranges.items() + # TODO(jansel): explore this further normalization + # if k in free_symbols + } + return self._normalize(index, var_ranges) + + def load(self, name: str, index: sympy.Expr) -> str: + self._reads.add(MemoryDep(name, *self.canonicalize(index))) + return f"load({name}, {sympy_str(index)})" + + def load_seed(self, name: str, index: int): + assert isinstance(index, int) + return self.load(name, sympy.Integer(index)) + + def store(self, name: str, index: sympy.Expr, value: str, mode=None) -> str: + self._writes.add(MemoryDep(name, *self.canonicalize(index), mode=mode)) + return f"store({name}, {sympy_str(index)}, {value}, {mode})" + + def store_reduction(self, name: str, index, value) -> str: + return self.store(name, index, f"store_reduction({value})") + + def index_expr(self, index: sympy.Expr, dtype) -> str: + self._index_exprs.add(IndexExprDep(*self.canonicalize(index))) + return f"index_expr({sympy_str(index)}, {dtype})" + + def bucketize( + self, + values, + offsets_name: str, + offsets_size: sympy.Expr, + indexing_dtype: torch.dtype, + right: bool, + ): + self._reads.add(StarDep(offsets_name)) + return f"bucketize({values}, {offsets_name}, {sympy_str(offsets_size)}, {indexing_dtype}, {right})" + + +class RecordLoadStore(V.KernelFormatterHandler): # type: ignore[name-defined] + def __init__(self, var_ranges: VarRanges, normalize: bool) -> None: + parent_handler = _RecordLoadStoreInner( + var_ranges=var_ranges, normalize=normalize + ) + super().__init__(parent_handler=parent_handler) + + +# TODO: check call sites +def var_builder(prefix: str) -> Tuple[VarRanges, Callable[[sympy.Expr], sympy.Symbol]]: + cnt = itertools.count() + var_ranges: VarRanges = {} + + def add_var(length: sympy.Expr) -> sympy.Symbol: + v = sympy_index_symbol(f"{prefix}{next(cnt)}") + var_ranges[v] = length + return v + + return var_ranges, add_var + + +def index_vars_no_squeeze(*argsizes: Tuple[sympy.Expr, ...], prefix: str): + var_ranges, add_var = var_builder(prefix) + args: List[List[sympy.Symbol]] = [] + for size in argsizes: + args.append(list(map(add_var, size))) + return args, var_ranges + + +def index_vars_squeeze(*argsizes: Tuple[sympy.Expr, ...], prefix: str = "d"): + from .ir import SqueezeView + + var_ranges, add_var = var_builder(prefix) + args: List[List[sympy.Expr]] = [] + new_sizes: List[List[sympy.Expr]] = [] + for size in argsizes: + new_size, reindex = SqueezeView.squeezer(size) + new_sizes.append(new_size) + args.append(reindex(list(map(add_var, new_size)))) + return args, var_ranges + + +def extract_read_writes( + fn: Callable[..., Any], + *argsizes: Tuple[sympy.Expr, ...], + normalize: bool = False, + prefix: str = "d", + hidden_args=(), +): + args, var_ranges = index_vars_squeeze(*argsizes, prefix=prefix) + + from .loop_body import LoopBody, MemoryUsageType + + if isinstance(fn, LoopBody): + # Fast path to avoid tracing when we already have a LoopBody + inner = _RecordLoadStoreInner(var_ranges=var_ranges, normalize=normalize) + name_to_index = fn.indexing_from_args([*args, *hidden_args]) + if fn.indirect_vars: + # mimic the `tmpX` naming tracing gives us + repl = {v: sympy.Symbol(f"tmp{i}") for i, v in enumerate(fn.indirect_vars)} + name_to_index = {k: sympy_subs(v, repl) for k, v in name_to_index.items()} + for entry in fn.memory_usage[MemoryUsageType.LOAD]: + inner.load(entry.buffer_name, name_to_index[entry.index_name]) + for entry in fn.memory_usage[MemoryUsageType.LOAD_SEED]: + inner.load_seed(entry.buffer_name, int(name_to_index[entry.index_name])) + for entry in fn.memory_usage[MemoryUsageType.STORE]: + inner.store( + entry.buffer_name, name_to_index[entry.index_name], None, entry.mode + ) + for entry in fn.memory_usage[MemoryUsageType.STORE_REDUCTION]: + inner.store_reduction( + entry.buffer_name, name_to_index[entry.index_name], None + ) + for entry in fn.memory_usage[MemoryUsageType.INDEX_EXPR]: + inner.index_expr(name_to_index[entry.index_name], None) + for entry in fn.memory_usage[MemoryUsageType.BUCKETIZE]: + inner.bucketize( + None, entry.buffer_name, name_to_index[entry.index_name], None, None + ) + # fn.memory_usage[MemoryUsageType.CHECK_BOUNDS] intentionally skipped + else: + # Slow path tracing the function + rw = RecordLoadStore(var_ranges, normalize=normalize) + with V.set_ops_handler(rw): + fn(*args, *hidden_args) + inner = rw.parent_handler + + if normalize: + range_vars = [] # Number of vars could differ due to normalization + else: + range_vars = [*itertools.chain.from_iterable(args)] + + return ReadWrites( + OrderedSet(inner._reads), + OrderedSet(inner._writes), + inner._index_exprs, + range_vars, + var_ranges, + ) + + +def extract_input_node_reduction_ranges( + input_node: "torch._inductor.ir.TensorBox", +) -> Tuple[Optional[List[sympy.Expr]], Optional[List[sympy.Expr]]]: + """ + Returns the size and reduction size of all inputs, if the sizes and reduction_sizes (if exist) are all the same. + It's possible that a node has multiple inputs, some are Reduction nodes and others are Pointwise nodes. + In this case, reduction_sizes of the Reduction nodes need to be the same. + Otherwise returns (None, None). + """ + + from .ir import ComputedBuffer, Loops + + if isinstance(input_node.data, ComputedBuffer): + # Input node has already been realized. Return its size and reduction_size. + size = input_node.get_size() + reduction_size = input_node.get_reduction_size() + if len(reduction_size) > 0: + return (size, reduction_size) + else: + return (None, None) + + if not isinstance(input_node.data.data, Loops): # type: ignore[attr-defined] + # Other IRNodes do not have reduction_ranges. + return (None, None) + + # There is one issue: what if there are views / permutations between the input node and its dependent realized nodes? + # The current method still uses reduction ranges from the dependent realized node, which is not ideal. + # Is there a way to check whether there are permutations inbetween? + reads = input_node.get_reads() + reduction_size = None + size = None + while reduction_size is None and len(reads) > 0: + seen: OrderedSet[str] = OrderedSet() + new_reads = [] + for read in reads: + if not isinstance(read, MemoryDep): + continue + if read.name in seen: + continue + seen.add(read.name) + buffer = V.graph.try_get_buffer(read.name) + if buffer is None: + continue + op = buffer.get_defining_op() + if op is None: + continue + + if isinstance(op, ComputedBuffer) and len(op.get_reduction_size()) > 0: + if reduction_size is None: + reduction_size = op.get_reduction_size() + size = op.get_size() + elif reduction_size != op.get_reduction_size() or size != op.get_size(): + return (None, None) + else: + new_reads.extend(op.get_reads()) + if reads == new_reads: + return (size, reduction_size) + else: + reads = new_reads + return (size, reduction_size) + + +def canonicalization_prefix(): + return "c" + + +# ops handler which computes all the free unbacked symbols for an IR +class FreeUnbackedSymbolsOpsHandler: + symbols: OrderedSet[sympy.Symbol] + + def __init__(self) -> None: + self.symbols = OrderedSet() + + def __getattr__(self, name: str) -> Callable[..., Any]: + def inner(*args, **kwargs): + for a in itertools.chain(args, kwargs.values()): + if isinstance(a, (sympy.Expr, sympy.logic.boolalg.Boolean)): + self.symbols |= free_unbacked_symbols(a) + + return inner + + def indirect_indexing( + self, index_var, size, check=True, wrap_neg=True + ) -> sympy.Symbol: + assert not isinstance(index_var, (sympy.Expr, sympy.logic.boolalg.Boolean)) + self.symbols |= free_unbacked_symbols(size) + return sympy_index_symbol(f"({str(index_var)})") + + def frexp(self, x): + return (None,) * 2 + + def scan(self, dtypes, combine_fn, values): + return (None,) * len(values) + + def sort(self, dtypes, values, stable, descending): + return (None,) * len(values) + + def reduction( + self, + dtype: torch.dtype, + src_dtype: torch.dtype, + reduction_type: ReductionType, + value: Union[None, Tuple[None, ...]], + ) -> Union[None, Tuple[None, ...]]: + num_values = reduction_num_outputs(reduction_type) + return (None,) * num_values if num_values > 1 else None + + +def _typecheck_FreeUnbackedSymbolsOpsHandler( + h: FreeUnbackedSymbolsOpsHandler, +) -> OpsHandler[None]: + return h + + +def extract_free_unbacked_symbols(fn: Callable[..., Any], index, rindex=None): + from .ir import FlexibleLayout + + args = [index, rindex] if rindex is not None else [index] + handler = FreeUnbackedSymbolsOpsHandler() + # NB: I cargo culted the allow_indexing patch here, I don't understand why + # people do this all over + with V.set_ops_handler(handler), patch.object( + FlexibleLayout, "allow_indexing", True + ): + fn(*args) + return handler.symbols diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/exc.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/exc.py new file mode 100644 index 0000000000000000000000000000000000000000..728f652032d52affff0bfe216e920a4a97cd51d3 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/exc.py @@ -0,0 +1,104 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import os +import tempfile +import textwrap +from functools import lru_cache + + +if os.environ.get("TORCHINDUCTOR_WRITE_MISSING_OPS") == "1": + + @lru_cache(None) + def _record_missing_op(target): + with open(f"{tempfile.gettempdir()}/missing_ops.txt", "a") as fd: + fd.write(str(target) + "\n") + +else: + + def _record_missing_op(target): # type: ignore[misc] + pass + + +class OperatorIssue(RuntimeError): + @staticmethod + def operator_str(target, args, kwargs): + lines = [f"target: {target}"] + [ + f"args[{i}]: {arg}" for i, arg in enumerate(args) + ] + if kwargs: + lines.append(f"kwargs: {kwargs}") + return textwrap.indent("\n".join(lines), " ") + + +class MissingOperatorWithoutDecomp(OperatorIssue): + def __init__(self, target, args, kwargs) -> None: + _record_missing_op(target) + super().__init__(f"missing lowering\n{self.operator_str(target, args, kwargs)}") + + +class MissingOperatorWithDecomp(OperatorIssue): + def __init__(self, target, args, kwargs) -> None: + _record_missing_op(target) + super().__init__( + f"missing decomposition\n{self.operator_str(target, args, kwargs)}" + + textwrap.dedent( + f""" + + There is a decomposition available for {target} in + torch._decomp.get_decompositions(). Please add this operator to the + `decompositions` list in torch._inductor.decomposition + """ + ) + ) + + +class LoweringException(OperatorIssue): + def __init__(self, exc: Exception, target, args, kwargs) -> None: + super().__init__( + f"{type(exc).__name__}: {exc}\n{self.operator_str(target, args, kwargs)}" + ) + + +class SubgraphLoweringException(RuntimeError): + pass + + +class InvalidCxxCompiler(RuntimeError): + def __init__(self) -> None: + from . import config + + super().__init__( + f"No working C++ compiler found in {config.__name__}.cpp.cxx: {config.cpp.cxx}" + ) + + +class CppWrapperCodeGenError(RuntimeError): + def __init__(self, msg: str) -> None: + super().__init__(f"C++ wrapper codegen error: {msg}") + + +class CppCompileError(RuntimeError): + def __init__(self, cmd: list[str], output: str) -> None: + if isinstance(output, bytes): + output = output.decode("utf-8") + + super().__init__( + textwrap.dedent( + """ + C++ compile error + + Command: + {cmd} + + Output: + {output} + """ + ) + .strip() + .format(cmd=" ".join(cmd), output=output) + ) + + +class CUDACompileError(CppCompileError): + pass diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/extern_node_serializer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/extern_node_serializer.py new file mode 100644 index 0000000000000000000000000000000000000000..6ed505d3e60ee9896aed5953ab114779ecd54dcd --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/extern_node_serializer.py @@ -0,0 +1,25 @@ +import json +from typing import List + +from torch._export.serde.aoti_schema import ExternKernelNode, ExternKernelNodes, Node +from torch._export.serde.serialize import _dataclass_to_dict, EnumEncoder +from torch._inductor.ir import ExternKernelNode as inductor_ExternKernelNode + + +def serialize_extern_kernel_node( + extern_kernel_node: inductor_ExternKernelNode, +) -> ExternKernelNode: + assert isinstance(extern_kernel_node.node, Node) + return ExternKernelNode( + name=extern_kernel_node.name, + node=extern_kernel_node.node, + ) + + +def extern_node_json_serializer( + extern_kernel_nodes: List[inductor_ExternKernelNode], +) -> str: + serialized_nodes = ExternKernelNodes( + nodes=[serialize_extern_kernel_node(node) for node in extern_kernel_nodes] + ) + return json.dumps(_dataclass_to_dict(serialized_nodes), cls=EnumEncoder) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/freezing.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/freezing.py new file mode 100644 index 0000000000000000000000000000000000000000..9b936bf7bb1b1b4ef00195874a8d51fc737d0b16 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/freezing.py @@ -0,0 +1,269 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import itertools +import logging +import weakref +from typing import Any, List, Optional, Tuple + +import torch +import torch.utils._pytree as pytree +from torch._dynamo.utils import dynamo_timed, lazy_format_graph_code +from torch._functorch.aot_autograd import MutationType +from torch._functorch.compile_utils import fx_graph_cse +from torch._inductor.constant_folding import constant_fold, replace_node_with_constant +from torch._inductor.fx_passes.freezing_patterns import freezing_passes +from torch._inductor.fx_passes.post_grad import view_to_reshape + +from . import config + + +aten = torch.ops.aten +prims = torch.ops.prims + +log = logging.getLogger(__name__) + + +def replace_params_with_constants( + gm: torch.fx.GraphModule, + flat_params: list[Any], + fw_metadata: torch._functorch.aot_autograd.ViewAndMutationMeta, +) -> List[int]: + """ + Replaces the parameters of a PyTorch GraphModule with constants wherever possible. + Returns a list of indices representing the input parameters that were not converted to constants. + """ + params = gm.graph.find_nodes(op="placeholder") + fake_inp_nodes = params[: len(params)] + preserved_arg_indices = [] + aliased_input_args = [ + out_info.base_idx + for out_info in fw_metadata.output_info + if out_info.base_idx is not None + ] + + # TODO (tmanlaibaatar) figure out why this is different + # from mutated_inp_runtime_indices + mutated_inps = [ + i + for i, m in enumerate(fw_metadata.input_info) + if m.mutation_type + in (MutationType.MUTATED_IN_GRAPH, MutationType.MUTATED_OUT_GRAPH) + ] + + for i, (real_input, node) in enumerate(zip(flat_params, fake_inp_nodes)): + if i in mutated_inps or i in aliased_input_args: + preserved_arg_indices.append(i) + continue + replace_node_with_constant(gm, node, real_input) + # add on non param inputs + preserved_arg_indices.extend(range(len(flat_params), len(params))) + # is this necessary ? + gm.recompile() + return preserved_arg_indices + + +def freeze( + dynamo_gm: torch.fx.GraphModule, + aot_autograd_gm: torch.fx.GraphModule, + example_inputs: List[torch._subclasses.FakeTensor], +) -> Tuple[torch.fx.GraphModule, List[int]]: + """ + Inlines parameters that are not mutated into constants and optimizes the graph through constant propagation + and other techniques. If enabled, the function also discards the original parameters of the module for memory efficiency. + + Assumes that this function is run in dynamo tracing post aot_autograd. + + Args: + dynamo_gm (torch.fx.GraphModule): The Dynamo constructed GraphModule. + aot_autograd_gm (torch.fx.GraphModule): The aot_autograd constructed GraphModule to be frozen. + example_inputs (List[torch.Tensor]): A list of example input tensors to be used in the freezing process. + + Returns: + Tuple[torch.fx.GraphModule, List[int]]: A tuple containing the frozen GraphModule and a list of indices + of the inputs that were preserved (not turned into constants). + """ + # We have convert conv's weight to channels last which may meet error for .view + # when doing fake_tensor_prop. So we need to convert view to reshape first. + # See the details in fx_codegen_and_compile of compile_fx.py. + view_to_reshape(aot_autograd_gm) + + if tracing_context := torch._guards.TracingContext.try_get(): + fw_metadata = tracing_context.fw_metadata + params_flat = tracing_context.params_flat + assert fw_metadata is not None and params_flat is not None + + preserved_arg_indices = replace_params_with_constants( + aot_autograd_gm, params_flat, fw_metadata + ) + else: + inputs = aot_autograd_gm.graph.find_nodes(op="placeholder") + preserved_arg_indices = list(range(len(inputs))) + + # TODO - further restrict cse ? right now needed to dedup aliasing ops + cse_graph = fx_graph_cse(aot_autograd_gm.graph) + aot_autograd_gm.graph = cse_graph + aot_autograd_gm.recompile() + + aot_example_inputs = [example_inputs[ind] for ind in preserved_arg_indices] + freezing_passes(aot_autograd_gm, aot_example_inputs) + + constant_fold(aot_autograd_gm) + # invalidate nn Modules + if config.freezing_discard_parameters: + invalidate_eager_modules() + discard_traced_gm_params(dynamo_gm) + + log.debug( + "%s", lazy_format_graph_code("FROZEN GRAPH", aot_autograd_gm, colored=True) + ) + + return aot_autograd_gm, preserved_arg_indices + + +class ErasedTensor(torch.Tensor): + @staticmethod + def __new__(cls, elem, name, owning_mod): + return super().__new__(cls, elem.to(device="meta")) + + def __init__(self, elem, name: Optional[str], mod) -> None: + self.erased_name = name + self.owning_mod_ref = weakref.ref(mod) + + @classmethod + def __torch_dispatch__(cls, func, types, args=(), kwargs=None): + erased_tensors = [ + e + for e in pytree.arg_tree_leaves(*args, **kwargs) + if isinstance(e, ErasedTensor) + ] + assert len(erased_tensors) > 0 + e = erased_tensors[0] + + raise RuntimeError( + f"Trying to run Pytorch Eager Module after Dynamo Freezing. " + "The original parameters have been discarded for memory efficiency. " + f"Found in op {func} for erased parameter {e.erased_name} of {e.owning_mod_ref()}" + ) + + +def invalidate_eager_modules(): + with torch.utils._python_dispatch._disable_current_modes(): + for ( + mod + ) in torch._guards.TracingContext.get().module_context.nn_modules.values(): + if not isinstance(mod, torch.nn.Module): + continue + + for attr_name, tensor in list( + itertools.chain( + mod.named_parameters(recurse=False), + mod.named_buffers(recurse=False), + ) + ): + with torch._dispatch.python.no_python_dispatcher(): + e_t = ErasedTensor(tensor, attr_name, mod) + if isinstance(tensor, torch.nn.Parameter): + e_t.requires_grad_(True) + e_t._is_param = True # type: ignore[attr-defined] + setattr(mod, attr_name, e_t) + + +def discard_traced_gm_params(mod: torch.fx.GraphModule): + with torch.utils._python_dispatch._disable_current_modes(): + for attr_name, tensor in list( + itertools.chain( + mod.named_parameters(recurse=False), mod.named_buffers(recurse=False) + ) + ): + with torch._dispatch.python.no_python_dispatcher(): + e_t = ErasedTensor(tensor, attr_name, mod) + if isinstance(tensor, torch.nn.Parameter): + e_t.requires_grad_(True) + e_t._is_param = True # type: ignore[attr-defined] + setattr(mod, attr_name, e_t) + + +def enforce_output_layout(gm: torch.fx.GraphModule): + """ + Make sure the output node's layout does not change due to compiler optimizations + by adding aten.as_strided nodes with the expected strides. + + Only used for inference so we can assume all graph outputs are model outputs. + """ + *_, output_node = gm.graph.nodes + out_list = output_node.args[0] + with gm.graph.inserting_before(output_node): + for n in out_list: + if not isinstance( + n.meta["val"], torch.Tensor + ) or not torch._prims_common.is_non_overlapping_and_dense(n.meta["val"]): + continue + + # add a node to enforce eager layout + ft = n.meta["val"] + new_node = gm.graph.call_function( + prims.inductor_force_stride_order.default, (n, ft.stride()) + ) + + # can not call + # n.replace_all_uses_with(new_node) + # since it will replace the usage of n in new_node itself. + output_node.replace_input_with(n, new_node) + + gm.graph.lint() + gm.recompile() + + +def enforce_as_strided_input_layout(gm: torch.fx.GraphModule): + """ + Make sure the as_strided node's input's layout does not change due to compiler + optimizations, because the as_strided strides info depends on input tensor stride info. + """ + + as_strided_ops = [ + torch.ops.aten.as_strided.default, + torch.ops.aten.as_strided_.default, + torch.ops.aten.as_strided_scatter.default, + ] + strided_nodes = [n for n in gm.graph.nodes if n.target in as_strided_ops] + for n in strided_nodes: + with gm.graph.inserting_before(n): + # add a node to enforce eager layout + ft = n.args[0].meta["val"] + new_node = gm.graph.call_function( + prims.inductor_force_stride_order.default, (n.args[0], ft.stride()) + ) + n.replace_input_with(n.args[0], new_node) + + gm.graph.lint() + gm.recompile() + + +def convert_conv_weights_to_channels_last(gm: torch.fx.GraphModule): + """ + Convert 4d convolution weight tensor to channels last format. + + This pass is performed before freezing so the added nodes can be constant + folded by freezing. + """ + with dynamo_timed("convert_conv_weights_to_channels_last"): + convs = [n for n in gm.graph.nodes if n.target == aten.convolution.default] + for conv in convs: + weight_node = conv.args[1] + if len(weight_node.meta["val"].size()) != 4 or weight_node.meta[ + "val" + ].is_contiguous(memory_format=torch.channels_last): + # not a 4d tensor or already channels last, skip + continue + + with gm.graph.inserting_before(conv): + new_node = gm.graph.call_function( + aten.clone.default, + (weight_node,), + {"memory_format": torch.channels_last}, + ) + conv.replace_input_with(weight_node, new_node) + + enforce_as_strided_input_layout(gm) + enforce_output_layout(gm) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/b2b_gemm.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/b2b_gemm.py new file mode 100644 index 0000000000000000000000000000000000000000..5a854b5b9d99413285118f351805f77dd94f7ab3 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/b2b_gemm.py @@ -0,0 +1,746 @@ +# mypy: allow-untyped-defs +import functools +from collections import deque +from typing import Dict, List, Set, Tuple + +import torch +from torch.utils._pytree import tree_map + +from ..._dynamo.utils import counters +from ..ir import ( + ComputedBuffer, + FixedLayout, + FlexibleLayout, + InputBuffer, + StorageBox, + Subgraph, + TensorBox, +) +from ..lowering import lowerings +from ..pattern_matcher import ( + Arg, + CallFunction, + Match, + PatternMatcherPass, + register_graph_pattern, +) +from ..select_algorithm import ( + autotune_select_algorithm, + ExternKernelChoice, + TritonTemplate, + TritonTemplateCaller, +) +from ..utils import ceildiv + + +B2B_GEMM_PASS = PatternMatcherPass( + pass_name="b2b_gemm_pass", +) + + +def b2b_gemm_grid(M, P, meta): + return (ceildiv(M, meta["BLOCK_SIZE_M"]) * ceildiv(P, meta["BLOCK_SIZE_P"]), 1, 1) + + +b2b_gemm_left_template = TritonTemplate( + name="b2b_gemm_left", + grid=b2b_gemm_grid, + debug=False, + source=r""" +{{def_kernel("A", "B", "C")}} + + + # B2B_GEMM_LEFT_TRITON_ENTRANCE + + # dynamic shapes + M = {{size("A", 0)}} + N = {{size("A", 1)}} + O = {{size("C", 0)}} + P = {{size("C", 1)}} + + # dynamic strides + stride_am = {{stride("A", 0)}} + stride_an = {{stride("A", 1)}} + stride_bn = {{stride("B", 0)}} + stride_bo = {{stride("B", 1)}} + stride_co = {{stride("C", 0)}} + stride_cp = {{stride("C", 1)}} + + # output block counts + num_m_block = tl.cdiv(M, BLOCK_SIZE_M) + num_p_block = tl.cdiv(P, BLOCK_SIZE_P) + + # internal block counts + num_n_block = tl.cdiv(N, BLOCK_SIZE_N) + num_o_block = tl.cdiv(O, BLOCK_SIZE_O) + + # output block ids + pid = tl.program_id(axis=0) + m_block_id = pid // num_p_block + p_block_id = pid % num_p_block + + # accumulator + acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_P), dtype=tl.float32) + + # main loop + offs_m = (m_block_id * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) + offs_p = (p_block_id * BLOCK_SIZE_P + tl.arange(0, BLOCK_SIZE_P)) + # (subgraph(A @ B) @ C) + offs_o = tl.arange(0, BLOCK_SIZE_O) + for _ in range(num_o_block): + c_mask = (offs_o[:, None] < O) & (offs_p[None, :] < P) + c_ptrs = C + (offs_o[:, None] * stride_co + offs_p[None, :] * stride_cp) + c = tl.load(c_ptrs, mask=c_mask, other=0.0).to(tl.float32) # BLOCK_SIZE_O * BLOCK_SIZE_P + acc_ab = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_O), dtype=tl.float32) + offs_n = tl.arange(0, BLOCK_SIZE_N) + for __ in range(num_n_block): + a_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N) + a_ptrs = A + (offs_m[:, None] * stride_am + offs_n[None, :] * stride_an) + a = tl.load(a_ptrs, mask=a_mask, other=0.0).to(tl.float32) # BLOCK_SIZE_M * BLOCK_SIZE_N + b_mask = (offs_n[:, None] < N) & (offs_o[None, :] < O) + b_ptrs = B + (offs_n[:, None] * stride_bn + offs_o[None, :] * stride_bo) + b = tl.load(b_ptrs, mask=b_mask, other=0.0).to(tl.float32) # BLOCK_SIZE_N * BLOCK_SIZE_O + acc_ab += tl.dot(a, b, out_dtype=tl.float32) + offs_n += BLOCK_SIZE_N + # apply the subgraph + {{ modification( + subgraph_number=0, + output_name="post_subgraph_acc_ab", + inner_mm="acc_ab" + ) | indent_except_first(2) }} + acc += tl.dot(post_subgraph_acc_ab, c, out_dtype=tl.float32) + offs_o += BLOCK_SIZE_O + + # type conversion + acc = acc.to(tl.float16) + + # store preparation + idx_m = offs_m[:, None] + idx_p = offs_p[None, :] + out_mask = (idx_m < M) & (idx_p < P) + + {{store_output(("idx_m", "idx_p"), "acc", "out_mask")}} +""", +) + + +b2b_gemm_right_template = TritonTemplate( + name="b2b_gemm_right", + grid=b2b_gemm_grid, + debug=False, + source=r""" +{{def_kernel("A", "B", "C")}} + + + # B2B_GEMM_RIGHT_TRITON_ENTRANCE + + # dynamic shapes + M = {{size("A", 0)}} + N = {{size("A", 1)}} + O = {{size("C", 0)}} + P = {{size("C", 1)}} + + # dynamic strides + stride_am = {{stride("A", 0)}} + stride_an = {{stride("A", 1)}} + stride_bn = {{stride("B", 0)}} + stride_bo = {{stride("B", 1)}} + stride_co = {{stride("C", 0)}} + stride_cp = {{stride("C", 1)}} + + # output block counts + num_m_block = tl.cdiv(M, BLOCK_SIZE_M) + num_p_block = tl.cdiv(P, BLOCK_SIZE_P) + + # internal block counts + num_n_block = tl.cdiv(N, BLOCK_SIZE_N) + num_o_block = tl.cdiv(O, BLOCK_SIZE_O) + + # output block ids + pid = tl.program_id(axis=0) + m_block_id = pid // num_p_block + p_block_id = pid % num_p_block + + # accumulator + acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_P), dtype=tl.float32) + + # main loop (two cases) + offs_m = (m_block_id * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) + offs_p = (p_block_id * BLOCK_SIZE_P + tl.arange(0, BLOCK_SIZE_P)) + # (A @ subgraph(B @ C)) + offs_n = tl.arange(0, BLOCK_SIZE_N) + for _ in range(num_n_block): + a_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N) + a_ptrs = A + (offs_m[:, None] * stride_am + offs_n[None, :] * stride_an) + a = tl.load(a_ptrs, mask=a_mask, other=0.0).to(tl.float32) # BLOCK_SIZE_M * BLOCK_SIZE_N + acc_bc = tl.zeros((BLOCK_SIZE_N, BLOCK_SIZE_P), dtype=tl.float32) + offs_o = tl.arange(0, BLOCK_SIZE_O) + for __ in range(num_o_block): + b_mask = (offs_n[:, None] < N) & (offs_o[None, :] < O) + b_ptrs = B + (offs_n[:, None] * stride_bn + offs_o[None, :] * stride_bo) + b = tl.load(b_ptrs, mask=b_mask, other=0.0).to(tl.float32) # BLOCK_SIZE_N * BLOCK_SIZE_O + c_mask = (offs_o[:, None] < O) & (offs_p[None, :] < P) + c_ptrs = C + (offs_o[:, None] * stride_co + offs_p[None, :] * stride_cp) + c = tl.load(c_ptrs, mask=c_mask, other=0.0).to(tl.float32) # BLOCK_SIZE_O * BLOCK_SIZE_P + acc_bc += tl.dot(b, c, out_dtype=tl.float32) + offs_o += BLOCK_SIZE_O + # apply the subgraph + {{ modification( + subgraph_number=0, + output_name="post_subgraph_acc_bc", + inner_mm="acc_bc" + ) | indent_except_first(2) }} + acc += tl.dot(a, post_subgraph_acc_bc, out_dtype=tl.float32) + offs_n += BLOCK_SIZE_N + + # type conversion + acc = acc.to(tl.float16) + + # store preparation + idx_m = offs_m[:, None] + idx_p = offs_p[None, :] + out_mask = (idx_m < M) & (idx_p < P) + + {{store_output(("idx_m", "idx_p"), "acc", "out_mask")}} +""", +) + + +# Note: load_ratio_left and load_ratio_right are only calculating numbers +# in the trivial subgraph case; i.e. (A @ (B @ C)) or ((A @ B) @ C) + + +def load_ratio_left( + M: int, N: int, O: int, P: int, m: int, n: int, o: int, p: int +) -> float: + """ + compute the ratio of estimated numbers of loads in baseline and b2bgemm + M, N, O, P are matrix sizes + m, n, o, p are block sizes + | | baseline (lower bound) | b2bgemm + | load | M * N + N * O + M * O + O * P | M / m * P / p * O / o * (o * p + N / n * (m * n + n * o)) + | store | M * O + M * P | M * P + b2bgemm is always better on stores, but for loads we need to find out beneficial cases using this function + """ + base = M * N + N * O + M * O + O * P + gemm = ( + ceildiv(M, m) + * ceildiv(P, p) + * ceildiv(O, o) + * (o * p + ceildiv(N, n) * (m * n + n * o)) + ) + return base / gemm + + +def load_ratio_right( + M: int, N: int, O: int, P: int, m: int, n: int, o: int, p: int +) -> float: + """ + compute the ratio of estimated numbers of loads in baseline and b2bgemm + M, N, O, P are matrix sizes + m, n, o, p are block sizes + | | baseline (lower bound) | b2bgemm + | load | N * O + O * P + M * N + N * P | M / m * P / p * N / n * (m * n + O / o * (n * o + o * p)) + | store | N * P + M * P | M * P + b2bgemm is always better on stores, but for loads we need to find out beneficial cases using this function + """ + base = N * O + O * P + M * N + N * P + gemm = ( + ceildiv(M, m) + * ceildiv(P, p) + * ceildiv(N, n) + * (m * n + ceildiv(O, o) * (n * o + o * p)) + ) + return base / gemm + + +# the block sizes are limited by hardware (the shared memory) +# intuitively, the optimization works when the intermediate matrix is large +# and we assign large block sizes to large dimensions +b2b_gemm_configs = [ + { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 16, + "BLOCK_SIZE_O": 16, + "BLOCK_SIZE_P": 16, + "num_stages": 4, + "num_warps": 8, + }, + { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_O": 32, + "BLOCK_SIZE_P": 32, + "num_stages": 2, + "num_warps": 4, + }, + { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_O": 64, + "BLOCK_SIZE_P": 64, + "num_stages": 2, + "num_warps": 4, + }, + { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 16, + "BLOCK_SIZE_O": 128, + "BLOCK_SIZE_P": 16, + "num_stages": 4, + "num_warps": 8, + }, + { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_O": 128, + "BLOCK_SIZE_P": 32, + "num_stages": 2, + "num_warps": 4, + }, + { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_O": 128, + "BLOCK_SIZE_P": 64, + "num_stages": 2, + "num_warps": 4, + }, + { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 16, + "BLOCK_SIZE_O": 16, + "BLOCK_SIZE_P": 128, + "num_stages": 4, + "num_warps": 8, + }, + { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_O": 32, + "BLOCK_SIZE_P": 128, + "num_stages": 2, + "num_warps": 4, + }, + { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_O": 64, + "BLOCK_SIZE_P": 128, + "num_stages": 2, + "num_warps": 4, + }, + { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_O": 16, + "BLOCK_SIZE_P": 128, + "num_stages": 4, + "num_warps": 8, + }, + { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_O": 32, + "BLOCK_SIZE_P": 128, + "num_stages": 2, + "num_warps": 4, + }, + { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_O": 64, + "BLOCK_SIZE_P": 128, + "num_stages": 2, + "num_warps": 4, + }, +] + + +def is_b2b_gemm_good_on( + is_left_assoc: bool, + A_node: torch.fx.Node, + B_node: torch.fx.Node, + C_node: torch.fx.Node, +) -> bool: + """ + checks whether the sizes are good for b2b_gemm + """ + # basic checks + if not all(["val" in A_node.meta, "val" in B_node.meta, "val" in C_node.meta]): + return False + A, B, C = ( + A_node.meta["val"], + B_node.meta["val"], + C_node.meta["val"], + ) # torch._subclasses.fake_tensor.FakeTensor + if not all([A.is_cuda, B.is_cuda, C.is_cuda]): + return False + if not all([len(A.shape) == 2, len(B.shape) == 2, len(C.shape) == 2]): + return False + if not ((A.shape[1] == B.shape[0]) and (B.shape[1] == C.shape[0])): + return False + # size checks: we only dispatch to B2B-GEMM when the average load ratio is > 1 + M, N = A.shape + O, P = C.shape + ratios = [] + if is_left_assoc: + for config in b2b_gemm_configs: + ratio = load_ratio_left( + M, + N, + O, + P, + config["BLOCK_SIZE_M"], + config["BLOCK_SIZE_N"], + config["BLOCK_SIZE_O"], + config["BLOCK_SIZE_P"], + ) + ratios.append(ratio) + else: + for config in b2b_gemm_configs: + ratio = load_ratio_right( + M, + N, + O, + P, + config["BLOCK_SIZE_M"], + config["BLOCK_SIZE_N"], + config["BLOCK_SIZE_O"], + config["BLOCK_SIZE_P"], + ) + ratios.append(ratio) + ratios.sort(reverse=True) + average_ratio = 1.0 + for r in ratios[:3]: # top 3 choices + average_ratio *= r + average_ratio = average_ratio ** (1 / 3) + return ( + average_ratio > 1 + ) # even if average_ratio is close to 1, the number of stores is always better + + +def unoptimized_b2b_gemm( + is_left_assoc: bool, + subgraph: Subgraph, + A: torch.Tensor, + B: torch.Tensor, + C: torch.Tensor, + *, + out: torch.Tensor, +) -> torch.Tensor: + """ + The unoptimized version is used as a fallback when the b2b_gemm kernel is not beneficial. + """ + if is_left_assoc: + torch.mm(subgraph.graph_module(torch.mm(A, B)), C, out=out) + else: + torch.mm(A, subgraph.graph_module(torch.mm(B, C)), out=out) + return out + + +unoptimized_choice = ExternKernelChoice(unoptimized_b2b_gemm) + + +def build_subgraph_buffer( + args: List[TensorBox], + subgraph: Subgraph, +): + """ + This function is adapted from ../kernel/flex_attention.py. + The goal is to take in the required args and produce the subgraph buffer + The subgraph buffer is a ComputedBuffer that will be inlined into the triton template + + Args: + args: The args that are passed into the subgraph + subgraph: The Subgraph ir for which to produce the output node + """ + cnt = 0 + env = {} + for node in subgraph.graph_module.graph.nodes: + if node.op == "placeholder": + env[node] = args[cnt] + cnt += 1 + elif node.op == "call_function": + # For call_function we use the default lowerings and pass in the + # already created TensorBoxes as args + args, kwargs = tree_map( + lambda x: env[x] if x in env else x, (node.args, node.kwargs) + ) + env[node] = lowerings[node.target](*args, **kwargs) + elif node.op == "output": + + def convert_output_node_to_buffer(output): + if output is None: + return None + output_node = output + output_buffer = env[output_node] + assert isinstance(output_buffer, TensorBox), ( + "The output node for B2B-GEMM's subgraph must be a TensorBox, but got: ", + type(output_buffer), + ) + assert isinstance(output_buffer.data, StorageBox), ( + "The output node for B2B-GEMM's subgraph must be a StorageBox, but got: ", + type(output_buffer), + ) + subgraph_buffer = ComputedBuffer( + name=None, + layout=FlexibleLayout( + device=output_buffer.data.get_device(), + dtype=output_buffer.data.get_dtype(), + size=output_buffer.data.get_size(), + ), + data=output_buffer.data.data, # type: ignore[arg-type] + ) + return subgraph_buffer + + # node.args[0] should be a single element representing the output of the subgraph + return tree_map(convert_output_node_to_buffer, node.args[0]) + + raise ValueError("B2B-GEMM was passed a subgraph with no output node!") + + +def create_placeholder( + name: str, dtype: torch.dtype, device: torch.device +) -> TensorBox: + """ + Creates a placeholder input buffers for producing subgraph_output + """ + input_buffer = InputBuffer(name, FixedLayout(device, dtype, [], [])) + return TensorBox.create(input_buffer) + + +def tuned_b2b_gemm( + is_left_assoc: bool, + subgraph: Subgraph, + A: torch._inductor.ir.TensorBox, + B: torch._inductor.ir.TensorBox, + C: torch._inductor.ir.TensorBox, + *, + layout=None, +) -> torch._inductor.ir.TensorBox: + # call .realize() to get rid of Pointwise + A.realize() + B.realize() + C.realize() + layout = FixedLayout(A.get_device(), A.get_dtype(), [A.shape[0], C.shape[1]]) + subgraph_buffer = build_subgraph_buffer( + [create_placeholder("inner_mm", A.get_dtype(), A.get_device())], + subgraph, + ) + choices: list[TritonTemplateCaller] = [] + for config in b2b_gemm_configs: + if is_left_assoc: + b2b_gemm_left_template.maybe_append_choice( + choices, + input_nodes=(A, B, C), + layout=layout, + subgraphs=[subgraph_buffer], + **config, + ) + else: + b2b_gemm_right_template.maybe_append_choice( + choices, + input_nodes=(A, B, C), + layout=layout, + subgraphs=[subgraph_buffer], + **config, + ) + # add the unoptimized choice to mitigate performance degradation + choices.append( + unoptimized_choice.bind( + (A, B, C), layout, is_left_assoc=is_left_assoc, subgraph=subgraph + ) + ) + # autotune + return autotune_select_algorithm("b2b_gemm", choices, [A, B, C], layout) + + +# match the inner mm of a potential b2b_gemm +@register_graph_pattern( + CallFunction(torch.ops.aten.mm, Arg(), Arg()), + pass_dict=B2B_GEMM_PASS, +) +def b2b_gemm_handler(match: Match, mat1: torch.fx.Node, mat2: torch.fx.Node) -> None: + # match.args: list[torch.fx.Node] + + def is_pointwise_node(node: torch.fx.Node) -> bool: + return ( + node.op == "call_function" + and isinstance(node.target, torch._ops.OpOverload) + and (torch.Tag.pointwise in node.target.tags) + ) + + def is_mm(node: torch.fx.Node) -> bool: + return node.target == torch.ops.aten.mm.default + + # the inner MM + inner_mm = match.nodes[-1] + + # find the (candidate) outer MM, which will be re-checked below to ensure every path reaches it + # In a real (A @ f(B @ C)), every path starting from (B @ C) must reach (A @ _). + outer_mm = None + node = inner_mm + while len(node.users) > 0: + node = next(iter(node.users)) + if is_mm(node): + outer_mm = node + break + elif is_pointwise_node(node): + continue + else: + break + if not outer_mm: + return + + # find the unique input node for outer_mm representing f(B @ C) in (A @ f(B @ C)) + # we call it the "f_node" + # when the pattern is simply (A @ (B @ C)), f_node is just inner_mm + f_node = inner_mm + while next(iter(f_node.users)) is not outer_mm: + f_node = next(iter(f_node.users)) + + def all_reach_via_pointwise_with_no_other_inputs( + src: torch.fx.Node, + dst: torch.fx.Node, + ) -> Tuple[bool, Set[torch.fx.Node]]: + """ + check whether every user path from src reaches dst via pointwise nodes, + with no other input nodes for the intermediates and dst; + return + (1) the Boolean value + (2) the subgraph node set including src and dst (which only makes sense when the Boolean value is True) + """ + visited: Set[torch.fx.Node] = set() + input_counter: Dict[torch.fx.Node, int] = {} + + all_reachable = True + queue = deque([src]) + while queue: + node = queue.popleft() + if node not in visited: + if node is dst: + visited.add(node) + elif (node is src) or is_pointwise_node(node): + for user in node.users.keys(): + # for nodes other than dst, bookkeep their users' input counts + if user not in input_counter: + input_counter[user] = len(user.all_input_nodes) + input_counter[user] -= 1 + # continue BFS + queue.append(user) + visited.add(node) + else: + all_reachable = False + break + + return ( + all_reachable and all(count == 0 for count in input_counter.values()), + visited, + ) + + # check inner_mm reaches f_node on every user path via pointwise nodes with no outside input_nodes + ok, subgraph_node_set = all_reach_via_pointwise_with_no_other_inputs( + inner_mm, f_node + ) + if not ok: + return + + # check inner_mm's inputs and f_node's outputs + if not (len(inner_mm.all_input_nodes) == 2 and len(f_node.users) == 1): + return + + # at this point, the nodes between inner_mm and f_node (both included) + # are all used internally inside (A @ subgraph(B @ C)) + # i.e. they neither have other users nor have other inputs + + # original graph and module + graph, module = inner_mm.graph, inner_mm.graph.owning_module + + # construct the new (sub)graph + subgraph_node_list: List[ + torch.fx.Node + ] = [] # ordered list of nodes used for node removal later + new_graph: torch.fx.Graph = torch.fx.Graph() + node_remapping: Dict[torch.fx.Node, torch.fx.Node] = {} + new_input_anchor: torch.fx.Node # inner_mm, to be changed to an input node + new_output_anchor: torch.fx.Node # f_node, to be used to construct an output node + new_input_node: torch.fx.Node + new_output_node: torch.fx.Node + for node in graph.nodes: # preserve the order of nodes + if node in subgraph_node_set: + subgraph_node_list.append(node) + new_node = new_graph.node_copy( + node, lambda x: node_remapping[x] if x in node_remapping else x + ) + node_remapping[node] = new_node + if node is inner_mm: + new_input_anchor = new_node + if node is f_node: + new_output_anchor = new_node + if new_input_anchor is not new_output_anchor: # subgraph is non-trivial + # update the input node + with new_graph.inserting_before(new_input_anchor): + new_input_node = new_graph.placeholder(name="subgraph_input") + new_input_node.meta.update(new_input_anchor.meta) + new_input_anchor.replace_all_uses_with(new_input_node) + new_graph.erase_node(new_input_anchor) + # add the output node + new_output_node = new_graph.output(new_output_anchor) + new_output_node.meta.update(new_output_anchor.meta) + else: # subgraph is trivial, e.g. (A @ (B @ C)) + # update the input node + with new_graph.inserting_before(new_input_anchor): + new_input_node = new_graph.placeholder(name="subgraph_input") + new_input_node.meta.update(new_input_anchor.meta) + new_input_anchor.replace_all_uses_with(new_input_node) + new_graph.erase_node(new_input_anchor) + # update the output node (don't use new_output_anchor since it has been erased) + new_output_node = new_graph.output(new_input_node) + new_output_node.meta.update(new_input_node.meta) + new_graph.lint() + + # construct the subgraph + subgraph = Subgraph( + name="subgraph", graph_module=torch.fx.GraphModule(module, new_graph) + ) + + # two cases + # (1) (subgraph(A @ B) @ C), called "left_assoc" + # (2) (A @ subgraph(B @ C)), called "right_assoc" + is_left_assoc = outer_mm.args[0] is f_node + + # find the nodes A, B, C and check the sizes + A: torch.fx.Node + B: torch.fx.Node + C: torch.fx.Node + if is_left_assoc: + A = inner_mm.args[0] # type: ignore[assignment] + B = inner_mm.args[1] # type: ignore[assignment] + C = outer_mm.args[1] # type: ignore[assignment] + else: + A = outer_mm.args[0] # type: ignore[assignment] + B = inner_mm.args[0] # type: ignore[assignment] + C = inner_mm.args[1] # type: ignore[assignment] + if not is_b2b_gemm_good_on(is_left_assoc, A, B, C): + return + + # finally update the original graph + counters["inductor"]["b2b_gemm"] += 1 + graph = match.graph + with graph.inserting_before(outer_mm): + function = functools.partial(tuned_b2b_gemm, is_left_assoc, subgraph) + function.__name__ = tuned_b2b_gemm.__name__ # type: ignore[attr-defined] + function._inductor_lowering_function = True # type: ignore[attr-defined] + replacement: torch.fx.Node = graph.call_function( + function, + (A, B, C), + match.kwargs, + ) + replacement.meta.update(outer_mm.meta) + outer_mm.replace_all_uses_with(replacement) + # erase unnecessary nodes + graph.erase_node(outer_mm) + for node in reversed(subgraph_node_list): + graph.erase_node(node) + graph.lint() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/binary_folding.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/binary_folding.py new file mode 100644 index 0000000000000000000000000000000000000000..bd1e0736d510faf10918530e0915d1c503dc09f6 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/binary_folding.py @@ -0,0 +1,276 @@ +# mypy: allow-untyped-defs +import functools +import itertools + +import torch + +from ..._dynamo.utils import counters +from ..pattern_matcher import Arg, CallFunction, KeywordArg +from .freezing_patterns import register_binary_folding_pattern + + +aten = torch.ops.aten +prims = torch.ops.prims + + +def mark_mixed_dtype_conv(conv): + conv_dtype = conv.meta["val"].dtype + if conv_dtype not in (torch.float16, torch.bfloat16): + return + + if not len(conv.users) == 1: + return + + conv_user = next(iter(conv.users.keys())) + if not isinstance(conv_user.meta["val"], torch.Tensor): + return + + if not conv_user.meta["val"].dtype == torch.float32: + return + + while conv_user.target in _binary_ops: + if not len(conv_user.users) == 1: + return + + conv_user = next(iter(conv_user.users.keys())) + + if conv_user.target != prims.convert_element_type.default: + return + + conv.meta["_allow_conv_mixed_dtype_folding"] = conv_dtype + + +def mark_mixed_dtype_allowed_convs(gm): + """ + Mark convolutions which we will binary fold even with mixed precision constants. We constant fold in the higher precision + for better accuracy and then recover the original precision after. + """ + for node in gm.graph.find_nodes( + op="call_function", target=aten.convolution.default + ): + mark_mixed_dtype_conv(node) + + +def recover_original_precision_folded_convs(gm): + """ + After binary folding conv weights and biases to a higher dtype, recover the original precision they were in. + """ + graph = gm.graph + for node in graph.find_nodes(op="call_function", target=aten.convolution.default): + orig_dtype = node.meta.get("_allow_conv_mixed_dtype_folding", None) + if orig_dtype is None: + continue + + with graph.inserting_before(node): + for idx in [1, 2]: + old_input = node.args[idx] + if old_input is None: + continue + + new_input = graph.create_node( + "call_function", + prims.convert_element_type.default, + (old_input, orig_dtype), + ) + node.replace_input_with(old_input, new_input) + + +_binary_ops = [aten.add.Tensor, aten.sub.Tensor, aten.mul.Tensor, aten.div.Tensor] + + +@functools.lru_cache(None) +def binary_folding_init(): + _conv_args = [Arg() for _ in range(9)] + _computation_ops = [aten.convolution.default] + _computation_calls = [CallFunction(aten.convolution.default, *_conv_args, _users=1)] + + """ + In order to fuse add/sub/mul/div with conv, the dimensions of its + constant tensor must satisfy the following: + - with resizing, broadcast to w/ weight/bias tensor shape + - broadcast to the conv output shape + It needs to have a shape that can resize to weight/bias + tensor shape because we need to run the op with the conv + weights/bias without changing their sizes. + It needs to broadcast to the conv output shape so that we do + accidentally change the shape of op output by pre-fusing it + compared to eager. + The only dimension value shared by weight/bias/conv output + is they all contain a dim with value = channels-out. In the + conv output tensor, this is in the second dimension, + so the pointwise op tensor may have a second dimension of + value == channels-out, but all the other dimensions have to be 1 + """ + + def _op_not_broadcasting_with_conv(weight_tensor, other_tensor): + # According to opDoesNotBroadCastWithConv of frozen_conv_folding.cpp + weight_shape = weight_tensor.shape + other_shape = other_tensor.shape + if len(weight_shape) < len(other_shape): + return False + if len(weight_shape) == len(other_shape) + 1: + # weight shape is [o, i, *], other_shape is [o, 1...]. + for i in reversed(range(len(other_shape))): + if i == 0 and weight_shape[0] == other_shape[i]: + continue + if other_shape[i] != 1: + return False + else: + # weight shape is [o, i, *], other_shape is [1, i, *] + for i in reversed(range(len(other_shape))): + if i == 1 and weight_shape[0] == other_shape[i]: + continue + if other_shape[i] != 1: + return False + return True + + def _check_conv_and_broadcast_op(conv_node, other): + # According to checkConvAndBroadcastingOpPreConditions of frozen_conv_folding.cpp. + # conv.weight + if conv_node.args[1].op != "get_attr": + return False + # conv.bias + if conv_node.args[1] is not None and conv_node.args[1].op != "get_attr": + return False + if ( + not isinstance(other, int) + and not isinstance(other, float) + and other.op != "get_attr" + ): + return False + + if not len(conv_node.args[1].users) == 1: + return False + + weight_meta_value = conv_node.args[1].meta.get("val") + if weight_meta_value is None: + return False + # Avoid fusing op that causes type promotion + # restricting to float avoids int/float difficulties with scalar overload + if not weight_meta_value.is_floating_point(): + return False + if isinstance(other, torch.fx.Node) and other.op == "get_attr": + other_meta_value = other.meta.get("val") + if not other_meta_value.is_floating_point(): # type: ignore[union-attr] + return False + if ( + torch.promote_types(other_meta_value.dtype, weight_meta_value.dtype) # type: ignore[union-attr] + != weight_meta_value.dtype + ): + if not conv_node.meta.get("_allow_conv_mixed_dtype_folding", False): + return False + + if ( + other_meta_value.dtype != torch.float # type: ignore[union-attr] + and weight_meta_value.dtype not in (torch.float16, torch.bfloat16) + ): + return False + + if not _op_not_broadcasting_with_conv(weight_meta_value, other_meta_value): + return False + else: + # TODO: support scalar case + return False + + return True + + def _is_foldable_pattern(match): + binary_node = match.output_node() + computation_node = binary_node.args[0] + other = binary_node.args[1] + if binary_node.args[0].target not in _computation_ops: + computation_node = binary_node.args[1] + other = binary_node.args[0] + if binary_node.args[0].target == aten.convolution.default: + return _check_conv_and_broadcast_op(computation_node, other) + + return False + + def resize_scalar_or_tensor_to_shape(graph, other, shape): + # TODO: support scalar case + if other.meta.get("val").numel() == 1: + # expand errors if the shape input has less # dims than the tensor input + res = graph.create_node( + "call_function", + aten.reshape.default, + (other, (1,)), + ) + res = graph.create_node( + "call_function", + aten.expand.default, + (res, shape), + ) + else: + res = graph.create_node( + "call_function", + aten.reshape.default, + (other, shape), + ) + return res + + def _create_new_conv_node(graph, conv_node, binary_node, other): + assert conv_node.target == aten.convolution.default + conv_args = list(conv_node.args) + weight_meta_value = conv_node.args[1].meta.get("val") + bias = conv_args[2] + if binary_node.target in [aten.add.Tensor, aten.sub.Tensor]: + other_reshape = resize_scalar_or_tensor_to_shape( + graph, other, (weight_meta_value.size(0),) + ) + new_bias = graph.create_node( + "call_function", + binary_node.target, + (0 if bias is None else bias, other_reshape), + ) + conv_args[2] = new_bias + else: + assert binary_node.target in [aten.mul.Tensor, aten.div.Tensor] + weight_broadcast_shape = [1 for _ in range(len(weight_meta_value.shape))] + weight_broadcast_shape[0] = weight_meta_value.size(0) + other_reshape1 = resize_scalar_or_tensor_to_shape( + graph, other, tuple(weight_broadcast_shape) + ) + new_weight = graph.create_node( + "call_function", binary_node.target, (conv_args[1], other_reshape1) + ) + new_weight.meta.update(conv_args[1].meta) + conv_args[1] = new_weight + if bias is not None: + other_reshape = resize_scalar_or_tensor_to_shape( + graph, other, (weight_meta_value.size(0),) + ) + new_bias = graph.create_node( + "call_function", binary_node.target, (bias, other_reshape) + ) + new_bias.meta.update(bias.meta) + conv_args[2] = new_bias + return graph.create_node("call_function", conv_node.target, tuple(conv_args)) + + for _computation_call, binary_op in itertools.product( + _computation_calls, _binary_ops + ): + + @register_binary_folding_pattern( + CallFunction(binary_op, _computation_call, KeywordArg("other")), + extra_check=_is_foldable_pattern, + ) + def folded_op(match, *args, **kwargs): + counters["inductor"]["binary_folding"] += 1 + other = kwargs.get("other") + binary_node = match.output_node() + computation_node = ( + binary_node.args[0] + if binary_node.args[0].target in _computation_ops + else binary_node.args[1] + ) + graph = match.graph + with graph.inserting_before(binary_node): + # TODO: support linear? + assert computation_node.target == aten.convolution.default + new_computation_node = _create_new_conv_node( + graph, computation_node, binary_node, other + ) + binary_node.replace_all_uses_with(new_computation_node) + new_computation_node.meta.update(computation_node.meta) + graph.erase_node(binary_node) + graph.erase_node(computation_node) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/ddp_fusion.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/ddp_fusion.py new file mode 100644 index 0000000000000000000000000000000000000000..6c4e57a1d505f6c83ed1e1883242a9e7fcdb0e8a --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/ddp_fusion.py @@ -0,0 +1,599 @@ +# Owner(s): ["oncall: distributed"] +import collections +import inspect +import logging +import math +import operator +from dataclasses import dataclass +from functools import partial +from typing import ( + Any, + Callable, + cast, + Dict, + Generator, + List, + Optional, + Set, + Tuple, + Union, +) + +import torch +import torch.fx as fx +from torch._dynamo.utils import counters +from torch.fx.passes.graph_transform_observer import GraphTransformObserver +from torch.fx.passes.shape_prop import _extract_tensor_metadata, TensorMetadata +from torch.utils._pytree import tree_flatten, tree_map, tree_unflatten + +from .. import config +from ..fx_utils import get_fake_args_kwargs +from ..virtualized import V + + +aten = torch.ops.aten +logger: logging.Logger = logging.getLogger("comm_fusion") + + +def move_block_after(block: List[fx.Node], target_node: fx.Node) -> None: + for node in block: + target_node.append(node) + target_node = node + + +def move_block_before(block: List[fx.Node], target_node: fx.Node) -> None: + for node in block: + target_node.prepend(node) + target_node = node + + +def call_function( + graph: fx.Graph, + target: Union[str, Callable[..., Any]], + args: Optional[Tuple[fx.node.Argument, ...]] = None, + kwargs: Optional[Dict[str, fx.node.Argument]] = None, +) -> fx.Node: + # We accept target as a str to avoid typing error as the type of + # a node.target is Union[str, Callable[..., Any]]. + # This also allows us to avoid writing check for every call. + if isinstance(target, str): + raise RuntimeError(f"Call function should not get a str target {target=}") + node = graph.call_function(target, args, kwargs) + _, args, kwargs = get_fake_args_kwargs(node) + with V.fake_mode: + node.meta["val"] = target(*args, **kwargs) + # node.meta["val"] may be a container. So we use tree_map here + # to recursively extract the tensor metadata. + node.meta["tensor_meta"] = tree_map( + _extract_tensor_metadata, (node.meta["val"],) + )[0] + return node + + +@dataclass(unsafe_hash=True) +class CommBlock: + shape: Union[torch.Size, List[torch.Size]] + node_list: List[fx.Node] + inputs: List[fx.Node] + wait_nodes: List[fx.Node] + comm_node: fx.Node + outputs: Set[fx.Node] + + +def get_comm_block(comm_node: fx.Node) -> Optional[CommBlock]: + """ + Given a collective node (e.g., allreduce), find out all the nodes belong to + this communcation. + + Args: + comm_node(fx.Node): The target communication/collective node. + Returns: + The CommBlock that encapsulates the related nodes (e.g., wait_node) of + the given comm_node. + """ + node_list = [] + wait_nodes = [] + inputs, _ = tree_flatten((comm_node.args, comm_node.kwargs)) + input_nodes = [inp for inp in inputs if isinstance(inp, fx.Node)] + wait_prefixes = "wait_tensor" + # If the users of the wait node are following items, we consinder them + # to be a part of the output. + intermediate_outputs = ("split", "reshape", "getitem", "detach", "alias") + + first_user = next(iter(comm_node.users)) + if ( + len(comm_node.users) == 1 + and first_user.target == torch.ops._c10d_functional.wait_tensor.default + ): + # Collective with only one output + node_list = [comm_node, first_user] + wait_nodes.append(first_user) + elif len(comm_node.users) > 1 and first_user.target == operator.getitem: + # Collective with only more than one output + node_list.append(comm_node) + for user in comm_node.users: + if user.target != operator.getitem: + return None + if len(user.users) != 1: + return None + wait_node = next(iter(user.users)) + if wait_node.target != torch.ops._c10d_functional.wait_tensor.default: + return None + wait_nodes.append(wait_node) + node_list.append(user) + node_list.extend(wait_nodes) + else: + return None + + # Identify all the outputs of this collective block. + outputs: Set[fx.Node] = set() + nodes = collections.deque(wait_nodes) + while nodes: + node = nodes.popleft() + for user in node.users: + if isinstance(user, fx.Node) and user.name.startswith(intermediate_outputs): + nodes.append(user) + node_list.append(user) + else: + outputs.add(node) + break + + tensor_meta = input_nodes[0].meta["tensor_meta"] + shape: Union[torch.Size, List[torch.Size]] + if isinstance(tensor_meta, TensorMetadata): + shape = tensor_meta.shape + elif isinstance(tensor_meta, (list, tuple)): + shape = [tm.shape for tm in tensor_meta] + else: + logger.warning("Unexpected type of tensor_meta %s", type(tensor_meta)) + return None + + return CommBlock( + shape=shape, + node_list=node_list, + wait_nodes=wait_nodes, + comm_node=comm_node, + inputs=input_nodes, + outputs=outputs, + ) + + +def get_all_comm_blocks( + graph: fx.Graph, + comm_ops: Tuple[torch._ops.OpOverload, ...], + comm_filter: Optional[Callable[..., bool]] = None, +) -> List[CommBlock]: + if comm_filter is None: + + def always_true(comm_block: CommBlock) -> bool: + return True + + comm_filter = always_true + + blocks = [] + for node in graph.nodes: + if node.target not in comm_ops: + continue + comm_block = get_comm_block(node) + if comm_block is not None and comm_filter(comm_block): + blocks.append(comm_block) + return blocks + + +def _fuse_allreduce_by_concat( + graph: fx.Graph, + last_input_node: fx.Node, + all_input_nodes: List[fx.Node], + last_comm_block: CommBlock, +) -> CommBlock: + """Given a list of inputs in order, create a fused allreduce using concat.""" + # Flatten all the inputs to the all_reduce nodes. + with graph.inserting_after(last_input_node): + cat_inputs = [] + for input_node in all_input_nodes: + assert isinstance(input_node.args[0], fx.Node) + input_node = input_node.args[0] + cat_inputs.append( + call_function(graph, aten.flatten.using_ints, (input_node,)) + ) + + # Concat all the flattened nodes. + with graph.inserting_after(cat_inputs[0]): + cat_node = call_function(graph, aten.cat, (cat_inputs,)) + + # Insert the fused div node and remove the input div nodes. + # This is an optimization and is not mandatory for fusion. + divisors = [div.args[1] for div in all_input_nodes] + assert all(divisor == divisors[0] for divisor in divisors) + with graph.inserting_after(cat_node): + div_node = call_function(graph, last_input_node.target, (cat_node, divisors[0])) + + # Create a new Comm/all_reduce node. + last_comm_node = last_comm_block.comm_node + last_wait_node = last_comm_block.wait_nodes[0] + with graph.inserting_after(div_node): + flatten_args, spec = tree_flatten((last_comm_node.args, last_comm_node.kwargs)) + flatten_args[0] = div_node + args, kwargs = tree_unflatten(flatten_args, spec) + fused_comm_node = call_function(graph, last_comm_node.target, args, kwargs) + + # Create a new Wait node. + with graph.inserting_after(fused_comm_node): + flatten_args, spec = tree_flatten((last_wait_node.args, last_wait_node.kwargs)) + flatten_args[0] = fused_comm_node + args, kwargs = tree_unflatten(flatten_args, spec) + fused_wait_node = call_function(graph, last_wait_node.target, args, kwargs) + + # Move the fused all_reduce and its args to right after the input node + nodes_to_move = cat_inputs + [cat_node, div_node, fused_comm_node, fused_wait_node] + move_block_after(nodes_to_move, last_input_node) + + return CommBlock( + shape=cast(TensorMetadata, cat_node.meta.get("tensor_meta")).shape, + node_list=[fused_comm_node, fused_wait_node], + wait_nodes=[fused_wait_node], + comm_node=fused_comm_node, + inputs=[div_node], + outputs={fused_wait_node}, + ) + + +def _fuse_with_coalesced_op( + graph: fx.Graph, + last_input_node: fx.Node, + all_input_nodes: List[fx.Node], + last_comm_block: CommBlock, +) -> CommBlock: + """Given a list of inputs in order, create a fused allreduce by coalesced.""" + last_comm_node = last_comm_block.comm_node + last_wait_node = last_comm_block.wait_nodes[0] + + # Insert the fused div node and remove the input div nodes. + # This is an optimization and is not mandatory for fusion. + dividends = [div.args[0] for div in all_input_nodes] + divisors = [div.args[1] for div in all_input_nodes] + assert all(divisor == divisors[0] for divisor in divisors) + with graph.inserting_before(last_input_node): + last_input_node = call_function( + graph, aten._foreach_div.Scalar, (dividends, divisors[0]) + ) + input_node = last_input_node + + # Create a new Comm/all_reduce_coalesced node. + with graph.inserting_after(last_comm_node): + flatten_args, spec = tree_flatten((last_comm_node.args, last_comm_node.kwargs)) + flatten_args[0] = input_node + args, kwargs = tree_unflatten(flatten_args, spec) + fused_comm_node = call_function( + graph, torch.ops._c10d_functional.all_reduce_coalesced.default, args, kwargs + ) + + # Create a new wait node. + getitem_nodes = [] + wait_nodes = [] + flatten_args, spec = tree_flatten((last_wait_node.args, last_wait_node.kwargs)) + for idx in range(len(all_input_nodes)): + with graph.inserting_after(fused_comm_node): + gi_node = call_function(graph, operator.getitem, (fused_comm_node, idx)) + getitem_nodes.append(gi_node) + flatten_args[0] = gi_node + args, kwargs = tree_unflatten(flatten_args, spec) + with graph.inserting_after(gi_node): + wait_nodes.append(call_function(graph, last_wait_node.target, args, kwargs)) + + # Move the new all_reduce_coalesced and its args to right after the input node + nodes_to_move = [fused_comm_node] + getitem_nodes + wait_nodes + move_block_after(nodes_to_move, last_input_node) + + return CommBlock( + shape=[ + tm.shape + for tm in cast( + List[TensorMetadata], fused_comm_node.meta.get("tensor_meta") + ) + ], + node_list=[fused_comm_node] + getitem_nodes + wait_nodes, + wait_nodes=wait_nodes, + comm_node=fused_comm_node, + inputs=[input_node], + outputs=set(wait_nodes), + ) + + +def _scatter_fused_allreduce_waits( + graph: fx.Graph, + fused_comm_block: CommBlock, + orig_comm_blocks: List[CommBlock], + node_indices: Dict[fx.Node, int], + split_and_reshape: bool = True, +) -> None: + """ + Scatters the result of the fused communication node to the original users. + If the fused method is concat splitting the output and reshape will be inserted, + before inserting getitem. Otherwise getitem will be used as the users of the + wait node. + """ + + # Before we mass up the order, we need to get the index of the last wait node + # in orig_comm_blocks. This index will be later used to determinee what users + # nodes need to be move to maintain a correct topological sort order. + last_wait_node_idx = 0 + for node in graph.nodes: + last_wait_node_idx = max( + node_indices.get(node, last_wait_node_idx), last_wait_node_idx + ) + if node == orig_comm_blocks[-1].wait_nodes[0]: + break + + if split_and_reshape: + fused_wait_node = fused_comm_block.wait_nodes[0] + with graph.inserting_after(fused_wait_node): + split_node = call_function( + graph, + aten.split, + ( + fused_wait_node, + [math.prod(cast(List[int], cb.shape)) for cb in orig_comm_blocks], + ), + ) + with graph.inserting_after(split_node): + fused_outputs = [] + for idx, comm_block in enumerate(orig_comm_blocks): + split_idx_node = call_function( + graph, operator.getitem, (split_node, idx) + ) + with graph.inserting_after(split_idx_node): + fused_outputs.append( + call_function( + graph, aten.reshape, (split_idx_node, comm_block.shape) + ) + ) + else: + fused_outputs = fused_comm_block.wait_nodes + + # Scatter the fused outputs. + incorrect_order_nodes = [] + for comm_block, fused_output in zip(orig_comm_blocks, fused_outputs): + # Some descendant users of the orig_comm_blocks may be scheduled before + # the fused all_reduce. For example, the user nodes of the very first + # all_reduce may be scheduled before the second all_reduce. Since the + # fused all_reduce is inserted right after the last all_reudce, the + # order can be wrong. + # `incorrect_order_nodes` records these nodes. + + orig_wait = comm_block.wait_nodes[0] + nodes = collections.deque(list(orig_wait.users)) + while nodes: + user_node = nodes.popleft() + if not isinstance(user_node, fx.Node): + continue + if node_indices[user_node] < last_wait_node_idx: + incorrect_order_nodes.append(user_node) + nodes.extend(list(user_node.users)) + + orig_wait.replace_all_uses_with(fused_output) + + last_fused_result = fused_outputs[0] + fused_outputs_set = set(fused_outputs) + for node in graph.nodes: + if node in fused_outputs_set: + last_fused_result = node + + # Move the incorrect_order_nodes to right after the last fused_result. + incorrect_order_nodes = sorted( + incorrect_order_nodes, key=lambda node: node_indices[node] + ) + move_block_after(incorrect_order_nodes, last_fused_result) + + +def _fuse_allreduce( + graph: fx.Graph, + comm_blocks: List[CommBlock], + node_indices: Dict[fx.Node, int], + use_concat: bool, +) -> CommBlock: + """Given a list of allreduce CommBlock, fuse the CommBlocks into one CommBlock.""" + + if len(comm_blocks) == 1: + return comm_blocks[0] + + # Find the last input node of all the CommBlocks. This node will be served + # as the inserting point of the new collective op. + last_input_node = comm_blocks[0].inputs[0] + last_input_index = -1 + all_input_nodes = [] + for comm_block in comm_blocks: + input_node = comm_block.inputs[0] + all_input_nodes.append(input_node) + index = node_indices[input_node] + if index >= last_input_index: + assert index != last_input_index + last_input_node = input_node + last_input_index = index + + if use_concat: + fused_comm_block = _fuse_allreduce_by_concat( + graph, last_input_node, all_input_nodes, comm_blocks[-1] + ) + else: + fused_comm_block = _fuse_with_coalesced_op( + graph, last_input_node, all_input_nodes, comm_blocks[-1] + ) + + _scatter_fused_allreduce_waits( + graph, fused_comm_block, comm_blocks, node_indices, split_and_reshape=use_concat + ) + + for comm_block in comm_blocks: + for wait in comm_block.wait_nodes: + graph.erase_node(wait) + graph.erase_node(comm_block.comm_node) + graph.eliminate_dead_code() + + return fused_comm_block + + +def _bucket_size_fusion( + graph: fx.Graph, comm_blocks: List[CommBlock], bucket_size_mb: int +) -> Generator[List[CommBlock], None, None]: + MB = 1024**2 + bucket_size = 1 * MB + bucket_cap_size = bucket_size_mb * MB + curr_size = 0 + curr_blocks = [] + + count = 0 + fuse_count = 0 + for i, block in enumerate(comm_blocks): + curr_blocks.append(block) + itemsize = block.comm_node.meta["tensor_meta"].dtype.itemsize + curr_size += cast(torch.Size, block.shape).numel() * itemsize + count += 1 + if curr_size < bucket_size and i != len(comm_blocks) - 1: + continue + + fuse_count += 1 + if torch.distributed.get_rank() == 0: + logger.info( + "DDP bucketing: block%d, count=%d, curr_size=%d, bucket_size=%d", + fuse_count, + count, + curr_size, + bucket_size, + ) + + # Set the debug counters + counters["inductor"]["ddp_buckets"] = fuse_count + yield curr_blocks + + bucket_size = bucket_cap_size + curr_blocks = [] + curr_size = 0 + count = 0 + + +def _fuse_ddp_communication( + graph: fx.Graph, algorithm_fn: Callable[..., Any], fusion_fn: Callable[..., Any] +) -> None: + for output in reversed(graph.nodes): + if output.op == "output": + break + + def ddp_reducer_filter(block: CommBlock) -> bool: + if ( + not isinstance(block.comm_node.args[0], fx.Node) + or block.comm_node.args[0].target != aten.div.Tensor + ): + return False + + if len(block.wait_nodes[0].users) != 1: + # gradient/wait node should only be used by one user + return False + + # Two cases: + # 1. gradient/wait node should be directly used by the output + # if gradient is None before bwd. + # 2. gradient/wait node should be directly used by copy_. + if ( + output not in block.wait_nodes[0].users + and next(iter(block.wait_nodes[0].users)).target != aten.copy_.default + ): + return False + + return True + + ops = ( + torch.ops._c10d_functional.all_reduce_.default, + torch.ops._c10d_functional.all_reduce.default, + ) + comm_blocks = get_all_comm_blocks(graph, ops, comm_filter=ddp_reducer_filter) + node_indices = {node: i for i, node in enumerate(graph.nodes)} + + for block in algorithm_fn(graph, comm_blocks): + fusion_fn(graph, block, node_indices) + + +def fuse_ddp_with_coalesced_op(graph: fx.Graph, bucket_size_mb: int) -> None: + _fuse_ddp_communication( + graph, + partial(_bucket_size_fusion, bucket_size_mb=bucket_size_mb), + partial(_fuse_allreduce, use_concat=False), + ) + + +def fuse_ddp_with_concat_op(graph: fx.Graph, bucket_size_mb: int) -> None: + _fuse_ddp_communication( + graph, + partial(_bucket_size_fusion, bucket_size_mb=bucket_size_mb), + partial(_fuse_allreduce, use_concat=True), + ) + + +def schedule_comm_wait(graph: fx.Graph) -> None: + """ + Delay the execution of wait tensors of allreduce until its first user. + + This algorithm considers the intermediate users, like split, getitem, + of the wait node and schedule those intermediate users as well. + This will result in a better overlapping result. + """ + ops = ( + torch.ops._c10d_functional.all_reduce_.default, + torch.ops._c10d_functional.all_reduce.default, + torch.ops._c10d_functional.all_reduce_coalesced.default, + torch.ops._c10d_functional.all_reduce_coalesced_.default, + ) + comm_blocks = get_all_comm_blocks(graph, ops) + if not comm_blocks: + return + + # Find all the end users. + allreduce_users: Set[fx.Node] = set() + for allreduce in comm_blocks: + for output in allreduce.outputs: + allreduce_users.update(output.users) + + node_indices = {node: i for i, node in enumerate(graph.nodes)} + for allreduce in comm_blocks: + # Find the earliest/first user -- target_node. + assert ( + len(allreduce.outputs) >= 1 + ), f"Found a allreduce that has zero outputs/users -- {allreduce}." + # Initialize the target node to avoid typing issues. + target_node = next(iter(next(iter(allreduce.outputs)).users)) + target_node_index = 2**31 + for user in (user for output in allreduce.outputs for user in output.users): + index = node_indices[user] + if index < target_node_index: + target_node = user + target_node_index = index + + # Move wait nodes and all the subsequent nodes in the comm_block to + # before the first user -- target_node. + wait_idx = -1 + for wait_idx, node in enumerate(allreduce.node_list): + if node == allreduce.wait_nodes[0]: + break + assert wait_idx >= 0 + move_block_before(allreduce.node_list[wait_idx:], target_node) + + +def fuse_ddp_communication( + graph: fx.Graph, passes: List[Union[Callable[..., None], str]], bucket_size_mb: int +) -> None: + for i, pa in enumerate(passes): + with GraphTransformObserver( + graph.owning_module, + f"fuse_ddp_communication_pass_{i}", + config.trace.log_url_for_graph_xform, + ): + if isinstance(pa, str): + func = globals()[pa] + else: + func = pa + if "bucket_size_mb" in { + v.name for v in inspect.signature(func).parameters.values() + }: + func(graph, bucket_size_mb=bucket_size_mb) + else: + func(graph) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/decompose_mem_bound_mm.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/decompose_mem_bound_mm.py new file mode 100644 index 0000000000000000000000000000000000000000..d6777987c0866258976d4e73205ba49084f92340 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/decompose_mem_bound_mm.py @@ -0,0 +1,153 @@ +# mypy: allow-untyped-defs +import logging +from typing import List + +import torch +from torch import Tensor +from torch._dynamo.utils import counters + +from .. import config +from ..pattern_matcher import Arg, CallFunction, Match, register_graph_pattern +from .split_cat import construct_pattern_matcher_pass + + +aten = torch.ops.aten +log = logging.getLogger(__name__) + +# TODO: need a better strategy for decomposing mm +MIN_FIRST_DIMENSION_DECOMPOSITION = 10240 +MAX_OTHER_DIMENSION_DECOMPOSITION = 32 + +min_first_dimension_decomposition = MIN_FIRST_DIMENSION_DECOMPOSITION +max_other_dimention_decomposition = MAX_OTHER_DIMENSION_DECOMPOSITION +if "decompose_mm_pass" in config.post_grad_fusion_options: + min_first_dimension_decomposition = config.post_grad_fusion_options[ + "decompose_mm_pass" + ].get("min_first_dimension_decomposition", MIN_FIRST_DIMENSION_DECOMPOSITION) + max_other_dimention_decomposition = config.post_grad_fusion_options[ + "decompose_mm_pass" + ].get("max_other_dimention_decomposition", MAX_OTHER_DIMENSION_DECOMPOSITION) + + +def check_device(a: Tensor, b: Tensor) -> bool: + return a.is_cuda and b.is_cuda + + +def realize_inputs(inputs: List[torch.fx.Node]): + for inp in inputs: + if isinstance(inp, torch.fx.node.Node): + inp.meta["inductor_realize_to_strides"] = True + + +def should_decompose_bmm(mat1, mat2) -> bool: + if is_node_meta_valid(mat1) and is_node_meta_valid(mat2): + mat1 = mat1.meta["val"] + mat2 = mat2.meta["val"] + else: + return False + if not check_device(mat1, mat2): + return False + else: + if len(mat1.shape) != 3 or len(mat2.shape) != 3: + return False + if mat1.shape[0] < min_first_dimension_decomposition: + return False + # 2 of m, n, k must be <= MAX_OTHER_DIMENSION_DECOMPOSITION + if (mat1.shape[1] < max_other_dimention_decomposition) + ( + mat1.shape[2] < max_other_dimention_decomposition + ) + (mat2.shape[2] < max_other_dimention_decomposition) < 2: + return False + return True + + +def should_decompose_mm(mat1, mat2) -> bool: + if is_node_meta_valid(mat1) and is_node_meta_valid(mat2): + mat1 = mat1.meta["val"] + mat2 = mat2.meta["val"] + else: + return False + return ( + check_device(mat1, mat2) + and len(mat1.shape) == 2 + and len(mat2.shape) == 2 + and mat1.shape[0] >= min_first_dimension_decomposition + and mat2.shape[0] < max_other_dimention_decomposition + and mat2.shape[1] < max_other_dimention_decomposition + ) + + +def is_node_meta_valid(node: torch.fx.Node): + return "val" in node.meta + + +def print_decompose_pattern(match: Match, inputs: List[torch.fx.Node]): + node = match.nodes[-1] + log.debug( + "Decompose %s with input shape: %s", + node.target, + ", ".join( + str(input.meta["val"].shape) if "val" in input.meta else "None" + for input in inputs + ), + ) + + +@register_graph_pattern( + CallFunction(aten.bmm, Arg(), Arg()), + pass_dict=construct_pattern_matcher_pass("decompose_mm_pass"), +) +def decompose_bmm(match: Match, mat1: torch.fx.Node, mat2: torch.fx.Node): + def repl(mat1, mat2): + return torch.sum(mat1[:, :, :, None] * mat2[:, None, :, :], dim=-2).to( + mat1.dtype + ) + + if should_decompose_bmm(mat1, mat2): + counters["inductor"]["decompose_bmm"] += 1 + match.replace_by_example(repl, [mat1, mat2]) + print_decompose_pattern(match, [mat1, mat2]) + realize_inputs([mat1, mat2]) + return + + +@register_graph_pattern( + CallFunction(aten.addmm, Arg(), Arg(), Arg()), + pass_dict=construct_pattern_matcher_pass("decompose_mm_pass"), +) +def decompose_addmm( + match: Match, + mat1: torch.fx.Node, + mat2: torch.fx.Node, + mat3: torch.fx.Node, +): + def repl(mat1, mat2, mat3): + return ( + torch.sum(mat2[:, :, None] * mat3[None, :, :], dim=-2).to(mat2.dtype) + mat1 + ) + + if should_decompose_mm(mat2, mat3): + counters["inductor"]["decompose_addmm"] += 1 + match.replace_by_example(repl, [mat1, mat2, mat3]) + print_decompose_pattern(match, [mat1, mat2, mat3]) + realize_inputs([mat1, mat2, mat3]) + return + + +@register_graph_pattern( + CallFunction(aten.mm, Arg(), Arg()), + pass_dict=construct_pattern_matcher_pass("decompose_mm_pass"), +) +def decompose_mm( + match: Match, + mat1: torch.fx.Node, + mat2: torch.fx.Node, +): + def repl(mat1, mat2): + return torch.sum(mat1[:, :, None] * mat2[None, :, :], dim=-2).to(mat1.dtype) + + if should_decompose_mm(mat1, mat2): + counters["inductor"]["decompose_mm"] += 1 + match.replace_by_example(repl, [mat1, mat2]) + print_decompose_pattern(match, [mat1, mat2]) + realize_inputs([mat1, mat2]) + return diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/dedupe_symint_uses.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/dedupe_symint_uses.py new file mode 100644 index 0000000000000000000000000000000000000000..f24b68b6c8f68fac86ee332ea9e610fdcf30badd --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/dedupe_symint_uses.py @@ -0,0 +1,80 @@ +# mypy: allow-untyped-defs +from dataclasses import dataclass +from typing import Union + +import torch +from torch import SymBool, SymFloat, SymInt +from torch.types import py_sym_types + + +@dataclass +class _SymExprHash: + """ + Hash for a py_sym_types that will use the underlying sympy expression + """ + + sym_obj: Union[SymInt, SymFloat, SymBool] + + def __hash__(self) -> int: + return hash((type(self.sym_obj), self.sym_obj.node.expr)) + + def __eq__(self, value) -> bool: + if not isinstance(value, _SymExprHash): + return False + return self.sym_obj.node.expr == value.sym_obj.node.expr + + +class _SymHashingDict: + """ + Wrapper around a dictionary that will convert sym types to hash with _SymExprHash and reuse + existing sym proxies. + + SymPy hash is not always reliable so optimistically hash sympy expression, and if those fail, + fallback to symnodes. + """ + + def __init__(self): + self.sym_hash_dict = {} + + def __setitem__(self, key, value): + self.sym_hash_dict.__setitem__(self._wrap_to_sym_expr_hash(key), value) + + def __getitem__(self, key): + return self.sym_hash_dict[self._wrap_to_sym_expr_hash(key)] + + def __contains__(self, key): + return self._wrap_to_sym_expr_hash(key) in self.sym_hash_dict + + def get(self, key, default=None): + return self.sym_hash_dict.get(self._wrap_to_sym_expr_hash(key), default) + + def _wrap_to_sym_expr_hash(self, key): + return _SymExprHash(key) if isinstance(key, py_sym_types) else key + + +def dedupe_symints(graph: torch.fx.Graph): + """ + Dedupes sym ints in the graph to nodes are resolvable to symint graph inputs. + + We only dedupe from graph inputs to avoid adding a potential dependency in the forward + from the backward. + + """ + + sym_dict = _SymHashingDict() + resolvable_from_input_symints = set() + + for node in graph.nodes: + val = node.meta.get("val", None) + if val is None or not isinstance(val, py_sym_types): + continue + + if node.op == "placeholder": + resolvable_from_input_symints.add(node) + sym_dict[val] = node + elif existing_node := sym_dict.get(val): + node.replace_all_uses_with(existing_node) + graph.erase_node(node) + elif all(n in resolvable_from_input_symints for n in node.all_input_nodes): + sym_dict[val] = node + resolvable_from_input_symints.add(node) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/efficient_conv_bn_eval.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/efficient_conv_bn_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..4845142caab347ce9f67c958772fe7b23fddc599 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/efficient_conv_bn_eval.py @@ -0,0 +1,406 @@ +# mypy: allow-untyped-defs +import torch +import torch.nn as nn +from torch._dynamo.utils import counters +from torch._inductor import config as inductor_config +from torch.func import functional_call + +from ..pattern_matcher import ( + CallFunctionVarArgs, + CallModuleVarArgs, + Match, + register_graph_pattern, +) +from .pre_grad import efficient_conv_bn_eval_pass + + +def efficient_conv_bn_eval( + bn: nn.modules.batchnorm._BatchNorm, conv: nn.modules.conv._ConvNd, x: torch.Tensor +): + """ + Implementation based on https://arxiv.org/abs/2305.11624 + "Efficient ConvBN Blocks for Transfer Learning and Beyond" + It leverages the associative law between convolution and affine transform, + i.e., normalize (weight conv feature) = (normalize weight) conv feature. + It works for Eval mode of ConvBN blocks during validation, and can be used + for **training** as well, but only if one sets `bn.training=False`. It + reduces memory footprint and computation cost, at the cost of slightly + reduced numerical stability. + Args: + bn (nn.modules.batchnorm._BatchNorm): a BatchNorm module. + conv (nn.modules.conv._ConvNd): a conv module + x (torch.Tensor): Input feature map. + """ + + assert bn.running_var is not None + + # These lines of code are designed to deal with various cases + # like bn without affine transform, and conv without bias + weight_on_the_fly = conv.weight + if conv.bias is not None: + bias_on_the_fly = conv.bias + else: + bias_on_the_fly = torch.zeros_like(bn.running_var) + + if bn.weight is not None: + bn_weight = bn.weight + else: + bn_weight = torch.ones_like(bn.running_var) + + if bn.bias is not None: + bn_bias = bn.bias + else: + bn_bias = torch.zeros_like(bn.running_var) + + # shape of [C_out, 1, 1, 1] in Conv2d + target_shape = [-1] + [1] * (conv.weight.ndim - 1) + if isinstance(conv, nn.modules.conv._ConvTransposeNd): + # for transposed conv, the C_out dimension should at index 1. + target_shape[:2] = [target_shape[1], target_shape[0]] + weight_coeff = torch.rsqrt(bn.running_var + bn.eps).reshape(target_shape) + # shape of [C_out, 1, 1, 1] in Conv2d + coefff_on_the_fly = bn_weight.view_as(weight_coeff) * weight_coeff + + # shape of [C_out, C_in, k, k] in Conv2d + weight_on_the_fly = weight_on_the_fly * coefff_on_the_fly + # shape of [C_out] in Conv2d + bias_on_the_fly = bn_bias + coefff_on_the_fly.flatten() * ( + bias_on_the_fly - bn.running_mean + ) + + input = x + params = {"weight": weight_on_the_fly, "bias": bias_on_the_fly} + output = functional_call(conv, params, input) + return output + + +def efficient_conv_bn_eval_decomposed( + bn_weight, + bn_bias, + bn_running_mean, + bn_running_var, + bn_eps, + conv: torch._ops.OpOverload, + conv_weight, + conv_bias, + x, + conv_remainging_args, +): + """ + Implementation based on https://arxiv.org/abs/2305.11624 + "Efficient ConvBN Blocks for Transfer Learning and Beyond" + It leverages the associative law between convolution and affine transform, + i.e., normalize (weight conv feature) = (normalize weight) conv feature. + It works for Eval mode of ConvBN blocks during validation, and can be used + for **training** as well, but only if one sets `bn.training=False`. It + reduces memory footprint and computation cost, at the cost of slightly + reduced numerical stability. + Args: + """ + assert bn_running_var is not None + + # These lines of code are designed to deal with various cases + # like bn without affine transform, and conv without bias + weight_on_the_fly = conv_weight + if conv_bias is not None: + bias_on_the_fly = conv_bias + else: + bias_on_the_fly = torch.zeros_like(bn_running_var) + + if bn_weight is not None: + bn_weight = bn_weight + else: + bn_weight = torch.ones_like(bn_running_var) + + if bn_bias is not None: + bn_bias = bn_bias + else: + bn_bias = torch.zeros_like(bn_running_var) + + # shape of [C_out, 1, 1, 1] in Conv2d + target_shape = [-1] + [1] * (conv_weight.ndim - 1) + if "conv_transpose" in conv.__str__(): + # for transposed conv, the C_out dimension should at index 1. + target_shape[:2] = [target_shape[1], target_shape[0]] + weight_coeff = torch.rsqrt(bn_running_var + bn_eps).reshape(target_shape) + # shape of [C_out, 1, 1, 1] in Conv2d + coefff_on_the_fly = bn_weight.view_as(weight_coeff) * weight_coeff + + # shape of [C_out, C_in, k, k] in Conv2d + weight_on_the_fly = weight_on_the_fly * coefff_on_the_fly + # shape of [C_out] in Conv2d + bias_on_the_fly = bn_bias + coefff_on_the_fly.flatten() * ( + bias_on_the_fly - bn_running_mean + ) + + input = x + return conv(*((input, weight_on_the_fly, bias_on_the_fly) + conv_remainging_args)) + + +@register_graph_pattern( + CallFunctionVarArgs( + [ + torch.nn.functional.batch_norm, + ] + ), + pass_dict=efficient_conv_bn_eval_pass, + extra_check=lambda match: not inductor_config.freezing + and inductor_config.efficient_conv_bn_eval_fx_passes, +) +def efficient_conv_bn_eval_graph_transform_inlined(match: Match, *args, **kwargs): + bn_node = match.nodes[0] + graph = match.graph + assert len(bn_node.args) == 8 + + # We can only use efficient conv-bn for eval mode with track_running_stats + # bn_node.args is `training` + if bn_node.args[-3]: + return + + # Check if the input is Conv + input_node = bn_node.args[0] + + if input_node.op != "call_function": # type: ignore[union-attr] + return + + input_fn = input_node.target # type: ignore[arg-type, union-attr] + supported_convs = [ + torch._C._nn.linear, + torch.conv1d, + torch.conv2d, + torch.conv3d, + torch.conv_transpose1d, + torch.conv_transpose2d, + torch.conv_transpose3d, + ] + + if not any(input_fn is cls for cls in supported_convs): + return + + conv_node = input_node + # Output of conv is used by other nodes, cannot optimize + if len(conv_node.users) > 1: # type: ignore[union-attr] + return + + counters["inductor"]["efficient_conv_bn_eval"] += 1 + + with graph.inserting_before(bn_node): + # prepare args for the fused function + bn_running_mean = bn_node.args[1] + bn_running_var = bn_node.args[2] + bn_weight = bn_node.args[3] + bn_bias = bn_node.args[4] + bn_eps = bn_node.args[7] + assert len(conv_node.args) >= 2 # type: ignore[union-attr] + conv_input = conv_node.args[0] # type: ignore[union-attr] + conv_weight = conv_node.args[1] # type: ignore[union-attr] + conv_bias = conv_node.args[2] if len(conv_node.args) >= 3 else None # type: ignore[union-attr] + conv_remainging_args = conv_node.args[3:] # type: ignore[union-attr] + args = ( + bn_weight, + bn_bias, + bn_running_mean, + bn_running_var, + bn_eps, + conv_node.target, # type: ignore[union-attr] + conv_weight, + conv_bias, + conv_input, + conv_remainging_args, + ) + + # create a new node + new_node = graph.create_node( + op="call_function", + target=efficient_conv_bn_eval_decomposed, + args=args, # type: ignore[arg-type] + name="efficient_conv_bn_eval", + ) + + # this node replaces the original conv + bn, and therefore + # should replace the uses of bn_node + bn_node.replace_all_uses_with(new_node) + # take care of the deletion order: + # delete bn_node first, and then conv_node + graph.erase_node(bn_node) + graph.erase_node(conv_node) # type: ignore[arg-type] + + return + + +@register_graph_pattern( + CallFunctionVarArgs( + [ + torch.ops.aten.batch_norm.default, + ] + ), + pass_dict=efficient_conv_bn_eval_pass, + extra_check=lambda match: not inductor_config.freezing + and inductor_config.efficient_conv_bn_eval_fx_passes, +) +def efficient_conv_bn_eval_graph_transform_decomposed(match: Match, *args, **kwargs): + bn_node = match.nodes[0] + graph = match.graph + assert len(bn_node.args) == 9 + + # We can only use efficient conv-bn for eval mode with track_running_stats + # bn_node.args is `training` + if bn_node.args[-4]: + return + + # Check if the input is Conv + input_node = bn_node.args[0] + + if input_node.op != "call_function": # type: ignore[union-attr] + return + + input_fn = input_node.target # type: ignore[arg-type, union-attr] + supported_convs = [ + torch.ops.aten.linear.default, + torch.ops.aten.conv1d.default, + torch.ops.aten.conv2d.default, + torch.ops.aten.conv3d.default, + torch.ops.aten.conv_transpose1d.default, + torch.ops.aten.conv_transpose2d.input, + torch.ops.aten.conv_transpose3d.input, + ] + + if not any(input_fn is cls for cls in supported_convs): + return + + conv_node = input_node + # Output of conv is used by other nodes, cannot optimize + if len(conv_node.users) > 1: # type: ignore[union-attr] + return + + counters["inductor"]["efficient_conv_bn_eval"] += 1 + + with graph.inserting_before(bn_node): + # prepare args for the fused function + bn_weight = bn_node.args[1] + bn_bias = bn_node.args[2] + bn_running_mean = bn_node.args[3] + bn_running_var = bn_node.args[4] + bn_eps = bn_node.args[7] + assert len(conv_node.args) >= 2 # type: ignore[union-attr] + conv_input = conv_node.args[0] # type: ignore[union-attr] + conv_weight = conv_node.args[1] # type: ignore[union-attr] + conv_bias = conv_node.args[2] if len(conv_node.args) >= 3 else None # type: ignore[union-attr] + conv_remainging_args = conv_node.args[3:] # type: ignore[union-attr] + args = ( + bn_weight, + bn_bias, + bn_running_mean, + bn_running_var, + bn_eps, + conv_node.target, # type: ignore[union-attr] + conv_weight, + conv_bias, + conv_input, + conv_remainging_args, + ) + + # create a new node + new_node = graph.create_node( + op="call_function", + target=efficient_conv_bn_eval_decomposed, + args=args, # type: ignore[arg-type] + name="efficient_conv_bn_eval", + ) + + # this node replaces the original conv + bn, and therefore + # should replace the uses of bn_node + bn_node.replace_all_uses_with(new_node) + # take care of the deletion order: + # delete bn_node first, and then conv_node + graph.erase_node(bn_node) + graph.erase_node(conv_node) # type: ignore[arg-type] + + return + + +@register_graph_pattern( + CallModuleVarArgs( + [ + nn.modules.batchnorm._BatchNorm, + nn.BatchNorm1d, + nn.BatchNorm2d, + nn.BatchNorm3d, + nn.SyncBatchNorm, + ], + ), + pass_dict=efficient_conv_bn_eval_pass, + extra_check=lambda match: not inductor_config.freezing + and inductor_config.efficient_conv_bn_eval_fx_passes, +) +def efficient_conv_bn_eval_graph_transform(match: Match, *args, **kwargs): + # We matched a BN node + bn_node = match.nodes[0] + graph = match.graph + gm = graph.owning_module + bn_mod = getattr(gm, bn_node.target) # type: ignore[arg-type] + + # We can only use efficient conv-bn for eval mode with track_running_stats + if not bn_mod.track_running_stats or bn_mod.training: + return + + # Check if the input is Conv + if bn_node.args: + input_node = bn_node.args[0] + else: + input_node = bn_node.kwargs["input"] + if input_node.op != "call_module": # type: ignore[union-attr] + return + if not hasattr(gm, input_node.target): # type: ignore[arg-type, union-attr] + return + input_mod = getattr(gm, input_node.target) # type: ignore[arg-type, union-attr] + supported_convs = [ + nn.Linear, + nn.Conv1d, + nn.Conv2d, + nn.Conv3d, + nn.ConvTranspose1d, + nn.ConvTranspose2d, + nn.ConvTranspose3d, + ] + if not any(isinstance(input_mod, cls) for cls in supported_convs): + return + conv_node = input_node + # Output of conv is used by other nodes, cannot optimize + if len(conv_node.users) > 1: # type: ignore[union-attr] + return + + # Find a pair of conv and bn computation nodes to optimize. + counters["inductor"]["efficient_conv_bn_eval"] += 1 + + with graph.inserting_before(conv_node): # type: ignore[arg-type] + # create `get_attr` node to access modules + # note that we directly call `create_node` to fill the `name` + # argument. `graph.get_attr` and + # `graph.call_function` does not allow the `name` argument. + conv_get_node = graph.create_node( + op="get_attr", target=conv_node.target, name="get_conv" # type: ignore[union-attr] + ) + bn_get_node = graph.create_node( + op="get_attr", target=bn_node.target, name="get_bn" + ) + if conv_node.args: # type: ignore[union-attr] + conv_input = conv_node.args[0] # type: ignore[union-attr] + else: + conv_input = conv_node.kwargs["input"] # type: ignore[union-attr] + # prepare args for the fused function + args = (bn_get_node, conv_get_node, conv_input) + # create a new node + new_node = graph.create_node( + op="call_function", + target=efficient_conv_bn_eval, + args=args, + name="efficient_conv_bn_eval", + ) + # this node replaces the original conv + bn, and therefore + # should replace the uses of bn_node + bn_node.replace_all_uses_with(new_node) + # take care of the deletion order: + # delete bn_node first, and then conv_node + graph.erase_node(bn_node) + graph.erase_node(conv_node) # type: ignore[arg-type] diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/freezing_patterns.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/freezing_patterns.py new file mode 100644 index 0000000000000000000000000000000000000000..572fcc9e9299d2cf4d0b1fa901568f5e86bf4cad --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/freezing_patterns.py @@ -0,0 +1,227 @@ +# mypy: allow-untyped-defs +import functools + +import torch +from torch._inductor.compile_fx import fake_tensor_prop + +from ..._dynamo.utils import counters +from .. import config +from ..pattern_matcher import ( + _return_true, + CallFunction, + fwd_only, + Ignored, + init_once_fakemode, + KeywordArg, + Match, + PatternMatcherPass, + register_graph_pattern, + register_replacement, + stable_topological_sort, +) + + +aten = torch.ops.aten + +# First pass_patterns[0] are applied, then [1], then [2] +pass_patterns = [ + PatternMatcherPass(), + PatternMatcherPass(), + PatternMatcherPass(), +] + +binary_folding_pass = PatternMatcherPass() + + +def freezing_passes(gm: torch.fx.GraphModule, aot_example_inputs): + """ + Passes that are applied to the graph to freeze pass. + """ + + from ..freezing import constant_fold + + lazy_init() + # We need a few rounds of binary folding to get rid of all the + # unnecessary nodes, but may need a good method to chose the rounds number. + # works like: conv+binary+binary. + binary_folding = counters["inductor"]["binary_folding"] + fake_tensor_prop(gm, aot_example_inputs, True) + + torch._inductor.fx_passes.binary_folding.mark_mixed_dtype_allowed_convs(gm) + for _ in range(4): + constant_fold(gm) + # Make sure meta['val'] is properly set for all nodes + fake_tensor_prop(gm, aot_example_inputs, True) + binary_folding_pass.apply(gm.graph) # type: ignore[arg-type] + # If we don't have binary folding, we don't need to run the pass again. + # TODO: remove the need to run fake_tensor_prop on the whole model. + if counters["inductor"]["binary_folding"] == binary_folding: + break + binary_folding = counters["inductor"]["binary_folding"] + + torch._inductor.fx_passes.binary_folding.recover_original_precision_folded_convs(gm) + + constant_fold(gm) + fake_tensor_prop(gm, aot_example_inputs, True) + + for pattern in pass_patterns: + pattern.apply(gm.graph) # type: ignore[arg-type] + + # The CPU weight packing always assume the conv's weight is channels last, + # So make sure the layout_optimization is on when doing it. + if ( + torch._C._has_mkldnn + and config.cpp.weight_prepack + and config.layout_optimization + ): + from .mkldnn_fusion import _eliminate_duplicate_packed_nodes + + _eliminate_duplicate_packed_nodes(gm) + + stable_topological_sort(gm.graph) + gm.recompile() + gm.graph.lint() + + +@init_once_fakemode +def lazy_init(): + if torch._C._has_mkldnn and config.cpp.weight_prepack: + from .mkldnn_fusion import _mkldnn_weight_pack_init + + _mkldnn_weight_pack_init() + + from .binary_folding import binary_folding_init + + addmm_patterns_init() + binary_folding_init() + + +def register_freezing_graph_pattern(pattern, extra_check=_return_true, pass_number=0): + return register_graph_pattern( + pattern, + extra_check=extra_check, + pass_dict=pass_patterns[pass_number], + ) + + +def register_binary_folding_pattern(pattern, extra_check=_return_true): + return register_graph_pattern( + pattern, + extra_check=extra_check, + pass_dict=binary_folding_pass, + ) + + +@functools.lru_cache(None) +def addmm_patterns_init(): + if torch.cuda.is_available(): + # workaround https://github.com/pytorch/pytorch/issues/97894 + device = "cuda" + else: + device = "cpu" + val = functools.partial(torch.empty, (10, 10), device=device, requires_grad=False) + + def check_concat_weights(match): + weight_inputs = ["w1", "w2"] + if "w3" in match.kwargs: + weight_inputs.append("w3") + + equal_shape_inputs = [weight_inputs] + + if "b1" in match.kwargs: + bias_inputs = ["b1", "b2"] + if "b3" in match.kwargs: + bias_inputs.append("b3") + + equal_shape_inputs.append(bias_inputs) + + for equal_shape_group in equal_shape_inputs: + inps = [match.kwargs[name] for name in equal_shape_group] + + if not all( + inp.op == "get_attr" + and inp.meta["val"].shape == inps[0].meta["val"].shape + for inp in inps + ): + return False + + return True + + def matmul_fuse_pattern(inp, w1, w2, w3): + return (inp @ w1, inp @ w2, inp @ w3) + + def matmul_replacement(inp, w1, w2, w3): + cat_t = torch.cat((w1, w2, w3), dim=1) + mm = inp @ cat_t + return mm.chunk(3, dim=1) + + register_replacement( + matmul_fuse_pattern, + matmul_replacement, + [val(), val(), val(), val()], + fwd_only, + pass_patterns[0], + extra_check=check_concat_weights, + exclusive_arg_names=("w1", "w2", "w3"), + ) + + def matmul_fuse_pattern_two(inp, w1, w2): + return (inp @ w1, inp @ w2) + + def matmul_replacement_two(inp, w1, w2): + cat_t = torch.cat((w1, w2), dim=1) + mm = inp @ cat_t + return mm.chunk(2, dim=1) + + register_replacement( + matmul_fuse_pattern_two, + matmul_replacement_two, + [val(), val(), val()], + fwd_only, + pass_patterns[0], + extra_check=check_concat_weights, + exclusive_arg_names=("w1", "w2"), + ) + + def addmm_fuse_pattern_second(inp, w1, w2, w3, b1, b2, b3): + return ( + aten.addmm(b1, inp, w1), + aten.addmm(b2, inp, w2), + aten.addmm(b3, inp, w3), + ) + + def addmm_fuse_replacement_second(inp, w1, w2, w3, b1, b2, b3): + cat_w = torch.cat((w1, w2, w3), dim=1) + cat_b = torch.cat((b1, b2, b3)) + return aten.addmm(cat_b, inp, cat_w).chunk(3, dim=1) + + register_replacement( + addmm_fuse_pattern_second, + addmm_fuse_replacement_second, + [val() for _ in range(7)], + fwd_only, + pass_patterns[0], + extra_check=check_concat_weights, + exclusive_arg_names=("w1", "w2", "w3", "b1", "b2", "b3"), + ) + + +def same_dtype(match): + return match.output_node().args[0].meta["val"].dtype == match.kwargs["dtype"] + + +@register_graph_pattern( + CallFunction( + torch.ops.prims.convert_element_type.default, + Ignored(), + KeywordArg("dtype"), + ), + pass_dict=pass_patterns[0], + extra_check=same_dtype, +) +def unnecessary_dtype_convert(match: Match, **kwargs): + """Remove unnecessary dtype conversion op, probably left as a result of Conv-Bn folding""" + graph = match.graph + node = match.output_node() + node.replace_all_uses_with(node.args[0]) # type: ignore[arg-type] + graph.erase_node(node) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/fuse_attention.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/fuse_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..1a3b681230e06d0c0f7089b7740cddefc0e50634 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/fuse_attention.py @@ -0,0 +1,909 @@ +# mypy: allow-untyped-defs +import functools +import inspect +import logging +import math + +import torch +from torch.nn.attention import sdpa_kernel, SDPBackend + +from ..._dynamo.utils import counters +from ..pattern_matcher import ( + filter_nodes, + fwd_only, + gen_register_replacement, + joint_fwd_bwd, +) + + +log = logging.getLogger(__name__) +aten = torch.ops.aten + + +if torch.version.hip: + + def _scaled_dot_product_attention(*args, **kwargs): + with sdpa_kernel(backends=[SDPBackend.MATH, SDPBackend.FLASH_ATTENTION]): + return aten.scaled_dot_product_attention(*args, **kwargs) + +else: + _scaled_dot_product_attention = aten.scaled_dot_product_attention + + +def _sfdp_pattern_1(query, key, value, inv_scale): + return ( + torch.matmul(query, key.transpose(-2, -1)) + .div(inv_scale) + .softmax(dim=-1) + .matmul(value) + ) + + +def _sfdp_replacement_1(query, key, value, inv_scale): + counters["inductor"]["fuse_attention"] += 1 + return _scaled_dot_product_attention( + query.contiguous(), + key.contiguous(), + value.contiguous(), + attn_mask=None, + dropout_p=0.0, + is_causal=False, + scale=1.0 / inv_scale, + ) + + +def _sfdp_pattern_2(query, key, value, scale_factor): + return ( + torch.matmul(query, key.transpose(-2, -1)) + .mul(scale_factor) + .softmax(dim=-1) + .matmul(value) + ) + + +def _sfdp_replacement_2(query, key, value, scale_factor): + counters["inductor"]["fuse_attention"] += 1 + return _scaled_dot_product_attention( + query.contiguous(), + key.contiguous(), + value.contiguous(), + attn_mask=None, + dropout_p=0.0, + is_causal=False, + scale=scale_factor, + ) + + +def _sfdp_pattern_3(query, key, value, inv_scale_factor, dropout_p): + return torch.nn.functional.dropout( + torch.matmul(query, key.transpose(-2, -1)) + .div(inv_scale_factor) + .softmax(dim=-1), + p=dropout_p, + ).matmul(value) + + +def _sfdp_replacement_3(query, key, value, inv_scale_factor, dropout_p): + counters["inductor"]["fuse_attention"] += 1 + return _scaled_dot_product_attention( + query.contiguous(), + key.contiguous(), + value.contiguous(), + attn_mask=None, + dropout_p=dropout_p, + is_causal=False, + scale=1.0 / inv_scale_factor, + ) + + +def _sfdp_pattern_4(query, key, value, scale_factor, dropout_p): + return torch.nn.functional.dropout( + torch.matmul(query, key.transpose(-2, -1)).mul(scale_factor).softmax(dim=-1), + p=dropout_p, + ).matmul(value) + + +def _sfdp_replacement_4(query, key, value, scale_factor, dropout_p): + counters["inductor"]["fuse_attention"] += 1 + return _scaled_dot_product_attention( + query.contiguous(), + key.contiguous(), + value.contiguous(), + attn_mask=None, + dropout_p=dropout_p, + is_causal=False, + scale=scale_factor, + ) + + +def _sfdp_pattern_5(query, key, value, attn_mask): + attn_weight = torch.softmax( + (query @ key.transpose(-2, -1) / math.sqrt(query.size(-1))) + attn_mask, dim=-1 + ) + # attn_weight = torch.dropout(attn_weight, dropout_p) + return attn_weight @ value + + +def _sfdp_replacement_5(query, key, value, attn_mask): + counters["inductor"]["fuse_attention"] += 1 + return _scaled_dot_product_attention( + query.contiguous(), + key.contiguous(), + value.contiguous(), + attn_mask=attn_mask.to(dtype=query.dtype), + dropout_p=0.0, + is_causal=False, + ) + + +def _sfdp_pattern_6(query, key, value, attn_mask, dropout_p): + attn_weight = torch.softmax( + (query @ key.transpose(-2, -1) / math.sqrt(query.size(-1))) + attn_mask, dim=-1 + ) + attn_weight = torch.dropout(attn_weight, dropout_p, True) + return attn_weight @ value + + +def _sfdp_replacement_6(query, key, value, attn_mask, dropout_p): + counters["inductor"]["fuse_attention"] += 1 + return _scaled_dot_product_attention( + query.contiguous(), + key.contiguous(), + value.contiguous(), + attn_mask=attn_mask.to(dtype=query.dtype), + dropout_p=dropout_p, + is_causal=False, + ) + + +def _sfdp_pattern_7(query, key, value, dropout_p): + # in real workloads inputs to matmul are permuted + # causing matmul to expand to a series of expand and clone calls + # we want the same to happen during pattern tracing + q = query.permute(0, 2, 1, 3) + k = key.permute(0, 2, 1, 3) + v = value.permute(0, 2, 1, 3) + div = q @ k.transpose(-2, -1) / math.sqrt(q.size(-1)) + div = div.to(torch.float32) + attn_weight = torch.softmax(div, dim=-1) + attn_weight = torch.dropout(attn_weight, dropout_p, True) + attn_weight = attn_weight.to(torch.float16) + return attn_weight @ v + + +def _sfdp_replacement_7(query, key, value, dropout_p): + # sdpa prefers inputs in permuted format + # it makes a copy to put them in this format + # if they aren't already + # to make replacement efficient ensure that inputs to sdpa + # are in required order + counters["inductor"]["fuse_attention"] += 1 + q = query.permute(0, 2, 1, 3) + k = key.permute(0, 2, 1, 3) + v = value.permute(0, 2, 1, 3) + return _scaled_dot_product_attention( + q, + k, + v, + attn_mask=None, # attn_mask, + dropout_p=dropout_p, + is_causal=False, + ) + + +def _sfdp_pattern_8(query, key, value): + # no dropout version of pattern 7 + q = query.permute(0, 2, 1, 3) + k = key.permute(0, 2, 1, 3) + v = value.permute(0, 2, 1, 3) + div = q @ k.transpose(-2, -1) / math.sqrt(q.size(-1)) + div = div.to(torch.float32) + attn_weight = torch.softmax(div, dim=-1) + attn_weight = attn_weight.to(torch.float16) + return attn_weight @ v + + +def _sfdp_replacement_8(query, key, value): + counters["inductor"]["fuse_attention"] += 1 + q = query.permute(0, 2, 1, 3) + k = key.permute(0, 2, 1, 3) + v = value.permute(0, 2, 1, 3) + return _scaled_dot_product_attention( + q, + k, + v, + attn_mask=None, # attn_mask, + dropout_p=0.0, + is_causal=False, + ) + + +def _sfdp_pattern_9(query, key, value, dropout_p): + q = query.permute(0, 2, 1, 3) + k = key.permute(0, 2, 1, 3) + v = value.permute(0, 2, 1, 3) + q = q / math.sqrt(q.size(-1)) + div = q @ k.transpose(-2, -1) + div = div.to(torch.float32) + attn_weight = torch.softmax(div, dim=-1) + attn_weight = torch.dropout(attn_weight, dropout_p, True) + attn_weight = attn_weight.to(torch.float16) + return attn_weight @ v + + +def _sfdp_replacement_9(query, key, value, dropout_p): + counters["inductor"]["fuse_attention"] += 1 + q = query.permute(0, 2, 1, 3) + k = key.permute(0, 2, 1, 3) + v = value.permute(0, 2, 1, 3) + return _scaled_dot_product_attention( + q, + k, + v, + attn_mask=None, # attn_mask, + dropout_p=dropout_p, + is_causal=False, + ) + + +def _sfdp_pattern_10(query, key, value): + # no dropout version of 9 + q = query.permute(0, 2, 1, 3) + k = key.permute(0, 2, 1, 3) + v = value.permute(0, 2, 1, 3) + q = q / math.sqrt(q.size(-1)) + div = q @ k.transpose(-2, -1) + div = div.to(torch.float32) + attn_weight = torch.softmax(div, dim=-1) + attn_weight = attn_weight.to(torch.float16) + return attn_weight @ v + + +def _sfdp_replacement_10(query, key, value): + counters["inductor"]["fuse_attention"] += 1 + q = query.permute(0, 2, 1, 3) + k = key.permute(0, 2, 1, 3) + v = value.permute(0, 2, 1, 3) + return _scaled_dot_product_attention( + q, + k, + v, + attn_mask=None, # attn_mask, + dropout_p=0.0, + is_causal=False, + ) + + +def _sfdp_pattern_11(query, key, value, inv_scale): + # Mainly for huggingface models + q = query.permute(0, 2, 1, 3) + k = key.permute(0, 2, 1, 3) + v = value.permute(0, 2, 1, 3) + return torch.matmul(q, k.transpose(-2, -1)).div(inv_scale).softmax(dim=-1).matmul(v) + + +def _sfdp_replacement_11(query, key, value, inv_scale): + counters["inductor"]["fuse_attention"] += 1 + return _scaled_dot_product_attention( + query.transpose(1, 2), + key.transpose(1, 2), + value.transpose(1, 2), + attn_mask=None, + dropout_p=0.0, + is_causal=False, + scale=1.0 / inv_scale, + ) + + +def _sfdp_pattern_12(query, key, value, inv_scale_factor, dropout_p): + q = query.permute(0, 2, 1, 3) + k = key.permute(0, 2, 1, 3) + v = value.permute(0, 2, 1, 3) + return torch.nn.functional.dropout( + torch.matmul(q, k.transpose(-2, -1)).div(inv_scale_factor).softmax(dim=-1), + p=dropout_p, + ).matmul(v) + + +def _sfdp_replacement_12(query, key, value, inv_scale_factor, dropout_p): + counters["inductor"]["fuse_attention"] += 1 + return _scaled_dot_product_attention( + query.transpose(1, 2), + key.transpose(1, 2), + value.transpose(1, 2), + attn_mask=None, + dropout_p=dropout_p, + is_causal=False, + scale=1.0 / inv_scale_factor, + ) + + +def _sfdp_pattern_13(query, key, value, dropout_p): + attn_weight = torch.bmm(query, key.transpose(1, 2)).softmax(dim=-1) + attn_weight = torch.nn.functional.dropout(attn_weight, p=dropout_p) + return torch.bmm(attn_weight, value) + + +def _sfdp_replacement_13(query, key, value, dropout_p): + counters["inductor"]["fuse_attention"] += 1 + return _scaled_dot_product_attention( + query.unsqueeze(0), + key.unsqueeze(0), + value.unsqueeze(0), + dropout_p=dropout_p, + scale=1.0, + ).squeeze(0) + + +def _sfdp_pattern_14(query, key, value, attn_mask, inv_scale): + # for BertLarge + # Permutations are needed to create clones in graph. + q = query.permute([0, 2, 1, 3]) + k = key.permute([0, 2, 1, 3]) + v = value.permute([0, 2, 1, 3]) + return ( + (torch.matmul(q, k.transpose(-2, -1)).div(inv_scale) + attn_mask) + .softmax(dim=-1) + .matmul(v) + ) + + +def _sfdp_replacement_14(query, key, value, attn_mask, inv_scale): + counters["inductor"]["fuse_attention"] += 1 + return _scaled_dot_product_attention( + query.transpose(1, 2), + key.transpose(1, 2), + value.transpose(1, 2), + attn_mask=attn_mask.to(dtype=query.dtype), + dropout_p=0.0, + is_causal=False, + scale=1.0 / inv_scale, + ) + + +def _sfdp_pattern_15(query, key, value, attn_mask, inv_scale): + # for DistilBert + # Permutations are needed to create clones in graph. + # Ref: https://github.com/pytorch/pytorch/issues/119911 + q = query.permute([0, 2, 1, 3]) + k = key.permute([0, 2, 1, 3]) + v = value.permute([0, 2, 1, 3]) + bs = q.size(0) + k_len = k.size(-2) + scores = q @ k.transpose(-2, -1) + scores = scores.div(inv_scale) + fill_value = torch.full((), -float("inf"), dtype=query.dtype, device=query.device) + attn_mask = (attn_mask == 0).view((bs, 1, 1, k_len)).expand_as(scores) + return torch.softmax(scores.masked_fill(attn_mask, fill_value), dim=-1) @ v + + +def _sfdp_replacement_15(query, key, value, attn_mask, inv_scale): + counters["inductor"]["fuse_attention"] += 1 + bs = query.size(0) + n_head = query.size(2) + q_len = query.size(1) + k_len = key.size(1) + # do attn_mask->logical_not() in _scaled_dot_product_attention + attn_mask = ( + (attn_mask == 1).view((bs, 1, 1, k_len)).expand((bs, n_head, q_len, k_len)) + ) + return _scaled_dot_product_attention( + query.transpose(1, 2), + key.transpose(1, 2), + value.transpose(1, 2), + attn_mask=attn_mask.to(dtype=torch.bool), + dropout_p=0.0, + is_causal=False, + scale=1.0 / inv_scale, + ) + + +def _sfdp_pattern_16(query, key, value, attn_mask, inv_scale, dropout_p): + # for BertLarge with dropout + q = query.permute([0, 2, 1, 3]) + k = key.permute([0, 2, 1, 3]) + v = value.permute([0, 2, 1, 3]) + return ( + torch.nn.functional.dropout( + (torch.matmul(q, k.transpose(-2, -1)).div(inv_scale) + attn_mask).softmax( + dim=-1 + ), + dropout_p, + ) + .to(dtype=query.dtype) + .matmul(v) + ) + + +def _sfdp_replacement_16(query, key, value, attn_mask, inv_scale, dropout_p): + counters["inductor"]["fuse_attention"] += 1 + return _scaled_dot_product_attention( + query.transpose(1, 2), + key.transpose(1, 2), + value.transpose(1, 2), + attn_mask=attn_mask.to(dtype=query.dtype), + dropout_p=dropout_p, + is_causal=False, + scale=1.0 / inv_scale, + ) + + +def _sfdp_pattern_17(query, key, value, attn_mask, inv_scale, dropout_p): + # for DistilBert with dropout + q = query.permute([0, 2, 1, 3]) + k = key.permute([0, 2, 1, 3]) + v = value.permute([0, 2, 1, 3]) + bs = q.size(0) + k_len = k.size(-2) + scores = q @ k.transpose(-2, -1) + scores = scores.div(inv_scale) + fill_value = torch.full((), -float("inf"), dtype=query.dtype, device=query.device) + attn_mask = (attn_mask == 0).view((bs, 1, 1, k_len)).expand_as(scores) + return ( + torch.nn.functional.dropout( + torch.softmax(scores.masked_fill(attn_mask, fill_value), dim=-1), dropout_p + ) + @ v + ) + + +def _sfdp_replacement_17(query, key, value, attn_mask, inv_scale, dropout_p): + counters["inductor"]["fuse_attention"] += 1 + bs = query.size(0) + n_head = query.size(2) + q_len = query.size(1) + k_len = key.size(1) + # do attn_mask->logical_not() in _scaled_dot_product_attention + attn_mask = ( + (attn_mask == 1).view((bs, 1, 1, k_len)).expand((bs, n_head, q_len, k_len)) + ) + return _scaled_dot_product_attention( + query.transpose(1, 2), + key.transpose(1, 2), + value.transpose(1, 2), + attn_mask=attn_mask.to(dtype=torch.bool), + dropout_p=dropout_p, + is_causal=False, + scale=1.0 / inv_scale, + ) + + +def _sfdp_pattern_18(query, key, value, causal_mask, dropout_p): + # for hf_GPT2 with dropout (introduces clone node) for inference + # it also returns permuted key & value + query = query.permute([0, 2, 1, 3]) + key = key.permute([0, 2, 1, 3]) + value = value.permute([0, 2, 1, 3]) + attn_weights = torch.matmul(query, key.permute(0, 1, 3, 2)) + inv_scale = torch.full( + [], + value.size(-1) ** 0.5, + dtype=attn_weights.dtype, + device=attn_weights.device, + ) + attn_weights = attn_weights.div(inv_scale) + causal_mask_value = torch.full( + (), torch.finfo(query.dtype).min, dtype=query.dtype, device=query.device + ) + attn_weights = torch.where(causal_mask, attn_weights, causal_mask_value) + return ( + ( + torch.nn.functional.dropout(attn_weights.softmax(dim=-1), dropout_p).matmul( + value + ) + ), + key, + value, + ) + + +def _sfdp_replacement_18(query, key, value, causal_mask, dropout_p): + counters["inductor"]["fuse_attention"] += 1 + permuted_key = key.transpose(1, 2) + permuted_value = value.transpose(1, 2) + return ( + _scaled_dot_product_attention( + query.transpose(1, 2), + permuted_key, + permuted_value, + attn_mask=causal_mask, + dropout_p=dropout_p, + is_causal=False, + scale=1.0 / math.sqrt(value.size(-1)), + ), + permuted_key, + permuted_value, + ) + + +def _sfdp_pattern_19(query, key, value, causal_mask, attn_mask, dropout_p): + # for token-classification+gpt2 / text-generation+gpt2 + attn_weights = torch.matmul(query, key.permute(0, 1, 3, 2)) + inv_scale = torch.full( + [], + value.size(-1) ** 0.5, + dtype=attn_weights.dtype, + device=attn_weights.device, + ) + attn_weights = attn_weights.div(inv_scale) + causal_mask_value = torch.full( + (), torch.finfo(query.dtype).min, dtype=query.dtype, device=query.device + ) + attn_weights = torch.where(causal_mask, attn_weights, causal_mask_value) + attn_weights = attn_weights + attn_mask + attn_weights = attn_weights.softmax(dim=-1).type(value.dtype) + return torch.nn.functional.dropout(attn_weights, dropout_p).matmul(value) + + +def _sfdp_replacement_19(query, key, value, causal_mask, attn_mask, dropout_p): + counters["inductor"]["fuse_attention"] += 1 + fill_value = torch.full((), -float("inf"), dtype=query.dtype, device=query.device) + attn_mask = torch.where(causal_mask, attn_mask, fill_value) + return _scaled_dot_product_attention( + query, + key, + value, + attn_mask=attn_mask, + dropout_p=dropout_p, + is_causal=False, + scale=1.0 / math.sqrt(value.size(-1)), + ) + + +def _sfdp_params_check(match): + assert all(k in match.kwargs for k in ("query", "key", "value")) + query = match.kwargs["query"].meta["val"] + key = match.kwargs["key"].meta["val"] + value = match.kwargs["value"].meta["val"] + if not (query.dtype == key.dtype == value.dtype) or not ( + query.device == key.device == value.device + ): + return False + add_mask_node = filter_nodes(match.nodes, aten.add.Tensor) + # Has attn_mask add. + if len(add_mask_node) > 0: + attn_mask_node = add_mask_node[0].args[1] + # attn_mask_node may be a float/int number. + if not hasattr(attn_mask_node, "meta"): + return False + attn_mask = attn_mask_node.meta["val"] # type: ignore[union-attr] + # Make sure attn_mask.dtype == query.dtype or attn_mask.dtype == torch.bool + # attn_mask.dtype == torch.float for models like albert. + if ( + not isinstance(attn_mask, torch.Tensor) + or not ( + attn_mask.dtype == query.dtype + or attn_mask.dtype == torch.bool + or attn_mask.dtype == torch.float + ) + or query.device != attn_mask.device + ): + return False + return True + + +def _sfdp_extra_check(scale_factor_op=None, disable_cuda=False): + def fn(match): + if ( + disable_cuda + and "query" in match.kwargs + and "cuda" in str(match.kwargs["query"].meta["val"].device) + ): + return False + if scale_factor_op is not None: + scale_factor_node = filter_nodes(match.nodes, scale_factor_op)[0] + # Note: args[1] of the scale_factor_node is always the scale_factor for the current patterns. + scale_factor = scale_factor_node.args[1] + # make sure the scale_factor a float/int. SymInt? + if not isinstance(scale_factor, (float, int)): + return False + return _sfdp_params_check(match) + + return fn + + +def partialize_and_update_signature(func, **kwargs): + """ + Equivalent to functools.partial but also updates the signature on returned function + """ + original_sig = inspect.signature(func) + parameters = original_sig.parameters + + new_parameters = { + key: value for key, value in parameters.items() if key not in kwargs + } + new_sig = inspect.Signature(parameters=list(new_parameters.values())) + + partial_func = functools.partial(func, **kwargs) + + def wrapper(*args, **kwargs): + return partial_func(*args, **kwargs) + + wrapper.__signature__ = new_sig # type: ignore[attr-defined] + wrapper.__name__ = func.__name__ + + return wrapper + + +def _get_sfdp_patterns(): + from .joint_graph import patterns + + if torch.cuda.is_available(): + # workaround https://github.com/pytorch/pytorch/issues/97894 + device = "cuda" + else: + device = "cpu" + + # sizes/values don't actually matter for initial trace + # once we get a possible match we re-trace with the actual values and verify the match still holds + g_inp = functools.partial( + torch.empty, (2, 4, 8, 16), device=device, requires_grad=True + ) + # attn_mask + b_inp = functools.partial(torch.empty, (1, 1, 8, 8), device=device) + m_inp = functools.partial(torch.empty, (2, 1, 1, 4), device=device) + # inv_scale + c_inp = functools.partial(torch.tensor, 2.0, device=device) + # workaround https://github.com/pytorch/pytorch/issues/97894 + # 0.113377 is a "magic" value that lets us recover the lost input arg relationship + d = {"dropout_p": 0.113377} + + # we could also generate all these patterns in 3d.. TODO + g_3d_inp = functools.partial( + torch.empty, (1024, 128, 128), device=device, requires_grad=True + ) + + # reshape in matmul decomposition generates a clone when batch_size>1 due to the memory layout change. + # however when batch_size=1, reshape does not change the memory layout, so clone would not be generated. + # here we need to trace with input of batch_size=1 to generate a pattern graph without clone. + g_bs1_inp = functools.partial( + torch.empty, (1, 4, 8, 16), device=device, requires_grad=True + ) + m_bs1_inp = functools.partial(torch.empty, (1, 1, 1, 4), device=device) + + # softmax will generate a dtype conversion on inputs if they are in half, + # but will not in float, so we generate a pattern for both + for dtype in [torch.float, torch.half]: + g = functools.partial(g_inp, dtype=dtype) + b = functools.partial(b_inp, dtype=dtype) + b_float = functools.partial(b_inp, dtype=torch.float) + b_bool = functools.partial(b_inp, dtype=torch.bool) + m = functools.partial(m_inp, dtype=dtype) + m_float = functools.partial(m_inp, dtype=torch.float) + m_bool = functools.partial(m_inp, dtype=torch.bool) + c = functools.partial(c_inp, dtype=dtype) + g_3d = functools.partial(g_3d_inp, dtype=dtype) + g_bs1 = functools.partial(g_bs1_inp, dtype=dtype) + m_bs1 = functools.partial(m_bs1_inp, dtype=dtype) + m_bs1_float = functools.partial(m_bs1_inp, dtype=torch.float) + m_bs1_bool = functools.partial(m_bs1_inp, dtype=torch.bool) + + candidates = [ + ( + _sfdp_pattern_1, + _sfdp_replacement_1, + [g(), g(), g(), c()], + {}, + _sfdp_extra_check(aten.div.Tensor), + ), + ( + _sfdp_pattern_2, + _sfdp_replacement_2, + [g(), g(), g(), c()], + {}, + _sfdp_extra_check(aten.mul.Tensor), + ), + ( + _sfdp_pattern_3, + _sfdp_replacement_3, + [g(), g(), g(), c()], + d, + _sfdp_extra_check(aten.div.Tensor), + ), + ( + _sfdp_pattern_4, + _sfdp_replacement_4, + [g(), g(), g(), c()], + d, + _sfdp_extra_check(aten.mul.Tensor), + ), + ( + _sfdp_pattern_5, + _sfdp_replacement_5, + [g(), g(), g(), b()], + {}, + _sfdp_params_check, + ), + ( + _sfdp_pattern_6, + _sfdp_replacement_6, + [g(), g(), g(), b()], + d, + _sfdp_params_check, + ), + ( + _sfdp_pattern_7, + _sfdp_replacement_7, + [g(), g(), g()], + d, + _sfdp_params_check, + ), + ( + _sfdp_pattern_8, + _sfdp_replacement_8, + [g(), g(), g()], + {}, + _sfdp_params_check, + ), + ( + _sfdp_pattern_9, + _sfdp_replacement_9, + [g(), g(), g()], + d, + _sfdp_params_check, + ), + ( + _sfdp_pattern_10, + _sfdp_replacement_10, + [g(), g(), g()], + {}, + _sfdp_params_check, + ), + ( + _sfdp_pattern_11, + _sfdp_replacement_11, + [g(), g(), g(), c()], + {}, + _sfdp_extra_check(aten.div.Tensor), + ), + ( + _sfdp_pattern_12, + _sfdp_replacement_12, + [g(), g(), g(), c()], + d, + _sfdp_extra_check(aten.div.Tensor), + ), + ( + _sfdp_pattern_13, + _sfdp_replacement_13, + [g_3d(), g_3d(), g_3d()], + d, + _sfdp_params_check, + ), + ( + _sfdp_pattern_14, + _sfdp_replacement_14, + [g(), g(), g(), m(), c()], + {}, + _sfdp_extra_check(aten.div.Tensor), + ), + ( + _sfdp_pattern_15, + _sfdp_replacement_15, + [g(), g(), g(), m(), c()], + {}, + _sfdp_extra_check(aten.div.Tensor), + ), + # TODO: Enable CUDA after solving Bert accuracy issue of calling efficient attention + ( + _sfdp_pattern_16, + _sfdp_replacement_16, + [g(), g(), g(), m(), c()], + d, + _sfdp_extra_check(aten.div.Tensor, disable_cuda=True), + ), + ( + _sfdp_pattern_16, + _sfdp_replacement_16, + [g_bs1(), g_bs1(), g_bs1(), m_bs1(), c()], + d, + _sfdp_extra_check(aten.div.Tensor, disable_cuda=True), + ), + ( + _sfdp_pattern_17, + _sfdp_replacement_17, + [g(), g(), g(), m(), c()], + d, + _sfdp_extra_check(aten.div.Tensor), + ), + ( + _sfdp_pattern_18, + _sfdp_replacement_18, + [g(), g(), g(), m_bool()], + d, + # CUDA AOT Inductor CI job's GPT2ForSequenceClassification accuracy test failed + _sfdp_extra_check(disable_cuda=True), + ), + ( + _sfdp_pattern_18, + _sfdp_replacement_18, + [g_bs1(), g_bs1(), g_bs1(), m_bs1_bool()], + d, + # CUDA AOT Inductor CI job's GPT2ForSequenceClassification accuracy test failed + _sfdp_extra_check(disable_cuda=True), + ), + ( + _sfdp_pattern_19, + _sfdp_replacement_19, + [g(), g(), g(), b_bool(), b_float()], + d, + _sfdp_params_check, + ), + ] + mask_fp32_patterns = ["pattern_16"] + if dtype == torch.half: + # Add inputs of bf16 q/k/v and fp32 mask, for models like albert. + candidates.append( + ( + _sfdp_pattern_16, + _sfdp_replacement_16, + [g(), g(), g(), m_float(), c()], + d, + _sfdp_extra_check(aten.div.Tensor, disable_cuda=True), + ) + ) + candidates.append( + ( + _sfdp_pattern_16, + _sfdp_replacement_16, + [g_bs1(), g_bs1(), g_bs1(), m_bs1_float(), c()], + d, + _sfdp_extra_check(aten.div.Tensor, disable_cuda=True), + ) + ) + + for pattern, replacement, args, workaround, extra_check in candidates: + # XXX: when adding a new pattern, re-run `gen_attention_patterns` so the pattern + # gets serialized to a python file and does not require tracing at runtime. + assert isinstance(workaround, dict) + name = pattern.__name__ + + if dtype != torch.float: + name += "_half" + if ( + any(p in name for p in mask_fp32_patterns) + and args[3].dtype == torch.float32 + ): + name += "_mask_fp32" + if args[0].size(0) == 1: + name += "_bs1" + + training_name = name + "_training" + yield training_name, { + "search_fn": pattern, + "replace_fn": replacement, + "example_inputs": args, + "trace_fn": joint_fwd_bwd, + "pass_dicts": patterns, + "extra_check": extra_check, + "scalar_workaround": workaround, + } + + if workaround: + assert len(workaround) == 1 and "dropout_p" in workaround + # functools.partial insufficient because we look at signature downstream + pattern = partialize_and_update_signature(pattern, dropout_p=0.0) + replacement = partialize_and_update_signature( + replacement, dropout_p=0.0 + ) + workaround = {} + + inference_name = name + "_inference" + yield inference_name, { + "search_fn": pattern, + "replace_fn": replacement, + "example_inputs": args, + "trace_fn": fwd_only, + "pass_dicts": patterns, + "extra_check": extra_check, + "scalar_workaround": workaround, + # with dropout turned into clone, we end up with a number of + # semantically identical graphs + "skip_duplicates": True, + } + + +@functools.lru_cache(None) +def _sfdp_init(): + for key, register_replacement_kwargs in _get_sfdp_patterns(): + gen_register_replacement(key, **register_replacement_kwargs) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/group_batch_fusion.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/group_batch_fusion.py new file mode 100644 index 0000000000000000000000000000000000000000..5311c9789fa59ed3c2f993b14083b2b9e698ffe2 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/group_batch_fusion.py @@ -0,0 +1,1317 @@ +# mypy: allow-untyped-defs +import collections +import logging +import operator +from collections import OrderedDict +from typing import ( + Any, + DefaultDict, + Deque, + Dict, + Iterable, + Iterator, + List, + Optional, + Set, + Tuple, +) + +import torch +from torch._dynamo.utils import counters, optimus_scuba_log +from torch._utils_internal import upload_graph +from torch.fx.passes.graph_transform_observer import GraphTransformObserver + +from .. import config +from ..pattern_matcher import ( + CallFunctionVarArgs, + get_arg_value, + stable_topological_sort, +) + + +try: + # importing this will register fbgemm lowerings for inductor + import deeplearning.fbgemm.fbgemm_gpu.fb.inductor_lowerings # noqa: F401 + + has_fbgemm = True +except Exception: + has_fbgemm = False + +aten = torch.ops.aten + +log = logging.getLogger(__name__) + +MIN_FUSE_SET_SIZE = 5 +MAX_FUSE_SET_SIZE = 300 +MAX_FUSE_SEARCH_DEPTH = 5 +# The maximum tensor size that can go into the fusion group +MAX_FUSE_TENSOR_SIZE_GROUP_LINEAR = 4096 +# Whether we only fuse nodes with same parent node +FUSE_NODES_WITH_SAME_PARENT = False +# Whether we enable the add broadcast in batch linear +SHAPE_BROADCAST_BATCH_LINEAR = False +# Whether we enable the fuse nodes with same users +Fuse_NODES_WITH_SAME_USERS = False + +# exclude these nodes from BFS +# excluding get item improves optimizer compilation time by 60s +SEARCH_EXCLUSIONS = {operator.getitem} + + +default_graph_search_options = { + "min_fuse_set_size": MIN_FUSE_SET_SIZE, + "max_fuse_set_size": MAX_FUSE_SET_SIZE, + "max_fuse_search_depth": MAX_FUSE_SEARCH_DEPTH, + "max_fuse_tensor_size_group_linear": MAX_FUSE_TENSOR_SIZE_GROUP_LINEAR, + "fuse_nodes_with_same_parent": FUSE_NODES_WITH_SAME_PARENT, + "shape_broadcast_batch_linear": SHAPE_BROADCAST_BATCH_LINEAR, + "fuse_nodes_with_same_users": Fuse_NODES_WITH_SAME_USERS, +} + +graph_search_options = default_graph_search_options + + +def update_stack_example_value(node, metadata, dim=0, op=torch.stack): + """ + Update the example value of the node in the graph to enable followup split cat opt. + """ + if node is not None and hasattr(node, "meta"): + if op == torch.stack: + example_value = torch.stack(metadata, dim=dim) + elif op == torch.unbind: + example_value = torch.unbind(metadata, dim=dim) # type: ignore[assignment] + else: + return + node.meta["example_value"] = example_value + + +def update_pointwise_example_value(pointwise_node, input, other, op): + """ + Update the example value of the add node in the graph to enable followup split cat opt. + """ + if pointwise_node is not None and hasattr(pointwise_node, "meta"): + if op == torch.add: + example_value = torch.add(input, other) + elif op == torch.mul: + example_value = torch.mul(input, other) + else: + return + pointwise_node.meta["example_value"] = example_value + + +class GroupBatchFusionBase: + def __init__(self, **kwargs) -> None: + self.graph_search_options = kwargs.pop( + "graph_search_options", default_graph_search_options + ) + + def match(self, node): + raise NotImplementedError("match called on base") + + def fuse(self, graph, subset): + raise NotImplementedError("fuse called on base") + + +PRE_GRAD_FUSIONS: Dict[str, GroupBatchFusionBase] = {} +POST_GRAD_FUSIONS: Dict[str, GroupBatchFusionBase] = {} + + +def register_fusion(name: str, pre_grad=True): + def decorator(fusion_cls: GroupBatchFusionBase): + if pre_grad: + PRE_GRAD_FUSIONS[name] = fusion_cls + else: + POST_GRAD_FUSIONS[name] = fusion_cls + return fusion_cls + + return decorator + + +def list_group_batch_fusions(pre_grad=True) -> List[str]: + if pre_grad: + return list(PRE_GRAD_FUSIONS.keys()) + else: + return list(POST_GRAD_FUSIONS.keys()) + + +def decompose_stack(graph: torch.fx.GraphModule, input_tensors: List[Any]) -> Any: + unsqueezed_inputs = [] + unsqueezed_inputs_meta = [] + for input_tensor in input_tensors: + unsqueezed_input = graph.call_function( + aten.unsqueeze, args=(input_tensor,), kwargs={"dim": 0} + ) + unsqueezed_inputs.append(unsqueezed_input) + unsqueezed_input.meta["val"] = aten.unsqueeze(input_tensor.meta["val"], dim=0) # type: ignore[assignment] + unsqueezed_inputs_meta.append(unsqueezed_input.meta["val"]) + stacked_inputs = graph.call_function( + aten.cat, args=(unsqueezed_inputs,), kwargs={"dim": 0} + ) + stacked_inputs.meta["val"] = aten.cat(unsqueezed_inputs_meta, dim=0) # type: ignore[assignment] + return stacked_inputs + + +class GroupFusion(GroupBatchFusionBase): + """ + Fuse ops in a group way, e.g, fuse mm/addmm of arbitrary input shapes with fbgemm.gmm. + """ + + +class BatchFusion(GroupBatchFusionBase): + """ + Fuse ops in a batch way, e.g, fuse mm/addmm of same input shapes with bmm. + """ + + +class BatchPointwiseOpsFusionFactory(BatchFusion): + def __init__(self, op, **kwargs) -> None: + super().__init__(**kwargs) + self.op = op + + +@register_fusion("batch_linear_post_grad", pre_grad=False) +class PostGradBatchLinearFusion(BatchFusion): + """ + Fuse ops in a batch way in post grad (aten level). + """ + + def _addmm_node_can_be_fused(self, node: torch.fx.Node) -> bool: + # pyre-fixme[7]: Incompatible return type + return ( + node.kwargs.get("beta", 1.0) == 1.0 and node.kwargs.get("alpha", 1.0) == 1.0 # type: ignore[return-value] + ) + + def _is_input_2d(self, input: torch.fx.Node) -> bool: + input_shapes = input.meta["val"].shape + return ( + len(input_shapes) == 2 + and isinstance(input_shapes[0], int) + and isinstance(input_shapes[1], int) + ) + + def match( + self, node: torch.fx.Node + ) -> Optional[Tuple[str, int, int, int, bool, str]]: + if CallFunctionVarArgs(aten.mm).match(node): + input_m, weight_m = node.args + bias_m = None + + elif CallFunctionVarArgs(aten.addmm.default).match( + node + ) and self._addmm_node_can_be_fused(node): + bias_m, input_m, weight_m = node.args + else: + return None + # get the user of the node + if self.graph_search_options.get("fuse_nodes_with_same_users", False): + users = [user.target for user in node.users.keys()] + else: + users = "" # type: ignore[assignment] + # only handle the cases where inputs are 2D tensors + if not self._is_input_2d(input_m) or not self._is_input_2d(weight_m): # type: ignore[arg-type] + return None + m, k = input_m.meta["val"].shape # type: ignore[union-attr] + n = weight_m.meta["val"].shape[1] # type: ignore[union-attr] + batch_key = ("batch_linear_post_grad", m, k, n, bias_m is not None, str(users)) + return batch_key + + def fuse(self, graph: torch.fx.GraphModule, subset: List[torch.fx.Node]): + batch_inputs = [] + batch_weights = [] + batch_biases = [] + batch_nodes = [] + batch_inputs_meta = [] + batch_weights_meta = [] + batch_biases_meta = [] + + for node in subset: + if CallFunctionVarArgs(aten.addmm.default).match(node): + bias, input, weight = node.args + elif CallFunctionVarArgs(aten.mm.default).match(node): + input, weight = node.args + bias = None + batch_nodes.append(node) + batch_inputs.append(input) # type: ignore[possibly-undefined] + batch_weights.append(weight) # type: ignore[possibly-undefined] + batch_biases.append(bias) # type: ignore[possibly-undefined] + batch_inputs_meta.append(input.meta) # type: ignore[possibly-undefined, union-attr] + batch_weights_meta.append(weight.meta) # type: ignore[possibly-undefined, union-attr] + if bias is not None: # type: ignore[possibly-undefined] + batch_biases_meta.append(bias.meta) # type: ignore[possibly-undefined, union-attr] + else: + batch_biases_meta.append(None) + + with graph.inserting_before(subset[-1]): + fused_inputs = decompose_stack(graph, batch_inputs) + fused_weights = decompose_stack(graph, batch_weights) + fused_inputs_meta_val = torch.stack( + [input["val"] for input in batch_inputs_meta] + ) + fused_weights_meta_val = torch.stack( + [weight["val"] for weight in batch_weights_meta] + ) + fused_bmm = graph.call_function( + aten.bmm, + args=(fused_inputs, fused_weights), + ) + fused_bmm.meta["val"] = aten.bmm( + fused_inputs_meta_val, fused_weights_meta_val + ) + for i, original_mm in enumerate(batch_nodes): + has_bias = False + with graph.inserting_after(fused_bmm): + new_mm = graph.call_function(aten.select, args=((fused_bmm, 0, i))) + new_mm.meta["val"] = aten.select(fused_bmm.meta["val"], 0, i) + if batch_biases[i]: + has_bias = True + # broadcast the bias to the same shape as the mm output + if self.graph_search_options.get( + "shape_broadcast_batch_linear", False + ): + broadcast_shape = torch.broadcast_shapes( + batch_biases_meta[i]["val"].shape, new_mm.meta["val"].shape + ) + broadcast_bias = graph.call_function( + aten.broadcast_to.default, + args=(batch_biases[i],), + kwargs={"size": broadcast_shape}, + ) + broadcast_bias.meta["val"] = aten.broadcast_to(batch_biases_meta[i]["val"], broadcast_shape) # type: ignore[assignment] + new_bias_add = graph.call_function( + aten.add.Tensor, args=((broadcast_bias, new_mm)) + ) + new_bias_add.meta["val"] = aten.add.Tensor( + broadcast_bias.meta["val"], new_mm.meta["val"] + ) + else: + new_bias_add = graph.call_function( + aten.add, args=((batch_biases[i], new_mm)) + ) + new_bias_add.meta["val"] = aten.add.Tensor( + batch_biases_meta[i]["val"], new_mm.meta["val"] + ) + new_mm_cont = new_bias_add if has_bias else new_mm # type: ignore[possibly-undefined] + original_mm.replace_all_uses_with(new_mm_cont) + new_mm_cont.meta.update(original_mm.meta) + graph.erase_node(original_mm) + counters["inductor"]["batch_linear_post_grad"] += 1 + + +@register_fusion("group_linear", pre_grad=False) +class GroupLinearFusion(GroupFusion): + def _addmm_node_can_be_fused(self, node: torch.fx.Node): + input_shape = node.args[1].meta["val"].shape # type: ignore[union-attr] + weight_shape = node.args[2].meta["val"].shape # type: ignore[union-attr] + return ( + node.kwargs.get("beta", 1.0) == 1.0 + and node.kwargs.get("alpha", 1.0) == 1.0 + and len(input_shape) == 2 + and len(weight_shape) == 2 + and all(x % 2 == 0 for x in input_shape + weight_shape) + and all( + shape <= self.graph_search_options["max_fuse_tensor_size_group_linear"] + for shape in input_shape + weight_shape + ) + ) + + def _mm_node_can_be_fused(self, node: torch.fx.Node): + input_shape = node.args[0].meta["val"].shape # type: ignore[union-attr] + weight_shape = node.args[1].meta["val"].shape # type: ignore[union-attr] + return ( + len(input_shape) == 2 + and len(weight_shape) == 2 + and all(x % 2 == 0 for x in input_shape + weight_shape) + and all( + shape <= self.graph_search_options["max_fuse_tensor_size_group_linear"] + for shape in input_shape + weight_shape + ) + ) + + def match(self, node: torch.fx.Node) -> Optional[Tuple[str, bool]]: + if CallFunctionVarArgs(aten.mm.default).match( + node + ) and self._mm_node_can_be_fused(node): + group_key = ("group_linear", True) + elif CallFunctionVarArgs(aten.addmm.default).match( + node + ) and self._addmm_node_can_be_fused(node): + bias = node.args[0] + group_key = ("group_linear", bias is None) + else: + group_key = None + return group_key + + def fuse(self, graph: torch.fx.GraphModule, subset: List[torch.fx.Node]): + group_inputs = [] + group_weights = [] + group_biases = [] + group_nodes = [] + for node in subset: + if CallFunctionVarArgs(aten.addmm.default).match(node): + bias, input, weight = node.args + else: + assert CallFunctionVarArgs(aten.mm.default).match(node) + input, weight = node.args + bias = None + + group_nodes.append(node) + group_inputs.append(input) + group_weights.append(weight) + group_biases.append(bias) + + if all(bias is None for bias in group_biases): + group_biases = None # type: ignore[assignment] + + with graph.inserting_before(subset[0]): + fused_mm = graph.call_function( + torch.ops.fbgemm.gmm.default, + args=(group_inputs, group_weights, group_biases), + kwargs={"smart_fused": True}, + ) + + for i, original_mm in enumerate(group_nodes): + with graph.inserting_after(fused_mm): + new_mm = graph.call_function(operator.getitem, args=(fused_mm, i)) + original_mm.replace_all_uses_with(new_mm) + new_mm.meta.update(original_mm.meta) + graph.erase_node(original_mm) + counters["inductor"]["group_linear"] += 1 + + +class BatchPointwiseMathOpsPostGradFusion(BatchPointwiseOpsFusionFactory): + """ + Batch pointwise math operator (e.g., add, mul) in post grad pass. + """ + + def __init__(self, op, **kwargs) -> None: + super().__init__(op, **kwargs) + self.op = op + + def _pointwise_node_can_be_fused(self, node: torch.fx.Node): + # note: we only consider the case where the inputs are tensors + # for mixed precision training, we need to make sure the inputs + # of the aten.cat when do the stack should be the same dtype + # otherwise, the output of the aten.cat may be not the same as + # its inputs, and cause dtype not same error in mm or addmm + input, other = node.args + return ( + input.meta["val"].shape == other.meta["val"].shape # type: ignore[union-attr] + if hasattr(input, "meta") + and hasattr(other, "meta") + and "val" in input.meta # type: ignore[union-attr] + and "val" in other.meta # type: ignore[union-attr] + else False + ) + + def match(self, node: torch.fx.Node): + if CallFunctionVarArgs(self.op).match( + node + ) and self._pointwise_node_can_be_fused(node): + alpha = node.kwargs.get("alpha", 1.0) + rounding_mode = node.kwargs.get("rounding_mode", None) + input, other = node.args + shape = list(input.meta["val"].shape) # type: ignore[union-attr] + if self.graph_search_options.get("fuse_nodes_with_same_parent", False): + # only consider the linear case so far + # pyre-fixme[16] + if input.target == aten.select or other.target == aten.select: # type: ignore[union-attr] + parent = ( + # pyre-fixme[16] + input.args[0] # type: ignore[union-attr] + # pyre-fixme[16] + if input.target == aten.select # type: ignore[union-attr] + else other.args[0] # type: ignore[union-attr] + ) + else: + parent = "" + else: + parent = "" + group_key = ( + "batch_aten_" + self.op.__name__.lower().split(".")[0], + str(shape), + str(input.meta["val"].dtype), # type: ignore[union-attr] + str(other.meta["val"].dtype), # type: ignore[union-attr] + str(alpha), + str(rounding_mode), + str(parent), + ) + else: + group_key = None + return group_key + + def fuse(self, graph: torch.fx.GraphModule, subset: List[torch.fx.Node]): + batch_inputs, batch_others = [], [] + alpha = subset[0].kwargs.get("alpha", 1.0) + batch_inputs_meta, batch_others_meta = [], [] + + for node in subset: + input, other = node.args + batch_inputs.append(input) + batch_others.append(other) + batch_inputs_meta.append(input.meta) # type: ignore[possibly-undefined, union-attr] + batch_others_meta.append(other.meta) # type: ignore[possibly-undefined, union-attr] + + with graph.inserting_before(subset[0]): + stack_inputs = decompose_stack(graph, batch_inputs) + stack_others = decompose_stack(graph, batch_others) + stack_inputs_meta = torch.stack( + [input["val"] for input in batch_inputs_meta] + ) + stack_others_meta = torch.stack( + [other["val"] for other in batch_others_meta] + ) + + batch_op = graph.call_function( + self.op, + args=(stack_inputs, stack_others), + kwargs={"alpha": alpha} if self.op == aten.add.Tensor else {}, + ) + batch_op.meta["val"] = self.op(stack_inputs_meta, stack_others_meta) + for i, original_add in enumerate(subset): + with graph.inserting_after(batch_op): + new_add = graph.call_function( + torch.ops.aten.select, args=((batch_op, 0, i)) + ) + original_add.replace_all_uses_with(new_add) + new_add.meta.update(original_add.meta) + graph.erase_node(original_add) + counters["inductor"][ + "batch_aten_" + self.op.__name__.lower().split(".")[0] + ] += 1 + + +@register_fusion("batch_linear_lhs") +class BatchLinearLHSFusion(BatchFusion): + """ + Batch linear left-hand side fusion. This pass tries to fuse the following patterns: + + torch.nn.functional.linear(x, w1), linear(x, w2),... * linear(x, wn) + -> torch.mm(x, torch.cat([w1, w2,... * wn]).transpose(0, 1)) + + We have a separate pass to eliminate contiguous transpose in a generic way. + """ + + def match(self, node: torch.fx.Node) -> Optional[Tuple[str, bool, Any]]: + if CallFunctionVarArgs(torch.nn.functional.linear).match( + node + ) and is_linear_node_can_be_fused(node): + input = get_arg_value(node, 0, "input") + bias = get_arg_value(node, 2, "bias") + group_key = ("batch_linear_lhs", bias is None, input) + else: + group_key = None + return group_key + + def fuse(self, graph: torch.fx.GraphModule, subset: List[torch.fx.Node]): + batch_nodes = [] + batch_input = None + batch_weights, batch_weights_meta = [], [] + batch_biases, batch_biases_meta = [], [] + split_sections = [] + for node in subset: + input = get_arg_value(node, 0, "input") + weight = get_arg_value(node, 1, "weight") + bias = get_arg_value(node, 2, "bias") + batch_nodes.append(node) + if batch_input is None: + batch_input = input + else: + assert batch_input is input + batch_weights.append(weight) + batch_weights_meta.append(weight.meta["example_value"]) + if bias: + batch_biases.append(bias) + batch_biases_meta.append(bias.meta["example_value"]) + split_sections.append(weight.meta["example_value"].shape[0]) + + with graph.inserting_before(subset[0]): + cat_weights = graph.call_function( + torch.cat, args=(batch_weights,), kwargs={"dim": 0} + ) + cat_weights.meta["example_value"] = torch.cat(batch_weights_meta, dim=0) + transposed_weights = graph.call_function( + torch.transpose, args=(cat_weights, 0, 1) + ) + transposed_weights.meta["example_value"] = torch.transpose( + cat_weights.meta["example_value"], 0, 1 + ) + if len(batch_biases) > 0: + cat_biases = graph.call_function( + torch.cat, args=(batch_biases,), kwargs={"dim": 0} + ) + cat_biases.meta["example_value"] = torch.cat(batch_biases_meta, dim=0) + fused_lhs = graph.call_function( + torch.addmm, + args=(cat_biases, batch_input, transposed_weights), + ) + fused_lhs.meta["example_value"] = torch.addmm( + cat_biases.meta["example_value"], + batch_input.meta["example_value"], # type: ignore[union-attr] + transposed_weights.meta["example_value"], + ) + else: + fused_lhs = graph.call_function( + torch.mm, + args=(batch_input, transposed_weights), + ) + fused_lhs.meta["example_value"] = torch.mm( + batch_input.meta["example_value"], # type: ignore[union-attr] + transposed_weights.meta["example_value"], + ) + fused_lhs_list = graph.call_function( + torch.split, args=(fused_lhs, split_sections), kwargs={"dim": 1} + ) + + for i, node in enumerate(batch_nodes): + with graph.inserting_after(fused_lhs_list): + new_node = graph.call_function( + operator.getitem, args=(fused_lhs_list, i) + ) + node.replace_all_uses_with(new_node) + new_node.meta.update(node.meta) + graph.erase_node(node) + counters["inductor"]["batch_linear_lhs"] += 1 + + +def is_node_meta_valid(node: Optional[torch.fx.Node]): + return node is None or "example_value" in node.meta or "val" in node.meta + + +# Poor person's check for if a node in the graph mutates its input. +# (the graph is torch IR, so we will see torch fns and python operators) +def _is_mutable_node(tgt): + if str(tgt).endswith("_"): + # e.g. torch.mul_, torch.Tensor.mul_ + return True + if ( + hasattr(tgt, "__module__") + and tgt.__module__ == "_operator" + and tgt.__name__.startswith("i") + ): + # e.g. operator.iand, operator.imul + return True + return False + + +def is_linear_node_can_be_fused(node: torch.fx.Node): + input = get_arg_value(node, 0, "input") + weight = get_arg_value(node, 1, "weight") + return ( + is_node_meta_valid(node) + and is_node_meta_valid(input) + and is_node_meta_valid(weight) + and len(input.meta["example_value"].shape) == 2 + and len(weight.meta["example_value"].shape) == 2 + # the mm -> bmm transform adds an unbind() op, + # which is not safe for autograd when the output of the mm is mutated. + # don't pattern match if any users of the mm mutate the input. + and not any(_is_mutable_node(user.target) for user in node.users) + ) + + +@register_fusion("batch_linear") +class PreGradBatchLinearFusion(BatchFusion): + """ + Batch linear fusion in pre grad pass. + Fuse linear with same size with torch.baddmm + """ + + def _getitem_args(self, getitem_node: torch.fx.Node): + if getitem_node.target != operator.__getitem__ or ( + getitem_node.op != "call_function" + ): + return None + return getitem_node.args[0] + + def match(self, node: torch.fx.Node): + if CallFunctionVarArgs(torch.nn.functional.linear).match( + node + ) and is_linear_node_can_be_fused(node): + input = get_arg_value(node, 0, "input") + weight = get_arg_value(node, 1, "weight") + bias = get_arg_value(node, 2, "bias") + if self.graph_search_options.get("fuse_nodes_with_same_users", False): + users = [user.target for user in node.users.keys()] + else: + users = "" # type: ignore[assignment] + group_key = ( + "batch_linear", + self._getitem_args(input), + str(input.meta["example_value"].shape), + str(weight.meta["example_value"].shape), + bias is None, + str(users), + ) + else: + group_key = None + return group_key + + def fuse(self, graph: torch.fx.GraphModule, subset: List[torch.fx.Node]): + batch_nodes = [] + batch_inputs = [] + batch_weights = [] + batch_biases = [] + batch_inputs_metadata = [] + batch_weights_metadata = [] + batch_biases_metadata = [] + for node in subset: + batch_nodes.append(node) + input = get_arg_value(node, 0, "input") + batch_inputs.append(input) + batch_inputs_metadata.append(input.meta["example_value"]) + weight = get_arg_value(node, 1, "weight") + batch_weights.append(weight) + batch_weights_metadata.append(weight.meta["example_value"]) + bias = get_arg_value(node, 2, "bias") + batch_biases.append(bias) + if bias is not None and hasattr(bias, "meta"): + batch_biases_metadata.append(bias.meta["example_value"]) + + with graph.inserting_before(subset[0]): + stack_inputs = graph.call_function( + torch.stack, args=(batch_inputs,), kwargs={"dim": 0} + ) + update_stack_example_value(stack_inputs, batch_inputs_metadata) + stack_weights = graph.call_function( + torch.stack, args=(batch_weights,), kwargs={"dim": 0} + ) + update_stack_example_value(stack_weights, batch_weights_metadata) + transpose_weight = graph.call_function( + torch.transpose, args=(stack_weights, 1, 2) + ) + transpose_weight.meta["example_value"] = torch.transpose( + stack_weights.meta["example_value"], 1, 2 + ) + if all(bias is None for bias in batch_biases): + bmm = graph.call_function( + torch.bmm, + args=(stack_inputs, transpose_weight), + ) + bmm.meta["example_value"] = torch.bmm( + stack_inputs.meta["example_value"], + transpose_weight.meta["example_value"], + ) + bmm_meta = bmm.meta["example_value"] + else: + stack_biases = graph.call_function( + torch.stack, args=(batch_biases,), kwargs={"dim": 0} + ) + update_stack_example_value(stack_biases, batch_biases_metadata) + unsqueeze_biases = graph.call_function( + torch.unsqueeze, args=(stack_biases, 1) + ) + unsqueeze_biases.meta["example_value"] = torch.unsqueeze( + stack_biases.meta["example_value"], 1 + ) + bmm = graph.call_function( + torch.baddbmm, + args=(unsqueeze_biases, stack_inputs, transpose_weight), + ) + try: + # it will have runtime error to broadcast when it has dynamic shape included + # in the meta data, so we need to skip the update meta data + bmm.meta["example_value"] = torch.baddbmm( + unsqueeze_biases.meta["example_value"], + stack_inputs.meta["example_value"], + transpose_weight.meta["example_value"], + ) + bmm_meta = bmm.meta["example_value"] + except Exception as e: + log.debug( + f" exception when update bmm meta data with stack error tracekey {e}" # noqa: G004 + ) + bmm_meta = None + + bmm = graph.call_function(torch.unbind, args=(bmm,), kwargs={"dim": 0}) + if bmm_meta is not None: + bmm.meta["example_value"] = torch.unbind(bmm_meta, dim=0) + for i, linear in enumerate(batch_nodes): + with graph.inserting_after(bmm): + getitem = graph.call_function(operator.getitem, args=(bmm, i)) + linear.replace_all_uses_with(getitem) + getitem.meta.update(linear.meta) + graph.erase_node(linear) + counters["inductor"]["batch_linear"] += 1 + + +@register_fusion("batch_layernorm") +class BatchLayernormFusion(BatchFusion): + """ + Batch layer norm fusion in pre grad pass + """ + + def match(self, node: torch.fx.Node): + if CallFunctionVarArgs(torch.nn.functional.layer_norm).match(node): + input = get_arg_value(node, 0, "input") + weight = get_arg_value(node, 2, "weight") + bias = get_arg_value(node, 3, "bias") + if self.graph_search_options.get("fuse_nodes_with_same_users", False): + users = [user.target for user in node.users.keys()] + else: + users = "" # type: ignore[assignment] + group_key = ( + ( + "batch_layernorm", + str(input.meta["example_value"].shape), + str(weight.meta["example_value"].shape) + if weight is not None + else "", + str(bias.meta["example_value"].shape) if bias is not None else "", + str(get_arg_value(node, 1, "normalized_shape")), + str(get_arg_value(node, 4, "eps")), + str(users), + ) + if "example_value" in input.meta + and is_node_meta_valid(weight) + and is_node_meta_valid(bias) + else None + ) + else: + group_key = None + return group_key + + def fuse(self, graph: torch.fx.GraphModule, subset: List[torch.fx.Node]): + group_inputs = [] + group_shapes = [] + group_weights = [] + group_biases = [] + group_epss = [] + group_nodes = [] + group_inputs_metadata = [] + group_biases_metadata = [] + group_weights_metadata = [] + for node in subset: + group_nodes.append(node) + input = get_arg_value(node, 0, "input") + group_inputs.append(input) + group_inputs_metadata.append(input.meta["example_value"]) + group_shapes.append(get_arg_value(node, 1, "normalized_shape")) + weight = get_arg_value(node, 2, "weight") + group_weights.append(weight) + if weight is not None and hasattr(weight, "meta"): + group_weights_metadata.append(weight.meta["example_value"]) + bias = get_arg_value(node, 3, "bias") + group_biases.append(bias) + if bias is not None and hasattr(bias, "meta"): + group_biases_metadata.append(bias.meta["example_value"]) + eps = get_arg_value(node, 4, "eps") + if eps is None: + eps = 1e-5 + group_epss.append(eps) + stack_dim = -1 - len(group_shapes[-1]) + + if all(bias is None for bias in group_biases): + group_biases = None # type: ignore[assignment] + if all(weight is None for weight in group_weights): + group_weights = None # type: ignore[assignment] + assert all( + eps == group_epss[0] for eps in group_epss + ), "all epsilon values must be equal" + + with graph.inserting_before(subset[0]): + stack_input = graph.call_function( + torch.stack, args=(group_inputs,), kwargs={"dim": stack_dim} + ) + update_stack_example_value(stack_input, group_inputs_metadata, stack_dim) + if group_weights is not None: + stack_weight = graph.call_function( + torch.stack, args=(group_weights,), kwargs={"dim": 0} + ) + update_stack_example_value(stack_weight, group_weights_metadata) + else: + stack_weight = None + if group_biases is not None: + stack_bias = graph.call_function( + torch.stack, args=(group_biases,), kwargs={"dim": 0} + ) + update_stack_example_value(stack_bias, group_biases_metadata) + else: + stack_bias = None + + batch_layer_norm = graph.call_function( + torch.nn.functional.layer_norm, + args=(stack_input, group_shapes[-1]), + kwargs={"eps": group_epss[-1]}, + ) + batch_layer_norm.meta["example_value"] = stack_input.meta["example_value"] + + if group_weights is not None and group_biases is not None: + previous_batch_layer_norm_meta = batch_layer_norm.meta["example_value"] + batch_layer_norm = graph.call_function( + torch.mul, args=(stack_weight, batch_layer_norm) + ) + update_pointwise_example_value( + batch_layer_norm, + stack_weight.meta["example_value"], + previous_batch_layer_norm_meta, + torch.mul, + ) + previous_batch_layer_norm_meta = batch_layer_norm.meta["example_value"] + batch_layer_norm = graph.call_function( + torch.add, args=(stack_bias, batch_layer_norm) + ) + update_pointwise_example_value( + batch_layer_norm, + stack_bias.meta["example_value"], + previous_batch_layer_norm_meta, + torch.add, + ) + elif group_weights is not None and group_biases is None: + previous_batch_layer_norm_meta = batch_layer_norm.meta["example_value"] + batch_layer_norm = graph.call_function( + torch.mul, args=(stack_weight, batch_layer_norm) + ) + update_pointwise_example_value( + batch_layer_norm, + stack_weight.meta["example_value"], + previous_batch_layer_norm_meta, + torch.mul, + ) + elif group_weights is None and group_biases is not None: + previous_batch_layer_norm_meta = batch_layer_norm.meta["example_value"] + batch_layer_norm = graph.call_function( + torch.add, args=(stack_bias, batch_layer_norm) + ) + update_pointwise_example_value( + batch_layer_norm, + stack_bias.meta["example_value"], + previous_batch_layer_norm_meta, + torch.add, + ) + + batch_layer_norm_unbind = graph.call_function( + torch.unbind, + args=(batch_layer_norm,), + kwargs={"dim": stack_dim}, + ) + update_stack_example_value( + batch_layer_norm_unbind, + batch_layer_norm.meta["example_value"], + op=torch.unbind, + dim=stack_dim, + ) + + for i, node in enumerate(group_nodes): + with graph.inserting_after(batch_layer_norm_unbind): + new_node = graph.call_function( + operator.getitem, args=(batch_layer_norm_unbind, i) + ) + node.replace_all_uses_with(new_node) + new_node.meta.update(node.meta) + graph.erase_node(node) + counters["inductor"]["batch_layernorm"] += 1 + + +class BatchPointwiseOpsPreGradFusion(BatchPointwiseOpsFusionFactory): + """ + Batch pointwise ops (e.g., sigmoid, relu, tanh) fusion in pre grad pass. + We fuse it in random place, and the introduced stack node may be merged in split cat. + """ + + def __init__(self, op, **kwargs) -> None: + super().__init__(op, **kwargs) + self.op = op + + def match(self, node: torch.fx.Node): + input = get_arg_value(node, 0, "input") + if CallFunctionVarArgs(self.op).match(node) and is_node_meta_valid(node): + if self.graph_search_options.get("fuse_nodes_with_same_parent", False): + # pyre-fixme[16] + parent = node.args[0] + parent = parent.target if parent is not None else "" # type: ignore[union-attr] + else: + parent = "" + # for relu op, we also use the inplace to construct the key + group_key = ( + "batch_" + self.op.__name__.lower().split(".")[0], + str(input.meta["example_value"].shape), + str(node.kwargs.get("inplace", False)), + str(parent), + ) + else: + group_key = None + return group_key + + def fuse(self, graph: torch.fx.GraphModule, subset: List[torch.fx.Node]): + batch_nodes = [] + batch_inputs = [] + batch_inputs_metadata = [] + + for node in subset: + batch_nodes.append(node) + input = get_arg_value(node, 0, "input") + batch_inputs.append(input) + batch_inputs_metadata.append(input.meta["example_value"]) + + with graph.inserting_before(subset[0]): + stack_inputs = graph.call_function( + torch.stack, args=(batch_inputs,), kwargs={"dim": 0} + ) + update_stack_example_value(stack_inputs, batch_inputs_metadata) + if self.op == torch.nn.functional.relu: + batch_op = graph.call_function( + self.op, + args=(stack_inputs,), + kwargs={"inplace": subset[0].kwargs.get("inplace", False)}, + ) + batch_op.meta["example_value"] = self.op( + stack_inputs.meta["example_value"], + inplace=subset[0].kwargs.get("inplace", False), + ) + else: + batch_op = graph.call_function( + self.op, + args=(stack_inputs,), + ) + batch_op.meta["example_value"] = self.op( + stack_inputs.meta["example_value"] + ) + unbind_op = graph.call_function( + torch.unbind, args=(batch_op,), kwargs={"dim": 0} + ) + unbind_op.meta["example_value"] = torch.unbind( + batch_op.meta["example_value"], dim=0 + ) + for i, node in enumerate(batch_nodes): + with graph.inserting_after(unbind_op): + getitem = graph.call_function(operator.getitem, args=(unbind_op, i)) + node.replace_all_uses_with(getitem) + getitem.meta.update(node.meta) + graph.erase_node(node) + counters["inductor"]["batch_" + self.op.__name__.lower().split(".")[0]] += 1 + + +class BatchPointwiseOpsPostGradFusion(BatchPointwiseOpsFusionFactory): + """ + Batch pointwise ops (e.g., sigmoid, relu, tanh) fusion in post grad pass. + The introduced stack node may be merged in split cat. + """ + + def __init__(self, op, **kwargs) -> None: + super().__init__(op, **kwargs) + self.op = op + + def match(self, node: torch.fx.Node): + input = get_arg_value(node, 0, "input") + if CallFunctionVarArgs(self.op).match(node) and is_node_meta_valid(node): + # for relu op, we also use the inplace to construct the key + # we batch the ops with same parent to enable followup split cat + parent = node.args[0] + parent = parent.target if self.graph_search_options.get("fuse_nodes_with_same_parent", False) else "" # type: ignore[union-attr] + group_key = ( + "batch_aten_" + self.op.__name__.lower().split(".")[0], + str(input.meta["val"].shape), + str(node.kwargs.get("inplace", False)), + # pyre-fixme[16] + str(parent), + ) + else: + group_key = None + return group_key + + def fuse(self, graph: torch.fx.GraphModule, subset: List[torch.fx.Node]): + batch_nodes = [] + batch_inputs = [] + batch_inputs_metadata = [] + + for node in subset: + batch_nodes.append(node) + input = get_arg_value(node, 0, "input") + batch_inputs.append(input) + batch_inputs_metadata.append(input.meta["val"]) + + with graph.inserting_before(subset[0]): + stack_inputs = decompose_stack(graph, batch_inputs) + update_stack_example_value(stack_inputs, batch_inputs_metadata) + batch_op = graph.call_function( + self.op, + args=(stack_inputs,), + ) + for i, node in enumerate(batch_nodes): + with graph.inserting_after(batch_op): + getitem = graph.call_function(aten.select, args=(batch_op, 0, i)) + node.replace_all_uses_with(getitem) + getitem.meta.update(node.meta) + graph.erase_node(node) + counters["inductor"][ + "batch_aten_" + self.op.__name__.lower().split(".")[0] + ] += 1 + + +@register_fusion("batch_tanh") +class BatchTanhPreGradFusion(BatchPointwiseOpsPreGradFusion): + def __init__(self, **kwargs) -> None: + super().__init__(torch.tanh, **kwargs) + + +@register_fusion("batch_sigmoid") +class BatchSigmoidPreGradFusion(BatchPointwiseOpsPreGradFusion): + def __init__(self, **kwargs) -> None: + super().__init__(torch.sigmoid, **kwargs) + + +@register_fusion("batch_relu") +class BatchReLuPreGradFusion(BatchPointwiseOpsPreGradFusion): + def __init__(self, **kwargs) -> None: + super().__init__(torch.nn.functional.relu, **kwargs) + + +@register_fusion("batch_aten_tanh", pre_grad=False) +class BatchTanhPostGradFusion(BatchPointwiseOpsPostGradFusion): + def __init__(self, **kwargs) -> None: + super().__init__(aten.tanh.default, **kwargs) + + +@register_fusion("batch_aten_sigmoid", pre_grad=False) +class BatchSigmoidPostGradFusion(BatchPointwiseOpsPostGradFusion): + def __init__(self, **kwargs) -> None: + super().__init__(aten.sigmoid.default, **kwargs) + + +@register_fusion("batch_aten_relu", pre_grad=False) +class BatchReLuPostGradFusion(BatchPointwiseOpsPostGradFusion): + def __init__(self, **kwargs) -> None: + super().__init__(aten.relu.default, **kwargs) + + +@register_fusion("batch_aten_add", pre_grad=False) +class BatchAddPostGradFusion(BatchPointwiseMathOpsPostGradFusion): + def __init__(self, **kwargs) -> None: + super().__init__(aten.add.Tensor, **kwargs) + + +@register_fusion("batch_aten_sub", pre_grad=False) +class BatchSubPostGradFusion(BatchPointwiseMathOpsPostGradFusion): + def __init__(self, **kwargs) -> None: + super().__init__(aten.sub.Tensor, **kwargs) + + +@register_fusion("batch_aten_div", pre_grad=False) +class BatchDivPostGradFusion(BatchPointwiseMathOpsPostGradFusion): + def __init__(self, **kwargs) -> None: + super().__init__(aten.div.Tensor, **kwargs) + + +@register_fusion("batch_aten_mul", pre_grad=False) +class BatchMulPostGradFusion(BatchPointwiseMathOpsPostGradFusion): + def __init__(self, **kwargs) -> None: + super().__init__(aten.mul.Tensor, **kwargs) + + +class _OrderedSet: + def __init__(self, param=None) -> None: + if param: + self.rep = OrderedDict(dict.fromkeys(param)) + else: + self.rep = OrderedDict() + + def __contains__(self, o) -> bool: + return o in self.rep + + def __len__(self) -> int: + return self.rep.__len__() + + def append(self, o): + self.rep[o] = None + + def __iter__(self): + return self.rep.keys().__iter__() + + +def find_independent_subset_greedy( + node_list: Iterable[torch.fx.Node], + graph_search_options: Dict[str, Any], +) -> Iterator[Iterable[torch.fx.Node]]: + """ + Yields a list of subsets of `node_list` where no element in the subset + depends on any other element in the subset. This results in a set of + independent nodes which can be fused together. + + The order of `node_list` is preserved within each subset so we can benefit + from split-cat elimination in later passes. + + During iteration it is only safe to mutate the graph by changing the nodes + that have been returned. + + graph_search_options: + - min_fuse_set_size: Minimum size of the subset to consider. Subsets below + this size will be ignored. + - max_fuse_set_size: Maximum size of the subset to consider. Subsets will + be broken to be at most this size. + """ + + # Compute all the children of `node` which are members of + # `interesting_nodes`. + def find_dependent_nodes(node, interesting_nodes): + visited_node_set: Set[torch.fx.Node] = {node} + dep_set: Set[torch.fx.Node] = set() + + work = [node] + while work: + node = work.pop() + for input_node in node.all_input_nodes: + if input_node in interesting_nodes: + dep_set.add(input_node) + + if input_node not in visited_node_set: + visited_node_set.add(input_node) + work.append(input_node) + + return dep_set + + min_fuse_set_size = graph_search_options["min_fuse_set_size"] + max_fuse_set_size = graph_search_options["max_fuse_set_size"] + + # node_list needs to be a set because we only track the nodes that are left + # in it (and we want to do the `in` on a set, not a list). But we want to + # keep the correct order. + node_list = _OrderedSet(node_list) + + cache: Dict[torch.fx.Node, Set[torch.fx.Node]] = {} + while node_list: + subset: List[torch.fx.Node] = [] + subset_deps: Set[torch.fx.Node] = set() + + next_round_node_list = _OrderedSet() + for node in node_list: + if len(subset) >= max_fuse_set_size or node in subset_deps: + next_round_node_list.append(node) + continue + + dep_set = cache.pop(node, None) + if dep_set is None: + dep_set = find_dependent_nodes(node, node_list) + + if not dep_set.intersection(subset): + subset.append(node) + subset_deps.update(dep_set) + else: + next_round_node_list.append(node) + cache[node] = dep_set + + if len(subset) >= min_fuse_set_size: + # Careful here - the caller uses the subsets to fuse nodes together + # so we need to clear any cache entry that contains one of the + # returned nodes because the dependency list could be different + # (larger) after the merge. + cache = {k: v for k, v in cache.items() if v.isdisjoint(subset)} + yield subset + + node_list = next_round_node_list + + +def get_fusion_candidates( + rule: GroupBatchFusionBase, root_node: torch.fx.Node, fused_set: Set[torch.fx.Node] +) -> DefaultDict[Any, List[torch.fx.Node]]: + """ + Search fusion candidates for a specific rule using BFS starting from the root node. + We only search the subgraph within graph_search_options["max_fuse_search_depth"]. + """ + q: Deque[Tuple[int, torch.fx.Node]] = collections.deque() + + candidate_dict: DefaultDict[Any, List[torch.fx.Node]] = collections.defaultdict( + list + ) + + if root_node.target in SEARCH_EXCLUSIONS: + return candidate_dict + + visited_set: Set[torch.fx.Node] = set() + + for next_node in root_node.all_input_nodes: + q.append((1, next_node)) + visited_set.add(next_node) + + while len(q) > 0: + depth, node = q.popleft() + + if node in fused_set: + continue + + key = rule.match(node) + if key is not None: + candidate_nodes = candidate_dict[key] + if node not in candidate_nodes: + candidate_nodes.append(node) + else: + if depth < rule.graph_search_options["max_fuse_search_depth"]: + for next_node in node.all_input_nodes: + if next_node not in visited_set: + visited_set.add(next_node) + q.append((depth + 1, next_node)) + + return candidate_dict + + +def apply_group_batch_fusion(graph: torch.fx.GraphModule, rule: GroupBatchFusionBase): + stable_topological_sort(graph) # type: ignore[arg-type] + fused_set: Set[torch.fx.Node] = set() + log_to_scuba = False + + for node in reversed(graph.nodes): + candidates = get_fusion_candidates(rule, node, fused_set) + + for key, candidate_nodes in candidates.items(): + if len(candidate_nodes) < rule.graph_search_options["min_fuse_set_size"]: + continue + + for subset in find_independent_subset_greedy( + candidate_nodes, rule.graph_search_options + ): + rule.fuse(graph, subset) + fused_set.update(subset) + log.debug( + f"{rule.__class__.__name__}: key = {key}; subset size = {len(list(subset))}" # noqa: G004 + ) + log_to_scuba = True + if log_to_scuba: + optimus_scuba_log[rule.__class__.__name__] = upload_graph(graph) + + +def generate_fusion_from_config(config_options: Dict[str, Any], pre_grad=True): + fusions: List[GroupBatchFusionBase] = [] + for name, options in config_options.items(): + # we skip all patterns from pattern_matcher passes (e.g., split_cat) + if name not in PRE_GRAD_FUSIONS and name not in POST_GRAD_FUSIONS: + continue + fusion_cls = PRE_GRAD_FUSIONS[name] if pre_grad else POST_GRAD_FUSIONS[name] + _options = graph_search_options.copy() + _options.update(options) + fusions.append(fusion_cls(graph_search_options=_options)) # type: ignore[operator] + return fusions + + +def group_batch_fusion_passes(graph: torch.fx.Graph, pre_grad=True): + fusions: List[GroupBatchFusionBase] = [] + # we keep all current pre grad fusions to keep + # current implementation, will remove this later + if pre_grad: + fusions += generate_fusion_from_config( + config.pre_grad_fusion_options, pre_grad=True + ) + else: + fbgemm_fusion_keys = [ + x + for x in config.post_grad_fusion_options + if config.post_grad_fusion_options[x].get("require_fbgemm", False) + ] + fbgemm_fusions = { + fusion: config.post_grad_fusion_options[fusion] + for fusion in fbgemm_fusion_keys + } + non_fbgemm_fusions = { + fusion: config.post_grad_fusion_options[fusion] + for fusion in config.post_grad_fusion_options.keys() + if fusion not in fbgemm_fusion_keys + } + fusions += generate_fusion_from_config(non_fbgemm_fusions, pre_grad=False) + if has_fbgemm: + fusions += generate_fusion_from_config(fbgemm_fusions, pre_grad=False) + + for i, rule in enumerate(fusions): + with GraphTransformObserver( + graph.owning_module, + f"group_batch_fusion_{i}", + config.trace.log_url_for_graph_xform, + ): + apply_group_batch_fusion(graph, rule) # type: ignore[arg-type] diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/joint_graph.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/joint_graph.py new file mode 100644 index 0000000000000000000000000000000000000000..d526f1bb6a64dec65ef8e5ea012fa31ad6aa7233 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/joint_graph.py @@ -0,0 +1,694 @@ +# mypy: allow-untyped-defs +import itertools +import logging +import typing +from collections import Counter +from typing import Any, Dict, List, Set, Union + +import torch +import torch._guards +import torch.utils._pytree as pytree +from torch._inductor.constant_folding import ConstantFolder +from torch._inductor.fx_passes.dedupe_symint_uses import _SymHashingDict +from torch.fx.experimental.symbolic_shapes import statically_known_true +from torch.fx.passes.graph_transform_observer import GraphTransformObserver +from torch.multiprocessing.reductions import StorageWeakRef + +from ...utils._ordered_set import OrderedSet +from .. import config +from ..pattern_matcher import ( + CallFunction, + init_once_fakemode, + KeywordArg, + Match, + MULTIPLE, + PatternMatcherPass, + register_graph_pattern, + stable_topological_sort, +) +from .replace_random import replace_random_passes + + +log = logging.getLogger(__name__) +patterns = PatternMatcherPass() +aten = torch.ops.aten +prims = torch.ops.prims + +pass_patterns = [ + patterns, + PatternMatcherPass(), +] + + +@init_once_fakemode +def lazy_init(): + from .fuse_attention import _sfdp_init + from .misc_patterns import _misc_patterns_init + from .pad_mm import _pad_mm_init + + _pad_mm_init() + _sfdp_init() + _misc_patterns_init() + + +def remove_no_ops( + gm: torch.fx.GraphModule, zeros: Set[torch.fx.Node], ones: Set[torch.fx.Node] +): + with torch.utils._python_dispatch._disable_current_modes(): + "Removes no-ops: (+ 0, - 0, * 1, / 1)" + graph = gm.graph + + def fake_tensors_eq(t1, t2, fields=("shape", "dtype", "device")): + if any(not isinstance(t, torch.Tensor) for t in (t1, t2)): + return False + for field in fields: + if getattr(t1, field) != getattr(t2, field): + return False + return True + + def replace_no_op(node, replace_input_index): + replacement = node.args[replace_input_index] + + # https://github.com/pytorch/pytorch/issues/86128 causes + # non-Tensor inputs even for ops with only Tensor inputs. + # TODO - decompose/type promote to avoid this + if not all(isinstance(arg, torch.fx.Node) for arg in node.args): + return + + if not fake_tensors_eq(node.meta["val"], replacement.meta["val"]): + if fake_tensors_eq( + node.meta["val"], + replacement.meta["val"], + ("shape", "device"), + ): + with graph.inserting_after(node): + replacement = graph.call_function( + torch.ops.prims.convert_element_type.default, + args=(replacement, node.meta["val"].dtype), + ) + else: + return + + node.replace_all_uses_with(replacement) + replacement.meta.update(node.meta) + graph.erase_node(node) + + for node in graph.find_nodes(op="call_function", target=aten.add.Tensor): + # TODO handle Tensor-Scalar adds, it's a different schema + if len(node.args) == 2: + if ( + not any(e in zeros for e in node.args) + or node.kwargs.get("alpha", 1) != 1 + ): + continue + + replace_index = 1 if node.args[0] in zeros else 0 + replace_no_op(node, replace_index) + + for node in graph.find_nodes(op="call_function", target=aten.sub.Tensor): + if len(node.args) == 2: + if node.args[1] not in zeros or node.kwargs.get("alpha", 1) != 1: + continue + + replace_no_op(node, 0) + + for node in graph.find_nodes(op="call_function", target=aten.mul.Tensor): + if len(node.args) == 2: + if not any(e in ones for e in node.args): + continue + + replace_input_index = 1 if node.args[0] in ones else 0 + replace_no_op(node, replace_input_index) + + for node in graph.find_nodes(op="call_function", target=aten.div.Tensor): + if len(node.args) == 2 and node.args[1] in ones: + replace_no_op(node, 0) + + # meta tensors returned from the graph have no data and can be replaced with empty_strided + for output_node in graph.find_nodes(op="output"): + had_meta_return = False + + def visit(n): + nonlocal had_meta_return + val = n.meta.get("val") + if isinstance(val, torch.Tensor) and val.device.type == "meta": + with graph.inserting_before(output_node): + n.replace_all_uses_with( + graph.call_function( + torch.ops.aten.empty_strided.default, + args=(val.size(), val.stride()), + kwargs={"dtype": val.dtype, "device": val.device}, + ) + ) + had_meta_return = True + + torch.fx.map_arg(output_node.args, visit) + if had_meta_return: + graph.eliminate_dead_code() + + +def remove_redundant_views(gm: torch.fx.GraphModule): + """ + Removes redundant views by reusing existing ones. + """ + with torch.utils._python_dispatch._disable_current_modes(): + # A dictionary mapping a tensor to all aliased views. + views: Dict[torch.fx.Node, Dict[torch.dtype, torch.fx.Node]] = {} + graph = gm.graph + + for node in graph.find_nodes( + op="call_function", target=torch.ops.aten.view.dtype + ): + src = node.args[0] + to_type = node.args[1] + existing_views = views.get(src) + is_needed = True + + if existing_views: + # Replace the view with the an existing view if available. + alias = existing_views.get(to_type) + if alias: + is_needed = False + node.replace_all_uses_with(alias) + alias.meta.update(node.meta) + graph.erase_node(node) + else: + from_type = src.meta["val"].dtype + existing_views = {from_type: src} + views[src] = existing_views + + if is_needed: + # Save the new alias but do not replace existing one. + existing_views.setdefault(to_type, node) + views[node] = existing_views + + # Clean up unused views. + while True: + unused_views = [alias for alias in views if not alias.users] + if len(unused_views) == 0: + break + for unused in unused_views: + views.pop(unused) + graph.erase_node(unused) + + +class UniformValueConstantFolder(ConstantFolder): + """ + Runs constant folding and replaces tensors that have a unifrom value + with a tensor constructor call: aten.full([shape], value, ...) + """ + + def __init__(self, gm, skip_constructors=False) -> None: + super().__init__(gm, skip_constructors) + self.node_storages_ptrs: Dict[torch.fx.Node, int] = {} + self.constant_data_ptrs: Dict[torch.fx.Node, StorageWeakRef] = {} + # we may constant fold a tensor which in the graph has a sym size + # see: [constant folding refining of symints] + self.node_replacements_shapes: Dict[torch.fx.Node, List[int]] = {} + + # initialize symint -> node mapping so that we can + # use symint nodes in full constructors + self.symint_nodes = _SymHashingDict() + for n in self.module.graph.nodes: + if "val" in n.meta and isinstance(n.meta["val"], torch.SymInt): + self.symint_nodes[n.meta["val"]] = n + + # reference from torch/_funtorch/partitioners.py:get_default_op_list + self.view_op_packets = [ + aten.squeeze, + aten.unsqueeze, + aten.alias, + aten.view, + aten.slice, + aten.t, + prims.broadcast_in_dim, + aten.expand, + aten.as_strided, + aten.permute, + ] + + self.indexing_op_packets = { + aten.slice, + } + + def _support_dynamic_shape(self): + return True + + def insertable_tensor_check(self, t: torch.Tensor) -> bool: + return True + + def add_node_replacement(self, node: torch.fx.Node, tensor: torch.Tensor) -> None: + self.node_replacements[node] = tensor.flatten()[0].item() + self.node_replacements_shapes[node] = node.meta["val"].shape + self.constant_data_ptrs[node] = StorageWeakRef(tensor.untyped_storage()) + + def insert_placerholder_values(self, env: Dict[torch.fx.Node, Any]) -> None: + for n in self.module.graph.find_nodes(op="placeholder"): + if "val" in n.meta and isinstance(n.meta["val"], torch.SymInt): + env[n] = n.meta["val"] + else: + env[n] = self.unknown_value + + def _deduce_value(self, node: torch.fx.Node): + # deduce value for full-like nodes + # 1. for constructors, substitute value is a tensor of size [1] + # 2. for view ops/indexing, substitute value is the same as the input + # 3. for pointwise ops, run node to get the substitute value + # 4. deal with some special ops + # otherwise, stop deduce value and return unknown value + + # TODO: cat, more indexing + # TODO - do on cpu to avoid syncs + + # single-elem attrs + if node.op == "get_attr" or ( + node.op == "call_function" + and node.target == torch.ops.aten.lift_fresh_copy.default + ): + out = super(ConstantFolder, self).run_node(node) + if isinstance(out, torch.Tensor) and out.numel() == 1: + return out + + # handle device_put op + if node.target == prims.device_put.default: + return super(ConstantFolder, self).run_node(node) + + # constructors ops + if ( + node.op == "call_function" + and node.target == aten.full.default + and len(node.args) == 2 + ): + args, kwargs = self.fetch_args_kwargs_from_env(node) + new_args = [[1], args[1]] + return aten.full.default(*new_args, **node.kwargs) + + # handle before view ops because this changes value + if node.target == aten.view.dtype: + return super(ConstantFolder, self).run_node(node) + + # view ops, return input tensor, the first argument + if hasattr(node.target, "overloadpacket") and ( + node.target.overloadpacket in self.view_op_packets + or node.target.overloadpacket in self.indexing_op_packets + ): + assert isinstance(node.args[0], torch.fx.Node) + return self.env[node.args[0]] + + # we don't want to return unknown value for symints so that we can + # still constant fold through their use in constructors or views + # if we see them in a pointwise node (e.g., tensor * symint) + # we will bail + if "val" in node.meta and isinstance(node.meta["val"], torch.SymInt): + return node.meta["val"] + + # pointwise ops + if isinstance(node.target, torch._ops.OpOverload) and ( + torch.Tag.pointwise in node.target.tags + or node.target is torch.ops.aten.scalar_tensor.default + ): + args, kwargs = self.fetch_args_kwargs_from_env(node) + flattened_inputs = pytree.arg_tree_leaves(*args, **kwargs) + + if any(isinstance(inp, torch.SymInt) for inp in flattened_inputs): + return self.unknown_value + + # we run the ops with dim 1, so remove memory_format to avoid error + kwargs = dict(kwargs) + kwargs.pop("memory_format", None) + + return node.target(*args, **kwargs) + + return self.unknown_value + + +def constant_fold_uniform_value(gm: torch.fx.GraphModule): + with torch.utils._python_dispatch._disable_current_modes(): + "Runs constant folding and replaces constants which can be constructed with a single `full` call. Calls into remove_no_ops." + aten = torch.ops.aten + + # Constant folding can leak memory, especially with repeated compilation, so we are only going to + # remove constants which can be replaced with a constructor. + cf = UniformValueConstantFolder(gm) + cf.run() + + node_replacements = cf.node_replacements + + # note: [constant folding refining of symints] + # constant folding will partially evaluate a graph such that values which have dependencies which + # are entirely known at compile time may also become compile time constants. in some cases, + # this will include symints which we had not yet previously deduced are guaranteed a + # constant value and is then deduced in constant folding. an example is: + # unbacked_symint_eq_11 = torch.full((), 11).item() + # torch.full((unbacked_symint_eq_11,), 0) + node_replacements_shapes = cf.node_replacements_shapes + + graph = gm.graph + + zeros = set() + ones = set() + + # Got failures in `test_is_set_to_cuda` if we change aliasing on constants, + # so just constant-ify if a Tensor is unaliased + constant_data_ptr_count: typing.Counter[StorageWeakRef] = Counter() + + for node in cf.node_replacements: + constant_data_ptr_count[cf.constant_data_ptrs[node]] += 1 + + for node, value in node_replacements.items(): + # we dont have a functional way right now of instantiating a non-contiguous tensor with full/zeros/ones right now + # hasn't shown up to be important yet + if "val" not in node.meta: + # This can only happen in AOTI + continue + + fake_tensor = node.meta["val"] + if not fake_tensor.is_contiguous(memory_format=torch.contiguous_format): + continue + + # TODO - not sure about lossy uint->python value->uint conversions + if fake_tensor.dtype in ( + torch.uint8, + torch.uint16, + torch.uint32, + torch.uint64, + ): + continue + + if constant_data_ptr_count[cf.constant_data_ptrs[node]] > 1: + continue + + with graph.inserting_after(node): + # the conversion from tensor and back to value can be lossy, just use the original full ctor value + if ( + node.op == "call_function" + and node.target == aten.full.default + and len(node.args) == 2 + ): + value = node.args[1] + + # refines symints, see [constant folding refining of symints] above + for runtime_size, compile_time_size in zip( + node_replacements_shapes[node], fake_tensor.shape + ): + torch._check(runtime_size == compile_time_size) + + # replace SymInt as Node before creating a new full node + # e.g. (1, s0) -> (1, arg0_1) + node_shape = node_replacements_shapes[node] + if not all( + not isinstance(s, torch.SymInt) or s in cf.symint_nodes + for s in node_shape + ): + continue + + shapes = [ + cf.symint_nodes[s] if isinstance(s, torch.SymInt) else s + for s in node_replacements_shapes[node] + ] + + # zeros and ones just get traced into full, so we insert those + new_node = graph.call_function( + aten.full.default, + args=(shapes, value), + kwargs={ + "dtype": fake_tensor.dtype, + "layout": torch.strided, + "device": fake_tensor.device, + "pin_memory": False, + }, + ) + + new_node.meta.update(node.meta) + node.replace_all_uses_with(new_node) + graph.erase_node(node) + + if value == 0: + zeros.add(new_node) + elif value == 1: + ones.add(new_node) + + remove_no_ops(gm, zeros, ones) + remove_redundant_views(gm) + + +def joint_graph_passes(graph: torch.fx.GraphModule): + """ + Run FX transformations on the joint forwards+backwards graph. + """ + lazy_init() + count = 0 + if config.joint_custom_pre_pass is not None: + with GraphTransformObserver( + graph, "joint_custom_pre_pass", config.trace.log_url_for_graph_xform + ): + config.joint_custom_pre_pass(graph.graph) + count += 1 + + from .post_grad import remove_noop_ops + + remove_noop_ops(graph.graph) + + if config.joint_graph_constant_folding: + with GraphTransformObserver( + graph, "constant_fold_uniform_value", config.trace.log_url_for_graph_xform + ): + constant_fold_uniform_value(graph) + + if config.pattern_matcher: + for patterns in pass_patterns: + count += patterns.apply(graph.graph) # type: ignore[arg-type] + + if not config.fallback_random: + count += replace_random_passes(graph) + + if config.joint_custom_post_pass is not None: + with GraphTransformObserver( + graph, "joint_custom_post_pass", config.trace.log_url_for_graph_xform + ): + config.joint_custom_post_pass(graph.graph) + count += 1 + + if count: + stable_topological_sort(graph.graph) + graph.graph.lint() + graph.recompile() + return graph + + +@register_graph_pattern( + CallFunction( + torch.ops.prims.iota.default, + KeywordArg("length"), + start=KeywordArg("start"), + step=KeywordArg("step"), + dtype=KeywordArg("dtype"), + device=KeywordArg("device"), + requires_grad=KeywordArg("requires_grad"), + ), + pass_dict=patterns, +) +def fix_iota_device(match: Match, length, start, step, dtype, device, requires_grad): + """ + Eager supports: + + aten.index(cuda_tensor, torch.arange(..., device="cpu")) + + But this results in an implicit host-device-copy and breaks cudagraphs. + Rewrite the arange to use CUDA. + """ + (node,) = match.nodes + user_devices: OrderedSet[torch.device] = OrderedSet() + for user in node.users: + if ( + user.op == "call_function" + and user.target in (aten.index.Tensor, aten.index_put.default) + and hasattr(user.meta.get("val"), "device") + ): + user_devices.add(user.meta["val"].device) # type: ignore[union-attr] + else: + return # bail out + + if len(user_devices) == 1 and "val" in node.meta: + (user_device,) = user_devices + if device.type != user_device.type: + repl = match.graph.call_function( + torch.ops.prims.iota.default, + (length,), + { + "start": start, + "step": step, + "dtype": dtype, + "device": user_device, + "requires_grad": requires_grad, + }, + ) + repl.meta.update(node.meta) + repl.meta["val"] = repl.meta["val"].to(user_device) + node.replace_all_uses_with(repl) + match.erase_nodes() + + +@register_graph_pattern( + CallFunction( + torch.ops.prims.convert_element_type.default, + CallFunction( + torch.ops.prims.convert_element_type.default, + KeywordArg("arg"), + KeywordArg("dtype1"), + ), + KeywordArg("dtype2"), + ), + pass_dict=patterns, +) +def pointless_convert(match: Match, arg, dtype1: torch.dtype, dtype2: torch.dtype): + """Remove chain of dtype conversions often created by AMP""" + graph = match.graph + node = match.output_node() + allowed = {torch.float16, torch.bfloat16, torch.float32, torch.float64} + if dtype1 in allowed and dtype2 in allowed: + repl = graph.call_function( + torch.ops.prims.convert_element_type.default, (arg, dtype2) + ) + repl.meta.update(node.meta) + node.replace_all_uses_with(repl) + match.erase_nodes() + + +@register_graph_pattern( + CallFunction(torch.ops.aten.view.default, KeywordArg("arg"), KeywordArg("size")), + pass_dict=patterns, +) +def pointless_view(match: Match, arg, size): + """Remove no-op view""" + node = match.output_node() + arg_size = list(node.args[0].meta["val"].shape) # type: ignore[union-attr] + if size == arg_size: + node.replace_all_uses_with(node.args[0]) # type: ignore[arg-type] + match.erase_nodes() + + +# When softmax is used with temperature or other scaling, we get the pattern +# +# scale(x) - scale(x).amax(dim, keepdim=True) +# +# which is expected to be at most zero, but we may end up with numerical +# discrepancies # between the recomputed values of scale(x) inside and out +# of the reduction, # depending on compiler optimizations, e.g. use of fma +# instructions. +# +# Here we replace it with the mathematically equivalent, +# +# scale(x - x.amax(dim, keepdim=True)) +# +# which is more stable as we only compute the scaling once. +# +# NOTE: This pattern must come after fused attention matching! + + +def _partial_softmax_pattern(linear_func, reverse=False, to_dtype=False): + # Allow matching inp * other and other * input + if reverse: + scaled = CallFunction( + linear_func, KeywordArg("other"), KeywordArg("inp"), _users=MULTIPLE + ) + else: + scaled = CallFunction( + linear_func, KeywordArg("inp"), KeywordArg("other"), _users=MULTIPLE + ) + if to_dtype: + scaled = CallFunction( + prims.convert_element_type, scaled, KeywordArg("dtype"), _users=MULTIPLE + ) + amax = CallFunction( + aten.amax.default, scaled, KeywordArg("dim"), KeywordArg("keepdim") + ) + return CallFunction(aten.sub.Tensor, scaled, amax) + + +def _other_is_broadcasted_in_dim(match): + # Check that the scaling factor is constant across the reduction dim, + # so scaling doesn't change which index corresponds to the maximum value + other = match.kwargs["other"] + if isinstance(other, (int, float)): + return True + + inp = match.kwargs["inp"] + if not all(isinstance(x, torch.fx.Node) for x in (inp, other)): + return False + + inp_example = inp.meta["val"] + other_example = other.meta["val"] + if isinstance(other_example, (torch.SymInt, torch.SymFloat)): + return True + + if not all(isinstance(x, torch.Tensor) for x in (inp_example, other_example)): + return False + + inp_ndim = inp_example.ndim + other_shape = other_example.shape + if inp_ndim < len(other_shape): + return False + + # Pad other_shape to the same ndim as inp + other_shape = [1] * (inp_ndim - len(other_shape)) + list(other_shape) + + dim = match.kwargs["dim"] + if isinstance(dim, int): + dim = (dim,) + + return all(statically_known_true(other_shape[d] == 1) for d in dim) + + +def mul_softmax_pattern(match: Match, *, inp, other, dim, keepdim, dtype=None): + def repl(inp, other): + if dtype is not None: + inp = inp.to(dtype) + + sign: Union[int, float, torch.Tensor] + if isinstance(other, (int, float, torch.SymInt, torch.SymFloat)): + sign = 1 if other >= 0 else -1 + else: + one = torch.scalar_tensor(1, dtype=inp.dtype, device=inp.device) + sign = torch.where(other >= 0, one, -one) + + inp = inp * sign + max_ = torch.amax(inp, dim=dim, keepdim=keepdim) + return (inp - max_) * (sign * other) + + match.replace_by_example(repl, [inp, other]) + + +for reverse, to_dtype in itertools.product((False, True), repeat=2): + register_graph_pattern( + _partial_softmax_pattern(aten.mul.Tensor, reverse=reverse, to_dtype=to_dtype), + pass_dict=pass_patterns[1], + extra_check=_other_is_broadcasted_in_dim, + )(mul_softmax_pattern) + + +def div_softmax_pattern(match: Match, *, inp, other, dim, keepdim, dtype=None): + def repl(inp, other): + if dtype is not None: + inp = inp.to(dtype) + + sign: Union[int, float, torch.Tensor] + if isinstance(other, (int, float, torch.SymInt, torch.SymFloat)): + sign = 1 if other >= 0 else -1 + else: + one = torch.scalar_tensor(1, dtype=inp.dtype, device=inp.device) + sign = torch.where(other >= 0, one, -one) + + inp = inp * sign + max_ = torch.amax(inp, dim=dim, keepdim=keepdim) + return (inp - max_) / (sign * other) + + match.replace_by_example(repl, [inp, other]) + + +for to_dtype in (False, True): + register_graph_pattern( + _partial_softmax_pattern(aten.div.Tensor, to_dtype=to_dtype), + pass_dict=pass_patterns[1], + extra_check=_other_is_broadcasted_in_dim, + )(div_softmax_pattern) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/micro_pipeline_tp.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/micro_pipeline_tp.py new file mode 100644 index 0000000000000000000000000000000000000000..9bb56590af5e3a7f43a485b9579e4631634b414f --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/micro_pipeline_tp.py @@ -0,0 +1,854 @@ +# mypy: allow-untyped-defs +import operator +from collections import defaultdict +from dataclasses import dataclass, field +from typing import Any, cast, Dict, List, Optional, Set + +import torch + +from .. import config, inductor_prims +from ..pattern_matcher import ( + CallFunction, + Ignored, + KeywordArg, + ListOf, + Match, + MULTIPLE, + PatternExpr, + PatternMatcherPass, +) + + +aten = torch.ops.aten +patterns = PatternMatcherPass() + + +def _is_backward(graph: torch.fx.Graph) -> bool: + placeholders = [] + for node in graph.nodes: + if node.op != "placeholder": + break + placeholders.append(node) + return not all(node.name.startswith("primal") for node in placeholders) + + +def _compute_mm_arithmetic_intensity(M: int, N: int, K: int) -> float: + return M * N * K / (M * K + N * K + M * N) + + +def _filter_nodes_by_target(nodes: List[torch.fx.Node], target) -> List[torch.fx.Node]: + return [x for x in nodes if x.target == target] + + +def _find_ancestors(node: torch.fx.Node) -> Set[torch.fx.Node]: + ancestors = set() + ancestors.add(node) + cur_nodes = [node] + while len(cur_nodes) > 0: + new_nodes = [] + for node in cur_nodes: + for inp in node.all_input_nodes: + if inp not in ancestors: + ancestors.add(inp) + new_nodes.append(inp) + cur_nodes = new_nodes + return {node for node in ancestors if node.op != "placeholder"} + + +def _get_tensor(node: torch.fx.Node) -> torch.Tensor: + val = node.meta["val"] + assert isinstance(val, torch.Tensor) + return val + + +@dataclass +class _AllGatherMatch: + match: Match + shard_node: torch.fx.Node + ag_node: torch.fx.Node + res_node: torch.fx.Node + gather_dim: int + group_name: str + + def replace_with(self, new_node: torch.fx.Node) -> None: + self.res_node.replace_all_uses_with(new_node) + + def erase(self) -> None: + for node in reversed(self.match.nodes): + if len(node.users) == 0: + node.graph.erase_node(node) + + +def find_all_gather_patterns(graph: torch.fx.Graph): + c10d = torch.ops._c10d_functional + + def make_zero_dim_all_gather_pattern(shard): + return CallFunction( + c10d.wait_tensor.default, + CallFunction( + c10d.all_gather_into_tensor.default, + shard, + Ignored(), + KeywordArg("group_name"), + ), + ) + + # Matches funcol.all_gather_tensor with gather_dim == 0 + zero_dim_all_gather_pattern = make_zero_dim_all_gather_pattern(KeywordArg("shard")) + + def make_all_gather_split_pattern(shard): + return CallFunction( + operator.getitem, + CallFunction( + aten.split.Tensor, + make_zero_dim_all_gather_pattern(shard), + Ignored(), + _users=MULTIPLE, + ), + Ignored(), + ) + + def make_cat_pattern(splits): + return CallFunction( + aten.cat.default, + ListOf(splits), + KeywordArg("gather_dim"), + ) + + # Matches funcol.all_gather_tensor with gather_dim > 0 + non_zero_dim_all_gather_pattern = make_cat_pattern( + make_all_gather_split_pattern(KeywordArg("shard")), + ) + + # Match a zero-dim all-gather in which the data is transferred as uint8 and + # viewed back as the original dtype. + zero_dim_type_erased_all_gather_pattern = CallFunction( + aten.view.dtype, + make_zero_dim_all_gather_pattern( + KeywordArg("shard"), + ), + Ignored(), + ) + + # Match a non-zero dim all-gather in which the data is transferred as uint8 + # and viewed back as the original dtype. + non_zero_dim_type_erased_all_gather_pattern = CallFunction( + aten.view.dtype, + make_cat_pattern( + CallFunction( + aten.view.dtype, + make_all_gather_split_pattern( + KeywordArg("shard"), + ), + Ignored(), + ), + ), + Ignored(), + ) + + # If two patterns with the same res_node_target have the same suffix, the + # longer pattern should appear first in the list. + # e.g. supposed we have (1) A -> B -> C -> D and (2) B -> C -> D, (1) + # should appear before (2) in the list. + res_node_target_to_patterns = { + aten.cat.default: [ + (non_zero_dim_all_gather_pattern, 0), + ], + aten.view.dtype: [ + (non_zero_dim_type_erased_all_gather_pattern, 0), + (zero_dim_type_erased_all_gather_pattern, 0), + ], + c10d.wait_tensor.default: [ + (zero_dim_all_gather_pattern, 0), + ], + } + + # Match in reverse to ensure longer patterns is prioritized + all_gathers = [] + visited_ag_nodes = set() + for node in reversed(graph.nodes): + for target, patterns in res_node_target_to_patterns.items(): + if node.target != target: + continue + for pattern, ag_node_idx in patterns: + match = pattern.match(node) + if not match: + continue + + assert isinstance(match, Match) + ag_node = match.nodes[ag_node_idx] + assert ag_node.target == c10d.all_gather_into_tensor.default + + if ag_node in visited_ag_nodes: + continue + visited_ag_nodes.add(ag_node) + + ag_match = _AllGatherMatch( + match=match, + shard_node=match.kwargs["shard"], + ag_node=ag_node, + res_node=node, + gather_dim=match.kwargs.get("gather_dim", 0), + group_name=match.kwargs["group_name"], + ) + all_gathers.append(ag_match) + + return list(reversed(all_gathers)) + + +@dataclass +class _ReduceScatterMatch: + match: Match + input_node: torch.fx.Node + rs_node: torch.fx.Node + res_node: torch.fx.Node + reduce_op: str + scatter_dim: int + group_name: str + + def replace_with(self, new_node: torch.fx.Node) -> None: + self.res_node.replace_all_uses_with(new_node) + + def erase(self) -> None: + for node in reversed(self.match.nodes): + if len(node.users) == 0: + node.graph.erase_node(node) + + +def find_reduce_scatter_patterns(graph: torch.fx.Graph): + c10d = torch.ops._c10d_functional + + def reduce_scatter_template(inp: PatternExpr): + return CallFunction( + c10d.wait_tensor.default, + CallFunction( + c10d.reduce_scatter_tensor.default, + inp, + KeywordArg("reduce_op"), + Ignored(), + KeywordArg("group_name"), + ), + ) + + # Matches funcol.reduce_scatter_tensor with scatter_dim == 0 + zero_dim_reduce_scatter_pattern = reduce_scatter_template(KeywordArg("input")) + + # Matches funcol.reduce_scatter_tensor with scatter_dim > 0 + non_zero_dim_reduce_scatter_pattern = reduce_scatter_template( + CallFunction( + aten.cat.default, + ListOf( + CallFunction( + operator.getitem, + CallFunction( + aten.split.Tensor, + KeywordArg("input"), + Ignored(), + KeywordArg("scatter_dim"), + _users=MULTIPLE, + ), + Ignored(), + ) + ), + ), + ) + + reduce_scatters = [] + for node in reversed(graph.nodes): + if node.target == c10d.wait_tensor.default: + if match := non_zero_dim_reduce_scatter_pattern.match(node): + assert isinstance(match, Match) + reduce_scatters.append( + _ReduceScatterMatch( + match=match, + input_node=match.kwargs["input"], + rs_node=match.nodes[-2], + res_node=node, + reduce_op=match.kwargs["reduce_op"], + scatter_dim=match.kwargs["scatter_dim"], + group_name=match.kwargs["group_name"], + ) + ) + elif match := zero_dim_reduce_scatter_pattern.match(node): + assert isinstance(match, Match) + reduce_scatters.append( + _ReduceScatterMatch( + match=match, + input_node=match.kwargs["input"], + rs_node=match.nodes[0], + res_node=node, + reduce_op=match.kwargs["reduce_op"], + scatter_dim=0, + group_name=match.kwargs["group_name"], + ) + ) + return list(reversed(reduce_scatters)) + + +@dataclass +class _Matmul: + nodes: List[torch.fx.Node] + arg_ancestor_nodes: Set[torch.fx.Node] = field(init=False) + A_node: torch.fx.Node + B_node: torch.fx.Node + + def __post_init__(self): + assert len(self.nodes) in (1, 3) + if len(self.nodes) == 1: + assert self.nodes[0].target in (aten.mm.default, aten._scaled_mm.default) + else: + assert self.nodes[0].target == aten.reshape.default + assert self.nodes[1].target in (aten.mm.default, aten._scaled_mm.default) + assert self.nodes[2].target == aten.reshape.default + self.arg_ancestor_nodes = _find_ancestors(self.B_node) + + def replace_with(self, new_node: torch.fx.Node) -> None: + """ + Replace the matmul with the new node. + """ + graph = new_node.graph + + # For 2D-matmuls, we simply replace the mm node with `new_node`. + if len(self.nodes) == 1: + mm_node = self.nodes[0] + assert mm_node.target in (aten.mm.default, aten._scaled_mm.default) + mm_node.replace_all_uses_with(new_node) + graph.erase_node(mm_node) + return + + # An ND-matmul is reshape -> mm -> reshape sequence. We first replace + # the second reshape node with `new_node`. Then, we ensure that the + # original mm node in the sequence ends up with zero users by replacing + # it with a reverse reshape of `new_node`. + graph = new_node.graph + assert len(self.nodes) == 3 + mm_node = self.nodes[1] + output_reshape_node = self.nodes[2] + + assert mm_node.target in (aten.mm.default, aten._scaled_mm.default) + assert output_reshape_node.target == aten.reshape.default + + output_reshape_node.replace_all_uses_with(new_node) + if len(mm_node.users) > 1: + with graph.inserting_after(new_node): + new_mm_node = graph.call_function( + aten.reshape.default, + args=(new_node, list(_get_tensor(mm_node).shape)), + ) + mm_node.replace_all_uses_with(new_mm_node) + + def erase(self) -> None: + for node in reversed(self.nodes): + if len(node.users) == 0: + node.graph.erase_node(node) + + @classmethod + def from_match(cls, match: List[torch.fx.Node]) -> "_Matmul": + assert len(match) in (1, 3) + assert match[0].target in ( + aten.mm.default, + aten.reshape.default, + ) + mm_node = match[0] if len(match) == 1 else match[1] + return _Matmul( + nodes=match, + A_node=cast(torch.fx.Node, match[0].args[0]), + B_node=cast(torch.fx.Node, mm_node.args[1]), + ) + + +@dataclass +class _ScaledMatmul(_Matmul): + A_scale_node: torch.fx.Node + B_scale_node: torch.fx.Node + bias_node: Optional[torch.fx.Node] + result_scale_node: Optional[torch.fx.Node] + out_dtype: Optional[torch.dtype] + use_fast_accum: bool + + def __post_init__(self): + super().__post_init__() + self.arg_ancestor_nodes |= _find_ancestors(self.A_scale_node) + self.arg_ancestor_nodes |= _find_ancestors(self.B_scale_node) + + @classmethod + def from_match(cls, match: List[torch.fx.Node]) -> "_ScaledMatmul": + assert len(match) in (1, 3) + assert match[0].target in ( + aten._scaled_mm.default, + aten.reshape.default, + ) + mm_node = match[0] if len(match) == 1 else match[1] + + def get_arg(node: torch.fx.Node, idx: int, default: Any) -> Any: + if idx >= len(node.args): + return default + return node.args[idx] + + return _ScaledMatmul( + nodes=match, + A_node=cast(torch.fx.Node, match[0].args[0]), + B_node=cast(torch.fx.Node, mm_node.args[1]), + A_scale_node=cast(torch.fx.Node, mm_node.args[2]), + B_scale_node=cast(torch.fx.Node, mm_node.args[3]), + bias_node=get_arg(mm_node, 4, None), + result_scale_node=get_arg(mm_node, 5, None), + out_dtype=get_arg(mm_node, 6, None), + use_fast_accum=get_arg(mm_node, 7, False), + ) + + +def _find_reshape_mm_reshape(node: torch.fx.Node) -> List[_Matmul]: + if node.target != aten.reshape.default: + return [] + + matches = [] + for mm_node in node.users: + if mm_node.target not in (aten.mm.default, aten._scaled_mm.default): + continue + for reshape_node in mm_node.users: + if reshape_node.target != aten.reshape.default: + continue + + # Since the reshape -> mm -> reshape pattern would be subsumed into + # the fused op, we only match the patterns where the shape of the + # second reshape is matches the mm result produced by the fused op. + matmul_input_node = cast(torch.fx.Node, node.args[0]) + B_node = cast(torch.fx.Node, mm_node.args[1]) + matmul_out_shape = torch.Size( + [ + *_get_tensor(matmul_input_node).shape[:-1], + _get_tensor(B_node).shape[-1], + ] + ) + if _get_tensor(reshape_node).shape != matmul_out_shape: + continue + matches.append([node, mm_node, reshape_node]) + # If for some rare reason mm_node is being reshaped by two + # different reshape nodes, we only include mm_node once in the + # parsing result. + break + + matmuls = [] + for match in matches: + mm_node = match[1] + if mm_node.target == aten.mm.default: + matmul = _Matmul.from_match(match) + matmuls.append(matmul) + elif mm_node.target == aten._scaled_mm.default: + matmul = _ScaledMatmul.from_match(match) + matmuls.append(matmul) + else: + raise AssertionError( + "Expect the node's target to be either aten.mm.default or " + f"aten._scaled_mm.default. Got {mm_node.target}." + ) + return matmuls + + +def _find_consumer_matmuls(node: torch.fx.Node) -> List[_Matmul]: + """ + Find the matmuls that use `node` as the lhs argument. + """ + matmuls = [] + for user in node.users: + # ND matmuls + if user.target == aten.reshape.default: + matmuls.extend(_find_reshape_mm_reshape(user)) + # 2D matmuls + elif user.target == aten.mm.default: + matmul = _Matmul.from_match(match=[user]) + matmuls.append(matmul) + elif user.target == aten._scaled_mm.default: + matmul = _ScaledMatmul.from_match([user]) + matmuls.append(matmul) + return matmuls + + +def _insert_fused_all_gather_matmul( + graph: torch.fx.Graph, + matmuls: List[_Matmul], + shard_node: torch.fx.Node, + gather_dim: int, + group_name: str, +) -> torch.fx.Node: + mm_types = set(map(type, matmuls)) + assert len(mm_types) == 1 + mm_type = next(iter(mm_types)) + if mm_type == _Matmul: + B_nodes = [matmul.B_node for matmul in matmuls] + return graph.call_function( + torch.ops.symm_mem.fused_all_gather_matmul.default, + args=(shard_node, B_nodes, gather_dim, group_name), + ) + elif mm_type == _ScaledMatmul: + scaled_matmuls = cast(List[_ScaledMatmul], matmuls) + return graph.call_function( + torch.ops.symm_mem.fused_all_gather_scaled_matmul.default, + args=( + shard_node, + [matmul.B_node for matmul in scaled_matmuls], + scaled_matmuls[0].A_scale_node, + [matmul.B_scale_node for matmul in scaled_matmuls], + gather_dim, + group_name, + [matmul.bias_node for matmul in scaled_matmuls], + [matmul.result_scale_node for matmul in scaled_matmuls], + [matmul.out_dtype for matmul in scaled_matmuls], + [matmul.use_fast_accum for matmul in scaled_matmuls], + ), + ) + else: + raise AssertionError(f"Unexpected matmul match type: {mm_type}") + + +def fuse_all_gather_matmul(all_gather: _AllGatherMatch) -> None: + """ + Fused the pattern + + A = all_gather_tensor(A_shard, gather_dim, group_name) + C_0 = torch.matmul(A, B_0) + C_1 = torch.matmul(A, B_1) + C_2 = torch.matmul(A, B_2) + ... + + into + + A, Cs = torch.ops.symm_mem.fused_all_gather_matmul( + A_shard, [B_0, B_1, B_2, ...], gather_dim, group_name, + ) + """ + if ( + not torch.distributed.is_available() + or not torch.distributed.is_nccl_available() + ): + return + + c10d = torch.ops._c10d_functional + from torch.distributed._symmetric_memory import ( + is_symm_mem_enabled_for_group, + restride_A_shard_for_fused_all_gather_matmul, + ) + + shard_node, ag_node, ag_res_node, gather_dim, group_name = ( + all_gather.shard_node, + all_gather.ag_node, + all_gather.res_node, + all_gather.gather_dim, + all_gather.group_name, + ) + + if not is_symm_mem_enabled_for_group(group_name): + return + + if gather_dim >= len(_get_tensor(shard_node).shape) - 1: + # Decomposing the matmul on the K dimension is not supported + return + + # Find consumer matmuls + matmuls = _find_consumer_matmuls(ag_res_node) + + # The matmuls are only fusible if non-A args don't depend on the all-gather + # result node + matmuls = [ + matmul + for matmul in matmuls + if all_gather.res_node not in matmul.arg_ancestor_nodes + ] + + if len(matmuls) == 0 or len(set(map(type, matmuls))) != 1: + return + + # Fuse the all_gather_tensor with the eligible matmuls + graph = ag_node.graph + with graph.inserting_before(ag_node): + if "val" in shard_node.meta: + restrided = restride_A_shard_for_fused_all_gather_matmul( + _get_tensor(shard_node), + gather_dim, + ) + shard_node = graph.call_function( + inductor_prims.force_stride_order, + args=(shard_node, restrided.stride()), + ) + + fused_node = _insert_fused_all_gather_matmul( + graph, matmuls, shard_node, gather_dim, group_name + ) + new_ag_node = graph.call_function( + operator.getitem, + args=(fused_node, 0), + ) + new_out_nodes = graph.call_function( + operator.getitem, + args=(fused_node, 1), + ) + for idx, matmul in enumerate(matmuls): + new_out_node = graph.call_function( + operator.getitem, + args=(new_out_nodes, idx), + ) + matmul.replace_with(new_out_node) + matmul.erase() + all_gather.replace_with(new_ag_node) + all_gather.erase() + + # Raise ancestors of non-A args that are topologically ordered between + # ag_res_node and the matmul above fused_node. + order = {node: idx for idx, node in enumerate(graph.nodes)} + nodes_to_raise = sorted( + {x for matmul in matmuls for x in matmul.arg_ancestor_nodes}, + key=lambda x: order[x], + ) + for node in nodes_to_raise: + if order[node] > order[fused_node]: + fused_node.prepend(node) + + +def _find_producer_matmul(node: torch.fx.Node) -> Optional[_Matmul]: + if node.target == aten.mm.default: + return _Matmul.from_match(match=[node]) + elif node.target == aten._scaled_mm.default: + return _ScaledMatmul.from_match(match=[node]) + elif node.target == aten.reshape.default: + reshape_node_1 = node + + mm_node = reshape_node_1.args[0] + assert isinstance(mm_node, torch.fx.Node) + if mm_node.target not in (aten.mm.default, aten._scaled_mm.default): + return None + + reshape_node_0 = mm_node.args[0] + assert isinstance(reshape_node_0, torch.fx.Node) + if reshape_node_0.target != aten.reshape.default: + return None + + if mm_node.target == aten.mm.default: + return _Matmul.from_match(match=[reshape_node_0, mm_node, reshape_node_1]) + elif mm_node.target == aten._scaled_mm.default: + return _ScaledMatmul.from_match( + match=[reshape_node_0, mm_node, reshape_node_1] + ) + return None + + +def _insert_fused_matmul_reduce_scatter( + graph: torch.fx.Graph, + matmul: _Matmul, + reduce_op: str, + scatter_dim: int, + group_name: str, +) -> torch.fx.Node: + if type(matmul) == _Matmul: + return graph.call_function( + torch.ops.symm_mem.fused_matmul_reduce_scatter.default, + args=(matmul.A_node, matmul.B_node, reduce_op, scatter_dim, group_name), + ) + elif type(matmul) == _ScaledMatmul: + return graph.call_function( + torch.ops.symm_mem.fused_scaled_matmul_reduce_scatter.default, + args=( + matmul.A_node, + matmul.B_node, + matmul.A_scale_node, + matmul.B_scale_node, + reduce_op, + scatter_dim, + group_name, + matmul.bias_node, + matmul.result_scale_node, + matmul.out_dtype, + matmul.use_fast_accum, + ), + ) + else: + raise AssertionError(f"Unexpected matmul match type: {type(matmul)}") + + +def fuse_matmul_reduce_scatter(reduce_scatter: _ReduceScatterMatch) -> None: + """ + Fused the pattern + + reduce_scatter_tensor(A @ B, scatter_dim, group_name) + + into + + torch.ops.symm_mem.fused_matmul_reduce_scatter( + A, B, scatter_dim, group_name, + ) + """ + if ( + not torch.distributed.is_available() + or not torch.distributed.is_nccl_available() + ): + return + + c10d = torch.ops._c10d_functional + from torch.distributed._symmetric_memory import ( + is_symm_mem_enabled_for_group, + restride_A_for_fused_matmul_reduce_scatter, + ) + + input_node, rs_node, rs_res_node, reduce_op, scatter_dim, group_name = ( + reduce_scatter.input_node, + reduce_scatter.rs_node, + reduce_scatter.res_node, + reduce_scatter.reduce_op, + reduce_scatter.scatter_dim, + reduce_scatter.group_name, + ) + + if not is_symm_mem_enabled_for_group(group_name): + return + + # Currently fused_matmul_reduce_scatter doesn't return the matmul result, + # so we can't apply the fusion if the matmul result is used by multiple + # users. This is not a fundamental limitation of the fused op and can be + # addressed if needed. + if len(input_node.users) != 1: + return + + matmul = _find_producer_matmul(input_node) + if matmul is None: + return + + if rs_res_node in matmul.arg_ancestor_nodes: + return + + graph = rs_res_node.graph + with graph.inserting_before(rs_res_node): + if "val" in matmul.A_node.meta: + restrided = restride_A_for_fused_matmul_reduce_scatter( + _get_tensor(matmul.A_node), + scatter_dim, + ) + matmul.A_node = graph.call_function( + inductor_prims.force_stride_order, + args=(matmul.A_node, restrided.stride()), + ) + + fused_node = _insert_fused_matmul_reduce_scatter( + graph, + matmul, + reduce_op, + scatter_dim, + group_name, + ) + reduce_scatter.replace_with(fused_node) + reduce_scatter.erase() + matmul.erase() + + order = {node: idx for idx, node in enumerate(graph.nodes)} + nodes_to_raise = sorted( + matmul.arg_ancestor_nodes, + key=lambda x: order[x], + ) + for node in nodes_to_raise: + if order[node] > order[fused_node]: + fused_node.prepend(node) + + +def _get_node_to_ancestors( + graph: torch.fx.Graph, +) -> Dict[torch.fx.Node, Set[torch.fx.Node]]: + """ + Compute the ancestors for all nodes in a graph. + """ + node_to_ancestors = defaultdict(set) + for node in graph.nodes: + node_to_ancestors[node] = set(node.all_input_nodes) + for dep in node.all_input_nodes: + node_to_ancestors[node] |= node_to_ancestors[dep] + + return node_to_ancestors + + +def _get_collective_to_overlappable_nodes( + graph: torch.fx.Graph, +) -> Dict[torch.fx.Node, List[torch.fx.Node]]: + """ + For each collective in the graph, find nodes that are neither ancestors nor + descendants of the collective. + """ + + def is_collective(node) -> bool: + # Only consider all-gather and reduce-scatter in the context of + # micro-pipeline TP. + return node.target in [ + torch.ops._c10d_functional.all_gather_into_tensor.default, + torch.ops._c10d_functional.reduce_scatter_tensor.default, + ] + + node_to_ancestors = _get_node_to_ancestors(graph) + collective_to_overlappable_nodes = defaultdict(list) + for node in graph.nodes: + if not is_collective(node): + continue + for x in graph.nodes: + if ( + node not in node_to_ancestors[x] + and x not in node_to_ancestors[node] + and x.op == "call_function" + ): + collective_to_overlappable_nodes[node].append(x) + + return collective_to_overlappable_nodes + + +def _get_unexposed_collectives(graph: torch.fx.Graph) -> List[torch.fx.Node]: + """ + Find all unexposed collectives in the graph. + + Because we don't have the runtime estimate, this function is a rough + estimation using the following strong/hand-wavy assumptions: + + - Only a predefined set of "compute intensive" operation can hide a collective. + - Any "compute intensive" operation can hide exactly one collective. + """ + + def _is_compute_intensive(node: torch.fx.Node) -> bool: + return node.target in [torch.ops.aten.mm.default] + + collective_to_overlapping_candidates = defaultdict(list) + available_nodes = set() + collective_to_overlappable_nodes = _get_collective_to_overlappable_nodes(graph) + for collective, overlappable_nodes in collective_to_overlappable_nodes.items(): + candidates = [x for x in overlappable_nodes if _is_compute_intensive(x)] + collective_to_overlapping_candidates[collective] = candidates + available_nodes |= set(candidates) + + unexposed_collectives = [] + for ( + collective, + overlapping_candidates, + ) in collective_to_overlapping_candidates.items(): + # Each collective consumes exactly one overlapping candidate + for x in overlapping_candidates: + if x in available_nodes: + unexposed_collectives.append(collective) + available_nodes.remove(x) + break + return unexposed_collectives + + +def micro_pipeline_tp_pass(graph: torch.fx.Graph): + all_gathers = find_all_gather_patterns(graph) + reduce_scatters = find_reduce_scatter_patterns(graph) + + # When a collective can be hidden through either simple overlapping or + # micro-pipeline TP, we prefer simple overlapping to avoid the overhead + # associated with decomposition. If reorder_for_compute_comm_overlap is + # enabled, we identify collectives that can be hidden through simple + # overlapping and exclude them from micro-pipeline TP candidates. + if config.reorder_for_compute_comm_overlap: + unexposed_collectives = _get_unexposed_collectives(graph) + all_gathers = [x for x in all_gathers if x.ag_node not in unexposed_collectives] + reduce_scatters = [ + x for x in reduce_scatters if x.rs_node not in unexposed_collectives + ] + + for all_gather in all_gathers: + fuse_all_gather_matmul(all_gather) + + for reduce_scatter in reduce_scatters: + fuse_matmul_reduce_scatter(reduce_scatter) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/misc_patterns.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/misc_patterns.py new file mode 100644 index 0000000000000000000000000000000000000000..0f608952a2fb2722934f822edf88281152b1bdcc --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/misc_patterns.py @@ -0,0 +1,131 @@ +# mypy: allow-untyped-defs +import functools +from typing import Dict, Set, Tuple + +import torch +from torch._dynamo.utils import counters +from torch._ops import OpOverload, OpOverloadPacket + +from ..pattern_matcher import fwd_only, register_replacement + + +aten = torch.ops.aten + + +@functools.lru_cache(None) +def _misc_patterns_init(): + from .joint_graph import patterns as joint_graph_patterns + from .post_grad import pass_patterns as post_grad_patterns_all + + post_grad_patterns = post_grad_patterns_all[1] # medium priority + + if torch.cuda.is_available(): + # workaround https://github.com/pytorch/pytorch/issues/97894 + device = "cuda" + else: + device = "cpu" + + # These patterns do 2 things + # 1. Since we know that index is completely unique, we can codegen it using + # stores instead of atomic adds, which is quite a bit faster. + # 2. Also, since we are guaranteed that they are completely within bounds, + # we can use unsafe indexing and skip debug asserts + def randperm_index_add_pattern(x, y): + index = torch.randperm(x.shape[0], device=x.device)[: y.shape[0]] + return torch.index_add(x, dim=0, source=y, index=index), index + + def randperm_index_add_replacement(x, y): + index = torch.randperm(x.shape[0], device=x.device)[: y.shape[0]] + return ( + torch.ops.aten._unsafe_index_put( + x, (index,), aten._unsafe_index(x, (index,)) + y, accumulate=False + ), + index, + ) + + register_replacement( + randperm_index_add_pattern, + randperm_index_add_replacement, + [torch.empty(4, 8, device=device), torch.empty(2, 8, device=device)], + fwd_only, + [post_grad_patterns, joint_graph_patterns], + ) + + def randperm_index_pattern(x, slice_shape): + index = torch.randperm(x.shape[0], device=x.device)[:slice_shape] + return torch.ops.aten.index(x, (index,)), index + + def randperm_index_replacement(x, slice_shape): + index = torch.randperm(x.shape[0], device=x.device)[:slice_shape] + return torch.ops.aten._unsafe_index(x, (index,)), index + + register_replacement( + randperm_index_pattern, + randperm_index_replacement, + [torch.empty(4, 8, device=device)], + fwd_only, + [post_grad_patterns, joint_graph_patterns], + scalar_workaround={"slice_shape": 42}, + ) + + +class NumpyCompatNormalization: + numpy_compat: Dict[str, Tuple[str, ...]] = { + "dim": ("axis",), + "keepdim": ("keepdims",), + "input": ("x", "a", "x1"), + "other": ("x2",), + } + inverse_mapping: Dict[str, str] + cache: Dict["torch.fx.graph.Target", Set[str]] + + def __init__(self) -> None: + self.cache = {} # callable -> tuple of replaceable args e.g. ["axis"] + self.inverse_mapping = {} + for actual_kwarg, numpy_kwargs in self.numpy_compat.items(): + for numpy_kwarg in numpy_kwargs: + assert numpy_kwarg not in self.inverse_mapping + self.inverse_mapping[numpy_kwarg] = actual_kwarg + + def __call__(self, graph: torch.fx.Graph): + for node in graph.nodes: + if node.op != "call_function": + continue + if isinstance(node.target, (OpOverload, OpOverloadPacket)): + # only applies to torch ops; e.g. torch.stack(axis=1) works, torch.ops.aten.stack(axis=1) doesn't. + continue + kwargs = node.kwargs + + if node.target in self.cache: + replaceable_kwargs = self.cache[node.target] + else: + signatures = torch.fx.operator_schemas.get_signature_for_torch_op( + node.target + ) + signatures = () if signatures is None else signatures + replaceable_kwargs = set() + for sig in signatures: + for param_name in sig.parameters.keys(): + if param_name in self.numpy_compat: + replaceable_kwargs.update(self.numpy_compat[param_name]) + + self.cache[node.target] = replaceable_kwargs + + if not replaceable_kwargs: + continue + + new_kwargs = {} + kwargs_changed = False + for k, v in kwargs.items(): + if k in replaceable_kwargs: + kwargs_changed = True + new_kwargs[self.inverse_mapping[k]] = v + else: + new_kwargs[k] = v + + if kwargs_changed: + node.kwargs = torch.fx.immutable_collections.immutable_dict(new_kwargs) + counters["inductor"]["numpy_compat_normalization"] += 1 + + +numpy_compat_normalization = NumpyCompatNormalization() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/mkldnn_fusion.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/mkldnn_fusion.py new file mode 100644 index 0000000000000000000000000000000000000000..156760a68e7e6978af1e9d67c89a37dfe963129a --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/mkldnn_fusion.py @@ -0,0 +1,1266 @@ +# mypy: allow-untyped-defs +import functools +import operator +from functools import reduce +from typing import Any, Tuple + +import torch +from torch.fx.experimental.symbolic_shapes import has_free_symbols + +from .. import ir +from ..lowering import lowerings as L +from ..pattern_matcher import ( + Arg, + CallFunction, + filter_nodes, + get_arg_value, + KeywordArg, + MULTIPLE, +) +from ..virtualized import ops, V +from .freezing_patterns import register_freezing_graph_pattern +from .post_grad import register_lowering_pattern +from .quantization import ( + _register_quantization_lowerings, + _register_quantization_weight_pack_pass, + _register_woq_lowerings, +) + + +if torch._C._has_mkldnn: + aten = torch.ops.aten + mkldnn = torch.ops.mkldnn + prims = torch.ops.prims + + _conv_args = [Arg() for _ in range(10)] + _linear_args = [Arg() for _ in range(6)] + _conv_transpose_args = [Arg() for _ in range(11)] + + def _conv_call(users=1): + return CallFunction( + mkldnn._convolution_pointwise.default, *_conv_args, _users=users + ) + + def _linear_call(users=1): + return CallFunction( + mkldnn._linear_pointwise.default, *_linear_args, _users=users + ) + + def _conv_transpose_call(users=1): + return CallFunction( + mkldnn._convolution_transpose_pointwise.default, + *_conv_transpose_args, + _users=users, + ) + + def _to_float(input_call, users=1): + return CallFunction( + prims.convert_element_type.default, + input_call, + KeywordArg("to_float"), + _users=users, + ) + + def _to_bf16(input_call): + return CallFunction( + prims.convert_element_type.default, + input_call, + KeywordArg("to_bf16"), + _users=1, + ) + + def _to_fp16(input_call): + return CallFunction( + prims.convert_element_type.default, + input_call, + KeywordArg("to_fp16"), + _users=1, + ) + + def _unary_fusion_pattern(unary_fusion, call_fn, users, lowp_dtype): + # only insert to_dtype if lowp_dtype is True + computation_call = ( + _to_float(call_fn(), users=users) if lowp_dtype else call_fn(users=users) + ) + out = unary_fusion(computation_call) + if lowp_dtype == torch.bfloat16: + return _to_bf16(out) + elif lowp_dtype == torch.float16: + return _to_fp16(out) + else: + return out + + def _gelu_fusion_1(computation_call): + return CallFunction( + aten.mul, + CallFunction(aten.mul, computation_call, 0.5), + CallFunction( + aten.add, + CallFunction( + aten.erf, + CallFunction(aten.mul, computation_call, 0.7071067811865476), + ), + 1, + ), + ) + + def _gelu_fusion_2(computation_call): + return CallFunction( + aten.mul, + CallFunction(aten.mul, computation_call, 0.5), + CallFunction( + aten.add, + CallFunction( + aten.tanh, + CallFunction( + aten.mul, + CallFunction( + aten.add, + computation_call, + CallFunction( + aten.mul, + CallFunction( + aten.mul, + CallFunction( + aten.mul, computation_call, computation_call + ), + computation_call, + ), + 0.044715, + ), + ), + 0.7978845608028654, + ), + ), + 1, + ), + ) + + def _hardswish_fusion(computation_call): + return CallFunction( + aten.div, + CallFunction( + aten.mul, + computation_call, + CallFunction( + aten.clamp_max, + CallFunction( + aten.clamp_min, CallFunction(aten.add, computation_call, 3), 0 + ), + 6, + ), + ), + 6, + ) + + def _silu_fusion(computation_call): + return CallFunction( + aten.mul, computation_call, CallFunction(aten.sigmoid, computation_call) + ) + + def _hardsigmoid_fusion(computation_call): + return CallFunction( + aten.div, + CallFunction( + aten.clamp_max, + CallFunction( + aten.clamp_min, CallFunction(aten.add, computation_call, 3), 0 + ), + 6, + ), + 6, + ) + + def _leaky_relu_fusion(computation_call): + return CallFunction( + aten.where, + CallFunction(aten.gt, computation_call, 0), + computation_call, + CallFunction(aten.mul, computation_call, KeywordArg("negative_slope")), + ) + + def _hardtanh_fusion(computation_call): + return CallFunction( + aten.clamp_max, + CallFunction(aten.clamp_min, computation_call, KeywordArg("min_value")), + KeywordArg("max_value"), + ) + + def _combined_fusion(computation_call, elementwise_op): + return CallFunction(elementwise_op, computation_call) + + # binary_op(other, computation_op) + def _binary_fusion_v1(computation_call, binary_fn): + return CallFunction(binary_fn, KeywordArg("other"), computation_call) + + # binary_op(computation_op, other) + def _binary_fusion_v2(computation_call, binary_fn): + return CallFunction(binary_fn, computation_call, KeywordArg("other")) + + def _is_single_computation_op(computation_op, lowp_dtype=None): + def fn(match): + computation_nodes = filter_nodes(match.nodes, computation_op) + + if lowp_dtype: + output_node_meta = match.output_node().meta.get("val") + if output_node_meta.dtype != lowp_dtype: + return False + + if len(computation_nodes) < 1: + return False + if any(n.args[-3] != "none" for n in computation_nodes): + return False + return True + + return fn + + def _is_valid_computation_unary_fusion(computation_op, lowp_dtype=None): + def fn(match): + matched = _is_single_computation_op(computation_op, lowp_dtype)(match) + computation_node = filter_nodes(match.nodes, computation_op)[0] + if lowp_dtype: + conversion_dtype_nodes = filter_nodes( + match.nodes, prims.convert_element_type.default + ) + if len(conversion_dtype_nodes) != 2: + return False + # fusion pattern is always in the form of computation_op + to_float32 + unary_op + to_bfloat16 + if computation_node == conversion_dtype_nodes[0].args[0]: + to_float = conversion_dtype_nodes[0].args[1] + to_lp = conversion_dtype_nodes[1].args[1] + else: + to_float = conversion_dtype_nodes[1].args[1] + to_lp = conversion_dtype_nodes[0].args[1] + matched = matched and to_float == torch.float and to_lp == lowp_dtype + return matched + + return fn + + def _register_unary_fusion_lowering( + pattern, unary_attr, computation_op, lowp_dtype=None + ): + @register_lowering_pattern( + pattern, + extra_check=_is_valid_computation_unary_fusion(computation_op, lowp_dtype), + ) + def fn(match, *args, **kwargs): + computation_args = list(args)[:-3] + [ + unary_attr.op_name, + unary_attr.scalars_attr, + unary_attr.algorithm_attr, + ] + return L[computation_op](*computation_args) + + return fn + + def _register_leaky_relu_fusion_lowering(pattern, computation_op, lowp_dtype=None): + @register_lowering_pattern( + pattern, extra_check=_is_single_computation_op(computation_op, lowp_dtype) + ) + def fn(match, *args, **kwargs): + negative_slope = kwargs.get("negative_slope") + if isinstance(negative_slope, ir.TensorBox): + matched = False + else: # inp is a Number + matched = True + if lowp_dtype: + dtype1 = kwargs.get("to_float") + dtype2 = ( + kwargs.get("to_bf16") + if lowp_dtype == torch.bfloat16 + else kwargs.get("to_fp16") + ) + matched = matched and dtype1 == torch.float and dtype2 == lowp_dtype + computation_args = list(args) + if matched: + computation_args = computation_args[:-3] + [ + "leaky_relu", + [negative_slope], + "", + ] + return L[computation_op](*computation_args) + else: + # computation_args += ["none", [], ""] + out = L[computation_op](*computation_args) + if lowp_dtype: + out = L[prims.convert_element_type.default](out, dtype=torch.float) + out = L[aten.where]( + L[aten.gt](out, 0), + out, + L[aten.mul](out, negative_slope), + ) + if lowp_dtype: + out = L[prims.convert_element_type.default](out, dtype=dtype2) # type: ignore[possibly-undefined] + return out + + return fn + + def _register_hardtanh_fusion_lowering(pattern, computation_op, lowp_dtype=None): + @register_lowering_pattern( + pattern, extra_check=_is_single_computation_op(computation_op, lowp_dtype) + ) + def fn(match, *args, **kwargs): + min_value = kwargs.get("min_value") + max_value = kwargs.get("max_value") + if isinstance(min_value, ir.TensorBox) or isinstance( + max_value, ir.TensorBox + ): + matched = False + else: # inp is a Number + assert max_value is not None + matched = min_value <= max_value + if lowp_dtype: + dtype1 = kwargs.get("to_float") + dtype2 = ( + kwargs.get("to_bf16") + if lowp_dtype == torch.bfloat16 + else kwargs.get("to_fp16") + ) + matched = matched and dtype1 == torch.float and dtype2 == lowp_dtype + computation_args = list(args) + if matched: + computation_args = computation_args[:-3] + [ + "hardtanh", + [min_value, max_value], + "", + ] + return L[computation_op](*computation_args) + else: + out = L[computation_op](*computation_args) + if lowp_dtype: + out = L[prims.convert_element_type.default](out, dtype=torch.float) + out = L[aten.clamp_max](L[aten.clamp_min](out, min_value), max_value) + if lowp_dtype: + out = L[prims.convert_element_type.default](out, dtype=dtype2) # type: ignore[possibly-undefined] + return out + + return fn + + _binary_attr = { + aten.add: "add", + ops.add: "add", + aten.sub: "sub", + ops.sub: "sub", + } + + def _is_valid_binary(match, fn): + binary_nodes = filter_nodes(match.nodes, fn) + if len(binary_nodes) < 1: + return False + + def get_meta_value(argument: torch.fx.node.Argument): + # Only torch.fx.Node is expected to have meta. + if isinstance(argument, torch.fx.Node): + return argument.meta.get("val", None) + return None + + if any( + not isinstance(get_meta_value(n.args[0]), torch.Tensor) + or not isinstance(get_meta_value(n.args[1]), torch.Tensor) + for n in binary_nodes + ): + return False + # check alpha is one. + if any( + get_arg_value(n, 2, kwarg_name="alpha") != 1.0 + and get_arg_value(n, 2, kwarg_name="alpha") is not None + for n in binary_nodes + ): + return False + if any( + get_meta_value(n.args[0]).size() != get_meta_value(n.args[1]).size() + or get_meta_value(n.args[0]).device != get_meta_value(n.args[1]).device + or get_meta_value(n.args[0]).dtype != get_meta_value(n.args[1]).dtype + for n in binary_nodes + ): + return False + # check args[0] and args[1] is not same + if any(n.args[0] == n.args[1] for n in binary_nodes): + return False + return True + + def _is_valid_computation_binary(computation_op, binary_op, other_index=None): + def fn(match): + if not _is_single_computation_op(computation_op)(match): + return False + if not _is_valid_binary(match, binary_op): + return False + return True + + return fn + + def _get_remaining_users(extra_input_node, compute_node): + # Think about this pattern: + # ReLU + # / \ + # Conv1 + # / \ + # Conv2 + # \ / + # Add + # Although, the extra input node (ReLU) has more than 1 users: Conv1 and Add. + # The Conv1 is the ancestor node of the current compute node (Conv2). + # This indicates that the buffer of ReLU has completed all its usage, + # So we can safely make changes to it now by doing Conv2->Add inplace fusion. + # Take above case as example: + # * extra_input_node: ReLU + # * compute_node: Conv2 + # _get_remaining_users will return the users of extra_input_node which are not + # ancestor node of compute_node. + def _is_ancestor_node(_current_node, _ancestor_node): + # Check whether _ancestor_node is the ancestor node of _current_node + _node_list = [_current_node] + _visited_nodes = set() + while len(_node_list) != 0: + _current_node = _node_list.pop(0) + if _current_node not in _visited_nodes: + _visited_nodes.add(_current_node) + if _current_node == _ancestor_node: + return True + elif isinstance( + _current_node, torch.fx.Node + ) and _current_node.op not in ["placeholder", "output", "get_attr"]: + for input in _current_node.all_input_nodes: + _node_list.append(input) # noqa: PERF402 + return False + + return [ + user + for user in list(extra_input_node.users) + if not _is_ancestor_node(compute_node, user) + ] + + def _is_valid_computation_binary_inplace(computation_op, binary_op, other_index): + def fn(match): + if not _is_valid_computation_binary(computation_op, binary_op)(match): + return False + binary_nodes = filter_nodes(match.nodes, binary_op) + + def _get_compute_node(_binary_node, _other_index): + assert ( + len(_binary_node.all_input_nodes) == 2 + ), "Binary node should have 2 input nodes." + _compute_index = 1 if (_other_index == 0) else 0 + return _binary_node.args[_compute_index] + + def _other_input_not_inplaceable(_binary_node, _other_index): + _compute_node = _get_compute_node(_binary_node, _other_index) + return ( + len( + _get_remaining_users( + _binary_node.args[_other_index], _compute_node + ) + ) + > 1 + or _binary_node.args[_other_index] == _compute_node.args[0] + ) + + if any(_other_input_not_inplaceable(n, other_index) for n in binary_nodes): + return False + if any( + n.args[other_index].op in ["placeholder", "output"] + for n in binary_nodes + ): + return False + return True + + return fn + + def _register_binary_unary_fusion_lowering( + pattern, + computation_op, + binary_op, + fusion_op, + unary_attr=None, + ): + @register_lowering_pattern( + pattern, extra_check=_is_valid_computation_binary(computation_op, binary_op) + ) + def fn(match, *args, **kwargs): + other = kwargs.get("other") + assert isinstance(other, ir.TensorBox) + binary_attr = _binary_attr[binary_op] + args_list = list(args) + computation_args = [args_list[0], other] + args_list[1:-3] + [binary_attr] + if len(args_list) > 6: + if unary_attr is not None: + computation_args += [ + 1.0, + unary_attr.op_name, + unary_attr.scalars_attr, + unary_attr.algorithm_attr, + ] + else: + computation_args += [1.0, None, [], None] + return L[fusion_op](*computation_args) + + return fn + + def _can_be_inplace(_other): + if isinstance(_other.data, ir.View): + return _can_be_inplace(_other.data) + else: + return not ( + isinstance(_other.data, ir.ReinterpretView) + or len(_other.get_inputs_that_alias_output()) > 0 + ) + + def _register_binary_unary_maybe_inplace_fusion_lowering( + pattern, + computation_op, + binary_op, + inplace_fusion_op, + outplace_fusion_op, + unary_attr=None, + other_index=None, + ): + @register_lowering_pattern( + pattern, + extra_check=_is_valid_computation_binary_inplace( + computation_op, binary_op, other_index + ), + ) + def fn(match, *args, **kwargs): + other = kwargs.get("other") + assert isinstance(other, ir.TensorBox) + binary_attr = _binary_attr[binary_op] + args_list = list(args) + computation_args = [args_list[0], other] + args_list[1:-3] + [binary_attr] + if len(args_list) > 6: + if unary_attr is not None: + computation_args += [ + 1.0, + unary_attr.op_name, + unary_attr.scalars_attr, + unary_attr.algorithm_attr, + ] + else: + computation_args += [1.0, None, [], None] + # Make sure the other is not an alias or mutation(fx side doesn't has such info). + other.realize() + if not _can_be_inplace(other): + return L[outplace_fusion_op](*computation_args) + return L[inplace_fusion_op](*computation_args) + + return fn + + computation_ops = [ + mkldnn._convolution_pointwise.default, + mkldnn._linear_pointwise.default, + mkldnn._convolution_transpose_pointwise.default, + ] + + class UnaryAttr: + def __init__( + self, op_name: str, scalars_attr=None, algorithm_attr=None + ) -> None: + self.op_name = op_name + self.scalars_attr = scalars_attr if scalars_attr else [] + self.algorithm_attr = algorithm_attr if algorithm_attr else "" + + def _register_unary_fusion(): + computation_call_fns = [_conv_call, _linear_call, _conv_transpose_call] + + def _unary_fusion_patterns(lowp_dtype): + replacement_unary_fusion_patterns = { + UnaryAttr("gelu", algorithm_attr="tanh"): [ + _unary_fusion_pattern(_gelu_fusion_2, call_fn, 4, lowp_dtype) + for call_fn in computation_call_fns + ], + UnaryAttr("gelu", algorithm_attr="none"): [ + _unary_fusion_pattern(_gelu_fusion_1, call_fn, 2, lowp_dtype) + for call_fn in computation_call_fns + ], + UnaryAttr("hardswish"): [ + _unary_fusion_pattern(_hardswish_fusion, call_fn, 2, lowp_dtype) + for call_fn in computation_call_fns + ], + UnaryAttr("hardsigmoid"): [ + _unary_fusion_pattern(_hardsigmoid_fusion, call_fn, 1, lowp_dtype) + for call_fn in computation_call_fns + ], + UnaryAttr("swish"): [ + _unary_fusion_pattern(_silu_fusion, call_fn, 2, lowp_dtype) + for call_fn in computation_call_fns + ], + } + if not lowp_dtype: + call_user1 = [call_fn(users=1) for call_fn in computation_call_fns] + replacement_unary_fusion_patterns.update( + { + UnaryAttr("relu"): [ + _combined_fusion(u, aten.relu) for u in call_user1 + ], + UnaryAttr("sigmoid"): [ + _combined_fusion(u, aten.sigmoid) for u in call_user1 + ], + UnaryAttr("tanh"): [ + _combined_fusion(u, aten.tanh) for u in call_user1 + ], + } + ) + + return replacement_unary_fusion_patterns + + for lowp_dtype in [torch.bfloat16, torch.float16, None]: + replace_patterns = _unary_fusion_patterns(lowp_dtype) + for unary_attr, patterns in replace_patterns.items(): + _register_unary_fusion_lowering( + patterns[0], unary_attr, computation_ops[0], lowp_dtype + ) + _register_unary_fusion_lowering( + patterns[1], unary_attr, computation_ops[1], lowp_dtype + ) + _register_unary_fusion_lowering( + patterns[2], unary_attr, computation_ops[2], lowp_dtype + ) + _leaky_relu_patterns = [ + _unary_fusion_pattern(_leaky_relu_fusion, call_fn, 3, lowp_dtype) + for call_fn in computation_call_fns + ] + for pattern, computation_op in zip(_leaky_relu_patterns, computation_ops): + _register_leaky_relu_fusion_lowering( + pattern, computation_op, lowp_dtype + ) + hardtanh_patterns = [ + _unary_fusion_pattern(_hardtanh_fusion, call_fn, 1, lowp_dtype) + for call_fn in computation_call_fns + ] + for pattern, computation_op in zip(hardtanh_patterns, computation_ops): + _register_hardtanh_fusion_lowering(pattern, computation_op, lowp_dtype) + + def _register_inplace_fusion(): + binary_ops = [aten.add, ops.add] + inplace_fusion_op = mkldnn._convolution_pointwise_.binary + outplace_fusion_op = mkldnn._convolution_pointwise.binary + conv_call = _conv_call(users=1) + conv_op = computation_ops[0] + for binary_op in binary_ops: + binary_v1 = _binary_fusion_v1(conv_call, binary_op) + binary_unary_v1 = _combined_fusion(binary_v1, aten.relu) + _register_binary_unary_maybe_inplace_fusion_lowering( + binary_unary_v1, + conv_op, + binary_op, + inplace_fusion_op, + outplace_fusion_op, + other_index=0, + unary_attr=UnaryAttr("relu"), + ) + _register_binary_unary_maybe_inplace_fusion_lowering( + binary_v1, + conv_op, + binary_op, + inplace_fusion_op, + outplace_fusion_op, + other_index=0, + ) + binary_v2 = _binary_fusion_v2(conv_call, binary_op) + binary_unary_v2 = _combined_fusion(binary_v2, aten.relu) + _register_binary_unary_maybe_inplace_fusion_lowering( + binary_unary_v2, + conv_op, + binary_op, + inplace_fusion_op, + outplace_fusion_op, + other_index=1, + unary_attr=UnaryAttr("relu"), + ) + _register_binary_unary_maybe_inplace_fusion_lowering( + binary_v2, + conv_op, + binary_op, + inplace_fusion_op, + outplace_fusion_op, + other_index=1, + ) + + def _register_binary_fusion(): + binary_ops = [aten.add, ops.add, aten.sub, ops.sub] + fusion_ops = [ + mkldnn._convolution_pointwise.binary, + mkldnn._linear_pointwise.binary, + ] + _computation_user_1 = [_conv_call(users=1), _linear_call(users=1)] + for computation_call, computation_op, fusion_op in zip( + _computation_user_1, computation_ops[:-1], fusion_ops + ): + for binary_op in binary_ops: + pattern = _binary_fusion_v2(computation_call, binary_op) + _register_binary_unary_fusion_lowering( + pattern, computation_op, binary_op, fusion_op + ) + + for binary_op in [aten.add, ops.add]: + pattern = _binary_fusion_v1(computation_call, binary_op) + _register_binary_unary_fusion_lowering( + pattern, computation_op, binary_op, fusion_op + ) + + def _register_binary_unary_fusion(): + binary_ops = [aten.add, ops.add, aten.sub, ops.sub] + fusion_ops = [mkldnn._convolution_pointwise.binary] + _computation_user_1 = [_conv_call(users=1)] + for computation_call, computation_op, fusion_op in zip( + _computation_user_1, computation_ops[:-1], fusion_ops + ): + for binary_op in binary_ops: + pattern_v1 = _combined_fusion( + _binary_fusion_v2(computation_call, binary_op), aten.relu + ) + _register_binary_unary_fusion_lowering( + pattern_v1, + computation_op, + binary_op, + fusion_op, + unary_attr=UnaryAttr("relu"), + ) + for binary_op in [aten.add, ops.add]: + pattern_v2 = _combined_fusion( + _binary_fusion_v1(computation_call, binary_op), aten.relu + ) + _register_binary_unary_fusion_lowering( + pattern_v2, + computation_op, + binary_op, + fusion_op, + unary_attr=UnaryAttr("relu"), + ) + + def _recover_linear(): + # convert reshape+linear+reshape to a single linear for applying fusion path. + @register_freezing_graph_pattern( + CallFunction( + aten.reshape.default, + CallFunction( + mkldnn._linear_pointwise.default, + CallFunction( + aten.reshape.default, + Arg(), + KeywordArg("reshape_1"), + _users=MULTIPLE, + ), + Arg(), + Arg(), + Arg(), + Arg(), + Arg(), + ), + KeywordArg("reshape_2"), + ), + pass_number=1, + ) + def reshape_linear_reshape_pattern(match, *args, **kwargs): + def get_val(val): + return val if isinstance(val, int) else val.meta.get("val") + + reshape_1 = kwargs.get("reshape_1") + reshape_2 = kwargs.get("reshape_2") + assert isinstance(reshape_1, list) + assert isinstance(reshape_2, list) + assert len(reshape_1) == 2 + + graph = match.graph + reshape_2_node = match.output_node() + linear_input_node = reshape_2_node.args[0].args[0].args[0] + # check linear's input's shape[:-1] == reshape_2[:-1] + # and check product(reshape_2[:-1]) == reshape_1[0] + can_remove_reshape = linear_input_node.meta.get("val").shape[ + :-1 + ] == torch.Size([get_val(val) for val in reshape_2[:-1]]) + can_remove_reshape = can_remove_reshape and ( + reduce( + operator.mul, + [get_val(val) for val in reshape_2[:-1]], + ) + == get_val(reshape_1[0]) + ) + + if can_remove_reshape: + repl = graph.call_function(mkldnn._linear_pointwise.default, args) + repl.meta.update(reshape_2_node.meta) + reshape_2_node.replace_all_uses_with(repl) + old_linear_node = reshape_2_node.args[0] + reshape_1_node = old_linear_node.args[0] + graph.erase_node(reshape_2_node) + graph.erase_node(old_linear_node) + if len(reshape_1_node.users) == 0: + graph.erase_node(reshape_1_node) + + def is_linear_add_bias(match): + add_node = match.output_node() + linear_node = add_node.args[0] + packed_weight_node = linear_node.args[1] + assert packed_weight_node.target == mkldnn._reorder_linear_weight + transpose_weight_node = packed_weight_node.args[0] + assert transpose_weight_node.target == aten.permute.default + weight_meta = transpose_weight_node.args[0].meta.get("val") + bias_node = add_node.args[1] + if isinstance(bias_node, int): + # we only folding bias if it is a constant + return False + bias_meta = add_node.args[1].meta.get("val") + if weight_meta is None or bias_meta is None: + return False + assert weight_meta.dtype in ( + torch.bfloat16, + torch.float16, + ) + if bias_meta.dtype != weight_meta.dtype: + return False + return ( + linear_node.args[2] is None + and bias_meta.dim() == 1 + and bias_meta.size(0) == weight_meta.size(1) + ) + + # convert linear+bias to a single linear for applying fusion path. + @register_freezing_graph_pattern( + CallFunction( + aten.add.Tensor, + CallFunction(mkldnn._linear_pointwise.default, *_linear_args), + Arg(), + ), + pass_number=1, + extra_check=is_linear_add_bias, + ) + def linear_bias_pattern(match, *args): + graph = match.graph + add_node = match.output_node() + linear_node = add_node.args[0] + new_args = list(linear_node.args) + new_args[2] = add_node.args[1] + repl = graph.call_function( + mkldnn._linear_pointwise.default, tuple(new_args) + ) + repl.meta.update(add_node.meta) + add_node.replace_all_uses_with(repl) + match.erase_nodes() + + def _is_packable_mkldnn_rnn_layer(match): + lstm_node = match.output_node() + POS_WEIGHTS = [1, 2] + POS_INPUTS = [0, 5, 6] + POS_ARGS = POS_WEIGHTS + POS_INPUTS + # Weights should be Constant + if any( + lstm_node.args[POS_WEIGHT].op != "get_attr" for POS_WEIGHT in POS_WEIGHTS + ): + return False + + # Meta info for weights and inputs should be available + if any(lstm_node.args[POS_ARG].meta.get("val") is None for POS_ARG in POS_ARGS): + return False + + # Check device + if any( + lstm_node.args[POS_ARG].meta.get("val").device.type != "cpu" + for POS_ARG in POS_ARGS + ): + return False + + # Check dtype + if any( + lstm_node.args[POS_ARG].meta.get("val").dtype == torch.bfloat16 + and not mkldnn._is_mkldnn_bf16_supported() + for POS_ARG in POS_ARGS + ): + return False + if any( + lstm_node.args[POS_ARG].meta.get("val").dtype == torch.float16 + and not mkldnn._is_mkldnn_fp16_supported() + for POS_ARG in POS_ARGS + ): + return False + + return True + + def _is_packable_convolution(match): + """ + Check if the node is supported for MKLDNN convolution. + """ + conv_node = match.output_node() + input_meta_value = conv_node.args[0].meta.get("val") + weight_meta_value = conv_node.args[1].meta.get("val") + if input_meta_value is None or weight_meta_value is None: + return False + input_size = input_meta_value.shape + if conv_node.args[1].op != "get_attr": + return False + for meta_value in [input_meta_value, weight_meta_value]: + if ( + meta_value is None + or meta_value.device.type != "cpu" + or (meta_value.dim() != 4 and meta_value.dim() != 5) + ): + return False + if ( + input_meta_value.dtype == torch.bfloat16 + or weight_meta_value.dtype == torch.bfloat16 + ): + if not mkldnn._is_mkldnn_bf16_supported(): + return False + if ( + input_meta_value.dtype == torch.float16 + or weight_meta_value.dtype == torch.float16 + ): + if not mkldnn._is_mkldnn_fp16_supported(): + return False + is_transposed = conv_node.args[-3] + if is_transposed: + # TODO: Support dynamic shape case for MKLDNN conv transpose. + if has_free_symbols(input_size): + return False + groups = conv_node.args[-1] + in_channels = weight_meta_value.size(0) + # doesn't support group_depthwise_conv_transpose. + if groups > 1 and groups == in_channels: + return False + # Port from: aten/src/ATen/native/Convolution.cpp:is_output_padding_big + output_paddings = conv_node.args[-2] + strides = conv_node.args[3] + if any( + output_padding >= stride + for output_padding, stride in zip(output_paddings, strides) + ): + return False + return True + + def _is_packable_linear(match): + """ + Check if the node is supported for MKLDNN linear. + """ + linear_node = match.output_node() + # mkldnn linear only supports beta=1or0 and alpha=1 + if linear_node.target == aten.addmm.default: + alpha = linear_node.kwargs.get("alpha", 1.0) + beta = linear_node.kwargs.get("beta", 1.0) + if (beta != 0.0 and beta != 1.0) or alpha != 1.0: + return False + # weight_idx is 1 for aten.mm and is 2 for aten.addmm + weight_idx = 2 if linear_node.target == aten.addmm.default else 1 + if linear_node.args[weight_idx].op != "get_attr": + return False + input_meta_value = linear_node.args[weight_idx - 1].meta.get("val") + weight_meta_value = linear_node.args[weight_idx].meta.get("val") + if input_meta_value is None or weight_meta_value is None: + return False + batch_size = input_meta_value.shape[0] + if ( + input_meta_value.dtype == torch.float64 + or weight_meta_value.dtype == torch.float64 + ): + return False + is_lp_weight = weight_meta_value.dtype in ( + torch.bfloat16, + torch.float16, + ) + # on x86, for fp32, mkl should be enabled and batch_size should not be a free symbol. + # on aarch64, use mkldnn op for fp32 as well if acl is enabled + if ( + not is_lp_weight + and not mkldnn._is_mkldnn_acl_supported() + and ((not torch._C.has_mkl) or has_free_symbols(batch_size)) + ): + return False + for meta_value in [input_meta_value, weight_meta_value]: + if ( + meta_value is None + or meta_value.device.type != "cpu" + or meta_value.dim() != 2 + ): + return False + if weight_idx == 2: + bias_meta_value = linear_node.args[0].meta.get("val") + if ( + bias_meta_value is None + or meta_value.device.type != "cpu" + or bias_meta_value.dim() != 1 + or bias_meta_value.size(0) != weight_meta_value.size(1) + ): + return False + + if ( + input_meta_value.dtype == torch.bfloat16 + or weight_meta_value.dtype == torch.bfloat16 + ): + if not mkldnn._is_mkldnn_bf16_supported(): + return False + if ( + input_meta_value.dtype == torch.float16 + or weight_meta_value.dtype == torch.float16 + ): + if not mkldnn._is_mkldnn_fp16_supported(): + return False + return True + + _aten_conv_args = ( + Arg(), + Arg(), + Arg(), + Arg(), + Arg(), + Arg(), + KeywordArg("is_transposed"), + Arg(), + Arg(), + ) + + _aten_mkldnn_rnn_layer_args = ( + Arg(), # input + Arg(), # weight0 + Arg(), # weight1 + Arg(), # weight2 + Arg(), # weight3 + Arg(), # hx_ + Arg(), # cx_ + KeywordArg("reverse"), # reverse + Arg(), # batch_sizes + Arg(), # mode + Arg(), # hidden_size + Arg(), # num_layers + Arg(), # has_biases + Arg(), # bidirectional + Arg(), # batch_first + Arg(), # train + ) + + def _register_weight_pack_pass(): + @register_freezing_graph_pattern( + CallFunction(aten.convolution.default, *_aten_conv_args), + extra_check=_is_packable_convolution, + ) + def convolution(match, *args, **kwargs): + is_transposed = kwargs.get("is_transposed") + assert isinstance(is_transposed, bool) + graph = match.graph + conv_node = match.output_node() + input_size = conv_node.args[0].meta.get("val").shape + with graph.inserting_before(conv_node): + constant_args = [args[4], args[3], args[5], args[-1]] + packed_weight_op = mkldnn._reorder_convolution_weight + packed_conv_op = mkldnn._convolution_pointwise.default + if is_transposed: + constant_args.insert(1, args[-2]) # output_padding + packed_weight_op = mkldnn._reorder_convolution_transpose_weight + packed_conv_op = mkldnn._convolution_transpose_pointwise.default + if not has_free_symbols(input_size): + packed_weight_inputs = ( + (args[1],) + tuple(constant_args) + (input_size,) + ) + packed_weight_node = graph.create_node( + "call_function", packed_weight_op, args=packed_weight_inputs + ) + else: + assert not is_transposed + # For dynamic shape case, we need to pack weight in runtime. + packed_weight_node = args[1] + packed_conv_inputs = ( + (args[0], packed_weight_node, args[2]) + + tuple(constant_args) + + ("none", [], "") + ) + packed_conv_node = graph.create_node( + "call_function", packed_conv_op, tuple(packed_conv_inputs) + ) + conv_node.replace_all_uses_with(packed_conv_node) + packed_conv_node.meta.update(conv_node.meta) + graph.erase_node(conv_node) + + @register_freezing_graph_pattern( + CallFunction(aten.mkldnn_rnn_layer.default, *_aten_mkldnn_rnn_layer_args), + extra_check=_is_packable_mkldnn_rnn_layer, + ) + def mkldnn_rnn_layer(match, *args, **kwargs): + def get_item(graph, node, index): + return graph.call_function(operator.getitem, (node, index)) + + graph = match.graph + lstm_node = match.output_node() + input = args[0] + weight0, weight1 = args[1:3] + reverse = kwargs.get("reverse") + packed_lstm_op = aten.mkldnn_rnn_layer.default + hidden_size = args[9] + has_biases = args[11] + batch_first = args[13] + with graph.inserting_before(lstm_node): + packed_weight_op = mkldnn._reorder_mkldnn_rnn_layer_weight.default + packed_weight_inputs = ( + weight0, + weight1, + hidden_size, + reverse, + has_biases, + batch_first, + ) + packed_weight_node = graph.create_node( + "call_function", packed_weight_op, packed_weight_inputs, {}, "name" + ) + packed_weight_items = [ + get_item(graph, packed_weight_node, i) for i in range(2) + ] + pack_lstm_inputs = ( + args[0], + *packed_weight_items, + args[3], + args[4], + args[5], + args[6], + reverse, + *args[7:], + ) + + packed_lstm_node = graph.create_node( + "call_function", packed_lstm_op, args=pack_lstm_inputs + ) + lstm_node.replace_all_uses_with(packed_lstm_node) + packed_lstm_node.meta.update(lstm_node.meta) + graph.erase_node(lstm_node) + + @register_freezing_graph_pattern( + CallFunction( + aten.addmm.default, + Arg(), + Arg(), + Arg(), + beta=KeywordArg("beta"), + alpha=KeywordArg("alpha"), + ), + extra_check=_is_packable_linear, + ) + @register_freezing_graph_pattern( + CallFunction(aten.mm.default, Arg(), Arg()), + extra_check=_is_packable_linear, + ) + def linear(match, *args, **kwargs): + graph = match.graph + linear_node = match.output_node() + input = args[0] if linear_node.target == aten.mm.default else args[1] + bias = ( + None + if linear_node.target == aten.mm.default + or ( + linear_node.target == aten.addmm.default + and linear_node.kwargs.get("beta", 1.0) == 0.0 + ) + else args[0] + ) + weight = args[1] if linear_node.target == aten.mm.default else args[2] + with graph.inserting_before(linear_node): + transpose_weight_node = graph.create_node( + "call_function", aten.permute.default, (weight, (1, 0)) + ) + weight_dtype = weight.meta.get("val").dtype + is_lp_weight = weight_dtype in ( + torch.bfloat16, + torch.float16, + ) + batch_size = input.meta.get("val").shape[0] + if has_free_symbols(batch_size): + assert ( + is_lp_weight or mkldnn._is_mkldnn_acl_supported() + ), f"only bf16/fp16 weight prepacking supports dynamic shape inputs but got {weight_dtype}" + # For bfloat16 dynamic shape path, using input size hint to pack weight for a better performance. + packed_weight_inputs = ( + transpose_weight_node, + batch_size.node.shape_env.size_hint(batch_size.node.expr) + if has_free_symbols(batch_size) + else batch_size, + ) + # MKL packed matrix can't be copied to a different address because the internal implementation + # depends on the alignment of internally-stored metadata. + # In aot mode, we need to firstly save the packed weight, when loading it, + # it will be in a different address which doesn't work. + # Disable MKL prepack linear in AOT mode + packed_weight_op = ( + mkldnn._reorder_linear_weight + if ( + is_lp_weight + or mkldnn._is_mkldnn_acl_supported() + or V.aot_compilation is True + ) + else torch.ops.mkl._mkl_reorder_linear_weight + ) + packed_weight_node = graph.create_node( + "call_function", packed_weight_op, args=packed_weight_inputs + ) + + packed_linear_inputs: Tuple[Any, ...] = (input, packed_weight_node) + if ( + is_lp_weight + or mkldnn._is_mkldnn_acl_supported() + or V.aot_compilation is True + ): + packed_linear_inputs += (bias, "none", [], "") + packed_linear_op = mkldnn._linear_pointwise.default + else: + packed_linear_inputs += (transpose_weight_node, bias, batch_size) + packed_linear_op = torch.ops.mkl._mkl_linear + packed_linear_node = graph.create_node( + "call_function", packed_linear_op, packed_linear_inputs + ) + linear_node.replace_all_uses_with(packed_linear_node) + packed_linear_node.meta.update(linear_node.meta) + graph.erase_node(linear_node) + + def _eliminate_duplicate_packed_nodes(gm): + """ + Combine packed weight nodes with the same inputs to reduce memory usage. + for example: + class Model(nn.Module): + def __init__(self) -> None: + super().__init__() + self.linear = nn.Linear(32, 32, bias=True) + + def forward(self, x): + return self.linear(self.linear(x)) + + the above's packed weight nodes are duplicate if two linear calls have same input size. + """ + if not (torch.backends.mkldnn.enabled and torch.backends.mkldnn.is_available()): + return gm + + packed_weight_ops = [ + torch._C._nn.mkldnn_reorder_conv2d_weight, + torch._C._nn.mkldnn_reorder_conv3d_weight, + mkldnn._reorder_convolution_transpose_weight, + mkldnn._reorder_linear_weight, + mkldnn._reorder_mkldnn_rnn_layer_weight, + ] + if torch._C.has_mkl: + packed_weight_ops.append(torch.ops.mkl._mkl_reorder_linear_weight) + + for node in gm.graph.nodes: + if node.target in packed_weight_ops and len(node.args[0].users) > 1: + for user_node in list(node.args[0].users.keys()): + if ( + user_node.target == node.target + and user_node != node + and user_node.args == node.args + ): + user_node.replace_all_uses_with(node) + gm.graph.erase_node(user_node) + + @functools.lru_cache(None) + def _mkldnn_fusion_init(): + # TODO: aarch64: enable op fusion for acl once it supports fused operators. Disabling it for now. + # Otherwise even the matmul or innerproduct can not be accelerated with acl + if ( + torch.backends.mkldnn.enabled + and torch.backends.mkldnn.is_available() + and not torch.ops.mkldnn._is_mkldnn_acl_supported() + ): + _register_unary_fusion() + _register_inplace_fusion() + _register_binary_unary_fusion() + _register_binary_fusion() + _register_quantization_lowerings() + _register_woq_lowerings() + + @functools.lru_cache(None) + def _mkldnn_weight_pack_init(): + if torch.backends.mkldnn.enabled and torch.backends.mkldnn.is_available(): + _register_weight_pack_pass() + _recover_linear() + _register_quantization_weight_pack_pass() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/numeric_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/numeric_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7069b100a38bd77d010d15268c5018082b56173b --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/numeric_utils.py @@ -0,0 +1,212 @@ +# mypy: allow-untyped-defs +import gc +import logging +import os +import random +import traceback + +import numpy + +import torch +import torch.optim as optim + +from .. import config + + +logger: logging.Logger = logging.getLogger(__name__) + +MAIN_RANDOM_SEED = 1337 + +# Set the CUBLAS_WORKSPACE_CONFIG environment variable +os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" + + +# If the two forward functions involve any non-deterministic operations, +# such as certain types of parallelism or asynchronous execution, +# this can also lead to different outputs. +def set_deterministic() -> None: + """Make torch manual seed deterministic.""" + + torch.manual_seed(MAIN_RANDOM_SEED) + random.seed(MAIN_RANDOM_SEED) + numpy.random.seed(MAIN_RANDOM_SEED) + torch.use_deterministic_algorithms(True) + + +def clean_memory() -> None: + """Clean memory to avoid OOM.""" + gc.collect() + torch.cuda.empty_cache() + + +# We compare the numerical results before and after pre/post grad fx passes +# transformation to make sure the numerical results are the same. +def compare_dict_tensors(dict_base, dict_control, precision): + if len(set(dict_base.keys())) != len(set(dict_control.keys())): + logger.warning("Mismatch keys found before and after pre/post grad fx passes.") + logger.debug("keys before pre/post grad fx passes %s", dict_base.keys()) + logger.debug("keys after pre/post grad fx passes %s", dict_control.keys()) + return False + is_allclose = True + for key in dict_base.keys(): + if key not in dict_control: + logger.warning( + "Mismatch parameter name %s does not exist after pre/post grad fx passes", + key, + ) + # Some parameters have `None`, and not every param has a valid .grad field, we skip them + if dict_base[key] is None or dict_control[key] is None: + continue + if not torch.allclose( + dict_base[key], + dict_control[key], + rtol=precision, + atol=precision, + equal_nan=True, + ): + logger.warning( + "Mismatch parameter values found before and after pre/post grad fx passes." + ) + logger.debug("value before pre/post grad fx passes %s", dict_base[key]) + logger.debug("value after pre/post grad fx passes %s", dict_control[key]) + is_allclose = False + return is_allclose + + +def compare_tuple_tensors(tuple_base, tuple_control, precision): + if len(tuple_base) != len(tuple_control): + logger.warning( + "Mismatch fw output length. before transformation: %s, after transformation: %s", + len(tuple_base), + len(tuple_control), + ) + return False + is_allclose = True + for i in range(len(tuple_base)): + # Some parameters have `None`, we skip them + if tuple_base[i] is None or tuple_control[i] is None: + continue + if not torch.allclose( + tuple_base[i], + tuple_control[i], + rtol=precision, + atol=precision, + equal_nan=True, + ): + logger.debug( + "forward output before pre/post grad fx passes %s", tuple_base[i] + ) + logger.debug( + "forward output after pre/post grad fx passes %s", tuple_control[i] + ) + is_allclose = False + return is_allclose + + +def compare_parameters(model_base, model_control, precision): + return compare_dict_tensors( + dict(model_base.named_parameters()), + dict(model_control.named_parameters()), + precision, + ) + + +def compare_forward_output(pred_base, pred_control, precision): + return compare_tuple_tensors( + pred_base, + pred_control, + precision, + ) + + +def compare_gradients(model_base, model_control, precision): + grad_base = {key: param.grad for key, param in model_base.named_parameters()} + grad_pt2 = {key: param.grad for key, param in model_control.named_parameters()} + return compare_dict_tensors( + grad_base, + grad_pt2, + precision, + ) + + +def run_model( + model_base, model_control, model_input, num_iterations=10, precision=1e-4 +): + clean_memory() + for i in range(num_iterations): + logger.info("start %s iteration", i) + set_deterministic() + pred_base = model_base(*model_input) + set_deterministic() + pred_control = model_control(*model_input) + + res = compare_parameters(model_base, model_control, precision) + logger.info("compare parameters. Numerical result : %s", res) + + res = compare_forward_output(pred_base, pred_control, precision) + logger.info("compare loss/predict. Numerical result : %s", res) + # tensor may not have a grad_fn + try: + _ = pred_base[0].sum().backward(retain_graph=True) + _ = pred_control[0].sum().backward(retain_graph=True) + res = compare_gradients(model_base, model_control, precision) + logger.info("compare param grad. Numerical result : %s", res) + except Exception: + logger.exception("Exception when comparing gradients") + traceback.print_exc() + + if config.fx_passes_numeric_check["requires_optimizer"]: + try: + optimizer_base = optim.SGD( + [param for name, param in model_base.named_parameters()], lr=0.01 + ) + optimizer_base.step() + + optimizer_control = optim.SGD( + [param for name, param in model_control.named_parameters()], lr=0.01 + ) + optimizer_control.step() + + res = compare_parameters(model_base, model_control, precision) + logger.info( + "compare parameters with optimizer added. Numerical result : %s", + res, + ) + except Exception as e: + logger.exception( + "Exception when optimizer is added to check parameter names" + ) + traceback.print_exc() + else: + logger.warning( + "no parameter with optimizer to compare with length %s before transformation" + " and the length %s after transformation", + len(dict(model_base.named_parameters())), + len(dict(model_control.named_parameters())), + ) + + +def numeric_check_if_enabled( + gm_before_fx_passes, + gm_after_fx_passes, + example_inputs, + num_iterations, + precision, +): + # need to topo-sort graphmodule before we run the model, + # otherwise it may fail as refer before def + # fail silently in order not to block the model run + try: + with torch.autograd.set_detect_anomaly(True): + run_model( + gm_before_fx_passes, + gm_after_fx_passes, + example_inputs, + num_iterations=num_iterations, + precision=precision, + ) + except Exception as e: + logger.warning( + "Runtime numeric check failed in pre grad fx passes with error: %s", e + ) + traceback.print_exc() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/pad_mm.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/pad_mm.py new file mode 100644 index 0000000000000000000000000000000000000000..87450b34e7ee2734f22f29fde2e1b995aa1128e8 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/pad_mm.py @@ -0,0 +1,881 @@ +# mypy: allow-untyped-defs +import functools +import itertools +import operator +import typing +from typing import Callable, List, Optional, Union + +import torch +import torch._inductor.runtime.runtime_utils +from torch import Tensor +from torch._dynamo.utils import counters +from torch._inductor import utils +from torch._inductor.autoheuristic.autoheuristic import ( + AHContext, + AutoHeuristic, + LocalFeedback, +) +from torch._inductor.autoheuristic.autoheuristic_utils import ( + context_add_strides, + context_add_using_tf32, + pad_mm_operations, + pad_mm_precondition, +) +from torch._subclasses.fake_tensor import FakeTensor +from torch.utils._mode_utils import no_dispatch + +from ...utils._triton import has_triton +from ..pattern_matcher import ( + fwd_only, + gen_register_replacement, + joint_fwd_bwd, + Match, + ReplaceFn, + SearchFn, +) + + +aten = torch.ops.aten + + +# This flag is only used for testing purpose. +# Changing it to True will ignore comparing do_bench times +# between original pattern and padded one. +_skip_do_bench_times = False + + +def fetch_fake_tensors(match, kwarg_names) -> List[Tensor]: + kwargs = match.kwargs + return [kwargs[name].meta["val"] for name in kwarg_names] + + +def unwrap_fake_args(*arg_names): + def decorator(func): + def wrapper(match): + fake_tensors = fetch_fake_tensors(match, arg_names) + return func(*fake_tensors) + + return wrapper + + return decorator + + +def get_alignment_size(x: Tensor) -> int: + return get_alignment_size_dtype(x.dtype) + + +def get_alignment_size_dtype(dtype: torch.dtype) -> int: + if dtype == torch.float16 or dtype == torch.half or dtype == torch.bfloat16: + return 8 + elif dtype == torch.float32 or dtype == torch.float: + return 4 + else: + return 0 + + +def check_device(a: Tensor, b: Tensor) -> bool: + return a.is_cuda and b.is_cuda + + +def check_dtype(a: Tensor, b: Tensor) -> bool: + return a.is_floating_point() and b.is_floating_point() + + +def should_pad_common( + mat1: Tensor, mat2: Tensor, input: Optional[Tensor] = None +) -> bool: + # It's fine we have symbolic shapes or strides as long as they + # have hints. Later, we will make sure we only pad non-symbolic dimensions. + def valid_shape_and_stride(t: Optional[Tensor]) -> bool: + if t is None: + return True + + symbolic_cnt = 0 + for x in t.size(): + if isinstance(x, int): + continue + elif utils.is_symbolic(x): + if not x.node.has_hint(): + return False + symbolic_cnt += 1 + else: + return False + # filter out cases where all dimentions are symbolic + if symbolic_cnt == len(t.size()): + return False + return all( + isinstance(x, int) or (utils.is_symbolic(x) and x.node.has_hint()) + for x in t.stride() + ) + + return ( + torch._inductor.config.shape_padding + and check_device(mat1, mat2) + and check_dtype(mat1, mat2) + and all(valid_shape_and_stride(t) for t in (mat1, mat2, input)) + ) + + +def get_padded_length(x: Union[int, torch.SymInt], alignment_size) -> int: + # we don't pad x if it is symbolic + if isinstance(x, torch.SymInt) or alignment_size == 0 or x % alignment_size == 0: + return 0 + + # ignore dim that can be squeezed away + if x == 1: + return 0 + + return int((x // alignment_size + 1) * alignment_size) - x + + +def pad_dim(x: Tensor, padded_length: int, dim: int) -> Tensor: + if padded_length == 0: + return x + pad = x.new_zeros(*x.shape[:dim], padded_length, *x.shape[dim + 1 :]) + return torch.cat([x, pad], dim=dim) + + +def addmm_pattern( + input: Tensor, mat1: Tensor, mat2: Tensor, beta: float, alpha: float +) -> Tensor: + return aten.addmm(input, mat1, mat2, beta=beta, alpha=alpha) + + +def should_pad_addmm(match: Match) -> bool: + mat1, mat2, input = fetch_fake_tensors(match, ("mat1", "mat2", "input")) + return should_pad_common(mat1, mat2, input) and should_pad_bench( + match, mat1, mat2, torch.ops.aten.addmm, input=input + ) + + +def pad_addmm( + input: Optional[Tensor], + mat1: Tensor, + mat2: Tensor, + m_padded_length: int, + k_padded_length: int, + n_padded_length: int, + beta=1.0, + alpha=1.0, + mat1_pre_padded: bool = False, + mat2_pre_padded: bool = False, +): + # for paddings, dim order is reversed for some reasons + # and for every dim, we need to specify left and right padding + if not mat1_pre_padded: + mat1 = pad_mat1( + mat1, m_padded_length=m_padded_length, k_padded_length=k_padded_length + ) + if not mat2_pre_padded: + mat2 = pad_mat2( + mat2, k_padded_length=k_padded_length, n_padded_length=n_padded_length + ) + + # the add broadcasts, so we only pad if the dimension != 1 + if input is not None: + if n_padded_length != 0: + if input.dim() == 2 and input.shape[1] != 1: + input = pad_dim(input, n_padded_length, 1) + elif input.dim() == 1 and input.shape[0] != 1: + input = pad_dim(input, n_padded_length, 0) + if m_padded_length != 0 and input.dim() == 2 and input.shape[0] != 1: + input = pad_dim(input, m_padded_length, 0) + + res = aten.addmm(input, mat1, mat2, beta=beta, alpha=alpha) + + if m_padded_length != 0: + res = res[:-m_padded_length, :] + if n_padded_length != 0: + res = res[:, :-n_padded_length] + return res + + +def addmm_replace( + input: Optional[Tensor], mat1: Tensor, mat2: Tensor, beta=1.0, alpha=1.0 +) -> Tensor: + k_padded_length = get_padded_length(mat1.shape[1], get_alignment_size(mat1)) + n_padded_length = get_padded_length(mat2.shape[1], get_alignment_size(mat2)) + m_padded_length = get_padded_length(mat1.shape[0], get_alignment_size(mat1)) + return pad_addmm( + input, + mat1, + mat2, + m_padded_length, + k_padded_length, + n_padded_length, + beta, + alpha, + ) + + +def is_mm_compute_bound(M: int, K: int, N: int, dtype: torch.dtype) -> bool: + denominator = M * K + N * K + M * N + if denominator == 0: + return False + arithmetic_intensity = (M * N * K) / denominator + + # we have experienced some large perf hits in this case, even in bandwidth bound regimes + if ( + dtype is torch.bfloat16 + and K > M + and K > N + and torch.cuda.get_device_capability() < (9, 0) + ): # doesnt repro on h100s: + return True + + # Fails with AMD + try: + machine_balance = ( + 1000 * utils.get_device_tflops(dtype) + ) / utils.get_gpu_dram_gbps() + except Exception: + return True + + # dram_gbps might be underestimating bandwidth because of cache. + # if we estimate machine balance too low we might miss some speedups, + # if we extimate too high there will be unnecessary compilation time increase. + # TODO - finetune coefficient here. As a reference point, Triton mm model assumes + # 80% of reads are in cache and cache is 4x faster than dram_gbps + machine_balance = machine_balance * 0.5 + + return arithmetic_intensity > machine_balance + + +@functools.lru_cache(None) +def get_pad_cache(): + return torch._inductor.codecache.LocalCache() + + +def get_cached_should_pad(key: str) -> bool: + return get_pad_cache().lookup(key) + + +def set_cached_should_pad(key: str, value: bool): + return get_pad_cache().set_value(key, value=value) + + +def get_cached_base_mm_benchmark_time(key: str) -> float: + return get_pad_cache().lookup(key) + + +def set_cached_base_mm_benchmark_time(key: str, value: float): + return get_pad_cache().set_value(key, value=value) + + +def should_pad_bench_key( + match, + mat1: Tensor, + mat2: Tensor, + op, + input: Optional[Tensor] = None, + is_base_time_key=False, +) -> str: + def tensor_key(t): + return (t.shape, t.stride(), t.dtype) + + tf32_key = ( + None if mat1.dtype != torch.float32 else torch.backends.cuda.matmul.allow_tf32 + ) + + def fmt_pad(name): + if is_base_time_key: + return None + return f"exclude_pad:{should_exclude_padding_time(match, name)}" + + key = ( + tensor_key(mat1), + tensor_key(mat2), + fmt_pad("mat1"), + fmt_pad("mat2"), + op, + input if input is None else tensor_key(input), + tf32_key, + ) + + key = str(key) + if is_base_time_key: + key = f"base mm time: {key}" + return key + + +def get_non_view_def(node): + if node.op == operator.getitem: + return get_non_view_def(node.args[0]) + + if ( + node.op == "call_function" + and isinstance(node.target, torch._ops.OpOverload) + and utils.is_view(node.target) + ): + return get_non_view_def(node.all_input_nodes[0]) + + return node + + +def should_exclude_padding_time(match, arg_name): + node_def = get_non_view_def(match.kwargs[arg_name]) + + # constant padding converts tensors to contiguous so even if the input tensor + # can be planned layout transform is not free. TODO - way to pad and preserve layout ? + if not fetch_fake_tensors(match, (arg_name,))[0].is_contiguous(): + return False + + # TODO - see issue https://githpub.com/pytorch/pytorch/issues/128889 + # We would only able to completely plan these out if we were only doing + # first dimension padding. non-first we would still need a copy + # because these outputs are fixed dense. + cannot_plan_output = [ + aten.mm.default, + aten.convolution.default, + aten.convolution_backward.default, + aten.bmm.default, + aten.addmm.default, + aten._scaled_dot_product_flash_attention.default, + aten._scaled_dot_product_efficient_attention.default, + ] + + if node_def.target in cannot_plan_output: + return False + + if ( + node_def.target == aten.cat.default + and len(node_def.all_input_nodes) + > torch._inductor.config.max_pointwise_cat_inputs + ): + return False + + # optimistically assume we should be able to memory plan away + # all non inputs + return node_def.op != "placeholder" + + +def should_pad(key: str, ori_time, pad_time) -> bool: + multiplier = 1.1 + # Shape padding introduces additional memory ops. Based on microbenchmarks, 1.1x represents a reasonable + # tradeoff between performance improvement from shape padding and overhead from additional memory ops + # TODO: Build a learned model which would be better than this heuristic + if "shape_padding_multiplier" in torch._inductor.config.post_grad_fusion_options: + multiplier = torch._inductor.config.post_grad_fusion_options[ + "shape_padding_multiplier" + ].get("value", 1.1) + counters["inductor"]["shape_padding_multiplier"] += 1 + should_pad = _skip_do_bench_times or ori_time > pad_time * multiplier + set_cached_should_pad(key, should_pad) + return should_pad + + +def should_pad_bench( + match, mat1: Tensor, mat2: Tensor, op, input: Optional[Tensor] = None +) -> bool: + do_bench = functools.partial( + torch._inductor.runtime.benchmarking.benchmarker.benchmark_gpu, + warmup=5, + ) + m_padded_length = 0 + n_padded_length = 0 + batchsize = 1 + with no_dispatch(): + if op is torch.ops.aten.mm or op is torch.ops.aten.addmm: + m = mat1.shape[0] + k = mat1.shape[1] + n = mat2.shape[1] + k_padded_length = get_padded_length(k, get_alignment_size(mat1)) + n_padded_length = get_padded_length(n, get_alignment_size(mat2)) + m_padded_length = get_padded_length(m, get_alignment_size(mat1)) + elif op is torch.ops.aten.bmm: + batchsize = mat1.shape[0] + m = mat1.shape[1] + k = mat1.shape[2] + n = mat2.shape[2] + k_padded_length = get_padded_length(k, get_alignment_size(mat1)) + m_padded_length = get_padded_length(m, get_alignment_size(mat1)) + n_padded_length = get_padded_length(n, get_alignment_size(mat2)) + else: + return False + + if m_padded_length == k_padded_length == n_padded_length == 0: + return False + + def realize_symbols(ds): + return [d if isinstance(d, int) else d.node.hint for d in ds] + + if any( + dim == 0 + for dim in itertools.chain( + realize_symbols(mat1.shape), realize_symbols(mat2.shape) + ) + ): + return False + + if torch._inductor.config.force_shape_pad: + return True + + if not has_triton(): + return False + + if not is_mm_compute_bound(m, k, n, mat1.dtype): + return False + + # We don't want to look up the cache for cases that are trivially false + # since it does file io + key = should_pad_bench_key(match, mat1, mat2, op, input) + + cached_pad = get_cached_should_pad(key) + if cached_pad is not None: + return cached_pad + + def realize_tensor(t): + if isinstance(t, FakeTensor): + size_hints = realize_symbols(t.size()) + stride_hint = realize_symbols(t.stride()) + real_size = ( + sum((d - 1) * s for d, s in zip(size_hints, stride_hint)) + 1 + ) + real_t = torch.randn(real_size, dtype=t.dtype, device=t.device) + return torch.as_strided(real_t, size_hints, stride_hint) + else: + return torch.randn_like(t) + + mat1 = realize_tensor(mat1) + mat2 = realize_tensor(mat2) + + # since we key on whether or not the inputs can be memory planned, set cache for the + # original time which is unaffected by whether or not the input can be planned + ori_time_key = should_pad_bench_key( + match, mat1, mat2, op, input, is_base_time_key=True + ) + ori_time = get_cached_base_mm_benchmark_time(ori_time_key) + if ori_time is None and op is torch.ops.aten.addmm and input is not None: + # realize bias for addmm + input = realize_tensor(input) + + mat1_pad = mat1 + mat2_pad = mat2 + + is_bmm = op is torch.ops.aten.bmm + + mat1_pre_padded = should_exclude_padding_time(match, "mat1") + fns = [] + if mat1_pre_padded and (m_padded_length or k_padded_length): + mat1_pad = pad_mat1( + mat1_pad, + m_padded_length=m_padded_length, + k_padded_length=k_padded_length, + is_bmm=is_bmm, + ) + + def write_pad(): + if is_bmm: + mat1_pad[:, -m_padded_length:, -k_padded_length:].fill_(0) + else: + mat1_pad[-m_padded_length:, -k_padded_length:].fill_(0) + + fns.append(write_pad) + + mat2_pre_padded = should_exclude_padding_time(match, "mat2") + if mat2_pre_padded and (k_padded_length or n_padded_length): + mat2_pad = pad_mat2( + mat2_pad, + k_padded_length=k_padded_length, + n_padded_length=n_padded_length, + is_bmm=is_bmm, + ) + + def write_pad(): + if is_bmm: + mat2_pad[:, -k_padded_length:, -n_padded_length:].fill_(0) + else: + mat2_pad[-k_padded_length:, -n_padded_length:].fill_(0) + + fns.append(write_pad) + + if op is torch.ops.aten.addmm: + input_pad = None + if input is not None and input.is_cuda: + input_pad = torch.randn_like(input) + fns.append( + lambda: pad_addmm( + input_pad, + mat1_pad, + mat2_pad, + m_padded_length, + k_padded_length, + n_padded_length, + mat1_pre_padded=mat1_pre_padded, + mat2_pre_padded=mat2_pre_padded, + ) + ) + elif op is torch.ops.aten.mm: + fns.append( + lambda: pad_mm( + mat1_pad, + mat2_pad, + m_padded_length, + k_padded_length, + n_padded_length, + mat1_pre_padded=mat1_pre_padded, + mat2_pre_padded=mat2_pre_padded, + ) + ) + else: + fns.append( + lambda: pad_bmm( + mat1_pad, + mat2_pad, + m_padded_length, + k_padded_length, + n_padded_length, + mat1_pre_padded=mat1_pre_padded, + mat2_pre_padded=mat2_pre_padded, + ) + ) + + def orig_bench_fn(): + if op is torch.ops.aten.bmm or op is torch.ops.aten.mm: + op(mat1, mat2) + else: + op(input, mat1, mat2) + + def pad_bench_fn(): + for fn in fns: + fn() + + if ( + torch._inductor.config.run_autoheuristic("pad_mm") + and op is torch.ops.aten.mm + ): + ah_should_pad = run_autoheuristic( + mat1, + mat2, + orig_bench_fn, + pad_bench_fn, + m_padded_length, + k_padded_length, + n_padded_length, + do_bench, + mat1_pre_padded, + mat2_pre_padded, + ori_time, + ori_time_key, + key, + ) + if ah_should_pad is not None: + return ah_should_pad + + if ori_time is None: + ori_time = do_bench(orig_bench_fn) + set_cached_base_mm_benchmark_time(ori_time_key, ori_time) + + pad_time = do_bench(pad_bench_fn) + return should_pad(key, ori_time, pad_time) + + +def get_context( + mat1: Tensor, + mat2: Tensor, + mat1_pre_padded: bool, + mat2_pre_padded: bool, + m_padded_length: int, + k_padded_length: int, + n_padded_length: int, +): + context = AHContext() + + context.add_feature("m", mat1.shape[0]) + context.add_feature("k", mat1.shape[1]) + context.add_feature("n", mat2.shape[1]) + + context_add_strides(context, "mat1", mat1.stride()) + context_add_strides(context, "mat2", mat2.stride()) + + context.add_feature("m_padded_length", m_padded_length) + context.add_feature("k_padded_length", k_padded_length) + context.add_feature("n_padded_length", n_padded_length) + + context.add_feature("mat1_align_size", get_alignment_size(mat1)) + context.add_feature("mat2_align_size", get_alignment_size(mat2)) + + context.add_feature("mat1_dtype", mat1.dtype, is_categorical=True) + context.add_feature("mat2_dtype", mat2.dtype, is_categorical=True) + + context.add_feature("prepadded_mat1", mat1_pre_padded, is_categorical=True) + context.add_feature("prepadded_mat2", mat2_pre_padded, is_categorical=True) + + context_add_using_tf32(context, mat1.dtype) + return context + + +def run_autoheuristic( + mat1: Tensor, + mat2: Tensor, + orig_bench_fn: Callable[[], None], + pad_bench_fn: Callable[[], None], + m_padded_length: int, + k_padded_length: int, + n_padded_length: int, + do_bench, + mat1_pre_padded: bool, + mat2_pre_padded: bool, + ori_time, + ori_time_key: str, + key: str, +) -> Optional[bool]: + def feedback_fn(choice: str): + if choice == orig_choice: + return do_bench(orig_bench_fn) + elif choice == pad_choice: + return do_bench(pad_bench_fn) + return None + + def fallback() -> str: + return "autotune" + + orig_choice = "orig" + pad_choice = "pad" + choices = [orig_choice, pad_choice] + feedback = LocalFeedback(feedback_fn) + context = get_context( + mat1, + mat2, + mat1_pre_padded, + mat2_pre_padded, + m_padded_length, + k_padded_length, + n_padded_length, + ) + name = "pad_mm" + autoheuristic = AutoHeuristic( + fallback=fallback, + choices=choices, + feedback=feedback, + context=context, + name=name, + augment_context=pad_mm_operations(), + precondition=pad_mm_precondition, + ) + choice = autoheuristic.get_choice() + choice2should_pad = {orig_choice: False, pad_choice: True, "autotune": None} + ah_should_pad = choice2should_pad.get(choice, None) + + if torch._inductor.config.collect_autoheuristic(name): + ah_ori_time = autoheuristic.get_collected_feedback(orig_choice) + ah_pad_time = autoheuristic.get_collected_feedback(pad_choice) + + # if precondition is not satisifed, autoheuristic does not collect data + if ah_ori_time is not None and ah_pad_time is not None: + if ori_time is None: + set_cached_base_mm_benchmark_time(ori_time_key, ah_ori_time) + return should_pad(key, ah_ori_time, ah_pad_time) + if ah_should_pad is not None: + set_cached_should_pad(key, ah_should_pad) + return ah_should_pad + + +def mm_pattern(mat1: Tensor, mat2: Tensor) -> Tensor: + return aten.mm(mat1, mat2) + + +def should_pad_mm(match: Match) -> bool: + mat1, mat2 = fetch_fake_tensors(match, ("mat1", "mat2")) + return should_pad_common(mat1, mat2) and should_pad_bench( + match, mat1, mat2, torch.ops.aten.mm + ) + + +def pad_mat1(mat1, *, m_padded_length, k_padded_length, is_bmm=False): + if m_padded_length == 0 and k_padded_length == 0: + return mat1 + elif k_padded_length != 0 and m_padded_length != 0: + # dim order is reversed for constant_pad_nd, for every dim we specify right and left padding + pad_arg = [0, k_padded_length, 0, m_padded_length] + if is_bmm: + pad_arg.extend((0, 0)) + return aten.constant_pad_nd(mat1, pad_arg) + elif m_padded_length != 0: + return pad_dim(mat1, m_padded_length, 0 if not is_bmm else 1) + else: + assert k_padded_length != 0 + return pad_dim(mat1, k_padded_length, 1 if not is_bmm else 2) + + +def pad_mat2(mat2, *, k_padded_length, n_padded_length, is_bmm=False): + if k_padded_length == 0 and n_padded_length == 0: + return mat2 + elif k_padded_length != 0 and n_padded_length != 0: + # dim order is reversed for constant_pad_nd, for every dim we specify right and left padding + pad_arg = [0, n_padded_length, 0, k_padded_length] + if is_bmm: + pad_arg.extend((0, 0)) + return aten.constant_pad_nd(mat2, pad_arg) + elif k_padded_length != 0: + return pad_dim(mat2, k_padded_length, 0 if not is_bmm else 1) + else: + assert n_padded_length != 0 + return pad_dim(mat2, n_padded_length, 1 if not is_bmm else 2) + + +def pad_mm( + mat1: Tensor, + mat2: Tensor, + m_padded_length: int, + k_padded_length: int, + n_padded_length: int, + mat1_pre_padded: bool = False, + mat2_pre_padded: bool = False, +) -> Tensor: + if not mat1_pre_padded: + mat1 = pad_mat1( + mat1, m_padded_length=m_padded_length, k_padded_length=k_padded_length + ) + if not mat2_pre_padded: + mat2 = pad_mat2( + mat2, k_padded_length=k_padded_length, n_padded_length=n_padded_length + ) + res = aten.mm(mat1, mat2) + if m_padded_length != 0: + res = res[:-m_padded_length, :] + if n_padded_length != 0: + res = res[:, :-n_padded_length] + return res + + +def mm_replace(mat1: Tensor, mat2: Tensor) -> Tensor: + k_padded_length = get_padded_length(mat1.shape[1], get_alignment_size(mat1)) + m_padded_length = get_padded_length(mat1.shape[0], get_alignment_size(mat1)) + n_padded_length = get_padded_length(mat2.shape[1], get_alignment_size(mat2)) + return pad_mm( + mat1, + mat2, + m_padded_length, + k_padded_length, + n_padded_length, + ) + + +def bmm_pattern(mat1: Tensor, mat2: Tensor) -> Tensor: + return aten.bmm(mat1, mat2) + + +def should_pad_bmm(match: Match) -> bool: + mat1, mat2 = fetch_fake_tensors(match, ("mat1", "mat2")) + return should_pad_common(mat1, mat2) and should_pad_bench( + match, mat1, mat2, torch.ops.aten.bmm + ) + + +def pad_bmm( + mat1: Tensor, + mat2: Tensor, + m_padded_length: int, + k_padded_length: int, + n_padded_length: int, + mat1_pre_padded: bool = False, + mat2_pre_padded: bool = False, +) -> Tensor: + if not mat1_pre_padded: + mat1 = pad_mat1( + mat1, + m_padded_length=m_padded_length, + k_padded_length=k_padded_length, + is_bmm=True, + ) + if not mat2_pre_padded: + mat2 = pad_mat2( + mat2, + k_padded_length=k_padded_length, + n_padded_length=n_padded_length, + is_bmm=True, + ) + res = aten.bmm(mat1, mat2) + if m_padded_length != 0: + res = res[:, :-m_padded_length, :] + if n_padded_length != 0: + res = res[:, :, :-n_padded_length] + return res + + +def bmm_replace(mat1: Tensor, mat2: Tensor) -> Tensor: + k_padded_length = get_padded_length(mat1.shape[2], get_alignment_size(mat1)) + n_padded_length = get_padded_length(mat2.shape[2], get_alignment_size(mat2)) + m_padded_length = get_padded_length(mat1.shape[1], get_alignment_size(mat1)) + return pad_bmm( + mat1, + mat2, + m_padded_length, + k_padded_length, + n_padded_length, + ) + + +@functools.lru_cache(None) +def _pad_mm_init(): + from .joint_graph import patterns + + if torch.cuda.is_available(): + # workaround https://github.com/pytorch/pytorch/issues/97894 + device = "cuda" + else: + device = "cpu" + + # sizes/values dont actually matter for initial trace + # once we get a possible match we re-trace with the actual values and verify the match still holds + + dim2a = functools.partial(torch.empty, (4, 4), device=device, requires_grad=True) + dim2b = functools.partial(torch.empty, (4, 4), device=device, requires_grad=True) + + dim3a = functools.partial(torch.empty, (4, 4, 4), device=device, requires_grad=True) + dim3b = functools.partial(torch.empty, (4, 4, 4), device=device, requires_grad=True) + + dim1a = functools.partial(torch.empty, (4), device=device, requires_grad=True) + + # workaround https://github.com/pytorch/pytorch/issues/97894 + # 0.113377 is a "magic" value that lets us recover the lost input arg relationship + rep = {"beta": 0.213377, "alpha": 0.113377} + + for pattern, replacement, args, workaround, extra_check in [ + ( + typing.cast(SearchFn, mm_pattern), + typing.cast(ReplaceFn, mm_replace), + [dim2a(), dim2b()], + {}, + should_pad_mm, + ), + ( + typing.cast(SearchFn, bmm_pattern), + typing.cast(ReplaceFn, bmm_replace), + [dim3a(), dim3b()], + {}, + should_pad_bmm, + ), + ( + typing.cast(SearchFn, addmm_pattern), + typing.cast(ReplaceFn, addmm_replace), + [dim1a(), dim2a(), dim2b()], + rep, + should_pad_addmm, + ), + ]: + assert isinstance(workaround, dict) # mypy is unable to infer the type properly + name = pattern.__name__ + + gen_register_replacement( + f"{name}_training", + pattern, + replacement, + args, + joint_fwd_bwd, + patterns, + extra_check=extra_check, + scalar_workaround=workaround, + ) + + gen_register_replacement( + f"{name}_inference", + pattern, + replacement, + args, + fwd_only, + patterns, + extra_check=extra_check, + scalar_workaround=workaround, + ) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/post_grad.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/post_grad.py new file mode 100644 index 0000000000000000000000000000000000000000..4e73931fcef23b606a56c977a36747ca1eefc3ad --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/post_grad.py @@ -0,0 +1,1318 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import functools +import itertools +import logging +import operator +from collections import Counter, defaultdict +from typing import Any, Dict, List, Optional, Set, TYPE_CHECKING, Union + +import torch +import torch._inductor as inductor +import torch.utils._pytree as pytree +from torch import fx +from torch._decomp import register_decomposition +from torch._dynamo.utils import counters, optimus_scuba_log +from torch._inductor import comms +from torch._inductor.virtualized import ops +from torch._prims_common import is_boolean_dtype, is_expandable_to, is_integer_dtype +from torch._utils_internal import upload_graph +from torch.fx.experimental.symbolic_shapes import statically_known_true, sym_eq +from torch.fx.passes.graph_transform_observer import GraphTransformObserver + +from .. import config, ir, pattern_matcher +from ..codegen.common import BackendFeature, has_backend_feature +from ..fx_utils import FakeTensorUpdater, get_fake_args_kwargs, get_node_storage +from ..lowering import lowerings as L +from ..pattern_matcher import ( + _return_true, + Arg, + CallFunction, + CallFunctionVarArgs, + filter_nodes, + get_arg_value, + get_mutation_region_id, + Ignored, + init_once_fakemode, + KeywordArg, + ListOf, + Match, + MULTIPLE, + PatternMatcherPass, + register_graph_pattern, + stable_topological_sort, +) +from ..utils import decode_device, get_gpu_type, is_pointwise_use +from ..virtualized import V +from .b2b_gemm import B2B_GEMM_PASS +from .ddp_fusion import fuse_ddp_communication +from .group_batch_fusion import group_batch_fusion_passes, POST_GRAD_FUSIONS +from .micro_pipeline_tp import micro_pipeline_tp_pass +from .pre_grad import is_same_dict, save_inductor_dict +from .reinplace import reinplace_inplaceable_ops +from .split_cat import POST_GRAD_PATTERNS + + +if TYPE_CHECKING: + from sympy import Expr + + +log = logging.getLogger(__name__) +aten = torch.ops.aten +prims = torch.ops.prims + +# First pass_patterns[0] are applied, then [1], then [2] +pass_patterns = [ + PatternMatcherPass(), + PatternMatcherPass(), + PatternMatcherPass(), +] + + +def post_grad_passes(gm: torch.fx.GraphModule, is_inference: bool): + """ + Passes that run on after grad. This is called once on the forwards + graph and once on the backwards graph. + + The IR here has been normalized and functionalized. + """ + if config.dce: + # has some issues with mutation in inference mode + gm.graph.eliminate_dead_code() + + if is_inference and config.reorder_for_locality: + reorder_for_locality(gm.graph) + + fake_tensor_updater = FakeTensorUpdater(gm.graph) + + if config.post_grad_custom_pre_pass is not None: + with GraphTransformObserver( + gm, "post_grad_custom_pre_pass", config.trace.log_url_for_graph_xform + ): + config.post_grad_custom_pre_pass(gm.graph) + + if config.pattern_matcher: + lazy_init() + optimus_scuba_log["before_recompile_post_grad"] = upload_graph(gm.graph) + group_batch_fusion_passes(gm.graph, pre_grad=False) + remove_noop_ops(gm.graph) + for patterns in pass_patterns: + patterns.apply(gm.graph) # type: ignore[arg-type] + for pass_name in config.post_grad_fusion_options: + # skip all patterns for group batch fusions + if pass_name in POST_GRAD_FUSIONS: + continue + pattern_matcher_pass = POST_GRAD_PATTERNS[pass_name] + inductor_before_change = save_inductor_dict( + [pattern_matcher_pass.pass_name] + ) + pattern_matcher_pass.apply(gm.graph) # type: ignore[arg-type] + if not is_same_dict(counters["inductor"], inductor_before_change): + optimus_scuba_log[ + f"{pattern_matcher_pass.pass_name}_post_grad" + ] = upload_graph(gm.graph) + if config.b2b_gemm_pass: + B2B_GEMM_PASS.apply(gm.graph) # type: ignore[arg-type] + + if config._micro_pipeline_tp: + micro_pipeline_tp_pass(gm.graph) + + if config._fuse_ddp_communication: + fuse_ddp_communication( + gm.graph, + config._fuse_ddp_communication_passes, + config._fuse_ddp_bucket_size, + ) + + if config.post_grad_custom_post_pass is not None: + with GraphTransformObserver( + gm, "post_grad_custom_post_pass", config.trace.log_url_for_graph_xform + ): + config.post_grad_custom_post_pass(gm.graph) + + stable_topological_sort(gm.graph) + + move_constructors_to_gpu(gm.graph) + + fake_tensor_updater.incremental_update() + + # Keep these last, since they introduces mutation. Look at + # ./fx_passes/README.md for a discussion of mutation invariants. + reinplace_inplaceable_ops(gm.graph) + decompose_auto_functionalized(gm.graph) + + comms.reinplace_fsdp_all_gather(gm.graph) + + gm.recompile() + optimus_scuba_log["after_recompile_post_grad"] = upload_graph(gm.graph) + gm.graph.lint() + + +@init_once_fakemode +def lazy_init(): + if torch._C._has_mkldnn: + from . import decompose_mem_bound_mm # noqa: F401 + from .mkldnn_fusion import _mkldnn_fusion_init + + _mkldnn_fusion_init() + + +def reorder_for_locality(graph: torch.fx.Graph): + def visit(other_node): + if ( + other_node.op == "call_function" + and other_node.target != operator.getitem + and all((n in seen_nodes) for n in other_node.users) + and get_mutation_region_id(graph, node) + == get_mutation_region_id(graph, other_node) + ): + # move node's producers right before it + node.prepend(other_node) + + seen_nodes = set() + + # only reorder nodes before the first copy_ in the graph. + # copy_ will appear at the end of functionalized graphs when there is mutation on inputs, + # and this reordering doesnt work well with mutation + first_copy = next( + iter(graph.find_nodes(op="call_function", target=torch.ops.aten.copy_.default)), + None, + ) + past_mutating_epilogue = True if first_copy is None else False + + for node in reversed(graph.nodes): + seen_nodes.add(node) + if not past_mutating_epilogue: + past_mutating_epilogue = node is first_copy + continue + + torch.fx.map_arg((node.args, node.kwargs), visit) + + +def register_lowering_pattern(pattern, extra_check=_return_true, pass_number=1): + """ + Register an aten to inductor IR replacement pattern + """ + return pattern_matcher.register_lowering_pattern( + pattern, extra_check, pass_dict=pass_patterns[pass_number] + ) + + +################################################################################ +# Actual patterns below this point. +# Priority of patterns is: +# - later output nodes first +# - order patterns are defined in +################################################################################ + + +def is_valid_mm_plus_mm(match: Match): + *b1, m1, k1 = match.kwargs["mat1"].meta.get("tensor_meta").shape + *b2, k2, n1 = match.kwargs["mat2"].meta.get("tensor_meta").shape + if k1 != k2: + return False + + *b1, m2, k3 = match.kwargs["mat3"].meta.get("tensor_meta").shape + *b2, k4, n2 = match.kwargs["mat4"].meta.get("tensor_meta").shape + if k3 != k4: + return False + + if m1 != m2 or n1 != n2: + return False + + return True + + +def scatter_upon_const_tensor_extra_check(m): + if not config.optimize_scatter_upon_const_tensor: + return False + full_shape = m.kwargs["shape"] + selector = m.kwargs["selector"] + dim = m.kwargs["dim"] + if dim < 0: + dim += len(full_shape) + + selector_ft = selector.meta["val"] + assert selector_ft.dim() == len(full_shape) + + for idx, select_sz, full_sz in zip( + itertools.count(), selector_ft.shape, full_shape + ): + if idx == dim: + continue + + # TODO: the pattern can be updated to support the case that index tensor + # is shorter. But that will need a more complex condition expression + # especially for multi-dimensional tensors. + # Skip it for now. + if isinstance(full_sz, fx.Node): + full_sz = full_sz.meta["val"] + if select_sz < full_sz: + return False + + # Actually we can support small size larger than 1. It would be a bit + # tedius. E.g., we load all the index values (not many) and compare + # them with the position in tensor to decide what value to return. + return selector_ft.size(dim) == 1 + + +@register_lowering_pattern( + CallFunction( + aten.scatter.value, + CallFunction( + aten.full, + KeywordArg("shape"), + KeywordArg("background_val"), + dtype=KeywordArg("dtype"), + ), + KeywordArg("dim"), + KeywordArg("selector"), + KeywordArg("val"), # scalar value + ), + extra_check=scatter_upon_const_tensor_extra_check, +) +def scatter_upon_const_tensor( + match: Match, shape, background_val, dtype, dim, selector, val +): + """ + Match the pattern of full+scatter into a pointwise. + + TODO: Right now the scatter value must be a scalar. But we could support it + when it is a tensor as well. + """ + from torch._inductor import metrics + + metrics.num_matches_for_scatter_upon_const_tensor += 1 + + selector_loader = selector.make_loader() + + def inner_fn(idx): + selector_idx = list(idx) + selector_idx[dim] = 0 + + selector = selector_loader(selector_idx) + return ops.where( + selector == ops.index_expr(idx[dim], torch.int64), + ops.constant(val, dtype), + ops.constant(background_val, dtype), + ) + + return ir.Pointwise.create( + device=selector.get_device(), + dtype=dtype, + inner_fn=inner_fn, + ranges=shape, + ) + + +@register_lowering_pattern( + CallFunction( + aten.add, + CallFunction(aten.mm, KeywordArg("mat1"), KeywordArg("mat2")), + CallFunction(aten.mm, KeywordArg("mat3"), KeywordArg("mat4")), + ), + extra_check=is_valid_mm_plus_mm, +) +def mm_plus_mm(match: Match, mat1, mat2, mat3, mat4): + return inductor.kernel.mm_plus_mm.tuned_mm_plus_mm(mat1, mat2, mat3, mat4) + + +def cuda_and_enabled_mixed_mm(match): + return ( + (config.use_mixed_mm or config.mixed_mm_choice != "default") + and getattr(match.kwargs["mat1"].meta.get("val"), "is_cuda", False) + and ( + match.kwargs["mat2_dtype"].itemsize + > match.kwargs["mat2"].meta.get("val").dtype.itemsize + ) + and has_backend_feature("cuda", BackendFeature.TRITON_TEMPLATES) + ) + + +def cuda_and_enabled_mixed_mm_and_not_int8(match): + return ( + cuda_and_enabled_mixed_mm(match) + and getattr(match.kwargs["mat1"].meta.get("val"), "is_cuda", False) + and getattr(match.kwargs["mat2"].meta.get("val"), "dtype", torch.int8) + != torch.int8 + ) # bitshift numerics in triton and pytorch don't match for torch.int8 + + +""" + this is intended to be used to unpack a [K,N] int4 tensor from a [K/2, N] uint4x2 tensor + (where the int4 and uint4x2 are represented with int8 and uint8 respectively) + where every other row of the int4 is packed with the row above it as: + uint4x2[k,n] = (8+int4[2*k,n])+(8+int4[2*k+1,n])<<4 + + unpack formulas: + int4[2*k,n]=(uint4x2[k,n] & 0xF) - 8 + int4[2*k+1,n]=(uint4x2[k,n] >> 4) - 8 + + thus matching on unpack formula: + torch.mm(mat1, torch.cat((mat2 & 0xF, mat2>>4),1).reshape(mat2_mm_shape).to(mat2_dtype).sub(8)) + + note: although the unpack formula in pytorch and the triton kernel is designed for a uint8 mat2, the behavior + of the kernel matches the pytorch formula for all dtypes except torch.int8 + where the bitwise numerics in triton do not match those in pytorch. +""" + + +@register_lowering_pattern( + CallFunction( + aten.mm.default, + KeywordArg("mat1"), + CallFunction( + aten.sub.Tensor, + CallFunction( + prims.convert_element_type.default, + CallFunction( + aten.reshape.default, + CallFunction( + aten.cat.default, + ListOf( + CallFunction( + aten.bitwise_and.Scalar, + KeywordArg("mat2"), + 0xF, + ), + # CallFunction( + # aten.__rshift__.Scalar, + # KeywordArg("mat2"), + # 4, + # ), + True, + ), + 1, + ), + KeywordArg("mat2_mm_shape"), + ), + KeywordArg("mat2_dtype"), + ), + 8, + ), + ), + extra_check=cuda_and_enabled_mixed_mm_and_not_int8, +) +def uint4x2_mixed_mm(match: Match, mat1, mat2, mat2_mm_shape, mat2_dtype): + return inductor.kernel.unpack_mixed_mm.tuned_uint4x2_mixed_mm( + mat1, mat2, mat2_mm_shape, mat2_dtype + ) + + +""" + torch.mm(mat1, mat2.to(mat2_dtype)) +""" + + +@register_lowering_pattern( + CallFunction( + aten.mm, + KeywordArg("mat1"), + CallFunction( + prims.convert_element_type.default, + KeywordArg("mat2"), + KeywordArg("mat2_dtype"), + ), + ), + extra_check=cuda_and_enabled_mixed_mm, +) +def mixed_mm(match: Match, mat1, mat2, mat2_dtype): + return inductor.kernel.mm.tuned_mixed_mm(mat1, mat2, mat2_dtype) + + +@register_graph_pattern( + CallFunction( + aten.cumsum.default, + CallFunction( + torch.ops.aten.full.default, + KeywordArg("shape"), + KeywordArg("fill_value"), + dtype=KeywordArg("dtype"), + layout=Ignored(), + device=KeywordArg("device"), + pin_memory=False, + _users=MULTIPLE, + ), + KeywordArg("dim"), + _users=MULTIPLE, + ), + pass_dict=pass_patterns[1], +) +def pointless_cumsum_replacement(match: Match, shape, fill_value, device, dtype, dim): + """Based on a pattern in OPTForCausalLM""" + + if is_integer_dtype(dtype) or is_boolean_dtype(dtype): + # cumsum promotes all integral types to int64 + dtype = torch.int64 + + def repl(*shape): + dim_size = shape[dim] + idx = torch.arange(1, dim_size + 1, device=device, dtype=dtype) + + inter_shape = [1] * len(shape) + inter_shape[dim] = dim_size + return (idx * fill_value).view(inter_shape).expand(shape) + + # only replace the output node, not all nodes + match.nodes = [match.output_node()] + match.replace_by_example(repl, list(shape)) + + +def shape_of_mm(a, b): + m, _ = a.get_size() + _, n = b.get_size() + return [m, n] + + +@register_lowering_pattern( + CallFunction(aten.cat, ListOf(CallFunction(aten.mm, Arg(), Arg())), Arg()), +) +def cat_mm(match, inputs, dim): + return cat_tuned_op(match, inputs, dim, op=L[aten.mm], shape_of=shape_of_mm) + + +@register_lowering_pattern( + CallFunction( + aten.cat, ListOf(CallFunction(aten.addmm, Arg(), Arg(), Arg())), Arg() + ), +) +def cat_addmm(match, inputs, dim): + def shape_of(bias, a, b): + m, _ = a.get_size() + _, n = b.get_size() + return [m, n] + + return cat_tuned_op(match, inputs, dim, op=L[aten.addmm], shape_of=shape_of) + + +def cat_tuned_op(match, inputs, dim, *, op, shape_of): + """ + Memory planning to remove cat. We can't use the stock memory + planner since autotuning matmuls needs to know the output layout. + """ + if len(inputs) == 1: + return op(*inputs[0]) + + # TODO(jansel): rewrite this as a bmm? + if dim < 0: + dim += len(shape_of(*inputs[0])) + assert dim in (0, 1) + notdim = 1 - dim + + new_size: Optional[Union[List[Expr], List[int]]] = None + offsets_start = [] + offsets_end = [] + + # compute output sizes + for i in range(len(inputs)): + shape = shape_of(*inputs[i]) + if new_size is None: + new_size = shape + else: + new_size[notdim] = V.graph.sizevars.guard_equals( # type: ignore[call-overload] + shape[notdim], new_size[notdim] + ) + new_size[dim] += shape[dim] + offsets_start.append(new_size[dim] - shape[dim]) + offsets_end.append(new_size[dim]) + + assert new_size is not None + dtype = functools.reduce( + torch.promote_types, + [x.get_dtype() for x in itertools.chain.from_iterable(inputs)], + ) + device = inputs[0][0].get_device() + kernel = ir.ConcatKernel( + name=None, + layout=ir.FixedLayout(device, dtype, new_size), + inputs=[], + ) + kernel_tensor = ir.TensorBox.create(kernel) + + for i in range(len(inputs)): + dst = ir.SliceView.create(kernel_tensor, dim, offsets_start[i], offsets_end[i]) + src = op(*inputs[i], layout=dst.get_layout()).data.data + assert isinstance(src, (ir.ExternKernelOut, ir.TemplateBuffer)) + src.layout = ir.NonOwningLayout(dst) + kernel.inputs.append(src) + + kernel.name = V.graph.register_buffer(kernel) + kernel.inputs = ir.ConcatKernel.unwrap_storage(kernel.inputs) + V.graph.register_operation(kernel) + return kernel_tensor + + +_cat_1 = CallFunction(aten.cat, Arg(), 1, _users=2) + + +@register_lowering_pattern( + CallFunction( + aten.cat, + [ + _cat_1, + CallFunction( + aten.slice, + _cat_1, + 1, + 0, + KeywordArg("size"), + ), + ], + 1, + ) +) +def cat_slice_cat(match, cat_input, size, dim=1): + """ + This is an example of a more complex pattern where cat_1 is used + multiple times inside the pattern. We fold 2 calls to cat into one. + + Matches: + cat_1: f32[1024, 4077] = torch.ops.aten.cat.default([add_26, primals_217], 1) + slice_1: f32[1024, 4077] = torch.ops.aten.slice.Tensor(cat_1, 0, 0, 9223372036854775807) + slice_2: f32[1024, 19] = torch.ops.aten.slice.Tensor(slice_1, 1, 0, 19) + cat_2: f32[1024, 4096] = torch.ops.aten.cat.default([cat_1, slice_2], 1) + + + Rewrite to: + slice_2 = torch.ops.aten.slice.Tensor(add_26, 1, 0, 19) + cat_2 = torch.ops.aten.cat.default([add_26, primals_217, slice2], 1) + """ + first, *rest = cat_input + # Optimization is optional, because we can just not fold the cat + # size should be within first.get_size()[dim] such that the optimization is valid. + # For negative `end`, we currently fallback to not optimizing. + if size >= 0 and V.graph.sizevars.statically_known_leq(size, first.get_size()[dim]): + # fold 2 cats into 1 cat + return L[aten.cat]( + [ + first, + *rest, + L[aten.slice](first, dim, 0, size), + ], + dim, + ) + else: + # don't expect to hit this case, just fall back + tmp = L[aten.cat](cat_input, dim) + return L[aten.cat]( + [ + tmp, + L[aten.slice](tmp, dim, 0, size), + ], + dim, + ) + + +def is_valid_splitwithsizes_cat(match): + split_nodes = filter_nodes(match.nodes, aten.split_with_sizes) + cat_nodes = filter_nodes(match.nodes, aten.cat) + get_item_nodes = filter_nodes(match.nodes, operator.getitem) + if len(split_nodes) != 1 or len(cat_nodes) != 1: + return False + split_node, cat_node = split_nodes[0], cat_nodes[0] + # The dim of split and cat should match for passthrough + if get_arg_value(split_node, 2, "dim") != get_arg_value(cat_node, 1, "dim"): + return False + get_item_args = { + get_arg_value(get_item_node, 1) for get_item_node in get_item_nodes + } + assert None not in get_item_args + split_sizes = get_arg_value(split_node, 1, "split_sizes") + # All parts of split should be included in the cat + if get_item_args != set(range(len(split_sizes))): + return False + # The order of get_item_args should same with cat_node used. + # For example, if the split_node like split_with_sizes(input, [2, 2, 3], 1), + # the cat node should be like cat([get_item(0), get_item(1), get_item(2)], 1). + cat_items_args_order = [ + get_arg_value(item_node, 1) for item_node in get_arg_value(cat_node, 0) + ] + if cat_items_args_order != list(range(len(split_sizes))): + return False + + return True + + +def same_meta(node1: torch.fx.Node, node2: torch.fx.Node): + """True if two nodes have the same metadata""" + val1 = node1.meta.get("val") + val2 = node2.meta.get("val") + return ( + val1 is not None + and val2 is not None + and statically_known_true(sym_eq(val1.size(), val2.size())) + and val1.layout == val2.layout + and val1.dtype == val2.dtype + and val1.device == val2.device + and ( + val1.layout != torch.strided + or statically_known_true(sym_eq(val1.stride(), val2.stride())) + ) + ) + + +noop_registry: Dict[Any, Any] = {} + + +def register_noop_decomp(targets, nop_arg=0): + def register_fun(cond): + register_decomposition(targets, registry=noop_registry, unsafe=True)( + (cond, nop_arg) # type: ignore[arg-type] + ) + return cond + + return register_fun + + +@register_noop_decomp(aten.slice) +def slice_noop(self, dim=0, start=None, end=None, step=1): + if start is None or end is None: + return False + if ( + statically_known_true(sym_eq(start, 0)) + and statically_known_true(end >= 2**63 - 1) + and statically_known_true(sym_eq(step, 1)) + ): + return True + return False + + +@register_noop_decomp(aten.slice_scatter, 1) +def slice_scatter_noop(self, src, dim=0, start=None, end=None, step=1): + if start is None: + start = 0 + if end is None: + end = 2**63 - 1 + if start == 0 and end >= 2**63 - 1 and step == 1: + return True + return False + + +@register_noop_decomp(aten.repeat) +def repeat_noop(self, repeats): + return all(r == 1 for r in repeats) + + +@register_noop_decomp(aten.constant_pad_nd) +def constant_pad_nd(x, padding, fill_value=0): + return all(p == 0 for p in padding) + + +@register_noop_decomp(torch.ops.prims.convert_element_type) +def convert_element_type_noop(x, dtype: torch.dtype): + return x.dtype == dtype + + +@register_noop_decomp(torch.ops.prims.device_put) +def device_put_noop(x, device): + return x.device == decode_device(device) + + +@register_noop_decomp([aten.ceil, aten.floor, aten.round, aten.trunc]) +def int_noop(x): + return is_integer_dtype(x.dtype) + + +@register_noop_decomp([aten.pow]) +def pow_noop(a, b): + return isinstance(b, int) and b == 1 + + +@register_noop_decomp([aten.cat], lambda args: args[0][0]) +def cat_noop(inputs, dim=0): + return len(inputs) == 1 + + +@register_noop_decomp(aten.view) +def view_noop(arg, size): + return arg.shape == size + + +# Note, we also always have a check for identical metadata, which is why these +# are safe +@register_noop_decomp([aten.copy], nop_arg=1) +@register_noop_decomp([aten.alias, aten.clone]) +def true_noop(*args, **kwargs): + return True + + +def remove_noop_ops(graph: torch.fx.Graph): + """ + Removes both operations that are essentially aten.clone and operations that are essentially aten.alias from the graph. + """ + inputs = set() + input_storages = set() + output_storages = set() + + for node in graph.find_nodes(op="placeholder"): + inputs.add(node) + input_storages.add(get_node_storage(node)) + + output_node = next(iter(reversed(graph.nodes))) + assert output_node.op == "output" + outputs = output_node.args[0] + if not isinstance(outputs, (list, tuple)): + # nested subgraphs can have singleton outputs + outputs = (outputs,) + for out in outputs: + if isinstance(out, torch.fx.Node): + output_storages.add(get_node_storage(out)) + + for node in graph.nodes: + if node.target in noop_registry: + cond, src_index = noop_registry[node.target] + if isinstance(src_index, int): + src = node.args[src_index] + else: + src = src_index(node.args) + if not isinstance(src, torch.fx.Node): + continue + # Don't introduce new aliasing between inputs and outputs. + # See fx_passes/README.md for a discussion of why this is + # necessary. + node_storage = get_node_storage(node) + src_storage = get_node_storage(src) + node_is_view = node_storage == src_storage + if ( + not node_is_view + and node_storage in output_storages + and (src_storage in input_storages or src_storage in output_storages) + ): + continue + + # Even if input and outputs are expected to alias, + # don't make "node is src" True + if ( + node_is_view + and node in output_node.args + and (src in inputs or src in output_node.args) + ): + continue + + is_valid, args, kwargs = get_fake_args_kwargs(node) + if not is_valid: + continue + if same_meta(node, src) and cond(*args, **kwargs): + node.replace_all_uses_with(src) + graph.erase_node(node) + + +def decompose_auto_functionalized(graph): + """Decomposes auto_functionalized and triton_kernel_wrapper_functional + nodes into clones and the underlying mutation node. + + We assume that the reinplacing pass runs before this; the reinplacing pass + tells us (via rewriting the arguments or .meta to those nodes) which + Tensors we should clone and which Tensors are safe to reinplace. + """ + graph_pass = PatternMatcherPass() + + @register_graph_pattern( + CallFunctionVarArgs(torch.ops.higher_order.auto_functionalized), + pass_dict=graph_pass, + ) + def _(match: Match, *args, **kwargs): + from torch._higher_order_ops.auto_functionalize import auto_functionalized_dense + + only_clone_these_tensors = tuple( + match.nodes[0].meta.get("only_clone_these_tensors", []) + ) + + flat_args, spec = pytree.tree_flatten((args, kwargs)) + + # NB: we combine (args, kwargs) into flat args for replacing. + # This is replace_by_example uses make_fx which does not support + # tracing a function with kwargs. + def decomp(*flat_args): + args, kwargs = pytree.tree_unflatten(flat_args, spec) + return auto_functionalized_dense(*args, only_clone_these_tensors, **kwargs) + + match.replace_by_example(decomp, flat_args, run_functional_passes=False) + + @register_graph_pattern( + CallFunctionVarArgs(torch.ops.higher_order.triton_kernel_wrapper_functional), + pass_dict=graph_pass, + ) + def _(match: Match, *args, **kwargs): + from torch._higher_order_ops.triton_kernel_wrap import ( + triton_kernel_wrapper_functional_dense, + ) + + flat_args, spec = pytree.tree_flatten((args, kwargs)) + + # NB: we combine (args, kwargs) into flat args for replacing. + # This is replace_by_example uses make_fx which does not support + # tracing a function with kwargs. + def decomp(*flat_args): + args, kwargs = pytree.tree_unflatten(flat_args, spec) + return (triton_kernel_wrapper_functional_dense(*args, **kwargs),) + + match.replace_by_example(decomp, flat_args, run_functional_passes=False) + + @register_graph_pattern( + CallFunctionVarArgs(torch.ops.higher_order.auto_functionalized_v2), + pass_dict=graph_pass, + ) + def _(match: Match, *args, **kwargs): + from torch._higher_order_ops.auto_functionalize import ( + auto_functionalized_v2_dense, + ) + + only_clone_these_bases = tuple( + match.nodes[0].meta.get("only_clone_these_tensors", []) + ) + + flat_args, spec = pytree.tree_flatten((args, kwargs)) + + # NB: we combine (args, kwargs) into flat args for replacing. + # This is replace_by_example uses make_fx which does not support + # tracing a function with kwargs. + def decomp(*flat_args): + args, kwargs = pytree.tree_unflatten(flat_args, spec) + return auto_functionalized_v2_dense(*args, only_clone_these_bases, **kwargs) + + match.replace_by_example(decomp, flat_args, run_functional_passes=False) + + graph_pass.apply(graph) + + for node in graph.find_nodes( + op="call_function", target=torch.ops.higher_order.auto_functionalized + ): + raise AssertionError("auto_functionalized was not removed") + + for node in graph.find_nodes( + op="call_function", target=torch.ops.higher_order.auto_functionalized_v2 + ): + raise AssertionError("auto_functionalized_v2 was not removed") + + for node in graph.find_nodes( + op="call_function", + target=torch.ops.higher_order.triton_kernel_wrapper_functional, + ): + raise AssertionError("triton_kernel_wrapper_functional was not removed") + + +@register_lowering_pattern( + CallFunction( + aten.cat, + ListOf( + CallFunction( + operator.getitem, + CallFunction( + aten.split_with_sizes, + KeywordArg("input_"), + Ignored(), + Ignored(), + _users=MULTIPLE, + ), + Ignored(), + ), + ), + Ignored(), + ), + pass_number=2, + extra_check=is_valid_splitwithsizes_cat, +) +def splitwithsizes_cat_replace(match, input_): + return input_ + + +def is_valid_cat_splitwithsizes(match): + cat_nodes = filter_nodes(match.nodes, aten.cat) + split_nodes = filter_nodes(match.nodes, aten.split_with_sizes) + if len(split_nodes) != 1 or len(cat_nodes) != 1: + return False + split_node, cat_node = split_nodes[0], cat_nodes[0] + + # the cat node has other users: can't eliminate + if len(cat_node.users) > 1: + return False + + # the dim of the cat and split should match + dim = get_arg_value(split_node, 2, "dim") + if dim != get_arg_value(cat_node, 1, "dim"): + return False + + cat_inputs = list(get_arg_value(cat_node, 0)) + split_sizes = get_arg_value(split_node, 1, "split_sizes") + # the number of input tensors in cat and the + # length of the split sizes should match + if len(cat_inputs) != len(split_sizes): + return False + + for cat_input, split_size in zip(cat_inputs, split_sizes): + # each cat input tensor's size along dim + # should match the corresponding split size + if "val" not in cat_input.meta: + return False + cat_input_size = cat_input.meta["val"].size(dim) + if cat_input_size != split_size: + return False + + return True + + +@register_lowering_pattern( + CallFunction( + aten.split_with_sizes, + CallFunction( + aten.cat, + KeywordArg("input_"), + Ignored(), + _users=MULTIPLE, + ), + Ignored(), + Ignored(), + ), + pass_number=2, + extra_check=is_valid_cat_splitwithsizes, +) +def cat_splitwithsizes_replace(match, input_): + return input_ + + +def view_to_reshape(gm): + """ + Replace view ops in the GraphModule to reshape ops. + """ + for nd in gm.graph.find_nodes( + op="call_function", target=torch.ops.aten.view.default + ): + nd.target = torch.ops.aten.reshape.default + + +def should_prefer_unfused_addmm(match): + inp = match.kwargs["inp"] + if not inp.meta["val"].is_cuda: + return False + + output = match.output_node() + return all(is_pointwise_use(use) for use in output.users) + + +@register_graph_pattern( + CallFunction(aten.addmm, KeywordArg("inp"), Arg(), Arg()), + pass_dict=pass_patterns[2], + extra_check=should_prefer_unfused_addmm, +) +def unfuse_bias_add_to_pointwise(match: Match, mat1, mat2, *, inp): + def repl(inp, x1, x2): + return x1 @ x2 + inp + + match.replace_by_example(repl, [inp, mat1, mat2]) + + +def is_valid_addmm_fusion(match): + mat1, mat2 = match.args + inp = match.kwargs["inp"] + + if not ( + isinstance(inp, torch.fx.Node) and isinstance(inp.meta["val"], torch.Tensor) + ): + return False # Input is a number + + in_shape = inp.meta["val"].shape + mm_shape = mat1.meta["val"].shape[0], mat2.meta["val"].shape[1] + matched = is_expandable_to(in_shape, mm_shape) + if not matched: + return False # Shape mismatch + + return not should_prefer_unfused_addmm(match) + + +@register_graph_pattern( + CallFunction( + aten.add, + CallFunction(aten.mm, Arg(), Arg()), + KeywordArg("inp"), + ), + pass_dict=pass_patterns[2], + extra_check=is_valid_addmm_fusion, +) +@register_graph_pattern( + CallFunction( + aten.add, + KeywordArg("inp"), + CallFunction(aten.mm, Arg(), Arg()), + ), + pass_dict=pass_patterns[2], + extra_check=is_valid_addmm_fusion, +) +def addmm(match, mat1, mat2, *, inp): + def repl(inp, mat1, mat2): + return aten.addmm(inp, mat1, mat2) + + match.replace_by_example(repl, [inp, mat1, mat2]) + + +def check_shape_cuda_and_fused_int_mm_mul_enabled(match): + return ( + config.force_fuse_int_mm_with_mul + and len(getattr(match.args[2].meta.get("val"), "shape", [])) == 2 + and getattr(match.args[2].meta.get("val"), "is_cuda", False) + ) + + +@register_lowering_pattern( + CallFunction( + prims.convert_element_type.default, + CallFunction( + aten.mul, + CallFunction( + aten._int_mm, + Arg(), + Arg(), + ), + Arg(), + ), + Arg(), + ), + check_shape_cuda_and_fused_int_mm_mul_enabled, +) +@register_lowering_pattern( + CallFunction( + aten.mul, + CallFunction( + aten._int_mm, + Arg(), + Arg(), + ), + Arg(), + ), + check_shape_cuda_and_fused_int_mm_mul_enabled, +) +def fused_int_mm_mul(match: Match, mat1, mat2, mat3, out_dtype=None): + return inductor.kernel.mm.tuned_fused_int_mm_mul(mat1, mat2, mat3, out_dtype) + + +def is_index_put_and_requires_h2d_sync_for_gpu_value(node): + from torch.fx.operator_schemas import normalize_function + + if node.target not in [ + torch.ops.aten.index_put.default, + torch.ops.aten.index_put_.default, + ]: + return False + # Inductor falls back to aten.index_put_. + # index_put_ will will call nonzero() and perform a H2D sync if + # any of its indices are bool/byte tensors + # However, it will short-circuit this H2D sync and run mask_fill_ + # if the value we are putting is a cpu scalar. + # Therefore, when inductor sees an index_put_ with byte tensor indices, + # it should *not* convert the cpu scalar value into a gpu tensor. + args_, kwargs_ = normalize_function(node.target, node.args, node.kwargs) # type: ignore[misc] + any_byte_bool_indices = False + indices = args_[1] + for i in indices: + if i is not None and i.meta["val"].dtype in [torch.bool, torch.int8]: + any_byte_bool_indices = True + + val = args_[2].meta["val"] + val_is_cpu_scalar = val.device.type == "cpu" and val.numel() == 1 + # If both these conditions hold, then converting the val + # to a gpu tensor will incur a H2D sync when inductor calls aten.index_put_ + return any_byte_bool_indices and val_is_cpu_scalar + + +class ConstructorMoverPass: + def __init__(self, target: str, allow_outputs: bool = False) -> None: + """ + Move constructors from cpu to the target_device. + + Sweeps through the module, looking for constructor nodes that can be moved + to the target_device. + + A constructor node can be moved to the target_device iff all of its users + can also be moved (tested by cannot_be_moved). Otherwise, all dependent + constructor nodes won't be moved. + + - target: target device type + - allow_outputs: allow outputs to be moved + """ + + self.target = target + self.allow_outputs = allow_outputs + + assert isinstance(target, str), ( + "target should be a string representing the device type. " + f"Got: {type(target).__name__}" + ) + + def allow_cpu_device(self, node: fx.Node) -> bool: + """ + Returns whether a node that returns a tensor on the target device may have + cpu tensors as input. + """ + return node.target in ( + torch.ops.aten.index.Tensor, + torch.ops.aten.index_put.default, + torch.ops.aten.index_put_.default, + torch.ops.aten.copy.default, + torch.ops.aten.copy_.default, + torch.ops.aten.slice_scatter.default, + ) + + def cannot_be_moved(self, node: fx.Node) -> bool: + """ + Returns whether a node can be moved to the target device. + + If this function returns False, it means that this node and all of its users + won't be moved into the target device. + """ + if node.target == "output": + return not self.allow_outputs + + if not ( + isinstance(node.target, torch._ops.OpOverload) + and node.target.namespace in ("prims", "aten") + ): + return True + if is_index_put_and_requires_h2d_sync_for_gpu_value(node): + return True + + return False + + def get_node_device(self, node: fx.Node) -> Optional[torch.device]: + """ + Get the device of a node. + """ + ten = node.meta.get("val") + return None if not isinstance(ten, torch.Tensor) else ten.device + + def get_cpu_indeg_count(self, graph: fx.Graph) -> Dict[fx.Node, int]: + """ + Get the number of cpu inputs to a node + """ + cpu_indeg: Dict[fx.Node, int] = Counter() + + for node in graph.nodes: + cpu_count = 0 + + def add_cpu_inp(node): + nonlocal cpu_count + device = self.get_node_device(node) + cpu_count += device is not None and device.type == "cpu" + + pytree.tree_map_only(fx.Node, add_cpu_inp, (node.args, node.kwargs)) + + if cpu_count: + cpu_indeg[node] = cpu_count + + return cpu_indeg + + def __call__(self, graph: fx.Graph) -> None: + target_devices = set() + constructors = [] + + for node in graph.nodes: + device = self.get_node_device(node) + if device and device.type == self.target: + target_devices.add(device) + + if not ( + isinstance(node.target, torch._ops.OpOverload) + and node.target.namespace in ("prims", "aten") + ): + continue + + if not torch._subclasses.fake_tensor._is_tensor_constructor(node.target): + continue + + if not node.kwargs.get("device") == torch.device("cpu"): + continue + + constructors.append(node) + + # not handling multiple target devices initially + if not constructors or len(target_devices) != 1: + return + + movable_constructors = self.find_movable_constructors(graph, constructors) + + for node in movable_constructors: + kwargs = node.kwargs.copy() + kwargs["device"] = next(iter(target_devices)) + node.kwargs = kwargs + + def find_movable_constructors( + self, graph: fx.Graph, constructors: List[fx.Node] + ) -> Set[fx.Node]: + """ + Starting from the cpu constructors, iterate through the graph and test that all of their + downstream uses can safely be moved to cpu. + """ + cpu_indeg: Dict[fx.Node, int] = self.get_cpu_indeg_count(graph) + + # which constructors cannot be moved to gpu + cannot_move_to_gpu: Set[fx.Node] = set() + + # For any node in the graph, which constructors does it have a dependency on + constructor_dependencies: Dict[fx.Node, Set[fx.Node]] = defaultdict(set) + + # if a cpu node has a dependency on two different cpu constructors, + # then if either constructor cannot be moved to gpu, the other cannot as well. + # In this case any node with a dependency on one will have a dependency on the other + equal_constructor_sets: Dict[fx.Node, Set[fx.Node]] = { + c: {c} for c in constructors + } + + def make_dependencies_equivalent( + set1: Set[fx.Node], set2: Set[fx.Node] + ) -> Set[fx.Node]: + # could use union find but not worth complexity here + set1.update(set2) + for obj in set1: + equal_constructor_sets[obj] = set1 + return set1 + + queue: List[fx.Node] = list(constructors) + + for c in queue: + constructor_dependencies[c].add(c) + + while queue: + node = queue.pop() + dependencies = constructor_dependencies[node] + + for user in node.users: + if self.cannot_be_moved(user): + cannot_move_to_gpu.update(dependencies) + break + + # this node was used on a op which takes in multiple devices and output a gpu + # tensor. we can convert its cpu input to gpu without making further changes + node_device = self.get_node_device(user) + if ( + self.allow_cpu_device(user) + and node_device + and node_device.type == self.target + ): + del cpu_indeg[user] + else: + # otherwise, we should continue look at its downstream uses + cpu_indeg[user] -= 1 + if cpu_indeg[user] == 0: + del cpu_indeg[user] + queue.append(user) + + unioned_set = make_dependencies_equivalent( + dependencies, constructor_dependencies[user] + ) + constructor_dependencies[user] = unioned_set + + for node in cpu_indeg: + if constructor_dependencies[node]: + cannot_move_to_gpu.update(constructor_dependencies[node]) + + all_cannot_move_to_gpu = cannot_move_to_gpu.copy() + for constructor in cannot_move_to_gpu: + all_cannot_move_to_gpu.update(equal_constructor_sets[constructor]) + + return set(constructors) - all_cannot_move_to_gpu + + +def move_constructors_to_gpu(graph: fx.Graph) -> None: + """ + Moves intermediary tensors which are constructed on the cpu to gpu when safe + """ + ConstructorMoverPass(get_gpu_type())(graph) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/pre_grad.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/pre_grad.py new file mode 100644 index 0000000000000000000000000000000000000000..eef058ab0b5aa22dfd74c6bda72aefba5b61b443 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/pre_grad.py @@ -0,0 +1,800 @@ +# mypy: allow-untyped-defs +import copy +import itertools +import logging +from typing import Dict, Optional + +import torch +import torch.nn as nn +from torch._dynamo.utils import counters, detect_fake_mode, optimus_scuba_log +from torch._utils_internal import upload_graph +from torch.fx.experimental.optimization import ( + matches_module_pattern, + replace_node_module, +) +from torch.fx.passes.graph_transform_observer import GraphTransformObserver +from torch.fx.passes.shape_prop import ShapeProp +from torch.nn import functional as F +from torch.nn.utils.fusion import fuse_conv_bn_eval, fuse_conv_bn_weights + +from .. import config +from ..fx_utils import matches_module_function_pattern +from ..pattern_matcher import ( + init_once_fakemode, + PatternMatcherPass, + stable_topological_sort, +) +from ..utils import is_cpu_device, pass_execution_and_save +from .group_batch_fusion import group_batch_fusion_passes, PRE_GRAD_FUSIONS +from .misc_patterns import numpy_compat_normalization +from .split_cat import PRE_GRAD_PATTERNS + + +log = logging.getLogger(__name__) + +efficient_conv_bn_eval_pass = PatternMatcherPass( + pass_name="efficient_conv_bn_eval_pass" +) + +fuse_split_linear_add_pass = PatternMatcherPass( + pass_name="fuse_split_linear_add_pass", +) +fuse_chunk_squeeze_cat_pass = PatternMatcherPass( + pass_name="fuse_chunk_squeeze_cat_pass", +) +remove_reshape_pass = PatternMatcherPass( + pass_name="remove_reshape_pass", +) + +# based on predispatch aten IR +normalization_pass_aten = PatternMatcherPass() +merge_splits_pass_aten = PatternMatcherPass() +split_cat_pass_aten = PatternMatcherPass() +unbind_stack_pass_aten = PatternMatcherPass() +merge_getitem_cat_pass_aten = PatternMatcherPass() +merge_stack_tahn_unbind_pass_aten = PatternMatcherPass() +mutate_cat_pass_aten = PatternMatcherPass() +remove_split_with_size_one_pass_aten = PatternMatcherPass() + + +def save_inductor_dict(pass_to_compare=None): + if not pass_to_compare: + pass_to_compare = list(config.pre_grad_fusion_options.keys()) + list( + config.post_grad_fusion_options.keys() + ) + return {p: dict(counters["inductor"]).get(p, 0) for p in pass_to_compare} + + +def is_same_dict(inductor_dict, optimus_dict): + for pass_name, count in optimus_dict.items(): + if count != dict(inductor_dict).get(pass_name, 0): + return False + return True + + +def normalize_node_kwargs_pass(graph): + return None + + +def fuse_parallel_linear_pass(graph): + return None + + +def remove_split_ops(graph, shape_prop): + return None + + +def fuse_chunk_reshape_unsqueeze_concat_pass(graph): + return None + + +def fuse_chunk_reshape_concat_pass(graph): + return None + + +def remove_noop_pass(graph): + return None + + +def stack_to_unsqueeze_pass(graph): + return None + + +@init_once_fakemode +def lazy_init(): + from . import efficient_conv_bn_eval, split_cat # noqa: F401 # noqa: F401 + + if config.is_fbcode(): + from . import fb # type: ignore[attr-defined] # noqa: F401 + + +def pre_grad_passes(gm: torch.fx.GraphModule, example_inputs=None): + """ + Apply passes on the input FX graph using Torch IR. + + WARNING: + The IR before grad is not functional or normalized, so it is harder + to write passes on this IR. Passes must be safe with respect to + aliasing and mutation and need to handle all possible arg schemas. + + Consider adding a new pass to post_grad.py or joint_graph.py which + are after functionalization and normalization. + """ + if config.pattern_matcher: + lazy_init() + if hasattr( + config, "fx_passes_numeric_check" + ) and config.fx_passes_numeric_check.get("pre_grad", False): + gm_before_fx_passes = gm.__copy__() + # explicitly run with predispatch atenIR based passes + if config.is_predispatch: + + def shape_prop(mod) -> None: + ShapeProp( + gm=mod, + # pyre-fixme[16]: Module `torch._dynamo.utils` has no attribute `detect_fake_mode` + fake_mode=detect_fake_mode(example_inputs), + ).propagate(*example_inputs) + + # normalization pass + pass_execution_and_save( + normalization_pass_aten.apply, + gm, + example_inputs, + "[Pre grad(predispatch IR)]Apply normalization pass", + ) + # normalize kwargs, must be called as the first pass + pass_execution_and_save( + normalize_node_kwargs_pass, + gm, + example_inputs, + "[Pre grad(predispatch IR)]Apply normalize_node_kwargs_pass", + ) + pass_execution_and_save( + remove_noop_pass, + gm, + example_inputs, + "[Pre grad(predispatch IR)]Apply remove_noop pass", + ) + pass_execution_and_save( + fuse_chunk_reshape_concat_pass, + gm, + example_inputs, + "[Pre grad(predispatch IR)] Apply fuse_chunk_reshape_concat_pass", + ) + pass_execution_and_save( + group_batch_fusion_passes, + gm, + example_inputs, + "[Pre grad(predispatch IR)] Apply group_batch_fusion", + ) + pass_execution_and_save( + normalize_node_kwargs_pass, + gm, + example_inputs, + "[Pre grad(predispatch IR)]Apply normalize_node_kwargs_pass", + ) + pass_execution_and_save( + fuse_chunk_squeeze_cat_pass.apply, + gm, + example_inputs, + "[Pre grad(predispatch IR)] Apply fuse_chunk_squeeze_cat_pass", + ) + pass_execution_and_save( + fuse_split_linear_add_pass.apply, + gm, + example_inputs, + "[Pre grad(predispatch IR)] Apply fuse_split_linear_add_pass", + ) + pass_execution_and_save( + remove_reshape_pass.apply, + gm, + example_inputs, + "[Pre grad(predispatch IR)] Apply remove_reshape_pass", + ) + pass_execution_and_save( + fuse_parallel_linear_pass, + gm, + example_inputs, + "[Pre grad(predispatch IR)] Apply fuse_parallel_linear_pass", + ) + pass_execution_and_save( + lambda graph: remove_split_ops(graph.owning_module, shape_prop), + gm, + example_inputs, + "[Pre grad(predispatch IR)] Apply remove_split_ops", + ) + # run before fuse_chunk_reshape_unsqueeze_concat_pass + pass_execution_and_save( + stack_to_unsqueeze_pass, + gm, + example_inputs, + "[Pre grad(predispatch IR)] Apply stack_to_unsqueeze_pass", + ) + pass_execution_and_save( + fuse_chunk_reshape_unsqueeze_concat_pass, + gm, + example_inputs, + "[Pre grad(predispatch IR)] Apply fuse_chunk_reshape_unsqueeze_concat_pass", + ) + # Remove noops at the end, which may be generated other passes. + pass_execution_and_save( + remove_noop_pass, + gm, + example_inputs, + "[Pre grad(predispatch IR)]Apply remove_noop pass", + ) + shape_prop(gm) + + else: + # We only log the graph with changes to avoid the excessive compilation time + # https://fb.workplace.com/groups/257735836456307/permalink/633533465543207/ + if example_inputs is not None: + gm = fuse_fx(gm, example_inputs) + numpy_compat_normalization(gm.graph) + optimus_scuba_log["before_recompile_pre_grad"] = upload_graph(gm.graph) + group_batch_fusion_passes(gm.graph, pre_grad=True) + for pass_name in config.pre_grad_fusion_options: + # skip all patterns for group batch fusions + if pass_name in PRE_GRAD_FUSIONS: + continue + pattern_matcher_pass = PRE_GRAD_PATTERNS[pass_name] + inductor_before_change = save_inductor_dict( + [pattern_matcher_pass.pass_name] + ) + # we support run same pattern multiple times, the default is to run only once + counter = config.pre_grad_fusion_options[pass_name].get("counter", 1) + for _ in range(counter): + pattern_matcher_pass.apply(gm.graph) # type: ignore[arg-type] + if not is_same_dict(counters["inductor"], inductor_before_change): + optimus_scuba_log[ + f"{pattern_matcher_pass.pass_name}_pre_grad" + ] = upload_graph(gm.graph) + # TODO: move efficient_conv_bn_eval_pass to the fusions dict too. + efficient_conv_bn_eval_pass.apply(gm.graph) # type: ignore[arg-type] + + if config.pre_grad_custom_pass is not None: + with GraphTransformObserver( + gm, "pre_grad_custom_pass", config.trace.log_url_for_graph_xform + ): + config.pre_grad_custom_pass(gm.graph) + stable_topological_sort(gm.graph) + + from .quantization import quant_lift_up + + quant_lift_up(gm) + + gm.graph.lint() + gm.recompile() + optimus_scuba_log["after_recompile_pre_grad"] = upload_graph(gm.graph) + + if ( + config.pattern_matcher + and hasattr(config, "fx_passes_numeric_check") + and config.fx_passes_numeric_check.get("pre_grad", False) + and example_inputs is not None + ): + from .numeric_utils import numeric_check_if_enabled + + gm_after_fx_passes = gm.__copy__() + numeric_check_if_enabled( + gm_before_fx_passes, # type: ignore[possibly-undefined] + gm_after_fx_passes, + example_inputs, + config.fx_passes_numeric_check.get("num_iterations", 1), + config.fx_passes_numeric_check.get("precision", 1e-4), + ) + + return gm + + +def fuse_fx(gm: torch.fx.GraphModule, example_inputs) -> torch.fx.GraphModule: + is_cpu = is_cpu_device(example_inputs) + # pyre-fixme[16]: Module `torch._dynamo.utils` has no attribute `detect_fake_mode` + fake_mode = detect_fake_mode(example_inputs) + + gm = sink_cat_after_pointwise(gm) + if config.permute_fusion and not is_cpu: + # For linear permute fusion, we need to check input info to identify + # and perform proper permutation/transpose + ShapeProp(gm, fake_mode=fake_mode).propagate(*example_inputs) + with GraphTransformObserver( + gm, "linear_permute_fusion", config.trace.log_url_for_graph_xform + ): + gm = linear_permute_fusion(gm) + with GraphTransformObserver( + gm, "permute_linear_fusion", config.trace.log_url_for_graph_xform + ): + gm = permute_linear_fusion(gm) + with GraphTransformObserver( + gm, "permute_matmul_fusion", config.trace.log_url_for_graph_xform + ): + gm = permute_matmul_fusion(gm) + + # make sure the autograd is disabled. + if torch.is_grad_enabled() or not is_cpu: + return gm + if config.freezing: + with GraphTransformObserver( + gm, "remove_identity", config.trace.log_url_for_graph_xform + ): + gm = remove_identity(gm) + with GraphTransformObserver( + gm, "fuse_conv_bn", config.trace.log_url_for_graph_xform + ): + gm = fuse_conv_bn(gm) + return gm + + +def fetch_attr(target: str, mod): + target_atoms = target.split(".") + attr_itr = mod + for i, atom in enumerate(target_atoms): + if not hasattr(attr_itr, atom): + raise RuntimeError( + f"Node referenced nonexistant target {'.'.join(target_atoms[:i])}" + ) + attr_itr = getattr(attr_itr, atom) + return attr_itr + + +def remove_identity(gm: torch.fx.GraphModule) -> torch.fx.GraphModule: + """ + Removes all identity layers from the module. + """ + + class IdentityRemover(torch.fx.Transformer): + def call_module(self, target, args, kwargs): + if isinstance(self.submodules[target], nn.Identity): + assert len(args) == 1 + return args[0] + else: + return super().call_module(target, args, kwargs) + + return IdentityRemover(gm).transform() + + +def fuse_conv_bn(gm: torch.fx.GraphModule, inplace=False) -> torch.fx.GraphModule: + """ + Fuses Convolution/BN layers for inference purposes. + """ + modules_patterns = [ + (torch.nn.Conv1d, torch.nn.BatchNorm1d), + (torch.nn.Conv2d, torch.nn.BatchNorm2d), + (torch.nn.Conv3d, torch.nn.BatchNorm3d), + ] + module_function_patterns = [ + (torch.nn.Conv1d, F.batch_norm), + (torch.nn.Conv2d, F.batch_norm), + (torch.nn.Conv3d, F.batch_norm), + ] + modules = dict(gm.named_modules()) + + class ConvBNFusion: + def __init__( + self, + bn_node, + conv_module, + bn_module=None, # For BN Module + bn_running_mean=None, # For Functional BN + bn_running_var=None, + bn_eps=None, + bn_weight=None, + bn_bias=None, + ) -> None: + self.bn_nodes = [ + bn_node, + ] + self.conv_module = conv_module + self.bn_module = bn_module + self.bn_running_mean = bn_running_mean + self.bn_running_var = bn_running_var + self.bn_eps = bn_eps + self.bn_weight = bn_weight + self.bn_bias = bn_bias + self.fusion_enabled = True + + def add_bn_node(self, bn_node): + self.bn_nodes.append(bn_node) + + def disable_fusion(self): + self.fusion_enabled = False + + def is_fusion_enabled(self): + return self.fusion_enabled + + conv_bn_to_fuse: Dict[int, ConvBNFusion] = {} + for pattern in modules_patterns: + conv_bn_to_fuse.clear() + for node in gm.graph.nodes: + if matches_module_pattern(pattern, node, modules): + if len(node.args[0].users) > 1: # Output of conv is used by other nodes + continue + conv = modules[node.args[0].target] + bn = modules[node.target] + eval_mode = all(not n.training for n in [conv, bn]) + if not eval_mode: + continue + if not bn.track_running_stats: + continue + + # Do hash based on the module name of conv + hash_id = hash(node.args[0].target) + if hash_id not in conv_bn_to_fuse: + conv_bn_to_fuse[hash_id] = ConvBNFusion(node, conv, bn) + else: + if bn == conv_bn_to_fuse[hash_id].bn_module: + # Do fusion if same bn module + conv_bn_to_fuse[hash_id].add_bn_node(node) + else: + # Disable the conv bn folding if conv shared by different bn + conv_bn_to_fuse[hash_id].disable_fusion() + + for conv_bn_fusion in conv_bn_to_fuse.values(): + if conv_bn_fusion.is_fusion_enabled(): + bn_nodes = conv_bn_fusion.bn_nodes + conv = conv_bn_fusion.conv_module + bn = conv_bn_fusion.bn_module + + fused_conv = fuse_conv_bn_eval(conv, bn) + for bn_node in bn_nodes: + replace_node_module(bn_node.args[0], modules, fused_conv) + bn_node.replace_all_uses_with(bn_node.args[0]) + gm.graph.erase_node(bn_node) + + gm.graph.lint() + for pattern in module_function_patterns: + conv_bn_to_fuse.clear() + for node in gm.graph.nodes: + if matches_module_function_pattern(pattern, node, modules): + # TODO: support kwargs. + if len(node.args) != 8: + continue + conv = modules[node.args[0].target] + bn_training = node.args[5] + bn_eps = node.args[7] + if conv.training or bn_training: + continue + if type(bn_eps) is not float: + continue + + def _used_by_same_conv_module(users): + conv_module_name = users[0].args[0].target + return all( + conv_module_name == user.args[0].target for user in users + ) + + bn_args_is_constant = all( + n.op == "get_attr" + and (len(n.users) == 1 or _used_by_same_conv_module(list(n.users))) + for n in node.args[1:5] + ) + if not bn_args_is_constant: + continue + bn_running_mean = fetch_attr(node.args[1].target, gm) + bn_running_var = fetch_attr(node.args[2].target, gm) + bn_weight = fetch_attr(node.args[3].target, gm) + bn_bias = fetch_attr(node.args[4].target, gm) + if bn_running_mean is None or bn_running_var is None: + continue + + # Do hash based on the module name of conv + hash_id = hash(node.args[0].target) + if hash_id not in conv_bn_to_fuse: + conv_bn_to_fuse[hash_id] = ConvBNFusion( + node, + conv, + bn_running_mean=bn_running_mean, + bn_running_var=bn_running_var, + bn_eps=bn_eps, + bn_weight=bn_weight, + bn_bias=bn_bias, + ) + else: + if ( + hash(bn_running_mean) + == hash(conv_bn_to_fuse[hash_id].bn_running_mean) + and hash(bn_running_var) + == hash(conv_bn_to_fuse[hash_id].bn_running_var) + and torch.allclose( + torch.tensor(bn_eps), + torch.tensor(conv_bn_to_fuse[hash_id].bn_eps), + ) + and hash(bn_weight) == hash(conv_bn_to_fuse[hash_id].bn_weight) + and hash(bn_bias) == hash(conv_bn_to_fuse[hash_id].bn_bias) + ): + # Do fusion if same functional bn + conv_bn_to_fuse[hash_id].add_bn_node(node) + else: + # Disable the conv bn folding if conv shared by different bn + conv_bn_to_fuse[hash_id].disable_fusion() + + for conv_bn_fusion in conv_bn_to_fuse.values(): + if conv_bn_fusion.is_fusion_enabled(): + bn_nodes = conv_bn_fusion.bn_nodes + conv = conv_bn_fusion.conv_module + bn_running_mean = conv_bn_fusion.bn_running_mean + bn_running_var = conv_bn_fusion.bn_running_var + bn_eps = conv_bn_fusion.bn_eps + bn_weight = conv_bn_fusion.bn_weight + bn_bias = conv_bn_fusion.bn_bias + + fused_conv = copy.deepcopy(conv) + fused_conv.weight, fused_conv.bias = fuse_conv_bn_weights( + fused_conv.weight, + fused_conv.bias, + bn_running_mean, + bn_running_var, + bn_eps, + bn_weight, + bn_bias, + ) + for bn_node in bn_nodes: + replace_node_module(bn_node.args[0], modules, fused_conv) + bn_node.replace_all_uses_with(bn_node.args[0]) + gm.graph.erase_node(bn_node) + gm.graph.lint() + gm.recompile() + + return gm + + +class NormalizedLinearNode: + def __init__(self, node: torch.fx.Node) -> None: + assert node.op == "call_function" + assert node.target in [torch.nn.functional.linear] + self.node: torch.fx.Node = node + + def get_input(self) -> torch.fx.Node: + if len(self.node.args) > 0: + return self.node.args[0] # type: ignore[return-value] + else: + return self.node.kwargs["input"] # type: ignore[return-value] + + def get_weight(self) -> torch.fx.Node: + if len(self.node.args) > 1: + return self.node.args[1] # type: ignore[return-value] + else: + return self.node.kwargs["weight"] # type: ignore[return-value] + + def get_bias(self) -> torch.fx.Node: + if len(self.node.args) > 2: + return self.node.args[2] # type: ignore[return-value] + else: + return self.node.kwargs["bias"] if "bias" in self.node.kwargs else None # type: ignore[return-value] + + +class NormalizedMatmulNode: + def __init__(self, node: torch.fx.Node) -> None: + assert node.op == "call_function" + assert node.target in [torch.bmm, torch.matmul] + self.node: torch.fx.Node = node + + def get_input(self) -> torch.fx.Node: + if len(self.node.args) > 0: + return self.node.args[0] # type: ignore[return-value] + else: + return self.node.kwargs["input"] # type: ignore[return-value] + + def get_other(self) -> torch.fx.Node: + if len(self.node.args) > 1: + return self.node.args[1] # type: ignore[return-value] + else: + return self.node.kwargs["other"] # type: ignore[return-value] + + +def check_permute(node: torch.fx.Node) -> bool: + ranks = len(node.meta["tensor_meta"].shape) + if len(node.args) > 3: + permutation = [node.args[i] % ranks for i in range(1, ranks + 1)] # type: ignore[operator] + elif ( + "permutation" in node.kwargs + and node.kwargs["permutation"] is not None + and len(node.kwargs["permutation"]) > 2 # type: ignore[arg-type] + ): + permutation = [i % ranks for i in node.kwargs["permutation"]] # type: ignore[union-attr] + else: + return False + allowed_permutation = list(range(ranks)) + allowed_permutation[-1] = ranks - 2 + allowed_permutation[-2] = ranks - 1 + return permutation == allowed_permutation + + +def sink_cat_after_pointwise(module: torch.fx.GraphModule) -> torch.fx.GraphModule: + def one_user(node): + users = list(node.users) + return users[0] if len(users) == 1 else None + + def is_view(node): + view = {"view"} + return node.op == "call_method" and node.target in view + + def is_pointwise_unary(node): + pointwise = {torch.relu, torch.tanh, "relu", "tanh"} + return node.op in {"call_function", "call_method"} and node.target in pointwise + + g = module.graph + for node in g.nodes: + if node.op != "call_function" or node.target != torch.cat: + continue + + cat_or_view = node + while True: + user = one_user(cat_or_view) + if not user or not is_view(user): + break + cat_or_view = user + + if user and is_pointwise_unary(user): + with g.inserting_before(node): + + def cat_args(tensors, dim=0): + return tensors, dim + + tensors, dim = cat_args(*node.args, **node.kwargs) + new_kwargs = { + name: val for name, val in user.kwargs.items() if name != "input" + } + new_tensors = [ + g.create_node(user.op, user.target, args=(arg,), kwargs=new_kwargs) + for arg in tensors + ] + new_cat = g.create_node( + "call_function", torch.cat, args=(new_tensors, dim) + ) + user.replace_all_uses_with(cat_or_view) + node.replace_all_uses_with(new_cat) + g.erase_node(user) + g.erase_node(node) + g.lint() + module.recompile() + return module + + +def linear_permute_fusion(module: torch.fx.GraphModule) -> torch.fx.GraphModule: + for node in module.graph.find_nodes(op="call_method", target="permute"): + if check_permute(node): + if len(node.args) > 0: + input_node = node.args[0] + else: + input_node = node.kwargs["input"] + if ( + input_node.op == "call_function" + and input_node.target == torch.nn.functional.linear + ): + normalized = NormalizedLinearNode(input_node) + input = normalized.get_input() + weight = normalized.get_weight() + bias = normalized.get_bias() + with module.graph.inserting_before(node): + fused_node = module.graph.call_function( + linear_transpose, args=(input, weight, bias) + ) + node.replace_all_uses_with(fused_node) + module.graph.erase_node(node) + if len(input_node.users) == 0: + module.graph.erase_node(input_node) + + module.graph.lint() + module.recompile() + return module + + +# Y1 = X * W^T + bias +# Y2 = Y1.permute(0, 2, 1) +# ----> +# Y2 = (W * X^T + bias.unsqueeze(-1))^T +def linear_transpose( + input: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] +) -> torch.Tensor: + if bias is None: + return torch.matmul(weight, input.transpose(-1, -2)) + return torch.matmul(weight, input.transpose(-1, -2)) + bias.unsqueeze(-1) + + +def permute_linear_fusion(module: torch.fx.GraphModule) -> torch.fx.GraphModule: + for node in module.graph.find_nodes( + op="call_function", target=torch.nn.functional.linear + ): + if len(node.args) > 0: + input_node = node.args[0] + else: + input_node = node.kwargs["input"] + if ( + input_node.op == "call_method" + and input_node.target == "permute" + and check_permute(input_node) + ): + normalized = NormalizedLinearNode(node) + if len(input_node.args) > 0: + input = input_node.args[0] + else: + input = input_node.kwargs["input"] + weight = normalized.get_weight() + bias = normalized.get_bias() + with module.graph.inserting_before(node): + fused_node = module.graph.call_function( + transpose_linear, args=(input, weight, bias) + ) + node.replace_all_uses_with(fused_node) + module.graph.erase_node(node) + if len(input_node.users) == 0: + module.graph.erase_node(input_node) + + module.graph.lint() + module.recompile() + return module + + +def permute_matmul_fusion(module: torch.fx.GraphModule) -> torch.fx.GraphModule: + for node in itertools.chain( + module.graph.find_nodes(op="call_function", target=torch.bmm), + module.graph.find_nodes(op="call_function", target=torch.matmul), + ): + normalized = NormalizedMatmulNode(node) + input_A_node = normalized.get_input() + input_B_node = normalized.get_other() + input_A = input_A_node + input_B = input_B_node + Atrans = Btrans = False + if ( + input_A_node.op == "call_method" + and input_A_node.target == "permute" + and check_permute(input_A_node) + ): + Atrans = True + if len(input_A_node.args) > 0: + input_A = input_A_node.args[0] # type: ignore[assignment] + else: + input_A = input_A_node.kwargs["input"] # type: ignore[assignment] + + if ( + input_B_node.op == "call_method" + and input_B_node.target == "permute" + and check_permute(input_B_node) + ): + Btrans = True + if len(input_B_node.args) > 0: + input_B = input_B_node.args[0] # type: ignore[assignment] + else: + input_B = input_B_node.kwargs["input"] # type: ignore[assignment] + + if Atrans or Btrans: + with module.graph.inserting_before(node): + fused_node = module.graph.call_function( + transpose_matmul, + args=(input_A, input_B, Atrans, Btrans), + ) + node.replace_all_uses_with(fused_node) + module.graph.erase_node(node) + if Atrans and len(input_A_node.users) == 0: + module.graph.erase_node(input_A_node) + if Btrans and len(input_B_node.users) == 0: + module.graph.erase_node(input_B_node) + + module.graph.lint() + module.recompile() + return module + + +# X1 = X.permute(0, 2, 1) +# Y1 = X1 * W1^T + bias1 +# ----> +# Y2 = X1.transpose(-1, -2) * W1^T + bias1 +def transpose_linear( + input: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] +) -> torch.Tensor: + if bias is None: + return torch.matmul(input.transpose(-1, -2), weight.t()) + return torch.matmul(input.transpose(-1, -2), weight.t()) + bias + + +def transpose_matmul( + A: torch.Tensor, B: torch.Tensor, Atrans: bool, Btrans: bool +) -> torch.Tensor: + if Atrans: + A = A.transpose(-1, -2) + if Btrans: + B = B.transpose(-1, -2) + return torch.matmul(A, B) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/quantization.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/quantization.py new file mode 100644 index 0000000000000000000000000000000000000000..6e17e2ff456fc626ea0ff4b1765fe20783242abb --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/quantization.py @@ -0,0 +1,2589 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import copy +import functools +import itertools +import math +import operator +from typing import Any, Tuple + +import torch +from torch._dynamo.utils import counters +from torch.fx.experimental.symbolic_shapes import has_free_symbols +from torch.fx.node import map_arg + +from ..lowering import lowerings as L, require_channels_last +from ..pattern_matcher import Arg, CallFunction, filter_nodes, KeywordArg, ListOf, Match +from ..utils import pad_listlike +from .freezing_patterns import register_freezing_graph_pattern +from .post_grad import register_lowering_pattern + + +aten = torch.ops.aten +prims = torch.ops.prims +quantized_decomposed = torch.ops.quantized_decomposed +quantized = torch.ops.quantized + +# Only for per tensor quant since permute may changes the channel idx +_PER_TENSOR_QUANTIZE_OPS = [ + quantized_decomposed.quantize_per_tensor.default, + quantized_decomposed.quantize_per_tensor.tensor, +] + +_VIEW_OPS = [ + aten.transpose.int, + aten.permute.default, + aten.view.default, +] + +""" +The quantization.py file primarily incorporates passes related to quantization fusion +in inductor, includes: +1. Dequant Promotion; +2. Conv/GEMM weight prepack with oneDNN Library; +3. Conv/GEMM quantization fusion with output quant node (if have); +4. Other pointwise operators' quantization fusion like: qmaxpool2d, qcat and more; + +It also involves int8-mixed-fp32 and int8-mixed-bf16 quantization. The main difference +of patterns for int8-mixed-bf16, comparing with int8-mixed-fp32, is +1. There is to(dtype=torch.bfloat16) node at the inputs of activation and weight for Conv/GEMM. +2. There is to(dtype=torch.float32) node at the outputs of Conv/GEMM before inputs to next quant node. +Refer to: https://github.com/pytorch/pytorch/issues/111640 for detail design of int8-mixed-bf16 +quantization. +""" + + +def _get_pattern_output_dtype(match: Match): + """ + Get the pattern's output dtype from node's meta + Assume only 1 output node in this matched pattern. + """ + pattern_output_nodes = match.output_nodes() + assert len(pattern_output_nodes) == 1 + output_node = pattern_output_nodes[0] + assert isinstance(output_node, torch.fx.Node) + output_dtype = output_node.meta["val"].dtype + assert output_dtype in [torch.uint8, torch.float32, torch.bfloat16] + return output_dtype + + +def _may_generate_pattern_with_dtype_convert( + pattern, dtype=Arg(), with_dtype_convert=True, users=1 +): + if with_dtype_convert: + return CallFunction( + prims.convert_element_type.default, + pattern, + dtype, + _users=users, + ) + else: + return pattern + + +def _may_generate_pattern_with_reshape(pattern, reshape_size=Arg(), with_reshape=True): + if with_reshape: + return CallFunction( + torch.ops.aten.reshape.default, + pattern, + reshape_size, + ) + else: + return pattern + + +def _generate_linear_t_pattern( + _dequant_per_channel_pattern, + dtype, +): + assert dtype in [torch.float32, torch.bfloat16] + t_pattern = CallFunction( + aten.permute.default, + _may_generate_pattern_with_dtype_convert( + _dequant_per_channel_pattern, + KeywordArg("autocast_wgt_dtype"), + dtype == torch.bfloat16, + ), + KeywordArg("permute_axes"), + ) + return t_pattern + + +def _unary_fusion_pattern(unary_fusion, call_fn, users, is_bf16): + # only insert to_dtype if is_bf16 is True + computation_call = _may_generate_pattern_with_dtype_convert( + call_fn, dtype=KeywordArg("to_float"), with_dtype_convert=is_bf16, users=users + ) + return unary_fusion(computation_call) + + +def get_dequantize_per_tensor_activation_pattern(is_tensor_overload=False): + dequantize_per_tensor_activation_pattern = CallFunction( + quantized_decomposed.dequantize_per_tensor.tensor + if is_tensor_overload + else quantized_decomposed.dequantize_per_tensor.default, + KeywordArg("x"), + KeywordArg("x_scale"), + KeywordArg("x_zp"), + KeywordArg("x_quant_min"), + KeywordArg("x_quant_max"), + KeywordArg("x_dq_dtype"), + ) + return dequantize_per_tensor_activation_pattern + + +dequantize_per_channel_weight_pattern = CallFunction( + quantized_decomposed.dequantize_per_channel.default, + KeywordArg("q_weight"), + KeywordArg("w_scale"), + KeywordArg("w_zp"), + KeywordArg("w_axis"), + KeywordArg("w_quant_min"), + KeywordArg("w_quant_max"), + KeywordArg("w_dtype"), +) + +dequantize_per_channel_to_bf16_weight_pattern = ( + _may_generate_pattern_with_dtype_convert( + dequantize_per_channel_weight_pattern, + KeywordArg("autocast_wgt_dtype"), + ) +) + +dequantize_per_channel_clone_weight_pattern = CallFunction( + aten.clone.default, + dequantize_per_channel_weight_pattern, + memory_format=KeywordArg("memory_format"), +) + +dequantize_per_channel_to_bf16_clone_weight_pattern = CallFunction( + aten.clone.default, + dequantize_per_channel_to_bf16_weight_pattern, + memory_format=KeywordArg("memory_format"), +) + + +def get_dequantize_qconv_pt2e_pattern(users=1): + return CallFunction( + torch.ops.onednn.qconv2d_pointwise.default, + KeywordArg("x"), + KeywordArg("x_scale"), # x_scale + KeywordArg("x_zp"), # x_zp + KeywordArg("packed_weight"), # packed_weight + KeywordArg("w_scale"), # w_scale + KeywordArg("w_zp"), # w_zp + KeywordArg("b"), # bias + KeywordArg("stride"), + KeywordArg("padding"), + KeywordArg("dilation"), + KeywordArg("groups"), + KeywordArg("output_scale"), # output_scale = 1.0 + KeywordArg("output_zero_point"), # output_zero_point = 0 + KeywordArg("output_dtype"), # output_dtype = None + KeywordArg("attr"), # attr = "none" + Arg(), # scalars + Arg(), # algorithm + _users=users, + ) + + +def get_qlinear_pt2e_pattern(x_scale_zp_are_tensors, users=1): + qlinear_op = ( + torch.ops.onednn.qlinear_pointwise.tensor + if x_scale_zp_are_tensors + else torch.ops.onednn.qlinear_pointwise.default + ) + return CallFunction( + qlinear_op, + KeywordArg("x"), + KeywordArg("x_scale"), + KeywordArg("x_zp"), + KeywordArg("packed_weight"), + KeywordArg("w_scale"), + KeywordArg("w_zp"), + KeywordArg("b"), + KeywordArg("output_scale"), + KeywordArg("output_zero_point"), + KeywordArg("output_dtype"), + KeywordArg("postop_name"), + KeywordArg("postop_args"), + KeywordArg("postop_algorithm"), + _users=users, + ) + + +dequantize_accum_pattern = CallFunction( + quantized_decomposed.dequantize_per_tensor.default, + KeywordArg("accum"), + KeywordArg("accum_scale"), + KeywordArg("accum_zp"), + Arg(), + Arg(), + KeywordArg("accum_dq_dtype"), +) + + +def generate_pattern_with_binary( + binary_post_op, + computation_call, + extra_input_pattern, + dtype_convert=False, + swap_inputs=False, +): + binary_pattern = ( + CallFunction( + binary_post_op, + extra_input_pattern, + computation_call, + ) + if swap_inputs + else CallFunction( + binary_post_op, + computation_call, + extra_input_pattern, + ) + ) + return _may_generate_pattern_with_dtype_convert( + binary_pattern, + KeywordArg("convert_dtype_after_inplace_add"), + dtype_convert, + ) + + +def generate_pattern_with_unary(computation_call, unary_post_op): + if unary_post_op is not None: + return CallFunction( + unary_post_op, + computation_call, + ) + return computation_call + + +def generate_pattern_with_output_quant(computation_call, with_dtype_convert=False): + quantized_op_output_pattern_pt2e = CallFunction( + quantized_decomposed.quantize_per_tensor.default, + _may_generate_pattern_with_dtype_convert( + computation_call, + Arg(), + with_dtype_convert, + ), + KeywordArg("o_inv_scale"), + KeywordArg("o_zp"), + KeywordArg("o_qmin"), + KeywordArg("o_qmax"), + KeywordArg("o_dtype"), + ) + return quantized_op_output_pattern_pt2e + + +def _check_node_kwarg_arg_value(check_node, kwarg_name, args_index, expected_value): + if kwarg_name in check_node.kwargs: + actual_value = check_node.kwargs[kwarg_name] + return actual_value == expected_value + else: + assert len(check_node.args) >= (args_index + 1) + actual_value = check_node.args[args_index] + return actual_value == expected_value + + +def _is_valid_quantized_conv2d_optimization_pattern(): + def fn(match): + output_dtype = _get_pattern_output_dtype(match) + if output_dtype in [torch.float32, torch.bfloat16]: + # Only keep matched pattern with same output_dtype + qconv_node_after_weight_prepack = filter_nodes( + match.nodes, torch.ops.onednn.qconv2d_pointwise + )[0] + return _check_node_kwarg_arg_value( + qconv_node_after_weight_prepack, "output_dtype", 13, output_dtype + ) + return True + + return fn + + +def _register_quantized_conv_lowering( + pattern, + pass_number, + computation_op, + unary_attr, +): + @register_lowering_pattern( + pattern, + extra_check=_is_valid_quantized_conv2d_optimization_pattern(), + pass_number=pass_number, + ) + def qconv(match: Match, *args, **kwargs): + # Activation QParams + x, x_scale, x_zp = ( + kwargs["x"], + kwargs["x_scale"], + kwargs["x_zp"], + ) + # Weight QParams + packed_weight, w_scale, w_zp = ( + kwargs["packed_weight"], + kwargs["w_scale"], + kwargs["w_zp"], + ) + # Conv Params + b, stride, padding, dilation, groups = ( + kwargs["b"], + kwargs["stride"], + kwargs["padding"], + kwargs["dilation"], + kwargs["groups"], + ) + output_dtype = _get_pattern_output_dtype(match) + assert output_dtype in [torch.uint8, torch.float32, torch.bfloat16] + # Output QParams + o_inv_scale = kwargs["o_inv_scale"] if output_dtype == torch.uint8 else 1.0 + o_zero_point = kwargs["o_zp"] if output_dtype == torch.uint8 else 0 + assert ( + kwargs["attr"] == "none" + ) # Expected no post op fused in weight prepack phase + if unary_attr.op_name == "hardtanh": + min_value = kwargs.get("min_value") + max_value = kwargs.get("max_value") + unary_attr.scalars_attr = [min_value, max_value] + + computation_args = ( + x, + x_scale, + x_zp, + packed_weight, + w_scale, + w_zp, + b, + stride, + padding, + dilation, + groups, + o_inv_scale, + o_zero_point, + output_dtype, + unary_attr.op_name, + unary_attr.scalars_attr, + unary_attr.algorithm_attr, + ) + counters["inductor"]["qconv2d_unary_matcher_count"] += 1 + counters["inductor"]["qconv2d_unary_matcher_nodes"] += len(match.nodes) + return L[computation_op](*computation_args) + + return qconv + + +def _is_valid_quantized_linear_optimization_pattern(): + def fn(match): + output_dtype = _get_pattern_output_dtype(match) + if output_dtype in [torch.float32, torch.bfloat16]: + # Only keep matched pattern with same output_dtype + qlinear_node_after_weight_prepack = filter_nodes( + match.nodes, torch.ops.onednn.qlinear_pointwise + )[0] + return _check_node_kwarg_arg_value( + qlinear_node_after_weight_prepack, "output_dtype", 9, output_dtype + ) + return True + + return fn + + +def _register_quantized_linear_lowering( + pattern, + pass_number, + computation_op, + unary_attr, +): + @register_lowering_pattern( + pattern, + extra_check=_is_valid_quantized_linear_optimization_pattern(), + pass_number=pass_number, + ) + def qlinear(match: Match, *args, **kwargs): + output_dtype = _get_pattern_output_dtype(match) + # Activation QParams + x, x_scale, x_zp = ( + kwargs["x"], + kwargs["x_scale"], + kwargs["x_zp"], + ) + # Weight QParams + packed_weight, w_scale, w_zp = ( + kwargs["packed_weight"], + kwargs["w_scale"], + kwargs["w_zp"], + ) + + # bias + b = kwargs["b"] if "b" in kwargs else None + + # Output QParams + o_inv_scale = kwargs["o_inv_scale"] if output_dtype == torch.uint8 else 1.0 + o_zero_point = kwargs["o_zp"] if output_dtype == torch.uint8 else 0 + assert ( + kwargs["postop_name"] == "none" + ) # Expected no post op fused in weight prepack phase + + computation_args = ( + x, + x_scale, + x_zp, + packed_weight, + w_scale, + w_zp, + b, + o_inv_scale, + o_zero_point, + output_dtype, + unary_attr.op_name, + unary_attr.scalars_attr, + unary_attr.algorithm_attr, + ) + counters["inductor"]["qlinear_unary_matcher_count"] += 1 + counters["inductor"]["qlinear_unary_matcher_nodes"] += len(match.nodes) + return L[computation_op](*computation_args) + + return qlinear + + +def _register_quantized_linear_binary_lowering( + pattern, + pass_number, + computation_op, + binary_unary_attr, +): + @register_lowering_pattern( + pattern, + extra_check=_is_valid_qlinear_binary_optimization_pattern(), + pass_number=pass_number, + ) + def qlinear_binary(match: Match, *args, **kwargs): + output_dtype = _get_pattern_output_dtype(match) + assert output_dtype is not None + # Activation QParams + x, x_scale, x_zp = ( + kwargs["x"], + kwargs["x_scale"], + kwargs["x_zp"], + ) + x2 = ( + kwargs["accum"] + if binary_unary_attr.binary_op_name == "sum" + else kwargs["other"] + ) + x2_scale = 1.0 + x2_zp = 0 + # Weight QParams + packed_weight, w_scale, w_zp = ( + kwargs["packed_weight"], + kwargs["w_scale"], + kwargs["w_zp"], + ) + # bias + b = kwargs["b"] if "b" in kwargs else None + # Output QParams + o_inv_scale = kwargs["o_inv_scale"] if output_dtype == torch.uint8 else 1.0 + o_zero_point = kwargs["o_zp"] if output_dtype == torch.uint8 else 0 + + x2.realize() + from .mkldnn_fusion import _can_be_inplace + + binary_op_name = binary_unary_attr.binary_op_name + + if binary_op_name == "sum" and not _can_be_inplace(x2): + # When we enable the GEMM Template, the output of QLinear + # will be reshaped from 2D back to 3D if the input is 3D. + # This causes _can_be_inplace(x2) to return False if x2 happens + # to be the output of QLinear in this scenario. + # Change the post op from sum to binary add for this case. + # Refer to test case: + # test_mkldnn_pattern_matcher.py::test_qlinear_dequant_promotion_cpu_input_dim_exceeds_2 + binary_op_name = "add" + + computation_args = ( + x, + x_scale, + x_zp, + packed_weight, + w_scale, + w_zp, + x2, + b, + o_inv_scale, + o_zero_point, + output_dtype, + x2_scale, + x2_zp, + binary_op_name, + binary_unary_attr.alpha, + binary_unary_attr.unary_op_name, + binary_unary_attr.scalars_attr, + binary_unary_attr.algorithm_attr, + ) + counters["inductor"]["qlinear_binary_matcher_count"] += 1 + counters["inductor"]["qlinear_binary_matcher_nodes"] += len(match.nodes) + return L[computation_op](*computation_args) + + return qlinear_binary + + +def _is_valid_qconv_binary_optimization_pattern(): + return _is_valid_quantized_op_binary_optimization_pattern( + torch.ops.onednn.qconv2d_pointwise + ) + + +def _is_valid_qlinear_binary_optimization_pattern(): + return _is_valid_quantized_op_binary_optimization_pattern( + torch.ops.onednn.qlinear_pointwise, + # we don't insert q-dq for extra input due to accuracy issues + extra_input_from_dequant=False, + ) + + +def _is_valid_quantized_op_binary_optimization_pattern( + qop, extra_input_from_dequant=True +): + # Check if it's a valid Binary Pattern for qconv2d and qlinear: + # * qop_pointwise should only has one users + # * If extra_input_from_dequant is True, extra input of binary node should come from dequant pattern + # * the two inputs of binary node should have attribute "meta" and should be tensors + # * the two inputs of binary node should have the same shape + # * All users of the extra input in this pattern should be + # ancestor nodes of the compute node, except for the binary node + # connected to the compute node. + def fn(match): + output_dtype = _get_pattern_output_dtype(match) + compute_node = filter_nodes(match.nodes, qop)[0] + # qop_pointwise should only have one user + if len(compute_node.users) != 1: + return False + binary_node_inputs = next(iter(compute_node.users)).args + assert len(binary_node_inputs) == 2, "Expects binary node with 2 inputs" + if output_dtype in [torch.float32, torch.bfloat16]: + extra_input_of_binary_node = None + for arg in binary_node_inputs: + if arg != compute_node: + extra_input_of_binary_node = arg + break + assert extra_input_of_binary_node is not None + # Extra input of binary node comes from dequant pattern + if extra_input_from_dequant and ( + (not isinstance(extra_input_of_binary_node, torch.fx.Node)) + or ( + extra_input_of_binary_node.target + != quantized_decomposed.dequantize_per_tensor.default + ) + ): + return False + + # the two inputs of binary node should have attribute "meta" and should be tensors + if not ( + hasattr(binary_node_inputs[0], "meta") + and isinstance(binary_node_inputs[0].meta.get("val", None), torch.Tensor) # type: ignore[union-attr] + ) or not ( + hasattr(binary_node_inputs[1], "meta") + and isinstance(binary_node_inputs[1].meta.get("val", None), torch.Tensor) # type: ignore[union-attr] + ): + return False + # the two inputs of binary node should have the same shape + if ( + binary_node_inputs[0].meta["val"].size() # type: ignore[union-attr] + != binary_node_inputs[1].meta["val"].size() # type: ignore[union-attr] + ): + return False + + # All users of the extra input in this pattern should be + # ancestor nodes of the compute node, except for the binary node + # connected to the compute node. + + from .mkldnn_fusion import _get_remaining_users + + extra_input_of_pattern = ( + match.kwargs["other"] + if "other" in match.kwargs + else ( + match.kwargs["accum"] + if output_dtype == torch.uint8 or (not extra_input_from_dequant) + else match.kwargs["accum_after_dequant"] + ) + ) + if ( + len(_get_remaining_users(extra_input_of_pattern, compute_node)) > 1 + or extra_input_of_pattern == compute_node.args[0] + ): + return False + return True + + return fn + + +def _register_quantized_conv_binary_lowering( + pattern, + pass_number, + computation_op, + binary_unary_attr, +): + @register_lowering_pattern( + pattern, + extra_check=_is_valid_qconv_binary_optimization_pattern(), + pass_number=pass_number, + ) + def qconv_binary(match: Match, *args, **kwargs): + output_dtype = _get_pattern_output_dtype(match) + assert output_dtype is not None + x, x_scale, x_zp = kwargs["x"], kwargs["x_scale"], kwargs["x_zp"] + accum = ( + kwargs["accum"] + if output_dtype == torch.uint8 + else kwargs["accum_after_dequant"] + ) + accum_scale = kwargs["accum_scale"] if output_dtype == torch.uint8 else 1.0 + accum_zp = kwargs["accum_zp"] if output_dtype == torch.uint8 else 0 + packed_weight, w_scale, w_zp = ( + kwargs["packed_weight"], + kwargs["w_scale"], + kwargs["w_zp"], + ) + b, stride, padding, dilation, groups = ( + kwargs["b"], + kwargs["stride"], + kwargs["padding"], + kwargs["dilation"], + kwargs["groups"], + ) + # Output QParams + o_inv_scale = kwargs["o_inv_scale"] if output_dtype == torch.uint8 else 1.0 + o_zero_point = kwargs["o_zp"] if output_dtype == torch.uint8 else 0 + + accum.realize() + from .mkldnn_fusion import _can_be_inplace + + assert _can_be_inplace( + accum + ), "QConv Binary Inplace Fusion requires accum is not an alias or mutation." + + computation_args = ( + x, + x_scale, + x_zp, + accum, + accum_scale, + accum_zp, + packed_weight, + w_scale, + w_zp, + b, + stride, + padding, + dilation, + groups, + o_inv_scale, + o_zero_point, + output_dtype, + binary_unary_attr.binary_op_name, + binary_unary_attr.alpha, + binary_unary_attr.unary_op_name, + binary_unary_attr.scalars_attr, + binary_unary_attr.algorithm_attr, + ) + counters["inductor"]["qconv2d_binary_matcher_count"] += 1 + counters["inductor"]["qconv2d_binary_matcher_nodes"] += len(match.nodes) + return L[computation_op](*computation_args) + + return qconv_binary + + +def _register_quantization_unary_fusion(): + from .mkldnn_fusion import ( + _gelu_fusion_1 as _gelu_fusion_erf, + _gelu_fusion_2 as _gelu_fusion_tanh, + _hardswish_fusion, + _hardtanh_fusion, + _silu_fusion, + ) + + class UnaryAttr: + def __init__( + self, op_name: str, scalars_attr=None, algorithm_attr=None + ) -> None: + self.op_name = op_name + self.scalars_attr = scalars_attr if scalars_attr else [] + self.algorithm_attr = algorithm_attr if algorithm_attr else "" + + for original_pattern_output_dtype in [torch.float32, torch.bfloat16]: + # QConv2d + # Priority 1 to match: QConv2d Unary pattern with int8 output + # If a pattern1 is a sub-set of pattern2, we should try to match pattern2 firstly. + # For example: pattern1 is qconv_fp32 -> relu, pattern2 is qconv_fp32 -> relu -> quant + is_bf16 = original_pattern_output_dtype == torch.bfloat16 + conv_unary_replace_patterns = { + UnaryAttr("none", [], ""): generate_pattern_with_output_quant( + get_dequantize_qconv_pt2e_pattern(1), + ), + UnaryAttr("relu", [], ""): generate_pattern_with_output_quant( + generate_pattern_with_unary( + get_dequantize_qconv_pt2e_pattern(1), aten.relu.default + ), + ), + UnaryAttr("hardtanh", [], ""): generate_pattern_with_output_quant( + _unary_fusion_pattern( + _hardtanh_fusion, + get_dequantize_qconv_pt2e_pattern(1), + 1, + is_bf16, + ), + with_dtype_convert=is_bf16, + ), + UnaryAttr("hardswish", [], ""): generate_pattern_with_output_quant( + _unary_fusion_pattern( + _hardswish_fusion, + get_dequantize_qconv_pt2e_pattern(1 if is_bf16 else 2), + 2, + is_bf16, + ), + with_dtype_convert=is_bf16, + ), + UnaryAttr("swish", [], ""): generate_pattern_with_output_quant( + _unary_fusion_pattern( + _silu_fusion, + get_dequantize_qconv_pt2e_pattern(1 if is_bf16 else 2), + 2, + is_bf16, + ), + with_dtype_convert=is_bf16, + ), + } + + for unary_attr, patterns in conv_unary_replace_patterns.items(): + # Register qconv2d pattern for ExternKernel Lowering + _register_quantized_conv_lowering( + patterns, + 1, # pass_number + torch.ops.onednn.qconv2d_pointwise, # computation_op + unary_attr, # unary_attr + ) + + # Priority 2 to match: QConv2d Unary pattern with fp32/bfloat16 output + conv_unary_replace_float_out_patterns = { + UnaryAttr("relu", [], ""): generate_pattern_with_unary( + get_dequantize_qconv_pt2e_pattern(1), aten.relu.default + ), + UnaryAttr("hardtanh", [], ""): _may_generate_pattern_with_dtype_convert( + _unary_fusion_pattern( + _hardtanh_fusion, + get_dequantize_qconv_pt2e_pattern(1), + 1, + is_bf16, + ), + Arg(), + is_bf16, + ), + UnaryAttr("hardswish", [], ""): _may_generate_pattern_with_dtype_convert( + _unary_fusion_pattern( + _hardswish_fusion, + get_dequantize_qconv_pt2e_pattern(1 if is_bf16 else 2), + 2, + is_bf16, + ), + Arg(), + is_bf16, + ), + UnaryAttr("swish", [], ""): _may_generate_pattern_with_dtype_convert( + _unary_fusion_pattern( + _silu_fusion, + get_dequantize_qconv_pt2e_pattern(1 if is_bf16 else 2), + 2, + is_bf16, + ), + Arg(), + is_bf16, + ), + } + + for unary_attr, patterns in conv_unary_replace_float_out_patterns.items(): + # Register qconv2d pattern for ExternKernel Lowering + _register_quantized_conv_lowering( + patterns, + 2, # pass_number + torch.ops.onednn.qconv2d_pointwise, # computation_op + unary_attr, # unary_attr + ) + + # QLinear + for x_scale_zp_are_tensors in (False, True): + qlinear_pattern = get_qlinear_pt2e_pattern(x_scale_zp_are_tensors) + # Priority 1 to match: QLinear Unary pattern with int8 output + linear_unary_replace_patterns = { + UnaryAttr("none", [], ""): generate_pattern_with_output_quant( + qlinear_pattern, + ), + UnaryAttr("relu", [], ""): generate_pattern_with_output_quant( + generate_pattern_with_unary(qlinear_pattern, aten.relu.default), + ), + UnaryAttr("gelu", [], "none"): generate_pattern_with_output_quant( + _unary_fusion_pattern( + _gelu_fusion_erf, + get_qlinear_pt2e_pattern( + x_scale_zp_are_tensors, 1 if is_bf16 else 2 + ), + 2, + is_bf16, + ), + with_dtype_convert=is_bf16, + ), + UnaryAttr("gelu", [], "tanh"): generate_pattern_with_output_quant( + _unary_fusion_pattern( + _gelu_fusion_tanh, + get_qlinear_pt2e_pattern( + x_scale_zp_are_tensors, 1 if is_bf16 else 4 + ), + 4, + is_bf16, + ), + with_dtype_convert=is_bf16, + ), + } + + for unary_attr, patterns in linear_unary_replace_patterns.items(): + _register_quantized_linear_lowering( + patterns, + 1, # pass_number + torch.ops.onednn.qlinear_pointwise, # computation_op + unary_attr, # unary_attr + ) + + # Priority 2 to match: QLinear Unary pattern with FP32/BF16 output + linear_unary_replace_float_out_patterns = { + UnaryAttr("relu", [], ""): generate_pattern_with_unary( + qlinear_pattern, aten.relu.default + ), + UnaryAttr("gelu", [], "none"): _may_generate_pattern_with_dtype_convert( + _unary_fusion_pattern( + _gelu_fusion_erf, + get_qlinear_pt2e_pattern( + x_scale_zp_are_tensors, 1 if is_bf16 else 2 + ), + 2, + is_bf16, + ), + Arg(), + is_bf16, + ), + UnaryAttr("gelu", [], "tanh"): _may_generate_pattern_with_dtype_convert( + _unary_fusion_pattern( + _gelu_fusion_tanh, + get_qlinear_pt2e_pattern( + x_scale_zp_are_tensors, 1 if is_bf16 else 4 + ), + 4, + is_bf16, + ), + Arg(), + is_bf16, + ), + } + + for unary_attr, patterns in linear_unary_replace_float_out_patterns.items(): + _register_quantized_linear_lowering( + patterns, + 2, # pass_number + torch.ops.onednn.qlinear_pointwise, # computation_op + unary_attr, # unary_attr + ) + + +def _register_quantization_binary_fusion(): + class BinaryUnaryAttr: + def __init__( + self, + binary_op_name: str, + alpha=None, + unary_op_name: str = "none", + scalars_attr=None, + algorithm_attr=None, + ) -> None: + self.binary_op_name = binary_op_name + self.alpha = alpha if alpha else 1.0 + self.unary_op_name = unary_op_name + self.scalars_attr = scalars_attr if scalars_attr else [] + self.algorithm_attr = algorithm_attr if algorithm_attr else "" + + for int8_mixed_bf16_with_inplace_add in [False, True]: + # Priority 1 to match: QConv2d Binary or Binary-Unary pattern with int8 output + binary_replace_patterns = { + BinaryUnaryAttr( + "sum", 1.0, "none", [], "" + ): generate_pattern_with_output_quant( + generate_pattern_with_binary( + aten.add.Tensor, + get_dequantize_qconv_pt2e_pattern(1), + dequantize_accum_pattern, + int8_mixed_bf16_with_inplace_add, + ), + ), + BinaryUnaryAttr( + "sum", 1.0, "relu", [], "" + ): generate_pattern_with_output_quant( + generate_pattern_with_unary( + generate_pattern_with_binary( + aten.add.Tensor, + get_dequantize_qconv_pt2e_pattern(1), + dequantize_accum_pattern, + int8_mixed_bf16_with_inplace_add, + ), + aten.relu.default, + ), + ), + } + + for binary_unary_attr, patterns in binary_replace_patterns.items(): + _register_quantized_conv_binary_lowering( + patterns, + 0, # pass_number + torch.ops.onednn.qconv2d_pointwise.binary, # computation_op + binary_unary_attr, # binary_unary_attr + ) + + # Priority 2 to match: QConv2d Binary-Unary pattern with fp32/bfloat16 output + binary_replace_float_out_patterns = { + BinaryUnaryAttr("sum", 1.0, "relu", [], ""): generate_pattern_with_unary( + generate_pattern_with_binary( + aten.add.Tensor, + get_dequantize_qconv_pt2e_pattern(1), + KeywordArg("accum_after_dequant"), + int8_mixed_bf16_with_inplace_add, + ), + aten.relu.default, + ), + } + + for ( + binary_unary_attr, + patterns, + ) in binary_replace_float_out_patterns.items(): + if int8_mixed_bf16_with_inplace_add: + _register_quantized_conv_binary_lowering( + patterns, + 0, # pass_number + torch.ops.onednn.qconv2d_pointwise.binary, # computation_op + binary_unary_attr, # binary_unary_attr + ) + else: + _register_quantized_conv_binary_lowering( + patterns, + 1, # pass_number + torch.ops.onednn.qconv2d_pointwise.binary, # computation_op + binary_unary_attr, # binary_unary_attr + ) + + # Priority 3: QConv2d Binary pattern with fp32/bfloat16 output + binary_replace_float_out_patterns = { + BinaryUnaryAttr("sum", 1.0, "none", [], ""): generate_pattern_with_binary( + aten.add.Tensor, + get_dequantize_qconv_pt2e_pattern(1), + KeywordArg("accum_after_dequant"), + int8_mixed_bf16_with_inplace_add, + ), + } + + for ( + binary_unary_attr, + patterns, + ) in binary_replace_float_out_patterns.items(): + _register_quantized_conv_binary_lowering( + patterns, + 1 if int8_mixed_bf16_with_inplace_add else 2, # pass_number + torch.ops.onednn.qconv2d_pointwise.binary, # computation_op + binary_unary_attr, # binary_unary_attr + ) + + # QLinear + r""" + Supported linear-binary(-unary) patterns + + linear(X) extra input + \ / + Add + | + Optional(relu) + | + Y + + 1. int8-mixed-fp32 + +---+---------------+-----------+------------------------------+---------+ + | # | Add type | Quant out | Pattern | Post op | + +---+---------------+-----------+------------------------------+---------+ + | 1 | In-/out-place | Yes | linear + fp32 -> (relu) -> q | add | + +---+---------------+-----------+------------------------------+---------+ + | 2 | In-/out-place | No | linear + fp32 -> (relu) | sum | + +---+---------------+-----------+------------------------------+---------+ + + 2. int8-mixed-bf16 + +---+----------+---------------+-----------+-----------------------------------------+---------+ + | # | X2 dtype | Add type | Quant out | Pattern | Post op | + +---+----------+---------------+-----------+-----------------------------------------+---------+ + | 1 | BF16 | In-/out-place | Yes | linear + bf16 -> (relu) -> q | add | + +---+----------+---------------+-----------+-----------------------------------------+---------+ + | 2 | BF16 | In-/out-place | No | linear + bf16 -> (relu) | sum | + +---+----------+---------------+-----------+-----------------------------------------+---------+ + | 3 | FP32 | Out-place | Yes | linear + fp32 -> (relu) -> q | add | + | | | In-place right| | | | + +---+----------+---------------+-----------+-----------------------------------------+---------+ + | 4 | FP32 | Out-place | No | linear + fp32 -> (relu) | sum | + | | | In-place right| | | | + +---+----------+---------------+-----------+-----------------------------------------+---------+ + | 5 | FP32 | In-place left | Yes | linear + fp32 -> to_bf16 -> (relu) -> q | add | + +---+----------+---------------+-----------+-----------------------------------------+---------+ + | 6 | FP32 | In-place left | No | linear + fp32 -> to_bf16 -> (relu) | add | + +---+----------+---------------+-----------+-----------------------------------------+---------+ + + Note + (1) The positions of linear and the extra input can be swapped. + (2) we don't insert q-dq before the extra input of linear-add by recipe. But if q-dq is found at the + extra input, we don't match that pattern because we cannot match all these patterns in 3 passes. + """ + for x_scale_zp_are_tensors in (False, True): + qlinear_binary_op = ( + torch.ops.onednn.qlinear_pointwise.binary_tensor + if x_scale_zp_are_tensors + else torch.ops.onednn.qlinear_pointwise.binary + ) + unary_postop_list = ["none", "relu"] + unary_postop_dict = { + "none": None, + "relu": aten.relu.default, + } + convert_dtype_after_binary_list = [False, True] + + # Priority 1 to match: QLinear Binary or Binary-Unary pattern with int8 output + # Covers case (1) of int8-mixed-fp32 and case (1)(3)(5) of int8-mixed-bf16, + # totally 3 patterns (2 are identical) + swap_binary_inputs_list = [False, True] + int8_mixed_bf16_list = [False, True] + combinations = itertools.product( + unary_postop_list, + int8_mixed_bf16_list, + swap_binary_inputs_list, + convert_dtype_after_binary_list, + ) + qlinear_binary_replace_patterns = {} + for unary_op, int8_mixed_bf16, swap_inputs, cvt_dtype_binary in combinations: + if not int8_mixed_bf16 and cvt_dtype_binary: + # No convert node after binary node if dtypes are all fp32 + continue + qlinear_binary_replace_patterns.update( + { + BinaryUnaryAttr( + "add", 1.0, unary_op, [], "" + ): generate_pattern_with_output_quant( + generate_pattern_with_unary( + generate_pattern_with_binary( + aten.add.Tensor, + get_qlinear_pt2e_pattern(x_scale_zp_are_tensors), + KeywordArg("other"), + # If fp32 extra input is inplace added to bf16 linear output, + # a to_bf16 node is inserted after binary + dtype_convert=cvt_dtype_binary, + swap_inputs=swap_inputs, + ), + unary_postop_dict[unary_op], + ), + ) + } + ) + for binary_unary_attr, patterns in qlinear_binary_replace_patterns.items(): + _register_quantized_linear_binary_lowering( + patterns, + 0, # pass_number + qlinear_binary_op, # computation_op + binary_unary_attr, # binary_unary_attr + ) + + # Priority 2.1 to match: QLinear Binary-Unary pattern with fp32/bfloat16 output + # Covers case (2) of int8-mixed-fp32 and case (2)(4) of int8-mixed-bf16, + # totally 2 patterns (2 are identical) + binary_replace_float_out_patterns = {} + for swap_binary_inputs in swap_binary_inputs_list: + binary_replace_float_out_patterns.update( + { + BinaryUnaryAttr( + "sum", 1.0, "relu", [], "" + ): generate_pattern_with_unary( + generate_pattern_with_binary( + aten.add.Tensor, + get_qlinear_pt2e_pattern(x_scale_zp_are_tensors), + KeywordArg("accum"), + dtype_convert=False, + swap_inputs=swap_binary_inputs, + ), + aten.relu.default, + ), + } + ) + for ( + binary_unary_attr, + patterns, + ) in binary_replace_float_out_patterns.items(): + _register_quantized_linear_binary_lowering( + patterns, + 1, # pass_number + qlinear_binary_op, # computation_op + binary_unary_attr, + ) + # Priority 2.2 to match: QLinear Binary-Unary pattern with fp32/bfloat16 output + # Covers case (6) of int8-mixed-bf16 + binary_replace_float_out_patterns = {} + for swap_binary_inputs in swap_binary_inputs_list: + binary_replace_float_out_patterns.update( + { + BinaryUnaryAttr( + "add", 1.0, "relu", [], "" + ): generate_pattern_with_unary( + generate_pattern_with_binary( + aten.add.Tensor, + get_qlinear_pt2e_pattern(x_scale_zp_are_tensors), + KeywordArg("other"), + dtype_convert=True, + swap_inputs=swap_binary_inputs, + ), + aten.relu.default, + ), + } + ) + for ( + binary_unary_attr, + patterns, + ) in binary_replace_float_out_patterns.items(): + _register_quantized_linear_binary_lowering( + patterns, + 1, # pass_number + qlinear_binary_op, # computation_op + binary_unary_attr, + ) + + # Priority 3.1: QLinear Binary pattern with fp32/bfloat16 output + # Covers case (2) of int8-mixed-fp32 and case (2)(4) of int8-mixed-bf16, + # totally 2 patterns (2 are identical) + binary_replace_float_out_patterns = {} + for swap_binary_inputs in swap_binary_inputs_list: + binary_replace_float_out_patterns.update( + { + BinaryUnaryAttr( + "sum", 1.0, "none", [], "" + ): generate_pattern_with_binary( + aten.add.Tensor, + get_qlinear_pt2e_pattern(x_scale_zp_are_tensors), + KeywordArg("accum"), + dtype_convert=False, + swap_inputs=swap_binary_inputs, + ), + } + ) + for ( + binary_unary_attr, + patterns, + ) in binary_replace_float_out_patterns.items(): + _register_quantized_linear_binary_lowering( + patterns, + 2, # pass_number + qlinear_binary_op, # computation_op + binary_unary_attr, + ) + # Priority 3.2: QLinear Binary pattern with fp32/bfloat16 output + # Covers (6) of int8-mixed-bf16 + binary_replace_float_out_patterns = {} + for swap_binary_inputs in swap_binary_inputs_list: + binary_replace_float_out_patterns.update( + { + BinaryUnaryAttr( + "add", 1.0, "none", [], "" + ): generate_pattern_with_binary( + aten.add.Tensor, + get_qlinear_pt2e_pattern(x_scale_zp_are_tensors), + KeywordArg("other"), + dtype_convert=True, + swap_inputs=swap_binary_inputs, + ), + } + ) + for ( + binary_unary_attr, + patterns, + ) in binary_replace_float_out_patterns.items(): + _register_quantized_linear_binary_lowering( + patterns, + 2, # pass_number + qlinear_binary_op, # computation_op + binary_unary_attr, + ) + + +def _is_valid_quantized_maxpool2d_optimization_pattern(): + def fn(match): + # Only match the pattern which max_pool2d_with_indices returns value + # instead of indices. + get_item_node = filter_nodes(match.nodes, operator.getitem)[0] + return get_item_node.args[1] == 0 + + return fn + + +def _register_quantized_maxpool2d_lowering( + pattern, + computation_op, +): + @register_lowering_pattern( + pattern, + extra_check=_is_valid_quantized_maxpool2d_optimization_pattern(), + ) + def qmaxpool2d(match: Match, *args, **kwargs): + x = kwargs["x"] + kernel_size = kwargs["kernel_size"] + stride = kwargs["stride"] if ("stride" in kwargs) else None + padding = kwargs["padding"] if ("padding" in kwargs) else 0 + dilation = kwargs["dilation"] if ("dilation" in kwargs) else 1 + ceil_mode = kwargs["ceil_mode"] if ("ceil_mode" in kwargs) else False + + if padding == 0: + padding = [0, 0] + if dilation == 1: + dilation = [1, 1] + if not stride: + stride = kernel_size + kernel_size = pad_listlike(kernel_size, 2) + stride = pad_listlike(stride, 2) + padding = pad_listlike(padding, 2) + dilation = pad_listlike(dilation, 2) + + assert len(kernel_size) == 2 + assert len(stride) == 2 + assert len(padding) == 2 + assert len(dilation) == 2 + + computation_args = ( + x, + kernel_size, + stride, + padding, + dilation, + ceil_mode, + ) + computation_args, _ = require_channels_last(computation_op, *computation_args) + counters["inductor"]["qmaxpool2d_matcher_count"] += 1 + counters["inductor"]["qmaxpool2d_matcher_nodes"] += len(match.nodes) + return L[computation_op](*computation_args) + + return qmaxpool2d + + +def _register_quantization_maxpool2d(): + # Currently, the default parameters are not in FX Graph generated by Dynamo export. + # So, if user defines nn.MaxPool2d with different assignment of default parameter, + # it will generate graph with different number of input nodes and hence + # different pattern to be matched. + # Refer to the issue: https://github.com/pytorch/pytorch/issues/105901 + max_pool2d_args_list = [ + [ + KeywordArg("stride"), + ], + [ + KeywordArg("stride"), + KeywordArg("padding"), + ], + [ + KeywordArg("stride"), + KeywordArg("padding"), + KeywordArg("dilation"), + ], + [ + KeywordArg("stride"), + KeywordArg("padding"), + KeywordArg("dilation"), + KeywordArg("ceil_mode"), + ], + ] + for max_pool2d_args in max_pool2d_args_list: + dequantize_maxpool2d_pattern = CallFunction( + aten.max_pool2d_with_indices.default, + get_dequantize_per_tensor_activation_pattern(), + KeywordArg("kernel_size"), + *max_pool2d_args, + ) + dequantize_lowmem_maxpool2d_pattern = CallFunction( + prims._low_memory_max_pool2d_with_offsets.default, + get_dequantize_per_tensor_activation_pattern(), + KeywordArg("kernel_size"), + *max_pool2d_args, + KeywordArg("offset_dtype"), + ) + dequantize_maxpool2d_get_item_pattern = CallFunction( + operator.getitem, + dequantize_maxpool2d_pattern, + Arg(), + ) + dequantize_lowmem_maxpool2d_get_item_pattern = CallFunction( + operator.getitem, + dequantize_lowmem_maxpool2d_pattern, + Arg(), + ) + _register_quantized_maxpool2d_lowering( + generate_pattern_with_output_quant(dequantize_maxpool2d_get_item_pattern), + quantized.max_pool2d.default, + ) + _register_quantized_maxpool2d_lowering( + generate_pattern_with_output_quant( + dequantize_lowmem_maxpool2d_get_item_pattern + ), + quantized.max_pool2d.default, + ) + + +def _is_input_output_same_scale_zp(check_node): + def fn(match): + # Ensure all the inputs and output has same scale and zero point + # Step 1: Check inputs/output zero point + # Get dequant nodes at input + dequant_nodes = filter_nodes( + match.nodes, quantized_decomposed.dequantize_per_tensor.default + ) + zero_points = [node.args[2] for node in dequant_nodes] + # Get quant nodes at output + quant_nodes = filter_nodes( + match.nodes, quantized_decomposed.quantize_per_tensor.default + ) + assert len(quant_nodes) == 1, "expect only 1 add node at output quant pattern" + zero_points.append(quant_nodes[0].args[2]) + if not all(zero_point == zero_points[0] for zero_point in zero_points): + return False + + # Step 2: Check inputs/output scale + scales = [node.args[1] for node in dequant_nodes] + scales.append(quant_nodes[0].args[1]) + if not all(math.isclose(scale, scales[0], rel_tol=1e-5) for scale in scales): # type: ignore[arg-type] + return False + + return True + + return fn + + +def _register_quantized_cat_lowering( + pattern, + computation_op, +): + @register_lowering_pattern( + pattern, + extra_check=_is_input_output_same_scale_zp(aten.cat.default), + ) + def qcat(match: Match, inputs, dim, **kwargs): + # inputs is with format: [[x1, x1_dq_dtype, x1_zp, x1_scale], ...] + uint8_inputs = [input[0] for input in inputs] + counters["inductor"]["qcat_matcher_count"] += 1 + counters["inductor"]["qcat_matcher_nodes"] += len(match.nodes) + return L[computation_op](uint8_inputs, dim) + + return qcat + + +_raw_dequantize_per_tensor_activation_pattern = CallFunction( + quantized_decomposed.dequantize_per_tensor.default, + Arg(), + Arg(), + Arg(), + Arg(), + Arg(), + Arg(), +) + + +def _register_quantization_cat(): + dequantize_cat_pattern = CallFunction( + aten.cat.default, + ListOf(_raw_dequantize_per_tensor_activation_pattern), + KeywordArg("dim"), + ) + _register_quantized_cat_lowering( + generate_pattern_with_output_quant(dequantize_cat_pattern), + aten.cat, + ) + + +def _register_quantized_reshape_lowering( + pattern, + computation_op, +): + @register_lowering_pattern( + pattern, + extra_check=_is_input_output_same_scale_zp(aten.reshape.default), + ) + def qreshape(match: Match, *args, **kwargs): + qx = kwargs["x"] + shape = kwargs["shape"] + counters["inductor"]["qreshape_matcher_count"] += 1 + counters["inductor"]["qreshape_matcher_nodes"] += len(match.nodes) + return L[computation_op](qx, shape) + + return qreshape + + +def _register_quantization_reshape(): + dequantize_reshape_pattern = CallFunction( + torch.ops.aten.reshape.default, + get_dequantize_per_tensor_activation_pattern(), + KeywordArg("shape"), + ) + _register_quantized_reshape_lowering( + generate_pattern_with_output_quant(dequantize_reshape_pattern), + aten.reshape, + ) + + +def _is_valid_woq_optimization_pattern(): + def fn(match): + assert all(k in match.kwargs for k in ("x", "weight", "scales")) + x = match.kwargs["x"].meta["val"] + weight = match.kwargs["weight"].meta["val"] + scales = match.kwargs["scales"].meta["val"] + return ( + # For now, we only support woq mm kernels + # with x.type=bfloat16 and w.type=int8 + x.dtype == torch.bfloat16 + and weight.dtype == torch.int8 + and scales.dtype == torch.bfloat16 + # _weight_int8pack_mm kernel only supports cpu now + # TODO: add cuda kernel support instead of calling mul+sum + and x.device.type == "cpu" + and x.device == weight.device + and x.device == scales.device + ) + + return fn + + +def _register_woq_lowering(pattern, computation_woq, computation_reshape): + @register_lowering_pattern( + pattern, + extra_check=_is_valid_woq_optimization_pattern(), + ) + def woq(match: Match, *args, **kwargs): + x = kwargs["x"] + weight = kwargs["weight"] + scales = kwargs["scales"] + counters["inductor"]["woq_matcher_count"] += 1 + counters["inductor"]["woq_matcher_nodes"] += len(match.nodes) + out_features = weight.get_size()[0] + origin_x_size = x.get_size() + x_shape = [-1, origin_x_size[-1]] + out_shape = origin_x_size[:-1] + [ + out_features, + ] + func1 = L[computation_reshape](x, x_shape) + func2 = L[computation_woq](func1, weight, scales) + return L[computation_reshape](func2, out_shape) + + return woq + + +def _register_woq_mm_int8_pattern1(): + # F.linear(x, weight.to(dtype=x.dtype)) * scales + # case of dispatching to mm, with x reshape + _woq_pattern = CallFunction( + aten.mul.Tensor, + CallFunction( + aten.reshape.default, + CallFunction( + aten.mm.default, + CallFunction(aten.reshape.default, KeywordArg("x"), Arg()), + CallFunction( + aten.permute.default, + CallFunction( + prims.convert_element_type.default, KeywordArg("weight"), Arg() + ), + Arg(), + ), + ), + Arg(), + ), + KeywordArg("scales"), + ) + _register_woq_lowering(_woq_pattern, aten._weight_int8pack_mm.default, aten.reshape) + + +def _register_woq_mm_int8_pattern2(): + # F.linear(x, weight.to(dtype=x.dtype)) * scales + # case of dispatching to mm, w/o x reshape + _woq_pattern = CallFunction( + aten.mul.Tensor, + CallFunction( + aten.reshape.default, + CallFunction( + aten.mm.default, + KeywordArg("x"), + CallFunction( + aten.permute.default, + CallFunction( + prims.convert_element_type.default, KeywordArg("weight"), Arg() + ), + Arg(), + ), + ), + Arg(), + ), + KeywordArg("scales"), + ) + _register_woq_lowering(_woq_pattern, aten._weight_int8pack_mm.default, aten.reshape) + + +def _register_woq_mm_int8_pattern3(): + # F.linear(x, weight.to(dtype=x.dtype)) * scales + # case of dispatching to bmm + _woq_pattern = CallFunction( + aten.mul.Tensor, + CallFunction( + aten.bmm.default, + CallFunction(aten.expand.default, KeywordArg("x"), Arg()), + CallFunction( + aten.expand.default, + CallFunction( + aten.permute.default, + CallFunction( + prims.convert_element_type.default, KeywordArg("weight"), Arg() + ), + Arg(), + ), + Arg(), + ), + ), + KeywordArg("scales"), + ) + _register_woq_lowering(_woq_pattern, aten._weight_int8pack_mm.default, aten.reshape) + + +def _register_quantization_lowerings(): + _register_quantization_unary_fusion() + _register_quantization_binary_fusion() + _register_quantization_maxpool2d() + _register_quantization_cat() + _register_quantization_reshape() + + +def _register_woq_lowerings(): + _register_woq_mm_int8_pattern1() + _register_woq_mm_int8_pattern2() + _register_woq_mm_int8_pattern3() + + +def _is_valid_dequant_promotion_pattern(dtype=torch.float32): + def _inner(match): + assert dtype in [torch.float32, torch.bfloat16] + dequant_pattern_end_node = match.output_node() + if dequant_pattern_end_node.target not in [ + quantized_decomposed.dequantize_per_tensor.default, + quantized_decomposed.dequantize_per_tensor.tensor, + prims.convert_element_type.default, + aten.reshape.default, + ]: + return False + + if dequant_pattern_end_node.target is aten.reshape.default: + dequant_node = ( + dequant_pattern_end_node.args[ + 0 + ] # pattern: linear <- reshape <- dequant + if dtype == torch.float32 + else dequant_pattern_end_node.args[0].args[ + 0 + ] # pattern: linear <- reshape <- to_bf16 <- dequant + ) + else: + dequant_node = ( + dequant_pattern_end_node # pattern: linear <- dequant + if dtype == torch.float32 + else dequant_pattern_end_node.args[ + 0 + ] # pattern: linear <- to_bf16 <- dequant + ) + + if ( + dequant_node.target + in [ + quantized_decomposed.dequantize_per_tensor.default, + quantized_decomposed.dequantize_per_tensor.tensor, + ] + and len(list(dequant_pattern_end_node.users)) > 1 + ): + # If dequant pattern has more than 1 users, then do dequant promoted + return True + return False + + return _inner + + +def _register_dequant_promotion_pass(pattern, pass_number, dtype=torch.float32): + @register_freezing_graph_pattern( + pattern, + extra_check=_is_valid_dequant_promotion_pattern(dtype), + pass_number=pass_number, + ) + def dequant_promotion(match: Match, *args, **kwargs): + # Dequant_promotion will transform + # graph 1: + # quant + # + - - - | - - - + + # | dequant | + # | / \ | + # | node1 node2 | + # + - | - - - | - + + # quant quant + # into: + # graph 2: + # quant + # + - - / - \ - - + + # |dequant dequant| + # | | | | + # | node1 node2 | + # + - | - - - | - + + # quant quant + # In graph 1, the dequant node is shared by node1 and node2, + # as a result, neither node1 nor node2 could form an int8 + # fusion pattern. + # After this transformation, the graph 2 could hit the int8 + # fusion pattern: dequant-node-quant, respectively for + # node1 and node2. + assert dtype in [torch.float32, torch.bfloat16] + + def clone_to_new_node(graph, source_node, user_node): + # Clone the source_node to a new node + # Replace user_node's input from source_node to new_node + assert ( + source_node.op == "call_function" + ), "clone_to_new_node only support node.op call_function" + with graph.inserting_before(user_node): + new_node = graph.call_function( + source_node.target, + args=source_node.args, + kwargs=source_node.kwargs, + ) + new_node.meta = copy.copy(source_node.meta) + user_node.replace_input_with(source_node, new_node) + return new_node + + # Find the start node and end node of a dequant pattern + # * End node should be the match.output_node() + # * Start node should be the node of dequantize_per_tensor + dequant_pattern_end_node = match.output_node() + assert dequant_pattern_end_node.target in [ + quantized_decomposed.dequantize_per_tensor.default, + quantized_decomposed.dequantize_per_tensor.tensor, + prims.convert_element_type.default, + aten.reshape.default, + ] + + # For a dequant pattern, we should expect see the node list as: + # * OPT(aten.reshape.default) + # * OPT(prims.convert_element_type.default) (to_bf16) + # * dequantize_per_tensor + def _find_first_node_in_dequant_pattern(_node): + if _node.target in [ + quantized_decomposed.dequantize_per_tensor.default, + quantized_decomposed.dequantize_per_tensor.tensor, + ]: + # For a dequant pattern, we expect the start node is a dequantize_per_tensor node + return _node + else: + assert ( + len(_node.args) >= 1 + ), "In in dequant pattern, each node should have more than 1 arg." + return _find_first_node_in_dequant_pattern(_node.args[0]) + + dequant_pattern_start_node = _find_first_node_in_dequant_pattern( + dequant_pattern_end_node + ) + + assert dequant_pattern_start_node.target in [ + quantized_decomposed.dequantize_per_tensor.default, + quantized_decomposed.dequantize_per_tensor.tensor, + ] + + # Clone the dequant pattern for each user node + graph = match.graph + user_node_list = list(dequant_pattern_end_node.users) + for user_node in user_node_list[1:]: + _source_node = dequant_pattern_end_node + _user_node = user_node + while _source_node != dequant_pattern_start_node.args[0]: + _user_node = clone_to_new_node(graph, _source_node, _user_node) + _source_node = _source_node.args[0] # type: ignore[assignment] + + counters["inductor"]["dequant_promotion_matcher_count"] += 1 + counters["inductor"]["dequant_promotion_matcher_nodes"] += len(match.nodes) + + +def _is_valid_dequant_conv2d_pattern(dtype): + def _inner(match): + # Here we do some further check to ensure: + # 1. It's a conv2d node with dim of 4, since we only support lowering of conv2d now. + # 2. The dequant pattern has only 1 user of conv2d node. + # If these conditions don't meet, we will not + # insert weight prepack node into the matched pattern. + conv_node = match.output_node() + assert conv_node.target is aten.convolution.default + input_meta_value = conv_node.args[0].meta.get("val") + weight_meta_value = conv_node.args[1].meta.get("val") + for meta_value in [input_meta_value, weight_meta_value]: + if ( + meta_value is None + or meta_value.device.type != "cpu" + or meta_value.dim() != 4 + ): + # Only support conv2d now + return False + + assert dtype in [torch.float32, torch.bfloat16] + + if dtype == torch.float32: + dequant_node = conv_node.args[0] + else: + convert_to_bf16 = conv_node.args[0] + dequant_node = convert_to_bf16.args[0] + + if len(list(dequant_node.users)) != 1: + # Ensure the dequant pattern only has 1 user + # since we will delete the dequant pattern here + return False + return True + + return _inner + + +def _register_qconv_weight_prepack_pass(pattern, pass_number, dtype=torch.float32): + @register_freezing_graph_pattern( + pattern, + extra_check=_is_valid_dequant_conv2d_pattern(dtype), + pass_number=pass_number, + ) + def qconv_weight_prepack(match: Match, *args, **kwargs): + """ + Match the pattern: + int8 activation + | + dequant_per_tensor + | + Conv2d <- optional(aten.clone.default) <- dequant_per_channel <- int8_weight + + Insert weight prepack node and change the pattern to: + int8 activation + | + onednn.qconv2d_pointwise <- onednn.qconv_prepack <- int8_weight + """ + assert dtype in [torch.float32, torch.bfloat16] + conv_node = match.output_node() + assert conv_node.target is aten.convolution.default + if dtype == torch.float32: + dequant_node = conv_node.args[0] + else: + convert_to_bf16 = conv_node.args[0] + dequant_node = convert_to_bf16.args[0] # type: ignore[union-attr] + has_clone_to_channel_last_node_in_pattern = ( + conv_node.args[1].target is aten.clone.default # type: ignore[union-attr] + ) + clone_node = ( + conv_node.args[1] if has_clone_to_channel_last_node_in_pattern else None + ) + + if dtype == torch.float32: + dequant_per_channel = ( + clone_node.args[0] # type: ignore[union-attr] + if has_clone_to_channel_last_node_in_pattern + else conv_node.args[1] + ) + else: + weight_to_bf16_node = ( + clone_node.args[0] # type: ignore[union-attr] + if has_clone_to_channel_last_node_in_pattern + else conv_node.args[1] + ) + dequant_per_channel = weight_to_bf16_node.args[0] # type: ignore[union-attr] + + assert ( + dequant_per_channel.target # type: ignore[union-attr] + is quantized_decomposed.dequantize_per_channel.default + ) + + # Activation QParams + qx, x_zp, x_scale = ( + kwargs["x"], + kwargs["x_zp"], + kwargs["x_scale"], + ) + + # Weight QParams + qw, w_scale, w_zp = ( + kwargs["q_weight"], + kwargs["w_scale"], + kwargs["w_zp"], + ) + + # Conv Params + bias, stride, padding, dilation, groups = ( + kwargs["b"], + kwargs["stride"], + kwargs["padding"], + kwargs["dilation"], + kwargs["groups"], + ) + + x_shape = qx.meta.get("tensor_meta").shape + if has_free_symbols(x_shape): + # For dynamic shape case, we can't get activation shape ahead of runtime. + x_shape = None + graph = match.graph + with graph.inserting_before(conv_node): + # Insert weight prepack node and the QConv node + packed_weight_inputs = ( + qw, + w_scale, + x_scale, + x_zp, + stride, + padding, + dilation, + groups, + x_shape, + ) + packed_weight_op = torch.ops.onednn.qconv_prepack + prepack_weight_node = graph.call_function( + packed_weight_op, args=packed_weight_inputs + ) + + new_args: Tuple[Any, ...] = ( + qx, + x_scale, + x_zp, + prepack_weight_node, + w_scale, + w_zp, + bias, + stride, + padding, + dilation, + groups, + 1.0, # output_scale + 0, # output_zero_point + dtype, # output_dtype + "none", # attr + [], # scalars + "", # algorithm + ) + new_conv_node = graph.call_function( + torch.ops.onednn.qconv2d_pointwise.default, args=new_args + ) + conv_node.replace_all_uses_with(new_conv_node) + new_conv_node.meta.update(conv_node.meta) + + # Erase the original conv node + graph.erase_node(conv_node) + # Erase the dequant pattern + if dtype == torch.bfloat16: + graph.erase_node(convert_to_bf16) # type: ignore[possibly-undefined, arg-type] + graph.erase_node(dequant_node) # type: ignore[arg-type] + # Erase the dequant per channel pattern + if clone_node is not None: + graph.erase_node(clone_node) # type: ignore[arg-type] + if dtype == torch.bfloat16: + graph.erase_node(weight_to_bf16_node) # type: ignore[possibly-undefined, arg-type] + graph.erase_node(dequant_per_channel) # type: ignore[arg-type] + counters["inductor"]["qconv2d_weight_prepack_matcher_count"] += 1 + counters["inductor"]["qconv2d_weight_prepack_matcher_nodes"] += len( + match.nodes + ) + + +def _generate_dequant_convolution_node_pattern( + _dequant_per_channel_pattern, dtype=torch.float32 +): + assert dtype in [torch.float32, torch.bfloat16] + dequant_convolution_node_pattern = CallFunction( + aten.convolution.default, + _may_generate_pattern_with_dtype_convert( + get_dequantize_per_tensor_activation_pattern(), + KeywordArg("autocast_act_dtype"), + dtype == torch.bfloat16, + ), + _dequant_per_channel_pattern, + KeywordArg("b"), + KeywordArg("stride"), + KeywordArg("padding"), + KeywordArg("dilation"), + KeywordArg("is_transposed"), + KeywordArg("out_padding"), + KeywordArg("groups"), + ) + return dequant_convolution_node_pattern + + +def _generate_qconv_weight_prepack_patterns(dtype=torch.float32): + assert dtype in [torch.float32, torch.bfloat16] + return ( + _generate_dequant_convolution_node_pattern( + dequantize_per_channel_weight_pattern + if dtype == torch.float32 + else dequantize_per_channel_to_bf16_weight_pattern, + dtype, + ), + # There is another pattern due to the pass of convert_conv_weights_to_channels_last + # https://github.com/pytorch/pytorch/blob/07107919297db3f8ab37f11c12666b6d6d5f692e/torch/_inductor/freezing.py#L338-L362. + # Depend on some heuristics, it may or may not insert to(channel_last) node + # between convolution and dequant_per_channel node + _generate_dequant_convolution_node_pattern( + dequantize_per_channel_clone_weight_pattern + if dtype == torch.float32 + else dequantize_per_channel_to_bf16_clone_weight_pattern, + dtype, + ), + ) + + +def _get_linear_node(match, input_dim_exceeds_two, input_contiguous): + output_reshape_node = None + if input_dim_exceeds_two: + if input_contiguous: + output_reshape_node = match.output_node() + assert output_reshape_node.target is aten.reshape.default + linear_node = output_reshape_node.args[0] + else: + linear_nodes = filter_nodes(match.nodes, aten.bmm.default) + assert len(linear_nodes) == 1 + linear_node = linear_nodes[0] + else: + linear_node = match.output_node() + + assert linear_node.target in ( + aten.addmm.default, + aten.mm.default, + aten.bmm.default, + ) + return linear_node, output_reshape_node + + +def _get_linear_dq_node( + linear_node, input_index, dtype, input_dim_exceeds_two, input_contiguous +): + act_reshape_node = None + activation_to_bf16_node = None + act_expand_node = None + if input_dim_exceeds_two: + if input_contiguous: + act_reshape_node = linear_node.args[input_index] + assert act_reshape_node.target is aten.reshape.default + if dtype == torch.float32: + # pattern: linear -> reshape -> dequant + dequant_node = act_reshape_node.args[0] + else: + # pattern: linear -> reshape -> to_bf16 -> dequant + activation_to_bf16_node = act_reshape_node.args[0] + dequant_node = activation_to_bf16_node.args[0] + else: + # bmm pattern decomposed from linear when input dim exceeds 2 and not contiguous + act_expand_node = linear_node.args[input_index] + assert act_expand_node.target is aten.expand.default + if dtype == torch.float32: + dequant_node = act_expand_node.args[0] + else: + activation_to_bf16_node = act_expand_node.args[0] + dequant_node = activation_to_bf16_node.args[0] + else: + if dtype == torch.float32: + # pattern: linear -> dequant + dequant_node = linear_node.args[input_index] + else: + # pattern: linear -> to_bf16 -> dequant + activation_to_bf16_node = linear_node.args[input_index] + dequant_node = activation_to_bf16_node.args[0] + return dequant_node, act_reshape_node, activation_to_bf16_node, act_expand_node + + +def _is_valid_dequant_linear_pattern(dtype, input_dim_exceeds_two, input_contiguous): + def _inner(match): + # Check dequant pattern has only 1 user. + ( + linear_node, + _, + ) = _get_linear_node(match, input_dim_exceeds_two, input_contiguous) + + input_index = 1 if linear_node.target is aten.addmm.default else 0 + assert dtype in [torch.float32, torch.bfloat16] + ( + dequant_node, + _, + _, + _, + ) = _get_linear_dq_node( + linear_node, input_index, dtype, input_dim_exceeds_two, input_contiguous + ) + + assert dequant_node.target in [ + quantized_decomposed.dequantize_per_tensor.default, + quantized_decomposed.dequantize_per_tensor.tensor, + ] + + if len(list(dequant_node.users)) != 1: + # Ensure the dequant pattern only has 1 user + # since we will delete the dequant pattern here + return False + + # Extra check for bmm pattern + if input_dim_exceeds_two and not input_contiguous: + # Check for act + # Act expand size should be exactly same as act size + act_expand_size = match.kwargs["act_expand_size"] + act_node = match.kwargs["x"] + if not ( + hasattr(act_node, "meta") + and isinstance(act_node.meta.get("val", None), torch.Tensor) + and (act_node.meta["val"].size() == torch.Size(act_expand_size)) + ): + return False + + # Check for wgt + # wgt permute dims should be [1, 0] + wgt_permute_dims = match.kwargs["permute_axes"] + if wgt_permute_dims != [1, 0]: + return False + + # Check below wgt size items: + # wgt before expand should with dim 2 + # Expand size should with dim 3 + # Expand size[0] should same as act size[0] + # Expand size[1] should same as wgt size[1] + # Expand size[2] should same as wgt size[0] + qweight_node = match.kwargs["q_weight"] + wgt_expand_size = match.kwargs["wgt_expand_size"] + if not ( + hasattr(qweight_node, "meta") + and isinstance(qweight_node.meta.get("val", None), torch.Tensor) + and len(qweight_node.meta["val"].size()) == 2 + and len(wgt_expand_size) == 3 + and wgt_expand_size[0] == act_node.meta["val"].size()[0] + and wgt_expand_size[1] == qweight_node.meta["val"].size()[1] + and wgt_expand_size[2] == qweight_node.meta["val"].size()[0] + ): + return False + + return True + + return _inner + + +def _register_qlinear_weight_prepack_pass( + pattern, + pass_number, + dtype=torch.float32, + input_dim_exceeds_two=False, + input_contiguous=True, +): + @register_freezing_graph_pattern( + pattern, + extra_check=_is_valid_dequant_linear_pattern( + dtype, input_dim_exceeds_two, input_contiguous + ), + pass_number=pass_number, + ) + def qlinear_weight_prepack(match: Match, *args, **kwargs): + """ + Match the pattern: + int8 activation + | + dequant_per_tensor + | + mm/addmm <- t <- dequant_per_channel <- int8_weight + + Insert weight prepack node and change the pattern to: + int8 activation + | + onednn.qlinear_pointwise <- onednn.qlinear_prepack <- int8_weight + """ + assert dtype in [torch.float32, torch.bfloat16] + ( + linear_node, + output_reshape_node, + ) = _get_linear_node(match, input_dim_exceeds_two, input_contiguous) + input_index = 1 if linear_node.target is aten.addmm.default else 0 + weight_index = input_index + 1 + + ( + dequant_node, + act_reshape_node, + activation_to_bf16_node, + act_expand_node, + ) = _get_linear_dq_node( + linear_node, input_index, dtype, input_dim_exceeds_two, input_contiguous + ) + + if input_dim_exceeds_two and not input_contiguous: + wgt_expand_node = linear_node.args[weight_index] + assert wgt_expand_node.target is aten.expand.default + t_node = wgt_expand_node.args[0] + else: + t_node = linear_node.args[weight_index] + + if dtype == torch.float32: + dequant_per_channel = t_node.args[0] + else: + weight_to_bf16_node = t_node.args[0] + dequant_per_channel = weight_to_bf16_node.args[0] + assert ( + dequant_per_channel.target + is quantized_decomposed.dequantize_per_channel.default + ) + + # Activation QParams + qx, x_zp, x_scale = ( + kwargs["x"], + kwargs["x_zp"], + kwargs["x_scale"], + ) + + # Weight QParams + qw, w_scale, w_zp = ( + kwargs["q_weight"], + kwargs["w_scale"], + kwargs["w_zp"], + ) + + # Params + bias = kwargs["b"] if "b" in kwargs else None + + x_shape = qx.meta.get("tensor_meta").shape + if has_free_symbols(x_shape): + # For dynamic shape case, we can't get activation shape ahead of runtime. + x_shape = None + graph = match.graph + with graph.inserting_before(linear_node): + # Insert weight prepack node and the qlinear node + packed_weight_inputs = ( + qw, + x_shape, + ) + packed_weight_op = torch.ops.onednn.qlinear_prepack + prepack_weight_node = graph.call_function( + packed_weight_op, args=packed_weight_inputs + ) + + new_args: Tuple[Any, ...] = ( + qx, + x_scale, + x_zp, + prepack_weight_node, + w_scale, + w_zp, + bias, + 1.0, # output_scale + 0, # output_zero_point + dtype, # output_dtype + "none", # post op name + [], # post op args + "", # post op algorithm + ) + Node = torch.fx.node.Node + if isinstance(x_scale, Node) and isinstance(x_zp, Node): + new_linear_node = graph.call_function( + torch.ops.onednn.qlinear_pointwise.tensor, args=new_args + ) + else: + new_linear_node = graph.call_function( + torch.ops.onednn.qlinear_pointwise.default, args=new_args + ) + if input_dim_exceeds_two: + if input_contiguous: + output_reshape_node.replace_all_uses_with(new_linear_node) + new_linear_node.meta.update(output_reshape_node.meta) + else: + if bias: + output_add_node_for_bias = match.output_node() + assert output_add_node_for_bias.target is aten.add.Tensor + output_add_node_for_bias.replace_all_uses_with(new_linear_node) + new_linear_node.meta.update(output_add_node_for_bias.meta) + else: + linear_node.replace_all_uses_with(new_linear_node) + new_linear_node.meta.update(linear_node.meta) + else: + linear_node.replace_all_uses_with(new_linear_node) + new_linear_node.meta.update(linear_node.meta) + + # Erase the original linear node + if input_dim_exceeds_two: + if input_contiguous: + graph.erase_node(output_reshape_node) + elif not input_contiguous and bias: + graph.erase_node(output_add_node_for_bias) # type: ignore[possibly-undefined] + graph.erase_node(linear_node) + if input_dim_exceeds_two: + if input_contiguous: + graph.erase_node(act_reshape_node) + else: + graph.erase_node(act_expand_node) + graph.erase_node(wgt_expand_node) # type: ignore[possibly-undefined] + if dtype == torch.bfloat16: + graph.erase_node(activation_to_bf16_node) + # Erase the dequant pattern + graph.erase_node(dequant_node) + # Erase the dequant per channel pattern + graph.erase_node(t_node) + if dtype == torch.bfloat16: + graph.erase_node(weight_to_bf16_node) # type: ignore[possibly-undefined] + graph.erase_node(dequant_per_channel) + + counters["inductor"]["qlinear_weight_prepack_matcher_count"] += 1 + counters["inductor"]["qlinear_weight_prepack_matcher_nodes"] += len( + match.nodes + ) + + +def _generate_dequant_linear_node_pattern( + _dequant_per_channel_pattern, + dtype=torch.float32, + input_dim_exceeds_two=False, + is_tensor_overload=False, +): + assert dtype in [torch.float32, torch.bfloat16] + t_pattern = _generate_linear_t_pattern(_dequant_per_channel_pattern, dtype) + dequant_linear_bias_pattern = _may_generate_pattern_with_reshape( + CallFunction( + aten.addmm.default, + KeywordArg("b"), + _may_generate_pattern_with_reshape( + _may_generate_pattern_with_dtype_convert( + get_dequantize_per_tensor_activation_pattern(is_tensor_overload), + KeywordArg("autocast_act_dtype"), + dtype == torch.bfloat16, + ), + KeywordArg("act_reshape_size"), + input_dim_exceeds_two, + ), + t_pattern, + ), + KeywordArg("output_reshape_size"), + input_dim_exceeds_two, + ) + dequant_linear_no_bias_pattern = _may_generate_pattern_with_reshape( + CallFunction( + aten.mm.default, + _may_generate_pattern_with_reshape( + _may_generate_pattern_with_dtype_convert( + get_dequantize_per_tensor_activation_pattern(is_tensor_overload), + KeywordArg("autocast_act_dtype"), + dtype == torch.bfloat16, + ), + KeywordArg("act_reshape_size"), + input_dim_exceeds_two, + ), + t_pattern, + ), + KeywordArg("output_reshape_size"), + input_dim_exceeds_two, + ) + return dequant_linear_bias_pattern, dequant_linear_no_bias_pattern + + +def _generate_dequant_bmm_node_pattern( + _dequant_per_channel_pattern, + dtype=torch.float32, + with_bias=False, + is_tensor_overload=False, +): + # When activation of linear dim exceed 2 and not contiguous + t_pattern = _generate_linear_t_pattern(_dequant_per_channel_pattern, dtype) + + assert dtype in [torch.float32, torch.bfloat16] + dequant_bmm_pattern = CallFunction( + aten.bmm.default, + CallFunction( + aten.expand.default, + _may_generate_pattern_with_dtype_convert( + get_dequantize_per_tensor_activation_pattern(is_tensor_overload), + KeywordArg("autocast_act_dtype"), + dtype == torch.bfloat16, + ), + KeywordArg("act_expand_size"), + ), + CallFunction( + aten.expand.default, + t_pattern, + KeywordArg("wgt_expand_size"), + ), + ) + + def _generate_pattern_with_output_add(_dequant_bmm_pattern, _with_bias): + if _with_bias: + return CallFunction( + aten.add.Tensor, + _dequant_bmm_pattern, + KeywordArg("b"), + ) + else: + return _dequant_bmm_pattern + + return _generate_pattern_with_output_add(dequant_bmm_pattern, with_bias) + + +def _generate_qlinear_weight_prepack_patterns( + dtype=torch.float32, + input_dim_exceeds_two=False, + input_contiguous=True, + with_bias=False, + is_tensor_overload=False, +): + if input_dim_exceeds_two and not input_contiguous: + return _generate_dequant_bmm_node_pattern( + dequantize_per_channel_weight_pattern, + dtype, + with_bias, + is_tensor_overload, + ) + else: + return _generate_dequant_linear_node_pattern( + dequantize_per_channel_weight_pattern, + dtype, + input_dim_exceeds_two, + is_tensor_overload, + ) + + +def _register_dequant_promotion(): + dequant_pattern_cases = itertools.product( + [torch.float32, torch.bfloat16], [True, False], [True, False] + ) + for dtype, input_dim_exceeds_two, is_tensor_overload in dequant_pattern_cases: + # 4 dequantization patterns will be matched based on the dtype and input dimension size. + # Case 1: int8-mixed-fp32, input dim size is 2 + # Case 2: int8-mixed-fp32, input dim size exceeds 2 + # Case 3: int8-mixed-bf16, input dim size is 2 + # Case 4: int8-mixed-bf16, input dim size exceeds 2 + # quant + # + - - - - | - - - - + + # | dequant | + # | | | + # | OPT(to_bf16) | + # | | | + # | OPT(reshape) | + # | / \ | + # | node1 node2 | + # + - - | - - - | - - + + # OPT(reshape) OPT(reshape) + # + - - | - - - | - - + + # OPT(to_fp32) OPT(to_fp32) + # + - - | - - - | - - + + # quant quant + _register_dequant_promotion_pass( + _may_generate_pattern_with_reshape( + _may_generate_pattern_with_dtype_convert( + get_dequantize_per_tensor_activation_pattern( + is_tensor_overload=is_tensor_overload + ), + KeywordArg("autocast_act_dtype"), + dtype == torch.bfloat16, + ), + KeywordArg("act_reshape_size"), + with_reshape=input_dim_exceeds_two, + ), + pass_number=0, + dtype=dtype, + ) # pass_number=0 to run before weight prepack + + +def _register_qconv_weight_prepack(): + for dtype in [torch.float32, torch.bfloat16]: + weight_prepack_patterns = _generate_qconv_weight_prepack_patterns(dtype) + for weight_prepack_pattern in weight_prepack_patterns: + # Register to pass_number 1, so we can do dequant promotion in pass_number 0. + _register_qconv_weight_prepack_pass( + weight_prepack_pattern, pass_number=1, dtype=dtype + ) + + +def _register_qlinear_weight_prepack(): + # 6 Linear related patterns will be matched based on the dtype, input dimension size and input contiguous. + # Then convert the pattern into a QLinear node with int8_fp32/bf16. + # Case 1: int8-mixed-fp32, input dim size is 2 + # Case 2: int8-mixed-fp32, input dim size exceeds 2 and contiguous + # Case 3: int8-mixed-bf16, input dim size is 2 + # Case 4: int8-mixed-bf16, input dim size exceeds 2 and contiguous + + # + - - - - | - - - - - - | - - - - - + + # | dq_per_tensor dq_per_channel | + # | | | | + # | OPT(to_bf16) OPT(to_bf16) | + # | | | | + # | OPT(reshape) permute | + # | \ / | + # | addmm/mm | + # | | | + # | OPT(reshape) | + + # Case 5: int8-mixed-fp32, input dim size exceeds 2 and not contiguous + # Case 6: int8-mixed-bf16, input dim size exceeds 2 and not contiguous + + # + - - - - | - - - - - - | - - - - - + + # | dq_per_tensor dq_per_channel | + # | | | | + # | OPT(to_bf16) OPT(to_bf16) | + # | | | | + # | expand permute | + # | \ | | + # | expand | + # | / | + # | bmm | + # | | | + # | OPT(add) | + + linear_weight_prepack_cases = itertools.product( + [torch.float32, torch.bfloat16], [True, False], [True, False] + ) + + # Step 1: register patterns from mm and addmm + for dtype, input_dim_exceeds_two, is_tensor_overload in linear_weight_prepack_cases: + weight_prepack_patterns = _generate_qlinear_weight_prepack_patterns( + dtype, + input_dim_exceeds_two, + is_tensor_overload=is_tensor_overload, + ) + for weight_prepack_pattern in weight_prepack_patterns: + # Register to pass_number 1, so we can do dequant promotion in pass_number 0. + _register_qlinear_weight_prepack_pass( + weight_prepack_pattern, + pass_number=1, + dtype=dtype, + input_dim_exceeds_two=input_dim_exceeds_two, + ) + + # Step 2: register patterns from bmm + # Linear might be decomposed into bmm when input dim exceeds 2 and not contiguous + # refer to: + # https://github.com/pytorch/pytorch/blob/ + # 80c07df659362a95da7cd4f3ec367abfdace38c4/torch/_decomp/decompositions.py#L3965-L3968 + # in this case, we can convert it back to qlinear + for dtype, with_bias, is_tensor_overload in itertools.product( + [torch.float32, torch.bfloat16], [True, False], [True, False] + ): + bmm_pattern = _generate_qlinear_weight_prepack_patterns( + dtype=dtype, + input_dim_exceeds_two=True, + input_contiguous=False, + with_bias=with_bias, + is_tensor_overload=is_tensor_overload, + ) + _register_qlinear_weight_prepack_pass( + bmm_pattern, + pass_number=1 + if with_bias + else 2, # if with_bias, there is an output add, so we should try to match it firstly + dtype=dtype, + input_dim_exceeds_two=True, + input_contiguous=False, + ) + + +@functools.lru_cache(None) +def _register_quantization_weight_pack_pass(): + # Step 1: Dequant promotion for int8-mixed-fp32/bf16 + _register_dequant_promotion() + + # Step 2: QConv weight prepack + _register_qconv_weight_prepack() + + # Step 3: QLinear weight prepack + _register_qlinear_weight_prepack() + + +def quant_lift_up(graph_module: torch.fx.GraphModule): + """ + Lift up the quant node before view like nodes. It can benefit performance + of Attention like block. For example, we have the pattern as: + + DQ + DQ LINEAR + LINEAR VIEW + VIEW PERMUTE + PERMUTE TRANSPOSE + Q Q + DQ DQ + Matmul + DIV + ADD + SOFTMAX + + We want to lift up the the quant nodes from matmul before view like nodes + as the output of Linear node. + + DQ + DQ LINEAR + LINEAR Q + Q VIEW + VIEW PERMUTE + PERMUTE TRANSPOSE + DQ DQ + Matmul + DIV + ADD + SOFTMAX + + It produces a DQ->LINEAR->Q pattern which can be fused by backend. + """ + + def is_view_op(node): + return node.op == "call_function" and node.target in _VIEW_OPS + + for node in graph_module.graph.nodes: + # Leslie: Here we verify that the quant node has exactly + # one input FX node, with constant scalar value for scale and zero point. + # For the case input of quant node has more than one input FX nodes, + # extend the implementation to lift up all the connected nodes + # before the view nodes to keep the topological order. + if ( + node.op == "call_function" + and node.target in _PER_TENSOR_QUANTIZE_OPS + and len(node.all_input_nodes) == 1 + and is_view_op(node.all_input_nodes[0]) + ): + quant_node = node + input_node_of_quant = quant_node.args[0] + + # Check the nodes along lift up path has only 1 user node + # Propagate view like node to find where to insert the new quant node + could_lift_up = True + current_node = quant_node + input_node = current_node.args[0] + while is_view_op(input_node): + if len(input_node.users) != 1: + could_lift_up = False + break + current_node = input_node + input_node = current_node.args[0] + + # Further check the input node of the first view node has only 1 user node + if could_lift_up and len(input_node.users) == 1: + # Replace dequant's input from quant to quant's input + quant_node.replace_all_uses_with(input_node_of_quant) + # Insert the new quant node + with graph_module.graph.inserting_before(current_node): + new_quant_node = graph_module.graph.node_copy(quant_node) + input_node.replace_all_uses_with(new_quant_node) + + # Update inputs of new_quant_node + def maybe_replace_node(n: torch.fx.Node) -> torch.fx.Node: + if n == input_node_of_quant: + return input_node + else: + return n + + new_args = map_arg(new_quant_node.args, maybe_replace_node) + new_kwargs = map_arg(new_quant_node.kwargs, maybe_replace_node) + new_quant_node.args = new_args # type: ignore[assignment] + new_quant_node.kwargs = new_kwargs # type: ignore[assignment] + graph_module.graph.erase_node(quant_node) + + graph_module.graph.lint() + graph_module.recompile() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/reinplace.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/reinplace.py new file mode 100644 index 0000000000000000000000000000000000000000..59706134f85feaef835f9be5e0ac11ce8e96c69b --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/reinplace.py @@ -0,0 +1,688 @@ +# mypy: allow-untyped-defs +import itertools +import logging +import operator +from collections import defaultdict +from dataclasses import dataclass +from typing import Any, Callable, Dict, List, Tuple + +import torch +from torch._higher_order_ops.triton_kernel_wrap import ( + kernel_side_table, + triton_kernel_wrapper_functional, +) +from torch._inductor import config, inductor_prims +from torch._inductor.fx_utils import get_node_storage, is_node_realized +from torch._inductor.lowering import ( + inplaceable_foreach_ops as inplaceable_foreach_ops_lowerings, +) +from torch._inductor.virtualized import V +from torch.fx.immutable_collections import immutable_dict +from torch.fx.passes.reinplace import _is_view_op +from torch.utils import _pytree as pytree + + +log = logging.getLogger(__name__) +aten = torch.ops.aten + + +@dataclass(frozen=True) +class InplaceableOp: + inplace_op: Callable[..., Any] + mutated_arg: int + extra_check: Callable[[torch.fx.Node], bool] = lambda node: True + + +_SCATTER_OP_TO_VIEW = { + torch.ops.aten.diagonal_scatter.default: torch.ops.aten.diagonal.default, + torch.ops.aten.select_scatter.default: torch.ops.aten.select.int, + torch.ops.aten.slice_scatter.default: torch.ops.aten.slice.Tensor, + torch.ops.aten.as_strided_scatter.default: torch.ops.aten.as_strided.default, +} +_VIEW_OP_TO_SCATTER = {v: k for k, v in _SCATTER_OP_TO_VIEW.items()} + + +def graph_call_function(graph: torch.fx.Graph, fn, *args, **kwargs): + fake_args, fake_kwargs = pytree.tree_map( + lambda node: node.meta["val"] if isinstance(node, torch.fx.Node) else node, + (args, kwargs), + ) + with V.fake_mode: + fake_result = fn(*fake_args, **fake_kwargs) + + node = graph.call_function(fn, args, kwargs) + node.meta["val"] = fake_result + return node + + +@dataclass +class ViewOp: + target: torch._ops.OpOverload + args: Tuple[Any, ...] + kwargs: Dict[str, Any] + + +def _inplace_generalized_scatter( + inp: torch.Tensor, src: torch.Tensor, view_ops: List[ViewOp] +) -> torch.Tensor: + tmp = inp + for view in view_ops: + fake_args, fake_kwargs = pytree.tree_map( + lambda node: node.meta["val"] if isinstance(node, torch.fx.Node) else node, + (view.args, view.kwargs), + ) + tmp = view.target(tmp, *fake_args, **fake_kwargs) + try: + tmp.copy_(src) + except RuntimeError as e: + raise RuntimeError( + f"shape error in scatter op, can not broadcast {src.shape} to {tmp.shape}" + ) from e + return inp + + +def _generalized_scatter( + inp: torch.Tensor, src: torch.Tensor, view_ops: List[ViewOp] +) -> torch.Tensor: + out = inp.clone() + return _inplace_generalized_scatter(out, src, view_ops) + + +def _decompose_scatter_functional_helper( + graph: torch.fx.Graph, + inp: torch.Tensor, + src: torch.Tensor, + view_ops: List[ViewOp], +) -> torch.fx.Node: + view_op, view_ops_tail = view_ops[0], view_ops[1:] + + if view_ops_tail: + view = graph_call_function( + graph, view_op.target, inp, *view_op.args, **view_op.kwargs + ) + src = _decompose_scatter_functional_helper(graph, view, src, view_ops[1:]) # type: ignore[assignment] + + return graph_call_function( + graph, + _VIEW_OP_TO_SCATTER[view_op.target], + inp, + src, + *view_op.args, + **view_op.kwargs, + ) + + +def _decompose_scatter_functional( + graph: torch.fx.Graph, node: torch.fx.Node +) -> torch.fx.Node: + """Decompose _generalized_scatter to a sequence of view_scatter operations + + e.g. _generalized_scatter(inp, src, [(aten.slice, 0, 0, 10), (aten.slice, 1, 10, -10)]) + + will become + + view = aten.slice(inp, 0, 0, 10) + view_updated = aten.slice_scatter(view, src, 1, 10, -10) + inp_updated = aten.slice_scatter(inp, view_updated, 0, 0, 10) + """ + assert node.target is _generalized_scatter + inp, src, view_ops = node.args + return _decompose_scatter_functional_helper(graph, *node.args) # type: ignore[arg-type] + + +def _decompose_scatter_mutating( + graph: torch.fx.Graph, node: torch.fx.Node +) -> torch.fx.Node: + """Decompose _generalized_scatter using mutations + + e.g. _generalized_scatter(inp, src, [(aten.slice, 0, 0, 10), (aten.slice, 1, 10, -10)]) + + will become + + inp_updated = aten.clone(inp) + slice1 = aten.slice(inp_updated, 0, 0, 10) + slice2 = aten.slice(slice1, 1, 10, -10) + slice2.copy_(src) + + """ + assert node.target in (_generalized_scatter, _inplace_generalized_scatter) + inp, src, view_ops = node.args + assert not node.kwargs + + if node.target is _generalized_scatter: + inp = graph_call_function(graph, aten.clone, inp) + + tmp = inp + for view in view_ops: # type: ignore[union-attr] + tmp = graph_call_function(graph, view.target, tmp, *view.args, **view.kwargs) # type: ignore[union-attr] + + graph_call_function(graph, aten.copy_.default, tmp, src) + return inp # type: ignore[return-value] + + +# View ops whose view_scatter op is lowered into mutations anyway, +# so is never a pessimisation to decompose. +_ALWAYS_MUTATING_SCATTER_OPS = { + aten.as_strided.default, + aten.diagonal.default, +} + + +def scatter_always_uses_mutation(node: torch.fx.Node) -> bool: + _, _, view_ops = node.args + return any(view.target in _ALWAYS_MUTATING_SCATTER_OPS for view in view_ops) # type: ignore[union-attr] + + +def should_reinplace_scatter(node: torch.fx.Node) -> bool: + """Choose between mutating and functional scatter decompositions + + Reinplacing view scatter ops can be pessimising as it blocks fusion with the + input or output tensor computations. However, it is still profitable if the + input and output would have been realized anyway. + + """ + inp, src, view_ops = node.args + + # Mutating scatter ops unconditionally realize input and output + if scatter_always_uses_mutation(node): + return True + + if is_node_realized(inp) and is_node_realized(node): # type: ignore[arg-type] + return True + + # If the output is copied back into the input, this forces both to be + # realized as the output is a user of the input + if inp.op in ("placeholder", "get_attr") and any( # type: ignore[union-attr] + user.target is aten.copy_.default and user.args[0] is inp for user in node.users + ): + return True + + # Otherwise, assume fusions will make functional variants profitable + return False + + +def decompose_generalized_scatter(graph: torch.fx.Graph) -> None: + """Replace _generalized_scatter with normal aten ops""" + for node in itertools.chain( + graph.find_nodes(op="call_function", target=_generalized_scatter), + graph.find_nodes(op="call_function", target=_inplace_generalized_scatter), + ): + use_mutation = ( + node.target is _inplace_generalized_scatter + or scatter_always_uses_mutation(node) + ) + + with graph.inserting_before(node): + if use_mutation: + new_node = _decompose_scatter_mutating(graph, node) + else: + new_node = _decompose_scatter_functional(graph, node) + + node.replace_all_uses_with(new_node) + graph.erase_node(node) + + +def canonicalize_view_scatter_ops(graph: torch.fx.Graph) -> None: + """ + This canonicalizes view scatter ops into a generalized form, defined as: + def scatter(inp, src, views): + tmp = inp.clone() + for view in views: + tmp = view(tmp) + tmp.copy_(src) + + We also fuse consecutive view scatter ops of the form + a = scatter(view2(self), src, [view1]) + b = scatter(self, a, [view2]) + which can be rewritten as + b = scatter(self, src, [view2, view1]) + a = view2(b) + + This is both more efficient as we only do a single scatter, and also + easier to reinplace since there is only one use of `self` + """ + + node_to_view_base: Dict[torch.fx.Node, torch.fx.Node] = {} + node_to_view_op: Dict[torch.fx.Node, List[ViewOp]] = defaultdict(list) + + def handle_views(node: torch.fx.Node): + inp = node.args[0] + node_to_view_base[node] = node_to_view_base.get(inp, inp) # type: ignore[arg-type] + node_to_view_op[node] = [ + *node_to_view_op[inp], # type: ignore[index] + ViewOp( + node.target, # type: ignore[arg-type] + args=node.args[1:], + kwargs=node.kwargs, + ), + ] + + def handle_view_scatter(node: torch.fx.Node): + assert len(node.args) >= 2 + inp, src = node.args[:2] + + scatter_view_op = ViewOp( + _SCATTER_OP_TO_VIEW[node.target], + args=node.args[2:], + kwargs=node.kwargs, + ) + + def can_fuse(): + if src.target is not _generalized_scatter: # type: ignore[union-attr] + return False + src_inp, src_src, src_scatter_view_op = src.args # type: ignore[union-attr] + + inp_base = node_to_view_base.get(inp, inp) # type: ignore[arg-type] + src_base = node_to_view_base.get(src_inp, src_inp) # type: ignore[arg-type] + return inp_base is src_base and node_to_view_op[src_inp] == [ # type: ignore[index] + *node_to_view_op[inp], # type: ignore[index] + scatter_view_op, + ] + + if not can_fuse(): + with graph.inserting_before(node): + new_node = graph_call_function( + graph, + _generalized_scatter, + inp, + src, + [scatter_view_op], + ) + node.replace_all_uses_with(new_node) + graph.erase_node(node) + return + + src_inp, src_src, src_scatter_view_op = src.args # type: ignore[union-attr] + with graph.inserting_before(src): # type: ignore[arg-type] + new_node = graph_call_function( + graph, + _generalized_scatter, + inp, + src_src, + [scatter_view_op, *src_scatter_view_op], # type: ignore[misc] + ) + node.replace_all_uses_with(new_node) + graph.erase_node(node) + + if src.users: # type: ignore[union-attr] + new_src = graph_call_function( + graph, + _SCATTER_OP_TO_VIEW[node.target], + new_node, + *node.args[2:], + **node.kwargs, + ) + + handle_views(new_src) + src.replace_all_uses_with(new_src) # type: ignore[union-attr] + + graph.erase_node(src) # type: ignore[arg-type] + + for node in graph.nodes: + if _is_view_op(node.target): + handle_views(node) + elif node.target in _SCATTER_OP_TO_VIEW: + handle_view_scatter(node) + + +inplaceable_ops = { + aten.index_put.default: InplaceableOp(aten.index_put_.default, 0), + aten._unsafe_index_put.default: InplaceableOp(inductor_prims._unsafe_index_put_, 0), + _generalized_scatter: InplaceableOp( + _inplace_generalized_scatter, + 0, + extra_check=should_reinplace_scatter, + ), +} + +try: + c10d_functional = torch.ops._c10d_functional + inplaceable_collective_ops = { + c10d_functional.all_reduce.default: InplaceableOp( + c10d_functional.all_reduce_.default, 0 + ), + c10d_functional.all_reduce_coalesced.default: InplaceableOp( + c10d_functional.all_reduce_coalesced_.default, 0 + ), + } + inplaceable_ops.update(inplaceable_collective_ops) +except AttributeError: + # _c10d_functional ops are only available when torch + # is built with USE_DISTRIBUTED=1. + pass + +inplaceable_foreach_ops: Dict[torch._ops.OpOverload, InplaceableOp] = {} +for outplace_op, inplace_op in inplaceable_foreach_ops_lowerings.items(): + inplaceable_foreach_ops[outplace_op] = InplaceableOp(inplace_op, 0) + + +inplaceable_triton_ops = {triton_kernel_wrapper_functional} + + +# Operators that don't depend on the tensor data +META_ONLY_OPS = { + aten.sym_size.int, + aten.sym_stride.int, + aten.sym_numel.default, + aten.sym_storage_offset.default, +} + + +def reinplace_inplaceable_ops_core(graph: torch.fx.Graph) -> None: + """ + Reinplaces in-placeable operations. + If there are no uses of a view of the mutated arg after the current node, + it is possible to inplace the op. + This above algorithm could be justified by observing side effects. While + we traverse the graph in forwards direction, only latter nodes could view + side effects of the current node. If the current node is not used later as + well as no view of this node is used later in the graph, then it is safe to + inplace as there would be no way to observe the side effects. + This condition is slightly different for graph inputs where they can only + be inplaced if the above condition is true and there's a copy_ in the + epilogue that signals that the caller wants to observe the mutation. + + Unlike JIT Inductor, AOTInductor currently unlifts weights and buffers from + input args, so instead of checking mutation on placeholder, AOTInductor + checks mutation on get_attr. This is subject to change in future. + """ + + copy_args_to_copy_nodes = {} + # maps argument to the first copy_ node that mutates it. + copy_nodes = {} + mutated_inputs = set() + storage_to_nodes = defaultdict(list) + node_order: Dict[Any, int] = {} + for i, node in enumerate(reversed(graph.nodes)): + node_order[node] = len(graph.nodes) - i - 1 + storage_to_nodes[get_node_storage(node)].append(node) + if node.target == aten.copy_.default and node.args[0].op in ( + "placeholder", + "get_attr", + ): + dst = node.args[0] + src = node.args[1] + # If the target is a getitem and it indexes a possible clone, + # then skip over it + if src.target == operator.getitem and ( + ( + src.args[0].target == triton_kernel_wrapper_functional + and src.args[0].kwargs["kwargs"][src.args[1]] == node.args[0] + ) + or (src.args[0].target in inplaceable_foreach_ops) + or (src.args[0].target == torch.ops.higher_order.auto_functionalized) + ): + src = src.args[0] + + copy_args_to_copy_nodes[(dst, src)] = node + copy_nodes[dst] = node + + mutated_inputs.add(node.args[0]) + + def any_use_of_views_after_node(node, shared_view_nodes, *, copy_node, mutated_arg): + node_loc = node_order[node] + copy_node_loc = node_order[copy_node] if copy_node is not None else None + + def is_meta_only_user(node): + if _is_view_op(node.target): + return all(is_meta_only_user(u) for u in node.users) + return node.target in META_ONLY_OPS + + for view in shared_view_nodes: + for user in view.users: + user_loc = node_order[user] + # Skip all users before node + if user_loc <= node_loc: + continue + # Ignore uses after the copy_ epilogue node, where the input + # has already been mutated anyway + if copy_node_loc is not None and copy_node_loc <= user_loc: + continue + # Reinplacing does not change shape metadata + if is_meta_only_user(user): + continue + # If our graph looks like: + # foo(mutated_arg) + # mutated_arg.copy_(other) + # then it's safe for us to reinplace foo because mutated_arg + # will get overwritten anyways. + if ( + user.target is torch.ops.aten.copy_.default + and mutated_arg is user.args[0] + ): + continue + return True + return False + + def can_inplace(node, mutated_arg): + if isinstance(mutated_arg, (list, tuple)): + unique_storages = {get_node_storage(arg) for arg in mutated_arg} + if len(unique_storages) != len(mutated_arg): + # at least two Tensors in mutated_arg alias each other, so we can't reinplace it. + # We can probably do better (that is, reinplace one of them and clone the other) + # but that requires more work and mutable List[Tensor] are not that common. + return False + return all(can_inplace(node, arg) for arg in mutated_arg) + + if get_node_storage(mutated_arg) is None: + return False + shared_view_nodes = storage_to_nodes[get_node_storage(mutated_arg)] + + if mutated_arg.op in ("placeholder", "get_attr"): + # Get the first copy_ node that mutates the mutated_arg. + copy_node = copy_nodes.get(mutated_arg, None) + if copy_node is None: + # There is no copy_ back to the candidate mutated_arg (which is a graph input). + # Therefore the semantics of the program are that it does not mutate + # mutated_arg, so we cannot re-inplace it. + return False + if any_use_of_views_after_node( + node, shared_view_nodes, copy_node=copy_node, mutated_arg=mutated_arg + ): + return False + + return True + elif any(view.op in ("placeholder", "get_attr") for view in shared_view_nodes): + # This should never happen in auto_functionalize_v2 non-inference mode, + # since all mutated_arg are bases. + + # If mutated arg is view of any of the inputs of the graph, + # do not allow for inplacing. + # This would require more sophisticated algorithm to handle + return False + else: + return not any_use_of_views_after_node( + node, shared_view_nodes, copy_node=None, mutated_arg=mutated_arg + ) + + def log_inplace_results( + node_name, + old_tensors_to_clone, + tensors_to_clone, + possibly_missed_reinplacing_opportunities, + ): + log.info( + "For node %s, attempted to reinplace %s. We were unable to reinplace %s; " + "%s (if non-empty) are possible missed reinplacing opportunities that may be bad for " + "memory usage and performance.", + node_name, + old_tensors_to_clone, + tensors_to_clone, + possibly_missed_reinplacing_opportunities, + ) + torch._dynamo.utils.counters["inductor"][ + "possibly_missed_reinplacing_opportunities" + ] += len(possibly_missed_reinplacing_opportunities) + + replace_dict: Dict[torch.fx.Node, torch.fx.Node] = {} + + def reinplace_and_refine_tensors_to_clone( + old_tensors_to_clone, kwargs, node_name, auto_functionalize_v2=False + ): + tensors_to_clone: List[str] = [] + storage_of_reinplaced_args = set() + possibly_missed_reinplacing_opportunities = [] + + def tensor_with_same_storage_already_reinplaced(arg): + if isinstance(arg, (list, tuple)): + return any( + get_node_storage(a) in storage_of_reinplaced_args for a in arg + ) + return get_node_storage(mutated_arg) in storage_of_reinplaced_args + + for arg in old_tensors_to_clone: + assert arg in kwargs + + mutated_arg = kwargs[arg] + + # Let's say we have: + # - op(x, y) that mutates both x and y + # - new_x, new_y = functional_op(x, y) is the functional variant + # If we are presented with functional_op(x, x), we must not reinplace + # this into op(x, x), because then it would be writing to the same Tensor. + # Instead, it's OK to reinplace one of them and to clone the other: + # >>> y = x.clone() + # >>> op(x, y) + # This also applies if we have views: functional_op(x, x[0]) + # should not reinplace into op(x, x[0]). + should_attempt_reinplace = not tensor_with_same_storage_already_reinplaced( + mutated_arg + ) + if should_attempt_reinplace and can_inplace(node, mutated_arg): + # In general, we probably do not need those optimizations. + copy_node = copy_args_to_copy_nodes.get((mutated_arg, node)) + if copy_node is not None: + replace_dict[copy_node] = copy_node.args[0] + if not auto_functionalize_v2: + for user in node.users: + # For auto_functionalize_v2, arg is the index of the base, where base at index i corresponds to + # output atindex size(out)+i. + # This used to compare string with integers before for auto_functionalize_v2. Not sure + # if it was needed for inplaceable_triton_ops? + if user.target == operator.getitem and user.args[1] == arg: + replace_dict[user] = mutated_arg + + if isinstance(mutated_arg, (list, tuple)): + for a in mutated_arg: + storage_of_reinplaced_args.add(get_node_storage(a)) + else: + storage_of_reinplaced_args.add(get_node_storage(mutated_arg)) + else: + if should_attempt_reinplace: + possibly_missed_reinplacing_opportunities.append(arg) + tensors_to_clone.append(arg) + + log_inplace_results( + node_name, + old_tensors_to_clone, + tensors_to_clone, + possibly_missed_reinplacing_opportunities, + ) + return tensors_to_clone + + for node in graph.nodes: + if (inplaceable_op := inplaceable_ops.get(node.target, None)) is not None: + mutated_arg = node.args[inplaceable_op.mutated_arg] + if can_inplace(node, mutated_arg) and inplaceable_op.extra_check(node): + # TODO(yifu): this doesn't properly remove copy epilogues for + # ops that mutate multiple inputs. Need to revise the copy + # node tracking logic to support the case. + copy_node = copy_args_to_copy_nodes.get((mutated_arg, node)) + if copy_node is not None: + replace_dict[copy_node] = copy_node.args[0] + node.target = inplaceable_op.inplace_op + elif node.target == torch.ops.higher_order.auto_functionalized_v2: + _mutable_op = node.args[0] + kwargs = node.kwargs + + all_bases = kwargs["_all_bases"] + bases_to_clone = range(len(all_bases)) + base_tensors_dct = dict(enumerate(all_bases)) + new_bases_to_clone: List[int] = reinplace_and_refine_tensors_to_clone( + bases_to_clone, + base_tensors_dct, + node.target, + auto_functionalize_v2=True, + ) + # Stash the metadata. There is a pass later on where we decompose + # auto_functionalized into clones + a mutable op; this metadata + # tells the decomp to only clone the following inputs + node.meta["only_clone_these_tensors"] = new_bases_to_clone + elif node.target == torch.ops.higher_order.auto_functionalized: + _mutable_op = node.args[0] + from torch._higher_order_ops.auto_functionalize import get_mutable_args + + tensors_to_clone, _ = get_mutable_args(_mutable_op) + # Don't try to reinplace Optional[Tensor] args that are None. + tensors_to_clone = [ + t for t in tensors_to_clone if node.kwargs[t] is not None + ] + tensors_to_clone = reinplace_and_refine_tensors_to_clone( + tensors_to_clone, + node.kwargs, + _mutable_op._name, + auto_functionalize_v2=False, + ) + + # Stash the metadata. There is a pass later on where we decompose + # auto_functionalized into clones + a mutable op; this metadata + # tells the decomp to only clone the following inputs + node.meta["only_clone_these_tensors"] = tensors_to_clone + elif node.target in inplaceable_triton_ops: + kernel_idx = node.kwargs["kernel_idx"] + kernel = kernel_side_table.get_kernel(kernel_idx) + from triton.runtime.autotuner import Autotuner + from triton.runtime.jit import JITFunction + + if isinstance(kernel, JITFunction): + kernel_name = kernel.fn.__name__ + elif isinstance(kernel, Autotuner): + if config.is_fbcode(): + # Autotuner has different implementations for AMD and NV + if torch.version.hip is None: + kernel_name = kernel.base_fn.__name__ + else: + kernel_name = kernel.fn.__name__ + else: + kernel_name = kernel.base_fn.__name__ + else: + raise AssertionError("Unknown triton kernel type") + + # inplaceable_triton_ops take an additional argument called + # tensors_to_clone which contain a list of tensors to clone + # This pass iterates over them and sees which ones are safe + # to eliminate (i.e. no longer need the clones) + tensors_to_clone = reinplace_and_refine_tensors_to_clone( + node.kwargs["tensors_to_clone"], node.kwargs["kwargs"], kernel_name + ) + + kwargs = dict(node.kwargs) + kwargs["tensors_to_clone"] = tensors_to_clone + node.kwargs = immutable_dict(kwargs) + elif ( + inplaceable_op := inplaceable_foreach_ops.get(node.target, None) + ) is not None: + mutated_args = node.args[inplaceable_op.mutated_arg] + + if not all((arg, node) in copy_args_to_copy_nodes for arg in mutated_args): + continue + + if can_inplace(node, mutated_args): + for arg in mutated_args: + copy_node = copy_args_to_copy_nodes[(arg, node)] + replace_dict[copy_node] = copy_node.args[0] + + node.target = inplaceable_op.inplace_op + for node, replacement in replace_dict.items(): + while replacement in replace_dict: + replacement = replace_dict[replacement] + replace_dict[node] = replacement + + node.replace_all_uses_with(replacement) + graph.erase_node(node) + + +def reinplace_inplaceable_ops(graph: torch.fx.Graph) -> None: + canonicalize_view_scatter_ops(graph) + reinplace_inplaceable_ops_core(graph) + decompose_generalized_scatter(graph) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/replace_random.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/replace_random.py new file mode 100644 index 0000000000000000000000000000000000000000..f56a90b86dd5c1c35ebba7c97fbff1f4497313c6 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/replace_random.py @@ -0,0 +1,145 @@ +# mypy: allow-untyped-defs +import collections +import logging + +import torch +from torch.fx.passes.graph_transform_observer import GraphTransformObserver +from torch.fx.passes.shape_prop import _extract_tensor_metadata + +from .. import config, inductor_prims +from ..pattern_matcher import ( + CallFunctionVarArgs, + Match, + PatternMatcherPass, + register_graph_pattern, +) +from ..virtualized import V + + +log = logging.getLogger(__name__) +patterns = PatternMatcherPass() +aten = torch.ops.aten + + +def replace_random_passes(gm: torch.fx.GraphModule): + """Modify the given FX graph to use backend-native random ops""" + if config.fallback_random: + return 0 + + count = patterns.apply(gm) + with GraphTransformObserver( + gm, "fuse_seed_creation_pass", config.trace.log_url_for_graph_xform + ): + count += fuse_seed_creation_pass(gm.graph) + + return count + + +def fuse_seed_creation_pass(graph: torch.fx.Graph): + """ + Horizontally fuse all the seed generation on each device + + a = inductor_seed(dev) + b = inductor_seed(dev) + + Becomes: + seeds = inductor_seeds(2, dev) + a = inductor_lookup_seed(seeds, 0) + b = inductor_lookup_seed(seeds, 1) + + We do this because seed creation is entirely launch overhead bound. + """ + device_seeds = collections.defaultdict(list) + for node in graph.nodes: + if CallFunctionVarArgs(inductor_prims.seed).match(node): + device_seeds[node.args[0]].append(node) + + if not device_seeds: + return 0 + + for device, seeds in device_seeds.items(): + with graph.inserting_before(seeds[0]): + combined = graph.call_function(inductor_prims.seeds, (len(seeds), device)) + with V.fake_mode: + combined.meta["val"] = torch.empty( + [len(seeds)], device=device, dtype=torch.int64 + ) + combined.meta["tensor_meta"] = _extract_tensor_metadata( + combined.meta["val"] + ) + + for idx, seed in enumerate(seeds): + with graph.inserting_before(seed): + new_seed = graph.call_function( + inductor_prims.lookup_seed, (combined, idx) + ) + seed.replace_all_uses_with(new_seed) + new_seed.meta.update(seed.meta) + graph.erase_node(seed) + + return len(device_seeds) + + +def default_kwargs(device): + return {} + + +def get_device(device): + if device is not None: + return device + return torch.empty([]).device # default device + + +@register_graph_pattern(CallFunctionVarArgs(aten.rand.default), pass_dict=patterns) +@register_graph_pattern(CallFunctionVarArgs(aten.rand.generator), pass_dict=patterns) +@register_graph_pattern(CallFunctionVarArgs(aten.randn.default), pass_dict=patterns) +@register_graph_pattern(CallFunctionVarArgs(aten.randn.generator), pass_dict=patterns) +def replace_random( + match: Match, + size, + *, + generator=None, + dtype=None, + device=None, + layout=None, + pin_memory=None, +): + if generator is not None: + return + + def replacement(size): + result = inductor_prims.random( + size, inductor_prims.seed(device), mode, **default_kwargs(device) + ) + if dtype is not None: + result = result.to(dtype) + return result + + mode = { + aten.rand: "rand", + aten.randn: "randn", + }[ + match.output_node().target.overloadpacket # type: ignore[union-attr] + ] # type: ignore[union-attr] + device = get_device(device) + match.replace_by_example(replacement, [size]) + + +@register_graph_pattern(CallFunctionVarArgs(aten.randint.low), pass_dict=patterns) +def replace_randint( + match: Match, + low, + high, + size, + *, + dtype=torch.int64, + device=None, + layout=None, + pin_memory=None, +): + def replacement(low, high, size): + result = inductor_prims.randint(low, high, size, inductor_prims.seed(device)) + return result.to(dtype) + + device = get_device(device) + match.replace_by_example(replacement, [low, high, size]) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/split_cat.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/split_cat.py new file mode 100644 index 0000000000000000000000000000000000000000..d0098a535ee295fe5c5e4cd97600b08a638a6f02 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_passes/split_cat.py @@ -0,0 +1,2514 @@ +# mypy: allow-untyped-defs +import itertools +import logging +import operator +from typing import Any, Callable, Dict, List, Optional, Sequence, Set, Tuple, Union +from typing_extensions import TypeAlias + +import torch +from torch._dynamo.utils import counters + +from ..pattern_matcher import ( + Arg, + CallFunction, + CallFunctionVarArgs, + CallMethodVarArgs, + FailedMatch, + get_arg_value, + Ignored, + KeywordArg, + ListOf, + Match, + MatchContext, + MULTIPLE, + PatternExpr, + PatternMatcherPass, + register_graph_pattern, + RepeatedExpr, +) +from .group_batch_fusion import is_node_meta_valid, POST_GRAD_FUSIONS, PRE_GRAD_FUSIONS + + +log = logging.getLogger(__name__) + +_Arguments: TypeAlias = Tuple[torch.fx.node.Argument, ...] +_TransformParam: TypeAlias = Tuple[ + Optional[_Arguments], + Optional[_Arguments], + Optional[_Arguments], + Optional[_Arguments], +] +_Range: TypeAlias = Tuple[int, int] + + +PRE_GRAD_PATTERNS: Dict[str, PatternMatcherPass] = {} +POST_GRAD_PATTERNS: Dict[str, PatternMatcherPass] = {} + +pre_grad_pass_names = [ + "normalization_pass", + "remove_split_with_size_one_pass", + "merge_getitem_cat_pass", + "merge_stack_tahn_unbind_pass", + "merge_splits_pass", + "mutate_cat_pass", + "split_cat_pass", + "unbind_stack_pass", + "split_cat_to_slices_pass", + "unbind_cat_to_view_pass", + "split_stack_to_cats_pass", + "unbind_stack_to_slices_pass", + "move_reshape_out_of_split_stack_pass", +] + +post_grad_pass_names = [ + "normalization_aten_pass", + "decompose_mm_pass", + "unbind_stack_aten_pass", + "shape_padding_multiplier", +] + +for pass_name in pre_grad_pass_names: + # exclude all passes from the group batch fusion + # they do not use pattern matcher + if pass_name in PRE_GRAD_FUSIONS: + continue + PRE_GRAD_PATTERNS[pass_name] = PatternMatcherPass( + pass_name=pass_name, + ) + +for pass_name in post_grad_pass_names: + # exclude all passes from the group batch fusion + # they do not use pattern matcher + if pass_name in POST_GRAD_FUSIONS: + continue + POST_GRAD_PATTERNS[pass_name] = PatternMatcherPass( + pass_name=pass_name, + ) + + +def construct_pattern_matcher_pass(pass_name: str): + """ + Return the specific pattern_matcher_pass given the pass name. + """ + if pass_name in PRE_GRAD_PATTERNS: + return PRE_GRAD_PATTERNS[pass_name] + else: + return POST_GRAD_PATTERNS[pass_name] + + +def _get_split_args_default(split_node): + input_kwarg = "tensor" + split_size_kwarg = "split_size_or_sections" + dim_kwarg = "dim" + default_dim_value = 0 + if split_node.op == "call_method": + split_size_kwarg = "split_size" + return ( + get_arg_value(split_node, 0, input_kwarg), + get_arg_value(split_node, 1, split_size_kwarg), + get_arg_value(split_node, 2, dim_kwarg) or default_dim_value, + ) + + +def _get_dim(node: Any): + assert isinstance(node, torch.fx.Node) + if "dim" in node.kwargs: + assert isinstance(node.kwargs["dim"], int) + return node.kwargs["dim"] + if node.target == torch.unbind: + if len(node.args) == 2: + assert isinstance(node.args[-1], int) + return node.args[-1] + return 0 # defaults to dim=0 + if node.target == torch.split: + if len(node.args) == 3: + assert isinstance(node.args[-1], int) + return node.args[-1] + return 0 # defaults to dim=0 + raise AssertionError( + f"Can't extract `dim` from {node.target} {node.args} {node.kwargs}" + ) + + +# noqa: W605 +# ############The pattern to be optimized is######### +# unbind (dim=0) +# / ... \ +# getitem getitem -> user=1 +# | | +# split split -> dim=1, user=1, split_section_size=1 +# | | +# getitem getitem -> user=1 +# \ / +# cat (dim=1) -> user=1 +# | + +# ################After transformation############# +# unbind (dim=0) +# / ... \ +# getitem getitem -> user=1 +# \ / +# cat (dim=1) -> user=1 +# | + + +def normalize_split_base( + match: Match, + _get_split_args: Callable[ + [torch.fx.Node], Tuple[Optional[torch.fx.Node], Optional[Any], Optional[int]] + ], +): + """ + Normalize split with split_size into split_with_sizes, so that we only deal with one type of split in + subsequent optimizations + """ + split_node = match.nodes[0] + graph = match.graph + split_input, split_size, split_dim = _get_split_args(split_node) + if split_input is None or split_dim is None or split_size is None: + log.debug("couldn't find split args") + return + if not is_node_meta_valid(split_node): + log.debug("example value absent for node: %s", split_node) + return + assert isinstance(split_node.meta["example_value"], (list, tuple)) + split_sections = [t.size()[split_dim] for t in split_node.meta["example_value"]] + + if any(isinstance(section, torch.SymInt) for section in split_sections): + # TODO dynamic_shapes with assume_static_by_default=False fails while AOT Autograd tracing. + return + if split_dim < 0: # Normalize split dim + split_dim += split_input.meta["example_value"].dim() + + new_args = (split_input, split_sections) + new_kwargs = {"dim": split_dim} + if ( + split_node.args == new_args + and split_node.kwargs == new_kwargs + and split_node.op == "call_function" + ): + return + + with graph.inserting_after(split_node): + new_split_node = graph.call_function( + torch.split, + args=new_args, + kwargs=new_kwargs, # type: ignore[arg-type] + ) + split_node.replace_all_uses_with(new_split_node) + new_split_node.meta.update(split_node.meta) + graph.erase_node(split_node) + counters["inductor"]["normalization_pass"] += 1 + + +@register_graph_pattern( + CallFunctionVarArgs(torch.split, users=MULTIPLE), + pass_dict=construct_pattern_matcher_pass("normalization_pass"), +) +@register_graph_pattern( + CallMethodVarArgs("split", users=MULTIPLE), + pass_dict=construct_pattern_matcher_pass("normalization_pass"), +) +def normalize_split_default(match: Match, *args, **kwargs): + return normalize_split_base(match, _get_split_args_default) + + +@register_graph_pattern( + CallFunctionVarArgs(torch.split, users=MULTIPLE), + pass_dict=construct_pattern_matcher_pass("remove_split_with_size_one_pass"), +) +@register_graph_pattern( + CallMethodVarArgs("split", users=MULTIPLE), + pass_dict=construct_pattern_matcher_pass("remove_split_with_size_one_pass"), +) +def remove_split_with_size_one(match: Match, *args, **kwargs): + graph = match.graph + split_node = match.nodes[0] + split_input, split_size, split_dim = _get_split_args_default(split_node) + if split_input is None or split_dim is None or split_size is None: + log.debug("couldn't find split args") + return + if not is_node_meta_valid(split_node): + log.debug("example value absent for node: %s", split_node) + return + assert isinstance(split_node.meta["example_value"], (list, tuple)) + split_sections = [t.size()[split_dim] for t in split_node.meta["example_value"]] + + if any(isinstance(section, torch.SymInt) for section in split_sections): + # TODO dynamic_shapes with assume_static_by_default=False fails while AOT Autograd tracing. + return + # remove the dummy split whose split sections size is one + if len(split_sections) == 1: + # find the grand children of the split_node + next_users = find_next_users(split_node) + user = next(iter(split_node.users.keys())) + # replace the users of grand child node with the input node + for next_user in next_users: + next_user.replace_input_with(user, split_input) + # erase the split node and its child + graph.erase_node(user) + graph.erase_node(split_node) + counters["inductor"]["remove_split_with_size_one_pass"] += 1 + + +@register_graph_pattern( + CallFunctionVarArgs(torch.unbind, users=MULTIPLE), + pass_dict=construct_pattern_matcher_pass("normalization_pass"), +) +@register_graph_pattern( + CallMethodVarArgs("unbind", users=MULTIPLE), + pass_dict=construct_pattern_matcher_pass("normalization_pass"), +) +def normalize_unbind_default(match: Match, *args, **kwargs): + node = match.nodes[0] + graph = match.graph + input = get_arg_value(node, 0, "input") + dim = get_arg_value(node, 1, "dim") + if dim is None: + axis = node.kwargs.get("axis") + if axis is not None: + dim = axis + else: + dim = 0 + if input is None: + log.debug("couldn't find unbind args") + return + if not is_node_meta_valid(input): + log.debug("example value absent for node: %s", input) + return + ndim = input.meta["example_value"].ndim + if dim < 0: # Normalize unbind dim + dim += ndim + with graph.inserting_after(node): + new_node = graph.call_function( + torch.unbind, + args=(input,), + kwargs={"dim": dim}, + ) + node.replace_all_uses_with(new_node) + new_node.meta.update(node.meta) + graph.erase_node(node) + counters["inductor"]["normalization_pass"] += 1 + + +@register_graph_pattern( + CallFunctionVarArgs(torch.cat, users=MULTIPLE), + pass_dict=construct_pattern_matcher_pass("normalization_pass"), +) +def normalize_cat_default(match: Match, *args, **kwargs): + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + cat_node = match.nodes[0] + graph = match.graph + tensors = get_arg_value(cat_node, 0, "tensors") + cat_dim = get_arg_value(cat_node, 1, "dim") + if cat_dim is None: + cat_axis = cat_node.kwargs.get("axis") + if cat_axis is not None: + cat_dim = cat_axis + else: + cat_dim = 0 + if tensors is None or cat_dim is None: + log.debug("couldn't find cat args") + return + assert isinstance(tensors, (list, tuple)) + for tensor in itertools.chain([cat_node], tensors): + if not is_node_meta_valid(tensor): + log.debug("example value absent for node: %s", tensor) + return + + ndim = cat_node.meta["example_value"].dim() + + def is_empty_tensor(x): + # special case where torch.cat supports cat'ing with an empty tensor + x_shape = x.meta["example_value"].shape + return len(x_shape) == 1 and guard_size_oblivious(x_shape[0] == 0) + + assert all( + ndim == x.meta["example_value"].dim() or is_empty_tensor(x) for x in tensors + ) + + if cat_dim < 0: # Normalize cat dim + cat_dim += ndim + + new_args = (tensors,) + new_kwargs = {"dim": cat_dim} + if ( + cat_node.args == new_args + and cat_node.kwargs == new_kwargs + and cat_node.op == "call_function" + ): + return + + with graph.inserting_after(cat_node): + new_cat_node = graph.call_function( + torch.cat, + args=new_args, + kwargs=new_kwargs, + ) + cat_node.replace_all_uses_with(new_cat_node) + new_cat_node.meta.update(cat_node.meta) + graph.erase_node(cat_node) + counters["inductor"]["normalization_pass"] += 1 + + +@register_graph_pattern( + CallFunctionVarArgs(torch.stack, users=MULTIPLE), + pass_dict=construct_pattern_matcher_pass("normalization_pass"), +) +def normalize_stack_default(match: Match, *args, **kwargs): + node = match.nodes[0] + graph = match.graph + tensors = get_arg_value(node, 0, "tensors") + dim = get_arg_value(node, 1, "dim") or 0 + if tensors is None or dim is None: + log.debug("couldn't find stack args") + return + assert isinstance(tensors, (list, tuple)) + + # A bug in pytorch, some nodes miss the example_value metadata + for tensor in itertools.chain([node], tensors): + if not is_node_meta_valid(tensor): + log.debug("example value absent for node: %s", tensor) + return + + ndim = node.meta["example_value"].dim() + if dim < 0: # Normalize dim + dim += ndim + + with graph.inserting_after(node): + new_node = graph.call_function( + node.target, # type: ignore[arg-type] + args=(tensors,), + kwargs={"dim": dim}, + ) + node.replace_all_uses_with(new_node) + new_node.meta.update(node.meta) + graph.erase_node(node) + counters["inductor"]["normalization_pass"] += 1 + + +def find_next_users(split_node: torch.fx.Node) -> List[torch.fx.Node]: + next_users = [] + for getitem_node in split_node.users.keys(): + for getitem_user in getitem_node.users.keys(): + if getitem_user not in next_users: + next_users.append(getitem_user) + return next_users + + +@register_graph_pattern( + CallMethodVarArgs("squeeze", users=MULTIPLE), + pass_dict=construct_pattern_matcher_pass("normalization_pass"), +) +def normalize_squeeze_default(match: Match, *args, **kwargs): + squeeze_node = match.nodes[0] + squeeze_input = get_arg_value(squeeze_node, 0) + + if "dim" in squeeze_node.kwargs: + assert len(squeeze_node.args) == 1 + dim = squeeze_node.kwargs["dim"] + elif len(squeeze_node.args) == 1: + # squeeze(Tensor) + dim = None + elif len(squeeze_node.args) == 2: + # squeeze(Tensor self, int dim) + # squeeze(Tensor self, int[] dim) + dim = squeeze_node.args[1] + else: + # squeeze(Tensor self, int[] dim) (called with varargs) + dim = squeeze_node.args[1:] + + if isinstance(dim, Sequence) and len(dim) == 1: + dim = dim[0] + + with match.graph.inserting_after(squeeze_node): + if dim is None: + new_squeeze_node = match.graph.call_function( + torch.squeeze, args=(squeeze_input,) + ) + else: + new_squeeze_node = match.graph.call_function( + torch.squeeze, args=(squeeze_input,), kwargs={"dim": dim} + ) + squeeze_node.replace_all_uses_with(new_squeeze_node) + new_squeeze_node.meta.update(squeeze_node.meta) + match.graph.erase_node(squeeze_node) + + +@register_graph_pattern( + CallMethodVarArgs("reshape", users=MULTIPLE), + pass_dict=construct_pattern_matcher_pass("normalization_pass"), +) +def normalize_reshape_default(match: Match, *args, **kwargs): + reshape_node = match.nodes[0] + if not is_node_meta_valid(reshape_node): + log.debug("example value absent for node: %s", reshape_node) + return + reshape_input = get_arg_value(reshape_node, 0) + + with match.graph.inserting_after(reshape_node): + new_reshape_node = match.graph.call_function( + torch.reshape, + args=(reshape_input, tuple(reshape_node.meta["example_value"].shape)), + ) + reshape_node.replace_all_uses_with(new_reshape_node) + new_reshape_node.meta.update(reshape_node.meta) + match.graph.erase_node(reshape_node) + + +class TorchSplit(CallFunction): + """ + Matches a call to torch.split if it is in a normalized form. Ensures that all users of + splits are unique getitems. + """ + + def __init__(self, arg, sizes, func=torch.split) -> None: + # using KeywordArg("dim") for `dim` checks they all match + super().__init__(func, arg, sizes, _users=MULTIPLE, dim=KeywordArg("dim")) + + def _match(self, node: torch.fx.Node, ctx: MatchContext): + m = super()._match(node, ctx) + if not m: + return m + split_sections = node.args[1] + if not isinstance(split_sections, (list, tuple)): + return FailedMatch("split not normalized") + # check users are all unique getitems + seen_idxs = set() + for user in node.users: + if not CallFunction(operator.getitem, Arg(), Arg()).match(user): + # This should ideally never happen. Split user should always be a getitem + return FailedMatch(f"user of split not a getitem: {user}") + if not isinstance(user.args[1], int): + return FailedMatch("only integer getitems are handled") + if user.args[1] in seen_idxs: + return FailedMatch(f"duplicate getitem {user.args[1]}") + if user.args[-1] < 0: # type: ignore[operator] + # This shouldn't ideally happen as dynamo normalizes indexes to positive + return FailedMatch("negative index") + seen_idxs.add(user.args[1]) + return m + + +@register_graph_pattern( + TorchSplit( + CallFunction( + operator.getitem, + TorchSplit( + KeywordArg("first_split_input"), + KeywordArg("first_split_sections"), + ), + Ignored(), + ), + KeywordArg("next_split_sections"), + ), + pass_dict=construct_pattern_matcher_pass("merge_splits_pass"), +) +def merge_splits( + match: Match, + first_split_input: torch.fx.Node, + first_split_sections: List[int], + next_split_sections: List[int], + # Note: dim is implicitly passed by TorchSplit, as it internally uses a pattern with dim + dim: int, +): + node = match.output_node() + # it is possible that the split has no users, + # we check the corner case and skip the pattern + if len(node.users.keys()) == 0: + return + graph = match.graph + first_split = node.args[0].args[0] # type: ignore[union-attr] + next_split_index = node.args[0].args[1] # type: ignore[union-attr] + + new_split_sections = list(first_split_sections) + new_split_sections[next_split_index : next_split_index + 1] = next_split_sections # type: ignore[operator, misc] + + first_split_dim = _get_dim(first_split) + + to_remove = [] + + with graph.inserting_before(first_split): # type: ignore[arg-type] + # Add the new split node + new_split = graph.call_function( + torch.split, + args=(first_split_input, new_split_sections), + kwargs={"dim": first_split_dim}, + ) + if is_node_meta_valid(first_split_input): + new_split.meta["example_value"] = torch.split( + first_split_input.meta["example_value"], + new_split_sections, + dim=first_split_dim, + ) + first_split_num_to_user = { + user.args[1]: user for user in first_split.users.keys() # type: ignore[union-attr] + } + + new_split_num = 0 + for split_num in range(len(first_split_sections)): + if split_num not in first_split_num_to_user: + new_split_num += 1 + continue + old_getitem = first_split_num_to_user[split_num] + if split_num != next_split_index: + old_getitem.update_arg(0, new_split) + old_getitem.update_arg(1, new_split_num) + new_split_num += 1 + else: + next_split_num_to_user = { + user.args[1]: user for user in node.users.keys() + } + # It is not necessary all getitems from the split node are used. + # We use the num of users to check the getitems to be merged. + for next_split_num in range(len(node.users.keys())): + with graph.inserting_after(new_split): + new_getitem = graph.call_function( + operator.getitem, args=(new_split, new_split_num) + ) + new_split_num += 1 + next_getitem = next_split_num_to_user[next_split_num] + new_getitem.meta.update(next_getitem.meta) + next_getitem.replace_all_uses_with(new_getitem) + to_remove.append(next_getitem) + to_remove.append(node) + to_remove.append(old_getitem) + + to_remove.append(first_split) # type: ignore[arg-type] + for node in to_remove: + graph.erase_node(node) + + counters["inductor"]["merge_splits_pass"] += 1 + + +class SplitCatSimplifier: + """ + Helper class to simplify split-cat pattern. In simple cases, both split and cat node can be removed in a "split->cat" + pattern. However, there are various cases where they can't and we need to simplify split/ add transforms before cat. + Some such cases are: + 1. Final node has additional args (not coming from the initial split) + 2. Shuffling of args between split/cat + 3. Some final nodes are non-(cat/stack) + 4. Split-dim != cat-dim (but equal split) + + Note that any combination of the above cases can happen. + + To deal with 1, 2, & 3 - we iterate over all users of split. And figure out common "ranges" that can be merged. + Then, we simplify the split accordingly. In the best case, split can be entirely removed. + + To deal with 4, we add some transformations (unflatten + movedim) (See `get_transform_params`). + + Finally, depending on final node being cat or stack, unsqueeze/flatten needs to be added. + + """ + + def simplify( + self, + graph: torch.fx.Graph, + split_node: torch.fx.Node, + split_sections: List[int], + ): + # Find the next users (i.e. users after the getitem) + next_users = find_next_users(split_node) + # Gather inputs of the next users. When inputs come from `split_node`, they are instead represented by + # a tuple indicating the split ranges. See `get_user_input_list` for more details + user_inputs_list = self.get_user_input_list(split_node, next_users) + # Simplify the split_sections based on user_inputs_list. In simpler cases, len(simplified_split_ranges) == 1 and + # we can simply replace the split node. Otherwise, we simplify it. + simplified_split_ranges = self.get_simplified_split_ranges( + split_sections, next_users, user_inputs_list + ) + if not simplified_split_ranges: # Simplification not possible + return + transform_params_list = self.get_transform_params( + split_node, next_users, user_inputs_list + ) + if not transform_params_list: + return + + # Start actual replacement + user_inputs_list_new = self.replace_split( + graph, split_node, split_sections, user_inputs_list, simplified_split_ranges + ) + self.replace_cat( + graph, split_node, next_users, user_inputs_list_new, transform_params_list # type: ignore[arg-type] + ) + self.erase_old_nodes(graph, split_node, next_users) # type: ignore[arg-type] + counters["inductor"]["unbind_stack_pass"] += 1 + + def get_user_input_list( + self, split_node: torch.fx.Node, next_users: List[torch.fx.Node] + ) -> List[List[Union[torch.fx.Node, _Range]]]: + """ + Returns list of inputs to the following user nodes, in order. The outer list represents the user node. The inner + list represents the inputs to that particular node. This list can either contain + - a tuple representing the ranges of get_items that should go into the cat (closed interval) + - torch.fx.Node representing "other" inputs (which are not coming from our split) + """ + user_inputs_list: List[List[Union[torch.fx.Node, _Range]]] = [] + for user in next_users: + if user.target in {torch.cat, torch.stack}: + user_inputs_list.append(self.get_merged_user_inputs(split_node, user)) + else: + user_inputs_list.append(self.get_non_cat_node_input(split_node, user)) # type: ignore[arg-type] + return user_inputs_list + + def get_merged_user_inputs( + self, split_node: torch.fx.Node, cat_node: torch.fx.Node + ) -> List[Union[torch.fx.Node, _Range]]: + user_inputs = get_arg_value(cat_node, 0, "tensors") + simplified_user_inputs = [] + split_users = set(split_node.users.keys()) + for user_input in user_inputs: + if user_input not in split_users: + simplified_user_inputs.append(user_input) + else: + # Add which "getitem" cat depends on + simplified_user_inputs.append(user_input.args[1]) + return self.merge_consecutive_inputs(simplified_user_inputs) + + def get_non_cat_node_input( + self, split_node: torch.fx.Node, node: torch.fx.Node + ) -> List[_Range]: + """ + Get input for a non cat node in the same format as `get_merged_user_inputs` + """ + node_input = [] + split_users = set(split_node.users.keys()) + for node_arg in node.all_input_nodes: + if node_arg in split_users: + getitem_num = get_arg_value(node_arg, 1) + node_input.append((getitem_num, getitem_num)) + return node_input + + def merge_consecutive_inputs( + self, inputs: List[Union[torch.fx.Node, int]] + ) -> List[Union[torch.fx.Node, _Range]]: + """ + Merge consecutive inputs going into a user node. + + For e.g. + [arg0, 0, 1, 2, arg1] -> [arg0, (0, 2), arg1] + """ + merged_ranges = [] + cur_range = None + for input_ in inputs: + if isinstance(input_, int): + if not cur_range: + cur_range = [input_, input_] + elif input_ == cur_range[1] + 1: + cur_range[1] += 1 + else: + merged_ranges.append(tuple(cur_range)) + cur_range = [input_, input_] + else: + if cur_range: + merged_ranges.append(tuple(cur_range)) + cur_range = None + merged_ranges.append(input_) # type: ignore[arg-type] + if cur_range: + merged_ranges.append(tuple(cur_range)) + return merged_ranges # type: ignore[return-value] + + def get_simplified_split_ranges( + self, + split_sections, + next_users, + user_inputs_list: List[List[Union[torch.fx.Node, _Range]]], + ) -> Optional[List[_Range]]: + ranges = set() + for user_node, user_inputs in zip(next_users, user_inputs_list): + ranges |= { + user_input + for user_input in user_inputs + if isinstance(user_input, tuple) + } + cumulative_sizes = [0] + torch.cumsum(torch.tensor(split_sections), 0).tolist() + split_ranges = sorted( + [(cumulative_sizes[r[0]], cumulative_sizes[r[1] + 1]) for r in ranges] + ) + + if not self.has_non_overlapping_ranges( + split_ranges, + ): # This need not be a strict condition + # However, we keep it now for simplicity. + return None + split_ranges = self.fill_gaps(split_ranges, 0, cumulative_sizes[-1]) + if len(split_sections) == len(split_ranges): # Simplification not possible + return None + counters["inductor"]["scmerge_split_sections_removed"] = len( + split_sections + ) - len(split_ranges) + return split_ranges + + def has_non_overlapping_ranges(self, ranges: List[_Range]) -> bool: + for range_, next_range in zip(ranges, ranges[1:]): + if range_[1] > next_range[0]: + return False + return True + + def fill_gaps(self, ranges: List[_Range], min_: int, max_: int) -> List[_Range]: + cur = min_ + filled_ranges = [] + for a, b in ranges: + if cur < a: + filled_ranges.append((cur, a)) + filled_ranges.append((a, b)) + cur = b + if filled_ranges[-1][1] < max_: + filled_ranges.append((filled_ranges[-1][1], max_)) + return filled_ranges + + def get_transform_params( + self, + split_node: torch.fx.Node, + next_users: List[torch.fx.Node], + user_inputs_list: List[List[Union[torch.fx.Node, _Range]]], + ) -> Optional[List[List[_TransformParam]]]: + """ + Figure out what transforms are needed for each input to each cat node. + + We replace a split node with an unflatten followed by a movedim + """ + split_dim = _get_dim(split_node) + split_sections = split_node.args[1] + transform_params_list: List[List[_TransformParam]] = [] + + for user_node, user_inputs in zip(next_users, user_inputs_list): + if user_node.target not in {torch.cat, torch.stack}: + transform_params_list.append([]) + continue + + cat_dim = get_arg_value(user_node, 1, "dim") + transform_params: List[_TransformParam] = [] + for user_input in user_inputs: + if split_dim == cat_dim and user_node.target == torch.cat: + # No transform needed + transform_params.append((None, None, None, None)) + elif isinstance(user_input, tuple): # Split being simplified + # Verify equal split + subset_split_sections = split_sections[ # type: ignore[index] + user_input[0] : user_input[1] + 1 + ] + # All sections should be equal + if len(set(subset_split_sections)) != 1: + return None + + num_splits = len(subset_split_sections) + unflatten_params = (split_dim, (num_splits, -1)) + movedim_params = ( + (split_dim, cat_dim) if split_dim != cat_dim else None + ) + transform_params.append( + (unflatten_params, movedim_params, None, None) + ) + elif ( + user_node.target == torch.stack or split_dim != cat_dim + ): # We need to unsqueeze inputs not coming through split + transform_params.append((None, None, (cat_dim,), None)) + else: # Non-split inputs + transform_params.append((None, None, None, None)) + transform_params_list.append(transform_params) + return transform_params_list + + def replace_split( + self, + graph: torch.fx.Graph, + split_node: torch.fx.Node, + split_sections: List[int], + user_inputs_list: List[List[Union[torch.fx.Node, _Range]]], + split_ranges: List[_Range], + ) -> List[List[torch.fx.Node]]: + """ + Replace the split node. It can either remove the split node if len(split_ranges) == 1, or simplify it + into a split with lesser sections if len(split_ranges) > 1. + + Returns the new `user_inputs_list`, with tuples replaced with new getitems from the newer split node. + """ + split_input = split_node.args[0] + split_dim = _get_dim(split_node) + if len(split_ranges) == 1: # We can completely eliminate the split node + split_items = [split_input] + else: + with graph.inserting_after(split_node): + new_split = graph.call_function( + torch.split, + args=( + split_input, + [r[1] - r[0] for r in split_ranges], + ), + kwargs={"dim": split_dim}, + ) + if is_node_meta_valid(split_input): # type: ignore[arg-type, union-attr] + new_split.meta["example_value"] = torch.split( + split_input.meta["example_value"], [r[1] - r[0] for r in split_ranges], dim=split_dim # type: ignore[union-attr] + ) + counters["inductor"]["scmerge_split_added"] += 1 + split_items = [] + with graph.inserting_after(new_split): + for i in range(len(split_ranges)): + getitem = graph.call_function(operator.getitem, args=(new_split, i)) + if is_node_meta_valid(new_split): + getitem.meta["example_value"] = new_split.meta["example_value"][ + i + ] + split_items.append(getitem) + # Now assign the right getitem to the right input + cumulative_sizes = [0] + torch.cumsum(torch.tensor(split_sections), 0).tolist() + new_user_inputs_list = [] + for user_inputs in user_inputs_list: + new_user_inputs = [] + for user_input in user_inputs: + if isinstance(user_input, tuple): + # Find the correct new getitem (present in split_items) + new_user_inputs.append( + split_items[ + split_ranges.index( + ( + cumulative_sizes[user_input[0]], + cumulative_sizes[user_input[1] + 1], + ) + ) + ] + ) + else: + new_user_inputs.append(user_input) + new_user_inputs_list.append(new_user_inputs) + return new_user_inputs_list # type: ignore[return-value] + + def replace_cat( + self, + graph: torch.fx.GraphModule, + split_node: torch.fx.Node, + next_users: List[torch.fx.Node], + user_inputs_list_new, + transform_params_list: List[List[_TransformParam]], + ): + split_dim = _get_dim(split_node) + split_users = split_node.users.keys() + new_cats = [] + for user_node, user_inputs_new, transform_params in zip( + next_users, user_inputs_list_new, transform_params_list + ): + if user_node.target not in {torch.cat, torch.stack}: + # Change the args and kwargs of non-cat/stack nodes. Replace old getitems (belonging to + # the original split node) with the newer getitems + next_cat_input = 0 + for input_node in user_node.all_input_nodes: + if input_node in split_users: + user_node.replace_input_with( + input_node, user_inputs_new[next_cat_input] + ) + next_cat_input += 1 + continue + + # Handle cat/stack user nodes + cat_dim = get_arg_value(user_node, 1, "dim") + user_inputs_new_transformed, user_inputs_new_transformed_meta = [], [] + # For `unsqueeze` transform, we will combine consecutive inputs with the same unsqueeze params, and stack them + to_stack, to_stack_meta = [], [] + stack_dim = None + with graph.inserting_before(user_node): + for user_input_new, transform_param in zip( + user_inputs_new, transform_params + ): + if not is_node_meta_valid(user_input_new): + log.debug("example value absent for node: %s", user_input_new) + return + # Apply transforms + ( + unflatten_params, + movedim_params, + unsqueeze_params, + flatten_params, + ) = transform_param + if unsqueeze_params and ( + stack_dim is None or stack_dim == unsqueeze_params[0] + ): + to_stack.append(user_input_new) + to_stack_meta.append(user_input_new.meta["example_value"]) + stack_dim = unsqueeze_params[0] + continue + elif to_stack: + stacked_input = graph.call_function( + torch.stack, args=(to_stack,), kwargs={"dim": stack_dim} + ) + stacked_input.meta["example_value"] = torch.stack(to_stack_meta, dim=stack_dim) # type: ignore[arg-type, union-attr] + to_stack, to_stack_meta = [], [] + stack_dim = None + user_inputs_new_transformed.append(stacked_input) + user_inputs_new_transformed_meta.append( + stacked_input.meta["example_value"] + ) + if unsqueeze_params: + to_stack.append(user_input_new) + stack_dim = unsqueeze_params[0] + to_stack_meta.append(user_input_new.meta["example_value"]) + continue + + if unflatten_params: + user_input_new_meta = user_input_new.meta["example_value"] + user_input_new = graph.call_function( + torch.unflatten, args=(user_input_new, *unflatten_params) + ) + user_input_new.meta["example_value"] = torch.unflatten(user_input_new_meta, *unflatten_params) # type: ignore[arg-type, possibly-undefined, union-attr] + if movedim_params: + user_input_new_meta = user_input_new.meta["example_value"] + user_input_new = graph.call_function( + torch.movedim, args=(user_input_new, *movedim_params) + ) + user_input_new.meta["example_value"] = torch.movedim(user_input_new_meta, *movedim_params) # type: ignore[arg-type, possibly-undefined, union-attr] + if flatten_params: + user_input_new_meta = user_input_new.meta["example_value"] + user_input_new = graph.call_function( + torch.flatten, args=(user_input_new, *flatten_params) + ) + user_input_new.meta["example_value"] = torch.flatten(user_input_new_meta, *flatten_params) # type: ignore[arg-type, possibly-undefined, union-attr] + user_inputs_new_transformed.append(user_input_new) + user_inputs_new_transformed_meta.append( + user_input_new.meta["example_value"] + ) + if to_stack: + stacked_input = graph.call_function( + torch.stack, args=(to_stack,), kwargs={"dim": stack_dim} + ) + stacked_input.meta["example_value"] = torch.stack(to_stack_meta, dim=stack_dim) # type: ignore[arg-type, union-attr] + user_inputs_new_transformed.append(stacked_input) + user_inputs_new_transformed_meta.append( + stacked_input.meta["example_value"] + ) + + with graph.inserting_after(user_node): + if len(user_inputs_new_transformed) > 1: + new_cat_node = graph.call_function( + torch.cat, + args=(user_inputs_new_transformed,), + kwargs={"dim": cat_dim}, + ) + new_cat_node.meta["example_value"] = torch.cat( + user_inputs_new_transformed_meta, dim=cat_dim + ) + counters["inductor"]["scmerge_cat_added"] += 1 + else: + new_cat_node = user_inputs_new_transformed[-1] + new_cat_node.meta[ + "example_value" + ] = user_inputs_new_transformed_meta[-1] + + if ( + user_node.target == torch.cat + and split_dim != cat_dim + and split_node.target == torch.split + ): + with graph.inserting_after(new_cat_node): + new_cat_node_meta = new_cat_node.meta["example_value"] + new_cat_node = graph.call_function( + torch.flatten, args=(new_cat_node, cat_dim, cat_dim + 1) + ) + new_cat_node.meta["example_value"] = torch.flatten(new_cat_node_meta, cat_dim, cat_dim + 1) # type: ignore[possibly-undefined, union-attr] + user_node.replace_all_uses_with(new_cat_node) + new_cats.append(new_cat_node) + + def erase_old_nodes( + self, + graph: torch.fx.GraphModule, + split_node: torch.fx.Node, + next_users: List[torch.fx.Node], + ): + to_remove = [split_node] + counters["inductor"]["scmerge_split_removed"] += 1 + to_remove.extend(split_node.users.keys()) + for next_user in next_users: + if next_user.target not in {torch.cat, torch.stack}: + continue + counters["inductor"]["scmerge_cat_removed"] += 1 + to_remove.append(next_user) + for node in reversed(to_remove): + if len(node.users.keys()) == 0: + graph.erase_node(node) + + +class UnbindCatRemover(SplitCatSimplifier): + """ + Helper class to merge Unbind->Cat/Stack. Many of the cases are similar to SplitCatSimplifier. + + Unbind can't be simplified like splits. So, we can only remove the unbind node. Other than this, + other cases like multiple users, additional args, dim mismatch are similar to `SplitCatSimplifier`, + hence we extend that class. + """ + + def remove_unbind( + self, + graph: torch.fx.Graph, + unbind_node: torch.fx.Node, + ): + if not is_node_meta_valid(unbind_node): + return + # we need to check if the getitem indices from unbind are consecutive and all go to the same cat node + # before we do the unbind remove, otherwise it will hit the error when we unbind part of them + getitem_indices = [] + for getitem_node in unbind_node.users.keys(): + getitem_indices.append(getitem_node.args[1]) + if not is_sorted_and_consecutive(getitem_indices) or len( # type: ignore[arg-type] + getitem_indices + ) != len( + unbind_node.meta["example_value"] + ): + return + num_unbind = len(getitem_indices) + split_sections = [1 for _ in range(num_unbind)] # type: ignore[operator, arg-type] + + super().simplify(graph, unbind_node, split_sections) + + def get_simplified_split_ranges( + self, + split_sections: List[int], + next_users: List[torch.fx.Node], + user_inputs_list: List[List[Union[torch.fx.Node, _Range]]], + ) -> Optional[List[_Range]]: + simplified_split_ranges = super().get_simplified_split_ranges( + split_sections, next_users, user_inputs_list + ) + if not simplified_split_ranges or len(simplified_split_ranges) != 1: + return None + return simplified_split_ranges + + def get_transform_params( + self, + split_node: torch.fx.Node, + next_users: List[torch.fx.Node], + user_inputs_list: List[List[Union[torch.fx.Node, _Range]]], + ) -> Optional[List[List[_TransformParam]]]: + """ + Figure out what transforms are needed for each input to each cat node. + + Here is the rough transforms we apply: + + x -> unbind -> stack => x -> movedim + + x -> unbind -> cat => x -> movedim -> flatten + + When cat/stack nodes have additional args: + + addn ---| addn -> unsqueeze ---| + x -> unbind -> stack => x -> movedim -> cat + + addn ---| addn ---| + x -> unbind -> cat => x -> movedim -> flatten -> cat + + (Note application of these depends on the dims as well) + + + """ + split_dim = _get_dim(split_node) + transform_params_list: List[List[_TransformParam]] = [] + for user_node, user_inputs in zip(next_users, user_inputs_list): + cat_dim = get_arg_value(user_node, 1, "dim") or 0 + transform_params: List[_TransformParam] = [] + for user_input in user_inputs: + if isinstance(user_input, tuple): + # User input is coming from unbind + movedim_params = ( + (split_dim, cat_dim) if split_dim != cat_dim else None + ) + flatten_params = None + if user_node.target == torch.cat: + flatten_params = (cat_dim, cat_dim + 1) + transform_params.append( + (None, movedim_params, None, flatten_params) + ) + elif ( + user_node.target == torch.stack + ): # We need to unsqueeze inputs not coming through unbind into cat + transform_params.append((None, None, (cat_dim,), None)) + else: # Non-unbind inputs + transform_params.append((None, None, None, None)) + transform_params_list.append(transform_params) + return transform_params_list + + +class GetItem(CallFunction): + def __init__(self, arg, index, _users=1) -> None: + super().__init__(operator.getitem, arg, index, _users=_users) + + def find_anchor_nodes(self, ctx: MatchContext, searched: Set[torch.fx.Node]): + # We generally match GetItem with arg being an Arg(). So, we never return the anchor + # nodes as the stored node in ctx.pattern_to_node is returned. Here we override find_anchor_nodes + # to not use ctx.pattern_to_node + for pattern in self.flat_args_kwargs[0]: + if isinstance(pattern, PatternExpr): + for other_node in pattern.find_anchor_nodes(ctx, searched): + if not isinstance(other_node, torch.fx.Node): + continue + for node in other_node.users: + if node not in searched: + if self._match_fns(node): + yield node + searched.add(node) + + +@register_graph_pattern( + RepeatedExpr( + CallFunction( + torch.squeeze, + GetItem( + TorchSplit( + KeywordArg("split_input"), + KeywordArg("split_sizes"), + ), + Ignored(), + ), + KeywordArg("dim"), + _users=MULTIPLE, + ), + ), + pass_dict=construct_pattern_matcher_pass("split_cat_pass"), +) +@register_graph_pattern( + RepeatedExpr( + CallFunction( + torch.squeeze, + GetItem( + TorchSplit( + KeywordArg("split_input"), + KeywordArg("split_sizes"), + ), + Ignored(), + ), + dim=KeywordArg("dim"), + _users=MULTIPLE, + ) + ), + pass_dict=construct_pattern_matcher_pass("split_cat_pass"), +) +def merge_split_squeeze( + match: Match, split_input: torch.fx.Node, split_sizes: List[int], dim: int +): + graph = match.graph + split = next(node for node in match.nodes if node.target == torch.split) + if not all(s == 1 for s in split_sizes): + return + if isinstance(dim, Sequence): + return + next_users = find_next_users(split) + if not all(node.target == torch.squeeze for node in next_users): + return + with graph.inserting_before(match.output_node()): + unbind = graph.call_function( + torch.unbind, args=(split_input,), kwargs={"dim": dim} + ) + if is_node_meta_valid(split_input): + unbind.meta["example_value"] = torch.unbind( + split_input.meta["example_value"], dim=dim + ) + for item_index, getitem_node in sorted( + [ + (getitem_node.args[1], getitem_node) + for getitem_node in split.users.keys() + ] + ): + squeeze = next(iter(getitem_node.users.keys())) + new_get_item = graph.call_function( + operator.getitem, args=(unbind, item_index) + ) + squeeze.replace_all_uses_with(new_get_item) + new_get_item.meta.update(squeeze.meta) + graph.erase_node(squeeze) + graph.erase_node(getitem_node) + graph.erase_node(split) + counters["inductor"]["split_cat_pass"] += 1 + + +getitem_unbind = ListOf( + GetItem( + CallFunction( + torch.unbind, + KeywordArg("unbind_input"), + dim=KeywordArg("dim"), + _users=MULTIPLE, + ), + Ignored(), + _users=MULTIPLE, + ), + partial=True, +) + + +@register_graph_pattern( + CallFunction([torch.stack, torch.cat], getitem_unbind, Ignored(), _users=MULTIPLE), + pass_dict=construct_pattern_matcher_pass("unbind_stack_pass"), +) +@register_graph_pattern( + CallFunction( + [torch.stack, torch.cat], getitem_unbind, dim=Ignored(), _users=MULTIPLE + ), + pass_dict=construct_pattern_matcher_pass("unbind_stack_pass"), +) +@register_graph_pattern( + CallFunction( + [torch.stack, torch.cat], tensors=getitem_unbind, dim=Ignored(), _users=MULTIPLE + ), + pass_dict=construct_pattern_matcher_pass("unbind_stack_pass"), +) +def merge_unbind_stack(match: Match, unbind_input: torch.fx.Node, dim: int): + unbind_node = next(node for node in match.nodes if node.target == torch.unbind) + UnbindCatRemover().remove_unbind(match.graph, unbind_node) + + +getitem_split = ListOf( + CallFunction( + operator.getitem, + TorchSplit( + Ignored(), + KeywordArg("split_sections"), + ), + Ignored(), + _users=MULTIPLE, + ), + partial=True, +) + + +reshape_getitem_split = ListOf( + CallFunction( + torch.reshape, + CallFunction( + operator.getitem, + TorchSplit( + Ignored(), + KeywordArg("split_sections"), + ), + Ignored(), + _users=MULTIPLE, + ), + Arg(), + _users=MULTIPLE, + ), + partial=True, +) + + +@register_graph_pattern( + CallFunction( + [torch.stack, torch.cat], + tensors=getitem_split, + dim=Ignored(), + _users=MULTIPLE, + ), + pass_dict=construct_pattern_matcher_pass("split_cat_pass"), +) +@register_graph_pattern( + CallFunction( + [torch.stack, torch.cat], + getitem_split, + dim=Ignored(), + _users=MULTIPLE, + ), + pass_dict=construct_pattern_matcher_pass("split_cat_pass"), +) +@register_graph_pattern( + CallFunction( + [torch.stack, torch.cat], + getitem_split, + Ignored(), + _users=MULTIPLE, + ), + pass_dict=construct_pattern_matcher_pass("split_cat_pass"), +) +def simplify_split_cat(match: Match, split_sections: List[int], dim: int): + if not isinstance(split_sections, (list, tuple)): # Unnormalized split + return + split_node = next(node for node in match.nodes if node.target == torch.split) + SplitCatSimplifier().simplify(match.graph, split_node, split_sections) + + +# noqa: W605 +# ############pattern to be optimized is######### + +# split_node(dim=1) +# / \ ... / \ +# getitem getitem getitem getitem -> user=1 +# \ / \ / +# cat (user=mul, dim=1) cat(user=mul, dim=1) +# | \ | \ + +# ################after transformation############# + +# split_node(dim=1) +# / ... \ +# getitem getitem +# | \ | \ + + +def has_same_parent_node(node: torch.fx.Node): + # the input nodes of the node should come from the same parent + prev_node = None + for getitem in node.args[0]: # type: ignore[union-attr] + if getitem.target != operator.getitem: # type: ignore[union-attr] + return False + if prev_node is None: + prev_node = getitem.args[0] # type: ignore[union-attr] + else: + if getitem.args[0] != prev_node: + return False + return True + + +def remove_zeros(split_sections: List[int]): + """ + Remove zeros from the list and get the index mapping dict from getitem + in split node to getitem in new split node + """ + new_split_sections, index_mapping = [], {} + idx = 0 + for i in range(len(split_sections)): + if split_sections[i] > 0: + new_split_sections.append(split_sections[i]) + index_mapping[i] = idx + idx += 1 + + return new_split_sections, index_mapping + + +def is_sorted_and_consecutive(arr: List[int]) -> bool: + # check if the array is sorted + if arr == sorted(arr): + # check if the differences between adjacent elements are all 1 + return all(x[1] - x[0] == 1 for x in zip(arr, arr[1:])) + else: + return False + + +def calculate_fused_tensor_size(split_node: torch.fx.Node, indices: List[int]) -> int: + """ + Calculate the fused tensor size in the indices + """ + fused_tensor_size = 0 + for i in range(len(split_node.args[1])): # type: ignore[arg-type] + if i in indices: + fused_tensor_size += split_node.args[1][i] # type: ignore[operator, assignment, index] + return fused_tensor_size + + +@register_graph_pattern( + CallFunction( + torch.cat, + getitem_split, + dim=Ignored(), + _users=MULTIPLE, + ), + pass_dict=construct_pattern_matcher_pass("merge_getitem_cat_pass"), +) +def merge_getitem_cat(match: Match, split_sections: List[int], dim: int): + if not isinstance(split_sections, (list, tuple)): # Unnormalized split + return + graph = match.graph + split_node = next(node for node in match.nodes if node.target == torch.split) + split_input, split_size, split_dim = _get_split_args_default(split_node) + # if the cat and split have different dims, return + # Find the next users (i.e. users after the getitem) + next_users = find_next_users(split_node) + # 'immutable_list' object does not support mutation. Create a new copy of it + split_sections = list(split_sections) + for cat_user in next_users: + if cat_user.target == torch.cat: + cat_dim = get_arg_value(cat_user, 1, "dim") + # check the all getitems in the cat_user from the same node + # check the input of the cat has all getitem from the split + # check all getitem only has one single user + if ( + split_dim != cat_dim + or not has_same_parent_node(cat_user) + or not all(len(arg.users) == 1 for arg in cat_user.args[0]) # type: ignore[union-attr] + ): + continue + # find the index of getitems to be cated/stacked + indices = [] + for arg in cat_user.args[0]: # type: ignore[union-attr] + indices.append(arg.args[1]) # type: ignore[union-attr] + # the gettitems to be merged must be consecutive, otherwise + # returned sliced tensor could be wrong + if not is_sorted_and_consecutive(indices): + continue + # update the arg of cat user, only keep the first getitem + cat_user.update_arg(0, cat_user.args[0][0]) # type: ignore[index] + # calculate the fused tensor sizes in the indices + fused_tensor_size = 0 + for i in range(len(split_node.args[1])): # type: ignore[arg-type] + if i in indices: + fused_tensor_size += split_node.args[1][i] # type: ignore[operator, assignment, index] + # update the split sections + split_sections[indices[0]] = calculate_fused_tensor_size( + split_node, indices + ) + # padding others with zeros to keep the same dict size + for i in indices[1:]: + split_sections[i] = 0 + # remove all unused indexes in the split_node + new_split_sections, index_mapping = remove_zeros(split_sections) + with graph.inserting_after(split_node): + new_split_node = graph.call_function( + torch.split, + args=(split_input, split_sections), + kwargs={"dim": split_dim}, + ) + split_node.replace_all_uses_with(new_split_node) + new_split_node.meta.update(split_node.meta) + # remove all unused getitem nodes + to_remove = [cat_user] + # dictionary keys changed during iteration + new_split_getitem_nodes = list(new_split_node.users.keys()) + for getitem_node in new_split_getitem_nodes: + if getitem_node.args[1] in indices[1:]: + to_remove.append(getitem_node) + # update meta data of getitem + elif getitem_node.args[1] == indices[0]: + cat_user.replace_all_uses_with(getitem_node) + getitem_node.meta.update(cat_user.meta) + else: + # update getitem index for new split node + getitem_node.update_arg(1, index_mapping[getitem_node.args[1]]) + graph.erase_node(split_node) + for getitem_node in to_remove: + graph.erase_node(getitem_node) + # update the split sections of new split node + new_split_node.update_arg(1, new_split_sections) + split_node = new_split_node + split_sections = new_split_sections + + counters["inductor"]["merge_getitem_cat_pass"] += 1 + + +# ############pattern to be optimized is######### + +# split_node(dim=1) -> user=multiple +# / \ ... / \ +# getitem getitem getitem getitem -> user=multiple +# \ \ / \ +# other_op /cat(user=mul, dim=1) other_op +# | + +# ################after transformation############# + +# split_node(dim=1) -> -> user=multiple +# / \ ... / \ +# getitem getitem getitem getitem -> user=multiple +# \ \ / \ +# other_op + + +@register_graph_pattern( + CallFunction( + torch.cat, + getitem_split, + dim=Ignored(), + _users=MULTIPLE, + ), + pass_dict=construct_pattern_matcher_pass("mutate_cat_pass"), +) +def mutate_cat_node(match: Match, split_sections: List[int], dim: int): + if not isinstance(split_sections, (list, tuple)): # Unnormalized split + return + graph = match.graph + split_node = next(node for node in match.nodes if node.target == torch.split) + split_input, split_size, split_dim = _get_split_args_default(split_node) + # if the cat and split have different dims, return + # Find the next users (i.e. users after the getitem) + next_users = find_next_users(split_node) + for cat_user in next_users: + if cat_user.target == torch.cat: + cat_dim = get_arg_value(cat_user, 1, "dim") or 0 + # check that all getitems in the cat_user from the same node + # check the input of the cat has all getitem from the split + if split_dim != cat_dim or not has_same_parent_node(cat_user): + continue + # find the index of getitems to be cat + indices, idx_to_getitem = [], {} + for getitem in cat_user.args[0]: # type: ignore[union-attr] + indices.append(getitem.args[1]) # type: ignore[union-attr] + idx_to_getitem[getitem.args[1]] = getitem # type: ignore[union-attr] + # the gettitems to be merged must be consecutive, otherwise + # returned sliced tensor could be wrong + if not is_sorted_and_consecutive(indices): + continue + # case 1: the cat uses all getitems from the split + if len(split_sections) == len(cat_user.args[0]): # type: ignore[arg-type] + # replace the users of the cat node to be the input of the split node + cat_user.replace_all_uses_with(split_node.args[0]) # type: ignore[arg-type] + # remove the cat node + graph.erase_node(cat_user) + counters["inductor"]["mutate_cat_pass"] += 1 + # case 2: the cat uses some getitems from the split + elif is_node_meta_valid(split_node.args[0]): # type: ignore[arg-type] + # check the split dim, and construct the slice tuple + start_fused_size = calculate_fused_tensor_size( + split_node, list(range(indices[0])) + ) + end_fused_size = start_fused_size + calculate_fused_tensor_size( + split_node, indices + ) + slice_list = [] + for i in range(len(split_node.args[0].meta["example_value"].shape)): # type: ignore[union-attr] + if i != split_dim: + slice_list.append(slice(None, None, None)) + else: + slice_list.append(slice(start_fused_size, end_fused_size, None)) + with graph.inserting_after(split_node): + slice_node = graph.call_function( + operator.getitem, + args=(split_node.args[0], tuple(slice_list)), + ) + cat_user.replace_all_uses_with(slice_node) + slice_node.meta.update(cat_user.meta) + + # remove the cat node + graph.erase_node(cat_user) + counters["inductor"]["mutate_cat_pass"] += 1 + + +@register_graph_pattern( + CallFunctionVarArgs(torch.ops.aten.cat.default, users=MULTIPLE), + pass_dict=construct_pattern_matcher_pass("normalization_aten_pass"), +) +def normalize_cat_default_aten(match: Match, *args, **kwargs): + cat_node = match.nodes[0] + graph = match.graph + tensors = get_arg_value(cat_node, 0, "tensors") + cat_dim = get_arg_value(cat_node, 1, "dim") + if cat_dim is None: + cat_axis = cat_node.kwargs.get("axis") + if cat_axis is not None: + cat_dim = cat_axis + else: + cat_dim = 0 + if tensors is None or cat_dim is None: + log.debug("couldn't find cat args") + return + assert isinstance(tensors, (list, tuple)) + for tensor in itertools.chain([cat_node], tensors): + if "val" not in tensor.meta: + log.debug("val absent for node: %s", tensor) + return + + ndim = cat_node.meta["val"].dim() + + def is_empty_tensor(x: torch.fx.Node) -> bool: + # special case where torch.ops.aten.cat.default supports cat'ing with an empty tensor + x_shape = x.meta["val"].shape + return len(x_shape) == 1 and x_shape[0] == 0 + + assert all(ndim == x.meta["val"].dim() or is_empty_tensor(x) for x in tensors) + + if cat_dim < 0: # Normalize cat dim + cat_dim += ndim + + with graph.inserting_after(cat_node): + new_cat_node = graph.call_function( + torch.ops.aten.cat.default, + args=(tensors,), + kwargs={"dim": cat_dim}, + ) + cat_node.replace_all_uses_with(new_cat_node) + new_cat_node.meta.update(cat_node.meta) + graph.erase_node(cat_node) + counters["inductor"]["normalization_aten_pass"] += 1 + + +@register_graph_pattern( + CallFunction( + torch.ops.aten.cat, + ListOf(CallFunctionVarArgs(torch.ops.aten.unsqueeze)), + _users=MULTIPLE, + ), + pass_dict=construct_pattern_matcher_pass("unbind_stack_aten_pass"), +) +def merge_unbind_stack_aten(match: Match, *args, **kwargs): + node = match.nodes[-1] + graph = match.graph + # pyre-fixme[6] + unsqueeze_nodes = list(node.args[0]) # type: ignore[arg-type] + cat_dim = get_arg_value(node, 1, "dim") + # check the unsqueeze nodes come from the select nodes + if not all( + get_arg_value(unsqueeze_node, 0, "input").target == torch.ops.aten.select + for unsqueeze_node in unsqueeze_nodes + ): + return + select_nodes = [ + get_arg_value(unsqueeze_node, 0, "input") for unsqueeze_node in unsqueeze_nodes + ] + parent_of_select_node = get_arg_value(select_nodes[0], 0, "input") + # check the target of select_nodes are the same + if not all( + select_node.target == torch.ops.aten.select for select_node in select_nodes + ): + return + # check the select nodes come from the same parent node + if not all( + get_arg_value(select_node, 0, "input") == parent_of_select_node + for select_node in select_nodes + ): + return + if len(unsqueeze_nodes) != len(select_nodes): + return + # check the select nodes have the same dim + if not all( + get_arg_value(select_node, 1, "dim") == cat_dim for select_node in select_nodes + ): + return + # check the select nodes have consecutive indices starting from 0 + if get_arg_value(select_nodes[0], 2, "index") != 0 or not is_sorted_and_consecutive( + [get_arg_value(select_node, 2, "index") for select_node in select_nodes] + ): + return + # check the users of parent of select node only from unsqueeze nodes that go to the cat node + # we simply check the number of users of the parent of select node + if len(parent_of_select_node.users.keys()) != len(node.args[0]): # type: ignore[arg-type] + return + node.replace_all_uses_with(parent_of_select_node) + graph.erase_node(node) + for unsqueeze_node in unsqueeze_nodes: + graph.erase_node(unsqueeze_node) + for select_node in select_nodes: + if len(select_node.users) == 0: + graph.erase_node(select_node) + counters["inductor"]["unbind_stack_aten_pass"] += 1 + + +def divide_into_consecutive_sublists(indices: List[int]) -> List[List[int]]: + n = len(indices) + if n <= 1: + return [indices] + + # Initialize the list of sublists + sublists = [] + + # Iterate over the indices + i = 0 + while i < n: + # Initialize the current sublist + sublist = [indices[i]] + + # Iterate over the remaining indices + j = i + 1 + while j < n and indices[j] == indices[j - 1] + 1: + # Add the next index to the current sublist + sublist.append(indices[j]) + j += 1 + + # Add the current sublist to the list of sublists + sublists.append(sublist) + # Move to the next index + i = j + + return sublists + + +def update_args_from_split_getitem( + graph: torch.fx.Graph, + node: torch.fx.Node, + getitem_indices: List[int], + parents_seen: List[torch.fx.Node], + new_cat_args: List[torch.fx.Node], + new_cat_args_meta: List[torch.fx.Node], + idx_to_getitems: Dict[int, torch.fx.Node], + threshold_to_cat: int = 2, +): + split_input, split_size, split_dim = _get_split_args_default(parents_seen[-1]) + # case 1: the number of getitems is the same as the split size, elimiate the split + if len(split_size) == len(getitem_indices) and is_sorted_and_consecutive( + getitem_indices + ): + # we can merge the getitems from the previous parent + new_cat_args.append(split_input) + new_cat_args_meta.append(split_input.meta["example_value"]) + else: + if len(getitem_indices) > 0: + # case 2: the number of getitems is smaller than the split size but larger than the threshold, and + # the indices of getitems are not all consecutive, we need to divide the indices into multiple groups + geitem_indices_sublist = divide_into_consecutive_sublists(getitem_indices) + for sublist in geitem_indices_sublist: + if len(sublist) >= threshold_to_cat: + # case 2: the number of getitems is smaller than the split size but larger than the threshold + # we need to slice the input of parent + start_fused_size = sum(split_size[: sublist[0]]) + end_fused_size = sum(split_size[: sublist[-1] + 1]) + slice_list = [] + for i in range(len(split_input.meta["example_value"].shape)): # type: ignore[union-attr] + if i != split_dim: + slice_list.append(slice(None, None, None)) + else: + slice_list.append( + slice(start_fused_size, end_fused_size, None) + ) + with graph.inserting_after(node): + slice_node = graph.call_function( + operator.getitem, + args=(split_input, tuple(slice_list)), + ) + slice_node.meta["example_value"] = split_input.meta[ + "example_value" + ][tuple(slice_list)] + new_cat_args.append(slice_node) + new_cat_args_meta.append(slice_node.meta["example_value"]) + else: + # case 3: the number of getitems is smaller than the threshold, no merge is done + # get the getitems based on the indexes + for i in sublist: + new_cat_args.append(idx_to_getitems[i]) + new_cat_args_meta.append( + idx_to_getitems[i].meta["example_value"] + ) + + +def reshape_cat_node( + graph: torch.fx.Graph, + cat_node: torch.fx.Node, + unbind_input: torch.fx.Node, + cat_dim: int, + unbind_dim: int, + cat_shape: torch.Size, +) -> torch.fx.Node: + if cat_dim != unbind_dim: + # construct the permute node args, which has the same shape as the slice node + # then it has the same dim as the unbind_input, i.e., shape of cat + 1 + with graph.inserting_after(cat_node): + permute_list = list(range(len(cat_shape) + 1)) + permute_list[unbind_dim], permute_list[cat_dim] = ( + permute_list[cat_dim], + permute_list[unbind_dim], + ) + permute_node = graph.call_function( + torch.permute, + args=(unbind_input, permute_list), + ) + permute_node.meta["example_value"] = torch.permute( + unbind_input.meta["example_value"], permute_list + ) # type: ignore[arg-type] + else: + permute_node = unbind_input + with graph.inserting_after(permute_node): + reshape_node = graph.call_function( + torch.reshape, args=(permute_node, tuple(cat_shape)) + ) + reshape_node.meta["example_value"] = torch.reshape( + permute_node.meta["example_value"], tuple(cat_shape) + ) # type: ignore[arg-type] + return reshape_node + + +def update_args_from_unbind_getitem( + graph: torch.fx.Graph, + node: torch.fx.Node, # cat or stack node + getitem_indices: List[int], + parents_seen: List[torch.fx.Node], + new_cat_args: List[torch.fx.Node], + new_cat_args_meta: List[torch.fx.Node], + idx_to_getitems: Dict[int, torch.fx.Node], + threshold_to_cat: int = 2, +): + unbind_input = get_arg_value(parents_seen[-1], 0, "input") # split or unbind input + unbind_dim = get_arg_value(parents_seen[-1], 1, "dim") # split or unbind dim + cat_dim = get_arg_value(node, 1, "dim") # cat or stack dim + # case 1: the number of getitems is the same as the split size, elimiate the split + size = list(unbind_input.meta["example_value"].shape)[unbind_dim] + if size == len(getitem_indices): + cat_shape = torch.cat( + [idx_to_getitems[i].meta["example_value"] for i in getitem_indices], + dim=cat_dim, + ).shape + # we can merge the getitems from the previous parent + reshape_node = reshape_cat_node( + graph, node, unbind_input, cat_dim, unbind_dim, cat_shape + ) + new_cat_args.append(reshape_node) + new_cat_args_meta.append(reshape_node.meta["example_value"]) + elif len(getitem_indices) >= threshold_to_cat and is_sorted_and_consecutive( + getitem_indices + ): + # case 2: the number of getitems is smaller than the split size but larger than the threshold + # we need to slice the input of parent + cat_shape = torch.cat( + [idx_to_getitems[i].meta["example_value"] for i in getitem_indices], + dim=cat_dim, + ).shape + slice_list = [] + for i in range(len(cat_shape) + 1): + if i != unbind_dim: + slice_list.append(slice(None, None, None)) # start, end, step + else: + slice_list.append( + slice(getitem_indices[0], getitem_indices[-1] + 1, None) + ) + with graph.inserting_after(node): + slice_node = graph.call_function( + operator.getitem, + args=(unbind_input, tuple(slice_list)), + ) + slice_node.meta["example_value"] = torch.narrow( + unbind_input.meta["example_value"], + unbind_dim, + getitem_indices[0], + getitem_indices[-1] - getitem_indices[0] + 1, + ) + reshape_node = reshape_cat_node( + graph, node, slice_node, cat_dim, unbind_dim, cat_shape + ) + new_cat_args.append(reshape_node) + new_cat_args_meta.append(reshape_node.meta["example_value"]) + else: + # case 3: the number of getitems is smaller than the threshold, no merge is done + # get the getitems based on the indexes + for i in getitem_indices: + new_cat_args.append(idx_to_getitems[i]) + new_cat_args_meta.append(idx_to_getitems[i].meta["example_value"]) + + +def construct_cat_args( + graph: torch.fx.Graph, + cat_or_stack_node: torch.fx.Node, + inputs: List[torch.fx.Node], + split_or_unbind_node: torch.fx.Node, + threshold_to_cat: int = 2, + run_update_func: Callable = update_args_from_split_getitem, # type: ignore[type-arg] +) -> Tuple[List[torch.fx.Node], List[torch.Tensor]]: + new_cat_args, parents_seen, getitem_indices, idx_to_getitems = [], [], [], {} # type: ignore[var-annotated] + new_cat_args_meta = [] # type: ignore[var-annotated] + for input in inputs: + if input.target != operator.getitem: + # update the last arg based on getitem_indices and parents_seens + if len(parents_seen) > 0: + run_update_func( # type: ignore[arg-type, union-attr] + graph, + cat_or_stack_node, + getitem_indices, + parents_seen, + new_cat_args, + new_cat_args_meta, + idx_to_getitems, # type: ignore[arg-type, union-attr] + threshold_to_cat, + ) + new_cat_args.append(input) + new_cat_args_meta.append(input.meta["example_value"]) + # reset the indices array + getitem_indices, idx_to_getitems = [], {} + else: + # get the parent node of the getitem input + parent, idx = input.args[0], input.args[1] # type: ignore[union-attr] + if parent.target != split_or_unbind_node.target: # type: ignore[union-attr] + new_cat_args.append(input) + new_cat_args_meta.append(input.meta["example_value"]) + continue + # cannot use parents_seen to check since the first item could be non getitem node + if len(parents_seen) == 0: + parents_seen.append(parent) + idx_to_getitems[idx] = input + getitem_indices.append(idx) + # case: we only have one getitem input, and it is in the last position + if input == inputs[-1]: + new_cat_args.append(input) + new_cat_args_meta.append(input.meta["example_value"]) + continue + # if it is the last input in the tensors, we also check if it can be optimized + if parent != parents_seen[-1] or input == inputs[-1]: + if input == inputs[-1]: + getitem_indices.append(idx) + idx_to_getitems[idx] = input + run_update_func( # type: ignore[arg-type, union-attr] + graph, + cat_or_stack_node, + getitem_indices, + parents_seen, + new_cat_args, + new_cat_args_meta, + idx_to_getitems, # type: ignore[arg-type, union-attr] + threshold_to_cat, + ) + # reset the indices array for the next parent + # remember to add the last element since it is the first + # item in this round of parent + # add the parent to the list of seen parents + parents_seen.append(parent) + getitem_indices, idx_to_getitems = [idx], {idx: input} + else: + getitem_indices.append(idx) + idx_to_getitems[idx] = input + return new_cat_args, new_cat_args_meta + + +def remove_split_unbind_children(graph: torch.fx.Graph, inputs: List[torch.fx.Node]): + nodes = set() + for input in inputs: + if input.target == operator.getitem: + nodes.add(input.args[0]) # type: ignore[union-attr] + if len(input.users.keys()) == 0: + graph.erase_node(input) + # check the split node to remove if it has no users + for node in nodes: + if len(node.users.keys()) == 0: # type: ignore[union-attr] + graph.erase_node(node) # type: ignore[arg-type] + + +# ############pattern to be optimized is######### + +# split_node(dim=1) -> user=multiple +# / \ ... / \ +# other inputs getitem getitem getitem -> user=multiple +# \ / \ +# cat(user=mul, dim=1) other_op +# | + +# ################after transformation############# + +# split_node(dim=1) other inputs -> -> user=multiple +# / \ +# cat (user=mul, dim=1, split_node) + + +@register_graph_pattern( + CallFunctionVarArgs(torch.cat, users=MULTIPLE), + pass_dict=construct_pattern_matcher_pass("split_cat_to_slices_pass"), +) +@register_graph_pattern( + CallFunction( + torch.cat, + getitem_split, + dim=Ignored(), + _users=MULTIPLE, + ), + pass_dict=construct_pattern_matcher_pass("split_cat_to_slices_pass"), +) +def split_cat_to_slices(match: Match, split_sections: List[int], dim: int): + if not isinstance(split_sections, (list, tuple)): # Unnormalized split + return + split_nodes = [node for node in match.nodes if node.target == torch.split] + if split_nodes: + split_node = next(node for node in split_nodes) + else: + # Handle the case where there are no nodes with a target of torch.split + return + split_dim = get_arg_value(split_node, 2, "dim") or 0 + graph = match.graph + threshold_to_cat = torch._inductor.config.pre_grad_fusion_options[ + "split_cat_to_slices_pass" + ].get("threshold_to_cat", 10) + # get the cat_node and check its inputs and meta data + next_users = find_next_users(split_node) + for cat_node in next_users: + if cat_node.target != torch.cat or not is_node_meta_valid(cat_node): + continue + cat_inputs = get_arg_value(cat_node, 0, "tensors") # type: ignore[union-attr] + new_cat_args, _ = construct_cat_args( + graph, + cat_node, + cat_inputs, + split_node, + threshold_to_cat, + update_args_from_split_getitem, + ) + # At least one node would be in the returned new_cat_args + # case 1: if new cat args has length 1, we can remove the cat node + if len(new_cat_args) == 1: + cat_node.replace_all_uses_with(new_cat_args[0]) + # remove inputs of cat_node if they have no users + cat_inputs = cat_node.args[0] # type: ignore[union-attr] + graph.erase_node(cat_node) + remove_split_unbind_children(graph, cat_inputs) # type: ignore[arg-type] + counters["inductor"]["split_cat_to_slices_pass"] += 1 + continue + if len(new_cat_args) > 1 and len(new_cat_args) < len(cat_inputs): + new_args = (new_cat_args,) + with graph.inserting_after(cat_node): + new_cat_node = graph.call_function( + torch.cat, + args=new_args, + # split and cat have the same dim + kwargs={"dim": split_dim}, + ) + cat_node.replace_all_uses_with(new_cat_node) + new_cat_node.meta.update(cat_node.meta) + # remove the cat node + graph.erase_node(cat_node) + remove_split_unbind_children(graph, cat_inputs) + counters["inductor"]["split_cat_to_slices_pass"] += 1 + + +# ############pattern to be optimized is######### + +# unbind(dim=0) -> user=multiple +# / \ ... / \ +# getitem getitem getitem getitem -> user=multiple +# \ / \ +# cat(user=mul, dim=1) other_op +# | + +# ################after transformation############# + +# input_of_unbind +# | \ +# slice +# | +# view +# | + + +@register_graph_pattern( + CallFunction( + torch.cat, + getitem_unbind, + dim=Ignored(), + _users=MULTIPLE, + ), + pass_dict=construct_pattern_matcher_pass("unbind_cat_to_view_pass"), +) +def unbind_cat_to_view(match: Match, unbind_input: torch.fx.Node, dim: int): + unbind_node = next(node for node in match.nodes if node.target == torch.unbind) + graph = match.graph + # get the cat_node and check its inputs and meta data + next_users = find_next_users(unbind_node) + threshold_to_cat = torch._inductor.config.pre_grad_fusion_options[ + "unbind_cat_to_view_pass" + ].get("threshold_to_cat", 10) + # get the cat_node and check its inputs and meta data + for cat_node in next_users: + if cat_node.target != torch.cat or not is_node_meta_valid(cat_node): + continue + inputs = get_arg_value(cat_node, 0, "tensors") # type: ignore[union-attr] + new_cat_args, new_cat_args_meta = construct_cat_args( + graph, + cat_node, + inputs, + unbind_node, + threshold_to_cat, + update_args_from_unbind_getitem, + ) + # get the view shape + # At least one node would be in the returned new_cat_args + # case 1: only one node in the new cat args, don't need to cat + if len(new_cat_args) == 1: + cat_node.replace_all_uses_with(new_cat_args[0]) + # remove inputs of cat_node if they have no users + cat_inputs = cat_node.args[0] # type: ignore[union-attr] + graph.erase_node(cat_node) + remove_split_unbind_children(graph, cat_inputs) # type: ignore[arg-type] + counters["inductor"]["unbind_cat_to_view_pass"] += 1 + continue + if len(new_cat_args) > 1 and len(new_cat_args) < len(inputs): + # get the view shape + cat_dim = get_arg_value(cat_node, 1, "dim") + with graph.inserting_after(cat_node): + new_cat_node = graph.call_function( + torch.cat, + args=(new_cat_args,), + kwargs={"dim": cat_dim}, + ) + new_cat_node.meta["example_value"] = torch.cat(new_cat_args_meta, dim=cat_dim) # type: ignore[arg-type] + cat_node.replace_all_uses_with(new_cat_node) + new_cat_node.meta.update(cat_node.meta) + # remove inputs of cat_node if they have no users + cat_inputs = cat_node.args[0] # type: ignore[union-attr] + graph.erase_node(cat_node) + remove_split_unbind_children(graph, cat_inputs) # type: ignore[arg-type] + counters["inductor"]["unbind_cat_to_view_pass"] += 1 + + +def reshape_cat_node_to_stack( + graph: torch.fx.Graph, + cat_node: torch.fx.Node, + stack_node: torch.fx.Node, + split_or_unbind_dim: int, +) -> None: + # reshape the cat node to the stack node shape + stack_shape = stack_node.meta["example_value"].shape + stack_dim = _get_dim(stack_node) + if stack_dim != split_or_unbind_dim: + # case 1: the stack dim is not the same as the split dim + # we need to reshape the split input before we do the reshape + reshape_list = list(stack_shape) + reshape_list[stack_dim], reshape_list[split_or_unbind_dim] = ( + reshape_list[split_or_unbind_dim], + reshape_list[stack_dim], + ) + reshape_node = graph.call_function( + torch.reshape, + args=(cat_node, tuple(reshape_list)), + ) + reshape_node.meta["example_value"] = torch.reshape( + cat_node.meta["example_value"], tuple(reshape_list) + ) + permute_list = list(range(len(stack_shape))) + permute_list[stack_dim], permute_list[split_or_unbind_dim] = ( + permute_list[split_or_unbind_dim], + permute_list[stack_dim], + ) + permute_node = graph.call_function( + torch.permute, + args=(reshape_node, permute_list), + ) + permute_node.meta["example_value"] = torch.permute( + reshape_node.meta["example_value"], permute_list + ) + else: + # case 2: the stack dim is the same as the split dim + # we can directly reshape the split input + permute_node = cat_node + reshape_node = graph.call_function( + torch.Tensor.view, + args=(permute_node, *stack_shape), # type: ignore[arg-type] + ) + stack_node.replace_all_uses_with(reshape_node) + reshape_node.meta.update(stack_node.meta) + stack_inputs = stack_node.args[0] # type: ignore[union-attr] + # remove stack node + graph.erase_node(stack_node) + # check the input of stack node, and remove nodes that have no users + remove_split_unbind_children(graph, stack_inputs) # type: ignore[arg-type] + + +def convert_reshape_cat_arg_to_stack( + graph: torch.fx.Graph, + cat_node: torch.fx.Node, + stack_node: torch.fx.Node, + stack_node_shape: torch.Size, + stack_dim: int, + split_dim: int, +) -> torch.fx.Node: + # reshape the cat node to the stack node shape + cat_shape = cat_node.meta["example_value"].shape + if stack_dim != split_dim: + permute_list = list(range(len(cat_shape))) + permute_list[stack_dim], permute_list[split_dim] = ( + permute_list[split_dim], + permute_list[stack_dim], + ) + permute_node = graph.call_function( + torch.permute, + args=(cat_node, permute_list), + ) + permute_node.meta["example_value"] = torch.permute( + cat_node.meta["example_value"], permute_list + ) + else: + permute_node = cat_node + reshape_node = graph.call_function( + torch.Tensor.view, + args=(permute_node, tuple(stack_node_shape)), # type: ignore[arg-type] + ) + reshape_node.meta["example_value"] = torch.Tensor.view( + permute_node.meta["example_value"], tuple(stack_node_shape) # type: ignore[arg-type] + ) + return reshape_node + + +# ############pattern to be optimized is######### +# | | +# split split (dim=1) +# / \ / \ +# getitem ... getitem other ops +# \ | / / +# stack(user=mul, dim=1 or 2) -> can be different dim +# | + +# ################after transformation############# + +# / \ ... / \ +# getitem getitem getitem getitem -> user=multiple +# \ / +# cat(user=mul, dim=1) cat_other_opts +# \ / +# cat +# | +# view +# | + + +@register_graph_pattern( + CallFunction( + torch.stack, + getitem_split, + dim=Ignored(), + _users=MULTIPLE, + ), + pass_dict=construct_pattern_matcher_pass("split_stack_to_cats_pass"), +) +def split_stack_to_cats(match: Match, split_sections: List[int], dim: int): + if not isinstance(split_sections, (list, tuple)): # Unnormalized split + return + split_node = next(node for node in match.nodes if node.target == torch.split) + split_dim = get_arg_value(split_node, 2, "dim") or 0 + graph = match.graph + threshold_to_cat = torch._inductor.config.pre_grad_fusion_options[ + "split_stack_to_cats_pass" + ].get("threshold_to_cat", 10) + # get the stack_node and check its inputs and meta data + next_users = find_next_users(split_node) + for stack_node in next_users: + if stack_node.target != torch.stack or not is_node_meta_valid(stack_node): + continue + inputs = get_arg_value(stack_node, 0, "tensors") # type: ignore[union-attr] + new_cat_args, new_cat_args_meta = construct_cat_args( + graph, + stack_node, + inputs, + split_node, + threshold_to_cat, + update_args_from_split_getitem, + ) + # At least one node would be in the returned new_cat_args + # case 1: only one node in the new cat args, don't need to cat + if len(new_cat_args) == 1: + reshape_cat_node_to_stack(graph, new_cat_args[0], stack_node, split_dim) + counters["inductor"]["split_stack_to_cats_pass"] += 1 + continue + if len(new_cat_args) > 1 and len(new_cat_args) < len(inputs): + with graph.inserting_after(stack_node): + cat_node = graph.call_function( + torch.cat, + args=(new_cat_args,), + kwargs={"dim": split_dim}, + ) + cat_node.meta["example_value"] = torch.cat( # type: ignore[arg-type] + new_cat_args_meta, dim=split_dim + ) + reshape_cat_node_to_stack(graph, cat_node, stack_node, split_dim) + counters["inductor"]["split_stack_to_cats_pass"] += 1 + + +# ############pattern to be optimized is######### + +# unbind(dim=1) -> user=multiple +# \ ... / \ +# others getitem getitem getitem -> user=multiple +# \ \ / \ +# stack(user=mul, dim=1) other_op +# | + +# ################after transformation############# + +# input_of_unbind +# | \ +# slice +# | +# view others +# | / +# stack +# | + + +@register_graph_pattern( + CallFunction( + torch.stack, + getitem_unbind, + dim=Ignored(), + _users=MULTIPLE, + ), + pass_dict=construct_pattern_matcher_pass("unbind_stack_to_slices_pass"), +) +def unbind_stack_to_slices(match: Match, unbind_input: torch.fx.Node, dim: int): + unbind_node = next(node for node in match.nodes if node.target == torch.unbind) + graph = match.graph + # get the cat_node and check its inputs and meta data + next_users = find_next_users(unbind_node) + threshold_to_cat = torch._inductor.config.pre_grad_fusion_options[ + "unbind_stack_to_slices_pass" + ].get("threshold_to_cat", 10) + # get the cat_node and check its inputs and meta data + for stack_node in next_users: + if stack_node.target != torch.stack or not is_node_meta_valid(stack_node): + continue + inputs = get_arg_value(stack_node, 0, "tensors") # type: ignore[union-attr] + new_cat_args, new_cat_args_meta = construct_cat_args( + graph, + stack_node, + inputs, + unbind_node, + threshold_to_cat, + update_args_from_unbind_getitem, + ) + unbind_dim = get_arg_value(unbind_node, 1, "dim") or 0 + # At least one node would be in the returned new_cat_args + # case 1: only one node in the new cat args, don't need to cat + if len(new_cat_args) == 1: + reshape_cat_node_to_stack(graph, new_cat_args[0], stack_node, unbind_dim) + counters["inductor"]["unbind_stack_to_slices_pass"] += 1 + continue + if len(new_cat_args) > 1 and len(new_cat_args) < len(inputs): + # get the view shape + cat_dim = get_arg_value(stack_node, 1, "dim") + with graph.inserting_after(stack_node): + new_cat_node = graph.call_function( + torch.cat, + args=(new_cat_args,), + kwargs={"dim": cat_dim}, + ) + new_cat_node.meta["example_value"] = torch.cat( + new_cat_args_meta, dim=cat_dim + ) + reshape_cat_node_to_stack(graph, new_cat_node, stack_node, unbind_dim) + counters["inductor"]["unbind_stack_to_slices_pass"] += 1 + + +# ############pattern to be optimized is######### +# input +# | +# split(dim=1) -> user=multiple +# \ \ +# others getitem getitem +# \ \ / +# reshape reshape reshape other_op +# \ \ / / +# stack(user=mul, dim=0) +# | + +# ################after transformation############# +# input +# | +# permute +# | +# reshape others +# | / +# cat (dim=0) +# | + + +def get_view_shape_list(cat_arg: torch.fx.Node, stack_dim: int) -> List[int]: + # cat_arg must be the split input + view_shape_list = [] + for user in cat_arg.users.keys(): + if user.target == torch.split: + for getitem in user.users.keys(): + if getitem.target == operator.getitem: + reshape_user = [ + user + for user in getitem.users.keys() + if user.target == torch.reshape + ] + if len(reshape_user) > 0: + view_shape_list = list( + reshape_user[0] + .meta["example_value"] + .unsqueeze(stack_dim) + .shape + ) + view_shape_list[stack_dim] = -1 + return view_shape_list + return view_shape_list + + +@register_graph_pattern( + CallFunction( + torch.stack, + reshape_getitem_split, + dim=Ignored(), + _users=MULTIPLE, + ), + pass_dict=construct_pattern_matcher_pass("move_reshape_out_of_split_stack_pass"), +) +def move_reshape_out_of_split_stack(match: Match, *args, **kwargs): + split_node = next(node for node in match.nodes if node.target == torch.split) + split_dim = _get_dim(split_node) + split_users = list(split_node.users.keys()) + stack_nodes = [node for node in match.nodes if node.target == torch.stack] + graph = match.graph + threshold_to_cat = torch._inductor.config.pre_grad_fusion_options[ + "move_reshape_out_of_split_stack_pass" + ].get("threshold_to_cat", 10) + for stack_node in stack_nodes: + if not is_node_meta_valid(stack_node): + log.debug("example value absent for node: %s", stack_node) + continue + stack_dim = _get_dim(stack_node) + stack_inputs = get_arg_value(stack_node, 0, "tensors") # type: ignore[union-attr] + inputs = [] + for stack_input in stack_inputs: + if stack_input.target != torch.reshape: + inputs.append(stack_input) + else: + inputs.append(stack_input.args[0]) # type: ignore[union-attr] + new_cat_args, new_cat_args_meta = construct_cat_args( + graph, + stack_node, + inputs, + split_node, + threshold_to_cat, + update_args_from_split_getitem, + ) + # At least one node would be in the returned new_cat_args + # case 1: only one node in the new cat args, don't need to cat + if len(new_cat_args) == 1: + reshape_node = convert_reshape_cat_arg_to_stack( + graph, + new_cat_args[0], + stack_node, + stack_node.meta["example_value"].shape, + stack_dim, + split_dim, + ) + stack_node.replace_all_uses_with(reshape_node) + # remove stack node + graph.erase_node(stack_node) + # check the input of stack node, and remove nodes that have no users + remove_split_unbind_children(graph, stack_inputs) # type: ignore[arg-type] + remove_split_unbind_children(graph, split_users) # type: ignore[arg-type] + counters["inductor"]["move_reshape_out_of_split_stack_pass"] += 1 + continue + if len(new_cat_args) > 1 and len(new_cat_args) < len(inputs): + # decompose the cat args into multiple stack nodes, i.e., we stack + # all the nodes exist in the stack inputs and reshape the rest followed by a cat + stack_node_input, stack_node_input_meta, cat_inputs = [], [], [] # type: ignore[var-annotated] + for cat_arg in new_cat_args: + if cat_arg not in stack_inputs: + if len(stack_node_input) > 0: + with graph.inserting_after(stack_node): + decomposed_stack_node = graph.call_function( + torch.stack, + args=(stack_node_input,), + kwargs={"dim": stack_dim}, + ) + decomposed_stack_node.meta["example_value"] = torch.stack( + stack_node_input_meta, dim=stack_dim + ) + cat_inputs.append(decomposed_stack_node) + # cat_arg must be the split input + view_shape_list = get_view_shape_list(cat_arg, stack_dim) + stack_node_shape = torch.reshape(cat_arg.meta["example_value"], tuple(view_shape_list)).shape # type: ignore[union-attr] + cat_inputs.append( + convert_reshape_cat_arg_to_stack( + graph, + cat_arg, + stack_node, + stack_node_shape, + stack_dim, + split_dim, + ) + ) + stack_node_input, stack_node_input_meta = [], [] + else: + stack_node_input.append(cat_arg) + stack_node_input_meta.append(cat_arg.meta["example_value"]) + + if len(stack_node_input) > 0: + with graph.inserting_after(stack_node): + decomposed_stack_node = graph.call_function( + torch.stack, + args=(stack_node_input,), + kwargs={"dim": stack_dim}, + ) + decomposed_stack_node.meta["example_value"] = torch.stack( + stack_node_input_meta, dim=stack_dim + ) + cat_inputs.append(decomposed_stack_node) + + with graph.inserting_after(stack_node): + cat_node = graph.call_function( + torch.cat, + args=(cat_inputs,), + kwargs={"dim": stack_dim}, + ) + stack_node.replace_all_uses_with(cat_node) + cat_node.meta.update(stack_node.meta) + graph.erase_node(stack_node) + remove_split_unbind_children(graph, stack_inputs) # type: ignore[arg-type] + remove_split_unbind_children(graph, split_users) # type: ignore[arg-type] + counters["inductor"]["move_reshape_out_of_split_stack_pass"] += 1 diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f1791004996204a0e2cb201c4a706f2e99408086 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/fx_utils.py @@ -0,0 +1,251 @@ +# mypy: allow-untyped-defs +import operator +from collections import defaultdict +from typing import Any, Callable, DefaultDict, Dict, Optional, Tuple, Type + +import sympy + +import torch +import torch.fx +from torch.fx.experimental.symbolic_shapes import ( + compute_unbacked_bindings, + rebind_unbacked, + statically_known_true, + sym_eq, +) +from torch.utils import _pytree as pytree +from torch.utils._pytree import tree_map + +from .virtualized import V + + +# Check the pattern: (nn.module, F.function/torch.Tensor.method) matched. +# Works for length 2 patterns with 1 module and 1 function/method. +def matches_module_function_pattern( + pattern: Tuple[Type[torch.nn.modules.Module], Callable[..., Any]], + node: torch.fx.node.Node, + modules: Dict[str, torch.nn.modules.Module], +) -> bool: + if len(node.args) == 0: + return False + if not isinstance(node.args[0], torch.fx.Node) or not isinstance( + node, torch.fx.Node + ): + return False + # the first node is call_module + if node.args[0].op != "call_module": + return False + if not isinstance(node.args[0].target, str): + return False + if node.args[0].target not in modules: + return False + if type(modules[node.args[0].target]) is not pattern[0]: + return False + # the second node is call_function or call_method + if node.op != "call_function" and node.op != "call_method": + return False + if node.target != pattern[1]: + return False + # make sure node.args[0] output is only used by current node. + if len(node.args[0].users) > 1: + return False + return True + + +class FakeTensorUpdater: + """ + The main idea here is that it's difficult to maintain accurate fake + tensors (our primary form of metadata) for each node in our graph as we + transform it. + + The most reliable way to obtain this information is by rerunning + faketensor propagation. However, in general, faketensor propagation is + fairly expensive. So, instead we'd like to only rerun faketensor + propagation on nodes that have changed. + + In order to detect which nodes have changed, we first hash its node, + target, and argument lists (which are immutable in FX). + + Then, whenever we call incremental_update, we check which FX nodes have a + new hash, and recompute the faketensor metadata for that node. Then, we + continue to recursively compute the faketensors for all users until the + fake tensors stop changing. + """ + + def __init__(self, graph: torch.fx.Graph) -> None: + self.processed_hashes = set() + self.graph = graph + + for node in self.graph.nodes: + self.processed_hashes.add(self.hash_node(node)) + + def hash_node(self, node: torch.fx.Node): + # todo(chilli): Not a great hash function + return (node, node.target, id(node.args), id(node.kwargs)) + + def incremental_update(self): + processed = set() + existing_storages: DefaultDict[Optional[int], int] = defaultdict(int) + for node in self.graph.nodes: + existing_storages[get_node_storage(node)] += 1 + + def is_intlist_same(new, old): + return statically_known_true(sym_eq(new, old)) + + def is_fake_tensor_same(new, old): + if type(new) != type(old): + return False + if isinstance(new, (list, tuple)): + if len(new) != len(old): + return False + return all( + is_fake_tensor_same(new_i, old_i) for new_i, old_i in zip(new, old) + ) + if new is None: + return old is None + if not isinstance(new, torch.Tensor): + assert isinstance( + new, (torch.SymInt, torch.SymBool, torch.SymFloat) + ), f"Unknown type {type(new)} in {self.graph}" + return ( + new.node.shape_env._maybe_evaluate_static( + sympy.Eq(new.node.expr, old.node.expr) + ) + == sympy.true + ) + if not is_intlist_same(new.shape, old.shape) or new.layout != old.layout: + return False + if new.layout == torch.strided and ( + not is_intlist_same(new.stride(), old.stride()) + or not statically_known_true( + new.storage_offset() == old.storage_offset() + ) + ): + return False + + if new.device != old.device: + return False + + if get_storage(new) == get_storage(old): + return True + + # This is the case where it returns a completely fresh storage that's used nowhere else. + if ( + existing_storages[get_storage(old)] == 1 + and get_storage(new) not in existing_storages + ): + return True + return False + + def should_process_node(node): + # node.target for nodes returning true from this function + # are called under fake mode and does not work for inductor + # lowerings. We check if the node.target is an aten operator + # or operator.getitem which is used when returning multiple + # tensors from an op. + return node.op == "call_function" and ( + isinstance(node.target, torch._ops.OpOverload) + or node.target == operator.getitem + ) + + to_process = set() + for node in self.graph.nodes: + if ( + self.hash_node(node) in self.processed_hashes + and id(node) not in to_process + ): + continue + + if not should_process_node(node): + continue + + is_valid, args, kwargs = get_fake_args_kwargs(node) + if not is_valid: + continue + with V.fake_mode: + new_fake_tensor = node.target(*args, **kwargs) + if "val" in node.meta and is_fake_tensor_same( + new_fake_tensor, node.meta["val"] + ): + continue + + rebind_unbacked(V.fake_mode.shape_env, node, new_fake_tensor) + + node.meta["val"] = new_fake_tensor + if (shape_env := V.fake_mode.shape_env) and ( + symbol_to_path := compute_unbacked_bindings(shape_env, new_fake_tensor) + ): + # Refresh the bindings to the new symbols + node.meta["unbacked_bindings"] = symbol_to_path + + existing_storages[get_node_storage(node)] += 1 + + to_process.update([id(user) for user in node.users]) + + self.processed_hashes.add(self.hash_node(node)) + + +def get_storage(t: torch.Tensor) -> int: + return t.untyped_storage()._cdata + + +def get_node_storage(node: torch.fx.Node) -> Optional[int]: + if "val" not in node.meta: + return None + if not isinstance(node.meta["val"], torch.Tensor): + return None + if not torch._C._has_storage(node.meta["val"]): + return None + return get_storage(node.meta["val"]) + + +def get_fake(x): + if isinstance(x, torch.fx.Node): + if "val" not in x.meta: + return x + return x.meta["val"] + return x + + +def get_fake_args_kwargs(x: torch.fx.Node) -> Tuple[bool, Tuple[Any], Dict[str, Any]]: + """ + First value returns a boolean if any of the input nodes don't have a faketensor. + """ + args, kwargs = tree_map(get_fake, (x.args, x.kwargs)) + if any( + isinstance(a, torch.fx.Node) for a in pytree.arg_tree_leaves(*args, **kwargs) + ): + return False, args, kwargs + return True, args, kwargs + + +def is_node_realized(node: torch.fx.Node) -> bool: + """Returns true if a node is always realized when lowered to inductor IR. + + NOTE: This may return some false negatives. e.g. it doesn't + handle buffers realized heuristically during lowering, or + buffers realized indirectly through view ops. + """ + from torch._inductor.lowering import fallbacks, needs_realized_inputs + + def is_buffer(node: torch.fx.Node) -> bool: + if node.op == "call_function" and node.target is operator.getitem: + # For nodes with multiple outputs, we get the fx graph: + # foo = torch.ops.aten.foo(...) + # getitem = foo[0] + # getitem_1 = foo[1] + # where we need to check if foo is a fallback kernel + return is_buffer(node.args[0]) # type: ignore[arg-type] + return node.op in ("placeholder", "output") or node.target in fallbacks + + if is_buffer(node): + return True + + def realizes_inputs(node: torch.fx.Node) -> bool: + return node.op == "output" or node.target in needs_realized_inputs + + if any(realizes_inputs(user) for user in node.users): + return True + + # Otherwise, assume node isn't realized + return False diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/graph.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/graph.py new file mode 100644 index 0000000000000000000000000000000000000000..3448ea6eb9620ad0094a7c86aa35244574f1ff2b --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/graph.py @@ -0,0 +1,1930 @@ +import functools +import itertools +import logging +import operator +import os +import re +import sys +import time +from collections import defaultdict +from contextlib import contextmanager +from types import ModuleType +from typing import ( + Any, + Callable, + DefaultDict, + Dict, + Iterable, + List, + NoReturn, + Optional, + Sequence, + Tuple, + TYPE_CHECKING, + Union, +) + +import sympy +from sympy import Expr + +import torch +import torch._logging +import torch.fx +from torch import device, Tensor +from torch._decomp import get_decompositions +from torch._dynamo.utils import defake, dynamo_timed +from torch._logging import LazyString, trace_structured +from torch._prims_common import make_channels_last_strides_for +from torch._subclasses.fake_tensor import FakeTensor +from torch.fx import GraphModule +from torch.fx.experimental._backward_state import BackwardState +from torch.fx.experimental.sym_node import magic_methods, method_to_operator +from torch.fx.experimental.symbolic_shapes import ( + free_unbacked_symbols, + has_free_symbols, + resolve_unbacked_bindings, + RuntimeAssert, + ShapeEnv, + SymTypes, +) +from torch.fx.graph import Graph +from torch.fx.node import Node +from torch.utils._mode_utils import no_dispatch +from torch.utils._ordered_set import OrderedSet +from torch.utils._sympy.numbers import int_oo + +from . import config, ir +from .codegen.common import ( + BackendFeature, + DeviceOpOverrides, + get_backend_features, + get_device_op_overrides, + get_wrapper_codegen_for_device, + init_backend_registration, +) +from .exc import ( + CppWrapperCodeGenError, + LoweringException, + MissingOperatorWithDecomp, + MissingOperatorWithoutDecomp, +) +from .ir import ( + Constant, + FixedLayout, + get_device_type, + InputBuffer, + Pointwise, + Reduction, + StorageBox, + TensorBox, + TorchBindObject, +) +from .lowering import ( + FALLBACK_ALLOW_LIST, + fallback_handler, + fallback_node_due_to_unsupported_type, + lowerings, + make_fallback, + maybe_layout_constraints, + needs_realized_inputs, + unsupported_output_tensor, +) +from .scheduler import BaseSchedulerNode +from .sizevars import SizeVarAllocator +from .utils import ( + convert_shape_to_inductor, + gather_origins, + get_cloned_parameter_buffer_name, + get_sympy_Expr_dtype, + maybe_get_suppress_shape_guards_ctx, + should_assume_input_aligned, +) +from .virtualized import NullHandler, V + + +if TYPE_CHECKING: + from torch._higher_order_ops.effects import _EffectType + from .codegen.wrapper import WrapperCodeGen + +from torch._inductor.codecache import output_code_log + + +log = logging.getLogger(__name__) +perf_hint_log = torch._logging.getArtifactLogger(__name__, "perf_hints") + +aten = torch.ops.aten + +_post_grad_graph_counter = itertools.count() + +if config.is_fbcode(): + from torch._inductor.fb.utils import log_module_code +else: + + def log_module_code(*args: Any, **kwargs: Any) -> None: + pass + + +def supported_dtype_of_cpp_wrapper(dtype: torch.device, cuda: bool) -> bool: + supported_dtype = { + torch.float32, + torch.float64, + torch.int64, + torch.int32, + torch.int16, + torch.int8, + torch.uint8, + torch.bool, + torch.bfloat16, + torch.complex32, + torch.complex64, + torch.complex128, + torch.float16, + } + if cuda: + supported_dtype.add(torch.float8_e4m3fn) + supported_dtype.add(torch.float8_e5m2) + supported_dtype.add(torch.float8_e4m3fnuz) + supported_dtype.add(torch.float8_e5m2fnuz) + + return dtype in supported_dtype + + +def may_get_constant_buffer_dtype(constant_buffer: sympy.Expr) -> Optional[torch.dtype]: + assert isinstance( + constant_buffer, (sympy.Symbol, sympy.Expr, sympy.core.numbers.Integer) + ), "get_constant_buffer_dtype only supports input of sympy.Symbol, sympy.Expr or sympy.core.numbers.Integer" + if isinstance(constant_buffer, sympy.core.numbers.Integer): + return torch.int64 + + if isinstance(constant_buffer, sympy.Expr): + return get_sympy_Expr_dtype(constant_buffer) + + if constant_buffer.is_integer: + return torch.int64 + elif constant_buffer.is_float: + return torch.float32 + else: + return None + + +def is_magic_method(op: Any) -> bool: + magic_ops = {method_to_operator(m) for m in magic_methods} + return op in magic_ops + + +def getattr_recursive( + obj: GraphModule, target: str +) -> Union[Tensor, torch._C.ScriptObject, GraphModule]: + target_atoms = target.split(".") + attr_itr = obj + for i, atom in enumerate(target_atoms): + if not hasattr(attr_itr, atom): + raise RuntimeError( + f"Node referenced nonexistent target {'.'.join(target_atoms[:i])}" + ) + attr_itr = getattr(attr_itr, atom) + return attr_itr + + +def mark_nodes_dislike_padding( + g: Graph, user_visible_outputs: Optional[Dict[str, None]] +) -> None: + """ + Nodes like convolution/convolution_backward want its input to be dense. + If we pad their inputs, we result in extra calls to copy kernels! On the other hand, padding usually helps reduction. + + The pass finds nodes that dislike padding. These are nodes that can be reached + from a convolution/convolution_backward in the backward direction without + going thru a reduction. + """ + if not config.comprehensive_padding: + return + ops_dislike_padding = { + aten.convolution, + aten.convolution_backward, + } + # what's a better way to collect the reduction ops? + ops_like_padding = { + aten.var_mean, + aten.sum, + aten.mean, + aten.prod, + aten.any, + aten.amin, + aten.amax, + aten.min, + aten.max, + aten.argmin, + aten.argmax, + aten.scatter_reduce, + } + + def _get_overload_packet( + node: torch.fx.Node, + ) -> Optional[torch._ops.OpOverloadPacket]: + return ( + node.target._overloadpacket + if node.op == "call_function" + # hasattr on OpOverloadPacket is slow, do isinstance first + and isinstance(node.target, torch._ops.OpOverload) + and hasattr(node.target, "_overloadpacket") + else None + ) + + for cur in reversed(g.nodes): + op = _get_overload_packet(cur) + if not op: + continue + if op in ops_dislike_padding: + cur.meta["dislike_padding"] = True + + if cur.meta.get("dislike_padding", False): + # propagate + for prior in cur.all_input_nodes: + prior_op = _get_overload_packet(prior) + if not prior_op: + continue + if prior_op not in ops_like_padding: + prior.meta["dislike_padding"] = True + # We only want to mark output nodes. So, move it after the above prior nodes process. + if ( + not config.pad_outputs + and user_visible_outputs + and cur.name in user_visible_outputs + ): + cur.meta["dislike_padding"] = True + + +class GraphLowering(torch.fx.Interpreter): + graph_outputs: List[ir.IRNode] + + def symbolic_sizes_strides( + self, ex: torch.Tensor + ) -> Tuple[Union[List[int], List[Expr]], Union[List[int], List[Expr]]]: + """ + Support dynamic shapes and dynamic strides by assigning variables + to each dimension. We duck-shape tensors, so if two tensors + have the same size they get assigned the same symbolic variable. + """ + if self.reuse_shape_env: + return convert_shape_to_inductor(ex.size()), convert_shape_to_inductor( + ex.stride() + ) + else: + from torch._dynamo.source import ConstantSource + + # TODO: this should not be needed once #93059 lands + # https://github.com/pytorch/pytorch/pull/94031#discussion_r1096044816 + # TODO: make a dedicated UnknownSource for this? + # NB: This is using the legacy default behavior from + # create_symbolic_sizes_strides_storage_offset but we hope we can + # just delete this entirely + source = ConstantSource( + f"__inductor_unknown_tensor_{len(self._shape_env.var_to_val)}" + ) + ( + size, + stride, + _, + ) = self._shape_env.create_symbolic_sizes_strides_storage_offset( + ex, + source, + ) + + size = [i.node.expr if isinstance(i, torch.SymInt) else i for i in size] + stride = [i.node.expr if isinstance(i, torch.SymInt) else i for i in stride] + return size, stride + + def static_sizes_strides( + self, ex: torch.Tensor + ) -> Tuple[List[sympy.Expr], List[sympy.Expr]]: + """ + Primarily used to weights + """ + size = [sympy.Integer(i) for i in ex.size()] + stride = [sympy.Integer(i) for i in ex.stride()] + return size, stride + + def __init__( + self, + gm: torch.fx.GraphModule, + example_inputs: Optional[List[torch.Tensor]] = None, + shape_env: Optional[ShapeEnv] = None, + graph_id: Optional[int] = None, + cpp_wrapper: bool = False, + aot_mode: bool = False, + user_visible_outputs: Optional[Dict[str, None]] = None, + layout_opt: Optional[bool] = None, + extern_node_serializer: Optional[ + Callable[[List[ir.ExternKernelNode]], Any] + ] = None, + is_inference: bool = False, + is_const_graph: bool = False, + const_output_index: Optional[Dict[str, int]] = None, + const_code: Optional[str] = None, + const_module: Optional["GraphLowering"] = None, + name: Optional[str] = None, + ) -> None: + super().__init__(gm) + self.example_inputs = example_inputs + self.layout_opt = ( + layout_opt + if layout_opt is not None + else self.decide_layout_opt(gm, is_inference=is_inference) + ) + self.num_channels_last_conv = 0 + self.is_inference = is_inference + self.is_const_graph = is_const_graph + self.const_code = const_code + self.const_module = const_module + + self.extra_traceback = False # we do our own error wrapping + if shape_env is None: + shape_env = ShapeEnv() + self.reuse_shape_env = False + else: + self._shape_env = shape_env + self.reuse_shape_env = True + self._shape_env = shape_env + # We are going to start code generating runtime asserts, so make sure + # you don't start adding new ones in the lowering process + shape_env.freeze_runtime_asserts() + # We're going to mutate ras_by_symbol as we finish generating them + self.ras_by_symbol: Dict[ + sympy.Symbol, List[RuntimeAssert] + ] = shape_env.deferred_runtime_asserts.copy() + self.bound_unbacked_symbols: OrderedSet[sympy.Symbol] = OrderedSet() + self.sizevars = SizeVarAllocator(shape_env) + self.graph_input_names: List[str] = [] + self.graph_inputs: Dict[str, TensorBox] = {} + self.graph_inputs_original: Dict[str, InputBuffer] = {} + self.zero_dim_cpu_tensor_list: OrderedSet[str] = OrderedSet() + self.device_types: OrderedSet[str] = ( + const_module.device_types if const_module else OrderedSet() + ) + self.device_idxs: OrderedSet[int] = ( + const_module.device_idxs if const_module else OrderedSet() + ) + self.cuda = False + self.buffers: List[ir.Buffer] = [] + self.operations: List[ir.Operation] = [] + self.const_output_index: Dict[str, int] = ( + const_output_index if const_output_index else {} + ) + self.folded_constants: OrderedSet[str] = ( + OrderedSet(const_output_index.keys()) + if const_output_index + else OrderedSet() + ) + self.constants: Dict[str, torch.Tensor] = ( + const_module.constants if const_module else {} + ) + self.torchbind_constants: Dict[str, torch._C.ScriptObject] = {} + self.constant_reprs: Dict[str, str] = {} + self.removed_operations: OrderedSet[str] = OrderedSet() + self.removed_buffers: OrderedSet[str] = OrderedSet() + self.removed_inplace_buffers: OrderedSet[str] = OrderedSet() + self.mutated_buffers: OrderedSet[str] = OrderedSet() + self.never_reuse_buffers: OrderedSet[str] = OrderedSet() + self.inplaced_to_remove: OrderedSet[str] = OrderedSet() + self.device_ops: DeviceOpOverrides = None # type: ignore[assignment] + self.wrapper_code: WrapperCodeGen = None # type: ignore[assignment] + # See `ProxyExecutor Design Note` in ir.py for more details + self.extern_kernel_nodes: List[ir.ExternKernelNode] = [] + + from torch._inductor.extern_node_serializer import extern_node_json_serializer + + self.extern_node_serializer: Callable[[List[ir.ExternKernelNode]], Any] = ( + extern_node_serializer + if config.is_fbcode() and extern_node_serializer + else extern_node_json_serializer + ) + + self.current_node: torch.fx.Node = None # type: ignore[assignment] + self.lists: Dict[str, List[str]] = {} + self.mutated_inputs: OrderedSet[str] = OrderedSet() + self.mutated_input_idxs: List[int] = [] + self.name_to_buffer: Dict[str, ir.Buffer] = {} + self.name_to_users: DefaultDict[str, List[ir.IRNode]] = defaultdict(list) + self.name_to_op: Dict[str, ir.Operation] = {} + self.creation_time = time.time() + self.name = name # type: ignore[assignment] + self.cpp_wrapper = cpp_wrapper + + # record multi_kernel choice for cpp_wrapper so the second pass knows + # which sub-kernel is picked. Copy cpp_wrapper to another variable + # since cpp_wrapper flag is OrderedSet to false for the first pass of codegen. + self.record_multi_kernel_choice = cpp_wrapper + self.multi_kernel_to_choice: Dict[str, int] = {} + + self.aot_mode = aot_mode + self.graph_id = graph_id + self.post_grad_graph_id = next(_post_grad_graph_counter) + self.scheduler: torch._inductor.scheduler.Scheduler = None # type: ignore[assignment] + self.nodes_prefer_channels_last = ( + self.find_nodes_prefer_channels_last() if self.layout_opt else OrderedSet() + ) + self._warned_fallback = {"aten.convolution_backward"} + self.user_visible_outputs = ( + user_visible_outputs if user_visible_outputs is not None else {} + ) + mark_nodes_dislike_padding(gm.graph, user_visible_outputs) + self.cache_key: str = "" # This is the cache key for the compiled artifact + self.cache_path: str = "" # This is the path in the filesystem where the compiled artifact is stored + self.cache_linemap: List[ + Tuple[int, str] + ] = ( + [] + ) # This is the linemap used by the profiler to mark custom compiled kernels getting run + # Used if lowering encounters cases where cudagraphs are not supported + self.disable_cudagraphs_reason: Optional[str] = None + + # only keeping one node per device for stack trace purposes + self.device_node_mapping: Dict[torch.device, torch.fx.Node] = {} + self.orig_gm: torch.fx.GraphModule = gm.__copy__() + self.dynamo_flat_name_to_original_fqn = self.module.meta.get( + "dynamo_flat_name_to_original_fqn", {} + ) + self.allocated_constant_name: Dict[str, str] = ( + const_module.allocated_constant_name if const_module is not None else {} + ) + init_backend_registration() + self.get_backend_features = functools.lru_cache(None)(get_backend_features) + + self.effectful_ops: Dict[_EffectType, ir.Buffer] = {} + self.aligned_inputs: OrderedSet[str] = OrderedSet() + self.no_fuse_buffer_names: OrderedSet[str] = OrderedSet() + + # Below field is related to printing debug intermediate tensor values info for debugging + self.all_codegen_kernel_names: OrderedSet[str] = OrderedSet() + + def has_feature( + self, device: Union[torch._inductor.ir.IRNode, device], feature: BackendFeature + ) -> bool: + assert isinstance(feature, BackendFeature), feature + return feature in self.get_backend_features(get_device_type(device)) + + @staticmethod + def decide_layout_opt(gm: GraphModule, *, is_inference: bool) -> bool: + """ + Decide if we should enable layout optimization for this graph based on + heuristics. + """ + if not config.layout_optimization: + return False + + if config.force_layout_optimization: + return True + + conv_nodes = [ + n for n in gm.graph.nodes if n.target == torch.ops.aten.convolution.default + ] + nconv = len(conv_nodes) + + if nconv == 0: + return False + + # For cpu backend and mkldnn enabled, we always use channels_last for better performance. + if ( + torch.backends.mkldnn.enabled + and torch.backends.mkldnn.is_available() + and all( + n.args[idx].meta["val"].device == torch.device("cpu") + for n in conv_nodes + for idx in [0, 1] + ) + ): + return True + + # Following models are skipped due to this: + # jx_nest_base + # volo_d1_224 + if len(list(gm.graph.nodes)) >= 300 * nconv: + log.debug("Skipped layout opt because only a few conv") + return False + + if any( + has_free_symbols(n.args[idx].meta["val"]) + for n in conv_nodes + for idx in [0, 1] + ): + log.debug( + "See perf regression with dynamic shape. Follow up in https://github.com/pytorch/pytorch/issues/102670" + ) + return False + + def is_grouped(n: Any) -> bool: + meta_val = n.args[1].meta["val"] # type: ignore[union-attr, operator] + assert isinstance(meta_val, torch.Tensor) + return n.args[-1] > 1 and meta_val.size(1) > 1 # type: ignore[union-attr, operator] + + def is_in_out_channel(n: torch.fx.Node) -> bool: + return ( + n.args[1].meta["val"].size(0) * 2 <= n.args[1].meta["val"].size(1) # type: ignore[union-attr, operator] + and n.args[1].meta["val"].size(2) > 1 # type: ignore[union-attr, operator] + ) + + def is_small_channel(n: torch.fx.Node) -> bool: + return ( + n.args[1].meta["val"].size(0) <= 64 # type: ignore[union-attr, operator] + and n.args[1].meta["val"].size(1) <= 64 # type: ignore[union-attr, operator] + ) + + # only grouped convolutions benchmarked as slower in conv samples for inference only + if is_inference: + from torch.utils.flop_counter import FlopCounterMode + + flop_counts: Dict[str, float] = defaultdict(float) + for node in conv_nodes: + success, args, kwargs = torch._inductor.fx_utils.get_fake_args_kwargs( + node + ) + + if success: + with FlopCounterMode(display=False) as flop_counter_mode: + with V.fake_mode: + node.target(*args, **kwargs) + + counted_flops = flop_counter_mode.get_total_flops() + if is_grouped(node): + node_type = "grouped" + elif is_small_channel(node): + node_type = "small" + elif is_in_out_channel(node): + node_type = "in_out" + else: + node_type = "default" + + flop_counts[node_type] += counted_flops + else: + log.debug("Conv inputs meta not found") + + # average benchmarked channels last speedup / slowdown, < 1 is speedup. + # taken from the set of convolution inputs in benchmarks/dynamo/microbenchmarks/operator_inp_logs/torchbench_train/ + # To regenerate these numbers follow https://gist.github.com/eellison/55d7a6ed6f39829d68ac56f95f4df5bb + GROUPED_MULTIPLIER = 1.358 + DEFAULT_MULTIPLIER = 0.823 + IN_OUT_MULTIPLIER = 0.725 + SMALL_MULTIPLIER = 0.783 + + total_flops = sum(flop_counts.values()) + # TODO - get different values per hardware + weighted_flops = ( + flop_counts["grouped"] * GROUPED_MULTIPLIER + + flop_counts["small"] * SMALL_MULTIPLIER + + flop_counts["in_out"] * IN_OUT_MULTIPLIER + + flop_counts["default"] * DEFAULT_MULTIPLIER + ) + do_layout_opt = weighted_flops <= total_flops + if not do_layout_opt: + log.debug( + "Skipped layout opt in inference because weighted flops indicate slowdown, default: %d, channels last: %d", + total_flops, + weighted_flops, + ) + return do_layout_opt + + # Channels last layout can dramatically hurt grouped conv perf. E.g. + # Conv with arguments like + # {"input_shape": [32, 224, 112, 112], "weight_shape": [224, 112, 3, 3], + # "stride": [2, 2], "padding": [1, 1], "groups": 2} + # slows down 31x using channels last.. + + # But a lot of timm models use depthwise separable convolution which will + # result in grouped convolution with in-channel size == 1. + # For those grouped convolution, channels last still helps a lot. + # E.g. + # Conv with arguments + # {"input_shape": [128, 58, 56, 56], "weight_shape": [58, 1, 3, 3], + # "stride": [2, 2], "padding": [1, 1], "groups": 58} + # get 1.86x speedup with channels last layout. + # + # The following heuristics skip using channels-last if the model contains + # grouped convolution with in-channels > 1. + if any(map(is_grouped, conv_nodes)): + log.debug( + "Skip layout opt because found grouped convolution with >1 in_channels!" + ) + return False + + # For some models that contain convolution with larger in-channel than out-channel, applying + # channels last hurts performance. + # Following models are skipped due to this: + # - pytorch_unet + # - phlippe_densenet (slightly worse) + # - Background_Matting (1.22x -> 0.821x) + # - pytorch_CycleGAN_and_pix2pix (1.597x -> 1.294x) + if any(map(is_in_out_channel, conv_nodes)): + log.debug( + "Skip layout opt because some convolutions have smaller out_channel" + ) + return False + + # Following models are skipped due to this: + # - functorch_maml_omniglot + if all(map(is_small_channel, conv_nodes)): + log.debug("Skip layout opt because all convolution channels are too small") + return False + + return True + + def qualify_name(self, name: str) -> str: + """Prepend the given name with the graph name if any.""" + if self.name is not None: + return f"{self.name}_{name}" + return name + + def make_subgraph( + self, + gm: torch.fx.GraphModule, + example_inputs: List[torch.Tensor], + subgraph_name: str, + ) -> "GraphLowering": + """ + Make a subgraph of the current graph with all inherited + parts, except the graph module (`gm`) and `example_inputs`. + The subgraphs are lowered separately, but intended to be + inlined in the parent graph's codegening. Hence the need + for maintaining the same `shape_env` and other properties. + The subgraph name is qualified by the parent graph's name. + """ + return GraphLowering( + gm=gm, + example_inputs=example_inputs, + shape_env=self._shape_env, + cpp_wrapper=self.cpp_wrapper, + aot_mode=self.aot_mode, + extern_node_serializer=self.extern_node_serializer, + is_inference=self.is_inference, + name=self.qualify_name(subgraph_name), + ) + + def find_nodes_prefer_channels_last(self) -> OrderedSet[Node]: + """ + The rule to decide if an node prefer channels last is simple. + 1. if it's input/output of a convolution + 2. if one of its user prefers channels last + + We have rule 1 because cudnn runs a faster convolution kernel for channels last inputs; + Rule 2 is also important. It makes sure that indirect inputs to convolution also prefers + channels last. + + Consider the scenario: conv -> batch-norm -> relu -> conv + Without rule 2, batch-norm output may use a contiguous layout. That will cause 2 extra copies: + 1. the output of batch-norm should be channels last initially since its input is a conv's output. + Forcing the batch-norm's output to be contiguous results in the first copy + 2. The second conv's input is initially contiguous. This layout is propagated from the batch-norm's output. + We need convert it to channels last layout which results in the second copy. + With rule 2, we makes sure all the tensors in the chain uses channels last layout. So both copies + can be saved. + """ + output_set: OrderedSet[Node] = OrderedSet() + for n in reversed(self.module.graph.nodes): + if n.target == torch.ops.aten.convolution.default: + output_set.add(n) + continue + + for user in n.users: + if user in output_set: + output_set.add(n) + break + + # need a second pass to add downstream nodes of those channel last nodes to the sets. + # This pass is especially needed to avoid mix-layout kernel inputs in backward pass. + # + # Let's say a conv-batchnorm 's output is passed to relu whose output is in turn returned + # from the fwd graph. Without this second pass, we will force relu's output to be contiguous. + # Then in the kernel in backward pass, the contiguous output of relu may be mix with other channels last + # tensors and passed to a kernel. + # + # This pass improve yolov3 training speedup from 1.116x (worse than disabling layout optimization speedup 1.196x) to 1.457x. + # It also improves dla102 training speedup from 1.240x (worse than disabling layout optimization speedup 1.523x) to 1.835x . + # This also helps the following models: + # - res2net101_26w_4s + # - res2net50_14w_8s + # - sebotnet33ts_256 + for n in self.module.graph.nodes: + if n in output_set: + output_set.update(n.users) + + return output_set + + def warn_fallback(self, name: str) -> None: + if name not in self._warned_fallback: + self._warned_fallback.add(name) + perf_hint_log.info("Using FallbackKernel: %s", name) + + def add_device_info(self, device: torch.device) -> None: + self.device_types.add(device.type) + if device.index is not None: + self.device_idxs.add(device.index) + if V.graph.current_node and device not in self.device_node_mapping: + self.device_node_mapping[device] = V.graph.current_node + + @property + def fake_mode(self) -> torch._subclasses.fake_tensor.FakeTensorMode: + return V.fake_mode + + def try_get_buffer( + self, buffer_name: str + ) -> Optional[Union[ir.TensorBox, ir.Buffer]]: + if buffer_name in self.name_to_buffer: + return self.name_to_buffer[buffer_name] + if buffer_name in self.graph_inputs: + return self.graph_inputs[buffer_name] + if buffer_name in self.constants: + data = V.graph.constants[buffer_name] + return ir.ConstantBuffer( + buffer_name, + ir.FixedLayout( + data.device, data.dtype, *V.graph.static_sizes_strides(data) + ), + ) + + return None + + def get_buffer(self, buffer_name: str) -> Union[ir.TensorBox, ir.Buffer]: + buf = self.try_get_buffer(buffer_name) + if buf is not None: + return buf + raise RuntimeError(f"Failed to find buffer matching name {buffer_name}") + + def get_dtype(self, buffer_name: str) -> torch.dtype: + if buffer_name in self.constants: + return self.constants[buffer_name].dtype + if buffer_name in self.name_to_buffer: + return self.name_to_buffer[buffer_name].get_dtype() + if buffer_name in self.graph_inputs: + return self.graph_inputs[buffer_name].get_dtype() + m = re.match(r"(as_strided|reinterpret_tensor)\(([a-zA-Z0-9_]+),", buffer_name) + if m: + return self.get_dtype(m.group(1)) + raise KeyError(f"could not find {buffer_name}") + + def get_numel(self, buffer_name: str) -> Union[int, Expr]: + from .ir import MultiOutputLayout + + if buffer_name in self.constants: + return self.constants[buffer_name].numel() + if buffer_name in self.name_to_buffer: + buf = self.name_to_buffer[buffer_name] + if isinstance(getattr(buf, "layout", None), MultiOutputLayout): + return 1 + return buf.get_numel() + if buffer_name in self.graph_inputs: + return self.graph_inputs[buffer_name].get_numel() + raise KeyError(f"could not find {buffer_name}") + + def run(self, *args: Any) -> Any: # type: ignore[override] + with dynamo_timed("GraphLowering.run"): + return super().run(*args) + + def register_operation(self, op: ir.Operation) -> str: + assert op.operation_name is None, f"Operation registered twice: {op}" + assert isinstance(op, ir.Operation) + name = self.qualify_name(f"op{len(self.operations)}") + self.operations.append(op) + self.name_to_op[name] = op + op.operation_name = name + return name + + def register_buffer(self, buffer: ir.Buffer, *, set_name: bool = False) -> str: + name = self.qualify_name(f"buf{len(self.buffers)}") + self.buffers.append(buffer) + self.name_to_buffer[name] = buffer + if ( + # Skip empty CPU tensor so that CUDA graphs can succeed, see https://github.com/pytorch/pytorch/pull/114144 + not (isinstance(buffer, ir.ComputedBuffer) and buffer.is_zero_elements()) + and buffer.get_device() is not None + ): + self.add_device_info(buffer.get_device()) + + if set_name: + buffer.name = name + return name + + def register_operation_list(self, operation_names: List[str]) -> str: + name = self.qualify_name("list_" + "_".join(operation_names)) + self.lists[name] = operation_names + return name + + def register_users_of( + self, node_output: Union[Iterable[ir.IRNode], ir.IRNode] + ) -> None: + def register(value: Union[Iterable[ir.IRNode], ir.IRNode]) -> None: + if isinstance(value, (list, tuple)): + for x in value: + register(x) + if isinstance(value, ir.TensorBox): + for read_name in value.get_read_names(): + self.name_to_users[read_name].append(value) + + register(node_output) + + def mark_buffer_mutated(self, name: str) -> None: + """ + When a buffer is mutated we need to make sure all the reads to + the old version are realized before the mutation happens. + """ + assert isinstance(name, str) + self.mutated_buffers.add(name) + + if name not in self.name_to_users: + return + + for user in self.name_to_users[name]: + user.realize() + + def get_original_value_of_constant(self, name: str) -> torch.Tensor: + """ + In AOTI, module buffers may have been mutated during the tracing and compilation. + Thus we need to read from previously stored original buffers, to make sure the + generated model.so uses correct initial values. + """ + assert name in self.allocated_constant_name and name in self.constants, ( + "Can not find the original value for " + name + ) + orig_name = get_cloned_parameter_buffer_name(self.allocated_constant_name[name]) + return ( + self.module.meta[orig_name] + if orig_name in self.module.meta + else self.constants[name] + ) + + def allocate_non_dup_const_name( + self, name: Optional[str], data: Union[Tensor] + ) -> str: + orig_name = name + if not config.aot_inductor.use_runtime_constant_folding: + for constant_name, value in self.constants.items(): + if ( + not data.is_mkldnn + and data.size() == value.size() + and data.stride() == value.stride() + and data.dtype == value.dtype + and data.device == value.device + and data.untyped_storage().data_ptr() + == value.untyped_storage().data_ptr() + and data.storage_offset() == value.storage_offset() + ): + return constant_name + + if name is None: + name = f"constant{len(self.constants)}" + assert name is not None + if name[0].isdigit(): + name = f"constant_{name}" + name = self.qualify_name(name) + # We may generate a var name for each constant in the codegen. + # Let's only keep sane characters. + prefix = re.sub(r"[^a-zA-Z0-9_]", "_", name) + name = prefix + cnt = 0 + while name in self.constants: + name = f"{prefix}_{cnt}" + cnt += 1 + self.constants[name] = data + self.constant_reprs[name] = ( + f"{data.device!r} {data.dtype!r} " + f"{tuple(data.size())!r} {tuple(data.stride())!r} " + f"{hash(data):x}" + ) + self.allocated_constant_name[name] = orig_name # type: ignore[assignment] + return name + + def add_tensor_constant( + self, data: Tensor, name: Optional[str] = None + ) -> TensorBox: + new_name = self.allocate_non_dup_const_name(name, data) + return TensorBox.create( + ir.ConstantBuffer( + new_name, + FixedLayout(data.device, data.dtype, *self.static_sizes_strides(data)), + ) + ) + + def constant_name(self, name: str, device_override: Optional[torch.device]) -> str: + """ + We AOT copy constants to the devices they are needed on. + If device_override doesn't match the constant's device, then + copy it and return a different name. + """ + if self.constants[name].device == device_override or device_override is None: + return name + with torch.utils._python_dispatch._disable_current_modes(): + # caller might have OrderedSet fake tensor mode which will create a fake tensor + # when calling .to, so unset modes here + return self.allocate_non_dup_const_name( + f"{name}_{device_override.type}{device_override.index or 0}", + self.constants[name].to(device_override), + ) + + def placeholder( + self, target: str, args: Tuple[object], kwargs: Dict[str, object] # type: ignore[override] + ) -> Union[Expr, TensorBox, None]: + example = super().placeholder(target, args, kwargs) # type: ignore[arg-type] + self.graph_input_names.append(target) + if isinstance(example, SymTypes): + expr = example.node.expr + self.graph_inputs[target] = expr + return expr + elif isinstance(example, (int, bool, float)): + expr = sympy.sympify(example) + self.graph_inputs[target] = expr + return expr + elif example is None: + return None + if isinstance(example, BackwardState): + # Ignored arg, must be unused + # Alternately we could filter this out in AotAutograd + return None + assert isinstance(example, torch.Tensor), example + # todo(chilli): We can remove the last check once we turn buffers into + # static shape tensors. That's a hack to workaround Inductor believing + # the buffer should be static but us passing in a fake tensor with + # symbolic shapes. + if not example._has_symbolic_sizes_strides: + # the first N inputs are weights + sizes, strides = self.static_sizes_strides(example) + else: + sizes, strides = self.symbolic_sizes_strides(example) # type: ignore[assignment] + # TODO(jansel): handle input aliasing + target = self.qualify_name(target) + tensor = TensorBox.create( + InputBuffer( + target, + FixedLayout(example.device, example.dtype, sizes, strides), + ) + ) + self.graph_inputs[target] = tensor + self.graph_inputs_original[target] = tensor.data.data + if self.current_node.users: # cudagraphs should work with an unused CPU input + self.add_device_info(example.device) + + # Note: [Input Alignment handling in Inductor] + # Alignment matters for generating efficient code. Some operations, + # e.g. vectorized loads, can only be performed on aligned inputs. + # + # But if we codegen assuming aligned inputs and then get unaligned + # inputs at runtime, then we are forced to clone - which is bad for + # both perf and memory usage. + # + # One option would be to guard on storage_offset%ALIGNMENT, and then + # codegen based on this. But storage_offset guards turned out to be + # expensive and cause recompiles; Instead, we're generating code + # based on the alignment of the example input without guarding. + with maybe_get_suppress_shape_guards_ctx(): + if should_assume_input_aligned(example): + self.aligned_inputs.add(target) + return tensor + + def call_function(self, target: Callable, args: Any, kwargs: Dict[str, Any]) -> Any: # type: ignore[type-arg, override] + if target is operator.getitem and isinstance(args[0], (list, tuple, dict)): + return super().call_function(target, args, kwargs) + + # hasattr on OpOverloadPacket is slow, check isinstance first + if not isinstance(target, torch._ops.OpOverloadPacket) and hasattr( + target, "_inductor_lowering_function" + ): + # passthrough lowerings from .pattern_matcher + return target(*args, **kwargs) + + if target not in lowerings: + assert isinstance( + target, torch._ops.OpOverload + ), f"{target} is not an OpOverload" + base_name = target.name().split(".")[0] + if base_name in FALLBACK_ALLOW_LIST: + make_fallback(target) + elif config.implicit_fallbacks: + error = ( + MissingOperatorWithDecomp + if get_decompositions([target]) + else MissingOperatorWithoutDecomp + ) + log.info( + "Creating implicit fallback for:\n%s", + error.operator_str(target, args, kwargs), + ) + make_fallback(target) + + elif get_decompositions([target]): + # There isn't a good way to dynamically patch this in + # since AOT Autograd already ran. The error message tells + # the user how to fix it. + raise MissingOperatorWithDecomp(target, args, kwargs) + else: + raise MissingOperatorWithoutDecomp(target, args, kwargs) + + try: + log.debug(" via %s", lowerings[target]) # type: ignore[index] + out = lowerings[target](*args, **kwargs) # type: ignore[index] + return out + except Exception as e: + raise LoweringException(e, target, args, kwargs).with_traceback( + e.__traceback__ + ) from None + + @staticmethod + def can_inline_constant(t: torch.Tensor) -> bool: + """ + True if this is a small constant attr that will be inlined. + """ + return len(t.shape) == 1 and t.shape[0] <= 8 + + def get_attr( + self, target: str, args: Tuple[()], kwargs: Dict[str, object] # type: ignore[override] + ) -> Union[Constant, TensorBox, ir.Subgraph, TorchBindObject]: + # this is a constant + value = getattr_recursive(self.module, target) # type: ignore[arg-type] + + if isinstance(value, torch.fx.GraphModule): + return ir.Subgraph(name=target, graph_module=value) + + if isinstance(value, torch._C.ScriptObject): + self.torchbind_constants[target] = value + self.constant_reprs[target] = "" + return TorchBindObject(target, value) + + assert isinstance(value, torch.Tensor) + if ( + config.aot_inductor.use_runtime_constant_folding + or config.always_keep_tensor_constants + or unsupported_output_tensor(value) + ): + return self.add_tensor_constant(value, target) + + with no_dispatch(): + if value.shape == (): + return Constant(value.item(), value.dtype, value.device) + if self.can_inline_constant(value): + log.debug("Inlining constant: %s ", str(target)) + # tensor lowering has constant inlining logic + from .lowering import tensor + + return tensor(value.tolist(), dtype=value.dtype, device=value.device) + + return self.add_tensor_constant(value, target) + + def call_module(self, target: Any, args: Any, kwargs: Any) -> NoReturn: + raise AssertionError + + def call_method(self, target: Any, args: Any, kwargs: Any) -> NoReturn: + raise AssertionError + + def output( + self, target: str, args: Tuple[object], kwargs: Dict[str, object] # type: ignore[override] + ) -> None: + result = super().output(target, args, kwargs) # type: ignore[arg-type] + if not isinstance(result, (tuple, list)): + # nested subgraphs can have singleton outputs + result = (result,) + assert isinstance(result, (tuple, list)), type(result) + assert all( + isinstance( + x, + ( + TensorBox, + ir.Constant, + type(None), + ir.ConstantBuffer, + sympy.Expr, + sympy.logic.boolalg.Boolean, + int, + ir.EffectfulKernel, + ), + ) + for x in result + ), result + + fx_node_args = V.graph.current_node.args[0] # type: ignore[arg-type] + if not isinstance(fx_node_args, (tuple, list)): + # nested subgraphs can have singleton outputs + fx_node_args = (fx_node_args,) + result = [ir.ExternKernel.realize_input(x) for x in result] + result_correct_strides = [] + + assert len(fx_node_args) == len(result) + for r, fx_node in zip(result, fx_node_args): + if not isinstance(r, (ir.TensorBox, ir.BaseView)): + result_correct_strides.append(r) + else: + # AOT Autograd tries to detect stride divergence of inductor from output metadata. + # Here, we try to avoid spurious divergence by matching insignificant strides such as + result_correct_strides.append( + self.try_match_insignificant_strides( + r, fx_node.meta["val"].stride() + ) + ) + + self.graph_outputs = result_correct_strides + value: ir.IRNode + for name, value in self.graph_inputs.items(): + assert isinstance( + value, (TensorBox, sympy.Expr) + ), f"Unsupported inductor graph input type: {type(value)}" + if not isinstance(value, TensorBox): + continue + value.realize() + assert isinstance(value, TensorBox) + value = value.data + assert isinstance(value, ir.StorageBox) + value_storage_box = value + value = value.data + if not isinstance(value, InputBuffer) or value.get_name() != name: + # one of our inputs was mutated, need to turn that into a copy + ir.MutationLayoutSHOULDREMOVE.realize_into( + value, self.graph_inputs_original[name] + ) + # replace output with mutated input + try: + ind = self.graph_outputs.index(value_storage_box) + self.graph_outputs[ind] = self.graph_inputs_original[name] + except ValueError: + pass + + self.finalize() + log.debug( + "Force channels last inputs for %d conv for the current graph with id %d", + self.num_channels_last_conv, + self.graph_id if self.graph_id is not None else -1, + ) + + def finalize(self) -> None: + for buf in self.buffers: + buf.decide_layout() + + @contextmanager + def set_current_node(self, node: torch.fx.Node): # type: ignore[no-untyped-def] + old = self.current_node + try: + self.current_node = node + yield + finally: + self.current_node = old + + def try_match_insignificant_strides( + self, + tensor: Union[ir.TensorBox, ir.BaseView], + meta_strides_inp: Tuple[Union[int, torch.SymInt], ...], + ) -> Union[ir.TensorBox, ir.BaseView]: + """ + Tries to match the strides of the tensor to those in the meta_strides. Strides of insignificant + dimensions - size 0 or 1 - will be updated. + + If there are real stride differences (NHWC vs NCHW) then the input will be returned. + """ + + # should have already been realized + assert torch._inductor.ir.is_storage_and_layout(tensor) + + meta_strides = [ + s.node.expr if isinstance(s, torch.SymInt) else s for s in meta_strides_inp + ] + + if all( + self.sizevars.statically_known_equals(s1, s2) + for s1, s2 in zip(meta_strides, tensor.get_stride()) + ): + return tensor # type: ignore[arg-type] + + def significant_strides_equal( + shape: Sequence[Union[Expr, int]], + meta_strides: Sequence[Union[Expr, int]], + tensor_strides: Sequence[Union[Expr, int]], + ) -> bool: + for dim, s1, s2 in zip(shape, meta_strides, tensor_strides): + if self.sizevars.statically_known_leq(dim, 1): # type: ignore[arg-type] + continue + + if not self.sizevars.statically_known_equals(s1, s2): + return False + + return True + + if not significant_strides_equal( + tensor.get_size(), meta_strides, tensor.get_stride() + ): + return tensor + + storage, old_layout = torch._inductor.ir.as_storage_and_layout(tensor) + new_stride = list(old_layout.stride) + for i, s in enumerate(tensor.get_size()): + if self.sizevars.statically_known_leq(s, 1): # type: ignore[arg-type] + new_stride[i] = meta_strides[i] + + new_layout = torch._inductor.ir.FixedLayout( + old_layout.device, + old_layout.dtype, + old_layout.size, + new_stride, + old_layout.offset, + ) + return ir.TensorBox(torch._inductor.ir.ReinterpretView(storage, new_layout)) + + def propagate_mutation( + self, + fx_node: torch.fx.Node, + old_args: Tuple[Any], + old_kwargs: Dict[str, Any], + new_args: Tuple[Any], + new_kwargs: Dict[str, Any], + ) -> None: + """Propagate mutations on new_args/new_kwargs back to old_args/old_kwargs. + + Assumes we may have cloned old_args/old_kwargs into new_args/new_kwargs + and then called fx_node(*new_args, **new_kwargs). + + If fx_node mutates any of new_args/new_kwargs, and they are different from + old_args/old_kwargs, then we need to update the original tensor. + """ + assert isinstance(fx_node.target, torch._ops.OpOverload) + assert len(old_args) == len(new_args) + assert len(old_kwargs) == len(new_kwargs) + + def maybe_propagate( + schema_arg: torch._C.Argument, old_arg: ir.IRNode, new_arg: ir.IRNode + ) -> None: + if old_arg is new_arg: + return + if schema_arg.alias_info is not None and schema_arg.alias_info.is_write: + # The lowering for copy_ is smart enough to "replace" old_arg with + # new_arg in all future uses so a copy_ kernel never gets emitted. + self.call_function(torch.ops.aten.copy_.default, (old_arg, new_arg), {}) + + schema = fx_node.target._schema + for idx, (old_arg, new_arg) in enumerate(zip(old_args, new_args)): + schema_arg = schema.arguments[idx] + maybe_propagate(schema_arg, old_arg, new_arg) + + schema_kwargs = {arg.name: arg for arg in schema.arguments} + + for key in old_kwargs.keys(): + old_arg = old_kwargs[key] + new_arg = new_kwargs[key] + schema_arg = schema_kwargs[key] + maybe_propagate(schema_arg, old_arg, new_arg) + + def run_node(self, n: torch.fx.Node) -> object: + def debug(msg: str) -> None: + log.debug("lowering %s %s", LazyString(n.format_node), msg) + + buffer_watermark = len(self.buffers) + operation_watermark = len(self.operations) + + origins = {n} + is_call_function = n.op == "call_function" + if is_call_function: + args, kwargs = self.fetch_args_kwargs_from_env(n) + origins |= gather_origins(args, kwargs) + with ir.IRNode.current_origins(origins), self.set_current_node( # type: ignore[arg-type] + n + ), V.set_current_node( + n + ): + if ( + n.op == "call_function" + and n.target is not operator.getitem + and fallback_node_due_to_unsupported_type(n) + ): + debug("fallback_handler") + result = fallback_handler(n.target, add_to_fallback_set=False)( + *args, **kwargs # type: ignore[possibly-undefined] + ) + elif n.op == "call_function" and ( + layout_constraints := maybe_layout_constraints(n.target) # type: ignore[arg-type] + ): + debug("layout_constraints") + old_args = args # type: ignore[possibly-undefined] + old_kwargs = kwargs # type: ignore[possibly-undefined] + args, kwargs = layout_constraints(n, *args, **kwargs) # type: ignore[index] + result = self.call_function(n.target, args, kwargs) # type: ignore[arg-type] + # layout_constraints are allowed to make new copies of the inputs. + # if they do, and if the target is mutable, then we need to + # write the new values back into the original inputs. + self.propagate_mutation(n, old_args, old_kwargs, args, kwargs) # type: ignore[possibly-undefined] + elif is_magic_method(n.target): + # TODO: this is sus, it probably should be handled in the + # lowerings themselves similarly to sym_size/sym-stride + # https://github.com/pytorch/pytorch/issues/127789 + debug("is_magic_method") + if isinstance( + n.meta["val"], (torch.SymInt, torch.SymFloat, torch.SymBool) + ): + result = n.meta["val"].node.expr + else: + result = super().run_node(n) + else: + debug("") + result = super().run_node(n) + + # require the same stride order for dense outputs, + # 1. user-land view() will not throw because inductor + # output different strides than eager + # long term the solution is to make view() always succeed + # with infallible strides. + # 2: as_strided ops, we need make sure its input has same size/stride with + # eager model to align with eager behavior. + as_strided_ops = [ + torch.ops.aten.as_strided.default, + torch.ops.aten.as_strided_.default, + torch.ops.aten.as_strided_scatter.default, + torch.ops.aten.resize.default, + torch.ops.aten.resize_as.default, + ] + is_output = any(user.op == "output" for user in n.users) + is_input_for_as_strided = any( + user.target in as_strided_ops for user in n.users + ) + + if n.meta.get("inductor_realize_to_strides", False) and isinstance( + result, TensorBox + ): + result.realize() + strides = n.meta["val"].stride() + sym_strides = torch._inductor.utils.any_is_symbolic(*strides) + if ( + not hasattr(result, "get_stride") + or result.get_stride() != strides + and not sym_strides + ): + stride_order = ir.get_stride_order(strides) + result = ir.ExternKernel.require_stride_order(result, stride_order) + if ( + is_output + and isinstance(result, TensorBox) + and isinstance(result.data, ir.BaseView) + ): + # Realize so that outputs are correctly aliased + result.realize() + + if (is_output or is_input_for_as_strided) and isinstance( + n.meta["val"], torch.Tensor + ): + strides = n.meta["val"].stride() + if len(strides): + allow_padding = ( + config.pad_outputs or n.name not in self.user_visible_outputs + ) and not is_input_for_as_strided + dense = torch._prims_common.is_non_overlapping_and_dense( + n.meta["val"] + ) + unbacked_symbols_in_strides = ( + len(free_unbacked_symbols(strides)) > 0 + ) + if ( + not unbacked_symbols_in_strides + and dense + and len(result.get_size()) == 4 + and n in self.nodes_prefer_channels_last + and n.name not in self.user_visible_outputs + and not is_input_for_as_strided + ): + strides = ir.FlexibleLayout.stride_ordered_for_memory_format( + result.get_size(), torch.channels_last + ) + if not unbacked_symbols_in_strides and len(strides): + # To avoid converting possible view ops to a copy kernel, we use the previous + # require_exact_strides to handle views. But ultimately it's better to require + # the right strides at the tensor definition. + if n.meta["val"]._is_view() or isinstance( + result.data, ir.BaseView + ): + result = ir.ExternKernel.require_stride_order( + result, + ir.get_stride_order(strides), + allow_padding=allow_padding, + ) + else: + strides = [ + s.node.expr if isinstance(s, torch.SymInt) else s + for s in strides + ] + result = ir.ExternKernel.require_exact_strides( + result, strides, allow_padding=allow_padding + ) + + # Realize if (1) any user need inputs realized, or (2) there is + # already too many reads and rematerializing can be bad. + num_users = len(OrderedSet(n.users)) + if num_users > 1 and isinstance(result, TensorBox): + for user in n.users: + if user.target in needs_realized_inputs: + result.realize_hint() + # This inclusion is somewhat controversial (from + # discussion between Horace, Natalia, and Elias). + # Currently, it's not very clear why this is helpful. + # The general idea here is that even though a node may + # have FlexibleLayout, we still often *treat* it as if + # it was contiguous. This appears to sometimes result in + # suboptimal behavior. + # + # When we do a better job selecting layout, we should + # revisit this. + need_fixed_layout = [ + torch.ops.aten.convolution_backward.default, + torch.ops.aten.mm.default, + torch.ops.aten._int_mm.default, + ] + need_fixed_channels_last_layout = [] + if not self.layout_opt: + need_fixed_layout.append(torch.ops.aten.convolution.default) + if torch._C._has_mkldnn: + need_fixed_layout += [ + torch.ops.mkldnn._linear_pointwise.default, + torch.ops.mkldnn._linear_pointwise.binary, + torch.ops.aten.mkldnn_rnn_layer.default, + torch.ops.onednn.qlinear_pointwise.default, + torch.ops.onednn.qlinear_pointwise.tensor, + torch.ops.onednn.qlinear_pointwise.binary, + torch.ops.onednn.qlinear_pointwise.binary_tensor, + ] + need_fixed_channels_last_layout += [ + torch.ops.mkldnn._convolution_pointwise.default, + torch.ops.mkldnn._convolution_pointwise.binary, + torch.ops.mkldnn._convolution_pointwise_.binary, + torch.ops.mkldnn._convolution_transpose_pointwise.default, + torch.ops.onednn.qconv2d_pointwise.default, + torch.ops.onednn.qconv2d_pointwise.binary, + ] + if torch._C.has_mkl: + need_fixed_layout += [torch.ops.mkl._mkl_linear.default] + if user.target in need_fixed_layout: + result = ir.ExternKernel.require_stride_order( + result, + ir.get_stride_order(n.meta["val"].stride()), + allow_padding=True, + ) + if ( + user.target in need_fixed_channels_last_layout + and n is user.args[0] + ): + result = ir.ExternKernel.require_stride_order( + result, + ir.get_stride_order( + make_channels_last_strides_for(n.meta["val"].shape) + ), + ) + if user.op == "output": + if isinstance(result.data.data, (Pointwise, Reduction)): + result.realize() + + # TODO(jansel): introduce a store vs inline choice + result.mark_reuse(len(n.users)) + + # Realize if the IRNode already has accumulated lots of reads + if isinstance(result, TensorBox) and result.has_exceeded_max_reads(): + # Prevent excessive accumulation in a computed buffer, when + # there are multiple branches each with small number of memory + # reads, but they converge to a user. + result.realize_hint() + + # Realize if a Pointwise has too much stuff to be inlined. + # As this may cause RecursionError during Inductor's evaluation. + if isinstance(result, TensorBox) and isinstance(result.data, StorageBox): + curr = result.data.data + if isinstance(curr, Pointwise): + # Use inner fn as a rough proxy. Good enough. + if curr.has_large_inner_fn(): + result.realize() + + # This is not complete, but it doesn't have to be: origin_node + # tracking is best effort. The logic here critically relies on direct + # TensorBox -> StorageBox denoting a non-view; we don't bother trying + # to get views to work. Feel free to add any extra cases as needed. + # + # Note: we can't YOLO tree_map over this result, because if there are + # buffers or a view involved, we might not be able to validly assign + # the origin_node here. + if isinstance(result, TensorBox) and isinstance(result.data, ir.StorageBox): + if isinstance(result.data.data, ir.Loops): + result.data.data.origin_node = n + elif isinstance(result.data.data, ir.Buffer): + result.data.data.origin_node = n + if isinstance(result.data.data, ir.ComputedBuffer) and isinstance( + result.data.data.data, ir.Loops + ): + result.data.data.data.origin_node = n + # Not really multi-output, can straightforwardly recurse in + elif ( + isinstance(result.data.data, ir.MultiOutput) + and not result.data.data.indices + ): + if isinstance(result.data.data.inputs[0], ir.Buffer): + result.data.data.inputs[0].origin_node = n + + self.register_users_of(result) + + new_unbacked_defs: OrderedSet[sympy.Symbol] = OrderedSet() + for buf in self.buffers[buffer_watermark:]: + new_unbacked_defs |= buf.get_unbacked_symbol_defs() + for op in self.operations[operation_watermark:]: + new_unbacked_defs |= op.get_unbacked_symbol_defs() + + def format_new_defs() -> str: + r = [] + for buf in self.buffers[buffer_watermark:]: + r.append( + f"unbacked_symbol_defs={buf.get_unbacked_symbol_defs()} in:\n{buf}\n" + ) + for op in self.operations[operation_watermark:]: + r.append( + f"unbacked_symbol_defs={op.get_unbacked_symbol_defs()} in:\n{op}\n" + ) + return "***\n".join(r) + + if n.op != "placeholder": + # Note [Backwards runtime asserts] + # Backwards poses an interesting problem for deferred runtime + # asserts. In the easy case, we may solely close over data + # dependent sized tensors, and there are no binding sites for + # unbacked SymInts. In this case, we can just drop all the + # runtime asserts on the floor: no non-placeholder bindings, no + # problem. + # + # However, it is *possible* for a fresh runtime assert to show up + # between forwards and backwards. Right now, the freezing process + # that happens when we lower forwards means that we will freeze + # runtime asserts, and then the moment the backwards lowering + # process attempts to add a new deferred runtime assert, we will + # fail. Let's say you remove that assert. Now when we get here, + # we need to make sure we actually emit these asserts (because we + # can't emit them in forwards, we already compiled it). So we + # have to do something here. But we don't want to reemit ALL + # deferred runtime asserts, we only want to emit the NEW ones. + # Therefore needing some sort of stratification in the ShapeEnv. + # This is all doable, it just hasn't been done yet. + shape_env = V.graph.sizevars.shape_env + + def make_assert(expr: Expr, msg: str) -> None: + assert_op = ir.AssertScalar(expr, msg) + self.register_buffer(assert_op, set_name=True) + self.register_operation(assert_op) + + for i0 in new_unbacked_defs: + ras = self.ras_by_symbol.pop(i0, []) + # NB: size-like not needed, we won't retrace + vr = shape_env.var_to_range[i0] + if not shape_env._default_unspecified_value_range().issubset(vr): + + def is_convertible(s: Expr) -> bool: + if s in (int_oo, -int_oo): + return False + try: + int(s) + return True + except TypeError: + return False + + if is_convertible(vr.lower): + make_assert(i0 >= vr.lower, f"{i0} >= {vr.lower}") + if is_convertible(vr.upper): + make_assert(i0 <= vr.upper, f"{i0} <= {vr.upper}") + + for ra in ras: + fvs = free_unbacked_symbols(ra.expr) + missing = fvs - self.bound_unbacked_symbols + if missing: + i1 = min(missing, key=str) + self.ras_by_symbol.setdefault(i1, []).append(ra) + else: + make_assert(ra.expr, f"{ra.expr}") + + self.bound_unbacked_symbols |= new_unbacked_defs + + unbacked_bindings = resolve_unbacked_bindings( + V.graph.sizevars.shape_env, n.meta.get("unbacked_bindings", {}) + ) + # When we do lowering, it is possible we reallocate unbacked SymInts. + # So we need to line up the unbacked SymInts when performing the test + # here + # + # In principle, we could permit lowering to introduce MORE unbacked + # SymInts: as long as all the old unbacked ones are accounted for, + # it's fine for inductor to introduce extra calls to item()/unbacked() + # whatever. This actually happens in practice when an unbacked SymInt + # gets memoized away; naively, when Inductor reprocesses a kernel, it + # doesn't know that the memo still applies, and ends up allocating a + # new symbol. However, this is generally a bad thing: we may still + # end up needing to test equalities on the symbols, and a fresh + # symbol is likely to hit lots of GuardOnDataDependent errors that + # we already know facts for. + renamed_unbacked_bindings = OrderedSet( + V.fake_mode.shape_env.unbacked_renamings.get(s, s) + for s in unbacked_bindings.keys() + ) + assert new_unbacked_defs >= renamed_unbacked_bindings, ( + f"failed {new_unbacked_defs} >= {renamed_unbacked_bindings} (inductor >= fx)\n" + f"fx node is: {n.format_node()}\n" + f"new operations are:\n\n{format_new_defs()}" + ) + + return result + + def validate_can_generate_cpp_wrapper(self) -> None: + if config.disable_cpp_codegen: + raise CppWrapperCodeGenError("C++ codegen is disabled") + + if sys.platform not in ["linux", "darwin", "win32"]: + raise CppWrapperCodeGenError(f"Unsupported platform {sys.platform}") + + for value in self.graph_inputs.values(): + dtype = None + if isinstance(value, TensorBox): + dtype = value.get_dtype() + elif isinstance( + value, (sympy.Symbol, sympy.Expr, sympy.core.numbers.Integer) + ): + dtype = may_get_constant_buffer_dtype(value) + + if not supported_dtype_of_cpp_wrapper(dtype, self.cuda): + raise CppWrapperCodeGenError(f"Unsupported input dtype {dtype}") + + def init_wrapper_code(self) -> None: + self.cuda = "cuda" in self.device_types + if self.cpp_wrapper: + self.validate_can_generate_cpp_wrapper() + + device_types = self.device_types.copy() + device_types.discard("cpu") + device_types.discard("meta") + # TODO(Eikan): Only support mixing cpu and other device now. + assert len(device_types) <= 1, "Does not support mixing {}".format( + "+".join(device_types) + ) + only_cpu = len(device_types) == 0 + device_type = "cpu" if only_cpu else device_types.pop() + + self.device_ops = get_device_op_overrides(device_type) + wrapper_code_gen_cls = get_wrapper_codegen_for_device( + device_type, self.cpp_wrapper + ) + assert wrapper_code_gen_cls is not None, f"Device {device_type} not supported" + self.wrapper_code = wrapper_code_gen_cls() + + if self.const_module: + # If we have const module, we could reuse the kernels + # This could avoid duplication and save time on doing recompilation (if Triton.) + self.wrapper_code._names_iter = self.const_module.wrapper_code._names_iter + self.wrapper_code.src_to_kernel = ( + self.const_module.wrapper_code.src_to_kernel + ) + + def codegen_with_cpp_wrapper(self) -> Tuple[str, List[Tuple[int, Node]]]: + """ + For CPU, the cpp wrapper codegen is done in one pass. + For GPU, the cpp wrapper codegen is done in two steps: JIT-compile the model with python + wrapper code and run it to generate autotuned kernel binaries in the first pass; and then + generate cpp wrapper code and compile it to a dynamic library in the second pass. + """ + if "cuda" in self.device_types: + # first pass + self.cpp_wrapper = False + # Although triton.store_cubin was OrderedSet in compile_fx, the backward pass didn't pick + # that up. In theory it should work by only setting triton.store_cubin to True here, + # but that will cause a problem when use_runtime_constant_folding is OrderedSet. + with config.patch({"triton.store_cubin": True}): + compiled = self.compile_to_module().call + + if not config.triton.autotune_at_compile_time: + + def materialize( + x: Union[torch.SymInt, torch.SymFloat, torch.Tensor] + ) -> Union[int, float, torch.Tensor]: + if x is None: + return None + elif isinstance(x, (torch.SymInt, torch.SymFloat)): + # Need concrete value to run dynamic shapes and tune the result + return x.node.hint + elif isinstance(x, FakeTensor): + return defake(x) + else: + assert isinstance( + x, torch.Tensor + ), "Unknown type when creating real inputs" + str(type(x)) + return x + + tracing_context = torch._guards.TracingContext.try_get() + if tracing_context is not None and not isinstance( + V.real_inputs, NullHandler + ): + if tracing_context.output_strides: + tracing_context.output_strides.clear() + + params_flat = [ + param + for param in tracing_context.params_flat # type: ignore[union-attr] + if param is not None + ] + real_inputs = [ + materialize(x) + for x in itertools.chain(params_flat, V.real_inputs) + ] + else: + # In the backward pass, V.real_inputs is not OrderedSet. + # Generating random inputs based on self.example_inputs sometimes can be problematic, + # e.g. illegal memory access. A comprehensive fix is to autotune in a separate process. + real_inputs = [ + materialize(x) + for x in ( + self.example_inputs + if isinstance(V.real_inputs, NullHandler) + else V.real_inputs + ) + ] + + if self.mutated_inputs: + from .compile_fx import clone_preserve_strides + + mutated_input_idxs = [ + idx + for idx, name in enumerate(self.graph_inputs) + if name in self.mutated_inputs + and isinstance(real_inputs[idx], torch.Tensor) + ] + for idx in mutated_input_idxs: + # clone mutated Tensor inputs to avoid mutating them in + # the first pass of the CPP wrapper-based compilation, as + # this will lead to a side effect on the example inputs: + # e.g. if torch.compile(f)(x) if called on input-mutating + # f, the inputs x will be mutated twice in the process: + # once here, and again when running the compiled model; + # this will also lead to a numerically incorrect output + mutated_inp = real_inputs[idx] + assert isinstance(mutated_inp, torch.Tensor) + real_inputs[idx] = clone_preserve_strides(mutated_inp) + del mutated_inp + + with torch.utils._python_dispatch._disable_current_modes(): + compiled(real_inputs) + del real_inputs + + # second pass + self.cpp_wrapper = True + self.removed_buffers.clear() + self.removed_operations.clear() + self.inplaced_to_remove.clear() + V.graph.sizevars.precomputed_replacements.clear() + V.graph.sizevars.inv_precomputed_replacements.clear() + with config.patch({"triton.autotune_at_compile_time": False}): + return self.codegen() + else: + # cpu + return self.codegen() + + def codegen(self) -> Tuple[str, List[Tuple[int, Node]]]: + from .scheduler import Scheduler + + self.init_wrapper_code() + + self.scheduler = Scheduler(self.operations) + V.debug.draw_orig_fx_graph(self.orig_gm, self.scheduler.nodes) + + self.wrapper_code.push_codegened_graph(self) + self.scheduler.codegen() + + log.debug( + "Finished codegen for all nodes. The list of kernel names available: %s", + V.graph.all_codegen_kernel_names, + ) + + result = self.wrapper_code.generate(self.is_inference) + self.wrapper_code.pop_codegened_graph() + return result + + def codegen_subgraph(self, parent_graph: "GraphLowering") -> None: + """ + This is a more compact version of the `codegen()` above + where we codegen this graph as a subgraph of some parent + graph. The parent graph is passed as an argument: the + intention is to inline codegening of the subgraph in + the parent graph's wrapper code (including the generated + kerenls). The wrapper code is not finalized (via `.generate()` + call), as this will be done in the parent graph's `codegen()`. + """ + from .scheduler import Scheduler + + self.wrapper_code = parent_graph.wrapper_code + self.device_ops = parent_graph.device_ops + self.cpp_wrapper = parent_graph.cpp_wrapper + + self.scheduler = Scheduler(self.operations) + self.scheduler.codegen() + + def count_bytes( + self, + ) -> Tuple[ + int, List[Tuple[BaseSchedulerNode, int]], List[Tuple[BaseSchedulerNode, float]] + ]: + total_bytes = 0 + node_counts = [] + node_runtimes = [] + for node in self.scheduler.nodes: + num_bytes = node.get_read_write_buffers_sizes() + total_bytes += num_bytes + node_counts.append((node, num_bytes // 4)) + node_runtimes.append((node, node.get_estimated_runtime())) + + return total_bytes, node_counts, node_runtimes + + @staticmethod + def save_output_code(code: str) -> None: + # No-op to be patched for unit tests + pass + + def compile_to_module(self) -> ModuleType: + with dynamo_timed( + "GraphLowering.compile_to_module", phase_name="code_gen", fwd_only=False + ): + return self._compile_to_module() + + def _compile_to_module(self) -> ModuleType: + from .codecache import PyCodeCache + + code, linemap = ( + self.codegen_with_cpp_wrapper() if self.cpp_wrapper else self.codegen() + ) + + GraphLowering.save_output_code(code) + output_code_log.debug("Output code: \n%s", code) + try: + linemap = [(line_no, node.stack_trace) for line_no, node in linemap] # type: ignore[misc] + key, path = PyCodeCache.write(code) + except Exception: + trace_structured( + "inductor_output_code", + # Just omit the filename, I still want the code though! + payload_fn=lambda: code, + ) + raise + else: + trace_structured( + "inductor_output_code", + lambda: {"filename": path}, + payload_fn=lambda: code, + ) + + mod = PyCodeCache.load_by_key_path( + key, + path, + linemap=linemap, # type: ignore[arg-type] + attrs={**self.constants, **self.torchbind_constants}, + ) + self.cache_key = key + self.cache_path = path + self.cache_linemap = linemap # type: ignore[assignment] + + # Logged twice as per https://github.com/pytorch/pytorch/pull/99038#discussion_r1167826029 + # TODO. Revisit this once the logging API is more mature + assert mod.__file__ is not None + + log_module_code(mod.__file__) + log.debug("Output code written to: %s", mod.__file__) + output_code_log.info("Output code written to: %s", mod.__file__) + if config.benchmark_kernel: + print(f"Compiled module path: {mod.__file__}", file=sys.stderr) + V.debug.output_code(mod.__file__) + V.debug.copy(os.path.splitext(mod.__file__)[0] + ".debug") + return mod + + def compile_to_fn(self) -> Any: + if self.aot_mode: + from .codecache import AotCodeCompiler + + assert self.cpp_wrapper, "AOT mode only supports C++ wrapper" + code, linemap = self.codegen_with_cpp_wrapper() + output_code_log.debug("Output code: \n%s", code) + + serialized_extern_kernel_nodes = None + if self.extern_kernel_nodes: + serialized_extern_kernel_nodes = self.extern_node_serializer( + self.extern_kernel_nodes + ) + output_code_log.debug( + "Serialized Extern Kernel Nodes: \n%s", + serialized_extern_kernel_nodes, + ) + + # Directly return the file path with the compiled code + return AotCodeCompiler.compile( + self, code, serialized_extern_kernel_nodes, cuda=self.cuda + ) + else: + return self.compile_to_module().call + + def get_output_names(self) -> List[str]: + return [ + node.get_name() + for node in self.graph_outputs + if not isinstance(node, ir.NoneAsConstantBuffer) + and not isinstance(node, ir.ShapeAsConstantBuffer) + ] + + def is_unspec_arg(self, name: str) -> bool: + # dynamo wraps unspec variable as 0d CPU tensor, + # need to convert to scalar during codegen (triton only) + return ( + name in self.graph_inputs.keys() + and self.graph_inputs[name].get_numel() == 1 + and self.graph_inputs[name].get_device().type == "cpu" + ) or name in self.zero_dim_cpu_tensor_list diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/hooks.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/hooks.py new file mode 100644 index 0000000000000000000000000000000000000000..9d8aeecd283185608260f0ffd16a19270f9e96b1 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/hooks.py @@ -0,0 +1,30 @@ +# mypy: allow-untyped-defs +import contextlib +from typing import Callable, List, TYPE_CHECKING + + +if TYPE_CHECKING: + import torch + +# Executed in the order they're registered +INTERMEDIATE_HOOKS: List[Callable[[str, "torch.Tensor"], None]] = [] + + +@contextlib.contextmanager +def intermediate_hook(fn): + INTERMEDIATE_HOOKS.append(fn) + try: + yield + finally: + INTERMEDIATE_HOOKS.pop() + + +def run_intermediate_hooks(name, val): + global INTERMEDIATE_HOOKS + hooks = INTERMEDIATE_HOOKS + INTERMEDIATE_HOOKS = [] + try: + for hook in hooks: + hook(name, val) + finally: + INTERMEDIATE_HOOKS = hooks diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/index_propagation.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/index_propagation.py new file mode 100644 index 0000000000000000000000000000000000000000..f4384d51b7d4adea0d55804090d684f9b7573b05 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/index_propagation.py @@ -0,0 +1,373 @@ +# mypy: allow-untyped-defs +"""This file implements the IndexPropagation ops handler, which wraps an +underlying handler to add a limited form of constant propagation, as well as +propagation of sympy expressions downstream of ops.index_expr calls. + +For example, say we have the IR: + + tmp0 = ops.index_expr(x, torch.int32) + tmp1 = ops.constant(2, torch.int32) + tmp2 = ops.mul(tmp0, tmp1) + tmp3 = ops.indirect_indexing(tmp2, x_size) + tmp4 = ops.load("buf0", tmp3) + +The underlying handler would just see: + + ops.load("buf0", x * 2) + +This is limited by the set of operators handled in the sympy expression +printers. So simple operations like minimum and maximum cannot be translated to +SymPy expressions yet, despite sympy.Min and sympy.Max existing. + +""" +import itertools +from dataclasses import dataclass +from typing import Any, Callable, Dict, Literal, Optional, overload, Tuple, Union +from typing_extensions import TypeAlias + +import sympy + +import torch +from torch._prims_common import dtype_to_type, is_integer_dtype +from torch.utils._sympy.functions import FloorDiv, ModularIndexing, Where +from torch.utils._sympy.value_ranges import bound_sympy, ValueRanges + +from .sizevars import evaluate_expr +from .utils import generate_assert +from .virtualized import V + + +_ExprType = Union[sympy.Expr, float, int, bool] + + +def _is_constant(val: _ExprType): + if isinstance(val, sympy.Basic): + return val.is_number + return isinstance(val, (int, float, bool)) + + +def upper_bound(val: _ExprType): + return bound_sympy(val).upper if isinstance(val, sympy.Expr) else val + + +@dataclass +class TypedExpr: + """A SymPy expression with associated type""" + + expr: _ExprType + dtype: torch.dtype + + def is_constant(self): + return _is_constant(self.expr) + + def __post_init__(self): + if _is_constant(self.expr): + self.expr = dtype_to_type(self.dtype)(self.expr) + + +class SymPyOps: + """An ops handler where all IR values are SymPy expressions + + When a value cannot be represented as a SymPy expression, the method is + either not defined, or returns NotImplemented + + """ + + @staticmethod + def identity(value: Any) -> Any: + return value + + @staticmethod + def constant(value: Union[int, float, bool], dtype: torch.dtype) -> TypedExpr: + return TypedExpr(value, dtype) + + @staticmethod + def index_expr(value: Union[sympy.Expr, int], dtype: torch.dtype) -> TypedExpr: + return TypedExpr(value, dtype) + + @staticmethod + def to_dtype( + value: TypedExpr, + dtype: torch.dtype, + src_dtype: Optional[torch.dtype] = None, + use_compute_types: bool = False, + ) -> TypedExpr: + return TypedExpr(value.expr, dtype) + + @staticmethod + def abs(x: TypedExpr) -> TypedExpr: + return TypedExpr(abs(x.expr), x.dtype) # type: ignore[arg-type] + + @staticmethod + def square(x: TypedExpr) -> TypedExpr: + return TypedExpr(x.expr * x.expr, x.dtype) + + @staticmethod + def add(x: TypedExpr, y: TypedExpr) -> TypedExpr: + result_type = torch.promote_types(x.dtype, y.dtype) + return TypedExpr(x.expr + y.expr, result_type) + + @staticmethod + def sub(x: TypedExpr, y: TypedExpr) -> TypedExpr: + result_type = torch.promote_types(x.dtype, y.dtype) + return TypedExpr(x.expr - y.expr, result_type) + + @staticmethod + def mul(x: TypedExpr, y: TypedExpr) -> TypedExpr: + result_type = torch.promote_types(x.dtype, y.dtype) + return TypedExpr(x.expr * y.expr, result_type) + + @staticmethod + def neg(x: TypedExpr) -> TypedExpr: + return TypedExpr(-x.expr, x.dtype) + + @staticmethod + def floordiv(x: TypedExpr, y: TypedExpr) -> TypedExpr: + result_type = torch.promote_types(x.dtype, y.dtype) + if not is_integer_dtype(result_type): + return NotImplemented + + return TypedExpr(FloorDiv(x.expr, y.expr), result_type) + + @staticmethod + def mod(x: TypedExpr, y: TypedExpr) -> Optional[TypedExpr]: + result_type = torch.promote_types(x.dtype, y.dtype) + if not is_integer_dtype(result_type): + return NotImplemented + + result_expr = ModularIndexing(x.expr, sympy.Integer(1), y.expr) + return TypedExpr(result_expr, result_type) + + @staticmethod + def remainder(x: TypedExpr, y: TypedExpr) -> Optional[TypedExpr]: + result_type = torch.promote_types(x.dtype, y.dtype) + if not is_integer_dtype(result_type): + return NotImplemented + + x_expr = sympy.sympify(x.expr) + y_expr = sympy.sympify(y.expr) + # In these cases, remainder in Python == remainder in C++, so this transformation + # is sound + if ( + x_expr.is_nonnegative is not None + and x_expr.is_nonnegative == y_expr.is_positive + ): + result_expr = ModularIndexing(x.expr, sympy.Integer(1), y.expr) + return TypedExpr(result_expr, result_type) + return NotImplemented + + @staticmethod + def minimum(x: TypedExpr, y: TypedExpr) -> TypedExpr: + result_type = torch.promote_types(x.dtype, y.dtype) + return TypedExpr(sympy.Min(x.expr, y.expr), result_type) + + @staticmethod + def maximum(x: TypedExpr, y: TypedExpr) -> TypedExpr: + result_type = torch.promote_types(x.dtype, y.dtype) + return TypedExpr(sympy.Max(x.expr, y.expr), result_type) + + +@dataclass +class IndexPropVar: + value: Any # Either an IR value, or TypedExpr if is_symbolic is true + is_symbolic: bool = False + + @staticmethod + def new_symbolic(expr: TypedExpr) -> "IndexPropVar": + return IndexPropVar(expr, is_symbolic=True) + + def __post_init__(self): + assert not self.is_symbolic or isinstance( + self.value, TypedExpr + ), "Symbolic IndexPropVar must contain a TypedExpr" + + +IndexPropResult: TypeAlias = Union[IndexPropVar, Tuple["IndexPropResult", ...]] + + +class IndexPropagation: + """Ops wrapper that tries to propagate constant and index_expr values through the computation. + + This aims to maximize the compile time simplification possible, and convert + indirect indexing from arange into normal static indexing. + + """ + + def __init__( + self, + inner: Any, + iter_ranges: Dict[sympy.Symbol, sympy.Expr], + indirect_var_ranges: Dict[sympy.Symbol, sympy.Expr], + ) -> None: + self._inner = inner + self.shape_env = V.graph.sizevars.shape_env + + var_to_range = { + k: ValueRanges(0, upper_bound(v) - 1) for k, v in iter_ranges.items() + } + self.var_to_range = tuple( + itertools.chain(self.shape_env.var_to_range.items(), var_to_range.items()) + ) + # NOTE: this is intentionally kept as a reference so the caller can + # update it in-place + self.indirect_var_ranges = indirect_var_ranges + + axioms = [] + for x, s in iter_ranges.items(): + axioms.append(0 <= x) + axioms.append(x < s) + self.axioms = tuple(axioms) + self.shape_env.get_axioms() + + def materialize_expr(self, expr: sympy.Expr, dtype: torch.dtype) -> Any: + # Construct a new constant/index_expr from the SymPy expression + if _is_constant(expr): + val = dtype_to_type(dtype)(expr) + return self._inner.constant(val, dtype) + return self._inner.index_expr(expr, dtype) + + def unwrap(self, a: Union[Any, IndexPropVar]) -> Any: + if isinstance(a, (list, tuple)): + return tuple(self.unwrap(v) for v in a) + + if not isinstance(a, IndexPropVar): + return a + + # Prefer the sympy representation if possible + if a.is_symbolic: + return self.materialize_expr(a.value.expr, a.value.dtype) + + return a.value + + def wrap(self, a) -> IndexPropResult: + if isinstance(a, (list, tuple)): + return tuple(self.wrap(v) for v in a) + return IndexPropVar(a) + + @overload + def fallback( + self, + name: Literal["indirect_indexing"], + args: Tuple[Any, ...], + kwargs: Dict[str, Any], + ) -> IndexPropVar: + ... + + @overload + def fallback( + self, name: str, args: Tuple[Any, ...], kwargs: Dict[str, Any] + ) -> IndexPropResult: + ... + + def fallback( + self, name: str, args: Tuple[Any, ...], kwargs: Dict[str, Any] + ) -> IndexPropResult: + # Fallback to the wrapped handler + new_args = [self.unwrap(a) for a in args] + new_kwargs = {k: self.unwrap(v) for k, v in kwargs.items()} + return self.wrap(getattr(self._inner, name)(*new_args, **new_kwargs)) + + def propagate_sympy( + self, name: str, args: Tuple[Any, ...], kwargs: Dict[str, Any] + ) -> IndexPropResult: + # Build a new SymPy expression from this ops call + def unwrap(a: Union[Any, IndexPropVar]) -> Any: + if not isinstance(a, IndexPropVar): + return a + return a.value + + new_args = [unwrap(a) for a in args] + new_kwargs = {k: unwrap(v) for k, v in kwargs.items()} + new_expr = getattr(SymPyOps, name)(*new_args, **new_kwargs) + is_valid_expr = new_expr is not NotImplemented and ( + # Inductor doesn't expect floating point in sympy expressions, but + # allow floating point constants to be propagated + new_expr.is_constant() + or new_expr.expr.is_integer + ) + if not is_valid_expr: + return self.fallback(name, args, kwargs) + return IndexPropVar.new_symbolic(new_expr) + + def __getattr__(self, name: str) -> Callable[..., IndexPropResult]: + def inner(*args: Any, **kwargs: Any) -> IndexPropResult: + if not hasattr(SymPyOps, name): + return self.fallback(name, args, kwargs) + + var_arguments = [ + a + for a in itertools.chain(args, kwargs.values()) + if isinstance(a, IndexPropVar) + ] + if not all(v.is_symbolic for v in var_arguments): + return self.fallback(name, args, kwargs) + + return self.propagate_sympy(name, args, kwargs) + + return inner + + def statically_true(self, e): + """ + Given some iter_ranges, return a function that given an expression, returns whether + it is true or false using value ranges, guard knowledge and runtime_asserts. + + FIXME I think this may not be entirely right, as we may not be able to use all runtime_asserts + If this is an issue, just use guards in `self.axioms`. + + The proper way of handling this would be to have a global shape_env that adds + runtime_asserts as they happen in the code. Then, it shuld be used in SimplifyIndexing + to perform wrap_expr and in CSEProxy.check_bounds to elide upper / lower bounds also + for indirect_indexing + """ + var_to_range = ( + *self.var_to_range, + *( + (k, ValueRanges(0, upper_bound(v) - 1)) + for k, v in self.indirect_var_ranges.items() + ), + ) + return evaluate_expr(self.shape_env, e, self.axioms, var_to_range) + + def indirect_indexing( + self, + index: Union[Any, IndexPropVar], + size: Any, + check: bool = True, + wrap_neg=True, + ) -> Any: + if isinstance(index, IndexPropVar) and index.is_symbolic: + # If we find something we can convert into a direct indexing we do so + # We still need to (perhaps) wrap the expression and add bound checks + # We want to do this "constant folding", as we don't allow to fuse + # kernels into indirect indexing + + expr = sympy.sympify(index.value.expr) + + # TODO Perhaps move this logic to the simplify indexing pass + def wrap_expr(expr): + # Positive, negative, mixed + if self.statically_true(0 <= expr): + return expr + elif self.statically_true(expr < 0): + return expr + size + else: + return Where(expr < 0, expr + size, expr) + + # Sometimes it's easier to prove 0 <= expr than the weaker -size <= expr + can_prove_lower = self.statically_true(0 <= expr) or self.statically_true( + -size <= expr + ) + can_prove_upper = self.statically_true(expr < size) + if wrap_neg: + expr = wrap_expr(expr) + if generate_assert(check): + self.fallback( + "check_bounds", + (expr, size), + dict(lower=not can_prove_lower, upper=not can_prove_upper), + ) + return expr + + indirect_var = self.fallback( + "indirect_indexing", (index, size, check, wrap_neg), {} + ).value + return indirect_var diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/inductor_prims.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/inductor_prims.py new file mode 100644 index 0000000000000000000000000000000000000000..82a23d3e60cf10eac46fc5f30b2a8d811ebe7360 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/inductor_prims.py @@ -0,0 +1,179 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import logging +from typing import Optional, Sequence + +import torch +from torch import _prims, Tensor + + +log = logging.getLogger(__name__) + + +def make_prim( + schema: str, + impl_aten, + return_type=_prims.RETURN_TYPE.NEW, + doc: str = "", + tags: Optional[Sequence[torch.Tag]] = None, +): + if isinstance(return_type, tuple): + + def meta(*args, **kwargs): + return tuple(_prims.TensorMeta(o) for o in impl_aten(*args, **kwargs)) + + else: + + def meta(*args, **kwargs): + return _prims.TensorMeta(impl_aten(*args, **kwargs)) + + return _prims._make_prim( + schema=schema, + return_type=return_type, + meta=meta, + impl_aten=impl_aten, + doc=doc, + tags=tags, + ) + + +def eager_force_stride(input_tensor: Tensor, stride) -> Tensor: + if input_tensor.stride() == stride: + return input_tensor + new_tensor = input_tensor.clone().as_strided( + input_tensor.shape, + stride, + ) + new_tensor.copy_(input_tensor) + return new_tensor + + +# Custom prims used for handling randomness +seed = make_prim( + "inductor_seed(Device device) -> Tensor", + lambda device: torch.randint(2**63 - 1, [], device=device), + doc="create a fresh seed (one per call) for use with inductor_rand", + tags=(torch.Tag.nondeterministic_seeded,), +) +seeds = make_prim( + "inductor_seeds(int count, Device device) -> Tensor", + lambda count, device: torch.randint(2**63 - 1, [count], device=device), + doc="Horizontal fusion of many inductor_seed() calls", + tags=(torch.Tag.nondeterministic_seeded,), +) +lookup_seed = make_prim( + # if inductor_lookup_seed changes, update partitioners.py + "inductor_lookup_seed(Tensor seeds, int index) -> Tensor", + lambda seeds, index: seeds[index], + doc="Extract a single seed from the result of inductor_seeds()", +) +random = make_prim( + "inductor_random(SymInt[] size, Tensor seed, str mode) -> Tensor", + lambda size, seed, mode: getattr(torch, mode)(size, device=seed.device), + doc="torch.rand()/torch.randn() using backend-specific RNG that can be fused", +) +randint = make_prim( + "inductor_randint(SymInt low, SymInt high, SymInt[] size, Tensor seed) -> Tensor", + lambda low, high, size, seed: torch.randint(low, high, size, device=seed.device), + doc="torch.randint() using backend-specific RNG that can be fused", +) +force_stride_order = make_prim( + "inductor_force_stride_order(Tensor input, SymInt[] stride) -> Tensor", + eager_force_stride, + doc="Force the stride order for input tensor. No-op if the input tensor already has the stride. Do a copy otherwise", +) +_unsafe_index_put_ = make_prim( + "_unsafe_index_put_(Tensor(a!) self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor(a!)", + lambda self, indices, values, accumulate=False: torch.ops.aten.index_put_( + self, indices, values, accumulate + ), + doc="Unsafe index_put_ (doesn't issue device asserts)", +) +fma = make_prim( + "fma(Tensor a, Tensor b, Tensor c) -> Tensor", + lambda a, b, c: (a * b) + c, + doc="Fused multiply add: fma(a, b, c) -> (a * b) + c without rounding after the multiplication", +) + + +def _low_memory_max_pool2d_with_offsets_aten( + self, + kernel_size, + stride, + padding, + dilation, + ceil_mode, +): + vals, indices = torch.ops.aten.max_pool2d_with_indices( + self, kernel_size, stride, padding, dilation, ceil_mode + ) + + input_width = self.shape[-1] + kernel_width = kernel_size[1] + + bh_shape = [1] * self.ndim + bh_shape[-2] = -1 + bh = torch.arange(indices.shape[-2], dtype=torch.int64, device=self.device).view( + bh_shape + ) + + bw_shape = [1] * self.ndim + bw_shape[-1] = -1 + bw = torch.arange(indices.shape[-1], dtype=torch.int64, device=self.device).view( + bw_shape + ) + + hbase = bh * stride[0] - padding[0] + wbase = bw * stride[1] - padding[1] + + ih = indices // input_width + iw = indices - (ih * input_width) + + h_inc = ih - hbase + w_inc = iw - wbase + + offsets = h_inc * kernel_width + w_inc + + return vals, offsets.to(torch.int8) + + +def _low_memory_max_pool2d_offsets_to_indices_aten( + offsets, kernel_width, input_width, stride, padding +): + offsets = offsets.to(torch.int64) + h_inc = offsets // kernel_width + w_inc = offsets - (h_inc * kernel_width) + + bh_shape = [1] * offsets.ndim + bh_shape[-2] = -1 + bh = torch.arange(offsets.shape[-2], dtype=torch.int64, device=offsets.device).view( + bh_shape + ) + + bw_shape = [1] * offsets.ndim + bw_shape[-1] = -1 + bw = torch.arange(offsets.shape[-1], dtype=torch.int64, device=offsets.device).view( + bw_shape + ) + + hbase = bh * stride[0] - padding[0] + wbase = bw * stride[1] - padding[1] + + ih = hbase + h_inc + iw = wbase + w_inc + return ih * input_width + iw + + +_low_memory_max_pool2d_with_offsets = make_prim( + "_low_memory_max_pool2d_with_offsets(Tensor self, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, SymInt[2] dilation, bool ceil_mode) -> (Tensor, Tensor)", # noqa: B950 + _low_memory_max_pool2d_with_offsets_aten, + return_type=(_prims.RETURN_TYPE.NEW, _prims.RETURN_TYPE.NEW), + doc="Instead of returning indices, returns indices offsets.", +) + +_low_memory_max_pool2d_offsets_to_indices = make_prim( + "_low_memory_max_pool2d_offsets_to_indices(Tensor self, SymInt kernel_w, SymInt input_w, SymInt[2] stride, SymInt[2] padding) -> Tensor", # noqa: B950 + _low_memory_max_pool2d_offsets_to_indices_aten, + doc="Convert small int offsets to regular indices.", +) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/ir.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/ir.py new file mode 100644 index 0000000000000000000000000000000000000000..0929996fe174523fe0b3aad44e2d20130ea7ba86 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/ir.py @@ -0,0 +1,6953 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import contextlib +import dataclasses +import functools +import itertools +import logging +import textwrap +import traceback +from contextlib import nullcontext +from functools import partial +from typing import ( + Any, + Callable, + ClassVar, + ContextManager, + Dict, + Iterable, + List, + Literal, + Optional, + overload, + Sequence, + Tuple, + TYPE_CHECKING, + TypeVar, + Union, +) +from typing_extensions import TypeAlias +from unittest.mock import patch + +import sympy +from sympy import Expr, Integer, Symbol + +import torch._export.serde.schema as export_schema +import torch._logging +import torch.fx +import torch.utils._pytree as pytree +from torch._dynamo.device_interface import get_interface_for_device +from torch._dynamo.utils import identity +from torch._export.serde.serialize import GraphModuleSerializer +from torch._higher_order_ops.auto_functionalize import can_auto_functionalize +from torch._inductor import metrics +from torch._prims_common import ( + compute_required_storage_length, + is_boolean_dtype, + is_float_dtype, + make_channels_last_strides_for, + StrideType, +) +from torch._subclasses.fake_tensor import get_schema_info +from torch.fx.experimental.symbolic_shapes import ( + CallMethodKey, + compute_unbacked_bindings, + DivideByKey, + free_unbacked_symbols, + rebind_unbacked, + resolve_unbacked_bindings, + SymTypes, +) +from torch.utils._ordered_set import OrderedSet +from torch.utils._sympy.functions import CleanDiv, FloorDiv, ModularIndexing +from torch.utils._sympy.symbol import SymT + +from . import config, dependencies +from .codegen.common import BackendFeature, index_prevent_reordering +from .dependencies import ( + extract_free_unbacked_symbols, + extract_input_node_reduction_ranges, + extract_read_writes, + var_builder, +) +from .loop_body import LoopBody +from .ops_handler import OpCounterCSE, OpCountResult +from .runtime.benchmarking import benchmarker +from .runtime.hints import ReductionHint +from .utils import ( + argsort, + cache_on_self, + ceildiv, + convert_shape_to_inductor, + convert_shape_to_symint, + developer_warning, + get_kernel_metadata, + is_dynamic, + is_gpu, + sympy_dot, + sympy_index_symbol, + sympy_index_symbol_with_prefix, + sympy_product, + sympy_subs, +) +from .virtualized import ops, OpsValue, V + + +if TYPE_CHECKING: + from .graph import GraphLowering + +_T = TypeVar("_T") +_U = TypeVar("_U") +_V = TypeVar("_V") + +_IntLike: TypeAlias = Union[int, Expr] + +log = logging.getLogger(__name__) +indent = functools.partial(textwrap.indent, prefix=" ") +aten = torch.ops.aten + +""" [Note: Inductor IR] + +Inductor's IR is produced by executing 'lowering' code (see lowering.py). Each +lowering is registered to a particular aten operator, and expects inputs that +correspond to the aten schema. However, in place of torch Tensor inputs, lowerings +expect Inductor TensorBox inputs. + +TensorBox IR represents torch tensors. Tensors are sometimes single objects owning +storage, and sometimes views of another Tensor's storage. Mutating tensor operations +(such as add_()) affect the underlying storage and any associated views. Other operations +(such as .t_()) update metadata about the current view but don't modify the underlying storage. + +To model this in Inductor, the IR distinguishes between TensorBox, View, StorageBox and Buffer. + +TensorBox is the top level IR construct that any lowering should produce and maps to a torch.Tensor +output from an operation. But just as torch.Tensors take different forms, TensorBox IR can +reference View IR or directly reference StorageBox IRs. + +Some Inductor lowerings produce new sets of 'Box'es, while others (such as .t() or other view ops) +may take an existing TensorBox and point it to a new underlying View IR. + +Tensors that directly own storage are represented as a chain of: +TensorBox -> StorageBox -> Buffer +where Buffer is a simple (1D) allocation, and StorageBox introduces the concept of a Layout. + +If you mutate the data of such a tensor, we swing the StorageBox pointer to point to a new buffer +(leaving the old buffer unmodified and functionalizing the operation). + +Tensors backed by views add one more indirection to the IR. +TensorBox -> View -> StorageBox -> Buffer +In these cases, the underlying StorageBox/Buffer will be shared with the pre-view TensorBox. + +Computation is represented by Operation nodes, with each operation producing 1 +or more output Buffers. In the case of mutations, these will be new Buffers that have the +mutated buffer listed in its get_mutation_names(). + +It is also possible to have an InputBuffer for which there is no corresponding Operation, +e.g. it may be a graph input or compile time constant. + +""" + + +_NodeOrNodes: TypeAlias = Union[ + int, + "TensorBox", + Dict[str, "TensorBox"], + "Symbol", + "IRNode", + Sequence[ + Optional[Union[int, Dict[str, "TensorBox"], "TensorBox", "Symbol", "IRNode"]] + ], +] + + +def validate_ir(node_or_nodes: Optional[_NodeOrNodes]) -> None: + def _check_tensorbox(nodes: Optional[_NodeOrNodes]) -> None: + # Could expand this to check deeper properties + # (e.g. TensorBox points to View or StorageBox) + if nodes is None: + pass + elif isinstance(nodes, (list, tuple)): + for node in nodes: + _check_tensorbox(node) + elif isinstance(nodes, dict): + for node in nodes.values(): + _check_tensorbox(node) + else: + assert isinstance( + nodes, + ( + torch._inductor.ir.ExpandView, + DynamicScalar, + AssertScalar, + TensorBox, + sympy.logic.boolalg.Boolean, + Expr, + int, + EffectfulKernel, + ), + ), f"Found {type(nodes)}, which is not a supported top level IR node. See [Note: Inductor IR]" + + # Be picky about the accepted data structure (don't use pytree here) + _check_tensorbox(node_or_nodes) + + +def ops_wrapper(name: str) -> Callable[..., OpsValue]: + assert isinstance(name, str) + + def fn(*args: object, **kwargs: object) -> OpsValue: + return getattr(ops, name)(*args, **kwargs) + + return fn + + +def inverse_reorder(order: Sequence[int]) -> Callable[[Sequence[_T]], Sequence[_T]]: + inv_order = dict(zip(order, range(len(order)))) + + def reindex(index: Sequence[_T]) -> Sequence[_T]: + assert len(index) == len(inv_order) + return [index[inv_order[i]] for i in range(len(index))] + + return reindex + + +def same_reorder(order: Sequence[int]) -> Callable[[Sequence[_T]], Sequence[_T]]: + def reindex(index: Sequence[_T]) -> Sequence[_T]: + assert len(index) == len(order) + return [index[order[i]] for i in range(len(index))] + + return reindex + + +def fuse_reindexing( + reindex1: Callable[[Sequence[_U]], Sequence[_V]], + reindex2: Callable[[Sequence[_T]], Sequence[_U]], +) -> Callable[[Sequence[_T]], Sequence[_V]]: + def reindex(index: Sequence[_T]) -> Sequence[_V]: + return reindex1(reindex2(index)) + + return reindex + + +NHWC_STRIDE_ORDER = [3, 0, 2, 1] +NHWDC_STRIDE_ORDER = [4, 0, 3, 2, 1] + + +def stride_order2fill_order( + order: Sequence[Union[int, Integer]] +) -> Sequence[Union[int, Integer]]: + """ + Convert stride order to fill order + For channel last format, + + stride order = [3, 0, 2, 1] and fill order = [1, 3, 2, 0] + """ + lookup = {pos: idx for idx, pos in enumerate(order)} + fill_order = [lookup[i] for i in range(len(order))] + return fill_order + + +def get_stride_order(seq: Sequence[Union[int, torch.SymInt, Expr]]) -> Sequence[int]: + """ + Convert strides to stride order + """ + sorted_idx: List[int] = argsort(seq) + out = [0 for _ in range(len(seq))] + for i, elem in enumerate(sorted_idx): + out[elem] = i + return out + + +@overload +def ir_node_to_tensor(x: Literal[None], guard_shape: bool = True) -> None: + ... + + +@overload +def ir_node_to_tensor(x: IRNode, guard_shape: bool = True) -> torch.Tensor: + ... + + +def ir_node_to_tensor( + x: Optional[IRNode], guard_shape: bool = True +) -> Optional[torch.Tensor]: + if x is None: + return None + + shape_fn: Callable[[Union[int, Expr]], Union[int, Expr]] + if not guard_shape: + shape_fn = V.graph.sizevars.size_hint + else: + shape_fn = identity + size = [shape_fn(s) for s in x.get_size()] + stride: StrideType + if is_storage_and_layout(x): + stride = [shape_fn(s) for s in x.get_layout().stride] # type: ignore[misc, union-attr] + else: + stride = FlexibleLayout.contiguous_strides(size) # type: ignore[assignment] + dtype = x.get_dtype() + device = x.get_device() + size = convert_shape_to_symint(size) + stride = convert_shape_to_symint(stride) + with V.graph.sizevars.shape_env.suppress_guards(): + t = torch.empty_strided( + size=size, stride=stride, dtype=dtype, device=device + ).zero_() + return t + + +def may_convert_to_optional( + value: Optional[Sequence[_T]], +) -> Optional[Sequence[Optional[_T]]]: + if isinstance(value, list) and not value: + # [None] makes sure the cpp wrapper codegen will generate something like + # {std::nullopt} instead of {} + return [None] + return value + + +def get_device_type(x: object) -> Optional[str]: + if get_device := getattr(x, "get_device", None): + return get_device_type(get_device()) + if isinstance(x, torch.device): + return x.type + return None + + +def is_triton(x: object) -> bool: + dtype = get_device_type(x) + return bool(dtype and is_gpu(dtype)) + + +def is_cpu(x: object) -> bool: + return get_device_type(x) == "cpu" + + +class IRNode: + _current_origins: ClassVar[OrderedSet[Any]] = OrderedSet() + + @staticmethod + @contextlib.contextmanager + def current_origins(origins: OrderedSet[torch.fx.Node]): + old = IRNode._current_origins + IRNode._current_origins = old | origins + try: + yield + finally: + IRNode._current_origins = old + + def __post_init__(self): + self.origins = OrderedSet(self._current_origins) + self.traceback = traceback.format_stack() if config.debug_ir_traceback else None + + def get_read_names(self) -> OrderedSet[str]: + raise NotImplementedError(f"NYI on {type(self)}") + + def get_traceback(self): + return self.traceback + + def get_defining_op(self): + raise NotImplementedError + + def common_repr(self, shorten=True): + origins = f"origins={getattr(self, 'origins', '')}" + if shorten and len(origins) > 64: + # this can get *very* long + origins = f"{origins[:61]}..." + return [origins] + + def str_helper(self, lines, shorten=True, multiline=True): + lines = lines + self.common_repr(shorten) + lines = list(map(str, lines)) + if multiline: + new_lines = indent(",\n".join(lines)) + return f"{type(self).__name__}(\n{new_lines}\n)" + else: + return f"{type(self).__name__}({lines})" + + def get_dtype(self): + return self.dtype + + def get_layout(self): + raise NotImplementedError(f"get_layout() is not implemented by {type(self)}!") + + def get_size(self): + raise NotImplementedError(f"get_size() is not implemented by {type(self)}!") + + @property + def shape(self): + return self.get_size() + + def get_numel(self): + return sympy_product(self.get_size()) + + def is_zero_elements(self): + return V.graph.sizevars.is_expr_static_and_true(sympy.Eq(self.get_numel(), 0)) # type: ignore[arg-type] + + def realize(self): + """ + If the IRNode refers to data which has not been materialized (e.g., + it is a Pointwise/Reduction that could potentially have more + compute fused into it), realize the IRNode into physical memory, + ending the possibility of fusing into it, but allowing, e.g., multiple + users to access the data without having to recompute. + + Check StorageBox.realize for a particularly notable implementation. + + TODO(ezyang): I think, in principle, every IRNode should have an + implementation of this, and most of the time no-op is OK, but you + really do have to audit each IRNode for this, so for now, raise + an error if it's not implemented. Note that some code in graph.py + will catch this thrown error and suppress it with a warning. + """ + raise NotImplementedError(f"realize NYI on {type(self)}") + + def codegen_reference(self, writer=None): + raise NotImplementedError(f"codegen_reference NYI on {type(self)}") + + # The abstract method declarations below serve to convince mypy that all IRNode instances have these functions + # defined, while having no effect at runtime. We cannot create stub implementations here because other parts of + # the code dynamically check for defined attributes. + get_device: Callable[[], torch.device] + dtype: torch.dtype + get_name: Callable[[], str] + get_reads: Callable[[], Any] + num_reads: Callable[[], int] + get_stride: Callable[[], Any] + get_storage_numel: Callable[[], Any] + has_exceeded_max_reads: Callable[[], bool] + make_loader: Callable[[], Callable[[Any], Any]] + make_indexer: Callable[[], Callable[[Any], Any]] + mark_reuse: Callable[[int], None] + realize_hint: Callable[[], None] + get_unbacked_symbol_uses: Callable[[], OrderedSet[sympy.Symbol]] + + +@dataclasses.dataclass +class Operation: + def __post_init__(self): + self.operation_name: Optional[str] = None + + def get_device(self): + raise NotImplementedError + + def get_origin_node(self): + assert hasattr(self, "origin_node") + return self.origin_node + + def get_origins(self): + assert hasattr(self, "origins") + return self.origins + + def get_operation_name(self) -> str: + assert self.operation_name is not None + return self.operation_name + + def is_extern(self): + return False + + def is_no_op(self): + return False + + def get_read_writes(self): + raise NotImplementedError + + def is_user_of(self, name): + return name in self.get_read_names() + + def get_read_names(self) -> OrderedSet[str]: + return OrderedSet(dep.name for dep in self.get_reads()) + + def get_reads(self): + return self.get_read_writes().reads + + def get_outputs(self) -> List[Buffer]: + raise NotImplementedError + + def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: + return OrderedSet() + + def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: + """ + Returns the unbacked symbols which are required to be in scope in + order to successfully perform codegen for this buffer. For example, + a buffer that corresponds to an extern kernel call that takes i0 as + an argument would return {i0} here. This is used to generate necessary + dependencies that ensure we actually bind i0 in codegen before you + try to use it. + + Note that this is NOT transitive; in particular, if this buffer takes + in as input another buffer with dynamic shape (e.g., (i0,)), we will + not report it here, because you will already have a dependency + on that buffer, which will eventually have a dependency on i0 if + necessary. + """ + return OrderedSet() + + def get_workspace_size(self): + """ + Gets extra global memory size needed by this buffer. + Some algorithms (e.g. group gemm) may require extra global memory in the generated code. + """ + return 0 + + +@dataclasses.dataclass +class Loops(IRNode): + device: torch.device + dtype: torch.dtype + inner_fn: Callable[..., Any] + ranges: List[Expr] + + def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: + return OrderedSet().union( + *(free_unbacked_symbols(e) for e in self.ranges), + self.inner_fn_free_unbacked_symbols(), + ) + + def __str__(self, names=("ranges",)): + return self.str_helper( + [ + f"'{self.device.type}'", + str(self.dtype), + self.inner_fn_str(), + ] + + [f"{name}={getattr(self, name)}" for name in names] + + [f"origin_node={self.origin_node!r}"] + ) + + def __post_init__(self): + super().__post_init__() + self.origin_node = None + + __repr__ = __str__ + + def get_device(self): + return self.device + + def get_origin_node(self): + return self.origin_node + + def get_size(self): + return self.ranges + + def get_pointwise_size(self): + return self.ranges + + def is_extern(self): + return False + + @classmethod + def create(cls, *args, **kwargs): + origin_node = kwargs.pop("origin_node", None) + tb = kwargs.pop("traceback", None) + r = cls(*args, **kwargs) + r.origin_node = origin_node + r.traceback = ( + tb or traceback.format_stack() if config.debug_ir_traceback else None + ) + return TensorBox.create(r) + + @staticmethod + def _index(ranges, prefix=SymT.INDEX): + return [ + sympy.Integer(0) if s == 1 else sympy_index_symbol_with_prefix(prefix, n) + for n, s in enumerate(ranges) + ] + + @cache_on_self + def inner_fn_opcount(self) -> OpCountResult: + opcounter = OpCounterCSE(V.MockHandler()) + with V.set_ops_handler(opcounter), patch.object( + FlexibleLayout, "allow_indexing", True + ): + self.inner_fn(*self.inner_fn_args()) + return opcounter.getvalue() + + def inner_fn_args(self): + return (self._index(self.ranges),) + + @cache_on_self + def inner_fn_str(self): + return V.KernelFormatterHandler.ir_to_string( + self.inner_fn, *self.inner_fn_args() + ) + + def has_large_inner_fn(self): + return self.inner_fn_opcount().num_ops > config.realize_opcount_threshold + + def inner_fn_free_unbacked_symbols(self): + index = self._index(self.ranges) + return extract_free_unbacked_symbols(self.inner_fn, index) + + def get_reads(self): + with patch.object(FlexibleLayout, "allow_indexing", True): + if self.get_reduction_type(): + return extract_read_writes( + self.make_loader(), + self.get_size(), + self.get_reduction_size(), + ).reads + else: + return extract_read_writes( + self.make_loader(), + self.get_size(), + ).reads + + def get_read_names(self) -> OrderedSet[str]: + return OrderedSet(self.inner_fn_opcount().read_buffers) + + def num_reads(self): + return len(self.inner_fn_opcount().read_buffers) + + def get_reduction_size(self): + raise NotImplementedError( + f"get_reduction_size() is not implemented by {type(self)}!" + ) + + def get_reduction_type(self): + raise NotImplementedError( + f"get_reduction_type() is not implemented by {type(self)}!" + ) + + def constant_to_device(self, device): + raise NotImplementedError( + f"constant_to_device() is not implemented by {type(self)}!" + ) + + +def nop_loader_fn(idx: Union[Expr, Sequence[Expr]], *, dtype: torch.dtype) -> OpsValue: + if dtype.is_floating_point: + return ops.constant(float("nan"), dtype) + else: + return ops.constant(0, dtype) + + +class Pointwise(Loops): + def make_loader(self): + # Make zero-element loops into a no-op + if self.is_zero_elements(): + return partial(nop_loader_fn, dtype=self.dtype) + + return self.inner_fn + + def get_reduction_size(self): + return [] + + def get_reduction_type(self): + return None + + def store_output(self, output_name, indexer, vars): + loader = self.make_loader() + return ops.store(output_name, indexer(vars), loader(vars)) + + def constant_to_device(self, device): + """Move this to a given device. Requires that all reads are to constants.""" + loader = self.make_loader() + loader = patch.object(ConstantBuffer, "override_device", device)(loader) + return Pointwise(device, self.dtype, loader, self.ranges) + + +@dataclasses.dataclass +class Scatter(Pointwise): + output_indexer: Callable[[List[Expr]], Expr] + scatter_mode: Optional[str] = None + + def constant_to_device(self, device): + """Move this to a given device. Requires that all reads are to constants.""" + loader = self.make_loader() + loader = patch.object(ConstantBuffer, "override_device", device)(loader) + return Scatter( + device, + self.dtype, + loader, + self.ranges, + self.output_indexer, + self.scatter_mode, + ) + + def store_output(self, output_name, indexer, vars): + loader = self.make_loader() + return ops.store( + output_name, + indexer(self.output_indexer(vars)), + loader(vars), + mode=self.scatter_mode, + ) + + +REDUCTION_COMBINE_FN: Dict[str, Callable[..., OpsValue]] = { + "any": ops_wrapper("logical_or"), + "max": ops_wrapper("maximum"), + "min": ops_wrapper("minimum"), + "prod": ops_wrapper("mul"), + "sum": ops_wrapper("add"), + "xor_sum": ops_wrapper("bitwise_xor"), +} + + +def get_reduction_combine_fn( + reduction_type: str, dtype: torch.dtype, arg_break_ties_left: bool = True +) -> Callable[..., object]: + if reduction_type in REDUCTION_COMBINE_FN: + return REDUCTION_COMBINE_FN[reduction_type] + + elif reduction_type in ("argmax", "argmin"): + + def argmax_combine_fn( + a: Tuple[object, object], b: Tuple[object, object] + ) -> Tuple[OpsValue, OpsValue]: + a_value, a_index = a + b_value, b_index = b + + if reduction_type == "argmin": + mask = ops.lt(a_value, b_value) + else: + mask = ops.gt(a_value, b_value) + + equal = ops.eq(a_value, b_value) + if is_float_dtype(dtype): + a_isnan = ops.ne(a_value, a_value) + b_isnan = ops.ne(b_value, b_value) + mask = ops.logical_or(mask, ops.gt(a_isnan, b_isnan)) + equal = ops.logical_or(equal, ops.logical_and(a_isnan, b_isnan)) + + tie = ( + ops.lt(a_index, b_index) + if arg_break_ties_left + else ops.gt(a_index, b_index) + ) + mask = ops.logical_or(mask, ops.logical_and(equal, tie)) + return ( + ops.where(mask, a_value, b_value), + ops.where(mask, a_index, b_index), + ) + + return argmax_combine_fn + + elif reduction_type == "welford_combine": + + def welford_combine_fn( + a: Tuple[OpsValue, OpsValue, OpsValue], + b: Tuple[OpsValue, OpsValue, OpsValue], + ) -> Tuple[OpsValue, OpsValue, OpsValue]: + a_mean, a_m2, a_weight = a + b_mean, b_m2, b_weight = b + + delta = b_mean - a_mean + new_weight = a_weight + b_weight + w2_over_w = b_weight / new_weight + return ( + a_mean + delta * w2_over_w, + a_m2 + b_m2 + delta * delta * a_weight * w2_over_w, + new_weight, + ) + + return welford_combine_fn + + else: + raise NotImplementedError(f"unknown reduction_type={reduction_type}") + + +def significant_strides_equal( + strides1: Sequence[_IntLike], strides2: Sequence[_IntLike], size: Sequence[_IntLike] +) -> bool: + """ + Returns true if the strides are equal, ignoring dimensions of size 1 . + """ + non_1_indices = [ + i + for i, dim in enumerate(size) + if V.graph.sizevars.size_hint(dim, fallback=2) != 1 + ] + strides1 = [V.graph.sizevars.size_hint(strides1[i]) for i in non_1_indices] + strides2 = [V.graph.sizevars.size_hint(strides2[i]) for i in non_1_indices] + return strides1 == strides2 + + +@dataclasses.dataclass +class Reduction(Loops): + reduction_ranges: List[Expr] + reduction_type: str + # self.dtype represents the dst dtype + src_dtype: torch.dtype + reduction_hint: ReductionHint + + def __str__(self) -> str: # type: ignore[override] + return Loops.__str__( # type: ignore[call-arg] + self, names=("ranges", "reduction_ranges", "reduction_type") + ) + + def __repr__(self) -> str: # type: ignore[override] + return self.__str__() + + def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: + return super().get_unbacked_symbol_uses() | OrderedSet().union( + *(free_unbacked_symbols(e) for e in self.reduction_ranges) + ) + + def get_reduction_size(self): + return self.reduction_ranges + + def get_reduction_type(self): + return self.reduction_type + + def store_reduction(self, output_name, indexer, vars, reduction_vars): + value = ops.reduction( + self.dtype, + self.src_dtype, + self.reduction_type, + self.inner_fn(vars, reduction_vars), + ) + return ops.store_reduction(output_name, indexer(vars), value) + + def index_length(self): + return len(self.ranges) + len(self.reduction_ranges) + + def inner_fn_args(self): + index = self._index(self.ranges) + rindex = self._index(self.reduction_ranges, SymT.RINDEX) + return (index, rindex) + + def inner_fn_free_unbacked_symbols(self): + index = self._index(self.ranges) + rindex = self._index(self.reduction_ranges, SymT.RINDEX) + return extract_free_unbacked_symbols(self.inner_fn, index, rindex) + + def constant_to_device(self, device): + """Move this to a given device. Requires that all reads are to constants.""" + loader = self.make_loader() + loader = patch.object(ConstantBuffer, "override_device", device)(loader) + return Reduction( + device, + self.dtype, + loader, + self.ranges, + self.reduction_ranges, + self.reduction_type, + self.src_dtype, + ReductionHint.DEFAULT, + ) + + @staticmethod + def num_splits( + device, + dst_dtype, + src_dtype, + inner_fn, + ranges, + reduction_ranges, + reduction_type, + reduction_numel, + input_node: Optional[IRNode] = None, + ): + def _is_static(x): + return isinstance(x, (int, sympy.Integer)) + + reduction_numel_hint = V.graph.sizevars.symbolic_hint(reduction_numel) + numel_hint = V.graph.sizevars.symbolic_hint(sympy_product(ranges)) + + should_split = ( + not V.graph.has_feature(device, BackendFeature.REDUCE_TO_SINGLE_ELEMENT) + and reduction_type + not in ( + "argmax", + "argmin", + ) + and config.split_reductions + # We don't support unbacked symints + and _is_static(reduction_numel_hint) + and _is_static(numel_hint) + ) + if not should_split: + return ReductionHint.DEFAULT, 1 + + device_interface = get_interface_for_device(get_device_type(device)) # type: ignore[arg-type] # next PR + device_properties = device_interface.Worker.get_device_properties(device) + if get_device_type(device) == "xpu": + num_sm = device_properties.gpu_subslice_count + else: + # default is cuda behavior + num_sm = device_properties.multi_processor_count + + min_elements_per_thread = 32 + max_elements_per_thread = 512 + threads_per_sm = 2048 + min_elements_per_device = min_elements_per_thread * num_sm * threads_per_sm + max_elements_per_device = max_elements_per_thread * num_sm * threads_per_sm + + def inner_reduction_splits(reduction_numel_hint, numel_hint): + # do heuristics that's close to eager mode for split inner reduction + # we leak reduction autotune configs here, and will need to refactor to avoid this later + num_warps = 8 + num_threads = 32 * num_warps + if numel_hint >= 2 * num_sm: # don't split if there are enough outputs + return 1 + if reduction_numel_hint <= 8192: + return 1 + if reduction_numel_hint * numel_hint <= min_elements_per_device: + split_size = min_elements_per_thread + elif reduction_numel_hint * numel_hint < max_elements_per_device: + target_blocks = num_sm * threads_per_sm // (2 * num_threads) + blocks_per_output = (target_blocks + numel_hint - 1) // numel_hint + tmp_split_size = ( + reduction_numel_hint + num_threads * blocks_per_output - 1 + ) // (num_threads * blocks_per_output) + divisors = sympy.divisors(reduction_numel_hint) + closest = min(divisors, key=lambda x: abs(x - tmp_split_size)) + if abs(closest - tmp_split_size) < 30: + # prefer even splits, but never smalle than min_elements_per_thread + split_size = max(closest, min_elements_per_thread) + else: + split_size = tmp_split_size + else: + divisors = sympy.divisors(reduction_numel_hint) + closest = min(divisors, key=lambda x: abs(x - max_elements_per_thread)) + if abs(closest - max_elements_per_thread) < 50: + # prefer even splits + split_size = closest + else: + split_size = max_elements_per_thread + return (reduction_numel_hint + split_size * num_threads - 1) // ( + split_size * num_threads + ) + + def outer_reduction_splits(reduction_numel_hint, numel_hint): + # TODO the best heuristic currently has XBLOCK (corresponding to numel_hint) 128 + # extend to even smaller number of outputs + num_warps = 8 + num_threads = num_warps * 32 + rvals_per_thread = 4 # comes from heuristics, refactor to not leak here + xvals_per_block = 128 + xblocks = (numel_hint + xvals_per_block - 1) // xvals_per_block + if reduction_numel_hint * numel_hint < min_elements_per_device: + split_size = min_elements_per_thread + elif reduction_numel_hint * numel_hint < max_elements_per_device: + target_blocks = num_sm * threads_per_sm // (num_threads) + target_blocks = (target_blocks + xblocks - 1) // xblocks + tmp_split_size = ( + reduction_numel_hint + rvals_per_thread * target_blocks - 1 + ) // (rvals_per_thread * target_blocks) + divisors = sympy.divisors(reduction_numel_hint) + closest = min(divisors, key=lambda x: abs(x - tmp_split_size)) + if abs(tmp_split_size - closest) < 20: + split_size = max(closest, min_elements_per_thread) + else: + split_size = tmp_split_size + else: + divisors = sympy.divisors(reduction_numel_hint) + closest = min(divisors, key=lambda x: abs(x - max_elements_per_thread)) + if abs(closest - max_elements_per_thread) < 50: + # prefer even splits + split_size = closest + else: + split_size = max_elements_per_thread + + return (reduction_numel_hint + rvals_per_thread * split_size - 1) // ( + rvals_per_thread * split_size + ) + + # easy cases + if numel_hint == 1: + split = inner_reduction_splits(reduction_numel_hint, numel_hint) + if split == 1: + # No need to split. + return ReductionHint.INNER, split + if input_node is not None and isinstance(input_node, TensorBox): + new_ranges, new_reduction_ranges = extract_input_node_reduction_ranges( + input_node + ) + if new_ranges is not None and new_reduction_ranges is not None: + extracted_numel_hint = V.graph.sizevars.symbolic_hint( + sympy_product(new_ranges + new_reduction_ranges) + ) + if reduction_numel_hint == extracted_numel_hint: + log.debug( + "Use previous IRNode's range and reduction_ranges instead of split. " + "current ranges: %s, current reduction ranges: %s, current split: %d, " + "new ranges: %s, new reduction ranges: %s", + ranges, + reduction_ranges, + split, + new_ranges, + new_reduction_ranges, + ) + # If the input_node or its dependent nodes are also Reduction nodes, + # use reduction_sizes of this node or its dependent nodes directly. + return ReductionHint.INNER, -1 + return ReductionHint.INNER, split + if ( + reduction_numel_hint <= min_elements_per_thread + or numel_hint >= num_sm * 2 * 32 + ): + return ReductionHint.DEFAULT, 1 + + r = Reduction( + device, + dst_dtype, + inner_fn, + ranges, + reduction_ranges, + reduction_type, + src_dtype, + ReductionHint.DEFAULT, + ) + + def get_read_indices(r): + cb = ComputedBuffer( + name=None, + layout=FlexibleLayout( + device=r.get_device(), + dtype=r.get_dtype(), + size=r.get_size(), + ), + data=r, + ) + read_writes = cb.get_read_writes() + # try finding the full size producer + # TODO this will fail for something like ((1, N) * (N, 1)).sum() + # this would also possibly be wrong for producers with the different contiguity but we hope those cases are rare + range_vars = [ + r + for r in read_writes.range_vars + if isinstance(r, sympy.Expr) and not isinstance(r, sympy.Number) + ] + indices = [] + changed = False + for md in sorted(read_writes.reads, key=lambda x: x.name): + if all(r in md.index.free_symbols for r in range_vars): + indices.append(md.index) + if md.name in V.graph.name_to_buffer: + buf = V.graph.name_to_buffer[md.name] + original_stride = buf.layout.stride + buf.decide_layout() + if buf.layout.stride != original_stride: + changed = True + return indices, changed + + indices, changed = get_read_indices(r) + if changed: + indices, _ = get_read_indices(r) + + if len(indices) == 0: + # TODO determine splits when all inputs are broadcast + return ReductionHint.DEFAULT, 1 + + (_, reduction_vars), ranges = dependencies.index_vars_squeeze( + r.get_size(), r.get_reduction_size() + ) + num_outer = 0 + num_inner = 0 + for i in indices: + i = V.graph.sizevars.simplify_with_ranges(i, ranges) + strides = V.graph.sizevars.stride_hints(i, reduction_vars, ranges.keys()) + outer = all(s > 1 for s in strides) + if outer: + num_outer += 1 + else: + num_inner += 1 + if num_inner > num_outer: + return ReductionHint.INNER, inner_reduction_splits( + reduction_numel_hint, numel_hint + ) + else: + return ReductionHint.OUTER, outer_reduction_splits( + reduction_numel_hint, numel_hint + ) + + @staticmethod + def _unroll_reduction_fn(inner_fn, reduction_ranges, reduction_type, src_dtype): + """Convert inner_fn from a reduction to an pointwise""" + reduction_ranges = [ + V.graph.sizevars.evaluate_static_shape(x) for x in reduction_ranges + ] + + combine_fn = get_reduction_combine_fn(reduction_type, src_dtype) + + def fn(index): + return functools.reduce( + combine_fn, + ( + value_fn(index, rindex) + for rindex in itertools.product( + *[range(x) for x in reduction_ranges] + ) + ), + ) + + if reduction_type in ("argmin", "argmax"): + flatten_index = FixedLayout( + None, # type: ignore[arg-type] + None, # type: ignore[arg-type] + reduction_ranges, + FlexibleLayout.contiguous_strides(reduction_ranges), + ).make_indexer() + + def value_fn(index, rindex): + rindex = [sympy.expand(i) for i in rindex] + return ( + inner_fn(index, rindex), + ops.index_expr(flatten_index(rindex), torch.int64), + ) + + return lambda index: fn(index)[1] + else: + value_fn = inner_fn + return fn + + @classmethod + def create( # type: ignore[override] + cls, + device: torch.device, + dst_dtype: torch.dtype, + src_dtype: torch.dtype, + inner_fn: Callable[..., Any], + ranges: List[Expr], + reduction_ranges: List[Expr], + reduction_type: str, + reduction_hint: ReductionHint = ReductionHint.DEFAULT, + input_node: Optional[IRNode] = None, + ): + reduction_numel = V.graph.sizevars.simplify(sympy_product(reduction_ranges)) + + if reduction_numel == 0: + # N.B. This is a hack to generate the literal of the given type + # Ideally, we should be fixing `def constant` in triton.py + # but it breaks due to hardcoded dtypes in other places + def py_cnst(val): + return ( + bool(val) + if dst_dtype == torch.bool + else float(val) + if dst_dtype.is_floating_point + else int(val) + ) + + rtypes_to_inits = { + "sum": py_cnst(0), + "xor_sum": py_cnst(0), + "prod": py_cnst(1), + "any": py_cnst(0), + # "all" is desugared to `!any(!val)` + } + + assert ( + reduction_type in rtypes_to_inits.keys() + ), f"{reduction_type} not supported for zero-dimension tensors!" + + def const_fn(index): + return ops.constant(rtypes_to_inits[reduction_type], dst_dtype) + + return Pointwise.create( + device=device, + dtype=src_dtype, + inner_fn=const_fn, + ranges=list(ranges), + ) + + if reduction_numel == 1: + # this reduction is actually a pointwise op + if reduction_type in ("argmin", "argmax"): + + def fn(index): + return ops.constant(0, dst_dtype) + + else: + + def fn(index): + reduction_index = [sympy.Integer(0) for _ in reduction_ranges] + return inner_fn(index, reduction_index) + + return Pointwise.create(device, dst_dtype, fn, ranges) + + if ( + isinstance(reduction_numel, sympy.Integer) + and V.graph.sizevars.size_hint(reduction_numel) + < config.unroll_reductions_threshold + and sympy_product(ranges) != 1 + ): + return Pointwise.create( + device, + dst_dtype, + cls._unroll_reduction_fn( + inner_fn, reduction_ranges, reduction_type, src_dtype + ), + ranges, + ) + + # triton doesn't support reduce to single element well, so break it up + hint, split = cls.num_splits( + device, + dst_dtype, + src_dtype, + inner_fn, + ranges, + reduction_ranges, + reduction_type, + reduction_numel, + input_node, + ) + # intermediate reduction in split can contain complex indexing, + # and num_splits will fail to correctly set the hint + # reuse the passed hint if available + if reduction_hint == ReductionHint.DEFAULT: + reduction_hint = hint + if split == -1: + assert input_node is not None + new_ranges, new_reduction_ranges = extract_input_node_reduction_ranges( + input_node # type: ignore[arg-type] + ) + assert new_ranges is not None + assert new_reduction_ranges is not None + return cls.create_multilayer_existing_ranges( + device, + dst_dtype, + src_dtype, + inner_fn, + ranges, + reduction_ranges, + new_ranges, + new_reduction_ranges, + reduction_type, + reduction_hint, + ) + elif split > 1: + # triton doesn't support reduce to single element well, so break it up + return cls.create_multilayer( + device, + dst_dtype, + src_dtype, + inner_fn, + ranges, + reduction_ranges, + reduction_type, + split, + reduction_hint, + ) + + return TensorBox.create( + Reduction( + device, + dst_dtype, + inner_fn, + ranges, + reduction_ranges, + reduction_type, + src_dtype, + reduction_hint, + ) + ) + + @staticmethod + def default_accumulator(reduction_type, dtype): + if reduction_type in ("max", "argmax"): + if is_float_dtype(dtype): + return float("-inf") + elif is_boolean_dtype(dtype): + return 0 + else: + return torch.iinfo(dtype).min + if reduction_type in ("min", "argmin"): + if is_float_dtype(dtype): + return float("inf") + elif is_boolean_dtype(dtype): + return 1 + else: + return torch.iinfo(dtype).max + + return { + "sum": 0, + "prod": 1, + "xor_sum": 0, + "any": 0, + "welford_reduce": (0, 0, 0), + "welford_combine": (0, 0, 0), + }[reduction_type] + + @staticmethod + def default_value(reduction_type, dtype): + if reduction_type == "welford_reduce": + return 0 + return Reduction.default_accumulator(reduction_type, dtype) + + @staticmethod + def _multilayer_second_step_hint( + split: int, numel_hint: int, reduction_hint: ReductionHint + ) -> ReductionHint: + if split == -1: + return reduction_hint + if split <= 512 and numel_hint <= 512 and reduction_hint == ReductionHint.OUTER: + return ReductionHint.OUTER_TINY + if ( + split <= 1024 + and numel_hint <= 256 + and reduction_hint == ReductionHint.OUTER + ): + return ReductionHint.OUTER_TINY + + return reduction_hint + + @classmethod + def _multilayer_wrap_loader( + cls, + loader, + reduction_ranges, + reduction_numel, + split, + block_size, + default, + ): + reindex = View.dynamic_reshape_indexer(reduction_ranges, [reduction_numel]) + need_mask = not V.graph.sizevars.is_expr_static_and_true( + sympy.Eq(reduction_numel % split, 0) # type: ignore[arg-type] + ) + + def wrapper_fn(index, reduction_index): + (reduction_index,) = reduction_index + *new_index, reduction_block = index + indices = block_size * reduction_block + reduction_index + + def body(): + return loader(new_index, reindex([indices])) + + if need_mask: + mask = ops.lt( + ops.index_expr(indices, torch.int32), + ops.index_expr(reduction_numel, torch.int32), + ) + return ops.masked(mask, body, default) + else: + return body() + + return wrapper_fn + + @classmethod + def _multilayer_wrap_loader_existing_ranges( + cls, + loader, + original_ranges, + original_reduction_ranges, + new_ranges, + new_reduction_ranges, + default, + ): + assert all( + r == 1 for r in original_ranges + ), f"Only enabled for numel_hint == 1, found {original_ranges=}" + reindex = View.dynamic_reshape_indexer( + original_reduction_ranges, tuple(new_ranges) + tuple(new_reduction_ranges) + ) + + def wrapper_fn(merged_index, new_reduction_index): + original_idx = merged_index[: len(original_ranges)] + new_index = merged_index[len(original_ranges) :] + return loader( + original_idx, + reindex(tuple(new_index) + tuple(new_reduction_index)), + ) + + return wrapper_fn + + @classmethod + def create_multilayer_helper( + cls, + device: torch.device, + dst_dtype: torch.dtype, + src_dtype: torch.dtype, + wrapper_fn: Callable[..., Any], + original_ranges: List[Expr], + original_reduction_ranges: List[Expr], + new_ranges: List[Expr], + new_reduction_ranges: List[Expr], + reduction_type: str, + split: int, + reduction_hint: ReductionHint, + ): + """ + Break a large reduction up into multiple smaller reductions + recursively + """ + # triton will automatically compute reductions in fp32 if reducing over fp16/bf16 + # within the kernel. keep the intermediate in fp32 so as to keep the whole reduction + # in fp32 and not reduce precision by breaking up the kernel into multiple layers + intermediate_dtype = ( + dst_dtype + if dst_dtype not in (torch.float16, torch.bfloat16) + else torch.float + ) + intermediate = Reduction.create( + device, + intermediate_dtype, + src_dtype, + wrapper_fn, + new_ranges, + new_reduction_ranges, + reduction_type, + reduction_hint, + ) + intermediate.realize() + intermediate_loader = intermediate.make_loader() + + def intermediate_fn(index, reduction_index): + return intermediate_loader([*index, *reduction_index]) + + numel_hint = V.graph.sizevars.size_hint(sympy_product(original_ranges)) + reduction_hint = cls._multilayer_second_step_hint( + split, numel_hint, reduction_hint + ) + + assert original_ranges == new_ranges[: len(original_ranges)] + return TensorBox.create( + Reduction( + device, + dst_dtype, + intermediate_fn, + original_ranges, + new_ranges[len(original_ranges) :], + reduction_type, + src_dtype, + reduction_hint, + ) + ) + + @classmethod + def create_multilayer( + cls, + device: torch.device, + dst_dtype: torch.dtype, + src_dtype: torch.dtype, + inner_fn: Callable[..., Any], + ranges: List[Expr], + reduction_ranges: List[Expr], + reduction_type: str, + split: int, + reduction_hint: ReductionHint, + ): + """ + Break a large reduction up into multiple smaller reductions + recursively + """ + # TODO(jansel): realize the reduction so we can do dynamic indexing + reduction_numel = sympy_product(reduction_ranges) + block_size = FloorDiv(reduction_numel + (split - 1), split) + default = cls.default_value(reduction_type, dst_dtype) + wrapper_fn = cls._multilayer_wrap_loader( + inner_fn, reduction_ranges, reduction_numel, split, block_size, default + ) + + return cls.create_multilayer_helper( + device, + dst_dtype, + src_dtype, + wrapper_fn, + ranges, + reduction_ranges, + [*ranges, split], # type: ignore[list-item] + [block_size], + reduction_type, + split, + reduction_hint, + ) + + @classmethod + def create_multilayer_existing_ranges( + cls, + device: torch.device, + dst_dtype: torch.dtype, + src_dtype: torch.dtype, + inner_fn: Callable[..., Any], + original_ranges: List[Expr], + original_reduction_ranges: List[Expr], + new_ranges: List[Expr], + new_reduction_ranges: List[Expr], + reduction_type: str, + reduction_hint: ReductionHint, + ): + """ + Break a large reduction up into multiple smaller reductions + recursively + """ + default = cls.default_value(reduction_type, dst_dtype) + wrapper_fn = cls._multilayer_wrap_loader_existing_ranges( + inner_fn, + original_ranges, + original_reduction_ranges, + new_ranges, + new_reduction_ranges, + default, + ) + return cls.create_multilayer_helper( + device, + dst_dtype, + src_dtype, + wrapper_fn, + original_ranges, + original_reduction_ranges, + [*original_ranges, *new_ranges], + new_reduction_ranges, + reduction_type, + -1, + reduction_hint, + ) + + +class WelfordReduction(Reduction): + output_index: int + + def __init__( + self, + device, + dtype, + inner_fns, + ranges, + reduction_ranges, + reduction_type, + reduction_hint, + output_index, + ): + if len(inner_fns) == 1: + loader = inner_fns[0] + else: + + def loader(idx, reduction_idx): + return tuple(fn(idx, reduction_idx) for fn in inner_fns) + + super().__init__( + device, + dtype, + loader, + ranges, + reduction_ranges, + reduction_type, + dtype, + reduction_hint, + ) + self.output_index = output_index + + def store_reduction(self, output_name, indexer, vars, reduction_vars): + values = ops.reduction( + self.dtype, + self.src_dtype, + self.reduction_type, + self.inner_fn(vars, reduction_vars), + ) + value = values[self.output_index] + return ops.store_reduction(output_name, indexer(vars), value) + + @classmethod + def create( # type: ignore[override] + cls, + device: torch.device, + dtype: torch.dtype, + inner_fns: Sequence[Callable[..., Any]], + ranges: List[Expr], + reduction_ranges: List[Expr], + reduction_type: str, + reduction_hint: ReductionHint = ReductionHint.DEFAULT, + ): + assert reduction_type in ("welford_reduce", "welford_combine") + + reduction_numel = V.graph.sizevars.simplify(sympy_product(reduction_ranges)) + + def const(val): + def inner_fn(idx): + return ops.constant( + val, + dtype, + ) + + return Pointwise.create( + device=device, + dtype=dtype, + inner_fn=inner_fn, + ranges=list(ranges), + ) + + if reduction_numel == 0: + mean = const(0) + m2 = const(0) + weight = const(0) + return mean, m2, weight + + if reduction_numel == 1: + + def copy(loader): + def inner_fn(idx): + reduction_index = [sympy.Integer(0) for _ in reduction_ranges] + return loader(idx, reduction_index) + + return Pointwise.create( + device=device, + dtype=dtype, + inner_fn=inner_fn, + ranges=list(ranges), + ) + + if reduction_type == "welford_reduce": + return copy(inner_fns[0]), const(0), const(1) + else: + return tuple(copy(fn) for fn in inner_fns) + + # TODO: Unrolled reduction + # if ( + # isinstance(reduction_numel, sympy.Integer) + # and V.graph.sizevars.size_hint(reduction_numel) + # < config.unroll_reductions_threshold + # and sympy_product(ranges) != 1 + # ): + # return Pointwise.create( + # device, + # dst_dtype, + # cls._unroll_reduction_fn( + # inner_fn, reduction_ranges, reduction_type, src_dtype + # ), + # ranges, + # ) + + # triton doesn't support reduce to single element well, so break it up + hint, split = Reduction.num_splits( + device, + dtype, + dtype, + inner_fns[0], + ranges, + reduction_ranges, + reduction_type=reduction_type, + reduction_numel=reduction_numel, + ) + # intermediate reduction in split can contain complex indexing, + # and num_splits will fail to correctly set the hint + # reuse the passed hint if available + if reduction_hint == ReductionHint.DEFAULT: + reduction_hint = hint + if split > 1: + # triton doesn't support reduce to single element well, so break it up + return cls.create_multilayer( + device, + dtype, + inner_fns, + ranges, + reduction_ranges, + reduction_type, + split, + reduction_hint, + ) + + results = [ + TensorBox.create( + WelfordReduction( + device, + dtype, + inner_fns, + ranges, + reduction_ranges, + reduction_type, + reduction_hint, + output_idx, + ) + ) + for output_idx in range(3) + ] + for t in results: + t.realize() + return results + + @staticmethod + def default_value(reduction_type, dtype): + return (0, 0, 0) + + @classmethod + def create_multilayer( # type: ignore[override] + cls, + device: torch.device, + dtype: torch.dtype, + inner_fns: Sequence[Callable[..., Any]], + ranges: List[Expr], + reduction_ranges: List[Expr], + reduction_type: str, + split: int, + reduction_hint: ReductionHint, + ): + """ + Break a large reduction up into multiple smaller reductions + recursively + """ + reduction_numel = sympy_product(reduction_ranges) + need_mask = not V.graph.sizevars.is_expr_static_and_true( + sympy.Eq(reduction_numel % split, 0) # type: ignore[arg-type] + ) + + if need_mask and reduction_type != "welford_combine": + # If we need mask, then "welford_reduce" doesn't work because + # masked inputs shouldn't count towards the welford weight + + def constant(idx, reduction_idx, value): + return ops.constant(value, dtype) + + return cls.create_multilayer( + device=device, + dtype=dtype, + inner_fns=( + inner_fns[0], + partial(constant, value=0), + partial(constant, value=1), + ), + ranges=ranges, + reduction_ranges=reduction_ranges, + reduction_type="welford_combine", + split=split, + reduction_hint=reduction_hint, + ) + + block_size = FloorDiv(reduction_numel + (split - 1), split) + intermediates = WelfordReduction.create( + device, + dtype, + tuple( + cls._multilayer_wrap_loader( + loader, + reduction_ranges, + reduction_numel, + split, + block_size, + default=0, + ) + for loader in inner_fns + ), + [*ranges, split], # type: ignore[list-item] + [block_size], + reduction_type, + reduction_hint, + ) + for i in intermediates: + i.realize() + + i_loaders = [i.make_loader() for i in intermediates] + + def intermediate_loader_fn(index, reduction_index, loader): + return loader([*index, *reduction_index]) + + numel_hint = V.graph.sizevars.size_hint(sympy_product(ranges)) + reduction_hint = cls._multilayer_second_step_hint( + split, numel_hint, reduction_hint + ) + return WelfordReduction.create( + device, + dtype, + tuple( + partial(intermediate_loader_fn, loader=i.make_loader()) + for i in intermediates + ), + ranges, + [split], # type: ignore[list-item] + # welford_reduce turns one input into three outputs, which are combined with welford_combine + "welford_combine", + reduction_hint, + ) + + +@dataclasses.dataclass +class Scan(Loops): + scan_ranges: List[Expr] + size: List[Expr] + combine_fn: Callable[[Tuple[Any, ...], Tuple[Any, ...]], Tuple[Any, ...]] + reindex: Callable[[List[Expr], List[Expr]], List[Expr]] + reduction_hint: ReductionHint + output_index: int + # output_index indexes the following tuples + dtypes: Tuple[torch.dtype, ...] + inner_fns: Tuple[Callable[..., Any], ...] + + # HACK we mimick reduction + + def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: + # TODO: Can combine_fn/reindex close over unbacked symbols? If so, we + # need to explicitly represent the closure so we can pull out unbacked + # symbols here + return ( + super().get_unbacked_symbol_uses() + | OrderedSet().union(*(free_unbacked_symbols(e) for e in self.scan_ranges)) + | OrderedSet().union(*(free_unbacked_symbols(e) for e in self.size)) + ) + + def __post_init__(self): + assert len(self.ranges) + len(self.scan_ranges) == len(self.size) + super().__post_init__() + + def store_reduction(self, output_name, indexer, vars, scan_vars): + idx = self.reindex(vars, scan_vars) + values = [inner_fn(idx) for inner_fn in self.inner_fns] + result = ops.scan(self.dtypes, self.combine_fn, values) + return ops.store(output_name, indexer(idx), result[self.output_index]) + + def get_reduction_type(self): + # return self.scan_op + return "custom" + + def get_reduction_size(self): + return self.scan_ranges + + def get_size(self): + return self.size + + def get_pointwise_size(self): + return self.ranges + + def index_length(self): + return len(self.ranges) + len(self.scan_ranges) + + def inner_fn_args(self): + index = self._index(self.ranges) + rindex = self._index(self.scan_ranges, SymT.RINDEX) + idx = self.reindex(index, rindex) + return (idx,) + + def inner_fn_free_unbacked_symbols(self): + index = self._index(self.ranges) + rindex = self._index(self.scan_ranges, SymT.RINDEX) + idx = self.reindex(index, rindex) + return extract_free_unbacked_symbols(self.inner_fn, idx) + + @classmethod + def create( + cls, + device: torch.device, + dtypes: Tuple[torch.dtype, ...], + inner_fns: Tuple[Callable[[List[Expr]], Any], ...], + size: List[Expr], + axis: int, + combine_fn: Callable[[Tuple[Any, ...], Tuple[Any, ...]], Tuple[Any, ...]], + reduction_hint: ReductionHint = ReductionHint.DEFAULT, + *, + # Whether we have the option to fallback to aten + can_fallback_to_aten: bool = True, + **kwargs, + ) -> List[Optional[TensorBox]]: + pointwise_ranges = [*size[:axis], *size[axis + 1 :]] + scan_ranges = [size[axis]] + + if not V.graph.has_feature(device, BackendFeature.SCAN): + return [None] * len(dtypes) + + if len(dtypes) > 1 and not V.graph.has_feature( + device, BackendFeature.TUPLE_REDUCTION + ): + return [None] * len(dtypes) + + sizevars = V.graph.sizevars + scan_numel = sizevars.simplify(sympy_product(scan_ranges)) + + assert len(dtypes) == len(inner_fns) + + # Scan with a single element is just a copy + if sizevars.is_expr_static_and_true(sympy.Le(scan_numel, 1)): # type: ignore[arg-type] + return [ + Pointwise.create( + device=device, + dtype=dtypes[output_index], + inner_fn=inner_fns[output_index], + ranges=size, + ) + for output_index in range(len(dtypes)) + ] + + reduction_hint, num_splits = cls.num_splits( + device=device, + dtype=dtypes[0], + inner_fn=inner_fns[0], + axis=axis, + pointwise_ranges=pointwise_ranges, + scan_ranges=scan_ranges, + combine_fn=combine_fn, + scan_numel=scan_numel, + ) + scan_type = Scan + if num_splits > 1: + supports_split = torch.version.hip is None and len(dtypes) == 1 + if not supports_split: + if can_fallback_to_aten: + # Fallback to ATen + return [None] * len(dtypes) + else: + num_splits = 1 + else: + scan_type = SplitScan + + def reindex(index, scan_index): + assert len(scan_index) == len(scan_ranges) + assert len(index) == len(pointwise_ranges) + return [*index[:axis], *scan_index, *index[axis:]] + + results = [ + TensorBox.create( + scan_type( + device=device, + dtype=dtypes[output_index], + dtypes=dtypes, + inner_fn=inner_fns[output_index], + inner_fns=inner_fns, + size=size, + ranges=pointwise_ranges, + scan_ranges=scan_ranges, + combine_fn=combine_fn, + reindex=reindex, + reduction_hint=reduction_hint, + output_index=output_index, + **kwargs, + ) + ) + for output_index in range(len(dtypes)) + ] + + for result in results: + result.realize() + + return results + + @classmethod + def num_splits( + cls, + device: torch.device, + dtype: torch.dtype, + inner_fn: Callable[[List[Expr]], Any], + axis: int, + pointwise_ranges: List[Expr], + scan_ranges: List[Expr], + combine_fn: Callable[[Tuple[Any, ...], Tuple[Any, ...]], Tuple[Any, ...]], + scan_numel: Expr, + ): + # TODO: custom splitting heuristic for scan + def wrapper_fn(idx, reduction_idx): + return inner_fn([*idx[:axis], *reduction_idx, *idx[axis:]]) + + return Reduction.num_splits( + device=device, + dst_dtype=dtype, + src_dtype=dtype, + inner_fn=wrapper_fn, + ranges=pointwise_ranges, + reduction_ranges=scan_ranges, + reduction_type="sum", + reduction_numel=scan_numel, + ) + + +# This signifies a scan op that should go through TritonSplitScanKernel codegen on CUDA. +@dataclasses.dataclass +class SplitScan(Scan): + pass + + +@dataclasses.dataclass +class Sort(Loops): + # Sorts a tuple of key, value pairs + sort_ranges: List[Expr] + size: List[Expr] + reindex: Callable[[List[Expr], List[Expr]], List[Expr]] + reduction_hint: ReductionHint + output_index: int + # output_index indexes the following tuples + dtypes: Tuple[torch.dtype, ...] + inner_fns: Tuple[Callable[..., Any], ...] + + stable: bool + descending: bool + + # HACK we mimick reduction + + def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: + return ( + super().get_unbacked_symbol_uses() + | OrderedSet().union(*(free_unbacked_symbols(e) for e in self.sort_ranges)) + | OrderedSet().union(*(free_unbacked_symbols(e) for e in self.size)) + ) + + def __post_init__(self): + assert len(self.ranges) + len(self.sort_ranges) == len(self.size) + super().__post_init__() + + def store_reduction(self, output_name, indexer, vars, sort_vars): + idx = self.reindex(vars, sort_vars) + values = [inner_fn(idx) for inner_fn in self.inner_fns] + result = ops.sort(self.dtypes, values, self.stable, self.descending) + return ops.store(output_name, indexer(idx), result[self.output_index]) + + def get_reduction_type(self): + return "sort" + + def get_reduction_size(self): + return self.sort_ranges + + def get_size(self): + return self.size + + def get_pointwise_size(self): + return self.ranges + + def index_length(self): + return len(self.ranges) + len(self.sort_ranges) + + def inner_fn_args(self): + index = self._index(self.ranges) + rindex = self._index(self.sort_ranges, SymT.RINDEX) + idx = self.reindex(index, rindex) + return (idx,) + + def inner_fn_free_unbacked_symbols(self): + index = self._index(self.ranges) + rindex = self._index(self.sort_ranges, SymT.RINDEX) + idx = self.reindex(index, rindex) + return extract_free_unbacked_symbols(self.inner_fn, idx) + + @classmethod + def create( + cls, + device: torch.device, + dtypes: Tuple[torch.dtype, ...], + inner_fns: Tuple[Callable[[List[Expr]], Any], ...], + size: List[Expr], + axis: int, + stable: bool, + descending: bool, + reduction_hint: ReductionHint = ReductionHint.DEFAULT, + **kwargs, + ) -> List[Optional[TensorBox]]: + pointwise_ranges = [*size[:axis], *size[axis + 1 :]] + sort_ranges = [size[axis]] + + if not V.graph.has_feature(device, BackendFeature.SORT): + return [None] * len(dtypes) + + sizevars = V.graph.sizevars + sort_numel = sizevars.simplify(sympy_product(sort_ranges)) + + # Heuristic, smallest rblock where triton usually outperforms aten.sort + # It also isn't bandwidth bound so fusion is unlikely to help. + max_rblock = 512 + is_persistent_kernel = ( + config.triton.persistent_reductions + and sizevars.is_expr_static_and_true(sympy.Le(sort_numel, max_rblock)) + ) + if not is_persistent_kernel: + # We only support persistent triton kernels + return [None] * len(dtypes) + + assert len(dtypes) == len(inner_fns) + + # Sort with a single element is just a copy + if sizevars.is_expr_static_and_true(sympy.Le(sort_numel, 1)): # type: ignore[arg-type] + return [ + Pointwise.create( + device=device, + dtype=dtypes[output_index], + inner_fn=inner_fns[output_index], + ranges=size, + ) + for output_index in range(len(dtypes)) + ] + + def reindex(index, sort_index): + assert len(sort_index) == len(sort_ranges) + assert len(index) == len(pointwise_ranges) + return [*index[:axis], *sort_index, *index[axis:]] + + results = [ + TensorBox.create( + Sort( + device=device, + dtype=dtypes[output_index], + dtypes=dtypes, + inner_fn=inner_fns[output_index], + inner_fns=inner_fns, + size=size, + ranges=pointwise_ranges, + sort_ranges=sort_ranges, + reindex=reindex, + reduction_hint=reduction_hint, + output_index=output_index, + stable=stable, + descending=descending, + **kwargs, + ) + ) + for output_index in range(len(dtypes)) + ] + + for result in results: + result.realize() + + return results + + +def is_storage_and_layout(x: IRNode) -> bool: + try: + as_storage_and_layout(x, freeze=False) + return True + except NotImplementedError: + return False + + +def is_contiguous_storage_and_layout(x: IRNode) -> bool: + try: + buffer, layout = as_storage_and_layout(x, freeze=False) + # pad the stride here so we will NOT claim an tensor as contiguous + # if a padding is gonna happen. + if layout.should_pad_strides(): + layout.pad_strides() + return layout.is_contiguous() + except NotImplementedError: + return False + + +def as_storage_and_layout( + x: IRNode, + freeze: bool = True, + want_contiguous: bool = False, + stride_order: Optional[Sequence[Union[int, Integer]]] = None, + allow_padding: bool = False, + exact_strides: Optional[Sequence[Union[int, Integer]]] = None, +) -> Tuple[StorageBox, Layout]: + """ + Try to simplify x into a StorageBox and a Layout. + + allow_padding only affect how we apply stride_order. When allow_padding + is True, we have the freedom to add padding when applying the stride_order. + """ + if isinstance(x, TensorBox): + return as_storage_and_layout( + x.data, + freeze=freeze, + want_contiguous=want_contiguous, + stride_order=stride_order, + allow_padding=allow_padding, + exact_strides=exact_strides, + ) + if isinstance(x, StorageBox) and isinstance(x.data, Buffer): + if freeze: + if want_contiguous: + x.data.freeze_layout() + assert x.data.layout.is_contiguous() + elif stride_order is not None: + x.data.freeze_layout_with_stride_order( + stride_order, allow_padding=allow_padding + ) + elif exact_strides is not None: + x.data.freeze_layout_with_exact_strides( + exact_strides, allow_padding=allow_padding + ) + else: + x.data.decide_layout() + return x, x.data.layout + if isinstance(x, ReinterpretView): + # making the base of x contiguous or stride_ordered will not necessarily make + # the ReinterpretView either, so don't pass along those arguments + buffer, _ = as_storage_and_layout( + x.data, + freeze=freeze, + ) + return buffer, x.layout + raise NotImplementedError + + +as_contiguous_storage_and_layout = functools.partial( + as_storage_and_layout, want_contiguous=True +) + + +def is_stride_order_storage_and_layout( + x: IRNode, stride_order: Sequence[Union[int, Integer]] +) -> bool: + try: + buffer, layout = as_storage_and_layout(x, freeze=False) + return layout.is_stride_ordered(stride_order) + except NotImplementedError: + return False + + +@dataclasses.dataclass +class BaseView(IRNode): + data: IRNode + + def get_unbacked_symbol_uses(self): + return self.data.get_unbacked_symbol_uses() + + def make_reindexer(self): + raise NotImplementedError(f"make_reindexer NYI on {self}") + + def make_indexer(self): + inner = self.data.make_indexer() + reindex = self.make_reindexer() + + def indexer(idx): + return inner(reindex(idx)) + + return indexer + + def make_loader(self): + inner = self.data.make_loader() + reindex = self.make_reindexer() + + def loader(idx): + return inner(reindex(idx)) + + return loader + + @property + def dtype(self): + return self.data.dtype + + def get_layout(self): + return self.data.get_layout() + + def get_device(self): + return self.data.get_device() + + def get_origin_node(self): + return None + + def get_name(self): + return self.data.get_name() + + def get_pointwise_size(self): + return self.get_size() + + def mark_reuse(self, users): + return self.data.mark_reuse(users) + + def has_exceeded_max_reads(self): + return self.data.has_exceeded_max_reads() + + def realize(self): + return self.data.realize() + + def realize_hint(self): + return self.data.realize_hint() + + def get_storage_numel(self): + return self.data.get_storage_numel() + + def is_extern(self): + return self.data.is_extern() # type: ignore[attr-defined] + + def is_module_buffer(self): + return self.data.is_module_buffer() # type: ignore[attr-defined] + + def get_read_names(self) -> OrderedSet[str]: + return self.data.get_read_names() + + def get_reads(self): + with patch.object(FlexibleLayout, "allow_indexing", True): + return extract_read_writes( + self.make_loader(), + self.get_size(), + ).reads + + def unwrap_view(self): + x: IRNode = self + while isinstance(x, BaseView): + x = x.data + return x + + def constant_to_device(self, device): + """Move this to a given device. Requires that all reads are to constants.""" + loader = self.make_loader() + loader = patch.object(ConstantBuffer, "override_device", device)(loader) + return Pointwise(device, self.get_dtype(), loader, self.get_size()) + + +@dataclasses.dataclass +class ExpandView(BaseView): + size: List[Expr] + + @staticmethod + def _normalize_size(x, new_size): + """Replace `-1` with correct sizes""" + sizevars = V.graph.sizevars + new_size = list(map(sympy.expand, new_size)) + old_size = x.get_size() + old_size = [None] * (len(new_size) - len(old_size)) + list(old_size) + assert len(new_size) == len(old_size) + for i in range(len(new_size)): + if new_size[i] == -1: + assert old_size[i] is not None + new_size[i] = old_size[i] + elif old_size[i] is None or old_size[i] == 1: + pass + else: + # Sanity check: Expect broadcast compatibility + # + # NB: new_size[i] == old_size[i] is expected to already be + # guarded because the meta formula was expected to have taught + # us this equality. + assert ( + sizevars.size_hint(new_size[i] - old_size[i], fallback=0) == 0 + ), "Broadcast failed in ExpandView({x.get_size()}, {new_size}) on dimension {i}" + return new_size + + @classmethod + def create(cls, x, new_size): + new_size = cls._normalize_size(x, new_size) + + if is_storage_and_layout(x): + storage, old_layout = as_storage_and_layout(x) + skip = len(new_size) - len(old_layout.size) + assert skip >= 0 + new_stride = [sympy.Integer(0)] * skip + for stride, size in zip(old_layout.stride, old_layout.size): + new_stride.append(stride if size != 1 else sympy.Integer(0)) + new_layout = FixedLayout( + old_layout.device, + old_layout.dtype, + list(new_size), + new_stride, + old_layout.offset, + ) + return ReinterpretView(storage, new_layout) + + return ExpandView(x, new_size) + + def get_size(self): + return self.size + + def make_reindexer(self): + target = self.get_size() + actual = self.data.get_size() + skip = len(target) - len(actual) + + def reindex(index): + index = list(index[skip:]) + assert len(index) == len(actual) + for i in range(len(actual)): + if actual[i] == 1: + # zero out broadcast dimension + index[i] = sympy.Integer(0) + return index + + return reindex + + +@dataclasses.dataclass +class PermuteView(BaseView): + dims: List[Expr] + + @classmethod + def create(cls, x, dims): + dims = cls._map_neg_dims(dims) + assert OrderedSet(dims) == OrderedSet(range(len(dims))) + + if is_storage_and_layout(x): + storage, old_layout = as_storage_and_layout(x) + new_layout = FixedLayout( + old_layout.device, + old_layout.dtype, + [old_layout.size[i] for i in dims], + [old_layout.stride[i] for i in dims], + old_layout.offset, + ) + return ReinterpretView(storage, new_layout) + + return PermuteView(x, dims) + + @classmethod + def _map_neg_dims(cls, dims): + return [dim if dim >= 0 else len(dims) + dim for dim in dims] + + def get_size(self): + assert OrderedSet(self._map_neg_dims(self.dims)) == OrderedSet( + range(len(self.dims)) + ) + size = self.data.get_size() + return [size[i] for i in self.dims] + + def make_reindexer(self): + inv = {j: i for i, j in enumerate(self.dims)} + inv = [inv[i] for i in range(len(self.dims))] # type: ignore[index] + assert OrderedSet(inv) == OrderedSet(range(len(self.dims))) + + def reindex(index): + return [index[i] for i in inv] + + return reindex + + +class SqueezeView(BaseView): + @classmethod + def create(cls, x, *, dim=None): + if is_storage_and_layout(x): + storage, old_layout = as_storage_and_layout(x) + new_size = [] + new_stride = [] + if dim is not None: + assert isinstance(dim, int), "expected integer dim argument" + assert 0 <= dim and dim < len(old_layout.size) + + for i, (size, stride) in enumerate(zip(old_layout.size, old_layout.stride)): + if dim is None: + if size != 1: + new_size.append(size) + new_stride.append(stride) + else: + if i != dim: + new_size.append(size) + new_stride.append(stride) + else: + assert size == 1, "expected squeezed size to be 1" + + new_layout = FixedLayout( + old_layout.device, + old_layout.dtype, + new_size, + new_stride, + old_layout.offset, + ) + return ReinterpretView(storage, new_layout) + + if dim is None: + # redirect to a generic view + return View.create(x, [s for s in x.get_size() if s != 1]) + else: + assert x.get_size()[dim] == 1 + return View.create(x, [s for i, s in enumerate(x.get_size()) if i != dim]) + + @staticmethod + def squeezer(size: Tuple[sympy.Expr, ...]): + new_size = [s for s in size if s != 1] + not_one = [i for i, s in enumerate(size) if s != 1] + length = len(size) + + def reindex(index: List[sympy.Expr]) -> Tuple[sympy.Expr, ...]: + assert len(index) == len(not_one), f"{index} {not_one}" + new_index = [sympy.Integer(0)] * length + for idx, s in zip(not_one, index): + new_index[idx] = s + return tuple(new_index) + + return new_size, reindex + + def __init__(self, data): + raise AssertionError("use SqueezeView.create()") + + +@dataclasses.dataclass +class GenericView(BaseView): + size: List[Expr] + reindex: Callable[..., Any] + + def make_reindexer(self): + return self.reindex + + def reindex_str(self): + index_old = [ + sympy_index_symbol_with_prefix(SymT.INDEX, n) for n in range(len(self.size)) + ] + index_new = list(self.reindex(index_old)) + return f"lambda {', '.join(map(str, index_old))}: {index_new}" + + def __str__(self) -> str: + return self.str_helper( + [self.data, f"size={self.size}", f"reindex={self.reindex_str()}"] + ) + + __repr__ = __str__ + + @classmethod + def create(cls, x, new_size, reindex): + return cls(x, list(new_size), reindex) + + def get_size(self): + return self.size + + +@dataclasses.dataclass +class View(GenericView): + @staticmethod + def handle_negative_index(idx, size): + idx = sympy.expand(idx) + size = sympy.expand(size) + evaluate_expr = V.graph.sizevars.shape_env.evaluate_expr + if evaluate_expr(sympy.Lt(idx, 0)): + idx = idx + size + return idx + + @classmethod + def create(cls, x, new_size): + assert isinstance(new_size, (tuple, list)) + old_size, new_size = cls.resolve_negative_size(x.get_size(), new_size) + + # Skip pointless views + if V.graph.sizevars.statically_known_list_equals(old_size, new_size): + return x + + unbacked_symbols_in_sizes = False + if ( + len(free_unbacked_symbols(old_size)) > 0 + or len(free_unbacked_symbols(new_size)) > 0 + ): + unbacked_symbols_in_sizes = True + + if 0 in new_size: + + def fake_reindex(index): + return tuple([0] * len(old_size)) + + return cls(x, list(new_size), fake_reindex) + # TODO: a new class for FixedTransferLayout that output layout is constrained by input layout + elif is_contiguous_storage_and_layout(x) or unbacked_symbols_in_sizes: + if unbacked_symbols_in_sizes and (not is_contiguous_storage_and_layout(x)): + # realize x; otherwise, the dynamic_reshape_indexer below will fail + # due to the size_hint's inability to process unbacked SymInts + x = ExternKernel.realize_input(x) + + storage, old_layout = as_contiguous_storage_and_layout(x) + new_layout = FixedLayout( + old_layout.device, + old_layout.dtype, + new_size, + FlexibleLayout.contiguous_strides(new_size), + old_layout.offset, + ) + return ReinterpretView(storage, new_layout) + + reindex = cls.dynamic_reshape_indexer(old_size, new_size) + return cls(x, list(new_size), reindex) + + @staticmethod + def resolve_negative_size(old_size, new_size): + new_size = [V.graph.sizevars.simplify(x) for x in new_size] + old_size = [V.graph.sizevars.simplify(x) for x in old_size] + + new_size = list(new_size) + for i in range(len(new_size)): + if new_size[i] == -1: + new_size[i] = sympy.Integer(1) + new_size[i] = CleanDiv(sympy_product(old_size), sympy_product(new_size)) + break + + V.graph.sizevars.guard_equals(sympy_product(old_size), sympy_product(new_size)) + return old_size, new_size + + @classmethod + def dynamic_reshape_indexer(cls, old_size, new_size): + try: + reindex = cls._dynamic_reshape_indexer(old_size, new_size) + except (AssertionError, IndexError): + # optimistic algorithm failed, lets do a fallback + flat = [sympy_product(old_size)] + reindex1 = cls._dynamic_reshape_indexer(old_size, flat) + reindex2 = cls._dynamic_reshape_indexer(flat, new_size) + reindex = fuse_reindexing(reindex1, reindex2) + return reindex + + @staticmethod + def _dynamic_reshape_indexer(old_size, new_size): + """ + Perform a reshape entirely by modifying indexing math + """ + size_hint = V.graph.sizevars.size_hint + # TODO: These symbols may not escape, if they don't assert so and + # treat them as temporary + vars = [ + sympy_index_symbol_with_prefix(SymT.VIEW, i) for i in range(len(new_size)) + ] + + stack_new = list(zip(vars, new_size)) + stack_old = list(old_size) + + view_expr = [] + while stack_new and stack_old: + size_old = stack_old.pop() + var, size_new = stack_new.pop() + if size_old == 1: + view_expr.append(sympy.Integer(0)) + stack_new.append((var, size_new)) # re-add + elif size_new == 1: + stack_old.append(size_old) # re-add + elif size_hint(size_new) == size_hint(size_old): + view_expr.append(var) + V.graph.sizevars.guard_equals(size_new, size_old) + elif size_hint(size_new) < size_hint(size_old): + while size_hint(size_new) < size_hint(size_old): + var2, size_new2 = stack_new.pop() + var = var2 * size_new + var + size_new = size_new * size_new2 + view_expr.append(var) + V.graph.sizevars.guard_equals(size_new, size_old) + elif size_hint(size_new) > size_hint(size_old): + divisor = sympy.Integer(1) + modulus = size_old + view_expr.append(ModularIndexing(var, divisor, modulus)) + divisor = divisor * modulus + while size_hint(size_new) > size_hint(size_old): + modulus = stack_old.pop() + view_expr.append(ModularIndexing(var, divisor, modulus)) + divisor = divisor * modulus + size_old = size_old * modulus + V.graph.sizevars.guard_equals(size_new, size_old) + else: + raise AssertionError + + while stack_old: + size_old = stack_old.pop() + V.graph.sizevars.guard_equals(size_old, 1) # type: ignore[arg-type] + view_expr.append(sympy.Integer(0)) + + while stack_new: + var, size_new = stack_new.pop() + V.graph.sizevars.guard_equals(size_new, 1) # type: ignore[arg-type] + + view_expr.reverse() + assert len(view_expr) == len(old_size) + + def reindex(index): + assert len(index) == len(vars), (len(index), len(vars)) + replacements = dict(zip(vars, index)) + return tuple(sympy_subs(x, replacements) for x in view_expr) # type: ignore[arg-type] + + return reindex + + +@dataclasses.dataclass +class ReinterpretView(BaseView): + """Pretend our storage has a different layout""" + + layout: Layout + + def __post_init__(self): + super().__post_init__() + if isinstance(self.data, BaseView): + self.data = self.data.unwrap_view() + + def __str__(self) -> str: + return self.str_helper( + [ + self.data, + self.layout, + ] + ) + + __repr__ = __str__ + + def get_name(self): + return self.data.get_name() + + def get_device(self): + return self.layout.device + + def get_origin_node(self): + return None + + @property + def dtype(self): + return self.layout.dtype + + def get_size(self): + return list(self.layout.size) + + def get_stride(self): + return list(self.layout.stride) + + def make_loader(self): + def loader(index): + indexer = self.layout.make_indexer() + tmp_loader = ops.load(self.get_name(), indexer(index)) + if self.layout.dtype != self.data.dtype: + return ops.to_dtype_bitcast(tmp_loader, self.dtype, self.data.dtype) + else: + return tmp_loader + + return loader + + def make_indexer(self): + return self.layout.make_indexer() + + def get_layout(self): + return self.layout + + def freeze_layout(self): + pass + + def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: + return ( + free_unbacked_symbols(self.layout.size) + | free_unbacked_symbols(self.layout.stride) + | free_unbacked_symbols(self.layout.offset) + ) + + def codegen_reference(self, writer=None): + # reinterpret_tensor is similar to as_strided except: + # - offset is added to the existing offset (rather than replacing it) + # - view tracking is disabled similar to unsafe_view + return V.graph.wrapper_code.codegen_reinterpret_view( + self.data, + self.layout.size, + self.layout.stride, + self.layout.offset, + writer, + dtype=self.layout.dtype, + ) + + def num_reads(self): + return 1 + + +@dataclasses.dataclass +class DtypeView(BaseView): + """Pretend our storage has a different type""" + + target_dtype: torch.dtype + + @classmethod + def create(cls, x, new_dtype): + if is_storage_and_layout(x): + storage, old_layout = as_storage_and_layout(x) + new_layout = FixedLayout( + old_layout.device, + new_dtype, + old_layout.size, + old_layout.stride, + old_layout.offset, + ) + return ReinterpretView(storage, new_layout) + return DtypeView(x, new_dtype) + + def __str__(self) -> str: + return self.str_helper([self.data, self.target_dtype]) + + __repr__ = __str__ + + @property + def dtype(self): + return self.target_dtype + + def get_size(self): + return self.data.get_size() + + def make_loader(self): + inner = self.data.make_loader() + + def loader(idx): + return ops.to_dtype_bitcast(inner(idx), self.target_dtype, self.data.dtype) + + return loader + + +class SliceView(View): + @classmethod + def normalize_start_end(cls, x, dim, start, end): + """ + Normalize start and end such that both are in the range + [0, x.get_size()[dim]] and start <= end. + """ + sizevars = V.graph.sizevars + dim_size = x.get_size()[dim] + + if any(free_unbacked_symbols(x) for x in (start, end, dim_size)): + + def clamp(x, lower, upper): + return sympy.Min(sympy.Max(x, lower), upper) + + else: + + def clamp(x, lower, upper): + return sizevars.evaluate_min(sizevars.evaluate_max(x, lower), upper) + + def clamp_wrap(val, lower, upper, default): + if val is None: + return default + val = cls.handle_negative_index(val, dim_size) + return clamp(val, lower, upper) + + start = clamp_wrap(start, 0, dim_size, 0) + end = clamp_wrap(end, start, dim_size, dim_size) + return start, end + + @classmethod + def create(cls, x, dim, start, end, step=1, clamp=True): + step = sympy.expand(step) + assert isinstance(step, sympy.Expr) or step > 0 + try: + if start == 0 and end >= 2**63 - 1 and step == 1: + return x + except TypeError: + pass + + sizevars = V.graph.sizevars + new_size = list(x.get_size()) + + # NB: Ordinarily we default to clamping. + # We only don't clamp for split_with_sizes. For split_with_sizes, sizes should be already valid + # failing in this situation is ok, since invalid sizes could trigger silent errors. + if clamp: + start, end = cls.normalize_start_end(x, dim, start, end) + + new_size[dim] = FloorDiv(end - start + (step - 1), step) + + if is_storage_and_layout(x): + # Fast path + storage, old_layout = as_storage_and_layout(x) + new_stride = list(old_layout.stride) + new_stride[dim] = new_stride[dim] * step + new_layout = FixedLayout( + old_layout.device, + old_layout.dtype, + new_size, + new_stride, + old_layout.offset + old_layout.stride[dim] * start, + ) + return ReinterpretView(storage, new_layout) + + def reindex(index): + assert len(index) == len(new_size), f"wrong ndim {index} {new_size}" + index = list(index) + index[dim] = index[dim] * step + start + return index + + # redirect to a generic view + return SliceView(x, size=new_size, reindex=reindex) + + +class BaseConstant(IRNode): + dtype: torch.dtype + device: torch.device + + def get_size(self): + return () + + def get_device(self): + return self.device + + def get_origin_node(self): + return None + + def mark_reuse(self, users): + pass + + def has_exceeded_max_reads(self): + return False + + def get_reads(self): + return () + + def is_extern(self): + return False + + +@dataclasses.dataclass +class Constant(BaseConstant): + value: Any + dtype: torch.dtype + device: torch.device + + def make_loader(self): + def loader(index): + return ops.constant(self.value, self.dtype) + + return loader + + def realize(self): + pass + + def constant_to_device(self, device): + return Constant(self.value, self.dtype, device) + + +@dataclasses.dataclass +class IndexingConstant(BaseConstant): + index: Any + dtype: torch.dtype + device: torch.device + + def make_loader(self): + def loader(index): + return ops.index_expr(self.index, self.dtype) + + return loader + + def constant_to_device(self, device): + return IndexingConstant(self.index, self.dtype, device) + + +def is_contiguous_strides_for_shape( + stride: Sequence[_IntLike], shape: Sequence[_IntLike] +) -> bool: + return all( + size == 1 or left == right + for left, right, size in zip( + stride, FlexibleLayout.contiguous_strides(shape), shape + ) + ) + + +def get_align_for_dtype(dtype: torch.dtype) -> int: + return config.padding_alignment_bytes // dtype.itemsize + + +@dataclasses.dataclass +class Layout(IRNode): + def __init__( + self, + device: torch.device, + dtype: torch.dtype, + size: List[Expr], + stride: Optional[Sequence[Union[Expr, int]]], + offset: Expr = Integer(0), + ): + assert stride is None or len(size) == len( + stride + ), f"size={size}, stride={stride}" + self.device = device + self.dtype = dtype + assert all(isinstance(s, (Expr, int)) for s in size) + self.size = size + self._stride = stride + self.offset = offset + + @property + def stride(self): + return self._stride + + def __str__(self) -> str: + offset = "" + if self.offset != 0: + offset = f", offset={self.offset}" + return ( + f"{type(self).__name__}('{self.device.type}', {self.dtype}, " + f"size={self.size}, stride={self.stride}{offset})" + ) + + __repr__ = __str__ + + def is_contiguous(self): + return is_contiguous_strides_for_shape(self.stride, self.size) + + @staticmethod + def is_channels_last_contiguous(shape, strides): + ndim = len(shape) + if ndim not in [4, 5] or shape[1] == 1: + return False + for left, right, size in zip( + strides, make_channels_last_strides_for(shape), shape # type: ignore[arg-type] + ): + if size != 1 and left != right: + return False + return True + + def is_transposed(self): + for left, right, size in zip( + self.stride, + reversed(FlexibleLayout.contiguous_strides(list(reversed(self.size)))), + self.size, + ): + if size != 1 and left != right: + return False + return True + + def is_stride_ordered(self, order): + assert len(self.stride) == len(order) + + # ignore dimensions of size 1, they dont affect layout + non_1_indices = [ + i + for i, dim in enumerate(self.size) + if V.graph.sizevars.size_hint(dim, fallback=2) != 1 + ] + + stride = [self.stride[i] for i in non_1_indices] + order = [order[i] for i in non_1_indices] + + def sorted_indices(arr): + sorted_arr = sorted(arr) + return [sorted_arr.index(element) for element in arr] + + # since we may have removed dimensions, need to re-sort & re-index order + order = sorted_indices(order) + + # reorder the stride given order + stride_ordered = [-1] * len(order) + for i in range(len(order)): + stride_ordered[order[i]] = V.graph.sizevars.size_hint(stride[i]) + # check if it is in ascending order + for i in range(len(order) - 1): + if stride_ordered[i] > stride_ordered[i + 1]: + return False + return True + + def is_channels_last_stride_ordered(self): + # create channels_last order(NCHW, NCDHW, the C is the first order). + order = [0] + list(reversed(range(1, len(self.stride) - 1))) + order = [len(order)] + order + return self.is_stride_ordered(order) + + @staticmethod + def _pad_strides(in_strides, size, dtype): + """ + The padding does not change stride order but makes sure all strides larger + than the threshold are multiple of align. + """ + align = get_align_for_dtype(dtype) + if len(in_strides) == 0: + return in_strides + + if not config.pad_channels_last and Layout.is_channels_last_contiguous( + size, in_strides + ): + return in_strides + + current_fx_node = V.get_current_node() + if hasattr(current_fx_node, "meta") and current_fx_node.meta.get( + "dislike_padding", False + ): + return in_strides + + # get_stride_order does not work with dynamic shape. Also we can not + # statically decide if a padding is needed or how much padding we should + # do for dynamic shape. + # + # Skip padding the strides for dynamic shape for now. + if not all( + isinstance(s, (int, sympy.Integer)) + for s in itertools.chain(in_strides, size) + ): + return in_strides + + stride_order = get_stride_order(in_strides) + fill_order = stride_order2fill_order(stride_order) + + new_strides = [0 for _ in range(len(in_strides))] + # since we pad when the layout is flexible, we can decide the + # smallest stride to be 1. + new_strides[fill_order[0]] = 1 + + padded = False + for rank, idx in enumerate(fill_order[1:], start=1): + prev_idx = fill_order[rank - 1] + stride = new_strides[prev_idx] * size[prev_idx] + + if stride > config.padding_stride_threshold and stride % align != 0: + stride = ceildiv(stride, align) * align + padded = True + new_strides[idx] = stride + + if not padded: + # Consider a tensor with shape [256, 1, 5, 5] + # Avoid strides like [25, 5, 5, 1] being padded to equivalent strides + # [25, 25, 5, 1]. + return in_strides + + metrics.num_comprehensive_padding += 1 + return new_strides + + def pad_strides(self): + assert isinstance(self, FlexibleLayout) + assert self._stride is not None + self._stride = self._pad_strides(self._stride, self.size, self.dtype) + + def should_pad_strides(self): + return config.comprehensive_padding and isinstance(self, FlexibleLayout) + + def as_fixed(self): + if isinstance(self, FixedLayout): + return self + + if self.should_pad_strides(): + self.pad_strides() + return FixedLayout( + self.device, + self.dtype, + self.size, + self.stride, + self.offset, + ) + + def make_indexer(self): + assert ( + FlexibleLayout.allow_indexing + ), f"convert {type(self).__name__} to FixedLayout first" + return self.as_fixed().make_indexer() + + def __eq__(self, other) -> bool: + return ( + self.device == other.device + and self.dtype == other.dtype + and self.size == other.size + and self.stride == other.stride + and self.offset == other.offset + ) + + def storage_size(self) -> sympy.Expr: + return compute_required_storage_length(self.size, self.stride, self.offset) # type: ignore[arg-type, return-value] + + +class FixedLayout(Layout): + """A Tensor layout we cannot change""" + + def __init__( + self, + device: torch.device, + dtype: torch.dtype, + size: Union[List[Expr], List[int]], + stride: Optional[Sequence[Union[Expr, int]]] = None, + offset: Union[Expr, int] = Integer(0), + ): + if stride is None: + stride = FlexibleLayout.contiguous_strides(size) + super().__init__( + device, + dtype, + size, # type: ignore[arg-type] + stride, + offset, # type: ignore[arg-type] + ) + + def make_indexer(self): + """A closure containing math to read a given element""" + + def indexer(index): + assert len(index) == len(self.stride) + assert len(index) == len(self.size) + result = self.offset + for idx, stride, sz in zip(index, self.stride, self.size): + if sz != 1: + result = result + idx * stride + return result + + return indexer + + +class FlexibleLayout(Layout): + """A Tensor layout we are allowed to change""" + + allow_indexing = False + + # WARNING! This doesn't handle zero size tensors correctly + @staticmethod + def contiguous_strides(sizes): + if len(sizes) == 0: + return [] + reversed_strides = [sympy.Integer(1)] + for size in reversed(sizes[1:]): + reversed_strides.append(size * reversed_strides[-1]) + return list(reversed(reversed_strides)) + + @staticmethod + def fill_ordered(sizes, order): + """ + Create a stride based on the order the dimensions should be filled in. + + In this format, channels last would be: + [1, 3, 2, 0] + """ + assert OrderedSet(range(len(sizes))) == OrderedSet(order), (sizes, order) + next_stride = sympy.Integer(1) + strides = [None] * len(order) + + for i in order: + strides[i] = next_stride + next_stride = next_stride * sizes[i] + return strides + + @staticmethod + def stride_ordered(sizes, order): + """ + Create a stride based on the sorted order of a permuted range. + + In this format, channels last would be: + [3, 0, 2, 1] + """ + assert OrderedSet(range(len(sizes))) == OrderedSet(order) + fill_order = stride_order2fill_order(order) + return FlexibleLayout.fill_ordered(sizes, fill_order) + + @staticmethod + def stride_ordered_for_memory_format(sizes, memory_format): + """ + Create a stride based on a memory format. + + Memory format is translasted into a stride order, + so channels_last is the same as: + FlexibleLayout.stride_ordered(sizes, [3, 0, 2, 1]) + + This interface does not support memory_format `torch.preserve_format` + which should be used to deduce a format from another source + """ + if memory_format == torch.channels_last: + return FlexibleLayout.stride_ordered(sizes, NHWC_STRIDE_ORDER) + elif memory_format == torch.channels_last_3d: + return FlexibleLayout.stride_ordered(sizes, NHWDC_STRIDE_ORDER) + elif memory_format == torch.contiguous_format: + return FlexibleLayout.contiguous_strides(sizes) + else: + log.debug( + "stride_ordered_for_memory_format, unsuppored memory_format: %s", + memory_format, + ) + raise NotImplementedError + + @staticmethod + def same_ordered(sizes, stride): + """ + Create a stride that has the same stride order as given stride + + For example, if given stride is [1000, 1, 100, 10], + the fill order should be [1, 3, 2, 0] + """ + assert len(sizes) == len(stride) + stride = [V.graph.sizevars.size_hint(x) for x in stride] + fill_order = sorted(range(len(stride)), key=stride.__getitem__) + return FlexibleLayout.fill_ordered(sizes, fill_order) + + def as_stride_order(self, order, allow_padding=False): + new_stride = self.stride_ordered(self.size, order) + if self.should_pad_strides() and allow_padding: + new_stride = self._pad_strides(new_stride, self.size, self.dtype) + + return FixedLayout( + self.device, + self.dtype, + self.size, + new_stride, + self.offset, + ) + + def as_exact_strides(self, exact_strides, allow_padding=False): + new_stride = exact_strides + if self.should_pad_strides() and allow_padding: + new_stride = self._pad_strides(new_stride, self.size, self.dtype) + + return FixedLayout( + self.device, + self.dtype, + self.size, + new_stride, + self.offset, + ) + + def as_fill_order(self, order): + new_stride = self.fill_ordered(self.size, order) + if self.should_pad_strides(): + new_stride = self._pad_strides(new_stride, self.size, self.dtype) + return FixedLayout( + self.device, + self.dtype, + self.size, + new_stride, + self.offset, + ) + + def as_same_order(self, stride): + new_stride = self.same_ordered(self.size, stride) + if self.should_pad_strides(): + new_stride = self._pad_strides(new_stride, self.size, self.dtype) + return FixedLayout( + self.device, + self.dtype, + self.size, + new_stride, + self.offset, + ) + + def __init__(self, device, dtype, size, stride_order=None): + if stride_order: + strides = FlexibleLayout.fill_ordered(size, stride_order) + else: + strides = FlexibleLayout.contiguous_strides(size) + super().__init__(device, dtype, size, strides) + + +class NonOwningLayout(Layout): + """Is a view into the storage of another tensor""" + + def __init__(self, view: Union[BaseView, TensorBox]): + layout = view.get_layout() + super().__init__( + layout.device, + layout.dtype, + layout.size, + layout.stride, + ) + self.view = view + + def make_indexer(self): + return self.as_fixed().make_indexer() + + def maybe_guard_aligned(self): + offset = self.view.get_layout().offset + if offset == 0: + return True + from .utils import ALIGNMENT + + return V.graph.sizevars.statically_known_multiple_of(offset, ALIGNMENT) # type: ignore[arg-type] + + +class NoneLayout(IRNode): + # This is janky, I figured out what fields to populate by just running + # the model I was interested in and adding properties/methods as needed. + # This doesn't inherit from Layout because Layout assumes you have stuff + # like sizes, but I don't really have anything here. + # + # If you have an ir.Node with NoneLayout, you probably need to setup + # dependencies manually in scheduler + + def __init__(self, device): + self.device = device + self.size = [0] + self.stride = [0] + + def storage_size(self): + return 0 + + def as_fixed(self): + return self + + +class MutationLayoutSHOULDREMOVE(Layout): + def __init__(self, target: IRNode): + super().__init__( + target.get_device(), + target.get_dtype(), + target.get_size(), + None, + ) + self.target = target + name = self.get_buffer().get_name() + V.graph.mark_buffer_mutated(name) + + @Layout.stride.getter # type: ignore[attr-defined] + def stride(self): + return self.real_layout().stride + + def storage_size(self) -> sympy.Expr: + return self.real_layout().storage_size() + + def get_buffer(self) -> Buffer: + def unwrap_views(target): + if isinstance(target, MutationLayoutSHOULDREMOVE): + return unwrap_views(target.target) + if isinstance(target, BaseView): + return unwrap_views(target.unwrap_view()) + if isinstance(target, MutableBox): + return unwrap_views(target.data) + return target + + result = unwrap_views(self.target) + assert isinstance( + result, Buffer + ), "MutationLayoutSHOULDREMOVE must refer to a buffer" + return result + + def real_layout(self): + return self.get_buffer().layout + + @classmethod + def realize_into(cls, src, dst, unsafe_alias=False): + dst.realize() + # NOTE: We must realize users of `dst` before we realize `src`, since + # realization order determines scheduling order. Otherwise, src's + # mutation would be scheduled before the existing users of dst! + V.graph.mark_buffer_mutated(dst.get_name()) + + if isinstance(src, TensorBox): + src = src.data + + # We copy the contents of src into dst. In most cases this should + # be fused into a single kernel by the scheduler. + # NOTE: We cannot change src's layout to mutate dst directly as this + # would alias src to dst, which is not correct as further mutations to + # dst would effect users of src. However if there are no more users of + # dst, we can alias src to dst. + src.realize_hint() + + if not unsafe_alias: + src = Pointwise.create( + device=src.get_device(), + dtype=src.get_dtype(), + inner_fn=src.make_loader(), + ranges=[ + V.graph.sizevars.guard_equals(a, b) + for a, b in zip(src.get_size(), dst.get_size()) + ], + ).data + + src.realize() + assert isinstance(src.data.layout, FlexibleLayout) + src.data.layout = MutationLayoutSHOULDREMOVE(dst) + return src.data + + def as_fixed(self): + return self + + def make_indexer(self): + return self.target.make_indexer() + + +@dataclasses.dataclass +class Buffer(IRNode): + # Name is sometimes None; e.g., ForceInPlace, where there isn't + # a meaningful name + name: Optional[str] + layout: Layout + + # Multi-output buffers will define 'outputs: List[Buffer]'. Confusingly, + # MultiOutput does NOT define this! + + def __post_init__(self): + super().__post_init__() + self.origin_node = None + + def make_indexer(self): + return self.layout.make_indexer() + + def get_name(self) -> str: + assert self.name, self + return self.name + + def get_device(self): + return self.layout.device + + def get_origin_node(self): + return self.origin_node + + def get_defining_op(self) -> Optional[Operation]: + return None + + @property + def dtype(self): + return getattr(self.layout, "dtype", None) + + def get_size(self): + return list(self.layout.size) + + def get_stride(self): + return list(self.layout.stride) + + def get_offset(self): + return self.layout.offset + + def get_layout(self): + return self.layout + + def get_storage_numel(self): + return self.get_numel() + + def is_extern(self): + return False + + def freeze_layout(self): + if not isinstance(self.layout, (MultiOutputLayout, NonOwningLayout)): + self.layout = self.layout.as_fixed() + + def freeze_layout_with_stride_order(self, order, allow_padding=False): + assert isinstance(self.layout, FlexibleLayout) + self.layout = self.layout.as_stride_order(order, allow_padding=allow_padding) + + def freeze_layout_with_fill_order(self, order): + assert isinstance(self.layout, FlexibleLayout) + self.layout = self.layout.as_fill_order(order) + + def freeze_layout_with_same_order(self, stride): + assert isinstance(self.layout, FlexibleLayout) + self.layout = self.layout.as_same_order(stride) + + def freeze_layout_with_exact_strides(self, exact_strides, allow_padding=False): + assert isinstance(self.layout, FlexibleLayout) + self.layout = self.layout.as_exact_strides( + exact_strides, allow_padding=allow_padding + ) + + def is_zero_elements(self): + return V.graph.sizevars.is_expr_static_and_true(sympy.Eq(self.get_numel(), 0)) # type: ignore[arg-type] + + def make_loader(self): + # Loading from a zero-element buffer is a no-op + if self.is_zero_elements(): + return partial(nop_loader_fn, dtype=self.get_dtype()) + + def loader(index): + indexer = self.layout.make_indexer() + return ops.load(self.name, indexer(index)) + + return loader + + def codegen_reference(self, writer=None): + return self.get_name() + + def decide_layout(self): + pass + + def get_inputs_that_alias_output(self): + if isinstance(self.layout, NonOwningLayout): + return [self.layout.view.get_name()] + return () + + def get_mutation_names(self): + if isinstance(self.layout, MutationLayoutSHOULDREMOVE): + return [self.layout.target.get_name()] + return () + + def get_read_names(self) -> OrderedSet[str]: + return OrderedSet([self.get_name()]) + + def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: + return OrderedSet() + + def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: + return OrderedSet() + + def realize(self): + pass + + def should_allocate(self): + # Returns False by default. + return False + + +@dataclasses.dataclass +class OperationBuffer(Buffer, Operation): + # An operation that produces a single output buffer + def get_outputs(self) -> List[Buffer]: + return [self] + + def get_defining_op(self) -> Operation: + return self + + def __post_init__(self): + Buffer.__post_init__(self) + Operation.__post_init__(self) + + +class InputBuffer(Buffer): + def num_reads(self): + return 1 + + +class ConstantBuffer(InputBuffer): + override_device: Optional[torch.device] = None + + def make_loader(self): + def loader(index): + indexer = self.layout.make_indexer() + return ops.load( + V.graph.constant_name(self.get_name(), self.override_device), + indexer(index), + ) + + return loader + + def constant_to_device(self, device): + return ConstantBuffer( + V.graph.constant_name(self.get_name(), device), self.layout + ) + + +class NoneAsConstantBuffer(IRNode): + def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: + return OrderedSet() + + def codegen_reference(self, writer=None): + return V.graph.wrapper_code.none_str + + +class ShapeAsConstantBuffer(IRNode): + def __init__(self, shape): + super().__init__() + self._shape = shape + + @property + def shape(self): + return self._shape + + def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: + return free_unbacked_symbols(self.shape) + + def codegen_reference(self, writer=None): + return V.graph.wrapper_code.expr_printer(V.graph.sizevars.simplify(self.shape)) + + +@dataclasses.dataclass +class ComputedBuffer(OperationBuffer): + data: Loops + + def get_computed_buffer_name(self): + """ + Returns self.name if it exists, otherwise returns the name of the data node if that exists. + If neither exist, returns None. + """ + if self.name is not None: + return self.name + if hasattr(self.data, "name"): + return self.data.name + return None + + def num_reads(self): + return self.data.num_reads() + + def get_read_names(self) -> OrderedSet[str]: + return self.data.get_read_names() + + def get_read_writes(self): + with patch.object(FlexibleLayout, "allow_indexing", True): + if self.data.get_reduction_type(): + return extract_read_writes( + self.get_store_function(), + self.data.get_pointwise_size(), + self.data.get_reduction_size(), + ) + else: + return extract_read_writes( + self.get_store_function(), + self.data.get_size(), + ) + + def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: + # Ordinarily, we'd like to just peek at the arguments list, + # but ComputedBuffers have no argument list. + # + # Morally, this logic needs to be synchronized with the + # KernelArgs.size calls, which are responsible for making symbols make + # there way as kernel arguments (and it is precisely passing in one of + # those symbols that establishes a dependency). However, we haven't + # started codegen yet so we can't directly reuse that logic. + # + # For now, I'm just yoloing with the size of the buffer. Not sure if + # it is enough. + # + # One thing you might wonder is if this is enough for a ComputedBuffer + # denoting a reduction over i0. Empirically, it is enough, but for an + # unusual reason: we only need accurate dependencies for item() call, + # but it's impossible to end up with a reduction over i0 from an + # item() call without a regular non-reduction buffer first. + return ( + free_unbacked_symbols(self.get_size()) + | free_unbacked_symbols(self.get_stride()) + | free_unbacked_symbols(self.get_offset()) + | self.data.get_unbacked_symbol_uses() + ) + + def make_loader(self): + # Inline constants and index_expressions + if ( + hasattr(self.data, "make_loader") + and self.name not in V.graph.mutated_buffers + and self.num_reads() == 0 + ): + # can be inlined + return self.data.make_loader() + return super().make_loader() + + def get_store_function(self): + indexer = self.layout.as_fixed().make_indexer() + if isinstance(self.data, (Reduction, Scan, Sort)): + return partial(self.data.store_reduction, self.name, indexer) + else: + assert isinstance(self.data, Pointwise) + return partial(self.data.store_output, self.name, indexer) + + def get_fill_order(self): + """ + If our layout is still flexible, try to determine the stride order based on stride orders of reads. + + TODO(jansel): A better algorithm here would look at downstream consumers of this + value and try to do global graph-level layout optimization. + This is also something just begging to be autotuned. + """ + if isinstance(self.layout, FlexibleLayout): + (index_vars, reduction_vars), _ = dependencies.index_vars_squeeze( + self.data.get_pointwise_size(), self.data.get_reduction_size() + ) + reads = self.get_read_writes().reads + # only consider reads to buffer of same size + # ignore StarDeps because they don't contribute stride information + assert all( + isinstance(r, (dependencies.StarDep, dependencies.MemoryDep)) + for r in reads + ) + reads = [ + sympy_subs( + r.index, {v: sympy.Integer(0) for v in reduction_vars if v != 0} + ) + for r in reads + if isinstance(r, dependencies.MemoryDep) + ] + + if reads: + if isinstance(self.data, (Scan, Sort)): + indices = self.data.reindex(index_vars, reduction_vars) + else: + indices = index_vars + stride_lengths = [ + V.graph.sizevars.stride_hints(expr, indices) for expr in reads # type: ignore[arg-type] + ] + from .scheduler import pick_loop_order + + return pick_loop_order(stride_lengths, self.get_size()) + + return None + + def decide_layout(self): + if isinstance(self.layout, FlexibleLayout): + order = self.get_fill_order() + if order: + self.freeze_layout_with_fill_order(order) + else: + self.freeze_layout() + + @cache_on_self + def get_default_sizes_body(self): + args, var_ranges = dependencies.index_vars_squeeze( + self.data.get_pointwise_size(), self.data.get_reduction_size(), prefix="q" + ) + with patch.object(ConstantBuffer, "override_device", self.get_device()): + body = LoopBody( + self.get_store_function(), + (args if self.get_reduction_type() else args[:1]), + var_ranges, + *args, + ) + index_vars = [] + reduce_vars: List[Any] = [] + index_size = [] + reduce_size = [] + for v, s in var_ranges.items(): + if v in args[0]: + assert not reduce_vars + index_vars.append(v) + index_size.append(s) + else: + assert v in args[1] + reduce_vars.append(v) + reduce_size.append(s) + return (index_size, reduce_size), body, (index_vars, reduce_vars) + + def simplify_and_reorder( + self, + extra_indexing_constraints: Optional[Tuple[Dict[Any, Any], List[Any]]] = None, + recompute_sizes_body_func: Optional[Callable[..., Any]] = None, + ): + """ + This is a main place where we do loop transformations in a + backend-agnostic way. + + Here we: + 1) Remove any 1 dimensions + 2) Fuse contiguous dimensions together + 3) Reorder dimensions based on stride orders + + Optional argument extra_indexing_constraints can be used to append additional + indexing expressions to existing ones derived from buffer's body. This can be useful + to fuse scheduler nodes with compatible ranges, e.g. (s0*s1*...,) and (s0, s1, s2, ...) + on CPU by preventing indexing simplifications and obtaining index/reduce ranges for + the scheduler node compatible with other nodes. + Optional argument recompute_sizes_body_func can be used to recompute sizes and body + on the default body. This can be useful to append additional loop transformations. + """ + ( + (index_size, reduce_size), + body, + (index_vars, reduce_vars), + ) = self.get_default_sizes_body() + + if recompute_sizes_body_func: + ( + (index_size, reduce_size), + body, + (index_vars, reduce_vars), + ) = recompute_sizes_body_func( + (index_size, reduce_size), body, (index_vars, reduce_vars) + ) + + index_formulas = [*body.indexing_exprs.values()] + if extra_indexing_constraints is not None: + assert ( + isinstance(extra_indexing_constraints, tuple) + and len(extra_indexing_constraints) == 2 + ) + extra_indexing_ranges, extra_indexing_expr = extra_indexing_constraints + assert isinstance(extra_indexing_ranges, dict) + assert isinstance(extra_indexing_expr, list) + assert all(isinstance(f, Expr) for f in extra_indexing_expr) + + expected_var_ranges = body.var_ranges + assert expected_var_ranges == extra_indexing_ranges, ( + expected_var_ranges, + extra_indexing_ranges, + ) + # remove already existing expressions + extra_indexing_expr = [ + e for e in extra_indexing_expr if e not in index_formulas + ] + index_formulas += extra_indexing_expr + + memory_addrs = [*body.get_write_exprs()] + if not V.graph.has_feature(self, BackendFeature.PREFER_STORE_LOOP_ORDER): + memory_addrs.extend(body.get_read_exprs()) + + def simplify_and_reorder(x_vars, support_vars, sizes, simplify_loops): + sizes, reindex0, reindex1 = self._apply_loop_reordering( + x_vars, support_vars, sizes, memory_addrs + ) + # for NHWC: reindex0([0,1,2,3]) = [0,2,3,1], reindex1([0,1,2,3]) = [0,3,2,1] + x_vars = reindex0(x_vars) + + if simplify_loops: + sizes, reindex2, prune = V.graph.sizevars._simplify_loops( + x_vars, + sizes, + index_prevent_reordering(index_formulas, x_vars, sizes), + ) + reindex = fuse_reindexing(reindex1, reindex2) + else: + reindex = reindex1 + return sizes, reindex, reindex1 + + support_vars = index_vars + reduce_vars + should_merge_loops = ( + self.get_device().type != "cuda" or not config.loop_ordering_after_fusion + ) + iter_ranges, iter_reindex, _ = simplify_and_reorder( + index_vars, + support_vars, + index_size, + should_merge_loops, + ) + + # Like iteration dimensions, we may also want to delay merging reduction dimensions. + # E.g., if we reduce a tensor [M, N, K] for its M and N dimensions followed by a pointwise + # kernel, merging M and N dimension too early makes it hard to decide what loop order + # we should pick for the piontwise kernel so that it is fusible with the reduction. + reduce_ranges, reduce_reindex, _ = simplify_and_reorder( + reduce_vars, support_vars, reduce_size, should_merge_loops + ) + + # retrace the loop body with simplification and reordering applied + (iter_vars, reduce_vars), var_ranges = dependencies.index_vars_no_squeeze( + iter_ranges, + reduce_ranges, + prefix="z", + ) + body = LoopBody( + body, + [iter_reindex(iter_vars), reduce_reindex(reduce_vars)], + var_ranges, + iter_vars, + reduce_vars, + ) + return (iter_ranges, reduce_ranges), body + + @staticmethod + def _apply_loop_reordering( + index_vars, + support_vars, + sizes, + memory_addrs, + priority_idx=None, + ): + """ + Shuffle the order of loops around to hopefully improve performance. + """ + from .scheduler import pick_loop_order + + if priority_idx is None: + priority_idx = [] + + try: + strides = [ + V.graph.sizevars.stride_hints(expr, index_vars, support_vars) + for expr in memory_addrs + ] + assert len(strides) == len(memory_addrs) and len(strides[0]) == len( + index_vars + ) + order = list(reversed(pick_loop_order(strides, sizes, priority_idx))) + except Exception: + if config.debug: + log.warning( + "Did not simplify complex index:\n%s\n%s", + dict(zip(index_vars, sizes)), + memory_addrs, + ) + order = list(range(len(sizes))) + sizes = [sizes[i] for i in order] + return sizes, same_reorder(order), inverse_reorder(order) + + def get_reduction_size(self): + return self.data.get_reduction_size() + + def get_reduction_type(self): + return self.data.get_reduction_type() + + def is_no_op(self): + return self.data.is_zero_elements() + + def should_allocate(self): + return True + + def constant_to_device(self, device): + """Move this to a given device. Requires that all reads are to constants.""" + return self.data.constant_to_device(device) + + +class TemplateBuffer(OperationBuffer): + """ + Represents a Triton (in the future other type) of template operator + that we can fuse an epilogue onto. + """ + + def __init__(self, layout, inputs, make_kernel_render): + super().__init__(name=None, layout=layout) + self.inputs = InputsKernel.unwrap_storage(inputs) + self.make_kernel_render = make_kernel_render + self.name = V.graph.register_buffer(self) + V.graph.register_operation(self) + + def get_read_writes(self): + return self.extract_read_writes(normalize=True) + + def extract_read_writes(self, normalize): + name = self.get_name() + indexer = self.layout.make_indexer() + + def dummy(index, rindex): + assert len(rindex) == 0 + return ops.store(name, indexer(index), "fake") + + deps = dependencies.extract_read_writes( + dummy, self.get_size(), (), normalize=normalize + ) + deps.reads = OrderedSet(dependencies.StarDep(x.get_name()) for x in self.inputs) + return deps + + def get_reduction_size(self): + return 1 + + def get_reduction_type(self): + return None + + def is_no_op(self): + return False + + def should_allocate(self): + return True + + def simplify_and_reorder( + self, + extra_indexing_constraints: Optional[Tuple[Dict[Any, Any], List[Any]]] = None, + recompute_sizes_body_func: Optional[Callable[..., Any]] = None, + ): + return ( + ( + self.get_size(), + (), + ), + None, + ) + + +class TritonTemplateBuffer(TemplateBuffer): + def __init__( + self, + layout, + inputs, + make_kernel_render, + debug_extra=None, + mutated_inputs: Optional[Iterable[IRNode]] = None, + ): + """ + NOTE:[TritonTemplates with multiple outputs] + We want the ability for TritonTemplates to output multiple tensors. Triton + kernels have no notion of outputs and this is done by creating tensors that + are then mutated by the kernel. Currenlty our STORE_OUTPUT codegen doesn't + support creating multinode outputs for triton templates. + We work around this by creating an extra input buffer during the lowering + and we mark them as mutated inputs. + """ + super().__init__(layout, inputs, make_kernel_render) + self.debug_extra = debug_extra + self.mutated_inputs = mutated_inputs + self.outputs: List[Buffer] = [self] + if mutated_inputs is not None: + # Ensure that the mutated inputs are only allowed for certain nodes + allowed_set = ( + torch.ops.higher_order.flex_attention, + torch.ops.higher_order.flex_attention_backward, + ) + current_node = V.graph.current_node.target + assert ( + current_node in allowed_set + ), f"Mutated inputs are only allowed for {allowed_set} but got {current_node}" + device = self.inputs[0].get_device() + self.outputs += [ + MutationOutput(NoneLayout(device), buf, self) for buf in mutated_inputs + ] + + def get_outputs(self) -> List[Buffer]: + return self.outputs + + def __str__(self) -> str: + out = f"TritonTemplateBuffer(layout={self.layout}, {self.debug_extra})" + return out + + +PrimitiveInfoType = Union[int, float, bool, str, List[Union[int, str, float, bool]]] + + +class ChoiceCaller: + """ + Represents a possible choice used in autotune_process.py. + During autotuning, self.benchmark() is first called to get benchmark result, + and if this choice is selected, self.output_node() is called to get the output_node. + + Children classes: TritonTemplateCaller, CUDATemplateCaller. + """ + + def __init__(self, name, input_nodes, layout): + super().__init__() + self.name = name + self.layout = layout + self.input_nodes = input_nodes + + def benchmark(self, *args, out) -> float: + algo = self.to_callable() + return benchmarker.benchmark(algo, args, {"out": out}) + + def call_name(self) -> str: + raise NotImplementedError + + def to_callable(self): + raise NotImplementedError + + def hash_key(self) -> str: + raise NotImplementedError + + def output_node(self) -> TensorBox: + raise NotImplementedError + + def info_dict(self) -> Dict[str, Union[PrimitiveInfoType, List[PrimitiveInfoType]]]: + """Information returned here is logged to the autotune log file when that is enabled.""" + return {} + + def autoheuristic_id(self) -> str: + return "unsupported_choice" + + +class TritonTemplateCallerBase(ChoiceCaller): + def get_make_kernel_render(self) -> Any: + raise NotImplementedError + + +class MultiTemplateBuffer(TritonTemplateBuffer): + """ + Represents a Buffer with multiple backing implementation choices. + + Choices can be TritonTemplates or ExternKernels. During scheduling if there is a potential + epilogue we will benchmark each of the choices with the epilogue to determine an implementation. + Otherwise, the fastest base choice will be chosen. + """ + + def __init__( + self, + layout: Layout, + inputs: List[IRNode], + choice_timings: Callable[[], Dict[ChoiceCaller, float]], + ): + super().__init__(layout=layout, inputs=inputs, make_kernel_render=None) + self._choice_timings_fn = choice_timings + self._choice_timings: Optional[Dict[ChoiceCaller, float]] = None + self.original_inputs = inputs + + @property + def choice_timings(self) -> Dict[ChoiceCaller, float]: + if self._choice_timings is None: + self._choice_timings = self._choice_timings_fn() + return self._choice_timings + + @contextlib.contextmanager + def swap_as_triton_caller(self, caller: TritonTemplateCallerBase): + assert isinstance(caller, torch._inductor.select_algorithm.TritonTemplateCaller) + assert self.layout == caller.layout + + render = self.make_kernel_render + self.make_kernel_render = caller.get_make_kernel_render() + try: + yield + finally: + self.make_kernel_render = render + + def finalize_as_triton_caller(self, caller: TritonTemplateCallerBase): + assert isinstance(caller, torch._inductor.select_algorithm.TritonTemplateCaller) + assert self.layout.size == caller.layout.size + assert self.layout.stride == caller.layout.stride + self.make_kernel_render = caller.get_make_kernel_render() + + def get_min_choice(self) -> Tuple[ChoiceCaller, float]: + min_choice = min(self.choice_timings, key=self.choice_timings.get) # type: ignore[arg-type] + return (min_choice, self.choice_timings[min_choice]) + + +class CUDATemplateBuffer(TemplateBuffer): + def __init__( + self, + layout, + inputs, + make_kernel_render, + workspace_size: int, + template: CUDATemplate, # type: ignore[name-defined] # noqa: F821 + ): + super().__init__(layout, inputs, make_kernel_render) + # Global memory (in bytes) needed for this template. + self.workspace_size = workspace_size + self.template = template + + def get_workspace_size(self): + return self.workspace_size if self.workspace_size is not None else 0 + + +class CppTemplateBuffer(TemplateBuffer): + def __init__(self, layout, inputs, make_kernel_render, template, choice): + super().__init__(layout, inputs, make_kernel_render) + self.template = template + self.choice = choice + + +@dataclasses.dataclass +class InputsKernel(OperationBuffer): + inputs: List[Buffer] + + def get_read_writes(self): + reads: OrderedSet[dependencies.Dep] = OrderedSet() + StarDep = dependencies.StarDep + for input in self.inputs: + if isinstance(input, list): + reads.update(StarDep(x.get_name()) for x in input) + else: + reads.add(StarDep(input.get_name())) + + writes: OrderedSet[dependencies.Dep] = OrderedSet( + StarDep(buf.get_name()) for buf in self.get_outputs() + ) + + return dependencies.ReadWrites( + reads=reads, + writes=writes, + index_exprs=OrderedSet(), + ) + + @classmethod + def unwrap_storage_for_input(cls, x): + if isinstance(x, TensorBox): + x = x.data + if isinstance(x, StorageBox): + x = x.data + if isinstance(x, BaseView) and not isinstance(x, ReinterpretView): + x = ExternKernel.realize_input(x) + if isinstance(x, TensorBox): + # when converting to ReinterpretView fails in the + # realize_input call above, the result will be wrapped + # into TensorBox / StorageBox pair as a result of the + # cls.copy_input call; so we should unwrap recursively + return cls.unwrap_storage_for_input(x) + if isinstance(x, TorchBindObject): + return x + assert isinstance(x, (Buffer, ReinterpretView)), x + return x + + @staticmethod + def unwrap_storage(inputs): + inputs_new = [] + for x in inputs: + if isinstance(x, list): + x = [InputsKernel.unwrap_storage_for_input(i) for i in x] + else: + x = InputsKernel.unwrap_storage_for_input(x) + inputs_new.append(x) + return inputs_new + + def is_extern(self): + return True + + def num_reads(self): + return 1 + + +class NopKernel(InputsKernel): + def is_no_op(self): + return True + + +class ConcatKernel(NopKernel): + """ + There isn't actually a real kernel for concat, we just change the + storage for the upstream data. + """ + + @classmethod + def create(cls, inputs, dim): + device = inputs[0].get_device() + dtype = inputs[0].get_dtype() + new_size = list(inputs[0].get_size()) + offsets_start = [0] + offsets_end = [new_size[dim]] + assert 0 <= dim < len(new_size) + for i in range(1, len(inputs)): + input_size = inputs[i].get_size() + offsets_start.append(new_size[dim]) + assert len(input_size) == len(new_size) + assert inputs[i].get_dtype() == dtype + assert inputs[i].get_device() == device + for j in range(len(new_size)): + if j == dim: + new_size[j] = new_size[j] + input_size[j] + else: + new_size[j] = V.graph.sizevars.guard_equals( + new_size[j], input_size[j] + ) + offsets_end.append(new_size[dim]) + + output_stride = FlexibleLayout.contiguous_strides(new_size) + # If any of the inputs is in CL format, use CL format for the output + for i in range(len(inputs)): + x = inputs[i] + if is_storage_and_layout(x): + layout = x.get_layout() + if isinstance( + layout, FixedLayout + ) and Layout.is_channels_last_contiguous(layout.size, layout.stride): + # use CL stride for the output + output_stride = make_channels_last_strides_for(new_size) + break + any_input_is_storage_and_layout = any(is_storage_and_layout(x) for x in inputs) + fx_node_args = V.graph.current_node.args[0] + assert isinstance(fx_node_args, list) + # If any of the inputs has meta tensor and the meta tensor is in CL format, use CL format for the output + if any_input_is_storage_and_layout is False and any( + "val" in arg.meta + and ( + arg.meta["val"].is_contiguous(memory_format=torch.channels_last) + or arg.meta["val"].is_contiguous(memory_format=torch.channels_last_3d) + ) + for arg in fx_node_args + ): + output_stride = make_channels_last_strides_for(new_size) + + concat_kernel = ConcatKernel( + name=None, + layout=FixedLayout( + device=device, + dtype=dtype, + size=new_size, + stride=output_stride, + ), + inputs=[], + ) + kernel = StorageBox(concat_kernel) + op_names = [] + for i in range(len(inputs)): + input_buffer = cls.realize_into( + inputs[i], + SliceView.create( + kernel, dim, offsets_start[i], offsets_end[i], clamp=False + ), + ) + concat_kernel.inputs.append(input_buffer) + + if isinstance(inputs[i].data, BaseView): + input_unwrapped = inputs[i].data.unwrap_view() + else: + input_unwrapped = inputs[i].data + + if ( + input_unwrapped.is_input_buffer() + and is_gpu(inputs[i].get_device().type) + and not is_dynamic(input_buffer) + ): + op_names.append(input_buffer.get_operation_name()) + + if len(op_names) > 1 and V.graph.has_feature(device, BackendFeature.FOREACH): + V.graph.register_operation_list(op_names) + + concat_kernel.name = V.graph.register_buffer(concat_kernel) + concat_kernel.inputs = cls.unwrap_storage(concat_kernel.inputs) + V.graph.register_operation(concat_kernel) + + return kernel + + @classmethod + def can_realize_into_without_copy(cls, src): + if isinstance(src, TensorBox): + # unwrap a TensorBox + return cls.can_realize_into_without_copy(src.data) + + return isinstance(src.data.layout, FlexibleLayout) and not isinstance( + src.data, ExternKernelAlloc + ) + + @classmethod + def realize_into(cls, src, dst): + # Attempt to turn this into a ReinterpretView rather than assert. + # This has concessions around layout, as as_storage_and_layout + # can cause us to go from flexible to fixed layout. + if not isinstance(dst, ReinterpretView): + if is_storage_and_layout(dst): + storage, layout = as_storage_and_layout(dst) + dst = ReinterpretView(storage, layout) + assert isinstance(dst, ReinterpretView), dst + if isinstance(src, TensorBox): + # unwrap a TensorBox + return cls.realize_into(src.data, dst) + if isinstance(src, StorageBox): + src.realize() + # ExternKernelAlloc has specific requirements for output layout, should create a copy + assert hasattr(src.data, "layout") + if cls.can_realize_into_without_copy(src): + src.data.layout = NonOwningLayout(dst) + return src.data + # introduce a copy + pw = Pointwise.create( + device=src.get_device(), + dtype=src.get_dtype(), + inner_fn=src.make_loader(), + ranges=[ + V.graph.sizevars.guard_equals(a, b) + for a, b in zip(src.get_size(), dst.get_size()) + ], + ) + return cls.realize_into(pw, dst) + + def should_allocate(self): + return True + + +@dataclasses.dataclass +class ExternKernel(InputsKernel): + constant_args: Tuple[Any, ...] = () + kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict) + output_view: Optional[ReinterpretView] = None + python_kernel_name: Optional[str] = None + cpp_kernel_name: Optional[str] = None + # FIXME: in some cases we sill need to explicitly pass in ordered_kwargs_for_cpp_kernel + # We shouldn't need to do this since the information can be retrieved from op_overload._schema. + ordered_kwargs_for_cpp_kernel: Iterable[str] = dataclasses.field( + default_factory=list + ) + op_overload: Optional[ + Union[torch._ops.OpOverload, torch._ops.HigherOrderOperator] + ] = None + arg_properties: Optional[List[Dict[str, Any]]] = None + kwarg_properties: Optional[Dict[str, Dict[str, Any]]] = None + unbacked_bindings: Dict[sympy.Symbol, pytree.KeyPath] = dataclasses.field( + default_factory=dict + ) + mutation_outputs: List[MutationOutput] = dataclasses.field(default_factory=list) + + def __init__( + self, + name, + layout, + inputs, + constant_args=(), + kwargs=None, + output_view=None, + python_kernel_name=None, + cpp_kernel_name=None, + ordered_kwargs_for_cpp_kernel=(), + op_overload=None, + ): + super().__init__( + name, + layout, + inputs, + ) + self.constant_args = constant_args + self.kwargs = kwargs if kwargs else {} + self.output_view = output_view + self.op_overload = op_overload + self.set_cpp_kernel_name(cpp_kernel_name) + self.set_python_kernel_name(python_kernel_name) + self.ordered_kwargs_for_cpp_kernel = ordered_kwargs_for_cpp_kernel + self.collect_arg_kwarg_properties() + self.unbacked_bindings = {} + self.mutation_outputs = [] + self.fx_node = V.graph.current_node + + def get_outputs(self) -> List[Buffer]: + return [self, *self.mutation_outputs] + + def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: + return OrderedSet() + + def collect_arg_kwarg_properties(self): + # if self.op_overload is torch._ops.OpOverload, we can use its schema to collect additional + # information for args and kwargs, e.g. type and default value, to help with the cpp wrapper codegen + self.arg_properties = ( + [ + { + "name": x.name, + "type": x.real_type, + "default_value": x.default_value, + } + for x in self.op_overload._schema.arguments + if not x.kwarg_only + ] + if isinstance(self.op_overload, torch._ops.OpOverload) + else [{} for i in range(len(self.inputs))] + ) + self.allarg_properties = ( + { + x.name: {"type": x.real_type, "default_value": x.default_value} + for x in self.op_overload._schema.arguments + } + if isinstance(self.op_overload, torch._ops.OpOverload) + else {} + ) + # FIXME: self.kwargs does not always match kwargs defined in schema, so sometimes + # ordered_kwargs_for_cpp_kernel is explicilty passed in. + if ( + isinstance(self.op_overload, torch._ops.OpOverload) + and not self.ordered_kwargs_for_cpp_kernel + ): + self.ordered_kwargs_for_cpp_kernel = [ + x.name for x in self.op_overload._schema.arguments if x.kwarg_only + ] + + def fill_non_provided_args(self, args, kwargs, convert_val_to_str=False): + # Previously, we want to maintain forward-compatibility by skipping + # default args in the serialized artifacts in fbcode. However, + # some of our shim interfaces require default values being OrderedSet. + # Discussed with Sherlock offline and we decided to allow serializing + # default args into the C++ wrapper code for now. We will refine this + # part if we see real FC requirement. More details related to FC + # can be found at: + # https://docs.google.com/document/d/1FzWm-sHYwmRi3x_g036kOxd99KaYquUsA-L5JwOn8ys/edit?usp=sharing + assert isinstance(args, (list, tuple)) + if isinstance(args, tuple): + args = list(args) + assert self.arg_properties, "ExternKernel.arg_properties should not be empty" + + n_args = len(args) + n_pos_args = len(self.arg_properties) + # For cpp wrapper, if some positional args are not provided, we need to check + # if they're in the kwargs or use their default value + if n_args < n_pos_args: + log.debug( + "%s has %d unprovided positional arguments. " + "Will check if they are in the keyword arguments or will use default values.", + self.op_overload, + n_pos_args - n_args, + ) + for i in range(n_args, n_pos_args): + arg_name = self.arg_properties[i]["name"] + args.append( + kwargs[arg_name] + if arg_name in kwargs + else self.arg_properties[i]["default_value"] + ) + return args + + def decide_layout(self): + if isinstance(self.layout, FlexibleLayout): + self.apply_constraint() + self.freeze_layout() + + def codegen_comment(self, wrapper): + origin_str, detailed_origin_str = get_kernel_metadata(self, wrapper) + if origin_str: + wrapper.writeline(origin_str) + + def codegen(self, wrapper): + raise NotImplementedError + + def set_cpp_kernel_name(self, cpp_kernel_name: Optional[str] = None): + self.cpp_kernel_name = cpp_kernel_name + self.cpp_kernel_overload_name = None + self.cpp_kernel_key = None + self.cpp_op_schema = None + if not V.graph.cpp_wrapper or not isinstance( + self.op_overload, torch._ops.OpOverload + ): + return + + kernel = self.op_overload + if self.cpp_kernel_name is None: + # Try to construct cpp_kernel_name from op_overload + if kernel.namespace == "aten": + # Calling with the default kernel name can lead to ambiguous behavior like the following example. + # repeat_interleave(const at::Tensor & repeats, c10::optional output_size=std::nullopt) + # repeat_interleave(const at::Tensor & self, int64_t repeats, + # c10::optional dim=std::nullopt, c10::optional output_size=std::nullopt) + opname = ( + kernel.__name__.split(".")[0] + if kernel._overloadname == "default" + else kernel.__name__.replace(".", "_") + ) + self.cpp_kernel_name = f"at::_ops::{opname}::call" + else: + self.cpp_kernel_name = kernel._schema.name + + # Set up info for runtime schema lookup + # TODO: The logics here may be further simplified. + from .codegen.wrapper import get_cpp_op_schema + + self.cpp_kernel_overload_name = kernel._schema.overload_name + self.cpp_kernel_key = f"{self.cpp_kernel_name.replace('::', '_')}_{self.cpp_kernel_overload_name}" # type: ignore[union-attr] + try: + self.cpp_op_schema = get_cpp_op_schema(kernel) + except Exception: + self.cpp_op_schema = "" + + def set_python_kernel_name(self, python_kernel_name: Optional[str]): + self.python_kernel_name = python_kernel_name + if python_kernel_name is not None: + return + + kernel = self.op_overload + if kernel is None: + pass + elif isinstance(kernel, torch._ops.HigherOrderOperator): + self.python_kernel_name = f"torch.ops.higher_order.{kernel.__name__}" + else: + self.python_kernel_name = ( + f"{kernel.__module__.replace('._ops.', '.ops.')}.{kernel.__name__}" + ) + + def get_kernel_name(self): + return ( + ( + V.graph.wrapper_code.get_c_shim_func_name(self.cpp_kernel_name) # type: ignore[attr-defined] + if config.abi_compatible + else self.cpp_kernel_name + ) + if V.graph.cpp_wrapper + else self.python_kernel_name + ) + + @staticmethod + def copy_input(x): + pw = Pointwise.create( + device=x.get_device(), + dtype=x.get_dtype(), + inner_fn=x.make_loader(), + ranges=x.get_size(), + origin_node=x.get_origin_node(), + traceback=x.get_traceback(), + ) + pw.realize() + return pw + + @classmethod + def process_kernel( + cls, kernel, *args, **kwargs + ) -> Tuple[ + Any, + List[Any], + List[Any], + Callable[[Any, Any], Any], + Optional[Dict[sympy.Symbol, pytree.KeyPath]], + ]: + binded_args = {"args": args, "kwargs": kwargs} + + args_flat, args_spec = pytree.tree_flatten(binded_args) + + is_arg_tensor = [] + tensor_args = [] + non_tensor_args: List[Any] = [] + for arg in args_flat: + is_arg_tensor.append(isinstance(arg, IRNode)) + if is_arg_tensor[-1]: + tensor_args.append(arg) + else: + if isinstance(arg, sympy.Expr): + arg = V.graph.sizevars.shape_env.create_symintnode(arg, hint=None) + non_tensor_args.append(arg) + + def unflatten_args(new_tensor_args, new_non_tensor_args): + result = [] + it_tensors = iter(new_tensor_args) + it_non_tensors = iter(new_non_tensor_args) + for is_tensor in is_arg_tensor: + if is_tensor: + result.append(next(it_tensors)) + else: + result.append(next(it_non_tensors)) + r = pytree.tree_unflatten(result, args_spec) + return r.get("args", []), r.get("kwargs", {}) + + tensor_args = [cls.realize_input(x) for x in tensor_args] + + # freeze layout otherwise our output stride calculation might + # become incorrect + for x in tensor_args: + if is_storage_and_layout(x): + as_storage_and_layout(x, freeze=True) + + # Rerun fake tensor propagation, because Inductor may have changed the + # strides of inputs and we need to determine accurately what the + # output stride will be. + example_args: List[Union[torch.Tensor, torch._C.ScriptObject]] = [] + + # We need to retain the constant values of fake tensors that we originally + # propagated the graph with, because for some operators running without a + # constant would trigger an error / DataDependentException + for x in tensor_args: + # if x is a view of a constant, we need to realize the view + # (we can't pass the constant into the kernel directly) + if not isinstance(x, BaseView) and x.get_name() in V.graph.constants: + example_args.append(V.graph.constants[x.get_name()]) + elif ( + not isinstance(x, BaseView) + and x.get_name() in V.graph.torchbind_constants + ): + example_args.append(V.graph.torchbind_constants[x.get_name()]) + else: + example_args.append(ir_node_to_tensor(x, guard_shape=True)) + + new_args, new_kwargs = unflatten_args(example_args, non_tensor_args) + example_output = kernel(*new_args, **new_kwargs) + + unbacked_bindings: Optional[Dict[sympy.Symbol, pytree.KeyPath]] = None + if shape_env := V.fake_mode.shape_env: + rebind_unbacked(shape_env, V.current_node, example_output) + unbacked_bindings = compute_unbacked_bindings( + shape_env, example_output, V.current_node.meta.get("val") + ) + + example_out_li = ( + [example_output] + if not isinstance(example_output, (list, tuple)) + else example_output + ) + for t in example_out_li: + if isinstance(t, torch.Tensor) and t.is_sparse: + msg = "sparsity not handled. Please file issue for sparse inference weights." + if stack_trace := V.graph.current_node.meta.get("stack_trace", None): + msg = f"{msg} Found from : \n {stack_trace}" + V.graph.disable_cudagraphs_reason = msg + + return ( + example_output, + tensor_args, + non_tensor_args, + unflatten_args, + unbacked_bindings, + ) + + @classmethod + def convert_to_reinterpret_view(cls, x): + """ + In order to pass this to an extern kernel we need a + ReinterpretView not a View. This allows us to avoid some + unneeded copies. + """ + assert isinstance(x, BaseView) + if isinstance(x, ReinterpretView): + return x + + # NOTE: Don't use extract_read_writes here as it fails when + # make_loader() inlines the computation + x_unwrap_view = x.unwrap_view() + buf = V.graph.get_buffer(x_unwrap_view.get_name()) + assert buf is not None + x_unwrap_view_fx_node = buf.get_origin_node() + # Prefer channels last format according to how the format is set from eager. + if ( + x_unwrap_view_fx_node is not None + and "val" in x_unwrap_view_fx_node.meta + and isinstance(x_unwrap_view.layout, FlexibleLayout) + and ( + x_unwrap_view_fx_node.meta["val"].is_contiguous( + memory_format=torch.channels_last + ) + or x_unwrap_view_fx_node.meta["val"].is_contiguous( + memory_format=torch.channels_last_3d + ) + ) + ): + x_unwrap_view.freeze_layout_with_same_order( + make_channels_last_strides_for(x_unwrap_view.get_size()) + ) + else: + x_unwrap_view.freeze_layout() + + index_args, var_ranges = dependencies.index_vars_squeeze( + x.get_size(), prefix="r" + ) + range_vars = index_args[0] + index = x.make_indexer()(range_vars) + + index = V.graph.sizevars.simplify_with_ranges(index, var_ranges) + strides = V.graph.sizevars.stride_vars(index, range_vars) + offset = V.graph.sizevars.offset_var(index, range_vars) + expected = sympy_dot(range_vars, strides) + offset + + if index != expected: + log.debug( + "convert_to_reinterpret_view failed: stride=%s offset=%s index=%s", + strides, + offset, + index, + ) + raise NotImplementedError + + return ReinterpretView( + data=x.data, + layout=FixedLayout( + device=x.get_device(), + dtype=x.get_dtype(), + size=x.get_size(), + stride=strides, + offset=offset, + ), + ) + + @classmethod + def realize_input(cls, x): + if x is None: + return NoneAsConstantBuffer() + if isinstance(x, (sympy.Expr, sympy.logic.boolalg.Boolean, int)): + return ShapeAsConstantBuffer(x) + if isinstance(x, Constant): + return V.graph.add_tensor_constant( + torch.tensor(x.value, dtype=x.get_dtype(), device=x.get_device()) + ) + if isinstance(x, ConstantBuffer): + return x + if isinstance(x, TensorBox): + return cls.realize_input(x.data) + if isinstance(x, ReinterpretView): + return ReinterpretView(cls.realize_input(x.data), x.get_layout()) + if isinstance(x, BaseView): + x.realize() + if is_storage_and_layout(x.unwrap_view()): + try: + return cls.convert_to_reinterpret_view(x) + except NotImplementedError: + pass + if isinstance(x, StorageBox): + # TODO(jansel): impose layout preference on realized buffer + x.realize() + return x + if isinstance(x, TorchBindObject): + return x + return cls.copy_input(x) + + @classmethod + def require_stride1(cls, x): + if is_storage_and_layout(x): + if len(x.get_stride()) == 0: + return x + for stride in x.get_stride(): + if stride == 1: + return x + return cls.copy_input(x) + + @classmethod + def require_strides( + cls, + x, + order: Optional[Sequence[int]] = None, + exact_strides: Optional[Sequence[_IntLike]] = None, + allow_padding=False, + ): + assert order is not None or exact_strides is not None + if x.get_numel() == 0: # Layout doesn't matter + return x + # require x to have the layout + if is_storage_and_layout(x): + while isinstance(x.get_layout(), NonOwningLayout): + x = x.get_layout().view + if isinstance(x.get_layout(), FlexibleLayout): + if order: + # If the the FlexibleLayout already has the size and stride in the required order, + # freeze it to a FixedLayout by using its current size and stride. + # The behavior of using its current size and stride or the given order can be different + # if the size and stride has ambiguilty, for example for a 4D input where the iC = 1: + # size=[s0, 1, 28, 28], stride=[784, 784, 28, 1]. If the required order is [3, 0, 2, 1] (channels last), + # the current size and stride already satisfies this order. + # However by freezing it to the required order, the layout will be changed to: + # size=[s0, 1, 28, 28], stride=[784, 1, 28, 1]), which is not actually necessary. + + # fix flexiblelayout to be FixedLayout with stride_order + as_storage_and_layout( + x, + freeze=True, + want_contiguous=False, + stride_order=get_stride_order( + V.graph.sizevars.size_hints(x.get_layout().stride) + ) + if is_stride_order_storage_and_layout(x, order) + else order, + allow_padding=allow_padding, + ) + return x + else: + # If the exact_strides is given, freeze the FlexibleLayout to a FixedLayout with the exact_strides. + as_storage_and_layout( + x, + freeze=True, + want_contiguous=False, + stride_order=None, + allow_padding=allow_padding, + exact_strides=exact_strides, + ) + return x + elif isinstance(x.get_layout(), FixedLayout) and ( + (order and x.get_layout().is_stride_ordered(order)) + or ( + exact_strides + and significant_strides_equal( + exact_strides, x.get_layout().stride, x.get_size() + ) + ) + ): + return x + elif isinstance(x.get_layout(), MutationLayoutSHOULDREMOVE): + if isinstance(x.get_layout().real_layout(), FlexibleLayout): + raise AssertionError( + "the MutationLayoutSHOULDREMOVE's real layout shouldn't be FlexibleLayout" + ) + elif isinstance(x.get_layout().real_layout(), FixedLayout) and ( + (order and x.get_layout().real_layout().is_stride_ordered(order)) + or ( + exact_strides + and significant_strides_equal( + exact_strides, + x.get_layout().real_layout().stride, + x.get_size(), + ) + ) + ): + return x + + # TODO - Storage to InputBuffer + if isinstance(x, InputBuffer) and ( + (order and x.get_layout().is_stride_ordered(order)) + or ( + exact_strides + and significant_strides_equal( + exact_strides, x.get_layout().stride, x.get_size() + ) + ) + ): + return x + if ( + isinstance(x, TensorBox) + and isinstance(x.data, BaseView) + and not isinstance(x.data, ReinterpretView) + and is_storage_and_layout(x.unwrap_view()) + and not isinstance(x.unwrap_view().data, ExternKernelAlloc) + ): + try: + x.data = cls.convert_to_reinterpret_view(x.data) + if order: + return cls.require_stride_order( + x, order, allow_padding=allow_padding + ) + elif exact_strides: + return cls.require_exact_strides( + x, exact_strides, allow_padding=allow_padding + ) + except NotImplementedError: + pass + # Although this is a clone, inductor is good about fusing clones into previous + # operations if they weren't realized and their layouts were flexible. + x = cls.copy_input(x) + as_storage_and_layout( + x, + freeze=True, + want_contiguous=False, + stride_order=order, + allow_padding=allow_padding, + exact_strides=exact_strides, + ) + if order: + assert is_stride_order_storage_and_layout(x, order) + return x + + @classmethod + def require_exact_strides(cls, x, exact_strides, allow_padding=False): + return cls.require_strides( + x, exact_strides=exact_strides, allow_padding=allow_padding + ) + + @classmethod + def require_stride_order(cls, x, order, allow_padding=False): + return cls.require_strides(x, order=order, allow_padding=allow_padding) + + @classmethod + def require_channels_last(cls, x): + return cls.require_stride_order(x, NHWC_STRIDE_ORDER) + + @classmethod + def require_channels_last_3d(cls, x): + return cls.require_stride_order(x, NHWDC_STRIDE_ORDER) + + @classmethod + def require_contiguous(cls, x): + return cls.require_stride_order(x, list(reversed(range(len(x.get_size()))))) + + def apply_constraint(self): + pass + + def codegen_const_args(self, names: Optional[List[str]] = None): + if V.graph.cpp_wrapper: + result = [] + # Aten ops follow the convention that tensor args are before non-tensor args, + # in which case the following 'len(self.inputs) + i' logic works. But this + # may not be true for other ops, and if that is the case, caller needs to + # pass in a list of const arg names for arg_properties lookup. + name_to_arg_properties = None + if names and self.arg_properties: + assert len(self.constant_args) == len( + names + ), "names passed to codegen_const_args does not match self.constant_args" + name_to_arg_properties = { + arg.get("name"): arg for arg in self.arg_properties + } + + for i, x in enumerate(self.constant_args): + if name_to_arg_properties is not None: + prop = name_to_arg_properties.get(names[i]) # type: ignore[index] + type_ = prop.get("type") if prop else None + else: + idx = len(self.inputs) + i + type_ = ( + self.arg_properties[idx].get("type") + if self.arg_properties and idx < len(self.arg_properties) + else None + ) + result.append( + V.graph.wrapper_code.val_to_arg_str(x, type_) # type: ignore[arg-type] + ) + return result + else: + return map(V.graph.wrapper_code.val_to_arg_str, self.constant_args) + + def codegen_args(self): + args = [] + for i, x in enumerate(self.inputs): + if isinstance(x, list): + names = [i.codegen_reference() for i in x] + codegen_reference = f'[{", ".join(names)}]' + args.append(codegen_reference) + else: + if V.graph.cpp_wrapper: + assert self.arg_properties and i < len( + self.arg_properties + ), "Invalid access to ExternKernel.arg_properties" + type_ = self.arg_properties[i].get("type") + args.append( + V.graph.wrapper_code.val_to_arg_str( # type: ignore[arg-type] + x, type_ + ) + ) + else: + args.append(x.codegen_reference()) + args.extend(self.codegen_const_args()) + return args + + def get_kwargs_value(self, arg_name): + if arg_name in self.kwargs: + return self.kwargs.get(arg_name) + if self.allarg_properties and self.allarg_properties.get(arg_name): + return self.allarg_properties.get(arg_name).get("default_value") # type: ignore[union-attr] + else: + raise AssertionError(f"{arg_name} not in self.allarg_properties") + + def codegen_kwargs(self, skip_out=False): + if V.graph.cpp_wrapper: + kwargs = [] + for arg_name in self.ordered_kwargs_for_cpp_kernel: + if skip_out and arg_name == "out": + # ExternKernelOut has its own logic for inserting the out parameter + continue + + v = self.get_kwargs_value(arg_name) + if isinstance(v, sympy.Expr): + kwargs.append(v) + else: + type_ = ( + self.allarg_properties.get(arg_name).get("type") # type: ignore[union-attr] + if self.allarg_properties and arg_name in self.allarg_properties + else None + ) + kwargs.append( + V.graph.wrapper_code.val_to_arg_str( # type: ignore[arg-type] + v, type_ + ) + ) + else: + kwargs = [ + f"{k}={V.graph.wrapper_code.val_to_arg_str(v)}" # type: ignore[misc] + for k, v in self.kwargs.items() + ] + return kwargs + + def codegen_size_asserts(self, wrapper): + if config.size_asserts and not V.graph.cpp_wrapper: + # comparing strides for 0 size tensor is tricky. Ignore them for now. + if sympy_product(self.get_size()) == 0: + return + size = V.graph.wrapper_code.codegen_shape_tuple(self.get_size()) + stride = V.graph.wrapper_code.codegen_shape_tuple(self.get_stride()) + wrapper.writeline( + f"assert_size_stride({self.get_name()}, {size}, {stride})" + ) + + def get_group_stride(self): + """ + get output sizes and strides, for template_codegen + """ + _size = self.get_size() + _stride = self.get_stride() + # iter_ranges = _size of output tensor, reduce_range = [] because no reduction + return [_size, []], _stride + + def canonicalize(self): + """ + Manually get canonicalization of the output index + """ + # manually generate index formula for conv + sizevars = V.graph.sizevars + sizes = self.get_size() + strides = self.get_stride() + strides = [sizevars.size_hint(x) for x in strides] + # TODO: I can't tell if the symbols here are temporary + index_vars = [sympy_index_symbol(f"d{i}") for i in range(len(sizes))] + # reorder index vars according to stride + index_order = sorted(range(len(strides)), key=strides.__getitem__, reverse=True) + lookup = {pos: idx for idx, pos in enumerate(index_order)} + order = [lookup[i] for i in range(len(lookup))] + index_vars = [index_vars[i] for i in order] + indexer = self.make_indexer() + index = indexer(index_vars) + + new_sizes, reindex, prune = V.graph.sizevars._simplify_loops( + index_vars, sizes, [index] + ) + + # assign new variables each dimension to deal with numbering mismatches + # d0, d1, d2 could become d0, d2 -- which won't match d0, d1 + _, add_var = var_builder("c") + replacement = dict(zip(index_vars, reindex([add_var(x) for x in new_sizes]))) + + index = sympy_subs(sympy.expand(index), replacement) # type: ignore[arg-type] + return index, tuple(new_sizes) + + def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: + # NB: It's not necessary to check regular inputs as we automatically + # have dependencies on them + r: OrderedSet[sympy.Symbol] = OrderedSet() + for arg in self.constant_args: + r |= maybe_free_unbacked_symbols(arg) + for arg in self.kwargs.values(): + r |= maybe_free_unbacked_symbols(arg) + return r + + def __str__(self) -> str: + kernel_name = getattr(self, "python_kernel_name", None) + lines = [ + f"python_kernel_name={kernel_name!r}", + ] + lines += [ + f"{field.name}={getattr(self, field.name)}" + for field in dataclasses.fields(self) + ] + lines.append(f"origin_node={self.origin_node!r}") + return self.str_helper(lines) + + __repr__ = __str__ + + +@dataclasses.dataclass +class ExternKernelOut(ExternKernel): + def codegen(self, wrapper): + self.codegen_comment(wrapper) + args = [*self.codegen_args(), *self.codegen_kwargs(skip_out=True)] + kernel_name = self.get_kernel_name() + if ( + V.graph.cpp_wrapper + and self.cpp_kernel_name == "torch::inductor::_mm_plus_mm" + ): + # For https://github.com/pytorch/pytorch/issues/128474 + kernel_name = ( + "aoti_torch__mm_plus_mm_out" + if config.abi_compatible + else "torch::inductor::_mm_plus_mm_out" + ) + else: + kernel_name = self.get_kernel_name() + wrapper.generate_extern_kernel_out( + kernel_name, + self.codegen_reference(), + self.output_view.codegen_reference() if self.output_view else None, + args, + ) + + def __init__( + self, + layout, + inputs, + constant_args=(), + kwargs=None, + output_view=None, + python_kernel_name=None, + cpp_kernel_name=None, + ordered_kwargs_for_cpp_kernel=(), + op_overload=None, + ): + super().__init__( + None, + layout, + self.unwrap_storage(inputs), + constant_args, + kwargs or {}, + None, + python_kernel_name, + cpp_kernel_name, + ordered_kwargs_for_cpp_kernel, + op_overload, + ) + self.name = V.graph.register_buffer(self) + V.graph.register_operation(self) + + def should_allocate(self): + return True + + +class RandomSeeds(ExternKernelOut): + def __init__(self, count: int, device: torch.device): + limits = torch.iinfo(torch.int64) + super().__init__( + layout=FixedLayout( + device=device, + dtype=torch.int64, + size=[count], + ), + inputs=[], + constant_args=[limits.min, limits.max, [count]], + python_kernel_name="aten.randint.low_out", + # FIXME: Ideally we should only use at::_ops::randint_low_out::call here, + # but the signature is different from is at::randint_out. Again, + # we can simplify the code when only keeping an ABI-compatible version. + cpp_kernel_name="at::_ops::randint_low_out::call" + if config.abi_compatible + else "at::randint_out", + op_overload=aten.randint.low_out, + ) + + +class ExternKernelAlloc(ExternKernel): + def codegen(self, wrapper): + self.codegen_comment(wrapper) + args = [*self.codegen_args(), *self.codegen_kwargs()] + V.graph.wrapper_code.generate_extern_kernel_alloc(self, args) + if isinstance(self.layout, Layout): + self.codegen_size_asserts(wrapper) + + def __init__( + self, + layout, + inputs, + constant_args=(), + kwargs=None, + python_kernel_name=None, + cpp_kernel_name=None, + ordered_kwargs_for_cpp_kernel=(), + op_overload=None, + ): + super().__init__( + None, + layout, + self.unwrap_storage(inputs), + constant_args, + kwargs or {}, + None, + python_kernel_name, + cpp_kernel_name, + ordered_kwargs_for_cpp_kernel, + op_overload, + ) + self.name = V.graph.register_buffer(self) + V.graph.register_operation(self) + + def should_allocate(self): + return False + + def apply_constraint(self): + raise NotImplementedError + + +class MutationOutput(Buffer): + """ + An output buffer that represents the mutation of a pre-existing buffer + """ + + def __init__(self, layout, mutated_node, mutating_node: Operation): + super().__init__(name=None, layout=layout) + mutated_node_name = mutated_node.get_name() + V.graph.mark_buffer_mutated(mutated_node_name) + self.mutation_names = [mutated_node_name] + self.mutating_node: Operation = mutating_node + self.name = V.graph.register_buffer(self) + + def get_defining_op(self) -> Operation: + return self.mutating_node + + def get_mutation_names(self): + return self.mutation_names + + def should_allocate(self): + return False + + +class UserDefinedTritonKernel(ExternKernel): + def get_kernel_and_configs(self): + from triton.runtime.autotuner import Autotuner + + from torch._higher_order_ops.triton_kernel_wrap import kernel_side_table + + kernel = kernel_side_table.get_kernel(self.kernel_idx) + configs = [] + if isinstance(kernel, Autotuner): + configs = kernel.configs + kernel = kernel.fn + return kernel, configs + + def codegen(self, wrapper): + kernel, configs = self.get_kernel_and_configs() + + # Definition of kernel + new_name, triton_meta = wrapper.define_user_defined_triton_kernel( + kernel, configs, self.kwargs + ) + raw_args = [ + self.get_kwargs_value(k) for k in self.ordered_kwargs_for_cpp_kernel + ] + + # NOTE: raw_args doesn't include autotuned args. + # But, kernel.constexprs includes indices of autotuned args. + # So, let's recalculate constexpr indices wrt to raw_args. + constexpr_indices = [] + for idx, kwarg in enumerate(self.ordered_kwargs_for_cpp_kernel): + if kernel.arg_names.index(kwarg) in kernel.constexprs: + constexpr_indices.append(idx) + + # Call to kernel + self.codegen_comment(wrapper) + wrapper.generate_user_defined_triton_kernel( + new_name, raw_args, self.grid, configs, triton_meta, constexpr_indices + ) + + def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: + # add unbacked symbols used in the grid to the ones used + # in the kwargs (the latter is generated by ExternKernel) + return super().get_unbacked_symbol_uses() | free_unbacked_symbols(self.grid) + + def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: + return OrderedSet() + + def __init__(self, *, kernel_idx, grid, kernel_args): + inputs = [] + kwargs = {} + constant_args = [] + for k, v in kernel_args.items(): + if isinstance(v, TensorBox): + t = InputsKernel.unwrap_storage_for_input(self.realize_input(v)) + inputs.append(t) + kwargs[k] = t + else: + constant_args.append(v) + kwargs[k] = v + + assert len(inputs) != 0 + self.device = inputs[0].get_device() + + super().__init__( + None, + NoneLayout(self.device), # type: ignore[arg-type] + inputs, + tuple(constant_args), + kwargs, + ) + self.kernel_idx = kernel_idx + self.grid = grid + + kernel, configs = self.get_kernel_and_configs() + # If we are autotuning, not all arguments will be passed + self.ordered_kwargs_for_cpp_kernel = [ + arg for arg in kernel.arg_names if arg in kernel_args + ] + + from torch._higher_order_ops.triton_kernel_wrap import identify_mutated_tensors + + autotuned_kwargs = configs[0].kwargs if len(configs) > 0 else {} + self.mutable_args = [ + kernel_args[key] + for key in identify_mutated_tensors( + kernel, {**kernel_args, **autotuned_kwargs} + ) + ] + + self.mutation_outputs = [ + MutationOutput(NoneLayout(self.device), buf, self) + for buf in self.mutable_args + ] + V.graph.register_operation(self) + + def get_outputs(self) -> List[Buffer]: + return list(self.mutation_outputs) + + def get_device(self) -> torch.device: + return self.device + + +class InplaceBernoulliFallback(ExternKernel): + """ + This needs to be a custom class to handle mutation properly + """ + + def codegen(self, wrapper): + (x,) = (t.codegen_reference() for t in self.inputs) + + if V.graph.cpp_wrapper and config.abi_compatible: + # Inductor doesn't really support aten Generator, so the Generator kwarg is always NULL here, + # which needs to be explicitly generated for cpp wrapper + wrapper.writeline( + f"{self.get_kernel_name()}({x}, {', '.join(map(repr, self.constant_args))}, NULL){wrapper.ending}" + ) + else: + wrapper.writeline( + f"{self.get_kernel_name()}({x}, {', '.join(map(repr, self.constant_args))}){wrapper.ending}" + ) + + def should_allocate(self): + return False + + def get_mutation_names(self): + return [self.inputs[0].get_name()] + + def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: + return OrderedSet() + + def __init__(self, op_overload, x, *constant_args): + super().__init__( + None, + NoneLayout(x.get_device()), # type: ignore[arg-type] + self.unwrap_storage([x]), + constant_args, + op_overload=op_overload, + ) + V.graph.mark_buffer_mutated(x.get_name()) + self.name = V.graph.register_buffer(self) + V.graph.register_operation(self) + if not config.abi_compatible: + # TODO: this should be simplified once we switch to ABI-compatible only + self.cpp_kernel_name = "at::native::bernoulli_" + + +# Used to deal with torch.complex types +class InplaceCopyFallback(ExternKernel): + """ + This needs to be a custom class to handle mutation properly + """ + + def codegen(self, wrapper): + (dst, src, non_blocking) = self.codegen_args() + wrapper.codegen_device_copy(src, dst) + + def should_allocate(self): + return False + + def get_mutation_names(self): + return [self.inputs[0].get_name()] + + def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: + return OrderedSet() + + def __init__( + self, + layout, + inputs, + constant_args, + ): + super().__init__( + None, + layout, + inputs, + constant_args, + python_kernel_name="aten.copy_", + cpp_kernel_name=( + "aoti_torch_copy_" if config.abi_compatible else "at::_ops::copy_::call" + ), + ) + V.graph.mark_buffer_mutated(inputs[0].get_name()) + self.name = V.graph.register_buffer(self) + V.graph.register_operation(self) + + @classmethod + def create(cls, dst, src, non_blocking: bool = False): + inputs = [cls.realize_input(t) for t in [dst, src]] + constant_args = (non_blocking,) + result = InplaceCopyFallback( + NoneLayout(dst.get_device()), # type: ignore[arg-type] + inputs, + constant_args, + ) + return result + + +class MutatingFirstArgExternKernel(ExternKernel): + """ + This needs to be a custom class to handle mutation properly + """ + + def codegen(self, wrapper): + argrefs = [ + *(t.codegen_reference() for t in self.inputs), + *map(repr, self.constant_args), + ] + wrapper.writeline( + f"{self.get_kernel_name()}({', '.join(argrefs)}){wrapper.ending}" + ) + + def should_allocate(self): + return False + + def get_mutation_names(self): + return [self.inputs[0].get_name()] + + def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: + return OrderedSet() + + def has_side_effects(self): + return True + + +class ResizeStorageBytes(MutatingFirstArgExternKernel): + def __init__(self, variable, new_size): + assert isinstance(new_size, int), "TODO: dynamic shapes" + super().__init__( + None, + NoneLayout(variable.get_device()), # type: ignore[arg-type] + self.unwrap_storage([variable]), + constant_args=(new_size,), + ) + V.graph.mark_buffer_mutated(variable.get_name()) + self.name = V.graph.register_buffer(self) + V.graph.register_operation(self) + self.python_kernel_name = "inductor_ops.resize_storage_bytes_" + self.cpp_kernel_name = "torch::inductor::resize_storage_bytes_" + V.graph.never_reuse_buffers.add(variable.data.get_name()) + + +class SetSourceTensorKernel(ExternKernelAlloc): + def __init__(self, self_tensor, storage_tensor): + self_tensor.freeze_layout() + super().__init__( + self_tensor.get_layout(), + [self_tensor, storage_tensor], + python_kernel_name="torch.ops.aten.set_.source_Tensor", + op_overload=torch.ops.aten.set_.source_Tensor, + ) + V.graph.never_reuse_buffers.add(self_tensor.data.get_name()) + V.graph.never_reuse_buffers.add(storage_tensor.get_name()) + V.graph.never_reuse_buffers.add(self.get_name()) + device = storage_tensor.get_device() + self.mutation_outputs = [ + MutationOutput(NoneLayout(device), self_tensor, self), + MutationOutput(NoneLayout(device), storage_tensor, self), + ] + + def get_inputs_that_alias_output(self): + return [self.inputs[0].get_name(), self.inputs[1].get_name()] + + +class ScatterFallback(ExternKernel): + """ + This needs to be a custom class to handle mutation properly. + This class handles both aten.scatter_ and aten.scatter_reduce_. + It also handle the case `src` being a scalar properly. + """ + + def codegen(self, wrapper): + reduce = self.kwargs["reduce"] + if V.graph.cpp_wrapper: + # Follow aten/src/ATen/native/ReductionType.h:get_operator_enum + get_operator_enum = {"add": "sum", "multiply": "prod"} + if reduce in get_operator_enum: + reduce = get_operator_enum[reduce] + + if self.src_is_tensor: + (x, index, src) = (t.codegen_reference() for t in self.inputs) + else: + (x, index) = (t.codegen_reference() for t in self.inputs) + src = self.constant_args[1] + wrapper.generate_scatter_fallback( + x, + [x, self.constant_args[0], index, src], + self.cpp_kernel_name, + self.python_kernel_name, + self.src_is_tensor, + reduce, + self.codegen_kwargs(), + ) + + def should_allocate(self): + return False + + def get_mutation_names(self): + return [self.inputs[0].get_name()] + + def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: + return OrderedSet() + + def __init__( + self, + op_overload, + x, + dim: int, + index, + src, + *, + reduce: Optional[str] = None, + include_self: bool = True, + ): + self.src_is_tensor = isinstance(src, TensorBox) + + constant_args: Tuple[Any, ...] + if self.src_is_tensor: + tensors = [self.realize_input(t) for t in [x, index, src]] + constant_args = (dim,) + else: + tensors = [self.realize_input(t) for t in [x, index]] + constant_args = (dim, src) + + super().__init__( + None, + NoneLayout(x.get_device()), # type: ignore[arg-type] + self.unwrap_storage(tensors), + constant_args, + {"reduce": reduce, "include_self": include_self}, + python_kernel_name=str(op_overload), + ordered_kwargs_for_cpp_kernel=["reduce", "include_self"], + op_overload=op_overload, + ) + V.graph.mark_buffer_mutated(x.get_name()) + self.name = V.graph.register_buffer(self) + V.graph.register_operation(self) + + +class IndexPutFallback(ExternKernel): + """ + This needs to be a custom class to handle mutation and indices properly + """ + + def codegen(self, wrapper): + (x, values, *valid_indices) = (t.codegen_reference() for t in self.inputs) + indices = [] + iter_valid_indices = iter(valid_indices) + for i, _ in enumerate(self.indices): + if self.indices[i] is not None: + indices.append(next(iter_valid_indices)) + else: + indices.append(V.graph.wrapper_code.none_str) + + wrapper.generate_index_put_fallback( + self.get_kernel_name(), x, indices, values, *self.codegen_const_args() + ) + + def should_allocate(self): + return False + + def get_mutation_names(self): + return [self.inputs[0].get_name()] + + def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: + return OrderedSet() + + def __init__(self, op_overload, x, indices, values, accumulate): + self.indices = indices + valid_indices = [i for i in indices if i is not None] + tensors = [self.realize_input(x) for x in [x, values, *valid_indices]] + cpp_kernel_name = ( + "aoti_torch_index_put_out" if config.abi_compatible else "at::index_put_out" + ) + super().__init__( + None, + NoneLayout(x.get_device()), # type: ignore[arg-type] + self.unwrap_storage(tensors), + (accumulate,), + python_kernel_name="aten.index_put_", + cpp_kernel_name=cpp_kernel_name, + op_overload=op_overload, + ) + V.graph.mark_buffer_mutated(self.inputs[0].get_name()) + self.name = V.graph.register_buffer(self) + V.graph.register_operation(self) + + +class DeviceCopy(ExternKernelOut): + @classmethod + def create(cls, x, device): + if ( + not x.is_extern() + and all(r in V.graph.constants for r in x.get_read_names()) + and not config.aot_inductor.use_runtime_constant_folding + ): + return x.constant_to_device(device) + + V.graph.add_device_info(device) + V.graph.add_device_info(x.get_device()) + + developer_warning("DeviceCopy in input program") + return DeviceCopy( + FlexibleLayout( + device=device, + dtype=x.get_dtype(), + size=x.get_size(), + ), + [cls.realize_input(x)], + ) + + def codegen(self, wrapper): + args = self.codegen_args() + assert len(args) == 1 + if self.output_view: + wrapper.codegen_device_copy(args[0], self.output_view.codegen_reference()) + else: + wrapper.codegen_device_copy(args[0], self.codegen_reference()) + + +class DynamicScalar(ExternKernel): + """ + The result of a call to aten._local_scalar_dense. + """ + + def get_reads(self): + return () + + def should_allocate(self): + return False + + def __init__(self, sym, keypath, data): + data.realize() + super().__init__(None, NoneLayout(torch.device("cpu")), self.unwrap_storage([data])) # type: ignore[arg-type] + self.sym = sym + self.keypath = keypath + + def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: + return OrderedSet([self.sym]) + + def codegen(self, wrapper): + wrapper.codegen_dynamic_scalar(self) + + +class AssertScalar(ExternKernel): + """ + The result of a call to aten._assert_scalar + """ + + def get_reads(self): + return () + + def should_allocate(self): + return False + + def __init__(self, scalar, msg): + super().__init__( + # Buffer(name, layotu) + None, + NoneLayout(torch.device("cpu")), # type: ignore[arg-type] + # InputsKernel(inputs) + [], + ) # type: ignore[arg-type] + self.scalar = scalar + self.msg = msg + + def has_side_effects(self): + return True + + def get_unbacked_symbol_uses(self): + return free_unbacked_symbols(self.scalar) + + def codegen(self, wrapper): + if V.graph.cpp_wrapper: + pass + else: + # NB: It is EXTREMELY important not to simplify the scalar under + # assertion here, because simplify is done with respect to + # runtime asserts. So if you have "u0 == 0" in the runtime + # asserts, if you subsequently try to simplify(u0 == 0), you will + # get True (because we've already runtime assert'ed that it's + # true). But we're code generating the actual runtime assert + # here!! + wrapper.writeline( + f"if not {V.graph.wrapper_code.codegen_python_sizevar(self.scalar, simplify=False)}:" + ) + wrapper.writeline(f" raise RuntimeError({repr(self.msg)})") + # No one should ever use this buffer, but for uniformity + # define the variable and assign it None + wrapper.writeline(f"{self.get_name()} = None") + + +@dataclasses.dataclass +class ExternKernelNode: + name: str + node: export_schema.Node + + +has_c_shim = OrderedSet( + [ + aten._embedding_bag.default, + aten._fft_c2c.default, + aten._scaled_dot_product_efficient_attention.default, + aten._scaled_dot_product_flash_attention.default, + aten._scaled_dot_product_cudnn_attention.default, + aten._scaled_mm.default, + aten.addmm.out, + aten.bmm.out, + aten.copy_.default, + aten.mm.out, + aten.repeat_interleave.Tensor, + aten.nonzero.default, + aten.view.dtype, + aten.view_as_real.default, + ] +) + + +class FallbackKernel(ExternKernelAlloc): + def __init__( + self, + layout, + kernel, + tensor_args, + nontensor_args, + unflatten_args, + kwargs=None, + *, + unbacked_bindings=None, + ): + if ( + kernel == aten.mul.Tensor + and len(tensor_args) == 1 + and len(nontensor_args) == 1 + ): + # When aten.mul.Tensor's second arg is constant, cpp wrapper expects + # to call mul_Scalar. A more proper fix is to do it in decomposition. + # See https://github.com/pytorch/pytorch/issues/123478 + kernel = aten.mul.Scalar + + super().__init__( + layout, + tuple(tensor_args), + tuple(nontensor_args), + op_overload=kernel, + ) + + # We need output buffers for generating kernel arguments in the + # abi-compatible mode, where we retrieve outputs by pass each individual + # output through the abi-compatible interface. + self.outputs: Sequence[Any] = [] + self.use_runtime_dispatch = False + self.unbacked_bindings = unbacked_bindings + + assert isinstance( + kernel, + ( + torch._ops.OpOverload, + torch._ops.HigherOrderOperator, + ), + ), f"Fails to create FallbackKernel for {kernel}: {type(kernel)} not supported" + self.op_overload = kernel + self.unflatten_args = unflatten_args + self.kwargs = {} if kwargs is None else kwargs + V.graph.warn_fallback(self.python_kernel_name) # type: ignore[arg-type] + + # args that are aliased + self.alias_names: List[str] = [] + # args that are mutated AND returned from the op + self.mutation_names: List[str] = [] + + if isinstance(self.op_overload, torch._ops.HigherOrderOperator): + # We assume here that HOPs with FallbackKernel are functional. + # This may not always be true! HOPs must individually opt-in to + # FallbackKernel, so please check this if you opt-in. + return + + if "_c10d_functional" in self.op_overload.name(): + # _c10d_functional kernels are lowered into _CollectiveKernel which + # derives from FallbackKernel for the cpp codegen. The kernels + # don't pass the can_auto_functionalize check, but their mutation + # is handled properly by _CollectiveKernel. + return + + schema = self.op_overload._schema + + # NOTE: [FallbackKernel supported operators] + # We only support three types of operators: + # - functional ops + # - view ops + # - inplace aten ops + # - mutating ops that are auto-functionalizable. That is, + # the operator may mutate any number of inputs, but its outputs + # may not alias any of the inputs. + # + # The unsupported cases usually do not show up here (because + # AOTAutograd functionalized them away); the only way for an in-place + # op to show up here is if a lowering or pass introduced it. + if torch._library.utils.mutates_and_returns_first_arg(self.op_overload): + self.mutation_names.append(tensor_args[0].get_name()) + return + + if schema.is_mutable and not can_auto_functionalize(kernel): + raise NotImplementedError( + f"NYI: Can't generate FallbackKernel for {kernel}" + ) + + schema_args = schema.arguments + args, kwargs = self.unflatten_args(self.inputs, self.constant_args) + + def handle_aliasing_and_mutation(info, arg): + # Assertions to make sure we didn't mismatch args + if isinstance(info.type, torch.ListType): + assert isinstance(arg, (list, tuple)) + is_optional_tensor = isinstance( + info.type, torch.OptionalType + ) and isinstance(info.type.getElementType(), torch.TensorType) + is_list_tensor = isinstance(info.type, torch.ListType) and isinstance( + info.type.getElementType(), torch.TensorType + ) + if is_optional_tensor or isinstance(info.type, torch.TensorType): + # PyTorch also accepts None and scalar types for args marked as "Tensor". + # We're not going to check all of them here. + assert not isinstance(arg, (tuple, list)) + + if arg is None: + return + if info.alias_info is None: + return + + def add_alias(t): + self.alias_names.append(t.get_name()) + if info.alias_info.is_write: + self.mutation_outputs.append( + MutationOutput(NoneLayout(t.get_device()), t, self) + ) + + if is_list_tensor: + for tensor_arg in arg: + add_alias(tensor_arg) + else: + assert isinstance(info.type, torch.TensorType) or is_optional_tensor + add_alias(arg) + + for info, arg in torch._library.utils.zip_schema(schema, args, kwargs): + handle_aliasing_and_mutation(info, arg) + + def codegen_unbacked_symbol_defs(self, wrapper): + if not hasattr(self, "unbacked_bindings"): + return + + unbacked_bindings = resolve_unbacked_bindings( + V.graph.sizevars.shape_env, self.unbacked_bindings + ) + + if not unbacked_bindings: + return + + for s, keypath in unbacked_bindings.items(): + + def go(expr, keypath): + if keypath == (): + return expr + + if ( + len(keypath) >= 2 + and isinstance(keypath[0], CallMethodKey) + and isinstance(keypath[1], pytree.SequenceKey) + ): + return go( + f"{expr}.{keypath[0].name}({keypath[1].idx})", keypath[2:] + ) + elif isinstance(keypath[0], CallMethodKey): + return go(f"{expr}.{keypath[0].name}()", keypath[1:]) + elif isinstance(keypath[0], pytree.SequenceKey): + return ( + go(f"std::get<{keypath[0].idx}>({expr})", keypath[1:]) + if V.graph.cpp_wrapper + else go(f"{expr}[{keypath[0].idx}]", keypath[1:]) + ) + elif isinstance(keypath[0], DivideByKey): + # TODO: need to assert divisibility + # TODO: this is invalid C++ codegen + return go(f"{expr}.__floordiv__({keypath[0].divisor})", keypath[1:]) + else: + raise AssertionError(f"unrecognized keypath {keypath}") + + def go_outer(): + if V.graph.cpp_wrapper and config.abi_compatible: + # Special handling for the top level buffer access, + # because self.get_name() is actually never bound; the + # individual output arguments are bound by + # generate_c_shim_fallback_kernel + if len(self.outputs) == 1: + return go(self.outputs[0].get_name(), keypath) + else: + assert isinstance(keypath[0], pytree.SequenceKey) + return go(self.outputs[keypath[0].idx].get_name(), keypath[1:]) + else: + return go(self.get_name(), keypath) + + wrapper.writeline( + f"{wrapper.codegen_unbacked_symbol_decl(s)} = {go_outer()}{wrapper.ending}" + ) + + def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: + if unbacked_bindings := getattr(self, "unbacked_bindings", None): + return resolve_unbacked_bindings( + V.graph.sizevars.shape_env, unbacked_bindings + ).keys() + else: + return OrderedSet() + + def codegen_args(self): + @dataclasses.dataclass + class Shim: + ref: Any + + def __repr__(self) -> str: + return self.ref + + tensor_args = [Shim(x.codegen_reference()) for x in self.inputs] + args, kwargs = self.unflatten_args(tensor_args, self.constant_args) + if V.graph.cpp_wrapper and isinstance(self.op_overload, torch._ops.OpOverload): + args = self.fill_non_provided_args(args, kwargs) + args = [ + V.graph.wrapper_code.val_to_arg_str(x, param.real_type) + for param, x in zip(self.op_overload._schema.arguments, args) + ] + else: + args = [V.graph.wrapper_code.val_to_arg_str(x) for x in args] + + # let self.codegen_kwargs handle kwargs + self.kwargs.update(kwargs) + return args + + @staticmethod + def find_device(tensor_args, example_output): + if tensor_args: + devices = [arg.get_device() for arg in tensor_args if arg.get_device()] + return devices[0] + if isinstance(example_output, torch.Tensor): + return example_output.device + if isinstance(example_output, (list, tuple)): + device_set = OrderedSet( + FallbackKernel.find_device(None, x) for x in example_output + ) + # Remove None + devices = [device for device in device_set if device] + if len(devices) == 1: + return devices[0] + for device in devices: + if is_gpu(device.type): + return device + return devices[0] + return None + + def has_side_effects(self): + if isinstance(self.op_overload, torch._ops.HigherOrderOperator): + return False + return get_schema_info(self.op_overload).is_mutable() + + def get_inputs_that_alias_output(self): + return self.alias_names + + def get_mutation_names(self): + assert len(self.mutation_names) <= 1 + return self.mutation_names + + # ProxyExecutor Design Note + # We export the ExternFallbackNodes (for custom ops) into a serialized file + # and run it with a host side proxy executor to address the ABI problem + # This is currently only implemented for fbcode. Eventually, we will also make this work for OSS. + # Detailed design doc can be found at + # https://docs.google.com/document/d/1wC4DOZFaYym2t1Esz0X5yxlLI3RDnSiyRbUus3bkJ64/edit?usp=sharing + def export_extern_kernel_node(self): + assert isinstance(self, FallbackKernel) + args, kwargs = self.unflatten_args(self.inputs, self.constant_args) + args = self.fill_non_provided_args(args, kwargs) + ordered_kwargs = [ + kwargs.get(key, None) for key in self.ordered_kwargs_for_cpp_kernel + ] + if not V.graph.aot_mode: + # No need to serialize in the cpp wrapper JIT mode + return [*args, *ordered_kwargs] + + serializer = GraphModuleSerializer(None, None) # type: ignore[arg-type] + named_arguments = serializer.serialize_inputs(self.op_overload, args, kwargs) # type: ignore[arg-type] + + # serialize_outputs + def handle_single_output(return_type, output): + if isinstance(return_type, torch.TensorType): + # For single Tensor + out = output + if isinstance(output, (list, tuple)): + assert len(output) == 1 + out = output[0] + return export_schema.Argument.create( + as_tensor=export_schema.TensorArgument(name=out.get_name()) + ) + elif isinstance(return_type, torch.ListType) and isinstance( + return_type.getElementType(), torch.TensorType + ): + # For single TensorList + return export_schema.Argument.create( + as_tensors=[ + export_schema.TensorArgument(name=out.get_name()) + for out in output + ] + ) + else: + raise RuntimeError(f"Unsupported return type {type(return_type)}") + + target = self.op_overload + returns = target._schema.returns # type: ignore[union-attr] + if len(returns) == 1: + return_type = returns[0].real_type + output_arguments = [handle_single_output(return_type, self.outputs)] + else: + # For tuple returns, e.g "-> (Tensor, Tensor)" or "-> (Tesnor, Tensor[])" + assert isinstance(self.outputs, tuple) + assert len(returns) == len(self.outputs) + output_arguments = [ + handle_single_output(return_schema.real_type, output) + for return_schema, output in zip(returns, self.outputs) + ] + + node = ExternKernelNode( + name=self.get_name(), + node=export_schema.Node( + target=self.op_overload.name(), # type: ignore[union-attr] + inputs=named_arguments, + outputs=output_arguments, + metadata={}, + ), + ) + + V.graph.extern_kernel_nodes.append(node) + + return [*args, *ordered_kwargs] + + def codegen(self, wrapper): + kernel = self.op_overload + if kernel.namespace == "aten": # type: ignore[union-attr] + # Aten Fallback Ops + assert isinstance(kernel, torch._ops.OpOverload) + if V.graph.cpp_wrapper: + from torchgen.aoti.fallback_ops import inductor_fallback_ops + + if config.abi_compatible and str(kernel) not in inductor_fallback_ops: + # C shim v2 is torchgen-ed, which should cover all aten ops. + # If you do hit a missed op, please update fallback_ops.py. + log.warning( + "%s is missing a c-shim implementation, using proxy executor as fallback", + kernel, + ) + self.use_runtime_dispatch = True + elif kernel.namespace == "_quantized": # type: ignore[union-attr] + # Internal Quantized Fallback Ops + assert isinstance(kernel, torch._ops.OpOverload) + if V.graph.cpp_wrapper: + if not config.abi_compatible: + self.use_runtime_dispatch = True + else: + # For non-aten OpOverload, i.e. custom ops + if V.graph.cpp_wrapper: + self.use_runtime_dispatch = True + + if self.use_runtime_dispatch: + self.codegen_comment(wrapper) + + exported_args = None + args = None + if config.abi_compatible: + exported_args = self.export_extern_kernel_node() + else: + args = [*self.codegen_args(), *self.codegen_kwargs()] + + wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed( + self.get_name(), + self.python_kernel_name, + self.cpp_kernel_name, + args, + self.cpp_op_schema, + self.cpp_kernel_key, + self.cpp_kernel_overload_name, + self.op_overload, + exported_args, + self.outputs, + ) + else: + self.codegen_comment(wrapper) + args = [*self.codegen_args(), *self.codegen_kwargs()] + V.graph.wrapper_code.generate_fallback_kernel(self, args) + if isinstance(self.layout, Layout): + self.codegen_size_asserts(wrapper) + + self.codegen_unbacked_symbol_defs(wrapper) + + @staticmethod + def tensor_to_layout(output: torch.Tensor): + return FixedLayout( + output.device, + output.dtype, + convert_shape_to_inductor(output.size()), + convert_shape_to_inductor(output.stride()), + ) + + @classmethod + def create(cls, kernel, *args, **kwargs): + fake_incorrect_kernels = (aten._fused_moving_avg_obs_fq_helper_functional,) + context: ContextManager[None] = ( + V.graph.fake_mode if kernel not in fake_incorrect_kernels else nullcontext() # type: ignore[assignment] + ) + with context: + ( + example_output, + tensor_args, + non_tensor_args, + unflatten_args, + unbacked_bindings, + ) = cls.process_kernel(kernel, *args, **kwargs) + + device = cls.find_device(tensor_args, example_output) + if example_output is None: + packed = cls( + NoneLayout(device), + kernel, + tensor_args, + non_tensor_args, + unflatten_args, + unbacked_bindings=unbacked_bindings, + ) + + else: + assert device, "Not sure where to find device info" + packed = cls( + MultiOutputLayout(device), + kernel, + tensor_args, + non_tensor_args, + unflatten_args, + unbacked_bindings=unbacked_bindings, + ) + + def generate_output(output, indices): + if isinstance(output, (list, tuple)): + return type(output)( + generate_output(output[i], indices + [(type(output), i)]) + for i in range(len(output)) + ) + elif isinstance(output, dict): + return { + key: generate_output(val, indices + [(type(output), key)]) + for key, val in output.items() + } + elif isinstance(output, torch.Tensor): + return MultiOutput( + cls.tensor_to_layout(output), + packed, + indices, + ) + elif isinstance(output, int): + return output + elif isinstance(output, torch.SymInt): + return output.node.expr + else: + assert ( + output is None + ), f"FallbackKernel output type {type(output)} is not supported" + return None + + outputs = generate_output(example_output, []) + if isinstance(outputs, (list, tuple, dict)): + packed.outputs = outputs # type: ignore[assignment] + else: + packed.outputs = [outputs] + return outputs + + def apply_constraint(self): + return super().apply_constraint() + + +@dataclasses.dataclass +class ComplexView(FallbackKernel): + """View a complex number as two dtyped numbers or vice versa""" + + def should_allocate(self): + return False + + def get_inputs_that_alias_output(self): + # Signal to codegen that our output buffer isn't safe to reuse + return [self.inputs[0].get_name()] + + def __init__( + self, + layout, + kernel, + tensor_args, + nontensor_args, + unflatten_args, + *, + unbacked_bindings=None, + ): + super().__init__( + layout, + kernel, + tensor_args, + nontensor_args, + unflatten_args, + unbacked_bindings=unbacked_bindings, + ) + + +@dataclasses.dataclass +class MultiOutputLayout(IRNode): + device: torch.device + + +class MultiOutput(ExternKernel): + # Given an input MultiOutputLayout buffer, indexes out an actual buffer + # from that result. This doesn't actually produce multiple outputs, + # that's MultiOutputLayout! + def codegen_list_tuple_access(self, basename, indices): + if len(indices) > 0: + itype, i = indices[0] + if issubclass(itype, list): + return self.codegen_list_tuple_access(f"{basename}[{i}]", indices[1:]) + elif issubclass(itype, tuple): + # cpp wrapper code needs to use std::get<> to access a tuple + tuple_access = V.graph.wrapper_code.codegen_tuple_access( + basename, self.get_name(), str(i) + ) + return self.codegen_list_tuple_access(tuple_access, indices[1:]) + elif issubclass(itype, dict): + return self.codegen_list_tuple_access(f"{basename}['{i}']", indices[1:]) + else: + raise AssertionError("non supported index type: ", itype) + else: + return basename + + def codegen(self, wrapper): + wrapper.codegen_multi_output( + self.get_name(), + self.codegen_list_tuple_access(self.inputs[0].get_name(), self.indices), + ) + + def __init__(self, layout, input, indices: List[Tuple[Any, ...]]): + super().__init__(None, layout, [input], ()) + self.name = V.graph.register_buffer(self) + V.graph.register_operation(self) + self.indices = indices + + def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: + return self.inputs[0].get_unbacked_symbol_uses() + + def should_allocate(self): + return False + + def get_inputs_that_alias_output(self): + return [ + inp.get_name() + for inp in self.inputs + if isinstance(inp, FallbackKernel) + and len(inp.get_inputs_that_alias_output()) > 0 + ] + + +@dataclasses.dataclass +class MutableBox(IRNode): + """ + TensorBox / StorageBox allow in-place mutation of Tensors + """ + + data: IRNode + + def __getattr__(self, name): + fn = getattr(self.data, name) + if callable(fn): + return fn + raise AttributeError(f"{type(self.data).__name__}.{name} not callable") + + def realize(self): + return self.data.realize() + + def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: + return self.data.get_unbacked_symbol_uses() + + def get_read_names(self) -> OrderedSet[str]: + return self.data.get_read_names() + + def get_defining_op(self): + return self.data.get_defining_op() + + def codegen_reference(self, writer=None): + return self.data.codegen_reference(writer) + + @property + def layout(self): + return self.data.get_layout() + + def get_layout(self): + return self.layout + + def get_size(self): + return self.data.get_size() + + @property + def dtype(self): + return self.data.dtype + + def __str__(self) -> str: + if isinstance(self.data, MutableBox): + line0 = f"{type(self).__name__}({type(self.data).__name__}(" + endl = "))" + inner = self.data.data + else: + line0 = f"{type(self).__name__}(" + inner = self.data + endl = ")" + + lines = [ + line0, + indent(str(inner)), + endl, + ] + return "\n".join(lines) + + __repr__ = __str__ + + +class TensorBox(MutableBox): + @staticmethod + def create(data): + return TensorBox(StorageBox(data)) + + +class StorageBox(MutableBox): + def is_input_buffer(self): + if isinstance(self.data, (InputBuffer, ReinterpretView)): + return self.data.get_name() in V.graph.graph_inputs + return False + + def is_module_buffer(self): + return ( + isinstance(self.data, (ConstantBuffer)) + and self.data.get_name() in V.graph.constants + ) + + def realize(self): + if isinstance( + self.data, + ( + ComputedBuffer, + InputsKernel, + InputBuffer, + ReinterpretView, + TemplateBuffer, + ), + ): + return self.data.get_name() + assert isinstance(self.data, (Pointwise, Reduction, Scan, Sort)), type( + self.data + ) + origin_node = self.data.get_origin_node() + traceback = self.data.get_traceback() + self.data = ComputedBuffer( + name=None, + layout=FlexibleLayout( + device=self.data.get_device(), + dtype=self.data.get_dtype(), + size=self.data.get_size(), + ), + data=self.data, + ) + self.data.name = V.graph.register_buffer(self.data) + V.graph.register_operation(self.data) + self.data.origins = self.origins + self.data.origin_node = origin_node + self.data.traceback = traceback + return self.data.name + + def realize_hint(self): + """ + Called on buffers we expect to be forced to realize later. + """ + if ( + isinstance(self.data, (Pointwise, Reduction)) + and self.data.inner_fn_opcount().nontrivial_read_count > 1 + ): + self.realize() + + def has_exceeded_max_reads(self): + return isinstance(self.data, Pointwise) and ( + self.num_reads() > config.realize_acc_reads_threshold + or self.has_large_inner_fn() + ) + + def should_realize_on_reuse(self, users): + """ + A heuristic to decide if we should realize a tensor + that is used multiple times. + """ + if users > 1 and isinstance(self.data, (Pointwise, Reduction)): + if is_cpu(self.data): + # Heuristic for realizing reused result of heavy ops on cpu + opcount = self.data.inner_fn_opcount() + heavy_ops = ["exp", "sigmoid"] # a list of heavy ops + if any(x in opcount.used_ops for x in heavy_ops): + return True + return ( + self.num_reads() > config.realize_reads_threshold + or self.has_large_inner_fn() + ) + return False + + def mark_reuse(self, users): + if self.should_realize_on_reuse(users): + self.realize() + + def num_reads(self): + return self.data.num_reads() + + +@dataclasses.dataclass +class Subgraph(IRNode): + name: str + graph_module: torch.fx.GraphModule + graph: Optional[GraphLowering] = None + + +def _has_aliased_buffers(buffers: Sequence[IRNode]) -> bool: + buffers = [ + buffer.unwrap_view() if isinstance(buffer, ReinterpretView) else buffer + for buffer in buffers + ] + # assuming the same buffer is represented by the same IRNode object + return len(OrderedSet(id(buffer) for buffer in buffers)) < len(buffers) + + +@dataclasses.dataclass +class Conditional(ExternKernel): + predicate: Optional[IRNode] = None + operands: Optional[List[TensorBox]] = None + true_subgraph: Optional[Subgraph] = None + false_subgraph: Optional[Subgraph] = None + outputs: Optional[List[MultiOutput]] = None + + def __init__( + self, + predicate: IRNode, + operands: List[TensorBox], + true_subgraph: Subgraph, + false_subgraph: Subgraph, + layout: MultiOutputLayout, + ): + self.predicate = predicate + self.operands = operands + self.true_subgraph = true_subgraph + self.false_subgraph = false_subgraph + + inputs = [] + if not isinstance(predicate, ShapeAsConstantBuffer): + inputs.append(predicate) + inputs.extend(operands) + + super().__init__( + name=None, + layout=layout, # type: ignore[arg-type] + inputs=inputs, # type: ignore[list-item] + ) + + self.name = V.graph.register_buffer(self) + V.graph.register_operation(self) + + @classmethod + def create( + cls, + predicate: TensorBox, + true_fn: Subgraph, + false_fn: Subgraph, + operands: List[TensorBox], + ): + predicate = cls.realize_input(predicate) + operands = [cls.realize_input(x) for x in operands] + + fx_operands = V.graph.current_node.args[-1] + fake_operands = [x.meta["val"] for x in fx_operands] # type: ignore[union-attr] + + for subgraph in (true_fn, false_fn): + if subgraph.graph is None: + # create and lower subgraphs + subgraph.graph = V.graph.make_subgraph( + gm=subgraph.graph_module, + example_inputs=fake_operands, + subgraph_name=subgraph.name, + ) + with V.set_graph_handler(subgraph.graph): + subgraph.graph.run(*fake_operands) + + true_outputs = true_fn.graph.graph_outputs # type: ignore[union-attr] + false_outputs = false_fn.graph.graph_outputs # type: ignore[union-attr] + + for name, outputs in (("true_fn", true_outputs), ("false_fn", false_outputs)): + if _has_aliased_buffers(true_outputs): + raise AssertionError( + "Output aliasing is currently not supported in compiled torch.cond. " + f"The outputs of the {name} subgraph of torch.cond are aliased: {outputs}" + ) + + # make sure true and false outputs are structurally equivalent + assert len(true_outputs) == len(false_outputs), (true_outputs, false_outputs) + for i, (to, fo) in enumerate(zip(true_outputs, false_outputs)): + assert to.get_size() == fo.get_size(), (i, to, fo) + assert to.get_stride() == fo.get_stride(), (i, to, fo) + assert to.get_device() == fo.get_device(), (i, to, fo) + assert to.get_dtype() == fo.get_dtype(), (i, to, fo) + assert to.get_layout().offset == fo.get_layout().offset, (i, to, fo) + + if not isinstance(predicate, ShapeAsConstantBuffer): + # use predicate device for consistent codegen-ing + device = predicate.get_device() + else: + # predicate is not a Tensor: use first operand's device + assert ( + len(operands) > 0 + ), "When predicate is not a Tensor, there must be at least one operand in torch.cond." + device = operands[0].get_device() + + conditional = Conditional( + predicate=predicate, + operands=operands, + true_subgraph=true_fn, + false_subgraph=false_fn, + layout=MultiOutputLayout(device), + ) + + outputs = [ + MultiOutput( + FixedLayout( + device=output.get_device(), + dtype=output.get_dtype(), + size=output.get_size(), + stride=output.get_stride(), + offset=output.get_layout().offset, + ), + conditional, + [(list, i)], + ) + # as the true and false outputs are equivalent, + # we can use either of them here as a "template" + for i, output in enumerate(true_outputs) + ] + + conditional.outputs = outputs + return outputs + + def codegen(self, wrapper): + wrapper.codegen_conditional(self) + + +@dataclasses.dataclass +class WhileLoop(ExternKernel): + carried_inputs: Optional[List[TensorBox]] = None + additional_inputs: Optional[List[TensorBox]] = None + cond_subgraph: Optional[Subgraph] = None + body_subgraph: Optional[Subgraph] = None + outputs: Optional[List[MultiOutput]] = None + + def __init__( + self, + carried_inputs: List[TensorBox], + additional_inputs: List[TensorBox], + cond_subgraph: Subgraph, + body_subgraph: Subgraph, + layout: MultiOutputLayout, + ): + self.carried_inputs = carried_inputs + self.additional_inputs = additional_inputs + self.cond_subgraph = cond_subgraph + self.body_subgraph = body_subgraph + + super().__init__( + name=None, + layout=layout, # type: ignore[arg-type] + inputs=carried_inputs + additional_inputs, # type: ignore[list-item] + ) + + self.name = V.graph.register_buffer(self) + V.graph.register_operation(self) + + @classmethod + def create( + cls, + cond_fn: Subgraph, + body_fn: Subgraph, + carried_inputs: List[TensorBox], + additional_inputs: List[TensorBox], + ): + carried_inputs = [cls.realize_input(x) for x in carried_inputs] + additional_inputs = [cls.realize_input(x) for x in additional_inputs] + all_inputs = carried_inputs + additional_inputs + + fx_all_inputs = V.graph.current_node.args[-2] + V.graph.current_node.args[-1] # type: ignore[operator] + fake_all_inputs = [x.meta["val"] for x in fx_all_inputs] # type: ignore[union-attr] + + for subgraph in (cond_fn, body_fn): + if subgraph.graph is None: + # create and lower subgraphs + subgraph.graph = V.graph.make_subgraph( + gm=subgraph.graph_module, + example_inputs=fx_all_inputs, # type: ignore[arg-type] + subgraph_name=subgraph.name, + ) + with V.set_graph_handler(subgraph.graph): + subgraph.graph.run(*fake_all_inputs) + + cond_outputs = cond_fn.graph.graph_outputs # type: ignore[union-attr] + body_outputs = body_fn.graph.graph_outputs # type: ignore[union-attr] + + if _has_aliased_buffers(body_outputs): + raise AssertionError( + "Output aliasing is currently not supported in compiled torch.while_loop. " + f"The outputs of the body_fn subgraph of torch.while_loop are aliased: {body_outputs}" + ) + + # make sure cond_fn returns a boolean scalar Tensor + assert len(cond_outputs) == 1, cond_outputs + assert cond_outputs[0].get_dtype() == torch.bool, cond_outputs + assert len(cond_outputs[0].get_size()) == 0, cond_outputs + + assert ( + len(all_inputs) > 0 + ), "torch.while_loop is assumed to have at least one operand." + + device = all_inputs[0].get_device() + + # make sure carried_inputs and body outputs are structurally equivalent + assert len(carried_inputs) == len(body_outputs), (carried_inputs, body_outputs) + for i, (op, bo) in enumerate(zip(carried_inputs, body_outputs)): + assert op.get_size() == bo.get_size(), (i, op, bo) + assert op.get_stride() == bo.get_stride(), (i, op, bo) + # assume all carried_inputs and outputs are on the same device + # as the MultiOutputLayout below requires single device + assert op.get_device() == bo.get_device() == device, (i, op, bo, device) + assert op.get_dtype() == bo.get_dtype(), (i, op, bo) + assert op.get_layout().offset == bo.get_layout().offset, (i, op, bo) + + while_loop = WhileLoop( + carried_inputs=carried_inputs, + additional_inputs=additional_inputs, + cond_subgraph=cond_fn, + body_subgraph=body_fn, + # asserted above that there is at least one operand + layout=MultiOutputLayout(device), + ) + + outputs = [ + MultiOutput( + FixedLayout( + device=output.get_device(), + dtype=output.get_dtype(), + size=output.get_size(), + stride=output.get_stride(), + offset=output.get_layout().offset, + ), + while_loop, + [(list, i)], + ) + for i, output in enumerate(body_outputs) + ] + + for inp, out in zip(carried_inputs, outputs): + if inp.get_name() in V.graph.graph_inputs: + # if a carried input of the while_loop is a graph input, + # it can be returned as is when the number of iterations + # is zero. due to this, we can't (generally) reuse the + # output buffers corresponding to the graph inputs, as + # the inputs may end up being mutated. + V.graph.never_reuse_buffers.add(out.get_name()) + + while_loop.outputs = outputs + return outputs + + def codegen(self, wrapper): + wrapper.codegen_while_loop(self) + + +class EffectfulKernel(FallbackKernel): + def __init__( + self, + layout, + kernel, + tensor_args, + nontensor_args, + unflatten_args, + kwargs=None, + *, + unbacked_bindings=None, + ): + super().__init__( + layout, + kernel, + tensor_args, + nontensor_args, + unflatten_args, + kwargs=None, + unbacked_bindings=unbacked_bindings, + ) + + from torch._higher_order_ops.effects import get_effect_key + + effect_type = get_effect_key(kernel, (*nontensor_args, *tensor_args), kwargs) + assert effect_type is not None + self.effect_type = effect_type + self.prev_effect_buffer = V.graph.effectful_ops.get(effect_type, None) + V.graph.effectful_ops[effect_type] = self + + def get_read_writes(self): + read_writes = super().get_read_writes() + + if self.prev_effect_buffer is not None: + read_writes.reads.add( + dependencies.StarDep(self.prev_effect_buffer.get_name()) + ) + + return read_writes + + def has_side_effects(self): + return True + + +@dataclasses.dataclass +class TorchBindObject(IRNode): + name: str + value: torch._C.ScriptObject + + def get_name(self): + return self.name + + def get_device(self): + return None # is there a device?? + + def codegen_reference(self, writer=None): + return self.name + + +class _CollectiveKernel(FallbackKernel): + def should_allocate(self): + return False + + def has_side_effects(self): + return True + + # This is identical to FallbackKernel.set_cpp_kernel(), minus the + # part that checks against input aliasing and mutation. + def set_cpp_kernel_name(self, cpp_kernel_name: Optional[str] = None): + from .codegen.wrapper import get_cpp_op_schema + + assert ( + type(self.op_overload) is torch._ops.OpOverload + ), "Setting cpp kernel needs a valid op_overload" + kernel = self.op_overload + self.cpp_kernel_name = kernel._schema.name + self.cpp_kernel_overload_name = kernel._schema.overload_name + self.cpp_kernel_key = f"{self.cpp_kernel_name.replace('::', '_')}_{self.cpp_kernel_overload_name}" # type: ignore[union-attr] + + self.cpp_op_schema = get_cpp_op_schema(kernel) + self.ordered_kwargs_for_cpp_kernel = [ + x.name for x in kernel._schema.arguments if x.kwarg_only + ] + + # NOTE: [In-Place Collective Safety] + # Between the initiation and completion of an in-place collective, the + # input buffers are subject to both volatile reads and volatile writes. + # They must not be read, written to or reused by another kernel. To ensure + # the constraints, we model collective -> wait_tensor as as two-step + # mutation of the input buffers. + @classmethod + def create_inplace( + cls, kernel, inputs: Union[TensorBox, List[TensorBox]], *args, **kwargs + ) -> None: + with V.graph.fake_mode: + ( + example_output, + tensor_args, + non_tensor_args, + unflatten_args, + unbacked_bindings, + ) = cls.process_kernel(kernel, inputs, *args, **kwargs) + assert not unbacked_bindings, f"{kernel} {unbacked_bindings}" + for tensor_arg in tensor_args: + tensor_arg.realize() + + device = tensor_args[0].get_device() + packed = cls( + NoneLayout(device), + kernel, + tensor_args, + non_tensor_args, + unflatten_args, + ) + + inps = pytree.tree_leaves(inputs) + packed.mutation_outputs.extend( + [MutationOutput(NoneLayout(device), buf, packed) for buf in inps] + ) + + # For inplace collective ops, the input is guaranteed to be alias of the returned value of op. + packed.alias_names.extend([inp.get_name() for inp in inps]) + if "out" in kwargs: + packed.mutation_outputs.append( + MutationOutput(NoneLayout(device), kwargs["out"], packed) + ) + # For out-variant collective ops, the `out=` arg is guaranteed to be alias of the returned value of op. + packed.alias_names.append(kwargs["out"].get_name()) + + # NOTE: [Out-of-Place Collective Safety] + # Between the initiation and completion of an out-of-place collective: + # + # Input buffers: + # - Are subject to volatile reads + # - Can be read by another kernel + # - Must not be written to or reused by another kernel + # + # Output buffers: + # - Are subject to volatile writes + # - Must not be read, written to or reused by another kernel + # + # To ensure the safety of input buffers without sacrificing read + # availability, we add input buffers as read deps of wait_tensor kernels. + # + # To ensure the safety of output buffers, we model wait_tensor as a + # mutation to the output buffer. Note we also assumes the user program being + # correct and the output buffer is not consumed by kernels other than + # wait_tensor. + # + # TODO(yifu): add a pre-grad pass to validate the correctness of collective + # usage in the user program. + @classmethod + def create_out_of_place( + cls, kernel, inputs: Union[TensorBox, List[TensorBox]], *args, **kwargs + ): + with V.graph.fake_mode: + ( + example_output, + tensor_args, + non_tensor_args, + unflatten_args, + unbacked_bindings, + ) = cls.process_kernel(kernel, inputs, *args, **kwargs) + assert not unbacked_bindings, f"{kernel}, {unbacked_bindings}" + for tensor_arg in tensor_args: + tensor_arg.realize() + + if isinstance(example_output, list): + device = cls.find_device(tensor_args, example_output) + packed = cls( + MultiOutputLayout(device), + kernel, + tensor_args, + non_tensor_args, + unflatten_args, + ) + packed.outputs = [ + MultiOutput( + cls.tensor_to_layout(tensor), + packed, + [(list, i)], + ) + for i, tensor in enumerate(example_output) + ] + return packed.outputs + else: + packed = cls( + cls.tensor_to_layout(example_output), + kernel, + tensor_args, + non_tensor_args, + unflatten_args, + ) + packed.outputs = [packed] + return packed + + +class _WaitKernel(_CollectiveKernel): + def get_volatile_reads(self): + inp = self.inputs[0] + if isinstance(inp, _CollectiveKernel): + # Out-of-place single-output + return [inp.inputs[0]] + elif isinstance(inp, MultiOutput): + # This can be two things: + # 1. Out-of-place multi-output coll + # 2. In-place coll with inputs coming from another MultiOutput + coll = inp.inputs[0] + # Case 1 + if isinstance(coll, _CollectiveKernel): + _, idx = inp.indices[0] + return [coll.inputs[idx]] + # Case 2 + return [] + else: + # In-place requires no additional deps handling for volatile + # reads since the inputs are mutated. + return [] + + @classmethod + def create_wait(cls, kernel, inp: TensorBox) -> None: + with V.graph.fake_mode: + ( + example_output, + tensor_args, + non_tensor_args, + unflatten_args, + unbacked_bindings, + ) = cls.process_kernel(kernel, inp) + assert not unbacked_bindings, f"{kernel} {unbacked_bindings}" + packed = cls( + NoneLayout(inp.get_device()), + kernel, + tensor_args, + non_tensor_args, + unflatten_args, + ) + packed.mutation_outputs.append( + MutationOutput(NoneLayout(inp.get_device()), inp, packed) + ) + + def get_read_writes(self): + read_writes = super().get_read_writes() + # See [Out-of-Place Collective Safety]. + volatile_reads = self.get_volatile_reads() + for vr in volatile_reads: + read_writes.reads.add(dependencies.StarDep(vr.get_name())) + return read_writes + + +# NB: recursive structure here reflects val_to_arg_str, avoid +# calling free_unbacked_symbols on "exotic" types that don't get pexpr +# treatment +def maybe_free_unbacked_symbols(s: object) -> OrderedSet[Symbol]: + if isinstance(s, (SymTypes, Expr)): + # This branch should be impossible in return position + return free_unbacked_symbols(s) + elif isinstance(s, (tuple, list)): + r: OrderedSet[sympy.Symbol] = OrderedSet() + for t in s: + r |= maybe_free_unbacked_symbols(t) + return r + elif isinstance(s, torch.Tensor): + # This branch is impossible in constant-args position + return free_unbacked_symbols(s) + else: + return OrderedSet() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/jagged_lowerings.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/jagged_lowerings.py new file mode 100644 index 0000000000000000000000000000000000000000..c96c9f4ae2d443779f7514e3077287c327c92123 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/jagged_lowerings.py @@ -0,0 +1,264 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +from typing import List, Optional, Tuple, Union + +import sympy + +import torch + +from .ir import Pointwise, TensorBox +from .lowering import fallback_handler, is_integer_type, register_lowering +from .virtualized import ops + + +# pyre-ignore[2,3] +def dense_idx_to_jagged_idx(batch_idx, seq_idx, offsets_loader, jagged_len): + # jagged_len + 1 is used as the upper bound, + # because the last sequence length may be zero + begin_idx = ops.indirect_indexing( + offsets_loader([batch_idx]), + jagged_len + 1, + ) + end_idx = offsets_loader([batch_idx + 1]) + jagged_idx = begin_idx + seq_idx + return jagged_idx, end_idx + + +def get_inverse_offsets( + offsets: TensorBox, + jagged_len: Union[int, sympy.Expr], + realize: bool = True, +) -> TensorBox: + """ + Returns "inverse_offsets" - the inverse of the offsets array. + offsets maps batch index (dense) to jagged index (i.e. offset into jagged tensor). + inverse_offsets maps jagged index to batch index. + + e.g. for offsets [0, 3, 4, 9, 10] this will return + inverse_offsets = [0, 0, 0, 1, 2, 2, 2, 2, 2, 3] + + For the given offsets, the computed inverse_offsets are cached + on the first call and reused in the further calls. + """ + + if hasattr(offsets, "inverse_offsets"): + # inverse_offsets are already computed + # for these offsets: can reuse + return offsets.inverse_offsets + + # ops.bucketize takes offsets.get_name() which doesn't exist on Pointwise + # kernels, i.e. we need to realize it before using. In other words, we need + # offsets to be in global memory so that we can binary search over the + # entire tensor + offsets.realize() + device: torch.device = offsets.get_device() + dtype: torch.dtype = offsets.get_dtype() + + # pyre-ignore[2,3] + def inner_fn(index): + idx = index[0] + bucket = ops.bucketize( + values=ops.index_expr(idx, dtype), + offsets_name=offsets.get_name(), + offsets_size=offsets.get_size()[0], + indexing_dtype=dtype, + right=True, + ) + # ops.bucketize above returns 1-based bucket indices, + # but we need 0-based, hence we subtract 1 from batch + return bucket - 1 + + inverse_offsets = Pointwise.create( + device=device, + dtype=dtype, + inner_fn=inner_fn, + ranges=[jagged_len], + ) + + if realize: + # "freeze" the node so that it doesn't get inlined downstream. + inverse_offsets.realize() + + # cache inverse_offsets for further reuse + offsets.inverse_offsets = inverse_offsets # type: ignore[attr-defined] + + return inverse_offsets + + +def jagged_idx_to_dense_idx( + jagged_idx, # pyre-ignore[2] + inverse_offsets_loader, # pyre-ignore[2] + offsets_loader, # pyre-ignore[2] + batch_size: Union[int, sympy.Expr], + max_seq_len: Union[int, sympy.Expr], + offsets_dtype: torch.dtype, +) -> Tuple[sympy.Expr, sympy.Expr]: + batch_idx = ops.indirect_indexing( + inverse_offsets_loader([jagged_idx]), + batch_size + 1, + ) + batch_start = offsets_loader([batch_idx]) + seq = ops.index_expr(jagged_idx, offsets_dtype) - batch_start + # check=False because there may be sequences longer than max_seq_len + seq_idx = ops.indirect_indexing(seq, max_seq_len, check=False) + return batch_idx, seq_idx + + +def register_jagged_ops(): + # pyre-ignore[56] + @register_lowering(torch.ops.aten._jagged_to_padded_dense_forward.default) + def _jagged_to_padded_dense_forward( + jagged_values: TensorBox, + jagged_offsets: List[TensorBox], + max_lengths: List[int], # list of ints/SymInts + padding_value: float = 0.0, + ) -> TensorBox: + device = jagged_values.get_device() + dtype = jagged_values.get_dtype() + + jagged_values_size = jagged_values.get_size() + + # only handle the common case of a single jagged dimension + if ( + len(jagged_offsets) != 1 + or device.type != "cuda" + or device != jagged_offsets[0].get_device() + or len(jagged_values_size) != 2 + or len(jagged_offsets[0].get_size()) != 1 + or len(max_lengths) != len(jagged_offsets) + or not is_integer_type(jagged_offsets[0]) + ): + return fallback_handler( + torch.ops.aten._jagged_to_padded_dense_forward.default, + add_to_fallback_set=False, + )( + jagged_values, + jagged_offsets, + max_lengths, + padding_value, + ) + + offsets: TensorBox = jagged_offsets[0] + offsets_len = offsets.get_size()[0] + offsets_dtype = offsets.get_dtype() + batch_size = offsets_len - 1 + max_seq_len = max_lengths[0] + embedding_len = jagged_values_size[1] + jagged_len = jagged_values_size[0] + + output_size = [batch_size, max_seq_len, embedding_len] + + values_loader = jagged_values.make_loader() + offsets_loader = offsets.make_loader() + + # pyre-ignore[2,3,53] + def inner_fn(index): + # dense tensor size: [B, N, D] + batch_idx, seq_idx, emb_idx = index + jagged_idx, end_idx = dense_idx_to_jagged_idx( + batch_idx=batch_idx, + seq_idx=seq_idx, + offsets_loader=offsets_loader, + jagged_len=jagged_len, + ) + return ops.masked( + ops.lt( + ops.index_expr(jagged_idx, offsets_dtype), + end_idx, + ), + lambda: values_loader([jagged_idx, emb_idx]), + padding_value, + ) + + return Pointwise.create( + device=device, + dtype=dtype, + inner_fn=inner_fn, + ranges=output_size, + ) + + def _dense_to_jagged_forward_impl( + fallback_op, # pyre-ignore[2] + dense: TensorBox, + jagged_offsets: List[TensorBox], + jagged_len: Optional[int] = None, + ) -> TensorBox: + device = dense.get_device() + dtype = dense.get_dtype() + + dense_size = dense.get_size() + + # only handle the common case of a single jagged dimension + if ( + len(jagged_offsets) != 1 + or device.type != "cuda" + or device != jagged_offsets[0].get_device() + or len(jagged_offsets[0].get_size()) != 1 + or len(dense_size) != 3 + or jagged_len is None + or not is_integer_type(jagged_offsets[0]) + ): + return fallback_handler(fallback_op, add_to_fallback_set=False)( + dense, + jagged_offsets, + jagged_len, + ) + + offsets: TensorBox = jagged_offsets[0] + offsets_dtype = offsets.get_dtype() + batch_size = dense_size[0] + max_seq_len = dense_size[1] + embedding_len = dense_size[-1] + + output_size = [jagged_len, embedding_len] + + dense_loader = dense.make_loader() + offsets_loader = offsets.make_loader() + + inverse_offsets = get_inverse_offsets( + offsets=offsets, + jagged_len=jagged_len, + ) + inverse_offsets_loader = inverse_offsets.make_loader() + + # pyre-ignore[2,3,53] + def inner_fn(index): + # jagged tensor size: [sum_B(N_B), D] + jagged_idx, emb_idx = index + batch_idx, seq_idx = jagged_idx_to_dense_idx( + jagged_idx=jagged_idx, + offsets_loader=offsets_loader, + inverse_offsets_loader=inverse_offsets_loader, + batch_size=batch_size, + max_seq_len=max_seq_len, + offsets_dtype=offsets_dtype, + ) + return ops.masked( + ops.lt( + ops.index_expr(seq_idx, offsets_dtype), + ops.index_expr(max_seq_len, offsets_dtype), + ), + lambda: dense_loader([batch_idx, seq_idx, emb_idx]), + 0.0, # jagged sequence longer than max_seq_len + ) + + return Pointwise.create( + device=device, + dtype=dtype, + inner_fn=inner_fn, + ranges=output_size, + ) + + # pyre-ignore[56] + @register_lowering(torch.ops.aten._padded_dense_to_jagged_forward) + def _dense_to_jagged_forward( + dense: TensorBox, + jagged_offsets: List[TensorBox], + jagged_len: Optional[int] = None, + ) -> TensorBox: + return _dense_to_jagged_forward_impl( + fallback_op=torch.ops.aten._padded_dense_to_jagged_forward.default, + dense=dense, + jagged_offsets=jagged_offsets, + jagged_len=jagged_len, + ) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b17d76e12794b8407063c84d5dbb55b3aac25c99 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/__init__.py @@ -0,0 +1 @@ +from . import mm, mm_common, mm_plus_mm, unpack_mixed_mm diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/bmm.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/bmm.py new file mode 100644 index 0000000000000000000000000000000000000000..428ff06d4a052fa38fec17df8ad7a1d6591b507c --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/bmm.py @@ -0,0 +1,192 @@ +# mypy: allow-untyped-defs +import logging + +import torch + +from .. import ir, lowering as L +from ..select_algorithm import ( + autotune_select_algorithm, + ExternKernelChoice, + TritonTemplate, +) +from ..utils import ( + ceildiv as cdiv, + use_aten_gemm_kernels, + use_cutlass_template, + use_triton_template, +) +from ..virtualized import V +from .mm import _is_static_problem +from .mm_common import addmm_epilogue, mm_args, mm_configs, mm_options + + +log = logging.getLogger(__name__) +aten = torch.ops.aten + + +def bmm_grid(b, m, n, meta): + return (cdiv(m, meta["BLOCK_M"]) * cdiv(n, meta["BLOCK_N"]), b, 1) + + +bmm_template = TritonTemplate( + name="bmm", + grid=bmm_grid, + source=r""" +{{def_kernel("A", "B")}} + M = {{size("A", -2)}} + N = {{size("B", -1)}} + K = {{size("A", -1)}} + + stride_aq = {{stride("A", 0)}} + stride_am = {{stride("A", 1)}} + stride_ak = {{stride("A", 2)}} + + stride_bq = {{stride("B", 0)}} + stride_bk = {{stride("B", 1)}} + stride_bn = {{stride("B", 2)}} + + # based on triton.ops.matmul + pid = tl.program_id(0) + grid_m = (M + BLOCK_M - 1) // BLOCK_M + grid_n = (N + BLOCK_N - 1) // BLOCK_N + + # re-order program ID for better L2 performance + width = GROUP_M * grid_n + group_id = pid // width + group_size = min(grid_m - group_id * GROUP_M, GROUP_M) + pid_m = group_id * GROUP_M + (pid % group_size) + pid_n = (pid % width) // (group_size) + + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + if (stride_am == 1 and stride_ak == M) or (stride_am == K and stride_ak == 1): + ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M) + else: + ram = rm % M + if (stride_bk == 1 and stride_bn == K) or (stride_bk == N and stride_bn == 1): + rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N) + else: + rbn = rn % N + + rk = tl.arange(0, BLOCK_K) + + idx_q = tl.program_id(1) # batch dimension for BMM + A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak + idx_q*stride_aq) + B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn + idx_q*stride_bq) + + acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE) + for k in range(K, 0, -BLOCK_K): + if EVEN_K: + a = tl.load(A) + b = tl.load(B) + else: + a = tl.load(A, mask=rk[None, :] < k, other=0.) + b = tl.load(B, mask=rk[:, None] < k, other=0.) + acc += tl.dot(a, b, allow_tf32=ALLOW_TF32) + A += BLOCK_K * stride_ak + B += BLOCK_K * stride_bk + + # rematerialize rm and rn to save registers + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + idx_q = tl.program_id(1) # batch dimension for BMM + idx_m = rm[:, None] + idx_n = rn[None, :] + mask = (idx_m < M) & (idx_n < N) + + # inductor generates a suffix + {{store_output(("idx_q", "idx_m", "idx_n"), "acc", "mask")}} +""", +) + +aten_bmm = ExternKernelChoice(torch.bmm, "at::bmm_out") +aten_baddbmm = ExternKernelChoice(torch.baddbmm, "at::baddbmm_out") + + +@L.register_lowering(aten.bmm) +def tuned_bmm(mat1, mat2, *, layout=None): + if all(x.get_device().type == "cpu" for x in [mat1, mat2]): + # decompose to small ops when memory bound + if mat1.get_size()[1] == 1 or mat2.get_size()[2] == 1: + mat1 = L.unsqueeze(mat1, -1) + mat2 = L.unsqueeze(mat2, 1) + return L.sum_(L.mul(mat1, mat2), axis=2) + + def is_valid_to_require_contiguous(t): + if not ir.is_storage_and_layout(t): + return True + _, layout = ir.as_storage_and_layout(t, freeze=False) + return isinstance(layout, ir.FlexibleLayout) + + def is_preferred_layout_as_bmm_input(sizes, strides): + # contiguous on one of the last two dims + return ( + strides[-1] == 1 and (sizes[-2] == 1 or strides[-2] >= sizes[-1]) + ) or (strides[-2] == 1 and (sizes[-1] == 1 or strides[-1] >= sizes[-2])) + + # Make the input of bmm contiguous + # if it is not contiguous on either of the last two dims, + # because bmm cpu implementation would do contiguous() if not. + # This is to avoid additional copies in bmm. + def may_require_contiguous(t, meta_t): + sizes = meta_t.meta["val"].size() + strides = meta_t.meta["val"].stride() + if not is_preferred_layout_as_bmm_input(sizes, strides): + t = ir.ExternKernel.require_contiguous(t) + return t + + if is_valid_to_require_contiguous(mat1): + meta_mat1 = V.graph.current_node.args[0] + mat1 = may_require_contiguous(mat1, meta_mat1) + if is_valid_to_require_contiguous(mat2): + meta_mat2 = V.graph.current_node.args[1] + mat2 = may_require_contiguous(mat2, meta_mat2) + + m, n, k, layout, mat1, mat2 = mm_args(mat1, mat2, layout=layout) + + # options to tune from + choices = [aten_bmm.bind((mat1, mat2), layout)] if use_aten_gemm_kernels() else [] + if use_triton_template(layout): + for config in mm_configs(m, n, k): + bmm_template.maybe_append_choice( + choices, + input_nodes=(mat1, mat2), + layout=layout, + **mm_options(config, m, n, k, layout), + ) + static_shape, is_nonzero = _is_static_problem([mat1, mat2], layout) + if static_shape and is_nonzero and use_cutlass_template(layout, m, n, k): + from ..codegen.cuda.gemm_template import CUTLASS3xGemmTemplate + + CUTLASS3xGemmTemplate.add_cutlass_gemm_choices(choices, layout, [mat1, mat2]) + + if len(choices) == 0: + log.warning("No choices for GEMM, using ATen backend as fallback") + choices.append(aten_bmm.bind((mat1, mat2), layout)) + + return autotune_select_algorithm("bmm", choices, [mat1, mat2], layout) + + +# Don't register this since it is slower than decomposing it +# @L.register_lowering(aten.baddbmm) +def tuned_baddbmm(inp, mat1, mat2, *, alpha=1, beta=1, layout=None): + m, n, k, layout, mat1, mat2, inp = mm_args(mat1, mat2, inp, layout=layout) + + # options to tune from + choices = ( + [aten_baddbmm.bind((inp, mat1, mat2), layout, alpha=alpha, beta=beta)] + if use_aten_gemm_kernels() + else [] + ) + if use_triton_template(layout): + for config in mm_configs(m, n, k): + bmm_template.maybe_append_choice( + choices, + input_nodes=(inp, mat1, mat2), + layout=layout, + **mm_options(config, m, n, k, layout), + prefix_args=1, + epilogue_fn=addmm_epilogue(layout.dtype, alpha, beta), + ) + + return autotune_select_algorithm("baddbmm", choices, [inp, mat1, mat2], layout) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/conv.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/conv.py new file mode 100644 index 0000000000000000000000000000000000000000..b69143fe0301545c53557886ed5701674e57565a --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/conv.py @@ -0,0 +1,679 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +from __future__ import annotations + +import functools +import logging +from typing import cast, List, Optional, Sequence, Tuple, TYPE_CHECKING, TypedDict + +import torch + +from .. import config, ir +from ..lowering import ( + add_layout_constraint, + constrain_to_fx_strides, + lowerings as L, + register_lowering, +) +from ..select_algorithm import ( + autotune_select_algorithm, + ExternKernelChoice, + TritonTemplate, +) +from ..utils import ( + ceildiv, + is_ones, + is_zeros, + pad_listlike, + sympy_product, + use_triton_template, +) +from ..virtualized import V +from .mm_common import filtered_configs + + +if TYPE_CHECKING: + from ..ir import TensorBox + +log = logging.getLogger(__name__) + + +aten = torch.ops.aten + + +def conv2d_grid(n, c, h, w, meta): + return ( + ceildiv(n * h * w, meta["BLOCK_M"]), + ceildiv(c, meta["BLOCK_N"]), + meta["GROUPS"], + ) + + +def conv3d_grid(n, c, d, h, w, meta): + return ( + ceildiv(n * d * h * w, meta["BLOCK_M"]), + ceildiv(c, meta["BLOCK_N"]), + meta["GROUPS"], + ) + + +# List of dictionaries to store the kernel configs. Configs that evaluate to true +# will be utilised on the target platform +kernel_configs = [ + # "BLOCK_M", "BLOCK_N", "BLOCK_K", "num_stages", "num_warps" + {"config": (64, 256, 16, 2, 4), "cond": True}, + {"config": (256, 64, 16, 2, 4), "cond": True}, + {"config": (1024, 16, 16, 1, 8), "cond": True}, + {"config": (128, 128, 32, 2, 8), "cond": True}, + {"config": (64, 64, 32, 2, 4), "cond": True}, + {"config": (64, 256, 32, 2, 8), "cond": True}, + {"config": (256, 64, 32, 2, 8), "cond": True}, +] + +# Create filtered list of configs based on conv +platform_configs = tuple( + cast(Tuple[int, int, int, int, int], config["config"]) + for config in kernel_configs + if config["cond"] +) + +# On ROCm convert num_stages to 1 as pipelining provides no benefit +if torch.version.hip: + platform_configs = tuple( + (config[0], config[1], config[2], 1, config[4]) for config in platform_configs + ) + +conv_configs = functools.partial( + filtered_configs, + configs=platform_configs, +) + +LOOP_BODY_2D = """ + idx_x_h = i - PADDING_H + idx_y_h * STRIDE_H + idx_x_w = j - PADDING_W + idx_y_w * STRIDE_W + idx_x_c = tl.arange(0, BLOCK_K) + k + + x_ptrs = x_base + ( + (idx_x_h * stride_xh)[:, None] + + (idx_x_w * stride_xw)[:, None] + + (idx_x_c * stride_xc)[None, :] + ) + mask_x = ( + (idx_n < BATCH)[:, None] + & (idx_x_h >= 0)[:, None] + & (idx_x_h < IN_H)[:, None] + & (idx_x_w >= 0)[:, None] + & (idx_x_w < IN_W)[:, None] + & (idx_x_c < GROUP_IN_C)[None, :] + ) + matrix_x = tl.load(x_ptrs, mask=mask_x, other=0.0) + + w_ptrs = w_base + ( + (idx_x_c * stride_wc_in)[:, None] + (i * stride_wh) + (j * stride_ww) + ) + mask_w = (idx_x_c[:, None] < GROUP_IN_C) & (idx_y_c[None, :] < GROUP_OUT_C) + matrix_w = tl.load(w_ptrs, mask=mask_w, other=0.0) + acc += tl.dot(matrix_x, matrix_w, allow_tf32=ALLOW_TF32) +""" + +""" +This is a relatively simple conv implementation that can likely be +improved. Many alternate conv versions can be found here: +https://github.com/pytorch/torchdynamo/pull/971 +""" +conv2d_template = TritonTemplate( + name="convolution2d", + grid=conv2d_grid, + source=r""" +{{def_kernel("X", "W")}} + # Tensor dimensions + BATCH = {{size("X", 0)}} + IN_C = {{size("X", 1)}} + IN_H = {{size("X", 2)}} + IN_W = {{size("X", 3)}} + OUT_C = {{size(None, 1)}} + OUT_H = {{size(None, 2)}} + OUT_W = {{size(None, 3)}} + + # Strides: + stride_xn = {{stride("X", 0)}} + stride_xc = {{stride("X", 1)}} + stride_xh = {{stride("X", 2)}} + stride_xw = {{stride("X", 3)}} + stride_wc_out = {{stride("W", 0)}} + stride_wc_in = {{stride("W", 1)}} + stride_wh = {{stride("W", 2)}} + stride_ww = {{stride("W", 3)}} + + nhw = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M) + idx_y_w = nhw % OUT_W + nh = nhw // OUT_W + idx_y_h = nh % OUT_H + idx_n = nh // OUT_H + idx_y_c = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N) + +{% if GROUPS == 1 %} + group = 0 + GROUP_IN_C = IN_C + GROUP_OUT_C = OUT_C +{% else %} + group = tl.program_id(2) + GROUP_IN_C = IN_C // GROUPS + GROUP_OUT_C = OUT_C // GROUPS +{% endif %} + + x_base = X + (group * stride_xc * GROUP_IN_C + idx_n * stride_xn)[:, None] + w_base = ( + W + (group * stride_wc_out * GROUP_OUT_C + idx_y_c * stride_wc_out)[None, :] + ) + + acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32) + +{% if UNROLL %} +{% for i in range(KERNEL_H) %} +{% for j in range(KERNEL_W) %} + i = {{i}} + j = {{j}} + for k in range(0, GROUP_IN_C, BLOCK_K): + """ + + LOOP_BODY_2D + + """ +{% endfor %} +{% endfor %} +{% else %} + # Could be simplified, but slightly slower: + # for i in range(KERNEL_H): + # for j in range(KERNEL_W): + # for k in range(0, GROUP_IN_C, BLOCK_K): + BLOCK_K_COUNT = (GROUP_IN_C + BLOCK_K - 1) // BLOCK_K + for ijk in range(KERNEL_H * KERNEL_W * BLOCK_K_COUNT): + k = (ijk % BLOCK_K_COUNT) * BLOCK_K + ij = ijk // BLOCK_K_COUNT + i = ij // KERNEL_W + j = ij % KERNEL_W + """ + + LOOP_BODY_2D + + """ +{% endif %} + + mask = ( + (idx_n < BATCH)[:, None] + & (idx_y_h < OUT_H)[:, None] + & (idx_y_w < OUT_W)[:, None] + & (idx_y_c < GROUP_OUT_C)[None, :] + ) + idx_n = idx_n[:, None] + idx_c = idx_y_c[None, :] + group * GROUP_OUT_C + idx_h = idx_y_h[:, None] + idx_w = idx_y_w[:, None] + + # inductor generates a suffix + {{store_output(("idx_n", "idx_c", "idx_h", "idx_w"), "acc", "mask")}} +""", +) + +LOOP_BODY_3D = """ + idx_x_d = d - PADDING_D + idx_y_d * STRIDE_D + idx_x_h = i - PADDING_H + idx_y_h * STRIDE_H + idx_x_w = j - PADDING_W + idx_y_w * STRIDE_W + idx_x_c = tl.arange(0, BLOCK_K) + k + + x_ptrs = x_base + ( + (idx_x_d * stride_xd)[:, None] + + (idx_x_h * stride_xh)[:, None] + + (idx_x_w * stride_xw)[:, None] + + (idx_x_c * stride_xc)[None, :] + ) + mask_x = ( + (idx_n < BATCH)[:, None] + & (idx_x_d >= 0)[:, None] + & (idx_x_d < IN_D)[:, None] + & (idx_x_h >= 0)[:, None] + & (idx_x_h < IN_H)[:, None] + & (idx_x_w >= 0)[:, None] + & (idx_x_w < IN_W)[:, None] + & (idx_x_c < GROUP_IN_C)[None, :] + ) + matrix_x = tl.load(x_ptrs, mask=mask_x, other=0.0) + + w_ptrs = w_base + ( + (idx_x_c * stride_wc_in)[:, None] + + (d * stride_wd) + (i * stride_wh) + (j * stride_ww) + ) + mask_w = (idx_x_c[:, None] < GROUP_IN_C) & (idx_y_c[None, :] < GROUP_OUT_C) + matrix_w = tl.load(w_ptrs, mask=mask_w, other=0.0) + acc += tl.dot(matrix_x, matrix_w, allow_tf32=ALLOW_TF32) +""" + +conv3d_template = TritonTemplate( + name="convolution3d", + grid=conv3d_grid, + source=r""" +{{def_kernel("X", "W")}} + # Tensor dimensions + BATCH = {{size("X", 0)}} + IN_C = {{size("X", 1)}} + IN_D = {{size("X", 2)}} + IN_H = {{size("X", 3)}} + IN_W = {{size("X", 4)}} + OUT_C = {{size(None, 1)}} + OUT_D = {{size(None, 2)}} + OUT_H = {{size(None, 3)}} + OUT_W = {{size(None, 4)}} + + # Strides: + stride_xn = {{stride("X", 0)}} + stride_xc = {{stride("X", 1)}} + stride_xd = {{stride("X", 2)}} + stride_xh = {{stride("X", 3)}} + stride_xw = {{stride("X", 4)}} + stride_wc_out = {{stride("W", 0)}} + stride_wc_in = {{stride("W", 1)}} + stride_wd = {{stride("W", 2)}} + stride_wh = {{stride("W", 3)}} + stride_ww = {{stride("W", 4)}} + + ndhw = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M) + idx_y_w = ndhw % OUT_W + ndh = ndhw // OUT_W + idx_y_h = ndh % OUT_H + nd = ndh // OUT_H + idx_y_d = nd % OUT_D + idx_n = nd // OUT_D + idx_y_c = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N) + +{% if GROUPS == 1 %} + group = 0 + GROUP_IN_C = IN_C + GROUP_OUT_C = OUT_C +{% else %} + group = tl.program_id(2) + GROUP_IN_C = IN_C // GROUPS + GROUP_OUT_C = OUT_C // GROUPS +{% endif %} + + x_base = X + (group * stride_xc * GROUP_IN_C + idx_n * stride_xn)[:, None] + w_base = ( + W + (group * stride_wc_out * GROUP_OUT_C + idx_y_c * stride_wc_out)[None, :] + ) + + acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32) + +{% if UNROLL %} +{% for d in range(KERNEL_D) %} +{% for i in range(KERNEL_H) %} +{% for j in range(KERNEL_W) %} + d = {{d}} + i = {{i}} + j = {{j}} + for k in range(0, GROUP_IN_C, BLOCK_K): + """ + + LOOP_BODY_3D + + """ +{% endfor %} +{% endfor %} +{% endfor %} +{% else %} + # Could be simplified, but slightly slower: + # for d in range(KERNEL_D): + # for i in range(KERNEL_H): + # for j in range(KERNEL_W): + # for k in range(0, GROUP_IN_C, BLOCK_K): + BLOCK_K_COUNT = (GROUP_IN_C + BLOCK_K - 1) // BLOCK_K + for dijk in range(KERNEL_D * KERNEL_H * KERNEL_W * BLOCK_K_COUNT): + k = (dijk % BLOCK_K_COUNT) * BLOCK_K + dij = dijk // BLOCK_K_COUNT + j = dij % KERNEL_W + di = dij // KERNEL_W + i = di % KERNEL_H + d = di // KERNEL_H + """ + + LOOP_BODY_3D + + """ +{% endif %} + + mask = ( + (idx_n < BATCH)[:, None] + & (idx_y_d < OUT_D)[:, None] + & (idx_y_h < OUT_H)[:, None] + & (idx_y_w < OUT_W)[:, None] + & (idx_y_c < GROUP_OUT_C)[None, :] + ) + idx_n = idx_n[:, None] + idx_c = idx_y_c[None, :] + group * GROUP_OUT_C + idx_d = idx_y_d[:, None] + idx_h = idx_y_h[:, None] + idx_w = idx_y_w[:, None] + + # inductor generates a suffix + {{store_output(("idx_n", "idx_c", "idx_d", "idx_h", "idx_w"), "acc", "mask")}} +""", +) + +aten_convolution = ExternKernelChoice( + torch.convolution, + "at::convolution", + has_out_variant=False, + op_overload=aten.convolution.default, +) + + +def conv1x1_via_mm(x, w, *, out): + w = torch.squeeze(torch.squeeze(w, -1), -1) + return torch.matmul( + x.permute(0, 2, 3, 1), w.permute(1, 0), out=out.permute(0, 2, 3, 1) + ) + + +aten_conv1x1_via_mm = ExternKernelChoice(conv1x1_via_mm, None) + + +class ConvLayoutParams(TypedDict): + stride: tuple[int, ...] + padding: tuple[int, ...] + dilation: tuple[int, ...] + transposed: bool + output_padding: tuple[int, ...] + groups: int + + +def conv_layout( + x: TensorBox, + weight: TensorBox, + bias: Optional[TensorBox], + stride: Sequence[int], + padding: tuple[int, ...], + dilation: tuple[int, ...], + transposed: bool, + output_padding: tuple[int, ...], + groups: int, +) -> ir.Layout: + """Determine output layout for a convolution""" + with V.graph.fake_mode: + output = torch.ops.aten.convolution( + ir.ir_node_to_tensor(x, guard_shape=True), + ir.ir_node_to_tensor(weight, guard_shape=True), + ir.ir_node_to_tensor(bias, guard_shape=True), + V.graph.sizevars.size_hints(stride), # type: ignore[arg-type] + V.graph.sizevars.size_hints(padding), # type: ignore[arg-type] + V.graph.sizevars.size_hints(dilation), # type: ignore[arg-type] + transposed, + V.graph.sizevars.size_hints(output_padding), # type: ignore[arg-type] + groups, + ) + sizes = ir.convert_shape_to_inductor(output.size()) + stride = ir.convert_shape_to_inductor(output.stride()) # type: ignore[assignment] + + return ir.FixedLayout( + x.get_device(), + x.get_dtype(), + sizes, + stride, + ) + + +def channels_last_order(rank): + order = list(reversed(range(rank))) + order.insert(1, order.pop(-1)) + return order + + +def convert_1x1_conv_to_mm(x, weight, bias): + # special case for 1x1 convolution, which is actually just a matmul + rank = len(weight.get_size()) + for _ in range(rank - 2): + weight = L[aten.squeeze](weight, dim=-1) + weight = L[aten.permute](weight, [1, 0]) + + x = ir.ExternKernel.require_stride_order(x, channels_last_order(rank)) + x_permute = list(range(rank)) + x_permute.append(x_permute.pop(1)) + x = L[aten.permute](x, x_permute) + *sizes, in_chan = x.get_size() + x = L[aten.reshape](x, [sympy_product(sizes), in_chan]) + if bias is None: + result = L[aten.mm](x, weight) + else: + result = L[aten.addmm](bias, x, weight) + result = L[aten.reshape](result, [*sizes, -1]) + result_permute = list(range(rank)) + result_permute.insert(1, result_permute.pop(-1)) + return L[aten.permute](result, result_permute) + + +@register_lowering(aten.convolution) +def convolution( + x: TensorBox, + weight: TensorBox, + bias: TensorBox, + stride: List[int], + padding: List[int], + dilation: List[int], + transposed: bool, + output_padding: List[int], + groups: int, +): + stride = tuple(stride) + padding = tuple(padding) + dilation = tuple(dilation) + output_padding = tuple(output_padding) + if not isinstance(groups, int): + groups = V.graph.sizevars.evaluate_static_shape(groups) + assert isinstance(groups, int) + + # Need use hint for triton template since the template does not + # work with a dynamic shape. + # + # No need to evaluate_static_shape for dilation and output_padding + # since the template is only used when dilation is 1 and output_padding + # is 0. + stride = tuple(V.graph.sizevars.evaluate_static_shapes(stride)) + padding = tuple(V.graph.sizevars.evaluate_static_shapes(padding)) + + kwargs: ConvLayoutParams = { + "stride": stride, + "padding": padding, + "dilation": dilation, + "transposed": transposed, + "output_padding": output_padding, + "groups": groups, + } + + if len(x.get_size()) == len(weight.get_size()) - 1: + # add batch dimension to simplify rest of function + return L[aten.squeeze]( + convolution(L[aten.expand](x, [1, *x.get_size()]), weight, bias, **kwargs), + dim=0, + ) + + out_chan, in_chan, *kernel_shape = V.graph.sizevars.evaluate_static_shapes( + weight.get_size() + ) + ndim = len(kernel_shape) + stride = pad_listlike(stride, ndim) + padding = pad_listlike(padding, ndim) + dilation = pad_listlike(dilation, ndim) + output_padding = pad_listlike(output_padding, ndim) + + def channels_last_conv(): + if V.graph.layout_opt and ndim == 2: + return True + + layout = conv_layout(x, weight, None, **kwargs) + req_stride_order = ir.get_stride_order( + V.graph.sizevars.size_hints(layout.stride) + ) + return req_stride_order == ir.NHWC_STRIDE_ORDER + + autotuning_gemm = config.max_autotune or config.max_autotune_gemm + + if ( + (config.conv_1x1_as_mm or (autotuning_gemm and channels_last_conv())) + and is_ones(kernel_shape) + and is_ones(stride) + and is_zeros(padding) + and is_ones(dilation) + and not transposed + and is_zeros(output_padding) + and groups == 1 + and V.graph.sizevars.statically_known_gt(sympy_product(x.get_size()), 0) + ): + return convert_1x1_conv_to_mm(x, weight, bias) + + if bias is not None and ir.get_device_type(x) != "cpu": + # peel off the bias, cudnn is slower with it + result = convolution(x, weight, None, **kwargs) + return L[aten.add]( + result, L[aten.view](bias, [result.get_size()[1]] + ndim * [1]) + ) + + x.realize() + weight.realize() + + # ndim can be 1 for convolution in models such as demucs + # TODO: check if it's beneficial to convert Conv1d to Conv2d and then + # apply channels last. + if V.graph.layout_opt and ndim == 2: + V.graph.num_channels_last_conv += 1 + x = ir.ExternKernel.require_channels_last(x) + # TODO maybe we can convert weights to channels last just once before + # running the model. + weight = ir.ExternKernel.require_channels_last(weight) + layout = conv_layout(x, weight, None, **kwargs) + else: + layout = conv_layout(x, weight, None, **kwargs) + req_stride_order = ir.get_stride_order( + V.graph.sizevars.size_hints(layout.stride) + ) + x = ir.ExternKernel.require_stride_order(x, req_stride_order) + weight = ir.ExternKernel.require_stride_order(weight, req_stride_order) + + ordered_kwargs_for_cpp_kernel = [ + "stride", + "padding", + "dilation", + "transposed", + "output_padding", + "groups", + ] + if bias is None: + args = [x, weight] + kwargs["bias"] = None # type: ignore[typeddict-unknown-key] + ordered_kwargs_for_cpp_kernel.insert(0, "bias") + else: + args = [x, weight, bias] + bias.realize() + bias.freeze_layout() + V.graph.sizevars.evaluate_static_shapes(bias.get_size()) + + choices = [] + if torch._inductor.utils._use_conv_autotune_backend("ATEN"): + choices = [ + aten_convolution.bind( + args, + layout, + ordered_kwargs_for_cpp_kernel, + **kwargs, + ) + ] + + if ( + torch._inductor.utils._use_conv_autotune_backend("TRITON") + and use_triton_template(layout) + # templates only support these: + and is_ones(dilation) + and not transposed + and is_zeros(output_padding) + # there are some odd models where this check fails (e.g. shufflenet_v2_x1_0) + and V.graph.sizevars.statically_known_equals(in_chan, x.get_size()[1]) # type: ignore[arg-type] + ): + if ( + is_ones(kernel_shape) + and is_ones(stride) + and is_zeros(padding) + and groups == 1 + ): + choices.append(aten_conv1x1_via_mm.bind(args, layout)) + + for cfg in conv_configs( + sympy_product([x.get_size()[0], *x.get_size()[2:]]), + out_chan, + in_chan, + ): + if ndim == 2: + conv2d_template.maybe_append_choice( + choices, + input_nodes=(x, weight), + layout=layout, + KERNEL_H=kernel_shape[0], + KERNEL_W=kernel_shape[1], + STRIDE_H=stride[0], + STRIDE_W=stride[1], + PADDING_H=padding[0], + PADDING_W=padding[1], + GROUPS=groups, + # TODO(jansel): try unroll for bigger kernels once fixed: + # https://github.com/openai/triton/issues/1254 + UNROLL=is_ones(kernel_shape), + ALLOW_TF32=torch.backends.cudnn.allow_tf32, + num_stages=cfg.num_stages, + num_warps=cfg.num_warps, + **cfg.kwargs, + ) + elif ndim == 3: + conv3d_template.maybe_append_choice( + choices, + input_nodes=(x, weight), + layout=layout, + KERNEL_D=kernel_shape[0], + KERNEL_H=kernel_shape[1], + KERNEL_W=kernel_shape[2], + STRIDE_D=stride[0], + STRIDE_H=stride[1], + STRIDE_W=stride[2], + PADDING_D=padding[0], + PADDING_H=padding[1], + PADDING_W=padding[2], + GROUPS=groups, + # TODO(jansel): try unroll for bigger kernels once fixed: + # https://github.com/openai/triton/issues/1254 + UNROLL=is_ones(kernel_shape), + ALLOW_TF32=torch.backends.cudnn.allow_tf32, + num_stages=cfg.num_stages, + num_warps=cfg.num_warps, + **cfg.kwargs, + ) + + return autotune_select_algorithm("convolution", choices, args, layout) + + +@register_lowering(aten._convolution) +def _convolution( + x, + weight, + bias, + stride, + padding, + dilation, + transposed, + output_padding, + groups, + benchmark, + deterministic, + cudnn_enabled, + allow_tf32, +): + return convolution( + x, weight, bias, stride, padding, dilation, transposed, output_padding, groups + ) + + +def constrain_conv_to_fx_strides(fx_node, *args, **kwargs): + assert fx_node.target == torch.ops.aten.convolution.default + if V.graph.layout_opt: + return args, kwargs + else: + return constrain_to_fx_strides(fx_node, *args, **kwargs) + + +add_layout_constraint(aten.convolution, constrain_conv_to_fx_strides) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/flex_attention.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/flex_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..567c4c5fefbf8688b8f5e4b1eac247535c8edb32 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/flex_attention.py @@ -0,0 +1,1843 @@ +# mypy: allow-untyped-defs +""" Triton Implementation of the flex_attention Kernel""" + +import logging +import math +from typing import Any, List, Optional, Sequence, Tuple + +import sympy + +import torch +from torch._inductor.virtualized import V +from torch.utils._pytree import tree_map + +from .. import config +from ..ir import ( + ComputedBuffer, + ExternKernel, + FixedLayout, + FlexibleLayout, + get_stride_order, + InputBuffer, + IRNode, + StorageBox, + stride_order2fill_order, + Subgraph, + TensorBox, +) +from ..lowering import empty, empty_strided, lowerings, register_lowering +from ..select_algorithm import autotune_select_algorithm, realize_inputs, TritonTemplate + + +log = logging.getLogger(__name__) +aten = torch.ops.aten +Expr = sympy.Expr + + +def construct_strides( + sizes: Sequence[int], + fill_order: Sequence[int], +) -> Sequence[int]: + """From a list of sizes and a fill order, construct the strides of the permuted tensor.""" + # Initialize strides + assert len(sizes) == len( + fill_order + ), "Length of sizes must match the length of the fill order" + strides = [0] * len(sizes) + + # Start with stride 1 for the innermost dimension + current_stride = 1 + + # Iterate through the fill order populating strides + for dim in fill_order: + strides[dim] = current_stride + current_stride *= sizes[dim] + + return strides + + +def flex_attention_grid(batch_size, q_heads, num_queries, d_model, meta): + """How is this kernel parallelized? + We create a grid of (batch_size * num_heads, ceil_div(n_queries, query_block_size), 1) + Each block is responsible for iterating over blocks of keys and values calculating + the final attention output. + """ + import triton + + return (triton.cdiv(num_queries, meta["BLOCK_M"]), batch_size * q_heads, 1) + + +def create_placeholder( + name: str, dtype: torch.dtype, device: torch.device +) -> TensorBox: + """Creates a placeholder input buffers for producing subgraph_output.""" + input_buffer = InputBuffer(name, FixedLayout(device, dtype, [], [])) + return TensorBox.create(input_buffer) + + +def maybe_realize(args: List[Optional[IRNode]]): + """Accepts a list of optional IRNodes and returns a list of realized IRNodes""" + return tree_map(lambda x: realize_inputs(x) if x is not None else None, args) + + +def get_float32_precision(): + if torch.get_float32_matmul_precision() == "highest" or torch.version.hip: + return "'ieee'" + else: + return "'tf32'" + + +def build_subgraph_buffer( + args: List[TensorBox], + subgraph: Subgraph, +): + """This function's goal is to take in the required args and produce the subgraph buffer + The subgraph buffer is a ComputedBuffer that will be inlined into the triton template + + Args: + args: The args that are passed into the subgraph. Contains both fixed and lifted inputs. + subgraph: The Subgraph ir for which to produce the output node + """ + cnt = 0 + env = {} + for node in subgraph.graph_module.graph.nodes: + # There are two classes of placeholder inpts that we need + # to handle differently. For the first n_scalar_inps inputs + # we expect that these placeholders were generated by the make_fx call + # in the flex Attention HOP. So we need to create a new placeholder + # TensorBox for each of these inputs. For the rest of the inputs we + # expect that these are lifted inputs that fill up the '*other_buffers' + # tuple and already have corresponding TensorBoxes passed in as args. + if node.op == "placeholder": + env[node] = args[cnt] + cnt += 1 + elif node.op == "call_function": + # For call_function we use the default lowerings and pass in the + # already created TensorBoxes as args + + args, kwargs = tree_map( + lambda x: env[x] if x in env else x, (node.args, node.kwargs) + ) + env[node] = lowerings[node.target](*args, **kwargs) + elif node.op == "output": + + def convert_output_node_to_buffer(output): + if output is None: + return None + output_node = output + output_buffer = env[output_node] + assert isinstance(output_buffer, TensorBox), ( + "The output node for flex attention's subgraph must be a TensorBox, but got: ", + type(output_buffer), + ) + assert isinstance(output_buffer.data, StorageBox), ( + "The output node for the flex attention subgraph must be a StorageBox, but got: ", + type(output_buffer), + ) + subgraph_buffer = ComputedBuffer( + name=None, + layout=FlexibleLayout( + device=output_buffer.data.get_device(), + dtype=output_buffer.data.get_dtype(), + size=output_buffer.data.get_size(), + ), + data=output_buffer.data.data, # type: ignore[arg-type] + ) + return subgraph_buffer + + # node.args[0] is either a single element or a list of elements + # representing all outputs of the function. + return tree_map(convert_output_node_to_buffer, node.args[0]) + + raise ValueError("FlexAttention was passed a subgraph with no output node!") + + +# Inner Triton functions shared by flex_attention & split-k decoding kernels. +compute_next_offset_func = r""" +@triton.jit +def get_offset_for_next_block(loop_iter, col_indices, total_blocks, SPARSE_BLOCK, SPARSE_BLOCK_MULTIPLE, BLOCK): + cur_block_idx = loop_iter // SPARSE_BLOCK_MULTIPLE + cur_block = tl.load(col_indices + cur_block_idx, eviction_policy="evict_last") + next_block = tl.load(col_indices + cur_block_idx + 1, eviction_policy="evict_last", mask=cur_block_idx + 1 < total_blocks) + needs_jump = (loop_iter + 1) % SPARSE_BLOCK_MULTIPLE == 0 + jump_to_block = (next_block - cur_block ) * SPARSE_BLOCK - (SPARSE_BLOCK_MULTIPLE - 1) * BLOCK + + offset = jump_to_block * needs_jump + (1 - needs_jump) * BLOCK + return offset +""" + +compute_flex_attention = r""" +{{def_kernel("Q", "K", "V", "LSE", "KV_NUM_BLKS", "KV_IDX", "FULL_KV_NUM_BLKS", "FULL_KV_IDX")}} + # Sub notation for this kernel: + # + # Q: Query, K: Key, V: Value + # M: Number of queries, N: Number of keys/values, D: Model dimension + # QK_HEAD_DIM: The dimension of the query and key embeddings + # V_HEAD_DIM: The dimension of the value embeddings + # z: Batch size, h: Number of heads, m: Number of queries per head, k: Number of keys per head + # GQA_SHARED_HEADS: number of query heads sharing one kv head in GQA setups. + # + # The following FULL_* and PARTIAL_* is defined in the block sparse mask grid, rather than the thread block grid. + # KV_NUM_BLKS: The number of KV blocks (that may or may not require masking) for each query. + # KV_IDX: The indices of KV blocks (that may or may not require masking) for each query. + # FULL_KV_NUM_BLKS: The number of fully unmasked KV blocks (so we don't need masking) for each query. + # FULL_KV_IDX: The indices of fully unmasked KV blocks (so we don't need masking) for each query. + # + # OUTPUT_LOGSUMEXP: We only need to store the logsumexp if we require grad + # + # (Modifiable) Performance tuning options + # BLOCK_M: The thread block size across the seqlen dim of Q. + # BLOCK_N: Iterate over BLOCK_N across the seqlen dim of K/V in each thread block. + + # The below are kernel options that can be applied for certain score_mods, + # or involve a numerics vs. perf tradeoff + # PRESCALE_QK: Whether to pre-scale QK by 1/sqrt(d) and change of base. Has + # about 20% more numerical error, but slightly faster. + # ROWS_GUARANTEED_SAFE: Is it guaranteed that at least one value in each row + # is not masked out? If so, we can skip an extra safety check + + tl.static_assert(SPARSE_Q_BLOCK_SIZE >= BLOCK_M and SPARSE_Q_BLOCK_SIZE % BLOCK_M == 0) + tl.static_assert(SPARSE_KV_BLOCK_SIZE >= BLOCK_N and SPARSE_KV_BLOCK_SIZE % BLOCK_N == 0) + + # Define strides of inputs + stride_qz, stride_qh, stride_qm, stride_qk = {{stride("Q")}} + stride_kz, stride_kh, stride_kn, stride_kk = {{stride("K")}} + stride_vz, stride_vh, stride_vn, stride_vk = {{stride("V")}} + + Z = {{size("Q", 0)}} + HQ = {{size("Q", 1)}} + Q_LEN = {{size("Q", 2)}} + KV_LEN = {{size("K", 2)}} + + MATMUL_PRECISION = Q.dtype.element_ty + + q_start = tl.program_id(0) + off_z = tl.program_id(1) // HQ + off_hq = tl.program_id(1) % HQ + off_hkv = off_hq // GQA_SHARED_HEADS + off_g = off_hq % GQA_SHARED_HEADS + + q_offset = off_z * stride_qz + off_hq * stride_qh + k_offset = off_z * stride_kz + off_hkv * stride_kh + v_offset = off_z * stride_vz + off_hkv * stride_vh + + Q = Q + q_offset + K = K + k_offset + V = V + v_offset + + SPARSE_Z = {{size("KV_NUM_BLKS", 0)}} + SPARSE_HQ = {{size("KV_NUM_BLKS", 1)}} + + sparse_idx_z = off_z % SPARSE_Z + sparse_idx_hq = off_hq % SPARSE_HQ + + SPARSE_Q_MULTIPLE: tl.constexpr = (SPARSE_Q_BLOCK_SIZE // BLOCK_M) + SPARSE_KV_MULTIPLE: tl.constexpr = (SPARSE_KV_BLOCK_SIZE // BLOCK_N) + + SPARSE_Q_BLOCK_CNT: tl.constexpr = tl.cdiv(Q_LEN, SPARSE_Q_BLOCK_SIZE) + SPARSE_KV_BLOCK_CNT: tl.constexpr = tl.cdiv(KV_LEN, SPARSE_KV_BLOCK_SIZE) + + # initialize pointer to m and l + m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf") + l_i = tl.zeros([BLOCK_M], dtype=tl.float32) + acc = tl.zeros([BLOCK_M, V_HEAD_DIM], dtype=tl.float32) + + offs_m = q_start * BLOCK_M + tl.arange(0, BLOCK_M) + + # KV_IDX and KV_NUM_BLKS are always contiguous. + sparse_hz_offset = sparse_idx_z * SPARSE_HQ + sparse_idx_hq + sparse_kv_num_blks_offset = sparse_hz_offset * SPARSE_Q_BLOCK_CNT + q_start // SPARSE_Q_MULTIPLE + sparse_kv_idx_offset = sparse_hz_offset * SPARSE_Q_BLOCK_CNT * SPARSE_KV_BLOCK_CNT + (q_start // SPARSE_Q_MULTIPLE) * SPARSE_KV_BLOCK_CNT # noqa: B950 + + Q_block_ptr = tl.make_block_ptr( + base=Q, + shape=(Q_LEN, QK_HEAD_DIM), + strides=(stride_qm, stride_qk), + offsets=(q_start * BLOCK_M, 0), + block_shape=(BLOCK_M, QK_HEAD_DIM), + order=(1, 0) + ) + + # load q: it stays in SRAM throughout the inner loop. + if IS_DIVISIBLE: + q = tl.load(Q_block_ptr) + else: + # boundary check is not free, so we only do it when necessary. + q = tl.load(Q_block_ptr, boundary_check=(0,), padding_option = "zero") + + # ~~~~~~~~~~~~~~ normal blocks ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + # We don't know anything "special" about these blocks, so we need to apply + # both score_mod and mask_mod to it + kv_indices = KV_IDX + sparse_kv_idx_offset + kv_start = tl.load(kv_indices) * SPARSE_KV_BLOCK_SIZE # first kv block we're loading + kv_num_blocks = tl.load(KV_NUM_BLKS + sparse_kv_num_blks_offset) + block_n_end = tl.minimum(kv_num_blocks * SPARSE_KV_MULTIPLE, tl.maximum(tl.cdiv(KV_LEN, BLOCK_N), 1)) + + K_block_ptr = tl.make_block_ptr( + base=K, + shape=(QK_HEAD_DIM, KV_LEN), + strides=(stride_kk, stride_kn), + offsets=(0, kv_start), + block_shape=(QK_HEAD_DIM, BLOCK_N), + order=(0, 1) + ) + V_block_ptr = tl.make_block_ptr( + base=V, + shape=(KV_LEN, V_HEAD_DIM), + strides=(stride_vn, stride_vk), + offsets=(kv_start, 0), + block_shape=(BLOCK_N, V_HEAD_DIM), + order=(1, 0) + ) + offs_n = kv_start + tl.arange(0, BLOCK_N) + + acc, l_i, m_i = forward_inner( + {{gen_argdefs()}}, + q, K_block_ptr, V_block_ptr, Q_LEN, KV_LEN, + acc, l_i, m_i, + off_z, off_hq, offs_m[:, None], offs_n[None, :], + kv_indices, kv_num_blocks, + 0, block_n_end, + MATMUL_PRECISION, + IS_FULL_BLOCKS=False, + ) + + # ~~~~~~~~~~~~~~ "full" blocks ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + # We know these blocks are guaranteed to be "full", so we don't need to + # apply mask_mod to them - only score_mod + if HAS_FULL_BLOCKS: + # FULL_KV_IDX and FULL_KV_NUM_BLKS are always contiguous. + kv_indices = FULL_KV_IDX + sparse_kv_idx_offset + kv_start = tl.load(kv_indices) * SPARSE_KV_BLOCK_SIZE # first kv block we're loading + kv_num_blocks = tl.load(FULL_KV_NUM_BLKS + sparse_kv_num_blks_offset) + block_n_end = tl.minimum(kv_num_blocks * SPARSE_KV_MULTIPLE, tl.maximum(tl.cdiv(KV_LEN, BLOCK_N), 1)) + + K_block_ptr = tl.make_block_ptr( + base=K, + shape=(QK_HEAD_DIM, KV_LEN), + strides=(stride_kk, stride_kn), + offsets=(0, kv_start), + block_shape=(QK_HEAD_DIM, BLOCK_N), + order=(0, 1) + ) + V_block_ptr = tl.make_block_ptr( + base=V, + shape=(KV_LEN, V_HEAD_DIM), + strides=(stride_vn, stride_vk), + offsets=(kv_start, 0), + block_shape=(BLOCK_N, V_HEAD_DIM), + order=(1, 0) + ) + offs_n = kv_start + tl.arange(0, BLOCK_N) + + acc, l_i, m_i = forward_inner( + {{gen_argdefs()}}, + q, K_block_ptr, V_block_ptr, Q_LEN, KV_LEN, + acc, l_i, m_i, + off_z, off_hq, offs_m[:, None], offs_n[None, :], + kv_indices, kv_num_blocks, + 0, block_n_end, + MATMUL_PRECISION, + IS_FULL_BLOCKS=True, + ) + + + # [Note] Handle fully masked out rows: + # Li will be the sum(e^(-inf)) == 0.0 for masked out rows, mi will be -inf. + # We set Li to 1.0 which will result in lse/out = 0.0 | after the log(li) + mi(0.0) step + l_i = tl.where(l_i == 0.0, 1, l_i) + + acc = acc / l_i[:, None] + idx_z = tl.program_id(1) // HQ + idx_hq = tl.program_id(1) % HQ + idx_m = offs_m[:, None] + idx_d = tl.arange(0, V_HEAD_DIM)[None, :] + + mask = idx_m < Q_LEN + # TODO generalize and add proper mask support + {{store_output(("idx_z", "idx_hq", "idx_m", "idx_d"), "acc", "mask")}} + + # TODO dont want to write this if we dont require grad + if OUTPUT_LOGSUMEXP: + off_hz = tl.program_id(1) + l_ptrs = LSE + off_hz * Q_LEN + offs_m + lse = m_i + tl.math.log2(l_i) + if IS_DIVISIBLE: + tl.store(l_ptrs, lse) + else: + tl.store(l_ptrs, lse, mask=offs_m < Q_LEN) + """ + + +compute_forward_inner = r""" +@triton.jit +def forward_inner( + {{gen_argdefs()}}, + q, K_block_ptr, V_block_ptr, Q_LEN, KV_LEN, + # accumulated values + acc, l_i, m_i, + # Offsets used as inputs to score_mod & mask_mod + # of size [BLOCK_M, BLOCK_N] or scalar. + off_z, off_h, offs_m, offs_n, + # blocksparse data + kv_indices, kv_num_blocks, + # start kv and end kv block + block_n_start, block_n_end, + MATMUL_PRECISION, + IS_FULL_BLOCKS, +): + # Redefines all kernel parameters (BLOCK_M, etc.) so we don't need to plumb them all through + {{gen_defines() | indent_except_first(1)}} + + SPARSE_KV_MULTIPLE: tl.constexpr = (SPARSE_KV_BLOCK_SIZE // BLOCK_N) + RCP_LN2: tl.constexpr = 1.44269504 + + if PRESCALE_QK: + q = (q * SM_SCALE * RCP_LN2).to(MATMUL_PRECISION) + + # loop over k, v and update accumulator until block_n_end + for start_n in range(block_n_start, block_n_end): + if IS_DIVISIBLE: + acc, l_i, m_i = forward_block_mn( + {{gen_argdefs()}}, + q, K_block_ptr, V_block_ptr, Q_LEN, KV_LEN, + # accumulated values + acc, l_i, m_i, + # Offsets + off_z, off_h, offs_m, offs_n, + MATMUL_PRECISION, RCP_LN2, + IS_FULL_BLOCKS, + ) + else: + # Benchmark shows even we applied mod & mask to each block for non divisible seqlen, + # it's on par or slightly faster than only applying to the last block in fwd. + # However, we choose different strategy for bwd, where we only apply mod & mask + # to the last block because it's faster a lot. + acc, l_i, m_i = forward_block_mn( + {{gen_argdefs()}}, + q, K_block_ptr, V_block_ptr, Q_LEN, KV_LEN, + # accumulated values + acc, l_i, m_i, + # Offsets + off_z, off_h, offs_m, offs_n, + MATMUL_PRECISION, RCP_LN2, + IS_FULL_BLOCKS, CHECK_BLOCK_BOUNDARY=True, + ) + + # update pointers + offset = get_offset_for_next_block( + start_n, kv_indices, kv_num_blocks, + SPARSE_KV_BLOCK_SIZE, SPARSE_KV_MULTIPLE, BLOCK_N + ) + + V_block_ptr = tl.advance(V_block_ptr, (offset, 0)) + K_block_ptr = tl.advance(K_block_ptr, (0, offset)) + + offs_n = offs_n + offset + + return acc, l_i, m_i + +""" + + +compute_forward_block_mn = r""" +@triton.jit +def forward_block_mn( + {{gen_argdefs()}}, + q, K_block_ptr, V_block_ptr, Q_LEN, KV_LEN, + # accumulated values + acc, l_i, m_i, + # Offsets + off_z, off_h, offs_m, offs_n, + MATMUL_PRECISION, RCP_LN2, + IS_FULL_BLOCKS, CHECK_BLOCK_BOUNDARY=False, +): + # Redefines all kernel parameters (BLOCK_M, etc.) so we don't need to plumb them all through + {{gen_defines() | indent_except_first(1)}} + + # -- load k -- + if IS_DIVISIBLE: + k = tl.load(K_block_ptr) + else: + k = tl.load(K_block_ptr, boundary_check=(1,), padding_option = "zero") + # -- compute qk --- + qk = tl.dot(q, k, input_precision=FLOAT32_PRECISION) # TODO: use cuda matmul when q_len <= 2. + if not PRESCALE_QK: + qk *= SM_SCALE + # ~~~~~~~~~~~~~~~~~~~ Apply score modification ~~~~~~~~~~~~~~~~~~~ + if CHECK_BLOCK_BOUNDARY: + # If this is the last block of a non divisible seqlen, we still need to load [BLOCK_M, BLOCK_N] elements, + # which is larger than the actual number of elements. To avoid access memory out of bound, + # we need to mask out the elements that are out of Q_LEN & KV_LEN. + m = offs_m % Q_LEN + n = offs_n % KV_LEN + else: + m = offs_m + n = offs_n + + {{ modification( + subgraph_number=0, + output_name="post_mod_scores", + score="qk", + b="off_z", + h="off_h", + m="m", + n="n", + out="qk" + ) | indent_except_first(1) }} + + if CHECK_BLOCK_BOUNDARY: + # Mask out the elements that are out of the KV_LEN for non divisible seqlen. + post_mod_scores = tl.where(offs_n < KV_LEN, post_mod_scores, float("-inf")) + + if not IS_FULL_BLOCKS: + {{ modification( + subgraph_number=1, + output_name="mask_mod_output", + score="qk", + b="off_z", + h="off_h", + m="m", + n="n", + ) | indent_except_first(2) }} + + if CHECK_BLOCK_BOUNDARY: + mask_mod_output = tl.where(offs_n < KV_LEN, mask_mod_output, float("-inf")) + # apply mask for partially unmasked blocks + post_mod_scores = tl.where(mask_mod_output, post_mod_scores, float("-inf")) + + # TODO: In the case that score_mod is linear, this can be LICMed + if not PRESCALE_QK: + post_mod_scores *= RCP_LN2 + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + # -- compute scaling constant --- + m_ij = tl.maximum(m_i, tl.max(post_mod_scores, 1)) + if not ROWS_GUARANTEED_SAFE: + masked_out_rows = (m_ij == float("-inf")) + m_ij_masked = tl.where(masked_out_rows, 0, m_ij) + else: + m_ij_masked = m_ij + + alpha = tl.math.exp2(m_i - m_ij_masked) + p = tl.math.exp2(post_mod_scores - m_ij_masked[:, None]) + + # NB: l_i update is pulled up here since it's a bit faster + # NB: For headdim=256, it's faster to move it back down to after m_i = + # m_ij + l_i = l_i * alpha + tl.sum(p, 1) + # # -- scale and update acc -- + acc = acc * alpha[:, None] + + if IS_DIVISIBLE: + v = tl.load(V_block_ptr) + else: + v = tl.load(V_block_ptr, boundary_check=(0,), padding_option = "zero") + acc = tl.dot(p.to(MATMUL_PRECISION), v, acc, input_precision=FLOAT32_PRECISION) + + # -- update m_i + m_i = m_ij + + return acc, l_i, m_i + +""" + + +flex_attention_template = TritonTemplate( + name="flex_attention", + grid=flex_attention_grid, + source=compute_flex_attention + + compute_forward_inner + + compute_next_offset_func + + compute_forward_block_mn, +) + + +def _use_flex_decoding(query, kernel_options): + # Decide which kernel to use, return true if use flex decoding kernel. + return ( + not kernel_options.get("FORCE_USE_FLEX_ATTENTION", False) + ) and V.graph.sizevars.evaluate_expr(sympy.Lt(query.get_size()[-2], 128)) + + +_h100_default_config = { + (torch.float32, 64): (128, 32, 4, 3), + (torch.float32, 128): (32, 64, 4, 3), + (torch.float32, 256): (32, 32, 4, 3), + (torch.bfloat16, 64): (128, 128, 4, 3), + (torch.bfloat16, 128): (128, 64, 8, 3), + (torch.bfloat16, 256): (64, 32, 4, 3), + (torch.float16, 64): (128, 128, 4, 3), + (torch.float16, 128): (128, 128, 8, 3), + (torch.float16, 256): (64, 32, 4, 3), +} + +_a100_default_config = { + (torch.float32, 64): (128, 32, 4, 3), + (torch.float32, 128): (128, 32, 4, 3), + (torch.float32, 256): (64, 16, 4, 3), + (torch.bfloat16, 64): (128, 64, 4, 3), + (torch.bfloat16, 128): (128, 64, 8, 3), + (torch.bfloat16, 256): (32, 64, 4, 3), + (torch.float16, 64): (128, 64, 4, 3), + (torch.float16, 128): (128, 64, 8, 3), + (torch.float16, 256): (32, 64, 4, 3), +} + + +def _get_default_config_fwd(query) -> Tuple[int, int, int, int]: + dtype = query.get_dtype() + head_dim = query.get_size()[-1] + default_config = None + + if head_dim <= 256 and torch.cuda.get_device_capability() >= (9, 0): # H100 + if dtype == torch.float32: + default_config = (64, 64, 4, 3) + else: + default_config = (128, 64, 4, 3) + default_config = _h100_default_config.get((dtype, head_dim), default_config) + elif head_dim <= 256 and torch.cuda.get_device_capability() >= (8, 0): # A100 + if dtype == torch.float32: + default_config = (64, 64, 4, 3) + else: + default_config = (128, 64, 4, 3) + default_config = _a100_default_config.get((dtype, head_dim), default_config) + else: # modest hardware or extremely large head_dim + if dtype == torch.float32: + default_config = (32, 16, 4, 3) + else: + default_config = (64, 32, 4, 3) + + return default_config + + +def _get_default_config_bwd(query) -> Tuple[int, int, int, int]: + head_dim = query.get_size()[-1] + dtype = query.get_dtype() + + if dtype == torch.float32: + return (16, 16, 4, 1) + if head_dim <= 256 and torch.cuda.get_device_capability() >= (9, 0): # H100 + if head_dim == 64: + return (64, 64, 4, 3) + elif head_dim == 128: + return (64, 128, 8, 3) + else: + return (64, 64, 4, 2) + elif torch.cuda.get_device_capability() >= (8, 0): # A100 + if head_dim == 64: + return (32, 128, 4, 3) + elif head_dim == 128: + return (64, 128, 8, 3) + else: + return (64, 64, 4, 2) + else: # modest hardware or extremely large head_dim + return (16, 16, 4, 1) + + +def create_num_blocks_fake_generator(sparse_indices): + # The idea here is that we need to create a real tensor with real data + # that's representative for benchmarking. + # For example, returning all zeros for the `kv_num_blocks` input would mean + # that we are computing 0 blocks for each row, which would provide bogus + # autotuning results. + # + # In this case, we choose to use min(16, max_block) blocks, because I + # (Horace) think it'll probably result in pretty representative performance. + # If it's too short then prefetching won't help. If it's too long then + # autotuning will take longer for no good reason. + def create_num_blocks_fake(x) -> torch.Tensor: + num_blocks_for_autotuning = min(16, sparse_indices.shape[-1]) + return torch.full( + x.get_size(), + int(num_blocks_for_autotuning), + dtype=x.get_dtype(), + device=x.get_device(), + ) + + return create_num_blocks_fake + + +def create_indices_fake(x) -> torch.Tensor: + indices = torch.arange( + 0, int(x.get_size()[-1]), dtype=x.get_dtype(), device=x.get_device() + ) + indices = indices.expand(x.get_size()).contiguous() + return indices + + +from torch._inductor.kernel.flex_decoding import create_flex_decoding_kernel + + +# TODO: We probably also need a layout constraint? +@register_lowering(torch.ops.higher_order.flex_attention, type_promotion_kind=None) +def flex_attention( + query, + key, + value, + subgraph, + block_mask, + scale, + kernel_options, + score_mod_other_buffers, + mask_mod_other_buffers, +): + ( + kv_num_blocks, + kv_indices, + full_kv_num_blocks, + full_kv_indices, + q_num_blocks, + q_indices, + full_q_num_blocks, + full_q_indices, + SPARSE_KV_BLOCK_SIZE, + SPARSE_Q_BLOCK_SIZE, + mask_graph, + ) = block_mask + placeholder_inps = [ + create_placeholder(name, dtype, query.get_device()) + for name, dtype in [ + ("score", query.get_dtype()), + ("b", torch.int32), + ("h", torch.int32), + ("m", torch.int32), + ("n", torch.int32), + ] + ] + subgraph_buffer = build_subgraph_buffer( + placeholder_inps + list(score_mod_other_buffers), subgraph + ) + mask_graph_placeholder_inps = [ + create_placeholder(name, dtype, query.get_device()) + for name, dtype in [ + ("b", torch.int32), + ("h", torch.int32), + ("m", torch.int32), + ("n", torch.int32), + ] + ] + mask_graph_buffer = build_subgraph_buffer( + mask_graph_placeholder_inps + list(mask_mod_other_buffers), mask_graph + ) + kernel_options = dict(kernel_options) + kernel_options.setdefault("FLOAT32_PRECISION", get_float32_precision()) + if _use_flex_decoding(query, kernel_options): + return create_flex_decoding_kernel( + query, + key, + value, + block_mask, + scale, + kernel_options, + subgraph_buffer, + mask_graph_buffer, + score_mod_other_buffers, + mask_mod_other_buffers, + ) + + ( + query, + key, + value, + kv_num_blocks, + kv_indices, + full_kv_num_blocks, + full_kv_indices, + q_num_blocks, + q_indices, + full_q_num_blocks, + full_q_indices, + ) = maybe_realize( + [ + query, + key, + value, + kv_num_blocks, + kv_indices, + full_kv_num_blocks, + full_kv_indices, + q_num_blocks, + q_indices, + full_q_num_blocks, + full_q_indices, + ] + ) + + Bq, Hq, seq_len_q, qk_head_dim = query.get_size() + Bkv, Hkv, seq_len_kv, v_head_dim = value.get_size() + assert Bq == Bkv, "Batch dimension must match" + B = Bq + + if seq_len_q % 128 != 0 or seq_len_kv % 128 != 0: + kernel_options.setdefault("IS_DIVISIBLE", False) + else: + kernel_options.setdefault("IS_DIVISIBLE", True) + + # Reuse query strides for output layout despite different last dimension. + # This works because only the last dim differs and we check it is contiguous. + q_strides = query.get_stride() + assert q_strides[-1] == 1, "Query must be contiguous in the last dimension" + + # Construct output layout with strides matching the query. + out_size = [B, Hq, seq_len_q, v_head_dim] + stride_order = get_stride_order(query.get_stride()) + fill_order = stride_order2fill_order(stride_order) + out_strides = construct_strides(out_size, fill_order) + + layout = FixedLayout( + query.get_device(), + query.get_dtype(), + [B, Hq, seq_len_q, v_head_dim], + stride=out_strides, + ) + # see NOTE:[TritonTemplates with multiple outputs] + logsumexp_shape = [B, Hq, seq_len_q] + logsumexp = empty_strided( + logsumexp_shape, + None, + dtype=torch.float32, # The logsumexp is always stored in fp32 regardless of the input dtype + device=query.get_device(), + ) + kernel_options.setdefault("SM_SCALE", scale) + + # Determine GQA broadcast factor. + gqa_shared_heads = Hq // Hkv + kernel_options.setdefault("GQA_SHARED_HEADS", gqa_shared_heads) + + # Inside of Triton kernel, only apply partial masking if partial blocks are computed. + # full_kv_num_blocks is None if partial blocks are not computed + has_full_blocks = full_kv_num_blocks is not None + kernel_options.setdefault("HAS_FULL_BLOCKS", has_full_blocks) + if not has_full_blocks: + full_kv_num_blocks, full_kv_indices = ( + empty(0, device=query.get_device()) for _ in range(2) + ) + kernel_options.setdefault("QK_HEAD_DIM", qk_head_dim) + kernel_options.setdefault("V_HEAD_DIM", v_head_dim) + + choices: List[Any] = [] + configs: List[Tuple[int, int, int, int]] = [] + configs.append(_get_default_config_fwd(query)) + if config.max_autotune: + configs += [ + (128, 64, 4, 3), + (128, 128, 4, 3), + (128, 128, 8, 2), + (64, 128, 4, 3), + (64, 64, 4, 3), + ] + + # Note, we don't need to pass in the captured buffers explicitly + # because they're implicitly added by the score_mod function + # We do need to explicitly pass it in for autotuning though. + + for BLOCK_M, BLOCK_N, num_warps, num_stages in configs: + if SPARSE_KV_BLOCK_SIZE % BLOCK_N != 0 or SPARSE_Q_BLOCK_SIZE % BLOCK_M != 0: + continue + # Work around https://github.com/pytorch/pytorch/issues/129625 + if num_stages == 2: + continue + + # Performance tuning + kernel_options.setdefault("BLOCK_M", BLOCK_M) + kernel_options.setdefault("BLOCK_N", BLOCK_N) + # Blocksparse options + kernel_options.setdefault("SPARSE_Q_BLOCK_SIZE", SPARSE_Q_BLOCK_SIZE) + kernel_options.setdefault("SPARSE_KV_BLOCK_SIZE", SPARSE_KV_BLOCK_SIZE) + + flex_attention_template.maybe_append_choice( + choices=choices, + input_nodes=[ + query, + key, + value, + logsumexp, + kv_num_blocks, + kv_indices, + full_kv_num_blocks, + full_kv_indices, + ], + layout=layout, + subgraphs=[ + subgraph_buffer, + mask_graph_buffer, + ], + mutated_inputs=[ + logsumexp, + ], + num_stages=num_stages, + num_warps=num_warps, + call_sizes=query.get_size(), + **kernel_options, + ) + inputs_for_autotuning = ( + [ + query, + key, + value, + logsumexp, + kv_num_blocks, + kv_indices, + full_kv_num_blocks, + full_kv_indices, + ] + + list(score_mod_other_buffers) + + list(mask_mod_other_buffers) + ) + input_gen_fns = { + 4: create_num_blocks_fake_generator(kv_indices), + 5: create_indices_fake, + 6: create_num_blocks_fake_generator(full_kv_indices), + 7: create_indices_fake, + } + return ( + autotune_select_algorithm( + "flex_attention", + choices, + inputs_for_autotuning, + layout, + input_gen_fns=input_gen_fns, + ), + logsumexp, + ) + + +# ---------------------------- Backward HOP Implementation ---------------------------- + + +def flex_attention_backward_grid( + batch_size, q_heads, num_queries, d_model, kv_heads, num_key_value, meta +): + """How is this kernel parallelized? + Currently this is only parallelizing over batch* kv_heads, but we can, and want to + parallelize over ceil_div(q_heads//kv_heads * num_key_value, key_value_block_size). + To do this will either require atomic updates to some grad values or to have a two pass kernel design. + """ + import triton + + return ( + triton.cdiv(num_queries, meta["BLOCK_M2"]) * (q_heads // kv_heads) + + triton.cdiv(num_key_value, meta["BLOCK_N1"]), + 1, + batch_size * kv_heads, + ) + + +flex_attention_backward_template = TritonTemplate( + name="flex_attention_backward", + grid=flex_attention_backward_grid, + source=r""" +{{def_kernel("Q", "K", "V", "LSE", "DELTA", "DO", "DQ", "DV", "KV_NUM_BLKS", "KV_IDX", "Q_NUM_BLKS", "Q_IDX", "FULL_KV_NUM_BLKS", "FULL_KV_IDX", "FULL_Q_NUM_BLKS", "FULL_Q_IDX")}} + # Sub notation for this kernel: + # + # Q: Query, K: Key, V: Value + # LSE: logsumexp (logsumexp is always stored in fp32 regardless of the input dtype) + # DELTA: Precomputed sum(OUT*DO, axis=-1) + # DO: Derivative of Output, DQ: Derivative of Query, DV: Derivative of Value + # DK: Derivative of Key, is the written to via the store_output call due to some limitations with + # inductor codegen + # M: Number of queries, N: Number of keys/values + # QK_HEAD_DIM: The dimension of the query and key embeddings + # V_HEAD_DIM: The dimension of the value embeddings + # z: Batch size, h: Number of heads, m: Number of queries or keys/values, d: Head dim + # GQA_SHARED_HEADS: number of query heads sharing one kv head in GQA setups. + # (Modifiable) Performance tuning options + # BLOCK_M1: when calculating DK & DV, iterate over BLOCK_M1 across the seqlen dim of Q in each thread block. + # BLOCK_N1: when calculating DK & DV, the thread block size across the seqlen dim of K/V. + # BLOCK_M2: when calculating DQ, the thread block size across the seqlen dim of Q. + # BLOCK_N2: when calculating DQ, iterate over BLOCK_N2 across the seqlen dim of K/V in each thread block. + # + # The following FULL_* and PARTIAL_* is defined in the block sparse mask grid, rather than the thread block grid. + # KV_NUM_BLKS: The number of KV blocks (that may or may not require masking) for each query. + # KV_IDX: The indices of KV blocks (that may or may not require masking) for each query. + # Q_NUM_BLKS: The number of Q blocks (that may or may not require masking) for each query. + # Q_IDX: The indices of Q blocks (that may or may not require masking) for each query. + # FULL_KV_NUM_BLKS: The number of fully unmasked KV blocks (so we don't need masking) for each query. + # FULL_KV_IDX: The indices of fully unmasked KV blocks (so we don't need masking) for each query. + # FULL_Q_NUM_BLKS: The number of fully unmasked Q blocks (so we don't need masking) for each query. + # FULL_Q_IDX: The indices of fully unmasked Q blocks (so we don't need masking) for each query. + + # The below are kernel options that can be applied for certain score_mods, + # or involve a numerics vs. perf tradeoff + # PRESCALE_QK: Whether to pre-scale QK by 1/sqrt(d) and change of base. Has + # about 20% more numerical error, but slightly faster. + + # Define strides of inputs + stride_qz, stride_qh, stride_qm, stride_qd = {{stride("Q")}} + stride_kz, stride_kh, stride_kn, stride_kd = {{stride("K")}} + stride_vz, stride_vh, stride_vn, stride_vd = {{stride("V")}} + stride_doz, stride_doh, stride_dom, stride_dod = {{stride("DO")}} + + stride_dqz, stride_dqh, stride_dqm, stride_dqd = {{stride("DQ")}} + stride_dvz, stride_dvh, stride_dvm, stride_dvd = {{stride("DV")}} + + Z = {{size("Q", 0)}} + HQ = {{size("Q", 1)}} + HKV = {{size("K", 1)}} + Q_LEN = {{size("Q", 2)}} + KV_LEN = {{size("K", 2)}} + + MATMUL_PRECISION = Q.dtype.element_ty + + pid = tl.program_id(0) + NUM_KV_BLOCKS = tl.cdiv(KV_LEN, BLOCK_N1) + NUM_Q_BLOCKS = tl.cdiv(Q_LEN, BLOCK_M2) + + off_hz = tl.program_id(2) + off_z = off_hz // HKV # batch idx + off_hkv = off_hz % HKV # kv head idx + + SPARSE_Z = {{size("KV_NUM_BLKS", 0)}} + SPARSE_HQ = {{size("KV_NUM_BLKS", 1)}} + + sparse_idx_z = off_z % SPARSE_Z + + k_adj = (stride_kh * off_hkv + stride_kz * off_z).to(tl.int64) + v_adj = (stride_vh * off_hkv + stride_vz * off_z).to(tl.int64) + dv_adj = (stride_dvh * off_hkv + stride_dvz * off_z).to(tl.int64) + + # offset K, V, DV pointers for batch/kv-head + K += k_adj + V += v_adj + DV += dv_adj + + RCP_LN2 = 1.44269504 + offs_k = tl.arange(0, QK_HEAD_DIM) + offs_v = tl.arange(0, V_HEAD_DIM) + + if pid >= NUM_KV_BLOCKS: + off_pid = pid - NUM_KV_BLOCKS + # THIS BLOCK DOES DQ + SPARSE_Q_MULTIPLE = (SPARSE_Q_BLOCK_SIZE // BLOCK_M2) + SPARSE_KV_MULTIPLE = (SPARSE_KV_BLOCK_SIZE // BLOCK_N2) + off_hq2 = off_pid // NUM_Q_BLOCKS + off_hkv * GQA_SHARED_HEADS + start_m2_block = off_pid % NUM_Q_BLOCKS + off_pid_mask = start_m2_block // SPARSE_Q_MULTIPLE + stride_kv_num_blks_h = {{stride("KV_NUM_BLKS", 1)}} + stride_kv_idx_h = {{stride("KV_IDX", 1)}} + stride_kv_idx_m = {{stride("KV_IDX", 2)}} + + sparse_idx_hq2 = off_hq2 % SPARSE_HQ + sparse_hz_offset = sparse_idx_z * SPARSE_HQ + sparse_idx_hq2 + + sparse_kv_num_blks_offset = sparse_hz_offset * stride_kv_num_blks_h + off_pid_mask + sparse_kv_idx_offset = sparse_hz_offset * stride_kv_idx_h + off_pid_mask * stride_kv_idx_m # noqa: B950 + + # Offset Q, DQ, DO, DELTA & LSE. These inputs are offseted by query heads. + q_adj2 = (stride_qh * off_hq2 + stride_qz * off_z).to(tl.int64) + do_adj2 = (stride_doh * off_hq2 + stride_doz * off_z).to(tl.int64) + dq_adj2 = (stride_dqh * off_hq2 + stride_dqz * off_z).to(tl.int64) + off_chz2 = ((off_z * HQ + off_hq2) * Q_LEN).to(tl.int64) + + Q2 = Q + q_adj2 + DO2 = DO + do_adj2 + # TODO: This does not work if DQ is not the same layout as Q (for example, + # if Q is broadcasted) + DQ2 = DQ + dq_adj2 + LSE2 = LSE + off_chz2 + DELTA2 = DELTA + off_chz2 + + dq = tl.zeros([BLOCK_M2, QK_HEAD_DIM], dtype=tl.float32) + + start_m2 = start_m2_block * BLOCK_M2 + offs_m2 = start_m2 + tl.arange(0, BLOCK_M2) + + # load Q and do: they stay in SRAM throughout the inner loop. + if IS_DIVISIBLE: + q = tl.load(Q2 + offs_m2[:, None] * stride_qm + offs_k[None, :] * stride_qd) + do = tl.load(DO2 + offs_m2[:, None] * stride_dom + offs_v[None, :] * stride_dod) + else: + q = tl.load(Q2 + offs_m2[:, None] * stride_qm + offs_k[None, :] * stride_qd, mask=offs_m2[:, None] < Q_LEN) + do = tl.load(DO2 + offs_m2[:, None] * stride_dom + offs_v[None, :] * stride_dod, mask=offs_m2[:, None] < Q_LEN) + + if PRESCALE_QK: + q = (q * SM_SCALE * RCP_LN2).to(MATMUL_PRECISION) + + if IS_DIVISIBLE: + Di = tl.load(DELTA2 + offs_m2) + lse = tl.load(LSE2 + offs_m2) + else: + Di = tl.load(DELTA2 + offs_m2, mask=offs_m2 < Q_LEN) + lse = tl.load(LSE2 + offs_m2, mask=offs_m2 < Q_LEN) + lse = tl.where(lse == -float("inf"), 0.0, lse) + lse = lse[:, None] + + # ~~~~~~~~~~~ fully unmasked blocks ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + # KV_IDX and KV_NUM_BLKS are always contiguous. + kv_indices = KV_IDX + sparse_kv_idx_offset + kv_start = tl.load(kv_indices) * SPARSE_KV_BLOCK_SIZE # first kv block we're loading + sparse_kv_num_blocks = tl.load(KV_NUM_BLKS + sparse_kv_num_blks_offset) + + offs_n2 = kv_start + tl.arange(0, BLOCK_N2) + dq = bwd_dq_inner( + {{gen_argdefs()}}, + K, V, + dq, q, do, Di, lse, + off_z, off_hq2, offs_m2, offs_n2, + stride_kn, stride_kd, stride_vn, stride_vd, + kv_indices, sparse_kv_num_blocks, + MATMUL_PRECISION, + IS_FULL_BLOCKS=False, + ) + + if HAS_FULL_BLOCKS: + # ~~~~~~~~~~~ partial unmasked blocks ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + # FULL_KV_IDX and FULL_KV_NUM_BLKS are always contiguous. + kv_indices = FULL_KV_IDX + sparse_kv_idx_offset + kv_start = tl.load(kv_indices) * SPARSE_KV_BLOCK_SIZE # first kv block we're loading + sparse_kv_num_blocks = tl.load(FULL_KV_NUM_BLKS + sparse_kv_num_blks_offset) + + offs_n2 = kv_start + tl.arange(0, BLOCK_N2) + dq = bwd_dq_inner( + {{gen_argdefs()}}, + K, V, + dq, q, do, Di, lse, + off_z, off_hq2, offs_m2, offs_n2, + stride_kn, stride_kd, stride_vn, stride_vd, + kv_indices, sparse_kv_num_blocks, + MATMUL_PRECISION, + IS_FULL_BLOCKS=True, + ) + + # Write back dQ. + dq_ptrs = DQ2 + offs_m2[:, None] * stride_dqm + offs_k[None, :] * stride_dqd + dq *= SM_SCALE + if IS_DIVISIBLE: + tl.store(dq_ptrs, dq) + else: + tl.store(dq_ptrs, dq, mask=offs_m2[:, None] < Q_LEN) + else: + # THIS BLOCK DOES DK & DV + SPARSE_Q_MULTIPLE = (SPARSE_Q_BLOCK_SIZE // BLOCK_M1) + SPARSE_KV_MULTIPLE = (SPARSE_KV_BLOCK_SIZE // BLOCK_N1) + + pid_mask = pid // SPARSE_KV_MULTIPLE + + stride_q_num_blks_h = {{stride("Q_NUM_BLKS", 1)}} + stride_q_idx_h = {{stride("Q_IDX", 1)}} + stride_q_idx_n = {{stride("Q_IDX", 2)}} + + dv = tl.zeros([BLOCK_N1, V_HEAD_DIM], dtype=tl.float32) + dk = tl.zeros([BLOCK_N1, QK_HEAD_DIM], dtype=tl.float32) + + start_n1 = pid * BLOCK_N1 + offs_n1 = start_n1 + tl.arange(0, BLOCK_N1) + + # load K and V: they stay in SRAM throughout the inner loop. + if IS_DIVISIBLE: + k = tl.load(K + offs_n1[:, None] * stride_kn + offs_k[None, :] * stride_kd) + v = tl.load(V + offs_n1[:, None] * stride_vn + offs_v[None, :] * stride_vd) + else: + k = tl.load(K + offs_n1[:, None] * stride_kn + offs_k[None, :] * stride_kd, mask=offs_n1[:, None] < KV_LEN) + v = tl.load(V + offs_n1[:, None] * stride_vn + offs_v[None, :] * stride_vd, mask=offs_n1[:, None] < KV_LEN) + if PRESCALE_QK: + k = (k * SM_SCALE * RCP_LN2).to(MATMUL_PRECISION) + + for off_g in range(0, GQA_SHARED_HEADS): + off_hq1 = off_hkv * GQA_SHARED_HEADS + off_g + + # Offset Q, DQ, DO, DELTA & LSE. These inputs are offseted by query heads. + q_adj1 = (stride_qh * off_hq1 + stride_qz * off_z).to(tl.int64) + do_adj1 = (stride_doh * off_hq1 + stride_doz * off_z).to(tl.int64) + dq_adj1 = (stride_dqh * off_hq1 + stride_dqz * off_z).to(tl.int64) + off_chz1 = ((off_z * HQ + off_hq1) * Q_LEN).to(tl.int64) + + Q1 = Q + q_adj1 + DO1 = DO + do_adj1 + # TODO: This does not work if DQ is not the same layout as Q (for example, + # if Q is broadcasted) + LSE1 = LSE + off_chz1 + DELTA1 = DELTA + off_chz1 + + sparse_idx_hq1 = off_hq1 % SPARSE_HQ + sparse_hz_offset = sparse_idx_z * SPARSE_HQ + sparse_idx_hq1 + + sparse_q_num_blks_offset = sparse_hz_offset * stride_q_num_blks_h + pid_mask + sparse_q_idx_offset = sparse_hz_offset * stride_q_idx_h + pid_mask * stride_q_idx_n # noqa: B950 + + # ~~~~~~~~~~~~~~~ fully unmasked blocks ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + # Q_IDX and Q_NUM_BLKS are always contiguous. + q_indices = Q_IDX + sparse_q_idx_offset + q_start = tl.load(q_indices) * SPARSE_Q_BLOCK_SIZE # first q block we're loading + sparse_q_num_blocks = tl.load(Q_NUM_BLKS + sparse_q_num_blks_offset) + + offs_m1 = q_start + tl.arange(0, BLOCK_M1) + dk, dv = bwd_dkdv_inner( + {{gen_argdefs()}}, + Q1, DO1, DELTA1, LSE1, + dk, dv, k, v, + off_z, off_hq1, offs_n1, offs_m1, + stride_qm, stride_qd, stride_dom, stride_dod, + q_indices, sparse_q_num_blocks, + MATMUL_PRECISION, + IS_FULL_BLOCKS=False, + ) + + + if HAS_FULL_BLOCKS: + # ~~~~~~~~~~~~~~~ fully unmasked blocks ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + # FULL_Q_IDX and FULL_Q_NUM_BLKS are always contiguous. + q_indices = FULL_Q_IDX + sparse_q_idx_offset + q_start = tl.load(q_indices) * SPARSE_Q_BLOCK_SIZE # first q block we're loading + sparse_q_num_blocks = tl.load(FULL_Q_NUM_BLKS + sparse_q_num_blks_offset) + + offs_m1 = q_start + tl.arange(0, BLOCK_M1) + dk, dv = bwd_dkdv_inner( + {{gen_argdefs()}}, + Q1, DO1, DELTA1, LSE1, + dk, dv, k, v, + off_z, off_hq1, offs_n1, offs_m1, + stride_qm, stride_qd, stride_dom, stride_dod, + q_indices, sparse_q_num_blocks, + MATMUL_PRECISION, + IS_FULL_BLOCKS=True, + ) + + # Write back dV and dK. + dv_ptrs = DV + offs_n1[:, None] * stride_dvm + offs_v[None, :] * stride_dvd + + index_n = offs_n1[:, None] + index_k = offs_k[None, :] + + if IS_DIVISIBLE: + tl.store(dv_ptrs, dv) + else: + tl.store(dv_ptrs, dv, mask=index_n < KV_LEN) + + dk *= SM_SCALE + mask = index_n < KV_LEN + {{store_output(("off_z", "off_hkv", "index_n", "index_k"), "dk", "mask", indent_width=8)}} + +@triton.jit +def bwd_dq_inner( + {{gen_argdefs()}}, + K, V, # pointers + dq, q, do, Di, lse, + off_z, off_hq, offs_m2, offs_n2, + stride_kn, stride_kd, stride_vn, stride_vd, + kv_indices, sparse_kv_num_blocks, + MATMUL_PRECISION, + IS_FULL_BLOCKS, +): + {{gen_defines() | indent_except_first(1) }} + SPARSE_KV_MULTIPLE: tl.constexpr = (SPARSE_KV_BLOCK_SIZE // BLOCK_N2) + RCP_LN2: tl.constexpr = 1.44269504 + Q_LEN = {{size("Q", 2)}} + KV_LEN = {{size("K", 2)}} + + offs_k = tl.arange(0, QK_HEAD_DIM) + offs_v = tl.arange(0, V_HEAD_DIM) + + kT_ptrs = K + offs_n2[None, :] * stride_kn + offs_k[:, None] * stride_kd + vT_ptrs = V + offs_n2[None, :] * stride_vn + offs_v[:, None] * stride_vd + # BLOCK_M2 must be a multiple of BLOCK_N2, otherwise the code wouldn't work. + tl.static_assert(BLOCK_M2 % BLOCK_N2 == 0) + + hi = tl.minimum(sparse_kv_num_blocks * SPARSE_KV_MULTIPLE, tl.maximum(tl.cdiv(KV_LEN, BLOCK_N2), 1)) + if not IS_DIVISIBLE: + if hi >= 1: + for start_n in range(0, hi - 1): + dq = bwd_dq_block_mn( + {{gen_argdefs()}}, + dq, q, kT_ptrs, vT_ptrs, do, Di, lse, Q_LEN, KV_LEN, + off_z, off_hq, offs_m2, offs_n2, + stride_kn, stride_kd, stride_vn, stride_vd, + kv_indices, sparse_kv_num_blocks, + MATMUL_PRECISION, RCP_LN2, + IS_FULL_BLOCKS, + ) + + # Increment pointers. + offset = get_offset_for_next_block( + start_n, kv_indices, sparse_kv_num_blocks, + SPARSE_KV_BLOCK_SIZE, SPARSE_KV_MULTIPLE, BLOCK_N2 + ) + + kT_ptrs += offset * stride_kn + vT_ptrs += offset * stride_vn + + offs_n2 += offset + + dq = bwd_dq_block_mn( + {{gen_argdefs()}}, + dq, q, kT_ptrs, vT_ptrs, do, Di, lse, Q_LEN, KV_LEN, + off_z, off_hq, offs_m2, offs_n2, + stride_kn, stride_kd, stride_vn, stride_vd, + kv_indices, sparse_kv_num_blocks, + MATMUL_PRECISION, RCP_LN2, + IS_FULL_BLOCKS, CHECK_BLOCK_BOUNDARY=True, + ) + else: + for start_n in range(0, hi): + dq = bwd_dq_block_mn( + {{gen_argdefs()}}, + dq, q, kT_ptrs, vT_ptrs, do, Di, lse, Q_LEN, KV_LEN, + off_z, off_hq, offs_m2, offs_n2, + stride_kn, stride_kd, stride_vn, stride_vd, + kv_indices, sparse_kv_num_blocks, + MATMUL_PRECISION, RCP_LN2, + IS_FULL_BLOCKS, + ) + + # Increment pointers. + offset = get_offset_for_next_block( + start_n, kv_indices, sparse_kv_num_blocks, + SPARSE_KV_BLOCK_SIZE, SPARSE_KV_MULTIPLE, BLOCK_N2 + ) + + kT_ptrs += offset * stride_kn + vT_ptrs += offset * stride_vn + + offs_n2 += offset + + return dq + + +@triton.jit +def bwd_dq_block_mn( + {{gen_argdefs()}}, + dq, q, kT_ptrs, vT_ptrs, do, Di, lse, Q_LEN, KV_LEN, + off_z, off_hq, offs_m2, offs_n2, + stride_kn, stride_kd, stride_vn, stride_vd, + kv_indices, sparse_kv_num_blocks, + MATMUL_PRECISION, RCP_LN2, + IS_FULL_BLOCKS, CHECK_BLOCK_BOUNDARY=False, +): + {{gen_defines() | indent_except_first(1)}} + + if IS_DIVISIBLE: + kT = tl.load(kT_ptrs) + else: + kT = tl.load(kT_ptrs, mask=offs_n2[None, :] < KV_LEN) + qk = tl.dot(q, kT, input_precision=FLOAT32_PRECISION) + if not PRESCALE_QK: + qk *= SM_SCALE + # ~~~~~~~~~~~~~~~~~~~ Apply score modification ~~~~~~~~~~~~~~~~~~~ + pre_mod_scores = qk + if CHECK_BLOCK_BOUNDARY: + m = offs_m2[:, None] % Q_LEN + n = offs_n2[None, :] % KV_LEN + else: + m = offs_m2[:, None] + n = offs_n2[None, :] + {{ modification( + subgraph_number=0, + output_name="post_mod_scores", + score="qk", + b="off_z", + h="off_hq", + m="m", + n="n", + out="qk" + ) | indent_except_first(1) }} + + if CHECK_BLOCK_BOUNDARY: + # Mask out the elements that are out of the KV_LEN for non divisible seqlen. + post_mod_scores = tl.where(offs_n2[None, :] < KV_LEN, post_mod_scores, float("-inf")) + + if not IS_FULL_BLOCKS: + {{ modification( + subgraph_number=2, + output_name="mask_mod_output", + score="qk", + b="off_z", + h="off_hq", + m="m", + n="n", + ) | indent_except_first(2) }} + + if CHECK_BLOCK_BOUNDARY: + mask_mod_output = tl.where(offs_n2[None, :] < KV_LEN, mask_mod_output, float("-inf")) + # apply mask for partial masked block + post_mod_scores = tl.where(mask_mod_output, post_mod_scores, float("-inf")) + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + if not PRESCALE_QK: + post_mod_scores *= RCP_LN2 + p = tl.math.exp2(post_mod_scores - lse) + # Compute dP and dS. + if IS_DIVISIBLE: + vT = tl.load(vT_ptrs) + else: + vT = tl.load(vT_ptrs, mask=offs_n2[None, :] < KV_LEN) + dp = tl.dot(do, vT, input_precision=FLOAT32_PRECISION) + ds = p * (dp - Di[:, None]) + # ~~~~~~~~~~~~~~~~~~~ Apply joint modification ~~~~~~~~~~~~~~~~~~~ + {{ modification( + subgraph_number=1, + output_name = "grad_scores", + score="pre_mod_scores", + b="off_z", + h="off_hq", + m="m", + n="n", + grad_score_mod="ds" + ) | indent_except_first(1) }} + if CHECK_BLOCK_BOUNDARY: + grad_scores = tl.where(offs_n2[None, :] < KV_LEN, grad_scores, 0.0) + + ds = grad_scores + + if not IS_FULL_BLOCKS: + if CHECK_BLOCK_BOUNDARY: + mask_mod_output = tl.where(offs_n2[None, :] < KV_LEN, mask_mod_output, float("-inf")) + # (grads) apply mask for partially unmasked block + ds = tl.where(mask_mod_output, ds, 0.0) + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + ds = ds.to(MATMUL_PRECISION) + # Compute dQ. + dq += tl.dot(ds, tl.trans(kT), input_precision=FLOAT32_PRECISION) + + return dq + + +@triton.jit +def bwd_dkdv_inner( + {{gen_argdefs()}}, + Q, DO, DELTA, LSE, # pointers + dk, dv, k, v, + off_z, off_hq, offs_n1, offs_m1, + stride_qm, stride_qd, stride_dom, stride_dod, + q_indices, sparse_q_num_blocks, + MATMUL_PRECISION, + IS_FULL_BLOCKS, +): + {{gen_defines() | indent_except_first(1) }} + SPARSE_Q_MULTIPLE: tl.constexpr = (SPARSE_Q_BLOCK_SIZE // BLOCK_M1) + RCP_LN2: tl.constexpr = 1.44269504 + Q_LEN = {{size("Q", 2)}} + KV_LEN = {{size("K", 2)}} + + offs_k = tl.arange(0, QK_HEAD_DIM) + offs_v = tl.arange(0, V_HEAD_DIM) + + qT_ptrs = Q + offs_m1[None, :] * stride_qm + offs_k[:, None] * stride_qd + do_ptrs = DO + offs_m1[:, None] * stride_dom + offs_v[None, :] * stride_dod + # BLOCK_N1 must be a multiple of BLOCK_M1, otherwise the code wouldn't work. + tl.static_assert(BLOCK_N1 % BLOCK_M1 == 0) + hi = tl.minimum(sparse_q_num_blocks * SPARSE_Q_MULTIPLE, tl.maximum(tl.cdiv(Q_LEN, BLOCK_M1), 1)) + + if not IS_DIVISIBLE: + if hi >= 1: + for start_m in range(0, hi - 1): + dk, dv = bwd_dkdv_block_mn( + {{gen_argdefs()}}, + dk, dv, qT_ptrs, k, v, do_ptrs, DELTA, LSE, Q_LEN, KV_LEN, + off_z, off_hq, offs_n1, offs_m1, + stride_qm, stride_qd, stride_dom, stride_dod, + q_indices, sparse_q_num_blocks, + MATMUL_PRECISION, RCP_LN2, + IS_FULL_BLOCKS, + ) + # Increment pointers. + offset = get_offset_for_next_block( + start_m, q_indices, sparse_q_num_blocks, + SPARSE_Q_BLOCK_SIZE, SPARSE_Q_MULTIPLE, BLOCK_M1 + ) + + qT_ptrs += offset * stride_qm + do_ptrs += offset * stride_dom + + offs_m1 += offset + + dk, dv = bwd_dkdv_block_mn( + {{gen_argdefs()}}, + dk, dv, qT_ptrs, k, v, do_ptrs, DELTA, LSE, Q_LEN, KV_LEN, + off_z, off_hq, offs_n1, offs_m1, + stride_qm, stride_qd, stride_dom, stride_dod, + q_indices, sparse_q_num_blocks, + MATMUL_PRECISION, RCP_LN2, + IS_FULL_BLOCKS, CHECK_BLOCK_BOUNDARY=True, + ) + else: + for start_m in range(0, hi): + dk, dv = bwd_dkdv_block_mn( + {{gen_argdefs()}}, + dk, dv, qT_ptrs, k, v, do_ptrs, DELTA, LSE, Q_LEN, KV_LEN, + off_z, off_hq, offs_n1, offs_m1, + stride_qm, stride_qd, stride_dom, stride_dod, + q_indices, sparse_q_num_blocks, + MATMUL_PRECISION, RCP_LN2, + IS_FULL_BLOCKS, + ) + # Increment pointers. + offset = get_offset_for_next_block( + start_m, q_indices, sparse_q_num_blocks, + SPARSE_Q_BLOCK_SIZE, SPARSE_Q_MULTIPLE, BLOCK_M1 + ) + + qT_ptrs += offset * stride_qm + do_ptrs += offset * stride_dom + + offs_m1 += offset + + return dk, dv + + +@triton.jit +def bwd_dkdv_block_mn( + {{gen_argdefs()}}, + dk, dv, qT_ptrs, k, v, do_ptrs, DELTA, LSE, Q_LEN, KV_LEN, + off_z, off_hq, offs_n1, offs_m1, + stride_qm, stride_qd, stride_dom, stride_dod, + q_indices, sparse_q_num_blocks, + MATMUL_PRECISION, RCP_LN2, + IS_FULL_BLOCKS, CHECK_BLOCK_BOUNDARY=False, +): + {{gen_defines() | indent_except_first(1) }} + + # Load LSE before computing qk to reduce pipeline stall. + if IS_DIVISIBLE: + qT = tl.load(qT_ptrs) + lse = tl.load(LSE + offs_m1) + else: + qT = tl.load(qT_ptrs, mask=offs_m1[None, :] < Q_LEN) + lse = tl.load(LSE + offs_m1, mask=offs_m1 < Q_LEN) + lse = tl.where(lse == -float("inf"), 0.0, lse) + qkT = tl.dot(k, qT, input_precision=FLOAT32_PRECISION) + if not PRESCALE_QK: + qkT *= SM_SCALE + # ~~~~~~~~~~~~~~~~~~~ Apply score modification ~~~~~~~~~~~~~~~~~~~ + if CHECK_BLOCK_BOUNDARY: + m = offs_m1[None, :] % Q_LEN + n = offs_n1[:, None] % KV_LEN + else: + m = offs_m1[None, :] + n = offs_n1[:, None] + pre_mod_scores = qkT + {{ modification( + subgraph_number=0, + output_name="post_mod_scores", + score="qkT", + b="off_z", + h="off_hq", + m="m", + n="n", + out="qkT" + ) | indent_except_first(1) }} + + if CHECK_BLOCK_BOUNDARY: + # Mask out the elements that are out of the KV_LEN for non divisible seqlen. + post_mod_scores = tl.where(offs_n1[:, None] < KV_LEN, post_mod_scores, float("-inf")) + + if not IS_FULL_BLOCKS: + {{ modification( + subgraph_number=2, + output_name="mask_mod_output", + score="qkT", + b="off_z", + h="off_hq", + m="m", + n="n", + ) | indent_except_first(2) }} + if CHECK_BLOCK_BOUNDARY: + mask_mod_output = tl.where(offs_n1[:, None] < KV_LEN, mask_mod_output, float("-inf")) + # (grads) apply mask for fully masked block + post_mod_scores = tl.where(mask_mod_output, post_mod_scores, float("-inf")) + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + if not PRESCALE_QK: + post_mod_scores *= RCP_LN2 + pT = tl.math.exp2(post_mod_scores - lse[None, :]) + if IS_DIVISIBLE: + do = tl.load(do_ptrs) + else: + do = tl.load(do_ptrs, mask=offs_m1[:, None] < Q_LEN) + # Compute dV. + ppT = pT + dv += tl.dot(ppT.to(MATMUL_PRECISION), do, input_precision=FLOAT32_PRECISION) + if IS_DIVISIBLE: + Di = tl.load(DELTA + offs_m1) + else: + Di = tl.load(DELTA + offs_m1, mask=offs_m1 < Q_LEN) + # Compute dP and dS. + dpT = tl.dot(v, tl.trans(do), input_precision=FLOAT32_PRECISION) + dsT = pT * (dpT - Di[None, :]) + # ~~~~~~~~~~~~~~~~~~~ Apply joint modification ~~~~~~~~~~~~~~~~~~~ + {{ modification( + subgraph_number=1, + output_name = "grad_scores", + score="pre_mod_scores", + b="off_z", + h="off_hq", + m="m", + n="n", + grad_score_mod="dsT" + ) | indent_except_first(1) }} + if CHECK_BLOCK_BOUNDARY: + grad_scores = tl.where(offs_n1[:, None] < KV_LEN, grad_scores, 0.0) + + dsT = grad_scores + if not IS_FULL_BLOCKS: + if CHECK_BLOCK_BOUNDARY: + mask_mod_output = tl.where(offs_n1[:, None] < KV_LEN, mask_mod_output, float("-inf")) + # (grads) apply mask for partially unmasked block + dsT = tl.where(mask_mod_output, dsT, 0.0) + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + dk += tl.dot(dsT.to(MATMUL_PRECISION), tl.trans(qT), input_precision=FLOAT32_PRECISION) + + return dk, dv + """ + + compute_next_offset_func, +) + + +# TODO: We probably also need a layout constraint? +@register_lowering( + torch.ops.higher_order.flex_attention_backward, type_promotion_kind=None +) +def flex_attention_backward(*args, **kwargs): + ( + query, + key, + value, + out, + logsumexp, + grad_out, + grad_logsumexp, + fw_graph, + joint_graph, + block_mask, + scale, + kernel_options, + score_mod_other_buffers, + mask_mod_other_buffers, + ) = args + ( + kv_num_blocks, + kv_indices, + full_kv_num_blocks, + full_kv_indices, + q_num_blocks, + q_indices, + full_q_num_blocks, + full_q_indices, + SPARSE_KV_BLOCK_SIZE, + SPARSE_Q_BLOCK_SIZE, + mask_graph, + ) = block_mask + + ( + query, + key, + value, + grad_out, + kv_num_blocks, + kv_indices, + full_kv_num_blocks, + full_kv_indices, + q_num_blocks, + q_indices, + full_q_num_blocks, + full_q_indices, + ) = maybe_realize( + [ + query, + key, + value, + grad_out, + kv_num_blocks, + kv_indices, + full_kv_num_blocks, + full_kv_indices, + q_num_blocks, + q_indices, + full_q_num_blocks, + full_q_indices, + ] + ) + + device = query.get_device() + dtype = query.get_dtype() + Bq, Hq, seq_len_q, qk_head_dim = query.get_size() + Bkv, Hkv, seq_len_kv, v_head_dim = value.get_size() + assert Bq == Bkv, "Batch dimension must match" + B = Bq + + kernel_options = dict(kernel_options) + kernel_options.setdefault("FLOAT32_PRECISION", get_float32_precision()) + if seq_len_q % 128 != 0 or seq_len_kv % 128 != 0: + kernel_options.setdefault("IS_DIVISIBLE", False) + else: + kernel_options.setdefault("IS_DIVISIBLE", True) + + fwd_placeholder_inps = [ + create_placeholder(name, dtype, device) + for name, dtype in [ + ("score", dtype), + ("b", torch.int32), + ("h", torch.int32), + ("m", torch.int32), + ("n", torch.int32), + ] + ] + fw_subgraph_buffer = build_subgraph_buffer( + fwd_placeholder_inps + list(score_mod_other_buffers), fw_graph + ) + + joint_placeholder_inps = fwd_placeholder_inps + [ + create_placeholder("grad_score_mod", dtype, device) + ] + joint_subgraph_buffer, *_ = build_subgraph_buffer( + joint_placeholder_inps + list(score_mod_other_buffers), joint_graph + ) + + mask_graph_placeholder_inps = [ + create_placeholder(name, dtype, query.get_device()) + for name, dtype in [ + ("b", torch.int32), + ("h", torch.int32), + ("m", torch.int32), + ("n", torch.int32), + ] + ] + mask_graph_buffer = build_subgraph_buffer( + mask_graph_placeholder_inps + list(mask_mod_other_buffers), mask_graph + ) + + layout_k = FixedLayout( + key.get_device(), + key.get_dtype(), + key.get_size(), + key.get_stride(), + ) + + # Create delta which will is needed for the bwd's kernel + grad_lse_exp2 = lowerings[aten.mul](grad_logsumexp, 1 / math.log(2)) + mul_delta = lowerings[aten.mul](out, grad_out) + delta = lowerings[aten.sum](mul_delta, axis=-1) + delta = lowerings[aten.sub](delta, grad_lse_exp2) + delta = ExternKernel.require_contiguous(delta) + + grad_lse_exp2, delta = maybe_realize([grad_lse_exp2, delta]) + + # see NOTE:[TritonTemplates with multiple outputs] + grad_query = empty_strided( + query.get_size(), query.get_stride(), dtype=dtype, device=device + ) + grad_value = empty_strided( + value.get_size(), value.get_stride(), dtype=dtype, device=device + ) + + kernel_options.setdefault("SM_SCALE", scale) + + # Determine GQA factor + gqa_shared_heads = Hq // Hkv + kernel_options.setdefault("GQA_SHARED_HEADS", gqa_shared_heads) + + # Inside of Triton kernel, only apply partial masking if partial blocks are computed. + # full_kv_num_blocks is torch.zeros([1, 1, 1]) if partial blocks are not computed. + has_full_blocks = full_kv_num_blocks is not None + kernel_options.setdefault("HAS_FULL_BLOCKS", has_full_blocks) + if not has_full_blocks: + full_kv_num_blocks, full_kv_indices, full_q_num_blocks, full_q_indices = ( + empty(0, device=query.get_device()) for _ in range(4) + ) + kernel_options.setdefault("QK_HEAD_DIM", qk_head_dim) + kernel_options.setdefault("V_HEAD_DIM", v_head_dim) + + choices: List[Any] = [] + configs: List[Tuple[int, int, int, int]] = [] + configs.append(_get_default_config_bwd(query)) + if config.max_autotune: + configs.extend( + [ + (BLOCK1, BLOCK2, w, s) + for BLOCK1 in [32, 64] + for BLOCK2 in [32, 64, 128] + for w in [4, 8] + for s in [1, 3, 4, 5] + if BLOCK2 % BLOCK1 == 0 + ] + ) + + for BLOCK1, BLOCK2, num_warps, num_stages in configs: + if ( + SPARSE_KV_BLOCK_SIZE % BLOCK1 != 0 + or SPARSE_Q_BLOCK_SIZE % BLOCK1 != 0 + or SPARSE_KV_BLOCK_SIZE % BLOCK2 != 0 + or SPARSE_Q_BLOCK_SIZE % BLOCK2 != 0 + ): + continue + + # Performance tuning + kernel_options.setdefault("BLOCK_M1", BLOCK1) + kernel_options.setdefault("BLOCK_N1", BLOCK2) + kernel_options.setdefault("BLOCK_M2", BLOCK2) + kernel_options.setdefault("BLOCK_N2", BLOCK1) + # Blocksparse options + kernel_options.setdefault("SPARSE_Q_BLOCK_SIZE", SPARSE_Q_BLOCK_SIZE) + kernel_options.setdefault("SPARSE_KV_BLOCK_SIZE", SPARSE_KV_BLOCK_SIZE) + + flex_attention_backward_template.maybe_append_choice( + choices=choices, + input_nodes=[ + query, + key, + value, + logsumexp, + delta, + grad_out, + grad_query, + grad_value, + kv_num_blocks, + kv_indices, + q_num_blocks, + q_indices, + full_kv_num_blocks, + full_kv_indices, + full_q_num_blocks, + full_q_indices, + ], + layout=layout_k, # We use store_output only for grad_key + subgraphs=[fw_subgraph_buffer, joint_subgraph_buffer, mask_graph_buffer], + mutated_inputs=[grad_query, grad_value], + call_sizes=query.get_size() + key.get_size()[1:3], + num_stages=num_stages, + num_warps=num_warps, + **kernel_options, + ) + inputs_for_autotuning = ( + [ + query, + key, + value, + logsumexp, + delta, + grad_out, + grad_query, + grad_value, + kv_num_blocks, + kv_indices, + q_num_blocks, + q_indices, + full_kv_num_blocks, + full_kv_indices, + full_q_num_blocks, + full_q_indices, + ] + + list(score_mod_other_buffers) + + list(mask_mod_other_buffers) + ) + input_gen_fns = { + 8: create_num_blocks_fake_generator(kv_indices), # kv_num_blocks + 9: create_indices_fake, + 10: create_num_blocks_fake_generator(q_indices), # q_num_blocks + 11: create_indices_fake, + 12: create_num_blocks_fake_generator(full_kv_indices), # full_kv_num_blocks + 13: create_indices_fake, + 14: create_num_blocks_fake_generator(full_q_indices), # full_q_num_blocks + 15: create_indices_fake, + } + + grad_key = autotune_select_algorithm( + "flex_attention_backward", + choices, + inputs_for_autotuning, + layout_k, + input_gen_fns=input_gen_fns, + ) + return ( + grad_query, + grad_key, + grad_value, + ) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/flex_decoding.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/flex_decoding.py new file mode 100644 index 0000000000000000000000000000000000000000..8a5b7c60eafddacfadf4ee25e7e3c943fb48697f --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/flex_decoding.py @@ -0,0 +1,570 @@ +# mypy: allow-untyped-defs +""" Triton Implementation of the flex_attention Kernel for short query length (FlexDecoding)""" +from typing import Any, List, Tuple + +import sympy + +import torch +from torch._inductor.virtualized import V + +from .. import config, ir +from ..ir import FixedLayout, FlexibleLayout +from ..lowering import empty, empty_strided, lowerings +from ..runtime.runtime_utils import is_power_of_2, next_power_of_2 +from ..select_algorithm import autotune_select_algorithm, TritonTemplate +from .flex_attention import ( + compute_forward_block_mn, + compute_forward_inner, + compute_next_offset_func, + create_indices_fake, + create_num_blocks_fake_generator, + maybe_realize, +) + + +aten = torch.ops.aten +prims = torch.ops.prims + + +def flex_decoding_grid(batch_size, kv_heads, gqa_group_size, n_keys, d_model, meta): + """How is this kernel parallelized? + We create a grid of (batch_size * kv_heads, SPLIT_KV, 1) + Each block is responsible for iterating over blocks of keys and values calculating + the local output for their tile of keys and values over all full length of query. + groups of SPLIT_KV blocks then combine their output to produce the final result. + """ + + return (batch_size * kv_heads, meta["SPLIT_KV"], 1) + + +flex_decoding_template = TritonTemplate( + name="flex_decoding", + grid=flex_decoding_grid, + source=r""" + {{def_kernel("Q", "K", "V", "M", "L", "KV_NUM_BLKS", "KV_IDX", "FULL_KV_NUM_BLKS", "FULL_KV_IDX")}} + # Sub notation for this kernel: + # Q: Query, K: Key, V: Value + # reduction buffers: M rowmax across local KV split, L local sumexp across local KV split + # M: Number of queries, N: Number of keys/values + # QK_HEAD_DIM: The dimension of the query and key embeddings + # V_HEAD_DIM: The dimension of the value embeddings + # BLOCK_M, QK_HEAD_DIM: M, and D dimemsion are always assigned to the same block + # z: Batch size, h: Number of heads, m: Number of queries per head, k: Number of keys per head t: Number of kv splits + # (Modifiable) Config options: + # SPLIT_KV: number of blocks K & V are split into + # TILE_KV: length of each local KV split + # BLOCK_M: block size that Q is padded along seqlen dim. + # BLOCK_N: block size of K & V along N dimension. + # GQA_SHARED_HEADS: number of query heads sharing one kv head in GQA setups. + # + # change of base out of the loop + # ROWS_GUARANTEED_SAFE: Is it guaranteed that at least one value in each row + # is not masked out? If so, we can skip an extra safety check + # SAFE_M_BOUNDARY: Is Q seqlen a multiple of BLOCK_M? If so, we can skip an extra boundary check for loading query. + # SAFE_N_BOUNDARY: Is KV seqlen a multiple of BLOCK_N? If so, we can skip an extra boundary check for loading key/value. + + # PRESCALE_QK: Whether to pre-scale QK by 1/sqrt(d) and change of base. + # + # SPARSE_KV_BLOCK_SIZE: sparse mask block size along KV seqlen dim. + # KV_NUM_BLKS: The number of KV blocks (that may or may not require masking) for each query. + # KV_IDX: The indices of KV blocks (that may or may not require masking) for each query. + # + # + # Output: ACC output accumulated across local KV split. + + tl.static_assert(SPARSE_KV_BLOCK_SIZE >= BLOCK_N and SPARSE_KV_BLOCK_SIZE % BLOCK_N == 0) + + # Define Q Strides + stride_qz, stride_qh, stride_qg, stride_qm, stride_qk = {{stride("Q")}} + stride_kz, stride_kh, stride_kn, stride_kk = {{stride("K")}} + stride_vz, stride_vh, stride_vn, stride_vk = {{stride("V")}} + stride_mz, stride_mt, stride_mh, stride_mm = {{stride("M")}} + stride_lz, stride_lt, stride_lh, stride_lm = {{stride("L")}} + + + Z = {{size("Q", 0)}} + HKV = {{size("Q", 1)}} + G: tl.constexpr = GQA_SHARED_HEADS + HQ = HKV * G + Q_LEN = {{size("Q", 3)}} + KV_LEN = {{size("K", 2)}} + + MATMUL_PRECISION = Q.dtype.element_ty + + # Make sure each split is a multiple of BLOCK_N + TILE_KV_OG = tl.cdiv(KV_LEN, SPLIT_KV) + TILE_KV = tl.cdiv(TILE_KV_OG, BLOCK_N) * BLOCK_N + TILE_KV_MULTIPLE: tl.constexpr = (TILE_KV // BLOCK_N) + + off_z = tl.program_id(0) // HKV + off_hkv = tl.program_id(0) % HKV + off_t = tl.program_id(1) + + q_offset = off_z * stride_qz + off_hkv * stride_qh + k_offset = off_z * stride_kz + off_hkv * stride_kh + v_offset = off_z * stride_vz + off_hkv * stride_vh + + SPARSE_Z = {{size("KV_NUM_BLKS", 0)}} + SPARSE_HQ = {{size("KV_NUM_BLKS", 1)}} + + sparse_idx_z = off_z % SPARSE_Z + # TODO: support masks not broadcasted along the head dimension. + tl.device_assert(SPARSE_HQ == 1) + sparse_idx_h = 0 + + SPARSE_KV_MULTIPLE: tl.constexpr = (SPARSE_KV_BLOCK_SIZE // BLOCK_N) + SPARSE_KV_BLOCK_CNT = tl.cdiv(KV_LEN, SPARSE_KV_BLOCK_SIZE) + + # initialize pointer to m and l + m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf") + l_i = tl.zeros([BLOCK_M], dtype=tl.float32) + acc = tl.zeros([BLOCK_M, V_HEAD_DIM], dtype=tl.float32) + + # initialize offsets + tl.device_assert(BLOCK_M % G == 0) + BLOCK_M_PER_HQ: tl.constexpr = BLOCK_M // G + off_g = tl.arange(0, G) # [G] + offs_g = tl.ravel(tl.broadcast_to(off_g[:, None], [G, BLOCK_M_PER_HQ])) # [BLOCK_M] + offs_hq = offs_g + off_hkv * G + off_m = tl.arange(0, BLOCK_M_PER_HQ) # [BLOCK_M_PER_HQ] + offs_m = tl.ravel(tl.broadcast_to(off_m[None, :], [G, BLOCK_M_PER_HQ])) # [BLOCK_M] + offs_d = tl.arange(0, QK_HEAD_DIM) + offs_vd = tl.arange(0, V_HEAD_DIM) + + # KV_IDX / FULL_KV_IDX and KV_NUM_BLKS / FULL_KV_NUM_BLKS are always contiguous. + sparse_hz_offset = sparse_idx_z * SPARSE_HQ + sparse_idx_h + + # Calculate KV blocks that belong this CTA. + block_n_start = off_t * TILE_KV_MULTIPLE # n_offset inside sparse block + block_n_end = block_n_start + TILE_KV_MULTIPLE # end BLOCK_N + + q_range = stride_qg * off_g[:, None, None] + stride_qm * off_m[None, :, None] + stride_qk * offs_d[None, None, :] + + if SAFE_M_BOUNDARY: + q = tl.load(Q + q_offset + q_range) + else: + mask = off_m[None, :, None] < Q_LEN + q = tl.load(Q + q_offset + q_range, mask) + + q = tl.reshape(q, [BLOCK_M, QK_HEAD_DIM]) + + + # ~~~~~~~~~~~~~~ normal blocks ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + # Apply both score_mod and mask_mod + + # find first kv block we are loading and the number of blocks we are loading + kv_indices = KV_IDX + sparse_hz_offset * SPARSE_KV_BLOCK_CNT + kv_num_blocks = tl.load(KV_NUM_BLKS + sparse_hz_offset) + indices_idx = block_n_start // SPARSE_KV_MULTIPLE + off_n_block_in_sparse = block_n_start % SPARSE_KV_MULTIPLE + off_n = tl.load(kv_indices + indices_idx) * SPARSE_KV_BLOCK_SIZE + off_n_block_in_sparse * BLOCK_N + # first kv block we're loading + + # last valid block according to sparse mask + block_n_last_valid = tl.minimum(kv_num_blocks * SPARSE_KV_MULTIPLE, tl.maximum(tl.cdiv(KV_LEN, BLOCK_N), 1)) + + K_block_ptr = tl.make_block_ptr( + base=K + k_offset, + shape=(QK_HEAD_DIM, KV_LEN), # (d, N) + strides=(stride_kk, stride_kn), + offsets=(0, off_n), + block_shape=(QK_HEAD_DIM, BLOCK_N), + order=(0, 1) + ) + V_block_ptr = tl.make_block_ptr( + base=V + v_offset, + shape=(KV_LEN, V_HEAD_DIM), + strides=(stride_vn, stride_vk), + offsets=(off_n, 0), + block_shape=(BLOCK_N, V_HEAD_DIM), + order=(1, 0) + ) + offs_n = tl.arange(0, BLOCK_N) + off_n + + acc, l_i, m_i = forward_inner( + {{gen_argdefs()}}, + q, K_block_ptr, V_block_ptr, Q_LEN, KV_LEN, + # accumulatd values + acc, l_i, m_i, + #offsets + off_z, offs_hq[:, None], offs_m[:, None], offs_n[None, :], + #block sparse data + kv_indices, kv_num_blocks, + block_n_start, block_n_end if block_n_end <= block_n_last_valid else block_n_last_valid, + MATMUL_PRECISION, + IS_FULL_BLOCKS=False, + ) + + + # ~~~~~~~~~~~~~~ "full" blocks ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + # We know these blocks are guaranteed to be "full", so we don't need to + # apply mask_mod to them - only score_mod + if HAS_FULL_BLOCKS: + kv_indices = FULL_KV_IDX + sparse_hz_offset * SPARSE_KV_BLOCK_CNT + kv_num_blocks = tl.load(FULL_KV_NUM_BLKS + sparse_hz_offset) + indices_idx = block_n_start // SPARSE_KV_MULTIPLE + off_n_block_in_sparse = block_n_start % SPARSE_KV_MULTIPLE + off_n = tl.load(kv_indices + indices_idx) * SPARSE_KV_BLOCK_SIZE + off_n_block_in_sparse * BLOCK_N + + # last valid block according to sparse mask + block_n_last_valid = tl.minimum(kv_num_blocks * SPARSE_KV_MULTIPLE, tl.maximum(tl.cdiv(KV_LEN, BLOCK_N), 1)) + + K_block_ptr = tl.make_block_ptr( + base=K + k_offset, + shape=(QK_HEAD_DIM, KV_LEN), # (d, N) + strides=(stride_kk, stride_kn), + offsets=(0, off_n), + block_shape=(QK_HEAD_DIM, BLOCK_N), + order=(0, 1) + ) + V_block_ptr = tl.make_block_ptr( + base=V + v_offset, + shape=(KV_LEN, V_HEAD_DIM), + strides=(stride_vn, stride_vk), + offsets=(off_n, 0), + block_shape=(BLOCK_N, V_HEAD_DIM), + order=(1, 0) + ) + offs_n = tl.arange(0, BLOCK_N) + off_n + + acc, l_i, m_i = forward_inner( + {{gen_argdefs()}}, + q, K_block_ptr, V_block_ptr, Q_LEN, KV_LEN, + # accumulatd values + acc, l_i, m_i, + #offsets + off_z, offs_hq[:, None], offs_m[:, None], offs_n[None, :], + #block sparse data + kv_indices, kv_num_blocks, + block_n_start, block_n_end if block_n_end <= block_n_last_valid else block_n_last_valid, + MATMUL_PRECISION, + IS_FULL_BLOCKS=True, + ) + + m_offset = off_t * stride_mt + off_z * stride_mz + l_offset = off_t * stride_lt + off_z * stride_lz + + M_block_ptr = tl.make_block_ptr( + base=M + m_offset, + shape=(G, Q_LEN), # (G, M) + strides=(stride_mh, stride_mm), + offsets=(off_hkv*G, 0), + block_shape=(G, BLOCK_M_PER_HQ), + order=(1, 0) + ) + L_block_ptr = tl.make_block_ptr( + base=L + l_offset, + shape=(G, Q_LEN), # (G, M) + strides=(stride_lh, stride_lm), + offsets=(off_hkv*G, 0), + block_shape=(G, BLOCK_M_PER_HQ), + order=(1, 0) + ) + + # Store output, logsumexp and rowmax for cross CTA reduction. (all in float32, even when input data are in fp16) + m_i = m_i.reshape(G, BLOCK_M_PER_HQ) + l_i = l_i.reshape(G, BLOCK_M_PER_HQ) + if SAFE_M_BOUNDARY: + tl.store(M_block_ptr, m_i) + tl.store(L_block_ptr, l_i) + else: + tl.store(M_block_ptr, m_i, boundary_check=(1,)) + tl.store(L_block_ptr, l_i, boundary_check=(1,)) + + # -- store output + idx_z = off_z + idx_t = off_t + idx_hq = off_hkv*G + off_g[:, None, None] + idx_m = off_m[None, :, None] + idx_d = offs_vd[None, None, :] + + mask = (idx_m < Q_LEN) + acc = acc.reshape(G, BLOCK_M_PER_HQ, V_HEAD_DIM) + {{store_output(("idx_z", "idx_t", "idx_hq", "idx_m", "idx_d"), "acc", "mask")}} + """ + + compute_forward_inner + + compute_next_offset_func + + compute_forward_block_mn, +) + + +def get_split_k(B: int, H: int, Mk: int, SM: int = 128) -> int: + """Heuristic for the number of splits from xformer""" + bh = max(B * H, 1) # NOTE: Handle B*h=0 case + split_k = SM // bh # Each SM should at least get one block. + split_k = max(split_k, 1) + + return split_k + + +def _get_decoding_default_config(key) -> Tuple[int, int, int]: + dtype = key.get_dtype() + head_dim = key.get_size()[-1] + sm_version = torch.cuda.get_device_capability() + default_config = (64, 2, 1) + if sm_version >= (9, 0): + if head_dim > 128 and dtype == torch.float32: + return default_config + return (64, 2, 3) + return default_config + + +def create_flex_decoding_kernel(*args, **kwargs): + ( + query, + key, + value, + block_mask, + scale, + kernel_options, + score_mod_subgraph, + mask_mod_subgraph, + score_mod_other_buffers, + mask_mod_other_buffers, + ) = args + ( + kv_num_blocks, + kv_indices, + full_kv_num_blocks, # full_kv_num_blocks, + full_kv_indices, # full_kv_indices, + _, # q_num_blocks + _, # q_indices + _, # full_q_num_blocks, + _, # full_q_indices, + SPARSE_KV_BLOCK_SIZE, + _, # SPARSE_Q_BLOCK_SIZE, + _, + ) = block_mask + + Bq, Hq, seq_len_q, qk_head_dim = query.get_size() + Bkv, Hkv, seq_len_kv, v_head_dim = value.get_size() + assert Bq == Bkv, "Batch dimension must match" + B = Bq + kernel_options = dict(kernel_options) + + # TODO: Fix flex decoding non-divisible case! + if seq_len_q % 128 != 0 or seq_len_kv % 128 != 0: + kernel_options.setdefault("IS_DIVISIBLE", False) + else: + kernel_options.setdefault("IS_DIVISIBLE", True) + + # Calculate GQA head sharing + gqa_shared_heads = Hq // Hkv + if not is_power_of_2(gqa_shared_heads): + raise ValueError( + "Number of shared query heads sharing the same KV head must be power of 2. " + ) + kernel_options.setdefault("GQA_SHARED_HEADS", gqa_shared_heads) + + # Determine if there are "full" blocks where we only need to apply score_mod, and can skip mask_mod + has_full_blocks = full_kv_num_blocks is not None + kernel_options.setdefault("HAS_FULL_BLOCKS", has_full_blocks) + if not has_full_blocks: + # Create a plackeholder full block list in case it is empty + full_kv_num_blocks, full_kv_indices = ( + empty(0, device=query.get_device()) for _ in range(2) + ) + + ( + query, + key, + value, + kv_num_blocks, + kv_indices, + full_kv_num_blocks, + full_kv_indices, + ) = maybe_realize( + [ + query, + key, + value, + kv_num_blocks, + kv_indices, + full_kv_num_blocks, + full_kv_indices, + ] + ) + + choices: List[Any] = [] + configs: List[Tuple[int, int, int]] = [] + configs.append(_get_decoding_default_config(key)) + # Note: max_autotune is not supported yet. Causes error in lowering the dynamic shape in reduction ops. + if config.max_autotune: + configs += [ + (64, 2, 2), + (32, 2, 3), + (128, 2, 3), + ] + # TODO: fix autotuning. + + kernel_options.setdefault("SM_SCALE", scale) + kernel_options.setdefault("SPLIT_KV", get_split_k(B, Hkv, seq_len_kv)) + MAX_SPLIT_KV = kernel_options["SPLIT_KV"] + + # create config dependent intermediate buffers + buf_ACC_shape = [B, MAX_SPLIT_KV, Hq, seq_len_q, v_head_dim] + buf_ML_shape = buf_ACC_shape[:-1] + buf_M = empty_strided( + buf_ML_shape, + None, + dtype=torch.float32, # The rowmax is always stored in fp32 regardless of the input dtype + device=query.get_device(), + ) + buf_L = empty_strided( + buf_ML_shape, + None, + dtype=torch.float32, # The intermediate sumexp is always stored in fp32 regardless of the input dtype + device=query.get_device(), + ) + + layout_acc = FixedLayout( + query.get_device(), + torch.float32, + buf_ACC_shape, + FlexibleLayout.contiguous_strides(buf_ACC_shape), + ) + + kernel_options.setdefault("QK_HEAD_DIM", qk_head_dim) + kernel_options.setdefault("V_HEAD_DIM", v_head_dim) + + kernel_options.setdefault( + "BLOCK_M", + ( + # m + # if V.graph.sizevars.evaluate_expr(sympy.Lt(query.get_size()[-2], 0)) + # else # Always use a BLOCK_M > 16 before Triton fix https://github.com/triton-lang/triton/pull/4061 is in pin + max( + next_power_of_2( + V.graph.sizevars.size_hint( + seq_len_q, fallback=torch._inductor.config.unbacked_symint_fallback # type: ignore[arg-type] + ) + * gqa_shared_heads + ), + 16, + ) + ), + ) + + query = ir.ExternKernel.realize_input(query) + stride_b, stride_hq, stride_seq_len_q, stride_qk_head_dim = query.get_stride() + + # Reshape query for GQA: [B, Hq, Mq, D] -> [B, Hkv, G, Mq, D] + gqa_query_shape = (B, Hkv, gqa_shared_heads, seq_len_q, qk_head_dim) + gqa_query_stride = ( + stride_b, + stride_hq * gqa_shared_heads, + stride_hq, + stride_seq_len_q, + stride_qk_head_dim, + ) + query = lowerings[aten.as_strided](query, gqa_query_shape, gqa_query_stride) + + V.graph.sizevars.guard_leq( + seq_len_q * gqa_shared_heads, sympy.Integer(kernel_options["BLOCK_M"]) + ) + + kernel_options.setdefault( + "SAFE_M_BOUNDARY", + ((seq_len_q * gqa_shared_heads) % kernel_options["BLOCK_M"]) == 0, + ) + # TODO: This feels sketchy + kernel_options.setdefault("SAFE_N_BOUNDARY", True) + + # Note, we don't need to pass in the captured buffers explicitly + # because they're implicitly added by the score_mod function + # We do need to explicitly pass it in for autotuning though. + for BLOCK_N, num_warps, num_stages in configs: + if SPARSE_KV_BLOCK_SIZE % BLOCK_N != 0: + continue + + # Performance tuning + kernel_options.setdefault("BLOCK_N", BLOCK_N) + kernel_options.setdefault("SPARSE_KV_BLOCK_SIZE", SPARSE_KV_BLOCK_SIZE) + + # Work around https://github.com/pytorch/pytorch/issues/129625 + if num_stages == 2: + continue + flex_decoding_template.maybe_append_choice( + choices=choices, + input_nodes=[ + query, + key, + value, + buf_M, + buf_L, + kv_num_blocks, + kv_indices, + full_kv_num_blocks, + full_kv_indices, + ], + layout=layout_acc, + subgraphs=[ + score_mod_subgraph, + mask_mod_subgraph, + ], + mutated_inputs=[buf_M, buf_L], + num_stages=num_stages, + num_warps=num_warps, + call_sizes=query.get_size(), + **kernel_options, + ) + + inputs_for_flex_decoding = ( + [ + query, + key, + value, + buf_M, + buf_L, + kv_num_blocks, + kv_indices, + full_kv_num_blocks, + full_kv_indices, + ] + + list(score_mod_other_buffers) + + list(mask_mod_other_buffers) + ) + + input_gen_fns = { + 5: create_num_blocks_fake_generator(kv_indices), + 6: create_indices_fake, + 7: create_num_blocks_fake_generator(full_kv_indices), + 8: create_indices_fake, + } + + buf_ACC = autotune_select_algorithm( + "flex_decoding", + choices, + inputs_for_flex_decoding, + layout_acc, + input_gen_fns=input_gen_fns, + ) + + # Reduction + + g_M = lowerings[aten.max](buf_M, dim=1, keepdim=True)[0] + # See [Note] Handle fully masked out rows: + # g_M Is the global max among split kv blocks. + masked_rows = lowerings[aten.eq](g_M, -float("inf")) + adj_M = lowerings[aten.sub](buf_M, g_M) + adj_M = lowerings[aten.where](masked_rows, 0, adj_M) + alpha = lowerings[aten.exp2](adj_M) + + buf_L = lowerings[aten.mul](buf_L, alpha) + g_L = lowerings[aten.sum](buf_L, axis=1) + masked_rows_squeezed = lowerings[aten.squeeze](masked_rows, dim=1) + g_L = lowerings[aten.where](masked_rows_squeezed, 1.0, g_L) + logsumexp = lowerings[aten.log2](g_L) + logsumexp = lowerings[aten.add](logsumexp, lowerings[aten.squeeze](g_M, dim=1)) + + alpha_unseq = lowerings[aten.unsqueeze](alpha, 4) + buf_ACC = lowerings[aten.mul](buf_ACC, alpha_unseq) + output = lowerings[aten.sum](buf_ACC, axis=1) + L_unseq = lowerings[aten.unsqueeze](g_L, 3) + output = lowerings[aten.div](output, L_unseq) + output = lowerings[prims.convert_element_type](output, query.get_dtype()) + + return ( + output, + logsumexp, + ) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/mm.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/mm.py new file mode 100644 index 0000000000000000000000000000000000000000..c2e09300f9b8a7eb6615618c8f34aebebe6e80a2 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/mm.py @@ -0,0 +1,776 @@ +# mypy: allow-untyped-defs +import functools +import logging +from typing import Any, Dict, List, Optional + +import torch +from torch._inductor.autoheuristic.autoheuristic import AutoHeuristicSelectAlgorithm +from torch._inductor.autoheuristic.autoheuristic_utils import ( + AHContext, + context_add_strides, + context_add_using_tf32, + get_mixedmm_precondition, + mixed_mm_operations, + mm_operations, +) +from torch._inductor.codegen.cpp_gemm_template import CppPackedGemmTemplate +from torch._inductor.virtualized import V + +from .. import config as inductor_config +from ..codegen.common import BackendFeature +from ..codegen.cuda.gemm_template import CUTLASS2xGemmTemplate, CUTLASS3xGemmTemplate +from ..codegen.rocm.ck_universal_gemm_template import CKGemmTemplate +from ..codegen.wrapper import WrapperCodeGen +from ..ir import FlexibleLayout, is_triton +from ..lowering import register_lowering +from ..select_algorithm import ( + autotune_select_algorithm, + ExternKernelChoice, + NoValidChoicesError, + TritonTemplate, +) +from ..utils import ( + get_gpu_shared_memory, + use_aten_gemm_kernels, + use_ck_template, + use_cpp_packed_gemm_template, + use_cutlass_template, + use_max_autotune, + use_triton_template, +) +from .mm_common import ( + addmm_epilogue, + extra_mm_configs, + int8_mm_configs, + mixed_mm_configs, + mm_args, + mm_configs, + mm_grid, + mm_options, + triton_config, +) + + +log = logging.getLogger(__name__) +aten = torch.ops.aten + +mm_template = TritonTemplate( + name="mm", + grid=mm_grid, + source=r""" +{{def_kernel("A", "B")}} + M = {{size("A", 0)}} + N = {{size("B", 1)}} + K = {{size("A", 1)}} + if M * N == 0: + # early exit due to zero-size input(s) + return + stride_am = {{stride("A", 0)}} + stride_ak = {{stride("A", 1)}} + stride_bk = {{stride("B", 0)}} + stride_bn = {{stride("B", 1)}} + + # based on triton.ops.matmul + pid = tl.program_id(0) + grid_m = (M + BLOCK_M - 1) // BLOCK_M + grid_n = (N + BLOCK_N - 1) // BLOCK_N + + # re-order program ID for better L2 performance + width = GROUP_M * grid_n + group_id = pid // width + group_size = min(grid_m - group_id * GROUP_M, GROUP_M) + pid_m = group_id * GROUP_M + (pid % group_size) + pid_n = (pid % width) // (group_size) + + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + if (stride_am == 1 and stride_ak == M) or (stride_am == K and stride_ak == 1): + ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M) + else: + ram = rm % M + if (stride_bk == 1 and stride_bn == K) or (stride_bk == N and stride_bn == 1): + rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N) + else: + rbn = rn % N + rk = tl.arange(0, BLOCK_K) + A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak) + B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn) + + acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE) + for k in range(K, 0, -BLOCK_K): + if EVEN_K: + a = tl.load(A) + b = tl.load(B) + else: + a = tl.load(A, mask=rk[None, :] < k, other=0.) + b = tl.load(B, mask=rk[:, None] < k, other=0.) + if B_PROLOGUE_CAST_TYPE is not None: + b = b.to(B_PROLOGUE_CAST_TYPE) + acc += tl.dot(a, b, allow_tf32=ALLOW_TF32) + A += BLOCK_K * stride_ak + B += BLOCK_K * stride_bk + + # rematerialize rm and rn to save registers + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + idx_m = rm[:, None] + idx_n = rn[None, :] + mask = (idx_m < M) & (idx_n < N) + + # inductor generates a suffix + {{store_output(("idx_m", "idx_n"), "acc", "mask")}} +""", +) + +aten_mm = ExternKernelChoice(torch.mm, "at::mm_out") + +aten_addmm = ExternKernelChoice( + torch.addmm, "at::addmm_out", op_overload=aten.addmm.default +) + +aten__int_mm = ExternKernelChoice(torch._int_mm, "at::_int_mm") + +aten__sparse_semi_structured_mm = ExternKernelChoice( + torch._sparse_semi_structured_mm, + "at::_sparse_semi_structured_mm", + has_out_variant=False, +) + + +def _is_int8_mat(mat): + return mat.get_dtype() in (torch.int8, torch.uint8) + + +def bias_addmm(inp, mat1, mat2, *, out=None, alpha=1, beta=1): + """ + Giving torch.addmm a 1D tensor calls a different (faster) cublasLt + kernel under the hood. There are a few shapes where this is slower, + but they are rare. + """ + if inp.stride(0) == 0 or inp.size(0) == 1: + return torch.addmm(inp[0], mat1, mat2, out=out, alpha=alpha, beta=beta) + return torch.addmm(inp, mat1, mat2, out=out, alpha=alpha, beta=beta) + + +aten_bias_addmm = ExternKernelChoice(bias_addmm, None) + + +@register_lowering(aten.mm, type_promotion_kind=None) +def tuned_mm(mat1, mat2, *, layout=None): + m, n, k, layout, mat1, mat2 = mm_args(mat1, mat2, layout=layout) + name = "mm" + + aten_layout = layout + if not use_max_autotune(): + aten_layout = FlexibleLayout( + device=layout.device, dtype=layout.dtype, size=layout.size + ) + + # options to tune from + choices = ( + [aten_mm.bind((mat1, mat2), aten_layout)] if use_aten_gemm_kernels() else [] + ) + static_shape, is_nonzero = _is_static_problem([mat1, mat2], layout) + if is_nonzero and use_triton_template(layout): + for config in mm_configs(m, n, k): + mm_template.maybe_append_choice( + choices, + input_nodes=(mat1, mat2), + layout=layout, + **mm_options(config, m, n, k, layout), + ) + if static_shape and is_nonzero and use_cutlass_template(layout, m, n, k): + CUTLASS3xGemmTemplate.add_cutlass_gemm_choices(choices, layout, [mat1, mat2]) + + if is_nonzero and use_ck_template(layout, m, n, k): + CKGemmTemplate.add_ck_gemm_choices(choices, layout, [mat1, mat2]) + + if use_cpp_packed_gemm_template(layout, mat1, mat2): + CppPackedGemmTemplate.add_choices( + choices, + layout, + [mat1, mat2], + ) + + input_nodes = [mat1, mat2] + if ( + is_nonzero + and use_triton_template(layout) + and torch._inductor.config.run_autoheuristic(name) + and is_triton(mat1) + ): + always_included = [] + if use_aten_gemm_kernels(): + always_included.append("extern_mm") + num_choices_before_extra_configs = len(choices) + for config in extra_mm_configs(m, n, k): + mm_template.maybe_append_choice( + choices, + input_nodes=(mat1, mat2), + layout=layout, + **mm_options(config, m, n, k, layout), + ) + + # using AutoHeuristic for ranking + ah_choices = mm_autoheuristic( + mat1, + mat2, + m, + n, + k, + choices, + name, + input_nodes, + mm_operations(), + None, + top_k=10, + always_included=always_included, + ) + if not torch._inductor.config.collect_autoheuristic(name): + # if we are collecting data, we do not want to modify choices + if ah_choices is not None and len(ah_choices) > 0: + # the order in which autoheuristic returns choices is not the same as + # as the order of choices, which affects things like epilogue fusion. + # once epilogue fusion benchmarks choices in sorted order, I think we can + # just use the order returned by autoheuristic + choices = [choice for choice in choices if choice in ah_choices] + else: + choices = choices[:num_choices_before_extra_configs] + + if ( + len(choices) == 0 + and not use_aten_gemm_kernels() + and inductor_config.autotune_fallback_to_aten + ): + log.warning("No choices for GEMM, using ATen backend as fallback") + return aten_mm.bind((mat1, mat2), aten_layout).output_node() + + try: + return autotune_select_algorithm(name, choices, [mat1, mat2], layout) + except NoValidChoicesError: + if not inductor_config.autotune_fallback_to_aten: + raise + log.warning("All choices for GEMM were invalid, using ATen backend as fallback") + return aten_mm.bind((mat1, mat2), aten_layout).output_node() + + +def _is_static_problem(inputs_tensors, layout): + # checks whether all input tensors and the output layout + # have a static shape by attempting to convert the dimensions + # to int + static_shape = True + static_size = WrapperCodeGen.statically_known_list_of_ints_or_none(layout.size) + if static_size is None: + nonzero = True + for s in layout.size: + sz = WrapperCodeGen.statically_known_int_or_none(s) + if sz is not None and sz == 0: + nonzero = False + break + return False, nonzero + numel = 1 + for dim in static_size: + numel *= dim + nonzero = numel > 0 + return static_shape, nonzero + + +@register_lowering(aten._int_mm, type_promotion_kind=None) +def tuned_int_mm(mat1, mat2, *, layout=None): + m, n, k, layout, mat1, mat2 = mm_args( + mat1, mat2, layout=layout, out_dtype=torch.int32 + ) + static_shape, is_nonzero = _is_static_problem([mat1, mat2], layout) + use_cutlass = static_shape and is_nonzero and use_cutlass_template(layout, m, n, k) + + choices = ( + [aten__int_mm.bind((mat1, mat2), layout)] if use_aten_gemm_kernels() else [] + ) + + # TODO: Re-enable eager mode implementation once cuBLAS is fixed + if use_cutlass or use_triton_template(layout, enable_int32=True): + choices = [] + + if use_cutlass: + CUTLASS3xGemmTemplate.add_cutlass_gemm_choices( + choices, layout, [mat1, mat2], fuseable=True, non_fuseable=True + ) + if is_nonzero and use_triton_template(layout, enable_int32=True): + for config in int8_mm_configs(m, n, k): + mm_template.maybe_append_choice( + choices, + input_nodes=(mat1, mat2), + layout=layout, + **mm_options(config, m, n, k, layout), + ) + if len(choices) == 0: + log.warning( + "No choices for integer GEMM avaialbe using configured backends, using ATen backend as fallback" + ) + choices = [aten__int_mm.bind((mat1, mat2), layout)] + + try: + return autotune_select_algorithm("int_mm", choices, [mat1, mat2], layout) + except NoValidChoicesError: + if not inductor_config.autotune_fallback_to_aten: + raise + log.warning("All choices for GEMM were invalid, using ATen backend as fallback") + choices = [aten__int_mm.bind((mat1, mat2), layout)] + return autotune_select_algorithm("int_mm", choices, [mat1, mat2], layout) + + +@register_lowering(aten.addmm, type_promotion_kind=None) +def tuned_addmm(inp, mat1, mat2, *, alpha=1, beta=1, layout=None): + ordered_kwargs_for_cpp_kernel = ("beta", "alpha") + m, n, k, layout, mat1, mat2, inp_expanded = mm_args(mat1, mat2, inp, layout=layout) + static_shape, is_nonzero = _is_static_problem([inp, mat1, mat2], layout) + if (not is_nonzero) or (not use_max_autotune()): + # Use a FlexibleLayout if we are not autotuning. + # This allows padding strides for the output. + from torch._inductor.ir import FixedLayout, FlexibleLayout + + if isinstance(layout, FixedLayout): + layout = FlexibleLayout( + device=layout.device, dtype=layout.dtype, size=layout.size + ) + choices = ( + [ + aten_addmm.bind( + (inp, mat1, mat2), + layout, + alpha=alpha, + beta=beta, + ) + ] + if use_aten_gemm_kernels() + else [] + ) + return autotune_select_algorithm("addmm", choices, [inp, mat1, mat2], layout) + + choices = ( + [ + aten_addmm.bind( + (inp_expanded, mat1, mat2), + layout, + alpha=alpha, + beta=beta, + ) + ] + if use_aten_gemm_kernels() + else [] + ) + + if ( + use_aten_gemm_kernels() + and inp_expanded.get_stride()[0] == 0 + and inp_expanded.get_device().type == "cuda" + and inductor_config.triton.autotune_cublasLt + ): + # unexpand inp to make sure fused addmm from cublasLt is used + choices.insert( + 0, + aten_bias_addmm.bind( + (inp_expanded, mat1, mat2), layout, alpha=alpha, beta=beta + ), + ) + + if is_nonzero and use_triton_template(layout): + for config in mm_configs(m, n, k): + mm_template.maybe_append_choice( + choices, + input_nodes=(inp_expanded, mat1, mat2), + layout=layout, + **mm_options(config, m, n, k, layout), + prefix_args=1, + epilogue_fn=addmm_epilogue(layout.dtype, alpha, beta), + ) + + if static_shape and is_nonzero and use_cutlass_template(layout, m, n, k): + # Filter out a known cause of CUDA illegal memory access errors + # broadcasting on the last dim of the bias term seems not to be working + # in the linear GEMM epilogue used by addmm. + if ( + WrapperCodeGen.statically_known_int_or_none(inp_expanded.layout.stride[-1]) + != 0 + ): + CUTLASS3xGemmTemplate.add_cutlass_gemm_choices( + choices, + layout, + [mat1, mat2, inp_expanded], + alpha=alpha, + beta=beta, + ) + + if is_nonzero and use_ck_template(layout, m, n, k): + CKGemmTemplate.add_ck_gemm_choices( + choices, + layout, + [mat1, mat2, inp_expanded], + alpha=alpha, + beta=beta, + ) + + if use_cpp_packed_gemm_template(layout, mat1, mat2): + CppPackedGemmTemplate.add_choices( + choices, + layout, + [inp_expanded, mat1, mat2], + alpha=alpha, + beta=beta, + has_bias=True, + ) + + add_aten_fallback = False + if len(choices) == 0: + log.warning("No choices for GEMM, using ATen backend as fallback") + add_aten_fallback = True + + if add_aten_fallback: + choices.append( + aten_addmm.bind( + (inp_expanded, mat1, mat2), + layout, + ordered_kwargs_for_cpp_kernel, + alpha=alpha, + beta=beta, + ) + ) + + if ( + inp_expanded.get_stride()[0] == 0 + and inp_expanded.get_device().type == "cuda" + and inductor_config.triton.autotune_cublasLt + ): + # unexpand inp to make sure fused addmm from cublasLt is used + choices.insert( + 0, + aten_bias_addmm.bind( + (inp_expanded, mat1, mat2), layout, alpha=alpha, beta=beta + ), + ) + try: + return autotune_select_algorithm( + "addmm", choices, [inp_expanded, mat1, mat2], layout + ) + except NoValidChoicesError: + if not inductor_config.autotune_fallback_to_aten: + raise + log.warning("All choices for GEMM were invalid, using ATen backend as fallback") + fallback_choice = aten_addmm.bind( + (inp, mat1, mat2), + layout, + ordered_kwargs_for_cpp_kernel, + alpha=alpha, + beta=beta, + ) + return fallback_choice.output_node() + + +@register_lowering(aten._sparse_semi_structured_mm, type_promotion_kind=None) +def tuned_sparse_semi_structured_mm( + mat1, mat1_meta, mat2, *, out_dtype=None, layout=None +): + from torch._inductor.select_algorithm import realize_inputs + + mat1, mat1_meta, mat2 = realize_inputs(mat1, mat1_meta, mat2) + m1, k1 = mat1.get_size() + m2, _ = mat1_meta.get_size() + k2, n = mat2.get_size() + m = V.graph.sizevars.guard_equals(m1, m2) + k = V.graph.sizevars.guard_equals(2 * k1, k2) + + if layout is None: + from torch._inductor.ir import FixedLayout + + layout = FixedLayout( + mat2.get_device(), + out_dtype if out_dtype else mat2.get_dtype(), + [m, n], + [n, 1], + ) + else: + assert out_dtype is None, "out_dtype is ignored if layout is specified." + + choices = ( + [ + aten__sparse_semi_structured_mm.bind( + (mat1, mat1_meta, mat2), layout, out_dtype=out_dtype + ) + ] + if use_aten_gemm_kernels() + else [] + ) + + if m * n != 0 and use_cutlass_template(layout, m, n, k): + CUTLASS2xGemmTemplate.add_cutlass_gemm_choices( + choices, layout, [mat1, mat2, mat1_meta], fuseable=True, non_fuseable=True + ) + + return autotune_select_algorithm( + "sparse_semi_structured_mm", choices, [mat1, mat1_meta, mat2], layout + ) + + +def fallback_mixed_mm(mat1, mat2, *, out): + return torch.mm(mat1, mat2.to(mat1.dtype), out=out) + + +aten_fallback_mixed_mm = ExternKernelChoice(fallback_mixed_mm, None) + + +@functools.lru_cache(None) +def _is_sm7x_or_older_gpu(index: Optional[int]) -> bool: + props = torch.cuda.get_device_properties(index or 0) + return props.major <= 7 + + +def dims_are_int(dims): + return all(isinstance(dim, int) for dim in dims) + + +def try_heuristic(m, n, k, choices, mat1, mat2, mat2_dtype, layout): + m, n, k = get_size_hints(mat1, mat2, m, n, k) + if not dims_are_int([m, n, k]): + return None + + if mat1.dtype != torch.float16: + return None + + # only use heuristic if we are running on an A100 + # torch.cuda.get_device_capability() >= (8, 0) returns true for A10G + # which does not have enough shared memory for one of the configs + if ( + not torch.cuda.get_device_capability() >= (8, 0) + ) or get_gpu_shared_memory() != 166912: + return None + + if m == 1 and (n % 16 != 0 or k % 16 != 0): + return None + + if m <= 16 and n >= 4096 and k >= 4096: + return triton_config( + BLOCK_M=16, + BLOCK_N=64, + BLOCK_K=128, + num_stages=5, + num_warps=4, + ) + elif m > 16 and m <= 32 and n >= 4096 and k >= 4096: + return triton_config( + BLOCK_M=32, + BLOCK_N=32, + BLOCK_K=128, + num_stages=5, + num_warps=4, + ) + elif m > 32 and m <= 64 and n >= 4096 and k >= 4096: + return triton_config( + BLOCK_M=64, + BLOCK_N=32, + BLOCK_K=128, + num_stages=5, + num_warps=4, + ) + return None + + +def mm_autoheuristic( + mat1, + mat2, + m, + n, + k, + choices, + name, + input_nodes, + ops, + precondition, + top_k: Optional[int] = None, + always_included=None, +): + m, n, k = get_size_hints(mat1, mat2, m, n, k) + if not dims_are_int([m, n, k]): + return None + mat1_stride, mat2_stride = get_size_hints_strides(mat1, mat2) + + def get_context(m, k, n, mat1, mat2, mat1_stride, mat2_stride): + context = AHContext() + context.add_feature("m", m) + context.add_feature("k", k) + context.add_feature("n", n) + context.add_feature("mat1_dtype", mat1.layout.dtype, is_categorical=True) + context.add_feature("mat2_dtype", mat2.layout.dtype, is_categorical=True) + context_add_strides(context, "mat1", mat1_stride) + context_add_strides(context, "mat2", mat2_stride) + context.add_feature( + "mat1_iscontig", mat1.layout.is_contiguous(), is_categorical=True + ) + context.add_feature( + "mat2_iscontig", mat2.layout.is_contiguous(), is_categorical=True + ) + if name == "mm": + # for mixed_mm, we only consider fp16 + context_add_using_tf32(context, mat1.layout.dtype) + return context + + def fallback(): + return None + + context = get_context(m, k, n, mat1, mat2, mat1_stride, mat2_stride) + autoheuristic = AutoHeuristicSelectAlgorithm( + fallback=fallback, + choices=choices, + input_nodes=input_nodes, + context=context, + name=name, + augment_context=ops, + precondition=precondition, + ) + + if top_k is not None: + # TODO: is there a cleaner way to ensure aten.mm is always included? + return autoheuristic.get_top_k_choices_caller( + top_k, always_included=always_included + ) + + return autoheuristic.get_choice_caller() + + +def get_size_hints(mat1, mat2, m, n, k): + if not isinstance(m, int) or not isinstance(k, int): + (m, k) = V.graph.sizevars.size_hints( + mat1.get_size(), + fallback=torch._inductor.config.unbacked_symint_fallback, + ) + + if not isinstance(n, int) or not isinstance(k, int): + (k, n) = V.graph.sizevars.size_hints( + mat2.get_size(), + fallback=torch._inductor.config.unbacked_symint_fallback, + ) + return m, n, k + + +def get_size_hints_strides(mat1, mat2): + mat1_stride = mat1.layout.stride + mat2_stride = mat2.layout.stride + strides = [mat1_stride, mat2_stride] + strides_hints = [] + for stride in strides: + if not isinstance(stride, int): + stride = V.graph.sizevars.size_hints( + stride, + fallback=torch._inductor.config.unbacked_symint_fallback, + ) + strides_hints.append(stride) + return strides_hints[0], strides_hints[1] + + +def tuned_mixed_mm(mat1, mat2, mat2_dtype): + m, n, k, layout, mat1, mat2 = mm_args(mat1, mat2, layout=None) + static_shape, is_nonzero = _is_static_problem([mat1, mat2], layout) + + fallback = aten_fallback_mixed_mm.bind((mat1, mat2), layout) + + choices = [fallback] + + # can't use triton kernel unless one of these is true or if running on v100 (numerical issues) + skip_triton = ( + ( + mat1.layout.dtype != torch.float32 + and not (mat2.layout.is_contiguous() or mat2.layout.is_transposed()) + ) + or _is_sm7x_or_older_gpu(layout.device.index) + or inductor_config.mixed_mm_choice == "aten" + or not V.graph.has_feature(layout.device, BackendFeature.TRITON_TEMPLATES) + or ( + mat1.layout.dtype == torch.float32 and torch.backends.cuda.matmul.allow_tf32 + ) + or (mat1.layout.dtype == torch.bfloat16 and mat2.layout.dtype == torch.uint8) + ) + + if inductor_config.mixed_mm_choice == "triton": + choices = [] + + if not skip_triton: + b_prologue_cast_type = f"tl.{mat2_dtype}".replace("torch.", "") + if static_shape and inductor_config.mixed_mm_choice == "heuristic": + choices = [] + config = try_heuristic(m, n, k, choices, mat1, mat2, mat2_dtype, layout) + if config is not None: + mm_template.maybe_append_choice( + choices, + input_nodes=(mat1, mat2), + layout=layout, + **mm_options(config, m, n, k, layout, b_prologue_cast_type), + ) + choices.append(fallback) + + has_int8_tensor = _is_int8_mat(mat1) or _is_int8_mat(mat2) + for config in mixed_mm_configs(m, n, k, has_int8_tensor=has_int8_tensor): + mm_template.maybe_append_choice( + choices, + input_nodes=(mat1, mat2), + layout=layout, + **mm_options(config, m, n, k, layout, b_prologue_cast_type), + ) + + if static_shape and is_nonzero and use_cutlass_template(layout, m, n, k): + CUTLASS3xGemmTemplate.add_cutlass_gemm_choices( + choices, layout, [mat1, mat2], fuseable=True, non_fuseable=True + ) + CUTLASS2xGemmTemplate.add_cutlass_gemm_choices( + choices, layout, [mat1, mat2], fuseable=True, non_fuseable=True + ) + + if skip_triton and not choices: + choices = [fallback] + + name = "mixed_mm" + input_nodes = [mat1, mat2] + if torch._inductor.config.run_autoheuristic(name): + choice = mm_autoheuristic( + mat1, + mat2, + m, + n, + k, + choices, + name, + input_nodes, + mixed_mm_operations(), + get_mixedmm_precondition, + ) + if ( + not skip_triton + and inductor_config.mixed_mm_choice == "heuristic" + and choice is not None + ): + choices.insert(0, choice) + return autotune_select_algorithm(name, choices, input_nodes, layout) + + +# This op is a special case of the int_mm op which we use based on the pattern +# _int_mm -> mul (defined in ../fx_passes/post_grad.py) in order to prevent +# realization of the int32 _int_mm output by forcing fusion with the mul op. +# This is only used when config.force_fuse_int_mm_with_mul = True +def tuned_fused_int_mm_mul(mat1, mat2, mat3, out_dtype, *, layout=None): + out_dtype = ( + torch.promote_types(mat3.get_dtype(), torch.int32) + if out_dtype is None + else out_dtype + ) + m, n, k, layout, mat1, mat2, mat3 = mm_args( + mat1, mat2, mat3, layout=layout, out_dtype=out_dtype + ) + choices: List[Dict[Any, Any]] = [] + for config in int8_mm_configs(m, n, k): + mm_template.maybe_append_choice( + choices, + input_nodes=(mat1, mat2, mat3), + layout=layout, + **dict(mm_options(config, m, n, k, layout), ACC_TYPE="tl.int32"), + suffix_args=1, + epilogue_fn=V.ops.mul, + ) + return autotune_select_algorithm("int_mm", choices, [mat1, mat2, mat3], layout) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/mm_common.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/mm_common.py new file mode 100644 index 0000000000000000000000000000000000000000..0c66da4ca4f314e09e996c2d9860c8f3b9365086 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/mm_common.py @@ -0,0 +1,466 @@ +# mypy: allow-untyped-defs +import functools +import itertools +import logging +from typing import cast, List, Tuple + +import sympy + +import torch +from torch._inductor.select_algorithm import realize_inputs +from torch._inductor.virtualized import V + +from .. import config as inductor_config +from ..runtime.runtime_utils import next_power_of_2 +from ..utils import ceildiv as cdiv + + +log = logging.getLogger(__name__) + + +def triton_config(num_stages, num_warps, **kwargs): + from triton import Config + + return Config(kwargs, num_stages=num_stages, num_warps=num_warps) + + +def filtered_configs( + m: int, + n: int, + k: int, + configs: List[Tuple[int, int, int, int, int]], + has_int8_tensor=False, +): + """Heuristic to shrink configs when they are bigger than the input size""" + + min_block_size = 16 + # block_k=16 seems to be causing issues + # see: https://github.com/triton-lang/triton/issues/2156#issuecomment-1695897424 + min_block_size_k = 32 if has_int8_tensor else 16 + m = max( + next_power_of_2( + V.graph.sizevars.size_hint( + m, fallback=torch._inductor.config.unbacked_symint_fallback # type: ignore[arg-type] + ) + ), + min_block_size, + ) + n = max( + next_power_of_2( + V.graph.sizevars.size_hint( + n, fallback=torch._inductor.config.unbacked_symint_fallback # type: ignore[arg-type] + ) + ), + min_block_size, + ) + k = max( + next_power_of_2( + V.graph.sizevars.size_hint( + k, fallback=torch._inductor.config.unbacked_symint_fallback # type: ignore[arg-type] + ) + ), + min_block_size_k, + ) + used = set() + for block_m, block_n, block_k, num_stages, num_warps in configs: + # shrink configs for small sizes + block_m = max(min(block_m, m), min_block_size) + block_n = max(min(block_n, n), min_block_size) + block_k = max(min(block_k, k), min_block_size_k) + # each warp computes 16x16 tile = 256 + num_warps = min(num_warps, block_m * block_n // 256) + if torch.version.hip: + for matrix_instr_nonkdim in [0, 16]: + if matrix_instr_nonkdim != 0 and ( + block_m % matrix_instr_nonkdim != 0 + or block_n % matrix_instr_nonkdim != 0 + ): + # block_m and block_n must be a multiple of matrix_instr_nonkdim + continue + if ( + block_m, + block_n, + block_k, + num_stages, + num_warps, + matrix_instr_nonkdim, + ) not in used: + used.add( + ( + block_m, + block_n, + block_k, + num_stages, + num_warps, + matrix_instr_nonkdim, + ) + ) + yield triton_config( + BLOCK_M=block_m, + BLOCK_N=block_n, + BLOCK_K=block_k, + num_stages=num_stages, + num_warps=num_warps, + matrix_instr_nonkdim=matrix_instr_nonkdim, + ) + else: + if (block_m, block_n, block_k, num_stages, num_warps, 0) not in used: + used.add((block_m, block_n, block_k, num_stages, num_warps, 0)) + yield triton_config( + BLOCK_M=block_m, + BLOCK_N=block_n, + BLOCK_K=block_k, + num_stages=num_stages, + num_warps=num_warps, + ) + + +# List of dictionaries to store the kernel configs. Configs that evaluate to true +# will be utilised on the target platform. The configs are as follows: +# (BLOCK_M, BLOCK_N, BLOCK_K, num_stages, num_warps) +mm_kernel_configs = ( + [ + {"config": (32, 32, 16, 1, 2), "cond": True}, + {"config": (32, 32, 128, 2, 4), "cond": torch.version.hip is None}, + {"config": (32, 64, 32, 5, 8), "cond": True}, + {"config": (64, 32, 32, 5, 8), "cond": True}, + {"config": (64, 32, 128, 5, 4), "cond": True}, + {"config": (64, 64, 16, 2, 4), "cond": True}, + {"config": (64, 64, 32, 2, 4), "cond": True}, + {"config": (64, 64, 64, 3, 8), "cond": True}, + {"config": (64, 64, 128, 5, 4), "cond": True}, + {"config": (64, 128, 32, 3, 4), "cond": True}, + {"config": (64, 128, 32, 4, 8), "cond": True}, + {"config": (64, 128, 64, 3, 4), "cond": True}, + {"config": (64, 128, 128, 4, 4), "cond": True}, + {"config": (128, 64, 32, 3, 4), "cond": True}, + {"config": (128, 64, 32, 4, 8), "cond": True}, + {"config": (128, 128, 32, 2, 8), "cond": True}, + {"config": (128, 128, 32, 3, 4), "cond": True}, + {"config": (128, 128, 64, 3, 4), "cond": True}, + {"config": (128, 128, 64, 5, 8), "cond": True}, + ] + if inductor_config.max_autotune_gemm_search_space != "EXHAUSTIVE" + else [ + {"config": (BLOCK_M, BLOCK_N, BLOCK_K, num_stages, num_warps), "cond": True} + for BLOCK_M, BLOCK_N, BLOCK_K in itertools.product( + [16, 32, 64, 128, 256], repeat=3 + ) + for num_stages in [1, 2, 3, 4, 5] + for num_warps in [2, 4, 8] + ] +) + +# these are only used in tuned_mm when AutoHeuristic is enabled +# the idea is that when AutoHeuristic collects data to learn a heuristic, more configs are autotuned +# when the learned heuristic is used, the learned heuristic reduces the number of configs down to 10 +# which saves compilation time (since less configs are autotuned) and potentially increase performance +# because the learned heuristic might predict a config that is not part mm_configs +extra_mm_kernel_configs = [ + {"config": (16, 32, 16, 3, 2), "cond": True}, + {"config": (16, 32, 32, 4, 2), "cond": True}, + {"config": (16, 32, 32, 5, 2), "cond": True}, + {"config": (64, 64, 128, 3, 4), "cond": True}, + {"config": (128, 64, 32, 2, 2), "cond": True}, + {"config": (128, 64, 64, 3, 8), "cond": True}, + {"config": (128, 64, 128, 4, 8), "cond": True}, + {"config": (128, 128, 32, 4, 4), "cond": True}, + {"config": (128, 128, 64, 3, 8), "cond": True}, + {"config": (128, 128, 64, 5, 4), "cond": True}, +] + +int8_mm_kernel_configs = [ + {"config": (64, 64, 32, 2, 4), "cond": True}, + {"config": (64, 128, 32, 3, 4), "cond": True}, + {"config": (128, 64, 32, 3, 4), "cond": True}, + {"config": (64, 128, 32, 4, 8), "cond": True}, + {"config": (128, 64, 32, 4, 8), "cond": True}, + {"config": (64, 32, 32, 5, 8), "cond": True}, + {"config": (32, 64, 32, 5, 8), "cond": True}, + {"config": (128, 128, 32, 2, 8), "cond": True}, + {"config": (64, 64, 64, 3, 8), "cond": True}, + # {"config": (32, 32, 128, 2, 4), "cond": True}, + # {"config": (64, 64, 16, 2, 4), "cond": True}, + # {"config": (32, 32, 16, 1, 2), "cond": True}, + {"config": (128, 256, 128, 3, 8), "cond": torch.version.hip is None}, + {"config": (256, 128, 128, 3, 8), "cond": torch.version.hip is None}, +] + +# Mixed precision kernel configs for small sizes of m for mm's like (16, 8192) x (8192, 8192). +mixed_mm_kernel_configs_small_m = [ + {"config": (16, 128, 256, 3, 4), "cond": True}, + {"config": (16, 128, 256, 5, 8), "cond": True}, +] + +mixed_mm_kernel_configs = ( + mm_kernel_configs + mixed_mm_kernel_configs_small_m + if inductor_config.max_autotune_gemm_search_space != "EXHAUSTIVE" + else mm_kernel_configs +) + +scaled_mm_kernel_configs = [ + {"config": (128, 256, 32, 3, 8), "cond": True}, + {"config": (256, 128, 32, 3, 8), "cond": True}, + {"config": (256, 64, 32, 4, 4), "cond": True}, + {"config": (64, 256, 32, 4, 4), "cond": True}, + {"config": (128, 128, 32, 4, 4), "cond": True}, + {"config": (128, 64, 32, 4, 4), "cond": True}, + {"config": (64, 128, 32, 4, 4), "cond": True}, + {"config": (128, 32, 32, 4, 4), "cond": True}, + {"config": (64, 32, 32, 5, 2), "cond": True}, + {"config": (256, 128, 128, 3, 8), "cond": True}, + {"config": (256, 64, 128, 4, 4), "cond": True}, + {"config": (64, 256, 128, 4, 4), "cond": True}, + {"config": (128, 128, 128, 4, 4), "cond": True}, + {"config": (128, 64, 64, 4, 4), "cond": True}, + {"config": (64, 128, 64, 4, 4), "cond": True}, + {"config": (128, 32, 64, 4, 4), "cond": True}, + {"config": (64, 32, 64, 5, 2), "cond": True}, + {"config": (16, 32, 32, 2, 2), "cond": True}, + {"config": (16, 64, 32, 2, 2), "cond": True}, + {"config": (16, 128, 32, 2, 4), "cond": True}, + {"config": (16, 256, 32, 2, 4), "cond": True}, + {"config": (16, 32, 64, 2, 2), "cond": True}, + {"config": (16, 64, 64, 2, 2), "cond": True}, + {"config": (16, 128, 64, 2, 4), "cond": True}, + {"config": (16, 256, 64, 2, 4), "cond": True}, + {"config": (32, 32, 32, 2, 2), "cond": True}, + {"config": (32, 64, 32, 2, 2), "cond": True}, + {"config": (32, 128, 32, 2, 4), "cond": True}, + {"config": (32, 256, 32, 2, 4), "cond": True}, + {"config": (32, 32, 64, 2, 2), "cond": True}, + {"config": (32, 64, 64, 2, 2), "cond": True}, + {"config": (32, 128, 64, 2, 4), "cond": True}, + {"config": (32, 256, 64, 2, 4), "cond": True}, + {"config": (16, 32, 32, 3, 2), "cond": True}, + {"config": (16, 64, 32, 3, 2), "cond": True}, + {"config": (16, 128, 32, 3, 4), "cond": True}, + {"config": (16, 256, 32, 3, 4), "cond": True}, + {"config": (16, 32, 64, 3, 2), "cond": True}, + {"config": (16, 64, 64, 3, 2), "cond": True}, + {"config": (16, 128, 64, 3, 4), "cond": True}, + {"config": (16, 256, 64, 3, 4), "cond": True}, + {"config": (32, 32, 32, 3, 2), "cond": True}, + {"config": (32, 64, 32, 3, 2), "cond": True}, + {"config": (32, 128, 32, 3, 4), "cond": True}, + {"config": (32, 256, 32, 3, 4), "cond": True}, + {"config": (32, 32, 64, 3, 2), "cond": True}, + {"config": (32, 64, 64, 3, 2), "cond": True}, + {"config": (32, 128, 64, 3, 4), "cond": True}, + {"config": (32, 256, 64, 3, 4), "cond": True}, + {"config": (16, 32, 32, 4, 2), "cond": True}, + {"config": (16, 64, 32, 4, 2), "cond": True}, + {"config": (16, 128, 32, 4, 4), "cond": True}, + {"config": (16, 256, 32, 4, 4), "cond": True}, + {"config": (16, 32, 64, 4, 2), "cond": True}, + {"config": (16, 64, 64, 4, 2), "cond": True}, + {"config": (16, 128, 64, 4, 4), "cond": True}, + {"config": (16, 256, 64, 4, 4), "cond": True}, + {"config": (32, 32, 32, 4, 2), "cond": True}, + {"config": (32, 64, 32, 4, 2), "cond": True}, + {"config": (32, 128, 32, 4, 4), "cond": True}, + {"config": (32, 256, 32, 4, 4), "cond": True}, + {"config": (32, 32, 64, 4, 2), "cond": True}, + {"config": (32, 64, 64, 4, 2), "cond": True}, + {"config": (32, 128, 64, 4, 4), "cond": True}, + {"config": (32, 256, 64, 4, 4), "cond": True}, + {"config": (16, 32, 32, 5, 2), "cond": True}, + {"config": (16, 64, 32, 5, 2), "cond": True}, + {"config": (16, 128, 32, 5, 4), "cond": True}, + {"config": (16, 256, 32, 5, 4), "cond": True}, + {"config": (16, 32, 64, 5, 2), "cond": True}, + {"config": (16, 64, 64, 5, 2), "cond": True}, + {"config": (16, 128, 64, 5, 4), "cond": True}, + {"config": (16, 256, 64, 5, 4), "cond": True}, + {"config": (32, 32, 32, 5, 2), "cond": True}, + {"config": (32, 64, 32, 5, 2), "cond": True}, + {"config": (32, 128, 32, 5, 4), "cond": True}, + {"config": (32, 256, 32, 5, 4), "cond": True}, + {"config": (32, 32, 64, 5, 2), "cond": True}, + {"config": (32, 64, 64, 5, 2), "cond": True}, + {"config": (32, 128, 64, 5, 4), "cond": True}, + {"config": (32, 256, 64, 5, 4), "cond": True}, + {"config": (16, 32, 32, 6, 2), "cond": True}, + {"config": (16, 64, 32, 6, 2), "cond": True}, + {"config": (16, 128, 32, 6, 4), "cond": True}, + {"config": (16, 256, 32, 6, 4), "cond": True}, + {"config": (16, 32, 64, 6, 2), "cond": True}, + {"config": (16, 64, 64, 6, 2), "cond": True}, + {"config": (16, 128, 64, 6, 4), "cond": True}, + {"config": (16, 256, 64, 6, 4), "cond": True}, + {"config": (32, 32, 32, 6, 2), "cond": True}, + {"config": (32, 64, 32, 6, 2), "cond": True}, + {"config": (32, 128, 32, 6, 4), "cond": True}, + {"config": (32, 256, 32, 6, 4), "cond": True}, + {"config": (32, 32, 64, 6, 2), "cond": True}, + {"config": (32, 64, 64, 6, 2), "cond": True}, + {"config": (32, 128, 64, 6, 4), "cond": True}, + {"config": (32, 256, 64, 6, 4), "cond": True}, +] + + +# Create filtered list of configs based on cond evaluation +mm_platform_configs = tuple( + cast(Tuple[int, int, int, int, int], config["config"]) + for config in mm_kernel_configs + if config["cond"] +) +extra_mm_platform_configs = tuple( + cast(Tuple[int, int, int, int, int], config["config"]) + for config in extra_mm_kernel_configs + if config["cond"] +) +int8_platform_configs = tuple( + cast(Tuple[int, int, int, int, int], config["config"]) + for config in int8_mm_kernel_configs + if config["cond"] +) +mixed_mm_platform_configs = tuple( + cast(Tuple[int, int, int, int, int], config["config"]) + for config in mixed_mm_kernel_configs + if config["cond"] +) +scaled_mm_platform_configs = tuple( + cast(Tuple[int, int, int, int, int], config["config"]) + for config in scaled_mm_kernel_configs + if config["cond"] +) + +# On ROCm convert num_stages to 0 to enable software pipelining +if torch.version.hip: + mm_platform_configs = tuple( + (config[0], config[1], config[2], 0, config[4]) + for config in mm_platform_configs + ) + extra_mm_platform_configs = tuple( + (config[0], config[1], config[2], 0, config[4]) + for config in extra_mm_platform_configs + ) + int8_platform_configs = tuple( + (config[0], config[1], config[2], 0, config[4]) + for config in mm_platform_configs + ) + mixed_mm_platform_configs = tuple( + (config[0], config[1], config[2], 0, config[4]) + for config in mixed_mm_platform_configs + ) + scaled_mm_platform_configs = tuple( + (config[0], config[1], config[2], 0, config[4]) + for config in scaled_mm_platform_configs + ) + +mm_configs = functools.partial( + filtered_configs, + configs=mm_platform_configs, +) + +extra_mm_configs = functools.partial( + filtered_configs, + configs=extra_mm_platform_configs, +) + +int8_mm_configs = functools.partial( + filtered_configs, + configs=int8_platform_configs, +) + +mixed_mm_configs = functools.partial( + filtered_configs, + configs=mixed_mm_platform_configs, +) + +scaled_mm_configs = functools.partial( + filtered_configs, + configs=scaled_mm_platform_configs, +) + + +def mm_grid(m, n, meta): + """ + The CUDA grid size for matmul triton templates. + """ + return (cdiv(m, meta["BLOCK_M"]) * cdiv(n, meta["BLOCK_N"]), 1, 1) + + +def acc_type(dtype): + if dtype in (torch.float16, torch.bfloat16): + return "tl.float32" + return f"tl.{dtype}".replace("torch.", "") + + +def mm_options(config, sym_m, sym_n, sym_k, layout, b_prologue_cast_type=None): + """ + Common options to matmul triton templates. + """ + even_k_symbolic = ( + # it isn't worth guarding on this + sympy.gcd(sym_k, config.kwargs["BLOCK_K"]) + == config.kwargs["BLOCK_K"] + ) + allow_tf32 = torch.backends.cuda.matmul.allow_tf32 and ( + not inductor_config.force_same_precision + or ((sym_m % 16) == 0 and (sym_n % 16) == 0 and (sym_k % 8) == 0) + ) + return dict( + GROUP_M=8, + EVEN_K=even_k_symbolic, + ALLOW_TF32=allow_tf32, + ACC_TYPE=acc_type(layout.dtype), + B_PROLOGUE_CAST_TYPE=b_prologue_cast_type, + num_stages=config.num_stages, + num_warps=config.num_warps, + **config.kwargs, + ) + + +def mm_args( + mat1, + mat2, + *others, + layout=None, + out_dtype=None, + use_4x2_dim=False, + mat2_transposed=False, +): + """ + Common arg processing for mm,bmm,addmm,etc + """ + mat1, mat2 = realize_inputs(mat1, mat2) + *b1, m, k1 = mat1.get_size() + if mat2_transposed: + *b2, n, k2 = mat2.get_size() + else: + *b2, k2, n = mat2.get_size() + b = [V.graph.sizevars.guard_equals(a, b) for a, b in zip(b1, b2)] + if use_4x2_dim: + k2 = k2 * 2 + k = V.graph.sizevars.guard_equals(k1, k2) + if layout is None: + from torch._inductor.ir import FixedLayout + + if out_dtype is None: + out_dtype = mat1.get_dtype() + + layout = FixedLayout( + mat1.get_device(), + out_dtype, + [*b, m, n], + ) + else: + assert out_dtype is None, "out_dtype is ignored if layout is specified." + from ..lowering import expand + + others = [realize_inputs(expand(x, layout.size)) for x in others] + + return [m, n, k, layout, mat1, mat2, *others] + + +def addmm_epilogue(dtype, alpha, beta): + def epilogue(acc, bias): + if alpha != 1: + acc = V.ops.mul(acc, V.ops.constant(alpha, dtype)) + if beta != 1: + bias = V.ops.mul(bias, V.ops.constant(beta, dtype)) + return V.ops.add(acc, bias) + + return epilogue diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/mm_plus_mm.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/mm_plus_mm.py new file mode 100644 index 0000000000000000000000000000000000000000..f432cbb13b6f14d1429aa6a4df0579015ef46975 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/mm_plus_mm.py @@ -0,0 +1,248 @@ +# mypy: allow-untyped-defs +import functools + +import torch + +from ..lowering import lowerings +from ..select_algorithm import ( + autotune_select_algorithm, + ExternKernelChoice, + TritonTemplate, +) +from ..utils import use_aten_gemm_kernels, use_triton_template +from ..virtualized import V +from .mm_common import mm_args, mm_grid, mm_options + + +aten = torch.ops.aten + +aten_mm_plus_mm = ExternKernelChoice( + torch.ops.inductor._mm_plus_mm, "torch::inductor::_mm_plus_mm" +) + +mm_plus_mm_template = TritonTemplate( + name="mm_plus_mm", + grid=mm_grid, + debug=False, + source=r""" +{{def_kernel("A", "B", "C", "D")}} + M = {{size("A", 0)}} + N = {{size("B", 1)}} + K1 = {{size("A", 1)}} + if M * N == 0: + # early exit due to zero-size input(s) + return + # K2 = {{size("C", 1)}} + stride_am = {{stride("A", 0)}} + stride_ak = {{stride("A", 1)}} + stride_bk = {{stride("B", 0)}} + stride_bn = {{stride("B", 1)}} + stride_cm = {{stride("C", 0)}} + stride_ck = {{stride("C", 1)}} + stride_dk = {{stride("D", 0)}} + stride_dn = {{stride("D", 1)}} + + # based on triton.ops.matmul + pid = tl.program_id(0) + grid_m = (M + BLOCK_M - 1) // BLOCK_M + grid_n = (N + BLOCK_N - 1) // BLOCK_N + + # re-order program ID for better L2 performance + width = GROUP_M * grid_n + group_id = pid // width + group_size = min(grid_m - group_id * GROUP_M, GROUP_M) + pid_m = group_id * GROUP_M + (pid % group_size) + pid_n = (pid % width) // (group_size) + + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + + if (((stride_am == 1 and stride_ak == M) or (stride_am == K1 and stride_ak == 1)) + and ((stride_cm == 1 and stride_ck == M) or (stride_cm == K1 and stride_ck == 1))): + ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M) + else: + ram = rm % M + + if (((stride_bk == 1 and stride_bn == K1) or (stride_bk == N and stride_bn == 1)) + and ((stride_dk == 1 and stride_dn == K1) or (stride_dk == N and stride_dn == 1))): + rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N) + else: + rbn = rn % N + + rk = tl.arange(0, BLOCK_K) + A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak) + B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn) + C = C + (ram[:, None] * stride_cm + rk[None, :] * stride_ck) + D = D + (rk[:, None] * stride_dk + rbn[None, :] * stride_dn) + + acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE) + for k1 in range(K1, 0, -BLOCK_K): + # First matmul with A @ B + if EVEN_K: + a = tl.load(A) + b = tl.load(B) + else: + a = tl.load(A, mask=rk[None, :] < k1, other=0.) + b = tl.load(B, mask=rk[:, None] < k1, other=0.) + acc += tl.dot(a, b, allow_tf32=ALLOW_TF32) + A += BLOCK_K * stride_ak + B += BLOCK_K * stride_bk + + for k2 in range(K1, 0, -BLOCK_K): + + # Second matmul with C @ D + if EVEN_K: + c = tl.load(C) + d = tl.load(D) + else: + c = tl.load(C, mask=rk[None, :] < k2, other=0.) + d = tl.load(D, mask=rk[:, None] < k2, other=0.) + acc += tl.dot(c, d, allow_tf32=ALLOW_TF32) + C += BLOCK_K * stride_ck + D += BLOCK_K * stride_dk + + + idx_m = rm[:, None] + idx_n = rn[None, :] + mask = (idx_m < M) & (idx_n < N) + + # inductor generates a suffix + {{store_output(("idx_m", "idx_n"), "acc", "mask")}} +""", +) + + +@functools.lru_cache(None) +def mm_configs(): + import triton + + # List of dictionaries to store the kernel configs. Configs that evaluate to true + # will be utilised on the target platform + mm_triton_configs = [ + { + "config": {"BLOCK_M": 64, "BLOCK_N": 64, "BLOCK_K": 32}, + "num_stages": 2, + "num_warps": 4, + "cond": True, + }, + { + "config": {"BLOCK_M": 64, "BLOCK_N": 64, "BLOCK_K": 32}, + "num_stages": 3, + "num_warps": 8, + "cond": True, + }, + { + "config": {"BLOCK_M": 64, "BLOCK_N": 64, "BLOCK_K": 32}, + "num_stages": 4, + "num_warps": 16, + "cond": True, + }, + { + "config": {"BLOCK_M": 64, "BLOCK_N": 32, "BLOCK_K": 32}, + "num_stages": 4, + "num_warps": 8, + "cond": True, + }, + { + "config": {"BLOCK_M": 32, "BLOCK_N": 64, "BLOCK_K": 32}, + "num_stages": 4, + "num_warps": 8, + "cond": True, + }, + { + "config": {"BLOCK_M": 128, "BLOCK_N": 128, "BLOCK_K": 32}, + "num_stages": 1, + "num_warps": 8, + "cond": True, + }, + { + "config": {"BLOCK_M": 64, "BLOCK_N": 64, "BLOCK_K": 64}, + "num_stages": 1, + "num_warps": 8, + "cond": True, + }, + { + "config": {"BLOCK_M": 32, "BLOCK_N": 32, "BLOCK_K": 128}, + "num_stages": 1, + "num_warps": 8, + "cond": torch.version.hip is None, + }, + { + "config": {"BLOCK_M": 64, "BLOCK_N": 64, "BLOCK_K": 16}, + "num_stages": 2, + "num_warps": 4, + "cond": True, + }, + { + "config": {"BLOCK_M": 32, "BLOCK_N": 32, "BLOCK_K": 16}, + "num_stages": 1, + "num_warps": 2, + "cond": True, + }, + ] + + # Filter out configs in which cond evaluates to true + # On ROCm convert num_stages to 1 as pipelining provides no benefit + if torch.version.hip: + filtered_configs = [ + triton.Config(c["config"], num_stages=1, num_warps=c["num_warps"]) + for c in mm_triton_configs + if c["cond"] + ] + else: + filtered_configs = [ + triton.Config( + c["config"], num_stages=c["num_stages"], num_warps=c["num_warps"] + ) + for c in mm_triton_configs + if c["cond"] + ] + + return filtered_configs + + +def tuned_mm_plus_mm(mat1, mat2, mat3, mat4, *, layout=None): + """ + Computes mm(mat1, mat2) + mm(mat3, mat4) + """ + m1, n1, k1, layout1, mat1, mat2 = mm_args(mat1, mat2, layout=layout) + m2, n2, _, layout2, mat3, mat4 = mm_args(mat3, mat4, layout=layout) + # Optimization is optional, because we can always just not do the fusion + if ( + m1 * n1 == 0 + or m2 * n2 == 0 + or not V.graph.sizevars.statically_known_list_equals( + mat1.get_size(), mat3.get_size() + ) + or not V.graph.sizevars.statically_known_list_equals( + mat2.get_size(), mat4.get_size() + ) + ): + # TODO(jansel): support different K values when this is fixed: + # https://github.com/openai/triton/issues/967 + return lowerings[aten.add]( + lowerings[aten.mm](mat1, mat2), lowerings[aten.mm](mat3, mat4) + ) + + assert layout1 == layout2 + # options to tune from + choices = ( + [aten_mm_plus_mm.bind((mat1, mat2, mat3, mat4), layout1)] + if use_aten_gemm_kernels() + else [] + ) + if use_triton_template(layout1): + for config in mm_configs(): + # see https://github.com/openai/triton/issues/1298 + # BLOCK_K = K causes llvm error + if V.graph.sizevars.statically_known_lt(config.kwargs["BLOCK_K"], k1): + mm_plus_mm_template.maybe_append_choice( + choices, + input_nodes=(mat1, mat2, mat3, mat4), + layout=layout1, + **mm_options(config, m1, n1, k1, layout1), + ) + + return autotune_select_algorithm( + "mm_plus_mm", choices, [mat1, mat2, mat3, mat4], layout1 + ) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/mm_scaled.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/mm_scaled.py new file mode 100644 index 0000000000000000000000000000000000000000..2f0d020716a199c802d07a19e906e20abb7e04ef --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/mm_scaled.py @@ -0,0 +1,311 @@ +import logging +from typing import Any, Dict, List, Optional, Tuple + +import sympy + +import torch + +from .. import config as inductor_config +from ..ir import ChoiceCaller, Layout, StorageBox, TensorBox +from ..lowering import add_layout_constraint, constrain_to_fx_strides, register_lowering +from ..select_algorithm import ( + autotune_select_algorithm, + ExternKernelChoice, + NoValidChoicesError, + realize_inputs, + TritonTemplate, +) +from ..utils import use_aten_gemm_kernels, use_triton_template +from .mm import _is_static_problem # TODO(yangsiyu) move to mm_common +from .mm_common import mm_args, mm_grid, scaled_mm_configs + + +log = logging.getLogger(__name__) +aten = torch.ops.aten + + +scaled_mm_template = TritonTemplate( + name="scaled_mm", + grid=mm_grid, + source=r""" +{{def_kernel("A", "B", "A_inverse_scale", "B_inverse_scale")}} + M = {{size("A", 0)}} + N = {{size("B", 1)}} + K = {{size("A", 1)}} + if M * N == 0: + # early exit due to zero-size input(s) + return + stride_am = {{stride("A", 0)}} + stride_ak = {{stride("A", 1)}} + stride_bk = {{stride("B", 0)}} + stride_bn = {{stride("B", 1)}} + + # based on triton.ops.matmul + pid = tl.program_id(0) + grid_m = (M + BLOCK_M - 1) // BLOCK_M + grid_n = (N + BLOCK_N - 1) // BLOCK_N + + # re-order program ID for better L2 performance + width = GROUP_M * grid_n + group_id = pid // width + group_size = min(grid_m - group_id * GROUP_M, GROUP_M) + pid_m = group_id * GROUP_M + (pid % group_size) + pid_n = (pid % width) // (group_size) + + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M) + rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N) + rk = tl.arange(0, BLOCK_K) + A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak) + B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn) + + acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE) + for k in range(K, 0, -BLOCK_K): + if EVEN_K: + a = tl.load(A) + b = tl.load(B) + else: + a = tl.load(A, mask=rk[None, :] < k, other=0.) + b = tl.load(B, mask=rk[:, None] < k, other=0.) + if B_PROLOGUE_CAST_TYPE is not None: + b = b.to(B_PROLOGUE_CAST_TYPE) + if USE_FAST_ACCUM: + acc = tl.dot(a, b, acc, out_dtype=ACC_TYPE) + else: + acc += tl.dot(a, b, out_dtype=ACC_TYPE) + A += BLOCK_K * stride_ak + B += BLOCK_K * stride_bk + + if SCALING_ROWWISE: + inv_a_scale_row = tl.load(A_inverse_scale + rm, mask=rm < M) + inv_b_scale_row = tl.load(B_inverse_scale + rn, mask=rn < N) + inv_scale_row = inv_a_scale_row[:, None] * inv_b_scale_row[None, :] + acc *= inv_scale_row + else: + # for tensor-wise scaling, the scales are scalars + inv_a_scale = tl.load(A_inverse_scale) + inv_b_scale = tl.load(B_inverse_scale) + inv_scale = inv_a_scale * inv_b_scale + acc *= inv_scale + + # rematerialize rm and rn to save registers + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + + idx_m = rm[:, None] + idx_n = rn[None, :] + mask = (idx_m < M) & (idx_n < N) + + # inductor generates a suffix + {{store_output(("idx_m", "idx_n"), "acc", "mask")}} +""", +) + + +# Inductor does not allow optional tensor input arguments currently (pass None as an +# input node to template choices), but since for _scaled_mm there is only one such arg +# (bias), work around by having a second template when bias is provided. +scaled_mm_bias_template = TritonTemplate( + name="scaled_mm_bias", + grid=mm_grid, + source=r""" +{{def_kernel("A", "B", "A_inverse_scale", "B_inverse_scale", "bias_ptr")}} + M = {{size("A", 0)}} + N = {{size("B", 1)}} + K = {{size("A", 1)}} + if M * N == 0: + # early exit due to zero-size input(s) + return + stride_am = {{stride("A", 0)}} + stride_ak = {{stride("A", 1)}} + stride_bk = {{stride("B", 0)}} + stride_bn = {{stride("B", 1)}} + + # based on triton.ops.matmul + pid = tl.program_id(0) + grid_m = (M + BLOCK_M - 1) // BLOCK_M + grid_n = (N + BLOCK_N - 1) // BLOCK_N + + # re-order program ID for better L2 performance + width = GROUP_M * grid_n + group_id = pid // width + group_size = min(grid_m - group_id * GROUP_M, GROUP_M) + pid_m = group_id * GROUP_M + (pid % group_size) + pid_n = (pid % width) // (group_size) + + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M) + rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N) + rk = tl.arange(0, BLOCK_K) + A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak) + B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn) + + acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE) + for k in range(K, 0, -BLOCK_K): + if EVEN_K: + a = tl.load(A) + b = tl.load(B) + else: + a = tl.load(A, mask=rk[None, :] < k, other=0.) + b = tl.load(B, mask=rk[:, None] < k, other=0.) + if B_PROLOGUE_CAST_TYPE is not None: + b = b.to(B_PROLOGUE_CAST_TYPE) + if USE_FAST_ACCUM: + acc = tl.dot(a, b, acc, out_dtype=ACC_TYPE) + else: + acc += tl.dot(a, b, out_dtype=ACC_TYPE) + A += BLOCK_K * stride_ak + B += BLOCK_K * stride_bk + + if SCALING_ROWWISE: + inv_a_scale_row = tl.load(A_inverse_scale + rm, mask=rm < M) + inv_b_scale_row = tl.load(B_inverse_scale + rn, mask=rn < N) + inv_scale_row = inv_a_scale_row[:, None] * inv_b_scale_row[None, :] + acc *= inv_scale_row + else: + # for tensor-wise scaling, the scales are scalars + inv_a_scale = tl.load(A_inverse_scale) + inv_b_scale = tl.load(B_inverse_scale) + inv_scale = inv_a_scale * inv_b_scale + acc *= inv_scale + + # rematerialize rm and rn to save registers + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + + # bias + bias = tl.load(bias_ptr + rn, mask=rn < N) + acc += bias + + idx_m = rm[:, None] + idx_n = rn[None, :] + mask = (idx_m < M) & (idx_n < N) + + # inductor generates a suffix + {{store_output(("idx_m", "idx_n"), "acc", "mask")}} +""", +) + + +aten__fp8_mm = ExternKernelChoice(torch._scaled_mm, "at::_scaled_mm") + + +def are_compatible_scales(size_a: List[int], size_b: List[int]) -> bool: + # Same sized scales are compatable + if len(size_a) == len(size_b): + return True + + # Both need to be scalars or len(1) tensors + if len(size_a) <= 1 and len(size_b) <= 1: + return True + + return False + + +def scaled_mm_options( # type: ignore[no-untyped-def] + config, # triton.Config + sym_m: sympy.core.numbers.Integer, + sym_n: sympy.core.numbers.Integer, + sym_k: sympy.core.numbers.Integer, + layout: Layout, + scale_a: StorageBox, + scale_b: StorageBox, + use_fast_accum: bool, + b_prologue_cast_type: Optional[str] = None, +) -> Dict[str, Any]: + even_k_symbolic = ( + sympy.gcd(sym_k, config.kwargs["BLOCK_K"]) == config.kwargs["BLOCK_K"] + ) + + size_a, size_b = scale_a.get_size(), scale_b.get_size() + assert are_compatible_scales(size_a, size_b), ( + "Expect scale_a and scale_b to be either both scalars (including single-element tensors) " + f"or 1-dimensional tensors with the same size. Got scale_a: {len(size_a)} and scale_b: {len(size_b)}." + ) + return dict( + GROUP_M=8, + EVEN_K=even_k_symbolic, + ACC_TYPE="tl.float32", + B_PROLOGUE_CAST_TYPE=b_prologue_cast_type, + USE_FAST_ACCUM=use_fast_accum, + num_stages=config.num_stages, + num_warps=config.num_warps, + # tensor-wise scaling if scalar scales + SCALING_ROWWISE=len(scale_a.get_size()) == 2, + **config.kwargs, + ) + + +add_layout_constraint(aten._scaled_mm.default, constrain_to_fx_strides) + + +@register_lowering(aten._scaled_mm.default, type_promotion_kind=None) # type: ignore[misc] +def tuned_scaled_mm( + mat_a: TensorBox, + mat_b: TensorBox, + scale_a: TensorBox, + scale_b: TensorBox, + bias: Optional[TensorBox] = None, + scale_result: Optional[TensorBox] = None, + out_dtype: Optional[torch.dtype] = None, + use_fast_accum: bool = False, + layout: Optional[Layout] = None, +) -> TensorBox: + m, n, k, layout, mat_a, mat_b = mm_args( + mat_a, mat_b, layout=layout, out_dtype=out_dtype + ) + scale_a, scale_b = realize_inputs(scale_a, scale_b) + + input_nodes: Tuple[Any, ...] + # workaround for Inductor not supporting optional tensor input arguments + if bias is None: + input_nodes = (mat_a, mat_b, scale_a, scale_b) + triton_template = scaled_mm_template + else: + bias = realize_inputs(bias) + input_nodes = (mat_a, mat_b, scale_a, scale_b, bias) + triton_template = scaled_mm_bias_template + + aten_choice = aten__fp8_mm.bind( + input_nodes, layout, out_dtype=out_dtype, use_fast_accum=use_fast_accum + ) + + choices: List[ChoiceCaller] = [] + if use_aten_gemm_kernels(): + choices.append(aten_choice) + + static_shape, is_nonzero = _is_static_problem([mat_a, mat_b], layout) + if is_nonzero and use_triton_template(layout, enable_float8=True): + for config in scaled_mm_configs(m, n, k): + if k == 16 and config.kwargs["BLOCK_M"] >= 64: + continue # Triton crashes in this case + kwargs = scaled_mm_options( + config, m, n, k, layout, scale_a, scale_b, use_fast_accum + ) + # possibly appends a TritonTemplateCaller to choices + triton_template.maybe_append_choice( + choices, + input_nodes=input_nodes, + layout=layout, + **kwargs, + ) + + if ( + len(choices) == 0 + and not use_aten_gemm_kernels() + and inductor_config.autotune_fallback_to_aten + ): + log.warning("No choices for scaled_mm, using ATen backend as fallback") + return aten_choice.output_node() + + try: + return autotune_select_algorithm("scaled_mm", choices, input_nodes, layout) + except NoValidChoicesError: + if not inductor_config.autotune_fallback_to_aten: + raise + log.warning( + "All choices for scaled_mm were invalid, using ATen backend as fallback" + ) + return aten_choice.output_node() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/unpack_mixed_mm.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/unpack_mixed_mm.py new file mode 100644 index 0000000000000000000000000000000000000000..674da97c165546acf8397f57e52930e60a7c195d --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/kernel/unpack_mixed_mm.py @@ -0,0 +1,87 @@ +# mypy: allow-untyped-defs +import logging +from typing import List, TYPE_CHECKING + +from ..select_algorithm import autotune_select_algorithm, TritonTemplate +from .mm_common import mm_args, mm_configs, mm_grid, mm_options + + +if TYPE_CHECKING: + from ..ir import ChoiceCaller + +log = logging.getLogger(__name__) + +uint4x2_mixed_mm_template = TritonTemplate( + name="uint4x2_mixed_mm", + grid=mm_grid, + source=r""" +{{def_kernel("A", "B")}} + M = {{size("A", 0)}} + N = {{size("B", 1)}} + K = {{size("A", 1)}} + stride_am = {{stride("A", 0)}} + stride_ak = {{stride("A", 1)}} + stride_bk = {{stride("B", 0)}} + stride_bn = {{stride("B", 1)}} + + # based on triton.ops.matmul + pid = tl.program_id(0) + grid_m = (M + BLOCK_M - 1) // BLOCK_M + grid_n = (N + BLOCK_N - 1) // BLOCK_N + + # re-order program ID for better L2 performance + width = GROUP_M * grid_n + group_id = pid // width + group_size = min(grid_m - group_id * GROUP_M, GROUP_M) + pid_m = group_id * GROUP_M + (pid % group_size) + pid_n = (pid % width) // (group_size) + + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M) + rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N) + rk = tl.arange(0, BLOCK_K) + A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak) + B = B + (rk[:, None]//2 * stride_bk + rbn[None, :] * stride_bn) + b_shifts = 4*(rk%2) + b_subs = 8*(1-(rk%2)) + + acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE) + for k in range(K, 0, -BLOCK_K): + if EVEN_K: + a = tl.load(A) + b = tl.load(B) + else: + a = tl.load(A, mask=rk[None, :] < k, other=0.) + b = tl.load(B, mask=rk[:, None] < k, other=0.) + b = ((b >> b_shifts[:, None]) & 0xF) - 8 + b = b.to(B_PROLOGUE_CAST_TYPE) + acc += tl.dot(a, b, allow_tf32=ALLOW_TF32) + A += BLOCK_K * stride_ak + B += BLOCK_K//2 * stride_bk + + # rematerialize rm and rn to save registers + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + idx_m = rm[:, None] + idx_n = rn[None, :] + mask = (idx_m < M) & (idx_n < N) + + # inductor generates a suffix + {{store_output(("idx_m", "idx_n"), "acc", "mask")}} +""", +) + + +def tuned_uint4x2_mixed_mm(mat1, mat2, mat2_mm_shape, mat2_dtype): + m, n, k, layout, mat1, mat2 = mm_args(mat1, mat2, layout=None, use_4x2_dim=True) + choices: List[ChoiceCaller] = [] + b_prologue_cast_type = f"tl.{mat2_dtype}".replace("torch.", "") + for config in mm_configs(m, n, k): + uint4x2_mixed_mm_template.maybe_append_choice( + choices, + input_nodes=(mat1, mat2), + layout=layout, + **mm_options(config, m, n, k, layout, b_prologue_cast_type), + ) + return autotune_select_algorithm("uint4x2_mixed_mm", choices, [mat1, mat2], layout) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/loop_body.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/loop_body.py new file mode 100644 index 0000000000000000000000000000000000000000..2f0b00b724e71fb8620d45f6601635f37772baf4 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/loop_body.py @@ -0,0 +1,594 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import functools +import itertools +import re +from enum import auto, Enum +from typing import Any, Callable, Dict, List, NamedTuple, Optional, Sequence, Tuple + +import sympy + +import torch.fx +from torch._dynamo.utils import identity +from torch.utils._sympy.symbol import SymT + +from . import config, dependencies +from .codegen.common import index_prevent_reordering +from .utils import cache_on_self, sympy_index_symbol_with_prefix, sympy_subs +from .virtualized import ops, V + + +class InterpreterShim(torch.fx.Interpreter): + @staticmethod + @functools.lru_cache(None) + def _dummy_gm(): + return torch.fx.symbolic_trace(identity) + + def __init__(self, graph, submodules): + # call super() with a placeholder to avoid constructing a + # GraphModule which is very expensive (it does codegen). + super().__init__(self._dummy_gm(), garbage_collect_values=False) + self.module = self # type: ignore[assignment] + self.graph = graph + self.submodules = submodules + self.extra_traceback = False + self.fetch_attr = submodules.__getitem__ # type: ignore[method-assign] + self.current_node = None + + def run_node(self, n: torch.fx.Node) -> Any: + self.current_node = n + return super().run_node(n) + + def run(self, *args, **kwargs): + with V.set_interpreter_handler(self): + return super().run(*args, **kwargs) + + +class MemoryEntry(NamedTuple): + index_name: str # LoopBody.indexing_exprs[index_name] + buffer_name: Optional[str] + mode: Optional[str] # V.ops.store(..., mode=mode) + + +class MemoryUsageType(Enum): + # These are 1:1 with the opcode generating the usage + LOAD = auto() + LOAD_SEED = auto() + STORE = auto() + STORE_REDUCTION = auto() + INDEX_EXPR = auto() + CHECK_BOUNDS = auto() + BUCKETIZE = auto() + + +class LoopBody: + """ + Captures the body of a Loops subclass into an FX graph. Persists any + indexing simplifications and makes it easier to analyze loop bodies. + """ + + indexing_exprs: Dict[str, sympy.Expr] + indexing_exprs_name: Dict[sympy.Expr, str] + submodules: Dict[str, Any] + subblocks: Dict[str, LoopBodyBlock] + indirect_vars: List[str] + indirect_var_ranges: Dict[sympy.Symbol, sympy.Expr] + root_block: LoopBodyBlock + memory_usage: Dict[MemoryUsageType, List[MemoryEntry]] + + def __init__(self, fn, args, var_ranges, iter_vars, reduce_vars): + super().__init__() + + _flat_sizes = tuple(var_ranges.values()) + self.sizes = ( + _flat_sizes[: len(iter_vars)], + _flat_sizes[len(iter_vars) :], + ) + + self.iter_vars = iter_vars + self.reduce_vars = reduce_vars + self.var_ranges = var_ranges + + if isinstance(fn, LoopBody): + self._init_with_copy(fn, args) + else: + self._init_with_tracing(fn, args) + + self.indexing = None + + def _init_with_tracing(self, fn, args): + """Do an FX trace of an arbitrary callable to construct self""" + self.indexing_exprs = {} + self.indexing_exprs_name = {} + self.submodules = {"get_index": self.get_index} + self.subblocks = {} + self.indirect_vars = [] + self.indirect_var_ranges: Dict[sympy.Symbol, sympy.Expr] = {} + self.memory_usage = {t: [] for t in MemoryUsageType} + self.root_block = LoopBodyBlock(self, fn, args) # traces + del self.indexing_exprs_name # not used after _init_with_tracing + + def _init_with_copy(self, other: LoopBody, args): + """ + _init_with_tracing() is slow, so this is a fast path in the case + where we are just reordering/merging/splitting the args of an + existing LoopBody. + """ + indexing_exprs = other.indexing_from_args(args) + self.indexing_exprs = { + name: V.graph.sizevars.simplify_with_ranges(expr, self.var_ranges) + for name, expr in indexing_exprs.items() + } + self.subblocks = {k: v.clone(self) for k, v in other.subblocks.items()} + self.indirect_vars = other.indirect_vars + self.indirect_var_ranges = other.indirect_var_ranges + self.memory_usage = other.memory_usage + self.root_block = other.root_block.clone(self) + + submodules = {**other.submodules} + submodules.pop("get_index") + self.submodules = { + "get_index": self.get_index, + **{k: v.clone(self) for k, v in submodules.items()}, # type: ignore[attr-defined] + } + + def merge_loops(self) -> LoopBody: + """ + Merge both iteration and reduction loops and return a new LoopBody. + """ + old_body = self + old_sizes = self.sizes + old_iter_vars, old_reduce_vars = old_body.vars + old_iter_sizes, old_reduce_sizes = old_sizes + + index_exprs = [*old_body.indexing_exprs.values()] + + iter_sizes, iter_reindex, _ = V.graph.sizevars._simplify_loops( + old_iter_vars, + old_iter_sizes, + index_prevent_reordering(index_exprs, old_iter_vars, old_iter_sizes), + ) + + reduce_sizes, reduce_reindex, _ = V.graph.sizevars._simplify_loops( + old_reduce_vars, + old_reduce_sizes, + index_prevent_reordering(index_exprs, old_reduce_vars, old_reduce_sizes), + ) + + # if iter_sizes == old_iter_sizes: + # # no dimensions get merged. + # return old_sizes, old_body + + # Note: if no dimension get merges, the symbol prefix will + # remain 'y'. But if we merge dimensions, we change prefix to + # 'z'. If this is an issue, we can always retrace the LoopBody + # to change symbol prefix to 'z'. + # + # There is indeed an issue due to symbol name conflicting. + # y0 maybe reused for the y dimension later. + ( + iter_vars, + reduce_vars, + ), var_ranges = dependencies.index_vars_no_squeeze( + iter_sizes, reduce_sizes, prefix="t" + ) + new_body = LoopBody( + old_body, + [iter_reindex(iter_vars), reduce_reindex(reduce_vars)], + var_ranges, + iter_vars, + reduce_vars, + ) + + # use the original symbol prefix + # Can try to optimize if this is a bottleneck for compilation time + (iter_vars2, reduce_vars2), var_ranges2 = dependencies.index_vars_no_squeeze( + iter_sizes, reduce_sizes, prefix="z" + ) + new_body2 = LoopBody( + new_body, (iter_vars2, reduce_vars2), var_ranges2, iter_vars2, reduce_vars2 + ) + return new_body2 + + def reorder_iter_loops(self, new_order) -> LoopBody: + """ + Reorder iteration loops and return a new LoopBody. + """ + from .ir import same_reorder + + old_body = self + old_sizes = self.sizes + assert len(old_sizes[0]) == len(new_order) + reorder_fn = same_reorder(new_order) + + iter_size, reduce_size = old_sizes + new_iter_size = reorder_fn(iter_size) + + new_sizes = (new_iter_size, reduce_size) + + (iter_vars, reduce_vars), var_ranges = dependencies.index_vars_no_squeeze( + *new_sizes, prefix="t" # type: ignore[arg-type] + ) + + inverse_order = {b: a for a, b in enumerate(new_order)} + inverse_order = [inverse_order[i] for i in range(len(new_order))] + + def new_body(*indices: Sequence[sympy.Expr]) -> Any: + index = list(itertools.chain(*indices)) + assert len(index) == len(iter_size) + len(reduce_size) + iter_idx = index[: len(iter_size)] + reduce_idx = index[len(iter_size) :] + iter_idx = [iter_idx[i] for i in inverse_order] + return old_body(iter_idx, reduce_idx) + + loop_body = LoopBody( + new_body, (iter_vars, reduce_vars), var_ranges, iter_vars, reduce_vars + ) + + # use the original symbol prefix so we can do multiple round of reordering + (iter_vars2, reduce_vars2), var_ranges2 = dependencies.index_vars_no_squeeze( + *new_sizes, prefix="z" # type: ignore[arg-type] + ) + new_body = LoopBody( + loop_body, (iter_vars2, reduce_vars2), var_ranges2, iter_vars2, reduce_vars2 + ) + return new_body + + @property + def vars(self): + assert self.iter_vars is not None + assert self.reduce_vars is not None + return self.iter_vars, self.reduce_vars + + @cache_on_self + def get_nodes(self): + all_graphs = itertools.chain( + (self.root_block.graph,), + (block.graph for block in self.subblocks.values()), + ) + return [node for graph in all_graphs for node in graph.nodes] + + @cache_on_self + def bounds(self): + # Doing a local import to avoid dumping all the code here + from .bounds import BoundVars + + return BoundVars(self) + + def get_read_expr(self, buffer_name): + # reversed to match old behavior + for entry in reversed(self.memory_usage[MemoryUsageType.LOAD]): + if entry.buffer_name == buffer_name: + return self.indexing_exprs[entry.index_name] + raise KeyError(buffer_name) + + def get_write_expr(self, buffer_name): + for entry in itertools.chain( + self.memory_usage[MemoryUsageType.STORE], + self.memory_usage[MemoryUsageType.STORE_REDUCTION], + ): + if entry.buffer_name == buffer_name: + return self.indexing_exprs[entry.index_name] + raise KeyError(buffer_name) + + def get_read_exprs(self): + return [ + self.indexing_exprs[entry.index_name] + for entry in self.memory_usage[MemoryUsageType.LOAD] + ] + + def get_write_exprs(self): + return [ + self.indexing_exprs[entry.index_name] + for entry in itertools.chain( + self.memory_usage[MemoryUsageType.STORE], + self.memory_usage[MemoryUsageType.STORE_REDUCTION], + ) + ] + + def debug_str(self): + lines = [f"var_ranges = {dict(self.var_ranges)}"] + lines.extend([f"{name} = {val}" for name, val in self.indexing_exprs.items()]) + lines.extend( + [ + block.debug_str(name) + for name, block in itertools.chain( + [("body", self.root_block)], self.subblocks.items() + ) + ] + ) + return "\n".join(lines) + + def is_memory_copy(self) -> bool: + """ + True of this contains only a single loads and store. + Note, this could involve a layout change. + """ + return ( + len(self.memory_usage[MemoryUsageType.LOAD]) == 1 + and len(self.memory_usage[MemoryUsageType.STORE]) == 1 + and len(self.submodules) == 1 # get_index + and self.root_block.contains_only_ops(("load", "store")) + ) + + __repr__ = debug_str + + def add_index_expr( + self, + expr: sympy.Expr, + mtype: MemoryUsageType, + buffer_name: Optional[str] = None, + mode: Optional[str] = None, + ): + name = self.indexing_exprs_name.get(expr) + if not name: + name = f"index{len(self.indexing_exprs)}" + self.indexing_exprs_name[expr] = name + self.indexing_exprs[name] = expr + self.memory_usage[mtype].append(MemoryEntry(name, buffer_name, mode)) + return name + + def add_submodule(self, block, prefix): + """Not actually for nn.Modules, but subblocks in generated code are mapped to FX call_module opcodes""" + if prefix[-1].isnumeric() and prefix not in self.submodules: + name = prefix + else: + name = f"{prefix}{len(self.submodules)}" + self.submodules[name] = block + return name + + def add_indirect(self, size): + var = sympy_index_symbol_with_prefix(SymT.INDIRECT, len(self.indirect_vars)) + assert var not in self.indirect_var_ranges + self.indirect_vars.append(var) + self.indirect_var_ranges[var] = size + return var + + def replace_indirect(self, old, new): + """Swap in a variable used in indirect indexing""" + if str(old) == str(new): + return + assert self.indexing is not None + self.indexing = {k: sympy_subs(v, {old: new}) for k, v in self.indexing.items()} + + def get_index(self, name): + assert self.indexing is not None + return self.indexing[name] + + def indexing_from_args(self, indices): + index = [*itertools.chain.from_iterable(indices)] + assert len(index) == len(self.var_ranges), (index, self.var_ranges) + assert all( + v not in self.var_ranges for v in index + ), f"{self.var_ranges=}, {indices=}" + replacements = dict(zip(self.var_ranges.keys(), index)) + return { + name: sympy_subs(expr, replacements) + for name, expr in self.indexing_exprs.items() + } + + def __call__(self, *indices): + self.indexing = self.indexing_from_args(indices) + result = self.root_block() + self.indexing = None + return result + + def bind_set_indirect_shim(self, var, size, check, wrap_neg): + def set_indirect(new_var): + self.replace_indirect( + var, V.ops.indirect_indexing(new_var, size, check, wrap_neg) + ) + + set_indirect.clone = functools.partial( # type: ignore[attr-defined] + LoopBody.bind_set_indirect_shim, + var=var, + size=size, + check=check, + wrap_neg=wrap_neg, + ) + return set_indirect + + def bind_scan_shim(self, combine_fn): + def shim(dtypes, values): + return V.ops.scan(dtypes, combine_fn, values) + + shim.clone = functools.partial(LoopBody.bind_scan_shim, combine_fn=combine_fn) # type: ignore[attr-defined] + return shim + + def bind_masked_shim(self, name): + def shim(mask, other): + return V.ops.masked(mask, self.subblocks[name], other) + + shim.clone = functools.partial(LoopBody.bind_masked_shim, name=name) # type: ignore[attr-defined] + return shim + + +class LoopBodyBlock: + """ + Captures the body of a Loops subclass into an FX graph. + In normal cases there will be a 1:1 mapping between LoopBody and + LoopBodyBlock, hower in the case of ops.masked() the masked out + operations will manifest as an extra LoopBodyBlock. + """ + + def __init__(self, body: LoopBody, fn: Callable[..., Any], args: List[Any]): + self.body = body + + def add_index(expr: sympy.Expr, mtype: MemoryUsageType, **kwargs): + return tracer.create_proxy( + "call_module", + "get_index", + (body.add_index_expr(expr, mtype, **kwargs),), + {}, + ) + + class CaptureIndexing(V.WrapperHandler): # type: ignore[name-defined] + self.name = "CaptureIndexing" + + def load(self, name: str, index: sympy.Expr): + index = add_index(index, MemoryUsageType.LOAD, buffer_name=name) + return self._inner.load(name, index) + + def load_seed(self, name: str, index: int): + assert isinstance(index, int) + body.add_index_expr( + sympy.Integer(index), MemoryUsageType.LOAD_SEED, buffer_name=name + ) + return self._inner.load_seed(name, index) + + def store(self, name, index, value, mode=None): + index = add_index( + index, MemoryUsageType.STORE, buffer_name=name, mode=mode + ) + return self._inner.store(name, index, value, mode) + + def store_reduction(self, name, index, value): + index = add_index( + index, MemoryUsageType.STORE_REDUCTION, buffer_name=name + ) + return self._inner.store_reduction(name, index, value) + + def reduction(self, dtype, src_dtype, reduction_type, value): + result = self._inner.reduction(dtype, src_dtype, reduction_type, value) + if "welford" in reduction_type: + return tuple(result[i] for i in range(3)) + return result + + def index_expr(self, index, dtype): + if isinstance(index, (int, sympy.Integer)): + return self._inner.constant(int(index), dtype) + index = add_index(index, MemoryUsageType.INDEX_EXPR) + return self._inner.index_expr(index, dtype) + + def check_bounds(self, index, size, lower, upper): + index = add_index(index, MemoryUsageType.CHECK_BOUNDS) + size = add_index(size, MemoryUsageType.CHECK_BOUNDS) + return self._inner.check_bounds(index, size, lower, upper) + + def bucketize( + self, + values, + offsets_name: str, + offsets_size: sympy.Expr, + indexing_dtype: torch.dtype, + right: bool, + ): + offsets_size = add_index( + offsets_size, MemoryUsageType.BUCKETIZE, buffer_name=offsets_name + ) + return self._inner.bucketize( + values, offsets_name, offsets_size, indexing_dtype, right + ) + + @staticmethod + def masked(mask_proxy, masked_body: Callable[..., Any], other_proxy): + """ + Recursively capture the masked out body in another LoopBodyBlock + """ + name = self.body.add_submodule(None, "masked_subblock") + self.body.submodules[name] = self.body.bind_masked_shim(name) + self.body.subblocks[name] = LoopBodyBlock(self.body, masked_body, []) + return tracer.create_proxy( + "call_module", name, (mask_proxy, other_proxy), {} + ) + + @staticmethod + def scan( + dtype_proxy, + combine_fn: Callable[ + [Tuple[Any, ...], Tuple[Any, ...]], Tuple[Any, ...] + ], + value_proxy, + ): + shim = self.body.bind_scan_shim(combine_fn) + name = self.body.add_submodule(shim, "scan") + result = tracer.create_proxy( + "call_module", + name, + (dtype_proxy, value_proxy), + {}, + ) + # Proxies are iterable, but some methods expect tuples/lists + return tuple(result[i] for i in range(len(value_proxy))) + + def sort(self, dtypes, values, stable, descending): + result = self._inner.sort(dtypes, values, stable, descending) + # Proxies are iterable, but some methods expect tuples/lists + return tuple(result[i] for i in range(len(values))) + + def frexp(self, value_proxy): + result = self._inner.frexp(value_proxy) + # Proxies are iterable, but some methods expect tuples/lists + return (result[0], result[1]) + + @staticmethod + def indirect_indexing(index_proxy, size, check=True, wrap_neg=True): + """ + Flow data from tensors into indexing formulas. + Introduce a call_module to update the indexing. + """ + + var = self.body.add_indirect(size) + set_indirect = self.body.bind_set_indirect_shim( + var, size, check, wrap_neg + ) + tracer.create_proxy( + "call_module", + self.body.add_submodule(set_indirect, f"set_{var}"), + (index_proxy,), + {}, + ) + return var + + @staticmethod + def output(result): + tracer.create_proxy("output", "output", (result,), {}) + + tracer = torch.fx.Tracer() + tracer.graph = torch.fx.Graph(tracer_cls=tracer.__class__) + proxy_ops = tracer.create_proxy("placeholder", "ops", (), {}) + + from .index_propagation import IndexPropagation + from .sizevars import SimplifyIndexing + + handler: Any = SimplifyIndexing( + CaptureIndexing(proxy_ops), self.body.var_ranges + ) + if config.constant_and_index_propagation: + handler = IndexPropagation( + handler, self.body.var_ranges, self.body.indirect_var_ranges + ) + + with V.set_ops_handler(handler): + # This indirection is just a cute way to get IndexPropagation to + # unwrap the return value. + ops.output(fn(*args)) + self.graph = tracer.graph + + def __call__(self): + graph = self.graph + submodules = self.body.submodules + + return InterpreterShim(graph, submodules).run(V.get_ops_handler()) + + def debug_str(self, name="block"): + code = torch.fx.GraphModule(self.body.submodules, self.graph).code + return re.sub( + # strip `; del var0` suffixes to make output prettier + r";[^\n]*", + "", + code.strip().replace("def forward(", f"def {name}("), + ) + + def contains_only_ops(self, allowed_ops) -> bool: + return all( + node.target in allowed_ops + for node in self.graph.find_nodes(op="call_method") + ) + + def clone(self, body: LoopBody): + """Shallow copy with a new parent LoopBody""" + copy = LoopBodyBlock.__new__(LoopBodyBlock) + copy.__dict__.update({**self.__dict__, "body": body}) + return copy diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/lowering.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/lowering.py new file mode 100644 index 0000000000000000000000000000000000000000..adf921913edfcdfc902ab70307edae5bf6c46528 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/lowering.py @@ -0,0 +1,6449 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import functools +import itertools +import logging +import math +import operator +import os +import warnings +from collections import defaultdict +from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union +from unittest.mock import patch + +import sympy + +import torch +import torch.ao.quantization.fx._decomposed +import torch.fx +import torch.utils._pytree as pytree +from torch._higher_order_ops.associative_scan import associative_scan_op +from torch._higher_order_ops.triton_kernel_wrap import triton_kernel_wrapper_mutation +from torch._prims_common import ( + canonicalize_dim, + canonicalize_dims, + check, + dtype_to_type, + elementwise_dtypes, + ELEMENTWISE_TYPE_PROMOTION_KIND, + get_computation_dtype, + is_boolean_dtype, + is_float_dtype, + is_integer_dtype, + Number, +) +from torch.fx.experimental.sym_node import magic_methods, method_to_operator +from torch.utils._sympy.functions import ( + CeilDiv, + FloorDiv, + Identity, + IntTrueDiv, + ModularIndexing, +) + +from .._dynamo.utils import import_submodule +from . import config, inductor_prims, ir, test_operators # NOQA: F401 +from .decomposition import decompositions, get_decompositions +from .ir import ( + DtypeView, + ExpandView, + IndexingConstant, + is_triton, + ops_wrapper, + PermuteView, + Pointwise, + Reduction, + SqueezeView, + TensorBox, + validate_ir, + View, +) +from .utils import ( + ceildiv, + decode_device, + is_dynamic, + is_gpu, + is_pointwise_use, + needs_fallback_due_to_atomic_add_limitations, + pad_listlike, + sympy_product, + use_scatter_fallback, +) +from .virtualized import ops, V + + +log = logging.getLogger(__name__) +lowerings: Dict[torch._ops.OpOverload, Callable[..., Any]] = {} +# Use maybe_layout_constraints to access this dict, we lazily register tag-based layout constraints +_maybe_layout_constraints: Dict[ + torch._ops.OpOverload, Optional[Callable[..., Any]] +] = {} +fallbacks: Set[torch._ops.OpOverload] = set() +aten = torch.ops.aten +tr_c10d = torch.ops.tr_c10d +prims = torch.ops.prims +needs_realized_inputs: Set[torch._ops.OpOverload] = set() +foreach_ops: Set[torch._ops.OpOverload] = set() +inplace_foreach_ops: Set[torch._ops.OpOverload] = set() +inplaceable_foreach_ops: Dict[torch._ops.OpOverload, torch._ops.OpOverload] = {} +quantized_decomposed = torch.ops.quantized_decomposed + + +def maybe_layout_constraints(fn: Callable[..., Any]) -> Optional[Callable[..., Any]]: + """Get layout constraints. Returns None if there are no layout constraints.""" + if not isinstance(fn, torch._ops.OpOverload): + # Only OpOverloads have layout constraints. + return None + if fn in _maybe_layout_constraints: + return _maybe_layout_constraints[fn] + # OpOverload with custom lowerings override tag-based layout constraints + if fn in lowerings: + _maybe_layout_constraints[fn] = None + return None + # We lazily register tag-based layout constraints. + + def handle_layout_constraint_tag(tag): + if tag is torch._C.Tag.needs_fixed_stride_order: + _maybe_layout_constraints[fn] = constrain_to_fx_strides + return _maybe_layout_constraints[fn] + elif tag is torch._C.Tag.flexible_layout: + _maybe_layout_constraints[fn] = None + return None + else: + raise AssertionError(f"Unknown layout constraint tag: {tag}") + + tag = get_layout_constraint_tag(fn) + return handle_layout_constraint_tag(tag) + + +def get_layout_constraint_tag(fn): + tags_by_priority = [ + torch._C.Tag.needs_fixed_stride_order, + torch._C.Tag.flexible_layout, + ] + for tag in tags_by_priority: + if tag in fn.tags: + return tag + return getattr(torch._C.Tag, config.custom_op_default_layout_constraint) + + +def assert_nyi(cond, msg): + if not cond: + raise NotImplementedError(f"inductor does not support {msg}") + + +def add_needs_realized_inputs(fn): + if isinstance(fn, (list, tuple, set)): + return [add_needs_realized_inputs(x) for x in fn] + needs_realized_inputs.add(fn) + if isinstance(fn, torch._ops.OpOverloadPacket): + needs_realized_inputs.update( + getattr(fn, overload) for overload in fn.overloads() + ) + + +def add_layout_constraint(fn, constraint): + if isinstance(fn, torch._ops.OpOverloadPacket): + for overload in fn.overloads(): + _maybe_layout_constraints[getattr(fn, overload)] = constraint + else: + _maybe_layout_constraints[fn] = constraint + + +add_needs_realized_inputs( + [ + aten.as_strided, + aten.as_strided_copy, + aten.avg_pool2d, + aten.avg_pool2d_backward, + aten.bmm, + aten.convolution, + aten.convolution_backward, + aten.max_pool2d_with_indices, + aten.max_pool2d_with_indices_backward, + aten.mm, + aten.upsample_nearest2d, + aten._upsample_nearest_exact2d, + aten._int_mm, + ] +) + +# TODO(jansel): ezyang says we won't need this in the future, try removing it +# based on https://github.com/pytorch/pytorch/blob/9e3eb329df8f701/c10/core/ScalarType.h#L28 +DTYPE_ID_LOOKUP = { + 0: torch.uint8, + 1: torch.int8, + 2: torch.int16, + 3: torch.int32, + 4: torch.int64, + 5: torch.float16, + 6: torch.float32, + 7: torch.float64, + 8: torch.complex32, + 9: torch.complex64, + 10: torch.complex32, + 11: torch.bool, + 15: torch.bfloat16, + # TODO(jansel): add quantized types? + # _(c10::qint8, QInt8) /* 12 */ + # _(c10::quint8, QUInt8) /* 13 */ + # _(c10::qint32, QInt32) /* 14 */ + # _(c10::quint4x2, QUInt4x2) /* 16 */ + # _(c10::quint2x4, QUInt2x4) /* 17 */ +} + + +def decode_dtype(dtype: int): + if not isinstance(dtype, int): + return dtype + assert dtype in DTYPE_ID_LOOKUP, f"id {dtype} missing from DTYPE_ID_LOOKUP" + dtype = DTYPE_ID_LOOKUP[dtype] + return dtype + + +def is_integer_type(x): + if isinstance(x, TensorBox): + return is_integer_dtype(x.get_dtype()) or is_boolean_dtype(x.get_dtype()) + elif isinstance(x, sympy.Expr): + return x.is_integer is True # type: ignore[attr-defined] + else: + return isinstance(x, int) + + +def is_boolean_type(x): + if isinstance(x, TensorBox): + return is_boolean_dtype(x.get_dtype()) + else: + return isinstance(x, bool) + + +def get_promoted_dtype(*args, type_promotion_kind: ELEMENTWISE_TYPE_PROMOTION_KIND): + def construct_input(inp): + if isinstance(inp, (Number, sympy.Basic)): + return inp + else: + assert hasattr(inp, "get_dtype") + dim = len(inp.get_size()) + # construct a tmp tensor to feed into torch.result_type + return torch.zeros([1] * dim, dtype=inp.get_dtype()) + + inps = [construct_input(arg) for arg in args] + _, dtype = elementwise_dtypes(*inps, type_promotion_kind=type_promotion_kind) + return dtype + + +def get_overloads(aten_fn): + if not isinstance(aten_fn, (list, tuple)): + aten_fn = [aten_fn] + else: + aten_fn = list(aten_fn) + + for fn in list(aten_fn): + if isinstance(fn, torch._ops.OpOverloadPacket): + for overload in fn.overloads(): + other_fn = getattr(fn, overload) + if other_fn not in lowerings: + aten_fn.append(other_fn) + + return aten_fn + + +def in_namespace(op, namespace): + if isinstance(op, torch._ops.OpOverloadPacket): + return namespace in op._qualified_op_name + elif isinstance(op, torch._ops.OpOverload): + return namespace in op.name() + return False + + +def transform_args(args, broadcast, type_promotion_kind, convert_input_to_bool): + indices = [i for i, x in enumerate(args) if isinstance(x, TensorBox)] + if (type_promotion_kind or convert_input_to_bool) and indices: + if convert_input_to_bool: + dtype = torch.bool + else: + # FIXME that's a crude approximation for promoting args + promoting_args = [ + a + for a in args + if isinstance(a, (Number, sympy.Basic)) + or getattr(a, "dtype", None) is not None + ] + dtype = get_promoted_dtype( + *promoting_args, type_promotion_kind=type_promotion_kind + ) + + # sometimes args are an immutable list so we can't mutate them + def promote(arg): + if isinstance(arg, TensorBox): + return to_dtype(arg, dtype) + elif isinstance(arg, ir.Constant): + return ir.Constant(arg.value, dtype, args[indices[0]].get_device()) + else: + return arg + + args = [promote(a) for a in args] + if broadcast and indices: + for i, x in zip(indices, broadcast_tensors(*[args[i] for i in indices])): + args[i] = x + for i in range(len(args)): + if isinstance(args[i], ir.Constant): + args[i] = ExpandView.create(args[i], list(args[indices[0]].get_size())) + + return args + + +def _register_foreach_lowering(aten_fn, decomp_fn): + """ + Add a foreach lowering to lowerings dict. + + Arguments: + aten_fn: torch.ops.aten.* fn we are lowering + decomp_fn: alternate implementation on our IR + broadcast: True to apply broadcasting to tensor inputs + type_promotion_kind: kind of type promotion applied to tensor inputs, `None` means no type promotion + convert_input_to_bool: some logical ops require inputs are converted to bool + """ + + @functools.wraps(decomp_fn) + def wrapped(*args, **kwargs): + assert len(args) <= 2 + out = decomp_fn(*args, **kwargs) + validate_ir(out) + return out + + aten_fns = get_overloads(aten_fn) + foreach_ops.update(aten_fns) + lowerings.update(dict.fromkeys(aten_fns, wrapped)) + return wrapped + + +def _register_lowering( + aten_fn, decomp_fn, broadcast, type_promotion_kind, convert_input_to_bool +): + """ + Add a lowering to lowerings dict + + Arguments: + aten_fn: torch.ops.aten.* fn we are lowering + decomp_fn: alternate implementation on our IR + broadcast: True to apply broadcasting to tensor inputs + type_promotion_kind: kind of type promotion applied to tensor inputs, `None` means no type promotion + convert_input_to_bool: some logical ops require inputs are converted to bool + """ + + @functools.wraps(decomp_fn) + def wrapped(*args, **kwargs): + args: Union[List[Any], Tuple[Any, ...], Dict[Any, Any]] = list(args) + unpacked = False + # TODO maybe we need to use pytrees here + if len(args) == 1 and isinstance(args[0], (list, tuple)): + unpacked = True + args = args[0] + + # kwargs tensors not supported yet unless it's a fallback op + if not all( + (fn in fallbacks or in_namespace(fn, "_c10d_functional")) for fn in aten_fn + ): + assert not any(isinstance(x, TensorBox) for x in kwargs.values()) + # explicitly assert for "out=" ops for better error messages + assert not any( + x == "out" for x in kwargs.keys() + ), "out= ops aren't yet supported" + + args = transform_args( + args, broadcast, type_promotion_kind, convert_input_to_bool + ) + + if unpacked: + args = [args] + + out = decomp_fn(*args, **kwargs) + validate_ir(out) + + return out + + aten_fn = get_overloads(aten_fn) + + lowerings.update(dict.fromkeys(aten_fn, wrapped)) + return wrapped + + +def register_lowering( + aten_fn, + broadcast=False, + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, + convert_input_to_bool=False, +): + """ + Shim to support decorator syntax. + """ + return functools.partial( + _register_lowering, + aten_fn, + broadcast=broadcast, + type_promotion_kind=type_promotion_kind, + convert_input_to_bool=convert_input_to_bool, + ) + + +def broadcast_symbolic_shapes(a, b): + """ + Broadcasting logic based on symbolic shapes. + + We give the shapes 0 and 1 concrete values, while all other shapes + are symbolic sympy formulas. + """ + output = [] + for x, y in itertools.zip_longest( + reversed(a), reversed(b), fillvalue=sympy.Integer(1) + ): + if y == 1: + output.append(x) + elif x == 1: + output.append(y) + else: + V.graph.sizevars.guard_equals(x, y) + if len(sympy.expand(y).free_symbols) < len(sympy.expand(x).free_symbols): + output.append(y) # prefer shorter formula + else: + output.append(x) + return tuple(reversed(output)) + + +def promote_constants(inputs, override_return_dtype=None, type_promotion_kind=None): + assert ( + override_return_dtype is None or type_promotion_kind is None + ), "only one of override_return_dtype or type_promotion_kind may be given" + + if override_return_dtype is None and type_promotion_kind is None: + type_promotion_kind = ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT + + if not any(isinstance(x, (sympy.Basic, int, float)) for x in inputs): + return inputs + if all(isinstance(x, (int, float, sympy.Basic)) for x in inputs): + dtype = override_return_dtype or get_promoted_dtype( + *inputs, type_promotion_kind=type_promotion_kind + ) + + def const_func(x): + if isinstance(x, sympy.Basic): + return ir.IndexingConstant(x, dtype, decode_device(None)) + else: + return ir.Constant(x, dtype, decode_device(None)) + + return [const_func(x) for x in inputs] + ex = next(x for x in inputs if isinstance(x, (TensorBox, ExpandView, ir.Constant))) + out = [] + for x in inputs: + if isinstance(x, (int, float)): + out.append( + ExpandView.create( + ir.Constant(x, ex.get_dtype(), ex.get_device()), list(ex.get_size()) + ) + ) + elif isinstance(x, sympy.Basic): + out.append( + ExpandView.create( + IndexingConstant(x, ex.get_dtype(), ex.get_device()), + list(ex.get_size()), + ) + ) + else: + out.append(x) + + return out + + +def make_pointwise( + fn, + override_return_dtype=None, + override_device=None, + override_fn_when_input_bool=None, + override_fn_when_gpu_float64=None, + allow_alpha=False, + triton_fallback=None, +): + def inner(*inputs: List[TensorBox], alpha=None): + if triton_fallback is not None and any(map(is_triton, inputs)): + assert not allow_alpha # not implemented + return triton_fallback(*inputs) + + inputs = promote_constants(inputs, override_return_dtype) + if allow_alpha: + if alpha is not None and alpha != 1: + inputs = list(inputs) + inputs[-1] = mul(inputs[-1], alpha) + else: + assert alpha is None + loaders = [x.make_loader() for x in inputs] + ranges = inputs[0].get_size() + dtype = override_return_dtype or inputs[0].get_dtype() + is_gpu_device = is_gpu(decode_device(inputs[0].get_device()).type) + + for other in inputs[1:]: + assert isinstance(other, ir.BaseConstant) or len(ranges) == len( + other.get_size() + ), f"ndim mismatch {fn} {ranges} {other.get_size()}" + + # in tracing, we will annotate pointwise nodes that correspond to the output of + # a pointwise node that would have been run in eager. intermediary pointwise nodes + # during decompositions are not annotated. + emulate_precision_casts = ( + V.graph is not None + and getattr(V.graph, "current_node", None) is not None + and V.graph.current_node.meta is not None + and V.graph.current_node.meta.get("low_precision_pointwise_barrier", False) + and dtype in (torch.bfloat16, torch.float16) + ) + + def inner_fn(index): + assert len(index) == len(ranges), f"wrong ndim {index} {ranges}" + if dtype == torch.bool and override_fn_when_input_bool is not None: + return override_fn_when_input_bool(*[load(index) for load in loaders]) + elif ( + override_fn_when_gpu_float64 + and is_gpu_device + and dtype == torch.float64 + ): + return override_fn_when_gpu_float64(*[load(index) for load in loaders]) + else: + inputs_loaded = [] + for load in loaders: + out = load(index) + if emulate_precision_casts: + downcast = ops.to_dtype(out, dtype, use_compute_types=False) + out = ops.to_dtype(downcast, dtype) + inputs_loaded.append(out) + + out = fn(*inputs_loaded) + if emulate_precision_casts: + # fp16/bf16 kernels are computed in fp32. Casting down to fp16/bf16 here, + # then upcasting again, to emulate casts that eager would do. + downcast = ops.to_dtype(out, dtype, use_compute_types=False) + return ops.to_dtype(downcast, dtype) + return out + + if not override_device: + device = None + for i in inputs: + if is_gpu(i.get_device().type): + device = i.get_device() + break + if not device: + device = inputs[0].get_device() + + device = override_device or device + + return Pointwise.create( + device=device, + dtype=dtype, + inner_fn=inner_fn, + ranges=ranges, + ) + + return inner + + +def make_foreach_pointwise(pw_fn, allow_alpha=False): + def inner(*inputs: List[List[TensorBox]], alpha=1): + # group by device, whether any of the inputs are dynamic, and whether their types match + # (proxy for type promotion) + def group_args(arg_pairs): + out = defaultdict(list) + for i, args in enumerate(arg_pairs): + use_foreach = ( + not is_dynamic(*args) or config.combo_kernel_foreach_dynamic_shapes + ) + device = None + for t in args: + if isinstance(t, TensorBox): + device = t.data.get_device() + break + assert ( + device is not None + ), "foreach op should have at least one tensor arg" + out[(device, use_foreach)].append((i, args)) + return out + + realize_outputs = ( + len(V.graph.current_node.users) == 0 + or V.graph.current_node.target in inplace_foreach_ops + ) + for node in V.graph.current_node.users: + for user in node.users: + if not (user.op == "call_function" and (user.target in foreach_ops)): + realize_outputs = True + + a_list_input = None + for input in inputs: + if isinstance(input, (list, tuple)): + a_list_input = input + break + assert ( + a_list_input is not None + ), "at least one input must be a list to a foreach op" + + # broadcast scalar inputs to match length of list inputs + broadcast_inputs = [] + for input in inputs: + if not isinstance(input, (list, tuple)): + broadcast_inputs.append([input] * len(a_list_input)) + else: + broadcast_inputs.append(input) + + groups = group_args(zip(*broadcast_inputs)) + + outputs = [None] * len(a_list_input) + for (device, use_foreach), group in groups.items(): + operation_list: List[str] = [] + for ( + output_ind, + args, + ) in group: + if allow_alpha: + output = pw_fn(*args, alpha=alpha) + else: + output = pw_fn(*args) + + outputs[output_ind] = output + + if ( + V.graph.has_feature(device, BackendFeature.FOREACH) + and use_foreach + and realize_outputs + ): + output.realize() + operation_list.append(output.get_operation_name()) + + if operation_list: + V.graph.register_operation_list(operation_list) + + assert all(x is not None for x in outputs) + return outputs + + return inner + + +def to_dtype(x: TensorBox, dtype: torch.dtype, copy=False): + src_dtype = x.get_dtype() + if src_dtype == dtype: + return clone(x) if copy else x + + def _to_dtype(x): + return ops.to_dtype(x, dtype, src_dtype=src_dtype) + + return make_pointwise(_to_dtype, override_return_dtype=dtype)(x) + + +@register_lowering(prims.convert_element_type, type_promotion_kind=None) +def _convert_element_type(x: TensorBox, dtype: torch.dtype): + if dtype.is_complex or x.get_dtype().is_complex: + if x.get_size(): + # Decompose since aa aten fallback is more friendly for c++ codegen. + # This decomposition doesn't work for empty tensor, which needs more investigation. + dst = empty_like(x, dtype=dtype) + ir.InplaceCopyFallback.create(dst, x) + return dst + else: + return fallback_handler( + prims.convert_element_type.default, add_to_fallback_set=False + )(x, dtype) + return to_dtype(x, dtype, copy=True) + + +def to_dtype_bitcast(x: TensorBox, dtype: torch.dtype, *, copy=False): + x_dtype = x.get_dtype() + if x_dtype == dtype: + return clone(x) if copy else x + + def _get_primitive_bitwidth(dtype): + if dtype.is_floating_point: + return torch.finfo(dtype).bits + else: + return torch.iinfo(dtype).bits + + src_bits = _get_primitive_bitwidth(x_dtype) + dst_bits = _get_primitive_bitwidth(dtype) + if src_bits != dst_bits: + # fallback to aten eager implementation for differing bitwidths + return fallback_handler(aten.view.dtype)(x, dtype) + else: + return TensorBox(DtypeView.create(x, dtype)) + + +@register_lowering(aten.view.dtype, type_promotion_kind=None) +def _view_dtype(x: TensorBox, dtype: torch.dtype): + if dtype.is_complex or x.get_dtype().is_complex: + return TensorBox.create( + ir.ComplexView.create(torch.ops.aten.view.dtype, x, dtype) + ) + return to_dtype_bitcast(x, dtype) + + +def to_device(x: TensorBox, device: torch.device, *, copy=False): + device = decode_device(device) + if x.get_device() == device: + return clone(x) if copy else x + return TensorBox.create(ir.DeviceCopy.create(x, device)) + + +@register_lowering(prims.device_put, type_promotion_kind=None) +def _device_put(x: TensorBox, device: torch.device): + return to_device(x, device, copy=True) + + +def register_pointwise( + aten_fn, + name=None, + broadcast=True, + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, + convert_input_to_bool=False, + override_return_dtype=None, + override_fn_when_input_bool=None, + allow_alpha=False, + use_libdevice_for_f64=False, + triton_fallback=None, +): + """A pointwise function that maps ops.{name} to inputs""" + name = name or aten_fn.__name__ + fn = ops_wrapper(name) + if use_libdevice_for_f64: + fn_libdevice = ops_wrapper("libdevice_" + name) + if override_fn_when_input_bool is not None: + override_fn_when_input_bool = ops_wrapper(override_fn_when_input_bool) + + fn = make_pointwise( + fn, + override_return_dtype=override_return_dtype, + override_fn_when_input_bool=override_fn_when_input_bool, + override_fn_when_gpu_float64=fn_libdevice if use_libdevice_for_f64 else None, # type: ignore[possibly-undefined] + allow_alpha=allow_alpha, + triton_fallback=triton_fallback, + ) + fn = register_lowering( + aten_fn, + broadcast=broadcast, + type_promotion_kind=type_promotion_kind, + convert_input_to_bool=convert_input_to_bool, + )(fn) + + if hasattr(prims, name): + register_lowering( + getattr(prims, name), + type_promotion_kind=None, + convert_input_to_bool=convert_input_to_bool, + )(fn) + return fn + + +def register_frexp(): + """A pointwise function that maps ops.frexp to inputs""" + name = "frexp" + frexp = ops_wrapper("frexp") + + def frexp0(*args, **kwargs): + return frexp(*args, **kwargs)[0] # type: ignore[index] # next PR + + def frexp1(*args, **kwargs): + return frexp(*args, **kwargs)[1] # type: ignore[index] # next PR + + pw_fns = [ + make_pointwise(frexp0), + make_pointwise(frexp1, override_return_dtype=torch.int32), + ] + + def fn(*args, **kwargs): + return pw_fns[0](*args, **kwargs), pw_fns[1](*args, **kwargs) + + fn = register_lowering( + aten.frexp, + )(fn) + + if hasattr(prims, name): + register_lowering( + getattr(prims, name), + type_promotion_kind=None, + )(fn) + return fn + + +register_frexp() + + +def register_foreach_pointwise( + aten_fn, + pointwise_lowering_fn, + allow_alpha=False, +): + fn = make_foreach_pointwise(pointwise_lowering_fn, allow_alpha=allow_alpha) + fn = _register_foreach_lowering(aten_fn, fn) + return fn + + +@register_lowering(aten.where, broadcast=False, type_promotion_kind=None) +def where(cond, a, b): + def fn(*args): + return ops.where(*args) + + if isinstance(a, (float, int)): + a = constant_like(a)(b) + if isinstance(b, (float, int)): + b = constant_like(b)(a) + + args = [cond, a, b] + dtype = get_promoted_dtype( + args[1], args[2], type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT + ) + indices = [i for i, x in enumerate(args) if isinstance(x, TensorBox)] + for i, x in zip(indices, broadcast_tensors(*[args[i] for i in indices])): + args[i] = x + for i in range(len(args)): + if isinstance(args[i], ir.Constant): + args[i] = ExpandView.create(args[i], list(args[indices[0]].get_size())) + return make_pointwise(fn, override_return_dtype=dtype)( + args[0], to_dtype(args[1], dtype), to_dtype(args[2], dtype) + ) + + +@register_lowering(aten.broadcast_tensors, broadcast=False, type_promotion_kind=None) +def broadcast_tensors(*inputs): + if len(inputs) == 1 and isinstance(inputs[0], (list, tuple)): + return broadcast_tensors(*inputs[0]) + target: List[sympy.Expr] = functools.reduce( + broadcast_symbolic_shapes, [x.get_size() for x in inputs], [] + ) + outputs = [] + for x in inputs: + sizes = x.get_size() + if len(sizes) != len(target) or any( + ((a == 1 and b != 1) or (a != 1 and b == 1)) for a, b in zip(sizes, target) + ): + x = expand(x, target) + outputs.append(x) + return outputs + + +@register_lowering([aten.alias, aten.detach, aten.detach_, aten.lift, prims.view_of]) +def nop(x): + return x # AOT autograd handles this for us + + +if hasattr(aten, "lift_fresh"): + register_lowering(aten.lift_fresh)(nop) + + +@register_lowering(aten.squeeze, type_promotion_kind=None) +def squeeze(x, dim=None): + assert isinstance(x, TensorBox) + if dim is None: + return TensorBox(SqueezeView.create(x.data)) + + dim = ( + V.graph.sizevars.evaluate_static_shape(dim) + if isinstance(dim, (int, sympy.Expr)) + else tuple(V.graph.sizevars.evaluate_static_shape(d) for d in dim) + ) + dim = canonicalize_dims(len(x.get_size()), dim) # type: ignore[call-overload] + dims = set((dim,) if not isinstance(dim, tuple) else dim) + + new_shape = [] + for d, s in enumerate(x.get_size()): + if not (d in dims and V.graph.sizevars.evaluate_expr(sympy.Eq(s, 1))): + new_shape.append(s) + + # squeeze does nothing if the size isn't 1 + return view(x, new_shape) if new_shape != x.get_size() else x + + +@register_lowering(aten.squeeze_copy, type_promotion_kind=None) +def squeeze_copy(x, dim=None): + return clone(squeeze(x, dim)) + + +@register_lowering([aten.squeeze_]) +def squeeze_(x, dim=None): + val = squeeze(x, dim) + assert isinstance(x, TensorBox) + assert isinstance(val, TensorBox) + x.data = val.data + return x + + +@register_lowering(aten.isinf) +def isinf(x): + if is_integer_type(x): + return full_like(x, False, dtype=torch.bool) + fn = ops_wrapper("isinf") + return make_pointwise(fn, override_return_dtype=torch.bool)(x) + + +@register_lowering(aten.isnan) +def isnan(x): + if is_integer_type(x): + return full_like(x, False, dtype=torch.bool) + fn = ops_wrapper("isnan") + return make_pointwise(fn, override_return_dtype=torch.bool)(x) + + +@register_lowering(aten.ceil) +def ceil(x): + if is_integer_type(x): + return clone(x) + fn = ops_wrapper("ceil") + return make_pointwise(fn)(x) + + +@register_lowering(aten.floor) +def floor(x): + if is_integer_type(x): + return clone(x) + fn = ops_wrapper("floor") + return make_pointwise(fn)(x) + + +@register_lowering(aten.round.default) +def round(x): + if is_integer_type(x): + return clone(x) + else: + fn = ops_wrapper("round") + return make_pointwise(fn)(x) + + +@register_lowering(aten.trunc) +def trunc(x): + if is_integer_type(x): + return clone(x) + fn = ops_wrapper("trunc") + return make_pointwise(fn)(x) + + +@register_lowering(aten.expand, type_promotion_kind=None) +def expand(x, sizes): + from torch.fx.experimental.symbolic_shapes import free_unbacked_symbols + + (x,) = promote_constants([x]) + if isinstance(x, ir.BaseConstant): + return ExpandView.create(x, tuple(sizes)) + assert isinstance(x, TensorBox) + assert isinstance(sizes, (list, tuple)) + if tuple(x.get_size()) == tuple(sizes): + return x + + if not free_unbacked_symbols(x.get_size()): + x_size_product = V.graph.sizevars.size_hint(sympy_product(x.get_size())) + # TODO: It would be better to realize the input if any of its sizes + # are unbacked, because typically the size will be non-zero. However, + # this cannot be done directly as below as we'll choke on the size_hint + # here + if x_size_product > 0 and not free_unbacked_symbols(sizes): + # maybe realize input before broadcasting it + x.mark_reuse( + V.graph.sizevars.size_hint(sympy_product(sizes)) // x_size_product + ) + return TensorBox(ExpandView.create(x.data, tuple(sizes))) + + +@register_lowering(prims.broadcast_in_dim, type_promotion_kind=None) +def broadcast_in_dim(a, shape, broadcast_dimensions): + s = list(shape) + for broadcast_dimension in broadcast_dimensions: + s[broadcast_dimension] = -1 + + v = a + for idx, x in enumerate(s): + if x != -1: + v = unsqueeze(v, idx) + + return expand(v, shape) + + +@register_lowering(aten.expand_as, type_promotion_kind=None) +def expand_as(x, y): + return expand(x, y.get_size()) + + +@register_lowering(aten.repeat) +def repeat(x, repeats): + old_size = list(x.get_size()) + if len(repeats) > len(old_size): + old_size = [sympy.Integer(1)] * (len(repeats) - len(old_size)) + old_size + x = view(x, list(old_size)) + assert len(repeats) == len(x.get_size()) + + new_size = list(x.get_size()) + + zero_tensor = False + for i in range(len(repeats)): + if repeats[i] == 0: + zero_tensor = True + new_size[i] = new_size[i] * repeats[i] + + if zero_tensor: + return empty(new_size, dtype=x.get_dtype(), device=x.get_device()) + if all((a == 1 or b == 1) for a, b in zip(repeats, old_size)): + return clone(expand(x, new_size)) + + x_loader: Callable[[Any], Any] + + def inner_fn(index): + assert len(index) == len(repeats) + index = list(index) + for i in range(len(repeats)): + if repeats[i] != 1: + if old_size[i] == 1: + index[i] = sympy.Integer(0) + else: + index[i] = ModularIndexing(index[i], 1, old_size[i]) + return x_loader(index) + + old_size_product = V.graph.sizevars.size_hint(sympy_product(old_size)) + if old_size_product > 0: + # maybe realize the input + x.mark_reuse( + V.graph.sizevars.size_hint(sympy_product(new_size)) // old_size_product + ) + + x_loader = x.make_loader() + return Pointwise.create( + device=x.get_device(), + dtype=x.get_dtype(), + inner_fn=inner_fn, + ranges=list(new_size), + ) + + +@register_lowering(aten._unsafe_view, type_promotion_kind=None) +@register_lowering(aten.view, type_promotion_kind=None) +@register_lowering(aten.reshape, type_promotion_kind=None) +def view(x, sizes): + assert isinstance(x, TensorBox) + assert isinstance(sizes, (list, tuple)) + return TensorBox(View.create(x.data, sizes)) + + +@register_lowering(aten.permute, type_promotion_kind=None) +def permute(x, dims): + assert isinstance(x, TensorBox) + assert isinstance(dims, (list, tuple)) + return TensorBox(PermuteView.create(x.data, tuple(dims))) + + +@register_lowering(aten.slice, type_promotion_kind=None) +def slice_(x, dim=0, start=0, end=2**63, step=1, clamp=True): + assert isinstance(x, TensorBox) + dim = _validate_dim(x, dim, 0) + return TensorBox(ir.SliceView.create(x.data, dim, start, end, step, clamp=clamp)) + + +@register_lowering(aten.as_strided, type_promotion_kind=None) +def as_strided(x, size, stride, storage_offset=None): + if isinstance(x, TensorBox) and isinstance(x.data, ir.BaseView): + # as_strided ignores views + x = x.data.unwrap_view() + x.realize() + if not ir.is_storage_and_layout(x): + raise NotImplementedError(f"unrealized as_strided({x}, ...)") + storage, old_layout = ir.as_storage_and_layout(x) + new_layout = ir.FixedLayout( + old_layout.device, + old_layout.dtype, + [sympy.expand(s) for s in size], + [sympy.expand(s) for s in stride], + sympy.expand(storage_offset or 0), + ) + return TensorBox(ir.ReinterpretView(storage, new_layout)) + + +@register_lowering(aten.as_strided_, type_promotion_kind=None) +def as_strided_(x, size, stride, storage_offset=None): + assert isinstance(x, TensorBox) + x.data = as_strided(x, size, stride, storage_offset).data + return x + + +@register_lowering(aten.as_strided_copy, type_promotion_kind=None) +def as_strided_copy(x, size, stride, storage_offset=None): + result = as_strided(x, size, stride, storage_offset) + return clone(result) + + +def pointwise_cat(inputs, dim=0): + # (inclusive, exclusive) + inputs_ranges: List[Tuple[sympy.Expr, sympy.Expr]] = [] + prev_end = 0 + for inp in inputs: + inputs_ranges.append((prev_end, prev_end + inp.get_size()[dim])) # type: ignore[arg-type] + prev_end = inputs_ranges[-1][-1] # type: ignore[assignment] + + inputs_loaders = [inp.make_loader() for inp in inputs] + + def inner_fn(idx): + idx_dim = ops.index_expr(idx[dim], torch.int64) + + masks = [] + masked_loads = [] + for i in range(len(inputs)): + start = ( + ops.constant(0, torch.int64) + if i == 0 + else ops.index_expr(inputs_ranges[i][0], torch.int64) + ) + end = ops.index_expr(inputs_ranges[i][1], torch.int64) + + start_cond = ops.ge(idx_dim, start) + end_cond = ops.lt(idx_dim, end) + if i == 0: + mask = end_cond + elif i == len(inputs) - 1: + mask = start_cond + else: + mask = ops.and_(start_cond, end_cond) + + masks.append(mask) + idx_load = list(idx) + + # if we're concatting [4], [2] + # when we index the second tensor for 5 we want to index 5 - 4 + # Use Identity to prevent expansion of index * stride to keep expression + # in same int bitwidth as shape + idx_load[dim] = Identity(idx_load[dim] - inputs_ranges[i][0]) + + masked_loads.append( + ops.masked( + mask, + lambda: inputs_loaders[i](idx_load), + 0.0, # this value should be unused + ), + ) + + next_val = masked_loads[-1] + for i in range((len(inputs)) - 2, -1, -1): + next_val = ops.where( + masks[i], + masked_loads[i], + next_val, + ) + return next_val + + new_size = list(inputs[0].get_size()) + new_size[dim] = inputs_ranges[-1][-1] + + return Pointwise.create( + device=inputs[0].get_device(), + dtype=inputs[0].get_dtype(), + inner_fn=inner_fn, + ranges=new_size, + ) + + +@register_lowering(quantized_decomposed.quantize_per_channel, type_promotion_kind=None) +def quantized_decomposed_quantize_per_channel( + input: TensorBox, + scales: TensorBox, + zero_points: TensorBox, + axis: int, + quant_min: int, + quant_max: int, + dtype: torch.dtype, +) -> TensorBox: + assert len(scales.get_size()) == 1, "expect scales 1 dim" + assert len(zero_points.get_size()) == 1, "expect zero_points 1 dim" + + if input.get_dtype() == torch.bfloat16: + input = to_dtype(input, torch.float32) + assert ( + input.get_dtype() == torch.float32 + ), f"Expecting input to have dtype torch.float32, but got dtype: {input.get_dtype()}" + assert axis < len( + input.get_size() + ), f"Expecting axis to be < {len(input.get_size())}" + + input_loader = input.make_loader() + scales_loader = scales.make_loader() + zero_points_loader = zero_points.make_loader() + + def inner_fn(idx): + channel_idx = (idx[axis],) + + input = input_loader(idx) + scale = scales_loader(channel_idx) + zero_point = zero_points_loader(channel_idx) + qmin, qmax = _create_constants(quant_min, quant_max, dtype=torch.float32) + + if scales.dtype != torch.float32: + scale = ops.to_dtype(scale, torch.float32) + if zero_points.dtype != torch.int32: + zero_point = ops.to_dtype(zero_point, torch.int32) + inv_scale = ops.reciprocal(scale) + val = ops.round(input * inv_scale) + zero_point + clamped = ops.maximum(qmin, ops.minimum(qmax, val)) + return ops.to_dtype(clamped, dtype) + + return Pointwise.create( + device=input.get_device(), + dtype=dtype, + inner_fn=inner_fn, + ranges=input.get_size(), + ) + + +@register_lowering( + quantized_decomposed.dequantize_per_channel, type_promotion_kind=None +) +def quantized_decomposed_dequantize_per_channel( + input: TensorBox, + scales: TensorBox, + zero_points: TensorBox, + axis: int, + quant_min: int, + quant_max: int, + dtype: torch.dtype, +) -> TensorBox: + assert len(scales.get_size()) == 1, "expect scales 1 dim" + assert len(zero_points.get_size()) == 1, "expect zero_points 1 dim" + assert ( + input.get_dtype() == dtype + ), f"Expecting input to have dtype {dtype}, but got dtype: {input.get_dtype()}" + assert axis < len( + input.get_size() + ), f"Expecting axis to be < {len(input.get_size())}" + + input_loader = input.make_loader() + scales_loader = scales.make_loader() + zero_points_loader = zero_points.make_loader() + + def inner_fn(idx): + channel_idx = (idx[axis],) + + input = input_loader(idx) + scale = scales_loader(channel_idx) + zero_point = zero_points_loader(channel_idx) + + if scales.dtype != torch.float32: + scale = ops.to_dtype(scale, torch.float32) + if zero_points.dtype != torch.float32: + zero_point = ops.to_dtype(zero_point, torch.float32) + val = ops.sub(ops.to_dtype(input, torch.float32), zero_point) * scale + return val + + return Pointwise.create( + device=input.get_device(), + dtype=torch.float32, + inner_fn=inner_fn, + ranges=input.get_size(), + ) + + +@register_lowering( + quantized_decomposed.quantize_per_tensor.default, type_promotion_kind=None +) +def quantized_decomposed_quantize_per_tensor_default( + input: TensorBox, + scale: float, + zero_point: int, + quant_min: int, + quant_max: int, + dtype: torch.dtype, +) -> TensorBox: + if input.get_dtype() == torch.bfloat16: + input = to_dtype(input, torch.float32) + assert ( + input.get_dtype() == torch.float32 + ), f"Expecting input to have dtype torch.float32, but got dtype: {input.get_dtype()}" + + input_loader = input.make_loader() + + def inner_fn(idx, scale, zero_point): + input = input_loader(idx) + inv_scale, zero_point = _create_constants( + 1.0 / scale, zero_point, dtype=torch.float32 + ) + val = ops.round(input * inv_scale) + zero_point + qmin, qmax = _create_constants(quant_min, quant_max, dtype=torch.float32) + clamped = ops.minimum(ops.maximum(val, qmin), qmax) + return ops.to_dtype(clamped, dtype) + + return Pointwise.create( + device=input.get_device(), + dtype=dtype, + inner_fn=functools.partial( + inner_fn, scale=float(scale), zero_point=int(zero_point) + ), + ranges=input.get_size(), + ) + + +@register_lowering( + quantized_decomposed.dequantize_per_tensor.default, type_promotion_kind=None +) +def quantized_decomposed_dequantize_per_tensor_default( + input: TensorBox, + scale: float, + zero_point: int, + quant_min: int, + quant_max: int, + dtype: torch.dtype, +) -> TensorBox: + assert ( + input.get_dtype() == dtype + ), f"Expecting input to have dtype {dtype}, but got dtype: {input.get_dtype()}" + + input_loader = input.make_loader() + + def inner_fn(idx, scale, zero_point): + input = input_loader(idx) + scale, zero_point = _create_constants(scale, zero_point, dtype=torch.float32) + val = ops.sub(ops.to_dtype(input, torch.float32), zero_point) * scale + return val + + return Pointwise.create( + device=input.get_device(), + dtype=torch.float32, + inner_fn=functools.partial( + inner_fn, scale=float(scale), zero_point=int(zero_point) + ), + ranges=input.get_size(), + ) + + +@register_lowering( + quantized_decomposed.quantize_per_tensor.tensor, type_promotion_kind=None +) +def quantized_decomposed_quantize_per_tensor_tensor( + input: TensorBox, + scale: TensorBox, + zero_point: TensorBox, + quant_min: int, + quant_max: int, + dtype: torch.dtype, +) -> TensorBox: + if input.get_dtype() == torch.bfloat16: + input = to_dtype(input, torch.float32) + assert ( + input.get_dtype() == torch.float32 + ), f"Expecting input to have dtype torch.float32, but got dtype: {input.get_dtype()}" + assert len(scale.get_size()) == 0 or ( + len(scale.get_size()) == 1 and scale.get_size()[0] == 1 + ), "expect scale as scalar tensor" + assert len(zero_point.get_size()) == 0 or ( + len(zero_point.get_size()) == 1 and zero_point.get_size()[0] == 1 + ), "expect zero_point as scalar tensor" + + input_loader = input.make_loader() + scale_loader = scale.make_loader() + zero_point_loader = zero_point.make_loader() + + def inner_fn(idx): + input = input_loader(idx) + _scale = scale_loader((0,) if len(scale.get_size()) == 1 else ()) + _zero_point = zero_point_loader((0,) if len(scale.get_size()) == 1 else ()) + if scale.dtype != torch.float32: + _scale = ops.to_dtype(_scale, torch.float32) + if zero_point.dtype != torch.float32: + _zero_point = ops.to_dtype(_zero_point, torch.float32) + val = ops.round(input * ops.reciprocal(_scale)) + _zero_point + qmin, qmax = _create_constants(quant_min, quant_max, dtype=torch.float32) + clamped = ops.minimum(ops.maximum(val, qmin), qmax) + return ops.to_dtype(clamped, dtype) + + return Pointwise.create( + device=input.get_device(), + dtype=dtype, + inner_fn=inner_fn, + ranges=input.get_size(), + ) + + +@register_lowering( + quantized_decomposed.dequantize_per_tensor.tensor, type_promotion_kind=None +) +def quantized_decomposed_dequantize_per_tensor_tensor( + input: TensorBox, + scale: TensorBox, + zero_point: TensorBox, + quant_min: int, + quant_max: int, + dtype: torch.dtype, +) -> TensorBox: + assert len(scale.get_size()) == 0 or ( + len(scale.get_size()) == 1 and scale.get_size()[0] == 1 + ), "expect scale as scalar tensor" + assert len(zero_point.get_size()) == 0 or ( + len(zero_point.get_size()) == 1 and zero_point.get_size()[0] == 1 + ), "expect zero_point as scalar tensor" + assert ( + input.get_dtype() == dtype + ), f"Expecting input to have dtype {dtype}, but got dtype: {input.get_dtype()}" + + input_loader = input.make_loader() + scale_loader = scale.make_loader() + zero_point_loader = zero_point.make_loader() + + def inner_fn(idx): + input = input_loader(idx) + _scale = scale_loader((0,) if len(scale.get_size()) == 1 else ()) + _zero_point = zero_point_loader((0,) if len(scale.get_size()) == 1 else ()) + if scale.dtype != torch.float32: + _scale = ops.to_dtype(_scale, torch.float32) + if zero_point.dtype != torch.float32: + _zero_point = ops.to_dtype(_zero_point, torch.float32) + val = ops.sub(ops.to_dtype(input, torch.float32), _zero_point) * _scale + return val + + return Pointwise.create( + device=input.get_device(), + dtype=torch.float32, + inner_fn=inner_fn, + ranges=input.get_size(), + ) + + +@register_lowering(aten.cat) +def cat(inputs, dim=0): + cpu_device = inputs[0].get_device().type == "cpu" + if cpu_device and all( + input.get_dtype() in [torch.int8, torch.uint8] for input in inputs + ): + # TODO Remove this fallback when we support vectorization + # code gen with uint8 data type directly. + for input in inputs: + input.realize() + if all(len(input.get_size()) == 4 for input in inputs): + inputs, _ = require_channels_last(aten.cat, *inputs) + return fallback_handler(aten.cat.default)(inputs, dim) + + if len(inputs) == 1: + return clone(inputs[0]) + + dim = _validate_dim(inputs[0], dim, 0) + dtype = get_promoted_dtype( + *inputs, type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT + ) + inputs = [to_dtype(inp, dtype) for inp in inputs] + + def unwrap_tensor(x: Union[TensorBox, ir.StorageBox]) -> ir.IRNode: + if isinstance(x, TensorBox): + if isinstance(x.data, ir.BaseView): + return x.data.unwrap_view() + else: + return x.data + + if isinstance(x, ir.StorageBox): + return x.data + + return x + + def is_reduction(t): + return isinstance(t, ir.ComputedBuffer) and isinstance(t.data, ir.Reduction) + + def can_fuse_reduction(t): + if isinstance(t, (TensorBox, ir.StorageBox)): + return can_fuse_reduction(unwrap_tensor(t)) + return ( + is_reduction(t) + or isinstance(t, ir.Pointwise) + and any( + can_fuse_reduction(V.graph.get_buffer(read)) + for read in t.get_read_names() + ) + ) + + # fusing reducutions into computed concat buffer can cause regressions. + fusable_reduction = any(can_fuse_reduction(t) for t in inputs) + + def should_lower_cat_input(x) -> bool: + # Unrealized inputs will not be storage and layouts, and we dont want to realize + # them in case we want to fuse + if ir.is_storage_and_layout(x): + storage, _ = ir.as_storage_and_layout(x, freeze=False) + return not ir.ConcatKernel.can_realize_into_without_copy(storage) + + if isinstance(x, (TensorBox, ir.StorageBox)): + return should_lower_cat_input(unwrap_tensor(x)) + + if isinstance(x, ir.Pointwise): + return True + + return False + + # TODO: We observed negative performance impact of pointwise_cat optimization on CPU so disabled it. + # We will revisit this later after enabling vectorization on index_expr. + if cpu_device: + return TensorBox(ir.ConcatKernel.create(inputs, dim)) + + def op_count(x): + if isinstance(x, (TensorBox, ir.StorageBox)): + return op_count(unwrap_tensor(x)) + + # this will correspond to a direct memory read + if not isinstance(x, ir.Pointwise): + return 0 + + count = x.inner_fn_opcount().num_ops + for read in x.get_read_names(): + count += op_count(V.graph.get_buffer(read)) + + return count + + # as of inputs increase, possibility for register spilling also increases + # past a certain threshold of inputs we only fuse if the if the input kernels + # are simple + # not sure if we want to expose to users via config since logic may change in future + MAX_COMPLEX_POINTWISE_CAT = 8 + MAX_SIMPLE_OP_COUNT = 2 + + def additional_pointwise_ops(op: torch._ops.OpOverload): + return op in (aten.cat.default, aten.constant_pad_nd.default) + + if len(inputs) <= MAX_COMPLEX_POINTWISE_CAT or ( + (len(inputs) <= config.max_pointwise_cat_inputs) + and all(op_count(t) <= MAX_SIMPLE_OP_COUNT for t in inputs) + ): + pointwise_uses = all( + is_pointwise_use(use, additional_pointwise_ops) + for use in V.current_node.users + ) + # fuse in case we will be used in a pointwise node, and there are any inputs we + # we can prevent materialization of. + fuse_pointwise_use = ( + any(should_lower_cat_input(inp) for inp in inputs) and pointwise_uses + ) + + # horizontal fuse in case all inputs will require a copy kernel anyway. + # only horizontally fuse pointwise kernels + horizontal_fuse_cat = all( + should_lower_cat_input(inp) for inp in inputs + ) and not any(can_fuse_reduction(t) for t in inputs) + if fuse_pointwise_use or (horizontal_fuse_cat and not fusable_reduction): + return pointwise_cat(inputs, dim) + + return TensorBox(ir.ConcatKernel.create(inputs, dim)) + + +@register_lowering(aten.diagonal, type_promotion_kind=None) +def diagonal(input, offset: int = 0, dim1: int = 0, dim2: int = 1): + original_shape = input.get_size() + num_dims = len(original_shape) + dim1 = canonicalize_dim(idx=dim1, rank=num_dims) + dim2 = canonicalize_dim(idx=dim2, rank=num_dims) + + check( + dim1 != dim2, lambda: f"diagonal dimensions cannot be identical {dim1}, {dim2}" + ) + + offset_negative = V.graph.sizevars.evaluate_expr(sympy.Lt(offset, 0)) + if offset_negative: + diag_size = V.graph.sizevars.evaluate_max( + V.graph.sizevars.evaluate_min( + original_shape[dim1] + offset, original_shape[dim2] + ), + 0, # type: ignore[arg-type] + ) + else: + diag_size = V.graph.sizevars.evaluate_max( + V.graph.sizevars.evaluate_min( + original_shape[dim1], original_shape[dim2] - offset + ), + 0, # type: ignore[arg-type] + ) + + base_idx = (0, 0) + if offset_negative: + base_idx = (-offset, 0) + else: + base_idx = (0, offset) + + sizes = [s for i, s in enumerate(original_shape) if i not in (dim1, dim2)] + sizes.append(diag_size) + + def reindexer(idx): + diag_idx = idx[-1] + original_idx = [0] * len(original_shape) + cur_dim = 0 + for d in range(num_dims): + if d == dim1: + original_idx[d] = diag_idx + base_idx[0] + elif d == dim2: + original_idx[d] = diag_idx + base_idx[1] + else: + original_idx[d] = idx[cur_dim] + cur_dim += 1 + + assert cur_dim == len(original_shape) - 2 + return original_idx + + return TensorBox(ir.GenericView.create(input, sizes, reindexer)) + + +@register_lowering(aten.diagonal_copy, type_promotion_kind=None) +def diagonal_copy(input, offset: int = 0, dim1: int = 0, dim2: int = 1): + return clone(diagonal(input, offset, dim1, dim2)) + + +@register_lowering(aten.diagonal_scatter, type_promotion_kind=None) +def diagonal_scatter(input, src, offset: int = 0, dim1: int = 0, dim2: int = 1): + output = clone(input) + target = diagonal(output, offset, dim1, dim2) + mutate_to(target, src) + return output + + +@register_lowering(aten.select, type_promotion_kind=None) +def select(x, dim, idx): + idx = View.handle_negative_index(idx, x.get_size()[dim]) + return squeeze(slice_(x, dim, idx, idx + 1), dim) + + +@register_lowering(aten.split, type_promotion_kind=None) +def split(x, sizes, dim=0, clamp=True): + dim = _validate_dim(x, dim, 0) + if isinstance(sizes, sympy.Expr): + # TODO: We don't have to guard on sizes per se, but the number + # of splits must stay constant + sizes = V.graph.sizevars.evaluate_static_shape(sizes) + if isinstance(sizes, (int, sympy.Integer)): + x_size = V.graph.sizevars.evaluate_static_shape(x.get_size()[dim]) + sizes = [sizes] * ((x_size + sizes - 1) // sizes) + result = [] + start = 0 + for size in sizes: + end = start + size + result.append(slice_(x, dim, start, end, clamp=clamp)) + start = end + return result + + +@register_lowering(aten.split_with_sizes, type_promotion_kind=None) +def split_with_sizes(x, sizes, dim=0): + return split(x, sizes, dim, clamp=False) + + +@register_lowering(aten.unbind, type_promotion_kind=None) +def unbind(x, dim=0): + dim = _validate_dim(x, dim, 0) + x_size = V.graph.sizevars.evaluate_static_shape(x.get_size()[dim]) + result = [] + for i in range(x_size): + result.append(select(x, dim, i)) + return result + + +@register_lowering(aten.unfold, type_promotion_kind=None) +def unfold(x, dimension, size, step): + sizes = x.get_size() + ndim = len(sizes) + dim = canonicalize_dim(ndim, dimension) + + if ndim == 0: + return slice_(unsqueeze(x, 0), end=size) + + dim_size = sizes[dim] + sizevars = V.graph.sizevars + sizevars.guard_leq(size, dim_size) + sizevars.guard_lt(0, step) # type: ignore[arg-type] + + new_dim_size = FloorDiv(dim_size - size, step) + 1 + if sizevars.size_hint(dim_size) > 0: + x.mark_reuse(sizevars.size_hint(CeilDiv(new_dim_size * size, dim_size))) + + out_size = [*sizes[:dim], new_dim_size, *sizes[dim + 1 :], size] + + def reindexer(idx): + dim_idx = idx[-1] + idx[dim] * step + return (*idx[:dim], dim_idx, *idx[dim + 1 : -1]) + + return TensorBox(ir.GenericView.create(x, out_size, reindexer)) + + +@register_lowering(aten.unsqueeze, type_promotion_kind=None) +def unsqueeze(x, dim): + dim = _validate_dim(x, dim, 1) + new_shape = list(x.get_size()) + new_shape.insert(dim, sympy.Integer(1)) + return view(x, new_shape) + + +@register_lowering(aten.unsqueeze_, type_promotion_kind=None) +def unsqueeze_(x, dim): + val = unsqueeze(x, dim) + assert isinstance(x, TensorBox) + assert isinstance(val, TensorBox) + x.data = val.data + return x + + +def _validate_dim(x, dim, offset=0): + dim = V.graph.sizevars.shape_env.evaluate_expr(sympy.sympify(dim)) + ndim = len(x.get_size()) + if dim < 0: + dim += ndim + offset + assert 0 <= dim < ndim + offset + return dim + + +@register_lowering(aten.glu) +def glu(x, dim=-1): + dim = _validate_dim(x, dim, 0) + # TODO: don't guard on static shape here + new_len = V.graph.sizevars.evaluate_static_shape(x.get_size()[dim]) // 2 + a = slice_(x, dim, 0, new_len) + b = slice_(x, dim, new_len, new_len * 2) + return mul(a, sigmoid(b)) + + +def fallback_handler(kernel, add_to_fallback_set=True): + if add_to_fallback_set: + fallbacks.add(kernel) + + def handler(*args, **kwargs): + def wrap_tensors(x): + return TensorBox.create(x) if isinstance(x, ir.IRNode) else x + + return pytree.tree_map( + wrap_tensors, ir.FallbackKernel.create(kernel, *args, **kwargs) + ) + + return handler + + +@functools.lru_cache(None) +def _warn_complex_not_supported(): + warnings.warn( + "Torchinductor does not support code generation for complex operators. Performance may be worse than eager." + ) + + +# There are some types (CPU) which we accept as input but not as +# output. +def unsupported_input_tensor(t: torch.Tensor, parent=None): + "Do not support reading or writing to this tensor" + if t.is_complex(): + # Complex views are supported with IR ComplexView + if parent and parent.target in ( + torch.ops.aten.view.dtype, + torch.ops.prims.convert_element_type.default, + ): + return False + _warn_complex_not_supported() + return True + return False + + +def unsupported_output_tensor(t: torch.Tensor, parent=None): + "Do not support writing tensor but can read from it" + if unsupported_input_tensor(t, parent): + return True + return t.is_cpu and config.disable_cpp_codegen + + +def fallback_node_due_to_unsupported_type(node: torch.fx.Node, allow_cpu_inputs=True): + # Custom fallback lowering + if node.target is aten.view_as_complex.default: + return False + + # We should be able to remove this special case once `disable_cpp_codegen` is killed. + if node.target is aten.lift_fresh_copy.default: + return False + + def check_skip_condition(node, parent, is_output): + if not isinstance(node, torch.fx.Node): + return False + + if "val" not in node.meta: + return False + + for meta in pytree.tree_leaves(node.meta["val"]): + if not isinstance(meta, torch._subclasses.FakeTensor): + continue + + if is_output: + if unsupported_output_tensor(meta, parent): + return True + else: + if unsupported_input_tensor(meta, parent): + return True + + return False + + # only skip codegen if there is a cpu output, not input + for arg in pytree.arg_tree_leaves(*node.args, **node.kwargs): + if check_skip_condition(arg, node, is_output=False): + return True + + return check_skip_condition(node, node, is_output=True) + + +def make_fallback(op, layout_constraint=None, warn=True): + assert op not in decompositions, f"both a fallback and a decomp for same op: {op}" + if ( + warn + and bool(os.getenv("CI")) + and get_decompositions([op]) + # if fallback_random, we allow not decomposing random + and not ( + config.fallback_random + and op in torch._decomp.decompositions_for_rng.extra_random_decomps + ) + ): + # Note: 'warn' is holdover from when this was a warning, but for ops that previously + # set warn=False we do not want a CI error. + # Ignore the 'suppress errors' configs in CI, as this particular warning happens on startup anyway and is not + # likely to be triggered preferentially on one CI config over another. + if torch._dynamo.config.suppress_errors: + torch._dynamo.config.suppress_errors = False + log.warning( + "A make_fallback error occurred in suppress_errors config," + " and suppress_errors is being disabled to surface it." + ) + raise AssertionError( + f"make_fallback({op}): a decomposition exists, we should switch to it." + " To fix this error, either add a decomposition to core_aten_decompositions (preferred)" + " or inductor_decompositions, and delete the corresponding `make_fallback` line." + " Get help from the inductor team if unsure, don't pick arbitrarily to unblock yourself.", + ) + + def register_fallback(op_overload): + add_needs_realized_inputs(op_overload) + if layout_constraint is not None: + add_layout_constraint(op_overload, layout_constraint) + return register_lowering(op_overload, type_promotion_kind=None)( + fallback_handler(op_overload) + ) + + if isinstance(op, torch._ops.OpOverloadPacket): + for ol in op.overloads(): + op_overload = getattr(op, ol) + register_fallback(op_overload) + elif isinstance(op, (torch._ops.OpOverload, torch._ops.HigherOrderOperator)): + register_fallback(op) + else: + raise RuntimeError(f"Unsupported fallback {op} with type {type(op)}") + + +def philox_rand_offset(shape): + """ + TorchInductor offset calculation differs from PyTorch eager offset + calculation for random ops (tl.rand vs torch.rand). In future, we should + strive for same impl for tl.rand and torch.rand. + """ + numel = 1 + for s in shape: + numel = numel * s + return tensor(numel, dtype=torch.int64) + + +@register_lowering(torch.ops.rngprims.philox_rand, type_promotion_kind=None) +def philox_rand(size, seed, offset, stride, device, dtype): + # stride arg is optional and will be used in future for distributed random + # ops. Currently, its unused. + random_pos = ir.FixedLayout( + device, + dtype, + size, + ir.FlexibleLayout.contiguous_strides(size), + ).make_indexer() + seed_loader = seed.make_loader() + offset_loader = offset.make_loader() + + def inner_fn(index): + # Both seed and offset in the philox_rand op are tensors. + # torch seed and offsets are of type int64, but tl.rand accepts int32 + seed_index_expr = ops.to_dtype(seed_loader([]), torch.int32) + offset_index_expr = ops.to_dtype(offset_loader([]), torch.int32) + # Get the offset'd position + rand_index_expr = ops.add( + ops.index_expr(random_pos(index), torch.int32), offset_index_expr + ) + result = ops.rand( + seed_index_expr, + rand_index_expr, + ) + return ops.to_dtype(result, dtype) + + random_values_node = Pointwise.create( + device=device, + dtype=dtype, + inner_fn=inner_fn, + ranges=list(size), + ) + + offset_node = philox_rand_offset(size) + return random_values_node, offset_node + + +@register_lowering(aten.native_dropout, type_promotion_kind=None) +def native_dropout(x, p, train): + if config.fallback_random: + return pytree.tree_map( + TensorBox.create, + ir.FallbackKernel.create(aten.native_dropout.default, x, p, train), + ) + else: + raise AssertionError("should be handled in replace_random.py") + + +@register_lowering(aten.bernoulli_, type_promotion_kind=None) +def bernoulli_(x, *args): + assert config.fallback_random or x.get_device() == torch.device( + "cpu" + ), "this should be handled in decomps unless config.fallback_random or the device is CPU" + x.realize() + op_overload = ( + aten.bernoulli_.float + if len(args) == 0 or isinstance(args[0], float) + else aten.bernoulli_.Tensor + ) + ir.InplaceBernoulliFallback(op_overload, x, *args) + return x + + +@register_lowering(aten.bernoulli.p, type_promotion_kind=None) +def bernoulli_p(x, *args): + assert config.fallback_random or x.get_device() == torch.device( + "cpu" + ), "this should be handled in decomps unless config.fallback_random or the device is CPU" + return bernoulli_(clone(x), *args) + + +# This shouldn't be called in general +@register_lowering(aten._foobar) +def _foobar(_): + raise AssertionError + + +@functools.lru_cache(1) +def _warn_triton_random(salt): + log.info("using triton random, expect difference from eager") + + +def warn_triton_random(): + # only warn once per graph + _warn_triton_random(V.graph.creation_time) + + +fallback_rand_default = fallback_handler(aten.rand.default) +fallback_rand_generator = fallback_handler(aten.rand.generator) +fallback_randn_default = fallback_handler(aten.randn.default) +fallback_randn_generator = fallback_handler(aten.randn.generator) +make_fallback(aten.randint) + + +@register_lowering(aten.rand) +def rand(*args, **kwargs): + if kwargs.get("generator", None) is not None: + return fallback_rand_generator(*args, **kwargs) + elif config.fallback_random: + kwargs.pop("generator", None) + return fallback_rand_default(*args, **kwargs) + raise AssertionError("should have been handled in replace_random.py") + + +@register_lowering(aten.randn) +def randn(*args, **kwargs): + if kwargs.get("generator", None) is not None: + return fallback_randn_generator(*args, **kwargs) + elif config.fallback_random: + kwargs.pop("generator", None) + return fallback_randn_default(*args, **kwargs) + raise AssertionError("should have been handled in replace_random.py") + + +@register_lowering(inductor_prims.force_stride_order, type_promotion_kind=None) +def inductor_force_stride_order(input_tensor, stride): + stride_order = ir.get_stride_order(stride) + return ir.ExternKernel.require_stride_order(input_tensor, stride_order) + + +@register_lowering(inductor_prims.seed, type_promotion_kind=None) +def inductor_seed(device: torch.device): + raise AssertionError("should be handled in fuse_seed_creation_pass()") + + +@register_lowering(inductor_prims.seeds, type_promotion_kind=None) +def inductor_seeds(count, device): + warn_triton_random() + return TensorBox.create(ir.RandomSeeds(count, decode_device(device))) + + +@register_lowering(inductor_prims.lookup_seed, type_promotion_kind=None) +def inductor_lookup_seed(seeds, index): + def inner_fn(_): + return ops.load_seed(seeds.get_name(), index) + + return Pointwise.create( + device=seeds.get_device(), + dtype=seeds.get_dtype(), + inner_fn=inner_fn, + ranges=[], + ) + + +@register_lowering(inductor_prims.random, type_promotion_kind=None) +def inductor_random(size: List[int], seed: TensorBox, mode: str, *, offset: int = 0): + assert not config.fallback_random + assert mode in ("rand", "randn") + size = [*size] + dtype = torch.float32 + device = seed.get_device() + random_pos = ir.FixedLayout( + device, dtype, size, ir.FlexibleLayout.contiguous_strides(size), offset=offset + ).make_indexer() + seed_loader = seed.make_loader() + + def inner_fn(index): + return getattr(ops, mode)( + seed_loader([]), + ops.index_expr(random_pos(index), torch.int32), + ) + + result = Pointwise.create( + device=device, + dtype=dtype, + inner_fn=inner_fn, + ranges=[*size], + ) + result.realize() + return result + + +@register_lowering(inductor_prims.randint, type_promotion_kind=None) +def inductor_randint( + low: int, high: int, size: List[int], seed: TensorBox, *, offset: int = 0 +): + assert not config.fallback_random + size = [*size] + dtype = torch.int64 + device = seed.get_device() + random_pos = ir.FixedLayout( + device, dtype, size, ir.FlexibleLayout.contiguous_strides(size), offset=offset + ).make_indexer() + seed_loader = seed.make_loader() + + def inner_fn(index): + return ops.randint64( + seed_loader([]), + ops.index_expr(random_pos(index), torch.int32), + ops.index_expr(low, torch.int64), + ops.index_expr(high, torch.int64), + ) + + return Pointwise.create( + device=device, + dtype=dtype, + inner_fn=inner_fn, + ranges=[*size], + ) + + +@register_lowering(aten.bucketize, type_promotion_kind=None) +def bucketize( + input: TensorBox, + boundaries: TensorBox, + *, + out_int32: bool = False, + right: bool = False, +): + assert len(boundaries.get_size()) == 1 + + if not ( + V.graph.has_feature(input, BackendFeature.BUCKETIZE) + and V.graph.has_feature(boundaries, BackendFeature.BUCKETIZE) + ): + return fallback_handler(aten.bucketize.Tensor, add_to_fallback_set=False)( + input, boundaries, out_int32=out_int32, right=right + ) + + # The entire boundaries tensor needs to be used by ops.bucketize, so we + # need to realize it into global memory; or in other words, we can't + # guarantee that boundaries.get_name() (used below) will exist unless + # we call boundaries.realize(). + boundaries.realize() + boundaries_size = boundaries.get_size()[0] + device = input.get_device() + input_loader = input.make_loader() + + index_dtype = torch.int32 if out_int32 else torch.int64 + + def inner_fn(index): + val = input_loader(index) + indices = ops.bucketize( + val, + boundaries.get_name(), + boundaries_size, + index_dtype, + right, + ) + + return indices + + return Pointwise.create( + device=device, + dtype=index_dtype, + inner_fn=inner_fn, + ranges=input.get_size(), + ) + + +def require_dense(_, *args, **kwargs): + args, kwargs = pytree.tree_map_only( + ir.IRNode, ir.ExternKernel.require_stride1, (args, kwargs) + ) + return args, kwargs + + +def require_contiguous(_, *args, **kwargs): + args, kwargs = pytree.tree_map_only( + ir.IRNode, ir.ExternKernel.require_contiguous, (args, kwargs) + ) + return args, kwargs + + +def require_channels_last(_, *args, **kwargs): + args, kwargs = pytree.tree_map_only( + ir.IRNode, ir.ExternKernel.require_channels_last, (args, kwargs) + ) + return args, kwargs + + +def constrain_to_fx_strides(fx_node, *args, **kwargs): + def apply_constraint(arg, fx_arg): + if isinstance(arg, ir.IRNode): + stride_order = ir.get_stride_order(fx_arg.meta["val"].stride()) + return ir.ExternKernel.require_stride_order(arg, stride_order) + return arg + + args = tuple( + apply_constraint(arg, fx_arg) for arg, fx_arg in zip(args, fx_node.args) + ) + kwargs = {k: apply_constraint(v, fx_node.kwargs[k]) for k, v in kwargs.items()} + return args, kwargs + + +# TODO(jansel): we should implement decomps or lowerings for these +# https://github.com/pytorch/torchdynamo/issues/327 +FALLBACK_ALLOW_LIST = { + "torchvision::roi_align", +} + + +def sdpa_constraint(fx_node, *args, **kwargs): + # sdpa requires dense last dimension] + + def apply_constraint(arg, fx_arg): + if not isinstance(arg, ir.IRNode): + return arg + + meta_val = fx_arg.meta["val"] + meta_stride = meta_val.stride() + + stride_order = ir.get_stride_order(meta_stride) + if stride_order and stride_order[-1] != 0: + # contiguous stride order + stride_order = list(reversed(range(len(arg.get_size())))) + + if not meta_val.is_cuda: + return ir.ExternKernel.require_stride_order(arg, stride_order) + + # This is the minimum alignment required by SDPA kernels for attention_bias. + # This value can be found in pytorch/aten/src/ATen/native/transformers/attention.cpp preprocess_mask + ALIGNMENT = 8 + + assert isinstance(arg, TensorBox) + if len(arg.get_size()) not in (3, 4): + return arg + + def is_aligned_realized_tensor(x): + aligned_strides = all( + (V.graph.sizevars.size_hint(x.get_stride()[i]) % ALIGNMENT) == 0 + for i in range(len(x.get_stride()) - 1) + ) + return ( + V.graph.sizevars.size_hint(x.get_stride()[-1]) + ) == 1 and aligned_strides + + try: + arg.get_stride() + if is_aligned_realized_tensor(arg): + return V.graph.try_match_insignificant_strides( + ir.ExternKernel.realize_input(arg), meta_stride + ) + except AttributeError: + pass + + def is_aligned(x): + return (V.graph.sizevars.size_hint(x.get_size()[-1]) % ALIGNMENT) == 0 + + if isinstance(arg.data, ir.BaseView): + if not is_aligned(arg): + if is_aligned(arg.unwrap_view()): + return V.graph.try_match_insignificant_strides( + ir.ExternKernel.realize_input(arg), meta_stride + ) + + return ir.ExternKernel.require_stride_order(arg, stride_order) + + args = tuple( + apply_constraint(arg, fx_arg) for arg, fx_arg in zip(args, fx_node.args) + ) + kwargs = {k: apply_constraint(v, fx_node.kwargs[k]) for k, v in kwargs.items()} + return args, kwargs + + +# WIP +make_fallback(aten._adaptive_avg_pool3d) # @isuruf +make_fallback(aten.adaptive_max_pool3d) # @isuruf +make_fallback(aten.fractional_max_pool3d) # @isuruf +make_fallback(aten.max_pool3d_with_indices) # @isuruf (can this one be implemented?) + + +# 1) Easy +make_fallback(aten.uniform, warn=False) +make_fallback(aten.exponential.default, warn=False) # (fails accuracy on test_torch.py) +make_fallback(aten._pdist_forward) # Has decomp. Needs benchmarks +make_fallback(aten.soft_margin_loss_backward, warn=False) # py_impl? +make_fallback(aten.searchsorted) # bucketized is implemented (see eager impl) + + +# 1.5) Easy or Impossible +make_fallback(aten._cdist_forward) # p=2 should be feasible +make_fallback(aten._cdist_backward) + +# 2) Medium +make_fallback(aten.max_unpool2d) +make_fallback(aten.max_unpool3d) +make_fallback(aten._trilinear) + + +# 3) Difficult +# Scans +# See the discussion at +# https://dev-discuss.pytorch.org/t/pytorch-sparse-gnn-compiler-rfc/1644/19 +make_fallback(aten.segment_reduce.default) +make_fallback(aten._segment_reduce_backward.default) + +# Histogram (need to implement Histogram IR) +make_fallback(aten.histc) +make_fallback(aten.histogram.bin_ct) +make_fallback(aten._histogramdd_bin_edges.default) +make_fallback(aten._histogramdd_from_bin_cts.default) + +# Need templated kernel +make_fallback(aten.addbmm) +make_fallback(aten._addmm_activation, warn=False) + +# Need templated kernel. Probably impossible to write efficiently +make_fallback(aten.convolution_backward, constrain_to_fx_strides) +make_fallback(aten._cudnn_rnn, require_dense) +make_fallback(aten._cudnn_rnn_backward, require_contiguous) + +# Haven't checked but sound difficult / impossible +make_fallback(aten._embedding_bag, require_contiguous) +make_fallback(aten._embedding_bag_forward_only, require_contiguous) +make_fallback(aten._embedding_bag_backward) +make_fallback(aten._embedding_bag_per_sample_weights_backward) +make_fallback(aten._embedding_bag_per_sample_weights_backward) +make_fallback(aten._fused_moving_avg_obs_fq_helper) +make_fallback(aten._fused_moving_avg_obs_fq_helper_functional) + + +# 4) Backwards (try py_impl'ing them) when fwd is written as a decomp +make_fallback(aten.max_pool3d_with_indices_backward) +make_fallback(aten._adaptive_avg_pool2d_backward, require_dense) +make_fallback(aten._adaptive_avg_pool3d_backward) +make_fallback(aten.adaptive_max_pool2d_backward) +make_fallback(aten.adaptive_max_pool3d_backward) +make_fallback(aten.fractional_max_pool2d_backward) +make_fallback(aten.fractional_max_pool3d_backward) +make_fallback(aten.replication_pad1d_backward) +make_fallback(aten.replication_pad2d_backward) +make_fallback(aten.upsample_linear1d_backward) +make_fallback(aten.upsample_bicubic2d_backward, require_contiguous) +make_fallback(aten.upsample_trilinear3d_backward) +make_fallback(aten.grid_sampler_2d_backward, require_dense) +make_fallback(aten._pdist_backward) + + +# 5) Impossible (missing triton/CPU features) + +# Sorting / Sorting-like +make_fallback(aten.sort) +make_fallback(aten.sort.stable) +make_fallback(aten.kthvalue) +make_fallback(aten.topk) +make_fallback(aten.mode) +make_fallback(aten.median) +make_fallback(aten.nanmedian) +make_fallback(aten.randperm) +# see: https://github.com/pytorch/pytorch/pull/121354 +make_fallback(aten.resize_) +make_fallback(aten.resize_as_) + +# Linalg +make_fallback(aten._linalg_det) +make_fallback(aten.linalg_householder_product) +make_fallback(aten.linalg_inv_ex) +make_fallback(aten.linalg_ldl_factor_ex) +make_fallback(aten.linalg_ldl_solve) +make_fallback(aten.linalg_lu) +make_fallback(aten.linalg_lu_factor_ex) +make_fallback(aten.linalg_lu_solve) +make_fallback(aten.linalg_matrix_exp) +make_fallback(aten.linalg_qr) +make_fallback(aten._linalg_slogdet) +make_fallback(aten._linalg_solve_ex) +make_fallback(aten.linalg_solve_triangular) +make_fallback(aten._linalg_svd) +make_fallback(aten.lu_unpack) +make_fallback(aten.ormqr) +make_fallback(aten._linalg_check_errors) +make_fallback(aten.linalg_pinv.atol_rtol_tensor) +make_fallback(aten._linalg_eigh) +make_fallback(aten.triangular_solve) +make_fallback(aten.linalg_cholesky_ex) +make_fallback(aten.cholesky_inverse) +make_fallback(aten.cholesky_solve) +make_fallback(aten.geqrf) +make_fallback(aten._fft_r2c) # needs complex as well + +# Data dependent (are these necessary?) +make_fallback(aten.nonzero.default) + +# Misc +make_fallback(aten.gcd.default, warn=False) +make_fallback(aten._thnn_fused_lstm_cell, require_dense) +make_fallback(torch._prims.rng_prims.run_and_save_rng_state) +make_fallback(torch._prims.rng_prims.run_with_rng_state) + +# Implmented / Half implemented +# Scans. Implemented for CUDA, missing CPU +make_fallback(aten.masked_scatter) +make_fallback(aten.masked_scatter_backward) + +# Complex number support +make_fallback(aten.view_as_complex, require_contiguous) +make_fallback(aten.angle) # needs complex + +# Needs efficentzerotensor +make_fallback(aten._efficientzerotensor) + +# Needs Sparse +make_fallback(aten._sparse_coo_tensor_with_dims_and_tensors) +make_fallback(aten.to_sparse) +make_fallback(aten._to_sparse) + +# Needs dimname support +make_fallback(aten.zeros.names) + +# 6) Pattern-matched +make_fallback( + aten._scaled_dot_product_efficient_attention.default, + sdpa_constraint, + warn=False, +) +make_fallback( + aten._scaled_dot_product_efficient_attention_backward.default, + sdpa_constraint, + warn=False, +) +make_fallback( + aten._scaled_dot_product_flash_attention.default, + sdpa_constraint, + warn=False, +) +make_fallback( + aten._scaled_dot_product_flash_attention_backward.default, + sdpa_constraint, + warn=False, +) +make_fallback( + aten._scaled_dot_product_cudnn_attention.default, + sdpa_constraint, + warn=False, +) +make_fallback( + aten._scaled_dot_product_cudnn_attention_backward.default, + sdpa_constraint, + warn=False, +) +make_fallback( + aten._scaled_dot_product_flash_attention_for_cpu.default, + sdpa_constraint, + warn=False, +) +make_fallback( + aten._scaled_dot_product_flash_attention_for_cpu_backward.default, + sdpa_constraint, + warn=False, +) +make_fallback(aten._flash_attention_forward.default, sdpa_constraint) +make_fallback(aten._flash_attention_backward.default, sdpa_constraint) +make_fallback(aten._efficient_attention_forward.default, sdpa_constraint) +make_fallback(aten._efficient_attention_backward.default, sdpa_constraint) + +# index_reduce requires fallback when use_scatter_fallback(...) returns True +make_fallback(aten.index_reduce) + + +# Register with type_promotion_kind None. +# For example, fp16.copy_(fp32) should **not** promote the first input's dtype. +@register_lowering(aten.copy, type_promotion_kind=None) +def copy(self, src, non_blocking=False): + x = src + if self.get_device() != src.get_device(): + x = to_device(x, self.get_device()) + if self.get_dtype() != src.get_dtype(): + x = to_dtype(x, self.get_dtype()) + + if self.get_size() != src.get_size(): + out = expand(x, self.get_size()) + return clone(out) + return clone(x) + + +@register_lowering(aten.clone) +def clone(x, *, memory_format=None): + # TODO(jansel): memory format + return Pointwise.create( + device=x.get_device(), + dtype=x.get_dtype(), + inner_fn=x.make_loader(), + ranges=list(x.get_size()), + ) + + +def clone_preserve_reinterpret_view(x): + reinterpret_view_layouts = [] + if isinstance(x, TensorBox) and isinstance(x.data, ir.ReinterpretView): + x = x.data # unwrap TensorBox + while isinstance(x, ir.ReinterpretView): + reinterpret_view_layouts.append(x.get_layout()) + x = x.data + x = TensorBox(x) + + x = clone(x) + + if reinterpret_view_layouts: + x = x.data # unwrap TensorBox + for layout in reinterpret_view_layouts[::-1]: + x = ir.ReinterpretView(x, layout) + x = TensorBox(x) + + return x + + +if hasattr(aten, "lift_fresh_copy"): + register_lowering(aten.lift_fresh_copy)(clone) + + +@register_lowering(prims.iota) +def iota( + length, + *, + start, + step, + dtype, + device, + requires_grad, +): + def fn(index): + return ops.index_expr(step * index[0] + start, dtype=dtype) + + return Pointwise.create( + device=decode_device(device), + dtype=dtype, + inner_fn=fn, + ranges=[length], + ) + + +@register_lowering(aten.select_scatter, type_promotion_kind=None) +def select_scatter(x, src, dim: int, index: int): + assert x.get_dtype() == src.get_dtype() + x_loader = x.make_loader() + dim = _validate_dim(x, dim, 0) + if V.graph.sizevars.evaluate_expr(sympy.Lt(index, 0)): + index = index + x.get_size()[dim] + V.graph.sizevars.guard_leq(0, index) # type: ignore[arg-type] + V.graph.sizevars.guard_lt(index, x.get_size()[dim]) # type: ignore[arg-type] + src = expand(unsqueeze(src, dim), x.get_size()) + src_loader = src.make_loader() + + def inner_fn(idx): + return ops.where( + ops.eq( + ops.index_expr(idx[dim], torch.int32), + ops.index_expr(index, torch.int32), + ), + src_loader(idx), + x_loader(idx), + ) + + return Pointwise.create( + device=x.get_device(), + dtype=x.get_dtype(), + inner_fn=inner_fn, + ranges=list(x.get_size()), + ) + + +@register_lowering(aten.slice_scatter, type_promotion_kind=None) +def slice_scatter(x, src, dim=0, start=None, end=None, step=1): + assert x.get_dtype() == src.get_dtype() + x_loader = x.make_loader() + dim = _validate_dim(x, dim, 0) + dim_size = x.get_size()[dim] + + start, end = ir.SliceView.normalize_start_end(x, dim, start, end) + + src_size = list(x.get_size()) + src_size[dim] = FloorDiv(end - start + (step - 1), step) + src = expand(src, src_size) + src_loader = src.make_loader() + + def inner_fn(idx): + if start == 0 and end == dim_size and step == 1: + # selecting every element is the same as just src.clone() + return src_loader(idx) + + idx_dim = ops.index_expr(idx[dim], torch.int64) + src_idx = list(idx) + src_idx[dim] = FloorDiv(idx[dim] - start, step) + + mask = [] + if start != 0: + mask.append( + ops.ge( + idx_dim, + ops.index_expr(sympy.expand(start), torch.int64), + ) + ) + if end != dim_size: + mask.append( + ops.lt( + idx_dim, + ops.index_expr(sympy.expand(end), torch.int64), + ) + ) + if step != 1: + mask.append( + ops.eq( + ops.index_expr( + ModularIndexing(idx[dim] - start, 1, step), torch.int64 + ), + ops.constant(0, torch.int64), + ) + ) + assert mask + mask = functools.reduce(ops.and_, mask) + src_val = ops.masked( + mask, + lambda: src_loader(src_idx), + 0 if is_integer_type(x) else 0.0, + ) + return ops.where( + mask, + src_val, + x_loader(idx), + ) + + return Pointwise.create( + device=x.get_device(), + dtype=x.get_dtype(), + inner_fn=inner_fn, + ranges=list(x.get_size()), + ) + + +def _unwrap(x): + if isinstance(x, (list, tuple)) and len(x) > 0: + return _unwrap(x[0]) + return x + + +@register_lowering([torch.tensor, aten.scalar_tensor]) +def tensor(data, *, dtype=None, device=None, layout=None, pin_memory=False): + assert_nyi(layout in (None, torch.strided), f"layout={layout}") + assert_nyi(not pin_memory, "pin_memory") + if isinstance(_unwrap(data), int): + dtype = dtype or torch.int64 + else: + dtype = dtype or torch.get_default_dtype() + + ranges: List[sympy.Expr] = [] + + if isinstance(data, sympy.Basic): + + def inner_fn(index): + return ops.index_expr(data, dtype) + + elif isinstance(data, (float, int)): + + def inner_fn(index): + return ops.constant(data, dtype) + + elif len(data) == 0 or isinstance(data[0], (float, int)) and len(data) <= 8: + # inline small tensors + ranges.append(sympy.Integer(len(data))) + + def inner_fn(index): + def binary_search(start, end): + assert start < end + if end - start == 1: + return ops.constant(data[start], dtype) + mid = (end - start) // 2 + start + return ops.where( + ops.lt( + ops.index_expr(index[0], torch.int64), + ops.constant(mid, torch.int64), + ), + binary_search(start, mid), + binary_search(mid, end), + ) + + if len(data) == 0: + return ops.constant(0, dtype) + return binary_search(0, len(data)) + + else: + return V.graph.add_tensor_constant( + torch.tensor(data, dtype=dtype, device=device) + ) + + return Pointwise.create( + device=decode_device(device), + dtype=dtype, + inner_fn=inner_fn, + ranges=ranges, + ) + + +@register_lowering(torch.as_tensor) +def as_tensor(data, dtype=None, device=None): + if isinstance(data, TensorBox): + if dtype is not None: + data = to_dtype(data, dtype) + if device is not None: + data = to_device(data, device) + return data + return tensor(data, dtype=dtype, device=device) + + +@register_lowering(torch.LongTensor) +def long_tensor(data): + return tensor(data, dtype=torch.int64) + + +@register_lowering(aten._local_scalar_dense) +def _local_scalar_dense(data): + from torch.fx.experimental.symbolic_shapes import resolve_unbacked_bindings + + # This is interesting! Most lowerings return tensors, so you can just + # return the buffer you allocated and it will get used (or not used, if + # it's dead.) But _local_scalar_dense (aka item) returns an int, + # not a Tensor, so you would have a type mismatch if you return a buffer; + # we are obligated to return a sympy expression instead. However, + # we need to actually codegen the .item() call somehow. We do this + # by registering a faux buffer for the DynamicScalar IR node, which is + # solely responsible for generating this .item(). The buffer is + # not used for anything (notice we discard it); at codegen time, + # the "buffer" just gets assigned None. + unbacked_bindings = resolve_unbacked_bindings( + V.graph.sizevars.shape_env, V.graph.current_node.meta["unbacked_bindings"] + ) + assert len(unbacked_bindings) == 1, unbacked_bindings + # NB: Have to be very careful here. V.graph.current_node.meta["val"] + # seemingly also contains a symbol which you want to do binding for, + # but it actually isn't. In particular, if we have later performed + # a deferred runtime assert saying that u0 == s0, you will actually + # see s0 from expr! This is bad because we need to actually generate + # the assert that says u0 == s0, so we need to know where to get u0 + # from (this call). In particular, we must use unbacked_bindings, which + # is guaranteed to have the original, unreplaced symbol in question. + # + # NB2: Another thing we have to be very careful about are symbol bindings + # that require nontrivial refinement, e.g., when you have a binding site + # x: Sym(u0 * 4) = y.item(). Here, the code generation must do a division + # in order to appropriately bind u0. This is communicated via the keypath + # in unbacked_bindings, and we need to hold onto it in order to generate + # code appropriately for this case. + binding_sym, keypath = next(iter(unbacked_bindings.items())) + buffer = ir.DynamicScalar(binding_sym, keypath, data) + buffer.name = V.graph.register_buffer(buffer) + V.graph.register_operation(buffer) + # NB: the replaced expr is OK to use directly downstream, we want + # simplifications in this case! + val = V.graph.current_node.meta["val"] + if isinstance(val, (torch.SymInt, torch.SymFloat, torch.SymBool)): + return val.node.expr + else: + return sympy.sympify(val) + + +@register_lowering(aten._assert_scalar) +def _assert_scalar(data, msg): + # NB: These will be handled at codegen time + # Not sure if we are guaranteed to be able to serve out truth from the + # deferred_runtime_asserts, TODO: try this assert out + # assert bool(data.scalar), data + return None + + +def _full(fill_value, device, dtype, size): + value = fill_value + if not isinstance(fill_value, (int, float)) and hasattr(value, "value"): + value = value.value + + if isinstance(value, (int, float)): + + def inner_fn(index): + return ops.constant(value, dtype) + + elif isinstance(value, sympy.Basic): + + def inner_fn(index): + return ops.index_expr(value, dtype) + + else: + assert len(value.get_size()) == 0 + value_loader = value.make_loader() + + def inner_fn(index): + return value_loader([]) + + return Pointwise.create( + device=device, + dtype=dtype, + inner_fn=inner_fn, + ranges=list(size), + ) + + +@register_lowering(aten.full_like, type_promotion_kind=None) +def full_like(x, fill_value, **kwargs): + return create_tensor_like(tensor_constructor(fill_value))(x, **kwargs) + + +def tensor_constructor(fill_value): + # torch.zeros, torch.ones, etc + def inner( + *size, + names=None, + dtype=None, + device=None, + layout=None, + pin_memory=False, + memory_format=None, + ): + assert_nyi(names is None, "named tensors") + assert_nyi(layout in (None, torch.strided), f"layout={layout}") + assert_nyi(not pin_memory, "pin_memory") + device = decode_device(device) + dtype = dtype or torch.get_default_dtype() + if len(size) == 1 and isinstance(size[0], (list, tuple, torch.Size)): + size = tuple(size[0]) + # See https://github.com/pytorch/pytorch/issues/118102 + # All sizes at lowering time should be sympy.Symbol, not SymInt! + for s in size: + assert not isinstance(s, torch.SymInt) + size = [sympy.expand(s) for s in size] + return _full(fill_value, device, dtype, size) + + return inner + + +@register_lowering([torch.empty, aten.empty]) +def empty( + *size, + names=None, + dtype=None, + layout=None, + device=None, + pin_memory=None, + memory_format=None, +): + assert_nyi(names is None, "named tensors") + device = decode_device(device) + if len(size) == 1 and isinstance(size[0], (list, tuple, torch.Size)): + size = tuple(size[0]) + return empty_strided( + size, None, dtype=dtype, layout=layout, device=device, pin_memory=pin_memory + ) + + +def create_tensor_like(creation_fn): + """ + Shim to convert X_like(...) into X(...). For example zeros_like() into zeros(). + """ + + def _constant_like( + x, *, dtype=None, device=None, layout=None, pin_memory=False, memory_format=None + ): + assert_nyi(not pin_memory, "pin_memory") + assert_nyi(layout in (None, torch.strided), f"layout={layout}") + if dtype is None: + dtype = x.get_dtype() + else: + dtype = decode_dtype(dtype) + device = device or x.get_device() + size = list(x.get_size()) + return creation_fn( + size, dtype=dtype, device=device, layout=layout, pin_memory=pin_memory + ) + + return _constant_like + + +def constant_like(fill_value): + return create_tensor_like(tensor_constructor(fill_value)) + + +empty_like = register_lowering(aten.empty_like)(create_tensor_like(empty)) +ones_like = create_tensor_like(tensor_constructor(1)) +zeros_like = create_tensor_like(tensor_constructor(0)) + + +def new_constant(fill_value): + def _new_constant( + x, size, *, dtype=None, layout=None, device=None, pin_memory=None + ): + assert isinstance(size, (list, tuple)) + assert_nyi(not pin_memory, "pin_memory") + assert_nyi(layout in (None, torch.strided), f"layout={layout}") + dtype = decode_dtype(dtype) or x.get_dtype() + device = device or x.get_device() + size = [sympy.Integer(s) for s in size] + return _full(fill_value, device, dtype, size) + + return _new_constant + + +@register_lowering(aten.new_empty) +def new_empty(x, size, *, dtype=None, layout=None, device=None, pin_memory=None): + if dtype is None: + dtype = x.get_dtype() + if device is None: + device = x.get_device() + return empty_strided( + size, None, dtype=dtype, layout=layout, device=device, pin_memory=pin_memory + ) + + +@register_lowering(aten.empty_strided) +def empty_strided( + size, stride, *, dtype=None, layout=None, device=None, pin_memory=None +): + assert isinstance(size, (list, tuple)) + assert isinstance(stride, (list, tuple, type(None))) + assert_nyi(not pin_memory, "pin_memory") + assert_nyi(layout in (None, torch.strided), f"layout={layout}") + dtype = decode_dtype(dtype) or torch.get_default_dtype() + device = device or torch.tensor(0.0).device + pointwise = _full(fill_value=0, device=device, dtype=dtype, size=size) + pointwise.realize() + buffer = pointwise.data.data + # explicitly set ranges to zeros in order to make a NopKernelSchedulerNode + buffer.data.ranges = [0] * len(size) + assert isinstance(buffer, ir.ComputedBuffer) + size = [sympy.expand(s) for s in size] + stride = ( + [sympy.expand(s) for s in stride] + if stride + else ir.FlexibleLayout.contiguous_strides(size) + ) + buffer.layout = ir.FixedLayout( + device=device, + dtype=dtype, + size=size, + stride=stride, + ) + return pointwise + + +@register_lowering(aten.new_empty_strided) +def new_empty_strided( + x, size, stride, *, dtype=None, layout=None, device=None, pin_memory=None +): + if dtype is None: + dtype = x.get_dtype() + if device is None: + device = x.get_device() + return empty_strided( + size, stride, dtype=dtype, layout=layout, device=device, pin_memory=pin_memory + ) + + +@register_lowering(prims.copy_strided.default) +def copy_strided(x, stride): + stride = [V.graph.sizevars.size_hint(s) for s in stride] + stride_order = sorted(range(len(stride)), key=stride.__getitem__) + return ir.ExternKernel.require_stride_order(x, stride_order) + + +@register_lowering([torch.full, aten.full]) +def full(size, fill_value, **kwargs): + assert kwargs.get("dtype") is not None, "dtype should be handled by decomposition" + return tensor_constructor(fill_value)(size, **kwargs) + + +@register_lowering(aten.gather, type_promotion_kind=None) +def gather(x, dim, index, sparse_grad=False): + # sparse_grad doesn't affect forward computation, + # and backward tracing is taken care of by AOT Autograd + assert isinstance(x, TensorBox) + if index.get_numel() == 0: + # Empty index case. Return an empty array with the same shape + return new_empty(x, index.get_size()) + + assert index.get_dtype() == torch.int64 + size = x.get_size() + offset = len(size) == 0 + dim = _validate_dim(x, dim, offset) + + if offset: + x = expand(x, [1]) + size = [1] + + x_loader = x.make_loader() + index_loader = index.make_loader() + + def fn(idx): + idx = list(idx) + gather_idx = ops.indirect_indexing(index_loader(idx), size[dim]) + if len(idx) == 0: + idx = [gather_idx] + else: + idx[dim] = gather_idx + return x_loader(idx) + + return Pointwise.create( + device=x.get_device(), + dtype=x.get_dtype(), + inner_fn=fn, + ranges=index.get_size(), + ) + + +@register_lowering(aten.embedding, type_promotion_kind=None) +def embedding(weight, indices, padding_idx=-1, scale_grad_by_freq=False, sparse=False): + assert not sparse + assert isinstance(weight, TensorBox) + assert isinstance(indices, TensorBox) + assert "int" in str(indices.get_dtype()) + + weight_loader = weight.make_loader() + indices_loader = indices.make_loader() + indices_ndim = len(indices.get_size()) + weight_size = weight.get_size() + new_size = [*indices.get_size(), *weight_size[1:]] + + def fn(idx): + assert len(idx) == len(new_size), f"{idx} != {new_size}" + var_index = indices_loader(idx[:indices_ndim]) + weight_idx = [ops.indirect_indexing(var_index, weight_size[0])] + [ + *idx[indices_ndim:] + ] + return weight_loader(weight_idx) + + return Pointwise.create( + device=weight.get_device(), + dtype=weight.get_dtype(), + inner_fn=fn, + ranges=new_size, + ) + + +def check_and_broadcast_indices(indices, device): + assert all( + i.get_dtype() in (torch.int64, torch.int32, torch.bool, torch.uint8) + for i in indices + if i is not None + ), f"indices must be int64, byte or bool. Got {[i.get_dtype() for i in indices if i is not None]}" + if any( + i.get_dtype() in (torch.bool, torch.uint8) for i in indices if i is not None + ): + raise NotImplementedError("Fallback for bool indices") + + valid_idxs = [i for i, x in enumerate(indices) if isinstance(x, TensorBox)] + assert len(valid_idxs) > 0, "requires at least 1 non-None index" + new_indices = [None] * len(indices) + for i, x in zip(valid_idxs, broadcast_tensors(*[indices[i] for i in valid_idxs])): + # Eager allows indices to be CPU tensor when running on CUDA + # FIXME: Calling to_device(x, device) should work but + # test_advancedindex_mixed_cpu_devices still fails + if x.get_device() != device: + raise NotImplementedError("Fallback when indices is on a different device") + new_indices[i] = x + return new_indices, valid_idxs + + +def index_output_size_and_inner_fn( + x_size, + indices, + tensor_indices, + tensor_size, + indices_loaders, + indexed_size, + x_loader, + check, +): + # Note that behavior of indexing differs when there are non consecutive + # tensors. In this case, the tensor index is pulled to the beginning. + # + # Suppose a = torch.arange(3 * 4 * 5 * 6 * 7).view(3, 4, 5, 6, 7) + # x = torch.tensor[1,2] + # Then, a[:,x,:,x,:] will have shape 2,3,5,7 as due to x,:,x then 2 will + # be pulled to the front. + non_consecutive_tensors = False + for previous, current in zip(tensor_indices, tensor_indices[1:]): + if current - previous != 1: + non_consecutive_tensors = True + + output_size = [x_size[i] for i, val in enumerate(indices) if val is None] + output_size = [*output_size, *x_size[len(output_size) + len(tensor_indices) :]] + + first_tensor_index = tensor_indices[0] + if non_consecutive_tensors: + output_size = tensor_size + output_size + else: + output_size = ( + output_size[:first_tensor_index] + + tensor_size + + output_size[first_tensor_index:] + ) + + def fn(idx): + assert len(idx) == len(output_size) + assert len(indices_loaders) == len(indexed_size) + + rank = len(tensor_size) + new_index = [] + first_tensor_index = tensor_indices[0] + start_offset = 0 if non_consecutive_tensors else first_tensor_index + next_idx = 0 + for i in range(tensor_indices[-1] + 1): + if i == start_offset: + next_idx += rank + if indices[i] is None: + assert next_idx < len(idx) + new_index.append(idx[next_idx]) + next_idx += 1 + else: + loader = indices_loaders[i] + assert loader is not None + size = indexed_size[i] + new_index.append( + ops.indirect_indexing( + loader(idx[start_offset : start_offset + rank]), + size, + check=check, + ) + ) + new_index = [ + *new_index, + *idx[next_idx:], + ] + return new_index if x_loader is None else x_loader(new_index) + + return output_size, fn + + +def index_impl(x, indices, check): + output_size, inner_fn, _ = index_impl_helper(x, indices, check) + + return Pointwise.create( + device=x.get_device(), + dtype=x.get_dtype(), + inner_fn=inner_fn, + ranges=output_size, + ) + + +def index_impl_helper(x, indices, check): + assert isinstance(indices, (list, tuple)) + x_loader = x.make_loader() + indices, tensor_indices = check_and_broadcast_indices(indices, x.get_device()) + assert len(tensor_indices) > 0, "Must have at least one valid idx" + + indices_loaders = [i.make_loader() if i is not None else None for i in indices] + # no guards on output size, all the guards are set in broadcast_tensors + + # We can use the first one since they are all required to be the same size + tensor_size = list(indices[tensor_indices[0]].get_size()) + + x_size = x.get_size() + + indexed_size = [x_size[i] for i in range(len(indices)) if indices[i] is not None] + if check and 0 in indexed_size and 0 not in tensor_size: + raise IndexError("index is out of bounds for dimension with size 0") + + indexed_size = [x_size[i] for i in range(len(indices))] + output_size, index_inner_fn = index_output_size_and_inner_fn( + x_size, + indices, + tensor_indices, + tensor_size, + indices_loaders, + indexed_size, + None, + check=check, + ) + + def inner_fn(idx): + return x_loader(index_inner_fn(idx)) + + return output_size, inner_fn, index_inner_fn + + +@register_lowering(aten.index, type_promotion_kind=None) +def index(x, indices): + try: + return index_impl(x, indices, check=True) + except NotImplementedError: + # Fallback to ATen for boolean indexing + x.realize() + return fallback_handler(aten.index.Tensor, add_to_fallback_set=False)( + x, indices + ) + + +@register_lowering(aten._unsafe_index, type_promotion_kind=None) +def _unsafe_index(x, indices): + return index_impl(x, indices, check=False) + + +# All the indexing decompositions are written in terms of index, index_put, and index_put_ +# We cannot have this lowering as a decomposition as it introduces +# mutation in the graph, which is bad for Aot Autograd. Aot Autograd runs dead +# code elimination and common subexpression elimination optimizations, which +# assume graphs to be side-effect free. More details at +# https://github.com/pytorch/torchdynamo/issues/1235 +# and +# https://github.com/pytorch/torchdynamo/issues/1863 +@register_lowering(aten.index_put) +def index_put(x, indices, values, accumulate=False): + return index_put_(clone(x), indices, values, accumulate) + + +@register_lowering(aten._unsafe_index_put) +def _unsafe_index_put(x, indices, values, accumulate=False): + return index_put_impl_(clone(x), indices, values, accumulate, check=False) + + +def index_put_as_masked_fill(self, indices, value, accumulate): + if value.get_device() != self.get_device(): + value = to_device(value, self.get_device()) + if accumulate: + value = add(self, value) + return mutate_to(self, where(indices[0], value, self)) + + +def index_put_fallback(self, indices, values, accumulate): + deterministic = torch.are_deterministic_algorithms_enabled() + if is_triton(values) and (accumulate or deterministic): + msg = ( + "index put with accumulate." + if not deterministic + else "deterministic index put." + ) + if stack_trace := V.graph.current_node.meta.get("stack_trace", None): + msg = f"{msg} Found from : \n {stack_trace}" + V.graph.disable_cudagraphs_reason = msg + + ir.IndexPutFallback(V.graph.current_node.target, self, indices, values, accumulate) + return self + + +@register_lowering(aten.index_put_, type_promotion_kind=None) +def index_put_(self, indices, values, accumulate=False): + return index_put_impl_(self, indices, values, accumulate, check=True) + + +@register_lowering(inductor_prims._unsafe_index_put_, type_promotion_kind=None) +def _unsafe_index_put_(self, indices, values, accumulate=False): + return index_put_impl_(self, indices, values, accumulate, check=False) + + +def index_put_impl_(self, indices, values, accumulate, check): + # Dispatch to masked fill for single boolean index with single value + if ( + values.get_numel() == 1 + and len(indices) == 1 + and indices[0].get_dtype() in {torch.bool, torch.uint8} + ): + mask = indices[0] + for _ in range(len(mask.get_size()), len(self.get_size())): + mask = unsqueeze(mask, -1) + return index_put_as_masked_fill(self, [mask], values, accumulate) + + # Fallback in torch deterministic mode + if torch.are_deterministic_algorithms_enabled(): + return index_put_fallback(self, indices, values, accumulate) + + # Fallback if there is a boolean index + for index in indices: + if index is not None and index.get_dtype() in {torch.bool, torch.uint8}: + return index_put_fallback(self, indices, values, accumulate) + + x_size = self.get_size() + x_ndim = len(x_size) + + if accumulate and needs_fallback_due_to_atomic_add_limitations(self.get_dtype()): + # self is an scalar Tensor + if x_ndim == 0: + self = view(self, [1]) + self = index_put_fallback(self, indices, values, accumulate) + if x_ndim == 0: + self = view(self, []) + return self + + values = to_dtype(values, self.get_dtype()) + + try: + # Note that code will only get here when dtype is uint32 + indices, tensor_indices = check_and_broadcast_indices( + indices, self.get_device() + ) + except NotImplementedError: + return index_put_fallback(self, indices, values, accumulate) + + indices_loaders = [i.make_loader() if i is not None else None for i in indices] + + assert isinstance(self, TensorBox) + self.realize() + + # self is an scalar Tensor + if x_ndim == 0: + self = view(self, [1]) + + # We can use the first one since they are all required to be the same size + tensor_size = list(indices[tensor_indices[0]].get_size()) + indexed_size = [x_size[i] for i in range(len(indices))] + + expected_vals_size, inner_fn = index_output_size_and_inner_fn( + x_size, + indices, + tensor_indices, + tensor_size, + indices_loaders, + indexed_size, + None, + check=check, + ) + + values = expand(values, expected_vals_size) + # all guards are set above during broadcast_tensors and expand + + scatter = ir.Scatter( + device=self.get_device(), + dtype=self.get_dtype(), + inner_fn=values.make_loader(), + ranges=expected_vals_size, # iter_ranges, + output_indexer=inner_fn, + scatter_mode="atomic_add" if accumulate else None, + ) + buffer = ir.ComputedBuffer( + None, + ir.MutationLayoutSHOULDREMOVE(self), + scatter, + ) + buffer.name = V.graph.register_buffer(buffer) + V.graph.register_operation(buffer) + + if x_ndim == 0: + self = view(self, []) + return self + + +fallback__unsafe_masked_index = fallback_handler( + aten._unsafe_masked_index.default, add_to_fallback_set=False +) + +fallback__unsafe_masked_index_put_accumulate = fallback_handler( + aten._unsafe_masked_index_put_accumulate.default, add_to_fallback_set=False +) + + +@register_lowering(aten._unsafe_masked_index, type_promotion_kind=None) +def _unsafe_masked_index(self, mask, indices, fill): + ranges, _, _unsafe_index_fn = index_impl_helper(self, indices, check=False) + mask_loader = mask.make_loader() + self_loader = self.make_loader() + + def inner_fn(idx): + if mask.dtype != torch.bool: + mask_val = ops.to_dtype(mask_loader(idx), torch.bool) + else: + mask_val = mask_loader(idx) + return ops.masked(mask_val, lambda: self_loader(_unsafe_index_fn(idx)), fill) + + return Pointwise.create( + device=self.get_device(), + dtype=self.get_dtype(), + inner_fn=inner_fn, + ranges=ranges, + ) + + +@register_lowering(aten._unsafe_masked_index_put_accumulate, type_promotion_kind=None) +def _unsafe_masked_index_put_accumulate(x, mask, indices, values): + masked_value = where(mask, values, 0) + shape = x.get_size() + clamped_indices = [ + clamp(indices[i], -shape[i], shape[i] - 1) if indices[i] else None + for i in range(len(indices)) + ] + # TODO: use a masked store for this. currently only triton + # supports masked stores and cpp backend does not. + return _unsafe_index_put(x, clamped_indices, masked_value, accumulate=True) + + +@make_pointwise +def clamp(a, min, max): + return ops.maximum(min, ops.minimum(max, a)) + + +@register_lowering(aten.as_strided_scatter, type_promotion_kind=None) +def as_strided_scatter(self, src, size, stride, storage_offset=None): + output = clone(self) + output_view = as_strided(output, size, stride, storage_offset) + copy_(output_view, src) + return output + + +@register_lowering(aten.scatter, type_promotion_kind=None) +def scatter(x, dim: int, index, src, **kwargs): + return scatter_(clone(x), dim, index, src, **kwargs) + + +def scatter_fallback( + op_overload: torch._ops.OpOverload, + self, + dim: int, + index, + src, + *, + reduce: Optional[str] = None, + include_self: bool = True, +): + src_is_tensor = isinstance(src, TensorBox) + if use_scatter_fallback( + op_overload, + reduce, + self.get_dtype(), + src.get_dtype() if src_is_tensor else type(src), + src.get_device().type if src_is_tensor else "not impl", + src_is_tensor, + ): + ir.ScatterFallback( + op_overload, + self, + dim, + index, + src, + reduce=reduce, + include_self=include_self, + ) + return self + + return None + + +@register_lowering(aten.scatter_, type_promotion_kind=None) +def scatter_(self, dim: int, index, src, *, reduce: Optional[str] = None): + assert reduce in {None, "add", "multiply"} + if reduce is None: + op_overload = getattr(aten.scatter_, V.graph.current_node.target._overloadname) # type: ignore[union-attr] + fallback_result = scatter_fallback( + op_overload, self, dim, index, src, reduce=reduce + ) + if fallback_result is not None: + return fallback_result + + if reduce == "add": + reduce = "sum" + elif reduce == "multiply": + reduce = "prod" + return scatter_reduce_(self, dim, index, src, reduce) + + +@register_lowering(aten.scatter_add, type_promotion_kind=None) +def scatter_add(x, dim: int, index, src): + return scatter_add_(clone(x), dim, index, src) + + +@register_lowering(aten.scatter_add_, type_promotion_kind=None) +def scatter_add_(x, dim: int, index, src): + return scatter_reduce_(x, dim, index, src, "sum") + + +@register_lowering(aten.scatter_reduce, type_promotion_kind=None) +def scatter_reduce(x, dim: int, index, src, reduction_type, **kwargs): + return scatter_reduce_(clone(x), dim, index, src, reduction_type, **kwargs) + + +@register_lowering(aten.scatter_reduce_, type_promotion_kind=None) +def scatter_reduce_(self, dim: int, index, src, reduce, *, include_self: bool = True): + assert reduce in {None, "sum", "prod", "mean", "amax", "amin"} + assert ( + len(aten.scatter_reduce_.overloads()) == 1 + and "two" in aten.scatter_reduce_.overloads() + ), "aten.scatter_reduce_.two is not the unique overload of aten.scatter_reduce_" + + if isinstance(src, Number): + src = full_like(self, src) + + fallback_result = scatter_fallback( + aten.scatter_reduce_.two, + self, + dim, + index, + src, + reduce=reduce, + include_self=include_self, + ) + + if fallback_result: + return fallback_result + + assert isinstance(self, TensorBox) + assert "int" in str(index.get_dtype()) + + ndim = len(self.get_size()) + if ndim == 0: + self = view(self, [1]) + + if isinstance(src, TensorBox) and len(src.get_size()) == 0: + src = view(src, [1]) + + if isinstance(index, TensorBox) and len(index.get_size()) == 0: + index = view(index, [1]) + + if index.get_numel() == 0: + return self + + dim = _validate_dim(self, dim) + + self.realize() + index_loader = index.make_loader() + src_loader = src.make_loader() if isinstance(src, TensorBox) else None + + def output_indexer(idx): + # self is captured from the end of the function, so it may have 0 dim + shape = self.get_size() + ndim = len(shape) + indirect_idx = list(idx) + indirect_idx[dim] = ops.indirect_indexing( + index_loader(idx), 1 if ndim == 0 else shape[dim], wrap_neg=False + ) + return indirect_idx + + def fn(idx): + if src_loader: + return src_loader(idx) + else: + # src is a scalar + return ops.constant(src, self.get_dtype()) + + def backend_reduce_str(reduce): + if reduce == "sum": + return "atomic_add" + else: + # TODO: Need to support more reduction type + assert reduce is None + return None + + if not include_self: + # zero out the corresponding elements first + zero_out = ir.Scatter( + device=self.get_device(), + dtype=self.get_dtype(), + inner_fn=lambda index: ops.constant(0, self.get_dtype()), + ranges=index.get_size(), + output_indexer=output_indexer, + scatter_mode=None, + ) + buffer = ir.ComputedBuffer( + None, + ir.MutationLayoutSHOULDREMOVE(self), + zero_out, + ) + buffer.name = V.graph.register_buffer(buffer) + V.graph.register_operation(buffer) + + # self[index[i][j][k]][j][k] += src[i][j][k] # if dim == 0 + # self[i][index[i][j][k]][k] += src[i][j][k] # if dim == 1 + # self[i][j][index[i][j][k]] += src[i][j][k] # if dim == 2 + scatter = ir.Scatter( + device=self.get_device(), + dtype=self.get_dtype(), + inner_fn=fn, + ranges=index.get_size(), + output_indexer=output_indexer, + scatter_mode=backend_reduce_str(reduce), + ) + buffer = ir.ComputedBuffer( + None, + ir.MutationLayoutSHOULDREMOVE(self), + scatter, + ) + buffer.name = V.graph.register_buffer(buffer) + V.graph.register_operation(buffer) + + if ndim == 0: + self = view(self, []) + return self + + +def upsample_nearestnd( + x, + output_size, + scales_x: Tuple[Optional[float], ...], + n: int = 2, + exact: bool = False, +): + x.realize_hint() # elements are reused + x_loader = x.make_loader() + i_sizes = x.get_size()[-n:] + batch = x.get_size()[:-n] + i_sizes = [V.graph.sizevars.evaluate_static_shape(i) for i in i_sizes] + + assert len(scales_x) == n + o_sizes = output_size + + inv_scales = [i / o for i, o in zip(i_sizes, o_sizes)] + for i, scale in enumerate(scales_x): + if scale is not None: + inv_scales[i] = 1.0 / scale + + def scale_fn(x, scale, size): + # Nearest Exact: input_index = round(scale * (output_index + 0.5) - 0.5) + # = floor(scale * (output_index + 0.5)) + # Nearest: input_index = floor(scale * output_index) + x = ops.index_expr(x, torch.float32) + if exact: + x = ops.add(x, ops.constant(0.5, torch.float32)) + x = ops.mul(x, ops.constant(scale, torch.float32)) + x = ops.to_dtype(x, torch.int32) + return ops.indirect_indexing(x, size, check=False) + + def fn(idx): + x = idx[-n:] + b = idx[:-n] + return x_loader( + [*b, *[scale_fn(i, s, size) for i, s, size in zip(x, inv_scales, i_sizes)]] + ) + + return Pointwise.create( + device=x.get_device(), + dtype=x.get_dtype(), + inner_fn=fn, + ranges=[*batch, *o_sizes], + ) + + +@register_lowering(aten.upsample_nearest1d.default) +def upsample_nearest1d(x, output_size, scales: Optional[float] = None): + return upsample_nearestnd(x, output_size, (scales,), n=1) + + +@register_lowering(aten._upsample_nearest_exact1d.default) +def _upsample_nearest_exact1d(x, output_size, scales: Optional[float] = None): + return upsample_nearestnd(x, output_size, (scales,), n=1, exact=True) + + +@register_lowering(aten.upsample_nearest2d.default) +def upsample_nearest2d( + x, output_size, scales_h: Optional[float] = None, scales_w: Optional[float] = None +): + return upsample_nearestnd(x, output_size, (scales_h, scales_w), n=2) + + +@register_lowering(aten._upsample_nearest_exact2d.default) +def _upsample_nearest_exact2d( + x, output_size, scales_h: Optional[float] = None, scales_w: Optional[float] = None +): + return upsample_nearestnd(x, output_size, (scales_h, scales_w), n=2, exact=True) + + +@register_lowering(aten.upsample_nearest3d.default) +def upsample_nearest3d( + x, + output_size, + scales_d: Optional[float] = None, + scales_h: Optional[float] = None, + scales_w: Optional[float] = None, +): + return upsample_nearestnd(x, output_size, (scales_d, scales_h, scales_w), n=3) + + +@register_lowering(aten._upsample_nearest_exact3d.default) +def _upsample_nearest_exact3d( + x, + output_size, + scales_d: Optional[float] = None, + scales_h: Optional[float] = None, + scales_w: Optional[float] = None, +): + return upsample_nearestnd( + x, output_size, (scales_d, scales_h, scales_w), n=3, exact=True + ) + + +def _create_constants(*args, dtype): + return tuple(ops.constant(a, dtype) for a in args) + + +@register_lowering(prims.rev.default) +def rev(x, dims): + # note - dims pre-canonicalized + x_loader = x.make_loader() + sizes = x.get_size() + + def loader(idx): + idx = list(idx) + assert len(idx) == len(sizes) + for dim in dims: + idx[dim] = (sizes[dim] - 1) - idx[dim] + + return x_loader(idx) + + return Pointwise.create( + device=x.get_device(), + dtype=x.get_dtype(), + inner_fn=loader, + ranges=sizes, + ) + + +@register_lowering(aten.constant_pad_nd, type_promotion_kind=None) +def constant_pad_nd(x, padding, fill_value=0): + assert (len(padding) % 2) == 0 + if all(p == 0 for p in padding): + return clone(x) + + sizes = x.get_size() + + bounds = list(reversed(list(zip(padding[::2], padding[1::2])))) + n = len(sizes) - len(bounds) + + # if padding is a complicated expression, hoist it + bounds_precomp: List[Tuple[sympy.Symbol, Any]] = [] + for l, h in bounds: + bounds_precomp.append((V.graph.sizevars.lookup_precomputed_size(l), h)) # type: ignore[arg-type] + + output_size = list(sizes[:n]) + mask_sizes = [] + for (low, high), size in zip(bounds, sizes[n:]): + mask_sizes.append(size) + output_size.append(sympy.expand(size + low + high)) + assert len(output_size) == len(sizes) + fill_value = dtype_to_type(x.get_dtype())(fill_value) + + def mask(index): + mask = [] + for idx, (low, high), length in zip(index[n:], bounds, mask_sizes): + if low != 0: + mask.append(range_mask_low(idx, 0)) + if high != 0: + mask.append(range_mask_high(idx, length)) + mask = functools.reduce(ops.and_, mask) + return ops.masked(mask, lambda: x_loader(index), fill_value) + + def offset_fn(index): + new_index = list(index[:n]) + for idx, (low, high) in zip(index[n:], bounds_precomp): + new_index.append(idx - low) + assert len(new_index) == len(index) + return mask(new_index) + + x_loader = x.make_loader() + return Pointwise.create( + device=x.get_device(), + dtype=x.get_dtype(), + inner_fn=offset_fn, + ranges=output_size, + ) + + +def range_mask_low(i: sympy.Expr, low: Union[sympy.Expr, int]): + return ops.ge( + ops.index_expr(i, torch.int64), + ops.index_expr(sympy.Integer(low), torch.int64), + ) + + +def range_mask_high(i: sympy.Expr, high: sympy.Expr): + return ops.lt( + ops.index_expr(i, torch.int64), + ops.index_expr(high, torch.int64), + ) + + +def range_mask(i: sympy.Expr, high: sympy.Expr, low: sympy.Expr): + return ops.and_( + range_mask_low(i, low), + range_mask_high(i, high), + ) + + +def constant_boundary_condition( + x, fill_value, padding=None, pad_fill_value=1.0, dim=None +): + h = x.get_size()[-dim:] + x_loader = x.make_loader() + padding_h = padding or [0] * dim + + def load(index): + prefix = index[:-dim] + ih = index[-dim:] + + mask = functools.reduce( + ops.and_, + [range_mask(ih[i], h[i] + padding_h[i], -padding_h[i]) for i in range(dim)], + ) + return ( + ops.masked( + mask, + lambda: constant_boundary_condition(x, pad_fill_value, dim=dim)( + [*prefix, *ih] + ), + fill_value, + ) + if padding + else ops.masked(mask, lambda: x_loader([*prefix, *ih]), fill_value) + ) + + return load + + +def pooling_size(x, i, kernel_size, stride, padding, ceil_mode): + x_out = FloorDiv( + x + 2 * padding[i] - (kernel_size[i] - 1) + (stride[i] - 1), stride[i] + ) + + if ceil_mode: + x_alt = FloorDiv( + x + 2 * padding[i] - (kernel_size[i] - 1) + 2 * (stride[i] - 1), stride[i] + ) + if V.graph.sizevars.size_hint((x_alt - 1) * stride[i] - x - padding[i]) >= 0: + # Sliding windows must start within the input or left padding + x_alt -= 1 # type: ignore[assignment] + V.graph.sizevars.guard_leq(0, x_alt * stride[i] - x - padding[i]) # type: ignore[arg-type] + if V.graph.sizevars.size_hint(x_out - x_alt) == 0: + # ceil mode is actually a no-op, lets guard on that + V.graph.sizevars.guard_equals(x_out, x_alt) + ceil_mode = False + else: + x_out = x_alt + return x_out, ceil_mode + + +def should_fallback_max_pool2d_with_indices(kernel_size, dilation): + kernel_size = pad_listlike(kernel_size, 2) + window_size = kernel_size[0] * kernel_size[1] + return (window_size > 25) or any(d > 1 for d in dilation) + + +def max_pool2d_checks( + x, kernel_size, stride, padding, dilation, *, assert_fallback=None +): + if padding == 0: + padding = [0, 0] + if dilation == 1: + dilation = [1, 1] + if not stride: + stride = kernel_size + + kernel_size = pad_listlike(kernel_size, 2) + stride = pad_listlike(stride, 2) + padding = pad_listlike(padding, 2) + dilation = pad_listlike(dilation, 2) + + assert isinstance(x, TensorBox) + assert len(kernel_size) == 2 + assert len(stride) == 2 + assert len(padding) == 2 + assert len(dilation) == 2 + assert len(x.get_size()) in (3, 4) + + use_fallback = should_fallback_max_pool2d_with_indices(kernel_size, dilation) + if assert_fallback is not None: + assert use_fallback == assert_fallback + + return kernel_size, stride, padding, dilation, use_fallback + + +@register_lowering(prims._low_memory_max_pool2d_with_offsets, type_promotion_kind=None) +def _low_memory_max_pool2d_with_offsets( + x, + kernel_size, + stride, + padding, + dilation, + ceil_mode=False, +): + # assert we are not on a fallback path, the inductor decomp should have guaranteed this + kernel_size, stride, padding, dilation, _ = max_pool2d_checks( + x, kernel_size, stride, padding, dilation, assert_fallback=False + ) + + x.realize_hint() + *batch, h, w = x.get_size() + + h_out, ceil_mode1 = pooling_size(h, 0, kernel_size, stride, padding, ceil_mode) + w_out, ceil_mode2 = pooling_size(w, 1, kernel_size, stride, padding, ceil_mode) + + dtype = x.dtype + min_value = ( + False + if dtype is torch.bool + else (float("-inf") if dtype.is_floating_point else torch.iinfo(dtype).min) + ) + + new_size = list(batch) + [h_out, w_out] + if padding[0] or padding[1] or ceil_mode1 or ceil_mode2: + x_loader = constant_boundary_condition(x, min_value, dim=2) + else: + x_loader = x.make_loader() + + def fn(idx, return_index): + *prefix, bh, bw = idx + maxval = None + maxindex = None + for h_inc, w_inc in itertools.product( + range(kernel_size[0]), range(kernel_size[1]) + ): + ih = bh * stride[0] + h_inc - padding[0] + iw = bw * stride[1] + w_inc - padding[1] + val = x_loader([*prefix, ih, iw]) + if return_index: + index = ops.index_expr(h_inc * kernel_size[1] + w_inc, torch.int8) + if maxindex is None: + maxindex = index + else: + maxindex = ops.where(ops.gt(val, maxval), index, maxindex) + if maxval is None: + maxval = val + else: + maxval = ops.maximum(val, maxval) + if return_index: + return maxindex + else: + return maxval + + out = Pointwise.create( + device=x.get_device(), + dtype=x.get_dtype(), + inner_fn=functools.partial(fn, return_index=False), + ranges=new_size, + ) + offsets = Pointwise.create( + device=x.get_device(), + dtype=torch.int8, + inner_fn=functools.partial(fn, return_index=True), + ranges=new_size, + ) + return out, offsets + + +@register_lowering( + prims._low_memory_max_pool2d_offsets_to_indices, type_promotion_kind=None +) +def _low_memory_max_pool2d_offsets_to_indices( + offsets, kernel_width, input_width, stride, padding +): + # TODO: Generalize to other max pooling flavors, and arbitrary dim + + offsets_loader = offsets.make_loader() + + def increments_to_index(h_inc, w_inc, bh, bw): + w_in = ops.index_expr(input_width, torch.int64) + hbase = ops.index_expr(bh * stride[0] - padding[0], torch.int64) + wbase = ops.index_expr(bw * stride[1] - padding[1], torch.int64) + ih = hbase + h_inc + iw = wbase + w_inc + return ih * w_in + iw + + def offsets_to_indices(idx): + *prefix, bh, bw = idx + offset = offsets_loader([*prefix, bh, bw]) + kw_const = ops.constant(kernel_width, torch.int32) + h_inc = offset // kw_const + w_inc = offset - (h_inc * kw_const) + return increments_to_index(h_inc, w_inc, bh, bw) + + indices = Pointwise.create( + device=offsets.get_device(), + dtype=torch.int64, + inner_fn=offsets_to_indices, + ranges=offsets.get_size(), + ) + return indices + + +# Fallback selected when we do not decompose to the low-memory path. +make_fallback(aten.max_pool2d_with_indices) + + +fallback_max_pool2d_with_indices_backward = fallback_handler( + aten.max_pool2d_with_indices_backward.default, + add_to_fallback_set=False, +) + + +@register_lowering(aten.max_pool2d_with_indices_backward, type_promotion_kind=None) +def max_pool2d_with_indices_backward( + grad_output, x, kernel_size, stride, padding, dilation, ceil_mode, indices +): + if padding == 0: + padding = [0, 0] + if dilation == 1: + dilation = [1, 1] + if not stride: + stride = kernel_size + + assert isinstance(x, TensorBox) + assert len(kernel_size) == 2 + assert len(stride) == 2 + assert len(padding) == 2 + assert len(dilation) == 2 + assert len(x.get_size()) in (3, 4) + + # we will read this many times, so make sure it is computed + grad_output.realize_hint() + try: + gO_stride = grad_output.get_stride() + except AttributeError: + # some classes don't have `get_stride` + # TODO will need a better way of determining if inputs are channels-last + gO_stride = None + if isinstance(x, TensorBox) and isinstance(x.data.data, Pointwise): # type: ignore[attr-defined] + data = x.data.data # type: ignore[attr-defined] + x_buffer = ir.ComputedBuffer( + name=None, + layout=ir.FlexibleLayout( + device=data.get_device(), + dtype=data.get_dtype(), + size=data.get_size(), + ), + data=data, + ) + x_buffer.decide_layout() + x_stride = x_buffer.get_stride() + else: + try: + x_stride = x.get_stride() + except AttributeError: + x_stride = None + + is_channels_last = (x_stride is not None and x_stride[1] == 1) or ( + gO_stride is not None and gO_stride[1] == 1 + ) + if any(d != 1 for d in dilation): + # dilation NYI + return fallback_max_pool2d_with_indices_backward( + grad_output, x, kernel_size, stride, padding, dilation, ceil_mode, indices + ) + + *batch, height, width = x.get_size() + *_, pooled_height, pooled_width = grad_output.get_size() + + indices_loader = indices.make_loader() + grad_loader = grad_output.make_loader() + new_size = list(x.get_size()) + + h_window_size = max( + max(h // stride[0] - max(0, (h - kernel_size[0]) // stride[0]), 1) + for h in range(kernel_size[0] * 2) + ) + w_window_size = max( + max(w // stride[1] - max(0, (w - kernel_size[1]) // stride[1]), 1) + for w in range(kernel_size[1] * 2) + ) + + window_size = h_window_size * w_window_size + + if window_size > 25: + # Kernel size too big. Results in hard-to-optimize Triton code. Use fallback. + return fallback_max_pool2d_with_indices_backward( + grad_output, x, kernel_size, stride, padding, dilation, ceil_mode, indices + ) + + indices_size = indices.get_size() + + def fn(idx): + *prefix, h, w = idx + index_test = ops.index_expr(h * width + w, torch.int32) + h = h + padding[0] + w = w + padding[1] + phstart = ops.index_expr( + FloorDiv(h - kernel_size[0] + stride[0], stride[0]), torch.int32 + ) + pwstart = ops.index_expr( + FloorDiv(w - kernel_size[1] + stride[1], stride[1]), torch.int32 + ) + phend = ops.index_expr(FloorDiv(h, stride[0]) + 1, torch.int32) + pwend = ops.index_expr(FloorDiv(w, stride[1]) + 1, torch.int32) + + phstart = ops.maximum(phstart, ops.constant(0, torch.int32)) + pwstart = ops.maximum(pwstart, ops.constant(0, torch.int32)) + phend = ops.minimum(phend, ops.index_expr(pooled_height, torch.int32)) + pwend = ops.minimum(pwend, ops.index_expr(pooled_width, torch.int32)) + + gradient = None + for ph_ in range(h_window_size): + for pw_ in range(w_window_size): + ph = ops.add(phstart, ops.constant(ph_, torch.int32)) + pw = ops.add(pwstart, ops.constant(pw_, torch.int32)) + grad_index = [ + *prefix, + ops.indirect_indexing( + ops.minimum(ph, ops.sub(phend, ops.constant(1, torch.int32))), + indices_size[-2], + check=False, + ), + ops.indirect_indexing( + ops.minimum(pw, ops.sub(pwend, ops.constant(1, torch.int32))), + indices_size[-1], + check=False, + ), + ] + + index_actual = indices_loader(grad_index) + grad_part = grad_loader(grad_index) + check = ops.eq(index_actual, index_test) + + if gradient is None: + # don't need mask for 0, 0 + gradient = ops.where( + check, grad_part, ops.constant(0.0, torch.float32) + ) + else: + mask = ops.and_( + ops.and_( + ops.lt(ph, phend), + ops.lt(pw, pwend), + ), + check, + ) + gradient = ops.where(mask, ops.add(gradient, grad_part), gradient) + assert gradient is not None + return gradient + + out = Pointwise.create( + device=grad_output.get_device(), + dtype=grad_output.get_dtype(), + inner_fn=fn, + ranges=new_size, + ) + if is_channels_last: + return ir.ExternKernel.require_channels_last(out) + else: + return out + + +def pad_adaptive_loader(x, pad_val=0.0): + *_, h, w = x.get_size() + x_loader = x.make_loader() + + def load(prefix, increments, start_indices, end_indices): + ih, iw = increments + h_start_index, w_start_index = start_indices + h_end_index, w_end_index = end_indices + + mask = ops.and_( + ops.lt( + ops.index_expr(h_start_index + ih, torch.int64), + ops.index_expr(h_end_index, torch.int64), + ), + ops.lt( + ops.index_expr(w_start_index + iw, torch.int64), + ops.index_expr(w_end_index, torch.int64), + ), + ) + + return ops.masked( + mask, + lambda: x_loader([*prefix, h_start_index + ih, w_start_index + iw]), + pad_val, + ) + + return load + + +def compute_indices_adaptive_pooling(start_index, end_index, h_in, w_in, h_out, w_out): + h_start_index = functools.partial(start_index, out_dim=h_out, inp_dim=h_in) + h_end_index = functools.partial(end_index, out_dim=h_out, inp_dim=h_in) + + w_start_index = functools.partial(start_index, out_dim=w_out, inp_dim=w_in) + w_end_index = functools.partial(end_index, out_dim=w_out, inp_dim=w_in) + + return h_start_index, h_end_index, w_start_index, w_end_index + + +def _adaptive_pooling_fn( + start_index, end_index, kernel_maxes, in_sizes, out_sizes, pooling_fn +): + h_in, w_in = in_sizes + h_out, w_out = out_sizes + + ( + h_start_index_fn, + h_end_index_fn, + w_start_index_fn, + w_end_index_fn, + ) = compute_indices_adaptive_pooling( + start_index, end_index, h_in, w_in, h_out, w_out + ) + + def fn(idx, loader): + *prefix, bh, bw = idx + + h_start_index = h_start_index_fn(bh) + h_end_index = h_end_index_fn(bh) + + w_start_index = w_start_index_fn(bw) + w_end_index = w_end_index_fn(bw) + + result = None + for ih, iw in itertools.product(range(kernel_maxes[0]), range(kernel_maxes[1])): + val = loader( + prefix, + [ih, iw], + [h_start_index, w_start_index], + [h_end_index, w_end_index], + ) + if result is None: + result = val + else: + result = pooling_fn(val, result) + return result + + return fn + + +def _adaptive_pooling_fn_with_idx( + start_index, end_index, kernel_maxes, in_sizes, out_sizes, pooling_fn +): + h_in, w_in = in_sizes + h_out, w_out = out_sizes + + ( + h_start_index_fn, + h_end_index_fn, + w_start_index_fn, + w_end_index_fn, + ) = compute_indices_adaptive_pooling( + start_index, end_index, h_in, w_in, h_out, w_out + ) + + def fn(idx, loader): + *prefix, bh, bw = idx + + h_start_index = h_start_index_fn(bh) + h_end_index = h_end_index_fn(bh) + + w_start_index = w_start_index_fn(bw) + w_end_index = w_end_index_fn(bw) + + maxval = None + maxindex = None + for ih, iw in itertools.product(range(kernel_maxes[0]), range(kernel_maxes[1])): + val = loader( + prefix, + [ih, iw], + [h_start_index, w_start_index], + [h_end_index, w_end_index], + ) + + index = ops.index_expr( + (h_start_index + ih) * w_in + w_start_index + iw, torch.int64 + ) + + if maxindex is None: + maxindex = index + else: + maxindex = ops.where(ops.gt(val, maxval), index, maxindex) + + if maxval is None: + maxval = val + else: + maxval = pooling_fn(val, maxval) + + return maxindex + + return fn + + +fallback_adaptive_avg_pool2d = fallback_handler( + aten._adaptive_avg_pool2d.default, add_to_fallback_set=False +) + + +@register_lowering(aten._adaptive_avg_pool2d) +def _adaptive_avg_pool2d(x, output_size): + assert isinstance(x, TensorBox) + assert len(output_size) == 2 + x.realize_hint() + + *batch, h_in, w_in = x.get_size() + + h_in = V.graph.sizevars.evaluate_static_shape(h_in) + w_in = V.graph.sizevars.evaluate_static_shape(w_in) + + h_out, w_out = output_size + + # no-op if the same input and output + if h_in == h_out and w_in == w_out: + return clone(x) + + if h_out == 0 or w_out == 0: + o_size = [*batch, h_out, w_out] + return empty(o_size, dtype=x.get_dtype(), device=x.get_device()) + if h_in % h_out == 0 and w_in % w_out == 0: + kernel_size = [h_in // h_out, w_in // w_out] + return avg_pool2d(x, kernel_size) + + h_kernel_max = ceildiv((h_in + h_out - 1), h_out) + w_kernel_max = ceildiv((w_in + w_out - 1), w_out) + + new_size = list(batch) + [h_out, w_out] + dtype = x.get_dtype() + + window_size = h_kernel_max * w_kernel_max + if window_size > 25: + # Kernel size too big. Results in hard-to-optimize Triton code. Use fallback. + return fallback_adaptive_avg_pool2d(x, output_size) + + def start_index(index, out_dim, inp_dim): + return FloorDiv((index * inp_dim), out_dim) + + def end_index(index, out_dim, inp_dim): + return FloorDiv((index + 1) * inp_dim + out_dim - 1, out_dim) + + fn_sum = _adaptive_pooling_fn( + start_index=start_index, + end_index=end_index, + kernel_maxes=[h_kernel_max, w_kernel_max], + in_sizes=[h_in, w_in], + out_sizes=[h_out, w_out], + pooling_fn=ops.add, + ) + + ones_loader = pad_adaptive_loader(ones_like(x)) + + def fn(idx): + return ops.truediv( + fn_sum(idx, pad_adaptive_loader(x)), fn_sum(idx, ones_loader) + ) + + rv = Pointwise.create( + device=x.get_device(), + dtype=dtype, + inner_fn=fn, + ranges=new_size, + ) + # TODO: should we force these to be realized? + return rv + + +fallback_adaptive_max_pool2d = fallback_handler( + aten.adaptive_max_pool2d.default, add_to_fallback_set=False +) + + +@register_lowering(aten.adaptive_max_pool2d) +def adaptive_max_pool2d(x, output_size): + assert isinstance(x, TensorBox) + assert len(output_size) == 2 + x.realize_hint() + + *batch, h_in, w_in = x.get_size() + + h_in = V.graph.sizevars.evaluate_static_shape(h_in) + w_in = V.graph.sizevars.evaluate_static_shape(w_in) + + h_out, w_out = output_size + + if h_out == 0 or w_out == 0: + o_size = [*batch, h_out, w_out] + return empty(o_size, dtype=x.get_dtype(), device=x.get_device()), empty( + o_size, dtype=torch.int64, device=x.get_device() + ) + if h_in % h_out == 0 and w_in % w_out == 0: + kernel_size = [h_in // h_out, w_in // w_out] + if should_fallback_max_pool2d_with_indices(kernel_size, dilation=[1, 1]): + return max_pool2d_with_indices(x, kernel_size) # type: ignore[name-defined] # noqa: F821 + else: + v, offsets = _low_memory_max_pool2d_with_offsets( + x, + kernel_size, + stride=kernel_size, + padding=[0, 0], + dilation=[1, 1], + ceil_mode=False, + ) + indices = _low_memory_max_pool2d_offsets_to_indices( + offsets, kernel_size[1], w_in, kernel_size, padding=[0, 0] + ) + return v, indices + + h_kernel_max = ceildiv((h_in + h_out - 1), h_out) + w_kernel_max = ceildiv((w_in + w_out - 1), w_out) + + new_size = list(batch) + [h_out, w_out] + dtype = x.get_dtype() + + window_size = h_kernel_max * w_kernel_max + if window_size > 25: + # Kernel size too big. Results in hard-to-optimize Triton code. Use fallback. + return fallback_adaptive_max_pool2d(x, output_size) + + def start_index(index, out_dim, inp_dim): + return FloorDiv((index * inp_dim), out_dim) + + def end_index(index, out_dim, inp_dim): + return FloorDiv((index + 1) * inp_dim + out_dim - 1, out_dim) + + inner_func_max_val = _adaptive_pooling_fn( + start_index=start_index, + end_index=end_index, + kernel_maxes=[h_kernel_max, w_kernel_max], + in_sizes=[h_in, w_in], + out_sizes=[h_out, w_out], + pooling_fn=ops.maximum, + ) + + inner_func_max_idx = _adaptive_pooling_fn_with_idx( + start_index=start_index, + end_index=end_index, + kernel_maxes=[h_kernel_max, w_kernel_max], + in_sizes=[h_in, w_in], + out_sizes=[h_out, w_out], + pooling_fn=ops.maximum, + ) + + def inner_fn_max_val(idx): + return inner_func_max_val(idx, pad_adaptive_loader(x, float("-inf"))) + + def inner_fn_max_idx(idx): + return inner_func_max_idx(idx, pad_adaptive_loader(x, float("-inf"))) + + rv = Pointwise.create( + device=x.get_device(), + dtype=dtype, + inner_fn=inner_fn_max_val, + ranges=new_size, + ) + ri = Pointwise.create( + device=x.get_device(), + dtype=torch.int64, + inner_fn=inner_fn_max_idx, + ranges=new_size, + ) + return rv, ri + + +fallback_fractional_max_pool2d = fallback_handler( + aten.fractional_max_pool2d.default, add_to_fallback_set=False +) + + +def _fractional_pooling_offsets(samples, in_sz, out_sz, kernel_sz, dim): + out_sz = out_sz[dim] + in_sz = in_sz[dim] + kernel_sz = kernel_sz[dim] + alpha = IntTrueDiv(in_sz - kernel_sz, out_sz - 1) + samples_loader = samples.make_loader() + + def load(prefix, i): + sample = samples_loader([*prefix, dim]) + i_expr = ops.index_expr(i, samples.get_dtype()) + alpha_expr = ops.index_expr(alpha, samples.get_dtype()) + seq_i = ops.floor((i_expr + sample) * alpha_expr) - ops.floor( + sample * alpha_expr + ) + seq_i = ops.to_dtype(seq_i, torch.int64) + + mask = ops.lt( + i_expr, + ops.index_expr(out_sz - 1, torch.int64), + ) + return ops.where(mask, seq_i, ops.index_expr(in_sz - kernel_sz, torch.int64)) + + return load + + +@register_lowering(aten.fractional_max_pool2d) +def fractional_max_pool2d(x, kernel_size, output_size, random_samples): + x.realize_hint() + *batch, inp_h, inp_w = x.get_size() + kernel_h, kernel_w = kernel_size + h_out, w_out = output_size + + if kernel_h * kernel_w >= 25: + return fallback_fractional_max_pool2d( + x, kernel_size, output_size, random_samples + ) + + gen_offsets_for_dim = functools.partial( + _fractional_pooling_offsets, + samples=random_samples, + in_sz=[inp_h, inp_w], + out_sz=output_size, + kernel_sz=kernel_size, + ) + + h_index_fn = gen_offsets_for_dim(dim=0) + w_index_fn = gen_offsets_for_dim(dim=1) + x_loader = x.make_loader() + + def fn(idx, return_index): + *prefix, bh, bw = idx + + h_start_index = ops.indirect_indexing(h_index_fn(prefix, bh), inp_h) + w_start_index = ops.indirect_indexing(w_index_fn(prefix, bw), inp_w) + + maxval = None + maxindex = None + for ih, iw in itertools.product(range(kernel_size[0]), range(kernel_size[1])): + val = x_loader([*prefix, h_start_index + ih, w_start_index + iw]) + if return_index: + index = ops.index_expr( + (h_start_index + ih) * inp_w + w_start_index + iw, torch.int64 + ) + if maxindex is None: + maxindex = index + else: + maxindex = ops.where( + ops.or_(ops.gt(val, maxval), ops.isnan(val)), index, maxindex + ) + if maxval is None: + maxval = val + else: + maxval = ops.maximum(val, maxval) + if return_index: + return maxindex + else: + return maxval + + new_size = list(batch) + [h_out, w_out] + rv = Pointwise.create( + device=x.get_device(), + dtype=x.get_dtype(), + inner_fn=functools.partial(fn, return_index=False), + ranges=new_size, + ) + + ri = Pointwise.create( + device=x.get_device(), + dtype=torch.int64, + inner_fn=functools.partial(fn, return_index=True), + ranges=new_size, + ) + return rv, ri + + +@register_lowering(aten.upsample_nearest2d_backward.default) +def upsample_nearest2d_backward( + x, output_size=None, input_size=None, scales_h=None, scales_w=None +): + x.realize_hint() + + *batch, inp_h, inp_w = x.get_size() + inp_h = V.graph.sizevars.evaluate_static_shape(inp_h) + inp_w = V.graph.sizevars.evaluate_static_shape(inp_w) + + *batch, out_h, out_w = input_size + + if inp_h % out_h == 0 and inp_w % out_w == 0: + return avg_pool2d(x, [inp_h // out_h, inp_w // out_w], divisor_override=1) + + h_kernel_max = ceildiv(inp_h, out_h) + w_kernel_max = ceildiv(inp_w, out_w) + + def start_index(index, out_dim, inp_dim): + return CeilDiv(index * inp_dim, sympy.sympify(out_dim)) + + def end_index(index, out_dim, inp_dim): + return start_index((index + 1), out_dim, inp_dim) + + fn_sum = _adaptive_pooling_fn( + start_index=start_index, + end_index=end_index, + kernel_maxes=[h_kernel_max, w_kernel_max], + in_sizes=[inp_h, inp_w], + out_sizes=[out_h, out_w], + pooling_fn=ops.add, + ) + + def fn(idx): + return fn_sum(idx, pad_adaptive_loader(x)) + + rv = Pointwise.create( + device=x.get_device(), + dtype=x.get_dtype(), + inner_fn=fn, + ranges=list(input_size), + ) + + return rv + + +fallback_avg_pool2d = fallback_handler( + aten.avg_pool2d.default, add_to_fallback_set=False +) +fallback_avg_pool3d = fallback_handler( + aten.avg_pool3d.default, add_to_fallback_set=False +) + + +@register_lowering(aten.avg_pool2d, type_promotion_kind=None) +def avg_pool2d( + x, + kernel_size, + stride=(), + padding=0, + ceil_mode=False, + count_include_pad=True, + divisor_override=None, +): + return _avg_poolnd( + x, + kernel_size, + stride, + padding, + ceil_mode, + count_include_pad, + divisor_override, + dim=2, + ) + + +@register_lowering(aten.avg_pool3d, type_promotion_kind=None) +def avg_pool3d( + x, + kernel_size, + stride=(), + padding=0, + ceil_mode=False, + count_include_pad=True, + divisor_override=None, +): + return _avg_poolnd( + x, + kernel_size, + stride, + padding, + ceil_mode, + count_include_pad, + divisor_override, + dim=3, + ) + + +def _avg_poolnd( + x, + kernel_size, + stride, + padding, + ceil_mode, + count_include_pad, + divisor_override, + dim, +): + if not stride: + stride = kernel_size + if not padding: + padding = [0] * dim + kernel_size = pad_listlike(kernel_size, dim) + stride = pad_listlike(stride, dim) + padding = pad_listlike(padding, dim) + + assert isinstance(x, TensorBox) + assert len(kernel_size) == dim + assert len(stride) == dim + assert len(padding) == dim + assert len(x.get_size()) in (dim + 1, dim + 2) + + x.realize_hint() + batch = x.get_size()[:-dim] + h = x.get_size()[-dim:] + + h_out, ceil_modes = zip( + *[ + pooling_size(h[i], i, kernel_size, stride, padding, ceil_mode) + for i in range(dim) + ] + ) + + if any(padding) or any(ceil_modes): + x_loader = constant_boundary_condition(x, 0.0, dim=dim) + had_padding = True + else: + x_loader = x.make_loader() + had_padding = False + + new_size = list(batch) + list(h_out) + dtype = x.get_dtype() + + window_size = functools.reduce(operator.mul, kernel_size) + if window_size > 25: + # Kernel size too big. Results in hard-to-optimize Triton code. Use fallback. + if dim == 2: + fallback = fallback_avg_pool2d + elif dim == 3: + fallback = fallback_avg_pool3d + else: + raise ValueError(f"Unknown dim: {dim}") + + return fallback( + x, + kernel_size, + stride, + padding, + ceil_mode, + count_include_pad, + divisor_override, + ) + + def fn_sum(idx, loader): + prefix = idx[:-dim] + b = idx[-dim:] + total = None + for ih in itertools.product(*[range(kernel_size[i]) for i in range(dim)]): + inp = [b[i] * stride[i] + ih[i] - padding[i] for i in range(dim)] + val = loader([*prefix, *inp]) + if total is None: + total = val + else: + total = ops.add(val, total) + return total + + if not had_padding or divisor_override: + if divisor_override: + scale = 1 / divisor_override + else: + scale = 1.0 / window_size + + def fn(idx): + return ops.mul(fn_sum(idx, x_loader), ops.constant(scale, dtype)) + + else: + + def fn(idx): + prefix = idx[:-dim] + bh = idx[-dim:] + + divide_factors = [] + for i in range(dim): + hstart = bh[i] * stride[i] - padding[i] + hend = sympy.Min(hstart + kernel_size[i], h[i] + padding[i]) + if not count_include_pad: + hstart = sympy.Max(hstart, 0) + hend = sympy.Min(hend, h[i]) + factor = ops.index_expr(hend - hstart, torch.int32) + divide_factors.append(factor) + divide_factor = functools.reduce(ops.mul, divide_factors) + return ops.truediv(fn_sum(idx, x_loader), divide_factor) + + rv = Pointwise.create( + device=x.get_device(), + dtype=dtype, + inner_fn=fn, + ranges=new_size, + ) + # TODO(jansel): should we force these to be realized? + return rv + + +fallback_avg_pool2d_backward = fallback_handler( + aten.avg_pool2d_backward.default, add_to_fallback_set=False +) + + +@register_lowering(aten.avg_pool2d_backward, type_promotion_kind=None) +def avg_pool2d_backward( + grad_output, + x, + kernel_size, + stride, + padding, + ceil_mode, + count_include_pad, + divisor_override=None, +): + assert divisor_override is None or divisor_override != 0, "divisor must be not zero" + if not stride: + stride = kernel_size + if not padding: + padding = [0, 0] + + assert isinstance(grad_output, TensorBox) + assert isinstance(x, TensorBox) + assert len(kernel_size) == 2 + assert len(stride) == 2 + assert len(padding) == 2 + assert len(x.get_size()) in (3, 4) + + grad_output.realize_hint() # we will read this many times, so make sure it is computed + + *batch, height, width = x.get_size() + + h_out, ceil_mode1 = pooling_size(height, 0, kernel_size, stride, padding, ceil_mode) + w_out, ceil_mode2 = pooling_size(width, 1, kernel_size, stride, padding, ceil_mode) + + grad_loader = grad_output.make_loader() + + had_padding = padding[0] or padding[1] or ceil_mode1 or ceil_mode2 + + *_, pooled_height, pooled_width = grad_output.get_size() + new_size = list(x.get_size()) + dtype = x.get_dtype() + + h_window_size = max( + max(h // stride[0] - max(0, (h - kernel_size[0]) // stride[0]), 1) + for h in range(kernel_size[0] * 2) + ) + w_window_size = max( + max(w // stride[1] - max(0, (w - kernel_size[1]) // stride[1]), 1) + for w in range(kernel_size[1] * 2) + ) + + window_size = h_window_size * w_window_size + if window_size > 25: + # Kernel size too big. Results in hard-to-optimize Triton code. Use fallback. + return fallback_avg_pool2d_backward( + grad_output, + x, + kernel_size, + stride, + padding, + ceil_mode, + count_include_pad, + divisor_override, + ) + + def compute_pool_size_without_padding(ph, pw): + """ + This computes the scaling factor that we will divide an element + by when `count_include_pad=False` + """ + stride_h = ops.constant(stride[0], torch.int32) + stride_w = ops.constant(stride[1], torch.int32) + pad_h = ops.constant(padding[0], torch.int32) + pad_w = ops.constant(padding[1], torch.int32) + kernel_h = ops.constant(kernel_size[0], torch.int32) + kernel_w = ops.constant(kernel_size[1], torch.int32) + hstart = ops.sub(ops.mul(ph, stride_h), pad_h) + wstart = ops.sub(ops.mul(pw, stride_w), pad_w) + hend = ops.minimum( + ops.add(hstart, kernel_h), + ops.add(ops.index_expr(height, torch.int32), pad_h), + ) + wend = ops.minimum( + ops.add(wstart, kernel_w), + ops.add(ops.index_expr(width, torch.int32), pad_w), + ) + hstart = ops.maximum(hstart, ops.constant(0, torch.int32)) + wstart = ops.maximum(wstart, ops.constant(0, torch.int32)) + hend = ops.minimum(hend, ops.index_expr(height, torch.int32)) + wend = ops.minimum(wend, ops.index_expr(width, torch.int32)) + divide_factor = ops.mul(ops.sub(hend, hstart), ops.sub(wend, wstart)) + return divide_factor + + def fn(idx): + *prefix, h, w = idx + h = h + padding[0] + w = w + padding[1] + phstart = ops.index_expr( + FloorDiv(h - kernel_size[0] + stride[0], stride[0]), torch.int32 + ) + pwstart = ops.index_expr( + FloorDiv(w - kernel_size[1] + stride[1], stride[1]), torch.int32 + ) + phend = ops.index_expr(FloorDiv(h, stride[0]) + 1, torch.int32) + pwend = ops.index_expr(FloorDiv(w, stride[1]) + 1, torch.int32) + + phstart = ops.maximum(phstart, ops.constant(0, torch.int32)) + pwstart = ops.maximum(pwstart, ops.constant(0, torch.int32)) + phend = ops.minimum(phend, ops.index_expr(pooled_height, torch.int32)) + pwend = ops.minimum(pwend, ops.index_expr(pooled_width, torch.int32)) + + gradient = None + for ph_ in range(h_window_size): + for pw_ in range(w_window_size): + ph = ops.add(phstart, ops.constant(ph_, torch.int32)) + pw = ops.add(pwstart, ops.constant(pw_, torch.int32)) + + if divisor_override is not None: + scale = divisor_override + elif count_include_pad or not had_padding: + scale = kernel_size[0] * kernel_size[1] + else: + scale = compute_pool_size_without_padding(ph, pw) + + part = ops.truediv( + grad_loader( + [ + *prefix, + ops.indirect_indexing( + ops.minimum( + ph, ops.sub(phend, ops.constant(1, torch.int32)) + ), + pooled_height, + check=False, + ), + ops.indirect_indexing( + ops.minimum( + pw, ops.sub(pwend, ops.constant(1, torch.int32)) + ), + pooled_width, + check=False, + ), + ] + ), + scale, + ) + + mask = ops.and_( + ops.lt(ph, phend), + ops.lt(pw, pwend), + ) + if gradient is None: + gradient = ops.where(mask, part, ops.constant(0.0, torch.float32)) + else: + gradient = ops.where(mask, ops.add(gradient, part), gradient) + assert gradient is not None + return gradient + + rv = Pointwise.create( + device=grad_output.get_device(), + dtype=dtype, + inner_fn=fn, + ranges=new_size, + ) + return rv + + +fallback_avg_pool3d_backward = fallback_handler( + aten.avg_pool3d_backward.default, add_to_fallback_set=False +) + + +@register_lowering(aten.avg_pool3d_backward, type_promotion_kind=None) +def avg_pool3d_backward( + grad_output, + x, + kernel_size, + stride, + padding, + ceil_mode, + count_include_pad, + divisor_override=None, +): + assert divisor_override is None or divisor_override != 0, "divisor must be not zero" + if not stride: + stride = kernel_size + if not padding: + padding = [0, 0, 0] + + assert isinstance(grad_output, TensorBox) + assert isinstance(x, TensorBox) + assert len(kernel_size) == 3 + assert len(stride) == 3 + assert len(padding) == 3 + assert len(x.get_size()) in (4, 5) + + grad_output.realize_hint() + + *batch, depth, height, width = x.get_size() + + d_out, ceil_mode_d = pooling_size(depth, 0, kernel_size, stride, padding, ceil_mode) + h_out, ceil_mode_h = pooling_size( + height, 1, kernel_size, stride, padding, ceil_mode + ) + w_out, ceil_mode_w = pooling_size(width, 2, kernel_size, stride, padding, ceil_mode) + + grad_loader = grad_output.make_loader() + had_padding = any(padding) or ceil_mode_d or ceil_mode_h or ceil_mode_w + + *_, pooled_depth, pooled_height, pooled_width = grad_output.get_size() + new_size = list(x.get_size()) + dtype = x.get_dtype() + + d_window_size, h_window_size, w_window_size = ( + max( + max(d // stride[i] - max(0, (d - kernel_size[i]) // stride[i]), 1) + for d in range(kernel_size[i] * 2) + ) + for i in range(3) + ) + + window_size = d_window_size * h_window_size * w_window_size + if window_size > 125: + # Kernel size too big. Results in hard-to-optimize Triton code. + return fallback_avg_pool3d_backward( + grad_output, + x, + kernel_size, + stride, + padding, + ceil_mode, + count_include_pad, + divisor_override, + ) + + def compute_pool_size_without_padding(pd, ph, pw): + stride_d, stride_h, stride_w = (ops.constant(s, torch.int32) for s in stride) + pad_d, pad_h, pad_w = (ops.constant(p, torch.int32) for p in padding) + kernel_d, kernel_h, kernel_w = ( + ops.constant(k, torch.int32) for k in kernel_size + ) + + dstart, hstart, wstart = ( + ops.sub(ops.mul(p, s), pad) + for p, s, pad in zip( + [pd, ph, pw], [stride_d, stride_h, stride_w], [pad_d, pad_h, pad_w] + ) + ) + dend, hend, wend = ( + ops.minimum( + ops.add(start, k), ops.add(ops.index_expr(dim, torch.int32), pad) + ) + for start, k, dim, pad in zip( + [dstart, hstart, wstart], + [kernel_d, kernel_h, kernel_w], + [depth, height, width], + [pad_d, pad_h, pad_w], + ) + ) + dstart, hstart, wstart = ( + ops.maximum(start, ops.constant(0, torch.int32)) + for start in [dstart, hstart, wstart] + ) + dend, hend, wend = ( + ops.minimum(end, ops.index_expr(dim, torch.int32)) + for end, dim in zip([dend, hend, wend], [depth, height, width]) + ) + divide_factor = ops.mul( + ops.mul(ops.sub(dend, dstart), ops.sub(hend, hstart)), ops.sub(wend, wstart) + ) + return divide_factor + + def fn(idx): + *prefix, d, h, w = idx + d, h, w = (v + pad for v, pad in zip([d, h, w], padding)) + + pdstart, phstart, pwstart = ( + ops.index_expr(FloorDiv(v - k + s, s), torch.int32) + for v, k, s in zip([d, h, w], kernel_size, stride) + ) + + pdend, phend, pwend = ( + ops.index_expr(FloorDiv(v, s) + 1, torch.int32) + for v, s in zip([d, h, w], stride) + ) + + pdstart, phstart, pwstart = ( + ops.maximum(pstart, ops.constant(0, torch.int32)) + for pstart in [pdstart, phstart, pwstart] + ) + pdend, phend, pwend = ( + ops.minimum(pend, ops.index_expr(pooled_dim, torch.int32)) + for pend, pooled_dim in zip( + [pdend, phend, pwend], [pooled_depth, pooled_height, pooled_width] + ) + ) + + gradient = None + # Iterate over the 3D region to accumulate gradients + for pd_ in range(d_window_size): + for ph_ in range(h_window_size): + for pw_ in range(w_window_size): + pd, ph, pw = ( + ops.add(pstart, ops.constant(p_, torch.int32)) + for pstart, p_ in zip( + [pdstart, phstart, pwstart], [pd_, ph_, pw_] + ) + ) + + if divisor_override is not None: + scale = divisor_override + elif count_include_pad or not had_padding: + scale = kernel_size[0] * kernel_size[1] * kernel_size[2] + else: + scale = compute_pool_size_without_padding(pd, ph, pw) + + part = ops.truediv( + grad_loader( + [ + *prefix, + ops.indirect_indexing( + ops.minimum( + pd, ops.sub(pdend, ops.constant(1, torch.int32)) + ), + pooled_depth, + check=False, + ), + ops.indirect_indexing( + ops.minimum( + ph, ops.sub(phend, ops.constant(1, torch.int32)) + ), + pooled_height, + check=False, + ), + ops.indirect_indexing( + ops.minimum( + pw, ops.sub(pwend, ops.constant(1, torch.int32)) + ), + pooled_width, + check=False, + ), + ] + ), + scale, + ) + + mask = ops.and_( + ops.and_(ops.lt(pd, pdend), ops.lt(ph, phend)), + ops.lt(pw, pwend), + ) + if gradient is None: + gradient = ops.where( + mask, part, ops.constant(0.0, torch.float32) + ) + else: + gradient = ops.where(mask, ops.add(gradient, part), gradient) + assert gradient is not None + return gradient + + rv = Pointwise.create( + device=grad_output.get_device(), + dtype=dtype, + inner_fn=fn, + ranges=new_size, + ) + return rv + + +def _validate_reduction_axis(x, axis): + size = x.get_size() + if isinstance(axis, int): + axis = [axis] + elif not axis: + axis = range(len(size)) + if len(size) == 0: + assert tuple(axis) in [(), (0,), (-1,)], f"invalid axis: {axis}" + return [] + axis = list(axis) + for i in range(len(axis)): + if axis[i] < 0: + axis[i] += len(size) if len(size) else 1 + assert 0 <= axis[i] < len(size) or (len(size) == 0 and axis[i] == 0) + assert len(set(axis)) == len(axis), "reduction axis not unique" + return axis + + +def _make_reduction_inner(x, *, axis, keepdims, dtype, override_return_dtype): + if dtype is not None: + x = to_dtype(x, dtype) + size = x.get_size() + axis = set(_validate_reduction_axis(x, axis)) + + kept_sizes = [] + kept_idx = [] + reduced_sizes = [] + reduced_idx = [] + for i in range(len(size)): + if i in axis: + reduced_idx.append(i) + reduced_sizes.append(size[i]) + else: + kept_idx.append(i) + kept_sizes.append(size[i]) + + def loader(index, reduction_index): + assert len(reduction_index) == len(reduced_idx) + if keepdims: + assert len(index) == len(size) + index = [index[i] for i in kept_idx] + assert len(index) == len(kept_idx) + new_index = [None] * (len(index) + len(reduction_index)) + for idx, var in itertools.chain( + zip(kept_idx, index), zip(reduced_idx, reduction_index) + ): + new_index[idx] = var + return inner_loader(new_index) + + if keepdims: + new_size = list(size) + for i in reduced_idx: + new_size[i] = sympy.Integer(1) + else: + new_size = kept_sizes + + inner_loader = x.make_loader() + return dict( + device=x.get_device(), + dst_dtype=override_return_dtype or x.get_dtype(), + src_dtype=x.get_dtype(), + inner_fn=loader, + ranges=new_size, + reduction_ranges=reduced_sizes, + ) + + +def make_reduction(reduction_type: str, override_return_dtype=None): + def inner(x, axis=None, keepdims=False, *, dtype=None): + kwargs = _make_reduction_inner( + x, + axis=axis, + keepdims=keepdims, + dtype=dtype, + override_return_dtype=override_return_dtype, + ) + result = Reduction.create(reduction_type=reduction_type, input_node=x, **kwargs) + if isinstance( + result.data.data, Reduction + ): # Only realize if reduction isn't unrolled + result.realize() + return result + + return inner + + +def _make_scan_inner(x, *, axis, dtype): + if dtype is not None: + x = to_dtype(x, dtype) + size = x.get_size() + axis = _validate_dim(x, axis) + + return dict( + device=x.get_device(), + dtypes=(x.get_dtype(),), + inner_fns=(x.make_loader(),), + size=x.get_size(), + axis=axis, + ) + + +@register_lowering(aten.mean) +def mean(x, axis=None, keepdim=False, *, dtype=None): + if dtype is not None: + x = to_dtype(x, dtype) + size = x.get_size() + axis = _validate_reduction_axis(x, axis) + # compute in higher-precision until end of mean lowering + output_dtype = x.get_dtype() + if output_dtype in (torch.float16, torch.bfloat16): + x = to_dtype(x, torch.float) + sum_result = sum_(x, axis, keepdim) + denom = sympy_product(size[i] for i in axis) + denom = ir.IndexingConstant(denom, x.get_dtype(), x.get_device()) + denom = ExpandView.create(denom, list(sum_result.get_size())) + return to_dtype(div(sum_result, denom), output_dtype) + + +def var_mean_sum_(x, axis, correction, keepdim, return_mean): + if correction is None: + correction = 1 + + size = x.get_size() + axis = _validate_reduction_axis(x, axis) + x_mean = mean(x, axis, keepdim=True) + if return_mean: + x_mean.realize() + + diffs = square(sub(x, x_mean)) + sum_result = sum_(diffs, axis, keepdim) + + denom = sympy_product(size[i] for i in axis) + if correction: + denom = sympy.Max(denom - correction, 0) + denom = ir.IndexingConstant(denom, x.get_dtype(), x.get_device()) + denom = ExpandView.create(denom, list(sum_result.get_size())) + x_var = div(sum_result, denom) + if not return_mean: + return (x_var,) + + x_mean = x_mean if keepdim else squeeze(x_mean, axis) + return x_var, x_mean + + +def use_two_step_variance(x, axis, keepdim): + # Instead of unrolling welford, just unroll the simpler two-step var + axis = _validate_reduction_axis(x, axis) + kwargs = _make_reduction_inner( + x, axis=axis, keepdims=keepdim, dtype=None, override_return_dtype=None + ) + + ranges = kwargs["ranges"] + reduction_numel = sympy_product(kwargs["reduction_ranges"]) + return ( + isinstance(reduction_numel, sympy.Integer) + and int(reduction_numel) < config.unroll_reductions_threshold + and sympy_product(ranges) != 1 + ) + + +def var_mean_welford_(x, axis, *, correction, keepdim, return_mean): + if correction is None: + correction = 1 + + kwargs = _make_reduction_inner( + x, axis=axis, keepdims=keepdim, dtype=None, override_return_dtype=None + ) + loader = kwargs.pop("inner_fn") + kwargs.pop("dst_dtype") + kwargs.pop("src_dtype") + + mean, m2, _ = ir.WelfordReduction.create( + inner_fns=(loader,), + reduction_type="welford_reduce", + dtype=x.get_dtype(), + **kwargs, + ) + m2.realize() + + dtype = x.get_dtype() + size = x.get_size() + axis = _validate_reduction_axis(x, axis) + rnumel = sympy_product(size[i] for i in axis) + + def get_constant_or_index_expr(x, dtype): + if isinstance(x, sympy.Expr) and not x.is_number: + return ops.to_dtype(ops.index_expr(x, torch.int64), dtype) + return ops.constant(x, dtype) + + def scale_fn(data): + c = get_constant_or_index_expr(correction, dtype) + N = get_constant_or_index_expr(rnumel, dtype) + zero = ops.constant(0, dtype) + return data / ops.maximum(zero, N - c) + + var = make_pointwise(scale_fn)(m2) + + if return_mean: + mean.realize() + return var, mean + return (var,) + + +def var_mean_helper_(x, *, axis, correction, keepdim, return_mean): + out_dtype = x.get_dtype() + compute_dtype = get_computation_dtype(out_dtype) + x = to_dtype(x, compute_dtype, copy=False) + kwargs = dict( + x=x, + axis=axis, + correction=correction, + keepdim=keepdim, + return_mean=return_mean, + ) + output = ( + var_mean_sum_(**kwargs) + if use_two_step_variance(x, axis=axis, keepdim=keepdim) + else var_mean_welford_(**kwargs) + ) + output = tuple(to_dtype(x, out_dtype, copy=False) for x in output) + return output[0] if not return_mean else output + + +@register_lowering([aten.var, prims.var]) +def var_(x, axis=None, *, correction=None, keepdim=False): + return var_mean_helper_( + x, axis=axis, correction=correction, keepdim=keepdim, return_mean=False + ) + + +@register_lowering(aten.var_mean) +def var_mean(x, axis=None, *, correction=None, keepdim=False): + return var_mean_helper_( + x, axis=axis, correction=correction, keepdim=keepdim, return_mean=True + ) + + +def pow_recursive(x, y, dtype): + if y < 0: + return pow_recursive(ops.reciprocal(x), -y, dtype) + if y == 0: + return ops.constant(1, dtype) + if y == 1: + return x + + result = pow_recursive(x, y // 2, dtype) + result = ops.mul(result, result) + if (y % 2) == 1: + result = ops.mul(result, x) + return result + + +@make_pointwise +def pow_native(a, b): + return ops.pow(a, b) + + +fallback_pow_tensor_tensor = fallback_handler( + aten.pow.Tensor_Tensor, add_to_fallback_set=False +) +fallback_pow_scalar = fallback_handler(aten.pow.Scalar, add_to_fallback_set=False) +fallback_pow_tensor_scalar = fallback_handler( + aten.pow.Tensor_Scalar, add_to_fallback_set=False +) + + +@register_lowering(aten.pow, broadcast=True) +def pow(a, b): + if isinstance(b, float) and b == int(b): + return pow(a, int(b)) + elif isinstance(b, float) and b == 0.5: + return sqrt(a) + elif isinstance(b, int) and b == 1: + return clone(a) + + # Type promotion ensures all tensor arguments have the same type + dtype = next(x.get_dtype() for x in (a, b) if isinstance(x, ir.TensorBox)) + is_integer_pow = is_integer_dtype(dtype) + + # Optimize away small fixed powers, or for integers avoid falling back to ATen + embed_exponent = isinstance(b, int) and ( + -32 < b < 32 or (is_integer_pow and b >= 0) + ) + if embed_exponent: + loader = a.make_loader() + + def fn(idx): + return pow_recursive(loader(idx), b, a.get_dtype()) + + return Pointwise.create( + device=a.get_device(), + dtype=a.get_dtype(), + inner_fn=fn, + ranges=a.get_size(), + ) + + if isinstance(a, Number): + if a == 1: + return full_like(b, 1) + if a == 2 and is_float_dtype(b.get_dtype()): + return exp2(b) + + if is_integer_pow: + # ops.pow doesn't work for integers + if isinstance(a, Number): + return fallback_pow_scalar(a, b) + elif isinstance(b, Number): + return fallback_pow_tensor_scalar(a, b) + else: + return fallback_pow_tensor_tensor(a, b) + + return pow_native(a, b) + + +def mutate_to(changed, val, unsafe_alias=False): + if isinstance(changed, TensorBox): + changed_data = changed.data + else: + changed_data = changed + if isinstance(val, TensorBox): + val = val.data + + if not isinstance(val, ir.StorageBox): + # introduce a copy to handle views + val = Pointwise.create( + device=changed.get_device(), + dtype=changed.get_dtype(), + inner_fn=val.make_loader(), + ranges=changed.get_size(), + ).data + assert isinstance(val, ir.StorageBox) + + if isinstance(changed_data, ir.StorageBox) and not ( + changed_data.is_input_buffer() + # In AOTI, module parameters and buffers are not lifted as graph inputs + or changed_data.is_module_buffer() + or isinstance(changed_data.data, ir.NopKernel) + ): + # Fast path, just swing the data pointer + val.realize() + changed_data.data = val.data + return changed + + ir.MutationLayoutSHOULDREMOVE.realize_into( + val, changed_data, unsafe_alias=unsafe_alias + ) + return changed + + +@register_lowering(aten.fill_) +def fill_(x, fill_value): + return mutate_to(x, full_like(x, fill_value)) + + +@register_lowering(aten.copy_, type_promotion_kind=None) +def copy_(dst, src, non_blocking=False): + if dst is src: + # dst.copy_(dst) can happen from the reinplacing pass + return dst + src = to_device(src, dst.get_device()) + src = to_dtype(src, dst.get_dtype()) + src = expand(src, dst.get_size()) + return mutate_to(dst, src) + + +@make_pointwise +def floordiv(a, b): + return ops.floordiv(a, b) + + +@make_pointwise +def truncdiv(a, b): + return ops.truncdiv(a, b) + + +@register_lowering(aten.div, broadcast=True) +def div_mode(a, b, rounding_mode=None): + both_integer = is_integer_type(a) and is_integer_type(b) + both_boolean = is_boolean_type(a) and is_boolean_type(b) + + # floordiv and truncdiv need special handling for integer tensors on Triton, + # see the discussion at https://github.com/openai/triton/issues/605 + if rounding_mode == "floor": + assert not both_boolean, "floordiv operands can not be boolean at the same time" + return floordiv(a, b) if both_integer else floor(div(a, b)) + if rounding_mode == "trunc": + assert not both_boolean, "truncdiv operands can not be boolean at the same time" + return truncdiv(a, b) if both_integer else trunc(div(a, b)) + return div(a, b) + + +@register_lowering([aten.mul], broadcast=True) +def mul(a, b): + both_bool = is_boolean_type(a) and is_boolean_type(b) + if both_bool: + return logical_and(a, b) + else: + fn = ops_wrapper(aten.mul.__name__) + return make_pointwise(fn)(a, b) + + +def get_constant_value(x: ir.IRNode) -> Optional[ir.Constant]: + """Try convert an arbitrary IR node into an ir.Constant value""" + + # First try unwrapping the IRNode to see if it is already an ir.Constant + # Optional step, but avoids unnecessary inner_fn evaluation. + if isinstance(x, ir.MutableBox): + return get_constant_value(x.data) + if isinstance(x, ir.BaseView): + return get_constant_value(x.unwrap_view()) + if isinstance(x, ir.Constant): + return x + + # If the unwrapped node is not an ir.Constant, try evaluating inner_fn + # to see if the returned value is from an `ops.constant` call + if not isinstance(x, ir.Loops): + return None + + handler = torch._inductor.ops_handler.ExtractConstantsHandler(x.get_device()) + with V.set_ops_handler(handler), patch.object( + ir.FlexibleLayout, "allow_indexing", True + ): + out = x.inner_fn(*x.inner_fn_args()) + + assert isinstance(out, torch._inductor.virtualized.OpsValue) + if isinstance(out.value, ir.Constant): + return out.value + return None + + +# NOTE: prims.div maps to a / b in C, so performs truncation division on +# integer inputs and true division for floating and complex inputs. +@register_lowering([prims.div], broadcast=True) +def div_prim(a, b): + is_integral = all(is_boolean_type(x) or is_integer_type(x) for x in [a, b]) + + if is_integral: + return truncdiv(a, b) + + if (divisor := get_constant_value(b)) is not None: + # Replace divide by constant with multiply by reciprocal + if divisor.value == 0: + reciprocal = math.copysign(float("inf"), divisor.value) + else: + reciprocal = 1.0 / divisor.value + return mul(a, reciprocal) + + def fn(*args): + return ops.truediv(*args) + + return make_pointwise(fn)(a, b) + + +@register_lowering( + [aten.true_divide, aten.div.Tensor], + broadcast=True, + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def div(a, b): + a, b = promote_constants( + (a, b), type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT + ) + return div_prim(a, b) + + +@register_lowering([aten.fmod, prims.fmod], broadcast=True) +def fmod(a, b): + is_integral = is_boolean_type(a) or is_integer_type(a) + + if is_integral: + + def fn(a, b): + return ops.mod(a, b) + + else: + + def fn(a, b): + return ops.fmod(a, b) + + return make_pointwise(fn)(a, b) + + +@register_lowering(aten.rsqrt) +def rsqrt(x): + dtype = x.get_dtype() + if is_integer_dtype(dtype) or is_boolean_dtype(dtype): + x = to_dtype(x, torch.get_default_dtype()) + + def _rsqrt(x): + return ops.rsqrt(x) + + return make_pointwise(_rsqrt)(x) + + +@register_lowering([aten.sum, prims.sum]) +def sum_(x, axis=None, keepdims=False, *, dtype=None): + if ( + is_integer_dtype(x.get_dtype()) or is_boolean_dtype(x.get_dtype()) + ) and dtype is None: + dtype = torch.int64 + + fn = make_reduction("sum", override_return_dtype=dtype) + return fn(x, axis, keepdims, dtype=dtype) + + +fallback_cumsum = fallback_handler(aten.cumsum.default) +fallback_cumprod = fallback_handler(aten.cumprod.default) +fallback_logcumsumexp = fallback_handler(aten.logcumsumexp.default) +fallback_cummax = fallback_handler(aten.cummax.default) +fallback_cummin = fallback_handler(aten.cummin.default) + + +@register_lowering(aten.cumsum) +def cumsum(x, axis=None, dtype=None): + if ( + is_integer_dtype(x.get_dtype()) or is_boolean_dtype(x.get_dtype()) + ) and dtype is None: + dtype = torch.int64 + + if len(x.get_size()) == 0: + assert axis in [0, -1] + dtype = dtype or x.get_dtype() + return to_dtype(x, dtype, copy=True) + + def combine_fn(a_tuple, b_tuple): + (a,) = a_tuple + (b,) = b_tuple + return (ops.add(a, b),) + + kwargs = _make_scan_inner(x, axis=axis, dtype=dtype) + (result,) = ir.Scan.create(**kwargs, combine_fn=combine_fn) + if result is None: + return fallback_cumsum(x, dim=axis, dtype=dtype) + return result + + +@register_lowering(aten.cumprod) +def cumprod(x, axis=None, dtype=None): + if ( + is_integer_dtype(x.get_dtype()) or is_boolean_dtype(x.get_dtype()) + ) and dtype is None: + dtype = torch.int64 + + if len(x.get_size()) == 0: + assert axis in [0, -1] + dtype = dtype or x.get_dtype() + return to_dtype(x, dtype, copy=True) + + def combine_fn(a_tuple, b_tuple): + (a,) = a_tuple + (b,) = b_tuple + return (ops.mul(a, b),) + + kwargs = _make_scan_inner(x, axis=axis, dtype=dtype) + (result,) = ir.Scan.create(**kwargs, combine_fn=combine_fn) + if result is None: + return fallback_cumprod(x, dim=axis, dtype=dtype) + return result + + +@register_lowering(aten.logcumsumexp) +def logcumsumexp(x, dim): + def log_add_exp_helper(a_tuple, b_tuple): + (a,) = a_tuple + (b,) = b_tuple + min_v = ops.minimum(a, b) + max_v = ops.maximum(a, b) + mask = (min_v != max_v) | (~ops.isinf(min_v)) + return (ops.where(mask, ops.log1p(ops.exp(min_v - max_v)) + max_v, a),) + + dtype = x.get_dtype() + if len(x.get_size()) == 0: + assert dim in [0, -1] + return clone(x) + + kwargs = _make_scan_inner(x, axis=dim, dtype=dtype) + (result,) = ir.Scan.create(**kwargs, combine_fn=log_add_exp_helper) + if result is None: + return fallback_logcumsumexp(x, dim=dim) + return result + + +@register_lowering(aten.cummax, type_promotion_kind=None) +def cummax(x, axis=None): + if len(x.get_size()) == 0: + assert axis in [0, -1] + return clone(x), empty_like(x, dtype=torch.int64) + + dtype = x.get_dtype() + combine_fn = ir.get_reduction_combine_fn( + "argmax", dtype=dtype, arg_break_ties_left=False + ) + + min_value = ( + False + if dtype is torch.bool + else ( + torch.finfo(dtype).min + if dtype.is_floating_point + else torch.iinfo(dtype).min + ) + ) + + kwargs = _make_scan_inner(x, axis=axis, dtype=dtype) + kwargs["dtypes"] = (dtype, torch.int64) + kwargs["inner_fns"] = (x.make_loader(), lambda _: "rindex") + values, indices = ir.Scan.create(**kwargs, combine_fn=combine_fn) # type: ignore[arg-type] # next PR + if values is None: + return fallback_cummax(x, dim=axis) + return values, indices + + +@register_lowering(aten.cummin, type_promotion_kind=None) +def cummin(x, axis=None): + if len(x.get_size()) == 0: + assert axis in [0, -1] + return clone(x), empty_like(x, dtype=torch.int64) + + dtype = x.get_dtype() + combine_fn = ir.get_reduction_combine_fn( + "argmin", dtype=dtype, arg_break_ties_left=False + ) + + max_value = ( + True + if dtype is torch.bool + else ( + torch.finfo(dtype).max + if dtype.is_floating_point + else torch.iinfo(dtype).max + ) + ) + + kwargs = _make_scan_inner(x, axis=axis, dtype=dtype) + kwargs["dtypes"] = (dtype, torch.int64) + kwargs["inner_fns"] = (x.make_loader(), lambda _: "rindex") + values, indices = ir.Scan.create(**kwargs, combine_fn=combine_fn) # type: ignore[arg-type] # next PR + if values is None: + return fallback_cummin(x, dim=axis) + return values, indices + + +@register_lowering(aten.prod) +def prod(x, axis=None, keepdims=False, *, dtype=None): + if ( + is_integer_dtype(x.get_dtype()) or is_boolean_dtype(x.get_dtype()) + ) and dtype is None: + dtype = torch.int64 + + fn = make_reduction("prod", override_return_dtype=dtype) + return fn(x, axis, keepdims, dtype=dtype) + + +@register_lowering(aten.any) +def reduce_any(x, dim=None, keepdim=False): + x = to_dtype(x, torch.bool) + return make_reduction("any")(x, axis=dim, keepdims=keepdim) + + +@register_lowering(aten.max, type_promotion_kind=None) +def reduce_max(x, dim=None, keepdim=False): + if dim is not None: + return ( + reduce_amax(x, axis=dim, keepdims=keepdim), + reduce_argmax(x, axis=dim, keepdims=keepdim), + ) + + return reduce_amax(x, axis=None, keepdims=keepdim) + + +@register_lowering(aten.min, type_promotion_kind=None) +def reduce_min(x, dim=None, keepdim=False): + if dim is not None: + return ( + reduce_amin(x, axis=dim, keepdims=keepdim), + reduce_argmin(x, axis=dim, keepdims=keepdim), + ) + + return reduce_amin(x, axis=None, keepdims=keepdim) + + +register_lowering(prims.xor_sum)(make_reduction("xor_sum")) +reduce_amax = register_lowering(aten.amax)(make_reduction("max")) +reduce_amin = register_lowering(aten.amin)(make_reduction("min")) +reduce_argmax = register_lowering(aten.argmax)( + make_reduction("argmax", override_return_dtype=torch.int64) +) +reduce_argmin = register_lowering(aten.argmin)( + make_reduction("argmin", override_return_dtype=torch.int64) +) + +add = register_pointwise( + aten.add, allow_alpha=True, override_fn_when_input_bool="logical_or" +) + +sort_fallback = fallback_handler(aten.sort.stable, add_to_fallback_set=False) + + +@register_lowering(aten.sort.stable, type_promotion_kind=None) +def sort_stable(x, *, stable=None, dim=-1, descending=False): + if stable is None: + stable = False + + shape = x.get_size() + device = x.get_device() + dim = canonicalize_dim(len(shape), dim) + if len(shape) == 0: + return clone(x), _full(0, device, torch.int64, shape) + + dim_size = shape[dim] if len(shape) else 1 + if not V.graph.sizevars.statically_known_lt(dim_size, torch.iinfo(torch.int16).max): + return sort_fallback(x, stable=stable, dim=dim, descending=descending) + + indices = iota( + dim_size, start=0, step=1, dtype=torch.int16, device=device, requires_grad=False + ) + view_shape = [1] * len(shape) + if len(shape): + view_shape[dim] = dim_size + indices = view(indices, view_shape) + indices = expand(indices, shape) + + values, indices = ir.Sort.create( + device=device, + dtypes=(x.dtype, indices.dtype), + inner_fns=(x.make_loader(), indices.make_loader()), + size=shape, + axis=dim, + stable=stable, + descending=descending, + ) + if values is None: + return sort_fallback(x, stable=stable, dim=dim, descending=descending) + + assert indices is not None + return values, to_dtype(indices, torch.int64) + + +@register_lowering(aten.sort.default, type_promotion_kind=None) +def sort(x, dim=-1, descending=False): + return sort_stable(x, stable=False, dim=dim, descending=descending) + + +def register_pointwise_numeric(op, name=None, triton_fallback=None): + return register_pointwise( + op, + name=name, + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + triton_fallback=triton_fallback, + ) + + +def register_pointwise_numeric_ldf64(op): + return register_pointwise( + op, + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + use_libdevice_for_f64=True, + ) + + +exp = register_pointwise_numeric_ldf64(aten.exp) +exp2 = register_pointwise_numeric(aten.exp2) +expm1 = register_pointwise_numeric(aten.expm1) +relu = register_pointwise(aten.relu) +sigmoid = register_pointwise_numeric_ldf64(aten.sigmoid) +sqrt = register_pointwise_numeric_ldf64(aten.sqrt) +square = register_pointwise(aten.square) +sub = register_pointwise(aten.sub, allow_alpha=True) +register_pointwise_numeric_ldf64(aten.cos) +register_pointwise_numeric_ldf64(aten.sin) +abs = register_pointwise(aten.abs) +bitwise_and = register_pointwise(aten.bitwise_and) +bitwise_left_shift = register_pointwise(aten.bitwise_left_shift) +bitwise_not = register_pointwise( + aten.bitwise_not, override_fn_when_input_bool="logical_not" +) +bitwise_or = register_pointwise(aten.bitwise_or) +bitwise_right_shift = register_pointwise(aten.bitwise_right_shift) +bitwise_xor = register_pointwise(aten.bitwise_xor) +register_pointwise_numeric(aten.lgamma) +erf = register_pointwise_numeric(aten.erf) +register_lowering( + aten.special_erf, type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT +)(erf) + +register_pointwise_numeric(aten.log1p) +register_pointwise_numeric(aten.tan) +register_pointwise_numeric(aten.tanh) +register_pointwise_numeric_ldf64(aten.log) +logical_and = register_pointwise( + aten.logical_and, + type_promotion_kind=None, + convert_input_to_bool=True, + override_return_dtype=torch.bool, +) +logical_not = register_pointwise( + aten.logical_not, + type_promotion_kind=None, + convert_input_to_bool=True, + override_return_dtype=torch.bool, +) +logical_or = register_pointwise( + aten.logical_or, + type_promotion_kind=None, + convert_input_to_bool=True, + override_return_dtype=torch.bool, +) +logical_xor = register_pointwise( + aten.logical_xor, + type_promotion_kind=None, + convert_input_to_bool=True, + override_return_dtype=torch.bool, +) +maximum = register_pointwise(aten.maximum) +minimum = register_pointwise(aten.minimum) +register_lowering(aten.clamp_min)(maximum) +register_lowering(aten.clamp_max)(minimum) +neg = register_pointwise(aten.neg) +abs = register_pointwise(aten.abs) +reciprocal = register_pointwise_numeric(aten.reciprocal) +register_pointwise(aten.remainder) +sign = register_pointwise(aten.sign, override_fn_when_input_bool="identity") +register_pointwise(aten.ceil) +register_pointwise(aten.signbit, override_return_dtype=torch.bool) + +register_lowering(aten._neg_view)(neg) + +register_pointwise(aten.le, override_return_dtype=torch.bool) +register_pointwise(aten.lt, override_return_dtype=torch.bool) +register_pointwise(aten.ge, override_return_dtype=torch.bool) +gt = register_pointwise(aten.gt, override_return_dtype=torch.bool) +register_pointwise(aten.eq, override_return_dtype=torch.bool) +register_pointwise(aten.ne, override_return_dtype=torch.bool) + +register_pointwise_numeric(aten.cosh) +register_pointwise_numeric(aten.sinh) +register_pointwise_numeric(aten.acos) +register_pointwise_numeric(aten.acosh) +register_pointwise_numeric(aten.asin) +register_pointwise_numeric(aten.asinh) +register_pointwise_numeric(aten.atan2) +register_pointwise_numeric(aten.atan) +register_pointwise_numeric(aten.atanh) +register_pointwise_numeric(aten.copysign) +register_pointwise_numeric(aten.erfc) +register_pointwise_numeric(aten.erfinv) +register_pointwise_numeric(aten.hypot) +register_pointwise_numeric(aten.log10) +register_pointwise_numeric(aten.log2) +register_pointwise_numeric(aten.nextafter) + +from .codegen.common import BackendFeature, pointwise_overrides_data + + +def _get_pointwise_overrides(ns, name): + data = pointwise_overrides_data[name] + op = getattr(ns, data.name, None) + if op is None: + return + + def make_triton_fallback(op): + if data.triton is None: + return fallback_handler(op) + + if isinstance(op, torch._ops.OpOverloadPacket): + for olname in op.overloads(): + ol = getattr(op, olname) + yield ol, data.type_promotion_kind, make_triton_fallback(ol) + else: + yield op, data.type_promotion_kind, make_triton_fallback(op) + + +for name in pointwise_overrides_data: + for op, type_promotion_kind, triton_fallback in _get_pointwise_overrides( + aten, name + ): + register_pointwise( + op, + name=name, + type_promotion_kind=type_promotion_kind, + triton_fallback=triton_fallback, + ) + + for op, type_promotion_kind, triton_fallback in _get_pointwise_overrides( + prims, name + ): + register_pointwise( + op, + name=name, + type_promotion_kind=type_promotion_kind, + triton_fallback=triton_fallback, + ) + + +foreach_add_list = register_foreach_pointwise( + aten._foreach_add.List, add, allow_alpha=True +) +foreach_add_scalar = register_foreach_pointwise( + aten._foreach_add.Scalar, add, allow_alpha=True +) +register_foreach_pointwise(aten._foreach_add.Tensor, add, allow_alpha=True) +foreach_mul_list = register_foreach_pointwise(aten._foreach_mul.List, mul) +foreach_mul_scalar = register_foreach_pointwise(aten._foreach_mul.Scalar, mul) +register_foreach_pointwise(aten._foreach_sub.List, sub) +register_foreach_pointwise(aten._foreach_sub.Scalar, sub) +register_foreach_pointwise(aten._foreach_neg.default, neg) +register_foreach_pointwise(aten._foreach_abs.default, abs) +register_foreach_pointwise(aten._foreach_pow.Scalar, pow) +register_foreach_pointwise(aten._foreach_pow.ScalarAndTensor, pow) +foreach_div_list = register_foreach_pointwise(aten._foreach_div.List, div) +foreach_div_scalar = register_foreach_pointwise(aten._foreach_div.Scalar, div) +register_foreach_pointwise(aten._foreach_sqrt, sqrt) +register_foreach_pointwise(aten._foreach_maximum.List, maximum) +register_foreach_pointwise(aten._foreach_maximum.Scalar, maximum) +register_foreach_pointwise(aten._foreach_minimum.List, minimum) +register_foreach_pointwise(aten._foreach_minimum.Scalar, minimum) +register_foreach_pointwise(aten._foreach_clamp_min.List, maximum) +register_foreach_pointwise(aten._foreach_clamp_min.Scalar, maximum) +register_foreach_pointwise(aten._foreach_clamp_max.List, minimum) +register_foreach_pointwise(aten._foreach_clamp_max.Scalar, minimum) +register_foreach_pointwise(aten._foreach_reciprocal, reciprocal) +register_foreach_pointwise(aten._foreach_sign, sign) +register_foreach_pointwise(aten._foreach_copy, copy) + + +# these are only encountered as outputs of the graph +# reinplacing epilogue copies improves compile time +# by removing extra buffers sent to the scheduler. +def register_foreach_inplace(aten_op, outplace_aten_op, outplace_op): + inplaceable_foreach_ops[outplace_aten_op] = aten_op + inplace_foreach_ops.add(aten_op) + + def fn(*args, **kwargs): + results = outplace_op(*args, **kwargs) + mut_results = [] + for arg, result in zip(args[0], results): + mut_results.append(mutate_to(arg, result, unsafe_alias=True)) + + return mut_results + + _register_foreach_lowering(aten_op, fn) + + +register_foreach_inplace( + aten._foreach_add_.List, aten._foreach_add.List, foreach_add_list +) +register_foreach_inplace( + aten._foreach_add_.Scalar, aten._foreach_add.Scalar, foreach_add_scalar +) +register_foreach_inplace( + aten._foreach_mul_.List, aten._foreach_mul.List, foreach_mul_list +) +register_foreach_inplace( + aten._foreach_mul_.Scalar, aten._foreach_mul.Scalar, foreach_mul_scalar +) +register_foreach_inplace( + aten._foreach_div_.List, aten._foreach_div.List, foreach_div_list +) +register_foreach_inplace( + aten._foreach_div_.Scalar, aten._foreach_div.Scalar, foreach_div_scalar +) + + +def register_inplace(aten_op, outplace_op): + @register_lowering(aten_op, type_promotion_kind=None) + def fn(*args, **kwargs): + result = outplace_op(*args, **kwargs) + result = to_dtype(result, args[0].get_dtype()) + return mutate_to(args[0], result) + + return fn + + +register_inplace(aten.add_, add) +register_inplace(aten.bitwise_and_, bitwise_and) +register_inplace(aten.bitwise_left_shift_, bitwise_left_shift) +register_inplace(aten.bitwise_not_, bitwise_not) +register_inplace(aten.bitwise_or_, bitwise_or) +register_inplace(aten.bitwise_right_shift_, bitwise_right_shift) +register_inplace(aten.bitwise_xor_, bitwise_xor) +register_inplace(aten.mul_, mul) +register_inplace(aten.div_.Tensor, div) +register_inplace(aten.div_.Tensor_mode, div_mode) +register_inplace(aten.logical_and_, logical_and) +register_inplace(aten.logical_not_, logical_not) +register_inplace(aten.logical_or_, logical_or) +register_inplace(aten.logical_xor_, logical_xor) +register_inplace(aten.sub_, sub) +register_inplace(aten.relu_, relu) +register_inplace(aten.sigmoid_, sigmoid) + + +register_lowering(aten.__and__)(bitwise_and) +register_lowering(aten.__lshift__)(bitwise_left_shift) +register_lowering(aten.__or__)(bitwise_or) +register_lowering(aten.__rshift__)(bitwise_right_shift) +register_lowering(aten.__xor__)(bitwise_xor) + +register_inplace(aten.__iand__, aten.__and__) +register_inplace(aten.__ilshift__, aten.__lshift__) +register_inplace(aten.__ior__, aten.__or__) +register_inplace(aten.__irshift__, aten.__rshift__) +register_inplace(aten.__ixor__, aten.__xor__) + + +@register_lowering(aten.sym_constrain_range) +def sym_constrain_range(a, min=None, max=None): + return None + + +@register_lowering(aten.sym_size.int) +def sym_size(a, dim): + val = V.graph.current_node.meta["val"] + # Note [Can val be an int?] + # ~~~~~~~~~~~~~~~~~~~~~~~~~ + # In principle, someone could construct an FX graph where + # a call to size/stride has a val that is a plain int (not + # SymInt). However, we will maintain the invariant that + # this is not possible: if you are constructing an FX graph + # where there is a call to size/stride that returns an + # int, but you KNOW that int must always be a constant, + # then you do not need trace that call at all (and just + # constant propagate the integer as is.) + assert isinstance(val, torch.SymInt) + return val.node.expr + + +@register_lowering(aten.sym_stride.int) +def sym_stride(a, dim): + val = V.graph.current_node.meta["val"] + # See Note [Can val be an int?] + assert isinstance(val, torch.SymInt) + return val.node.expr + + +@register_lowering(aten.sym_numel) +def sym_numel(a): + return a.get_numel() + + +for method, func in magic_methods.items(): + register_lowering(method_to_operator(method))(func) + + +@register_lowering(aten._foobar) +def foobar(self, *args, **kwargs): + raise NotImplementedError("Helpful for debugging") + + +@register_lowering(torch.ops._inductor_test.realize) +def _realize(x): + x.realize() + return clone(x) + + +@register_lowering(torch.ops.inductor.resize_storage_bytes_) +def resize_storage_bytes_(variable, new_size): + variable.realize() + ir.ResizeStorageBytes(variable, new_size) + return variable + + +@register_lowering(torch.ops.aten.set_.source_Tensor) +def set__source_tensor(self, source_tensor): + self.realize() + source_tensor.realize() + return TensorBox.create(ir.SetSourceTensorKernel(self, source_tensor)) + + +if hasattr(torch.ops.fsdp, "set_"): + + @register_lowering(torch.ops.fsdp.set_.default) + def fsdp_set_(self, source_tensor): + self.realize() + source_tensor.realize() + ir.SetSourceTensorKernel(self, source_tensor) + + +@register_lowering(torch.ops.aten.resize) +def resize(x, size, *, memory_format=None): + assert isinstance(x, TensorBox) + assert isinstance(size, (list, tuple)) + + if memory_format is None: + memory_format = torch.contiguous_format + if memory_format == torch.preserve_format: + raise RuntimeError(f"unsupported memory format: {memory_format}") + + if memory_format == torch.channels_last: + assert len(size) == 4 + if memory_format == torch.channels_last_3d: + assert len(size) == 5 + + old_numel = x.get_numel() + dtype = x.get_dtype() + device = x.get_device() + + if isinstance(x.data, ir.BaseView): + x.data = x.data.unwrap_view() + + if ( + torch.are_deterministic_algorithms_enabled() + and torch.utils.deterministic.fill_uninitialized_memory # type: ignore[attr-defined] + ): + if is_float_dtype(dtype): + uninitalized_val = float("nan") + elif is_integer_dtype(dtype): + uninitalized_val = torch.iinfo(dtype).max + else: + uninitalized_val = True + else: + # using zero as that is what empty does + uninitalized_val = 0.0 + + if V.graph.sizevars.statically_known_equals(old_numel, 0): # type: ignore[arg-type] + return full(size, uninitalized_val, dtype=dtype, device=device) + + x_flat = as_strided( + x, + [ + old_numel, + ], + [ + 1, + ], + ) + flat_loader = x_flat.make_loader() + out_stride = ir.FlexibleLayout.stride_ordered_for_memory_format(size, memory_format) + out_indexer = ir.FixedLayout(device, dtype, size, out_stride).make_indexer() + + def inner_fn(idx): + flat_index = out_indexer(idx) + flat_index_expr = ops.index_expr(flat_index, torch.int64) + limit = ops.index_expr(old_numel, torch.int64) + mask = ops.lt(flat_index_expr, limit) + return ops.masked(mask, lambda: flat_loader([flat_index]), uninitalized_val) + + out = Pointwise.create( + device=device, dtype=dtype, inner_fn=inner_fn, ranges=list(size) + ) + return out + + +from torch._higher_order_ops.auto_functionalize import auto_functionalized + + +make_fallback(auto_functionalized) + + +@register_lowering(triton_kernel_wrapper_mutation) +def triton_kernel_wrap_(*, kernel_idx, constant_args_idx, grid, kwargs): + from torch._higher_order_ops.triton_kernel_wrap import kernel_side_table + + constant_args = kernel_side_table.get_constant_args(constant_args_idx) + ir.UserDefinedTritonKernel( + kernel_idx=kernel_idx, + grid=grid, + kernel_args={**kwargs, **constant_args}, + ) + return {key: val for key, val in kwargs.items() if isinstance(val, TensorBox)} + + +@register_lowering(torch.ops.higher_order.cond) +def cond(pred, true_fn, false_fn, operands): + if is_triton(pred) or any(map(is_triton, operands)): + msg = "control flow operator: torch.cond." + if stack_trace := V.graph.current_node.meta.get("stack_trace", None): + msg = f"{msg} Found from : \n {stack_trace}" + V.graph.disable_cudagraphs_reason = msg + + result = ir.Conditional.create(pred, true_fn, false_fn, operands) + return list(map(TensorBox.create, result)) + + +@register_lowering(torch.ops.higher_order.while_loop) +def while_loop(cond_fn, body_fn, carried_inputs, additional_inputs): + if any(map(is_triton, carried_inputs + additional_inputs)): + msg = "control flow operator: torch.while_loop." + if stack_trace := V.graph.current_node.meta.get("stack_trace", None): + msg = f"{msg} Found from : \n {stack_trace}" + V.graph.disable_cudagraphs_reason = msg + + result = ir.WhileLoop.create(cond_fn, body_fn, carried_inputs, additional_inputs) + return list(map(TensorBox.create, result)) + + +@register_lowering(associative_scan_op, type_promotion_kind=None) +def associative_scan(combine_fn: ir.Subgraph, input, dim: int): + from .subgraph_lowering import InputDescriptor, lower_pointwise_subgraph + + subgraph_inputs = [ + InputDescriptor(dtype=x.get_dtype(), device=x.get_device()) + for x in itertools.chain(input, input) + ] + lowered_combine_fn = lower_pointwise_subgraph(combine_fn, subgraph_inputs) # type: ignore[var-annotated] + + def wrapped_combine_fn(lhs, rhs): + return lowered_combine_fn( + *pytree.tree_leaves(lhs), + *pytree.tree_leaves(rhs), + ) + + kwargs = _make_scan_inner(input[0], axis=dim, dtype=None) + kwargs["dtypes"] = tuple(x.get_dtype() for x in input) + kwargs["inner_fns"] = tuple(x.make_loader() for x in input) + result = ir.Scan.create( + combine_fn=wrapped_combine_fn, + can_fallback_to_aten=False, + **kwargs, + ) + if result[0] is None: + raise RuntimeError("Unable to generate code for associative_scan op") + return result + + +@register_lowering(torch.ops.prims._sink_tokens.default) +def _sink_tokens(tokens): + return None + + +@register_lowering(torch.ops.higher_order.with_effects) +def with_effects(token, op, *args, **kwargs): + result = ir.EffectfulKernel.create(op, *args, **kwargs) + + from torch._higher_order_ops.effects import get_effect_key + + effect_type = get_effect_key(op, args, kwargs) + assert effect_type is not None + effectful_kernel = V.graph.effectful_ops[effect_type] + + if result is None: + return (effectful_kernel,) + + result = pytree.tree_map_only(ir.MultiOutput, TensorBox.create, result) + if not isinstance(result, (list, tuple)): + return (effectful_kernel, result) + else: + return (effectful_kernel, *result) + + +try: + import torch.distributed._functional_collectives + + _c10d_functional = torch.ops._c10d_functional + + @register_lowering(_c10d_functional.all_reduce) + def _all_reduce(inp, reduce_op, group_name): + inp = clone(inp) + if config.reorder_for_compute_comm_overlap: + # The horizontal fusion of this clone often severely delays the + # scheduling of the all_reduce_ node. Horizontally fusing this + # clone can almost never out-perform scheduling the all_reduce_ + # earlier. Also in most cases, this clone is eliminated via + # in-place reuse. Therefore, we tell the scheduler to not fuse it. + inp.realize() + V.graph.no_fuse_buffer_names.add(inp.get_name()) + ir._CollectiveKernel.create_inplace( + _c10d_functional.all_reduce_.default, inp, reduce_op, group_name + ) + return inp + + @register_lowering(_c10d_functional.all_reduce_) + def _all_reduce_(inp, reduce_op, group_name): + ir._CollectiveKernel.create_inplace( + _c10d_functional.all_reduce_.default, inp, reduce_op, group_name + ) + return inp + + @register_lowering(_c10d_functional.all_reduce_coalesced) + def _all_reduce_coalesced(inputs, reduce_op, group_name): + inputs = [clone(inp) for inp in inputs] + ir._CollectiveKernel.create_inplace( + _c10d_functional.all_reduce_coalesced_.default, + inputs, + reduce_op, + group_name, + ) + return inputs + + @register_lowering(_c10d_functional.all_reduce_coalesced_) + def _all_reduce_coalesced_(inputs, reduce_op, group_name): + ir._CollectiveKernel.create_inplace( + _c10d_functional.all_reduce_coalesced_.default, + inputs, + reduce_op, + group_name, + ) + return inputs + + @register_lowering(_c10d_functional.all_gather_into_tensor) + def _all_gather_into_tensor(inp, group_size, group_name): + return ir.TensorBox.create( + ir._CollectiveKernel.create_out_of_place( + _c10d_functional.all_gather_into_tensor.default, + inp, + group_size, + group_name, + ) + ) + + @register_lowering(_c10d_functional.all_gather_into_tensor_coalesced) + def _all_gather_into_tensor_coalesced(inputs, group_size, group_name): + return pytree.tree_map( + ir.TensorBox.create, + ir._CollectiveKernel.create_out_of_place( + _c10d_functional.all_gather_into_tensor_coalesced.default, + inputs, + group_size, + group_name, + ), + ) + + @register_lowering(_c10d_functional.all_gather_into_tensor_out) + def _all_gather_into_tensor_out(inp, group_size, group_name, *, out): + ir._CollectiveKernel.create_inplace( + _c10d_functional.all_gather_into_tensor_out.default, + inp, + group_size, + group_name, + out=out, + ) + return out + + @register_lowering(_c10d_functional.reduce_scatter_tensor) + def _reduce_scatter_tensor(inp, reduce_op, group_size, group_name): + return ir.TensorBox.create( + ir._CollectiveKernel.create_out_of_place( + _c10d_functional.reduce_scatter_tensor.default, + inp, + reduce_op, + group_size, + group_name, + ) + ) + + @register_lowering(_c10d_functional.reduce_scatter_tensor_coalesced) + def _reduce_scatter_tensor_coalesced(inputs, reduce_op, group_size, group_name): + return pytree.tree_map( + ir.TensorBox.create, + ir._CollectiveKernel.create_out_of_place( + _c10d_functional.reduce_scatter_tensor_coalesced.default, + inputs, + reduce_op, + group_size, + group_name, + ), + ) + + @register_lowering(_c10d_functional.all_to_all_single) + def _all_to_all_single(inp, output_split_sizes, input_split_sizes, group_name): + return ir.TensorBox.create( + ir._CollectiveKernel.create_out_of_place( + _c10d_functional.all_to_all_single.default, + inp, + output_split_sizes, + input_split_sizes, + group_name, + ) + ) + + @register_lowering(_c10d_functional.broadcast) + def _broadcast(inp, src, group_name): + inp = clone(inp) + ir._CollectiveKernel.create_inplace( + _c10d_functional.broadcast_.default, inp, src, group_name + ) + return inp + + @register_lowering(_c10d_functional.broadcast_) + def _broadcast_(inp, src, group_name): + ir._CollectiveKernel.create_inplace( + _c10d_functional.broadcast_.default, inp, src, group_name + ) + return inp + + @register_lowering(_c10d_functional.wait_tensor) + def _wait_tensor(inp): + ir._WaitKernel.create_wait(_c10d_functional.wait_tensor.default, inp) + return inp + + @register_lowering(torch.ops._dtensor.shard_dim_alltoall) + def _shard_dim_alltoall(inp, gather_dim, shard_dim, group_name): + return ir.TensorBox.create( + ir._CollectiveKernel.create_out_of_place( + torch.ops._dtensor.shard_dim_alltoall.default, + inp, + gather_dim, + shard_dim, + group_name, + ) + ) + +except (AttributeError, ImportError): + log.info( + "Inductor support for distributed collectives depends on building torch.distributed" + ) + +# populate lowerings defined in kernel/* +from . import kernel + + +import_submodule(kernel) + +from . import quantized_lowerings + + +quantized_lowerings.register_quantized_ops() +quantized_lowerings.register_woq_mm_ops() + +from . import mkldnn_lowerings + + +mkldnn_lowerings.register_onednn_fusion_ops() + +from . import jagged_lowerings + + +jagged_lowerings.register_jagged_ops() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/metrics.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..fe77279800e3da5ae581cb684c340726b9b3827d --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/metrics.py @@ -0,0 +1,436 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import csv +import dataclasses +import inspect +import os +import re +from dataclasses import dataclass +from functools import lru_cache +from typing import Dict, List, Set, Tuple, TYPE_CHECKING + +from torch._inductor import config +from torch._inductor.utils import get_benchmark_name + + +# Prevent circular import +if TYPE_CHECKING: + from torch._inductor.scheduler import BaseSchedulerNode + +# counter for tracking how many kernels have been generated +generated_kernel_count = 0 +generated_cpp_vec_kernel_count = 0 +num_bytes_accessed = 0 +nodes_num_elem: List[ + Tuple[ + BaseSchedulerNode, + int, + ] +] = [] +node_runtimes: List[Tuple[BaseSchedulerNode, float]] = [] + +# counters for tracking fusions +ir_nodes_pre_fusion = 0 + +# counters for tracking to_dtype inserted +cpp_to_dtype_count = 0 + + +@dataclasses.dataclass +class CppOuterLoopFusedCount: + inner_kernel_number: int + local_buffer_number: int = 0 + + +# The length counts the number of outer loop fusions. +cpp_outer_loop_fused_inner_counts: List[CppOuterLoopFusedCount] = [] + +num_comprehensive_padding = 0 +num_matches_for_scatter_upon_const_tensor = 0 + +num_loop_reordering = 0 + + +# reset all counters +def reset(): + global generated_kernel_count + global generated_cpp_vec_kernel_count + global num_bytes_accessed, nodes_num_elem + global ir_nodes_pre_fusion + global cpp_to_dtype_count + global cpp_outer_loop_fused_inner_counts + global num_comprehensive_padding + global num_matches_for_scatter_upon_const_tensor + global num_loop_reordering + + generated_kernel_count = 0 + generated_cpp_vec_kernel_count = 0 + num_bytes_accessed = 0 + nodes_num_elem.clear() + node_runtimes.clear() + ir_nodes_pre_fusion = 0 + cpp_to_dtype_count = 0 + cpp_outer_loop_fused_inner_counts.clear() + num_comprehensive_padding = 0 + num_matches_for_scatter_upon_const_tensor = 0 + num_loop_reordering = 0 + + +@dataclass +class CachedMetricsDeltas: + """ + The subset of metrics we want update across cache hits, e.g., the + FxGraphCache. + """ + + generated_kernel_count: int + generated_cpp_vec_kernel_count: int + ir_nodes_pre_fusion: int + cpp_to_dtype_count: int + num_bytes_accessed: int + num_matches_for_scatter_upon_const_tensor: int + + +def get_metric_fields(): + return [field.name for field in dataclasses.fields(CachedMetricsDeltas)] + + +class CachedMetricsHelper: + """ + A helper class to help calculate and apply counter deltas for those + metrics we want to save with cache entries (e.g., FxGraphCache) and + apply on a cache hit. + """ + + def __init__(self) -> None: + self.cached_metrics = {} + for metric in get_metric_fields(): + self.cached_metrics[metric] = globals()[metric] + + def get_deltas(self) -> CachedMetricsDeltas: + delta_metrics = {} + for metric in get_metric_fields(): + delta_metrics[metric] = globals()[metric] - self.cached_metrics[metric] + + return CachedMetricsDeltas(**delta_metrics) + + @staticmethod + def apply_deltas(delta: CachedMetricsDeltas): + for metric in get_metric_fields(): + globals()[metric] += getattr(delta, metric) + + +REGISTERED_METRIC_TABLES: Dict[str, MetricTable] = {} + + +@dataclass +class MetricTable: + table_name: str + column_names: List[str] + + num_rows_added: int = 0 + + def add_row(self, row_fn): + if self.table_name not in enabled_metric_tables(): + return + + row_dict = row_fn() + assert len(self.column_names) == len( + row_dict + ), f"{len(self.column_names)} v.s. {len(row_dict)}" + assert set(self.column_names) == set( + row_dict.keys() + ), f"{set(self.column_names)} v.s. {set(row_dict.keys())}" + + row = [ + get_benchmark_name(), + ] + row += [row_dict[column_name] for column_name in self.column_names] + self._write_row(row) + + def output_filename(self): + return f"metric_table_{self.table_name}.csv" + + def write_header(self): + filename = self.output_filename() + with open(filename, "w") as fd: + writer = csv.writer(fd, lineterminator="\n") + writer.writerow(["model_name"] + self.column_names) + + def _write_row(self, row): + filename = self.output_filename() + if self.num_rows_added == 0 and not os.path.exists(filename): + self.write_header() + + self.num_rows_added += 1 + + for idx, orig_val in enumerate(row): + if isinstance(orig_val, float): + new_val = f"{orig_val:.6f}" + elif orig_val is None: + new_val = "" + else: + new_val = orig_val + row[idx] = new_val + + with open(filename, "a") as fd: + writer = csv.writer(fd, lineterminator="\n") + writer.writerow(row) + + @staticmethod + def register_table(name, column_names): + table = MetricTable(name, column_names) + REGISTERED_METRIC_TABLES[name] = table + + +MetricTable.register_table( + "slow_fusion", + [ + "kernel1_path", + "kernel1_latency", + "kernel2_path", + "kernel2_latency", + "fused_kernel_path", + "fused_kernel_latency", + "slow_down_ratio", + ], +) + +# track the fusion statistics for each graph +MetricTable.register_table( + "graph_stats", + [ + "graph_id", + "num_nodes_before_fusion", + "num_nodes_after_fusion", + ], +) + +# track the perf difference between persistent reduction and non-persistent +# reductions +MetricTable.register_table( + "persistent_red_perf", + [ + "kernel1_name", + "kernel2_name", + "kernel1_latency", + "kernel2_latency", + "size_hints", + "reduction_hint", + "speedup", + ], +) + +# Log the fusion failures due to indexing mismatch +MetricTable.register_table( + "fusion_failure_due_to_indexing_mismatch", + [ + "pre_grad_graph_id", + "post_grad_graph_id", + "node1_name", + "node2_name", + "node1_debug_str", + "node2_debug_str", + "common_buffer_names", + "failure_reason", + ], +) + +# Log metadata for pointwise/reduction kernels. E.g., model name, kernel path, numel, rnumel, reduction hint +MetricTable.register_table( + "kernel_metadata", + [ + "kernel_name", + "kernel_path", + "kernel_category", # pointwise/reduction/foreach etc. + "size_hints", + "reduction_hint", + "line_of_code", + "num_load", + "num_store", + "num_for_loop", + "num_atomic_add", + "num_args", + # xyz numel can be different to size_hints since size_hints are rounded + # up to the nearest power of 2. + # Inductor kernel will burn in the xyz numel in kernel code for static + # shape kernels. + # Logging them will be helpful to find unaligned shape for reduction + "xnumel", + "ynumel", + "rnumel", + "kernel_args_num_gb", + ], +) + + +def _parse_kernel_fn_code(kernel_module_code): + """ + The kernel_module_code is the python module that contains kernel function code. + kernel function is the proper triton kernel function annotated with + @triton.jit + """ + from .codecache import PyCodeCache + from .wrapper_benchmark import get_triton_kernel + + mod = PyCodeCache.load(kernel_module_code) + kernel = get_triton_kernel(mod) + # kernel is a CachingAutotune; kernel.fn is the JITFunction; + # kernel.fn.fn is the function being decorate by triton.jit + return inspect.getsource(kernel.fn.fn) + + +def _parse_kernel_line_of_code(proper_kernel_fn_code): + """ + Return the line of code for the kernel excluding the decorators. + """ + return len(proper_kernel_fn_code.splitlines()) + + +def _parse_size_hints(kernel_module_code, kernel_category): + if kernel_category == "foreach": + # foreach kernel does not have size_hints + return None + m = re.search(r"size_hints=(\[[0-9, ]*\]),", kernel_module_code) + assert m, "size_hints missing!" + return m.group(1) + + +def _parse_reduction_hint(kernel_category, kernel_module_code): + if kernel_category not in ("reduction", "persistent_reduction"): + return None + m = re.search(r"reduction_hint=ReductionHint\.(\w*),", kernel_module_code) + assert m, "reduction_hint not found in kernel source code!" + return m.group(1) + + +def _count_pattern(proper_kernel_fn_code, pattern): + return proper_kernel_fn_code.count(pattern) + + +def _count_args(proper_kernel_fn_code): + def_line = proper_kernel_fn_code.splitlines()[0] + assert def_line.startswith("def ") + start_idx = def_line.index("(") + end_idx = def_line.index("):") + decl_csv = def_line[start_idx + 1 : end_idx] + comps = decl_csv.split(",") + return len(comps) + + +def _parse_proper_kernel_fn_code(kernel_fn_code): + """ + Skip decorators. + """ + start_pos = kernel_fn_code.index("def ") + return kernel_fn_code[start_pos:] + + +def _parse_numel(proper_kernel_fn_code, numel_arg_name): + m = re.search(f"{numel_arg_name} = ([\\d]+)", proper_kernel_fn_code) + if m: + return int(m.group(1)) + else: + return None + + +def _parse_kernel_args_num_gb(kernel_fn_code, kernel_category): + """ + inductor meta looks like: + inductor_meta={... 'mutated_arg_names': [], 'no_x_dim': False, 'kernel_num_gb': 2.0}, + """ + m = re.search(r".kernel_num_gb.:\s*([0-9.]+)", kernel_fn_code) + if m: + return float(m.group(1)) + else: + """ + There are a few cases that kernel_num_gdb field can be missing: + 1. the field will be missing if config.benchmark_kernel and + config.profile_bandwidth are false + 2. even if config.benchmark_kernel or config.profile_bandwidth is true. + foreach kernel does not have kernel_num_gb field in the metadata + """ + return None + + +def log_kernel_metadata(kernel_name, kernel_path, kernel_module_code): + """ + An utility to log kernel metadata. We may parse metadata from kernel source code here. + + It's fine to parse the generated kernel code here since the logging is + disabled by default. It would hurt compilation time. + """ + from .wrapper_benchmark import get_kernel_category_by_source_code + + kernel_category = get_kernel_category_by_source_code(kernel_module_code) + reduction_hint = _parse_reduction_hint(kernel_category, kernel_module_code) + size_hints = _parse_size_hints(kernel_module_code, kernel_category) + kernel_fn_code = _parse_kernel_fn_code(kernel_module_code) + + proper_kernel_fn_code = _parse_proper_kernel_fn_code(kernel_fn_code) + + # the line of code excluding the decortors + kernel_line_of_code = _parse_kernel_line_of_code(proper_kernel_fn_code) + + get_metric_table("kernel_metadata").add_row( + lambda: { + "kernel_name": kernel_name, + "kernel_path": kernel_path, + "kernel_category": kernel_category, + "size_hints": size_hints, + "reduction_hint": reduction_hint, + "line_of_code": kernel_line_of_code, + "num_load": _count_pattern(proper_kernel_fn_code, "tl.load"), + "num_store": _count_pattern(proper_kernel_fn_code, "tl.store"), + "num_for_loop": _count_pattern(proper_kernel_fn_code, "for "), + "num_atomic_add": _count_pattern(proper_kernel_fn_code, "tl.atomic_add"), + "num_args": _count_args(proper_kernel_fn_code), + "xnumel": _parse_numel(proper_kernel_fn_code, "xnumel"), + "ynumel": _parse_numel(proper_kernel_fn_code, "ynumel"), + "rnumel": _parse_numel(proper_kernel_fn_code, "rnumel"), + "kernel_args_num_gb": _parse_kernel_args_num_gb( + kernel_fn_code, kernel_category + ), + } + ) + + +def purge_old_log_files(): + """ + Purge the old log file at the beginning when the benchmark script runs. + Should do it in the parent process rather than the child processes running + each individual model. + """ + for name, table in REGISTERED_METRIC_TABLES.items(): + if name in enabled_metric_tables(): + filename = table.output_filename() + if os.path.exists(filename): + os.unlink(filename) + + table.write_header() + + +@lru_cache +def enabled_metric_tables() -> Set[str]: + config_str = config.enabled_metric_tables + + enabled = set() + for name in config_str.split(","): + name = name.strip() + if not name: + continue + assert ( + name in REGISTERED_METRIC_TABLES + ), f"Metric table name {name} is not registered" + enabled.add(name) + return enabled + + +def is_metric_table_enabled(name): + return name in enabled_metric_tables() + + +def get_metric_table(name): + assert name in REGISTERED_METRIC_TABLES, f"Metric table {name} is not defined" + return REGISTERED_METRIC_TABLES[name] diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/mkldnn_ir.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/mkldnn_ir.py new file mode 100644 index 0000000000000000000000000000000000000000..12634c632b80a9e6c9dd52ea9b8ddf2b190b1943 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/mkldnn_ir.py @@ -0,0 +1,1881 @@ +# mypy: allow-untyped-defs +from typing import Any, List, Optional + +import sympy + +import torch +from torch._prims_common import make_channels_last_strides_for +from torch.utils._ordered_set import OrderedSet + +from .ir import ( + ExternKernelAlloc, + FixedLayout, + FlexibleLayout, + ir_node_to_tensor, + IRNode, + is_contiguous_storage_and_layout, + Layout, + may_convert_to_optional, + MultiOutput, + MultiOutputLayout, + MutationOutput, + NoneLayout, + TensorBox, +) +from .utils import convert_shape_to_inductor, pad_listlike +from .virtualized import V + + +def _prepare_convolution_fusion_create( + cls, + x: "TensorBox", + weight: "TensorBox", + bias: "TensorBox", + padding: List[int], + stride: List[int], + dilation: List[int], + groups: int, + transposed: bool = False, + output_padding: Optional[List[int]] = None, +): + """ + This function is a helper function to prepare inputs, layout and constant args + for convolution post-op fusion's create function, including deciding the output + layout (channels first or channels last), realizing inputs and make them etc. The + function only supports the CPU device since conv post-op fusion kernel is only + supported on CPU right now. + """ + + # Port from aten/src/ATen/native/ConvUtils.h: _conv_input_size + def _conv_input_size( + output_size, weight_size, padding, output_padding, stride, dilation, groups + ): + assert len(output_size) == len(weight_size), "Expect input dim == weight dim" + dim = len(output_size) + assert dim > 2, "Expect input dim > 2" + + BATCH_DIM = 0 + WEIGHT_INPUT_CHANNELS_DIM = 1 + input_size = [] + input_size.append(output_size[BATCH_DIM]) + input_size.append(weight_size[WEIGHT_INPUT_CHANNELS_DIM] * groups) + for d in range(2, dim): + kernel = (weight_size[d] - 1) * dilation[d - 2] + 1 + input_size_d = ( + (output_size[d] - 1) * stride[d - 2] + - (padding[d - 2] * 2) + + kernel + + output_padding[d - 2] + ) + input_size.append(input_size_d) + return list(map(int, input_size)) + + # The size of prepacked_weight is the prepacked weight size of deconv: + # Groups > 1: [g*o, i/g, ...] + # Groups == 1: [o, i, ...] + # Returns original weight size in [i, o, ...] + def _original_deconv_weight_size( + prepacked_weight, + groups, + ): + prepacked_weight_size = prepacked_weight.size() + dim = len(prepacked_weight_size) + assert dim > 2, "Expect weight dim > 2" + if groups > 1: + weight_size = [] + weight_size.append(prepacked_weight_size[1] * groups) + weight_size.append(prepacked_weight_size[0] / groups) + for d in range(2, dim): + weight_size.append(prepacked_weight_size[d]) + else: + weight_size = prepacked_weight.transpose(0, 1).size() + return weight_size + + x.realize() + weight.realize() + if bias is not None: + bias.realize() + with V.graph.fake_mode: + # TODO cleaned up the fake_tensor trace as Linear implementation + x_fake = ir_node_to_tensor(x, guard_shape=True) + weight_fake = ir_node_to_tensor(weight, guard_shape=True) + dims = len(x_fake.size()) - 2 + assert 0 < len(padding) <= dims + assert 0 < len(dilation) <= dims + assert 0 < len(stride) <= dims + padding = pad_listlike(padding, dims) + dilation = pad_listlike(dilation, dims) + stride = pad_listlike(stride, dims) + if output_padding is None: + output_padding = pad_listlike([0], dims) + else: + assert 0 < len(output_padding) <= dims + output_padding = pad_listlike(output_padding, dims) + assert isinstance(groups, (int, sympy.core.numbers.Integer)) + if transposed: + # When transposed, the size of the prepacked oneDNN weight is different + # from the PyTorch weight. We're not able to run aten conv with such + # size. We infer the output size from the input params here: + weight_size = _original_deconv_weight_size(weight_fake, groups) + input_size = x_fake.size() + output_size = _conv_input_size( + input_size, + weight_size, + padding, + output_padding, + stride, + dilation, + groups, + ) + else: + bias_fake = ( + ir_node_to_tensor(bias, guard_shape=True) if bias is not None else bias + ) + output = torch.ops.aten.convolution( + x_fake, + weight_fake, + bias_fake, + stride, + padding, + dilation, + transposed, + output_padding, + groups, + ) + output_size = output.size() + + req_stride_order = [0] + list(reversed(range(1, len(stride) + 1))) + req_stride_order = [len(req_stride_order)] + req_stride_order + + x = cls.require_stride_order(x, req_stride_order) + + # We won't do weight prepack for Conv if dynamic_shapes. + # In static shape cases, since weight is prepacked, we'll always force output to be channels last in the Conv kernel. + # In dynamic shape cases, for input with channels = 1, like tensor of size (s0, 1, 28, 28) and stride (784, 784, 28, 1), + # x = cls.require_stride_order(x, req_stride_order) where req_stride_order is in the channels last order + # won't change the stride of this tensor since stride for dimensions of size 1 is ignored. While in Conv kernel, + # this tensor is considered as channels first and the output will be in contiguous format. + # To align the behavior of the Conv kernel, we set the output_stride in such case to be contiguous instead of channels last. + dynamic_shapes = not all(isinstance(i, int) for i in (output_size)) + if dynamic_shapes and is_contiguous_storage_and_layout(x): + output_stride = FlexibleLayout.contiguous_strides(output_size) + else: + output_stride = make_channels_last_strides_for(output_size) + + assert x.get_device().type == "cpu" and weight.get_device().type == "cpu" + inputs = [x, weight] + + kernel_layout = FixedLayout( + x.get_device(), + x.get_dtype(), + convert_shape_to_inductor(output_size), + convert_shape_to_inductor(output_stride), + ) + constant_args = [padding, stride, dilation, groups] + if transposed: + constant_args.insert(1, output_padding) + + if bias is not None: + inputs.append(bias) + else: + constant_args.insert(0, bias) + return inputs, constant_args, kernel_layout, req_stride_order + + +def _prepare_linear_fusion_create( + cls, + x: "TensorBox", + weight: "TensorBox", + bias: "TensorBox", +): + """ + This function is a helper function to prepare inputs, layout and constant args + for linear post-op fusion's create function. The function only supports the CPU device + since linear post-op fusion kernel is only supported on CPU right now. + """ + x.realize() + weight.realize() + if bias is not None: + bias.realize() + + *m, _ = x.get_size() + # The weight has been transposed during the qlinear weight prepack process. + # https://github.com/pytorch/pytorch/blob/4979f9c0d72490970e2019bb1d2284f83d93f76b/ + # aten/src/ATen/native/quantized/cpu/qlinear_prepack.cpp#L291 + _, oc = weight.get_size() + output_size = list(m) + [oc] + req_stride_order = list(reversed(range(len(x.get_size())))) + + x = cls.require_stride_order(x, req_stride_order) + assert x.get_device().type == "cpu" and weight.get_device().type == "cpu" + inputs = [x, weight] + + output_stride = FlexibleLayout.contiguous_strides(output_size) + kernel_layout = FixedLayout( + x.get_device(), + x.get_dtype(), + output_size, + output_stride, + ) + constant_args: List[Any] = [] + + if bias is not None: + inputs.append(bias) + else: + constant_args.insert(0, bias) + return inputs, constant_args, kernel_layout, req_stride_order + + +class ConvolutionUnary(ExternKernelAlloc): + def __init__( + self, + layout, + inputs, + constant_args=(), + ) -> None: + super().__init__( + layout, + inputs, + constant_args, + None, + op_overload=torch.ops.mkldnn._convolution_pointwise.default, + ) + self.cpp_op_schema = """ + at::Tensor( + const at::Tensor& input_t, + const at::Tensor& weight_t, + const std::optional& bias_opt, + at::IntArrayRef padding, + at::IntArrayRef stride, + at::IntArrayRef dilation, + int64_t groups, + c10::string_view attr, + torch::List> scalars, + std::optional algorithm)""" + + def codegen(self, wrapper): + wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed( + self.get_name(), + self.python_kernel_name, + self.cpp_kernel_name, + self.codegen_args(), + self.cpp_op_schema, + self.cpp_kernel_key, + op_overload=self.op_overload, + raw_args=[*self.inputs, *self.constant_args], + ) + if isinstance(self.layout, Layout): + self.codegen_size_asserts(wrapper) + + @classmethod + def create( + cls, + x: "TensorBox", + weight: "TensorBox", + bias: "TensorBox", + padding_: List[int], + stride_: List[int], + dilation_: List[int], + groups: int, + attr, + scalars: Optional[List[Any]], + algorithm, + ): + (inputs, constant_args, kernel_layout, _) = _prepare_convolution_fusion_create( + cls, x, weight, bias, padding_, stride_, dilation_, groups + ) + constant_args = constant_args + [ + attr, + may_convert_to_optional(scalars), + algorithm, + ] + return ConvolutionUnary( + layout=kernel_layout, + inputs=inputs, + constant_args=constant_args, + ) + + +class ConvolutionBinary(ExternKernelAlloc): + def __init__( + self, + layout, + inputs, + constant_args=(), + cpp_constant_args=(), + ) -> None: + super().__init__( + layout, + inputs, + constant_args, + None, + op_overload=torch.ops.mkldnn._convolution_pointwise.binary, + ) + self.cpp_op_schema = """ + at::Tensor( + const at::Tensor& input_t, + const at::Tensor& other_t, + const at::Tensor& weight_t, + const std::optional& bias_opt, + at::IntArrayRef padding, + at::IntArrayRef stride, + at::IntArrayRef dilation, + int64_t groups, + c10::string_view binary_attr, + std::optional alpha, + std::optional unary_attr, + torch::List> unary_scalars, + std::optional unary_algorithm)""" + self.cpp_constant_args = cpp_constant_args + + def codegen(self, wrapper): + wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed( + self.get_name(), + self.python_kernel_name, + self.cpp_kernel_name, + self.codegen_args(), + self.cpp_op_schema, + self.cpp_kernel_key, + self.cpp_kernel_overload_name, + self.op_overload, + [*self.inputs, *self.constant_args], + ) + if isinstance(self.layout, Layout): + self.codegen_size_asserts(wrapper) + + @classmethod + def create( + cls, + x: "TensorBox", + other: "TensorBox", + weight: "TensorBox", + bias: "TensorBox", + padding_: List[int], + stride_: List[int], + dilation_: List[int], + groups: int, + binary_attr: str, + binary_alpha: Optional[float], + unary_attr: Optional[str], + unary_scalars: Optional[List[Any]], + unary_algorithm: Optional[str], + ): + ( + inputs, + constant_args, + kernel_layout, + req_stride_order, + ) = _prepare_convolution_fusion_create( + cls, x, weight, bias, padding_, stride_, dilation_, groups + ) + other = cls.require_stride_order(other, req_stride_order) + inputs.insert(1, other) + constant_args = constant_args + [ + binary_attr, + binary_alpha, + unary_attr, + may_convert_to_optional(unary_scalars), + unary_algorithm, + ] + return ConvolutionBinary( + layout=kernel_layout, + inputs=inputs, + constant_args=constant_args, + ) + + +class ConvolutionBinaryInplace(ExternKernelAlloc): + def __init__( + self, + kernel_layout, + inputs, + constant_args=(), + ) -> None: + # Due to constrain of op.call, other (Tensor&) should be at input[0] + reordered_inputs = [inputs[1], inputs[0]] + inputs[2:] + + super().__init__( + kernel_layout, + reordered_inputs, + constant_args, + None, + op_overload=torch.ops.mkldnn._convolution_pointwise_.binary, + ) + # TODO: op.call: input[0] should be at::Tensor& + self.cpp_op_schema = """ + at::Tensor&( + at::Tensor& other_t, + const at::Tensor& input_t, + const at::Tensor& weight_t, + const std::optional& bias_opt, + at::IntArrayRef padding, + at::IntArrayRef stride, + at::IntArrayRef dilation, + int64_t groups, + c10::string_view binary_attr, + std::optional alpha, + std::optional unary_attr, + torch::List> unary_scalars, + std::optional unary_algorithm)""" + + self.mutation_outputs = [ + MutationOutput(NoneLayout(inputs[0].get_device()), inputs[0], self), + MutationOutput(NoneLayout(inputs[1].get_device()), inputs[1], self), + ] + + def codegen(self, wrapper): + wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed( + self.get_name(), + self.python_kernel_name, + self.cpp_kernel_name, + self.codegen_args(), + self.cpp_op_schema, + self.cpp_kernel_key, + self.cpp_kernel_overload_name, + self.op_overload, + [*self.inputs, *self.constant_args], + ) + + def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: + return OrderedSet() + + @classmethod + def create( + cls, + x: "TensorBox", + other: "TensorBox", + weight: "TensorBox", + bias: "TensorBox", + padding_: List[int], + stride_: List[int], + dilation_: List[int], + groups: int, + binary_attr: str, + binary_alpha: Optional[float], + unary_attr: Optional[str], + unary_scalars: Optional[List[Any]], + unary_algorithm: Optional[str], + ): + ( + inputs, + constant_args, + _, + req_stride_order, + ) = _prepare_convolution_fusion_create( + cls, x, weight, bias, padding_, stride_, dilation_, groups + ) + other = cls.require_stride_order(other, req_stride_order) + inputs.insert(1, other) + constant_args = constant_args + [ + binary_attr, + binary_alpha, + unary_attr, + may_convert_to_optional(unary_scalars), + unary_algorithm, + ] + packed = ConvolutionBinaryInplace( + kernel_layout=NoneLayout(inputs[1].get_device()), # type: ignore[arg-type] + inputs=inputs, + constant_args=constant_args, + ) + # This op mutates in place which means that the result is not the + # target but rather the input that is being mutated + # init reorders the inputs, so inputs[1] becomes packed.inputs[0] + return packed.inputs[0] + + +class ConvolutionTransposeUnary(ExternKernelAlloc): + def __init__( + self, + layout, + inputs, + constant_args=(), + ) -> None: + super().__init__( + layout, + inputs, + constant_args, + None, + op_overload=torch.ops.mkldnn._convolution_transpose_pointwise.default, + ) + self.cpp_op_schema = """ + at::Tensor( + const at::Tensor& input_t, + const at::Tensor& weight_t, + const std::optional& bias_opt, + at::IntArrayRef padding, + at::IntArrayRef output_padding, + at::IntArrayRef stride, + at::IntArrayRef dilation, + int64_t groups, + c10::string_view attr, + torch::List> scalars, + std::optional algorithm)""" + + def codegen(self, wrapper): + wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed( + self.get_name(), + self.python_kernel_name, + self.cpp_kernel_name, + self.codegen_args(), + self.cpp_op_schema, + self.cpp_kernel_key, + ) + + @classmethod + def create( + cls, + x: "TensorBox", + weight: "TensorBox", + bias: "TensorBox", + padding_: List[int], + output_padding_: List[int], + stride_: List[int], + dilation_: List[int], + groups_: int, + attr, + scalars: Optional[List[Any]], + algorithm, + ): + transposed = True + ( + inputs, + constant_args, + kernel_layout, + _, + ) = _prepare_convolution_fusion_create( + cls, + x, + weight, + bias, + padding_, + stride_, + dilation_, + groups_, + transposed, + output_padding_, + ) + constant_args = constant_args + [ + attr, + may_convert_to_optional(scalars), + algorithm, + ] + return ConvolutionTransposeUnary( + layout=kernel_layout, + inputs=inputs, + constant_args=constant_args, + ) + + +class QConvPointWisePT2E(ExternKernelAlloc): + def __init__( + self, + layout, + inputs, + constant_args=(), + ) -> None: + """ + if bias is not None + - inputs = [x, w, b, weight_scale, weight_zp] + - const_args is: [stride, padding, dilation, groups, x_scale, x_zp, o_scale, o_zp, + fp32_output, unary_attr, unary_scalars, unary_algorithm] + else + - inputs = [x, w, weight_scale, weight_zp] + - const_args is: [bias, stride, padding, dilation, groups, x_scale, x_zp, o_scale, o_zp, + fp32_output, unary_attr, unary_scalars, unary_algorithm] + """ + self.has_bias = len(inputs) == 5 + super().__init__( + layout, + inputs, + constant_args, + None, + op_overload=torch.ops.onednn.qconv2d_pointwise.default, + ) + self.cpp_op_schema = """ + at::Tensor( + at::Tensor act, + double act_scale, + int64_t act_zero_point, + at::Tensor weight, + at::Tensor weight_scales, + at::Tensor weight_zero_points, + std::optional bias, + torch::List stride, + torch::List padding, + torch::List dilation, + int64_t groups, + double output_scale, + int64_t output_zero_point, + std::optional output_dtype, + c10::string_view attr, + torch::List> scalars, + std::optional algorithm)""" + + def codegen(self, wrapper): + # Parser the inputs and constant + # The raw_args setup can be skipped if there is a C shim implementation + args = [x.codegen_reference() for x in self.inputs] + const_arg_names = [ + "x_scale", + "x_zero_point", + "stride", + "padding", + "dilation", + "groups", + "output_scale", + "output_zero_point", + "output_dtype", + "attr", + "scalars", + "algorithm", + ] + if not self.has_bias: + const_arg_names.insert(2, "bias") + const_args = list(self.codegen_const_args(const_arg_names)) + + x = args[0] + x_raw = self.inputs[0] + packed_weight = args[1] + packed_weight_raw = self.inputs[1] + bias = args[2] if self.has_bias else const_args[2] + bias_raw = self.inputs[2] if self.has_bias else self.constant_args[2] + w_scale, w_zp = args[-2], args[-1] + w_scale_raw, w_zp_raw = self.inputs[-2], self.inputs[-1] + ( + x_scale, + x_zp, + ) = const_args[:2] + ( + x_scale_raw, + x_zp_raw, + ) = self.constant_args[:2] + ( + stride, + padding, + dilation, + groups, + o_scale, + o_zp, + output_dtype, + unary_attr, + unary_scalars, + unary_algorithm, + ) = const_args[-10:] + ( + stride_raw, + padding_raw, + dilation_raw, + groups_raw, + o_scale_raw, + o_zp_raw, + output_dtype_raw, + unary_attr_raw, + unary_scalars_raw, + unary_algorithm_raw, + ) = self.constant_args[-10:] + codegen_args = ( + x, + x_scale, + x_zp, + packed_weight, + w_scale, + w_zp, + bias, + stride, + padding, + dilation, + groups, + o_scale, + o_zp, + output_dtype, + unary_attr, + unary_scalars, + unary_algorithm, + ) + raw_args = ( + x_raw, + x_scale_raw, + x_zp_raw, + packed_weight_raw, + w_scale_raw, + w_zp_raw, + bias_raw, + stride_raw, + padding_raw, + dilation_raw, + groups_raw, + o_scale_raw, + o_zp_raw, + output_dtype_raw, + unary_attr_raw, + unary_scalars_raw, + unary_algorithm_raw, + ) + wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed( + self.get_name(), + self.python_kernel_name, + self.cpp_kernel_name, + codegen_args, + self.cpp_op_schema, + self.cpp_kernel_key, + op_overload=self.op_overload, + raw_args=raw_args, + ) + if isinstance(self.layout, Layout): + self.codegen_size_asserts(wrapper) + + @classmethod + def create( + cls, + qx: "TensorBox", + x_scale: float, + x_zero_point: int, + qw: "TensorBox", # qw + w_scale: "TensorBox", + w_zero_point: "TensorBox", + bias: "TensorBox", + stride: List[int], + padding: List[int], + dilation: List[int], + groups: int, + output_scale: float, + output_zero_point: int, + output_dtype, + attr, + scalars, + algorithm, + ): + transposed = False + output_padding = None + (inputs, constant_args, kernel_layout, _) = _prepare_convolution_fusion_create( + cls, + qx, + qw, + bias, + padding, + stride, + dilation, + groups, + transposed, + output_padding, + ) + # swap padding and stride to align with functional conv arg order + if bias is None: + constant_args[1], constant_args[2] = constant_args[2], constant_args[1] + else: + constant_args[0], constant_args[1] = constant_args[1], constant_args[0] + + w_scale.realize() + w_zero_point.realize() + inputs = inputs + [w_scale, w_zero_point] + + constant_args = ( + [ + x_scale, + x_zero_point, + ] + + constant_args + + [ + output_scale, + output_zero_point, + output_dtype, + attr, + may_convert_to_optional(scalars), + algorithm, + ] + ) + + assert output_dtype is not None + if output_dtype in [torch.float32, torch.bfloat16]: + # in _prepare_convolution_fusion_create, we use x.dtype (uint8) to create kernel_layout + # if we set output_dtype is not None, the output buf should be output_dtype instead of uint8. + kernel_layout.dtype = output_dtype + + return QConvPointWisePT2E( + layout=kernel_layout, + inputs=inputs, + constant_args=constant_args, + ) + + +class QConvPointWiseBinaryPT2E(ExternKernelAlloc): + def __init__( + self, + layout, + inputs, + constant_args=(), + ) -> None: + """ + Needs input/weight/output qparams + if bias is not None + - inputs = [x, w, b, accum, w_scale, w_zp] + - const_args = [stride, padding, dilation, groups, x_scale, x_zp, accum_scale, accum_zp, o_scale, o_zp, + fp32_output, binary_attr, aplha, unary_attr, unary_scalars, unary_algorithm] + else + - inputs = [x, w, accum, w_scale, w_zp] + - const_args = const_args is: [bias, stride, padding, dilation, groups, x_scale, x_zp, accum_scale, + accum_zp, o_scale, o_zp, fp32_output, binary_attr, aplha, unary_attr, unary_scalars, unary_algorithm] + """ + self.has_bias = len(inputs) == 6 + self.idx_for_inplace_sum = 3 if self.has_bias else 2 + super().__init__( + layout, + inputs, + constant_args, + None, + op_overload=torch.ops.onednn.qconv2d_pointwise.binary, + ) + self.cpp_op_schema = """ + at::Tensor( + at::Tensor act, + double act_scale, + int64_t act_zero_point, + at::Tensor accum, + double accum_scale, + int64_t accum_zero_point, + at::Tensor weight, + at::Tensor weight_scales, + at::Tensor weight_zero_points, + std::optional bias, + torch::List stride, + torch::List padding, + torch::List dilation, + int64_t groups, + double output_scale, + int64_t output_zero_point, + std::optional output_dtype, + c10::string_view binary_attr, + std::optional alpha, + std::optional attr, + torch::List> scalars, + std::optional algorithm)""" + + def codegen(self, wrapper): + # Parser the inputs and constant + # The raw_args setup can be skipped if there is a C shim implementation + args = [x.codegen_reference() for x in self.inputs] + const_arg_names = [ + "x_scale", + "x_zero_point", + "accum_scale", + "accum_zero_point", + "stride", + "padding", + "dilation", + "groups", + "output_scale", + "output_zero_point", + "output_dtype", + "binary_attr", + "alpha", + "unary_attr", + "unary_scalars", + "unary_algorithm", + ] + if not self.has_bias: + const_arg_names.insert(4, "bias") + const_args = list(self.codegen_const_args(const_arg_names)) + + x = args[0] + x_raw = self.inputs[0] + packed_weight = args[1] + packed_weight_raw = self.inputs[1] + bias = args[2] if self.has_bias else const_args[4] + bias_raw = self.inputs[2] if self.has_bias else self.constant_args[4] + accum, w_scale, w_zp = args[-3], args[-2], args[-1] + accum_raw, w_scale_raw, w_zp_raw = ( + self.inputs[-3], + self.inputs[-2], + self.inputs[-1], + ) + ( + x_scale, + x_zp, + accum_scale, + accum_zp, + ) = const_args[:4] + ( + x_scale_raw, + x_zp_raw, + accum_scale_raw, + accum_zp_raw, + ) = self.constant_args[:4] + ( + stride, + padding, + dilation, + groups, + o_scale, + o_zp, + output_dtype, + binary_attr, + alpha, + unary_attr, + unary_scalars, + unary_algorithm, + ) = const_args[-12:] + ( + stride_raw, + padding_raw, + dilation_raw, + groups_raw, + o_scale_raw, + o_zp_raw, + output_dtype_raw, + binary_attr_raw, + alpha_raw, + unary_attr_raw, + unary_scalars_raw, + unary_algorithm_raw, + ) = self.constant_args[-12:] + conv_args = ( + x, + x_scale, + x_zp, + accum, + accum_scale, + accum_zp, + packed_weight, + w_scale, + w_zp, + bias, + stride, + padding, + dilation, + groups, + o_scale, + o_zp, + output_dtype, + binary_attr, + alpha, + unary_attr, + unary_scalars, + unary_algorithm, + ) + raw_args = ( + x_raw, + x_scale_raw, + x_zp_raw, + accum_raw, + accum_scale_raw, + accum_zp_raw, + packed_weight_raw, + w_scale_raw, + w_zp_raw, + bias_raw, + stride_raw, + padding_raw, + dilation_raw, + groups_raw, + o_scale_raw, + o_zp_raw, + output_dtype_raw, + binary_attr_raw, + alpha_raw, + unary_attr_raw, + unary_scalars_raw, + unary_algorithm_raw, + ) + wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed( + self.get_name(), + self.python_kernel_name, + self.cpp_kernel_name, + conv_args, + self.cpp_op_schema, + self.cpp_kernel_key, + self.cpp_kernel_overload_name, + op_overload=self.op_overload, + raw_args=raw_args, + ) + if isinstance(self.layout, Layout): + self.codegen_size_asserts(wrapper) + + def get_mutation_names(self): + return [self.inputs[self.idx_for_inplace_sum].get_name()] + + def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: + return OrderedSet() + + @classmethod + def create( + cls, + qx: "TensorBox", + x_scale, + x_zero_point, + qaccum: "TensorBox", + accum_scale, + accum_zero_point, + qw: "TensorBox", # packed_weight + w_scale, + w_zero_point, + bias: "TensorBox", + stride: List[int], + padding: List[int], + dilation: List[int], + groups: int, + output_scale: "TensorBox", + output_zero_point: "TensorBox", + output_dtype, + binary_attr, + alpha, + unary_attr, + unary_scalars, + unary_algorithm, + ): + transposed = False + output_padding = None + ( + inputs, + constant_args, + kernel_layout, + req_stride_order, + ) = _prepare_convolution_fusion_create( + cls, + qx, + qw, + bias, + padding, + stride, + dilation, + groups, + transposed, + output_padding, + ) + + qaccum = cls.require_stride_order(qaccum, req_stride_order) + inputs.append(qaccum) + + # swap padding and stride to align with functional conv arg order + if bias is None: + constant_args[1], constant_args[2] = constant_args[2], constant_args[1] + else: + constant_args[0], constant_args[1] = constant_args[1], constant_args[0] + + w_scale.realize() + w_zero_point.realize() + inputs = inputs + [w_scale, w_zero_point] + constant_args = ( + [ + x_scale, + x_zero_point, + accum_scale, + accum_zero_point, + ] + + constant_args + + [ + output_scale, + output_zero_point, + output_dtype, + binary_attr, + alpha, + unary_attr, + may_convert_to_optional(unary_scalars), + unary_algorithm, + ] + ) + + assert ( + binary_attr == "sum" + ), "For now, only post op sum is supported in QConvPointWiseBinaryPT2E." + + V.graph.mark_buffer_mutated(qaccum.get_name()) + packed = QConvPointWiseBinaryPT2E( + layout=NoneLayout(qaccum.get_device()), + inputs=inputs, + constant_args=constant_args, + ) + + # Return accum since it has been inplace changed. + return packed.inputs[packed.idx_for_inplace_sum] + + +class MKLPackedLinear(ExternKernelAlloc): + def __init__( + self, + layout, + inputs, + constant_args=(), + ) -> None: + super().__init__( + layout, + inputs, + constant_args, + None, + op_overload=torch.ops.mkl._mkl_linear.default, + ) + self.cpp_op_schema = """ + at::Tensor( + const at::Tensor& self, + const at::Tensor& mkl_weight_t, + const at::Tensor& origin_weight_t, + const std::optional& bias_opt, + const int64_t prepack_batch_size)""" + + def codegen(self, wrapper): + wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed( + self.get_name(), + self.python_kernel_name, + self.cpp_kernel_name, + self.codegen_args(), + self.cpp_op_schema, + self.cpp_kernel_key, + ) + + @classmethod + def create(cls, x, packed_w, orig_w, B, batch_size): + x = cls.require_stride1(cls.realize_input(x)) + orig_w = cls.require_stride1(cls.realize_input(orig_w)) + *m, _ = x.get_size() + oc, _ = orig_w.get_size() + output_size = list(m) + [oc] + output_stride = FlexibleLayout.contiguous_strides(output_size) + inputs = [x, packed_w, orig_w] + constant_args = [batch_size] + if B is not None: + inputs += [B] + else: + constant_args.insert(0, None) + + return MKLPackedLinear( + layout=FixedLayout( + x.get_device(), x.get_dtype(), output_size, output_stride + ), + inputs=inputs, + constant_args=constant_args, + ) + + +class LinearUnary(ExternKernelAlloc): + def __init__( + self, + layout, + inputs, + constant_args=(), + ) -> None: + super().__init__( + layout, + inputs, + constant_args, + None, + op_overload=torch.ops.mkldnn._linear_pointwise.default, + ) + self.cpp_kernel_key = "linear_pointwise" + self.cpp_op_schema = """ + at::Tensor( + const at::Tensor& input_t, + const at::Tensor& weight_t, + const std::optional& bias_opt, + c10::string_view attr, + torch::List> scalars, + std::optional algorithm)""" + + def codegen(self, wrapper): + wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed( + self.get_name(), + self.python_kernel_name, + self.cpp_kernel_name, + self.codegen_args(), + self.cpp_op_schema, + self.cpp_kernel_key, + ) + + @classmethod + def create(cls, x, w, B, attr, scalars, algorithm): + x = cls.require_contiguous(cls.realize_input(x)) + w = cls.require_contiguous(cls.realize_input(w)) + + *m, ic = x.get_size() + oc, ic = w.get_size() + inputs = [x, w] + constant_args = [attr, scalars if scalars else [-1], algorithm] + if B is not None: + B = cls.require_contiguous(cls.realize_input(B)) + inputs.append(B) + else: + constant_args.insert(0, None) + + return LinearUnary( + layout=FlexibleLayout( + device=x.get_device(), + dtype=x.get_dtype(), + size=list(m) + [oc], + ), + inputs=inputs, + constant_args=constant_args, + ) + + def apply_constraint(self): + pass + + +class LinearBinary(ExternKernelAlloc): + kernel = "torch.ops.mkldnn._linear_pointwise.binary" + + def __init__( + self, + layout, + inputs, + constant_args=(), + ) -> None: + super().__init__( + layout, + inputs, + constant_args, + None, + op_overload=torch.ops.mkldnn._linear_pointwise.binary, + ) + self.cpp_op_schema = """ + at::Tensor( + const at::Tensor& input_t, + const at::Tensor& other_t, + const at::Tensor& weight_t, + const std::optional& bias_opt, + c10::string_view attr) + """ + + def codegen(self, wrapper): + wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed( + self.get_name(), + self.python_kernel_name, + self.cpp_kernel_name, + self.codegen_args(), + self.cpp_op_schema, + self.cpp_kernel_key, + self.cpp_kernel_overload_name, + ) + + @classmethod + def create(cls, x, y, w, B, attr): + x = cls.require_contiguous(cls.realize_input(x)) + y = cls.require_contiguous(cls.realize_input(y)) + w = cls.require_contiguous(cls.realize_input(w)) + + *m, ic = x.get_size() + oc, ic = w.get_size() + + inputs = [x, y, w] + constant_args = [attr] + if B is not None: + B = cls.require_contiguous(cls.realize_input(B)) + inputs.append(B) + else: + constant_args.insert(0, B) + + return LinearBinary( + layout=FlexibleLayout( + device=x.get_device(), + dtype=x.get_dtype(), + size=list(m) + [oc], + ), + inputs=inputs, + constant_args=constant_args, + ) + + def apply_constraint(self): + pass + + +class QLinearPointwisePT2E(ExternKernelAlloc): + def __init__( + self, + layout, + inputs, + constant_args=(), + has_bias=True, + x_scale_zp_are_tensors=False, + ) -> None: + """ + if bias is not None + - inputs = [x, w, b, weight_scale, weight_zp] + - const_args is: [x_scale, x_zp, o_scale, o_zp, + fp32_output, unary_attr, unary_scalars, unary_algorithm] + else + - inputs = [x, w, weight_scale, weight_zp] + - const_args is: [bias, x_scale, x_zp, o_scale, o_zp, + fp32_output, unary_attr, unary_scalars, unary_algorithm] + """ + self.has_bias = has_bias + self.x_scale_zp_are_tensors = x_scale_zp_are_tensors + super().__init__( + layout, + inputs, + constant_args, + None, + op_overload=torch.ops.onednn.qlinear_pointwise.tensor + if x_scale_zp_are_tensors + else torch.ops.onednn.qlinear_pointwise.default, + ) + x_scale_type_str, x_zp_type_str = ( + ("at::Tensor", "at::Tensor") + if x_scale_zp_are_tensors + else ("double", "int64_t") + ) + self.cpp_op_schema = f""" + at::Tensor( + at::Tensor act, + {x_scale_type_str} act_scale, + {x_zp_type_str} act_zero_point, + at::Tensor weight, + at::Tensor weight_scales, + at::Tensor weight_zero_points, + std::optional bias, + double output_scale, + int64_t output_zero_point, + std::optional output_dtype, + c10::string_view post_op_name, + torch::List> post_op_args, + c10::string_view post_op_algorithm)""" + + def codegen(self, wrapper): + # Parser the inputs and constant + # The raw_args setup can be skipped if there is a C shim implementation + args = [x.codegen_reference() for x in self.inputs] + const_args = [] + const_args.extend(self.codegen_const_args()) + + x = args[0] + x_raw = self.inputs[0] + packed_weight = args[1] + packed_weight_raw = self.inputs[1] + bias = args[2] if self.has_bias else const_args[0] + bias_raw = self.inputs[2] if self.has_bias else self.constant_args[0] + w_scale, w_zp = args[-2], args[-1] + w_scale_raw, w_zp_raw = self.inputs[-2], self.inputs[-1] + if self.x_scale_zp_are_tensors: + assert len(args) >= 4 + x_scale, x_zp = args[-4], args[-3] + x_scale_raw, x_zp_raw = self.inputs[-4], self.inputs[-3] + ( + o_scale, + o_zp, + output_dtype, + unary_attr, + unary_scalars, + unary_algorithm, + ) = const_args[-6:] + ( + o_scale_raw, + o_zp_raw, + output_dtype_raw, + unary_attr_raw, + unary_scalars_raw, + unary_algorithm_raw, + ) = self.constant_args[-6:] + else: + assert len(const_args) >= 8 + ( + x_scale, + x_zp, + o_scale, + o_zp, + output_dtype, + unary_attr, + unary_scalars, + unary_algorithm, + ) = const_args[-8:] + ( + x_scale_raw, + x_zp_raw, + o_scale_raw, + o_zp_raw, + output_dtype_raw, + unary_attr_raw, + unary_scalars_raw, + unary_algorithm_raw, + ) = self.constant_args[-8:] + + codegen_args = ( + x, + x_scale, + x_zp, + packed_weight, + w_scale, + w_zp, + bias, + o_scale, + o_zp, + output_dtype, + unary_attr, + unary_scalars, + unary_algorithm, + ) + raw_args = ( + x_raw, + x_scale_raw, + x_zp_raw, + packed_weight_raw, + w_scale_raw, + w_zp_raw, + bias_raw, + o_scale_raw, + o_zp_raw, + output_dtype_raw, + unary_attr_raw, + unary_scalars_raw, + unary_algorithm_raw, + ) + wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed( + self.get_name(), + self.python_kernel_name, + self.cpp_kernel_name, + codegen_args, + self.cpp_op_schema, + self.cpp_kernel_key, + self.cpp_kernel_overload_name, + self.op_overload, + raw_args, + ) + if isinstance(self.layout, Layout): + self.codegen_size_asserts(wrapper) + + @classmethod + def create( + cls, + qx: "TensorBox", + x_scale: float, + x_zero_point: int, + qw: "TensorBox", # packed_weight + w_scale: "TensorBox", + w_zero_point: "TensorBox", + bias: "TensorBox", + output_scale: float, + output_zero_point: int, + output_dtype, + post_op_name, + post_op_args, + post_op_algorithm, + ): + (inputs, constant_args, kernel_layout, _) = _prepare_linear_fusion_create( + cls, + qx, + qw, + bias, + ) + + if isinstance(x_scale, TensorBox) and isinstance(x_zero_point, TensorBox): + x_scale.realize() + x_zero_point.realize() + inputs = inputs + [x_scale, x_zero_point] + x_scale_zp_are_tensors = True + else: + assert isinstance(x_scale, float) and isinstance(x_zero_point, int) + constant_args = constant_args + [x_scale, x_zero_point] + x_scale_zp_are_tensors = False + w_scale.realize() + w_zero_point.realize() + inputs = inputs + [w_scale, w_zero_point] + constant_args = constant_args + [ + output_scale, + output_zero_point, + output_dtype, + post_op_name, + may_convert_to_optional(post_op_args), + post_op_algorithm, + ] + + assert output_dtype is not None + if output_dtype in [torch.float32, torch.bfloat16]: + # in _prepare_linear_fusion_create, we use x.dtype (uint8) to create kernel_layout + # if we set fp32_output, the output buf should be dtype float32 instead of uint8. + kernel_layout.dtype = output_dtype + + return QLinearPointwisePT2E( + layout=kernel_layout, + inputs=inputs, + constant_args=constant_args, + has_bias=(bias is not None), + x_scale_zp_are_tensors=x_scale_zp_are_tensors, + ) + + +class QLinearPointwiseBinaryPT2E(ExternKernelAlloc): + def __init__( + self, + layout, + inputs, + constant_args=(), + has_bias=True, + x_scale_zp_are_tensors=False, + ) -> None: + """ + if bias is not None + - inputs = [x, w, b, weight_scale, weight_zp, x2] + - const_args is: [x_scale, x_zp, o_scale, o_zp, + fp32_output, binary_attr, aplha, unary_attr, unary_scalars, unary_algorithm] + else + - inputs = [x, w, weight_scale, weight_zp, x2] + - const_args is: [bias, x_scale, x_zp, o_scale, o_zp, + fp32_output, binary_attr, aplha, unary_attr, unary_scalars, unary_algorithm] + """ + self.has_bias = has_bias + self.x_scale_zp_are_tensors = x_scale_zp_are_tensors + super().__init__( + layout, + inputs, + constant_args, + None, + op_overload=torch.ops.onednn.qlinear_pointwise.binary_tensor + if x_scale_zp_are_tensors + else torch.ops.onednn.qlinear_pointwise.binary, + ) + x_scale_type_str, x_zp_type_str = ( + ("at::Tensor", "at::Tensor") + if x_scale_zp_are_tensors + else ("double", "int64_t") + ) + self.cpp_op_schema = f""" + at::Tensor( + at::Tensor act, + {x_scale_type_str} act_scale, + {x_zp_type_str} act_zero_point, + at::Tensor weight, + at::Tensor weight_scales, + at::Tensor weight_zero_points, + std::optional other, + std::optional bias, + double inv_output_scale, + int64_t output_zero_point, + std::optional output_dtype, + double other_scale, + int64_t other_zero_point, + c10::string_view binary_post_op, + double binary_alpha, + c10::string_view unary_post_op, + torch::List> unary_post_op_args, + c10::string_view unary_post_op_algorithm)""" + + def codegen(self, wrapper): + # Parser the inputs and constant + # The raw_args setup can be skipped if there is a C shim implementation + args = [x.codegen_reference() for x in self.inputs] + const_args = [] + const_args.extend(self.codegen_const_args()) + + x = args[0] + x_raw = self.inputs[0] + packed_weight = args[1] + packed_weight_raw = self.inputs[1] + bias = args[2] if self.has_bias else const_args[0] + bias_raw = self.inputs[2] if self.has_bias else self.constant_args[0] + w_scale, w_zp, other = args[-3], args[-2], args[-1] + w_scale_raw, w_zp_raw, other_raw = ( + self.inputs[-3], + self.inputs[-2], + self.inputs[-1], + ) + if self.x_scale_zp_are_tensors: + assert len(args) >= 5 + x_scale, x_zp = args[-5], args[-4] + x_scale_raw, x_zp_raw = self.inputs[-5], self.inputs[-4] + ( + o_scale, + o_zp, + output_dtype, + other_scale, + other_zp, + binary_attr, + alpha, + unary_attr, + unary_scalars, + unary_algorithm, + ) = const_args[-10:] + ( + o_scale_raw, + o_zp_raw, + output_dtype_raw, + other_scale_raw, + other_zp_raw, + binary_attr_raw, + alpha_raw, + unary_attr_raw, + unary_scalars_raw, + unary_algorithm_raw, + ) = self.constant_args[-10:] + else: + assert len(const_args) >= 8 + ( + x_scale, + x_zp, + o_scale, + o_zp, + output_dtype, + other_scale, + other_zp, + binary_attr, + alpha, + unary_attr, + unary_scalars, + unary_algorithm, + ) = const_args[-12:] + ( + x_scale_raw, + x_zp_raw, + o_scale_raw, + o_zp_raw, + output_dtype_raw, + other_scale_raw, + other_zp_raw, + binary_attr_raw, + alpha_raw, + unary_attr_raw, + unary_scalars_raw, + unary_algorithm_raw, + ) = self.constant_args[-12:] + + codegen_args = ( + x, + x_scale, + x_zp, + packed_weight, + w_scale, + w_zp, + other, + bias, + o_scale, + o_zp, + output_dtype, + other_scale, + other_zp, + binary_attr, + alpha, + unary_attr, + unary_scalars, + unary_algorithm, + ) + raw_args = ( + x_raw, + x_scale_raw, + x_zp_raw, + packed_weight_raw, + w_scale_raw, + w_zp_raw, + other_raw, + bias_raw, + o_scale_raw, + o_zp_raw, + output_dtype_raw, + other_scale_raw, + other_zp_raw, + binary_attr_raw, + alpha_raw, + unary_attr_raw, + unary_scalars_raw, + unary_algorithm_raw, + ) + wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed( + self.get_name(), + self.python_kernel_name, + self.cpp_kernel_name, + codegen_args, + self.cpp_op_schema, + self.cpp_kernel_key, + self.cpp_kernel_overload_name, + self.op_overload, + raw_args, + ) + if isinstance(self.layout, Layout): + self.codegen_size_asserts(wrapper) + + def get_mutation_names(self): + binary_post_op = self.constant_args[-5] + if binary_post_op == "sum": + return [self.inputs[-1].get_name()] + else: + return [] + + @classmethod + def create( + cls, + qx: "TensorBox", + x_scale: float, + x_zero_point: int, + qw: "TensorBox", # packed_weight + w_scale: "TensorBox", + w_zero_point: "TensorBox", + other: "TensorBox", + bias: "TensorBox", + output_scale: float, + output_zero_point: int, + output_dtype, + other_scale, + other_zp, + binary_post_op, + binary_alpha, + unary_post_op, + unary_post_op_args, + unary_post_op_algorithm, + ): + ( + inputs, + constant_args, + kernel_layout, + req_stride_order, + ) = _prepare_linear_fusion_create( + cls, + qx, + qw, + bias, + ) + + if isinstance(x_scale, TensorBox) and isinstance(x_zero_point, TensorBox): + x_scale.realize() + x_zero_point.realize() + inputs = inputs + [x_scale, x_zero_point] + x_scale_zp_are_tensors = True + else: + assert isinstance(x_scale, float) and isinstance(x_zero_point, int) + constant_args = constant_args + [x_scale, x_zero_point] + x_scale_zp_are_tensors = False + w_scale.realize() + w_zero_point.realize() + inputs = inputs + [w_scale, w_zero_point] + if binary_post_op == "sum": + other = cls.require_stride_order(other, req_stride_order) + inputs.append(other) + constant_args = constant_args + [ + output_scale, + output_zero_point, + output_dtype, + other_scale, + other_zp, + binary_post_op, + binary_alpha, + unary_post_op, + may_convert_to_optional(unary_post_op_args), + unary_post_op_algorithm, + ] + + if binary_post_op == "sum": + V.graph.mark_buffer_mutated(other.get_name()) + packed = QLinearPointwiseBinaryPT2E( + layout=NoneLayout(other.get_device()), + inputs=inputs, + constant_args=constant_args, + has_bias=(bias is not None), + x_scale_zp_are_tensors=x_scale_zp_are_tensors, + ) + # Return other since it has been inplace changed. + return packed.inputs[-1] + + assert output_dtype is not None + if output_dtype in [torch.float32, torch.bfloat16]: + # in _prepare_linear_fusion_create, we use x.dtype (uint8) to create kernel_layout + # if we set fp32_output, the output buf should be dtype float32 instead of uint8. + kernel_layout.dtype = output_dtype + + return QLinearPointwiseBinaryPT2E( + layout=kernel_layout, + inputs=inputs, + constant_args=constant_args, + has_bias=(bias is not None), + x_scale_zp_are_tensors=x_scale_zp_are_tensors, + ) + + +class MkldnnRnnLayer(ExternKernelAlloc): + def __init__( + self, + layout, + inputs, + constant_args=(), + ) -> None: + super().__init__( + layout, + inputs, + constant_args, + None, + op_overload=torch.ops.aten.mkldnn_rnn_layer.default, + ) + + @classmethod + def create( + cls, + x: "TensorBox", + w0: "TensorBox", + w1: "TensorBox", + w2: "TensorBox", + w3: "TensorBox", + hx: "TensorBox", + cx: "TensorBox", + reverse: bool, + batch_sizes: List[int], + mode: int, + hidden_size: int, + num_layers: int, + has_biases: bool, + bidirectional: bool, + batch_first: bool, + train: bool, + ): + x = cls.require_stride1(cls.realize_input(x)) + # If batch_first, x has been permuted in lstm before entering the mkldnn_rnn_layer. + # Make sure x is contiguous in batch_first case. + x.freeze_layout() + w0 = cls.require_stride1(cls.realize_input(w0)) + w1 = cls.require_stride1(cls.realize_input(w1)) + w2 = cls.require_stride1(cls.realize_input(w2)) + w3 = cls.require_stride1(cls.realize_input(w3)) + hx = cls.require_stride1(cls.realize_input(hx)) + hx.freeze_layout() + cx = cls.require_stride1(cls.realize_input(cx)) + cx.freeze_layout() + + input_size = x.get_size() + assert len(input_size) == 3, "Expect lstm input to be 3D" + # batch_first is handled in the lstm OP. When entering + # rnn_layer here, we'll always have batch_first = False + seq_length, mini_batch, input_size = input_size + output_shape = [seq_length, mini_batch, hidden_size] + + hy_shape = hx.get_size() + cy_shape = cx.get_size() + + res: List[IRNode] = [] + + inputs = [x, w0, w1, w2, w3, hx, cx] + constant_args = [ + reverse, + batch_sizes, + mode, + hidden_size, + num_layers, + has_biases, + bidirectional, + batch_first, + train, + ] + + packed = MkldnnRnnLayer( + MultiOutputLayout(x.get_device()), + inputs=inputs, + constant_args=constant_args, + ) + + def get_strides_of_lstm_output(output_shape, batch_first): + assert len(output_shape) == 3, "Expect output_shape to be 3D" + return FlexibleLayout.contiguous_strides(output_shape) + + output_sizes = [output_shape, hy_shape, cy_shape] + output_strides = [ + get_strides_of_lstm_output(output_shape, batch_first), + FlexibleLayout.contiguous_strides(hy_shape), + FlexibleLayout.contiguous_strides(cy_shape), + ] + output_ir = [ + MultiOutput( + FixedLayout( + x.get_device(), + x.get_dtype(), + output_size, + output_stride, + ), + packed, + [(tuple, i)], + ) + for i, (output_size, output_stride) in enumerate( + zip(output_sizes, output_strides) + ) + ] + + return output_ir diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/mkldnn_lowerings.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/mkldnn_lowerings.py new file mode 100644 index 0000000000000000000000000000000000000000..a9cc0bc8299ebfce4bc67628ac21af27a847a936 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/mkldnn_lowerings.py @@ -0,0 +1,1087 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import functools +from typing import List, Optional + +import torch +import torch.utils._pytree as pytree +from torch._inductor.kernel.mm_common import mm_args + +from . import ir +from .codegen.cpp_gemm_template import CppPackedGemmTemplate +from .codegen.cpp_utils import create_epilogue_with_attr +from .ir import TensorBox +from .lowering import ( + add, + add_needs_realized_inputs, + aten, + permute, + register_lowering, + to_dtype, + view, +) +from .select_algorithm import ( + autotune_select_algorithm, + ChoiceCaller, + ExternKernelChoice, +) +from .utils import use_aten_gemm_kernels, use_cpp_packed_gemm_template, use_max_autotune +from .virtualized import ops, V + + +def register_onednn_fusion_ops(): + if torch._C._has_mkldnn: + from . import mkldnn_ir + + aten_mkldnn_linear_unary = ExternKernelChoice( + torch.ops.mkldnn._linear_pointwise, + "mkldnn::_linear_pointwise", + has_out_variant=False, + kernel_creator=mkldnn_ir.LinearUnary.create, + ) + aten_mkldnn_linear_binary = ExternKernelChoice( + torch.ops.mkldnn._linear_pointwise.binary, + "mkldnn::_linear_pointwise", + has_out_variant=False, + kernel_creator=mkldnn_ir.LinearBinary.create, + ) + aten_mkldnn_qlinear_unary = ExternKernelChoice( + torch.ops.onednn.qlinear_pointwise, + "onednn::qlinear_pointwise", + has_out_variant=False, + kernel_creator=mkldnn_ir.QLinearPointwisePT2E.create, + ) + aten_mkldnn_qlinear_binary = ExternKernelChoice( + torch.ops.onednn.qlinear_pointwise.binary, + "onednn::qlinear_pointwise", + has_out_variant=False, + kernel_creator=mkldnn_ir.QLinearPointwiseBinaryPT2E.create, + ) + cpu_needs_realized_inputs = [ + torch.ops.mkldnn._convolution_pointwise, + torch.ops.mkldnn._convolution_pointwise_, + torch.ops.mkldnn._convolution_transpose_pointwise, + torch.ops.mkldnn._linear_pointwise, + aten.mkldnn_rnn_layer.default, + torch.ops.onednn.qconv2d_pointwise, + ] + + @register_lowering(torch.ops.mkldnn._convolution_pointwise) + def convolution_unary( + x: TensorBox, + weight: TensorBox, + bias: TensorBox, + padding, + stride, + dilation, + groups, + attr, + scalars, + algorithm, + ): + return TensorBox.create( + mkldnn_ir.ConvolutionUnary.create( + x, + weight, + bias, + padding, + stride, + dilation, + groups, + attr, + scalars, + algorithm, + ) + ) + + @register_lowering(torch.ops.mkldnn._convolution_pointwise.binary) + def convolution_binary( + x: TensorBox, + other: TensorBox, + weight: TensorBox, + bias: TensorBox, + padding, + stride, + dilation, + groups, + binary_attr, + binary_alpha, + unary_attr, + unary_scalars, + unary_algorithm, + ): + return TensorBox.create( + mkldnn_ir.ConvolutionBinary.create( + x, + other, + weight, + bias, + padding, + stride, + dilation, + groups, + binary_attr, + binary_alpha, + unary_attr, + unary_scalars, + unary_algorithm, + ) + ) + + @register_lowering(torch.ops.mkldnn._convolution_pointwise_.binary) + def convolution_binary_inplace( + x: TensorBox, + other: TensorBox, + weight: TensorBox, + bias: TensorBox, + padding, + stride, + dilation, + groups, + binary_attr, + binary_alpha, + unary_attr, + unary_scalars, + unary_algorithm, + ): + return TensorBox.create( + mkldnn_ir.ConvolutionBinaryInplace.create( + x, + other, + weight, + bias, + padding, + stride, + dilation, + groups, + binary_attr, + binary_alpha, + unary_attr, + unary_scalars, + unary_algorithm, + ) + ) + + @register_lowering(torch.ops.mkldnn._linear_pointwise) + def linear_unary( + x: TensorBox, + w: TensorBox, + b: TensorBox, + attr, + scalars, + algorithm, + layout=None, + ): + x_size = x.get_size() + if len(x_size) > 2: + # GEMM template needs 2D input, normalize input shape here + x = view(x, [-1, x_size[-1]]) + if b is not None: + b = ir.ExternKernel.realize_input(b) + choices: List[ChoiceCaller] = [] + if use_max_autotune(): + transposed_w = permute(w, [1, 0]) + *_, layout, x, transposed_w = mm_args(x, transposed_w, layout=layout) + if use_cpp_packed_gemm_template(layout, x, transposed_w): + + def epilogue_creator(buf): + return create_epilogue_with_attr( + buf, attr, scalars=scalars, algorithm=algorithm + ) + + kwargs = dict( + has_bias=b is not None, + trans_w=True, + epilogue_creator=None if attr == "none" else epilogue_creator, + ) + if b is not None: + kwargs["input_indices"] = [2, 0, 1] # type: ignore[assignment] + CppPackedGemmTemplate.add_choices( + choices, + layout, + [x, w] if b is None else [x, w, b], + **kwargs, # type: ignore[arg-type] + ) + if len(choices) == 0 or use_aten_gemm_kernels(): + kwargs = dict(attr=attr, scalars=scalars, algorithm=algorithm) + if b is None: + kwargs["B"] = None + choices.append( + aten_mkldnn_linear_unary.bind( + [x, w] if b is None else [x, w, b], + layout, + **kwargs, + ) + ) + assert w.get_name() in V.graph.constants + input_gen_fns = { + 1: lambda x: V.graph.constants[x.get_name()], + } + result = autotune_select_algorithm( + "linear_unary", + choices, + [x, w] if b is None else [x, w, b], + layout, + input_gen_fns=input_gen_fns, + ) + if len(x_size) > 2: + result = view(result, (*x_size[:-1], result.get_size()[-1])) + return result + + @register_lowering(torch.ops.mkldnn._linear_pointwise.binary) + def linear_binary( + x: TensorBox, y: TensorBox, w: TensorBox, b: TensorBox, attr, layout=None + ): + x_size = x.get_size() + if len(x_size) > 2: + # GEMM template needs 2D input, normalize input shape here + x = view(x, [-1, x_size[-1]]) + y_size = y.get_size() + if len(y_size) > 2: + y = view(y, [-1, y_size[-1]]) + if b is not None: + b = ir.ExternKernel.realize_input(b) + choices: List[ChoiceCaller] = [] + if use_max_autotune(): + transposed_w = permute(w, [1, 0]) + *_, layout, x, transposed_w, y = mm_args( + x, transposed_w, y, layout=layout + ) + if use_cpp_packed_gemm_template(layout, x, transposed_w): + + def epilogue_creator(buf): + return create_epilogue_with_attr(buf, attr, other=y) + + kwargs = dict( + has_bias=b is not None, + trans_w=True, + epilogue_creator=epilogue_creator, + ) + kwargs["input_indices"] = [0, 2, 1] if b is None else [3, 0, 2, 1] + CppPackedGemmTemplate.add_choices( + choices, + layout, + [x, y, w] if b is None else [x, y, w, b], + **kwargs, # type: ignore[arg-type] + ) + if len(choices) == 0 or use_aten_gemm_kernels(): + kwargs = dict(attr=attr) + if b is None: + kwargs["B"] = None + choices.append( + aten_mkldnn_linear_binary.bind( + [x, y, w] if b is None else [x, y, w, b], + layout, + **kwargs, + ) + ) + assert w.get_name() in V.graph.constants + input_gen_fns = { + 2: lambda x: V.graph.constants[x.get_name()], + } + result = autotune_select_algorithm( + "linear_binary", + choices, + [x, y, w] if b is None else [x, y, w, b], + layout, + input_gen_fns=input_gen_fns, + ) + if len(x_size) > 2: + result = view(result, (*x_size[:-1], result.get_size()[-1])) + return result + + @register_lowering(torch.ops.mkldnn._convolution_transpose_pointwise) + def convolution_transpose_unary( + x: TensorBox, + weight: TensorBox, + bias: TensorBox, + padding, + output_padding, + stride, + dilation, + groups, + attr, + scalars, + algorithm, + ): + return TensorBox.create( + mkldnn_ir.ConvolutionTransposeUnary.create( + x, + weight, + bias, + padding, + output_padding, + stride, + dilation, + groups, + attr, + scalars, + algorithm, + ) + ) + + @register_lowering(aten.mkldnn_rnn_layer.default) + def mkldnn_rnn_layer( + x: TensorBox, + w0: TensorBox, + w1: TensorBox, + w2: TensorBox, + w3: TensorBox, + hx: TensorBox, + cx: TensorBox, + reverse: bool, + batch_sizes: List[int], + mode: int, + hidden_size: int, + num_layers: int, + has_biases: bool, + bidirectional: bool, + batch_first: bool, + train: bool, + ): + return pytree.tree_map( + TensorBox.create, + mkldnn_ir.MkldnnRnnLayer.create( + x, + w0, + w1, + w2, + w3, + hx, + cx, + reverse, + batch_sizes, + mode, + hidden_size, + num_layers, + has_biases, + bidirectional, + batch_first, + train, + ), + ) + + @register_lowering(torch.ops.onednn.qconv2d_pointwise, type_promotion_kind=None) + def qconvolution_unary( + x: TensorBox, + x_scale, + x_zp, + packed_weight: TensorBox, + w_scale: TensorBox, + w_zp: TensorBox, + bias: TensorBox, + stride, + padding, + dilation, + groups, + o_inv_scale, + o_zero_point, + output_dtype, + attr, + scalars, + algorithm, + ): + return TensorBox.create( + mkldnn_ir.QConvPointWisePT2E.create( + x, + x_scale, + x_zp, + packed_weight, + w_scale, + w_zp, + bias, + stride, + padding, + dilation, + groups, + o_inv_scale, + o_zero_point, + output_dtype, + attr, + scalars, + algorithm, + ) + ) + + @register_lowering( + torch.ops.onednn.qconv2d_pointwise.binary, type_promotion_kind=None + ) + def qconvolution_binary( + x: TensorBox, + x_scale, + x_zp, + accum: TensorBox, + accum_scale, + accum_zp, + packed_weight: TensorBox, + w_scale: TensorBox, + w_zp: TensorBox, + bias: TensorBox, + stride, + padding, + dilation, + groups, + o_inv_scale, + o_zero_point, + output_dtype, + binary_attr, + alpha, + unary_attr, + unary_scalars, + unary_algorithmm, + ): + if ( + binary_attr == "sum" + and output_dtype in [torch.float32, torch.bfloat16] + and accum.get_dtype() in [torch.float32, torch.bfloat16] + and accum.get_dtype() != output_dtype + ): + # For int8-mixed-bf16 quantization and inplace add, + # there is case when accum dtype is float32 but output dtype is bfloat16. + # Since the accum will be inplaced changed with post op sum, + # we will do accum dtype convertion here. + accum = to_dtype(accum, output_dtype) + return TensorBox.create( + mkldnn_ir.QConvPointWiseBinaryPT2E.create( + x, + x_scale, + x_zp, + accum, + accum_scale, + accum_zp, + packed_weight, + w_scale, + w_zp, + bias, + stride, + padding, + dilation, + groups, + o_inv_scale, + o_zero_point, + output_dtype, + binary_attr, + alpha, + unary_attr, + unary_scalars, + unary_algorithmm, + ) + ) + + @register_lowering(torch.ops.onednn.qlinear_pointwise, type_promotion_kind=None) + def qlinear_unary( + x: TensorBox, + x_scale, + x_zp, + packed_weight: TensorBox, + w_scale: TensorBox, + w_zp: TensorBox, + bias: TensorBox, + o_scale, + o_zero_point, + output_dtype, + attr, + scalars, + algorithm, + layout=None, + ): + x_size = x.get_size() + if len(x_size) > 2: + # GEMM template needs 2D input, normalize input shape here + x = view(x, [-1, x_size[-1]]) + if not isinstance(x_scale, ir.TensorBox): + assert type(x_scale) == float + x_scale = V.graph.add_tensor_constant( + torch.tensor(x_scale, dtype=torch.float32), name="x_scale" + ) + else: + x_scale.realize() + if not isinstance(x_zp, ir.TensorBox): + assert type(x_zp) == int + x_zp = V.graph.add_tensor_constant( + torch.tensor(x_zp, dtype=torch.int32), name="x_zp" + ) + else: + x_zp.realize() + + # When channels less than 8, w_scale/w_zp is Pointwise instead of ConstantBuffer + # Refer to https://github.com/pytorch/pytorch/blob + # /f353d17755ed23b02924c962a86ff99a3405fe10/torch/_inductor/graph.py#L570-L577 + w_scale.realize() + w_zp.realize() + if w_zp.get_dtype() != torch.int32 and isinstance( + ir.InputsKernel.unwrap_storage_for_input(w_zp), + ir.ConstantBuffer, + ): + # W_zp might be a ConstantBuffer with int64, convert it to int32 + w_zp_tensor = V.graph.constants[w_zp.get_name()].to(torch.int32) + w_zp = V.graph.add_tensor_constant( + torch.tensor(w_zp_tensor, dtype=torch.int32), name=w_zp.get_name() + ) + + bias_dtype = None if bias is None else bias.get_dtype() + + choices: List[ChoiceCaller] = [] + if use_max_autotune(): + *_, layout, x, packed_weight = mm_args( + x, packed_weight, layout=layout, out_dtype=output_dtype + ) + if ( + isinstance( + ir.InputsKernel.unwrap_storage_for_input(x_zp), + ir.ConstantBuffer, + ) + and len(x_zp.get_layout().size) == 0 # Per tensor quant of act + and isinstance( + ir.InputsKernel.unwrap_storage_for_input(w_zp), + ir.ConstantBuffer, + ) + and torch.equal( + torch.zeros_like(V.graph.constants[w_zp.get_name()]), + V.graph.constants[w_zp.get_name()], + ) # We only compensate MatrixB and assume B_zp is 0 to avoid the compensation of MatrixA + and use_cpp_packed_gemm_template(layout, x, packed_weight) + ): + W_tensor = V.graph.constants[packed_weight.get_name()].to_dense() + weight_compens_tensor = torch.sum(W_tensor.to(torch.float), dim=0) + weight_compens = V.graph.add_tensor_constant( + weight_compens_tensor, + name=packed_weight.get_name() + "_BMatrixCompens", + ) + + def epilogue_creator(input_buffer): + # Epilogue to convert from s32 to f32 for u8s8f32 + assert output_dtype in [ + torch.float32, + torch.bfloat16, + torch.uint8, + ] + input_loader = input_buffer.make_loader() + weight_compens_loader = weight_compens.make_loader() + x_scale_loader = x_scale.make_loader() + w_scale_loader = w_scale.make_loader() + x_zp_loader = x_zp.make_loader() + nonlocal bias + bias_loader = None + if bias is not None: + bias_loader = bias.make_loader() + + def inner_fn(index): + nonlocal bias + input = input_loader(index) + # MicroKernel Output is with int32 + # cvt to FP32 before doing compensation + input = ops.to_dtype(input, torch.float32) + weight_compens_index = (index[-1],) + _x_scale = x_scale_loader(()) + _x_zp = x_zp_loader(()) + _w_scale = w_scale_loader(weight_compens_index) + _weight_compo = weight_compens_loader(weight_compens_index) + # Step 1: Doing compensation to cvt fp32 + temp = ops.mul( + ops.mul( + input, + _x_scale, + ), + _w_scale, + ) + temp = ops.sub( + temp, + ops.mul( + ops.mul( + ops.mul( + _x_scale, + _w_scale, + ), + _x_zp, + ), + _weight_compo, + ), + ) + # Step 2: add Bias if applicable + if bias is not None: + _bias = bias_loader(weight_compens_index) + nonlocal bias_dtype + assert bias_dtype in [torch.float32, torch.bfloat16] + if bias_dtype == torch.bfloat16: + _bias = ops.to_dtype(_bias, torch.float32) + temp = ops.add(temp, _bias) + + return temp + + output_buf = ir.Pointwise( + device=input_buffer.get_device(), + dtype=torch.float32, # Hardcode to FP32 for u8s8f32 + inner_fn=inner_fn, + ranges=input_buffer.get_size(), + ) + + # Step 3: Doing the unary post op fusion + if attr != "none": + output_buf = create_epilogue_with_attr( + output_buf, attr, scalars=scalars, algorithm=algorithm + ) + + # Step 4: Cast output to Target Dtype + if output_dtype == torch.bfloat16: + output_cast_loader = output_buf.make_loader() + + def inner_fn_cast_output_to_bf16(index): + input = output_cast_loader(index) + return ops.to_dtype(input, output_dtype) + + output_buf = ir.Pointwise( + device=output_buf.get_device(), + dtype=output_dtype, + inner_fn=inner_fn_cast_output_to_bf16, + ranges=output_buf.get_size(), + ) + elif output_dtype == torch.uint8: + from .lowering import _create_constants + + requant_input_loader = output_buf.make_loader() + + def inner_fn_requant(index, scale, zero_point): + input = requant_input_loader(index) + inv_scale, zero_point = _create_constants( + 1.0 / scale, zero_point, dtype=torch.float32 + ) + val = ops.round(input * inv_scale) + zero_point + qmin, qmax = _create_constants( + 0, 255, dtype=torch.float32 + ) + clamped = ops.minimum(ops.maximum(val, qmin), qmax) + return ops.to_dtype(clamped, torch.uint8) + + output_buf = ir.Pointwise( + device=output_buf.get_device(), + dtype=output_dtype, + inner_fn=functools.partial( + inner_fn_requant, + scale=float(o_scale), + zero_point=int(o_zero_point), + ), + ranges=output_buf.get_size(), + ) + + return output_buf + + assert x.get_dtype() == torch.uint8 + CppPackedGemmTemplate.add_choices( + choices, + layout, + [x, x_scale, x_zp, packed_weight, w_scale, w_zp] + if bias is None + else [x, x_scale, x_zp, packed_weight, w_scale, w_zp, bias], + has_bias=bias is not None, + epilogue_creator=epilogue_creator, + input_indices=[0, 3, 1, 2, 4, 5] + if bias is None + else [6, 0, 3, 1, 2, 4, 5], + ) + if len(choices) == 0 or use_aten_gemm_kernels(): + kwargs = dict( + output_scale=o_scale, + output_zero_point=o_zero_point, + output_dtype=output_dtype, + post_op_name=attr, + post_op_args=scalars, + post_op_algorithm=algorithm, + ) + if bias is None: + kwargs["bias"] = None + choices.append( + aten_mkldnn_qlinear_unary.bind( + (x, x_scale, x_zp, packed_weight, w_scale, w_zp) + if bias is None + else (x, x_scale, x_zp, packed_weight, w_scale, w_zp, bias), + layout, + **kwargs, + ) + ) + assert packed_weight.get_name() in V.graph.constants + input_gen_fns = { + 3: lambda x: V.graph.constants[x.get_name()], + 4: lambda x: V.graph.constants[x.get_name()], + 5: lambda x: V.graph.constants[x.get_name()], + 6: lambda x: V.graph.constants[x.get_name()], # For bias + } + result = autotune_select_algorithm( + "qlinear_unary", + choices, + [x, x_scale, x_zp, packed_weight, w_scale, w_zp] + if bias is None + else [x, x_scale, x_zp, packed_weight, w_scale, w_zp, bias], + layout, + input_gen_fns=input_gen_fns, + ) + if len(x_size) > 2: + result = view(result, (*x_size[:-1], result.get_size()[-1])) + return result + + @register_lowering( + torch.ops.onednn.qlinear_pointwise.binary, type_promotion_kind=None + ) + @register_lowering( + torch.ops.onednn.qlinear_pointwise.binary_tensor, type_promotion_kind=None + ) + def qlinear_binary( + x: TensorBox, + x_scale, + x_zp, + packed_weight: TensorBox, + w_scale: TensorBox, + w_zp: TensorBox, + x2: TensorBox, + bias: TensorBox, + o_scale, + o_zero_point, + output_dtype, + x2_scale, + x2_zp, + binary_attr, + alpha, + unary_attr, + unary_scalars, + unary_algorithmm, + layout=None, + ): + x_size = x.get_size() + x2_size = x2.get_size() + assert len(x_size) == len(x2_size) + if len(x_size) > 2 and binary_attr == "add": + # GEMM template needs 2D input, normalize input shape here + x = view(x, [-1, x_size[-1]]) + x2 = view(x2, [-1, x2_size[-1]]) + if not isinstance(x_scale, ir.TensorBox): + assert type(x_scale) == float + x_scale = V.graph.add_tensor_constant( + torch.tensor(x_scale, dtype=torch.float32), name="x_scale" + ) + else: + x_scale.realize() + if not isinstance(x_zp, ir.TensorBox): + assert type(x_zp) == int + x_zp = V.graph.add_tensor_constant( + torch.tensor(x_zp, dtype=torch.int32), name="x_zp" + ) + else: + x_zp.realize() + + # When channels less than 8, w_scale/w_zp is Pointwise instead of ConstantBuffer + # Refer to https://github.com/pytorch/pytorch/blob + # /f353d17755ed23b02924c962a86ff99a3405fe10/torch/_inductor/graph.py#L570-L577 + w_scale.realize() + w_zp.realize() + if w_zp.get_dtype() != torch.int32 and isinstance( + ir.InputsKernel.unwrap_storage_for_input(w_zp), + ir.ConstantBuffer, + ): + w_zp_tensor = V.graph.constants[w_zp.get_name()].to(torch.int32) + w_zp = V.graph.add_tensor_constant( + torch.tensor(w_zp_tensor, dtype=torch.int32), name=w_zp.get_name() + ) + if binary_attr == "sum": + if output_dtype in [ + torch.float32, + torch.bfloat16, + ] and x2.get_dtype() in [torch.float32, torch.bfloat16]: + if x2.get_dtype() != output_dtype: + # For int8-mixed-bf16 quantization and inplace add, + # there is case when accum dtype is float32 but output dtype is bfloat16. + # Since the accum will be inplaced changed with post op sum, + # we will do accum dtype convertion here. + x2 = to_dtype(x2, output_dtype) + else: + assert ( + x2.get_dtype() == output_dtype + ), "dtype of accum for qlinear post op sum should be the same as output" + x2_dtype = x2.get_dtype() + bias_dtype = bias.get_dtype() if bias is not None else None + choices: List[ChoiceCaller] = [] + if ( + use_max_autotune() and binary_attr == "add" + ): # Support inplace sum fusion + *_, layout, x, packed_weight, x2 = mm_args( + x, packed_weight, x2, layout=layout, out_dtype=output_dtype + ) + if ( + isinstance( + ir.InputsKernel.unwrap_storage_for_input(x_zp), + ir.ConstantBuffer, + ) + and len(x_zp.get_layout().size) == 0 # Per tensor quant of act + and isinstance( + ir.InputsKernel.unwrap_storage_for_input(w_zp), + ir.ConstantBuffer, + ) + and torch.equal( + torch.zeros_like(V.graph.constants[w_zp.get_name()]), + V.graph.constants[w_zp.get_name()], + ) # We only compensate MatrixB and assume B_zp is 0 to avoid the compensation of MatrixA + and use_cpp_packed_gemm_template(layout, x, packed_weight) + ): + W_tensor = V.graph.constants[packed_weight.get_name()] + W_tensor = W_tensor.to_dense() + weight_compens_tensor = torch.sum(W_tensor.to(torch.float), dim=0) + weight_compens = V.graph.add_tensor_constant( + weight_compens_tensor, + name=packed_weight.get_name() + "_BMatrixCompens", + ) + + def epilogue_creator(input_buffer): + # Epilogue to convert from s32 to f32 for u8s8f32 + assert output_dtype in [ + torch.float32, + torch.bfloat16, + torch.uint8, + ] + + input_loader = input_buffer.make_loader() + x2_loader = x2.make_loader() + weight_compens_loader = weight_compens.make_loader() + x_scale_loader = x_scale.make_loader() + w_scale_loader = w_scale.make_loader() + x_zp_loader = x_zp.make_loader() + nonlocal bias + bias_loader = None + if bias is not None: + bias_loader = bias.make_loader() + + def inner_fn(index): + nonlocal bias + input = input_loader(index) + _x2 = x2_loader(index) + _x_scale = x_scale_loader(()) + _x_zp = x_zp_loader(()) + + # MicroKernel Output is with int32 + # cvt to FP32 before doing compensation + input = ops.to_dtype(input, torch.float32) + weight_compens_index = (index[-1],) + _w_scale = w_scale_loader(weight_compens_index) + _weight_compens = weight_compens_loader( + weight_compens_index + ) + # Step 1: Doing compensation to cvt fp32 + temp = ops.mul( + ops.mul( + input, + _x_scale, + ), + _w_scale, + ) + temp = ops.sub( + temp, + ops.mul( + ops.mul( + ops.mul( + _x_scale, + _w_scale, + ), + _x_zp, + ), + _weight_compens, + ), + ) + + # Step 2: add Bias if applicable + if bias is not None: + _bias = bias_loader(weight_compens_index) + nonlocal bias_dtype + assert bias_dtype in [torch.float32, torch.bfloat16] + if bias_dtype == torch.bfloat16: + _bias = ops.to_dtype(_bias, torch.float32) + temp = ops.add(temp, _bias) + + # Step 3: Binary add + nonlocal x2_dtype + assert x2_dtype in [torch.float32, torch.bfloat16] + if x2_dtype == torch.bfloat16: + _x2 = ops.to_dtype(_x2, torch.float32) + temp = ops.add(temp, _x2) + + return temp + + output_buf = ir.Pointwise( + device=input_buffer.get_device(), + dtype=torch.float32, # Hardcode to FP32 for u8s8f32 + inner_fn=inner_fn, + ranges=input_buffer.get_size(), + ) + + # Step 4: Unary post op if has + if unary_attr != "none": + output_buf = create_epilogue_with_attr( + output_buf, + unary_attr, + scalars=unary_scalars, + algorithm=unary_algorithmm, + ) + + # Step 5: Cast output to Target Dtype + if output_dtype == torch.bfloat16: + output_cast_loader = output_buf.make_loader() + + def inner_fn_cast_output_to_bf16(index): + input = output_cast_loader(index) + return ops.to_dtype(input, output_dtype) + + output_buf = ir.Pointwise( + device=output_buf.get_device(), + dtype=output_dtype, + inner_fn=inner_fn_cast_output_to_bf16, + ranges=output_buf.get_size(), + ) + elif output_dtype == torch.uint8: + from .lowering import _create_constants + + requant_input_loader = output_buf.make_loader() + + def inner_fn_requant(index, scale, zero_point): + input = requant_input_loader(index) + inv_scale, zero_point = _create_constants( + 1.0 / scale, zero_point, dtype=torch.float32 + ) + val = ops.round(input * inv_scale) + zero_point + qmin, qmax = _create_constants( + 0, 255, dtype=torch.float32 + ) + clamped = ops.minimum(ops.maximum(val, qmin), qmax) + return ops.to_dtype(clamped, torch.uint8) + + output_buf = ir.Pointwise( + device=output_buf.get_device(), + dtype=torch.uint8, + inner_fn=functools.partial( + inner_fn_requant, + scale=float(o_scale), + zero_point=int(o_zero_point), + ), + ranges=output_buf.get_size(), + ) + + return output_buf + + CppPackedGemmTemplate.add_choices( + choices, + layout, + [x, x_scale, x_zp, packed_weight, w_scale, w_zp, x2] + if bias is None + else [x, x_scale, x_zp, packed_weight, w_scale, w_zp, x2, bias], + has_bias=bias is not None, + epilogue_creator=epilogue_creator, + # Reorder bias and x2 + input_indices=[0, 3, 1, 2, 4, 5, 6] + if bias is None + else [7, 0, 3, 1, 2, 4, 5, 6], + ) + + if len(choices) == 0 or use_aten_gemm_kernels(): + kwargs = dict( + output_scale=o_scale, + output_zero_point=o_zero_point, + output_dtype=output_dtype, + other_scale=x2_scale, + other_zp=x2_zp, + binary_post_op=binary_attr, + binary_alpha=alpha, + unary_post_op=unary_attr, + unary_post_op_args=unary_scalars, + unary_post_op_algorithm=unary_algorithmm, + ) + if bias is None: + kwargs["bias"] = None + choices.append( + aten_mkldnn_qlinear_binary.bind( + (x, x_scale, x_zp, packed_weight, w_scale, w_zp, x2) + if bias is None + else (x, x_scale, x_zp, packed_weight, w_scale, w_zp, x2, bias), + layout, + **kwargs, + ) + ) + assert packed_weight.get_name() in V.graph.constants + input_gen_fns = { + 3: lambda x: V.graph.constants[x.get_name()], + 4: lambda x: V.graph.constants[x.get_name()], + 5: lambda x: V.graph.constants[x.get_name()], + } + if bias is not None: + input_gen_fns[7] = lambda x: V.graph.constants[x.get_name()] # For bias + result = autotune_select_algorithm( + "qlinear_binary", + choices, + [x, x_scale, x_zp, packed_weight, w_scale, w_zp, x2] + if bias is None + else [x, x_scale, x_zp, packed_weight, w_scale, w_zp, x2, bias], + layout, + input_gen_fns=input_gen_fns, + ) + if len(x_size) > 2 and binary_attr == "add": + result = view(result, (*x_size[:-1], result.get_size()[-1])) + return result + + if torch._C.has_mkl: + aten_mkl_linear = ExternKernelChoice( + torch.ops.mkl._mkl_linear, + "mkl::_mkl_linear", + has_out_variant=False, + kernel_creator=mkldnn_ir.MKLPackedLinear.create, + ) + cpu_needs_realized_inputs.append(torch.ops.mkl._mkl_linear) + + @register_lowering(torch.ops.mkl._mkl_linear) + def mkl_packed_linear( + x: TensorBox, + packed_w: TensorBox, + orig_w: TensorBox, + b: Optional[TensorBox], + batch_size, + *, + layout=None, + ): + choices: List[ChoiceCaller] = [] + if use_max_autotune(): + transposed_w = permute(orig_w, [1, 0]) + *_, layout, x, transposed_w = mm_args( + x, transposed_w, layout=layout + ) + if use_cpp_packed_gemm_template(layout, x, transposed_w): + CppPackedGemmTemplate.add_choices( + choices, + layout, + [x, packed_w, orig_w], + trans_w=True, + input_indices=[0, 2], + ) + + if len(choices) == 0 or use_aten_gemm_kernels(): + choices.append( + aten_mkl_linear.bind( + (x, packed_w, orig_w), layout, B=None, batch_size=batch_size + ) + ) + + assert packed_w.get_name() in V.graph.constants + assert orig_w.get_name() in V.graph.constants + # packed_w is a mkldnn tensor which we can't generate directly + # so we use the weights from the original tensor in autotune. + input_gen_fns = { + 1: lambda x: V.graph.constants[x.get_name()], + 2: lambda x: V.graph.constants[x.get_name()], + } + result: TensorBox = autotune_select_algorithm( + "packed_linear", + choices, + [x, packed_w, orig_w], + layout, + input_gen_fns=input_gen_fns, + ) + if b is not None: + result = add(result, b) + return result + + add_needs_realized_inputs(cpu_needs_realized_inputs) + else: + pass diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/ops_handler.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/ops_handler.py new file mode 100644 index 0000000000000000000000000000000000000000..c47ee1026ab919f0d4c619a8e89278268f503d52 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/ops_handler.py @@ -0,0 +1,1093 @@ +# mypy: allow-untyped-defs +import itertools +from typing import ( + Any, + Callable, + Dict, + Generic, + List, + Literal, + NamedTuple, + Optional, + Tuple, + TypeVar, + Union, +) +from typing_extensions import Protocol +from unittest.mock import patch + +import sympy + +import torch +import torch.utils._pytree as pytree + +from ..utils._ordered_set import OrderedSet +from .utils import IndentedBuffer, reduction_num_outputs, sympy_index_symbol, sympy_str + + +T = TypeVar("T") +StoreMode = Optional[Literal["atomic_add"]] +ReductionType = Literal[ + "argmax", + "argmin", + "welford_reduce", + "welford_combine", + "any", + "max", + "min", + "prod", + "sum", + "xor_sum", +] + + +def _arg_str(a) -> str: + if isinstance(a, sympy.Expr): + return sympy_str(a) + return str(a) + + +# NB: This is not done as a parent class, because our ops handlers +# implementations make heavy use of __getattr__ magic, and pre-existing +# stubs for methods would interfere with this mechanism. +# +# TODO: A superclass that does desugaring for operations like +# reciprocal/square might be useful. +class OpsHandler(Protocol[T]): + """ + Protocol describing the set of valid operations on ``torch._inductor.virtualized.ops``, + as well as the contract for op handlers. The type T signifies the domain + of the abstract analysis AKA what all of the functions return / take as arguments + anywhere compute occurs. + + While these operators are typically dtype polymorphic (e.g., you can use mul + on both integers and floats), they do NOT do promotion and usually return the + same dtype as the input. You are expected to have handled type promotion + during ATen decompositions. Most operators correspond exactly to pointwise + operations as defined by torch, so when in doubt about semantics, check the + corresponding torch documentation. These are all scalar operations (so they + are defined to operate on a single element at a time.) + + For convenience, many operators take a src_dtype which indicates what the dtype + of the input argument is. Although in principle this can be derived by an + analysis, providing this for ops where it is useful helps avoid having to repeatedly + recompute dtype in code generation. + + Note that this often describes a class of static methods, for stateless + ops handlers. + + Handlers are often defined using ``__getattr__`` metaprogramming, which means + that you cannot declare that a type implements a protocol by inheriting from + it (as the type stubs count as attribute declarations and impede the getattr + magic method from being called). Instead, define a function that casts an + argument of your type to the protocol, which is sufficient to induce mypy to + test that the protocol is implemented correctly. Search for ``_typecheck_`` + in this file to see some examples. If you see an obscure error where a + class doesn't implement a Protocol, but mypy doesn't say why, check to see + that ``__getattr__`` is typed correctly (typically, it is not possible to + type ``__getattr__`` without typing it as ``Callable[..., Any]``) + """ + + def constant(self, value: Union[bool, float, int], dtype: torch.dtype) -> T: + """Produces a scalar constant of type dtype.""" + ... + + def load_seed(self, name: str, offset: T): + """Computes inductor_prims.lookup_seed.""" + ... + + def rand(self, seed: T, offset: T) -> T: + """Computes inductor_prims.random with mode="rand". offset has dtype int32.""" + ... + + def randn(self, seed: T, offset: T) -> T: + """Computes inductor_prims.random with mode="randn". offset has dtype int32.""" + ... + + def randint64(self, seed: T, offset: T, low: T, high: T) -> T: + """Computes inductor_prims.randint. offset has dtype int32.""" + ... + + def masked(self, mask: T, body: Callable[[], T], other: T) -> T: + """ + Computes body, but only perform loads/stores if the boolean mask + evaluates to true. For example, you would use this if you needed to + perform an indirect load that may not be valid on some elements; + without masking, invalid accesses can cause IMAs. When mask is true, + the result is the result of body; otherwise it is other. Here, `other` + needs to be a constant. + + Contrast this with ops.where, which can multiplex between two values + that have been unconditionally computed. + """ + ... + + def where(self, condition: T, input: T, other: T) -> T: + """ + Computes torch.where: when condition is true, return input; otherwise return other. + """ + ... + + def index_expr(self, expr: sympy.Expr, dtype: torch.dtype) -> T: + """ + Converts a sympy expression into a scalar of type dtype. expr is typically + an indexing expression, thus the name; however, it can also be used in + non-indexing situations. + """ + ... + + def to_dtype( + self, + x: T, + dtype: torch.dtype, + src_dtype: Optional[torch.dtype] = None, + use_compute_types=True, + ) -> T: + """ + Convert x to dtype. src_dtype can be optionally set to specify what the original + dtype of x was, which can improve code generation (used by torch to(dtype=dtype)). + """ + ... + + def trunc_to_int(self, x: T, dtype: torch.dtype) -> T: + """ + Convert x to dtype with truncation semantics (similar to how the int + constructor works in Python). In Inductor codegen, this just decays + to trunc and then to_dtype, but this composite operation helps + roundtrips for Sympy evaluation. + + dtype is taken as an explicit parameter because the desired output + dtype is typically the index dtype, which may vary between int32 and + int64 depending on if we've shown that all the indexing operations can + be done in int32. + """ + ... + + def ceil_to_int(self, x: T, dtype: torch.dtype) -> T: + """ + Convert x to dtype with ceiling semantics. See also trunc_to_int. + """ + ... + + def floor_to_int(self, x: T, dtype: torch.dtype) -> T: + """ + Convert x to dtype with ceiling semantics. See also trunc_to_int. + """ + ... + + def round_to_int(self, x: T, dtype: torch.dtype) -> T: + """ + Convert x to dtype with round-to-even semantics. See also trunc_to_int. + """ + ... + + def to_dtype_bitcast(self, x: T, dtype: torch.dtype, src_dtype: torch.dtype) -> T: + """ + Reinterpret cast x to dtype (reinterpreting the bits in memory as another dtype.) + src_dtype must be the original type of x. + """ + ... + + def identity(self, x: T) -> T: + """ + Returns x as is. This is used to trigger CSE. + """ + ... + + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + # These operations are only available in a "kernel" context. Check + # torch._inductor.codegen.common.CSEProxy for their typical implementation + # in op handler (routing to their respective implementations in the kernel + # handler) + # + # Importantly, inside a kernel, indexing and mask variables are available + # in scope, which are typically used by sympy.Expr indexing. + + def indirect_indexing( + self, x: T, size: sympy.Expr, check: bool = True, wrap_neg=True + ) -> sympy.Expr: + """ + Convert an integral x into a sympy.Expr that can be subsequently used in + indexing computation. 'size' represents an upper bound on the what valid + indexes can be; when 'check' is True, we check that the x is in bounds. + + NB: This is typically mandatory to implement for any analysis, because you + MUST return a valid sympy.Expr of some sort (even if it's a meaningless symbol). + """ + ... + + def load(self, name: str, index: sympy.Expr) -> T: + """ + Load from the memory location 'name', offset by some indexing expression 'index'. + """ + ... + + def store( + self, + name: str, + index: sympy.Expr, + value: T, + mode: StoreMode = None, + ) -> None: + """ + Store 'value' to the memory location 'name' offset by 'expr'. If + specified, 'mode' can require the store to be an atomic addition. + """ + ... + + # TODO: Better explain how the "collective" semantics of these ops; + # remember that the input value is a scalar, you can't reduce on it in the + # traditional sense! + def reduction( + self, + dtype: torch.dtype, + src_dtype: torch.dtype, + reduction_type: ReductionType, + value: T, + ) -> Union[T, Tuple[T, ...]]: + """ + Perform a 'reduction_type' reduction on 'value' of dtype 'src_dtype', + using 'dtype' as the accumulation dtype for the reduction. The result + is an intermediate computation which should be stored to the final + location using 'ops.store_reduction'. + + Valid reduction types are . For Welford reduction types, this + function returns multiple outputs; consult reduction_num_outputs to + determine the amount in metaprogramming applications. + """ + ... + + # TODO: in practice, this seems to actually return None, but not returning + # a T makes common __getattr__ idioms not type correctly. Figure out if + # this should be returning something. + def store_reduction(self, name: str, index: sympy.Expr, value: T) -> T: + """ + Store the fully accumulated result of 'reduction' to the memory + location 'name' offset by 'expr'. + """ + ... + + def scan( + self, + dtypes: Tuple[torch.dtype, ...], + combine_fn: Callable[[Tuple[T, ...], Tuple[T, ...]], Tuple[T, ...]], + values: Tuple[T, ...], + ) -> Tuple[T, ...]: + """ + Perform an associative scan on 'value'. + """ + # TODO: Improve the description with some pseudocode + ... + + def sort( + self, + dtypes: Tuple[torch.dtype, ...], + values: Tuple[T, ...], + stable: bool, + descending: bool, + ) -> Tuple[T, ...]: + """ + Sort values along the reduction dimension. + """ + ... + + def bucketize( + self, + values: T, + offsets_name: str, + offsets_size: sympy.Expr, + indexing_dtype: torch.dtype, + right: bool, + ) -> T: + # See [Note: Inductor bucketize op] + ... + + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + # The following ops have semantics that correspond exactly to the torch + # operation with the same corresponding name. + + def abs(self, x0: T) -> T: + ... + + def exp(self, x0: T) -> T: + ... + + def exp2(self, x0: T) -> T: + ... + + def expm1(self, x0: T) -> T: + ... + + def sqrt(self, x0: T) -> T: + ... + + def relu(self, x0: T) -> T: + ... + + def minimum(self, x0: T, x1: T) -> T: + ... + + def maximum(self, x0: T, x1: T) -> T: + ... + + def cos(self, x0: T) -> T: + ... + + def sin(self, x0: T) -> T: + ... + + def lgamma(self, x0: T) -> T: + ... + + def erf(self, x0: T) -> T: + ... + + def cosh(self, x0: T) -> T: + ... + + def sinh(self, x0: T) -> T: + ... + + def acos(self, x0: T) -> T: + ... + + def acosh(self, x0: T) -> T: + ... + + def asin(self, x0: T) -> T: + ... + + def asinh(self, x0: T) -> T: + ... + + def atan2(self, x0: T, x1: T) -> T: + ... + + def atan(self, x0: T) -> T: + ... + + def atanh(self, x0: T) -> T: + ... + + def copysign(self, x0: T, x1: T) -> T: + ... + + def erfc(self, x0: T) -> T: + ... + + def erfinv(self, x0: T) -> T: + ... + + def frexp(self, x0: T): + ... + + def hypot(self, x0: T, x1: T) -> T: + ... + + def log10(self, x0: T) -> T: + ... + + def log2(self, x0: T) -> T: + ... + + def nextafter(self, x0: T, x1: T) -> T: + ... + + def logical_and(self, x0: T, x1: T) -> T: + ... + + def logical_not(self, x0: T) -> T: + ... + + def logical_or(self, x0: T, x1: T) -> T: + ... + + def logical_xor(self, x0: T, x1: T) -> T: + ... + + def bitwise_and(self, x0: T, x1: T) -> T: + ... + + def bitwise_not(self, x0: T) -> T: + ... + + def bitwise_or(self, x0: T, x1: T) -> T: + ... + + def bitwise_xor(self, x0: T, x1: T) -> T: + ... + + def bitwise_left_shift(self, x0: T, x1: T) -> T: + ... + + def bitwise_right_shift(self, x0: T, x1: T) -> T: + ... + + def rsqrt(self, x0: T) -> T: + ... + + def log1p(self, x0: T) -> T: + ... + + def tan(self, x0: T) -> T: + ... + + def tanh(self, x0: T) -> T: + ... + + def sigmoid(self, x0: T) -> T: + ... + + def signbit(self, x0: T) -> T: + ... + + def fmod(self, x0: T, x1: T) -> T: + ... + + def log(self, x0: T) -> T: + ... + + def isinf(self, x0: T) -> T: + ... + + def isnan(self, x0: T) -> T: + ... + + # NB: this returns a float, like the torch operation + # This rounds half to even to break ties + def round(self, x0: T) -> T: + ... + + # NB: this returns a float, like the torch operation + def floor(self, x0: T) -> T: + ... + + def sign(self, x0: T) -> T: + ... + + # NB: this returns a float, like the torch operation + def trunc(self, x0: T) -> T: + ... + + # NB: this returns a float, like the torch operation + def ceil(self, x0: T) -> T: + ... + + def neg(self, x0: T) -> T: + ... + + def reciprocal(self, x0: T) -> T: + ... + + def eq(self, x0: T, x1: T) -> T: + ... + + def ne(self, x0: T, x1: T) -> T: + ... + + def lt(self, x0: T, x1: T) -> T: + ... + + def gt(self, x0: T, x1: T) -> T: + ... + + def le(self, x0: T, x1: T) -> T: + ... + + def ge(self, x0: T, x1: T) -> T: + ... + + def add(self, x0: T, x1: T) -> T: + ... + + def sub(self, x0: T, x1: T) -> T: + ... + + def mul(self, x0: T, x1: T) -> T: + ... + + # NB: this returns a float, like the torch operation + def pow(self, x0: T, x1: T) -> T: + ... + + def and_(self, x0: T, x1: T) -> T: + ... + + def or_(self, x0: T, x1: T) -> T: + ... + + def xor(self, x0: T, x1: T) -> T: + ... + + # These are metaprogrammed by MockHandler._init_cls + def lshift(self, x0: T, x1: T) -> T: + ... + + def rshift(self, x0: T, x1: T) -> T: + ... + + def getitem(self, x0: T, x1: T) -> T: + # TODO: this is probably just illegal lol + ... + + def matmul(self, x0: T, x1: T) -> T: + # TODO: this is probably just illegal lol + ... + + def invert(self, x0: T) -> T: + ... + + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + # These are "special" operators. These only exist if the target + # language actually supports the operator. Keep this in sync with + # pointwise_overrides_data. + + def airy_ai(self, x: T) -> T: + ... + + def bessel_j0(self, x: T) -> T: + ... + + def bessel_j1(self, x: T) -> T: + ... + + def bessel_y0(self, x: T) -> T: + ... + + def bessel_y1(self, x: T) -> T: + ... + + def digamma(self, x: T) -> T: + ... + + def erfcx(self, x: T) -> T: + ... + + def fma(self, x: T, y: T, z: T) -> T: + ... + + def igamma(self, x: T, y: T) -> T: + ... + + def igammac(self, x: T, y: T) -> T: + ... + + def gammainc(self, x: T, y: T) -> T: + ... + + def gammaincc(self, x: T, y: T) -> T: + ... + + def i0(self, x: T) -> T: + ... + + def i0e(self, x: T) -> T: + ... + + def i1(self, x: T) -> T: + ... + + def i1e(self, x: T) -> T: + ... + + def log_ndtr(self, x: T) -> T: + ... + + def modified_bessel_i0(self, x: T) -> T: + ... + + def modified_bessel_i1(self, x: T) -> T: + ... + + def modified_bessel_k0(self, x: T) -> T: + ... + + def modified_bessel_k1(self, x: T) -> T: + ... + + def ndtr(self, x: T) -> T: + ... + + def ndtri(self, x: T) -> T: + ... + + def polygamma(self, x: T, y: T) -> T: + ... + + def scaled_modified_bessel_k0(self, x: T) -> T: + ... + + def scaled_modified_bessel_k1(self, x: T) -> T: + ... + + def spherical_bessel_j0(self, x: T) -> T: + ... + + def zeta(self, x: T, y: T) -> T: + ... + + def chebyshev_polynomial_t(self, x: T, y: T) -> T: + ... + + def chebyshev_polynomial_u(self, x: T, y: T) -> T: + ... + + def chebyshev_polynomial_v(self, x: T, y: T) -> T: + ... + + def chebyshev_polynomial_w(self, x: T, y: T) -> T: + ... + + def legendre_polynomial_p(self, x: T, y: T) -> T: + ... + + def shifted_chebyshev_polynomial_t(self, x: T, y: T) -> T: + ... + + def shifted_chebyshev_polynomial_u(self, x: T, y: T) -> T: + ... + + def shifted_chebyshev_polynomial_v(self, x: T, y: T) -> T: + ... + + def shifted_chebyshev_polynomial_w(self, x: T, y: T) -> T: + ... + + def hermite_polynomial_h(self, x: T, y: T) -> T: + ... + + def hermite_polynomial_he(self, x: T, y: T) -> T: + ... + + def laguerre_polynomial_l(self, x: T, y: T) -> T: + ... + + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + # These operators are a bit special, because they are conventionally + # natively supported in both Python and C, but the semantics differ so + # care must be taken + + def truncdiv(self, x0: T, x1: T) -> T: + """C-style trunc division between integers only. Computes the true + division of two numbers and rounds the result to zero. + """ + ... + + def floordiv(self, x0: T, x1: T) -> T: + """Python-style floor division between integers only. Computes the + true division of two numbers and floors the result. If you want + floor division for floats, do regular truediv and floor the result. + """ + ... + + def truediv(self, x0: T, x1: T) -> T: + """True division between floats. Integer inputs are NOT valid. To + do Python-style (int, int) -> float division, use int_truediv""" + ... + + def int_truediv(self, x0: T, x1: T) -> T: + """True division between integers. This is NOT the same as promoting + to float and doing integer division, there is a bespoke algorithm for + doing the division in higher precision than the above. + """ + ... + + def div(self, x0: T, x1: T) -> T: + """TODO: to be removed. This renders as / no matter what the backend is + which is incoherent.""" + ... + + def mod(self, x0: T, x1: T) -> T: + """C-style modulus, take sign from LHS (x0).""" + ... + + def remainder(self, x0: T, x1: T) -> T: + """Python-style modulus, take sign from RHS (x1).""" + ... + + def round_decimal(self, x0: T, x1: T) -> T: + """Python-style round with decimal argument""" + ... + + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + # In CUDA, optimized implementations of other mathematical operations are + # offered separately via libdevice for double precision computation (in + # Triton, these go to tl.math rather than tl). We lower to these + # operators when doing FP64 on CUDA. Note that some operators + # unconditional go to tl.math. + # + # TODO(ezyang): Is this really the best way to do this? What if we have + # abs internally route to tl.math automatically when given a double + # precision input? One reason is that when doing codegen, we often don't + # know what the dtype of the inputs are! (In principle we do know, but + # for many analyses it's not conveniently available.) + + def libdevice_abs(self, x0: T) -> T: + ... + + def libdevice_exp(self, x0: T) -> T: + ... + + def libdevice_sqrt(self, x0: T) -> T: + ... + + def libdevice_cos(self, x0: T) -> T: + ... + + def libdevice_sin(self, x0: T) -> T: + ... + + def libdevice_sigmoid(self, x0: T) -> T: + ... + + def libdevice_log(self, x0: T) -> T: + ... + + +class NoopHandler: + def __getattr__(self, name): + if name == "name": + return "NoopHandler" + + def inner(*args, **kwargs): + return None + + return inner + + @staticmethod + def masked(mask, body, other) -> None: + return None + + @staticmethod + def frexp(x) -> Tuple[None, None]: + return (None, None) + + @staticmethod + def scan(dtypes, combine_fn, values) -> Tuple[None, ...]: + return (None,) * len(values) + + @staticmethod + def sort(dtypes, values, stable, descending) -> Tuple[None, ...]: + return (None,) * len(values) + + @staticmethod + def indirect_indexing(index_var, size, check=True, wrap_neg=True) -> sympy.Symbol: + return sympy.Integer(0) + + +# Use mypy to check protocol implemented correctly +def _typecheck_NoopHandler(h: NoopHandler) -> OpsHandler[None]: + return h + + +class MockHandler: + def __getattr__(self, name): + if name == "name": + return "MockHandler" + + def inner(*args, **kwargs): + fargs = [_arg_str(a) for a in args] + fargs.extend(f"{k}={v}" for k, v in kwargs.items()) + return f"ops.{name}({', '.join(fargs)})" + + return inner + + @staticmethod + def masked(mask, body, other) -> str: + return f"ops.masked({mask}, {body()}, {other})" + + @staticmethod + def frexp(x): + return (f"ops.frexp({x})[0]", f"ops.frexp({x})[1]") + + @staticmethod + def scan(dtypes, combine_fn, values): + return tuple( + f"ops.scan({dtypes}, {combine_fn}, {values})[{i}]" + for i in range(len(values)) + ) + + @staticmethod + def sort(dtypes, values, stable, descending): + return tuple( + f"ops.sort({dtypes}, {values}, stable={stable}, descending={descending})[{i}]" + for i in range(len(values)) + ) + + @staticmethod + def indirect_indexing(index_var, size, check=True, wrap_neg=True) -> sympy.Symbol: + return sympy_index_symbol(str(index_var)) + + @classmethod + def _init_cls(cls): + def make_handler(format_string): + @staticmethod # type: ignore[misc] + def inner(*args): + return format_string.format(*args) + + return inner + + for name, format_string in { + "add": "{} + {}", + "sub": "{} - {}", + "mul": "{} * {}", + "floordiv": "{} // {}", + "truediv": "{} / {}", + "mod": "{} % {}", # careful, depending on target semantics varies + "pow": "{} ** {}", + "lshift": "{} << {}", + "rshift": "{} >> {}", + "and_": "{} & {}", + "or_": "{} | {}", + "xor": "{} ^ {}", + "eq": "{} == {}", + "ne": "{} != {}", + "lt": "{} < {}", + "gt": "{} > {}", + "le": "{} <= {}", + "ge": "{} >= {}", + "neg": "-{}", + }.items(): + setattr(cls, name, make_handler(format_string)) + + +MockHandler._init_cls() + + +# Use mypy to check protocol implemented correctly +def _typecheck_MockHandler(h: MockHandler) -> OpsHandler[str]: + return h + + +class KernelFormatterHandler: + def __init__(self, parent_handler): + self.parent_handler = parent_handler + self.output = IndentedBuffer(1) + self.var_counter = itertools.count() + + @staticmethod + def ir_to_string(ir_fn, index, rindex=None) -> str: + from .ir import FlexibleLayout + from .virtualized import V + + args = [index, rindex] if rindex is not None else [index] + names = ["index", "rindex"] if rindex is not None else ["index"] + formatter = KernelFormatterHandler(MockHandler()) + + with formatter.output.indent(-1): + formatter.output.writeline(f"def inner_fn({', '.join(names)}):") + for name, arg in zip(names, args): + if arg: + lhs = ", ".join( + [ + str("_" if isinstance(v, (int, sympy.Integer)) else v) + for v in arg + ] + ) + formatter.output.writeline(f"{lhs} = {name}") + + with V.set_ops_handler(formatter), patch.object( + FlexibleLayout, "allow_indexing", True + ): + result = ir_fn(*args) + return formatter.getvalue(result) + + def __getattr__(self, name) -> Callable[..., Any]: + def inner(*args, **kwargs): + line = getattr(self.parent_handler, name)(*args, **kwargs) + if name == "indirect_indexing": + return line + + def write(line): + # replace line with a new variable name + varname = f"tmp{next(self.var_counter)}" + self.output.writeline(f"{varname} = {line}") + return varname + + return pytree.tree_map(write, line) + + return inner + + def reduction( + self, + dtype: torch.dtype, + src_dtype: torch.dtype, + reduction_type: ReductionType, + value: Union[str, Tuple[str, ...]], + ) -> Union[str, Tuple[str, ...]]: + line = self.parent_handler.reduction(dtype, src_dtype, reduction_type, value) + num_values = reduction_num_outputs(reduction_type) + varnames = [f"tmp{next(self.var_counter)}" for _ in range(num_values)] + self.output.writeline(f"{','.join(varnames)} = {line}") + return tuple(varnames) if num_values > 1 else varnames[0] + + def getvalue(self, result): + self.output.writeline(f"return {result}") + return self.output.getvalue() + + +# Use mypy to check protocol implemented correctly +def _typecheck_KernelFormatterHandler(h: KernelFormatterHandler) -> OpsHandler[str]: + return h + + +class WrapperHandler(Generic[T]): + def __init__(self, inner: OpsHandler[T]): + self._inner = inner + + def __getattr__(self, item): + return getattr(self._inner, item) + + +# Use mypy to check protocol implemented correctly +def _typecheck_WrapperHandler(h: WrapperHandler[T]) -> OpsHandler[T]: + return h + + +class AddParenHandler(WrapperHandler[T]): + def __getattr__(self, name): + def inner(*args, **kwargs): + val = getattr(self._inner, name)(*args, **kwargs) + return f"({val})" + + return inner + + +# Use mypy to check protocol implemented correctly +def _typecheck_AddParenHandler(h: AddParenHandler[T]) -> OpsHandler[T]: + return h + + +class OpCountResult(NamedTuple): + num_ops: int + used_ops: OrderedSet[str] + read_buffers: List[str] + nontrivial_read_count: int + + +class OpCounterCSE: + """Shim to count how many ops are used""" + + def __init__(self, inner): + super().__init__() + self.parent_handler = inner + self.op_count = 0 + self.var_names = {} + self._used_ops: OrderedSet[str] = OrderedSet() + self._read_names: List[str] = [] + self._nontrivial_read_count = 0 + + def __getattr__(self, name): + def inner(*args, **kwargs): + return pytree.tree_map( + self._update_count, getattr(self.parent_handler, name)(*args, **kwargs) + ) + + self._used_ops.add(name) + return inner + + def _update_count(self, val): + varname = self.var_names.get(val) + if not varname: + varname = f"tmp{self.op_count}" + self.op_count += 1 + self.var_names[val] = varname + return varname + + def indirect_indexing(self, *args, **kwargs): + self._used_ops.add("indirect_indexing") + return self.parent_handler.indirect_indexing(*args, **kwargs) + + def load(self, name: str, index: sympy.Expr) -> str: + val = self.parent_handler.load(name, index) + if val not in self.var_names: + self._used_ops.add("load") + self._read_names.append(name) + if not isinstance(index, (sympy.Integer, int)): + self._nontrivial_read_count += 1 + return self._update_count(val) + + def load_seed(self, name: str, offset: T): + val = self.parent_handler.load_seed(name, offset) + if val not in self.var_names: + self._used_ops.add("load_seed") + self._read_names.append(name) + return self._update_count(val) + + def bucketize( + self, + values, + offsets_name: str, + offsets_size: sympy.Expr, + indexing_dtype: torch.dtype, + right: bool, + ): + val = self.parent_handler.bucketize( + values, offsets_name, offsets_size, indexing_dtype, right + ) + if val not in self.var_names: + self._used_ops.add("bucketize") + self._read_names.append(offsets_name) + return self._update_count(val) + + def getvalue(self): + return OpCountResult( + self.op_count, self._used_ops, self._read_names, self._nontrivial_read_count + ) + + +def _typecheck_OpCounterCSE(h: OpCounterCSE) -> OpsHandler[str]: + return h + + +class ExtractConstantsHandler(NoopHandler): + def __init__(self, device): + self.device = device + + def constant(self, value: Any, dtype: torch.dtype) -> "torch._inductor.ir.Constant": + from torch._inductor import ir + + return ir.Constant(value=value, dtype=dtype, device=self.device) + + +def _typecheck_ExtractConstantsHandler(h: ExtractConstantsHandler) -> OpsHandler[Any]: + return h + + +class SimpleCSEHandler(WrapperHandler[T]): + """Wraps the underlying handler with a CSE pass + + NOTE: Compared to codegen level CSE this is simplified as it + doesn't support stores which require load cache invalidation. + """ + + def __init__(self, inner: OpsHandler[T]): + super().__init__(inner) + self.cse_cache: Dict[str, Union[T, Tuple[T, ...]]] = {} + self.mock = MockHandler() + + def indirect_indexing(self, *args, **kwargs) -> sympy.Expr: + return super().indirect_indexing(*args, **kwargs) # type: ignore[misc] + + def store(self, *args, **kwargs) -> T: + raise NotImplementedError("store not implemented") + + def store_reduction(self, *args, **kwargs) -> T: + raise NotImplementedError("store not implemented") + + def __getattr__(self, name) -> Callable[..., Any]: + def inner(*args, **kwargs): + key = getattr(self.mock, name)(*args, **kwargs) + val = self.cse_cache.get(key) + if val is not None: + return val + + val = getattr(self._inner, name)(*args, **kwargs) + self.cse_cache[key] = val + return val + + return inner + + +def _typecheck_SimpleCSEHandler(h: SimpleCSEHandler[Any]) -> OpsHandler[Any]: + return h diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/optimize_indexing.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/optimize_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..96bf8641f3c9a62b3c61fe769132717ef493cf7f --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/optimize_indexing.py @@ -0,0 +1,120 @@ +# mypy: allow-untyped-defs +import math + +import sympy + +import torch +from torch.utils._sympy.value_ranges import ValueRanges + +from .loop_body import LoopBody +from .utils import dominated_nodes + + +def val_expressable_in_32_bits(val): + if getattr(val, "is_Boolean", False): + return True + + if isinstance(val, sympy.Expr): + assert val.is_number + if val.is_Integer or val.is_Boolean: + val = int(val) + else: + val = float(val) + + # bound within mantissa + if isinstance(val, float): + return val <= (2**24) and val >= -(2**24) + + if isinstance(val, int): + iinfo = torch.iinfo(torch.int32) + return val <= iinfo.max and val >= iinfo.min + + raise TypeError(f"Unexpected value {val}") + + +def range_expressable_in_32_bits(range): + return val_expressable_in_32_bits(range.lower) and val_expressable_in_32_bits( + range.upper + ) + + +def try_to_reduce_precision(node, bounds, indirect_vars, indices, replacement_vals): + # if a downstream use of a node explicitly converts to int32, or float16/float32/float64, + # then it's precision is set for that chain of uses, and we don't need to consider those + # dominated values + def skip_filter(node): + return node.target == "to_dtype" and node.args[2] in ( + torch.int32, + torch.float32, + torch.float64, + ) + + # TODO - there are dominated uses whose dtype does not depend on whether + # we reduce the precision here, e.g. add(int64, int64) one of the args can be reduced to + # int32 without changing the output precision of the node. this case hasn't shown up + for dominated in dominated_nodes([node], skip_filter): + if dominated.target in ["store", "output"]: + continue + + if isinstance(dominated.target, str) and "set_indirect" in dominated.target: + idx = int(dominated.target[len("set_indirect") :]) + indirect_var = indirect_vars[idx] + + # We check that we can compute all the indices it's involved in with int32 + for index, expr in indices.items(): + if indirect_var in expr.free_symbols: + index_val = replacement_vals[index] + + if math.isinf(index_val.lower) or math.isinf(index_val.upper): + return + + # all indices are integers, so make sure that we + # use the bounds of integers instead of floats. + # TODO - not sure if we should be doing int/float casts while tracing, + # might interfere with sympy. + + index_val_int = ValueRanges[sympy.Expr]( + int(index_val.lower), int(index_val.upper) + ) + if not range_expressable_in_32_bits(index_val_int): + return + + if not range_expressable_in_32_bits(bounds[dominated]): + return + + args = list(node.args) + args[2] = torch.int32 + node.args = tuple(args) + + +def indexing_dtype_strength_reduction(loop_body: LoopBody): + """ + Performs Value Range Analysis on LoopBody's fx graph to reduce precision of + intermediaries from int64 to int32 + """ + bv = loop_body.bounds() + + int64_dtype_nodes = [ + node + for node in loop_body.get_nodes() + if ( + node.target == "to_dtype" + and node.args[2] == torch.int64 + and node not in bv.unbounded_vars + ) + ] + if not int64_dtype_nodes: + return + + bounds = bv.get_bounds() + + # TODO - if dominated node of one to_dtype is not expressible in int32, + # we should short circuit another to_dtype node if that node also dominates + for node in int64_dtype_nodes: + try_to_reduce_precision( + node, + bounds, + loop_body.indirect_vars, + loop_body.indexing_exprs, + bv.replacement_vals, + ) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/package/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/package/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c088562100cdae1b3808437d845501b639822862 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/package/__init__.py @@ -0,0 +1 @@ +from .package import load_package, package_aoti diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/package/build_package.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/package/build_package.py new file mode 100644 index 0000000000000000000000000000000000000000..9205b9ced254275018472108485173eba9479f11 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/package/build_package.py @@ -0,0 +1,15 @@ +build_package_contents = """ +import os +from pathlib import Path + +from torch._inductor.package.package import compile_so + +curr_dir = Path(__file__).parent +aoti_files = [ + os.path.join(root, file) + for root, dirs, files in os.walk(curr_dir) + for file in files +] + +output_so = compile_so(curr_dir, aoti_files, curr_dir) +""" diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/package/package.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/package/package.py new file mode 100644 index 0000000000000000000000000000000000000000..d1304293e3cd88bdeaa704c558ffefd3f3ef885c --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/package/package.py @@ -0,0 +1,237 @@ +import glob +import json +import os +import shlex +import subprocess +import tempfile +import zipfile +from pathlib import Path +from typing import Callable, List, Optional, Union + +import torch +import torch._inductor +import torch.utils._pytree as pytree +from torch._inductor import config, exc +from torch._inductor.cpp_builder import BuildOptionsBase, CppBuilder +from torch.export._tree_utils import reorder_kwargs + +from .build_package import build_package_contents +from .pt2_archive_constants import AOTINDUCTOR_DIR, ARCHIVE_VERSION + + +class PT2ArchiveWriter: + def __init__(self, archive_path: str) -> None: + self.archive_path: str = archive_path + self.archive_file: Optional[zipfile.ZipFile] = None + + def __enter__(self) -> "PT2ArchiveWriter": + assert self.archive_file is None + self.archive_file = zipfile.ZipFile( + self.archive_path, "w", compression=zipfile.ZIP_STORED + ) + self.writestr("version", str(ARCHIVE_VERSION)) + self.writestr("archive_format", "pt2") + return self + + def __exit__(self, *args) -> None: # type: ignore[no-untyped-def] + assert self.archive_file is not None + self.archive_file.close() + self.archive_file = None + return None + + def writestr(self, name: str, data: Union[bytes, str]) -> None: + assert self.archive_file is not None + self.archive_file.writestr(name, data) + + def write_file(self, name: str, file_path: str) -> None: + """ + Copy a file into the archive. + name: The destination file inside the archive. + file_path: The source file on disk. + """ + assert Path(file_path).is_file(), f"{file_path} is not a valid file path" + assert self.archive_file is not None + self.archive_file.write(file_path, arcname=name) + + +class PT2ArchiveReader: + def __init__(self, archive_path: str) -> None: + self.archive_path: str = archive_path + self.archive_file: Optional[zipfile.ZipFile] = None + + def __enter__(self) -> "PT2ArchiveReader": + self.archive_file = zipfile.ZipFile( + self.archive_path, "r", compression=zipfile.ZIP_STORED + ) + return self + + def __exit__(self, *args) -> None: # type: ignore[no-untyped-def] + if self.archive_file is not None: + self.archive_file.close() + return None + + def read(self, name: str) -> bytes: + assert self.archive_file is not None + return self.archive_file.read(name) + + def extract_to_path(self, member: str, path: str) -> str: + assert self.archive_file is not None + return self.archive_file.extract(member, path) + + def extractall(self, path: str) -> None: + assert self.archive_file is not None + self.archive_file.extractall(path) + + def get_file_names(self) -> List[str]: + assert self.archive_file is not None + return self.archive_file.namelist() + + +def _run_command_and_check(cmd: str) -> None: + cmd = shlex.split(cmd) + try: + subprocess.run(cmd, check=True) + except subprocess.CalledProcessError as e: + raise exc.CppCompileError(cmd, e.output) from e + + +def compile_so(aoti_dir: str, aoti_files: List[str], so_path: str) -> str: + def get_aoti_file_with_suffix(suffix: str) -> str: + for file in aoti_files: + if file.endswith(suffix): + return file + raise RuntimeError(f"Unable to find file with suffix {suffix}") + + # Compile all the files into a .so + cpp_file = os.path.join(aoti_dir, get_aoti_file_with_suffix(".cpp")) + consts_o = os.path.join(aoti_dir, get_aoti_file_with_suffix(".o")) + + file_name = os.path.splitext(cpp_file)[0] + + # Parse compile flags and build the .o file + with open(file_name + "_compile_flags.json") as f: + compile_flags = json.load(f) + + compile_options = BuildOptionsBase(**compile_flags) + object_builder = CppBuilder( + name=file_name, + sources=cpp_file, + BuildOption=compile_options, + ) + compile_cmd = object_builder.get_command_line() + output_o = object_builder.get_target_file_path() + + _run_command_and_check(compile_cmd) + + # Parse linker flags and build the .so file + with open(file_name + "_linker_flags.json") as f: + linker_flags = json.load(f) + + linker_options = BuildOptionsBase(**linker_flags) + so_builder = CppBuilder( + name=os.path.split(so_path)[-1], + sources=[output_o, consts_o], + BuildOption=linker_options, + output_dir=so_path, + ) + link_cmd = so_builder.get_command_line() + output_so = so_builder.get_target_file_path() + + _run_command_and_check(link_cmd) + + # mmapped weights + serialized_weights_filename = file_name + "_serialized_weights.bin" + if serialized_weights_filename in aoti_files: + with open(serialized_weights_filename, "rb") as f_weights: + serialized_weights = f_weights.read() + + with open(output_so, "a+b") as f_so: + so_size = f_so.tell() + # Page align the weights + f_so.write(b" " * (16384 - so_size % 16384)) + f_so.write(serialized_weights) + + return output_so + + +def package_aoti(aoti_output_dir: str) -> str: + """ + Saves the AOTInductor generated files to the PT2Archive format. + """ + + # Add a makefile and python script + build_package_filename = "build_package.py" + with open(os.path.join(aoti_output_dir, build_package_filename), "w") as f: + f.write(build_package_contents) + + with open(os.path.join(aoti_output_dir, "Makefile"), "w") as f: + f.write(f"all:\n\tpython3 {build_package_filename}\n") + + if config.aot_inductor.output_path.endswith(".so"): + raise RuntimeError( + "Unable to save package as a .so. It should be a .pt2 format or a directory." + ) + elif config.aot_inductor.output_path.endswith(".pt2"): + # Save using the PT2 packaging format + # (https://docs.google.com/document/d/1jLPp8MN8Whs0-VW9PmJ93Yg02W85tpujvHrTa1pc5x8/edit#heading=h.v2y2jgnwc56a) + archive_path = config.aot_inductor.output_path + + with PT2ArchiveWriter(archive_path) as archive_writer: + package_files = glob.glob(f"{aoti_output_dir}/*") + + for path in package_files: + filename = os.path.basename(path) + archive_writer.write_file(f"{AOTINDUCTOR_DIR}{filename}", path) + + return archive_path + + else: + # Directly put the files into the directory, without any archiving + return aoti_output_dir + + +def load_package(path: str, device: str) -> Callable: # type: ignore[type-arg] + if path.endswith(".so"): + raise RuntimeError( + "Unable to load .so. It should be a .pt2 format or a directory." + ) + + elif path.endswith(".pt2"): + so_path = os.path.splitext(path)[0] + with PT2ArchiveReader(path) as archive_reader: + file_names = archive_reader.get_file_names() + + with tempfile.TemporaryDirectory() as tmp_dir: + archive_reader.extractall(tmp_dir) + file_names = archive_reader.get_file_names() + aoti_files = [ + file for file in file_names if file.startswith(AOTINDUCTOR_DIR) + ] + + so_path = compile_so(tmp_dir, aoti_files, so_path) + + else: + assert os.path.isdir(path), "Must specify a directory or a .pt2 file" + aoti_files = [ + os.path.join(root, file) + for root, dirs, files in os.walk(path) + for file in files + ] + so_path = compile_so(path, aoti_files, path) + + if device == "cpu": + runner = torch._C._aoti.AOTIModelContainerRunnerCpu(so_path, 1) # type: ignore[call-arg] + elif device == "cuda" or device.startswith("cuda:"): + runner = torch._C._aoti.AOTIModelContainerRunnerCuda(so_path, 1, device) # type: ignore[assignment, call-arg] + else: + raise RuntimeError("Unsupported device " + device) + + def optimized(*args, **kwargs): # type: ignore[no-untyped-def] + call_spec = runner.get_call_spec() # type: ignore[attr-defined] + in_spec = pytree.treespec_loads(call_spec[0]) + out_spec = pytree.treespec_loads(call_spec[1]) + flat_inputs = pytree.tree_flatten((args, reorder_kwargs(kwargs, in_spec)))[0] + flat_outputs = runner.run(flat_inputs) # type: ignore[attr-defined] + return pytree.tree_unflatten(flat_outputs, out_spec) + + return optimized diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/package/pt2_archive_constants.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/package/pt2_archive_constants.py new file mode 100644 index 0000000000000000000000000000000000000000..7f0c91d2fcd4806c7b85b5e3b78ec53937a3b098 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/package/pt2_archive_constants.py @@ -0,0 +1,16 @@ +ARCHIVE_ROOT_NAME = "package" +ARCHIVE_FORMAT_PATH = "archive_format" +MODELS_DIR = "models/" +MODELS_FILENAME_FORMAT = "models/{}.json" +AOTINDUCTOR_DIR = "data/aotinductor/" +WEIGHTS_DIR = "data/weights/" +WEIGHT_FILENAME_PREFIX = "weight_" +CONSTANTS_DIR = "data/constants/" +TENSOR_CONSTANT_FILENAME_PREFIX = "tensor_" +CUSTOM_OBJ_FILENAME_PREFIX = "custom_obj_" +SAMPLE_INPUTS_DIR = "data/sample_inputs/" +SAMPLE_INPUTS_FILENAME_FORMAT = "data/sample_inputs/{}.pt" +EXTRA_DIR = "extra/" +MODULE_INFO_PATH = "extra/module_info.json" + +ARCHIVE_VERSION = 0 diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/pattern_matcher.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/pattern_matcher.py new file mode 100644 index 0000000000000000000000000000000000000000..e43d37fd37b1a757949406ce5e906b8b08cf719b --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/pattern_matcher.py @@ -0,0 +1,2005 @@ +# mypy: allow-untyped-decorators +""" +# Inductor Pattern Matcher + +The pattern matcher enables search/replace within an FX graph. + +The main entrypoint to the pattern matcher is register_replacement(). Given a +search function and a replacement function this will register a replacement with +a pass (such as torch._inductor.fx_passes.joint_graph.patterns). + +Internally the pattern matcher represents patterns as a graph (a DAG). Creating +new patterns manually as a graph is cumbersome and error-prone so the standard +way to create patterns (using register_replacement()) is to provide a search +function and a replacement function which is traced and converted into a graph. + +Because the search functions are built somewhat generic (they tend to ignore +tensor sizes, for example) register_replacement() allows you to specify an +`extra_check` function which performs additional checks to verify that the +matched pattern fully matches before returning it. + +## Precompiled Patterns + +New patterns are added using register_replacement(). Patterns added in this way +can have a compile-time overhead because they need to be traced before +use. Patterns can be precompiled and added using gen_register_replacement() +instead. To do this you call gen_register_replacement() instead of +register_replacement(). The arguments are the same except for an additional +unique name which is used as a lookup key. + +## Internals + +The match DAG is represented by a graph of `PatternExpr` nodes. Each PatternExpr +implements a `_match` method which returns either a `Match` object for a +successful match or a `FailedMatch` object for a failure to match. +""" + +from __future__ import annotations + +import contextlib +import dataclasses +import functools +import importlib +import inspect +import itertools +import logging +import operator +import os +import re +import textwrap +import typing +from abc import ABC, abstractmethod +from collections import defaultdict +from pathlib import Path +from typing import ( + Any, + Callable, + DefaultDict, + Dict, + Generator, + Iterable, + List, + Mapping, + NoReturn, + Optional, + Protocol, + Sequence, + Set, + Tuple, + Type, + TypeVar, + Union, +) +from typing_extensions import Self, TypeGuard + +import torch +import torch._guards +import torch.fx +import torch.utils._pytree as pytree +from torch._dispatch.python import enable_python_dispatcher +from torch._dynamo.utils import counters +from torch._inductor.config import trace as trace_config +from torch._prims_common import is_integer_dtype +from torch._subclasses.fake_tensor import unset_fake_temporarily +from torch.fx.experimental.proxy_tensor import make_fx +from torch.fx.experimental.symbolic_shapes import guard_size_oblivious +from torch.fx.immutable_collections import immutable_dict, immutable_list +from torch.fx.passes.graph_transform_observer import GraphTransformObserver + +from .._functorch import config as functorch_config +from .._functorch.aot_autograd import aot_function, make_boxed_func +from .._functorch.partitioners import default_partition +from .._subclasses import FakeTensor, FakeTensorMode +from ..fx import Transformer +from . import config +from .decomposition import select_decomp_table +from .lowering import fallback_node_due_to_unsupported_type + + +log = logging.getLogger(__name__) +aten = torch.ops.aten +prims = torch.ops.prims + +Constant = Any +NodeOrConstant = Union[Constant, torch.fx.Node] + + +class SearchFn(Protocol): + __name__: str + + def __call__(self, *args: Any, **kwargs: Any) -> Any: + ... + + +class ReplaceFn(Protocol): + def __call__(self, *args: Any, **kwargs: Any) -> Any: + ... + + +class TraceFn(Protocol): + def __call__( + self, fn: Union[SearchFn, ReplaceFn], *args: Any, **kwargs: Any + ) -> torch.fx.GraphModule: + ... + + +T = TypeVar("T") + +# What's a better name for this? +FnsType = Union[torch.fx.node.Target, str] + + +class Multiple: + def __init__(self) -> None: + # Ensure we're really a singleton. + assert "MULTIPLE" not in globals() or self is MULTIPLE + + +# Sentinel indicating multiple quantities can be matched +MULTIPLE = Multiple() + + +class Match: + """ + Represents a successfully matched pattern. + + The `Match` object is returned to represent a successfully matched + pattern. Included in the Match are the pattern that was matched, the graph + nodes matched, and any args that were used during the matching. + + The args and kwargs are specific to the type of pattern that was matched and + provide hints about what was matched. + """ + + pattern: PatternExpr + args: List[Any] + kwargs: Dict[str, Any] + nodes: List[torch.fx.Node] + targets: Dict[_TargetExpr, torch.fx.node.Target] + ctx: MatchContext + replacement_graph: Optional[torch.fx.Graph] + + def __init__( + self, + ctx: MatchContext, + pattern: PatternExpr, + args: Optional[Sequence[Any]] = None, + kwargs: Optional[Dict[str, Any]] = None, + ) -> None: + super().__init__() + self.pattern = pattern + # The input nodes that must be passed in to the result + self.args = list(args or []) + self.kwargs = kwargs or {} + # The nodes matched in this expression + self.nodes = [] + # Mapping CallFunction to the node.target + self.targets = {} + self.ctx = ctx + self.replacement_graph = None + + @property + def graph(self) -> torch.fx.Graph: + return self.ctx.graph + + def extend(self, other: Match) -> None: + if self.kwargs: + for key in set(self.kwargs.keys()) & set(other.kwargs.keys()): + if self.kwargs[key] != other.kwargs[key]: + raise FailedMatch("kwarg mismatch: {}", key) + self.args.extend(other.args) + self.nodes.extend(other.nodes) + self.kwargs.update(other.kwargs) + self.targets.update(other.targets) + + def bundle(self) -> Match: + # Wrap args in an extra list + self.args = [tuple(self.args)] if self.args else [] + return self + + def __repr__(self) -> str: + return f"Match(..., {self.args}, {self.kwargs})" + + def erase_nodes(self) -> None: + graph = self.graph + for n in reversed(self.nodes): + if not n._erased and not n.users: + graph.erase_node(n) + + def output_nodes(self) -> List[Optional[torch.fx.Node]]: + return [ + (self.ctx.pattern_to_node[p] if p is not None else None) + for p in self.ctx.outputs + ] + + def output_node(self) -> torch.fx.Node: + return next(p for p in self.output_nodes() if p) + + def replace_with_graph( + self, replacement_graph: torch.fx.Graph, args: Sequence[Any] + ) -> None: + ReplacementPatternEntry.replace_with_graph( + self, self.ctx.graph, replacement_graph, args + ) + + def replace_by_example( + self, + replacement_fn: ReplaceFn, + args: Sequence[Any], + trace_fn: Optional[TraceFn] = None, + run_functional_passes: bool = True, + ) -> None: + """Replace with a graph generated by tracing the replacement_fn. + + Args: + run_functional_passes (bool). If we should run passes that + assume functional IR (like DCE, remove_noop_ops), on the + replacement graph. + + """ + from torch._inductor.virtualized import NullHandler, V + + context = ( + V.fake_mode + if (not isinstance(V.fake_mode, NullHandler) or (V.fake_mode is None)) + else contextlib.nullcontext() + ) + + with context: + if trace_fn is None: + trace_fn = functools.partial( + fwd_only, run_functional_passes=run_functional_passes + ) + replacement = trace_fn( + replacement_fn, torch.fx.map_arg(args, lambda arg: arg.meta["val"]) # type: ignore[arg-type] + ) + ReplacementPatternEntry.replace_with_graph( + self, + self.ctx.graph, + replacement, + args, + ) + + +class FailedMatch(RuntimeError): + """ + Represents a unsuccessful match. + + The `FailedMatch` object is returned to represent a failure to match a + pattern. + """ + + format_string: str + + def __init__(self, format_string: str, *args: Any, **kwargs: Any) -> None: + self.format_string = format_string + # We want to construct error messages lazily instead of eagerly, as + # constructing them eagerly can significantly worsen compile times. + if len(format_string) > 200: + raise RuntimeError( + f"Format string too long - use lazy construction of strings instead. Format string is\n {format_string}" + ) + self.args = args + self.kwargs = kwargs + + def __str__(self) -> str: + return self.format_string.format(*self.args, **self.kwargs) + + def __bool__(self) -> bool: + return False + + +MatchResult = Union[Match, FailedMatch] + + +def is_match(m: MatchResult) -> TypeGuard[Match]: + """ + TypeGuards cannot act on `self`. Thus this function exists to let mypy + recognize FailedMatch.__bool__ as a TypeGuard. + """ + return bool(m) + + +class MatchContext: + """ + Internal state needed while running PatternExpr._match(). + """ + + outputs: List[Optional[PatternExpr]] + pattern_to_node: Dict[PatternExpr, Optional[torch.fx.Node]] + graph: torch.fx.Graph + exclusive_node_set: List[NodeOrConstant] + + def __init__( + self, + outputs: List[Optional[PatternExpr]], + pattern_to_node: Optional[Dict[PatternExpr, torch.fx.Node]] = None, + *, + graph: torch.fx.Graph, + ) -> None: + self.outputs = outputs + self.pattern_to_node = {} if pattern_to_node is None else dict(pattern_to_node) + self.graph = graph + self.exclusive_node_set = [] + + def match(self, pattern: PatternExpr, node: NodeOrConstant) -> MatchResult: + """wrapper to check reused nodes in patterns""" + if pattern in self.pattern_to_node: + if self.pattern_to_node[pattern] == node: + return Match(self, pattern) # already checked this node + else: + return FailedMatch("repeated pattern differs") + m = pattern._match(node, self) + assert pattern not in self.pattern_to_node + self.pattern_to_node[pattern] = node if m else None + return m + + def filter_multi_user_patterns(self) -> Dict[PatternExpr, torch.fx.Node]: + return { + pattern: node + for pattern, node in self.pattern_to_node.items() + if pattern.has_multiple_users() and node is not None + } + + +class PatternExpr(ABC): + """ + Base class for types of patterns. + """ + + @abstractmethod + def _match(self, node: torch.fx.Node, ctx: MatchContext) -> MatchResult: + ... + + def match(self, node: torch.fx.Node) -> MatchResult: + try: + return MatchContext([self], graph=node.graph).match(self, node) + except FailedMatch as e: + return e + + def has_multiple_users(self) -> bool: + return False + + def __repr__(self) -> str: + return self.__class__.__name__ + "()" + + def find_anchor_nodes( + self, ctx: MatchContext, searched: Set[torch.fx.Node] + ) -> Generator[Optional[torch.fx.Node], None, None]: + if self in ctx.pattern_to_node: + yield ctx.pattern_to_node[self] + + def pattern_eq(self, other: Any) -> bool: + """ + Compare two `PatternExpr`s and return true if they are the + same. Note this is NOT matching a pattern - it is comparing the pattern + structures (for debugging). + """ + return isinstance(other, self.__class__) + + +class Arg(PatternExpr): + """ + Capture an arg which will become an input to the handler. Args are + passed in depth first order. + """ + + def _match(self, node: NodeOrConstant, ctx: MatchContext) -> MatchResult: + return Match(ctx, self, args=[node]) # matches anything + + +class Ignored(PatternExpr): + """ + Match an arg, but don't pass it to handler + """ + + def _match(self, node: NodeOrConstant, ctx: MatchContext) -> MatchResult: + return Match(ctx, self) # matches anything + + def __repr__(self) -> str: + return "*" + + def pretty_print(self, pp: PatternPrettyPrinter) -> str: + return "Ignored()" + + +class KeywordArg(PatternExpr): + """ + Capture a kwarg which will become an input to the handler. + """ + + def __init__(self, name: str) -> None: + super().__init__() + self.name = name + + def __repr__(self) -> str: + return f"KeywordArg({self.name!r})" + + def _match(self, node: NodeOrConstant, ctx: MatchContext) -> MatchResult: + return Match(ctx, self, kwargs={self.name: node}) # matches anything + + def pattern_eq(self, other: Any) -> bool: + other = typing.cast(Self, other) # super makes sure this is true + return super().pattern_eq(other) and self.name == other.name + + +class ExclusiveKeywordArg(PatternExpr): + """ + Capture a kwarg which will become an input to the handler. + """ + + name: str + + def __init__(self, name: str) -> None: + super().__init__() + self.name = name + + def __repr__(self) -> str: + return f"ExclusiveKeywordArg({self.name!r})" + + def _match(self, node: NodeOrConstant, ctx: MatchContext) -> MatchResult: + if node in ctx.exclusive_node_set: + return FailedMatch("exclusive arg appears twice") + + ctx.exclusive_node_set.append(node) + return Match(ctx, self, kwargs={self.name: node}) # matches anything + + def pattern_eq(self, other: Any) -> bool: + other = typing.cast(Self, other) # super makes sure this is true + return super().pattern_eq(other) and self.name == other.name + + +class _TargetExpr(PatternExpr): + """ + Base class for filtering match by node.target + """ + + fns: List[FnsType] + fns_set: Set[FnsType] + + def __init__( + self, fns: Union[FnsType, Sequence[FnsType]], users: Union[Multiple, int] = 1 + ) -> None: + super().__init__() + fns = [fns] if callable(fns) or isinstance(fns, str) else list(fns) + for fn in fns: + if isinstance(fn, torch._ops.OpOverloadPacket): + fns.extend(getattr(fn, overload) for overload in fn.overloads()) + + self.fns = fns + self.fns_set = set(fns) + self.users = users + + @property + @abstractmethod + def op(self) -> str: + ... + + def fns_repr(self) -> str: + first_repr = self.fns[0] + if not isinstance(first_repr, str): + first_repr = first_repr.__name__ + + if len(self.fns) > 1: + return f"[{first_repr}, ...]" + elif self.fns[0] is getattr(torch, first_repr, None): + return f"torch.{first_repr}" + elif isinstance(self.fns[0], torch._ops.OpOverload): + return str(self.fns[0]) + else: + return first_repr + + def __repr__(self) -> str: + if self.users is MULTIPLE: + comma_users = ", MULTIPLE" + elif self.users != 1: + comma_users = f", {self.users})" + else: + comma_users = "" + return f"{self.__class__.__name__}({self.fns_repr()}{comma_users})" + + def has_multiple_users(self) -> bool: + return isinstance(self.users, Multiple) or self.users > 1 + + def find_anchor_nodes( + self, ctx: MatchContext, searched: Set[torch.fx.Node] + ) -> Generator[Optional[torch.fx.Node], None, None]: + raise NotImplementedError + + def _match_fns(self, node: torch.fx.Node) -> bool: + return ( + isinstance(node, torch.fx.Node) + and node.op == self.op + and extract_target(node) in self.fns_set + ) + + def _match_users(self, node: torch.fx.Node, ctx: MatchContext) -> bool: + return ( + self in ctx.outputs + or self.users is MULTIPLE + or len(node.users) == self.users + ) + + def pattern_eq(self, other: Any) -> bool: + other = typing.cast(Self, other) # super makes sure this is true + return ( + super().pattern_eq(other) + and self.op == other.op + and self.fns == other.fns + and self.users == other.users + ) + + +_SimpleSpec = Tuple[Any, ...] + + +class _TargetArgsExpr(_TargetExpr): + """ + Base class for filtering match by node.{target,args,kwargs} + """ + + def __init__( + self, + fns: Union[torch.fx.node.Target, str, Sequence[Any]], + *args: Any, + _users: Union[int, Multiple] = 1, + **kwargs: Any, + ) -> None: + super().__init__(fns, _users) + self.args = tuple(args) + self.kwargs = dict(kwargs) + if any( + isinstance(x, (dict, list, tuple)) + for x in itertools.chain(args, kwargs.values()) + ): + self.flatten = self.pytree_flatten + else: + self.flatten = self.simple_flatten + self.flat_args_kwargs = self.flatten(self.args, self.kwargs) + + @staticmethod + def simple_flatten( + args: Sequence[Any], kwargs: Mapping[Any, Any] + ) -> Tuple[Sequence[Any], Union[_SimpleSpec, pytree.TreeSpec]]: + values = (*args, *kwargs.values()) + spec = (len(args), *kwargs.keys()) + return values, spec + + @staticmethod + def pytree_flatten( + args: Sequence[Any], kwargs: Mapping[Any, Any] + ) -> Tuple[Sequence[Any], Union[_SimpleSpec, pytree.TreeSpec]]: + def norm_spec(s: pytree.TreeSpec) -> pytree.TreeSpec: + if s.type is None: + return s + mapping = {immutable_list: list, tuple: list, immutable_dict: dict} + return pytree.TreeSpec( + mapping.get(s.type, s.type), + s.context, + list(map(norm_spec, s.children_specs)), + ) + + flat, spec = pytree.tree_flatten([args, kwargs]) + spec = norm_spec(spec) + return flat, spec + + def __repr__(self) -> str: + args = [ + self.fns_repr(), + *map(repr, self.args), + *[f"{k}={v}" for k, v in self.kwargs.items()], + ] + if self.users is MULTIPLE: + args.append("_users=MULTIPLE") + elif self.users != 1: + args.append(f"_users={self.users}") + return f"{self.__class__.__name__}({', '.join(args)})" + + def pretty_print(self, pp: PatternPrettyPrinter) -> str: + args = [ + self.fns_repr(), + *(pp.pretty_print(x) for x in self.args), + *[f"{k}={pp.pretty_print(v)}" for k, v in self.kwargs.items()], + ] + if self.users is MULTIPLE: + args.append("_users=MULTIPLE") + elif self.users != 1: + args.append(f"_users={self.users}") + + joiner_str = ", " + return f"{self.__class__.__name__}({joiner_str.join(args)})" + + def _match(self, node: torch.fx.Node, ctx: MatchContext) -> MatchResult: + if not self._match_fns(node) or len(node.args) != len(self.args): + return FailedMatch("function_mismatch: node={}, pattern={}", node, self) + + if not self._match_users(node, ctx): + return FailedMatch("multiple_users {}", self) + + _args = node.args + _kwargs = node.kwargs + if len(_kwargs) < len(self.kwargs): + from torch.fx.operator_schemas import normalize_function + + normalized_args_and_kwargs = normalize_function( + node.target, node.args, node.kwargs # type: ignore[arg-type] + ) + + if normalized_args_and_kwargs is None: + return FailedMatch("function_mismatch: node={}, pattern={}", node, self) + else: + _args, _kwargs = normalized_args_and_kwargs + if len(_args) == len(self.args) and len(_kwargs) >= len(self.kwargs): + _kwargs = {i: _kwargs[i] for i in _kwargs if i in self.kwargs} + else: + return FailedMatch( + "function_mismatch: node={}, pattern={}", node, self + ) + else: + _kwargs = {i: _kwargs[i] for i in _kwargs if i in self.kwargs} + + node_items, node_spec = self.flatten(_args, _kwargs) + self_items, self_spec = self.flat_args_kwargs + if node_spec != self_spec: + return FailedMatch("args_structure {} {}", node_spec, self_spec) + assert len(node_items) == len(self_items) + + m = Match(ctx, self) + for i, pattern, child_node in zip(itertools.count(), self_items, node_items): + if isinstance(pattern, PatternExpr): + child_match = ctx.match(pattern, child_node) + if not is_match(child_match): + return child_match + m.extend(child_match) + elif isinstance(child_node, torch.fx.Node) or child_node != pattern: + return FailedMatch( + "constant_args: {} {!r}!={pattern!r}", node, child_node + ) + m.nodes.append(node) + m.targets[self] = node.target + return m + + def find_anchor_nodes( + self, ctx: MatchContext, searched: Set[torch.fx.Node] + ) -> Generator[Optional[torch.fx.Node], None, None]: + """ + This is used when we are matching a pattern with multiple outputs. + There is a partial match (stored in ctx) and we want to walk + this pattern to find a connection to an already-matched node. + + Yields candidate nodes that `self._match` might like. + """ + if self in ctx.pattern_to_node: + yield ctx.pattern_to_node[self] + return + + for pattern in self.flat_args_kwargs[0]: + if isinstance(pattern, PatternExpr): + for other_node in pattern.find_anchor_nodes(ctx, searched): + if not isinstance(other_node, torch.fx.Node): + continue + for node in other_node.users: + if node not in searched: + if self._match_fns(node): + yield node + searched.add(node) + + def pattern_eq(self, other: Any) -> bool: + other = typing.cast(Self, other) # super makes sure this is true + return ( + super().pattern_eq(other) + and self.flat_args_kwargs[1] == other.flat_args_kwargs[1] + and all( + a.pattern_eq(b) if isinstance(a, PatternExpr) else a == b + for a, b in zip(self.flat_args_kwargs[0], other.flat_args_kwargs[0]) + ) + ) + + +class CallFunction(_TargetArgsExpr): + """ + Matches a call_function node in the FX graphs: `fns[i](*args, **kwargs)` + """ + + op = "call_function" + + +class CallMethod(_TargetArgsExpr): + """ + Matches a call_method node in the FX graphs: `fns[i].method(*args, **kwargs)` + """ + + op = "call_method" + + +class CallModule(_TargetArgsExpr): + """ + Matches a call_module node in the FX graphs: `module(*args, **kwargs)` + """ + + op = "call_module" + + +class _TargetExprVarArgs(_TargetExpr): + """ + Matches a call_function node with any arguments which are passed into the pattern + """ + + def _match(self, node: torch.fx.Node, ctx: MatchContext) -> MatchResult: + if not self._match_fns(node): + return FailedMatch("function_mismatch") + + if not self._match_users(node, ctx): + return FailedMatch("multiple_users") + + m = Match(ctx, self) + m.nodes.append(node) + m.targets[self] = node.target + m.args.extend(node.args) + m.kwargs.update(node.kwargs) + return m + + +class CallFunctionVarArgs(_TargetExprVarArgs): + op = "call_function" + + +class CallMethodVarArgs(_TargetExprVarArgs): + op = "call_method" + + +class CallModuleVarArgs(_TargetExprVarArgs): + op = "call_module" + + +class ListOf(PatternExpr): + """ + Matches a repeated pattern + """ + + def __init__(self, pattern: PatternExpr, partial: bool = False) -> None: + super().__init__() + assert isinstance(pattern, PatternExpr) + self.pattern = pattern + self.partial = partial + + def __repr__(self) -> str: + return f"{self.__class__.__name__}({self.pattern})" + + def _match(self, node: List[torch.fx.Node], ctx: MatchContext) -> MatchResult: # type: ignore[override] + if not isinstance(node, (list, tuple)) or len(node) == 0: + return FailedMatch("non_list") + m = Match(ctx, self) + # Propagating patterns with multiple users will ensure we don't revisit + # the same nodes + pattern_to_node = ctx.filter_multi_user_patterns() + matched = False + for i, child_node in enumerate(node): + child_ctx = MatchContext( + ctx.outputs, pattern_to_node, graph=child_node.graph + ) + child_match = child_ctx.match(self.pattern, child_node) + pattern_to_node = child_ctx.filter_multi_user_patterns() + if not is_match(child_match): + if not self.partial: + return FailedMatch("list[{}]: {}", i, child_match) + continue + matched = True + m.extend(child_match.bundle()) + if not matched: + return FailedMatch("list: no_match") + return m.bundle() + + def pattern_eq(self, other: Any) -> bool: + other = typing.cast(Self, other) # super makes sure this is true + return ( + super().pattern_eq(other) + and self.pattern.pattern_eq(other.pattern) + and self.partial == other.partial + ) + + +class MultiOutputPattern(PatternExpr): + outputs: List[Optional[PatternExpr]] + + def __init__(self, outputs: Sequence[Optional[PatternExpr]]) -> None: + super().__init__() + assert isinstance(outputs[0], _TargetExpr) + assert all(x is None or isinstance(x, PatternExpr) for x in outputs), outputs + self.outputs = list(outputs) + self.op = outputs[0].op + + @property + def fns(self) -> Union[Callable[..., Any], str, Sequence[Any]]: + # This cast is checked above in __init__() + output = typing.cast(_TargetExpr, self.outputs[0]) + return output.fns + + def __repr__(self) -> str: + return f"{self.__class__.__name__}({self.outputs})" + + def pretty_print(self, pp: PatternPrettyPrinter) -> str: + args = [pp.pretty_print(x) for x in self.outputs] + joiner_str = f",\n{' '}" + str_out = f"{self.__class__.__name__}([{joiner_str.join(args)}" + str_out = f"{str_out}\n])" + return str_out + + def _match(self, node: torch.fx.Node, ctx: MatchContext) -> MatchResult: + output = typing.cast(_TargetExpr, self.outputs[0]) + m = ctx.match(output, node) + if not is_match(m): + return m + + for pattern in self.outputs[1:]: + if pattern is None: + continue + child_match = self._match_from_anchors(pattern, ctx) + if not is_match(child_match): + return child_match + m.extend(child_match) + + return m + + def _match_from_anchors( + self, pattern: PatternExpr, ctx: MatchContext + ) -> MatchResult: + prior = dict(ctx.pattern_to_node) + m: MatchResult = FailedMatch("no anchor found") + for node in pattern.find_anchor_nodes(ctx, set()): + m = ctx.match(pattern, node) + if is_match(m): + return m + # revert any partial matches + ctx.pattern_to_node = dict(prior) + return m + + def match(self, node: torch.fx.Node) -> MatchResult: + try: + return MatchContext(self.outputs, graph=node.graph).match(self, node) + except FailedMatch as e: + return e + + def pattern_eq(self, other: Any) -> bool: + other = typing.cast(Self, other) # super makes sure this is true + return ( + super().pattern_eq(other) + and len(self.outputs) == len(other.outputs) + and all( + a.pattern_eq(b) if isinstance(a, PatternExpr) else a == b + for a, b in zip(self.outputs, other.outputs) + ) + ) + + +class RepeatedExpr(PatternExpr): + """ + Checks for a repeated pattern. Useful for repeated operations after a node such as `split` or `unbind` + """ + + def __init__(self, inner_pattern: _TargetExpr) -> None: + super().__init__() + self.inner_pattern = inner_pattern + self.op = inner_pattern.op + + @property + def fns(self) -> Sequence[FnsType]: + return self.inner_pattern.fns + + def _match(self, node: torch.fx.Node, ctx: MatchContext) -> MatchResult: + m = ctx.match(self.inner_pattern, node) + if not is_match(m): + return m + ctx.pattern_to_node.pop( + self.inner_pattern, + ) + # Check all anchor nodes match the pattern + for anchor_node in self.inner_pattern.find_anchor_nodes(ctx, set()): + anchor_m = MatchContext([self], graph=node.graph).match( + self.inner_pattern, anchor_node + ) + if not is_match(anchor_m): + return anchor_m + m.extend(anchor_m) + return m + + def pattern_eq(self, other: Any) -> bool: + other = typing.cast(Self, other) # super makes sure this is true + return super().pattern_eq(other) and self.inner_pattern.pattern_eq( + other.inner_pattern + ) + + +class PatternPrettyPrinter: + """ + Serializes Patterns to executable python. + XXX: currently only used and tested for fuse attention patterns. May not cover + all patterns. + """ + + def __init__(self) -> None: + self.namespace = torch.fx.graph._Namespace() + self.memoized_objs_names: Dict[PatternExpr, str] = {} + self.memoized_objs_pp: Dict[PatternExpr, str] = {} + + @staticmethod + @functools.lru_cache(None) + def run(obj: PatternExpr, output_name: str = "output") -> str: + """ + Serializes obj to python code with obj written out to `output_name` + """ + + pp = PatternPrettyPrinter() + assert hasattr(obj, "pretty_print") + out_str = obj.pretty_print(pp=pp) + + output = [] + for key in pp.memoized_objs_names: + output.append(f"{pp.memoized_objs_names[key]} = {pp.memoized_objs_pp[key]}") + + output.append(f"{output_name} = {out_str}") + + return "\n".join(output) + + def pretty_print(self, obj: Any) -> str: + if isinstance(obj, _TargetArgsExpr): + if memoized_name := self.memoized_objs_names.get(obj): + return memoized_name + else: + return self.memoize(obj) + if hasattr(obj, "pretty_print"): + return obj.pretty_print(self) + + return repr(obj) + + def memoize(self, obj: _TargetArgsExpr) -> str: + obj_str = obj.pretty_print(self) + obj_name = obj.fns_repr() + for prefix in ("aten.", "torch.", "prims."): + obj_name = obj_name.replace(prefix, "") + + tmp_name = self.namespace.create_name(obj_name, None) + self.memoized_objs_names[obj] = tmp_name + self.memoized_objs_pp[obj] = obj_str + return tmp_name + + +class _PassDictsType(Protocol): + def __getitem__(self, k: Tuple[str, torch.fx.node.Target]) -> List[PatternEntry]: + ... + + +@dataclasses.dataclass +class PatternEntry: + pattern: PatternExpr + extra_check: Callable[[Match], bool] + + def apply(self, match: Match, graph: torch.fx.Graph, node: torch.fx.Node) -> None: + raise NotImplementedError + + def register( + self, + pass_dicts: Union[_PassDictsType, Sequence[_PassDictsType]], + target: Union[torch.fx.node.Target, None] = None, + prepend: bool = False, + ) -> None: + if target is None: + assert hasattr(self.pattern, "fns") + for fn in self.pattern.fns: + self.register(pass_dicts, fn, prepend=prepend) + elif isinstance(pass_dicts, (dict, PatternMatcherPass)): + assert hasattr(self.pattern, "op") + if prepend: + pass_dicts[(self.pattern.op, target)].insert(0, self) + else: + pass_dicts[(self.pattern.op, target)].append(self) + else: + pass_dicts = typing.cast(Sequence[_PassDictsType], pass_dicts) + for x in pass_dicts: + self.register(x, target, prepend=prepend) + + +@dataclasses.dataclass +class LoweringPatternEntry(PatternEntry): + handler: Callable[..., Any] + + def apply(self, match: Match, graph: torch.fx.Graph, node: torch.fx.Node) -> None: + handler = functools.wraps(self.handler)(functools.partial(self.handler, match)) + with graph.inserting_before(node): + replacement = graph.call_function(handler, tuple(match.args), match.kwargs) + replacement.meta.update(node.meta) + node.replace_all_uses_with(replacement) + assert match.nodes[-1] is node + match.erase_nodes() + + +@dataclasses.dataclass +class GraphPatternEntry(PatternEntry): + """ + A pattern that runs a function on the FX graph + """ + + handler: Callable[..., Any] + + def apply(self, match: Match, graph: torch.fx.Graph, node: torch.fx.Node) -> None: + with graph.inserting_before(node): + self.handler(match, *match.args, **match.kwargs) + + +@dataclasses.dataclass +class ReplacementPatternEntry(PatternEntry): + normalize_args: Callable[..., List[Any]] + + @staticmethod + def replace_with_graph( + match: Match, + graph: torch.fx.Graph, + replacement_graph: Union[torch.fx.Graph, torch.fx.GraphModule], + args: Sequence[torch.fx.Node], + ) -> None: + class Replacer(torch.fx.Interpreter): + call_method = None # type: ignore[assignment] + call_module = None # type: ignore[assignment] + get_attr = None # type: ignore[assignment] + + def run_node(self, node: torch.fx.Node) -> Any: + if node.op in ("placeholder", "output"): + return super().run_node(node) + if node.op == "call_function": + target = node.target + args, kwargs = self.fetch_args_kwargs_from_env(node) + result = graph.call_function(target, args, kwargs) # type: ignore[arg-type] + if "val" in node.meta and "val" not in result.meta: + result.meta["val"] = node.meta["val"] + if isinstance(node.meta["val"], torch.Tensor): + assert "tensor_meta" in node.meta + result.meta["tensor_meta"] = node.meta["tensor_meta"] + return result + raise NotImplementedError(f"unhandled {node}") + + output_nodes = match.output_nodes() + + if len(output_nodes) == 1: + last_node = output_nodes[0] + else: + assert output_nodes[0] + nodes = list(output_nodes[0].graph.nodes) + indices = [ + (nodes.index(n), n) + for n in output_nodes + if isinstance(n, torch.fx.Node) + ] + last_node = min(indices, key=operator.itemgetter(0))[1] + + def percolate_tags( + node: torch.fx.Node, + tag_name: str, + tag_value: str, + input_stops: Set[torch.fx.Node], + ) -> None: + queue = [node] + visited = set() + + while queue: + arg = queue.pop() + if ( + arg not in visited + and arg not in input_stops + and hasattr(arg, "meta") + ): + visited.add(arg) + arg.meta[tag_name] = tag_value + queue.extend(arg.all_input_nodes) + + with graph.inserting_before(last_node): + replacement = Replacer(replacement_graph).run(*args) # type: ignore[arg-type] + if isinstance(replacement, torch.fx.Node): + replacement = [replacement] + + def maybe_getitem(node: torch.fx.Node) -> Any: + if node.op != "call_function": + return None + if node.target != operator.getitem: + return None + assert len(node.args) == 2 + return node.args[1] + + def replace( + old: Union[torch.fx.Node, None], + new: Union[torch.fx.Node, Sequence[torch.fx.Node], None], + ) -> None: + if old is None: + assert new is None + return + assert isinstance(old, torch.fx.Node) + if new is None: + old.replace_all_uses_with(None) # type: ignore[arg-type] + graph.erase_node(old) + return + if isinstance(new, torch.fx.Node): + if "val" not in new.meta: + new.meta.update(old.meta) + + # Preserve the recompute tags in the replacement graph. We + # look at the recompute tags of the original output node to + # propagate the tag from the output all the way to the input + # args (named as args in the replace_with_graph). + # Note that this is best effort. Since patterns are from + # many to many, there is no easy way to correctly map the + # recomputable tags. It is possible in some scenarios that we + # incorrectly tag some nodes as recomputables. + for tag_name in ["recompute", "ac_graph_id"]: + if tag_name in old.meta: + percolate_tags(new, tag_name, old.meta[tag_name], set(args)) + + old.replace_all_uses_with(new) + graph.erase_node(old) + return + + # `new` is not a node: it's a list of nodes. + # + # This happens when we want to replace a node that has a single + # packed return with multiple unpacked returns. We need to do + # some graph surgery here. + # + # Example: + # def original_graph(x): + # a = op(x) + # b = a[0] + # c = a[1] + # ... + # + # Assume that we want to replace op(x) with the graph + # def new_op(x): + # w = x + 1 + # z = x + 2 + # return (w, z) + # + # We need to replace `op` with the contents of `new_op`, + # and then rewrite a[0] to be w and a[1] to be z, as so: + # def new_graph(x): + # w = x + 1 + # z = x + 2 + # b = w + # c = z + # ... + old_uses = list(old.users.keys()) + for user in old_uses: + idx = maybe_getitem(user) + if idx is None: + raise AssertionError("can't handle") + replace(user, new[idx]) # type: ignore[index] + graph.erase_node(old) + + if len(output_nodes) == len(replacement): + for old, new in zip(output_nodes, replacement): + replace(old, new) + else: + assert len(output_nodes) == 1 + replace(output_nodes[0], replacement) + + match.erase_nodes() + + def apply(self, match: Match, graph: torch.fx.Graph, node: torch.fx.Node) -> None: + assert match.replacement_graph is not None + self.replace_with_graph( + match, + graph, + match.replacement_graph, + self.normalize_args(*match.args, **match.kwargs), + ) + + +def _return_true(match: Match) -> bool: + return True + + +def log_trace_failure(search_fn: Callable[..., Any], e: RuntimeError) -> None: + log.info( + "Replacement pattern %s failed to apply due to shape mismatch: %s", + search_fn.__name__, + e, + ) + + +def register_replacement( + search_fn: SearchFn, + replace_fn: ReplaceFn, + example_inputs: Iterable[Any], + trace_fn: TraceFn, + pass_dicts: Union[_PassDictsType, Sequence[_PassDictsType]], + extra_check: Callable[[Match], bool] = _return_true, + scalar_workaround: Union[Dict[str, Union[float, int]], None] = None, + exclusive_arg_names: Sequence[str] = (), + search_fn_pattern: Union[PatternExpr, None] = None, +) -> bool: + """ + Create a replacement rule based on example functions that get traced + to create patterns. This supports both training and inference when + run on a joint forward+backward graph. + + Args: + search_fn: traced to give original pattern + replace_fn: traced to give replacement graph + example_inputs: example inputs for initial trace + trace_fn: fwd_only or joint_fwd_bwd + pass_dict: dict of passes to register to + extra_check: additional check to run on match(using real shapes) + """ + argnames_static = [*inspect.signature(search_fn).parameters.keys()] + + def check_fn(match: Match) -> bool: + """ + Often shapes get burned into the pattern, so our initial match ran with + `ignore_types=(int, ...)`. + + Recheck the match with the correct shapes. + """ + argnames = list(argnames_static) + for name in argnames: + if name not in match.kwargs: + raise RuntimeError( + f"Not all inputs to pattern found in match.kwargs. Perhaps one " + f"of the inputs is unused? argnames={argnames}, match.kwargs={match.kwargs}" + ) + + args = list( + torch.fx.map_arg( # type: ignore[arg-type] + [match.kwargs[name] for name in argnames], lambda n: n.meta["val"] + ) + ) + sym_args: List[torch.SymInt] = [] + with torch._dynamo.utils.detect_fake_mode(args): + for i, grad in enumerate(requires_grad): + if isinstance(args[i], torch.Tensor): + if grad and is_integer_dtype(args[i].dtype): + return False + + args[i] = torch.empty_strided( + args[i].size(), + args[i].stride(), + dtype=args[i].dtype, + device=args[i].device, + requires_grad=grad, + ) + for v in itertools.chain(args[i].shape, args[i].stride()): + if isinstance(v, torch.SymInt) and all( + guard_size_oblivious(v != a) for a in sym_args + ): + sym_args.append(v) + + # If we were given a pre-traced pattern then use that instead of + # retracing. Note that this means the pattern has to be independent + # of its args. + specific_pattern = search_fn_pattern + + if not specific_pattern: + if sym_args: + # AOT Autograd and make fx will dedupe symbolic shape size + # accesses of sym ints that appear as inputs + # We don't want the sym_size uses to interfere with pattern matching + # so we provide them as inputs. + # Later, when we actually do the replacement, the symbolic shape + # sizes will get re-traced and added to the graph. + + def search_fn_new(*args_new: Any) -> Any: + return search_fn(*args_new[len(args_new) - len(args) :]) + + try: + specific_graph = trace_fn(search_fn_new, sym_args + args) + except RuntimeError as e: + log_trace_failure(search_fn, e) + return False + + # correct argnames in the graph + sym_arg_names = [] + for i, placeholder in zip( + range(len(sym_args) + len(args)), + specific_graph.graph.nodes, + ): + if i < len(sym_args): + sym_arg_names.append(placeholder.target) + continue + + with specific_graph.graph.inserting_after(placeholder): + new_node = specific_graph.graph.placeholder( + argnames[i - len(sym_args)] + ) + new_node.target = new_node.name + placeholder.replace_all_uses_with(new_node) + specific_graph.graph.erase_node(placeholder) + + argnames = sym_arg_names + argnames + else: + try: + specific_graph = trace_fn(search_fn, args) + except RuntimeError as e: + log_trace_failure(search_fn, e) + return False + + specific_pattern = fx_to_pattern( + specific_graph, + argnames=argnames, + exclusive_arg_names=exclusive_arg_names, + scalar_workaround=scalar_workaround, + ) + + node = match.output_nodes()[0] + assert node is not None + specific_pattern_match = specific_pattern.match(node) + + if is_match(specific_pattern_match) and extra_check(specific_pattern_match): + # trace the pattern using the shapes from the user program + match.replacement_graph = trace_fn(replace_fn, args) # type: ignore[assignment] + return True + return False + + def normalize_args(**kwargs: Any) -> List[Any]: + args = [] + for name in argnames_static: + args.append(kwargs.pop(name)) + for i in range(1, len(kwargs) + 1): + if f"tangents_{i}" not in kwargs: + break + args.append(kwargs.pop(f"tangents_{i}")) + assert not kwargs, f"leftover kwargs: {kwargs!r}" + return args + + if trace_fn is joint_fwd_bwd: + # If inference mode is enabled during compilation, assume that we don't + # want to match on any training graph patterns + if torch.is_inference_mode_enabled(): + return False + + # TODO: Revisit the functionalize_rng_ops for lowmem dropout + with functorch_config.patch(functionalize_rng_ops=False): + requires_grad: List[bool] = [ + isinstance(x, torch.Tensor) and x.requires_grad for x in example_inputs + ] + if search_fn_pattern is None: + pattern = gen_pattern( + search_fn, + example_inputs, + trace_fn, + scalar_workaround, + exclusive_arg_names, + ) + else: + pattern = search_fn_pattern + + pattern_repr = PatternPrettyPrinter.run(pattern) + assert pattern_repr not in _seen_patterns + _seen_patterns.add(pattern_repr) + pattern = ReplacementPatternEntry( + pattern=pattern, + extra_check=check_fn, + normalize_args=normalize_args, + ) + pattern.register(pass_dicts) + return pattern.pattern + + +_serialized_patterns: Set[str] = set() + + +def _serialize_pattern( + unique_name: str, + search_fn: SearchFn, + example_inputs: Iterable[Any], + trace_fn: TraceFn, + scalar_workaround: Union[Dict[str, Union[float, int]], None], +) -> PatternExpr: + def get_file_template() -> str: + auto_generated_msg = textwrap.dedent( + """\ + # This is an auto-generated file. Please do not modify it by hand. + # To re-generate, run: + # cd ~/pytorch && python torchgen/fuse/gen_patterns.py + """ + ) + + file_template = textwrap.dedent( + """\ + # mypy: ignore-errors + + # noqa: F401, E501 + {msg} + import torch + import torch._inductor + + aten = torch.ops.aten + prims = torch.ops.prims + + """ + ).format(msg=auto_generated_msg) + + pattern_matcher_imports = [] + for name in dir(torch._inductor.pattern_matcher): + attr = getattr(torch._inductor.pattern_matcher, name) + if isinstance(attr, type) and issubclass(attr, (PatternExpr, _TargetExpr)): + pattern_matcher_imports.append(name) + + formatted_imports = ",\n ".join(pattern_matcher_imports) + formatted_imports = f"from torch._inductor.pattern_matcher import (\n {formatted_imports},\n)\n" + return f"{file_template}{formatted_imports}" + + if not SERIALIZED_PATTERN_PATH.is_dir(): + raise RuntimeError( + f"Could not find serialized patterns directory at {SERIALIZED_PATTERN_PATH}" + ) + + pattern_name = search_fn.__name__ + + from torch._functorch import config as functorch_config + + with functorch_config.patch(functionalize_rng_ops=False): + pattern = gen_pattern(search_fn, example_inputs, trace_fn, scalar_workaround) + + serialized_pattern = PatternPrettyPrinter.run(pattern, output_name=unique_name) + if pattern_name not in _serialized_patterns: + write_mode = "w" + _serialized_patterns.add(pattern_name) + else: + write_mode = "a" + + file_template = get_file_template() + + with open(SERIALIZED_PATTERN_PATH / f"{pattern_name}.py", write_mode) as f: + if write_mode == "w": + f.write(file_template) + else: + f.write("\n\n") + f.write(serialized_pattern) + f.write("\n") + + return pattern + + +SERIALIZED_PATTERN_PATH = Path(__file__).parent / "fx_passes" / "serialized_patterns" + +# This is the set of serialized patterns that we've registered. Used by +# test_serialized_patterns_up_to_date() to ensure the patterns are up +# to date. +_known_precompiled_patterns: List[ + Tuple[ + Any, + Iterable[Any], + Callable[[Callable[..., Any], Iterable[Any]], torch.fx.GraphModule], + Any, + PatternExpr, + ] +] = [] + + +def gen_register_replacement( + unique_name: str, + search_fn: SearchFn, + replace_fn: ReplaceFn, + example_inputs: Iterable[Any], + trace_fn: TraceFn, + pass_dicts: Union[_PassDictsType, Sequence[_PassDictsType]], + extra_check: Callable[[Match], bool] = _return_true, + scalar_workaround: Union[Dict[str, Union[float, int]], None] = None, + exclusive_arg_names: Sequence[str] = (), + skip_duplicates: bool = False, +) -> None: + # Make sure the example_inputs is materialized. + example_inputs = tuple(example_inputs) + + if "PYTORCH_GEN_PATTERNS" in os.environ: + pat = _serialize_pattern( + unique_name, search_fn, example_inputs, trace_fn, scalar_workaround + ) + else: + pattern_name = search_fn.__name__ + m = importlib.import_module( + f"torch._inductor.fx_passes.serialized_patterns.{pattern_name}" + ) + if not m or not hasattr(m, unique_name): + log.warning( + "Precompiled pattern %r not found. Run torchgen/fuse/gen_patterns.py.", + unique_name, + ) + pat = getattr(m, unique_name) + + for arg in pytree.tree_iter(example_inputs): + if isinstance(arg, FakeTensor) and arg.constant is not None: + # This can be a problem - small fake tensors (e.g. `tensor(2)`) will + # hold onto their original constant value - and by stashing it here + # will cause a memory leak if the constant value is on GPU. + # Since this is just an optimization we can clear it out. + arg.constant = None + + if PatternPrettyPrinter.run(pat) in _seen_patterns and skip_duplicates: + return + _known_precompiled_patterns.append( + (search_fn, example_inputs, trace_fn, scalar_workaround, pat) + ) + register_replacement( + search_fn, + replace_fn, + example_inputs, + trace_fn, + pass_dicts, + extra_check, + scalar_workaround, + exclusive_arg_names, + search_fn_pattern=pat, + ) + + +@functorch_config.patch(functionalize_rng_ops=False) +def gen_pattern( + search_fn: SearchFn, + example_inputs: Sequence[Any], + trace_fn: TraceFn, + scalar_workaround: Union[Dict[str, Union[float, int]], None] = None, + exclusive_arg_names: Sequence[str] = (), +) -> PatternExpr: + argnames = [*inspect.signature(search_fn).parameters.keys()] + + if scalar_workaround is None: + scalar_workaround = {} + flat_inputs = [] + input_idx = 0 # Positional arguments index + + for argname in argnames: + if argname in scalar_workaround: + flat_inputs.append(scalar_workaround[argname]) + else: + flat_inputs.append(example_inputs[input_idx]) + input_idx += 1 + + search_gm = trace_fn(search_fn, flat_inputs) + return fx_to_pattern( + search_gm, + ignore_types=(int, float, list, torch.device, torch.dtype), + argnames=argnames, + scalar_workaround=scalar_workaround, + exclusive_arg_names=exclusive_arg_names, + ) + + +def register_lowering_pattern( + pattern: PatternExpr, + extra_check: Callable[[Match], bool] = _return_true, + *, + pass_dict: _PassDictsType, + prepend: bool = False, +) -> Callable[[Callable[..., Any]], Callable[..., Any]]: + """ + Register an aten to inductor IR replacement pattern. The decorated + function is saved and then called a lowering time allowing direct + pattern to inductor IR conversion. + """ + + def decorator(handler: Callable[..., Any]) -> Callable[..., Any]: + assert callable(handler) + LoweringPatternEntry( + pattern=pattern, extra_check=extra_check, handler=handler + ).register(pass_dict, prepend=prepend) + handler._inductor_lowering_function = True # type: ignore[attr-defined] + return handler + + return decorator + + +def register_graph_pattern( + pattern: PatternExpr, + extra_check: Callable[[Match], bool] = _return_true, + *, + pass_dict: _PassDictsType, + prepend: bool = False, +) -> Callable[[Callable[..., Any]], Callable[..., Any]]: + """ + Register a pattern that runs a function on the FX graph, allowing + custom transformation code. + """ + + def decorator(handler: Callable[..., Any]) -> Callable[..., Any]: + assert callable(handler) + GraphPatternEntry( + pattern=pattern, extra_check=extra_check, handler=handler + ).register(pass_dict, prepend=prepend) + return handler + + return decorator + + +def is_start_of_fx_graph(graph: torch.fx.Graph, node: torch.fx.Node) -> bool: + # first node in the graph + return node is next(iter(graph.nodes)) + + +# match: copy_, relu_, _set_grad_enabled, manual_seed, _enter_autocast, etc +# doesn't match: __rshift__, etc +_mutation_op_re = re.compile(r"(? bool: + if node.op == "call_function": + if _mutation_op_re.search(node.target.__name__): # type: ignore[union-attr] + return True + elif node.op == "call_method": + if _mutation_op_re.search(node.target): # type: ignore[union-attr, arg-type] + return True + return node.kwargs.get("out") is not None + + +def same_mutation_regions(a: torch.fx.Node, b: torch.fx.Node) -> bool: + assert "mutation_region_id" in a.meta + assert "mutation_region_id" in b.meta + return a.meta["mutation_region_id"] == b.meta["mutation_region_id"] + + +def get_mutation_region_id(graph: torch.fx.Graph, node: torch.fx.Node) -> int: + n = node + while "mutation_region_id" not in n.meta and not is_start_of_fx_graph(graph, n): + n = n.prev + mutation_region_id = n.meta.get("mutation_region_id", 0) + while n is not node: + n = n.next + if is_mutation_op(n): + mutation_region_id += 1 + n.meta["mutation_region_id"] = mutation_region_id + return mutation_region_id + + +def should_compute_mutation_region_ids(graph: torch.fx.GraphModule) -> bool: + return "mutation_region_id" not in next(iter(graph.nodes)).meta + + +def compute_mutation_region_ids(graph: torch.fx.GraphModule) -> None: + mutation_region_id = 0 + for nd in graph.nodes: + if is_mutation_op(nd): + mutation_region_id += 1 + nd.meta["mutation_region_id"] = mutation_region_id + + +class PatternMatcherPass: + def __init__( + self, + pass_name: Optional[str] = None, + ) -> None: + super().__init__() + self.patterns: DefaultDict[ + Tuple[str, torch.fx.node.Target], List[PatternEntry] + ] = defaultdict(list) + self.pass_name = pass_name + + def __getitem__(self, item: Tuple[str, torch.fx.node.Target]) -> List[PatternEntry]: + return self.patterns[item] + + def apply(self, gm: torch.fx.GraphModule) -> int: + if not self.patterns: + return 0 + if isinstance(gm, torch.fx.GraphModule): + graph = gm.graph + elif isinstance(gm, torch.fx.Graph): + graph = gm + gm = graph.owning_module + else: + raise RuntimeError( + f"The input to PatternMatcherPass must be a GraphModule or a Graph, but got {type(gm)}" + ) + if should_compute_mutation_region_ids(graph): # type: ignore[arg-type] + compute_mutation_region_ids(graph) # type: ignore[arg-type] + get_mutation_region_id_partial = functools.partial( + get_mutation_region_id, graph + ) + count = 0 + nodes = [] + has_call_module = False + for op, target in self.patterns: + if op == "call_module": + has_call_module = True + else: + nodes.append(graph.find_nodes(op=op, target=target, sort=False)) + if has_call_module: + nodes.append(graph.find_nodes(op="call_module", sort=False)) + pass_name = self.pass_name if self.pass_name is not None else "pattern_matcher" + with GraphTransformObserver( + gm, pass_name, trace_config.log_url_for_graph_xform + ): + for node in sorted(itertools.chain.from_iterable(nodes), reverse=True): + target = extract_target(node) + if node.op == "call_module": + if (node.op, target) not in self.patterns: + continue + + # conservatively not applying pattern for cpu input, + # since some of the patterns induce codegen and split nodes. + # Note: we will only skip cpu compute if disable_cpp_codegen=True + if fallback_node_due_to_unsupported_type(node, allow_cpu_inputs=False): + continue + + for entry in self.patterns[(node.op, target)]: + if node._erased: + break + m = entry.pattern.match(node) + # pattern match crosses mutation barrier - discard + if ( + is_match(m) + and len(set(map(get_mutation_region_id_partial, m.nodes))) != 1 # type: ignore[possibly-undefined] + ): + continue + if os.environ.get("TORCHINDUCTOR_PATTERN_MATCH_DEBUG") == node.name: + log.warning("%s%s %s %s", node, node.args, m, entry.pattern) + if is_match(m) and entry.extra_check(m): + count += 1 + entry.apply(m, graph, node) # type: ignore[arg-type] + counters["inductor"]["pattern_matcher_count"] += 1 + counters["inductor"]["pattern_matcher_nodes"] += len(m.nodes) + return count + + def clear(self) -> None: + self.patterns.clear() + + +def _not_implemented(*args: Any, **kwargs: Any) -> NoReturn: + raise NotImplementedError + + +def fx_to_pattern( + gm: Union[torch.fx.GraphModule, torch.fx.Graph], + ignore_types: Sequence[Type[Any]] = (), + argnames: Sequence[str] = (), + scalar_workaround: Union[Dict[str, Union[float, int]], None] = None, + exclusive_arg_names: Sequence[str] = (), +) -> PatternExpr: + """ + Convert an FX graph into a PatternExpr. This is useful for simple + patterns that can only match single functions and fixed-length lists. + """ + # scalar_workaround is a hack to capture dropout_p + # see https://github.com/pytorch/pytorch/issues/97894 + scalar_workaround = scalar_workaround or {} + inv_scalar_workaround = {v: k for k, v in scalar_workaround.items()} + assert len(inv_scalar_workaround) == len(scalar_workaround) + + def process_arg(x: T) -> Union[T, KeywordArg, Ignored]: + if isinstance(x, (float, int)) and x in inv_scalar_workaround: + return KeywordArg(inv_scalar_workaround[x]) + if type(x) in ignore_types: + return Ignored() + if isinstance(x, list) and all(isinstance(y, Ignored) for y in x) and x: + return Ignored() + return x + + argnum = itertools.count() + + class Converter(torch.fx.Interpreter): + call_method = _not_implemented + call_module = _not_implemented + get_attr = _not_implemented + + def placeholder( + self, target: str, args: Sequence[Any], kwargs: Mapping[str, Any] # type: ignore[override] + ) -> Union[ExclusiveKeywordArg, KeywordArg]: + n = next(argnum) + if n < len(argnames): + name = argnames[n] + elif argnames: + assert target.startswith("tangent") + name = target + else: + target = re.sub(r"_\d+$", "", target) # de-mangle arg name + name = target + if name in exclusive_arg_names: + return ExclusiveKeywordArg(name) + else: + return KeywordArg(name) + + def call_function( + self, target: str, args: Sequence[Any], kwargs: Mapping[str, Any] # type: ignore[override] + ) -> PatternExpr: + args, kwargs = pytree.tree_map(process_arg, (args, kwargs)) + if list in ignore_types: + # Handle a burned in tensor size which are now [Ignored(), Ignored(), ...] + args = [process_arg(a) for a in args] + kwargs = {k: process_arg(a) for k, a in kwargs.items()} + return CallFunction(target, *args, **kwargs) + + def run_node(self, n: torch.fx.Node) -> Any: + rv = super().run_node(n) + if n.op == "output" and isinstance(rv, tuple): + assert len(rv) == len(n.args[0]) # type: ignore[arg-type] + for r, arg in zip(rv, n.args[0]): # type: ignore[arg-type] + r.users = len(arg.users) + else: + rv.users = len(n.users) + return rv + + pattern = Converter(gm).run() # type: ignore[arg-type] + if not isinstance(pattern, PatternExpr): + return MultiOutputPattern(pytree.tree_leaves(pattern)) + return pattern + + +@torch.no_grad() +def fwd_only( + fn: Callable[..., Any], + args: Sequence[Any], + *, + run_functional_passes: bool = True, + get_decomp_fn: Optional[Callable[..., Any]] = None, +) -> torch.fx.GraphModule: + """Build a normalized inference graph, for use with fx_to_pattern""" + # TODO - look into using aot autograd, asserting no mutating ops here + with enable_python_dispatcher(): + decompositions = ( + get_decomp_fn() if get_decomp_fn is not None else select_decomp_table() + ) + gm = make_fx(fn, decompositions, tracing_mode="real")(*args) + + from .fx_passes.post_grad import remove_noop_ops + + if run_functional_passes: + remove_noop_ops(gm.graph) + gm.graph.eliminate_dead_code() + + gm.recompile() + return gm + + +@torch.enable_grad() +def joint_fwd_bwd(fn: Callable[..., Any], args: Sequence[Any]) -> torch.fx.GraphModule: + """Build a normalized training graph, for use with fx_to_pattern""" + gm: Optional[torch.fx.GraphModule] = None + + def record_joint_graph( + joint_graph: torch.fx.GraphModule, inputs: Sequence[Any], **kwargs: Any + ) -> Tuple[torch.fx.GraphModule, torch.fx.GraphModule]: + nonlocal gm + assert not gm + gm = clone_graph(joint_graph) + return default_partition(joint_graph, inputs, **kwargs) + + with torch._guards.tracing(None): + aot_function( + fn, + lambda g, i: make_boxed_func(g), + partition_fn=record_joint_graph, + decompositions=select_decomp_table(), + keep_inference_input_mutations=True, + enable_log=False, + )(*args) + assert gm + + from .fx_passes.post_grad import remove_noop_ops + + remove_noop_ops(gm.graph) + + from .fx_passes.joint_graph import pointless_view + + matcher_pass = PatternMatcherPass() + + pattern = CallFunction( + torch.ops.aten.view.default, KeywordArg("arg"), KeywordArg("size") + ) + GraphPatternEntry( + pattern=pattern, handler=pointless_view, extra_check=_return_true + ).register(matcher_pass.patterns) + matcher_pass.apply(gm.graph) # type: ignore[arg-type] + + # remove in/out specs + gm.graph._codegen = torch.fx.graph.CodeGen() + gm.graph.eliminate_dead_code() + gm.recompile() + return gm + + +def _args(n: torch.fx.Node) -> List[torch.fx.node.Argument]: + args: List[torch.fx.node.Argument] = [] + torch.fx.map_arg((n.args, n.kwargs), args.append) + return args + + +def stable_topological_sort(graph: torch.fx.Graph) -> None: + # Nodes are in exactly one of these three collections: + + # - Nodes in `pending` are waiting to be processed (in reverse order): + pending = list(reversed(graph.nodes)) + + # - Nodes in `ready` have been processed and are already in the correct + # order. + ready = set() + + # - `waiting` is a mapping from a dependency to nodes which depend on that + # dependency. + waiting = defaultdict(list) + + # The cursor indicates the last processed node so we can add new nodes + # after it. + cursor = None + while pending: + node = pending.pop() + waiting_for = [x for x in _args(node) if x not in ready] + if waiting_for: + # We have unprocessed input nodes. Might as well wait for the last + # arg so an already sorted list will only recheck this node once. + waiting[waiting_for[-1]].append(node) + else: + ready.add(node) + if cursor and cursor.next is not node: + cursor.append(node) + cursor = node + # Mark the nodes that have been waiting for this node to finish as + # ready to check again. + pending.extend(reversed(waiting.pop(node, ()))) + + assert not waiting and len(ready) == len(graph.nodes) + + +def init_once_fakemode(fn: Callable[..., Any]) -> Callable[[], Any]: + """Wrapper around lazy init functions in fx_passes/""" + + @functools.lru_cache(None) + @functools.wraps(fn) + def lazy_init() -> Any: + counters_ref = counters["inductor"].copy() + + with torch._guards.tracing(None), unset_fake_temporarily(), FakeTensorMode(): + result = fn() + + # clear view matches encountered during tracing + counters["inductor"] = counters_ref + + return result + + return lazy_init + + +def config_flag(name: str) -> Callable[[Match], Any]: + """Function for extra_check to put pass behind a flag""" + + def flag_check(match: Match) -> Any: + return getattr(config, name) + + return flag_check + + +def clone_graph(input_graph: torch.fx.GraphModule) -> torch.fx.GraphModule: + class CopyGraph(Transformer): + def run_node(self, old_node: torch.fx.Node) -> torch.fx.Node: + new_node = super().run_node(old_node) + if isinstance(new_node, torch.fx.Proxy): + new_node.node.meta.update(old_node.meta) + new_node.node.name = self.new_graph._graph_namespace.create_name( + old_node.name, None + ) + return new_node + + return CopyGraph(input_graph).transform() + + +_seen_patterns: Set[str] = set() + + +def get_arg_value( + node: torch.fx.Node, arg_number: int, kwarg_name: Optional[str] = None +) -> Any: + return ( + node.args[arg_number] + if len(node.args) > arg_number + else node.kwargs.get(kwarg_name) # type: ignore[arg-type] + ) + + +def filter_nodes(nodes: Iterable[torch.fx.Node], fn: Any) -> List[torch.fx.Node]: + fns = [fn] + if isinstance(fn, torch._ops.OpOverloadPacket): + fns.extend([getattr(fn, overload) for overload in fn.overloads()]) + + return [node for node in nodes if node.target in fns] + + +def extract_target(node: torch.fx.Node) -> torch.fx.node.Target: + """For call_function and call_method, we directly use the target function; + For call_module, the target is string, and we treat the module class + as a function. + """ + if node.op == "call_module": + return getattr(node.graph.owning_module, node.target).__class__ # type: ignore[arg-type] + return node.target diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/quantized_lowerings.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/quantized_lowerings.py new file mode 100644 index 0000000000000000000000000000000000000000..80910e67d3a61b7ba1ae115371c6b59786eff205 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/quantized_lowerings.py @@ -0,0 +1,92 @@ +# mypy: allow-untyped-defs +import logging + +import torch +from torch._inductor.kernel.mm_common import mm_args + +from . import config as inductor_config, lowering +from .codegen.cpp_gemm_template import CppPackedGemmTemplate +from .codegen.cpp_utils import create_epilogue_with_attr +from .lowering import expand, register_lowering +from .select_algorithm import ( + autotune_select_algorithm, + ExternKernelChoice, + realize_inputs, +) +from .utils import use_aten_gemm_kernels, use_cpp_packed_gemm_template + + +log = logging.getLogger(__name__) + +aten__weight_int8pack_mm = ExternKernelChoice( + torch._weight_int8pack_mm, "at::_weight_int8pack_mm", has_out_variant=False +) + + +quantized = torch.ops.quantized +_quantized = torch.ops._quantized +aten = torch.ops.aten + + +def register_quantized_ops(): + lowering.add_needs_realized_inputs( + [ + quantized.max_pool2d, + _quantized.wrapped_fbgemm_pack_gemm_matrix_fp16, + _quantized.wrapped_fbgemm_linear_fp16_weight, + ] + ) + + lowering.make_fallback(quantized.max_pool2d) + lowering.make_fallback(_quantized.wrapped_fbgemm_pack_gemm_matrix_fp16) + lowering.make_fallback(_quantized.wrapped_fbgemm_linear_fp16_weight) + + +def register_woq_mm_ops(): + @register_lowering(aten._weight_int8pack_mm, type_promotion_kind=None) + def int8pack_mm(input, weight, scale, *, layout=None): + _, _, _, layout, mat1, mat2 = mm_args( + input, weight, layout=layout, mat2_transposed=True + ) + assert ( + mat1.get_dtype() in [torch.bfloat16, torch.float16, torch.float] + and mat2.get_dtype() == torch.int8 + ) + aten_layout = layout + + # options to tune from + choices = ( + [aten__weight_int8pack_mm.bind((mat1, mat2, scale), aten_layout)] + if use_aten_gemm_kernels() + else [] + ) + + # scale is applied as an epilogue, and the scale tensor is expanded (with a view op) + # for broadcasting, as it's 1D. + def _mul_epilogue(buf): + return create_epilogue_with_attr( + buf, "mul", other=realize_inputs(expand(scale, layout.size)) + ) + + if use_cpp_packed_gemm_template(aten_layout, mat1, mat2, mat2_transposed=True): + CppPackedGemmTemplate.add_choices( + choices, + aten_layout, + [mat1, mat2, scale], + trans_w=True, + epilogue_creator=_mul_epilogue, + ) + + if ( + len(choices) == 0 + and inductor_config.autotune_fallback_to_aten + and not use_aten_gemm_kernels() + ): + log.warning("No choices for GEMM, using ATen backend as fallback") + return aten__weight_int8pack_mm.bind( + (mat1, mat2, scale), aten_layout + ).output_node() + + return autotune_select_algorithm( + "_weight_int8pack_mm", choices, [mat1, mat2, scale], aten_layout + ) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/remote_cache.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/remote_cache.py new file mode 100644 index 0000000000000000000000000000000000000000..14963a1bf5d4358b012f69bb97411f3d610f6bbe --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/remote_cache.py @@ -0,0 +1,198 @@ +from __future__ import annotations + +import json +import os +import typing +from abc import abstractmethod +from typing import Any, Callable, Dict, Generic, List, Optional, Type, TypeVar, Union +from typing_extensions import override, TypeAlias + +from torch._inductor import config + + +try: + import redis +except ImportError: + redis = None # type: ignore[assignment] + + +if config.is_fbcode(): + from rfe.scubadata.scubadata_py3 import ( # type: ignore[import-not-found] + Sample as Sample_, + ) + + Sample: TypeAlias = Sample_ +else: + Sample: TypeAlias = Type[object] # type: ignore[misc,no-redef] + + +_T = TypeVar("_T") +_U = TypeVar("_U") + + +class RemoteCacheBackend(Generic[_T]): + """ + A backend implementation for accessing a remote/distributed cache. Only + works with bytes in/out. For structured data use a RemoteCache. + """ + + @abstractmethod + def get(self, key: str) -> Optional[_T]: + pass + + @abstractmethod + def put(self, key: str, data: _T) -> None: + pass + + +# Serde that encodes from _T to _U and decodes from _U to _T. +class RemoteCacheSerde(Generic[_T, _U]): + @abstractmethod + def encode(self, data: _T) -> _U: + pass + + @abstractmethod + def decode(self, data: _U) -> _T: + pass + + +JsonDataTy = Optional[ + Union[int, float, str, bool, Dict[str, "JsonDataTy"], List["JsonDataTy"]] +] + + +class RemoteCacheJsonSerde(RemoteCacheSerde[JsonDataTy, bytes]): + def encode(self, data: JsonDataTy) -> bytes: + return bytes(json.dumps(data), "ascii") + + def decode(self, data: bytes) -> JsonDataTy: + return json.loads(data) + + +class RemoteCachePassthroughSerde(RemoteCacheSerde[_T, _T]): + def encode(self, data: _T) -> _T: + return data + + def decode(self, data: _T) -> _T: + return data + + +class RemoteCache(Generic[_T]): + backend_override_cls: Optional[Callable[[], RemoteCacheBackend[Any]]] = None + + def __init__( + self, backend: RemoteCacheBackend[_U], serde: RemoteCacheSerde[_T, _U] + ) -> None: + # Support for testing. + if (override_cls := self.__class__.backend_override_cls) is not None: + self.backend = override_cls() + else: + self.backend = backend + self.serde = serde + + def get(self, key: str) -> Optional[_T]: + sample = self._create_sample() + result = self._get(key, sample) + self._log_sample(sample) + return result + + def put(self, key: str, value: _T) -> None: + sample = self._create_sample() + self._put(key, value, sample) + self._log_sample(sample) + + def _decode(self, data: _U, sample: Optional[Sample]) -> _T: + return self.serde.decode(data) + + def _encode(self, value: _T, sample: Optional[Sample]) -> Any: # returns _U + return self.serde.encode(value) + + def _get(self, key: str, sample: Optional[Sample]) -> Optional[_T]: + if data := self.backend.get(key): + return self._decode(data, sample) + return None + + def _put(self, key: str, value: _T, sample: Optional[Sample]) -> None: + data = self._encode(value, sample) + self.backend.put(key, data) + + def _create_sample(self) -> Optional[Sample]: + return None + + def _log_sample(self, sample: Optional[Sample]) -> None: + pass + + +class RedisRemoteCacheBackend(RemoteCacheBackend[bytes]): + """ + A Redis implementation of a remote/distributed cache. + """ + + _key_fmt: str + _redis: Optional[redis.Redis] = None + + def __init__(self, cache_id: str) -> None: + if not redis: + # We had trouble importing redis - just skip init. + return + + self._key_fmt = f"pt2:{cache_id}:{{key}}" + self._redis = redis.Redis( + host=os.environ.get("TORCHINDUCTOR_REDIS_HOST", "localhost"), + port=int(os.environ.get("TORCHINDUCTOR_REDIS_PORT", 6379)), + ) + + def __get_key(self, key: str) -> str: + return self._key_fmt.format(key=key) + + @override + def get(self, key: str) -> Optional[bytes]: + if not self._redis: + # Either redis wasn't found or we already had some trouble... + return None + + try: + value = self._redis.get(self.__get_key(key)) + except redis.exceptions.ConnectionError: + # Redis is lazy and doesn't actually attempt to connect until the + # first use. Mark is as unavailable now. + self._redis = None + return None + + # In theory redis.get() can return an Awaitable as well... + assert value is None or isinstance(value, bytes) + return value + + @override + def put(self, key: str, data: bytes) -> None: + if not self._redis: + # Either redis wasn't found or we already had some trouble... + return + + try: + self._redis.set(self.__get_key(key), data) + except redis.exceptions.ConnectionError: + # Redis is lazy and doesn't actually attempt to connect until the + # first use. Mark is as unavailable now. + self._redis = None + + +class RedisRemoteCache(RemoteCache[JsonDataTy]): + def __init__(self, key: str) -> None: + # Special test handling: If we're just going to override the backend + # anyway don't require redis + if self.__class__.backend_override_cls: + # This is totally bogus but it works for now... + backend = typing.cast(RemoteCacheBackend[bytes], None) + else: + backend = RedisRemoteCacheBackend(key) + serde = RemoteCacheJsonSerde() + super().__init__(backend, serde) + + +class RemoteAutotuneCache(RedisRemoteCache): + pass + + +class RemoteFxGraphCache(RedisRemoteCache): + pass diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/scheduler.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/scheduler.py new file mode 100644 index 0000000000000000000000000000000000000000..564a9b4ccfd8337e13c52c005c6ba5149fd85825 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/scheduler.py @@ -0,0 +1,3727 @@ +# mypy: disallow-untyped-defs +from __future__ import annotations + +import collections +import dataclasses +import functools +import itertools +import logging +import math +import operator +import os +import pprint +import textwrap +import traceback +import typing +from typing import ( + Any, + Callable, + Counter, + DefaultDict, + Dict, + Generic, + List, + Optional, + Sequence, + Set, + Tuple, + TypeVar, + Union, +) + +import sympy + +import torch +import torch._inductor.async_compile # noqa: F401 required to warm up AsyncCompile pools +from torch._dynamo.utils import counters, dynamo_timed +from torch._inductor.metrics import get_metric_table, is_metric_table_enabled +from torch.fx.experimental.symbolic_shapes import free_unbacked_symbols +from torch.utils._ordered_set import OrderedSet +from torch.utils._sympy.symbol import free_symbol_is_type, SymT +from torch.utils._triton import has_triton + +from . import comms, config, dependencies, ir, metrics +from .codecache import write_text +from .codegen.common import BackendFeature, get_scheduling_for_device, Kernel +from .comm_analysis import estimate_nccl_collective_runtime +from .dependencies import Dep, MemoryDep, StarDep, WeakDep +from .ir import ComputedBuffer, MultiOutput, MultiOutputLayout +from .loop_body import LoopBody +from .runtime.runtime_utils import green_text, red_text +from .sizevars import SimplifyIndexing +from .utils import ( + cache_on_self, + cmp, + device_need_guard, + get_device_tflops, + get_dtype_size, + get_gpu_dram_gbps, + IndentedBuffer, + is_collective, + is_gpu, + is_wait, + sympy_product, +) +from .virtualized import V + + +log = logging.getLogger(__name__) +fusion_log = torch._logging.getArtifactLogger(__name__, "fusion") +loop_ordering_log = torch._logging.getArtifactLogger(__name__, "loop_ordering") + + +@dataclasses.dataclass +class SchedulerBuffer: + scheduler: Scheduler + node: ir.Buffer + defining_op: BaseSchedulerNode + users: List[NodeUser] = dataclasses.field(default_factory=list) + + def __hash__(self) -> int: + return hash(self.node.name) + + def debug_str(self) -> str: + result = IndentedBuffer() + name = self.get_name() + result.writeline(f"{name}: {type(self.node).__name__}") + result.writeline(f"{name}.layout = {self.node.layout}") + if self.get_aliases(): + result.writeline(f"{name}.aliases = {pformat(self.get_aliases())}") + if self.get_mutations(): + result.writeline(f"{name}.mutations = {pformat(self.get_mutations())}") + + if len(self.users) <= 1: + result.writeline(f"{name}.users = {self.users}") + else: + result.writeline(f"{name}.users = [") + with result.indent(1): + for user in self.users: + result.writeline(f"{user},") + result.writeline("]") + return result.getrawvalue() + + def get_name(self) -> str: + return self.node.get_name() + + def allocate(self) -> None: + assert self.node is not None + if not self.node.should_allocate(): + return + + if self.node.get_inputs_that_alias_output() or self.node.get_mutation_names(): + V.graph.wrapper_code.codegen_allocation(self.node) + return + + # hacky check for if V.kernel is a real kernel or NullHandler + if ( + hasattr(V.kernel, "args") + and self.get_name() in V.kernel.inplace_update_buffers + ): + V.graph.wrapper_code.codegen_inplace_reuse( + self.scheduler.name_to_buf[ + V.kernel.inplace_update_buffers[self.get_name()] + ].node, + self.node, + ) + else: + V.graph.wrapper_code.codegen_allocation(self.node) + + def can_free(self) -> bool: + # There's no real allocated buffer, no need to free it + assert self.node is not None + if isinstance(self.node.layout, ir.NoneLayout): + return False + for use in self.users: + if isinstance(use.node, OutputNode): + return False + return True + + def set_users(self, users: List[NodeUser]) -> None: + # deduplicate + result: Dict[int, NodeUser] = {} + for use in users: + if id(use.node) in result: + result[id(use.node)] = use.merge(result[id(use.node)]) + else: + result[id(use.node)] = use + self.users = list(result.values()) + + def get_aliases(self) -> Sequence[str]: + assert self.node is not None + return self.node.get_inputs_that_alias_output() + + def get_mutations(self) -> List[str]: + assert self.node is not None + return self.node.get_mutation_names() + + +class BaseSchedulerNode: + group: Tuple[torch.device, Tuple[Tuple[sympy.Expr, ...], ...]] + read_writes: dependencies.ReadWrites + unmet_dependencies: OrderedSet[Dep] + # .min_order and .max_order are only relevant for "grouped" nodes such as FusedSchedulerNode. + # e.g. if the FusedSchedulerNode includes nodes (op_1, op_2, op_3), and op_X is X-th node + # in `self.scheduler.nodes`, then for this FusedSchedulerNode, .min_order is 1 and .max_order is 3. + # For non-"grouped" nodes (i.e. regular SchedulerNode), + # .min_order = .max_order = X if this node is X-th node in `self.scheduler.nodes`. + min_order: int + max_order: int + + def __init__(self, scheduler: Scheduler) -> None: + self.scheduler: Scheduler = scheduler + + def _init_from_node(self, node: ir.Operation) -> None: + self.node: Optional[ir.Operation] = node + self.ancestors: OrderedSet[str] = OrderedSet() + self.last_usage: OrderedSet[ + str + ] = OrderedSet() # buffers that won't be used after this kernel + self.written = False + self.outputs: List[SchedulerBuffer] = [ + SchedulerBuffer( + scheduler=self.scheduler, + node=output, + defining_op=self, + ) + for output in node.get_outputs() + ] + self.outputs_by_name: Dict[str, SchedulerBuffer] = { + buf.get_name(): buf for buf in self.outputs + } + + def __repr__(self) -> str: + return f"{type(self).__name__}(name={self.get_name()!r})" + + def debug_str(self) -> str: + """Longer form printout for trace logs""" + name = self.get_name() + buf = IndentedBuffer() + buf.splice( + f"""\ +{name}: {type(self).__name__}({type(getattr(self, 'node', None)).__name__}) +{name}.writes = {pformat(self.read_writes.writes)} +{name}.unmet_dependencies = {pformat(self.unmet_dependencies)} +{name}.met_dependencies = {pformat(self.read_writes.reads - self.unmet_dependencies)} +{name}.outputs = [ + """ + ) + with buf.indent(): + for out in self.get_outputs(): + buf.splice(out.debug_str()) + buf.writeline("]") + + try: + buf.splice(self.debug_str_extra()) + except Exception: + log.warning("Ignoring error in debug_str()", exc_info=True) + + return buf.getrawvalue().rstrip() + + def debug_str_extra(self) -> str: + return "" + + def debug_str_short(self) -> str: + maybe_data = getattr(self.node, "data", None) + data_str = "" + if isinstance(maybe_data, torch._inductor.ir.Pointwise): + data_str = ", " + maybe_data.str_helper( + [maybe_data.get_size()], shorten=False, multiline=False + ) + elif isinstance(maybe_data, torch._inductor.ir.Reduction): + data_str = ", " + maybe_data.str_helper( + [maybe_data.get_reduction_size(), maybe_data.get_reduction_type()], + shorten=False, + multiline=False, + ) + return f"{self}{data_str}" + + def log_details(self) -> None: + log.info( + "%s: unmet_dependencies = %s, writes = %s", + self, + self.unmet_dependencies, + self.read_writes.writes, + ) + + def reorder_loops_by_dep_pair( + self, self_dep: MemoryDep, other_dep: MemoryDep + ) -> None: + return + + def update_mutated_names(self, renames: Dict[str, str]) -> None: + self.set_read_writes(self.read_writes.rename(renames)) + + def add_fake_dep(self, dep: Dep) -> None: + self.set_read_writes(self.read_writes.with_read(dep)) + + def has_aliasing_or_mutation(self) -> bool: + return any( + buf.get_aliases() or buf.get_mutations() for buf in self.get_outputs() + ) + + def set_read_writes(self, rw: dependencies.ReadWrites) -> None: + self.read_writes = rw + self.unmet_dependencies = self.read_writes.reads + self.prune_deps() + + def set_last_usage( + self, future_used_buffers: OrderedSet[str], mutation_real_name: Dict[str, str] + ) -> None: + used_buffers = self.used_or_aliased_buffer_names() + used_buffers = OrderedSet([mutation_real_name.get(k, k) for k in used_buffers]) + self.last_usage = used_buffers - future_used_buffers + + def mark_run(self) -> None: + for buf in self.outputs: + buf.allocate() + + def used_buffer_names(self) -> OrderedSet[str]: + return OrderedSet( + dep.name + for dep in itertools.chain(self.read_writes.reads, self.read_writes.writes) + ) + + def used_or_aliased_buffer_names(self) -> OrderedSet[str]: + used_names: OrderedSet[str] = OrderedSet() + + deps = [ + dep.name + for dep in itertools.chain(self.read_writes.reads, self.read_writes.writes) + ] + while len(deps) > 0: + dep = deps.pop() + used_names.add(dep) + if V.graph.name_to_buffer.get(dep): + for alias in V.graph.name_to_buffer[dep].get_inputs_that_alias_output(): + if alias not in used_names: + deps.append(alias) + return used_names + + def prune_deps(self) -> None: + self.unmet_dependencies = OrderedSet( + dep + for dep in self.unmet_dependencies + if dep.name not in self.scheduler.available_buffer_names + ) + + def prune_weak_deps(self) -> None: + # Prune weak dependencies on operations that have been removed + def should_prune(dep: Dep) -> bool: + if not isinstance(dep, WeakDep): + return False + op = self.scheduler.name_to_buf[dep.name].defining_op + return op.get_name() in V.graph.removed_operations + + to_remove = OrderedSet( + dep for dep in self.read_writes.reads if should_prune(dep) + ) + self.set_read_writes(self.read_writes.remove_reads(to_remove)) + + def prune_redundant_deps( + self, name_to_fused_node: Dict[str, BaseSchedulerNode] + ) -> None: + _prune_redundant_deps(self, name_to_fused_node, self.scheduler.name_to_buf) + + def get_name(self) -> str: + assert self.node is not None + return self.node.get_operation_name() + + def get_first_name(self) -> str: + return self.get_name() + + def get_operation_names(self) -> OrderedSet[str]: + return OrderedSet(node.get_name() for node in self.get_nodes()) + + def get_buffer_names(self) -> OrderedSet[str]: + return OrderedSet(out.get_name() for out in self.outputs) + + def get_nodes(self) -> Sequence[BaseSchedulerNode]: + return [self] + + def get_outputs(self) -> Sequence[SchedulerBuffer]: + return self.outputs + + def get_output(self, buf_name: str) -> SchedulerBuffer: + return self.outputs_by_name[buf_name] + + def get_device(self) -> torch.device: + assert self.node is not None + return self.node.get_device() + + def is_reduction(self) -> bool: + return False + + def is_split_scan(self) -> bool: + return False + + def is_template(self) -> bool: + return False + + def is_extern(self) -> bool: + return False + + def is_foreach(self) -> bool: + return False + + def can_inplace(self, read_dep: dependencies.Dep) -> bool: + return False + + def has_side_effects(self) -> bool: + return False + + def decide_inplace_update(self) -> None: + """ + Decide if there should be inplace updates for the node + and record the decision in the active kernel. + """ + from .codegen.wrapper import buffer_reuse_key + + if not ( + isinstance(self, (SchedulerNode,)) + and config.inplace_buffers + and V.graph.has_feature(self.get_device(), BackendFeature.INPLACE_BUFFERS) + and ( + not isinstance(V.kernel, torch._inductor.codegen.simd.SIMDKernel) + or getattr(V.kernel, "mutations", None) is not None + ) + # hacky check for if V.kernel is a real kernel or NullHandler + and hasattr(V.kernel, "args") + ): + return + + ordered_reads = sorted(self.read_writes.reads, key=lambda x: x.name) + + for buf in self.get_outputs(): + buf_node = buf.node + assert buf_node is not None + if ( + not buf_node.should_allocate() + or buf_node.get_inputs_that_alias_output() + or buf_node.get_mutation_names() + or buf.get_name() in V.graph.removed_buffers + ): + continue + + for read in ordered_reads: + input_buf: Optional[SchedulerBuffer] = self.scheduler.name_to_buf.get( + read.name + ) + if ( + input_buf + and V.graph.wrapper_code.can_reuse(input_buf, self) + and not isinstance(input_buf.defining_op, NopKernelSchedulerNode) + ): + assert input_buf.users is not None + remaining_uses = [ + x + for x in input_buf.users + if x.node.get_name() not in self.scheduler.completed_operations + ] + if ( + len(remaining_uses) == 1 + and remaining_uses[0].can_inplace + and remaining_uses[0].node is self + and input_buf.node is not None + and not isinstance( + input_buf.node.get_layout(), + ( + ir.MultiOutputLayout, + ir.MutationLayoutSHOULDREMOVE, + ), + ) + and not ( + isinstance( + input_buf.defining_op.node, + (ir.FallbackKernel, ir.MultiOutput), + ) + and len(input_buf.node.get_inputs_that_alias_output()) > 0 + ) + and buffer_reuse_key(input_buf.node) + == buffer_reuse_key(buf.node) + ): + # if there isn't a triton kernel, then we don't need to call triton-specific things. + # but TODO this might be a convenient place to signal to the Collective kernels to inplace + # (and, can we make "kernel" less generic of a name?) + V.kernel.args.make_inplace(input_buf.get_name(), buf.get_name()) + # mutations not tracked in cpp kernels + if isinstance( + V.kernel, torch._inductor.codegen.simd.SIMDKernel + ): + V.kernel.mutations.add(input_buf.get_name()) + V.kernel.mutations.add(buf.get_name()) + + # update last usage of reused node + self.last_usage.discard(input_buf.get_name()) + + V.kernel.inplace_update_buffers[ + buf.get_name() + ] = input_buf.get_name() + break + + def codegen_originating_info( + self, buffer: IndentedBuffer, only_once: bool = True + ) -> None: + if not config.comment_origin: + return + + if only_once and self.written: + return + assert self.node is not None + origins = self.node.get_origins() + out_lines = [] + + for o in origins: + if o.op == "output": + # These are boring and samey + continue + + out_lines.append("") + # TODO(voz): Should the pragma be constant somewhere? + out_lines.append("#pragma CMT ORIGIN:") + op_info_str = f"#pragma CMT {o.op} {o.target}" + if "seq_nr" in o.meta: + op_info_str = op_info_str + f" seq_nr:{o.meta['seq_nr']}" + out_lines.append(op_info_str) + if "stack_trace" in o.meta: + stack_trace = f"{o.meta['stack_trace']}" + stack_trace_last_line = stack_trace.split("|")[-1] + out_lines.append( + "#pragma CMT " + + stack_trace_last_line.replace("{", "{{") + .replace("}", "}}") + .replace("\n", "\\") + ) + out_lines.append("#pragma CMT END ORIGIN") + out_lines.append("") + + if len(out_lines) == 0: + return + + # TODO(voz): Ostensibly, we should not need this. But there are cases where C++ codegen does + # not use BracesBuffer, so we have no good indicator of a C++ buffer atm. + buffer.writelines(out_lines) + self.written = True + + def get_read_write_buffers_sizes(self) -> int: + """ + Counting the number of bytes accessed for a kernel is + surprisingly tricky. In particular, there is a differentiation + between 'theoretical' memory accesses and practical memory + accesses. For example, a layernorm kernel may actually access an + input 3 times, but in theory, it only needs to access its input + once (and may be optimized to do so through say, persistent + reductions) + + Another example is that even though a buffer is passed in, we may + not access the entire buffer. This may occur if we are accessing + a slice of the buffer. Another tricky case is for indirect + indexing, where the amount of bytes accessed depends on the + values of the input. + + What this function aims to compute is the memory accesses for + worst-case inputs, best-case optimization. What this means is + that for each buffer we compute the amount of potential accesses in two ways and take the minimum. + + 1. Numel in ranges multiplied by number of deps the buffer has + 2. The buffer size + """ + if isinstance(self, NopKernelSchedulerNode): + return 0 + if isinstance(self, ExternKernelSchedulerNode) and isinstance( + self.node, MultiOutput + ): + # todo: Calculate this - it's kinda annoying. + return 0 + + def try_size_hint(s: sympy.Expr) -> int: + return V.graph.sizevars.size_hint(s, fallback=0) + + if isinstance(self, SchedulerNode): + node_numel = try_size_hint( + sympy_product(self.get_ranges()[0]) + * sympy_product(self.get_ranges()[1]), + ) + else: + node_numel = int(1e9) + buf_accesses = collections.defaultdict(list) + for dep in self.read_writes.reads | self.read_writes.writes: + buf_accesses[dep.name].append(dep) + + reads = OrderedSet(dep.name for dep in self.read_writes.reads) + writes = OrderedSet(dep.name for dep in self.read_writes.writes) + + def is_materialized(buf: str, snodes: Sequence[BaseSchedulerNode]) -> bool: + users = self.scheduler.name_to_buf[buf].users + buf_uses = OrderedSet(user.node for user in users) + return len(buf_uses - OrderedSet(snodes)) > 0 + + if isinstance(self, FusedSchedulerNode): + removed_buffers = OrderedSet( + dep for dep in writes if not is_materialized(dep, self.snodes) + ) + writes = writes - removed_buffers + reads = reads - removed_buffers + node_bytes = 0 + + for buf_name in reads | writes: + buf_accessed_elems = sum(node_numel for dep in buf_accesses[buf_name]) + buf: Union[ir.Buffer, ir.TensorBox] + if buf_name in V.graph.name_to_buffer: + buf = V.graph.name_to_buffer[buf_name] + elif buf_name in V.graph.graph_inputs: + buf = V.graph.graph_inputs[buf_name] + else: + continue + + def get_buf_bytes(buf: Optional[Union[ir.Buffer, ir.TensorBox]]) -> int: + if not buf: + return 0 + # Kind of a lazy way to get the MultiOutput nodes corresponding to + # a MultiOutputLayout + if isinstance(buf.layout, MultiOutputLayout): + users = self.scheduler.name_to_buf[buf.get_name()].users + tot = 0 + for user in users: + assert isinstance(user.node, BaseSchedulerNode) + if isinstance(user.node.node, MultiOutput): + for sched_buf in user.node.get_outputs(): + tot += get_buf_bytes(sched_buf.node) + else: + # Buf is a MultiOutputLayout but not all of its + # users are MultiOutputs... + # TODO: Figure out what's going on + return 0 + return tot + elif isinstance(buf.layout, ir.NoneLayout): + return sum( + get_buf_bytes(V.graph.get_buffer(mut_name)) + for mut_name in buf.get_mutation_names() + ) + else: + buf_elems = try_size_hint(sympy_product(buf.get_size())) + return get_dtype_size(buf.get_dtype()) * min( + buf_accessed_elems, buf_elems + ) + + node_bytes += get_buf_bytes(buf) + + return node_bytes + + def get_estimated_runtime(self) -> float: + """ + Returns estimated op runtime in nanoseconds (ns) + """ + buf = self.get_nodes()[0].get_outputs()[0] + layout = buf.node.get_layout() + dtype = buf.node.get_dtype() + + if layout.device is not None and not is_gpu(layout.device.type): + # default to no reordering based on runtime + return 0 + + # Collective kernels + if is_collective(self.node): + assert isinstance(self.node, ir.IRNode) + try: + return estimate_nccl_collective_runtime(self.node) + except ValueError as e: + # We don't know how to estimate runtime for this collective, + # falling back to 0 + log.info(e) + return 0 + + elif is_wait(self.node): + # ir.Wait is only used for collective ops. + # The time needed for the collective op is already estimated and considered + # when we are processing the collective op IR node, so ir.Wait takes 0 time + # since it doesn't take extra time to get the result after the collective is completed. + return 0 + + try: + gpu_memory_bandwidth = get_gpu_dram_gbps() + gpu_flops = get_device_tflops(dtype) * 10**12 + except Exception: + return 0 + + if isinstance(self, ExternKernelSchedulerNode): + assert isinstance(self.node, ir.ExternKernel), f"{type(self.node)=}" + op = kernel_name_to_op.get( + getattr(self.node, "python_kernel_name", ""), None + ) + + # if there is a resolved op, dry-run using fake mode and record flop count + if op is not None: + from torch._subclasses.fake_tensor import FakeTensorMode + from torch.utils.flop_counter import FlopCounterMode + + if any( + len(free_unbacked_symbols(n.get_numel())) > 0 + for n in self.node.inputs + ): + # Tensor has unbacked symints, we don't know how to estimate + # runtime for that today + return 0 + + with FakeTensorMode() as fake_mode, FlopCounterMode( + display=False + ) as flop_counter_mode, V.set_current_node( + self.node.fx_node + ), V.set_fake_mode( + fake_mode + ): + from .ir import ir_node_to_tensor + + fake_inputs = [ + ir_node_to_tensor(input, guard_shape=False) + for input in self.node.inputs + ] + cls = self.node.__class__ + cls.process_kernel(op, *fake_inputs, **self.node.kwargs) + + # TODO(xmfan): find a better heuristic to model FLOPS/latency relationship + factor = 1.0 + counted_flops = flop_counter_mode.get_total_flops() + counted_bytes = self.get_read_write_buffers_sizes() + compute_time = (factor * counted_flops / gpu_flops) * 1e9 + transfer_time = counted_bytes / gpu_memory_bandwidth + + # Return estimated runtime in nanoseconds + return max(compute_time, transfer_time) + + elif isinstance(self, FusedSchedulerNode) or isinstance( + self.node, ComputedBuffer + ): + # Return estimated runtime in nanoseconds (bytes / gbps) + return self.get_read_write_buffers_sizes() / gpu_memory_bandwidth + + return 0 + + def get_template_node(self) -> Optional[ir.TemplateBuffer]: + return None + + +class WhyNoFuse: + # TODO when we drop support for Python < 3.10, we can use + # @dataclass(slots=True) instead of manually specifying __slots__. + __slots__ = ["node1", "node2", "reason", "args"] + reason: str + args: Tuple[Any, ...] + + def __init__(self, node1: BaseSchedulerNode, node2: BaseSchedulerNode) -> None: + self.node1 = node1 + self.node2 = node2 + + def __call__(self, reason: str, *args: Any) -> None: + self.reason = reason + self.args = args + fusion_log.debug(self) + + def __str__(self) -> str: + return f"cannot fuse {self.node1.get_name()} with {self.node2.get_name()}: " + ( + self.reason % self.args + ) + + +def pformat(obj: Any) -> str: + if isinstance(obj, OrderedSet): + # pformat has trouble with sets of sympy exprs + obj = sorted(obj, key=str) + result = pprint.pformat(obj, indent=4) + if "\n" in result: + return f"\n{textwrap.indent(result, ' ' * 4)}" + return result + + +class OutputNode: + def __init__(self, dep: StarDep) -> None: + self.unmet_dependencies = OrderedSet([dep]) + + def is_reduction(self) -> bool: + return False + + def get_inputs_that_alias_output(self) -> Sequence[str]: + return () + + def get_name(self) -> str: + return "OUTPUT" + + __repr__ = get_name + + +def _prune_redundant_deps( + node: BaseSchedulerNode, + name_to_fused_node: Dict[str, BaseSchedulerNode], + name_to_buf: Dict[str, SchedulerBuffer], +) -> None: + """ + Prunes weakdeps intended for mutation ordering + on an upstream fused node if after fusion there is another dependency + on the fused upstream node, making the weakdep redundant + + In essence this enforces an ordering on fusions. As fusions occur, weakdeps will + be incrementally removed, enabling other fusions, ensuring they are fused in order. + """ + name_to_dep_count: Counter[str] = collections.Counter() + + for dep in node.unmet_dependencies: + if not isinstance(dep, WeakDep): + op = name_to_buf[dep.name].defining_op + name_to_dep_count[name_to_fused_node[op.get_name()].get_name()] += 1 + + def should_prune(dep: Dep) -> bool: + if isinstance(dep, WeakDep): + op_name = name_to_buf[dep.name].defining_op.get_name() + is_redundant = name_to_dep_count[name_to_fused_node[op_name].get_name()] > 0 + # These can occur because fused nodes always gather deps from their snodes + # If B has a weakdep on A + # B gets fused with C, then any time BC is fused, the weakdep will reappear + is_self_dep = name_to_fused_node[op_name] == node + return is_redundant or is_self_dep + else: + return False + + deps_to_prune = OrderedSet( + dep for dep in node.unmet_dependencies if should_prune(dep) + ) + + if deps_to_prune: + node.unmet_dependencies = node.unmet_dependencies - deps_to_prune + node.set_read_writes(node.read_writes.remove_reads(deps_to_prune)) + + +# TODO(xmfan): reuse: an existing mapping for this if it exists, or formalize this into ir.py:ExternKernel +kernel_name_to_op = { + "extern_kernels.convolution": torch.ops.aten.convolution, + "extern_kernels.mm": torch.ops.aten.mm, + "extern_kernels.bmm": torch.ops.aten.bmm, + "extern_kernels.addmm": torch.ops.aten.addmm, +} + + +class ExternKernelSchedulerNode(BaseSchedulerNode): + def __init__(self, scheduler: Scheduler, node: ir.Operation) -> None: + super().__init__(scheduler) + self._init_from_node(node) + self.set_read_writes(node.get_read_writes()) + + def debug_str_extra(self) -> str: + return f"{self.get_name()}.node.kernel = {getattr(self.node, 'python_kernel_name', None)}" + + def is_extern(self) -> bool: + return True + + def has_side_effects(self) -> bool: + assert self.node is not None + return hasattr(self.node, "has_side_effects") and self.node.has_side_effects() + + +class NopKernelSchedulerNode(BaseSchedulerNode): + def __init__(self, scheduler: Scheduler, node: ir.Operation) -> None: + super().__init__(scheduler) + self._init_from_node(node) + self.set_read_writes(node.get_read_writes()) + + +class SchedulerNode(BaseSchedulerNode): + def __init__( + self, + scheduler: Scheduler, + node: Union[ir.ComputedBuffer, ir.TemplateBuffer], + ) -> None: + super().__init__(scheduler) + self._init_from_node(node) + self._compute_attrs() + + def _compute_attrs( + self, + extra_indexing_constraints: Optional[Tuple[Dict[Any, Any], List[Any]]] = None, + recompute_sizes_body_func: Optional[Callable[..., Any]] = None, + ) -> None: + assert isinstance(self.node, (ir.ComputedBuffer, ir.TemplateBuffer)) + self._sizes, self._body = self.node.simplify_and_reorder( + extra_indexing_constraints=extra_indexing_constraints, + recompute_sizes_body_func=recompute_sizes_body_func, + ) + + group_fn = self.scheduler.get_backend(self.node.get_device()).group_fn + self.group = (self.node.get_device(), group_fn(self._sizes)) + + # Don't normalize since normalization will merge loops which + # makes it hard to decide new loop orders. + should_normalize = ( + not config.loop_ordering_after_fusion + or self.node.get_device().type != "cuda" + ) + + if isinstance(self.node, ir.TemplateBuffer): + self.set_read_writes( + self.node.extract_read_writes(normalize=should_normalize) + ) + else: + self.set_read_writes( + dependencies.extract_read_writes( + self._body, *self._sizes, normalize=should_normalize + ) + ) + + def recompute_size_and_body( + self, + extra_indexing_constraints: Optional[Tuple[Dict[Any, Any], List[Any]]] = None, + recompute_sizes_body_func: Optional[Callable[..., Any]] = None, + ) -> None: + self._compute_attrs( + extra_indexing_constraints=extra_indexing_constraints, + recompute_sizes_body_func=recompute_sizes_body_func, + ) + + def refresh_dependencies(self, normalize: bool) -> None: + # Fake dependencies are added manually. They can not be analyzed from + # extract_read_writes. Find them out and apply manually. + fake_deps = { + dep for dep in self.read_writes.reads if isinstance(dep, (WeakDep, StarDep)) + } + + # don't normalize since the loop order may need to be further changed + # later + self.set_read_writes( + dependencies.extract_read_writes( + self._body, *self._sizes, normalize=normalize + ).with_read(fake_deps) + ) + + def apply_new_loop_order(self, new_order: Sequence[int]) -> None: + self._body = self._body.reorder_iter_loops( + new_order, + ) + self._sizes = self._body.sizes + + self.refresh_dependencies(normalize=False) + + def reorder_loops_by_dep_pair( + self, self_dep: MemoryDep, other_dep: MemoryDep + ) -> None: + new_order = None + self_sizes = self._sizes[0] + if len(self_sizes) == self_dep.num_vars == other_dep.num_vars: + new_order = self_dep.decide_loop_order_to_match(other_dep) + + if new_order: + metrics.num_loop_reordering += 1 + loop_ordering_log.debug( + "Reorder loops for %s with order %s", self.get_name(), new_order + ) + self.apply_new_loop_order(new_order) + else: + loop_ordering_log.debug( + "Don't reordering %s because we can not decide the suitable loop order", + self.get_name(), + ) + + def debug_str_extra(self) -> str: + name = self.get_name() + lines = [ + f"{name}.group.device = {self.group[0]}", + f"{name}.group.iteration = {self.group[1]}", + f"{name}.sizes = {self._sizes}", + ] + for dep in self.read_writes.reads_and_writes(): + if not isinstance(dep, WeakDep): + buf_name = dep.name + buf = V.graph.get_buffer(buf_name) + lines.append(f"{buf_name}_layout = {pformat(buf.layout)}") + if isinstance(self._body, LoopBody): + lines.append(f"class {name}_loop_body:") + lines.append(textwrap.indent(self._body.debug_str(), " ")) + + assert self.node is not None + if ir.is_triton(self.node.get_device()): + lines.extend(debug_triton_code(self)) + + return "\n".join(lines) + + def get_ranges(self) -> Sequence[Sequence[sympy.Expr]]: + return self._sizes + + def is_reduction(self) -> bool: + assert isinstance( + self.node, (ir.ComputedBuffer, ir.TemplateBuffer) + ), f"{type(self.node)=}" + return bool(self.node.get_reduction_type()) + + def is_split_scan(self) -> bool: + assert isinstance( + self.node, (ir.ComputedBuffer, ir.TemplateBuffer) + ), f"{type(self.node)=}" + return isinstance(self.node, ir.ComputedBuffer) and isinstance( + self.node.data, ir.SplitScan + ) + + def is_template(self) -> bool: + return isinstance(self.node, ir.TemplateBuffer) + + def get_template_node(self) -> Optional[ir.TemplateBuffer]: + return self.node if isinstance(self.node, ir.TemplateBuffer) else None + + def run(self, *index_vars: Sequence[sympy.Expr]) -> None: + self.decide_inplace_update() + self.mark_run() + self.codegen(index_vars) + + def ranges_from_index_vars( + self, index_vars: Sequence[Sequence[sympy.Expr]] + ) -> Dict[sympy.Expr, sympy.Expr]: + sizes = self._sizes + assert sum(map(len, sizes)) == sum(map(len, index_vars)) + var_ranges = dict( + zip( + itertools.chain.from_iterable(index_vars), + itertools.chain.from_iterable(sizes), + ) + ) + return var_ranges + + def codegen(self, index_vars: Sequence[Sequence[sympy.Expr]]) -> None: + var_ranges = self.ranges_from_index_vars(index_vars) + try: + with V.set_ops_handler( + SimplifyIndexing(V.get_ops_handler(), var_ranges) + ), V.kernel.set_current_node(self): + self._body(*index_vars) + except Exception: + log.fatal("Error in codegen for %s", self.node) + raise + + @cache_on_self + def pointwise_read_writes(self) -> dependencies.ReadWrites: + """ + Get the memory dependencies in the non-reduction axis. + """ + sizes, reduction_sizes = self._sizes + return dependencies.extract_read_writes( + self._body, sizes, hidden_args=[[sympy.Integer(0)] * len(reduction_sizes)] + ) + + def can_inplace(self, read_dep: dependencies.Dep) -> bool: + if self.is_template(): + return False + if any(out.get_aliases() for out in self.get_outputs()): + return False + if len(self.read_writes.writes) == 1 and isinstance( + read_dep, dependencies.MemoryDep + ): + write_dep = next(iter(self.read_writes.writes)) + assert isinstance(write_dep, dependencies.MemoryDep), f"{type(write_dep)=}" + return read_dep.index == write_dep.index and read_dep.size == write_dep.size + return False + + @cache_on_self + def _get_atomic_add_buffers(self) -> OrderedSet[str]: + buffers_store_as_atomic_add: OrderedSet[str] = OrderedSet() + if isinstance(self._body, LoopBody): + for node in self._body.get_nodes(): + if ( + node.op == "call_method" + and node.target == "store" + and ( + ("mode" in node.kwargs and node.kwargs["mode"] == "atomic_add") + or (len(node.args) == 5 and node.args[4] == "atomic_add") + ) + ): + buffers_store_as_atomic_add.add( + node.kwargs["name"] + if "name" in node.kwargs + else (node.args[1] if len(node.args) >= 2 else "") + ) + return buffers_store_as_atomic_add + + +def refresh_group_node_dependencies(group_snode: BaseSchedulerNode) -> None: + snodes = group_snode.snodes # type: ignore[attr-defined] + group_snode.set_read_writes( + dependencies.ReadWrites.merge_list([x.read_writes for x in snodes]) + ) + + group_snode.unmet_dependencies = ( + OrderedSet( + dep + for dep in OrderedSet.union(*[x.unmet_dependencies for x in snodes]) + if dep.name not in group_snode.get_buffer_names() + ) + - group_snode.read_writes.writes + ) + + +def init_group_node( + group_snode: BaseSchedulerNode, + scheduler: Scheduler, + snodes: List[BaseSchedulerNode], +) -> None: + assert isinstance(group_snode, (FusedSchedulerNode, GroupedSchedulerNode)) + group_snode.snodes = snodes + group_snode.scheduler = scheduler + group_snode.node = None + group_snode.ancestors = OrderedSet.union( + *[x.ancestors for x in snodes if x.ancestors is not None] + ) + + refresh_group_node_dependencies(group_snode) + + group_snode.min_order = min(x.min_order for x in group_snode.snodes) + group_snode.max_order = max(x.max_order for x in group_snode.snodes) + group_snode.outputs_by_name = { + buf.get_name(): buf for buf in group_snode.get_outputs() + } + + +class FusedSchedulerNode(BaseSchedulerNode): + """ + This is a "fake" scheduler node that represents a group of scheduler nodes + that are meant to be fused together. The way it does this is by maintaining + its unmet dependencies as the union of its constituent nodes. + """ + + snodes: List[BaseSchedulerNode] + + @classmethod + def fuse( + cls, node1: BaseSchedulerNode, node2: BaseSchedulerNode + ) -> FusedSchedulerNode: + assert node1.scheduler is node2.scheduler + assert isinstance(node1, (SchedulerNode, FusedSchedulerNode)) + assert isinstance(node2, (SchedulerNode, FusedSchedulerNode)) + nodes = list(itertools.chain(node1.get_nodes(), node2.get_nodes())) + return cls(node1.scheduler, nodes) + + def reorder_loops_by_dep_pair( + self, self_dep: MemoryDep, other_dep: MemoryDep + ) -> None: + if self.is_template(): + # We can not really reorder loops for a triton template + return + self_sizes = None + for snode in self.snodes: + assert isinstance(snode, SchedulerNode) + if self_sizes is not None and self_sizes != snode._sizes[0]: + loop_ordering_log.debug( + "Can not reorder fused node due to different sizes" + ) + return + self_sizes = snode._sizes[0] + new_order = None + + assert self_sizes is not None + if len(self_sizes) == self_dep.num_vars == other_dep.num_vars: + new_order = self_dep.decide_loop_order_to_match(other_dep) + + if not new_order: + loop_ordering_log.debug( + "Dont reordering fused node %s because we can not decide the suitable loop order", + self.get_name(), + ) + return + metrics.num_loop_reordering += 1 + loop_ordering_log.debug( + "Reorder loops for fused node %s with order %s", self.get_name(), new_order + ) + for snode in self.snodes: + assert isinstance(snode, SchedulerNode) + snode.apply_new_loop_order(new_order) # type: ignore[arg-type] + + refresh_group_node_dependencies(self) + + def __init__(self, scheduler: Scheduler, snodes: List[BaseSchedulerNode]) -> None: + super().__init__(scheduler) + init_group_node(self, scheduler, snodes) + self.users: List[NodeUser] = [] + self.group = max(snodes, key=lambda x: int(x.is_reduction())).group + + @cache_on_self + def get_name(self) -> str: + return "_".join([x.get_name() for x in self.snodes]) + + def get_first_name(self) -> str: + return self.snodes[0].get_name() + + @cache_on_self + def get_buffer_names(self) -> OrderedSet[str]: + return OrderedSet.union(*[x.get_buffer_names() for x in self.snodes]) + + def get_outputs(self) -> List[SchedulerBuffer]: + result: List[SchedulerBuffer] = [] + for node in self.snodes: + result.extend(node.get_outputs()) + return result + + def debug_str_extra(self) -> str: + lines = [ + f"{self.get_name()}.snodes[{i}] =\n{node.debug_str()}" + for i, node in enumerate(self.snodes) + ] + node = self.snodes[0].node + if node is not None: + device = node.get_device() + if ir.is_triton(device): + lines.extend(debug_triton_code(self)) + + return textwrap.indent("\n".join(lines).rstrip(), " ") + + def debug_str_short(self) -> str: + snodes_str = [node.debug_str_short() for node in self.snodes] + return f"{self}, snodes: {snodes_str}" + + def set_last_usage( + self, future_used_buffers: OrderedSet[str], mutation_real_name: Dict[str, str] + ) -> None: + # Set self.last_usage using the global information + # This will be used for inter-kernel optimisations + super().set_last_usage(future_used_buffers, mutation_real_name) + # Set self.last_usage on the snodes + # This will be used for optimisations within the kernel + future_used_buffers: OrderedSet[str] = OrderedSet() + for node in reversed(self.snodes): + node.set_last_usage(future_used_buffers, mutation_real_name) + future_used_buffers.update(node.last_usage) + + @cache_on_self + def used_buffer_names(self) -> OrderedSet[str]: + return OrderedSet.union(*[x.used_buffer_names() for x in self.snodes]) + + @cache_on_self + def used_or_aliased_buffer_names(self) -> OrderedSet[str]: + return OrderedSet.union( + *[x.used_or_aliased_buffer_names() for x in self.snodes] + ) + + def get_nodes(self) -> Sequence[BaseSchedulerNode]: + return self.snodes + + def __repr__(self) -> str: + return f"{type(self).__name__}(nodes={self.get_name()})" + + @cache_on_self + def is_reduction(self) -> bool: + return any(x.is_reduction() for x in self.snodes) + + @cache_on_self + def is_split_scan(self) -> bool: + return any(x.is_split_scan() for x in self.snodes) + + @cache_on_self + def is_template(self) -> bool: + return any(x.is_template() for x in self.snodes) + + @cache_on_self + def get_template_node(self) -> Optional[ir.TemplateBuffer]: + for node in self.snodes: + if node.is_template(): + return node.get_template_node() + return None + + def get_device(self) -> torch.device: + return self.group[0] + + @cache_on_self + def has_aliasing_or_mutation(self) -> bool: + return any(x.has_aliasing_or_mutation() for x in self.snodes) + + # None of these need to be implemented, as a FusedSchedulerNode is just an + # abstraction for scheduling purposes + def update_mutated_names(self, renames: Dict[str, str]) -> None: + raise NotImplementedError + + def add_fake_dep(self, name: Dep) -> None: + raise NotImplementedError + + def can_inplace(self, read_dep: dependencies.Dep) -> bool: + raise NotImplementedError + + def debug_str(self) -> str: + """Longer form printout for trace logs""" + name = self.get_name() + node_typestr = ",".join(type(n).__name__ for n in self.snodes) + buf = IndentedBuffer() + buf.splice( + f"""\ +{name}: {type(self).__name__}({node_typestr}) +{name}.writes = {pformat(self.read_writes.writes)} +{name}.unmet_dependencies = {pformat(self.unmet_dependencies)} +{name}.met_dependencies = {pformat(self.read_writes.reads - self.unmet_dependencies)} +{name}.outputs = [ + """ + ) + with buf.indent(): + for out in self.get_outputs(): + buf.splice(out.debug_str()) + buf.writeline("]") + + try: + buf.splice(self.debug_str_extra()) + except Exception: + log.warning("Ignoring error in debug_str()", exc_info=True) + + return buf.getrawvalue().rstrip() + + +class ForeachKernelSchedulerNode(FusedSchedulerNode): + """ + This is a schedular node that consists of a set of scheduler nodes that + has no data dependencies among them and can be executed in parallel. + """ + + def get_consumer_subnode_for( + self, producer: BaseSchedulerNode + ) -> Optional[BaseSchedulerNode]: + for buf in producer.get_outputs(): + if buf.get_name() in self.read_to_node: + return self.read_to_node[buf.get_name()] + + return None + + def get_producer_subnode_for( + self, consumer: BaseSchedulerNode + ) -> Optional[BaseSchedulerNode]: + producers = set() + for rd in consumer.read_writes.reads: + if rd.name not in self.scheduler.name_to_buf: + continue + + node_name = self.scheduler.name_to_buf[rd.name].defining_op.get_name() + if node_name in self.name_to_node: + producers.add(self.name_to_node[node_name]) + + # Don't permit fusion if there are multiple subnodes + # that this consumer reads from + if len(producers) == 1: + return next(iter(producers)) + else: + return None + + @classmethod + def can_fuse(cls, producer: BaseSchedulerNode, consumer: BaseSchedulerNode) -> bool: + why = WhyNoFuse(producer, consumer) + if producer.is_foreach() and consumer.is_foreach(): + producer = typing.cast(ForeachKernelSchedulerNode, producer) + consumer = typing.cast(ForeachKernelSchedulerNode, consumer) + foreach_match = len(producer.snodes) == len(consumer.snodes) + if not foreach_match: + why("foreach do not have same length") + return foreach_match and all( + producer.scheduler.can_fuse(l, r) + for l, r in zip(producer.snodes, consumer.snodes) + ) + elif consumer.is_foreach(): + if producer.is_reduction(): + why( + "candidate producer is a reduction, foreach ops cannot be fused with reductions currently" + ) + return False + + consumer = typing.cast(ForeachKernelSchedulerNode, consumer) + consumer_subnode = consumer.get_consumer_subnode_for(producer) + if consumer_subnode is not None: + return consumer.scheduler.can_fuse(producer, consumer_subnode) + + why("candidate producer is not dep of any foreach consumer") + return False + + elif producer.is_foreach(): + if consumer.is_reduction(): + why( + "candidate consumer is a reduction, foreach ops cannot be fused with reductions currently" + ) + return False + + producer = typing.cast(ForeachKernelSchedulerNode, producer) + producer_subnode = producer.get_producer_subnode_for(consumer) + if producer_subnode is not None: + return producer.scheduler.can_fuse(producer_subnode, consumer) + + why("candidate consumer has no dep in any foreach producer") + return False + + raise AssertionError( + "At least one node passed to ForeachKernelSchedulerNode.can_fuse should be a foreach node" + ) + + @classmethod + def fuse( + cls, producer: BaseSchedulerNode, consumer: BaseSchedulerNode + ) -> ForeachKernelSchedulerNode: + assert producer.is_foreach() or consumer.is_foreach() + if producer.is_foreach(): + producer = typing.cast(ForeachKernelSchedulerNode, producer) + use_custom_partition_algo = producer.use_custom_partition_algo + enable_autotune = producer.enable_autotune + else: + consumer = typing.cast(ForeachKernelSchedulerNode, consumer) + use_custom_partition_algo = consumer.use_custom_partition_algo + enable_autotune = consumer.enable_autotune + prev_node_1 = None + prev_node_2 = None + fused_nodes: List[BaseSchedulerNode] + if producer.is_foreach() and consumer.is_foreach(): + producer = typing.cast(ForeachKernelSchedulerNode, producer) + consumer = typing.cast(ForeachKernelSchedulerNode, consumer) + fused_nodes = [ + FusedSchedulerNode.fuse(l, r) + for l, r in zip(producer.snodes, consumer.snodes) + ] + elif producer.is_foreach(): + producer = typing.cast(ForeachKernelSchedulerNode, producer) + producer_subnode = producer.get_producer_subnode_for(consumer) + fused_nodes = [] + prev_node_1 = producer + prev_node_2 = None + for node in producer.snodes: + if node is producer_subnode: + new_node = FusedSchedulerNode.fuse(node, consumer) + prev_node_2 = new_node + fused_nodes.append(new_node) + else: + fused_nodes.append(node) + + elif consumer.is_foreach(): + consumer = typing.cast(ForeachKernelSchedulerNode, consumer) + consumer_subnode = consumer.get_consumer_subnode_for(producer) + fused_nodes = [] + prev_node_1 = consumer + prev_node_2 = None + + for node in consumer.snodes: + if node is consumer_subnode: + new_node = FusedSchedulerNode.fuse(producer, node) + prev_node_2 = new_node + fused_nodes.append(new_node) + else: + fused_nodes.append(node) + else: + raise AssertionError( + "At least one node passed to ForeachKernelSchedulerNode.fuse should be a foreach node" + ) + + return cls( + producer.scheduler, + fused_nodes, + use_custom_partition_algo=use_custom_partition_algo, + prev_node_1=prev_node_1, + prev_node_2=prev_node_2, + enable_autotune=enable_autotune, + ) + + def __init__( + self, + scheduler: Scheduler, + snodes: List[BaseSchedulerNode], + use_custom_partition_algo: bool, + prev_node_1: Optional[BaseSchedulerNode] = None, + prev_node_2: Optional[BaseSchedulerNode] = None, + enable_autotune: bool = False, + ) -> None: + self.read_to_node = {} + self.name_to_node = {} + + if prev_node_1 is None or prev_node_2 is None: + super().__init__(scheduler, snodes) + + for node in snodes: + for read in node.read_writes.reads: + self.read_to_node[read.name] = node + + for name in node.get_operation_names(): + self.name_to_node[name] = node + else: + self.scheduler = scheduler + self.snodes = snodes + self.node = None + self.users: List[NodeUser] = [] + + self.set_read_writes( + dependencies.ReadWrites.merge_list( + [prev_node_1.read_writes, prev_node_2.read_writes] + ) + ) + + self.unmet_dependencies = ( + OrderedSet( + dep + for dep in OrderedSet.union( + prev_node_1.unmet_dependencies, prev_node_2.unmet_dependencies + ) + if dep.name not in self.get_buffer_names() + ) + - self.read_writes.writes + ) + + self.min_order = min([prev_node_1.min_order, prev_node_2.min_order]) + self.max_order = max([prev_node_1.max_order, prev_node_2.max_order]) + + if prev_node_1.is_foreach(): + assert isinstance(prev_node_1, ForeachKernelSchedulerNode) + foreach_node, other_node = prev_node_1, prev_node_2 + else: + assert isinstance(prev_node_2, ForeachKernelSchedulerNode) + foreach_node, other_node = prev_node_2, prev_node_1 + + self.ancestors = foreach_node.ancestors + self.ancestors.update(other_node.ancestors) + + self.name_to_node = foreach_node.name_to_node + for name in other_node.get_operation_names(): + self.name_to_node[name] = other_node + + self.use_custom_partition_algo = use_custom_partition_algo + self.group = (snodes[0].get_device(), ((sympy.Expr("combo_kernel"),),)) + self.origins: OrderedSet[torch.fx.Node] = OrderedSet() + self.enable_autotune = enable_autotune + + @classmethod + def combinable_nodes( + cls, nodes: List[BaseSchedulerNode] + ) -> List[BaseSchedulerNode]: + extern = [x for x in nodes if isinstance(x, ExternKernelSchedulerNode)] + if extern: + log.debug( + "ComboKernels: %d external nodes are filtered %s", + len(extern), + [node.node.get_origins() for node in extern if node.node is not None], + ) + filtered_nodes = [ + x + for x in nodes + if not isinstance(x, (NopKernelSchedulerNode, ExternKernelSchedulerNode)) + ] + foreach_nodes = [ + x for x in filtered_nodes if isinstance(x, ForeachKernelSchedulerNode) + ] + if foreach_nodes: + log.debug("ComboKernels: %d foreach nodes are filtered", len(foreach_nodes)) + filtered_nodes = [ + x for x in filtered_nodes if not isinstance(x, ForeachKernelSchedulerNode) + ] + template_nodes = [x for x in filtered_nodes if x.is_template()] + if template_nodes: + log.debug( + "ComboKernels: %d template nodes are filtered", {len(template_nodes)} + ) + filtered_nodes = [x for x in filtered_nodes if x not in template_nodes] + return filtered_nodes + + @staticmethod + def _default_group_nodes_for_combo_kernels( + scheduler: Scheduler, + ) -> List[List[BaseSchedulerNode]]: + """ + Returns a list of lists of nodes that are to be grouped together. + """ + sorted_nodes = scheduler._topological_sort_nodes() + grouped_nodes = [] + max_num_nodes = 8 + for nodes in sorted_nodes: + grouped_nodes.extend( + [ + nodes[i : i + max_num_nodes] + for i in range(0, len(nodes), max_num_nodes) + ] + ) + + return grouped_nodes + + group_algorithm_for_combo_kernels: Callable[ + [Scheduler], List[List[BaseSchedulerNode]] + ] = _default_group_nodes_for_combo_kernels + + @staticmethod + def set_group_algorithm_for_combo_kernels( + custom_group_algorithm: Callable[[Scheduler], List[List[BaseSchedulerNode]]] + ) -> None: + ForeachKernelSchedulerNode.group_algorithm_for_combo_kernels = ( + custom_group_algorithm + ) + + @staticmethod + def group_nodes_for_combo_kernels( + scheduler: Scheduler, + ) -> List[List[BaseSchedulerNode]]: + return ForeachKernelSchedulerNode.group_algorithm_for_combo_kernels(scheduler) + + def mark_run(self) -> None: + raise NotImplementedError + + def codegen(self) -> None: + assert isinstance(self.node, ir.ComputedBuffer), f"{type(self.node)=}" + self.node.get_store_function()(self.node.make_loader()()) + + def is_foreach(self) -> bool: + return True + + def get_subkernel_nodes(self) -> List[BaseSchedulerNode]: + """Returns a list of nodes which comprise the combo kernel. + These nodes may be vertically fused.""" + return list(self.snodes) + + def get_nodes(self) -> Sequence[BaseSchedulerNode]: + """Returns all nodes contained in this kernel, unpacking fused nodes + into their constituent scheduler nodes.""" + return list(itertools.chain.from_iterable(x.get_nodes() for x in self.snodes)) + + def get_first_name(self) -> str: + return self.snodes[0].get_first_name() + + def prune_redundant_deps( + self, name_to_fused_node: Dict[str, BaseSchedulerNode] + ) -> None: + _prune_redundant_deps(self, name_to_fused_node, self.scheduler.name_to_buf) + + for node in self.snodes: + node.prune_redundant_deps(name_to_fused_node) + + +class GroupedSchedulerNode(BaseSchedulerNode): + """ + This is a "fake" scheduler node that represents a group of scheduler nodes + that are meant to be *grouped* together (it does not allow another node to be scheduled + in between its constituent nodes, nor does it allow another node to fuse into any of its constituent nodes). + The way it does this is by maintaining its unmet dependencies as the union of its constituent nodes. + Fusion will still happen among the nodes within each GroupedSchedulerNode. + At codegen time, this scheduler node will be unpacked and codegen is called on each constituent node. + """ + + snodes: List[BaseSchedulerNode] + + @classmethod + def create(cls, snodes: List[BaseSchedulerNode]) -> GroupedSchedulerNode: + scheduler = snodes[0].scheduler + assert all(node.scheduler is scheduler for node in snodes) + grouped_snode = cls(scheduler, snodes) # type: ignore[arg-type] + for snode in snodes: + scheduler.name_to_fused_node[snode.get_name()] = grouped_snode + scheduler.name_to_fused_node[grouped_snode.get_name()] = grouped_snode + return grouped_snode + + def __init__(self, scheduler: Scheduler, snodes: List[BaseSchedulerNode]) -> None: + super().__init__(scheduler) + init_group_node(self, scheduler, snodes) + + def unpack(self) -> List[BaseSchedulerNode]: + """ + Do fusion among nodes within this GroupedSchedulerNode, + and then unpack this GroupedSchedulerNode into regular nodes. + """ + for snode in self.snodes: + self.scheduler.name_to_fused_node[snode.get_name()] = snode + del self.scheduler.name_to_fused_node[self.get_name()] + return self.scheduler.fuse_nodes(self.snodes) + + def add_fake_dep(self, fake_dep: Dep) -> None: + self.set_read_writes(self.read_writes.with_read(fake_dep)) + self.unmet_dependencies.add(fake_dep) + + @cache_on_self + def get_name(self) -> str: + return "_".join([x.get_name() for x in self.snodes]) + + def get_first_name(self) -> str: + return self.snodes[0].get_name() + + @cache_on_self + def get_buffer_names(self) -> OrderedSet[str]: + return OrderedSet.union(*[x.get_buffer_names() for x in self.snodes]) + + def get_outputs(self) -> List[SchedulerBuffer]: + result: List[SchedulerBuffer] = [] + for node in self.snodes: + result.extend(node.get_outputs()) + return result + + def get_nodes(self) -> Sequence[BaseSchedulerNode]: + return self.snodes + + @classmethod + def can_fuse(cls, producer: BaseSchedulerNode, consumer: BaseSchedulerNode) -> bool: + # GroupedSchedulerNode cannot be fused with another node + return False + + +def pick_loop_order( + stride_lengths: List[List[int]], + sizes: List[sympy.Expr], + priority_idx: Tuple[int, ...] = (), +) -> List[int]: + """ + A heuristic to decide loop iteration orders. This has not been well + tuned and may be something we should autotune. + """ + + @functools.cmp_to_key + def index_cmp(a: int, b: int) -> int: + if sizes[a] == 1 or sizes[b] == 1: + # 1-sizes don't matter, just move them to the end + return cmp(sizes[a] == 1, sizes[b] == 1) + + # Take abs, otherwise flipped dimensions are treated as smaller + # strides than contiguous dims + stride_len_a = [abs(sl[a]) for sl in stride_lengths] + stride_len_b = [abs(sl[b]) for sl in stride_lengths] + + # equivalent to + # np.logical_or(stride_lengths[:, b] == 0, stride_lengths[:, a] < stride_lengths[:, b]).all() + a_first = sum( + sl_b == 0 or sl_a < sl_b for sl_a, sl_b in zip(stride_len_a, stride_len_b) + ) + b_first = sum( + sl_a == 0 or sl_b < sl_a for sl_a, sl_b in zip(stride_len_a, stride_len_b) + ) + if a_first > b_first: + return -1 + if b_first > a_first: + return 1 + + # otherwise contiguous + return cmp(b, a) + + order = list(reversed(range(len(stride_lengths[0])))) + if len(priority_idx) > 0: + # if we have priority node, only use that node's order + stride_lengths = [stride_lengths[pi] for pi in priority_idx] + if config.pick_loop_orders: + order.sort(key=index_cmp) + return order + + +@dataclasses.dataclass +class NodeUser: + node: Union[BaseSchedulerNode, OutputNode] + can_inplace: bool = False + + # A weak user must be scheduled after a given node, but doesn't actually + # use the result + is_weak: bool = False + + def __hash__(self) -> int: + return hash((self.node.get_name(), self.can_inplace, self.is_weak)) + + def __eq__(self, other: object) -> bool: + return ( + isinstance(other, NodeUser) + and self.get_name() == other.get_name() + and self.can_inplace == other.can_inplace + and self.is_weak == other.is_weak + ) + + def get_name(self) -> str: + return self.node.get_name() + + def merge(self, other: NodeUser) -> NodeUser: + assert self.node is other.node + return NodeUser( + self.node, + self.can_inplace and other.can_inplace, + self.is_weak and other.is_weak, + ) + + +_post_grad_graph_counter = itertools.count() + + +class Scheduler: + __dep_size_hint_cache: Dict[Dep, int] + + def __init__(self, nodes: List[ir.Operation]) -> None: + with dynamo_timed("Scheduler.__init__"): + self._init(nodes) + + def _init(self, nodes: List[ir.Operation]) -> None: + super().__init__() + self.__dep_size_hint_cache = {} + V.graph.scheduler = self + self.backends: Dict[torch.device, BaseScheduling] = {} + self.post_grad_graph_id = next(_post_grad_graph_counter) + + self.completed_operations: OrderedSet[str] = OrderedSet() + self.available_buffer_names = OrderedSet( + [ + *V.graph.graph_inputs.keys(), + *V.graph.constants.keys(), + *V.graph.torchbind_constants.keys(), + ] + ) + + self.nodes = [self.create_scheduler_node(n) for n in nodes] + self.update_zero_dim_cpu_tensor() + # some new constants could have been created above + self.available_buffer_names.update(V.graph.constants.keys()) + for node in self.nodes: + node.prune_deps() + + self.name_to_node: Dict[str, BaseSchedulerNode] = { + n.get_name(): n for n in self.nodes + } + self.name_to_buf: Dict[str, SchedulerBuffer] = { + buf.get_name(): buf for node in self.nodes for buf in node.get_outputs() + } + self.name_to_fused_node: Dict[str, BaseSchedulerNode] = self.name_to_node.copy() + + # mutation_real_name: Maps back to the original name for codegen + # Example: + # If you mutate buf0 inside of buf1's kernel, then: + # mutation_real_name = {"buf0" : "buf1"} + # all subsequent uses of buf0 become buf1's usage in dependency graph + self.mutation_real_name: Dict[str, str] = {} + + # We handle mutation by renaming modified versions of the same + # buffer in the dependency graph to prevent cycles. + # mutation_renames: tracks the current name for a given buffer + # (changed once per mutation) + # Example: + # If you mutate buf0 inside of buf1's kernel, then: + # mutation_renames = {"buf1" : "buf0"} + # in codegen we only use buf0, never buf1 + self.mutation_renames: Dict[str, str] = {} + + self.compute_dependencies() + self.nodes = self.topological_sort_schedule(self.nodes) + self.dead_node_elimination() + self.name_to_fused_node = {n.get_name(): n for n in self.nodes} + self.compute_ancestors() + if config.reorder_for_compute_comm_overlap: + self.nodes = comms.decide_global_ordering_of_comms( + self.nodes, + self.name_to_buf, + self.name_to_fused_node, + ) + + metrics.ir_nodes_pre_fusion += len(self.nodes) + V.debug.ir_pre_fusion(self.nodes) + self.num_orig_nodes = len(self.nodes) + self.create_foreach_nodes() + self.nodes = self.topological_sort_schedule(self.nodes) + self.logged_slow_fusion: OrderedSet[Tuple[str, str]] = OrderedSet() + if config._pre_fusion_custom_pass is not None: + self.nodes = config._pre_fusion_custom_pass(self.nodes) + self.nodes = self.fuse_nodes(self.nodes) + self.merge_loops() + self.finalize_multi_template_buffers() + if config.reorder_for_compute_comm_overlap: + self.nodes = comms.reorder_compute_and_comm_for_overlap(self.nodes) + if config.combo_kernels: + self.create_combo_kernel_nodes(num_ck_nodes=None) + self.process_grouped_nodes() + self.compute_last_usage() + V.debug.ir_post_fusion(self.nodes) + V.debug.graph_diagram(self.nodes) + self.debug_draw_graph() + + # used during codegen: + self.current_device: Optional[torch.device] = None + self.buffer_names_to_free: OrderedSet[str] = OrderedSet() + + # fx graph node to the position it appears in the graph + # for debug attribution + self.origin_to_index: Dict[torch.fx.Node, int] = {} + + get_metric_table("graph_stats").add_row( + lambda: { + "graph_id": self.post_grad_graph_id, + "num_nodes_before_fusion": self.num_orig_nodes, + "num_nodes_after_fusion": len(self.nodes), + } + ) + + def get_current_device_or_throw(self) -> torch.device: + if device := self.current_device: + return device + else: + raise RuntimeError("No current device") + + def debug_draw_graph(self) -> None: + """Generate an image of the graph for debugging""" + if os.environ.get("INDUCTOR_WRITE_SCHEDULER_GRAPH", None) == "1": + from .debug import draw_buffers + + draw_buffers(self.nodes, print_graph=True) + + def debug_print_nodes(self, label: str) -> None: + if log.isEnabledFor(logging.INFO): + log.info("%s:", label) + for node in self.nodes: + node.log_details() + + def create_scheduler_node(self, node: ir.Operation) -> BaseSchedulerNode: + assert ( + node.get_origins() is not None + ), "All nodes passed to scheduling must have an origin" + if node.is_no_op(): + return NopKernelSchedulerNode(self, node) + elif isinstance(node, (ir.ComputedBuffer, ir.TemplateBuffer)): + return SchedulerNode(self, node) + elif isinstance(node, ir.ExternKernel): + return ExternKernelSchedulerNode(self, node) + else: + raise NotImplementedError(node) + + def create_foreach_nodes(self) -> None: + removed_node_names: OrderedSet[str] = OrderedSet() + fe_nodes = [] + kept_node_names = self.name_to_fused_node.keys() + + for names in V.graph.lists.values(): + names = [ + name + for name in names + if name in kept_node_names + and not isinstance(self.name_to_node[name], NopKernelSchedulerNode) + ] + if not names: + # All nodes eliminated + continue + + removed_node_names.update(names) + snodes = [self.name_to_node[name] for name in names] + + enable_autotune = config.combo_kernels_autotune > 1 + fe_node = ForeachKernelSchedulerNode( + self, + snodes, + use_custom_partition_algo=False, + enable_autotune=enable_autotune, + ) + + fe_nodes.append(fe_node) + + for name in names: + self.name_to_fused_node[name] = fe_node + + self.nodes = [ + node for node in self.nodes if node.get_name() not in removed_node_names + ] + list(fe_nodes) + + def compute_dependencies(self) -> None: + """ + Create dependency edges between nodes, handling aliasing and + mutation properly. + """ + + T = TypeVar("T") + + class DedupList(Generic[T]): + """ + This data structure behaves like a list except it makes sure the + elements remain unique. + Normally one could use a OrderedSet/dict for this purpose however + the list in question gets elements appended as it is being + iterated over which means that we need to keep the list + semantics. + """ + + def __init__( + self, + items: Optional[List[T]] = None, + membership: Optional[OrderedSet[T]] = None, + ) -> None: + self.items = items or [] + self.membership = membership or OrderedSet() + + def append(self, node_user: T) -> None: + if node_user in self.membership: + return + self.items.append(node_user) + self.membership.add(node_user) + + def __add__(self, other: DedupList[T]) -> DedupList[T]: + new_membership = OrderedSet.union(self.membership, other.membership) + new_items = self.items + [ + x for x in other.items if x not in self.membership + ] + return DedupList(new_items, new_membership) + + name_to_users: DefaultDict[str, DedupList[NodeUser]] = collections.defaultdict( + DedupList + ) + + # handle aliasing by using python aliasing in name_to_users + # if foo aliases bar then we will make name_to_users["foo"] point + # to the same python list as name_to_users["bar"] + for node in self.nodes: + for buf1 in node.get_outputs(): + buf1_name = buf1.get_name() + for buf2_name in buf1.get_aliases(): + if buf1_name in name_to_users and buf2_name in name_to_users: + # merge the two + list1 = name_to_users[buf1_name] + list2 = name_to_users[buf2_name] + combined = list1 + list2 + for key in name_to_users.keys(): + if ( + name_to_users[key] is list1 + or name_to_users[key] is list2 + ): + name_to_users[key] = combined + elif buf1_name in name_to_users: + name_to_users[buf2_name] = name_to_users[buf1_name] + else: + name_to_users[buf1_name] = name_to_users[buf2_name] + + def rename(n: str) -> str: + if n in self.mutation_renames: + return rename(self.mutation_renames[n]) + return n + + def add_user( + used_by_name: str, + user_node: Union[BaseSchedulerNode, OutputNode], + can_inplace: bool = False, + is_weak: bool = False, + ) -> None: + name_to_users[rename(used_by_name)].append( + NodeUser(user_node, can_inplace, is_weak) + ) + + unbacked_symbol_to_origin_node: Dict[sympy.Symbol, Optional[str]] = {} + + # NB: None means that the dependency is on an input. Don't actually + # generate a dependency because if we do, Inductor will start trying + # to free the unbacked int but that's pointless + for name, val in V.graph.graph_inputs.items(): + if isinstance(val, sympy.Expr): + for fs in val.free_symbols: + unbacked_symbol_to_origin_node[fs] = None + + for node in self.nodes: + log.debug("scheduling %s", node.node) + + # unbacked symbols don't follow ordinary buffer dependencies, so + # we track their def/uses separately + assert node.node is not None + unbacked_symbol_defs = sorted( + node.node.get_unbacked_symbol_defs(), key=lambda x: x.name + ) + for s in unbacked_symbol_defs: + assert isinstance(s, sympy.Symbol) + # Pick the first definer as canonical. There may be multiple + # because if a MultiOutputLayout buffer propagates an unbacked + # symint to multiple outputs, they will all claim to def it. + if s not in unbacked_symbol_to_origin_node: + unbacked_symbol_to_origin_node[s] = node.get_name() + + unbacked_symbol_uses = sorted( + node.node.get_unbacked_symbol_uses(), key=lambda x: x.name + ) + # if a kernel takes unbacked symints, register dependencies + for s in unbacked_symbol_uses: + assert ( + s in unbacked_symbol_to_origin_node + ), f"{s} not in {unbacked_symbol_to_origin_node}" + if (r := unbacked_symbol_to_origin_node[s]) is not None: + for buf in self.name_to_node[r].get_outputs(): + node.add_fake_dep(StarDep(buf.get_name())) + + if ( + len(node.read_writes.writes) == 1 + and (dep := next(iter(node.read_writes.writes))) + and isinstance(dep, MemoryDep) + ): + node_mode = dep.mode + else: + node_mode = None + + # Handle output mutations + for buf in node.get_outputs(): + # a node will mutate either 0 or 1 buffers + assert len(buf.get_mutations()) <= 1 + for alt_name in buf.get_mutations(): + alt_name = rename(alt_name) + # this node must run after the prior writer + add_user(alt_name, node) + node.add_fake_dep(StarDep(alt_name, mode=node_mode)) + for user in name_to_users[alt_name].items: + if user.get_name() == node.get_name(): + continue + + assert isinstance(user.node, BaseSchedulerNode) + for other_name in user.node.get_buffer_names(): + # this node must run after all prior readers + other_name = rename(other_name) + node.add_fake_dep( + WeakDep(other_name, mutating_buf=buf.get_name()) + ) + add_user(other_name, node, is_weak=True) + + # add normal non-mutation dependencies + for read in node.read_writes.reads: + if not isinstance(read, WeakDep): + add_user(read.name, node, node.can_inplace(read)) + + node.update_mutated_names(self.mutation_renames) + + # update our renaming scheme for the next iteration + for buf in node.get_outputs(): + for alt_name in buf.get_mutations(): + self.mutation_renames[rename(alt_name)] = buf.get_name() + self.mutation_renames[alt_name] = buf.get_name() + self.mutation_real_name[ + buf.get_name() + ] = self.mutation_real_name.get(alt_name, alt_name) + + # make sure outputs aren't dead-code-eliminated + for buf_name in V.graph.get_output_names(): + log.debug("scheduling output %s", buf_name) + add_user(buf_name, OutputNode(StarDep(buf_name))) + + # make sure unbacked symints aren't dead-code-eliminated + for out in V.graph.graph_outputs: + for s in out.get_unbacked_symbol_uses(): + assert ( + s in unbacked_symbol_to_origin_node + ), f"{s} not in {unbacked_symbol_to_origin_node.keys()}" + if r := unbacked_symbol_to_origin_node[s]: + for buf_name in self.name_to_node[r].get_buffer_names(): + log.debug( + "scheduling output %s for unbacked symint %s", buf_name, s + ) + add_user(buf_name, OutputNode(StarDep(buf_name))) + + # make sure input mutation isn't dead-code-eliminated + for name in self.mutation_renames: + if name in V.graph.graph_inputs: + add_user(name, OutputNode(StarDep(name))) + V.graph.mutated_inputs.add(name) + elif name in V.graph.constants: + # In AOTI, module parameters and buffers are not lifted as graph inputs + add_user(name, OutputNode(StarDep(name))) + + inp_names = { + name: index for index, name in enumerate(V.graph.graph_inputs.keys()) + } + V.graph.mutated_input_idxs = [ + inp_names[name] for name in V.graph.mutated_inputs + ] + + # copy users information onto the nodes + for node in self.nodes: + for buf in node.get_outputs(): + buf.set_users(name_to_users[buf.get_name()].items) + + def dead_node_elimination(self) -> None: + """ + Remove any nodes without users + """ + # self.nodes is in topological order, so by iterating in reverse order + # we have visited (and potentially removed) all users before visiting a + # given node. + updated_nodes = [] + for node in reversed(self.nodes): + + def can_eliminate_user(user: NodeUser) -> bool: + return user.is_weak or user.get_name() in V.graph.removed_operations + + active_buffers = False + for buf in node.get_outputs(): + can_eliminate = all(can_eliminate_user(u) for u in buf.users) + if can_eliminate: + log.debug("removed dead buffer: %s", buf.get_name()) + V.graph.removed_buffers.add(buf.get_name()) + else: + active_buffers = True + + can_eliminate = not node.has_side_effects() and not active_buffers + + if not can_eliminate: + updated_nodes.append(node) + else: + # dead code + log.debug("removed dead operation: %s", node.get_name()) + V.graph.removed_operations.add(node.get_name()) + + self.nodes = list(reversed(updated_nodes)) + + # Prune any WeakDeps no longer needed + for node in self.nodes: + node.prune_weak_deps() + + def topological_sort_schedule( + self, nodes: List[BaseSchedulerNode] + ) -> List[BaseSchedulerNode]: + """ + Ensure nodes is in topologically sorted order + """ + seen: OrderedSet[BaseSchedulerNode] = OrderedSet() + name_to_node: Dict[str, BaseSchedulerNode] = dict() + result: List[BaseSchedulerNode] = [] + + def visit(n: BaseSchedulerNode) -> None: + if n not in seen: + seen.add(n) + for dep in sorted(n.unmet_dependencies, key=lambda d: d.name): + # We only care about doing toposort within `nodes` + if dep.name not in name_to_node: + continue + visit(name_to_node[dep.name]) + result.append(n) + + for node in nodes: + for name in node.get_buffer_names(): + name_to_node[name] = node + for node in nodes: + visit(node) + return result + + def _get_unmet_dep_nodes(self, snode: BaseSchedulerNode) -> List[BaseSchedulerNode]: + unmet_deps = set() + if isinstance( + snode, + ( + SchedulerNode, + ExternKernelSchedulerNode, + NopKernelSchedulerNode, + FusedSchedulerNode, + ), + ): + for dep in snode.unmet_dependencies: + unmet_deps.add(dep.name) + else: + raise RuntimeError( + f"get_unmet_dep_nodes is not implemented for {type(snode)}." + ) + unmet_dep_ops = (self.name_to_buf[dep].defining_op for dep in unmet_deps) + return list({self.name_to_fused_node[n.get_name()] for n in unmet_dep_ops}) + + def _topological_sort_nodes(self) -> List[List[BaseSchedulerNode]]: + """ + Sort nodes by their topological order, return a list of node lists. + """ + order = [] + nodes = dict.fromkeys(self.nodes, 0) + children: Dict[Any, Any] = {} + for node in self.nodes: + deps = self._get_unmet_dep_nodes(node) + nodes[node] = len(deps) + for dep in deps: + c = children.get(dep, []) + c.append(node) + children[dep] = c + + zero_deg_nodes = [n for n, v in nodes.items() if v == 0] + while zero_deg_nodes: + order.append(zero_deg_nodes) + for n in zero_deg_nodes: + for user in children.get(n, []): + nodes[user] -= 1 + nodes.pop(n) + zero_deg_nodes = [n for n, v in nodes.items() if v == 0] + assert not nodes, "Topological sort failed!" + return order + + def compute_ancestors(self) -> None: + """ + Populate each node.ancestors + """ + # note self.nodes is topologically sorted + name_to_ancestors: Dict[str, OrderedSet[str]] = {} + for node in self.nodes: + ancestors: OrderedSet[str] = OrderedSet() + for dep in node.unmet_dependencies: + dep_node_name = self.name_to_buf[dep.name].defining_op.get_name() + ancestors.add(dep_node_name) + ancestors |= name_to_ancestors[dep_node_name] + name_to_ancestors[node.get_name()] = ancestors + node.ancestors = ancestors + + for order, node in enumerate(self.nodes): + node.min_order = order + node.max_order = order + + def merge_loops(self) -> None: + for node in self.nodes: + if not config.loop_ordering_after_fusion: + continue + + # Even for CPU, if we are using the halide backend, we still need + # the merge loops steps below + if not isinstance(node, (SchedulerNode, FusedSchedulerNode)) or ( + node.get_device().type != "cuda" and config.cpu_backend != "halide" + ): + continue + for snode in node.get_nodes(): + # merge loops for the scheduler node + if not isinstance(snode, SchedulerNode) or snode.is_template(): + continue + + snode._body = snode._body.merge_loops() + snode._sizes = snode._body.sizes + + # merge_loops is called after loop reordering. + # We still need retain fake dependencies since codegen the + # estimated amount of memory access rely on them. + snode.refresh_dependencies(normalize=True) + + # Note that for CPU backend, merging loops will change + # snode.group. It's fine for Triton backend. + # But if we simplify update snode.group like this: + # group_fn = self.get_backend(snode.node.get_device()).group_fn + # snode.group = (snode.node.get_device(), group_fn(snode._sizes)) + # There is still an issue due to different snode in a + # FusedSchedulerNode having different merged loops. + # Skip CPU backend for now. + + def fuse_nodes(self, nodes: List[BaseSchedulerNode]) -> List[BaseSchedulerNode]: + """ + Combine eligible nodes into FusedSchedulerNodes. + """ + for i in range(10): + old_len = len(nodes) + fusion_log.debug( + "===== attempting fusion (%d/10): %d nodes =====", + i + 1, + old_len, + ) + nodes = self.fuse_nodes_once(nodes) + new_len = len(nodes) + fusion_log.debug( + "completed fusion round (%d/10): fused %d nodes into %d nodes\n", + i + 1, + old_len, + new_len, + ) + if new_len == old_len or new_len == 1: + fusion_log.debug("===== fusion complete (%d iterations) =====", i + 1) + break + return nodes + + def process_grouped_nodes(self) -> None: + """ + Unpack GroupedSchedulerNode into regular nodes. + """ + new_nodes: List[BaseSchedulerNode] = [] + for node in self.nodes: + new_nodes.extend( + node.unpack() if isinstance(node, GroupedSchedulerNode) else [node] + ) + self.nodes = new_nodes + + def benchmark_fused_nodes( + self, nodes: Sequence[BaseSchedulerNode] + ) -> Tuple[float, str]: + """ + Benchmark fused list of nodes and return the execution time + in milliseconds on randomly generated inputs. + """ + assert len(nodes) > 0 + device = nodes[0].get_device() + self.current_device = device + backend = self.get_backend(device) + return backend.benchmark_fused_nodes(nodes) + + def finalize_multi_template_buffers(self) -> None: + def replace_operation_buffer( + orig_node: ir.MultiTemplateBuffer, new_node: ir.OperationBuffer + ) -> None: + replaced_buf_name = new_node.get_name() + orig_buf_name = orig_node.get_name() + assert isinstance(orig_buf_name, str) and isinstance(replaced_buf_name, str) + + replaced_op_name = new_node.get_operation_name() + orig_op_name = orig_node.get_operation_name() + assert isinstance(orig_op_name, str) and isinstance(replaced_op_name, str) + + del V.graph.name_to_buffer[replaced_buf_name] + new_node.name = orig_buf_name + + del V.graph.name_to_op[replaced_op_name] + new_node.operation_name = orig_op_name + + orig = V.graph.buffers.index(orig_node) + V.graph.buffers.remove(new_node) + V.graph.buffers[orig] = new_node + V.graph.name_to_buffer[orig_buf_name] = new_node + + orig = V.graph.operations.index(orig_node) + V.graph.operations.remove(new_node) + V.graph.operations[orig] = new_node + V.graph.name_to_op[orig_op_name] = new_node + + for i, node in enumerate(self.nodes): + if isinstance(node, SchedulerNode) and isinstance( + node.node, ir.MultiTemplateBuffer + ): + multi_node = node.node + min_node_unfused, _ = multi_node.get_min_choice() + + if isinstance( + min_node_unfused, + torch._inductor.ir.TritonTemplateCallerBase, + ): + node.node.finalize_as_triton_caller(min_node_unfused) + continue + + out_tensorbox = min_node_unfused.output_node() + out_storage = out_tensorbox.data + assert isinstance(out_storage, ir.StorageBox) + out_buffer = out_storage.data + assert isinstance(out_buffer, ir.OperationBuffer) + + out_buffer.layout = multi_node.layout + replace_operation_buffer(multi_node, out_buffer) + new_scheduler_node = self.create_scheduler_node(out_buffer) + + self.nodes[i] = new_scheduler_node + self.name_to_node[node.get_name()] = new_scheduler_node + self.name_to_fused_node[node.get_name()] = new_scheduler_node + + for new_out, old_out in zip( + new_scheduler_node.get_outputs(), node.get_outputs() + ): + self.name_to_buf[old_out.get_name()] = new_out + new_out.users = old_out.users + + new_scheduler_node.min_order = node.min_order + new_scheduler_node.max_order = node.max_order + new_scheduler_node.last_usage = node.last_usage + + def _any_atomic_add(self, node_list: Sequence[BaseSchedulerNode]) -> bool: + return any( + hasattr(n.node, "data") + and n.node is not None + and hasattr(n.node.data, "scatter_mode") + and n.node.data.scatter_mode == "atomic_add" + for n in node_list + ) + + def speedup_by_fusion( + self, node1: BaseSchedulerNode, node2: BaseSchedulerNode + ) -> bool: + """ + If config.benchmark_fusion is False, always return True. + Otherwise, return True if fusion can brings speedup. + """ + + is_multi_template = node1.is_template() and isinstance( + node1.get_template_node(), ir.MultiTemplateBuffer + ) + if not config.benchmark_fusion and not is_multi_template: + return True + + if ( + node1.is_template() + and not isinstance(node1.get_template_node(), ir.TritonTemplateBuffer) + or node1.is_foreach() + or node2.is_foreach() + ): + # TODO support benchmarking epilogue fusion + return True + + node_list_1 = node1.get_nodes() + device = node_list_1[0].get_device() + + # don't support benchmark fusion for CPU right now. + if device.type == "cpu": + return True + + node_list_2 = node2.get_nodes() + node_list_fused = list(itertools.chain(node_list_1, node_list_2)) + + # We can not accurately benchmark kernel using atomic_add + # due to how we generate random integer inputs. + # Skip benchmarking them by allowing fusion. + if self._any_atomic_add(node_list_fused): + return True + + from triton.compiler.errors import CompilationError + + why = WhyNoFuse(node1, node2) + + def log_fusion(ms_fused: float, ms1: float, ms2: float) -> None: + if fusion_log.isEnabledFor(logging.DEBUG): + if ms_fused < ms1 + ms2: + fusion_log.debug( + "can fuse (benchmark): fusing %s with %s cause %sx speedup", + node1.get_buffer_names(), + node2.get_buffer_names(), + green_text(f"{(ms1 + ms2) / ms_fused:.3f}"), + ) + else: + fusion_log.debug( + "cannot fuse (benchmark): fusing %s with %s cause %sx slowdown", + node1.get_buffer_names(), + node2.get_buffer_names(), + red_text(f"{ms_fused / (ms1 + ms2):.3f}"), + ) + + if isinstance(node1, SchedulerNode) and isinstance( + node1.node, ir.MultiTemplateBuffer + ): + multi_node = node1.node + choice_timings = multi_node.choice_timings + + _, ms1 = multi_node.get_min_choice() + ms2, path2 = self.benchmark_fused_nodes(node_list_2) + + min_ms_fused = float("inf") + ms_fused_choice = None + + triton_choices = 0 + + for choice, unfused_time in sorted( + choice_timings.items(), key=lambda x: x[1] + ): + if not isinstance(choice, torch._inductor.ir.TritonTemplateCallerBase): + continue + + if unfused_time >= ms1 + ms2: + break + + triton_choices += 1 + if triton_choices > config.max_epilogue_benchmarked_choices: + break + + # TODO - parallel compile triton templates + # TODO - should prune/skip choices that are not within certain % of best choice + with node1.node.swap_as_triton_caller(choice): + ms_fused, _ = self.benchmark_fused_nodes(node_list_fused) + + if ms_fused < min_ms_fused: + min_ms_fused = ms_fused + ms_fused_choice = choice + + log_fusion(min_ms_fused, ms1, ms2) + + # after we do a fusion, we finalize a triton template. + # TODO - could preserve multi template and choices for subsequent fusions + if min_ms_fused < (ms1 + ms2) and ms_fused_choice is not None: + node1.node.finalize_as_triton_caller(ms_fused_choice) + return True + else: + return False + else: + try: + ms1, path1 = self.benchmark_fused_nodes(node_list_1) + if math.isinf(ms1): + why("register spilling of the first kernel") + return False + ms2, path2 = self.benchmark_fused_nodes(node_list_2) + if math.isinf(ms2): + why("register spilling of the second kernel") + return False + ms_fused, path_fused = self.benchmark_fused_nodes(node_list_fused) + if math.isinf(ms_fused): + why("register spilling of the fused kernel") + return False + except CompilationError as e: + # workaround triton issue: https://github.com/openai/triton/issues/2151 + if "Loop-carried variable" in str(e): + return True # allow fusion + else: + raise + + log_fusion(ms_fused, ms1, ms2) + if ( + is_metric_table_enabled("slow_fusion") + and ms_fused >= ms1 + ms2 + and (path1, path2) not in self.logged_slow_fusion + ): + self.logged_slow_fusion.add((path1, path2)) + get_metric_table("slow_fusion").add_row( + lambda: { + "kernel1_path": path1, + "kernel1_latency": ms1, + "kernel2_path": path2, + "kernel2_latency": ms2, + "fused_kernel_path": path_fused, + "fused_kernel_latency": ms_fused, + "slow_down_ratio": ms_fused / (ms1 + ms2), + } + ) + return ms_fused < ms1 + ms2 + + def fuse_nodes_once( + self, nodes: List[BaseSchedulerNode] + ) -> List[BaseSchedulerNode]: + """ + Combine eligible nodes into FusedSchedulerNodes. + + This relies on two key functions to control the logic: + - self.can_fuse(): checks if a fusion is legal + - self.score_fusion(): assigns priority to a given fusion + """ + fused_nodes = OrderedSet(nodes) + if fusion_log.isEnabledFor(logging.DEBUG): + fusion_log.debug("fuse_nodes_once, candidates:") + for node in fused_nodes: + fusion_log.debug(" " + node.debug_str_short()) # noqa: G003 + for node1, node2 in self.get_possible_fusions(nodes): + node1 = self.name_to_fused_node[node1.get_first_name()] + node2 = self.name_to_fused_node[node2.get_first_name()] + if self.can_fuse(node1, node2) and not self.will_fusion_create_cycle( + node1, node2 + ): + if not self.speedup_by_fusion(node1, node2): + continue + fusion_log.debug( + "fusing %s with %s", node1.get_name(), node2.get_name() + ) + + # above can_fuse asserts that node2 has the same device + device = node1.get_device() + node3 = self.get_backend(device).fuse(node1, node2) + fused_nodes.remove(node1) + fused_nodes.remove(node2) + fused_nodes.add(node3) + self.name_to_fused_node.update( + {n.get_name(): node3 for n in node3.get_nodes()} + ) + nodes = sorted(fused_nodes, key=lambda x: x.min_order) + nodes = self.topological_sort_schedule(nodes) + self.prune_redundant_deps(nodes) + return nodes + + def create_combo_kernel_nodes(self, num_ck_nodes: Optional[int] = None) -> None: + """ + Groups parallel nodes + """ + fused_nodes = set(self.nodes) + count = 0 + num_nodes_orig = len(self.nodes) + log.debug("ComboKernels: Generating with num_ck_nodes = %d...", num_ck_nodes) + for num, node_list in enumerate( + ForeachKernelSchedulerNode.group_nodes_for_combo_kernels(self) + ): + node_list = ForeachKernelSchedulerNode.combinable_nodes(node_list) + if len(node_list) < 2: + continue + if num_ck_nodes is not None and count > num_ck_nodes: + break + if not self.speedup_by_combo_kernel(node_list): + log.debug("ComboKernels: Not speeding up %d-th group", num) + continue + count += 1 + enable_autotune = config.combo_kernels_autotune > 0 + group_snode = ForeachKernelSchedulerNode( + node_list[0].scheduler, + node_list, + use_custom_partition_algo=True, + enable_autotune=enable_autotune, + ) + log.info( + "ComboKernels: Combining %d nodes for %d-th group", + len(node_list), + num, + ) + for node in node_list: + fused_nodes.remove(node) + fused_nodes.add(group_snode) + self.name_to_fused_node.update( + {n.get_name(): group_snode for n in group_snode.get_nodes()} + ) + self.nodes = sorted(fused_nodes, key=lambda x: x.min_order) + self.nodes = self.topological_sort_schedule(self.nodes) + log.info( + "Generated ComboKernel nodes: %d ComboKernels, totally %d -> %d nodels", + count, + num_nodes_orig, + len(self.nodes), + ) + self.prune_redundant_deps(self.nodes) + + def prune_redundant_deps(self, nodes: List[BaseSchedulerNode]) -> None: + for node in nodes: + node.prune_redundant_deps(self.name_to_fused_node) + + def get_possible_fusions( + self, nodes: List[BaseSchedulerNode] + ) -> List[Tuple[BaseSchedulerNode, BaseSchedulerNode]]: + """ + Helper to find all legal fusion opportunities, sorted by self.score_fusion() + """ + possible_fusions = [] + seen: OrderedSet[Tuple[BaseSchedulerNode, BaseSchedulerNode]] = OrderedSet() + + def check_all_pairs(nodes: List[BaseSchedulerNode]) -> None: + for node1_index, node1 in enumerate(nodes): + for node2 in nodes[node1_index + 1 :]: + key = (node1, node2) + if key in seen: + continue + seen.add(key) + + if self.can_fuse(node1, node2): + possible_fusions.append(key) + elif (node2.is_template() or node2.is_foreach()) and self.can_fuse( + node2, node1 + ): + # foreach fusions and epilogue fusions are order dependent + possible_fusions.append((node2, node1)) + + buffer_names_grouping = collections.defaultdict(list) + for node in nodes: + for buf in node.used_buffer_names(): + buffer_names_grouping[buf].append(node) + for node_grouping in buffer_names_grouping.values(): + check_all_pairs(node_grouping) + + if config.aggressive_fusion: + group_grouping = collections.defaultdict(list) + for node in nodes: + group = getattr(node, "group", None) + if group: + group_grouping[group].append(node) + for node_grouping in group_grouping.values(): + check_all_pairs(node_grouping) + + possible_fusions = self.get_possible_fusions_with_highest_priority( + possible_fusions + ) + possible_fusions.sort(key=self.score_fusion_key, reverse=True) + fusion_log.debug("found %d possible fusions", len(possible_fusions)) + return possible_fusions + + def will_fusion_create_cycle( + self, node1: BaseSchedulerNode, node2: BaseSchedulerNode + ) -> bool: + """ + Finds whether there's a path from node1 to node2 (or vice-versa) + caused indirectly by other fusions. + """ + # since we are just returning boolean here, use slightly faster, unordered set + visited: Set[FusedSchedulerNode] = set() + + def found_path(node: BaseSchedulerNode) -> bool: + # only fused nodes can introduce new ancestors. + if isinstance(node, FusedSchedulerNode) and node not in visited: + visited.add(node) + if node.get_operation_names().issubset(combined_ancestors): + # All fusion outputs are in ancestors of node1 and node2, thus + # cannot introduce new path: + # + # 1. if output is neither descendent of node1 or node2, the + # output cannot introduce a path + # 2. due to [can_fuse]: if WLOG output is descendent of node1, it cannot be + # on path(node1->node2), hence it cannot be ancestor of node2 + # 3. due to [acyclic]: if WLOG output is descendent of node1, it cannot be + # ancestor of node1 + return False + else: + # continue DFS of new ancestors introduced by the fusion + return bool(combined_names & node.ancestors) or any( + found_path(self.name_to_fused_node[n]) + for n in node.ancestors - combined_ancestors + ) + return False + + # as above - use slightly faster, unordered set + combined_names = ( + node1.get_operation_names()._dict.keys() + | node2.get_operation_names()._dict.keys() + ) + combined_ancestors = ( + node1.ancestors._dict.keys() | node2.ancestors._dict.keys() + ) - combined_names + cycle = any(found_path(self.name_to_fused_node[n]) for n in combined_ancestors) + if cycle: + WhyNoFuse(node1, node2)("will create cycle") + return cycle + + def can_fusion_increase_peak_memory( + self, node1: BaseSchedulerNode, node2: BaseSchedulerNode + ) -> bool: + """ + This function prevents fusion for nodes that can increase memory + footprint. This problem is more common in horizontal fusion, where nodes + that are far apart in the original order get fused, lengthening the live + intervals of tensors. This is very evident in models with activation + checkpointing, where the recomputed nodes from different checkpointed + regions get fused and significantly increase the memory footprint. + + The current attempt is a quick, possibly hacky, heuristic to prevent the + fusion of nodes that are far away in the original order. + + A better but difficult to implement heurisitic would be to use live + intervals of the buffers, find region of peak pressure in the original + program and prevent fusion that crosses that peak region. We might need + special care or good approximation in this implementation, as fusion of + node changes live intervals, and re-computing live intervals and peak + memory after each fusion can introduce large compilation overhead. + """ + proximity_score = max( + abs(node1.min_order - node2.max_order), + abs(node2.min_order - node1.max_order), + ) + return proximity_score > 64 + + def decide_fusion_fail_reason( + self, + node1: BaseSchedulerNode, + node2: BaseSchedulerNode, + common_buf_names: Tuple[str, ...], + ) -> str: + """ + Try to decide reasons why fusion fail due to no shared memory even though + there are common buffers. + """ + reasons = {} + node1_name2dep = {dep.name: dep for dep in node1.read_writes.reads_and_writes()} + node2_name2dep = {dep.name: dep for dep in node2.read_writes.reads_and_writes()} + + for buf_name in common_buf_names: + buf = V.graph.get_buffer(buf_name) + lhs_dep = node1_name2dep[buf_name] + rhs_dep = node2_name2dep[buf_name] + + if lhs_dep.get_numel() != rhs_dep.get_numel(): + reasons[ + buf_name + ] = f"different numel: {lhs_dep.get_numel()} v.s. {rhs_dep.get_numel()}" + continue + + # same numel but different MemoryDep.size. Should be broadcasting + if sympy_product(lhs_dep.size) != sympy_product(rhs_dep.size): + reasons[buf_name] = "broadcast" + continue + + if not isinstance(lhs_dep, MemoryDep) or not isinstance(rhs_dep, MemoryDep): + reasons[ + buf_name + ] = f"not MemoryDep: {type(lhs_dep)} v.s. {type(rhs_dep)}" + continue + + lhs_off = lhs_dep.get_offset() + rhs_off = rhs_dep.get_offset() + if lhs_off != rhs_off: + # One example is in transformer, we use a concatenated linear layer + # to project Q/K/V and then split the result. The 3 splits will + # point to the same buffer with different offsets. + reasons[buf_name] = f"different offset: {lhs_off} v.s. {rhs_off}" + continue + + if ( + lhs_dep.normalize_with_stride_order() + == rhs_dep.normalize_with_stride_order() + ): + reasons[buf_name] = f"Mismatch loop orders: {lhs_dep} v.s. {rhs_dep}" + continue + + # Add more rules here + reasons[ + buf_name + ] = f"Unknown reason: {lhs_dep} v.s. {rhs_dep}. Layout: {buf.layout}" + + return str(reasons) + + def has_shared_data_after_reordering_loop( + self, node1: BaseSchedulerNode, node2: BaseSchedulerNode + ) -> bool: + """ + Right now just greedily reorder the loop of node1 to be compatible with node2, + but ideally we should have some heuristics to reorder the loop for node2 + to be compatibile with node1 if that's more efficient. + """ + + # TODO Don't do loop reordering for CPU for now. + # Should debug more why it does not work for CPU codegen + if not config.loop_ordering_after_fusion or any( + n.get_device().type == "cpu" for n in [node1, node2] + ): + return False + + node1_buffer_names = node1.read_writes.buffer_names() + node2_buffer_names = node2.read_writes.buffer_names() + # Fast path: no common buffers. + common_buffer_names = node1_buffer_names & node2_buffer_names + if not common_buffer_names: + return False + + node1_name2dep = {dep.name: dep for dep in node1.read_writes.reads_and_writes()} + node2_name2dep = {dep.name: dep for dep in node2.read_writes.reads_and_writes()} + + # Find the commons buffers that has different loop orders + candidates = [] + for buffer_name in common_buffer_names: + lhs_dep = node1_name2dep[buffer_name] + rhs_dep = node2_name2dep[buffer_name] + if ( + lhs_dep.normalize_with_stride_order() + == rhs_dep.normalize_with_stride_order() + ): + candidates.append( + ( + V.graph.sizevars.size_hint(lhs_dep.get_numel(), fallback=0), + lhs_dep, + rhs_dep, + ) + ) + + if len(candidates) == 0: + return False + + # Pick the largest buffer to guide the loop reordering + numel, lhs_dep, rhs_dep = sorted(candidates, reverse=True, key=lambda x: x[0])[ + 0 + ] + + if lhs_dep.num_vars != rhs_dep.num_vars: + # this can happen due to we don't merge loops. + # We can not do loop reordering in this case right now + # Simply returning true if the two Deps are the same after + # normalization (merging loops) + return lhs_dep.normalize() == rhs_dep.normalize() + + # Only reorder loops for pointwise for now + if not node1.is_reduction(): + node1.reorder_loops_by_dep_pair(lhs_dep, rhs_dep) + elif not node2.is_reduction(): + node2.reorder_loops_by_dep_pair(rhs_dep, lhs_dep) + else: + loop_ordering_log.debug( + "Don't reorder loops since both nodes are reductions: %s v.s. %s", + node1.get_name(), + node2.get_name(), + ) + + return self.score_fusion_memory(node1, node2) > 0 + + def can_fuse(self, node1: BaseSchedulerNode, node2: BaseSchedulerNode) -> bool: + """ + Determine if it is possible to combine node1 and node2 into a + single fused node. + """ + + if node1 is node2: + return False + + why = WhyNoFuse(node1, node2) + + if isinstance(node1, GroupedSchedulerNode) or isinstance( + node2, GroupedSchedulerNode + ): + why("grouped node must not be fused with other nodes") + return False + if ( + isinstance(node1, (ExternKernelSchedulerNode, NopKernelSchedulerNode)) + and not node1.is_template() + ): + why("node1 is extern or nop") + return False + if ( + isinstance(node2, (ExternKernelSchedulerNode, NopKernelSchedulerNode)) + and not node2.is_template() + ): + why("node2 is extern or nop") + return False + + if node2.get_operation_names() & node1.ancestors: + why("node1 must go before node2") + return False + + if node2.is_template(): + why("templates can only fuse epilogues") + return False + if node1.is_template() and ( + node2.has_aliasing_or_mutation() + or node2.is_reduction() + or not config.epilogue_fusion + ): + why("template epilogue not satisfied") + return False + + if ( + node1.get_buffer_names() | node2.get_buffer_names() + ) & V.graph.no_fuse_buffer_names: + why("fusion for buffer explicit disabled") + return False + + device = node1.get_device() + device2 = node2.get_device() + if device != device2: + why("device mismatch (%s vs %s)", device, device2) + return False + del device2 + + no_shared_data = self.score_fusion_memory(node1, node2) == 0 + if no_shared_data: + no_shared_data = not self.has_shared_data_after_reordering_loop( + node1, node2 + ) + + loop_ordering_log.debug( + "%s and %s has%s shared data", + node1.get_name(), + node2.get_name(), + " no" if no_shared_data else "", + ) + if no_shared_data and ( + not config.aggressive_fusion or node1.is_reduction() or node2.is_reduction() + ): + if is_metric_table_enabled("fusion_failure_due_to_indexing_mismatch"): + common_buf_names = ( + node1.read_writes.buffer_names() & node2.read_writes.buffer_names() + ) + if len(common_buf_names) > 0: + get_metric_table("fusion_failure_due_to_indexing_mismatch").add_row( + lambda: { + "pre_grad_graph_id": V.graph.graph_id, + "post_grad_graph_id": V.graph.post_grad_graph_id, + "node1_name": node1.get_name(), + "node2_name": node2.get_name(), + "node1_debug_str": write_text(node1.debug_str()), + "node2_debug_str": write_text(node2.debug_str()), + "common_buffer_names": list(common_buf_names), + "failure_reason": self.decide_fusion_fail_reason( + node1, node2, common_buf_names + ), + } + ) + + why("no shared data due to indexing mismatch") + return False + why("no shared data") + return False # heuristic not needed for correctness + + if ( + not node1.is_foreach() + and not node2.is_foreach() + and len(node1.get_nodes()) + len(node2.get_nodes()) > config.max_fusion_size + ): + why("exceeds max fusion") + return False # heuristic not needed for correctness + + if node1.get_operation_names() & node2.ancestors: + # node2 depends on node1 outputs + if not self.can_fuse_vertical(node1, node2): + return False + return self.get_backend(device).can_fuse_vertical(node1, node2) + else: # nodes don't depend on each other, but may have common reads + if self.can_fusion_increase_peak_memory(node1, node2): + why("will increase peak memory") + return False + return self.get_backend(device).can_fuse_horizontal(node1, node2) + + def can_fuse_vertical( + self, node1: BaseSchedulerNode, node2: BaseSchedulerNode + ) -> bool: + """ + Check if it is legal to fuse a consumer (node2) into a producer (node1). + + We can fuse them if all the reads of node2 either match + corresponding writes in node1, or are written by nodes that can + be scheduled before the fusion of node1 and node2. + """ + node1_buf_names = node1.get_buffer_names() + node1_op_names = node1.get_operation_names() + computed_deps: OrderedSet[Dep] = OrderedSet() + why = WhyNoFuse(node1, node2) + + for cd in node1.read_writes.writes: + if not isinstance(cd, MemoryDep): + continue + for rd in node2.unmet_dependencies: + if self.fusable_read_and_write(rd, cd): + computed_deps.add(rd) + + for dep in node2.unmet_dependencies: + if isinstance(dep, WeakDep) and self.fusable_weak_dep(dep, node1, node2): + computed_deps.add(dep) + + remaining_deps = OrderedSet( + dep.name for dep in node2.unmet_dependencies - computed_deps + ) + if remaining_deps & node1_buf_names: + # MemoryDeps didn't match and read different locations of the same buffer. + # Examples here include: + # - MemoryDep("foo", x) != MemoryDep("foo", x + 1) + # - MemoryDep("foo", x) != StarDep("foo") + why("memory deps did not match") + return False + for name in remaining_deps: + op_name = self.name_to_buf[name].defining_op.get_name() + if node1_op_names & self.name_to_fused_node[op_name].ancestors: + why("intermediate nodes between node1 & node2") + return False + + return True + + def fusable_weak_dep( + self, weak_dep: WeakDep, node1: BaseSchedulerNode, node2: BaseSchedulerNode + ) -> bool: + if weak_dep.name not in node1.get_buffer_names(): + return False + + # A weak dep can be fused if and only if the fused operation acts inplace + # on the buffer being mutated. i.e. the same index is being read then mutated + mutating_writes = [ + write + for write in node2.read_writes.writes + if write.name == weak_dep.mutating_buf + ] + if len(mutating_writes) != 1: + return False + write = mutating_writes[0] + assert isinstance(write, MemoryDep) + + if free_symbol_is_type(write.index, SymT.TMP): + return False + + real_name = self.mutation_real_name[weak_dep.mutating_buf] + relevant_reads = [ + read for read in node1.read_writes.reads if read.name == real_name + ] + return all( + isinstance(read, MemoryDep) + and not free_symbol_is_type(read.index, SymT.TMP) + and read.index == write.index + and read.size == write.size + for read in relevant_reads + ) + + # StarDep doesn't match MemoryDep, different indices don't match + # However, broadcasting sometimes strips dimensions, and if that's the case + # we still can match unmet dep + # if there's indirect indexing, don't match it + def fusable_read_and_write(self, read: Dep, write: MemoryDep) -> bool: + if isinstance(read, MemoryDep): + if read.mode == write.mode and write.mode is not None: + return True + read_name = self.mutation_renames.get(read.name, read.name) + + if ( + read_name != write.name + or free_symbol_is_type(read.index, SymT.TMP) + or free_symbol_is_type(write.index, SymT.TMP) + ): + return False + + if config.loop_ordering_after_fusion and read.num_vars != write.num_vars: + # Need merge loops if we do loop ordering after fusion since + # we have not merged the loops yet when creating the scheduler + # nodes. + read = read.normalize() + write = write.normalize() + + return ( + read.index == write.index + and len(read.size) >= len(write.size) + and read.size[: len(write.size)] == write.size + ) + elif isinstance(read, StarDep): + read_name = self.mutation_renames.get(read.name, read.name) + write_name = self.mutation_renames.get(write.name, write.name) + if ( + read.mode == write.mode + and write.mode is not None + and read_name == write_name + ): + return True + return False + + def score_fusion( + self, node1: BaseSchedulerNode, node2: BaseSchedulerNode + ) -> Tuple[bool, bool, int, int]: + """ + Assign a score (higher comes first) to the fusion of node1 + and node2. When different fusions conflict with each other, + this is the way we decide what order to run them in. + + Our current score is based on: + - Estimate of the saved memory operations + - Fusions closer together in original order + """ + memory_score = self.score_fusion_memory(node1, node2) + proximity_score = -max( + abs(node1.min_order - node2.max_order), + abs(node2.min_order - node1.max_order), + ) + return ( + node1.is_template() == config.epilogue_fusion_first and memory_score > 0, + node1.is_reduction() == node2.is_reduction() and memory_score > 0, + memory_score, + proximity_score, + ) + + def dep_size_hint(self, dep: Dep) -> int: + res = 0 + if dep not in self.__dep_size_hint_cache: + try: + if not dep.has_unbacked_symbols(): + res = dep.numbytes_hint() + except KeyError: + # In at least one test (test/inductor/test_torchbind.py) we + # create a StarDep that doesn't exist in the graph and calling + # `has_unbacked_symbols()` throws an error. + pass + self.__dep_size_hint_cache[dep] = res + else: + res = self.__dep_size_hint_cache[dep] + return res + + def score_fusion_memory( + self, node1: BaseSchedulerNode, node2: BaseSchedulerNode + ) -> int: + """ + The first term in our fusion score that estimates number of saved + memory operations. + """ + node1_dep_len = len(node1.read_writes.reads) + len(node1.read_writes.writes) + node2_dep_len = len(node1.read_writes.reads) + len(node2.read_writes.writes) + + # optimization: iter over smaller set + if max(node1_dep_len, node2_dep_len) * 4 > min(node1_dep_len, node2_dep_len): + if node1_dep_len > node2_dep_len: + tmp = node1 + node1 = node2 + node2 = tmp + + deps = [] + for dep in node1.read_writes.reads | node1.read_writes.writes: + if dep in node2.read_writes.reads or dep in node2.read_writes.writes: + deps.append(dep) + + return sum(self.dep_size_hint(dep) for dep in deps) + + common_memory_deps = (node1.read_writes.reads | node1.read_writes.writes) & ( + node2.read_writes.reads | node2.read_writes.writes + ) + return sum(self.dep_size_hint(dep) for dep in common_memory_deps) + + def get_possible_fusions_with_highest_priority( + self, possible_fusions: List[Tuple[BaseSchedulerNode, BaseSchedulerNode]] + ) -> List[Tuple[BaseSchedulerNode, BaseSchedulerNode]]: + # Group the possible fusions based on their priority from the backend. + # Only return the group of possible fusions with highest priority. + if len(possible_fusions) == 0: + return possible_fusions + possible_fusions_group_by_priority: Dict[ + int, List[Tuple[BaseSchedulerNode, BaseSchedulerNode]] + ] = {} + + for node1, node2 in possible_fusions: + assert node1.get_device() == node2.get_device() + device = node1.get_device() + fusion_pair_priority = int( + self.get_backend(device).get_fusion_pair_priority(node1, node2) + ) + if fusion_pair_priority not in possible_fusions_group_by_priority: + possible_fusions_group_by_priority[fusion_pair_priority] = [ + (node1, node2), + ] + else: + possible_fusions_group_by_priority[fusion_pair_priority].append( + (node1, node2) + ) + # return the possible fusions with highest priority + possible_fusions_with_highest_priority = min( + possible_fusions_group_by_priority.items(), key=operator.itemgetter(0) + )[1] + assert len(possible_fusions_with_highest_priority) > 0 + return possible_fusions_with_highest_priority + + def score_fusion_key( + self, nodes: Tuple[BaseSchedulerNode, BaseSchedulerNode] + ) -> Tuple[bool, bool, int, int]: + """ + Shim for list.sort(key=...) + """ + node1, node2 = nodes + return self.score_fusion(node1, node2) + + def compute_last_usage(self) -> None: + """ + Populate node.last_usage recursively (also for the nodes within a FusedSchedulerNode) + """ + + future_used_buffers: OrderedSet[str] = OrderedSet(V.graph.get_output_names()) + + for node in reversed(self.nodes): + node.set_last_usage(future_used_buffers, self.mutation_real_name) + future_used_buffers.update(node.last_usage) + + def free_buffers(self) -> None: + """Free any buffers that are no longer needed""" + for name in sorted( + self.buffer_names_to_free + - V.graph.removed_buffers + - V.graph.wrapper_code.freed + ): + if name in self.name_to_buf: + buf = self.name_to_buf[name] + if buf.can_free(): + V.graph.wrapper_code.codegen_free(buf.node) + elif name in V.graph.graph_inputs: + storage = V.graph.graph_inputs[name].data + assert isinstance(storage, ir.StorageBox) and storage.is_input_buffer() + V.graph.wrapper_code.codegen_free(storage.data) + + self.buffer_names_to_free.clear() + + def remove_kernel_local_buffers(self) -> None: + """ + Any buffers that are both created and have a last use in the + same kernel can be removed. + """ + + fused_node_names = OrderedSet( + self.name_to_buf[buf].defining_op.get_name() + for buf in V.kernel.store_buffer_names + if buf in self.name_to_buf + ) + names_to_remove = [] + for out_buf in V.kernel.store_buffer_names: + if out_buf not in self.name_to_buf: + # Aux buffers created during kernel codegen + names_to_remove.append(out_buf) + continue + users = self.name_to_buf[out_buf].users + assert users is not None + users = OrderedSet(user.get_name() for user in users if not user.is_weak) + if users.issubset(fused_node_names): + names_to_remove.append(out_buf) + + def remove_filter(n: str) -> bool: + return ( + n not in V.kernel.must_keep_buffers + and n not in V.kernel.args.input_buffers + and n not in self.mutation_renames + and n not in self.mutation_real_name + ) + + names_to_remove = list(filter(remove_filter, names_to_remove)) + + for name in names_to_remove: + if name in V.kernel.args.inplace_buffers: + buf = V.kernel.args.inplace_buffers[name] + if isinstance(buf, str) and buf.startswith("REMOVED"): + continue + remove = all(n in names_to_remove for n in buf.other_names) + if remove: + self.remove_inplace_buffer(name) + V.kernel.inplaced_to_remove.add(name) + else: + self.remove_buffer(name) + + def remove_buffer(self, name: str) -> None: + # Assign a special value instead of deleting the entry + # because we still rely on output_buffers's length to + # generate unique arg name. + log.debug("remove_buffer(%r)", name) + V.kernel.args.output_buffers[name] = "REMOVED" + V.kernel.removed_buffers.add(name) + + def remove_inplace_buffer(self, name: str) -> None: + log.debug("removing_inplace_buffer(%r)", name) + inner_name = V.kernel.args.inplace_buffers[name].inner_name + V.kernel.args.inplace_buffers[name] = inner_name.replace( + "in_out_ptr", "REMOVED" + ) + V.kernel.removed_buffers.add(name) + + def flush(self) -> None: + for backend in self.backends.values(): + backend.flush() + self.free_buffers() + + def codegen_extern_call(self, scheduler_node: ExternKernelSchedulerNode) -> None: + assert isinstance(scheduler_node, ExternKernelSchedulerNode) + # 'decide_inplace_update' stores the inplace update decisions in + # the current kernel from where 'allocate' retrieve those decisions. + # We have to make sure there is a non-NULL kernel handler to store + # those inplace update decisions. + counters["inductor"]["extern_calls"] += 1 + with V.set_kernel_handler(Kernel(increase_kernel_count=False)): + scheduler_node.decide_inplace_update() + scheduler_node.mark_run() + node = scheduler_node.node + assert isinstance(node, ir.ExternKernel), f"{type(node)=}" + node.codegen(V.graph.wrapper_code) + self.free_buffers() + + def create_backend(self, device: torch.device) -> BaseScheduling: + assert ( + not is_gpu(device.type) or device.index is not None + ), f"{device} should have been normalized in lowering" + V.graph.add_device_info(device) + + device_scheduling = get_scheduling_for_device(device.type) + if device_scheduling is None: + raise RuntimeError(f"Unsupported device type: {device.type}") + + if not has_triton(): + if ( + device.type == "cuda" + and (device_props := torch.cuda.get_device_properties(device)).major < 7 + ): + raise RuntimeError( + f"Found {device_props.name} which is too old to be supported by the triton GPU compiler, which is used as the backend. Triton only supports devices of CUDA Capability >= 7.0, but your device is of CUDA capability {device_props.major}.{device_props.minor}" # noqa: B950 + ) + elif is_gpu(device.type): + raise RuntimeError( + "Cannot find a working triton installation. Either the package is not installed or it is too old. More information on installing Triton can be found at https://github.com/openai/triton" # noqa: B950 + ) + + return device_scheduling(self) + + def get_backend(self, device: torch.device) -> BaseScheduling: + if device not in self.backends: + self.backends[device] = self.create_backend(device) + return self.backends[device] + + def enter_context(self, node: BaseSchedulerNode) -> None: + def get_order(n: torch.fx.Node) -> int: + if n not in self.origin_to_index: + self.origin_to_index.update({n: i for i, n in enumerate(n.graph.nodes)}) + return self.origin_to_index[n] + + # Use a dict to have ordering + origins = { + (get_order(e), e): None + for n in node.get_nodes() + if n.node is not None + for e in n.node.get_origins() + } + origins = list(origins.keys()) + if origins: + _, last = max(origins, key=operator.itemgetter(0)) + V.graph.wrapper_code.enter_context(last) + + def codegen(self) -> None: + with dynamo_timed("Scheduler.codegen"): + return self._codegen() + + def _codegen(self) -> None: + if config.check_stack_no_cycles_TESTING_ONLY: + import torch._dynamo.convert_frame + + stack = traceback.extract_stack() + seen = set() + for frame in reversed(stack): + # This is where maybe_cprofile is + if ( + frame.name == "_compile_inner" + and frame.filename == torch._dynamo.convert_frame.__file__ + ): + break + key = (frame.filename, frame.lineno) + assert key not in seen, ( + f"Duplicate stack frame {frame.filename}:{frame.lineno}; " + "did you add a decorator to one of the functions in this stack " + "trace? If so, try using a context manager instead." + ) + seen.add(key) + + for node in self.nodes: + try: + log.debug( + "Generating code for node %s with estimated runtime %f", + node.get_name(), + node.get_estimated_runtime(), + ) + except Exception as e: + log.debug( + "Generating code for node %s with estimated runtime 0.0", + node.get_name(), + ) + + self.enter_context(node) + + if not isinstance(node, NopKernelSchedulerNode) and ( + device := node.get_device() + ): + if ( + device != self.current_device + or node.is_extern() + or node.is_template() + ): + self.flush() + if device != self.current_device: + if self.current_device and device_need_guard( + self.current_device.type + ): + V.graph.wrapper_code.codegen_device_guard_exit() + if device_need_guard(device.type): + assert device.index is not None, "device should have an index" + V.graph.wrapper_code.codegen_device_guard_enter(device.index) + + self.current_device = device + + self.buffer_names_to_free.update(node.last_usage) + + if node.is_template(): + node, *epilogue = node.get_nodes() + self.get_backend(device).codegen_template(node, epilogue) + elif node.is_extern(): + node = typing.cast(ExternKernelSchedulerNode, node) + self.codegen_extern_call(node) + elif node.is_foreach(): + node = typing.cast(ForeachKernelSchedulerNode, node) + backend_ = self.get_backend(device) + from .codegen.cuda_combined_scheduling import CUDACombinedScheduling + from .codegen.simd import SIMDScheduling + + if isinstance(backend_, (SIMDScheduling, CUDACombinedScheduling)): + backend = backend_ + else: + raise AssertionError(f"{type(self)=}") + backend.codegen_combo_kernel(node) + elif isinstance(node, (FusedSchedulerNode, SchedulerNode)): + self.get_backend(device).codegen_node(node) + else: + assert isinstance(node, NopKernelSchedulerNode) + node.mark_run() + + if config.triton.debug_sync_kernel: + self.get_backend(device).codegen_sync() + + self.available_buffer_names.update(node.get_buffer_names()) + self.completed_operations.update(node.get_operation_names()) + + if not isinstance(node, NopKernelSchedulerNode): + device = node.get_device() + if device is not None and self.get_backend(device).ready_to_flush(): + self.flush() + + if self.current_device and device_need_guard(self.current_device.type): + # exit the outermost CUDA device guard. this is + # important for nested indentation codegen-ing. + V.graph.wrapper_code.codegen_device_guard_exit() + + self.flush() + + def benchmark_combo_kernel( + self, node_list: Sequence[BaseSchedulerNode] + ) -> Tuple[float, float, str]: + """ + Benchmark fused list of nodes and return the execution time + in milliseconds on randomly generated inputs. + """ + device = node_list[0].get_device() + V.graph.scheduler = self + self.current_device = device + backend = self.get_backend(device) + return backend.benchmark_combo_kernel(node_list) + + def speedup_by_combo_kernel(self, nodes: List[BaseSchedulerNode]) -> bool: + """ + If config.benchmark_fusion is False, always return True. + Otherwise, return True if fusion can brings speedup. + """ + if not config.benchmark_combo_kernel: + return True + + subkernel_nodes = nodes + device = subkernel_nodes[0].get_device() + + # don't support benchmark fusion for CPU right now. + if device.type == "cpu": + return True + + from triton.compiler.errors import CompilationError + + ms1, path1_list = 0.0, [] + for i, snode in enumerate(subkernel_nodes): + node_list = snode.get_nodes() + # We can not accurately benchmark kernel using atomic_add + # due to how we generate random integer inputs. + if self._any_atomic_add(node_list): + fusion_log.debug( + "ComboKernel: benchmarking may not accurate due to atomic_add" + ) + + try: + ms, path = self.benchmark_fused_nodes(node_list) + if math.isinf(ms): + fusion_log.debug( + "ComboKernel benchmark: register spilling of %d-th subkernel", + i, + ) + return False + except CompilationError as e: + # workaround triton issue: https://github.com/openai/triton/issues/2151 + if "Loop-carried variable" in str(e): + fusion_log.debug( + "ComboKernel benchmark: return True because of loop-carried variable" + ) + return True # allow fusion + else: + raise + ms1 += ms + path1_list.append(path) + + try: + ms2, ms2_clone, path2_list = self.benchmark_combo_kernel(subkernel_nodes) + except CompilationError as e: + # workaround triton issue: https://github.com/openai/triton/issues/2151 + if "Loop-carried variable" in str(e): + fusion_log.debug( + "ComboKernel benchmark: return True because of loop-carried variable" + ) + return True # allow fusion + else: + raise + + # small kernels are very likely to have speedup but hard to benchmark. So we skip benchmarking. + small_kernel = ms2 - ms2_clone < 0.3 or ms1 < 0.3 + if fusion_log.isEnabledFor(logging.DEBUG): + if ms1 > ms2 or small_kernel: + fusion_log.debug( + "can fuse (benchmark): fusing causes %sx speedup", + green_text(f"{ms1 / ms2:.3f}"), + ) + else: + fusion_log.debug( + "cannot fuse (benchmark): fusing causes %sx slowdown", + red_text(f"{ms1 / ms2:.3f}"), + ) + # ms1 returned by benchmark_fused_nodes discounted clone time + return ms2 - ms2_clone < ms1 or small_kernel + + def get_buffer_layout(self, buf_name: str) -> ir.Layout: + buf = self.name_to_buf[buf_name] + assert buf.node is not None + return buf.node.get_layout() + + def update_zero_dim_cpu_tensor(self) -> None: + for node in self.nodes: + if node.get_device() and is_gpu(node.get_device().type): + for read in node.read_writes.reads: + buffer = V.graph.name_to_buffer.get(read.name) + if ( + buffer + and buffer.get_device() + and buffer.get_device().type == "cpu" + and not isinstance(buffer.layout, MultiOutputLayout) + and buffer.get_size() == [] + ): + V.graph.zero_dim_cpu_tensor_list.add(read.name) + + +class BaseScheduling: + @classmethod + def get_backend_features(cls, device: torch.device) -> Sequence[BackendFeature]: + """Return a set of .codegen.common.BackendFeature()""" + return () + + def can_fuse_vertical( + self, node1: BaseSchedulerNode, node2: BaseSchedulerNode + ) -> bool: + """ + Check whether node1 and node2 can be vertically fused or not. + """ + raise NotImplementedError + + def can_fuse_horizontal( + self, node1: BaseSchedulerNode, node2: BaseSchedulerNode + ) -> bool: + """ + Check whether node1 and node2 can be horizontally fused or not. + """ + raise NotImplementedError + + def fuse( + self, node1: BaseSchedulerNode, node2: BaseSchedulerNode + ) -> FusedSchedulerNode: + """ + Fuse two nodes + """ + if node1.is_foreach() or node2.is_foreach(): + return ForeachKernelSchedulerNode.fuse(node1, node2) + else: + return FusedSchedulerNode.fuse(node1, node2) + + def group_fn( + self, sizes: Sequence[Sequence[sympy.Expr]] + ) -> Tuple[Tuple[sympy.Expr, ...], ...]: + """ + Process the iteration sizes in case a transformation needs to be applied. + """ + raise NotImplementedError + + def codegen_template( + self, + template_node: BaseSchedulerNode, + epilogue_nodes: Sequence[BaseSchedulerNode], + ) -> Optional[str]: + """ + Given a template node, generate a kernel. + + This function is only available for triton now. If the third-party backend behaves as a sub-class + of TritonScheduling, it can override it or reuse it. + """ + raise NotImplementedError + + def codegen_node(self, node: Union[FusedSchedulerNode, SchedulerNode]) -> None: + """ + Generate a kernel given a list of pre-fused nodes. + """ + raise NotImplementedError + + def codegen_sync(self) -> None: + """ + Generate synchronization code for the kernel. This method depends on the hardware characteristics. + """ + raise NotImplementedError + + def ready_to_flush(self) -> bool: + """ + Check whether the backend is requesting the scheduler to flush the generated kernel. + If not supported, please return False. + """ + return False + + def flush(self) -> None: + """ + Flush the generated kernel and python wrapper code to the source code file. + """ + raise NotImplementedError + + def benchmark_fused_nodes( + self, nodes: Sequence[BaseSchedulerNode] + ) -> Tuple[float, str]: + """ + Benchmark fused list of nodes and return the execution time + in milliseconds on randomly generated inputs. + """ + raise NotImplementedError + + def get_fusion_pair_priority( + self, node1: BaseSchedulerNode, node2: BaseSchedulerNode + ) -> int: + """ + Return an unsigned integer which represents the priority of this fusion pair. + The smaller is with higher priority. + """ + return 0 + + def benchmark_combo_kernel( + self, node_list: Sequence[BaseSchedulerNode] + ) -> Tuple[float, float, str]: + """ + Benchmark the list of nodes to combine and return the execution time + and memory copy time in milliseconds on randomly generated inputs. + """ + raise NotImplementedError + + +def debug_triton_code(node: Union[SchedulerNode, FusedSchedulerNode]) -> List[str]: + lines = [] + multi_template = node.get_template_node() + assert multi_template is None or isinstance(multi_template, ir.MultiTemplateBuffer) + if multi_template and multi_template.make_kernel_render is None: + lines.append(f"{node.get_name()} Unfinalized multi template buffer") + else: + from torch._inductor.codegen.cuda_combined_scheduling import ( + CUDACombinedScheduling, + ) + + from .codegen.simd import SIMDScheduling + + snodes = (node,) if isinstance(node, SchedulerNode) else node.snodes + device = snodes[0].get_device() + backend = node.scheduler.get_backend(device) + assert isinstance(backend, (SIMDScheduling, CUDACombinedScheduling)) + V.graph.scheduler.current_device = device + + # Don't increment kernel count when generating debug string. + # This will confuse some unit tests that check the number of + # generated kernels. + old_generated_kernel_count = metrics.generated_kernel_count + triton_code = backend.generate_kernel_code_from_nodes(snodes).strip() + metrics.generated_kernel_count = old_generated_kernel_count + + lines.append(f"{node.get_name()} Triton code:") + lines.append(textwrap.indent(triton_code, " ")) + return lines diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/select_algorithm.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/select_algorithm.py new file mode 100644 index 0000000000000000000000000000000000000000..67d9651a1e39fae326a190102fe8a23aee29fcd0 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/select_algorithm.py @@ -0,0 +1,1743 @@ +# mypy: allow-untyped-defs +import builtins +import contextlib +import functools +import inspect +import itertools +import json +import logging +import math +import operator +import os +import sys +import textwrap +import time +from collections import namedtuple +from concurrent.futures import as_completed, ThreadPoolExecutor +from io import StringIO +from typing import Any, Callable, Dict, List, Optional, Tuple, Union +from unittest.mock import patch + +import sympy +from filelock import FileLock + +import torch +import torch._inductor.async_compile # noqa: F401 required to warm up AsyncCompile pools +from torch._dynamo.testing import rand_strided +from torch._dynamo.utils import counters, identity, preserve_rng_state + +from . import config, ir +from .autotune_process import TensorMeta, TritonBenchmarkRequest +from .codecache import code_hash, PersistentCache, PyCodeCache +from .codegen.common import IndentedBuffer, KernelTemplate +from .codegen.triton import ( + gen_common_triton_imports, + texpr, + TritonKernel, + TritonPrinter, + TritonScheduling, +) +from .codegen.triton_utils import config_of, signature_to_meta +from .exc import CUDACompileError +from .ir import ChoiceCaller, PrimitiveInfoType +from .runtime.benchmarking import benchmarker +from .runtime.hints import DeviceProperties +from .utils import ( + FakeIndentedBuffer, + get_dtype_size, + Placeholder, + restore_stdout_stderr, + sympy_dot, + sympy_index_symbol, + sympy_product, + unique, +) +from .virtualized import V + + +log = logging.getLogger(__name__) + +# correctness checks struggle with fp16/tf32 +VERIFY: Dict[str, Any] = {} +PRINT_AUTOTUNE = True +DEBUG = False + + +class KernelNamespace: + pass + + +# these objects are imported from the generated wrapper code +extern_kernels = KernelNamespace() + + +class PartialRender: + """ + Some parts of a template need to be generated at the end, but + inserted into the template at the start. This allows doing a bunch + of replacements after the initial render. + """ + + def __init__(self, code, replacement_hooks) -> None: + super().__init__() + self.code = code + self.replacement_hooks = replacement_hooks + + def finalize_hook(self, hook_key: str, strict=True) -> None: + if hook_key not in self.replacement_hooks: + if strict: + raise RuntimeError( + f"{hook_key} not registered in self.replacement_hooks" + ) + else: + return + assert ( + self.replacement_hooks[hook_key] is not None + ), "hook_key can only be called once" + self.code = self.code.replace(hook_key, self.replacement_hooks[hook_key]()) + self.replacement_hooks[hook_key] = None + + def finalize_all(self) -> str: + for key, fn in self.replacement_hooks.items(): + self.code = self.code.replace(key, fn()) + return self.code + + +# This is used to store info needed for lowering each subgraph in triton +# templates +SubgraphInfo = namedtuple( + "SubgraphInfo", + [ + "body", + "template_mask", + "template_out", + ], +) + + +class TritonTemplateKernel(TritonKernel): + def __init__( + self, + kernel_name, + input_nodes, + output_node, + defines, + num_stages, + num_warps, + grid_fn, + meta, + call_sizes, + use_jit=False, + prefix_args=0, + suffix_args=0, + epilogue_fn=identity, + subgraphs: Optional[List[ir.ComputedBuffer]] = None, + *, + index_dtype, + ) -> None: + super().__init__( + sympy_product(output_node.get_size()), + sympy.Integer(1), + index_dtype=index_dtype, + ) + self.input_nodes = input_nodes + self.output_node = output_node + self.named_input_nodes = {} # type: ignore[var-annotated] + self.defines = defines + self.kernel_name = kernel_name + self.use_jit = use_jit + self.num_stages = num_stages + self.num_warps = num_warps + self.grid_fn = grid_fn + self.meta = meta + self.call_sizes = call_sizes + # for templates with fixed epilogues + self.prefix_args = prefix_args + self.suffix_args = suffix_args + self.epilogue_fn = epilogue_fn + self.render_hooks = {} # type: ignore[var-annotated] + self.triton_meta: Optional[Dict[str, object]] = None + # For Templated Attention this can be a list of ir.Subgraph + self.subgraphs: Optional[List[ir.ComputedBuffer]] = subgraphs + + # The following attributes (body, template_mask, output_val) are all + # used for triton kernel codegen. + # They are swapped onto the TritonTemplateKernel object by + # `set_subgraph_body` + self.subgraph_bodies: Dict[str, SubgraphInfo] = {} + + self.body: IndentedBuffer = FakeIndentedBuffer() + self.template_mask: Optional[str] = None + self.template_out: Optional[str] = None + + @contextlib.contextmanager + def set_subgraph_body(self, body_name: str): + old_body, old_mask, old_out = self.body, self.template_mask, self.template_out + assert body_name in self.subgraph_bodies, body_name + self.body, self.template_mask, self.template_out = self.subgraph_bodies[ + body_name + ] + yield + self.subgraph_bodies[body_name] = SubgraphInfo( + self.body, self.template_mask, self.template_out + ) + self.body, self.template_mask, self.template_out = old_body, old_mask, old_out + + @contextlib.contextmanager + def create_subgraph_body(self, body_name: str): + assert body_name not in self.subgraph_bodies + self.subgraph_bodies[body_name] = SubgraphInfo(IndentedBuffer(), None, None) + with self.set_subgraph_body(body_name): + yield + + def need_numel_args(self): + return False + + def estimate_kernel_num_bytes(self): + """ + Estimate the total number of bytes this kernel takes. + For in/out nodes, sizes are counted twice: once for reading and + once for writing. + """ + ninplace_args = len(unique(self.args.inplace_buffers.values())) + num_bytes = [] + for i, inp in enumerate(itertools.chain(self.input_nodes, (self.output_node,))): + size = V.graph.sizevars.size_hints(inp.get_size()) + numel = functools.reduce(operator.mul, size, 1) + dtype_size = get_dtype_size(inp.get_dtype()) + num_bytes.append(numel * dtype_size * (1 + int(i < ninplace_args))) + return sum(num_bytes) + + def jit_lines(self): + if self.use_jit: + return "@triton.jit" + + argdefs, _, signature, _ = self.args.python_argdefs() + triton_meta = { + "signature": signature_to_meta(signature, size_dtype=self.index_dtype), + "device": DeviceProperties.create(self.output_node.get_device()), + "constants": {}, + } + triton_meta["configs"] = [config_of(signature)] + for arg_num in triton_meta["configs"][0].equal_to_1: # type: ignore[index] + triton_meta["constants"][arg_num] = 1 # type: ignore[index] + matrix_instr_nonkdim = self.meta.get("matrix_instr_nonkdim", 0) + if matrix_instr_nonkdim != 0: + triton_meta["matrix_instr_nonkdim"] = matrix_instr_nonkdim + + self.triton_meta = triton_meta + + inductor_meta = { + "kernel_name": str(Placeholder.DESCRIPTIVE_NAME), + **TritonKernel.inductor_meta_common(), + } + if config.profile_bandwidth or config.benchmark_kernel: + num_gb = self.estimate_kernel_num_bytes() / 1e9 + inductor_meta["kernel_num_gb"] = num_gb + return f""" + @triton_heuristics.template( + num_stages={self.num_stages}, + num_warps={self.num_warps}, + triton_meta={triton_meta!r}, + inductor_meta={inductor_meta!r}, + ) + @triton.jit + """ + + def gen_argdefs(self): + def hook(): + # python_argdefs() cannot be run until after the rest of the template lazily adds more args + arg_defs, *_ = self.args.python_argdefs() + return f"{', '.join(arg_defs)}" + + self.render_hooks[""] = hook + return "" + + def gen_defines(self): + return self.defines + + def def_kernel(self, *argnames): + """ + Hook called from template code to generate function def and + needed args. + """ + assert all(isinstance(x, str) for x in argnames) + renames = IndentedBuffer(initial_indent=1) + + named_args = self.input_nodes[ + self.prefix_args : len(self.input_nodes) - self.suffix_args + ] + + assert len(argnames) == len(named_args), ( + len(argnames), + len(named_args), + self.prefix_args, + len(self.input_nodes), + ) + + for input_node in self.input_nodes[: self.prefix_args]: + # get args in correct order + self.args.input(input_node.get_name()) + + for name, input_node in zip(argnames, named_args): + arg_name = f"arg_{name}" + self.named_input_nodes[name] = input_node + self.args.input_buffers[input_node.get_name()] = arg_name + + # The args may be duplicated, so renaming must be after args are de-duplicated. + for name in argnames: + input_node = self.named_input_nodes[name] + arg_name = self.args.input_buffers[input_node.get_name()] + if input_node.get_layout().offset == 0: + renames.writeline(f"{name} = {arg_name}") + else: + offset = texpr(self.rename_indexing(input_node.get_layout().offset)) + renames.writeline(f"{name} = {arg_name} + {offset}") + + for input_node in self.input_nodes[len(self.input_nodes) - self.suffix_args :]: + # get args in correct order + self.args.input(input_node.get_name()) + + def hook(): + # python_argdefs() cannot be run until after the rest of the template lazily adds more args + arg_defs, *_ = self.args.python_argdefs() + code = IndentedBuffer() + code.splice(gen_common_triton_imports()) + code.splice(self.jit_lines()) + code.writeline(f"def {self.kernel_name}({', '.join(arg_defs)}):") + with code.indent(): + code.splice(self.defines) + code.splice(renames.getvalue()) + return code.getvalue() + + assert "" not in self.render_hooks + self.render_hooks[""] = hook + return "" + + def size(self, name: str, index: int): + """ + Hook called from template code to get the size of an arg. + Will add needed args to pass it in if it is dynamic. + """ + assert isinstance(index, int) + if name is None: + val = self.output_node.get_size()[index] + else: + assert isinstance(name, str) + val = self.named_input_nodes[name].get_size()[index] + return texpr(self.rename_indexing(val)) + + def stride(self, name, index=None): + """ + Hook called from template code to get the stride of an arg. + Will add needed args to pass it in if it is dynamic. + """ + if name is None: + val = self.output_node.get_stride() + else: + assert isinstance(name, str) + val = self.named_input_nodes[name].get_stride() + + if isinstance(index, int): + return texpr(self.rename_indexing(val[index])) + else: + return ", ".join([texpr(self.rename_indexing(i)) for i in val]) + + def modification( + self, subgraph_number: int, output_name: str, **fixed_inputs + ) -> str: + """This creates a modification function for a subgraph. + To use this inside a template, the first argument should specify which subgraph to codegen for + + Args: + subgraph_number (int): The index of the subgraph in self.subgraphs + """ + num = 0 + while f"mod_{subgraph_number}_{num}" in self.subgraph_bodies: + num += 1 + with self.create_subgraph_body(f"mod_{subgraph_number}_{num}"): + assert isinstance(subgraph_number, int) + assert isinstance(self.subgraphs, list) + assert ( + self.body.getvalue() == "" + ), "Body should be clear before adding a modification" + assert subgraph_number < len( + self.subgraphs + ), f"Invalid subgraph number provided to create_modification, {subgraph_number} must be < {len(self.subgraphs)}" + + subgraph = self.subgraphs[subgraph_number] + + def add_input(name): + return self.args.input(name) + + name = f"PlaceholderSubstitution_{subgraph_number}" + + class PlaceholderSubstitution(V.WrapperHandler): # type: ignore[name-defined] + self.name = name + + def load(self, name: str, index: sympy.Expr): + if name not in fixed_inputs: + # If it's not a fixed input, it's a load from a captured + # tensor + var = add_input(name) + return f"tl.load({var} + {index})" + + return f"({fixed_inputs[name]})" + + def indirect_indexing(self, index_var, size, check, wrap_neg=True): + return sympy_index_symbol(str(index_var)) + + with V.set_ops_handler(PlaceholderSubstitution(V.ops)): + assert isinstance( + subgraph, ir.ComputedBuffer + ), f"Expected the subgraph to be a ComputedBuffer, got {type(subgraph)}" + if isinstance(subgraph.data, ir.InputBuffer): + out = subgraph.data.make_loader()(()) + else: + out = subgraph.data.inner_fn(()) + + self.codegen_body() + self.body.writeline(f"{output_name} = {out.value}") + + body_val = self.body.getvalue() + self.cse.invalidate(set()) # type: ignore[arg-type] + return body_val + + def store_output( + self, + indices: Union[List[Any], Tuple[Any]], + val: str, + mask: Optional[str] = None, + indent_width: int = 4, + ): + """Stores the final output and appends any epilogue fusions if the buffer hasn't been optimized away. + + Args: + indices (Union[List, Tuple]): The index for each dimension of the output. The dot product of + these indices and output strides must match `val`. + val (str): The value to store. + mask (Optional[str]): An optional mask to use for the store operation. If provided, this mask + will be applied to the store. + indent_width (int): The number of spaces to use for indentation. This is used when the call to + store_output is indented in the kernel definition. + """ + with self.create_subgraph_body(""): + assert isinstance(indices, (list, tuple)) + assert isinstance(val, str) + assert isinstance(mask, (str, type(None))) + assert self.template_mask is None + indices = list(map(TritonPrinter.paren, indices)) + index_symbols = [sympy.Symbol(x, integer=True) for x in indices] + lengths = [ + V.graph.sizevars.simplify(s) for s in self.output_node.get_size() + ] + assert len(indices) == len(lengths) + + # glue to make generated code use same indexing from template + for name, range_tree_entry in zip( + indices, self.range_trees[0].construct_entries(lengths) + ): + range_tree_entry.set_name(name) + contiguous_index = sympy_dot( + ir.FlexibleLayout.contiguous_strides(lengths), index_symbols + ) + contiguous_index = self.rename_indexing(contiguous_index) + self.body.writeline("xindex = " + texpr(contiguous_index)) + self.range_trees[0].lookup( + sympy.Integer(1), sympy_product(lengths) + ).set_name("xindex") + self.template_mask = mask + self.template_out = val + self.template_indices = indices + output_index = self.output_node.get_layout().make_indexer()(index_symbols) + output_index = self.rename_indexing(output_index) + if output_index == contiguous_index: + output_index = sympy.Symbol("xindex", integer=True) + + epilogue_args = [val] + for input_node in itertools.chain( + self.input_nodes[: self.prefix_args], + self.input_nodes[len(self.input_nodes) - self.suffix_args :], + ): + input_node.freeze_layout() + epilogue_args.append(input_node.make_loader()(index_symbols)) + + V.ops.store( + self.output_node.get_name(), + output_index, + self.epilogue_fn(*epilogue_args), + ) + self.codegen_body() + + def hook(): + # more stuff might have been added since the codegen_body above + self.codegen_body() + + return textwrap.indent(self.body.getvalue(), " " * indent_width).strip() + + assert "" not in self.render_hooks + self.render_hooks[""] = hook + return "" + + def render(self, template, kwargs): + return PartialRender( + template.render(**self.template_env(), **kwargs), + self.render_hooks, + ) + + def make_load(self, name, indices, mask): + """ + Optional helper called from template code to generate the code + needed to load from an tensor. + """ + assert isinstance(indices, (list, tuple)) + assert isinstance(name, str) + assert isinstance(mask, str) + stride = self.named_input_nodes[name].get_stride() + indices = list(map(TritonPrinter.paren, indices)) + assert len(indices) == len(stride) + index = " + ".join( + f"{texpr(self.rename_indexing(s))} * {i}" for s, i in zip(stride, indices) + ) + return f"tl.load({name} + ({index}), {mask}, other=0.0)" + + def template_env(self): + """ + Generate the namespace visible in the template. + """ + return { + fn.__name__: fn + for fn in [ + self.def_kernel, + self.size, + self.stride, + self.store_output, + self.make_load, + self.modification, + self.gen_argdefs, + self.gen_defines, + ] + } + + def indexing( + self, + index: sympy.Expr, + *, + dense_indexing=False, + copy_shape=None, + override_mask=None, + block_ptr=False, + ): + """ + Override the default indexing to use our custom mask and force + dense indexing. + """ + return super().indexing( + index, + dense_indexing=False, + # We pass template_out as the shape to broadcast the indexing to as + # the mask might be broadcast to the output shape + copy_shape=self.template_out, + override_mask=self.template_mask, + block_ptr=block_ptr, + ) + + def codegen_range_tree(self): + pass # ignore default codegen + + def call_kernel(self, name: str, node: Optional[ir.IRNode] = None): + wrapper = V.graph.wrapper_code + _, call_args, _, arg_types = self.args.python_argdefs() + if V.graph.cpp_wrapper: + # In the cpp_wrapper case, we have to compute CUDA launch grid at runtime + # if any dynamic dimension is involved. We rely on the Python version + # of the grid function to generate those grid configs, which may contain + # symbolic values. The wrapper will use cexpr to print out C++ code + # appropriately for the grid configs. + grid = self.call_sizes + [self.meta] + wrapper.generate_kernel_call( + name, + call_args, + grid=self.grid_fn(*grid), + arg_types=arg_types, + triton_meta=self.triton_meta, + ) + else: + wrapper.add_import_once(f"import {self.grid_fn.__module__}") + meta = wrapper.add_meta_once(self.meta) + grid = self.call_sizes + [meta] + wrapper.generate_kernel_call( + name, + call_args, + grid=grid, + grid_fn=f"{self.grid_fn.__module__}.{self.grid_fn.__name__}", + arg_types=arg_types, + triton_meta=self.triton_meta, + ) + + +@functools.lru_cache(None) +def _jinja2_env(): + try: + import jinja2 + + return jinja2.Environment( + undefined=jinja2.StrictUndefined, + ) + except ImportError: + return None + + +class TritonTemplate(KernelTemplate): + index_counter = itertools.count() + all_templates: Dict[str, "TritonTemplate"] = {} + + def __init__(self, name: str, grid: Any, source: str, debug=False) -> None: + super().__init__(name) + self.grid = grid + self.template = self._template_from_string(source) + assert name not in self.all_templates, "duplicate template name" + self.all_templates[name] = self + self.debug = debug + + def generate( # type: ignore[override] + self, + input_nodes, + layout, + num_stages, + num_warps, + prefix_args=0, + suffix_args=0, + epilogue_fn=identity, + subgraphs=None, + mutated_inputs=None, + call_sizes=None, + **kwargs, + ): + """This function generates a TritonTemplateCaller + + Args: + input_nodes: List of input nodes + layout: Output layout + num_stages: Number of stages for triton launch + num_warps: Number of warps for triton launch + prefix_args: Number of input nodes to be passed as arguments + suffix_args: Number of input nodes to be passed as arguments + epilogue_fn: Optional epilogue function to be called on the output + subgraphs: Optional subgraphs to be passed as arguments, these will be inlined + into the triton template string + mutated_inputs: Optional list of input nodes that are mutated by the kernel, this is helpful + if you need to return multiple outputs. You can pass them as inputs and mark them as + being mutated by the kernel. + """ + assert self.template, "requires jinja2" + defines = StringIO() + for name, val in kwargs.items(): + defines.write(f"{name} : tl.constexpr = {val}\n") + defines = defines.getvalue() + + fake_out = ir.Buffer("buf_out", layout) + kernel_name = f"triton_{self.name}" + + numel = sympy_product(layout.size) + buffers = itertools.chain(input_nodes, (fake_out,)) + if not TritonScheduling.can_use_32bit_indexing(numel, buffers): + raise NotImplementedError( + "64-bit indexing is not yet implemented for triton templates" + ) + + if call_sizes is None: + call_sizes = layout.size + + kernel_options = dict( + input_nodes=input_nodes, + defines=defines, + num_stages=num_stages, + num_warps=num_warps, + grid_fn=self.grid, + meta=kwargs, + call_sizes=call_sizes, + prefix_args=prefix_args, + suffix_args=suffix_args, + epilogue_fn=epilogue_fn, + index_dtype="tl.int32", + subgraphs=subgraphs, + ) + + with patch.object( + V.graph, "get_dtype", self._fake_get_dtype(fake_out) + ), TritonTemplateKernel( + kernel_name=kernel_name, + output_node=fake_out, + use_jit=False, + **kernel_options, + ) as kernel: + try: + template = kernel.render(self.template, kwargs) + with kernel.set_subgraph_body(""): + code = template.finalize_all() + except ZeroDivisionError: + # TODO(nmacchioni): fix sympy division by zero + return None + if self.debug: + print("Generated Code:\n", code) + extra = ( + "-".join( + [ + *[ + f"{kwarg}={repr(kwargs[kwarg])}" + for kwarg in sorted(kwargs.keys()) + ], + f"num_stages={num_stages}", + f"num_warps={num_warps}", + ] + ) + + "-" + ) + mod = PyCodeCache.load(code, extra) + + input_call_args = tuple(kernel.args.input_buffers.keys()) + output_call_args = tuple(kernel.args.output_buffers.keys()) + + # We expect the input_buffer order to be [*input_nodes, *captured_buffers] + expected_input_args = tuple(unique(x.get_name() for x in input_nodes)) + expected_output_args = (fake_out.get_name(),) + assert input_call_args[: len(expected_input_args)] == expected_input_args, ( + input_call_args, + expected_input_args, + ) + assert output_call_args == expected_output_args, ( + output_call_args, + expected_output_args, + ) + + full_input_nodes = tuple([V.graph.get_buffer(k) for k in input_call_args]) + extra_args = V.graph.sizevars.size_hints( + map(sympy.expand, tuple(kernel.args.sizevars.keys())), + fallback=config.unbacked_symint_fallback, + ) + + kernel_hash_name = f"triton_{self.name}_{next(self.index_counter)}" + + def make_kernel_render(out_node): + kernel = TritonTemplateKernel( + kernel_name=str(Placeholder.KERNEL_NAME), + output_node=out_node, + use_jit=False, + **kernel_options, + ) + render = functools.partial( + kernel.render, + self.template, + kwargs, + ) + return kernel, render + + # create the BenchmarkRequest + assert mod.__file__ is not None + grid = self.grid( + *V.graph.sizevars.size_hints( + call_sizes, + fallback=config.unbacked_symint_fallback, + ), + kwargs, + ) + bmreq = TritonBenchmarkRequest( + module_path=mod.__file__, + module_cache_key=mod.key, + kernel_name=kernel_name, + grid=grid, + extra_args=extra_args, + num_stages=num_stages, + num_warps=num_warps, + matrix_instr_nonkdim=kwargs.get("matrix_instr_nonkdim", 0), + input_tensor_meta=TensorMeta.from_irnodes(full_input_nodes), # type: ignore[arg-type] + output_tensor_meta=TensorMeta.from_irnodes(layout), + ) + + return TritonTemplateCaller( + kernel_hash_name, + full_input_nodes, + layout, + make_kernel_render, + extra.strip("-").replace("-", ", "), + bmreq, + log_info={ + "tile_shape": str( + ( + kwargs.get("BLOCK_M", -1), + kwargs.get("BLOCK_K", -1), + kwargs.get("BLOCK_N", -1), + ) + ), + "num_stages": num_stages, + "num_warps": num_warps, + "allow_tf32": str(kwargs.get("ALLOW_TF32", None)), + "acc_type": str(kwargs.get("ACC_TYPE", None)), + }, + mutated_inputs=mutated_inputs, + ) + + +class ExternKernelChoice: + def __init__( + self, + kernel, + cpp_kernel=None, + *, + name=None, + has_out_variant=True, + op_overload=None, + use_fallback_kernel=False, + kernel_creator=None, + ) -> None: + super().__init__() + name = name or kernel.__name__ + assert callable(kernel) + assert not hasattr(extern_kernels, name), f"duplicate extern kernel: {name}" + self.name = name + self.cpp_kernel_name = cpp_kernel + self.has_out_variant = has_out_variant + setattr(extern_kernels, name, kernel) + self.op_overload = op_overload + self.use_fallback_kernel = use_fallback_kernel + self.kernel_creator = kernel_creator + + def to_callable(self): + return getattr(extern_kernels, self.name) + + def call_name(self): + return f"extern_kernels.{self.name}" + + @functools.lru_cache(None) # noqa: B019 + def hash_key(self): + fn = self.to_callable() + parts = [ + self.name, + getattr(fn, "__name__", ""), + getattr(fn, "__module__", ""), + ] + try: + parts.append(inspect.getsource(fn)) + except Exception: + pass + return code_hash("-".join(parts)) + + def bind( + self, + input_nodes, + layout, + ordered_kwargs_for_cpp_kernel=(), + **kwargs, + ): + self.ordered_kwargs_for_cpp_kernel = ordered_kwargs_for_cpp_kernel + return ExternKernelCaller( + self, input_nodes, layout, kwargs, has_out_variant=self.has_out_variant + ) + + +class TritonTemplateCaller(ir.TritonTemplateCallerBase): + def __init__( + self, + name, + input_nodes, + layout, + make_kernel_render, + debug_extra, + bmreq, + log_info: Optional[ + Dict[str, Union[PrimitiveInfoType, List[PrimitiveInfoType]]] + ] = None, + mutated_inputs=None, + ) -> None: + super().__init__(name, input_nodes, layout) + self.make_kernel_render = make_kernel_render + self.debug_extra = debug_extra + self.bmreq: TritonBenchmarkRequest = bmreq + if log_info is None: + log_info = {} + self.log_info: Dict[str, Any] = log_info + self.log_info.update( + { + "backend": "Triton", + "grid": str(self.bmreq.grid), + "num_stages": self.bmreq.num_stages, + "num_warps": self.bmreq.num_warps, + } + ) + self.mutated_inputs = mutated_inputs + + def benchmark(self, *args, out): + assert self.bmreq is not None + return self.bmreq.benchmark(*args, output_tensor=out) + + def precompile(self): + assert self.bmreq is not None + self.bmreq.precompile() + + def __str__(self) -> str: + return f"TritonTemplateCaller({self.bmreq.module_path}, {self.debug_extra})" + + def call_name(self): + return f"template_kernels.{self.name}" + + def hash_key(self): + return "-".join( + [ + self.name.rsplit("_", 1)[0], + self.bmreq.module_cache_key, + ] + ) + + def output_node(self): + return ir.TensorBox.create( + ir.TritonTemplateBuffer( + layout=self.layout, + inputs=self.input_nodes, + make_kernel_render=self.make_kernel_render, + debug_extra=self.debug_extra, + mutated_inputs=self.mutated_inputs, + ) + ) + + def info_dict(self) -> Dict[str, Union[PrimitiveInfoType, List[PrimitiveInfoType]]]: + """Information returned here is logged to the autotune log file when that is enabled.""" + return self.log_info + + def get_make_kernel_render(self): + return self.make_kernel_render + + def autoheuristic_id(self): + type_name = "triton" + info = self.info_dict() + # TODO(AlnisM): Does tile_shape always exist? + tile = info["tile_shape"] + tile_vals = eval(tile) # type: ignore[arg-type] + BLOCK_M = tile_vals[0] + BLOCK_K = tile_vals[1] + BLOCK_N = tile_vals[2] + num_stages = info["num_stages"] + num_warps = info["num_warps"] + return f"type={type_name}_BLOCK-M={BLOCK_M}_BLOCK-K={BLOCK_K}_BLOCK-N={BLOCK_N}_numstages={num_stages}_numwarps={num_warps}" + + +class ExternKernelCaller(ChoiceCaller): + def __init__( + self, + choice: ExternKernelChoice, + input_nodes, + layout, + kwargs=None, + *, + has_out_variant=True, + ) -> None: + super().__init__(choice.name, input_nodes, layout) + self.choice = choice + self.kwargs = kwargs or {} + self.has_out_variant = has_out_variant + + def __str__(self) -> str: + return f"ExternKernelCaller({self.choice.call_name()})" + + def benchmark(self, *args, out): + if out.numel() == 0: + # no need to run the kerrnel of do benchmarking + return 0.0 + if self.has_out_variant: + return super().benchmark(*args, out=out) + else: + algo = self.to_callable() + out_new = algo(*args) + torch._C._dynamo.guards.assert_size_stride( + out_new, tuple(out.size()), tuple(out.stride()) + ) + out.copy_(out_new) # for correctness checking + return benchmarker.benchmark(algo, args, {}) + + def to_callable(self): + fn = self.choice.to_callable() + if self.kwargs: + return functools.partial(fn, **self.kwargs) + else: + return fn + + def hash_key(self): + return "-".join( + [ + self.choice.name, + *[ + f"{kwarg}={repr(self.kwargs[kwarg])}" + for kwarg in sorted(self.kwargs.keys()) + ], + self.choice.hash_key(), + ] + ) + + def output_node(self): + if config.abi_compatible and self.choice.use_fallback_kernel: + assert ( + self.choice.op_overload is not None + ), "Please provide an op_overload to use ir.FallbackKernel" + inner = ir.FallbackKernel.create( + self.choice.op_overload, *self.input_nodes, **self.kwargs + ) + elif self.choice.kernel_creator is not None: + inner = self.choice.kernel_creator(*self.input_nodes, **self.kwargs) + else: + cls = ir.ExternKernelOut if self.has_out_variant else ir.ExternKernelAlloc + inner = cls( + layout=self.layout, + inputs=self.input_nodes, + python_kernel_name=self.choice.call_name(), + cpp_kernel_name=self.choice.cpp_kernel_name, + ordered_kwargs_for_cpp_kernel=self.choice.ordered_kwargs_for_cpp_kernel, + op_overload=self.choice.op_overload, + kwargs=self.kwargs, + ) + + return ir.TensorBox.create(inner) + + def info_dict(self) -> Dict[str, Union[PrimitiveInfoType, List[PrimitiveInfoType]]]: + """Information returned here is logged to the autotune log file when that is enabled.""" + return { + "backend": "extern", + "kernel_call_name": self.choice.call_name(), + } + + def autoheuristic_id(self): + return f"extern_{self.choice.name}" + + +@functools.lru_cache(None) +def get_mm_log_filename() -> Optional[str]: + mm_file_name = os.environ.get("TORCHINDUCTOR_MM_LOGGING_FILE", None) + if not mm_file_name: + return None + + if "json" not in mm_file_name: + mm_file_name = f"{mm_file_name}.json" + + return mm_file_name + + +def append_to_log(filename, data): + lock_file = filename.replace(".json", ".lock") + lock = FileLock(lock_file) + with lock: + try: + with open(filename) as f: + log_data = json.load(f) + except (FileNotFoundError, json.JSONDecodeError): + log_data = [] + + log_data.append(data) + + with open(filename, "w") as f: + json.dump(log_data, f, indent=4) + + +class DataProcessorChoiceCallerWrapper: + def __init__(self, wrapped, preprocessor, postprocessor) -> None: + self._wrapped = wrapped + if preprocessor is not None: + self._preprocessor = preprocessor + else: + self._preprocessor = lambda x, y: (x, y) + if postprocessor is not None: + self._postprocessor = postprocessor + else: + self._postprocessor = lambda x: x + + def __getattr__(self, name): + return getattr(self._wrapped, name) + + def benchmark(self, *args, out) -> float: + new_args, new_out = self._preprocessor(args, out) + result = self._wrapped.benchmark(*new_args, out=new_out) + new_out = self._postprocessor(new_out) + if out is not new_out: + out.copy_(new_out) + return result + + def output_node(self) -> ir.TensorBox: + result = self._wrapped.output_node() + return self._postprocessor(result) + + def __repr__(self) -> str: + return f"DataProcessorChoiceCallerWrapper({self._wrapped})" + + +class DataProcessorTemplateWrapper: + """ + A wrapper class for a kernel template. + + This class together with `DataProcessorChoiceCallerWrapper` provides a convenient way to + preprocess and postprocess data before and after using the wrapped template. A typical + usage is to reorder or filter the input nodes in order to match the expected input of other + kernel choices like a ATen kernel. A more complicated usage is to prepack the weights. + See the example from :mod:`cpp_gemm_template` for more details. + """ + + def __init__( + self, + wrapped_template_cls, + preprocessor, + postprocessor, + **kwargs, + ) -> None: + if preprocessor is not None: + self._preprocessor = preprocessor + else: + self._preprocessor = lambda x, y: (x, y) + if postprocessor is not None: + self._postprocessor = postprocessor + else: + self._postprocessor = lambda x: x + assert "input_nodes" in kwargs + assert "layout" in kwargs + kwargs["input_nodes"], kwargs["layout"] = preprocessor( + kwargs["input_nodes"], kwargs["layout"] + ) + self._wrapped = wrapped_template_cls(**kwargs) + + def __getattr__(self, name): + return getattr(self._wrapped, name) + + def maybe_append_choice(self, choices, **kwargs): + return type(self._wrapped).maybe_append_choice(self, choices, **kwargs) + + def generate(self, **kwargs): + choice_caller = self._wrapped.generate(**kwargs) + return DataProcessorChoiceCallerWrapper( + choice_caller, self._preprocessor, self._postprocessor + ) + + def __repr__(self) -> str: + return f"DataProcessorTemplateWrapper({self._wrapped})" + + +class ErrorFromChoice(RuntimeError): + def __init__(self, msg, choice: ChoiceCaller, inputs_str) -> None: + msg += f"\nFrom choice {choice}\n{inputs_str}" + super().__init__(msg) + self.choice = choice + + +class NoValidChoicesError(RuntimeError): + pass + + +@functools.lru_cache(None) +def get_env_num_workers() -> Optional[int]: + if "TORCHINDUCTOR_COMPILE_THREADS" in os.environ: + return int(os.environ["TORCHINDUCTOR_COMPILE_THREADS"]) + return None + + +def create_inputs_key(input_nodes) -> str: + return repr([AlgorithmSelectorCache.key_of(x) for x in input_nodes]) + + +def create_precompile_key( + name: str, inputs_key: str, choices: List[ChoiceCaller] +) -> str: + return ":".join( + [ + name, + inputs_key, + torch.get_float32_matmul_precision(), + ] + + [choice.hash_key() for choice in choices] + ) + + +class AlgorithmSelectorCache(PersistentCache): + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + + # the autotuning will get occur in the scheduler, so there is + # no guarantee that the first lowering for a given key will also be the + # first to benchmark it. share a single precompilation function for all lowerings + # of a particular key + self.precompile_cache: Dict[str, Callable[[], None]] = {} + # list of callbacks that are called after benchmarking + self.feedback_saver_fns: List[ + Callable[ + [Dict[ChoiceCaller, float], str, List[Any], List[ChoiceCaller]], None + ] + ] = [] + + def __call__( + self, + name, + choices: List[ChoiceCaller], + input_nodes, + layout, + # optional dict mapping arg indices to the functions + # generating a torch.Tensor for that input from the + # corresponding ir.Buffer. if passed for a given + # arg, the function will be called instead of + # generating a random torch.Tensor for benchmarking. + input_gen_fns: Optional[Dict[int, Callable[[ir.Buffer], torch.Tensor]]] = None, + precompilation_timeout_seconds: int = 60 * 60, + return_multi_template=False, + ): + from .codegen.cuda.cuda_kernel import CUDATemplateCaller + + # Templates selected with input_gen_fns require specific input data to avoid IMA + # Passing custom input gen fns to benchmark_fusion NYI, so skip deferred template selection + # TODO(jgong5): support multi-template on CPU + if input_gen_fns is not None or layout.device.type == "cpu": + return_multi_template = False + + # TODO - assert that we have not mutating kernels here + + # TODO(nmacchioni): remove once CI tests are fixed + choices = [choice for choice in choices if choice is not None] + + if mm_file_name := get_mm_log_filename(): + M, K = input_nodes[-2].get_size()[:2] + N = input_nodes[-1].get_size()[-1] + append_to_log(mm_file_name, {"invoke": str((M, K, N))}) + + if len(choices) == 0: + backend_config = ( + "max_autotune_gemm_backends" + if name != "convolution" + else "max_autotune_conv_backends" + ) + raise NoValidChoicesError( + f"No choices to select, please consider adding ATEN into {backend_config} " + "config (defined in torch/_inductor/config.py) to allow at least one choice. " + ) + log.debug("Max autotune selects from %s choices.", str(len(choices))) + + if len(choices) == 1: + if not isinstance(choices[0], CUDATemplateCaller): + # CUDATemplateCaller still needs to go through autotuning process to retrieve workspace size. + return choices[0].output_node() + + @functools.lru_cache(None) + def make_benchmark_fn(): + return self.make_benchmark_fn(choices, input_nodes, layout, input_gen_fns) + + inputs_key = create_inputs_key(input_nodes) + + def precompile(choices) -> Callable[[], None]: + def no_op(*args, **kwargs): + return + + if ( + precompilation_timeout_seconds is None + or precompilation_timeout_seconds <= 0 + ): + return no_op + + env_workers = get_env_num_workers() + num_workers = env_workers if env_workers is not None else (len(choices)) + + if num_workers <= 0: + return no_op + + # https://github.com/python/cpython/issues/106905 + if ( + sys.version_info.major == 3 + and sys.version_info.minor == 11 + and sys.version_info.micro <= 8 + ): + return no_op + + # check local and global cache before precompiling + timings = self.lookup( + choices, + name, + inputs_key, + benchmark=None, + ) + + if timings: + return no_op + + precompile_key = create_precompile_key(name, inputs_key, choices) + if precompile_func := self.precompile_cache.get(precompile_key): + return precompile_func + + log.info( + "Multithreaded precompilation for %d choices using %d worker threads", + len(choices), + num_workers, + ) + + # In rare circumstances, because python threads inherit global state, + # thread pool executor can race and leave stdout/stderr in a state + # different than the original values. we explicitly restore the state + # here to avoid this issue. + + initial_stdout = sys.stdout + initial_stderr = sys.stderr + + def precompile_with_captured_stdout(choice): + with restore_stdout_stderr(initial_stdout, initial_stderr): + return choice.precompile() + + executor = ThreadPoolExecutor(max_workers=num_workers) + + futures = {} + for c in choices: + if hasattr(c, "precompile"): + future = executor.submit(precompile_with_captured_stdout, c) + futures[future] = c + + @functools.lru_cache(None) + @restore_stdout_stderr(initial_stdout, initial_stderr) + def wait_on_futures(): + counters["inductor"]["select_algorithm_precompile"] += 1 + for future in as_completed( + futures, + timeout=precompilation_timeout_seconds, + ): + if e := future.exception(): + log.error( + "Exception %s for benchmark choice %s", e, futures[future] + ) + + executor.shutdown(wait=True) + + self.precompile_cache[precompile_key] = wait_on_futures + + return wait_on_futures + + def autotune(choices): + return make_benchmark_fn()(choices) + + if config.autotune_in_subproc: + from .autotune_process import tuning_pool + + # do the optional warmup + tuning_pool.initialize() + + def do_autotuning(precompile_fn): + precompile_start_ts = time.time() + precompile_fn() + precompile_elapse = time.time() - precompile_start_ts + + autotune_start_ts = time.time() + timings = self.lookup( + choices, + name, + inputs_key, + autotune, + ) + autotune_elapse = time.time() - autotune_start_ts + + if timings and all( + not math.isfinite(timing) for timing in timings.values() + ): + raise NoValidChoicesError + + if make_benchmark_fn.cache_info().currsize: + counters["inductor"]["select_algorithm_autotune"] += 1 + + if ( + make_benchmark_fn.cache_info().currsize + or log.getEffectiveLevel() == logging.DEBUG + or config.trace.log_autotuning_results + ): + self.log_results( + name, input_nodes, timings, autotune_elapse, precompile_elapse + ) + + for feedback_fn in self.feedback_saver_fns: + feedback_fn(timings, name, input_nodes, choices) + + return timings + + precompile_fn = precompile(choices) + + if return_multi_template and (config.max_autotune or config.max_autotune_gemm): + + def get_timings(): + timings = do_autotuning(precompile_fn) + min_extern_choice = float("inf") + for choice, timing in timings.items(): + if isinstance(choice, ExternKernelCaller): + min_extern_choice = min(min_extern_choice, timing) + + timings = { + choice: time + for choice, time in timings.items() + if ( + time <= min_extern_choice + or not isinstance(choice, ExternKernelCaller) + ) + } + + return timings + + return torch._inductor.ir.TensorBox.create( + torch._inductor.ir.MultiTemplateBuffer( + layout, + input_nodes, + get_timings, + ) + ) + + # TODO - dont want to precompile if we have a cache hit + timings = do_autotuning(precompile_fn) + if timings == {} or choices[0] not in timings: + return choices[0].output_node() + + selected_key = builtins.min(timings, key=timings.__getitem__) + selected_time = timings[selected_key] + selected_choice = selected_key.output_node() + log.debug("selected choice: %s", str(selected_choice)) + return selected_choice + + @classmethod + def make_benchmark_fn( + cls, + choices, + input_nodes, + layout, + input_gen_fns=None, + ): + if input_gen_fns is None: + input_gen_fns = {} + + def get_inputs(): + # de-duplicate args + unique_example_inputs = { + x.get_name(): input_gen_fns.get(i, cls.benchmark_example_value)(x) + for i, x in enumerate(input_nodes) + } + example_inputs = list(unique_example_inputs.values()) + example_inputs_extern = [ + unique_example_inputs[input_node.get_name()] + if unique_example_inputs[input_node.get_name()].is_mkldnn + else torch.as_strided( + unique_example_inputs[input_node.get_name()], + V.graph.sizevars.size_hints( + input_node.get_size(), + fallback=config.unbacked_symint_fallback, + ), + V.graph.sizevars.size_hints( + input_node.get_stride(), + fallback=config.unbacked_symint_fallback, + ), + V.graph.sizevars.size_hint( + input_node.get_layout().offset, + fallback=config.unbacked_symint_fallback, + ), + ) + for input_node in input_nodes + ] + + out = cls.benchmark_example_value(layout) + out_extern = torch.as_strided( + out, out.size(), out.stride(), V.graph.sizevars.size_hint(layout.offset) + ) + expected = None + if VERIFY: + choices[0].benchmark(*example_inputs_extern, out=out_extern) + expected = out_extern.clone() + + return example_inputs, example_inputs_extern, out, out_extern, expected + + if DEBUG: + print(f"{len(choices)} tuning requests:") + + def debug_str(example_inputs, out): + def tensor_repr(x): + return ( + f"torch.empty_strided({tuple(x.size())!r}, {tuple(x.stride())!r}, " + f"dtype={x.dtype!r}, device={x.device.type!r})" + ) + + lines = [ + "inputs = [", + ] + for x in example_inputs: + lines.append(f" {tensor_repr(x)},") + lines += ["]", f"out = {tensor_repr(out)}", ""] + return "\n".join(lines) + + def benchmark_choice_in_current_process( + choice, example_inputs, example_inputs_extern, out, out_extern, expected + ): + out.zero_() + if isinstance(choice, ExternKernelCaller): + # aten kernels want the offset baked in for sliced tensors + result = choice.benchmark(*example_inputs_extern, out=out_extern) + else: + # triton templates want the base pointer for sliced tensors + result = choice.benchmark(*example_inputs, out=out) + if VERIFY and expected is not None: + torch.testing.assert_close(out_extern, expected, **VERIFY) + if torch.cuda.is_available(): + torch.cuda.synchronize() # shake out any CUDA errors + return result + + def benchmark_in_current_process(choices): + inputs = get_inputs() + example_inputs, _, out, _, _ = inputs + timings = {} + for choice in choices: + try: + timing = benchmark_choice_in_current_process(choice, *inputs) + except CUDACompileError as e: + log.error( + "CUDA compilation error during autotuning: \n%s. \nIgnoring this choice.", + str(e), + ) + timing = float("inf") + except NotImplementedError as e: + log.warning("Not yet implemented: %s", e) + timing = float("inf") + except RuntimeError as e: + msg = str(e) + if "invalid argument" in msg: + msg += "\n\nThis may mean this GPU is too small for max_autotune mode.\n\n" + else: + if "illegal memory access" in msg: + msg += "\n\nEither error in template or triton bug.\n" + log.error( + "Runtime error during autotuning: \n%s. \nIgnoring this choice.", + msg, + ) + timing = float("inf") + except AssertionError as e: + raise AssertionError( # noqa: B904 + f"Incorrect result from choice {choice}\n\n{e}" + ) + except Exception as e: + try: + from triton.runtime.autotuner import OutOfResources + + if isinstance(e, OutOfResources): + log.warning(e) + timing = float("inf") + else: + raise e + except ImportError: + raise e from None + + timings[choice] = timing + + return timings + + def benchmark_in_sub_process(choices): + from . import autotune_process + + # only benchmark triton kernel in sub process for now. + # ATen/Extern kernel are still benchmarked in the current process. + extern = [c for c in choices if isinstance(c, ExternKernelCaller)] + triton = [c for c in choices if not isinstance(c, ExternKernelCaller)] + + timings = benchmark_in_current_process(extern) + timings.update(autotune_process.benchmark_in_sub_process(triton)) + return timings + + benchmark = ( + benchmark_in_sub_process + if config.autotune_in_subproc + else benchmark_in_current_process + ) + + return benchmark + + @staticmethod + def log_results( + name: str, + input_nodes: List[ir.IRNode], + timings: Dict[ChoiceCaller, float], + elapse: float, + precompile_elapse: float, + ): + V.debug.log_autotuning_results( + name, input_nodes, timings, elapse, precompile_elapse + ) + if not (config.max_autotune or config.max_autotune_gemm) or not PRINT_AUTOTUNE: + return + sizes = ", ".join( + [ + "x".join( + map( + str, + V.graph.sizevars.size_hints( + n.get_size(), fallback=config.unbacked_symint_fallback + ), + ) + ) + for n in input_nodes + ] + ) + + n = None if log.getEffectiveLevel() == logging.DEBUG else 10 + top_k = sorted(timings, key=timings.__getitem__)[:n] + best = top_k[0] + + def get_choice_info(choice): + if isinstance(choice, torch._inductor.select_algorithm.ExternKernelCaller): + return {"type": "cublas", "time": timings[choice]} + + assert isinstance( + choice, torch._inductor.select_algorithm.TritonTemplateCaller + ) + + info = choice.info_dict() + tile = info["tile_shape"] + + tile_vals = eval(tile) # type: ignore[arg-type] + BLOCK_M = tile_vals[0] + BLOCK_K = tile_vals[1] + BLOCK_N = tile_vals[2] + + return { + "type": "triton", + "time": timings[choice], + "BLOCK_M": BLOCK_M, + "BLOCK_K": BLOCK_K, + "BLOCK_N": BLOCK_N, + "num_stages": info["num_stages"], + "num_warps": info["num_warps"], + } + + mm_filename = get_mm_log_filename() + if mm_filename and "mm" in name: + M, K = input_nodes[-2].get_size()[:2] + N = input_nodes[-1].get_size()[-1] + + out_dict = { + str((M, K, N)): [get_choice_info(choice) for choice in timings.keys()] + } + + append_to_log(mm_filename, out_dict) + + best_time = timings[best] + sys.stderr.write(f"AUTOTUNE {name}({sizes})\n") + for choice in top_k: + result = timings[choice] + if result: + kernel_info = ( + choice.debug_extra if hasattr(choice, "debug_extra") else "" + ) + sys.stderr.write( + f" {choice.name} {result:.4f} ms {best_time / result:.1%} {kernel_info}\n" + ) + else: + sys.stderr.write( + f" {choice.name} {result:.4f} ms \n" + ) + + autotune_type_str = ( + "SubProcess" if config.autotune_in_subproc else "SingleProcess" + ) + sys.stderr.write( + f"{autotune_type_str} AUTOTUNE benchmarking takes {elapse:.4f} seconds and {precompile_elapse:.4f}" + " seconds precompiling\n" + ) + + @staticmethod + def benchmark_example_value(node): + """ + Convert an ir.Buffer into a concrete torch.Tensor we can use for + benchmarking. + """ + if isinstance(node, ir.Layout): + node = ir.Buffer("fake", node) + # triton templates want the base tensor. + if isinstance(node, ir.BaseView): + node = node.unwrap_view() + return AlgorithmSelectorCache.generate_example_value( + V.graph.sizevars.size_hints( + node.get_size(), + fallback=config.unbacked_symint_fallback, + ), + V.graph.sizevars.size_hints( + node.get_stride(), + fallback=config.unbacked_symint_fallback, + ), + node.get_device(), + node.get_dtype(), + node.layout.offset, + ) + + @staticmethod + def generate_example_value(size, stride, device, dtype, extra_size): + # preserve rng states to avoid the rand_strided call below changes + # the rng states for the real model code. + with preserve_rng_state(): + return rand_strided( + size, + stride, + device=device, + dtype=dtype, + extra_size=extra_size, + ) + + @staticmethod + def key_of(node): + """ + Extract the pieces of an ir.Buffer that we should invalidate cached + autotuning results on. + """ + sizevars = V.graph.sizevars + return ( + node.get_device().type, + str(node.get_dtype()), + *sizevars.size_hints( + node.get_size(), + fallback=config.unbacked_symint_fallback, + ), + *sizevars.size_hints( + node.get_stride(), + fallback=config.unbacked_symint_fallback, + ), + sizevars.size_hint( + node.get_layout().offset, + fallback=config.unbacked_symint_fallback, + ), + ) + + def add_feedback_saver( + self, + fn: Callable[ + [Dict[ChoiceCaller, float], str, List[Any], List[ChoiceCaller]], None + ], + ): + self.feedback_saver_fns.append(fn) + + +_ALGORITHM_SELECTOR_CACHE: Optional[AlgorithmSelectorCache] = None + + +def autotune_select_algorithm(*args, **kwargs): + global _ALGORITHM_SELECTOR_CACHE + if _ALGORITHM_SELECTOR_CACHE is None: + _ALGORITHM_SELECTOR_CACHE = AlgorithmSelectorCache() + + if "return_multi_template" not in kwargs: + kwargs[ + "return_multi_template" + ] = torch._inductor.config.benchmark_epilogue_fusion + + return _ALGORITHM_SELECTOR_CACHE(*args, **kwargs) + + +def add_feedback_saver( + fn: Callable[[Dict[ChoiceCaller, float], str, List[Any], List[ChoiceCaller]], None] +): + global _ALGORITHM_SELECTOR_CACHE + if _ALGORITHM_SELECTOR_CACHE is None: + _ALGORITHM_SELECTOR_CACHE = AlgorithmSelectorCache() + _ALGORITHM_SELECTOR_CACHE.add_feedback_saver(fn) + + +def realize_inputs(*args): + if len(args) == 1: + return ir.ExternKernel.require_stride1(ir.ExternKernel.realize_input(args[0])) + return [realize_inputs(x) for x in args] + + +# ensure lowering is imported so that `extern_kernels.*` is populated +from . import lowering # noqa: F401 diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/sizevars.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/sizevars.py new file mode 100644 index 0000000000000000000000000000000000000000..8d3c6d411d278c2ef893ad06f8195dbd2096572c --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/sizevars.py @@ -0,0 +1,892 @@ +# mypy: allow-untyped-defs +import functools +import itertools +import logging +from typing import ( + Any, + Callable, + cast, + Dict, + Iterable, + List, + Optional, + Sequence, + Set, + Tuple, + Union, +) + +import sympy +from sympy import Expr + +from torch.fx.experimental.symbolic_shapes import free_unbacked_symbols, ShapeEnv +from torch.utils._sympy.functions import FloorDiv, ModularIndexing +from torch.utils._sympy.symbol import symbol_is_type, SymT +from torch.utils._sympy.value_ranges import bound_sympy, IntInfinity, ValueRanges + +from .runtime.runtime_utils import is_power_of_2 +from .utils import ( + has_free_symbols, + sympy_index_symbol, + sympy_index_symbol_with_prefix, + sympy_subs, + VarRanges, +) +from .virtualized import V + + +log = logging.getLogger(__name__) + + +def evaluate_expr( + shape_env: ShapeEnv, + expr: Union[sympy.Basic, bool], + axioms: Optional[Tuple[sympy.Expr]] = None, + var_to_range: Optional[Tuple[Tuple[sympy.Symbol, ValueRanges[Any]]]] = None, +) -> bool: + if expr in (True, False): + return bool(expr) + + try: + simplified = shape_env._maybe_evaluate_static( + expr, + axioms=axioms, + var_to_range=var_to_range, + ) + if simplified is not None: + return bool(simplified) + except Exception: + log.debug("Could not simplify %s", expr, exc_info=True) + + return False + + +# This class is a little awkward, because ShapeEnv is doing most of the heavy +# lifting and in some cases we should be directly passing through to ShapeEnv, +# but there is some extra inductor logic that needs to be handled here +class SizeVarAllocator: + def __init__(self, shape_env=None) -> None: + super().__init__() + if shape_env is None: + shape_env = ShapeEnv() + self.shape_env = shape_env + self.var_to_val = self.shape_env.var_to_val + self.replacements: Dict[sympy.Symbol, Expr] = self.shape_env.replacements + # Maps of dynamic sizes that have to be precomputed on the host to the kernel args. + # The basic idea is if we have some complicated sympy expression + # f(s0), we may choose to precompute it on the host and then replace + # all occurrences of that sympy expression with ps0, so that when we + # codegen we simply reference ps0 directly without repeating + # f(s0). Unlike regular size variables, ps variables cannot be + # guarded upon; so if we are asked to guard on a Sympy expression + # which potentially could have already had a precomputed replacement + # on it, we are obligated to invert the precomputed replacements + # (inv_precomputed_replacements). + self.precomputed_replacements: Dict[Expr, sympy.Symbol] = {} + self.inv_precomputed_replacements: Dict[sympy.Symbol, Expr] = {} + self.stride_vars = self.make_stride_vars_cache() + self.simplify_with_ranges = self.make_simplify_with_ranges_cache() + self._simplify_loops = self.make_simplify_loops_cache() + + def simplify(self, expr: Expr): + return sympy.expand(expr).xreplace(self.replacements) + + def make_simplify_with_ranges_cache(self) -> Callable[[Expr, VarRanges], Expr]: + """ + self._simplify_with_ranges() can be expensive, cache its results + """ + cache: Dict[Tuple[Any, ...], Expr] = {} + replacement_count = len(self.replacements) + + def simplify_with_ranges(expr: Expr, var_ranges: VarRanges) -> Expr: + nonlocal replacement_count + if replacement_count != len(self.replacements): + # new replacements invalidates cached results + cache.clear() + replacement_count = len(self.replacements) + key = (expr, *var_ranges.items()) + result = cache.get(key, None) + if result is None: + result = self._simplify_with_ranges(expr, var_ranges) + cache[key] = result + return result + + return simplify_with_ranges + + def make_simplify_loops_cache(self): + """ + self._simplify_with_ranges() can be expensive, cache its results + """ + cache: Dict[Tuple[Any, ...], Any] = {} + replacement_count = len(self.replacements) + + def simplify_loops(index_vars, sizes, index_formulas): + nonlocal replacement_count + if replacement_count != len(self.replacements): + # new replacements invalidates cached results + cache.clear() + replacement_count = len(self.replacements) + key = (*index_vars, *sizes, *index_formulas) + result = cache.get(key, None) + if result is None: + result = self._simplify_loops_impl(index_vars, sizes, index_formulas) + cache[key] = result + return result + + return simplify_loops + + def _simplify_with_ranges(self, expr: Expr, var_ranges: VarRanges) -> Expr: + """ + Simplify indexing expression with knowledge of the ranges of + iteration variables. + """ + + expr = join_dimensions(self.simplify(expr)) + original_expr = expr + + var_to_range = dict(self.shape_env.var_to_range) + var_to_range.update( + { + k: ValueRanges( + 0, max(0, v - 1) if not has_free_symbols([v]) else IntInfinity() + ) + for k, v in var_ranges.items() + } + ) + for var in expr.free_symbols: + if var not in var_to_range: + var_to_range[var] = ValueRanges(0, IntInfinity()) + + var_to_range_tuple = cast( + Tuple[Tuple[sympy.Symbol, ValueRanges[sympy.Expr]]], + tuple(var_to_range.items()), + ) + + axioms = [] + for var, upper_bound in var_ranges.items(): + axioms.append(0 <= var) + axioms.append(var < upper_bound) + axioms = tuple(axioms) + self.shape_env.get_axioms() + + def statically_known(expr): + evaluated = self.shape_env._maybe_evaluate_static( + expr, + axioms=axioms, + var_to_range=var_to_range_tuple, + ) + return bool(evaluated) + + def remove_zero_terms(base, divisor): + """Symbols smaller than the divisor are zero""" + if not statically_known(base >= 0): + return base + + for v in base.free_symbols: + if v in var_ranges: + # var smaller than divisor can be removed + # if the rest is guaranteed to be multiple of divisor + rest = sympy.Wild("_rest", exclude=[v]) + m = base.match(v + rest) + if m and v not in m[rest].free_symbols: + gcd = sympy.gcd(m[rest], divisor) + if gcd == divisor: + if statically_known(v < divisor): + base = m[rest] + return base + + def visit_indexing_div(base, divisor): + return FloorDiv(remove_zero_terms(base, divisor), divisor) + + def visit_modular_indexing(base, divisor, modulus): + base = remove_zero_terms(base, divisor) + + can_remove_mod = statically_known(base >= 0) and statically_known( + base < modulus * divisor + ) + + if can_remove_mod: + return FloorDiv(base, divisor) + return ModularIndexing(base, divisor, modulus) + + if expr.has(ModularIndexing): + expr = expr.replace( + ModularIndexing( + sympy.Wild("base", integer=True), + sympy.Wild("divisor", integer=True), + sympy.Wild("modulus", integer=True), + ), + visit_modular_indexing, + ) + + if expr.has(FloorDiv): + expr = expr.replace( + FloorDiv( + sympy.Wild("base", integer=True), + sympy.Wild("divisor", integer=True), + ), + visit_indexing_div, + ) + + if expr != original_expr: + return self._simplify_with_ranges(expr, var_ranges) + return expr + + def _simplify_loops_impl( + self, index_vars: List[sympy.Symbol], sizes, index_formulas + ): + """ + Try to remove as many axis from loop iterations as possible, by: + 1) removing size==1 dimensions + 2) fuse contiguous dimensions into a single loop + If channel_last = True, we will prevent the last dim fused with other dims + """ + sizes = list(map(self.simplify, sizes)) + + strides = [ + # index_formulas may contain boolean expressions (e.g. s0 < 10), + # for which "strides" don't make sense so we ignore them here. + # NOTE: These expressions may still block merging dims in the sound + # substitution test performed in can_merge_dims. + self.stride_vars(x, index_vars) + if isinstance(x, sympy.Expr) + else [0] * len(index_vars) + for x in index_formulas + ] + assert len(sizes) == len(strides[0]), (len(sizes), len(strides[0])) + + for i in range(len(sizes)): + if sizes[i] == 1: + # remove dim + sizes[i] = None + + def can_merge_dims(a, b): + for k in range(len(strides)): + if self.simplify(strides[k][a] * sizes[a]) == self.simplify( + strides[k][b] + ): + # approximate test passed, try sound version + va = index_vars[a] + vb = index_vars[b] + m1 = sympy_index_symbol("_merge_tester1") + m2 = sympy_index_symbol("_merge_tester2") + # NOTE: can't sub vb=0 here in case va * vb appears in the expression, + # in which case both expr1 and expr2 would be zero! + expr1 = sympy_subs(index_formulas[k], {va: m1 * sizes[a], vb: m2}) + expr2 = sympy_subs(index_formulas[k], {va: 0, vb: (m1 + m2)}) + if self.simplify(expr1) == self.simplify(expr2): + continue + return False + return True + + changed = True + while changed: + changed = False + for i, j in itertools.product( + reversed(range(len(sizes))), reversed(range(len(sizes))) + ): + if i == j or sizes[i] is None or sizes[j] is None: + continue + if can_merge_dims(i, j): + changed = True + sizes[i] = sizes[i] * sizes[j] + sizes[j] = None + + def reindex(index): + it = list(reversed(index)) + new_index = [] + for size in sizes: + if size is None: + new_index.append(sympy.Integer(0)) + else: + new_index.append(it.pop()) + assert not it + return new_index + + def prune(index): + assert len(index) == len(sizes) + return [i for i, s in zip(index, sizes) if s is not None] + + return [x for x in sizes if x is not None], reindex, prune + + # Note - [On Statically Known] + # + # The statically_known_* family of functions below replaces a prior system, called maybe_guard_*. The prior system + # operated by providing essentially a question, where the size hinted values were evaluated. If the condition was + # true, we add a guard and return True, otherwise, False. + # + # def maybe_guard_foo(args): + # if size_hinted_check(args): + # return False # No guard, no optim + # guard(args) # Make a guard + # return True # Safe to apply optimization + # + # The prior system incurred a guard, and green lit an optimization. + # + # The new system works in reverse - in the new system, if we know that the inputs are static, and evaluate the + # condition as true, we green light the optimization, and we do not incur a guard. If we cannot prove that, we + # return False. + # + # def maybe_guard_foo(args): + # if all_static(args): + # return True # Safe to apply optimization + # else: + # return False # No guard, no optim + + # See Note - [On Statically Known] + + def is_expr_static_and_true(self, expr: Union[sympy.Basic, bool]) -> bool: + return evaluate_expr(self.shape_env, expr) + + def statically_known_equals( + self, left: Union[Expr, int], right: Union[Expr, int] + ) -> bool: + """ + Returns a bool indicating if it is sound to optimize as if left and right are equal. + """ + return self.is_expr_static_and_true(sympy.Eq(left, right)) # type: ignore[arg-type] + + # See Note - [On Statically Known] + def statically_known_list_equals(self, left: List[Expr], right: List[Expr]) -> bool: + """ + Returns a bool indicating if it is sound to optimize as if left and right lists are equal. + """ + return len(left) == len(right) and all( + self.statically_known_equals(l, r) for l, r in zip(left, right) + ) + + # See Note - [On Statically Known] + def statically_known_leq(self, left: Expr, right: Union[Expr, int]) -> bool: + """ + Returns a bool indicating if it is sound to optimize as if left is less than or equal to right. + """ + expr = left <= right + return self.is_expr_static_and_true(expr) + + # See Note - [On Statically Known] + def statically_known_geq(self, left: Expr, right: Union[Expr, int]) -> bool: + """ + Returns a bool indicating if it is sound to optimize as if left is greater than or equal to right. + """ + expr = left >= right + return self.is_expr_static_and_true(expr) + + # See Note - [On Statically Known] + def statically_known_lt(self, left: Expr, right: Union[Expr, int]) -> bool: + """ + Returns a bool indicating if it is sound to optimize as if left is less than right. + """ + expr = left < right + return self.is_expr_static_and_true(expr) + + # See Note - [On Statically Known] + def statically_known_gt(self, left: Expr, right: Union[Expr, int]) -> bool: + """ + Returns a bool indicating if it is sound to optimize as if left is greater than right. + """ + expr = left > right + return self.is_expr_static_and_true(expr) + + # See Note - [On Statically Known] + def statically_known_multiple_of( + self, numerator: Expr, denominator: Union[Expr, int] + ) -> bool: + """ + Return a bool indicating if it is sound to optimize for the numerator being a multiple of the denominator. + """ + if free_unbacked_symbols(numerator) or free_unbacked_symbols(denominator): + return False + expr = sympy.Eq(numerator % denominator, 0) + return self.is_expr_static_and_true(expr) # type: ignore[arg-type] + + # See Note - [On Statically Known] + def statically_known_power_of_2(self, expr: Expr) -> bool: + """ + Returns a bool indicating if x is known to be a power of 2. + """ + return isinstance(expr, sympy.Integer) and is_power_of_2(int(expr)) + + # The guard functions require you to ALREADY KNOW that a particular + # condition holds. If you don't know (you want to guard on an expression + # being a particular value, and then get access to that value), use + # the evaluate functions. + + def guard_equals(self, left: Expr, right: Expr) -> Expr: + if isinstance(left, Expr): + left = sympy_subs(left, self.inv_precomputed_replacements) # type: ignore[arg-type] + if isinstance(right, Expr): + right = sympy_subs(right, self.inv_precomputed_replacements) # type: ignore[arg-type] + assert self.shape_env.evaluate_expr(sympy.Eq(left, right)) + return left + + def guard_leq(self, left: Expr, right: Expr) -> None: + return self.guard_lt(left, right + 1) + + def guard_lt(self, left: Expr, right: Expr) -> None: + assert self.shape_env.evaluate_expr(sympy.Lt(left, right)) + + def guarded_order(self, seq): + """ + Return the order of a sequence as a permutation of range(len(seq)) and guard on that order not changing. + """ + seq = [*map(self.remove_precomputed_replacements, seq)] + seq = [(self.size_hint(var), orig_idx, var) for orig_idx, var in enumerate(seq)] + seq.sort() + order = [-1] * len(seq) + last_var = None + for new_index, (_, orig_index, var) in enumerate(seq): + order[orig_index] = new_index + if last_var is not None: + self.guard_leq(last_var, var) + last_var = var + return order + + # The evaluate functions evaluate some symbolic sympy expression + # (NB: not necessarily an Expr) and return what the concrete result + # is, guarding on the expression being that result + + # NB: write evaluate_expr(sympy.Lt(a, b)) rather than evaluate_expr(a < b) + # as this will ensure that you actually have a sympy'ified expression, + # and will prevent you from incorrectly writing evaluate_expr(a == b) + # which does the wrong thing if a or b is a sympy expression + def evaluate_expr(self, left: Union[Expr, sympy.logic.boolalg.Boolean]) -> bool: + assert isinstance(left, (Expr, sympy.logic.boolalg.Boolean)), type(left) + return self.shape_env.evaluate_expr(sympy.sympify(left)) + + def evaluate_min(self, left: Expr, right: Expr) -> Expr: + """return the smaller of left and right, and guard on that choice""" + if isinstance(left, Expr): + left = sympy_subs(left, self.inv_precomputed_replacements) # type: ignore[arg-type] + if isinstance(right, Expr): + right = sympy_subs(right, self.inv_precomputed_replacements) # type: ignore[arg-type] + try: + lv = self.size_hint(left) + rv = self.size_hint(right) + except TypeError: # unbacked symints + if left == right or self.statically_known_leq(left, right): + return left + if self.statically_known_leq(right, left): + return right + gcd = sympy.gcd(left, right) + if left == gcd: # handle `min(10*u0, u0)` etc + return left + if right == gcd: + return right + raise TypeError( + f"evaluate_min({left}, {right}) with unbacked symints" + ) from None + if lv <= rv: + self.guard_leq(left, right) + return left + else: + self.guard_leq(right, left) + return right + + def evaluate_max(self, left: Expr, right: Expr) -> Expr: + """return the larger of left and right, and guard on that choice""" + # Always choose the opposite of eval min for consistency + # This means min(a, b) and max(a, b) produce the same guards + min_val = self.evaluate_min(left, right) + return right if min_val is left else left + + def evaluate_static_shape(self, left: Union[Expr, int]) -> int: + if isinstance(left, int): + return left + right = self.size_hint(left) + self.guard_equals(left, sympy.Integer(right)) + return int(right) + + def evaluate_static_shapes(self, left: Sequence[Union[Expr, int]]) -> List[int]: + return [self.evaluate_static_shape(x) for x in left] + + def remove_precomputed_replacements(self, expr: Expr) -> Expr: + if any(symbol_is_type(s, SymT.PRECOMPUTED_SIZE) for s in expr.free_symbols): # type: ignore[attr-defined] + return sympy_subs(expr, self.inv_precomputed_replacements) # type: ignore[arg-type] + return expr + + def symbolic_hint(self, expr: Union[Expr, int]) -> Union[Expr, int]: + if isinstance(expr, int): + return expr + # Substitute all hints into expr, but leave unbacked symints alone + expr = self.simplify(expr) + if not isinstance(expr, Expr): + assert isinstance(expr, int) + return expr + free_symbols = expr.free_symbols + if not free_symbols: + try: + return int(expr) # type: ignore[return-value] + except TypeError: + return expr # inf/nan/I + expr = self.remove_precomputed_replacements(expr) + return sympy_subs(expr, self.var_to_val) + + def size_hint( + self, expr: Union[Expr, int], *, fallback: Optional[int] = None + ) -> int: + out = self.symbolic_hint(expr) + if not isinstance(out, (int, sympy.Integer)) and fallback is not None: + # Use the provided heuristic fallback hint + unbacked_sym_vrs = { + s: self.shape_env.var_to_range.get(s, None) for s in out.free_symbols + } + if all(vr is not None for vr in unbacked_sym_vrs.values()): + hint_vr = bound_sympy(out, unbacked_sym_vrs) # type: ignore[arg-type] + if isinstance(hint_vr.lower, (int, sympy.Integer)): + fallback = max(fallback, int(hint_vr.lower)) + if isinstance(hint_vr.upper, (int, sympy.Integer)): + fallback = min(fallback, int(hint_vr.upper)) + return fallback + + try: + return int(out) + except Exception: + log.debug("failed on: %s", out) + raise + + def size_hints( + self, + exprs: Iterable[Expr], + *, + fallback: Optional[int] = None, + ) -> Tuple[int, ...]: + return tuple(self.size_hint(x, fallback=fallback) for x in exprs) + + def _lru_cache(self, fn, maxsize=None): + """ + Wrapper around functools.lru_cache that clears when replacements + has been invalidated. + """ + fn_cache = functools.lru_cache(maxsize)(fn) + prior_len = len(self.replacements) + + @functools.wraps(fn) + def wrapper(*args, **kwargs): + nonlocal prior_len + if prior_len != len(self.replacements): + prior_len = len(self.replacements) + fn_cache.cache_clear() + return fn_cache(*args, **kwargs) + + return wrapper + + def make_stride_vars_cache(self): + cache = self._lru_cache(self._stride_vars) + + def stride_vars( + index: Expr, + vars: Sequence[sympy.Symbol], + support_vars: Optional[Sequence[sympy.Symbol]] = None, + ) -> List[Expr]: + if not support_vars: + support_vars = vars + return cache(index, tuple(vars), tuple(support_vars)) + + return stride_vars + + def _stride_vars( + self, + index: Expr, + vars: Sequence[sympy.Symbol], + support_vars: Sequence[sympy.Symbol], + ) -> List[Expr]: + """Convert an indexing expression back into strides + + NOTE: This is only valid if the index is a standard strided offset + calculation. e.g. 10 * ModularIndexing(i0 + 1, 1, 2) would give a + stride of -10 because the index wraps around after the first element + + """ + strides = [] + index = self.simplify(index) + # remove any offset + index = index - sympy_subs( + index, {v: sympy.Integer(0) for v in support_vars if v != 0} + ) + for i in range(len(vars)): + # drop all the other dims + index_dim = sympy_subs( + index, + { + support_vars[j]: sympy.Integer(0) + for j in range(len(support_vars)) + if vars[i] != support_vars[j] and support_vars[j] != 0 + }, + ) + v = vars[i] + if v == 0: + strides.append(sympy.Integer(0)) + else: + # TODO(jansel): should we use sympy.diff here? + strides.append( + sympy_subs(index_dim, {v: sympy.Integer(1)}) + - sympy_subs(index_dim, {v: sympy.Integer(0)}) + ) + return strides + + def offset_var(self, index: Expr, vars: List[sympy.Symbol]) -> Expr: + """Extract offset part of an indexing expression""" + index = self.simplify(index) + return sympy_subs(index, {v: sympy.Integer(0) for v in vars if v != 0}) + + def stride_hints( + self, + index: Expr, + vars: Sequence[sympy.Symbol], + support_vars: Optional[Sequence[sympy.Symbol]] = None, + ) -> List[int]: + for v in index.free_symbols: + if symbol_is_type(v, SymT.INDIRECT): # type: ignore[attr-defined] + index = sympy_subs(index, {v: 0}) # type: ignore[dict-item] + result = [] + for s in self.stride_vars(index, vars, support_vars): + try: + result.append(self.size_hint(s)) + except TypeError: + result.append(0) + return result + + def stride_order(self, index: Expr, vars: List[sympy.Symbol]) -> List[int]: + strides = tuple(map(abs, self.stride_hints(index, vars))) + order = list(range(len(strides))) + order.sort(key=lambda x: (strides[x] == 0, strides[x])) + return order + + def lookup_precomputed_size(self, expr: Expr) -> Expr: + if ( + isinstance(expr, (int, sympy.Symbol, sympy.Number)) + or expr.is_number + or expr.is_symbol + ): + return expr + expr = self.remove_precomputed_replacements(expr) + if expr not in self.precomputed_replacements: + sym = sympy_index_symbol_with_prefix( + SymT.PRECOMPUTED_SIZE, len(self.precomputed_replacements) + ) + self.precomputed_replacements[expr] = sym + self.inv_precomputed_replacements[sym] = expr + return self.precomputed_replacements[expr] + + def free_symbols(self) -> Set[sympy.Symbol]: + return set(self.var_to_val.keys()) - set(self.replacements.keys()) + + def combine_modular_indexing_pairs(self, index: sympy.Expr) -> sympy.Expr: + """ + A pair of special ModularIndexing can be combined. + + E.g. ModularIndexing(ModularIndexing(x, 1, a), 1, b) + We can simplify this to ModuleIndexing(x, 1, b), if + 1. x is non negative integer + 2. a and b are positive integers + 3. a is a multiple of b. + """ + + def _check_args(x, div, mod, is_first): + if not isinstance(div, sympy.Integer) or not isinstance(mod, sympy.Integer): + return False + if div != 1: + return False + if mod <= 0: + return False + + if is_first: + # first ModularIndexing should conatins a nested ModularIndex + if not isinstance(x, ModularIndexing): + return False + else: + # second ModularIndexing should constains a non-negative + # symbol + if not isinstance(x, sympy.Symbol) or not self.statically_known_geq( + x, 0 + ): + return False + return True + + if isinstance(index, ModularIndexing): + x, div, mod = index.args + + if not _check_args(x, div, mod, True): + return index + + x2, div2, mod2 = x.args + + if not _check_args(x2, div2, mod2, False): + return index + + if mod2 % mod != 0: + return index + + return ModularIndexing(x2, 1, mod) + + return index + + def expand_floor_div( + self, index: sympy.Expr + ) -> Union[bool, Tuple[sympy.Expr, sympy.Expr]]: + """ + Expand the FloorDiv to the entire expression so that the expression may + be simplfied. + + E.g., for a 2D contiguous tensor with shape [a, 2 * b], and index variables + x1, x2, index expression 'x1 * 2b + x2' can be easily combined. + But index expression 'x1 * b + x2 // 2' can not. + By expanding the FloorDiv to the entire expression, we get + '(x1 * 2b + x2) // 2'. This transformation allows us to merge loops + for the numerator! + + Return false if this optimization can be applied; + Return the new expression and the denominator otherwise. + The original expression will be equivalent to 'new_expression // denominator' + """ + if not isinstance(index, sympy.Add): + return False + terms = index.args + + if len(terms) < 2: + return False + floor_div_index = -1 + varlist = [] + factorlist = [] + for idx, term in enumerate(terms): + if isinstance(term, sympy.Mul): + # For dynamic shape, term like '2*s1*x1' has 3 child nodes. + # - A integer for 2 + # - A symbol for s1 + # - A symbol for x1 + # Skip for now. + if len(term.args) != 2: + return False + factor, var = term.args + varlist.append(var) + factorlist.append(factor) + if not isinstance(factor, sympy.Integer) or not isinstance( + var, sympy.Symbol + ): + return False + # It's easier to reason about the correceness of the transformation + # for non-negative integers. + if not self.statically_known_geq(var, 0): + return False + elif isinstance(term, FloorDiv): + var, factor = term.args + if not isinstance(factor, sympy.Integer) or not isinstance( + var, sympy.Symbol + ): + return False + if not self.statically_known_geq(var, 0): + return False + if floor_div_index >= 0: + # can not handle multi FloorDiv yet + return False + + floor_div_index = idx + varlist.append(var) + # this factor is denominator + factorlist.append(factor) + else: + return False + + if floor_div_index < 0: + return False + + # Construct the new expression and remember the denominator + denominator = factorlist[floor_div_index] + new_index = sympy.Integer(0) + + for var, factor, idx in zip(varlist, factorlist, itertools.count()): + if idx == floor_div_index: + new_index += var + else: + new_index += (factor * denominator) * var + + return new_index, denominator + + +def join_dimensions(expr: Expr) -> Expr: + if not isinstance(expr, sympy.Add) or not expr.has(ModularIndexing): + return expr # fast exit path + return _join_dimensions_cached(expr) + + +@functools.lru_cache(256) +def _join_dimensions_cached(expr: Expr) -> Expr: + """ + ModularIndexing(i0, 1, 32) + 32 * ModularIndexing(i0, 32, 4) + becomes + ModularIndexing(i0, 1, 128) + ModularIndexing(i0, 1, 32) + 32 * FloorDiv(i0, 32) + becomes i0 + + + This type of pattern can come from view operations + """ + assert isinstance(expr, sympy.Add) + + scale = sympy.Wild("scale", exclude=[0], integer=True) + base = sympy.Wild("base", integer=True) + divisor = sympy.Wild("divisor", integer=True) + mod1 = sympy.Wild("modulus", integer=True) + mod2 = sympy.Wild("modulus2", integer=True) + for term1 in expr.args: + m1 = term1.match(scale * ModularIndexing(base, divisor, mod1)) + if m1: + for term2 in expr.args: + m2 = term2.match( + m1[scale] + * m1[mod1] + * ModularIndexing(m1[base], m1[divisor] * m1[mod1], mod2) + ) + if m2 and term1 != term2: + expr = join_dimensions( + expr + - term1 + - term2 + + m1[scale] + * ModularIndexing(m1[base], m1[divisor], m1[mod1] * m2[mod2]) + ) + return expr + for term1 in expr.args: + m1 = term1.match(scale * ModularIndexing(base, divisor, mod1)) + if m1: + for term2 in expr.args: + m2 = term2.match( + m1[scale] * m1[mod1] * FloorDiv(m1[base], m1[divisor] * m1[mod1]) + ) + if m2 is not None: # in case of success we get an empty dict here + expr = join_dimensions( + expr + - term1 + - term2 + + m1[scale] * FloorDiv(m1[base], m1[divisor]) + ) + return expr + return expr + + +class SimplifyIndexing(V.WrapperHandler): # type: ignore[name-defined] + """ + A wrapper around .virtualize.ops that uses var range information to + simplify ModularIndexing/FloorDiv. + """ + + def __init__(self, inner, var_ranges: VarRanges) -> None: + super().__init__(inner) + self.name = "SimplifyIndexing" + self._simplify: Callable[ + [Expr], Expr + ] = lambda index: V.graph.sizevars.simplify_with_ranges(index, var_ranges) + + def load(self, name: str, index: sympy.Expr): + return self._inner.load(name, self._simplify(index)) + + def store(self, name, index, value, mode=None): + return self._inner.store(name, self._simplify(index), value, mode=mode) + + def store_reduction(self, name, index, value): + return self._inner.store_reduction(name, self._simplify(index), value) + + def index_expr(self, index, dtype): + return self._inner.index_expr(self._simplify(index), dtype) + + def check_bounds(self, index, size, lower, upper): + return self._inner.check_bounds(self._simplify(index), size, lower, upper) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/subgraph_lowering.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/subgraph_lowering.py new file mode 100644 index 0000000000000000000000000000000000000000..21145375ede8fa1eb803955c435564238148fb1c --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/subgraph_lowering.py @@ -0,0 +1,155 @@ +"""Utilities for lowering subgraphs used by higher order operators + +""" + +import functools +import operator +from dataclasses import dataclass +from typing import Any, Callable, Dict, List, Optional, Tuple, TypeVar, Union +from typing_extensions import ParamSpec + +import torch + +from . import ir +from .exc import SubgraphLoweringException +from .ops_handler import SimpleCSEHandler +from .sizevars import SizeVarAllocator +from .virtualized import ops, V, WrapperHandler + + +T = TypeVar("T") +_P = ParamSpec("_P") + + +class PointwiseSubgraphLowering(torch.fx.Interpreter): + graph_outputs: Optional[List[ir.IRNode]] + + def __init__( + self, + gm: torch.fx.GraphModule, + root_graph_lowering: "torch._inductor.graph.GraphLowering", + ) -> None: + super().__init__(gm) + self.graph_outputs = None + self.root_graph = root_graph_lowering + + @property + def sizevars(self) -> SizeVarAllocator: + return self.root_graph.sizevars + + def mark_buffer_mutated(self, name: str) -> None: + raise SubgraphLoweringException("Mutations are not supported in this context") + + def register_buffer(self, buffer: ir.Buffer) -> str: + raise SubgraphLoweringException( + "Buffer creation is not supported in this context" + ) + + def call_function( + self, + target: Callable[[Any], Any], # type: ignore[override] + args: Any, + kwargs: Dict[str, Any], + ) -> Any: + from .lowering import lowerings + + if target is operator.getitem and isinstance(args[0], (list, tuple, dict)): + return super().call_function(target, args, kwargs) + + assert isinstance(target, torch._ops.OpOverload) + + if target not in lowerings: + raise SubgraphLoweringException( + f"{target} not supported in subgraph, (missing lowering)" + ) + + if torch.Tag.pointwise not in target.tags: + raise SubgraphLoweringException( + f"Only pointwise operators are supported in this context, but got {target}" + ) + + return lowerings[target](*args, **kwargs) + + def output(self, target: str, args: Tuple[Any], kwargs: Dict[str, Any]) -> None: # type: ignore[override] + assert len(args) == 1 + self.graph_outputs = args[0] + + +@dataclass +class InputDescriptor: + dtype: torch.dtype + device: torch.device + + +class TracingOpsHandler(WrapperHandler[T]): + def __init__(self, tracer: torch.fx.Tracer, num_inputs: int) -> None: + parent = tracer.create_proxy("placeholder", "ops", (), {}) + super().__init__(parent) + self.tracer = tracer + + self.placeholders = [ + self.tracer.create_proxy("placeholder", f"input{i}", (), {}) + for i in range(num_inputs) + ] + + def placeholder(self, idx: int) -> torch.fx.Proxy: + return self.placeholders[idx] + + def output(self, *args: Tuple[object]) -> torch.fx.Node: + return self.tracer.create_node( + "output", "output", (tuple(self.tracer.create_arg(a) for a in args),), {} + ) + + +def lower_pointwise_subgraph( + subgraph: ir.Subgraph, inputs: List[InputDescriptor] +) -> Callable[_P, Any]: + # Lower subgraph to ir.Pointwise nodes + def fake_inner_fn( + loop_idx: int, input_idx: int + ) -> Union[ir.Expr, ir.TensorBox, None]: + return ops.placeholder(input_idx) + + graph_inputs = [ + ir.Pointwise.create( + device=desc.device, + dtype=desc.dtype, + inner_fn=functools.partial(fake_inner_fn, input_idx=i), + ranges=[], + ) + for i, desc in enumerate(inputs) + ] + gm = subgraph.graph_module + pw_subgraph = PointwiseSubgraphLowering(gm, root_graph_lowering=V.graph) + with V.set_graph_handler(pw_subgraph): # type: ignore[arg-type] + pw_subgraph.run(*graph_inputs) + + # Combine multiple pointwise computations into a single graph module + # Do this by tracing through each individually and doing CSE + tracer = torch.fx.Tracer() + tracer.graph = torch.fx.Graph(tracer_cls=tracer.__class__) + trace_ops = SimpleCSEHandler(TracingOpsHandler(tracer, len(inputs))) + assert pw_subgraph.graph_outputs is not None + + with V.set_ops_handler(trace_ops): + output_irs = [] + + for out_var in pw_subgraph.graph_outputs: + assert isinstance(out_var, ir.TensorBox), type(out_var) + assert out_var.get_size() == [] + assert isinstance(out_var.data, ir.StorageBox) + assert isinstance(out_var.data.data, ir.Pointwise) + + idx = () + ir_out = out_var.data.data.inner_fn(idx) + + output_irs.append(ir_out) + + ops.output(*output_irs) + + lowered_gm = torch.fx.GraphModule({}, tracer.graph) + + def inner_fn(*args: _P.args, **kwargs: _P.kwargs) -> Any: + return lowered_gm(V.get_ops_handler(), *args, **kwargs) + + return inner_fn diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/test_case.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/test_case.py new file mode 100644 index 0000000000000000000000000000000000000000..53a791685c67ed005f8d88f8d64661187e63e0bb --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/test_case.py @@ -0,0 +1,35 @@ +# mypy: allow-untyped-defs +import contextlib +import os + +from torch._dynamo.test_case import ( + run_tests as dynamo_run_tests, + TestCase as DynamoTestCase, +) +from torch._inductor import config +from torch._inductor.utils import fresh_inductor_cache + + +def run_tests(needs=()): + dynamo_run_tests(needs) + + +class TestCase(DynamoTestCase): + """ + A base TestCase for inductor tests. Enables FX graph caching and isolates + the cache directory for each test. + """ + + def setUp(self): + super().setUp() + self._inductor_test_stack = contextlib.ExitStack() + self._inductor_test_stack.enter_context(config.patch({"fx_graph_cache": True})) + if ( + os.environ.get("INDUCTOR_TEST_DISABLE_FRESH_CACHE") != "1" + and os.environ.get("TORCH_COMPILE_DEBUG") != "1" + ): + self._inductor_test_stack.enter_context(fresh_inductor_cache()) + + def tearDown(self): + super().tearDown() + self._inductor_test_stack.close() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/test_operators.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/test_operators.py new file mode 100644 index 0000000000000000000000000000000000000000..a5c1d401f2d0207503273dc709b01e50a288fe1c --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/test_operators.py @@ -0,0 +1,27 @@ +# mypy: allow-untyped-defs +import torch.library +from torch import Tensor +from torch.autograd import Function + + +if not torch._running_with_deploy(): + _test_lib_def = torch.library.Library("_inductor_test", "DEF") + _test_lib_def.define( + "realize(Tensor self) -> Tensor", tags=torch.Tag.pt2_compliant_tag + ) + + _test_lib_impl = torch.library.Library("_inductor_test", "IMPL") + for dispatch_key in ("CPU", "CUDA", "Meta"): + _test_lib_impl.impl("realize", lambda x: x.clone(), dispatch_key) + + class Realize(Function): + @staticmethod + def forward(ctx, x): + return torch.ops._inductor_test.realize(x) + + @staticmethod + def backward(ctx, grad_output): + return grad_output + + def realize(x: Tensor) -> Tensor: + return Realize.apply(x) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b17f3a68559a60d0d84f814739cc3b325b07594c --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/utils.py @@ -0,0 +1,2037 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import collections +import contextlib +import dataclasses +import enum +import functools +import inspect +import io +import itertools +import logging +import math +import operator +import os +import platform +import shutil +import sys +import tempfile +import textwrap +import time +import unittest +from datetime import datetime +from io import StringIO +from typing import ( + Any, + Callable, + Dict, + Generic, + Iterable, + List, + NamedTuple, + Optional, + Protocol, + Sequence, + Set, + TypeVar, + Union, + ValuesView, +) +from typing_extensions import Concatenate, ParamSpec +from unittest import mock + +import sympy + +import torch + + +GPU_TYPES = ["cuda", "xpu"] + + +# defines here before import torch._dynamo is for avoiding circular import +# when get_gpu_type is imported from dynamo +@functools.lru_cache(None) +def get_gpu_type(): + avail_gpus = [x for x in GPU_TYPES if getattr(torch, x).is_available()] + assert len(avail_gpus) <= 1 + gpu_type = "cuda" if len(avail_gpus) == 0 else avail_gpus.pop() + return gpu_type + + +from torch._dynamo.device_interface import get_interface_for_device +from torch._dynamo.utils import detect_fake_mode +from torch.autograd import DeviceType +from torch.autograd.profiler_util import EventList +from torch.fx.passes.graph_transform_observer import GraphTransformObserver +from torch.fx.passes.shape_prop import ShapeProp +from torch.utils._sympy.functions import ( + CeilDiv, + CleanDiv, + FloorDiv, + Identity, + ModularIndexing, +) +from torch.utils._sympy.symbol import make_symbol, SymT +from torch.utils._sympy.value_ranges import bound_sympy, ValueRanges + +from . import config +from .runtime.runtime_utils import ceildiv as runtime_ceildiv + + +_IS_WINDOWS = sys.platform == "win32" + +log = logging.getLogger(__name__) + +_T = TypeVar("_T") +VarRanges = Dict[sympy.Expr, sympy.Expr] +InputType = Union[torch.Tensor, int] + + +GPU_ALIGN_BYTES = 16 +ALIGNMENT = 16 + +ALIGN_BYTES = 64 +assert (ALIGN_BYTES & (ALIGN_BYTES - 1)) == 0 and ALIGN_BYTES >= 8, "must be power of 2" + + +def _align(nbytes): + """Round up to the nearest multiple of ALIGN_BYTES""" + return (nbytes + ALIGN_BYTES - 1) & -ALIGN_BYTES + + +def _is_aligned(v: sympy.Expr): + """v can be statically proven to be a multiple of ALIGN_BYTES""" + if isinstance(v, (sympy.Add, sympy.Max)): + return all(map(_is_aligned, v.args)) + return isinstance(v, align) or sympy.gcd(v, ALIGN_BYTES) == ALIGN_BYTES + + +class align(sympy.Function): + """Symbolically round up to the nearest multiple of ALIGN_BYTES""" + + nargs = (1,) + is_integer = True + + @classmethod + def eval(cls, value): + if isinstance(value, (int, sympy.Integer)): + return _align(int(value)) + if _is_aligned(value): + return value + + +def do_bench_using_profiling(fn: Callable[[], Any], warmup=25, rep=100) -> float: + """ + Returns benchmark results by examining torch profiler events. + This could be more accurate as it doesn't count CPU side overhead. + However, this also requires manually excluding irrelevant event, e.g. + vectorized_elementwise_kernel which is used to fill L2 cache, + various CUDA events, etc, so could also be fragile. + """ + + fn() + torch.cuda.synchronize() + cache = torch.empty(int(256e6 // 4), dtype=torch.int, device="cuda") + + # Estimate the runtime of the function + start_event = torch.cuda.Event(enable_timing=True) + end_event = torch.cuda.Event(enable_timing=True) + start_event.record() + for _ in range(5): + cache.zero_() + fn() + end_event.record() + torch.cuda.synchronize() + estimate_ms = start_event.elapsed_time(end_event) / 5 + + # compute number of warmup and repeat + n_warmup = max(1, int(warmup / estimate_ms)) + n_repeat = max(1, int(rep / estimate_ms)) + + # Warm-up + for _ in range(n_warmup): + fn() + + with torch.profiler.profile( + activities=[ + torch.profiler.ProfilerActivity.CUDA, + ] + ) as p: + # Benchmark + for i in range(n_repeat): + # we clear the L2 cache before each run + cache.zero_() + # record time of `fn` + fn() + # Record clocks + torch.cuda.synchronize() + + log.debug("raw events") + log.debug(p.key_averages().table(sort_by="self_device_time_total", row_limit=-1)) + + filtered_events = EventList( + [ + event + for event in p.events() + if event.device_type == DeviceType.CUDA and event.name != "Context Sync" + ] + ) + if len(filtered_events) % n_repeat != 0: + raise RuntimeError( + "Failed to divide all profiling events into #repeat groups. " + "#CUDA events: %d, #repeats: %s", + len(filtered_events), + n_repeat, + ) + num_event_per_group = len(filtered_events) / n_repeat + actual_events = EventList( + [ + event + for i, event in enumerate(filtered_events) + if i % num_event_per_group != 0 + ] + ) + actual_events._build_tree() + actual_events = actual_events.key_averages() + + log.debug("profiling time breakdown") + log.debug(actual_events.table(row_limit=-1)) + + res = sum(event.device_time_total for event in actual_events) / 1000.0 / n_repeat + log.debug("profiling results: %s ms", res) + return res + + +@functools.lru_cache(None) +def has_torchvision_roi_align() -> bool: + try: + from torchvision.ops import roi_align # noqa: F401 + + torch._C._dispatch_has_kernel_for_dispatch_key("torchvision::nms", "Meta") + return roi_align is not None and hasattr( + getattr(torch.ops, "torchvision", None), "roi_align" + ) + except ImportError: + return False + except RuntimeError as e: + assert "torchvision::nms does not exist" in str(e) + return False + + +def decode_device(device: Union[Optional[torch.device], str]) -> torch.device: + if device is None: + return torch.tensor(0.0).device # default device + if isinstance(device, str): + device = torch.device(device) + if device.type not in ("cpu", "meta") and device.index is None: + device_interface = get_interface_for_device(device.type) + return torch.device(device.type, index=device_interface.Worker.current_device()) + return device + + +def sympy_product(it): + return functools.reduce(operator.mul, it, sympy.Integer(1)) + + +def sympy_dot(seq1, seq2): + assert len(seq1) == len(seq2) + return sympy.expand(sum(a * b for a, b in zip(seq1, seq2))) + + +def unique(it: Iterable[_T]) -> ValuesView[_T]: + return {id(x): x for x in it}.values() + + +def ceildiv( + numer: Union[int, sympy.Expr], denom: Union[int, sympy.Expr] +) -> Union[int, sympy.Expr]: + if isinstance(numer, sympy.Expr) or isinstance(denom, sympy.Expr): + return CeilDiv(sympy.sympify(numer), sympy.sympify(denom)) + # TODO: There is a bug in a call to this function, to repro: + # python benchmarks/dynamo/huggingface.py --inductor -d cuda --accuracy + # --amp --only YituTechConvBert --dynamic-shapes + assert isinstance(numer, int) and isinstance( + denom, int + ), f"{numer}: {type(numer)}, {denom}: {type(denom)}" + return runtime_ceildiv(numer, denom) + + +def _type_of(key): + # Use the function here to get rid of dependencies on the Triton during the codegen. + # Refer to Triton implementation here: + # https://github.com/openai/triton/blob/98b5945d2aef679e00ebca8e07c35c3658ec76de/python/triton/runtime/jit.py#L238 + # `None` is nullptr. Implicitly convert to *i8. + if key is None: + return "*i8" + dtype_str = str(key).split(".")[-1] + tys = { + "bool": "i1", + "float8e4nv": "fp8e4nv", + "float8e5": "fp8e5", + "float8e4b15": "fp8e4b15", + "float8e4b15x4": "fp8e4b15x4", + "float8_e4m3fn": "fp8e4nv", + "float8_e5m2": "fp8e5", + "float16": "fp16", + "bfloat16": "bf16", + "float32": "fp32", + "float64": "fp64", + "int8": "i8", + "int16": "i16", + "int32": "i32", + "int64": "i64", + "uint8": "u8", + "uint16": "u16", + "uint32": "u32", + "uint64": "u64", + } + # reinterpret can create triton type + for v in list(tys.values()): + tys[v] = v + return key if isinstance(key, str) else f"*{tys[dtype_str]}" + + +def convert_shape_to_inductor( + lst: Iterable[Union[int, torch.SymInt]] +) -> List[sympy.Expr]: + """ + Gets the shape and stride of a tensor. For non-symbolic tensors, this is + trivial. But for symbolic tensors, we need to map from SymIntNode into + sympy.Expr. + """ + return [sympy.sympify(i) for i in lst] + + +def convert_shape_to_symint( + lst: Iterable[Union[int, sympy.Expr]] +) -> List[Union[int, torch.SymInt]]: + """ + Takes a list of shapes from Inductor and converts them into symints (or just + ints if all shapes are static). + """ + from .virtualized import V + + return [ + i + if isinstance(i, int) + else int(i) + if isinstance(i, sympy.Integer) + else V.graph.sizevars.shape_env.create_symintnode(i, hint=None) + for i in lst + ] + + +def is_view(op: torch._ops.OpOverload): + """ + Does this op overload have aliasing + """ + assert isinstance(op, torch._ops.OpOverload) + return any(a.alias_info is not None for a in op._schema.arguments) + + +def is_pointwise_use( + use, is_pointwise_fn: Optional[Callable[[torch._ops.OpOverload], bool]] = None +): + """ + Do all uses of this op have torch.Tag.pointwise or return True for optional `is_pointwise_fn` + + Uses in views ops will follow the views uses + """ + + if not use.op == "call_function": + return False + + if not ( + isinstance(use.target, torch._ops.OpOverload) or use.target is operator.getitem + ): + return False + + if use.target is operator.getitem or is_view(use.target): + return all(is_pointwise_use(u, is_pointwise_fn) for u in use.users) + + return torch.Tag.pointwise in use.target.tags or ( + is_pointwise_fn is not None and is_pointwise_fn(use.target) + ) + + +def gen_gm_and_inputs(target, args, kwargs): + g = torch.fx.Graph() + g_args = [] + a_args = [] + for n, arg in enumerate(args): + if isinstance(arg, torch.Tensor): + g_args.append(g.placeholder(f"arg{n}")) + a_args.append(arg) + else: + g_args.append(arg) + assert all(not isinstance(x, torch.Tensor) for x in kwargs.values()) + node = g.call_function(target, tuple(g_args), kwargs) + if ( + len(target._schema.returns) == 1 + and str(target._schema.returns[0].type) == "Tensor" + ): + node = (node,) # type: ignore[assignment] + g.output(node) + + gm = torch.fx.GraphModule({}, g) + return gm, a_args + + +def synchronize(device: str = "cuda"): + if device == "cpu": + return + device_interface = get_interface_for_device(device) + if device_interface.is_available(): + device_interface.synchronize() + + +def timed( + model: Callable[..., Any], example_inputs, times: int = 1, device: str = "cuda" +) -> float: + synchronize(device) + torch.manual_seed(1337) + t0 = time.perf_counter() + for _ in range(times): + result = model(*example_inputs) + synchronize(device) + t1 = time.perf_counter() + # GC the result after timing + assert result is not None # type: ignore[possibly-undefined] + return t1 - t0 + + +def print_performance( + fn, args=(), times=10, repeat=10, baseline=1.0, device: str = "cuda" +): + timings = torch.tensor([timed(fn, args, times, device) for _ in range(repeat)]) + took = torch.median(timings) / times + print(f"{took / baseline:.6f}") + return took + + +def precompute_method(obj: Any, method: str): + """Replace obj.method() with a new method that returns a precomputed constant.""" + result = getattr(obj, method)() + setattr(obj, method, lambda: result) + + +def precompute_methods(obj: Any, methods: List[str]): + """Replace methods with new methods that returns a precomputed constants.""" + for method in methods: + precompute_method(obj, method) + + +def cmp(a, b) -> int: + return int(a > b) - int(a < b) + + +def pad_listlike(x, size): + if len(x) == 1: + return type(x)([x[0]]) * size + else: + return x + + +# Used to ensure that iterating over a set is deterministic +def tuple_sorted(x): + if len(x) == 0: + return [] + + def sort_func(elem): + if isinstance(elem, str): + return elem + else: + # We expect `elem` to be `scheduler.BaseSchedulerNode` type here, + # but we are not able to do isinstance assert because of circular dependency + return elem.get_name() + + return sorted(x, key=sort_func) + + +P = ParamSpec("P") +RV = TypeVar("RV", covariant=True) + + +class CachedMethod(Protocol, Generic[P, RV]): + @staticmethod + def clear_cache(self) -> None: + ... + + def __call__(self, *args: P.args, **kwargs: P.kwargs) -> RV: + ... + + +# See https://github.com/python/mypy/issues/13222#issuecomment-1193073470 to understand the type signature +def cache_on_self(fn: Callable[Concatenate[Any, P], RV]) -> CachedMethod[P, RV]: + key = f"__{fn.__name__}_cache" + + @functools.wraps(fn) + def wrapper(self): + if not hasattr(self, key): + setattr(self, key, fn(self)) + return getattr(self, key) + + def clear_cache(self): + if hasattr(self, key): + delattr(self, key) + + wrapper.clear_cache = clear_cache # type: ignore[attr-defined] + return wrapper # type: ignore[return-value] + + +def aggregate_origins(node_schedule): + from . import ir + + if isinstance(node_schedule, list): + return functools.reduce( + operator.or_, + [ + node.node.origins + for node in node_schedule + if hasattr(node, "node") and node.node + ], + set(), + ) + elif isinstance(node_schedule, ir.ExternKernel): + return node_schedule.origins + else: + return set() + + +def get_fused_kernel_name(node_schedule, descriptive_names): + all_origins = aggregate_origins(node_schedule) + if descriptive_names == "original_aten": + # Bases the kernel name off of the top-level aten operator (i.e. pre-decompositions) + sources = [ + origin.meta["original_aten"]._overloadpacket.__name__ + for origin in all_origins + if origin.op == "call_function" + and "original_aten" in origin.meta + and origin.meta["original_aten"] is not None + ] + sources = sorted(set(sources)) + elif descriptive_names == "torch": + # Bases the kernel name off of the top-level "torch" operator (i.e. post-dynamo graph) + sources = [] + for origin in all_origins: + if origin.op == "call_function" and "source_fn_stack" in origin.meta: + source_fn = origin.meta["source_fn_stack"][-1] + if isinstance(source_fn[1], str): + sources.append(source_fn[1]) + else: + sources.append(source_fn[1].__name__) + sources = sorted(set(sources)) + elif descriptive_names == "inductor_node": + sources = [ + origin.name for origin in all_origins if origin.op == "call_function" + ] + else: + raise NotImplementedError + sources = sources + return "_".join(["fused"] + sources) + + +def get_kernel_metadata(node_schedule, wrapper): + all_origins = aggregate_origins(node_schedule) + inductor_nodes = [origin for origin in all_origins if origin.op == "call_function"] + + from_node_dict = collections.defaultdict(list) + original_aten_dict = collections.defaultdict(list) + + # Attempt to sort `inductor_nodes` topologically. Note that the case + # where `inductor_nodes` contains nodes from multiple graph instances + # is not supported. An example of this is conditional statements. + single_graph = None + if len(inductor_nodes): + unique_graphs = {n.graph for n in inductor_nodes} + if len(unique_graphs) == 1: + single_graph = inductor_nodes[0].graph + # create a map of idx -> node and cache it + if not hasattr(single_graph, "_inductor_kernel_metadata_node_to_idx_map"): + node_to_idx_map = {} + for idx, n in enumerate(single_graph.nodes): + node_to_idx_map[n] = idx + single_graph._inductor_kernel_metadata_node_to_idx_map = node_to_idx_map + inductor_nodes.sort( + key=lambda n: single_graph._inductor_kernel_metadata_node_to_idx_map[n] + ) + + for node in inductor_nodes: + if "original_aten" in node.meta and node.meta["original_aten"] is not None: + key = str(node.meta["original_aten"]._overloadpacket) + original_aten_dict[key].append(node.name) + if "from_node" in node.meta: + key = node.meta["from_node"][0][0] + from_node_dict[key].append(node.name) + sort_str = "Topologically Sorted" if single_graph is not None else "Unsorted" + metadata = ( + f"{wrapper.comment} {sort_str} Source Nodes: [{', '.join(from_node_dict.keys())}], " + f"Original ATen: [{', '.join(original_aten_dict.keys())}]" + ) + + # trace back to original node here + detailed_metadata = [f"{wrapper.comment} Source node to ATen node mapping:"] + for original_node, nodes in sorted(from_node_dict.items()): + detailed_metadata.append( + f"{wrapper.comment} {original_node} => {', '.join(sorted(nodes))}" + ) + + # print the aot_autograd graph fragment + if single_graph is not None: + detailed_metadata.append(f"{wrapper.comment} Graph fragment:") + for n in inductor_nodes: + # TODO(future): maybe refactor torch/fx/graph.py to make it easy to + # generate python code for graph fragments + detailed_metadata.append(f"{wrapper.comment} {n.format_node()}") + + return metadata, "\n".join(detailed_metadata) + + +def dominated_nodes( + initial_queue: Iterable[torch.fx.Node], skip_filter=None +) -> Set[torch.fx.Node]: + """Returns the set of nodes whose values depend on those within initial_queue""" + initial_queue = list(initial_queue) + dominated_set = set(initial_queue) + + while initial_queue: + node = initial_queue.pop() + for user in node.users: + if skip_filter and skip_filter(user): + continue + if user not in dominated_set: + dominated_set.add(user) + initial_queue.append(user) + + return dominated_set + + +def gather_origins(args, kwargs): + import itertools + + from . import ir + + def is_unrealized_node(n): + if isinstance(n, ir.TensorBox): + return is_unrealized_node(n.data) + if isinstance(n, ir.StorageBox): + return is_unrealized_node(n.data) + return isinstance(n, ir.IRNode) and isinstance(n, ir.Pointwise) + + kwarg_origins = [val.origins for val in kwargs.values() if is_unrealized_node(val)] + arg_origins = [arg.origins for arg in args if is_unrealized_node(arg)] + return set(itertools.chain(*arg_origins, *kwarg_origins)) + + +def sympy_str(expr: sympy.Expr) -> str: + """ + Normal sympy str is very slow, this is a lot faster. The result are + somewhat worse, as it doesn't do as much simplification. So don't + use this for final codegen. + """ + if isinstance(expr, sympy.Symbol): + return expr.name + if isinstance(expr, sympy.Add): + return " + ".join(map(sympy_str, expr.args)) + if isinstance(expr, sympy.Mul): + return " * ".join(map(sympy_str, expr.args)) + + if isinstance(expr, (ModularIndexing, CleanDiv, FloorDiv, Identity)): + return f"{expr.func.__name__}({', '.join(map(sympy_str, expr.args))})" + return str(expr) + + +def get_bounds_index_expr(index): + from .virtualized import V + + # If this expression does not come from an FX node, we compute its bounds + if ( + config.compute_all_bounds + and (fx_node := getattr(V.interpreter, "current_node", None)) + and fx_node.target != "index_expr" + ): + return bound_sympy(index) + else: + return ValueRanges.unknown() + + +def sympy_index_symbol_with_prefix(prefix: SymT, idx: int) -> sympy.Symbol: + """ + Used to generate an integer-nonnegative symbol. + """ + # This should never be used for creating shape/stride symbols, as those + # should all be allocated before Inductor. + assert prefix != SymT.SIZE + # NOTE: shape symbols are positive (> 0), but index variables are only + # non-negative (>= 0). + return make_symbol(prefix, idx, integer=True, nonnegative=True) + + +def generate_assert(check): + return (check or config.debug_index_asserts) and config.assert_indirect_indexing + + +def sympy_index_symbol(name: str) -> sympy.Symbol: + """ + Used to generate an integer-nonnegative symbol. + """ + # This should never be used for creating shape/stride symbols, as those + # should all be allocated before Inductor. + assert name[0] != "s" + # NOTE: shape symbols are positive (> 0), but index variables are only + # non-negative (>= 0). + return sympy.Symbol(name, integer=True, nonnegative=True) + + +def sympy_subs(expr: sympy.Expr, replacements: Dict[sympy.Expr, Any]) -> sympy.Expr: + """ + When the passed replacement symbol v is a string, it is converted to a symbol with name v that + have the same replaced expression integer and nonnegative properties. + """ + + def to_symbol(replaced, replacement): + assert isinstance(replaced, sympy.Expr) + if isinstance(replacement, str): + return sympy.Symbol( + replacement, + integer=replaced.is_integer, # type: ignore[attr-defined] + nonnegative=replaced.is_nonnegative, # type: ignore[attr-defined] + ) + else: + return replacement + + # xreplace is faster than subs, but is way more picky + return sympy.sympify(expr).xreplace( + {k: to_symbol(k, v) for k, v in replacements.items()} + ) + + +def is_symbolic(a: Any) -> bool: + return isinstance(a, torch.SymInt) or ( + isinstance(a, torch.Tensor) + and any(is_symbolic(x) for x in itertools.chain(a.size(), a.stride())) + ) + + +def any_is_symbolic(*args: Any) -> bool: + return any(is_symbolic(a) for a in args) + + +def get_first_incompatible_cudagraph_node(gm): + from torch.fx.experimental.symbolic_shapes import free_unbacked_symbols + + forbidden_set = { + "aten._fused_moving_avg_obs_fq_helper.default", + "aten._fused_moving_avg_obs_fq_helper_functional.default", + "aten.multinomial.default", + "fbgemm.dense_to_jagged.default", + "fbgemm.jagged_to_padded_dense.default", + "run_and_save_rng_state", + "run_with_rng_state", + "aten._local_scalar_dense", + # Technically, it's not necessary to ban this, because an + # assert_scalar with constant arguments can be validly run + # with CUDA graphs, but the operator is also pointless with + # constant arguments, so might as well ban + "aten._assert_scalar", + } + if torch.are_deterministic_algorithms_enabled(): + forbidden_set.update( + { + "aten._unsafe_index_put.default", + "aten._unsafe_masked_index_put_accumulate.default", + "aten.index_put.default", + "aten.index_put_.default", + "aten.scatter.src", + "aten.scatter.reduce", + "aten.scatter.value_reduce", + "aten.scatter_add_", + "aten.scatter_add.default", + "aten.scatter_reduce.two", + "aten.scatter_reduce_.two", + "aten.scatter_reduce.two_out", + } + ) + for node in gm.graph.nodes: + if str(node.target) in forbidden_set: + return node + if (val := node.meta.get("val")) is not None and free_unbacked_symbols(val): + return node + return None + + +def has_incompatible_cudagraph_ops(gm): + return get_first_incompatible_cudagraph_node(gm) is not None + + +def output_node(gm: torch.fx.GraphModule): + """Get the output node from an FX graph""" + last_node = next(iter(reversed(gm.graph.nodes))) + assert last_node.op == "output" + return last_node + + +_registered_caches: List[Any] = [] + + +def clear_on_fresh_inductor_cache(obj: Any): + """ + Use this decorator to register any caches that should be cache_clear'd + with fresh_inductor_cache(). + """ + if not hasattr(obj, "cache_clear") or not callable(obj.cache_clear): + raise AttributeError(f"{obj} does not have a cache_clear method") + + _registered_caches.append(obj) + return obj + + +def clear_inductor_caches(): + """ + Clear all registered caches. + """ + for obj in _registered_caches: + obj.cache_clear() + + +@contextlib.contextmanager +def fresh_inductor_cache(cache_entries=None, dir=None, delete=True): + """ + Contextmanager that provides a clean tmp cachedir for inductor. + + Optionally, pass a dict as 'cache_entries' to get a list of filenames and sizes + generated with this cache instance. + """ + clear_inductor_caches() + + inductor_cache_dir = tempfile.mkdtemp(dir=dir) + try: + with mock.patch.dict( + os.environ, {"TORCHINDUCTOR_CACHE_DIR": inductor_cache_dir} + ): + log.debug("Using inductor cache dir %s", inductor_cache_dir) + triton_cache_dir = os.path.join(inductor_cache_dir, "triton") + with mock.patch.dict(os.environ, {"TRITON_CACHE_DIR": triton_cache_dir}): + yield + if isinstance(cache_entries, dict): + assert len(cache_entries) == 0, "expected empty cache_entries dict" + if os.path.exists(triton_cache_dir): + files = os.listdir(triton_cache_dir) + cache_entries.update( + { + f: os.path.getsize(os.path.join(triton_cache_dir, f)) + for f in files + if ".lock" not in f + } + ) + if delete: + shutil.rmtree(inductor_cache_dir) + except Exception: + if not _IS_WINDOWS: + """ + Windows can't delete the loaded modules, because the modules binaries are opened. + TODO: discuss if have better solution to handle this issue. + """ + log.warning("on error, temporary cache dir kept at %s", inductor_cache_dir) + raise + finally: + clear_inductor_caches() + + +def argsort(seq) -> List[int]: + # preserve original order for equal strides + getter = seq.__getitem__ + a_r = range(len(seq)) + return list(reversed(sorted(a_r, key=getter, reverse=True))) # noqa: C413 + + +@functools.lru_cache(8) +def get_dtype_size(dtype): + return torch.empty((), dtype=dtype).element_size() + + +class LineContext(NamedTuple): + context: Any + + +class IndentedBuffer: + tabwidth = 4 + + def __init__(self, initial_indent=0): + self._lines = [] + self._indent = initial_indent + + def getvaluewithlinemap(self) -> tuple[str, list[tuple[int, LineContext]]]: + buf = StringIO() + p = 1 + linemap = [] + for line in self._lines: + if isinstance(line, DeferredLineBase): + line = line() + if line is None: + continue + elif isinstance(line, LineContext): + linemap.append((p, line.context)) + continue + assert isinstance(line, str) + buf.write(line) + buf.write("\n") + p += 1 + line.count("\n") + return buf.getvalue(), linemap + + def getvalue(self) -> str: + v, _ = self.getvaluewithlinemap() + return v + + def getrawvalue(self) -> str: + buf = StringIO() + for line in self._lines: + if isinstance(line, DeferredLineBase): + line = line() + if line is None: + continue + elif isinstance(line, LineContext): + continue + assert isinstance(line, str) + # backslash implies line continuation + if line.endswith("\\"): + buf.write(line[:-1]) + else: + buf.write(line) + buf.write("\n") + return buf.getvalue() + + def clear(self): + self._lines.clear() + + def __bool__(self): + return bool(self._lines) + + def prefix(self): + return " " * (self._indent * self.tabwidth) + + def newline(self): + self.writeline("\n") + + def writeline(self, line): + if isinstance(line, LineContext): + self._lines.append(line) + elif isinstance(line, DeferredLineBase): + self._lines.append(line.with_prefix(self.prefix())) + elif line.strip(): + self._lines.append(f"{self.prefix()}{line}") + else: + self._lines.append("") + + def writelines(self, lines): + for line in lines: + self.writeline(line) + + def indent(self, offset=1): + @contextlib.contextmanager + def ctx(): + self._indent += offset + try: + yield + finally: + self._indent -= offset + + return ctx() + + def do_indent(self, offset=1): + self._indent += offset + + def do_unindent(self, offset=1): + self._indent -= offset + + def splice(self, other_code, strip=False): + if isinstance(other_code, IndentedBuffer): + dedent = float("inf") + for line in other_code._lines: + if not isinstance(line, LineContext) and line: + dedent = min(dedent, len(line) - len(line.lstrip())) + if math.isinf(dedent): + dedent = 0 + for line in other_code._lines: + if isinstance(line, LineContext): + self._lines.append(line) + else: + IndentedBuffer.writeline(self, line[int(dedent) :]) + else: + other_code = textwrap.dedent(other_code) + if strip: + other_code = other_code.lstrip() + if not other_code: + return + other_code = other_code.rstrip() + for line in other_code.split("\n"): + self.writeline(line) + + def map(self, func: Callable[[Any], Any]) -> IndentedBuffer: + res = IndentedBuffer(initial_indent=self._indent) + res._lines = [func(line) for line in self._lines] + return res + + def __repr__(self): + return f"{type(self)}({self.getvalue()})" + + def __add__(self, other): + assert self._indent == other._indent + res = IndentedBuffer(initial_indent=self._indent) + res.writelines(self._lines) + res.writelines(other._lines) + return res + + +class FakeIndentedBuffer(IndentedBuffer): + def __init__(self) -> None: + super().__init__() + + def __getattribute__(self, name): + if name == "__class__": # Allow access to the class attribute + return object.__getattribute__(self, name) + raise RuntimeError( + f"Tried to call self.{name} on FakeIndentedBuffer. This buffer" + "is currently used on TritonTemplateKernel to prevent actual" + "writes to the body without explicitly specifying the body with" + "`TritonTemplateKernel.set_subgraph_body(name)`" + ) + + +@contextlib.contextmanager +def restore_stdout_stderr(initial_stdout, initial_stderr): + try: + yield + finally: + sys.stdout = initial_stdout + sys.stderr = initial_stderr + + +class DeferredLineBase: + """A line that can be 'unwritten' at a later time""" + + def __init__(self, line): + if not line.strip(): + line = "" + self.line = line + + def __call__(self) -> Optional[str]: + """Returns either self.line or None to indicate the line has been 'unwritten'""" + raise NotImplementedError + + def _new_line(self, line: str) -> DeferredLineBase: + """Returns a new deferred line with the same condition""" + raise NotImplementedError + + def with_prefix(self, prefix): + return self._new_line(f"{prefix}{self.line}") + + def lstrip(self): + return self._new_line(self.line.lstrip()) + + def __getitem__(self, index): + return self._new_line(self.line[index]) + + def __bool__(self): + return bool(self.line) + + def __len__(self): + return len(self.line) + + +@functools.lru_cache(None) +def is_big_gpu(index) -> bool: + min_sms = 68 # 3080 + avail_sms = torch.cuda.get_device_properties(index).multi_processor_count + if avail_sms < min_sms: + log.warning( + "Not enough SMs to use max_autotune_gemm mode", + extra={"min_sms": min_sms, "avail_sms": avail_sms}, + ) + return False + return True + + +def use_max_autotune() -> bool: + return config.max_autotune or config.max_autotune_gemm + + +def _use_template_for_cuda(layout, allowed_layout_dtypes: List[torch.dtype]) -> bool: + return ( + use_max_autotune() + and layout.device.type == "cuda" + and layout.dtype in allowed_layout_dtypes + and is_big_gpu(layout.device.index or 0) + ) + + +def _use_autotune_backend(backend: str) -> bool: + return backend.upper() in [ + x.strip() for x in config.max_autotune_gemm_backends.upper().split(",") + ] + + +def _use_conv_autotune_backend(backend: str) -> bool: + return backend.upper() in [ + x.strip() for x in config.max_autotune_conv_backends.upper().split(",") + ] + + +def use_triton_template(layout, *, enable_int32=False, enable_float8=False): + from .codegen.common import BackendFeature, has_backend_feature + + layout_dtypes = [torch.float16, torch.bfloat16, torch.float32] + if enable_int32: + layout_dtypes = [torch.float16, torch.bfloat16, torch.float32, torch.int32] + if enable_float8: + layout_dtypes.extend([torch.float8_e4m3fn, torch.float8_e5m2]) + return ( + _use_template_for_cuda(layout, layout_dtypes) + and _use_autotune_backend("TRITON") + and has_backend_feature(layout.device, BackendFeature.TRITON_TEMPLATES) + ) + + +def use_cutlass_template(layout, m, n, k): + from .virtualized import V + + gemm_size = V.graph.sizevars.size_hint(m * n * k, fallback=-1) + if gemm_size <= 0 or gemm_size < config.cuda.cutlass_backend_min_gemm_size: + return False + from .codegen.cuda.cutlass_utils import try_import_cutlass + + # Do not use cutlass template on ROCm + if torch.version.hip: + return False + + layout_dtypes = [torch.float16, torch.bfloat16, torch.float32, torch.int32] + res = _use_template_for_cuda(layout, layout_dtypes) and _use_autotune_backend( + "CUTLASS" + ) + + if res: + if not try_import_cutlass(): + log.warning( + "Failed to import CUTLASS lib. Please check whether " + "_inductor.config.cuda.cutlass_dir is set correctly. " + "Skipping CUTLASS backend for now." + ) + return False + return res + + +@functools.lru_cache(None) +def _rocm_native_device_arch_name(device): + return torch.cuda.get_device_properties(device).gcnArchName + + +@functools.lru_cache(None) +def try_import_ck_lib(): + try: + import ck4inductor # type: ignore[import] + from ck4inductor.universal_gemm.gen_instances import ( # type: ignore[import] + gen_ops_library, + gen_ops_preselected, + ) + from ck4inductor.universal_gemm.op import ( # type: ignore[import] + CKGemmOperation, + ) + + package_dirname = os.path.dirname(ck4inductor.__file__) + except ImportError: + + def gen_ops_library(): + return [] + + def gen_ops_preselected(): + return [] + + class CKGemmOperation: # type: ignore[no-redef] + pass + + package_dirname = None + return package_dirname, gen_ops_library, gen_ops_preselected, CKGemmOperation + + +def use_ck_template(layout, m, n, k): + # config knobs check 1 + if not use_max_autotune(): + return False + # config knobs check 2 + if not _use_autotune_backend("CK"): + return False + # platform check + if not torch.version.hip: + return False + # tensors must be on GPU + if not layout.device.type == "cuda": + return False + # hardware check + # if config arch list is not specified, get the native arch from the device properties + native_arch = _rocm_native_device_arch_name(layout.device) + requested_archs = {k.split(":")[0]: k for k in config.rocm.arch} or { + native_arch.split(":")[0]: native_arch + } + requested_supported_archs = [ + requested_archs[k] + for k in requested_archs.keys() & config.rocm.ck_supported_arch + ] + if not requested_supported_archs: + return False + # supported input dtypes + if layout.dtype not in [torch.float16, torch.bfloat16]: + return False + # TBD: investigate if we need to disable backend based on number of available CUs similar to `is_big_gpu` + # check if shape is static and gemm size is not 0 + from .virtualized import V + + gemm_size = V.graph.sizevars.size_hint(m * n * k, fallback=-1) + if gemm_size <= 0: + return False + # TBD: investigate if backend needs to be disabled for small gemms similar to CUTLASS + + ck_package_dirname, _, _, _ = try_import_ck_lib() + + if not ck_package_dirname: + log.warning("Please pip install Composable Kernel package") + return False + + if not config.rocm.ck_dir: + log.warning("Please set TORCHINDUCTOR_CK_DIR env variable") + return False + + if ck_package_dirname != config.rocm.ck_dir: + log.warning("Invalid path to CK library") + return False + + return True + + +def _use_template_for_cpu(layout): + return use_max_autotune() and layout.device.type == "cpu" + + +def use_cpp_packed_gemm_template(layout, mat1, mat2, mat2_transposed=False): + from . import ir + from .codegen.cpp_micro_gemm import create_micro_gemm + from .codegen.cpp_utils import get_gemm_template_output_and_compute_dtype + from .kernel.mm_common import mm_args + + if not _use_template_for_cpu(layout) or not _use_autotune_backend("CPP"): + return False + + if not config.cpp.weight_prepack: + return False + + int8_gemm = mat1.get_dtype() == torch.uint8 + layout_dtypes = [torch.float32, torch.bfloat16, torch.half, torch.uint8] + m, n, k, layout, mat1, mat2 = mm_args( + mat1, + mat2, + out_dtype=layout.dtype if int8_gemm else None, + mat2_transposed=mat2_transposed, + ) + + # TODO(jgong5): support dynamic shapes for n or k + if has_free_symbols((n, k)): + return False + if isinstance(mat2, ir.BaseView): + mat2 = mat2.unwrap_view() + + output_dtype, _ = get_gemm_template_output_and_compute_dtype(mat1.get_dtype()) + micro_gemm = create_micro_gemm( + "micro_gemm", + m, + n, + k, + input_dtype=mat1.get_dtype(), + input2_dtype=mat2.get_dtype(), + output_dtype=output_dtype, + num_threads=parallel_num_threads(), + ) + + def is_last_dim_stride1(x): + x.freeze_layout() + return x.get_stride()[-1] == 1 + + return ( + layout.dtype in layout_dtypes + and micro_gemm is not None + and is_last_dim_stride1(mat1) # TODO(jgong5): support transposed input + and isinstance(mat2, ir.StorageBox) + and mat2.is_module_buffer() + ) + + +def use_aten_gemm_kernels(): + return not use_max_autotune() or _use_autotune_backend("ATEN") + + +class DebugDirManager: + counter = itertools.count(0) + prev_debug_name: str + + def __init__(self) -> None: + self.id = next(DebugDirManager.counter) + + def __enter__(self): + self.prev_debug_name = torch._dynamo.config.debug_dir_root + self.new_name = f"{self.prev_debug_name}_tmp_{self.id}" + torch._dynamo.config.debug_dir_root = self.new_name + + def __exit__(self, *args): + shutil.rmtree(self.new_name) + torch._dynamo.config.debug_dir_root = self.prev_debug_name + + +def run_and_get_code(fn, *args, **kwargs): + from .graph import GraphLowering + + source_codes: List[str] = [] + + def save_output_code(code: str): + source_codes.append(code) + + with mock.patch.object(GraphLowering, "save_output_code", save_output_code): + torch._dynamo.reset() + result = fn(*args, **kwargs) + return result, source_codes + + +def run_fw_bw_and_get_code(fn): + def run_with_backward(): + result = fn() + result.sum().backward() + return result + + return run_and_get_code(run_with_backward) + + +def get_code(fn, *args, **kwargs): + """Get the inductor-generated code, but skip any actual compilation or running.""" + from .graph import GraphLowering + + source_codes: List[str] = [] + + def save_output_code(code: str): + source_codes.append(code) + + def patched_compile_to_module(self: GraphLowering): + class DummyModule: + """This is empty to replace the generated triton module""" + + def __init__(self) -> None: + pass + + def call(self, *args, **kwargs): + # Don't do anything when called + pass + + code, _ = ( + self.codegen_with_cpp_wrapper() if self.cpp_wrapper else self.codegen() + ) + # Skip all the actual compiling. + nonlocal save_output_code + save_output_code(code) + + return DummyModule() + + with mock.patch.object( + GraphLowering, "compile_to_module", patched_compile_to_module + ), mock.patch.object(GraphLowering, "save_output_code", save_output_code): + torch._dynamo.reset() + # Note the return here is None + _ = fn(*args, **kwargs) + + return source_codes + + +def get_triton_code(fn, *args, **kwargs): + source_codes = get_code(fn, *args, **kwargs) + # Can have two outputs if backwards was eagerly compiled + assert ( + 1 <= len(source_codes) <= 2 + ), f"expected one or two code outputs got {len(source_codes)}" + return source_codes[0] + + +def run_and_get_triton_code(fn, *args, **kwargs): + _, source_codes = run_and_get_code(fn, *args, **kwargs) + # Can have two outputs if backwards was eagerly compiled + assert ( + 1 <= len(source_codes) <= 2 + ), f"expected one or two code outputs got {len(source_codes)}" + return source_codes[0] + + +def run_and_get_graph_lowering(fn, *args, **kwargs): + from torch._inductor.codecache import CompiledFxGraph + from torch._inductor.graph import GraphLowering + + real_init = CompiledFxGraph.__init__ + graph_lowerings = [] + + def fake_init(*args, **kwargs): + real_init(*args, **kwargs) + graph = args[2] + assert isinstance(graph, GraphLowering) + graph_lowerings.append(graph) + + with mock.patch.object(CompiledFxGraph, "__init__", fake_init): + result = fn(*args, **kwargs) + + return result, graph_lowerings + + +@contextlib.contextmanager +def override_lowering(aten_op, override_fn): + """ + Override the lowering of aten_op with override_fn. + The first argument of override_fn is the original lowering fn. + """ + from torch._inductor import lowering + + orig_fn = lowering.lowerings[aten_op] + try: + lowering.lowerings[aten_op] = functools.partial(override_fn, orig_fn) + yield + finally: + lowering.lowerings[aten_op] = orig_fn + + +def add_scheduler_init_hook(pre_fn, post_fn=None): + """ + Add hook functions to be called at the beginning and end of Scheduler.__init__. + Used for unit tests. + """ + from torch._inductor.scheduler import Scheduler + + orig_fn = Scheduler.__init__ + + def wrapper(scheduler, nodes): + pre_fn(scheduler, nodes) + out = orig_fn(scheduler, nodes) + if post_fn: + post_fn(scheduler, nodes) + return out + + return unittest.mock.patch.object(Scheduler, "__init__", wrapper) + + +def developer_warning(msg): + """ + Warnings that will be actionable for PyTorch developers, but not + end users. Allows us to easily disable them in stable releases but + keep them on for nightly builds. + """ + if config.developer_warnings: + log.warning(msg) + else: + log.info(msg) + + +def get_benchmark_name(): + """ + An experimental API used only when config.benchmark_kernel is true. + + The benchmark name is only available at codegen time. So we can not + directly call it in benchmark_all_kernels which is run after codegen. + + The function assumes the argument after --only is the benchmark name. + It works for torchbench.py/hugginface.py/timm_models.py. But for ad-hoc + scripts, this function may return None. + + There are 2 flavors of --only argument we need handle: + 1. --only model_name + 2. --only=model_name + """ + try: + idx = sys.argv.index("--only") + if ( + idx + 1 < len(sys.argv) + and len(sys.argv[idx + 1]) > 0 + and sys.argv[idx + 1][0] != "-" + ): + return sys.argv[idx + 1] + except ValueError: + pass + + for arg in sys.argv: + if arg.startswith("--only="): + return arg[len("--only=") :] + + +def is_ones(items): + return all(x == 1 for x in items) + + +def is_zeros(items): + return all(x == 0 for x in items) + + +def is_cpu_device(inputs): + return all( + item.device == torch.device("cpu") + for item in inputs + if isinstance(item, torch.Tensor) + ) + + +def get_sympy_Expr_dtype(val: sympy.Expr) -> torch.dtype: + assert isinstance( + val, sympy.Expr + ), "only support sympy.Expr as input to get_sympy_Expr_dtype" + if val.is_integer: # type: ignore[attr-defined] + return torch.int64 + else: + return torch.float64 + + +@contextlib.contextmanager +def maybe_profile(should_profile, *args, **kwargs): + if should_profile: + with torch.profiler.profile(*args, **kwargs) as p: + yield p + else: + yield + + +def parallel_num_threads(): + threads = config.cpp.threads + if threads < 1: + threads = torch.get_num_threads() + return threads + + +@functools.lru_cache(None) +def get_device_tflops(dtype): + from triton.testing import get_max_simd_tflops, get_max_tensorcore_tflops + + assert dtype in (torch.float16, torch.bfloat16, torch.float32) + + if inspect.signature(get_max_simd_tflops).parameters.get("clock_rate"): + # Triton API change in https://github.com/openai/triton/pull/2293 + from torch._utils_internal import max_clock_rate + + sm_clock = max_clock_rate() + if dtype in (torch.float16, torch.bfloat16): + return get_max_tensorcore_tflops(dtype, sm_clock) + + if torch.backends.cuda.matmul.allow_tf32: + return get_max_tensorcore_tflops(torch.float32, sm_clock) + else: + return get_max_simd_tflops(torch.float32, sm_clock) + else: + if dtype in (torch.float16, torch.bfloat16): + return get_max_tensorcore_tflops(dtype) + + if torch.backends.cuda.matmul.allow_tf32: + return get_max_tensorcore_tflops(torch.float32) + else: + return get_max_simd_tflops(torch.float32) + + +@functools.lru_cache(None) +def get_gpu_dram_gbps(): + from triton.testing import get_dram_gbps + + return get_dram_gbps() + + +def get_gpu_shared_memory(): + from triton.runtime import driver + + return driver.active.utils.get_device_properties(0).get("max_shared_mem", 0) + + +def is_welford_reduction(reduction_type): + return reduction_type.startswith("welford") + + +def reduction_num_outputs(reduction_type): + return 3 if is_welford_reduction(reduction_type) else 1 + + +def is_linux() -> bool: + return platform.system() == "Linux" + + +def is_windows(): + return sys.platform == "win32" + + +def has_free_symbols(itr: Iterable[Any]): + return any(isinstance(x, sympy.Expr) and not x.is_number for x in itr) + + +def is_dynamic(*args): + from . import ir + + for t in args: + if isinstance(t, ir.TensorBox): + if has_free_symbols(t.data.get_size()) or ( + hasattr(t.data, "get_stride") and has_free_symbols(t.data.get_stride()) + ): + return True + elif isinstance(t, (ir.StorageBox, ir.BaseView, ir.ComputedBuffer)): + assert hasattr(t, "get_size") and hasattr(t, "get_stride") + if has_free_symbols(t.get_size()) or has_free_symbols(t.get_stride()): + return True + elif not isinstance(t, ir.IRNode): + continue + else: + raise TypeError(f"unexpected type for is_dynamic {type(t)}") + + return False + + +# Placeholder strings used in triton codegen. +class Placeholder(enum.Enum): + # The placeholder for the actual name of a triton kernel. + # e.g. for "def triton_" it would be "triton_" + KERNEL_NAME = "KERNEL_NAME" + + # The descriptive name of the triton kernel; when unique_kernel_names = False, this + # placeholder will be replaced with a string with more information. + DESCRIPTIVE_NAME = "DESCRIPTIVE_NAME" + + +def pass_execution_and_save(func, gm, inp, msg): + from .pattern_matcher import stable_topological_sort + + with tempfile.NamedTemporaryFile( + mode="w", + encoding="utf-8", + delete=False, + ) as f: + before_io = io.StringIO() + after_io = io.StringIO() + ShapeProp(gm=gm, fake_mode=detect_fake_mode(inp)).propagate(*inp) + print(f"Before:\n{gm.graph}", file=f) + print(gm.graph, file=before_io) + start_time = datetime.now() + with GraphTransformObserver(gm, msg, config.trace.log_url_for_graph_xform): + func(gm.graph) + time_elapsed = datetime.now() - start_time + # recompile graph + stable_topological_sort(gm.graph) + gm.graph.lint() + gm.recompile() + + print(f"After:\n{gm.graph}", file=f) + print(gm.graph, file=after_io) + t = before_io.getvalue() == after_io.getvalue() + log.info( + "%s, save before/after graph to %s, graph before/after are the same = %s, time elapsed = %s", + msg, + f.name, + t, + time_elapsed, + ) + + +def is_collective(node, op=None): + from . import ir + + return type(node) == ir._CollectiveKernel and (op is None or node.op_overload is op) + + +def is_wait(node): + from . import ir + + return type(node) == ir._WaitKernel + + +def contains_collective(snode): + from torch._inductor.scheduler import BaseSchedulerNode, GroupedSchedulerNode + + assert isinstance(snode, BaseSchedulerNode) + if isinstance(snode, GroupedSchedulerNode): + return any(contains_collective(x) for x in snode.snodes) + else: + return is_collective(snode.node) + + +def contains_wait(snode): + from torch._inductor.scheduler import BaseSchedulerNode, GroupedSchedulerNode + + assert isinstance(snode, BaseSchedulerNode) + if isinstance(snode, GroupedSchedulerNode): + return any(contains_wait(x) for x in snode.snodes) + else: + return is_wait(snode.node) + + +def is_fallback_op(node, op): + from . import ir + + if isinstance(op, torch._ops.OpOverload): + op = {op} + return isinstance(node, ir.FallbackKernel) and node.op_overload in op + + +def buf_name_to_fused_snode(buf_name, name_to_buf, name_to_fused_node): + return name_to_fused_node[name_to_buf[buf_name].defining_op.get_name()] + + +def find_recursive_deps_of_node( + snode, collected_node_set, name_to_buf, name_to_fused_node, criteria_cb=None +): + if criteria_cb and criteria_cb(snode): + return + collected_node_set.add(snode) + for dep in snode.unmet_dependencies: + defining_op_for_dep = buf_name_to_fused_snode( + dep.name, name_to_buf, name_to_fused_node + ) + if defining_op_for_dep in collected_node_set: + continue + find_recursive_deps_of_node( + defining_op_for_dep, + collected_node_set, + name_to_buf, + name_to_fused_node, + criteria_cb=criteria_cb, + ) + + +def find_recursive_users_of_node( + snode, collected_node_set, name_to_buf, name_to_fused_node, criteria_cb=None +): + if criteria_cb and criteria_cb(snode): + return + collected_node_set.add(snode) + for o in snode.get_outputs(): + for user in o.users: + assert user.node is not None + if user.node.get_name() == "OUTPUT": + continue + if user.node.get_name() not in name_to_fused_node: + continue + user_op = name_to_fused_node[user.node.get_name()] + if user_op in collected_node_set: + continue + find_recursive_users_of_node( + user_op, + collected_node_set, + name_to_buf, + name_to_fused_node, + criteria_cb=criteria_cb, + ) + + +def num_fw_fixed_arguments(dynamo_gm_num_inputs: int, aot_fw_gm_num_inputs: int): + "Computes the number of inputs to the aot fw graph which have fixed addresses (params and buffers)" + num_rng_seed_offset_inputs = ( + 2 if torch._functorch.config.functionalize_rng_ops else 0 + ) + # AOT won't lift any parameters if we're inlining NN Modules + # however desugaring subclasses will still add arguments + # resulted in extra fixed inputs https://github.com/pytorch/pytorch/issues/130502 + if ( + torch._dynamo.config.inline_inbuilt_nn_modules + and not torch._dynamo.utils.is_parameter_freezing() + ): + return 0 + + return aot_fw_gm_num_inputs - dynamo_gm_num_inputs - num_rng_seed_offset_inputs + + +def count_tangents(fx_g: torch.fx.GraphModule): + """ + Infers which inputs are static for a backwards graph + """ + + def is_saved_tensor(x): + return ( + "tangents" not in x.name + and "bwd_seed" not in x.name + and "bwd_base_offset" not in x.name + ) + + arg_count = 0 + static_arg_idxs = [] + for n in fx_g.graph.nodes: + if n.op == "placeholder": + if is_saved_tensor(n): + static_arg_idxs.append(arg_count) + arg_count += 1 + + assert static_arg_idxs == list(range(len(static_arg_idxs))) + return len(static_arg_idxs) + + +@dataclasses.dataclass +class BoxedBool: + value: bool + + def __bool__(self): + return self.value + + @staticmethod + def disable(obj): + if isinstance(obj, BoxedBool): + obj.value = False + return obj + return False + + +@contextlib.contextmanager +def collect_defined_kernels(kernel_list): + from .codegen.wrapper import WrapperCodeGen + + orig_define_kernel = WrapperCodeGen.define_kernel + + def new_define_kernel(wrapper, name, kernel_code, metadata, *args, **kwargs): + nonlocal kernel_list + kernel_list.append(kernel_code) + return orig_define_kernel(wrapper, name, kernel_code, metadata, *args, **kwargs) + + with unittest.mock.patch.object(WrapperCodeGen, "define_kernel", new_define_kernel): + yield + + +def get_cloned_parameter_buffer_name(name: str): + return name + "__original__" + + +def is_gpu(device: str): + assert isinstance(device, str) or device is None, device + return device in ["cuda", "xpu"] + + +def device_need_guard(device: str): + assert isinstance(device, str) + return is_gpu(device) + + +def needs_fallback_due_to_atomic_add_limitations(dtype): + # tl.atomic_add does NOT support the following types + return dtype in {torch.int64, torch.bool, torch.bfloat16} + + +def use_scatter_fallback( + op_overload: torch._ops.OpOverload, + reduction_type, + self_dtype, + src_dtype, + src_device_type, + src_is_tensor, +): + if ( + op_overload.overloadpacket + in (torch.ops.aten.scatter_reduce_, torch.ops.aten.scatter_reduce) + and reduction_type is None + ): + return False + + reduce_ty = ( + "add" if op_overload.overloadpacket == torch.ops.aten.scatter_ else "sum" + ) + + return ( + reduction_type not in {None, reduce_ty} + or ( + src_is_tensor + and is_gpu(src_device_type) + and needs_fallback_due_to_atomic_add_limitations(src_dtype) + ) + or ( + op_overload.overloadpacket == torch.ops.aten.scatter_reduce_ + and reduction_type == "sum" + and src_is_tensor + and src_device_type == "cpu" + and config.cpp.fallback_scatter_reduce_sum + and (config.cpp.dynamic_threads or parallel_num_threads() != 1) + ) + or (reduction_type == reduce_ty and self_dtype in {torch.bool, torch.int64}) + or torch.are_deterministic_algorithms_enabled() + ) + + +def dump_node_schedule(node_schedule): + """ + An API that can be used in pdb to dump a node_schedule. + Right mainly dump the read/write dependencies but can add more as needed. + """ + from torch._inductor.codegen.simd import DisableReduction, EnableReduction + from torch._inductor.scheduler import SchedulerNode + + print(f"Node schedule with {len(node_schedule)} nodes") + for idx, node in enumerate(node_schedule): + print(f" {idx:3}:") + if node is EnableReduction: + print("enable reduction") + elif node is DisableReduction: + print("disable reduction") + elif isinstance(node, SchedulerNode): + is_red = node.is_reduction() + print(f"{'red' if is_red else 'pw'} scheduler node") + if is_red: + assert node.node is not None + print(f"original reduction hint {node.node.data.reduction_hint}") # type: ignore[attr-defined] + print("ReadDep:") + for dep in node.read_writes.reads: + print(dep) + print("WriteDep:") + for dep in node.read_writes.writes: + print(dep) + else: + raise RuntimeError(f"Unrecognized node type: {type(node)}") + + +def tensor_is_aligned(tensor: torch.Tensor): + # See Note: [Input Alignment handling in Inductor] + # Right now, we don't try to guard on the alignment of the storage offset. + # When this comment was written, non-symbolic storage_offsets are not guarded on + # but symbolic storage_offsets are. For consistency, we suppress guard creation + # upon performing this check: that ensures that we don't add recompiles when we + # add this logic. + from torch.fx.experimental.symbolic_shapes import statically_known_true + + return statically_known_true( + (tensor.storage_offset() * get_dtype_size(tensor.dtype)) % GPU_ALIGN_BYTES == 0 + ) + + +def should_assume_input_aligned(example_input: torch.Tensor): + # See Note: [Input Alignment handling in Inductor] + + # right now, we only care about alignment for cuda tensors. + if not is_gpu(example_input.device.type): + return False + return config.assume_aligned_inputs or tensor_is_aligned(example_input) + + +def maybe_get_suppress_shape_guards_ctx(): + # Try to get TracingContext.try_get().fake_mode.shape_env.suppress_guards() + # If it's not available, return a nullcontext. + + # If we're dealing with cudagraphs, we might not have a tracing_context + tracing_context = torch._guards.TracingContext.try_get() + if not tracing_context: + return contextlib.nullcontext() + + # In standalone inductor compile mode, we might not have a shape_env attached to the fake mode + shape_env = tracing_context.fake_mode.shape_env + if not shape_env: + return contextlib.nullcontext() + + return shape_env.suppress_guards() + + +def run_and_get_cpp_code(fn, *args, **kwargs): + # We use the patch context manager instead of using it as a decorator. + # In this way, we can ensure that the attribute is patched and unpatched correctly + # even if this run_and_get_cpp_code function is called multiple times. + with unittest.mock.patch.object(config, "debug", True): + torch._dynamo.reset() + import io + import logging + + log_capture_string = io.StringIO() + ch = logging.StreamHandler(log_capture_string) + from torch._inductor.codecache import output_code_log + + output_code_log.addHandler(ch) + prev_level = output_code_log.level + output_code_log.setLevel(logging.DEBUG) + result = fn(*args, **kwargs) + s = log_capture_string.getvalue() + output_code_log.setLevel(prev_level) + output_code_log.removeHandler(ch) + return result, s + + +def shape_env_from_inputs(inputs: List[torch.Tensor]): + shape_env = None + fake_mode = detect_fake_mode(inputs) + + # TODO(voz): It would be nice to enable this assert, but there are lots of tests that + # pass in real inputs for now. + # if len(inputs) > 0: + # assert fake_mode is not None, breakpoint() + + if fake_mode is not None: + return fake_mode.shape_env + + # When there are no tensor inputs, get shape_env from the first SymInt. + for input in inputs: + if isinstance(input, torch.SymInt): + return input.node.shape_env + + # TODO(voz): Should we always have one anyway? + return None + + +def align_inputs_from_check_idxs( + model: Callable[[List[InputType]], Any], + inputs_to_check: Sequence[int], +) -> Callable[[List[InputType]], Any]: + if len(inputs_to_check) == 0: + return model + + def run(new_inputs: List[InputType]): + copy_misaligned_inputs(new_inputs, inputs_to_check) + return model(new_inputs) + + return run + + +def clone_preserve_strides(x: torch.Tensor): + needed_size = ( + sum((shape - 1) * stride for shape, stride in zip(x.size(), x.stride())) + 1 + ) + buffer = torch.as_strided(x, (needed_size,), (1,)).clone() + return torch.as_strided(buffer, x.size(), x.stride()) + + +def copy_misaligned_inputs( + new_inputs: List[InputType], check_inputs_idxs: Sequence[int] +) -> None: + for i in check_inputs_idxs: + _inp = new_inputs[i] + assert isinstance(_inp, torch.Tensor) + if _inp.data_ptr() % ALIGNMENT: + new_inputs[i] = clone_preserve_strides(_inp) + + +def remove_unaligned_input_idxs( + inputs: List[InputType], + static_input_idxs: Sequence[int], +): + """ + We require all inputs to be aligned, so introduce a copy for any + that aren't. + """ + aligned_static_input_idxs = [] + for idx in static_input_idxs: + input = inputs[idx] + if isinstance(input, torch.Tensor) and (input.data_ptr() % ALIGNMENT) == 0: + aligned_static_input_idxs.append(idx) + if len(aligned_static_input_idxs) != len(static_input_idxs): + return aligned_static_input_idxs + return static_input_idxs + + +def set_tracing_context_output_strides(example_inputs, compiled_graph): + # Return the output strides to the caller via TracingContext + context = torch._guards.TracingContext.try_get() + if context is not None and context.output_strides is not None: + assert len(context.output_strides) == 0 + shape_env = shape_env_from_inputs(example_inputs) + for exprs in compiled_graph.output_strides: + if exprs is None: + context.output_strides.append(None) + else: + context.output_strides.append( + tuple( + ( + shape_env.evaluate_symexpr(e) + if shape_env is not None + else int(e) + ) + for e in exprs + ) + ) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/virtualized.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/virtualized.py new file mode 100644 index 0000000000000000000000000000000000000000..b00a94c5f2cee32534f94730b34dc7bffa7a9c56 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/virtualized.py @@ -0,0 +1,361 @@ +# mypy: allow-untyped-defs +""" +This file provides a number of "global" variables/handlers that are actually +thread local and dynamically scoped, with Inductor patching them to various +implementations depending on the situation. + +These handlers are interacted with in a fairly stylized way. Typically, +we will import V from this module:: + + from .virtualized import V + +Various handlers are accessible as attributes on this module; for example, +you might access ``V.graph.sizevars.size_hint`` to resolve a size hint associated with +a number. + +There are a few distinct usage patterns for virtualized global variables: + +1. Implicit argument passing. Examples: ``V.current_node``, ``V.aot_compilation``. + Use ``V.set_current_node`` to change what the current node is while we're + executing some region of code, so code inside that region can query ``V.current_node`` + to find out what it is. This is often more convenient than manually threading + the current node as an argument through all call stacks. + +2. Per-compilation global state. Examples: ``V.fake_mode``, ``V.graph``. For a + given ``compile_fx`` invocation, these typically don't change, but they are + associated with some internal state so they cannot just be global functions. + We install these objects at the beginning of compilation and then you can + conveniently access them without having to pass them around. + +3. Alternate define-by-run interpretations. Examples: ``V.ops``, ``V.kernel``. + A commonly used IR in Inductor is define-by-run: instead of maintaining + explicit syntax data structures, we instead represent loop bodies as + callable functions, which internally invoke operations defined on + ``V.ops``. To perform semantic analysis, print or code generate these + operations, we dynamically patch ``V.ops`` with an alternate handler with + the intended semantics and then run the callable function. For example, to + extract out a traditional (FX) graph representation of the define-by-run + IR, simply install a handler that records each ``ops`` call to a graph. + + TODO: Define a parent class / protocol that defines all of the operations + V.ops is expected to support. + +It is typically an error to access a virtualized global without having installed +an appropriate handler (you will get a NullHandler), although in some cases we +provide a default implementation. + +One last thing: although most virtualized globals are accessed via ``V``, ``ops`` is +ubiquitous enough to have its own top level variable, so you will typically see +``ops.constant(...)`` rather than ``V.ops.constant(...)``. In fact, these are not +equivalent; the former interface supports arithmetic overloads like ``x + y`` +instead of forcing ``ops.add(x, y)``, so it should be preferred. + +Some operators are seemingly unused, but they are implicitly used by ops_wrapper. +In particular, we typically have an operator for every basic pointwise PyTorch operation +supported. +""" + +from __future__ import annotations + +from contextlib import AbstractContextManager, contextmanager +from threading import local +from typing import Any, Callable, Generic, List, Type, TYPE_CHECKING, TypeVar, Union + +from .ops_handler import ( # noqa: F401 + KernelFormatterHandler, + MockHandler, + OpsHandler, + ReductionType, + StoreMode, + WrapperHandler, +) + + +if TYPE_CHECKING: + import torch + from torch._inductor.codegen.cpp_utils import LocalBufferContext + from torch._inductor.debug import DebugContext + from torch._inductor.graph import GraphLowering + from torch._inductor.loop_body import InterpreterShim + from torch._subclasses import FakeTensorMode + +threadlocal = local() + +T = TypeVar("T") + + +class NullHandler: + """ + Sentinel indicating that a global variable is unset ala None. Typically, + attempting to access the global variable before it's set is an error, but with + NullHandler it won't fail until you try to access an attribute on it. + """ + + +class Virtualized(Generic[T]): + """ + Implements a global variable that redirects via thread local variable + (NB: construct this class to create the global variable; this is not + a singleton class!) + + This allows us to swap in different op implementations in codegen. + + NB: Despite the fact that we typically call these "handlers" (e.g., NullHandler is + the default value of the variable), we sometimes use these variables to + store other things, like booleans. + """ + + def __init__(self, vname: str, default: Union[Callable[[], T], Type[NullHandler]]): + self._key: str = f"__torchinductor_{vname}" + self._default = default + + def _set_handler(self, value: T) -> AbstractContextManager[None]: + prior = self._get_handler() + setattr(threadlocal, self._key, value) + + @contextmanager + def ctx(): + try: + yield + finally: + self._set_handler(prior) + + return ctx() + + def _get_handler(self) -> T: + try: + return getattr(threadlocal, self._key) + except AttributeError: + # TODO: To be honest, I feel we probably should just error in this + # case, instead of making a null handler that will probably error + # when you getattr on it + return self._default() # type: ignore[return-value] + + def __getattr__(self, name: str) -> Any: + return getattr(self._get_handler(), name) + + +class NullKernelHandler(NullHandler): + """ + We need access `V.kernel.removed_buffers` in DeferredLine class when there + is no kernel in the context. This happens when codegening the wrapper. + Initialize `removed_buffers` and `inplaced_to_remove` explicitly so we don't + need call 'getattr' with default value which is error prone to typo in + attribute name. + """ + + def __init__(self): + super().__init__() + self.removed_buffers = set() + self.inplaced_to_remove = set() + self.index_dtype = "tl.int64" + + +_ops: Virtualized[OpsHandler[Any]] = Virtualized("ops", MockHandler) +_graph: Virtualized[GraphLowering] = Virtualized("graph", NullHandler) +_real_inputs: Virtualized[List[torch.Tensor]] = Virtualized("real_inputs", NullHandler) +_fake_mode: Virtualized[FakeTensorMode] = Virtualized("fake_mode", NullHandler) +_kernel: Virtualized[NullKernelHandler] = Virtualized( + "kernel", NullKernelHandler +) # TODO: improve type +_debug: Virtualized[DebugContext] = Virtualized("debug", NullHandler) +_interpreter: Virtualized[InterpreterShim] = Virtualized("interpreter", NullHandler) +_aot_compilation: Virtualized[bool] = Virtualized("aot_compilation", NullHandler) +_current_node: Virtualized[torch.fx.Node] = Virtualized("current_node", NullHandler) +_local_buffer_context: Virtualized[LocalBufferContext] = Virtualized( + "local_buffer_context", NullHandler +) + + +class OpsValue: + """The return type of most ops calls. + + This exists so we can overload magic methods, and write mathematical + expressions much more fluently. So instead of + + ops.add(ops.mul(ops.mul(ops.sub(ops.mul(_Ap2, x), _Ap3), x), x), _1) + + we can write + + (_Ap2 * x - _Ap3) * x * x + _1 + + """ + + value: Any + + def __init__(self, value): + self.value = value + + def __str__(self): + return str(self.value) + + def __repr__(self): + return f"OpsValue({self.value!r})" + + def __add__(self, other): + return ops.add(self, other) + + def __mul__(self, other): + return ops.mul(self, other) + + def __sub__(self, other): + return ops.sub(self, other) + + def __neg__(self): + return ops.neg(self) + + def __truediv__(self, other): + return ops.truediv(self, other) + + def __floordiv__(self, other): + return ops.floordiv(self, other) + + def __mod__(self, other): + return ops.mod(self, other) + + def __pow__(self, other): + return ops.pow(self, other) + + def __lt__(self, other): + return ops.lt(self, other) + + def __le__(self, other): + return ops.le(self, other) + + def __eq__(self, other): + return ops.eq(self, other) + + def __ne__(self, other): + return ops.ne(self, other) + + def __gt__(self, other): + return ops.gt(self, other) + + def __ge__(self, other): + return ops.ge(self, other) + + def __and__(self, other): + return ops.bitwise_and(self, other) + + def __or__(self, other): + return ops.bitwise_or(self, other) + + def __xor__(self, other): + return ops.bitwise_xor(self, other) + + def __invert__(self): + return ops.bitwise_not(self) + + def __rshfit__(self, n): + return ops.bitwise_right_shift(self, n) + + def __lshift__(self, n): + return ops.bitwise_left_shift(self, n) + + +class OpsWrapper: + """This wraps any returned IR values into an `OpsValue` instance, so that we + can overload the magic methods for writing mathematical expressions fluently. + """ + + def __getattr__(self, name): + def inner(*args, **kwargs): + new_args = [OpsWrapper._unwrap(a) for a in args] + new_kwargs = {k: OpsWrapper._unwrap(v) for k, v in kwargs.items()} + return OpsWrapper._wrap(getattr(_ops, name)(*new_args, **new_kwargs)) + + return inner + + @staticmethod + def _unwrap(x): + if isinstance(x, (list, tuple)): + return tuple(OpsWrapper._unwrap(v) for v in x) + if isinstance(x, OpsValue): + return x.value + return x + + @staticmethod + def _wrap(x): + if isinstance(x, (list, tuple)): + return tuple(OpsValue(v) for v in x) + return OpsValue(x) + + @staticmethod + def indirect_indexing(index, size, check=True, wrap_neg=True): + # Returns a sympy value, not IR value + index = OpsWrapper._unwrap(index) + return _ops.indirect_indexing(index, size, check, wrap_neg) + + +ops = OpsWrapper() + + +class _V: + MockHandler = MockHandler + KernelFormatterHandler = KernelFormatterHandler + WrapperHandler = WrapperHandler + + set_ops_handler: Callable[[Any], Any] = _ops._set_handler + get_ops_handler: Callable[[], Any] = _ops._get_handler + set_graph_handler: Callable[[GraphLowering], Any] = _graph._set_handler + set_real_inputs: Callable[[Any], Any] = _real_inputs._set_handler + get_real_inputs: Callable[[], Any] = _real_inputs._get_handler + set_fake_mode: Callable[[Any], Any] = _fake_mode._set_handler + get_fake_mode: Callable[[], Any] = _fake_mode._get_handler + set_kernel_handler: Callable[[Any], Any] = _kernel._set_handler + set_debug_handler: Callable[[Any], Any] = _debug._set_handler + set_interpreter_handler: Callable[[Any], Any] = _interpreter._set_handler + set_aot_compilation: Callable[[bool], Any] = _aot_compilation._set_handler + get_aot_compilation: Callable[[], Any] = _aot_compilation._get_handler + set_current_node: Callable[[Any], Any] = _current_node._set_handler + get_current_node: Callable[[], Any] = _current_node._get_handler + set_local_buffer_context: Callable[[Any], Any] = _local_buffer_context._set_handler + get_local_buffer_context: Callable[[], Any] = _local_buffer_context._get_handler + + @property + def ops(self) -> OpsHandler[Any]: + """The operator handler specific to the current codegen task""" + return _ops._get_handler() + + @property + def graph(self) -> GraphLowering: + """The graph currently being generated""" + return _graph._get_handler() + + @property + def real_inputs(self): + """non-fake example inputs""" + return _real_inputs._get_handler() + + @property + def fake_mode(self): + """The graph currently being generated""" + return _fake_mode._get_handler() + + @property + def kernel(self): + """The kernel currently being generated""" + return _kernel._get_handler() + + @property + def debug(self): + return _debug._get_handler() + + @property + def interpreter(self): + return _interpreter._get_handler() + + @property + def aot_compilation(self): + return _aot_compilation._get_handler() + + @property + def current_node(self): + return _current_node._get_handler() + + @property + def local_buffer_context(self): + return _local_buffer_context._get_handler() + + +V = _V() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/wrapper_benchmark.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/wrapper_benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..bdd1f0fc95b7f905eab8457a733d4b036849c443 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_inductor/wrapper_benchmark.py @@ -0,0 +1,315 @@ +# mypy: allow-untyped-defs +import dataclasses +import tempfile +from collections import defaultdict + +import torch +from torch.autograd import DeviceType + +from .runtime.benchmarking import benchmarker +from .runtime.runtime_utils import create_bandwidth_info_str, get_num_bytes + + +_kernel_category_choices = [ + "foreach", + "persistent_reduction", + "pointwise", + "reduction", + "split_scan", + "template", +] + + +def get_kernel_category_by_source_code(src_code): + """ + Similar to get_kernel_category but use the source code. Call this API + if we have not compile the src_code to module yet. + """ + choices = [ + ch for ch in _kernel_category_choices if f"@triton_heuristics.{ch}" in src_code + ] + if len(choices) == 1: + return choices[0] + else: + return "unknown" + + +def get_kernel_category(kernel_mod): + """ + Given the module defining a triton kernel, return the category of the kernel. + Category can be one of: + - pointwise + - reduction + - persistent_reduction + + Currently we simply decide the category depending on what decorator is imported + by the kernel. + """ + choices = [ch for ch in _kernel_category_choices if ch in kernel_mod.__dict__] + if len(choices) == 1: + return choices[0] + else: + return "unknown" + + +def get_triton_kernel(mod): + from torch._inductor.runtime.triton_heuristics import CachingAutotuner + + cand_list = [ + v + for k, v in mod.__dict__.items() + if k.startswith("triton_") and isinstance(v, CachingAutotuner) + ] + assert len(cand_list) == 1 + return cand_list[0] + + +def benchmark_all_kernels(benchmark_name, benchmark_all_configs): + """ + An experimental API used only when config.benchmark_kernel is true. + + Run the kernel benchmarks for all the kernels cached in PyCodeCache. + Used in the compiled modules. + + Put this method here rather than codegen it for convenience since its implementation + does not change based on different graph modules being compiled. + """ + from torch._inductor.codecache import PyCodeCache + + nfound = 0 + for kernel_key, kernel_mod in PyCodeCache.cache.items(): + if not hasattr(kernel_mod, "get_args") or not hasattr(kernel_mod, "call"): + continue + + triton_kernel = get_triton_kernel(kernel_mod) + kernel_category = get_kernel_category(kernel_mod) + args = kernel_mod.get_args() + num_in_out_ptrs = len( + [ + arg_name + for arg_name in triton_kernel.fn.arg_names + if arg_name.startswith("in_out_ptr") + ] + ) + num_gb = triton_kernel.inductor_meta.get("kernel_num_gb", None) + if num_gb is None: + num_gb = get_num_bytes(*args, num_in_out_args=num_in_out_ptrs) / 1e9 + + def get_info_str(ms, n_regs, n_spills, shared, prefix=""): + if not any(x is None for x in [n_regs, n_spills, shared]): + kernel_detail_str = ( + f" {n_regs:3} regs {n_spills:3} spills {shared:8} shared mem" + ) + else: + kernel_detail_str = "" + + gb_per_s = num_gb / (ms / 1e3) + return create_bandwidth_info_str( + ms, num_gb, gb_per_s, prefix=prefix, suffix=kernel_detail_str + ) + + kernel_desc = ( + f"{benchmark_name:20} {kernel_category[:3].upper()} {kernel_key[:10]}" + ) + if benchmark_all_configs: + assert hasattr(kernel_mod, "benchmark_all_configs") + bench_result = kernel_mod.benchmark_all_configs(args) + print(kernel_desc) + for launcher, ms in bench_result.items(): + print( + f" {get_info_str(ms, launcher.n_regs, launcher.n_spills, launcher.shared)} @ {launcher.config}" + ) + else: + ms = benchmarker.benchmark_gpu( + lambda: kernel_mod.call(args), rep=40, fast_flush=True + ) + assert ( + len(triton_kernel.launchers) == 1 + ), "Autotuner should have selected the best config" + launcher = triton_kernel.launchers[0] + print( + get_info_str( + ms, + launcher.n_regs, + launcher.n_spills, + launcher.shared, + prefix=f"{kernel_desc} ", + ) + ) + + nfound += 1 + if nfound == 0: + print( + "No kernel with benchmark functionality found. Make sure you run inductor with config.benchmark_kernel being True" + ) + + +@dataclasses.dataclass +class ProfileEvent: + category: str + key: str + self_device_time_ms: float + # the benchmark is run multiple times and we average the count across all the + # runs. It should be an integer but define a float just in case. + count: float + + +def parse_profile_event_list( + benchmark_name, event_list, wall_time_ms, nruns, device_name +): + def get_self_device_time(ev): + """ + ev.self_device_time_total is in microsecond. Convert to millisecond. + """ + return ev.self_device_time_total / 1000 / nruns + + all_events = defaultdict(list) + + def add_event(ev, category): + profile_ev = ProfileEvent( + category=category, + key=ev.key, + self_device_time_ms=get_self_device_time(ev), + count=ev.count / nruns, # average across all runs + ) + all_events[category].append(profile_ev) + + for ev in event_list: + assert not ev.is_legacy, "Don't support the legacy profiler" + if ev.device_type == DeviceType.CPU: + # ignore the event on CPU side + continue + + category = "unknown" + if ev.key.startswith("triton_"): + if ev.key.startswith("triton_poi"): + category = "triton_pointwise" + elif ev.key.startswith("triton_red"): + category = "triton_reduction" + elif ev.key.startswith("triton_per"): + category = "triton_persistent_reduction" + else: + category = "triton_unknown" + + add_event(ev, category) + + def report_category(category, profile_events): + from tabulate import tabulate + + profile_events.sort(key=lambda ev: ev.self_device_time_ms, reverse=True) + + rows = [] + total_time = 0.0 + print(f"\n == {category} category kernels == ") + for ev in profile_events: + total_time += ev.self_device_time_ms + percent = f"{ev.self_device_time_ms / wall_time_ms * 100:.2f}%" + rows.append([ev.key[:120], ev.self_device_time_ms, ev.count, percent]) + rows.append( + ["Total", total_time, "", f"{total_time / wall_time_ms * 100:.2f}%"] + ) + print( + tabulate( + rows, + headers=[ + "Kernel", + f"Self {device_name.upper()} TIME (ms)", + "Count", + "Percent", + ], + ) + ) + return total_time + + def report(): + category_list = [ + "triton_pointwise", + "triton_reduction", + "triton_persistent_reduction", + "triton_unknown", + "unknown", + ] + assert set(all_events.keys()).issubset( + set(category_list) + ), f"{list(all_events.keys())}" + + per_category_wall_time = {} + total_device_ms = 0.0 + for category in category_list: + if category in all_events: + _time = report_category(category, all_events[category]) + per_category_wall_time[category] = _time + total_device_ms += _time + + device_busy_percent = f"{total_device_ms / wall_time_ms * 100:.2f}%" + print( + f"\nPercent of time when {device_name.upper()} is busy: {device_busy_percent}" + ) + print(f"Total wall time {wall_time_ms:.3f} ms") + + # output such a line so we can gather such line from all compiled modules from all + # benchmarks and tabulate it! + # Columns: benchmark_name, pointwise_percent, reduction_percent, persistent_reduction_percent, + # unknown_category_percent, device_busy_percent, wall_time_ms + tabulate_line = f"Output for tabulate: {benchmark_name}" + for category in category_list: + percent = ( + f"{per_category_wall_time.get(category, 0.0) / wall_time_ms * 100:.2f}%" + ) + tabulate_line += f", {percent}" + tabulate_line += f", {device_busy_percent}, {wall_time_ms:.3f}ms" + + print(tabulate_line) + + report() + + +def compiled_module_main(benchmark_name, benchmark_compiled_module_fn): + """ + This is the function called in __main__ block of a compiled module. + """ + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument( + "--benchmark-kernels", + "-k", + action="store_true", + help="Whether to benchmark each individual kernels", + ) + parser.add_argument( + "--benchmark-all-configs", + "-c", + action="store_true", + help="Whether to benchmark each individual config for a kernel", + ) + parser.add_argument( + "--profile", + "-p", + action="store_true", + help="Whether to profile the compiled module", + ) + args = parser.parse_args() + + if args.benchmark_kernels: + benchmark_all_kernels(benchmark_name, args.benchmark_all_configs) + else: + times = 10 + repeat = 10 + wall_time_ms = benchmark_compiled_module_fn(times=times, repeat=repeat) * 1000 + + if not args.profile: + return + + with torch.profiler.profile(record_shapes=True) as p: + benchmark_compiled_module_fn(times=times, repeat=repeat) + + path = f"{tempfile.gettempdir()}/compiled_module_profile.json" + p.export_chrome_trace(path) + print(f"Profiling result for a compiled module of benchmark {benchmark_name}:") + print(f"Chrome trace for the profile is written to {path}") + event_list = p.key_averages(group_by_input_shape=True) + print(event_list.table(sort_by="self_device_time_total", row_limit=10)) + parse_profile_event_list( + benchmark_name, event_list, wall_time_ms, times * repeat, p.use_device + ) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8d90efa40e58841a11a25569ca6722b791894999 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/__init__.py @@ -0,0 +1,55 @@ +# mypy: allow-untyped-defs + +import torch._C._lazy +from torch.utils._pytree import tree_flatten, tree_unflatten + +from .closure import add_step_closure, run_step_closures + + +def mark_step(device: str = "", wait=False): + """Triggers a mark step, which amounts to + - collecting a group of 'live' lazy tensors to index into the compilation cache + (lowering/compiling their IR graphs if not cached) + - kicking off execution of the compiled function + - (optionally, wait=True) waiting for cpu-side execution to complete (does not sync the accelerator) + """ + # TODO(whc) expand this to include backend hooks and align with XLA backend needs + torch._C._lazy._mark_step(device, [], wait=wait) + + run_step_closures() + + +def wait_device_ops(devices=None): + """Waits for all the async operations on the given devices to complete. + Args: + devices (string..., optional): The devices whose async ops need to be waited + for. 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closure): + """Run closure function + + Args: + closure: callable function to run + """ + closure() + + def __call__(self, closures): + for closure in closures: + self.run(closure) + + +class AsyncClosureHandler(ClosureHandler): + """Handler for Asynchronous Step Closures + Args: + max_queue_size: The maximum length of the closure queue after which + the training loop will block until closures are evaluated. + By default, a reasonable limit of a maximum of 100 on the queue. + This value can be set using the `XLA_MAX_ASYNC_QUEUE` environment + variable. + """ + + def __init__(self, max_queue_size=100): + super().__init__() + self._closure_queue: Queue = Queue( + int(os.environ.get("LTC_MAX_ASYNC_QUEUE", max_queue_size)) + ) + self._closure_exception: Queue = Queue() + self._closure_lock = threading.Lock() + self._closure_event_loop_finished = threading.Event() + self._closure_event_loop = None + + def start_event_loop(self): + """Start closure event loop if not started""" + if self._closure_event_loop is None: + + def event_loop(): + # Run loop until closure event is set and closure queue is empty + while True: + try: + closure = self._closure_queue.get(block=True, timeout=3) + closure() + self._closure_queue.task_done() + except EmptyQueue: + with self._closure_lock: + if self._closure_queue.empty(): + self._closure_event_loop_finished.set() + return + except Exception as e: + self._closure_exception.put(e) + return + + self._closure_event_loop = threading.Thread(target=event_loop) + self._closure_event_loop.start() + + def run(self, closure): + with self._closure_lock: + self._closure_queue.put(closure, block=True) + if ( + self._closure_event_loop is None + or not self._closure_event_loop.is_alive() + ): + try: + e = self._closure_exception.get(block=False) + raise RuntimeError( + "Cannot run asynchronous closure due to previously raised exception" + ) from e + except EmptyQueue: + self._closure_event_loop = None + self.start_event_loop() + + +def add_step_closure(closure, args=(), run_async=False): + """Adds a closure to the list of the ones to be run at the end of the step. + Many times during model training there is the need to print/report (print to + console, post to tensorboard, etc...) information which require the content of + intermediary tensors to be inspected. + Inspecting different tensors content in different points of the model code + requires many executions and typically causes performance issues. + Adding a step closure will ensure that it will be run after the barrier, when + all the live tensors will be already materialized to device data. + Live tensors which will include the ones captured by the closure arguments. + So using `add_step_closure()` will ensure a single execution will be + performed, even when multiple closures are queued, requiring multiple tensors + to be inspected. + Step closures will be run sequentially in the order they have been queued. + Note that even though using this API the execution will be optimized, it is + advised to throttle the printing/reporting events once every N steps. + Args: + closure (callable): The function to be called. + args (tuple): The arguments to be passed to the closure. + run_async: If True, run the closure asynchronously. + """ + devctx = get_device_context() + closures_type = "async_step_closures" if run_async else "step_closures" + step_closures = getattr(devctx, closures_type, None) + if step_closures is None: + step_closures = [] + setattr(devctx, closures_type, step_closures) + step_closures.append(lambda a=args: closure(*a)) + + +def run_step_closures(): + devctx = get_device_context() + async_step_closures = getattr(devctx, "async_step_closures", None) + if async_step_closures is not None: + devctx.async_step_closures = [] + async_closure_handler = getattr(devctx, "async_closure_handler", None) + if async_closure_handler is None: + async_closure_handler = AsyncClosureHandler() + devctx.async_closure_handler = async_closure_handler + async_closure_handler(async_step_closures) + + step_closures = getattr(devctx, "step_closures", None) + if step_closures is not None: + devctx.step_closures = [] + closure_handler = getattr(devctx, "closure_handler", None) + if closure_handler is None: + closure_handler = ClosureHandler() + devctx.closure_handler = closure_handler + closure_handler(step_closures) + return devctx diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/computation.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/computation.py new file mode 100644 index 0000000000000000000000000000000000000000..17a61e36cb9f2a46461d14caa3c1a3ff6e8c9094 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/computation.py @@ -0,0 +1,27 @@ +# mypy: allow-untyped-defs +import torch._C._lazy +import torch._C._lazy_ts_backend + + +def get_tensors_ts_device_data_node(tensors): + """Return tensor ids and eager tensors for DeviceData nodes in the + IR for the passed in lazy tensors. + + TODO: This API is currently ts backend specific. We are working on + generalizing it to all backends including XLA. + """ + return torch._C._lazy_ts_backend._get_tensors_ts_device_data_node(tensors) + + +def get_graph_hash(tensors): + """Return the graph hash for the passed in lazy tensors""" + return torch._C._lazy._get_graph_hash(tensors) + + +def run_cached_graph(hash_str, graph_inputs): + """Running the cached computation graph with the given inputs + + TODO: This API is currently ts backend specific. We are working on + generalizing it to all backends including XLA. + """ + return torch._C._lazy_ts_backend._run_cached_graph(hash_str, graph_inputs) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/config.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/config.py new file mode 100644 index 0000000000000000000000000000000000000000..f7ebca12de7fc44c27a2b3ae7c2ed1c7d8097c99 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/config.py @@ -0,0 +1,17 @@ +# mypy: allow-untyped-defs +import torch._C._lazy + + +def get_force_fallback(): + """Get the config used to force LTC fallback""" + return torch._C._lazy._get_force_fallback() + + +def set_force_fallback(configval): + """Set the config used to force LTC fallback""" + torch._C._lazy._set_force_fallback(configval) + + +def set_reuse_ir(val: bool): + """Set the config to reuse IR nodes for faster tracing""" + torch._C._lazy._set_reuse_ir(val) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/debug.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/debug.py new file mode 100644 index 0000000000000000000000000000000000000000..84534fb232509f0c9bbe722820bd1ae649d53e07 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/debug.py @@ -0,0 +1,22 @@ +# mypy: allow-untyped-defs +import torch._C._lazy + + +def render_ir_graph(tensors): + """Return a text dump of the LTC IR graph in dot format for the tensors. + The text can be processed by tools like dot to be rendered in pdf,png etc.""" + return torch._C._lazy._get_tensors_dot(tensors) + + +def dump_ir(tensors, ir_format): + """Return a dump of the tensors in the specified format. + Valid format are + - text: for LTC IR + - backend: for the activate backend IR + """ + if ir_format == "text": + return torch._C._lazy._get_tensors_text(tensors) + elif ir_format == "backend": + return torch._C._lazy._get_tensors_backend(tensors) + else: + raise RuntimeError(f"Unrecognized IR format: {ir_format}") diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/device_context.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/device_context.py new file mode 100644 index 0000000000000000000000000000000000000000..e09fdab3f7458cc6a410a1736b89e68b4a4eef17 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/device_context.py @@ -0,0 +1,26 @@ +# mypy: allow-untyped-defs +import threading +from typing import Any, Dict + +import torch._C._lazy + + +class DeviceContext: + _CONTEXTS: Dict[str, Any] = {} + _CONTEXTS_LOCK = threading.Lock() + + def __init__(self, device): + self.device = device + + +def get_device_context(device=None): + if device is None: + device = torch._C._lazy._get_default_device_type() + else: + device = str(device) + with DeviceContext._CONTEXTS_LOCK: + devctx = DeviceContext._CONTEXTS.get(device, None) + if devctx is None: + devctx = DeviceContext(device) + DeviceContext._CONTEXTS[device] = devctx + return devctx diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/extract_compiled_graph.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/extract_compiled_graph.py new file mode 100644 index 0000000000000000000000000000000000000000..f46eea4eee9b79033aa22ce2bcc77ba9f650c622 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/extract_compiled_graph.py @@ -0,0 +1,225 @@ +# mypy: allow-untyped-defs +import copy +import dataclasses +import itertools +import os +from typing import Any, Callable, Dict, List + +import torch +import torch._lazy as lazy +import torch._lazy.metrics as metrics +from torch import fx +from torch._lazy import computation, debug as lazy_debug +from torch._lazy.tensor_factory_functions import tensor_factory_functions + + +debug = os.environ.get("debug_extract_compiled_graph") is not None + + +@dataclasses.dataclass +class GraphInputMatcher: + """ + The GraphInputMatcher class setup the graph inputs for future calls after lazy tracing. + Specifically, those graph inputs corresponding to method parameters should be replaced with the + arguments for the current call. + + tensor_id_to_arg_idx maps the tensor id to the parameter index. + graph_input_tensor_ids, graph_input_ivalues list the tensor_id and ivalue for each of the + TS/XLA graph inputs. + """ + + tensor_id_to_arg_idx: Dict[int, int] + graph_input_tensor_ids: List[int] + # there are 2 categories of graph_input_tensors. + # Category 1: those whose id are not found in tensor_id_to_arg_idx. These are + # most likely const tensors and we can get its content from graph_input_tensors + # Category 2: those whose id are found in tensor_id_to_arg_idx. We should get + # the tensor from method arguments + graph_input_ivalues: List[Any] + + # get the real graph input tensors + def __call__(self, args): + real_input = [] + for tensor_id, traced_ivalue in zip( + self.graph_input_tensor_ids, self.graph_input_ivalues + ): + arg_idx = self.tensor_id_to_arg_idx.get(tensor_id, None) + if arg_idx is None: + inp = traced_ivalue + else: + inp = args[arg_idx] + real_input.append(inp) + return real_input + + +class ReturnValueHandler: + r""" + When ltc_sync_multi is called on multi tensors, the compiled graph + will contain output only for unique tensors - if a tensor appears multiple + times in the input to _ltc_sync_multi, only the first occurance matters. + + However from python level, we still expect multi tensors returned with duplciation + even if the TS graph dedup the output. e.g. for method: + + def forward(self, a): + return a, a + + the TS graph captured by LTC will return a single tensor, but Python method expects 2. + + This class dedup the lazy tensors first to get the index that will be used + to duplicate the eager tensors later. + """ + + def __init__(self, lazy_out_list): + self.index: List[List[int]] = [] + self.total_count = len(lazy_out_list) + + tensor_id_to_idx: Dict[int, int] = {} + for dup_idx, lazy_tensor in enumerate(lazy_out_list): + uniq_idx = tensor_id_to_idx.get(id(lazy_tensor), None) + if uniq_idx is not None: + self.index[uniq_idx].append(dup_idx) + else: + uniq_idx = len(self.index) + self.index.append([dup_idx]) + tensor_id_to_idx[id(lazy_tensor)] = uniq_idx + + def duplicate_eager_tensors(self, eager_tensor_list): + duplicated_list = [None] * self.total_count + assert len(eager_tensor_list) == len(self.index) + + for uniq_idx, eager_tensor in enumerate(eager_tensor_list): + for dup_idx in self.index[uniq_idx]: + duplicated_list[dup_idx] = eager_tensor + return duplicated_list + + +def force_lazy_device(model: fx.GraphModule): + """ + Factory methods in a Fx graph may create tensors for a specific eager devices. + If we take no actions, those eager tensors will be mixed with lazy tensors and + cause crash. This method overwrite those eager device to lazy device. + """ + + def tolazydevice(dev): + if isinstance(dev, torch.device): + return torch.device("lazy", index=dev.index) + return dev + + def hasDeviceArg(args, kwargs): + return any( + isinstance(arg, torch.device) + for arg in itertools.chain(args, kwargs.values()) + ) + + for nd in model.graph.nodes: + nd.args = tuple(tolazydevice(arg) for arg in nd.args) + nd.kwargs = {k: tolazydevice(v) for k, v in nd.kwargs.items()} + + # For torchbench like yolov3, hf_Bart, dynamo generates Fx graph that return + # eager tensors on the default device + # (check https://gist.github.com/shunting314/eabdf6c769c59bc384469717b8f9bb7f for yolove, + # and https://gist.github.com/shunting314/8d5e2d9348a3258959d3954186c48814 for hf_Bart). + # To force those tensors on the lazy device, we can not simply override + # the device argument since there is no explicit device argument. + # What we are doing here is, for the list of covered tensor factory methods + # we add a lazy device argument explicity. + # + # TODO: This solution is no ideal since we may miss some factory methods. In future + # when we support lazy mode, this method can be replaced by that. + if nd.target in tensor_factory_functions and not hasDeviceArg( + nd.args, nd.kwargs + ): + kwargs = dict(nd.kwargs) # nd.kwargs is immutable. make a mutable copy. + kwargs["device"] = torch.device("lazy") + nd.kwargs = kwargs + + model.recompile() + + +def get_fallback_ops(): + fallback_ops = [] + for opname in metrics.counter_names(): + if "aten::" not in opname: + continue + val = int(metrics.counter_value(opname)) + if val > 0: + fallback_ops.append(f"{opname}={val}") + + return fallback_ops + + +def extract_compiled_graph(model: fx.GraphModule, example_inputs) -> Callable: + """ + Optimize an eager model with LTC and returns a wrapper to execute the + compiled graph directly without retracing. It depends on other mechanisms + like TorchDynamo guards to guarantee the returned wrapper is only called + when it's safe. + """ + lazy_args = [arg.to(device="lazy") for arg in example_inputs] + args_tensor_ids = [lazy.get_tensor_id(lazy_arg) for lazy_arg in lazy_args] + tensor_id_to_arg_idx = {tensor_id: i for i, tensor_id in enumerate(args_tensor_ids)} + lazy_model = copy.deepcopy(model).to(device=torch.device("lazy")) + force_lazy_device(lazy_model) + + # This line executes lazy tracing and enable us extracting compiled graph later + metrics.reset() + lazy_out = lazy_model(*lazy_args) + fallback_ops = get_fallback_ops() + metrics.reset() + + if len(fallback_ops) > 0: + raise RuntimeError( + f"Fail to extact the compiled graph because of fallback: {','.join(fallback_ops)}" + ) + + if not isinstance(lazy_out, (tuple, list)): + lazy_out = (lazy_out,) + + args_and_out = tuple(lazy_args) + tuple(lazy_out) + return_value_handler = ReturnValueHandler(args_and_out) + if debug: + print("Fx code:\n", model.code) + print("LTC IR:", lazy_debug.dump_ir(args_and_out, "text")) + + # TODO: this part is TS backend specific for now and will be generalized to + # support XLA + ( + graph_input_tensor_ids, + graph_input_ivalues, + ) = computation.get_tensors_ts_device_data_node(args_and_out) + assert len(graph_input_tensor_ids) == len(graph_input_ivalues) + graph_input_matcher = GraphInputMatcher( + tensor_id_to_arg_idx, graph_input_tensor_ids, graph_input_ivalues + ) + + graph_hash = computation.get_graph_hash(args_and_out) + + if debug: + print("graph_hash", graph_hash) + print(f"args_tensor_ids {args_tensor_ids}") + print("tensor ids from device data:", graph_input_tensor_ids) + + # sync the list of output tensors so the computation graph for these + # tensors will be cached. Those computation graphs can be retrieved + # by graph hash later. + lazy.sync_multi(args_and_out, []) + + def optimized_mod(*args): + if len(args_and_out) == 0: + return () + graph_input = graph_input_matcher(args) + res = return_value_handler.duplicate_eager_tensors( + computation.run_cached_graph(graph_hash, graph_input) + ) + + assert len(res) == len(args_and_out) + for i, arg in enumerate(args): + # only copy those tensors that get inplace updated + if arg is not res[i]: + arg.copy_(res[i]) + + # skip the args + return res[len(args) :] + + return optimized_mod diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/ir_cache.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/ir_cache.py new file mode 100644 index 0000000000000000000000000000000000000000..a6e654566f29bce166eb52e721b694f3b1f7862b --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/ir_cache.py @@ -0,0 +1,14 @@ +# mypy: allow-untyped-defs +import torch._C._lazy + + +def dump(dot_file_name: str): + """Dump TrieCache in the dot format""" + return torch._C._lazy._dump_ir_cache(dot_file_name) + + +def reset(): + """Clear TrieCache. This is needed in testing to avoid + node reusing between different tests. + """ + return torch._C._lazy._clear_ir_cache() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/metrics.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..a77981feb90dbd74eb0a31ae86fe661a758a494a --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/metrics.py @@ -0,0 +1,22 @@ +# mypy: allow-untyped-defs +import torch._C._lazy + + +def reset(): + """Resets all metric counters.""" + torch._C._lazy._reset_metrics() + + +def counter_names(): + """Retrieves all the currently active counter names.""" + return torch._C._lazy._counter_names() + + +def counter_value(name: str): + """Return the value of the counter with the speficied name""" + return torch._C._lazy._counter_value(name) + + +def metrics_report(): + """Return the combined (lazy core and backend) metric report""" + return torch._C._lazy._metrics_report() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/tensor_factory_functions.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/tensor_factory_functions.py new file mode 100644 index 0000000000000000000000000000000000000000..3b8ddc8b11c7e036ba6beac440d04eb1835b26d4 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/tensor_factory_functions.py @@ -0,0 +1,49 @@ +import torch + + +""" +tensor_factory_functions defines the list of torch functions that create tensors. +The list is grabbed by searching thru native_functions.yaml by the following +regular expression: + + cat native_functions.yaml | grep 'func:' | grep -v "Tensor.*->" | grep "[-]>.*Tensor" + +It's possible that new tensor factory functions are added making this list stale. +Use at your own risk or regenerate the list. +""" +tensor_factory_functions = ( + torch._cudnn_init_dropout_state, + torch.arange, + torch.bartlett_window, + torch.blackman_window, + torch._empty_affine_quantized, + torch.empty_strided, + torch.eye, + torch.full, + torch.from_file, + torch.hann_window, + torch.hamming_window, + torch.kaiser_window, + torch.linspace, + torch.logspace, + torch.ones, + torch.scalar_tensor, + torch.rand, + torch.randint, + torch.randn, + torch.randperm, + torch.range, + torch._efficientzerotensor, + torch.zeros, + torch.tril_indices, + torch.triu_indices, + # Note: the following functions match the regular expression search above but + # they are not available in the torch module. Comment out. + # torch._sparse_coo_tensor_with_dims, + # torch.fft_fftfreq, + # torch.fft_rfftfreq, +) + ( + # torch.tensor is special since it's not in native_functions.yaml + # add it separately + torch.tensor, +) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/ts_backend.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/ts_backend.py new file mode 100644 index 0000000000000000000000000000000000000000..5c6ce13746e913db8e27081b8b0dcf8f4e0d4c88 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_lazy/ts_backend.py @@ -0,0 +1,7 @@ +# mypy: allow-untyped-defs +import torch._C._lazy_ts_backend + + +def init(): + """Initializes the lazy Torchscript backend""" + torch._C._lazy_ts_backend._init() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/__init__.py new file mode 100644 index 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a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/autograd.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/autograd.py new file mode 100644 index 0000000000000000000000000000000000000000..75997ec63eb19243143c1e1d824405ed85bc10d1 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/autograd.py @@ -0,0 +1,241 @@ +# mypy: allow-untyped-defs +import dataclasses +from dataclasses import dataclass +from typing import Any, Callable, Dict, Optional, Protocol + +from torch import _C, _ops, autograd, Tensor +from torch.utils import _pytree + +from . import utils + + +class InfoProtocol(Protocol): + _backward_fn: Optional[Callable] + _setup_context_fn: Optional[Callable] + + +@dataclasses.dataclass +class Info: + _backward_fn: Optional[Callable] + _setup_context_fn: Optional[Callable] + + +def make_autograd_impl(op: _ops.OpOverload, info: InfoProtocol) -> Callable: + name: str = f"GeneratedBackwardFor_{op._namespace}_{op._opname}_{op._overloadname}" + + has_kwarg_only_args = utils.has_kwarg_only_args(op._schema) + + @dataclass + class Metadata: + keyset: _C.DispatchKeySet + keyword_only_args: Dict[str, Any] + + def forward_no_grad(*args): + metadata = args[-1] + args = args[:-1] + + with _C._AutoDispatchBelowAutograd(): + keyset = metadata.keyset + kwargs = metadata.keyword_only_args + result = op.redispatch(keyset & _C._after_autograd_keyset, *args, **kwargs) + return result + + def forward(ctx, *args): + metadata = args[-1] + args = args[:-1] + + with _C._AutoDispatchBelowAutograd(): + keyset = metadata.keyset + kwargs = metadata.keyword_only_args + result = op.redispatch(keyset & _C._after_autograd_keyset, *args, **kwargs) + if info._setup_context_fn: + # The Dispatcher will remove args that are equal to their default + # values from (args, kwargs). We're going to add it back so that + # the user can access them. + # + # This is OK to do: The Dispatcher removed the args for serialization + # FC/BC reasons (that is, a graph will not store args that are equal + # to their default values), but that doesn't matter here. If the user + # adds a new default arg, then they must update + # their setup_context (along with the rest of their operator + # registrations) + args, kwargs = utils.fill_defaults(op._schema, args, kwargs) + + if has_kwarg_only_args: + info._setup_context_fn( + ctx=ctx, inputs=args, keyword_only_inputs=kwargs, output=result + ) + else: + info._setup_context_fn(ctx=ctx, inputs=args, output=result) + return result + + def backward(ctx, *grads): + if info._backward_fn: + try: + prev_needs_input_grad = ctx.needs_input_grad + ctx.needs_input_grad = ctx.needs_input_grad[:-1] + result = info._backward_fn(ctx, *grads) + finally: + ctx.needs_input_grad = prev_needs_input_grad + if isinstance(result, tuple): + return (*result, None) + return result, None + raise RuntimeError( + f"Trying to backward through {op} but no autograd " + f"formula was registered. " + f"Please use register_autograd to add one." + ) + + Generated = type( + name, + (autograd.Function,), + { + "forward": staticmethod(forward), + "backward": staticmethod(backward), + }, + ) + + schema = op._schema + if any( + utils.is_tensorlist_like_type(a.type) + for a in (*schema.arguments, *schema.returns) + ): + Generated = supports_tensorlist(Generated) + + # The dispatcher passes any keyword-only-args as kwargs and the + # rest of the args (even if specified as kwargs) as args. + def autograd_impl(keyset, *args, **keyword_only_args): + if _C.is_grad_enabled() and _pytree.tree_any_only( + Tensor, lambda x: x.requires_grad, args, not_list_of_tensor + ): + result = Generated.apply(*args, Metadata(keyset, keyword_only_args)) # type: ignore[attr-defined] + else: + result = forward_no_grad(*args, Metadata(keyset, keyword_only_args)) + return result + + return autograd_impl + + +def supports_tensorlist(cls: Any) -> Any: + """Allows a given autograd.Function class to support List[Tensor] inputs/outputs. + + Regular autograd.Function has a constraint that it only directly supports autograd for + Tensors. Applying @supports_tensorlist enables an autograd.Function to support + autograd for List[Tensor] inputs and outputs. + """ + orig_forward = cls.forward + orig_backward = cls.backward + orig_apply = cls.apply + + @dataclass + class Metadata: + input_spec: spec_t + output_spec: Optional[spec_t] = None + result_is_tuple: Optional[bool] = None + + def new_forward(ctx, *args): + metadata = args[-1] + args = args[:-1] + if not isinstance(metadata, Metadata): + raise NotImplementedError( + "NYI: calling supports_tensorlist autograd.Function.forward directly. " + "You should probably be calling .apply instead. " + "Please file an issue if not." + ) + args = unflatten(list(args), metadata.input_spec) + result = orig_forward(ctx, *args) + metadata.result_is_tuple = isinstance(result, tuple) + if not metadata.result_is_tuple: + result = (result,) + flat_result, output_spec = flatten(result, not_list_of_tensor) + metadata.output_spec = output_spec + + if hasattr(ctx, "_pt_metadata"): + raise RuntimeError( + "Please don't set ctx._pt_metadata; PyTorch uses it to store info" + ) + ctx._pt_metadata = metadata + + return tuple(flat_result) + + def new_backward(ctx, *grads): + if not hasattr(ctx, "_pt_metadata"): + raise NotImplementedError( + "NYI: calling supports_tensorlist autograd.Function.backward directly. " + "This will automatically get called by PyTorch autograd. " + "Please file an issue if you need this." + ) + + metadata = ctx._pt_metadata + grads = unflatten(list(grads), metadata.output_spec) + + # If the user's input is ([x, y, z], w), + # then needs_input_grad is (bool, bool, bool, bool, bool). + # We need to + # 1. get rid of the additional bool (which comes from the extra + # `metadata input`) + # 2. unflatten to get the right structure. + prev_needs_input_grad = ctx.needs_input_grad + try: + ctx.needs_input_grad = unflatten( + list(ctx.needs_input_grad[:-1]), metadata.input_spec + ) + grad_inputs = orig_backward(ctx, *grads) + finally: + ctx.needs_input_grad = prev_needs_input_grad + + if not isinstance(grad_inputs, tuple): + grad_inputs = (grad_inputs,) + # Assume that any Nones in the backward are Tensors. + # If the forward has an arg that is [1, 2, 3], the backward should + # return None as the grad. + # If the forward has an arg that is [tensor, tensor], the backward + # may return [None, None], [grad, None], [None, grad], or [grad, grad]. + flat_grad_inputs, grad_inputs_spec = flatten( + grad_inputs, not_list_of_optional_tensor + ) + if grad_inputs_spec != metadata.input_spec: + raise RuntimeError( + f"Expected the return from backward to be of the same structure " + f"as the inputs. Got: {grad_inputs_spec} (return from backward), " + f"{metadata.input_spec} (inputs)" + ) + return tuple(flat_grad_inputs + [None]) + + def new_apply(*args): + flat_args, input_spec = flatten(args, is_leaf=not_list_of_tensor) + metadata = Metadata(input_spec) + result = orig_apply(*flat_args, metadata) # type: ignore[misc] + assert metadata.output_spec is not None + result = unflatten(list(result), metadata.output_spec) + if not metadata.result_is_tuple: + assert isinstance(result, tuple) + assert len(result) == 1 + return result[0] + return result + + cls.forward = new_forward + cls.backward = new_backward + cls.apply = new_apply + return cls + + +def not_list_of_tensor(tree): + if isinstance(tree, tuple): + return False + if isinstance(tree, list): + return any(not isinstance(l, Tensor) for l in tree) + return True + + +def not_list_of_optional_tensor(tree): + if isinstance(tree, tuple): + return False + if isinstance(tree, list): + return any(l is not None and not isinstance(l, Tensor) for l in tree) + return True + + +flatten = _pytree.tree_flatten +unflatten = _pytree.tree_unflatten +spec_t = _pytree.TreeSpec diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/custom_ops.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/custom_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..2eb45a78a5edce949c2946d27019e8322d73b0ae --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/custom_ops.py @@ -0,0 +1,835 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import inspect +import logging +import weakref +from contextlib import contextmanager +from typing import ( + Any, + Callable, + Dict, + Iterable, + Iterator, + List, + Optional, + Sequence, + Set, + Tuple, + Union, +) + +import torch +from torch import _C, _ops, Tensor +from torch.utils._exposed_in import exposed_in + +from . import autograd, utils + + +device_types_t = Optional[Union[str, Sequence[str]]] +log = logging.getLogger(__name__) + + +@exposed_in("torch.library") +def custom_op( + name: str, + fn: Optional[Callable] = None, + /, + *, + mutates_args: Union[str, Iterable[str]], + device_types: device_types_t = None, + schema: Optional[str] = None, +) -> Callable: + """Wraps a function into custom operator. + + Reasons why you may want to create a custom op include: + - Wrapping a third-party library or custom kernel to work with PyTorch + subsystems like Autograd. + - Preventing torch.compile/export/FX tracing from peeking inside your function. + + This API is used as a decorator around a function (please see examples). + The provided function must have type hints; these are needed to interface + with PyTorch's various subsystems. + + Args: + name (str): A name for the custom op that looks like "{namespace}::{name}", + e.g. "mylib::my_linear". The name is used as the op's stable identifier + in PyTorch subsystems (e.g. torch.export, FX graphs). + To avoid name collisions, please use your project name as the namespace; + e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. + mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. + This MUST be accurate, otherwise, the behavior is undefined. If "unknown", + it pessimistically assumes that all inputs to the operator are being mutated. + device_types (None | str | Sequence[str]): The device type(s) the function + is valid for. If no device type is provided, then the function + is used as the default implementation for all device types. + Examples: "cpu", "cuda". + When registering a device-specific implementation for an operator that accepts no Tensors, + we require the operator to have a "device: torch.device argument". + schema (None | str): A schema string for the operator. If None + (recommended) we'll infer a schema for the operator from its type + annotations. We recommend letting us infer a schema unless you + have a specific reason not to. + Example: "(Tensor x, int y) -> (Tensor, Tensor)". + + .. note:: + We recommend not passing in a ``schema`` arg and instead letting us infer + it from the type annotations. It is error-prone to write your own schema. + You may wish to provide your own schema if our interpretation of + the type annotation is not what you want. + For more info on how to write a schema string, see + `here `_ + + Examples:: + >>> import torch + >>> from torch import Tensor + >>> from torch.library import custom_op + >>> import numpy as np + >>> + >>> @custom_op("mylib::numpy_sin", mutates_args=()) + >>> def numpy_sin(x: Tensor) -> Tensor: + >>> x_np = x.cpu().numpy() + >>> y_np = np.sin(x_np) + >>> return torch.from_numpy(y_np).to(device=x.device) + >>> + >>> x = torch.randn(3) + >>> y = numpy_sin(x) + >>> assert torch.allclose(y, x.sin()) + >>> + >>> # Example of a custom op that only works for one device type. + >>> @custom_op("mylib::numpy_sin_cpu", mutates_args=(), device_types="cpu") + >>> def numpy_sin_cpu(x: Tensor) -> Tensor: + >>> x_np = x.numpy() + >>> y_np = np.sin(x_np) + >>> return torch.from_numpy(y_np) + >>> + >>> x = torch.randn(3) + >>> y = numpy_sin_cpu(x) + >>> assert torch.allclose(y, x.sin()) + >>> + >>> # Example of a custom op that mutates an input + >>> @custom_op("mylib::numpy_sin_inplace", mutates_args={"x"}, device_types="cpu") + >>> def numpy_sin_inplace(x: Tensor) -> None: + >>> x_np = x.numpy() + >>> np.sin(x_np, out=x_np) + >>> + >>> x = torch.randn(3) + >>> expected = x.sin() + >>> numpy_sin_inplace(x) + >>> assert torch.allclose(x, expected) + >>> + >>> # Example of a factory function + >>> @torch.library.custom_op("mylib::bar", mutates_args={}, device_types="cpu") + >>> def bar(device: torch.device) -> Tensor: + >>> return torch.ones(3) + >>> + >>> bar("cpu") + + """ + + def inner(fn): + import torch + + if schema is None: + schema_str = torch.library.infer_schema(fn, mutates_args=mutates_args) + else: + schema_str = schema + + namespace, opname = name.split("::") + result = CustomOpDef(namespace, opname, schema_str, fn) + if schema is not None: + # Check that schema's alias annotations match those of `mutates_args`. + expected = set() + for arg in result._opoverload._schema.arguments: + if arg.alias_info is not None and arg.alias_info.is_write: + expected.add(arg.name) + if expected != set(mutates_args): + raise ValueError( + f"Attempted to create a custom op with `mutates_args={mutates_args}` " + f"and `schema={schema}. The schema suggests that the op mutates {expected}" + f"which is different from what was provided to us in `mutates_args`. " + f"Please make these consistent." + ) + result.register_kernel(device_types)(fn) + return result + + if fn is None: + return inner + return inner(fn) + + +class CustomOpDef: + """CustomOpDef is a wrapper around a function that turns it into a custom op. + + It has various methods for registering additional behavior for this + custom op. + + You should not instantiate CustomOpDef directly; instead, use the + :func:`torch.library.custom_op` API. + """ + + def __init__(self, namespace: str, name: str, schema: str, fn: Callable) -> None: + # Fields used to interface with the PyTorch dispatcher + self._namespace = namespace + self._name = name + self._schema = schema + + self._init_fn = fn + + self._backend_fns: Dict[Union[str, None], Callable] = {} + self._abstract_fn: Optional[Callable] = None + self._setup_context_fn: Optional[Callable] = None + self._backward_fn: Optional[Callable] = None + self._torch_dispatch_fns: Dict[type, Callable] = {} + self._vmap_fn: Optional[Callable] = None + + self._lib = get_library_allowing_overwrite(self._namespace, self._name) + self._register_to_dispatcher() + self._disabled_kernel: Set = set() + OPDEFS[self._qualname] = self + + @property + def _qualname(self) -> str: + return f"{self._namespace}::{self._name}" + + def __repr__(self) -> str: + return f"" + + @contextmanager + def set_kernel_enabled(self, device_type: str, enabled: bool = True): + """ + Disable or re-enable an already registered kernel for this custom operator. + + If the kernel is already disabled/enabled, this is a no-op. + + Note: + If a kernel is first disabled and then registered, it is disabled until enabled again. + + Args: + device_type (str): The device type to disable/enable the kernel for. + disable (bool): Whether to disable or enable the kernel. + + Example: + >>> inp = torch.randn(1) + >>> + >>> # define custom op `f`. + >>> @custom_op("mylib::f", mutates_args=()) + >>> def f(x: Tensor) -> Tensor: + >>> return torch.zeros(1) + >>> + >>> print(f(inp)) # tensor([0.]), default kernel + >>> + >>> @f.register_kernel("cpu") + >>> def _(x): + >>> return torch.ones(1) + >>> + >>> print(f(inp)) # tensor([1.]), CPU kernel + >>> + >>> # temporarily disable the CPU kernel + >>> with f.set_kernel_enabled("cpu", enabled = False): + >>> print(f(inp)) # tensor([0.]) with CPU kernel disabled + + """ + action = "enable" if enabled else "disable" + originally_disabled = device_type in self._disabled_kernel + if device_type not in self._backend_fns: + log.warning( + "Attempted to %s kernel for %s but no kernel was registered for this device type.", + action, + device_type, + ) + + if not enabled: + if originally_disabled: + log.warning( + "Attempted to disable kernel for %s but it was already disabled.", + device_type, + ) + else: + self._disabled_kernel.add(device_type) + else: # enable the kernel + if not originally_disabled: + log.warning( + "Attempted to enable kernel for %s but it was already enabled.", + device_type, + ) + else: + self._disabled_kernel.remove(device_type) + + try: + yield + finally: + # restore original state + if originally_disabled: + self._disabled_kernel.add(device_type) + else: + self._disabled_kernel.discard(device_type) + + def register_kernel( + self, device_types: device_types_t, fn: Optional[Callable] = None, / + ) -> Callable: + """Register an implementation for a device type for this operator. + + Some valid device_types are: "cpu", "cuda", "xla", "mps", "ipu", "xpu". + This API may be used as a decorator. + + Args: + fn (Callable): The function to register as the implementation for + the given device types. + device_types (str | Sequence[str]): The device device_types to register an impl to. + + Examples:: + >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) + >>> import torch + >>> from torch import Tensor + >>> from torch.library import custom_op + >>> import numpy as np + >>> + >>> # Create a custom op that works on cpu + >>> @custom_op("mylib::numpy_sin", mutates_args=(), device_types="cpu") + >>> def numpy_sin(x: Tensor) -> Tensor: + >>> x_np = x.numpy() + >>> y_np = np.sin(x_np) + >>> return torch.from_numpy(y_np) + >>> + >>> # Add implementations for the cuda device + >>> @numpy_sin.register_kernel("cuda") + >>> def _(x): + >>> x_np = x.cpu().numpy() + >>> y_np = np.sin(x_np) + >>> return torch.from_numpy(y_np).to(device=x.device) + >>> + >>> x_cpu = torch.randn(3) + >>> x_cuda = x_cpu.cuda() + >>> assert torch.allclose(numpy_sin(x_cpu), x_cpu.sin()) + >>> assert torch.allclose(numpy_sin(x_cuda), x_cuda.sin()) + + """ + + def inner(fn): + if device_types is None or isinstance(device_types, str): + dtypes: List[Union[str, None]] = [device_types] + else: + dtypes = list(device_types) + for device_type in dtypes: + if device_type not in self._backend_fns: + + def backend_impl(*args, **kwargs): + # Checks the assumption that outputs cannot alias + # inputs or other outputs. + storages = { + id(tensor.untyped_storage()) + for tensor in iter_tensors(args, kwargs) + } + + result = self._backend_fns[device_type](*args, **kwargs) + + tuple_result = result + if not isinstance(result, tuple): + tuple_result = (result,) + for tensor in iter_tensors(tuple_result, {}): + key = id(tensor.untyped_storage()) + if id(tensor.untyped_storage()) in storages: + fn = self._backend_fns[device_type] + module = inspect.getmodule(fn) + raise RuntimeError( + f"{self._name} (with implementation in {module}): " + f"The output of this custom operator (1) must not " + f"also be an input to this custom operator and " + f"(2) may not alias any inputs to this custom operator " + f"or other returns. " + f"The most common way to trigger this error is if " + f"we have y = custom_op(x) and y and x are the same Tensor. " + f"Please instead return a clone of the offending output " + f"tensor(s) (e.g. return x.clone()) or refactor the custom " + f"operator to not return y." + ) + storages.add(key) + return result + + if device_type is None: + self._lib.impl( + self._name, backend_impl, "CompositeExplicitAutograd" + ) + else: + self._lib.impl( + self._name, + backend_impl, + _C._dispatch_key_for_device(device_type), + ) + + # Wrap function to choose between the default implementation or the device-specific + # implementation depending on if the kernel is disabled. + @torch._disable_dynamo + def wrapped_fn(*args, **kwargs): + if device_type in self._disabled_kernel: + return self._init_fn(*args, **kwargs) + else: + return fn(*args, **kwargs) + + self._backend_fns[device_type] = wrapped_fn + return fn + + if device_types is not None and not utils.has_tensor_arg( + self._opoverload._schema + ): + device_arg_index = utils.get_device_arg_index(self._opoverload._schema) + if device_arg_index is None: + raise ValueError( + "Functions without tensor inputs are required to have a `device: torch.device` argument" + ) + self._register_backend_select_dispatcher(device_arg_index) + + # See NOTE: [Supporting decorator and non-decorator usage] + if fn is None: + return inner + return inner(fn) + + def register_fake(self, fn: Callable, /) -> Callable: + r"""Register a FakeTensor implementation for this custom op. + + This is necessary to get the operator to work efficiently with torch.compile. + + The Fake impl (sometimes also known as a meta kernel or abstract impl) + specifies the behavior of this operator on Tensors that carry no data. + Given some input Tensors with certain properties + (sizes/strides/storage_offset/device), it specifies what the properties of + the output Tensors are. + + Please see :func:`torch.library.impl_abstract` for more details. + + Args: + fn (Callable): The function to register as the FakeTensor + implementation. + + Examples: + >>> import torch + >>> import numpy as np + >>> from torch import Tensor + >>> + >>> # Example 1: an operator without data-dependent output shape + >>> @torch.library.custom_op("mylib::linear", mutates_args=()) + >>> def linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: + >>> return (x @ weight.t()) + bias + >>> + >>> @linear.register_fake + >>> def _(x, weight, bias): + >>> assert x.dim() == 2 + >>> assert weight.dim() == 2 + >>> assert bias.dim() == 1 + >>> assert x.shape[1] == weight.shape[1] + >>> assert weight.shape[0] == bias.shape[0] + >>> assert x.device == weight.device + >>> return x.new_empty(x.size(0), weight.size(0)) + >>> + >>> x = torch.randn(2, 2) + >>> weight = torch.randn(2, 2) + >>> bias = torch.randn(2) + >>> # xdoctest: +SKIP("Requires Python <= 3.11") + >>> out = torch.compile(linear, fullgraph=True)(x, weight, bias) + >>> # xdoctest: +SKIP("Requires Python <= 3.11") + >>> assert torch.allclose(out, torch.nn.functional.linear(x, weight, bias)) + >>> + >>> # Example 2: an operator with data-dependent output shape + >>> @torch.library.custom_op("mylib::nonzero", mutates_args=()) + >>> def nonzero(x: Tensor) -> Tensor: + >>> x_np = x.cpu().numpy() + >>> res = np.stack(np.nonzero(x_np), axis=1) + >>> return torch.tensor(res, device=x.device) + >>> + >>> @nonzero.register_fake + >>> def _(x): + >>> # Number of nonzero-elements is data-dependent. + >>> # Since we cannot peek at the data in an abstract impl, + >>> # we use the ctx object to construct a new symint that + >>> # represents the data-dependent size. + >>> ctx = torch.library.get_ctx() + >>> nnz = ctx.new_dynamic_size() + >>> shape = [nnz, x.dim()] + >>> result = x.new_empty(shape, dtype=torch.int64) + >>> return result + >>> + >>> x = torch.tensor([0, 1, 2, 0, 0, 1]) + >>> # xdoctest: +SKIP("Requires Python <= 3.11") + >>> out = torch.compile(nonzero, fullgraph=True)(x) + >>> # xdoctest: +SKIP("Requires Python <= 3.11") + >>> assert torch.allclose(out, x.nonzero()) + + """ + self._abstract_fn = fn + return fn + + def register_torch_dispatch( + self, torch_dispatch_class: Any, fn: Optional[Callable] = None, / + ) -> Callable: + r"""Registers a torch_dispatch rule for the given operator and ``torch_dispatch_class``. + + This allows for open registration to specify the behavior between the operator + and the ``torch_dispatch_class`` without needing to modify the ``torch_dispatch_class`` + or the operator directly. + + Please see :func:`torch.library.register_torch_dispatch` for examples and more details. + """ + + def register(fn): + if torch_dispatch_class not in self._torch_dispatch_fns: + + def inner(*args, **kwargs): + return self._torch_dispatch_fns[torch_dispatch_class]( + *args, **kwargs + ) + + self._lib._register_torch_dispatch_rule( + self._name, torch_dispatch_class, inner + ) + self._torch_dispatch_fns[torch_dispatch_class] = fn + return fn + + if fn is None: + return register + else: + return register(fn) + + def register_autograd( + self, + backward: Callable, + /, + *, + setup_context: Optional[Callable] = None, + ) -> None: + r"""Register a backward formula for this custom op. + + In order for an operator to work with autograd, you need to register + a backward formula: + 1. You must tell us how to compute gradients during the backward pass + by providing us a "backward" function. + 2. If you need any values from the forward to compute gradients, you can + use `setup_context` to save values for backward. + + ``backward_fn`` runs during the backward pass. It accepts ``(ctx, *grads)``: + - ``grads`` is one or more gradients. The number of gradients matches + the number of outputs of the operator. + The ``ctx`` object is `the same ctx object `_ used by + :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the + same as :meth:`torch.autograd.Function.backward`. + + ``setup_context(ctx, inputs, output)`` runs during the forward pass. + Please save quantities needed for backward onto the ``ctx`` object via + either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` + or assigning them as attributes of ``ctx``. If your custom op has + kwarg-only arguments, we expect the signature of ``setup_context`` + to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. + + Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, + they may not directly access :meth:`torch.Tensor.data_ptr` and they must + not depend on or mutate global state. If you need a non-traceable backward, + you can make it a separate custom_op that you call inside ``backward_fn``. + + Examples: + >>> import torch + >>> import numpy as np + >>> from torch import Tensor + >>> + >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) + >>> def numpy_sin(x: Tensor) -> Tensor: + >>> x_np = x.cpu().numpy() + >>> y_np = np.sin(x_np) + >>> return torch.from_numpy(y_np).to(device=x.device) + >>> + >>> def setup_context(ctx, inputs, output) -> Tensor: + >>> x, = inputs + >>> ctx.save_for_backward(x) + >>> + >>> def backward(ctx, grad): + >>> x, = ctx.saved_tensors + >>> return grad * x.cos() + >>> + >>> numpy_sin.register_autograd(backward, setup_context=setup_context) + >>> + >>> x = torch.randn(3, requires_grad=True) + >>> y = numpy_sin(x) + >>> grad_x, = torch.autograd.grad(y, x, torch.ones_like(y)) + >>> assert torch.allclose(grad_x, x.cos()) + >>> + >>> # Example with a keyword-only arg + >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) + >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: + >>> x_np = x.cpu().numpy() + >>> y_np = x_np * val + >>> return torch.from_numpy(y_np).to(device=x.device) + >>> + >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: + >>> ctx.val = keyword_only_inputs["val"] + >>> + >>> def backward(ctx, grad): + >>> return grad * ctx.val + >>> + >>> numpy_mul.register_autograd(backward, setup_context=setup_context) + >>> + >>> x = torch.randn(3, requires_grad=True) + >>> y = numpy_mul(x, val=3.14) + >>> grad_x, = torch.autograd.grad(y, x, torch.ones_like(y)) + >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) + + """ + schema = self._opoverload._schema + if not utils.is_functional_schema(schema): + raise RuntimeError( + f"Cannot register autograd formula for non-functional operator " + f"{self} with schema {schema}. Please create " + f"a functional operator and register an autograd formula for that." + ) + + self._backward_fn = backward + self._setup_context_fn = setup_context + + def _register_to_dispatcher(self) -> None: + lib = self._lib + schema_str = self._name + self._schema + cpp_schema = _C.parse_schema(schema_str) + if utils.has_kwarg_only_tensors(cpp_schema): + # If you want to support this, the progression is: + # - supporting kwarg-only Tensors that are non-differentiable + # - supporting kwarg-only Tensors (regardless of differentiability) + raise NotImplementedError( + f"custom_op with kwarg-only Tensor args. Please make your " + f"tensors not kwarg-only. Got: {schema_str}" + ) + + lib.define( + schema_str, + tags=[_C.Tag.pt2_compliant_tag, _C.Tag.needs_fixed_stride_order], + ) + self._opoverload = utils.lookup_op(self._qualname) + + def fake_impl(*args, **kwargs): + if self._abstract_fn is None: + if utils.can_generate_trivial_fake_impl(self._opoverload): + return None + raise RuntimeError( + f"There was no fake impl registered for {self}. " + f"This is necessary for torch.compile/export/fx tracing to work. " + f"Please use `{self._init_fn.__name__}.register_fake` to add an " + f"fake impl." + ) + return self._abstract_fn(*args, **kwargs) + + lib._register_fake(self._name, fake_impl, _stacklevel=4) + + autograd_impl = autograd.make_autograd_impl(self._opoverload, self) + lib.impl(self._name, autograd_impl, "Autograd", with_keyset=True) + + schema = self._opoverload._schema + if schema.is_mutable: + + def adinplaceorview_impl(keyset, *args, **kwargs): + for arg, val in utils.zip_schema(schema, args, kwargs): + if not arg.alias_info: + continue + if not arg.alias_info.is_write: + continue + if isinstance(val, Tensor): + torch.autograd.graph.increment_version(val) + elif isinstance(val, (tuple, list)): + for v in val: + if isinstance(v, Tensor): + torch.autograd.graph.increment_version(v) + with _C._AutoDispatchBelowADInplaceOrView(): + return self._opoverload.redispatch( + keyset & _C._after_ADInplaceOrView_keyset, *args, **kwargs + ) + + lib.impl( + self._name, + adinplaceorview_impl, + "ADInplaceOrView", + with_keyset=True, + ) + + def _register_backend_select_dispatcher(self, device_arg_index: int): + """ + Switch on the device argument to select the correct backend to dispatch to. + """ + + def backend_select(keyset, *args, **kwargs): + device = args[device_arg_index].type + if device not in self._backend_fns: + raise RuntimeError( + f"{self._name} does not have a kernel registered for {device}. " + "Please use register_kernel to do so." + ) + dispatch_key = _C._dispatch_key_for_device(device) + dispatch_key = getattr(_C.DispatchKey, dispatch_key) + return self._opoverload.redispatch( + _C.DispatchKeySet(dispatch_key), *args, **kwargs + ) + + self._lib.impl(self._name, backend_select, "BackendSelect", with_keyset=True) + + def __call__(self, *args, **kwargs): + return self._opoverload(*args, **kwargs) + + def register_vmap( + self, + func: Optional[Callable] = None, + ): + r"""Register a vmap implementation to support :func:`torch.vmap` for this custom op. + + This API may be used as a decorator. + + In order for an operator to work with :func:`torch.vmap`, you may need to register a + vmap implementation in the following signature: + + ``vmap_func(info, in_dims: Tuple[Optional[int]], *args, **kwargs)``, + + where ``*args`` and ``**kwargs`` are the arguments and kwargs for ``op``. + + It specifies how do we compute the batched version of ``op`` given inputs with an additional + dimension (specified by ``in_dims``). + + For each arg in ``args``, ``in_dims`` has a corresponding ``Optional[int]``. It is ``None`` + if the arg is not a Tensor or if the arg is not being vmapped over, otherwise, it is an integer + specifying what dimension of the Tensor is being vmapped over. + + ``info`` is a collection of additional metadata that may be helpful: + ``info.batch_size`` specifies the size of the dimension being vmapped over, while + ``info.randomness`` is the ``randomness`` option that was passed to :func:`torch.vmap`. + + The return of the function ``func`` is a tuple of ``(output, out_dims)``. Similar to ``in_dims``, + ``out_dims`` should be of the same structure as ``output`` and contain one ``out_dim`` + per output that specifies if the output has the vmapped dimension and what index it is in. + + Examples: + >>> import torch + >>> import numpy as np + >>> from torch import Tensor + >>> from typing import Tuple + >>> + >>> def to_numpy(tensor): + >>> return tensor.cpu().numpy() + >>> + >>> lib = torch.library.Library("mylib", "FRAGMENT") + >>> @torch.library.custom_op("mylib::numpy_cube", mutates_args=()) + >>> def numpy_cube(x: Tensor) -> Tuple[Tensor, Tensor]: + >>> x_np = to_numpy(x) + >>> dx = torch.tensor(3 * x_np ** 2, device=x.device) + >>> return torch.tensor(x_np ** 3, device=x.device), dx + >>> + >>> def numpy_cube_vmap(info, in_dims, x): + >>> result = numpy_cube(x) + >>> return result, (in_dims[0], in_dims[0]) + >>> + >>> numpy_cube.register_vmap(numpy_cube_vmap) + >>> + >>> x = torch.randn(3) + >>> torch.vmap(numpy_cube)(x) + >>> + >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) + >>> def numpy_mul(x: Tensor, y: Tensor) -> Tensor: + >>> return torch.tensor(to_numpy(x) * to_numpy(y), device=x.device) + >>> + >>> @numpy_mul.register_vmap + >>> def numpy_mul_vmap(info, in_dims, x, y): + >>> x_bdim, y_bdim = in_dims + >>> x = x.movedim(x_bdim, -1) if x_bdim is not None else x.unsqueeze(-1) + >>> y = y.movedim(y_bdim, -1) if y_bdim is not None else y.unsqueeze(-1) + >>> result = x * y + >>> result = result.movedim(-1, 0) + >>> return result, 0 + >>> + >>> + >>> x = torch.randn(3) + >>> y = torch.randn(3) + >>> torch.vmap(numpy_mul)(x, y) + """ + from torch._functorch.autograd_function import custom_function_call_vmap_helper + from torch._functorch.pyfunctorch import retrieve_current_functorch_interpreter + + def register(func): + need_register = self._vmap_fn is None + self._vmap_fn = func + + if need_register: + + def wrapped_func(keyset, *args, **kwargs): + interpreter = retrieve_current_functorch_interpreter() + return custom_function_call_vmap_helper( + interpreter, self._vmap_fn, self._opoverload, *args, **kwargs + ) + + self._lib.impl( + self._name, wrapped_func, "FuncTorchBatched", with_keyset=True + ) + + if func is None: + return register + else: + return register(func) + + +# NOTE: [Supporting decorator and non-decorator usage] +# +# Some APIs may be both used as a decorator and not as a decorator. +# For example: +# +# >>> def fn(x): +# >>> return x.sin() +# >>> +# >>> # Usage 1: not as a decorator +# >>> numpy_sin.register_kernel("cuda", fn) +# >>> +# >>> # Usage 2: as a decorator +# >>> @numpy_sin.register_kernel("cuda") +# >>> def fn2(x): +# >>> return x.sin +# +# The way we support this is that `register_kernel` accepts an optional `fn`. +# If `fn` is provided (Usage 1), then we know that the user is using it not +# as a decorator. +# If `fn` is not provided (Usage 2), then `register_kernel` needs to return a +# decorator. + + +OPDEF_TO_LIB: Dict[str, "torch.library.Library"] = {} +OPDEFS: weakref.WeakValueDictionary = weakref.WeakValueDictionary() + + +def get_library_allowing_overwrite( + namespace: str, name: str +) -> "torch.library.Library": + qualname = f"{namespace}::{name}" + + if qualname in OPDEF_TO_LIB: + OPDEF_TO_LIB[qualname]._destroy() + del OPDEF_TO_LIB[qualname] + + lib = torch.library.Library(namespace, "FRAGMENT") # noqa: TOR901 + OPDEF_TO_LIB[qualname] = lib + return lib + + +def iter_tensors( + args: Tuple[Any], kwargs: Dict[str, Any], allowed_nesting: int = 1 +) -> Iterator[Tensor]: + def check(arg): + if isinstance(arg, Tensor): + yield arg + elif allowed_nesting > 0 and isinstance(arg, (tuple, list)): + yield from iter_tensors(tuple(arg), {}, allowed_nesting - 1) + + for arg in args: + yield from check(arg) + for kwarg in kwargs.values(): + yield from check(kwarg) + + +def _maybe_get_opdef( + op: Union[CustomOpDef, _ops.OpOverload, str] +) -> Optional[CustomOpDef]: + if isinstance(op, CustomOpDef): + return op + if isinstance(op, _ops.OpOverload): + op = op._name + assert isinstance(op, str) + if op in OPDEFS: + return OPDEFS[op] + return None diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/fake_class_registry.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/fake_class_registry.py new file mode 100644 index 0000000000000000000000000000000000000000..3c2689f5328f55dfbf37f1a2f6584aa328c6ce83 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/fake_class_registry.py @@ -0,0 +1,320 @@ +# mypy: allow-untyped-defs +import logging +from typing import Any, Dict, Optional, Protocol, Tuple, Union + +import torch +from torch._library.utils import parse_namespace + + +log = logging.getLogger(__name__) + + +class FakeScriptObject: + def __init__(self, wrapped_obj: Any, script_class_name: str, x: torch.ScriptObject): + self.wrapped_obj = wrapped_obj + + # The fully qualified name of the class of original script object + self.script_class_name = script_class_name + self.real_obj = x + + +class FakeScriptMethod: + def __init__( + self, + self_fake_obj: FakeScriptObject, + method_name: str, + schema: Optional[torch.FunctionSchema], + ): + self.self_fake_obj = self_fake_obj + self.method_name = method_name + self.schema = schema + + def __call__(self, *args, **kwargs): + from torch._higher_order_ops.torchbind import call_torchbind + + return call_torchbind(self.self_fake_obj, self.method_name, *args, **kwargs) + + +class HasStaticMethodFromReal(Protocol): + @classmethod + def from_real(cls, real_obj: torch.ScriptObject): + pass + + +class FakeClassRegistry: + def __init__(self) -> None: + self._registered_class: Dict[str, Any] = {} + + def has_impl(self, full_qualname: str) -> bool: + return full_qualname in self._registered_class + + def get_impl(self, full_qualname: str) -> Any: + self._check_registered(full_qualname) + return self._registered_class[full_qualname] + + def register(self, full_qualname: str, fake_class=None) -> None: + if self.has_impl(full_qualname): + log.warning( + "%s is already registered. Previous fake class is overridden with %s.", + full_qualname, + fake_class, + ) + self._registered_class[full_qualname] = fake_class + + def deregister(self, full_qualname: str) -> Any: + if not self.has_impl(full_qualname): + log.warning( + "Cannot deregister %s. Please use register_fake_class to register it first." + " Or do you dereigster it twice?", + full_qualname, + ) + else: + return self._registered_class.pop(full_qualname) + + def clear(self) -> None: + self._registered_class.clear() + + def _check_registered(self, full_qualname: str) -> None: + if full_qualname not in self._registered_class: + raise RuntimeError( + f"{full_qualname} is not registered. Please use register_fake_class to register it first." + ) + + +global_fake_class_registry = FakeClassRegistry() + + +# TODO: add this check at compile time for __obj_flatten__. +def _check_valid_flat_script_obj(flat_x): + if not isinstance(flat_x, tuple): + raise RuntimeError("Expect flat x to be a tuple.") + + for tp in flat_x: + if not isinstance(tp, tuple): + raise RuntimeError("Expect flat x to be a tuple of tuples.") + + if not len(tp) == 2 or not isinstance(tp[0], str): + raise RuntimeError( + "Expect element of flat x to be a tuple of two elements with first element being a string" + ) + + +def tracing_with_real(x: torch.ScriptObject) -> bool: + if not hasattr(x, "tracing_mode"): + return False + + assert x.tracing_mode() in [ + "real", + "fake", + ], f"tracing_mode can be either real or fake but got {x.tracing_mode()}" + return x.tracing_mode() == "real" + + +def maybe_to_fake_obj( + fake_mode, x: torch.ScriptObject +) -> Union[FakeScriptObject, torch.ScriptObject]: + import torch.utils._pytree as pytree + from torch.utils._python_dispatch import _disable_current_modes + + # When tracing with real mode, people should implement meta kernels that can + # handle the case of real script object + fake tensor inputs. + if tracing_with_real(x): + return x + + # x.__obj_flatten__() could be calling some tensor operations inside but we don't + # want to call these ops in surrounding dispatch modes when executing it. + # Otherwise, for example, the fake tensor modes will error out when the tensors inside + # script obeject execute some operations like clone if allow_non_fake_input flag is set. + with _disable_current_modes(): + flat_x = x.__obj_flatten__() # type: ignore[attr-defined] + + _check_valid_flat_script_obj(flat_x) + + fake_flattened = pytree.tree_map_only( + torch.Tensor, + lambda t: fake_mode.from_tensor(t), + flat_x, + ) + + fake_x = _find_fake_class_for_script_object(x).__obj_unflatten__(fake_flattened) + + fake_x_wrapped = FakeScriptObject(fake_x, x._type().qualified_name(), x) # type: ignore[attr-defined] + + for name in x._method_names(): # type: ignore[attr-defined] + attr = getattr(fake_x, name, None) + if attr: + if not callable(attr): + raise RuntimeError(f"Expect {name} to be a callable but got {attr}.") + + real_attr = getattr(x, name) # type: ignore[attr-defined] + + # real attr sometimes is not torch.ScriptMethod thus doesn't have schema e.g. __init___ or __eq__ + method_schema: Optional[torch.FunctionSchema] = None + if isinstance(real_attr, torch.ScriptMethod): + method_schema = real_attr.schema # type: ignore[attr-defined] + + setattr( + fake_x_wrapped, + name, + FakeScriptMethod(fake_x_wrapped, name, method_schema), + ) + else: + override_skip_list = {"__obj_flatten__", "__get_state__", "__set_state__"} + if name not in override_skip_list: + log.warning("fake object of %s doesn't implement method %s.", x, name) + return fake_x_wrapped + + +def register_fake_class(qualname, fake_class: Optional[HasStaticMethodFromReal] = None): + r"""Register a fake implementation for this class. + + It's in the same spirit of registering a fake implementation for + an operator but with the difference that it + associates a fake class with the original torch bind class (registered + with torch::class_). In this way, torch.compile can handle them properly + in components such as Dynamo and AOTAutograd. + + This API may be used as a decorator (see example). For the fake class, users + are required to provide a from_real classmethod that takes a real object and + returns an instance of the fake class. All tensors in the fake object should also + be properly fakified with to_fake_tensor() in from_real. + + + Examples: + # For a custom class Foo defined in test_custom_class_registration.cpp: + + TORCH_LIBRARY(_TorchScriptTesting, m) { + m.class_("_TensorQueue") + .def(torch::init()) + .def("push", &TensorQueue::push) + .def("pop", &TensorQueue::pop) + .def("top", &TensorQueue::top) + .def("size", &TensorQueue::size) + .def("clone_queue", &TensorQueue::clone_queue) + .def("__obj_flatten__", &TensorQueue::__obj_flatten__) + .def_pickle( + // __getstate__ + [](const c10::intrusive_ptr& self) + -> c10::Dict { + return self->serialize(); + }, + // __setstate__ + [](c10::Dict data) + -> c10::intrusive_ptr { + return c10::make_intrusive(std::move(data)); + }); + }; + # We could register a fake class FakeTensorQueue in Python as follows: + import torch + + @torch._library.register_fake_class("_TorchScriptTesting::_TensorQueue") + class FakeTensorQueue: + def __init__(self, queue): + self.queue = queue + + @classmethod + def __obj_unflatten__(cls, flattened_ctx): + return cls(**dict(ctx)) + + def push(self, x): + self.queue.append(x) + + def pop(self): + return self.queue.pop(0) + + def size(self): + return len(self.queue) + + In this example, the original TensorQeue need to addd a __obj_flatten__ method + to the class TensorQueue and the flattend result is passed into FakeTensorQueue's + __obj_unflatten__ as inputs to create a fake class. This protocol allows pytorch to look + at the contents of the script object and properly handle them in the subsystems + like dynamo, aot_aotugrad or more. + """ + + def inner(fake_class: HasStaticMethodFromReal): + ns, name = parse_namespace(qualname) + + # This also checks whether the refered torch::class_ exists. + torchbind_class = torch._C._get_custom_class_python_wrapper(ns, name) + + from_method = getattr(fake_class, _CONVERT_FROM_REAL_NAME, None) + if not from_method: + raise RuntimeError( + f"{fake_class} doesn't define a classmethod {_CONVERT_FROM_REAL_NAME}." + ) + + if not isinstance(fake_class.__dict__[_CONVERT_FROM_REAL_NAME], classmethod): + raise RuntimeError( + f"{_CONVERT_FROM_REAL_NAME} method is not a classmethod." + ) + + global_fake_class_registry.register(_full_qual_class_name(qualname), fake_class) + return fake_class + + if fake_class is None: + return inner + return inner(fake_class) + + +def deregister_fake_class(qualname): + return global_fake_class_registry.deregister(_full_qual_class_name(qualname)) + + +def has_fake_class(full_qualname) -> bool: + return global_fake_class_registry.has_impl(full_qualname) + + +def find_fake_class(full_qualname) -> Optional[Any]: + if not has_fake_class(full_qualname): + return None + return global_fake_class_registry.get_impl(full_qualname) + + +def _full_qual_class_name(qualname: str) -> str: + ns, name = parse_namespace(qualname) + return "__torch__.torch.classes." + ns + "." + name + + +# Return the namespace and class name from fully qualified name. +def _ns_and_class_name(full_qualname: str) -> Tuple[str, str]: + splits = full_qualname.split(".") + assert len(splits) == 5 + _torch, torch_ns, classes, ns, class_name = splits + return ns, class_name + + +def _find_fake_class_for_script_object(x: torch.ScriptObject) -> Any: + full_qualname = x._type().qualified_name() # type: ignore[attr-defined] + ns, class_name = _ns_and_class_name(full_qualname) + fake_class = find_fake_class(full_qualname) + if fake_class is None: + raise RuntimeError( + f" ScriptObject's {full_qualname} haven't registered a fake class." + f" Please use register_fake_class({ns}::{class_name}) to annotate a fake class for the script obj." + f" Specifically, create a python class that implements a fake version for all the methods" + f" that're used in the program and put annotated class in the program e.g. after loading the library." + f" The fake methods can be written in the same way as a meta kernel for an operator but need to additionally" + f" simulate the object's states. Be sure to add a {_CONVERT_FROM_REAL_NAME} classmethod" + f" to enable creating a fake obj from a real one." + ) + return fake_class + + +_CONVERT_FROM_REAL_NAME = "__obj_unflatten__" + + +def _fake_obj_from_real(fake_mode, x) -> Any: + fake_class = _find_fake_class_for_script_object(x) + + from_real_method = getattr(fake_class, _CONVERT_FROM_REAL_NAME, None) + if not from_real_method: + raise RuntimeError( + f"{fake_class} must define a classmethod {_CONVERT_FROM_REAL_NAME}" + f" that converts the real object to the fake object." + ) + + # from_real defined by user need the ctx to fakify the tensor states. + ctx = torch._library.fake_impl.FakeImplCtx(fake_mode, None) + with torch._library.fake_impl.set_ctx_getter(lambda: ctx): + return fake_class.from_real(x) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/fake_impl.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/fake_impl.py new file mode 100644 index 0000000000000000000000000000000000000000..a972e8da89eb796db7bdd4e48043cff0cab38bcc --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/fake_impl.py @@ -0,0 +1,207 @@ +# mypy: allow-untyped-defs +import contextlib +import functools +from typing import Callable, Optional +from typing_extensions import deprecated + +import torch +from torch._library.utils import Kernel, RegistrationHandle + + +class FakeImplHolder: + """A holder where one can register an fake impl to.""" + + def __init__(self, qualname: str): + self.qualname: str = qualname + self.kernel: Optional[Kernel] = None + self.lib: Optional[torch.library.Library] = None + + def register(self, func: Callable, source: str) -> RegistrationHandle: + """Register an fake impl. + + Returns a RegistrationHandle that one can use to de-register this + fake impl. + """ + if self.kernel is not None: + raise RuntimeError( + f"register_fake(...): the operator {self.qualname} " + f"already has an fake impl registered at " + f"{self.kernel.source}." + ) + if torch._C._dispatch_has_kernel_for_dispatch_key(self.qualname, "Meta"): + raise RuntimeError( + f"register_fake(...): the operator {self.qualname} " + f"already has an DispatchKey::Meta implementation via a " + f"pre-existing torch.library or TORCH_LIBRARY registration. " + f"Please either remove that registration or don't call " + f"register_fake." + ) + + if torch._C._dispatch_has_kernel_for_dispatch_key( + self.qualname, "CompositeImplicitAutograd" + ): + raise RuntimeError( + f"register_fake(...): the operator {self.qualname} " + f"already has an implementation for this device type via a " + f"pre-existing registration to " + f"DispatchKey::CompositeImplicitAutograd." + f"CompositeImplicitAutograd operators do not need an fake " + f"impl; " + f"instead, the operator will decompose into its constituents " + f"and those " + f"can have fake impls defined on them." + ) + + # Store the kernel in this holder + self.kernel = Kernel(func, source) + + # Also register the fake impl to Meta key + if self.lib is None: + ns = self.qualname.split("::")[0] + self.lib = torch.library.Library(ns, "FRAGMENT") # noqa: TOR901 + meta_kernel = construct_meta_kernel(self.qualname, self) + self.lib.impl(self.qualname, meta_kernel, "Meta") + + def deregister_fake_class(): + if self.lib: + self.lib._destroy() + self.lib = None + self.kernel = None + + return RegistrationHandle(deregister_fake_class) + + +def construct_meta_kernel(qualname: str, fake_impl_holder: FakeImplHolder) -> Callable: + assert fake_impl_holder.kernel is not None + + @functools.wraps(fake_impl_holder.kernel.func) + def meta_kernel(*args, **kwargs): + assert fake_impl_holder.kernel is not None + source = fake_impl_holder.kernel.source + + def error_on_ctx(): + raise RuntimeError( + f"Attempted to call get_ctx() for the meta implementation " + f"for {qualname} (implemented at {source})" + f"You have presumably called get_ctx() because the operator " + f"has a data-dependent output shape; if so, there is no " + f"such meta implementation and this error is the correct " + f"behavior." + ) + + with set_ctx_getter(error_on_ctx): + return fake_impl_holder.kernel(*args, **kwargs) + + return meta_kernel + + +def get_none(): + return None + + +global_ctx_getter: Callable = get_none + + +@contextlib.contextmanager +def set_ctx_getter(ctx_getter): + global global_ctx_getter + prev = global_ctx_getter + try: + global_ctx_getter = ctx_getter + yield + finally: + global_ctx_getter = prev + + +class FakeImplCtx: + """ + Context object for writing fake implementations for custom operators. + """ + + def __init__(self, _fake_mode, _op): + self._fake_mode = _fake_mode + self._shape_env = _fake_mode.shape_env + self._op = _op + + @deprecated( + "`create_unbacked_symint` is deprecated, please use `new_dynamic_size` instead", + category=FutureWarning, + ) + def create_unbacked_symint(self, *, min=2, max=None) -> torch.SymInt: + return self.new_dynamic_size(min=min, max=max) + + def new_dynamic_size(self, *, min=0, max=None) -> torch.SymInt: + """Constructs a new symint (symbolic int) representing a data-dependent value. + + This is useful for writing the fake implementation (which is necessary + for torch.compile) for a CustomOp where an output Tensor has a size + that depends on the data of the input Tensors. + + Args: + min (int): A statically known inclusive lower bound for this symint. Default: 0 + max (Optional[int]): A statically known inclusive upper bound for this + symint. Default: None + + .. warning: + + It is important that the ``min`` and ``max`` (if not None) values are set + correctly, otherwise, there will be undefined behavior under + torch.compile. The default value of ``min`` is 2 due to torch.compile + specializing on 0/1 sizes. + + You must also verify that your implementation on concrete Tensors + (e.g. CPU/CUDA) only returns Tensors where the size that corresponds + to the symint also has respects these constraint. + The easiest way to do this is to add an assertion in the CPU/CUDA/etc + implementation that the size follows these bounds. + + Example:: + + >>> # An operator with data-dependent output shape + >>> lib = torch.library.Library("mymodule", "FRAGMENT") + >>> lib.define("mymodule::custom_nonzero(Tensor x) -> Tensor") + >>> + >>> @torch.library.register_fake("mymodule::custom_nonzero") + >>> def _(x): + >>> # Number of nonzero-elements is data-dependent. + >>> # Since we cannot peek at the data in an fake impl, + >>> # we use the ctx object to construct a new symint that + >>> # represents the data-dependent size. + >>> ctx = torch.library.get_ctx() + >>> nnz = ctx.new_dynamic_size() + >>> shape = [nnz, x.dim()] + >>> result = x.new_empty(shape, dtype=torch.int64) + >>> return result + >>> + >>> @torch.library.impl(lib, "custom_nonzero", "CPU") + >>> def _(x): + >>> x_np = x.numpy() + >>> res = np.stack(np.nonzero(x_np), axis=1) + >>> return torch.tensor(res, device=x.device) + + """ + if ( + self._shape_env is None + or not self._shape_env.allow_dynamic_output_shape_ops + ): + raise torch._subclasses.fake_tensor.DynamicOutputShapeException(self._op) + + if isinstance(min, torch.SymInt) or isinstance(max, torch.SymInt): + raise ValueError( + f"ctx.new_dynamic_size(min={min}, max={max}): expected " + f"min and max to be statically known ints but got SymInt. " + f"This is not supported." + ) + + if min < 0: + raise ValueError( + f"ctx.new_dynamic_size(min={min}, ...): expected min to be " + f"greater than or equal to 0: this API can only create " + f"non-negative sizes." + ) + + result = self._shape_env.create_unbacked_symint() + torch.fx.experimental.symbolic_shapes._constrain_range_for_size( + result, min=min, max=max + ) + return result diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/infer_schema.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/infer_schema.py new file mode 100644 index 0000000000000000000000000000000000000000..b2eeb24521d382516830cd4ebe289672eecfeae8 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/infer_schema.py @@ -0,0 +1,271 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import inspect +import typing +from typing import List, Optional, Sequence, Union # noqa: F401 + +import torch +from torch import device, dtype, Tensor, types +from torch.utils._exposed_in import exposed_in + + +@exposed_in("torch.library") +def infer_schema( + prototype_function: typing.Callable, + /, + *, + mutates_args, + op_name: Optional[str] = None, +) -> str: + r"""Parses the schema of a given function with type hints. The schema is inferred from the + function's type hints, and can be used to define a new operator. + + We make the following assumptions: + + * None of the outputs alias any of the inputs or each other. + * | String type annotations "device, dtype, Tensor, types" without library specification are + | assumed to be torch.*. Similarly, string type annotations "Optional, List, Sequence, Union" + | without library specification are assumed to be typing.*. + * | Only the args listed in ``mutates_args`` are being mutated. If ``mutates_args`` is "unknown", + | it assumes that all inputs to the operator are being mutates. + + Callers (e.g. the custom ops API) are responsible for checking these assumptions. + + Args: + prototype_function: The function from which to infer a schema for from its type annotations. + op_name (Optional[str]): The name of the operator in the schema. If ``name`` is None, then the + name is not included in the inferred schema. Note that the input schema to + ``torch.library.Library.define`` requires a operator name. + mutates_args ("unknown" | Iterable[str]): The arguments that are mutated in the function. + + Returns: + The inferred schema. + + Example: + >>> def foo_impl(x: torch.Tensor) -> torch.Tensor: + >>> return x.sin() + >>> + >>> infer_schema(foo_impl, op_name="foo", mutates_args={}) + foo(Tensor x) -> Tensor + >>> + >>> infer_schema(foo_impl, mutates_args={}) + (Tensor x) -> Tensor + """ + UNKNOWN_MUTATES = "unknown" + sig = inspect.signature(prototype_function) + + def error_fn(what): + raise ValueError( + f"infer_schema(func): {what} " f"Got func with signature {sig})" + ) + + def convert_type_string(annotation_type: str): + try: + return eval(annotation_type) + except Exception as e: + error_fn( + f"Unsupported type annotation {annotation_type}. It is not a type." + ) + + params = [] + seen_args = set() + saw_kwarg_only_arg = False + for idx, (name, param) in enumerate(sig.parameters.items()): + if not supported_param(param): + error_fn("We do not support positional-only args, varargs, or varkwargs.") + + if param.kind == inspect.Parameter.KEYWORD_ONLY: + # The first time we see a kwarg-only arg, add "*" to the schema. + if not saw_kwarg_only_arg: + params.append("*") + saw_kwarg_only_arg = True + + if param.annotation is inspect.Parameter.empty: + error_fn(f"Parameter {name} must have a type annotation.") + + # The annotation might be converted to a string by annotation, + # we convert it to the actual type. + annotation_type = param.annotation + if type(annotation_type) == str: + annotation_type = convert_type_string(annotation_type) + + if annotation_type not in SUPPORTED_PARAM_TYPES.keys(): + if annotation_type.__origin__ is tuple: + list_type = tuple_to_list(annotation_type) + example_type_str = "\n\n" + # Only suggest the list type if this type is supported. + if list_type in SUPPORTED_PARAM_TYPES.keys(): + example_type_str = f"For example, {list_type}.\n\n" + error_fn( + f"Parameter {name} has unsupported type {param.annotation}. " + f"We do not support Tuple inputs in schema. As a workaround, please try to use List instead. " + f"{example_type_str}" + f"The valid types are: {SUPPORTED_PARAM_TYPES.keys()}." + ) + else: + error_fn( + f"Parameter {name} has unsupported type {param.annotation}. " + f"The valid types are: {SUPPORTED_PARAM_TYPES.keys()}." + ) + + schema_type = SUPPORTED_PARAM_TYPES[annotation_type] + if type(mutates_args) == str: + if mutates_args != UNKNOWN_MUTATES: + raise ValueError( + "mutates_args must either be a sequence of the names of " + "the arguments that are mutated or the string 'unknown'. " + ) + if schema_type.startswith("Tensor"): + schema_type = f"Tensor(a{idx}!){schema_type[len('Tensor'):]}" + elif name in mutates_args: + if not schema_type.startswith("Tensor"): + error_fn( + f"Parameter {name} is in mutable_args but only Tensors or collections of Tensors can be mutated" + ) + schema_type = f"Tensor(a{idx}!){schema_type[len('Tensor'):]}" + seen_args.add(name) + if param.default is inspect.Parameter.empty: + params.append(f"{schema_type} {name}") + else: + default_repr = None + if param.default is None or isinstance(param.default, (int, float, bool)): + default_repr = str(param.default) + elif isinstance(param.default, (str, torch.device)): + default_repr = f'"{param.default}"' + elif isinstance(param.default, torch.dtype): + dtype_repr = str(param.default) + torch_dot = "torch." + assert dtype_repr.startswith(torch_dot) + default_repr = dtype_repr[len(torch_dot) :] + else: + error_fn( + f"Parameter {name} has an unsupported default value type {type(param.default)}. " + f"Please file an issue on GitHub so we can prioritize this." + ) + params.append(f"{schema_type} {name}={default_repr}") + if mutates_args != UNKNOWN_MUTATES: + mutates_args_not_seen = set(mutates_args) - seen_args + if len(mutates_args_not_seen) > 0: + error_fn( + f"{mutates_args_not_seen} in mutates_args were not found in " + f"the custom op's signature. " + f"mutates_args should contain the names of all args that the " + f"custom op mutates, or just the string 'unknown' if you don't know." + ) + return_annotation = sig.return_annotation + if type(return_annotation) == str: + return_annotation = convert_type_string(return_annotation) + ret = parse_return(return_annotation, error_fn) + if op_name is not None: + return f"{op_name}({', '.join(params)}) -> {ret}" + return f"({', '.join(params)}) -> {ret}" + + +def derived_types( + base_type, cpp_type, list_base, optional_base_list, optional_list_base +): + result = [ + (base_type, cpp_type), + (typing.Optional[base_type], f"{cpp_type}?"), + ] + + def derived_seq_types(typ): + return [ + typing.Sequence[typ], # type: ignore[valid-type] + typing.List[typ], # type: ignore[valid-type] + ] + + if list_base: + for seq_typ in derived_seq_types(base_type): + result.append((seq_typ, f"{cpp_type}[]")) # type: ignore[valid-type] + if optional_base_list: + for seq_typ in derived_seq_types(typing.Optional[base_type]): + result.append((seq_typ, f"{cpp_type}?[]")) # type: ignore[valid-type] + if optional_list_base: + for seq_typ in derived_seq_types(base_type): # type: ignore[valid-type] + result.append((typing.Optional[seq_typ], f"{cpp_type}[]?")) # type: ignore[valid-type] + return result + + +def get_supported_param_types(): + data = [ + # (python type, schema type, type[] variant, type?[] variant, type[]? variant + (Tensor, "Tensor", True, True, False), + (int, "SymInt", True, False, True), + (float, "float", True, False, True), + (bool, "bool", True, False, True), + (str, "str", False, False, False), + (types.Number, "Scalar", True, False, False), + (dtype, "ScalarType", False, False, False), + (device, "Device", False, False, False), + ] + result = [] + for line in data: + result.extend(derived_types(*line)) + return dict(result) + + +SUPPORTED_RETURN_TYPES = { + Tensor: "Tensor", + typing.List[Tensor]: "Tensor[]", + int: "SymInt", + float: "float", + bool: "bool", + types.Number: "Scalar", +} + + +def parse_return(annotation, error_fn): + if annotation is None: + return "()" + + if annotation is inspect.Parameter.empty: + error_fn("No return type annotation was provided. Please add one.") + + origin = typing.get_origin(annotation) + if origin is not tuple: + if annotation not in SUPPORTED_RETURN_TYPES.keys(): + error_fn( + f"Return has unsupported type {annotation}. " + f"The valid types are: {SUPPORTED_RETURN_TYPES}." + ) + return SUPPORTED_RETURN_TYPES[annotation] + + args = typing.get_args(annotation) + for arg in args: + if arg not in SUPPORTED_RETURN_TYPES: + error_fn( + f"Return has unsupported type {annotation}. " + f"The valid types are: {SUPPORTED_RETURN_TYPES}." + ) + + return "(" + ", ".join([SUPPORTED_RETURN_TYPES[arg] for arg in args]) + ")" + + +SUPPORTED_PARAM_TYPES = get_supported_param_types() + + +def supported_param(param: inspect.Parameter) -> bool: + return param.kind in ( + inspect.Parameter.POSITIONAL_OR_KEYWORD, + inspect.Parameter.KEYWORD_ONLY, + ) + + +def tuple_to_list(tuple_type: typing.Type[typing.Tuple]) -> typing.Type[typing.List]: + """ + Convert `tuple_type` into a list type with the same type arguments. Assumes that `tuple_type` is typing.Tuple type. + """ + type_args = getattr(tuple_type, "__args__", None) + # Account for different python versions, e.g. python 3.8 would give () + # but python 3.12 would give None. + if tuple_type is typing.Tuple or type_args == () or type_args is None: + # Handle the case of an empty tuple type + return typing.List + elif len(type_args) == 1: + # General case: create a List with the same type arguments + return typing.List[type_args[0]] # type: ignore[valid-type] + elif len(type_args) == 2 and type_args[1] is Ellipsis: # type: ignore[valid-type] + return typing.List[type_args[0]] # type: ignore[valid-type] + else: + return typing.List[typing.Union[tuple(type_args)]] # type: ignore[misc] diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/simple_registry.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/simple_registry.py new file mode 100644 index 0000000000000000000000000000000000000000..cfef278679ea56f4d5d15589467ffd47c3edaef4 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/simple_registry.py @@ -0,0 +1,85 @@ +# mypy: allow-untyped-defs +from typing import Callable, Optional + +from .fake_impl import FakeImplHolder +from .utils import RegistrationHandle + + +__all__ = ["SimpleLibraryRegistry", "SimpleOperatorEntry", "singleton"] + + +class SimpleLibraryRegistry: + """Registry for the "simple" torch.library APIs + + The "simple" torch.library APIs are a higher-level API on top of the + raw PyTorch DispatchKey registration APIs that includes: + - fake impl + + Registrations for these APIs do not go into the PyTorch dispatcher's + table because they may not directly involve a DispatchKey. For example, + the fake impl is a Python function that gets invoked by FakeTensor. + Instead, we manage them here. + + SimpleLibraryRegistry is a mapping from a fully qualified operator name + (including the overload) to SimpleOperatorEntry. + """ + + def __init__(self): + self._data = {} + + def find(self, qualname: str) -> "SimpleOperatorEntry": + if qualname not in self._data: + self._data[qualname] = SimpleOperatorEntry(qualname) + return self._data[qualname] + + +singleton: SimpleLibraryRegistry = SimpleLibraryRegistry() + + +class SimpleOperatorEntry: + """This is 1:1 to an operator overload. + + The fields of SimpleOperatorEntry are Holders where kernels can be + registered to. + """ + + def __init__(self, qualname: str): + self.qualname: str = qualname + self.fake_impl: FakeImplHolder = FakeImplHolder(qualname) + self.torch_dispatch_rules: GenericTorchDispatchRuleHolder = ( + GenericTorchDispatchRuleHolder(qualname) + ) + + # For compatibility reasons. We can delete this soon. + @property + def abstract_impl(self): + return self.fake_impl + + +class GenericTorchDispatchRuleHolder: + def __init__(self, qualname): + self._data = {} + self.qualname = qualname + + def register( + self, torch_dispatch_class: type, func: Callable + ) -> RegistrationHandle: + if self.find(torch_dispatch_class): + raise RuntimeError( + f"{torch_dispatch_class} already has a `__torch_dispatch__` rule registered for {self.qualname}" + ) + self._data[torch_dispatch_class] = func + + def deregister(): + del self._data[torch_dispatch_class] + + return RegistrationHandle(deregister) + + def find(self, torch_dispatch_class): + return self._data.get(torch_dispatch_class, None) + + +def find_torch_dispatch_rule(op, torch_dispatch_class: type) -> Optional[Callable]: + return singleton.find(op.__qualname__).torch_dispatch_rules.find( + torch_dispatch_class + ) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/triton.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/triton.py new file mode 100644 index 0000000000000000000000000000000000000000..d2caa4924529ef67b3ed7729129fc38e6120c0f9 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/triton.py @@ -0,0 +1,233 @@ +import contextlib +import threading +from typing import Callable, Generator, Iterable, Optional, Union + +from .custom_ops import custom_op +from .infer_schema import infer_schema + + +def triton_op( + name: str, + fn: Optional[Callable] = None, + /, + *, + mutates_args: Union[str, Iterable[str]], + schema: Optional[str] = None, +) -> Callable: + """Create a custom operator whose implementation is backed by 1+ triton kernels. + + Use this instead of :func:`torch.library.custom_op` when the implementation + consists of 1+ triton kernels. :func:`torch.library.custom_op` treats + custom operators as opaque (:func:`torch.compile` and + :func:`torch.export.export` will never trace into them), but ``triton_op`` + makes the implementation visible to these subsystems, allowing them + to optimize the triton kernel(s). + + Note that ``fn`` must only consist of calls to PyTorch-understood + operators and triton kernels. Any triton kernels called inside ``fn`` + must be wrapped in a call to :func:`torch._library.capture_triton``. + + Args: + name (str): A name for the custom op that looks like "{namespace}::{name}", + e.g. "mylib::my_linear". The name is used as the op's stable identifier + in PyTorch subsystems (e.g. torch.export, FX graphs). + To avoid name collisions, please use your project name as the namespace; + e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. + mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. + This MUST be accurate, otherwise, the behavior is undefined. If "unknown", + it pessimistically assumes that all inputs to the operator are being mutated. + schema (None | str): A schema string for the operator. If None + (recommended) we'll infer a schema for the operator from its type + annotations. We recommend letting us infer a schema unless you + have a specific reason not to. + Example: "(Tensor x, int y) -> (Tensor, Tensor)". + + Example:: + + >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) + >>> import torch + >>> from torch._library import triton_op, capture_triton + >>> + >>> import triton + >>> from triton import language as tl + >>> + >>> @triton.jit + >>> def add_kernel( + >>> in_ptr0, + >>> in_ptr1, + >>> out_ptr, + >>> n_elements, + >>> BLOCK_SIZE: "tl.constexpr", + >>> ): + >>> pid = tl.program_id(axis=0) + >>> block_start = pid * BLOCK_SIZE + >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) + >>> mask = offsets < n_elements + >>> x = tl.load(in_ptr0 + offsets, mask=mask) + >>> y = tl.load(in_ptr1 + offsets, mask=mask) + >>> output = x + y + >>> tl.store(out_ptr + offsets, output, mask=mask) + >>> + >>> @triton_op("mylib::add", mutates_args={}) + >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: + >>> output = torch.empty_like(x) + >>> n_elements = output.numel() + >>> + >>> def grid(meta): + >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) + >>> + >>> # NB: we need to wrap the triton kernel in a call to capture_triton + >>> capture_triton(add_kernel)[grid](x, y, output, n_elements, 16) + >>> return output + >>> + >>> @torch.compile + >>> def f(x, y): + >>> return add(x, y) + >>> + >>> x = torch.randn(3, device="cuda") + >>> y = torch.randn(3, device="cuda") + >>> + >>> z = f(x, y) + >>> assert torch.allclose(z, x + y) + + """ + + def dec(fn: Callable) -> Callable: + def backend_fn(*args, **kwargs): # type: ignore[no-untyped-def] + # Optimization: we're passing regular Tensors into the triton kernel, so + # no need to go through HOP dispatch + with set_capture_triton_enabled(False): + return fn(*args, **kwargs) + + result = custom_op( + name, + backend_fn, + mutates_args=mutates_args, + schema=infer_schema(fn, mutates_args=mutates_args), + ) + from .._subclasses.functional_tensor import FunctionalTensorMode + + # We require that the user pass us a function that is make_fx traceable, + # so we can just register it as the Fake/meta kernel. + result.register_fake(fn) + + # We decompose the operator when FunctionalTensorMode is active. + # The goal is to decompose the operator in AOTDispatcher. + # - With torch.compile, this means that the backend (usually Inductor) + # can see a call to the triton kernel(s) and so it can directly optimize + # them by inlining them into the lowering process. + # - With post-dispatch torch.export, this means that there will + # be a call(s) to the triton_kernel_wrapper_functional HOP in the + # graph (that we have yet to figure out how to serialize). + def functional_decomp( # type: ignore[no-untyped-def] + mode, _, types, args, kwargs + ): + with mode: + return fn(*args, **kwargs) + + result.register_torch_dispatch(FunctionalTensorMode, functional_decomp) + return result + + if fn is None: + return dec + else: + return dec(fn) + + +capture_triton_enabled = threading.local() +capture_triton_enabled_default = True + + +@contextlib.contextmanager +def set_capture_triton_enabled(enabled: bool) -> Generator[None, None, None]: + """If triton kernels annotated with @capture_triton should dispatch via HOP + or go straight to the triton kernel execution. + + We have this switch because eager-mode performance of HOP dispatch is slow + enough to matter (~1ms) and we know that capture_triton isn't necessary in + some situations (eager-mode with regular Tensors) + """ + try: + prev = is_capture_triton_enabled() + capture_triton_enabled.value = enabled + yield + finally: + capture_triton_enabled.value = prev + + +def is_capture_triton_enabled() -> bool: + return getattr(capture_triton_enabled, "value", capture_triton_enabled_default) + + +def capture_triton(triton_kernel: Callable, /) -> Callable: + """Allows capture of a triton kernel into a graph via make_fx or + non-strict export (coming soon). + + These technologies perform Dispatcher-based tracing (via + ``__torch_dispatch__``) and cannot see calls to raw triton kernels. + The ``capture_triton`` API returns a new callable that can actually + be traced into a graph. + + Examples: + + >>> # xdoctest: +SKIP + >>> import torch + >>> import triton + >>> from triton import language as tl + >>> from torch.fx.experimental.proxy_tensor import make_fx + >>> from torch._higher_order_ops.triton_kernel_wrap import capture_triton + >>> + >>> @triton.jit + >>> def add_kernel( + >>> in_ptr0, + >>> in_ptr1, + >>> out_ptr, + >>> n_elements, + >>> BLOCK_SIZE: "tl.constexpr", + >>> ): + >>> pid = tl.program_id(axis=0) + >>> block_start = pid * BLOCK_SIZE + >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) + >>> mask = offsets < n_elements + >>> x = tl.load(in_ptr0 + offsets, mask=mask) + >>> y = tl.load(in_ptr1 + offsets, mask=mask) + >>> output = x + y + >>> tl.store(out_ptr + offsets, output, mask=mask) + >>> + >>> def add(x, y): + >>> output = torch.empty_like(x) + >>> n_elements = output.numel() + >>> + >>> def grid_fn(meta): + >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) + >>> + >>> capture_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) + >>> return output + >>> + >>> x = torch.randn(3, device="cuda") + >>> y = torch.randn(3, device="cuda") + >>> gm = make_fx(add)(x, y) + >>> print(gm.code) + >>> # def forward(self, x_1, y_1): + >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) + >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( + >>> # kernel_idx = 0, constant_args_idx = 0, + >>> # grid = [(1, 1, 1)], kwargs = { + >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, + >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 + >>> # }) + >>> # return empty_like + + """ + from triton.runtime.autotuner import Autotuner + from triton.runtime.jit import JITFunction + + from torch._higher_order_ops.triton_kernel_wrap import TraceableTritonKernelWrapper + + if not isinstance(triton_kernel, (JITFunction, Autotuner)): + raise RuntimeError( + "capture_triton only works on functions annotated with triton.jit or triton.autotune" + ) + if not is_capture_triton_enabled(): + return triton_kernel + return TraceableTritonKernelWrapper(triton_kernel, None, None) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7b8eec899d77e445428b278dc96c765d7b0f9ae6 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_library/utils.py @@ -0,0 +1,318 @@ +# mypy: allow-untyped-defs +import dataclasses +import inspect +import sys +from typing import Any, Callable, Dict, Iterable, Tuple, Union + +import torch +from torch import _C, _utils_internal +from torch._ops import OpOverload + + +@dataclasses.dataclass +class Kernel: + """Models a (function, source location)""" + + func: Callable + source: str + + def __call__(self, *args, **kwargs): + return self.func(*args, **kwargs) + + +class RegistrationHandle: + """Does something when someone calls .destroy() on it""" + + def __init__(self, on_destroy: Callable): + self._on_destroy = on_destroy + + def destroy(self) -> None: + self._on_destroy() + + +def get_source(stacklevel: int) -> str: + """Get a string that represents the caller. + + Example: "/path/to/foo.py:42" + + Use stacklevel=1 to get the caller's source + Use stacklevel=2 to get the caller's caller's source + etc. + """ + frame = inspect.getframeinfo(sys._getframe(stacklevel)) + source = f"{frame.filename}:{frame.lineno}" + return source + + +def parse_namespace(qualname: str) -> Tuple[str, str]: + splits = qualname.split("::") + if len(splits) != 2: + raise ValueError( + f"Expected `qualname` to be of the form " + f'"namespace::name", but got {qualname}. ' + f"The qualname passed to the torch.library APIs must consist " + f"of a namespace and a name, e.g. aten::sin" + ) + return splits[0], splits[1] + + +def lookup_op(qualname: str) -> OpOverload: + namespace, name = parse_namespace(qualname) + if "." in name: + name, overload = name.split(".") + else: + overload = "default" + ns = getattr(torch.ops, namespace) + packet = getattr(ns, name) + return getattr(packet, overload) + + +def is_builtin(op: OpOverload) -> bool: + assert isinstance(op, OpOverload) + return op.namespace in {"aten", "prim", "prims"} + + +def is_functional_schema(schema: Any) -> bool: + """Check if the schema is functional. + + An operator is functional if: + - it does not mutate any of its inputs + - it does not return a view on any of its inputs + - it has at least one return + """ + + def is_functional(schema): + if schema.is_mutable: + return False + rets = schema.returns + is_non_mutating_view = len(rets) > 0 and any( + r.alias_info is not None and not r.alias_info.is_write for r in rets + ) + if is_non_mutating_view: + return False + if not schema.returns: + return False + return True + + if isinstance(schema, torch._C.FunctionSchema): + return is_functional(schema) + + # Lazy import because not all PyTorch builds have torchgen + from torchgen.model import FunctionSchema + + if isinstance(schema, str): + schema = FunctionSchema.parse(schema) + assert isinstance(schema, FunctionSchema) + return is_functional(schema) + + +# should be torch._C.JitType but that annotation is busted +def is_tensorlist_like_type(typ: Any) -> bool: + return ( + typ == _C.ListType(_C.TensorType.get()) + or typ == _C.ListType(_C.OptionalType(_C.TensorType.get())) + or typ == _C.OptionalType(_C.ListType(_C.TensorType.get())) + or typ == _C.OptionalType(_C.ListType(_C.OptionalType(_C.TensorType.get()))) + ) + + +# should be torch._C.JitType but that annotation is busted +def is_tensor_like_type(typ: Any) -> bool: + return typ == _C.TensorType.get() or typ == _C.OptionalType(_C.TensorType.get()) + + +def mutates_and_returns_first_arg(op: OpOverload): + """Check if an op is an inplace aten op, i.e. it mutates and returns the first arg. + + TODO: torchgen/model.py's FunctionSchema.parse is the source of truth for this, + but not all PyTorch builds have torchgen (due to the yaml dependency being weird). + Figure this out. + + Example: add_(Tensor(a!) x, Tensor y) -> Tensor(a) + """ + if op.namespace != "aten": + return False + schema = op._schema + if not len(schema.returns) == 1: + return False + if schema.returns[0].alias_info is None: + return False + alias_set = schema.returns[0].alias_info.after_set + if len(alias_set) != 1: + return False + loc = next(iter(alias_set)) + if len(schema.arguments) < 1: + return False + first_arg = schema.arguments[0] + if first_arg.alias_info is None: + return False + if not first_arg.alias_info.is_write: + return False + alias_set = first_arg.alias_info.after_set + if len(alias_set) != 1: + return False + if loc != next(iter(alias_set)): + return False + for arg in schema.arguments[1:]: + if arg.alias_info is not None: + return False + return True + + +def fill_defaults(schema, args, kwargs): + new_args = [] + new_kwargs = {} + for i in range(len(schema.arguments)): + info = schema.arguments[i] + if info.kwarg_only: + if info.name in kwargs: + new_kwargs[info.name] = kwargs[info.name] + else: + new_kwargs[info.name] = info.default_value + else: + if i < len(args): + new_args.append(args[i]) + else: + new_args.append(info.default_value) + return tuple(new_args), new_kwargs + + +def zip_schema( + schema: _C.FunctionSchema, args: Tuple[Any, ...], kwargs: Dict[str, Any] +) -> Iterable[Tuple[_C.Argument, Any]]: + """zips schema.arguments and (args, kwargs) together. + + Assumes that (args, kwargs) were the inputs to some torch._ops.OpOverload: + that is, kwargs must be keyword-only arguments and default values may be omitted. + """ + assert len(schema.arguments) >= len(args) + len(kwargs) + for i in range(len(schema.arguments)): + info = schema.arguments[i] + if info.kwarg_only: + if info.name in kwargs: + yield info, kwargs[info.name] + continue + if i >= len(args): + # args that are equal to their default values are not populated + # if they are followed by args that are equal to their defaults. + # Skip these. + continue + yield info, args[i] + return + + +def hop_schema_from_fx_node(node): + from torchgen.gen_schema_utils import FunctionSchemaGen + + hop = node.target + if not isinstance(hop, torch._ops.HigherOrderOperator): + raise RuntimeError("fx_node's target must be a hop.") + + def _collect_example_val(node): + meta_val = node.meta.get("val", None) + if meta_val is None: + assert node.op == "get_attr" + meta_val = getattr(node.graph.owning_module, node.target) + return meta_val + + example_inputs = [] + for arg in node.args: + if isinstance(arg, (torch.fx.Node, torch.fx.node.Node)): + example_inputs.append(_collect_example_val(arg)) + elif isinstance( + arg, (torch.fx.immutable_collections.immutable_list, list, tuple) + ): + example_inputs.append([_collect_example_val(x) for x in arg]) + else: + raise RuntimeError(f"Unsupported arg type {type(arg)}") + + # Bound the arguments to make sure number of inputs are correct + bound_args: inspect.BoundArguments = inspect.signature(hop.__call__).bind( + *example_inputs + ) + + # We treat example_output as a single value in return. This is to differentiate 1. return a single val + # vs 2. return a tuple with one element. + example_output = _collect_example_val(node) + return FunctionSchemaGen.from_example( + hop._name, tuple(bound_args.arguments.items()), (list(example_output),) + ) + + +def can_generate_trivial_fake_impl(op: OpOverload) -> bool: + assert isinstance(op, OpOverload) + if is_builtin(op): + # We control the built-ins. These may (in rare cases) + # do input metadata mutation (which we have banned on custom ops) + return False + schema = op._schema + # It's suspicious if the op is not mutable but returns nothing, so we return False out of an abundance of caution + if not schema.is_mutable: + return False + if len(schema.returns) > 0: + return False + # If the op returns nothing, then it has a trivial fake impl. + return True + + +def requires_set_python_module() -> bool: + """If an op was defined in C++ and extended from Python using the + torch.library APIs, returns if we require that there have been a + m.set_python_module("mylib.ops") call from C++ that associates + the C++ op with a python module. + """ + return getattr(_utils_internal, "REQUIRES_SET_PYTHON_MODULE", True) + + +def handle_dispatch_mode(curr_mode, op_overload, *args, **kwargs): + assert isinstance(curr_mode, torch.utils._python_dispatch.TorchDispatchMode) + overload_types = [] + args_flattened, _ = torch.utils._pytree.tree_flatten((args, kwargs.values())) + for a in args_flattened: + # TODO: need to double check the semantics of the "types" argument to torch_dispatch. + # It's generated in PyInterpreter.cpp, but seems to be generated in two places, + # where in one case we only include tensors with the python key, and in another + # we include **all** tensors. + if isinstance(a, torch.Tensor) and torch._C._dispatch_keys(a).has( + torch._C.DispatchKey.Python + ): + overload_types.append(type(a)) + # TODO: check that I got these args correct (in C++, we pass in "0000"??) + + return curr_mode.__torch_dispatch__(op_overload, overload_types, args, kwargs) + + +def has_kwarg_only_args(schema: _C.FunctionSchema): + return any(a.kwarg_only for a in schema.arguments) + + +def has_kwarg_only_tensors(schema: _C.FunctionSchema): + for a in schema.arguments: + if not (is_tensor_like_type(a.type) or is_tensorlist_like_type(a.type)): + continue + if not a.kwarg_only: + continue + return True + return False + + +def has_tensor_arg(schema: _C.FunctionSchema) -> bool: + """ + Given a schema, returns True if the schema has a Tensor arg. + A Tensor arg is any arg with a type annotation that might involve Tensor. + """ + return any( + (is_tensor_like_type(a.type) or is_tensorlist_like_type(a.type)) + for a in schema.arguments + ) + + +def get_device_arg_index(schema: _C.FunctionSchema) -> Union[int, None]: + """ + Given a schema, returns the id of the `device: torch.device` argument. + If it does not exist, returns None. + """ + for index, arg in enumerate(schema.arguments): + if arg.type is _C.DeviceObjType.get() and arg.name == "device": + return index + return None diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_logging/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_logging/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0531869ae2fdf3306ef36e17562040c0aeb37f61 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_logging/__init__.py @@ -0,0 +1,17 @@ +# Top level logging module for torch logging +# Design doc: https://docs.google.com/document/d/1ZRfTWKa8eaPq1AxaiHrq4ASTPouzzlPiuquSBEJYwS8/edit# +# Simple setup for onboarding (see above doc for more detail): +# 1. register any top-level log qualified name for your module in torch._logging._registrations (see there for examples) +# 2. register any artifacts ( below) in torch._logging._registrations +# a. call getArtifactLogger(__name__, ) at your logging site instead of the standard logger to log your artifact +import torch._logging._registrations + +from ._internal import ( + _init_logs, + DEFAULT_LOGGING, + getArtifactLogger, + LazyString, + set_logs, + trace_structured, + warning_once, +) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_logging/_internal.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_logging/_internal.py new file mode 100644 index 0000000000000000000000000000000000000000..f11af08e3b768d9b1f776c7591d9b1afa6bc41af --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_logging/_internal.py @@ -0,0 +1,1162 @@ +# mypy: allow-untyped-defs +import functools +import hashlib +import itertools +import json +import logging +import os +import os.path +import pathlib +import re +import sys +import tempfile +from dataclasses import dataclass, field +from importlib import __import__ +from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union +from weakref import WeakSet + +import torch._logging.structured +from torch._utils_internal import log_trace_structured_event +from torch.utils._traceback import CapturedTraceback + + +log = logging.getLogger(__name__) + +# This is a synthetic logger which doesn't correspond to an actual logger, +# but handles all of our "tracing" logging, which is structured and doesn't go +# to stderr but always goes to a dedicated log file. We don't put these +# loggers in the classic module hierarchy, because we don't want a suppression +# of logs to also cause a trace to get suppressed (traces typically are not +# collected, unless we are in prod, in which case they always are collected.) +# +# TODO: Maybe we should allow for some sub-hierarchy so you can control which +# traces you want to collect, for performance reasons. +# +# See https://docs.google.com/document/d/1CX_hJ0PNy9f3R1y8TJrfkSeLkvGjjjLU84BSXgS2AZ8/edit +trace_log = logging.getLogger("torch.__trace") + +DEFAULT_LOG_LEVEL = logging.WARNING +LOG_ENV_VAR = "TORCH_LOGS" +LOG_OUT_ENV_VAR = "TORCH_LOGS_OUT" +LOG_FORMAT_ENV_VAR = "TORCH_LOGS_FORMAT" +TRACE_ENV_VAR = "TORCH_TRACE" + + +@dataclass +class LogRegistry: + # shorthand name to log qualified name + # Note: this only contains loggers registered + # from register_log + # e.g. "dynamo" -> "torch._dynamo" + log_alias_to_log_qnames: Dict[str, List[str]] = field(default_factory=dict) + + # artifact logger qualified names, + # this is populated lazily, as calls to getArtifactLogger + # currently formatted as .__ + # e.g. "torch._dynamo.convert_frame.__guards" + artifact_log_qnames: Set[str] = field(default_factory=set) + + # child logs of registered logs if specified via open + # registration by the user (ie placing "torch._dynamo.output_graph" in the env var) + # these need to be tracked so their levels can be reset properly + # e.g. "torch._dynamo.output_graph" + child_log_qnames: Set[str] = field(default_factory=set) + + # artifact names, populated by register_artifact + # e.g. "guards" + artifact_names: Set[str] = field(default_factory=set) + + # Artifacts that should be visible by default in the error message + visible_artifacts: Set[str] = field(default_factory=set) + + # A short description of each artifact + artifact_descriptions: Dict[str, str] = field(default_factory=dict) + + # artifacts which are not displayed unless explicitly named in the + # settings. Ex. output_code is NOT displayed even if the inductor + # log level is set to DEBUG. It must be explicitly named in the settings + off_by_default_artifact_names: Set[str] = field(default_factory=set) + + # logging format string for artifacts + artifact_log_formatters: Dict[str, logging.Formatter] = field(default_factory=dict) + + def is_artifact(self, name): + return name in self.artifact_names + + def is_log(self, alias): + return alias in self.log_alias_to_log_qnames + + # register a log with an alias + def register_log(self, alias, log_qnames: Union[str, List[str]]): + if isinstance(log_qnames, str): + log_qnames = [log_qnames] + self.log_alias_to_log_qnames[alias] = log_qnames + + # register an artifact name + def register_artifact_name( + self, name, description, visible, off_by_default, log_format + ): + self.artifact_names.add(name) + if visible: + self.visible_artifacts.add(name) + self.artifact_descriptions[name] = description + + # if off by default, don't enable it + # when log_name's log_level is set to DEBUG + if off_by_default: + self.off_by_default_artifact_names.add(name) + + if log_format is not None: + self.artifact_log_formatters[name] = logging.Formatter(log_format) + + # register the qualified name of an artifact log + # this is needed to know which logs need to be reset + # whenever the log_state is changed + def register_artifact_log(self, artifact_log_qname): + self.artifact_log_qnames.add(artifact_log_qname) + + def register_child_log(self, log_qname): + self.child_log_qnames.add(log_qname) + + # flattens all the qnames together (TODO: consider memoizing?) + def get_log_qnames(self) -> Set[str]: + return { + qname + for qnames in self.log_alias_to_log_qnames.values() + for qname in qnames + } + + def get_artifact_log_qnames(self): + return set(self.artifact_log_qnames) + + def get_child_log_qnames(self): + return set(self.child_log_qnames) + + def is_off_by_default(self, artifact_qname): + return artifact_qname in self.off_by_default_artifact_names + + +@dataclass +class LogState: + # qualified log names -> currently set log level + log_qname_to_level: Dict[str, str] = field(default_factory=dict) + + # the set of currently enabled artifacts + artifact_names: Set[str] = field(default_factory=set) + + def enable_artifact(self, artifact_name): + self.artifact_names.add(artifact_name) + + def is_artifact_enabled(self, name): + return name in self.artifact_names + + def enable_log(self, log_qnames, log_level): + if isinstance(log_qnames, str): + log_qnames = [log_qnames] + for log_qname in log_qnames: + self.log_qname_to_level[log_qname] = log_level + + def get_log_level_pairs(self): + """Returns all qualified module names for which the user requested + explicit logging settings. + + .. warning: + + This function used to return all loggers, regardless of whether + or not the user specified them or not; it now only returns logs + which were explicitly mentioned by the user (and torch, which + always is implicitly requested when we initialize our logging + subsystem.) + """ + return self.log_qname_to_level.items() + + def clear(self): + self.log_qname_to_level.clear() + self.artifact_names.clear() + + +log_registry = LogRegistry() +log_state = LogState() + +# sample usage: torch._logging.set_logs(**torch._logging.DEFAULT_LOGGING) +DEFAULT_LOGGING = { + "dynamo": logging.INFO, + "aot": logging.INFO, + "inductor": logging.INFO, + "fsdp": logging.INFO, + "ddp_graphs": True, + "graph_breaks": True, + "guards": True, + "recompiles": True, + "dynamic": logging.INFO, +} + + +def set_logs( + *, + all: Optional[int] = None, + dynamo: Optional[int] = None, + aot: Optional[int] = None, + autograd: Optional[int] = None, + dynamic: Optional[int] = None, + inductor: Optional[int] = None, + distributed: Optional[int] = None, + c10d: Optional[int] = None, + ddp: Optional[int] = None, + fsdp: Optional[int] = None, + dtensor: Optional[int] = None, + onnx: Optional[int] = None, + bytecode: bool = False, + aot_graphs: bool = False, + aot_joint_graph: bool = False, + ddp_graphs: bool = False, + graph: bool = False, + graph_code: bool = False, + graph_breaks: bool = False, + graph_sizes: bool = False, + guards: bool = False, + recompiles: bool = False, + recompiles_verbose: bool = False, + trace_source: bool = False, + trace_call: bool = False, + trace_bytecode: bool = False, + output_code: bool = False, + kernel_code: bool = False, + schedule: bool = False, + perf_hints: bool = False, + post_grad_graphs: bool = False, + onnx_diagnostics: bool = False, + fusion: bool = False, + overlap: bool = False, + export: Optional[int] = None, + modules: Optional[Dict[str, Union[int, bool]]] = None, + cudagraphs: bool = False, + sym_node: bool = False, + compiled_autograd: bool = False, + compiled_autograd_verbose: bool = False, + cudagraph_static_inputs: bool = False, + benchmarking: bool = False, +): + """ + Sets the log level for individual components and toggles individual log + artifact types. + + .. warning:: This feature is a prototype and may have compatibility + breaking changes in the future. + + .. note:: The ``TORCH_LOGS`` environment variable has complete precedence + over this function, so if it was set, this function does nothing. + + A component is a set of related features in PyTorch. All of the log + messages emitted from a given component have their own log levels. If the + log level of a particular message has priority greater than or equal to its + component's log level setting, it is emitted. Otherwise, it is suppressed. + This allows you to, for instance, silence large groups of log messages that + are not relevant to you and increase verbosity of logs for components that + are relevant. The expected log level values, ordered from highest to lowest + priority, are: + + * ``logging.CRITICAL`` + * ``logging.ERROR`` + * ``logging.WARNING`` + * ``logging.INFO`` + * ``logging.DEBUG`` + * ``logging.NOTSET`` + + See documentation for the Python ``logging`` module for more information on + log levels: ``_ + + An artifact is a particular type of log message. Each artifact is assigned + to a parent component. A component can emit many different kinds of + artifacts. In general, an artifact is emitted if either its corresponding + setting in the argument list below is turned on or if its parent component + is set to a log level less than or equal to the log level of the artifact. + + Keyword args: + all (:class:`Optional[int]`): + The default log level for all components. Default: ``logging.WARN`` + + dynamo (:class:`Optional[int]`): + The log level for the TorchDynamo component. Default: ``logging.WARN`` + + aot (:class:`Optional[int]`): + The log level for the AOTAutograd component. Default: ``logging.WARN`` + + autograd (:class:`Optional[int]`): + The log level for autograd. Default: ``logging.WARN`` + + inductor (:class:`Optional[int]`): + The log level for the TorchInductor component. Default: ``logging.WARN`` + + dynamic (:class:`Optional[int]`): + The log level for dynamic shapes. Default: ``logging.WARN`` + + distributed (:class:`Optional[int]`): + Whether to log c10d communication operations and other debug info from PyTorch Distributed components. + Default: ``logging.WARN`` + + c10d (:class:`Optional[int]`): + Whether to log c10d communication operations related debug info in PyTorch Distributed components. + Default: ``logging.WARN`` + + ddp (:class:`Optional[int]`): + Whether to log debug info related to ``DistributedDataParallel``(DDP) from PyTorch Distributed components. + Default: ``logging.WARN`` + + fsdp (:class:`Optional[int]`): + Whether to log debug info related to ``FullyShardedDataParallel``(FSDP) in PyTorch Distributed components. + Default: ``logging.WARN`` + + dtensor (:class:`Optional[int]`): + Whether to log debug info related to ``DTensor``(DTensor) in PyTorch Distributed components. + Default: ``logging.WARN`` + + onnx (:class:`Optional[int]`): + The log level for the ONNX exporter component. Default: ``logging.WARN`` + + bytecode (:class:`bool`): + Whether to emit the original and generated bytecode from TorchDynamo. + Default: ``False`` + + aot_graphs (:class:`bool`): + Whether to emit the graphs generated by AOTAutograd. Default: ``False`` + + aot_joint_graph (:class:`bool`): + Whether to emit the joint forward-backward graph generated by AOTAutograd. Default: ``False`` + + ddp_graphs (:class:`bool`): + Whether to emit graphs generated by DDPOptimizer. Default: ``False`` + + graph (:class:`bool`): + Whether to emit the graph captured by TorchDynamo in tabular format. + Default: ``False`` + + graph_code (:class:`bool`): + Whether to emit the python source of the graph captured by TorchDynamo. + Default: ``False`` + + graph_breaks (:class:`bool`): + Whether to emit the graph breaks encountered by TorchDynamo. + Default: ``False`` + + graph_sizes (:class:`bool`): + Whether to emit tensor sizes of the graph captured by TorchDynamo. + Default: ``False`` + + guards (:class:`bool`): + Whether to emit the guards generated by TorchDynamo for each compiled + function. Default: ``False`` + + recompiles (:class:`bool`): + Whether to emit a guard failure reason and message every time + TorchDynamo recompiles a function. Default: ``False`` + + recompiles_verbose (:class:`bool`): + Whether to emit all guard failure reasons when TorchDynamo recompiles + a function, even those that are not actually run. Default: ``False`` + + trace_source (:class:`bool`): + Whether to emit when TorchDynamo begins tracing a new line. Default: ``False`` + + trace_call (:class:`bool`): + Whether to emit detailed line location when TorchDynamo creates an FX node + corresponding to function call. Python 3.11+ only. Default: ``False`` + + trace_bytecode (:class:`bool`): + Whether to emit bytecode instructions and traced stack state as TorchDynamo + traces bytecode. Default: ``False`` + + output_code (:class:`bool`): + Whether to emit the TorchInductor output code on a per-graph basis. Default: ``False`` + + kernel_code (:class:`bool`): + Whether to emit the TorchInductor output code on a per-kernel bases. Default: ``False`` + + schedule (:class:`bool`): + Whether to emit the TorchInductor schedule. Default: ``False`` + + perf_hints (:class:`bool`): + Whether to emit the TorchInductor perf hints. Default: ``False`` + + post_grad_graphs (:class:`bool`): + Whether to emit the graphs generated by after post grad passes. Default: ``False`` + + onnx_diagnostics (:class:`bool`): + Whether to emit the ONNX exporter diagnostics in logging. Default: ``False`` + + fusion (:class:`bool`): + Whether to emit detailed Inductor fusion decisions. Default: ``False`` + + overlap (:class:`bool`): + Whether to emit detailed Inductor compute/comm overlap decisions. Default: ``False`` + + sym_node (:class:`bool`): + Whether to emit debug info for various SymNode opterations. Default: ``False`` + + export (:class:`Optional[int]`): + The log level for export. Default: ``logging.WARN`` + + benchmarking (:class:`bool`): + Whether to emit detailed Inductor benchmarking information. Default: ``False`` + + modules (dict): + This argument provides an alternate way to specify the above log + component and artifact settings, in the format of a keyword args + dictionary given as a single argument. There are two cases + where this is useful (1) if a new log component or artifact has + been registered but a keyword argument for it has not been added + to this function and (2) if the log level for an unregistered module + needs to be set. This can be done by providing the fully-qualified module + name as the key, with the log level as the value. Default: ``None`` + + cudagraph_static_inputs (:class:`bool`): + Whether to emit debug info for cudagraph static input detection. Default: ``False`` + + + Example:: + + >>> # xdoctest: +SKIP + >>> import logging + + # The following changes the "dynamo" component to emit DEBUG-level + # logs, and to emit "graph_code" artifacts. + + >>> torch._logging.set_logs(dynamo=logging.DEBUG, graph_code=True) + + # The following enables the logs for a different module + + >>> torch._logging.set_logs(modules={"unregistered.module.name": logging.DEBUG}) + """ + # ignore if env var is set + if LOG_ENV_VAR in os.environ: + log.warning( + "Using TORCH_LOGS environment variable for log settings, ignoring call to set_logs" + ) + return + + log_state.clear() + + modules = modules or {} + + def _set_logs(**kwargs): + for alias, val in itertools.chain(kwargs.items(), modules.items()): # type: ignore[union-attr] + if val is None: + continue + + if log_registry.is_artifact(alias): + if not isinstance(val, bool): + raise ValueError( + f"Expected bool to enable artifact {alias}, received {val}" + ) + + if val: + log_state.enable_artifact(alias) + elif log_registry.is_log(alias) or alias in log_registry.child_log_qnames: + if val not in logging._levelToName: + raise ValueError( + f"Unrecognized log level for log {alias}: {val}, valid level values " + f"are: {','.join([str(k) for k in logging._levelToName.keys()])}" + ) + + log_state.enable_log( + log_registry.log_alias_to_log_qnames.get(alias, alias), val + ) + else: + raise ValueError( + f"Unrecognized log or artifact name passed to set_logs: {alias}" + ) + + _init_logs() + + _set_logs( + torch=all, + dynamo=dynamo, + aot=aot, + autograd=autograd, + inductor=inductor, + dynamic=dynamic, + bytecode=bytecode, + aot_graphs=aot_graphs, + aot_joint_graph=aot_joint_graph, + ddp_graphs=ddp_graphs, + distributed=distributed, + c10d=c10d, + ddp=ddp, + fsdp=fsdp, + dtensor=dtensor, + graph=graph, + graph_code=graph_code, + graph_breaks=graph_breaks, + graph_sizes=graph_sizes, + guards=guards, + recompiles=recompiles, + recompiles_verbose=recompiles_verbose, + trace_source=trace_source, + trace_call=trace_call, + trace_bytecode=trace_bytecode, + output_code=output_code, + kernel_code=kernel_code, + schedule=schedule, + perf_hints=perf_hints, + post_grad_graphs=post_grad_graphs, + onnx=onnx, + onnx_diagnostics=onnx_diagnostics, + fusion=fusion, + overlap=overlap, + sym_node=sym_node, + export=export, + cudagraphs=cudagraphs, + compiled_autograd=compiled_autograd, + compiled_autograd_verbose=compiled_autograd_verbose, + cudagraph_static_inputs=cudagraph_static_inputs, + benchmarking=benchmarking, + ) + + +def get_loggers(): + """ + Returns: a list of all registered loggers + """ + return [logging.getLogger(qname) for qname in log_registry.get_log_qnames()] + + +def register_log(setting_name, log_name): + """ + Enables a log to be controlled by the env var and user API with the setting_name + Args: + setting_name: the shorthand name used in the env var and user API + log_name: the log name that the setting_name is associated with + """ + log_registry.register_log(setting_name, log_name) + + +def register_artifact( + setting_name, description, visible=False, off_by_default=False, log_format=None +): + """ + Enables an artifact to be controlled by the env var and user API with name + Args: + setting_name: the shorthand name used in the env var and user API + description: A description of what this outputs + visible: Whether it gets suggested to users by default + off_by_default: whether this artifact should be logged when the ancestor loggers + are enabled at level DEBUG + """ + log_registry.register_artifact_name( + setting_name, description, visible, off_by_default, log_format + ) + + +def getArtifactLogger(module_qname, artifact_name): + if artifact_name not in log_registry.artifact_names: + raise ValueError( + f"Artifact name: {repr(artifact_name)} not registered," + f"please call register_artifact({repr(artifact_name)}) in torch._logging.registrations." + ) + qname = module_qname + f".__{artifact_name}" + log = logging.getLogger(qname) + log.artifact_name = artifact_name # type: ignore[attr-defined] + log_registry.register_artifact_log(qname) + configure_artifact_log(log) + return log + + +INCR_VERBOSITY_CHAR = "+" +DECR_VERBOSITY_CHAR = "-" +VERBOSITY_REGEX = ( + "(" + + "|".join([re.escape(INCR_VERBOSITY_CHAR), re.escape(DECR_VERBOSITY_CHAR)]) + + "?)" +) + + +def configure_artifact_log(log): + # If the artifact is off by default, then it should only be logged when explicitly + # enabled; set propagate to False so that this artifact is not propagated + # to its ancestor logger + if log_registry.is_off_by_default(log.artifact_name): + log.propagate = False + + # enable artifact logging when explicitly enabled + if log_state.is_artifact_enabled(log.artifact_name): + log.setLevel(logging.DEBUG) + log.propagate = True + + +# match a comma separated list of loggable names (whitespace allowed after commas) +def _gen_settings_regex(): + return re.compile(r"((\+|-)?[\w\.]+,\s*)*(\+|-)?[\w\.]+?") + + +def _validate_settings(settings): + return re.fullmatch(_gen_settings_regex(), settings) is not None + + +def help_message(verbose=False): + def pad_to(s, length=30): + assert len(s) <= length + return s + " " * (length - len(s)) + + if verbose: + printed_artifacts = log_registry.artifact_names + else: + printed_artifacts = log_registry.visible_artifacts + + if verbose: + heading = "All registered names" + else: + heading = "Visible registered names (use TORCH_LOGS='+help' for full list)" + lines = ( + ["all"] + + sorted(log_registry.log_alias_to_log_qnames.keys()) + + sorted( + [ + f"{pad_to(name)}\t{log_registry.artifact_descriptions[name]}" + for name in printed_artifacts + ] + ) + ) + setting_info = " " + "\n ".join(lines) + examples = """ +Examples: + TORCH_LOGS="+dynamo,aot" will set the log level of TorchDynamo to + logging.DEBUG and AOT to logging.INFO + + TORCH_LOGS="-dynamo,+inductor" will set the log level of TorchDynamo to + logging.ERROR and TorchInductor to logging.DEBUG + + TORCH_LOGS="aot_graphs" will enable the aot_graphs artifact + + TORCH_LOGS="+dynamo,schedule" will enable set the log level of TorchDynamo + to logging.DEBUG and enable the schedule artifact + + TORCH_LOGS="+some.random.module,schedule" will set the log level of + some.random.module to logging.DEBUG and enable the schedule artifact + + TORCH_LOGS_FORMAT="%(levelname)s: %(message)s" or any provided format + string will set the output format + Valid keys are "levelname", "message", "pathname", "levelno", "lineno", + "filename" and "name". + + TORCH_LOGS_OUT=/tmp/output.txt will output the logs to /tmp/output.txt as + well. This is useful when the output is long. +""" # flake8: noqa: B950 + msg = f""" +TORCH_LOGS Info +{examples} + +{heading} +{setting_info} +""" + return msg + + +def _invalid_settings_err_msg(settings, verbose=False): + valid_settings = ", ".join( + ["all"] + + list(log_registry.log_alias_to_log_qnames.keys()) + + list(log_registry.artifact_names) + ) + msg = f""" +Invalid log settings: {settings}, must be a comma separated list of fully +qualified module names, registered log names or registered artifact names. +For more info on various settings, try TORCH_LOGS="help" +Valid settings: +{valid_settings} +""" + return msg + + +@functools.lru_cache +def _parse_log_settings(settings): + if settings == "": + return {} + + if settings == "help": + raise ValueError(help_message(verbose=False)) + elif settings == "+help": + raise ValueError(help_message(verbose=True)) + if not _validate_settings(settings): + raise ValueError(_invalid_settings_err_msg(settings)) + + settings = re.sub(r"\s+", "", settings) + log_names = settings.split(",") + + def get_name_level_pair(name): + clean_name = name.replace(INCR_VERBOSITY_CHAR, "") + clean_name = clean_name.replace(DECR_VERBOSITY_CHAR, "") + + if name[0] == INCR_VERBOSITY_CHAR: + level = logging.DEBUG + elif name[0] == DECR_VERBOSITY_CHAR: + level = logging.ERROR + else: + level = logging.INFO + + return clean_name, level + + log_state = LogState() + + for name in log_names: + name, level = get_name_level_pair(name) + + if name == "all": + name = "torch" + + if log_registry.is_log(name): + assert level is not None + log_qnames = log_registry.log_alias_to_log_qnames[name] + log_state.enable_log(log_qnames, level) + elif log_registry.is_artifact(name): + log_state.enable_artifact(name) + elif _is_valid_module(name): + if not _has_registered_parent(name): + log_registry.register_log(name, name) + else: + log_registry.register_child_log(name) + log_state.enable_log(name, level) + else: + raise ValueError(_invalid_settings_err_msg(settings)) + + return log_state + + +def _is_valid_module(qname): + try: + __import__(qname) + return True + except ImportError: + return False + + +def _update_log_state_from_env(): + global log_state + log_setting = os.environ.get(LOG_ENV_VAR, None) + if log_setting is not None: + log_state = _parse_log_settings(log_setting) + + +def _has_registered_parent(log_qname): + cur_log = logging.getLogger(log_qname) + + registered_log_qnames = log_registry.get_log_qnames() + + while cur_log.parent: + if cur_log.name in registered_log_qnames: + return True + cur_log = cur_log.parent + + return False + + +def make_module_path_relative(abs_path): + """ + Given an absolute filepath corresponding to a Python module which was + loaded via normal import mechanisms using sys.path, convert it into + a relative path relative to one of the Python search paths. + """ + + abs_path = pathlib.Path(abs_path).resolve() + + for path in sys.path: + try: + rel_path = abs_path.relative_to(path) + except ValueError: + continue + else: + return str(rel_path) + + return str(abs_path) + + +# apply custom formats to artifacts when necessary +class TorchLogsFormatter(logging.Formatter): + def __init__(self, *, trace: bool = False): + super().__init__() + self._is_trace = trace + + def format(self, record): + artifact_name = getattr(logging.getLogger(record.name), "artifact_name", None) + if artifact_name is not None: + artifact_formatter = log_registry.artifact_log_formatters.get( + artifact_name, None + ) + if artifact_formatter is not None: + return artifact_formatter.format(record) + + record.message = record.getMessage() + record.asctime = self.formatTime(record, "%m%d %H:%M:%S") + + # exception handling - copied from logging.Formatter.format + s = record.message + if record.exc_info: + # Cache the traceback text to avoid converting it multiple times + # (it's constant anyway) + if not record.exc_text: + record.exc_text = self.formatException(record.exc_info) + if record.exc_text: + if s[-1:] != "\n": + s = s + "\n" + s = s + record.exc_text + if record.stack_info: + if s[-1:] != "\n": + s = s + "\n" + s = s + self.formatStack(record.stack_info) + + record.rankprefix = "" + if not self._is_trace and dist.is_available() and dist.is_initialized(): + record.rankprefix = f"[rank{dist.get_rank()}]:" + + record.traceid = "" + if ( + not self._is_trace + and (trace_id := torch._guards.CompileContext.current_trace_id()) + is not None + ): + record.traceid = f" [{trace_id}]" + + glog_level_to_abbr = { + "DEBUG": "V", # V is for VERBOSE in glog + "INFO": "I", + "WARNING": "W", + "ERROR": "E", + "CRITICAL": "C", + } + + shortlevel = glog_level_to_abbr.get(record.levelname, record.levelname) + + record.artifactprefix = "" + if artifact_name is not None: + record.artifactprefix = f" [__{artifact_name}]" + + filepath = make_module_path_relative(record.pathname) + + prefix = ( + f"{record.rankprefix}{shortlevel}{record.asctime}.{int(record.msecs*1000):06d} {record.process} " + f"{filepath}:" + f"{record.lineno}]{record.traceid}{record.artifactprefix}" + ) + if self._is_trace: + assert s == "" + try: + r = f"{prefix} {json.dumps(record.metadata)}" + except TypeError: + log.warning("failing metadata: %r", record.metadata) + raise + if record.payload is not None: + r += "".join(f"\n\t{l}" for l in record.payload.split("\n")) + return r + else: + lines = s.split("\n") + return "\n".join(f"{prefix} {l}" for l in lines) + + +def _default_formatter(): + fmt = os.environ.get(LOG_FORMAT_ENV_VAR, None) + if fmt is None: + return TorchLogsFormatter() + else: + if fmt in ("short", "basic"): + fmt = logging.BASIC_FORMAT + return logging.Formatter(fmt) + + +DEFAULT_FORMATTER = _default_formatter() + + +def _setup_handlers(create_handler_fn, log): + debug_handler = _track_handler(create_handler_fn()) + debug_handler.setFormatter(DEFAULT_FORMATTER) + debug_handler.setLevel(logging.DEBUG) + log.addHandler(debug_handler) + + +handlers = WeakSet() # type: ignore[var-annotated] + + +# mark handlers that we've created +# so we don't modify user handlers +def _track_handler(handler): + handlers.add(handler) + return handler + + +def _is_torch_handler(handler): + return handler in handlers + + +# clears all torch handlers on specified loggers +def _clear_handlers(log): + to_remove = [handler for handler in log.handlers if _is_torch_handler(handler)] + for handler in to_remove: + log.removeHandler(handler) + + +def _reset_logs(): + # reset all registered logs + for log_qname in log_registry.get_log_qnames(): + log = logging.getLogger(log_qname) + log.setLevel(logging.WARNING) + log.propagate = False + _clear_handlers(log) + + # reset all artifact and child logs + for artifact_log_qname in itertools.chain( + log_registry.get_artifact_log_qnames(), log_registry.get_child_log_qnames() + ): + log = logging.getLogger(artifact_log_qname) + log.setLevel(logging.NOTSET) + log.propagate = True + + trace_log.propagate = False + _clear_handlers(trace_log) + + +def _get_log_state(): + return log_state + + +def _set_log_state(state): + global log_state + log_state = state + + +def _init_logs(log_file_name=None): + _reset_logs() + _update_log_state_from_env() + + out = os.environ.get(LOG_OUT_ENV_VAR, None) + if out is not None: + log_file_name = out + + # First, reset all known (registered) loggers to NOTSET, so that they + # respect their parent log level + for log_qname in log_registry.get_log_qnames(): + # But not the top level torch level: this defaults to WARNING so + # that our log messages don't leak to the lower levels + if log_qname == "torch": + continue + log = logging.getLogger(log_qname) + log.setLevel(logging.NOTSET) + + # Now, for all loggers which the user requested to have non-standard + # logging behavior, modify their log levels + for log_qname, level in log_state.get_log_level_pairs(): + log = logging.getLogger(log_qname) + log.setLevel(level) + + # Finally, setup handlers for all registered loggers + for log_qname in log_registry.get_log_qnames(): + log = logging.getLogger(log_qname) + _setup_handlers( + logging.StreamHandler, + log, + ) + + if log_file_name is not None: + _setup_handlers( + lambda: logging.FileHandler(log_file_name), + log, + ) + + # configure artifact loggers, note: this must happen last + # since the levels of ancestor loggers are taken into account + for artifact_log_qname in log_registry.get_artifact_log_qnames(): + log = logging.getLogger(artifact_log_qname) + configure_artifact_log(log) + + # Setup handler for the special trace_log, with different default + # configuration + trace_dir_name = os.environ.get(TRACE_ENV_VAR, None) + # This handler may remove itself if trace_dir_name is None and we are not + # actually in an FB environment. This allows us to defer actually + # initializing it until we actually need to log anything. This is + # important because JK initializes a C++ singleton, which will pork our + # process if we subsequently fork. + handler = LazyTraceHandler(trace_dir_name) + # This log is ALWAYS at debug level. We will additionally test if there + # are any handlers before deciding to actually call logging on this. Do + # not manually call + trace_log.setLevel(logging.DEBUG) + trace_log_handler = _track_handler(handler) + trace_log_handler.setFormatter(TorchLogsFormatter(trace=True)) + trace_log.addHandler(trace_log_handler) + + +class LazyTraceHandler(logging.StreamHandler): + """Like FileHandler, but the file is allocated lazily only upon the first log message""" + + def __init__(self, root_dir: Optional[str]): + # This is implemented in the same way that delay is implemented on + # FileHandler + self.root_dir = root_dir + logging.Handler.__init__(self) + self.stream = None + self._builtin_open = open + + # cloned from FileHandler in cpython + def close(self): + self.acquire() + try: + try: + if self.stream: + try: + self.flush() + finally: + stream = self.stream + self.stream = None + if hasattr(stream, "close"): + stream.close() + finally: + # Issue #19523: call unconditionally to + # prevent a handler leak when delay is set + # Also see Issue #42378: we also rely on + # self._closed being set to True there + logging.StreamHandler.close(self) + finally: + self.release() + + def emit(self, record): + if self.stream is None: + ok = False + if self.root_dir is None: + TRACE_LOG_DIR = "/logs" + open_func = self._builtin_open + + import torch.version as torch_version + + if ( + hasattr(torch_version, "git_version") + and os.getenv("MAST_HPC_JOB_NAME") is None + ): + log.info( + "LazyTraceHandler: disabled because not fbcode or conda on mast" + ) + elif not torch._utils_internal.justknobs_check("pytorch/trace:enable"): + log.info( + "LazyTraceHandler: disabled because justknobs_check('pytorch/trace:enable') returned False" + ) + elif not os.path.exists(TRACE_LOG_DIR): + log.info( + "LazyTraceHandler: disabled because %s does not exist", + TRACE_LOG_DIR, + ) + elif not os.access(TRACE_LOG_DIR, os.W_OK): + log.info( + "LazyTraceHandler: disabled because %s is not writeable", + TRACE_LOG_DIR, + ) + else: + self.root_dir = TRACE_LOG_DIR + + if self.root_dir is not None: + os.makedirs(self.root_dir, exist_ok=True) + ranksuffix = "" + if dist.is_available() and dist.is_initialized(): + ranksuffix = f"rank_{dist.get_rank()}_" + self.stream = tempfile.NamedTemporaryFile( + mode="w+", + suffix=".log", + prefix=f"dedicated_log_torch_trace_{ranksuffix}", + dir=self.root_dir, + delete=False, + ) + log.info("LazyTraceHandler: logging to %s", self.stream.name) + else: + # We go poof, remove and no-op + trace_log.removeHandler(self) + return + if self.stream: + super().emit(record) + + +@functools.lru_cache(None) +def warning_once(logger_obj, *args, **kwargs): + """ + This function is similar to `logger.warning()`, but will emit the warning with the same message only once + Note: The cache is for the function arguments, so 2 different callers using the same arguments will hit the cache. + The assumption here is that all warning messages are unique across the code. If they aren't then need to switch to + another type of cache that includes the caller frame information in the hashing function. + """ + logger_obj.warning(*args, **kwargs) + + +class LazyString: + def __init__(self, func, *args, **kwargs): + self.func = func + self.args = args + self.kwargs = kwargs + + def __str__(self): + return self.func(*self.args, **self.kwargs) + + +def trace_structured( + name: str, + # NB: metadata expected to be dict so adding more info is forward compatible + # Tuple[str, int] is a special case for string interning + metadata_fn: Callable[[], Union[Dict[str, Any], Tuple[str, int]]] = dict, + *, + payload_fn: Callable[[], Optional[Union[str, object]]] = lambda: None, + suppress_context: bool = False, + expect_trace_id: bool = True, # Whether or not we expect to have a current trace id +): + """ + metadata is an arbitrary JSON compatible struct, but it's expected to not be + too long (e.g., less than 1MB) + + payload is an arbitrary string, which can be arbitrarily long (but expected to have + newlines so no lines are too long) + """ + assert "name" not in ["rank", "frame_id", "frame_compile_id", "attempt"] + assert callable( + metadata_fn + ), f"metadata_fn should be callable, but got {type(metadata_fn)}" + assert callable( + payload_fn + ), f"payload_fn should be callable, but got {type(payload_fn)}" + # trace_log never propagates and is ALWAYS DEBUG, so also check that there + # are handlers instead of checking the log level + if trace_log.handlers: + record: Dict[str, object] = {} + record[name] = metadata_fn() + if not suppress_context: + # TODO: Actually, the rank probably should just be emitted once at + # the top, and not repeatedly spammed in all the logs, since it + # never changes and we assume no interleaving + if dist.is_available() and dist.is_initialized(): + record["rank"] = dist.get_rank() + if ( + trace_id := torch._guards.CompileContext.current_trace_id() + ) is not None: + record["frame_id"] = trace_id.compile_id.frame_id + record["frame_compile_id"] = trace_id.compile_id.frame_compile_id + record["attempt"] = trace_id.attempt + else: + if expect_trace_id: + # Record the stack of the log call to better diagnose why we + # don't have a frame id for it + record["stack"] = torch._logging.structured.from_traceback( + CapturedTraceback.extract(skip=1).summary() + ) + payload = payload_fn() + if payload is not None: + if not isinstance(payload, str): + if isinstance(payload, list): + # special case to look better + payload = "[\n" + ",\n".join(json.dumps(i) for i in payload) + "\n]" + else: + # force newlines so we are unlikely to overflow line limit + payload = json.dumps(payload, indent=0) + h = hashlib.md5() + h.update(payload.encode("utf-8")) + record["has_payload"] = h.hexdigest() + trace_log.debug( + "", extra={"metadata": record, "payload": payload}, stacklevel=2 + ) + log_trace_structured_event(name, record) + + +import torch._guards +import torch._utils_internal +import torch.distributed as dist diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_logging/_registrations.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_logging/_registrations.py new file mode 100644 index 0000000000000000000000000000000000000000..e2bdedf349a0ac406f77d452c66bcc99a4d3d7e7 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_logging/_registrations.py @@ -0,0 +1,192 @@ +# flake8: noqa: B950 +from ._internal import register_artifact, register_log + + +DYNAMIC = [ + "torch.fx.experimental.symbolic_shapes", + "torch.fx.experimental.sym_node", + "torch.fx.experimental.recording", +] +DISTRIBUTED = [ + "torch.distributed", + "torch._dynamo.backends.distributed", + "torch.nn.parallel.distributed", +] + +register_log("dynamo", ["torch._dynamo", *DYNAMIC]) +register_log("fake_tensor", ["torch._subclasses.fake_tensor"]) +register_log("aot", ["torch._functorch.aot_autograd", "torch._functorch._aot_autograd"]) +register_log("autograd", "torch.autograd") +register_log("inductor", ["torch._inductor", "torch._inductor.cudagraph_trees"]) + +register_artifact( + "cudagraphs", + "Logs information from wrapping inductor generated code with cudagraphs.", +) + +register_log("dynamic", DYNAMIC) +register_log("torch", "torch") +register_log("distributed", DISTRIBUTED) +register_log( + "c10d", ["torch.distributed.distributed_c10d", "torch.distributed.rendezvous"] +) +register_log( + "ddp", ["torch.nn.parallel.distributed", "torch._dynamo.backends.distributed"] +) +register_log("pp", ["torch.distributed.pipelining"]) +register_log("fsdp", ["torch.distributed.fsdp", "torch.distributed._composable.fsdp"]) +register_log("dtensor", ["torch.distributed._tensor", "torch.distributed.tensor"]) +register_log("onnx", "torch.onnx") +register_log( + "export", + [ + "torch._dynamo", + "torch.export", + "torch.export.dynamic_shapes", + *DYNAMIC, + "torch._export.converter", + "torch._export.non_strict_utils", + ], +) + +register_artifact( + "guards", + "This prints the guards for every compiled Dynamo frame. It does not tell you where the guards come from.", + visible=True, +) +register_artifact("verbose_guards", "", off_by_default=True) +register_artifact( + "bytecode", + "Prints the original and modified bytecode from Dynamo. Mostly useful if you're debugging our bytecode generation in Dynamo.", + off_by_default=True, +) +register_artifact( + "graph", + "Prints the dynamo traced graph (prior to AOTDispatch) in a table. If you prefer python code use `graph_code` instead. ", +) +register_artifact("graph_code", "Like `graph`, but gives you the Python code instead.") +register_artifact( + "graph_sizes", "Prints the sizes of all FX nodes in the dynamo graph." +) +register_artifact( + "trace_source", + "As we execute bytecode, prints the file name / line number we are processing and the actual source code. Useful with `bytecode`", +) +register_artifact( + "trace_call", + "Like trace_source, but it will give you the per-expression blow-by-blow if your Python is recent enough.", +) +register_artifact( + "trace_bytecode", + "As we trace bytecode, prints the instruction and the current stack.", +) +register_artifact( + "aot_graphs", + "Prints the FX forward and backward graph generated by AOTDispatch, after partitioning. Useful to understand what's being given to Inductor", + visible=True, +) +register_artifact( + "aot_joint_graph", + "Print FX joint graph from AOTAutograd, prior to partitioning. Useful for debugging partitioning", +) +register_artifact( + "aot_graphs_effects", + "Prints the FX forward and backward graph generated by AOTDispatch, useful for debugging effects processing.", + visible=True, +) +register_artifact( + "post_grad_graphs", + "Prints the FX graph generated by post grad passes. Useful to understand what's being given to Inductor after post grad passes", +) +register_artifact( + "compiled_autograd", + "Prints various logs in compiled_autograd, including but not limited to the graphs. Useful for debugging compiled_autograd.", + visible=True, +) +register_artifact( + "compiled_autograd_verbose", + "Will affect performance. Prints compiled_autograd logs with C++ info e.g. autograd node -> fx node mapping", + off_by_default=True, +) +register_artifact( + "ddp_graphs", + "Only relevant for compiling DDP. DDP splits into multiple graphs to trigger comms early. This will print each individual graph here.", +) +register_artifact( + "recompiles", + "Prints the reason why we recompiled a graph. Very, very useful.", + visible=True, +) +register_artifact( + "recompiles_verbose", + "Prints all guard checks that fail during a recompilation. " + "At runtime, Dynamo will stop at the first failed check for each failing guard. " + "So not all logged failing checks are actually ran by Dynamo.", + visible=True, + off_by_default=True, +) +register_artifact( + "graph_breaks", + "Prints whenever Dynamo decides that it needs to graph break (i.e. create a new graph). Useful for debugging why torch.compile has poor performance", + visible=True, +) +register_artifact( + "not_implemented", + "Prints log messages whenever we return NotImplemented in a multi-dispatch, letting you trace through each object we attempted to dispatch to", +) +register_artifact( + "output_code", + "Prints the code that Inductor generates (either Triton or C++)", + off_by_default=True, + visible=True, +) +register_artifact( + "kernel_code", + "Prints the code that Inductor generates (on a per-kernel basis)", + off_by_default=True, + visible=True, +) +register_artifact( + "schedule", + "Inductor scheduler information. Useful if working on Inductor fusion algo", + off_by_default=True, +) +register_artifact("perf_hints", "", off_by_default=True) +register_artifact("onnx_diagnostics", "", off_by_default=True) +register_artifact( + "fusion", + "Detailed Inductor fusion decisions. More detailed than 'schedule'", + off_by_default=True, +) +register_artifact( + "loop_ordering", + "Logs related to loop ordering", + off_by_default=True, +) +register_artifact( + "overlap", + "Detailed Inductor compute/comm overlap decisions", + off_by_default=True, +) +register_artifact( + "sym_node", + "Logs extra info for various SymNode operations", + off_by_default=True, +) +register_artifact( + "trace_shape_events", + "Logs traces for every ShapeEnv operation that we record for replay", + off_by_default=True, +) +register_artifact( + "cudagraph_static_inputs", + "Logs static inputs handling in dynamo, AOT, and cudagraphs", + off_by_default=True, +) +register_artifact( + "benchmarking", + "Detailed Inductor benchmarking information.", + off_by_default=True, +) + +register_artifact("custom_format_test_artifact", "Testing only", log_format="") diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_logging/scribe.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_logging/scribe.py new file mode 100644 index 0000000000000000000000000000000000000000..18745e468b5e6a74695179f560405346fe04f1c6 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_logging/scribe.py @@ -0,0 +1,61 @@ +from typing import Callable, List, Union +from typing_extensions import TypeAlias + + +try: + from fbscribelogger import make_scribe_logger # type: ignore[import-untyped] +except ImportError: + TAtom: TypeAlias = Union[int, float, bool, str] + TField: TypeAlias = Union[TAtom, List[TAtom]] + TLazyField: TypeAlias = Union[TField, Callable[[], TField]] + + def make_scribe_logger(name: str, thrift_src: str) -> Callable[..., None]: + def inner(**kwargs: TLazyField) -> None: + pass + + return inner + + +open_source_signpost = make_scribe_logger( + "TorchOpenSourceSignpost", + """ +struct TorchOpenSourceSignpostLogEntry { + + # The commit SHA that triggered the workflow, e.g., 02a6b1d30f338206a71d0b75bfa09d85fac0028a. Derived from GITHUB_SHA. + 4: optional string commit_sha; + + # Commit date (not author date) of the commit in commit_sha as timestamp, e.g., 1724208105. Increasing if merge bot is used, though not monotonic; duplicates occur when stack is landed. + 5: optional i64 commit_date; + + # The fully-formed ref of the branch or tag that triggered the workflow run, e.g., refs/pull/133891/merge or refs/heads/main. Derived from GITHUB_REF. + 6: optional string github_ref; + + # Indicates if branch protections or rulesets are configured for the ref that triggered the workflow run. Derived from GITHUB_REF_PROTECTED. + 7: optional bool github_ref_protected; + + # A unique number for each attempt of a particular workflow run in a repository, e.g., 1. Derived from GITHUB_RUN_ATTEMPT. + 8: optional string github_run_attempt; + + # A unique number for each workflow run within a repository, e.g., 19471190684. Derived from GITHUB_RUN_ID. + 9: optional string github_run_id; + + # A unique number for each run of a particular workflow in a repository, e.g., 238742. Derived from GITHUB_RUN_NUMBER. + 10: optional string github_run_number_str; + + # The name of the current job. Derived from JOB_NAME, e.g., linux-jammy-py3.8-gcc11 / test (default, 3, 4, amz2023.linux.2xlarge). + 11: optional string job_name; + + # The GitHub user who triggered the job. Derived from GITHUB_TRIGGERING_ACTOR. + 12: optional string github_triggering_actor; + 13: optional string name; # Event name + 14: optional string parameters; # Parameters (JSON data) + 16: optional string subsystem; # Subsystem the event is associated with + + # The unit timestamp in second for the Scuba Time Column override + 17: optional i64 time; + + # The weight of the record according to current sampling rate + 18: optional i64 weight; +} +""", # noqa: B950 +) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_logging/structured.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_logging/structured.py new file mode 100644 index 0000000000000000000000000000000000000000..2a99a7a849622822d43b9e0d7b7bd9f9c9dca0b1 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_logging/structured.py @@ -0,0 +1,57 @@ +""" +Utilities for converting data types into structured JSON for dumping. +""" + +import traceback +from typing import Any, Dict, List, Sequence, Set + +import torch._logging._internal + + +INTERN_TABLE: Dict[str, int] = {} + + +DUMPED_FILES: Set[str] = set() + + +def intern_string(s: str) -> int: + r = INTERN_TABLE.get(s, None) + if r is None: + r = len(INTERN_TABLE) + INTERN_TABLE[s] = r + torch._logging._internal.trace_structured( + "str", lambda: (s, r), suppress_context=True + ) + return r + + +def dump_file(filename: str) -> None: + if "eval_with_key" not in filename: + return + if filename in DUMPED_FILES: + return + DUMPED_FILES.add(filename) + from torch.fx.graph_module import _loader + + torch._logging._internal.trace_structured( + "dump_file", + metadata_fn=lambda: { + "name": filename, + }, + payload_fn=lambda: _loader.get_source(filename), + ) + + +def from_traceback(tb: Sequence[traceback.FrameSummary]) -> List[Dict[str, Any]]: + r = [] + for frame in tb: + # dict naming convention here coincides with + # python/combined_traceback.cpp + r.append( + { + "line": frame.lineno, + "name": frame.name, + "filename": intern_string(frame.filename), + } + ) + return r diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..89f6ccfec07a557d9c73a3b85c4554ef74b05b25 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/__init__.py @@ -0,0 +1,34 @@ +# mypy: ignore-errors + +from . import fft, linalg, random +from ._dtypes import * # noqa: F403 +from ._funcs import * # noqa: F403 +from ._getlimits import finfo, iinfo +from ._ndarray import ( + array, + asarray, + ascontiguousarray, + can_cast, + from_dlpack, + ndarray, + newaxis, + result_type, +) +from ._ufuncs import * # noqa: F403 +from ._util import AxisError, UFuncTypeError + + +from math import pi, e # usort: skip + + +all = all +alltrue = all + +any = any +sometrue = any + +inf = float("inf") +nan = float("nan") + +False_ = False +True_ = True diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..13c5c4c8226679213f8a1fb53e96492867432fc6 Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/__pycache__/__init__.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/__pycache__/_binary_ufuncs_impl.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/__pycache__/_binary_ufuncs_impl.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6b461453878c0d1ab3e31af80a5dbcc81b23d1d6 Binary files /dev/null and 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+1,85 @@ +# mypy: ignore-errors + +"""Export torch work functions for binary ufuncs, rename/tweak to match numpy. +This listing is further exported to public symbols in the `torch._numpy/_ufuncs.py` module. +""" + +import torch +from torch import ( # noqa: F401 + add, + arctan2, + bitwise_and, + bitwise_left_shift as left_shift, + bitwise_or, + bitwise_right_shift as right_shift, + bitwise_xor, + copysign, + divide, + eq as equal, + float_power, + floor_divide, + fmax, + fmin, + fmod, + gcd, + greater, + greater_equal, + heaviside, + hypot, + lcm, + ldexp, + less, + less_equal, + logaddexp, + logaddexp2, + logical_and, + logical_or, + logical_xor, + maximum, + minimum, + multiply, + nextafter, + not_equal, + pow as power, + remainder, + remainder as mod, + subtract, + true_divide, +) + +from . import _dtypes_impl, _util + + +# work around torch limitations w.r.t. numpy +def matmul(x, y): + # work around: + # - RuntimeError: expected scalar type Int but found Double + # - RuntimeError: "addmm_impl_cpu_" not implemented for 'Bool' + # - RuntimeError: "addmm_impl_cpu_" not implemented for 'Half' + dtype = _dtypes_impl.result_type_impl(x, y) + is_bool = dtype == torch.bool + is_half = (x.dtype == torch.float16 or y.dtype == torch.float16) and ( + x.is_cpu or y.is_cpu + ) + + work_dtype = dtype + if is_bool: + work_dtype = torch.uint8 + if is_half: + work_dtype = torch.float32 + + x = _util.cast_if_needed(x, work_dtype) + y = _util.cast_if_needed(y, work_dtype) + + result = torch.matmul(x, y) + + if work_dtype != dtype: + result = result.to(dtype) + + return result + + +# a stub implementation of divmod, should be improved after +# https://github.com/pytorch/pytorch/issues/90820 is fixed in pytorch +def divmod(x, y): + return x // y, x % y diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_casting_dicts.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_casting_dicts.py new file mode 100644 index 0000000000000000000000000000000000000000..3c859b855dd5d806b4e9e08bb61ada58f4c14c90 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_casting_dicts.py @@ -0,0 +1,1368 @@ +# mypy: ignore-errors + +import torch + + +# These two dicts are autogenerated with autogen/gen_dtypes.py, +# using numpy version 1.24.3. + +_can_cast_dict = { + "no": { + torch.float16: { + torch.float16: True, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.float32: { + torch.float16: False, + torch.float32: True, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.float64: { + torch.float16: False, + torch.float32: False, + torch.float64: True, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.complex64: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: True, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.complex128: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.uint8: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: True, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.uint16: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: True, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.uint32: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: True, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.uint64: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: True, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.int8: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: True, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.int16: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: True, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.int32: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: True, + torch.int64: False, + torch.bool: False, + }, + torch.int64: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: True, + torch.bool: False, + }, + torch.bool: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: True, + }, + }, + "equiv": { + torch.float16: { + torch.float16: True, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.float32: { + torch.float16: False, + torch.float32: True, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.float64: { + torch.float16: False, + torch.float32: False, + torch.float64: True, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.complex64: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: True, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.complex128: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.uint8: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: True, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.uint16: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: True, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.uint32: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: True, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.uint64: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: True, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.int8: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: True, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.int16: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: True, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.int32: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: True, + torch.int64: False, + torch.bool: False, + }, + torch.int64: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: True, + torch.bool: False, + }, + torch.bool: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: False, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: True, + }, + }, + "safe": { + torch.float16: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.float32: { + torch.float16: False, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.float64: { + torch.float16: False, + torch.float32: False, + torch.float64: True, + torch.complex64: False, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.complex64: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: True, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.complex128: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: False, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.uint8: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: False, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: False, + }, + torch.uint16: { + torch.float16: False, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: False, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: False, + torch.int16: False, + torch.int32: True, + torch.int64: True, + torch.bool: False, + }, + torch.uint32: { + torch.float16: False, + torch.float32: False, + torch.float64: True, + torch.complex64: False, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: True, + torch.uint64: True, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: True, + torch.bool: False, + }, + torch.uint64: { + torch.float16: False, + torch.float32: False, + torch.float64: True, + torch.complex64: False, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: True, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.int8: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: False, + }, + torch.int16: { + torch.float16: False, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: False, + }, + torch.int32: { + torch.float16: False, + torch.float32: False, + torch.float64: True, + torch.complex64: False, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: True, + torch.int64: True, + torch.bool: False, + }, + torch.int64: { + torch.float16: False, + torch.float32: False, + torch.float64: True, + torch.complex64: False, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: True, + torch.bool: False, + }, + torch.bool: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: True, + }, + }, + "same_kind": { + torch.float16: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.float32: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.float64: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.complex64: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: True, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.complex128: { + torch.float16: False, + torch.float32: False, + torch.float64: False, + torch.complex64: True, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: False, + torch.int16: False, + torch.int32: False, + torch.int64: False, + torch.bool: False, + }, + torch.uint8: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: False, + }, + torch.uint16: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: False, + }, + torch.uint32: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: False, + }, + torch.uint64: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: False, + }, + torch.int8: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: False, + }, + torch.int16: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: False, + }, + torch.int32: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: False, + }, + torch.int64: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: False, + torch.uint16: False, + torch.uint32: False, + torch.uint64: False, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: False, + }, + torch.bool: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: True, + }, + }, + "unsafe": { + torch.float16: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: True, + }, + torch.float32: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: True, + }, + torch.float64: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: True, + }, + torch.complex64: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: True, + }, + torch.complex128: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: True, + }, + torch.uint8: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: True, + }, + torch.uint16: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: True, + }, + torch.uint32: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: True, + }, + torch.uint64: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: True, + }, + torch.int8: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: True, + }, + torch.int16: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: True, + }, + torch.int32: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: True, + }, + torch.int64: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: True, + }, + torch.bool: { + torch.float16: True, + torch.float32: True, + torch.float64: True, + torch.complex64: True, + torch.complex128: True, + torch.uint8: True, + torch.uint16: True, + torch.uint32: True, + torch.uint64: True, + torch.int8: True, + torch.int16: True, + torch.int32: True, + torch.int64: True, + torch.bool: True, + }, + }, +} + + +_result_type_dict = { + torch.float16: { + torch.float16: torch.float16, + torch.float32: torch.float32, + torch.float64: torch.float64, + torch.complex64: torch.complex64, + torch.complex128: torch.complex128, + torch.uint8: torch.float16, + torch.uint16: torch.float32, + torch.uint32: torch.float64, + torch.uint64: torch.float64, + torch.int8: torch.float16, + torch.int16: torch.float32, + torch.int32: torch.float64, + torch.int64: torch.float64, + torch.bool: torch.float16, + }, + torch.float32: { + torch.float16: torch.float32, + torch.float32: torch.float32, + torch.float64: torch.float64, + torch.complex64: torch.complex64, + torch.complex128: torch.complex128, + torch.uint8: torch.float32, + torch.uint16: torch.float32, + torch.uint32: torch.float64, + torch.uint64: torch.float64, + torch.int8: torch.float32, + torch.int16: torch.float32, + torch.int32: torch.float64, + torch.int64: torch.float64, + torch.bool: torch.float32, + }, + torch.float64: { + torch.float16: torch.float64, + torch.float32: torch.float64, + torch.float64: torch.float64, + torch.complex64: torch.complex128, + torch.complex128: torch.complex128, + torch.uint8: torch.float64, + torch.uint16: torch.float64, + torch.uint32: torch.float64, + torch.uint64: torch.float64, + torch.int8: torch.float64, + torch.int16: torch.float64, + torch.int32: torch.float64, + torch.int64: torch.float64, + torch.bool: torch.float64, + }, + torch.complex64: { + torch.float16: torch.complex64, + torch.float32: torch.complex64, + torch.float64: torch.complex128, + torch.complex64: torch.complex64, + torch.complex128: torch.complex128, + torch.uint8: torch.complex64, + torch.uint16: torch.complex64, + torch.uint32: torch.complex128, + torch.uint64: torch.complex128, + torch.int8: torch.complex64, + torch.int16: torch.complex64, + torch.int32: torch.complex128, + torch.int64: torch.complex128, + torch.bool: torch.complex64, + }, + torch.complex128: { + torch.float16: torch.complex128, + torch.float32: torch.complex128, + torch.float64: torch.complex128, + torch.complex64: torch.complex128, + torch.complex128: torch.complex128, + torch.uint8: torch.complex128, + torch.uint16: torch.complex128, + torch.uint32: torch.complex128, + torch.uint64: torch.complex128, + torch.int8: torch.complex128, + torch.int16: torch.complex128, + torch.int32: torch.complex128, + torch.int64: torch.complex128, + torch.bool: torch.complex128, + }, + torch.uint8: { + torch.float16: torch.float16, + torch.float32: torch.float32, + torch.float64: torch.float64, + torch.complex64: torch.complex64, + torch.complex128: torch.complex128, + torch.uint8: torch.uint8, + torch.uint16: torch.uint16, + torch.uint32: torch.uint32, + torch.uint64: torch.uint64, + torch.int8: torch.int16, + torch.int16: torch.int16, + torch.int32: torch.int32, + torch.int64: torch.int64, + torch.bool: torch.uint8, + }, + torch.uint16: { + torch.float16: torch.float32, + torch.float32: torch.float32, + torch.float64: torch.float64, + torch.complex64: torch.complex64, + torch.complex128: torch.complex128, + torch.uint8: torch.uint16, + torch.uint16: torch.uint16, + torch.uint32: torch.uint32, + torch.uint64: torch.uint64, + torch.int8: torch.int32, + torch.int16: torch.int32, + torch.int32: torch.int32, + torch.int64: torch.int64, + torch.bool: torch.uint16, + }, + torch.uint32: { + torch.float16: torch.float64, + torch.float32: torch.float64, + torch.float64: torch.float64, + torch.complex64: torch.complex128, + torch.complex128: torch.complex128, + torch.uint8: torch.uint32, + torch.uint16: torch.uint32, + torch.uint32: torch.uint32, + torch.uint64: torch.uint64, + torch.int8: torch.int64, + torch.int16: torch.int64, + torch.int32: torch.int64, + torch.int64: torch.int64, + torch.bool: torch.uint32, + }, + torch.uint64: { + torch.float16: torch.float64, + torch.float32: torch.float64, + torch.float64: torch.float64, + torch.complex64: torch.complex128, + torch.complex128: torch.complex128, + torch.uint8: torch.uint64, + torch.uint16: torch.uint64, + torch.uint32: torch.uint64, + torch.uint64: torch.uint64, + torch.int8: torch.float64, + torch.int16: torch.float64, + torch.int32: torch.float64, + torch.int64: torch.float64, + torch.bool: torch.uint64, + }, + torch.int8: { + torch.float16: torch.float16, + torch.float32: torch.float32, + torch.float64: torch.float64, + torch.complex64: torch.complex64, + torch.complex128: torch.complex128, + torch.uint8: torch.int16, + torch.uint16: torch.int32, + torch.uint32: torch.int64, + torch.uint64: torch.float64, + torch.int8: torch.int8, + torch.int16: torch.int16, + torch.int32: torch.int32, + torch.int64: torch.int64, + torch.bool: torch.int8, + }, + torch.int16: { + torch.float16: torch.float32, + torch.float32: torch.float32, + torch.float64: torch.float64, + torch.complex64: torch.complex64, + torch.complex128: torch.complex128, + torch.uint8: torch.int16, + torch.uint16: torch.int32, + torch.uint32: torch.int64, + torch.uint64: torch.float64, + torch.int8: torch.int16, + torch.int16: torch.int16, + torch.int32: torch.int32, + torch.int64: torch.int64, + torch.bool: torch.int16, + }, + torch.int32: { + torch.float16: torch.float64, + torch.float32: torch.float64, + torch.float64: torch.float64, + torch.complex64: torch.complex128, + torch.complex128: torch.complex128, + torch.uint8: torch.int32, + torch.uint16: torch.int32, + torch.uint32: torch.int64, + torch.uint64: torch.float64, + torch.int8: torch.int32, + torch.int16: torch.int32, + torch.int32: torch.int32, + torch.int64: torch.int64, + torch.bool: torch.int32, + }, + torch.int64: { + torch.float16: torch.float64, + torch.float32: torch.float64, + torch.float64: torch.float64, + torch.complex64: torch.complex128, + torch.complex128: torch.complex128, + torch.uint8: torch.int64, + torch.uint16: torch.int64, + torch.uint32: torch.int64, + torch.uint64: torch.float64, + torch.int8: torch.int64, + torch.int16: torch.int64, + torch.int32: torch.int64, + torch.int64: torch.int64, + torch.bool: torch.int64, + }, + torch.bool: { + torch.float16: torch.float16, + torch.float32: torch.float32, + torch.float64: torch.float64, + torch.complex64: torch.complex64, + torch.complex128: torch.complex128, + torch.uint8: torch.uint8, + torch.uint16: torch.uint16, + torch.uint32: torch.uint32, + torch.uint64: torch.uint64, + torch.int8: torch.int8, + torch.int16: torch.int16, + torch.int32: torch.int32, + torch.int64: torch.int64, + torch.bool: torch.bool, + }, +} diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_dtypes.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_dtypes.py new file mode 100644 index 0000000000000000000000000000000000000000..27799adaf56376433ccc27e2181e4751dccde685 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_dtypes.py @@ -0,0 +1,453 @@ +# mypy: ignore-errors + +""" Define analogs of numpy dtypes supported by pytorch. +Define the scalar types and supported dtypes and numpy <--> torch dtype mappings. +""" +import builtins + +import torch + +from . import _dtypes_impl + + +# ### Scalar types ### + + +class generic: + name = "generic" + + def __new__(cls, value): + # NumPy scalars are modelled as 0-D arrays + # so a call to np.float32(4) produces a 0-D array. + + from ._ndarray import asarray, ndarray + + if isinstance(value, str) and value in ["inf", "nan"]: + value = {"inf": torch.inf, "nan": torch.nan}[value] + + if isinstance(value, ndarray): + return value.astype(cls) + else: + return asarray(value, dtype=cls) + + +################## +# abstract types # +################## + + +class number(generic): + name = "number" + + +class integer(number): + name = "integer" + + +class inexact(number): + name = "inexact" + + +class signedinteger(integer): + name = "signedinteger" + + +class unsignedinteger(integer): + name = "unsignedinteger" + + +class floating(inexact): + name = "floating" + + +class complexfloating(inexact): + name = "complexfloating" + + +_abstract_dtypes = [ + "generic", + "number", + "integer", + "signedinteger", + "unsignedinteger", + "inexact", + "floating", + "complexfloating", +] + +# ##### concrete types + +# signed integers + + +class int8(signedinteger): + name = "int8" + typecode = "b" + torch_dtype = torch.int8 + + +class int16(signedinteger): + name = "int16" + typecode = "h" + torch_dtype = torch.int16 + + +class int32(signedinteger): + name = "int32" + typecode = "i" + torch_dtype = torch.int32 + + +class int64(signedinteger): + name = "int64" + typecode = "l" + torch_dtype = torch.int64 + + +# unsigned integers + + +class uint8(unsignedinteger): + name = "uint8" + typecode = "B" + torch_dtype = torch.uint8 + + +class uint16(unsignedinteger): + name = "uint16" + typecode = "H" + torch_dtype = torch.uint16 + + +class uint32(signedinteger): + name = "uint32" + typecode = "I" + torch_dtype = torch.uint32 + + +class uint64(signedinteger): + name = "uint64" + typecode = "L" + torch_dtype = torch.uint64 + + +# floating point + + +class float16(floating): + name = "float16" + typecode = "e" + torch_dtype = torch.float16 + + +class float32(floating): + name = "float32" + typecode = "f" + torch_dtype = torch.float32 + + +class float64(floating): + name = "float64" + typecode = "d" + torch_dtype = torch.float64 + + +class complex64(complexfloating): + name = "complex64" + typecode = "F" + torch_dtype = torch.complex64 + + +class complex128(complexfloating): + name = "complex128" + typecode = "D" + torch_dtype = torch.complex128 + + +class bool_(generic): + name = "bool_" + typecode = "?" + torch_dtype = torch.bool + + +# name aliases +_name_aliases = { + "intp": int64, + "int_": int64, + "intc": int32, + "byte": int8, + "short": int16, + "longlong": int64, # XXX: is this correct? + "ulonglong": uint64, + "ubyte": uint8, + "half": float16, + "single": float32, + "double": float64, + "float_": float64, + "csingle": complex64, + "singlecomplex": complex64, + "cdouble": complex128, + "cfloat": complex128, + "complex_": complex128, +} +# We register float_ = float32 and so on +for name, obj in _name_aliases.items(): + vars()[name] = obj + + +# Replicate this NumPy-defined way of grouping scalar types, +# cf tests/core/test_scalar_methods.py +sctypes = { + "int": [int8, int16, int32, int64], + "uint": [uint8, uint16, uint32, uint64], + "float": [float16, float32, float64], + "complex": [complex64, complex128], + "others": [bool_], +} + + +# Support mappings/functions + +_names = {st.name: st for cat in sctypes for st in sctypes[cat]} +_typecodes = {st.typecode: st for cat in sctypes for st in sctypes[cat]} +_torch_dtypes = {st.torch_dtype: st for cat in sctypes for st in sctypes[cat]} + + +_aliases = { + "u1": uint8, + "i1": int8, + "i2": int16, + "i4": int32, + "i8": int64, + "b": int8, # XXX: srsly? + "f2": float16, + "f4": float32, + "f8": float64, + "c8": complex64, + "c16": complex128, + # numpy-specific trailing underscore + "bool_": bool_, +} + + +_python_types = { + int: int64, + float: float64, + complex: complex128, + builtins.bool: bool_, + # also allow stringified names of python types + int.__name__: int64, + float.__name__: float64, + complex.__name__: complex128, + builtins.bool.__name__: bool_, +} + + +def sctype_from_string(s): + """Normalize a string value: a type 'name' or a typecode or a width alias.""" + if s in _names: + return _names[s] + if s in _name_aliases.keys(): + return _name_aliases[s] + if s in _typecodes: + return _typecodes[s] + if s in _aliases: + return _aliases[s] + if s in _python_types: + return _python_types[s] + raise TypeError(f"data type {s!r} not understood") + + +def sctype_from_torch_dtype(torch_dtype): + return _torch_dtypes[torch_dtype] + + +# ### DTypes. ### + + +def dtype(arg): + if arg is None: + arg = _dtypes_impl.default_dtypes().float_dtype + return DType(arg) + + +class DType: + def __init__(self, arg): + # a pytorch object? + if isinstance(arg, torch.dtype): + sctype = _torch_dtypes[arg] + elif isinstance(arg, torch.Tensor): + sctype = _torch_dtypes[arg.dtype] + # a scalar type? + elif issubclass_(arg, generic): + sctype = arg + # a dtype already? + elif isinstance(arg, DType): + sctype = arg._scalar_type + # a has a right attribute? + elif hasattr(arg, "dtype"): + sctype = arg.dtype._scalar_type + else: + sctype = sctype_from_string(arg) + self._scalar_type = sctype + + @property + def name(self): + return self._scalar_type.name + + @property + def type(self): + return self._scalar_type + + @property + def kind(self): + # https://numpy.org/doc/stable/reference/generated/numpy.dtype.kind.html + return _torch_dtypes[self.torch_dtype].name[0] + + @property + def typecode(self): + return self._scalar_type.typecode + + def __eq__(self, other): + if isinstance(other, DType): + return self._scalar_type == other._scalar_type + try: + other_instance = DType(other) + except TypeError: + return False + return self._scalar_type == other_instance._scalar_type + + @property + def torch_dtype(self): + return self._scalar_type.torch_dtype + + def __hash__(self): + return hash(self._scalar_type.name) + + def __repr__(self): + return f'dtype("{self.name}")' + + __str__ = __repr__ + + @property + def itemsize(self): + elem = self.type(1) + return elem.tensor.element_size() + + def __getstate__(self): + return self._scalar_type + + def __setstate__(self, value): + self._scalar_type = value + + +typecodes = { + "All": "efdFDBbhil?", + "AllFloat": "efdFD", + "AllInteger": "Bbhil", + "Integer": "bhil", + "UnsignedInteger": "B", + "Float": "efd", + "Complex": "FD", +} + + +# ### Defaults and dtype discovery + + +def set_default_dtype(fp_dtype="numpy", int_dtype="numpy"): + """Set the (global) defaults for fp, complex, and int dtypes. + + The complex dtype is inferred from the float (fp) dtype. It has + a width at least twice the width of the float dtype, + i.e., it's complex128 for float64 and complex64 for float32. + + Parameters + ---------- + fp_dtype + Allowed values are "numpy", "pytorch" or dtype_like things which + can be converted into a DType instance. + Default is "numpy" (i.e. float64). + int_dtype + Allowed values are "numpy", "pytorch" or dtype_like things which + can be converted into a DType instance. + Default is "numpy" (i.e. int64). + + Returns + ------- + The old default dtype state: a namedtuple with attributes ``float_dtype``, + ``complex_dtypes`` and ``int_dtype``. These attributes store *pytorch* + dtypes. + + Notes + ------------ + This functions has a side effect: it sets the global state with the provided dtypes. + + The complex dtype has bit width of at least twice the width of the float + dtype, i.e. it's complex128 for float64 and complex64 for float32. + + """ + if fp_dtype not in ["numpy", "pytorch"]: + fp_dtype = dtype(fp_dtype).torch_dtype + if int_dtype not in ["numpy", "pytorch"]: + int_dtype = dtype(int_dtype).torch_dtype + + if fp_dtype == "numpy": + float_dtype = torch.float64 + elif fp_dtype == "pytorch": + float_dtype = torch.float32 + else: + float_dtype = fp_dtype + + complex_dtype = { + torch.float64: torch.complex128, + torch.float32: torch.complex64, + torch.float16: torch.complex64, + }[float_dtype] + + if int_dtype in ["numpy", "pytorch"]: + int_dtype = torch.int64 + else: + int_dtype = int_dtype + + new_defaults = _dtypes_impl.DefaultDTypes( + float_dtype=float_dtype, complex_dtype=complex_dtype, int_dtype=int_dtype + ) + + # set the new global state and return the old state + old_defaults = _dtypes_impl.default_dtypes + _dtypes_impl._default_dtypes = new_defaults + return old_defaults + + +def issubclass_(arg, klass): + try: + return issubclass(arg, klass) + except TypeError: + return False + + +def issubdtype(arg1, arg2): + # cf https://github.com/numpy/numpy/blob/v1.24.0/numpy/core/numerictypes.py#L356-L420 + + # We also accept strings even if NumPy doesn't as dtypes are serialized as their + # string representation in dynamo's graph + def str_to_abstract(t): + if isinstance(t, str) and t in _abstract_dtypes: + return globals()[t] + return t + + arg1 = str_to_abstract(arg1) + arg2 = str_to_abstract(arg2) + + if not issubclass_(arg1, generic): + arg1 = dtype(arg1).type + if not issubclass_(arg2, generic): + arg2 = dtype(arg2).type + return issubclass(arg1, arg2) + + +__all__ = ["dtype", "DType", "typecodes", "issubdtype", "set_default_dtype", "sctypes"] +__all__ += list(_names.keys()) # noqa: PLE0605 +__all__ += list(_name_aliases.keys()) # noqa: PLE0605 +__all__ += _abstract_dtypes # noqa: PLE0605 diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_dtypes_impl.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_dtypes_impl.py new file mode 100644 index 0000000000000000000000000000000000000000..7dfe6d4787bb27d236a8525df0fc1444de36625c --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_dtypes_impl.py @@ -0,0 +1,217 @@ +# mypy: ignore-errors + +"""Dtypes/scalar type implementaions with torch dtypes. + +Here `dtype` is always a torch.dtype, this module knows nothing about +scalar types, wrapper dtypes or anything like that. PyTorch only. +""" +from collections import namedtuple + +import torch + + +# defaults : mimic NumPy, allow user control +DefaultDTypes = namedtuple( + "DefaultDTypes", ["float_dtype", "complex_dtype", "int_dtype"] +) + +# a global state +# We set it the first time we call default_dtypes() to avoid importing +# torch._dynamo.config and create a circular reference +_default_dtypes = None + + +def default_dtypes(): + global _default_dtypes + if _default_dtypes is None: + import torch._dynamo.config as config + + _default_dtypes = DefaultDTypes( + float_dtype=getattr(torch, config.numpy_default_float), + complex_dtype=getattr(torch, config.numpy_default_complex), + int_dtype=getattr(torch, config.numpy_default_int), + ) + assert isinstance(_default_dtypes.float_dtype, torch.dtype) + assert isinstance(_default_dtypes.complex_dtype, torch.dtype) + assert isinstance(_default_dtypes.int_dtype, torch.dtype) + return _default_dtypes + + +def get_default_dtype_for(dtype): + """Default scalar type given sctype category.""" + if dtype == torch.bool: + return dtype + if dtype.is_complex: + return default_dtypes().complex_dtype + if dtype.is_floating_point: + return default_dtypes().float_dtype + # else, it must be (some) integer + return default_dtypes().int_dtype + + +from . import _casting_dicts as _cd + + +def can_cast_impl(from_torch_dtype, to_torch_dtype, casting): + return _cd._can_cast_dict[casting][from_torch_dtype][to_torch_dtype] + + +def result_type_impl(*tensors): + # NB: torch dtypes here + dtyp = tensors[0].dtype + if len(tensors) == 1: + return dtyp + + for curr in tensors[1:]: + dtyp = _cd._result_type_dict[dtyp][curr.dtype] + + return dtyp + + +def python_type_for_torch(dtyp): + """Get a python scalar type a torch dtype""" + if dtyp.is_floating_point: + typ = float + elif dtyp.is_complex: + typ = complex + elif dtyp == torch.bool: + typ = bool + else: + typ = int + return typ + + +# ### NEP 50 helpers ### + +_SCALAR_TYPES = (int, bool, float, complex) + +_SCALAR_AND_SYMBOLIC_TYPES = ( + *_SCALAR_TYPES, + torch.SymInt, + torch.SymFloat, + torch.SymBool, +) + +_NEP50_FUNCS_TENSOR_ONLY = ( + "minimum", + "maximum", + "logaddexp", + "logaddexp2", + "lcm", + "gcd", + "hypot", + "heaviside", + "fmod", + "fmin", + "fmax", + "copysign", + "arctan2", +) + + +def is_scalar(x): + return isinstance(x, _SCALAR_TYPES) + + +def is_scalar_or_symbolic(x): + return isinstance(x, _SCALAR_AND_SYMBOLIC_TYPES) + + +def _dtype_for_scalar(py_type): + return { + bool: torch.bool, + torch.SymBool: torch.bool, + int: torch.int64, + torch.SymInt: torch.int64, + float: torch.float64, + torch.SymFloat: torch.float64, + complex: torch.complex128, + }[py_type] + + +def _dtype_for_scalar_or_tensor(x): + return x.dtype if isinstance(x, torch.Tensor) else _dtype_for_scalar(type(x)) + + +def is_float_or_fp_tensor(x): + return _dtype_for_scalar_or_tensor(x).is_floating_point + + +def is_complex_or_complex_tensor(x): + return _dtype_for_scalar_or_tensor(x).is_complex + + +def _category(dtype): + return { + torch.bool: 0, + torch.SymBool: 0, + # int + torch.uint8: 1, + torch.int8: 1, + torch.int16: 1, + torch.int32: 1, + torch.int64: 1, + torch.SymInt: 1, + # float + torch.float16: 2, + torch.float32: 2, + torch.float64: 2, + torch.SymFloat: 2, + # complex + torch.complex64: 3, + torch.complex128: 3, + }[dtype] + + +def nep50_to_tensors(x1, x2, handle_weaks, function_name): + """If either of inputs is a python scalar, type-promote with NEP 50.""" + + def to_tensor(scalar, dtype=None): + if dtype is None: + dtype = _dtype_for_scalar(type(scalar)) + dtype = get_default_dtype_for(dtype) + return torch.as_tensor(scalar, dtype=dtype) + + x1_is_weak = not isinstance(x1, torch.Tensor) + x2_is_weak = not isinstance(x2, torch.Tensor) + if not handle_weaks or (x1_is_weak and x2_is_weak): + x1 = to_tensor(x1) if x1_is_weak else x1 + x2 = to_tensor(x2) if x2_is_weak else x2 + return x1, x2 + + # scalar tensor: NEP 50 + assert x1_is_weak != x2_is_weak + + weak, not_weak = (x1, x2) if x1_is_weak else (x2, x1) + + # find the dtype for the weak's type + weak_dtype = _dtype_for_scalar(type(weak)) + + cat_weak = _category(weak_dtype) + cat_not_weak = _category(not_weak.dtype) + + dt = not_weak.dtype if cat_weak <= cat_not_weak else None + + # special-case complex + float32 + if weak_dtype.is_complex and not_weak.dtype == torch.float32: + dt = torch.complex64 + + # detect overflows: in PyTorch, uint8(-1) wraps around to 255, + # while NEP50 mandates an exception. + # + # Note that we only check if each element of the binop overflows, + # not the result. Consider, e.g. `uint8(100) + 200`. Operands are OK + # in uint8, but the result overflows and wrap around 255. + # Numpy emits a RuntimeWarning, PyTorch does not, and we do not either. + if cat_weak == 1 and cat_not_weak == 1: + # integers + iinfo = torch.iinfo(not_weak.dtype) + if not (iinfo.min <= weak <= iinfo.max): + raise OverflowError( + f"Python integer {weak} out of bounds for {not_weak.dtype}" + ) + if weak_dtype != dt or function_name in _NEP50_FUNCS_TENSOR_ONLY: + # finally, can make `weak` into a 0D tensor, if both parameters are required to be tensor. + weak = to_tensor(weak, dt) + + return (weak, not_weak) if x1_is_weak else (not_weak, weak) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_funcs.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_funcs.py new file mode 100644 index 0000000000000000000000000000000000000000..c0341c4588ed5359ff68a90c8c53e667eedeb12d --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_funcs.py @@ -0,0 +1,76 @@ +# mypy: ignore-errors + +import inspect +import itertools + +from . import _funcs_impl, _reductions_impl +from ._normalizations import normalizer + + +# _funcs_impl.py contains functions which mimic NumPy's eponymous equivalents, +# and consume/return PyTorch tensors/dtypes. +# They are also type annotated. +# Pull these functions from _funcs_impl and decorate them with @normalizer, which +# - Converts any input `np.ndarray`, `torch._numpy.ndarray`, list of lists, Python scalars, etc into a `torch.Tensor`. +# - Maps NumPy dtypes to PyTorch dtypes +# - If the input to the `axis` kwarg is an ndarray, it maps it into a tuple +# - Implements the semantics for the `out=` arg +# - Wraps back the outputs into `torch._numpy.ndarrays` + + +def _public_functions(mod): + def is_public_function(f): + return inspect.isfunction(f) and not f.__name__.startswith("_") + + return inspect.getmembers(mod, is_public_function) + + +# We fill in __all__ in the loop below +__all__ = [] + +# decorate implementer functions with argument normalizers and export to the top namespace +for name, func in itertools.chain( + _public_functions(_funcs_impl), _public_functions(_reductions_impl) +): + if name in ["percentile", "quantile", "median"]: + decorated = normalizer(func, promote_scalar_result=True) + elif name == "einsum": + # normalized manually + decorated = func + else: + decorated = normalizer(func) + + decorated.__qualname__ = name + decorated.__name__ = name + vars()[name] = decorated + __all__.append(name) + + +""" +Vendored objects from numpy.lib.index_tricks +""" + + +class IndexExpression: + """ + Written by Konrad Hinsen + last revision: 1999-7-23 + + Cosmetic changes by T. Oliphant 2001 + """ + + def __init__(self, maketuple): + self.maketuple = maketuple + + def __getitem__(self, item): + if self.maketuple and not isinstance(item, tuple): + return (item,) + else: + return item + + +index_exp = IndexExpression(maketuple=True) +s_ = IndexExpression(maketuple=False) + + +__all__ += ["index_exp", "s_"] diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_funcs_impl.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_funcs_impl.py new file mode 100644 index 0000000000000000000000000000000000000000..94d593684a19e54a9437e92f09c77a79273d7eb6 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_funcs_impl.py @@ -0,0 +1,2056 @@ +# mypy: ignore-errors + +"""A thin pytorch / numpy compat layer. + +Things imported from here have numpy-compatible signatures but operate on +pytorch tensors. +""" +# Contents of this module ends up in the main namespace via _funcs.py +# where type annotations are used in conjunction with the @normalizer decorator. +from __future__ import annotations + +import builtins +import itertools +import operator +from typing import Optional, Sequence, TYPE_CHECKING + +import torch + +from . import _dtypes_impl, _util + + +if TYPE_CHECKING: + from ._normalizations import ( + ArrayLike, + ArrayLikeOrScalar, + CastingModes, + DTypeLike, + NDArray, + NotImplementedType, + OutArray, + ) + + +def copy( + a: ArrayLike, order: NotImplementedType = "K", subok: NotImplementedType = False +): + return a.clone() + + +def copyto( + dst: NDArray, + src: ArrayLike, + casting: Optional[CastingModes] = "same_kind", + where: NotImplementedType = None, +): + (src,) = _util.typecast_tensors((src,), dst.dtype, casting=casting) + dst.copy_(src) + + +def atleast_1d(*arys: ArrayLike): + res = torch.atleast_1d(*arys) + if isinstance(res, tuple): + return list(res) + else: + return res + + +def atleast_2d(*arys: ArrayLike): + res = torch.atleast_2d(*arys) + if isinstance(res, tuple): + return list(res) + else: + return res + + +def atleast_3d(*arys: ArrayLike): + res = torch.atleast_3d(*arys) + if isinstance(res, tuple): + return list(res) + else: + return res + + +def _concat_check(tup, dtype, out): + if tup == (): + raise ValueError("need at least one array to concatenate") + + """Check inputs in concatenate et al.""" + if out is not None and dtype is not None: + # mimic numpy + raise TypeError( + "concatenate() only takes `out` or `dtype` as an " + "argument, but both were provided." + ) + + +def _concat_cast_helper(tensors, out=None, dtype=None, casting="same_kind"): + """Figure out dtypes, cast if necessary.""" + + if out is not None or dtype is not None: + # figure out the type of the inputs and outputs + out_dtype = out.dtype.torch_dtype if dtype is None else dtype + else: + out_dtype = _dtypes_impl.result_type_impl(*tensors) + + # cast input arrays if necessary; do not broadcast them agains `out` + tensors = _util.typecast_tensors(tensors, out_dtype, casting) + + return tensors + + +def _concatenate( + tensors, axis=0, out=None, dtype=None, casting: Optional[CastingModes] = "same_kind" +): + # pure torch implementation, used below and in cov/corrcoef below + tensors, axis = _util.axis_none_flatten(*tensors, axis=axis) + tensors = _concat_cast_helper(tensors, out, dtype, casting) + return torch.cat(tensors, axis) + + +def concatenate( + ar_tuple: Sequence[ArrayLike], + axis=0, + out: Optional[OutArray] = None, + dtype: Optional[DTypeLike] = None, + casting: Optional[CastingModes] = "same_kind", +): + _concat_check(ar_tuple, dtype, out=out) + result = _concatenate(ar_tuple, axis=axis, out=out, dtype=dtype, casting=casting) + return result + + +def vstack( + tup: Sequence[ArrayLike], + *, + dtype: Optional[DTypeLike] = None, + casting: Optional[CastingModes] = "same_kind", +): + _concat_check(tup, dtype, out=None) + tensors = _concat_cast_helper(tup, dtype=dtype, casting=casting) + return torch.vstack(tensors) + + +row_stack = vstack + + +def hstack( + tup: Sequence[ArrayLike], + *, + dtype: Optional[DTypeLike] = None, + casting: Optional[CastingModes] = "same_kind", +): + _concat_check(tup, dtype, out=None) + tensors = _concat_cast_helper(tup, dtype=dtype, casting=casting) + return torch.hstack(tensors) + + +def dstack( + tup: Sequence[ArrayLike], + *, + dtype: Optional[DTypeLike] = None, + casting: Optional[CastingModes] = "same_kind", +): + # XXX: in numpy 1.24 dstack does not have dtype and casting keywords + # but {h,v}stack do. Hence add them here for consistency. + _concat_check(tup, dtype, out=None) + tensors = _concat_cast_helper(tup, dtype=dtype, casting=casting) + return torch.dstack(tensors) + + +def column_stack( + tup: Sequence[ArrayLike], + *, + dtype: Optional[DTypeLike] = None, + casting: Optional[CastingModes] = "same_kind", +): + # XXX: in numpy 1.24 column_stack does not have dtype and casting keywords + # but row_stack does. (because row_stack is an alias for vstack, really). + # Hence add these keywords here for consistency. + _concat_check(tup, dtype, out=None) + tensors = _concat_cast_helper(tup, dtype=dtype, casting=casting) + return torch.column_stack(tensors) + + +def stack( + arrays: Sequence[ArrayLike], + axis=0, + out: Optional[OutArray] = None, + *, + dtype: Optional[DTypeLike] = None, + casting: Optional[CastingModes] = "same_kind", +): + _concat_check(arrays, dtype, out=out) + + tensors = _concat_cast_helper(arrays, dtype=dtype, casting=casting) + result_ndim = tensors[0].ndim + 1 + axis = _util.normalize_axis_index(axis, result_ndim) + return torch.stack(tensors, axis=axis) + + +def append(arr: ArrayLike, values: ArrayLike, axis=None): + if axis is None: + if arr.ndim != 1: + arr = arr.flatten() + values = values.flatten() + axis = arr.ndim - 1 + return _concatenate((arr, values), axis=axis) + + +# ### split ### + + +def _split_helper(tensor, indices_or_sections, axis, strict=False): + if isinstance(indices_or_sections, int): + return _split_helper_int(tensor, indices_or_sections, axis, strict) + elif isinstance(indices_or_sections, (list, tuple)): + # NB: drop split=..., it only applies to split_helper_int + return _split_helper_list(tensor, list(indices_or_sections), axis) + else: + raise TypeError("split_helper: ", type(indices_or_sections)) + + +def _split_helper_int(tensor, indices_or_sections, axis, strict=False): + if not isinstance(indices_or_sections, int): + raise NotImplementedError("split: indices_or_sections") + + axis = _util.normalize_axis_index(axis, tensor.ndim) + + # numpy: l%n chunks of size (l//n + 1), the rest are sized l//n + l, n = tensor.shape[axis], indices_or_sections + + if n <= 0: + raise ValueError + + if l % n == 0: + num, sz = n, l // n + lst = [sz] * num + else: + if strict: + raise ValueError("array split does not result in an equal division") + + num, sz = l % n, l // n + 1 + lst = [sz] * num + + lst += [sz - 1] * (n - num) + + return torch.split(tensor, lst, axis) + + +def _split_helper_list(tensor, indices_or_sections, axis): + if not isinstance(indices_or_sections, list): + raise NotImplementedError("split: indices_or_sections: list") + # numpy expects indices, while torch expects lengths of sections + # also, numpy appends zero-size arrays for indices above the shape[axis] + lst = [x for x in indices_or_sections if x <= tensor.shape[axis]] + num_extra = len(indices_or_sections) - len(lst) + + lst.append(tensor.shape[axis]) + lst = [ + lst[0], + ] + [a - b for a, b in zip(lst[1:], lst[:-1])] + lst += [0] * num_extra + + return torch.split(tensor, lst, axis) + + +def array_split(ary: ArrayLike, indices_or_sections, axis=0): + return _split_helper(ary, indices_or_sections, axis) + + +def split(ary: ArrayLike, indices_or_sections, axis=0): + return _split_helper(ary, indices_or_sections, axis, strict=True) + + +def hsplit(ary: ArrayLike, indices_or_sections): + if ary.ndim == 0: + raise ValueError("hsplit only works on arrays of 1 or more dimensions") + axis = 1 if ary.ndim > 1 else 0 + return _split_helper(ary, indices_or_sections, axis, strict=True) + + +def vsplit(ary: ArrayLike, indices_or_sections): + if ary.ndim < 2: + raise ValueError("vsplit only works on arrays of 2 or more dimensions") + return _split_helper(ary, indices_or_sections, 0, strict=True) + + +def dsplit(ary: ArrayLike, indices_or_sections): + if ary.ndim < 3: + raise ValueError("dsplit only works on arrays of 3 or more dimensions") + return _split_helper(ary, indices_or_sections, 2, strict=True) + + +def kron(a: ArrayLike, b: ArrayLike): + return torch.kron(a, b) + + +def vander(x: ArrayLike, N=None, increasing=False): + return torch.vander(x, N, increasing) + + +# ### linspace, geomspace, logspace and arange ### + + +def linspace( + start: ArrayLike, + stop: ArrayLike, + num=50, + endpoint=True, + retstep=False, + dtype: Optional[DTypeLike] = None, + axis=0, +): + if axis != 0 or retstep or not endpoint: + raise NotImplementedError + if dtype is None: + dtype = _dtypes_impl.default_dtypes().float_dtype + # XXX: raises TypeError if start or stop are not scalars + return torch.linspace(start, stop, num, dtype=dtype) + + +def geomspace( + start: ArrayLike, + stop: ArrayLike, + num=50, + endpoint=True, + dtype: Optional[DTypeLike] = None, + axis=0, +): + if axis != 0 or not endpoint: + raise NotImplementedError + base = torch.pow(stop / start, 1.0 / (num - 1)) + logbase = torch.log(base) + return torch.logspace( + torch.log(start) / logbase, + torch.log(stop) / logbase, + num, + base=base, + ) + + +def logspace( + start, + stop, + num=50, + endpoint=True, + base=10.0, + dtype: Optional[DTypeLike] = None, + axis=0, +): + if axis != 0 or not endpoint: + raise NotImplementedError + return torch.logspace(start, stop, num, base=base, dtype=dtype) + + +def arange( + start: Optional[ArrayLikeOrScalar] = None, + stop: Optional[ArrayLikeOrScalar] = None, + step: Optional[ArrayLikeOrScalar] = 1, + dtype: Optional[DTypeLike] = None, + *, + like: NotImplementedType = None, +): + if step == 0: + raise ZeroDivisionError + if stop is None and start is None: + raise TypeError + if stop is None: + # XXX: this breaks if start is passed as a kwarg: + # arange(start=4) should raise (no stop) but doesn't + start, stop = 0, start + if start is None: + start = 0 + + # the dtype of the result + if dtype is None: + dtype = ( + _dtypes_impl.default_dtypes().float_dtype + if any(_dtypes_impl.is_float_or_fp_tensor(x) for x in (start, stop, step)) + else _dtypes_impl.default_dtypes().int_dtype + ) + work_dtype = torch.float64 if dtype.is_complex else dtype + + # RuntimeError: "lt_cpu" not implemented for 'ComplexFloat'. Fall back to eager. + if any(_dtypes_impl.is_complex_or_complex_tensor(x) for x in (start, stop, step)): + raise NotImplementedError + + if (step > 0 and start > stop) or (step < 0 and start < stop): + # empty range + return torch.empty(0, dtype=dtype) + + result = torch.arange(start, stop, step, dtype=work_dtype) + result = _util.cast_if_needed(result, dtype) + return result + + +# ### zeros/ones/empty/full ### + + +def empty( + shape, + dtype: Optional[DTypeLike] = None, + order: NotImplementedType = "C", + *, + like: NotImplementedType = None, +): + if dtype is None: + dtype = _dtypes_impl.default_dtypes().float_dtype + return torch.empty(shape, dtype=dtype) + + +# NB: *_like functions deliberately deviate from numpy: it has subok=True +# as the default; we set subok=False and raise on anything else. + + +def empty_like( + prototype: ArrayLike, + dtype: Optional[DTypeLike] = None, + order: NotImplementedType = "K", + subok: NotImplementedType = False, + shape=None, +): + result = torch.empty_like(prototype, dtype=dtype) + if shape is not None: + result = result.reshape(shape) + return result + + +def full( + shape, + fill_value: ArrayLike, + dtype: Optional[DTypeLike] = None, + order: NotImplementedType = "C", + *, + like: NotImplementedType = None, +): + if isinstance(shape, int): + shape = (shape,) + if dtype is None: + dtype = fill_value.dtype + if not isinstance(shape, (tuple, list)): + shape = (shape,) + return torch.full(shape, fill_value, dtype=dtype) + + +def full_like( + a: ArrayLike, + fill_value, + dtype: Optional[DTypeLike] = None, + order: NotImplementedType = "K", + subok: NotImplementedType = False, + shape=None, +): + # XXX: fill_value broadcasts + result = torch.full_like(a, fill_value, dtype=dtype) + if shape is not None: + result = result.reshape(shape) + return result + + +def ones( + shape, + dtype: Optional[DTypeLike] = None, + order: NotImplementedType = "C", + *, + like: NotImplementedType = None, +): + if dtype is None: + dtype = _dtypes_impl.default_dtypes().float_dtype + return torch.ones(shape, dtype=dtype) + + +def ones_like( + a: ArrayLike, + dtype: Optional[DTypeLike] = None, + order: NotImplementedType = "K", + subok: NotImplementedType = False, + shape=None, +): + result = torch.ones_like(a, dtype=dtype) + if shape is not None: + result = result.reshape(shape) + return result + + +def zeros( + shape, + dtype: Optional[DTypeLike] = None, + order: NotImplementedType = "C", + *, + like: NotImplementedType = None, +): + if dtype is None: + dtype = _dtypes_impl.default_dtypes().float_dtype + return torch.zeros(shape, dtype=dtype) + + +def zeros_like( + a: ArrayLike, + dtype: Optional[DTypeLike] = None, + order: NotImplementedType = "K", + subok: NotImplementedType = False, + shape=None, +): + result = torch.zeros_like(a, dtype=dtype) + if shape is not None: + result = result.reshape(shape) + return result + + +# ### cov & corrcoef ### + + +def _xy_helper_corrcoef(x_tensor, y_tensor=None, rowvar=True): + """Prepare inputs for cov and corrcoef.""" + + # https://github.com/numpy/numpy/blob/v1.24.0/numpy/lib/function_base.py#L2636 + if y_tensor is not None: + # make sure x and y are at least 2D + ndim_extra = 2 - x_tensor.ndim + if ndim_extra > 0: + x_tensor = x_tensor.view((1,) * ndim_extra + x_tensor.shape) + if not rowvar and x_tensor.shape[0] != 1: + x_tensor = x_tensor.mT + x_tensor = x_tensor.clone() + + ndim_extra = 2 - y_tensor.ndim + if ndim_extra > 0: + y_tensor = y_tensor.view((1,) * ndim_extra + y_tensor.shape) + if not rowvar and y_tensor.shape[0] != 1: + y_tensor = y_tensor.mT + y_tensor = y_tensor.clone() + + x_tensor = _concatenate((x_tensor, y_tensor), axis=0) + + return x_tensor + + +def corrcoef( + x: ArrayLike, + y: Optional[ArrayLike] = None, + rowvar=True, + bias=None, + ddof=None, + *, + dtype: Optional[DTypeLike] = None, +): + if bias is not None or ddof is not None: + # deprecated in NumPy + raise NotImplementedError + xy_tensor = _xy_helper_corrcoef(x, y, rowvar) + + is_half = (xy_tensor.dtype == torch.float16) and xy_tensor.is_cpu + if is_half: + # work around torch's "addmm_impl_cpu_" not implemented for 'Half'" + dtype = torch.float32 + + xy_tensor = _util.cast_if_needed(xy_tensor, dtype) + result = torch.corrcoef(xy_tensor) + + if is_half: + result = result.to(torch.float16) + + return result + + +def cov( + m: ArrayLike, + y: Optional[ArrayLike] = None, + rowvar=True, + bias=False, + ddof=None, + fweights: Optional[ArrayLike] = None, + aweights: Optional[ArrayLike] = None, + *, + dtype: Optional[DTypeLike] = None, +): + m = _xy_helper_corrcoef(m, y, rowvar) + + if ddof is None: + ddof = 1 if bias == 0 else 0 + + is_half = (m.dtype == torch.float16) and m.is_cpu + if is_half: + # work around torch's "addmm_impl_cpu_" not implemented for 'Half'" + dtype = torch.float32 + + m = _util.cast_if_needed(m, dtype) + result = torch.cov(m, correction=ddof, aweights=aweights, fweights=fweights) + + if is_half: + result = result.to(torch.float16) + + return result + + +def _conv_corr_impl(a, v, mode): + dt = _dtypes_impl.result_type_impl(a, v) + a = _util.cast_if_needed(a, dt) + v = _util.cast_if_needed(v, dt) + + padding = v.shape[0] - 1 if mode == "full" else mode + + if padding == "same" and v.shape[0] % 2 == 0: + # UserWarning: Using padding='same' with even kernel lengths and odd + # dilation may require a zero-padded copy of the input be created + # (Triggered internally at pytorch/aten/src/ATen/native/Convolution.cpp:1010.) + raise NotImplementedError("mode='same' and even-length weights") + + # NumPy only accepts 1D arrays; PyTorch requires 2D inputs and 3D weights + aa = a[None, :] + vv = v[None, None, :] + + result = torch.nn.functional.conv1d(aa, vv, padding=padding) + + # torch returns a 2D result, numpy returns a 1D array + return result[0, :] + + +def convolve(a: ArrayLike, v: ArrayLike, mode="full"): + # NumPy: if v is longer than a, the arrays are swapped before computation + if a.shape[0] < v.shape[0]: + a, v = v, a + + # flip the weights since numpy does and torch does not + v = torch.flip(v, (0,)) + + return _conv_corr_impl(a, v, mode) + + +def correlate(a: ArrayLike, v: ArrayLike, mode="valid"): + v = torch.conj_physical(v) + return _conv_corr_impl(a, v, mode) + + +# ### logic & element selection ### + + +def bincount(x: ArrayLike, /, weights: Optional[ArrayLike] = None, minlength=0): + if x.numel() == 0: + # edge case allowed by numpy + x = x.new_empty(0, dtype=int) + + int_dtype = _dtypes_impl.default_dtypes().int_dtype + (x,) = _util.typecast_tensors((x,), int_dtype, casting="safe") + + return torch.bincount(x, weights, minlength) + + +def where( + condition: ArrayLike, + x: Optional[ArrayLikeOrScalar] = None, + y: Optional[ArrayLikeOrScalar] = None, + /, +): + if (x is None) != (y is None): + raise ValueError("either both or neither of x and y should be given") + + if condition.dtype != torch.bool: + condition = condition.to(torch.bool) + + if x is None and y is None: + result = torch.where(condition) + else: + result = torch.where(condition, x, y) + return result + + +# ###### module-level queries of object properties + + +def ndim(a: ArrayLike): + return a.ndim + + +def shape(a: ArrayLike): + return tuple(a.shape) + + +def size(a: ArrayLike, axis=None): + if axis is None: + return a.numel() + else: + return a.shape[axis] + + +# ###### shape manipulations and indexing + + +def expand_dims(a: ArrayLike, axis): + shape = _util.expand_shape(a.shape, axis) + return a.view(shape) # never copies + + +def flip(m: ArrayLike, axis=None): + # XXX: semantic difference: np.flip returns a view, torch.flip copies + if axis is None: + axis = tuple(range(m.ndim)) + else: + axis = _util.normalize_axis_tuple(axis, m.ndim) + return torch.flip(m, axis) + + +def flipud(m: ArrayLike): + return torch.flipud(m) + + +def fliplr(m: ArrayLike): + return torch.fliplr(m) + + +def rot90(m: ArrayLike, k=1, axes=(0, 1)): + axes = _util.normalize_axis_tuple(axes, m.ndim) + return torch.rot90(m, k, axes) + + +# ### broadcasting and indices ### + + +def broadcast_to(array: ArrayLike, shape, subok: NotImplementedType = False): + return torch.broadcast_to(array, size=shape) + + +# This is a function from tuples to tuples, so we just reuse it +from torch import broadcast_shapes + + +def broadcast_arrays(*args: ArrayLike, subok: NotImplementedType = False): + return torch.broadcast_tensors(*args) + + +def meshgrid(*xi: ArrayLike, copy=True, sparse=False, indexing="xy"): + ndim = len(xi) + + if indexing not in ["xy", "ij"]: + raise ValueError("Valid values for `indexing` are 'xy' and 'ij'.") + + s0 = (1,) * ndim + output = [x.reshape(s0[:i] + (-1,) + s0[i + 1 :]) for i, x in enumerate(xi)] + + if indexing == "xy" and ndim > 1: + # switch first and second axis + output[0] = output[0].reshape((1, -1) + s0[2:]) + output[1] = output[1].reshape((-1, 1) + s0[2:]) + + if not sparse: + # Return the full N-D matrix (not only the 1-D vector) + output = torch.broadcast_tensors(*output) + + if copy: + output = [x.clone() for x in output] + + return list(output) # match numpy, return a list + + +def indices(dimensions, dtype: Optional[DTypeLike] = int, sparse=False): + # https://github.com/numpy/numpy/blob/v1.24.0/numpy/core/numeric.py#L1691-L1791 + dimensions = tuple(dimensions) + N = len(dimensions) + shape = (1,) * N + if sparse: + res = () + else: + res = torch.empty((N,) + dimensions, dtype=dtype) + for i, dim in enumerate(dimensions): + idx = torch.arange(dim, dtype=dtype).reshape( + shape[:i] + (dim,) + shape[i + 1 :] + ) + if sparse: + res = res + (idx,) + else: + res[i] = idx + return res + + +# ### tri*-something ### + + +def tril(m: ArrayLike, k=0): + return torch.tril(m, k) + + +def triu(m: ArrayLike, k=0): + return torch.triu(m, k) + + +def tril_indices(n, k=0, m=None): + if m is None: + m = n + return torch.tril_indices(n, m, offset=k) + + +def triu_indices(n, k=0, m=None): + if m is None: + m = n + return torch.triu_indices(n, m, offset=k) + + +def tril_indices_from(arr: ArrayLike, k=0): + if arr.ndim != 2: + raise ValueError("input array must be 2-d") + # Return a tensor rather than a tuple to avoid a graphbreak + return torch.tril_indices(arr.shape[0], arr.shape[1], offset=k) + + +def triu_indices_from(arr: ArrayLike, k=0): + if arr.ndim != 2: + raise ValueError("input array must be 2-d") + # Return a tensor rather than a tuple to avoid a graphbreak + return torch.triu_indices(arr.shape[0], arr.shape[1], offset=k) + + +def tri( + N, + M=None, + k=0, + dtype: Optional[DTypeLike] = None, + *, + like: NotImplementedType = None, +): + if M is None: + M = N + tensor = torch.ones((N, M), dtype=dtype) + return torch.tril(tensor, diagonal=k) + + +# ### equality, equivalence, allclose ### + + +def isclose(a: ArrayLike, b: ArrayLike, rtol=1.0e-5, atol=1.0e-8, equal_nan=False): + dtype = _dtypes_impl.result_type_impl(a, b) + a = _util.cast_if_needed(a, dtype) + b = _util.cast_if_needed(b, dtype) + return torch.isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan) + + +def allclose(a: ArrayLike, b: ArrayLike, rtol=1e-05, atol=1e-08, equal_nan=False): + dtype = _dtypes_impl.result_type_impl(a, b) + a = _util.cast_if_needed(a, dtype) + b = _util.cast_if_needed(b, dtype) + return torch.allclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan) + + +def _tensor_equal(a1, a2, equal_nan=False): + # Implementation of array_equal/array_equiv. + if a1.shape != a2.shape: + return False + cond = a1 == a2 + if equal_nan: + cond = cond | (torch.isnan(a1) & torch.isnan(a2)) + return cond.all().item() + + +def array_equal(a1: ArrayLike, a2: ArrayLike, equal_nan=False): + return _tensor_equal(a1, a2, equal_nan=equal_nan) + + +def array_equiv(a1: ArrayLike, a2: ArrayLike): + # *almost* the same as array_equal: _equiv tries to broadcast, _equal does not + try: + a1_t, a2_t = torch.broadcast_tensors(a1, a2) + except RuntimeError: + # failed to broadcast => not equivalent + return False + return _tensor_equal(a1_t, a2_t) + + +def nan_to_num( + x: ArrayLike, copy: NotImplementedType = True, nan=0.0, posinf=None, neginf=None +): + # work around RuntimeError: "nan_to_num" not implemented for 'ComplexDouble' + if x.is_complex(): + re = torch.nan_to_num(x.real, nan=nan, posinf=posinf, neginf=neginf) + im = torch.nan_to_num(x.imag, nan=nan, posinf=posinf, neginf=neginf) + return re + 1j * im + else: + return torch.nan_to_num(x, nan=nan, posinf=posinf, neginf=neginf) + + +# ### put/take_along_axis ### + + +def take( + a: ArrayLike, + indices: ArrayLike, + axis=None, + out: Optional[OutArray] = None, + mode: NotImplementedType = "raise", +): + (a,), axis = _util.axis_none_flatten(a, axis=axis) + axis = _util.normalize_axis_index(axis, a.ndim) + idx = (slice(None),) * axis + (indices, ...) + result = a[idx] + return result + + +def take_along_axis(arr: ArrayLike, indices: ArrayLike, axis): + (arr,), axis = _util.axis_none_flatten(arr, axis=axis) + axis = _util.normalize_axis_index(axis, arr.ndim) + return torch.take_along_dim(arr, indices, axis) + + +def put( + a: NDArray, + indices: ArrayLike, + values: ArrayLike, + mode: NotImplementedType = "raise", +): + v = values.type(a.dtype) + # If indices is larger than v, expand v to at least the size of indices. Any + # unnecessary trailing elements are then trimmed. + if indices.numel() > v.numel(): + ratio = (indices.numel() + v.numel() - 1) // v.numel() + v = v.unsqueeze(0).expand((ratio,) + v.shape) + # Trim unnecessary elements, regardless if v was expanded or not. Note + # np.put() trims v to match indices by default too. + if indices.numel() < v.numel(): + v = v.flatten() + v = v[: indices.numel()] + a.put_(indices, v) + return None + + +def put_along_axis(arr: ArrayLike, indices: ArrayLike, values: ArrayLike, axis): + (arr,), axis = _util.axis_none_flatten(arr, axis=axis) + axis = _util.normalize_axis_index(axis, arr.ndim) + + indices, values = torch.broadcast_tensors(indices, values) + values = _util.cast_if_needed(values, arr.dtype) + result = torch.scatter(arr, axis, indices, values) + arr.copy_(result.reshape(arr.shape)) + return None + + +def choose( + a: ArrayLike, + choices: Sequence[ArrayLike], + out: Optional[OutArray] = None, + mode: NotImplementedType = "raise", +): + # First, broadcast elements of `choices` + choices = torch.stack(torch.broadcast_tensors(*choices)) + + # Use an analog of `gather(choices, 0, a)` which broadcasts `choices` vs `a`: + # (taken from https://github.com/pytorch/pytorch/issues/9407#issuecomment-1427907939) + idx_list = [ + torch.arange(dim).view((1,) * i + (dim,) + (1,) * (choices.ndim - i - 1)) + for i, dim in enumerate(choices.shape) + ] + + idx_list[0] = a + return choices[idx_list].squeeze(0) + + +# ### unique et al. ### + + +def unique( + ar: ArrayLike, + return_index: NotImplementedType = False, + return_inverse=False, + return_counts=False, + axis=None, + *, + equal_nan: NotImplementedType = True, +): + (ar,), axis = _util.axis_none_flatten(ar, axis=axis) + axis = _util.normalize_axis_index(axis, ar.ndim) + + result = torch.unique( + ar, return_inverse=return_inverse, return_counts=return_counts, dim=axis + ) + + return result + + +def nonzero(a: ArrayLike): + return torch.nonzero(a, as_tuple=True) + + +def argwhere(a: ArrayLike): + return torch.argwhere(a) + + +def flatnonzero(a: ArrayLike): + return torch.flatten(a).nonzero(as_tuple=True)[0] + + +def clip( + a: ArrayLike, + min: Optional[ArrayLike] = None, + max: Optional[ArrayLike] = None, + out: Optional[OutArray] = None, +): + return torch.clamp(a, min, max) + + +def repeat(a: ArrayLike, repeats: ArrayLikeOrScalar, axis=None): + return torch.repeat_interleave(a, repeats, axis) + + +def tile(A: ArrayLike, reps): + if isinstance(reps, int): + reps = (reps,) + return torch.tile(A, reps) + + +def resize(a: ArrayLike, new_shape=None): + # implementation vendored from + # https://github.com/numpy/numpy/blob/v1.24.0/numpy/core/fromnumeric.py#L1420-L1497 + if new_shape is None: + return a + + if isinstance(new_shape, int): + new_shape = (new_shape,) + + a = a.flatten() + + new_size = 1 + for dim_length in new_shape: + new_size *= dim_length + if dim_length < 0: + raise ValueError("all elements of `new_shape` must be non-negative") + + if a.numel() == 0 or new_size == 0: + # First case must zero fill. The second would have repeats == 0. + return torch.zeros(new_shape, dtype=a.dtype) + + repeats = -(-new_size // a.numel()) # ceil division + a = concatenate((a,) * repeats)[:new_size] + + return reshape(a, new_shape) + + +# ### diag et al. ### + + +def diagonal(a: ArrayLike, offset=0, axis1=0, axis2=1): + axis1 = _util.normalize_axis_index(axis1, a.ndim) + axis2 = _util.normalize_axis_index(axis2, a.ndim) + return torch.diagonal(a, offset, axis1, axis2) + + +def trace( + a: ArrayLike, + offset=0, + axis1=0, + axis2=1, + dtype: Optional[DTypeLike] = None, + out: Optional[OutArray] = None, +): + result = torch.diagonal(a, offset, dim1=axis1, dim2=axis2).sum(-1, dtype=dtype) + return result + + +def eye( + N, + M=None, + k=0, + dtype: Optional[DTypeLike] = None, + order: NotImplementedType = "C", + *, + like: NotImplementedType = None, +): + if dtype is None: + dtype = _dtypes_impl.default_dtypes().float_dtype + if M is None: + M = N + z = torch.zeros(N, M, dtype=dtype) + z.diagonal(k).fill_(1) + return z + + +def identity(n, dtype: Optional[DTypeLike] = None, *, like: NotImplementedType = None): + return torch.eye(n, dtype=dtype) + + +def diag(v: ArrayLike, k=0): + return torch.diag(v, k) + + +def diagflat(v: ArrayLike, k=0): + return torch.diagflat(v, k) + + +def diag_indices(n, ndim=2): + idx = torch.arange(n) + return (idx,) * ndim + + +def diag_indices_from(arr: ArrayLike): + if not arr.ndim >= 2: + raise ValueError("input array must be at least 2-d") + # For more than d=2, the strided formula is only valid for arrays with + # all dimensions equal, so we check first. + s = arr.shape + if s[1:] != s[:-1]: + raise ValueError("All dimensions of input must be of equal length") + return diag_indices(s[0], arr.ndim) + + +def fill_diagonal(a: ArrayLike, val: ArrayLike, wrap=False): + if a.ndim < 2: + raise ValueError("array must be at least 2-d") + if val.numel() == 0 and not wrap: + a.fill_diagonal_(val) + return a + + if val.ndim == 0: + val = val.unsqueeze(0) + + # torch.Tensor.fill_diagonal_ only accepts scalars + # If the size of val is too large, then val is trimmed + if a.ndim == 2: + tall = a.shape[0] > a.shape[1] + # wrap does nothing for wide matrices... + if not wrap or not tall: + # Never wraps + diag = a.diagonal() + diag.copy_(val[: diag.numel()]) + else: + # wraps and tall... leaving one empty line between diagonals?! + max_, min_ = a.shape + idx = torch.arange(max_ - max_ // (min_ + 1)) + mod = idx % min_ + div = idx // min_ + a[(div * (min_ + 1) + mod, mod)] = val[: idx.numel()] + else: + idx = diag_indices_from(a) + # a.shape = (n, n, ..., n) + a[idx] = val[: a.shape[0]] + + return a + + +def vdot(a: ArrayLike, b: ArrayLike, /): + # 1. torch only accepts 1D arrays, numpy flattens + # 2. torch requires matching dtype, while numpy casts (?) + t_a, t_b = torch.atleast_1d(a, b) + if t_a.ndim > 1: + t_a = t_a.flatten() + if t_b.ndim > 1: + t_b = t_b.flatten() + + dtype = _dtypes_impl.result_type_impl(t_a, t_b) + is_half = dtype == torch.float16 and (t_a.is_cpu or t_b.is_cpu) + is_bool = dtype == torch.bool + + # work around torch's "dot" not implemented for 'Half', 'Bool' + if is_half: + dtype = torch.float32 + elif is_bool: + dtype = torch.uint8 + + t_a = _util.cast_if_needed(t_a, dtype) + t_b = _util.cast_if_needed(t_b, dtype) + + result = torch.vdot(t_a, t_b) + + if is_half: + result = result.to(torch.float16) + elif is_bool: + result = result.to(torch.bool) + + return result + + +def tensordot(a: ArrayLike, b: ArrayLike, axes=2): + if isinstance(axes, (list, tuple)): + axes = [[ax] if isinstance(ax, int) else ax for ax in axes] + + target_dtype = _dtypes_impl.result_type_impl(a, b) + a = _util.cast_if_needed(a, target_dtype) + b = _util.cast_if_needed(b, target_dtype) + + return torch.tensordot(a, b, dims=axes) + + +def dot(a: ArrayLike, b: ArrayLike, out: Optional[OutArray] = None): + dtype = _dtypes_impl.result_type_impl(a, b) + is_bool = dtype == torch.bool + if is_bool: + dtype = torch.uint8 + + a = _util.cast_if_needed(a, dtype) + b = _util.cast_if_needed(b, dtype) + + if a.ndim == 0 or b.ndim == 0: + result = a * b + else: + result = torch.matmul(a, b) + + if is_bool: + result = result.to(torch.bool) + + return result + + +def inner(a: ArrayLike, b: ArrayLike, /): + dtype = _dtypes_impl.result_type_impl(a, b) + is_half = dtype == torch.float16 and (a.is_cpu or b.is_cpu) + is_bool = dtype == torch.bool + + if is_half: + # work around torch's "addmm_impl_cpu_" not implemented for 'Half'" + dtype = torch.float32 + elif is_bool: + dtype = torch.uint8 + + a = _util.cast_if_needed(a, dtype) + b = _util.cast_if_needed(b, dtype) + + result = torch.inner(a, b) + + if is_half: + result = result.to(torch.float16) + elif is_bool: + result = result.to(torch.bool) + return result + + +def outer(a: ArrayLike, b: ArrayLike, out: Optional[OutArray] = None): + return torch.outer(a, b) + + +def cross(a: ArrayLike, b: ArrayLike, axisa=-1, axisb=-1, axisc=-1, axis=None): + # implementation vendored from + # https://github.com/numpy/numpy/blob/v1.24.0/numpy/core/numeric.py#L1486-L1685 + if axis is not None: + axisa, axisb, axisc = (axis,) * 3 + + # Check axisa and axisb are within bounds + axisa = _util.normalize_axis_index(axisa, a.ndim) + axisb = _util.normalize_axis_index(axisb, b.ndim) + + # Move working axis to the end of the shape + a = torch.moveaxis(a, axisa, -1) + b = torch.moveaxis(b, axisb, -1) + msg = "incompatible dimensions for cross product\n(dimension must be 2 or 3)" + if a.shape[-1] not in (2, 3) or b.shape[-1] not in (2, 3): + raise ValueError(msg) + + # Create the output array + shape = broadcast_shapes(a[..., 0].shape, b[..., 0].shape) + if a.shape[-1] == 3 or b.shape[-1] == 3: + shape += (3,) + # Check axisc is within bounds + axisc = _util.normalize_axis_index(axisc, len(shape)) + dtype = _dtypes_impl.result_type_impl(a, b) + cp = torch.empty(shape, dtype=dtype) + + # recast arrays as dtype + a = _util.cast_if_needed(a, dtype) + b = _util.cast_if_needed(b, dtype) + + # create local aliases for readability + a0 = a[..., 0] + a1 = a[..., 1] + if a.shape[-1] == 3: + a2 = a[..., 2] + b0 = b[..., 0] + b1 = b[..., 1] + if b.shape[-1] == 3: + b2 = b[..., 2] + if cp.ndim != 0 and cp.shape[-1] == 3: + cp0 = cp[..., 0] + cp1 = cp[..., 1] + cp2 = cp[..., 2] + + if a.shape[-1] == 2: + if b.shape[-1] == 2: + # a0 * b1 - a1 * b0 + cp[...] = a0 * b1 - a1 * b0 + return cp + else: + assert b.shape[-1] == 3 + # cp0 = a1 * b2 - 0 (a2 = 0) + # cp1 = 0 - a0 * b2 (a2 = 0) + # cp2 = a0 * b1 - a1 * b0 + cp0[...] = a1 * b2 + cp1[...] = -a0 * b2 + cp2[...] = a0 * b1 - a1 * b0 + else: + assert a.shape[-1] == 3 + if b.shape[-1] == 3: + cp0[...] = a1 * b2 - a2 * b1 + cp1[...] = a2 * b0 - a0 * b2 + cp2[...] = a0 * b1 - a1 * b0 + else: + assert b.shape[-1] == 2 + cp0[...] = -a2 * b1 + cp1[...] = a2 * b0 + cp2[...] = a0 * b1 - a1 * b0 + + return torch.moveaxis(cp, -1, axisc) + + +def einsum(*operands, out=None, dtype=None, order="K", casting="safe", optimize=False): + # Have to manually normalize *operands and **kwargs, following the NumPy signature + # We have a local import to avoid poluting the global space, as it will be then + # exported in funcs.py + from ._ndarray import ndarray + from ._normalizations import ( + maybe_copy_to, + normalize_array_like, + normalize_casting, + normalize_dtype, + wrap_tensors, + ) + + dtype = normalize_dtype(dtype) + casting = normalize_casting(casting) + if out is not None and not isinstance(out, ndarray): + raise TypeError("'out' must be an array") + if order != "K": + raise NotImplementedError("'order' parameter is not supported.") + + # parse arrays and normalize them + sublist_format = not isinstance(operands[0], str) + if sublist_format: + # op, str, op, str ... [sublistout] format: normalize every other argument + + # - if sublistout is not given, the length of operands is even, and we pick + # odd-numbered elements, which are arrays. + # - if sublistout is given, the length of operands is odd, we peel off + # the last one, and pick odd-numbered elements, which are arrays. + # Without [:-1], we would have picked sublistout, too. + array_operands = operands[:-1][::2] + else: + # ("ij->", arrays) format + subscripts, array_operands = operands[0], operands[1:] + + tensors = [normalize_array_like(op) for op in array_operands] + target_dtype = _dtypes_impl.result_type_impl(*tensors) if dtype is None else dtype + + # work around 'bmm' not implemented for 'Half' etc + is_half = target_dtype == torch.float16 and all(t.is_cpu for t in tensors) + if is_half: + target_dtype = torch.float32 + + is_short_int = target_dtype in [torch.uint8, torch.int8, torch.int16, torch.int32] + if is_short_int: + target_dtype = torch.int64 + + tensors = _util.typecast_tensors(tensors, target_dtype, casting) + + from torch.backends import opt_einsum + + try: + # set the global state to handle the optimize=... argument, restore on exit + if opt_einsum.is_available(): + old_strategy = torch.backends.opt_einsum.strategy + old_enabled = torch.backends.opt_einsum.enabled + + # torch.einsum calls opt_einsum.contract_path, which runs into + # https://github.com/dgasmith/opt_einsum/issues/219 + # for strategy={True, False} + if optimize is True: + optimize = "auto" + elif optimize is False: + torch.backends.opt_einsum.enabled = False + + torch.backends.opt_einsum.strategy = optimize + + if sublist_format: + # recombine operands + sublists = operands[1::2] + has_sublistout = len(operands) % 2 == 1 + if has_sublistout: + sublistout = operands[-1] + operands = list(itertools.chain.from_iterable(zip(tensors, sublists))) + if has_sublistout: + operands.append(sublistout) + + result = torch.einsum(*operands) + else: + result = torch.einsum(subscripts, *tensors) + + finally: + if opt_einsum.is_available(): + torch.backends.opt_einsum.strategy = old_strategy + torch.backends.opt_einsum.enabled = old_enabled + + result = maybe_copy_to(out, result) + return wrap_tensors(result) + + +# ### sort and partition ### + + +def _sort_helper(tensor, axis, kind, order): + if tensor.dtype.is_complex: + raise NotImplementedError(f"sorting {tensor.dtype} is not supported") + (tensor,), axis = _util.axis_none_flatten(tensor, axis=axis) + axis = _util.normalize_axis_index(axis, tensor.ndim) + + stable = kind == "stable" + + return tensor, axis, stable + + +def sort(a: ArrayLike, axis=-1, kind=None, order: NotImplementedType = None): + # `order` keyword arg is only relevant for structured dtypes; so not supported here. + a, axis, stable = _sort_helper(a, axis, kind, order) + result = torch.sort(a, dim=axis, stable=stable) + return result.values + + +def argsort(a: ArrayLike, axis=-1, kind=None, order: NotImplementedType = None): + a, axis, stable = _sort_helper(a, axis, kind, order) + return torch.argsort(a, dim=axis, stable=stable) + + +def searchsorted( + a: ArrayLike, v: ArrayLike, side="left", sorter: Optional[ArrayLike] = None +): + if a.dtype.is_complex: + raise NotImplementedError(f"searchsorted with dtype={a.dtype}") + + return torch.searchsorted(a, v, side=side, sorter=sorter) + + +# ### swap/move/roll axis ### + + +def moveaxis(a: ArrayLike, source, destination): + source = _util.normalize_axis_tuple(source, a.ndim, "source") + destination = _util.normalize_axis_tuple(destination, a.ndim, "destination") + return torch.moveaxis(a, source, destination) + + +def swapaxes(a: ArrayLike, axis1, axis2): + axis1 = _util.normalize_axis_index(axis1, a.ndim) + axis2 = _util.normalize_axis_index(axis2, a.ndim) + return torch.swapaxes(a, axis1, axis2) + + +def rollaxis(a: ArrayLike, axis, start=0): + # Straight vendor from: + # https://github.com/numpy/numpy/blob/v1.24.0/numpy/core/numeric.py#L1259 + # + # Also note this function in NumPy is mostly retained for backwards compat + # (https://stackoverflow.com/questions/29891583/reason-why-numpy-rollaxis-is-so-confusing) + # so let's not touch it unless hard pressed. + n = a.ndim + axis = _util.normalize_axis_index(axis, n) + if start < 0: + start += n + msg = "'%s' arg requires %d <= %s < %d, but %d was passed in" + if not (0 <= start < n + 1): + raise _util.AxisError(msg % ("start", -n, "start", n + 1, start)) + if axis < start: + # it's been removed + start -= 1 + if axis == start: + # numpy returns a view, here we try returning the tensor itself + # return tensor[...] + return a + axes = list(range(0, n)) + axes.remove(axis) + axes.insert(start, axis) + return a.view(axes) + + +def roll(a: ArrayLike, shift, axis=None): + if axis is not None: + axis = _util.normalize_axis_tuple(axis, a.ndim, allow_duplicate=True) + if not isinstance(shift, tuple): + shift = (shift,) * len(axis) + return torch.roll(a, shift, axis) + + +# ### shape manipulations ### + + +def squeeze(a: ArrayLike, axis=None): + if axis == (): + result = a + elif axis is None: + result = a.squeeze() + else: + if isinstance(axis, tuple): + result = a + for ax in axis: + result = a.squeeze(ax) + else: + result = a.squeeze(axis) + return result + + +def reshape(a: ArrayLike, newshape, order: NotImplementedType = "C"): + # if sh = (1, 2, 3), numpy allows both .reshape(sh) and .reshape(*sh) + newshape = newshape[0] if len(newshape) == 1 else newshape + return a.reshape(newshape) + + +# NB: cannot use torch.reshape(a, newshape) above, because of +# (Pdb) torch.reshape(torch.as_tensor([1]), 1) +# *** TypeError: reshape(): argument 'shape' (position 2) must be tuple of SymInts, not int + + +def transpose(a: ArrayLike, axes=None): + # numpy allows both .transpose(sh) and .transpose(*sh) + # also older code uses axes being a list + if axes in [(), None, (None,)]: + axes = tuple(reversed(range(a.ndim))) + elif len(axes) == 1: + axes = axes[0] + return a.permute(axes) + + +def ravel(a: ArrayLike, order: NotImplementedType = "C"): + return torch.flatten(a) + + +def diff( + a: ArrayLike, + n=1, + axis=-1, + prepend: Optional[ArrayLike] = None, + append: Optional[ArrayLike] = None, +): + axis = _util.normalize_axis_index(axis, a.ndim) + + if n < 0: + raise ValueError(f"order must be non-negative but got {n}") + + if n == 0: + # match numpy and return the input immediately + return a + + if prepend is not None: + shape = list(a.shape) + shape[axis] = prepend.shape[axis] if prepend.ndim > 0 else 1 + prepend = torch.broadcast_to(prepend, shape) + + if append is not None: + shape = list(a.shape) + shape[axis] = append.shape[axis] if append.ndim > 0 else 1 + append = torch.broadcast_to(append, shape) + + return torch.diff(a, n, axis=axis, prepend=prepend, append=append) + + +# ### math functions ### + + +def angle(z: ArrayLike, deg=False): + result = torch.angle(z) + if deg: + result = result * (180 / torch.pi) + return result + + +def sinc(x: ArrayLike): + return torch.sinc(x) + + +# NB: have to normalize *varargs manually +def gradient(f: ArrayLike, *varargs, axis=None, edge_order=1): + N = f.ndim # number of dimensions + + varargs = _util.ndarrays_to_tensors(varargs) + + if axis is None: + axes = tuple(range(N)) + else: + axes = _util.normalize_axis_tuple(axis, N) + + len_axes = len(axes) + n = len(varargs) + if n == 0: + # no spacing argument - use 1 in all axes + dx = [1.0] * len_axes + elif n == 1 and (_dtypes_impl.is_scalar(varargs[0]) or varargs[0].ndim == 0): + # single scalar or 0D tensor for all axes (np.ndim(varargs[0]) == 0) + dx = varargs * len_axes + elif n == len_axes: + # scalar or 1d array for each axis + dx = list(varargs) + for i, distances in enumerate(dx): + distances = torch.as_tensor(distances) + if distances.ndim == 0: + continue + elif distances.ndim != 1: + raise ValueError("distances must be either scalars or 1d") + if len(distances) != f.shape[axes[i]]: + raise ValueError( + "when 1d, distances must match " + "the length of the corresponding dimension" + ) + if not (distances.dtype.is_floating_point or distances.dtype.is_complex): + distances = distances.double() + + diffx = torch.diff(distances) + # if distances are constant reduce to the scalar case + # since it brings a consistent speedup + if (diffx == diffx[0]).all(): + diffx = diffx[0] + dx[i] = diffx + else: + raise TypeError("invalid number of arguments") + + if edge_order > 2: + raise ValueError("'edge_order' greater than 2 not supported") + + # use central differences on interior and one-sided differences on the + # endpoints. This preserves second order-accuracy over the full domain. + + outvals = [] + + # create slice objects --- initially all are [:, :, ..., :] + slice1 = [slice(None)] * N + slice2 = [slice(None)] * N + slice3 = [slice(None)] * N + slice4 = [slice(None)] * N + + otype = f.dtype + if _dtypes_impl.python_type_for_torch(otype) in (int, bool): + # Convert to floating point. + # First check if f is a numpy integer type; if so, convert f to float64 + # to avoid modular arithmetic when computing the changes in f. + f = f.double() + otype = torch.float64 + + for axis, ax_dx in zip(axes, dx): + if f.shape[axis] < edge_order + 1: + raise ValueError( + "Shape of array too small to calculate a numerical gradient, " + "at least (edge_order + 1) elements are required." + ) + # result allocation + out = torch.empty_like(f, dtype=otype) + + # spacing for the current axis (NB: np.ndim(ax_dx) == 0) + uniform_spacing = _dtypes_impl.is_scalar(ax_dx) or ax_dx.ndim == 0 + + # Numerical differentiation: 2nd order interior + slice1[axis] = slice(1, -1) + slice2[axis] = slice(None, -2) + slice3[axis] = slice(1, -1) + slice4[axis] = slice(2, None) + + if uniform_spacing: + out[tuple(slice1)] = (f[tuple(slice4)] - f[tuple(slice2)]) / (2.0 * ax_dx) + else: + dx1 = ax_dx[0:-1] + dx2 = ax_dx[1:] + a = -(dx2) / (dx1 * (dx1 + dx2)) + b = (dx2 - dx1) / (dx1 * dx2) + c = dx1 / (dx2 * (dx1 + dx2)) + # fix the shape for broadcasting + shape = [1] * N + shape[axis] = -1 + a = a.reshape(shape) + b = b.reshape(shape) + c = c.reshape(shape) + # 1D equivalent -- out[1:-1] = a * f[:-2] + b * f[1:-1] + c * f[2:] + out[tuple(slice1)] = ( + a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)] + ) + + # Numerical differentiation: 1st order edges + if edge_order == 1: + slice1[axis] = 0 + slice2[axis] = 1 + slice3[axis] = 0 + dx_0 = ax_dx if uniform_spacing else ax_dx[0] + # 1D equivalent -- out[0] = (f[1] - f[0]) / (x[1] - x[0]) + out[tuple(slice1)] = (f[tuple(slice2)] - f[tuple(slice3)]) / dx_0 + + slice1[axis] = -1 + slice2[axis] = -1 + slice3[axis] = -2 + dx_n = ax_dx if uniform_spacing else ax_dx[-1] + # 1D equivalent -- out[-1] = (f[-1] - f[-2]) / (x[-1] - x[-2]) + out[tuple(slice1)] = (f[tuple(slice2)] - f[tuple(slice3)]) / dx_n + + # Numerical differentiation: 2nd order edges + else: + slice1[axis] = 0 + slice2[axis] = 0 + slice3[axis] = 1 + slice4[axis] = 2 + if uniform_spacing: + a = -1.5 / ax_dx + b = 2.0 / ax_dx + c = -0.5 / ax_dx + else: + dx1 = ax_dx[0] + dx2 = ax_dx[1] + a = -(2.0 * dx1 + dx2) / (dx1 * (dx1 + dx2)) + b = (dx1 + dx2) / (dx1 * dx2) + c = -dx1 / (dx2 * (dx1 + dx2)) + # 1D equivalent -- out[0] = a * f[0] + b * f[1] + c * f[2] + out[tuple(slice1)] = ( + a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)] + ) + + slice1[axis] = -1 + slice2[axis] = -3 + slice3[axis] = -2 + slice4[axis] = -1 + if uniform_spacing: + a = 0.5 / ax_dx + b = -2.0 / ax_dx + c = 1.5 / ax_dx + else: + dx1 = ax_dx[-2] + dx2 = ax_dx[-1] + a = (dx2) / (dx1 * (dx1 + dx2)) + b = -(dx2 + dx1) / (dx1 * dx2) + c = (2.0 * dx2 + dx1) / (dx2 * (dx1 + dx2)) + # 1D equivalent -- out[-1] = a * f[-3] + b * f[-2] + c * f[-1] + out[tuple(slice1)] = ( + a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)] + ) + + outvals.append(out) + + # reset the slice object in this dimension to ":" + slice1[axis] = slice(None) + slice2[axis] = slice(None) + slice3[axis] = slice(None) + slice4[axis] = slice(None) + + if len_axes == 1: + return outvals[0] + else: + return outvals + + +# ### Type/shape etc queries ### + + +def round(a: ArrayLike, decimals=0, out: Optional[OutArray] = None): + if a.is_floating_point(): + result = torch.round(a, decimals=decimals) + elif a.is_complex(): + # RuntimeError: "round_cpu" not implemented for 'ComplexFloat' + result = torch.complex( + torch.round(a.real, decimals=decimals), + torch.round(a.imag, decimals=decimals), + ) + else: + # RuntimeError: "round_cpu" not implemented for 'int' + result = a + return result + + +around = round +round_ = round + + +def real_if_close(a: ArrayLike, tol=100): + if not torch.is_complex(a): + return a + if tol > 1: + # Undocumented in numpy: if tol < 1, it's an absolute tolerance! + # Otherwise, tol > 1 is relative tolerance, in units of the dtype epsilon + # https://github.com/numpy/numpy/blob/v1.24.0/numpy/lib/type_check.py#L577 + tol = tol * torch.finfo(a.dtype).eps + + mask = torch.abs(a.imag) < tol + return a.real if mask.all() else a + + +def real(a: ArrayLike): + return torch.real(a) + + +def imag(a: ArrayLike): + if a.is_complex(): + return a.imag + return torch.zeros_like(a) + + +def iscomplex(x: ArrayLike): + if torch.is_complex(x): + return x.imag != 0 + return torch.zeros_like(x, dtype=torch.bool) + + +def isreal(x: ArrayLike): + if torch.is_complex(x): + return x.imag == 0 + return torch.ones_like(x, dtype=torch.bool) + + +def iscomplexobj(x: ArrayLike): + return torch.is_complex(x) + + +def isrealobj(x: ArrayLike): + return not torch.is_complex(x) + + +def isneginf(x: ArrayLike, out: Optional[OutArray] = None): + return torch.isneginf(x) + + +def isposinf(x: ArrayLike, out: Optional[OutArray] = None): + return torch.isposinf(x) + + +def i0(x: ArrayLike): + return torch.special.i0(x) + + +def isscalar(a): + # We need to use normalize_array_like, but we don't want to export it in funcs.py + from ._normalizations import normalize_array_like + + try: + t = normalize_array_like(a) + return t.numel() == 1 + except Exception: + return False + + +# ### Filter windows ### + + +def hamming(M): + dtype = _dtypes_impl.default_dtypes().float_dtype + return torch.hamming_window(M, periodic=False, dtype=dtype) + + +def hanning(M): + dtype = _dtypes_impl.default_dtypes().float_dtype + return torch.hann_window(M, periodic=False, dtype=dtype) + + +def kaiser(M, beta): + dtype = _dtypes_impl.default_dtypes().float_dtype + return torch.kaiser_window(M, beta=beta, periodic=False, dtype=dtype) + + +def blackman(M): + dtype = _dtypes_impl.default_dtypes().float_dtype + return torch.blackman_window(M, periodic=False, dtype=dtype) + + +def bartlett(M): + dtype = _dtypes_impl.default_dtypes().float_dtype + return torch.bartlett_window(M, periodic=False, dtype=dtype) + + +# ### Dtype routines ### + +# vendored from https://github.com/numpy/numpy/blob/v1.24.0/numpy/lib/type_check.py#L666 + + +array_type = [ + [torch.float16, torch.float32, torch.float64], + [None, torch.complex64, torch.complex128], +] +array_precision = { + torch.float16: 0, + torch.float32: 1, + torch.float64: 2, + torch.complex64: 1, + torch.complex128: 2, +} + + +def common_type(*tensors: ArrayLike): + is_complex = False + precision = 0 + for a in tensors: + t = a.dtype + if iscomplexobj(a): + is_complex = True + if not (t.is_floating_point or t.is_complex): + p = 2 # array_precision[_nx.double] + else: + p = array_precision.get(t, None) + if p is None: + raise TypeError("can't get common type for non-numeric array") + precision = builtins.max(precision, p) + if is_complex: + return array_type[1][precision] + else: + return array_type[0][precision] + + +# ### histograms ### + + +def histogram( + a: ArrayLike, + bins: ArrayLike = 10, + range=None, + normed=None, + weights: Optional[ArrayLike] = None, + density=None, +): + if normed is not None: + raise ValueError("normed argument is deprecated, use density= instead") + + if weights is not None and weights.dtype.is_complex: + raise NotImplementedError("complex weights histogram.") + + is_a_int = not (a.dtype.is_floating_point or a.dtype.is_complex) + is_w_int = weights is None or not weights.dtype.is_floating_point + if is_a_int: + a = a.double() + + if weights is not None: + weights = _util.cast_if_needed(weights, a.dtype) + + if isinstance(bins, torch.Tensor): + if bins.ndim == 0: + # bins was a single int + bins = operator.index(bins) + else: + bins = _util.cast_if_needed(bins, a.dtype) + + if range is None: + h, b = torch.histogram(a, bins, weight=weights, density=bool(density)) + else: + h, b = torch.histogram( + a, bins, range=range, weight=weights, density=bool(density) + ) + + if not density and is_w_int: + h = h.long() + if is_a_int: + b = b.long() + + return h, b + + +def histogram2d( + x, + y, + bins=10, + range: Optional[ArrayLike] = None, + normed=None, + weights: Optional[ArrayLike] = None, + density=None, +): + # vendored from https://github.com/numpy/numpy/blob/v1.24.0/numpy/lib/twodim_base.py#L655-L821 + if len(x) != len(y): + raise ValueError("x and y must have the same length.") + + try: + N = len(bins) + except TypeError: + N = 1 + + if N != 1 and N != 2: + bins = [bins, bins] + + h, e = histogramdd((x, y), bins, range, normed, weights, density) + + return h, e[0], e[1] + + +def histogramdd( + sample, + bins=10, + range: Optional[ArrayLike] = None, + normed=None, + weights: Optional[ArrayLike] = None, + density=None, +): + # have to normalize manually because `sample` interpretation differs + # for a list of lists and a 2D array + if normed is not None: + raise ValueError("normed argument is deprecated, use density= instead") + + from ._normalizations import normalize_array_like, normalize_seq_array_like + + if isinstance(sample, (list, tuple)): + sample = normalize_array_like(sample).T + else: + sample = normalize_array_like(sample) + + sample = torch.atleast_2d(sample) + + if not (sample.dtype.is_floating_point or sample.dtype.is_complex): + sample = sample.double() + + # bins is either an int, or a sequence of ints or a sequence of arrays + bins_is_array = not ( + isinstance(bins, int) or builtins.all(isinstance(b, int) for b in bins) + ) + if bins_is_array: + bins = normalize_seq_array_like(bins) + bins_dtypes = [b.dtype for b in bins] + bins = [_util.cast_if_needed(b, sample.dtype) for b in bins] + + if range is not None: + range = range.flatten().tolist() + + if weights is not None: + # range=... is required : interleave min and max values per dimension + mm = sample.aminmax(dim=0) + range = torch.cat(mm).reshape(2, -1).T.flatten() + range = tuple(range.tolist()) + weights = _util.cast_if_needed(weights, sample.dtype) + w_kwd = {"weight": weights} + else: + w_kwd = {} + + h, b = torch.histogramdd(sample, bins, range, density=bool(density), **w_kwd) + + if bins_is_array: + b = [_util.cast_if_needed(bb, dtyp) for bb, dtyp in zip(b, bins_dtypes)] + + return h, b + + +# ### odds and ends + + +def min_scalar_type(a: ArrayLike, /): + # https://github.com/numpy/numpy/blob/maintenance/1.24.x/numpy/core/src/multiarray/convert_datatype.c#L1288 + + from ._dtypes import DType + + if a.numel() > 1: + # numpy docs: "For non-scalar array a, returns the vector's dtype unmodified." + return DType(a.dtype) + + if a.dtype == torch.bool: + dtype = torch.bool + + elif a.dtype.is_complex: + fi = torch.finfo(torch.float32) + fits_in_single = a.dtype == torch.complex64 or ( + fi.min <= a.real <= fi.max and fi.min <= a.imag <= fi.max + ) + dtype = torch.complex64 if fits_in_single else torch.complex128 + + elif a.dtype.is_floating_point: + for dt in [torch.float16, torch.float32, torch.float64]: + fi = torch.finfo(dt) + if fi.min <= a <= fi.max: + dtype = dt + break + else: + # must be integer + for dt in [torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64]: + # Prefer unsigned int where possible, as numpy does. + ii = torch.iinfo(dt) + if ii.min <= a <= ii.max: + dtype = dt + break + + return DType(dtype) + + +def pad(array: ArrayLike, pad_width: ArrayLike, mode="constant", **kwargs): + if mode != "constant": + raise NotImplementedError + value = kwargs.get("constant_values", 0) + # `value` must be a python scalar for torch.nn.functional.pad + typ = _dtypes_impl.python_type_for_torch(array.dtype) + value = typ(value) + + pad_width = torch.broadcast_to(pad_width, (array.ndim, 2)) + pad_width = torch.flip(pad_width, (0,)).flatten() + + return torch.nn.functional.pad(array, tuple(pad_width), value=value) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_getlimits.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_getlimits.py new file mode 100644 index 0000000000000000000000000000000000000000..b0c46094e8782f8309ba6cfa15cb59f93cf667a0 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_getlimits.py @@ -0,0 +1,15 @@ +# mypy: ignore-errors + +import torch + +from . import _dtypes + + +def finfo(dtyp): + torch_dtype = _dtypes.dtype(dtyp).torch_dtype + return torch.finfo(torch_dtype) + + +def iinfo(dtyp): + torch_dtype = _dtypes.dtype(dtyp).torch_dtype + return torch.iinfo(torch_dtype) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_ndarray.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_ndarray.py new file mode 100644 index 0000000000000000000000000000000000000000..73d60b24b5c37291020ad68d295e171a9d72d055 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_ndarray.py @@ -0,0 +1,592 @@ +# mypy: ignore-errors + +from __future__ import annotations + +import builtins +import math +import operator +from typing import Sequence + +import torch + +from . import _dtypes, _dtypes_impl, _funcs, _ufuncs, _util +from ._normalizations import ( + ArrayLike, + normalize_array_like, + normalizer, + NotImplementedType, +) + + +newaxis = None + +FLAGS = [ + "C_CONTIGUOUS", + "F_CONTIGUOUS", + "OWNDATA", + "WRITEABLE", + "ALIGNED", + "WRITEBACKIFCOPY", + "FNC", + "FORC", + "BEHAVED", + "CARRAY", + "FARRAY", +] + +SHORTHAND_TO_FLAGS = { + "C": "C_CONTIGUOUS", + "F": "F_CONTIGUOUS", + "O": "OWNDATA", + "W": "WRITEABLE", + "A": "ALIGNED", + "X": "WRITEBACKIFCOPY", + "B": "BEHAVED", + "CA": "CARRAY", + "FA": "FARRAY", +} + + +class Flags: + def __init__(self, flag_to_value: dict): + assert all(k in FLAGS for k in flag_to_value.keys()) # sanity check + self._flag_to_value = flag_to_value + + def __getattr__(self, attr: str): + if attr.islower() and attr.upper() in FLAGS: + return self[attr.upper()] + else: + raise AttributeError(f"No flag attribute '{attr}'") + + def __getitem__(self, key): + if key in SHORTHAND_TO_FLAGS.keys(): + key = SHORTHAND_TO_FLAGS[key] + if key in FLAGS: + try: + return self._flag_to_value[key] + except KeyError as e: + raise NotImplementedError(f"{key=}") from e + else: + raise KeyError(f"No flag key '{key}'") + + def __setattr__(self, attr, value): + if attr.islower() and attr.upper() in FLAGS: + self[attr.upper()] = value + else: + super().__setattr__(attr, value) + + def __setitem__(self, key, value): + if key in FLAGS or key in SHORTHAND_TO_FLAGS.keys(): + raise NotImplementedError("Modifying flags is not implemented") + else: + raise KeyError(f"No flag key '{key}'") + + +def create_method(fn, name=None): + name = name or fn.__name__ + + def f(*args, **kwargs): + return fn(*args, **kwargs) + + f.__name__ = name + f.__qualname__ = f"ndarray.{name}" + return f + + +# Map ndarray.name_method -> np.name_func +# If name_func == None, it means that name_method == name_func +methods = { + "clip": None, + "nonzero": None, + "repeat": None, + "round": None, + "squeeze": None, + "swapaxes": None, + "ravel": None, + # linalg + "diagonal": None, + "dot": None, + "trace": None, + # sorting + "argsort": None, + "searchsorted": None, + # reductions + "argmax": None, + "argmin": None, + "any": None, + "all": None, + "max": None, + "min": None, + "ptp": None, + "sum": None, + "prod": None, + "mean": None, + "var": None, + "std": None, + # scans + "cumsum": None, + "cumprod": None, + # advanced indexing + "take": None, + "choose": None, +} + +dunder = { + "abs": "absolute", + "invert": None, + "pos": "positive", + "neg": "negative", + "gt": "greater", + "lt": "less", + "ge": "greater_equal", + "le": "less_equal", +} + +# dunder methods with right-looking and in-place variants +ri_dunder = { + "add": None, + "sub": "subtract", + "mul": "multiply", + "truediv": "divide", + "floordiv": "floor_divide", + "pow": "power", + "mod": "remainder", + "and": "bitwise_and", + "or": "bitwise_or", + "xor": "bitwise_xor", + "lshift": "left_shift", + "rshift": "right_shift", + "matmul": None, +} + + +def _upcast_int_indices(index): + if isinstance(index, torch.Tensor): + if index.dtype in (torch.int8, torch.int16, torch.int32, torch.uint8): + return index.to(torch.int64) + elif isinstance(index, tuple): + return tuple(_upcast_int_indices(i) for i in index) + return index + + +# Used to indicate that a parameter is unspecified (as opposed to explicitly +# `None`) +class _Unspecified: + pass + + +_Unspecified.unspecified = _Unspecified() + +############################################################### +# ndarray class # +############################################################### + + +class ndarray: + def __init__(self, t=None): + if t is None: + self.tensor = torch.Tensor() + elif isinstance(t, torch.Tensor): + self.tensor = t + else: + raise ValueError( + "ndarray constructor is not recommended; prefer" + "either array(...) or zeros/empty(...)" + ) + + # Register NumPy functions as methods + for method, name in methods.items(): + fn = getattr(_funcs, name or method) + vars()[method] = create_method(fn, method) + + # Regular methods but coming from ufuncs + conj = create_method(_ufuncs.conjugate, "conj") + conjugate = create_method(_ufuncs.conjugate) + + for method, name in dunder.items(): + fn = getattr(_ufuncs, name or method) + method = f"__{method}__" + vars()[method] = create_method(fn, method) + + for method, name in ri_dunder.items(): + fn = getattr(_ufuncs, name or method) + plain = f"__{method}__" + vars()[plain] = create_method(fn, plain) + rvar = f"__r{method}__" + vars()[rvar] = create_method(lambda self, other, fn=fn: fn(other, self), rvar) + ivar = f"__i{method}__" + vars()[ivar] = create_method( + lambda self, other, fn=fn: fn(self, other, out=self), ivar + ) + + # There's no __idivmod__ + __divmod__ = create_method(_ufuncs.divmod, "__divmod__") + __rdivmod__ = create_method( + lambda self, other: _ufuncs.divmod(other, self), "__rdivmod__" + ) + + # prevent loop variables leaking into the ndarray class namespace + del ivar, rvar, name, plain, fn, method + + @property + def shape(self): + return tuple(self.tensor.shape) + + @property + def size(self): + return self.tensor.numel() + + @property + def ndim(self): + return self.tensor.ndim + + @property + def dtype(self): + return _dtypes.dtype(self.tensor.dtype) + + @property + def strides(self): + elsize = self.tensor.element_size() + return tuple(stride * elsize for stride in self.tensor.stride()) + + @property + def itemsize(self): + return self.tensor.element_size() + + @property + def flags(self): + # Note contiguous in torch is assumed C-style + return Flags( + { + "C_CONTIGUOUS": self.tensor.is_contiguous(), + "F_CONTIGUOUS": self.T.tensor.is_contiguous(), + "OWNDATA": self.tensor._base is None, + "WRITEABLE": True, # pytorch does not have readonly tensors + } + ) + + @property + def data(self): + return self.tensor.data_ptr() + + @property + def nbytes(self): + return self.tensor.storage().nbytes() + + @property + def T(self): + return self.transpose() + + @property + def real(self): + return _funcs.real(self) + + @real.setter + def real(self, value): + self.tensor.real = asarray(value).tensor + + @property + def imag(self): + return _funcs.imag(self) + + @imag.setter + def imag(self, value): + self.tensor.imag = asarray(value).tensor + + # ctors + def astype(self, dtype, order="K", casting="unsafe", subok=True, copy=True): + if order != "K": + raise NotImplementedError(f"astype(..., order={order} is not implemented.") + if casting != "unsafe": + raise NotImplementedError( + f"astype(..., casting={casting} is not implemented." + ) + if not subok: + raise NotImplementedError(f"astype(..., subok={subok} is not implemented.") + if not copy: + raise NotImplementedError(f"astype(..., copy={copy} is not implemented.") + torch_dtype = _dtypes.dtype(dtype).torch_dtype + t = self.tensor.to(torch_dtype) + return ndarray(t) + + @normalizer + def copy(self: ArrayLike, order: NotImplementedType = "C"): + return self.clone() + + @normalizer + def flatten(self: ArrayLike, order: NotImplementedType = "C"): + return torch.flatten(self) + + def resize(self, *new_shape, refcheck=False): + # NB: differs from np.resize: fills with zeros instead of making repeated copies of input. + if refcheck: + raise NotImplementedError( + f"resize(..., refcheck={refcheck} is not implemented." + ) + if new_shape in [(), (None,)]: + return + + # support both x.resize((2, 2)) and x.resize(2, 2) + if len(new_shape) == 1: + new_shape = new_shape[0] + if isinstance(new_shape, int): + new_shape = (new_shape,) + + if builtins.any(x < 0 for x in new_shape): + raise ValueError("all elements of `new_shape` must be non-negative") + + new_numel, old_numel = math.prod(new_shape), self.tensor.numel() + + self.tensor.resize_(new_shape) + + if new_numel >= old_numel: + # zero-fill new elements + assert self.tensor.is_contiguous() + b = self.tensor.flatten() # does not copy + b[old_numel:].zero_() + + def view(self, dtype=_Unspecified.unspecified, type=_Unspecified.unspecified): + if dtype is _Unspecified.unspecified: + dtype = self.dtype + if type is not _Unspecified.unspecified: + raise NotImplementedError(f"view(..., type={type} is not implemented.") + torch_dtype = _dtypes.dtype(dtype).torch_dtype + tview = self.tensor.view(torch_dtype) + return ndarray(tview) + + @normalizer + def fill(self, value: ArrayLike): + # Both Pytorch and NumPy accept 0D arrays/tensors and scalars, and + # error out on D > 0 arrays + self.tensor.fill_(value) + + def tolist(self): + return self.tensor.tolist() + + def __iter__(self): + return (ndarray(x) for x in self.tensor.__iter__()) + + def __str__(self): + return ( + str(self.tensor) + .replace("tensor", "torch.ndarray") + .replace("dtype=torch.", "dtype=") + ) + + __repr__ = create_method(__str__) + + def __eq__(self, other): + try: + return _ufuncs.equal(self, other) + except (RuntimeError, TypeError): + # Failed to convert other to array: definitely not equal. + falsy = torch.full(self.shape, fill_value=False, dtype=bool) + return asarray(falsy) + + def __ne__(self, other): + return ~(self == other) + + def __index__(self): + try: + return operator.index(self.tensor.item()) + except Exception as exc: + raise TypeError( + "only integer scalar arrays can be converted to a scalar index" + ) from exc + + def __bool__(self): + return bool(self.tensor) + + def __int__(self): + return int(self.tensor) + + def __float__(self): + return float(self.tensor) + + def __complex__(self): + return complex(self.tensor) + + def is_integer(self): + try: + v = self.tensor.item() + result = int(v) == v + except Exception: + result = False + return result + + def __len__(self): + return self.tensor.shape[0] + + def __contains__(self, x): + return self.tensor.__contains__(x) + + def transpose(self, *axes): + # np.transpose(arr, axis=None) but arr.transpose(*axes) + return _funcs.transpose(self, axes) + + def reshape(self, *shape, order="C"): + # arr.reshape(shape) and arr.reshape(*shape) + return _funcs.reshape(self, shape, order=order) + + def sort(self, axis=-1, kind=None, order=None): + # ndarray.sort works in-place + _funcs.copyto(self, _funcs.sort(self, axis, kind, order)) + + def item(self, *args): + # Mimic NumPy's implementation with three special cases (no arguments, + # a flat index and a multi-index): + # https://github.com/numpy/numpy/blob/main/numpy/core/src/multiarray/methods.c#L702 + if args == (): + return self.tensor.item() + elif len(args) == 1: + # int argument + return self.ravel()[args[0]] + else: + return self.__getitem__(args) + + def __getitem__(self, index): + tensor = self.tensor + + def neg_step(i, s): + if not (isinstance(s, slice) and s.step is not None and s.step < 0): + return s + + nonlocal tensor + tensor = torch.flip(tensor, (i,)) + + # Account for the fact that a slice includes the start but not the end + assert isinstance(s.start, int) or s.start is None + assert isinstance(s.stop, int) or s.stop is None + start = s.stop + 1 if s.stop else None + stop = s.start + 1 if s.start else None + + return slice(start, stop, -s.step) + + if isinstance(index, Sequence): + index = type(index)(neg_step(i, s) for i, s in enumerate(index)) + else: + index = neg_step(0, index) + index = _util.ndarrays_to_tensors(index) + index = _upcast_int_indices(index) + return ndarray(tensor.__getitem__(index)) + + def __setitem__(self, index, value): + index = _util.ndarrays_to_tensors(index) + index = _upcast_int_indices(index) + + if not _dtypes_impl.is_scalar(value): + value = normalize_array_like(value) + value = _util.cast_if_needed(value, self.tensor.dtype) + + return self.tensor.__setitem__(index, value) + + take = _funcs.take + put = _funcs.put + + def __dlpack__(self, *, stream=None): + return self.tensor.__dlpack__(stream=stream) + + def __dlpack_device__(self): + return self.tensor.__dlpack_device__() + + +def _tolist(obj): + """Recursively convert tensors into lists.""" + a1 = [] + for elem in obj: + if isinstance(elem, (list, tuple)): + elem = _tolist(elem) + if isinstance(elem, ndarray): + a1.append(elem.tensor.tolist()) + else: + a1.append(elem) + return a1 + + +# This is the ideally the only place which talks to ndarray directly. +# The rest goes through asarray (preferred) or array. + + +def array(obj, dtype=None, *, copy=True, order="K", subok=False, ndmin=0, like=None): + if subok is not False: + raise NotImplementedError("'subok' parameter is not supported.") + if like is not None: + raise NotImplementedError("'like' parameter is not supported.") + if order != "K": + raise NotImplementedError + + # a happy path + if ( + isinstance(obj, ndarray) + and copy is False + and dtype is None + and ndmin <= obj.ndim + ): + return obj + + if isinstance(obj, (list, tuple)): + # FIXME and they have the same dtype, device, etc + if obj and all(isinstance(x, torch.Tensor) for x in obj): + # list of arrays: *under torch.Dynamo* these are FakeTensors + obj = torch.stack(obj) + else: + # XXX: remove tolist + # lists of ndarrays: [1, [2, 3], ndarray(4)] convert to lists of lists + obj = _tolist(obj) + + # is obj an ndarray already? + if isinstance(obj, ndarray): + obj = obj.tensor + + # is a specific dtype requested? + torch_dtype = None + if dtype is not None: + torch_dtype = _dtypes.dtype(dtype).torch_dtype + + tensor = _util._coerce_to_tensor(obj, torch_dtype, copy, ndmin) + return ndarray(tensor) + + +def asarray(a, dtype=None, order="K", *, like=None): + return array(a, dtype=dtype, order=order, like=like, copy=False, ndmin=0) + + +def ascontiguousarray(a, dtype=None, *, like=None): + arr = asarray(a, dtype=dtype, like=like) + if not arr.tensor.is_contiguous(): + arr.tensor = arr.tensor.contiguous() + return arr + + +def from_dlpack(x, /): + t = torch.from_dlpack(x) + return ndarray(t) + + +def _extract_dtype(entry): + try: + dty = _dtypes.dtype(entry) + except Exception: + dty = asarray(entry).dtype + return dty + + +def can_cast(from_, to, casting="safe"): + from_ = _extract_dtype(from_) + to_ = _extract_dtype(to) + + return _dtypes_impl.can_cast_impl(from_.torch_dtype, to_.torch_dtype, casting) + + +def result_type(*arrays_and_dtypes): + tensors = [] + for entry in arrays_and_dtypes: + try: + t = asarray(entry).tensor + except (RuntimeError, ValueError, TypeError): + dty = _dtypes.dtype(entry) + t = torch.empty(1, dtype=dty.torch_dtype) + tensors.append(t) + + torch_dtype = _dtypes_impl.result_type_impl(*tensors) + return _dtypes.dtype(torch_dtype) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_normalizations.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_normalizations.py new file mode 100644 index 0000000000000000000000000000000000000000..ef60dd18900480559686a4f2bb43b6e5e9a605ef --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_normalizations.py @@ -0,0 +1,259 @@ +# mypy: ignore-errors + +""" "Normalize" arguments: convert array_likes to tensors, dtypes to torch dtypes and so on. +""" +from __future__ import annotations + +import functools +import inspect +import operator +import typing + +import torch + +from . import _dtypes, _dtypes_impl, _util + + +ArrayLike = typing.TypeVar("ArrayLike") +Scalar = typing.Union[int, float, complex, bool] +ArrayLikeOrScalar = typing.Union[ArrayLike, Scalar] + +DTypeLike = typing.TypeVar("DTypeLike") +AxisLike = typing.TypeVar("AxisLike") +NDArray = typing.TypeVar("NDArray") +CastingModes = typing.TypeVar("CastingModes") +KeepDims = typing.TypeVar("KeepDims") + +# OutArray is to annotate the out= array argument. +# +# This one is special is several respects: +# First, It needs to be an NDArray, and we need to preserve the `result is out` +# semantics. Therefore, we cannot just extract the Tensor from the out array. +# So we never pass the out array to implementer functions and handle it in the +# `normalizer` below. +# Second, the out= argument can be either keyword or positional argument, and +# as a positional arg, it can be anywhere in the signature. +# To handle all this, we define a special `OutArray` annotation and dispatch on it. +# +OutArray = typing.TypeVar("OutArray") + +try: + from typing import NotImplementedType +except ImportError: + NotImplementedType = typing.TypeVar("NotImplementedType") + + +def normalize_array_like(x, parm=None): + from ._ndarray import asarray + + return asarray(x).tensor + + +def normalize_array_like_or_scalar(x, parm=None): + if _dtypes_impl.is_scalar_or_symbolic(x): + return x + return normalize_array_like(x, parm) + + +def normalize_optional_array_like_or_scalar(x, parm=None): + if x is None: + return None + return normalize_array_like_or_scalar(x, parm) + + +def normalize_optional_array_like(x, parm=None): + # This explicit normalizer is needed because otherwise normalize_array_like + # does not run for a parameter annotated as Optional[ArrayLike] + return None if x is None else normalize_array_like(x, parm) + + +def normalize_seq_array_like(x, parm=None): + return tuple(normalize_array_like(value) for value in x) + + +def normalize_dtype(dtype, parm=None): + # cf _decorators.dtype_to_torch + torch_dtype = None + if dtype is not None: + dtype = _dtypes.dtype(dtype) + torch_dtype = dtype.torch_dtype + return torch_dtype + + +def normalize_not_implemented(arg, parm): + if arg != parm.default: + raise NotImplementedError(f"'{parm.name}' parameter is not supported.") + + +def normalize_axis_like(arg, parm=None): + from ._ndarray import ndarray + + if isinstance(arg, ndarray): + arg = operator.index(arg) + return arg + + +def normalize_ndarray(arg, parm=None): + # check the arg is an ndarray, extract its tensor attribute + if arg is None: + return arg + + from ._ndarray import ndarray + + if not isinstance(arg, ndarray): + raise TypeError(f"'{parm.name}' must be an array") + return arg.tensor + + +def normalize_outarray(arg, parm=None): + # almost normalize_ndarray, only return the array, not its tensor + if arg is None: + return arg + from ._ndarray import ndarray + + # Dynamo can pass torch tensors as out arguments, + # wrap it in an ndarray before processing + if isinstance(arg, torch.Tensor): + arg = ndarray(arg) + + if not isinstance(arg, ndarray): + raise TypeError(f"'{parm.name}' must be an array") + return arg + + +def normalize_casting(arg, parm=None): + if arg not in ["no", "equiv", "safe", "same_kind", "unsafe"]: + raise ValueError( + f"casting must be one of 'no', 'equiv', 'safe', 'same_kind', or 'unsafe' (got '{arg}')" + ) + return arg + + +normalizers = { + "ArrayLike": normalize_array_like, + "ArrayLikeOrScalar": normalize_array_like_or_scalar, + "Optional[ArrayLike]": normalize_optional_array_like, + "Sequence[ArrayLike]": normalize_seq_array_like, + "Optional[ArrayLikeOrScalar]": normalize_optional_array_like_or_scalar, + "Optional[NDArray]": normalize_ndarray, + "Optional[OutArray]": normalize_outarray, + "NDArray": normalize_ndarray, + "Optional[DTypeLike]": normalize_dtype, + "AxisLike": normalize_axis_like, + "NotImplementedType": normalize_not_implemented, + "Optional[CastingModes]": normalize_casting, +} + + +def maybe_normalize(arg, parm): + """Normalize arg if a normalizer is registered.""" + normalizer = normalizers.get(parm.annotation, None) + return normalizer(arg, parm) if normalizer else arg + + +# ### Return value helpers ### + + +def maybe_copy_to(out, result, promote_scalar_result=False): + # NB: here out is either an ndarray or None + if out is None: + return result + elif isinstance(result, torch.Tensor): + if result.shape != out.shape: + can_fit = result.numel() == 1 and out.ndim == 0 + if promote_scalar_result and can_fit: + result = result.squeeze() + else: + raise ValueError( + f"Bad size of the out array: out.shape = {out.shape}" + f" while result.shape = {result.shape}." + ) + out.tensor.copy_(result) + return out + elif isinstance(result, (tuple, list)): + return type(result)( + maybe_copy_to(o, r, promote_scalar_result) for o, r in zip(out, result) + ) + else: + raise AssertionError # We should never hit this path + + +def wrap_tensors(result): + from ._ndarray import ndarray + + if isinstance(result, torch.Tensor): + return ndarray(result) + elif isinstance(result, (tuple, list)): + result = type(result)(wrap_tensors(x) for x in result) + return result + + +def array_or_scalar(values, py_type=float, return_scalar=False): + if return_scalar: + return py_type(values.item()) + else: + from ._ndarray import ndarray + + return ndarray(values) + + +# ### The main decorator to normalize arguments / postprocess the output ### + + +def normalizer(_func=None, *, promote_scalar_result=False): + def normalizer_inner(func): + @functools.wraps(func) + def wrapped(*args, **kwds): + sig = inspect.signature(func) + params = sig.parameters + first_param = next(iter(params.values())) + + # NumPy's API does not have positional args before variadic positional args + if first_param.kind == inspect.Parameter.VAR_POSITIONAL: + args = [maybe_normalize(arg, first_param) for arg in args] + else: + # NB: extra unknown arguments: pass through, will raise in func(*args) below + args = ( + tuple( + maybe_normalize(arg, parm) + for arg, parm in zip(args, params.values()) + ) + + args[len(params.values()) :] + ) + + kwds = { + name: maybe_normalize(arg, params[name]) if name in params else arg + for name, arg in kwds.items() + } + + result = func(*args, **kwds) + + # keepdims + bound_args = None + if "keepdims" in params and params["keepdims"].annotation == "KeepDims": + # keepdims can be in any position so we need sig.bind + bound_args = sig.bind(*args, **kwds).arguments + if bound_args.get("keepdims", False): + # In this case the first arg is the initial tensor and + # the second arg is (optionally) the axis + tensor = args[0] + axis = bound_args.get("axis") + result = _util.apply_keepdims(result, axis, tensor.ndim) + + # out + if "out" in params: + # out can be in any position so we need sig.bind + if bound_args is None: + bound_args = sig.bind(*args, **kwds).arguments + out = bound_args.get("out") + result = maybe_copy_to(out, result, promote_scalar_result) + result = wrap_tensors(result) + + return result + + return wrapped + + if _func is None: + return normalizer_inner + else: + return normalizer_inner(_func) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_reductions_impl.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_reductions_impl.py new file mode 100644 index 0000000000000000000000000000000000000000..57dc8e660cad14f2b0e89f3acedf918e7d216818 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_reductions_impl.py @@ -0,0 +1,459 @@ +# mypy: ignore-errors + +""" Implementation of reduction operations, to be wrapped into arrays, dtypes etc +in the 'public' layer. + +Anything here only deals with torch objects, e.g. "dtype" is a torch.dtype instance etc +""" +from __future__ import annotations + +import functools +from typing import Optional, TYPE_CHECKING + +import torch + +from . import _dtypes_impl, _util + + +if TYPE_CHECKING: + from ._normalizations import ( + ArrayLike, + AxisLike, + DTypeLike, + KeepDims, + NotImplementedType, + OutArray, + ) + + +def _deco_axis_expand(func): + """ + Generically handle axis arguments in reductions. + axis is *always* the 2nd arg in the function so no need to have a look at its signature + """ + + @functools.wraps(func) + def wrapped(a, axis=None, *args, **kwds): + if axis is not None: + axis = _util.normalize_axis_tuple(axis, a.ndim) + + if axis == (): + # So we insert a length-one axis and run the reduction along it. + # We cannot return a.clone() as this would sidestep the checks inside the function + newshape = _util.expand_shape(a.shape, axis=0) + a = a.reshape(newshape) + axis = (0,) + + return func(a, axis, *args, **kwds) + + return wrapped + + +def _atleast_float(dtype, other_dtype): + """Return a dtype that is real or complex floating-point. + + For inputs that are boolean or integer dtypes, this returns the default + float dtype; inputs that are complex get converted to the default complex + dtype; real floating-point dtypes (`float*`) get passed through unchanged + """ + if dtype is None: + dtype = other_dtype + if not (dtype.is_floating_point or dtype.is_complex): + return _dtypes_impl.default_dtypes().float_dtype + return dtype + + +@_deco_axis_expand +def count_nonzero(a: ArrayLike, axis: AxisLike = None, *, keepdims: KeepDims = False): + return a.count_nonzero(axis) + + +@_deco_axis_expand +def argmax( + a: ArrayLike, + axis: AxisLike = None, + out: Optional[OutArray] = None, + *, + keepdims: KeepDims = False, +): + if a.is_complex(): + raise NotImplementedError(f"argmax with dtype={a.dtype}.") + + axis = _util.allow_only_single_axis(axis) + + if a.dtype == torch.bool: + # RuntimeError: "argmax_cpu" not implemented for 'Bool' + a = a.to(torch.uint8) + + return torch.argmax(a, axis) + + +@_deco_axis_expand +def argmin( + a: ArrayLike, + axis: AxisLike = None, + out: Optional[OutArray] = None, + *, + keepdims: KeepDims = False, +): + if a.is_complex(): + raise NotImplementedError(f"argmin with dtype={a.dtype}.") + + axis = _util.allow_only_single_axis(axis) + + if a.dtype == torch.bool: + # RuntimeError: "argmin_cpu" not implemented for 'Bool' + a = a.to(torch.uint8) + + return torch.argmin(a, axis) + + +@_deco_axis_expand +def any( + a: ArrayLike, + axis: AxisLike = None, + out: Optional[OutArray] = None, + keepdims: KeepDims = False, + *, + where: NotImplementedType = None, +): + axis = _util.allow_only_single_axis(axis) + axis_kw = {} if axis is None else {"dim": axis} + return torch.any(a, **axis_kw) + + +@_deco_axis_expand +def all( + a: ArrayLike, + axis: AxisLike = None, + out: Optional[OutArray] = None, + keepdims: KeepDims = False, + *, + where: NotImplementedType = None, +): + axis = _util.allow_only_single_axis(axis) + axis_kw = {} if axis is None else {"dim": axis} + return torch.all(a, **axis_kw) + + +@_deco_axis_expand +def amax( + a: ArrayLike, + axis: AxisLike = None, + out: Optional[OutArray] = None, + keepdims: KeepDims = False, + initial: NotImplementedType = None, + where: NotImplementedType = None, +): + if a.is_complex(): + raise NotImplementedError(f"amax with dtype={a.dtype}") + + return a.amax(axis) + + +max = amax + + +@_deco_axis_expand +def amin( + a: ArrayLike, + axis: AxisLike = None, + out: Optional[OutArray] = None, + keepdims: KeepDims = False, + initial: NotImplementedType = None, + where: NotImplementedType = None, +): + if a.is_complex(): + raise NotImplementedError(f"amin with dtype={a.dtype}") + + return a.amin(axis) + + +min = amin + + +@_deco_axis_expand +def ptp( + a: ArrayLike, + axis: AxisLike = None, + out: Optional[OutArray] = None, + keepdims: KeepDims = False, +): + return a.amax(axis) - a.amin(axis) + + +@_deco_axis_expand +def sum( + a: ArrayLike, + axis: AxisLike = None, + dtype: Optional[DTypeLike] = None, + out: Optional[OutArray] = None, + keepdims: KeepDims = False, + initial: NotImplementedType = None, + where: NotImplementedType = None, +): + assert dtype is None or isinstance(dtype, torch.dtype) + + if dtype == torch.bool: + dtype = _dtypes_impl.default_dtypes().int_dtype + + axis_kw = {} if axis is None else {"dim": axis} + return a.sum(dtype=dtype, **axis_kw) + + +@_deco_axis_expand +def prod( + a: ArrayLike, + axis: AxisLike = None, + dtype: Optional[DTypeLike] = None, + out: Optional[OutArray] = None, + keepdims: KeepDims = False, + initial: NotImplementedType = None, + where: NotImplementedType = None, +): + axis = _util.allow_only_single_axis(axis) + + if dtype == torch.bool: + dtype = _dtypes_impl.default_dtypes().int_dtype + + axis_kw = {} if axis is None else {"dim": axis} + return a.prod(dtype=dtype, **axis_kw) + + +product = prod + + +@_deco_axis_expand +def mean( + a: ArrayLike, + axis: AxisLike = None, + dtype: Optional[DTypeLike] = None, + out: Optional[OutArray] = None, + keepdims: KeepDims = False, + *, + where: NotImplementedType = None, +): + dtype = _atleast_float(dtype, a.dtype) + + axis_kw = {} if axis is None else {"dim": axis} + result = a.mean(dtype=dtype, **axis_kw) + + return result + + +@_deco_axis_expand +def std( + a: ArrayLike, + axis: AxisLike = None, + dtype: Optional[DTypeLike] = None, + out: Optional[OutArray] = None, + ddof=0, + keepdims: KeepDims = False, + *, + where: NotImplementedType = None, +): + in_dtype = dtype + dtype = _atleast_float(dtype, a.dtype) + tensor = _util.cast_if_needed(a, dtype) + result = tensor.std(dim=axis, correction=ddof) + return _util.cast_if_needed(result, in_dtype) + + +@_deco_axis_expand +def var( + a: ArrayLike, + axis: AxisLike = None, + dtype: Optional[DTypeLike] = None, + out: Optional[OutArray] = None, + ddof=0, + keepdims: KeepDims = False, + *, + where: NotImplementedType = None, +): + in_dtype = dtype + dtype = _atleast_float(dtype, a.dtype) + tensor = _util.cast_if_needed(a, dtype) + result = tensor.var(dim=axis, correction=ddof) + return _util.cast_if_needed(result, in_dtype) + + +# cumsum / cumprod are almost reductions: +# 1. no keepdims +# 2. axis=None flattens + + +def cumsum( + a: ArrayLike, + axis: AxisLike = None, + dtype: Optional[DTypeLike] = None, + out: Optional[OutArray] = None, +): + if dtype == torch.bool: + dtype = _dtypes_impl.default_dtypes().int_dtype + if dtype is None: + dtype = a.dtype + + (a,), axis = _util.axis_none_flatten(a, axis=axis) + axis = _util.normalize_axis_index(axis, a.ndim) + + return a.cumsum(axis=axis, dtype=dtype) + + +def cumprod( + a: ArrayLike, + axis: AxisLike = None, + dtype: Optional[DTypeLike] = None, + out: Optional[OutArray] = None, +): + if dtype == torch.bool: + dtype = _dtypes_impl.default_dtypes().int_dtype + if dtype is None: + dtype = a.dtype + + (a,), axis = _util.axis_none_flatten(a, axis=axis) + axis = _util.normalize_axis_index(axis, a.ndim) + + return a.cumprod(axis=axis, dtype=dtype) + + +cumproduct = cumprod + + +def average( + a: ArrayLike, + axis=None, + weights: ArrayLike = None, + returned=False, + *, + keepdims=False, +): + if weights is None: + result = mean(a, axis=axis) + wsum = torch.as_tensor(a.numel() / result.numel(), dtype=result.dtype) + else: + if not a.dtype.is_floating_point: + a = a.double() + + # axis & weights + if a.shape != weights.shape: + if axis is None: + raise TypeError( + "Axis must be specified when shapes of a and weights differ." + ) + if weights.ndim != 1: + raise TypeError( + "1D weights expected when shapes of a and weights differ." + ) + if weights.shape[0] != a.shape[axis]: + raise ValueError( + "Length of weights not compatible with specified axis." + ) + + # setup weight to broadcast along axis + weights = torch.broadcast_to(weights, (a.ndim - 1) * (1,) + weights.shape) + weights = weights.swapaxes(-1, axis) + + # do the work + result_dtype = _dtypes_impl.result_type_impl(a, weights) + numerator = sum(a * weights, axis, dtype=result_dtype) + wsum = sum(weights, axis, dtype=result_dtype) + result = numerator / wsum + + # We process keepdims manually because the decorator does not deal with variadic returns + if keepdims: + result = _util.apply_keepdims(result, axis, a.ndim) + + if returned: + if wsum.shape != result.shape: + wsum = torch.broadcast_to(wsum, result.shape).clone() + return result, wsum + else: + return result + + +# Not using deco_axis_expand as it assumes that axis is the second arg +def quantile( + a: ArrayLike, + q: ArrayLike, + axis: AxisLike = None, + out: Optional[OutArray] = None, + overwrite_input=False, + method="linear", + keepdims: KeepDims = False, + *, + interpolation: NotImplementedType = None, +): + if overwrite_input: + # raise NotImplementedError("overwrite_input in quantile not implemented.") + # NumPy documents that `overwrite_input` MAY modify inputs: + # https://numpy.org/doc/stable/reference/generated/numpy.percentile.html#numpy-percentile + # Here we choose to work out-of-place because why not. + pass + + if not a.dtype.is_floating_point: + dtype = _dtypes_impl.default_dtypes().float_dtype + a = a.to(dtype) + + # edge case: torch.quantile only supports float32 and float64 + if a.dtype == torch.float16: + a = a.to(torch.float32) + + if axis is None: + a = a.flatten() + q = q.flatten() + axis = (0,) + else: + axis = _util.normalize_axis_tuple(axis, a.ndim) + + # FIXME(Mario) Doesn't np.quantile accept a tuple? + # torch.quantile does accept a number. If we don't want to implement the tuple behaviour + # (it's deffo low prio) change `normalize_axis_tuple` into a normalize_axis index above. + axis = _util.allow_only_single_axis(axis) + + q = _util.cast_if_needed(q, a.dtype) + + return torch.quantile(a, q, axis=axis, interpolation=method) + + +def percentile( + a: ArrayLike, + q: ArrayLike, + axis: AxisLike = None, + out: Optional[OutArray] = None, + overwrite_input=False, + method="linear", + keepdims: KeepDims = False, + *, + interpolation: NotImplementedType = None, +): + # np.percentile(float_tensor, 30) : q.dtype is int64 => q / 100.0 is float32 + if _dtypes_impl.python_type_for_torch(q.dtype) == int: + q = q.to(_dtypes_impl.default_dtypes().float_dtype) + qq = q / 100.0 + + return quantile( + a, + qq, + axis=axis, + overwrite_input=overwrite_input, + method=method, + keepdims=keepdims, + interpolation=interpolation, + ) + + +def median( + a: ArrayLike, + axis=None, + out: Optional[OutArray] = None, + overwrite_input=False, + keepdims: KeepDims = False, +): + return quantile( + a, + torch.as_tensor(0.5), + axis=axis, + overwrite_input=overwrite_input, + out=out, + keepdims=keepdims, + ) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_ufuncs.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_ufuncs.py new file mode 100644 index 0000000000000000000000000000000000000000..0543aad2f7a6ba551d8ce7483e1dc8e0474ac57e --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_ufuncs.py @@ -0,0 +1,334 @@ +# mypy: ignore-errors + +from __future__ import annotations + +from typing import Optional + +import torch + +from . import _binary_ufuncs_impl, _dtypes_impl, _unary_ufuncs_impl, _util +from ._normalizations import ( + ArrayLike, + ArrayLikeOrScalar, + CastingModes, + DTypeLike, + normalizer, + NotImplementedType, + OutArray, +) + + +def _ufunc_postprocess(result, out, casting): + if out is not None: + result = _util.typecast_tensor(result, out.dtype.torch_dtype, casting) + result = torch.broadcast_to(result, out.shape) + return result + + +# ############# Binary ufuncs ###################### + +_binary = [ + name + for name in dir(_binary_ufuncs_impl) + if not name.startswith("_") and name not in ["torch", "matmul", "divmod", "ldexp"] +] + + +NEP50_FUNCS = ( + "add", + "subtract", + "multiply", + "floor_divide", + "true_divide", + "divide", + "remainder", + "bitwise_and", + "bitwise_or", + "bitwise_xor", + "bitwise_left_shift", + "bitwise_right_shift", + "hypot", + "arctan2", + "logaddexp", + "logaddexp2", + "heaviside", + "copysign", + "fmax", + "minimum", + "fmin", + "maximum", + "fmod", + "gcd", + "lcm", + "pow", +) + + +def deco_binary_ufunc(torch_func): + """Common infra for binary ufuncs. + + Normalize arguments, sort out type casting, broadcasting and delegate to + the pytorch functions for the actual work. + """ + + @normalizer + def wrapped( + x1: ArrayLikeOrScalar, + x2: ArrayLikeOrScalar, + /, + out: Optional[OutArray] = None, + *, + where: NotImplementedType = True, + casting: Optional[CastingModes] = "same_kind", + order: NotImplementedType = "K", + dtype: Optional[DTypeLike] = None, + subok: NotImplementedType = False, + signature: NotImplementedType = None, + extobj: NotImplementedType = None, + ): + if dtype is not None: + + def cast(x, dtype): + if isinstance(x, torch.Tensor): + return _util.typecast_tensor(x, dtype, casting) + else: + return torch.as_tensor(x, dtype=dtype) + + x1 = cast(x1, dtype) + x2 = cast(x2, dtype) + elif isinstance(x1, torch.Tensor) and isinstance(x2, torch.Tensor): + dtype = _dtypes_impl.result_type_impl(x1, x2) + x1, x2 = _util.typecast_tensors((x1, x2), dtype, casting) + else: + x1, x2 = _dtypes_impl.nep50_to_tensors( + x1, x2, torch_func.__name__ in NEP50_FUNCS, torch_func.__name__ + ) + + result = torch_func(x1, x2) + + return _ufunc_postprocess(result, out, casting) + + wrapped.__qualname__ = torch_func.__name__ + wrapped.__name__ = torch_func.__name__ + + return wrapped + + +# matmul's signature is _slightly_ different from other ufuncs: +# - no where=... +# - additional axis=..., axes=... +# - no NEP50 scalars in or out +@normalizer +def matmul( + x1: ArrayLike, + x2: ArrayLike, + /, + out: Optional[OutArray] = None, + *, + casting: Optional[CastingModes] = "same_kind", + order: NotImplementedType = "K", + dtype: Optional[DTypeLike] = None, + subok: NotImplementedType = False, + signature: NotImplementedType = None, + extobj: NotImplementedType = None, + axes: NotImplementedType = None, + axis: NotImplementedType = None, +): + if dtype is None: + dtype = _dtypes_impl.result_type_impl(x1, x2) + x1, x2 = _util.typecast_tensors((x1, x2), dtype, casting) + + result = _binary_ufuncs_impl.matmul(x1, x2) + + result = _ufunc_postprocess(result, out, casting) + return result + + +# ldexp casting is special : the dtype of the result == dtype of the 1st arg +@normalizer +def ldexp( + x1: ArrayLikeOrScalar, + x2: ArrayLikeOrScalar, + /, + out: Optional[OutArray] = None, + *, + where: NotImplementedType = True, + casting: Optional[CastingModes] = "same_kind", + order: NotImplementedType = "K", + dtype: Optional[DTypeLike] = None, + subok: NotImplementedType = False, + signature: NotImplementedType = None, + extobj: NotImplementedType = None, +): + if dtype is not None: + if isinstance(x1, torch.Tensor): + x1 = _util.typecast_tensor(x1, dtype, casting) + else: + x1 = torch.as_tensor(x1, dtype=dtype) + else: + if not isinstance(x1, torch.Tensor): + x1 = torch.as_tensor(x1) + x1 = _util.cast_int_to_float(x1) + + x2 = torch.as_tensor(x2) + # the second arg must be integer + if _dtypes_impl._category(x2.dtype) != 1: + raise ValueError("ldexp 2nd arg must be integer") + + result = _binary_ufuncs_impl.ldexp(x1, x2) + + if x1.dtype == torch.float16: + # torch.ldexp(f16, int) -> f32, undo it + result = result.to(torch.float16) + + return _ufunc_postprocess(result, out, casting) + + +# nin=2, nout=2 +@normalizer +def divmod( + x1: ArrayLike, + x2: ArrayLike, + out1: Optional[OutArray] = None, + out2: Optional[OutArray] = None, + /, + out: tuple[Optional[OutArray], Optional[OutArray]] = (None, None), + *, + where: NotImplementedType = True, + casting: Optional[CastingModes] = "same_kind", + order: NotImplementedType = "K", + dtype: Optional[DTypeLike] = None, + subok: NotImplementedType = False, + signature: NotImplementedType = None, + extobj: NotImplementedType = None, +): + # make sure we either have no out arrays at all, or there is either + # out1, out2, or out=tuple, but not both + num_outs = sum(x is not None for x in [out1, out2]) + if num_outs == 1: + raise ValueError("both out1 and out2 need to be provided") + elif num_outs == 2: + o1, o2 = out + if o1 is not None or o2 is not None: + raise TypeError( + "cannot specify 'out' as both a positional and keyword argument" + ) + else: + out1, out2 = out + + if dtype is None: + dtype = _dtypes_impl.result_type_impl(x1, x2) + x1, x2 = _util.typecast_tensors((x1, x2), dtype, casting) + + quot, rem = _binary_ufuncs_impl.divmod(x1, x2) + + quot = _ufunc_postprocess(quot, out1, casting) + rem = _ufunc_postprocess(rem, out2, casting) + return quot, rem + + +# +# Attach ufuncs to this module, for a further export to the public namespace in __init__.py +# +for name in _binary: + ufunc = getattr(_binary_ufuncs_impl, name) + vars()[name] = deco_binary_ufunc(ufunc) + + +def modf(x, /, *args, **kwds): + quot, rem = divmod(x, 1, *args, **kwds) + return rem, quot + + +_binary = _binary + ["divmod", "modf", "matmul", "ldexp"] + + +# ############# Unary ufuncs ###################### + + +_unary = [ + name + for name in dir(_unary_ufuncs_impl) + if not name.startswith("_") and name != "torch" +] + + +# these are ufunc(int) -> float +_fp_unary = [ + "arccos", + "arccosh", + "arcsin", + "arcsinh", + "arctan", + "arctanh", + "cbrt", + "cos", + "cosh", + "deg2rad", + "degrees", + "exp", + "exp2", + "expm1", + "log", + "log10", + "log1p", + "log2", + "rad2deg", + "radians", + "reciprocal", + "sin", + "sinh", + "sqrt", + "square", + "tan", + "tanh", + "trunc", +] + + +def deco_unary_ufunc(torch_func): + """Common infra for unary ufuncs. + + Normalize arguments, sort out type casting, broadcasting and delegate to + the pytorch functions for the actual work. + """ + + @normalizer + def wrapped( + x: ArrayLike, + /, + out: Optional[OutArray] = None, + *, + where=True, + casting: Optional[CastingModes] = "same_kind", + order="K", + dtype: Optional[DTypeLike] = None, + subok: NotImplementedType = False, + signature=None, + extobj=None, + ): + if dtype is not None: + x = _util.typecast_tensor(x, dtype, casting) + + if torch_func.__name__ in _fp_unary: + x = _util.cast_int_to_float(x) + + result = torch_func(x) + result = _ufunc_postprocess(result, out, casting) + return result + + wrapped.__qualname__ = torch_func.__name__ + wrapped.__name__ = torch_func.__name__ + + return wrapped + + +# +# Attach ufuncs to this module, for a further export to the public namespace in __init__.py +# +for name in _unary: + ufunc = getattr(_unary_ufuncs_impl, name) + vars()[name] = deco_unary_ufunc(ufunc) + + +__all__ = _binary + _unary # noqa: PLE0605 diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_unary_ufuncs_impl.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_unary_ufuncs_impl.py new file mode 100644 index 0000000000000000000000000000000000000000..9b7fe08ab4b8cf6b2e892b8956fff00d93c08d11 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_unary_ufuncs_impl.py @@ -0,0 +1,72 @@ +# mypy: ignore-errors + +"""Export torch work functions for unary ufuncs, rename/tweak to match numpy. +This listing is further exported to public symbols in the `_numpy/_ufuncs.py` module. +""" + +import torch +from torch import ( # noqa: F401 + absolute as fabs, + arccos, + arccosh, + arcsin, + arcsinh, + arctan, + arctanh, + bitwise_not, + bitwise_not as invert, + ceil, + conj_physical as conjugate, + cos, + cosh, + deg2rad, + deg2rad as radians, + exp, + exp2, + expm1, + floor, + isfinite, + isinf, + isnan, + log, + log10, + log1p, + log2, + logical_not, + negative, + rad2deg, + rad2deg as degrees, + reciprocal, + round as fix, + round as rint, + sign, + signbit, + sin, + sinh, + sqrt, + square, + tan, + tanh, + trunc, +) + + +# special cases: torch does not export these names +def cbrt(x): + return torch.pow(x, 1 / 3) + + +def positive(x): + return +x + + +def absolute(x): + # work around torch.absolute not impl for bools + if x.dtype == torch.bool: + return x + return torch.absolute(x) + + +# TODO set __name__ and __qualname__ +abs = absolute +conj = conjugate diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_util.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_util.py new file mode 100644 index 0000000000000000000000000000000000000000..3005dbd79da1128d41d9a5e7067efc337c44c7f0 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/_util.py @@ -0,0 +1,261 @@ +# mypy: ignore-errors + +"""Assorted utilities, which do not need anything other then torch and stdlib. +""" + +import operator + +import torch + +from . import _dtypes_impl + + +# https://github.com/numpy/numpy/blob/v1.23.0/numpy/distutils/misc_util.py#L497-L504 +def is_sequence(seq): + if isinstance(seq, str): + return False + try: + len(seq) + except Exception: + return False + return True + + +class AxisError(ValueError, IndexError): + pass + + +class UFuncTypeError(TypeError, RuntimeError): + pass + + +def cast_if_needed(tensor, dtype): + # NB: no casting if dtype=None + if dtype is not None and tensor.dtype != dtype: + tensor = tensor.to(dtype) + return tensor + + +def cast_int_to_float(x): + # cast integers and bools to the default float dtype + if _dtypes_impl._category(x.dtype) < 2: + x = x.to(_dtypes_impl.default_dtypes().float_dtype) + return x + + +# a replica of the version in ./numpy/numpy/core/src/multiarray/common.h +def normalize_axis_index(ax, ndim, argname=None): + if not (-ndim <= ax < ndim): + raise AxisError(f"axis {ax} is out of bounds for array of dimension {ndim}") + if ax < 0: + ax += ndim + return ax + + +# from https://github.com/numpy/numpy/blob/main/numpy/core/numeric.py#L1378 +def normalize_axis_tuple(axis, ndim, argname=None, allow_duplicate=False): + """ + Normalizes an axis argument into a tuple of non-negative integer axes. + + This handles shorthands such as ``1`` and converts them to ``(1,)``, + as well as performing the handling of negative indices covered by + `normalize_axis_index`. + + By default, this forbids axes from being specified multiple times. + Used internally by multi-axis-checking logic. + + Parameters + ---------- + axis : int, iterable of int + The un-normalized index or indices of the axis. + ndim : int + The number of dimensions of the array that `axis` should be normalized + against. + argname : str, optional + A prefix to put before the error message, typically the name of the + argument. + allow_duplicate : bool, optional + If False, the default, disallow an axis from being specified twice. + + Returns + ------- + normalized_axes : tuple of int + The normalized axis index, such that `0 <= normalized_axis < ndim` + """ + # Optimization to speed-up the most common cases. + if type(axis) not in (tuple, list): + try: + axis = [operator.index(axis)] + except TypeError: + pass + # Going via an iterator directly is slower than via list comprehension. + axis = tuple([normalize_axis_index(ax, ndim, argname) for ax in axis]) + if not allow_duplicate and len(set(map(int, axis))) != len(axis): + if argname: + raise ValueError(f"repeated axis in `{argname}` argument") + else: + raise ValueError("repeated axis") + return axis + + +def allow_only_single_axis(axis): + if axis is None: + return axis + if len(axis) != 1: + raise NotImplementedError("does not handle tuple axis") + return axis[0] + + +def expand_shape(arr_shape, axis): + # taken from numpy 1.23.x, expand_dims function + if type(axis) not in (list, tuple): + axis = (axis,) + out_ndim = len(axis) + len(arr_shape) + axis = normalize_axis_tuple(axis, out_ndim) + shape_it = iter(arr_shape) + shape = [1 if ax in axis else next(shape_it) for ax in range(out_ndim)] + return shape + + +def apply_keepdims(tensor, axis, ndim): + if axis is None: + # tensor was a scalar + shape = (1,) * ndim + tensor = tensor.expand(shape).contiguous() + else: + shape = expand_shape(tensor.shape, axis) + tensor = tensor.reshape(shape) + return tensor + + +def axis_none_flatten(*tensors, axis=None): + """Flatten the arrays if axis is None.""" + if axis is None: + tensors = tuple(ar.flatten() for ar in tensors) + return tensors, 0 + else: + return tensors, axis + + +def typecast_tensor(t, target_dtype, casting): + """Dtype-cast tensor to target_dtype. + + Parameters + ---------- + t : torch.Tensor + The tensor to cast + target_dtype : torch dtype object + The array dtype to cast all tensors to + casting : str + The casting mode, see `np.can_cast` + + Returns + ------- + `torch.Tensor` of the `target_dtype` dtype + + Raises + ------ + ValueError + if the argument cannot be cast according to the `casting` rule + + """ + can_cast = _dtypes_impl.can_cast_impl + + if not can_cast(t.dtype, target_dtype, casting=casting): + raise TypeError( + f"Cannot cast array data from {t.dtype} to" + f" {target_dtype} according to the rule '{casting}'" + ) + return cast_if_needed(t, target_dtype) + + +def typecast_tensors(tensors, target_dtype, casting): + return tuple(typecast_tensor(t, target_dtype, casting) for t in tensors) + + +def _try_convert_to_tensor(obj): + try: + tensor = torch.as_tensor(obj) + except Exception as e: + mesg = f"failed to convert {obj} to ndarray. \nInternal error is: {str(e)}." + raise NotImplementedError(mesg) # noqa: B904 + return tensor + + +def _coerce_to_tensor(obj, dtype=None, copy=False, ndmin=0): + """The core logic of the array(...) function. + + Parameters + ---------- + obj : tensor_like + The thing to coerce + dtype : torch.dtype object or None + Coerce to this torch dtype + copy : bool + Copy or not + ndmin : int + The results as least this many dimensions + is_weak : bool + Whether obj is a weakly typed python scalar. + + Returns + ------- + tensor : torch.Tensor + a tensor object with requested dtype, ndim and copy semantics. + + Notes + ----- + This is almost a "tensor_like" coersion function. Does not handle wrapper + ndarrays (those should be handled in the ndarray-aware layer prior to + invoking this function). + """ + if isinstance(obj, torch.Tensor): + tensor = obj + else: + # tensor.dtype is the pytorch default, typically float32. If obj's elements + # are not exactly representable in float32, we've lost precision: + # >>> torch.as_tensor(1e12).item() - 1e12 + # -4096.0 + default_dtype = torch.get_default_dtype() + torch.set_default_dtype(_dtypes_impl.get_default_dtype_for(torch.float32)) + try: + tensor = _try_convert_to_tensor(obj) + finally: + torch.set_default_dtype(default_dtype) + + # type cast if requested + tensor = cast_if_needed(tensor, dtype) + + # adjust ndim if needed + ndim_extra = ndmin - tensor.ndim + if ndim_extra > 0: + tensor = tensor.view((1,) * ndim_extra + tensor.shape) + + # copy if requested + if copy: + tensor = tensor.clone() + + return tensor + + +def ndarrays_to_tensors(*inputs): + """Convert all ndarrays from `inputs` to tensors. (other things are intact)""" + from ._ndarray import ndarray + + if len(inputs) == 0: + return ValueError() + elif len(inputs) == 1: + input_ = inputs[0] + if isinstance(input_, ndarray): + return input_.tensor + elif isinstance(input_, tuple): + result = [] + for sub_input in input_: + sub_result = ndarrays_to_tensors(sub_input) + result.append(sub_result) + return tuple(result) + else: + return input_ + else: + assert isinstance(inputs, tuple) # sanity check + return ndarrays_to_tensors(inputs) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/fft.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/fft.py new file mode 100644 index 0000000000000000000000000000000000000000..b7d2f8365dbbd96c30c047d743dda806f12cb211 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/fft.py @@ -0,0 +1,130 @@ +# mypy: ignore-errors + +from __future__ import annotations + +import functools + +import torch + +from . import _dtypes_impl, _util +from ._normalizations import ArrayLike, normalizer + + +def upcast(func): + """NumPy fft casts inputs to 64 bit and *returns 64-bit results*.""" + + @functools.wraps(func) + def wrapped(tensor, *args, **kwds): + target_dtype = ( + _dtypes_impl.default_dtypes().complex_dtype + if tensor.is_complex() + else _dtypes_impl.default_dtypes().float_dtype + ) + tensor = _util.cast_if_needed(tensor, target_dtype) + return func(tensor, *args, **kwds) + + return wrapped + + +@normalizer +@upcast +def fft(a: ArrayLike, n=None, axis=-1, norm=None): + return torch.fft.fft(a, n, dim=axis, norm=norm) + + +@normalizer +@upcast +def ifft(a: ArrayLike, n=None, axis=-1, norm=None): + return torch.fft.ifft(a, n, dim=axis, norm=norm) + + +@normalizer +@upcast +def rfft(a: ArrayLike, n=None, axis=-1, norm=None): + return torch.fft.rfft(a, n, dim=axis, norm=norm) + + +@normalizer +@upcast +def irfft(a: ArrayLike, n=None, axis=-1, norm=None): + return torch.fft.irfft(a, n, dim=axis, norm=norm) + + +@normalizer +@upcast +def fftn(a: ArrayLike, s=None, axes=None, norm=None): + return torch.fft.fftn(a, s, dim=axes, norm=norm) + + +@normalizer +@upcast +def ifftn(a: ArrayLike, s=None, axes=None, norm=None): + return torch.fft.ifftn(a, s, dim=axes, norm=norm) + + +@normalizer +@upcast +def rfftn(a: ArrayLike, s=None, axes=None, norm=None): + return torch.fft.rfftn(a, s, dim=axes, norm=norm) + + +@normalizer +@upcast +def irfftn(a: ArrayLike, s=None, axes=None, norm=None): + return torch.fft.irfftn(a, s, dim=axes, norm=norm) + + +@normalizer +@upcast +def fft2(a: ArrayLike, s=None, axes=(-2, -1), norm=None): + return torch.fft.fft2(a, s, dim=axes, norm=norm) + + +@normalizer +@upcast +def ifft2(a: ArrayLike, s=None, axes=(-2, -1), norm=None): + return torch.fft.ifft2(a, s, dim=axes, norm=norm) + + +@normalizer +@upcast +def rfft2(a: ArrayLike, s=None, axes=(-2, -1), norm=None): + return torch.fft.rfft2(a, s, dim=axes, norm=norm) + + +@normalizer +@upcast +def irfft2(a: ArrayLike, s=None, axes=(-2, -1), norm=None): + return torch.fft.irfft2(a, s, dim=axes, norm=norm) + + +@normalizer +@upcast +def hfft(a: ArrayLike, n=None, axis=-1, norm=None): + return torch.fft.hfft(a, n, dim=axis, norm=norm) + + +@normalizer +@upcast +def ihfft(a: ArrayLike, n=None, axis=-1, norm=None): + return torch.fft.ihfft(a, n, dim=axis, norm=norm) + + +@normalizer +def fftfreq(n, d=1.0): + return torch.fft.fftfreq(n, d) + + +@normalizer +def rfftfreq(n, d=1.0): + return torch.fft.rfftfreq(n, d) + + +@normalizer +def fftshift(x: ArrayLike, axes=None): + return torch.fft.fftshift(x, axes) + + +@normalizer +def ifftshift(x: ArrayLike, axes=None): + return torch.fft.ifftshift(x, axes) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/linalg.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/linalg.py new file mode 100644 index 0000000000000000000000000000000000000000..093851142dbca5ab714126a532c0618dee298c45 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/linalg.py @@ -0,0 +1,239 @@ +# mypy: ignore-errors + +from __future__ import annotations + +import functools +import math +from typing import Sequence + +import torch + +from . import _dtypes_impl, _util +from ._normalizations import ArrayLike, KeepDims, normalizer + + +class LinAlgError(Exception): + pass + + +def _atleast_float_1(a): + if not (a.dtype.is_floating_point or a.dtype.is_complex): + a = a.to(_dtypes_impl.default_dtypes().float_dtype) + return a + + +def _atleast_float_2(a, b): + dtyp = _dtypes_impl.result_type_impl(a, b) + if not (dtyp.is_floating_point or dtyp.is_complex): + dtyp = _dtypes_impl.default_dtypes().float_dtype + + a = _util.cast_if_needed(a, dtyp) + b = _util.cast_if_needed(b, dtyp) + return a, b + + +def linalg_errors(func): + @functools.wraps(func) + def wrapped(*args, **kwds): + try: + return func(*args, **kwds) + except torch._C._LinAlgError as e: + raise LinAlgError(*e.args) # noqa: B904 + + return wrapped + + +# ### Matrix and vector products ### + + +@normalizer +@linalg_errors +def matrix_power(a: ArrayLike, n): + a = _atleast_float_1(a) + return torch.linalg.matrix_power(a, n) + + +@normalizer +@linalg_errors +def multi_dot(inputs: Sequence[ArrayLike], *, out=None): + return torch.linalg.multi_dot(inputs) + + +# ### Solving equations and inverting matrices ### + + +@normalizer +@linalg_errors +def solve(a: ArrayLike, b: ArrayLike): + a, b = _atleast_float_2(a, b) + return torch.linalg.solve(a, b) + + +@normalizer +@linalg_errors +def lstsq(a: ArrayLike, b: ArrayLike, rcond=None): + a, b = _atleast_float_2(a, b) + # NumPy is using gelsd: https://github.com/numpy/numpy/blob/v1.24.0/numpy/linalg/umath_linalg.cpp#L3991 + # on CUDA, only `gels` is available though, so use it instead + driver = "gels" if a.is_cuda or b.is_cuda else "gelsd" + return torch.linalg.lstsq(a, b, rcond=rcond, driver=driver) + + +@normalizer +@linalg_errors +def inv(a: ArrayLike): + a = _atleast_float_1(a) + result = torch.linalg.inv(a) + return result + + +@normalizer +@linalg_errors +def pinv(a: ArrayLike, rcond=1e-15, hermitian=False): + a = _atleast_float_1(a) + return torch.linalg.pinv(a, rtol=rcond, hermitian=hermitian) + + +@normalizer +@linalg_errors +def tensorsolve(a: ArrayLike, b: ArrayLike, axes=None): + a, b = _atleast_float_2(a, b) + return torch.linalg.tensorsolve(a, b, dims=axes) + + +@normalizer +@linalg_errors +def tensorinv(a: ArrayLike, ind=2): + a = _atleast_float_1(a) + return torch.linalg.tensorinv(a, ind=ind) + + +# ### Norms and other numbers ### + + +@normalizer +@linalg_errors +def det(a: ArrayLike): + a = _atleast_float_1(a) + return torch.linalg.det(a) + + +@normalizer +@linalg_errors +def slogdet(a: ArrayLike): + a = _atleast_float_1(a) + return torch.linalg.slogdet(a) + + +@normalizer +@linalg_errors +def cond(x: ArrayLike, p=None): + x = _atleast_float_1(x) + + # check if empty + # cf: https://github.com/numpy/numpy/blob/v1.24.0/numpy/linalg/linalg.py#L1744 + if x.numel() == 0 and math.prod(x.shape[-2:]) == 0: + raise LinAlgError("cond is not defined on empty arrays") + + result = torch.linalg.cond(x, p=p) + + # Convert nans to infs (numpy does it in a data-dependent way, depending on + # whether the input array has nans or not) + # XXX: NumPy does this: https://github.com/numpy/numpy/blob/v1.24.0/numpy/linalg/linalg.py#L1744 + return torch.where(torch.isnan(result), float("inf"), result) + + +@normalizer +@linalg_errors +def matrix_rank(a: ArrayLike, tol=None, hermitian=False): + a = _atleast_float_1(a) + + if a.ndim < 2: + return int((a != 0).any()) + + if tol is None: + # follow https://github.com/numpy/numpy/blob/v1.24.0/numpy/linalg/linalg.py#L1885 + atol = 0 + rtol = max(a.shape[-2:]) * torch.finfo(a.dtype).eps + else: + atol, rtol = tol, 0 + return torch.linalg.matrix_rank(a, atol=atol, rtol=rtol, hermitian=hermitian) + + +@normalizer +@linalg_errors +def norm(x: ArrayLike, ord=None, axis=None, keepdims: KeepDims = False): + x = _atleast_float_1(x) + return torch.linalg.norm(x, ord=ord, dim=axis) + + +# ### Decompositions ### + + +@normalizer +@linalg_errors +def cholesky(a: ArrayLike): + a = _atleast_float_1(a) + return torch.linalg.cholesky(a) + + +@normalizer +@linalg_errors +def qr(a: ArrayLike, mode="reduced"): + a = _atleast_float_1(a) + result = torch.linalg.qr(a, mode=mode) + if mode == "r": + # match NumPy + result = result.R + return result + + +@normalizer +@linalg_errors +def svd(a: ArrayLike, full_matrices=True, compute_uv=True, hermitian=False): + a = _atleast_float_1(a) + if not compute_uv: + return torch.linalg.svdvals(a) + + # NB: ignore the hermitian= argument (no pytorch equivalent) + result = torch.linalg.svd(a, full_matrices=full_matrices) + return result + + +# ### Eigenvalues and eigenvectors ### + + +@normalizer +@linalg_errors +def eig(a: ArrayLike): + a = _atleast_float_1(a) + w, vt = torch.linalg.eig(a) + + if not a.is_complex() and w.is_complex() and (w.imag == 0).all(): + w = w.real + vt = vt.real + return w, vt + + +@normalizer +@linalg_errors +def eigh(a: ArrayLike, UPLO="L"): + a = _atleast_float_1(a) + return torch.linalg.eigh(a, UPLO=UPLO) + + +@normalizer +@linalg_errors +def eigvals(a: ArrayLike): + a = _atleast_float_1(a) + result = torch.linalg.eigvals(a) + if not a.is_complex() and result.is_complex() and (result.imag == 0).all(): + result = result.real + return result + + +@normalizer +@linalg_errors +def eigvalsh(a: ArrayLike, UPLO="L"): + a = _atleast_float_1(a) + return torch.linalg.eigvalsh(a, UPLO=UPLO) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/random.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/random.py new file mode 100644 index 0000000000000000000000000000000000000000..b10a4c667c8c6376dfef7663c2b18d91e6582d99 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/random.py @@ -0,0 +1,191 @@ +# mypy: ignore-errors + +"""Wrapper to mimic (parts of) np.random API surface. + +NumPy has strict guarantees on reproducibility etc; here we don't give any. + +Q: default dtype is float64 in numpy + +""" +from __future__ import annotations + +import functools +from math import sqrt +from typing import Optional + +import torch + +from . import _dtypes_impl, _util +from ._normalizations import array_or_scalar, ArrayLike, normalizer + + +__all__ = [ + "seed", + "random_sample", + "sample", + "random", + "rand", + "randn", + "normal", + "choice", + "randint", + "shuffle", + "uniform", +] + + +def use_numpy_random(): + # local import to avoid ref cycles + import torch._dynamo.config as config + + return config.use_numpy_random_stream + + +def deco_stream(func): + @functools.wraps(func) + def inner(*args, **kwds): + if not use_numpy_random(): + return func(*args, **kwds) + else: + import numpy + + from ._ndarray import ndarray + + f = getattr(numpy.random, func.__name__) + + # numpy funcs accept numpy ndarrays, unwrap + args = tuple( + arg.tensor.numpy() if isinstance(arg, ndarray) else arg for arg in args + ) + kwds = { + key: val.tensor.numpy() if isinstance(val, ndarray) else val + for key, val in kwds.items() + } + + value = f(*args, **kwds) + + # `value` can be either numpy.ndarray or python scalar (or None) + if isinstance(value, numpy.ndarray): + value = ndarray(torch.as_tensor(value)) + + return value + + return inner + + +@deco_stream +def seed(seed=None): + if seed is not None: + torch.random.manual_seed(seed) + + +@deco_stream +def random_sample(size=None): + if size is None: + size = () + dtype = _dtypes_impl.default_dtypes().float_dtype + values = torch.empty(size, dtype=dtype).uniform_() + return array_or_scalar(values, return_scalar=size == ()) + + +def rand(*size): + if size == (): + size = None + return random_sample(size) + + +sample = random_sample +random = random_sample + + +@deco_stream +def uniform(low=0.0, high=1.0, size=None): + if size is None: + size = () + dtype = _dtypes_impl.default_dtypes().float_dtype + values = torch.empty(size, dtype=dtype).uniform_(low, high) + return array_or_scalar(values, return_scalar=size == ()) + + +@deco_stream +def randn(*size): + dtype = _dtypes_impl.default_dtypes().float_dtype + values = torch.randn(size, dtype=dtype) + return array_or_scalar(values, return_scalar=size == ()) + + +@deco_stream +def normal(loc=0.0, scale=1.0, size=None): + if size is None: + size = () + dtype = _dtypes_impl.default_dtypes().float_dtype + values = torch.empty(size, dtype=dtype).normal_(loc, scale) + return array_or_scalar(values, return_scalar=size == ()) + + +@deco_stream +def shuffle(x): + # no @normalizer because we do not cast e.g. lists to tensors + from ._ndarray import ndarray + + if isinstance(x, torch.Tensor): + tensor = x + elif isinstance(x, ndarray): + tensor = x.tensor + else: + raise NotImplementedError("We do not random.shuffle lists in-place") + + perm = torch.randperm(tensor.shape[0]) + xp = tensor[perm] + tensor.copy_(xp) + + +@deco_stream +def randint(low, high=None, size=None): + if size is None: + size = () + if not isinstance(size, (tuple, list)): + size = (size,) + if high is None: + low, high = 0, low + values = torch.randint(low, high, size=size) + return array_or_scalar(values, int, return_scalar=size == ()) + + +@deco_stream +@normalizer +def choice(a: ArrayLike, size=None, replace=True, p: Optional[ArrayLike] = None): + # https://stackoverflow.com/questions/59461811/random-choice-with-pytorch + if a.numel() == 1: + a = torch.arange(a) + + # TODO: check a.dtype is integer -- cf np.random.choice(3.4) which raises + + # number of draws + if size is None: + num_el = 1 + elif _util.is_sequence(size): + num_el = 1 + for el in size: + num_el *= el + else: + num_el = size + + # prepare the probabilities + if p is None: + p = torch.ones_like(a) / a.shape[0] + + # cf https://github.com/numpy/numpy/blob/main/numpy/random/mtrand.pyx#L973 + atol = sqrt(torch.finfo(p.dtype).eps) + if abs(p.sum() - 1.0) > atol: + raise ValueError("probabilities do not sum to 1.") + + # actually sample + indices = torch.multinomial(p, num_el, replacement=replace) + + if _util.is_sequence(size): + indices = indices.reshape(size) + + samples = a[indices] + + return samples diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/testing/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/testing/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..05e73b12e29f8e6608647a3f16fabab39fbfb582 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/testing/__init__.py @@ -0,0 +1,20 @@ +# mypy: ignore-errors + +from .utils import ( + _gen_alignment_data, + assert_, + assert_allclose, + assert_almost_equal, + assert_array_almost_equal, + assert_array_equal, + assert_array_less, + assert_equal, + assert_raises_regex, + assert_warns, + HAS_REFCOUNT, + IS_WASM, + suppress_warnings, +) + + +# from .testing import assert_allclose # FIXME diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/testing/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/testing/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ff3dd74bb4298a312bdbd686c685a2bee5647c15 Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/testing/__pycache__/__init__.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/testing/__pycache__/utils.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/testing/__pycache__/utils.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4159df73d95663d052f4f4e6d75bfc5253218139 Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/testing/__pycache__/utils.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/testing/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/testing/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..3af3f2275a92c21c0c3c4904f27ca60b2aa11a23 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_numpy/testing/utils.py @@ -0,0 +1,2392 @@ +# mypy: ignore-errors + +""" +Utility function to facilitate testing. + +""" +import contextlib +import gc +import operator +import os +import platform +import pprint +import re +import shutil +import sys +import warnings +from functools import wraps +from io import StringIO +from tempfile import mkdtemp, mkstemp +from warnings import WarningMessage + +import torch._numpy as np +from torch._numpy import arange, asarray as asanyarray, empty, float32, intp, ndarray + + +__all__ = [ + "assert_equal", + "assert_almost_equal", + "assert_approx_equal", + "assert_array_equal", + "assert_array_less", + "assert_string_equal", + "assert_", + "assert_array_almost_equal", + "build_err_msg", + "decorate_methods", + "print_assert_equal", + "verbose", + "assert_", + "assert_array_almost_equal_nulp", + "assert_raises_regex", + "assert_array_max_ulp", + "assert_warns", + "assert_no_warnings", + "assert_allclose", + "IgnoreException", + "clear_and_catch_warnings", + "temppath", + "tempdir", + "IS_PYPY", + "HAS_REFCOUNT", + "IS_WASM", + "suppress_warnings", + "assert_array_compare", + "assert_no_gc_cycles", + "break_cycles", + "IS_PYSTON", +] + + +verbose = 0 + +IS_WASM = platform.machine() in ["wasm32", "wasm64"] +IS_PYPY = sys.implementation.name == "pypy" +IS_PYSTON = hasattr(sys, "pyston_version_info") +HAS_REFCOUNT = getattr(sys, "getrefcount", None) is not None and not IS_PYSTON + + +def assert_(val, msg=""): + """ + Assert that works in release mode. + Accepts callable msg to allow deferring evaluation until failure. + + The Python built-in ``assert`` does not work when executing code in + optimized mode (the ``-O`` flag) - no byte-code is generated for it. + + For documentation on usage, refer to the Python documentation. + + """ + __tracebackhide__ = True # Hide traceback for py.test + if not val: + try: + smsg = msg() + except TypeError: + smsg = msg + raise AssertionError(smsg) + + +def gisnan(x): + return np.isnan(x) + + +def gisfinite(x): + return np.isfinite(x) + + +def gisinf(x): + return np.isinf(x) + + +def build_err_msg( + arrays, + err_msg, + header="Items are not equal:", + verbose=True, + names=("ACTUAL", "DESIRED"), + precision=8, +): + msg = ["\n" + header] + if err_msg: + if err_msg.find("\n") == -1 and len(err_msg) < 79 - len(header): + msg = [msg[0] + " " + err_msg] + else: + msg.append(err_msg) + if verbose: + for i, a in enumerate(arrays): + if isinstance(a, ndarray): + # precision argument is only needed if the objects are ndarrays + # r_func = partial(array_repr, precision=precision) + r_func = ndarray.__repr__ + else: + r_func = repr + + try: + r = r_func(a) + except Exception as exc: + r = f"[repr failed for <{type(a).__name__}>: {exc}]" + if r.count("\n") > 3: + r = "\n".join(r.splitlines()[:3]) + r += "..." + msg.append(f" {names[i]}: {r}") + return "\n".join(msg) + + +def assert_equal(actual, desired, err_msg="", verbose=True): + """ + Raises an AssertionError if two objects are not equal. + + Given two objects (scalars, lists, tuples, dictionaries or numpy arrays), + check that all elements of these objects are equal. An exception is raised + at the first conflicting values. + + When one of `actual` and `desired` is a scalar and the other is array_like, + the function checks that each element of the array_like object is equal to + the scalar. + + This function handles NaN comparisons as if NaN was a "normal" number. + That is, AssertionError is not raised if both objects have NaNs in the same + positions. This is in contrast to the IEEE standard on NaNs, which says + that NaN compared to anything must return False. + + Parameters + ---------- + actual : array_like + The object to check. + desired : array_like + The expected object. + err_msg : str, optional + The error message to be printed in case of failure. + verbose : bool, optional + If True, the conflicting values are appended to the error message. + + Raises + ------ + AssertionError + If actual and desired are not equal. + + Examples + -------- + >>> np.testing.assert_equal([4,5], [4,6]) + Traceback (most recent call last): + ... + AssertionError: + Items are not equal: + item=1 + ACTUAL: 5 + DESIRED: 6 + + The following comparison does not raise an exception. There are NaNs + in the inputs, but they are in the same positions. + + >>> np.testing.assert_equal(np.array([1.0, 2.0, np.nan]), [1, 2, np.nan]) + + """ + __tracebackhide__ = True # Hide traceback for py.test + + num_nones = sum([actual is None, desired is None]) + if num_nones == 1: + raise AssertionError(f"Not equal: {actual} != {desired}") + elif num_nones == 2: + return True + # else, carry on + + if isinstance(actual, np.DType) or isinstance(desired, np.DType): + result = actual == desired + if not result: + raise AssertionError(f"Not equal: {actual} != {desired}") + else: + return True + + if isinstance(desired, str) and isinstance(actual, str): + assert actual == desired + return + + if isinstance(desired, dict): + if not isinstance(actual, dict): + raise AssertionError(repr(type(actual))) + assert_equal(len(actual), len(desired), err_msg, verbose) + for k in desired.keys(): + if k not in actual: + raise AssertionError(repr(k)) + assert_equal(actual[k], desired[k], f"key={k!r}\n{err_msg}", verbose) + return + if isinstance(desired, (list, tuple)) and isinstance(actual, (list, tuple)): + assert_equal(len(actual), len(desired), err_msg, verbose) + for k in range(len(desired)): + assert_equal(actual[k], desired[k], f"item={k!r}\n{err_msg}", verbose) + return + + from torch._numpy import imag, iscomplexobj, isscalar, ndarray, real, signbit + + if isinstance(actual, ndarray) or isinstance(desired, ndarray): + return assert_array_equal(actual, desired, err_msg, verbose) + msg = build_err_msg([actual, desired], err_msg, verbose=verbose) + + # Handle complex numbers: separate into real/imag to handle + # nan/inf/negative zero correctly + # XXX: catch ValueError for subclasses of ndarray where iscomplex fail + try: + usecomplex = iscomplexobj(actual) or iscomplexobj(desired) + except (ValueError, TypeError): + usecomplex = False + + if usecomplex: + if iscomplexobj(actual): + actualr = real(actual) + actuali = imag(actual) + else: + actualr = actual + actuali = 0 + if iscomplexobj(desired): + desiredr = real(desired) + desiredi = imag(desired) + else: + desiredr = desired + desiredi = 0 + try: + assert_equal(actualr, desiredr) + assert_equal(actuali, desiredi) + except AssertionError: + raise AssertionError(msg) # noqa: B904 + + # isscalar test to check cases such as [np.nan] != np.nan + if isscalar(desired) != isscalar(actual): + raise AssertionError(msg) + + # Inf/nan/negative zero handling + try: + isdesnan = gisnan(desired) + isactnan = gisnan(actual) + if isdesnan and isactnan: + return # both nan, so equal + + # handle signed zero specially for floats + array_actual = np.asarray(actual) + array_desired = np.asarray(desired) + + if desired == 0 and actual == 0: + if not signbit(desired) == signbit(actual): + raise AssertionError(msg) + + except (TypeError, ValueError, NotImplementedError): + pass + + try: + # Explicitly use __eq__ for comparison, gh-2552 + if not (desired == actual): + raise AssertionError(msg) + + except (DeprecationWarning, FutureWarning) as e: + # this handles the case when the two types are not even comparable + if "elementwise == comparison" in e.args[0]: + raise AssertionError(msg) # noqa: B904 + else: + raise + + +def print_assert_equal(test_string, actual, desired): + """ + Test if two objects are equal, and print an error message if test fails. + + The test is performed with ``actual == desired``. + + Parameters + ---------- + test_string : str + The message supplied to AssertionError. + actual : object + The object to test for equality against `desired`. + desired : object + The expected result. + + Examples + -------- + >>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 1]) # doctest: +SKIP + >>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 2]) # doctest: +SKIP + Traceback (most recent call last): + ... + AssertionError: Test XYZ of func xyz failed + ACTUAL: + [0, 1] + DESIRED: + [0, 2] + + """ + __tracebackhide__ = True # Hide traceback for py.test + import pprint + + if not (actual == desired): + msg = StringIO() + msg.write(test_string) + msg.write(" failed\nACTUAL: \n") + pprint.pprint(actual, msg) + msg.write("DESIRED: \n") + pprint.pprint(desired, msg) + raise AssertionError(msg.getvalue()) + + +def assert_almost_equal(actual, desired, decimal=7, err_msg="", verbose=True): + """ + Raises an AssertionError if two items are not equal up to desired + precision. + + .. note:: It is recommended to use one of `assert_allclose`, + `assert_array_almost_equal_nulp` or `assert_array_max_ulp` + instead of this function for more consistent floating point + comparisons. + + The test verifies that the elements of `actual` and `desired` satisfy. + + ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` + + That is a looser test than originally documented, but agrees with what the + actual implementation in `assert_array_almost_equal` did up to rounding + vagaries. An exception is raised at conflicting values. For ndarrays this + delegates to assert_array_almost_equal + + Parameters + ---------- + actual : array_like + The object to check. + desired : array_like + The expected object. + decimal : int, optional + Desired precision, default is 7. + err_msg : str, optional + The error message to be printed in case of failure. + verbose : bool, optional + If True, the conflicting values are appended to the error message. + + Raises + ------ + AssertionError + If actual and desired are not equal up to specified precision. + + See Also + -------- + assert_allclose: Compare two array_like objects for equality with desired + relative and/or absolute precision. + assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal + + Examples + -------- + >>> from torch._numpy.testing import assert_almost_equal + >>> assert_almost_equal(2.3333333333333, 2.33333334) + >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) + Traceback (most recent call last): + ... + AssertionError: + Arrays are not almost equal to 10 decimals + ACTUAL: 2.3333333333333 + DESIRED: 2.33333334 + + >>> assert_almost_equal(np.array([1.0,2.3333333333333]), + ... np.array([1.0,2.33333334]), decimal=9) + Traceback (most recent call last): + ... + AssertionError: + Arrays are not almost equal to 9 decimals + + Mismatched elements: 1 / 2 (50%) + Max absolute difference: 6.666699636781459e-09 + Max relative difference: 2.8571569790287484e-09 + x: torch.ndarray([1.0000, 2.3333], dtype=float64) + y: torch.ndarray([1.0000, 2.3333], dtype=float64) + + """ + __tracebackhide__ = True # Hide traceback for py.test + from torch._numpy import imag, iscomplexobj, ndarray, real + + # Handle complex numbers: separate into real/imag to handle + # nan/inf/negative zero correctly + # XXX: catch ValueError for subclasses of ndarray where iscomplex fail + try: + usecomplex = iscomplexobj(actual) or iscomplexobj(desired) + except ValueError: + usecomplex = False + + def _build_err_msg(): + header = "Arrays are not almost equal to %d decimals" % decimal + return build_err_msg([actual, desired], err_msg, verbose=verbose, header=header) + + if usecomplex: + if iscomplexobj(actual): + actualr = real(actual) + actuali = imag(actual) + else: + actualr = actual + actuali = 0 + if iscomplexobj(desired): + desiredr = real(desired) + desiredi = imag(desired) + else: + desiredr = desired + desiredi = 0 + try: + assert_almost_equal(actualr, desiredr, decimal=decimal) + assert_almost_equal(actuali, desiredi, decimal=decimal) + except AssertionError: + raise AssertionError(_build_err_msg()) # noqa: B904 + + if isinstance(actual, (ndarray, tuple, list)) or isinstance( + desired, (ndarray, tuple, list) + ): + return assert_array_almost_equal(actual, desired, decimal, err_msg) + try: + # If one of desired/actual is not finite, handle it specially here: + # check that both are nan if any is a nan, and test for equality + # otherwise + if not (gisfinite(desired) and gisfinite(actual)): + if gisnan(desired) or gisnan(actual): + if not (gisnan(desired) and gisnan(actual)): + raise AssertionError(_build_err_msg()) + else: + if not desired == actual: + raise AssertionError(_build_err_msg()) + return + except (NotImplementedError, TypeError): + pass + if abs(desired - actual) >= np.float64(1.5 * 10.0 ** (-decimal)): + raise AssertionError(_build_err_msg()) + + +def assert_approx_equal(actual, desired, significant=7, err_msg="", verbose=True): + """ + Raises an AssertionError if two items are not equal up to significant + digits. + + .. note:: It is recommended to use one of `assert_allclose`, + `assert_array_almost_equal_nulp` or `assert_array_max_ulp` + instead of this function for more consistent floating point + comparisons. + + Given two numbers, check that they are approximately equal. + Approximately equal is defined as the number of significant digits + that agree. + + Parameters + ---------- + actual : scalar + The object to check. + desired : scalar + The expected object. + significant : int, optional + Desired precision, default is 7. + err_msg : str, optional + The error message to be printed in case of failure. + verbose : bool, optional + If True, the conflicting values are appended to the error message. + + Raises + ------ + AssertionError + If actual and desired are not equal up to specified precision. + + See Also + -------- + assert_allclose: Compare two array_like objects for equality with desired + relative and/or absolute precision. + assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal + + Examples + -------- + >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP + >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP + ... significant=8) + >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP + ... significant=8) + Traceback (most recent call last): + ... + AssertionError: + Items are not equal to 8 significant digits: + ACTUAL: 1.234567e-21 + DESIRED: 1.2345672e-21 + + the evaluated condition that raises the exception is + + >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) + True + + """ + __tracebackhide__ = True # Hide traceback for py.test + import numpy as np + + (actual, desired) = map(float, (actual, desired)) + if desired == actual: + return + # Normalized the numbers to be in range (-10.0,10.0) + # scale = float(pow(10,math.floor(math.log10(0.5*(abs(desired)+abs(actual)))))) + scale = 0.5 * (np.abs(desired) + np.abs(actual)) + scale = np.power(10, np.floor(np.log10(scale))) + try: + sc_desired = desired / scale + except ZeroDivisionError: + sc_desired = 0.0 + try: + sc_actual = actual / scale + except ZeroDivisionError: + sc_actual = 0.0 + msg = build_err_msg( + [actual, desired], + err_msg, + header="Items are not equal to %d significant digits:" % significant, + verbose=verbose, + ) + try: + # If one of desired/actual is not finite, handle it specially here: + # check that both are nan if any is a nan, and test for equality + # otherwise + if not (gisfinite(desired) and gisfinite(actual)): + if gisnan(desired) or gisnan(actual): + if not (gisnan(desired) and gisnan(actual)): + raise AssertionError(msg) + else: + if not desired == actual: + raise AssertionError(msg) + return + except (TypeError, NotImplementedError): + pass + if np.abs(sc_desired - sc_actual) >= np.power(10.0, -(significant - 1)): + raise AssertionError(msg) + + +def assert_array_compare( + comparison, + x, + y, + err_msg="", + verbose=True, + header="", + precision=6, + equal_nan=True, + equal_inf=True, + *, + strict=False, +): + __tracebackhide__ = True # Hide traceback for py.test + from torch._numpy import all, array, asarray, bool_, inf, isnan, max + + x = asarray(x) + y = asarray(y) + + def array2string(a): + return str(a) + + # original array for output formatting + ox, oy = x, y + + def func_assert_same_pos(x, y, func=isnan, hasval="nan"): + """Handling nan/inf. + + Combine results of running func on x and y, checking that they are True + at the same locations. + + """ + __tracebackhide__ = True # Hide traceback for py.test + x_id = func(x) + y_id = func(y) + # We include work-arounds here to handle three types of slightly + # pathological ndarray subclasses: + # (1) all() on `masked` array scalars can return masked arrays, so we + # use != True + # (2) __eq__ on some ndarray subclasses returns Python booleans + # instead of element-wise comparisons, so we cast to bool_() and + # use isinstance(..., bool) checks + # (3) subclasses with bare-bones __array_function__ implementations may + # not implement np.all(), so favor using the .all() method + # We are not committed to supporting such subclasses, but it's nice to + # support them if possible. + if (x_id == y_id).all().item() is not True: + msg = build_err_msg( + [x, y], + err_msg + f"\nx and y {hasval} location mismatch:", + verbose=verbose, + header=header, + names=("x", "y"), + precision=precision, + ) + raise AssertionError(msg) + # If there is a scalar, then here we know the array has the same + # flag as it everywhere, so we should return the scalar flag. + if isinstance(x_id, bool) or x_id.ndim == 0: + return bool_(x_id) + elif isinstance(y_id, bool) or y_id.ndim == 0: + return bool_(y_id) + else: + return y_id + + try: + if strict: + cond = x.shape == y.shape and x.dtype == y.dtype + else: + cond = (x.shape == () or y.shape == ()) or x.shape == y.shape + if not cond: + if x.shape != y.shape: + reason = f"\n(shapes {x.shape}, {y.shape} mismatch)" + else: + reason = f"\n(dtypes {x.dtype}, {y.dtype} mismatch)" + msg = build_err_msg( + [x, y], + err_msg + reason, + verbose=verbose, + header=header, + names=("x", "y"), + precision=precision, + ) + raise AssertionError(msg) + + flagged = bool_(False) + + if equal_nan: + flagged = func_assert_same_pos(x, y, func=isnan, hasval="nan") + + if equal_inf: + flagged |= func_assert_same_pos( + x, y, func=lambda xy: xy == +inf, hasval="+inf" + ) + flagged |= func_assert_same_pos( + x, y, func=lambda xy: xy == -inf, hasval="-inf" + ) + + if flagged.ndim > 0: + x, y = x[~flagged], y[~flagged] + # Only do the comparison if actual values are left + if x.size == 0: + return + elif flagged: + # no sense doing comparison if everything is flagged. + return + + val = comparison(x, y) + + if isinstance(val, bool): + cond = val + reduced = array([val]) + else: + reduced = val.ravel() + cond = reduced.all() + + # The below comparison is a hack to ensure that fully masked + # results, for which val.ravel().all() returns np.ma.masked, + # do not trigger a failure (np.ma.masked != True evaluates as + # np.ma.masked, which is falsy). + if not cond: + n_mismatch = reduced.size - int(reduced.sum(dtype=intp)) + n_elements = flagged.size if flagged.ndim != 0 else reduced.size + percent_mismatch = 100 * n_mismatch / n_elements + remarks = [ + f"Mismatched elements: {n_mismatch} / {n_elements} ({percent_mismatch:.3g}%)" + ] + + # with errstate(all='ignore'): + # ignore errors for non-numeric types + with contextlib.suppress(TypeError, RuntimeError): + error = abs(x - y) + if np.issubdtype(x.dtype, np.unsignedinteger): + error2 = abs(y - x) + np.minimum(error, error2, out=error) + max_abs_error = max(error) + remarks.append( + "Max absolute difference: " + array2string(max_abs_error.item()) + ) + + # note: this definition of relative error matches that one + # used by assert_allclose (found in np.isclose) + # Filter values where the divisor would be zero + nonzero = bool_(y != 0) + if all(~nonzero): + max_rel_error = array(inf) + else: + max_rel_error = max(error[nonzero] / abs(y[nonzero])) + remarks.append( + "Max relative difference: " + array2string(max_rel_error.item()) + ) + + err_msg += "\n" + "\n".join(remarks) + msg = build_err_msg( + [ox, oy], + err_msg, + verbose=verbose, + header=header, + names=("x", "y"), + precision=precision, + ) + raise AssertionError(msg) + except ValueError: + import traceback + + efmt = traceback.format_exc() + header = f"error during assertion:\n\n{efmt}\n\n{header}" + + msg = build_err_msg( + [x, y], + err_msg, + verbose=verbose, + header=header, + names=("x", "y"), + precision=precision, + ) + raise ValueError(msg) # noqa: B904 + + +def assert_array_equal(x, y, err_msg="", verbose=True, *, strict=False): + """ + Raises an AssertionError if two array_like objects are not equal. + + Given two array_like objects, check that the shape is equal and all + elements of these objects are equal (but see the Notes for the special + handling of a scalar). An exception is raised at shape mismatch or + conflicting values. In contrast to the standard usage in numpy, NaNs + are compared like numbers, no assertion is raised if both objects have + NaNs in the same positions. + + The usual caution for verifying equality with floating point numbers is + advised. + + Parameters + ---------- + x : array_like + The actual object to check. + y : array_like + The desired, expected object. + err_msg : str, optional + The error message to be printed in case of failure. + verbose : bool, optional + If True, the conflicting values are appended to the error message. + strict : bool, optional + If True, raise an AssertionError when either the shape or the data + type of the array_like objects does not match. The special + handling for scalars mentioned in the Notes section is disabled. + + Raises + ------ + AssertionError + If actual and desired objects are not equal. + + See Also + -------- + assert_allclose: Compare two array_like objects for equality with desired + relative and/or absolute precision. + assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal + + Notes + ----- + When one of `x` and `y` is a scalar and the other is array_like, the + function checks that each element of the array_like object is equal to + the scalar. This behaviour can be disabled with the `strict` parameter. + + Examples + -------- + The first assert does not raise an exception: + + >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], + ... [np.exp(0),2.33333, np.nan]) + + Use `assert_allclose` or one of the nulp (number of floating point values) + functions for these cases instead: + + >>> np.testing.assert_allclose([1.0,np.pi,np.nan], + ... [1, np.sqrt(np.pi)**2, np.nan], + ... rtol=1e-10, atol=0) + + As mentioned in the Notes section, `assert_array_equal` has special + handling for scalars. Here the test checks that each value in `x` is 3: + + >>> x = np.full((2, 5), fill_value=3) + >>> np.testing.assert_array_equal(x, 3) + + Use `strict` to raise an AssertionError when comparing a scalar with an + array: + + >>> np.testing.assert_array_equal(x, 3, strict=True) + Traceback (most recent call last): + ... + AssertionError: + Arrays are not equal + + (shapes (2, 5), () mismatch) + x: torch.ndarray([[3, 3, 3, 3, 3], + [3, 3, 3, 3, 3]]) + y: torch.ndarray(3) + + The `strict` parameter also ensures that the array data types match: + + >>> x = np.array([2, 2, 2]) + >>> y = np.array([2., 2., 2.], dtype=np.float32) + >>> np.testing.assert_array_equal(x, y, strict=True) + Traceback (most recent call last): + ... + AssertionError: + Arrays are not equal + + (dtypes dtype("int64"), dtype("float32") mismatch) + x: torch.ndarray([2, 2, 2]) + y: torch.ndarray([2., 2., 2.]) + """ + __tracebackhide__ = True # Hide traceback for py.test + assert_array_compare( + operator.__eq__, + x, + y, + err_msg=err_msg, + verbose=verbose, + header="Arrays are not equal", + strict=strict, + ) + + +def assert_array_almost_equal(x, y, decimal=6, err_msg="", verbose=True): + """ + Raises an AssertionError if two objects are not equal up to desired + precision. + + .. note:: It is recommended to use one of `assert_allclose`, + `assert_array_almost_equal_nulp` or `assert_array_max_ulp` + instead of this function for more consistent floating point + comparisons. + + The test verifies identical shapes and that the elements of ``actual`` and + ``desired`` satisfy. + + ``abs(desired-actual) < 1.5 * 10**(-decimal)`` + + That is a looser test than originally documented, but agrees with what the + actual implementation did up to rounding vagaries. An exception is raised + at shape mismatch or conflicting values. In contrast to the standard usage + in numpy, NaNs are compared like numbers, no assertion is raised if both + objects have NaNs in the same positions. + + Parameters + ---------- + x : array_like + The actual object to check. + y : array_like + The desired, expected object. + decimal : int, optional + Desired precision, default is 6. + err_msg : str, optional + The error message to be printed in case of failure. + verbose : bool, optional + If True, the conflicting values are appended to the error message. + + Raises + ------ + AssertionError + If actual and desired are not equal up to specified precision. + + See Also + -------- + assert_allclose: Compare two array_like objects for equality with desired + relative and/or absolute precision. + assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal + + Examples + -------- + the first assert does not raise an exception + + >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], + ... [1.0,2.333,np.nan]) + + >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], + ... [1.0,2.33339,np.nan], decimal=5) + Traceback (most recent call last): + ... + AssertionError: + Arrays are not almost equal to 5 decimals + + Mismatched elements: 1 / 3 (33.3%) + Max absolute difference: 5.999999999994898e-05 + Max relative difference: 2.5713661239633743e-05 + x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) + y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) + + >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], + ... [1.0,2.33333, 5], decimal=5) + Traceback (most recent call last): + ... + AssertionError: + Arrays are not almost equal to 5 decimals + + x and y nan location mismatch: + x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) + y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) + + """ + __tracebackhide__ = True # Hide traceback for py.test + from torch._numpy import any as npany, float_, issubdtype, number, result_type + + def compare(x, y): + try: + if npany(gisinf(x)) or npany(gisinf(y)): + xinfid = gisinf(x) + yinfid = gisinf(y) + if not (xinfid == yinfid).all(): + return False + # if one item, x and y is +- inf + if x.size == y.size == 1: + return x == y + x = x[~xinfid] + y = y[~yinfid] + except (TypeError, NotImplementedError): + pass + + # make sure y is an inexact type to avoid abs(MIN_INT); will cause + # casting of x later. + dtype = result_type(y, 1.0) + y = asanyarray(y, dtype) + z = abs(x - y) + + if not issubdtype(z.dtype, number): + z = z.astype(float_) # handle object arrays + + return z < 1.5 * 10.0 ** (-decimal) + + assert_array_compare( + compare, + x, + y, + err_msg=err_msg, + verbose=verbose, + header=("Arrays are not almost equal to %d decimals" % decimal), + precision=decimal, + ) + + +def assert_array_less(x, y, err_msg="", verbose=True): + """ + Raises an AssertionError if two array_like objects are not ordered by less + than. + + Given two array_like objects, check that the shape is equal and all + elements of the first object are strictly smaller than those of the + second object. An exception is raised at shape mismatch or incorrectly + ordered values. Shape mismatch does not raise if an object has zero + dimension. In contrast to the standard usage in numpy, NaNs are + compared, no assertion is raised if both objects have NaNs in the same + positions. + + + + Parameters + ---------- + x : array_like + The smaller object to check. + y : array_like + The larger object to compare. + err_msg : string + The error message to be printed in case of failure. + verbose : bool + If True, the conflicting values are appended to the error message. + + Raises + ------ + AssertionError + If actual and desired objects are not equal. + + See Also + -------- + assert_array_equal: tests objects for equality + assert_array_almost_equal: test objects for equality up to precision + + + + Examples + -------- + >>> np.testing.assert_array_less([1.0, 1.0, np.nan], [1.1, 2.0, np.nan]) + >>> np.testing.assert_array_less([1.0, 1.0, np.nan], [1, 2.0, np.nan]) + Traceback (most recent call last): + ... + AssertionError: + Arrays are not less-ordered + + Mismatched elements: 1 / 3 (33.3%) + Max absolute difference: 1.0 + Max relative difference: 0.5 + x: torch.ndarray([1., 1., nan], dtype=float64) + y: torch.ndarray([1., 2., nan], dtype=float64) + + >>> np.testing.assert_array_less([1.0, 4.0], 3) + Traceback (most recent call last): + ... + AssertionError: + Arrays are not less-ordered + + Mismatched elements: 1 / 2 (50%) + Max absolute difference: 2.0 + Max relative difference: 0.6666666666666666 + x: torch.ndarray([1., 4.], dtype=float64) + y: torch.ndarray(3) + + >>> np.testing.assert_array_less([1.0, 2.0, 3.0], [4]) + Traceback (most recent call last): + ... + AssertionError: + Arrays are not less-ordered + + (shapes (3,), (1,) mismatch) + x: torch.ndarray([1., 2., 3.], dtype=float64) + y: torch.ndarray([4]) + + """ + __tracebackhide__ = True # Hide traceback for py.test + assert_array_compare( + operator.__lt__, + x, + y, + err_msg=err_msg, + verbose=verbose, + header="Arrays are not less-ordered", + equal_inf=False, + ) + + +def assert_string_equal(actual, desired): + """ + Test if two strings are equal. + + If the given strings are equal, `assert_string_equal` does nothing. + If they are not equal, an AssertionError is raised, and the diff + between the strings is shown. + + Parameters + ---------- + actual : str + The string to test for equality against the expected string. + desired : str + The expected string. + + Examples + -------- + >>> np.testing.assert_string_equal('abc', 'abc') # doctest: +SKIP + >>> np.testing.assert_string_equal('abc', 'abcd') # doctest: +SKIP + Traceback (most recent call last): + File "", line 1, in + ... + AssertionError: Differences in strings: + - abc+ abcd? + + + """ + # delay import of difflib to reduce startup time + __tracebackhide__ = True # Hide traceback for py.test + import difflib + + if not isinstance(actual, str): + raise AssertionError(repr(type(actual))) + if not isinstance(desired, str): + raise AssertionError(repr(type(desired))) + if desired == actual: + return + + diff = list( + difflib.Differ().compare(actual.splitlines(True), desired.splitlines(True)) + ) + diff_list = [] + while diff: + d1 = diff.pop(0) + if d1.startswith(" "): + continue + if d1.startswith("- "): + l = [d1] + d2 = diff.pop(0) + if d2.startswith("? "): + l.append(d2) + d2 = diff.pop(0) + if not d2.startswith("+ "): + raise AssertionError(repr(d2)) + l.append(d2) + if diff: + d3 = diff.pop(0) + if d3.startswith("? "): + l.append(d3) + else: + diff.insert(0, d3) + if d2[2:] == d1[2:]: + continue + diff_list.extend(l) + continue + raise AssertionError(repr(d1)) + if not diff_list: + return + msg = f"Differences in strings:\n{''.join(diff_list).rstrip()}" + if actual != desired: + raise AssertionError(msg) + + +import unittest + + +class _Dummy(unittest.TestCase): + def nop(self): + pass + + +_d = _Dummy("nop") + + +def assert_raises_regex(exception_class, expected_regexp, *args, **kwargs): + """ + assert_raises_regex(exception_class, expected_regexp, callable, *args, + **kwargs) + assert_raises_regex(exception_class, expected_regexp) + + Fail unless an exception of class exception_class and with message that + matches expected_regexp is thrown by callable when invoked with arguments + args and keyword arguments kwargs. + + Alternatively, can be used as a context manager like `assert_raises`. + + Notes + ----- + .. versionadded:: 1.9.0 + + """ + __tracebackhide__ = True # Hide traceback for py.test + return _d.assertRaisesRegex(exception_class, expected_regexp, *args, **kwargs) + + +def decorate_methods(cls, decorator, testmatch=None): + """ + Apply a decorator to all methods in a class matching a regular expression. + + The given decorator is applied to all public methods of `cls` that are + matched by the regular expression `testmatch` + (``testmatch.search(methodname)``). Methods that are private, i.e. start + with an underscore, are ignored. + + Parameters + ---------- + cls : class + Class whose methods to decorate. + decorator : function + Decorator to apply to methods + testmatch : compiled regexp or str, optional + The regular expression. Default value is None, in which case the + nose default (``re.compile(r'(?:^|[\\b_\\.%s-])[Tt]est' % os.sep)``) + is used. + If `testmatch` is a string, it is compiled to a regular expression + first. + + """ + if testmatch is None: + testmatch = re.compile(rf"(?:^|[\\b_\\.{os.sep}-])[Tt]est") + else: + testmatch = re.compile(testmatch) + cls_attr = cls.__dict__ + + # delayed import to reduce startup time + from inspect import isfunction + + methods = [_m for _m in cls_attr.values() if isfunction(_m)] + for function in methods: + try: + if hasattr(function, "compat_func_name"): + funcname = function.compat_func_name + else: + funcname = function.__name__ + except AttributeError: + # not a function + continue + if testmatch.search(funcname) and not funcname.startswith("_"): + setattr(cls, funcname, decorator(function)) + return + + +def _assert_valid_refcount(op): + """ + Check that ufuncs don't mishandle refcount of object `1`. + Used in a few regression tests. + """ + if not HAS_REFCOUNT: + return True + + import gc + + import numpy as np + + b = np.arange(100 * 100).reshape(100, 100) + c = b + i = 1 + + gc.disable() + try: + rc = sys.getrefcount(i) + for j in range(15): + d = op(b, c) + assert_(sys.getrefcount(i) >= rc) + finally: + gc.enable() + del d # for pyflakes + + +def assert_allclose( + actual, + desired, + rtol=1e-7, + atol=0, + equal_nan=True, + err_msg="", + verbose=True, + check_dtype=False, +): + """ + Raises an AssertionError if two objects are not equal up to desired + tolerance. + + Given two array_like objects, check that their shapes and all elements + are equal (but see the Notes for the special handling of a scalar). An + exception is raised if the shapes mismatch or any values conflict. In + contrast to the standard usage in numpy, NaNs are compared like numbers, + no assertion is raised if both objects have NaNs in the same positions. + + The test is equivalent to ``allclose(actual, desired, rtol, atol)`` (note + that ``allclose`` has different default values). It compares the difference + between `actual` and `desired` to ``atol + rtol * abs(desired)``. + + .. versionadded:: 1.5.0 + + Parameters + ---------- + actual : array_like + Array obtained. + desired : array_like + Array desired. + rtol : float, optional + Relative tolerance. + atol : float, optional + Absolute tolerance. + equal_nan : bool, optional. + If True, NaNs will compare equal. + err_msg : str, optional + The error message to be printed in case of failure. + verbose : bool, optional + If True, the conflicting values are appended to the error message. + + Raises + ------ + AssertionError + If actual and desired are not equal up to specified precision. + + See Also + -------- + assert_array_almost_equal_nulp, assert_array_max_ulp + + Notes + ----- + When one of `actual` and `desired` is a scalar and the other is + array_like, the function checks that each element of the array_like + object is equal to the scalar. + + Examples + -------- + >>> x = [1e-5, 1e-3, 1e-1] + >>> y = np.arccos(np.cos(x)) + >>> np.testing.assert_allclose(x, y, rtol=1e-5, atol=0) + + """ + __tracebackhide__ = True # Hide traceback for py.test + + def compare(x, y): + return np.isclose(x, y, rtol=rtol, atol=atol, equal_nan=equal_nan) + + actual, desired = asanyarray(actual), asanyarray(desired) + header = f"Not equal to tolerance rtol={rtol:g}, atol={atol:g}" + + if check_dtype: + assert actual.dtype == desired.dtype + + assert_array_compare( + compare, + actual, + desired, + err_msg=str(err_msg), + verbose=verbose, + header=header, + equal_nan=equal_nan, + ) + + +def assert_array_almost_equal_nulp(x, y, nulp=1): + """ + Compare two arrays relatively to their spacing. + + This is a relatively robust method to compare two arrays whose amplitude + is variable. + + Parameters + ---------- + x, y : array_like + Input arrays. + nulp : int, optional + The maximum number of unit in the last place for tolerance (see Notes). + Default is 1. + + Returns + ------- + None + + Raises + ------ + AssertionError + If the spacing between `x` and `y` for one or more elements is larger + than `nulp`. + + See Also + -------- + assert_array_max_ulp : Check that all items of arrays differ in at most + N Units in the Last Place. + spacing : Return the distance between x and the nearest adjacent number. + + Notes + ----- + An assertion is raised if the following condition is not met:: + + abs(x - y) <= nulp * spacing(maximum(abs(x), abs(y))) + + Examples + -------- + >>> x = np.array([1., 1e-10, 1e-20]) + >>> eps = np.finfo(x.dtype).eps + >>> np.testing.assert_array_almost_equal_nulp(x, x*eps/2 + x) # doctest: +SKIP + + >>> np.testing.assert_array_almost_equal_nulp(x, x*eps + x) # doctest: +SKIP + Traceback (most recent call last): + ... + AssertionError: X and Y are not equal to 1 ULP (max is 2) + + """ + __tracebackhide__ = True # Hide traceback for py.test + import numpy as np + + ax = np.abs(x) + ay = np.abs(y) + ref = nulp * np.spacing(np.where(ax > ay, ax, ay)) + if not np.all(np.abs(x - y) <= ref): + if np.iscomplexobj(x) or np.iscomplexobj(y): + msg = "X and Y are not equal to %d ULP" % nulp + else: + max_nulp = np.max(nulp_diff(x, y)) + msg = "X and Y are not equal to %d ULP (max is %g)" % (nulp, max_nulp) + raise AssertionError(msg) + + +def assert_array_max_ulp(a, b, maxulp=1, dtype=None): + """ + Check that all items of arrays differ in at most N Units in the Last Place. + + Parameters + ---------- + a, b : array_like + Input arrays to be compared. + maxulp : int, optional + The maximum number of units in the last place that elements of `a` and + `b` can differ. Default is 1. + dtype : dtype, optional + Data-type to convert `a` and `b` to if given. Default is None. + + Returns + ------- + ret : ndarray + Array containing number of representable floating point numbers between + items in `a` and `b`. + + Raises + ------ + AssertionError + If one or more elements differ by more than `maxulp`. + + Notes + ----- + For computing the ULP difference, this API does not differentiate between + various representations of NAN (ULP difference between 0x7fc00000 and 0xffc00000 + is zero). + + See Also + -------- + assert_array_almost_equal_nulp : Compare two arrays relatively to their + spacing. + + Examples + -------- + >>> a = np.linspace(0., 1., 100) + >>> res = np.testing.assert_array_max_ulp(a, np.arcsin(np.sin(a))) # doctest: +SKIP + + """ + __tracebackhide__ = True # Hide traceback for py.test + import numpy as np + + ret = nulp_diff(a, b, dtype) + if not np.all(ret <= maxulp): + raise AssertionError( + f"Arrays are not almost equal up to {maxulp:g} " + f"ULP (max difference is {np.max(ret):g} ULP)" + ) + return ret + + +def nulp_diff(x, y, dtype=None): + """For each item in x and y, return the number of representable floating + points between them. + + Parameters + ---------- + x : array_like + first input array + y : array_like + second input array + dtype : dtype, optional + Data-type to convert `x` and `y` to if given. Default is None. + + Returns + ------- + nulp : array_like + number of representable floating point numbers between each item in x + and y. + + Notes + ----- + For computing the ULP difference, this API does not differentiate between + various representations of NAN (ULP difference between 0x7fc00000 and 0xffc00000 + is zero). + + Examples + -------- + # By definition, epsilon is the smallest number such as 1 + eps != 1, so + # there should be exactly one ULP between 1 and 1 + eps + >>> nulp_diff(1, 1 + np.finfo(x.dtype).eps) # doctest: +SKIP + 1.0 + """ + import numpy as np + + if dtype: + x = np.asarray(x, dtype=dtype) + y = np.asarray(y, dtype=dtype) + else: + x = np.asarray(x) + y = np.asarray(y) + + t = np.common_type(x, y) + if np.iscomplexobj(x) or np.iscomplexobj(y): + raise NotImplementedError("_nulp not implemented for complex array") + + x = np.array([x], dtype=t) + y = np.array([y], dtype=t) + + x[np.isnan(x)] = np.nan + y[np.isnan(y)] = np.nan + + if not x.shape == y.shape: + raise ValueError(f"x and y do not have the same shape: {x.shape} - {y.shape}") + + def _diff(rx, ry, vdt): + diff = np.asarray(rx - ry, dtype=vdt) + return np.abs(diff) + + rx = integer_repr(x) + ry = integer_repr(y) + return _diff(rx, ry, t) + + +def _integer_repr(x, vdt, comp): + # Reinterpret binary representation of the float as sign-magnitude: + # take into account two-complement representation + # See also + # https://randomascii.wordpress.com/2012/02/25/comparing-floating-point-numbers-2012-edition/ + rx = x.view(vdt) + if not (rx.size == 1): + rx[rx < 0] = comp - rx[rx < 0] + else: + if rx < 0: + rx = comp - rx + + return rx + + +def integer_repr(x): + """Return the signed-magnitude interpretation of the binary representation + of x.""" + import numpy as np + + if x.dtype == np.float16: + return _integer_repr(x, np.int16, np.int16(-(2**15))) + elif x.dtype == np.float32: + return _integer_repr(x, np.int32, np.int32(-(2**31))) + elif x.dtype == np.float64: + return _integer_repr(x, np.int64, np.int64(-(2**63))) + else: + raise ValueError(f"Unsupported dtype {x.dtype}") + + +@contextlib.contextmanager +def _assert_warns_context(warning_class, name=None): + __tracebackhide__ = True # Hide traceback for py.test + with suppress_warnings() as sup: + l = sup.record(warning_class) + yield + if not len(l) > 0: + name_str = f" when calling {name}" if name is not None else "" + raise AssertionError("No warning raised" + name_str) + + +def assert_warns(warning_class, *args, **kwargs): + """ + Fail unless the given callable throws the specified warning. + + A warning of class warning_class should be thrown by the callable when + invoked with arguments args and keyword arguments kwargs. + If a different type of warning is thrown, it will not be caught. + + If called with all arguments other than the warning class omitted, may be + used as a context manager: + + with assert_warns(SomeWarning): + do_something() + + The ability to be used as a context manager is new in NumPy v1.11.0. + + .. versionadded:: 1.4.0 + + Parameters + ---------- + warning_class : class + The class defining the warning that `func` is expected to throw. + func : callable, optional + Callable to test + *args : Arguments + Arguments for `func`. + **kwargs : Kwargs + Keyword arguments for `func`. + + Returns + ------- + The value returned by `func`. + + Examples + -------- + >>> import warnings + >>> def deprecated_func(num): + ... warnings.warn("Please upgrade", DeprecationWarning) + ... return num*num + >>> with np.testing.assert_warns(DeprecationWarning): + ... assert deprecated_func(4) == 16 + >>> # or passing a func + >>> ret = np.testing.assert_warns(DeprecationWarning, deprecated_func, 4) + >>> assert ret == 16 + """ + if not args: + return _assert_warns_context(warning_class) + + func = args[0] + args = args[1:] + with _assert_warns_context(warning_class, name=func.__name__): + return func(*args, **kwargs) + + +@contextlib.contextmanager +def _assert_no_warnings_context(name=None): + __tracebackhide__ = True # Hide traceback for py.test + with warnings.catch_warnings(record=True) as l: + warnings.simplefilter("always") + yield + if len(l) > 0: + name_str = f" when calling {name}" if name is not None else "" + raise AssertionError(f"Got warnings{name_str}: {l}") + + +def assert_no_warnings(*args, **kwargs): + """ + Fail if the given callable produces any warnings. + + If called with all arguments omitted, may be used as a context manager: + + with assert_no_warnings(): + do_something() + + The ability to be used as a context manager is new in NumPy v1.11.0. + + .. versionadded:: 1.7.0 + + Parameters + ---------- + func : callable + The callable to test. + \\*args : Arguments + Arguments passed to `func`. + \\*\\*kwargs : Kwargs + Keyword arguments passed to `func`. + + Returns + ------- + The value returned by `func`. + + """ + if not args: + return _assert_no_warnings_context() + + func = args[0] + args = args[1:] + with _assert_no_warnings_context(name=func.__name__): + return func(*args, **kwargs) + + +def _gen_alignment_data(dtype=float32, type="binary", max_size=24): + """ + generator producing data with different alignment and offsets + to test simd vectorization + + Parameters + ---------- + dtype : dtype + data type to produce + type : string + 'unary': create data for unary operations, creates one input + and output array + 'binary': create data for unary operations, creates two input + and output array + max_size : integer + maximum size of data to produce + + Returns + ------- + if type is 'unary' yields one output, one input array and a message + containing information on the data + if type is 'binary' yields one output array, two input array and a message + containing information on the data + + """ + ufmt = "unary offset=(%d, %d), size=%d, dtype=%r, %s" + bfmt = "binary offset=(%d, %d, %d), size=%d, dtype=%r, %s" + for o in range(3): + for s in range(o + 2, max(o + 3, max_size)): + if type == "unary": + + def inp(): + return arange(s, dtype=dtype)[o:] + + out = empty((s,), dtype=dtype)[o:] + yield out, inp(), ufmt % (o, o, s, dtype, "out of place") + d = inp() + yield d, d, ufmt % (o, o, s, dtype, "in place") + yield out[1:], inp()[:-1], ufmt % ( + o + 1, + o, + s - 1, + dtype, + "out of place", + ) + yield out[:-1], inp()[1:], ufmt % ( + o, + o + 1, + s - 1, + dtype, + "out of place", + ) + yield inp()[:-1], inp()[1:], ufmt % (o, o + 1, s - 1, dtype, "aliased") + yield inp()[1:], inp()[:-1], ufmt % (o + 1, o, s - 1, dtype, "aliased") + if type == "binary": + + def inp1(): + return arange(s, dtype=dtype)[o:] + + inp2 = inp1 + out = empty((s,), dtype=dtype)[o:] + yield out, inp1(), inp2(), bfmt % (o, o, o, s, dtype, "out of place") + d = inp1() + yield d, d, inp2(), bfmt % (o, o, o, s, dtype, "in place1") + d = inp2() + yield d, inp1(), d, bfmt % (o, o, o, s, dtype, "in place2") + yield out[1:], inp1()[:-1], inp2()[:-1], bfmt % ( + o + 1, + o, + o, + s - 1, + dtype, + "out of place", + ) + yield out[:-1], inp1()[1:], inp2()[:-1], bfmt % ( + o, + o + 1, + o, + s - 1, + dtype, + "out of place", + ) + yield out[:-1], inp1()[:-1], inp2()[1:], bfmt % ( + o, + o, + o + 1, + s - 1, + dtype, + "out of place", + ) + yield inp1()[1:], inp1()[:-1], inp2()[:-1], bfmt % ( + o + 1, + o, + o, + s - 1, + dtype, + "aliased", + ) + yield inp1()[:-1], inp1()[1:], inp2()[:-1], bfmt % ( + o, + o + 1, + o, + s - 1, + dtype, + "aliased", + ) + yield inp1()[:-1], inp1()[:-1], inp2()[1:], bfmt % ( + o, + o, + o + 1, + s - 1, + dtype, + "aliased", + ) + + +class IgnoreException(Exception): + "Ignoring this exception due to disabled feature" + + +@contextlib.contextmanager +def tempdir(*args, **kwargs): + """Context manager to provide a temporary test folder. + + All arguments are passed as this to the underlying tempfile.mkdtemp + function. + + """ + tmpdir = mkdtemp(*args, **kwargs) + try: + yield tmpdir + finally: + shutil.rmtree(tmpdir) + + +@contextlib.contextmanager +def temppath(*args, **kwargs): + """Context manager for temporary files. + + Context manager that returns the path to a closed temporary file. Its + parameters are the same as for tempfile.mkstemp and are passed directly + to that function. The underlying file is removed when the context is + exited, so it should be closed at that time. + + Windows does not allow a temporary file to be opened if it is already + open, so the underlying file must be closed after opening before it + can be opened again. + + """ + fd, path = mkstemp(*args, **kwargs) + os.close(fd) + try: + yield path + finally: + os.remove(path) + + +class clear_and_catch_warnings(warnings.catch_warnings): + """Context manager that resets warning registry for catching warnings + + Warnings can be slippery, because, whenever a warning is triggered, Python + adds a ``__warningregistry__`` member to the *calling* module. This makes + it impossible to retrigger the warning in this module, whatever you put in + the warnings filters. This context manager accepts a sequence of `modules` + as a keyword argument to its constructor and: + + * stores and removes any ``__warningregistry__`` entries in given `modules` + on entry; + * resets ``__warningregistry__`` to its previous state on exit. + + This makes it possible to trigger any warning afresh inside the context + manager without disturbing the state of warnings outside. + + For compatibility with Python 3.0, please consider all arguments to be + keyword-only. + + Parameters + ---------- + record : bool, optional + Specifies whether warnings should be captured by a custom + implementation of ``warnings.showwarning()`` and be appended to a list + returned by the context manager. Otherwise None is returned by the + context manager. The objects appended to the list are arguments whose + attributes mirror the arguments to ``showwarning()``. + modules : sequence, optional + Sequence of modules for which to reset warnings registry on entry and + restore on exit. To work correctly, all 'ignore' filters should + filter by one of these modules. + + Examples + -------- + >>> import warnings + >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP + ... modules=[np.core.fromnumeric]): + ... warnings.simplefilter('always') + ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') + ... # do something that raises a warning but ignore those in + ... # np.core.fromnumeric + """ + + class_modules = () + + def __init__(self, record=False, modules=()): + self.modules = set(modules).union(self.class_modules) + self._warnreg_copies = {} + super().__init__(record=record) + + def __enter__(self): + for mod in self.modules: + if hasattr(mod, "__warningregistry__"): + mod_reg = mod.__warningregistry__ + self._warnreg_copies[mod] = mod_reg.copy() + mod_reg.clear() + return super().__enter__() + + def __exit__(self, *exc_info): + super().__exit__(*exc_info) + for mod in self.modules: + if hasattr(mod, "__warningregistry__"): + mod.__warningregistry__.clear() + if mod in self._warnreg_copies: + mod.__warningregistry__.update(self._warnreg_copies[mod]) + + +class suppress_warnings: + """ + Context manager and decorator doing much the same as + ``warnings.catch_warnings``. + + However, it also provides a filter mechanism to work around + https://bugs.python.org/issue4180. + + This bug causes Python before 3.4 to not reliably show warnings again + after they have been ignored once (even within catch_warnings). It + means that no "ignore" filter can be used easily, since following + tests might need to see the warning. Additionally it allows easier + specificity for testing warnings and can be nested. + + Parameters + ---------- + forwarding_rule : str, optional + One of "always", "once", "module", or "location". Analogous to + the usual warnings module filter mode, it is useful to reduce + noise mostly on the outmost level. Unsuppressed and unrecorded + warnings will be forwarded based on this rule. Defaults to "always". + "location" is equivalent to the warnings "default", match by exact + location the warning warning originated from. + + Notes + ----- + Filters added inside the context manager will be discarded again + when leaving it. Upon entering all filters defined outside a + context will be applied automatically. + + When a recording filter is added, matching warnings are stored in the + ``log`` attribute as well as in the list returned by ``record``. + + If filters are added and the ``module`` keyword is given, the + warning registry of this module will additionally be cleared when + applying it, entering the context, or exiting it. This could cause + warnings to appear a second time after leaving the context if they + were configured to be printed once (default) and were already + printed before the context was entered. + + Nesting this context manager will work as expected when the + forwarding rule is "always" (default). Unfiltered and unrecorded + warnings will be passed out and be matched by the outer level. + On the outmost level they will be printed (or caught by another + warnings context). The forwarding rule argument can modify this + behaviour. + + Like ``catch_warnings`` this context manager is not threadsafe. + + Examples + -------- + + With a context manager:: + + with np.testing.suppress_warnings() as sup: + sup.filter(DeprecationWarning, "Some text") + sup.filter(module=np.ma.core) + log = sup.record(FutureWarning, "Does this occur?") + command_giving_warnings() + # The FutureWarning was given once, the filtered warnings were + # ignored. All other warnings abide outside settings (may be + # printed/error) + assert_(len(log) == 1) + assert_(len(sup.log) == 1) # also stored in log attribute + + Or as a decorator:: + + sup = np.testing.suppress_warnings() + sup.filter(module=np.ma.core) # module must match exactly + @sup + def some_function(): + # do something which causes a warning in np.ma.core + pass + """ + + def __init__(self, forwarding_rule="always"): + self._entered = False + + # Suppressions are either instance or defined inside one with block: + self._suppressions = [] + + if forwarding_rule not in {"always", "module", "once", "location"}: + raise ValueError("unsupported forwarding rule.") + self._forwarding_rule = forwarding_rule + + def _clear_registries(self): + if hasattr(warnings, "_filters_mutated"): + # clearing the registry should not be necessary on new pythons, + # instead the filters should be mutated. + warnings._filters_mutated() + return + # Simply clear the registry, this should normally be harmless, + # note that on new pythons it would be invalidated anyway. + for module in self._tmp_modules: + if hasattr(module, "__warningregistry__"): + module.__warningregistry__.clear() + + def _filter(self, category=Warning, message="", module=None, record=False): + if record: + record = [] # The log where to store warnings + else: + record = None + if self._entered: + if module is None: + warnings.filterwarnings("always", category=category, message=message) + else: + module_regex = module.__name__.replace(".", r"\.") + "$" + warnings.filterwarnings( + "always", category=category, message=message, module=module_regex + ) + self._tmp_modules.add(module) + self._clear_registries() + + self._tmp_suppressions.append( + (category, message, re.compile(message, re.IGNORECASE), module, record) + ) + else: + self._suppressions.append( + (category, message, re.compile(message, re.IGNORECASE), module, record) + ) + + return record + + def filter(self, category=Warning, message="", module=None): + """ + Add a new suppressing filter or apply it if the state is entered. + + Parameters + ---------- + category : class, optional + Warning class to filter + message : string, optional + Regular expression matching the warning message. + module : module, optional + Module to filter for. Note that the module (and its file) + must match exactly and cannot be a submodule. This may make + it unreliable for external modules. + + Notes + ----- + When added within a context, filters are only added inside + the context and will be forgotten when the context is exited. + """ + self._filter(category=category, message=message, module=module, record=False) + + def record(self, category=Warning, message="", module=None): + """ + Append a new recording filter or apply it if the state is entered. + + All warnings matching will be appended to the ``log`` attribute. + + Parameters + ---------- + category : class, optional + Warning class to filter + message : string, optional + Regular expression matching the warning message. + module : module, optional + Module to filter for. Note that the module (and its file) + must match exactly and cannot be a submodule. This may make + it unreliable for external modules. + + Returns + ------- + log : list + A list which will be filled with all matched warnings. + + Notes + ----- + When added within a context, filters are only added inside + the context and will be forgotten when the context is exited. + """ + return self._filter( + category=category, message=message, module=module, record=True + ) + + def __enter__(self): + if self._entered: + raise RuntimeError("cannot enter suppress_warnings twice.") + + self._orig_show = warnings.showwarning + self._filters = warnings.filters + warnings.filters = self._filters[:] + + self._entered = True + self._tmp_suppressions = [] + self._tmp_modules = set() + self._forwarded = set() + + self.log = [] # reset global log (no need to keep same list) + + for cat, mess, _, mod, log in self._suppressions: + if log is not None: + del log[:] # clear the log + if mod is None: + warnings.filterwarnings("always", category=cat, message=mess) + else: + module_regex = mod.__name__.replace(".", r"\.") + "$" + warnings.filterwarnings( + "always", category=cat, message=mess, module=module_regex + ) + self._tmp_modules.add(mod) + warnings.showwarning = self._showwarning + self._clear_registries() + + return self + + def __exit__(self, *exc_info): + warnings.showwarning = self._orig_show + warnings.filters = self._filters + self._clear_registries() + self._entered = False + del self._orig_show + del self._filters + + def _showwarning( + self, message, category, filename, lineno, *args, use_warnmsg=None, **kwargs + ): + for cat, _, pattern, mod, rec in (self._suppressions + self._tmp_suppressions)[ + ::-1 + ]: + if issubclass(category, cat) and pattern.match(message.args[0]) is not None: + if mod is None: + # Message and category match, either recorded or ignored + if rec is not None: + msg = WarningMessage( + message, category, filename, lineno, **kwargs + ) + self.log.append(msg) + rec.append(msg) + return + # Use startswith, because warnings strips the c or o from + # .pyc/.pyo files. + elif mod.__file__.startswith(filename): + # The message and module (filename) match + if rec is not None: + msg = WarningMessage( + message, category, filename, lineno, **kwargs + ) + self.log.append(msg) + rec.append(msg) + return + + # There is no filter in place, so pass to the outside handler + # unless we should only pass it once + if self._forwarding_rule == "always": + if use_warnmsg is None: + self._orig_show(message, category, filename, lineno, *args, **kwargs) + else: + self._orig_showmsg(use_warnmsg) + return + + if self._forwarding_rule == "once": + signature = (message.args, category) + elif self._forwarding_rule == "module": + signature = (message.args, category, filename) + elif self._forwarding_rule == "location": + signature = (message.args, category, filename, lineno) + + if signature in self._forwarded: + return + self._forwarded.add(signature) + if use_warnmsg is None: + self._orig_show(message, category, filename, lineno, *args, **kwargs) + else: + self._orig_showmsg(use_warnmsg) + + def __call__(self, func): + """ + Function decorator to apply certain suppressions to a whole + function. + """ + + @wraps(func) + def new_func(*args, **kwargs): + with self: + return func(*args, **kwargs) + + return new_func + + +@contextlib.contextmanager +def _assert_no_gc_cycles_context(name=None): + __tracebackhide__ = True # Hide traceback for py.test + + # not meaningful to test if there is no refcounting + if not HAS_REFCOUNT: + yield + return + + assert_(gc.isenabled()) + gc.disable() + gc_debug = gc.get_debug() + try: + for i in range(100): + if gc.collect() == 0: + break + else: + raise RuntimeError( + "Unable to fully collect garbage - perhaps a __del__ method " + "is creating more reference cycles?" + ) + + gc.set_debug(gc.DEBUG_SAVEALL) + yield + # gc.collect returns the number of unreachable objects in cycles that + # were found -- we are checking that no cycles were created in the context + n_objects_in_cycles = gc.collect() + objects_in_cycles = gc.garbage[:] + finally: + del gc.garbage[:] + gc.set_debug(gc_debug) + gc.enable() + + if n_objects_in_cycles: + name_str = f" when calling {name}" if name is not None else "" + raise AssertionError( + "Reference cycles were found{}: {} objects were collected, " + "of which {} are shown below:{}".format( + name_str, + n_objects_in_cycles, + len(objects_in_cycles), + "".join( + "\n {} object with id={}:\n {}".format( + type(o).__name__, + id(o), + pprint.pformat(o).replace("\n", "\n "), + ) + for o in objects_in_cycles + ), + ) + ) + + +def assert_no_gc_cycles(*args, **kwargs): + """ + Fail if the given callable produces any reference cycles. + + If called with all arguments omitted, may be used as a context manager: + + with assert_no_gc_cycles(): + do_something() + + .. versionadded:: 1.15.0 + + Parameters + ---------- + func : callable + The callable to test. + \\*args : Arguments + Arguments passed to `func`. + \\*\\*kwargs : Kwargs + Keyword arguments passed to `func`. + + Returns + ------- + Nothing. The result is deliberately discarded to ensure that all cycles + are found. + + """ + if not args: + return _assert_no_gc_cycles_context() + + func = args[0] + args = args[1:] + with _assert_no_gc_cycles_context(name=func.__name__): + func(*args, **kwargs) + + +def break_cycles(): + """ + Break reference cycles by calling gc.collect + Objects can call other objects' methods (for instance, another object's + __del__) inside their own __del__. On PyPy, the interpreter only runs + between calls to gc.collect, so multiple calls are needed to completely + release all cycles. + """ + + gc.collect() + if IS_PYPY: + # a few more, just to make sure all the finalizers are called + gc.collect() + gc.collect() + gc.collect() + gc.collect() + + +def requires_memory(free_bytes): + """Decorator to skip a test if not enough memory is available""" + import pytest + + def decorator(func): + @wraps(func) + def wrapper(*a, **kw): + msg = check_free_memory(free_bytes) + if msg is not None: + pytest.skip(msg) + + try: + return func(*a, **kw) + except MemoryError: + # Probably ran out of memory regardless: don't regard as failure + pytest.xfail("MemoryError raised") + + return wrapper + + return decorator + + +def check_free_memory(free_bytes): + """ + Check whether `free_bytes` amount of memory is currently free. + Returns: None if enough memory available, otherwise error message + """ + env_var = "NPY_AVAILABLE_MEM" + env_value = os.environ.get(env_var) + if env_value is not None: + try: + mem_free = _parse_size(env_value) + except ValueError as exc: + raise ValueError( # noqa: B904 + f"Invalid environment variable {env_var}: {exc}" + ) + + msg = ( + f"{free_bytes/1e9} GB memory required, but environment variable " + f"NPY_AVAILABLE_MEM={env_value} set" + ) + else: + mem_free = _get_mem_available() + + if mem_free is None: + msg = ( + "Could not determine available memory; set NPY_AVAILABLE_MEM " + "environment variable (e.g. NPY_AVAILABLE_MEM=16GB) to run " + "the test." + ) + mem_free = -1 + else: + msg = ( + f"{free_bytes/1e9} GB memory required, but {mem_free/1e9} GB available" + ) + + return msg if mem_free < free_bytes else None + + +def _parse_size(size_str): + """Convert memory size strings ('12 GB' etc.) to float""" + suffixes = { + "": 1, + "b": 1, + "k": 1000, + "m": 1000**2, + "g": 1000**3, + "t": 1000**4, + "kb": 1000, + "mb": 1000**2, + "gb": 1000**3, + "tb": 1000**4, + "kib": 1024, + "mib": 1024**2, + "gib": 1024**3, + "tib": 1024**4, + } + + size_re = re.compile( + r"^\s*(\d+|\d+\.\d+)\s*({})\s*$".format("|".join(suffixes.keys())), + re.IGNORECASE, + ) + + m = size_re.match(size_str.lower()) + if not m or m.group(2) not in suffixes: + raise ValueError(f"value {size_str!r} not a valid size") + return int(float(m.group(1)) * suffixes[m.group(2)]) + + +def _get_mem_available(): + """Return available memory in bytes, or None if unknown.""" + try: + import psutil + + return psutil.virtual_memory().available + except (ImportError, AttributeError): + pass + + if sys.platform.startswith("linux"): + info = {} + with open("/proc/meminfo") as f: + for line in f: + p = line.split() + info[p[0].strip(":").lower()] = int(p[1]) * 1024 + + if "memavailable" in info: + # Linux >= 3.14 + return info["memavailable"] + else: + return info["memfree"] + info["cached"] + + return None + + +def _no_tracing(func): + """ + Decorator to temporarily turn off tracing for the duration of a test. + Needed in tests that check refcounting, otherwise the tracing itself + influences the refcounts + """ + if not hasattr(sys, "gettrace"): + return func + else: + + @wraps(func) + def wrapper(*args, **kwargs): + original_trace = sys.gettrace() + try: + sys.settrace(None) + return func(*args, **kwargs) + finally: + sys.settrace(original_trace) + + return wrapper + + +def _get_glibc_version(): + try: + ver = os.confstr("CS_GNU_LIBC_VERSION").rsplit(" ")[1] + except Exception as inst: + ver = "0.0" + + return ver + + +_glibcver = _get_glibc_version() + + +def _glibc_older_than(x): + return _glibcver != "0.0" and _glibcver < x diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..26b3f298e361870f7bf09ea52633d57fefe38aed --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims/__init__.py @@ -0,0 +1,3113 @@ +# mypy: allow-untyped-defs +import operator +from enum import Enum +from functools import partial, reduce +from typing import Callable, List, Optional, Sequence, Tuple, Type, Union + +import torch +import torch._prims_common as utils +import torch.library +from torch import sym_float, Tensor +from torch._C import _get_default_device +from torch._higher_order_ops.effects import new_token_tensor +from torch._library.utils import is_functional_schema +from torch._prims.debug_prims import register_debug_prims +from torch._prims.rng_prims import register_rng_prims +from torch._prims_common import ( + Dim, + DimsSequenceType, + DimsType, + IntLike, + Number, + NumberType, + RETURN_TYPE, + ShapeType, + StrideType, + TensorLike, + TensorLikeType, + type_to_dtype, +) +from torch._prims_common.wrappers import backwards_not_supported +from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode +from torch.overrides import handle_torch_function, has_torch_function +from torch.utils._pytree import tree_flatten, tree_map, tree_unflatten + + +prim = torch.library.Library("prims", "DEF") +prim_impl = torch.library.Library("prims", "IMPL", "CompositeExplicitAutograd") +prim_backend_select_impl = torch.library.Library("prims", "IMPL", "BackendSelect") +prim_autograd_impl = torch.library.Library("prims", "IMPL", "Autograd") +prim_meta_impl = torch.library.Library("prims", "IMPL", "Meta") + +# Experimental module containing prototype "primitive" operations. + +__all__ = [ + # + # Common datastructures and helpers + # + "RETURN_TYPE", + # + # Elementwise unary prims + # + "abs", + "acos", + "acosh", + "asin", + "asinh", + "atan", + "atanh", + "cos", + "cosh", + "bessel_i0", + "bessel_i0e", + "bessel_i1", + "bessel_i1e", + "bessel_j0", + "bessel_j1", + "bitwise_not", + "cbrt", + "ceil", + "conj_physical", + "digamma", + "erf", + "erf_inv", + "erfc", + "erfcx", + "exp", + "expm1", + "exp2", + "fill", + "floor", + "imag", + "isfinite", + "lgamma", + "log", + "log1p", + "log2", + "log10", + "ndtri", + "neg", + "real", + "reciprocal", + "round", + "sign", + "signbit", + "sin", + "sinh", + "spherical_bessel_j0", + "sqrt", + "tan", + "tanh", + "trunc", + # + # Elementwise binary prims + # + "add", + "atan2", + "bitwise_and", + "bitwise_or", + "bitwise_xor", + # 'complex', # needs custom meta + "div", + "eq", + "fmax", + "fmin", + "fmod", + "frexp", + "gcd", + "ge", + "gt", + "hypot", + "igamma", + "igammac", + "le", + "lt", + "maximum", + "minimum", + "mul", + "ne", + "nextafter", + "pow", + "remainder", + "rsqrt", + "shift_left", + "shift_right_arithmetic", + "shift_right_logical", # not implemented + "sub", + "zeta", + # + # View prims + # + "as_strided", + "broadcast_in_dim", + "collapse_view", + "conj", + "expand_dims", + "slice", + "slice_in_dim", # implemented using slice -- make this a ref? + "split_dim", + "squeeze", + "transpose", + "view_of", + "view_element_type", + # + # Functionalized view mutations + # + "as_strided_scatter", + # + # Shape prims + # + "collapse", + "cat", + "reshape", + "rev", + # + # Conditional prims + # + "where", + # + # Data conversion and movement prims + # + "clone", + "convert_element_type", + "device_put", + "item", + "maximum_value", + "minimum_value", + "copy_strided", + # + # Inplace prims + # + "copy_to", + "resize", + # "_set", # Commented out, see note below + # + # Reduction prims + # + "amax", + "amin", + "prod", + "sum", + "xor_sum", + "var", + # + # Tensor Creation Prims + # + "empty_strided", + "empty_permuted", + "scalar_tensor", + "iota", + # + # Linear algebra (linalg) Prims + # + "svd", + # + # Randomness Prims + # + "normal", + "_uniform_helper", + # + # FFT prims + # + "fft_r2c", + "fft_c2c", + "fft_c2r", + # + # prims for making/sinking tokens + # + "_make_token", + "_sink_tokens", +] + + +def TensorMeta( + tensorlike: Optional[Union[NumberType, torch.Tensor]] = None, + *, + shape: Optional[ShapeType] = None, + strides: Optional[StrideType] = None, + dtype: Optional[torch.dtype] = None, + device: Optional[Union[torch.device, str]] = None, +): + if isinstance(tensorlike, Number): + assert not shape and (shape is None or isinstance(shape, Sequence)) + assert not strides and (strides is None or isinstance(strides, Sequence)) + inferred_shape: Tuple[int, ...] = () + inferred_strides: Tuple[int, ...] = () + inferred_dtype = type_to_dtype(type(tensorlike)) + inferred_device = torch.device("cpu") + # TODO: This looks wrong, a number that is wrapped into a tensor + # needs to behave differently than a scalar tensor for type + # promotion purposes + elif tensorlike is not None: + assert isinstance(tensorlike, torch.Tensor) + inferred_shape = tuple(tensorlike.shape) + inferred_strides = tuple(tensorlike.stride()) + inferred_dtype = tensorlike.dtype + inferred_device = tensorlike.device + else: + # If no tensorlike "example" is given then all metadata + # must be provided explicitly + assert shape is not None + assert strides is not None + assert dtype is not None + assert device is not None + + shape = inferred_shape if shape is None else tuple(shape) # type: ignore[possibly-undefined] + strides = inferred_strides if strides is None else tuple(strides) # type: ignore[possibly-undefined] + dtype = inferred_dtype if dtype is None else dtype # type: ignore[possibly-undefined] + device = inferred_device if device is None else device # type: ignore[possibly-undefined] + + if isinstance(device, str): + device = torch.device(device) + + return torch.empty_strided(shape, strides, dtype=dtype, device=device) + + +def _make_prim( + *, + schema: str, + return_type: Union[RETURN_TYPE, Tuple[RETURN_TYPE, ...]], + meta: Callable, + impl_aten: Callable, + doc: str, + tags: Optional[Sequence[torch.Tag]] = None, + use_old_custom_ops_api: bool = False, + register_conj_neg_fallthrough: bool = False, +): + """ + Creates a primitive operation. + + """ + + def _prim_impl(*args, **kwargs): + # always run the meta function because aten implementation will + # typically accept more inputs (e.g., it will do promotion and + # broadcasting) which we want to reject + meta(*args, **kwargs) + return impl_aten(*args, **kwargs) + + # Right now prims don't support autograd (we can and should add an + # argument that provides an implementation for backward here.) Because we + # don't have derivative formulas, we must setup a custom autograd function + # that raises an error if backwards is invoked + def _autograd_impl(*args, **kwargs): + return backwards_not_supported(_prim)(*args, **kwargs) + + def _backend_select_impl(*args, **kwargs): + if kwargs.get("device") and kwargs["device"].type == "meta": + return meta(*args, **kwargs) + if any(isinstance(x, torch.device) and x.type == "meta" for x in args): + return meta(*args, **kwargs) + else: + return _prim_impl(*args, **kwargs) + + name = schema.split("(")[0] + schema = schema[len(name) :] + + # register non-functional ops with old custom ops API + cpp_schema = torch._C.parse_schema(name + schema) + if use_old_custom_ops_api or not is_functional_schema(cpp_schema): + prim.define(name + schema, tags=torch.Tag.pt2_compliant_tag) + prim_impl.impl(name, _prim_impl) + prim_autograd_impl.impl(name, _autograd_impl) + prim_meta_impl.impl(name, meta) + else: + mutates_args = [] + for arg in cpp_schema.arguments: + if arg.alias_info is not None and arg.alias_info.is_write: + mutates_args.append(arg.name) + prim_def = torch.library.custom_op( + "prims::" + name, + _prim_impl, + mutates_args=tuple(mutates_args), + schema=schema, + ) + prim_def.register_fake(meta) + + # all view ops get conj/neg fallthroughs + if return_type == RETURN_TYPE.VIEW or register_conj_neg_fallthrough: + prim_def._lib.impl(name, torch.library.fallthrough_kernel, "Conjugate") + prim_def._lib.impl(name, torch.library.fallthrough_kernel, "Negative") + + _prim_packet = getattr(torch._ops.ops.prims, name) + _prim = _prim_packet.default + if tags: + _prim._tags = tags + elif aten_packet := getattr(torch.ops.aten, name, None): + overload_tags = [ + getattr(aten_packet, overload).tags for overload in aten_packet.overloads() + ] + tags_intersection = set(overload_tags[0]) + tags_intersection.intersection_update(*overload_tags[1:]) + + # dont inadvertently add to prim ops + tags_intersection.discard(torch.Tag.core) + # causes errors with python ref executor tests, none of the + # data dependent pytorch ops actually decompose to prims + tags_intersection.discard(torch.Tag.data_dependent_output) + + # iter over first tags for determinism + _prim._tags = tuple(t for t in overload_tags[0] if t in tags_intersection) + + from torch._subclasses.fake_tensor import contains_tensor_types + + if not any(contains_tensor_types(a.type) for a in _prim._schema.arguments) or str( + _prim + ) in [ + # See https://github.com/pytorch/pytorch/issues/103532 + "prims.device_put.default" + ]: + prim_backend_select_impl.impl(name, _backend_select_impl) + + for p in (_prim_packet, _prim): + p.__doc__ = doc + p.return_type = return_type # type: ignore[attr-defined] + + p.schema = schema + p.prim_impl = _prim_impl + p.prim_meta_impl = meta + p.impl_aten = impl_aten + + return _prim + + +class ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND(Enum): + DEFAULT = (0,) + INT_TO_FLOAT = (2,) + ALWAYS_BOOL = (3,) + COMPLEX_TO_FLOAT = (4,) + + +# TODO: implement dtype validation here, too, or on the corresponding refs +def _prim_elementwise_meta( + *args, + type_promotion: ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND, + args_with_fixed_dtypes: Optional[Tuple[TensorLikeType, ...]] = None, +) -> FakeTensor: + """ + Meta function for elementwise operations that produce outputs in the same dtype + as their inputs. + + Stride logic is currently incorrect. + """ + + assert len(args) > 0 + + utils.check_same_dtype(*args) + + args_ = list(args) + if args_with_fixed_dtypes is not None: + args_ = list(args_with_fixed_dtypes) + args_ + + utils.check_same_device(*args_, allow_cpu_scalar_tensors=True) + utils.check_same_shape(*args_, allow_cpu_scalar_tensors=True) + + l2p_perm = utils.compute_elementwise_output_logical_to_physical_perm(*args_) + shape = utils.extract_shape(*args_, allow_cpu_scalar_tensors=True) + + # Acquires the dtype + dtype = None + scalar_type = None + for arg in args: + if isinstance(arg, TensorLike): + if not utils.is_cpu_scalar_tensor(arg): + dtype = arg.dtype + break + else: + dtype = arg.dtype + elif isinstance(arg, Number): + scalar_type = type(arg) + + if dtype is None and scalar_type is not None: + dtype = utils.type_to_dtype(scalar_type) + + # Acquires the device (if it exists) or number + device = None + number = None + for arg in args_: + if isinstance(arg, TensorLike): + if utils.is_cpu_scalar_tensor(arg): + if device is None: + device = arg.device + # keep going, in case there is a cuda tensor later + else: + device = arg.device + break + + elif isinstance(arg, Number): + if number is None: + number = arg + + # NOTE: type promotion behavior here is mostly hidden from tests because + # references will typically handle the type promotion properly even if this doesn't + # (but getting it wrong will cause too many casts to be inserted in traces!) + if device is not None: + assert dtype is not None + if type_promotion == ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT: + dtype = dtype + elif type_promotion == ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.ALWAYS_BOOL: + dtype = torch.bool + elif type_promotion == ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.INT_TO_FLOAT: + if utils.is_integer_dtype(dtype) or utils.is_boolean_dtype(dtype): + dtype = torch.get_default_dtype() + elif type_promotion == ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT: + if utils.is_complex_dtype(dtype): + dtype = utils.corresponding_real_dtype(dtype) + else: + dtype = dtype + + assert shape is not None + return torch.empty_permuted(shape, l2p_perm, device=device, dtype=dtype) # type: ignore[return-value] + + # Number case + # TODO: fix number type promotion (bool, complex->float) + + # For now for symint/float, just implementing the common / simple cases of (int,float,symint,symfloat) + seen_float = False + if isinstance(number, (torch.SymInt, torch.SymFloat)): + for a in args: + assert isinstance(a, (int, float, torch.SymInt, torch.SymFloat)), "NYI" + seen_float = seen_float or isinstance(a, (float, torch.SymFloat)) + if seen_float: + number = sym_float(number) + + return TensorMeta(number) # type: ignore[arg-type] + + +def _complex_only_elementwise_meta(*args, **kwargs): + torch._check( + utils.is_complex_dtype(args[0].dtype), lambda: "Only complex dtype is supported" + ) + return _prim_elementwise_meta(*args, **kwargs) + + +def _make_elementwise_unary_prim( + name: str, *, type_promotion: ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND, **kwargs +): + """ + Creates an elementwise unary prim. + """ + + return _make_prim( + schema=f"{name}(Tensor self) -> Tensor", + meta=partial(_prim_elementwise_meta, type_promotion=type_promotion), + return_type=RETURN_TYPE.NEW, + **kwargs, + ) + + +def _make_elementwise_binary_prim( + name: str, *, type_promotion: ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND, **kwargs +): + """ + Creates an elementwise binary prim. + """ + + return _make_prim( + schema=f"{name}(Tensor self, Tensor other) -> Tensor", + meta=partial(_prim_elementwise_meta, type_promotion=type_promotion), + return_type=RETURN_TYPE.NEW, + **kwargs, + ) + + +def _not_impl(*args, **kwargs): + raise NotImplementedError + + +# +# Elementwise unary operations +# + + +abs = _make_elementwise_unary_prim( + "abs", + impl_aten=torch.abs, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT, +) + +acos = _make_elementwise_unary_prim( + "acos", + impl_aten=torch.acos, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +acosh = _make_elementwise_unary_prim( + "acosh", + impl_aten=torch.acosh, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +asin = _make_elementwise_unary_prim( + "asin", + impl_aten=torch.asin, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +asinh = _make_elementwise_unary_prim( + "asinh", + impl_aten=torch.asinh, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +atan = _make_elementwise_unary_prim( + "atan", + impl_aten=torch.atan, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +atanh = _make_elementwise_unary_prim( + "atanh", + impl_aten=torch.atanh, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +cos = _make_elementwise_unary_prim( + "cos", + impl_aten=torch.cos, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +cosh = _make_elementwise_unary_prim( + "cosh", + impl_aten=torch.cosh, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +bessel_j0 = _make_elementwise_unary_prim( + "bessel_j0", + impl_aten=torch.special.bessel_j0, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +bessel_j1 = _make_elementwise_unary_prim( + "bessel_j1", + impl_aten=torch.special.bessel_j1, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +bessel_i0 = _make_elementwise_unary_prim( + "bessel_i0", + impl_aten=torch.i0, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +bessel_i0e = _make_elementwise_unary_prim( + "bessel_i0e", + impl_aten=torch.special.i0e, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +bessel_i1 = _make_elementwise_unary_prim( + "bessel_i1", + impl_aten=torch.special.i1, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +bessel_i1e = _make_elementwise_unary_prim( + "bessel_i1e", + impl_aten=torch.special.i1e, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +bitwise_not = _make_elementwise_unary_prim( + "bitwise_not", + impl_aten=torch.bitwise_not, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + + +def _cbrt_aten(a: torch.Tensor) -> Tensor: + torch._check( + not a.is_complex(), + lambda: "cbrt: Complex inputs not supported. Consider calling torch.pow(a, 1.0/3.0)", + ) + # Returns the real cubic root of the number. + # Note that if a < 0, pow(a, (1. / 3.)) returns th complex number + # exp(1/3 * log(a)) = exp(1/3 * (log(abs(a)) + pi*i)) = cbrt(abs(a)) * e^{pi/3*i} + # which is a complex number. + # For more info see the section Note in + # https://en.cppreference.com/w/cpp/numeric/math/cbrt + return torch.copysign(torch.pow(a.abs(), 1 / 3), a) + + +cbrt = _make_elementwise_unary_prim( + "cbrt", + impl_aten=_cbrt_aten, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +ceil = _make_elementwise_unary_prim( + "ceil", + impl_aten=torch.ceil, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + + +def _conj_physical_meta(input: TensorLikeType) -> TensorLikeType: + if not input.dtype.is_complex: + raise RuntimeError("prims.conj_physical is only defined for complex dtypes") + + strides = utils.compute_elementwise_output_strides(input) + return TensorMeta(input, strides=strides) + + +conj_physical = _make_prim( + schema="conj_physical(Tensor self) -> Tensor", + meta=_conj_physical_meta, + impl_aten=torch._conj_physical, + doc="Returns the physical conjugation of a complex tensor", + return_type=RETURN_TYPE.NEW, +) + + +def _clone_meta( + input: TensorLikeType, *, memory_format: torch.memory_format = torch.preserve_format +) -> TensorLikeType: + if memory_format != torch.preserve_format: + return torch.empty( + input.shape, + dtype=input.dtype, + layout=input.layout, + device=input.device, + memory_format=memory_format, + ) + + # memory_format == torch.preserve_format + strides = utils.compute_elementwise_output_strides(input) + return torch.empty_strided( + input.shape, + strides, + dtype=input.dtype, + layout=input.layout, + device=input.device, + ) + + +clone = _make_prim( + schema="clone(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor", + meta=_clone_meta, + impl_aten=torch.clone, + doc="Returns the copy of a tensor", + return_type=RETURN_TYPE.NEW, + register_conj_neg_fallthrough=True, +) + +digamma = _make_elementwise_unary_prim( + "digamma", + impl_aten=torch.digamma, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +erf = _make_elementwise_unary_prim( + "erf", + impl_aten=torch.erf, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +erf_inv = _make_elementwise_unary_prim( + "erf_inv", + impl_aten=torch.special.erfinv, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +erfc = _make_elementwise_unary_prim( + "erfc", + impl_aten=torch.special.erfc, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +erfcx = _make_elementwise_unary_prim( + "erfcx", + impl_aten=torch.special.erfcx, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +exp = _make_elementwise_unary_prim( + "exp", + impl_aten=torch.exp, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +expm1 = _make_elementwise_unary_prim( + "expm1", + impl_aten=torch.special.expm1, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +exp2 = _make_elementwise_unary_prim( + "exp2", + impl_aten=torch.special.exp2, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + + +def _fill_meta(a: TensorLikeType, value: NumberType) -> TensorLikeType: + return _prim_elementwise_meta( + a, type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT + ) + + +# NOTE: fill uses _make_prim directly because it has a value parameter +fill = _make_prim( + schema="fill(Tensor self, Scalar value) -> Tensor", + return_type=RETURN_TYPE.NEW, + meta=_fill_meta, + impl_aten=torch.fill, + doc="", +) + +floor = _make_elementwise_unary_prim( + "floor", + impl_aten=torch.floor, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +imag = _make_prim( + schema="imag(Tensor(a) self) -> Tensor(a)", + meta=partial( + _complex_only_elementwise_meta, + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT, + ), + return_type=RETURN_TYPE.VIEW, + impl_aten=torch.imag, + doc="", +) + +isfinite = _make_elementwise_unary_prim( + "isfinite", + impl_aten=torch.isfinite, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.ALWAYS_BOOL, +) + +lgamma = _make_elementwise_unary_prim( + "lgamma", + impl_aten=torch.lgamma, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +log = _make_elementwise_unary_prim( + "log", + impl_aten=torch.log, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +log1p = _make_elementwise_unary_prim( + "log1p", + impl_aten=torch.log1p, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +log2 = _make_elementwise_unary_prim( + "log2", + impl_aten=torch.log2, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +log10 = _make_elementwise_unary_prim( + "log10", + impl_aten=torch.log10, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +real = _make_prim( + schema="real(Tensor(a) self) -> Tensor(a)", + meta=partial( + _complex_only_elementwise_meta, + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT, + ), + return_type=RETURN_TYPE.VIEW, + impl_aten=torch.real, + doc="", +) + +reciprocal = _make_elementwise_unary_prim( + "reciprocal", + impl_aten=torch.reciprocal, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +ndtri = _make_elementwise_unary_prim( + "ndtri", + impl_aten=torch.special.ndtri, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +neg = _make_elementwise_unary_prim( + "neg", + impl_aten=torch.neg, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +round = _make_elementwise_unary_prim( + "round", + impl_aten=torch.round, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +rsqrt = _make_elementwise_unary_prim( + "rsqrt", + impl_aten=torch.rsqrt, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +sign = _make_elementwise_unary_prim( + "sign", + impl_aten=torch.sign, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +signbit = _make_elementwise_unary_prim( + "signbit", + impl_aten=torch.signbit, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +sin = _make_elementwise_unary_prim( + "sin", + impl_aten=torch.sin, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +sinh = _make_elementwise_unary_prim( + "sinh", + impl_aten=torch.sinh, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +spherical_bessel_j0 = _make_elementwise_unary_prim( + "spherical_bessel_j0", + impl_aten=torch.special.spherical_bessel_j0, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +sqrt = _make_elementwise_unary_prim( + "sqrt", + impl_aten=torch.sqrt, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +tan = _make_elementwise_unary_prim( + "tan", + impl_aten=torch.tan, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +tanh = _make_elementwise_unary_prim( + "tanh", + impl_aten=torch.tanh, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +trunc = _make_elementwise_unary_prim( + "trunc", + impl_aten=torch.trunc, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +# +# Elementwise binary operations +# + +add = _make_elementwise_binary_prim( + name="add", + impl_aten=torch.add, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +atan2 = _make_elementwise_binary_prim( + name="atan2", + impl_aten=torch.atan2, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +bitwise_and = _make_elementwise_binary_prim( + "bitwise_and", + impl_aten=torch.bitwise_and, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +bitwise_or = _make_elementwise_binary_prim( + "bitwise_or", + impl_aten=torch.bitwise_or, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +bitwise_xor = _make_elementwise_binary_prim( + "bitwise_xor", + impl_aten=torch.bitwise_xor, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +# TODO: complex needs a special meta to account for its float -> complex behavior +# complex = _make_elementwise_binary_prim( +# impl_aten=torch.complex, +# doc="", +# ) + + +# div prim performs truncation division on integer inputs +# and true division for floating and complex inputs +def _div_aten(a, b): + is_integral = isinstance(a, (bool, int, torch.SymInt)) or ( + isinstance(a, torch.Tensor) and utils.is_integer_dtype(a.dtype) + ) + + if is_integral: + return torch.div(a, b, rounding_mode="trunc") + else: + return torch.true_divide(a, b) + + +div = _make_elementwise_binary_prim( + "div", + impl_aten=_div_aten, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +eq = _make_elementwise_binary_prim( + "eq", + impl_aten=torch.eq, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.ALWAYS_BOOL, +) + +fmax = _make_elementwise_binary_prim( + "fmax", + impl_aten=torch.fmax, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +fmin = _make_elementwise_binary_prim( + "fmin", + impl_aten=torch.fmin, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +fmod = _make_elementwise_binary_prim( + "fmod", + impl_aten=torch.fmod, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + + +gcd = _make_elementwise_binary_prim( + "gcd", + impl_aten=torch.gcd, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + + +ge = _make_elementwise_binary_prim( + "ge", + impl_aten=torch.ge, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.ALWAYS_BOOL, +) + +gt = _make_elementwise_binary_prim( + "gt", + impl_aten=torch.gt, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.ALWAYS_BOOL, +) + +hypot = _make_elementwise_binary_prim( + "hypot", + impl_aten=torch.hypot, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +igamma = _make_elementwise_binary_prim( + "igamma", + impl_aten=torch.special.gammainc, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +igammac = _make_elementwise_binary_prim( + "igammac", + impl_aten=torch.special.gammaincc, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +le = _make_elementwise_binary_prim( + "le", + impl_aten=torch.le, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.ALWAYS_BOOL, +) + +lt = _make_elementwise_binary_prim( + "lt", + impl_aten=torch.lt, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.ALWAYS_BOOL, +) + + +# Note: the following impls are because torch.maximum and torch.minimum do not support scalar inputs +def _maximum_aten( + a: Union[TensorLikeType, NumberType], b: Union[TensorLikeType, NumberType] +) -> TensorLikeType: + if isinstance(a, TensorLike) and isinstance(b, Number): + b = scalar_tensor(b, dtype=a.dtype, device=a.device) + elif isinstance(b, TensorLike) and isinstance(a, Number): + a = scalar_tensor(a, dtype=b.dtype, device=b.device) + + return torch.maximum(a, b) # type: ignore[arg-type] + + +maximum = _make_elementwise_binary_prim( + "maximum", + impl_aten=_maximum_aten, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + + +def _minimum_aten( + a: Union[TensorLikeType, NumberType], b: Union[TensorLikeType, NumberType] +) -> TensorLikeType: + if isinstance(a, TensorLike) and isinstance(b, Number): + b = scalar_tensor(b, dtype=a.dtype, device=a.device) + elif isinstance(b, TensorLike) and isinstance(a, Number): + a = scalar_tensor(a, dtype=b.dtype, device=b.device) + + return torch.minimum(a, b) # type: ignore[arg-type] + + +minimum = _make_elementwise_binary_prim( + "minimum", + impl_aten=_minimum_aten, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +mul = _make_elementwise_binary_prim( + "mul", + impl_aten=torch.mul, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +ne = _make_elementwise_binary_prim( + "ne", + impl_aten=torch.ne, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.ALWAYS_BOOL, +) + +nextafter = _make_elementwise_binary_prim( + "nextafter", + impl_aten=torch.nextafter, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +pow = _make_elementwise_binary_prim( + "pow", + impl_aten=torch.pow, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +remainder = _make_elementwise_binary_prim( + "remainder", + impl_aten=torch.remainder, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + + +shift_left = _make_elementwise_binary_prim( + "shift_left", + impl_aten=torch.bitwise_left_shift, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +shift_right_arithmetic = _make_elementwise_binary_prim( + "shift_right_arithmetic", + impl_aten=torch.bitwise_right_shift, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +shift_right_logical = _not_impl + +sub = _make_elementwise_binary_prim( + "sub", + impl_aten=torch.sub, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +zeta = _make_elementwise_binary_prim( + "zeta", + impl_aten=torch.special.zeta, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + + +# +# View operations +def _as_strided_meta( + a: TensorLikeType, size: ShapeType, stride: StrideType, storage_offset: int +) -> TensorLikeType: + assert len(size) == len(stride) + assert storage_offset >= 0 + utils.validate_strides(stride) + utils.validate_shape(size) + + if reduce(operator.mul, size) == 0: + # NOTE: This special case is to avoid having to acquire the storage below + # as_strided to shapes with no elements are trivially valid, so it's OK + pass + elif isinstance(a, torch.Tensor): + utils.check_in_bounds_for_storage( + a._typed_storage(), size, stride, storage_offset + ) + + return torch.as_strided(a, size, stride, storage_offset) + + +def _as_strided_aten( + a: Tensor, size: ShapeType, stride: StrideType, storage_offset: int +) -> Tensor: + return torch.as_strided(a, size, stride, storage_offset) + + +_as_strided_doc = """ + Creates a view of the tensor with the given shape (size), strides (stride) and + storage offset (storage_offset). +""" + +as_strided = _make_prim( + schema="as_strided(Tensor(a!) a, SymInt[] size, SymInt[] stride, SymInt storage_offset) -> Tensor(a!)", + meta=_as_strided_meta, + impl_aten=_as_strided_aten, + return_type=RETURN_TYPE.VIEW, + doc=_as_strided_doc, +) + + +def _broadcast_in_dim_meta( + a: TensorLikeType, shape: ShapeType, broadcast_dimensions: Sequence[int] +): + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + # Type checks + assert isinstance(a, TensorLike) + assert isinstance(shape, Sequence) + assert isinstance(broadcast_dimensions, Sequence) + + # every dimension must be accounted for + assert a.ndim == len(broadcast_dimensions) + + # broadcast shape must have weakly more dimensions + assert len(shape) >= a.ndim + + # broadcast_dimensions must be an ascending sequence + # (no relative reordering of dims) of integers and + # each dimension must be within the new shape + def _greater_than_reduce(acc, x): + assert isinstance(x, Dim) + assert x > acc + assert x < len(shape) + + return x + + reduce(_greater_than_reduce, broadcast_dimensions, -1) + + # shape must be broadcastable to + for idx, new_idx in enumerate(broadcast_dimensions): + if not guard_size_oblivious(a.shape[idx] == 1): + torch._check( + a.shape[idx] == shape[new_idx], + lambda: f"{a.shape[idx]} must be broadcastable to {shape[new_idx]}", + ) + + new_strides = [] + original_idx = 0 + for idx in range(len(shape)): + if idx in broadcast_dimensions: + # Assigns a stride of zero to dimensions + # which were actually broadcast + if guard_size_oblivious(a.shape[original_idx] != shape[idx]): + new_strides.append(0) + else: + new_strides.append(a.stride()[original_idx]) + original_idx = original_idx + 1 + else: + if guard_size_oblivious(shape[idx] != 1): + new_strides.append(0) + elif original_idx == a.ndim: + new_strides.append(1) + else: + new_strides.append(a.stride()[original_idx] * a.size()[original_idx]) + + return a.as_strided(shape, new_strides, a.storage_offset()) + + +def _broadcast_in_dim_aten(a, shape, broadcast_dimensions): + s = list(shape) + for broadcast_dimension in broadcast_dimensions: + s[broadcast_dimension] = -1 + + v = a + for idx, x in enumerate(s): + if x != -1: + v = v.unsqueeze(idx) + + return v.expand(shape) + + +_broadcast_in_dim_doc = """ + Creates a view of a with the specified shape. + + Allows adding dimensions of any length and broadcasting + dimensions of length one in a to any length. + + The location of the broadcast dimensions must be specified + using the broadcast_dimensions argument. Changing the + relative order of dimensions is not supported. + """ + +broadcast_in_dim = _make_prim( + schema="broadcast_in_dim(Tensor(a) a, SymInt[] shape, int[] broadcast_dimensions) -> Tensor(a)", + meta=_broadcast_in_dim_meta, + impl_aten=_broadcast_in_dim_aten, + return_type=RETURN_TYPE.VIEW, + doc=_broadcast_in_dim_doc, +) + + +def _validate_collapse_args(a: Tensor, start: int, end: int) -> None: + # Special-case for zero dimensional tensors + ndim = max(1, a.dim()) + utils.validate_idx(ndim, start) + utils.validate_idx(ndim, end) + + # Verifies end is strictly greater than start + # (Collapse requires a non-empty interval) + torch._check_value( + end >= start, + lambda: f"Attempting to collapse but end, {end}, is less than start, {start}!", + ) + + +def _collapsed_shape(shape: ShapeType, start: int, end: int) -> Tuple[int, ...]: + """ + Returns the shape of a with dims in [start, end) merged into a single dimension. + """ + # Special-case for zero dimensional tensors + shape = (1,) if len(shape) == 0 else tuple(shape) + + dim_length = 1 + for s in shape[start : end + 1]: + dim_length = dim_length * s + + return shape[0:start] + (dim_length,) + shape[end + 1 :] + + +def _collapse_view_helper( + a: TensorLikeType, start: int, end: int +) -> Tuple[Optional[ShapeType], Optional[StrideType]]: + assert isinstance(a, TensorLike) + + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + _validate_collapse_args(a, start, end) + + # Special-case for zero dimensional tensors + if a.ndim == 0: + shape = (1,) + strides = (1,) + else: + shape = a.shape # type: ignore[assignment] + strides = a.stride() # type: ignore[assignment] + + if a.ndim == 0 or (end == start): + return shape, strides + + length = shape[end] + stride = strides[end] + for idx in range(end - 1, start - 1, -1): + if guard_size_oblivious(shape[idx] == 0) or guard_size_oblivious( + shape[idx + 1] == 0 + ): + length = 0 + stride = 0 + break + + if guard_size_oblivious(shape[idx] == 1): + continue + + length = length * shape[idx] + if guard_size_oblivious(stride < strides[idx]): + stride = stride + else: + stride = strides[idx] + + if ( + guard_size_oblivious(a.numel() > 0) + and guard_size_oblivious(shape[idx + 1] != 1) + and not guard_size_oblivious( + strides[idx] == strides[idx + 1] * shape[idx + 1] + ) + ): + return None, None + + new_shape = shape[:start] + (length,) + shape[end + 1 :] + new_strides = strides[:start] + (stride,) + strides[end + 1 :] + + # NOTE: when the input has no elements it's restrided as if it were contiguous + if guard_size_oblivious(a.numel() == 0): + new_strides = utils.make_contiguous_strides_for(new_shape) + + return new_shape, new_strides + + +def _collapse_view_meta(a: TensorLikeType, start: int, end: int) -> TensorLikeType: + new_shape, new_strides = _collapse_view_helper(a, start, end) + + if new_shape is None: + msg = "Attempting to view a collapsed tensor, but no such view exists!" + raise ValueError(msg) + + assert new_strides is not None + return a.as_strided(new_shape, new_strides, a.storage_offset()) + + +def _collapse_view_aten(a: Tensor, start: int, end: int) -> Tensor: + new_shape = _collapsed_shape(a.shape, start, end) + return a.view(new_shape) + + +_collapse_view_doc = """ + Creates a view of a with the dimensions between + start (inclusive) and end (exclusive) merged into a + single dimension. + + If it's not possible to take such a view then an error + is thrown. See collapse instead. + + The dimensions can be merged if and only if + they are all "nested" with each other. That is, they all + have the property that + + stride[i] = stride[i+1] * shape[i+1] + + for all i in [start, end - 1). + """ + +collapse_view = _make_prim( + schema="collapse_view(Tensor(a) a, int start, int end) -> Tensor(a)", + meta=_collapse_view_meta, + impl_aten=_collapse_view_aten, + return_type=RETURN_TYPE.VIEW, + doc=_collapse_view_doc, +) + + +def _conj_meta(a: TensorLikeType) -> TensorLikeType: + if not a.dtype.is_complex: + raise RuntimeError("Expected complex dtype in prims.conj") + out = a.as_strided(a.shape, a.stride(), a.storage_offset()) + torch._C._set_conj(out, not a.is_conj()) + return out + + +_conj_doc = """ +Returns a conjugated view of the original tensor +""" + +conj = _make_prim( + schema="conj(Tensor(a) a) -> Tensor(a)", + meta=_conj_meta, + impl_aten=torch.conj, + return_type=RETURN_TYPE.VIEW, + doc=_conj_doc, +) + + +def expand_dims( + a: TensorLikeType, dimensions: DimsSequenceType, ndim=None +) -> TensorLikeType: + """ + Creates a view of a with a.ndim + len(dimensions) dimensions, with new + dimensions of length one at the dimensions specified by dimensions. + """ + if ndim is not None: + # TODO: this is only here to support the unsqueeze ref + dims = sorted(utils.canonicalize_dims(ndim, dimensions)) # type: ignore[arg-type] + else: + dims = sorted(utils.canonicalize_dims(a.ndim, dimensions)) # type: ignore[arg-type] + if len(set(dims)) != len(dims): + msg = f"Received duplicate dimensions to expand in {str(dimensions)}" + raise ValueError(msg) + + new_shape = list(a.shape) + for idx in dims: + new_shape.insert(idx, 1) + + broadcast_dimensions = [ + idx for idx in range(len(new_shape)) if idx not in dimensions + ] + return broadcast_in_dim(a, new_shape, broadcast_dimensions) + + +# Note: saves the Python slice object because we're about to clobber its name with the slice prim +pyslice: Type[slice] = slice # type: ignore[has-type] + + +def _slice_meta( + a: TensorLikeType, + start_indices: DimsSequenceType, + limit_indices: DimsSequenceType, + strides: Optional[StrideType] = None, +) -> TensorLikeType: + _strides = strides if strides is not None else [1] * len(start_indices) + + if a.ndim != len(start_indices): + msg = f"Attempting to slice tensor of rank {a.ndim} with start_indices of length {len(start_indices)}!" + raise ValueError(msg) + + if a.ndim != len(limit_indices): + msg = f"Attempting to slice tensor of rank {a.ndim} with limit_indices of length {len(limit_indices)}!" + raise ValueError(msg) + + if a.ndim != len(_strides): + msg = f"Attempting to slice tensor of rank {a.ndim} with strides of length {len(limit_indices)}!" + raise ValueError(msg) + + for x, y in zip(start_indices, a.shape): + if x < 0: + msg = f"Attempting to slice a tensor with a negative start index of {x}!" + raise ValueError(msg) + if x > y: + msg = ( + f"Attempting to slice a tensor but a start index in {start_indices} is greater than" + f" the length of its corresponding dimension in shape {a.shape}" + ) + raise ValueError(msg) + + for x, y, z in zip(limit_indices, a.shape, start_indices): + if x < 0: + msg = f"Attempting to slice a tensor with a negative stop index of {x}!" + raise ValueError(msg) + if x > y: + msg = ( + f"Attempting to slice a tensor but a stop index in {limit_indices} is greater than the length of " + f" its corresponding dimension in shape {a.shape}" + ) + raise ValueError(msg) + if x < z: + msg = ( + f"Attempting to slice a tensor but a start index in {x} is greater than " + f" its corresponding stop index {z}" + ) + + for x in _strides: + if x <= 0: + msg = f"Attempting to slice a tensor with a non-positive step of {x}!" + raise ValueError(msg) + + new_shape = [] + for x, y, z in zip(start_indices, limit_indices, _strides): + new_shape.append(1 + (y - x - 1) // z) + + new_strides = [] + for x, y in zip(a.stride(), _strides): + new_strides.append(x * y) + + return a.as_strided(new_shape, new_strides, a.storage_offset()) + + +def _slice_aten( + a: Tensor, + start_indices: DimsSequenceType, + limit_indices: DimsSequenceType, + strides: Optional[StrideType] = None, +) -> Tensor: + _strides = strides if strides is not None else [1] * len(start_indices) + + slices = [] + for start, stop, step in zip(start_indices, limit_indices, _strides): + slices.append(pyslice(start, stop, step)) + + return operator.getitem(a, slices) # type: ignore[call-overload] + + +_slice_doc = """ + Creates a view of a "bounding box" within the tensor. + + The bounding box is specified independently in each of the tensor's dimensions. + start_indices and limit_indices describe the box's boundaries for their corresponding + dimensions. If strides is specified then they specify the step size between elements + in their corresponding dimension. + + This operation is analogous to slicing in NumPy, but does not permit slices where + the stop indices are less than the start indices. + """ + +slice = _make_prim( + schema="slice(Tensor(a) a, SymInt[] start_indices, SymInt[] limit_indices, SymInt[]? strides=None) -> Tensor(a)", + meta=_slice_meta, + impl_aten=_slice_aten, + return_type=RETURN_TYPE.VIEW, + doc=_slice_doc, +) + + +def _slice_in_dim_meta( + a: TensorLikeType, + start_index: int, + limit_index: int, + stride: int = 1, + axis: int = 0, +) -> TensorLikeType: + if axis < 0: + msg = f"slice_in_dim: received a negative axis {axis}" + raise ValueError(msg) + if axis >= a.ndim: + msg = f"slice_in_dim: axis {axis} is greater or equal to the rank {a.ndim} of the tensor" + raise ValueError(msg) + + if start_index < 0: + msg = f"slice_in_dim: received a negative start_index {start_index}" + raise ValueError(msg) + + if start_index > a.shape[axis]: + msg = f"slice_in_dim: start_index is greater than the length {start_index} of dimension {axis}" + raise ValueError(msg) + + if limit_index > a.shape[axis]: + msg = f"slice_in_dim: limit_index is greater than the length {limit_index} of dimension {axis}" + raise ValueError(msg) + + if limit_index < start_index: + msg = f"slice_in_dim: received a limit_index {limit_index} less than the start_index {start_index}" + raise ValueError(msg) + + if stride < 0: + msg = f"slice_in_dim: received a non-positive stride of {stride}!" + raise ValueError(msg) + + start_indices = [0] * a.ndim + limit_indices = list(a.shape) + strides = [1] * a.ndim + + start_indices[axis] = start_index + limit_indices[axis] = limit_index + strides[axis] = stride + + return _slice_meta(a, start_indices, limit_indices, strides) + + +def _slice_in_dim_aten( + a: Tensor, + start_index: int, + limit_index: int, + stride: int = 1, + axis: int = 0, +) -> Tensor: + start_indices = [0] * a.ndim + limit_indices = list(a.shape) + strides = [1] * a.ndim + + start_indices[axis] = start_index + limit_indices[axis] = limit_index + strides[axis] = stride + + return slice(a, start_indices, limit_indices, strides) + + +_slice_in_dim_doc = """ + Convenience wrapper for slicing just one dimension using slice. + """ + +# TODO: make stride SymInt +slice_in_dim = _make_prim( + schema="slice_in_dim(Tensor(a) a, SymInt start_index, SymInt limit_index, int stride=1, int axis=0) -> Tensor(a)", + meta=_slice_in_dim_meta, + impl_aten=_slice_in_dim_aten, + return_type=RETURN_TYPE.VIEW, + doc=_slice_in_dim_doc, +) + + +def _split_dim_meta(a: TensorLikeType, dim: int, outer_length: int) -> TensorLikeType: + assert isinstance(a, TensorLike) + utils.validate_idx(a.ndim, dim) + utils.validate_dim_length(outer_length) + + # Verifies the dim can be split with the specified lhs_length + inner_length = a.shape[dim] // outer_length + + if (a.shape[dim] % outer_length) != 0: + msg = ( + f"Attempting to split dimension of length {a.shape[dim]}, " + f"but outer length of {outer_length} divides it with a remainder!" + ) + raise ValueError(msg) + + new_shape: List[int] = [] + new_strides: List[int] = [] + for idx in range(a.ndim): + if idx == dim: + new_shape.extend((outer_length, inner_length)) + new_strides.extend((a.stride()[idx] * inner_length, a.stride()[idx])) + else: + new_shape.append(a.shape[idx]) + new_strides.append(a.stride()[idx]) + + return a.as_strided(new_shape, new_strides, a.storage_offset()) + + +def _split_dim_aten(a: Tensor, dim: int, outer_length: int) -> Tensor: + inner_length = a.shape[dim] // outer_length + new_shape = a.shape[0:dim] + (outer_length, inner_length) + a.shape[dim + 1 :] + + return a.view(new_shape) + + +_split_dim_doc = """ + Creates a view of a with the given dimension (of length l) split + into two dimensions, with the outer of the two having + length outer_length and the inner of the two having computed + length inner_length such outer_length * inner_length = l. + """ + +# TODO: consider renaming split_dim_view +split_dim = _make_prim( + schema="split_dim(Tensor(a) a, int dim, SymInt outer_length) -> Tensor(a)", + meta=_split_dim_meta, + impl_aten=_split_dim_aten, + return_type=RETURN_TYPE.VIEW, + doc=_split_dim_doc, +) + + +# Note: allows dimensions to be specified redundantly +def _squeeze_meta(a: TensorLikeType, dimensions: Sequence) -> TensorLikeType: + assert isinstance(a, TensorLike) + + for idx in dimensions: + utils.validate_idx(a.ndim, idx) + assert a.shape[idx] == 1 + + new_shape = [] + new_strides = [] + for idx in range(len(a.shape)): + if idx in dimensions: + continue + + new_shape.append(a.shape[idx]) + new_strides.append(a.stride()[idx]) + + return a.as_strided(new_shape, new_strides, a.storage_offset()) + + +_squeeze_doc = """ + Creates a view of the tensor with the specified dimensions removed. + + The removed dimensions must each have length one. + """ + +squeeze = _make_prim( + schema="squeeze(Tensor(a) a, int[] dimensions) -> Tensor(a)", + meta=_squeeze_meta, + impl_aten=torch.squeeze, + return_type=RETURN_TYPE.VIEW, + doc=_squeeze_doc, +) + + +def _transpose_meta(a: TensorLikeType, permutation: DimsSequenceType) -> TensorLikeType: + if a.ndim != len(permutation): + msg = f"Attempting to permute a tensor of rank {a.ndim}, but received a permutation of length {len(permutation)}!" + raise ValueError(msg) + + if not utils.is_valid_permutation(a.ndim, permutation): + msg = f"Received an invalid permutation, {permutation}!" + raise ValueError(msg) + + new_shape = [0] * a.ndim + new_strides = [0] * a.ndim + for idx, dim in enumerate(permutation): + new_shape[idx] = a.shape[dim] + new_strides[idx] = a.stride()[dim] + + return a.as_strided(tuple(new_shape), tuple(new_strides), a.storage_offset()) + + +def _transpose_aten(a: Tensor, permutation: DimsSequenceType) -> Tensor: + return torch.permute(a, permutation) + + +_transpose_doc = """ + Creates a view of the tensor with its dimensions permuted. + + The length of the permutation must be the rank of the tensor, + and each element of the permutation specifies the new order + for the corresponding dimension. + """ + +transpose = _make_prim( + schema="transpose(Tensor(a) a, int[] permutation) -> Tensor(a)", + meta=_transpose_meta, + impl_aten=_transpose_aten, + return_type=RETURN_TYPE.VIEW, + doc=_transpose_doc, +) + + +def _view_of_meta(a: TensorLikeType) -> TensorLikeType: + return a.as_strided(a.shape, a.stride(), a.storage_offset()) + + +def _view_of_aten(a: Tensor) -> Tensor: + return a.view(a.shape) + + +_view_of_doc = """ + Creates a view of the tensor. + """ + +view_of = _make_prim( + schema="view_of(Tensor(a) a) -> Tensor(a)", + meta=_view_of_meta, + impl_aten=_view_of_aten, + return_type=RETURN_TYPE.VIEW, + doc=_view_of_doc, +) + + +def _view_element_type_meta(a: TensorLikeType, dtype: torch.dtype) -> TensorLikeType: + return a.view(dtype) + + +def _view_element_type_aten(a: Tensor, dtype: torch.dtype) -> Tensor: + return a.view(dtype) + + +_view_element_type_doc = """ + Creates a view of the tensor with a different dtype. + """ + +view_element_type = _make_prim( + schema="view_of_dtype(Tensor(a) a, ScalarType dtype) -> Tensor(a)", + meta=_view_element_type_meta, + impl_aten=_view_element_type_aten, + return_type=RETURN_TYPE.VIEW, + doc=_view_element_type_doc, +) + +# +# Functionalized view mutations +# + + +def _as_strided_scatter_meta( + input: TensorLikeType, + src: TensorLikeType, + size: ShapeType, + stride: StrideType, + storage_offset: int, +) -> TensorLikeType: + utils.validate_shape(size) + utils.validate_strides(stride) + + required_size = utils.compute_required_storage_length(size, stride, storage_offset) + torch._check( + input.numel() >= required_size, + lambda: ( + f"as_strided_scatter: sizes {size}, strides {stride}, storage offset {storage_offset} " + f" and itemsize {input.element_size()} requiring a storage size of " + f"{required_size * input.element_size()} are out of bounds " + f"for storage of size {input.numel() * input.element_size()}" + ), + ) + torch._check( + utils.is_same_shape(src.shape, size), + lambda: f"expected src to have a size equal to the slice of self. src size = {src.shape}, slice size = {size}", + ) + + return utils.clone_preserve_strides(input) + + +_as_strided_scatter_doc = """ + Creates a new tensor equivalent to ``out = input.clone()`` after mutation by + ``out.as_strided(size, stride, storage_offset).copy_(src)``. +""" + +as_strided_scatter = _make_prim( + schema="as_strided_scatter(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt storage_offset) -> Tensor", + meta=_as_strided_scatter_meta, + impl_aten=torch.as_strided_scatter, + return_type=RETURN_TYPE.NEW, + doc=_as_strided_scatter_doc, +) + + +# +# Shape operations +# + + +def _collapse_meta(a: Tensor, start: int, end: int) -> Tensor: + # Special-case for zero dimensional tensors + _validate_collapse_args(a, start, end) + new_shape = _collapsed_shape(a.shape, start, end) + return a.new_empty(new_shape) + + +def _collapse_aten(a: Tensor, start: int, end: int) -> Tensor: + new_shape = _collapsed_shape(a.shape, start, end) + out = a.new_empty(new_shape) + with torch.no_grad(): + out.view_as(a).copy_(a) + return out + + +_collapse_doc = """ +Collapse a span of neighboring dimensions into one. + +See collapse_view for the corresponding view operation. +""" +collapse = _make_prim( + schema="collapse(Tensor a, int start, int end) -> Tensor", + meta=_collapse_meta, + impl_aten=_collapse_aten, + return_type=RETURN_TYPE.NEW, + doc=_collapse_doc, +) + + +# TODO: review stride logic +# NB: unlike torch.cat, this is more strict about empty tensors and dim is +# never negative +def _cat_meta(tensors: Sequence[TensorLikeType], dim: int) -> TensorLikeType: + # Verifies same shape (except in the concat dimension) + assert dim >= 0 + shape = tensors[0].shape + concat_length = 0 + for tensor_idx, tensor in enumerate(tensors): + assert len(shape) == len(tensor.shape) + for idx, (common_length, length) in enumerate(zip(shape, tensor.shape)): + if idx == dim: + concat_length = concat_length + length + else: + torch._check( + length == common_length, + lambda: f"Sizes of tensors must match except in dimension {dim}. " + f"Expected {common_length} but got {length} for tensor number " + f"{tensor_idx} in the list", + ) + + new_shape = list(tensors[0].shape).copy() + new_shape[dim] = concat_length + return TensorMeta( + tensors[0], + shape=new_shape, + strides=utils.make_contiguous_strides_for(new_shape), + ) + + +def _cat_aten(tensors: Union[Tuple[Tensor, ...], List[Tensor]], dim: int) -> Tensor: + return torch.cat(tensors, dim) + + +_cat_doc = """ + Concatenates tensors along the specified dimension. + + The tensors' shapes must have the same rank and same length for other dimensions. + """ + +cat = _make_prim( + schema="cat(Tensor[] tensors, int dim) -> Tensor", + meta=_cat_meta, + impl_aten=_cat_aten, + return_type=RETURN_TYPE.NEW, + doc=_cat_doc, +) + + +def _reshape_meta(a: TensorLikeType, shape: ShapeType): + assert isinstance(a, TensorLike) + utils.validate_shape(shape) + + # Validates the tensor and the requested shape have the + # same number of elements + numel = reduce(operator.mul, shape) + if numel != a.numel(): + msg = f"Attempting to reshape a tensor with {a.numel()} elements to a shape with {numel} elements!" + raise ValueError(msg) + + return TensorMeta(a, shape=shape, strides=utils.make_contiguous_strides_for(shape)) + + +def _reshape_aten(a: Tensor, shape: ShapeType) -> Tensor: + return a.reshape(shape).contiguous().clone() + + +_reshape_doc = """ + Creates a contiguous tensor with the specified shape + containing a copy of the data in a. + """ +reshape = _make_prim( + schema="reshape(Tensor a, SymInt[] shape) -> Tensor", + meta=_reshape_meta, + impl_aten=_reshape_aten, + return_type=RETURN_TYPE.NEW, + doc=_reshape_doc, +) + + +def _rev_meta(a: TensorLikeType, dims: DimsSequenceType) -> TensorLikeType: + utils.validate_dimension_indices(a.ndim, dims) + return torch.empty_like(a, memory_format=torch.preserve_format) + + +_rev_doc = """ + Reverses the order of elements along the given dimensions. + """ + +rev = _make_prim( + schema="rev(Tensor a, int[] dims) -> Tensor", + meta=_rev_meta, + impl_aten=torch.flip, + return_type=RETURN_TYPE.NEW, + doc=_rev_doc, +) + +# +# Conditional prims +# + + +def _where_meta( + pred: TensorLikeType, a: TensorLikeType, b: TensorLikeType +) -> TensorLikeType: + return _prim_elementwise_meta( + a, + b, + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, + args_with_fixed_dtypes=(pred,), + ) + + +_where_doc = """ + Selects elements from a and b according to pred. + + Where pred is true the result contains the element from a, and + where pred is false the result contains the element from b. + """ + +where = _make_prim( + schema="where(Tensor pred, Tensor a, Tensor b) -> Tensor", + meta=_where_meta, + impl_aten=torch.where, + return_type=RETURN_TYPE.NEW, + doc=_where_doc, +) + + +# +# Type conversions +# +def _convert_element_type_meta(a: TensorLikeType, dtype: torch.dtype) -> TensorLikeType: + # Type checks + assert isinstance(a, TensorLike) + assert isinstance(dtype, torch.dtype) + + # dtype conversion preserves dense strides + if torch._prims_common.is_non_overlapping_and_dense(a): + strides = a.stride() + else: + strides = utils.compute_elementwise_output_strides(a) + + return TensorMeta(a, strides=strides, dtype=dtype) + + +def _convert_element_type_aten(a: Tensor, dtype: torch.dtype) -> Tensor: + # Propagates requires grad when possible + if not utils.is_grad_dtype(dtype): + requires_grad = False + else: + # TODO: update meta objects so this can be acquired directly + try: + requires_grad = a.requires_grad + except Exception as e: + requires_grad = False + + result = torch.empty_like( + a, device=a.device, dtype=dtype, requires_grad=requires_grad + ) + with torch.no_grad(): + return copy_to(result, a) + + +_convert_element_type_doc = """ + Creates a copy of a tensor with the given dtype. + """ + +convert_element_type = _make_prim( + schema="convert_element_type(Tensor a, ScalarType dtype) -> Tensor", + meta=_convert_element_type_meta, + impl_aten=_convert_element_type_aten, + return_type=RETURN_TYPE.NEW, + doc=_convert_element_type_doc, + tags=(torch.Tag.pointwise,), +) + + +def _device_put_meta( + a: TensorLikeType, device: Union[str, torch.device] +) -> TensorLikeType: + assert isinstance(a, TensorLike) + assert isinstance(device, (str, torch.device)) + + return TensorMeta(a, device=utils.canonicalize_device(device)) + + +def _device_put_aten(a: Tensor, device: Union[str, torch.device]) -> Tensor: + return a.to(device) + + +_device_put_doc = """ + Creates a copy of a tensor on the given device. + """ + +device_put = _make_prim( + schema="device_put(Tensor a, Device device) -> Tensor", + meta=_device_put_meta, + impl_aten=_device_put_aten, + return_type=RETURN_TYPE.NEW, + doc=_device_put_doc, +) + + +# NOTE: need to model meta scalars +# See https://github.com/pytorch/pytorch/issues/78070 +def _item_meta(a: TensorLikeType) -> FakeTensor: + number_type = utils.dtype_to_type(a.dtype) + return TensorMeta(number_type(-1)) + + +_item_doc = """ + Converts a tensor with one element to a Python number. +""" + +# TODO: create a new return type for scalars? +# FIXME: currently returns integers for boolean tensors +# https://github.com/pytorch/pytorch/issues/78071 +item = _make_prim( + schema="item(Tensor a) -> Scalar", + meta=_item_meta, + impl_aten=torch.Tensor.item, + return_type=RETURN_TYPE.NEW, + doc=_item_doc, +) + + +# NOTE: need to model meta scalars +# See https://github.com/pytorch/pytorch/issues/78070 +def _maximum_value_meta(dtype: torch.dtype) -> FakeTensor: + number_type = utils.dtype_to_type(dtype) + return TensorMeta(number_type(-1)) + + +def _maximum_value_aten(dtype: torch.dtype): + if dtype == torch.bool: + return True + elif dtype.is_complex or dtype.is_floating_point: + return torch.finfo(dtype).max + else: + return torch.iinfo(dtype).max + + +_maximum_value_doc = """ + Return the maximum finite value for a dtype. +""" + +# TODO: create a new return type for scalars? +# FIXME: currently returns integers for boolean tensors +# https://github.com/pytorch/pytorch/issues/78071 +maximum_value = _make_prim( + schema="maximum_value(ScalarType dtype) -> Scalar", + meta=_maximum_value_meta, + impl_aten=_maximum_value_aten, + return_type=RETURN_TYPE.NEW, + doc=_maximum_value_doc, +) + + +# NOTE: need to model meta scalars +# See https://github.com/pytorch/pytorch/issues/78070 +def _minimum_value_meta(dtype: torch.dtype) -> FakeTensor: + number_type = utils.dtype_to_type(dtype) + return TensorMeta(number_type(-1)) + + +def _minimum_value_aten(dtype: torch.dtype): + if dtype == torch.bool: + return False + elif dtype.is_complex or dtype.is_floating_point: + return torch.finfo(dtype).min + else: + return torch.iinfo(dtype).min + + +_minimum_value_doc = """ + Return the minimum finite value for a dtype. +""" + +# TODO: create a new return type for scalars? +# FIXME: currently returns integers for boolean tensors +# https://github.com/pytorch/pytorch/issues/78071 +minimum_value = _make_prim( + schema="minimum_value(ScalarType dtype) -> Scalar", + meta=_minimum_value_meta, + impl_aten=_minimum_value_aten, + return_type=RETURN_TYPE.NEW, + doc=_minimum_value_doc, +) + +# +# Inplace operators +# + + +def _copy_to_meta(a: TensorLikeType, b: TensorLikeType): + assert isinstance(a, TensorLike) + assert isinstance(b, TensorLike) + + # Validates the cast is safe + # TODO: move this as an option on the reference + # a_typ = utils.dtype_to_type(a.dtype) + # b_typ = utils.dtype_to_type(b.dtype) + # if a_typ is not utils.get_higher_type(a_typ, b_typ): + # raise RuntimeError(str(b.dtype), " can't be cast safely to ", str(a.dtype), "!") + + # Validates the tensors have the same number of elements + if a.numel() != b.numel(): + msg = f"Attempting to copy {b.numel()} elements to a tensor with {a.numel()} elements!" + raise RuntimeError(msg) + + return a + + +def _copy_to_aten(a: Tensor, b: Tensor) -> Tensor: + return a.copy_(b) + + +_copy_to_doc = """ + Copies the data in b to a and returns the modified a. + """ + +# TODO: Remove safe casting and implement on reference instead +copy_to = _make_prim( + schema="copy_to(Tensor(a!) a, Tensor b) -> Tensor(a!)", + meta=_copy_to_meta, + impl_aten=_copy_to_aten, + return_type=RETURN_TYPE.INPLACE, + doc=_copy_to_doc, + register_conj_neg_fallthrough=True, +) + + +def _copy_strided_meta(a: TensorLikeType, stride: ShapeType): + assert isinstance(a, TensorLike) + return torch.empty_strided( + a.shape, + stride, + dtype=a.dtype, + layout=a.layout, + device=a.device, + requires_grad=a.requires_grad, + ) + + +def _copy_strided_aten(a: Tensor, stride: ShapeType) -> Tensor: + out = torch.empty_strided( + a.size(), + stride=stride, + dtype=a.dtype, + layout=a.layout, + device=a.device, + requires_grad=a.requires_grad, + ) + out.copy_(a) + return out + + +_copy_strided_doc = """ + Copies the data in a to a new tensor, the new tensor has same shape with a size, but has different stride. + """ + + +copy_strided = _make_prim( + schema="copy_strided(Tensor a, SymInt[] stride) -> Tensor", + meta=_copy_strided_meta, + impl_aten=_copy_strided_aten, + return_type=RETURN_TYPE.NEW, + doc=_copy_strided_doc, +) + + +def _resize_meta(a: TensorLikeType, shape: ShapeType): + return a.resize_(shape) + + +def _resize_aten(a: Tensor, shape: ShapeType) -> Tensor: + return a.resize_(shape) + + +_resize_doc = """ + Gives a tensor with no elements a new shape, returning the modified tensor. + + The tensor's strides are contiguous and its values are unitialized. + """ + +# TODO: review support arbitrary resizes +resize = _make_prim( + schema="resize(Tensor(a!) a, SymInt[] shape) -> Tensor(a!)", + meta=_resize_meta, + impl_aten=_resize_aten, + return_type=RETURN_TYPE.INPLACE, + doc=_resize_doc, +) + + +def _reduction_meta(inp, dims, *, output_dtype=None): + """ + Meta function for single output reduction operations + Stride logic is incorrect + """ + assert isinstance(inp, TensorLike) + if output_dtype is None: + output_dtype = inp.dtype + output_shape = utils.compute_reduction_output_shape(inp.shape, dims) + return TensorMeta( + shape=output_shape, + strides=utils.make_contiguous_strides_for(output_shape), + dtype=output_dtype, + device=inp.device, + ) + + +def _var_reduction_meta(inp, dims, correction): + if utils.is_complex_dtype(inp.dtype): + output_dtype = utils.corresponding_real_dtype(inp.dtype) + else: + output_dtype = inp.dtype + return _reduction_meta(inp, dims, output_dtype=output_dtype) + + +_sum_doc = """ + Computes the sum of elements in the input tensor over the list of dimensions + specified in the dim argument + """ +_xor_sum_doc = """ + Computes the xor sum of elements in the input tensor over the list of dimensions + specified in the dim argument + """ +_prod_doc = """ + Computes the product of elements in the input tensor over the list of dimensions + specified in the dim argument + """ +_amax_doc = """ + Computes the maximum value of elements in the input tensor over the list of dimensions + specified in the dim argument + """ +_amin_doc = """ + Computes the minimum value of elements in the input tensor over the list of dimensions + specified in the dim argument + """ +_var_doc = """ + Computes the biased variance of x over the list of dimensions specified in the dim argument + """ + + +def _make_reduction_prim(name: str, impl_aten, doc): + """Creates a reduction prim.""" + return _make_prim( + schema=f"{name}(Tensor inp, int[]? dims, *, ScalarType? output_dtype=None) -> Tensor", + meta=_reduction_meta, + impl_aten=impl_aten, + return_type=RETURN_TYPE.NEW, + doc=doc, + ) + + +def _make_var_reduction_prim(name: str, impl_aten, doc): + """Creates a reduction prim.""" + return _make_prim( + schema=f"{name}(Tensor inp, int[]? dims, float? correction=1, *, ScalarType? output_dtype=None) -> Tensor", + meta=_var_reduction_meta, + impl_aten=impl_aten, + return_type=RETURN_TYPE.NEW, + doc=doc, + ) + + +sum = _make_reduction_prim( + name="sum", + impl_aten=torch.sum, + doc=_sum_doc, +) + + +def _xor_sum_aten( + inp: TensorLikeType, + dims: Optional[DimsSequenceType], + *, + dtype: Optional[torch.dtype] = None, +) -> Tensor: + raise NotImplementedError("xor_sum only implemented with inductor") + + +xor_sum = _make_reduction_prim( + name="xor_sum", + impl_aten=_xor_sum_aten, + doc=_xor_sum_doc, +) + + +def _prod_aten( + inp: TensorLikeType, + dims: Optional[DimsSequenceType], + *, + dtype: Optional[torch.dtype] = None, +) -> Tensor: + if dims is not None: + if len(dims) == 0: + return inp.clone() + for d in sorted(dims, reverse=True): + assert d >= 0 + inp = torch.prod(inp, d, dtype=dtype) + return inp + else: + return torch.prod(inp, dims, dtype=dtype) + + +prod = _make_reduction_prim( + name="prod", + impl_aten=_prod_aten, + doc=_prod_doc, +) + + +# torch.var, but correction is not kwarg-only +def torch_var(input, dim=None, correction=1, **kwargs): + return torch.var(input, dim=dim, correction=correction, **kwargs) + + +var = _make_var_reduction_prim( + name="var", + impl_aten=torch_var, + doc=_var_doc, +) + +amax = _make_reduction_prim( + name="amax", + impl_aten=torch.amax, + doc=_amax_doc, +) + +amin = _make_reduction_prim( + name="amin", + impl_aten=torch.amin, + doc=_amin_doc, +) + + +_iota_doc = """ + Constructs a 1-D tensor t where ``t[i] == start + i * step``. +""" + + +# TODO: layout, pin_memory, memory_format +# TODO: model requires_grad on TensorMeta +def _iota_meta( + length: int, + *, + start: int, + step: int, + dtype: torch.dtype, + device: torch.device, + requires_grad: bool, +) -> TensorLikeType: + torch._check( + utils.is_integer_dtype(dtype), + lambda: "prims.iota only supports integer dtypes", + ) + torch._check(step != 0, lambda: "step must be nonzero") + return torch.empty( + length, + dtype=dtype, + device=device, + requires_grad=requires_grad, + ) + + +def _iota_aten( + length: int, + *, + start: int, + step: int, + dtype: torch.dtype, + device: torch.device, + requires_grad: bool, +) -> TensorLikeType: + end = start + length * step + return torch.arange( + start, end, step, dtype=dtype, device=device, requires_grad=requires_grad + ) + + +iota = _make_prim( + schema="iota(SymInt length, *, SymInt start, SymInt step, ScalarType dtype, Device device, bool requires_grad) -> Tensor", # noqa: B950 + return_type=RETURN_TYPE.NEW, + meta=_iota_meta, + impl_aten=_iota_aten, + doc=_iota_doc, +) + + +# TODO: layout, pin_memory, memory_format +# TODO: model requires_grad on TensorMeta +def _empty_meta( + shape: ShapeType, *, dtype: torch.dtype, device: torch.device, requires_grad: bool +) -> TensorLikeType: + strides = utils.make_contiguous_strides_for(shape) + return TensorMeta(shape=shape, strides=strides, dtype=dtype, device=device) + + +def _empty_aten( + shape: ShapeType, *, dtype: torch.dtype, device: torch.device, requires_grad: bool +) -> Tensor: + return torch.empty(shape, dtype=dtype, device=device, requires_grad=requires_grad) + + +_empty_doc = """ + Creates a tensor with uninitialized values and the specified shape, dtype, and device. +""" + +empty = _make_prim( + schema="empty(SymInt[] shape, *, ScalarType dtype, Device device, bool requires_grad) -> Tensor", + meta=_empty_meta, + impl_aten=_empty_aten, + return_type=RETURN_TYPE.NEW, + doc=_empty_doc, +) + + +def _empty_strided_meta( + shape: ShapeType, + strides: StrideType, + *, + dtype: torch.dtype, + device: torch.device, + requires_grad: bool, +) -> TensorLikeType: + return TensorMeta(shape=shape, strides=strides, dtype=dtype, device=device) + + +_empty_strided_doc = """ + Creates a tensor with uninitialized values. +""" + +# TODO: add layout, pin_memory +empty_strided = _make_prim( + schema="empty_strided(SymInt[] shape, SymInt[] strides, *, ScalarType dtype, Device device, bool requires_grad) -> Tensor", + return_type=RETURN_TYPE.NEW, + meta=_empty_strided_meta, + impl_aten=torch.empty_strided, + doc=_empty_strided_doc, +) + + +def _empty_permuted_meta( + shape: ShapeType, + physical_layout: DimsSequenceType, + *, + dtype: torch.dtype, + device: torch.device, + requires_grad: bool, +) -> TensorLikeType: + p_strides = utils.make_contiguous_strides_for([shape[l] for l in physical_layout]) + dim = len(shape) + torch._check( + len(physical_layout) == dim, + lambda: ( + "Number of dimensions in the tensor input does not match the " + f"length of the physical layout; i.e. len(size) = {dim} " + f"is not equal to len(physical_layout) = {len(physical_layout)}" + ), + ) + strides = [0] * len(shape) + seen_dims = set() + for p, l in enumerate(physical_layout): + torch._check( + 0 <= l < dim, + lambda: ( + f"Dimension out of range (expected to be between 0 and {dim - 1}, but got " + f"{l} at index {p}). NB: negative dims " + "not currently supported; file an issue if you want it." + ), + ) + torch._check(l not in seen_dims, lambda: "Duplicate dim not allowed") + strides[l] = p_strides[p] + seen_dims.add(l) + return TensorMeta( + shape=shape, + strides=strides, + dtype=dtype, + device=device, + ) + + +_empty_permuted_doc = """ + Creates a tensor with uninitialized values according to some physical layout, + that is guaranteed to be non-overlapping and dense. +""" + +# TODO: add layout, pin_memory +empty_permuted = _make_prim( + schema="empty_permuted(SymInt[] shape, int[] physical_layout, *, ScalarType dtype, Device device, bool requires_grad) -> Tensor", # noqa: B950 + return_type=RETURN_TYPE.NEW, + meta=_empty_permuted_meta, + impl_aten=torch.empty_permuted, + doc=_empty_permuted_doc, +) + + +def _full_meta( + shape: ShapeType, + fill_value: NumberType, + *, + dtype: torch.dtype, + device: torch.device, + requires_grad: bool, +) -> TensorLikeType: + strides = utils.make_contiguous_strides_for(shape) + return TensorMeta(shape=shape, strides=strides, dtype=dtype, device=device) + + +def _full_aten( + shape: ShapeType, + fill_value: NumberType, + *, + dtype: torch.dtype, + device: torch.device, + requires_grad: bool, +) -> Tensor: + # Note that Mypy thinks torch.full can't accept a complex fill_value + return torch.full( + shape, fill_value, dtype=dtype, device=device, requires_grad=requires_grad # type: ignore[arg-type] + ) + + +_full_doc = """ + Creates a tensor filled with the given fill value, and with the specified shape, dtype, and device. +""" + +# TODO: add layout +full = _make_prim( + schema="full(SymInt[] shape, Scalar fill_value, *, ScalarType dtype, Device device, bool requires_grad) -> Tensor", + meta=_full_meta, + impl_aten=_full_aten, + return_type=RETURN_TYPE.NEW, + doc=_full_doc, +) + + +def _full_like_meta( + a: TensorLikeType, + fill_value: NumberType, + *, + dtype: torch.dtype, + device: torch.device, + requires_grad: bool, +) -> TensorLikeType: + strides = utils.compute_elementwise_output_strides(a) + if a.numel() == 0: + strides = a.stride() + + return TensorMeta(a, strides=strides, dtype=dtype, device=device) + + +def _full_like_aten( + a: Tensor, + fill_value: NumberType, + *, + dtype: torch.dtype, + device: torch.device, + requires_grad: bool, +) -> Tensor: + # Note that Mypy thinks torch.full can't accept a complex fill_value + return torch.full_like( + a, fill_value, dtype=dtype, device=device, requires_grad=requires_grad # type: ignore[arg-type] + ) + + +_full_like_doc = """ + Creates a tensor filled with the given fill value, and the same shape, dtype, and device as the + given tensor by default. The dtype and device settings can be overridden + by specifying them explicitly. +""" + +full_like = _make_prim( + schema="full_like(Tensor a, Scalar fill_value, *, ScalarType dtype, Device device, bool requires_grad) -> Tensor", + meta=_full_like_meta, + impl_aten=_full_like_aten, + return_type=RETURN_TYPE.NEW, + doc=_full_like_doc, +) + + +def _scalar_tensor_meta( + scalar: NumberType, + *, + dtype: torch.dtype, + device: torch.device, +) -> TensorLikeType: + shape: ShapeType = [] + strides = utils.make_contiguous_strides_for(shape) + return TensorMeta(scalar, shape=shape, strides=strides, dtype=dtype, device=device) + + +def _scalar_tensor_aten( + scalar: NumberType, + *, + dtype: torch.dtype, + device: torch.device, +) -> Tensor: + if isinstance(scalar, complex) and ( + dtype is None or not utils.is_complex_dtype(dtype) + ): + raise TypeError("Complex scalar requires complex tensor dtype.") + # Note that Mypy thinks torch.scalar can't accept a complex scalar + return torch.scalar_tensor(scalar, dtype=dtype, device=device) # type: ignore[arg-type] + + +_scalar_tensor_doc = """ + Wraps a Number into a Tensor with the specified dtype and device. +""" + +# TODO: add layout and pin_memory support +scalar_tensor = _make_prim( + schema="scalar_tensor(Scalar s, *, ScalarType? dtype=None, Device? device=None) -> Tensor", + meta=_scalar_tensor_meta, + impl_aten=_scalar_tensor_aten, + return_type=RETURN_TYPE.NEW, + doc=_scalar_tensor_doc, +) + + +# +# Linear algebra (linalg) prims +# + + +def _svd_meta( + A: TensorLikeType, *, full_matrices: bool +) -> Tuple[TensorLikeType, TensorLikeType, TensorLikeType]: + utils.check_is_matrix(A, "linalg.svd") + utils.check_fp_or_complex(A.dtype, "linalg.svd", allow_low_precision_dtypes=False) + + A_shape = A.shape + batch = A_shape[:-2] + m, n = A_shape[-2:] + k = min(m, n) + + shape_U = batch + (m, m if full_matrices else k) + strides_U = utils.make_contiguous_strides_for(shape_U, row_major=False) + U = TensorMeta(shape=shape_U, strides=strides_U, dtype=A.dtype, device=A.device) + + shape_S = batch + (k,) + strides_S = utils.make_contiguous_strides_for(shape_S) + S = TensorMeta( + shape=shape_S, + strides=strides_S, + dtype=utils.corresponding_real_dtype(A.dtype) if A.is_complex() else A.dtype, + device=A.device, + ) + + shape_Vh = batch + (n if full_matrices else k, n) + # The CPU backend returns V, but the cuSolver backend returns V^H + # TODO The MAGMA backend returns V, so this is wrong if used with the MAGMA backend + is_cuda = A.device.type == "cuda" + strides_Vh = utils.make_contiguous_strides_for(shape_Vh, row_major=is_cuda) + Vh = TensorMeta(shape=shape_Vh, strides=strides_Vh, dtype=A.dtype, device=A.device) + # Also makes sure this is CUDA or HIP: + # https://pytorch.org/docs/stable/notes/hip.html#checking-for-hip + if A.numel() != 0 and Vh.is_complex() and torch.cuda.is_available(): + Vh = Vh.conj() + return U, S, Vh + + +def _svd_aten( + A: TensorLikeType, *, full_matrices: bool +) -> Tuple[Tensor, Tensor, Tensor]: + return torch.linalg.svd(A, full_matrices=full_matrices) + + +_svd_doc = """ + Returns the SVD of a matrix or batch of matrices. + + The `full_matrices` flag controls whether the full or reduced SVD decomposition is returned. +""" + +svd = _make_prim( + schema="svd(Tensor A, *, bool full_matrices) -> (Tensor U, Tensor S, Tensor Vh)", + meta=_svd_meta, + impl_aten=_svd_aten, + return_type=(RETURN_TYPE.NEW, RETURN_TYPE.NEW, RETURN_TYPE.NEW), + doc=_svd_doc, +) + + +# +# Randomness Prims +# + + +def _normal_meta( + shape: ShapeType, + *, + mean: Union[float, complex], + std: float, + dtype: torch.dtype, + device: torch.device, + requires_grad: bool, + generator: Optional[torch.Generator] = None, +) -> TensorLikeType: + torch._check( + std >= 0.0, + lambda: f"expected non-negative standard deviation, but got std={std}", + ) + + torch._check( + utils.is_float_dtype(dtype) or utils.is_complex_dtype(dtype), + lambda: f"expected a floating-point or complex dtype, but got dtype={dtype}", + ) + + strides = utils.make_contiguous_strides_for(shape) + return TensorMeta(shape=shape, strides=strides, dtype=dtype, device=device) + + +def _normal_aten( + shape: ShapeType, + *, + mean: Union[float, complex], + std: float, + dtype: torch.dtype, + device: torch.device, + requires_grad: bool, + generator: Optional[torch.Generator] = None, +) -> Tensor: + a = torch.empty(shape, dtype=dtype, device=device, requires_grad=requires_grad) + with torch.no_grad(): + # NOTE: normal_ is incorrectly annotated to expect mean to be a float + a.normal_(mean, std, generator=generator) # type: ignore[arg-type] + return a + + +_normal_doc = """ + Constructs a tensor filled with values drawn from a normal distribution with the specified mean + and standard deviation. + + Only supports floating-point types. +""" + +normal = _make_prim( + schema=( + "normal(SymInt[] shape, *, Scalar mean, Scalar std, ScalarType dtype, Device device, bool requires_grad, Generator? generator=None) -> Tensor" # noqa: B950 + ), + return_type=RETURN_TYPE.NEW, + meta=_normal_meta, + impl_aten=_normal_aten, + doc=_normal_doc, +) + + +def _uniform_meta( + shape: ShapeType, + *, + low: float, + high: float, + dtype: torch.dtype, + device: torch.device, + generator: Optional[torch.Generator] = None, +) -> TensorLikeType: + strides = utils.make_contiguous_strides_for(shape) + return TensorMeta(shape=shape, strides=strides, dtype=dtype, device=device) + + +def _uniform_aten( + shape: ShapeType, + *, + low: float, + high: float, + dtype: torch.dtype, + device: torch.device, + generator: Optional[torch.Generator] = None, +) -> Tensor: + a = torch.empty(shape, dtype=dtype, device=device) + a.uniform_(low, high, generator=generator) + return a + + +_uniform_doc = """ + Constructs a tensor filled with values drawn uniformly from low to high. +""" + +# TODO: we should more seriously review randomness modeling and prims +_uniform_helper = _make_prim( + schema=( + "uniform(SymInt[] shape, *, Scalar low, Scalar high, ScalarType dtype, Device device, Generator? generator=None) -> Tensor" + ), + return_type=RETURN_TYPE.NEW, + meta=_uniform_meta, + impl_aten=_uniform_aten, + doc=_uniform_doc, +) + +# +# FFT prims +# + + +def _fft_r2c_meta( + input: TensorLike, + *, + dim: DimsSequenceType, + onesided: bool, +) -> TensorLikeType: + dim = utils.canonicalize_dims(input.ndim, dim) + utils.validate_no_repeating_dims(dim) + + shape = list(input.shape) + if onesided: + last_dim = dim[-1] + shape[last_dim] = shape[last_dim] // 2 + 1 + + dtype = utils.corresponding_complex_dtype(input.dtype) + strides = utils.make_contiguous_strides_for(shape) + return TensorMeta(shape=shape, strides=strides, dtype=dtype, device=input.device) + + +def _fft_r2c_aten( + input: TensorLike, + *, + dim: DimsSequenceType, + onesided: bool, +) -> TensorLikeType: + normalization = 0 # No normalization + return torch._fft_r2c(input, dim, normalization, onesided) + + +_fft_r2c_doc = """ + Performs a real to complex Fast Fourier Transform +""" + + +fft_r2c = _make_prim( + schema="fft_r2c(Tensor self, *, int[] dim, bool onesided) -> Tensor", + meta=_fft_r2c_meta, + impl_aten=_fft_r2c_aten, + return_type=RETURN_TYPE.NEW, + doc=_fft_r2c_doc, +) + + +def _fft_c2c_meta( + input: TensorLike, + *, + dim: DimsSequenceType, + forward: bool, +) -> TensorLikeType: + dim = utils.canonicalize_dims(input.ndim, dim) + utils.validate_no_repeating_dims(dim) + + shape = input.shape + strides = utils.make_contiguous_strides_for(shape) + return TensorMeta( + shape=shape, strides=strides, dtype=input.dtype, device=input.device + ) + + +def _fft_c2c_aten( + input: TensorLike, + *, + dim: DimsSequenceType, + forward: bool, +) -> TensorLikeType: + normalization = 0 # No normalization + return torch._fft_c2c(input, dim, normalization, forward) + + +_fft_c2c_doc = """ + Performs either a Fast Fourier Transform, or its inverse +""" + + +fft_c2c = _make_prim( + schema="fft_c2c(Tensor self, *, int[] dim, bool forward) -> Tensor", + meta=_fft_c2c_meta, + impl_aten=_fft_c2c_aten, + return_type=RETURN_TYPE.NEW, + doc=_fft_c2c_doc, +) + + +def _fft_c2r_meta( + input: TensorLike, + *, + dim: DimsSequenceType, + last_dim_size: int, +) -> TensorLikeType: + dim = utils.canonicalize_dims(input.ndim, dim) + utils.validate_no_repeating_dims(dim) + + shape = list(input.shape) + shape[dim[-1]] = last_dim_size + dtype = utils.corresponding_real_dtype(input.dtype) + strides = utils.make_contiguous_strides_for(shape) + return TensorMeta(shape=shape, strides=strides, dtype=dtype, device=input.device) + + +def _fft_c2r_aten( + input: TensorLike, + *, + dim: DimsSequenceType, + last_dim_size: int, +) -> TensorLikeType: + normalization = 0 # No normalization + return torch._fft_c2r(input, dim, normalization, last_dim_size) + + +_fft_c2r_doc = """ + Performs a complex to real Inverse Fast Fourier Transform +""" + + +fft_c2r = _make_prim( + schema="fft_c2r(Tensor self, *, int[] dim, SymInt last_dim_size) -> Tensor", + meta=_fft_c2r_meta, + impl_aten=_fft_c2r_aten, + return_type=RETURN_TYPE.NEW, + doc=_fft_c2r_doc, +) + + +def _frexp_meta(self: TensorLikeType) -> Tuple[TensorLikeType, TensorLikeType]: + torch._check( + self.dtype.is_floating_point, + lambda: "torch.frexp() only supports floating-point dtypes", + ) + return torch.empty_like(self), torch.empty_like(self, dtype=torch.int32) + + +frexp = _make_prim( + schema="frexp(Tensor self) -> (Tensor mantissa, Tensor exponent)", + meta=_frexp_meta, + return_type=(RETURN_TYPE.NEW, RETURN_TYPE.NEW), + impl_aten=torch.frexp, + doc="", +) + + +def _make_token_aten() -> TensorLikeType: + return new_token_tensor() + + +_make_token = _make_prim( + schema="_make_token() -> Tensor", + meta=_make_token_aten, + return_type=RETURN_TYPE.NEW, + impl_aten=_make_token_aten, + doc="Creates a token used for keeping track of side effects.", +) + + +def _sink_tokens_aten(tokens) -> None: + pass + + +_sink_tokens = _make_prim( + schema="_sink_tokens(Tensor[] tokens) -> ()", + meta=_sink_tokens_aten, + return_type=RETURN_TYPE.NONE, + impl_aten=_sink_tokens_aten, + doc="Sink all of the tokens which were previously used for keeping track of side effects.", +) + + +register_rng_prims() +register_debug_prims() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims/context.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims/context.py new file mode 100644 index 0000000000000000000000000000000000000000..7026d02790715b01bfd33a7190062d4c83b7ba4f --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims/context.py @@ -0,0 +1,146 @@ +# mypy: allow-untyped-defs +import functools +from contextlib import nullcontext +from typing import Any, Callable, Dict, Optional, Sequence + +import torch +import torch._decomp +import torch._prims +import torch._refs +import torch._refs.nn +import torch._refs.nn.functional +import torch._refs.special +import torch.overrides +from torch._prims_common import torch_function_passthrough + + +@functools.lru_cache(None) +def torch_to_refs_map(): + """ + Mapping of torch API functions to torch._refs functions. + E.g. torch_to_refs_map()[torch.add] == torch._refs.add + """ + modules = [ + (torch, torch._refs), + (torch.nn, torch._refs.nn), + (torch.nn.functional, torch._refs.nn.functional), + (torch.special, torch._refs.special), + (torch.fft, torch._refs.fft), + (torch.linalg, torch._refs.linalg), + ] + r: Dict[Any, Any] = { + torch.Tensor.__invert__: torch._refs.bitwise_not, + torch.Tensor.__xor__: torch._refs.bitwise_xor, + torch.Tensor.__and__: torch._refs.bitwise_and, + torch.Tensor.__or__: torch._refs.bitwise_or, + torch.Tensor.__eq__: torch._refs.eq, + torch.Tensor.__rsub__: torch._refs.rsub, + torch.Tensor.__rtruediv__: torch._refs.rtruediv, + torch.Tensor.__floordiv__: torch._refs.floor_divide, + torch.Tensor.__rfloordiv__: torch._refs.rfloordiv, + torch.Tensor.__pow__: torch._refs.pow, + torch.Tensor.__rpow__: torch._refs.rpow, + torch.Tensor.new_empty: torch._refs.new_empty, + torch.Tensor.new_full: torch._refs.new_full, + torch.Tensor.new_zeros: torch._refs.new_zeros, + torch.Tensor.new_ones: torch._refs.new_ones, + torch.Tensor.fill_: torch._refs.fill_, + torch.Tensor.zero_: torch._refs.zero_, + torch.Tensor.to: torch._refs.to, + torch.Tensor.sum_to_size: torch._refs.sum_to_size, + # TODO: Should these methods be mapped some other way? + torch.Tensor.copy_: torch._prims.copy_to, + torch.Tensor.resize: torch._prims.resize, + } + for mod_torch, mod_refs in modules: + for s in mod_refs.__all__: # type: ignore[attr-defined] + r[mod_torch.__dict__.get(s)] = mod_refs.__dict__.get(s) + + # Support remapping torch.Tensor.foo to _refs.foo + for s in dir(torch.Tensor): + if s in torch._refs.__all__: + r[getattr(torch.Tensor, s)] = torch._refs.__dict__.get(s) + + # Support conversions + for s in torch._refs._conversions.__all__: + tensor_attr = getattr(torch.Tensor, s, None) or getattr(torch, s) + r[tensor_attr] = torch._refs._conversions.__dict__.get(s) + + return r + + +@functools.lru_cache(None) +def all_prims(): + """ + Set of all prim functions, e.g., torch._prims.add in all_prims() + """ + return {torch._prims.__dict__.get(s) for s in torch._prims.__all__} + + +class TorchRefsMode(torch.overrides.TorchFunctionMode): + """ + Switches the interpretation of torch.* functions and Tensor methods to + use PrimTorch refs in torch._refs. (Direct calls to _refs are unaffected.) + + >>> # xdoctest: +SKIP + >>> with TorchRefsMode(): + ... torch.add(x, y) # calls torch._refs.add(x, y) + + By default, this context manager will fall back on the torch.* if the + ref does not exist; set strict=True to error if this occurs. + If the ref exists we still would like to fall back on the torch.* sometimes, + this behavior can be customized by passing a function to should_fallback_fn. + """ + + def __init__( + self, + strict=False, + should_fallback_fn=lambda *_: False, + prims_mode_cls=nullcontext, + ): + self.strict = strict + self.should_fallback_fn = should_fallback_fn + self.prims_mode_cls = prims_mode_cls + + def __torch_function__( + self, + orig_func: Callable, + types: Sequence, + args: Sequence[Any] = (), + kwargs: Optional[Dict] = None, + ): + if kwargs is None: + kwargs = {} + # For primitive operations, run them as is without interception + # Unless we are in prims_mode, in which case we want to use nvprims + if orig_func in torch_function_passthrough or orig_func in all_prims(): + with self.prims_mode_cls(): + return orig_func(*args, **kwargs) + mapping = torch_to_refs_map() + func = mapping.get(orig_func, None) + + # For torch.ops.aten.*, use registered decompositions from torch._decomp + # torch._decomp.decomposition_table provides a mapping from + # torch.ops.aten.* to torch._refs or torch._decomp.decompositions + # implementations. + # There're other ways to implement this functionality, + # see https://github.com/pytorch/pytorch/pull/82657#discussion_r939776417 + if func is None and isinstance(orig_func, torch._ops.OpOverload): + func = torch._decomp.decomposition_table.get(orig_func, None) + elif func is None and isinstance(orig_func, torch._ops.OpOverloadPacket): + default = getattr(orig_func, "default", None) + if default is not None: + func = torch._decomp.decomposition_table.get(default, None) + + if func is not None: + # If the ref exists query whether we should use it or not + if self.should_fallback_fn(self, orig_func, func, args, kwargs): + return orig_func(*args, **kwargs) + # torch calls inside func should be interpreted as refs calls + with self: + return func(*args, **kwargs) + if self.strict: + raise RuntimeError( + f"no _refs support for {torch.overrides.resolve_name(orig_func)}" + ) + return orig_func(*args, **kwargs) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims/debug_prims.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims/debug_prims.py new file mode 100644 index 0000000000000000000000000000000000000000..6958cbcef283dbc7eda544760640126e7524bb2e --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims/debug_prims.py @@ -0,0 +1,54 @@ +# mypy: allow-untyped-defs +import contextlib +from typing import Optional + +import torch +from torch.utils._content_store import ContentStoreReader + + +LOAD_TENSOR_READER: Optional[ContentStoreReader] = None + + +@contextlib.contextmanager +def load_tensor_reader(loc): + global LOAD_TENSOR_READER + assert LOAD_TENSOR_READER is None + # load_tensor is an "op", and we will play merry hell on + # Inductor's memory planning if we return a tensor that + # aliases another tensor that we previously returned from + # an operator. So unlike standard ContentStoreReader use, + # we disable the cache so that you always get fresh storages + # (no aliasing for you!) + LOAD_TENSOR_READER = ContentStoreReader(loc, cache=False) + try: + yield + finally: + LOAD_TENSOR_READER = None + + +def register_debug_prims(): + torch.library.define( + "debugprims::load_tensor", + "(str name, int[] size, int[] stride, *, ScalarType dtype, Device device) -> Tensor", + ) + + @torch.library.impl("debugprims::load_tensor", "BackendSelect") + def load_tensor_factory(name, size, stride, dtype, device): + if LOAD_TENSOR_READER is None: + from torch._dynamo.testing import rand_strided + + return rand_strided(size, stride, dtype, device) + else: + from torch._dynamo.utils import clone_input + + # device argument here takes care of coercion + r = LOAD_TENSOR_READER.read_tensor(name, device=device) + assert list(r.size()) == size, f"{r.size()} != {size}" + assert list(r.stride()) == stride, f"{r.stride()} != {stride}" + assert r.device == device, f"{r.device} != {device}" + + # Unlike the other properties, we will do coercions for dtype + # mismatch + if r.dtype != dtype: + r = clone_input(r, dtype=dtype) + return r diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims/executor.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims/executor.py new file mode 100644 index 0000000000000000000000000000000000000000..a020c1db75b2eacb4cb4c6ca809586258e74dcd8 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims/executor.py @@ -0,0 +1,60 @@ +# mypy: allow-untyped-defs +from typing import Callable, Optional + +from torch._prims.context import TorchRefsMode +from torch.fx import GraphModule +from torch.fx.experimental.proxy_tensor import make_fx, wrapper_and_args_for_make_fx + + +def execute( + gm: GraphModule, + *args, + executor: str = "aten", + executor_parameters: Optional[dict] = None, +): + """ + Prototype ATen executor. + + Just executes the context's graph. + """ + + if executor == "aten": + return gm.forward(*args) + + msg = f"Received unexpected value for 'executor': {executor}. Allowed values are: aten." + raise ValueError(msg) + + +def make_traced(fn: Callable): + """ + Returns a function that, when called, will + trace its torch operations to prims and then + execute those prims on the requested trace executor + (possibly lowering them to that trace executor first). + + Only supports the torch operations defined in _torch_to_reference_map + in context.py and operations with positional args. All args must + be tensors. + In the near future all these restrictions will be lifted. + + Example usage: + + def foo(a, b): + return torch.add(a, b) + + traced_foo = make_traced(foo) + + a = torch.randn((1, 2, 3, 4, 5), device='cuda') + b = torch.randn((1, 2, 3, 4, 5), device='cuda') + result = traced_foo(a, b, executor='aten') + """ + + def _traced(*args, executor="aten", **kwargs): + # TODO: caching + wrapped, all_args = wrapper_and_args_for_make_fx(fn, args, kwargs) + + with TorchRefsMode(): + gm = make_fx(wrapped)(all_args) + return execute(gm, all_args, executor=executor) + + return _traced diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims/rng_prims.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims/rng_prims.py new file mode 100644 index 0000000000000000000000000000000000000000..bbbdb8958f9adb7c215a404f28db8272c0b563e2 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims/rng_prims.py @@ -0,0 +1,319 @@ +# mypy: allow-untyped-defs +from typing import Optional, Tuple + +import torch +import torch.utils._pytree as pytree +from torch import _prims +from torch._C import DispatchKey +from torch._higher_order_ops.utils import autograd_not_implemented +from torch._ops import HigherOrderOperator +from torch._prims_common import CUDARngStateHelper, make_contiguous_strides_for +from torch._subclasses.fake_tensor import FakeTensorMode +from torch.fx.experimental.proxy_tensor import ( + disable_proxy_modes_tracing, + ProxyTorchDispatchMode, + track_tensor_tree, +) +from torch.types import _device, _dtype + + +def throw_on_non_cuda(device): + raise RuntimeError( + f"You are trying to functionalize a {device.type} RNG operator but {device.type} does not " + f"use Philox/counter-based RNG. Therefore, functionalizing a {device.type} RNG operator is " + "not supported. We are discussing the possibility of a Philox-based RNG implementation for CPU." + ) + + +def register_rng_prim(name, schema, impl_aten, impl_meta, doc, tags=None): + rngprim_def = torch.library.custom_op( + "rngprims::" + name, impl_aten, mutates_args=(), schema=schema + ) + rngprim_def.register_fake(impl_meta) + + prim_packet = getattr(torch._ops.ops.rngprims, name) + prim = prim_packet.default + if tags: + prim._tags = tags + + for p in (prim_packet, prim): + p.__doc__ = doc + p.return_type = torch._prims_common.RETURN_TYPE.NEW # type: ignore[attr-defined] + + p.schema = name + schema + p.impl_aten = impl_aten + p.prim_meta_impl = impl_meta + + +# Philox rand offsets could be shared in future with other philox ops, so +# keeping these functions in global scope. +def philox_rand_offset_meta( + shape: torch.Size, +): + return _prims.TensorLike(torch.tensor(0, dtype=torch.int64)) + + +def philox_rand_offset( + shape: torch.Size, +): + # For impl, look at the function calc_execution_policy in the file + # aten/src/ATen/native/cuda/DistributionTemplates.h. The impl was copied at + # commit hash 72aa0667bd16707d50eb8fa337092a1f5d11dfb6 + numel_scalar = 1 + for dim_size in shape: + numel_scalar *= dim_size + numel = torch.scalar_tensor(numel_scalar, dtype=torch.int64) + + block_size = 256 + unroll = 4 + curand4_engine_calls = 4 + device_property = torch.cuda.get_device_properties(torch.cuda.current_device()) + blocks_per_sm = device_property.max_threads_per_multi_processor // block_size + grid_size = (numel + block_size - 1) // block_size + grid_size = min(grid_size, device_property.multi_processor_count * blocks_per_sm) + offset = ( + (numel - 1) // (block_size * grid_size * unroll) + 1 + ) * curand4_engine_calls + return offset + + +def register_philox_rand(): + name = "philox_rand" + schema = "(SymInt[] size, Tensor seed, Tensor offset, int[]? stride, Device? device=None, ScalarType? dtype=None) -> (Tensor, Tensor)" # noqa: B950 + + def _philox_rand_meta( + shape: torch.Size, + seed: torch.Tensor, + offset: torch.Tensor, + stride: Optional[Tuple[int, ...]], + device: _device, + dtype: _dtype, + ): + # stride arg will be useful for distributed usecase. Currently, its unused. + assert stride is None + stride = make_contiguous_strides_for(shape) + random_values = _prims.TensorMeta( + shape=shape, strides=stride, dtype=dtype, device=device + ) + offset = philox_rand_offset_meta(shape) + return (random_values, offset) + + def _philox_rand( + shape: torch.Size, + seed: torch.Tensor, + offset: torch.Tensor, + stride: Optional[Tuple[int, ...]], + device: _device, + dtype: _dtype, + ): + # stride arg will be useful for distributed usecase. Currently, its unused. + assert stride is None + if device.type == "cpu": + devices = [] + else: + devices = [device] + + if device.type != "cuda": + raise throw_on_non_cuda(device) + + with torch.random.fork_rng(devices): + CUDARngStateHelper.set_torch_state_tensor(seed, offset) + random_values = torch.rand(shape, device=device, dtype=dtype) + + return random_values, philox_rand_offset(shape) + + register_rng_prim( + name=name, + schema=schema, + impl_aten=_philox_rand, + impl_meta=_philox_rand_meta, + doc="Philox based stateless rand operator", + tags=(torch.Tag.nondeterministic_seeded,), + ) + + +def get_device(args, kwargs): + if kwargs.get("device"): + device = kwargs.get("device") + if isinstance(device, str): + device = torch.device(device) + return device.type + + devices = {arg.device.type for arg in args if isinstance(arg, torch.Tensor)} + if any(dev == "cuda" for dev in devices): + return "cuda" + elif any(dev == "xpu" for dev in devices): + return "xpu" + elif any(dev == "hpu" for dev in devices): + return "hpu" + elif any(dev == "cpu" for dev in devices): + return "cpu" + return None + + +def register_run_and_save_rng_state_op(): + class RunAndSaveRngState(HigherOrderOperator): + def __init__(self): + super().__init__("run_and_save_rng_state") + + def __call__(self, op, *args, **kwargs): + return super().__call__(op, *args, **kwargs) + + run_and_save_rng_state = RunAndSaveRngState() + + run_and_save_rng_state.py_impl(DispatchKey.Autograd)( + autograd_not_implemented(run_and_save_rng_state, deferred_error=True) + ) + + @run_and_save_rng_state.py_impl(DispatchKey.CUDA) + def impl_cuda(op, *args, **kwargs): + return torch.cuda.get_rng_state(), op(*args, **kwargs) + + @run_and_save_rng_state.py_impl(DispatchKey.CPU) + def impl_cpu(op, *args, **kwargs): + return torch.get_rng_state(), op(*args, **kwargs) + + @run_and_save_rng_state.py_impl(DispatchKey.HPU) + def impl_hpu(op, *args, **kwargs): + if hasattr(torch, "hpu"): + return torch.hpu.get_rng_state(), op(*args, **kwargs) + raise RuntimeError("functionalize a hpu RNG operator is not supported.") + + @run_and_save_rng_state.py_impl(DispatchKey.XPU) + def impl_xpu(op, *args, **kwargs): + return torch.xpu.get_rng_state(), op(*args, **kwargs) + + @run_and_save_rng_state.py_impl(DispatchKey.BackendSelect) + def impl_backend_select(op, *args, **kwargs): + impl_map = { + "cuda": impl_cuda, + "cpu": impl_cpu, + "hpu": impl_hpu, + "xpu": impl_xpu, + } + device = get_device(args, kwargs) + assert device in impl_map, f"Backend not supported for {device}" + impl = impl_map[device] + return impl(op, *args, **kwargs) + + @run_and_save_rng_state.py_impl(FakeTensorMode) + def impl_fake_tensor_mode(mode, op, *args, **kwargs): + # Check device to call the right impl + with mode: + return impl_backend_select(op, *args, **kwargs) + + @run_and_save_rng_state.py_impl(ProxyTorchDispatchMode) + def impl_proxy_dispatch_mode(mode, op, *args, **kwargs): + out = impl_backend_select(op, *args, **kwargs) + proxy_args = pytree.tree_map(mode.tracer.unwrap_proxy, (op, *args)) + proxy_kwargs = pytree.tree_map(mode.tracer.unwrap_proxy, kwargs) + out_proxy = mode.tracer.create_proxy( + "call_function", run_and_save_rng_state, proxy_args, proxy_kwargs + ) + return track_tensor_tree(out, out_proxy, constant=None, tracer=mode.tracer) + + return run_and_save_rng_state + + +def register_run_with_rng_state_op(): + class RunWithRngState(HigherOrderOperator): + def __init__(self): + super().__init__("run_with_rng_state") + + def __call__(self, rng_state, op, *args, **kwargs): + return super().__call__(rng_state, op, *args, **kwargs) + + run_with_rng_state = RunWithRngState() + + run_with_rng_state.py_impl(DispatchKey.Autograd)( + autograd_not_implemented(run_with_rng_state, deferred_error=True) + ) + + @run_with_rng_state.py_impl(DispatchKey.CUDA) + def impl_cuda(rng_state, op, *args, **kwargs): + current_state = torch.cuda.get_rng_state() + torch.cuda.set_rng_state(rng_state.cpu()) + out = op(*args, **kwargs) + torch.cuda.set_rng_state(current_state) + return out + + @run_with_rng_state.py_impl(DispatchKey.CPU) + def impl_cpu(rng_state, op, *args, **kwargs): + current_state = torch.get_rng_state() + torch.set_rng_state(rng_state) + out = op(*args, **kwargs) + torch.set_rng_state(current_state) + return out + + @run_with_rng_state.py_impl(DispatchKey.HPU) + def impl_hpu(rng_state, op, *args, **kwargs): + if hasattr(torch, "hpu"): + current_state = torch.hpu.get_rng_state() + torch.hpu.set_rng_state(rng_state) + out = op(*args, **kwargs) + torch.hpu.set_rng_state(current_state) + return out + raise RuntimeError("functionalize a hpu RNG operator is not supported.") + + @run_with_rng_state.py_impl(DispatchKey.XPU) + def impl_xpu(rng_state, op, *args, **kwargs): + current_state = torch.xpu.get_rng_state() + torch.xpu.set_rng_state(rng_state) + out = op(*args, **kwargs) + torch.xpu.set_rng_state(current_state) + return out + + @run_with_rng_state.py_impl(ProxyTorchDispatchMode) + def impl_proxy_dispatch_mode(mode, rng_state, op, *args, **kwargs): + # TODO: you don't need to do this, the dispatch here already disabled + # it + with disable_proxy_modes_tracing(): + out = run_with_rng_state(rng_state, op, *args, **kwargs) + proxy_args = pytree.tree_map(mode.tracer.unwrap_proxy, (rng_state, op, *args)) + proxy_kwargs = pytree.tree_map(mode.tracer.unwrap_proxy, kwargs) + out_proxy = mode.tracer.create_proxy( + "call_function", run_with_rng_state, proxy_args, proxy_kwargs + ) + return track_tensor_tree(out, out_proxy, constant=None, tracer=mode.tracer) + + @run_with_rng_state.py_impl(DispatchKey.BackendSelect) + def impl_backend_select(rng_state, op, *args, **kwargs): + impl_map = { + "cuda": impl_cuda, + "cpu": impl_cpu, + "hpu": impl_hpu, + "xpu": impl_xpu, + } + device = get_device(args, kwargs) + assert device in impl_map, f"Backend not supported for {device}" + impl = impl_map[device] + return impl(rng_state, op, *args, **kwargs) + + @run_with_rng_state.py_impl(FakeTensorMode) + def impl_fake_tensor_mode(mode, rng_state, op, *args, **kwargs): + # Skip setting the set_rng_state as it does not work well with fake tensors. + # And it does not matter for the fake tensor mode. + with mode: + return op(*args, **kwargs) + + @run_with_rng_state.py_functionalize_impl + def impl_functional(ctx, rng_state, op, *args, **kwargs): + unwrapped_rng_state = ctx.unwrap_tensors(rng_state) + unwrapped_args = ctx.unwrap_tensors(args) + unwrapped_kwargs = ctx.unwrap_tensors(kwargs) + + with ctx.redispatch_to_next(): + out = run_with_rng_state( + unwrapped_rng_state, op, *unwrapped_args, **unwrapped_kwargs + ) + return ctx.wrap_tensors(out) + + return run_with_rng_state + + +run_and_save_rng_state = register_run_and_save_rng_state_op() +run_with_rng_state = register_run_with_rng_state_op() + + +def register_rng_prims(): + register_philox_rand() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims_common/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims_common/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..61d0ba13b88f1515fa1cdd4534b166e55beaf826 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims_common/__init__.py @@ -0,0 +1,1996 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import operator +import warnings +from contextlib import nullcontext +from enum import Enum +from functools import reduce +from typing import ( + Any, + Callable, + cast, + List, + NamedTuple, + Optional, + overload, + Sequence, + Tuple, + Type, + TYPE_CHECKING, + Union, +) +from typing_extensions import deprecated, TypeAlias + + +if TYPE_CHECKING: + # Import the following modules during type checking to enable code intelligence features, + # such as auto-completion in tools like pylance, even when these modules are not explicitly + # imported in user code. + + import sympy + +import torch +from torch import sym_float, sym_int, sym_max + + +ShapeType: TypeAlias = Union[torch.Size, List[int], Tuple[int, ...]] +StrideType: TypeAlias = Union[List[int], Tuple[int, ...]] +DimsType: TypeAlias = Union[int, List[int], Tuple[int, ...]] +DimsSequenceType: TypeAlias = Union[List[int], Tuple[int, ...]] +# TODO: Type[torch.SymInt], Type[torch.SymFloat] +NumberTypeType: TypeAlias = Union[Type[bool], Type[int], Type[float], Type[complex]] +# TODO: This needs a lot more type annotations +# NumberType = Union[bool, int, float, complex, torch.SymInt, torch.SymFloat] +NumberType: TypeAlias = Union[bool, int, float, complex] +RealNumberType: TypeAlias = Union[bool, int, float] + +Number = (bool, int, float, complex, torch.SymInt, torch.SymFloat, torch.SymBool) +# I don't call it Integral because numbers.Integral includes bool, but IntLike +# does not +Dim = int +IntLike = (int, torch.SymInt) +FloatLike = (float, torch.SymFloat) +BoolLike = (bool, torch.SymBool) +IntWithoutSymInt = int +FloatWithoutSymFloat = float +DeviceLikeType: TypeAlias = Union[str, torch.device, int] +Tensor = torch.Tensor + + +torch_function_passthrough = { + torch.device, + torch.sym_not, + torch.sym_float, + torch.sym_int, + torch.sym_max, + torch.sym_min, + torch._sym_sqrt, # type: ignore[attr-defined] + torch.sym_ite, + torch.Tensor.dim, + torch.Tensor.ndim.__get__, # type: ignore[attr-defined] + torch.Tensor.numel, + torch.Tensor.size, + torch.Tensor.storage_offset, + torch.Tensor.stride, + torch.Tensor.dtype.__get__, # type: ignore[attr-defined] + torch.Tensor.is_sparse.__get__, # type: ignore[attr-defined] + torch.Tensor.shape.__get__, # type: ignore[attr-defined] + torch.Tensor.device.__get__, # type: ignore[attr-defined] + torch.Tensor.requires_grad.__get__, # type: ignore[attr-defined] + torch.Tensor.layout.__get__, # type: ignore[attr-defined] + torch.Tensor.is_contiguous, + # For TorchRefsMode only + torch.Tensor.__format__, + torch.Tensor.__repr__, + torch.Tensor.requires_grad.__get__, # type: ignore[attr-defined] + torch.Tensor.__getitem__, +} + + +TensorLikeType = torch.Tensor +TensorLike = torch.Tensor +TensorSequenceType: TypeAlias = Union[List[TensorLikeType], Tuple[TensorLikeType, ...]] +TensorOrNumberLikeType: TypeAlias = Union[TensorLikeType, NumberType] + +CustomOutParamAnnotation = "__custom_out_param__" + + +def same_shape(a: ShapeType, b: ShapeType, *, allow_rhs_unbacked=False) -> bool: + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + if len(a) != len(b): + return False + + for x, y in zip(a, b): + if allow_rhs_unbacked: + # TODO: We should check that the symbols are consistent + # with each other + if isinstance(y, torch.SymInt): + continue + # NB: Naively, you would not expect to have to do an oblivious guard + # here because there is seemingly no broadcasting here, but in fact we + # use this in some situations to determine if we need to do an expand + # on the tensor because they don't line up, so you can definitely end + # up trying to prove u0 != 1 in this situation. See + # python test/test_proxy_tensor.py -k test_cumsum_unbacked + if guard_size_oblivious(x != y): + return False + + return True + + +def _maybe_get_pytype(t): + if t is torch.SymFloat: + return float + elif t is torch.SymInt: + return int + elif t is torch.SymBool: + return bool + else: + return t + + +# TODO: look at using torch.testing.assert_close instead with an option +# to just compare metadata +def compare_tensor_meta( + a: TensorLikeType, + b: TensorLikeType, + check_strides=False, + *, + allow_rhs_unbacked=False, + check_conj=True, +): + """ + Checks that two tensor likes have the same shape, + dtype and device. + + In the future this will validate additional metadata, like + strides. + """ + assert isinstance(a, TensorLike) + assert isinstance(b, TensorLike) + + if not same_shape(a.shape, b.shape, allow_rhs_unbacked=allow_rhs_unbacked): + msg = f"Shapes {a.shape} and {b.shape} are not equal!" + raise AssertionError(msg) + + if a.dtype != b.dtype: + msg = f"Dtypes {a.dtype} and {b.dtype} are not equal!" + raise AssertionError(msg) + + if a.device != b.device: + # Handles special cuda:0 vs cuda case + # TODO: we should review why this happens and see about fixing it + if (str(a.device) == "cuda:0" or str(a.device) == "cuda") and ( + str(b.device) == "cuda:0" or str(b.device) == "cuda" + ): + pass + else: + msg = f"Devices {a.device} and {b.device} are not equal!" + raise AssertionError(msg) + + # Stride checking is currently disabled, see https://github.com/pytorch/pytorch/issues/78050 + if check_strides: + same_strides, idx = check_significant_strides(a, b) + if not same_strides: + msg = f"Stride mismatch! Strides are {a.stride()} and {b.stride()} (mismatched at {idx})!" + raise RuntimeError(msg) + + if a.storage_offset() != b.storage_offset(): + msg = f"Storage offset mismatch! Storage offsets are {a.storage_offset()} and {b.storage_offset()}!" + raise RuntimeError(msg) + + if check_conj: + if a.is_conj() != b.is_conj(): + raise RuntimeError( + f"Conj mismatch! is_conj is set to {a.is_conj()} and {b.is_conj()}" + ) + + if a.is_neg() != b.is_neg(): + raise RuntimeError( + f"Neg mismatch! is_neg is set to {a.is_neg()} and {b.is_neg()}" + ) + + +def _check_strides_helper( + a: TensorLikeType, b: TensorLikeType, *, only_cuda=True, significant_only=True +) -> Tuple[bool, Optional[int]]: + # NOTE: only on CUDA because CPU elementwise strides are incorrect in PyTorch + # See https://github.com/pytorch/pytorch/issues/77553 + # Only compares strides that are "meaningful" -- strides for dimensions with length > 1 + # and for tensors with more than one element + if ( + not only_cuda or a.device.type == "cuda" or b.device.type == "cuda" + ) and a.numel() > 0: + for idx in range(a.ndim): + check = not significant_only or a.shape[idx] > 1 + if a.stride()[idx] != b.stride()[idx] and check: + return False, idx + + return True, None + + +def check_significant_strides( + a: TensorLikeType, b: TensorLikeType, *, only_cuda=True +) -> Tuple[bool, Optional[int]]: + return _check_strides_helper(a, b, only_cuda=only_cuda, significant_only=True) + + +def check_all_strides( + a: TensorLikeType, b: TensorLikeType, *, only_cuda=True +) -> Tuple[bool, Optional[int]]: + return _check_strides_helper(a, b, only_cuda=only_cuda, significant_only=False) + + +# This function is equivalent to compute_contiguous() from TensorImpl.cpp +def is_contiguous(a: TensorLikeType) -> bool: + """ + Tests whether a tensor is contiguous or not. + + Tensors are contiguous when they have no elements, + one element, or when they have "nested" strides. + """ + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + if guard_size_oblivious(a.numel() < 2): + return True + + expected_stride = 1 + for x, y in reversed(tuple(zip(a.shape, a.stride()))): + # Skips checking strides when a dimension has length 1 + if guard_size_oblivious(x == 1): + continue + + if guard_size_oblivious(y != expected_stride): + return False + expected_stride = expected_stride * x + + return True + + +# This function is equivalent to compute_channels_last_contiguous_2d() in TensorImpl.cpp +def is_channels_last_contiguous_2d(a: Tensor) -> bool: + # NHWC or not channels last 2D contiguous + if a.ndim != 4: + return False + + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + expected_stride = 1 + for idx in (1, 3, 2, 0): + length = a.shape[idx] + if guard_size_oblivious(length == 1): + continue + + stride = a.stride()[idx] + if guard_size_oblivious(stride != expected_stride): + return False + + expected_stride *= length + + return True + + +def is_channels_last_contiguous_3d(a: Tensor) -> bool: + # NDHWC or not channels last 3D contiguous + if a.ndim != 5: + return False + + expected_stride = 1 + for idx in (1, 4, 3, 2, 0): + length = a.shape[idx] + if length == 1: + continue + + stride = a.stride()[idx] + if stride != expected_stride: + return False + + expected_stride *= length + + return True + + +_memory_formats = { + torch.contiguous_format, + torch.preserve_format, + torch.channels_last, + torch.channels_last_3d, +} + + +def validate_memory_format(memory_format: torch.memory_format): + torch._check( + memory_format in _memory_formats, + lambda: f"Received unknown memory format {memory_format}!", + ) + + +def is_contiguous_for_memory_format( # type: ignore[return] + a: Tensor, *, memory_format: torch.memory_format +) -> bool: + validate_memory_format(memory_format) + + if memory_format == torch.contiguous_format: + return is_contiguous(a) + if memory_format == torch.channels_last: + return is_channels_last_contiguous_2d(a) + if memory_format == torch.channels_last_3d: + return is_channels_last_contiguous_3d(a) + + torch._check( + False, + lambda: f"is_contiguous received unsupported memory format {memory_format}", + ) + + +# NOTE: that tensors with no elements and channels last is ??? +def is_channels_last_contiguous(a: Tensor) -> bool: + """ + True when a tensor is channels-last contiguous. + + This requires that: + + - the tensor is conceptually either 4 (NHWC) or 5 (NDHWC) dimensions + - if we name the tensor's dimensions NCHW or NCDHW, then the strides are such that the + stride of the 'C' dimension (Cs) is 1 and the strides corresponding to + each dimension (Xs) can be ordered Cs <= Ws <= Hs <= (Ds) <= Ns and are + "nested" -- so Ws = Cs * Cl, where Cl is the length of the 'C' dimension, + for example. + """ + return is_channels_last_contiguous_2d(a) or is_channels_last_contiguous_3d(a) + + +def is_non_overlapping_and_dense(a: Tensor) -> bool: + """ + True when a tensor is non-overlapping and dense. + + A tensor is non-overlapping and dense when there exists a permutation of + its dimensions that is contiguous. + """ + + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + if a.is_sparse: + return False + + # Short-circuits if the tensor is already contiguous or channels-last contiguous + if is_contiguous(a) or is_channels_last_contiguous(a): + return True + + # The following is equivalent to compute_non_overlapping_and_dense in TensorImpl.cpp + + # Short-circuits for tensors of rank one, which are + # non-overlapping and "dense" if their stride is one + if a.ndim == 1: + return a.stride()[0] == 1 + + # Checks that there exists a permutation of the strides s.t. the tensor would be contiguous + # Sorts (length, stride) pairs by stride + # + # This sort is done in a size-oblivious way, which helps if we do a + # comparison like 2048*u0 > u0; we just want this to return True + # (and not worry about what if u0 is zero). + class K(NamedTuple): + size: int + stride: int + + def __lt__(self, other): + return guard_size_oblivious(self.stride < other.stride) + + def __gt__(self, other): + return guard_size_oblivious(self.stride > other.stride) + + def __le__(self, other): + return guard_size_oblivious(self.stride <= other.stride) + + def __ge__(self, other): + return guard_size_oblivious(self.stride >= other.stride) + + def __eq__(self, other): + return guard_size_oblivious(self.stride == other.stride) + + lengths_and_strides = sorted(map(K, a.shape, a.stride())) + + expected_stride = 1 + for length, stride in lengths_and_strides: + if guard_size_oblivious(length == 1): + continue + + if stride != expected_stride: + return False + + expected_stride *= length + + return True + + +# NOTE: Based on the implementation in TensorIterator.cpp, but note that +# the note [Computing output strides] is incorrect, because it +# says that strides will be preserved even if they are not +# "non overlapping and dense", but this is incorrect. The +# output of elementwise operations are always given +# non overlapping and dense strides. +# This is also INCORRECT because it does not model TensorIterator's +# short-circuit, which can cause different strides. +def compute_elementwise_output_logical_to_physical_perm( + *tensors, _skip_checks=False +) -> List[int]: + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + if not _skip_checks and len(tensors) == 0: + msg = "Can't compute elementwise output strides for zero tensors!" + raise ValueError(msg) + + if not _skip_checks: + check_same_shape(*tensors, allow_cpu_scalar_tensors=True) + + # Filters the tensors to actual tensors + if not _skip_checks: + tensors = tuple( + a + for a in tensors + if isinstance(a, TensorLike) and not is_cpu_scalar_tensor(a) + ) + + # Short-circuits for CPU scalar case + if len(tensors) == 0: + return [] + + # Short-circuits for shapes with zero or one dimensions + # TODO: are these necessary? + ndim = tensors[0].ndim + if ndim == 0: + return [] + if ndim == 1: + return [0] + + # Short-circuits if contiguous or channels last, following the fake fast path. + # This reduces the number of guards we end up making + is_contiguous = True + is_channels_last = True + for t in tensors: + is_contiguous = is_contiguous and t.is_contiguous( + memory_format=torch.contiguous_format + ) + is_channels_last = is_channels_last and t.is_contiguous( + memory_format=torch.channels_last + ) + + if is_contiguous and not is_channels_last: + return list(range(ndim)) + + if is_channels_last and not is_contiguous: + return [0, *list(range(2, ndim)), 1] + + shape = tensors[0].shape + + def should_swap(idx_a, idx_b): + for tensor in tensors: + stride_a = tensor.stride()[idx_a] + stride_b = tensor.stride()[idx_b] + + if guard_size_oblivious(stride_a == 0) or guard_size_oblivious( + stride_b == 0 + ): + continue + + if guard_size_oblivious(stride_a < stride_b): + return -1 + + if guard_size_oblivious(stride_a > stride_b): + return 1 + + # stride_a == stride_b + if guard_size_oblivious(shape[idx_a] > shape[idx_b]): + return 1 + + # Note: this case is hit if all strides are zero, + # or all strides are equal and all dimensions have the same length + return 0 + + # The "sort" order for the permutation is back-to-front, but + # the natural order for permutations is front-to-back. Do the + # sorting back-to-front and then reverse it on output. + # + # also, note this returns the logical to physical shape permutation + perm = list(reversed(range(ndim))) + + # insertion sort with support for ambiguous comparisons + for i in range(1, ndim): + dim1 = i + for dim0 in reversed(range(i)): + comparison = should_swap(perm[dim0], perm[dim1]) + if comparison > 0: + perm[dim0], perm[dim1] = perm[dim1], perm[dim0] + dim1 = dim0 + elif comparison < 0: + break + + return list(reversed(perm)) + + +def compute_elementwise_output_strides(*tensors) -> Tuple[int, ...]: + """ + Computes the output strides for elementwise operations. + """ + if len(tensors) == 0: + msg = "Can't compute elementwise output strides for zero tensors!" + raise ValueError(msg) + + check_same_shape(*tensors, allow_cpu_scalar_tensors=True) + + # Filters the tensors to actual tensors + tensors = tuple( + a for a in tensors if isinstance(a, TensorLike) and not is_cpu_scalar_tensor(a) + ) + + # Short-circuits for CPU scalar case + if len(tensors) == 0: + return () + + ndim = tensors[0].ndim + shape = tensors[0].shape + + if ndim == 0: + return () + if ndim == 1: + return (1,) + + logical_to_physical_perm = compute_elementwise_output_logical_to_physical_perm( + *tensors, _skip_checks=True + ) + permuted_shape = apply_perm(shape, logical_to_physical_perm) # to physical + + new_strides = make_contiguous_strides_for(permuted_shape) + permuted_strides = apply_perm( + new_strides, invert_perm(logical_to_physical_perm) + ) # to logical + + return tuple(permuted_strides) + + +# Identity permutation is [0, 1, 2] +def apply_perm(inp, perm): + ndim = len(inp) + permuted_inp = [-1] * ndim + for idx, x in enumerate(perm): + permuted_inp[idx] = inp[x] + return permuted_inp + + +def invert_perm(perm): + ndim = len(perm) + new_perm = [-1] * ndim + for idx, x in enumerate(perm): + new_perm[x] = idx + return new_perm + + +# +# Common helper functions +# + + +def validate_dim_length(length: int): + """ + Validates that an object represents a valid + dimension length. + """ + + if isinstance(length, (int, torch.SymInt)): + torch._check_is_size(length) + else: + # sometimes called with sympy expression by inductor + assert length >= 0 + + +def validate_shape(shape: ShapeType): + """ + Validates that a sequence represents a valid shape. + """ + + assert isinstance(shape, Sequence), type(shape) + for l in shape: + validate_dim_length(l) + + +def validate_strides(strides: StrideType): + """ + Verifies the object specifies valid strides. + """ + + assert isinstance(strides, Sequence) + for stride in strides: + assert stride >= 0 + + +def validate_idx(rank: int, idx: int): + """ + Validates that idx is a valid index for the given shape. + Assumes the index is already canonicalized. + """ + + assert isinstance(idx, Dim) + assert isinstance(rank, Dim) + + assert idx >= 0 and idx < rank or idx == 0 + + +def validate_dimension_indices(rank: int, indices: DimsSequenceType): + for idx in indices: + validate_idx(rank, idx) + + +def validate_exclusive_idx(rank: int, ex_idx: int): + """ + Validates that ex_idx is a valid exclusive index + for the given shape. + """ + + assert isinstance(ex_idx, Dim) + assert isinstance(rank, Dim) + assert ex_idx > 0 and ex_idx <= rank + + +# "Wraps" a dim (up to one time) for the given rank, allowing dims to be +# specified using negative indices. If `wrap_scalar` is true then scalar +# tensors of rank 0 will allow dimensions in the range [-1, 0]. Otherwise, +# idx should be in the range [-rank, rank-1]. +def canonicalize_dim(rank: int, idx: int, wrap_scalar: bool = True) -> int: + if rank < 0: + msg = f"Rank cannot be negative but got {rank}" + raise IndexError(msg) + + if rank == 0: + if not wrap_scalar: + msg = f"Dimension specified as {idx} but tensor has no dimensions" + raise IndexError(msg) + rank = 1 + + if idx >= 0 and idx < rank: + return idx + + if idx < 0: + _idx = idx + rank + else: + _idx = idx + + if _idx < 0 or _idx >= rank: + # Same error message as in aten/src/ATen/WrapDimUtils.h:49 + msg = f"Dimension out of range (expected to be in range of [{-rank}, {rank - 1}], but got {idx})" + raise IndexError(msg) + + return _idx + + +# Takes a dimension or sequence of dimensions and "wraps" them, +# mapping negative offsets to positive ones +@overload +def canonicalize_dims( + rank: int, indices: Sequence[int], wrap_scalar: bool = True +) -> Tuple[int, ...]: + pass + + +@overload +def canonicalize_dims(rank: int, indices: int, wrap_scalar: bool = True) -> int: + pass + + +def canonicalize_dims(rank, indices, wrap_scalar=True): + if isinstance(indices, Dim): + return canonicalize_dim(rank, indices, wrap_scalar) + + return tuple(canonicalize_dim(rank, x, wrap_scalar) for x in indices) + + +def is_valid_permutation(rank: int, perm: DimsSequenceType) -> bool: + """ + Validates that perm is a permutation of length rank. + """ + + return isinstance(perm, Sequence) and sorted(perm) == list(range(rank)) + + +def is_same_shape(a: Sequence, b: Sequence) -> bool: + """ + Compares two shapes a and b, returning True if they are the same + (their ranks and corresponding lengths match) and False otherwise. + """ + + return tuple(a) == tuple(b) + + +def is_cpu_scalar_tensor(a: Any) -> bool: + return isinstance(a, TensorLike) and a.ndim == 0 and a.device.type == "cpu" + + +def check_same_device(*args, allow_cpu_scalar_tensors): + """ + Checks that all Tensors in args have the same device. + + Raises a RuntimeError when: + - args contains an object whose type is not Tensor or Number + - two Tensor objects in args have different devices, unless one is a CPU scalar tensor and allow_cpu_scalar_tensors is True + """ + # Short-circuits if all (one or fewer) arguments are trivially on the same device + if len(args) <= 1: + return + + # Note: cannot initialize device to the first arg's device (it may not have one) + device = None + for arg in args: + if isinstance(arg, Number): + continue + elif isinstance(arg, TensorLike): + if allow_cpu_scalar_tensors and is_cpu_scalar_tensor(arg): + continue + + if device is None: + device = arg.device + + if device != arg.device: + msg = ( + "Tensor on device " + + str(arg.device) + + " is not on the expected device " + + str(device) + + "!" + ) + raise RuntimeError(msg) + else: + msg = ( + "Unexpected type when checking for same device, " + str(type(arg)) + "!" + ) + raise RuntimeError(msg) + + +def canonicalize_device(device: DeviceLikeType) -> torch.device: + if isinstance(device, torch.device): + return device + + assert isinstance(device, str) + return torch.device(device) + + +# Asserts if any of the following are true: +# - a non-scalar or non-Tensor is given +# - the shape of any tensors is distinct +def check_same_shape(*args, allow_cpu_scalar_tensors: bool): + """ + Checks that all Tensors in args have the same shape. + + Raises a RuntimeError when: + - args contains an object whose type is not Tensor or Number + - two Tensor objects in args have different devices + """ + shape = None + + for arg in args: + if isinstance(arg, Number): + continue + elif isinstance(arg, TensorLike): + if allow_cpu_scalar_tensors and is_cpu_scalar_tensor(arg): + continue + + if shape is None: + shape = arg.shape + + if not is_same_shape(shape, arg.shape): + msg = f"Shape {arg.shape} is not the expected shape {shape}!" + raise RuntimeError(msg) + else: + msg = ( + "Unexpected type when checking for same shape, " + str(type(arg)) + "!" + ) + raise RuntimeError(msg) + + +# Acquires a common shape, if it exists, from one or more tensor arguments, +# filtering number arguments +def extract_shape(*args, allow_cpu_scalar_tensors: bool) -> Optional[ShapeType]: + shape = None + scalar_shape = None + + for arg in args: + if isinstance(arg, Number): + continue + elif isinstance(arg, TensorLike): + if allow_cpu_scalar_tensors and is_cpu_scalar_tensor(arg): + scalar_shape = arg.shape + continue + + if shape is None: + shape = arg.shape + + if not is_same_shape(shape, arg.shape): + return None + else: + return None + + return shape if shape is not None else scalar_shape + + +# Extracts dimensions that might be passed either as a list/tuple or as varargs. +# A typical case is Tensor.permute . +def extract_dims_from_varargs( + dims: Union[DimsSequenceType, Tuple[DimsSequenceType, ...]] +) -> DimsSequenceType: + if dims and isinstance(dims[0], Sequence): + assert len(dims) == 1 + dims = cast(Tuple[DimsSequenceType], dims) + return dims[0] + else: + return cast(DimsSequenceType, dims) + + +def extract_shape_from_varargs( + shape: Union[ShapeType, Tuple[ShapeType]], + validate=True, +) -> Tuple[int, ...]: + """ + Returns a shape from varargs. + + In PyTorch, operations that accept shapes often accept them as varargs, like + foo(*shape). However a user can pass the shape as a sequence of integers, + like this: + + foo(1, 2, 3) + + or as a sequence of integers + + foo((1, 2, 3)) + + In the first case shape will be a tuple of integers, and in the second case it's a tuple + containing a tuple of integers. This validates those inputs and canonicalizes them + to a tuple of integers. + """ + + # Handles tuple unwrapping + if len(shape) == 1 and isinstance(shape[0], Sequence): + shape = shape[0] + + if validate: + validate_shape(shape) # type: ignore[arg-type] + return shape # type: ignore[return-value] + + +def infer_size_shapes(a: ShapeType, b: ShapeType) -> Tuple[int, ...]: + ndim = max(len(a), len(b)) + expandedSizes = [0] * ndim + + for i in range(ndim - 1, -1, -1): + offset = ndim - 1 - i + dimA = len(a) - 1 - offset + dimB = len(b) - 1 - offset + sizeA = a[dimA] if dimA >= 0 else 1 + sizeB = b[dimB] if dimB >= 0 else 1 + + torch._check( + (sizeA == sizeB) or (sizeA == 1) or (sizeB == 1), + lambda: ( + f"The size of tensor a ({sizeA}) must match the size of " + f"tensor b ({sizeB}) at non-jagged dimension {i}" + ), + ) + + # 1s map to the other size (even 0) + expandedSizes[i] = sizeB if sizeA == 1 else sizeA + + return tuple(expandedSizes) + + +def infer_size(shape: ShapeType, numel: int) -> Tuple[int, ...]: + """ + Infers the size of a dim with size -1, if it exists. + Also checks that new shape is compatible with the number of elements. + """ + dim = None + newsize = 1 + for i, d in enumerate(shape): + if d == -1: + torch._check(dim is None, lambda: "only one dimension can be inferred") + dim = i + elif d >= 0: + newsize *= d + else: + torch._check(False, lambda: f"invalid shape dimension {d}") + if dim is None: + torch._check( + numel == newsize, + lambda: f"shape '{list(shape)}' is invalid for input of size {numel}", + ) + else: + from torch.fx.experimental.symbolic_shapes import definitely_true + + torch._check( + newsize != 0, + lambda: ( + f"cannot reshape tensor of 0 elements into shape {list(shape)} because the " + f"unspecified dimension size -1 can be any value and is ambiguous" + if definitely_true(numel == 0) + else f"shape '{list(shape)}' is invalid for input of size {numel}" + ), + ) + torch._check( + numel % newsize == 0, + lambda: f"shape '{list(shape)}' is invalid for input of size {numel}", + ) + # Convert to list to produce a compatible error message with core + # PyTorch, which prints sequences in square brackets. + shape = list(shape) + shape[dim] = numel // newsize + # NB: This is pretty important when you have unbacked SymInts. + # Suppose you have (i0, 12) resizing into (2, -1, 12). The old + # range for i0 is typically [2, inf], which means if you divide + # by two the new range should be [1, inf]. But this is bad news + # if you have an unbacked SymInt: we need to reapply the unsound + # assumption that the size is >= 2. + torch._check_is_size(shape[dim]) + return tuple(shape) + + +_integer_dtypes = ( + torch.uint8, + torch.uint16, + torch.uint32, + torch.uint64, + torch.int8, + torch.int16, + torch.int32, + torch.int64, +) +_low_precision_dtypes = (torch.float16, torch.bfloat16, torch.complex32) +_complex_dtypes = (torch.complex32, torch.complex64, torch.complex128) + + +def is_boolean_dtype(dtype: torch.dtype) -> bool: + assert isinstance(dtype, torch.dtype) + return dtype is torch.bool + + +def is_integer_dtype(dtype: torch.dtype) -> bool: + assert isinstance(dtype, torch.dtype) + return dtype in _integer_dtypes + + +def is_low_precision_dtype(dtype: torch.dtype) -> bool: + assert isinstance(dtype, torch.dtype) + return dtype in _low_precision_dtypes + + +def is_float_dtype(dtype: torch.dtype) -> bool: + assert isinstance(dtype, torch.dtype) + return dtype.is_floating_point + + +def is_complex_dtype(dtype: torch.dtype) -> bool: + assert isinstance(dtype, torch.dtype) + return dtype in _complex_dtypes + + +def is_grad_dtype(dtype: torch.dtype) -> bool: + """ + Checks if the dtype can require a gradient. + """ + return dtype.is_floating_point or is_complex_dtype(dtype) + + +_complex_to_real_dtype_map = { + torch.complex128: torch.float64, + torch.complex64: torch.float32, + torch.complex32: torch.float16, +} + +_real_to_complex_dtype_map = { + torch.float16: torch.complex32, + torch.bfloat16: torch.complex64, + torch.float32: torch.complex64, + torch.float64: torch.complex128, +} + + +def corresponding_real_dtype(dtype: torch.dtype) -> torch.dtype: + return _complex_to_real_dtype_map[dtype] + + +def corresponding_complex_dtype(dtype: torch.dtype) -> torch.dtype: + return _real_to_complex_dtype_map[dtype] + + +def dtype_to_type(dtype: torch.dtype) -> type: + """ + Computes the corresponding Python type (AKA "type kind") for the + given dtype. + """ + assert isinstance(dtype, torch.dtype) + + if dtype is torch.bool: + return bool + if dtype in _integer_dtypes: + return int + if dtype.is_floating_point: + return float + if dtype in _complex_dtypes: + return complex + + raise ValueError("Invalid dtype!") + + +def dtype_to_type_ctor(dtype: torch.dtype) -> Callable[[NumberType], NumberType]: + """ + Computes the corresponding Python type constructor for the + given dtype. + """ + assert isinstance(dtype, torch.dtype) + + if dtype is torch.bool: + return lambda x: bool(x) + if dtype in _integer_dtypes: + return sym_int + if dtype.is_floating_point: + return sym_float + if dtype in _complex_dtypes: + # TODO: type error here is real, replace with sym_complex + return lambda x: complex(x) # type: ignore[arg-type] + + raise ValueError("Invalid dtype!") + + +def type_to_dtype(typ: type) -> torch.dtype: + """ + Computes the corresponding dtype for a Number type. + """ + + assert isinstance(typ, type) + + if typ in (bool, torch.SymBool): + return torch.bool + if typ in (int, torch.SymInt): + return torch.long + if typ in (float, torch.SymFloat): + return torch.get_default_dtype() + # TODO: sym_complex_float? + if typ is complex: + return corresponding_complex_dtype(torch.get_default_dtype()) + + raise ValueError(f"Invalid type {typ}!") + + +def get_dtype(x: Union[torch.Tensor, NumberType]): + if isinstance(x, torch.Tensor): + return x.dtype + else: + return type_to_dtype(type(x)) + + +_ordered_types = (bool, int, float, complex) + + +def check_fp_or_complex( + dtype: torch.dtype, fn_name: str, allow_low_precision_dtypes: bool = True +): + """ + Checks whether the input is floating point or complex. + If allow_low_precision_dtypes is True, it allows having float16, bfloat16, and complex32 + """ + torch._check( + is_float_dtype(dtype) or is_complex_dtype(dtype), + lambda: f"{fn_name}: Expected a floating point or complex tensor as input. Got {dtype}", + ) + torch._check( + allow_low_precision_dtypes or not is_low_precision_dtype(dtype), + lambda: f"{fn_name}: Half precision dtypes not supported. Got {dtype}", + ) + + +def check_is_matrix(A: TensorLikeType, f_name: str, arg_name: str = "A"): + torch._check( + len(A.shape) >= 2, + lambda: f"{f_name}: The input tensor {arg_name} must have at least 2 dimensions.", + ) + + +def get_higher_type(a: type, b: type) -> type: + """ + Returns the higher of the two given Number types. + + The types are ordered bool -> int -> float -> complex. + """ + a, b = _maybe_get_pytype(a), _maybe_get_pytype(b) + # Type checking + if a not in _ordered_types or b not in _ordered_types: + raise RuntimeError(f"Expected builtin numeric types, found {a}, {b}") + + if a is b: + return a + + for typ in _ordered_types: + if a is typ: + return b + if b is typ: + return a + + raise ValueError("Unknown Python scalar type!") + + +# Returns the higher of two torch datatypes a and b or, if the two +# are not ordered relative to each other, the next +# higher datatype +def get_higher_dtype( + a: Optional[Union[torch.dtype, TensorLikeType, NumberType]], + b: Optional[Union[torch.dtype, TensorLikeType, NumberType]], +) -> Optional[torch.dtype]: + """ + Computes the "lowest" datatype that is weakly + "higher" than both a and b. + """ + + # Type checking + assert a is None or isinstance(a, (torch.dtype, TensorLike, Number)) + assert b is None or isinstance(b, (torch.dtype, TensorLike, Number)) + + def _extract_dtype( + x: Optional[Union[torch.dtype, TensorLikeType, NumberType]] + ) -> Optional[torch.dtype]: + if x is None: + return None + if isinstance(x, torch.dtype): + return x + if isinstance(x, TensorLike): + return x.dtype + if isinstance(x, Number): + return type_to_dtype(type(x)) + + raise RuntimeError("Unexpected type given to _extract_dtype!") + + a, b = _extract_dtype(a), _extract_dtype(b) + + if a is b: + return a + + if a is None: + return b + + if b is None: + return a + + ordered_datatypes = ( + (torch.bool,), + (torch.uint8, torch.int8), + (torch.int16,), + (torch.int32,), + (torch.int64,), + (torch.float16, torch.bfloat16), + (torch.float32,), + (torch.float64,), + (torch.complex32,), + (torch.complex64,), + (torch.complex128,), + ) + + for idx, dtypes in enumerate(ordered_datatypes): + if a in dtypes and b in dtypes: + return ordered_datatypes[idx + 1][0] + if a in dtypes: + return b + if b in dtypes: + return a + + raise RuntimeError("Unexpected termination!") + + +def check_pin_memory(pin_memory: bool): + torch._check_not_implemented( + not pin_memory, lambda: "PrimTorch does not support pinned memory" + ) + + +def check_layout(layout: torch.layout): + torch._check_not_implemented( + layout == torch.strided, lambda: f"PrimTorch doesn't support layout={layout}" + ) + + +# TODO: maybe unify with can_cast_to? +def is_weakly_lesser_type(a: type, b: type) -> bool: + """ + Compares two types, a and b, returning True if a is weakly "less" than b. + + The comparison is determined by the following type ordering: bool, int, float, complex. + """ + + a, b = _maybe_get_pytype(a), _maybe_get_pytype(b) + + if a not in _ordered_types or b not in _ordered_types: + raise RuntimeError(f"Expected builtin numeric types, found {a}, {b}") + + for typ in _ordered_types: + if a == typ: + return True + if b == typ: + return False + + raise RuntimeError("Unexpected termination!") + + +def can_safe_cast_to(*, cast_to: torch.dtype, cast_from: torch.dtype) -> bool: + for fn in (is_complex_dtype, is_float_dtype, is_integer_dtype, is_boolean_dtype): + if fn(cast_to): + return True + if fn(cast_from): + return False + + raise ValueError(f"Received unknown dtypes {cast_to}, {cast_from}!") + + +def check_same_dtype(*args): + """ + Checks that all Tensors in args have the same device and that all Numbers have the + same corresponding Python type. + + Raises a RuntimeError when: + - args contains an object whose type is not Tensor or Number + - two Tensors objects in args have different dtypes + - two Number objects in args have different types + - there are Tensors and Numbers in args, and one of those Tensors corresponding + Python types is different from the type of one of those Numbers + """ + full_dtype = None + scalar_type = None + + for arg in args: + if isinstance(arg, Number): + # Scalar type checking is disabled (and may be removed in the future) + continue + # if scalar_type is None: + # scalar_type = type(arg) + + # if scalar_type is not type(arg): + # msg = ( + # "Scalar of type " + # + str(type(arg)) + # + " is not the expected type of " + # + str(scalar_type) + # + "!" + # ) + # raise RuntimeError(msg) + elif isinstance(arg, TensorLike): + if full_dtype is None: + full_dtype = arg.dtype + if scalar_type is None: + scalar_type = dtype_to_type(arg.dtype) + + if full_dtype is not arg.dtype: + msg = ( + "Tensor with dtype " + + str(arg.dtype) + + " is not the expected dtype of " + + str(full_dtype) + + "!" + ) + raise RuntimeError(msg) + + arg_type = dtype_to_type(arg.dtype) + if arg_type is not scalar_type: + msg = ( + "Tensor with corresponding Python type " + + str(arg_type) + + " is not the expected type of " + + str(scalar_type) + + "!" + ) + raise RuntimeError(msg) + else: + msg = ( + "Unexpected type when checking for same dtype, " + str(type(arg)) + "!" + ) + raise RuntimeError(msg) + + +# Maps datatypes to their computation types for elementwise operations +_computation_dtype_map = { + torch.bfloat16: torch.float32, + torch.float16: torch.float32, + torch.complex32: torch.complex64, +} + + +def get_computation_dtype(dtype: torch.dtype) -> torch.dtype: + return _computation_dtype_map.get(dtype, dtype) + + +_cpu_acc_type_map = { + torch.bfloat16: torch.float64, + torch.float16: torch.float64, + torch.float32: torch.float64, + torch.complex32: torch.complex128, + torch.complex64: torch.complex128, +} + + +def get_acc_type(dtype: torch.dtype, device: torch.device) -> torch.dtype: + # Equivalent to at::toAccumulateType, prefer computation_dtype where possible + if device.type == "cpu": + return _cpu_acc_type_map.get(dtype, dtype) + else: + return get_computation_dtype(dtype) + + +class ELEMENTWISE_TYPE_PROMOTION_KIND(Enum): + DEFAULT = (0,) + NO_OPMATH = (1,) + INT_TO_FLOAT = (2,) + ALWAYS_BOOL = (3,) + COMPLEX_TO_FLOAT = (4,) + BOOL_TO_LONG = (5,) + + +class REDUCTION_OUTPUT_TYPE_KIND(Enum): + SAME = (0,) + COMPLEX_TO_FLOAT = (1,) # for complex types outputs corresponding real type + KEEP_PROMOTED_TYPE = (2,) # keep output in opmath type, needed for mean + ALWAYS_BOOL = (3,) + + +# Describes the return type of the primitive: +# +# - NEW, a new tensor is created +# - VIEW, a view of an input tensor is returned +# - INPLACE, one or more input tensors is modified +# +# these descriptors are mututally exclusive and exhaustive. +class RETURN_TYPE(Enum): + NEW = (0,) + VIEW = (1,) + INPLACE = (2,) + NONE = (3,) + + +# TODO: when NumberType contains the sym types, can simplify this +def number_type( + x: Union[NumberType, torch.SymInt, torch.SymFloat, torch.SymBool] +) -> Type: + if isinstance(x, torch.SymInt): + return int + elif isinstance(x, torch.SymFloat): + return float + elif isinstance(x, torch.SymBool): + return bool + else: + return type(x) + + +def expr_type(x: sympy.Basic) -> Type: + import sympy + + if x.kind is sympy.core.kind.BooleanKind: + return bool + elif x.is_integer: # type: ignore[attr-defined] + return int + else: + # NB: Not strictly correct, but we don't support SymPy complex or bool. + return float + + +# TODO: document type promotion kinds +def elementwise_dtypes( + *_args, + type_promotion_kind: ELEMENTWISE_TYPE_PROMOTION_KIND, +) -> Tuple[torch.dtype, torch.dtype]: + """ + Computes the computation and result dtypes for elementwise type promotion + on the given arguments and with the given elementwise type promotion kind. + + Note that not all inputs to an elementwise operation necessarily participate in type promotion. + For example, the "alpha" parameter of torch.add does not participate in type promotion, + although it may be cast to the Python type corresponding to the computation dtype that + the type promotion algorithm determines. + + Default elementwise type promotion, which all other type promotion kinds tweak (see below), + first decides which of four ordered types to use: + + bool -> integer -> floating point -> complex + + The selected type is the "lowest" type in the above list such that all number arguments + have a weakly "lower" type and all tensor arguments have a weakly lower corresponding + type for their dtype. + + Once the type is determined, the particular result dtype is found. The dtypes are + partially ordered as follows: + + bool -> uint8, int8 -> int16 -> int32 -> int64 -> + float16, bfloat16 -> float32 -> float64 -> complex32 -> complex64 -> complex128 + + The result dtype is selected by: + - if no tensor's dtype has the same corresponding type as the one selected, + then the result dtype is the (default) dtype corresponding to the selected type + (for example, 1.5 + an integer tensor has a result dtype of the default floating point dtype) + - if the result type is complex then the dtype is: + - the default complex dtype if there are no floating point or complex tensors + - if there are floating point or complex tensors with one or more dimensions, then + the complex dtype corresponding to the highest corresponding complex dtype among those tensors + (for example, double + cfloat -> cdouble) + - if there are only floating point or complex tensors with zero dimensions, then + the complex dtype corresponding to the highest corresponding complex dtype among those tensors + - if the first two cases do not apply, the result dtype is the highest dtype among + all tensors with one or more dimensions of the output type, and if there are no such + tensors then it's the highest dtype among all tensors with zero dimensions of the output type + (for example, long + half -> half, even if the half tensor has zero dimensions) + + The "corresponding complex dtypes" are: + float16 -> complex32 + bfloat16 -> complex64 + float32 -> complex64 + float64 -> complex128 + complex32 -> complex32 + complex64 -> complex64 + complex128 -> complex128 + + The DEFAULT type promotion kind computes per above, and then uses the result dtype to pick a computation + dtype by mapping low precision floating point and complex dtypes as follows: + + float16 -> float32 + bfloat16 -> float32 + complex32 -> complex64 + + This is referred to as "op math", and the NO_OPMATH type promotion kind disables this mapping, making the + computation dtype the same as the result dtype when it's selected. NO_OPMATH is appropriate for kernels + which perform no mathematical operations on their tensors (see below for examples). + + The INT_TO_FLOAT type promotion kind maps boolean and integer result dtypes to the default floating point dtype, + and computation dtypes to the appropriate op math dtype. + + The COMPLEX_TO_FLOAT type promotion kind maps complex result dtypes to the corresponding float dtype, following this + mapping: + + complex32 -> float16 + complex64 -> float32 + complex128 -> float64 + + Note that COMPLEX_TO_FLOAT derives the computation dtype as the DEFAULT setting does. + + The BOOL_TO_LONG type promotion kind maps boolean computation and result dtypes to long. + + The ALWAYS_BOOL type promotion kind always sets the result dtype to bool. + + Example operators for each type promotion option: + DEFAULT : add + NO_OPMATH : where, nextafter, cat + INT_TO_FLOAT : sin + COMPLEX_TO_FLOAT : abs + BOOL_TO_LONG : pow + ALWAYS_BOOL : eq + + """ + + args = tuple(x for x in _args if x is not None) + + highest_type: type = bool + + # Import sympy locally, as importing it eagerly at a module level is too slow + # See https://dev-discuss.pytorch.org/t/delving-into-what-happens-when-you-import-torch/1589 + import sympy + + for x in args: + if not isinstance(x, (Number, TensorLike, sympy.Basic)): + msg = f"Unexpected type {str(type(x))} when computing elementwise type promotion!" + raise ValueError(msg) + + if isinstance(x, Number): + highest_type = get_higher_type(highest_type, number_type(x)) + elif isinstance(x, sympy.Basic): + highest_type = get_higher_type(highest_type, expr_type(x)) + else: + # x is a TensorLike + highest_type = get_higher_type(highest_type, dtype_to_type(x.dtype)) + + result_dtype = None + + def _find_highest_dtype_filtered( + args, filter, *, float_as_complex=False + ) -> Optional[torch.dtype]: + zero_dim_tensor_dtype = None + one_plus_dim_tensor_dtype = None + for x in args: + if isinstance(x, TensorLike) and filter(x.dtype): + _dtype = x.dtype + if float_as_complex and is_float_dtype(_dtype): + _dtype = corresponding_complex_dtype(_dtype) + if x.ndim == 0: + zero_dim_tensor_dtype = get_higher_dtype( + zero_dim_tensor_dtype, _dtype + ) + else: + # x.ndim > 0 + one_plus_dim_tensor_dtype = get_higher_dtype( + one_plus_dim_tensor_dtype, _dtype + ) + + # Prefers dtype of tensors with one or more dimensions + if one_plus_dim_tensor_dtype is not None: + return one_plus_dim_tensor_dtype + + return zero_dim_tensor_dtype + + if highest_type is float: + result_dtype = _find_highest_dtype_filtered(args, is_float_dtype) + result_dtype = ( + torch.get_default_dtype() if result_dtype is None else result_dtype + ) + elif highest_type is complex: + result_dtype = _find_highest_dtype_filtered( + args, + lambda x: is_float_dtype(x) or is_complex_dtype(x), + float_as_complex=True, + ) + if result_dtype is None: + result_dtype = corresponding_complex_dtype(torch.get_default_dtype()) + elif highest_type is int: + result_dtype = _find_highest_dtype_filtered(args, is_integer_dtype) + result_dtype = torch.long if result_dtype is None else result_dtype + else: + # highest_type is bool + result_dtype = torch.bool + + if type_promotion_kind is ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT: + return get_computation_dtype(result_dtype), result_dtype + elif type_promotion_kind is ELEMENTWISE_TYPE_PROMOTION_KIND.NO_OPMATH: + return result_dtype, result_dtype + elif type_promotion_kind is ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT: + if is_integer_dtype(result_dtype) or is_boolean_dtype(result_dtype): + result_dtype = torch.get_default_dtype() + return get_computation_dtype(result_dtype), result_dtype + elif type_promotion_kind is ELEMENTWISE_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT: + # NOTE: computation can still occur in a complex dtype + computation_dtype = get_computation_dtype(result_dtype) + if is_complex_dtype(result_dtype): + result_dtype = corresponding_real_dtype(result_dtype) + return computation_dtype, result_dtype + elif type_promotion_kind is ELEMENTWISE_TYPE_PROMOTION_KIND.BOOL_TO_LONG: + if is_boolean_dtype(result_dtype): + return torch.long, torch.long + return get_computation_dtype(result_dtype), result_dtype + elif type_promotion_kind is ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL: + return get_computation_dtype(result_dtype), torch.bool + else: + raise ValueError(f"Unknown type promotion kind {str(type_promotion_kind)}") + + +def reduction_dtypes( + arg, + output_dtype_kind: REDUCTION_OUTPUT_TYPE_KIND, + dtype: Optional[torch.dtype] = None, +) -> Tuple[torch.dtype, Optional[torch.dtype]]: + # even though some reductions, like amin or amax, don't strictly require type promotion, + # all the math ops (including comparisons) are still defined only for a computation type, + # so promotion will still happen. We are doing it explicitly here + inp_dtype = dtype if dtype is not None else arg.dtype + computation_dtype = get_computation_dtype(inp_dtype) + if ( + output_dtype_kind == REDUCTION_OUTPUT_TYPE_KIND.SAME + or output_dtype_kind == REDUCTION_OUTPUT_TYPE_KIND.COMPLEX_TO_FLOAT + ): + result_dtype = dtype if dtype else arg.dtype + if ( + output_dtype_kind == REDUCTION_OUTPUT_TYPE_KIND.COMPLEX_TO_FLOAT + and is_complex_dtype(result_dtype) + ): + result_dtype = corresponding_real_dtype(result_dtype) + elif output_dtype_kind == REDUCTION_OUTPUT_TYPE_KIND.KEEP_PROMOTED_TYPE: + result_dtype = None + else: # ALWAYS_BOOL + result_dtype = torch.bool + return computation_dtype, result_dtype + + +# This function's logic is borrowed from the following functions defined in C++: +# batched_matrix_contiguous_strides and contiguous_strides +def make_contiguous_strides_for( + shape: ShapeType, row_major: bool = True +) -> Tuple[int, ...]: + """ + Returns the strides of a contiguous tensor if row_major + If row_major=True, it returns the strides of a contiguous batch of Fortran-contiguous matrices + This is often used when calling external libraries like BLAS/LAPACK/cuSolver... + """ + # contiguous_strides from c10/util/strides.h + validate_shape(shape) + if not shape: + return () + + from torch.fx.experimental.symbolic_shapes import is_nested_int + + multiplier = 1 + strides = [] + for l in reversed(shape): + strides.append(multiplier) + multiplier *= l if is_nested_int(l) else sym_max(l, 1) + + result = tuple(reversed(strides)) + + # batched_matrix_contiguous_strides from aten/src/ATen/native/LinearAlgebraUtils.h + if row_major: + return result + else: + if len(shape) < 2: + return result + return result[:-2] + (1, max(shape[-2], 1)) + + +def make_channels_last_1d_strides_for(shape: ShapeType) -> Tuple[int, ...]: + torch._check( + len(shape) == 3, + lambda: "Only tensors of rank 3 can use the channels_last_1d memory format", + ) + + multiplier = 1 + strides = [0] * 3 + for idx in (1, -1, 0): + # NOTE: intentionally divergence from make_contiguous_strides_for + # This is consistent with eager + strides[idx] = multiplier + multiplier *= shape[idx] + + return tuple(strides) + + +def make_channels_last_2d_strides_for(shape: ShapeType) -> Tuple[int, ...]: + # TODO: maybe inform the user of channels_last_3d if rank of the tensor is 5? + torch._check( + len(shape) == 4, + lambda: "Only tensors of rank 4 can use the channels_last memory format", + ) + + multiplier = 1 + strides = [0] * 4 + for idx in (1, -1, -2, 0): + # NOTE: intentionally divergence from make_contiguous_strides_for + # This is consistent with eager + strides[idx] = multiplier + multiplier *= shape[idx] + + return tuple(strides) + + +def make_channels_last_3d_strides_for(shape: ShapeType) -> Tuple[int, ...]: + torch._check( + len(shape) == 5, + lambda: "Only tensors of rank 5 can use the channels_last_3d memory format", + ) + + multiplier = 1 + strides = [0] * 5 + for idx in (1, -1, -2, -3, 0): + # NOTE: intentionally divergence from make_contiguous_strides_for + # This is consistent with eager + strides[idx] = multiplier + multiplier *= shape[idx] + + return tuple(strides) + + +def make_channels_last_strides_for(shape: ShapeType) -> Tuple[int, ...]: + ndim = len(shape) if isinstance(shape, Sequence) else 1 + if ndim == 3: + return make_channels_last_1d_strides_for(shape) + elif ndim == 4: + return make_channels_last_2d_strides_for(shape) + elif ndim == 5: + return make_channels_last_3d_strides_for(shape) + else: + raise RuntimeError( + f"no channels last format strides exist in {ndim} dimensions" + ) + + +def compute_reduction_output_shape( + shape: ShapeType, dimensions: Sequence +) -> Tuple[int, ...]: + for idx in dimensions: + validate_idx(len(shape), idx) + + new_shape = [] + for idx in range(len(shape)): + if idx in dimensions: + continue + + new_shape.append(shape[idx]) + + return tuple(new_shape) + + +def validate_no_repeating_dims(dims: Sequence): + if len(dims) != len(set(dims)): + raise RuntimeError("duplicate value in the list of dims") + + +def reduction_dims(shape: ShapeType, dims: Optional[Sequence]) -> Tuple[int, ...]: + if dims is None: + return tuple(range(len(shape))) + dims = tuple(canonicalize_dim(len(shape), idx) for idx in dims) + validate_no_repeating_dims(dims) + return dims + + +def set_correction( + unbiased: Optional[bool] = None, + correction: Optional[NumberType] = None, +) -> float: + if correction is not None and unbiased is not None: + raise RuntimeError("cannot specify both correction and unbiased arguments") + elif correction is None and unbiased is None: + correction = 1.0 + elif correction is None and unbiased is not None: + correction = 0.0 if unbiased is False else 1.0 + # NB: we don't actually support symint here, but it's harmless to accept + if not isinstance(correction, (IntLike, FloatLike)): + raise ValueError("correction argument should be integer or float") + if correction < 0: + raise ValueError("correction argument should be non-negative") + return sym_float(correction) + + +def compute_required_storage_length( + shape: ShapeType, strides: StrideType, storage_offset: int +) -> int: + """Computes the minimum storage size to hold the given tensor geometry. + + Example + ======= + + This is the size of a newly allocated tensor's storage, in units of elements + + >>> t = torch.empty((10, 20)) + >>> compute_required_storage_length(t.shape, t.stride(), t.storage_offset()) + 200 + + >>> # xdoctest: +SKIP(failing) + >>> t2 = torch.empty_strided((1, 2, 3), (5, 7, 11)) + >>> size = compute_required_storage_length(t2.shape, t2.stride(), t2.storage_offset()) + >>> size == t.storage().size() + True + + A valid tensor may have a larger storage size, but never smaller + + >>> slice = torch.empty(100)[20:40] + >>> slice.storage().size() + 100 + + >>> compute_required_storage_length(slice.shape, slice.stride(), slice.storage_offset()) + 40 + + """ + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + # Short-circuits if the shape has no elements + if guard_size_oblivious(reduce(operator.mul, shape, 1) == 0): + return 0 + + max_offset = sum((x - 1) * y for x, y in zip(shape, strides)) + # +1 to account for the first element which offsets are taken from + return 1 + storage_offset + max_offset + + +def check_in_bounds_for_storage( + a: torch.TypedStorage, shape: ShapeType, strides: StrideType, storage_offset: int +): + """ + Determines if the given shape, strides, and offset are valid for the given storage. + """ + + required_length = compute_required_storage_length(shape, strides, storage_offset) + if a.size() < required_length: + msg = ( + f"Can't view a storage of size {a.size()} with an offset of {storage_offset}, " + f"shape of {str(shape)}, and strides of {str(strides)}, " + f"which requires a storage of size {required_length}" + ) + raise ValueError(msg) + + +# NOTE: This function should ideally be removed, but some Meta internal models +# packaged with `torch.package` are using it, so it will have to be removed +# at some point in the future when those models no longer use this function. +@deprecated( + "`torch._prims_common.check` is deprecated and will be removed in the future. " + "Please use `torch._check*` functions instead.", + category=FutureWarning, +) +def check( + b: bool, s: Callable[[], str], exc_type: Type[Exception] = RuntimeError +) -> None: + """ + Helper function for raising an error_type (default: RuntimeError) if a boolean condition fails. + Error message is a callable producing a string (to avoid wasting time + string formatting in non-error case, and also to make it easier for torchdynamo + to trace.) + + .. note:: This function is planned for removal in the future. Please use + `torch._check*` functions instead. + """ + torch._check_with(exc_type, b, s) + + +# This combines is_channels_last_strides_2d and is_channels_last_strides_3d in +# c10/core/MemoryFormat.h into one function +def are_strides_like_channels_last( + shape: Sequence[int], strides: Sequence[int] +) -> bool: + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + ndim = len(shape) + + if ndim == 4: + # Check for channels_last_2d + dim_order = [1, 3, 2, 0] + elif ndim == 5: + # Check for channels_last_3d + dim_order = [1, 4, 3, 2, 0] + else: + return False + + if guard_size_oblivious(strides[1] == 0): + return False + + min = 0 + for d in dim_order: + if guard_size_oblivious(shape[d] == 0): + return False + if guard_size_oblivious(strides[d] < min): + return False + if d == 0 and min == strides[1]: + return False + min = strides[d] + if guard_size_oblivious(strides[d] > 1): + min *= shape[d] + return True + + +def suggest_memory_format(x: TensorLikeType) -> torch.memory_format: + if x.layout != torch.strided: + return torch.contiguous_format + + if are_strides_like_channels_last(x.shape, x.stride()): + return torch.channels_last if x.ndim == 4 else torch.channels_last_3d + + return torch.contiguous_format + + +def prod(xs: Sequence[NumberType]) -> NumberType: + """Product of elements in input sequence. Returns 1 for empty sequence""" + return reduce(operator.mul, xs, 1) + + +def is_expandable_to(shape: ShapeType, desired: ShapeType) -> bool: + """Checks if a shape can be expanded to another shape. + This is equivalent to checking if the two shapes are broadcastable. + """ + # This is a Python implementation of + # aten/src/ATen/ExpandUtils.h:is_expandable_to + if len(shape) > len(desired): + return False + for i in range(len(shape)): + if shape[-i - 1] != desired[-i - 1] and shape[-i - 1] != 1: + return False + return True + + +def mask_tensor(mask: TensorLikeType, t: TensorLikeType): + """ + Similar to torch.where(mask, t, 0) but if t is boolean, + result is also boolean and not promoted to int. + """ + # torch.where(mask, t, False) is equivalent + # but feels hacky and might break in the future + if t.dtype is torch.bool: + return mask.logical_and(t) + else: + return torch.where(mask, t, 0) + + +def get_aten_op(fn: Callable, name: str): + """ + Given the __module__ of reference and its name, it returns + (our best guess of) the ATen name of the associated operation + + Note: In ATen, the __name__ of a function within a module often + starts by the module name. E.g. linalg_eigh, or special_zeta + """ + module = fn.__module__ + prefix = "torch._refs" + assert module.startswith(prefix) + module = module[len(prefix) :] + # We want to go from .special / .nn.functional + # to special and special_ / nn_functional_ + if module: + module = module[1:] + module = module.replace(".", "_") + module = module + "_" + return getattr(torch._ops.ops.aten, f"{module}{name}") + + +def dtype_or_default(dtype: Optional[torch.dtype]) -> torch.dtype: + return dtype if dtype is not None else torch.get_default_dtype() + + +def device_or_default(device: Optional[DeviceLikeType]) -> DeviceLikeType: + return device if device is not None else torch.device("cpu") + + +def layout_or_default(layout: Optional[torch.layout]) -> torch.layout: + return layout if layout is not None else torch.strided + + +def clone_preserve_strides(x): + needed_size = compute_required_storage_length( + x.size(), x.stride(), x.storage_offset() + ) + # Our eager implementations for *_scatter ops are all primitives w.r.t autograd, + # so these as_strided() calls are not seen by autograd. + # We need to mimic this behavior in our ref/prim implementations. + # TODO: a better way to handle this would be with a new op, "_unsafe_as_strided" + # We should revisit this when we add a compositional as_strided op, + # and also as part of https://github.com/pytorch/pytorch/issues/90507 + try: + old = torch._C._dispatch_tls_is_dispatch_key_excluded( + torch._C.DispatchKey.ADInplaceOrView + ) + torch._C._dispatch_tls_set_dispatch_key_excluded( + torch._C.DispatchKey.ADInplaceOrView, True + ) + buffer = torch.as_strided(x, (needed_size,), (1,), 0).clone() + return torch.as_strided(buffer, x.size(), x.stride(), x.storage_offset()) + finally: + torch._C._dispatch_tls_set_dispatch_key_excluded( + torch._C.DispatchKey.ADInplaceOrView, old + ) + + +def alert_not_deterministic(caller: str): + if torch.are_deterministic_algorithms_enabled(): + if torch.is_deterministic_algorithms_warn_only_enabled(): + warnings.warn( + f"{caller} does not have a deterministic implementation, but you set " + f"'torch.use_deterministic_algorithms(True, warn_only=True)'. " + f"You can file an issue at https://github.com/pytorch/pytorch/issues " + f"to help us prioritize adding deterministic support for this operation." + ) + else: + torch._check( + False, + lambda: ( + f"{caller} does not have a deterministic implementation, but you set " + f"'torch.use_deterministic_algorithms(True)'. You can turn off " + f"determinism just for this operation, or you can use the " + f"'warn_only=True' option, if that's acceptable for your application. " + f"You can also file an issue at https://github.com/pytorch/pytorch/issues " + f"to help us prioritize adding deterministic support for this operation." + ), + ) + + +class CUDARngStateHelper: + @staticmethod + def get_torch_state_as_tuple(fake_mode=nullcontext()): + if not torch.cuda.is_available(): + raise RuntimeError("CUDA not available") + + with fake_mode: + seed = torch.tensor(torch.cuda.initial_seed()) + offset = torch.tensor(torch.cuda._get_rng_state_offset()) + return seed, offset + + @staticmethod + def set_torch_state_tensor(seed, offset): + # Rng state is [64-bit seed, 64-bit offset] + seed_portion = seed.reshape([1]).view(torch.uint8) + offset_portion = offset.reshape([1]).view(torch.uint8) + new_state = torch.cat([seed_portion, offset_portion]) + torch.cuda.set_rng_state(new_state) + + @staticmethod + def set_new_offset(relative_offset): + torch.cuda._set_rng_state_offset(relative_offset.item()) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims_common/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims_common/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3ee6e90980ab05c0627f17ba7e4345fd5c6c1c4b Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims_common/__pycache__/__init__.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims_common/__pycache__/wrappers.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims_common/__pycache__/wrappers.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7f1631fd02f4ade79b9798dcf40d9d6648d06b26 Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims_common/__pycache__/wrappers.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims_common/wrappers.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims_common/wrappers.py new file mode 100644 index 0000000000000000000000000000000000000000..a89ea7cb9997e68f5c42b765ff7e49a48a662848 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_prims_common/wrappers.py @@ -0,0 +1,430 @@ +# mypy: allow-untyped-defs +import inspect +import warnings +from functools import wraps +from typing import Callable, NamedTuple, Optional, overload, Sequence, Tuple, TypeVar +from typing_extensions import ParamSpec + +import torch +import torch._prims_common as utils +from torch._prims_common import ( + CustomOutParamAnnotation, + ELEMENTWISE_TYPE_PROMOTION_KIND, + Number, + NumberType, + ShapeType, + TensorLike, + TensorLikeType, +) +from torch.utils import _pytree as pytree +from torch.utils._pytree import tree_flatten, tree_unflatten + + +_T = TypeVar("_T") +_P = ParamSpec("_P") + + +@overload +def _maybe_convert_to_dtype(a: TensorLikeType, dtype: torch.dtype) -> TensorLikeType: + pass + + +@overload +def _maybe_convert_to_dtype(a: NumberType, dtype: torch.dtype) -> NumberType: + pass + + +@overload +def _maybe_convert_to_dtype(a: Sequence, dtype: torch.dtype) -> Sequence: + pass + + +@overload +def _maybe_convert_to_dtype(a: None, dtype: torch.dtype) -> None: + pass + + +# TODO: implement ref.cast with an option to enforce safe casting +def _maybe_convert_to_dtype(a, dtype): + if isinstance(a, TensorLike): + if a.dtype != dtype: + return a.to(dtype) + return a + if isinstance(a, Number): + return utils.dtype_to_type_ctor(dtype)(a) # type: ignore[arg-type] + if isinstance(a, Sequence): + return tuple(_maybe_convert_to_dtype(x, dtype) for x in a) + # Passthrough None because some functions wrapped with type promotion + # wrapper might have optional args + if a is None: + return None + + raise ValueError(f"Received type {type(a)} that is neither a tensor or a number!") + + +def _maybe_convert_to_type(a: NumberType, typ: type) -> NumberType: + if not isinstance(a, Number): + msg = f"Found unknown type {type(a)} when trying to convert scalars!" + raise ValueError(msg) + if not utils.is_weakly_lesser_type(type(a), typ): + msg = f"Scalar {a} of type {type(a)} cannot be safely cast to type {typ}!" + raise ValueError(msg) + + return typ(a) + + +def _annotation_has_type(*, typ, annotation): + if hasattr(annotation, "__args__"): + for a in annotation.__args__: + if _annotation_has_type(typ=typ, annotation=a): + return True + return False + + return typ is annotation + + +class elementwise_type_promotion_wrapper: + """ + Adds elementwise type promotion to a Python reference implementation. + + Takes two kwargs, type_promoting_args and type_promotion_kind. + + type_promoting_args must be a string Sequence specifiying the argument names of all + arguments that participate in type promotion (and should be type promoted). If the + arg specifies a Sequence-type then every element of the Sequence will participate in + type promotion. + + type_promotion_kind must be one of the kinds specified by ELEMENTWISE_TYPE_PROMOTION_KIND. + See its documentation for details. + + The return_dtype will be coerced to the wrapped function's dtype arg if it is available and + not None. + + Other type promotion behavior, like validating the Python type of scalar arguments, must + be handled separately. + """ + + def __init__( + self, + *, + type_promotion_kind: ELEMENTWISE_TYPE_PROMOTION_KIND, + type_promoting_args: Optional[Sequence[str]] = None, + ): + self.type_promoting_arg_names = type_promoting_args + self.type_promotion_kind = type_promotion_kind + + def __call__(self, fn: Callable) -> Callable: + sig = inspect.signature(fn) + + @wraps(fn) + def _fn(*args, **kwargs): + bound = sig.bind(*args, **kwargs) + type_promoting_args = tuple( + bound.arguments[x] + for x in self.type_promoting_arg_names # type: ignore[union-attr] + if x in bound.arguments.keys() + ) + + flattened_type_promoting_args = pytree.arg_tree_leaves(*type_promoting_args) + compute_dtype, result_dtype = utils.elementwise_dtypes( + *flattened_type_promoting_args, + type_promotion_kind=self.type_promotion_kind, + ) + + promoted_args = { + x: _maybe_convert_to_dtype(bound.arguments[x], compute_dtype) + for x in self.type_promoting_arg_names # type: ignore[union-attr] + if x in bound.arguments.keys() + } + bound.arguments.update(promoted_args) + + result = fn(**bound.arguments) + + # Override the return_dtype if a dtype arg is present and not None + if "dtype" in bound.arguments: + maybe_dtype = bound.arguments["dtype"] + if maybe_dtype: # dtype cannot be None + result_dtype = maybe_dtype + + if isinstance(result, TensorLike): + return _maybe_convert_to_dtype(result, result_dtype) + if isinstance(result, Sequence): + return tuple(_maybe_convert_to_dtype(x, result_dtype) for x in result) + raise AssertionError(f"Unhandled result type: {type(result)}") + + _fn.__signature__ = sig # type: ignore[attr-defined] + return _fn + + +# Returns True if resize is necessary +def _resize_output_check(out: TensorLikeType, shape: ShapeType): + # If the shapes are correct there's nothing to do + if utils.same_shape(out.shape, shape): + return False + if out.numel() != 0: + msg = ( + f"An output with one or more elements was resized since it had shape {str(out.shape)} " + "which does not match the required output shape {str(shape)}. " + "This behavior is deprecated, and in a future PyTorch release outputs will not " + "be resized unless they have zero elements. " + "You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0)." + ) + warnings.warn(msg) + return True + + +# TODO: handle tuples of tensors +def _maybe_resize_out( + out: TensorLikeType, + shape: ShapeType, + memory_format: Optional[torch.memory_format] = None, +): + if _resize_output_check(out, shape): + return out.resize_(shape, memory_format=memory_format) + else: + return out + + +def is_cpu_scalar(x: TensorLikeType) -> bool: + return x.dim() == 0 and x.device.type == "cpu" + + +def _safe_copy_out( + *, copy_from: TensorLikeType, copy_to: TensorLikeType, exact_dtype: bool = False +): + # Checks same device + if not is_cpu_scalar(copy_from) and copy_from.device != copy_to.device: + msg = ( + f"Attempting to copy from device {copy_from.device} " + f"to device {copy_to.device}, but cross-device copies are not allowed!" + ) + raise RuntimeError(msg) + + # Checks safe cast + if exact_dtype: + torch._check( + copy_from.dtype == copy_to.dtype, + lambda: f"Expected out tensor to have dtype {copy_from.dtype} " + f"but got {copy_to.dtype} instead", + ) + else: + torch._check( + utils.can_safe_cast_to(cast_from=copy_from.dtype, cast_to=copy_to.dtype), + lambda: f"Attempting to cast from {copy_from.dtype} to out tensor with dtype {copy_to.dtype}, " + "but this can't be cast because it is not safe!", + ) + + return copy_to.copy_(copy_from) + + +def out_wrapper( + *out_names: str, + exact_dtype: bool = False, + pass_is_out: bool = False, + preserve_memory_format: bool = False, +) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]: + # The wrapped function needs to convert the output parameters to ensure + # compatibility between the Python API (which always uses "out" as the + # parameter name and may be a tuple) and the Aten API (which may have + # multiple output parameters and use different parameter names such as + # "grad_input", "indices" or "values".) + + default_out_names = ("out",) + if len(out_names) == 0: + # Use default in out name + out_names = default_out_names + + is_tensor = len(out_names) == 1 + + def maybe_compute_memory_format(t): + return utils.suggest_memory_format(t) if preserve_memory_format else None + + def _out_wrapper(fn: Callable[_P, _T]) -> Callable[_P, _T]: + """ + Adds the out parameter to a Python reference. + """ + out_type = ( + TensorLikeType + if is_tensor + else Tuple[tuple(TensorLikeType for _ in range(len(out_names)))] + ) + return_type = ( + TensorLikeType + if is_tensor + else NamedTuple( + f"return_types_{fn.__name__}", [(o, TensorLikeType) for o in out_names] + ) + ) + + sig = inspect.signature(fn) + factory_kwargs = ("device", "dtype") + is_factory_fn = all(p in sig.parameters for p in factory_kwargs) + + @wraps(fn) + def _fn(*args: _P.args, out=None, **kwargs: _P.kwargs): + if is_factory_fn and out is not None: + for k in factory_kwargs: + out_attr = getattr(out, k) + if k not in kwargs: + kwargs[k] = out_attr + if pass_is_out: + result = fn(*args, is_out=(out is not None), **kwargs) # type: ignore[arg-type] + else: + result = fn(*args, **kwargs) + assert ( + isinstance(result, TensorLike) + and is_tensor + or isinstance(result, Tuple) # type: ignore[arg-type] + and len(result) == len(out_names) # type: ignore[arg-type] + ) + if out is not None: + # Naively you might expect this assert to be true, but + # it's not: + # + # assert type(out) == type(result) + # + # The reason is that functions under this wrapper can + # get registered to the Meta dispatch key, and that + # means they can be executed in a context where tensor + # subclasses are disabled (with no_dispatch), which is a + # handy way for an is-a tensor subclass (e.g., + # FakeTensor) to have the normal meta backend create a + # meta tensor, to be wrapped once it gets returned. + # In this situation, you will get a FakeTensor as + # the output tensor, but not the result--which will + # be a normal meta tensor, but this is perfectly + # harmless. + if is_tensor: + assert isinstance(out, TensorLike) + # These two operations are done in-place + _maybe_resize_out( + out, result.shape, maybe_compute_memory_format(result) # type: ignore[union-attr] + ) + _safe_copy_out(copy_from=result, copy_to=out, exact_dtype=exact_dtype) # type: ignore[arg-type] + else: + assert isinstance(out, Tuple) # type: ignore[arg-type] + torch._check_type( + len(out) == len(result), # type: ignore[arg-type] + lambda: f"expected tuple of {len(result)} elements but got {len(out)}", # type: ignore[arg-type] + ) + for r, o in zip(result, out): # type: ignore[arg-type] + # These two operations are done in-place + _maybe_resize_out(o, r.shape, maybe_compute_memory_format(r)) + _safe_copy_out(copy_from=r, copy_to=o, exact_dtype=exact_dtype) # type: ignore[arg-type] + else: + out = result + # mypy does not see through the definition of out_type given that it's in a different scope + return out if is_tensor else return_type(*out) # type: ignore[operator] + + out_param = inspect.Parameter( + "out", + kind=inspect.Parameter.KEYWORD_ONLY, + default=None, + annotation=out_type, + ) + # Mark that the function now returns a tuple + assert isinstance(sig.return_annotation, str) or sig.return_annotation in ( + sig.empty, + out_type, + ) + params = *sig.parameters.values(), out_param + + # If there's a Parameter.VAR_KEYWORD parameter (like **kwds), it must appear + # after the out= parameter, which is Parameter.KEYWORD_ONLY. Sorting by + # Parameter.kind guarantees that all the parameters are in legal order. + params = sorted(params, key=lambda p: p.kind) + + _fn.__signature__ = inspect.Signature( # type: ignore[attr-defined] + parameters=params, return_annotation=return_type # type: ignore[arg-type] + ) + + _fn.__annotations__ = dict(getattr(fn, "__annotations__", {})) + _fn.__annotations__["out"] = out_type + _fn.__annotations__["return"] = return_type + + # In the special case of having a single tensor out parameter with a + # name other than out, add a special annotation to name the parameter + if is_tensor and out_names != default_out_names: + _fn.__annotations__[CustomOutParamAnnotation] = out_names[0] + + # Add an indicator attribute that can be used in special cases + # where having a function wrapped by `out_wrapper` is not desirable e.g. + # jit + _fn._torch_decompositions_out_wrapper = f"This function is wrapped by {out_wrapper.__module__}.out_wrapper" # type: ignore[attr-defined] + + return _fn + + return _out_wrapper + + +def _maybe_remove_out_wrapper(fn: Callable): + return inspect.unwrap( + fn, + stop=lambda f: not hasattr(f, "_torch_decompositions_out_wrapper"), + ) + + +def backwards_not_supported(prim): + def redispatch_prim(args, kwargs): + with torch._C._AutoDispatchBelowAutograd(): + old = torch._C._dispatch_tls_is_dispatch_key_excluded( + torch._C.DispatchKey.ADInplaceOrView + ) + return prim(*args, **kwargs) + + class BackwardsNotSupported(torch.autograd.Function): + @staticmethod + def forward(ctx, args_spec, *flat_args): + args, kwargs = tree_unflatten(flat_args, args_spec) # type: ignore[arg-type] + return redispatch_prim(args, kwargs) + + @staticmethod + def backward(ctx, *args): + raise RuntimeError("backwards not supported on prim") + + @wraps(prim) + def _autograd_impl(*args, **kwargs): + flat_args, args_spec = tree_flatten((args, kwargs)) + if torch.is_grad_enabled() and any( + a.requires_grad for a in flat_args if isinstance(a, torch.Tensor) + ): + # TODO: There is a subtle bug here: prims like copy_to + # return their input argument after mutating it; and custom + # autograd function will incorrectly turn the result into + # a view which will fail test_python_ref_executor tests. + # At the moment, we sidestep this by observing that the + # unit tests don't ever try to run the executor with + # autograd, so we don't exercise the buggy case, but if + # you ever want to feed autograd through this, be aware + # of it! We need a way of properly implementing autograd + # for mutating operations in Python to do this. + return BackwardsNotSupported.apply(args_spec, *flat_args) + else: + return redispatch_prim(args, kwargs) + + return _autograd_impl + + +# TODO: when tracing this will add torch tensors and not TensorMeta objects +# to the trace -- we should fix this by adding a tracing context and NumberMeta classes +# TODO: this wrapper is currently untested +def elementwise_unary_scalar_wrapper(fn: Callable) -> Callable: + """ + Allows unary operators that accept tensors to work with Python numbers. + """ + sig = inspect.signature(fn) + + @wraps(fn) + def _fn(*args, **kwargs): + if len(args) > 0 and isinstance(args[0], Number): + dtype = utils.type_to_dtype(type(args[0])) + args_ = list(args) + args_[0] = torch.tensor(args[0], dtype=dtype) + result = fn(*args_, **kwargs) + assert isinstance(result, torch.Tensor) + return result.item() + + return fn(*args, **kwargs) + + _fn.__signature__ = sig # type: ignore[attr-defined] + return _fn diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..15b18fefacb777ac84615877426288eabdc7e507 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/__init__.py @@ -0,0 +1,6545 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import builtins +import collections +import inspect +import itertools +import math +import operator +import warnings +from collections.abc import Iterable +from enum import Enum +from functools import partial, reduce, singledispatch, wraps +from typing import Any, Callable, Dict, List, Optional, overload, Sequence, Tuple, Union + +import torch +import torch._prims as prims +import torch._prims_common as utils +import torch.utils._pytree as pytree +from torch import sym_float, sym_int +from torch._prims_common import ( + BoolLike, + DeviceLikeType, + Dim, + DimsSequenceType, + DimsType, + dtype_to_type, + ELEMENTWISE_TYPE_PROMOTION_KIND, + FloatLike, + FloatWithoutSymFloat, + IntLike, + is_weakly_lesser_type, + Number, + NumberType, + RealNumberType, + REDUCTION_OUTPUT_TYPE_KIND, + ShapeType, + StrideType, + TensorLike, + TensorLikeType, + TensorOrNumberLikeType, + TensorSequenceType, +) +from torch._prims_common.wrappers import ( + _maybe_convert_to_dtype, + _maybe_resize_out, + _safe_copy_out, + elementwise_type_promotion_wrapper, + elementwise_unary_scalar_wrapper, + out_wrapper, +) + + +# Experimental module containing prototype Python references for existing +# PyTorch operations. + +__all__ = [ + # + # Elementwise Unary References + # + "abs", + "acos", + "acosh", + "asinh", + "asin", + "atan", + "atanh", + "bitwise_not", + # "cbrt", # No corresponding torch operation + "ceil", + "conj_physical", + "cos", + "cosh", + "count_nonzero", + "deg2rad", + "digamma", + "erf", + "erfinv", + "erfc", + "exp", + "expm1", + "exponential", + "exp2", + "fill", + "fill_", + "floor", + "frac", + "geometric", + "index_add", + "index_copy", + "index_copy_", + "index_select", + "index_fill", + "index_fill_", + "isfinite", + "isinf", + "isposinf", + "isneginf", + "isnan", + "isreal", + "i0", + "lerp", + "lgamma", + "log", + "log1p", + "log2", + "log10", + "log_normal", + "log_softmax", + "mvlgamma", + "norm", + "normal", + "nan_to_num", + "neg", + "positive", + "rad2deg", + "reciprocal", + "round", # TODO: model kwargs + "sigmoid", + "sgn", + "sign", + "signbit", + "sin", + "sinc", + "sinh", + "softmax", + "sqrt", + "square", + "tan", + "tanh", + "trace", + "trunc", + # + # Elementwise Binary References + # + "add", + "atan2", + "bitwise_and", + "bitwise_left_shift", + "bitwise_or", + "bitwise_right_shift", + "bitwise_xor", + "clamp_min", + "clamp_max", + "copysign", + "div", + "eq", + "float_power", + "floor_divide", + "fmax", + "fmin", + "fmod", + "gcd", + "ge", + "gt", + "heaviside", + "hypot", + "igamma", + "igammac", + "imag", + "isclose", + "lcm", + # 'ldexp', + "le", + "logaddexp", + "logaddexp2", + "logical_and", + "logical_not", + "logical_or", + "logical_xor", + "logsumexp", + "lt", + # 'max', # implement with reductions + "maximum", + # 'min', # implement with reductions + "minimum", + "mul", + "ne", + "nextafter", + # 'polar', # abs, cos, sin + "pow", + "real", + "rpow", + "remainder", + "rsub", + "rtruediv", + "rfloordiv", + "sub", + "true_divide", + "trunc_divide", + "xlogy", + # + # Elementwise Ternary References + # + "addcdiv", + "addcmul", + "clamp", + # + # Conditional references + # + "masked_fill", + "masked_fill_", + "where", + # + # Data conversion and movement references + # + "clone", + "copy_to", # TODO: add OpInfo (or implement .to) + "item", + "to", + # + # Reduction ops + # + "all", + "amax", + "amin", + "any", + "cumsum", + "cumprod", + "mean", + "dot", + "vdot", + "std", + "std_mean", + "sum", + "sum_to_size", + "prod", + "var", + "var_mean", + # + # Linear algebra ops + # + "addr", + # + # View & Shape Ops + # + "alias", + "alias_copy", + "atleast_1d", + "atleast_2d", + "atleast_3d", + "as_strided", + "as_strided_copy", + "as_strided_scatter", + "block_diag", + "broadcast_shapes", + "broadcast_tensors", + "broadcast_to", + "cat", + "chunk", + "column_stack", + "conj", + "constant_pad_nd", + "contiguous", + "diag_embed", + "diag", + "diagonal", + "diagonal_copy", + "diagonal_scatter", + "dsplit", + "dstack", + "expand", + "expand_as", + "expand_copy", + "flatten", + "flip", + "fliplr", + "flipud", + "hsplit", + "hstack", + "meshgrid", + "movedim", + "narrow", + "narrow_copy", + "native_group_norm", + "native_layer_norm", + "permute", + "ravel", + "repeat", + "reshape", + "reshape_as", + "roll", + "rot90", + "rsqrt", + "stack", + "swap_axes", # alias for transpose + "squeeze", + "t", + "t_copy", + "T", + "take_along_dim", + "tensor_split", + "transpose", + "unfold", + "unfold_copy", + "unsqueeze", + "unsqueeze_copy", + "view", + "view_as", + "view_copy", + "vsplit", + "vstack", + "view_as_complex", + "unflatten", + "unbind", + "triu", + "tril", + "triu_indices", + "tril_indices", + # + # Tensor Creation + # + "arange", + "cauchy", + "empty", + "empty_like", + "empty_permuted", + "empty_strided", + "eye", + "full", + "full_like", + "linspace", + "logspace", + "new_empty", + "new_empty_strided", + "new_full", + "new_ones", + "new_zeros", + "ones", + "ones_like", + "randn", + "scalar_tensor", + "zero", + "zeros", + "zeros_like", + # + # Test-related functions + # + "allclose", + "equal", + # + # Statistical operations + # + "bucketize", + # + # Misc + # + "is_complex", + "renorm", + "stft", + "istft", +] + +Tensor = torch.Tensor +DispatchKey = torch._C.DispatchKey # type: ignore[attr-defined] +aten = torch._ops.ops.aten + +# Note that the docstrings for the public methods from this file are in +# torch/_torch_docs.py + + +def is_noncontiguous_supported(device): + return device is None or device.type != "hpu" + + +def handle_noncontiguous_outputs(input_tlist, output): + device = None + from torch._subclasses.fake_tensor import FakeTensor + + for t in input_tlist: + if isinstance(t, FakeTensor): + device = t.fake_device + break + + if not is_noncontiguous_supported(device): + output = output.contiguous() + + return output + + +def _broadcast_shapes(*_shapes): + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + shapes = tuple( + (x,) if isinstance(x, IntLike) else x + for x in filter(lambda x: x is not None, _shapes) + ) + + # Short-circuits on no input + if len(shapes) == 0: + return None + + # Type checking + # TODO: make common validations available as utils + for shape in shapes: + assert isinstance(shape, Sequence) + + # Computes common shape + common_shape = [ + 1, + ] * reduce(max, (len(shape) for shape in shapes)) + for arg_idx, shape in enumerate(shapes): + for idx in range(-1, -1 - len(shape), -1): + if guard_size_oblivious(common_shape[idx] == 1): + if shape[idx] < 0: + raise ValueError( + "Attempting to broadcast a dimension with negative length!" + ) + common_shape[idx] = shape[idx] + elif guard_size_oblivious(shape[idx] != 1): + if common_shape[idx] != shape[idx]: + raise RuntimeError( + f"Attempting to broadcast a dimension of length {shape[idx]} at {idx}! " + f"Mismatching argument at index {arg_idx} had {shape}; but expected shape " + f"should be broadcastable to {common_shape}" + ) + + return common_shape + + +def _maybe_broadcast(*args, preserve_cpu_scalar_tensors=True): + # Computes common shape + common_shape = _broadcast_shapes( + *(t.shape if isinstance(t, TensorLike) else None for t in args) + ) + + def __maybe_broadcast(x, shape): + if x is None: + return None + elif isinstance(x, Number): + return x + elif isinstance(x, TensorLike): + if preserve_cpu_scalar_tensors and utils.is_cpu_scalar_tensor(x): + return x + + if not utils.same_shape(x.shape, common_shape): + return x.expand(common_shape) + + return x + else: + raise RuntimeError( + "Unexpected type when broadcasting: " + str(type(x)) + "!" + ) + + return tuple(__maybe_broadcast(x, common_shape) for x in args) + + +# Utilities should come BEFORE this import +from torch._decomp import register_decomposition + + +# +# Elementwise unary references +# + +infer_aten_op = object() + + +# TODO: add type promotion support +def _make_elementwise_unary_reference( + type_promotion_kind, + *, + aten_op=infer_aten_op, + extra_meta=None, +) -> Callable: + def inner(prim: Callable): + nonlocal aten_op + + @wraps(prim) + @out_wrapper() + @elementwise_unary_scalar_wrapper + @elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=type_promotion_kind, + ) + def _ref(a: TensorLikeType) -> TensorLikeType: + if extra_meta is not None: + extra_meta(a) + + output = prim(a) + return handle_noncontiguous_outputs([a], output) + + if aten_op is infer_aten_op: + aten_op = utils.get_aten_op(prim, prim.__name__) + if aten_op is not None: + register_decomposition(aten_op)(_ref) + + return _ref + + return inner + + +def _make_alias(fn, name): + """ + This function defines an alias of another function and sets its __name__ argument. + It also sets its __module__ argument to the module of the caller. + Note that when naively doing `alias = fn`, we have that `alias.__name__ == "fn"`, and + `alias.__module__ == fn.__module__`. + """ + + def _fn(*args, **kwargs): + return fn(*args, **kwargs) + + _fn.__name__ = name + _fn.__module__ = inspect.currentframe().f_back.f_globals["__name__"] # type: ignore[union-attr] + return _fn + + +def _make_inplace(fn): + """ + Given a function with out variant (i.e. using `out_wrapper()), it returns its in-place variant + See https://github.com/pytorch/pytorch/wiki/Developer-FAQ#how-do-in-place-operations-work-in-pytorch + """ + + # nb. We use the name of the first argument used in the unary references + @wraps(fn) + def _fn(a, *args, **kwargs): + return fn(a, *args, out=a, **kwargs) + + inplace_name = f"{fn.__name__}_" + _fn.__name__ = inplace_name + _fn = register_decomposition(getattr(aten, inplace_name))(_fn) # type: ignore[assignment] + + # We access the __all__ attribute of the module where fn is defined + # There may be a cleaner way of doing this... + from inspect import getmodule + + _all = getmodule(fn).__all__ # type: ignore[union-attr] + if inplace_name not in _all: + _all.append(inplace_name) + return _fn + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT) +def abs(a): + return prims.abs(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def acos(a): + return prims.acos(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def acosh(a): + return prims.acosh(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def asin(a): + return prims.asin(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def asinh(a): + return prims.asinh(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def atan(a): + return prims.atan(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def atanh(a): + return prims.atanh(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT) +def bitwise_not(a): + return prims.bitwise_not(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT) +def ceil(a): + return prims.ceil(a) + + +@register_decomposition(aten.is_complex) +def is_complex(input: TensorLikeType): + return utils.is_complex_dtype(input.dtype) + + +@register_decomposition(aten.conj_physical) +@out_wrapper() +def conj_physical(input: TensorLikeType): + if not utils.is_complex_dtype(input.dtype): + return input + return prims.conj_physical(input) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def cos(a): + return prims.cos(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def cosh(a): + return prims.cosh(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def digamma(a): + return prims.digamma(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def erf(a): + return prims.erf(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def erfinv(a): + return prims.erf_inv(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def erfc(a): + return prims.erfc(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def exp(a): + return prims.exp(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def expm1(a): + return prims.expm1(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def exp2(a): + return prims.exp2(a) + + +# Fill has its own implementation because it has a value parameter +# CompositeImplicitAutograd - don't register decomp +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a,"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.NO_OPMATH, +) +def fill(a: TensorLikeType, value: NumberType) -> TensorLikeType: + assert isinstance(a, TensorLike) + assert isinstance(value, Number) + + python_type = utils.dtype_to_type(a.dtype) + if not utils.is_weakly_lesser_type(type(value), python_type): + msg = f"value argument of type {type(value)} cannot be safely cast to type {python_type}!" + raise ValueError(msg) + + return prims.fill(a, value) + + +def fill_(a: TensorLikeType, value: NumberType) -> TensorLikeType: + r = prims.fill(a, value) + prims.copy_to(a, r) + return a + + +@register_decomposition(aten.zero) +@out_wrapper() +def zero(input: TensorLikeType) -> TensorLikeType: + return torch.zeros_like(input) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT) +def floor(a): + return prims.floor(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT) +def frac(x: TensorLikeType) -> TensorLikeType: + trunc_x = torch.mul(torch.floor(torch.abs(x)), torch.sign(x)) + return torch.sub(x, trunc_x) + + +# imag does not use _make_elementwise_unary_reference because it does not support out +def imag(a: TensorLikeType) -> TensorLikeType: + assert isinstance(a, TensorLike) + torch._check( + utils.is_complex_dtype(a.dtype), lambda: "imag only supports complex tensors." + ) + return prims.imag(a) + + +@_make_elementwise_unary_reference( + ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL, + aten_op=None, # CompositeImplicitAutograd +) +def isfinite(a: TensorLikeType) -> TensorLikeType: + if utils.is_float_dtype(a.dtype) or utils.is_complex_dtype(a.dtype): + return prims.isfinite(a) + + return ones_like(a, dtype=torch.bool) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL) +def isinf(a: TensorLikeType) -> TensorLikeType: + if utils.is_complex_dtype(a.dtype): + return torch.logical_or(isinf(torch.real(a)), isinf(torch.imag(a))) + if utils.is_float_dtype(a.dtype): + return torch.abs(a) == float("inf") + return torch.zeros_like(a, dtype=torch.bool) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL) +def isposinf(a: TensorLikeType) -> TensorLikeType: + torch._check( + not utils.is_complex_dtype(a.dtype), + lambda: f"Complex dtype is not supported for isposinf, got dtype {a.dtype}", + ) + if utils.is_float_dtype(a.dtype): + return a == float("inf") + return torch.zeros_like(a, dtype=torch.bool) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL) +def isneginf(a: TensorLikeType) -> TensorLikeType: + torch._check( + not utils.is_complex_dtype(a.dtype), + lambda: f"Complex dtype is not supported for isneginf, got dtype {a.dtype}", + ) + if utils.is_float_dtype(a.dtype): + return a == float("-inf") + return torch.zeros_like(a, dtype=torch.bool) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL) +def isnan(a: TensorLikeType) -> TensorLikeType: + return prims.ne(a, a) + + +# alias +mvlgamma = _make_alias(torch.special.multigammaln, "mvlgamma") # type: ignore[has-type] + + +@_make_elementwise_unary_reference( + ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL, + aten_op=None, # CompositeImplicitAutograd +) +def isreal(a: TensorLikeType) -> TensorLikeType: + if utils.is_complex_dtype(a.dtype): + return torch.imag(a) == 0 + return torch.ones_like(a, dtype=torch.bool) + + +# TODO: if this is special maybe it should be defined there and imported here? +@_make_elementwise_unary_reference( + ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, aten_op=aten.i0 +) +def i0(a): + return prims.bessel_i0(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def lgamma(a): + return prims.lgamma(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def log(a): + return prims.log(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def log1p(a): + return prims.log1p(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def log2(a): + return prims.log2(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def log10(a): + return prims.log10(a) + + +# CompositeImplicitAutograd - don't register decomp +@out_wrapper() +def log_softmax( + a: TensorLikeType, + dim: int, + dtype: Optional[torch.dtype] = None, +) -> TensorLikeType: + result_dtype = dtype or a.dtype + computation_dtype = utils.get_computation_dtype(result_dtype) + a_ = _maybe_convert_to_dtype(a, computation_dtype) + return _maybe_convert_to_dtype(a_ - logsumexp(a_, dim, keepdim=True), result_dtype) # type: ignore[return-value] + + +@register_decomposition(aten.logsumexp) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("self",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def logsumexp( + self: TensorLikeType, dim: DimsType, keepdim: bool = False +) -> TensorLikeType: + if not isinstance(dim, Iterable): + dim = (dim,) + if self.numel() == 0: + return torch.sum(torch.exp(self), dim, keepdim).log() + maxes = torch.amax(torch.real(self), dim, keepdim=True) + maxes = torch.masked_fill(maxes, maxes.abs() == float("inf"), 0) + maxes_squeezed = maxes if keepdim else torch.squeeze(maxes, dim) + result = torch.sum(torch.exp(self - maxes), dim, keepdim) + return result.log().add(maxes_squeezed) + + +@register_decomposition(aten.nan_to_num) +@out_wrapper() +def nan_to_num( + a: TensorLikeType, + nan: Optional[NumberType] = 0.0, + posinf: Optional[NumberType] = None, + neginf: Optional[NumberType] = None, +) -> TensorLikeType: + assert isinstance(a, TensorLike) + + if utils.is_boolean_dtype(a.dtype) or utils.is_integer_dtype(a.dtype): + return a.clone() + + if nan is None: + nan = 0.0 + + if posinf is None: + posinf = torch.finfo(a.dtype).max + + if neginf is None: + neginf = torch.finfo(a.dtype).min + + result = torch.where(torch.isnan(a), nan, a) # type: ignore[call-overload] + result = torch.where(torch.isneginf(a), neginf, result) # type: ignore[call-overload] + result = torch.where(torch.isposinf(a), posinf, result) # type: ignore[call-overload] + return result + + +def _neg_meta(a: TensorLikeType): + torch._check( + a.dtype is not torch.bool, + lambda: ( + "Negation, the `-` operator, on a bool tensor is not supported. " + "If you are trying to invert a mask, use the `~` or `logical_not()` " + "operator instead." + ), + ) + + +@_make_elementwise_unary_reference( + ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, extra_meta=_neg_meta +) +def neg(a): + return prims.neg(a) + + +# positive does not use _make_elementwise_unary_reference because it does not support out +# CompositeImplicitAutograd - don't register decomp +def positive(a: TensorLikeType) -> TensorLikeType: + assert isinstance(a, TensorLike) + if a.dtype is torch.bool: + msg = "positive does not support bool tensors." + raise RuntimeError(msg) + return a + + +# real does not use _make_elementwise_unary_reference because it does not support out +def real(a: TensorLikeType) -> TensorLikeType: + assert isinstance(a, TensorLike) + if utils.is_complex_dtype(a.dtype): + return prims.real(a) + return a + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def reciprocal(a): + return prims.reciprocal(a) + + +@register_decomposition(aten.round) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def round(a: TensorLikeType, *, decimals: int = 0) -> TensorLikeType: + if decimals == 0: + return prims.round(a) + else: + ten_pow = 10**decimals + ten_neg_pow = 10 ** (-decimals) + return prims.mul(prims.round(prims.mul(a, ten_pow)), ten_neg_pow) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def rsqrt(a): + return prims.rsqrt(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def sigmoid(a: TensorLikeType) -> TensorLikeType: + return true_divide(1, add(1, exp(neg(a)))) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT) +def sgn(a): + if utils.is_complex_dtype(a.dtype): + a_abs = a.abs() + return torch.where(a_abs == 0, 0, a / a_abs) + else: + return a.sign() + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT) +def sign(a): + return prims.sign(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL) +def signbit(a): + return prims.signbit(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def sin(a): + return prims.sin(a) + + +# Autograd note: This will give the right first derivative at zero (by chance), +# but not the right second derivative +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def sinc(a): + a = math.pi * a + return torch.where(a == 0, 1, torch.sin(a) / a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def sinh(a): + return prims.sinh(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def sqrt(a): + return prims.sqrt(a) + + +@_make_elementwise_unary_reference( + ELEMENTWISE_TYPE_PROMOTION_KIND.BOOL_TO_LONG, + aten_op=None, # CompositeImplicitAutograd, +) +def square(a: TensorLikeType) -> TensorLikeType: + return mul(a, a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def tan(a): + return prims.tan(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def tanh(a): + return prims.tanh(a) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT) +def trunc(a): + return prims.trunc(a) + + +# TODO: register this as a real ref/decomposition once TorchInductor supports complex! +def view_as_complex(self: TensorLikeType) -> TensorLikeType: + input_dtype = self.dtype + torch._check( + utils.is_float_dtype(input_dtype), + lambda: f"view_as_complex is only supported for floating point" + f"tensors, but got a tensor of scalar type: {input_dtype}", + ) + sizes = self.size() + torch._check( + len(sizes) != 0, + lambda: "Input tensor must have one or more dimensions", + ) + torch._check( + sizes[-1] == 2, + lambda: "Tensor must have a last dimension of size 2", + ) + + old_strides = self.stride() + torch._check( + old_strides[-1] == 1, + lambda: "Tensor must have a last dimension with stride 1", + ) + dims = old_strides[:-1] + torch._check( + builtins.all(stride % 2 == 0 for stride in dims), + lambda: "Tensor must have a stride divisible by 2 for all but last dimension", + ) + torch._check( + self.storage_offset() % 2 == 0, + lambda: "Tensor must have a storage_offset divisible by 2", + ) + return prims.view_element_type( + self, utils.corresponding_complex_dtype(input_dtype) + ).squeeze(-1) + + +def _make_elementwise_binary_reference( + type_promotion_kind, + aten_op=infer_aten_op, + name=None, + has_out=True, + supports_lhs_python_scalar=True, + supports_rhs_python_scalar=True, + supports_two_python_scalars=False, + should_register_decomposition=True, +) -> Callable: + def inner(prim: Callable): + nonlocal aten_op, name + if name is None: + name = prim.__name__ + + @wraps(prim) + @elementwise_type_promotion_wrapper( + type_promoting_args=("a", "b"), + type_promotion_kind=type_promotion_kind, + ) + def _ref( + a: Union[Tensor, NumberType], + b: Union[Tensor, NumberType], + ) -> Tensor: + torch._check_value( + supports_lhs_python_scalar or not isinstance(a, Number), + lambda: f"{name}: Received a lhs Python scalar to an elementwise binary " + "operation that does not accept lhs scalars!", + ) + torch._check_value( + supports_rhs_python_scalar or not isinstance(b, Number), + lambda: f"{name}: Received a rhs Python scalar to an elementwise binary " + "operation that does not accept rhs scalars!", + ) + torch._check_value( + supports_two_python_scalars + or not (isinstance(a, Number) and isinstance(b, Number)), + lambda: f"{name}: Receive two Number inputs to an elementwise binary operation!", + ) + a, b = _maybe_broadcast(a, b) + output = prim(a, b) + return handle_noncontiguous_outputs([a, b], output) + + if has_out: + _ref = out_wrapper()(_ref) # type: ignore[assignment] + + _ref.__name__ = name + if aten_op is infer_aten_op: + aten_op = utils.get_aten_op(prim, name) + if aten_op is not None and should_register_decomposition: + register_decomposition(aten_op)(_ref) + + return _ref + + return inner + + +# Add has its own implementation because it has an alpha argument +@register_decomposition(aten.add) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a", "b"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def add( + a: Union[TensorLikeType, NumberType], + b: Union[TensorLikeType, NumberType], + *, + alpha: Optional[NumberType] = None, +): + """ + Reference implementation of torch.add + """ + + a, b = _maybe_broadcast(a, b) + + if alpha is not None: + dtype = a.dtype if isinstance(a, TensorLike) else b.dtype # type: ignore[union-attr] + python_type = utils.dtype_to_type(dtype) + if python_type != bool and not utils.is_weakly_lesser_type( + type(alpha), python_type + ): + msg = f"alpha argument of type {type(alpha)} cannot be safely cast to type {python_type}!" + raise ValueError(msg) + if isinstance(b, TensorLike): + b = prims.mul(b, alpha) + else: + b = b * alpha + + output = prims.add(a, b) + return handle_noncontiguous_outputs([a, b], output) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + supports_lhs_python_scalar=False, + supports_rhs_python_scalar=False, +) +def atan2(a, b): + return prims.atan2(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def bitwise_and(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.bitwise_and(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def bitwise_left_shift(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.shift_left(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def bitwise_or(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.bitwise_or(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def bitwise_right_shift(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.shift_right_arithmetic(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def bitwise_xor(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.bitwise_xor(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + supports_lhs_python_scalar=False, +) +def copysign( + a: Union[TensorLikeType, NumberType], b: Union[TensorLikeType, NumberType] +): + if isinstance(b, Number) and isinstance(a, Tensor): + b = scalar_tensor(b, dtype=a.dtype, device=a.device) + elif isinstance(a, Tensor) and isinstance(b, Tensor) and a.device != b.device: + msg = f"Expected divisor (b) to be on the same device ({a.device}) as dividend (a), but it is found on {b.device}!" + raise RuntimeError(msg) + return where(signbit(b), neg(abs(a)), abs(a)) + + +# complex = _make_elementwise_binary_reference(prims.complex, type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT) + + +@register_decomposition(aten.div) +@out_wrapper() +def div( + a: Union[TensorLikeType, NumberType], + b: Union[TensorLikeType, NumberType], + *, + rounding_mode: Optional[str] = None, +): + """ + Reference implementation of torch.div + """ + if rounding_mode is None: + return true_divide(a, b) + elif rounding_mode == "trunc": + return trunc_divide(a, b) + elif rounding_mode == "floor": + return floor_divide(a, b) + else: + msg = f"div expected rounding_mode to be one of None, 'trunc', or 'floor' but found {rounding_mode}." + raise ValueError(msg) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL, + supports_lhs_python_scalar=False, +) +def eq(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.eq(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.BOOL_TO_LONG, +) +def pow( + a: Union[TensorLikeType, NumberType], + b: Union[TensorLikeType, NumberType], +) -> TensorLikeType: + assert isinstance(a, TensorLikeType) or isinstance(b, TensorLikeType) + + if isinstance(b, Number): + if b == 1.0: + return a.clone() # type: ignore[return-value,union-attr] + elif b == 2.0: + return a * a # type: ignore[return-value] + elif b == 0.5: + return torch.sqrt(a) # type: ignore[arg-type] + elif isinstance(a, Number): + if a == 1.0: + return torch.fill(b, True) + if a == 2.0 and ( + utils.is_float_dtype(b.dtype) or utils.is_complex_dtype(b.dtype) + ): + return torch.exp2(b) + + return prims.pow(a, b) + + +# Float power has its own implementation because it has unique type promotion. +# CompositeImplicitAutograd - don't register decomp +@out_wrapper() +def float_power( + a: Union[TensorLikeType, NumberType], + b: Union[TensorLikeType, NumberType], +) -> Tensor: + if isinstance(a, Number) and isinstance(b, Number): + raise ValueError( + "Receive two Number inputs to an elementwise binary operation!" + ) + + # Handles type promotion + dtype = utils.get_higher_dtype(a, b) + assert dtype is not None + if utils.is_complex_dtype(dtype): + dtype = torch.complex128 + else: + dtype = torch.float64 + + # Float power has the following contiguous cast behavior to be + # consistent with its C++ impl + a = _maybe_convert_to_dtype(a, dtype) + b = _maybe_convert_to_dtype(b, dtype) + + a, b = _maybe_broadcast(a, b) + return pow(a, b) + + +# >>> a = torch.tensor(-0.2500, dtype=torch.float64) +# tensor(-0.250000000000000, dtype=torch.float64) +# +# >>> b = torch.tensor(-0.0010, dtype=torch.float64) +# tensor(-0.001000000000000, dtype=torch.float64) +# +# Note: In this case, casting float to double will expand the float mantissa with zeros, +# while creating a double generates a distinct mantissa. +# >>> torch.tensor(-0.001).to(dtype=torch.float64) +# tensor(-0.001000000047497, dtype=torch.float64) +# +# Floor Division +# The difference is caused because torch.remainder(a, b) = -0.001. +# +# >>> torch.floor(torch.true_divide(a, b)) +# tensor(250., dtype=torch.float64) +# +# >>> torch.div(a, b, rounding_mode='floor') +# tensor(249., dtype=torch.float64) +# +# Definition: a // b = (a - remainder(a, b)) / b +# >>> torch.true_divide(torch.sub(a, torch.remainder(a, b)), b) +# tensor(249., dtype=torch.float64) +# +# For reference, see CPython's implementation: +# https://github.com/python/cpython/blob/ace008c531dd685a30c1dd68f9b5ba35f20171cf/Objects/floatobject.c#L636 + + +@_make_elementwise_binary_reference( + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, + supports_two_python_scalars=True, + should_register_decomposition=False, +) +def floor_divide( + a: Union[TensorLikeType, NumberType], b: Union[TensorLikeType, NumberType] +): + # Wrap scalars because some references only accept tensor arguments. + if isinstance(a, Number) and isinstance(b, Number): + a = scalar_tensor(a) + b = scalar_tensor(b) + elif isinstance(b, Number) and isinstance(a, Tensor): + b = scalar_tensor(b, dtype=a.dtype, device=a.device) + elif isinstance(a, Number) and isinstance(b, Tensor): + a = scalar_tensor(a, dtype=b.dtype, device=b.device) + elif isinstance(a, Tensor) and isinstance(b, Tensor) and a.device != b.device: + if a.device == torch.device("cpu"): + msg = f"Expected divisor (b) to be on the same device ({a.device}) as dividend (a), but it is found on {b.device}!" + raise RuntimeError(msg) + else: + b = prims.device_put(b, device=a.device) + + assert isinstance(a, Tensor) and isinstance(b, Tensor) + dtype = a.dtype + if utils.is_float_dtype(dtype): + return _floor_divide_float(a, b) + elif utils.is_integer_dtype(dtype): + return _floor_divide_integer(a, b) + else: + torch._check(False, lambda: f"{dtype} not supported for floor_divide") + + +def _floor_divide_integer(a: Tensor, b: Tensor) -> Tensor: + a, b = _maybe_broadcast(a, b) + + if not a.dtype.is_signed: + return prims.div(a, b) + + # Convert truncation to flooring: + offset = (torch.signbit(a) != torch.signbit(b)).logical_and(torch.fmod(a, b) != 0) + return prims.div(a, b) - _maybe_convert_to_dtype(offset, a.dtype) + + +def _floor_divide_float(a: Tensor, b: Tensor) -> Tensor: + mod = fmod(a, b) + div = true_divide(sub(a, mod), b) + + # Ensure that the remainder has the same sign as denominator + different_signed_inputs = bitwise_xor(lt(a, 0), lt(b, 0)) + non_zero_remainder = ne(mod, 0) + mask = bitwise_and(non_zero_remainder, different_signed_inputs) + div = where(mask, sub(div, 1), div) + + # Map quotient to nearest integer value + floor_div = floor(div) + mask = gt(sub(div, floor_div), 0.5) + floor_div = where(mask, add(floor_div, 1), floor_div) + + basic_div = true_divide(a, b) + zero_tensor = scalar_tensor(0, dtype=basic_div.dtype, device=basic_div.device) + + # If quotient is zero, copy signbit from true_divide quotient + floor_div = where(ne(div, 0), floor_div, copysign(zero_tensor, basic_div)) + + # If denominator is zero, then follow true_divide behavior + return where(ne(b, 0), floor_div, basic_div) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, + supports_lhs_python_scalar=False, + supports_rhs_python_scalar=False, +) +def fmax(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.fmax(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, + supports_lhs_python_scalar=False, + supports_rhs_python_scalar=False, +) +def fmin(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.fmin(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, + supports_lhs_python_scalar=False, + supports_rhs_python_scalar=True, +) +def fmod(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.fmod(a, b) + + +@register_decomposition(aten.frexp) +@out_wrapper("mantissa", "exponent") +def frexp(self: TensorLikeType) -> Tuple[TensorLikeType, TensorLikeType]: + return torch.return_types.frexp(prims.frexp(self)) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, + supports_lhs_python_scalar=False, + supports_rhs_python_scalar=False, +) +def gcd(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.gcd(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL, + supports_lhs_python_scalar=False, +) +def ge(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.ge(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL, + supports_lhs_python_scalar=False, +) +def gt(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.gt(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, + supports_lhs_python_scalar=False, + supports_rhs_python_scalar=False, +) +def heaviside(input: TensorLikeType, values: TensorLikeType) -> TensorLikeType: + input_eq_zero = torch.eq(input, 0) + input_lt_zero = torch.logical_or(torch.lt(input, 0), torch.isnan(input)) + zeros_and_ones = torch.where(input_lt_zero, 0, 1) + output = torch.where(input_eq_zero, values, zeros_and_ones) + return output + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, + supports_lhs_python_scalar=False, + supports_rhs_python_scalar=False, +) +def hypot(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.hypot(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + supports_lhs_python_scalar=False, + supports_rhs_python_scalar=False, +) +def igamma(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.igamma(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + supports_lhs_python_scalar=False, + supports_rhs_python_scalar=False, +) +def igammac(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.igammac(a, b) + + +def _check_close_args( + name: str, + a: TensorLikeType, + b: TensorLikeType, + rtol: float, + atol: float, +) -> None: + torch._check_value( + a.dtype == b.dtype, + lambda: f"{name}: Attempting to compare tensors of different dtypes {a.dtype} and {b.dtype}!", + ) + torch._check( + rtol >= 0, + lambda: f"{name}: rtol must be greater than or equal to zero, but got {rtol}!", + ) + torch._check( + atol >= 0, + lambda: f"{name}: atol must be greater than or equal to zero, but got {atol}!", + ) + + +# CompositeImplicitAutograd - don't register decomp +def isclose( + a: TensorLikeType, + b: TensorLikeType, + rtol: float = 1e-05, + atol: float = 1e-08, + equal_nan: bool = False, +) -> TensorLikeType: + _check_close_args(name="torch.isclose", a=a, b=b, rtol=rtol, atol=atol) + + close = eq(a, b) + if equal_nan and (utils.is_float_dtype(a.dtype) or utils.is_complex_dtype(a.dtype)): + close = logical_or(close, logical_and(isnan(a), isnan(b))) + + # Note: In case of zero tolerances the closeness inequality degenerates to an equality check. + # In this case, the short-circuit prevents false positives as detailed in the paragraph below. + if atol == 0 and rtol == 0: + return close + + # Note [closeness error computation] + # atol and rtol are provided as doubles, so the computation + # rtol * other will produce a float or complex tensor. + # When the difference (self - other) is compared to it then the + # tensor representing the difference will also be cast to float or complex. + # However, since (self - other) in uint8 is very likely to produce a + # negative value, this moves the cast forward so the difference is + # always computed in a float or complex type. + # If the values of the integer tensors cannot be exactly represented + # by the default scalar type then this may cause an incorrect result. + if not utils.is_float_dtype(a.dtype) and not utils.is_complex_dtype(a.dtype): + a = prims.convert_element_type(a, torch.get_default_dtype()) + b = prims.convert_element_type(b, torch.get_default_dtype()) + + allowed_error = add(atol, abs(mul(b, rtol))) + actual_error = abs(sub(a, b)) + + # Computes finite closeness + result = logical_or( + close, logical_and(isfinite(actual_error), le(actual_error, allowed_error)) + ) + + return result + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, + supports_lhs_python_scalar=False, + supports_rhs_python_scalar=False, +) +def lcm(a: TensorLikeType, b: TensorLikeType): + dtype = a.dtype + # promoting to int32 to maintain 100% consistency with C++ and to + # prevent overflow in case of int8 and int16 + promote_to_int = dtype in (torch.int8, torch.int16) + if promote_to_int: + a = prims.convert_element_type(a, torch.int32) + b = prims.convert_element_type(b, torch.int32) + + g = torch.gcd(a, b) + # Avoid division by zero in case gcd(0, 0) == 0 + g = torch.where(g == 0, 1, g) + res = torch.abs(prims.div(a, g) * b) + return res if not promote_to_int else prims.convert_element_type(res, dtype) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL, + supports_lhs_python_scalar=False, +) +def le(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.le(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, + supports_lhs_python_scalar=False, + supports_rhs_python_scalar=False, +) +def logaddexp(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + # Nb. this implementation does not distribute the gradients evenly when a == b + mask = torch.real(a) >= torch.real(b) + max_ = torch.where(mask, a, b) + min_ = torch.where(mask, b, a) + inf_mask = torch.logical_and( + torch.logical_not(torch.isfinite(torch.real(a))), torch.real(a) == torch.real(b) + ) + if utils.is_complex_dtype(a.dtype) or utils.is_complex_dtype(b.dtype): + # are you wondering what this bunch of codes are for? edge cases! + neg_min_mask = torch.real(min_) < 0 + inf_vals = torch.where( + neg_min_mask, min_, torch.log(torch.exp(min_) + torch.exp(max_)) + ) + non_nan_vals = torch.where( + inf_mask, inf_vals, max_ + torch.log1p(torch.exp(min_ - max_)) + ) + # the type for full_like does not include tensor yet + nan_mask = torch.isnan(min_) + return torch.where(nan_mask, complex(float("nan"), float("nan")), non_nan_vals) # type: ignore[call-overload] + else: + return torch.where(inf_mask, a, max_ + torch.log1p(torch.exp(min_ - max_))) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, + supports_lhs_python_scalar=False, + supports_rhs_python_scalar=False, +) +def logaddexp2(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + torch._check( + not (utils.is_complex_dtype(a.dtype) or utils.is_complex_dtype(b.dtype)), + lambda: "logaddexp2 doesn't support complex dtypes", + ) + # Nb. this implementation does not distribute the gradients evenly when a == b + mask = a >= b + max_ = torch.where(mask, a, b) + min_ = torch.where(mask, b, a) + inf_mask = torch.logical_and(torch.isinf(a), a == b) + inv_log_2 = 1.0 / math.log(2) + result = max_ + torch.log1p(torch.exp2(min_ - max_)) * inv_log_2 + return torch.where(inf_mask, a, result) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL, +) +def logical_and(a: TensorLikeType, b: TensorLikeType): + if not utils.is_boolean_dtype(a.dtype): + a = a != 0 + if not utils.is_boolean_dtype(b.dtype): + b = b != 0 + return a & b + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL) +def logical_not(a: TensorLikeType): + if not utils.is_boolean_dtype(a.dtype): + return a == 0 + return ~a + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL, +) +def logical_or(a: TensorLikeType, b: TensorLikeType): + if not utils.is_boolean_dtype(a.dtype): + a = a != 0 + if not utils.is_boolean_dtype(b.dtype): + b = b != 0 + return bitwise_or(a, b) + + +# TODO: skip unnecessary conversion of long to float +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL, +) +def logical_xor(a: TensorLikeType, b: TensorLikeType): + if not utils.is_boolean_dtype(a.dtype): + a = a != 0 + if not utils.is_boolean_dtype(b.dtype): + b = b != 0 + return a ^ b + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL, + supports_lhs_python_scalar=False, +) +def lt(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.lt(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def maximum(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.maximum(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def minimum(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.minimum(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, + supports_two_python_scalars=True, +) +def mul(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.mul(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL, + supports_lhs_python_scalar=False, +) +def ne(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.ne(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.NO_OPMATH, + supports_lhs_python_scalar=False, + supports_rhs_python_scalar=False, +) +def nextafter(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.nextafter(a, b) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def remainder(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.remainder(a, b) + + +# reverse sub +@register_decomposition(aten.rsub) +@out_wrapper() +def rsub( + a: Union[TensorLikeType, NumberType], + b: Union[TensorLikeType, NumberType], + alpha: NumberType = 1, +): + if isinstance(a, Number): + msg = "Received a Number for the first argument, but expected a Tensor" + raise ValueError(msg) + + return torch.sub(b, a, alpha=alpha) + + +# TODO: consider refactoring this with add impl +# sub has its own implementation because it has an alpha argument +@register_decomposition(aten.sub) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a", "b"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def sub( + a: Union[TensorLikeType, NumberType], + b: Union[TensorLikeType, NumberType], + *, + alpha: NumberType = 1, +): + """ + Reference implementation of torch.sub + """ + + a, b = _maybe_broadcast(a, b) + + if isinstance(a, TensorLike) and isinstance(b, TensorLike): + torch._check( + not utils.is_boolean_dtype(a.dtype) and not utils.is_boolean_dtype(b.dtype), + lambda: ( + "Subtraction, the `-` operator, with two bool tensors is not supported. " + "Use the `^` or `logical_xor()` operator instead." + ), + ) + + if alpha != 1: + dtype = a.dtype if isinstance(a, TensorLike) else b.dtype # type: ignore[union-attr] + python_type = utils.dtype_to_type(dtype) + if not utils.is_weakly_lesser_type(type(alpha), python_type): + msg = f"alpha argument of type {type(alpha)} cannot be safely cast to type {python_type}!" + raise ValueError(msg) + if isinstance(b, torch.Tensor): + b = prims.mul(b, alpha) + else: + # Carefully not to use prims.mul if b is a scalar / symint. + # prims.mul always returns a tensor, + # which will mess with type promotion. + b = b * alpha + + output = prims.sub(a, b) + return handle_noncontiguous_outputs([a, b], output) + + +@_make_elementwise_binary_reference( + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + name="true_divide", + aten_op=None, # CompositeImplicitAutograd + supports_two_python_scalars=True, +) +def true_divide(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.div(a, b) + + +@register_decomposition(aten.xlogy) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a", "b"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def xlogy(a: Union[TensorLikeType, NumberType], b: Union[TensorLikeType, NumberType]): + torch._check( + isinstance(a, TensorLike) or isinstance(b, TensorLike), + lambda: 'Expected either argument a or b to be a Tensor"', + ) + + # Operations like eq and log do not handle scalar values, so we convert them to scalar_tensors. + if isinstance(b, TensorLike) and isinstance(a, Number): + a = scalar_tensor(a, dtype=b.dtype, device=b.device) + elif isinstance(a, TensorLike) and isinstance(b, Number): + b = scalar_tensor(b, dtype=a.dtype, device=a.device) + + # mypy: expected "Tensor" + assert isinstance(a, TensorLike) + assert isinstance(b, TensorLike) + rhs = torch.where(torch.eq(a, 0), 0, torch.mul(a, torch.log(b))) + return torch.where(torch.isnan(b), float("nan"), rhs) + + +@_make_elementwise_binary_reference( + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, + aten_op=None, # CompositeImplicitAutograd + supports_two_python_scalars=True, +) +def trunc_divide( + a: Union[TensorLikeType, NumberType], b: Union[TensorLikeType, NumberType] +): + dtype = utils.get_dtype(a) + if utils.is_integer_dtype(dtype): + return prims.div(a, b) + + return trunc(prims.div(a, b)) + + +# +# Elementwise Ternary References +# + + +@register_decomposition(aten.addcdiv) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("self", "tensor1", "tensor2"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def addcdiv( + self: TensorLikeType, + tensor1: TensorLikeType, + tensor2: TensorLikeType, + *, + value: NumberType = 1, +) -> TensorLikeType: + """ + Reference implementation of torch.addcdiv + """ + if value is not None: + dtype = self.dtype # no scalars allowed, see add + python_type = utils.dtype_to_type(dtype) + torch._check_value( + utils.is_weakly_lesser_type(type(value), python_type), + lambda: f"value argument of type {type(value)} cannot be safely cast to type {python_type}!", + ) + + return self + value * tensor1 / tensor2 + + +@register_decomposition(aten.addcmul) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("self", "tensor1", "tensor2"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def addcmul( + self: TensorLikeType, + tensor1: TensorLikeType, + tensor2: TensorLikeType, + *, + value: NumberType = 1, +) -> TensorLikeType: + """ + Reference implementation of torch.addcmul + """ + if value is not None: + dtype = self.dtype # no scalars allowed, see add + python_type = utils.dtype_to_type(dtype) + torch._check_value( + utils.is_weakly_lesser_type(type(value), python_type), + lambda: f"value argument of type {type(value)} cannot be safely cast to type {python_type}!", + ) + + return self + value * tensor1 * tensor2 + + +@register_decomposition(aten.clamp) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a", "min", "max"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def clamp( + a: TensorLikeType, + min: Optional[TensorOrNumberLikeType] = None, + max: Optional[TensorOrNumberLikeType] = None, +) -> TensorLikeType: + # NOTE: grad behavior with implementation `where` is not consistent on `nan` + if min is None and max is None: + msg = "clamp called but both min and max are none!" + raise ValueError(msg) + if min is not None: + a_isnan = torch.isnan(a) + condition = torch.bitwise_or(torch.ge(a, min), a_isnan) # type: ignore[arg-type] + # we should also propagate `nan` coming from boundaries. However, that's + # not necessary since `ge` would already `False` when either operands has + # a `nan`. So this line below is redundant + # `condition = bitwise_and(condition, bitwise_not(isnan(min)))` + a = torch.where(condition, a, min) # type: ignore[arg-type] + if max is not None: + a_isnan = torch.isnan(a) + # same as above, no need to adjust `nan` from `max` + condition = torch.bitwise_or(torch.le(a, max), a_isnan) # type: ignore[arg-type] + a = torch.where(condition, a, max) # type: ignore[arg-type] + + return a + + +@register_decomposition(aten.clamp_min) +@out_wrapper() +def clamp_min( + self: TensorLikeType, + min: Optional[TensorOrNumberLikeType] = None, +) -> TensorLikeType: + return torch.clamp(self, min=min) # type: ignore[arg-type] + + +@register_decomposition(aten.clamp_max) +@out_wrapper() +def clamp_max( + self: TensorLikeType, + max: Optional[TensorOrNumberLikeType] = None, +) -> TensorLikeType: + return torch.clamp(self, max=max) # type: ignore[arg-type] + + +# +# Conditional references +# + + +# https://pytorch.org/docs/stable/generated/torch.where.html +# TODO: implement alternate where +@register_decomposition(aten.where) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a", "b"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.NO_OPMATH, +) +def where( + pred: Tensor, + a: Optional[TensorOrNumberLikeType] = None, + b: Optional[TensorOrNumberLikeType] = None, +): + """ """ + + if a is None or b is None: + raise NotImplementedError + + utils.check_same_device(pred, a, b, allow_cpu_scalar_tensors=True) + torch._check( + pred.dtype is torch.bool, + lambda: f"expected predicate to be bool, got {pred.dtype}", + ) + + pred, a, b = _maybe_broadcast(pred, a, b) + return prims.where(pred, a, b) + + +# +# Data Movement References +# +@register_decomposition(aten.clone) +@out_wrapper() +def clone( + a: TensorLikeType, *, memory_format: torch.memory_format = torch.preserve_format +) -> TensorLikeType: + result = prims.clone(a, memory_format=memory_format) + return result + + +def copy_to(a: Tensor, b: Tensor, *, allow_cross_device=True): + if not allow_cross_device and a.device != b.device: + msg = f"Attempting to copy from device {b.device} to device {a.device}, but cross-device copies are not allowed!" + raise RuntimeError(msg) + + return prims.copy_to(a, b) + + +@register_decomposition(aten.item) +def item(a: TensorLikeType) -> NumberType: + if a.numel() != 1: + msg = f"Can't convert a tensor with {a.numel()} elements to a number!" + raise ValueError(msg) + + # NOTE: explicit conversion is necessary for bool! + # See https://github.com/pytorch/pytorch/issues/78071 + number_type = utils.dtype_to_type(a.dtype) + return number_type(prims.item(a)) + + +# fast path when `to` returns an alias to input. This mimics the same function in aten +def _to_will_alias( + a: TensorLikeType, + device: Optional[DeviceLikeType] = None, + dtype: Optional[torch.dtype] = None, + copy: Optional[bool] = None, + layout: Optional[torch.layout] = None, + memory_format: Optional[torch.memory_format] = None, + pin_memory: Optional[bool] = False, + non_blocking: bool = False, # not using non_blocking +) -> bool: + return ( + not copy + and (device is None or a.device == device) + and (dtype is None or a.dtype == dtype) + and (layout is None or a.layout == layout) + # is_pinned issue #84925 + # and (pin_memory is None or pin_memory == a.is_pinned()) + and ( + memory_format is None + or memory_format == torch.preserve_format + or utils.is_contiguous_for_memory_format(a, memory_format=memory_format) + ) + ) + + +@singledispatch +def _to_dispatch(*args, **kwargs): + raise NotImplementedError + + +@_to_dispatch.register +def _to_device( + device: torch.device, + dtype: torch.dtype, + non_blocking: bool = False, + copy: bool = False, + memory_format: Optional[torch.memory_format] = None, +) -> Dict[str, Any]: + kwargs = { + "device": device, + "dtype": dtype, + "non_blocking": non_blocking, + "copy": copy, + "memory_format": memory_format, + } + return kwargs + + +@_to_dispatch.register +def _to_device_str( + device: str, + dtype: torch.dtype, + non_blocking: bool = False, + copy: bool = False, + memory_format: Optional[torch.memory_format] = None, +) -> Dict[str, Any]: + kwargs = { + "device": torch.device(device), + "dtype": dtype, + "non_blocking": non_blocking, + "copy": copy, + "memory_format": memory_format, + } + return kwargs + + +@_to_dispatch.register +def _to_dtype( + dtype: torch.dtype, + non_blocking: bool = False, + copy: bool = False, + memory_format: Optional[torch.memory_format] = None, +) -> Dict[str, Any]: + kwargs = { + "dtype": dtype, + "non_blocking": non_blocking, + "copy": copy, + "memory_format": memory_format, + } + return kwargs + + +@_to_dispatch.register +def _to_other( + other: Tensor, + non_blocking: bool = False, + copy: bool = False, + memory_format: Optional[torch.memory_format] = None, +) -> Dict[str, Any]: + device = other.device + dtype = other.dtype + layout = other.layout + # is_pinned issue #84925 + # pin_memory = other.is_pinned() + kwargs = { + "device": device, + "dtype": dtype, + "layout": layout, + "non_blocking": non_blocking, + "copy": copy, + "memory_format": memory_format, + } + return kwargs + + +# remove to_kwargs that is already present in `a` +def _canonicalize_to_arguments(a: Tensor, to_kwargs: dict): + options_to_check = ["dtype", "device", "layout", "memory_format"] + # "device" option could be passed a str instead torch.device + if "device" in to_kwargs and isinstance(to_kwargs["device"], str): + to_kwargs["device"] = torch.device(to_kwargs["device"]) + + for kw in options_to_check: + if kw in to_kwargs: + if ( + (kw == "memory_format" and to_kwargs[kw] is torch.preserve_format) + or ( + kw == "device" + and to_kwargs[kw].type == a.device.type + and ( + not to_kwargs[kw].index or to_kwargs[kw].index == a.device.index + ) + ) + or ( + getattr(a, kw, None) == to_kwargs[kw] + ) # this also handles {"memory_format": None} + ): + to_kwargs.pop(kw) + + +def to(a: TensorLikeType, *args, **kwargs) -> TensorLikeType: + # handled dispatch via positional arguments + if len(args) != 0: + kwargs = _to_dispatch(*args, **kwargs) + + # TODO: is_pinned is not currently supported in refs or fake_tensor + # https://github.com/pytorch/pytorch/issues/84925 + assert "pin_memory" not in kwargs + _canonicalize_to_arguments(a, kwargs) + + if _to_will_alias(a, **kwargs): + return a + + copy = kwargs.pop("copy") if "copy" in kwargs else False + non_blocking = kwargs.pop("non_blocking") if "non_blocking" in kwargs else False + + # short-circuit to `prims.convert_element_type` when `to` is just a dtype change + if ( + (copy or (kwargs.get("dtype", a.dtype) != a.dtype)) + and (not non_blocking) + and ("memory_format" not in kwargs) + and ("device" not in kwargs) + and ("layout" not in kwargs) + # is_pinned issue #84925 + # and ("pin_memory" not in kwargs) + ): + return prims.convert_element_type(a, kwargs.get("dtype", a.dtype)) + + result = torch.empty_like(a, **kwargs) + # TODO: non_blocking should be handled by `copy_to` + copy_to(result, a) + return result + + +# +# Reduction references +# + + +def _reduction( + a: TensorLikeType, + prim: Callable, + *, + has_identity: bool = True, + accepts_dim_tuple: bool = True, # to handle min/argmin that accept single dim only + dims: Optional[DimsType] = None, + keepdims: bool = False, + dtype: Optional[torch.dtype] = None, # should be specified for ops that support it + out: Optional[Tensor] = None, + output_dtype_kind: REDUCTION_OUTPUT_TYPE_KIND, +) -> TensorLikeType: # it is usually SAME, but I want + # ref writers to actually think about what to put here + assert isinstance(a, TensorLike) + if a.ndim > 64: + raise RuntimeError( + f"Received a tensor with {a.ndim} dimensions, but only tensors with up to 64 dims are supported!" + ) + + if out is not None: + assert isinstance(out, TensorLike) + if dtype is not None: + # TODO - this is true for eager mode currently, but it's wrong behavior for complex norms + if dtype != out.dtype: + raise RuntimeError( + "dtype argument and out dtype must match in reduction" + ) + if not accepts_dim_tuple: + assert dims is None or isinstance(dims, Dim) + if isinstance(dims, Dim): + dims = (dims,) # type: ignore[assignment] + dims = utils.reduction_dims(a.shape, dims) + if not has_identity: + valid_shape = a.ndim == 0 or builtins.all(a.shape[i] for i in dims) + if not valid_shape: + raise RuntimeError( + "reducing over zero-size dimension for reduction operation without identity" + ) + computation_dtype, result_dtype = utils.reduction_dtypes( + a, output_dtype_kind, dtype + ) + a = _maybe_convert_to_dtype(a, computation_dtype) # type: ignore[method-assign] + result = prim(a, dims) + if keepdims: + output_shape = [a.shape[i] if i not in dims else 1 for i in range(a.ndim)] + broadcast_dims = [i for i in range(a.ndim) if i not in dims] + result = prims.broadcast_in_dim(result, output_shape, broadcast_dims) + + if out is not None: + assert result_dtype is not None + if dtype is not None and result_dtype != out.dtype: + raise RuntimeError( + "Expected the dtype of reduction result and out to match" + ) + out = _maybe_resize_out(out, result.shape) + return _safe_copy_out(copy_from=result, copy_to=out) # type: ignore[arg-type] + + if result.dtype != result_dtype and result_dtype is not None: + result = prims.convert_element_type(result, result_dtype) + + return result + + +def _make_copy_from_view(fn): + """ + Given a view function (e.g. torch.diagonal) generates its copy variant (e.g. torch.diagonal_copy) + """ + aten_fn = getattr(aten, fn.__name__) + annotations = getattr(fn, "__annotations__", {}) + fn = out_wrapper()(aten_fn) + + @wraps(fn) + def _fn(*args, out=None, **kwargs): + result = fn(*args, out=out, **kwargs) + if out is not None: + return result + + return pytree.tree_map( + lambda x: x.clone(memory_format=torch.contiguous_format), + result, + ) + + copy_name = f"{fn.__name__}_copy" + _fn.__name__ = copy_name + _fn.__annotations__.update(annotations) + register_decomposition(getattr(aten, copy_name))(_fn) + return _fn + + +@register_decomposition(aten.all) +@out_wrapper() +def all( + a: TensorLikeType, + dim: Optional[DimsType] = None, + keepdim: bool = False, +) -> TensorLikeType: + result = torch.logical_not(torch.any(torch.logical_not(a), dim, keepdim=keepdim)) + + if a.dtype == torch.uint8: + result = result.to(dtype=torch.uint8) + + return result + + +@register_decomposition(aten.any) +@out_wrapper() +def any( + a: TensorLikeType, + dim: Optional[DimsType] = None, + keepdim: bool = False, +) -> TensorLikeType: + a_ = _maybe_convert_to_dtype(a, torch.bool) + if isinstance(dim, (list, tuple)) and len(dim) == 0: + result = a_.clone() + else: + result = a_.sum(dim=dim, keepdim=keepdim).ne(False) + + # Preserves uint8 -- probably a legacy mask thing + if a.dtype is torch.uint8: + return prims.convert_element_type(result, torch.uint8) + + return result + + +@register_decomposition([aten.sum.dim_IntList, aten.sum.IntList_out]) +def sum( + a: TensorLikeType, + dim: Union[Optional[int], Optional[List[int]]] = None, + keepdim: bool = False, + *, + dtype: Optional[torch.dtype] = None, + out: Optional[Tensor] = None, +) -> TensorLikeType: + if dtype is None: + if out is not None: + dtype = out.dtype + elif utils.is_boolean_dtype(a.dtype) or utils.is_integer_dtype(a.dtype): + dtype = torch.int64 + else: + dtype = a.dtype + # reduces over all dimensions if dim=() is passed + if dim == () or dim == []: + dim = None + return _reduction( + a, + prims.sum, + dims=dim, + keepdims=keepdim, + dtype=dtype, + out=out, + output_dtype_kind=REDUCTION_OUTPUT_TYPE_KIND.SAME, + ) + + +def sum_to_size( + a: Tensor, + *shape, +) -> Tensor: + shape = utils.extract_shape_from_varargs(shape, validate=False) + torch._check( + utils.is_expandable_to(shape, a.shape), + lambda: f'sum_to_size: size "{shape}" is not expandable to size "{a.shape}"', + ) + # In ATen scalar tensors are sent through sum and the result is returned as + # type promoted + if utils.is_same_shape(shape, a.shape) and len(shape) > 0: + return prims.view_of(a) + leading_dims = a.ndim - len(shape) + reduce_dims = tuple(range(leading_dims)) + tuple( + i + for i in range(leading_dims, len(shape)) + if shape[i - leading_dims] == 1 and a.shape[i] != 1 + ) + return torch.sum(a, dim=reduce_dims, keepdim=True, dtype=None) + + +@register_decomposition(aten.prod) +def prod( + a: TensorLikeType, + dim: Union[Optional[int], Optional[List[int]]] = None, + keepdim: bool = False, + *, + dtype=None, + out: Optional[Tensor] = None, +) -> TensorLikeType: + if dtype is None: + if out is not None: + dtype = out.dtype + elif utils.is_boolean_dtype(a.dtype) or utils.is_integer_dtype(a.dtype): + dtype = torch.int64 + else: + dtype = a.dtype + # reduces over all dimensions if dim=() is passed + if dim == () or dim == []: + dim = None + return _reduction( + a, + prims.prod, + dims=dim, + keepdims=keepdim, + dtype=dtype, + out=out, + output_dtype_kind=REDUCTION_OUTPUT_TYPE_KIND.SAME, + ) + + +@register_decomposition(aten.amin) +def amin( + a: TensorLikeType, + dim: Optional[DimsType] = None, + keepdim: bool = False, + *, + out: Optional[Tensor] = None, +) -> TensorLikeType: + # reduces over all dimensions if dim=() is passed + if dim == () or dim == []: + dim = None + + return _reduction( + a, + prims.amin, + dims=dim, + keepdims=keepdim, + dtype=None, + out=out, + has_identity=False, + output_dtype_kind=REDUCTION_OUTPUT_TYPE_KIND.SAME, + ) + + +@register_decomposition(aten.amax) +def amax( + a: TensorLikeType, + dim: Optional[DimsType] = None, + keepdim: bool = False, + *, + out: Optional[Tensor] = None, +) -> TensorLikeType: + # reduces over all dimensions if dim=() is passed + if dim == () or dim == []: + dim = None + + return _reduction( + a, + prims.amax, + dims=dim, + keepdims=keepdim, + dtype=None, + out=out, + has_identity=False, + output_dtype_kind=REDUCTION_OUTPUT_TYPE_KIND.SAME, + ) + + +def _dim_var_dispatch(dim=None, unbiased=None): + # There's the following overload of torch.var: + # var(Tensor self, bool unbiased=True) -> (Tensor, Tensor) + # We need to explicitly convert bool dims to unbiased arg + if unbiased is None and isinstance(dim, bool): + unbiased = dim + dim = None + return dim, unbiased + + +@register_decomposition(aten.var) +@out_wrapper() +def var( + a: TensorLikeType, + dim: Optional[DimsType] = None, + unbiased: Optional[bool] = None, + keepdim: bool = False, + *, + correction: Optional[NumberType] = None, +) -> TensorLikeType: + dim, unbiased = _dim_var_dispatch(dim, unbiased) + correction = utils.set_correction(unbiased, correction) + # reduces over all dimensions if dim=() is passed + if dim == () or dim == []: + dim = None + + result = _reduction( + a, + partial(prims.var, correction=correction), + dims=dim, + keepdims=keepdim, + dtype=None, + out=None, + has_identity=True, + output_dtype_kind=REDUCTION_OUTPUT_TYPE_KIND.COMPLEX_TO_FLOAT, + ) + return result + + +@register_decomposition(aten.std) +@out_wrapper() +def std( + a: TensorLikeType, + dim: Union[Optional[int], Optional[List[int]]] = None, + unbiased: Optional[bool] = None, + keepdim: bool = False, + *, + correction: Optional[NumberType] = None, +) -> TensorLikeType: + dim, unbiased = _dim_var_dispatch(dim, unbiased) + correction = utils.set_correction(unbiased, correction) + + opmath_dtype, dtype = utils.reduction_dtypes( + a, REDUCTION_OUTPUT_TYPE_KIND.COMPLEX_TO_FLOAT + ) + a = _maybe_convert_to_dtype(a, opmath_dtype) + a_var = torch.var(a, dim, correction=correction, keepdim=keepdim) + a_std = torch.sqrt(a_var) + assert dtype is not None + return _maybe_convert_to_dtype(a_std, dtype) + + +@register_decomposition(aten.mean) +def mean( + a: TensorLikeType, + dim: Optional[DimsType] = None, + keepdim: bool = False, + *, + dtype=None, + out=None, +) -> TensorLikeType: + # reduces over all dimensions if dim=() is passed + if dim == () or dim == []: + dim = None + orig_dtype = dtype + if dtype is None: + dtype = a.dtype + # can't use out wrapper because of this argument + torch._check( + out is None or out.dtype == dtype, + lambda: f"Expected out tensor to have dtype {dtype}, but got {out.dtype} instead", + ) + result = _reduction( + a, + prims.sum, + dims=dim, + keepdims=keepdim, + dtype=dtype, + out=None, + output_dtype_kind=REDUCTION_OUTPUT_TYPE_KIND.KEEP_PROMOTED_TYPE, + ) + torch._check( + utils.is_float_dtype(dtype) or utils.is_complex_dtype(dtype), + lambda: ( + f"mean(): could not infer output dtype. " + f"{'Input' if orig_dtype is None else 'Optional'} dtype must be either " + f"a floating point or complex dtype. Got: {dtype}" + ), + ) + if isinstance(dim, Dim): + dim = (dim,) # type: ignore[assignment] + dims = utils.reduction_dims(a.shape, dim) # type: ignore[arg-type] + nelem = 1 if a.ndim == 0 else reduce(operator.mul, (a.shape[i] for i in dims), 1) + result = true_divide(result, nelem) + result_dtype = a.dtype if dtype is None else dtype + result = _maybe_convert_to_dtype(result, result_dtype) # type: ignore[method-assign] + if out is not None: + assert isinstance(out, TensorLike) + out = _maybe_resize_out(out, result.shape) + return _safe_copy_out(copy_from=result, copy_to=out) # type: ignore[arg-type] + return result + + +@register_decomposition(aten.std_mean) +@out_wrapper("out0", "out1") +def std_mean( + a: TensorLikeType, + dim: Optional[DimsType] = None, + *, + unbiased: Optional[bool] = None, + keepdim: bool = False, + correction: Optional[NumberType] = None, +): + dim, unbiased = _dim_var_dispatch(dim, unbiased) + correction = utils.set_correction(unbiased, correction) + opmath_dtype, dtype = utils.reduction_dtypes( + a, REDUCTION_OUTPUT_TYPE_KIND.COMPLEX_TO_FLOAT + ) + original_dtype = a.dtype + a = _maybe_convert_to_dtype(a, opmath_dtype) + a_var, a_mean = torch.var_mean(a, dim, correction=correction, keepdim=keepdim) + a_std = torch.sqrt(a_var) + assert dtype is not None + return ( + _maybe_convert_to_dtype(a_std, dtype), + _maybe_convert_to_dtype(a_mean, original_dtype), + ) + + +@register_decomposition(aten.var_mean) +@out_wrapper("out0", "out1") +def var_mean( + a: TensorLikeType, + dim: Optional[DimsType] = None, + unbiased: Optional[bool] = None, + keepdim: bool = False, + *, + correction: Optional[NumberType] = None, +): + dim, unbiased = _dim_var_dispatch(dim, unbiased) + v = var(a, dim, unbiased, keepdim, correction=correction) + m = mean(a, dim, keepdim) + return v, m + + +@register_decomposition(aten.addr) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("self", "vec1", "vec2"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def addr( + self: TensorLikeType, + vec1: TensorLikeType, + vec2: TensorLikeType, + *, + beta: NumberType = 1, + alpha: NumberType = 1, +) -> TensorLikeType: + torch._check( + vec1.ndim == 1, + lambda: f"addr: Expected 1-D argument vec1, but got {vec1.ndim}-D", + ) + torch._check( + vec2.ndim == 1, + lambda: f"addr: Expected 1-D argument vec2, but got {vec2.ndim}-D", + ) + for arg, arg_name in ((alpha, "alpha"), (beta, "beta")): + if isinstance(arg, bool): + torch._check( + utils.is_boolean_dtype(self.dtype) + and utils.is_boolean_dtype(vec1.dtype) + and utils.is_boolean_dtype(vec2.dtype), + lambda: f"Boolean {arg_name} only supported for Boolean results.", + ) + self = self.expand(vec1.shape[0], vec2.shape[0]) + if utils.is_boolean_dtype(self.dtype): + # Integers are accepted for booleans + torch._check( + is_weakly_lesser_type(type(beta), int), + lambda: f"expected bool/int beta but got {type(beta)}", + ) + torch._check( + is_weakly_lesser_type(type(alpha), int), + lambda: f"expected bool/int alpha but got {type(beta)}", + ) + if not beta: + return torch.outer(vec1, vec2) if alpha else torch.full_like(self, False) + else: + return torch.logical_or( + self, + torch.outer(vec1, vec2) if alpha else torch.full_like(self, False), + ) + else: + torch._check( + is_weakly_lesser_type(type(beta), dtype_to_type(self.dtype)), + lambda: f"cannot safely convert {type(beta)} to {self.dtype}", + ) + torch._check( + is_weakly_lesser_type(type(alpha), dtype_to_type(self.dtype)), + lambda: f"cannot safely convert {type(alpha)} to {self.dtype}", + ) + if beta == 0: + # This means NaNs from self are dropped if beta is zero + return alpha * torch.outer(vec1, vec2) + else: + return beta * self + alpha * torch.outer(vec1, vec2) + + +# CompositeImplicitAutograd - don't register decomp +def atleast_1d( + arg: Union[TensorLikeType, Sequence[TensorLikeType]], *args: TensorLikeType +) -> Union[TensorLikeType, Tuple[TensorLikeType, ...]]: + """Reference implementation of :func:`torch.atleast_1d`.""" + if not args and isinstance(arg, collections.abc.Sequence): + args_ = arg + else: + assert not isinstance(arg, collections.abc.Sequence) + args_ = (arg,) + args + res = tuple(a if a.ndim >= 1 else unsqueeze(a, 0) for a in args_) + return res if len(res) > 1 else res[0] + + +# Helper function with assert to avoid MyPy error +# of incompatible type passed to unsqueeze +def _unsqueeze_atleast( + at_least_fn: Callable, dim: int, arg: TensorLikeType +) -> TensorLikeType: + arg_ = at_least_fn(arg) + assert isinstance(arg_, TensorLike) + return unsqueeze(arg_, dim) + + +# CompositeImplicitAutograd - don't register decomp +def atleast_2d( + arg: Union[TensorLikeType, Sequence[TensorLikeType]], *args: TensorLikeType +) -> Union[TensorLikeType, Tuple[TensorLikeType, ...]]: + """Reference implementation of :func:`torch.atleast_2d`.""" + if not args and isinstance(arg, collections.abc.Sequence): + args_ = arg + else: + assert not isinstance(arg, collections.abc.Sequence) + args_ = (arg,) + args + unsqueeze_atleast_1d = partial(_unsqueeze_atleast, atleast_1d, 0) + res = tuple(a if a.ndim >= 2 else unsqueeze_atleast_1d(a) for a in args_) + return res if len(res) > 1 else res[0] + + +# CompositeImplicitAutograd - don't register decomp +def atleast_3d( + arg: Union[TensorLikeType, Sequence[TensorLikeType]], *args: TensorLikeType +) -> Union[TensorLikeType, Tuple[TensorLikeType, ...]]: + """Reference implementation of :func:`torch.atleast_3d`.""" + if not args and isinstance(arg, collections.abc.Sequence): + args_ = arg + else: + assert not isinstance(arg, collections.abc.Sequence) + args_ = (arg,) + args + unsqueeze_atleast_2d = partial(_unsqueeze_atleast, atleast_2d, -1) + res = tuple(a if a.ndim >= 3 else unsqueeze_atleast_2d(a) for a in args_) + return res if len(res) > 1 else res[0] + + +def as_strided( + a: TensorLikeType, + size: ShapeType, + stride: StrideType, + storage_offset: Optional[int] = None, +) -> TensorLikeType: + storage_offset_int = ( + storage_offset if storage_offset is not None else a.storage_offset() + ) + return prims.as_strided(a, size, stride, storage_offset_int) + + +@register_decomposition(aten.as_strided_scatter) +@out_wrapper() +def as_strided_scatter( + input: TensorLikeType, + src: TensorLikeType, + size: ShapeType, + stride: StrideType, + storage_offset: Optional[int] = None, +) -> TensorLikeType: + storage_offset_int = 0 if storage_offset is None else storage_offset + return prims.as_strided_scatter(input, src, size, stride, storage_offset_int) + + +def broadcast_shapes(*shapes) -> ShapeType: + return torch.Size(_broadcast_shapes(*shapes)) + + +@aten.broadcast_tensors.default.py_impl(DispatchKey.CompositeImplicitAutograd) +@aten.broadcast_tensors.default.py_impl(DispatchKey.Meta) +def broadcast_tensors(*tensors) -> List[TensorLikeType]: + if len(tensors) == 1 and not isinstance(tensors[0], Tensor): + tensors = tensors[0] + return list(_maybe_broadcast(*tensors, preserve_cpu_scalar_tensors=False)) + + +# CompositeImplicitAutograd - don't register decomp +def broadcast_to(a: TensorLikeType, size: ShapeType) -> TensorLikeType: + start = len(size) - len(a.shape) + dims = tuple(range(start, len(a.shape) + start)) + return prims.broadcast_in_dim(a, size, dims) + + +@register_decomposition(aten.cat) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("tensors",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.NO_OPMATH, +) +def cat(tensors: TensorSequenceType, dim: int = 0) -> TensorLikeType: + def cat_compute_output_memory_format(inputs): + format = None + for t in inputs: + f = utils.suggest_memory_format(t) + if f == torch.contiguous_format: + return f + if format is not None and format != f: + return torch.contiguous_format + format = f + assert format is not None + return format + + if len(tensors) == 0: + msg = "cat expects at least one tensor, but received zero!" + raise ValueError(msg) + + for tensor in tensors: + assert isinstance(tensor, TensorLike) + + utils.check_same_device(*tensors, allow_cpu_scalar_tensors=False) + + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + # This is a bit tricky. Naively, you would expect to just pick one + # arbitrary tensor and check that all tensors match this tensor. However, + # there is legacy behavior which says that if you have a 1-D empty tensor + # (0,), this is permissible. So you can't assume that all the tensors + # have same dimensionality, and you can't assume that the first tensor is + # the correct stencil. + # + # We'll implement this in a few passes. First, we will try to infer the + # ndim of the cat output. If this ndim != 1, then we know that all ndim = + # 1 inputs must be empty, or are errors. If this ndim == 1, then life + # is easy (the legacy special case coincides with regular handling). + # + # NB: The regular implementation of cat just filters out empty inputs, + # but we do it slightly different here for better handling for unbacked + # SymInts + + example = None + for i, t in enumerate(tensors): + if example is None: + if t.ndim != 1: + example = t + else: + if t.ndim != 1: + torch._check( + t.ndim == example.ndim, + lambda: "Number of dimensions of tensors must match. " + f"Expected {example.ndim}-D tensors, but got {t.ndim}-D for " + f"tensor number {i} in the list", + ) + + if example is None: + # example is None if everything is 1-D. If so, just arbitrarily pick + # the first one + example = tensors[0] + + shape = example.shape + filtered = [] + for tensor_idx, tensor in enumerate(tensors): + if len(shape) != len(tensor.shape): + assert tensor.ndim == 1 # we've already checked this above + # Don't suggest the legacy behavior in the error message + torch._check( + # NB: it is not enough to simply assert that tensor.shape[0] == 0; + # this MUST be true even under guard size oblivious. + # Effectively, we must actually know that the shape is zero, + # passing an unbacked SymInt which we will defer a runtime + # assert on won't cut it. This is a policy decision (size + # oblivious semantics say that u0 tensors never are inferred + # to be zero size, even if they must be that for the cat to go + # through), and is load bearing for our Inductor lowerings + # (which assume that size oblivious tests are OK to determine + # if a shape is permissibly zero.) + guard_size_oblivious(tensor.shape[0] == 0), + lambda: f"Number of dimensions of tensors must match. " + f"Expected {example.ndim}-D tensors, but got 1-D for " + f"tensor number {tensor_idx} in the list", + ) + else: + # Remove inputs that are 1-D, zero size + if tensor.ndim == 1 and guard_size_oblivious(tensor.shape[0] == 0): + continue + # Don't bother checking size match, prims.cat will handle it + filtered.append(tensor) + + memory_format = cat_compute_output_memory_format(tensors) + + if len(filtered) == 0: + t = tensors[0] + + # TODO: fix this to work with meta tensors + try: + # BUG? This looks like it wants to call builtins.any() but is + # actually calling .any() (in this file). Changing to builtins.any() + # causes tests to fail: + # PYTORCH_OPINFO_SAMPLE_INPUT_INDEX=4 python test/test_ops.py -k \ + # TestFakeTensorCUDA.test_fake_crossref_backward_amp_cat_cuda_float32 + requires_grad = bool(any(x.requires_grad for x in tensors)) # type: ignore[arg-type] + except Exception: + requires_grad = False # type: ignore[assignment] + + return empty( + (0,), + dtype=t.dtype, + device=t.device, + requires_grad=requires_grad, + memory_format=memory_format, + ) + + dim = utils.canonicalize_dim(filtered[0].ndim, dim) + utils.validate_idx(filtered[0].ndim, dim) + + return prims.cat(filtered, dim).clone(memory_format=memory_format) + + +# CompositeImplicitAutograd - don't register decomp +@out_wrapper() +def column_stack(tensors: TensorSequenceType) -> TensorLikeType: + aligned_tensors = tuple( + x if x.ndim > 1 else x.reshape((x.numel(), 1)) for x in tensors + ) + return cat(aligned_tensors, 1) + + +def conj(input: TensorLikeType) -> TensorLikeType: + if not utils.is_complex_dtype(input.dtype): + return input + if input.is_sparse: + return torch.conj_physical(input) + return prims.conj(input) + + +# This replicates at::constant_pad_nd, defined in ATen/native/PadNd.cpp +@register_decomposition(aten.constant_pad_nd) +@out_wrapper() +def constant_pad_nd( + input: TensorLikeType, pad: List[int], value: NumberType = 0 +) -> TensorLikeType: + torch._check( + len(pad) % 2 == 0, + lambda: f"Length of pad must be even but instead it equals {len(pad)}", + ) + + input_sizes = input.shape + l_inp = len(input_sizes) + + l_pad = len(pad) // 2 + l_diff = l_inp - l_pad + + torch._check( + l_inp >= l_pad, + lambda: "Length of pad should be no more than twice the number of " + f"dimensions of the input. Pad length is {len(pad)} while the input has " + f"{l_inp} dimensions.", + ) + + c_input = input + for i in range(l_diff, l_inp): + pad_idx = 2 * (l_inp - i - 1) + if pad[pad_idx] < 0: + c_input = c_input.narrow(i, -pad[pad_idx], c_input.shape[i] + pad[pad_idx]) + + if pad[pad_idx + 1] < 0: + c_input = c_input.narrow(i, 0, c_input.shape[i] + pad[pad_idx + 1]) + + # If all the pads are negative we can return the result. + # Avoid early exiting if all pads = 0 to prevent specialization on export. + # During export, raw if statements are specialized on the input, meaning + # that we lose a branch depending on the example input used to export. + # Here, this is either the case where all pads = 0, or the case where at + # least one pad > 0 and the rest are >= 0. + # Avoiding the early exit when all pads = 0 ensures we can export + # constant_pad_nd for cases when all pads >= 0. + # Note: if any pads are negative, this code specializes due to the if statements above. + if builtins.all(p < 0 for p in pad): + return c_input.clone() + + new_shape = list(input_sizes[:l_diff]) + + for i in range(l_pad): + pad_idx = len(pad) - ((i + 1) * 2) + new_dim = input_sizes[l_diff + i] + pad[pad_idx] + pad[pad_idx + 1] + torch._check( + new_dim > 0, + lambda: f"The input size {input_sizes[l_diff + i]}, plus negative padding " + f"{pad[pad_idx]} and {pad[pad_idx + 1]} resulted in a negative output size, " + f"which is invalid. Check dimension {l_diff + i} of your input.", + ) + new_shape.append(new_dim) + + memory_format = utils.suggest_memory_format(input) + output = torch.empty( + new_shape, + dtype=input.dtype, + device=input.device, + requires_grad=input.requires_grad, + memory_format=memory_format, + ) + + if value == 0 and input.dtype == torch.bool: + value = False + # torch.fill isn't typed to allow complex values + output = torch.fill(output, value) # type: ignore[arg-type] + + c_output = output + for i in range(l_diff, l_inp): + pad_idx = 2 * (l_inp - i - 1) + if pad[pad_idx] >= 0: + c_output = c_output.narrow( + i, pad[pad_idx], c_output.shape[i] - pad[pad_idx] + ) + if pad[pad_idx + 1] >= 0: + c_output = c_output.narrow(i, 0, c_output.shape[i] - pad[pad_idx + 1]) + + prims.copy_to(c_output, c_input) + return output + + +def contiguous( + a: Tensor, *, memory_format: torch.memory_format = torch.contiguous_format +) -> Tensor: + torch._check( + memory_format != torch.preserve_format, + lambda: "preserve memory format is unsupported by the contiguous operator", + ) + + if utils.is_contiguous_for_memory_format(a, memory_format=memory_format): + return a + + return torch.clone(a, memory_format=memory_format) + + +@out_wrapper() +def dstack(tensors: TensorSequenceType) -> TensorLikeType: + torch._check(len(tensors) > 0, lambda: "dstack expects a non-empty TensorList") + aligned_tensors = atleast_3d(*tensors) + return cat(aligned_tensors, 2) + + +@register_decomposition(aten.expand) +def expand(a: Tensor, *shape) -> Tensor: + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + # NOTE: cannot use utils.extract_shape_from_varargs here + # because that also validates the shape, but the shape + # given to expand may be "invalid" + if len(shape) == 1 and isinstance(shape[0], Sequence): + shape = tuple(shape[0]) + + torch._check( + len(shape) >= len(a.shape), + lambda: "expand: the requested shape has too few dimensions!", + ) + + offset = len(shape) - len(a.shape) + shape_ = list(shape) + for idx, x in enumerate(a.shape): + offset_idx = idx + offset + requested_length = shape[offset_idx] + torch._check( + guard_size_oblivious(requested_length == x) + or guard_size_oblivious(x == 1) + or requested_length == -1, + lambda: f"expand: attempting to expand a dimension of length {x}!", + ) + + shape_[offset_idx] = requested_length if requested_length != -1 else x + + # At this point shape must be valid + utils.validate_shape(shape_) + + return prims.broadcast_in_dim( + a, shape_, tuple(range(offset, len(a.shape) + offset)) + ) + + +# CompositeImplicitAutograd - don't register decomp +def expand_as(a: Tensor, b: Tensor) -> Tensor: + return a.expand(b.shape) + + +def chunk(a: TensorLikeType, chunks: int, dim: int = 0) -> Tuple[TensorLikeType, ...]: + if chunks <= 0: + msg = f"Expected at least one chunk, but got {chunks}!" + raise ValueError(msg) + + dim = utils.canonicalize_dim(a.ndim, dim) + length = a.shape[dim] + chunk_size = math.ceil(length / chunks) + full_chunks = math.floor(length / chunk_size) + tail_chunk_size = length % chunk_size + + result = [] + for i in range(full_chunks): + result.append(narrow(a, dim, i * chunk_size, chunk_size)) + + if tail_chunk_size != 0: + result.append(narrow(a, dim, full_chunks * chunk_size, tail_chunk_size)) + + return tuple(result) + + +# Note: flatten, unlike other shape operators, returns the input tensor on a no-op (unless +# a 0D tensor is flattened, in which case it's returned in 1D) +# CompositeImplicitAutograd - don't register decomp +def flatten(a: TensorLikeType, start_dim: int = 0, end_dim: int = -1) -> TensorLikeType: + start_dim = utils.canonicalize_dim(a.ndim, start_dim) + end_dim = utils.canonicalize_dim(a.ndim, end_dim) + + # Short-circuits on no-op + if start_dim == end_dim and a.ndim != 0: + return a + + # Tries to take a view + # TODO: we could look at directing collapse_view to skip its meta function here (unsafe_collapse_view) + new_shape, new_strides = prims._collapse_view_helper(a, start_dim, end_dim) + if new_shape is not None: + return prims.collapse_view(a, start_dim, end_dim) + + # Makes a copy if it can't make a view + return prims.collapse(a, start_dim, end_dim) + + +@register_decomposition(aten.flip) +@out_wrapper() +def flip(a: TensorLikeType, dims: DimsSequenceType) -> TensorLikeType: + if not isinstance(dims, tuple) and not isinstance(dims, list): + raise ValueError("dims has to be a sequence of ints") + dims = utils.canonicalize_dims(a.ndim, dims) # type: ignore[assignment] + utils.validate_no_repeating_dims(dims) + return prims.rev(a, dims) + + +# CompositeImplicitAutograd - don't register decomp +def fliplr(a: TensorLikeType) -> TensorLikeType: + if a.ndim < 2: + raise RuntimeError("Input must be >= 2-d.") + + return flip(a, (1,)) + + +# CompositeImplicitAutograd - don't register decomp +def flipud(a: TensorLikeType) -> TensorLikeType: + if a.ndim < 1: + raise RuntimeError("Input must be >= 1-d.") + + return flip(a, (0,)) + + +# CompositeImplicitAutograd - don't register decomp +def narrow( + a: TensorLikeType, dim: int, start: Union[int, TensorLikeType], length: int +) -> TensorLikeType: + # Supports Tensor overload that was added for XLA: + # https://github.com/pytorch/pytorch/issues/31558 + if isinstance(start, TensorLike): + torch._check( + start.dim() == 0 and utils.is_integer_dtype(start.dtype), + lambda: "start must be an 0-dim integral Tensor.", + ) + start = start.item() # type: ignore[assignment] + torch._check(a.dim() > 0, lambda: "narrow() cannot be applied to a 0-dim tensor.") + torch._check(length >= 0, lambda: "narrow(): length must be non-negative.") + dim = utils.canonicalize_dim(a.ndim, dim) + dim_length = a.size(dim) + torch._check_with( + IndexError, + -dim_length <= start and start <= dim_length, # type: ignore[arg-type] + lambda: f"start out of range (expected to be in range of [{-dim_length}, {dim_length}], but got {start})", + ) + if start < 0: + start = start + dim_length + torch._check( + start <= dim_length - length, # type: ignore[arg-type] + lambda: f"start ({start}) + length ({length}) exceeds dimension size ({dim_length}).", + ) + return prims.slice_in_dim(a, start, start + length, axis=dim) + + +def _normalize( + a: Tensor, norm_dims: DimsType, eps: float +) -> Tuple[Tensor, Tensor, Tensor]: + """Computes mean and 1/std of a tensor along norm_dims. + + Used as a helper function for normalization layers. + + Args: + a (Tensor): input tensor + norm_dims (DimsType): dimensions to normalize over + eps (float): epsilon for numerical stability + + Returns: + out (Tensor): normalized tensor. + mean (Tensor): mean of the tensor along norm_dims. + rstd (Tensor): 1/std of the tensor along norm_dims. + """ + norm_dims = utils.canonicalize_dims(a.ndim, norm_dims) + computation_dtype = utils.get_computation_dtype(a.dtype) + a_acc = _maybe_convert_to_dtype(a, computation_dtype) + assert isinstance(a_acc, TensorLike) # to avoid mypy error for var_mean + biased_var, mean = torch.var_mean( + a_acc, dim=norm_dims, unbiased=False, keepdim=True + ) + rstd = torch.rsqrt(biased_var + eps) + out = (a - mean) * rstd + return out, mean, rstd + + +# add all specified dimensions +def _unsqueeze_multiple(x: TensorLikeType, dimensions: List[int]) -> TensorLikeType: + for dim in sorted(dimensions): + x = torch.unsqueeze(x, dim) + return x + + +@register_decomposition(aten.native_group_norm.default) +def native_group_norm( + input: Tensor, + weight: Optional[Tensor], + bias: Optional[Tensor], + batch_size: int, + num_channels: int, + flattened_inner_size: int, + num_groups: int, + eps: float, +) -> Tuple[Tensor, Tensor, Tensor]: + torch._check( + input.ndim >= 2, + lambda: f"Expected at least 2 dimensions for input tensor but received {input.ndim}", + ) + torch._check( + num_channels % num_groups == 0, + lambda: "Expected number of channels in input to be divisible by num_groups, " + + f"but got input of shape {input.shape} and num_groups = {num_groups}", + ) + + # num_channels / num_groups and flattened inner dimension are the reduction axes + reduction_dims = [2, 3] + input_reshaped = torch.reshape( + input, + [batch_size, num_groups, num_channels // num_groups, flattened_inner_size], + ) + out, mean, rstd = _normalize(input_reshaped, reduction_dims, eps) + out = out.view(input.shape) + + broadcast_dims = [0] + list(range(2, input.ndim)) + unsqueeze_bias = None + if bias is not None: + unsqueeze_bias = _unsqueeze_multiple(bias, broadcast_dims) + unsqueeze_weight = None + if weight is not None: + unsqueeze_weight = _unsqueeze_multiple(weight, broadcast_dims) + + if unsqueeze_weight is not None: + out = out * unsqueeze_weight + if unsqueeze_bias is not None: + out = out + unsqueeze_bias + + out = _maybe_convert_to_dtype(out, input.dtype) # type: ignore[assignment] + mean = _maybe_convert_to_dtype(mean, input.dtype) # type: ignore[assignment] + rstd = _maybe_convert_to_dtype(rstd, input.dtype) # type: ignore[assignment] + + # remove broadcast dimensions from mean and rstd + mean = torch.squeeze(mean, reduction_dims) + rstd = torch.squeeze(rstd, reduction_dims) + return (out, mean, rstd) + + +@register_decomposition(aten.native_layer_norm) +@out_wrapper("out0", "out1", "out2") +def native_layer_norm( + input: Tensor, + normalized_shape: ShapeType, + weight: Optional[Tensor], + bias: Optional[Tensor], + eps: float, +) -> Tuple[Tensor, Tensor, Tensor]: + normalized_ndim = len(normalized_shape) + torch._check( + normalized_ndim >= 1, + lambda: "Expected normalized_shape to be at least 1-dimensional, i.e., " + + "containing at least one element, but got normalized_shape = " + + str(normalized_shape), + ) + # torch.Size([1, 2, 3]) == [1, 2, 3] evaluates to False + # while torch.Size([1, 2, 3]) == (1, 2, 3) is True + # therefore we use tuple(normalized_shape) + torch._check( + weight is None or weight.shape == tuple(normalized_shape), + lambda: "Expected weight to be of same shape as normalized_shape, but got " + + "weight of shape " + + str(weight.shape) # type: ignore[union-attr] + + " and normalized_shape = " + + str(normalized_shape), + ) + torch._check( + bias is None or bias.shape == tuple(normalized_shape), + lambda: "Expected bias to be of same shape as normalized_shape, but got " + + "bias of shape " + + str(bias.shape) # type: ignore[union-attr] + + " and normalized_shape = " + + str(normalized_shape), + ) + torch._check( + input.ndim >= normalized_ndim + and input.shape[(input.ndim - normalized_ndim) :] == tuple(normalized_shape), + lambda: "Given normalized_shape=" + + str(normalized_shape) + + ", expected input with shape " + + str(normalized_shape) + + ", but got input of size " + + str(input.shape), + ) + + input = input.contiguous() + if weight is not None: + weight = weight.contiguous() + if bias is not None: + bias = bias.contiguous() + + axis = input.ndim - normalized_ndim + reduction_dims = list(range(axis, input.ndim)) + out, mean, rstd = _normalize(input, reduction_dims, eps) + + if weight is None and bias is not None: + out = out + bias + elif weight is not None and bias is None: + out = out * weight + elif weight is not None and bias is not None: + out = out * weight + bias + + out = _maybe_convert_to_dtype(out, input.dtype) # type: ignore[assignment] + if input.device.type == "cpu": + mean = _maybe_convert_to_dtype(mean, input.dtype) # type: ignore[assignment] + rstd = _maybe_convert_to_dtype(rstd, input.dtype) # type: ignore[assignment] + return (out, mean, rstd) + + +# TODO: Adding this as a meta function causes functorch tests to fail when compiled with debug mode. +# test/test_eager_transforms.py::TestFunctionalizeCPU::test_functionalize_fx_transpose_simple_cpu +@register_decomposition(aten.permute) +def permute(a: TensorLikeType, *dims) -> TensorLikeType: + _permutation = utils.canonicalize_dims( + a.ndim, utils.extract_dims_from_varargs(dims) + ) + return prims.transpose(a, _permutation) + + +@register_decomposition(aten.renorm) +@out_wrapper() +def renorm( + input: TensorLikeType, p: RealNumberType, dim: int, maxnorm: RealNumberType +) -> TensorLikeType: + torch._check(not isinstance(p, complex), lambda: "renorm: p must be real-valued") + torch._check(p > 0, lambda: "renorm: non-positive norm not supported") + torch._check( + not isinstance(maxnorm, complex), lambda: "renorm: maxnorm must be real-valued" + ) + torch._check( + maxnorm >= 0, lambda: f"renorm: expected maxnorm to be >= 0 but got {maxnorm}" + ) + ndim = input.ndim + torch._check( + ndim > 1, + lambda: f"renorm: input needs at least 2 dimensions, got {ndim} dimensions", + ) + + dim = utils.canonicalize_dim(ndim, dim) + reduce_dims = list(range(ndim)) + del reduce_dims[dim] + + # For half and bfloat16, calculate norm in float precision then cast + # normalization factor to half + acc_type = utils.get_computation_dtype(input.dtype) + if acc_type != input.dtype: + norm = torch.linalg.vector_norm( + input, p, reduce_dims, keepdim=True, dtype=acc_type + ) + else: + norm = torch.linalg.vector_norm(input, p, reduce_dims, keepdim=True) + + eps = 1e-7 + norm_factor = torch.where(norm > maxnorm, maxnorm / (norm + eps), 1.0) + if acc_type != input.dtype: + norm_factor = prims.convert_element_type(norm_factor, input.dtype) + return (input * norm_factor).contiguous() + + +# CompositeImplicitAutograd - don't register decomp +@aten.stft.center.py_impl(DispatchKey.CompositeImplicitAutograd) +def stft( + input: Tensor, + n_fft: int, + hop_length: Optional[int] = None, + win_length: Optional[int] = None, + window: Optional[Tensor] = None, + center: bool = True, + pad_mode: str = "reflect", + normalized: bool = False, + onesided: Optional[bool] = None, + return_complex: Optional[bool] = None, +) -> Tensor: + torch._check( + window is None or window.device == input.device, + lambda: ( + f"stft input and window must be on the same device but got self on {input.device}" + + f" and window on {window.device}" # type: ignore[union-attr] + ), + ) + + hop_length_ = hop_length if hop_length is not None else n_fft // 4 + win_length_ = win_length if win_length is not None else n_fft + + if return_complex is None: + return_complex_ = input.is_complex() or ( + window is not None and utils.is_complex_dtype(window.dtype) + ) + torch._check( + return_complex_, + ( + "stft requires the return_complex parameter be given for real inputs, " + + "and will further require that return_complex=True in a future PyTorch release." + ), + ) + else: + return_complex_ = return_complex + + torch._check( + utils.is_float_dtype(input.dtype) or utils.is_complex_dtype(input.dtype), + lambda: "stft expected a tensor of floating point or complex values", + ) + torch._check(1 <= input.ndim <= 2, lambda: "stft expected a 1D or 2D tensor") + + original_ndim = input.ndim + if original_ndim == 1: + input = input.unsqueeze(0) + + if center: + extra_dims = 3 - input.ndim + pad_amount = n_fft // 2 + extended_shape = [*itertools.repeat(1, extra_dims), *input.shape] + input = aten.pad(input.view(extended_shape), [pad_amount, pad_amount], pad_mode) + input = input.view(input.size()[extra_dims:]) + + batch = input.size(0) + length = input.size(1) + torch._check( + 0 < n_fft <= length, + lambda: f"stft expected 0 < n_fft <= {length}, but got n_fft={n_fft}", + ) + torch._check( + hop_length_ > 0, + lambda: f"stft expected hop_length > 0 but got hop_length={hop_length_}", + ) + torch._check( + 0 < win_length_ <= n_fft, + lambda: f"stft expected 0 < win_length <= n_fft but got win_length={win_length_}", + ) + torch._check( + window is None or window.shape == (win_length_,), + lambda: ( + f"expected a 1D window tensor of size equal to win_length={win_length_}, " + + f"but got window with size {window.shape}" # type: ignore[union-attr] + ), + ) + + if win_length_ < n_fft: + if window is None: + window = torch.ones(win_length_, dtype=input.dtype, device=input.device) + left = (n_fft - win_length_) // 2 + window = aten.constant_pad_nd(window, [left, n_fft - win_length_ - left]) + + input = input.unfold(dimension=-1, size=n_fft, step=hop_length_) + if window is not None: + input = input * window + + complex_fft = utils.is_complex_dtype(input.dtype) + onesided = onesided if onesided is not None else not complex_fft + norm = "ortho" if normalized else None + if onesided: + torch._check( + not complex_fft, + lambda: "Cannot have onesided output if window or input is complex", + ) + out = torch.fft.rfft(input, dim=-1, norm=norm) + else: + out = torch.fft.fft(input, dim=-1, norm=norm) + + out.transpose_(1, 2) + + if original_ndim == 1: + out = out.squeeze_(0) + + return out if return_complex_ else torch.view_as_real(out) + + +# CompositeImplicitAutograd - don't register decomp +@aten.istft.default.py_impl(DispatchKey.CompositeImplicitAutograd) +def istft( + input: Tensor, + n_fft: int, + hop_length: Optional[int] = None, + win_length: Optional[int] = None, + window: Optional[Tensor] = None, + center: bool = True, + normalized: bool = False, + onesided: Optional[bool] = None, + length: Optional[int] = None, + return_complex=False, +) -> Tensor: + torch._check( + window is None or window.device == input.device, + lambda: ( + f"istft input and window must be on the same device but got self on {input.device}" + + f" and window on {window.device}" # type: ignore[union-attr] + ), + ) + + hop_length_ = hop_length if hop_length is not None else n_fft // 4 + win_length_ = win_length if win_length is not None else n_fft + + torch._check( + utils.is_complex_dtype(input.dtype), + lambda: ( + "istft input and window must be on the same device but got self on " + + f"{input.device} and window on {window.device}" # type: ignore[union-attr] + ), + ) + n_frames = input.size(-1) + fft_size = input.size(-2) + + expected_output_signal_len = n_fft + hop_length_ * (n_frames - 1) + torch._check(input.numel() > 0, lambda: "istft input tensor cannot be empty") + torch._check( + 2 <= input.ndim <= 3, + lambda: f"istft expected a tensor with 2 or 3 dimensions, but got {input.ndim}", + ) + onesided_ = onesided if onesided is not None else fft_size != n_fft + + if onesided_: + torch._check( + n_fft // 2 + 1 == fft_size, + lambda: ( + "istft expected the frequency dimension (3rd to the last) of the input tensor " + + "to match n_fft / 2 + 1 when onesided=True, but got {fft_size}" + ), + ) + else: + torch._check( + n_fft == fft_size, + lambda: ( + "istft expected the frequency dimension (3rd to the last) of the input tensor " + + "to match n_fft when onesided=False, but got {fft_size}", + ), + ) + + torch._check( + 0 < hop_length_ <= win_length_, + lambda: "istft expected 0 < hop_length <= win_length", + ) + torch._check( + 0 < win_length_ <= n_fft, lambda: "istft expected 0 < win_length <= n_fft" + ) + torch._check( + window is None or window.shape == (win_length_,), + lambda: "Invalid window shape. window has to be 1D and length of `win_length`", + ) + + if window is None: + real_dtype = utils.corresponding_real_dtype(input.dtype) + window_ = torch.ones(win_length_, dtype=real_dtype, device=input.device) + else: + window_ = window + + if win_length_ != n_fft: + left = (n_fft - win_length_) // 2 + window_ = aten.constant_pad_nd(window_, (left, n_fft - win_length_ - left), 0) + + original_ndim = input.ndim + if input.ndim == 2: + input = input.unsqueeze(0) + + input = input.transpose(1, 2) + norm = "ortho" if normalized else None + if return_complex: + torch._check( + not onesided_, + lambda: "cannot have onesided output if window or input is complex", + ) + input = torch.fft.ifft(input, dim=-1, norm=norm) + else: + torch._check( + window is None or not utils.is_complex_dtype(window.dtype), + lambda: "Complex windows are incompatible with return_complex=False", + ) + if not onesided_: + input = input.narrow(dim=-1, start=0, length=n_fft // 2 + 1) + input = torch.fft.irfft(input, dim=-1, norm=norm) + + assert input.size(2) == n_fft + + y_tmp = input * window_.view([1, 1, n_fft]) + y = aten.unfold_backward( + y_tmp, + input_sizes=(y_tmp.size(0), expected_output_signal_len), + dim=1, + size=n_fft, + step=hop_length_, + ) + window_envelop = aten.unfold_backward( + window_.pow(2).expand((1, n_frames, n_fft)), + input_sizes=(y_tmp.size(0), expected_output_signal_len), + dim=1, + size=n_fft, + step=hop_length_, + ) + + assert expected_output_signal_len == y.size(1) + assert expected_output_signal_len == window_envelop.size(1) + + start = n_fft // 2 if center else 0 + if length is not None: + end = start + length + elif center: + end = expected_output_signal_len - n_fft // 2 + else: + end = expected_output_signal_len + + length = max(0, end - start) + y = y.narrow(dim=1, start=start, length=length) + window_envelop = window_envelop.narrow(dim=1, start=start, length=length) + + y = y / window_envelop + if original_ndim == 2: + y = y.squeeze(0) + + if end > expected_output_signal_len: + warnings.warn( + "The length of signal is shorter than the length parameter. Result is being " + + "padded with zeros in the tail. Please check your center and hop_length settings" + ) + y = aten.constant_pad_nd(y, (0, end - expected_output_signal_len), 0) + return y + + +# Get the new shape and stride after applying unfold to an input tensor +def _get_unfold_shape_stride( + a_shape: ShapeType, a_stride: StrideType, dimension: int, size: int, step: int +): + a_ndim = len(a_shape) + dim = utils.canonicalize_dim(a_ndim, dimension, wrap_scalar=True) + max_size = 1 if a_ndim == 0 else a_shape[dim] + last_stride = 1 if a_ndim == 0 else a_stride[dim] + + torch._check( + size <= max_size, + lambda: f"Maximum size for tensor at dimension {dim} is {max_size} but size is {size}", + ) + + torch._check( + step > 0, + lambda: f"Step is {step} but must be > 0", + ) + + shape = list(a_shape) + strides = list(a_stride) + shape.append(size) + strides.append(last_stride) + if dim < a_ndim: + shape[dim] = (shape[dim] - size) // step + 1 + strides[dim] *= step + return shape, strides + + +@register_decomposition(aten.repeat) +@out_wrapper() +def repeat(a: Tensor, *repeat_shape) -> Tensor: + repeat_shape = utils.extract_shape_from_varargs(repeat_shape, validate=False) + torch._check( + len(repeat_shape) >= len(a.shape), + lambda: "repeat: Number of dimensions of repeat dims can not be smaller than number of dimensions of tensor", + ) + + if len(repeat_shape) == 0: + return torch.clone(a) + + num_new_dimensions = len(repeat_shape) - a.ndim + padded_shape = [1] * num_new_dimensions + for dim_size in a.shape: + padded_shape.append(dim_size) + + target_shape = tuple( + padded_size * repeat_size + for padded_size, repeat_size in zip(padded_shape, repeat_shape) + ) + + # return an empty tensor if one of the repeat_shape dimensions is zero + if 0 in repeat_shape: + return torch.empty( + target_shape, + dtype=a.dtype, + device=a.device, + requires_grad=a.requires_grad, + memory_format=utils.suggest_memory_format(a), + ) + + urtensor_shape = target_shape + urtensor_stride = utils.make_contiguous_strides_for(target_shape) + for dim, dim_size in enumerate(padded_shape): + # repeat each dimension by using unfold_copy operation + urtensor_shape, urtensor_stride = _get_unfold_shape_stride( + urtensor_shape, urtensor_stride, dim, dim_size, max(dim_size, 1) + ) + + # derive permute order by sorting urtensor strides + enumerated_stride = list(enumerate(urtensor_stride)) + enumerated_stride.sort(key=operator.itemgetter(1), reverse=True) + permute_order, sorted_stride = zip(*enumerated_stride) + + # add new and expand dimensions according to urtensor + repeat_xtensor = a.expand(urtensor_shape) + + # clone tensor to concretize expanded dimensions + cloned_result = torch.clone(repeat_xtensor) + + # transpose axis so strides are in sorted order + permuted_result = cloned_result.permute(permute_order) + + # reshape to get contiguous tensor with correct target shape + return permuted_result.reshape(target_shape) + + +def _reshape_view_helper(a: TensorLikeType, *shape, allow_copy: bool) -> TensorLikeType: + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious, sym_eq + + # Creates a valid shape + shape = utils.extract_shape_from_varargs(shape, validate=False) + # Reshape may be given a shape with a -1 length + # This indicates that the dimension's length should be inferred + shape = utils.infer_size(shape, a.numel()) + + # Special-cases tensors with no elements + if guard_size_oblivious(a.numel() == 0): + return as_strided(a, shape, utils.make_contiguous_strides_for(shape)) + + # Special-cases reshaping zero dim tensors + if a.ndim == 0: + _a = a + for length in shape: + assert length == 1 + _a = unsqueeze(_a, -1) + if _a is a: + return prims.view_of(a) + else: + return _a + + # Special-cases reshaping to zero dim tensors + if len(shape) == 0: + _a = a + for length in a.shape: + assert length == 1 + _a = squeeze(_a, -1) + if _a is a: + return prims.view_of(a) + else: + return _a + + if a.is_contiguous(): + # Special-cases for nd_to_1d + if len(shape) == 1 and a.ndim > 1: + return torch.as_strided(a, [a.numel()], [1]) + # Special-cases for 1d_to_2d + if len(shape) == 2 and a.ndim == 1: + dim0 = shape[0] + dim1 = shape[1] + return torch.as_strided(a, [dim0, dim1], [dim1, 1]) + + # Handles general case: a 1+D tensor reshaped into a distinct 1+D shape + + # NOTE [Reshape Algorithm] + # This algorithm works by attempting to greedily construct the desired dimensions in + # the output shape, left to right. It does this by, conceptually, accumulating + # dimensions of the original tensor, also left to right, until the dimension + # can be constructed using prims.split_dim. + # The algorithm also has special handling for tail squeezes/unsqueezes, like + # if a reshape from (5, 5) to (5, 5, 1) or vice versa. + # + # This algorithm does not flatten the original tensor and then split dims as appropriate + # because that would create copies more often than this algorithm. flatten is the only + # operation below which can create a view or a copy, and while it prefers creating + # views it may sometimes create a copy if the tensor's strides do not permit a view. + # As a result, this algorithm tries to minimize flattening. + # + # Note that a better version of this algorithm may exist. Regions which could be + # flattened without creating a copy can be identified in advance, and that might + # allow fewer flatten calls or faster short-circuiting to make a copy. + idx = 0 + a_ = a + for length in shape: + # Handles tail unsqueezes + if idx >= a_.ndim: + assert length == 1 + last_dim = a_.ndim - 1 + # NOTE: using split_dim instead of unsqueeze may seem silly here, + # but it's necessary to get the strides correct + a_ = prims.split_dim(a_, last_dim, a_.shape[last_dim]) + idx = idx + 1 + continue + + # Skips dimensions that are already the correct length + if guard_size_oblivious(length == a_.shape[idx]): + idx = idx + 1 + continue + + # Gathers enough original dimensions such that this new dimension can be created + # Note that this accumulation will terminate because we've verified a and the shape + # specify the same number of elements above + accum = a_.shape[idx] + end = idx + while guard_size_oblivious(accum % length != 0): + end = end + 1 + accum = accum * a_.shape[end] + if end != idx: + # NOTE: in this case multiple dimensions must be flatten to create the desired dimension + # This flattening is why reshape sometimes creates a copy -- because flattening + # may return a view of a copy + + # Checks if collapse can be a view and short-circuits to copying reshape if it can't + new_shape, new_strides = prims._collapse_view_helper(a_, idx, end) + if new_shape is None: + if allow_copy: + return prims.reshape(a, shape) + + msg = f"Cannot view a tensor with shape {a.shape} and strides {a.stride()} as a tensor with shape {shape}!" + raise ValueError(msg) + + a_ = flatten(a_, idx, end) + + # Splits the (possibly flattened) dimension to create the desired dim length + if guard_size_oblivious(accum != length): + a_ = prims.split_dim(a_, idx, length) + + idx = idx + 1 + + # Squeezes tail + while idx < a_.ndim: + torch._check( + a_.shape[idx] == 1, + lambda: f"a.size({idx}) expected to be 1 but got {a_.shape[idx]}", + ) + a_ = squeeze(a_, idx) + + if a_ is a: + return prims.view_of(a) + else: + return a_ + + +# CompositeImplicitAutograd - don't register decomp +# NOTE: shape is a vararg because Tensor.reshape can be called with as +# Tensor.reshape(a, b, c) or Tensor.reshape((a, b, c)) Function call +# torch.reshape doesn't support unpacked shapes +def reshape(a: TensorLikeType, *shape: ShapeType) -> TensorLikeType: + return _reshape_view_helper(a, *shape, allow_copy=True) + + +# CompositeImplicitAutograd - don't register decomp +def reshape_as(self: TensorLikeType, other: TensorLikeType) -> TensorLikeType: + return self.reshape(other.size()) + + +@register_decomposition(aten.roll) +@out_wrapper() +def roll(a: TensorLikeType, shifts: DimsType, dims: DimsType = ()) -> TensorLikeType: + """Reference implementation of :func:`torch.roll`.""" + dims = utils.canonicalize_dims(a.ndim, dims) + # ATen specifies int[1] type for shifts and dims which expands integers to tuples of length 1 + if not isinstance(shifts, Iterable): + shifts = (shifts,) + if not isinstance(dims, Iterable): + dims = (dims,) + + # Avoid modulo by zero + if a.numel() == 0: + # Keeping this as ref for now as FakeTensor runs into some issues with complex tensors + return a.clone() + + if a.dim() == 0 and len(dims) > 0: + raise IndexError( + f"Dimension specified as {dims[0]} but tensor has no dimensions" + ) + + len_shifts = len(shifts) + len_dims = len(dims) + if len_shifts != 1 or len_dims != 1: + if len_shifts == 0: + raise RuntimeError("`shifts` required") + # Takes care of the case when dims is not specified (default) + # By default, the tensor is flattened before shifting, after which the original shape is restored + if len_dims == 0 and len_shifts == 1: + return torch.roll(torch.flatten(a), shifts, 0).view(a.shape) + if len_shifts != len_dims: + raise RuntimeError( + f"shifts and dimensions must align. shifts: {len_shifts}, dims: {len_dims}" + ) + assert len_dims > 1 + tail_shifts = shifts[1:] + tail_dims = dims[1:] + first_dim_rolled = torch.roll(a, (shifts[0],), dims[0]) + return torch.roll(first_dim_rolled, tail_shifts, tail_dims) + + # This path is taken when only one dimension is rolled + # For example to get `first_dim_rolled` above + dim = dims[0] + size = a.shape[dim] + start = (size - shifts[0]) % size + idx = torch.arange(size, device=a.device) + return a.index_select(dim, torch.fmod(start + idx, size)) + + +@register_decomposition(aten.rot90) +@out_wrapper() +def rot90( + a: TensorLikeType, k: int = 1, dims: DimsSequenceType = (0, 1) +) -> TensorLikeType: + """Reference implementation of :func:`torch.rot90`.""" + if len(dims) != 2: + raise RuntimeError( + f"expected total rotation dims == 2, but got dims = {len(dims)}" + ) + if a.ndim < 2: + raise RuntimeError(f"expected total dims >= 2, but got total dims = {a.ndim}") + + # Do this after the initial checks to be compatible with the behavior in + # core. + dims = utils.canonicalize_dims(a.ndim, dims) + + if dims[0] == dims[1]: + raise RuntimeError( + f"expected rotation dims to be different, but got dim0 = {dims[0]} and dim1 = {dims[1]}" + ) + k = k % 4 # Rotation direction is from the second towards the first axis for k < 0 + if k == 1: + return torch.transpose(torch.flip(a, (dims[1],)), dims[0], dims[1]) + elif k == 2: + return torch.flip(a, dims) + elif k == 3: + return torch.transpose(torch.flip(a, (dims[0],)), dims[0], dims[1]) + else: + return a.clone(memory_format=torch.contiguous_format) + + +def _check_stack_inputs(tensors: TensorSequenceType) -> None: + entry_shape = tensors[0].shape + for i in range(1, len(tensors)): + assert tensors[i].shape == entry_shape, ( + f"stack expects each tensor to be equal size, but got {entry_shape} at entry 0 " + f"and {tensors[i].shape} at entry {i}" + ) + + +@register_decomposition(aten.stack) +@out_wrapper() +def stack(tensors: TensorSequenceType, dim: int = 0) -> TensorLikeType: + assert len(tensors) > 0, "stack expects a non-empty TensorList" + wrapped_dim = utils.canonicalize_dim(tensors[0].ndim + 1, dim) + # Refs need sparse support to check other condition + if wrapped_dim < tensors[0].ndim: # and not tensors[0].is_sparse: + _check_stack_inputs(tensors) + result_sizes = list(tensors[0].shape) + result_sizes.insert(wrapped_dim, len(tensors)) + out = torch.cat(tensors, wrapped_dim) + return out.view(result_sizes) + + # If dim == tensors[0].ndim, view cannot efficiently handle it + return torch.cat([t.unsqueeze(wrapped_dim) for t in tensors], dim) + + +# CompositeImplicitAutograd - don't register decomp +@out_wrapper() +def softmax( + a: TensorLikeType, + dim: int, + dtype: Optional[torch.dtype] = None, +) -> TensorLikeType: + result_dtype = dtype or a.dtype + computation_dtype = utils.get_computation_dtype(result_dtype) + a_ = _maybe_convert_to_dtype(a, computation_dtype) + if a.numel() == 0: + a_exp = exp(a_) + else: + a_max = amax(a_, dim, keepdim=True) + a_exp = exp(a_ - a_max) + return _maybe_convert_to_dtype( + true_divide(a_exp, sum(a_exp, dim, keepdim=True)), result_dtype + ) # type: ignore[return-value] + + +# CompositeImplicitAutograd - don't register decomp +@out_wrapper() +def hstack(tensors: TensorSequenceType) -> TensorLikeType: + torch._check(len(tensors) > 0, lambda: "hstack expects a non-empty TensorList") + aligned_tensors = atleast_1d(*tensors) + if aligned_tensors[0].ndim == 1: + return cat(aligned_tensors, 0) + return cat(aligned_tensors, 1) + + +# CompositeImplicitAutograd - don't register decomp +@out_wrapper() +def vstack(tensors: TensorSequenceType) -> TensorLikeType: + torch._check(len(tensors) > 0, lambda: "vstack expects a non-empty TensorList") + aligned_tensors = atleast_2d(*tensors) + return cat(aligned_tensors, 0) + + +# CompositeImplicitAutograd - don't register decomp +def unflatten(a: TensorLikeType, dim: int, sizes: ShapeType) -> TensorLikeType: + dim = utils.canonicalize_dim(a.ndim, dim) + torch._check(len(sizes) != 0, lambda: "unflatten: sizes must be non-empty") + return a.view(tuple(a.shape[:dim]) + tuple(sizes) + tuple(a.shape[dim + 1 :])) + + +@register_decomposition(aten.unbind) +def unbind(t: TensorLikeType, dim: int = 0) -> TensorSequenceType: + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + dim = utils.canonicalize_dim(t.ndim, dim) + torch._check_index( + len(t.shape) > 0, + lambda: "Dimension specified as 0 but tensor has no dimensions", + ) + if guard_size_oblivious(t.shape[dim] == 0): + return () + else: + return tuple( + torch.squeeze(s, dim) for s in torch.tensor_split(t, t.shape[dim], dim) + ) + + +@out_wrapper() +def index_copy(x: TensorLike, dim: int, index: TensorLike, tensor: TensorLike): + return x.clone(memory_format=torch.contiguous_format).index_copy_( + dim, index, tensor + ) + + +def index_copy_(x: TensorLike, dim: int, index: TensorLike, tensor: TensorLike): + dim = utils.canonicalize_dims(x.ndim, dim) + torch._check( + index.ndim <= 1, + lambda: f"Index should have dimension 1 or 0 (got {index.ndim})", + ) + # Treat scalars as elements of \R^1 + y = x.unsqueeze(0) if x.ndim == 0 else x + idx = (slice(None),) * dim + (index,) + y[idx] = tensor + return x + + +@register_decomposition(aten.index_fill) +@out_wrapper() +def index_fill( + x: TensorLike, dim: int, index: TensorLike, value: Union[NumberType, TensorLike] +): + return _index_fill(x, dim, index, value, inplace=False) + + +@register_decomposition(aten.index_fill_) +def index_fill_( + x: TensorLike, dim: int, index: TensorLike, value: Union[NumberType, TensorLike] +): + return _index_fill(x, dim, index, value, inplace=True) + + +def _index_fill( + x: TensorLike, + dim: int, + index: TensorLike, + value: Union[NumberType, TensorLike], + *, + inplace: bool, +): + torch._check( + index.ndim <= 1, + lambda: f"Index should have dimension 1 or 0 (got {index.ndim})", + ) + if isinstance(value, TensorLike): + torch._check( + value.ndim == 0, + lambda: "Only supports 0-dimensional value tensor. " # type: ignore[union-attr] + f"Got a tensor with {value.ndim} dimensions.", + ) # type: ignore[arg-type] + else: + value = torch.scalar_tensor( + value, dtype=x.dtype, layout=x.layout, device=x.device # type: ignore[arg-type] + ) + + # index_copy has some unnecessary preconditions when x is a scalar. We do this to work through them + zero_dim = x.ndim == 0 + y = x.unsqueeze(0) if zero_dim else x + # index_copy does not broadcast on value so we have to do it manually + shape = list(y.shape) + shape[dim] = index.numel() + value = value.expand(shape) + index_copy = Tensor.index_copy_ if inplace else torch.index_copy + out = index_copy(y, dim, index, value) # type: ignore[operator] + if inplace: + return x + else: + if zero_dim: + # The clone is necessary so that it returns a fresh tensor rather than a view + out = out.squeeze(0).clone() + # index_fill preserves the strides. index_copy always returns contiguous tensors + if out.stride() != x.stride(): + new_out = torch.empty_like(x) + new_out.copy_(out) + out = new_out + return out + + +@out_wrapper() +def index_add( + x: TensorLike, + dim: int, + index: TensorLike, + tensor: TensorLike, + *, + alpha: NumberType = 1, +): + # index_add always returns a new contiguous tensor + return x.clone(memory_format=torch.contiguous_format).index_add_( + dim, index, tensor, alpha=alpha # type: ignore[arg-type] + ) + + +@register_decomposition(aten.index_select) +@out_wrapper() +def index_select(x: TensorLike, dim: int, index: TensorLike): + dim = utils.canonicalize_dims(x.ndim, dim) + torch._check( + index.ndim <= 1, + lambda: f"Index should have dimension 1 or 0 (got {index.ndim})", + ) + if index.ndim == 0: + index = index.unsqueeze(0) + if x.ndim == 0: + # Treat scalars as elements of \R^1 + # We cannot use x[idx] here as it accesses item() (??), hence this awkward construction + return torch.empty_like(x).index_copy(0, index, x.expand_as(index)) + + idx = (slice(None),) * dim + (index,) + return x[idx] + + +@register_decomposition(aten.squeeze.dims) +def squeeze(a: TensorLikeType, dim: Optional[DimsType] = None) -> TensorLikeType: + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + if dim is None: + dims = tuple(idx for idx, size in enumerate(a.shape) if size == 1) + return prims.squeeze(a, dims) if dims else prims.view_of(a) + + ndim = a.ndim + dim = utils.canonicalize_dims(ndim, dim) + dims = (dim,) if isinstance(dim, Dim) else dim + # Short-circuits if the tensor has no dimensions + if ndim == 0: + assert len(dims) == 0 or dims == (0,) + return prims.view_of(a) + + # Note: squeeze does not modify tensors when the given dim is not a dimension of length 1 + dims = tuple(d for d in dims if guard_size_oblivious(a.shape[d] == 1)) + if len(dims) == 0: + return prims.view_of(a) + if len(dims) == 1: + return prims.squeeze(a, dims) + dims_list = list(dims) + dims_list = sorted(dims_list, reverse=True) + for i in dims_list: + a = squeeze(a, i) + return a + + +# Note: does not work with TensorMetas because of data-dependent control-flow +# CompositeImplicitAutograd - don't register decomp +def tensor_split( + a: TensorLikeType, + indices_or_sections: Union[Tensor, DimsType], + dim: int = 0, +) -> Tuple[TensorLikeType, ...]: + _dim = utils.canonicalize_dim(a.ndim, dim) + if a.ndim == 0: + msg = "tensor_split: received a rank zero tensor, but expected a tensor of rank one or greater!" + raise ValueError(msg) + + # If indices_or_sections is a tensor, it must be a CPU Long tensor + if isinstance(indices_or_sections, TensorLike): + if not indices_or_sections.device.type == "cpu": + msg = ( + f"tensor_split: if indices_or_sections is a tensor it must be on the CPU, " + f"but received one on {indices_or_sections.device}" + ) + raise ValueError(msg) + if indices_or_sections.dtype != torch.long: + msg = "tensor_split: if indices_or_sections is a tensor it must have long dtype, " + f" but received one with dtype {indices_or_sections.dtype}" + raise ValueError(msg) + + # Case 0 -- indices_or_sections is an integer or a scalar tensor n and a is split along dim into n parts of equal-ish length + if isinstance(indices_or_sections, IntLike) or ( + isinstance(indices_or_sections, TensorLike) and indices_or_sections.ndim == 0 + ): + sections: int = ( + indices_or_sections # type: ignore[assignment] + if isinstance(indices_or_sections, Number) + else indices_or_sections.item() + ) + + if sections <= 0: + msg = f"tensor_split: number of sections must be greater than 0, but was {sections}" + raise ValueError(msg) + + splits = [] + dim_size = a.shape[_dim] + min_split_size = math.floor(dim_size / sections) + num_splits_one_extra = dim_size % sections + start_idx = 0 + for split_idx in range(sections): + split_size = ( + min_split_size + 1 + if (split_idx < num_splits_one_extra) + else min_split_size + ) + s = prims.slice_in_dim(a, start_idx, start_idx + split_size, axis=_dim) + splits.append(s) + start_idx = start_idx + split_size + + return tuple(splits) + # Case 1 -- indices_or_sections is a sequence of integers or a 1D tensor describing the splits + else: + indices = indices_or_sections + if isinstance(indices_or_sections, TensorLike): + if indices_or_sections.ndim != 1: + msg = "tensor_split: non-scalar indices_or_sections tensors must have only one dimension, " + f"but received a tensor with {indices_or_sections.ndim} dimensions" + raise ValueError(msg) + + indices = indices_or_sections.tolist() + + splits = [] + start_idx = 0 + for x in indices: + splits.append(prims.slice_in_dim(a, start_idx, x, axis=_dim)) + start_idx = x + splits.append(prims.slice_in_dim(a, start_idx, a.shape[_dim], axis=_dim)) + return tuple(splits) + + +# CompositeImplicitAutograd - don't register decomp +def hsplit( + a: TensorLikeType, indices_or_sections: DimsType +) -> Tuple[TensorLikeType, ...]: + torch._check( + a.ndim >= 1, + lambda: ( + "torch.hsplit requires a tensor with at least 1 dimension, but got a tensor with " + + str(a.ndim) + + " dimensions!" + ), + ) + dim = 0 if a.ndim == 1 else 1 + if isinstance(indices_or_sections, IntLike): + split_size = indices_or_sections + torch._check( + (split_size != 0 and a.shape[dim] % split_size == 0), + lambda: ( + "torch.hsplit attempted to split along dimension " + + str(dim) + + ", but the size of the dimension " + + str(a.shape[dim]) + + " is not divisible by the split_size " + + str(split_size) + + "!" + ), + ) + return tensor_split(a, split_size, dim) + + torch._check_type( + isinstance(indices_or_sections, (list, tuple)), + lambda: ( + "hsplit(): received an invalid combination of arguments. " + "Expected indices_or_sections to be of type int, list of ints or tuple of ints " + f"but got type {type(indices_or_sections)}" + ), + ) + + split_sizes = indices_or_sections + return tensor_split(a, split_sizes, dim) + + +# CompositeImplicitAutograd - don't register decomp +def vsplit( + a: TensorLikeType, indices_or_sections: DimsType +) -> Tuple[TensorLikeType, ...]: + torch._check( + a.ndim >= 2, + lambda: ( + "torch.vsplit requires a tensor with at least 2 dimension, but got a tensor with " + + str(a.ndim) + + " dimensions!" + ), + ) + if isinstance(indices_or_sections, IntLike): + split_size = indices_or_sections + torch._check( + (split_size != 0 and a.shape[0] % split_size == 0), + lambda: ( + f"torch.vsplit attempted to split along dimension 0" + f", but the size of the dimension " + f"{a.shape[0]}" + f" is not divisible by the split_size " + f"{split_size}" + f"!" + ), + ) + return tensor_split(a, split_size, 0) + + torch._check_type( + isinstance(indices_or_sections, (list, tuple)), + lambda: ( + "vsplit(): received an invalid combination of arguments. " + "Expected indices_or_sections to be of type int, list of ints or tuple of ints " + f"but got type {type(indices_or_sections)}" + ), + ) + + split_sizes = indices_or_sections + return tensor_split(a, split_sizes, 0) + + +@register_decomposition(aten.diag.out) +@out_wrapper() +def diag( + self: TensorLikeType, + offset: int = 0, +) -> TensorLikeType: + ndim = self.dim() + torch._check( + ndim in (1, 2), lambda: f"diag(): Supports 1D or 2D tensors. Got {ndim}D" + ) + if ndim == 1: + return torch.diag_embed(self, offset) + else: + return torch.diagonal_copy(self, offset) + + +@register_decomposition(aten.diagonal_scatter) +@out_wrapper() +def diagonal_scatter( + input: TensorLikeType, + src: TensorLikeType, + offset: int = 0, + dim1: int = 0, + dim2: int = 1, +) -> TensorLikeType: + out = utils.clone_preserve_strides(input) + diag = out.diagonal(offset, dim1, dim2) + torch._check( + diag.shape == src.shape, + lambda: "expected src to have a size equal to the diagonal of the input." + f"Got {src.shape} for a diagonal of shape {diag.shape}", + ) + copy_to(diag, src) + return out + + +@register_decomposition(aten.diagonal) +def diagonal( + self: TensorLikeType, + offset: int = 0, + dim1: int = 0, + dim2: int = 1, +) -> TensorLikeType: + """ + Reference implementation of torch.diagonal + """ + num_dims = self.dim() + dim1 = utils.canonicalize_dim(idx=dim1, rank=num_dims) + dim2 = utils.canonicalize_dim(idx=dim2, rank=num_dims) + + torch._check( + dim1 != dim2, lambda: f"diagonal dimensions cannot be identical {dim1}, {dim2}" + ) + + storage_offset = self.storage_offset() + + if offset >= 0: + diag_size = max(min(self.size()[dim1], self.size()[dim2] - offset), 0) + else: + diag_size = max(min(self.size()[dim1] + offset, self.size()[dim2]), 0) + + if diag_size > 0: + if offset >= 0: + storage_offset += offset * self.stride()[dim2] + else: + storage_offset -= offset * self.stride()[dim1] + + sizes = [s for i, s in enumerate(self.size()) if i not in (dim1, dim2)] + sizes.append(diag_size) + + strides = [s for i, s in enumerate(self.stride()) if i not in (dim1, dim2)] + strides.append(self.stride()[dim1] + self.stride()[dim2]) + + result = self.as_strided(size=sizes, stride=strides, storage_offset=storage_offset) + + return result + + +@register_decomposition(aten.diag_embed) +@out_wrapper() +def diag_embed( + t: TensorLikeType, + offset: int = 0, + dim1: int = -2, + dim2: int = -1, +) -> TensorLikeType: + """ + Reference implementation of torch.diag_embed + """ + # convert from negative dims + rank = t.ndim + 1 + dim1 = utils.canonicalize_dim(rank=rank, idx=dim1) + dim2 = utils.canonicalize_dim(rank=rank, idx=dim2) + + # as per the docs, exchanging dims is equivalent to changing the sign of + # offset + if dim1 > dim2: + dim1, dim2 = dim2, dim1 + offset = -offset + + torch._check( + dim1 != dim2, lambda: f"diagonal dimensions cannot be identical {dim1}, {dim2}" + ) + + # as per the docs, the size of last dim is placed at dim1 and dim2 + last_dim = t.size(-1) + + if offset != 0: + # add padding to match the new size + t_shape = list(t.shape) + t_shape[-1] = builtins.abs(offset) + z = torch.zeros(t_shape, dtype=t.dtype, device=t.device, requires_grad=False) + pair = (z, t) if offset > 0 else (t, z) + t = torch.cat(pair, dim=-1) + # make sure the diagonal always has the same size + last_dim += builtins.abs(offset) + + # preserve original data, but place 1 at dim1 and move last dim to dim2 + t = t.unsqueeze(dim1).movedim(-1, dim2) + + # generate ranges shifting indices based on offset + a_range = torch.arange(last_dim, device=t.device, dtype=torch.int64) + b_range = torch.arange( + offset, last_dim + offset, device=t.device, dtype=torch.int64 + ) + + # broadcast + cond = a_range == b_range.unsqueeze(-1) + cond_shape = [last_dim if i in (dim1, dim2) else 1 for i in range(len(t.shape))] + cond = cond.reshape(cond_shape) + + # aten.diag_embed always returns a new contiguous tensor + # contiguous() is needed to correctly model the output stride + return utils.mask_tensor(cond, t).contiguous() + + +@register_decomposition(aten.block_diag) +@out_wrapper() +def _block_diag_iterable(tensors: List[TensorLikeType]) -> TensorLikeType: + """ + Reference implementation of torch.block_diag + """ + tensors_2d = [ + tensor.view(1, -1) if tensor.dim() <= 1 else tensor for tensor in tensors + ] + + ncols = builtins.sum(tensor.shape[1] for tensor in tensors_2d) + device = tensors_2d[0].device + + result = [] + + col_start = 0 + for i, tensor in enumerate(tensors_2d): + torch._check( + tensor.dim() == 2, + lambda: "Input tensors must have 2 or fewer dimensions. " + f"Input {i} has {tensor.dim()} dimensions", + ) + torch._check( + tensor.device == device, + lambda: "Input tensors must all be on the same device. " + f"Input 0 is on device {device} and input {i} is on device {tensor.device}.", + ) + row, col = tensor.shape + left = torch.zeros((row, col_start), device=device, dtype=tensor.dtype) + right = torch.zeros( + (row, ncols - col_start - col), device=device, dtype=tensor.dtype + ) + result += [torch.cat((left, tensor, right), dim=1)] + col_start += col + + return torch.cat(result, dim=0) + + +def block_diag(*tensors: List[TensorLikeType]) -> TensorLikeType: + """ + This is used as an input to PythonRefInfo. `torch.block_diag` + expects arguments splatted, but `aten.block_diag` expects only + one argument that is a list of Tensors. + """ + return _block_diag_iterable(tensors) # type: ignore[arg-type] + + +# CompositeImplicitAutograd - don't register decomp +def dsplit(a: TensorLikeType, sections: DimsType) -> TensorSequenceType: + if a.ndim < 3: + raise RuntimeError( + f"torch.dsplit requires a tensor with at least 3 dimension, but got a tensor with {a.ndim} dimensions!" + ) + if isinstance(sections, IntLike) and (sections == 0 or a.shape[2] % sections != 0): + raise RuntimeError( + "torch.dsplit attempted to split along dimension 2, " + + f"but the size of the dimension {a.shape[2]} is not divisible by the split_size {sections}!" + ) + return tensor_split(a, sections, 2) + + +@register_decomposition(aten.t.default) +def t(a: TensorLikeType): + # TODO: Add sparse support + # if a.is_sparse: + # sparse_dim = a.sparse_dim() + # dense_dim = a.dense_dim() + # if not (sparse_dim <= 2 and dense_dim == 0): + # raise RuntimeError( + # f"t() expects a tensor with <= 2 sparse and 0 dense dimensions, but got {sparse_dim} sparse and" + # f"{dense_dim} dense dimensions" + # ) + if a.ndim > 2: + raise RuntimeError( + f"t() expects a tensor with <= 2 dimensions, but self is {a.ndim}D" + ) + return torch.transpose(a, 0, 0 if a.ndim < 2 else 1) + + +# CompositeImplicitAutograd - don't register decomp +def T(a: TensorLikeType) -> TensorLikeType: + # n != 2 && n != 0 is deprecated in regular PyTorch. + torch._check( + a.ndim in (0, 2), + lambda: ( + "The use of `x.T` on tensors of dimension other than 0 or 2 " + "to reverse their shape is not supported." + ), + ) + return a.t() + + +@register_decomposition(aten.alias) +def alias(a: TensorLikeType) -> TensorLikeType: + return prims.view_of(a) + + +@register_decomposition(aten.transpose) +def transpose(a: TensorLikeType, dim0: int, dim1: int) -> TensorLikeType: + _dim0, _dim1 = utils.canonicalize_dims(a.ndim, (dim0, dim1)) # type: ignore[misc] + + if a.ndim <= 1 or dim0 == dim1: + return aten.alias.default(a) + + _permutation = list(range(0, a.ndim)) + _permutation[_dim0] = _dim1 + _permutation[_dim1] = _dim0 + return torch.permute(a, _permutation) + + +# Aliases for transpose +swap_axes = transpose + + +@register_decomposition(aten.unfold) +def unfold( + self: TensorLikeType, dimension: int, size: int, step: int +) -> TensorLikeType: + shape, strides = _get_unfold_shape_stride( + self.shape, self.stride(), dimension, size, step + ) + return self.as_strided(shape, strides) + + +@register_decomposition(aten.unfold_copy) +@out_wrapper() +def unfold_copy(self: TensorLikeType, dimension: int, size: int, step: int): + return self.unfold(dimension, size, step).clone( + memory_format=torch.contiguous_format + ) + + +def _cumsumprod_common( + func, + init, + a: TensorLikeType, + dim: int, + *, + dtype: Optional[torch.dtype] = None, + out: Optional[Tensor] = None, +) -> TensorLikeType: + # We implement all the kwargs of a reduction. ATen just handles dtype + # nb. This decomposition may not be as efficient as a backend-specific implementation + ndim = a.ndim + dim = utils.canonicalize_dim(ndim, dim) + if ndim == 0: + return func(a.unsqueeze(0), dim=0, dtype=dtype, out=out) + a = a.unsqueeze(dim + 1) + rg = torch.arange(a.shape[dim], device=a.device) + mask = rg.unsqueeze(1) <= rg + for _ in range(ndim - dim - 1): + mask = mask.unsqueeze(-1) + masked_a = torch.where(mask, a, init) + return func(masked_a, dim=dim, dtype=dtype, out=out) + + +@register_decomposition(aten.cumsum) +def cumsum( + a: TensorLikeType, + dim: int, + *, + dtype: Optional[torch.dtype] = None, + out: Optional[Tensor] = None, +) -> TensorLikeType: + return _cumsumprod_common(func=sum, init=0, a=a, dim=dim, dtype=dtype, out=out) + + +@register_decomposition(aten.cumprod) +def cumprod( + a: TensorLikeType, + dim: int, + *, + dtype: Optional[torch.dtype] = None, + out: Optional[Tensor] = None, +) -> TensorLikeType: + return _cumsumprod_common(func=prod, init=1, a=a, dim=dim, dtype=dtype, out=out) + + +# Note: although squeeze is documented as having the out= kwarg it doesn't +@register_decomposition(aten.unsqueeze) +def unsqueeze(a: TensorLikeType, dim: int) -> TensorLikeType: + # Note that unsqueeze canonicalizes with rank + 1 because it allows + # a new innermost dimension to be specified + ndim = a.ndim + 1 + dim = utils.canonicalize_dim(ndim, dim) + return prims.expand_dims(a, (dim,), ndim=ndim) + + +# NOTE: shape is a vararg because Tensor.reshape can be called with as +# Tensor.view(a, b, c) or Tensor.view((a, b, c)) Function call torch.view +# doesn't support unpacked shapes +# TODO: Turn this into a decomposition (currently fails on reshape meta tests) +@register_decomposition(aten.view.default) +def view(a: TensorLikeType, *shape: ShapeType) -> TensorLikeType: + return _reshape_view_helper(a, *shape, allow_copy=False) + + +# CompositeImplicitAutograd - don't register decomp +def view_as(self: TensorLikeType, other: TensorLikeType) -> TensorLikeType: + return self.view(other.size()) + + +# CompositeImplicitAutograd - don't register decomp +def ravel(a: TensorLikeType) -> TensorLikeType: + return reshape(a, (-1,)) + + +# CompositeImplicitAutograd - don't register decomp +# missing ref impl. for aten.gather +@out_wrapper() +def take_along_dim( + a: torch.Tensor, indices: torch.Tensor, dim: Optional[int] = None +) -> torch.Tensor: + torch._check( + a.ndim == indices.ndim, + lambda: ( + "torch.take_along_dim(): input and indices should have the same " + f"number of dimensions, but got {a.ndim} dimensions for input, and " + f"{indices.ndim} dimensions for indices" + ), + ) + + torch._check( + utils.is_integer_dtype(indices.dtype), + lambda: ( + "torch.take_along_dim(): dtype of indices should be int but got " + f"{indices.dtype} instead" + ), + ) + + if dim is None: + return torch.gather(a.view(-1), 0, indices.view(-1)) + else: + self_sizes = list(a.shape) + self_sizes[dim] = indices.size(dim) + broadcast_shape = utils.infer_size_shapes(self_sizes, indices.size()) + indices_broadcast = broadcast_to(indices, broadcast_shape) + + indices_sizes = list(indices.shape) + indices_sizes[dim] = a.size(dim) + broadcast_shape = utils.infer_size_shapes(indices_sizes, a.size()) + self_broadcast = broadcast_to(a, broadcast_shape) + + return torch.gather(self_broadcast, dim, indices_broadcast) + + +@out_wrapper() +def empty( + *shape, + dtype: Optional[torch.dtype] = None, + layout: torch.layout = torch.strided, + device: Optional[DeviceLikeType] = None, + requires_grad: bool = False, + pin_memory: bool = False, + memory_format: torch.memory_format = torch.contiguous_format, +) -> TensorLikeType: + torch._check( + memory_format != torch.preserve_format, + lambda: "torch.empty: the Preserve memory format is not supported", + ) + + shape = utils.extract_shape_from_varargs(shape) + + if memory_format == torch.contiguous_format: + strides = utils.make_contiguous_strides_for(shape) + elif memory_format == torch.channels_last_3d: + strides = utils.make_channels_last_3d_strides_for(shape) + else: # memory_format == torch.channels_last + torch._check( + memory_format == torch.channels_last, + lambda: f"torch.empty: received an unknown memory format {memory_format}!", + ) + strides = utils.make_channels_last_2d_strides_for(shape) + + return torch.empty_strided( + shape, + strides, + dtype=dtype, + layout=layout, + device=device, + pin_memory=pin_memory, + requires_grad=requires_grad, + ) + + +@out_wrapper() +def empty_permuted( + shape, + physical_layout, + dtype: Optional[torch.dtype] = None, + layout: torch.layout = torch.strided, + device: Optional[DeviceLikeType] = None, + requires_grad: bool = False, + pin_memory: bool = False, +) -> TensorLikeType: + return prims.empty_permuted( + shape, + physical_layout, + dtype=dtype, + device=device, + requires_grad=requires_grad, + ) + + +@register_decomposition(aten.new_empty) +@out_wrapper() +def new_empty( + a: TensorLikeType, + size: ShapeType, + *, + dtype: Optional[torch.dtype] = None, + layout: Optional[torch.layout] = None, + device: Optional[DeviceLikeType] = None, + pin_memory: bool = False, +) -> TensorLikeType: + dtype = a.dtype if dtype is None else dtype + layout = a.layout if layout is None else layout + device = a.device if device is None else device + + return torch.empty( + size, + dtype=dtype, + device=device, + pin_memory=pin_memory, + layout=layout, + ) + + +@register_decomposition(aten.new_empty_strided) +@out_wrapper() +def new_empty_strided( + a: TensorLikeType, + size: ShapeType, + stride: StrideType, + *, + dtype: Optional[torch.dtype] = None, + layout: Optional[torch.layout] = None, + device: Optional[DeviceLikeType] = None, + pin_memory: bool = False, +) -> TensorLikeType: + """ + Reference implementation of torch.Tensor.new_empty_strided + """ + + dtype = a.dtype if dtype is None else dtype + layout = a.layout if layout is None else layout + device = a.device if device is None else device + + return torch.empty_strided( + size, + stride, + dtype=dtype, + device=device, + pin_memory=pin_memory, + layout=layout, + ) + + +@register_decomposition(aten.zeros.default) +@out_wrapper() +def zeros( + *size, + dtype: Optional[torch.dtype] = None, + layout: torch.layout = torch.strided, + device: Optional[DeviceLikeType] = None, + pin_memory: bool = False, + requires_grad: bool = False, +) -> TensorLikeType: + size = utils.extract_shape_from_varargs(size) + + if dtype is None: + dtype = torch.get_default_dtype() + + return torch.full( + size, + False if dtype == torch.bool else 0, + dtype=dtype, + layout=layout, + device=device, + pin_memory=pin_memory, + requires_grad=requires_grad, + ) + + +@register_decomposition(aten.new_zeros) +@out_wrapper() +def new_zeros( + a: TensorLikeType, + size: ShapeType, + *, + dtype: Optional[torch.dtype] = None, + layout: Optional[torch.layout] = None, + device: Optional[DeviceLikeType] = None, + pin_memory: bool = False, + requires_grad: bool = False, +) -> TensorLikeType: + dtype = a.dtype if dtype is None else dtype + layout = a.layout if layout is None else layout + device = a.device if device is None else device + + return torch.full( + size, + False if (dtype or a.dtype) == torch.bool else 0, + dtype=dtype, + layout=layout, + device=device, + pin_memory=pin_memory, + requires_grad=requires_grad, + ) + + +@register_decomposition(aten.ones.default) +@out_wrapper() +def ones( + *size, + dtype: Optional[torch.dtype] = None, + layout: torch.layout = torch.strided, + device: Optional[DeviceLikeType] = None, + pin_memory: bool = False, + requires_grad: bool = False, +) -> TensorLikeType: + size = utils.extract_shape_from_varargs(size) + + if dtype is None: + dtype = torch.get_default_dtype() + + return torch.full( + size, + True if dtype == torch.bool else 1, + dtype=dtype, + layout=layout, + device=device, + pin_memory=pin_memory, + requires_grad=requires_grad, + ) + + +@register_decomposition(aten.new_ones) +@out_wrapper() +def new_ones( + a: TensorLikeType, + size: ShapeType, + *, + dtype: Optional[torch.dtype] = None, + layout: Optional[torch.layout] = None, + device: Optional[DeviceLikeType] = None, + pin_memory: bool = False, + requires_grad: bool = False, +) -> TensorLikeType: + dtype = a.dtype if dtype is None else dtype + layout = a.layout if layout is None else layout + device = a.device if device is None else device + + return torch.full( + size, + True if (dtype or a.dtype) == torch.bool else 1, + dtype=dtype, + layout=layout, + device=device, + pin_memory=pin_memory, + requires_grad=requires_grad, + ) + + +@register_decomposition(aten.new_full) +@out_wrapper() +def new_full( + a: TensorLikeType, + size: ShapeType, + fill_value: NumberType, + *, + dtype: Optional[torch.dtype] = None, + layout: Optional[torch.layout] = None, + device: Optional[DeviceLikeType] = None, + pin_memory: bool = False, +) -> TensorLikeType: + dtype = a.dtype if dtype is None else dtype + layout = a.layout if layout is None else layout + device = a.device if device is None else device + + return torch.full( + size, + fill_value, + dtype=dtype, + layout=layout, + device=device, + pin_memory=pin_memory, + ) + + +@register_decomposition(aten.empty_like) +@out_wrapper() +def empty_like( + a: TensorLikeType, + *, + dtype: Optional[torch.dtype] = None, + device: Optional[DeviceLikeType] = None, + layout: Optional[torch.layout] = None, + pin_memory: bool = False, + requires_grad: bool = False, + memory_format: torch.memory_format = torch.preserve_format, +) -> TensorLikeType: + dtype = a.dtype if dtype is None else dtype + layout = a.layout if layout is None else layout + device = a.device if device is None else device + + if memory_format != torch.preserve_format: + return torch.empty( + a.shape, + dtype=dtype, + layout=layout, + device=device, + requires_grad=requires_grad, + pin_memory=pin_memory, + memory_format=memory_format, + ) + + # memory_format == torch.preserve_format + logical_to_physical_perm = ( + utils.compute_elementwise_output_logical_to_physical_perm(a) + ) + # identity perm is [2, 1, 0] + return torch.empty_permuted( + a.shape, + logical_to_physical_perm, + dtype=dtype, + layout=layout, + device=device, + pin_memory=pin_memory, + requires_grad=requires_grad, + ) + + +@register_decomposition([aten.arange.start_step, aten.arange.start_out]) +@out_wrapper() +def arange( + start: NumberType = 0, + end: Optional[NumberType] = None, + step: NumberType = 1, + *, + dtype: Optional[torch.dtype] = None, + layout: torch.layout = torch.strided, + device: Optional[DeviceLikeType] = None, + pin_memory: bool = False, + requires_grad: bool = False, +) -> TensorLikeType: + utils.check_layout(layout) + utils.check_pin_memory(pin_memory) + device = torch.device(utils.device_or_default(device)) + + assert not isinstance(start, complex) + assert not isinstance(end, complex) + assert not isinstance(step, complex) + + # Case: torch.arange(5) + if end is None: + end = start + start = 0 + torch._check(step != 0, lambda: "step must be nonzero") + if step > 0: + torch._check( + end >= start, + lambda: "upper bound and lower bound inconsistent with step sign", + ) + elif step < 0: + torch._check( + end <= start, + lambda: "upper bound and lower bound inconsistent with step sign", + ) + + def is_finite(x): + return not isinstance(x, FloatWithoutSymFloat) or math.isfinite(x) + + torch._check( + is_finite(start) and is_finite(end), + lambda: f"unsupported range: {start} -> {end}", + ) + torch._check( + is_finite(step), + lambda: f"step must be finite but got {step}", + ) + + args = (start, end, step) + integer_args = builtins.all(isinstance(arg, IntLike) for arg in args) + + if dtype is None: + dtype = torch.int64 if integer_args else torch.get_default_dtype() + + is_integer = utils.is_integer_dtype(dtype) + if is_integer or integer_args: + xstart = sym_int(start) + xend = sym_int(end) + xstep = sym_int(step) + + # For int64 we truncate arguments to int before calculating length, but + # other integral dtypes we don't. Weird... but needed to match ATen shapes. + if dtype == torch.int64 or integer_args: + # Uses floordiv to avoid ceil in inductor. + sgn = bool(xstep > 0) - bool(xstep < 0) # type: ignore[possibly-undefined] + length = (xend - xstart + xstep - sgn) // xstep # type: ignore[possibly-undefined] + else: + length = math.ceil((end - start) / step) + + if is_integer: + return prims.iota( + length, + start=xstart, # type: ignore[possibly-undefined] + step=xstep, # type: ignore[possibly-undefined] + dtype=dtype, + device=device, + requires_grad=requires_grad, + ) + + index = prims.iota( + length, + start=0, + step=1, + dtype=torch.int64, + device=device, + requires_grad=False, + ) + + computation_dtype = ( + torch.long if integer_args else utils.get_acc_type(dtype, device) + ) + index = _maybe_convert_to_dtype(index, computation_dtype) + result = start + step * index + result = _maybe_convert_to_dtype(result, dtype) + + if requires_grad: + result.requires_grad_(True) + return result + + +@register_decomposition(aten.lerp) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("start", "end", "weight"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def lerp(start: Tensor, end: Tensor, weight: Union[Tensor, NumberType]): + inputs = [start, end] + if isinstance(weight, Number): + weight = start.new_full((), weight) # type: ignore[arg-type] + else: + inputs.append(weight) + assert isinstance(weight, Tensor) # mypy + # We implement it this way for numerical stability. We assume (in the stability optimisation) + # that 0 <= weight <= 1. We take the abs to deal with complex numbers + # We want to perform operations near zero, which is where floating points are most precise + # thus, we perform the following optimisation: + # If weight.abs() >= 0.5: + # return (1 - weight) * (start - end) + end + mask = weight.abs() >= 0.5 + coeff = torch.where(mask, weight - 1, weight) + base = torch.where(mask, end, start) + output = coeff * (end - start) + base + # make sure the decomposition output's stride is same as non-decomposition path. + stride = utils.compute_elementwise_output_strides(*_maybe_broadcast(*inputs)) + if output.stride() != stride: + output = prims.copy_strided(output, stride) + + return handle_noncontiguous_outputs(inputs, output) + + +@register_decomposition(aten.linspace) +@out_wrapper() +def linspace( + start: Union[NumberType, TensorLikeType], + end: Union[NumberType, TensorLikeType], + steps: NumberType, + *, + dtype: Optional[torch.dtype] = None, + device: Optional[DeviceLikeType] = None, + layout: torch.layout = torch.strided, + pin_memory: bool = False, + requires_grad: bool = False, +) -> TensorLikeType: + if isinstance(start, TensorLikeType): + torch._check( + start.dim() == 0, + lambda: "linspace only supports 0-dimensional start and end tensors", + ) + start = _maybe_convert_to_dtype(start, torch.float64) + if isinstance(end, TensorLikeType): + torch._check( + end.dim() == 0, + lambda: "linspace only supports 0-dimensional start and end tensors", + ) + end = _maybe_convert_to_dtype(end, torch.float64) + + if builtins.any(isinstance(arg, complex) for arg in (start, end, steps)): + default_complex_dtype = utils.corresponding_complex_dtype( + torch.get_default_dtype() + ) + if dtype is None: + dtype = default_complex_dtype + else: + torch._check( + utils.is_complex_dtype(dtype), + lambda: f"linspace(): inferred dtype {default_complex_dtype} can't be safely cast to passed dtype {dtype}", + ) + else: + dtype = dtype or torch.get_default_dtype() + assert isinstance(dtype, torch.dtype) + + # steps does not participate in the computation of the dtype + torch._check_type( + isinstance(steps, IntLike), + lambda: f"received an invalid combination of arguments - got \ +({type(start).__name__}, {type(end).__name__}, {type(steps).__name__})", + ) + assert isinstance(steps, IntLike) # for mypy + torch._check(steps >= 0, lambda: "number of steps must be non-negative") + + factory_kwargs = { + "layout": layout, + "device": device, + "pin_memory": pin_memory, + "requires_grad": requires_grad, + } + if steps == 0: + return torch.full((0,), 0, dtype=dtype, **factory_kwargs) # type: ignore[arg-type] + if steps == 1: + if isinstance(start, TensorLikeType): + return torch.empty((steps,), dtype=dtype, **factory_kwargs).copy_(start) # type: ignore[arg-type] + else: + return torch.full((steps,), start, dtype=dtype, **factory_kwargs) # type: ignore[arg-type] + + # Perform in arange in int because some backends like ATen or Triton do not support all the dtypes + rg = torch.arange(0, steps, **factory_kwargs) # type: ignore[arg-type] + + # Small types need to be computed in higher precision as this is, at heart, an associative scan + dtype_red = ( + torch.int64 + if (utils.is_boolean_dtype(dtype) or utils.is_integer_dtype(dtype)) + else dtype + ) + computation_dtype, _ = utils.reduction_dtypes( + rg, REDUCTION_OUTPUT_TYPE_KIND.SAME, dtype_red + ) + cast_rg = partial(_maybe_convert_to_dtype, dtype=computation_dtype) + + # We implement torch.lerp without performing rg / (steps - 1) explicitly + # With this we get out[0] == start, out[-1] == end + step = (end - start) / (steps - 1) + out = torch.where( + rg < steps / 2, + start + step * cast_rg(rg), # type: ignore[arg-type,operator] + end - step * cast_rg((steps - 1) - rg), # type: ignore[arg-type,operator] + ) + return _maybe_convert_to_dtype(out, dtype) # type: ignore[return-value] + + +@register_decomposition(aten.logspace) +@out_wrapper() +def logspace( + start: Union[NumberType, TensorLikeType], + end: Union[NumberType, TensorLikeType], + steps: NumberType, + base: NumberType = 10, + *, + dtype: Optional[torch.dtype] = None, + device: Optional[DeviceLikeType] = None, + layout: torch.layout = torch.strided, + pin_memory: bool = False, + requires_grad: bool = False, +) -> TensorLikeType: + if dtype is None: + dtype = torch.get_default_dtype() + + # NB: NumPy doesn't have this cast + if prims.utils.is_integer_dtype(dtype): + if isinstance(start, FloatLike): + start = sym_int(start) + elif isinstance(start, TensorLikeType): + torch._check( + start.dim() == 0, + lambda: "logspace only supports 0-dimensional start and end tensors", + ) + start = _maybe_convert_to_dtype(start, dtype) + if isinstance(end, FloatLike): + end = sym_int(end) + elif isinstance(end, TensorLikeType): + torch._check( + end.dim() == 0, + lambda: "logspace only supports 0-dimensional start and end tensors", + ) + end = _maybe_convert_to_dtype(end, dtype) + + if builtins.any(isinstance(arg, complex) for arg in (start, end, steps)): + default_complex_dtype = utils.corresponding_complex_dtype( + torch.get_default_dtype() + ) + dtype = default_complex_dtype + _dtype = None # torch.linspace will update the correct dtype + else: + _dtype = torch.float64 + + assert not isinstance(base, complex) # for mypy + if base < 0: + raise NotImplementedError + ret = torch.linspace( # type: ignore[misc] + start, # type: ignore[arg-type] + end, # type: ignore[arg-type] + steps, # type: ignore[arg-type] + dtype=_dtype, + layout=layout, + device=device, + pin_memory=pin_memory, + requires_grad=requires_grad, + ) + return _maybe_convert_to_dtype(torch.pow(base, ret), dtype) # type: ignore[arg-type,return-value] + + +@overload +def meshgrid(tensors: Sequence[TensorLikeType], indexing: str): + pass + + +@overload +def meshgrid(*tensors: TensorLikeType, indexing: str): + pass + + +@register_decomposition(aten.meshgrid) # type: ignore[misc] +def meshgrid( + *tensors: Union[TensorLikeType, List[TensorLikeType], Tuple[TensorLikeType]], + indexing: str, +) -> List[TensorLikeType]: + # This ref simultaneously handles two overloads (see stubs above) + # The `indexing` argument is currently optional for torch.meshgrid, but we + # plan to make the argument required: https://github.com/pytorch/pytorch/issues/50276 + if isinstance(tensors[0], (list, tuple)): + assert len(tensors) == 1 + tensors = tuple(tensors[0]) + + torch._check( + builtins.all(isinstance(a, TensorLike) for a in tensors), + lambda: "meshgrid expects its inputs to be tensors", + ) + + torch._check(len(tensors) > 0, lambda: "meshgrid expects a non-empty TensorList") + + for i in range(len(tensors) - 1): + torch._check( + tensors[i].dtype == tensors[i + 1].dtype, # type: ignore[union-attr] + lambda: "meshgrid expects all tensors to have the same dtype", + ) + torch._check( + tensors[i].device == tensors[i + 1].device, # type: ignore[union-attr] + lambda: "meshgrid expects all tensors to have the same device", + ) + + swap_first_and_second_tensors = False + if indexing == "xy": + swap_first_and_second_tensors = len(tensors) >= 2 + if swap_first_and_second_tensors: + tensors = (tensors[1], tensors[0], *tensors[2:]) + else: + torch._check( + indexing == "ij", + lambda: ( + 'torch.meshgrid: indexing must be one of "xy" or "ij", ' + f"but received: {indexing}" + ), + ) + + result_shape: List[int] = [] + for t in tensors: + assert isinstance(t, TensorLike) # mypy + torch._check( + t.ndim == 0 or t.ndim == 1, + lambda: f"torch.meshgrid: Expected 0D or 1D tensor in the tensor list but got: {t}", + ) + result_shape.append(t.numel()) + + grids: List[TensorLikeType] = [] + for i, t in enumerate(tensors): + assert isinstance(t, TensorLike) # mypy + if t.ndim == 0: + t = t.view((1,)) + grids.append(prims.broadcast_in_dim(t, result_shape, (i,))) + + if swap_first_and_second_tensors: + # Swap outputs if we originally swapped at the beginning + grids[0], grids[1] = grids[1], grids[0] + + return grids + + +# CompositeImplicitAutograd - don't register decomp +def movedim( + input: TensorLikeType, + source: Union[int, DimsSequenceType], + destination: Union[int, DimsSequenceType], +) -> TensorLikeType: + """ + Reference implementation of torch.movedim + """ + if type(source) is int: + source = (source,) + if type(destination) is int: + destination = (destination,) + + # Converts to list to produce a compatible error message with core PyTorch, + # which prints sequences in square brackets. + torch._check( + len(source) == len(destination), # type: ignore[arg-type] + lambda: ( + "movedim: Invalid source or destination dims: source " # type: ignore[arg-type] + f"({list(source)} dims) should contain the same number " # type: ignore[arg-type] + f"of dims as destination ({list(destination)} dims)" # type: ignore[arg-type] + ), + ) + + rank = input.ndim + ss = tuple(utils.canonicalize_dims(rank=rank, indices=source)) # type: ignore[arg-type] + ds = tuple(utils.canonicalize_dims(rank=rank, indices=destination)) # type: ignore[arg-type] + + sss = set(ss) + dss = set(ds) + + # See above on why this converts to list in error messages. + torch._check( + len(ss) == len(sss), + lambda: f"movedim: repeated dim in `source` ({list(source)})", # type: ignore[arg-type] + ) + torch._check( + len(ds) == len(dss), + lambda: f"movedim: repeated dim in `destination` ({list(destination)})", # type: ignore[arg-type] + ) + + m = dict(zip(ds, ss)) + dims = [] + si = 0 # source index + for di in range(rank): + # check if the destination index is in the mapping + s = m.get(di) + if s is not None: + # insert source index if found + dims.append(s) + else: + # insert source index sequentially, skipping indices from the mapping + while si in sss: + si += 1 + dims.append(si) + si += 1 + + result = torch.permute(input, tuple(dims)) + + return result + + +# NOTE: for convenience, shape can be a tuple of ints or a tuple containing a tuple of ints +@register_decomposition(aten.empty_strided) +@out_wrapper() +def empty_strided( + shape: Union[ShapeType, Tuple[ShapeType]], + strides: StrideType, + *, + dtype: Optional[torch.dtype] = None, + device: Optional[DeviceLikeType] = None, + layout: torch.layout = torch.strided, + requires_grad: bool = False, + pin_memory: bool = False, +) -> TensorLikeType: + # Layout == strided, pin_memory is False + utils.check_layout(layout) + utils.check_pin_memory(pin_memory) + + shape = utils.extract_shape_from_varargs(shape) + dtype = torch.get_default_dtype() if dtype is None else dtype + device = torch.device("cpu") if device is None else device + + return prims.empty_strided( + shape, + strides, + dtype=dtype, + device=device, + requires_grad=requires_grad, + ) + + +@register_decomposition(aten.eye) +@out_wrapper() +def eye( + n: int, + m: Optional[int] = None, + *, + dtype: Optional[torch.dtype] = None, + layout: torch.layout = torch.strided, + device: Optional[DeviceLikeType] = None, + pin_memory: bool = False, + requires_grad: bool = False, # TODO: unused +) -> TensorLikeType: + """ + Reference implementation of torch.eye + """ + if m is None: + m = n + + torch._check(n >= 0, lambda: f"n must be greater or equal to 0, got {n}") + torch._check(m >= 0, lambda: f"m must be greater or equal to 0, got {m}") + + range_n = torch.arange(n, dtype=torch.int64, device=device, requires_grad=False) + range_m = torch.arange(m, dtype=torch.int64, device=device, requires_grad=False) + + cond = range_n.unsqueeze(-1) == range_m + if dtype is torch.bool: + return cond + else: + one = torch.ones( + (1,), + dtype=dtype, + layout=layout, + device=device, + pin_memory=pin_memory, + requires_grad=False, + ) + return torch.where(cond, one, 0) + # TODO: Use requires_grad. All refs taking the requires_grad kwarg must + # return a leaf tensor. + # result.requires_grad_(requires_grad) + + +@register_decomposition([aten.full.default, aten.full.out]) +@out_wrapper() +def full( + shape: ShapeType, + fill_value: NumberType, + *, + dtype: Optional[torch.dtype] = None, + layout: torch.layout = torch.strided, + device: Optional[DeviceLikeType] = None, + pin_memory: bool = False, + requires_grad: bool = False, +) -> TensorLikeType: + utils.check_layout(layout) + utils.check_pin_memory(pin_memory) + + dtype = dtype if dtype is not None else utils.type_to_dtype(type(fill_value)) + device = device if device is not None else torch.device("cpu") + + e = empty( + shape, + dtype=dtype, + layout=layout, + device=device, + pin_memory=pin_memory, + requires_grad=requires_grad, + ) + return torch.fill(e, fill_value) # type: ignore[arg-type] + + +def full_like( + a: TensorLikeType, + fill_value: NumberType, + *, + dtype: Optional[torch.dtype] = None, + layout: Optional[torch.layout] = None, + device: Optional[DeviceLikeType] = None, + pin_memory: bool = False, + requires_grad: bool = False, + memory_format: torch.memory_format = torch.preserve_format, +) -> TensorLikeType: + e = torch.empty_like( + a, + dtype=dtype, + layout=layout, + device=device, + pin_memory=pin_memory, + requires_grad=requires_grad, + memory_format=memory_format, + ) + return fill(e, fill_value) + + +@register_decomposition(aten.zeros_like) +@out_wrapper() +def zeros_like( + a: TensorLikeType, + *, + dtype: Optional[torch.dtype] = None, + layout: Optional[torch.layout] = None, + device: Optional[DeviceLikeType] = None, + pin_memory: bool = False, + requires_grad: bool = False, + memory_format: torch.memory_format = torch.preserve_format, +) -> TensorLikeType: + return torch.full_like( + a, + False if (dtype or a.dtype) == torch.bool else 0, + dtype=dtype, + layout=layout, + device=device, + pin_memory=pin_memory, + requires_grad=requires_grad, + memory_format=memory_format, + ) + + +@register_decomposition(aten.ones_like) +@out_wrapper() +def ones_like( + a: TensorLikeType, + *, + dtype: Optional[torch.dtype] = None, + layout: Optional[torch.layout] = None, + device: Optional[DeviceLikeType] = None, + pin_memory: bool = False, + requires_grad: bool = False, + memory_format: torch.memory_format = torch.preserve_format, +) -> TensorLikeType: + return torch.full_like( + a, + True if (dtype or a.dtype) == torch.bool else 1, + dtype=dtype, + layout=layout, + device=device, + pin_memory=pin_memory, + requires_grad=requires_grad, + memory_format=memory_format, + ) + + +@register_decomposition(aten.randn.default) +@out_wrapper() +def randn( + *shape, + dtype: Optional[torch.dtype] = None, + device: Optional[DeviceLikeType] = None, + layout: Optional[torch.layout] = None, + requires_grad: bool = False, + pin_memory: bool = False, +) -> TensorLikeType: + utils.check_pin_memory(pin_memory) + + shape_ = utils.extract_shape_from_varargs(shape) + + dtype = utils.dtype_or_default(dtype) + device = utils.device_or_default(device) + + return prims.normal( + shape_, + mean=0.0, + std=1.0, + dtype=dtype, + device=device, + requires_grad=requires_grad, + ) + + +def scalar_tensor( + a: NumberType, + *, + dtype: Optional[torch.dtype] = None, + layout: torch.layout = torch.strided, + device: Optional[DeviceLikeType] = None, + pin_memory: bool = False, +) -> TensorLikeType: + utils.check_layout(layout) + utils.check_pin_memory(pin_memory) + dtype = dtype if dtype is not None else utils.type_to_dtype(type(a)) + device = device if device is not None else torch.device("cpu") + return prims.scalar_tensor(a, dtype=dtype, device=device) + + +# +# Randomness References +# + + +def _uniform_helper( + shape: ShapeType, + low: Union[bool, int, float] = 0.0, + high: Union[bool, int, float] = 1.0, + *, + dtype: torch.dtype, + device: DeviceLikeType, +) -> TensorLikeType: + utils.validate_shape(shape) + + assert isinstance(low, Number) + assert isinstance(high, Number) + low = sym_float(low) + high = sym_float(high) + + assert isinstance(dtype, torch.dtype) + device = utils.canonicalize_device(device) + + return prims._uniform_helper(shape, low=low, high=high, dtype=dtype, device=device) + + +@register_decomposition(aten.masked_fill) +@out_wrapper() +def masked_fill(a: TensorLikeType, mask: TensorLikeType, value: TensorOrNumberLikeType): + python_type = utils.dtype_to_type(a.dtype) + if isinstance(value, Number): + value_type = type(value) + else: + # NOTE: Could not use value = item(value) as it resulted in + # RuntimeError: Cannot cast FakeTensor(cpu) to number + value_ndim = value.ndim + torch._check( + value_ndim == 0, + lambda: f"only supports a 0-dimensional value tensor, but got tensor with {value_ndim} dimension", + ) + # `masked_fill` allows cpu scalar to be moved to cuda, xpu and hpu but not otherwise. + is_cpu_scalar = ( + a.device.type + in ["cuda", "xpu", torch._C._get_privateuse1_backend_name(), "hpu"] + and value.device.type == "cpu" + ) + torch._check( + is_cpu_scalar or value.device == a.device, + lambda: "Expected `value` to be on same device as `a`", + ) + value_type = utils.dtype_to_type(value.dtype) + + if value_type is complex: + # only downcasting from complex to lower type is not allowed. + # We allow casting `value` to lower type for other case + # Eg. float -> int. + # Ref: https://github.com/pytorch/pytorch/issues/79195 + torch._check( + utils.is_weakly_lesser_type(value_type, python_type), + lambda: f"could not convert to type {python_type} without overflow", + ) + + # Since `where` allows type-promotion, + # cast value to correct type before passing to `where` + value = _maybe_convert_to_dtype(value, a.dtype) + r = torch.where(mask, value, a) # type: ignore[arg-type] + + # aten.mask_fill always return a new contiguous tensor + # contiguous() is needed to correctly model the output stride + return r.contiguous() + + +@register_decomposition(aten.masked_fill_) +def masked_fill_( + a: TensorLikeType, mask: TensorLikeType, value: TensorOrNumberLikeType +) -> TensorLikeType: + b = torch.masked_fill(a, mask, value) # type: ignore[arg-type] + a.copy_(b) + return a + + +# CompositeImplicitAutograd - don't register decomp +def allclose( + a: TensorLikeType, + b: TensorLikeType, + rtol: float = 1e-05, + atol: float = 1e-08, + equal_nan: bool = False, +) -> bool: + """ + Reference implementation of torch.allclose + """ + _check_close_args(name="torch.allclose", a=a, b=b, rtol=rtol, atol=atol) + + return bool( + torch.all(torch.isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan)).item() + ) + + +def equal(a: TensorLikeType, b: TensorLikeType) -> bool: + utils.check_same_device(a, b, allow_cpu_scalar_tensors=False) + utils.check_same_dtype(a, b) + + # Shape check + if a.ndim != b.ndim: + return False + + for x, y in zip(a.shape, b.shape): + if x != y: + return False + + # Short-circuits if there are no elements to validate + if a.numel() == 0: + return True + + return item(all(eq(a, b))) # type: ignore[return-value] + + +@register_decomposition(aten.norm) +@out_wrapper(exact_dtype=True) +def norm( + input: TensorLikeType, + p: Optional[Union[float, str]] = "fro", + dim: Optional[DimsType] = None, + keepdim: bool = False, + *, + dtype: Optional[torch.dtype] = None, +) -> TensorLikeType: + # In these cases we compute the "Frobenius norm" + if ( + p == "fro" and (dim is None or isinstance(dim, Dim) or len(dim) <= 2) + ) or p is None: + p = 2 + if isinstance(dim, Dim): + dim = [dim] + if isinstance(p, str): + # Here we either call the nuclear norm, or we call matrix_norm with some arguments + # that will throw an error + if dim is None: + dim = tuple(range(input.ndim)) + return torch.linalg.matrix_norm(input, p, dim, keepdim, dtype=dtype) + else: + return torch.linalg.vector_norm(input, p, dim, keepdim, dtype=dtype) + + +@register_decomposition(aten.trace) +@out_wrapper() +def trace(self: TensorLikeType) -> TensorLikeType: + torch._check( + self.ndim == 2, lambda: "expected a matrix, but got tensor with dim {self.ndim}" + ) + return torch.sum(torch.diag(self, 0)) + + +def _make_r_binary_op(base_op): + def rop( + a: Union[TensorLikeType, NumberType], + b: Union[TensorLikeType, NumberType], + ) -> TensorLikeType: + return base_op(b, a) + + return rop + + +rtruediv = _make_r_binary_op(true_divide) +rfloordiv = _make_r_binary_op(floor_divide) +rpow = _make_r_binary_op(pow) + + +@register_decomposition(aten.triu) +@out_wrapper() +def triu(a: TensorLikeType, diagonal: int = 0) -> TensorLikeType: + torch._check( + a.ndim >= 2, lambda: "triu: input tensor must have at least 2 dimensions" + ) + h, w = a.shape[-2:] + mask = ( + torch.arange(w, device=a.device).unsqueeze(-2) + - torch.arange(h, device=a.device).unsqueeze(-1) + ) >= diagonal + + # aten.triu always returns a new contiguous tensor + # contiguous() is needed to correctly model the output stride + return utils.mask_tensor(mask, a).contiguous() + + +@register_decomposition(aten.tril) +@out_wrapper() +def tril(a: TensorLikeType, diagonal: int = 0) -> TensorLikeType: + torch._check( + a.ndim >= 2, lambda: "tril: input tensor must have at least 2 dimensions" + ) + h, w = a.shape[-2:] + mask = ( + torch.arange(w, device=a.device).unsqueeze(-2) + - torch.arange(h, device=a.device).unsqueeze(-1) + ) <= diagonal + + # aten.tril always returns a new contiguous tensor + # contiguous() is needed to correctly model the output stride + return utils.mask_tensor(mask, a).contiguous() + + +# This is based on get_tril_size in aten/src/ATen/native/TensorFactories.h +# The components of the matrix that belong to the lower triangle with offset +# form a pentagon that can be broken down into a top trapezoid and a bottom +# rectangle. For the implementation of tril_indices, we need the sizes of +# both of these, as well as the length of the top side of the trapezoid. +def _get_tril_sizes(row: int, col: int, offset: int) -> Tuple[int, int, int]: + if row == 0 or col == 0: + return 0, 0, 0 + + m_first_row = min(col, 1 + offset) if offset > 0 else int(row + offset > 0) + m_last_row = max(0, min(col, row + offset)) + n_row_all = max(0, min(row, row + offset)) + n_row_trapezoid = m_last_row - m_first_row + 1 + + # Number of elements in top trapezoid + trapezoid_size = (m_first_row + m_last_row) * n_row_trapezoid // 2 + # Number of elements in bottom rectangle + diff_row = n_row_all - n_row_trapezoid + rectangle_size = max(0, diff_row * col) + + return trapezoid_size, rectangle_size, m_first_row + + +def _trilu_checks( + name: str, + row: int, + col: int, + dtype: torch.dtype, + layout: torch.layout, + pin_memory: bool, +): + torch._check(row >= 0, lambda: f"row must be non-negative, got {row}") + torch._check(col >= 0, lambda: f"col must be non-negative, got {col}") + torch._check( + dtype in (torch.int32, torch.int64), + lambda: f"\"{name}\" not implemented for '{dtype}'", + ) + + +# This is based on tril_indices_cuda in aten/src/ATen/native/cuda/TensorFactories.cu +@register_decomposition(aten.tril_indices) +@out_wrapper() +def tril_indices( + row: int, + col: int, + offset: int = 0, + *, + dtype: torch.dtype = torch.long, + layout: torch.layout = torch.strided, + device: DeviceLikeType = "cpu", + pin_memory: bool = False, +) -> TensorLikeType: + _trilu_checks("tril_indices", row, col, dtype, layout, pin_memory) + + trapezoid_size, rectangle_size, m_first_row = _get_tril_sizes(row, col, offset) + row_offset = max(0, -offset) + + arange_kw = partial( + torch.arange, layout=layout, device=device, pin_memory=pin_memory + ) + + # first we do the indices for top trapezoid + xs1 = arange_kw(0, trapezoid_size, dtype=torch.float64) + b = m_first_row - 0.5 + row_inds1 = torch.floor(-b + torch.sqrt(b * b + 2 * xs1)) + col_inds1 = torch.floor(xs1 - (2 * m_first_row - 1 + row_inds1) * row_inds1 * 0.5) + row_inds1 = _maybe_convert_to_dtype(row_inds1 + row_offset, dtype) + col_inds1 = _maybe_convert_to_dtype(col_inds1, dtype) + + # then bottom rectangle + xs2 = arange_kw(0, rectangle_size, dtype=dtype) + row_inds2 = xs2 // col + (col - m_first_row + 1 + row_offset) + col_inds2 = xs2 % col + + return torch.stack( + (torch.cat((row_inds1, row_inds2)), torch.cat((col_inds1, col_inds2))) + ) + + +# Similar to _get_tril_sizes above, but here there is a top trapezoid and +# a bottom rectangle instead. Note that you can't reduce this to +# _get_tril_sizes(col, row, -offset) because that would correspond to +# decomposing into a left trapezoid and right rectangle. +def _get_triu_sizes(row: int, col: int, offset: int) -> Tuple[int, int, int]: + if row == 0 or col == 0: + return 0, 0, 0 + + m_first_row = max(0, col - offset) if offset > 0 else col + + # Number of elements in top rectangle + rectangle_size = max(0, min(row, -offset) * col) + + # Number of elements in bottom trapezoid + trapezoid_size_tril, rectangle_size_tril, _ = _get_tril_sizes(row, col, offset - 1) + triu_size = row * col - (trapezoid_size_tril + rectangle_size_tril) + trapezoid_size = triu_size - rectangle_size + + return trapezoid_size, rectangle_size, m_first_row + + +@register_decomposition(aten.triu_indices) +@out_wrapper() +def triu_indices( + row: int, + col: int, + offset: int = 0, + *, + dtype: torch.dtype = torch.long, + layout: torch.layout = torch.strided, + device: DeviceLikeType = "cpu", + pin_memory: bool = False, +) -> TensorLikeType: + _trilu_checks("triu_indices", row, col, dtype, layout, pin_memory) + + trapezoid_size, rectangle_size, m_first_row = _get_triu_sizes(row, col, offset) + col_offset = max(0, offset) + + arange_kw = partial( + torch.arange, layout=layout, device=device, pin_memory=pin_memory + ) + + # indices for top rectangle + xs2 = arange_kw(0, rectangle_size, dtype=dtype) + row_inds2 = xs2 // col + col_inds2 = xs2 % col + + # bottom trapezoid + xs1 = arange_kw(0, trapezoid_size, dtype=torch.float64) + b = -0.5 - m_first_row + row_inds1 = torch.floor(-b - torch.sqrt(b * b - 2 * xs1)) + col_inds1 = torch.floor(xs1 - ((2 * m_first_row - 1 - row_inds1) * row_inds1) * 0.5) + row_inds1 = _maybe_convert_to_dtype(row_inds1, dtype) + col_inds1 = _maybe_convert_to_dtype(col_inds1, dtype) + + if col: + row_inds1 = row_inds1 + (rectangle_size // col) + col_inds1 = col_inds1 + col_offset + + return torch.stack( + (torch.cat((row_inds2, row_inds1)), torch.cat((col_inds2, col_inds1))) + ) + + +@register_decomposition(aten.bucketize) +@out_wrapper(exact_dtype=True) +def bucketize( + a: TensorOrNumberLikeType, + boundaries: TensorLikeType, + *, + out_int32: bool = False, + right: bool = False, +): + torch._check( + boundaries.dim() == 1, + lambda: f"boundaries tensor must be 1 dimension but got dim({boundaries.dim()})", + ) + + a = a if isinstance(a, torch.Tensor) else torch.tensor(a) + out_dtype = torch.int32 if out_int32 else torch.int64 + n_boundaries = boundaries.shape[-1] + if n_boundaries == 0: + return torch.zeros_like(a) + # We are trying to find the bucket (defined by pairs of consecutive elements of `boundaries`) + # each element of `a` belongs to. We use binary search to achieve logarithimic complexity, + # but each step of the search is done "in parallel" over all elements of `a` + # can't use int32 as indexes, so we have to do all computations with int64 and convert at the end + start = torch.zeros(a.shape, device=a.device, dtype=torch.int64) + end = start + n_boundaries + # Max depth of the binary search + # Since we can't break out of the loop at different points for different elements of a, + # we just do the max amount of iterations that binary search requires and add condition + # tensor (cond_update below) to stop updating once the search terminates + + # For first iteration through loop we can skip some checks, we have separate implementation + mid = start + (end - start) // 2 + mid_val = boundaries[mid] + if right: + cond_mid = mid_val > a + else: + cond_mid = mid_val >= a + start = torch.where(cond_mid, start, mid + 1) + + if n_boundaries > 1: + cond_update = torch.ones_like(a, dtype=torch.bool) + niters = int(math.log2(n_boundaries)) + for _ in range(niters): + end = torch.where(cond_mid & cond_update, mid, end) + cond_update = start < end + # start might end up pointing to 1 past the end, we guard against that + mid = torch.where(cond_update, start + (end - start) // 2, 0) + mid_val = boundaries[mid] + # If right is true, the buckets are closed on the *left* + # (i.e., we are doing the equivalent of std::upper_bound in C++) + # Otherwise they are closed on the right (std::lower_bound) + if right: + cond_mid = mid_val > a + else: + cond_mid = mid_val >= a + start = torch.where((~cond_mid) & cond_update, mid + 1, start) + + return start.to(dtype=out_dtype) + + +@register_decomposition(aten.cauchy) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("self",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def cauchy(self, median=0, sigma=1, generator=None): + assert generator is None + torch._check( + not utils.is_complex_dtype(self.dtype) + and not utils.is_integer_dtype(self.dtype) + and not utils.is_boolean_dtype(self.dtype), + lambda: f"Cauchy distribution is a continuous probability distribution. \ + dtype must be a floating point but you specified {self.dtype}", + ) + torch._check( + sigma > 0.0, + lambda: f"cauchy_ expects sigma > 0.0, but found sigma={sigma}", + ) + return median + sigma * torch.tan(math.pi * (torch.rand_like(self) - 0.5)) + + +@register_decomposition(aten.exponential) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("self",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def exponential(self, rate=1, generator=None): + assert generator is None + torch._check( + not utils.is_complex_dtype(self.dtype) + and not utils.is_integer_dtype(self.dtype) + and not utils.is_boolean_dtype(self.dtype), + lambda: f"Exponential distribution is a continuous probability distribution. \ + dtype must be a floating point but you specified {self.dtype}", + ) + torch._check( + rate > 0.0, + lambda: f"exponential_ expects lambda > 0.0, but found lambda={rate}", + ) + + uniform_val = torch.rand_like(self) + + # copying numerics of transformation::exponential see comment: + # curand_uniform has (0,1] bounds. log(1) is 0 and exponential excludes 0. + # we need log to be not 0, and not underflow when converted to half + # fast __logf approximation can underflow, so set log to -epsilon/2 for 1 or close to 1 args + epsilon = torch.finfo(uniform_val.dtype).eps / 2 + condition = uniform_val >= 1.0 - epsilon + log_uniform = torch.where(condition, -epsilon, torch.log(uniform_val)) + + return -1 / rate * log_uniform + + +@register_decomposition(aten.geometric) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("self",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def geometric(self, p, generator=None): + assert generator is None + # TODO: fix inductor rand_like for integer, bool dtypes + torch._check( + not utils.is_complex_dtype(self.dtype) + and not utils.is_boolean_dtype(self.dtype), + lambda: f"geometric not implemented for {self.dtype}", + ) + torch._check( + 0 < p and p < 1, + lambda: f"geometric_ expects p to be in (0, 1), but got p={p}", + ) + return torch.floor(torch.log1p(-torch.rand_like(self)) / math.log1p(-p)) + 1 + + +@register_decomposition(aten.log_normal) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("self",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def log_normal(self, mean=1, std=2, generator=None): + assert generator is None + torch._check( + not utils.is_complex_dtype(self.dtype) + and not utils.is_integer_dtype(self.dtype) + and not utils.is_boolean_dtype(self.dtype), + lambda: f"log_normal not implemented for {self.dtype}", + ) + torch._check( + 0 < std, + lambda: f"log_normal_ expects std > 0.0, but found std={std}", + ) + return torch.exp(std * torch.randn_like(self) + mean) + + +# TODO: add support for functionalization aten.normal_functional +# NOTE: the device and dtype will be ignored when shape is None +@register_decomposition(aten.normal) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=( + "mean", + "std", + ), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def normal( + mean=0, + std=1, + size=None, + *, + generator=None, + dtype=None, + layout=None, + device=None, + pin_memory=None, +): + assert layout is None or layout == torch.strided + + if not isinstance(std, TensorLike): + torch._check( + std >= 0, lambda: f"normal expects std >= 0.0, but found std {std}" + ) + + if size is None: + tensors = tuple(t for t in (mean, std) if isinstance(t, TensorLike)) + torch._check( + len(tensors) > 0, + lambda: "normal expects that either mean or std is a tensor, or size is defined", + ) + torch._check( + layout is None and pin_memory is None, + lambda: "Cannot pass layout, or pin_memory without size", + ) + + size = _broadcast_shapes(*(t.shape for t in tensors)) + dtype = tensors[0].dtype + device = tensors[0].device + else: + torch._check( + not isinstance(mean, TensorLike) and not isinstance(std, TensorLike), + lambda: "normal expects mean and std to be scalars when size is defined", + ) + dtype = torch.get_default_dtype() if dtype is None else dtype + device = torch.device("cpu") if device is None else device + + normal_samples = prims.normal( + size, + mean=0.0, + std=1.0, + dtype=dtype, + device=device, + requires_grad=False, + generator=generator, + ) + return std * normal_samples + mean + + +@register_decomposition(aten.normal_) +def normal_(self, mean=0, std=1, *, generator=None): + return normal(mean, std, self.shape, out=self, generator=generator) + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def rad2deg(self: TensorLikeType): + torch._check( + not utils.is_complex_dtype(self.dtype), + lambda: "rad2deg is not supported for complex tensors.", + ) + M_180_PI = 57.295779513082320876798154814105170332405472466564 + return self * M_180_PI + + +@_make_elementwise_unary_reference(ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT) +def deg2rad(self: TensorLikeType): + torch._check( + not utils.is_complex_dtype(self.dtype), + lambda: "deg2rad is not supported for complex tensors.", + ) + M_PI_180 = 0.017453292519943295769236907684886127134428718885417 + return self * M_PI_180 + + +@register_decomposition(aten.count_nonzero) +@out_wrapper() +def count_nonzero(self, dim: Optional[DimsType] = None): + return (self != 0).sum(dim) + + +def _dot_check(self, other): + torch._check( + self.dim() == 1 and other.dim() == 1, + lambda: f"1D tensors expected, but got {self.dim()}D and {other.dim()}D tensors", + ) + + def numel_error(): + return ( + f"inconsistent tensor size, expected tensor [{self.numel()}] and src [{other.numel()}] to have the" + f"same number of elements, but got {self.numel()} and {other.numel()} elements respectively" + ) + + torch._check(self.numel() == other.numel(), numel_error) + + +@register_decomposition(aten.dot) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("self", "other"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def dot(self, other): + if self.is_complex(): + if self.is_conj(): + if other.is_conj(): + return torch.dot(self.conj(), other.conj()).conj() + else: + return torch.vdot(self.conj(), other) + elif other.is_conj(): + return torch.vdot(other.conj(), self) + + _dot_check(self, other) + return (self * other).sum() + + +@register_decomposition(aten.vdot) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("self", "other"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def vdot(self, other): + if not self.is_complex(): + return torch.dot(self, other) + + if self.is_conj(): + if other.is_conj(): + return torch.vdot(other.conj(), self.conj()) + else: + return torch.dot(self.conj(), other) + elif other.is_conj(): + return torch.dot(self, other.conj()).conj() + + _dot_check(self, other) + # The decomposition fails if you do self.conj()... not sure why + return (self.conj_physical() * other).sum() + + +@register_decomposition(aten.select_scatter) +@out_wrapper() +def select_scatter(x: TensorLikeType, src: TensorLikeType, dim: int, index: int): + dim = utils.canonicalize_dim(x.ndim, dim) + mask_shape = [1] * x.ndim + mask_shape[dim] = -1 + if index < 0: + index = index + x.shape[dim] + mask = torch.arange(x.shape[dim], device=x.device).view(mask_shape) == index + src = torch.unsqueeze(src, dim).expand(x.shape) + return torch.where(mask, src, x) + + +# inplace +abs_ = _make_inplace(abs) +acos_ = _make_inplace(acos) +acosh_ = _make_inplace(acosh) +add_ = _make_inplace(add) +addcmul_ = _make_inplace(addcmul) +addcdiv_ = _make_inplace(addcdiv) +asin_ = _make_inplace(asin) +asinh_ = _make_inplace(asinh) +atan_ = _make_inplace(atan) +atanh_ = _make_inplace(atanh) +atan2_ = _make_inplace(atan2) +bitwise_and_ = _make_inplace(bitwise_and) +bitwise_left_shift_ = _make_inplace(bitwise_left_shift) +bitwise_not_ = _make_inplace(bitwise_not) +bitwise_or_ = _make_inplace(bitwise_or) +bitwise_right_shift_ = _make_inplace(bitwise_right_shift) +bitwise_xor_ = _make_inplace(bitwise_xor) +ceil_ = _make_inplace(ceil) +clamp_ = _make_inplace(clamp) +clamp_min_ = _make_inplace(clamp_min) +clamp_max_ = _make_inplace(clamp_max) +conj_physical_ = _make_inplace(conj_physical) +copysign_ = _make_inplace(copysign) +cos_ = _make_inplace(cos) +cosh_ = _make_inplace(cosh) +cumsum_ = _make_inplace(cumsum) +cumprod_ = _make_inplace(cumprod) +deg2rad_ = _make_inplace(deg2rad) +digamma_ = _make_inplace(digamma) +div_ = _make_inplace(div) +eq_ = _make_inplace(eq) +erf_ = _make_inplace(erf) +erfc_ = _make_inplace(erfc) +erfinv_ = _make_inplace(erfinv) +exp_ = _make_inplace(exp) +exp2_ = _make_inplace(exp2) +expm1_ = _make_inplace(expm1) +float_power_ = _make_inplace(float_power) +floor_ = _make_inplace(floor) +floor_divide_ = _make_inplace(floor_divide) +fmod_ = _make_inplace(fmod) +frac_ = _make_inplace(frac) +gcd_ = _make_inplace(gcd) +ge_ = _make_inplace(ge) +gt_ = _make_inplace(gt) +heaviside_ = _make_inplace(heaviside) +hypot_ = _make_inplace(hypot) +igamma_ = _make_inplace(igamma) +igammac_ = _make_inplace(igammac) +i0_ = _make_inplace(i0) +lcm_ = _make_inplace(lcm) +le_ = _make_inplace(le) +lerp_ = _make_inplace(lerp) +lgamma_ = _make_inplace(lgamma) +log10_ = _make_inplace(log10) +log1p_ = _make_inplace(log1p) +log2_ = _make_inplace(log2) +log_ = _make_inplace(log) +logical_and_ = _make_inplace(logical_and) +logical_not_ = _make_inplace(logical_not) +logical_or_ = _make_inplace(logical_or) +logical_xor_ = _make_inplace(logical_xor) +lt_ = _make_inplace(lt) +mul_ = _make_inplace(mul) +mvlgamma_ = _make_inplace(mvlgamma) +nan_to_num_ = _make_inplace(nan_to_num) +ne_ = _make_inplace(ne) +neg_ = _make_inplace(neg) +nextafter_ = _make_inplace(nextafter) +pow_ = _make_inplace(pow) +rad2deg_ = _make_inplace(rad2deg) +reciprocal_ = _make_inplace(reciprocal) +remainder_ = _make_inplace(remainder) +rsqrt_ = _make_inplace(rsqrt) +sgn_ = _make_inplace(sgn) +sigmoid_ = _make_inplace(sigmoid) +sign_ = _make_inplace(sign) +sin_ = _make_inplace(sin) +sinc_ = _make_inplace(sinc) +sinh_ = _make_inplace(sinh) +sqrt_ = _make_inplace(sqrt) +square_ = _make_inplace(square) +sub_ = _make_inplace(sub) +tan_ = _make_inplace(tan) +tanh_ = _make_inplace(tanh) +tril_ = _make_inplace(tril) +triu_ = _make_inplace(triu) +true_divide_ = _make_inplace(true_divide) +trunc_ = _make_inplace(trunc) +xlogy_ = _make_inplace(xlogy) +cauchy_ = _make_inplace(cauchy) +exponential_ = _make_inplace(exponential) +geometric_ = _make_inplace(geometric) +log_normal_ = _make_inplace(log_normal) +zero_ = _make_inplace(zero) + +alias_copy = _make_copy_from_view(aten.alias) +as_strided_copy = _make_copy_from_view(aten.as_strided) +diagonal_copy = _make_copy_from_view(aten.diagonal) +expand_copy = _make_copy_from_view(aten.expand) +# TODO: This must return a sparse tensor if the input is sparse, but refs have +# no sparse support. See narrow_copy_sparse in core. +narrow_copy = _make_copy_from_view(aten.narrow) +t_copy = _make_copy_from_view(aten.t) +unsqueeze_copy = _make_copy_from_view(aten.unsqueeze) +view_copy = _make_copy_from_view(aten.view) + + +# xref: isStorage in torch/csrc/DynamicTypes.cpp +def _isStorage(obj): + return isinstance(obj, (torch.TypedStorage, torch.UntypedStorage)) + + +# xref: compute_sizes in torch/csrc/utils/tensor_new.cpp +def _compute_sizes(seq, scalar_type): + MAX_DIMS = 128 + is_storage = _isStorage(seq) + sizes = [] + # TODO: this is inaccurate, we actually test PySequence_Check + while isinstance(seq, (list, tuple)): + length = len(seq) + if is_storage: + length //= scalar_type.itemsize + sizes.append(length) + if len(sizes) > MAX_DIMS: + raise ValueError(f"too many dimensions '{type(seq).__name__}'") + if length == 0: + break + try: + handle = seq[0] + except Exception: + raise ValueError( # noqa: B904 + f"could not determine the shape of object type '{type(seq).__name__}'" + ) + seq = handle + + return sizes + + +# xref: infer_scalar_type in torch/csrc/utils/tensor_new.cpp +def _infer_scalar_type(obj): + if isinstance(obj, FloatLike): + return torch.get_default_dtype() + if isinstance(obj, IntLike) and not isinstance(obj, bool): # careful! + return torch.int64 + if isinstance(obj, BoolLike): + return torch.bool + if isinstance(obj, complex): + default_dtype = torch.get_default_dtype() + if default_dtype is torch.float: + return torch.cfloat + elif default_dtype is torch.double: + return torch.cdouble + elif default_dtype is torch.half: + return torch.chalf + else: + raise RuntimeError("invalid default scalar type for complex") + if isinstance(obj, torch.Tensor): + return obj.dtype + if isinstance(obj, str): + raise TypeError(f"new(): invalid data type '{type(obj).__name__}'") + # TODO: this is inaccurate, we actually test PySequence_Check + if isinstance(obj, (list, tuple)): + scalarType = None + length = len(obj) + # match NumPy semantics, except use default tensor type instead of + # double. + if length == 0: + return torch.get_default_dtype() + for i in range(length): + cur_item = obj[i] + # TODO: test this + """ + if cur_item is obj: + raise TypeError("new(): self-referential lists are incompatible") + """ + item_scalarType = _infer_scalar_type(cur_item) # recurse! + if scalarType is not None: + scalarType = torch.promote_types(scalarType, item_scalarType) + else: + scalarType = item_scalarType + if scalarType is torch.cdouble: + # this won't change (unless we hit undefined, but that will + # fail later) + return scalarType + return scalarType + raise RuntimeError(f"Could not infer dtype of {type(obj).__name__}") + + +# Analogous to recursive_store +# xref: recursive_store in torch/csrc/utils/tensor_new.cpp +def _recursive_build( + scalarType: torch.dtype, obj: Union[TensorOrNumberLikeType, TensorSequenceType] +): + if isinstance(obj, Tensor) and obj.numel() == 1: + return obj.detach().to(dtype=scalarType, device="cpu", copy=True).view(()) + elif isinstance(obj, Tensor): + # It is invalid to call ".tensor([...])" with a non-scalar tensor in eager mode + # >>> torch.tensor([torch.randn(2)]) + # ValueError: only one element tensors can be converted to Python scalars + # + # But it is possible with a NumPy array + # >>> torch.tensor([np.random.uniform(size=(2,))]).shape + # torch.Size([1, 2]) + return obj.detach().to(dtype=scalarType, device="cpu", copy=True) + elif isinstance(obj, Number): + return torch.scalar_tensor(obj, dtype=scalarType) + + # seq can be a list of tensors + seq = obj + return torch.stack([_recursive_build(scalarType, item) for item in seq]) + + +# xref: internal_new_from_data in torch/csrc/utils/tensor_new.cpp +def _internal_new_from_data( + options, + scalar_type, + device_opt, + data, + copy_variables, + copy_numpy, + type_inference, + pin_memory=False, +): + if isinstance(data, torch.Tensor): + torch._check( + not pin_memory, lambda: "Can't pin tensor constructed from a variable" + ) + var = data + if copy_variables: + var = var.detach() + inferred_scalar_type = var.dtype if type_inference else scalar_type + device = device_opt if device_opt is not None else var.device + return var.to( + device=device, + dtype=inferred_scalar_type, + non_blocking=False, + copy=copy_variables, + ) + + # TODO + if hasattr(data, "__cuda_array_interface__"): + return NotImplemented + + # TODO: test for numpy input with PyArray_Check + + device = device_opt if device_opt is not None else options["device"] + inferred_scalar_type = _infer_scalar_type(data) if type_inference else scalar_type + + # NB: Don't need to avoid tracing, as we aren't going to do any manual + # pointer filling tricks + if _isStorage(data): + return NotImplemented + else: + if torch.device(device).type == "meta": + return NotImplemented + + # In the C implementation, we would directly start poking the memory + # of a freshly allocated CPU tensor. Here, we're going to do an + # alternate, heinously slow implementation: turn each individual + # scalar into a tensor, and then repeatedly cat them together + tensor = _recursive_build(inferred_scalar_type, data) + + tensor = tensor.to(device, inferred_scalar_type, non_blocking=False, copy=False) + + # NB: lift_fresh is not needed, because we built the tensor from scalars + # guaranteeing a fresh tensor in this case + return tensor + + +# xref: tensor_ctor in torch/csrc/utils/tensor_new.cpp +def tensor(data, *, dtype=None, device=None, pin_memory=False, requires_grad=False): + # TODO (or not): support names kwarg + if isinstance(data, torch.Tensor): + warnings.warn( + "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() " + "or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor)" + ) + type_inference = dtype is None + new_tensor = _internal_new_from_data( + # device="cpu" because that's what you get with torch.tensor(2) no + # device by default + {"device": "cpu"}, # TODO: use torch.get_default_tensor_type + dtype if dtype is not None else torch.get_default_dtype(), + device, + data, + copy_variables=True, + copy_numpy=True, + type_inference=type_inference, + pin_memory=pin_memory, + ) + new_tensor.detach_() + if requires_grad: + new_tensor.requires_grad_(requires_grad) + return new_tensor + + +# Views +# We can't model these as above, as the pattern of doing `op(a, out=a)` does not work for a view function +# given that it does not reshape the input (it just copies the result into it) + +# squeeze_ = _make_inplace(squeeze) +# t_ = _make_inplace(t) +# transpose_ = _make_inplace(transpose) +# unsqueeze_ = _make_inplace(unsqueeze) + + +import torch._refs._conversions +import torch._refs.fft +import torch._refs.linalg +import torch._refs.nn.functional +import torch._refs.special diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/__pycache__/_conversions.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/__pycache__/_conversions.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d7e3630d4ef779c0ab0f5ee539584b58e05e6ac9 Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/__pycache__/_conversions.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/__pycache__/fft.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/__pycache__/fft.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7bde6075022081d0d435799abe779a9189d98ac6 Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/__pycache__/fft.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/_conversions.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/_conversions.py new file mode 100644 index 0000000000000000000000000000000000000000..8092469741981efce3c53f424e3b2fb83a38e8eb --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/_conversions.py @@ -0,0 +1,119 @@ +# mypy: allow-untyped-defs +import torch +import torch._prims_common as utils + +# Utilities should come BEFORE this import +from torch._decomp import register_decomposition +from torch._prims_common import TensorLikeType +from torch._prims_common.wrappers import out_wrapper +from torch._refs import _broadcast_shapes + + +# Data conversion references. +# +# Note: this module breaks the usual _refs to torch naming scheme where +# _refs.foo.bar is a ref for torch.foo.bar. The following definitions are not +# part of _refs/__init__.py to avoid name clashes with Python builtin types +# (like int). + +__all__ = [ + # dtypes + "bfloat16", + "bool", + "byte", + "cdouble", + "cfloat", + "chalf", + "char", + "double", + "float", + "half", + "int", + "long", + "short", + # misc + "complex", + "polar", +] + + +def _make_conversion_method(name: str, dtype: torch.dtype): + def fn( + self: TensorLikeType, memory_format: torch.memory_format = torch.preserve_format + ) -> TensorLikeType: + return self.to(dtype, memory_format=memory_format) # type: ignore[call-overload] + + fn.__name__ = name + return fn + + +bfloat16 = _make_conversion_method("bfloat16", torch.bfloat16) + +bool = _make_conversion_method("bool", torch.bool) + +byte = _make_conversion_method("byte", torch.uint8) + +cdouble = _make_conversion_method("cdouble", torch.cdouble) + +cfloat = _make_conversion_method("cfloat", torch.cfloat) + +chalf = _make_conversion_method("chalf", torch.complex32) + +char = _make_conversion_method("char", torch.int8) + +double = _make_conversion_method("double", torch.double) + +float = _make_conversion_method("float", torch.float) + +half = _make_conversion_method("half", torch.half) + +int = _make_conversion_method("int", torch.int) + +long = _make_conversion_method("long", torch.long) + +short = _make_conversion_method("short", torch.short) + + +@register_decomposition(torch._ops.ops.aten.complex) +# Note: complex has type promotion tests disabled due to different semantics. +# exact_dtype is for compat with complex_check_dtype from core. +@out_wrapper(exact_dtype=True) +def complex(real: TensorLikeType, imag: TensorLikeType) -> TensorLikeType: + allowed_dtypes = (torch.float32, torch.float64, torch.float16) + torch._check( + real.dtype in allowed_dtypes and imag.dtype in allowed_dtypes, + lambda: ( + f"Expected both inputs to be Half, Float or Double tensors but got " + f"{real.dtype} and {imag.dtype}" + ), + ) + torch._check( + real.dtype == imag.dtype, + lambda: ( + f"Expected object of scalar type {real.dtype} but got " + f"scalar type {imag.dtype} for second argument" + ), + ) + result_dtype = utils.corresponding_complex_dtype(real.dtype) # type: ignore[arg-type] + common_shape = _broadcast_shapes(real.shape, imag.shape) + result = real.new_empty( + common_shape, + dtype=result_dtype, + layout=real.layout, + device=real.device, + # pin_memory=real.is_pinned(), # NYI + ) + result.real = real + result.imag = imag + return result + + +@register_decomposition(torch._ops.ops.aten.polar) +# Note: polar has type promotion tests disabled due to different semantics. +# exact_dtype is for compat with complex_check_dtype from core. +@out_wrapper(exact_dtype=True) +def polar(abs: TensorLikeType, angle: TensorLikeType) -> TensorLikeType: + result = torch.complex(abs, angle) + result.real = abs * torch.cos(angle) + result.imag = abs * torch.sin(angle) + return result diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/fft.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/fft.py new file mode 100644 index 0000000000000000000000000000000000000000..2558dcf6da0fc5c30ad205abc269b92f2007fac8 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/fft.py @@ -0,0 +1,590 @@ +import math +from typing import Iterable, List, Literal, NamedTuple, Optional, Sequence, Tuple, Union + +import torch +import torch._prims as prims +import torch._prims_common as utils +from torch._decomp import register_decomposition +from torch._prims_common import DimsType, ShapeType, TensorLikeType +from torch._prims_common.wrappers import _maybe_convert_to_dtype, out_wrapper + + +__all__ = [ + # Transforms + "fft", + "fft2", + "fftn", + "hfft", + "hfft2", + "hfftn", + "rfft", + "rfft2", + "rfftn", + "ifft", + "ifft2", + "ifftn", + "ihfft", + "ihfft2", + "ihfftn", + "irfft", + "irfft2", + "irfftn", + # Helpers + "fftshift", + "ifftshift", +] + +NormType = Union[None, Literal["forward", "backward", "ortho"]] +_NORM_VALUES = {None, "forward", "backward", "ortho"} +aten = torch._ops.ops.aten + + +def _apply_norm( + x: TensorLikeType, norm: NormType, signal_numel: int, forward: bool +) -> TensorLikeType: + """Apply normalization to the un-normalized FFT result""" + torch._check(norm in _NORM_VALUES, lambda: f"Invalid normalization mode: {norm}") + + if norm == "ortho": + return x * (1 / math.sqrt(signal_numel)) + + normalize = (not forward and (norm is None or norm == "backward")) or ( + forward and norm == "forward" + ) + return x * (1 / signal_numel) if normalize else x + + +def _promote_type_fft( + dtype: torch.dtype, require_complex: bool, device: torch.device +) -> torch.dtype: + """Helper to promote a dtype to one supported by the FFT primitives""" + if dtype.is_complex: + return dtype + + # Promote integral to default float type + if not dtype.is_floating_point: + dtype = torch.get_default_dtype() + + allowed_types = [torch.float32, torch.float64] + maybe_support_half = device.type in ["cuda", "meta"] + + if maybe_support_half: + allowed_types.append(torch.float16) + torch._check(dtype in allowed_types, lambda: f"Unsupported dtype {dtype}") + + if require_complex: + dtype = utils.corresponding_complex_dtype(dtype) + + return dtype + + +def _maybe_promote_tensor_fft( + t: TensorLikeType, require_complex: bool = False +) -> TensorLikeType: + """Helper to promote a tensor to a dtype supported by the FFT primitives""" + cur_type = t.dtype + new_type = _promote_type_fft(cur_type, require_complex, t.device) + return _maybe_convert_to_dtype(t, new_type) # type: ignore[return-value] + + +def _resize_fft_input( + x: TensorLikeType, dims: Tuple[int, ...], sizes: Tuple[int, ...] +) -> TensorLikeType: + """ + Fixes the shape of x such that x.size(dims[i]) == sizes[i], + either by zero-padding, or by slicing x starting from 0. + """ + assert len(dims) == len(sizes) + must_copy = False + x_sizes = x.shape + pad_amount = [0] * len(x_sizes) * 2 + for i in range(len(dims)): + if sizes[i] == -1: + continue + + if x_sizes[dims[i]] < sizes[i]: + must_copy = True + pad_idx = len(pad_amount) - 2 * dims[i] - 1 + pad_amount[pad_idx] = sizes[i] - x_sizes[dims[i]] + + if x_sizes[dims[i]] > sizes[i]: + x = x.narrow(dims[i], 0, sizes[i]) + + return torch.constant_pad_nd(x, pad_amount) if must_copy else x + + +def _fft_c2r( + func_name: str, + input: TensorLikeType, + n: Optional[int], + dim: int, + norm: NormType, + forward: bool, +) -> TensorLikeType: + """Common code for performing any complex to real FFT (irfft or hfft)""" + input = _maybe_promote_tensor_fft(input, require_complex=True) + dims = (utils.canonicalize_dim(input.ndim, dim, wrap_scalar=False),) + last_dim_size = n if n is not None else 2 * (input.shape[dim] - 1) + torch._check( + last_dim_size >= 1, + lambda: f"Invalid number of data points ({last_dim_size}) specified", + ) + + if n is not None: + input = _resize_fft_input(input, dims=dims, sizes=(last_dim_size // 2 + 1,)) + + if forward: + input = torch.conj(input) + + output = prims.fft_c2r(input, dim=dims, last_dim_size=last_dim_size) + return _apply_norm(output, norm=norm, signal_numel=last_dim_size, forward=forward) + + +def _fft_r2c( + func_name: str, + input: TensorLikeType, + n: Optional[int], + dim: int, + norm: NormType, + forward: bool, + onesided: bool, +) -> TensorLikeType: + """Common code for performing any real to complex FFT (rfft or ihfft)""" + torch._check( + not input.dtype.is_complex, + lambda: f"{func_name} expects a floating point input tensor, but got {input.dtype}", + ) + input = _maybe_promote_tensor_fft(input) + dims = (utils.canonicalize_dim(input.ndim, dim, wrap_scalar=False),) + dim_size = n if n is not None else input.shape[dim] + torch._check( + dim_size >= 1, lambda: f"Invalid number of data points ({dim_size}) specified" + ) + + if n is not None: + input = _resize_fft_input(input, dims, (n,)) + + ret = prims.fft_r2c(input, dim=dims, onesided=onesided) + ret = _apply_norm(ret, norm, dim_size, forward) + return ret if forward else torch.conj(ret) + + +def _fft_c2c( + func_name: str, + input: TensorLikeType, + n: Optional[int], + dim: int, + norm: NormType, + forward: bool, +) -> TensorLikeType: + """Common code for performing any complex to complex FFT (fft or ifft)""" + torch._check( + input.dtype.is_complex, + lambda: f"{func_name} expects a complex input tensor, but got {input.dtype}", + ) + dims = (utils.canonicalize_dim(input.ndim, dim, wrap_scalar=False),) + dim_size = n if n is not None else input.shape[dim] + torch._check( + dim_size >= 1, lambda: f"Invalid number of data points ({dim_size}) specified" + ) + + if n is not None: + input = _resize_fft_input(input, dims, (n,)) + + ret = prims.fft_c2c(input, dim=dims, forward=forward) + return _apply_norm(ret, norm, dim_size, forward) + + +@register_decomposition(aten.fft_fft) +@out_wrapper() +def fft( + input: TensorLikeType, + n: Optional[int] = None, + dim: int = -1, + norm: NormType = None, +) -> TensorLikeType: + if input.dtype.is_complex: + return _fft_c2c("fft", input, n, dim, norm, forward=True) + else: + return _fft_r2c("fft", input, n, dim, norm, forward=True, onesided=False) + + +@register_decomposition(aten.fft_ifft) +@out_wrapper() +def ifft( + input: TensorLikeType, + n: Optional[int] = None, + dim: int = -1, + norm: NormType = None, +) -> TensorLikeType: + if input.dtype.is_complex: + return _fft_c2c("ifft", input, n, dim, norm, forward=False) + else: + return _fft_r2c("ifft", input, n, dim, norm, forward=False, onesided=False) + + +@register_decomposition(aten.fft_rfft) +@out_wrapper() +def rfft( + input: TensorLikeType, + n: Optional[int] = None, + dim: int = -1, + norm: NormType = None, +) -> TensorLikeType: + return _fft_r2c("rfft", input, n, dim, norm, forward=True, onesided=True) + + +@register_decomposition(aten.fft_irfft) +@out_wrapper() +def irfft( + input: TensorLikeType, + n: Optional[int] = None, + dim: int = -1, + norm: NormType = None, +) -> TensorLikeType: + return _fft_c2r("irfft", input, n, dim, norm, forward=False) + + +@register_decomposition(aten.fft_hfft) +@out_wrapper() +def hfft( + input: TensorLikeType, + n: Optional[int] = None, + dim: int = -1, + norm: NormType = None, +) -> TensorLikeType: + return _fft_c2r("hfft", input, n, dim, norm, forward=True) + + +@register_decomposition(aten.fft_ihfft) +@out_wrapper() +def ihfft( + input: TensorLikeType, + n: Optional[int] = None, + dim: int = -1, + norm: NormType = None, +) -> TensorLikeType: + return _fft_r2c("ihfft", input, n, dim, norm, forward=False, onesided=True) + + +class _ShapeAndDims(NamedTuple): + shape: Tuple[int, ...] + dims: Tuple[int, ...] + + +def _canonicalize_fft_shape_and_dim_args( + input: TensorLikeType, shape: Optional[ShapeType], dim: Optional[DimsType] +) -> _ShapeAndDims: + """Convert the shape and dim arguments into a canonical form where neither are optional""" + input_dim = input.ndim + input_sizes = input.shape + + if dim is not None: + if not isinstance(dim, Sequence): + dim = (dim,) + ret_dims = utils.canonicalize_dims(input_dim, dim, wrap_scalar=False) + + # Check dims are unique + torch._check( + len(set(ret_dims)) == len(ret_dims), lambda: "FFT dims must be unique" + ) + + if shape is not None: + if not isinstance(shape, Sequence): + shape = (shape,) + + # Has shape, might have dim + torch._check( + dim is None or len(dim) == len(shape), + lambda: "When given, dim and shape arguments must have the same length", + ) + transform_ndim = len(shape) + + torch._check( + transform_ndim <= input_dim, + lambda: f"Got shape with {transform_ndim} values but input tensor " + f"only has {input_dim} dimensions.", + ) + + # If shape is given, dims defaults to the last len(shape) dimensions + if dim is None: + ret_dims = tuple(range(input_dim - transform_ndim, input_dim)) + + # Translate any -1 values in shape to the default length + ret_shape = tuple( + s if s != -1 else input_sizes[d] for (s, d) in zip(shape, ret_dims) # type: ignore[possibly-undefined] + ) + elif dim is None: + # No shape, no dim + ret_dims = tuple(range(input_dim)) + ret_shape = tuple(input_sizes) + else: + # No shape, has dim + ret_shape = tuple(input_sizes[d] for d in ret_dims) # type: ignore[possibly-undefined] + + for n in ret_shape: + torch._check(n > 0, lambda: f"Invalid number of data points ({n}) specified") + + return _ShapeAndDims(shape=ret_shape, dims=ret_dims) # type: ignore[possibly-undefined] + + +def _prod(xs: Iterable[int]) -> int: + """Compute product of a list""" + prod = 1 + for x in xs: + prod *= x + return prod + + +def _fftn_c2c( + function_name: str, + input: TensorLikeType, + shape: Tuple[int, ...], + dim: Tuple[int, ...], + norm: NormType, + forward: bool, +) -> TensorLikeType: + """Common code for n-dimensional complex to complex FFTs (fftn or ifftn)""" + torch._check( + input.dtype.is_complex, + lambda: f"{function_name} expects a complex input tensor, " + f"but got {input.dtype}", + ) + x = _resize_fft_input(input, dim, shape) + output = prims.fft_c2c(x, dim=dim, forward=forward) + return _apply_norm(output, norm=norm, signal_numel=_prod(shape), forward=forward) + + +@register_decomposition(aten.fft_fftn) +@out_wrapper() +def fftn( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = None, + norm: NormType = None, +) -> TensorLikeType: + (shape, dim) = _canonicalize_fft_shape_and_dim_args(input, s, dim) + x = _maybe_promote_tensor_fft(input, require_complex=True) + return _fftn_c2c("fftn", x, shape, dim, norm, forward=True) + + +@register_decomposition(aten.fft_ifftn) +@out_wrapper() +def ifftn( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = None, + norm: NormType = None, +) -> TensorLikeType: + (shape, dim) = _canonicalize_fft_shape_and_dim_args(input, s, dim) + x = _maybe_promote_tensor_fft(input, require_complex=True) + return _fftn_c2c("ifftn", x, shape, dim, norm, forward=False) + + +@register_decomposition(aten.fft_rfftn) +@out_wrapper() +def rfftn( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = None, + norm: NormType = None, +) -> TensorLikeType: + torch._check( + not input.dtype.is_complex, + lambda: f"rfftn expects a real-valued input tensor, but got {input.dtype}", + ) + shape, dim = _canonicalize_fft_shape_and_dim_args(input, s, dim) + input = _maybe_promote_tensor_fft(input, require_complex=False) + input = _resize_fft_input(input, dim, shape) + out = prims.fft_r2c(input, dim=dim, onesided=True) + return _apply_norm(out, norm=norm, signal_numel=_prod(shape), forward=True) + + +@register_decomposition(aten.fft_ihfftn) +@out_wrapper() +def ihfftn( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = None, + norm: NormType = None, +) -> TensorLikeType: + torch._check( + not input.dtype.is_complex, + lambda: f"ihfftn expects a real-valued input tensor, but got {input.dtype}", + ) + shape, dim = _canonicalize_fft_shape_and_dim_args(input, s, dim) + torch._check(len(shape) > 0, lambda: "ihfftn must transform at least one axis") + input = _maybe_promote_tensor_fft(input, require_complex=False) + input = _resize_fft_input(input, dim, shape) + + tmp = prims.fft_r2c(input, dim=dim[-1:], onesided=True) + + if len(dim) == 1: + tmp = _apply_norm(tmp, norm=norm, signal_numel=shape[0], forward=False) + return prims.conj(tmp) + + tmp = prims.conj_physical(tmp) + tmp = prims.fft_c2c(tmp, dim=dim[:-1], forward=False) + return _apply_norm(tmp, norm=norm, signal_numel=_prod(shape), forward=False) + + +class _CanonicalizeC2rReturn(NamedTuple): + shape: Tuple[int, ...] + dim: Tuple[int, ...] + last_dim_size: int + + +def _canonicalize_fft_c2r_shape_and_dim_args( + fname: str, + input: TensorLikeType, + s: Optional[ShapeType], + dim: Optional[DimsType], +) -> _CanonicalizeC2rReturn: + """Canonicalize shape and dim arguments for n-dimensional c2r transforms, + as well as calculating the last_dim_size which is shape[dim[-1]] for the output""" + (shape, dim) = _canonicalize_fft_shape_and_dim_args(input, s, dim) + torch._check(len(shape) > 0, lambda: f"{fname} must transform at least one axis") + + if s is None or s[-1] == -1: + last_dim_size = 2 * (input.shape[dim[-1]] - 1) + else: + last_dim_size = shape[-1] + + torch._check( + last_dim_size >= 1, + lambda: f"Invalid number of data points ({last_dim_size}) specified", + ) + + shape_list = list(shape) + shape_list[-1] = last_dim_size // 2 + 1 + return _CanonicalizeC2rReturn( + shape=tuple(shape_list), dim=dim, last_dim_size=last_dim_size + ) + + +@register_decomposition(aten.fft_irfftn) +@out_wrapper() +def irfftn( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = None, + norm: NormType = None, +) -> TensorLikeType: + shape, dim, last_dim_size = _canonicalize_fft_c2r_shape_and_dim_args( + "irfftn", input, s, dim + ) + input = _maybe_promote_tensor_fft(input, require_complex=True) + input = _resize_fft_input(input, dim, shape) + out = prims.fft_c2r(input, dim=dim, last_dim_size=last_dim_size) + return _apply_norm(out, norm, _prod(out.shape[d] for d in dim), forward=False) + + +@register_decomposition(aten.fft_hfftn) +@out_wrapper() +def hfftn( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = None, + norm: NormType = None, +) -> TensorLikeType: + shape, dim, last_dim_size = _canonicalize_fft_c2r_shape_and_dim_args( + "hfftn", input, s, dim + ) + input = _maybe_promote_tensor_fft(input, require_complex=True) + input = _resize_fft_input(input, dim, shape) + + tmp = prims.fft_c2c(input, dim=dim[:-1], forward=True) if len(dim) > 1 else input + tmp = _apply_norm(tmp, norm, _prod(shape[:-1]), forward=True) + tmp = prims.conj_physical(tmp) + out = prims.fft_c2r(tmp, dim=dim[-1:], last_dim_size=last_dim_size) + return _apply_norm(out, norm, last_dim_size, forward=True) + + +@register_decomposition(aten.fft_fft2) +@out_wrapper() +def fft2( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = (-2, -1), + norm: NormType = None, +) -> TensorLikeType: + return torch.fft.fftn(input, s=s, dim=dim, norm=norm) + + +@register_decomposition(aten.fft_ifft2) +@out_wrapper() +def ifft2( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = (-2, -1), + norm: NormType = None, +) -> TensorLikeType: + return torch.fft.ifftn(input, s=s, dim=dim, norm=norm) + + +@register_decomposition(aten.fft_rfft2) +@out_wrapper() +def rfft2( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = (-2, -1), + norm: NormType = None, +) -> TensorLikeType: + return torch.fft.rfftn(input, s=s, dim=dim, norm=norm) + + +@register_decomposition(aten.fft_irfft2) +@out_wrapper() +def irfft2( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = (-2, -1), + norm: NormType = None, +) -> TensorLikeType: + return torch.fft.irfftn(input, s=s, dim=dim, norm=norm) + + +@register_decomposition(aten.fft_hfft2) +@out_wrapper() +def hfft2( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = (-2, -1), + norm: NormType = None, +) -> TensorLikeType: + return torch.fft.hfftn(input, s=s, dim=dim, norm=norm) + + +@register_decomposition(aten.fft_ihfft2) +@out_wrapper() +def ihfft2( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = (-2, -1), + norm: NormType = None, +) -> TensorLikeType: + return torch.fft.ihfftn(input, s=s, dim=dim, norm=norm) + + +def _default_alldims(dim: Optional[DimsType], x: TensorLikeType) -> List[int]: + """Convert Optional[DimsType] to a simple list, defaulting to all dimensions""" + if dim is None: + return list(range(x.ndim)) + elif not isinstance(dim, Sequence): + return [dim] + else: + return list(dim) + + +@register_decomposition(aten.fft_fftshift) +def fftshift(input: TensorLikeType, dim: Optional[DimsType] = None) -> TensorLikeType: + dims = _default_alldims(dim, input) + shift = [input.shape[d] // 2 for d in dims] + return torch.roll(input, shift, dims) + + +@register_decomposition(aten.fft_ifftshift) +def ifftshift(input: TensorLikeType, dim: Optional[DimsType] = None) -> TensorLikeType: + dims = _default_alldims(dim, input) + shift = [(input.shape[d] + 1) // 2 for d in dims] + return torch.roll(input, shift, dims) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/linalg/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/linalg/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6585f57e3d643bfd58c0b208bf33e3a1ee732d8c --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/linalg/__init__.py @@ -0,0 +1,309 @@ +# mypy: allow-untyped-defs +from functools import partial +from typing import Optional, Tuple, Union + +import torch +import torch._prims as prims +import torch._prims_common as utils +import torch._refs as refs +import torch._refs.linalg as linalg +from torch import Tensor +from torch._prims_common import ( + check_fp_or_complex, + check_is_matrix, + Dim, + DimsType, + ELEMENTWISE_TYPE_PROMOTION_KIND, + IntLike, + TensorLikeType, +) +from torch._prims_common.wrappers import ( + _maybe_convert_to_dtype, + elementwise_type_promotion_wrapper, + out_wrapper, +) + + +__all__ = [ + "diagonal", + "matrix_norm", + "norm", + "svd", + "svdvals", + "vector_norm", + "vecdot", + "cross", +] + + +def _check_norm_dtype(dtype: Optional[torch.dtype], x_dtype: torch.dtype, fn_name: str): + """ + Checks related to the dtype kwarg in `linalg.*norm` functions + """ + if dtype is not None: + torch._check( + utils.is_float_dtype(dtype) or utils.is_complex_dtype(dtype), + lambda: f"{fn_name}: dtype should be floating point or complex. Got {dtype}", + ) + torch._check( + utils.is_complex_dtype(dtype) == utils.is_complex_dtype(x_dtype), + lambda: "{fn_name}: dtype should be {d} for {d} inputs. Got {dtype}".format( + fn_name=fn_name, + d="complex" if utils.is_complex_dtype(x_dtype) else "real", + dtype=dtype, + ), + ) + torch._check( + utils.get_higher_dtype(dtype, x_dtype) == dtype, + lambda: f"{fn_name}: the dtype of the input ({x_dtype}) should be convertible " + "without narrowing to the specified dtype ({dtype})", + ) + + +import operator + +# Utilities should come BEFORE this import +from torch._decomp import register_decomposition +from torch._decomp.decompositions import pw_cast_for_opmath + + +@register_decomposition(torch._ops.ops.aten.linalg_cross) +@out_wrapper() +@pw_cast_for_opmath +def cross(a: Tensor, b: Tensor, dim: int = -1): + torch._check( + a.ndim == b.ndim, + lambda: "linalg.cross: inputs must have the same number of dimensions.", + ) + torch._check( + a.size(dim) == 3 and b.size(dim) == 3, + lambda: f"linalg.cross: inputs dim {dim} must have length 3, got {a.size(dim)} and {b.size(dim)}", + ) + a, b = torch.broadcast_tensors(a, b) + dim = utils.canonicalize_dim(a.ndim, dim) + idx = torch.arange(3, device=a.device) + return a.index_select(dim, (idx + 1) % 3) * b.index_select( + dim, (idx + 2) % 3 + ) - a.index_select(dim, (idx + 2) % 3) * b.index_select(dim, (idx + 1) % 3) + + +def diagonal( + input: TensorLikeType, + *, + offset: int = 0, + dim1: int = -2, + dim2: int = -1, +) -> TensorLikeType: + return torch.diagonal(input, offset=offset, dim1=dim1, dim2=dim2) + + +@register_decomposition(torch._ops.ops.aten.linalg_vector_norm) +@out_wrapper(exact_dtype=True) +def vector_norm( + x: TensorLikeType, + ord: Union[float, int] = 2, + dim: Optional[DimsType] = None, + keepdim: bool = False, + *, + dtype: Optional[torch.dtype] = None, +) -> Tensor: + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + # Checks + check_fp_or_complex(x.dtype, "linalg.vector_norm") + + if isinstance(dim, Dim): + dim = [dim] # type: ignore[assignment] + + if guard_size_oblivious(x.numel() == 0) and (ord < 0.0 or ord == float("inf")): + torch._check( + dim is not None and len(dim) != 0, + lambda: f"linalg.vector_norm cannot compute the {ord} norm on an empty tensor " + "because the operation does not have an identity", + ) + shape = x.shape + assert dim is not None # mypy does not seem to be able to see through check? + for d in dim: + torch._check( + shape[d] != 0, + lambda: f"linalg.vector_norm cannot compute the {ord} norm on the " + f"dimension {d} because this dimension is empty and the " + "operation does not have an identity", + ) + _check_norm_dtype(dtype, x.dtype, "linalg.vector_norm") + + computation_dtype, result_dtype = utils.reduction_dtypes( + x, utils.REDUCTION_OUTPUT_TYPE_KIND.COMPLEX_TO_FLOAT, dtype + ) + + to_result_dtype = partial(_maybe_convert_to_dtype, dtype=result_dtype) + + # Implementation + if ord == 0.0: + return torch.sum(torch.ne(x, 0.0), dim=dim, keepdim=keepdim, dtype=result_dtype) + elif ord == float("inf"): + return to_result_dtype(torch.amax(torch.abs(x), dim=dim, keepdim=keepdim)) # type: ignore[return-value,arg-type] + elif ord == float("-inf"): + return to_result_dtype(torch.amin(torch.abs(x), dim=dim, keepdim=keepdim)) # type: ignore[return-value,arg-type] + else: + # From here on the computation dtype is important as the reduction is non-trivial + x = _maybe_convert_to_dtype(x, computation_dtype) # type: ignore[assignment] + reduce_sum = partial(torch.sum, dim=dim, keepdim=keepdim) + + is_ord_even = ord % 2 == 0 if isinstance(ord, IntLike) else ord % 2.0 == 0.0 + if not (is_ord_even and utils.is_float_dtype(x.dtype)): + x = torch.abs(x) + return to_result_dtype(torch.pow(reduce_sum(torch.pow(x, ord)), 1.0 / ord)) # type: ignore[return-value] + + +def _backshift_permutation(dim0, dim1, ndim): + # Auxiliary function for matrix_norm + # Computes the permutation that moves the two given dimensions to the back + ret = [i for i in range(ndim) if i != dim0 and i != dim1] + ret.extend((dim0, dim1)) + return ret + + +def _inverse_permutation(perm): + # Given a permutation, returns its inverse. It's equivalent to argsort on an array + return [i for i, j in sorted(enumerate(perm), key=operator.itemgetter(1))] + + +# CompositeImplicitAutograd +@out_wrapper(exact_dtype=True) +def matrix_norm( + A: TensorLikeType, + ord: Union[float, str] = "fro", + dim: DimsType = (-2, -1), + keepdim: bool = False, + *, + dtype: Optional[torch.dtype] = None, +) -> TensorLikeType: + # shape + check_is_matrix(A, "linalg.matrix_norm") + # dim + dim = utils.canonicalize_dims(A.ndim, dim) + if isinstance(dim, Dim): + dim = (dim,) # type: ignore[assignment] + torch._check( + len(dim) == 2, lambda: "linalg.matrix_norm: dim must be a 2-tuple. Got {dim}" + ) + torch._check( + dim[0] != dim[1], + lambda: "linalg.matrix_norm: dims must be different. Got ({dim[0]}, {dim[1]})", + ) + # dtype arg + _check_norm_dtype(dtype, A.dtype, "linalg.matrix_norm") + + if isinstance(ord, str): + # ord + torch._check( + ord in ("fro", "nuc"), + lambda: "linalg.matrix_norm: Order {ord} not supported.", + ) + # dtype + check_fp_or_complex( + A.dtype, "linalg.matrix_norm", allow_low_precision_dtypes=ord != "nuc" + ) + + if ord == "fro": + return vector_norm(A, 2, dim, keepdim, dtype=dtype) + else: # ord == "nuc" + if dtype is not None: + A = _maybe_convert_to_dtype(A, dtype) # type: ignore[assignment] + perm = _backshift_permutation(dim[0], dim[1], A.ndim) + result = torch.sum(svdvals(prims.transpose(A, perm)), -1, keepdim) + if keepdim: + inv_perm = _inverse_permutation(perm) + result = prims.transpose(torch.unsqueeze(result, -1), inv_perm) + return result + else: + # ord + abs_ord = abs(ord) + torch._check( + abs_ord in (2, 1, float("inf")), + lambda: "linalg.matrix_norm: Order {ord} not supported.", + ) + # dtype + check_fp_or_complex( + A.dtype, "linalg.matrix_norm", allow_low_precision_dtypes=ord != 2 + ) + + max_min = partial(torch.amax if ord > 0.0 else torch.amin, keepdim=keepdim) + + if abs_ord == 2.0: + if dtype is not None: + A = _maybe_convert_to_dtype(A, dtype) # type: ignore[assignment] + perm = _backshift_permutation(dim[0], dim[1], A.ndim) + result = max_min(svdvals(prims.transpose(A, perm)), dim=-1) + if keepdim: + inv_perm = _inverse_permutation(perm) + result = prims.transpose(torch.unsqueeze(result, -1), inv_perm) + return result + else: # 1, -1, inf, -inf + dim0, dim1 = dim + if abs_ord == float("inf"): + dim0, dim1 = dim1, dim0 + if not keepdim and (dim0 < dim1): + dim1 -= 1 + return max_min( + vector_norm(A, 1.0, dim=dim0, keepdim=keepdim, dtype=dtype), dim1 + ) + + +# CompositeImplicitAutograd +@out_wrapper(exact_dtype=True) +def norm( + A: TensorLikeType, + ord: Optional[Union[float, str]] = None, + dim: Optional[DimsType] = None, + keepdim: bool = False, + *, + dtype: Optional[torch.dtype] = None, +) -> TensorLikeType: + if dim is not None: + if isinstance(dim, Dim): + dim = (dim,) # type: ignore[assignment] + torch._check( + len(dim) in (1, 2), + lambda: "linalg.norm: If dim is specified, it must be of length 1 or 2. Got {dim}", + ) + elif ord is not None: + torch._check( + A.ndim in (1, 2), + lambda: "linalg.norm: If dim is not specified but ord is, the input must be 1D or 2D. Got {A.ndim}D", + ) + + if ord is not None and ( + (dim is not None and len(dim) == 2) or (dim is None and A.ndim == 2) + ): + if dim is None: + dim = (0, 1) + return matrix_norm(A, ord, dim, keepdim, dtype=dtype) + else: + if ord is None: + ord = 2.0 + return vector_norm(A, ord, dim, keepdim, dtype=dtype) # type: ignore[arg-type] + + +# CompositeImplicitAutograd +@out_wrapper("U", "S", "Vh", exact_dtype=True) +def svd(A: TensorLikeType, full_matrices: bool = True) -> Tuple[Tensor, Tensor, Tensor]: + return prims.svd(A, full_matrices=full_matrices) + + +# CompositeImplicitAutograd +@out_wrapper(exact_dtype=True) +def svdvals(A: TensorLikeType) -> Tensor: + return svd(A, full_matrices=False)[1] + + +# CompositeImplicitAutograd +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("x", "y"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def vecdot(x: Tensor, y: Tensor, dim: int = -1) -> Tensor: + check_fp_or_complex(x.dtype, "linalg.vecdot") + return (x.conj() * y).sum(dim=dim) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/linalg/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/linalg/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d990d57153520e7d1e888b3ec0ee150169812924 Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/linalg/__pycache__/__init__.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/nn/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/nn/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b7414d43515a6eebe92ab2c204b334cc345fff51 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/nn/__init__.py @@ -0,0 +1,4 @@ +from typing import List + + +__all__: List[str] = [] diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/nn/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/nn/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..269c5d6c91719f62b0ef3ad5a1ab47b14e7a09c7 Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/nn/__pycache__/__init__.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/nn/functional/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/nn/functional/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c8087408da93c1cf44deb4679002df51be2f8932 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/nn/functional/__init__.py @@ -0,0 +1,1279 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import math +from functools import wraps +from typing import Callable, Optional, Union + +import torch +import torch._prims as prims +import torch._prims_common as utils +import torch._refs as refs +from torch._decomp import register_decomposition +from torch._prims_common import ( + ELEMENTWISE_TYPE_PROMOTION_KIND, + NumberType, + ShapeType, + TensorLike, + TensorLikeType, +) +from torch._prims_common.wrappers import ( + elementwise_type_promotion_wrapper, + elementwise_unary_scalar_wrapper, + out_wrapper, +) +from torch._refs import _make_inplace + + +__all__ = [ + "alpha_dropout", + "celu", + "celu_", + "channel_shuffle", + "dropout", + "elu", + "elu_", + "gelu", + "glu", + "group_norm", + "hardshrink", + "hardtanh", + "hinge_embedding_loss", + "huber_loss", + "l1_loss", + "layer_norm", + "leaky_relu", + "log_softmax", + "margin_ranking_loss", + "mish", + "mish_", + "mse_loss", + "nll_loss", + "pairwise_distance", + "pdist", + "poisson_nll_loss", + "prelu", + "relu", + "relu6", + "selu", + "selu_", + "smooth_l1_loss", + "softmax", + "softmin", + "softplus", + "softshrink", + "tanhshrink", + "threshold", + "threshold_", + "triplet_margin_loss", +] + +Tensor = torch.Tensor +aten = torch._ops.ops.aten +DispatchKey = torch._C.DispatchKey # type: ignore[attr-defined] + + +def _dropout_helper( + self: TensorLikeType, + val: float, +) -> TensorLikeType: + """ + Helper function for all dropout-type operators. During training, + some of the elements of the input tensor are randomly masked. + + Returns the masked tensor of the boolean values. + + """ + + return ( + refs._uniform_helper( + self.shape, low=0.0, high=1.0, dtype=torch.float32, device=self.device + ) + < val + ) + + +@register_decomposition(aten.alpha_dropout) +def alpha_dropout( + self: TensorLikeType, p: float = 0.5, training: bool = False, inplace: bool = False +) -> TensorLikeType: + if inplace: + raise NotImplementedError + + if not training: + return self + + torch._check( + p <= 1 and p >= 0, + lambda: f"dropout probability has to be between 0 and 1, but got, {p}", + ) + + if p == 1: + return torch.zeros_like(self) + + if p == 0: + return self + + dropout_mask = _dropout_helper(self, 1 - p) + + # From paper: Self-Normalizing Neural Networks (https://arxiv.org/pdf/1706.02515.pdf) + # alpha = - SELU.alpha * SELU.scale, here + # SELU.alpha = 1.6732632423543772848170429916717 and + # SELU.scale = 1.0507009873554804934193349852946 + alpha = -1.7580993408473766 + + a = 1.0 / math.sqrt((alpha * alpha * p + 1) * (1 - p)) + b = torch.logical_not(dropout_mask) + b = b * (alpha * a) + alpha * a * p + dropout_mask = a * dropout_mask + + return self * dropout_mask + b + + +def _inplace_wrapper(fn): + """ + Given a nn.functional non-linearity, implements its `inplace: bool` argument + """ + + # nb. We use the name of the first argument used in the unary references + @wraps(fn) + def _fn(a, *args, inplace=False, **kwargs): + if inplace: + torch._check( + "out" not in kwargs, + lambda: "Cannot set inplace=True and pass out= at the same time", + ) + return fn(a, *args, inplace=False, out=a, **kwargs) + else: + return fn(a, *args, inplace=False, **kwargs) + + return _fn + + +# celu is implemented specially because it has an alpha argument +# celu is very similar to elu +@register_decomposition(aten.celu) +@_inplace_wrapper +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def celu( + a: TensorLikeType, alpha: Optional[NumberType] = None, inplace: bool = False +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.celu + """ + + if inplace: + raise NotImplementedError + + rhs: TensorLikeType + if alpha is not None: + python_type = utils.dtype_to_type(a.dtype) + if not utils.is_weakly_lesser_type(type(alpha), python_type): + msg = f"alpha argument of type {type(alpha)} cannot be safely cast to type {python_type}!" + raise ValueError(msg) + rhs = alpha * torch.expm1(torch.true_divide(a, alpha)) # type: ignore[arg-type] + else: + rhs = torch.expm1(a) + + return torch.where(a > 0, a, rhs) + + +@_inplace_wrapper +@out_wrapper() +def dropout( + a: TensorLikeType, p: float = 0.5, training: bool = True, inplace: bool = False +) -> TensorLikeType: + if inplace: + raise NotImplementedError + + if not training: + return a + + torch._check( + p <= 1 and p >= 0, + lambda: f"dropout probability has to be between 0 and 1, but got, {p}", + ) + + if p == 1: + return torch.zeros_like(a) + + if p == 0: + return a + + scale = 1 / (1 - p) + dropout_mask = _dropout_helper(a, 1 - p) + + return a * dropout_mask * scale + + +@register_decomposition(aten.elu) +@_inplace_wrapper +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def elu( + a: TensorLikeType, + alpha: NumberType = 1.0, + scale: NumberType = 1.0, + input_scale: NumberType = 1.0, + inplace: bool = False, +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.elu + """ + if inplace: + raise NotImplementedError + + # nb. This should be factored out into a can_cast aux function + python_type = utils.dtype_to_type(a.dtype) + torch._check( + utils.is_weakly_lesser_type(type(input_scale), python_type), + lambda: f"input_scale argument of type {type(input_scale)} cannot be safely cast to type {python_type}!", + ) + torch._check( + utils.is_weakly_lesser_type(type(scale), python_type), + lambda: f"scale argument of type {type(scale)} cannot be safely cast to type {python_type}!", + ) + torch._check( + utils.is_weakly_lesser_type(type(alpha), python_type), + lambda: f"alpha argument of type {type(alpha)} cannot be safely cast to type {python_type}!", + ) + + return torch.where(a > 0, scale * a, (alpha * scale) * torch.expm1(a * input_scale)) + + +@register_decomposition(aten.relu) +@_inplace_wrapper +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def relu(a: TensorLikeType, inplace: bool = False) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.relu + """ + + if inplace: + raise NotImplementedError + + return torch.where(torch.le(a, 0), 0, a) + + +@register_decomposition(aten.channel_shuffle) +@out_wrapper() +def channel_shuffle(input: TensorLikeType, groups: int) -> TensorLikeType: + """ + Reference implementation of :func:`torch.nn.functional.channel_shuffle`. + """ + from torch._meta_registrations import device_hint + + torch._check( + input.dim() > 2, + lambda: f"channel_shuffle expects input with > 2 dims, but got input with sizes {list(input.size())}", + ) + c = input.shape[1] + torch._check( + groups > 0, + lambda: f"Number of groups to divide channels in must be positive. Value of groups:{groups}", + ) + torch._check( + (c % groups) == 0, + lambda: f"Number of channels must be divisible by groups. Got {c} channels and {groups} groups.", + ) + n = input.shape[0] + cg = c // groups + dhw = input.shape[2:] + + if input.numel() == 0 or ( + device_hint(input) == "cuda" and (groups == 1 or groups == c) + ): + return input.view(input.shape) + + return ( + input.reshape(n, groups, cg, *dhw) + .transpose(1, 2) + .reshape(input.shape) + .contiguous() + ) + + +def group_norm( + input: Tensor, + num_groups: int, + weight: Optional[Tensor] = None, + bias: Optional[Tensor] = None, + eps: float = 1e-5, +) -> Tensor: + """ + Reference implementation of :func:`torch.nn.functional.group_norm`. + """ + torch._check( + input.ndim >= 2, + lambda: f"Expected at least 2 dimensions for input tensor but received {input.ndim}", + ) + + batch_size = input.shape[0] + num_channels = input.shape[1] + torch._check( + num_channels % num_groups == 0, + lambda: "Expected number of channels in input to be divisible by num_groups, " + + f"but got input of shape {input.shape} and num_groups = {num_groups}", + ) + + # input shape is (N, C, *), so we flatten all inner dimensions except (N, C) + flattened_inner_size = 1 + for dim_length in input.shape[2:]: + flattened_inner_size *= dim_length + + return torch.native_group_norm( + input, + weight, + bias, + batch_size, + num_channels, + flattened_inner_size, + num_groups, + eps, + )[0] + + +def layer_norm( + input: Tensor, + normalized_shape: ShapeType, + weight: Optional[Tensor] = None, + bias: Optional[Tensor] = None, + eps: float = 1e-5, +) -> Tensor: + """ + Reference implementation of :func:`torch.nn.functional.layer_norm`. + """ + return torch.native_layer_norm(input, normalized_shape, weight, bias, eps)[0] + + +@register_decomposition(aten.leaky_relu) +@_inplace_wrapper +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def leaky_relu( + a: TensorLikeType, negative_slope: float = 0.01, inplace: bool = False +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.leaky_relu + """ + + if inplace: + raise NotImplementedError + + python_type = utils.dtype_to_type(a.dtype) + if not utils.is_weakly_lesser_type(type(negative_slope), python_type): + msg = f"negative_slope argument of type {type(negative_slope)} cannot be safely cast to type {python_type}!" + raise ValueError(msg) + return torch.where(torch.gt(a, 0), a, torch.mul(a, negative_slope)) + + +@register_decomposition(aten.mish) +@_inplace_wrapper +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def mish(a: TensorLikeType, inplace: bool = False) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.mish + """ + + if inplace: + raise NotImplementedError + return a * torch.tanh(torch.nn.functional.softplus(a)) + + +@register_decomposition(aten.selu) +@_inplace_wrapper +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def selu(a: TensorLikeType, inplace: bool = False) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.selu + """ + if inplace: + raise NotImplementedError + + alpha = 1.6732632423543772848170429916717 + scale = 1.0507009873554804934193349852946 + + rhs = alpha * torch.expm1(a) + + return scale * torch.where(a > 0, a, rhs) + + +# Forwarding alias: the functional variant doesn't support the out kwarg +# CompositeImplicitAutograd - don't register decomp +def softmax( + a: TensorLikeType, + dim: Optional[int] = None, + _stacklevel: int = 3, # for compat when using TorchRefsMode(strict=True) + dtype: Optional[torch.dtype] = None, +) -> TensorLikeType: + # The error is for compat with regular PyTorch, which has this behavior + # deprecated. For PrimTorch, it's fine to drop support for deprecated + # behavior because it requires explicit opt in. This error is to inform + # users how to update their calls. + torch._check(dim is not None, lambda: "implicit dim not supported, use dim=X") + return torch.softmax(a=a, dim=dim, dtype=dtype) # type: ignore[call-overload] + + +# CompositeImplicitAutograd - don't register decomp +def softmin( + a: TensorLikeType, + dim: Optional[int] = None, + _stacklevel: int = 3, # for compat when using TorchRefsMode(strict=True) + dtype: Optional[torch.dtype] = None, +) -> TensorLikeType: + # The error is for compat with regular PyTorch, which has this behavior + # deprecated. For PrimTorch, it's fine to drop support for deprecated + # behavior because it requires explicit opt in. This error is to inform + # users how to update their calls. + torch._check(dim is not None, lambda: "implicit dim not supported, use dim=X") + return torch.softmax(a=-a, dim=dim, dtype=dtype) # type: ignore[call-overload] + + +# softplus is implemented specially because it has beta and threshold arguments +@register_decomposition(aten.softplus) +@_inplace_wrapper +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def softplus( + a: TensorLikeType, + beta: Optional[NumberType] = None, + threshold: NumberType = 20, + inplace: bool = False, +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.softplus + """ + + if inplace: + raise NotImplementedError + + rhs: TensorLikeType + if beta is not None: + python_type = utils.dtype_to_type(a.dtype) + if not utils.is_weakly_lesser_type(type(beta), python_type): + msg = f"beta argument of type {type(beta)} cannot be safely cast to type {python_type}!" + raise ValueError(msg) + scaled_input = a * beta + rhs = torch.true_divide(torch.log1p(torch.exp(scaled_input)), beta) # type: ignore[arg-type] + + else: + scaled_input = a + rhs = torch.log1p(torch.exp(scaled_input)) + + return torch.where(scaled_input > threshold, a, rhs) + + +@aten.hardshrink.default.py_impl(DispatchKey.Autograd) +@register_decomposition(aten.hardshrink) +@out_wrapper() +def hardshrink(a: TensorLikeType, lambd: float = 0.5): + # Formula for reference, + # hardshrink(x) = x if x > lambd + # = x if x < -lambd + # = 0 otherwise + return torch.where(torch.abs(a) <= lambd, 0, a) + + +@aten.softshrink.default.py_impl(DispatchKey.Autograd) +@register_decomposition(aten.softshrink) +@out_wrapper() +def softshrink(a: TensorLikeType, lambd: float = 0.5): + # Formula for reference, + # softshrink(x) = x - lambd if x > lambd + # = x + lambd if x < -lambd + # = 0 otherwise + torch._check( + lambd >= 0, + lambda: f"lambda must be greater or equal to 0, but found to be {lambd}", + ) + # We implement this in one torch.where to generate better code in the backward + # see https://github.com/pytorch/pytorch/pull/107052#discussion_r1293748211 + return torch.where(torch.abs(a) > lambd, a - torch.sign(a) * lambd, 0) + + +# Losses +def _reduction_int_to_str(reduction: int) -> str: + from torch._decomp.decompositions import Reduction + + if reduction == Reduction.NONE.value: + return "none" + elif reduction == Reduction.MEAN.value: + return "mean" + elif reduction == Reduction.SUM.value: + return "sum" + else: + raise ValueError(f"{reduction} is not a valid value for reduction") + + +def _apply_loss_reduction(loss: TensorLikeType, reduction: str) -> TensorLikeType: + if reduction == "sum": + return torch.sum(loss) + elif reduction == "mean": + return torch.mean(loss) + else: # reduction == "none" + return loss + + +def _check_reduction_value(reduction: str): + if reduction not in ("mean", "sum", "none"): + raise ValueError(f"{reduction} is not a valid value for reduction") + + +# This helper function maps depreciated arguments, "size_average" and "reduce" +# to their corresponding "reduction" string argument +def _get_string_reduction_arg( + *, size_average: Optional[bool], reduce: Optional[bool] +) -> str: + if size_average is None: + size_average = True + if reduce is None: + reduce = True + if size_average and reduce: + ret = "mean" + elif reduce: + ret = "sum" + else: + ret = "none" + return ret + + +# CompositeImplicitAutograd - don't register decomp +@elementwise_type_promotion_wrapper( + type_promoting_args=("input", "target"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT, +) +def l1_loss( + input: TensorLikeType, + target: TensorLikeType, + size_average: Optional[bool] = None, + reduce: Optional[bool] = None, + reduction: str = "mean", +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.l1_loss + """ + if size_average is not None or reduce is not None: + # TODO: Raise exception instead of converting value. This is only for + # primTorch since it can drop support for deprecated arguments. + # msg = "size_average and reduce args are deprecated, please use reduction argument." + reduction = _get_string_reduction_arg(size_average=size_average, reduce=reduce) + _check_reduction_value(reduction) + loss = torch.abs(input - target) + return _apply_loss_reduction(loss, reduction) + + +@elementwise_type_promotion_wrapper( + type_promoting_args=("input", "target"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT, +) +def smooth_l1_loss( + input: TensorLikeType, + target: TensorLikeType, + size_average: Optional[bool] = None, + reduce: Optional[bool] = None, + reduction: str = "mean", + beta: float = 1.0, +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.smooth_l1_loss + """ + if size_average is not None or reduce is not None: + # TODO: Raise exception instead of converting value. This is only for + # primTorch since it can drop support for deprecated arguments. + # msg = "size_average and reduce args are deprecated, please use reduction argument." + reduction = _get_string_reduction_arg(size_average=size_average, reduce=reduce) + _check_reduction_value(reduction) + + if beta == 0.0: + return torch.nn.functional.l1_loss( + input, target, size_average=size_average, reduce=reduce, reduction=reduction + ) + else: + loss = torch.abs(input - target) + loss = torch.where(loss < beta, 0.5 * loss**2 / beta, loss - 0.5 * beta) + return _apply_loss_reduction(loss, reduction) + + +# Forwarding alias: the functional variant doesn't support the out kwarg +# CompositeImplicitAutograd - don't register decomp +def log_softmax( + a: TensorLikeType, + dim: Optional[int] = None, + _stacklevel: int = 3, # for compat when using TorchRefsMode(strict=True) + dtype: Optional[torch.dtype] = None, +) -> TensorLikeType: + # The error is for compat with regular PyTorch, which has this behavior + # deprecated. For PrimTorch, it's fine to drop support for deprecated + # behavior because it requires explicit opt in. This error is to inform + # users how to update their calls. + torch._check(dim is not None, lambda: "implicit dim not supported, use dim=X") + return torch.log_softmax(a=a, dim=dim, dtype=dtype) # type: ignore[call-overload] + + +@register_decomposition(aten.margin_ranking_loss) +def margin_ranking_loss( + input1: TensorLikeType, + input2: TensorLikeType, + target: TensorLikeType, + margin: float = 0.0, + reduction: str = "mean", +) -> TensorLikeType: + # loss_without_reduction = max(0, -target * (input1 - input2) + margin) + if input1.ndim != input2.ndim or input1.ndim != target.ndim: + raise RuntimeError( + "margin_ranking_loss : All input tensors should have same dimension but got sizes: " + f"input1: {input1.shape}, input2: {input2.shape}, target: {target.shape} " + ) + _check_reduction_value(reduction) + loss = torch.clamp_min(-target * (input1 - input2) + margin, 0) + return _apply_loss_reduction(loss, reduction) + + +@elementwise_type_promotion_wrapper( + type_promoting_args=("input", "target"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT, +) +def mse_loss( + input: TensorLikeType, + target: TensorLikeType, + size_average: Optional[bool] = None, + reduce: Optional[bool] = None, + reduction: str = "mean", +) -> TensorLikeType: + if size_average is not None or reduce is not None: + # TODO: Raise exception instead of converting value. This is only for + # primTorch since it can drop support for deprecated arguments. + # msg = "size_average and reduce args are deprecated, please use reduction argument." + reduction = _get_string_reduction_arg(size_average=size_average, reduce=reduce) + _check_reduction_value(reduction) + loss = torch.pow(input - target, 2) + return _apply_loss_reduction(loss, reduction) + + +@register_decomposition(aten.hinge_embedding_loss) +def hinge_embedding_loss( + input: TensorLikeType, + target: TensorLikeType, + margin: float = 1.0, + reduction: str = "mean", +) -> TensorLikeType: + # loss_without_reduction = input if y == 1 + # = max(0, margin - input) if y == -1 + _check_reduction_value(reduction) + margin_clamp = torch.clamp_min(margin - input, 0) + output_margin = torch.where(target != 1, margin_clamp, 0) + output_self = torch.where(target != -1, input, 0) + loss = output_margin + output_self + return _apply_loss_reduction(loss, reduction) + + +def _nll_loss_nd( + input: TensorLikeType, + target: TensorLikeType, + weight: Optional[TensorLikeType], + reduction: str, + ignore_index: int, +) -> TensorLikeType: + torch._check( + input.ndim > 0 and input.ndim <= 3, + lambda: f"Expected input dimension to be either [1, 2, 3] but received {input.ndim}.", + ) + + torch._check( + (input.ndim == 1) or (input.shape[0] == target.shape[0]), + lambda: f"Expected input batch size {input.shape[0]} to match target batch size {target.shape[0]}.", + ) + + _check_reduction_value(reduction) + + flat_target = torch.flatten(target) + ignore_classes_mask = torch.eq(flat_target, ignore_index) + + # TODO: Enable data-dependent checks with debug mode + # TODO: This check does not work with FakeTensor inputs; See Issue #85834 + # Explicit cast for class_check to bool; See Issue #78071 + """ + from torch._subclasses.fake_tensor import FakeTensor + num_classes = input.shape[1] if input.ndim > 1 else input.shape[0] + valid_classes_mask = torch.logical_and( + (flat_target >= 0), (flat_target < num_classes) + ) + class_check = torch.all(torch.logical_or(ignore_classes_mask, valid_classes_mask)) + torch._check( + isinstance(target, FakeTensor) or bool(class_check.item()), + lambda: "A target class is out-of-bounds and not the ignore index.", + ) + """ + + ignore_class_weight = torch.scalar_tensor(0, dtype=input.dtype, device=input.device) + class_weight = ( + torch.scalar_tensor(1, dtype=input.dtype, device=input.device) + if weight is None + else weight[flat_target] + ) + current_weight = torch.where( + ignore_classes_mask, + ignore_class_weight, + class_weight, + ) + + if input.ndim == 1: + # implicit batch size = 1 + # input (1 batch size, C classes) + loss = -input[target] * current_weight + elif input.ndim == 2: + # input (N batch size, C classes) + batch_size = input.shape[0] + loss = -input[torch.arange(batch_size), target] * current_weight + else: + # 3D case (N batch size, C classe, K dimensions) + # input (N batch size, C classes, K) + batch_size = input.shape[0] + extent = input.shape[2] + numel = batch_size * extent + indices = torch.arange(numel) + bdx = indices // extent + kdx = indices % extent + loss = -input[bdx, flat_target, kdx] * current_weight + loss = torch.reshape(loss, target.shape) + + if reduction == "none": + return loss + elif reduction == "sum": + return torch.sum(loss) + else: + # calculate weighted mean of the loss function + return torch.sum(loss) / torch.sum(current_weight) + + +@register_decomposition(aten.nll_loss) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("input",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def nll_loss( + input: TensorLikeType, + target: TensorLikeType, + weight: Optional[TensorLikeType] = None, + size_average: Optional[bool] = None, + ignore_index: int = -100, + reduce: Optional[bool] = None, + reduction: str = "mean", +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.nll_loss + """ + torch._check( + input.ndim > 0, + lambda: f"Expected input tensor to have 1 or more dimensions (got {input.ndim})", + ) + + # TODO: raise exception instead of converting value + # msg = "size_average and reduce args are deprecated, please use reduction argument." + # Convert these options for consistency with the eager mode + if size_average is not None or reduce is not None: + reduction = _get_string_reduction_arg(size_average=size_average, reduce=reduce) + + # The expected behavior when the target and input have zero elements: + # reduction = 'none' --- tensor([]) + # reduction = 'sum' --- tensor(0.) + # reduction = 'mean' --- tensor(nan) + # Mean reduction on empty tensors produces NaN. See the discussion in + # https://github.com/pytorch/pytorch/pull/64572#issuecomment-926504162 + if input.numel() == 0 and target.numel() == 0: + if reduction == "none": + return torch.zeros_like(target) + elif reduction == "sum": + return torch.empty_like(target) + else: + return torch.full_like(target, float("nan")) + + # The _nll_loss_nd helper function handles the most common cases. + # ndim == 1 (Single Example) + # => Batch Size: 1, Input: (C), Target: () + # ndim == 2 (k = 1) + # => Batch Size: N, Input: (N, C), Target: (N) + # ndim == 3 (k > 1) + # => Batch Size: N, Input: (N, C, K), Target: (N, K) + if input.ndim <= 3: + return _nll_loss_nd(input, target, weight, reduction, ignore_index) + + # For ndim > 3, we reshape the input and target to 3-D case. + # Input (N batch-size, C classes, k-dimensions) + # Target (N batch-size, k-dimensions) + torch._check( + input.ndim > 0 and target.ndim > 0 and target.shape[1:] == input.shape[2:], + lambda: ( + "Expected input and target to both have ndim > 0 and " + "target.shape[1:] == input.shape[2:], but got " + f"target.shape {target.shape} and input.shape {input.shape}" + ), + ) + + batch_size = input.shape[0] + num_classes = input.shape[1] + out_size = [batch_size] + list(target.shape[1:]) + + input = torch.reshape(input, [batch_size, num_classes, -1]) + target = torch.reshape(target, [batch_size, -1]) + if reduction != "none": + return _nll_loss_nd(input, target, weight, reduction, ignore_index) + else: + result = _nll_loss_nd(input, target, weight, reduction, ignore_index) + # reshape flattened inner-dim to original k-dimensions + return torch.reshape(result, out_size) + + +# TODO: This ref supports int reduction and out kwarg to be compatible with ATen: +# https://github.com/pytorch/pytorch/issues/83931 +# TODO: Could be rewritten to support complex: +# https://github.com/pytorch/pytorch/pull/85041 +@register_decomposition(aten.huber_loss) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("input", "target"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def huber_loss( + input: TensorLikeType, + target: TensorLikeType, + reduction: Union[str, int] = "mean", + delta: float = 1.0, +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.huber_loss + """ + if type(reduction) is int: + reduction = _reduction_int_to_str(reduction) + _check_reduction_value(reduction) # type: ignore[arg-type] + torch._check( + delta > 0, + lambda: "huber_loss does not support non-positive values for delta.", + ) + z = (input - target).abs() + loss = torch.where(z < delta, 0.5 * z * z, delta * (z - 0.5 * delta)) + return _apply_loss_reduction(loss, reduction) # type: ignore[arg-type] + + +# tanhshrink does not use _make_elementwise_unary_reference because it does not support out +@elementwise_unary_scalar_wrapper +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def tanhshrink(a: TensorLikeType) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.tanhshrink + """ + if not isinstance(a, TensorLike): + raise RuntimeError( + "Expected a tensor input for an elementwise unary operation!" + ) + return a - torch.tanh(a) + + +@register_decomposition(aten.threshold) +@_inplace_wrapper +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def threshold( + a: TensorLikeType, + threshold: NumberType, + value: Union[bool, int, float], + inplace: bool = False, +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.threshold + """ + + if inplace: + raise NotImplementedError + + return torch.where(a <= threshold, value, a) + + +# CompositeImplicitAutograd - don't register decomp +# No elementwise type promotion - core op doesn't explicitly type promote +def triplet_margin_loss( + anchor: TensorLikeType, + positive: TensorLikeType, + negative: TensorLikeType, + margin: float = 1.0, + p: float = 2, + eps: float = 1e-6, + swap: bool = False, + size_average: Optional[bool] = None, + reduce: Optional[bool] = None, + reduction: str = "mean", +) -> TensorLikeType: + if size_average is not None or reduce is not None: + # TODO: Raise exception instead of converting value. This is only for + # primTorch since it can drop support for deprecated arguments. + # msg = "size_average and reduce args are deprecated, please use reduction argument." + reduction = _get_string_reduction_arg(size_average=size_average, reduce=reduce) + + if margin <= 0: + raise ValueError(f"margin must be greater than 0, got {margin}") + + # torch.nn.functional.triplet_margin_with_distance_loss has no ref defined + # since it's a pure Python implementation. Use this helper instead. + return _triplet_margin_with_distance_loss( + anchor=anchor, + positive=positive, + negative=negative, + distance_function=lambda x, y: torch.pairwise_distance(x, y, p, eps), + margin=margin, + swap=swap, + reduction=reduction, + ) + + +# Pure Python impl - don't register decomp and don't add a ref. Defined as a +# helper here since triplet_margin_loss can be nicely implemented with it. +def _triplet_margin_with_distance_loss( + anchor: TensorLikeType, + positive: TensorLikeType, + negative: TensorLikeType, + *, + distance_function: Optional[ + Callable[[TensorLikeType, TensorLikeType], TensorLikeType] + ] = None, + margin: float = 1.0, + swap: bool = False, + reduction: str = "mean", +) -> TensorLikeType: + _check_reduction_value(reduction) + + a_dim = anchor.ndim + p_dim = positive.ndim + n_dim = negative.ndim + torch._check( + a_dim == p_dim and p_dim == n_dim, + lambda: ( + f"The anchor, positive, and negative tensors are expected to have " + f"the same number of dimensions, but got: anchor {a_dim}D, " + f"positive {p_dim}D, and negative {n_dim}D inputs" + ), + ) + + if distance_function is None: + distance_function = torch.pairwise_distance + + dist_pos = distance_function(anchor, positive) + dist_neg = distance_function(anchor, negative) + # The distance swap is described in the paper "Learning shallow + # convolutional feature descriptors with triplet losses" by V. Balntas, E. + # Riba et al. If True, and if the positive example is closer to the + # negative example than the anchor is, swaps the positive example and the + # anchor in the loss computation. + if swap: + dist_swap = distance_function(positive, negative) + dist_neg = torch.minimum(dist_neg, dist_swap) + loss = torch.clamp_min(margin + dist_pos - dist_neg, 0) + return _apply_loss_reduction(loss, reduction) + + +@register_decomposition(aten.hardtanh) +@_inplace_wrapper +@out_wrapper() +@elementwise_unary_scalar_wrapper +@elementwise_type_promotion_wrapper( + type_promoting_args=("a"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def hardtanh( + a: TensorLikeType, + min_val: NumberType = -1, + max_val: NumberType = 1, + inplace: bool = False, +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.hardtanh + """ + if inplace: + raise NotImplementedError + if utils.is_boolean_dtype(a.dtype): + raise RuntimeError("Bool inputs not supported for hardtanh") + + # preserve legacy behavior of boundaries not causing type promotion + if utils.is_integer_dtype(a.dtype): + min_val = int(min_val) # type: ignore[arg-type] + max_val = int(max_val) # type: ignore[arg-type] + if not (a.dtype != torch.uint8 or (min_val >= 0 and max_val >= 0)): + raise RuntimeError( + "Cannot do hardtanh on an unsigned type with negative limits" + ) + + if min_val > max_val: # type: ignore[operator] + raise ValueError("min_val cannot be greater than max_val") + + return torch.clamp(a, min_val, max_val) # type: ignore[arg-type] + + +@register_decomposition(aten.gelu) +@out_wrapper() +@elementwise_unary_scalar_wrapper +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def gelu(a: TensorLikeType, approximate: str = "none") -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.gelu + """ + if not isinstance(a, TensorLike): + raise RuntimeError( + "Expected a tensor input for an elementwise unary operation!" + ) + M_SQRT2 = 1.41421356237309504880 + M_SQRT1_2 = 0.70710678118654752440 + M_2_SQRTPI = 1.12837916709551257390 + if approximate == "tanh": + kBeta = M_SQRT2 * M_2_SQRTPI * 0.5 + kKappa = 0.044715 + a_cube = a * a * a + inner = kBeta * (a + kKappa * a_cube) + return 0.5 * a * (1 + torch.tanh(inner)) + elif approximate == "none": + kAlpha = M_SQRT1_2 + return a * 0.5 * (1 + torch.erf(a * kAlpha)) + else: + raise RuntimeError("approximate argument must be either none or tanh.") + + +# CompositeImplicitAutograd - don't register decomp +@elementwise_type_promotion_wrapper( + type_promoting_args=("input", "target"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def poisson_nll_loss( + input: TensorLikeType, + target: TensorLikeType, + log_input: bool = True, + full: bool = False, + size_average: Optional[bool] = None, + eps: float = 1e-8, + reduce: Optional[bool] = None, + reduction: str = "mean", +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.poisson_nll_loss + """ + if size_average is not None or reduce is not None: + # TODO: Raise exception instead of converting value. This is only for + # primTorch since it can drop support for deprecated arguments. + # msg = "size_average and reduce args are deprecated, please use reduction argument." + reduction = _get_string_reduction_arg(size_average=size_average, reduce=reduce) + _check_reduction_value(reduction) + if log_input: + loss = torch.exp(input) - target * input + else: + loss = input - target * torch.log(input + eps) + + if full: + stirling_term = ( + target * torch.log(target) - target + 0.5 * torch.log(2 * torch.pi * target) + ) + # avoid inplace add + loss = loss + stirling_term.masked_fill(target <= 1, 0) + return _apply_loss_reduction(loss, reduction) + + +@register_decomposition(aten.prelu) +@elementwise_type_promotion_wrapper( + type_promoting_args=("a", "weight"), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def prelu(a: TensorLikeType, weight: TensorLikeType) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.prelu + """ + torch._check( + isinstance(a, TensorLike), + lambda: f"prelu: Expected `a` to be tensor, but got: {type(a)}", + ) + torch._check( + isinstance(weight, TensorLike), + lambda: f"prelu: Expected `weight` to be tensor, but got: {type(weight)}", + ) + + if weight.numel() != 1: + torch._check(a.ndim > 0, lambda: "Not allow zero-dim input tensor.") + channel_size = a.shape[1] if a.ndim >= 2 else 1 + torch._check( + weight.numel() == channel_size, + lambda: f"Mismatch of parameter numbers and input channel size. Found parameter numbers =" + f" {weight.numel()} and channel size = {channel_size}.", + ) + + torch._check( + weight.ndim == 0 or weight.ndim == 1, + lambda: f"prelu: Expected `weight` to be a scalar or 1D tensor, but got: " + f"ndim = {weight.ndim}", + ) + if a.ndim == 0: + weight = weight[0] if weight.ndim == 1 else weight + else: + weight = prims.broadcast_in_dim( + weight, a.shape, () if weight.ndim == 0 else (0 if a.ndim == 1 else 1,) + ) + + return torch.where(a > 0, a, a * weight) + + +@register_decomposition(aten.relu6) +@_inplace_wrapper +@out_wrapper() +def relu6(a: TensorLikeType, inplace: bool = False) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.relu6 + """ + if inplace: + raise NotImplementedError + + # See https://github.com/pytorch/pytorch/pull/81142#discussion_r918220126 + # It may be better to use clamp here, but we use hardtanh to replicate + # the behavior of the existing implementation + return torch.nn.functional.hardtanh(a, 0, 6) + + +@register_decomposition(aten.glu) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def glu(a: TensorLikeType, dim: int = -1) -> TensorLikeType: + dim = utils.canonicalize_dims(a.ndim, dim) + torch._check( + a.shape[dim] % 2 == 0, + lambda: f"Halving dimension must be even, but dimension {dim} is size {a.shape[dim]}", + ) + b, c = torch.tensor_split(a, 2, dim) + + return b * torch.sigmoid(c) + + +@register_decomposition(aten.pairwise_distance) +@out_wrapper() +def pairwise_distance( + x1: TensorLikeType, + x2: TensorLikeType, + p: NumberType = 2.0, + eps: NumberType = 1e-6, + keepdim=False, +) -> TensorLikeType: + return torch.linalg.vector_norm(x1 - x2 + eps, ord=p, dim=-1, keepdim=keepdim) + + +@register_decomposition(aten.pdist) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def pdist(a: TensorLikeType, p: float = 2) -> TensorLikeType: + torch._check(a.ndim == 2, lambda: f"pdist only supports 2D tensors, got: {a.ndim}D") + torch._check(p >= 0, lambda: "pdist only supports non-negative p values") + # For p == 2 we can use an efficient implementation, but other values of p + # require creating a much bigger tensor for an intermediate step + if p == 2: + aTa = torch.mm(a, a.T) + aTa_diag = torch.diag(aTa) + t = torch.sqrt(torch.clamp(aTa_diag + aTa_diag.unsqueeze(-1) - 2 * aTa, min=0)) + else: + t = torch.linalg.vector_norm(a.unsqueeze(1) - a, ord=p, dim=2) + i = torch.triu_indices(t.shape[0], t.shape[1], offset=1, device=a.device) + return t.flatten().index_select(0, i[0] * t.shape[0] + i[1]) + + +@register_decomposition(aten.pixel_shuffle) +@out_wrapper() +def pixel_shuffle(self: Tensor, upscale_factor: int): + torch._check( + self.dim() >= 3, + lambda: f"pixel_shuffle expects input to have at least 3 dimensions, but got input with {self.dim} dimension(s)", + ) + batch = self.shape[:-3] + C_out = self.shape[-3] // upscale_factor**2 + HW_out = (self.shape[-2] * upscale_factor, self.shape[-1] * upscale_factor) + n = len(batch) + B_dims = range(n) + C_dim, r1_dim, r2_dim, H_dim, W_dim = range(n, n + 5) + return ( + self.view( + *batch, + C_out, + upscale_factor, + upscale_factor, + self.shape[-2], + self.shape[-1], + ) + .permute(*B_dims, C_dim, H_dim, r1_dim, W_dim, r2_dim) + .reshape(*batch, C_out, *HW_out) + .clone(memory_format=utils.suggest_memory_format(self)) + ) + + +@register_decomposition(aten.pixel_unshuffle) +@out_wrapper() +def pixel_unshuffle(self: Tensor, downscale_factor: int): + torch._check( + self.dim() >= 3, + lambda: f"pixel_unshuffle expects input to have at least 3 dimensions, but got input with {self.dim} dimension(s)", + ) + batch = self.shape[:-3] + C_out = self.shape[-3] * downscale_factor**2 + HW_out = (self.shape[-2] // downscale_factor, self.shape[-1] // downscale_factor) + n = len(batch) + B_dims = range(n) + C_dim, H_dim, r1_dim, W_dim, r2_dim = range(n, n + 5) + return ( + self.view( + *batch, + self.shape[-3], + HW_out[0], + downscale_factor, + HW_out[1], + downscale_factor, + ) + .permute(*B_dims, C_dim, r1_dim, r2_dim, H_dim, W_dim) + .reshape(*batch, C_out, *HW_out) + .clone(memory_format=utils.suggest_memory_format(self)) + ) + + +# Needed as aten.{celu_,elu_...} exist (even if they don't have the in-place kwarg) +celu_ = _make_inplace(celu) +elu_ = _make_inplace(elu) +mish_ = _make_inplace(mish) +selu_ = _make_inplace(selu) +threshold_ = _make_inplace(threshold) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/nn/functional/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/nn/functional/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9bbafb6f6660f16dc26c1c0897cbce0aab5d7717 Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/nn/functional/__pycache__/__init__.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/special/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/special/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3599f2fd85c1406c006640ea3e994b9864b605bb --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/special/__init__.py @@ -0,0 +1,236 @@ +# mypy: allow-untyped-defs +import math +from typing import Optional, Union + +import torch +import torch._prims as prims +import torch._prims_common as utils +import torch._refs as refs +from torch import Tensor +from torch._decomp import register_decomposition +from torch._prims_common import ( + ELEMENTWISE_TYPE_PROMOTION_KIND, + Number, + NumberType, + TensorLike, + TensorLikeType, +) +from torch._prims_common.wrappers import elementwise_type_promotion_wrapper, out_wrapper +from torch._refs import ( + _make_alias, + _make_elementwise_binary_reference, + _make_elementwise_unary_reference, +) + + +__all__ = [ + "bessel_j0", + "bessel_j1", + "entr", + "erfcx", + "expit", + "i0e", + "i1", + "i1e", + "log_ndtr", + "logit", + "log_softmax", + "multigammaln", + "ndtr", + "ndtri", + "softmax", + "spherical_bessel_j0", + "xlog1py", + "zeta", +] +aten = torch._ops.ops.aten + + +@_make_elementwise_unary_reference( + ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def bessel_j0(a: TensorLikeType) -> TensorLikeType: + return prims.bessel_j0(a) + + +@_make_elementwise_unary_reference( + ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def bessel_j1(a: TensorLikeType) -> TensorLikeType: + return prims.bessel_j1(a) + + +@register_decomposition(aten.special_entr) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def entr(a: TensorLikeType) -> TensorLikeType: + return torch.where( + torch.isnan(a), + a, + torch.where(a > 0, -a * torch.log(a), torch.where(a == 0, 0, -torch.inf)), + ) + + +@register_decomposition(aten.special_erfcx) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def erfcx(a: TensorLikeType) -> TensorLikeType: + return prims.erfcx(a) + + +# alias for sigmoid +expit = _make_alias(torch.sigmoid, "expit") + + +@_make_elementwise_unary_reference( + ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def i0e(a: TensorLikeType) -> TensorLikeType: + return prims.bessel_i0e(a) + + +@_make_elementwise_unary_reference( + ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def i1(a: TensorLikeType) -> TensorLikeType: + return prims.bessel_i1(a) + + +@_make_elementwise_unary_reference( + ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def i1e(a: TensorLikeType) -> TensorLikeType: + return prims.bessel_i1e(a) + + +@register_decomposition(aten.special_log_ndtr) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def log_ndtr(a: TensorLikeType) -> TensorLikeType: + # Note: M_SQRT1_2 is the value of 1 / sqrt(2) + M_SQRT1_2 = 0.707106781186547524400844362104849039 + t = a * M_SQRT1_2 + return torch.where( + a < 1.0, + torch.log(torch.special.erfcx(-t) / 2) - t * t, + torch.log1p(-torch.erfc(t) / 2), + ) + + +@register_decomposition(aten.logit) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("self",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def logit(self: TensorLikeType, eps: Optional[float] = None) -> TensorLikeType: + if eps is None: + eps = -1.0 + lo = eps + hi = 1 - eps + self = torch.clamp(self, lo, hi) + return torch.log(torch.true_divide(self, torch.sub(1, self))) + + +@register_decomposition(aten.special_xlog1py) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a", "b"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def xlog1py(a: Union[TensorLikeType, NumberType], b: Union[TensorLikeType, NumberType]): + torch._check( + isinstance(a, TensorLike) or isinstance(b, TensorLike), + lambda: 'Expected either argument a or b to be a Tensor"', + ) + + # Operations like eq and log do not handle scalar values, so we convert them to scalar_tensors. + if isinstance(a, TensorLike) and isinstance(b, Number): + b = refs.scalar_tensor(b, dtype=a.dtype, device=a.device) + elif isinstance(b, TensorLike) and isinstance(a, Number): + a = refs.scalar_tensor(a, dtype=b.dtype, device=b.device) + + # mypy: expected "Tensor" + assert isinstance(a, TensorLike) + assert isinstance(b, TensorLike) + rhs = torch.where(torch.eq(a, 0), 0, torch.mul(a, torch.log1p(b))) + return torch.where(torch.isnan(b), float("nan"), rhs) + + +@register_decomposition(aten.mvlgamma) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def multigammaln(a: TensorLikeType, p: int) -> TensorLikeType: + c = 0.25 * p * (p - 1) * math.log(math.pi) + b = 0.5 * torch.arange(start=(1 - p), end=1, step=1, dtype=a.dtype, device=a.device) + return torch.sum(torch.lgamma(a.unsqueeze(-1) + b), dim=-1) + c + + +@register_decomposition(aten.special_ndtr) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def ndtr(a: TensorLikeType) -> TensorLikeType: + # Note: M_SQRT1_2 is the value of 1 / sqrt(2) + M_SQRT1_2 = 0.707106781186547524400844362104849039 + a_sqrt_2 = a * M_SQRT1_2 + return (1 + torch.erf(a_sqrt_2)) * 0.5 + + +@register_decomposition(aten.special_ndtri) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def ndtri(a: TensorLikeType) -> TensorLikeType: + return prims.ndtri(a) + + +# Forwarding alias: the special variant doesn't support the out kwarg +# CompositeImplicitAutograd - don't register decomp +def log_softmax( + a: TensorLikeType, + dim: int, + dtype: Optional[torch.dtype] = None, +) -> TensorLikeType: + return torch.log_softmax(a=a, dim=dim, dtype=dtype) # type: ignore[call-overload] + + +# Forwarding alias: the special variant doesn't support the out kwarg +# CompositeImplicitAutograd - don't register decomp +def softmax( + a: TensorLikeType, + dim: int, + dtype: Optional[torch.dtype] = None, +) -> TensorLikeType: + return torch.softmax(a=a, dim=dim, dtype=dtype) # type: ignore[call-overload] + + +@_make_elementwise_unary_reference( + ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def spherical_bessel_j0(a: TensorLikeType) -> TensorLikeType: + return prims.spherical_bessel_j0(a) + + +# TODO: add docstring +@_make_elementwise_binary_reference( + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def zeta(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.zeta(a, b) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/special/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/special/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b63d0206b300b37a818fe2fc0e348a35d436f920 Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_refs/special/__pycache__/__init__.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_strobelight/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_strobelight/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_strobelight/cli_function_profiler.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_strobelight/cli_function_profiler.py new file mode 100644 index 0000000000000000000000000000000000000000..72e44ec42ac3a47d447ababc8185d35636f8360a --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_strobelight/cli_function_profiler.py @@ -0,0 +1,311 @@ +# mypy: disallow-untyped-defs + +import functools +import logging +import os +import re +import subprocess +import time +from threading import Lock +from timeit import default_timer as timer +from typing import Any, List, Optional, Sequence + + +logger = logging.getLogger("strobelight_function_profiler") + +console_handler = logging.StreamHandler() +formatter = logging.Formatter( + "%(name)s, line %(lineno)d, %(asctime)s, %(levelname)s: %(message)s" +) +console_handler.setFormatter(formatter) + +logger.addHandler(console_handler) +logger.setLevel(logging.INFO) +logger.propagate = False + + +class StrobelightCLIProfilerError(Exception): + """ + Raised when an error happens during strobelight profiling + """ + + +def _pid_namespace_link(pid: Optional[int] = None) -> str: + """Returns the link to the process's namespace, example: pid:[4026531836]""" + PID_NAMESPACE_PATH = "/proc/{}/ns/pid" + pid = pid or os.getpid() + return os.readlink(PID_NAMESPACE_PATH.format(pid)) + + +def _pid_namespace(pid: Optional[int] = None) -> int: + """Returns the process's namespace id""" + pid = pid or os.getpid() + link = _pid_namespace_link(pid) + return int(link[link.find("[") + 1 : -1]) + + +def _command_to_string(command: Sequence[str]) -> str: + return " ".join(command) + + +class StrobelightCLIFunctionProfiler: + """ + Note: this is a Meta only tool. + + StrobelightCLIFunctionProfiler can be used to profile a python function and + generate a strobelight link with the results. It works on meta servers but + does not requries an fbcode target. + When stop_at_error is false(default), error during profiling does not prevent + the work function from running. + + Check function_profiler_example.py for an example. + """ + + # This lock is used to make sure only one thread is running the profiler at any point. + _lock = Lock() + + def __init__( + self, + *, + stop_at_error: bool = False, + max_profile_duration_sec: int = 60 * 10, + sample_each: float = 1e7, # sample each sample_each cycles. + run_user_name: str = "pytorch-strobelight-ondemand", + timeout_wait_for_running_sec: int = 60, + timeout_wait_for_finished_sec: int = 60, + recorded_env_variables: Optional[List[str]] = None, + sample_tags: Optional[List[str]] = None, + stack_max_len: int = 127, + async_stack_max_len: int = 127, + ): + self.stop_at_error = stop_at_error + self.max_profile_duration_sec = max_profile_duration_sec + self.sample_each = sample_each + self.run_user_name = run_user_name + self.timeout_wait_for_running_sec = timeout_wait_for_running_sec + self.timeout_wait_for_finished_sec = timeout_wait_for_finished_sec + # Results of the most recent run. + # Tracks the strobelight run id of the most recent run + self.current_run_id: Optional[int] = None + self.profile_result: Optional[List[str]] = None + self.sample_tags = sample_tags + + def _run_async(self) -> None: + processId = os.getpid() + namespace = _pid_namespace(processId) + command = [ + "strobeclient", + "run", + "--profiler", + "pyperf", + "--event", + "cycles", + "--async", + "--sample-interval", + f"{int(self.sample_each)}", + "--duration-ms", + f"{int(self.max_profile_duration_sec * 1000)}", + "--pid", + f"{namespace}:{processId}", + ] + + if self.sample_tags: + command.append("--sample-tags") + command.append(",".join(self.sample_tags)) + + logger.debug("running command: %s", _command_to_string(command)) + result = subprocess.run(command, capture_output=True) + output = result.stderr.decode("utf-8") + logger.debug("output:\n{%s}", output) + + if result.returncode != 0: + raise StrobelightCLIProfilerError( + f"failed to start strobelight profiling, error in run_async:{output}" + ) + + if match := re.search(r"INFO Run Id: (-?\d+)", output): + self.current_run_id = int(match.group(1)) + return + + raise StrobelightCLIProfilerError( + f"failed to start strobelight profiling, unexpected result {output}" + ) + + def _wait_for_running(self, counter: int = 0) -> None: + if counter > 20: + raise StrobelightCLIProfilerError( + "wait_for_running called more than 20 times" + ) + + command = ["strobeclient", "getRunStatus", "--run-id", f"{self.current_run_id}"] + logger.debug("running command: %s", _command_to_string(command)) + result = subprocess.run(command, capture_output=True) + output = result.stderr.decode("utf-8") + logger.debug("output:\n{%s}", output) + + if result.returncode != 0: + raise StrobelightCLIProfilerError( + f"failed to start strobelight profiling, error in wait_for_running:{output}" + ) + + if match := re.search("Profile run status: (.*)", output): + current_status = match.group(1) + if current_status == "RUNNING": + return + elif current_status == "PREPARING": + time.sleep(10) + self._wait_for_running(counter + 1) + return + else: + raise StrobelightCLIProfilerError(f"unexpected {current_status} phase") + + raise StrobelightCLIProfilerError(f"unexpected output\n: {output} ") + + def _stop_run(self) -> None: + command = ["strobeclient", "stopRun", "--run-id", str(self.current_run_id)] + logger.debug("running command: %s", _command_to_string(command)) + result = subprocess.run(command, capture_output=True) + output = result.stderr.decode("utf-8") + logger.debug("output:\n{%s}", output) + + if result.returncode != 0: + raise StrobelightCLIProfilerError( + f"failed to stop strobelight profiling, return code is not 0 :{output}" + ) + + if match := re.search("INFO ::1:(.*)", output): + current_status = match.group(1) + if current_status.__contains__("Success!"): + return + else: + raise StrobelightCLIProfilerError( + f"failed to stop strobelight profiling, got {current_status} result" + ) + + raise StrobelightCLIProfilerError(f"unexpected output\n: {output} ") + + def _get_results(self) -> None: + command = ["strobeclient", "getRunStatus", "--run-id", str(self.current_run_id)] + logger.debug("running command: %s", _command_to_string(command)) + result = subprocess.run(command, capture_output=True) + output = result.stderr.decode("utf-8") + logger.debug("output:\n{%s}", output) + + if result.returncode != 0: + raise StrobelightCLIProfilerError( + f"failed to extract profiling results, return code is not 0 : {output}" + ) + + if match := re.search("INFO ::1:(.*)", output): + current_status = match.group(1) + if current_status.__contains__("Profile run status: PROCESSING"): + time.sleep(10) + self._get_results() + return + elif not current_status.__contains__("Profile run finished with SUCCESS"): + raise StrobelightCLIProfilerError( + f"failed to extract profiling results, unexpected response {output}" + ) + + self.profile_result = [] + for item in re.findall( + r"(Total samples(.*)|GraphProfiler(.*)|Icicle view \(python stack\)(.*))", + output, + ): + self.profile_result += item[0] + logger.info(item[0]) + + def _stop_strobelight_no_throw( + self, + collect_results: bool, + ) -> None: + try: + # call stop run + self._stop_run() + logger.info("strobelight profiling stopped") + + logger.debug("collection stopped") + + if not collect_results: + return + + self._get_results() + except Exception as error: + logger.warning("error during stop_strobelight", exc_info=True) + + # Return true if strobelight started and is running. Never throw. + def _start_strobelight(self) -> bool: + strobelight_started = False + try: + self._run_async() + strobelight_started = True + logger.info("strobelight run id is: %s", self.current_run_id) + self._wait_for_running() + logger.info("strobelight profiling running") + return True + + except Exception as error: + logger.warning("error during start_strobelight:", exc_info=True) + if strobelight_started: + self._stop_strobelight_no_throw(collect_results=False) + return False + + def profile(self, work_function: Any, *args: Any, **kwargs: Any) -> Any: + self.current_run_id = None + self.profile_result = None + + if locked := StrobelightCLIFunctionProfiler._lock.acquire(False): + if not locked: + if self.stop_at_error: + raise StrobelightCLIProfilerError("concurrent runs not supported") + + logger.warning("concurrent runs not supported") + return work_function(*args, **kwargs) + + started = self._start_strobelight() + if not started: + if self.stop_at_error: + StrobelightCLIFunctionProfiler._lock.release() + raise StrobelightCLIProfilerError( + "failed to start strobelight profiling" + ) + result = work_function(*args, **kwargs) + StrobelightCLIFunctionProfiler._lock.release() + return result + + try: + logger.debug("collection started") + start = timer() + result = work_function(*args, **kwargs) + end = timer() + total_time = end - start # Time in seconds, e.g. 5.38091952400282 + logger.info("work function took %s seconds", total_time) + self._stop_strobelight_no_throw(collect_results=True) + StrobelightCLIFunctionProfiler._lock.release() + return result + except Exception as error: + logger.warning("work function throw exception", exc_info=True) + self._stop_strobelight_no_throw(collect_results=False) + StrobelightCLIFunctionProfiler._lock.release() + raise error + + +# A function decorator that wraps profile, if no profiler is provided one with +# default args is created. A function can be annotated as: +# @strobelight() +# @strobelight(profiler = StrobelightFunctionProfiler(stop_at_error=True,..)) +# @strobelight(stop_at_error=True,...) +def strobelight( + profiler: Optional[StrobelightCLIFunctionProfiler] = None, **kwargs: Any +) -> Any: + if not profiler: + profiler = StrobelightCLIFunctionProfiler(**kwargs) + + def strobelight_inner(work_function: Any) -> Any: + @functools.wraps(work_function) + def wrapper_function(*args: Any, **kwargs: Any) -> Any: + return profiler.profile(work_function, *args, **kwargs) + + return wrapper_function + + return strobelight_inner diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_strobelight/compile_time_profiler.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_strobelight/compile_time_profiler.py new file mode 100644 index 0000000000000000000000000000000000000000..197fcad97165e1891116518dfad44dc67d56eac2 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_strobelight/compile_time_profiler.py @@ -0,0 +1,156 @@ +# mypy: disallow-untyped-defs + +import logging +import os +from datetime import datetime +from socket import gethostname +from typing import Any, Optional + +from torch._strobelight.cli_function_profiler import StrobelightCLIFunctionProfiler + + +logger = logging.getLogger("strobelight_compile_time_profiler") + +console_handler = logging.StreamHandler() +formatter = logging.Formatter( + "%(name)s, line %(lineno)d, %(asctime)s, %(levelname)s: %(message)s" +) +console_handler.setFormatter(formatter) + +logger.addHandler(console_handler) +logger.setLevel(logging.INFO) +logger.propagate = False + + +class StrobelightCompileTimeProfiler: + success_profile_count: int = 0 + failed_profile_count: int = 0 + ignored_profile_runs: int = 0 + inside_profile_compile_time: bool = False + enabled: bool = False + # A unique identifier that is used as the run_user_name in the strobelight profile to + # associate all compile time profiles together. + identifier: Optional[str] = None + + current_phase: Optional[str] = None + + profiler: Optional[Any] = None + + max_stack_length: int = int( + os.environ.get("COMPILE_STROBELIGHT_MAX_STACK_LENGTH", 127) + ) + max_profile_time: int = int( + os.environ.get("COMPILE_STROBELIGHT_MAX_PROFILE_TIME", 60 * 30) + ) + # Collect sample each x cycles. + sample_each: int = int( + float(os.environ.get("COMPILE_STROBELIGHT_SAMPLE_RATE", 1e7)) + ) + + @classmethod + def enable(cls, profiler_class: Any = StrobelightCLIFunctionProfiler) -> None: + if cls.enabled: + logger.info("compile time strobelight profiling already enabled") + return + + logger.info("compile time strobelight profiling enabled") + + if profiler_class is StrobelightCLIFunctionProfiler: + import shutil + + if not shutil.which("strobeclient"): + logger.info( + "strobeclient not found, cant enable compile time strobelight profiling, seems" + "like you are not on a FB machine." + ) + return + + cls.enabled = True + cls._cls_init() + # profiler_class should have public API similar to that of StrobelightCLIFunctionProfiler. + # we have pass different functionProfilerClass for meta-internal fbcode targets. + cls.profiler = profiler_class( + sample_each=cls.sample_each, + max_profile_duration_sec=cls.max_profile_time, + stack_max_len=cls.max_stack_length, + async_stack_max_len=cls.max_stack_length, + run_user_name="pt2-profiler/" + + os.environ.get("USER", os.environ.get("USERNAME", "")), + sample_tags={cls.identifier}, + ) + + @classmethod + def _cls_init(cls) -> None: + cls.identifier = "{date}{pid}{hostname}".format( + date=datetime.now().strftime("%Y-%m-%d-%H:%M:%S"), + pid=os.getpid(), + hostname=gethostname(), + ) + + logger.info("Unique sample tag for this run is: %s", cls.identifier) + logger.info( + "You can use the following link to access the strobelight profile at the end of the run: %s", + ( + "https://www.internalfb.com/intern/scuba/query/?dataset=pyperf_experime" + "ntal%2Fon_demand&drillstate=%7B%22purposes%22%3A[]%2C%22end%22%3A%22no" + "w%22%2C%22start%22%3A%22-30%20days%22%2C%22filterMode%22%3A%22DEFAULT%" + "22%2C%22modifiers%22%3A[]%2C%22sampleCols%22%3A[]%2C%22cols%22%3A[%22n" + "amespace_id%22%2C%22namespace_process_id%22]%2C%22derivedCols%22%3A[]%" + "2C%22mappedCols%22%3A[]%2C%22enumCols%22%3A[]%2C%22return_remainder%22" + "%3Afalse%2C%22should_pivot%22%3Afalse%2C%22is_timeseries%22%3Afalse%2C" + "%22hideEmptyColumns%22%3Afalse%2C%22timezone%22%3A%22America%2FLos_Ang" + "eles%22%2C%22compare%22%3A%22none%22%2C%22samplingRatio%22%3A%221%22%2" + "C%22metric%22%3A%22count%22%2C%22aggregation_field%22%3A%22async_stack" + "_complete%22%2C%22top%22%3A10000%2C%22aggregateList%22%3A[]%2C%22param" + "_dimensions%22%3A[%7B%22dim%22%3A%22py_async_stack%22%2C%22op%22%3A%22" + "edge%22%2C%22param%22%3A%220%22%2C%22anchor%22%3A%220%22%7D]%2C%22orde" + "r%22%3A%22weight%22%2C%22order_desc%22%3Atrue%2C%22constraints%22%3A[[" + "%7B%22column%22%3A%22sample_tags%22%2C%22op%22%3A%22all%22%2C%22value%" + f"22%3A[%22[%5C%22{cls.identifier}%5C%22]%22]%7D]]%2C%22c_constraints%22%3A[[]]%2C%22b" + "_constraints%22%3A[[]]%2C%22ignoreGroupByInComparison%22%3Afalse%7D&vi" + "ew=GraphProfilerView&&normalized=1712358002&pool=uber" + ), + ) + + @classmethod + def _log_stats(cls) -> None: + logger.info( + "%s strobelight success runs out of %s non-recursive compilation events.", + cls.success_profile_count, + cls.success_profile_count + cls.failed_profile_count, + ) + + # TODO use threadlevel meta data to tags to record phases. + @classmethod + def profile_compile_time( + cls, func: Any, phase_name: str, *args: Any, **kwargs: Any + ) -> Any: + if not cls.enabled: + return func(*args, **kwargs) + + if cls.profiler is None: + logger.error("profiler is not set") + return + + if cls.inside_profile_compile_time: + cls.ignored_profile_runs += 1 + logger.info( + "profile_compile_time is requested for phase: %s while already in running phase: %s, recursive call ignored", + phase_name, + cls.current_phase, + ) + return func(*args, **kwargs) + + cls.inside_profile_compile_time = True + cls.current_phase = phase_name + + work_result = cls.profiler.profile(func, *args, **kwargs) + + if cls.profiler.profile_result is not None: + cls.success_profile_count += 1 + else: + cls.failed_profile_count += 1 + + cls._log_stats() + cls.inside_profile_compile_time = False + return work_result diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cdc42f39cbddaf5bdc919cef88d5f049fdba2634 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/__init__.py @@ -0,0 +1,17 @@ +import torch +from torch._subclasses.fake_tensor import ( + DynamicOutputShapeException, + FakeTensor, + FakeTensorMode, + UnsupportedFakeTensorException, +) +from torch._subclasses.fake_utils import CrossRefFakeMode + + +__all__ = [ + "FakeTensor", + "FakeTensorMode", + "UnsupportedFakeTensorException", + "DynamicOutputShapeException", + "CrossRefFakeMode", +] diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/_fake_tensor_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/_fake_tensor_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..d07b7f39053e31a134bd50b149026b87b1e35683 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/_fake_tensor_utils.py @@ -0,0 +1,258 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Dict, List, Optional, Type, TYPE_CHECKING, Union + +import torch +from torch import SymInt +from torch.fx.experimental.sym_node import SymNode +from torch.types import py_sym_types, PySymType +from torch.utils._backport_slots import dataclass_slots + + +if TYPE_CHECKING: + import sympy + + from torch.fx.experimental.symbolic_shapes import ShapeEnv + + from .fake_tensor import _DispatchCacheKey, _MetadataIntLike + + +@dataclass_slots +@dataclass(frozen=True) +class _DeconstructedSymNode: + """ + Represents a SymNode without the associated ShapeEnv + """ + + # n.b. keep the same protocol as SymNode + _expr: sympy.Expr + pytype: type + _hint: Optional[Union[int, float, bool]] + constant: Optional[Union[int, float, bool]] + fx_node: torch.fx.Node + + @staticmethod + def from_node(node: SymNode) -> _DeconstructedSymNode: + return _DeconstructedSymNode( + node._expr, node.pytype, node._hint, node.constant, node.fx_node + ) + + def extract(self, shape_env: ShapeEnv) -> SymNode: + return SymNode( + self._expr, shape_env, self.pytype, self._hint, self.constant, self.fx_node + ) + + def __str__(self) -> str: + return str(self._expr) + + def __repr__(self) -> str: + return f"_DeconstructedSymNode{{{self._expr!r}, {self.pytype!r}, {self._hint!r}, {self.constant!r}, {self.fx_node!r}}}" + + def __eq__(self, other: object) -> bool: + raise NotImplementedError + + def __hash__(self) -> int: + raise NotImplementedError + + # _value_eq to match SymNode + def _value_eq(self, other: object) -> bool: + if isinstance(other, (SymNode, _DeconstructedSymNode)): + return ( + self._expr == other._expr + and self.pytype == other.pytype + and self._hint == other._hint + and self.constant == other.constant + and self.fx_node == other.fx_node + ) + else: + return False + + # _value_hash to match SymNode + def _value_hash(self) -> int: + return hash((self._expr, self.pytype, self._hint, self.constant, self.fx_node)) + + +@dataclass_slots +@dataclass(frozen=True) +class _DeconstructedSymType: + """ + Represents a SymInt, SymFloat, SymBool without the associated ShapeEnv + """ + + ty: Type[PySymType] + node: _DeconstructedSymNode + + @staticmethod + def from_sym_type(value: PySymType) -> _DeconstructedSymType: + return _DeconstructedSymType(type(value), value.node) + + def extract(self, shape_env: ShapeEnv) -> PySymType: + return self.ty(self.node.extract(shape_env)) + + def __str__(self) -> str: + return f"{self.ty}({self.node})" + + def __repr__(self) -> str: + return f"_DeconstructedSymType({self.ty}, {self.node!r})" + + def __eq__(self, other: object) -> bool: + return NotImplemented + + def __hash__(self) -> int: + return NotImplemented + + +@dataclass_slots +@dataclass(frozen=True) +class _InputBackref: + value: int + + +@dataclass_slots +@dataclass +class _PySymInputStub: + """ + Represents a SymInt in the cached key. Needed because SymInt doesn't + support __eq__ or __hash__ directly. + """ + + # value can be: + # PySymType: This is the 'normal' SymInt value, wrapped so we can use + # hash/eq as value hash/eq (normally SymInt does object + # hash/eq). + # _DeconstructedSymType: This is used when storing the _PySymInputStub in + # the cache to avoid cyclic ShapeEnv references. + # _InputBackref: This is a back-reference to a previous _PySymInputStub in + # the key. + value: Union[PySymType, _DeconstructedSymType, _InputBackref] + + def __init__( + self, value: Union[PySymType, _DeconstructedSymType, _InputBackref] + ) -> None: + # For inputs (values in the `key`) we need to keep the PySymType intact + # - this way if we need to reuse it as an output we can properly copy + # the original value. + self.value = value + + def strip_shape_env(self) -> None: + if isinstance(self.value, py_sym_types): + self.value = _DeconstructedSymType.from_sym_type(self.value) + + def extract(self, shape_env: ShapeEnv) -> PySymType: + if isinstance(self.value, _DeconstructedSymType): + return self.value.extract(shape_env) + else: + # We should never see an _InputBackref here - anyone extracting a + # value should be pulling from the original entry (the one this + # backref points at). + assert not isinstance(self.value, _InputBackref) + return self.value + + def __str__(self) -> str: + return str(self.value) + + def __repr__(self) -> str: + return f"_PySymInputStub({self.value!r})" + + def __eq__(self, other: object) -> bool: + if not isinstance(other, _PySymInputStub): + return False + elif isinstance(self.value, _InputBackref) or isinstance( + other.value, _InputBackref + ): + return self.value == other.value + else: + return self.value.node._value_eq(other.value.node) + + def __hash__(self) -> int: + if isinstance(self.value, _InputBackref): + return hash(self.value) + else: + return self.value.node._value_hash() + + +@dataclass_slots +@dataclass +class _SymIntOutputStub: + """ + Represents a SymInt in the cached output. + """ + + # This is either an `int` which represents the index in the key to copy the + # SymNode from or it's the deconstructed SymNode itself. + value: Union[int, _DeconstructedSymNode] + + def __init__(self, value: SymInt, key_path: Optional[int]) -> None: + if key_path is None: + self.value = _DeconstructedSymNode.from_node(value.node) + else: + self.value = key_path + + def extract(self, key: _DispatchCacheKey, shape_env: ShapeEnv) -> SymInt: + if isinstance(self.value, _DeconstructedSymNode): + return SymInt(self.value.extract(shape_env)) + else: + src = key.key[self.value] + assert isinstance(src, _PySymInputStub) and isinstance(src.value, SymInt) + return src.value + + def __repr__(self) -> str: + return f"_SymIntOutputStub({self.value!r})" + + def __eq__(self, other: object) -> bool: + raise NotImplementedError + + def __hash__(self) -> int: + raise NotImplementedError + + +@dataclass_slots +@dataclass +class _CacheKeyState: + """ + State used while building our cache key. + """ + + # We track the SymNodes so when we get the output we can see if it exactly + # matches one of the inputs so we can uncache it properly. + sym_node_lookup: Dict[int, int] # id(SymNode) -> index + + # There are cases where we're asked to perform an op when we have no + # ShapeEnv on the FakeTensorMode - but for SymNodes we MUST have a + # ShapeEnv. So as we scan if we see a SymNode (with a ShapeEnv) we record it + # here. + shape_env: Optional[ShapeEnv] + + def __init__(self, shape_env: Optional[ShapeEnv] = None) -> None: + self.sym_node_lookup = {} + self.shape_env = shape_env + + def cache_on_shape_env(self) -> bool: + """ + Returns true if the CacheKey needs to be cached on the ShapeEnv + rather than the global cache. + + If our inputs contain a SymNode then we can't cache this operation on + the global cache because the cached output will implicitly depend on + guard values which might not be true on some other ShapeEnv. So unless + we're also going to cache the guards we need to cache this operation on + the ShapeEnv instead of globally. + """ + return bool(self.sym_node_lookup) + + def convert_sym_int(self, result: List[object], arg: SymInt) -> None: + node_id = id(arg.node) + if node_id in self.sym_node_lookup: + result.append(_InputBackref(self.sym_node_lookup[node_id])) + else: + self.sym_node_lookup[node_id] = len(result) + if self.shape_env is None: + self.shape_env = arg.node.shape_env + result.append(_PySymInputStub(arg)) + + def convert_output(self, arg: _MetadataIntLike) -> _MetadataIntLike: + if isinstance(arg, SymInt): + return _SymIntOutputStub(arg, self.sym_node_lookup.get(id(arg.node), None)) + else: + return arg diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/fake_impls.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/fake_impls.py new file mode 100644 index 0000000000000000000000000000000000000000..1ba37b6f06bddfe7ecdd5d0a4a5b0e28c2e361e7 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/fake_impls.py @@ -0,0 +1,959 @@ +# mypy: ignore-errors + +import functools +import itertools +import math +import sys +from typing import Callable, Union + +import torch +import torch._custom_op +import torch._logging +from torch._dispatch.python import no_python_dispatcher +from torch._ops import OpOverload +from torch._prims_common import ( + elementwise_dtypes, + ELEMENTWISE_TYPE_PROMOTION_KIND, + is_boolean_dtype, + is_float_dtype, + is_integer_dtype, +) +from torch._subclasses.fake_tensor import ( + DataDependentOutputException, + DynamicOutputShapeException, + FakeTensor, + in_kernel_invocation_manager, + run_fallback_kernel, + UnsupportedOperatorException, +) +from torch.fx.operator_schemas import normalize_function +from torch.utils._stats import count_label + + +pytree = torch.utils._pytree + +__all__ = [ + "op_implementations_checks", + "get_fast_op_impls", + "stride_incorrect_op", + "has_meta", +] + +op_implementations_dict = {} +op_implementations_checks = [] + + +aten = torch._ops.ops.aten + + +def ordered_set(*items): + return dict.fromkeys(items, True) + + +# This function indicates if the backend device +# supports non-contiguous tensors +def is_noncontiguous_supported(device): + return device.type != "hpu" + + +_like_tensor_constructors = ordered_set( + aten.empty_like.default, + aten.empty_like.out, + aten.full_like.default, + aten.full_like.out, + aten.ones_like.default, + aten.ones_like.out, + aten.rand_like.default, + aten.rand_like.out, + aten.randn_like.default, + aten.randn_like.out, + aten.randint_like.default, + aten.randint_like.out, + aten.randint_like.low_dtype, + aten.randint_like.low_dtype_out, + aten.zeros_like.default, + aten.zeros_like.out, + aten.new_empty.default, + aten.new_empty.out, + aten.new_empty_strided.default, + aten.new_empty_strided.out, + aten.new_full.default, + aten.new_full.out, + aten.new_zeros.default, + aten.new_zeros.out, + aten.new_ones.default, + aten.new_ones.out, +) + + +_device_not_kwarg_ops = ordered_set( + aten._resize_output_.default, + aten._nested_tensor_from_tensor_list.default, + aten._nested_tensor_from_tensor_list.out, + aten.pin_memory.default, + aten.to.device, + aten.to.prim_Device, + aten.is_pinned.default, + aten._pin_memory.default, + aten._pin_memory.out, + aten._resize_output.default, + aten._resize_output.out, +) + +# this op is never actually used +_non_kwarg_device_constructors = (aten._list_to_tensor,) + + +def contains_tensor_types(type): + tensor_type = torch._C.TensorType.get() + return type.isSubtypeOf(tensor_type) or any( + contains_tensor_types(e) for e in type.containedTypes() + ) + + +@functools.lru_cache(None) +def _is_tensor_constructor(func: OpOverload): + assert isinstance(func, OpOverload) + schema = func._schema + if any(contains_tensor_types(arg.type) for arg in schema.arguments): + return False + # TODO: no real reason to restrict multiple outputs + return ( + len(schema.returns) == 1 and schema.returns[0].type is torch._C.TensorType.get() + ) + + +def register_op_impl(run_impl_check: Union[Callable[[OpOverload], bool], OpOverload]): + def impl_decorator(op_impl): + if isinstance(run_impl_check, OpOverload): + assert ( + run_impl_check not in op_implementations_dict + ), f"duplicate registration: {run_impl_check}" + op_implementations_dict[run_impl_check] = op_impl + elif isinstance(run_impl_check, (list, tuple)): + for op in run_impl_check: + register_op_impl(op)(op_impl) + else: + assert callable(run_impl_check) + op_implementations_checks.append((run_impl_check, op_impl)) + + return op_impl + + return impl_decorator + + +@register_op_impl(op_implementations_dict.__contains__) +def dispatch_to_op_implementations_dict(fake_mode, func, *args, **kwargs): + return op_implementations_dict[func](fake_mode, func, *args, **kwargs) + + +@register_op_impl(_is_tensor_constructor) +@register_op_impl([*_like_tensor_constructors]) +def constructors(fake_mode, func, *args, **kwargs): + assert func not in _non_kwarg_device_constructors + _, new_kwargs = normalize_function( + func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True + ) + if "names" in kwargs: + raise UnsupportedOperatorException( + "torch.compile doesn't support named tensors" + ) + + if func in _like_tensor_constructors: + default_device = new_kwargs["input"].device + # TODO: file issue + args = (new_kwargs.pop("input"),) + else: + # cpu is default device if none is specified + default_device = torch.device("cpu") + args = () + out_device = new_kwargs.pop("device", None) + out_device = out_device if out_device is not None else default_device + new_kwargs["device"] = torch.device("meta") + # _like constructors have fake tensor inputs (maybe this causes the non-like + # to fail? hmmm) + with in_kernel_invocation_manager(fake_mode): + r = func(*args, **new_kwargs) + return FakeTensor(fake_mode, r, out_device) + + +@register_op_impl(aten.is_pinned.default) +def non_kwarg_is_pinned(fake_mode, func, *args, **kwargs): + _, new_kwargs = normalize_function( + func, args, kwargs, normalize_to_only_use_kwargs=True + ) + inp = new_kwargs.pop("input") + # we'll ignore device argument because it is deprecated and not + # actually used by is_pinned. + with in_kernel_invocation_manager(fake_mode): + r = func(inp) + return r + + +@register_op_impl(aten.to.prim_Device) +@register_op_impl(aten.to.device) +def non_kwarg_to(fake_mode, func, *args, **kwargs): + _, new_kwargs = normalize_function( + func, args, kwargs, normalize_to_only_use_kwargs=True + ) + input_device = new_kwargs["device"] + out_device = input_device if input_device else new_kwargs["input"].device + new_kwargs["device"] = torch.device("meta") + inp = new_kwargs.pop("input") + with in_kernel_invocation_manager(fake_mode): + r = func(inp, **new_kwargs) + # TODO: I think this does the wrong thing if r is inp + return fake_mode.fake_tensor_converter.from_meta_and_device( + fake_mode, r, out_device + ) + + +def stride_incorrect_op(op): + if op.namespace not in ("aten", "prims"): + return False + if op is aten._fft_c2c.default: + return False + + op_name = op.name() + if "fft" in op_name: + return True + return False + + +# These operators have meta implementations with incorrect strides +@register_op_impl(stride_incorrect_op) +def wordaround_stride_incorrect_op(fake_mode, func, *args, **kwargs): + # This is a workaround for meta implmentations with incorrect strides + + def is_symbolic(x): + if isinstance(x, FakeTensor): + return x._has_symbolic_sizes_strides + if isinstance(x, (torch.SymInt, torch.SymFloat, torch.SymBool)): + return True + return False + + # For static shapes, we can fall back to eager for the real strides + if fake_mode.allow_fallback_kernels: + require_dynamic = any( + is_symbolic(x) for x in itertools.chain(args, kwargs.values()) + ) + if not require_dynamic: + flat_args, args_spec = pytree.tree_flatten((args, kwargs)) + return run_fallback_kernel(fake_mode, func, flat_args, args_spec, None) + + raise UnsupportedOperatorException(func) + + +# Dont default to default device handling, +# since the device of `the_template` is ignored +@register_op_impl(aten.resize_as_.default) +def resize_as_(fake_mode, func, *args, **kwargs): + with in_kernel_invocation_manager(fake_mode): + return func(*args, **kwargs) + + +@register_op_impl(aten._sparse_coo_tensor_with_dims_and_tensors.default) +def _sparse_coo_tensor_with_dims_and_tensors(fake_mode, func, *args, **kwargs): + # TODO: remove me + return constructors(fake_mode, func, *args, **kwargs) + + +# index.Tensor data-dependent in only some conditions +@register_op_impl( + lambda func: torch.Tag.dynamic_output_shape in func.tags + and func + not in [aten.index.Tensor, aten.nonzero.default, aten.repeat_interleave.Tensor] +) +def dyn_shape(fake_mode, func, *args, **kwargs): + raise DynamicOutputShapeException(func) + + +def _unique( + fake_mode, func, arg, dim, sorted=True, return_inverse=False, return_counts=False +): + if ( + fake_mode.shape_env is None + or not fake_mode.shape_env.allow_dynamic_output_shape_ops + ): + # Without symints/symfloats, cannot handle this + raise DynamicOutputShapeException(func) + + # Do not use a memo for unique_dim + if dim is not None or (nnz := arg.unique_memo) is None: + # Avoid importing sympy at a module level + from torch.fx.experimental.symbolic_shapes import ( + _constrain_range_for_size, + has_free_symbols, + ) + + if not has_free_symbols(arg.numel()) and arg.numel() == 0: + # If numel is zero, then the output size must be zero. + # In this case, we must not allocate an unbacked SymInt, + # because if we do, it will immediately get refined to + # zero, but this will be inconsistent with size oblivious + # tests (which will continue to claim that the unbacked + # symint cannot equal zero). We could also unconditionally + # allocate an unbacked SymInt and not refine its range, + # but this seems more precise. + nnz = 0 + else: + nnz = fake_mode.shape_env.create_unbacked_symint() + + maxval = sys.maxsize - 1 + + numel = arg.numel() if dim is None else arg.size(dim) + if not has_free_symbols(numel): + maxval = int(numel) + + _constrain_range_for_size(nnz, max=maxval) + + if dim is None: + arg.unique_memo = nnz + + if dim is None: + ret = [arg.new_empty((nnz,))] + else: + ret = [arg.new_empty(*arg.shape[:dim], nnz, *arg.shape[dim + 1 :])] + + return_if_dim_and_cpu = dim is not None and arg.fake_device == torch.device("cpu") + if return_inverse or return_if_dim_and_cpu: + inverse = arg.new_empty(arg.shape if dim is None else (arg.shape[dim],)) + else: + inverse = arg.new_empty(0) + ret.append(inverse) + + if return_counts or return_if_dim_and_cpu: + counts = arg.new_empty(ret[0].shape if dim is None else (ret[0].shape[dim],)) + else: + counts = arg.new_empty(0) + ret.append(counts) + + return tuple(ret) + + +@register_op_impl(aten._unique2.default) +def unique2( + fake_mode, func, arg, sorted=True, return_inverse=False, return_counts=False +): + return _unique(fake_mode, func, arg, None, sorted, return_inverse, return_counts) + + +@register_op_impl(aten.unique_dim.default) +def unique_dim( + fake_mode, func, arg, dim, sorted=True, return_inverse=False, return_counts=False +): + return _unique( + fake_mode, + func, + arg, + # normalize dim to be non-negative + dim if dim >= 0 else dim % max(arg.ndim, 1), + sorted, + return_inverse, + return_counts, + ) + + +@register_op_impl(aten.repeat_interleave.Tensor) +def repeat_interleave_tensor(fake_mode, func, repeats, output_size=None): + if output_size is None: + if ( + fake_mode.shape_env is None + or not fake_mode.shape_env.allow_dynamic_output_shape_ops + ): + raise DynamicOutputShapeException(func) + + output_size = fake_mode.shape_env.create_unbacked_symint() + + # Avoid importing sympy at a module level + from torch.fx.experimental.symbolic_shapes import _constrain_range_for_size + + _constrain_range_for_size(output_size) + # TODO: consider a memo + return repeats.new_empty(output_size) + + +@register_op_impl(torch.ops.aten.item.default) +@register_op_impl(torch.ops.aten._local_scalar_dense.default) +def local_scalar_dense(fake_mode, func, arg): + if (r := arg.item_memo) is not None: + return r + if fake_mode.shape_env is None or ( + not fake_mode.shape_env.allow_scalar_outputs + and not fake_mode.allow_scalar_outputs + ): + # Without symints/symfloats, cannot handle this + raise DataDependentOutputException(func) + if is_float_dtype(arg.dtype): + r = fake_mode.shape_env.create_unbacked_symfloat() + elif is_integer_dtype(arg.dtype): + r = fake_mode.shape_env.create_unbacked_symint() + elif is_boolean_dtype(arg.dtype): + r = fake_mode.shape_env.create_unbacked_symbool() + else: + raise NotImplementedError(f"local_scalar_dense/item NYI for {arg.dtype}") + arg.item_memo = r + return r + + +@register_op_impl(torch.ops.aten.nonzero.default) +def nonzero(fake_mode, func, arg): + if ( + fake_mode.shape_env is None + or not fake_mode.shape_env.allow_dynamic_output_shape_ops + ): + # Without symints/symfloats, cannot handle this + raise DynamicOutputShapeException(func) + + if (nnz := arg.nonzero_memo) is None: + # Avoid importing sympy at a module level + from torch.fx.experimental.symbolic_shapes import ( + _constrain_range_for_size, + has_free_symbols, + ) + + if not has_free_symbols(arg.numel()) and arg.numel() == 0: + # If numel is zero, then the output size must be zero. + # In this case, we must not allocate an unbacked SymInt, + # because if we do, it will immediately get refined to + # zero, but this will be inconsistent with size oblivious + # tests (which will continue to claim that the unbacked + # symint cannot equal zero). We could also unconditionally + # allocate an unbacked SymInt and not refine its range, + # but this seems more precise. + nnz = 0 + else: + nnz = fake_mode.shape_env.create_unbacked_symint() + + maxval = sys.maxsize - 1 + + if not has_free_symbols(arg.numel()): + maxval = int(arg.numel()) + + _constrain_range_for_size(nnz, max=maxval) + + arg.nonzero_memo = nnz + + return arg.new_empty((nnz, arg.dim()), dtype=torch.int64) + + +@register_op_impl(torch.ops.aten.masked_select.default) +def masked_select(fake_mode, func, self, mask): + if ( + fake_mode.shape_env is None + or not fake_mode.shape_env.allow_dynamic_output_shape_ops + ): + # Without symints/symfloats, cannot handle this + raise DynamicOutputShapeException(func) + + nnz = fake_mode.shape_env.create_unbacked_symint() + + # see nonzero for commentary + maxval = sys.maxsize - 1 + + # Avoid importing sympy at a module level + from torch.fx.experimental.symbolic_shapes import ( + _constrain_range_for_size, + has_free_symbols, + ) + from torch.utils._sympy.numbers import IntInfinity + from torch.utils._sympy.value_ranges import bound_sympy + + # If num elements is expressed symbolically, calculate + # the concrete value based on upper bounds. Otherwise, + # we can set max val directly. + if not has_free_symbols(self.numel()): + num_elements = int(self.numel()) + else: + prod_node = math.prod(self.shape).node + prod_range = bound_sympy(prod_node.expr, prod_node.shape_env.var_to_range) + if isinstance(prod_range.upper, IntInfinity): + num_elements = sys.maxsize - 1 + else: + num_elements = prod_range.upper + if num_elements > 2: + maxval = num_elements + + _constrain_range_for_size(nnz, max=maxval) + + return self.new_empty((nnz,)) + + +# NB: this must be ordered after local_scalar_dense +@register_op_impl(lambda func: torch.Tag.data_dependent_output in func.tags) +def data_dep(fake_mode, func, *args, **kwargs): + raise DataDependentOutputException(func) + + +# Bool Indices get Expanded as Masks +# See: IndexingUtils.h:expandTensors +def check_no_bool_index_tensors(func, self, indices): + for index in indices: + if index is not None and index.dtype in (torch.bool, torch.uint8): + raise DynamicOutputShapeException(func) + + +def run_and_return_new_tensor_of_input_device(fake_mode, func, args, kwargs): + _, new_kwargs = normalize_function( + func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True + ) + + out_device = new_kwargs["input"].device + with in_kernel_invocation_manager(fake_mode): + out = func(*args, **kwargs) + if not is_noncontiguous_supported(out_device): + out = out.new_empty(out.shape) + + if out is new_kwargs["input"]: + return out # copy_ + return FakeTensor(fake_mode, out, out_device) + + +_is_builtin_namespaces = ordered_set("aten", "prims", "prim") + + +def is_builtin(op): + return op.namespace in _is_builtin_namespaces + + +def has_meta(func): + return torch._C._dispatch_has_computed_kernel_for_dispatch_key(func.name(), "Meta") + + +@register_op_impl( + lambda func: is_builtin(func) and "foreach" in func.name() and has_meta(func) +) +def foreach_run_and_map_input_device(fake_mode, func, *args, **kwargs): + tensor_lists = [] + for arg in itertools.chain(args, kwargs.values()): + if ( + isinstance(arg, (list, tuple)) + and len(arg) + and isinstance(arg[0], torch.Tensor) + ): + tensor_lists.append(arg) + + try: + with in_kernel_invocation_manager(fake_mode): + out_meta = func(*args, **kwargs) + except NotImplementedError as not_implemented_error: + return NotImplemented + + if not out_meta: + return out_meta + + assert tensor_lists + out_fake = [] + + for i, meta_t in enumerate(out_meta): + device, _ = FakeTensor._find_common_device(func, [tl[i] for tl in tensor_lists]) + out_fake.append( + fake_mode.fake_tensor_converter.from_meta_and_device( + fake_mode, meta_t, device + ) + ) + + return out_fake + + +# Dont default to default device handling, +# Since op can take in non-zero sized cpu +# index tensors with cuda self +@register_op_impl(aten.index.Tensor) +def index_tensor(fake_mode, func, *args, **kwargs): + from torch._meta_registrations import meta_index_Tensor + + _, new_kwargs = normalize_function( + func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True + ) + + out_device = new_kwargs["input"].device + # ensure nonzero call goes to fake tensor + with fake_mode: + out = meta_index_Tensor(*args, **kwargs) + return out.to(out_device) + + +# Can take mixed meta/non-meta arguments; the meta registration +# will roughly do the right thing even when given real devices +@register_op_impl(aten._embedding_bag.default) +def embedding_bag(fake_mode, func, *args, **kwargs): + from torch._meta_registrations import meta_embedding_bag + + with fake_mode: + return meta_embedding_bag(*args, **kwargs) + + +# takes in multiple-devices, dont default to default device handling +@register_op_impl(aten._unsafe_index_put.default) +@register_op_impl(aten.copy.default) +@register_op_impl(aten.copy_.default) +@register_op_impl(aten.slice_scatter.default) +def multi_device_op_default(fake_mode, func, *args, **kwargs): + return run_and_return_new_tensor_of_input_device(fake_mode, func, args, kwargs) + + +# same with multi_device_op_default, but return the input +@register_op_impl(aten.copy.out) +@register_op_impl(aten.slice_scatter.out) +def multi_device_op_out(fake_mode, func, *args, **kwargs): + with in_kernel_invocation_manager(fake_mode): + out = func(*args, **kwargs) + + _, new_kwargs = normalize_function( + func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True + ) + + return new_kwargs["input"] + + +@register_op_impl(aten.index_put.default) +@register_op_impl(aten.index_put_.default) +def index_put_impl(fake_mode, func, *args, **kwargs): + _, new_kwargs = normalize_function( + func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True + ) + + values = new_kwargs["values"] + self_device = new_kwargs["input"].fake_device + torch._check( + self_device == values.fake_device or (values.ndim == 0 and values.numel() == 1), + lambda: f"Mismatching {func} device between self ({self_device}) and values ({values.device})", + ) + + out = run_and_return_new_tensor_of_input_device(fake_mode, func, args, kwargs) + if func is aten.index_put_.default: + return new_kwargs["input"] + else: + return out + + +@register_op_impl(aten._nested_tensor_from_tensor_list.default) +@register_op_impl(aten._nested_tensor_from_tensor_list.out) +@register_op_impl(aten._nested_view_from_buffer.default) +@register_op_impl(aten._nested_view_from_buffer_copy.default) +def nested_tensors_unsupported(fake_mode, func, *args, **kwargs): + raise UnsupportedOperatorException( + "torch.compile does not support strided NestedTensor" + ) + + +@register_op_impl( + [ + x + for x in _device_not_kwarg_ops + if x + not in ( + # these are already registered elsewhere + aten.is_pinned.default, + aten.to.device, + aten.to.prim_Device, + aten._nested_tensor_from_tensor_list.default, + aten._nested_tensor_from_tensor_list.out, + ) + ] +) +def nyi(fake_mode, func, *args, **kwargs): + assert func not in _device_not_kwarg_ops, f"NYI: {func}" + + +@register_op_impl([aten.convolution.default, aten.convolution_backward.default]) +def conv(fake_mode, func, *args, **kwargs): + _, kwargs = normalize_function( + func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True + ) + device = kwargs["input"].fake_device + # need to re-enable mode so the tensors report fake device + with fake_mode: + # if the input is unsqueezed is done in Convolution.cpp we get segfault + k = kwargs["weight"].ndim + batch = kwargs["input"].shape[0] + + # Avoid importing sympy at a module level + from torch.fx.experimental.symbolic_shapes import has_hint + + if not has_hint(batch): + # TODO: We can make this a little more faithful with best effort + # channels last detection (but only if it's statically obvious!) + mem_fmt = None + elif k == 3 and not kwargs["input"].is_mkldnn and not kwargs["input"].is_xpu: + mem_fmt = None + else: + if func is aten.convolution.default: + conv_backend = torch._C._select_conv_backend(**kwargs) + else: + conv_backend = torch._C._select_conv_backend( + kwargs["input"], + kwargs["weight"], + bias=None, + stride=kwargs["stride"], + padding=kwargs["padding"], + dilation=kwargs["dilation"], + transposed=kwargs["transposed"], + output_padding=kwargs["output_padding"], + groups=kwargs["groups"], + bias_sizes=kwargs["bias_sizes"], + ) + mem_fmt = torch._C._conv_determine_backend_memory_format( + kwargs["input"], kwargs["weight"], conv_backend + ) + + def convert(t, mem_fmt): + if t is None: + return t + if mem_fmt is not None: + t = t.to(memory_format=mem_fmt) + return FakeTensor(fake_mode, t, device) + + with in_kernel_invocation_manager(fake_mode): + out = func(**kwargs) + + if func is aten.convolution.default: + return convert(out, mem_fmt) + else: + return ( + convert(out[0], mem_fmt), + convert(out[1], mem_fmt), + convert(out[2], None), + ) + + +@register_op_impl(torch.ops.aten._pack_padded_sequence.default) +def _pack_padded_sequence(fake_mode, func, inputs, lengths, batch_first): + if ( + fake_mode.shape_env is None + or not fake_mode.shape_env.allow_dynamic_output_shape_ops + ): + # Without symints/symfloats, cannot handle this + raise DynamicOutputShapeException(func) + + new_batch_size = fake_mode.shape_env.create_unbacked_symint() + + from torch.fx.experimental.symbolic_shapes import _constrain_range_for_size + + _constrain_range_for_size(new_batch_size) + + if not batch_first: + # Inputs should have shape (batch_size, seq_len, *) + inputs = inputs.transpose(0, 1) + + res_size = inputs.shape[1:] + packed_data = inputs.new_empty(res_size) + batch_size = inputs.new_empty((new_batch_size,)) + return (packed_data, batch_size) + + +FAST_OP_IMPLEMENTATIONS = {} + + +# Unlike register_op_impl, these don't do the slow iteration for +# run_impl_check, and these run BEFORE decompositions +def register_fast_op_impl(func: OpOverload): + def impl_decorator(op_impl): + FAST_OP_IMPLEMENTATIONS[func] = op_impl + return op_impl + + return impl_decorator + + +# infer_size_impl in ExpandUtils +def infer_size(a, b): + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + dimsA = len(a) + dimsB = len(b) + ndim = max(dimsA, dimsB) + expandedSizes = [0] * ndim + for i in range(ndim - 1, -1, -1): + offset = ndim - 1 - i + dimA = dimsA - 1 - offset + dimB = dimsB - 1 - offset + sizeA = a[dimA] if dimA >= 0 else 1 + sizeB = b[dimB] if dimB >= 0 else 1 + + # NB: It is very important to test for broadcasting, before testing + # sizeA == sizeB. This is because the broadcasting tests are likely + # to be statically known (in particular, if sizeA/sizeB is unbacked + # but size-like, we will unsoundly assume they never equal 1), but + # the sizeA == sizeB test may not be statically known. However, once + # we have established that no broadcasting is happening, the + # sizeA == sizeB is now expect_true and we can defer it as a runtime + # assert (this works because Python will return the terminal + # expression of an or statement as-is, without bool()'ing it; if this + # were not the case, we'd need to write this using torch.sym_or() or + # something like that). + torch._check( + guard_size_oblivious(sizeA == 1) + or guard_size_oblivious(sizeB == 1) + or sizeA == sizeB, + lambda: f"The size of tensor a ({sizeA}) " + f"must match the size of tensor b ({sizeB}) " + f"at non-singleton dimension {i})", + ) + expandedSizes[i] = sizeB if guard_size_oblivious(sizeA == 1) else sizeA + return tuple(expandedSizes) + + +def make_fast_binary_impl(slow_ref): + def fast_binary_impl(mode, *args, **kwargs): + def slow(msg): + count_label(f"slow {msg}") + with mode: + return slow_ref(*args, **kwargs) + + count_label("attempt fast") + + # Fast path (based off of TensorIterator fast path). + # Unfortunately, there is no way to easily deduplicate + # this with either the TensorIterator C++ implementation + # (which we don't want to SymIntify, and also the algorithm + # here is slightly different from TensorIterator to allow + # for broadcasting), nor the PrimTorch implementation + # (which does not actually implement a fast path.) + + operands = args + + # compute_shape + has_scalars = False + has_tensors = False + final_shape = None + for op in operands: + shape = op.shape if isinstance(op, torch.Tensor) else () + if len(shape) == 0: + has_scalars = True + else: + has_tensors = True + if final_shape is None: + final_shape = shape + # TODO: Minor optimization: track if the shapes + # were equal so you can skip the equality check + # below if unnecessary + final_shape = infer_size(final_shape, shape) + assert final_shape is not None + + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious, sym_eq + + # Do some extra safety checks to see if the output + # stride is obvious + for op in operands: + if ( + isinstance(op, torch.Tensor) + and len(op.shape) == len(final_shape) + and guard_size_oblivious(sym_eq(op.shape, final_shape)) + ): + break + else: + return slow("both tensors nontrivially broadcast") + + # compute_types + cpu = torch.device("cpu") + common_device = cpu + common_dtype = None + output_dtype = None + has_different_input_dtypes = False + for op in operands: + if not isinstance(op, torch.Tensor): + # Use elementwise_dtypes for the tricky case + has_different_input_dtypes = True + continue + if common_device == cpu and not op.device.type == "cpu": + common_device = op.device + # Slightly simplified here as target_dtype cannot vary + if common_dtype is None: + common_dtype = op.dtype + elif common_dtype != op.dtype: + has_different_input_dtypes = True + + if has_different_input_dtypes: + # compute promotion + # TODO: we don't need the compute type + _, common_dtype = elementwise_dtypes( + *operands, type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT + ) + + # check all tensors on same device + # cpu scalars are assumed allow + current_cpu_scalars_on_non_cpu = 0 + max_cpu_scalars_on_non_cpu = 1 # hard coded atm + for op in operands: + if not isinstance(op, torch.Tensor): + continue + if common_device != cpu and op.dim() == 0 and op.device == cpu: + if current_cpu_scalars_on_non_cpu >= max_cpu_scalars_on_non_cpu: + return slow("error") + current_cpu_scalars_on_non_cpu += 1 + elif op.device != common_device: + return slow("error") + + # compute_fast_setup_type + is_contiguous = True + is_channels_last = True + # TODO: is_non-overlapping_and_dense (not bound from Python + # no inplace, no out, everything defined + + if is_noncontiguous_supported(common_device): + for op in operands: + if not isinstance(op, torch.Tensor): + continue + is_contiguous = is_contiguous and op.is_contiguous( + memory_format=torch.contiguous_format + ) + is_channels_last = is_channels_last and op.is_contiguous( + memory_format=torch.channels_last + ) + if is_contiguous: + # do contiguous + count_label("fast is_contiguous") + return FakeTensor( + mode, + torch.empty( + final_shape, + dtype=common_dtype, + device="meta", + memory_format=torch.contiguous_format, + ), + device=common_device, + ) + if is_channels_last: + count_label("fast channels_last") + # do channels last + return FakeTensor( + mode, + torch.empty( + final_shape, + dtype=common_dtype, + device="meta", + memory_format=torch.channels_last, + ), + device=common_device, + ) + + return slow("no contiguity match") + + return fast_binary_impl + + +# disable the python dispatcher to avoid decomposing detach() further +# (proxy_mode should still decompose detach() though) +def fast_detach(fake_mode, x): + with no_python_dispatcher(), in_kernel_invocation_manager(fake_mode): + out = torch.ops.aten.detach.default(x) + return FakeTensor(fake_mode, out, x.device) + + +@functools.lru_cache(None) +def get_fast_op_impls(): + import torch._refs + + register_fast_op_impl(torch.ops.aten.add.Tensor)( + make_fast_binary_impl(torch._refs.add) + ) + register_fast_op_impl(torch.ops.aten.sub.Tensor)( + make_fast_binary_impl(torch._refs.sub) + ) + register_fast_op_impl(torch.ops.aten.mul.Tensor)(make_fast_binary_impl(torch._refs.mul)) # type: ignore[has-type] + register_fast_op_impl(torch.ops.aten.div.Tensor)( + make_fast_binary_impl(torch._refs.div) + ) + register_fast_op_impl(torch.ops.aten.detach.default)(fast_detach) + return FAST_OP_IMPLEMENTATIONS diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/fake_tensor.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/fake_tensor.py new file mode 100644 index 0000000000000000000000000000000000000000..4867b43e84c2ca4190333c77b7566fa22bbb207d --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/fake_tensor.py @@ -0,0 +1,2431 @@ +# mypy: allow-untyped-decorators +from __future__ import annotations + +import atexit +import contextlib +import dataclasses +import functools +import logging +import math +import os +import traceback +import typing +import weakref +from collections import defaultdict +from dataclasses import dataclass +from typing import ( + Any, + Callable, + cast, + Dict, + Generator, + Iterable, + List, + Literal, + Mapping, + Optional, + Sequence, + Set, + Tuple, + Type, + TYPE_CHECKING, + TypeVar, + Union, +) +from typing_extensions import Self, TypeGuard +from weakref import ReferenceType + +import torch +from torch import SymBool, SymFloat, SymInt, Tensor +from torch._C._functorch import is_functorch_wrapped_tensor, is_legacy_batchedtensor +from torch._prims_common import suggest_memory_format +from torch._subclasses.meta_utils import ( + assert_eq, + assert_metadata_eq, + is_sparse_any, + is_sparse_compressed, + MetaConverter, +) +from torch._utils import render_call +from torch.fx.immutable_collections import immutable_dict +from torch.fx.operator_schemas import normalize_function +from torch.multiprocessing.reductions import StorageWeakRef +from torch.overrides import TorchFunctionMode +from torch.types import IntLikeType, py_sym_types +from torch.utils._backport_slots import dataclass_slots +from torch.utils._mode_utils import no_dispatch +from torch.utils._python_dispatch import ( + is_traceable_wrapper_subclass, + TorchDispatchMode, +) +from torch.utils._pytree import PyTree, tree_map, tree_map_, TreeSpec +from torch.utils._stats import count +from torch.utils._traceback import CapturedTraceback + +from ._fake_tensor_utils import _CacheKeyState, _PySymInputStub, _SymIntOutputStub + + +if TYPE_CHECKING: + from types import TracebackType + + from torch._guards import Source + from torch._ops import OpOverload + from torch.fx.experimental.symbolic_shapes import ShapeEnv, SymbolicContext + +log = logging.getLogger(__name__) + +# TODO: Hack to unblock https://github.com/pytorch/pytorch/pull/108186 +# Proper fix tracked by https://github.com/pytorch/pytorch/issues/120105 +try: + not_implemented_log = torch._logging.getArtifactLogger(__name__, "not_implemented") +except ValueError as e: + if "'not_implemented' not registered" in str(e): + import logging as not_implemented_log + else: + raise e + + +class _Unassigned: + pass + + +_UNASSIGNED = _Unassigned() + +DimList = List + +pytree = torch.utils._pytree +T = TypeVar("T") + +aten = torch._ops.ops.aten + +CONSTANT_NUMEL_LIMIT = 1 + +RECURSION_COUNT = 0 + + +# Small helper that increments recursion count, and +# resets it when the object goes out of scope. Useful +# if you don't want to increase indentation which is +# what a context manager would do. +class IncrementRecursionCount: + def __init__(self) -> None: + global RECURSION_COUNT + RECURSION_COUNT += 1 + + def __del__(self) -> None: + global RECURSION_COUNT + RECURSION_COUNT -= 1 + + +@dataclass +class UnsupportedFakeTensorException(RuntimeError): + reason: str + + +@dataclass +class DynamicOutputShapeException(RuntimeError): + func: OpOverload + + +@dataclass +class DataDependentOutputException(RuntimeError): + func: OpOverload + + +@dataclass +class UnsupportedOperatorException(RuntimeError): + func: OpOverload + + +def ordered_set(*items: T) -> Dict[T, Literal[True]]: + return dict.fromkeys(items, True) + + +@contextlib.contextmanager +def unset_fake_temporarily() -> Generator[Optional[TorchDispatchMode], None, None]: + old = torch._C._unset_dispatch_mode(torch._C._TorchDispatchModeKey.FAKE) + try: + yield old + finally: + if old is not None: + torch._C._set_dispatch_mode(old) + + +def get_plain_tensors(subclass: Tensor) -> List[Tensor]: + assert is_traceable_wrapper_subclass(subclass) + plain_tensors: List[Tensor] = [] + todo = [subclass] + while todo: + curr = todo.pop() + if not is_traceable_wrapper_subclass(curr): + assert isinstance(curr, Tensor) + plain_tensors.append(curr) + continue + + inner_keys, _ = curr.__tensor_flatten__() + for key in reversed(inner_keys): + todo.append(getattr(curr, key)) + + return plain_tensors + + +def is_fake(x: object) -> TypeGuard[Tensor]: + if isinstance(x, FakeTensor): + return True + if is_traceable_wrapper_subclass(x): + attrs, _ = type(x).__tensor_flatten__(x) + flattened_tensors = [getattr(x, attr) for attr in attrs] + all_fake = all(is_fake(x) for x in flattened_tensors) + any_fake = any(is_fake(x) for x in flattened_tensors) + assert all_fake == any_fake, "got mixed fake and real tensors!" + return all_fake + elif isinstance(x, Tensor) and torch._is_functional_tensor(x): + reapply_views = torch._C._functionalization_reapply_views_tls() + unwrapped = torch._C._functorch._unwrap_functional_tensor(x, reapply_views) + return is_fake(unwrapped) + elif isinstance(x, Tensor) and is_functorch_wrapped_tensor(x): + unwrapped = torch._C._functorch.get_unwrapped(x) + return is_fake(unwrapped) + return False + + +def maybe_get_fake_mode(t: object) -> Optional[FakeTensorMode]: + if isinstance(t, FakeTensor): + return t.fake_mode + if is_traceable_wrapper_subclass(t): + inner_tensor_names, _ = t.__tensor_flatten__() + modes = [ + maybe_get_fake_mode(getattr(t, t_name)) for t_name in inner_tensor_names + ] + m = modes[0] + assert all(m is x for x in modes) + return m + elif isinstance(t, Tensor) and torch._is_functional_tensor(t): + reapply_views = torch._C._functionalization_reapply_views_tls() + unwrapped = torch._C._functorch._unwrap_functional_tensor(t, reapply_views) + return maybe_get_fake_mode(unwrapped) + elif isinstance(t, Tensor) and is_functorch_wrapped_tensor(t): + unwrapped = torch._C._functorch.get_unwrapped(t) + return maybe_get_fake_mode(unwrapped) + return None + + +@functools.lru_cache(None) +def get_schema_info(func: OpOverload) -> torch._C._SchemaInfo: + return torch._C._SchemaInfo(func._schema) + + +# many of the decompositions registered to torch/_prims do not at the moment model +# aliasing or strides, so as an incremental step, just enable the decompositions in +# torch/_decomp/decompositions.py. +# decomps are used for aot autograd tracing so we would like to unify on their +# implementation and add additional testing to them +@functools.lru_cache(None) +def torch_decomp_decompositions(func: OpOverload) -> bool: + from torch._decomp import decomposition_table + + decompositions = torch._decomp.decompositions + # Note that the function in the decomposition table might be + # different from the one in the module because of the difference + # in out handling in aten API and torch public API + return decomposition_table[func].__module__.startswith( + "torch._decomp" + ) and decomposition_table[func].__name__ in dir(decompositions) + + +def tree_flatten_only(ty: Type[T], tree: PyTree) -> List[T]: + flat_vals = pytree.tree_leaves(tree) + return [elem for elem in flat_vals if isinstance(elem, ty)] + + +def _is_plain_tensor(t: object) -> bool: + return ( + type(t) is Tensor + and t.layout == torch.strided + and not ( + t.is_sparse + or t.is_nested + or is_functorch_wrapped_tensor(t) + or is_legacy_batchedtensor(t) + or torch._is_functional_tensor(t) + ) + ) + + +# Similar to `MetaConverter`, this is a class for converting +# multiple tensors into fake tensors which share the same view/storage +# structure. Like `MetaConverter`, it uses `WeakIdRef` to +# hold a weak reference for all memoized tensors. +class FakeTensorConverter: + @property + def tensor_memo( + self, + ) -> weakref.WeakValueDictionary: + # not valid until py3.10 + # weakref.WeakValueDictionary["torch._subclasses.meta_utils.MetaTensorId", Optional["FakeTensor"]] + return self.meta_converter.tensor_memo + + meta_converter: MetaConverter + constant_storage_mapping: Dict[StorageWeakRef, List[ReferenceType]] + export: bool + + def __init__(self, *, copy_data: bool = False, export: bool = False) -> None: + self.meta_converter = MetaConverter(copy_data=copy_data) + self.export = export + + # map from to storage to corresponding constant tensors + self.constant_storage_mapping = {} + + def add_constant_storage_mapping(self, fake_tensor: FakeTensor) -> None: + # when you have a constant, aliased tensor: + # const_tensor.add_(torch.rand([1])) + # all aliases of it must become no longer const + assert isinstance(fake_tensor, FakeTensor) and fake_tensor.constant is not None + weak_st = StorageWeakRef(fake_tensor.constant._typed_storage()) + + # we need a map from a weak storage to all of its corresponding + # constant tensors. python doesn't have the weak value equivalent + # of defaultdict(list), so we are using a WeakValueDictionary as one + if weak_st not in self.constant_storage_mapping: + self.constant_storage_mapping[weak_st] = [] + self.constant_storage_mapping[weak_st].append(weakref.ref(fake_tensor)) + + def invalidate_constant_aliases(self, tensor: Tensor) -> None: + assert not isinstance(tensor, FakeTensor) + + weak_st = StorageWeakRef(tensor._typed_storage()) + if weak_st not in self.constant_storage_mapping: + return + + for weak_tensor_ref in self.constant_storage_mapping[weak_st]: + ten = weak_tensor_ref() + if ten is not None: + ten._fix_weakref() + ten.constant = None + + del self.constant_storage_mapping[weak_st] + + def _get_memo(self, t: Tensor) -> Optional[FakeTensor]: + tid = self.meta_converter.describer.lookup_tensor.get(t) + if tid is None: + return None + return self.tensor_memo.get(tid) + + def set_tensor_memo(self, t: Tensor, v: FakeTensor) -> None: + tid = self.meta_converter.describer.get_tensor_id(t) + self.meta_converter.tensor_memo[tid] = v + + # You can have a real tensor that you need to convert into a fake tensor. + # If you have a meta tensor already, call from_meta_and_device. + # + # You're allowed to pass a meta tensor to be turned into a fake + # tensor; although an odd thing to do, this can occur if you're doing + # cross ref testing and the inner test is already operating on meta tensors. + def from_real_tensor( + self, + fake_mode: FakeTensorMode, + t: Tensor, + make_constant: bool = False, + shape_env: Optional[ShapeEnv] = None, + *, + source: Optional[Source] = None, + symbolic_context: Optional[SymbolicContext] = None, + trace: bool = True, + ) -> FakeTensor: + # see note [Tensor Fakification and Symbol Caching] + if not symbolic_context and not source and shape_env: + if tracing_context := torch._guards.TracingContext.try_get(): + if t in tracing_context.tensor_to_context: + symbolic_context = tracing_context.tensor_to_context[t] + from torch.fx.experimental.symbolic_shapes import ( + StatefulSymbolicContext, + ) + + assert isinstance(symbolic_context, StatefulSymbolicContext) + source = symbolic_context.tensor_source + + maybe_memo = self._get_memo(t) + if maybe_memo is not None: + return maybe_memo + existing_device = t.device + # not yet supported in metatensors + if t.is_quantized: + raise UnsupportedFakeTensorException("quantized nyi in meta tensors") + if type(t) is torch.nn.Parameter: + assert not make_constant + + def mk_fake_tensor(make_meta_t: Callable[[], object]) -> FakeTensor: + # NB: don't use in_kernel_invocation_manager. to + # ensure FakeTensor can internally do constant computation + # as necessary. Invocation manager is "more correct" as + # it works for more operators in make_meta_t, but + # invariant is that make_meta_t only calls factories + # for which it is not strictly necessary to use the + # invocation manager (I think!) + with no_dispatch(): + return FakeTensor( + fake_mode, + make_meta_t(), + existing_device, + # TODO: callback might be used in recursive contexts, in + # which case using t is wrong! BUG! + constant=t if make_constant else None, + ) + + out = self.meta_converter( + t, + shape_env=shape_env, + callback=mk_fake_tensor, + source=source, + symbolic_context=symbolic_context, + trace=trace, + ) + if out is NotImplemented: + raise UnsupportedFakeTensorException("meta converter nyi") + + from torch._dynamo.source import RandomValueSource + + value = None + if ( + not self.export + and _is_plain_tensor(t) # mostly, we want to know if item() works + and t.dim() == 0 + and t.device.type == "cpu" + # All integer types are fair game, because signed overflow is UB + # (and even int64 can overflow, since integers in Python are + # arbitrary precision). But only float64 is OK for float, because + # switching between float32 and float64 changes semantics in an + # observable way without hitting UB. + and t.dtype + in [torch.int64, torch.int32, torch.int16, torch.int8, torch.float64] + and source is not None + # Impede setting up item() on things coming from random. These + # are not "real" item() calls, instead UnspecializedPythonVariable + # is unsafely pretending an int is a tensor, which can sometimes + # implicitly cause an item call. The problem is this is pretty + # unsound: there's no reason substituting an int with a Tensor is + # going to give the same results. Today, you mostly get around + # this by typically not having capture_scalar_outputs on and graph + # breaking when someone tries to use the unspec variable in an + # int-y context. But allowing it through here would break that. + # So don't. + # + # Once random values are setup to be represented as + # SymNodeVariable, this condition can be removed. To check if + # you've done it right, this is a good test: + # + # PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py -k + # TestReductionsCPU.test_dim_reduction_fns_fn_name_amax_cpu_bfloat16 + and not isinstance(source, RandomValueSource) + # In Dynamo, shape_env is never none (even with static shapes). + # However, FakeTensorMode can be used by hand and in some cases + # ShapeEnv is not allocated. + and shape_env is not None + ): + from torch._dynamo.source import CallMethodItemSource, FloatTensorSource + from torch.fx.experimental.symbolic_shapes import DimDynamic + + with no_dispatch(): + value = t.item() + if not math.isnan(value): + # Peephole strip out unnecessary torch.as_tensor(x).item() + if isinstance(source, FloatTensorSource): + item_source = source.base + else: + item_source = CallMethodItemSource(source) + symbol = shape_env.create_unspecified_symbol( + value, + source=item_source, + dynamic_dim=DimDynamic.DYNAMIC, + ) + # NB: reusing item_memo here ensures that we invalidate on + # mutation + if t.dtype == torch.int64: + out.item_memo = shape_env.create_symintnode( + symbol, + hint=value, + source=item_source, + ) + elif t.dtype == torch.float64: + out.item_memo = shape_env.create_symfloatnode( + symbol, + hint=value, + source=item_source, + ) + if make_constant: + self.add_constant_storage_mapping(out) + # NB: meta_converter set the memo + return out + + # If you specify the device, it MUST be a meta tensor. + def from_meta_and_device( + self, fake_mode: FakeTensorMode, t: Tensor, device: torch.device + ) -> FakeTensor: + assert ( + t.device.type == "meta" + ), f"tensor's device must be `meta`, got {t.device.type} instead" + # This is a bit abusive (this is not the "real" tensor) but whatever, + # the meta tensor should be fresh so there's no way to get it wrong + maybe_memo = self._get_memo(t) + if maybe_memo is not None: + return maybe_memo + out = FakeTensor(fake_mode, t, device) + self.set_tensor_memo(t, out) + return out + + +@functools.lru_cache(None) +def init_gpu_context() -> None: + # Backward will error with cuda Fake Tensors if no cuda tensors have been initialized first + if torch.cuda.is_available(): + ( + torch.empty(1, device="cuda") + if torch.version.hip is None + else torch.zeros(1, device="cuda") + ) + + if torch.xpu.is_available(): + (torch.empty(1, device="xpu")) + + +@contextlib.contextmanager +def in_kernel_invocation_manager( + fake_mode: FakeTensorMode, +) -> Generator[None, None, None]: + # See: note [Fake Tensor Dispatch Keys] + prev_in_kernel = fake_mode.in_kernel_invocation + meta_in_tls = torch._C._meta_in_tls_dispatch_include() + assert meta_in_tls == prev_in_kernel, f"{meta_in_tls}, {prev_in_kernel}" + + with torch._C._DisableTorchDispatch(): + fake_mode.in_kernel_invocation = True + # Unfortunately _set_meta_in_tls_dispatch_include(False) can leave + # `Dense` turned on (because it's implied by `Meta`) + with torch._C._PreserveDispatchKeyGuard(): + torch._C._set_meta_in_tls_dispatch_include(True) + try: + yield + finally: + fake_mode.in_kernel_invocation = prev_in_kernel + # torch._C._set_meta_in_tls_dispatch_include(prev_in_kernel) + + +# Return if the function allows Python numbers to bind to Tensors +def should_allow_numbers_as_tensors(func: OpOverload) -> bool: + return torch._C._should_allow_numbers_as_tensors( + func.name().split("::")[-1].split(".")[0] + ) + + +class FakeTensorConfig: + debug = os.environ.get("TORCH_FAKE_TENSOR_DEBUG", "0") == "1" + + +# This memorizes the unbacked SymInt representing quantities like the number +# of nonzero elements in this tensor. There is one instance of the descriptor +# per particular quantity to memoize. +# +# Memoization is helpful if you do something like x[mask] and y[mask]; +# mask.nonzero() gets repeatedly called and should give a consistent unbacked +# SymInt. It needs to be invalidated in the same way constant is. +# +# Making this a descriptor may seem overly fancy, but actually it's the most +# convenient way to make sure we have access to FakeTensor during access, +# which is required for testing version counter and epoch validity +class SymIntMemoDescriptor: + _name: str + + # By default, SymInts in this memo are invalidated across versions/epochs. + # nested_ints however are preserved across epochs and across versions. + # Preserving across versions is okay for nested int since the association + # of a nested int is agnostic to the underlying data and nested ints are not + # shared across multiple distinct tensors. + _is_nested_int: bool + + def __init__(self, *, is_nested_int: bool = False) -> None: + self._is_nested_int = is_nested_int + + def __set_name__(self, owner: str, name: str) -> None: + self._name = name + + def _memo(self, obj: FakeTensor) -> str: + return f"_{self._name}" + + def _memo_vc(self, obj: FakeTensor) -> str: + return f"_{self._name}_vc" + + # When we retrace, we need to invalidate all the memos so that we can + # accurately identify the first time unbacked SymInts are allocated. + # This is only relevant for inputs; for intermediates, they will get fresh + # fake tensors so you won't have a memo anyway + def _memo_epoch(self, obj: FakeTensor) -> str: + return f"_{self._name}_epoch" + + def __get__( + self, obj: FakeTensor, objtype: Optional[Type[FakeTensor]] = None + ) -> Optional[torch.SymInt]: + if (r := getattr(obj, self._memo(obj))) is None: + return None + # Version counter based tracking isn't 100% sound but it's close + # enough + if ( + not self._is_nested_int and getattr(obj, self._memo_vc(obj)) != obj._version + ) or ( + not self._is_nested_int + and getattr(obj, self._memo_epoch(obj)) != obj.fake_mode.epoch + ): + setattr(obj, self._memo(obj), None) + return None + return r + + def __set__(self, obj: FakeTensor, value: Optional[torch.SymInt]) -> None: + if value is None: + setattr(obj, self._memo(obj), None) + setattr(obj, self._memo_vc(obj), None) + setattr(obj, self._memo_epoch(obj), None) + elif not obj.is_inference() or self._is_nested_int: + setattr(obj, self._memo(obj), value) + if not self._is_nested_int: + setattr(obj, self._memo_vc(obj), obj._version) + setattr(obj, self._memo_epoch(obj), obj.fake_mode.epoch) + + +class FakeTensor(Tensor): + """ + Meta tensors give you the ability to run PyTorch code without having to + actually do computation through tensors allocated on a `meta` device. + Because the device is `meta`, meta tensors do not model device propagation. + FakeTensor extends MetaTensors to also carry an additional `fake_device` + which tracks devices that would have been used. + """ + + fake_device: torch.device + fake_mode: FakeTensorMode + constant: Optional[Tensor] + real_tensor: Optional[Tensor] + + # TODO: Generalize this as needed, e.g., into a trie of memos, if + # you do something like x[0].item() (x[0] is fresh each time, so + # memo mechanism here won't work) + nonzero_memo = SymIntMemoDescriptor() + item_memo = SymIntMemoDescriptor() + unique_memo = SymIntMemoDescriptor() + + # We expect nested_int_memo to be None when an offsets is a graph + # intermediate, or an input that has never been associated with a + # nested int. + nested_int_memo = SymIntMemoDescriptor(is_nested_int=True) + + # Indicates to our torch_dispatch dispatching infra that + # this is an "infra" mode with lower dispatching precedence. + _mode_key = torch._C._TorchDispatchModeKey.FAKE + + @property + def device(self) -> torch.device: + if self.fake_mode.in_kernel_invocation: + return torch.device("meta") + else: + return self.fake_device + + @device.setter + def device(self, _: torch.device) -> None: + raise NotImplementedError + + # Note: [Fake Tensor Dispatch Keys] + # In order to model the behavior of device-specific autocast + # and autograd logic, we update the dispatch keys of FakeTensors + # to reflect their fake device. This includes the BackendComponent + # (DispatchKey::Meta -> DispatchKey::CUDA), and also the BackendComponent + # related Autocast and Autograd keys. __torch_dispatch__ sits below + # Autocast and Autograd, and is only invoked when we are at the + # kernel for the BackendComponent. Then, we add Meta to the + # thread-local dispatch include set to hit the meta kernel + # instead of the kernel of the BackendComponent for the fake device. + # The `device_for_backend_keys` does that below + # NOTE: this probably will not do the right thing for backends + # that have dispatch keys which are higher than the "meta" key: + # https://github.com/pytorch/pytorch/blob/main/c10/core/DispatchKey.h#L189 + + # We don't support named tensors; graph break + @property + def names(self) -> List[str]: + raise UnsupportedFakeTensorException( + "torch.compile doesn't support named tensors" + ) + + @names.setter + def names(self, _: List[str]) -> None: + raise NotImplementedError + + @staticmethod + def __new__( + cls, + fake_mode: FakeTensorMode, + elem: Tensor, + device: torch.device, + constant: Optional[Tensor] = None, + real_tensor: Optional[Tensor] = None, + ) -> Self: + self = Tensor._make_subclass( + cls, + elem, + elem.requires_grad, + dispatch_device=True, + device_for_backend_keys=device, + ) + if not fake_mode._allow_unsafe_data_ptr_access: + torch._C._set_throw_on_mutable_data_ptr(self) + else: + torch._C._set_warn_deprecated_on_mutable_data_ptr(self) + + assert elem.device.type == "meta", elem.device.type + device = device if isinstance(device, torch.device) else torch.device(device) + # NB: it is fine, if a little confusing, for device to be meta + # (we are faking a meta tensor in that case). However, it often + # indicates some sort of confusion (e.g., you accidentally passed + # in a meta tensor when you should have passed in the real tensor). + # So by default we disallow meta, and if you are working in a situation + # where it is helpful (e.g., crossref testing) you can turn it back + # on + if not fake_mode.allow_meta: + assert device.type != "meta" + # normalize device. + if device.type in ["cuda", "xpu"]: + init_gpu_context() + + if ( + device.type + in ["cuda", "hpu", "xpu", torch._C._get_privateuse1_backend_name()] + and device.index is None + ): + if getattr(torch, device.type).is_initialized(): + device = torch.device( + f"{device.type}:{getattr(torch, device.type).current_device()}" + ) + else: + device = torch.device(f"{device.type}:0") + self.fake_device = device + self.fake_mode = fake_mode + self.constant = constant + assert not isinstance(real_tensor, FakeTensor) + self.real_tensor = real_tensor + self.nonzero_memo = None + self.item_memo = None + self.unique_memo = None + self.nested_int_memo = None + + if FakeTensorConfig.debug: + self._debug_trace = CapturedTraceback.extract() # type: ignore[attr-defined] + return self + + # In some circumstances, a conventional Tensor constructor + # will get rewritten to call into FakeTensor. We must provide an + # __init__ method that can accept the Python interpreters initialization + # in such a situation; we must also be able to handle direct fake + # tensor construction via FakeTensor(). + # + # In particular, the __init__ call will look funny in the following case: + # + # with FakeTensorMode(): + # x = Tensor([1, 2, 3]) + # + # this desugars into: + # + # with FakeTensorMode(): + # x = Tensor.__new__([1, 2, 3]) + # # NB: x is a fake tensor, because of the mode! + # x.__init__([1, 2, 3]) # not the normal fake tensor args! + # + def __init__(self, *args: object, **kwargs: object) -> None: + super().__init__() + + @staticmethod + def from_tensor(t: Tensor, fake_mode: FakeTensorMode) -> FakeTensor: + return fake_mode.from_tensor(t) + + @classmethod + @count + def __torch_dispatch__( + cls, + func: OpOverload, + types: Sequence[Type], + args: Sequence[object] = (), + kwargs: Mapping[str, object] = immutable_dict(), + ) -> object: + # need to handle here to avoid infinite recursion + # see [in_kernel_invocation] + if func == torch.ops.prim.device.default: + assert len(args) == 1 and isinstance(args[0], FakeTensor) + if args[0].fake_mode.in_kernel_invocation: + return torch.device("meta") + else: + return args[0].fake_device + + # this handler must be done inside FakeTensor subclass, not mode, because + # we can end up dispatching here when we have a fake tensor with + # symbolic sizes running under in_kernel_invocation_manager. + # The subclass is asked to handle this query because size (not + # sym_size) was called, but we are unable to serve it directly because + # there are symbolic sizes in the class. The use of + # in_kernel_invocation_manager means it's incorrect to activate a + # mode to actually handle this (this caused + # https://github.com/pytorch/pytorch/issues/122772). + if handler := _DISPATCH_META_HANDLERS.get(func): + return handler(args) + + # Because fake mode can return NotImplemented (if it sees a subclass + # it doesn't know how to deal with), this test here is important + # because the next dispatch after a fake mode will attempt to use + # subclasses of tensors to dispatch, and any FakeTensor arguments + # will be considered eligible. + unrecognized_types = [ + t for t in types if not issubclass(t, FakeTensor) and t is not Tensor + ] + if unrecognized_types: + not_implemented_log.debug( + "FakeTensor unrecognized subclass(es): %s", unrecognized_types + ) + return NotImplemented + + fake_mode = None + for arg in pytree.arg_tree_leaves(*args, **kwargs): + if isinstance(arg, FakeTensor): + fake_mode = arg.fake_mode + break + + assert fake_mode is not None + + # If the fake mode is already active, don't try to reapply it! + # NotImplemented is the right thing to return here, because the + # typical situation this can occur is if ProxyTensorMode returned a + # NotImplemented because of a not implemented subclass; we may have + # unluckily attempted to hit FakeTensor's dispatch first, + # NotImplemented lets us keep chaining until we find the actual + # subclass + maybe_cur_fake_mode = torch._C._get_dispatch_mode( + torch._C._TorchDispatchModeKey.FAKE + ) + if maybe_cur_fake_mode: + not_implemented_log.debug( + "FakeTensor mode already active: %s in %s", + fake_mode, + maybe_cur_fake_mode, + ) + return NotImplemented + + assert not fake_mode.in_kernel_invocation + + with fake_mode: + return func(*args, **kwargs) + + @staticmethod + def _find_common_device( + func: OpOverload, flat_args: Sequence[object] + ) -> Tuple[torch.device, bool]: + # Returns: (common_device, has_scalar_only_inputs) + + # cpu - zero-dim tensors can be called in cuda kernels, + # so overwrite the common_device if it the only existing + # device comes from a cpu zero-dim tensor + common_device = None + has_scalar_only_inputs = False + is_cpu_zero_dim = None + + def cpu_zero_dim(t: Tensor) -> bool: + return t.device.type == "cpu" and t.dim() == 0 + + def merge_devices(t: object) -> None: + nonlocal common_device + nonlocal is_cpu_zero_dim + if not isinstance(t, FakeTensor): + return + + if common_device is None: + common_device = t.device + is_cpu_zero_dim = cpu_zero_dim(t) + return + + t_is_cpu_zero_dim = cpu_zero_dim(t) + if t.device == common_device: + if is_cpu_zero_dim: + is_cpu_zero_dim = t_is_cpu_zero_dim + return + + # mismatching devices ! + # if current tensor is cpu 0 dim, defer to existing device + if t_is_cpu_zero_dim: + return + + # current device is from cpu 0 dim tensor, overwrite + if is_cpu_zero_dim: + common_device = t.device + is_cpu_zero_dim = t_is_cpu_zero_dim + return + + # mismatching devices of non-zero dim tensors, throw + # This might be valid behavior and need to be explicitly modeled, e.g. reshape_as + raise RuntimeError( + f"Unhandled FakeTensor Device Propagation for {func}, found two different devices {common_device}, {t.device}" + ) + + for arg in flat_args: + merge_devices(arg) + + # some functions that allow Python numbers to bind to Tensors + # if we have failed to find a device, and we're running one of these operators, + # we must have scalar only inputs + if should_allow_numbers_as_tensors(func) and common_device is None: + # ops with scalar only inputs always have result on cpu + has_scalar_only_inputs = True + common_device = torch.device("cpu") + + assert common_device is not None, f"Could not find common device for {func}" + + return common_device, has_scalar_only_inputs + + def get_nested_int( + self, + *, + coeff: Union[int, torch.SymInt] = 1, + ) -> torch.SymInt: + if self.nested_int_memo is None: + self.nested_int_memo = self.fake_mode.create_symbolic_nested_int( + nt_tensor_id=None + ) + return self.nested_int_memo * coeff + + # Similar to FunctionalTensor.tolist + def tolist(self) -> Any: + if self.dim() == 0: + return self.item() + elif self.dim() == 1: + return [elem.item() for elem in self] + else: + return [elem.tolist() for elem in self] + + +_MetadataIntLike = Union[IntLikeType, "_PySymInputStub", "_SymIntOutputStub"] + + +@dataclass_slots +@dataclass +class TensorMetadata: + """ + The Tensor metadata relevant to hashing FakeTensors when caching. + """ + + dtype: torch.dtype + shape: Tuple[_MetadataIntLike, ...] + stride: Tuple[_MetadataIntLike, ...] + device: torch.device + layout: torch.layout + memory_format: Optional[torch.memory_format] + storage_offset: _MetadataIntLike + storage_bytes: Optional[_MetadataIntLike] + requires_grad: bool + is_quantized: bool + is_conj: bool + is_neg: bool + is_inference: bool + is_sparse: bool # read: is sparse COO + is_coalesced: Optional[bool] + dense_dim: Optional[int] + sparse_dim: Optional[int] + + def _flatten_into( + self, + result: List[object], + mode: FakeTensorMode, + state: _CacheKeyState, + ) -> None: + # Flatten the TensorMetadata out into `result`. Make sure to call + # state.convert_sym_int() on any SymInts. + for field in dataclasses.fields(self): + value = getattr(self, field.name) + if isinstance(value, (tuple, list, torch.Size)): + # This will recursively flatten the iterable, calling + # convert_sym_int() as necessary. + mode._prep_args_for_hash(result, value, state) + elif isinstance(value, SymInt): + state.convert_sym_int(result, value) + else: + result.append(value) + + +def extract_tensor_metadata(t: Tensor) -> TensorMetadata: + """ + Extract the TensorMetadata of a tensor. + """ + memory_format: Optional[torch.memory_format] = suggest_memory_format(t) + # Don't call is_contiguous() on a Tensor which has symbolic sizes or things + # will go badly (guards will be messed up?) + if ( + t._has_symbolic_sizes_strides + or is_sparse_any(t) + or not t.is_contiguous(memory_format=memory_format) + ): + memory_format = None + + storage_offset = t.storage_offset() + + return TensorMetadata( + t.dtype, + t.shape, + t.stride() if t.layout == torch.strided else (), + t.device, + t.layout, + memory_format, + storage_offset, + # Only set storage_bytes for tensors that have storage (not sparse) + t.untyped_storage().nbytes() if not is_sparse_any(t) else None, + t.requires_grad, + t.is_quantized, + t.is_conj(), + t.is_neg(), + t.is_inference(), + t.is_sparse, + t.is_coalesced() if t.is_sparse else None, + t.dense_dim() if is_sparse_any(t) else None, + t.sparse_dim() if is_sparse_any(t) else None, + ) + + +@dataclass_slots +@dataclass +class _DispatchCacheKey: + """ + Key for the FakeTensor dispatch cache. + """ + + key: Tuple[object, ...] + hashvalue: int + + def __init__(self, tup: Tuple[object, ...]) -> None: + self.key = tup + self.hashvalue = hash(tup) + + def __eq__(self, other: object) -> bool: + return isinstance(other, _DispatchCacheKey) and self.key == other.key + + def __hash__(self) -> int: + return self.hashvalue + + def strip_shape_env(self) -> None: + # We need to strip the ShapeEnv from any values before we store in the + # cache so the cache doesn't keep our ShapeEnvs alive. + for v in self.key: + if isinstance(v, _PySymInputStub): + v.strip_shape_env() + + +@dataclass_slots +@dataclass(frozen=True) +class _DispatchCacheEntry: + """ + Entry type for the FakeTensor dispatch cache. Accounts for two possibilities: + 1) The op is inplace, and a hit means we need to alias the argument at a + given index. + 2) We need to synthesize a new FakeTensor given tensor metadata. For view + ops, we further capture the index of the arg to alias. + """ + + inplace_idx: Optional[int] + metadata: Optional[TensorMetadata] + view_idx: Optional[int] + + +@dataclass_slots +@dataclass(frozen=True) +class _BypassDispatchCache(Exception): + """ + Signals cases that should skip FakeTensor caching. + """ + + reason: str + + +@dataclass_slots +@dataclass(frozen=True) +class DispatchCacheInfo: + """ + Information about the state of the FakeTensor dispatch cache. + """ + + hits: int + misses: int + bypasses: Dict[str, int] + size: int + + +# We keep one instantiation of `fake_tensor_converter` active +# for the duration of `with FakeTensorMode()`. +# This allows accurate storage aliasing across invocation of +# different operators. While this will keep all freshly allocated +# tensors alive during `FakeTensorMode`, there will no be no +# new allocations of Tensors which have non-meta storage so +# memory should not significantly increase. + + +class FakeTensorMode(TorchDispatchMode): + cache: Dict[_DispatchCacheKey, _DispatchCacheEntry] = {} + cache_hits: int = 0 + cache_misses: int = 0 + cache_bypasses: Dict[str, int] = defaultdict(int) + # Every time you retrace using the same fake tensor mode, you should + # advance the epoch so we don't reuse unbacked memos + epoch: int = 0 + in_kernel_invocation: bool = False + static_shapes: bool + shape_env: Optional[ShapeEnv] + _stack: Optional[str] + allow_meta: bool + + # NestedTensor uses a tensor_id_counter to uniquely identify offsets. + # This counter is incremented when an offsets is used to create an NJT + # for the first time. To avoid mutating eager state if we construct NJT + # during tracing, we maintain a separate counter on the FakeTensorMode. + # The initial count is set to the current eager tensor_id_counter value + # upon initialization, and every time you retrace using the same fake tensor + # mode, you should reset the counter to the initial count. + nt_tensor_id_counter: int = -1 + nt_tensor_id_initial_count: int = -1 + + def __init__( + self, + *, + allow_fallback_kernels: bool = True, + allow_non_fake_inputs: bool = False, + shape_env: Optional[ShapeEnv] = None, + static_shapes: Optional[bool] = None, + # TODO: This is a temporary measure, see + # https://github.com/pytorch/pytorch/pull/126245#discussion_r1604185748 + # We're currently solely using this to impede population of + # item_memo for 0d scalar tensor inputs when export, because this + # causes things that used to be deferred runtime asserts to turn into + # guards, and then the guards are just lost. We can potentially fix + # this by ensuring guards also get put in the graph, but this is + # pending a rework of how deferred runtime asserts in export. Once + # that's done, we can remove this. + export: bool = False, + ) -> None: + log.debug("create_mode 0x%x", id(self)) + super().__init__() + self.allow_fallback_kernels = allow_fallback_kernels + + import torch._dynamo.config + import torch._functorch.config + + self.propagate_real_tensors = ( + torch._functorch.config.fake_tensor_propagate_real_tensors + ) + self.fake_tensor_converter = FakeTensorConverter( + copy_data=self.propagate_real_tensors, + export=export, + ) + + if static_shapes is not None: + self.static_shapes = static_shapes + else: + self.static_shapes = shape_env is None + + # This is temporarily patched to True in Dynamo to grandfather in some + # places where we unconditionally allow scalar outputs, TO BE REMOVED + self.allow_scalar_outputs = False + + self._allow_unsafe_data_ptr_access = ( + torch._functorch.config.fake_tensor_allow_unsafe_data_ptr_access + ) + self.allow_meta = torch._functorch.config.fake_tensor_allow_meta + self.cache_enabled = ( + torch._dynamo.config.fake_tensor_cache_enabled + and not self.propagate_real_tensors + ) + self.cache_crosscheck_enabled = ( + torch._dynamo.config.fake_tensor_cache_crosscheck_enabled + ) + + # A flag that controls, whether we want to invoke ops on mix of + # real weights/global variables and fake inputs + self.allow_non_fake_inputs = allow_non_fake_inputs + + # [in_kernel_invocation] + # when FakeTensor is invoked in user code, .device should return + # the fake_device of the tensor so that code such as as `if x.is_cuda` + # or torch.zeros([10, 10], device=x.device) continues to execute as if + # the FakeTensor were real. However, within kernel execution, we return + # the `Meta` device because all computation within the kernels should + # behave as if the Tensors are on meta devices. Kernels should allocate + # new tensors on meta devices, and checks like `is_meta` should return true. + # within python refs, we always return the real device by defining + # the device property + self.in_kernel_invocation = False + + # True if we enter'ed and actually enabled fake tensor mode, + # false if it was a no-op. Not thread safe but neither is + # in_kernel_invocation + # If another fake mode was already active when we enter, we also stash it here. + # That way when we exit, we know to re-enable the previous fake mode. + self.enter_stack: List[ + Tuple[bool, Optional[TorchDispatchMode], Optional[bool]] + ] = [] + + self.shape_env = shape_env + + self._stack_trace = traceback.extract_stack() + self._stack = None + + # Indicates to our torch_dispatch dispatching infra that + # this is an "infra" mode with lower dispatching precedence. + self._mode_key = torch._C._TorchDispatchModeKey.FAKE + + import torch.nested._internal.nested_tensor + + self.nt_tensor_id_initial_count = ( + torch.nested._internal.nested_tensor._tensor_id_counter + ) + self.nt_tensor_id_counter = self.nt_tensor_id_initial_count + + def reset_nt_tensor_id_counter(self) -> None: + self.nt_tensor_id_counter = self.nt_tensor_id_initial_count + + # Typically, there is only one fake tensor mode and you test for it by + # doing an isinstance test. However, in some situations, there might be + # TWO fake tensor modes. The canonical example of this is exporting + # a fake model: there is an outer fake mode created by the user, and + # an inner fake mode created by Dynamo. The two phase process is required + # because the outer fake mode typically won't have a ShapeEnv, even if + # the user is interested in exporting with dynamic shapes (so the inner + # fake mode will actually have a ShapeEnv and swap in symbolic sizes.) + # + # In this case, it's insufficient to test only one FakeTensor: you need + # to distinguish between our fake tensor and other fake tensors. That's + # what this function does. + def is_our_fake(self, t: object) -> TypeGuard[FakeTensor]: + return isinstance(t, FakeTensor) and t.fake_mode is self + + # If we should avoid device init. This changes the behavior of various APIs: + # - We avoid constant-prop on Tensors with ops that move them to another device + # - We change the torch.tensor ctor contract to never materialize + # tensors on device + # (see NOTE: [torch.tensor, lift_fresh, and device movement]) + @property + def avoid_device_init(self) -> bool: + if torch.xpu._is_compiled(): + assert not torch.cuda._is_compiled() + return not torch.xpu.is_available() + + return not torch.cuda.is_available() + + @property + def stack(self) -> str: + if self._stack is None: + self._stack = "".join(traceback.format_list(self._stack_trace)) + return self._stack + + @count + def __torch_dispatch__( + self, + func: OpOverload, + types: Sequence[Type], + args: Sequence[object] = (), + kwargs: Mapping[str, object] = immutable_dict(), + ) -> object: + # FakeTensorMode should not be set when we're inside of it. + assert ( + torch._C._get_dispatch_mode(torch._C._TorchDispatchModeKey.FAKE) is None + ), func + try: + return self.dispatch(func, types, args, kwargs) + except TypeError: + log.exception("fake tensor raised TypeError") + raise + + # No-op if FakeTensorMode is already in use + def __enter__(self) -> Self: + import torch.nested._internal.nested_tensor + + prev_only_lift_cpu_tensors = None + if self.avoid_device_init: + # See NOTE: [torch.tensor, lift_fresh, and device movement] + prev_only_lift_cpu_tensors = torch._C._only_lift_cpu_tensors() + torch._C._set_only_lift_cpu_tensors(True) + maybe_prev_fake_mode = torch._C._unset_dispatch_mode(self._mode_key) + if self is not maybe_prev_fake_mode: + self.enter_stack.append( + (True, maybe_prev_fake_mode, prev_only_lift_cpu_tensors) + ) + return super().__enter__() + else: + # no-op (still need to re-set the fake mode though since we unset it) + torch._C._set_dispatch_mode(self) + self.enter_stack.append((False, None, prev_only_lift_cpu_tensors)) + return self + + def __exit__( + self, + a: Optional[Type[BaseException]], + b: Optional[BaseException], + c: Optional[TracebackType], + ) -> None: + ( + live, + maybe_prev_fake_mode, + maybe_prev_only_lift_cpu_tensors, + ) = self.enter_stack.pop() + if live: + out = super().__exit__(a, b, c) + # Re-enable the previous fake mode, if there was one. + if maybe_prev_fake_mode is not None: + torch._C._set_dispatch_mode(maybe_prev_fake_mode) + if maybe_prev_only_lift_cpu_tensors is not None: + torch._C._set_only_lift_cpu_tensors(maybe_prev_only_lift_cpu_tensors) + + @classmethod + def is_infra_mode(cls) -> bool: + return True + + @classmethod + def cache_info(cls) -> DispatchCacheInfo: + """ + Query the state of the dispatch cache. + """ + return DispatchCacheInfo( + FakeTensorMode.cache_hits, + FakeTensorMode.cache_misses, + dict(FakeTensorMode.cache_bypasses), + len(FakeTensorMode.cache), + ) + + @classmethod + def cache_clear(cls) -> None: + """ + Clear the dispatch cache. + """ + cls.cache_hits = 0 + cls.cache_misses = 0 + cls.cache_bypasses.clear() + cls.cache.clear() + + def _cached_dispatch_impl( + self, + func: OpOverload, + types: Sequence[Type], + args: Sequence[object], + kwargs: Mapping[str, object], + ) -> object: + """ + Lookup a cache entry for the given arguments. If none exists, dispatch + and cache the result (if the result is eligible for caching). + """ + output: object = _UNASSIGNED + try: + state = _CacheKeyState(self.shape_env) + key = self._cache_key(state, func, args, kwargs) + if state.cache_on_shape_env(): + assert state.shape_env is not None + cache = state.shape_env.fake_tensor_cache + else: + cache = FakeTensorMode.cache + entry = cache.get(key, None) + if entry is not None: + output = self._output_from_cache_entry(state, entry, key, func, args) + FakeTensorMode.cache_hits += 1 + if self.cache_crosscheck_enabled: + # For debugging / testing: Validate that the output synthesized + # from the cache matches the output created by normal dispatch. + self._crosscheck_cache_output(output, func, types, args, kwargs) + else: + self._validate_cache_key(func, args, kwargs) + output = self._dispatch_impl(func, types, args, kwargs) + entry = self._make_cache_entry(state, key, func, args, kwargs, output) + key.strip_shape_env() + cache[key] = entry + FakeTensorMode.cache_misses += 1 + except _BypassDispatchCache as e: + FakeTensorMode.cache_bypasses[e.reason] += 1 + + if output is _UNASSIGNED: + output = self._dispatch_impl(func, types, args, kwargs) + + return output + + def _cache_key( + self, + state: _CacheKeyState, + func: OpOverload, + args: Sequence[object], + kwargs: Mapping[str, object], + ) -> _DispatchCacheKey: + """ + Create a cache key given the dispatch args. Raises _BypassDispatchCache + for any situation that precludes caching. + """ + key_values = [ + func, + # Capture the default_dtype mode since that can affect the output tensor, + # e.g., when operating on constant float values. + torch.get_default_dtype(), + # Capture the current device to support, e.g., cache tensor creation, + # where there isn't necessarily a tensor to take the device from. + torch._C._get_default_device(), + # We want to create tensors from cached metadata only when the inference + # mode is the same. + torch.is_inference_mode_enabled(), + # Shape env settings could affect behavior. One example seen in the wild: + # Disallowing dynamic shapes can introduce a DynamicOutputShapeException + # where it wasn't seen on a previous instance of the same op. + self.shape_env.settings if self.shape_env else None, + ] + # Translate any FakeTensor args to metadata. + if args: + self._prep_args_for_hash(key_values, args, state) + if kwargs: + self._prep_args_for_hash(key_values, kwargs, state) + return _DispatchCacheKey(tuple(key_values)) + + def _validate_cache_key( + self, + func: OpOverload, + args: Sequence[object], + kwargs: Mapping[str, object], + ) -> None: + """ + Validate that the cache key generated by _cache_key will be + reasonable. + """ + # Avoid caching for any ops that would require a more sophisticated + # caching implementation, e.g., data dependent ops or ops that modify + # the inputs. + if torch.Tag.data_dependent_output in func.tags: + raise _BypassDispatchCache("data dependent output") + + if torch.Tag.dynamic_output_shape in func.tags: + raise _BypassDispatchCache("dynamic output shape") + + if torch.Tag.inplace_view in func.tags: + raise _BypassDispatchCache("inplace view") + + if func == aten._unsafe_view.default: + raise _BypassDispatchCache("unsafe view") + + if func in self.lift_fns: + raise _BypassDispatchCache("lift") + + if func.name() == "inductor::resize_storage_bytes_": + raise _BypassDispatchCache("inductor::resize_storage_bytes_") + + if not torch._library.utils.is_builtin(func): + raise _BypassDispatchCache("non-builtin") + + # In order to handle storage aliasing, we need to establish the alias + # for any view op on a cache hit. But CompositeImplicitAutograd ops may + # or may not alias the input, so just punt on caching these. + if func.is_view and torch._C._dispatch_has_kernel_for_dispatch_key( + func.name(), torch._C.DispatchKey.CompositeImplicitAutograd + ): + raise _BypassDispatchCache("CompositeImplicitAutograd") + + def _prep_args_for_hash( + self, + result: List[object], + args: Union[Mapping[str, object], Sequence[object], Iterable[object]], + state: _CacheKeyState, + ) -> None: + """ + Translate the provided args into a form suitable for caching at FakeTensor + dispatch, i.e., convert unhashable types like lists & dicts into tuples and + convert FakeTensors into metadata. Raises _BypassDispatchCache to signal + unsupported cases that should bypass caching. + """ + if isinstance(args, dict): + self._prep_args_for_hash(result, args.keys(), state) + self._prep_args_for_hash(result, args.values(), state) + return + + for arg in args: + if isinstance(arg, FakeTensor): + if not self.is_our_fake(arg): + raise _BypassDispatchCache("not our fake") + if arg.constant is not None: + raise _BypassDispatchCache("constant attribute") + if is_sparse_any(arg): + raise _BypassDispatchCache(f"{arg.layout} tensor") + # FIXME: For now back out caching when there are symbolic nbytes + # - this doesn't seem to play nice with set(). See T196779132 for examples. + if isinstance(arg.untyped_storage().nbytes(), SymInt): + raise _BypassDispatchCache("symbolic nbytes") + metadata = extract_tensor_metadata(arg) + metadata._flatten_into(result, self, state) + elif isinstance(arg, Tensor): + raise _BypassDispatchCache("non-fake tensor") + elif isinstance(arg, SymInt): + state.convert_sym_int(result, arg) + elif isinstance(arg, (SymBool, SymFloat)): + raise _BypassDispatchCache("symbolic shape") + elif isinstance(arg, (list, tuple, dict)): + self._prep_args_for_hash(result, arg, state) + else: + # It's important to capture the type of the arg since, e.g., 1 and 1.0 + # hash to the same value, but can produce different dtypes for the + # output tensor. + result.append(type(arg)) + result.append(arg) + + def _make_cache_entry( + self, + state: _CacheKeyState, + key: _DispatchCacheKey, + func: OpOverload, + args: Sequence[object], + kwargs: Mapping[str, object], + output: Optional[FakeTensor], + ) -> _DispatchCacheEntry: + """ + Make a cache entry object for the given 'output' Tensor. Raises + _BypassDispatchCache if the output tensor has characteristics that + prevent caching it. + """ + if output is None: + return _DispatchCacheEntry(inplace_idx=None, metadata=None, view_idx=None) + + # Some ops return tuples of Tensors, but it's rare, so avoid + # the complexity of caching other types. + if not isinstance(output, FakeTensor): + raise _BypassDispatchCache("non-FakeTensor output") + + # Avoid caching FakeTensors with constants attached since those + # can be invalidated. + if output.constant is not None: + raise _BypassDispatchCache("constant attribute") + + # TODO: support caching sparse outputs? + if output.is_sparse: + raise _BypassDispatchCache("sparse output") + + if is_sparse_compressed(output): + raise _BypassDispatchCache("sparse compressed output") + + # Can an in-place op really reference a kwarg? If so, then we need + # to extend the implementation to handle it. + for kval in kwargs.values(): + if id(kval) == id(output): + raise _BypassDispatchCache("kwarg aliases output") + + # If this is an in-place op, the entry records which input arg is aliased. + for idx in range(len(args)): + if id(args[idx]) == id(output): + return _DispatchCacheEntry( + inplace_idx=idx, metadata=None, view_idx=None + ) + + # Otherwise, create an entry that records the output tensor's metadata. + view_idx = None + if func.is_view: + idxs = [i for i, t in enumerate(args) if isinstance(t, Tensor)] + assert len(idxs) == 1 + view_idx = idxs[0] + + metadata = extract_tensor_metadata(output) + metadata.shape = tuple(state.convert_output(v) for v in metadata.shape) + metadata.stride = tuple(state.convert_output(v) for v in metadata.stride) + metadata.storage_offset = state.convert_output(metadata.storage_offset) + metadata.storage_bytes = ( + None + if metadata.storage_bytes is None + else state.convert_output(metadata.storage_bytes) + ) + + entry = _DispatchCacheEntry( + inplace_idx=None, + metadata=metadata, + view_idx=view_idx, + ) + + # N.B.: Some checks for bypassing the cache would be performed on the + # output tensor synthesized from the cached metadata. As an optimization, + # we can synthesize a tensor here and do the checks on that instance. + # This approach keeps the (more frequent) cache-hit path as lightweight + # as possible. + synth_output = self._output_from_cache_entry(state, entry, key, func, args) + + # Make sure the dispatch_key_set from the synthesized output tensor will + # be the same. + synth_key_set = torch._C._dispatch_key_set(synth_output) + key_set = torch._C._dispatch_key_set(output) + if synth_key_set != key_set: + raise _BypassDispatchCache("dispatch_key_set mismatch") + + return entry + + def _output_from_cache_entry( + self, + state: _CacheKeyState, + entry: _DispatchCacheEntry, + key: _DispatchCacheKey, + func: OpOverload, + args: Sequence[object], + ) -> Optional[FakeTensor]: + """ + Create a new FakeTensor from the cache entry. + """ + if entry.inplace_idx is not None: + # This is an in-place op; return the aliased arg. + inplace_arg = args[entry.inplace_idx] + assert isinstance(inplace_arg, FakeTensor) + return inplace_arg + + # Synthesize a new FakeTensor with the cached metadata. + metadata = entry.metadata + if metadata is None: + return None + + assert not is_sparse_any(metadata) + + def check_value( + value: _MetadataIntLike, state: _CacheKeyState + ) -> Union[IntLikeType]: + if isinstance(value, _SymIntOutputStub): + assert state.shape_env is not None + return value.extract(key, state.shape_env) + else: + assert not isinstance(value, _PySymInputStub) + return value + + shape = tuple(check_value(v, state) for v in metadata.shape) + stride = tuple(check_value(v, state) for v in metadata.stride) + storage_offset = check_value(metadata.storage_offset, state) + storage_bytes = ( + None + if metadata.storage_bytes is None + else check_value(metadata.storage_bytes, state) + ) + + maybe_suppress: Callable[[], typing.ContextManager] = contextlib.nullcontext + if self.shape_env is not None: + maybe_suppress = self.shape_env.suppress_guards + + with in_kernel_invocation_manager(self), maybe_suppress(): + empty = torch.empty_strided( + shape, + stride, + dtype=metadata.dtype, + layout=metadata.layout, + device="meta", + requires_grad=metadata.requires_grad, + ) + + if metadata.is_conj: + torch._C._set_conj(empty, True) + if metadata.is_neg: + torch._C._set_neg(empty, True) + + if func.is_view: + # For view ops, the storage should be the same as the tensor input. + view_arg = args[cast(int, entry.view_idx)] + assert isinstance(view_arg, FakeTensor) + storage = view_arg.untyped_storage() + with in_kernel_invocation_manager(self), maybe_suppress(): + empty.set_(storage, storage_offset, shape, stride) + + return FakeTensor(self, empty, metadata.device) + + def _crosscheck_cache_output( + self, + output: Optional[FakeTensor], + func: OpOverload, + types: Sequence[Type], + args: Sequence[object], + kwargs: Mapping[str, object], + ) -> None: + """ + Helper to validate that the output synthesized from the cache matches + the output created by normal dispatch. + """ + try: + true_output = self._dispatch_impl(func, types, args, kwargs) + except Exception as e: + raise RuntimeError( + f"FakeTensor cache crosscheck failure: func={func}, " + f"args={args}, kwargs={kwargs}: Dispatch raised={e}" + ) from e + try: + if (true_output is not None) and (output is not None): + assert_metadata_eq(assert_eq, true_output, output) + else: + assert true_output is None + assert output is None + except Exception as e: + raise RuntimeError( + f"FakeTensor cache crosscheck failure: func={func}, " + f"args={args}, kwargs={kwargs}" + ) from e + + def dispatch( + self, + func: OpOverload, + types: Sequence[Type], + args: Sequence[object] = (), + kwargs: Mapping[str, object] = immutable_dict(), + ) -> object: + kwargs = kwargs or {} + with no_dispatch(): + log.debug("%s %s %s", func, args, kwargs) + + if func in _DISPATCH_META_HANDLERS: + return _DISPATCH_META_HANDLERS[func](args) + + if log.getEffectiveLevel() <= logging.DEBUG: + log.debug( + "%sFakeTensorMode.__torch_dispatch__: %s", " " * RECURSION_COUNT, func + ) + # NOTE: incr is intentionally unused for a RAII pattern + incr = IncrementRecursionCount() + + # Some attribute queries that can be serviced directly + # See Note [is_coalesced is dispatched] + if func in _DISPATCH_HANDLE_DIRECTLY: + # NB: no_dispatch is ok here too, this func is very simple + with in_kernel_invocation_manager(self): + return func(*args, **kwargs) + + if self.cache_enabled: + return self._cached_dispatch_impl(func, types, args, kwargs) + else: + return self._dispatch_impl(func, types, args, kwargs) + + def _dispatch_impl( + self, + func: OpOverload, + types: Sequence[Type], + args: Sequence[object], + kwargs: Mapping[str, object], + ) -> Optional[FakeTensor]: + flat_args, args_spec = pytree.tree_flatten((args, kwargs)) + + # DO NOT PUT LOGIC BEFORE UNRECOGNIZED TYPE CHECKING + # We must throw NotImplemented in case of unrecognized types to handle subclasses. + # Throwing the exception will pass the control to the next __torch_dispatch__. + # See [subclass inputs] below + # NB: If you're seeing a mysterious infinite loop involving fake + # tensor, it might be related to this line. Though I'm not sure + # how you'll know to read this comment, as this line won't show up + # in the stack trace. + has_unrecognized_types = _check_for_subclass(flat_args) + if has_unrecognized_types: + unrecognized_types = [ + type(x) for x in flat_args if _check_for_subclass_arg(x) + ] + not_implemented_log.debug( + "FakeTensorMode unrecognized subclass(es): %s", unrecognized_types + ) + return NotImplemented + + flat_arg_fake_tensors = [t for t in flat_args if self.is_our_fake(t)] + has_symbolic_sizes = any( + i._has_symbolic_sizes_strides for i in flat_arg_fake_tensors + ) or any(isinstance(a, SymInt) for a in flat_args) + + converter = self.fake_tensor_converter + + is_lift_func = func in self.lift_fns + + # To constant propagate through these functions: + # 1, If this is a lift due to a torch.tensor call, + # the input tensor is guaranteed to be a + # constant, so we keep a copy of the original argument along so + # we can query it if we're asked to item() it at some later point. + # (Note that you can always call a lift fn manually, so we do + # have to check if there are any fake tensors!) + # 2, Some functions that allow Python numbers to bind to Tensors, e.g, torch.div + if (is_lift_func and not flat_arg_fake_tensors) or ( + should_allow_numbers_as_tensors(func) + and not has_symbolic_sizes + and not flat_arg_fake_tensors + ): + assert all( + t.constant is not None for t in flat_arg_fake_tensors + ), f"{func} should not have fake inputs without constants" + const_flat_args = [ + a.constant if self.is_our_fake(a) else a for a in flat_args + ] + const_args, const_kwargs = pytree.tree_unflatten(const_flat_args, args_spec) + out = func(*const_args, **const_kwargs) + if type(out) is Tensor and self.may_turn_const(out): + # NB: not in_kernel_invocation_manager because we're doing real + # compute here + # NB: no_dispatch() here is VERY DANGEROUS (like, segfault + # dangerous) if this is actually a wrapper subclass tensor, + # therefore the exact type test above + with no_dispatch(): + out = out.clone() + return converter.from_real_tensor(self, out, make_constant=True) + + # if we are in the dispatch mode, we will enter this function even if the inputs + # are not FakeTensors. For now, throw if any non-Fake Tensor inputs + # and just support constructors. + + # this is generated from torch.tensor(), which does not use the + # dispatcher, to allow wrapper subclasses to wrap the new tensor + if is_lift_func: + assert len(kwargs) == 0 and len(args) == 1, f"{args} {kwargs}" + + if type(args[0]) is Tensor: + return converter.from_real_tensor(self, args[0]) + + # If we are trying to avoid device init, then we need to avoid constant + # prop on constant tensors for ops that change devices. + avoiding_device_init = False + if self.avoid_device_init: + if ( + func == torch.ops.aten._to_copy.default + and "device" in kwargs + and kwargs["device"] != "cpu" + ): + avoiding_device_init = True + if func == torch.ops.prims.device_put.default: + avoiding_device_init = True + + # Recompute flat_arg_fake_tensors here again in case some of the inputs + # were real tensors and fakified in validate_and_convert_non_fake_tensors + (flat_args, flat_arg_fake_tensors) = self.validate_and_convert_non_fake_tensors( + func, converter, flat_args, args_spec + ) + del args, kwargs # Invalidated + + # The current constant handling only support tracing systems + # (aot autograd, torchdynamo) where each operation is run consecutively. + # Because each operation is run in order, we can trace out and support + # sequences like: x = torch.tensor(0.); y = x.add_(1) + # Whenver a constant is written to but with inputs that cannot be evaluated + # statically, such as random_(), we invalidate all constants that alias the input + # We will rely on functionalization for use of fake tensors constants as persistent + # objects on an FX Graph. + + # We dispatch size/stride/numel on the FakeTensor not its constant, so bail on inplace_view + all_constant = all(e.constant is not None for e in flat_arg_fake_tensors) + if ( + torch.Tag.nondeterministic_seeded not in func.tags + and torch.Tag.inplace_view not in func.tags + and all_constant + and len(flat_arg_fake_tensors) != 0 + and not has_symbolic_sizes + and not avoiding_device_init + ): + const_flat_args = [ + a.constant if self.is_our_fake(a) else a for a in flat_args + ] + const_args, const_kwargs = pytree.tree_unflatten(const_flat_args, args_spec) + + # NB: not in_kernel_invocation_manager(self) as we want to do REAL + # compute + with no_dispatch(): + out = func(*const_args, **const_kwargs) + + flat_out = pytree.tree_leaves(out) + flat_out_tensors = [t for t in flat_out if isinstance(t, Tensor)] + all_constant = all(self.may_turn_const(t) for t in flat_out_tensors) + + if all_constant: + return pytree.tree_map_only( + Tensor, + lambda t: converter.from_real_tensor(self, t, make_constant=True), + out, + ) + + # we weren't able to turn outputs to constants, + # so invalidate all constants that might be aliases of the outputs + for ten in flat_out_tensors: + converter.invalidate_constant_aliases(ten) + + # we are falling through to running non constant tensors, any input constant that + # is written to must be invalidated + args, kwargs = pytree.tree_unflatten(flat_args, args_spec) + self.invalidate_written_to_constants(func, flat_arg_fake_tensors, args, kwargs) + + def maybe_to_real_tensor(t: T) -> Optional[Union[T, Tensor]]: + if isinstance(t, FakeTensor): + return t.real_tensor + elif isinstance(t, py_sym_types): + assert self.shape_env is not None + return t.node.pytype( + t.node.expr.xreplace(self.shape_env.var_to_val).xreplace( + self.shape_env.unbacked_var_to_val + ) + ) + else: + return t + + from torch.fx.experimental.symbolic_shapes import ( + compute_unbacked_bindings, + free_unbacked_symbols, + ) + + nil = object() + + real_out = nil + if ( + self.propagate_real_tensors + and all(e.real_tensor is not None for e in flat_arg_fake_tensors) + # TODO: Handle SymFloat/SymBool + and not any( + ( + isinstance(a, SymInt) + and (syms := free_unbacked_symbols(a)) + and self.shape_env is not None + and any(s not in self.shape_env.unbacked_var_to_val for s in syms) + ) + for a in flat_args + ) + ): + real_flat_args = [maybe_to_real_tensor(a) for a in flat_args] + real_args, real_kwargs = pytree.tree_unflatten(real_flat_args, args_spec) + real_out = func(*real_args, **real_kwargs) + elif self.propagate_real_tensors: + # This can happen occasionally legitimately, specifically when you + # are inside the meta of a data dependent operation and you create + # a tensor on an unbacked SymInt; at this point in time we don't + # know what the unbacked SymInt is, but we will know later. + # However, if there's a bug in the condition above, this condition + # will also trigger. + log.debug( + "propagate_real_tensors skipped %s(%s, %s) %s", + func, + flat_arg_fake_tensors, + flat_args, + self.shape_env.unbacked_var_to_val if self.shape_env else None, + ) + + def maybe_propagate_real_tensors(fake_out: T) -> T: + import sympy + + def go(t: object, real_t: Tensor) -> None: + if isinstance(t, FakeTensor): + # NB: unconditionally overwrite + t.real_tensor = real_t + elif isinstance(t, py_sym_types) and free_unbacked_symbols(t): + if isinstance(t.node.expr, sympy.Symbol): + assert self.shape_env is not None + self.shape_env.set_unbacked_var_to_val(t.node.expr, real_t) + + if real_out is not nil: + tree_map_(go, fake_out, real_out) + + # If a data-dependent op is used in a decomposition, we + # may need to get the unbacked settings "early" + # TODO: Is this really needed? + compute_unbacked_bindings(self.shape_env, fake_out, peek=True) + + return fake_out + + # Try for fastpath + if has_symbolic_sizes: + fast_impl = get_fast_op_impls().get(func) + if fast_impl is not None: + return maybe_propagate_real_tensors(fast_impl(self, *args, **kwargs)) + + # If there's a Python meta, prefer that over the decomposition + from torch._decomp import meta_table as meta_table + + if func not in meta_table and not self.cpp_meta_supports_symint(func): + from torch._decomp import decomposition_table + + # Prefer Python decompositions over C++ ones + if func in decomposition_table and ( + has_symbolic_sizes + or ( + # TODO: Remove these exclusions, so that we can remove + # this leg entirely + torch_decomp_decompositions(func) + and all(not is_sparse_any(e) for e in flat_arg_fake_tensors) + ) + ): + with self: + return decomposition_table[func](*args, **kwargs) + + with self: + # Decomposes CompositeImplicitAutograd ops + r = func.decompose(*args, **kwargs) + if r is not NotImplemented: + return r + + # prims already wrap FakeTensor inputs to FakeTensor outputs + # and do device logic, we dont need do anything but run them + # and ensure that Meta kernels are dispatched to (see) + # Fake Tensor Dispatch Keys + # TODO - we should be use the prim aten impl + # TODO - fix prims complex ops + if ( + "prims::" in func._schema.name + and hasattr(func, "prim_meta_impl") + and not stride_incorrect_op(func) + ): + with self: + return maybe_propagate_real_tensors( + func.prim_meta_impl(*args, **kwargs) + ) + + # Users can register FakeTensor rules for custom operators + # Call them if they exist. + maybe_fake_impl = torch._library.simple_registry.singleton.find( + func.name() + ).fake_impl.kernel + if maybe_fake_impl: + ctx = torch._library.fake_impl.FakeImplCtx(self, func) + with torch._library.fake_impl.set_ctx_getter(lambda: ctx), self: + result = maybe_fake_impl(*args, **kwargs) + return maybe_propagate_real_tensors(result) + + # special handling for funcs registered through `register_op_impl`, + # e.g., manipulating args on constructor calls to construct meta tensors + # and then afterwards wrapping them to a FakeTensor + for run_impl_check, op_impl in op_implementations_checks: + if run_impl_check(func): + op_impl_out = op_impl(self, func, *args, **kwargs) + if op_impl_out is not NotImplemented: + return maybe_propagate_real_tensors(op_impl_out) + + def maybe_run_unsafe_fallback( + error: Optional[RuntimeError] = None, + ) -> Optional[FakeTensor]: + # We infer the meta of a custom ops that return None to just + # return None. custom ops are not allowed to mutate metadata + # of their inputs, so this is safe. + if torch._library.utils.can_generate_trivial_fake_impl(func): + return None + # no meta kernel registered, fallback to kernel for the device + if has_symbolic_sizes or not self.can_run_unsafe_fallback(func): + raise UnsupportedOperatorException(func) + if error is None: + error = UnsupportedOperatorException(func) + return run_fallback_kernel(self, func, flat_args, args_spec, error) + + # Optimization: If there is no Meta kernel, it takes a surprisingly long + # amount of time to catch the NotImplementedError, so we check it here. + if not has_meta(func): + fallback = maybe_run_unsafe_fallback() + return maybe_propagate_real_tensors(fallback) + + # run kernel registered to meta for func, which include + # python meta registrations, prims, decomps, and c++ meta fns (structured kernels) + # It's possible that the kernel will return NotImplementedError + try: + with in_kernel_invocation_manager(self): + r = func(*args, **kwargs) + except NotImplementedError as not_implemented_error: + return maybe_run_unsafe_fallback(not_implemented_error) + except Exception: + log.exception("failed while attempting to run meta for %s", func) + raise + + return maybe_propagate_real_tensors( + self.wrap_meta_outputs_with_default_device_logic( + r, func, flat_args, device=kwargs.get("device") + ) + ) + + # WARNING: DO NOT add any additional namespaces/operators here if they refer to operators + # outside of the pytorch/pytorch library! Any pre-existing things here + # are either in the pytorch/pytorch library or have been grandfathered in. + # The fallback does not always work and MAY CRASH and emit unreadable error messages + # so it should not be allowed by default. + _can_run_unsafe_fallback_allowed_namespaces = ordered_set( + "debugprims", + "prims", + "aten", + "xla", + "vision", + "torchtext", + "torchaudio", + "quantized", + ) + + def can_run_unsafe_fallback(self, func: OpOverload) -> bool: + if not self.allow_fallback_kernels: + return False + # It's OK to try the fallback for built-in ops (e.g. aten, prims) + # because we control and test these but the fallback leads to unexpected behavior + # in user-defined custom ops + return ( + func.namespace in self._can_run_unsafe_fallback_allowed_namespaces + or func.name() == "fbgemm::gmm" + ) + + def validate_and_convert_non_fake_tensors( + self, + func: OpOverload, + converter: FakeTensorConverter, + flat_args: Sequence[object], + args_spec: TreeSpec, + ) -> Tuple[List[object], List[FakeTensor]]: + """ + Checks if the list of tensors are fake tensors. + If not, try to convert them to fake tensors. + Returns the original args, kwargs, and a flattened list of (args, kwargs) that are fake tensors. + """ + flat_arg_fake_tensors: List[FakeTensor] = [] + + def validate(x: T) -> Union[T, FakeTensor]: + if not isinstance(x, Tensor): + return x + + nonlocal flat_arg_fake_tensors + if not self.is_our_fake(x): + if torch.Tag.inplace_view in func.tags: + args, kwargs = pytree.tree_unflatten(flat_args, args_spec) + raise AssertionError( + f"Can't call metadata mutating ops on non-Fake Tensor inputs. Found in {render_call(func, args, kwargs)}" + ) + if not self.allow_non_fake_inputs: + if isinstance(x, FakeTensor) and x.fake_mode is not self: + raise AssertionError("Mixing fake modes NYI") + args, kwargs = pytree.tree_unflatten(flat_args, args_spec) + raise AssertionError( + f"Please convert all Tensors to FakeTensors first or instantiate FakeTensorMode " + f"with 'allow_non_fake_inputs'. Found in {render_call(func, args, kwargs)}" + ) + + out = converter.from_real_tensor(self, x) + else: + out = x + + flat_arg_fake_tensors.append(out) + return out + + validated_args = [validate(a) for a in flat_args] + return validated_args, flat_arg_fake_tensors + + def wrap_meta_outputs_with_default_device_logic( + self, + r: object, + func: OpOverload, + flat_args: Sequence[object], + device: torch.device, + ) -> PyTree: + converter = self.fake_tensor_converter + + # Lazily initialized, in case there are no tensor returns + common_device = None + has_scalar_only_inputs = False + + def wrap(e: T) -> Union[T, FakeTensor]: + nonlocal common_device + nonlocal has_scalar_only_inputs + + if not isinstance(e, Tensor): + return e + + if common_device is None: + ( + common_device, + has_scalar_only_inputs, + ) = FakeTensor._find_common_device(func, flat_args) + + is_our_fake = self.is_our_fake(e) + if is_our_fake: + torch._check( + e.device == common_device, + lambda: f"FakeTensor is wrapped to wrong device, found {e.device}, expected {common_device}", + ) + return cast(T, e) + elif converter is not None: + if has_scalar_only_inputs: + # Under FakeTensorMode, op accepts scalar only inputs, such as aten.add/sub/mul/div, + # returns a real scalar tensor on CPU. See TensorMeta() in _prims/__init__.py for details. + # We thus directly convert real tensor to fake tensor. + return converter.from_real_tensor(self, e) + else: + return converter.from_meta_and_device( + self, e, device or common_device + ) + else: + return e + + return tree_map(wrap, r) + + def create_symbolic_nested_int( + self, *, nt_tensor_id: Optional[int] = None + ) -> torch.SymInt: + # See Note: [Creating symbolic nested int] + # Returned nested int always has coeff=1; multiply the result by coeff if needed + import torch.nested._internal.nested_tensor + + if nt_tensor_id is None: + nt_tensor_id = self.nt_tensor_id_counter + assert self.enter_stack, "should only called while FakeTensorMode is active" + self.nt_tensor_id_counter += 1 + hint = torch._C._get_nested_int(nt_tensor_id, 1) + + src = torch._dynamo.source.EphemeralSource("intermediate_offsets_or_lengths") + assert self.shape_env is not None + ret = self.shape_env.create_symintnode( + sym=self.shape_env.create_symbol( + val=hint, + source=src, + ), + hint=hint, + source=src, + ) + return ret + + _cpp_meta_supports_symint = ordered_set( + aten.empty.memory_format, + aten.empty_strided.default, + aten.as_strided_scatter.default, + aten.as_strided.default, + aten.as_strided_.default, + aten.zeros.default, + aten.detach.default, + aten.view_as_real.default, + aten.view_as_complex.default, + aten.set_.source_Storage_storage_offset, + aten._sparse_coo_tensor_with_dims_and_tensors.default, + ) + + def cpp_meta_supports_symint(self, func: OpOverload) -> bool: + if torch.Tag.view_copy in func.tags: + return True + return func in self._cpp_meta_supports_symint + + lift_fns = ordered_set(aten.lift_fresh.default, aten.lift_fresh_copy.default) + + def may_turn_const(self, t: Tensor) -> bool: + return ( + t.numel() <= CONSTANT_NUMEL_LIMIT + and not is_sparse_any(t) + and not self.is_our_fake(t) + and not t.device.type == "meta" + ) + + def invalidate_written_to_constants( + self, + func: OpOverload, + flat_arg_fake_tensors: Sequence[FakeTensor], + args: Sequence[object], + kwargs: Mapping[str, object], + ) -> None: + any_constant = any(e.constant is not None for e in flat_arg_fake_tensors) + schema_info = get_schema_info(func) + if any_constant and schema_info.is_mutable(): + _, new_kwargs = normalize_function( # type: ignore[misc] + func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True # type: ignore[arg-type] + ) + for k, v in new_kwargs.items(): + k = k if (k != "input" or schema_info.has_argument(k)) else "self" + if ( + self.is_our_fake(v) + and schema_info.is_mutable(k) + and v.constant is not None + ): + self.fake_tensor_converter.invalidate_constant_aliases(v.constant) + + def from_tensor( + self, + tensor: Tensor, + *, + static_shapes: Optional[bool] = None, + source: Optional[Source] = None, + symbolic_context: Optional[SymbolicContext] = None, + trace: bool = True, + ) -> FakeTensor: + shape_env: Optional[ShapeEnv] = self.shape_env + if static_shapes is None: + static_shapes = self.static_shapes + if static_shapes: + assert ( + symbolic_context is None + ), "cannot set both static_shapes and symbolic_context" + shape_env = None + return self.fake_tensor_converter.from_real_tensor( + self, + tensor, + shape_env=shape_env, + source=source, + symbolic_context=symbolic_context, + trace=trace, + ) + + +_StoragePointer = object + + +# NB: returns fake tensors +def run_fallback_kernel( + fake_mode: FakeTensorMode, + func: OpOverload, + flat_args: Sequence[object], + args_spec: PyTree, + orig_not_implemented_exception: RuntimeError, +) -> FakeTensor: + # these should all be supported, just to be safe + # avoid fallback for operators which inplace modify metadata + # because the input fake tensors would be umodified + if torch.Tag.inplace_view in func.tags: + raise orig_not_implemented_exception + + inp_impls = {} + + # Don't use in_kernel_invocation_manager(fake_mode) as we want to do + # REAL compute (not with meta device) + with no_dispatch(): + + def to_real_tensor(e: T) -> Union[T, Tensor]: + if fake_mode.is_our_fake(e): + out = torch.zeros_like(e, device=e.fake_device) + if e.is_sparse: + out._coalesced_(e.is_coalesced()) + inp_impls[id(out)] = e + return out + return e + + flat_args = [to_real_tensor(a) for a in flat_args] + args, kwargs = pytree.tree_unflatten(flat_args, args_spec) + + r = func(*args, **kwargs) + + storages: Set[_StoragePointer] = set() + + for e in flat_args: + if isinstance(e, Tensor): + if not is_sparse_any(e): + storages.add(e._typed_storage()._cdata) + + # TODO: also check metadata change on inputs + # proper aliasing/metadata relationship between outputs and inputs will + # not be set up, bc of conversion to device, unless we can reuse an + # input impl + + def map_out(e: T) -> Union[T, FakeTensor]: + if id(e) not in inp_impls and ( + isinstance(e, Tensor) + and not is_sparse_any(e) + and e._typed_storage()._cdata in storages + ): + raise orig_not_implemented_exception + + if isinstance(e, Tensor): + if id(e) in inp_impls: + return inp_impls[id(e)] + else: + return fake_mode.fake_tensor_converter.from_real_tensor(fake_mode, e) + else: + return e + + return pytree.tree_map(map_out, r) + + +# Just for use to allow copying a module to fake tensors, +# does not apply elsewhere +class FakeCopyMode(TorchFunctionMode): + def __init__(self, fake_mode: FakeTensorMode) -> None: + self.fake_mode = fake_mode + + def __torch_function__( + self, + func: OpOverload, + types: Sequence[Type], + args: Sequence[object] = (), + kwargs: Optional[Mapping[str, object]] = None, + ) -> FakeTensor: + kwargs = kwargs if kwargs else {} + + # clone will get called in Parameter deepcopy + if func == torch._C.TensorBase.clone: + assert isinstance(args[0], Tensor) + return func( + self.fake_mode.from_tensor(args[0], static_shapes=True), **kwargs + ) + elif func == Tensor.__deepcopy__: + assert len(args) == 2 and len(kwargs) == 0 + tensor = cast(Tensor, args[0]) + memo = cast(Dict[int, FakeTensor], args[1]) + + if id(tensor) in memo: + return memo[id(tensor)] + + out = self.fake_mode.from_tensor(tensor, static_shapes=True) + memo[id(tensor)] = out + return out + else: + with torch._C.DisableTorchFunctionSubclass(): + return func(*args, **kwargs) + + +def _device_handler(args: Sequence[object]) -> torch.device: + # NB: Don't use is_our_fake, just serve the fake information + # as is. Notice we don't use 'self'; we use args[0].fake_mode + # because they may not be the same. It would also be possible + # to return NotImplemented here, in which case the FakeTensor + # handler on args[0] would handle it, but we're being nice and + # short-circuiting quickly. + assert len(args) == 1 and isinstance(args[0], FakeTensor) + if args[0].fake_mode.in_kernel_invocation: + return torch.device("meta") + else: + return args[0].fake_device + + +# [subclass inputs] +# Suppose we enable fake tensor mode. This means that fake tensor +# mode will run first. But what if we do an operation that +# involves a tensor subclass that will desugar into normal tensor +# operations? Without returning NotImplemented, fake tensor mode will run first, +# decide that a conversion was made (since there was a non fake +# tensor argument), and report an error that converting non +# fake tensor is not supported. What we actually wanted to happen +# was to give the subclass a chance to figure out what it wants to +# before erroring out. Returning NotImplemented here allows this. +def _check_for_subclass(flat_args: Sequence[object]) -> bool: + return any(_check_for_subclass_arg(x) for x in flat_args) + + +def _check_for_subclass_arg(x: object) -> bool: + return ( + not isinstance(x, FakeTensor) + and isinstance(x, Tensor) + and type(x) is not Tensor + and type(x) is not torch.nn.Parameter + ) + + +_DISPATCH_META_HANDLERS = { + torch.ops.prim.device.default: _device_handler, + torch.ops.aten.size.default: lambda args: tuple( + int(s) for s in cast(Tensor, args[0]).size() + ), + torch.ops.aten.stride.default: lambda args: tuple( + int(s) for s in cast(Tensor, args[0]).stride() + ), + torch.ops.aten.storage_offset.default: lambda args: int( + cast(Tensor, args[0]).storage_offset() + ), +} + +_DISPATCH_HANDLE_DIRECTLY = ordered_set( + torch.ops.aten.is_coalesced.default, + torch.ops.aten.dense_dim.default, + torch.ops.aten.sparse_dim.default, +) + +from torch._subclasses.fake_impls import ( # noqa: F401 + _device_not_kwarg_ops, + _is_tensor_constructor, + _like_tensor_constructors, + contains_tensor_types, + get_fast_op_impls, + has_meta, + op_implementations_checks, + stride_incorrect_op, +) + + +@atexit.register +def dump_cache_stats() -> None: + log.info("FakeTensor cache stats:") + log.info(" cache_hits: %s", FakeTensorMode.cache_hits) + log.info(" cache_misses: %s", FakeTensorMode.cache_misses) + bypasses = FakeTensorMode.cache_bypasses + if bypasses: + log.info(" cache_bypasses:") + width = max(len(k) for k in bypasses) + for k, v in sorted(bypasses.items(), key=lambda i: -i[1]): + log.info(" %-*s %s", width + 1, f"{k}:", v) diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/fake_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/fake_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..28fc7a4028917fcd348d1d9ea655c04617d6e4c8 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/fake_utils.py @@ -0,0 +1,197 @@ +# mypy: ignore-errors + +import functools +import warnings +from typing import Callable, Union + +import torch +import torch.utils._pytree as pytree +from torch._ops import OpOverload +from torch._subclasses.fake_tensor import ( + FakeTensorMode, + tree_flatten_only, + UnsupportedFakeTensorException, +) +from torch.utils._python_dispatch import TorchDispatchMode + + +aten = torch._ops.ops.aten + + +def outputs_alias_inputs(outputs, inputs): + input_storages = { + inp._typed_storage()._cdata + for inp in tree_flatten_only(torch.Tensor, inputs) + if torch._C._has_storage(inp) + } + return any( + torch._C._has_storage(out) and out._typed_storage()._cdata in input_storages + for out in tree_flatten_only(torch.Tensor, outputs) + ) + + +def outputs_are_inputs(outputs, inputs): + input_ids = {id(inp) for inp in tree_flatten_only(torch.Tensor, inputs)} + return any(id(out) in input_ids for out in tree_flatten_only(torch.Tensor, outputs)) + + +def output_alias_each_other(outputs): + storages = set() + for out in tree_flatten_only(torch.Tensor, outputs): + if not torch._C._has_storage(out): + continue + stor = out._typed_storage()._cdata + if stor in storages: + return True + storages.add(stor) + return False + + +def is_sdpa_error(func, idx, e): + if ( + ( + func is aten._scaled_dot_product_flash_attention.default + or func is aten._flash_attention_forward.default + ) + and idx in (6, 7) + and "Devices" in repr(e) + ): + return True + if ( + ( + func is aten._scaled_dot_product_efficient_attention.default + or func is aten._efficient_attention_forward.default + ) + and idx in (2, 3) + and "Devices" in repr(e) + ): + return True + if ( + func is aten._scaled_dot_product_cudnn_attention.default + and idx in (6, 7) + and "Devices" in repr(e) + ): + return True + return False + + +class CrossRefFakeMode(TorchDispatchMode): + def __init__( + self, + ignore_op_fn: Union[Callable[[OpOverload], bool], None] = None, + *, + check_strides=True, + check_aliasing=True, + ): + super().__init__() + self.ignore_op_fn = ( + ignore_op_fn if ignore_op_fn is not None else lambda fn: False + ) + self.check_strides = check_strides + self.check_aliasing = check_aliasing + + def __torch_dispatch__(self, func, types, args=(), kwargs=None): + kwargs = kwargs or {} + + fake_r = None + + # empty_like excluded for now due to sparse complex + # aten._to_dense.default this one is getting called with csc + if ( + func + not in ( + aten.lift_fresh.default, + aten.lift_fresh_copy.default, + aten.set_.source_Storage_storage_offset, + ) + and not self.ignore_op_fn(func) + and torch.Tag.dynamic_output_shape not in func.tags + and torch.Tag.inplace_view not in func.tags + and torch.Tag.data_dependent_output not in func.tags + ): + # Do not import symbolic_shapes at the top of the module as it imports sympy and that's slow + from torch.fx.experimental.symbolic_shapes import ShapeEnv + + try: + # TODO: enable_python_dispatcher() here + with FakeTensorMode(shape_env=ShapeEnv()) as fake_mode: + fake_args, fake_kwargs = pytree.tree_map_only( + torch.Tensor, + functools.partial(fake_mode.from_tensor, static_shapes=True), + (args, kwargs), + ) + with warnings.catch_warnings(): + fake_r = func(*fake_args, **fake_kwargs) + except UnsupportedFakeTensorException: + pass + + context = ( + f"When comparing the output of {func} on FakeTensor and concrete Tensors, " + f"found" + ) + r = func(*args, **kwargs) + if fake_r is not None: + r_flat = pytree.tree_leaves(r) + f_flat = pytree.tree_leaves(fake_r) + assert len(f_flat) == len( + r_flat + ), f"{context} mismatch in number of returns {len(f_flat)} != {len(r_flat)}" + + if self.check_aliasing: + r_aliasing = outputs_alias_inputs(r, (args, kwargs)) + f_aliasing = outputs_alias_inputs(fake_r, (fake_args, fake_kwargs)) + assert ( + r_aliasing == f_aliasing + ), f"{context} mismatch in outputs_alias_inputs check {f_aliasing} != {r_aliasing}" + + r_identity_eq = outputs_are_inputs(r, (args, kwargs)) + f_identity_eq = outputs_are_inputs(fake_r, (fake_args, fake_kwargs)) + assert ( + r_identity_eq == f_identity_eq + ), f"{context} mismatch in outputs_are_inputs check {f_identity_eq} != {r_identity_eq}" + + r_output_alias_each_other = output_alias_each_other(r) + f_output_alias_each_other = output_alias_each_other(fake_r) + assert r_output_alias_each_other == f_output_alias_each_other, ( + f"{context} mismatch in outputs_alias_each_other check " + f"{f_output_alias_each_other} != {r_output_alias_each_other}" + ) + + for idx, (r_out, fake_out) in enumerate( + zip(pytree.tree_leaves(r), pytree.tree_leaves(fake_r)) + ): + r_is_ten = isinstance(r_out, torch.Tensor) + assert r_is_ten == isinstance( + fake_out, torch.Tensor + ), f"{context} mismatched number of tensor outputs" + if r_is_ten: + assert r_out.requires_grad == fake_out.requires_grad, ( + f"{context} mismatched requires_grad-ness of outputs. " + f"This usually means that you have added autograd support " + f"for your operator at a dispatch key other than Autograd, " + f"which will lead to problems" + ) + if torch._C._has_storage(r_out): + r_offset = r_out.storage_offset() + f_offset = fake_out.storage_offset() + assert ( + r_offset == f_offset + ), f"{context} mismatched storage offset" + + try: + torch._prims.utils.compare_tensor_meta( + r_out, + fake_out, + check_strides=self.check_strides, + allow_rhs_unbacked=True, + ) + except Exception as e: + if is_sdpa_error(func, idx, e): + continue + error_message = ( + f"{context} mismatched tensor metadata: {e}" + if len(r_flat) == 1 + else f"{context} mismatched tensor metadata for output[{idx}]: {e}" + ) + raise RuntimeError(error_message) from e + return r diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/functional_tensor.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/functional_tensor.py new file mode 100644 index 0000000000000000000000000000000000000000..7bfa16a295a5d2624d6f00865c0d1b5fdd352490 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/functional_tensor.py @@ -0,0 +1,807 @@ +# mypy: allow-untyped-defs +import contextlib +import warnings +import weakref +from abc import ABC, abstractmethod +from typing import Any, Callable, ContextManager, Dict, List, Optional, Tuple, Union + +import torch +import torch._inductor.config as inductor_config +import torch.utils._pytree as pytree +from torch._C import _functionalization_reapply_views_tls as _reapply_views +from torch._ops import _get_dispatch_mode_pre_dispatch +from torch._subclasses.meta_utils import is_sparse_any +from torch.utils._python_dispatch import ( + _detect_infra_mode, + _disable_infra_mode, + return_and_correct_aliasing, + TorchDispatchMode, +) + + +not_implemented_log = torch._logging.getArtifactLogger(__name__, "not_implemented") + + +# NOTE Some special handling for tensor conversion during export is needed. +# Normally, when tracing through the model with tensor.to(), the maybe-aliasing +# relationship between input and output tensors will be baked into the graph. +# For example, if we got a tensor with device cpu and call tensor.to("cpu"), +# it will become a no-op in the graph. For a whole graph capture, this is not +# sound so we need to do something different. Instead, in export we will try to +# preserve the tensor conversion by forcing a non-semantic-breaking aten::_to_copy +# operator to be traced in the graph, and subsequently banning mutations on all +# such converted tensors. +# In addition to patching .to() method call in functionalization, we will have to +# patch other similar methods like float() and cpu(), because they intentionally +# don't fall back to .to() methods, but have the same behavior as .to() according to +# pytorch document. https://pytorch.org/docs/stable/generated/torch.Tensor.float.html +# thus we simply force them to go through .to() call. +def _conversion_method_template(**extra_kwargs): + def _(self, *args, **kwargs): + return self.to(*args, **{**kwargs, **extra_kwargs}) + + return _ + + +class FunctionalTensor(torch.Tensor): + """ + Functional tensors represent tensors that will remove mutations + from a program. If you perform a mutable operation on a functional tensor, + it will re-dispatch to the functional variant of that operation. + + Historically, functionalization is implemented in C++ in the dispatcher. + This class is a lightweight python shim around the C++ functionalization logic. + + FunctionalTensor is required to be used with a corresponding + FunctionalTensormode active, because it relies + on using the mode for dispatch (which can properly handle factory functions). + """ + + elem: torch.Tensor + # Indicates to our torch_dispatch dispatching infra that + # this is an "infra" mode with lower dispatching precedence. + _mode_key = torch._C._TorchDispatchModeKey.FUNCTIONAL + + # Note: The reason we add these extra keys to our FunctionalTensor subclass + # is to mirror the behavior of C++ functionalization (we can choose to change this + # later, as long as it doesn't break anything). + # FunctionalTensorWrapper copies **all** dispatch keys from the inner tensor + # to the wrapper, excluding functorch and python dispatch keys. + # Here I'm trying to re-use the keyset the functorch wrapper subclasses copy, + # except that they don't include ZeroTensor so I'm manually adding it in. + _extra_dispatch_keys = torch._C._additional_keys_to_prop_for_wrapper_tensors.add( + torch._C.DispatchKey.ZeroTensor + ) + + # These are all aten ops that correspond to metadata queries. + # We want FunctionalTensor to be able to handle them directly. + metadata_fns = [ + torch.ops.aten.is_contiguous.default, # type: ignore[has-type] + torch.ops.aten.is_contiguous.memory_format, # type: ignore[has-type] + torch.ops.aten.is_strides_like_format.default, # type: ignore[has-type] + torch.ops.aten.is_non_overlapping_and_dense.default, # type: ignore[has-type] + torch.ops.aten.size.default, # type: ignore[has-type] + torch.ops.aten.sym_size.default, # type: ignore[has-type] + torch.ops.aten.stride.default, # type: ignore[has-type] + torch.ops.aten.sym_stride.default, # type: ignore[has-type] + torch.ops.aten.storage_offset.default, # type: ignore[has-type] + torch.ops.aten.sym_storage_offset.default, # type: ignore[has-type] + torch.ops.aten.numel.default, # type: ignore[has-type] + torch.ops.aten.sym_numel.default, # type: ignore[has-type] + torch.ops.aten.dim.default, # type: ignore[has-type] + torch.ops.prim.device.default, # type: ignore[has-type] + ] + + # These are ops that claim to be functional, but actually are maybe-mutating/maybe-aliasing + # TODO (tmanlaibaatar) make it a tag + maybe_aliasing_or_mutating_ops = [ + torch.ops.aten.dropout.default, # type: ignore[has-type] + torch.ops.aten.batch_norm.default, # type: ignore[has-type] + torch.ops.aten.native_batch_norm.default, # type: ignore[has-type] + torch.ops.aten._batch_norm_impl_index.default, # type: ignore[has-type] + torch.ops.aten.cudnn_batch_norm.default, # type: ignore[has-type] + torch.ops.aten.miopen_batch_norm.default, # type: ignore[has-type] + torch.ops.aten.atleast_1d.default, # type: ignore[has-type] + torch.ops.aten.atleast_2d.default, # type: ignore[has-type] + torch.ops.aten.atleast_3d.default, # type: ignore[has-type] + torch.ops.aten.cartesian_prod.default, # type: ignore[has-type] + torch.ops.aten.conj_physical.default, # type: ignore[has-type] + torch.ops.aten.alpha_dropout.default, # type: ignore[has-type] + torch.ops.aten.feature_dropout.default, # type: ignore[has-type] + torch.ops.aten.feature_alpha_dropout.default, # type: ignore[has-type] + torch.ops.aten.unsafe_chunk.default, # type: ignore[has-type] + ] + + # Used by auto_functionalize to determine base of tensors during inference mode. + _inference_mode_base: Optional["FunctionalTensor"] = None + + def __new__(cls, elem, mode): + assert torch._is_functional_tensor(elem) + + # In general, we'd like our functional tensor subclass to only be in charge of functionalization, + # and defer to the inner subclass for all other functionality. + # Example: If our inner tensor is a ZeroTensor, we would want to defer running the ZeroTensor fallback + # until after we redispatch to our inner ZeroTensor. + # However, there are a few keys that we need to mirror between the inner and outer tensors. + # Conjugate + # Negative + # Why? These keys are used to test metadata queries, like `.is_conj()` and `.is_neg()`. + # We **need** calls to is_conj() to return the same thing on the outer and inner tensors, + # Because user code / framework code that branches like so needs to do the same thing + # when it sees the outer FunctionalTensor: + # if (x.is_conj()) { + # return at::view_as_real(x.resolve_conj()); + # } else { + # return at::view_as_real(x); + # } + extra_dispatch_keys = ( + FunctionalTensor._extra_dispatch_keys & torch._C._dispatch_keys(elem) + ) + + out = torch.Tensor._make_wrapper_subclass( # type: ignore[arg-type, attr-defined] + # TODO: right now, _make_wrapper_subclass's dynamic shape interaction is not great. + # Calling the overload that has kwargs causes us to go down the first overload path, + # which will **always** specialize sizes. + # We should probably eventually fix this so that the first overload can just handle dynamic shapes. + cls, + elem.shape, # sizes + elem.stride() if not is_sparse_any(elem) else None, # strides + ( + elem.storage_offset() if not is_sparse_any(elem) else None + ), # storage_offset + None, # memory_format + elem.dtype, # dtype + elem.layout, # layout + elem.device, # device + False, # pin_memory + elem.requires_grad, # requires_grad + None, # dispatch_sizes_strides_policy + False, # dispatch_device + False, # dispatch_layout + extra_dispatch_keys, # _extra_dispatch_keys + ) + torch._C._set_throw_on_mutable_data_ptr(out) + out.elem = elem + + if ( + torch.is_inference_mode_enabled() + and torch._inductor.config.enable_auto_functionalized_v2 + ): + if out.is_base_tensor(): + out._inference_mode_base = None + # This assumes that the FunctionalTensor.elem does not change its storage after this point. + # Otherwise this would be invalid. + mode._storage_to_base[out.elem.untyped_storage()] = out + else: + out._inference_mode_base = mode._storage_to_base[ + out.elem.untyped_storage() + ] + assert out._inference_mode_base is not None + return out + + def __torch_dispatch__(self, func, types, args=(), kwargs=None): + unrecognized_types = [ + t + for t in types + if t not in [torch.Tensor, torch._subclasses.FakeTensor, FunctionalTensor] + ] + if unrecognized_types: + not_implemented_log.debug( + "FunctionalTensor unrecognized subclass(es): %s", unrecognized_types + ) + return NotImplemented + + if kwargs is None: + kwargs = {} + + # FunctionalTensor needs to plumb all metadata requests to the inner tensor. + # In theory we don't have to do this - but if we want to service metadata requests here, + # we need to carefully make sure all metadata is accurate (including metadata mutations) + if func in FunctionalTensor.metadata_fns: + # All metadata accesses should be plumbed to the inner tensor, that way we don't have to worry + # about the problem of keeping metadata in sync between the wrapper and inner tensor. + # This also alleviates us from having to manually handle metadata mutations on the wrapper. + assert len(kwargs) == 0 + if func in [ + torch.ops.aten.is_strides_like_format.default, + torch.ops.aten.is_contiguous.memory_format, + ]: + assert len(args) == 2 and isinstance(args[0], FunctionalTensor) + return func(torch._from_functional_tensor(args[0].elem), args[1]) + assert len(args) == 1 and isinstance(args[0], FunctionalTensor) + + return func(torch._from_functional_tensor(args[0].elem)) + # Originally I tried to implement my subclass without giving it a torch_dispatch, but I gave up: + # - _make_wrapper_subclass requires a __torch_dispatch__ + # - If we want to use _make_subclass(), we have a problem: the subclass will share a TensorImpl with the inner tensor, + # which is of type FunctionalTensorWrapper! We explicitly do not want our wrapper to be a FunctionalTensorWrapper. + # - If we use the default tensor.__new__(), we have another problem: it returns inner_tensor.alias(), + # which causes every subclass created above autograd to have autograd view metadata + # (in addition to also being a FunctionalTensorWrapper). + raise RuntimeError( + "Attempting to use FunctionalTensor on its own. Instead, please use it with a corresponding FunctionalTensorMode()" + ) + + def __repr__(self): + return f"FunctionalTensor({repr(self.elem)})" + + @staticmethod + def to_functional(x): + # We will do the wrapping for the user. + + assert not torch._is_functional_tensor(x) + # The only autograd metadata we care about on the FunctionalTensor is: + # - requires_grad (so autograd runs) + # - is_leaf (so that mutations on graph inputs that are not leaves are allowed by the autograd engine) + # this is handled by FunctionalTensor.to_functional + x_functional = torch._to_functional_tensor(x) + # Technically the FunctionalTensormode here is unnecessary, + # but it avoids spurious NotImplemented logs during `ProxyTorchDispatchMode` tracing. + # _mirror_autograd_meta_to queries tensor sizes, + # and otherwise the sym_size() call will go to the proxy mode before hitting + # FunctionalTensor.__torch_dispatch__ + + functional_mode = _detect_infra_mode(torch._C._TorchDispatchModeKey.FUNCTIONAL) + assert functional_mode is not None + + with functional_mode: + torch._mirror_autograd_meta_to(x, x_functional) # type: ignore[attr-defined] + out = FunctionalTensor(x_functional, functional_mode) + torch._mirror_autograd_meta_to(x_functional, out) # type: ignore[attr-defined] + return out + + def from_functional(self): + torch._sync(self) + return torch._from_functional_tensor(self.elem) + + def is_base_tensor(self) -> bool: + return torch._is_functional_tensor_base(self.elem) + + def replace_(self, output) -> None: + torch._functionalize_replace(self.elem, output) + + def commit_update(self) -> None: + torch._functionalize_commit_update(self.elem) + + def sync(self) -> None: + torch._functionalize_sync(self.elem) + + def mark_mutation_hidden_from_autograd(self) -> None: + torch._functionalize_mark_mutation_hidden_from_autograd(self.elem) + + def tolist(self) -> Any: + if self.elem.dim() == 0: + return self.elem.item() + elif self.elem.dim() == 1: + return [elem.item() for elem in self.elem] + else: + return [elem.tolist() for elem in self.elem] + + def to(self, *args, **kwargs): + if _detect_infra_mode(torch._C._TorchDispatchModeKey.FUNCTIONAL).export: + # If copy is specified as pos arg, it's always the second one. + if len([arg for arg in args if isinstance(arg, bool)]) <= 1: + return super().to(*args, **{**kwargs, "copy": True}) + return super().to(*args, **kwargs) + + def cuda(self, device=None, *args, **kwargs): + device = device or torch.cuda.current_device() + if len(args) > 0: + return self.to(device, *args, **kwargs) + else: + return self.to(device=device, **kwargs) + + char = _conversion_method_template(dtype=torch.int8) + cpu = _conversion_method_template(device=torch.device("cpu")) + bfloat16 = _conversion_method_template(dtype=torch.bfloat16) + byte = _conversion_method_template(dtype=torch.uint8) + double = _conversion_method_template(dtype=torch.float64) + float = _conversion_method_template(dtype=torch.float32) + bool = _conversion_method_template(dtype=torch.bool) + half = _conversion_method_template(dtype=torch.float16) + int = _conversion_method_template(dtype=torch.int32) + long = _conversion_method_template(dtype=torch.int64) + + # TODO(sparse-team): fixes #133174 but can we do without the relay? + def to_dense(self): + return self.elem.to_dense() + + @property + def layout(self): + return self.elem.layout + + +class FunctionalTensorMode(TorchDispatchMode): + def __init__(self, pre_dispatch=False, export=False, _allow_token_discovery=False): + super().__init__() + self.export = export + self.is_on_stack = False + self.enter_stack = [] + # Indicates to our torch_dispatch dispatching infra that + # this is an "infra" mode with lower dispatching precedence. + self._mode_key = torch._C._TorchDispatchModeKey.FUNCTIONAL + self.pre_dispatch = pre_dispatch + # This will be turned off later for pre-dispatch functionalization + self._dispatch_key = torch._C.DispatchKey.PreDispatch if pre_dispatch else None # type: ignore[attr-defined] + # Map of effect type (ex. _EffectType.ORDERED) to a token. The tokens help keep + # track of the ordering between side effectful operations. + self._tokens: Dict[Any, torch.Tensor] = {} + + # Filled after forward tracing. + self._tokens_forward_output: Dict[Any, torch.Tensor] = {} + + # Functionalization runs twice in AOTAutograd, once in + # `run_functionalized_fw_and_collect_metadata` to collect metadata to + # see which tensors need to be functionalized and discover how many + # tokens we need, and another time in `make_fx` which does the actual + # tracing to replace ops with their functional variants and handling + # side-effectful ops. In the second stage there should be no token + # discovery. This flag distinguishes between the two stages. + self._allow_token_discovery = _allow_token_discovery + + self._storage_to_base: weakref.WeakKeyDictionary[ + torch.storage.UntypedStorage, Optional[FunctionalTensor] + ] = weakref.WeakKeyDictionary() + + # No-op if FunctionalTensorMode is already in use + def __enter__(self): + def _get_prev_mode(): + if self._dispatch_key == torch._C.DispatchKey.PreDispatch: + return _get_dispatch_mode_pre_dispatch( + torch._C._TorchDispatchModeKey.FUNCTIONAL + ) + return torch._C._get_dispatch_mode( + torch._C._TorchDispatchModeKey.FUNCTIONAL + ) + + if _get_prev_mode() is None: + self.enter_stack.append(True) + return super().__enter__() + else: + self.enter_stack.append(False) + return self + + def __exit__(self, a, b, c): + is_on_stack = self.enter_stack.pop() + if is_on_stack: + super().__exit__(a, b, c) + + def __torch_dispatch__(self, func, types, args=(), kwargs=None): + if kwargs is None: + kwargs = {} + + if self.export: + # We need to make sure that we don't decompose to() as usual in export mode, + # because it can get optimized away. Instead we always replace it with _to_copy(). + if func == torch.ops.aten.to.dtype_layout: + kwargs.pop("copy", None) + return self.__torch_dispatch__( + torch.ops.aten._to_copy.default, types, args, kwargs + ) + if func == torch.ops.aten.to.dtype: + schema = tuple(arg.name for arg in func._schema.arguments) + for arg, name in zip(args[1:], schema[1:]): + kwargs[name] = arg + kwargs.pop("copy", None) + return self.__torch_dispatch__( + torch.ops.aten._to_copy.default, types, args[:1], kwargs + ) + + unrecognized_types = [ + t + for t in types + if not issubclass(t, torch._subclasses.FakeTensor) + and t not in [torch.Tensor, FunctionalTensor] + ] + + if unrecognized_types: + not_implemented_log.debug( + "FunctionalTensor unrecognized subclass(es): %s", unrecognized_types + ) + return NotImplemented + + def _can_decompose(func): + # See https://github.com/pytorch/pytorch/pull/115258#issuecomment-1900755832 + # Never decompose dropout in export + if self.export and func == torch.ops.aten.dropout.default: + return False + + # We unconditionally decompose ops that are maybe aliasing or mutating ops + if func in FunctionalTensor.maybe_aliasing_or_mutating_ops: + return True + + # (1) we unconditionally decompose maybe-aliasing or maybe-mutating ops, + # because we must know statically of an op mutates or aliasing in order to functionalize it properly + # (2) for mutating ops that have CompositeImplicit decomps, we choose to decompose them today. + # In theory, we could walk this back and avoid decomposing them later if we need to. + alias_info_present = any(arg.alias_info for arg in func._schema.arguments) + if alias_info_present or func._schema.is_mutable: + return True + + # If we are here, it means we are seeing functional composite op. + # For pre-dispatch IR or export inference IR, we wont' decompose them + if (self.export or self.pre_dispatch) and func._can_decompose(): + if func.namespace not in ["aten", "prim"]: + # TODO (tmanlaibaatar) check if the op is PT2 compliant + warnings.warn( + f"At pre-dispatch tracing, we assume that any custom op marked with " + f"CompositeImplicitAutograd and have functional schema are safe to not decompose. " + f"Found {func} to be one such op." + ) + return False + + # in normal torch.compile IR, we decompose functional composite ops + return True + + if ( + func not in FunctionalTensor.metadata_fns + and _can_decompose(func) + # Not all funcs from __torch_dispatch__ are actual dispatcher ops, + # e.g. prim.device + and torch._C._dispatch_has_kernel(func.name()) + ): + with self: + r = func.decompose(*args, **kwargs) + if r is not NotImplemented: + return r + + def wrap(x): + # Only wrap our outputs in subclasses if the inner functionalization call + # also wrapped outputs into FunctionalTensorWrappers. + # When can this happen? e.g. `torch.div(2, 2)` + assert not isinstance(x, FunctionalTensor) + if isinstance(x, torch.Tensor) and torch._is_functional_tensor(x): + return FunctionalTensor(x, self) + return x + + def unwrap(x): + return x.elem + + from torch._higher_order_ops.auto_functionalize import ( + can_auto_functionalize, + do_auto_functionalize, + do_auto_functionalize_v2, + ) + + if can_auto_functionalize( + func + ) and not torch._C._dispatch_has_kernel_for_dispatch_key( + func.name(), torch._C.DispatchKey.Functionalize + ): + # it doesn't matter what mode we use here because + # the implementation of do_auto_functionalize doesn't + # interact with FunctionalTensorMode at all + if self.export or not inductor_config.enable_auto_functionalized_v2: + return do_auto_functionalize(func, args, kwargs) + else: + return do_auto_functionalize_v2(func, args, kwargs) + + from torch._higher_order_ops.effects import handle_effects, has_effects + + if has_effects(func, args, kwargs): + assert not torch._C._dispatch_has_kernel_for_dispatch_key( + func.name(), torch._C.DispatchKey.Functionalize + ) + return handle_effects( + self._allow_token_discovery, self._tokens, func, args, kwargs + ) + + args_unwrapped, kwargs_unwrapped = pytree.tree_map_only( + FunctionalTensor, unwrap, (args, kwargs) + ) + + # Expectation: functionalization should not **already** be enabled above our mode. + # Why would that be bad? when we return a FunctionalTensor here, we don't want functionalization + # to run above this mode and further wrap that output in **another** C++ FunctionalTensorWrapper. + is_included = torch._C._dispatch_tls_is_dispatch_key_included( + torch._C.DispatchKey.Functionalize + ) + is_excluded = torch._C._dispatch_tls_is_dispatch_key_excluded( + torch._C.DispatchKey.Functionalize + ) + assert is_excluded or not is_included + include_to_set = ( + torch._C._dispatch_tls_local_include_set() + | torch._C.DispatchKeySet(torch._C.DispatchKey.Functionalize) + ) + exclude_to_set = ( + torch._C._dispatch_tls_local_exclude_set().remove( + torch._C.DispatchKey.Functionalize + ) + - FunctionalTensor._extra_dispatch_keys + ) + + # All we want to do here is re-use the existing C++ functionalization logic. + # This requires swizzling our TLS dispatch keys so that the Functionalize key is active. + with torch._C._ForceDispatchKeyGuard(include_to_set, exclude_to_set): + try: + # By default for python functionalization (for AOTAutograd), we reapply views. + old_apply_views = torch._functionalize_enable_reapply_views(True) # type: ignore[attr-defined] + + # Sometimes these functions cannot be directly dispatched to functionalize key + # because args are sometimes not functional tensors for some reason? + if func in FunctionalTensor.metadata_fns: + outs_unwrapped = func(*args_unwrapped, **kwargs_unwrapped) + outs_wrapped = pytree.tree_map_only( + torch.Tensor, wrap, outs_unwrapped + ) + else: + # When we dispatch to the C++ functionalization kernel, we might need to jump back to the + # PreDispatch mode stack afterwards, to handle any other PreDispatch modes underneath + # FunctionalTensorMode. If we call func() directly, we would need to exclude PreDispatch + # from the TLS in order to avoid infinite looping, but this would prevent us from coming + # back to PreDispatch later + outs_unwrapped = func._op_dk( + torch._C.DispatchKey.Functionalize, + *args_unwrapped, + **kwargs_unwrapped, + ) + # We don't allow any mutation on result of dropout or _to_copy + if self.export: + if func in ( + torch.ops.aten.dropout.default, + torch.ops.aten._to_copy.default, + ): + torch._freeze_functional_tensor(outs_unwrapped) # type: ignore[attr-defined] + outs_wrapped = pytree.tree_map_only( + torch.Tensor, wrap, outs_unwrapped + ) + finally: + torch._disable_functionalization() + torch._functionalize_enable_reapply_views(old_apply_views) # type: ignore[attr-defined] + + is_included = torch._C._dispatch_tls_is_dispatch_key_included( + torch._C.DispatchKey.Functionalize + ) + is_excluded = torch._C._dispatch_tls_is_dispatch_key_excluded( + torch._C.DispatchKey.Functionalize + ) + assert is_excluded or not is_included + + if ( + # If no outputs are our functional subclass, then don't try to fix up aliasing + not any( + isinstance(x, FunctionalTensor) + for x in pytree.tree_leaves(outs_wrapped) + ) + # Since lift_fresh lifts its argument into a functional tensor, we can skip the + # aliasing correction step. Otherwise, we would be setting the storage of a + # lifted tensor to that of an unlifted tensor. + # Ref: https://github.com/pytorch/pytorch/issues/111506 + or func == torch.ops.aten.lift_fresh.default + ): + return outs_wrapped + # for metadata mutations, need to manually mutate the metadata of the FunctionalTensor wrapper + if ( + torch.Tag.inplace_view in func.tags + and func is not torch.ops.aten.set_.source_Tensor + ): + with torch.utils._mode_utils.no_dispatch(): + func(*args, **kwargs) + # Wrapper tensor subclasses do not have correct aliasing info! Use this util to manually correct the output aliasing. + # inplace ops like `aten.add_()` are expected to return inputs **directly**, instead of creating fresh tensor objects. + # Use this util to figure out the right thing to return. + # If none of our inputs were wrapped, then we have no FunctionalTensor outputs that we need to fix up storages for. + return return_and_correct_aliasing(func, args, kwargs, outs_wrapped) + + @classmethod + def is_infra_mode(cls) -> bool: + return True + + +@contextlib.contextmanager +def disable_functional_mode(): + return _disable_infra_mode(torch._C._TorchDispatchModeKey.FUNCTIONAL) + + +# This is similar to torch.func.functionalize, but: +# - It uses FunctionalTensorMode, and FunctionalTensor (a python subclass). +# One important advantage to using this mode is that it will let us +# run functionalization underneath __torch_dispatch__, +# which we need in AOTAutograd. +# - Doing so means that it does not automatically compose with other +# functorch transforms, since these transforms always run above __torch_dispatch__. +# That's why this util lives here, and not in functorch. +def dispatch_functionalize(func, mode: FunctionalTensorMode = FunctionalTensorMode()): + # TODO: pull these from aot autograd + def to_fun(t): + if isinstance(t, torch.Tensor): + return FunctionalTensor.to_functional(t) + return t + + def from_fun(t): + if not isinstance(t, FunctionalTensor): + # quick sanity assert + if isinstance(t, torch.Tensor): + assert not torch._is_functional_tensor(t) + return t + torch._sync(t) + return torch._from_functional_tensor(t.elem) + + def inner(*args, **kwargs): + disable_above = torch._C._ExcludeDispatchKeyGuard( + torch._C.DispatchKeySet(torch._C.DispatchKey.Functionalize) + ) + with disable_above, mode: + func_args = pytree.tree_map_only(torch.Tensor, to_fun, args) + func_kwargs = pytree.tree_map_only(torch.Tensor, to_fun, kwargs) + func_outputs = func(*func_args, **func_kwargs) + outputs = pytree.tree_map_only(FunctionalTensor, from_fun, func_outputs) + + return outputs + + return inner + + +class BaseFunctionalizeAPI(ABC): + @abstractmethod + def wrap_tensors(self, args: Tuple[Any]) -> Tuple[Any]: + pass + + @abstractmethod + def unwrap_tensors( + self, args: Union[torch.Tensor, Tuple[torch.Tensor, ...]] + ) -> Any: + pass + + @abstractmethod + def functionalize(self, inner_f: Callable) -> Callable: + pass + + @abstractmethod + def redispatch_to_next(self) -> ContextManager: + pass + + @abstractmethod + def replace(self, input_tensor, output_tensor) -> None: + pass + + @abstractmethod + def commit_update(self, tensor) -> None: + pass + + @abstractmethod + def sync(self, tensor) -> None: + pass + + @abstractmethod + def mark_mutation_hidden_from_autograd(self, tensor) -> None: + pass + + +class PythonFunctionalizeAPI(BaseFunctionalizeAPI): + def __init__( + self, mode: Optional[FunctionalTensorMode] = None, pre_dispatch: bool = False + ) -> None: + super().__init__() + self.mode = mode if mode else FunctionalTensorMode() + self.pre_dispatch = pre_dispatch + + def wrap_tensors(self, args: Tuple[Any]) -> Tuple[Any]: + with self.mode: + return torch.utils._pytree.tree_map_only( + torch.Tensor, FunctionalTensor.to_functional, args + ) + + def unwrap_tensors( + self, args: Union[torch.Tensor, Tuple[torch.Tensor, ...], List[torch.Tensor]] + ) -> Any: + return torch.utils._pytree.tree_map_only( + FunctionalTensor, FunctionalTensor.from_functional, args + ) + + def functionalize(self, inner_f: Callable) -> Callable: + return dispatch_functionalize(inner_f, self.mode) + + def redispatch_to_next(self) -> ContextManager: + # [NOTE] We don't do anything here because at the time + # we exercise this path, we would have already popped the + # FunctionalTensorMode from mode stack. Since FunctionalTensorMode + # is now stateful, it is better to explicitly pass in correct mode + # directly instead of globally setting it. + return contextlib.nullcontext() + + def replace(self, input_tensor, output_tensor) -> None: + assert isinstance(input_tensor, FunctionalTensor) + assert not isinstance(output_tensor, FunctionalTensor) + input_tensor.replace_(output_tensor) + + def commit_update(self, tensor) -> None: + assert isinstance(tensor, FunctionalTensor) + tensor.commit_update() + + def sync(self, tensor) -> None: + assert isinstance(tensor, FunctionalTensor) + tensor.sync() + + def mark_mutation_hidden_from_autograd(self, tensor) -> None: + assert isinstance(tensor, FunctionalTensor) + tensor.mark_mutation_hidden_from_autograd() + + +class CppFunctionalizeAPI(BaseFunctionalizeAPI): + def wrap_tensors(self, args: Tuple[Any]) -> Tuple[Any]: + from torch._functorch.eager_transforms import _wrap_all_tensors_to_functional + + return _wrap_all_tensors_to_functional(args, level=0) + + def unwrap_tensors( + self, args: Union[torch.Tensor, Tuple[torch.Tensor, ...]] + ) -> Union[torch.Tensor, Tuple[torch.Tensor, ...]]: + from torch._functorch.eager_transforms import ( + _unwrap_all_tensors_from_functional, + ) + + return _unwrap_all_tensors_from_functional(args, reapply_views=_reapply_views()) + + def functionalize(self, inner_f: Callable) -> Callable: + return torch.func.functionalize(inner_f) + + def redispatch_to_next(self) -> ContextManager: + return torch._C._ExcludeDispatchKeyGuard( + torch._C.DispatchKeySet(torch._C.DispatchKey.Functionalize) + ) + + def replace(self, input_tensor, output_tensor) -> None: + torch._functionalize_replace(input_tensor, output_tensor) + + def commit_update(self, tensor) -> None: + torch._functionalize_commit_update(tensor) + + def sync(self, tensor) -> None: + torch._functionalize_sync(tensor) + + def mark_mutation_hidden_from_autograd(self, tensor) -> None: + torch._functionalize_mark_mutation_hidden_from_autograd(tensor) + + +class FunctorchFunctionalizeAPI(BaseFunctionalizeAPI): + def __init__(self, interpreter): + self.interpreter = interpreter + + def wrap_tensors(self, args: Tuple[Any]) -> Tuple[Any]: + from torch._functorch.eager_transforms import _wrap_all_tensors_to_functional + + return _wrap_all_tensors_to_functional(args, level=self.interpreter.level()) + + def unwrap_tensors( + self, args: Union[torch.Tensor, Tuple[torch.Tensor, ...]] + ) -> Union[torch.Tensor, Tuple[torch.Tensor, ...]]: + from torch._functorch.eager_transforms import ( + _unwrap_all_tensors_from_functional, + ) + + return _unwrap_all_tensors_from_functional( + args, reapply_views=self.interpreter.functionalize_add_back_views() + ) + + def functionalize(self, inner_f: Callable) -> Callable: + return torch.func.functionalize( + inner_f, + remove=( + "mutations_and_views" + if self.interpreter.functionalize_add_back_views() + else "mutations" + ), + ) + + def redispatch_to_next(self) -> ContextManager: + return self.interpreter.lower() + + def replace(self, input_tensor, output_tensor) -> None: + torch._functionalize_replace(input_tensor, output_tensor) + + def commit_update(self, tensor) -> None: + torch._functionalize_commit_update(tensor) + + def sync(self, tensor) -> None: + torch._functionalize_sync(tensor) + + def mark_mutation_hidden_from_autograd(self, tensor) -> None: + torch._functionalize_mark_mutation_hidden_from_autograd(tensor) + + +def mb_unwrap_functional_tensor(tensor: torch.Tensor): + if isinstance(tensor, FunctionalTensor): + return torch._from_functional_tensor(tensor.elem) + return tensor diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/meta_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/meta_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..702165406f2bd20b75b05cd7729f59b682d8d4df --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/meta_utils.py @@ -0,0 +1,1677 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import contextlib +import dataclasses +import warnings +import weakref +from dataclasses import dataclass +from typing import ( + Any, + Callable, + ClassVar, + ContextManager, + Dict, + List, + Optional, + Tuple, + Type, + TYPE_CHECKING, + Union, +) +from typing_extensions import TypeAlias + +import torch +from torch._C._autograd import CreationMeta +from torch._C._functorch import ( + _add_batch_dim, + _unwrap_functional_tensor, + _wrap_functional_tensor, + get_unwrapped, + is_batchedtensor, + is_functorch_wrapped_tensor, + is_gradtrackingtensor, + is_legacy_batchedtensor, + maybe_get_bdim, + maybe_get_level, + peek_interpreter_stack, +) +from torch._logging import trace_structured +from torch.utils._mode_utils import no_dispatch +from torch.utils._python_dispatch import is_traceable_wrapper_subclass +from torch.utils.weak import WeakIdKeyDictionary + + +if TYPE_CHECKING: + from torch._C._functorch import CInterpreter + from torch._guards import Source + + # Import here to avoid cycle + from torch._subclasses.fake_tensor import FakeTensorMode + + # Import the following modules during type checking to enable code intelligence features, + # Do not import unconditionally, as they import sympy and importing sympy is very slow + from torch.fx.experimental.symbolic_shapes import ShapeEnv, SymbolicContext + +DimList = List + + +def safe_is_leaf(t): + try: + return t.is_leaf + except RuntimeError: + # inference mode can trigger this + return False + + +def safe_grad(t): + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", "The .grad attribute of a Tensor") + return t.grad + + +def assert_eq(a, b): + assert a == b, f"{a} != {b}" + + +def assert_metadata_eq( + assert_eq, + m1: Union[MetaTensorDesc, torch.Tensor], + m2: torch.Tensor, + *, + skip_symbolic=False, + skip_leaf=False, +): + if isinstance(m1, torch.Tensor): + m1 = MetaTensorDescriber().describe_tensor(m1) + + def go(m1, m2): + assert_eq(m1.dtype, m2.dtype) + if not skip_symbolic: + assert_eq(m1.shape, m2.shape) + assert_eq(m1.requires_grad, m2.requires_grad) + if not skip_leaf: + assert_eq(m1.is_leaf, m2.is_leaf) + # MetaTensorDesc doesn't store grad_fn; inferred from leaf + # assert_eq(m1.grad_fn is None, m2.grad_fn is None) + assert_eq(m1.is_sparse, m2.is_sparse) + assert_eq(m1.is_inference, m2.is_inference()) + assert_eq(m1.is_conj, m2.is_conj()) + assert_eq(m1.is_neg, m2.is_neg()) + assert_eq(m1.grad is not None, safe_grad(m2) is not None) + if m1.grad is not None: + go(m1.grad, safe_grad(m2)) + # TODO: move "assert_eq(m1.layout, m2.layout)" out of sparse + # branches (but not ready for prime time yet)... + if m1.is_sparse: + assert_eq(m1.layout, m2.layout) + assert_eq(m1.dense_dim, m2.dense_dim()) + assert_eq(m1.sparse_dim, m2.sparse_dim()) + assert_eq(m1.is_coalesced, m2.is_coalesced()) + elif is_sparse_compressed(m1): + assert_eq(m1.layout, m2.layout) + assert_eq(m1.dense_dim, m2.dense_dim()) + assert_eq(m1.sparse_dim, m2.sparse_dim()) + else: + if not skip_symbolic: + assert_eq(m1.stride, m2.stride()) + assert_eq(m1.storage_offset, m2.storage_offset()) + assert_eq(m1.is_view, m2._is_view()) + if m1.is_view: + go(m1.base, m2._base) + # TODO: test if is resizable (no direct query for this atm) + # TODO: audit AutogradMeta to see if it matches + # TODO: test forward AD + + return go(m1, m2) + + +def is_sparse_coo(t): + return isinstance(t, torch.Tensor) and t.layout is torch.sparse_coo + + +def is_sparse_compressed_layout(layout): + return layout in { + torch.sparse_csr, + torch.sparse_csc, + torch.sparse_bsr, + torch.sparse_bsc, + } + + +def is_sparse_compressed(t): + return isinstance(t, torch.Tensor) and is_sparse_compressed_layout(t.layout) + + +def is_sparse_any(t): + return is_sparse_coo(t) or is_sparse_compressed(t) + + +# Don't use id() directly, because those can get reallocated over time. +MetaStorageId: TypeAlias = int +MetaTensorId: TypeAlias = int + + +DESCRIBER_NEXT_ID = 0 + + +class MetaTensorDescriber: + """ + Given a Tensor/Storage, generate a MetaTensorDesc/MetaStorageDesc + for it, which is enough information to reconstruct a meta tensor/fake tensor + corresponding to a Tensor as faithfully as possible. + + This is a stateful conversion object because we keep track of the IDs + of the tensors/storages passed to us, so we can consistently give + the same ID when we see the same tensor/storage. + """ + + def __init__(self, *, copy_data=False): + global DESCRIBER_NEXT_ID + self.id = DESCRIBER_NEXT_ID + DESCRIBER_NEXT_ID += 1 + self.next_tensor_id: MetaTensorId = 0 + self.next_storage_id: MetaStorageId = 0 + # Tensor -> int + self.lookup_tensor = WeakIdKeyDictionary() + # Storage -> int + self.lookup_storage = WeakIdKeyDictionary() + self.copy_data = copy_data + self.traced_tensors = set() + self.traced_storages = set() + + def get_tensor_id(self, t: torch.Tensor): + if t not in self.lookup_tensor: + self.lookup_tensor[t] = self.next_tensor_id + self.next_tensor_id += 1 + return self.lookup_tensor[t] + + def get_storage_id(self, s: torch.UntypedStorage): + if s not in self.lookup_storage: + self.lookup_storage[s] = self.next_storage_id + self.next_storage_id += 1 + return self.lookup_storage[s] + + def describe_storage(self, s: torch.UntypedStorage, *, trace: bool = False): + r = MetaStorageDesc( + id=self.get_storage_id(s), + size=s.size(), + # NB: We don't do the copy yet; copy happens when we start + # creating the new storages + data=s if self.copy_data else None, + ) + if trace and r.id not in self.traced_storages: + trace_structured( + "describe_storage", + metadata_fn=lambda: r.as_json(self.id), + ) + self.traced_storages.add(r.id) + return r + + def describe_tensor( + self, t: torch.Tensor, *, recurse: bool = True, trace: bool = False + ): + is_leaf = safe_is_leaf(t) + is_view = t._is_view() + is_sparse = t.is_sparse + layout = t.layout + is_nested = t.is_nested + is_traceable_wrapper_subclass_v = is_traceable_wrapper_subclass(t) + is_functorch_wrapped = is_functorch_wrapped_tensor(t) + is_mkldnn = t.is_mkldnn + is_batchedtensor_v = is_batchedtensor(t) + is_legacy_batchedtensor_v = is_legacy_batchedtensor(t) + is_gradtrackingtensor_v = is_gradtrackingtensor(t) + is_functorch_batched_or_grad = is_batchedtensor_v or is_gradtrackingtensor_v + is_functional = torch._is_functional_tensor(t) + + storage = None + # NB: For compatibility, I default this to zero, as sometimes people + # still have stuffed zero into storage offset even though the tensor + # doesn't meaningfully have an offset + storage_offset = 0 + if not ( + is_sparse + or is_sparse_compressed_layout(layout) + or (is_nested and not is_traceable_wrapper_subclass_v) + or is_mkldnn + # TODO: TBH, functorch wrapped tensors probably should have + # storage associated with them + or is_functorch_wrapped + or is_legacy_batchedtensor_v + ): + # NB: We actually don't use storage to do views, but might as well + # put it in for accuracy + storage = self.describe_storage(t.untyped_storage(), trace=trace) + storage_offset = t.storage_offset() # type: ignore[assignment] + + stride = None + if not ( + is_sparse + or is_sparse_compressed_layout(layout) + or (is_nested and not is_traceable_wrapper_subclass_v) + ): + # stride/storage_offset are called from is_functorch_wrapped, + # view_from_base, empty_create_subclass, + # sym_sizes_strides_storage_offset (empty_create) + stride = t.stride() + + # NB: this technically should refer to functorch unwrapped tensor, but + # I am (perhaps abusively) using it to store both the functorch and + # non-functorch functional tensor + unwrapped = None + autograd_meta_from = None + current_level = None + if is_batchedtensor_v or is_gradtrackingtensor_v: + unwrapped = self.describe_tensor(get_unwrapped(t), trace=trace) + # xla and lazy tensors present as functional tensors, but we want them + # to be handled specially + elif is_functional and t.device.type not in ("xla", "lazy"): + if t._is_view(): + raise RuntimeError( + "Cannot safely fakify a view because this process drops the view information right now." + ) + if not is_functorch_wrapped: + torch._sync(t) + unwrapped = self.describe_tensor( + torch._from_functional_tensor(t), trace=trace + ) + autograd_meta_from = t + else: + reapply_views = torch._C._functionalization_reapply_views_tls() + # NB: has side effects! + unwrapped = self.describe_tensor( + _unwrap_functional_tensor(t, reapply_views), trace=trace + ) + # TODO: It's pretty suspicious that functional tensors don't have + # valid level and thus we just grab whatever the current level + # is + current_level = torch._C._functorch.current_level() + + maybe_functorch_stack = None + if is_functorch_wrapped: + with torch._functorch.pyfunctorch.temporarily_clear_interpreter_stack() as maybe_functorch_stack: + pass + + attrs = None + ctx = None + type_v = None + if is_traceable_wrapper_subclass_v: + assert hasattr(t, "__tensor_flatten__") + raw_attrs, ctx = t.__tensor_flatten__() + attrs = { + attr: self.describe_tensor(getattr(t, attr), trace=trace) + for attr in raw_attrs + } + type_v = type(t) + + from torch.nested._internal.nested_tensor import _tensor_symint_registry + + # TODO: Is it important to enable torch.inference_mode before querying + # these values? + r = MetaTensorDesc( + id=self.get_tensor_id(t), + storage=storage, + is_inference=t.is_inference(), + is_leaf=is_leaf, + requires_grad=t.requires_grad, + # NB: ndim should be OK too but there is a disaster at + # python test/dynamo/test_subclasses.py -k test_user_overidden_property_unsupported + # Actually, this means that we have a little bit of a problem + # here, which is that there is some sensitivity to how exactly an + # access is done if you have a __torch_function__ subclass. Maybe + # should disable torch function before doing accesses? + ndim=t.dim(), + dtype=t.dtype, + is_sparse=is_sparse, + is_mkldnn=is_mkldnn, + is_functorch_wrapped=is_functorch_wrapped, + is_batchedtensor=is_batchedtensor_v, + is_legacy_batchedtensor=is_legacy_batchedtensor_v, + is_gradtrackingtensor=is_gradtrackingtensor_v, + is_view=is_view, + is_conj=t.is_conj(), + is_neg=t.is_neg(), + is_parameter=isinstance(t, torch.nn.Parameter), + is_traceable_wrapper_subclass=is_traceable_wrapper_subclass_v, + is_nested=is_nested, + nested_int=( + _tensor_symint_registry[t].node.nested_int() + if t in _tensor_symint_registry + else None + ), + is_functional=is_functional, + layout=layout, + device=t.device, + size=t.size(), + stride=stride, + storage_offset=storage_offset, + dynamo_dynamic_indices=list(getattr(t, "_dynamo_dynamic_indices", set())), + sparse_dim=( + t.sparse_dim() if t.is_sparse or is_sparse_compressed(t) else None + ), + dense_dim=t.dense_dim() if t.is_sparse or is_sparse_compressed(t) else None, + is_coalesced=t.is_coalesced() if t.is_sparse else None, + # TODO: I actually think recursing here is correct, but we have at + # least an infinite cycle from base -> values -> base + # https://github.com/pytorch/pytorch/issues/122089 + crow_indices=( + self.describe_tensor(t.crow_indices(), recurse=False, trace=trace) + if recurse and t.layout in {torch.sparse_csr, torch.sparse_bsr} + else None + ), + col_indices=( + self.describe_tensor(t.col_indices(), recurse=False, trace=trace) + if recurse and t.layout in {torch.sparse_csr, torch.sparse_bsr} + else None + ), + ccol_indices=( + self.describe_tensor(t.ccol_indices(), recurse=False, trace=trace) + if recurse and t.layout in {torch.sparse_csc, torch.sparse_bsc} + else None + ), + row_indices=( + self.describe_tensor(t.row_indices(), recurse=False, trace=trace) + if recurse and t.layout in {torch.sparse_csc, torch.sparse_bsc} + else None + ), + values=( + self.describe_tensor(t.values(), recurse=False, trace=trace) + if recurse and is_sparse_compressed(t) + else None + ), + grad=( + self.describe_tensor(safe_grad(t), trace=trace) + if safe_grad(t) is not None + else None + ), + creation_meta=( + torch._C._autograd._get_creation_meta(t) if t._is_view() else None + ), + unwrapped=unwrapped, + level=( + maybe_get_level(t) + if is_batchedtensor_v or is_gradtrackingtensor_v + else None + ), + bdim=maybe_get_bdim(t) if is_batchedtensor_v else None, + base=( + self.describe_tensor(t._base, trace=trace) + if recurse and t._is_view() and t._base is not None + else None + ), + fake_mode=torch._subclasses.fake_tensor.maybe_get_fake_mode(t), + view_func=t._view_func_unsafe, + attrs=attrs, + ctx=ctx, + type=type_v, + # NB: even if functorch is enabled, don't actually save the + # interpreter stack here unless we are actually functorch wrapped; + # it's irrelevant for non-functorch stuff + functorch_stack=maybe_functorch_stack, + autograd_meta_from=autograd_meta_from, + current_level=current_level, + data=t if self.copy_data else None, + ) + if trace and r.id not in self.traced_tensors: + trace_structured( + "describe_tensor", + metadata_fn=lambda: r.as_json(self.id), + ) + self.traced_tensors.add(r.id) + return r + + +@dataclass(frozen=True) +class MetaStorageDesc: + id: MetaStorageId + size: int + # NB: this is only populated with copy_data True, it is not directly + # serializable in JSON, you want to do something special here anyway + data: Optional[torch.UntypedStorage] + + def as_json(self, describer_id): + return { + "id": self.id, + "describer_id": describer_id, + "size": self.size if isinstance(self.size, int) else repr(self.size), + } + + +@dataclass(frozen=True) +class MetaTensorDesc: + id: MetaTensorId + ndim: int + dtype: torch.dtype + device: torch.device + + # NB: Sometimes, size, stride and storage_offset contain SymInt, in which + # case this is NOT serializable. That only happens when you're + # re-fakeifying a fake tensor with an existing ShapeEnv... maybe we + # can get rid of this use case entirely. Notably, even if we are + # fakeifying a real tensor into a fake tensor with symbolic shapes, the + # size here is NOT dynamic + # NB: These also contain SymInt because wrap_meta_outputs_with_default_device_logic + # goes through this codepath. But it really should not LOL. + # NB: size could potentially be None as you can override it and make it + # throw an error, but we don't currently have any subclasses that do this + # except C++ nested tensor but we're going to have nested int to make this + # defined on NJT + size: Tuple[int, ...] + dynamo_dynamic_indices: List[int] + + layout: torch.layout = torch.strided + is_inference: bool = False + is_leaf: bool = False + requires_grad: bool = False + is_sparse: bool = False + is_mkldnn: bool = False + is_functorch_wrapped: bool = False + is_batchedtensor: bool = False + is_legacy_batchedtensor: bool = False + is_gradtrackingtensor: bool = False + is_view: bool = False + is_nested: bool = False + # We eagerly symbolicize the associated nested int for e.g. offsets / lengths + # metadata if that offsets is already associated with a nested int. + # See test_construct_from_jagged_with_input_offsets_mixed_case. + nested_int: Optional[int] = None + is_traceable_wrapper_subclass: bool = False + is_functional: bool = False + is_conj: bool = False + is_neg: bool = False + is_parameter: bool = False + stride: Optional[Tuple[int, ...]] = None + storage_offset: int = 0 + # NB: We have a choice whether or not to store the id or a direct pointer + # to the data structure. For ease of use, we store the data structure, + # but this means that when we serialize, we have to swizzle these pointers + # back into ids (so we have accurate aliasing relationships) + storage: Optional[MetaStorageDesc] = None + sparse_dim: Optional[int] = None # is_sparse, is_sparse_compressed + dense_dim: Optional[int] = None # is_sparse, is_sparse_compressed + is_coalesced: Optional[bool] = None # is_sparse + crow_indices: Optional[MetaTensorDesc] = None # is_sparse_compressed + col_indices: Optional[MetaTensorDesc] = None # is_sparse_compressed + ccol_indices: Optional[MetaTensorDesc] = None # is_sparse_compressed + row_indices: Optional[MetaTensorDesc] = None # is_sparse_compressed + values: Optional[MetaTensorDesc] = None # is_sparse_compressed + unwrapped: Optional[MetaTensorDesc] = None # is_functorch_wrapped + bdim: Optional[int] = None # is_functorch_wrapped + base: Optional[MetaTensorDesc] = None # is_view + attrs: Optional[Dict[str, MetaTensorDesc]] = None # is_traceable_wrapper_subclass + creation_meta: Optional[CreationMeta] = None + grad: Optional[MetaTensorDesc] = None + + # Everything below is NOT serializable, need some more work + + _UNSERIALIZABLE: ClassVar[List[str]] = [ + "ctx", + "type", + "fake_mode", + "view_func", + "level", + "current_level", + "functorch_stack", + "autograd_meta_from", + "data", + "nested_int", + ] + + ctx: Optional[object] = None # is_traceable_wrapper_subclass + type: Optional[Type] = None # is_traceable_wrapper_subclass + fake_mode: Optional[FakeTensorMode] = None + view_func: Optional[ + Callable[ + [ + torch.Tensor, + Callable[[int], int], + Callable[[torch.Tensor], torch.Tensor], + ], + torch.Tensor, + ] + ] = None + # level looks serializable, but actually it is meaningless without + # the functorch_stack below + level: Optional[int] = None # is_functorch_wrapped + current_level: Optional[int] = None + functorch_stack: Optional[List[CInterpreter]] = None + autograd_meta_from: Optional[torch.Tensor] = None + + # This is only populated on copy_data, and typically is not used at all, + # except for some of our meta-ification paths that don't properly use + # storage (pro-tip: you should use storage) + data: Optional[torch.Tensor] = None + + # Faithfully serializing functorch tensors will not be too difficult. + # We only need to consider grad/vmap interpreters, and their internal + # state is only bools (mostly what the grad enabled/disabled state + # should be in the lower layer). Beyond that, tensors just need to + # precisely indicate which particular interpreter they correspond + # to (we then replace level with a pointer to the interpreter stack.) + # However, this use of functorch is very "non-lexical" so it's not + # entirely clear how to make it all lexical again, so we haven't done + # it for now. + + # NB: This will reference numeric IDs, and it is assumed that you've + # already serialized everything this recursively references + def as_json(self, describer_id): + def json(k, v): + # Some best-effort debugging serialization for unserializable + # fields (feel free to add other special cases as appropriate) + if k in ["data", "autograd_meta_from"]: + return None # never repr these + if k in set(MetaTensorDesc._UNSERIALIZABLE): + return repr(v) + if isinstance(v, (torch.device, torch.dtype, torch.layout)): + return repr(v) + if isinstance(v, torch.SymInt): + return repr(v) + if isinstance(v, (tuple, list)): + return [json(k, v1) for v1 in v] + if isinstance(v, (MetaStorageDesc, MetaTensorDesc)): + return v.id + if isinstance(v, CreationMeta): + return str(v) + if k == "attrs" and isinstance(v, dict): + return {k1: v1.id for k1, v1 in v.items()} + return v + + r = { + field.name: json(field.name, getattr(self, field.name)) + for field in dataclasses.fields(self) + if not ( + getattr(self, field.name) is field.default + or ( + field.name == "dynamo_dynamic_indices" + and not getattr(self, field.name) + ) + ) + } + r.update({"describer_id": describer_id}) + return r + + @property + def shape(self): + return self.size + + +# A more faithful reproduction would do a copy on the entire +# storage, but this needs to be done carefully because the +# underlying storage could have larger extent than is implied +# by size/stride. The real fix is to properly call +# meta_storage recursively here. +# +# These "safe" functions are intended to be used under no_dispatch() mode. +# The no_dispatch() here is intended to prevent ambient fake tensor mode from +# fakeifying the operation. But if we are given an honest to goodness +# FakeTensor as src, we MUST NOT run the copy/clone operation. A better way +# to do this would be to not use no_dispatch and instead just disable fake +# tensor mode only (allowing for subclass dispatch to occur) +def _safe_copy(dst, src): + if type(src) is not torch.Tensor: + return + dst.copy_(src) + + +def _safe_clone(src): + if type(src) is not torch.Tensor: + return None + return src.clone() + + +# This is a class for converting multiple tensors into meta tensors which +# share the same view/storage structure. The operation model is you allocate +# one of these, and then call it repeatedly on all the tensors you want to +# convert. It's important to use the same object for tensors you want to +# share storage because this is how we correlate shared storages to the same +# meta storages. This class will hold weak references to cached tenosrs +# and tensor storages. +class MetaConverter: + def __init__(self, *, copy_data: bool = False): + # Maps MetaStorageId to UntypedStorage + self.storage_memo: weakref.WeakValueDictionary = weakref.WeakValueDictionary() + # Maps MetaTensorId to torch.Tensor (typically a meta tensor or + # FakeTensor) + self.tensor_memo: weakref.WeakValueDictionary = weakref.WeakValueDictionary() + self.hit = 0 + self.miss = 0 + self.del_hook = None + self.arg_cnt = 0 + # Ensures real_storage/real_tensor are populated on the resulting + # metaified storage/tensor. The naming of this attribute is load + # bearing: FakeTensor relies on real tensor being set to exactly this + # value + self.copy_data = copy_data + self.describer = MetaTensorDescriber(copy_data=copy_data) + + def successful(self): + return self.hit > 0 and self.miss == 0 + + def get_tensor_memo(self, t: MetaTensorDesc): + return self.tensor_memo.get(t.id, None) + + def set_tensor_memo(self, t: MetaTensorDesc, v): + self.tensor_memo[t.id] = v + + def get_storage_memo(self, s: MetaStorageDesc): + return self.storage_memo.get(s.id, None) + + def set_storage_memo(self, s: MetaStorageDesc, v): + self.storage_memo[s.id] = v + + def meta_storage(self, s: MetaStorageDesc, callback): + # If we are fakeifying a tensor that has a secretly-zero-sized storage, + # Need to make sure to resize the meta storage too. + if self.get_storage_memo(s) is None: + r_s = callback( + lambda: torch.empty(s.size, dtype=torch.uint8, device="meta"), + ).untyped_storage() + if self.copy_data: + # NB: no_dispatch is needed because internally storage copy is + # implemented as Tensor operations + with torch.no_grad(), no_dispatch(): + assert s.data is not None + r_s.real_storage = s.data.clone() + self.set_storage_memo(s, r_s) + return r_s + else: + return self.get_storage_memo(s) + + # This function assumes that it's possible to do the conversion + # NB: name here is used in a conventional way by Dynamo; it corresponds + # precisely to the Source.name() of the tensor we're fakeifying and + # corresponds to a valid Python expression. When we construct sub-names + # as part of this process, we will maintain this invariant! (Even though + # other users of this may not need it this property to be upheld.) + def meta_tensor( + self, + t: MetaTensorDesc, + shape_env: Optional[ShapeEnv] = None, + callback=lambda t: t(), + source: Optional[Source] = None, + symbolic_context: Optional[SymbolicContext] = None, + ): + if source is None: + from torch._dynamo.source import ConstantSource + + # TODO: make a dedicated UnknownSource for this? + source = ConstantSource( + f"__meta_utils_unknown_tensor{len(self.tensor_memo)}" + ) + + # This indicates you set no_dispatch() before calling into this + # function. This is an error: we may be creating fake tensors and + # will perform operations on them which need fake tensor mode to + # be active. You will segfault if you are in a no_dispatch() block. + assert not torch._C._dispatch_tls_local_exclude_set().has( + torch._C.DispatchKey.Python + ) + arg_cnt = self.arg_cnt + self.arg_cnt += 1 + + # When we make as_strided calls, we end up generating a guard + # that the new as_strided tensor is in bounds for the old storage + # for the base (since as_strided calls can "bust" out of their + # bounding box.) This guard is unnecessary: if a user is able + # to provide us a tensor with the view base setup this way, we + # don't need to produce a guard, because the fact that they + # were able to produce the view base means its in bounds. + # + # Now, ordinarily, this guard would be harmless. However, the + # generated guard refers to variables bound on the base variable. + # At the moment, Dynamo doesn't actually guard on x._base, because + # according to Voz this results in a lot of spurious invalidations, + # and also if the user doesn't directly make use of _base, its + # pointless anyway (because programs should be parametric over + # whether or not the input tensor is a view or not--unless you're + # mutating the input, but that's a whole 'nother ballgame). So + # for expediency, we suppress these guards so we don't have to + # deal with this (yet, anyway.) + # + # NB: An old version of this code suppressed guards for ALL operations + # happening during meta conversion, not just as_strided calls. + # This is too aggressive: we do duck sizing and 0/1 simplification + # as we allocate variables, and we do need to register guards for + # these cases. + maybe_suppress: Callable[[], Any] = contextlib.nullcontext + if shape_env is not None: + maybe_suppress = shape_env.suppress_guards + + def sym_sizes_strides_storage_offset( + t: MetaTensorDesc, src, symbolic_context=symbolic_context + ) -> Tuple[Tuple[int, ...], Tuple[int, ...], int]: + assert t.stride is not None + if shape_env is not None: + fake_mode = t.fake_mode + if fake_mode is not None and fake_mode.shape_env is shape_env: + # Don't reallocate the sizes; the shape envs are the same, + # so reuse the old sizes/strides/etc + return (t.size, t.stride, t.storage_offset) + else: + # TODO: deduplicate this + t_size = tuple( + shape_env._maybe_specialize_sym_int_with_hint(sz) + for sz in t.size + ) + t_stride = tuple( + shape_env._maybe_specialize_sym_int_with_hint(sd) + for sd in t.stride + ) + t_storage_offset = shape_env._maybe_specialize_sym_int_with_hint( + t.storage_offset + ) + return shape_env._create_symbolic_sizes_strides_storage_offset( + t_size, + t_stride, + t_storage_offset, + [d in t.dynamo_dynamic_indices for d in range(t.ndim)], + src, + symbolic_context=symbolic_context, + ) + else: + return (t.size, t.stride, t.storage_offset) + + def empty_create( + inner_t: MetaTensorDesc, inner_src, symbolic_context=symbolic_context + ): + ( + inner_sizes, + inner_strides, + inner_storage_offset, + ) = sym_sizes_strides_storage_offset(inner_t, inner_src, symbolic_context) + return torch.empty_strided( + inner_sizes, + inner_strides, + dtype=inner_t.dtype, + device="meta", + ) + + # Creates a subclass instance with empty inner tensors according to the specified + # symbolic context. + def empty_create_subclass( + t: MetaTensorDesc, + outer_size, + outer_stride, + symbolic_context=symbolic_context, + callback=callback, + source=source, + ): + from torch._dynamo.source import AttrSource + from torch.fx.experimental.symbolic_shapes import SubclassSymbolicContext + + assert t.attrs is not None + assert t.type is not None + # NB: t.ctx could be None if the subclass in question has no + # meaningful context + + # Note: transform_subclass will use __tensor_unflatten__ to generate + # a fresh subclass wrapper with outer sizes / strides according to the + # outer symbolic context (passed in to this function). Inner size / stride + # / storage offset symbols are allocated according to the appropriate inner + # symbolic contexts, after which the checks in transform_subclass() will + # relate them to the outer metadata as possible. + # + # Morally, the code here is same as transform_subclass, but we've + # written it from scratch to read EmptyCreateSubclass + outer_size = outer_size if outer_size is not None else t.size + outer_stride = outer_stride if outer_stride is not None else t.stride + + assert symbolic_context is None or isinstance( + symbolic_context, SubclassSymbolicContext + ) + + def _empty_create_subclass( + t, outer_size, outer_stride, symbolic_context, callback, source + ): + # We are hitting plain meta_desc tensor so actually + # create a tensor here. + if t.attrs is None: + return self.meta_tensor( + t, + shape_env=shape_env, + callback=callback, + source=source, + symbolic_context=symbolic_context, + ) + + inner_tensors = {} + for attr, meta_tensor_desc in t.attrs.items(): + current_context = None + if symbolic_context is not None: + current_context = symbolic_context.inner_contexts[attr] + + current_source = AttrSource(source, attr) + new_empty_tensor = _empty_create_subclass( + meta_tensor_desc, + meta_tensor_desc.size, + meta_tensor_desc.stride, + current_context, + callback, + current_source, + ) + inner_tensors[attr] = new_empty_tensor + + return t.type.__tensor_unflatten__( + inner_tensors, t.ctx, outer_size, outer_stride + ) + + sub = _empty_create_subclass( + t, outer_size, outer_stride, symbolic_context, callback, source + ) + + # NB: Purposefully guard here to simplify the inner / outer symbols. + # Using sym_eq() for symbolic comparison can result in an expression that's too + # difficult to guard on, so we use == here. + assert sub.shape == outer_size, ( + f"Expected return value from {t.type}__tensor_unflatten__() to have " + f"shape equal to {outer_size}, but got: {sub.shape}" + ) + assert sub.stride() == outer_stride, ( + f"Expected return value from {t.type}__tensor_unflatten__() to have " + f"stride equal to {outer_stride}, but got: {sub.stride()}" + ) + + return sub + + # Returns an all-dynamic symbolic context used for metafying the given tensor with + # fully dynamic dims. This is useful when fake-ifying intermediate tensors in + # closed-over ViewFunc state, as we don't have symbolic contexts for them, but we + # don't want to over-specialize during view replay. + def all_dynamic_symbolic_context( + t: MetaTensorDesc, source, shape_env, callback + ): + from torch._dynamo.source import AttrSource + from torch.fx.experimental.symbolic_shapes import ( + DimDynamic, + StatelessSymbolicContext, + SubclassSymbolicContext, + ) + + view_base_context: Optional[SymbolicContext] = None + if t.is_view: + assert t.base is not None + view_base_context = all_dynamic_symbolic_context( + t.base, AttrSource(source, "_base"), shape_env, callback + ) + + t_symbolic_context: SymbolicContext + t_dynamic_sizes = [DimDynamic.DYNAMIC] * t.ndim + if t.is_traceable_wrapper_subclass: + assert t.attrs is not None + inner_contexts: Dict[str, SymbolicContext] = {} + for attr, inner in t.attrs.items(): + assert isinstance(attr, str) + inner_contexts[attr] = all_dynamic_symbolic_context( + inner, AttrSource(source, attr), shape_env, callback + ) + t_symbolic_context = SubclassSymbolicContext( + dynamic_sizes=t_dynamic_sizes, + constraint_sizes=[None] * t.ndim, + inner_contexts=inner_contexts, # type: ignore[arg-type] + tensor_source=source, + view_base_context=view_base_context, + ) + else: + t_symbolic_context = StatelessSymbolicContext( + dynamic_sizes=t_dynamic_sizes, + constraint_sizes=[None] * t.ndim, + view_base_context=view_base_context, + ) + + return t_symbolic_context + + # Returns a fake-ified version of an input view tensor t, given an already fake-ified + # base. At a high level, we want two things: + # 1. fake_t should have the same view relationship to the given fake base as the + # input t has to its _base. + # 2. fake_t should have symbolic sizes / strides / storage offset according to the + # appropriate symbolic context (i.e. from the automatic dynamic algorithm). + # + # We currently take different strategies across view types: + # * For dense -> dense views, accomplish both (1) and (2) simultaneously via an + # as_strided() call on the fake-ified base, passing symbolic metadata. + # * For views involving subclasses, perform view replay using view funcs to + # achieve (1). It's necessary for (2) to swap out any closed-over state in + # the view funcs with symbolicized SymInts and fake-ified tensors. Doing this + # avoids specialization (and thus over-eager simplification of symbols) that + # could occur during view replay on the fake-ified base. + # + # Examples: + # * t.unsqueeze(-1) with dense t is a dense -> dense view. It can be modeled + # with an as_strided() call on the fake base passing symbolic metadata. + # * sub.select(dim=0, index=3) is a subclass -> subclass view. The index arg + # is made symbolic to avoid invalid specialization and view replay is then + # done to reconstruct the view. + # * _nested_from_jagged(values, offsets) is a dense -> subclass view + # that returns a subclass instance from a dense values tensor. The offsets + # tensor is closed over in the view func, as it can be considered view metadata. + # First, the offsets tensor is fake-ified according to the inner symbolic + # context and with the correct relationship to the outer size / stride metadata. + # Then view replay is done, swapping in the fake offsets so the view replay output + # is fully fake with no invalid specialization. + def view_from_base( + base: torch.Tensor, t: MetaTensorDesc, source=source, shape_env=shape_env + ): + # fake-ify t's metadata according to the outer symbolic context + (sizes, strides, storage_offset) = sym_sizes_strides_storage_offset( + t, source + ) + if ( + not t.is_traceable_wrapper_subclass + and not is_traceable_wrapper_subclass(base) + ): + # Dense -> Dense view case uses as_strided() to construct view relationship. + # TODO: Change this logic to use view replay for consistency? + # It's likely there is no view func available. + with maybe_suppress(): + return base.as_strided(sizes, strides, storage_offset) + + from torch._dynamo.source import EphemeralSource + from torch.fx.experimental.symbolic_shapes import ( + StatelessSymbolicContext, + sym_eq, + ) + + def symint_visitor_fn(s): + nonlocal symbolic_context + from torch.fx.experimental.symbolic_shapes import DimDynamic + + all_static_sizes = ( + symbolic_context is not None + and isinstance(symbolic_context, StatelessSymbolicContext) + and all( + x is DimDynamic.STATIC for x in symbolic_context.dynamic_sizes + ) + ) + # Can't just rely on shape env being None - dynamo always initializes it + if all_static_sizes or shape_env is None: + return s + + # NB: The symbol here is expected to be simplified out because we a priori + # allocate inner and outer symbols according to the appropriate symbolic + # contexts and prefer those over this symbol during symbol simplification + # (via usage of EphemeralSource below). This -shouldn't- happen, but if + # this symbol somehow leaks out beyond the view tensor's shape metadata, our + # assumption of it being simplified out will fail and it may be guarded on, + # which will hard error. + sym_source = EphemeralSource("symint_visitor_fn") + + symbol = shape_env.create_symbol(s, sym_source, positive=None) + return shape_env.create_symintnode(symbol, hint=s, source=sym_source) + + real_to_fake_mapping = {} + if t.is_traceable_wrapper_subclass: + assert t.attrs is not None + # NB: t.ctx could be None if the subclass in question has no + # meaningful context + assert t.type is not None + + # Fake-ify t naively here; this is only done so we can get fake-ified inner + # tensors with the correct relationships to the outer sizes / strides for use + # in view replay. It's done beforehand here because it's not easy to do when + # visiting tensors one-by-one during view replay. + # + # Example: + # Consider a Dense -> NJT view. NJT has (values, offsets) components and we + # want a view of values with the offsets closed over. As the offsets component + # is needed to describe the output view, it's important that it's fakeified + # correctly. + fake_t = empty_create_subclass( + t, outer_size=sizes, outer_stride=strides + ) + attrs, _ = fake_t.__tensor_flatten__() + for attr in attrs: + real_to_fake_mapping[t.attrs[attr].id] = getattr(fake_t, attr) + + def tensor_visitor_fn( + visited_t: torch.Tensor, + # These arguments are never passed, we just use them to close + # over these relevant values + shape_env=shape_env, + callback=callback, + ): + # It's possible to close over an undefined tensor (e.g. NJT's lengths). + if visited_t is None: + return None + + # NB: visited_t being a Tensor here is very naughty! Should + # have already been described + + # Fake inner tensors of view subclasses will come from the mapping built above. + visited_id = self.describer.get_tensor_id(visited_t) + fake_visited_t = real_to_fake_mapping.get(visited_id, None) + if fake_visited_t is not None: + return fake_visited_t + + visited_desc = self.describer.describe_tensor(visited_t) + + # For other closed-over tensor state, fake-ify it as all dynamic with an + # ephemeral source. This avoids invalid specialization during view replay. + # If we find that in practice the usage of ephemeral sources isn't enough + # to guarantee that we don't have guards on these symbols, we may need to + # explicitly suppress guards (as is done for _base in the dense -> dense + # view case). + temp_source = EphemeralSource("tensor_visitor_fn") + return self.meta_tensor( + visited_desc, + shape_env, + callback, + source=temp_source, + symbolic_context=all_dynamic_symbolic_context( + visited_desc, temp_source, shape_env, callback + ), + ) + + # Replay the view, swapping out any non-symbolic SymInts or real tensors + # for symbolic SymInts or fake tensors. + assert t.view_func is not None + # NB: we do NOT suppress guards here, we need to remove ephemeral + # sources + fake_t = t.view_func(base, symint_visitor_fn, tensor_visitor_fn) + + # Ensure the output has symbolic shapes according to the outer symbolic context. + # These checks should simplify out any symbols created for closed-over view func + # SymInts. + torch._check(sym_eq(fake_t.size(), sizes)) + torch._check(sym_eq(fake_t.stride(), strides)) + torch._check(sym_eq(fake_t.storage_offset(), storage_offset)) + return fake_t + + if self.get_tensor_memo(t) is None: + GRAD_TENSOR_SENTINEL_VALUE = -2 + + with torch.inference_mode(t.is_inference): + if t.is_sparse: + is_leaf = t.is_leaf + + # The lambda function below is similar to + # `t.to(device='meta')` except the latter + # preserves nnz value + r = callback( + lambda: torch.ops.aten._sparse_coo_tensor_with_dims( + t.sparse_dim, + t.dense_dim, + t.size, + dtype=t.dtype, + layout=torch.sparse_coo, + device="meta", + ) + ) + if self.copy_data: + # Pray that sparse clone doesn't lose information + assert t.data is not None + with torch.no_grad(), no_dispatch(): + r.real_tensor = _safe_clone(t.data) + assert safe_is_leaf(r), "the callback you passed in doesn't detach" + # Note [is_coalesced is dispatched] + # Strangely enough, is_coalesced() is a dispatched operator, + # which means that it will get caught by fake tensor mode. + # Ordinarily this would error, but there's some logic in + # fake tensor ensure this doesn't happen. + r._coalesced_(t.is_coalesced) + if t.requires_grad: + r.requires_grad = True + if t.requires_grad and not is_leaf: + # This should probably use DelayedError, + # but clone is fine for now for sparse tensors. + # (DelayedError does not work for sparse because it causes + # the Fake sparse tensor to "lose" its fakeness) + r = r.clone() + with torch.enable_grad(): + r._coalesced_(t.is_coalesced) + elif is_sparse_compressed_layout(t.layout): + is_leaf = t.is_leaf + + if t.layout in {torch.sparse_bsr, torch.sparse_bsc}: + assert t.sparse_dim is not None + assert t.dense_dim is not None + assert t.values is not None + batch_dim = t.ndim - t.sparse_dim - t.dense_dim + blocksize = t.values.shape[batch_dim + 1 : batch_dim + 3] + else: + blocksize = () + if t.layout in {torch.sparse_csr, torch.sparse_bsr}: + assert t.crow_indices is not None + index_dtype = t.crow_indices.dtype + else: + assert t.ccol_indices is not None + index_dtype = t.ccol_indices.dtype + + r = callback( + lambda: torch.ops.aten._sparse_compressed_tensor_with_dims( + 0, + t.dense_dim, + t.shape, + blocksize, + index_dtype, + layout=t.layout, + dtype=t.dtype, + device="meta", + ) + ) + if self.copy_data: + # Pray sparse clone doesn't lose information + assert t.data is not None + with torch.no_grad(), no_dispatch(): + r.real_tensor = _safe_clone(t.data) + assert safe_is_leaf(r), "the callback you passed in doesn't detach" + if t.requires_grad: + r.requires_grad = True + if t.requires_grad and not is_leaf: + r = torch._C._functions.DelayedError( + "Internal error: Tried to backward() through example input", + 1, + )(r) + elif t.is_nested and not t.is_traceable_wrapper_subclass: + # TODO: Handle this better in Dynamo? + # There are checks there now, but this can still be triggered by a dense + # tensor graph input that is a view of a strided NT. + from torch._dynamo.exc import unimplemented + + unimplemented( + "strided nested tensors are not supported by meta conversion" + ) + elif t.is_mkldnn: + is_leaf = t.is_leaf + sizes, strides, _storage_offset = sym_sizes_strides_storage_offset( + t, source + ) + # TODO: This doesn't seem right, where's the MKLDNN'ness + # lol + r = callback( + lambda: torch.empty_strided( + sizes, strides, dtype=t.dtype, device="meta" + ) + ) + if self.copy_data: + with torch.no_grad(), no_dispatch(): + assert t.size is not None + assert t.stride is not None + r.real_tensor = torch.empty_strided( + t.size, t.stride, dtype=t.dtype, device=t.device + ) + assert t.data is not None + _safe_copy(r.real_tensor, t.data) + assert safe_is_leaf(r), "the callback you passed in doesn't detach" + if t.requires_grad: + r.requires_grad = True + if t.requires_grad and not is_leaf: + r = torch._C._functions.DelayedError( + "Internal error: Tried to backward() through example input", + 1, + )(r) + elif t.is_functorch_wrapped: + if t.is_view: + from torch._dynamo.exc import unimplemented + + unimplemented( + "view functorch tensors are not supported by meta conversion" + ) + + # Wraps a functorch tensor class (BatchedTensor, GradTrackingTensor) + # in a FakeTensor + def _to_fake_tensor(t: MetaTensorDesc): + # TODO: why aren't the recursive calls going to + # meta_tensor + if t.is_batchedtensor: + assert t.unwrapped is not None + assert t.level is not None + assert t.bdim is not None + ft = _to_fake_tensor(t.unwrapped) + lvl = t.level + bdim = t.bdim + # You cannot create functorch tensors without + # having the ambient funtorch interpreter stack + # available, as the level refers to things in the + # stack + with torch._functorch.pyfunctorch.temporarily_restore_interpreter_stack( + t.functorch_stack + ): + r = _add_batch_dim(ft, bdim, lvl) + elif t.is_gradtrackingtensor: + assert t.unwrapped is not None + assert t.level is not None + disable_functorch = torch._C._DisableFuncTorch + with disable_functorch(): + ft = _to_fake_tensor(t.unwrapped) + lvl = t.level + if lvl == GRAD_TENSOR_SENTINEL_VALUE: + r = ft + else: + with torch._functorch.pyfunctorch.temporarily_restore_interpreter_stack( + t.functorch_stack + ): + r = torch._C._functorch._wrap_for_grad(ft, lvl) + + is_leaf = t.is_leaf + if t.requires_grad and safe_is_leaf(r): + r.requires_grad = True + elif t.requires_grad and not is_leaf: + r = torch._C._functions.DelayedError( # type: ignore[assignment] + "Internal error: Tried to backward() through example input", + 1, + )( + r # type: ignore[arg-type] + ) + elif t.is_functional: + assert t.unwrapped is not None + assert t.current_level is not None + ft = self.meta_tensor( + t.unwrapped, + shape_env=shape_env, + callback=callback, + # NB: reuse these exactly, we treat the + # functional tensor as "invisible". + # TODO: Actually this all probably doesn't + # work, take a closer look. + source=source, + symbolic_context=symbolic_context, + ) + r = _wrap_functional_tensor(ft, t.current_level) + # TODO: is_leaf/requires_grad? + else: + assert t.stride is not None + + sizes = t.size + strides = t.stride + r = callback( + lambda: torch.empty_strided( + sizes, + strides, + dtype=t.dtype, + device="meta", + ) + ) + if self.copy_data: + with torch.no_grad(), no_dispatch(): + r.real_tensor = torch.empty_strided( # type: ignore[attr-defined] + t.size, + t.stride, + dtype=t.dtype, + device=t.device, + ) + assert t.data is not None + _safe_copy(r.real_tensor, t.data) # type: ignore[attr-defined] + return r + + r = _to_fake_tensor(t) + + elif t.is_functional and t.device.type not in ["xla", "lazy"]: + assert t.unwrapped is not None + assert not t.is_functorch_wrapped # handled above + unwrapped = self.meta_tensor( + t.unwrapped, + shape_env=shape_env, + callback=callback, + source=source, + symbolic_context=symbolic_context, + ) + r = torch._to_functional_tensor(unwrapped) + torch._mirror_autograd_meta_to(t.autograd_meta_from, r) # type: ignore[attr-defined] + + elif t.is_view: + # Construct views in two steps: recursively meta-fy their + # base, and then create view(s) off that. NB: doing it + # directly from storage is WRONG because this won't cause + # version counters to get shared. + + assert t.base is not None + + base_symbolic_context = None + if shape_env and symbolic_context is not None: + from torch.fx.experimental.symbolic_shapes import ( + StatelessSymbolicContext, + ) + + assert isinstance(symbolic_context, StatelessSymbolicContext) + # NB: This should generally be set when the input is a view, + # but the exception right now is for fake-ifying grads, which is + # a work in progress. + if symbolic_context.view_base_context is not None: + base_symbolic_context = symbolic_context.view_base_context + + base = self.meta_tensor( + t.base, + shape_env, + callback, + source=torch._dynamo.source.AttrSource(source, "_base"), + symbolic_context=base_symbolic_context, + ) + + def is_c_of_r(complex_dtype, real_dtype): + return ( + utils.is_complex_dtype(complex_dtype) + and utils.corresponding_real_dtype(complex_dtype) + == real_dtype + ) + + # In some situations, MetaConverter may be called in a + # context where autograd is disabled. For the _is_view + # assert to pass, we have to setup the autograd view + # metadata anyway. Do this by reenabling the + # ADInplaceOrView key. This is kind of a hack. + old_exclude = torch._C._dispatch_tls_is_dispatch_key_excluded( + torch._C.DispatchKey.ADInplaceOrView + ) + torch._C._dispatch_tls_set_dispatch_key_excluded( + torch._C.DispatchKey.ADInplaceOrView, False + ) + try: + if base.dtype == t.dtype: + pass + elif is_c_of_r(base.dtype, t.dtype): + base = torch.view_as_real(base) + elif is_c_of_r(t.dtype, base.dtype): + base = torch.view_as_complex(base) + else: + # This is not guaranteed to succeed. If it fails, it + # means there is another dtype-converting view function + # that hasn't been handled here + base = base.view(t.dtype) + + # This is very tricky. Naively, you might expect this + # to hold: + # + # if t.requires_grad and not safe_is_leaf(t) + # assert t._base.requires_grad + # + # But it's not true! As you can see in the following + # program: + # + # x = torch.zeros(4) + # y = x.view(1, 4) + # y.requires_grad = True + # z = y.view(1, 1, 4) + # assert z._base is x + # + # So we may have to do *two* views out of the base to + # recreate this situation. + if t.is_leaf: + # Leaf views that track view metadata are created by + # creating a view inside a no_grad block + with torch.no_grad(): + r = view_from_base(base, t) + # As it's a leaf, we can directly assign requires_grad + r.requires_grad = t.requires_grad + else: + if t.base.requires_grad == t.requires_grad: + # Easy case, just run the view op + with torch.enable_grad(): + r = view_from_base(base, t) + + # NB: We don't actaully faithfully replicate + # autograd connectivity, but that doesn't matter + # today. See following for more info: + # https://gist.github.com/soulitzer/e03f015b314c3f5fcf80888c69390913 + else: + # Obscure case. Create a leaf view and give it the + # correct requires_grad, then do the final view. + # NB: Can't have a non-leaf without requiring grad! + assert t.requires_grad + with torch.no_grad(): + mid = base.view(base.shape) + mid.requires_grad = t.requires_grad + with torch.enable_grad(): + r = view_from_base(mid, t) + # The CreationMeta influences whether or not inplace + # mutation is an error or not. So we need to make + # sure we properly propagate this as well. + assert t.creation_meta is not None + torch._C._autograd._set_creation_meta(r, t.creation_meta) + finally: + torch._C._dispatch_tls_set_dispatch_key_excluded( + torch._C.DispatchKey.ADInplaceOrView, old_exclude + ) + + else: + is_leaf = t.is_leaf + + # Graph-Break for wrapped tensors + if ( + not (t.is_batchedtensor or t.is_gradtrackingtensor) + and t.is_functorch_wrapped + ) or t.is_legacy_batchedtensor: + return NotImplemented + + ( + sizes, + strides, + storage_offset, + ) = sym_sizes_strides_storage_offset(t, source, symbolic_context) + + # If we have a subclass that desugars into dense tensors, + # perform our callback on each inner tensor. + if t.is_traceable_wrapper_subclass: + r = empty_create_subclass( + t, outer_size=sizes, outer_stride=strides + ) + else: + r = callback( + lambda: torch.empty_strided( + sizes, + strides, + dtype=t.dtype, + device="meta", + ) + ) + if self.copy_data: + with torch.no_grad(), no_dispatch(): + assert t.size is not None + assert t.stride is not None + r.real_tensor = torch.empty_strided( + t.size, t.stride, dtype=t.dtype, device=t.device + ) + _safe_copy(r.real_tensor, t.data) + + assert safe_is_leaf(r), "the callback you passed in doesn't detach" + if t.requires_grad: + r.requires_grad = t.requires_grad + if not is_leaf: + # Fake up some autograd history. + # Note: we *used* to call .clone() here to mock up some autograd history. + # This is bad for subclasses. + # Consider the case where you have a wrapper subclass that is contiguous, + # but its inner tensor is noncontiguous(). + # .clone() (or other ops) will have the side effect of changing + # the metadata of the inner tensor. + # So instead, we now have a dedicated fn to set autograd history, + # without inadvertently changing other metadata. + r = torch._C._functions.DelayedError( + "Internal error: Tried to backward() through example input", + 1, + )(r) + + s = t.storage + assert s is not None + if s.id not in self.storage_memo and ( + r.is_nested + or ( + r.stride() == strides + and r.storage_offset() == storage_offset + ) + ): + # You're normal and happy, install the fresh storage into the memo + self.set_storage_memo(s, r.untyped_storage()) + if self.copy_data: + r.untyped_storage().real_storage = ( + r.real_tensor.untyped_storage() + ) + else: + # You're in crazy town; somehow you gave us a tensor + # that wasn't a view, but had nonzero storage offset, + # nontrivial strides (such that clone() couldn't + # preserve them), or already aliases with another + # tensor's storage. The most typical way to end + # up here is with set_. So use set_ to bludgeon this + # in. + r_s = self.meta_storage(s, callback=callback) + # NB: In principle, this should always work, but there + # is some subtle difference in the autograd metadata + # that means we will backprop the set_ call, even if + # r is declared as an input to grad. + # See https://github.com/pytorch/pytorch/issues/87956 + # for the reproducer. + # NB: The in_kernel_invocation_manager here is necessary + # for fake tensor. If we run the set_ call with fake + # tensor on, r will improperly report that it is NOT a + # meta tensor but a cpu tensor, and then the set_ call + # will fail due to device mismatch. no_dispatch() is + # not enough, because the fake tensor will still claim + # to be a CPU tensor and you'll end up in the CPU + # kernel. Arguably this is a hack; a cleaner way to + # solve this is to have a FakeStorage concept which + # would report it's CPU device--no problem now! But + # this is difficult to do because we don't have storage + # subclasses. Relevant test is + # DynamicShapesFunctionTests::test_add_dynamic_shapes in + # test/dynamo/test_dynamic_shapes.py + maybe_fake_mgr: ContextManager[None] = contextlib.nullcontext() + from torch._subclasses.fake_tensor import ( + in_kernel_invocation_manager, + maybe_get_fake_mode, + ) + + mb_fake_mode = maybe_get_fake_mode(r) + if mb_fake_mode is not None: + maybe_fake_mgr = in_kernel_invocation_manager(mb_fake_mode) + with torch.no_grad(), maybe_suppress(): + with maybe_fake_mgr: + r.set_(r_s, storage_offset, sizes, strides) + if self.copy_data: + with torch.no_grad(), no_dispatch(): + r.real_tensor.set_( + r_s.real_storage, + t.storage_offset, + t.size, + t.stride, + ) + + if t.grad is not None: + from torch._dynamo.source import AttrSource + + # TODO: Use a valid grad-specific symbolic context instead of recycling + # the one from t. This isn't correct if e.g. t._is_view() != t.grad._is_view(). + r.grad = self.meta_tensor( + t.grad, + shape_env, + callback, + source=AttrSource(source, "grad"), + symbolic_context=symbolic_context, + ) + torch._C._set_conj(r, t.is_conj) + torch._C._set_neg(r, t.is_neg) + # This can be skipped if necessary for performance reasons + skip_leaf = ( + t.is_gradtrackingtensor and t.level == GRAD_TENSOR_SENTINEL_VALUE + ) + assert_metadata_eq(assert_eq, t, r, skip_symbolic=True, skip_leaf=skip_leaf) + # Thanks to storage resizing, it's possible to end up with a tensor + # that advertises a real size, but has a storage that actually has zero bytes. + # Need to reflect this in the generated FakeTensor. + if t.storage is not None and t.storage.size == 0: + r.untyped_storage().resize_(0) + + if t.is_parameter: + r._is_param = True + + # See Note: [Creating symbolic nested int] + if t.nested_int is not None: + r.nested_int_memo = r.fake_mode.create_symbolic_nested_int( + nt_tensor_id=t.nested_int + ) + + self.set_tensor_memo(t, r) + + return self.get_tensor_memo(t) + + def __call__( + self, + t, + shape_env=None, + *, + callback=lambda t: t(), + source=None, + symbolic_context=None, + # Controls whether or not we should dump the tensor metadata to structured logs + # when source is not None. Because we refakify after Dynamo is done, + # we don't want to dump info again from AOTAutograd, it is redundant. + trace=True, + ): + # TODO: zero tensors? We appear to have eliminated them by + # excluding complex for now + + # Filter out cases we don't support + # TODO: This can probably be simplified quite a bit + if isinstance(t, torch.Tensor): + if ( + # Lazy tensors are not supported. Note that XLA is + # implemented on top of lazy tensor, not excluded here; we + # have some special handling for it; this is for XLA Dynamo + # integration + t.device.type == "lazy" + or + # Quantization is not supported + t.is_quantized + or + # Views out of sparse tensors not currently supported (plain + # sparse is supported htough) + (t._is_view() and t._base is not None and t._base.is_sparse) + ): + self.miss += 1 + return NotImplemented + else: + self.hit += 1 + elif torch.overrides.is_tensor_like(t): + self.miss += 1 + return NotImplemented + else: + # non-Tensor types don't count as hit or miss + return t + + if source is None: + trace = False + + # Describe the tensor. NB: do NOT disable ambient modes, we may need + # to query them when figuring out what to put in here + t_desc = self.describer.describe_tensor(t, trace=trace) + + if trace: + trace_structured( + "describe_source", + metadata_fn=lambda: { + "describer_id": self.describer.id, + "id": t_desc.id, + "source": source.name(), + }, + ) + + # Do the meta-fication. Here, we disable all the ambient modes, to + # better simulate what would be like to re-fakeify from a fresh + # process + with contextlib.ExitStack() as exit_stack: + exit_stack.enter_context(torch._dispatch.python.suspend_functionalization()) + st = peek_interpreter_stack() + if st is not None: + exit_stack.enter_context( + torch._functorch.pyfunctorch.temporarily_clear_interpreter_stack() + ) + + r = self.meta_tensor( + t_desc, + shape_env=shape_env, + callback=callback, + source=source, + symbolic_context=symbolic_context, + ) + + if type(t) is torch.nn.Parameter: + # NB: Cannot directly use Parameter constructor + # because that would force a detach, not desirable + r._is_param = True + + # TODO: return the description for later + return r + + +import torch._prims_common as utils diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/schema_check_mode.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/schema_check_mode.py new file mode 100644 index 0000000000000000000000000000000000000000..d7ad9ebd28170c1d458b8a20223b2d3f3b409245 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_subclasses/schema_check_mode.py @@ -0,0 +1,230 @@ +# mypy: ignore-errors + +from collections import namedtuple +from copy import deepcopy +from itertools import combinations + +import torch +from torch.fx.operator_schemas import normalize_function +from torch.utils import _pytree as pytree +from torch.utils._python_dispatch import TorchDispatchMode +from torch.utils._pytree import tree_map + + +# Named Tuples used within SchemaCheckMode +Mutation = namedtuple("Mutation", ["op_name", "arg_name"]) +Aliasing = namedtuple("Aliasing", ["op_name", "arg_name", "output_number"]) + +# Simplified naming for C++ classes +SchemaArgument = torch._C._SchemaArgument +SchemaArgType = torch._C._SchemaArgType +SchemaInfo = torch._C._SchemaInfo + +# This TorchDispatchMode Subclass is used to verify op schemas +# This TorchDispatchMode Scubclass currently: +# - Records the called ops +# - Checks for mutations on all inputs +# - Checks for aliasing on all inputs + + +# move these 2 functions here to avoid numpy dependency in testing/_internal/common_utils.py + + +def is_iterable_of_tensors(iterable): + # Tensor itself is iterable so we check this first + if isinstance(iterable, torch.Tensor): + return False + try: + if len(iterable) == 0: + return False + for t in iter(iterable): + if not isinstance(t, torch.Tensor): + return False + except TypeError as te: + return False + return True + + +def clone_inputs(args): + inputs = [] + + for arg in args: + if isinstance(arg, torch.Tensor): + inputs.append(arg.detach().clone()) + elif is_iterable_of_tensors(arg): + inputs.append([t.detach().clone() for t in arg]) + else: + inputs.append(arg) + + return inputs + + +class SchemaCheckMode(TorchDispatchMode): + def __init__(self) -> None: + # Information recorded for testing purposes. For example: + # - incorrect schemas + # - overly conservative schemas + self.ops = [] + self.mutated = [] + self.aliasing = [] + + def reset_cache(self): + self.ops.clear() + self.mutated.clear() + self.aliasing.clear() + + def display_ops(self): + print(*self.ops, sep=",") + + def __torch_dispatch__(self, func, types, args=(), kwargs=None): + def bitwise_equal(lhs, rhs): + if lhs.is_quantized: + # TODO: This is only OK if can't have NaN quantized; idk if + # this is actually true + return torch.equal(lhs, rhs) + else: + return torch.allclose(lhs, rhs, equal_nan=True) + + def has_mutated(before, after, md): + are_tensors = type(before) == torch.Tensor and type(after) == torch.Tensor + if ( + are_tensors + and before.layout != torch.sparse_csr + and after.layout != torch.sparse_csr + ): + return not ( + before.size() == after.size() + and bitwise_equal(before, after) + and md[0] == after.stride() + and md[1] == after._typed_storage()._cdata + ) + return False + + def has_aliased(lhs, rhs): + try: + return torch._C._overlaps(lhs, rhs) + except Exception as exception: + if str(exception).startswith("Cannot inspect value of type "): + return False + else: + raise exception + + def standardize_name(name): + return name if name != "self" else "input" + + def unwrap(e): + if isinstance(e, torch.Tensor) and not type(e) == torch.Tensor: + try: + return e.elem + except AttributeError as t: + return e + return e + + def parse_metadata(e): + if isinstance(e, torch.Tensor): + if not type(e) == torch.Tensor: + try: + current = e.elem + return ( + deepcopy(current.stride()), + current._typed_storage()._cdata, + ) + except AttributeError as t: + return None + # Sparse CSR tensors do not have strides or storage + elif e.layout != torch.sparse_csr: + return (deepcopy(e.stride()), e._typed_storage()._cdata) + return None + + self.ops.append(func._schema.name) + + # Clone and process arguments and outputs + pre_arguments = normalize_function( + func, args, kwargs, normalize_to_only_use_kwargs=True + ).kwargs + + c_p_args = dict(zip(pre_arguments.keys(), clone_inputs(pre_arguments.values()))) + cloned_arguments = { + name: tree_map(unwrap, c_p_args.get(name)) for name in c_p_args + } + cloned_metadata = { + name: [ + parse_metadata(a) for a in pytree.tree_leaves(pre_arguments.get(name)) + ] + for name in pre_arguments + } + + out = func(*args, **kwargs) + arguments = { + name: tree_map(unwrap, pre_arguments.get(name)) for name in pre_arguments + } + tuple_out = out if isinstance(out, tuple) else (out,) + tuple_out = tree_map(unwrap, tuple_out) + + schema_info = SchemaInfo(func._schema) + schema_info.add_argument_values(pre_arguments) + + # Process arguments with outputs + for i in range(len(func._schema.arguments)): + arg = func._schema.arguments[i] + name = standardize_name(arg.name) + if arguments.get(name) is not None: + before = cloned_arguments.get(name) + md = cloned_metadata.get(name) + after = arguments.get(name) + for j in range(len(tuple_out)): + # aten::_unsafe_view is intended to have incorrect aliasing notation (hence unsafe) + unsafe_ops = ("aten::_unsafe_view", "aten::unsafe_split") + if ( + has_aliased(tuple_out[j], after) + and func._schema.name not in unsafe_ops + ): + if not schema_info.may_contain_alias( + SchemaArgument(SchemaArgType.output, j), + SchemaArgument(SchemaArgType.input, i), + ): + raise RuntimeError( + f"Argument {name} is not defined to alias output but was aliasing" + ) + else: + self.aliasing.append( + Aliasing(func._schema.name, name, f"output_{j}") + ) + if after is tuple_out[j] and isinstance(after, torch.Tensor): + # Only mutable ops e.g. (add_, add.out) are allowed to directly return inputs. + if not schema_info.is_mutable( + SchemaArgument(SchemaArgType.input, i) + ) and func not in [ + torch.ops.aten.lift.default, + torch.ops.aten.lift_fresh.default, + ]: + raise RuntimeError( + f"""\ +Dispatcher operators below autograd are not allowed to directly return inputs. +However, we found that `outputs[{str(j)}] is {name}""" + ) + if any( + has_mutated(a, b, c) + for a, b, c in zip( + pytree.tree_leaves(before), pytree.tree_leaves(after), md + ) + ): + if not schema_info.is_mutable( + SchemaArgument(SchemaArgType.input, i) + ): + raise RuntimeError( + f"Argument {name} is not defined as mutable but was mutated" + ) + else: + self.mutated.append(Mutation(func._schema.name, name)) + + # Aliasing between outputs + for i, j in combinations(range(len(func._schema.returns)), 2): + if has_aliased(tuple_out[i], tuple_out[j]): + if not schema_info.may_contain_alias( + SchemaArgument(SchemaArgType.output, i), + SchemaArgument(SchemaArgType.output, j), + ): + raise RuntimeError(f"Outputs {i} and {j} alias unexpectedly") + + return out diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..060306d6d064ce0b8978b53f1566c7a265d08d71 Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/__pycache__/__init__.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/packaging/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/packaging/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..22809cfd5dc25792d77070c269fc8d111a12eed0 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/packaging/__init__.py @@ -0,0 +1,15 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +__title__ = "packaging" +__summary__ = "Core utilities for Python packages" +__uri__ = "https://github.com/pypa/packaging" + +__version__ = "23.2" + +__author__ = "Donald Stufft and individual contributors" +__email__ = "donald@stufft.io" + +__license__ = "BSD-2-Clause or Apache-2.0" +__copyright__ = "2014 %s" % __author__ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/packaging/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/packaging/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ac51f1a9cdf56da047600e02bf9bdc25b22703c9 Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/packaging/__pycache__/__init__.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/packaging/__pycache__/_structures.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/packaging/__pycache__/_structures.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cc90af1621c516cf017fe8bda94c58f3cd651966 Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/packaging/__pycache__/_structures.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/packaging/__pycache__/version.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/packaging/__pycache__/version.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..64b6f8f1719785f0fa7281f8ef97650012b71c5a Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/packaging/__pycache__/version.cpython-311.pyc differ diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/packaging/_structures.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/packaging/_structures.py new file mode 100644 index 0000000000000000000000000000000000000000..90a6465f9682c886363eea5327dac64bf623a6ff --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/packaging/_structures.py @@ -0,0 +1,61 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + + +class InfinityType: + def __repr__(self) -> str: + return "Infinity" + + def __hash__(self) -> int: + return hash(repr(self)) + + def __lt__(self, other: object) -> bool: + return False + + def __le__(self, other: object) -> bool: + return False + + def __eq__(self, other: object) -> bool: + return isinstance(other, self.__class__) + + def __gt__(self, other: object) -> bool: + return True + + def __ge__(self, other: object) -> bool: + return True + + def __neg__(self: object) -> "NegativeInfinityType": + return NegativeInfinity + + +Infinity = InfinityType() + + +class NegativeInfinityType: + def __repr__(self) -> str: + return "-Infinity" + + def __hash__(self) -> int: + return hash(repr(self)) + + def __lt__(self, other: object) -> bool: + return True + + def __le__(self, other: object) -> bool: + return True + + def __eq__(self, other: object) -> bool: + return isinstance(other, self.__class__) + + def __gt__(self, other: object) -> bool: + return False + + def __ge__(self, other: object) -> bool: + return False + + def __neg__(self: object) -> InfinityType: + return Infinity + + +NegativeInfinity = NegativeInfinityType() diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/packaging/version.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/packaging/version.py new file mode 100644 index 0000000000000000000000000000000000000000..5faab9bd0dcf28847960162b2b4f13a8a556ef20 --- /dev/null +++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/_vendor/packaging/version.py @@ -0,0 +1,563 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. +""" +.. testsetup:: + + from packaging.version import parse, Version +""" + +import itertools +import re +from typing import Any, Callable, NamedTuple, Optional, SupportsInt, Tuple, Union + +from ._structures import Infinity, InfinityType, NegativeInfinity, NegativeInfinityType + +__all__ = ["VERSION_PATTERN", "parse", "Version", "InvalidVersion"] + +LocalType = Tuple[Union[int, str], ...] + +CmpPrePostDevType = Union[InfinityType, NegativeInfinityType, Tuple[str, int]] +CmpLocalType = Union[ + NegativeInfinityType, + Tuple[Union[Tuple[int, str], Tuple[NegativeInfinityType, Union[int, str]]], ...], +] +CmpKey = Tuple[ + int, + Tuple[int, ...], + CmpPrePostDevType, + CmpPrePostDevType, + CmpPrePostDevType, + CmpLocalType, +] +VersionComparisonMethod = Callable[[CmpKey, CmpKey], bool] + + +class _Version(NamedTuple): + epoch: int + release: Tuple[int, ...] + dev: Optional[Tuple[str, int]] + pre: Optional[Tuple[str, int]] + post: Optional[Tuple[str, int]] + local: Optional[LocalType] + + +def parse(version: str) -> "Version": + """Parse the given version string. + + >>> parse('1.0.dev1') + + + :param version: The version string to parse. + :raises InvalidVersion: When the version string is not a valid version. + """ + return Version(version) + + +class InvalidVersion(ValueError): + """Raised when a version string is not a valid version. + + >>> Version("invalid") + Traceback (most recent call last): + ... + packaging.version.InvalidVersion: Invalid version: 'invalid' + """ + + +class _BaseVersion: + _key: Tuple[Any, ...] + + def __hash__(self) -> int: + return hash(self._key) + + # Please keep the duplicated `isinstance` check + # in the six comparisons hereunder + # unless you find a way to avoid adding overhead function calls. + def __lt__(self, other: "_BaseVersion") -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key < other._key + + def __le__(self, other: "_BaseVersion") -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key <= other._key + + def __eq__(self, other: object) -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key == other._key + + def __ge__(self, other: "_BaseVersion") -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key >= other._key + + def __gt__(self, other: "_BaseVersion") -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key > other._key + + def __ne__(self, other: object) -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key != other._key + + +# Deliberately not anchored to the start and end of the string, to make it +# easier for 3rd party code to reuse +_VERSION_PATTERN = r""" + v? + (?: + (?:(?P[0-9]+)!)? # epoch + (?P[0-9]+(?:\.[0-9]+)*) # release segment + (?P
                                          # pre-release
+            [-_\.]?
+            (?Palpha|a|beta|b|preview|pre|c|rc)
+            [-_\.]?
+            (?P[0-9]+)?
+        )?
+        (?P                                         # post release
+            (?:-(?P[0-9]+))
+            |
+            (?:
+                [-_\.]?
+                (?Ppost|rev|r)
+                [-_\.]?
+                (?P[0-9]+)?
+            )
+        )?
+        (?P                                          # dev release
+            [-_\.]?
+            (?Pdev)
+            [-_\.]?
+            (?P[0-9]+)?
+        )?
+    )
+    (?:\+(?P[a-z0-9]+(?:[-_\.][a-z0-9]+)*))?       # local version
+"""
+
+VERSION_PATTERN = _VERSION_PATTERN
+"""
+A string containing the regular expression used to match a valid version.
+
+The pattern is not anchored at either end, and is intended for embedding in larger
+expressions (for example, matching a version number as part of a file name). The
+regular expression should be compiled with the ``re.VERBOSE`` and ``re.IGNORECASE``
+flags set.
+
+:meta hide-value:
+"""
+
+
+class Version(_BaseVersion):
+    """This class abstracts handling of a project's versions.
+
+    A :class:`Version` instance is comparison aware and can be compared and
+    sorted using the standard Python interfaces.
+
+    >>> v1 = Version("1.0a5")
+    >>> v2 = Version("1.0")
+    >>> v1
+    
+    >>> v2
+    
+    >>> v1 < v2
+    True
+    >>> v1 == v2
+    False
+    >>> v1 > v2
+    False
+    >>> v1 >= v2
+    False
+    >>> v1 <= v2
+    True
+    """
+
+    _regex = re.compile(r"^\s*" + VERSION_PATTERN + r"\s*$", re.VERBOSE | re.IGNORECASE)
+    _key: CmpKey
+
+    def __init__(self, version: str) -> None:
+        """Initialize a Version object.
+
+        :param version:
+            The string representation of a version which will be parsed and normalized
+            before use.
+        :raises InvalidVersion:
+            If the ``version`` does not conform to PEP 440 in any way then this
+            exception will be raised.
+        """
+
+        # Validate the version and parse it into pieces
+        match = self._regex.search(version)
+        if not match:
+            raise InvalidVersion(f"Invalid version: '{version}'")
+
+        # Store the parsed out pieces of the version
+        self._version = _Version(
+            epoch=int(match.group("epoch")) if match.group("epoch") else 0,
+            release=tuple(int(i) for i in match.group("release").split(".")),
+            pre=_parse_letter_version(match.group("pre_l"), match.group("pre_n")),
+            post=_parse_letter_version(
+                match.group("post_l"), match.group("post_n1") or match.group("post_n2")
+            ),
+            dev=_parse_letter_version(match.group("dev_l"), match.group("dev_n")),
+            local=_parse_local_version(match.group("local")),
+        )
+
+        # Generate a key which will be used for sorting
+        self._key = _cmpkey(
+            self._version.epoch,
+            self._version.release,
+            self._version.pre,
+            self._version.post,
+            self._version.dev,
+            self._version.local,
+        )
+
+    def __repr__(self) -> str:
+        """A representation of the Version that shows all internal state.
+
+        >>> Version('1.0.0')
+        
+        """
+        return f""
+
+    def __str__(self) -> str:
+        """A string representation of the version that can be rounded-tripped.
+
+        >>> str(Version("1.0a5"))
+        '1.0a5'
+        """
+        parts = []
+
+        # Epoch
+        if self.epoch != 0:
+            parts.append(f"{self.epoch}!")
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self.release))
+
+        # Pre-release
+        if self.pre is not None:
+            parts.append("".join(str(x) for x in self.pre))
+
+        # Post-release
+        if self.post is not None:
+            parts.append(f".post{self.post}")
+
+        # Development release
+        if self.dev is not None:
+            parts.append(f".dev{self.dev}")
+
+        # Local version segment
+        if self.local is not None:
+            parts.append(f"+{self.local}")
+
+        return "".join(parts)
+
+    @property
+    def epoch(self) -> int:
+        """The epoch of the version.
+
+        >>> Version("2.0.0").epoch
+        0
+        >>> Version("1!2.0.0").epoch
+        1
+        """
+        return self._version.epoch
+
+    @property
+    def release(self) -> Tuple[int, ...]:
+        """The components of the "release" segment of the version.
+
+        >>> Version("1.2.3").release
+        (1, 2, 3)
+        >>> Version("2.0.0").release
+        (2, 0, 0)
+        >>> Version("1!2.0.0.post0").release
+        (2, 0, 0)
+
+        Includes trailing zeroes but not the epoch or any pre-release / development /
+        post-release suffixes.
+        """
+        return self._version.release
+
+    @property
+    def pre(self) -> Optional[Tuple[str, int]]:
+        """The pre-release segment of the version.
+
+        >>> print(Version("1.2.3").pre)
+        None
+        >>> Version("1.2.3a1").pre
+        ('a', 1)
+        >>> Version("1.2.3b1").pre
+        ('b', 1)
+        >>> Version("1.2.3rc1").pre
+        ('rc', 1)
+        """
+        return self._version.pre
+
+    @property
+    def post(self) -> Optional[int]:
+        """The post-release number of the version.
+
+        >>> print(Version("1.2.3").post)
+        None
+        >>> Version("1.2.3.post1").post
+        1
+        """
+        return self._version.post[1] if self._version.post else None
+
+    @property
+    def dev(self) -> Optional[int]:
+        """The development number of the version.
+
+        >>> print(Version("1.2.3").dev)
+        None
+        >>> Version("1.2.3.dev1").dev
+        1
+        """
+        return self._version.dev[1] if self._version.dev else None
+
+    @property
+    def local(self) -> Optional[str]:
+        """The local version segment of the version.
+
+        >>> print(Version("1.2.3").local)
+        None
+        >>> Version("1.2.3+abc").local
+        'abc'
+        """
+        if self._version.local:
+            return ".".join(str(x) for x in self._version.local)
+        else:
+            return None
+
+    @property
+    def public(self) -> str:
+        """The public portion of the version.
+
+        >>> Version("1.2.3").public
+        '1.2.3'
+        >>> Version("1.2.3+abc").public
+        '1.2.3'
+        >>> Version("1.2.3+abc.dev1").public
+        '1.2.3'
+        """
+        return str(self).split("+", 1)[0]
+
+    @property
+    def base_version(self) -> str:
+        """The "base version" of the version.
+
+        >>> Version("1.2.3").base_version
+        '1.2.3'
+        >>> Version("1.2.3+abc").base_version
+        '1.2.3'
+        >>> Version("1!1.2.3+abc.dev1").base_version
+        '1!1.2.3'
+
+        The "base version" is the public version of the project without any pre or post
+        release markers.
+        """
+        parts = []
+
+        # Epoch
+        if self.epoch != 0:
+            parts.append(f"{self.epoch}!")
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self.release))
+
+        return "".join(parts)
+
+    @property
+    def is_prerelease(self) -> bool:
+        """Whether this version is a pre-release.
+
+        >>> Version("1.2.3").is_prerelease
+        False
+        >>> Version("1.2.3a1").is_prerelease
+        True
+        >>> Version("1.2.3b1").is_prerelease
+        True
+        >>> Version("1.2.3rc1").is_prerelease
+        True
+        >>> Version("1.2.3dev1").is_prerelease
+        True
+        """
+        return self.dev is not None or self.pre is not None
+
+    @property
+    def is_postrelease(self) -> bool:
+        """Whether this version is a post-release.
+
+        >>> Version("1.2.3").is_postrelease
+        False
+        >>> Version("1.2.3.post1").is_postrelease
+        True
+        """
+        return self.post is not None
+
+    @property
+    def is_devrelease(self) -> bool:
+        """Whether this version is a development release.
+
+        >>> Version("1.2.3").is_devrelease
+        False
+        >>> Version("1.2.3.dev1").is_devrelease
+        True
+        """
+        return self.dev is not None
+
+    @property
+    def major(self) -> int:
+        """The first item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").major
+        1
+        """
+        return self.release[0] if len(self.release) >= 1 else 0
+
+    @property
+    def minor(self) -> int:
+        """The second item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").minor
+        2
+        >>> Version("1").minor
+        0
+        """
+        return self.release[1] if len(self.release) >= 2 else 0
+
+    @property
+    def micro(self) -> int:
+        """The third item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").micro
+        3
+        >>> Version("1").micro
+        0
+        """
+        return self.release[2] if len(self.release) >= 3 else 0
+
+
+def _parse_letter_version(
+    letter: Optional[str], number: Union[str, bytes, SupportsInt, None]
+) -> Optional[Tuple[str, int]]:
+
+    if letter:
+        # We consider there to be an implicit 0 in a pre-release if there is
+        # not a numeral associated with it.
+        if number is None:
+            number = 0
+
+        # We normalize any letters to their lower case form
+        letter = letter.lower()
+
+        # We consider some words to be alternate spellings of other words and
+        # in those cases we want to normalize the spellings to our preferred
+        # spelling.
+        if letter == "alpha":
+            letter = "a"
+        elif letter == "beta":
+            letter = "b"
+        elif letter in ["c", "pre", "preview"]:
+            letter = "rc"
+        elif letter in ["rev", "r"]:
+            letter = "post"
+
+        return letter, int(number)
+    if not letter and number:
+        # We assume if we are given a number, but we are not given a letter
+        # then this is using the implicit post release syntax (e.g. 1.0-1)
+        letter = "post"
+
+        return letter, int(number)
+
+    return None
+
+
+_local_version_separators = re.compile(r"[\._-]")
+
+
+def _parse_local_version(local: Optional[str]) -> Optional[LocalType]:
+    """
+    Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve").
+    """
+    if local is not None:
+        return tuple(
+            part.lower() if not part.isdigit() else int(part)
+            for part in _local_version_separators.split(local)
+        )
+    return None
+
+
+def _cmpkey(
+    epoch: int,
+    release: Tuple[int, ...],
+    pre: Optional[Tuple[str, int]],
+    post: Optional[Tuple[str, int]],
+    dev: Optional[Tuple[str, int]],
+    local: Optional[LocalType],
+) -> CmpKey:
+
+    # When we compare a release version, we want to compare it with all of the
+    # trailing zeros removed. So we'll use a reverse the list, drop all the now
+    # leading zeros until we come to something non zero, then take the rest
+    # re-reverse it back into the correct order and make it a tuple and use
+    # that for our sorting key.
+    _release = tuple(
+        reversed(list(itertools.dropwhile(lambda x: x == 0, reversed(release))))
+    )
+
+    # We need to "trick" the sorting algorithm to put 1.0.dev0 before 1.0a0.
+    # We'll do this by abusing the pre segment, but we _only_ want to do this
+    # if there is not a pre or a post segment. If we have one of those then
+    # the normal sorting rules will handle this case correctly.
+    if pre is None and post is None and dev is not None:
+        _pre: CmpPrePostDevType = NegativeInfinity
+    # Versions without a pre-release (except as noted above) should sort after
+    # those with one.
+    elif pre is None:
+        _pre = Infinity
+    else:
+        _pre = pre
+
+    # Versions without a post segment should sort before those with one.
+    if post is None:
+        _post: CmpPrePostDevType = NegativeInfinity
+
+    else:
+        _post = post
+
+    # Versions without a development segment should sort after those with one.
+    if dev is None:
+        _dev: CmpPrePostDevType = Infinity
+
+    else:
+        _dev = dev
+
+    if local is None:
+        # Versions without a local segment should sort before those with one.
+        _local: CmpLocalType = NegativeInfinity
+    else:
+        # Versions with a local segment need that segment parsed to implement
+        # the sorting rules in PEP440.
+        # - Alpha numeric segments sort before numeric segments
+        # - Alpha numeric segments sort lexicographically
+        # - Numeric segments sort numerically
+        # - Shorter versions sort before longer versions when the prefixes
+        #   match exactly
+        _local = tuple(
+            (i, "") if isinstance(i, int) else (NegativeInfinity, i) for i in local
+        )
+
+    return epoch, _release, _pre, _post, _dev, _local
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/amp/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/amp/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..263908eff68bab7c7d9a123bd77a5f5c048b8c5a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/amp/__init__.py
@@ -0,0 +1,9 @@
+from .autocast_mode import (
+    _enter_autocast,
+    _exit_autocast,
+    autocast,
+    custom_bwd,
+    custom_fwd,
+    is_autocast_available,
+)
+from .grad_scaler import GradScaler
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/amp/autocast_mode.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/amp/autocast_mode.py
new file mode 100644
index 0000000000000000000000000000000000000000..6aba6bbad42efc8ea28447f52c067c1bb9c41186
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/amp/autocast_mode.py
@@ -0,0 +1,513 @@
+# mypy: allow-untyped-defs
+import collections
+import functools
+import warnings
+from typing import Any, Optional
+
+import torch
+from torch.types import _dtype
+
+
+try:
+    import numpy as np
+
+    HAS_NUMPY = True
+except ModuleNotFoundError:
+    HAS_NUMPY = False
+    np = None  # type: ignore[assignment]
+
+__all__ = [
+    "autocast_decorator",
+    "autocast",
+    "is_autocast_available",
+    "custom_fwd",
+    "custom_bwd",
+]
+
+
+def is_autocast_available(device_type: str) -> bool:
+    r"""
+    Return a bool indicating if autocast is available on :attr:`device_type`.
+
+    Args:
+        device_type(str):  Device type to use. Possible values are: 'cuda', 'cpu', 'xpu' and so on.
+            The type is the same as the `type` attribute of a :class:`torch.device`.
+            Thus, you may obtain the device type of a tensor using `Tensor.device.type`.
+    """
+    return torch._C._is_autocast_available(device_type)
+
+
+def autocast_decorator(autocast_instance, func):
+    @functools.wraps(func)
+    def decorate_autocast(*args, **kwargs):
+        with autocast_instance:
+            return func(*args, **kwargs)
+
+    decorate_autocast.__script_unsupported = "@autocast() decorator is not supported in script mode"  # type: ignore[attr-defined]
+    return decorate_autocast
+
+
+class autocast:
+    r"""
+    Instances of :class:`autocast` serve as context managers or decorators that
+    allow regions of your script to run in mixed precision.
+
+    In these regions, ops run in an op-specific dtype chosen by autocast
+    to improve performance while maintaining accuracy.
+    See the :ref:`Autocast Op Reference` for details.
+
+    When entering an autocast-enabled region, Tensors may be any type.
+    You should not call ``half()`` or ``bfloat16()`` on your model(s) or inputs when using autocasting.
+
+    :class:`autocast` should wrap only the forward pass(es) of your network, including the loss
+    computation(s).  Backward passes under autocast are not recommended.
+    Backward ops run in the same type that autocast used for corresponding forward ops.
+
+    Example for CUDA Devices::
+
+        # Creates model and optimizer in default precision
+        model = Net().cuda()
+        optimizer = optim.SGD(model.parameters(), ...)
+
+        for input, target in data:
+            optimizer.zero_grad()
+
+            # Enables autocasting for the forward pass (model + loss)
+            with torch.autocast(device_type="cuda"):
+                output = model(input)
+                loss = loss_fn(output, target)
+
+            # Exits the context manager before backward()
+            loss.backward()
+            optimizer.step()
+
+    See the :ref:`Automatic Mixed Precision examples` for usage (along with gradient scaling)
+    in more complex scenarios (e.g., gradient penalty, multiple models/losses, custom autograd functions).
+
+    :class:`autocast` can also be used as a decorator, e.g., on the ``forward`` method of your model::
+
+        class AutocastModel(nn.Module):
+            ...
+            @torch.autocast(device_type="cuda")
+            def forward(self, input):
+                ...
+
+    Floating-point Tensors produced in an autocast-enabled region may be ``float16``.
+    After returning to an autocast-disabled region, using them with floating-point
+    Tensors of different dtypes may cause type mismatch errors.  If so, cast the Tensor(s)
+    produced in the autocast region back to ``float32`` (or other dtype if desired).
+    If a Tensor from the autocast region is already ``float32``, the cast is a no-op,
+    and incurs no additional overhead.
+    CUDA Example::
+
+        # Creates some tensors in default dtype (here assumed to be float32)
+        a_float32 = torch.rand((8, 8), device="cuda")
+        b_float32 = torch.rand((8, 8), device="cuda")
+        c_float32 = torch.rand((8, 8), device="cuda")
+        d_float32 = torch.rand((8, 8), device="cuda")
+
+        with torch.autocast(device_type="cuda"):
+            # torch.mm is on autocast's list of ops that should run in float16.
+            # Inputs are float32, but the op runs in float16 and produces float16 output.
+            # No manual casts are required.
+            e_float16 = torch.mm(a_float32, b_float32)
+            # Also handles mixed input types
+            f_float16 = torch.mm(d_float32, e_float16)
+
+        # After exiting autocast, calls f_float16.float() to use with d_float32
+        g_float32 = torch.mm(d_float32, f_float16.float())
+
+    CPU Training Example::
+
+        # Creates model and optimizer in default precision
+        model = Net()
+        optimizer = optim.SGD(model.parameters(), ...)
+
+        for epoch in epochs:
+            for input, target in data:
+                optimizer.zero_grad()
+
+                # Runs the forward pass with autocasting.
+                with torch.autocast(device_type="cpu", dtype=torch.bfloat16):
+                    output = model(input)
+                    loss = loss_fn(output, target)
+
+                loss.backward()
+                optimizer.step()
+
+
+    CPU Inference Example::
+
+        # Creates model in default precision
+        model = Net().eval()
+
+        with torch.autocast(device_type="cpu", dtype=torch.bfloat16):
+            for input in data:
+                # Runs the forward pass with autocasting.
+                output = model(input)
+
+    CPU Inference Example with Jit Trace::
+
+        class TestModel(nn.Module):
+            def __init__(self, input_size, num_classes):
+                super().__init__()
+                self.fc1 = nn.Linear(input_size, num_classes)
+            def forward(self, x):
+                return self.fc1(x)
+
+        input_size = 2
+        num_classes = 2
+        model = TestModel(input_size, num_classes).eval()
+
+        # For now, we suggest to disable the Jit Autocast Pass,
+        # As the issue: https://github.com/pytorch/pytorch/issues/75956
+        torch._C._jit_set_autocast_mode(False)
+
+        with torch.cpu.amp.autocast(cache_enabled=False):
+            model = torch.jit.trace(model, torch.randn(1, input_size))
+        model = torch.jit.freeze(model)
+        # Models Run
+        for _ in range(3):
+            model(torch.randn(1, input_size))
+
+    Type mismatch errors *in* an autocast-enabled region are a bug; if this is what you observe,
+    please file an issue.
+
+    ``autocast(enabled=False)`` subregions can be nested in autocast-enabled regions.
+    Locally disabling autocast can be useful, for example, if you want to force a subregion
+    to run in a particular ``dtype``.  Disabling autocast gives you explicit control over
+    the execution type.  In the subregion, inputs from the surrounding region
+    should be cast to ``dtype`` before use::
+
+        # Creates some tensors in default dtype (here assumed to be float32)
+        a_float32 = torch.rand((8, 8), device="cuda")
+        b_float32 = torch.rand((8, 8), device="cuda")
+        c_float32 = torch.rand((8, 8), device="cuda")
+        d_float32 = torch.rand((8, 8), device="cuda")
+
+        with torch.autocast(device_type="cuda"):
+            e_float16 = torch.mm(a_float32, b_float32)
+            with torch.autocast(device_type="cuda", enabled=False):
+                # Calls e_float16.float() to ensure float32 execution
+                # (necessary because e_float16 was created in an autocasted region)
+                f_float32 = torch.mm(c_float32, e_float16.float())
+
+            # No manual casts are required when re-entering the autocast-enabled region.
+            # torch.mm again runs in float16 and produces float16 output, regardless of input types.
+            g_float16 = torch.mm(d_float32, f_float32)
+
+    The autocast state is thread-local.  If you want it enabled in a new thread, the context manager or decorator
+    must be invoked in that thread.  This affects :class:`torch.nn.DataParallel` and
+    :class:`torch.nn.parallel.DistributedDataParallel` when used with more than one GPU per process
+    (see :ref:`Working with Multiple GPUs`).
+
+    Args:
+        device_type(str, required):  Device type to use. Possible values are: 'cuda', 'cpu', 'xpu' and 'hpu'.
+                                     The type is the same as the `type` attribute of a :class:`torch.device`.
+                                     Thus, you may obtain the device type of a tensor using `Tensor.device.type`.
+        enabled(bool, optional):  Whether autocasting should be enabled in the region.
+            Default: ``True``
+        dtype(torch_dtype, optional):  Data type for ops run in autocast. It uses the default value
+            (``torch.float16`` for CUDA and ``torch.bfloat16`` for CPU), given by
+            :func:`~torch.get_autocast_dtype`, if :attr:`dtype` is ``None``.
+            Default: ``None``
+        cache_enabled(bool, optional):  Whether the weight cache inside autocast should be enabled.
+            Default: ``True``
+    """
+
+    def __init__(
+        self,
+        device_type: str,
+        dtype: Optional[_dtype] = None,
+        enabled: bool = True,
+        cache_enabled: Optional[bool] = None,
+    ):
+        if not isinstance(device_type, str):
+            raise ValueError(
+                f"Expected `device_type` of type `str`, got: `{type(device_type)}`"
+            )
+        if dtype is None:
+            dtype = torch.get_autocast_dtype(device_type)
+        if torch._jit_internal.is_scripting():
+            self._enabled = enabled
+            self.device = device_type
+            self.fast_dtype = dtype
+            assert dtype is not None
+            return
+        self.device = device_type
+        if not is_autocast_available(self.device):
+            raise RuntimeError(
+                f"User specified an unsupported autocast device_type '{self.device}'"
+            )
+        self.custom_backend_name = torch._C._get_privateuse1_backend_name()
+        self.fast_dtype = torch.get_autocast_dtype(self.device)
+        if self.device == self.custom_backend_name:
+            necessary_funcs = [
+                "get_amp_supported_dtype",
+            ]
+            message = f"Tried to use AMP with the `{self.custom_backend_name}` backend, but the backend has not "
+            message += "registered a module or  the module miss some necessary funcs. The backend should register "
+            message += "a module by `torch._register_device_module`, and the module must have these funcs: \n"
+            message += "`get_amp_supported_dtype() -> List[torch.dtype]`. \n"
+
+            assert hasattr(torch, self.custom_backend_name), message
+            self.custom_device_mod = getattr(torch, self.custom_backend_name)
+            for func in necessary_funcs:
+                assert hasattr(self.custom_device_mod, func), (
+                    message + f"But the func `{func}` is missing. \n"
+                )
+
+        self._cache_enabled = torch.is_autocast_cache_enabled()
+        if (
+            enabled
+            and torch.cuda.amp.common.amp_definitely_not_available()
+            and self.device == "cuda"
+        ):
+            warnings.warn(
+                "User provided device_type of 'cuda', but CUDA is not available. Disabling"
+            )
+            enabled = False
+        if dtype is not None:
+            self.fast_dtype = dtype
+        if cache_enabled is not None:
+            self._cache_enabled = cache_enabled
+
+        if self.device == "cpu":
+            supported_dtype = [torch.bfloat16, torch.float16]
+            if self.fast_dtype not in supported_dtype and enabled:
+                error_message = "In CPU autocast, but the target dtype is not supported. Disabling autocast.\n"
+                error_message += "CPU Autocast only supports dtype of "
+                error_message += (
+                    ", ".join(str(dtype) for dtype in supported_dtype) + " currently."
+                )
+                warnings.warn(error_message)
+                enabled = False
+        elif self.device == "xpu":
+            supported_dtype = [torch.bfloat16, torch.float16]
+            if self.fast_dtype not in supported_dtype:
+                error_message = "In XPU autocast, but the target dtype is not supported. Disabling autocast.\n"
+                error_message += "XPU Autocast only supports dtypes of torch.bfloat16 and torch.float16 currently."
+                warnings.warn(error_message)
+                enabled = False
+        elif self.device == "ipu":
+            supported_dtypes = [torch.bfloat16, torch.float16]
+            if self.fast_dtype not in supported_dtypes:
+                error_message = "In IPU autocast, but the target dtype is not supported. Disabling autocast.\n"
+                error_message += "IPU Autocast only supports dtypes of torch.bfloat16 and torch.float16 currently."
+                warnings.warn(error_message)
+                enabled = False
+        elif self.device == "hpu":
+            supported_dtype = [torch.bfloat16, torch.float16]
+            if self.fast_dtype not in supported_dtype:
+                error_message = "In HPU autocast, but the target dtype is not supported. Disabling autocast.\n"
+                error_message += "HPU Autocast only supports dtypes of torch.bfloat16 and torch.float16 currently."
+                warnings.warn(error_message)
+                enabled = False
+        elif self.device == self.custom_backend_name:
+            supported_dtype = self.custom_device_mod.get_amp_supported_dtype()
+            if self.fast_dtype not in supported_dtype:
+                error_message = f"In {self.custom_backend_name} autocast, but the target dtype is not supported. "
+                error_message += f"Disabling autocast.\n {self.custom_backend_name} Autocast only supports dtypes of "
+                error_message += (
+                    ", ".join(str(dtype) for dtype in supported_dtype) + " currently."
+                )
+                warnings.warn(error_message)
+                enabled = False
+        elif self.device == "cuda":
+            if (
+                enabled
+                and self.fast_dtype == torch.bfloat16
+                and not torch.cuda.is_bf16_supported()
+            ):
+                raise RuntimeError(
+                    "Current CUDA Device does not support bfloat16. Please switch dtype to float16."
+                )
+        elif self.device == "mps":
+            supported_dtype = [torch.float16]
+            if self.fast_dtype not in supported_dtype:
+                error_message = "In MPS autocast, but the target dtype is not supported. Disabling autocast.\n"
+                error_message += (
+                    "MPS Autocast only supports dtype of torch.bfloat16 currently."
+                )
+                warnings.warn(error_message)
+                enabled = False
+        elif self.device == "xla":
+            supported_dtype = [torch.float16, torch.bfloat16]
+            if self.fast_dtype not in supported_dtype:
+                error_message = "In XLA autocast, but the target dtype is not supported. Disabling autocast.\n"
+                error_message += (
+                    "XLA Autocast only supports dtype of torch.bfloat16 currently."
+                )
+                warnings.warn(error_message)
+                enabled = False
+        self._enabled = enabled
+
+    def __enter__(self):
+        if torch._jit_internal.is_scripting():
+            assert self.fast_dtype is not None
+            return self
+
+        self.prev_cache_enabled = torch.is_autocast_cache_enabled()
+        self.prev = torch.is_autocast_enabled(self.device)
+        self.prev_fastdtype = torch.get_autocast_dtype(self.device)
+        torch.set_autocast_enabled(self.device, self._enabled)
+        torch.set_autocast_dtype(self.device, self.fast_dtype)  # type: ignore[arg-type]
+        torch.autocast_increment_nesting()
+        torch.set_autocast_cache_enabled(self._cache_enabled)
+
+    def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any):  # type: ignore[override]
+        if torch._jit_internal.is_scripting():
+            return
+
+        # Drop the cache when we exit to a nesting level that's outside any instance of autocast.
+        if torch.autocast_decrement_nesting() == 0:
+            torch.clear_autocast_cache()
+        torch.set_autocast_enabled(self.device, self.prev)
+        torch.set_autocast_dtype(self.device, self.prev_fastdtype)
+        torch.set_autocast_cache_enabled(self.prev_cache_enabled)
+        return False
+
+    def __call__(self, func):
+        if torch._jit_internal.is_scripting():
+            return func
+        return autocast_decorator(self, func)
+
+
+# These functions aren't meant for public usage.
+# They are what we trace into a graph during pre_dispatch tracing
+# when we encounter an autocast context manager.
+def _enter_autocast(*vals):
+    # For pre-dispatch tracing, if a TorchFunction mode is active, we'll want to trace this into a graph.
+    if torch._C._is_torch_function_mode_enabled():
+        return torch.overrides.handle_torch_function(
+            torch.amp._enter_autocast, [], *vals
+        )
+    mode = torch.amp.autocast(*vals)
+    mode.__enter__()
+    return mode
+
+
+def _exit_autocast(mode):
+    if torch._C._is_torch_function_mode_enabled():
+        return torch.overrides.handle_torch_function(torch.amp._exit_autocast, [], mode)
+    mode.__exit__(None, None, None)
+
+
+# Casts Tensors and containers of Tensors.  Special-cases passthroughs for strings and np.ndarrays, which
+# may be falsely detected as "Iterables."
+def _cast(value, device_type: str, dtype: _dtype):
+    if isinstance(value, torch.Tensor):
+        is_eligible = (
+            value.is_floating_point()
+            and value.device.type == device_type
+            and (value.dtype is not torch.float64)
+        )
+        return value.to(dtype) if is_eligible else value
+    elif isinstance(value, (str, bytes)):
+        return value
+    elif HAS_NUMPY and isinstance(value, np.ndarray):
+        return value
+    elif isinstance(value, collections.abc.Mapping):
+        return {
+            _cast(k, device_type, dtype): _cast(v, device_type, dtype)
+            for k, v in value.items()
+        }
+    elif isinstance(value, collections.abc.Iterable):
+        iterable = (_cast(v, device_type, dtype) for v in value)
+        if isinstance(value, (list, tuple)):
+            return type(value)(iterable)
+        else:
+            return iterable
+    else:
+        return value
+
+
+def custom_fwd(
+    fwd=None,
+    *,
+    device_type: str,
+    cast_inputs: Optional[_dtype] = None,
+):
+    """
+    Create a helper decorator for ``forward`` methods of custom autograd functions.
+
+    Autograd functions are subclasses of :class:`torch.autograd.Function`.
+    See the :ref:`example page` for more detail.
+
+    Args:
+        device_type(str):  Device type to use. 'cuda', 'cpu', 'xpu' and so on.
+            The type is the same as the `type` attribute of a :class:`torch.device`.
+            Thus, you may obtain the device type of a tensor using `Tensor.device.type`.
+        cast_inputs (:class:`torch.dtype` or None, optional, default=None):  If not ``None``,
+            when ``forward`` runs in an autocast-enabled region, casts incoming
+            floating-point Tensors to the target dtype (non-floating-point Tensors are not affected),
+            then executes ``forward`` with autocast disabled.
+            If ``None``, ``forward``'s internal ops execute with the current autocast state.
+
+    .. note::
+        If the decorated ``forward`` is called outside an autocast-enabled region,
+        :func:`custom_fwd` is a no-op and ``cast_inputs`` has no effect.
+    """
+    if not isinstance(device_type, str):
+        raise ValueError(
+            f"Expected `device_type` of type `str`, got: `{type(device_type)}`"
+        )
+    if fwd is None:
+        return functools.partial(
+            custom_fwd, device_type=device_type, cast_inputs=cast_inputs
+        )
+
+    @functools.wraps(fwd)
+    def decorate_fwd(*args, **kwargs):
+        args[0]._dtype = torch.get_autocast_dtype(device_type)
+        if cast_inputs is None:
+            args[0]._fwd_used_autocast = torch.is_autocast_enabled(device_type)
+            return fwd(*args, **kwargs)
+        else:
+            autocast_context = torch.is_autocast_enabled(device_type)
+            args[0]._fwd_used_autocast = False
+            if autocast_context:
+                with autocast(device_type=device_type, enabled=False):
+                    return fwd(
+                        *_cast(args, device_type, cast_inputs),
+                        **_cast(kwargs, device_type, cast_inputs),
+                    )
+            else:
+                return fwd(*args, **kwargs)
+
+    return decorate_fwd
+
+
+# Autograd ensures incoming gradients are the same type as forward outputs.  Allowing a separate
+# cast_inputs argument on custom_bwd is unnecessary and could cause errors if it doesn't match
+# cast_inputs supplied to custom_fwd.
+def custom_bwd(bwd=None, *, device_type: str):
+    """Create a helper decorator for backward methods of custom autograd functions.
+
+    Autograd functions are subclasses of :class:`torch.autograd.Function`.
+    Ensures that ``backward`` executes with the same autocast state as ``forward``.
+    See the :ref:`example page` for more detail.
+
+    Args:
+        device_type(str):  Device type to use. 'cuda', 'cpu', 'xpu' and so on.
+            The type is the same as the `type` attribute of a :class:`torch.device`.
+            Thus, you may obtain the device type of a tensor using `Tensor.device.type`.
+    """
+
+    if not isinstance(device_type, str):
+        raise ValueError(
+            f"Expected `device_type` of type `str`, got: `{type(device_type)}`"
+        )
+    if bwd is None:
+        return functools.partial(custom_bwd, device_type=device_type)
+
+    @functools.wraps(bwd)
+    def decorate_bwd(*args, **kwargs):
+        with autocast(
+            device_type=device_type,
+            enabled=args[0]._fwd_used_autocast,
+            dtype=args[0]._dtype,
+        ):
+            return bwd(*args, **kwargs)
+
+    return decorate_bwd
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/amp/grad_scaler.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/amp/grad_scaler.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e99b223adb441fecd227129664cc463572e147c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/amp/grad_scaler.py
@@ -0,0 +1,685 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import inspect
+import warnings
+from collections import abc, defaultdict
+from enum import Enum
+from typing import Any, cast, Dict, Iterable, List, Optional, overload, Tuple, Union
+
+import torch
+
+
+__all__ = ["OptState", "GradScaler"]
+
+
+class _MultiDeviceReplicator:
+    """Lazily serves copies of a tensor to requested devices.
+
+    Copies are cached per-device.
+    """
+
+    def __init__(self, master_tensor: torch.Tensor) -> None:
+        self.master = master_tensor
+        self._per_device_tensors: Dict[torch.device, torch.Tensor] = {}
+
+    def get(self, device: torch.device) -> torch.Tensor:
+        retval = self._per_device_tensors.get(device, None)
+        if retval is None:
+            retval = self.master.to(device=device, non_blocking=True, copy=True)
+            self._per_device_tensors[device] = retval
+        return retval
+
+
+# Defines default_factory for GradScaler's _per_optimizer_states defaultdict,
+# as well as associated "enum" values.  Prefers defining these at top level because
+# - Lambdas can't be pickled, so we don't want to supply a lambda as the factory.
+# - Defining READY, UNSCALED, STEPPED and _refresh_per_optimizer_state within GradScaler
+#   causes a circular reference, which we'd rather avoid.
+class OptState(Enum):
+    READY = 0
+    UNSCALED = 1
+    STEPPED = 2
+
+
+def _refresh_per_optimizer_state() -> Dict[str, Any]:
+    return {"stage": OptState.READY, "found_inf_per_device": {}}
+
+
+class GradScaler:
+    """An instance ``scaler`` of :class:`GradScaler`.
+
+    Helps perform the steps of gradient scaling
+    conveniently.
+
+    * ``scaler.scale(loss)`` multiplies a given loss by ``scaler``'s current scale factor.
+    * ``scaler.step(optimizer)`` safely unscales gradients and calls ``optimizer.step()``.
+    * ``scaler.update()`` updates ``scaler``'s scale factor.
+
+    Example::
+
+        # Creates a GradScaler once at the beginning of training.
+        scaler = GradScaler()
+
+        for epoch in epochs:
+            for input, target in data:
+                optimizer.zero_grad()
+                output = model(input)
+                loss = loss_fn(output, target)
+
+                # Scales loss.  Calls backward() on scaled loss to create scaled gradients.
+                scaler.scale(loss).backward()
+
+                # scaler.step() first unscales gradients of the optimizer's params.
+                # If gradients don't contain infs/NaNs, optimizer.step() is then called,
+                # otherwise, optimizer.step() is skipped.
+                scaler.step(optimizer)
+
+                # Updates the scale for next iteration.
+                scaler.update()
+
+    See the :ref:`Automatic Mixed Precision examples` for usage
+    (along with autocasting) in more complex cases like gradient clipping, gradient accumulation, gradient penalty,
+    and multiple losses/optimizers.
+
+    ``scaler`` dynamically estimates the scale factor each iteration.  To minimize gradient underflow,
+    a large scale factor should be used.  However, ``float16`` values can "overflow" (become inf or NaN) if
+    the scale factor is too large.  Therefore, the optimal scale factor is the largest factor that can be used
+    without incurring inf or NaN gradient values.
+    ``scaler`` approximates the optimal scale factor over time by checking the gradients for infs and NaNs during every
+    ``scaler.step(optimizer)`` (or optional separate ``scaler.unscale_(optimizer)``, see :meth:`unscale_`).
+
+    * If infs/NaNs are found, ``scaler.step(optimizer)`` skips the underlying ``optimizer.step()`` (so the params
+      themselves remain uncorrupted) and ``update()`` multiplies the scale by ``backoff_factor``.
+
+    * If no infs/NaNs are found, ``scaler.step(optimizer)`` runs the underlying ``optimizer.step()`` as usual.
+      If ``growth_interval`` unskipped iterations occur consecutively, ``update()`` multiplies the scale by
+      ``growth_factor``.
+
+    The scale factor often causes infs/NaNs to appear in gradients for the first few iterations as its
+    value calibrates.  ``scaler.step`` will skip the underlying ``optimizer.step()`` for these
+    iterations.  After that, step skipping should occur rarely (once every few hundred or thousand iterations).
+
+    Args:
+        device (str, optional, default="cuda"): Device type to use. Possible values are: 'cuda' and 'cpu'.
+            The type is the same as the `type` attribute of a :class:`torch.device`.
+            Thus, you may obtain the device type of a tensor using `Tensor.device.type`.
+        init_scale (float, optional, default=2.**16):  Initial scale factor.
+        growth_factor (float, optional, default=2.0):  Factor by which the scale is multiplied during
+            :meth:`update` if no inf/NaN gradients occur for ``growth_interval`` consecutive iterations.
+        backoff_factor (float, optional, default=0.5):  Factor by which the scale is multiplied during
+            :meth:`update` if inf/NaN gradients occur in an iteration.
+        growth_interval (int, optional, default=2000):  Number of consecutive iterations without inf/NaN gradients
+            that must occur for the scale to be multiplied by ``growth_factor``.
+        enabled (bool, optional):  If ``False``, disables gradient scaling. :meth:`step` simply
+            invokes the underlying ``optimizer.step()``, and other methods become no-ops.
+            Default: ``True``
+    """
+
+    def __init__(
+        self,
+        device: str = "cuda",
+        init_scale: float = 2.0**16,
+        growth_factor: float = 2.0,
+        backoff_factor: float = 0.5,
+        growth_interval: int = 2000,
+        enabled: bool = True,
+    ) -> None:
+        self._device = device
+        self._enabled = enabled
+        if self._device == "cuda":
+            if enabled and torch.cuda.amp.common.amp_definitely_not_available():
+                warnings.warn(
+                    "torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling."
+                )
+                self._enabled = False
+
+        if self._enabled:
+            assert growth_factor > 1.0, "The growth factor must be > 1.0."
+            assert backoff_factor < 1.0, "The backoff factor must be < 1.0."
+
+            self._init_scale = init_scale
+            # self._scale will be lazily initialized during the first call to scale()
+            self._scale: Optional[torch.Tensor] = None
+            self._growth_factor = growth_factor
+            self._backoff_factor = backoff_factor
+            self._growth_interval = growth_interval
+            self._init_growth_tracker = 0
+            # self._growth_tracker will be lazily initialized during the first call to scale()
+            self._growth_tracker: Optional[torch.Tensor] = None
+            self._per_optimizer_states: Dict[int, Dict[str, Any]] = defaultdict(
+                _refresh_per_optimizer_state
+            )
+
+    def _check_scale_growth_tracker(
+        self, funcname: str
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        fix = "This may indicate your script did not use scaler.scale(loss or outputs) earlier in the iteration."
+        assert self._scale is not None, (
+            f"Attempted {funcname} but _scale is None.  " + fix
+        )
+        assert self._growth_tracker is not None, (
+            f"Attempted {funcname} but _growth_tracker is None.  " + fix
+        )
+        return (self._scale, self._growth_tracker)
+
+    def _lazy_init_scale_growth_tracker(self, dev: torch.device) -> None:
+        assert self._growth_tracker is None, "_growth_tracker initialized before _scale"
+        self._scale = torch.full((), self._init_scale, dtype=torch.float32, device=dev)
+        self._growth_tracker = torch.full(
+            (), self._init_growth_tracker, dtype=torch.int32, device=dev
+        )
+
+    @overload
+    def scale(self, outputs: torch.Tensor) -> torch.Tensor:
+        ...
+
+    @overload
+    def scale(self, outputs: List[torch.Tensor]) -> List[torch.Tensor]:
+        ...
+
+    @overload
+    def scale(self, outputs: Tuple[torch.Tensor, ...]) -> Tuple[torch.Tensor, ...]:
+        ...
+
+    @overload
+    def scale(self, outputs: Iterable[torch.Tensor]) -> Iterable[torch.Tensor]:
+        ...
+
+    def scale(
+        self,
+        outputs: Union[torch.Tensor, Iterable[torch.Tensor]],
+    ) -> Union[torch.Tensor, Iterable[torch.Tensor]]:
+        """
+        Multiplies ('scales') a tensor or list of tensors by the scale factor.
+
+        Returns scaled outputs.  If this instance of :class:`GradScaler` is not enabled, outputs are returned
+        unmodified.
+
+        Args:
+            outputs (Tensor or iterable of Tensors):  Outputs to scale.
+        """
+        if not self._enabled:
+            return outputs
+
+        # Short-circuit for the common case.
+        if isinstance(outputs, torch.Tensor):
+            if self._scale is None:
+                self._lazy_init_scale_growth_tracker(outputs.device)
+            assert self._scale is not None
+            return outputs * self._scale.to(device=outputs.device, non_blocking=True)
+
+        # Invoke the more complex machinery only if we're treating multiple outputs.
+        stash: List[
+            _MultiDeviceReplicator
+        ] = []  # holds a reference that can be overwritten by apply_scale
+
+        def apply_scale(val: Union[torch.Tensor, Iterable[torch.Tensor]]):
+            if isinstance(val, torch.Tensor):
+                if len(stash) == 0:
+                    if self._scale is None:
+                        self._lazy_init_scale_growth_tracker(val.device)
+                    assert self._scale is not None
+                    stash.append(_MultiDeviceReplicator(self._scale))
+                return val * stash[0].get(val.device)
+            if isinstance(val, abc.Iterable):
+                iterable = map(apply_scale, val)
+                if isinstance(val, (list, tuple)):
+                    return type(val)(iterable)
+                return iterable
+            raise ValueError("outputs must be a Tensor or an iterable of Tensors")
+
+        return apply_scale(outputs)
+
+    def _unscale_grads_(
+        self,
+        optimizer: torch.optim.Optimizer,
+        inv_scale: torch.Tensor,
+        found_inf: torch.Tensor,
+        allow_fp16: bool,
+    ) -> Dict[torch.device, torch.Tensor]:
+        per_device_inv_scale = _MultiDeviceReplicator(inv_scale)
+        per_device_found_inf = _MultiDeviceReplicator(found_inf)
+
+        # To set up _amp_foreach_non_finite_check_and_unscale_, split grads by device and dtype.
+        # There could be hundreds of grads, so we'd like to iterate through them just once.
+        # However, we don't know their devices or dtypes in advance.
+
+        # https://stackoverflow.com/questions/5029934/defaultdict-of-defaultdict
+        # Google says mypy struggles with defaultdicts type annotations.
+        per_device_and_dtype_grads: Dict[
+            torch.device, Dict[torch.dtype, List[torch.Tensor]]
+        ] = defaultdict(lambda: defaultdict(list))
+        with torch.no_grad():
+            for group in optimizer.param_groups:
+                for param in group["params"]:
+                    assert isinstance(param, torch.Tensor)
+                    if param.grad is None:
+                        continue
+                    if (not allow_fp16) and param.grad.dtype == torch.float16:
+                        raise ValueError("Attempting to unscale FP16 gradients.")
+                    if param.grad.is_sparse:
+                        # is_coalesced() == False means the sparse grad has values with duplicate indices.
+                        # coalesce() deduplicates indices and adds all values that have the same index.
+                        # For scaled fp16 values, there's a good chance coalescing will cause overflow,
+                        # so we should check the coalesced _values().
+                        if param.grad.dtype is torch.float16:
+                            param.grad = param.grad.coalesce()
+                        to_unscale = param.grad._values()
+                    else:
+                        to_unscale = param.grad
+
+                    # TODO: is there a way to split by device and dtype without appending in the inner loop?
+                    per_device_and_dtype_grads[to_unscale.device][
+                        to_unscale.dtype
+                    ].append(to_unscale)
+
+            for device, per_dtype_grads in per_device_and_dtype_grads.items():
+                for grads in per_dtype_grads.values():
+                    torch._amp_foreach_non_finite_check_and_unscale_(
+                        grads,
+                        per_device_found_inf.get(device),
+                        per_device_inv_scale.get(device),
+                    )
+
+        return per_device_found_inf._per_device_tensors
+
+    def unscale_(self, optimizer: torch.optim.Optimizer) -> None:
+        """
+        Divides ("unscales") the optimizer's gradient tensors by the scale factor.
+
+        :meth:`unscale_` is optional, serving cases where you need to
+        :ref:`modify or inspect gradients`
+        between the backward pass(es) and :meth:`step`.
+        If :meth:`unscale_` is not called explicitly,  gradients will be unscaled  automatically during :meth:`step`.
+
+        Simple example, using :meth:`unscale_` to enable clipping of unscaled gradients::
+
+            ...
+            scaler.scale(loss).backward()
+            scaler.unscale_(optimizer)
+            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
+            scaler.step(optimizer)
+            scaler.update()
+
+        Args:
+            optimizer (torch.optim.Optimizer):  Optimizer that owns the gradients to be unscaled.
+
+        .. note::
+            :meth:`unscale_` does not incur a CPU-GPU sync.
+
+        .. warning::
+            :meth:`unscale_` should only be called once per optimizer per :meth:`step` call,
+            and only after all gradients for that optimizer's assigned parameters have been accumulated.
+            Calling :meth:`unscale_` twice for a given optimizer between each :meth:`step` triggers a RuntimeError.
+
+        .. warning::
+            :meth:`unscale_` may unscale sparse gradients out of place, replacing the ``.grad`` attribute.
+        """
+        if not self._enabled:
+            return
+
+        self._check_scale_growth_tracker("unscale_")
+
+        optimizer_state = self._per_optimizer_states[id(optimizer)]
+
+        if optimizer_state["stage"] is OptState.UNSCALED:
+            raise RuntimeError(
+                "unscale_() has already been called on this optimizer since the last update()."
+            )
+        elif optimizer_state["stage"] is OptState.STEPPED:
+            raise RuntimeError("unscale_() is being called after step().")
+
+        # FP32 division can be imprecise for certain compile options, so we carry out the reciprocal in FP64.
+        assert self._scale is not None
+        inv_scale = self._scale.double().reciprocal().float()
+        found_inf = torch.full((), 0.0, dtype=torch.float32, device=self._scale.device)
+
+        optimizer_state["found_inf_per_device"] = self._unscale_grads_(
+            optimizer, inv_scale, found_inf, False
+        )
+        optimizer_state["stage"] = OptState.UNSCALED
+
+    def _maybe_opt_step(
+        self,
+        optimizer: torch.optim.Optimizer,
+        optimizer_state: Dict[str, Any],
+        *args: Any,
+        **kwargs: Any,
+    ) -> Optional[float]:
+        retval: Optional[float] = None
+        if not sum(v.item() for v in optimizer_state["found_inf_per_device"].values()):
+            retval = optimizer.step(*args, **kwargs)
+        return retval
+
+    def step(
+        self, optimizer: torch.optim.Optimizer, *args: Any, **kwargs: Any
+    ) -> Optional[float]:
+        """Invoke ``unscale_(optimizer)`` followed by parameter update, if gradients are not infs/NaN.
+
+        :meth:`step` carries out the following two operations:
+
+        1.  Internally invokes ``unscale_(optimizer)`` (unless :meth:`unscale_` was explicitly called for ``optimizer``
+            earlier in the iteration).  As part of the :meth:`unscale_`, gradients are checked for infs/NaNs.
+        2.  If no inf/NaN gradients are found, invokes ``optimizer.step()`` using the unscaled
+            gradients.  Otherwise, ``optimizer.step()`` is skipped to avoid corrupting the params.
+
+        ``*args`` and ``**kwargs`` are forwarded to ``optimizer.step()``.
+
+        Returns the return value of ``optimizer.step(*args, **kwargs)``.
+
+        Args:
+            optimizer (torch.optim.Optimizer):  Optimizer that applies the gradients.
+            args:  Any arguments.
+            kwargs:  Any keyword arguments.
+
+        .. warning::
+            Closure use is not currently supported.
+        """
+        if not self._enabled:
+            return optimizer.step(*args, **kwargs)
+
+        if "closure" in kwargs:
+            raise RuntimeError(
+                "Closure use is not currently supported if GradScaler is enabled."
+            )
+
+        self._check_scale_growth_tracker("step")
+
+        optimizer_state = self._per_optimizer_states[id(optimizer)]
+
+        if optimizer_state["stage"] is OptState.STEPPED:
+            raise RuntimeError(
+                "step() has already been called since the last update()."
+            )
+
+        retval: Optional[float] = None
+
+        if getattr(optimizer, "_step_supports_amp_scaling", False):
+            # This optimizer has customized scale-handling logic, so we can call optimizer.step() directly.
+            # The contract with custom optimizers is that their step() should accept an additional,
+            # optional grad_scaler kwarg.  We append self to the kwargs so the custom optimizer has full information:
+            # it can query its own state, invoke unscale_ on itself, etc
+            # The contract above is being deprecated to avoid introducing `grad_scaler: GradScaler` argument
+            # to `Optimizer.step`. The new behavior is going to add two Tensor attributes of `grad_scale`
+            # and `found_inf` to the passed optimizer so that the optimizer can utilize those
+            # to skip the parameter updates or unscale gradients before updating parameters in
+            # the fused kernel, e.g. `FusedAdamMathFunctor`.
+            # In this behavior, `GradScaler._check_inf_per_device` is called if `OptState.READY`,
+            # while the method is expected to be called by users side, i.e. their optimizers.
+            kwargs_ = kwargs
+            has_grad_scaler_kwarg = (
+                "grad_scaler" in inspect.signature(optimizer.step).parameters
+            )
+            if has_grad_scaler_kwarg:
+                warnings.warn(
+                    "GradScaler is going to stop passing itself as a keyword argument to the passed "
+                    "optimizer. In the near future GradScaler registers `grad_scale: Tensor` and "
+                    "`found_inf: Tensor` to the passed optimizer and let the optimizer use them directly.",
+                    FutureWarning,
+                )
+                kwargs_.update({"grad_scaler": self})
+            else:
+                if optimizer_state["stage"] is OptState.READY:
+                    self._check_inf_per_device(optimizer)
+                scaler = self._get_scale_async()
+                assert scaler is not None
+                found_inf = cast(
+                    torch.Tensor,
+                    sum(
+                        [  # noqa: C419
+                            t.to(scaler.device, non_blocking=True)
+                            for t in optimizer_state["found_inf_per_device"].values()
+                        ]
+                    ),
+                )
+                # Take the product of the scales, if the user has already set `optimizer.grad_scale`.
+                optimizer.grad_scale = (  # type: ignore[attr-defined]
+                    getattr(optimizer, "grad_scale", None)
+                    if optimizer_state["stage"] == OptState.UNSCALED
+                    else scaler * getattr(optimizer, "grad_scale", 1)
+                )
+                optimizer.found_inf = found_inf  # type: ignore[attr-defined]
+            retval = optimizer.step(*args, **kwargs_)
+            optimizer_state["stage"] = OptState.STEPPED
+            if not has_grad_scaler_kwarg:
+                del optimizer.grad_scale  # type: ignore[attr-defined]
+                del optimizer.found_inf  # type: ignore[attr-defined]
+            return retval
+
+        if optimizer_state["stage"] is OptState.READY:
+            self.unscale_(optimizer)
+
+        assert (
+            len(optimizer_state["found_inf_per_device"]) > 0
+        ), "No inf checks were recorded for this optimizer."
+
+        retval = self._maybe_opt_step(optimizer, optimizer_state, *args, **kwargs)
+
+        optimizer_state["stage"] = OptState.STEPPED
+
+        return retval
+
+    def update(self, new_scale: Optional[Union[float, torch.Tensor]] = None) -> None:
+        """Update the scale factor.
+
+        If any optimizer steps were skipped the scale is multiplied by ``backoff_factor``
+        to reduce it. If ``growth_interval`` unskipped iterations occurred consecutively,
+        the scale is multiplied by ``growth_factor`` to increase it.
+
+        Passing ``new_scale`` sets the new scale value manually. (``new_scale`` is not
+        used directly, it's used to fill GradScaler's internal scale tensor. So if
+        ``new_scale`` was a tensor, later in-place changes to that tensor will not further
+        affect the scale GradScaler uses internally.)
+
+        Args:
+            new_scale (float or :class:`torch.Tensor`, optional, default=None):  New scale factor.
+
+        .. warning::
+            :meth:`update` should only be called at the end of the iteration, after ``scaler.step(optimizer)`` has
+            been invoked for all optimizers used this iteration.
+
+        .. warning::
+            For performance reasons, we do not check the scale factor value to avoid synchronizations,
+            so the scale factor is not guaranteed to be above 1. If the scale falls below 1 and/or
+            you are seeing NaNs in your gradients or loss, something is likely wrong. For example,
+            bf16-pretrained models are often incompatible with AMP/fp16 due to differing dynamic ranges.
+        """
+        if not self._enabled:
+            return
+
+        _scale, _growth_tracker = self._check_scale_growth_tracker("update")
+
+        if new_scale is not None:
+            assert self._scale is not None
+            # Accept a new user-defined scale.
+            if isinstance(new_scale, float):
+                self._scale.fill_(new_scale)
+            else:
+                reason = "new_scale should be a float or a 1-element torch.cuda.FloatTensor or \
+                    torch.FloatTensor with requires_grad=False."
+                assert new_scale.device.type == self._device, reason
+                assert new_scale.numel() == 1, reason
+                assert new_scale.requires_grad is False, reason
+                self._scale.copy_(new_scale)
+        else:
+            # Consume shared inf/nan data collected from optimizers to update the scale.
+            # If all found_inf tensors are on the same device as self._scale, this operation is asynchronous.
+            found_infs = [
+                found_inf.to(device=_scale.device, non_blocking=True)
+                for state in self._per_optimizer_states.values()
+                for found_inf in state["found_inf_per_device"].values()
+            ]
+
+            assert len(found_infs) > 0, "No inf checks were recorded prior to update."
+
+            found_inf_combined = found_infs[0]
+            if len(found_infs) > 1:
+                for i in range(1, len(found_infs)):
+                    found_inf_combined += found_infs[i]
+
+            torch._amp_update_scale_(
+                _scale,
+                _growth_tracker,
+                found_inf_combined,
+                self._growth_factor,
+                self._backoff_factor,
+                self._growth_interval,
+            )
+
+        # To prepare for next iteration, clear the data collected from optimizers this iteration.
+        self._per_optimizer_states = defaultdict(_refresh_per_optimizer_state)
+
+    def _get_scale_async(self) -> Optional[torch.Tensor]:
+        return self._scale
+
+    def get_scale(self) -> float:
+        """Return a Python float containing the current scale, or 1.0 if scaling is disabled.
+
+        .. warning::
+            :meth:`get_scale` incurs a CPU-GPU sync.
+        """
+        if self._enabled:
+            return (
+                self._init_scale
+                if (scale := self._get_scale_async()) is None
+                else cast(float, scale.item())
+            )
+        return 1.0
+
+    def get_growth_factor(self) -> float:
+        r"""Return a Python float containing the scale growth factor."""
+        return self._growth_factor
+
+    def set_growth_factor(self, new_factor: float) -> None:
+        r"""Set a new scale growth factor.
+
+        Args:
+            new_scale (float):  Value to use as the new scale growth factor.
+        """
+        self._growth_factor = new_factor
+
+    def get_backoff_factor(self) -> float:
+        r"""Return a Python float containing the scale backoff factor."""
+        return self._backoff_factor
+
+    def set_backoff_factor(self, new_factor: float) -> None:
+        r"""Set a new scale backoff factor.
+
+        Args:
+            new_scale (float):  Value to use as the new scale backoff factor.
+        """
+        self._backoff_factor = new_factor
+
+    def get_growth_interval(self) -> int:
+        r"""Return a Python int containing the growth interval."""
+        return self._growth_interval
+
+    def set_growth_interval(self, new_interval: int) -> None:
+        r"""Set a new growth interval.
+
+        Args:
+            new_interval (int):  Value to use as the new growth interval.
+        """
+        self._growth_interval = new_interval
+
+    def _get_growth_tracker(self) -> int:
+        if self._enabled:
+            return (
+                self._init_growth_tracker
+                if self._growth_tracker is None
+                else cast(int, self._growth_tracker.item())
+            )
+        return 0
+
+    def is_enabled(self) -> bool:
+        r"""Return a bool indicating whether this instance is enabled."""
+        return self._enabled
+
+    def state_dict(self) -> Dict[str, Any]:
+        r"""Return the state of the scaler as a :class:`dict`.
+
+        It contains five entries:
+
+        * ``"scale"`` - a Python float containing the current scale
+        * ``"growth_factor"`` - a Python float containing the current growth factor
+        * ``"backoff_factor"`` - a Python float containing the current backoff factor
+        * ``"growth_interval"`` - a Python int containing the current growth interval
+        * ``"_growth_tracker"`` - a Python int containing the number of recent consecutive unskipped steps.
+
+        If this instance is not enabled, returns an empty dict.
+
+        .. note::
+           If you wish to checkpoint the scaler's state after a particular iteration, :meth:`state_dict`
+           should be called after :meth:`update`.
+        """
+        if self._enabled:
+            return {
+                "scale": self.get_scale(),
+                "growth_factor": self._growth_factor,
+                "backoff_factor": self._backoff_factor,
+                "growth_interval": self._growth_interval,
+                "_growth_tracker": self._get_growth_tracker(),
+            }
+        return {}
+
+    def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
+        r"""Load the scaler state.
+
+        If this instance is disabled, :meth:`load_state_dict` is a no-op.
+
+        Args:
+           state_dict(dict): scaler state.  Should be an object returned from a call to :meth:`state_dict`.
+        """
+        if not self._enabled:
+            return
+
+        if len(state_dict) == 0:
+            raise RuntimeError(
+                "The source state dict is empty, possibly because it was saved "
+                "from a disabled instance of GradScaler."
+            )
+
+        self._init_scale = cast(float, state_dict["scale"])
+        if self._scale is not None:
+            self._scale.fill_(state_dict["scale"])
+        self._growth_factor = cast(float, state_dict["growth_factor"])
+        self._backoff_factor = cast(float, state_dict["backoff_factor"])
+        self._growth_interval = cast(int, state_dict["growth_interval"])
+        self._init_growth_tracker = cast(int, state_dict["_growth_tracker"])
+        if self._growth_tracker is not None:
+            self._growth_tracker.fill_(state_dict["_growth_tracker"])
+
+    def __getstate__(self) -> Dict[str, Any]:
+        state = self.__dict__.copy()
+        if self._enabled:
+            assert len(self._per_optimizer_states) == 0, (
+                "A GradScaler instance may only be pickled at the beginning "
+                "of an iteration, or at the end after scaler.update()."
+            )
+            # Pickling _scale and _growth_tracker Tensors directly triggers
+            # "warnings.warn("pickle support for Storage will be removed in 1.5..."
+            # so instead, we set the unpickled instance up to reinitialize them lazily.
+            state["_init_scale"] = self.get_scale()
+            state["_init_growth_tracker"] = self._get_growth_tracker()
+            state["_scale"] = None
+            state["_growth_tracker"] = None
+        return state
+
+    def __setstate__(self, state: Dict[str, Any]) -> None:
+        self.__dict__.update(state)
+
+    def _check_inf_per_device(self, optimizer: torch.optim.Optimizer) -> Dict[str, Any]:
+        _scale, _ = self._check_scale_growth_tracker("_check_inf_per_device")
+
+        dummy_inv_scale = torch.full((), 1.0, dtype=torch.float32, device=_scale.device)
+        found_inf = torch.full((), 0.0, dtype=torch.float32, device=_scale.device)
+
+        self._per_optimizer_states[id(optimizer)][
+            "found_inf_per_device"
+        ] = self._unscale_grads_(optimizer, dummy_inv_scale, found_inf, True)
+
+        return self._per_optimizer_states[id(optimizer)]["found_inf_per_device"]
+
+    def _found_inf_per_device(self, optimizer: torch.optim.Optimizer) -> Dict[str, Any]:
+        return self._per_optimizer_states[id(optimizer)]["found_inf_per_device"]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c54493da25e857307745ca78f207b101c22366d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/__init__.py
@@ -0,0 +1,19 @@
+# mypy: allow-untyped-defs
+# torch.ao is a package with a lot of interdependencies.
+# We will use lazy import to avoid cyclic dependencies here.
+
+
+__all__ = [
+    "nn",
+    "ns",
+    "quantization",
+    "pruning",
+]
+
+
+def __getattr__(name):
+    if name in __all__:
+        import importlib
+
+        return importlib.import_module("." + name, __name__)
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a60b90d88b90399e8ca53135c37b40075ff9a271
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/__init__.py
@@ -0,0 +1,22 @@
+# mypy: allow-untyped-defs
+# We are exposing all subpackages to the end-user.
+# Because of possible inter-dependency, we want to avoid
+# the cyclic imports, thus implementing lazy version
+# as per https://peps.python.org/pep-0562/
+
+import importlib
+
+
+__all__ = [
+    "intrinsic",
+    "qat",
+    "quantizable",
+    "quantized",
+    "sparse",
+]
+
+
+def __getattr__(name):
+    if name in __all__:
+        return importlib.import_module("." + name, __name__)
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..644a07f20d3234f61ba668f7af82f6db62dd223a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/__init__.py
@@ -0,0 +1,40 @@
+# mypy: allow-untyped-defs
+from .modules import *  # noqa: F403
+from .modules.fused import _FusedModule  # noqa: F403
+
+
+# # Subpackages
+# from . import qat  # noqa: F403
+# from . import quantized  # noqa: F403
+
+__all__ = [
+    "ConvBn1d",
+    "ConvBn2d",
+    "ConvBn3d",
+    "ConvBnReLU1d",
+    "ConvBnReLU2d",
+    "ConvBnReLU3d",
+    "ConvReLU1d",
+    "ConvReLU2d",
+    "ConvReLU3d",
+    "LinearReLU",
+    "BNReLU2d",
+    "BNReLU3d",
+    "LinearBn1d",
+    "LinearLeakyReLU",
+    "LinearTanh",
+    "ConvAdd2d",
+    "ConvAddReLU2d",
+]
+
+
+# We are exposing all subpackages to the end-user.
+# Because of possible inter-dependency, we want to avoid
+# the cyclic imports, thus implementing lazy version
+# as per https://peps.python.org/pep-0562/
+def __getattr__(name):
+    if name in __all__:
+        import importlib
+
+        return importlib.import_module("." + name, __name__)
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/modules/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..132137b7357378fe29ef9a63310a554725aea86a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/modules/__init__.py
@@ -0,0 +1,41 @@
+from .fused import (  # noqa: F401
+    _FusedModule,
+    BNReLU2d,
+    BNReLU3d,
+    ConvAdd2d,
+    ConvAddReLU2d,
+    ConvBn1d,
+    ConvBn2d,
+    ConvBn3d,
+    ConvBnReLU1d,
+    ConvBnReLU2d,
+    ConvBnReLU3d,
+    ConvReLU1d,
+    ConvReLU2d,
+    ConvReLU3d,
+    LinearBn1d,
+    LinearLeakyReLU,
+    LinearReLU,
+    LinearTanh,
+)
+
+
+__all__ = [
+    "ConvBn1d",
+    "ConvBn2d",
+    "ConvBn3d",
+    "ConvBnReLU1d",
+    "ConvBnReLU2d",
+    "ConvBnReLU3d",
+    "ConvReLU1d",
+    "ConvReLU2d",
+    "ConvReLU3d",
+    "LinearReLU",
+    "BNReLU2d",
+    "BNReLU3d",
+    "LinearBn1d",
+    "LinearLeakyReLU",
+    "LinearTanh",
+    "ConvAdd2d",
+    "ConvAddReLU2d",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/modules/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/modules/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/modules/__pycache__/fused.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/modules/__pycache__/fused.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/modules/fused.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/modules/fused.py
new file mode 100644
index 0000000000000000000000000000000000000000..010b9a701a33530a03ea8c066f4f6355b937d077
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/modules/fused.py
@@ -0,0 +1,245 @@
+# mypy: allow-untyped-defs
+import torch
+from torch.nn import (
+    BatchNorm1d,
+    BatchNorm2d,
+    BatchNorm3d,
+    Conv1d,
+    Conv2d,
+    Conv3d,
+    Linear,
+    ReLU,
+)
+from torch.nn.utils.parametrize import type_before_parametrizations
+
+
+__all__ = [
+    "ConvReLU1d",
+    "ConvReLU2d",
+    "ConvReLU3d",
+    "LinearReLU",
+    "ConvBn1d",
+    "ConvBn2d",
+    "ConvBnReLU1d",
+    "ConvBnReLU2d",
+    "ConvBn3d",
+    "ConvBnReLU3d",
+    "BNReLU2d",
+    "BNReLU3d",
+    "LinearBn1d",
+    "LinearLeakyReLU",
+    "LinearTanh",
+    "ConvAdd2d",
+    "ConvAddReLU2d",
+]
+
+
+# Used for identifying intrinsic modules used in quantization
+class _FusedModule(torch.nn.Sequential):
+    pass
+
+
+class ConvReLU1d(_FusedModule):
+    r"""This is a sequential container which calls the Conv1d and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, relu):
+        assert (
+            type_before_parametrizations(conv) == Conv1d
+            and type_before_parametrizations(relu) == ReLU
+        ), f"Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(relu)}"
+        super().__init__(conv, relu)
+
+
+class ConvReLU2d(_FusedModule):
+    r"""This is a sequential container which calls the Conv2d and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, relu):
+        assert (
+            type_before_parametrizations(conv) == Conv2d
+            and type_before_parametrizations(relu) == ReLU
+        ), f"Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(relu)}"
+        super().__init__(conv, relu)
+
+
+class ConvReLU3d(_FusedModule):
+    r"""This is a sequential container which calls the Conv3d and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, relu):
+        assert (
+            type_before_parametrizations(conv) == Conv3d
+            and type_before_parametrizations(relu) == ReLU
+        ), f"Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(relu)}"
+        super().__init__(conv, relu)
+
+
+class LinearReLU(_FusedModule):
+    r"""This is a sequential container which calls the Linear and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, linear, relu):
+        assert (
+            type_before_parametrizations(linear) == Linear
+            and type_before_parametrizations(relu) == ReLU
+        ), f"Incorrect types for input modules{type_before_parametrizations(linear)}{type_before_parametrizations(relu)}"
+        super().__init__(linear, relu)
+
+
+class ConvBn1d(_FusedModule):
+    r"""This is a sequential container which calls the Conv 1d and Batch Norm 1d modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, bn):
+        assert (
+            type_before_parametrizations(conv) == Conv1d
+            and type_before_parametrizations(bn) == BatchNorm1d
+        ), f"Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(bn)}"
+        super().__init__(conv, bn)
+
+
+class ConvBn2d(_FusedModule):
+    r"""This is a sequential container which calls the Conv 2d and Batch Norm 2d modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, bn):
+        assert (
+            type_before_parametrizations(conv) == Conv2d
+            and type_before_parametrizations(bn) == BatchNorm2d
+        ), f"Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(bn)}"
+        super().__init__(conv, bn)
+
+
+class ConvBnReLU1d(_FusedModule):
+    r"""This is a sequential container which calls the Conv 1d, Batch Norm 1d, and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, bn, relu):
+        assert (
+            type_before_parametrizations(conv) == Conv1d
+            and type_before_parametrizations(bn) == BatchNorm1d
+            and type_before_parametrizations(relu) == ReLU
+        ), f"Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(bn)}{type_before_parametrizations(relu)}"  # noqa: B950
+        super().__init__(conv, bn, relu)
+
+
+class ConvBnReLU2d(_FusedModule):
+    r"""This is a sequential container which calls the Conv 2d, Batch Norm 2d, and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, bn, relu):
+        assert (
+            type_before_parametrizations(conv) == Conv2d
+            and type_before_parametrizations(bn) == BatchNorm2d
+            and type_before_parametrizations(relu) == ReLU
+        ), f"Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(bn)}{type_before_parametrizations(relu)}"  # noqa: B950
+        super().__init__(conv, bn, relu)
+
+
+class ConvBn3d(_FusedModule):
+    r"""This is a sequential container which calls the Conv 3d and Batch Norm 3d modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, bn):
+        assert (
+            type_before_parametrizations(conv) == Conv3d
+            and type_before_parametrizations(bn) == BatchNorm3d
+        ), f"Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(bn)}"
+        super().__init__(conv, bn)
+
+
+class ConvBnReLU3d(_FusedModule):
+    r"""This is a sequential container which calls the Conv 3d, Batch Norm 3d, and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, bn, relu):
+        assert (
+            type_before_parametrizations(conv) == Conv3d
+            and type_before_parametrizations(bn) == BatchNorm3d
+            and type_before_parametrizations(relu) == ReLU
+        ), f"Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(bn)}{type_before_parametrizations(relu)}"  # noqa: B950
+        super().__init__(conv, bn, relu)
+
+
+class BNReLU2d(_FusedModule):
+    r"""This is a sequential container which calls the BatchNorm 2d and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, batch_norm, relu):
+        assert (
+            type_before_parametrizations(batch_norm) == BatchNorm2d
+            and type_before_parametrizations(relu) == ReLU
+        ), f"Incorrect types for input modules{type_before_parametrizations(batch_norm)}{type_before_parametrizations(relu)}"
+        super().__init__(batch_norm, relu)
+
+
+class BNReLU3d(_FusedModule):
+    r"""This is a sequential container which calls the BatchNorm 3d and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, batch_norm, relu):
+        assert (
+            type_before_parametrizations(batch_norm) == BatchNorm3d
+            and type_before_parametrizations(relu) == ReLU
+        ), f"Incorrect types for input modules{type_before_parametrizations(batch_norm)}{type_before_parametrizations(relu)}"
+        super().__init__(batch_norm, relu)
+
+
+class LinearBn1d(_FusedModule):
+    r"""This is a sequential container which calls the Linear and BatchNorm1d modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, linear, bn):
+        assert (
+            type_before_parametrizations(linear) == Linear
+            and type_before_parametrizations(bn) == BatchNorm1d
+        ), f"Incorrect types for input modules{type_before_parametrizations(linear)}{type_before_parametrizations(bn)}"
+        super().__init__(linear, bn)
+
+
+class LinearLeakyReLU(_FusedModule):
+    r"""This is a sequential container which calls the Linear and LeakyReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, linear, leaky_relu):
+        assert (
+            type(linear) == Linear and type(leaky_relu) == torch.nn.LeakyReLU
+        ), f"Incorrect types for input modules{type(linear)}{type(leaky_relu)}"
+        super().__init__(linear, leaky_relu)
+
+
+class LinearTanh(_FusedModule):
+    r"""This is a sequential container which calls the Linear and Tanh modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, linear, tanh):
+        assert (
+            type(linear) == Linear and type(tanh) == torch.nn.Tanh
+        ), f"Incorrect types for input modules{type(linear)}{type(tanh)}"
+        super().__init__(linear, tanh)
+
+
+class ConvAdd2d(_FusedModule):
+    r"""This is a sequential container which calls the Conv2d modules with extra Add.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, add):
+        super().__init__(conv)
+        self.add = add
+
+    def forward(self, x1, x2):
+        return self.add(self[0](x1), x2)
+
+
+class ConvAddReLU2d(_FusedModule):
+    r"""This is a sequential container which calls the Conv2d, add, Relu.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, add, relu):
+        super().__init__(conv)
+        self.add = add
+        self.relu = relu
+
+    def forward(self, x1, x2):
+        return self.relu(self.add(self[0](x1), x2))
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d79bdbfe83209f18b17cc8c7b245f322871d6c0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/__init__.py
@@ -0,0 +1 @@
+from .modules import *  # noqa: F403
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/modules/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..18534bbc588e7480ac6529c6648c5976eadaea3a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/modules/__init__.py
@@ -0,0 +1,32 @@
+from .conv_fused import (
+    ConvBn1d,
+    ConvBn2d,
+    ConvBn3d,
+    ConvBnReLU1d,
+    ConvBnReLU2d,
+    ConvBnReLU3d,
+    ConvReLU1d,
+    ConvReLU2d,
+    ConvReLU3d,
+    freeze_bn_stats,
+    update_bn_stats,
+)
+from .linear_fused import LinearBn1d
+from .linear_relu import LinearReLU
+
+
+__all__ = [
+    "LinearReLU",
+    "LinearBn1d",
+    "ConvReLU1d",
+    "ConvReLU2d",
+    "ConvReLU3d",
+    "ConvBn1d",
+    "ConvBn2d",
+    "ConvBn3d",
+    "ConvBnReLU1d",
+    "ConvBnReLU2d",
+    "ConvBnReLU3d",
+    "update_bn_stats",
+    "freeze_bn_stats",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/modules/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/modules/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/modules/__pycache__/linear_relu.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/modules/__pycache__/linear_relu.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/modules/conv_fused.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/modules/conv_fused.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c700b30f247205e33718528fa68257bb9074e6c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/modules/conv_fused.py
@@ -0,0 +1,1050 @@
+# mypy: allow-untyped-defs
+import math
+from typing import TypeVar
+
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.qat as nnqat
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import init
+from torch.nn.modules.utils import _pair, _single, _triple
+from torch.nn.parameter import Parameter
+from torch.nn.utils import fuse_conv_bn_weights
+
+
+__all__ = [
+    "ConvBn1d",
+    "ConvBnReLU1d",
+    "ConvReLU1d",
+    "ConvBn2d",
+    "ConvBnReLU2d",
+    "ConvReLU2d",
+    "ConvBn3d",
+    "ConvBnReLU3d",
+    "ConvReLU3d",
+    "update_bn_stats",
+    "freeze_bn_stats",
+]
+_BN_CLASS_MAP = {
+    1: nn.BatchNorm1d,
+    2: nn.BatchNorm2d,
+    3: nn.BatchNorm3d,
+}
+
+
+MOD = TypeVar("MOD", bound=nn.modules.conv._ConvNd)
+
+
+class _ConvBnNd(nn.modules.conv._ConvNd, nni._FusedModule):
+    _version = 2
+    _FLOAT_MODULE = MOD
+
+    def __init__(
+        self,
+        # ConvNd args
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride,
+        padding,
+        dilation,
+        transposed,
+        output_padding,
+        groups,
+        bias,
+        padding_mode,
+        # BatchNormNd args
+        # num_features: out_channels
+        eps=1e-05,
+        momentum=0.1,
+        # affine: True
+        # track_running_stats: True
+        # Args for this module
+        freeze_bn=False,
+        qconfig=None,
+        dim=2,
+    ):
+        nn.modules.conv._ConvNd.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            transposed,
+            output_padding,
+            groups,
+            False,
+            padding_mode,
+        )
+        assert qconfig, "qconfig must be provided for QAT module"
+        self.qconfig = qconfig
+        self.freeze_bn = freeze_bn if self.training else True
+        self.bn = _BN_CLASS_MAP[dim](out_channels, eps, momentum, True, True)
+        self.weight_fake_quant = self.qconfig.weight()
+        if bias:
+            self.bias = Parameter(torch.empty(out_channels))
+        else:
+            self.register_parameter("bias", None)
+        self.reset_bn_parameters()
+
+        # this needs to be called after reset_bn_parameters,
+        # as they modify the same state
+        if self.training:
+            if freeze_bn:
+                self.freeze_bn_stats()
+            else:
+                self.update_bn_stats()
+        else:
+            self.freeze_bn_stats()
+
+        self._enable_slow_path_for_better_numerical_stability = False
+
+    def reset_running_stats(self):
+        self.bn.reset_running_stats()
+
+    def reset_bn_parameters(self):
+        self.bn.reset_running_stats()
+        init.uniform_(self.bn.weight)
+        init.zeros_(self.bn.bias)
+        # note: below is actually for conv, not BN
+        if self.bias is not None:
+            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
+            bound = 1 / math.sqrt(fan_in)
+            init.uniform_(self.bias, -bound, bound)
+
+    def reset_parameters(self):
+        super().reset_parameters()
+
+    def update_bn_stats(self):
+        self.freeze_bn = False
+        self.bn.training = True
+        return self
+
+    def freeze_bn_stats(self):
+        self.freeze_bn = True
+        self.bn.training = False
+        return self
+
+    def _forward(self, input):
+        if self._enable_slow_path_for_better_numerical_stability:
+            return self._forward_slow(input)
+        return self._forward_approximate(input)
+
+    def _forward_approximate(self, input):
+        """Approximated method to fuse conv and bn. It requires only one forward pass.
+        conv_orig = conv / scale_factor where scale_factor = bn.weight / running_std
+        """
+        assert self.bn.running_var is not None
+        running_std = torch.sqrt(self.bn.running_var + self.bn.eps)
+        scale_factor = self.bn.weight / running_std
+        weight_shape = [1] * len(self.weight.shape)
+        weight_shape[0] = -1
+        bias_shape = [1] * len(self.weight.shape)
+        bias_shape[1] = -1
+        scaled_weight = self.weight_fake_quant(
+            self.weight * scale_factor.reshape(weight_shape)
+        )
+        # using zero bias here since the bias for original conv
+        # will be added later
+        if self.bias is not None:
+            zero_bias = torch.zeros_like(self.bias, dtype=input.dtype)
+        else:
+            zero_bias = torch.zeros(
+                self.out_channels, device=scaled_weight.device, dtype=input.dtype
+            )
+        conv = self._conv_forward(input, scaled_weight, zero_bias)
+        conv_orig = conv / scale_factor.reshape(bias_shape)
+        if self.bias is not None:
+            conv_orig = conv_orig + self.bias.reshape(bias_shape)
+        conv = self.bn(conv_orig)
+        return conv
+
+    def _forward_slow(self, input):
+        """
+        A more accurate but slow method to compute conv bn fusion, following https://arxiv.org/pdf/1806.08342.pdf
+        It requires two forward passes but handles the case bn.weight == 0
+
+        Conv: Y = WX + B_c
+        Conv without bias: Y0 = WX = Y - B_c, Y = Y0 + B_c
+
+        Batch statistics:
+          mean_Y = Y.mean()
+                 = Y0.mean() + B_c
+          var_Y = (Y - mean_Y)^2.mean()
+                = (Y0 - Y0.mean())^2.mean()
+        BN (r: bn.weight, beta: bn.bias):
+          Z = r * (Y - mean_Y) / sqrt(var_Y + eps) + beta
+            = r * (Y0 - Y0.mean()) / sqrt(var_Y + eps) + beta
+
+        Fused Conv BN training (std_Y = sqrt(var_Y + eps)):
+          Z = (r * W / std_Y) * X + r * (B_c - mean_Y) / std_Y + beta
+            = (r * W / std_Y) * X - r * Y0.mean() / std_Y + beta
+
+        Fused Conv BN inference (running_std = sqrt(running_var + eps)):
+          Z = (r * W / running_std) * X - r * (running_mean - B_c) / running_std + beta
+
+        QAT with fused conv bn:
+          Z_train = fake_quant(r * W / running_std) * X * (running_std / std_Y) - r * Y0.mean() / std_Y + beta
+                  = conv(X, fake_quant(r * W / running_std)) * (running_std / std_Y) - r * Y0.mean() / std_Y + beta
+          Z_inference = conv(X, fake_quant(r * W / running_std)) - r * (running_mean - B_c) / running_std + beta
+        """
+
+        assert self.bn.running_var is not None
+        assert self.bn.running_mean is not None
+
+        # using zero bias here since the bias for original conv
+        # will be added later
+        zero_bias = torch.zeros(
+            self.out_channels, device=self.weight.device, dtype=input.dtype
+        )
+
+        weight_shape = [1] * len(self.weight.shape)
+        weight_shape[0] = -1
+        bias_shape = [1] * len(self.weight.shape)
+        bias_shape[1] = -1
+
+        if self.bn.training:
+            # needed to compute batch mean/std
+            conv_out = self._conv_forward(input, self.weight, zero_bias)
+            # update bn statistics
+            with torch.no_grad():
+                conv_out_bias = (
+                    conv_out
+                    if self.bias is None
+                    else conv_out + self.bias.reshape(bias_shape)
+                )
+                self.bn(conv_out_bias)
+
+        # fused conv + bn without bias using bn running statistics
+        running_std = torch.sqrt(self.bn.running_var + self.bn.eps)
+        scale_factor = self.bn.weight / running_std
+        scaled_weight = self.weight_fake_quant(
+            self.weight * scale_factor.reshape(weight_shape)
+        )
+        # fused conv without bias for inference: (r * W / running_std) * X
+        conv_bn = self._conv_forward(input, scaled_weight, zero_bias)
+
+        if self.bn.training:
+            avg_dims = [0] + list(range(2, len(self.weight.shape)))
+            batch_mean = conv_out.mean(avg_dims)  # type: ignore[possibly-undefined]
+            batch_var = torch.square(conv_out - batch_mean.reshape(bias_shape)).mean(
+                avg_dims
+            )
+            batch_std = torch.sqrt(batch_var + self.bn.eps)
+
+            # scale to use batch std in training mode
+            # conv(X, r * W / std_Y) = conv(X, r * W / running_std) * (running_std / std_Y)
+            unscale_factor = running_std / batch_std
+            conv_bn *= unscale_factor.reshape(bias_shape)
+
+            fused_mean = batch_mean
+            fused_std = batch_std
+        else:
+            fused_mean = self.bn.running_mean - (
+                self.bias if self.bias is not None else 0
+            )
+            fused_std = running_std
+
+        # fused bias = beta - r * mean / std
+        fused_bias = self.bn.bias - self.bn.weight * fused_mean / fused_std
+        conv_bn += fused_bias.reshape(bias_shape)
+
+        # HACK to let conv bias participate in loss to avoid DDP error (parameters
+        #   were not used in producing loss)
+        if self.bias is not None:
+            conv_bn += (self.bias - self.bias).reshape(bias_shape)
+
+        return conv_bn
+
+    def extra_repr(self):
+        # TODO(jerryzh): extend
+        return super().extra_repr()
+
+    def forward(self, input):
+        return self._forward(input)
+
+    def train(self, mode=True):
+        """
+        Batchnorm's training behavior is using the self.training flag. Prevent
+        changing it if BN is frozen. This makes sure that calling `model.train()`
+        on a model with a frozen BN will behave properly.
+        """
+        self.training = mode
+        if not self.freeze_bn:
+            for module in self.children():
+                module.train(mode)
+        return self
+
+    # ===== Serialization version history =====
+    #
+    # Version 1/None
+    #   self
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #   |--- gamma : Tensor
+    #   |--- beta : Tensor
+    #   |--- running_mean : Tensor
+    #   |--- running_var : Tensor
+    #   |--- num_batches_tracked : Tensor
+    #
+    # Version 2
+    #   self
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #   |--- bn : Module
+    #        |--- weight : Tensor (moved from v1.self.gamma)
+    #        |--- bias : Tensor (moved from v1.self.beta)
+    #        |--- running_mean : Tensor (moved from v1.self.running_mean)
+    #        |--- running_var : Tensor (moved from v1.self.running_var)
+    #        |--- num_batches_tracked : Tensor (moved from v1.self.num_batches_tracked)
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        version = local_metadata.get("version", None)
+        if version is None or version == 1:
+            # BN related parameters and buffers were moved into the BN module for v2
+            v2_to_v1_names = {
+                "bn.weight": "gamma",
+                "bn.bias": "beta",
+                "bn.running_mean": "running_mean",
+                "bn.running_var": "running_var",
+                "bn.num_batches_tracked": "num_batches_tracked",
+            }
+            for v2_name, v1_name in v2_to_v1_names.items():
+                if prefix + v1_name in state_dict:
+                    state_dict[prefix + v2_name] = state_dict[prefix + v1_name]
+                    state_dict.pop(prefix + v1_name)
+                elif prefix + v2_name in state_dict:
+                    # there was a brief period where forward compatibility
+                    # for this module was broken (between
+                    # https://github.com/pytorch/pytorch/pull/38478
+                    # and https://github.com/pytorch/pytorch/pull/38820)
+                    # and modules emitted the v2 state_dict format while
+                    # specifying that version == 1. This patches the forward
+                    # compatibility issue by allowing the v2 style entries to
+                    # be used.
+                    pass
+                elif strict:
+                    missing_keys.append(prefix + v2_name)
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            strict,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a qat module from a float module or qparams_dict
+
+        Args: `mod` a float module, either produced by torch.ao.quantization utilities
+        or directly from user
+        """
+        # The ignore is because _FLOAT_MODULE is a TypeVar here where the bound
+        # has no __name__ (code is fine though)
+        assert type(mod) == cls._FLOAT_MODULE, (
+            "qat."
+            + cls.__name__
+            + ".from_float only works for "
+            + cls._FLOAT_MODULE.__name__  # type: ignore[attr-defined]
+        )
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        assert mod.qconfig, "Input float module must have a valid qconfig"
+        qconfig = mod.qconfig
+        conv, bn = mod[0], mod[1]
+        qat_convbn = cls(
+            conv.in_channels,
+            conv.out_channels,
+            conv.kernel_size,
+            conv.stride,
+            conv.padding,
+            conv.dilation,
+            conv.groups,
+            conv.bias is not None,
+            conv.padding_mode,
+            bn.eps,
+            bn.momentum,
+            False,
+            qconfig,
+        )
+        qat_convbn.weight = conv.weight
+        qat_convbn.bias = conv.bias
+        qat_convbn.bn.weight = bn.weight
+        qat_convbn.bn.bias = bn.bias
+        qat_convbn.bn.running_mean = bn.running_mean
+        qat_convbn.bn.running_var = bn.running_var
+        # mypy error: Cannot determine type of 'num_batches_tracked'
+        qat_convbn.bn.num_batches_tracked = bn.num_batches_tracked  # type: ignore[has-type]
+        return qat_convbn
+
+    def to_float(self):
+        cls = type(self)
+        conv = cls._FLOAT_CONV_MODULE(  # type: ignore[attr-defined]
+            self.in_channels,
+            self.out_channels,
+            self.kernel_size,
+            self.stride,
+            self.padding,
+            self.dilation,
+            self.groups,
+            self.bias is not None,
+            self.padding_mode,
+        )
+        conv.weight = torch.nn.Parameter(self.weight.detach())
+        if self.bias is not None:
+            conv.bias = torch.nn.Parameter(self.bias.detach())
+
+        if cls._FLOAT_BN_MODULE:  # type: ignore[attr-defined]
+            # fuse bn into conv
+            assert self.bn.running_var is not None and self.bn.running_mean is not None
+            conv.weight, conv.bias = fuse_conv_bn_weights(
+                conv.weight,
+                conv.bias,
+                self.bn.running_mean,
+                self.bn.running_var,
+                self.bn.eps,
+                self.bn.weight,
+                self.bn.bias,
+            )
+
+        if cls._FLOAT_RELU_MODULE:  # type: ignore[attr-defined]
+            modules = []
+            modules.append(conv)
+            relu = cls._FLOAT_RELU_MODULE()  # type: ignore[attr-defined]
+            modules.append(relu)
+            conv_relu = cls._FUSED_FLOAT_MODULE(*modules)  # type: ignore[attr-defined]
+            conv_relu.train(self.training)
+            return conv_relu
+        else:
+            conv.train(self.training)
+            return conv
+
+
+class ConvBn1d(_ConvBnNd, nn.Conv1d):
+    r"""
+    A ConvBn1d module is a module fused from Conv1d and BatchNorm1d,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Conv1d` and
+    :class:`torch.nn.BatchNorm1d`.
+
+    Similar to :class:`torch.nn.Conv1d`, with FakeQuantize modules initialized
+    to default.
+
+    Attributes:
+        freeze_bn:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    _FLOAT_BN_MODULE = nn.BatchNorm1d
+    _FLOAT_RELU_MODULE: None = None
+    _FLOAT_MODULE = nni.ConvBn1d
+    _FLOAT_CONV_MODULE = nn.Conv1d
+
+    def __init__(
+        self,
+        # Conv1d args
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=None,
+        padding_mode="zeros",
+        # BatchNorm1d args
+        # num_features: out_channels
+        eps=1e-05,
+        momentum=0.1,
+        # affine: True
+        # track_running_stats: True
+        # Args for this module
+        freeze_bn=False,
+        qconfig=None,
+    ):
+        kernel_size = _single(kernel_size)
+        stride = _single(stride)
+        padding = _single(padding)
+        dilation = _single(dilation)
+        _ConvBnNd.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            False,
+            _single(0),
+            groups,
+            bias,
+            padding_mode,
+            eps,
+            momentum,
+            freeze_bn,
+            qconfig,
+            dim=1,
+        )
+
+
+class ConvBnReLU1d(ConvBn1d):
+    r"""
+    A ConvBnReLU1d module is a module fused from Conv1d, BatchNorm1d and ReLU,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Conv1d` and
+    :class:`torch.nn.BatchNorm1d` and :class:`torch.nn.ReLU`.
+
+    Similar to `torch.nn.Conv1d`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    # base class defines _FLOAT_MODULE as "ConvBn1d"
+    _FLOAT_MODULE = nni.ConvBnReLU1d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE = nn.Conv1d
+    _FLOAT_BN_MODULE = nn.BatchNorm1d
+    _FLOAT_RELU_MODULE = nn.ReLU  # type: ignore[assignment]
+    # module class after fusing bn into conv
+    _FUSED_FLOAT_MODULE = nni.ConvReLU1d
+
+    def __init__(
+        self,
+        # Conv1d args
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=None,
+        padding_mode="zeros",
+        # BatchNorm1d args
+        # num_features: out_channels
+        eps=1e-05,
+        momentum=0.1,
+        # affine: True
+        # track_running_stats: True
+        # Args for this module
+        freeze_bn=False,
+        qconfig=None,
+    ):
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            padding_mode,
+            eps,
+            momentum,
+            freeze_bn,
+            qconfig,
+        )
+
+    def forward(self, input):
+        return F.relu(ConvBn1d._forward(self, input))
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(mod, use_precomputed_fake_quant)
+
+
+class ConvReLU1d(nnqat.Conv1d, nni._FusedModule):
+    r"""A ConvReLU1d module is a fused module of Conv1d and ReLU, attached with
+    FakeQuantize modules for weight for
+    quantization aware training.
+
+    We combined the interface of :class:`~torch.nn.Conv1d` and
+    :class:`~torch.nn.BatchNorm1d`.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    _FLOAT_MODULE = nni.ConvReLU1d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE = nn.Conv1d
+    _FLOAT_BN_MODULE: None = None
+    _FLOAT_RELU_MODULE = nn.ReLU
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        qconfig=None,
+    ):
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+            qconfig=qconfig,
+        )
+        assert qconfig, "qconfig must be provided for QAT module"
+        self.qconfig = qconfig
+        self.weight_fake_quant = self.qconfig.weight()
+
+    def forward(self, input):
+        return F.relu(
+            self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+class ConvBn2d(_ConvBnNd, nn.Conv2d):
+    r"""
+    A ConvBn2d module is a module fused from Conv2d and BatchNorm2d,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Conv2d` and
+    :class:`torch.nn.BatchNorm2d`.
+
+    Similar to :class:`torch.nn.Conv2d`, with FakeQuantize modules initialized
+    to default.
+
+    Attributes:
+        freeze_bn:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    _FLOAT_MODULE = nni.ConvBn2d
+    _FLOAT_CONV_MODULE = nn.Conv2d
+    _FLOAT_BN_MODULE = nn.BatchNorm2d
+    _FLOAT_RELU_MODULE: None = None
+
+    def __init__(
+        self,
+        # ConvNd args
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=None,
+        padding_mode="zeros",
+        # BatchNorm2d args
+        # num_features: out_channels
+        eps=1e-05,
+        momentum=0.1,
+        # affine: True
+        # track_running_stats: True
+        # Args for this module
+        freeze_bn=False,
+        qconfig=None,
+    ):
+        kernel_size = _pair(kernel_size)
+        stride = _pair(stride)
+        padding = _pair(padding)
+        dilation = _pair(dilation)
+        _ConvBnNd.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            False,
+            _pair(0),
+            groups,
+            bias,
+            padding_mode,
+            eps,
+            momentum,
+            freeze_bn,
+            qconfig,
+            dim=2,
+        )
+
+
+class ConvBnReLU2d(ConvBn2d):
+    r"""
+    A ConvBnReLU2d module is a module fused from Conv2d, BatchNorm2d and ReLU,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Conv2d` and
+    :class:`torch.nn.BatchNorm2d` and :class:`torch.nn.ReLU`.
+
+    Similar to `torch.nn.Conv2d`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    # base class defines _FLOAT_MODULE as "ConvBn2d"
+    _FLOAT_MODULE = nni.ConvBnReLU2d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE = nn.Conv2d
+    _FLOAT_BN_MODULE = nn.BatchNorm2d
+    _FLOAT_RELU_MODULE = nn.ReLU  # type: ignore[assignment]
+    # module class after fusing bn into conv
+    _FUSED_FLOAT_MODULE = nni.ConvReLU2d
+
+    def __init__(
+        self,
+        # Conv2d args
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=None,
+        padding_mode="zeros",
+        # BatchNorm2d args
+        # num_features: out_channels
+        eps=1e-05,
+        momentum=0.1,
+        # affine: True
+        # track_running_stats: True
+        # Args for this module
+        freeze_bn=False,
+        qconfig=None,
+    ):
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            padding_mode,
+            eps,
+            momentum,
+            freeze_bn,
+            qconfig,
+        )
+
+    def forward(self, input):
+        return F.relu(ConvBn2d._forward(self, input))
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(mod, use_precomputed_fake_quant)
+
+
+class ConvReLU2d(nnqat.Conv2d, nni._FusedModule):
+    r"""A ConvReLU2d module is a fused module of Conv2d and ReLU, attached with
+    FakeQuantize modules for weight for
+    quantization aware training.
+
+    We combined the interface of :class:`~torch.nn.Conv2d` and
+    :class:`~torch.nn.BatchNorm2d`.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    _FLOAT_MODULE = nni.ConvReLU2d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE = nn.Conv2d
+    _FLOAT_BN_MODULE: None = None
+    _FLOAT_RELU_MODULE = nn.ReLU
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        qconfig=None,
+    ):
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+            qconfig=qconfig,
+        )
+        assert qconfig, "qconfig must be provided for QAT module"
+        self.qconfig = qconfig
+        self.weight_fake_quant = self.qconfig.weight()
+
+    def forward(self, input):
+        return F.relu(
+            self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+class ConvBn3d(_ConvBnNd, nn.Conv3d):
+    r"""
+    A ConvBn3d module is a module fused from Conv3d and BatchNorm3d,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Conv3d` and
+    :class:`torch.nn.BatchNorm3d`.
+
+    Similar to :class:`torch.nn.Conv3d`, with FakeQuantize modules initialized
+    to default.
+
+    Attributes:
+        freeze_bn:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    _FLOAT_MODULE = nni.ConvBn3d
+    _FLOAT_CONV_MODULE = nn.Conv3d
+    _FLOAT_BN_MODULE = nn.BatchNorm3d
+    _FLOAT_RELU_MODULE: None = None
+
+    def __init__(
+        self,
+        # ConvNd args
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=None,
+        padding_mode="zeros",
+        # BatchNorm3d args
+        # num_features: out_channels
+        eps=1e-05,
+        momentum=0.1,
+        # affine: True
+        # track_running_stats: True
+        # Args for this module
+        freeze_bn=False,
+        qconfig=None,
+    ):
+        kernel_size = _triple(kernel_size)
+        stride = _triple(stride)
+        padding = _triple(padding)
+        dilation = _triple(dilation)
+        _ConvBnNd.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            False,
+            _triple(0),
+            groups,
+            bias,
+            padding_mode,
+            eps,
+            momentum,
+            freeze_bn,
+            qconfig,
+            dim=3,
+        )
+
+
+class ConvBnReLU3d(ConvBn3d):
+    r"""
+    A ConvBnReLU3d module is a module fused from Conv3d, BatchNorm3d and ReLU,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Conv3d` and
+    :class:`torch.nn.BatchNorm3d` and :class:`torch.nn.ReLU`.
+
+    Similar to `torch.nn.Conv3d`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    _FLOAT_MODULE = nni.ConvBnReLU3d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE = nn.Conv3d
+    _FLOAT_BN_MODULE = nn.BatchNorm3d
+    _FLOAT_RELU_MODULE = nn.ReLU  # type: ignore[assignment]
+    # module class after fusing bn into conv
+    _FUSED_FLOAT_MODULE = nni.ConvReLU3d
+
+    def __init__(
+        self,
+        # Conv3d args
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=None,
+        padding_mode="zeros",
+        # BatchNorm3d args
+        # num_features: out_channels
+        eps=1e-05,
+        momentum=0.1,
+        # affine: True
+        # track_running_stats: True
+        # Args for this module
+        freeze_bn=False,
+        qconfig=None,
+    ):
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            padding_mode,
+            eps,
+            momentum,
+            freeze_bn,
+            qconfig,
+        )
+
+    def forward(self, input):
+        return F.relu(ConvBn3d._forward(self, input))
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+class ConvReLU3d(nnqat.Conv3d, nni._FusedModule):
+    r"""A ConvReLU3d module is a fused module of Conv3d and ReLU, attached with
+    FakeQuantize modules for weight for
+    quantization aware training.
+
+    We combined the interface of :class:`~torch.nn.Conv3d` and
+    :class:`~torch.nn.BatchNorm3d`.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    _FLOAT_MODULE = nni.ConvReLU3d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE = nn.Conv3d
+    _FLOAT_BN_MODULE: None = None
+    _FLOAT_RELU_MODULE = nn.ReLU
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        qconfig=None,
+    ):
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+            qconfig=qconfig,
+        )
+        assert qconfig, "qconfig must be provided for QAT module"
+        self.qconfig = qconfig
+        self.weight_fake_quant = self.qconfig.weight()
+
+    def forward(self, input):
+        return F.relu(
+            self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+def update_bn_stats(mod):
+    if type(mod) in {
+        ConvBnReLU1d,
+        ConvBnReLU2d,
+        ConvBnReLU3d,
+        ConvBn1d,
+        ConvBn2d,
+        ConvBn3d,
+    }:
+        mod.update_bn_stats()
+
+
+def freeze_bn_stats(mod):
+    if type(mod) in {
+        ConvBnReLU1d,
+        ConvBnReLU2d,
+        ConvBnReLU3d,
+        ConvBn1d,
+        ConvBn2d,
+        ConvBn3d,
+    }:
+        mod.freeze_bn_stats()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_fused.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_fused.py
new file mode 100644
index 0000000000000000000000000000000000000000..75d46cf3f7d66cf3d4b090f72d086107616c11b7
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_fused.py
@@ -0,0 +1,193 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import init
+from torch.nn.parameter import Parameter
+from torch.nn.utils.fusion import fuse_linear_bn_weights
+
+
+__all__ = [
+    "LinearBn1d",
+]
+
+
+class LinearBn1d(nn.modules.linear.Linear, nni._FusedModule):
+    r"""
+    A LinearBn1d module is a module fused from Linear and BatchNorm1d, attached
+    with FakeQuantize modules for weight, used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Linear` and
+    :class:torch.nn.BatchNorm1d`.
+
+    Similar to :class:`torch.nn.Linear`, with FakeQuantize modules initialized
+    to default.
+
+    Attributes:
+        freeze_bn:
+        weight_fake_quant: fake quant module for weight
+
+    """
+
+    def __init__(
+        self,
+        # Linear args
+        in_features,
+        out_features,
+        bias=True,
+        # BatchNorm1d args
+        # num_features: out_features
+        eps=1e-05,
+        momentum=0.1,
+        # affine: True
+        # track_running_stats: True
+        # Args for this module
+        freeze_bn=False,
+        qconfig=None,
+    ):
+        nn.modules.linear.Linear.__init__(self, in_features, out_features, bias)
+        assert qconfig, "qconfig must be provided for QAT module"
+        self.qconfig = qconfig
+        self.freeze_bn = freeze_bn if self.training else True
+        self.bn = nn.BatchNorm1d(out_features, eps, momentum, True, True)
+        self.weight_fake_quant = self.qconfig.weight()
+        if bias:
+            self.bias = Parameter(torch.empty(out_features))
+        else:
+            self.register_parameter("bias", None)
+        self.reset_bn_parameters()
+
+        # this needs to be called after reset_bn_parameters,
+        # as they modify the same state
+        if self.training:
+            if freeze_bn:
+                self.freeze_bn_stats()
+            else:
+                self.update_bn_stats()
+        else:
+            self.freeze_bn_stats()
+
+    def reset_running_stats(self):
+        self.bn.reset_running_stats()
+
+    def reset_bn_parameters(self):
+        self.bn.reset_running_stats()
+        init.uniform_(self.bn.weight)
+        init.zeros_(self.bn.bias)
+
+    def reset_parameters(self):
+        super().reset_parameters()
+
+    def update_bn_stats(self):
+        self.freeze_bn = False
+        self.bn.training = True
+        return self
+
+    def freeze_bn_stats(self):
+        self.freeze_bn = True
+        self.bn.training = False
+        return self
+
+    def forward(self, input):
+        assert self.bn.running_var is not None
+
+        # Scale the linear weights by BN's running statistics to reduce
+        # weight jitter, see https://arxiv.org/pdf/1806.08342.pdf, page 18
+        # for motivation.
+        #
+        # Instead of
+        #
+        #   x1 = F.linear(x0, fq(w), b)
+        #   x2 = self.bn(x1)
+        #
+        # We have
+        #
+        #   # scale the weight by previous batch's running statistics
+        #   scale_factor = bn.w / bn.running_std_from_prev_batch
+        #   # do the linear transformation without bias
+        #   x1_scaled = F.linear(x0, fq(w * scale_factor), 0)
+        #   # reverse the scaling and add original bias
+        #   x1_orig = x1_scaled / scale_factor + b
+        #   x2 = self.bn(x1_orig)
+
+        running_std = torch.sqrt(self.bn.running_var + self.bn.eps)
+        scale_factor = self.bn.weight / running_std
+        weight_shape = [1] * len(self.weight.shape)
+        weight_shape[0] = -1
+        bias_shape = [1] * len(self.weight.shape)
+        bias_shape[1] = -1
+        scaled_weight = self.weight_fake_quant(
+            self.weight * scale_factor.reshape(weight_shape)
+        )
+        if self.bias is not None:
+            zero_bias = torch.zeros_like(self.bias)
+        else:
+            zero_bias = torch.zeros(self.out_features, device=scaled_weight.device)
+        linear_out = F.linear(input, scaled_weight, zero_bias)
+        linear_out_orig = linear_out / scale_factor.reshape(bias_shape)
+        if self.bias is not None:
+            linear_out_orig = linear_out_orig + self.bias.reshape(bias_shape)
+        bn_out = self.bn(linear_out_orig)
+        return bn_out
+
+    def train(self, mode=True):
+        """
+        Batchnorm's training behavior is using the self.training flag. Prevent
+        changing it if BN is frozen. This makes sure that calling `model.train()`
+        on a model with a frozen BN will behave properly.
+        """
+        self.training = mode
+        if not self.freeze_bn:
+            for module in self.children():
+                module.train(mode)
+        return self
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a qat module from a float module or qparams_dict
+
+        Args: `mod' a float module, either produced by torch.ao.quantization
+        utilities or directly from user
+        """
+        assert type(mod) == nni.LinearBn1d, (
+            "qat."
+            + cls.__name__
+            + ".from_float only works for "
+            + nni.LinearBn1d.__name__
+        )
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        assert mod.qconfig, "Input float module must have a valid config"
+        qconfig = mod.qconfig
+        linear, bn = mod[0], mod[1]
+        qat_linearbn = cls(
+            linear.in_features,
+            linear.out_features,
+            linear.bias is not None,
+            bn.eps,
+            bn.momentum,
+            False,
+            qconfig,
+        )
+        qat_linearbn.weight = linear.weight
+        qat_linearbn.bias = linear.bias
+        qat_linearbn.bn.weight = bn.weight
+        qat_linearbn.bn.bias = bn.bias
+        qat_linearbn.bn.running_mean = bn.running_mean
+        qat_linearbn.bn.running_var = bn.running_var
+        qat_linearbn.bn.num_batches_tracked = bn.num_batches_tracked
+        return qat_linearbn
+
+    def to_float(self):
+        linear = torch.nn.Linear(self.in_features, self.out_features)
+        assert self.bn.running_var is not None and self.bn.running_mean is not None
+        linear.weight, linear.bias = fuse_linear_bn_weights(
+            self.weight,
+            self.bias,
+            self.bn.running_mean,
+            self.bn.running_var,
+            self.bn.eps,
+            self.bn.weight,
+            self.bn.bias,
+        )
+        return linear
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py
new file mode 100644
index 0000000000000000000000000000000000000000..9cf0d4bba898c019c7cf71932d8554dea8103af5
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py
@@ -0,0 +1,51 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.qat as nnqat
+import torch.nn.functional as F
+
+
+class LinearReLU(nnqat.Linear, nni._FusedModule):
+    r"""
+    A LinearReLU module fused from Linear and ReLU modules, attached with
+    FakeQuantize modules for weight, used in
+    quantization aware training.
+
+    We adopt the same interface as :class:`torch.nn.Linear`.
+
+    Similar to `torch.ao.nn.intrinsic.LinearReLU`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight: fake quant module for weight
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> m = nn.qat.LinearReLU(20, 30)
+        >>> input = torch.randn(128, 20)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 30])
+    """
+    _FLOAT_MODULE = nni.LinearReLU  # type: ignore[assignment]
+
+    def __init__(self, in_features, out_features, bias=True, qconfig=None):
+        super().__init__(in_features, out_features, bias, qconfig)
+
+    def forward(self, input):
+        return F.relu(F.linear(input, self.weight_fake_quant(self.weight), self.bias))
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(mod, use_precomputed_fake_quant)
+
+    def to_float(self):
+        linear = torch.nn.Linear(
+            self.in_features, self.out_features, self.bias is not None
+        )
+        linear.weight = torch.nn.Parameter(self.weight.detach())
+        if self.bias is not None:
+            linear.bias = torch.nn.Parameter(self.bias.detach())
+        relu = torch.nn.ReLU()
+        return torch.ao.nn.intrinsic.LinearReLU(linear, relu)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6af3b4aeee893966323cc4e73a27ff41814fc251
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/__init__.py
@@ -0,0 +1,15 @@
+from .modules import *  # noqa: F403
+
+
+__all__ = [
+    "BNReLU2d",
+    "BNReLU3d",
+    "ConvReLU1d",
+    "ConvReLU2d",
+    "ConvReLU3d",
+    "LinearReLU",
+    "LinearLeakyReLU",
+    "LinearTanh",
+    "ConvAdd2d",
+    "ConvAddReLU2d",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d79bdbfe83209f18b17cc8c7b245f322871d6c0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/__init__.py
@@ -0,0 +1 @@
+from .modules import *  # noqa: F403
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7a6c3c57c7828861b574e76b134aee2c23f0aad
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/__init__.py
@@ -0,0 +1,6 @@
+from .linear_relu import LinearReLU
+
+
+__all__ = [
+    "LinearReLU",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py
new file mode 100644
index 0000000000000000000000000000000000000000..1515e005cc7b398d275123ea1e779873bff9ce7c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py
@@ -0,0 +1,60 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.quantized.dynamic as nnqd
+
+
+__all__ = ["LinearReLU"]
+
+
+class LinearReLU(nnqd.Linear):
+    r"""
+    A LinearReLU module fused from Linear and ReLU modules that can be used
+    for dynamic quantization.
+    Supports both, FP16 and INT8 quantization.
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.dynamic.Linear`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.dynamic.Linear
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> m = nn.intrinsic.quantized.dynamic.LinearReLU(20, 30)
+        >>> input = torch.randn(128, 20)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 30])
+    """
+    _FLOAT_MODULE = nni.LinearReLU  # type: ignore[assignment]
+
+    def __init__(self, in_features, out_features, bias=True, dtype=torch.qint8):
+        super().__init__(in_features, out_features, bias, dtype)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self._packed_params.dtype == torch.qint8:
+            # TODO check if we should set reduce_rage = True by default here
+            Y = torch.ops.quantized.linear_relu_dynamic(
+                x, self._packed_params._packed_params, reduce_range=True
+            )
+        elif self._packed_params.dtype == torch.float16:
+            Y = torch.ops.quantized.linear_relu_dynamic_fp16(
+                x, self._packed_params._packed_params
+            )
+        else:
+            raise RuntimeError("Unsupported dtype on dynamic quantized linear relu!")
+        return Y.to(x.dtype)
+
+    def _get_name(self):
+        return "DynamicQuantizedLinearReLU"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+    @classmethod
+    def from_reference(cls, ref_qlinear_relu):
+        return super().from_reference(ref_qlinear_relu[0])
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/modules/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7fa4dcec2597e18c002489405894ea7251d5156
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/modules/__init__.py
@@ -0,0 +1,18 @@
+from .bn_relu import BNReLU2d, BNReLU3d
+from .conv_add import ConvAdd2d, ConvAddReLU2d
+from .conv_relu import ConvReLU1d, ConvReLU2d, ConvReLU3d
+from .linear_relu import LinearLeakyReLU, LinearReLU, LinearTanh
+
+
+__all__ = [
+    "LinearReLU",
+    "ConvReLU1d",
+    "ConvReLU2d",
+    "ConvReLU3d",
+    "BNReLU2d",
+    "BNReLU3d",
+    "LinearLeakyReLU",
+    "LinearTanh",
+    "ConvAdd2d",
+    "ConvAddReLU2d",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/modules/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/modules/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/modules/bn_relu.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/modules/bn_relu.py
new file mode 100644
index 0000000000000000000000000000000000000000..78009d1c76f4ac2e099e25e63c8201ad1f087920
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/modules/bn_relu.py
@@ -0,0 +1,105 @@
+# mypy: allow-untyped-defs
+
+import torch
+import torch.ao.nn.intrinsic
+import torch.ao.nn.intrinsic.qat
+import torch.ao.nn.quantized as nnq
+
+
+__all__ = ["BNReLU2d", "BNReLU3d"]
+
+
+class BNReLU2d(nnq.BatchNorm2d):
+    r"""
+    A BNReLU2d module is a fused module of BatchNorm2d and ReLU
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.BatchNorm2d`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.BatchNorm2d
+
+    """
+    _FLOAT_MODULE = torch.ao.nn.intrinsic.BNReLU2d
+
+    def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None):
+        super().__init__(
+            num_features, eps=eps, momentum=momentum, device=device, dtype=dtype
+        )
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+        return torch.ops.quantized.batch_norm2d_relu(
+            input,
+            self.weight,
+            self.bias,
+            self.running_mean,
+            self.running_var,
+            self.eps,
+            self.scale,
+            self.zero_point,
+        )
+
+    def _get_name(self):
+        return "QuantizedBNReLU2d"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        # TODO: Add qat support for BNReLU2d
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+    @classmethod
+    def from_reference(cls, bn_relu, output_scale, output_zero_point):
+        return super().from_reference(bn_relu[0], output_scale, output_zero_point)
+
+
+class BNReLU3d(nnq.BatchNorm3d):
+    r"""
+    A BNReLU3d module is a fused module of BatchNorm3d and ReLU
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.BatchNorm3d`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.BatchNorm3d
+
+    """
+    _FLOAT_MODULE = torch.ao.nn.intrinsic.BNReLU3d
+
+    def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None):
+        super().__init__(
+            num_features, eps=eps, momentum=momentum, device=device, dtype=dtype
+        )
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 5:
+            raise ValueError("Input shape must be `(N, C, D, H, W)`!")
+        return torch.ops.quantized.batch_norm3d_relu(
+            input,
+            self.weight,
+            self.bias,
+            self.running_mean,
+            self.running_var,
+            self.eps,
+            self.scale,
+            self.zero_point,
+        )
+
+    def _get_name(self):
+        return "QuantizedBNReLU3d"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        # TODO: Add qat support for BNReLU3d
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+    @classmethod
+    def from_reference(cls, bn_relu, output_scale, output_zero_point):
+        return super().from_reference(bn_relu[0], output_scale, output_zero_point)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/modules/conv_add.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/modules/conv_add.py
new file mode 100644
index 0000000000000000000000000000000000000000..66299394091b7c9b13334f87c5efcde9a359b5d8
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/modules/conv_add.py
@@ -0,0 +1,145 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.ao.nn.intrinsic
+import torch.ao.nn.intrinsic.qat
+import torch.ao.nn.quantized as nnq
+import torch.nn.functional as F
+
+
+_reverse_repeat_padding = nnq.modules.conv._reverse_repeat_padding
+
+
+class ConvAdd2d(nnq.Conv2d):
+    r"""
+    A ConvAdd2d module is a fused module of Conv2d and Add
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Conv2d`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.Conv2d
+
+    """
+    _FLOAT_MODULE = torch.ao.nn.intrinsic.ConvAdd2d  # type: ignore[assignment]
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+            device=device,
+            dtype=dtype,
+        )
+
+    def forward(self, input, extra_input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+        if self.padding_mode != "zeros":
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return torch.ops.quantized.conv2d_add(
+            input, extra_input, self._packed_params, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedConvAdd2d"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+    @classmethod
+    def from_reference(cls, ref_qconv, output_scale, output_zero_point):
+        return super().from_reference(ref_qconv[0], output_scale, output_zero_point)
+
+
+class ConvAddReLU2d(nnq.Conv2d):
+    r"""
+    A ConvAddReLU2d module is a fused module of Conv2d, Add and Relu
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Conv2d`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.Conv2d
+
+    """
+    _FLOAT_MODULE = torch.ao.nn.intrinsic.ConvAddReLU2d  # type: ignore[assignment]
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+            device=device,
+            dtype=dtype,
+        )
+
+    def forward(self, input, extra_input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+        if self.padding_mode != "zeros":
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return torch.ops.quantized.conv2d_add_relu(
+            input, extra_input, self._packed_params, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedConvAddReLU2d"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+    @classmethod
+    def from_reference(cls, ref_qconv, output_scale, output_zero_point):
+        return super().from_reference(ref_qconv[0], output_scale, output_zero_point)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/modules/conv_relu.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/modules/conv_relu.py
new file mode 100644
index 0000000000000000000000000000000000000000..25d695859180fc5ccb9b2f53b107e438c547de4a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/modules/conv_relu.py
@@ -0,0 +1,263 @@
+# mypy: allow-untyped-defs
+
+import torch
+import torch.ao.nn.intrinsic
+import torch.ao.nn.intrinsic.qat
+import torch.ao.nn.quantized as nnq
+import torch.nn.functional as F
+from torch.nn.utils import fuse_conv_bn_weights
+
+
+__all__ = [
+    "ConvReLU1d",
+    "ConvReLU2d",
+    "ConvReLU3d",
+]
+
+_reverse_repeat_padding = nnq.modules.conv._reverse_repeat_padding
+
+
+# TODO: factor out the common parts to ConvNd
+class ConvReLU1d(nnq.Conv1d):
+    r"""
+    A ConvReLU1d module is a fused module of Conv1d and ReLU
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Conv1d`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.Conv1d
+
+    """
+    _FLOAT_MODULE = torch.ao.nn.intrinsic.ConvReLU1d  # type: ignore[assignment]
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+            device=device,
+            dtype=dtype,
+        )
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 3:
+            raise ValueError("Input shape must be `(N, C, L)`!")
+        if self.padding_mode != "zeros":
+            # Padding in Conv1d is stored as (p, p), need to get (p,)
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding[:1])
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return torch.ops.quantized.conv1d_relu(
+            input, self._packed_params, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedConvReLU1d"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        if type(mod) == torch.ao.nn.intrinsic.qat.ConvBnReLU1d:
+            assert mod.bn.running_var is not None and mod.bn.running_mean is not None
+            mod.weight, mod.bias = fuse_conv_bn_weights(
+                mod.weight,
+                mod.bias,
+                mod.bn.running_mean,
+                mod.bn.running_var,
+                mod.bn.eps,
+                mod.bn.weight,
+                mod.bn.bias,
+            )
+        return super().from_float(mod, use_precomputed_fake_quant)
+
+    @classmethod
+    def from_reference(cls, ref_qconv, output_scale, output_zero_point):
+        assert (
+            type(ref_qconv) != torch.ao.nn.intrinsic.ConvBnReLU1d
+        ), "BatchNorm1d should be fused into Conv1d before converting to reference module"
+        return super().from_reference(ref_qconv[0], output_scale, output_zero_point)
+
+
+class ConvReLU2d(nnq.Conv2d):
+    r"""
+    A ConvReLU2d module is a fused module of Conv2d and ReLU
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Conv2d`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.Conv2d
+
+    """
+    _FLOAT_MODULE = torch.ao.nn.intrinsic.ConvReLU2d  # type: ignore[assignment]
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+            device=device,
+            dtype=dtype,
+        )
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+        if self.padding_mode != "zeros":
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return torch.ops.quantized.conv2d_relu(
+            input, self._packed_params, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedConvReLU2d"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        if type(mod) == torch.ao.nn.intrinsic.qat.ConvBnReLU2d:
+            assert mod.bn.running_var is not None and mod.bn.running_mean is not None
+            mod.weight, mod.bias = fuse_conv_bn_weights(
+                mod.weight,
+                mod.bias,
+                mod.bn.running_mean,
+                mod.bn.running_var,
+                mod.bn.eps,
+                mod.bn.weight,
+                mod.bn.bias,
+            )
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+    @classmethod
+    def from_reference(cls, ref_qconv, output_scale, output_zero_point):
+        assert (
+            type(ref_qconv) != torch.ao.nn.intrinsic.ConvBnReLU2d
+        ), "BatchNorm2d should be fused into Conv2d before converting to reference module"
+        return super().from_reference(ref_qconv[0], output_scale, output_zero_point)
+
+
+class ConvReLU3d(nnq.Conv3d):
+    r"""
+    A ConvReLU3d module is a fused module of Conv3d and ReLU
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Conv3d`.
+
+    Attributes: Same as torch.ao.nn.quantized.Conv3d
+
+    """
+    _FLOAT_MODULE = torch.ao.nn.intrinsic.ConvReLU3d  # type: ignore[assignment]
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        assert padding_mode != "reflect", "Conv3d does not support reflection padding"
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+            device=device,
+            dtype=dtype,
+        )
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 5:
+            raise ValueError("Input shape must be `(N, C, D, H, W)`!")
+        if self.padding_mode != "zeros":
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return torch.ops.quantized.conv3d_relu(
+            input, self._packed_params, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedConvReLU3d"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        if type(mod) == torch.ao.nn.intrinsic.qat.ConvBnReLU3d:
+            assert mod.bn.running_var is not None and mod.bn.running_mean is not None
+            mod.weight, mod.bias = fuse_conv_bn_weights(
+                mod.weight,
+                mod.bias,
+                mod.bn.running_mean,
+                mod.bn.running_var,
+                mod.bn.eps,
+                mod.bn.weight,
+                mod.bn.bias,
+            )
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+    @classmethod
+    def from_reference(cls, ref_qconv, output_scale, output_zero_point):
+        assert (
+            type(ref_qconv) != torch.ao.nn.intrinsic.ConvBnReLU3d
+        ), "BatchNorm3d should be fused into Conv3d before converting to reference module"
+        return super().from_reference(ref_qconv[0], output_scale, output_zero_point)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py
new file mode 100644
index 0000000000000000000000000000000000000000..5173ed813bf0db093470c2f273b12e0f974b3ac1
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py
@@ -0,0 +1,187 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.quantized as nnq
+from torch.ao.nn.quantized.modules.utils import _quantize_weight
+
+
+__all__ = [
+    "LinearReLU",
+    "LinearLeakyReLU",
+    "LinearTanh",
+]
+
+
+class LinearReLU(nnq.Linear):
+    r"""
+    A LinearReLU module fused from Linear and ReLU modules
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Linear`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.Linear
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> m = nn.intrinsic.LinearReLU(20, 30)
+        >>> input = torch.randn(128, 20)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 30])
+    """
+    _FLOAT_MODULE = nni.LinearReLU  # type: ignore[assignment]
+
+    def __init__(self, in_features, out_features, bias=True, dtype=torch.qint8):
+        super().__init__(in_features, out_features, bias, dtype)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.ops.quantized.linear_relu(
+            x, self._packed_params._packed_params, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedLinearReLU"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(mod, use_precomputed_fake_quant)
+
+    @classmethod
+    def from_reference(cls, ref_linear_relu, output_scale, output_zero_point):
+        return super().from_reference(
+            ref_linear_relu[0], output_scale, output_zero_point
+        )
+
+
+class LinearLeakyReLU(nnq.Linear):
+    r"""
+    For onednn backend only
+    A LinearLeakyReLU module fused from Linear and LeakyReLU modules
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Linear`.
+    Attributes:
+        Same as torch.ao.nn.quantized.Linear
+        + negative_slope
+    Examples::
+        >>> # xdoctest: +SKIP
+        >>> m = nn.intrinsic.LinearLeakyReLU(20, 30, 0.01)
+        >>> input = torch.randn(128, 20)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 30])
+    """
+    _FLOAT_MODULE = nni.LinearLeakyReLU  # type: ignore[assignment]
+
+    def __init__(
+        self, in_features, out_features, negative_slope, bias=True, dtype=torch.qint8
+    ):
+        super().__init__(in_features, out_features, bias, dtype)
+        self.negative_slope = negative_slope
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.ops.quantized.linear_leaky_relu(
+            x,
+            self._packed_params._packed_params,
+            self.scale,
+            self.zero_point,
+            self.negative_slope,
+        )
+
+    def _get_name(self):
+        return "QuantizedLinearLeakyReLU"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        assert (
+            type(mod) == nni.LinearLeakyReLU
+        ), "Input float module should be LinearLeakyReLU"
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        activation_post_process = mod.activation_post_process
+        leaky_relu = mod[1]
+        mod = mod[0]
+        weight_post_process = mod.qconfig.weight()
+        weight_post_process(mod.weight)
+        dtype = weight_post_process.dtype
+        act_scale, act_zp = activation_post_process.calculate_qparams()  # type: ignore[union-attr,operator]
+        assert dtype == torch.qint8, "Weight observer must have dtype torch.qint8"
+        qweight = _quantize_weight(mod.weight.float(), weight_post_process)
+        qlinear_leaky_relu = cls(
+            mod.in_features, mod.out_features, leaky_relu.negative_slope, dtype=dtype
+        )
+        qlinear_leaky_relu.set_weight_bias(qweight, mod.bias)
+        qlinear_leaky_relu.scale = float(act_scale)
+        qlinear_leaky_relu.zero_point = int(act_zp)
+        return qlinear_leaky_relu
+
+    @classmethod
+    def from_reference(cls, ref_mod, output_scale, output_zero_point):
+        linear = ref_mod[0]
+        leaky_relu = ref_mod[1]
+        qlinear_leaky_relu = cls(
+            linear.in_features, linear.out_features, leaky_relu.negative_slope
+        )
+        qweight = linear.get_quantized_weight()
+        qlinear_leaky_relu.set_weight_bias(qweight, linear.bias)
+        qlinear_leaky_relu.scale = float(output_scale)
+        qlinear_leaky_relu.zero_point = int(output_zero_point)
+        return qlinear_leaky_relu
+
+
+class LinearTanh(nnq.Linear):
+    r"""
+    A LinearTanh module fused from Linear and Tanh modules
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Linear`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.Linear
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> m = nn.intrinsic.LinearTanh(20, 30)
+        >>> input = torch.randn(128, 20)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 30])
+    """
+    _FLOAT_MODULE = nni.LinearTanh  # type: ignore[assignment]
+
+    def __init__(self, in_features, out_features, bias=True, dtype=torch.qint8):
+        super().__init__(in_features, out_features, bias, dtype)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.ops.quantized.linear_tanh(
+            x, self._packed_params._packed_params, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedLinearTanh"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        assert type(mod) == nni.LinearTanh, "Input float module should be LinearTanh"
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        activation_post_process = mod.activation_post_process
+        mod = mod[0]
+        weight_post_process = mod.qconfig.weight()
+        weight_post_process(mod.weight)
+        dtype = weight_post_process.dtype
+        act_scale, act_zp = activation_post_process.calculate_qparams()  # type: ignore[union-attr,operator]
+        assert dtype == torch.qint8, "Weight observer must have dtype torch.qint8"
+        qweight = _quantize_weight(mod.weight.float(), weight_post_process)
+        qlinear_tanh = cls(mod.in_features, mod.out_features, dtype=dtype)
+        qlinear_tanh.set_weight_bias(qweight, mod.bias)
+        qlinear_tanh.scale = float(act_scale)
+        qlinear_tanh.zero_point = int(act_zp)
+        return qlinear_tanh
+
+    @classmethod
+    def from_reference(cls, ref_mod, output_scale, output_zero_point):
+        linear = ref_mod[0]
+        qlinear_tanh = cls(linear.in_features, linear.out_features)
+        qweight = linear.get_quantized_weight()
+        qlinear_tanh.set_weight_bias(qweight, linear.bias)
+        qlinear_tanh.scale = float(output_scale)
+        qlinear_tanh.zero_point = int(output_zero_point)
+        return qlinear_tanh
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d79bdbfe83209f18b17cc8c7b245f322871d6c0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/__init__.py
@@ -0,0 +1 @@
+from .modules import *  # noqa: F403
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c531197afc4fb145873f5e212fd6ae7969cf73a1
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/dynamic/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/dynamic/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d79bdbfe83209f18b17cc8c7b245f322871d6c0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/dynamic/__init__.py
@@ -0,0 +1 @@
+from .modules import *  # noqa: F403
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/dynamic/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/dynamic/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..1a9c6b861ff12fe67b1c8185e93d7702b43eda92
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/dynamic/modules/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/dynamic/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..dca71fcf09b019f3e197576eb415ba4fd54fa28a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/dynamic/modules/__init__.py
@@ -0,0 +1,4 @@
+from .linear import Linear
+
+
+__all__ = ["Linear"]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/dynamic/modules/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/dynamic/modules/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/dynamic/modules/__pycache__/linear.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/dynamic/modules/__pycache__/linear.cpython-311.pyc
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index 0000000000000000000000000000000000000000..d6cdfa08684d7633da02dc11f34361415325367a
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/dynamic/modules/linear.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/dynamic/modules/linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..3dd5a284e9293389793b40b3ad60517a2f6b6ef1
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/dynamic/modules/linear.py
@@ -0,0 +1,35 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+__all__ = ["Linear"]
+
+
+class Linear(torch.ao.nn.qat.Linear):
+    r"""
+    A linear module attached with FakeQuantize modules for weight,
+    used for dynamic quantization aware training.
+
+    We adopt the same interface as `torch.nn.Linear`, please see
+    https://pytorch.org/docs/stable/nn.html#torch.nn.Linear
+    for documentation.
+
+    Similar to `torch.nn.Linear`, with FakeQuantize modules initialized to
+    default.
+    """
+
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        bias=True,
+        qconfig=None,
+        device=None,
+        dtype=None,
+    ) -> None:
+        super().__init__(in_features, out_features, bias, qconfig, device, dtype)
+        if not torch.ao.quantization.qconfig._activation_is_memoryless(qconfig):
+            raise ValueError(
+                "Dynamic QAT requires a memoryless observer."
+                + "This means a MovingAverage observer with averaging constant equal to 1"
+            )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/modules/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e28e0968a60d7612ebbd26d5f607b4407c2d380
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/modules/__init__.py
@@ -0,0 +1,13 @@
+from .conv import Conv1d, Conv2d, Conv3d
+from .embedding_ops import Embedding, EmbeddingBag
+from .linear import Linear
+
+
+__all__ = [
+    "Linear",
+    "Conv1d",
+    "Conv2d",
+    "Conv3d",
+    "Embedding",
+    "EmbeddingBag",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/modules/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/modules/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/modules/conv.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/modules/conv.py
new file mode 100644
index 0000000000000000000000000000000000000000..3be5a7cec8162e0e70bb73e5c830546ce6542917
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/modules/conv.py
@@ -0,0 +1,310 @@
+# mypy: allow-untyped-defs
+from typing import Tuple, TypeVar, Union
+
+import torch
+import torch.nn as nn
+from torch.ao.nn.intrinsic import _FusedModule
+from torch.nn.common_types import _size_1_t, _size_2_t, _size_3_t
+from torch.nn.modules.utils import _pair, _single, _triple
+
+
+__all__ = ["Conv1d", "Conv2d", "Conv3d"]
+
+MOD = TypeVar("MOD", bound=nn.modules.conv._ConvNd)
+
+
+class _ConvNd(nn.modules.conv._ConvNd):
+    _FLOAT_MODULE = MOD
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: Tuple[int, ...],
+        stride: Tuple[int, ...],
+        padding: Tuple[int, ...],
+        dilation: Tuple[int, ...],
+        transposed: bool,
+        output_padding: Tuple[int, ...],
+        groups: int,
+        bias: bool,
+        padding_mode: str,
+        qconfig=None,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        nn.modules.conv._ConvNd.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            transposed,
+            output_padding,
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+        assert qconfig, "qconfig must be provided for QAT module"
+        self.qconfig = qconfig
+        self.weight_fake_quant = qconfig.weight(factory_kwargs=factory_kwargs)
+
+    def forward(self, input):
+        return self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
+
+    @staticmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a qat module from a float module
+
+        Args:
+           `mod`: a float module, either produced by torch.ao.quantization utilities
+           or directly from user
+        """
+        assert type(mod) == cls._FLOAT_MODULE, (
+            "qat."
+            + cls.__name__
+            + ".from_float only works for "
+            + cls._FLOAT_MODULE.__name__  # type: ignore[attr-defined]
+        )
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        assert mod.qconfig, "Input float module must have a valid qconfig"
+        if issubclass(type(mod), _FusedModule):
+            mod = mod[0]  # type: ignore[index]
+        qconfig = mod.qconfig
+        qat_conv = cls(
+            mod.in_channels,
+            mod.out_channels,
+            mod.kernel_size,
+            stride=mod.stride,
+            padding=mod.padding,
+            dilation=mod.dilation,
+            groups=mod.groups,
+            bias=mod.bias is not None,
+            padding_mode=mod.padding_mode,
+            qconfig=qconfig,
+        )
+        qat_conv.weight = mod.weight
+        qat_conv.bias = mod.bias
+        return qat_conv
+
+    def to_float(self):
+        """This works for both single qat conv, and the qat conv - relu modules
+        to convert the qat module to a floating point module
+        """
+        cls = type(self)
+        conv = cls._FLOAT_CONV_MODULE(  # type: ignore[attr-defined, operator]
+            self.in_channels,
+            self.out_channels,
+            self.kernel_size,  # type: ignore[arg-type]
+            self.stride,  # type: ignore[arg-type]
+            self.padding,  # type: ignore[arg-type]
+            self.dilation,  # type: ignore[arg-type]
+            self.groups,
+            self.bias is not None,
+            self.padding_mode,
+        )
+        conv.weight = torch.nn.Parameter(self.weight.detach())
+        if self.bias is not None:
+            conv.bias = torch.nn.Parameter(self.bias.detach())
+        # conv relu
+        if issubclass(cls, _FusedModule):
+            modules = [conv]
+            assert hasattr(cls, "_FLOAT_RELU_MODULE")
+            relu = cls._FLOAT_RELU_MODULE()  # type: ignore[attr-defined]
+            modules.append(relu)
+            fused = cls._FLOAT_MODULE(*modules)  # type: ignore[arg-type, attr-defined, operator]
+            fused.train(self.training)
+            return fused
+        else:
+            return conv
+
+
+class Conv1d(_ConvNd, nn.Conv1d):
+    r"""
+    A Conv1d module attached with FakeQuantize modules for weight,
+    used for quantization aware training.
+
+    We adopt the same interface as :class:`~torch.nn.Conv1d`
+
+    Similar to :class:`~torch.nn.Conv2d`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+    """
+    _FLOAT_MODULE = nn.Conv1d
+    _FLOAT_CONV_MODULE = nn.Conv1d
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: _size_1_t,
+        stride: _size_1_t = 1,
+        padding: Union[str, _size_1_t] = 0,
+        dilation: _size_1_t = 1,
+        groups: int = 1,
+        bias: bool = True,
+        padding_mode: str = "zeros",
+        qconfig=None,
+        device=None,
+        dtype=None,
+    ) -> None:
+        kernel_size_ = _single(kernel_size)
+        stride_ = _single(stride)
+        padding_ = padding if isinstance(padding, str) else _single(padding)
+        dilation_ = _single(dilation)
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size_,
+            stride=stride_,
+            padding=padding_,
+            dilation=dilation_,
+            transposed=False,
+            output_padding=_single(0),
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+            qconfig=qconfig,
+            device=device,
+            dtype=dtype,
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(
+            cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+class Conv2d(_ConvNd, nn.Conv2d):
+    r"""
+    A Conv2d module attached with FakeQuantize modules for weight,
+    used for quantization aware training.
+
+    We adopt the same interface as `torch.nn.Conv2d`, please see
+    https://pytorch.org/docs/stable/nn.html?highlight=conv2d#torch.nn.Conv2d
+    for documentation.
+
+    Similar to `torch.nn.Conv2d`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+    """
+    _FLOAT_MODULE = nn.Conv2d
+    _FLOAT_CONV_MODULE = nn.Conv2d
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: _size_2_t,
+        stride: _size_2_t = 1,
+        padding: Union[str, _size_2_t] = 0,
+        dilation: _size_2_t = 1,
+        groups: int = 1,
+        bias: bool = True,
+        padding_mode: str = "zeros",
+        qconfig=None,
+        device=None,
+        dtype=None,
+    ) -> None:
+        kernel_size_ = _pair(kernel_size)
+        stride_ = _pair(stride)
+        padding_ = padding if isinstance(padding, str) else _pair(padding)
+        dilation_ = _pair(dilation)
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size_,
+            stride=stride_,
+            padding=padding_,
+            dilation=dilation_,
+            transposed=False,
+            output_padding=_pair(0),
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+            qconfig=qconfig,
+            device=device,
+            dtype=dtype,
+        )
+
+    def forward(self, input):
+        return self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(
+            cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+class Conv3d(_ConvNd, nn.Conv3d):
+    r"""
+    A Conv3d module attached with FakeQuantize modules for weight,
+    used for quantization aware training.
+
+    We adopt the same interface as `torch.nn.Conv3d`, please see
+    https://pytorch.org/docs/stable/nn.html?highlight=conv3d#torch.nn.Conv3d
+    for documentation.
+
+    Similar to `torch.nn.Conv3d`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+    """
+    _FLOAT_MODULE = nn.Conv3d
+    _FLOAT_CONV_MODULE = nn.Conv3d
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: _size_3_t,
+        stride: _size_3_t = 1,
+        padding: Union[str, _size_3_t] = 0,
+        dilation: _size_3_t = 1,
+        groups: int = 1,
+        bias: bool = True,
+        padding_mode: str = "zeros",
+        qconfig=None,
+        device=None,
+        dtype=None,
+    ) -> None:
+        kernel_size_ = _triple(kernel_size)
+        stride_ = _triple(stride)
+        padding_ = padding if isinstance(padding, str) else _triple(padding)
+        dilation_ = _triple(dilation)
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size_,
+            stride=stride_,
+            padding=padding_,
+            dilation=dilation_,
+            transposed=False,
+            output_padding=_triple(0),
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+            qconfig=qconfig,
+            device=device,
+            dtype=dtype,
+        )
+
+    def forward(self, input):
+        return self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(
+            cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/modules/embedding_ops.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/modules/embedding_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..27ad7c9db437afd97f879e50eacb4b7a19d960a9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/modules/embedding_ops.py
@@ -0,0 +1,248 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+
+
+__all__ = ["Embedding", "EmbeddingBag"]
+
+
+class Embedding(nn.Embedding):
+    r"""
+    An embedding bag module attached with FakeQuantize modules for weight,
+    used for quantization aware training.
+
+    We adopt the same interface as `torch.nn.Embedding`, please see
+    https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html#torch.nn.Embedding
+    for documentation.
+
+    Similar to `torch.nn.Embedding`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight: fake quant module for weight
+    """
+    _FLOAT_MODULE = nn.Embedding
+
+    def __init__(
+        self,
+        num_embeddings,
+        embedding_dim,
+        padding_idx=None,
+        max_norm=None,
+        norm_type=2.0,
+        scale_grad_by_freq=False,
+        sparse=False,
+        _weight=None,
+        device=None,
+        dtype=None,
+        qconfig=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(
+            num_embeddings,
+            embedding_dim,
+            padding_idx,
+            max_norm,
+            norm_type,
+            scale_grad_by_freq,
+            sparse,
+            _weight,
+            **factory_kwargs,
+        )
+        assert qconfig, "qconfig must be provided for QAT module"
+        assert qconfig.weight().qscheme == torch.per_channel_affine_float_qparams, (
+            "Embedding weights requires a qscheme of torch.per_channel_affine_float_qparams Got "
+            + str(qconfig.weight().qscheme)
+        )
+        self.qconfig = qconfig
+        self.weight_fake_quant = qconfig.weight(factory_kwargs=factory_kwargs)
+
+    def forward(self, input) -> Tensor:
+        return F.embedding(
+            input,
+            self.weight_fake_quant(self.weight),
+            self.padding_idx,
+            self.max_norm,
+            self.norm_type,
+            self.scale_grad_by_freq,
+            self.sparse,
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a qat module from a float module
+
+        Args: `mod` a float module, either produced by torch.ao.quantization utilities
+        or directly from user
+        """
+        assert type(mod) == cls._FLOAT_MODULE, (
+            " qat."
+            + cls.__name__
+            + ".from_float only works for "
+            + cls._FLOAT_MODULE.__name__
+        )
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        assert mod.qconfig, "Input float module must have a valid qconfig"
+        weight_qscheme = mod.qconfig.weight().qscheme  # type: ignore[union-attr, operator]
+        assert weight_qscheme == torch.per_channel_affine_float_qparams, (
+            "Embedding weights requires a qscheme of torch.per_channel_affine_float_qparams Got "
+            + str(weight_qscheme)
+        )
+
+        qconfig = mod.qconfig
+        qat_embedding_bag = cls(
+            mod.num_embeddings,
+            mod.embedding_dim,
+            mod.padding_idx,
+            mod.max_norm,
+            mod.norm_type,
+            mod.scale_grad_by_freq,
+            mod.sparse,
+            mod.weight,
+            qconfig=qconfig,
+        )
+
+        return qat_embedding_bag
+
+    def to_float(self):
+        embedding_bag = torch.nn.Embedding(
+            self.num_embeddings,
+            self.embedding_dim,
+            self.padding_idx,
+            self.max_norm,
+            self.norm_type,
+            self.scale_grad_by_freq,
+            self.sparse,
+            None,
+        )
+        embedding_bag.weight = torch.nn.Parameter(self.weight.detach())
+        embedding_bag.train(self.training)
+        return embedding_bag
+
+
+class EmbeddingBag(nn.EmbeddingBag):
+    r"""
+    An embedding bag module attached with FakeQuantize modules for weight,
+    used for quantization aware training.
+
+    We adopt the same interface as `torch.nn.EmbeddingBag`, please see
+    https://pytorch.org/docs/stable/generated/torch.nn.EmbeddingBag.html#torch.nn.EmbeddingBag
+    for documentation.
+
+    Similar to `torch.nn.EmbeddingBag`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight: fake quant module for weight
+    """
+    _FLOAT_MODULE = nn.EmbeddingBag
+
+    def __init__(
+        self,
+        num_embeddings,
+        embedding_dim,
+        max_norm=None,
+        norm_type=2.0,
+        scale_grad_by_freq=False,
+        mode="mean",
+        sparse=False,
+        _weight=None,
+        include_last_offset=False,
+        padding_idx=None,
+        qconfig=None,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(
+            num_embeddings,
+            embedding_dim,
+            max_norm,
+            norm_type,
+            scale_grad_by_freq,
+            mode,
+            sparse,
+            _weight,
+            include_last_offset,
+            padding_idx,
+            **factory_kwargs,
+        )
+        assert qconfig, "qconfig must be provided for QAT module"
+        assert qconfig.weight().qscheme == torch.per_channel_affine_float_qparams, (
+            "Embedding Bag weights requires a qscheme of torch.per_channel_affine_float_qparams Got "
+            + str(qconfig.weight().qscheme)
+        )
+        self.qconfig = qconfig
+        self.weight_fake_quant = qconfig.weight(factory_kwargs=factory_kwargs)
+
+    def forward(self, input, offsets=None, per_sample_weights=None) -> Tensor:
+        return F.embedding_bag(
+            input,
+            self.weight_fake_quant(self.weight),
+            offsets,
+            self.max_norm,
+            self.norm_type,
+            self.scale_grad_by_freq,
+            self.mode,
+            self.sparse,
+            per_sample_weights,
+            self.include_last_offset,
+            self.padding_idx,
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a qat module from a float module
+
+        Args: `mod` a float module, either produced by torch.ao.quantization utilities
+        or directly from user
+        """
+        assert type(mod) == cls._FLOAT_MODULE, (
+            " qat."
+            + cls.__name__
+            + ".from_float only works for "
+            + cls._FLOAT_MODULE.__name__
+        )
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        assert mod.qconfig, "Input float module must have a valid qconfig"
+        weight_qscheme = mod.qconfig.weight().qscheme  # type: ignore[union-attr, operator]
+        assert weight_qscheme == torch.per_channel_affine_float_qparams, (
+            "Embedding Bag weights requires a qscheme of torch.per_channel_affine_float_qparams Got "
+            + str(weight_qscheme)
+        )
+
+        qconfig = mod.qconfig
+        qat_embedding_bag = cls(
+            mod.num_embeddings,
+            mod.embedding_dim,
+            mod.max_norm,
+            mod.norm_type,
+            mod.scale_grad_by_freq,
+            mod.mode,
+            mod.sparse,
+            mod.weight,
+            mod.include_last_offset,
+            mod.padding_idx,
+            qconfig=qconfig,
+        )
+
+        return qat_embedding_bag
+
+    def to_float(self):
+        embedding_bag = torch.nn.EmbeddingBag(
+            self.num_embeddings,
+            self.embedding_dim,
+            self.max_norm,
+            self.norm_type,
+            self.scale_grad_by_freq,
+            self.mode,
+            self.sparse,
+            None,
+            self.include_last_offset,
+            self.padding_idx,
+        )
+        embedding_bag.weight = torch.nn.Parameter(self.weight.detach())
+        embedding_bag.train(self.training)
+        return embedding_bag
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/modules/linear.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/modules/linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..ede488e66b0b97fe645c804fdc27c023f736d154
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/qat/modules/linear.py
@@ -0,0 +1,96 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.ao.nn.intrinsic import LinearReLU
+from torch.nn.utils.parametrize import (
+    is_parametrized,
+    transfer_parametrizations_and_params,
+    type_before_parametrizations,
+)
+
+
+__all__ = ["Linear"]
+
+
+class Linear(nn.Linear):
+    r"""
+    A linear module attached with FakeQuantize modules for weight,
+    used for quantization aware training.
+
+    We adopt the same interface as `torch.nn.Linear`, please see
+    https://pytorch.org/docs/stable/nn.html#torch.nn.Linear
+    for documentation.
+
+    Similar to `torch.nn.Linear`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight: fake quant module for weight
+    """
+    _FLOAT_MODULE = nn.Linear
+
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        bias=True,
+        qconfig=None,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(in_features, out_features, bias, **factory_kwargs)
+        assert qconfig, "qconfig must be provided for QAT module"
+        self.qconfig = qconfig
+        self.weight_fake_quant = qconfig.weight(factory_kwargs=factory_kwargs)
+
+    def forward(self, input):
+        return F.linear(input, self.weight_fake_quant(self.weight), self.bias)
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a qat module from a float module or qparams_dict
+        Args: `mod` a float module, either produced by torch.ao.quantization utilities
+        or directly from user
+        """
+        assert type_before_parametrizations(mod) == cls._FLOAT_MODULE, (
+            " qat."
+            + cls.__name__
+            + ".from_float only works for "
+            + cls._FLOAT_MODULE.__name__
+        )
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        assert mod.qconfig, "Input float module must have a valid qconfig"
+        if type_before_parametrizations(mod) == LinearReLU:
+            mod = mod[0]
+
+        qconfig = mod.qconfig
+        qat_linear = cls(
+            mod.in_features,
+            mod.out_features,
+            bias=mod.bias is not None,
+            qconfig=qconfig,
+        )
+
+        if is_parametrized(mod, "weight"):
+            transfer_parametrizations_and_params(mod, qat_linear, "weight")
+        else:
+            qat_linear.weight = mod.weight
+
+        if is_parametrized(mod, "bias"):
+            transfer_parametrizations_and_params(mod, qat_linear, "bias")
+        else:
+            qat_linear.bias = mod.bias
+
+        return qat_linear
+
+    def to_float(self):
+        linear = torch.nn.Linear(
+            self.in_features, self.out_features, self.bias is not None
+        )
+        linear.weight = torch.nn.Parameter(self.weight.detach())
+        if self.bias is not None:
+            linear.bias = torch.nn.Parameter(self.bias.detach())
+        linear.train(self.training)
+        return linear
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantizable/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantizable/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d79bdbfe83209f18b17cc8c7b245f322871d6c0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantizable/__init__.py
@@ -0,0 +1 @@
+from .modules import *  # noqa: F403
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantizable/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantizable/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantizable/modules/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantizable/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..221107660158171ada5d1823cc193666c9e152e7
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantizable/modules/__init__.py
@@ -0,0 +1,9 @@
+from .activation import MultiheadAttention
+from .rnn import LSTM, LSTMCell
+
+
+__all__ = [
+    "LSTM",
+    "LSTMCell",
+    "MultiheadAttention",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantizable/modules/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantizable/modules/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantizable/modules/activation.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantizable/modules/activation.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e6118eb206b9b67afe031d2733e541ac14fde22
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantizable/modules/activation.py
@@ -0,0 +1,550 @@
+# mypy: allow-untyped-defs
+import warnings
+from typing import Optional, Tuple
+
+import torch
+import torch.jit  # this is needed to avoid a circular import
+import torch.nn.functional as F
+from torch import nn, Tensor
+
+
+__all__ = ["MultiheadAttention"]
+
+
+class MultiheadAttention(nn.MultiheadAttention):
+    _FLOAT_MODULE = nn.MultiheadAttention
+
+    r"""Quantizable implementation of the MultiheadAttention.
+
+    Note::
+        Please, refer to :class:`~torch.nn.MultiheadAttention` for more
+        information
+
+    Allows the model to jointly attend to information from different
+    representation subspaces.
+    See reference: Attention Is All You Need
+
+    The original MHA module is not quantizable.
+    This reimplements it by explicitly instantiating the linear layers.
+
+    .. math::
+        \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
+        \text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)
+
+    Args:
+        embed_dim: total dimension of the model.
+        num_heads: parallel attention heads.
+        dropout: a Dropout layer on attn_output_weights. Default: 0.0.
+        bias: add bias as module parameter. Default: True.
+        add_bias_kv: add bias to the key and value sequences at dim=0.
+        add_zero_attn: add a new batch of zeros to the key and
+                       value sequences at dim=1.
+        kdim: total number of features in key. Default: None.
+        vdim: total number of features in value. Default: None.
+        batch_first: If ``True``, then the input and output tensors are provided
+            as (batch, seq, feature). Default: ``False`` (seq, batch, feature).
+
+    Note that if :attr:`kdim` and :attr:`vdim` are None, they will be set
+    to :attr:`embed_dim` such that query, key, and value have the same
+    number of features.
+
+    Examples::
+
+        >>> import torch.ao.nn.quantizable as nnqa
+        >>> multihead_attn = nnqa.MultiheadAttention(embed_dim, num_heads)
+        >>> attn_output, attn_output_weights = multihead_attn(query, key, value)
+
+    Note::
+        Please, follow the quantization flow to convert the quantizable MHA.
+    """
+    __constants__ = ["batch_first"]
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        dropout: float = 0.0,
+        bias: bool = True,
+        add_bias_kv: bool = False,
+        add_zero_attn: bool = False,
+        kdim: Optional[int] = None,
+        vdim: Optional[int] = None,
+        batch_first: bool = False,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(
+            embed_dim,
+            num_heads,
+            dropout,
+            bias,
+            add_bias_kv,
+            add_zero_attn,
+            kdim,
+            vdim,
+            batch_first,
+            **factory_kwargs,
+        )
+        self.linear_Q = nn.Linear(
+            self.embed_dim, self.embed_dim, bias=bias, **factory_kwargs
+        )
+        self.linear_K = nn.Linear(
+            self.kdim, self.embed_dim, bias=bias, **factory_kwargs
+        )
+        self.linear_V = nn.Linear(
+            self.vdim, self.embed_dim, bias=bias, **factory_kwargs
+        )
+        # for the type: ignore, see https://github.com/pytorch/pytorch/issues/58969
+        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=bias, **factory_kwargs)  # type: ignore[assignment]
+
+        # Functionals
+        self.q_scaling_product = torch.ao.nn.quantized.FloatFunctional()
+        # note: importing torch.ao.nn.quantized at top creates a circular import
+
+        # Quant/Dequant
+        self.quant_attn_output = torch.ao.quantization.QuantStub()
+        self.quant_attn_output_weights = torch.ao.quantization.QuantStub()
+        self.dequant_q = torch.ao.quantization.DeQuantStub()
+        self.dequant_k = torch.ao.quantization.DeQuantStub()
+        self.dequant_v = torch.ao.quantization.DeQuantStub()
+
+    def _get_name(self):
+        return "QuantizableMultiheadAttention"
+
+    @classmethod
+    def from_float(cls, other):
+        assert type(other) == cls._FLOAT_MODULE
+        assert hasattr(other, "qconfig"), "The float module must have 'qconfig'"
+        # Setting the dropout to 0.0!
+        observed = cls(
+            other.embed_dim,
+            other.num_heads,
+            other.dropout,
+            (other.in_proj_bias is not None),
+            (other.bias_k is not None),
+            other.add_zero_attn,
+            other.kdim,
+            other.vdim,
+            other.batch_first,
+        )
+        observed.bias_k = other.bias_k
+        observed.bias_v = other.bias_v
+        observed.qconfig = other.qconfig
+
+        # Set the linear weights
+        # for the type: ignores, see https://github.com/pytorch/pytorch/issues/58969
+        observed.out_proj.weight = other.out_proj.weight  # type: ignore[has-type]
+        observed.out_proj.bias = other.out_proj.bias  # type: ignore[has-type]
+        if other._qkv_same_embed_dim:
+            # Use separate params
+            bias = other.in_proj_bias
+            _start = 0
+            _end = _start + other.embed_dim
+            weight = other.in_proj_weight[_start:_end, :]
+            if bias is not None:
+                bias = torch.nn.Parameter(bias[_start:_end], bias.requires_grad)
+            observed.linear_Q.weight = torch.nn.Parameter(weight, weight.requires_grad)
+            observed.linear_Q.bias = bias
+
+            bias = other.in_proj_bias
+            _start = _end
+            _end = _start + other.embed_dim
+            weight = other.in_proj_weight[_start:_end, :]
+            if bias is not None:
+                bias = torch.nn.Parameter(bias[_start:_end], bias.requires_grad)
+            observed.linear_K.weight = torch.nn.Parameter(weight, weight.requires_grad)
+            observed.linear_K.bias = bias
+
+            bias = other.in_proj_bias
+            _start = _end
+            weight = other.in_proj_weight[_start:, :]
+            if bias is not None:
+                bias = torch.nn.Parameter(bias[_start:], bias.requires_grad)
+            observed.linear_V.weight = torch.nn.Parameter(weight, weight.requires_grad)
+            observed.linear_V.bias = bias
+        else:
+            observed.linear_Q.weight = nn.Parameter(other.q_proj_weight)
+            observed.linear_K.weight = nn.Parameter(other.k_proj_weight)
+            observed.linear_V.weight = nn.Parameter(other.v_proj_weight)
+            if other.in_proj_bias is None:
+                observed.linear_Q.bias = None  # type: ignore[assignment]
+                observed.linear_K.bias = None  # type: ignore[assignment]
+                observed.linear_V.bias = None  # type: ignore[assignment]
+            else:
+                observed.linear_Q.bias = nn.Parameter(
+                    other.in_proj_bias[0 : other.embed_dim]
+                )
+                observed.linear_K.bias = nn.Parameter(
+                    other.in_proj_bias[other.embed_dim : (other.embed_dim * 2)]
+                )
+                observed.linear_V.bias = nn.Parameter(
+                    other.in_proj_bias[(other.embed_dim * 2) :]
+                )
+        observed.eval()
+        # Explicit prepare
+        observed = torch.ao.quantization.prepare(observed, inplace=True)
+        return observed
+
+    @torch.jit.unused
+    def dequantize(self):
+        r"""Utility to convert the quantized MHA back to float.
+
+        The motivation for this is that it is not trivial to conver the weights
+        from the format that is used in the quantized version back to the
+        float.
+        """
+        fp = self._FLOAT_MODULE(
+            self.embed_dim,
+            self.num_heads,
+            self.dropout,
+            (self.linear_Q._weight_bias()[1] is not None),
+            (self.bias_k is not None),
+            self.add_zero_attn,
+            self.kdim,
+            self.vdim,
+            self.batch_first,
+        )
+        assert fp._qkv_same_embed_dim == self._qkv_same_embed_dim
+        if self.bias_k is not None:
+            fp.bias_k = nn.Parameter(self.bias_k.dequantize())
+        if self.bias_v is not None:
+            fp.bias_v = nn.Parameter(self.bias_v.dequantize())
+
+        # Set the linear weights
+        # Note: Because the linear layers are quantized, mypy does not nkow how
+        # to deal with them -- might need to ignore the typing checks.
+        # for the type: ignore[has-type], see https://github.com/pytorch/pytorch/issues/58969
+        w, b = self.out_proj._weight_bias()  # type: ignore[operator, has-type]
+        fp.out_proj.weight = nn.Parameter(w.dequantize())
+        if b is not None:
+            fp.out_proj.bias = nn.Parameter(b)
+
+        wQ, bQ = self.linear_Q._weight_bias()  # type: ignore[operator]
+        wQ = wQ.dequantize()
+        wK, bK = self.linear_K._weight_bias()  # type: ignore[operator]
+        wK = wK.dequantize()
+        wV, bV = self.linear_V._weight_bias()  # type: ignore[operator]
+        wV = wV.dequantize()
+        if fp._qkv_same_embed_dim:
+            # Use separate params
+            _start = 0
+            _end = _start + fp.embed_dim
+            fp.in_proj_weight[_start:_end, :] = wQ
+            if fp.in_proj_bias is not None:
+                assert all(bQ == 0)
+                fp.in_proj_bias[_start:_end] = bQ
+
+            _start = _end
+            _end = _start + fp.embed_dim
+            fp.in_proj_weight[_start:_end, :] = wK
+            if fp.in_proj_bias is not None:
+                assert all(bK == 0)
+                fp.in_proj_bias[_start:_end] = bK
+
+            _start = _end
+            fp.in_proj_weight[_start:, :] = wV
+            if fp.in_proj_bias is not None:
+                assert all(bV == 0)
+                fp.in_proj_bias[_start:] = bV
+        else:
+            fp.q_proj_weight = nn.Parameter(wQ)
+            fp.k_proj_weight = nn.Parameter(wK)
+            fp.v_proj_weight = nn.Parameter(wV)
+            if fp.in_proj_bias is None:
+                self.linear_Q.bias = None
+                self.linear_K.bias = None
+                self.linear_V.bias = None
+            else:
+                fp.in_proj_bias[0 : fp.embed_dim] = bQ
+                fp.in_proj_bias[fp.embed_dim : (fp.embed_dim * 2)] = bK
+                fp.in_proj_bias[(fp.embed_dim * 2) :] = bV
+
+        return fp
+
+    @classmethod
+    def from_observed(cls, other):
+        # The whole flow is float -> observed -> quantized
+        # This class does float -> observed only
+        # See nn.quantized.MultiheadAttention
+        raise NotImplementedError(
+            "It looks like you are trying to prepare an "
+            "MHA module. Please, see "
+            "the examples on quantizable MHAs."
+        )
+
+    def forward(
+        self,
+        query: Tensor,
+        key: Tensor,
+        value: Tensor,
+        key_padding_mask: Optional[Tensor] = None,
+        need_weights: bool = True,
+        attn_mask: Optional[Tensor] = None,
+        average_attn_weights: bool = True,
+        is_causal: bool = False,
+    ) -> Tuple[Tensor, Optional[Tensor]]:
+        r"""
+        Note::
+            Please, refer to :func:`~torch.nn.MultiheadAttention.forward` for more
+            information
+
+        Args:
+            query, key, value: map a query and a set of key-value pairs to an output.
+                See "Attention Is All You Need" for more details.
+            key_padding_mask: if provided, specified padding elements in the key will
+                be ignored by the attention. When given a binary mask and a value is True,
+                the corresponding value on the attention layer will be ignored.
+            need_weights: output attn_output_weights.
+            attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all
+                the batches while a 3D mask allows to specify a different mask for the entries of each batch.
+
+        Shape:
+            - Inputs:
+            - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is
+              the embedding dimension. :math:`(N, L, E)` if ``batch_first`` is ``True``.
+            - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is
+              the embedding dimension. :math:`(N, S, E)` if ``batch_first`` is ``True``.
+            - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is
+              the embedding dimension. :math:`(N, S, E)` if ``batch_first`` is ``True``.
+            - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length.
+              If a BoolTensor is provided, the positions with the
+              value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.
+            - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.
+              3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,
+              S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
+              positions. If a BoolTensor is provided, positions with ``True``
+              is not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
+              is provided, it will be added to the attention weight.
+            - is_causal: If specified, applies a causal mask as attention mask. Mutually exclusive with providing attn_mask.
+              Default: ``False``.
+            - average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across
+              heads. Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an
+              effect when ``need_weights=True.``. Default: True (i.e. average weights across heads)
+
+            - Outputs:
+            - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size,
+              E is the embedding dimension. :math:`(N, L, E)` if ``batch_first`` is ``True``.
+            - attn_output_weights: If ``average_attn_weights=True``, returns attention weights averaged
+              across heads of shape :math:`(N, L, S)`, where N is the batch size, L is the target sequence length,
+              S is the source sequence length. If ``average_attn_weights=False``, returns attention weights per
+              head of shape :math:`(N, num_heads, L, S)`.
+        """
+        return self._forward_impl(
+            query,
+            key,
+            value,
+            key_padding_mask,
+            need_weights,
+            attn_mask,
+            average_attn_weights,
+            is_causal,
+        )
+
+    def _forward_impl(
+        self,
+        query: Tensor,
+        key: Tensor,
+        value: Tensor,
+        key_padding_mask: Optional[Tensor] = None,
+        need_weights: bool = True,
+        attn_mask: Optional[Tensor] = None,
+        average_attn_weights: bool = True,
+        is_causal: bool = False,
+    ) -> Tuple[Tensor, Optional[Tensor]]:
+        # This version will not deal with the static key/value pairs.
+        # Keeping it here for future changes.
+        #
+        # TODO: This method has some duplicate lines with the
+        # `torch.nn.functional.multi_head_attention`. Will need to refactor.
+        static_k = None
+        static_v = None
+
+        if attn_mask is not None and is_causal:
+            raise AssertionError("Only allow causal mask or attn_mask")
+
+        if is_causal:
+            raise AssertionError("causal mask not supported by AO MHA module")
+
+        if self.batch_first:
+            query, key, value = (x.transpose(0, 1) for x in (query, key, value))
+
+        tgt_len, bsz, embed_dim_to_check = query.size()
+        assert self.embed_dim == embed_dim_to_check
+        # allow MHA to have different sizes for the feature dimension
+        assert key.size(0) == value.size(0) and key.size(1) == value.size(1)
+
+        head_dim = self.embed_dim // self.num_heads
+        assert (
+            head_dim * self.num_heads == self.embed_dim
+        ), "embed_dim must be divisible by num_heads"
+        scaling = float(head_dim) ** -0.5
+
+        q = self.linear_Q(query)
+        k = self.linear_K(key)
+        v = self.linear_V(value)
+
+        q = self.q_scaling_product.mul_scalar(q, scaling)
+
+        if attn_mask is not None:
+            if attn_mask.dtype == torch.uint8:
+                warnings.warn(
+                    "Byte tensor for `attn_mask` in `nn.MultiheadAttention` is deprecated. "
+                    "Use bool tensor instead.",
+                    stacklevel=3,
+                )
+                attn_mask = attn_mask.to(torch.bool)
+            assert (
+                attn_mask.is_floating_point() or attn_mask.dtype == torch.bool
+            ), f"Only float and bool types are supported for attn_mask, not {attn_mask.dtype}"
+
+            if attn_mask.dim() == 2:
+                attn_mask = attn_mask.unsqueeze(0)
+                if list(attn_mask.size()) != [1, query.size(0), key.size(0)]:
+                    raise RuntimeError("The size of the 2D attn_mask is not correct.")
+            elif attn_mask.dim() == 3:
+                if list(attn_mask.size()) != [
+                    bsz * self.num_heads,
+                    query.size(0),
+                    key.size(0),
+                ]:
+                    raise RuntimeError("The size of the 3D attn_mask is not correct.")
+            else:
+                raise RuntimeError(
+                    f"attn_mask's dimension {attn_mask.dim()} is not supported"
+                )
+            # attn_mask's dim is 3 now.
+
+        # convert ByteTensor key_padding_mask to bool
+        if key_padding_mask is not None and key_padding_mask.dtype == torch.uint8:
+            warnings.warn(
+                "Byte tensor for `key_padding_mask` in `nn.MultiheadAttention` is deprecated. "
+                "Use bool tensor instead.",
+                stacklevel=3,
+            )
+            key_padding_mask = key_padding_mask.to(torch.bool)
+        if self.bias_k is not None and self.bias_v is not None:
+            if static_k is None and static_v is None:
+                # Explicitly assert that bias_k and bias_v are not None
+                # in a way that TorchScript can understand.
+                bias_k = self.bias_k
+                assert bias_k is not None
+                bias_v = self.bias_v
+                assert bias_v is not None
+
+                k = torch.cat([k, bias_k.repeat(1, bsz, 1)])
+                v = torch.cat([v, bias_v.repeat(1, bsz, 1)])
+                if attn_mask is not None:
+                    attn_mask = F.pad(attn_mask, (0, 1))
+                if key_padding_mask is not None:
+                    key_padding_mask = F.pad(key_padding_mask, (0, 1))
+            else:
+                assert static_k is None, "bias cannot be added to static key."
+                assert static_v is None, "bias cannot be added to static value."
+        else:
+            assert self.bias_k is None
+            assert self.bias_v is None
+
+        q = q.contiguous().view(tgt_len, bsz * self.num_heads, head_dim).transpose(0, 1)
+        if k is not None:
+            k = k.contiguous().view(-1, bsz * self.num_heads, head_dim).transpose(0, 1)
+        if v is not None:
+            v = v.contiguous().view(-1, bsz * self.num_heads, head_dim).transpose(0, 1)
+
+        if static_k is not None:
+            assert static_k.size(0) == bsz * self.num_heads
+            assert static_k.size(2) == head_dim
+            k = static_k
+
+        if static_v is not None:
+            assert static_v.size(0) == bsz * self.num_heads
+            assert static_v.size(2) == head_dim
+            v = static_v
+
+        src_len = k.size(1)
+
+        if key_padding_mask is not None:
+            assert key_padding_mask.size(0) == bsz
+            assert key_padding_mask.size(1) == src_len
+
+        if self.add_zero_attn:
+            src_len += 1
+            k_zeros = torch.zeros((k.size(0), 1) + k.size()[2:])
+            if k.is_quantized:
+                k_zeros = torch.quantize_per_tensor(
+                    k_zeros, k.q_scale(), k.q_zero_point(), k.dtype
+                )
+            k = torch.cat([k, k_zeros], dim=1)
+            v_zeros = torch.zeros((v.size(0), 1) + k.size()[2:])
+            if v.is_quantized:
+                v_zeros = torch.quantize_per_tensor(
+                    v_zeros, v.q_scale(), v.q_zero_point(), v.dtype
+                )
+            v = torch.cat([v, v_zeros], dim=1)
+
+            if attn_mask is not None:
+                attn_mask = F.pad(attn_mask, (0, 1))
+            if key_padding_mask is not None:
+                key_padding_mask = F.pad(key_padding_mask, (0, 1))
+
+        # Leaving the quantized zone here
+        q = self.dequant_q(q)
+        k = self.dequant_k(k)
+        v = self.dequant_v(v)
+        attn_output_weights = torch.bmm(q, k.transpose(1, 2))
+        assert list(attn_output_weights.size()) == [
+            bsz * self.num_heads,
+            tgt_len,
+            src_len,
+        ]
+
+        if attn_mask is not None:
+            if attn_mask.dtype == torch.bool:
+                attn_output_weights.masked_fill_(attn_mask, float("-inf"))
+            else:
+                attn_output_weights += attn_mask
+
+        if key_padding_mask is not None:
+            attn_output_weights = attn_output_weights.view(
+                bsz, self.num_heads, tgt_len, src_len
+            )
+            attn_output_weights = attn_output_weights.masked_fill(
+                key_padding_mask.unsqueeze(1).unsqueeze(2),
+                float("-inf"),
+            )
+            attn_output_weights = attn_output_weights.view(
+                bsz * self.num_heads, tgt_len, src_len
+            )
+
+        attn_output_weights = F.softmax(attn_output_weights, dim=-1)
+        attn_output_weights = F.dropout(
+            attn_output_weights, p=self.dropout, training=self.training
+        )
+
+        attn_output = torch.bmm(attn_output_weights, v)
+        assert list(attn_output.size()) == [bsz * self.num_heads, tgt_len, head_dim]
+        if self.batch_first:
+            attn_output = attn_output.view(bsz, tgt_len, self.embed_dim)
+        else:
+            attn_output = (
+                attn_output.transpose(0, 1)
+                .contiguous()
+                .view(tgt_len, bsz, self.embed_dim)
+            )
+
+        # Reentering the quantized zone
+        attn_output = self.quant_attn_output(attn_output)
+        # for the type: ignore[has-type], see https://github.com/pytorch/pytorch/issues/58969
+        attn_output = self.out_proj(attn_output)  # type: ignore[has-type]
+        attn_output_weights = self.quant_attn_output_weights(attn_output_weights)
+
+        if need_weights:
+            # average attention weights over heads
+            attn_output_weights = attn_output_weights.view(
+                bsz, self.num_heads, tgt_len, src_len
+            )
+            if average_attn_weights:
+                attn_output_weights = attn_output_weights.mean(dim=1)
+            return attn_output, attn_output_weights
+        else:
+            return attn_output, None
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantizable/modules/rnn.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantizable/modules/rnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a520f5b250db07deb2fd34f6b520eaa8521533e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantizable/modules/rnn.py
@@ -0,0 +1,499 @@
+"""
+We will recreate all the RNN modules as we require the modules to be decomposed
+into its building blocks to be able to observe.
+"""
+
+# mypy: allow-untyped-defs
+
+import numbers
+import warnings
+from typing import Optional, Tuple
+
+import torch
+from torch import Tensor
+
+
+__all__ = ["LSTMCell", "LSTM"]
+
+
+class LSTMCell(torch.nn.Module):
+    r"""A quantizable long short-term memory (LSTM) cell.
+
+    For the description and the argument types, please, refer to :class:`~torch.nn.LSTMCell`
+
+    Examples::
+
+        >>> import torch.ao.nn.quantizable as nnqa
+        >>> rnn = nnqa.LSTMCell(10, 20)
+        >>> input = torch.randn(6, 10)
+        >>> hx = torch.randn(3, 20)
+        >>> cx = torch.randn(3, 20)
+        >>> output = []
+        >>> for i in range(6):
+        ...     hx, cx = rnn(input[i], (hx, cx))
+        ...     output.append(hx)
+    """
+    _FLOAT_MODULE = torch.nn.LSTMCell
+
+    def __init__(
+        self,
+        input_dim: int,
+        hidden_dim: int,
+        bias: bool = True,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.input_size = input_dim
+        self.hidden_size = hidden_dim
+        self.bias = bias
+
+        self.igates = torch.nn.Linear(
+            input_dim, 4 * hidden_dim, bias=bias, **factory_kwargs
+        )
+        self.hgates = torch.nn.Linear(
+            hidden_dim, 4 * hidden_dim, bias=bias, **factory_kwargs
+        )
+        self.gates = torch.ao.nn.quantized.FloatFunctional()
+
+        self.input_gate = torch.nn.Sigmoid()
+        self.forget_gate = torch.nn.Sigmoid()
+        self.cell_gate = torch.nn.Tanh()
+        self.output_gate = torch.nn.Sigmoid()
+
+        self.fgate_cx = torch.ao.nn.quantized.FloatFunctional()
+        self.igate_cgate = torch.ao.nn.quantized.FloatFunctional()
+        self.fgate_cx_igate_cgate = torch.ao.nn.quantized.FloatFunctional()
+
+        self.ogate_cy = torch.ao.nn.quantized.FloatFunctional()
+
+        self.initial_hidden_state_qparams: Tuple[float, int] = (1.0, 0)
+        self.initial_cell_state_qparams: Tuple[float, int] = (1.0, 0)
+        self.hidden_state_dtype: torch.dtype = torch.quint8
+        self.cell_state_dtype: torch.dtype = torch.quint8
+
+    def forward(
+        self, x: Tensor, hidden: Optional[Tuple[Tensor, Tensor]] = None
+    ) -> Tuple[Tensor, Tensor]:
+        if hidden is None or hidden[0] is None or hidden[1] is None:
+            hidden = self.initialize_hidden(x.shape[0], x.is_quantized)
+        hx, cx = hidden
+
+        igates = self.igates(x)
+        hgates = self.hgates(hx)
+        gates = self.gates.add(igates, hgates)
+
+        input_gate, forget_gate, cell_gate, out_gate = gates.chunk(4, 1)
+
+        input_gate = self.input_gate(input_gate)
+        forget_gate = self.forget_gate(forget_gate)
+        cell_gate = self.cell_gate(cell_gate)
+        out_gate = self.output_gate(out_gate)
+
+        fgate_cx = self.fgate_cx.mul(forget_gate, cx)
+        igate_cgate = self.igate_cgate.mul(input_gate, cell_gate)
+        fgate_cx_igate_cgate = self.fgate_cx_igate_cgate.add(fgate_cx, igate_cgate)
+        cy = fgate_cx_igate_cgate
+
+        # TODO: make this tanh a member of the module so its qparams can be configured
+        tanh_cy = torch.tanh(cy)
+        hy = self.ogate_cy.mul(out_gate, tanh_cy)
+        return hy, cy
+
+    def initialize_hidden(
+        self, batch_size: int, is_quantized: bool = False
+    ) -> Tuple[Tensor, Tensor]:
+        h, c = torch.zeros((batch_size, self.hidden_size)), torch.zeros(
+            (batch_size, self.hidden_size)
+        )
+        if is_quantized:
+            (h_scale, h_zp) = self.initial_hidden_state_qparams
+            (c_scale, c_zp) = self.initial_cell_state_qparams
+            h = torch.quantize_per_tensor(
+                h, scale=h_scale, zero_point=h_zp, dtype=self.hidden_state_dtype
+            )
+            c = torch.quantize_per_tensor(
+                c, scale=c_scale, zero_point=c_zp, dtype=self.cell_state_dtype
+            )
+        return h, c
+
+    def _get_name(self):
+        return "QuantizableLSTMCell"
+
+    @classmethod
+    def from_params(cls, wi, wh, bi=None, bh=None):
+        """Uses the weights and biases to create a new LSTM cell.
+
+        Args:
+            wi, wh: Weights for the input and hidden layers
+            bi, bh: Biases for the input and hidden layers
+        """
+        assert (bi is None) == (bh is None)  # Either both None or both have values
+        input_size = wi.shape[1]
+        hidden_size = wh.shape[1]
+        cell = cls(input_dim=input_size, hidden_dim=hidden_size, bias=(bi is not None))
+        cell.igates.weight = torch.nn.Parameter(wi)
+        if bi is not None:
+            cell.igates.bias = torch.nn.Parameter(bi)
+        cell.hgates.weight = torch.nn.Parameter(wh)
+        if bh is not None:
+            cell.hgates.bias = torch.nn.Parameter(bh)
+        return cell
+
+    @classmethod
+    def from_float(cls, other, use_precomputed_fake_quant=False):
+        assert type(other) == cls._FLOAT_MODULE
+        assert hasattr(other, "qconfig"), "The float module must have 'qconfig'"
+        observed = cls.from_params(
+            other.weight_ih, other.weight_hh, other.bias_ih, other.bias_hh
+        )
+        observed.qconfig = other.qconfig
+        observed.igates.qconfig = other.qconfig
+        observed.hgates.qconfig = other.qconfig
+        return observed
+
+
+class _LSTMSingleLayer(torch.nn.Module):
+    r"""A single one-directional LSTM layer.
+
+    The difference between a layer and a cell is that the layer can process a
+    sequence, while the cell only expects an instantaneous value.
+    """
+
+    def __init__(
+        self,
+        input_dim: int,
+        hidden_dim: int,
+        bias: bool = True,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.cell = LSTMCell(input_dim, hidden_dim, bias=bias, **factory_kwargs)
+
+    def forward(self, x: Tensor, hidden: Optional[Tuple[Tensor, Tensor]] = None):
+        result = []
+        seq_len = x.shape[0]
+        for i in range(seq_len):
+            hidden = self.cell(x[i], hidden)
+            result.append(hidden[0])  # type: ignore[index]
+        result_tensor = torch.stack(result, 0)
+        return result_tensor, hidden
+
+    @classmethod
+    def from_params(cls, *args, **kwargs):
+        cell = LSTMCell.from_params(*args, **kwargs)
+        layer = cls(cell.input_size, cell.hidden_size, cell.bias)
+        layer.cell = cell
+        return layer
+
+
+class _LSTMLayer(torch.nn.Module):
+    r"""A single bi-directional LSTM layer."""
+
+    def __init__(
+        self,
+        input_dim: int,
+        hidden_dim: int,
+        bias: bool = True,
+        batch_first: bool = False,
+        bidirectional: bool = False,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.batch_first = batch_first
+        self.bidirectional = bidirectional
+        self.layer_fw = _LSTMSingleLayer(
+            input_dim, hidden_dim, bias=bias, **factory_kwargs
+        )
+        if self.bidirectional:
+            self.layer_bw = _LSTMSingleLayer(
+                input_dim, hidden_dim, bias=bias, **factory_kwargs
+            )
+
+    def forward(self, x: Tensor, hidden: Optional[Tuple[Tensor, Tensor]] = None):
+        if self.batch_first:
+            x = x.transpose(0, 1)
+        if hidden is None:
+            hx_fw, cx_fw = (None, None)
+        else:
+            hx_fw, cx_fw = hidden
+        hidden_bw: Optional[Tuple[Tensor, Tensor]] = None
+        if self.bidirectional:
+            if hx_fw is None:
+                hx_bw = None
+            else:
+                hx_bw = hx_fw[1]
+                hx_fw = hx_fw[0]
+            if cx_fw is None:
+                cx_bw = None
+            else:
+                cx_bw = cx_fw[1]
+                cx_fw = cx_fw[0]
+            if hx_bw is not None and cx_bw is not None:
+                hidden_bw = hx_bw, cx_bw
+        if hx_fw is None and cx_fw is None:
+            hidden_fw = None
+        else:
+            hidden_fw = torch.jit._unwrap_optional(hx_fw), torch.jit._unwrap_optional(
+                cx_fw
+            )
+        result_fw, hidden_fw = self.layer_fw(x, hidden_fw)
+
+        if hasattr(self, "layer_bw") and self.bidirectional:
+            x_reversed = x.flip(0)
+            result_bw, hidden_bw = self.layer_bw(x_reversed, hidden_bw)
+            result_bw = result_bw.flip(0)
+
+            result = torch.cat([result_fw, result_bw], result_fw.dim() - 1)
+            if hidden_fw is None and hidden_bw is None:
+                h = None
+                c = None
+            elif hidden_fw is None:
+                (h, c) = torch.jit._unwrap_optional(hidden_bw)
+            elif hidden_bw is None:
+                (h, c) = torch.jit._unwrap_optional(hidden_fw)
+            else:
+                h = torch.stack([hidden_fw[0], hidden_bw[0]], 0)  # type: ignore[list-item]
+                c = torch.stack([hidden_fw[1], hidden_bw[1]], 0)  # type: ignore[list-item]
+        else:
+            result = result_fw
+            h, c = torch.jit._unwrap_optional(hidden_fw)  # type: ignore[assignment]
+
+        if self.batch_first:
+            result.transpose_(0, 1)
+
+        return result, (h, c)
+
+    @classmethod
+    def from_float(cls, other, layer_idx=0, qconfig=None, **kwargs):
+        r"""
+        There is no FP equivalent of this class. This function is here just to
+        mimic the behavior of the `prepare` within the `torch.ao.quantization`
+        flow.
+        """
+        assert hasattr(other, "qconfig") or (qconfig is not None)
+
+        input_size = kwargs.get("input_size", other.input_size)
+        hidden_size = kwargs.get("hidden_size", other.hidden_size)
+        bias = kwargs.get("bias", other.bias)
+        batch_first = kwargs.get("batch_first", other.batch_first)
+        bidirectional = kwargs.get("bidirectional", other.bidirectional)
+
+        layer = cls(input_size, hidden_size, bias, batch_first, bidirectional)
+        layer.qconfig = getattr(other, "qconfig", qconfig)
+        wi = getattr(other, f"weight_ih_l{layer_idx}")
+        wh = getattr(other, f"weight_hh_l{layer_idx}")
+        bi = getattr(other, f"bias_ih_l{layer_idx}", None)
+        bh = getattr(other, f"bias_hh_l{layer_idx}", None)
+
+        layer.layer_fw = _LSTMSingleLayer.from_params(wi, wh, bi, bh)
+
+        if other.bidirectional:
+            wi = getattr(other, f"weight_ih_l{layer_idx}_reverse")
+            wh = getattr(other, f"weight_hh_l{layer_idx}_reverse")
+            bi = getattr(other, f"bias_ih_l{layer_idx}_reverse", None)
+            bh = getattr(other, f"bias_hh_l{layer_idx}_reverse", None)
+            layer.layer_bw = _LSTMSingleLayer.from_params(wi, wh, bi, bh)
+        return layer
+
+
+class LSTM(torch.nn.Module):
+    r"""A quantizable long short-term memory (LSTM).
+
+    For the description and the argument types, please, refer to :class:`~torch.nn.LSTM`
+
+    Attributes:
+        layers : instances of the `_LSTMLayer`
+
+    .. note::
+        To access the weights and biases, you need to access them per layer.
+        See examples below.
+
+    Examples::
+
+        >>> import torch.ao.nn.quantizable as nnqa
+        >>> rnn = nnqa.LSTM(10, 20, 2)
+        >>> input = torch.randn(5, 3, 10)
+        >>> h0 = torch.randn(2, 3, 20)
+        >>> c0 = torch.randn(2, 3, 20)
+        >>> output, (hn, cn) = rnn(input, (h0, c0))
+        >>> # To get the weights:
+        >>> # xdoctest: +SKIP
+        >>> print(rnn.layers[0].weight_ih)
+        tensor([[...]])
+        >>> print(rnn.layers[0].weight_hh)
+        AssertionError: There is no reverse path in the non-bidirectional layer
+    """
+    _FLOAT_MODULE = torch.nn.LSTM
+
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int,
+        num_layers: int = 1,
+        bias: bool = True,
+        batch_first: bool = False,
+        dropout: float = 0.0,
+        bidirectional: bool = False,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.input_size = input_size
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.bias = bias
+        self.batch_first = batch_first
+        self.dropout = float(dropout)
+        self.bidirectional = bidirectional
+        self.training = False  # Default to eval mode. If we want to train, we will explicitly set to training.
+        num_directions = 2 if bidirectional else 1
+
+        if (
+            not isinstance(dropout, numbers.Number)
+            or not 0 <= dropout <= 1
+            or isinstance(dropout, bool)
+        ):
+            raise ValueError(
+                "dropout should be a number in range [0, 1] "
+                "representing the probability of an element being "
+                "zeroed"
+            )
+        if dropout > 0:
+            warnings.warn(
+                "dropout option for quantizable LSTM is ignored. "
+                "If you are training, please, use nn.LSTM version "
+                "followed by `prepare` step."
+            )
+            if num_layers == 1:
+                warnings.warn(
+                    "dropout option adds dropout after all but last "
+                    "recurrent layer, so non-zero dropout expects "
+                    f"num_layers greater than 1, but got dropout={dropout} "
+                    f"and num_layers={num_layers}"
+                )
+
+        layers = [
+            _LSTMLayer(
+                self.input_size,
+                self.hidden_size,
+                self.bias,
+                batch_first=False,
+                bidirectional=self.bidirectional,
+                **factory_kwargs,
+            )
+        ]
+        for layer in range(1, num_layers):
+            layers.append(
+                _LSTMLayer(
+                    self.hidden_size,
+                    self.hidden_size,
+                    self.bias,
+                    batch_first=False,
+                    bidirectional=self.bidirectional,
+                    **factory_kwargs,
+                )
+            )
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, x: Tensor, hidden: Optional[Tuple[Tensor, Tensor]] = None):
+        if self.batch_first:
+            x = x.transpose(0, 1)
+
+        max_batch_size = x.size(1)
+        num_directions = 2 if self.bidirectional else 1
+        if hidden is None:
+            zeros = torch.zeros(
+                num_directions,
+                max_batch_size,
+                self.hidden_size,
+                dtype=torch.float,
+                device=x.device,
+            )
+            zeros.squeeze_(0)
+            if x.is_quantized:
+                zeros = torch.quantize_per_tensor(
+                    zeros, scale=1.0, zero_point=0, dtype=x.dtype
+                )
+            hxcx = [(zeros, zeros) for _ in range(self.num_layers)]
+        else:
+            hidden_non_opt = torch.jit._unwrap_optional(hidden)
+            if isinstance(hidden_non_opt[0], Tensor):
+                hx = hidden_non_opt[0].reshape(
+                    self.num_layers, num_directions, max_batch_size, self.hidden_size
+                )
+                cx = hidden_non_opt[1].reshape(
+                    self.num_layers, num_directions, max_batch_size, self.hidden_size
+                )
+                hxcx = [
+                    (hx[idx].squeeze(0), cx[idx].squeeze(0))
+                    for idx in range(self.num_layers)
+                ]
+            else:
+                hxcx = hidden_non_opt
+
+        hx_list = []
+        cx_list = []
+        for idx, layer in enumerate(self.layers):
+            x, (h, c) = layer(x, hxcx[idx])
+            hx_list.append(torch.jit._unwrap_optional(h))
+            cx_list.append(torch.jit._unwrap_optional(c))
+        hx_tensor = torch.stack(hx_list)
+        cx_tensor = torch.stack(cx_list)
+
+        # We are creating another dimension for bidirectional case
+        # need to collapse it
+        hx_tensor = hx_tensor.reshape(-1, hx_tensor.shape[-2], hx_tensor.shape[-1])
+        cx_tensor = cx_tensor.reshape(-1, cx_tensor.shape[-2], cx_tensor.shape[-1])
+
+        if self.batch_first:
+            x = x.transpose(0, 1)
+
+        return x, (hx_tensor, cx_tensor)
+
+    def _get_name(self):
+        return "QuantizableLSTM"
+
+    @classmethod
+    def from_float(cls, other, qconfig=None):
+        assert isinstance(other, cls._FLOAT_MODULE)
+        assert hasattr(other, "qconfig") or qconfig
+        observed = cls(
+            other.input_size,
+            other.hidden_size,
+            other.num_layers,
+            other.bias,
+            other.batch_first,
+            other.dropout,
+            other.bidirectional,
+        )
+        observed.qconfig = getattr(other, "qconfig", qconfig)
+        for idx in range(other.num_layers):
+            observed.layers[idx] = _LSTMLayer.from_float(
+                other, idx, qconfig, batch_first=False
+            )
+
+        # Prepare the model
+        if other.training:
+            observed.train()
+            observed = torch.ao.quantization.prepare_qat(observed, inplace=True)
+        else:
+            observed.eval()
+            observed = torch.ao.quantization.prepare(observed, inplace=True)
+        return observed
+
+    @classmethod
+    def from_observed(cls, other):
+        # The whole flow is float -> observed -> quantized
+        # This class does float -> observed only
+        raise NotImplementedError(
+            "It looks like you are trying to convert a "
+            "non-quantizable LSTM module. Please, see "
+            "the examples on quantizable LSTMs."
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..77e97d8595282f3d69963ee129fa473249e3ae29
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/__init__.py
@@ -0,0 +1,39 @@
+from . import functional
+from .modules import *  # noqa: F403
+from .modules import MaxPool2d
+
+
+__all__ = [
+    "BatchNorm2d",
+    "BatchNorm3d",
+    "Conv1d",
+    "Conv2d",
+    "Conv3d",
+    "ConvTranspose1d",
+    "ConvTranspose2d",
+    "ConvTranspose3d",
+    "DeQuantize",
+    "ELU",
+    "Embedding",
+    "EmbeddingBag",
+    "GroupNorm",
+    "Hardswish",
+    "InstanceNorm1d",
+    "InstanceNorm2d",
+    "InstanceNorm3d",
+    "LayerNorm",
+    "LeakyReLU",
+    "Linear",
+    "LSTM",
+    "MultiheadAttention",
+    "Quantize",
+    "ReLU6",
+    "Sigmoid",
+    "Softmax",
+    "Dropout",
+    "PReLU",
+    # Wrapper modules
+    "FloatFunctional",
+    "FXFloatFunctional",
+    "QFunctional",
+]
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/dynamic/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/dynamic/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d79bdbfe83209f18b17cc8c7b245f322871d6c0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/dynamic/__init__.py
@@ -0,0 +1 @@
+from .modules import *  # noqa: F403
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/dynamic/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/dynamic/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/dynamic/modules/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/dynamic/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..969fd6f121f5ddb72ed2e8e158e3ee7e990cfd0c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/dynamic/modules/__init__.py
@@ -0,0 +1,26 @@
+from .conv import (
+    Conv1d,
+    Conv2d,
+    Conv3d,
+    ConvTranspose1d,
+    ConvTranspose2d,
+    ConvTranspose3d,
+)
+from .linear import Linear
+from .rnn import GRU, GRUCell, LSTM, LSTMCell, RNNCell
+
+
+__all__ = [
+    "Linear",
+    "LSTM",
+    "GRU",
+    "LSTMCell",
+    "RNNCell",
+    "GRUCell",
+    "Conv1d",
+    "Conv2d",
+    "Conv3d",
+    "ConvTranspose1d",
+    "ConvTranspose2d",
+    "ConvTranspose3d",
+]
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa86dcdd93e17dfcb2458aa575c5931a36e1b100
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py
@@ -0,0 +1,520 @@
+# mypy: allow-untyped-defs
+r"""Dynamically quantized convolution modules."""
+
+import warnings
+
+import torch
+import torch.ao.nn.quantized as nnq
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+from torch._ops import ops
+from torch.ao.nn.quantized.modules.conv import _reverse_repeat_padding
+from torch.nn.common_types import _size_1_t
+from torch.nn.modules.utils import _pair, _single, _triple
+
+
+__all__ = [
+    "Conv1d",
+    "Conv2d",
+    "Conv3d",
+    "ConvTranspose1d",
+    "ConvTranspose2d",
+    "ConvTranspose3d",
+]
+
+
+class Conv1d(nnq.Conv1d):
+    r"""A dynamically quantized conv module with floating point tensors as inputs and outputs.
+
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.Conv1d` and :class:`~torch.ao.nn.quantized.dynamic.Conv1d` and
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+
+    See :class:`~torch.nn.Conv1d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> m = nn.quantized.dynamic.Conv1d(16, 33, 3, stride=2)
+        >>> input = torch.randn(20, 16, 100)
+        >>> output = m(input)
+
+    """
+
+    _FLOAT_MODULE = nn.Conv1d
+    _NNIQAT_CONV_BN_MODULE = None  # type: ignore[assignment]
+    _NNI_CONV_RELU_MODULE = None  # type: ignore[assignment]
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: _size_1_t,
+        stride: _size_1_t = 1,
+        padding: _size_1_t = 0,
+        dilation: _size_1_t = 1,
+        groups: int = 1,
+        bias: bool = True,
+        padding_mode: str = "zeros",
+        device=None,
+        dtype=None,
+        reduce_range=True,
+    ):
+        warnings.warn(
+            f"The current implementation of the {self._get_name()} module has poor numerical accuracy and its use is not recommended"  # noqa: B950
+        )
+        factory_kwargs = {"device": device, "dtype": dtype}
+        kernel_size = _single(kernel_size)
+        stride = _single(stride)
+        padding = padding if isinstance(padding, str) else _single(padding)
+        dilation = _single(dilation)
+
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "DynamicQuantizedConv1d"
+
+    def forward(self, input: Tensor, reduce_range: bool = True) -> Tensor:
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 3:
+            raise ValueError("Input shape must be `(N, C, L)`!")
+        if self.padding_mode != "zeros":
+            # Padding in Conv1d is stored as (p, p), need to get (p,)
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding[:1])
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return ops.quantized.conv1d_dynamic(input, self._packed_params, reduce_range)
+
+
+class Conv2d(nnq.Conv2d):
+    r"""A dynamically quantized conv module with floating point tensors as inputs and outputs.
+
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.Conv2d` and :class:`~torch.ao.nn.quantized.dynamic.Conv2d` and
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+
+    See :class:`~torch.nn.Conv2d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> # With square kernels and equal stride
+        >>> m = nn.quantized.dynamic.Conv2d(16, 33, 3, stride=2)
+        >>> # non-square kernels and unequal stride and with padding
+        >>> m = nn.quantized.dynamic.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
+        >>> # non-square kernels and unequal stride and with padding and dilation
+        >>> m = nn.quantized.dynamic.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1))
+        >>> input = torch.randn(20, 16, 50, 100)
+        >>> output = m(input)
+
+    """
+    _FLOAT_MODULE = nn.Conv2d
+    _NNIQAT_CONV_BN_MODULE = None  # type: ignore[assignment]
+    _NNI_CONV_RELU_MODULE = None  # type: ignore[assignment]
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        warnings.warn(
+            f"The current implementation of the {self._get_name()} module "
+            "has poor numerical accuracy and its use is not recommended"
+        )
+        factory_kwargs = {"device": device, "dtype": dtype}
+        kernel_size = _pair(kernel_size)
+        stride = _pair(stride)
+        padding = _pair(padding)
+        dilation = _pair(dilation)
+
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "DynamicQuantizedConv2d"
+
+    def forward(self, input: Tensor, reduce_range: bool = True) -> Tensor:
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+        if self.padding_mode != "zeros":
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return ops.quantized.conv2d_dynamic(input, self._packed_params, reduce_range)
+
+
+class Conv3d(nnq.Conv3d):
+    r"""A dynamically quantized conv module with floating point tensors as inputs and outputs.
+
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.Conv3d` and :class:`~torch.ao.nn.quantized.dynamic.Conv3d` and
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+
+    See :class:`~torch.nn.Conv3d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> # With square kernels and equal stride
+        >>> m = nn.quantized.dynamic.Conv3d(16, 33, 3, stride=2)
+        >>> # non-square kernels and unequal stride and with padding
+        >>> m = nn.quantized.dynamic.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2))
+        >>> # non-square kernels and unequal stride and with padding and dilation
+        >>> m = nn.quantized.dynamic.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), dilation=(1, 2, 2))
+        >>> input = torch.randn(20, 16, 56, 56, 56)
+        >>> output = m(input)
+
+    """
+    _FLOAT_MODULE = nn.Conv3d
+    _NNIQAT_CONV_BN_MODULE = None  # type: ignore[assignment]
+    _NNI_CONV_RELU_MODULE = None  # type: ignore[assignment]
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        warnings.warn(
+            f"The current implementation of the {self._get_name()} module has poor numerical accuracy and its use is not recommended"  # noqa: B950
+        )
+        assert padding_mode != "reflect", "Conv3d does not support reflection padding"
+        factory_kwargs = {"device": device, "dtype": dtype}
+        kernel_size = _triple(kernel_size)
+        stride = _triple(stride)
+        padding = _triple(padding)
+        dilation = _triple(dilation)
+        super()._init(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            False,
+            _triple(0),
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "DynamicQuantizedConv3d"
+
+    def forward(self, input: Tensor, reduce_range: bool = True) -> Tensor:
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 5:
+            raise ValueError("Input shape must be `(N, C, D, H, W)`!")
+        if self.padding_mode != "zeros":
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return ops.quantized.conv3d_dynamic(input, self._packed_params, reduce_range)
+
+
+class ConvTranspose1d(nnq.ConvTranspose1d):
+    r"""A dynamically quantized transposed convolution module with floating point tensors as inputs and outputs.
+
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.ConvTranspose1d`.
+
+    For special notes, please, see :class:`~torch.ao.nn.quantized.dynamic.Conv1d`
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+    See :class:`~torch.nn.ConvTranspose1d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> # With square kernels and equal stride
+        >>> m = nndq.ConvTranspose1d(16, 33, 3, stride=2)
+        >>> # non-square kernels and unequal stride and with padding
+        >>> m = nndq.ConvTranspose1d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
+        >>> output = m(input)
+        >>> # exact output size can be also specified as an argument
+        >>> downsample = nndq.Conv1d(16, 16, 3, stride=2, padding=1)
+        >>> upsample = nndq.ConvTranspose1d(16, 16, 3, stride=2, padding=1)
+        >>> h = downsample(input)
+        >>> h.size()
+        torch.Size([1, 16, 6])
+        >>> output = upsample(h, output_size=input.size())
+        >>> output.size()
+        torch.Size([1, 16, 12])
+    """
+
+    _FLOAT_MODULE = nn.ConvTranspose1d
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        output_padding=0,
+        groups=1,
+        bias=True,
+        dilation=1,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        warnings.warn(
+            f"The current implementation of the {self._get_name()} module has poor numerical accuracy and its use is not recommended"  # noqa: B950
+        )
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            output_padding,
+            groups,
+            bias,
+            dilation,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "DynamicQuantizedConvTranspose1d"
+
+    def forward(self, input: Tensor, reduce_range: bool = True) -> Tensor:
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 3:
+            raise ValueError("Input shape must be `(N, C, L)`!")
+        return torch.ops.quantized.conv_transpose1d_dynamic(
+            input, self._packed_params, reduce_range
+        )
+
+
+class ConvTranspose2d(nnq.ConvTranspose2d):
+    r"""A dynamically quantized transposed convolution module with floating point tensors as inputs and outputs.
+
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.ConvTranspose2d`.
+
+    For special notes, please, see :class:`~torch.ao.nn.quantized.dynamic.Conv2d`
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+    See :class:`~torch.nn.ConvTranspose2d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> # With square kernels and equal stride
+        >>> m = nnq.ConvTranspose2d(16, 33, 3, stride=2)
+        >>> # non-square kernels and unequal stride and with padding
+        >>> m = nnq.ConvTranspose2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
+        >>> output = m(input)
+        >>> # exact output size can be also specified as an argument
+        >>> downsample = nnq.Conv2d(16, 16, 3, stride=2, padding=1)
+        >>> upsample = nnq.ConvTranspose2d(16, 16, 3, stride=2, padding=1)
+        >>> h = downsample(input)
+        >>> h.size()
+        torch.Size([1, 16, 6, 6])
+        >>> output = upsample(h, output_size=input.size())
+        >>> output.size()
+        torch.Size([1, 16, 12, 12])
+    """
+
+    _FLOAT_MODULE = nn.ConvTranspose2d
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        output_padding=0,
+        groups=1,
+        bias=True,
+        dilation=1,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        warnings.warn(
+            f"The current implementation of the {self._get_name()} module has poor numerical accuracy and its use is not recommended"  # noqa: B950
+        )
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            output_padding,
+            groups,
+            bias,
+            dilation,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "DynamicQuantizedConvTranspose2d"
+
+    def forward(self, input: Tensor, reduce_range: bool = True) -> Tensor:
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+        return ops.quantized.conv_transpose2d_dynamic(
+            input, self._packed_params, reduce_range
+        )
+
+
+class ConvTranspose3d(nnq.ConvTranspose3d):
+    r"""A dynamically quantized transposed convolution module with floating point tensors as inputs and outputs.
+
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.ConvTranspose3d`.
+
+    For special notes, please, see :class:`~torch.ao.nn.quantized.dynamic.Conv3d`
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+    See :class:`~torch.nn.ConvTranspose3d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> # With cubic kernels and equal stride
+        >>> m = nnq.ConvTranspose3d(16, 33, 3, stride=2)
+        >>> # non-cubic kernels and unequal stride and with padding
+        >>> m = nnq.ConvTranspose3d(16, 33, (3, 3, 5), stride=(2, 1, 1), padding=(4, 2, 2))
+        >>> output = m(input)
+        >>> # exact output size can be also specified as an argument
+        >>> downsample = nnq.Conv3d(16, 16, 3, stride=2, padding=1)
+        >>> upsample = nnq.ConvTranspose3d(16, 16, 3, stride=2, padding=1)
+        >>> h = downsample(input)
+        >>> h.size()
+        torch.Size([1, 16, 6, 6, 6])
+        >>> output = upsample(h, output_size=input.size())
+        >>> output.size()
+        torch.Size([1, 16, 12, 12, 12])
+    """
+
+    _FLOAT_MODULE = nn.ConvTranspose3d
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        output_padding=0,
+        groups=1,
+        bias=True,
+        dilation=1,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        warnings.warn(
+            f"The current implementation of the {self._get_name()} module has poor numerical accuracy and its use is not recommended"  # noqa: B950
+        )
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            output_padding,
+            groups,
+            bias,
+            dilation,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "DynamicQuantizedConvTranspose3d"
+
+    def forward(self, input: Tensor, reduce_range: bool = True) -> Tensor:
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 5:
+            raise ValueError("Input shape must be `(N, C, T, H, W)`!")
+        return ops.quantized.conv_transpose3d_dynamic(
+            input, self._packed_params, reduce_range
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..a94b308da267ec886906cb904768d959b2336b5d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py
@@ -0,0 +1,165 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.quantized as nnq
+from torch.ao.nn.quantized.modules.utils import _quantize_weight
+
+
+__all__ = [
+    "Linear",
+]
+
+
+class Linear(nnq.Linear):
+    r"""
+    A dynamic quantized linear module with floating point tensor as inputs and outputs.
+    We adopt the same interface as `torch.nn.Linear`, please see
+    https://pytorch.org/docs/stable/nn.html#torch.nn.Linear for documentation.
+
+    Similar to :class:`torch.nn.Linear`, attributes will be randomly
+    initialized at module creation time and will be overwritten later
+
+    Attributes:
+        weight (Tensor): the non-learnable quantized weights of the module which are of
+                         shape :math:`(\text{out\_features}, \text{in\_features})`.
+        bias (Tensor): the non-learnable floating point bias of the module of shape
+                       :math:`(\text{out\_features})`. If :attr:`bias` is ``True``,
+                       the values are initialized to zero.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> m = nn.quantized.dynamic.Linear(20, 30)
+        >>> input = torch.randn(128, 20)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 30])
+    """
+    # version used in this class is different from the parent class nnq.Linear
+    _version = 4
+
+    def __init__(self, in_features, out_features, bias_=True, dtype=torch.qint8):
+        super().__init__(in_features, out_features, bias_, dtype=dtype)
+        # We don't muck around with buffers or attributes or anything here
+        # to keep the module simple. *everything* is simply a Python attribute.
+        # Serialization logic is explicitly handled in the below serialization and
+        # deserialization modules
+        self.version = 4
+
+    def forward(self, x):
+        # Note that we can handle self.bias == None case.
+        if self._packed_params.dtype == torch.qint8:
+            if self.version is None or self.version < 4:
+                Y = torch.ops.quantized.linear_dynamic(
+                    x, self._packed_params._packed_params
+                )
+            else:
+                Y = torch.ops.quantized.linear_dynamic(
+                    x, self._packed_params._packed_params, reduce_range=True
+                )
+        elif self._packed_params.dtype == torch.float16:
+            Y = torch.ops.quantized.linear_dynamic_fp16(
+                x, self._packed_params._packed_params
+            )
+        else:
+            raise RuntimeError("Unsupported dtype on dynamic quantized linear!")
+        return Y.to(x.dtype)
+
+    def _get_name(self):
+        return "DynamicQuantizedLinear"
+
+    def extra_repr(self):
+        extra_repr_str = f"in_features={self.in_features}, out_features={self.out_features}, dtype={self._packed_params.dtype}"
+        if self._packed_params.dtype == torch.qint8:
+            extra_repr_str += f", qscheme={self.weight().qscheme()}"
+        return extra_repr_str
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        version = local_metadata.get("version", None)
+        self.version = version
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a dynamic quantized module from a float module or qparams_dict
+
+        Args:
+            mod (Module): a float module, either produced by torch.ao.quantization
+                          utilities or provided by the user
+        """
+        float_modules = [
+            torch.nn.Linear,
+            torch.nn.modules.linear.NonDynamicallyQuantizableLinear,
+            torch.ao.nn.intrinsic.modules.fused.LinearReLU,
+            torch.ao.nn.qat.dynamic.Linear,
+        ]
+
+        assert (
+            type(mod) in float_modules
+        ), "nn.quantized.dynamic.Linear.from_float only works for one of" + str(
+            [float_mod.__name__ for float_mod in float_modules]
+        )
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        if type(mod) == nni.LinearReLU:
+            mod = mod[0]
+        if mod.qconfig is not None and mod.qconfig.weight is not None:
+            weight_observer = mod.qconfig.weight()
+        else:
+            # We have the circular import issues if we import the qconfig in the beginning of this file:
+            # https://github.com/pytorch/pytorch/pull/24231. The current workaround is to postpone the
+            # import until we need it.
+            from torch.ao.quantization.qconfig import default_dynamic_qconfig
+
+            weight_observer = default_dynamic_qconfig.weight()
+        dtype = weight_observer.dtype
+        assert dtype in [torch.qint8, torch.float16], (
+            "The only supported dtypes for "
+            f"dynamic quantized linear are qint8 and float16 got: {dtype}"
+        )
+        weight_observer(mod.weight)
+        if dtype == torch.qint8:
+            qweight = _quantize_weight(mod.weight.float(), weight_observer)
+        elif dtype == torch.float16:
+            qweight = mod.weight.float()
+        else:
+            raise RuntimeError(
+                "Unsupported dtype specified for dynamic quantized Linear!"
+            )
+        qlinear = cls(mod.in_features, mod.out_features, dtype=dtype)
+        qlinear.set_weight_bias(qweight, mod.bias)
+        return qlinear
+
+    @classmethod
+    def from_reference(cls, ref_qlinear):
+        """Create a (fbgemm/qnnpack) dynamic quantized module from a reference quantized
+        module
+        Args:
+            ref_qlinear (Module): a reference quantized  module, either produced by
+            torch.ao.quantization functions or provided by the user
+        """
+        qlinear = cls(
+            ref_qlinear.in_features,
+            ref_qlinear.out_features,
+            dtype=ref_qlinear.weight_dtype,
+        )
+        qweight = ref_qlinear.get_quantized_weight()
+        bias = ref_qlinear.bias
+        qlinear.set_weight_bias(qweight, bias)
+        return qlinear
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..30e958e13d785fdafed5f423978af4ae8b6dca22
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py
@@ -0,0 +1,1359 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import numbers
+import warnings
+from typing_extensions import deprecated
+
+import torch
+import torch.nn as nn
+from torch import Tensor  # noqa: F401
+from torch._jit_internal import Dict, List, Optional, Tuple, Union  # noqa: F401
+from torch.ao.nn.quantized.modules.utils import _quantize_weight
+from torch.nn.utils.rnn import PackedSequence
+
+
+__all__ = [
+    "pack_weight_bias",
+    "PackedParameter",
+    "RNNBase",
+    "LSTM",
+    "GRU",
+    "RNNCellBase",
+    "RNNCell",
+    "LSTMCell",
+    "GRUCell",
+    "apply_permutation",
+]
+
+
+def _apply_permutation(tensor: Tensor, permutation: Tensor, dim: int = 1) -> Tensor:
+    return tensor.index_select(dim, permutation)
+
+
+@deprecated(
+    "`apply_permutation` is deprecated, please use `tensor.index_select(dim, permutation)` instead",
+    category=FutureWarning,
+)
+def apply_permutation(tensor: Tensor, permutation: Tensor, dim: int = 1) -> Tensor:
+    return _apply_permutation(tensor, permutation, dim)
+
+
+def pack_weight_bias(qweight, bias, dtype):
+    if dtype == torch.qint8:
+        # for each layer, for each direction we need to quantize and pack
+        # weights and pack parameters in this order:
+        #
+        #   w_ih, w_hh
+        packed_weight = torch.ops.quantized.linear_prepack(qweight, bias)
+
+        return packed_weight
+    else:
+        # for each layer, for each direction we need to quantize and pack
+        # weights and pack parameters in this order:
+        #
+        #   packed_ih, packed_hh, b_ih, b_hh
+        packed_weight = torch.ops.quantized.linear_prepack_fp16(qweight, bias)
+
+        return packed_weight
+
+
+class PackedParameter(torch.nn.Module):
+    def __init__(self, param):
+        super().__init__()
+        self.param = param
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "param"] = self.param
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        self.param = state_dict[prefix + "param"]
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+
+class RNNBase(torch.nn.Module):
+    _FLOAT_MODULE = nn.RNNBase
+
+    _version = 2
+
+    def __init__(
+        self,
+        mode,
+        input_size,
+        hidden_size,
+        num_layers=1,
+        bias=True,
+        batch_first=False,
+        dropout=0.0,
+        bidirectional=False,
+        dtype=torch.qint8,
+    ):
+        super().__init__()
+
+        self.mode = mode
+        self.input_size = input_size
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.bias = bias
+        self.batch_first = batch_first
+        self.dropout = float(dropout)
+        self.bidirectional = bidirectional
+        self.dtype = dtype
+        self.version = 2
+        self.training = False
+        num_directions = 2 if bidirectional else 1
+
+        # "type: ignore" is required since ints and Numbers are not fully comparable
+        # https://github.com/python/mypy/issues/8566
+        if (
+            not isinstance(dropout, numbers.Number)
+            or not 0 <= dropout <= 1  # type: ignore[operator]
+            or isinstance(dropout, bool)
+        ):
+            raise ValueError(
+                "dropout should be a number in range [0, 1] "
+                "representing the probability of an element being "
+                "zeroed"
+            )
+        if dropout > 0 and num_layers == 1:  # type: ignore[operator]
+            warnings.warn(
+                "dropout option adds dropout after all but last "
+                "recurrent layer, so non-zero dropout expects "
+                f"num_layers greater than 1, but got dropout={dropout} and "
+                f"num_layers={num_layers}"
+            )
+
+        if mode == "LSTM":
+            gate_size = 4 * hidden_size
+        elif mode == "GRU":
+            gate_size = 3 * hidden_size
+        else:
+            raise ValueError("Unrecognized RNN mode: " + mode)
+
+        _all_weight_values = []
+        for layer in range(num_layers):
+            for direction in range(num_directions):
+                layer_input_size = (
+                    input_size if layer == 0 else hidden_size * num_directions
+                )
+
+                w_ih = torch.randn(gate_size, layer_input_size).to(torch.float)
+                w_hh = torch.randn(gate_size, hidden_size).to(torch.float)
+                b_ih = torch.randn(gate_size).to(torch.float)
+                b_hh = torch.randn(gate_size).to(torch.float)
+                if dtype == torch.qint8:
+                    w_ih = torch.quantize_per_tensor(
+                        w_ih, scale=0.1, zero_point=0, dtype=torch.qint8
+                    )
+                    w_hh = torch.quantize_per_tensor(
+                        w_hh, scale=0.1, zero_point=0, dtype=torch.qint8
+                    )
+                    packed_ih = torch.ops.quantized.linear_prepack(w_ih, b_ih)
+                    packed_hh = torch.ops.quantized.linear_prepack(w_hh, b_hh)
+                    if self.version is None or self.version < 2:
+                        cell_params = (
+                            torch.ops.quantized.make_quantized_cell_params_dynamic(
+                                packed_ih, packed_hh, b_ih, b_hh
+                            )
+                        )
+                    else:
+                        cell_params = (
+                            torch.ops.quantized.make_quantized_cell_params_dynamic(
+                                packed_ih, packed_hh, b_ih, b_hh, True
+                            )
+                        )
+                else:
+                    packed_ih = torch.ops.quantized.linear_prepack_fp16(w_ih, b_ih)
+                    packed_hh = torch.ops.quantized.linear_prepack_fp16(w_hh, b_hh)
+                    cell_params = torch.ops.quantized.make_quantized_cell_params_fp16(
+                        packed_ih, packed_hh
+                    )
+
+                _all_weight_values.append(PackedParameter(cell_params))
+        self._all_weight_values = torch.nn.ModuleList(_all_weight_values)
+
+    def _get_name(self):
+        return "DynamicQuantizedRNN"
+
+    def extra_repr(self):
+        s = "{input_size}, {hidden_size}"
+        if self.num_layers != 1:
+            s += ", num_layers={num_layers}"
+        if self.bias is not True:
+            s += ", bias={bias}"
+        if self.batch_first is not False:
+            s += ", batch_first={batch_first}"
+        if self.dropout != 0:
+            s += ", dropout={dropout}"
+        if self.bidirectional is not False:
+            s += ", bidirectional={bidirectional}"
+        return s.format(**self.__dict__)
+
+    def __repr__(self):
+        # We don't want to show `ModuleList` children, hence custom
+        # `__repr__`. This is the same as nn.Module.__repr__, except the check
+        # for the `PackedParameter` and `nn.ModuleList`.
+        # You should still override `extra_repr` to add more info.
+        extra_lines = []
+        extra_repr = self.extra_repr()
+        # empty string will be split into list ['']
+        if extra_repr:
+            extra_lines = extra_repr.split("\n")
+        child_lines = []
+        for key, module in self._modules.items():
+            if isinstance(module, (PackedParameter, nn.ModuleList)):
+                continue
+            mod_str = repr(module)
+            mod_str = nn.modules.module._addindent(mod_str, 2)
+            child_lines.append("(" + key + "): " + mod_str)
+        lines = extra_lines + child_lines
+
+        main_str = self._get_name() + "("
+        if lines:
+            # simple one-liner info, which most builtin Modules will use
+            if len(extra_lines) == 1 and not child_lines:
+                main_str += extra_lines[0]
+            else:
+                main_str += "\n  " + "\n  ".join(lines) + "\n"
+
+        main_str += ")"
+        return main_str
+
+    def check_input(self, input: Tensor, batch_sizes: Optional[Tensor]) -> None:
+        expected_input_dim = 2 if batch_sizes is not None else 3
+        if input.dim() != expected_input_dim:
+            raise RuntimeError(
+                f"input must have {expected_input_dim} dimensions, got {input.dim()}"
+            )
+        if self.input_size != input.size(-1):
+            raise RuntimeError(
+                f"input.size(-1) must be equal to input_size. Expected {self.input_size}, got {input.size(-1)}"
+            )
+
+    def get_expected_hidden_size(
+        self, input: Tensor, batch_sizes: Optional[Tensor]
+    ) -> Tuple[int, int, int]:
+        if batch_sizes is not None:
+            mini_batch = int(batch_sizes[0])
+        else:
+            mini_batch = input.size(0) if self.batch_first else input.size(1)
+        num_directions = 2 if self.bidirectional else 1
+        expected_hidden_size = (
+            self.num_layers * num_directions,
+            mini_batch,
+            self.hidden_size,
+        )
+        return expected_hidden_size
+
+    def check_hidden_size(
+        self,
+        hx: Tensor,
+        expected_hidden_size: Tuple[int, int, int],
+        msg: str = "Expected hidden size {}, got {}",
+    ) -> None:
+        if hx.size() != expected_hidden_size:
+            raise RuntimeError(msg.format(expected_hidden_size, list(hx.size())))
+
+    def check_forward_args(
+        self, input: Tensor, hidden: Tensor, batch_sizes: Optional[Tensor]
+    ) -> None:
+        self.check_input(input, batch_sizes)
+        expected_hidden_size = self.get_expected_hidden_size(input, batch_sizes)
+        self.check_hidden_size(
+            hidden, expected_hidden_size, msg="Expected hidden size {}, got {}"
+        )
+
+    def permute_hidden(self, hx: Tensor, permutation: Optional[Tensor]) -> Tensor:
+        if permutation is None:
+            return hx
+        return _apply_permutation(hx, permutation)
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        version = local_metadata.get("version", None)
+        self.version = version
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    def set_weight_bias(self, weight_bias_dict):
+        def weight_bias_name(ihhh, layer, suffix):
+            weight_name = f"weight_{ihhh}_l{layer}{suffix}"
+            bias_name = f"bias_{ihhh}_l{layer}{suffix}"
+            return weight_name, bias_name
+
+        num_directions = 2 if self.bidirectional else 1
+        # TODO: dedup with __init__ of RNNBase
+        _all_weight_values = []
+        for layer in range(self.num_layers):
+            for direction in range(num_directions):
+                suffix = "_reverse" if direction == 1 else ""
+                w_ih_name, b_ih_name = weight_bias_name("ih", layer, suffix)
+                w_hh_name, b_hh_name = weight_bias_name("hh", layer, suffix)
+                w_ih = weight_bias_dict[w_ih_name]
+                b_ih = weight_bias_dict[b_ih_name]
+                w_hh = weight_bias_dict[w_hh_name]
+                b_hh = weight_bias_dict[b_hh_name]
+                if w_ih.dtype == torch.qint8:
+                    packed_ih = torch.ops.quantized.linear_prepack(w_ih, b_ih)
+                    packed_hh = torch.ops.quantized.linear_prepack(w_hh, b_hh)
+                    if self.version is None or self.version < 2:
+                        cell_params = (
+                            torch.ops.quantized.make_quantized_cell_params_dynamic(
+                                packed_ih, packed_hh, b_ih, b_hh
+                            )
+                        )
+                    else:
+                        cell_params = (
+                            torch.ops.quantized.make_quantized_cell_params_dynamic(
+                                packed_ih, packed_hh, b_ih, b_hh, True
+                            )
+                        )
+                else:
+                    packed_ih = torch.ops.quantized.linear_prepack_fp16(w_ih, b_ih)
+                    packed_hh = torch.ops.quantized.linear_prepack_fp16(w_hh, b_hh)
+                    cell_params = torch.ops.quantized.make_quantized_cell_params_fp16(
+                        packed_ih, packed_hh
+                    )
+
+                _all_weight_values.append(PackedParameter(cell_params))
+        self._all_weight_values = torch.nn.ModuleList(_all_weight_values)
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        assert type(mod) in {
+            torch.nn.LSTM,
+            torch.nn.GRU,
+        }, "nn.quantized.dynamic.RNNBase.from_float only works for nn.LSTM and nn.GRU"
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+
+        if mod.qconfig is not None and mod.qconfig.weight is not None:
+            weight_observer_method = mod.qconfig.weight
+        else:
+            # We have the circular import issues if we import the qconfig in the beginning of this file:
+            # https://github.com/pytorch/pytorch/pull/24231. The current workaround is to postpone the
+            # import until we need it.
+            from torch.ao.quantization.qconfig import default_dynamic_qconfig
+
+            weight_observer_method = default_dynamic_qconfig.weight
+
+        dtype = weight_observer_method().dtype
+        supported_scalar_types = [torch.qint8, torch.float16]
+        if dtype not in supported_scalar_types:
+            raise RuntimeError(
+                f"Unsupported dtype for dynamic RNN quantization: {dtype}"
+            )
+        # RNNBase can be either LSTM or GRU
+        qRNNBase: Union[LSTM, GRU]
+        if mod.mode == "LSTM":
+            qRNNBase = LSTM(
+                mod.input_size,
+                mod.hidden_size,
+                mod.num_layers,
+                mod.bias,
+                mod.batch_first,
+                mod.dropout,
+                mod.bidirectional,
+                dtype,
+            )
+        elif mod.mode == "GRU":
+            qRNNBase = GRU(
+                mod.input_size,
+                mod.hidden_size,
+                mod.num_layers,
+                mod.bias,
+                mod.batch_first,
+                mod.dropout,
+                mod.bidirectional,
+                dtype,
+            )
+        else:
+            raise NotImplementedError(
+                "Only LSTM/GRU is supported for QuantizedRNN for now"
+            )
+
+        num_directions = 2 if mod.bidirectional else 1
+
+        assert mod.bias
+
+        _all_weight_values = []
+        for layer in range(qRNNBase.num_layers):
+            for direction in range(num_directions):
+                suffix = "_reverse" if direction == 1 else ""
+
+                def retrieve_weight_bias(ihhh):
+                    weight_name = f"weight_{ihhh}_l{layer}{suffix}"
+                    bias_name = f"bias_{ihhh}_l{layer}{suffix}"
+                    weight = getattr(mod, weight_name)
+                    bias = getattr(mod, bias_name)
+                    return weight, bias
+
+                weight_ih, bias_ih = retrieve_weight_bias("ih")
+                weight_hh, bias_hh = retrieve_weight_bias("hh")
+
+                if dtype == torch.qint8:
+
+                    def quantize_and_pack(w, b):
+                        weight_observer = weight_observer_method()
+                        weight_observer(w)
+                        qweight = _quantize_weight(w.float(), weight_observer)
+                        packed_weight = torch.ops.quantized.linear_prepack(qweight, b)
+                        return packed_weight
+
+                    packed_ih = quantize_and_pack(weight_ih, bias_ih)
+                    packed_hh = quantize_and_pack(weight_hh, bias_hh)
+                    if qRNNBase.version is None or qRNNBase.version < 2:
+                        cell_params = (
+                            torch.ops.quantized.make_quantized_cell_params_dynamic(
+                                packed_ih, packed_hh, bias_ih, bias_hh
+                            )
+                        )
+                    else:
+                        cell_params = (
+                            torch.ops.quantized.make_quantized_cell_params_dynamic(
+                                packed_ih, packed_hh, bias_ih, bias_hh, True
+                            )
+                        )
+
+                elif dtype == torch.float16:
+                    packed_ih = torch.ops.quantized.linear_prepack_fp16(
+                        weight_ih.float(), bias_ih
+                    )
+                    packed_hh = torch.ops.quantized.linear_prepack_fp16(
+                        weight_hh.float(), bias_hh
+                    )
+
+                    cell_params = torch.ops.quantized.make_quantized_cell_params_fp16(
+                        packed_ih, packed_hh
+                    )
+                else:
+                    raise RuntimeError(
+                        "Unsupported dtype specified for dynamic quantized LSTM!"
+                    )
+
+                _all_weight_values.append(PackedParameter(cell_params))
+        qRNNBase._all_weight_values = torch.nn.ModuleList(_all_weight_values)
+
+        return qRNNBase
+
+    def _weight_bias(self):
+        # Returns a dict of weights and biases
+        weight_bias_dict: Dict[str, Dict] = {"weight": {}, "bias": {}}
+        count = 0
+        num_directions = 2 if self.bidirectional else 1
+        for layer in range(self.num_layers):
+            for direction in range(num_directions):
+                suffix = "_reverse" if direction == 1 else ""
+                key_name1 = f"weight_ih_l{layer}{suffix}"
+                key_name2 = f"weight_hh_l{layer}{suffix}"
+                # packed weights are part of torchbind class, CellParamsSerializationType
+                # Within the packed weight class, the weight and bias are accessible as Tensors
+                packed_weight_bias = self._all_weight_values[
+                    count
+                ].param.__getstate__()[0][4]
+                weight_bias_dict["weight"][key_name1] = packed_weight_bias[
+                    0
+                ].__getstate__()[0][0]
+                weight_bias_dict["weight"][key_name2] = packed_weight_bias[
+                    1
+                ].__getstate__()[0][0]
+                key_name1 = f"bias_ih_l{layer}{suffix}"
+                key_name2 = f"bias_hh_l{layer}{suffix}"
+                weight_bias_dict["bias"][key_name1] = packed_weight_bias[
+                    0
+                ].__getstate__()[0][1]
+                weight_bias_dict["bias"][key_name2] = packed_weight_bias[
+                    1
+                ].__getstate__()[0][1]
+                count = count + 1
+        return weight_bias_dict
+
+    def get_weight(self):
+        return self._weight_bias()["weight"]
+
+    def get_bias(self):
+        return self._weight_bias()["bias"]
+
+
+class LSTM(RNNBase):
+    r"""
+    A dynamic quantized LSTM module with floating point tensor as inputs and outputs.
+    We adopt the same interface as `torch.nn.LSTM`, please see
+    https://pytorch.org/docs/stable/nn.html#torch.nn.LSTM for documentation.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> rnn = nn.LSTM(10, 20, 2)
+        >>> input = torch.randn(5, 3, 10)
+        >>> h0 = torch.randn(2, 3, 20)
+        >>> c0 = torch.randn(2, 3, 20)
+        >>> output, (hn, cn) = rnn(input, (h0, c0))
+    """
+    _FLOAT_MODULE = nn.LSTM
+
+    __overloads__ = {"forward": ["forward_packed", "forward_tensor"]}
+
+    def __init__(self, *args, **kwargs):
+        super().__init__("LSTM", *args, **kwargs)
+
+    def _get_name(self):
+        return "DynamicQuantizedLSTM"
+
+    def forward_impl(
+        self,
+        input: Tensor,
+        hx: Optional[Tuple[Tensor, Tensor]],
+        batch_sizes: Optional[Tensor],
+        max_batch_size: int,
+        sorted_indices: Optional[Tensor],
+    ) -> Tuple[Tensor, Tuple[Tensor, Tensor]]:
+        if hx is None:
+            num_directions = 2 if self.bidirectional else 1
+            zeros = torch.zeros(
+                self.num_layers * num_directions,
+                max_batch_size,
+                self.hidden_size,
+                dtype=input.dtype,
+                device=input.device,
+            )
+            hx = (zeros, zeros)
+        else:
+            # Each batch of the hidden state should match the input sequence that
+            # the user believes he/she is passing in.
+            hx = self.permute_hidden(hx, sorted_indices)
+
+        self.check_forward_args(input, hx, batch_sizes)
+
+        _all_params = [m.param for m in self._all_weight_values]
+        if batch_sizes is None:
+            result = torch.quantized_lstm(
+                input,
+                hx,
+                _all_params,
+                self.bias,
+                self.num_layers,
+                float(self.dropout),
+                self.training,
+                self.bidirectional,
+                self.batch_first,
+                dtype=self.dtype,
+                use_dynamic=True,
+            )
+        else:
+            result = torch.quantized_lstm(
+                input,
+                batch_sizes,
+                hx,
+                _all_params,
+                self.bias,
+                self.num_layers,
+                float(self.dropout),
+                self.training,
+                self.bidirectional,
+                dtype=self.dtype,
+                use_dynamic=True,
+            )
+        output = result[0]
+        hidden = result[1:]
+
+        return output, hidden
+
+    @torch.jit.export
+    def forward_tensor(
+        self, input: Tensor, hx: Optional[Tuple[Tensor, Tensor]] = None
+    ) -> Tuple[Tensor, Tuple[Tensor, Tensor]]:
+        batch_sizes = None
+        max_batch_size = input.size(0) if self.batch_first else input.size(1)
+        sorted_indices = None
+        unsorted_indices = None
+
+        output, hidden = self.forward_impl(
+            input, hx, batch_sizes, max_batch_size, sorted_indices
+        )
+
+        return output, self.permute_hidden(hidden, unsorted_indices)
+
+    @torch.jit.export
+    def forward_packed(
+        self, input: PackedSequence, hx: Optional[Tuple[Tensor, Tensor]] = None
+    ) -> Tuple[PackedSequence, Tuple[Tensor, Tensor]]:
+        input_, batch_sizes, sorted_indices, unsorted_indices = input
+        max_batch_size = int(batch_sizes[0])
+
+        output_, hidden = self.forward_impl(
+            input_, hx, batch_sizes, max_batch_size, sorted_indices
+        )
+
+        output = PackedSequence(output_, batch_sizes, sorted_indices, unsorted_indices)
+        return output, self.permute_hidden(hidden, unsorted_indices)
+
+    # "type: ignore" is required due to issue #43072
+    def permute_hidden(  # type: ignore[override]
+        self,
+        hx: Tuple[Tensor, Tensor],
+        permutation: Optional[Tensor],
+    ) -> Tuple[Tensor, Tensor]:
+        if permutation is None:
+            return hx
+        return _apply_permutation(hx[0], permutation), _apply_permutation(
+            hx[1], permutation
+        )
+
+    # "type: ignore" is required due to issue #43072
+    def check_forward_args(  # type: ignore[override]
+        self,
+        input: Tensor,
+        hidden: Tuple[Tensor, Tensor],
+        batch_sizes: Optional[Tensor],
+    ) -> None:
+        self.check_input(input, batch_sizes)
+        expected_hidden_size = self.get_expected_hidden_size(input, batch_sizes)
+
+        self.check_hidden_size(
+            hidden[0], expected_hidden_size, "Expected hidden[0] size {}, got {}"
+        )
+        self.check_hidden_size(
+            hidden[1], expected_hidden_size, "Expected hidden[1] size {}, got {}"
+        )
+
+    @torch.jit.ignore
+    def forward(self, input, hx=None):
+        if isinstance(input, PackedSequence):
+            return self.forward_packed(input, hx)
+        else:
+            return self.forward_tensor(input, hx)
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+    @classmethod
+    def from_reference(cls, ref_mod):
+        assert hasattr(ref_mod, "weight_ih_l0_dtype"), "We are assuming weight_ih_l0 "
+        "exists in LSTM, may need to relax the assumption to support the use case"
+        qmod = cls(
+            ref_mod.input_size,
+            ref_mod.hidden_size,
+            ref_mod.num_layers,
+            ref_mod.bias,
+            ref_mod.batch_first,
+            ref_mod.dropout,
+            ref_mod.bidirectional,
+            # assuming there is layer 0, which should be OK
+            ref_mod.weight_ih_l0_dtype,
+        )
+        qmod.set_weight_bias(ref_mod.get_quantized_weight_bias_dict())
+        return qmod
+
+
+class GRU(RNNBase):
+    r"""Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.
+
+
+    For each element in the input sequence, each layer computes the following
+    function:
+
+    .. math::
+        \begin{array}{ll}
+            r_t = \sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\
+            z_t = \sigma(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \\
+            n_t = \tanh(W_{in} x_t + b_{in} + r_t \odot (W_{hn} h_{(t-1)}+ b_{hn})) \\
+            h_t = (1 - z_t) \odot n_t + z_t \odot h_{(t-1)}
+        \end{array}
+
+    where :math:`h_t` is the hidden state at time `t`, :math:`x_t` is the input
+    at time `t`, :math:`h_{(t-1)}` is the hidden state of the layer
+    at time `t-1` or the initial hidden state at time `0`, and :math:`r_t`,
+    :math:`z_t`, :math:`n_t` are the reset, update, and new gates, respectively.
+    :math:`\sigma` is the sigmoid function, and :math:`\odot` is the Hadamard product.
+
+    In a multilayer GRU, the input :math:`x^{(l)}_t` of the :math:`l` -th layer
+    (:math:`l >= 2`) is the hidden state :math:`h^{(l-1)}_t` of the previous layer multiplied by
+    dropout :math:`\delta^{(l-1)}_t` where each :math:`\delta^{(l-1)}_t` is a Bernoulli random
+    variable which is :math:`0` with probability :attr:`dropout`.
+
+    Args:
+        input_size: The number of expected features in the input `x`
+        hidden_size: The number of features in the hidden state `h`
+        num_layers: Number of recurrent layers. E.g., setting ``num_layers=2``
+            would mean stacking two GRUs together to form a `stacked GRU`,
+            with the second GRU taking in outputs of the first GRU and
+            computing the final results. Default: 1
+        bias: If ``False``, then the layer does not use bias weights `b_ih` and `b_hh`.
+            Default: ``True``
+        batch_first: If ``True``, then the input and output tensors are provided
+            as (batch, seq, feature). Default: ``False``
+        dropout: If non-zero, introduces a `Dropout` layer on the outputs of each
+            GRU layer except the last layer, with dropout probability equal to
+            :attr:`dropout`. Default: 0
+        bidirectional: If ``True``, becomes a bidirectional GRU. Default: ``False``
+
+    Inputs: input, h_0
+        - **input** of shape `(seq_len, batch, input_size)`: tensor containing the features
+          of the input sequence. The input can also be a packed variable length
+          sequence. See :func:`torch.nn.utils.rnn.pack_padded_sequence`
+          for details.
+        - **h_0** of shape `(num_layers * num_directions, batch, hidden_size)`: tensor
+          containing the initial hidden state for each element in the batch.
+          Defaults to zero if not provided. If the RNN is bidirectional,
+          num_directions should be 2, else it should be 1.
+
+    Outputs: output, h_n
+        - **output** of shape `(seq_len, batch, num_directions * hidden_size)`: tensor
+          containing the output features h_t from the last layer of the GRU,
+          for each `t`. If a :class:`torch.nn.utils.rnn.PackedSequence` has been
+          given as the input, the output will also be a packed sequence.
+          For the unpacked case, the directions can be separated
+          using ``output.view(seq_len, batch, num_directions, hidden_size)``,
+          with forward and backward being direction `0` and `1` respectively.
+
+          Similarly, the directions can be separated in the packed case.
+        - **h_n** of shape `(num_layers * num_directions, batch, hidden_size)`: tensor
+          containing the hidden state for `t = seq_len`
+
+          Like *output*, the layers can be separated using
+          ``h_n.view(num_layers, num_directions, batch, hidden_size)``.
+
+    Shape:
+        - Input1: :math:`(L, N, H_{in})` tensor containing input features where
+          :math:`H_{in}=\text{input\_size}` and `L` represents a sequence length.
+        - Input2: :math:`(S, N, H_{out})` tensor
+          containing the initial hidden state for each element in the batch.
+          :math:`H_{out}=\text{hidden\_size}`
+          Defaults to zero if not provided. where :math:`S=\text{num\_layers} * \text{num\_directions}`
+          If the RNN is bidirectional, num_directions should be 2, else it should be 1.
+        - Output1: :math:`(L, N, H_{all})` where :math:`H_{all}=\text{num\_directions} * \text{hidden\_size}`
+        - Output2: :math:`(S, N, H_{out})` tensor containing the next hidden state
+          for each element in the batch
+
+    Attributes:
+        weight_ih_l[k] : the learnable input-hidden weights of the :math:`\text{k}^{th}` layer
+            (W_ir|W_iz|W_in), of shape `(3*hidden_size, input_size)` for `k = 0`.
+            Otherwise, the shape is `(3*hidden_size, num_directions * hidden_size)`
+        weight_hh_l[k] : the learnable hidden-hidden weights of the :math:`\text{k}^{th}` layer
+            (W_hr|W_hz|W_hn), of shape `(3*hidden_size, hidden_size)`
+        bias_ih_l[k] : the learnable input-hidden bias of the :math:`\text{k}^{th}` layer
+            (b_ir|b_iz|b_in), of shape `(3*hidden_size)`
+        bias_hh_l[k] : the learnable hidden-hidden bias of the :math:`\text{k}^{th}` layer
+            (b_hr|b_hz|b_hn), of shape `(3*hidden_size)`
+
+    .. note::
+        All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`
+        where :math:`k = \frac{1}{\text{hidden\_size}}`
+
+    .. note::
+        The calculation of new gate :math:`n_t` subtly differs from the original paper and other frameworks.
+        In the original implementation, the Hadamard product :math:`(\odot)` between :math:`r_t` and the
+        previous hidden state :math:`h_{(t-1)}` is done before the multiplication with the weight matrix
+        `W` and addition of bias:
+
+        .. math::
+            \begin{aligned}
+                n_t = \tanh(W_{in} x_t + b_{in} + W_{hn} ( r_t \odot h_{(t-1)} ) + b_{hn})
+            \end{aligned}
+
+        This is in contrast to PyTorch implementation, which is done after :math:`W_{hn} h_{(t-1)}`
+
+        .. math::
+            \begin{aligned}
+                n_t = \tanh(W_{in} x_t + b_{in} + r_t \odot (W_{hn} h_{(t-1)}+ b_{hn}))
+            \end{aligned}
+
+        This implementation differs on purpose for efficiency.
+
+    .. include:: ../cudnn_persistent_rnn.rst
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> rnn = nn.GRU(10, 20, 2)
+        >>> input = torch.randn(5, 3, 10)
+        >>> h0 = torch.randn(2, 3, 20)
+        >>> output, hn = rnn(input, h0)
+    """
+    _FLOAT_MODULE = nn.GRU
+
+    __overloads__ = {"forward": ["forward_packed", "forward_tensor"]}
+
+    def __init__(self, *args, **kwargs):
+        super().__init__("GRU", *args, **kwargs)
+
+    def _get_name(self):
+        return "DynamicQuantizedGRU"
+
+    def check_forward_args(
+        self, input: Tensor, hidden: Tensor, batch_sizes: Optional[Tensor]
+    ) -> None:
+        self.check_input(input, batch_sizes)
+        expected_hidden_size = self.get_expected_hidden_size(input, batch_sizes)
+
+        self.check_hidden_size(
+            hidden, expected_hidden_size, "Expected hidden size {}, got {}"
+        )
+
+    def forward_impl(
+        self,
+        input: Tensor,
+        hx: Optional[Tensor],
+        batch_sizes: Optional[Tensor],
+        max_batch_size: int,
+        sorted_indices: Optional[Tensor],
+    ) -> Tuple[Tensor, Tensor]:
+        if hx is None:
+            num_directions = 2 if self.bidirectional else 1
+            zeros = torch.zeros(
+                self.num_layers * num_directions,
+                max_batch_size,
+                self.hidden_size,
+                dtype=input.dtype,
+                device=input.device,
+            )
+            hx = zeros
+        else:
+            # Each batch of the hidden state should match the input sequence that
+            # the user believes he/she is passing in.
+            hx = self.permute_hidden(hx, sorted_indices)
+
+        self.check_forward_args(input, hx, batch_sizes)
+
+        _all_params = [m.param for m in self._all_weight_values]
+        if batch_sizes is None:
+            result = torch.quantized_gru(
+                input,
+                hx,
+                _all_params,
+                self.bias,
+                self.num_layers,
+                self.dropout,
+                self.training,
+                self.bidirectional,
+                self.batch_first,
+            )
+        else:
+            result = torch.quantized_gru(
+                input,
+                batch_sizes,
+                hx,
+                _all_params,
+                self.bias,
+                self.num_layers,
+                self.dropout,
+                self.training,
+                self.bidirectional,
+            )
+        output = result[0]
+        hidden = result[1]
+
+        return output, hidden
+
+    @torch.jit.export
+    def forward_tensor(
+        self, input: Tensor, hx: Optional[Tensor] = None
+    ) -> Tuple[Tensor, Tensor]:
+        batch_sizes = None
+        max_batch_size = input.size(0) if self.batch_first else input.size(1)
+        sorted_indices = None
+        unsorted_indices = None
+
+        output, hidden = self.forward_impl(
+            input, hx, batch_sizes, max_batch_size, sorted_indices
+        )
+
+        return output, self.permute_hidden(hidden, unsorted_indices)
+
+    @torch.jit.export
+    def forward_packed(
+        self, input: PackedSequence, hx: Optional[Tensor] = None
+    ) -> Tuple[PackedSequence, Tensor]:
+        input_, batch_sizes, sorted_indices, unsorted_indices = input
+        max_batch_size = int(batch_sizes[0])
+        output_, hidden = self.forward_impl(
+            input_, hx, batch_sizes, max_batch_size, sorted_indices
+        )
+
+        output = PackedSequence(output_, batch_sizes, sorted_indices, unsorted_indices)
+        return output, self.permute_hidden(hidden, unsorted_indices)
+
+    def permute_hidden(self, hx: Tensor, permutation: Optional[Tensor]) -> Tensor:
+        if permutation is None:
+            return hx
+        return _apply_permutation(hx, permutation)
+
+    @torch.jit.ignore
+    def forward(self, input, hx=None):
+        if isinstance(input, PackedSequence):
+            return self.forward_packed(input, hx)
+        else:
+            return self.forward_tensor(input, hx)
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+    @classmethod
+    def from_reference(cls, ref_mod):
+        assert hasattr(ref_mod, "weight_ih_l0_dtype"), "We are assuming weight_ih_l0 "
+        "exists in LSTM, may need to relax the assumption to support the use case"
+        qmod = cls(
+            ref_mod.input_size,
+            ref_mod.hidden_size,
+            ref_mod.num_layers,
+            ref_mod.bias,
+            ref_mod.batch_first,
+            ref_mod.dropout,
+            ref_mod.bidirectional,
+            # assuming there is layer 0, which should be OK
+            ref_mod.weight_ih_l0_dtype,
+        )
+        qmod.set_weight_bias(ref_mod.get_quantized_weight_bias_dict())
+        return qmod
+
+
+class RNNCellBase(torch.nn.Module):
+    # _FLOAT_MODULE = nn.CellRNNBase
+    __constants__ = ["input_size", "hidden_size", "bias"]
+
+    def __init__(
+        self, input_size, hidden_size, bias=True, num_chunks=4, dtype=torch.qint8
+    ):
+        super().__init__()
+        self.input_size = input_size
+        self.hidden_size = hidden_size
+        self.bias = bias
+        self.weight_dtype = dtype
+        if bias:
+            self.bias_ih = torch.randn(num_chunks * hidden_size).to(dtype=torch.float)
+            self.bias_hh = torch.randn(num_chunks * hidden_size).to(dtype=torch.float)
+        else:
+            self.register_parameter("bias_ih", None)
+            self.register_parameter("bias_hh", None)
+
+        weight_ih = torch.randn(num_chunks * hidden_size, input_size).to(torch.float)
+        weight_hh = torch.randn(num_chunks * hidden_size, hidden_size).to(torch.float)
+        if dtype == torch.qint8:
+            weight_ih = torch.quantize_per_tensor(
+                weight_ih, scale=1, zero_point=0, dtype=torch.qint8
+            )
+            weight_hh = torch.quantize_per_tensor(
+                weight_hh, scale=1, zero_point=0, dtype=torch.qint8
+            )
+
+        if dtype == torch.qint8:
+            # for each layer, for each direction we need to quantize and pack
+            # weights and pack parameters in this order:
+            #
+            #   w_ih, w_hh
+            packed_weight_ih = torch.ops.quantized.linear_prepack(
+                weight_ih, self.bias_ih
+            )
+            packed_weight_hh = torch.ops.quantized.linear_prepack(
+                weight_hh, self.bias_hh
+            )
+        else:
+            # for each layer, for each direction we need to quantize and pack
+            # weights and pack parameters in this order:
+            #
+            #   packed_ih, packed_hh, b_ih, b_hh
+            packed_weight_ih = torch.ops.quantized.linear_prepack_fp16(
+                weight_ih, self.bias_ih
+            )
+            packed_weight_hh = torch.ops.quantized.linear_prepack_fp16(
+                weight_hh, self.bias_hh
+            )
+
+        self._packed_weight_ih = packed_weight_ih
+        self._packed_weight_hh = packed_weight_hh
+
+    def _get_name(self):
+        return "DynamicQuantizedRNNBase"
+
+    def extra_repr(self):
+        s = "{input_size}, {hidden_size}"
+        if "bias" in self.__dict__ and self.bias is not True:
+            s += ", bias={bias}"
+        if "nonlinearity" in self.__dict__ and self.nonlinearity != "tanh":
+            s += ", nonlinearity={nonlinearity}"
+        return s.format(**self.__dict__)
+
+    def check_forward_input(self, input):
+        if input.size(1) != self.input_size:
+            raise RuntimeError(
+                f"input has inconsistent input_size: got {input.size(1)}, expected {self.input_size}"
+            )
+
+    def check_forward_hidden(
+        self, input: Tensor, hx: Tensor, hidden_label: str = ""
+    ) -> None:
+        if input.size(0) != hx.size(0):
+            raise RuntimeError(
+                f"Input batch size {input.size(0)} doesn't match hidden{hidden_label} batch size {hx.size(0)}"
+            )
+
+        if hx.size(1) != self.hidden_size:
+            raise RuntimeError(
+                f"hidden{hidden_label} has inconsistent hidden_size: got {hx.size(1)}, expected {self.hidden_size}"
+            )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        assert type(mod) in {
+            torch.nn.LSTMCell,
+            torch.nn.GRUCell,
+            torch.nn.RNNCell,
+        }, "nn.quantized.dynamic.RNNCellBase.from_float \
+                                 only works for nn.LSTMCell, nn.GRUCell and nn.RNNCell"
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+
+        if mod.qconfig is not None and mod.qconfig.weight is not None:
+            weight_observer_method = mod.qconfig.weight
+        else:
+            # We have the circular import issues if we import the qconfig in the beginning of this file:
+            # https://github.com/pytorch/pytorch/pull/24231. The current workaround is to postpone the
+            # import until we need it.
+            from torch.ao.quantization.qconfig import default_dynamic_qconfig
+
+            weight_observer_method = default_dynamic_qconfig.weight
+
+        dtype = weight_observer_method().dtype
+        supported_scalar_types = [torch.qint8, torch.float16]
+        if dtype not in supported_scalar_types:
+            raise RuntimeError(
+                f"Unsupported dtype for dynamic RNN quantization: {dtype}"
+            )
+
+        qRNNCellBase: Union[LSTMCell, GRUCell, RNNCell]
+
+        if type(mod) == torch.nn.LSTMCell:
+            qRNNCellBase = LSTMCell(
+                mod.input_size, mod.hidden_size, bias=mod.bias, dtype=dtype
+            )
+        elif type(mod) == torch.nn.GRUCell:
+            qRNNCellBase = GRUCell(
+                mod.input_size, mod.hidden_size, bias=mod.bias, dtype=dtype
+            )
+        elif type(mod) == torch.nn.RNNCell:
+            qRNNCellBase = RNNCell(
+                mod.input_size,
+                mod.hidden_size,
+                bias=mod.bias,
+                nonlinearity=mod.nonlinearity,
+                dtype=dtype,
+            )
+        else:
+            raise NotImplementedError(
+                "Only LSTMCell, GRUCell and RNNCell \
+            are supported for QuantizedRNN for now"
+            )
+
+        assert mod.bias
+
+        def _observe_and_quantize_weight(weight):
+            if dtype == torch.qint8:
+                weight_observer = weight_observer_method()
+                weight_observer(weight)
+                qweight = _quantize_weight(weight.float(), weight_observer)
+                return qweight
+            else:
+                return weight.float()
+
+        qRNNCellBase._packed_weight_ih = pack_weight_bias(
+            _observe_and_quantize_weight(mod.weight_ih), mod.bias_ih, dtype
+        )
+        qRNNCellBase._packed_weight_hh = pack_weight_bias(
+            _observe_and_quantize_weight(mod.weight_hh), mod.bias_hh, dtype
+        )
+        return qRNNCellBase
+
+    @classmethod
+    def from_reference(cls, ref_mod):
+        assert hasattr(ref_mod, "weight_ih_dtype"), "We are assuming weight_ih "
+        "exists in reference module, may need to relax the assumption to support the use case"
+        if hasattr(ref_mod, "nonlinearity"):
+            qmod = cls(
+                ref_mod.input_size,
+                ref_mod.hidden_size,
+                ref_mod.bias,
+                ref_mod.nonlinearity,
+                dtype=ref_mod.weight_ih_dtype,
+            )
+        else:
+            qmod = cls(
+                ref_mod.input_size,
+                ref_mod.hidden_size,
+                ref_mod.bias,
+                dtype=ref_mod.weight_ih_dtype,
+            )
+        weight_bias_dict = {
+            "weight": {
+                "weight_ih": ref_mod.get_quantized_weight_ih(),
+                "weight_hh": ref_mod.get_quantized_weight_hh(),
+            },
+            "bias": {
+                "bias_ih": ref_mod.bias_ih,
+                "bias_hh": ref_mod.bias_hh,
+            },
+        }
+        qmod.set_weight_bias(weight_bias_dict)
+        return qmod
+
+    def _weight_bias(self):
+        # Returns a dict of weights and biases
+        weight_bias_dict: Dict[str, Dict] = {"weight": {}, "bias": {}}
+        w1, b1 = self._packed_weight_ih.__getstate__()[0]
+        w2, b2 = self._packed_weight_hh.__getstate__()[0]
+        # TODO: these can be simplified to one level? e.g. using weight_ih as key
+        # directly
+        weight_bias_dict["weight"]["weight_ih"] = w1
+        weight_bias_dict["weight"]["weight_hh"] = w2
+        weight_bias_dict["bias"]["bias_ih"] = b1
+        weight_bias_dict["bias"]["bias_hh"] = b2
+        return weight_bias_dict
+
+    def get_weight(self):
+        return self._weight_bias()["weight"]
+
+    def get_bias(self):
+        return self._weight_bias()["bias"]
+
+    def set_weight_bias(self, weight_bias_dict):
+        # TODO: these can be simplified to one level? e.g. using weight_ih as key
+        # directly
+        self._packed_weight_ih = pack_weight_bias(
+            weight_bias_dict["weight"]["weight_ih"],
+            weight_bias_dict["bias"]["bias_ih"],
+            self.weight_dtype,
+        )
+        self._packed_weight_hh = pack_weight_bias(
+            weight_bias_dict["weight"]["weight_hh"],
+            weight_bias_dict["bias"]["bias_hh"],
+            self.weight_dtype,
+        )
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "_packed_weight_ih"] = self._packed_weight_ih
+        destination[prefix + "_packed_weight_hh"] = self._packed_weight_hh
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        self._packed_weight_ih = state_dict.pop(prefix + "_packed_weight_ih")
+        self._packed_weight_hh = state_dict.pop(prefix + "_packed_weight_hh")
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+
+class RNNCell(RNNCellBase):
+    r"""An Elman RNN cell with tanh or ReLU non-linearity.
+    A dynamic quantized RNNCell module with floating point tensor as inputs and outputs.
+    Weights are quantized to 8 bits. We adopt the same interface as `torch.nn.RNNCell`,
+    please see https://pytorch.org/docs/stable/nn.html#torch.nn.RNNCell for documentation.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> rnn = nn.RNNCell(10, 20)
+        >>> input = torch.randn(6, 3, 10)
+        >>> hx = torch.randn(3, 20)
+        >>> output = []
+        >>> for i in range(6):
+        ...     hx = rnn(input[i], hx)
+        ...     output.append(hx)
+    """
+    __constants__ = ["input_size", "hidden_size", "bias", "nonlinearity"]
+
+    def __init__(
+        self, input_size, hidden_size, bias=True, nonlinearity="tanh", dtype=torch.qint8
+    ):
+        super().__init__(input_size, hidden_size, bias, num_chunks=1, dtype=dtype)
+        self.nonlinearity = nonlinearity
+
+    def _get_name(self):
+        return "DynamicQuantizedRNNCell"
+
+    def forward(self, input: Tensor, hx: Optional[Tensor] = None) -> Tensor:
+        self.check_forward_input(input)
+        if hx is None:
+            hx = torch.zeros(
+                input.size(0), self.hidden_size, dtype=input.dtype, device=input.device
+            )
+        self.check_forward_hidden(input, hx, "")
+        if self.nonlinearity == "tanh":
+            ret = torch.ops.quantized.quantized_rnn_tanh_cell_dynamic(
+                input,
+                hx,
+                self._packed_weight_ih,
+                self._packed_weight_hh,
+                self.bias_ih,
+                self.bias_hh,
+            )
+        elif self.nonlinearity == "relu":
+            ret = torch.ops.quantized.quantized_rnn_relu_cell_dynamic(
+                input,
+                hx,
+                self._packed_weight_ih,
+                self._packed_weight_hh,
+                self.bias_ih,
+                self.bias_hh,
+            )
+        else:
+            ret = input  # TODO: remove when jit supports exception flow
+            raise RuntimeError(f"Unknown nonlinearity: {self.nonlinearity}")
+        return ret
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+class LSTMCell(RNNCellBase):
+    r"""A long short-term memory (LSTM) cell.
+
+    A dynamic quantized LSTMCell module with floating point tensor as inputs and outputs.
+    Weights are quantized to 8 bits. We adopt the same interface as `torch.nn.LSTMCell`,
+    please see https://pytorch.org/docs/stable/nn.html#torch.nn.LSTMCell for documentation.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> rnn = nn.LSTMCell(10, 20)
+        >>> input = torch.randn(6, 3, 10)
+        >>> hx = torch.randn(3, 20)
+        >>> cx = torch.randn(3, 20)
+        >>> output = []
+        >>> for i in range(6):
+        ...     hx, cx = rnn(input[i], (hx, cx))
+        ...     output.append(hx)
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, num_chunks=4, **kwargs)  # type: ignore[misc]
+
+    def _get_name(self):
+        return "DynamicQuantizedLSTMCell"
+
+    def forward(
+        self, input: Tensor, hx: Optional[Tuple[Tensor, Tensor]] = None
+    ) -> Tuple[Tensor, Tensor]:
+        self.check_forward_input(input)
+        if hx is None:
+            zeros = torch.zeros(
+                input.size(0), self.hidden_size, dtype=input.dtype, device=input.device
+            )
+            hx = (zeros, zeros)
+        self.check_forward_hidden(input, hx[0], "[0]")
+        self.check_forward_hidden(input, hx[1], "[1]")
+        return torch.ops.quantized.quantized_lstm_cell_dynamic(
+            input,
+            hx,
+            self._packed_weight_ih,
+            self._packed_weight_hh,
+            self.bias_ih,
+            self.bias_hh,
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+class GRUCell(RNNCellBase):
+    r"""A gated recurrent unit (GRU) cell
+
+    A dynamic quantized GRUCell module with floating point tensor as inputs and outputs.
+    Weights are quantized to 8 bits. We adopt the same interface as `torch.nn.GRUCell`,
+    please see https://pytorch.org/docs/stable/nn.html#torch.nn.GRUCell for documentation.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> rnn = nn.GRUCell(10, 20)
+        >>> input = torch.randn(6, 3, 10)
+        >>> hx = torch.randn(3, 20)
+        >>> output = []
+        >>> for i in range(6):
+        ...     hx = rnn(input[i], hx)
+        ...     output.append(hx)
+    """
+
+    def __init__(self, input_size, hidden_size, bias=True, dtype=torch.qint8):
+        super().__init__(input_size, hidden_size, bias, num_chunks=3, dtype=dtype)
+
+    def _get_name(self):
+        return "DynamicQuantizedGRUCell"
+
+    def forward(self, input: Tensor, hx: Optional[Tensor] = None) -> Tensor:
+        self.check_forward_input(input)
+        if hx is None:
+            hx = torch.zeros(
+                input.size(0), self.hidden_size, dtype=input.dtype, device=input.device
+            )
+        self.check_forward_hidden(input, hx, "")
+        return torch.ops.quantized.quantized_gru_cell_dynamic(
+            input,
+            hx,
+            self._packed_weight_ih,
+            self._packed_weight_hh,
+            self.bias_ih,
+            self.bias_hh,
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/functional.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/functional.py
new file mode 100644
index 0000000000000000000000000000000000000000..c251cef874d3791a2ccf056733a4c0a730830811
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/functional.py
@@ -0,0 +1,778 @@
+# mypy: allow-untyped-defs
+r""" Functional interface (quantized)."""
+import warnings
+from typing import List, Optional
+
+import torch
+from torch import Tensor
+from torch.jit.annotations import BroadcastingList2
+from torch.nn.modules.utils import _pair, _triple
+
+from .modules.utils import _pair_from_first
+
+
+# Although some of the functions and docstrings are mirrored from the torch.nn,
+# we want to have them here for future changes.
+
+__all__ = [
+    "avg_pool2d",
+    "avg_pool3d",
+    "adaptive_avg_pool2d",
+    "adaptive_avg_pool3d",
+    "conv1d",
+    "conv2d",
+    "conv3d",
+    "interpolate",
+    "linear",
+    "max_pool1d",
+    "max_pool2d",
+    "celu",
+    "leaky_relu",
+    "hardtanh",
+    "hardswish",
+    "threshold",
+    "elu",
+    "hardsigmoid",
+    "clamp",
+    "upsample",
+    "upsample_bilinear",
+    "upsample_nearest",
+]
+
+
+def avg_pool2d(
+    input,
+    kernel_size,
+    stride=None,
+    padding=0,
+    ceil_mode=False,
+    count_include_pad=True,
+    divisor_override=None,
+):
+    r"""
+    Applies 2D average-pooling operation in :math:`kH \times kW` regions by step size
+    :math:`sH \times sW` steps. The number of output features is equal to the number of
+    input planes.
+
+    .. note:: The input quantization parameters propagate to the output.
+
+    See :class:`~torch.ao.nn.quantized.AvgPool2d` for details and output shape.
+
+    Args:
+        input: quantized input tensor :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`
+        kernel_size: size of the pooling region. Can be a single number or a
+          tuple `(kH, kW)`
+        stride: stride of the pooling operation. Can be a single number or a
+          tuple `(sH, sW)`. Default: :attr:`kernel_size`
+        padding: implicit zero paddings on both sides of the input. Can be a
+          single number or a tuple `(padH, padW)`. Default: 0
+        ceil_mode: when True, will use `ceil` instead of `floor` in the formula
+            to compute the output shape. Default: ``False``
+        count_include_pad: when True, will include the zero-padding in the
+            averaging calculation. Default: ``True``
+        divisor_override: if specified, it will be used as divisor, otherwise
+             size of the pooling region will be used. Default: None
+    """
+    if not input.is_quantized:
+        raise ValueError("Input to 'quantized.avg_pool2d' must be quantized!")
+    return torch.nn.functional.avg_pool2d(
+        input,
+        kernel_size,
+        stride,
+        padding,
+        ceil_mode,
+        count_include_pad,
+        divisor_override,
+    )
+
+
+def avg_pool3d(
+    input,
+    kernel_size,
+    stride=None,
+    padding=0,
+    ceil_mode=False,
+    count_include_pad=True,
+    divisor_override=None,
+):
+    r"""
+    Applies 3D average-pooling operation in :math:`kD \ times kH \times kW` regions by step size
+    :math:`sD \times sH \times sW` steps. The number of output features is equal to the number of
+    input planes.
+
+    .. note:: The input quantization parameters propagate to the output.
+
+    Args:
+        input: quantized input tensor :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`
+        kernel_size: size of the pooling region. Can be a single number or a
+          tuple `(kD, kH, kW)`
+        stride: stride of the pooling operation. Can be a single number or a
+          tuple `(sD, sH, sW)`. Default: :attr:`kernel_size`
+        padding: implicit zero paddings on both sides of the input. Can be a
+          single number or a tuple `(padD, padH, padW)`. Default: 0
+        ceil_mode: when True, will use `ceil` instead of `floor` in the formula
+            to compute the output shape. Default: ``False``
+        count_include_pad: when True, will include the zero-padding in the
+            averaging calculation. Default: ``True``
+        divisor_override: if specified, it will be used as divisor, otherwise
+             size of the pooling region will be used. Default: None
+    """
+    if not input.is_quantized:
+        raise ValueError("Input to 'quantized.avg_pool3d' must be quantized!")
+    return torch.nn.functional.avg_pool3d(
+        input,
+        kernel_size,
+        stride,
+        padding,
+        ceil_mode,
+        count_include_pad,
+        divisor_override,
+    )
+
+
+def adaptive_avg_pool2d(input: Tensor, output_size: BroadcastingList2[int]) -> Tensor:
+    r"""
+    Applies a 2D adaptive average pooling over a quantized input signal composed
+    of several quantized input planes.
+
+    .. note:: The input quantization parameters propagate to the output.
+
+    See :class:`~torch.ao.nn.quantized.AdaptiveAvgPool2d` for details and output shape.
+
+    Args:
+        output_size: the target output size (single integer or
+                     double-integer tuple)
+    """
+    if not input.is_quantized:
+        raise ValueError(
+            "Input to 'quantized.functional.adaptive_avg_pool2d' must be quantized!"
+        )
+    return torch.nn.functional.adaptive_avg_pool2d(input, output_size)
+
+
+def adaptive_avg_pool3d(input: Tensor, output_size: BroadcastingList2[int]) -> Tensor:
+    r"""
+    Applies a 3D adaptive average pooling over a quantized input signal composed
+    of several quantized input planes.
+
+    .. note:: The input quantization parameters propagate to the output.
+
+    See :class:`~torch.ao.nn.quantized.AdaptiveAvgPool3d` for details and output shape.
+
+    Args:
+        output_size: the target output size (single integer or
+                     double-integer tuple)
+    """
+    if not input.is_quantized:
+        raise ValueError(
+            "Input to 'quantized.functional.adaptive_avg_pool3d' must be quantized!"
+        )
+    return torch.nn.functional.adaptive_avg_pool3d(input, output_size)
+
+
+def conv1d(
+    input,
+    weight,
+    bias,
+    stride=1,
+    padding=0,
+    dilation=1,
+    groups=1,
+    padding_mode="zeros",
+    scale=1.0,
+    zero_point=0,
+    dtype=torch.quint8,
+):
+    r"""
+    Applies a 1D convolution over a quantized 1D input composed of several input
+    planes.
+
+    See :class:`~torch.ao.nn.quantized.Conv1d` for details and output shape.
+
+    Args:
+        input: quantized input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iW)`
+        weight: quantized filters of shape :math:`(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , iW)`
+        bias: **non-quantized** bias tensor of shape :math:`(\text{out\_channels})`. The tensor type must be `torch.float`.
+        stride: the stride of the convolving kernel. Can be a single number or a
+          tuple `(sW,)`. Default: 1
+        padding: implicit paddings on both sides of the input. Can be a
+          single number or a tuple `(padW,)`. Default: 0
+        dilation: the spacing between kernel elements. Can be a single number or
+          a tuple `(dW,)`. Default: 1
+        groups: split input into groups, :math:`\text{in\_channels}` should be divisible by the
+          number of groups. Default: 1
+        padding_mode: the padding mode to use. Only "zeros" is supported for quantized convolution at the moment. Default: "zeros"
+        scale: quantization scale for the output. Default: 1.0
+        zero_point: quantization zero_point for the output. Default: 0
+        dtype: quantization data type to use. Default: ``torch.quint8``
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> from torch.ao.nn.quantized import functional as qF
+        >>> filters = torch.randn(33, 16, 3, dtype=torch.float)
+        >>> inputs = torch.randn(20, 16, 50, dtype=torch.float)
+        >>> bias = torch.randn(33, dtype=torch.float)
+        >>>
+        >>> scale, zero_point = 1.0, 0
+        >>> dtype_inputs = torch.quint8
+        >>> dtype_filters = torch.qint8
+        >>>
+        >>> q_filters = torch.quantize_per_tensor(filters, scale, zero_point, dtype_filters)
+        >>> q_inputs = torch.quantize_per_tensor(inputs, scale, zero_point, dtype_inputs)
+        >>> qF.conv1d(q_inputs, q_filters, bias, padding=1, scale=scale, zero_point=zero_point)
+    """  # noqa: E501
+    if padding_mode != "zeros":
+        raise NotImplementedError("Only zero-padding is supported!")
+    if input.dtype != torch.quint8:
+        raise NotImplementedError(
+            "Only torch.quint8 is supported for activation tensor!"
+        )
+    if weight.dtype != torch.qint8:
+        raise NotImplementedError("Only torch.qint8 is supported for weight tensor!")
+    if input.ndim != 3:
+        raise ValueError("Input shape must be `(N, C, L)`!")
+    stride = _pair_from_first(stride)
+    padding = _pair_from_first(padding)
+    dilation = _pair_from_first(dilation)
+
+    packed_params = torch.ops.quantized.conv1d_prepack(
+        weight, bias, stride, padding, dilation, groups
+    )
+    return torch.ops.quantized.conv1d(input, packed_params, scale, zero_point)
+
+
+def conv2d(
+    input,
+    weight,
+    bias,
+    stride=1,
+    padding=0,
+    dilation=1,
+    groups=1,
+    padding_mode="zeros",
+    scale=1.0,
+    zero_point=0,
+    dtype=torch.quint8,
+):
+    r"""
+    Applies a 2D convolution over a quantized 2D input composed of several input
+    planes.
+
+    See :class:`~torch.ao.nn.quantized.Conv2d` for details and output shape.
+
+    Args:
+        input: quantized input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`
+        weight: quantized filters of shape :math:`(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kH , kW)`
+        bias: **non-quantized** bias tensor of shape :math:`(\text{out\_channels})`. The tensor type must be `torch.float`.
+        stride: the stride of the convolving kernel. Can be a single number or a
+          tuple `(sH, sW)`. Default: 1
+        padding: implicit paddings on both sides of the input. Can be a
+          single number or a tuple `(padH, padW)`. Default: 0
+        dilation: the spacing between kernel elements. Can be a single number or
+          a tuple `(dH, dW)`. Default: 1
+        groups: split input into groups, :math:`\text{in\_channels}` should be divisible by the
+          number of groups. Default: 1
+        padding_mode: the padding mode to use. Only "zeros" is supported for quantized convolution at the moment. Default: "zeros"
+        scale: quantization scale for the output. Default: 1.0
+        zero_point: quantization zero_point for the output. Default: 0
+        dtype: quantization data type to use. Default: ``torch.quint8``
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> from torch.ao.nn.quantized import functional as qF
+        >>> filters = torch.randn(8, 4, 3, 3, dtype=torch.float)
+        >>> inputs = torch.randn(1, 4, 5, 5, dtype=torch.float)
+        >>> bias = torch.randn(8, dtype=torch.float)
+        >>>
+        >>> scale, zero_point = 1.0, 0
+        >>> dtype_inputs = torch.quint8
+        >>> dtype_filters = torch.qint8
+        >>>
+        >>> q_filters = torch.quantize_per_tensor(filters, scale, zero_point, dtype_filters)
+        >>> q_inputs = torch.quantize_per_tensor(inputs, scale, zero_point, dtype_inputs)
+        >>> qF.conv2d(q_inputs, q_filters, bias, padding=1, scale=scale, zero_point=zero_point)
+    """  # noqa: E501
+    if padding_mode != "zeros":
+        raise NotImplementedError("Only zero-padding is supported!")
+    if input.dtype != torch.quint8:
+        raise NotImplementedError(
+            "Only torch.quint8 is supported for activation tensor!"
+        )
+    if weight.dtype != torch.qint8:
+        raise NotImplementedError("Only torch.qint8 is supported for weight tensor!")
+    if input.ndim != 4:
+        raise ValueError("Input shape must be `(N, C, H, W)`!")
+    stride = _pair(stride)
+    padding = _pair(padding)
+    dilation = _pair(dilation)
+
+    packed_params = torch.ops.quantized.conv2d_prepack(
+        weight, bias, stride, padding, dilation, groups
+    )
+    return torch.ops.quantized.conv2d(input, packed_params, scale, zero_point)
+
+
+def conv3d(
+    input,
+    weight,
+    bias,
+    stride=1,
+    padding=0,
+    dilation=1,
+    groups=1,
+    padding_mode="zeros",
+    scale=1.0,
+    zero_point=0,
+    dtype=torch.quint8,
+):
+    r"""
+    Applies a 3D convolution over a quantized 3D input composed of several input
+    planes.
+
+    See :class:`~torch.ao.nn.quantized.Conv3d` for details and output shape.
+
+    Args:
+        input: quantized input tensor of shape
+          :math:`(\text{minibatch} , \text{in\_channels} , iD , iH , iW)`
+        weight: quantized filters of shape
+          :math:`(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kD , kH , kW)`
+        bias: **non-quantized** bias tensor of shape
+          :math:`(\text{out\_channels})`. The tensor type must be `torch.float`.
+        stride: the stride of the convolving kernel. Can be a single number or a
+          tuple `(sD, sH, sW)`. Default: 1
+        padding: implicit paddings on both sides of the input. Can be a
+          single number or a tuple `(padD, padH, padW)`. Default: 0
+        dilation: the spacing between kernel elements. Can be a single number or
+          a tuple `(dD, dH, dW)`. Default: 1
+        groups: split input into groups, :math:`\text{in\_channels}` should be
+          divisible by the number of groups. Default: 1
+        padding_mode: the padding mode to use. Only "zeros" is supported for
+          quantized convolution at the moment. Default: "zeros"
+        scale: quantization scale for the output. Default: 1.0
+        zero_point: quantization zero_point for the output. Default: 0
+        dtype: quantization data type to use. Default: ``torch.quint8``
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> from torch.ao.nn.quantized import functional as qF
+        >>> filters = torch.randn(8, 4, 3, 3, 3, dtype=torch.float)
+        >>> inputs = torch.randn(1, 4, 5, 5, 5, dtype=torch.float)
+        >>> bias = torch.randn(8, dtype=torch.float)
+        >>>
+        >>> scale, zero_point = 1.0, 0
+        >>> dtype_inputs = torch.quint8
+        >>> dtype_filters = torch.qint8
+        >>>
+        >>> q_filters = torch.quantize_per_tensor(filters, scale, zero_point, dtype_filters)
+        >>> q_inputs = torch.quantize_per_tensor(inputs, scale, zero_point, dtype_inputs)
+        >>> qF.conv3d(q_inputs, q_filters, bias, padding=1, scale=scale, zero_point=zero_point)
+    """  # noqa: E501
+    if padding_mode != "zeros":
+        raise NotImplementedError("Only zero-padding is supported!")
+    if input.dtype != torch.quint8:
+        raise NotImplementedError(
+            "Only torch.quint8 is supported for activation tensor!"
+        )
+    if weight.dtype != torch.qint8:
+        raise NotImplementedError("Only torch.qint8 is supported for weight tensor!")
+    if input.ndim != 5:
+        raise ValueError("Input shape must be `(N, C, D, H, W)`!")
+    stride = _triple(stride)
+    padding = _triple(padding)
+    dilation = _triple(dilation)
+
+    packed_params = torch.ops.quantized.conv3d_prepack(
+        weight, bias, stride, padding, dilation, groups
+    )
+    return torch.ops.quantized.conv3d(input, packed_params, scale, zero_point)
+
+
+def interpolate(
+    input, size=None, scale_factor=None, mode="nearest", align_corners=None
+):
+    r"""Down/up samples the input to either the given :attr:`size` or the given
+    :attr:`scale_factor`
+
+    See :func:`torch.nn.functional.interpolate` for implementation details.
+
+    The input dimensions are interpreted in the form:
+    `mini-batch x channels x [optional depth] x [optional height] x width`.
+
+    .. note:: The input quantization parameters propagate to the output.
+
+    .. note:: Only 2D/3D input is supported for quantized inputs
+
+    .. note:: Only the following modes are supported for the quantized inputs:
+
+        - `bilinear`
+        - `nearest`
+
+    Args:
+        input (Tensor): the input tensor
+        size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]):
+            output spatial size.
+        scale_factor (float or Tuple[float]): multiplier for spatial size. Has to match input size if it is a tuple.
+        mode (str): algorithm used for upsampling:
+            ``'nearest'`` | ``'bilinear'``
+        align_corners (bool, optional): Geometrically, we consider the pixels of the
+            input and output as squares rather than points.
+            If set to ``True``, the input and output tensors are aligned by the
+            center points of their corner pixels, preserving the values at the corner pixels.
+            If set to ``False``, the input and output tensors are aligned by the corner
+            points of their corner pixels, and the interpolation uses edge value padding
+            for out-of-boundary values, making this operation *independent* of input size
+            when :attr:`scale_factor` is kept the same. This only has an effect when :attr:`mode`
+            is ``'bilinear'``.
+            Default: ``False``
+    """
+    if not input.is_quantized:
+        raise ValueError("Input to 'quantized.interpolate' must be quantized!")
+    return torch.nn.functional.interpolate(
+        input, size, scale_factor, mode, align_corners
+    )
+
+
+def linear(
+    input: Tensor,
+    weight: Tensor,
+    bias: Optional[Tensor] = None,
+    scale: Optional[float] = None,
+    zero_point: Optional[int] = None,
+) -> Tensor:
+    r"""
+    Applies a linear transformation to the incoming quantized data:
+    :math:`y = xA^T + b`.
+    See :class:`~torch.ao.nn.quantized.Linear`
+
+    .. note::
+
+      Current implementation packs weights on every call, which has penalty on performance.
+      If you want to avoid the overhead, use :class:`~torch.ao.nn.quantized.Linear`.
+
+    Args:
+      input (Tensor): Quantized input of type `torch.quint8`
+      weight (Tensor): Quantized weight of type `torch.qint8`
+      bias (Tensor): None or fp32 bias of type `torch.float`
+      scale (double): output scale. If None, derived from the input scale
+      zero_point (long): output zero point. If None, derived from the input zero_point
+
+    Shape:
+        - Input: :math:`(N, *, in\_features)` where `*` means any number of
+          additional dimensions
+        - Weight: :math:`(out\_features, in\_features)`
+        - Bias: :math:`(out\_features)`
+        - Output: :math:`(N, *, out\_features)`
+    """
+    if scale is None:
+        scale = input.q_scale()
+    if zero_point is None:
+        zero_point = input.q_zero_point()
+    _packed_params = torch.ops.quantized.linear_prepack(weight, bias)
+    return torch.ops.quantized.linear(input, _packed_params, scale, zero_point)
+
+
+def max_pool1d(
+    input,
+    kernel_size,
+    stride=None,
+    padding=0,
+    dilation=1,
+    ceil_mode=False,
+    return_indices=False,
+):
+    r"""Applies a 1D max pooling over a quantized input signal composed of
+    several quantized input planes.
+
+    .. note:: The input quantization parameters are propagated to the output.
+
+    See :class:`~torch.ao.nn.quantized.MaxPool1d` for details.
+    """
+    if return_indices:
+        raise NotImplementedError("return_indices is not yet implemented!")
+    if stride is None:
+        stride = torch.jit.annotate(List[int], [])
+    return torch.nn.functional.max_pool1d(
+        input,
+        kernel_size,
+        stride,
+        padding,
+        dilation,
+        ceil_mode=ceil_mode,
+        return_indices=return_indices,
+    )
+
+
+def max_pool2d(
+    input,
+    kernel_size,
+    stride=None,
+    padding=0,
+    dilation=1,
+    ceil_mode=False,
+    return_indices=False,
+):
+    r"""Applies a 2D max pooling over a quantized input signal composed of
+    several quantized input planes.
+
+    .. note:: The input quantization parameters are propagated to the output.
+
+    See :class:`~torch.ao.nn.quantized.MaxPool2d` for details.
+    """
+    if return_indices:
+        raise NotImplementedError("return_indices is not yet implemented!")
+    if stride is None:
+        stride = torch.jit.annotate(List[int], [])
+    return torch.nn.functional.max_pool2d(
+        input,
+        kernel_size,
+        stride,
+        padding,
+        dilation,
+        ceil_mode=ceil_mode,
+        return_indices=return_indices,
+    )
+
+
+def celu(input: Tensor, scale: float, zero_point: int, alpha: float = 1.0) -> Tensor:
+    r"""celu(input, scale, zero_point, alpha=1.) -> Tensor
+
+    Applies the quantized CELU function element-wise.
+
+    .. math::
+        \text{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x / \alpha) - 1))
+
+    Args:
+        input: quantized input
+        alpha: the :math:`\alpha` value for the CELU formulation. Default: 1.0
+    """
+    if not input.is_quantized:
+        raise ValueError("Input to 'quantized.celu' must be quantized!")
+    return torch.ops.quantized.celu(input, scale, zero_point, alpha)
+
+
+def leaky_relu(
+    input: Tensor,
+    negative_slope: float = 0.01,
+    inplace: bool = False,
+    scale: Optional[float] = None,
+    zero_point: Optional[int] = None,
+):
+    r"""
+    Quantized version of the.
+    leaky_relu(input, negative_slope=0.01, inplace=False, scale, zero_point) -> Tensor
+
+    Applies element-wise,
+    :math:`\text{LeakyReLU}(x) = \max(0, x) + \text{negative\_slope} * \min(0, x)`
+
+    Args:
+        input: Quantized input
+        negative_slope: The slope of the negative input
+        inplace: Inplace modification of the input tensor
+        scale, zero_point: Scale and zero point of the output tensor.
+
+    See :class:`~torch.nn.LeakyReLU` for more details.
+    """
+    if scale is not None and zero_point is not None:
+        assert not inplace, "Cannot rescale with `inplace`"
+        output = torch._empty_affine_quantized(
+            input.shape, scale=scale, zero_point=int(zero_point), dtype=input.dtype
+        )
+        torch._C._nn.leaky_relu(input, negative_slope, out=output)
+        return output
+    if inplace:
+        result = torch._C._nn.leaky_relu_(input, negative_slope)
+    else:
+        result = torch._C._nn.leaky_relu(input, negative_slope)
+    return result
+
+
+def hardtanh(
+    input: Tensor, min_val: float = -1.0, max_val: float = 1.0, inplace: bool = False
+) -> Tensor:
+    r"""This is the quantized version of :func:`~torch.nn.functional.hardtanh`."""
+    if not input.is_quantized:
+        raise ValueError("Input to 'quantized.hardtanh' must be quantized!")
+    if inplace:
+        return torch._C._nn.hardtanh_(input, min_val, max_val)
+    return torch._C._nn.hardtanh(input, min_val, max_val)
+
+
+def hardswish(input: Tensor, scale: float, zero_point: int) -> Tensor:
+    r"""This is the quantized version of :func:`~torch.nn.functional.hardswish`.
+
+    Args:
+        input: quantized input
+        scale: quantization scale of the output tensor
+        zero_point: quantization zero point of the output tensor
+    """
+    if not input.is_quantized:
+        raise ValueError("Input to 'quantized.hardswish' must be quantized!")
+    return torch._ops.ops.quantized.hardswish(input, scale, zero_point)
+
+
+def threshold(input: Tensor, threshold: float, value: float) -> Tensor:
+    r"""Applies the quantized version of the threshold function element-wise:
+
+    .. math::
+        x = \begin{cases}
+                x & \text{if~} x > \text{threshold} \\
+                \text{value} & \text{otherwise}
+            \end{cases}
+
+    See :class:`~torch.nn.Threshold` for more details.
+    """
+    if not input.is_quantized:
+        raise ValueError("Input to 'quantized.threshold' must be quantized!")
+    if threshold is None:
+        raise ValueError("Input to 'threshold' must be specified!")
+    if value is None:
+        raise ValueError("Input to 'value' must be specified!")
+    return torch._ops.ops.quantized.threshold(input, threshold, value)
+
+
+def elu(input: Tensor, scale: float, zero_point: int, alpha: float = 1.0) -> Tensor:
+    r"""This is the quantized version of :func:`~torch.nn.functional.elu`.
+
+    Args:
+        input: quantized input
+        scale: quantization scale of the output tensor
+        zero_point: quantization zero point of the output tensor
+        alpha: the alpha constant
+    """
+    if not input.is_quantized:
+        raise ValueError("Input to 'quantized.elu' must be quantized!")
+    return torch.ops.quantized.elu(input, scale, zero_point, alpha)
+
+
+def hardsigmoid(input: Tensor, inplace: bool = False) -> Tensor:
+    r"""This is the quantized version of :func:`~torch.nn.functional.hardsigmoid`."""
+    if not input.is_quantized:
+        raise ValueError("Input to 'quantized.hardsigmoid' must be quantized!")
+    if inplace:
+        return torch._C._nn.hardsigmoid_(input)  # type: ignore[attr-defined]
+    return torch._C._nn.hardsigmoid(input)
+
+
+def clamp(input: Tensor, min_: float, max_: float) -> Tensor:
+    r"""float(input, min\_, max\_) -> Tensor
+
+    Applies the clamp function element-wise.
+    See :class:`~torch.ao.nn.quantized.clamp` for more details.
+
+    Args:
+        input: quantized input
+        min_: minimum value for clamping
+        max_: maximum value for clamping
+    """
+    if not input.is_quantized:
+        raise ValueError("Input to 'quantized.clamp' must be quantized!")
+    return torch.clamp(input, min_, max_)
+
+
+def upsample(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
+    r"""Upsamples the input to either the given :attr:`size` or the given
+    :attr:`scale_factor`
+
+    .. warning::
+        This function is deprecated in favor of
+        :func:`torch.ao.nn.quantized.functional.interpolate`.
+        This is equivalent with ``nn.quantized.functional.interpolate(...)``.
+
+    See :func:`torch.nn.functional.interpolate` for implementation details.
+
+    The input dimensions are interpreted in the form:
+    `mini-batch x channels x [optional depth] x [optional height] x width`.
+
+    .. note:: The input quantization parameters propagate to the output.
+
+    .. note:: Only 2D input is supported for quantized inputs
+
+    .. note:: Only the following modes are supported for the quantized inputs:
+
+        - `bilinear`
+        - `nearest`
+
+    Args:
+        input (Tensor): quantized input tensor
+        size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]):
+            output spatial size.
+        scale_factor (float or Tuple[float]): multiplier for spatial size. Has to be an integer.
+        mode (str): algorithm used for upsampling:
+            ``'nearest'`` | ``'bilinear'``
+        align_corners (bool, optional): Geometrically, we consider the pixels of the
+            input and output as squares rather than points.
+            If set to ``True``, the input and output tensors are aligned by the
+            center points of their corner pixels, preserving the values at the corner pixels.
+            If set to ``False``, the input and output tensors are aligned by the corner
+            points of their corner pixels, and the interpolation uses edge value padding
+            for out-of-boundary values, making this operation *independent* of input size
+            when :attr:`scale_factor` is kept the same. This only has an effect when :attr:`mode`
+            is ``'bilinear'``.
+            Default: ``False``
+
+    .. warning::
+        With ``align_corners = True``, the linearly interpolating modes
+        (`bilinear`) don't proportionally align the
+        output and input pixels, and thus the output values can depend on the
+        input size. This was the default behavior for these modes up to version
+        0.3.1. Since then, the default behavior is ``align_corners = False``.
+        See :class:`~torch.nn.Upsample` for concrete examples on how this
+        affects the outputs.
+    """
+    warnings.warn(
+        "nn.quantized.functional.upsample is deprecated. Use nn.quantized.functional.interpolate instead."
+    )
+    return interpolate(input, size, scale_factor, mode, align_corners)
+
+
+def upsample_bilinear(input, size=None, scale_factor=None):
+    r"""Upsamples the input, using bilinear upsampling.
+
+    .. warning::
+        This function is deprecated in favor of
+        :func:`torch.ao.nn.quantized.functional.interpolate`.
+        This is equivalent with
+        ``nn.quantized.functional.interpolate(..., mode='bilinear', align_corners=True)``.
+
+    .. note:: The input quantization parameters propagate to the output.
+
+    .. note:: Only 2D inputs are supported
+
+    Args:
+        input (Tensor): quantized input
+        size (int or Tuple[int, int]): output spatial size.
+        scale_factor (int or Tuple[int, int]): multiplier for spatial size
+    """
+    # DeprecationWarning is ignored by default
+    warnings.warn(
+        "nn.quantized.functional.upsample_bilinear is deprecated. Use nn.quantized.functional.interpolate instead."
+    )
+    return interpolate(input, size, scale_factor, mode="bilinear", align_corners=True)
+
+
+def upsample_nearest(input, size=None, scale_factor=None):
+    r"""Upsamples the input, using nearest neighbours' pixel values.
+
+    .. warning::
+        This function is deprecated in favor of
+        :func:`torch.ao.nn.quantized.functional.interpolate`.
+        This is equivalent with ``nn.quantized.functional.interpolate(..., mode='nearest')``.
+
+    .. note:: The input quantization parameters propagate to the output.
+
+    .. note:: Only 2D inputs are supported
+
+    Args:
+        input (Tensor): quantized input
+        size (int or Tuple[int, int] or Tuple[int, int, int]): output spatial
+            size.
+        scale_factor (int): multiplier for spatial size. Has to be an integer.
+    """
+    # DeprecationWarning is ignored by default
+    warnings.warn(
+        "nn.quantized.functional.upsample_nearest is deprecated. Use nn.quantized.functional.interpolate instead."
+    )
+    return interpolate(input, size, scale_factor, mode="nearest")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3bad8c49350f56e5e58235570799a8d0968296d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/__init__.py
@@ -0,0 +1,162 @@
+# mypy: allow-untyped-defs
+import torch
+
+# The quantized modules use `torch.nn` and `torch.ao.nn.quantizable`
+# packages. However, the `quantizable` package uses "lazy imports"
+# to avoid circular dependency.
+# Hence we need to include it here to make sure it is resolved before
+# they are used in the modules.
+import torch.ao.nn.quantizable
+from torch.nn.modules.pooling import MaxPool2d
+
+from .activation import (
+    ELU,
+    Hardswish,
+    LeakyReLU,
+    MultiheadAttention,
+    PReLU,
+    ReLU6,
+    Sigmoid,
+    Softmax,
+)
+from .batchnorm import BatchNorm2d, BatchNorm3d
+from .conv import (
+    Conv1d,
+    Conv2d,
+    Conv3d,
+    ConvTranspose1d,
+    ConvTranspose2d,
+    ConvTranspose3d,
+)
+from .dropout import Dropout
+from .embedding_ops import Embedding, EmbeddingBag
+from .functional_modules import FloatFunctional, FXFloatFunctional, QFunctional
+from .linear import Linear
+from .normalization import (
+    GroupNorm,
+    InstanceNorm1d,
+    InstanceNorm2d,
+    InstanceNorm3d,
+    LayerNorm,
+)
+from .rnn import LSTM
+
+
+__all__ = [
+    "BatchNorm2d",
+    "BatchNorm3d",
+    "Conv1d",
+    "Conv2d",
+    "Conv3d",
+    "ConvTranspose1d",
+    "ConvTranspose2d",
+    "ConvTranspose3d",
+    "DeQuantize",
+    "ELU",
+    "Embedding",
+    "EmbeddingBag",
+    "GroupNorm",
+    "Hardswish",
+    "InstanceNorm1d",
+    "InstanceNorm2d",
+    "InstanceNorm3d",
+    "LayerNorm",
+    "LeakyReLU",
+    "Linear",
+    "LSTM",
+    "MultiheadAttention",
+    "Quantize",
+    "ReLU6",
+    "Sigmoid",
+    "Softmax",
+    "Dropout",
+    "PReLU",
+    # Wrapper modules
+    "FloatFunctional",
+    "FXFloatFunctional",
+    "QFunctional",
+]
+
+
+class Quantize(torch.nn.Module):
+    r"""Quantizes an incoming tensor
+
+    Args:
+     `scale`: scale of the output Quantized Tensor
+     `zero_point`: zero_point of output Quantized Tensor
+     `dtype`: data type of output Quantized Tensor
+     `factory_kwargs`: Dictionary of kwargs used for configuring initialization
+         of internal buffers. Currently, `device` and `dtype` are supported.
+         Example: `factory_kwargs={'device': 'cuda', 'dtype': torch.float64}`
+         will initialize internal buffers as type `torch.float64` on the current CUDA device.
+         Note that `dtype` only applies to floating-point buffers.
+
+    Examples::
+        >>> t = torch.tensor([[1., -1.], [1., -1.]])
+        >>> scale, zero_point, dtype = 1.0, 2, torch.qint8
+        >>> qm = Quantize(scale, zero_point, dtype)
+        >>> # xdoctest: +SKIP
+        >>> qt = qm(t)
+        >>> print(qt)
+        tensor([[ 1., -1.],
+                [ 1., -1.]], size=(2, 2), dtype=torch.qint8, scale=1.0, zero_point=2)
+    """
+
+    scale: torch.Tensor
+    zero_point: torch.Tensor
+
+    def __init__(self, scale, zero_point, dtype, factory_kwargs=None):
+        factory_kwargs = torch.nn.factory_kwargs(factory_kwargs)
+        super().__init__()
+        self.register_buffer("scale", torch.tensor([scale], **factory_kwargs))
+        self.register_buffer(
+            "zero_point",
+            torch.tensor(
+                [zero_point],
+                dtype=torch.long,
+                **{k: v for k, v in factory_kwargs.items() if k != "dtype"},
+            ),
+        )
+        self.dtype = dtype
+
+    def forward(self, X):
+        return torch.quantize_per_tensor(
+            X, float(self.scale), int(self.zero_point), self.dtype
+        )
+
+    @staticmethod
+    def from_float(mod, use_precomputed_fake_quant=False):
+        assert hasattr(mod, "activation_post_process")
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        return Quantize(
+            scale.float().item(),
+            zero_point.long().item(),
+            mod.activation_post_process.dtype,
+        )
+
+    def extra_repr(self):
+        return f"scale={self.scale}, zero_point={self.zero_point}, dtype={self.dtype}"
+
+
+class DeQuantize(torch.nn.Module):
+    r"""Dequantizes an incoming tensor
+
+    Examples::
+        >>> input = torch.tensor([[1., -1.], [1., -1.]])
+        >>> scale, zero_point, dtype = 1.0, 2, torch.qint8
+        >>> qm = Quantize(scale, zero_point, dtype)
+        >>> # xdoctest: +SKIP
+        >>> quantized_input = qm(input)
+        >>> dqm = DeQuantize()
+        >>> dequantized = dqm(quantized_input)
+        >>> print(dequantized)
+        tensor([[ 1., -1.],
+                [ 1., -1.]], dtype=torch.float32)
+    """
+
+    def forward(self, Xq):
+        return Xq.dequantize()
+
+    @staticmethod
+    def from_float(mod, use_precomputed_fake_quant=False):
+        return DeQuantize()
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/activation.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/activation.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7e37fd80baaa752d8721204e6df40599ca9021d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/activation.py
@@ -0,0 +1,343 @@
+# mypy: allow-untyped-defs
+from warnings import warn
+
+import torch
+
+
+__all__ = [
+    "ReLU6",
+    "Hardswish",
+    "ELU",
+    "LeakyReLU",
+    "Sigmoid",
+    "Softmax",
+    "MultiheadAttention",
+    "PReLU",
+]
+
+
+class ReLU6(torch.nn.ReLU):
+    r"""Applies the element-wise function:
+
+    :math:`\text{ReLU6}(x) = \min(\max(x_0, x), q(6))`, where :math:`x_0` is the
+    zero_point, and :math:`q(6)` is the quantized representation of number 6.
+
+    Args:
+        inplace: can optionally do the operation in-place. Default: ``False``
+
+    Shape:
+        - Input: :math:`(N, *)` where `*` means, any number of additional
+          dimensions
+        - Output: :math:`(N, *)`, same shape as the input
+
+    .. image:: ../scripts/activation_images/ReLU6.png
+
+    Examples::
+
+        >>> m = nn.quantized.ReLU6()
+        >>> input = torch.randn(2)
+        >>> # xdoctest: +SKIP
+        >>> input = torch.quantize_per_tensor(input, 1.0, 0, dtype=torch.qint32)
+        >>> output = m(input)
+    """
+
+    def __init__(self, inplace=False):
+        super().__init__(inplace)
+        self.inplace = inplace
+
+    def forward(self, input):
+        return torch.ops.quantized.relu6(input, self.inplace)
+
+    def _get_name(self):
+        return "QuantizedReLU6"
+
+    @staticmethod
+    def from_float(mod, use_precomputed_fake_quant=False):
+        return ReLU6(mod.inplace)
+
+
+class Hardswish(torch.nn.Hardswish):
+    r"""This is the quantized version of :class:`~torch.nn.Hardswish`.
+
+    Args:
+        scale: quantization scale of the output tensor
+        zero_point: quantization zero point of the output tensor
+    """
+
+    def __init__(self, scale, zero_point, device=None, dtype=None):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.register_buffer("scale", torch.tensor(scale, **factory_kwargs))
+        self.register_buffer("zero_point", torch.tensor(zero_point, **factory_kwargs))
+
+    def forward(self, input):
+        return torch.ops.quantized.hardswish(input, self.scale, self.zero_point)
+
+    def _get_name(self):
+        return "QuantizedHardswish"
+
+    @staticmethod
+    def from_float(mod, use_precomputed_fake_quant=False):
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        return Hardswish(float(scale), int(zero_point))
+
+    @classmethod
+    def from_reference(cls, mod, scale, zero_point):
+        return cls(float(scale), int(zero_point))
+
+
+class ELU(torch.nn.ELU):
+    r"""This is the quantized equivalent of :class:`~torch.nn.ELU`.
+
+    Args:
+        scale: quantization scale of the output tensor
+        zero_point: quantization zero point of the output tensor
+        alpha: the alpha constant
+    """
+
+    def __init__(self, scale, zero_point, alpha=1.0):
+        super().__init__(alpha)
+        self.scale = scale
+        self.zero_point = zero_point
+
+    def forward(self, input):
+        return torch.ao.nn.quantized.functional.elu(
+            input, self.scale, self.zero_point, self.alpha
+        )
+
+    def _get_name(self):
+        return "QuantizedELU"
+
+    @staticmethod
+    def from_float(mod, use_precomputed_fake_quant=False):
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        return ELU(float(scale), int(zero_point), mod.alpha)
+
+    @classmethod
+    def from_reference(cls, mod, scale, zero_point):
+        return cls(float(scale), int(zero_point), mod.alpha)
+
+
+class LeakyReLU(torch.nn.LeakyReLU):
+    r"""This is the quantized equivalent of :class:`~torch.nn.LeakyReLU`.
+
+    Args:
+        scale: quantization scale of the output tensor
+        zero_point: quantization zero point of the output tensor
+        negative_slope: Controls the angle of the negative slope. Default: 1e-2
+    """
+
+    def __init__(
+        self,
+        scale: float,
+        zero_point: int,
+        negative_slope: float = 1e-2,
+        inplace: bool = False,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(negative_slope, inplace)
+        self.register_buffer("scale", torch.tensor(scale, **factory_kwargs))
+        self.register_buffer("zero_point", torch.tensor(zero_point, **factory_kwargs))
+
+    def forward(self, input):
+        return torch.ops.quantized.leaky_relu(
+            input, self.negative_slope, self.inplace, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedLeakyReLU"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        return cls(float(scale), int(zero_point), mod.negative_slope, mod.inplace)
+
+    @classmethod
+    def from_reference(cls, mod, scale, zero_point):
+        return cls(float(scale), int(zero_point), mod.negative_slope, mod.inplace)
+
+
+class Sigmoid(torch.nn.Sigmoid):
+    r"""This is the quantized equivalent of :class:`~torch.nn.Sigmoid`.
+
+    Args:
+        scale: quantization scale of the output tensor
+        zero_point: quantization zero point of the output tensor
+    """
+
+    def __init__(self, output_scale: float, output_zero_point: int):
+        super().__init__()
+        self.output_scale = output_scale
+        self.output_zero_point = output_zero_point
+
+    def forward(self, input):
+        return torch.ops.quantized.sigmoid(
+            input, self.output_scale, self.output_zero_point
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        (
+            output_scale,
+            output_zero_point,
+        ) = mod.activation_post_process.calculate_qparams()
+        return cls(float(output_scale), int(output_zero_point))
+
+
+class Softmax(torch.nn.Softmax):
+    r"""This is the quantized version of :class:`~torch.nn.Softmax`.
+
+    Args:
+        dim: A dimension along which Softmax will be computed (so every slice along dim will sum to 1).
+        scale: quantization scale of the output tensor
+        zero_point: quantization zero point of the output tensor
+    """
+
+    def __init__(self, dim=None, scale=1.0, zero_point=0):
+        super().__init__()
+        self.dim = dim
+        self.scale = scale
+        self.zero_point = zero_point
+
+    def forward(self, input):
+        dim = self.dim
+        if dim is None:
+            stacklevel = 3
+            # Note: adding the mypy ignore on _get_softmax_dim seems less bad
+            # than making `_get_softmax_dim` an official API.
+            dim = torch.nn.functional._get_softmax_dim(  # type: ignore[attr-defined]
+                "softmax", input.dim(), stacklevel
+            )
+        return torch.ops.quantized.softmax(input, dim, self.scale, self.zero_point)
+
+    def _get_name(self):
+        return "QuantizedSoftmax"
+
+    @staticmethod
+    def from_float(mod, use_precomputed_fake_quant=False):
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        return Softmax(mod.dim, float(scale), int(zero_point))
+
+    @classmethod
+    def from_reference(cls, mod, scale, zero_point):
+        return cls(mod.dim, float(scale), int(zero_point))
+
+
+class MultiheadAttention(torch.ao.nn.quantizable.MultiheadAttention):
+    _FLOAT_MODULE = torch.ao.nn.quantizable.MultiheadAttention
+
+    def _get_name(self):
+        return "QuantizedMultiheadAttention"
+
+    @classmethod
+    def from_float(cls, other):
+        # The whole flow is float -> observed -> quantized
+        # This class does observed -> quantized only
+        raise NotImplementedError(
+            "It looks like you are trying to convert a "
+            "non-observed MHA module. Please, see "
+            "the examples on quantizable MHAs."
+        )
+
+    @classmethod
+    def from_observed(cls, other):
+        converted = torch.ao.quantization.convert(
+            other,
+            mapping=None,
+            inplace=False,
+            remove_qconfig=True,
+            convert_custom_config_dict=None,
+        )
+        converted.__class__ = cls
+        # Remove the parameters for the bias_k and bias_v to quantize them
+        # TODO: This is a potential source of accuracy drop.
+        #       quantized cat takes the scale and zp of the first
+        #       element, which might lose the precision in the bias_k
+        #       and the bias_v (which are cat'ed with k/v being first).
+        if converted.bias_k is not None:
+            bias_k = converted._parameters.pop("bias_k")
+            sc, zp = torch._choose_qparams_per_tensor(bias_k, reduce_range=False)
+            bias_k = torch.quantize_per_tensor(bias_k, sc, zp, torch.quint8)
+            setattr(converted, "bias_k", bias_k)  # noqa: B010
+
+        if converted.bias_v is not None:
+            bias_v = converted._parameters.pop("bias_v")
+            sc, zp = torch._choose_qparams_per_tensor(
+                bias_k, reduce_range=False  # type: ignore[possibly-undefined]
+            )
+            bias_v = torch.quantize_per_tensor(bias_v, sc, zp, torch.quint8)
+            setattr(converted, "bias_v", bias_v)  # noqa: B010
+
+        del converted.in_proj_weight
+        del converted.in_proj_bias
+
+        return converted
+
+
+class PReLU(torch.nn.Module):
+    r"""This is the quantized equivalent of :class:`~torch.nn.PReLU`.
+
+    Args:
+        scale: quantization scale of the output tensor
+        zero_point: quantization zero point of the output tensor
+        num_parameters: number of parameters: 1, or the number of channels at input. Default: 1
+    """
+
+    def __init__(
+        self, output_scale: float, output_zero_point: int, num_parameters: int = 1
+    ) -> None:
+        super().__init__()
+        self.num_parameters = num_parameters
+        self.scale = output_scale
+        self.zero_point = output_zero_point
+        w = torch.randn(num_parameters, dtype=torch.float)
+        qw = torch.quantize_per_tensor(w, scale=1.0, zero_point=0, dtype=torch.quint8)
+        self.set_weight(qw)
+
+    def set_weight(self, w: torch.Tensor) -> None:
+        self.weight = w
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        return torch.ops.quantized.prelu(
+            input, self.weight, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedPReLU"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        qprelu = cls(float(scale), int(zero_point), mod.num_parameters)
+        float_wt = mod.weight.float()
+        observer = mod.qconfig.weight()
+        observer(float_wt)
+        if observer.dtype != torch.quint8:
+            warn(
+                f"PReLU's weight observer should have dtype quint8 but got {observer.dtype}"
+            )
+        wt_scale, wt_zp = observer.calculate_qparams()
+        qweight = torch.quantize_per_tensor(
+            float_wt, float(wt_scale), int(wt_zp), torch.quint8
+        )
+        qprelu.set_weight(qweight)
+        return qprelu
+
+    @classmethod
+    def from_reference(cls, mod, scale, zero_point):
+        qprelu = cls(float(scale), int(zero_point), mod.num_parameters)
+        float_wt = mod.weight.float()
+        observer = mod.qconfig.weight()
+        observer(float_wt)
+        if observer.dtype != torch.quint8:
+            warn(
+                f"PReLU's weight observer should have dtype quint8 but got {observer.dtype}"
+            )
+        wt_scale, wt_zp = observer.calculate_qparams()
+        qweight = torch.quantize_per_tensor(
+            float_wt, float(wt_scale), int(wt_zp), torch.quint8
+        )
+        qprelu.set_weight(qweight)
+        return qprelu
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/batchnorm.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/batchnorm.py
new file mode 100644
index 0000000000000000000000000000000000000000..345a17e0db9c693e6fa52ffd518f6b8b207ee904
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/batchnorm.py
@@ -0,0 +1,128 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.ao.nn.intrinsic as nni
+
+
+__all__ = ["BatchNorm2d", "BatchNorm3d"]
+
+
+class _BatchNorm(torch.nn.modules.batchnorm._BatchNorm):
+    def __init__(
+        self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(num_features, eps, momentum, True, True, **factory_kwargs)
+        self.register_buffer("scale", torch.tensor(1.0, **factory_kwargs))
+        self.register_buffer("zero_point", torch.tensor(0, **factory_kwargs))
+
+    @staticmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        activation_post_process = mod.activation_post_process
+        if type(mod) == cls._NNI_BN_RELU_MODULE:
+            mod = mod[0]
+        scale, zero_point = activation_post_process.calculate_qparams()
+        new_mod = cls(mod.num_features, mod.eps)
+        new_mod.weight = mod.weight
+        new_mod.bias = mod.bias
+        new_mod.running_mean = mod.running_mean
+        new_mod.running_var = mod.running_var
+        new_mod.scale = scale
+        new_mod.zero_point = zero_point
+        return new_mod
+
+    @classmethod
+    def from_reference(cls, bn, output_scale, output_zero_point):
+        qbn = cls(
+            bn.num_features,
+            bn.eps,
+            bn.momentum,
+            device=bn.weight.device,
+            dtype=bn.weight.dtype,
+        )
+        qbn.weight = bn.weight
+        qbn.bias = bn.bias
+        qbn.running_mean = bn.running_mean
+        qbn.running_var = bn.running_var
+        qbn.scale = output_scale
+        qbn.zero_point = output_zero_point
+        return qbn
+
+
+class BatchNorm2d(_BatchNorm):
+    r"""This is the quantized version of :class:`~torch.nn.BatchNorm2d`."""
+
+    _NNI_BN_RELU_MODULE = nni.BNReLU2d
+
+    def __init__(
+        self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(num_features, eps, momentum, **factory_kwargs)
+
+    def _get_name(self):
+        return "QuantizedBatchNorm2d"
+
+    def _check_input_dim(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        # disabling this since this is not symbolically traceable
+        # self._check_input_dim(input)
+        return torch.ops.quantized.batch_norm2d(
+            input,
+            self.weight,
+            self.bias,
+            self.running_mean,
+            self.running_var,
+            self.eps,
+            self.scale,
+            self.zero_point,
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return _BatchNorm.from_float(
+            cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+class BatchNorm3d(_BatchNorm):
+    r"""This is the quantized version of :class:`~torch.nn.BatchNorm3d`."""
+
+    _NNI_BN_RELU_MODULE = nni.BNReLU3d
+
+    def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(num_features, eps, momentum, **factory_kwargs)
+
+    def _get_name(self):
+        return "QuantizedBatchNorm3d"
+
+    def _check_input_dim(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 5:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        # disabling this since this is not symbolically traceable
+        # self._check_input_dim(input)
+        return torch.ops.quantized.batch_norm3d(
+            input,
+            self.weight,
+            self.bias,
+            self.running_mean,
+            self.running_var,
+            self.eps,
+            self.scale,
+            self.zero_point,
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return _BatchNorm.from_float(
+            cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/conv.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/conv.py
new file mode 100644
index 0000000000000000000000000000000000000000..48ca18028acf946c48924f66b5bd0a53054711e4
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/conv.py
@@ -0,0 +1,1243 @@
+# mypy: allow-untyped-defs
+r"""Quantized convolution modules."""
+
+from typing import List, Optional, TypeVar
+
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.intrinsic.qat as nniqat
+import torch.nn as nn
+import torch.nn.functional as F
+from torch._ops import ops
+from torch.nn.common_types import _size_1_t
+from torch.nn.modules.utils import _pair, _single, _triple
+from torch.nn.utils import fuse_conv_bn_weights
+
+from .utils import _quantize_weight, WeightedQuantizedModule
+
+
+__all__ = [
+    "Conv1d",
+    "Conv2d",
+    "Conv3d",
+    "ConvTranspose1d",
+    "ConvTranspose2d",
+    "ConvTranspose3d",
+]
+
+_SUPPORTED_PADDING = {"zeros", "reflect"}
+
+
+def _reverse_repeat_padding(padding: List[int]) -> List[int]:
+    _reversed_padding_repeated_twice: List[int] = []
+    N = len(padding)
+    for idx in range(N):
+        for _ in range(2):
+            _reversed_padding_repeated_twice.append(padding[N - idx - 1])
+    return _reversed_padding_repeated_twice
+
+
+class _ConvNd(WeightedQuantizedModule):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        # All subclasses have this signature - See PR #49702s
+        raise NotImplementedError
+
+    def _init(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride,
+        padding,
+        dilation,
+        transposed,
+        output_padding,
+        groups,
+        bias,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+
+        if in_channels % groups != 0:
+            raise ValueError("in_channels must be divisible by groups")
+        if out_channels % groups != 0:
+            raise ValueError("out_channels must be divisible by groups")
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.padding = padding
+        self.dilation = dilation
+        self.transposed = transposed
+        self.output_padding = output_padding
+        self.groups = groups
+        if padding_mode not in _SUPPORTED_PADDING:
+            raise ValueError(
+                f"'padding_mode' {padding_mode} is not supported by quantized convolution"
+            )
+        self.padding_mode = padding_mode
+        # Initialize as NCHW. set_weight will internally transpose to NHWC.
+        if self.transposed:
+            weight_shape = [in_channels, out_channels // self.groups]
+        else:
+            weight_shape = [out_channels, in_channels // self.groups]
+        qweight = torch._empty_affine_quantized(
+            weight_shape + list(kernel_size),
+            scale=1,
+            zero_point=0,
+            dtype=torch.qint8,
+            **{k: v for k, v in factory_kwargs.items() if k != "dtype"},
+        )
+        bias_float = (
+            torch.zeros(
+                out_channels,
+                dtype=torch.float,
+                **{k: v for k, v in factory_kwargs.items() if k != "dtype"},
+            )
+            if bias
+            else None
+        )
+
+        self.set_weight_bias(qweight, bias_float)
+        self.scale = 1.0
+        self.zero_point = 0
+
+    def set_weight_bias(self, qweight, bias_float):
+        raise NotImplementedError
+
+    def bias(self):
+        raise NotImplementedError
+
+    def _weight_bias(self):
+        raise NotImplementedError
+
+    def extra_repr(self):
+        s = (
+            "{in_channels}, {out_channels}, kernel_size={kernel_size}"
+            ", stride={stride}, scale={scale}, zero_point={zero_point}"
+        )
+        if self.padding != (0,) * len(self.padding):
+            s += ", padding={padding}"
+        if self.dilation != (1,) * len(self.dilation):
+            s += ", dilation={dilation}"
+        if self.output_padding != (0,) * len(self.output_padding):
+            s += ", output_padding={output_padding}"
+        if self.groups != 1:
+            s += ", groups={groups}"
+        if self.bias() is None:
+            s += ", bias=False"
+        return s.format(**self.__dict__)
+
+    # ===== Serialization methods =====
+    # The special consideration here is that we have to unpack the weights into
+    # their regular QTensor form for serialization. Packed weights should not
+    # live outside the process in which they were created, rather they should be
+    # derived from the QTensor weight.
+    #   self
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #
+    # TODO: maybe change to this when https://github.com/pytorch/pytorch/pull/32958 is landed
+    #   self
+    #   |--- _packed_params : Conv2dPackedParamsBase or Conv3dPackedParamsBase
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        (w, b) = self._weight_bias()
+        destination[prefix + "weight"] = w
+        destination[prefix + "bias"] = b
+        destination[prefix + "scale"] = torch.tensor(self.scale)
+        destination[prefix + "zero_point"] = torch.tensor(self.zero_point)
+
+    @torch.jit.export
+    def __getstate__(self):
+        (w, b) = self._weight_bias()
+        return (
+            self.in_channels,
+            self.out_channels,
+            self.kernel_size,
+            self.stride,
+            self.padding,
+            self.dilation,
+            self.transposed,
+            self.output_padding,
+            self.groups,
+            self.padding_mode,
+            w,
+            b,
+            self.scale,
+            self.zero_point,
+            self.training,
+        )
+
+    # ===== Deserialization methods =====
+    # Counterpart to the serialization methods, we must pack the serialized
+    # QTensor weight into its packed format for use by the FBGEMM ops.
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        self.set_weight_bias(state_dict[prefix + "weight"], state_dict[prefix + "bias"])
+        state_dict.pop(prefix + "weight")
+        state_dict.pop(prefix + "bias")
+        self.scale = float(state_dict[prefix + "scale"])
+        state_dict.pop(prefix + "scale")
+        self.zero_point = int(state_dict[prefix + "zero_point"])
+        state_dict.pop(prefix + "zero_point")
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    @torch.jit.export
+    def __setstate__(self, state):
+        self.in_channels = state[0]
+        self.out_channels = state[1]
+        self.kernel_size = state[2]
+        self.stride = state[3]
+        self.padding = state[4]
+        self.dilation = state[5]
+        self.transposed = state[6]
+        self.output_padding = state[7]
+        self.groups = state[8]
+        self.padding_mode = state[9]
+        self.set_weight_bias(state[10], state[11])
+        self.scale = state[12]
+        self.zero_point = state[13]
+        self.training = state[14]
+
+    def __deepcopy__(self, memo):
+        new_instance = type(self).__new__(type(self))
+        torch.nn.Module.__init__(new_instance)
+        state = self.__getstate__()
+        new_instance.__setstate__(state)
+        return new_instance
+
+    def __copy__(self):
+        return self.__deepcopy__({})
+
+    @classmethod
+    def get_qconv(cls, mod, activation_post_process, weight_post_process=None):
+        r"""Creates a qconv object and returns it."""
+        if weight_post_process is None:
+            weight_post_process = mod.qconfig.weight()
+        weight_post_process(mod.weight)
+        assert (
+            weight_post_process.dtype == torch.qint8
+        ), "Weight observer must have a dtype of qint8"
+        qweight = _quantize_weight(mod.weight.float(), weight_post_process)
+        # the __init__ call used is the one from derived classes and not the one from _ConvNd
+        qconv = cls(
+            mod.in_channels,
+            mod.out_channels,
+            mod.kernel_size,
+            mod.stride,
+            mod.padding,
+            mod.dilation,
+            mod.groups,
+            mod.bias is not None,
+            mod.padding_mode,
+        )
+        qconv.set_weight_bias(qweight, mod.bias)
+        if (
+            activation_post_process is None
+            or activation_post_process.dtype == torch.float
+        ):
+            return qconv  # dynamic quantization doesn't need scale/zero_point
+        else:
+            act_scale, act_zp = activation_post_process.calculate_qparams()
+            qconv.scale = float(act_scale)
+            qconv.zero_point = int(act_zp)
+            return qconv
+
+    @staticmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        if hasattr(mod, "weight_fake_quant"):
+            # assert type(mod) == cls.__QAT_MODULE, " nnq." + cls.__name__ + \
+            # ".from_float only works for " + cls.__QAT_MODULE.__name__
+            if type(mod) == cls._NNIQAT_CONV_BN_MODULE:
+                mod.weight, mod.bias = fuse_conv_bn_weights(
+                    mod.weight,
+                    mod.bias,
+                    mod.bn.running_mean,
+                    mod.bn.running_var,
+                    mod.bn.eps,
+                    mod.bn.weight,
+                    mod.bn.bias,
+                )
+            assert hasattr(
+                mod, "activation_post_process"
+            ), "Input QAT module must have observer attached"
+            weight_post_process = mod.weight_fake_quant
+            activation_post_process = mod.activation_post_process
+        else:
+            assert type(mod) == cls._FLOAT_MODULE, (
+                " nnq."
+                + cls.__name__
+                + ".from_float only works for "
+                + cls._FLOAT_MODULE.__name__
+                + " but got:"
+                + str(type(mod))
+            )
+            assert hasattr(
+                mod, "qconfig"
+            ), "Input float module must have qconfig defined."
+            activation_post_process = (
+                None
+                if not hasattr(mod, "activation_post_process")
+                else mod.activation_post_process
+            )
+            if type(mod) in [
+                cls._NNI_CONV_RELU_MODULE,
+                cls._NNI_CONV_ADD_MODULE,
+                cls._NNI_CONV_ADD_RELU_MODULE,
+            ]:
+                mod = mod[0]
+            weight_post_process = mod.qconfig.weight()
+        return cls.get_qconv(mod, activation_post_process, weight_post_process)
+
+    @classmethod
+    def from_reference(cls, ref_qconv, output_scale, output_zero_point):
+        r"""Create a (fbgemm/qnnpack) quantized module from a reference quantized module
+        Args:
+            ref_qconv (Module): a reference quantized  module, either produced by torch.ao.quantization
+                                utilities or provided by the user
+            output_scale (float): scale for output Tensor
+            output_zero_point (int): zero point for output Tensor
+        """
+        qconv = cls(
+            ref_qconv.in_channels,
+            ref_qconv.out_channels,
+            ref_qconv.kernel_size,  # type: ignore[arg-type]
+            ref_qconv.stride,  # type: ignore[arg-type]
+            ref_qconv.padding,  # type: ignore[arg-type]
+            ref_qconv.dilation,  # type: ignore[arg-type]
+            ref_qconv.groups,
+            ref_qconv.bias is not None,  # type: ignore[arg-type]
+            ref_qconv.padding_mode,
+            device=ref_qconv.weight.device,
+            dtype=ref_qconv.weight.dtype,
+        )
+        qweight = ref_qconv.get_quantized_weight()
+        qconv.set_weight_bias(qweight, ref_qconv.bias)
+        qconv.scale = float(output_scale)
+        qconv.zero_point = int(output_zero_point)
+        return qconv
+
+
+class Conv1d(_ConvNd):
+    r"""Applies a 1D convolution over a quantized input signal composed of
+    several quantized input planes.
+
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.Conv1d`.
+
+    .. note::
+        Only `zeros` is supported for the :attr:`padding_mode` argument.
+
+    .. note::
+        Only `torch.quint8` is supported for the input data type.
+
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+
+    See :class:`~torch.nn.Conv1d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2)
+        >>> input = torch.randn(20, 16, 100)
+        >>> # quantize input to quint8
+        >>> # xdoctest: +SKIP
+        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0,
+        ...                                     dtype=torch.quint8)
+        >>> output = m(q_input)
+
+    """
+
+    _FLOAT_MODULE = nn.Conv1d
+    _NNIQAT_CONV_BN_MODULE = nniqat.ConvBn1d
+    _NNI_CONV_RELU_MODULE = nni.ConvReLU1d
+    _NNI_CONV_ADD_MODULE: None = None
+    _NNI_CONV_ADD_RELU_MODULE: None = None
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: _size_1_t,
+        stride: _size_1_t = 1,
+        padding: _size_1_t = 0,
+        dilation: _size_1_t = 1,
+        groups: int = 1,
+        bias: bool = True,
+        padding_mode: str = "zeros",
+        device=None,
+        dtype=None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        kernel_size = _single(kernel_size)
+        stride = _single(stride)
+        padding = padding if isinstance(padding, str) else _single(padding)
+        dilation = _single(dilation)
+
+        # Subclasses of _ConvNd needs to call _init rather than __init__. See
+        # discussion on PR #49702
+        super()._init(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            False,
+            _single(0),
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "QuantizedConv1d"
+
+    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
+        if self.padding_mode == "zeros":
+            self._packed_params = torch.ops.quantized.conv1d_prepack(
+                w, b, self.stride, self.padding, self.dilation, self.groups
+            )
+        else:
+            self._packed_params = torch.ops.quantized.conv1d_prepack(
+                w, b, self.stride, _pair(0), self.dilation, self.groups
+            )
+
+    def _weight_bias(self):
+        w, b = torch.ops.quantized.conv1d_unpack(self._packed_params)
+        return w, b
+
+    def weight(self):
+        return self._weight_bias()[0]
+
+    def bias(self):
+        return self._weight_bias()[1]
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 3:
+            raise ValueError("Input shape must be `(N, C, L)`!")
+        if self.padding_mode != "zeros":
+            # Padding in Conv1d is stored as (p, p), need to get (p,)
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding[:1])
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return ops.quantized.conv1d(
+            input, self._packed_params, self.scale, self.zero_point
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Creates a quantized module from a float module or qparams_dict.
+
+        Args:
+            mod (Module): a float module, either produced by torch.ao.quantization
+              utilities or provided by the user
+        """
+        return _ConvNd.from_float(
+            cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+class Conv2d(_ConvNd):
+    r"""Applies a 2D convolution over a quantized input signal composed of
+    several quantized input planes.
+
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.Conv2d`.
+
+    .. note::
+        Only `zeros` is supported for the :attr:`padding_mode` argument.
+
+    .. note::
+        Only `torch.quint8` is supported for the input data type.
+
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+
+    See :class:`~torch.nn.Conv2d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> # With square kernels and equal stride
+        >>> m = nn.quantized.Conv2d(16, 33, 3, stride=2)
+        >>> # non-square kernels and unequal stride and with padding
+        >>> m = nn.quantized.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
+        >>> # non-square kernels and unequal stride and with padding and dilation
+        >>> m = nn.quantized.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1))
+        >>> input = torch.randn(20, 16, 50, 100)
+        >>> # quantize input to quint8
+        >>> # xdoctest: +SKIP
+        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
+        >>> output = m(q_input)
+
+    """
+    _FLOAT_MODULE = nn.Conv2d
+    _NNIQAT_CONV_BN_MODULE = nniqat.ConvBn2d
+    _NNI_CONV_RELU_MODULE = nni.ConvReLU2d
+    _NNI_CONV_ADD_MODULE = nni.ConvAdd2d
+    _NNI_CONV_ADD_RELU_MODULE = nni.ConvAddReLU2d
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        kernel_size = _pair(kernel_size)
+        stride = _pair(stride)
+        padding = _pair(padding)
+        dilation = _pair(dilation)
+        # Subclasses of _ConvNd need to call _init rather than __init__. See
+        # discussion on PR #49702
+        super()._init(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            False,
+            _pair(0),
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "QuantizedConv2d"
+
+    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
+        if self.padding_mode == "zeros":
+            self._packed_params = torch.ops.quantized.conv2d_prepack(
+                w, b, self.stride, self.padding, self.dilation, self.groups
+            )
+        else:
+            self._packed_params = torch.ops.quantized.conv2d_prepack(
+                w, b, self.stride, _pair(0), self.dilation, self.groups
+            )
+
+    def _weight_bias(self):
+        return self._packed_params.unpack()
+
+    def weight(self):
+        return self._weight_bias()[0]
+
+    def bias(self):
+        return self._weight_bias()[1]
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+        if self.padding_mode != "zeros":
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return ops.quantized.conv2d(
+            input, self._packed_params, self.scale, self.zero_point
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Creates a quantized module from a float module or qparams_dict.
+
+        Args:
+            mod (Module): a float module, either produced by torch.ao.quantization
+              utilities or provided by the user
+        """
+        return _ConvNd.from_float(
+            cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+class Conv3d(_ConvNd):
+    r"""Applies a 3D convolution over a quantized input signal composed of
+    several quantized input planes.
+
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.Conv3d`.
+
+    .. note::
+        Only `zeros` is supported for the :attr:`padding_mode` argument.
+
+    .. note::
+        Only `torch.quint8` is supported for the input data type.
+
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+
+    See :class:`~torch.nn.Conv3d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> # With square kernels and equal stride
+        >>> m = nn.quantized.Conv3d(16, 33, 3, stride=2)
+        >>> # non-square kernels and unequal stride and with padding
+        >>> m = nn.quantized.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2))
+        >>> # non-square kernels and unequal stride and with padding and dilation
+        >>> m = nn.quantized.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), dilation=(1, 2, 2))
+        >>> input = torch.randn(20, 16, 56, 56, 56)
+        >>> # quantize input to quint8
+        >>> # xdoctest: +SKIP
+        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
+        >>> output = m(q_input)
+
+    """
+    _FLOAT_MODULE = nn.Conv3d
+    _NNIQAT_CONV_BN_MODULE = nniqat.ConvBn3d
+    _NNI_CONV_RELU_MODULE = nni.ConvReLU3d
+    _NNI_CONV_ADD_MODULE: None = None
+    _NNI_CONV_ADD_RELU_MODULE: None = None
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        assert padding_mode != "reflect", "Conv3d does not support reflection padding"
+        factory_kwargs = {"device": device, "dtype": dtype}
+        kernel_size = _triple(kernel_size)
+        stride = _triple(stride)
+        padding = _triple(padding)
+        dilation = _triple(dilation)
+        # Subclasses of _ConvNd need to call _init rather than __init__. See
+        # discussion on PR #49702
+        super()._init(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            False,
+            _triple(0),
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "QuantizedConv3d"
+
+    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
+        if self.padding_mode == "zeros":
+            self._packed_params = torch.ops.quantized.conv3d_prepack(
+                w, b, self.stride, self.padding, self.dilation, self.groups
+            )
+        else:
+            self._packed_params = torch.ops.quantized.conv3d_prepack(
+                w, b, self.stride, _triple(0), self.dilation, self.groups
+            )
+
+    def _weight_bias(self):
+        return self._packed_params.unpack()
+
+    def weight(self):
+        return self._weight_bias()[0]
+
+    def bias(self):
+        return self._weight_bias()[1]
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 5:
+            raise ValueError("Input shape must be `(N, C, D, H, W)`!")
+        if self.padding_mode != "zeros":
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return ops.quantized.conv3d(
+            input, self._packed_params, self.scale, self.zero_point
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Creates a quantized module from a float module or qparams_dict.
+
+        Args:
+            mod (Module): a float module, either produced by torch.ao.quantization
+              utilities or provided by the user
+        """
+        return _ConvNd.from_float(
+            cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+# === Transposed Convolutions ===
+MOD = TypeVar("MOD", bound=nn.modules.conv._ConvNd)
+
+
+class _ConvTransposeNd(_ConvNd):
+    _FLOAT_MODULE = MOD
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride,
+        padding,
+        dilation,
+        transposed,
+        output_padding,
+        groups,
+        bias,
+        padding_mode,
+        device=None,
+        dtype=None,
+    ):
+        if padding_mode != "zeros":
+            raise ValueError(
+                f'Only "zeros" padding mode is supported for {self.__class__.__name__}'
+            )
+        factory_kwargs = {"device": device, "dtype": dtype}
+        # Subclasses of _ConvNd need to call _init rather than __init__. See
+        # discussion on PR #49702
+        super()._init(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            transposed,
+            output_padding,
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _input_padding(
+        self, kernel_size: List[int], dilation: List[int], padding: List[int]
+    ) -> List[int]:
+        res = torch.jit.annotate(List[int], [])
+        for kdx in range(len(kernel_size)):
+            pad = dilation[kdx] * (kernel_size[kdx] - 1) - padding[kdx]
+            res.append(pad)
+        return res
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Creates a quantized module from a float module or qparams_dict.
+        Args:
+            mod (Module): a float module, either produced by torch.ao.quantization
+              utilities or provided by the user
+        """
+        # derived classes override cls._FLOAT_MODULE attribute
+        msg = (
+            " nnq."
+            + cls.__name__
+            + ".from_float only works for "
+            + cls._FLOAT_MODULE.__name__  # type: ignore[attr-defined]
+        )
+        assert type(mod) == cls._FLOAT_MODULE, msg
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined."
+        weight_post_process = mod.qconfig.weight()
+        weight_post_process(mod.weight)
+        assert (
+            weight_post_process.dtype == torch.qint8
+        ), "Weight observer must have a dtype of qint8"
+        qweight = _quantize_weight(mod.weight.float(), weight_post_process)
+        # the __init__ call used is the one from derived classes and not the one from _ConvTransposeNd
+        qconv = cls(
+            mod.in_channels,
+            mod.out_channels,
+            mod.kernel_size,  # type: ignore[call-arg]
+            mod.stride,
+            mod.padding,
+            mod.output_padding,
+            mod.groups,
+            mod.bias is not None,
+            mod.dilation,
+            mod.padding_mode,
+        )
+        qconv.set_weight_bias(qweight, mod.bias)
+        if (
+            not hasattr(mod, "activation_post_process")
+            or mod.activation_post_process.dtype == torch.float
+        ):
+            return qconv  # dynamic quantization doesn't need scale/zero_point
+        else:
+            act_scale, act_zp = mod.activation_post_process.calculate_qparams()
+            qconv.scale = float(act_scale)
+            qconv.zero_point = int(act_zp)
+            return qconv
+
+    @staticmethod
+    def from_reference(cls, ref_qconvt, output_scale, output_zero_point):
+        r"""Create a (fbgemm/qnnpack) quantized module from a reference quantized module
+        Args:
+            ref_qconvt (Module): a reference quantized  module, either produced by torch.ao.quantization
+                                 utilities or provided by the user
+            output_scale (float): scale for output Tensor
+            output_zero_point (int): zero point for output Tensor
+        """
+        qconv = cls(
+            ref_qconvt.in_channels,
+            ref_qconvt.out_channels,
+            ref_qconvt.kernel_size,  # type: ignore[arg-type]
+            ref_qconvt.stride,  # type: ignore[arg-type]
+            ref_qconvt.padding,  # type: ignore[arg-type]
+            ref_qconvt.output_padding,  # type: ignore[arg-type]
+            ref_qconvt.groups,
+            ref_qconvt.bias is not None,  # type: ignore[arg-type]
+            ref_qconvt.dilation,  # type: ignore[arg-type]
+            ref_qconvt.padding_mode,
+            device=ref_qconvt.weight.device,
+            dtype=ref_qconvt.weight.dtype,
+        )
+        qweight = ref_qconvt.get_quantized_weight()
+        qconv.set_weight_bias(qweight, ref_qconvt.bias)
+        qconv.scale = float(output_scale)
+        qconv.zero_point = int(output_zero_point)
+        return qconv
+
+
+class ConvTranspose1d(_ConvTransposeNd):
+    r"""Applies a 1D transposed convolution operator over an input image
+    composed of several input planes.
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.ConvTranspose1d`.
+
+    .. note:: Currently only the QNNPACK engine is implemented.
+        Please, set the `torch.backends.quantized.engine = 'qnnpack'`
+
+    For special notes, please, see :class:`~torch.ao.nn.quantized.Conv1d`
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+    See :class:`~torch.nn.ConvTranspose2d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> torch.backends.quantized.engine = 'qnnpack'
+        >>> from torch.ao.nn import quantized as nnq
+        >>> # With square kernels and equal stride
+        >>> m = nnq.ConvTranspose1d(16, 33, 3, stride=2)
+        >>> # non-square kernels and unequal stride and with padding
+        >>> m = nnq.ConvTranspose1d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
+        >>> input = torch.randn(20, 16, 50)
+        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
+        >>> output = m(q_input)
+        >>> # exact output size can be also specified as an argument
+        >>> input = torch.randn(1, 16, 12)
+        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
+        >>> downsample = nnq.Conv1d(16, 16, 3, stride=2, padding=1)
+        >>> upsample = nnq.ConvTranspose1d(16, 16, 3, stride=2, padding=1)
+        >>> h = downsample(q_input)
+        >>> h.size()
+        torch.Size([1, 16, 6])
+        >>> # xdoctest: +SKIP("FIXME: output_size is not a parameter)
+        >>> output = upsample(h, output_size=input.size())
+        >>> output.size()
+        torch.Size([1, 16, 12])
+    """
+
+    _FLOAT_MODULE = nn.ConvTranspose1d
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        output_padding=0,
+        groups=1,
+        bias=True,
+        dilation=1,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        kernel_size = _single(kernel_size)
+        stride = _single(stride)
+        padding = _single(padding)
+        dilation = _single(dilation)
+        output_padding = _single(output_padding)
+
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            True,
+            output_padding,
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "QuantizedConvTranspose1d"
+
+    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
+        self._packed_params = torch.ops.quantized.conv_transpose1d_prepack(
+            w,
+            b,
+            self.stride,
+            self.padding,
+            self.output_padding,
+            self.dilation,
+            self.groups,
+        )
+
+    def _weight_bias(self):
+        w, b = torch.ops.quantized.conv_transpose1d_unpack(self._packed_params)
+        return w, b
+
+    def weight(self):
+        (w, _) = self._weight_bias()
+        return w
+
+    def bias(self):
+        (_, b) = self._weight_bias()
+        return b
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 3:
+            raise ValueError("Input shape must be `(N, C, L)`!")
+        return torch.ops.quantized.conv_transpose1d(
+            input, self._packed_params, self.scale, self.zero_point
+        )
+
+    @classmethod
+    def from_reference(cls, ref_qconvt, output_scale, output_zero_point):
+        return _ConvTransposeNd.from_reference(
+            cls, ref_qconvt, output_scale, output_zero_point
+        )
+
+
+class ConvTranspose2d(_ConvTransposeNd):
+    r"""Applies a 2D transposed convolution operator over an input image
+    composed of several input planes.
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.ConvTranspose2d`.
+
+    For special notes, please, see :class:`~torch.ao.nn.quantized.Conv2d`
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+    See :class:`~torch.nn.ConvTranspose2d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> # QNNPACK or FBGEMM as backend
+        >>> torch.backends.quantized.engine = 'qnnpack'
+        >>> # With square kernels and equal stride
+        >>> import torch.ao.nn.quantized as nnq
+        >>> m = nnq.ConvTranspose2d(16, 33, 3, stride=2)
+        >>> # non-square kernels and unequal stride and with padding
+        >>> m = nnq.ConvTranspose2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
+        >>> input = torch.randn(20, 16, 50, 100)
+        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
+        >>> output = m(q_input)
+        >>> # exact output size can be also specified as an argument
+        >>> input = torch.randn(1, 16, 12, 12)
+        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
+        >>> downsample = nnq.Conv2d(16, 16, 3, stride=2, padding=1)
+        >>> upsample = nnq.ConvTranspose2d(16, 16, 3, stride=2, padding=1)
+        >>> h = downsample(q_input)
+        >>> h.size()
+        torch.Size([1, 16, 6, 6])
+        >>> # xdoctest: +SKIP("FIXME: output_size is not a parameter)
+        >>> output = upsample(h, output_size=input.size())
+        >>> output.size()
+        torch.Size([1, 16, 12, 12])
+    """
+
+    _FLOAT_MODULE = nn.ConvTranspose2d
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        output_padding=0,
+        groups=1,
+        bias=True,
+        dilation=1,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        kernel_size = _pair(kernel_size)
+        stride = _pair(stride)
+        padding = _pair(padding)
+        dilation = _pair(dilation)
+        output_padding = _pair(output_padding)
+
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            True,
+            output_padding,
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "QuantizedConvTranspose2d"
+
+    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
+        self._packed_params = torch.ops.quantized.conv_transpose2d_prepack(
+            w,
+            b,
+            self.stride,
+            self.padding,
+            self.output_padding,
+            self.dilation,
+            self.groups,
+        )
+
+    def _weight_bias(self):
+        w, b = torch.ops.quantized.conv2d_unpack(self._packed_params)
+        return w, b
+
+    def weight(self):
+        (w, _) = self._weight_bias()
+        return w
+
+    def bias(self):
+        (_, b) = self._weight_bias()
+        return b
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+        return ops.quantized.conv_transpose2d(
+            input, self._packed_params, self.scale, self.zero_point
+        )
+
+    @classmethod
+    def from_reference(cls, ref_qconvt, output_scale, output_zero_point):
+        return _ConvTransposeNd.from_reference(
+            cls, ref_qconvt, output_scale, output_zero_point
+        )
+
+
+class ConvTranspose3d(_ConvTransposeNd):
+    r"""Applies a 3D transposed convolution operator over an input image
+    composed of several input planes.
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.ConvTranspose3d`.
+
+    .. note:: Currently only the FBGEMM engine is implemented.
+        Please, set the `torch.backends.quantized.engine = 'fbgemm'`
+
+    For special notes, please, see :class:`~torch.ao.nn.quantized.Conv3d`
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+    See :class:`~torch.nn.ConvTranspose3d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> torch.backends.quantized.engine = 'fbgemm'
+        >>> from torch.ao.nn import quantized as nnq
+        >>> # With cubic kernels and equal stride
+        >>> m = nnq.ConvTranspose3d(16, 33, 3, stride=2)
+        >>> # non-cubic kernels and unequal stride and with padding
+        >>> m = nnq.ConvTranspose3d(16, 33, (3, 3, 5), stride=(2, 1, 1), padding=(4, 2, 2))
+        >>> input = torch.randn(20, 16, 50, 100, 100)
+        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
+        >>> output = m(q_input)
+        >>> # exact output size can be also specified as an argument
+        >>> input = torch.randn(1, 16, 12, 12, 12)
+        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
+        >>> downsample = nnq.Conv3d(16, 16, 3, stride=2, padding=1)
+        >>> upsample = nnq.ConvTranspose3d(16, 16, 3, stride=2, padding=1)
+        >>> h = downsample(q_input)
+        >>> h.size()
+        torch.Size([1, 16, 6, 6, 6])
+        >>> # xdoctest: +SKIP("FIXME: output_size is not a parameter)
+        >>> output = upsample(h, output_size=input.size())
+        >>> output.size()
+        torch.Size([1, 16, 12, 12, 12])
+    """
+
+    _FLOAT_MODULE = nn.ConvTranspose3d
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        output_padding=0,
+        groups=1,
+        bias=True,
+        dilation=1,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        kernel_size = _triple(kernel_size)
+        stride = _triple(stride)
+        padding = _triple(padding)
+        dilation = _triple(dilation)
+        output_padding = _triple(output_padding)
+
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            True,
+            output_padding,
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "QuantizedConvTranspose3d"
+
+    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
+        self._packed_params = torch.ops.quantized.conv_transpose3d_prepack(
+            w,
+            b,
+            self.stride,
+            self.padding,
+            self.output_padding,
+            self.dilation,
+            self.groups,
+        )
+
+    def _weight_bias(self):
+        w, b = torch.ops.quantized.conv3d_unpack(self._packed_params)
+        return w, b
+
+    def weight(self):
+        (w, _) = self._weight_bias()
+        return w
+
+    def bias(self):
+        (_, b) = self._weight_bias()
+        return b
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 5:
+            raise ValueError("Input shape must be `(N, C, T, H, W)`!")
+        return ops.quantized.conv_transpose3d(
+            input, self._packed_params, self.scale, self.zero_point
+        )
+
+    @classmethod
+    def from_reference(cls, ref_qconvt, output_scale, output_zero_point):
+        return _ConvTransposeNd.from_reference(
+            cls, ref_qconvt, output_scale, output_zero_point
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/dropout.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/dropout.py
new file mode 100644
index 0000000000000000000000000000000000000000..3744ca30d5a49ba92cbb86690f2683af02d594fe
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/dropout.py
@@ -0,0 +1,30 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+__all__ = ["Dropout"]
+
+
+class Dropout(torch.nn.Dropout):
+    r"""This is the quantized equivalent of :class:`~torch.nn.Dropout`.
+        And this is a placeholder to enable models where fp32 tensors
+        had dropout to work with quantized tensors in train and eval mode.
+
+    Args:
+        p: probability of an element to be zeroed
+        inplace: can optionally do the operation in-place. Default: ``False``
+    """
+
+    def forward(self, input):
+        return input
+
+    def _get_name(self):
+        return "QuantizedDropout"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return cls(mod.p, mod.inplace)
+
+    @classmethod
+    def from_reference(cls, mod, scale, zero_point):
+        return cls(mod.p, mod.inplace)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ae04ed66b5e8ede8f9ebe8985590f055176ffeb
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py
@@ -0,0 +1,405 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import torch
+import torch.nn as nn
+from torch import Tensor  # noqa: F401
+from torch._jit_internal import List, Optional  # noqa: F401
+
+from .utils import _hide_packed_params_repr, _quantize_weight
+
+
+__all__ = ["EmbeddingPackedParams", "Embedding", "EmbeddingBag"]
+
+
+class EmbeddingPackedParams(torch.nn.Module):
+    _version = 1
+
+    def __init__(self, num_embeddings, embedding_dim, dtype=torch.quint8):
+        super().__init__()
+        self.dtype = dtype
+        if self.dtype in [torch.quint8, torch.quint4x2]:
+            scales = torch.ones(num_embeddings, dtype=torch.float)
+            zero_points = torch.zeros(num_embeddings, dtype=torch.float)
+            wq = torch._empty_per_channel_affine_quantized(
+                [num_embeddings, embedding_dim],
+                scales=scales,
+                zero_points=zero_points,
+                axis=0,
+                dtype=self.dtype,
+            )
+            self.set_weight(wq)
+        else:
+            raise NotImplementedError(
+                f"Unsupported dtype on quantized embedding! Supports quint8 and quint4x2. Got dtype: {dtype}"
+            )
+
+    @torch.jit.export
+    def set_weight(self, weight: torch.Tensor) -> None:
+        if self.dtype in [torch.quint8, torch.quint4x2]:
+            self._packed_weight = torch.ops.quantized.embedding_bag_prepack(weight)
+        else:
+            raise NotImplementedError(
+                "Unsupported dtype for quantized embedding prepack! Supports quint8 and quint4x2."
+            )
+
+    @torch.jit.export
+    def _weight(self):
+        if self.dtype in [torch.quint8, torch.quint4x2]:
+            return torch.ops.quantized.embedding_bag_unpack(self._packed_weight)
+        else:
+            raise NotImplementedError(
+                "Unsupported dtype for quantized embedding unpack! Supports quint8 and quint4x2."
+            )
+
+    def forward(self, x):
+        return x
+
+    # Version 1
+    #   self
+    #   |--- _packed_weight : Tensor representing weight of EmbeddingPackedParamsBase
+    #   |--- dtype : torch.dtype
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "dtype"] = self.dtype
+        destination[prefix + "_packed_weight"] = self._weight()
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        self.dtype = state_dict[prefix + "dtype"]
+        state_dict.pop(prefix + "dtype")
+
+        weight = state_dict[prefix + "_packed_weight"]
+        state_dict.pop(prefix + "_packed_weight")
+        self.set_weight(weight)
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    def __repr__(self):
+        return self._weight().__repr__()
+
+
+class Embedding(torch.nn.Module):
+    r"""
+    A quantized Embedding module with quantized packed weights as inputs.
+    We adopt the same interface as `torch.nn.Embedding`, please see
+    https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html for documentation.
+
+    Similar to :class:`~torch.nn.Embedding`, attributes will be randomly
+    initialized at module creation time and will be overwritten later
+
+    Attributes:
+        weight (Tensor): the non-learnable quantized weights of the module of
+                         shape :math:`(\text{num\_embeddings}, \text{embedding\_dim})`.
+
+    Examples::
+        >>> m = nn.quantized.Embedding(num_embeddings=10, embedding_dim=12)
+        >>> indices = torch.tensor([9, 6, 5, 7, 8, 8, 9, 2, 8])
+        >>> output = m(indices)
+        >>> print(output.size())
+        torch.Size([9, 12])
+
+    """
+    _version = 1
+
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+        padding_idx: Optional[int] = None,
+        max_norm: Optional[float] = None,
+        norm_type: float = 2.0,
+        scale_grad_by_freq: bool = False,
+        sparse: bool = False,
+        _weight: Optional[Tensor] = None,
+        dtype=torch.quint8,
+    ) -> None:
+        super().__init__()
+        self.num_embeddings = num_embeddings
+        self.embedding_dim = embedding_dim
+        self.dtype = dtype
+
+        if _weight is None:
+            scales = torch.ones(num_embeddings, dtype=torch.float)
+            zero_points = torch.zeros(num_embeddings, dtype=torch.float)
+            qweight = torch._empty_per_channel_affine_quantized(
+                [num_embeddings, embedding_dim],
+                scales=scales,
+                zero_points=zero_points,
+                axis=0,
+                dtype=torch.quint8,
+            )
+        else:
+            assert list(_weight.shape) == [
+                num_embeddings,
+                embedding_dim,
+            ], "Shape of weight does not match num_embeddings and embedding_dim"
+            qweight = _weight
+
+        self._packed_params = EmbeddingPackedParams(
+            num_embeddings, embedding_dim, dtype
+        )
+        self._packed_params.set_weight(qweight)
+
+    def forward(self, indices: Tensor) -> Tensor:
+        if self.dtype == torch.quint4x2:
+            return torch.ops.quantized.embedding_4bit(
+                self._packed_params._packed_weight, indices
+            )
+        else:
+            return torch.ops.quantized.embedding_byte(
+                self._packed_params._packed_weight, indices
+            )
+
+    def _get_name(self):
+        return "QuantizedEmbedding"
+
+    def __repr__(self):
+        return _hide_packed_params_repr(self, EmbeddingPackedParams)
+
+    def extra_repr(self):
+        extra_repr_str = (
+            f"num_embeddings={self.num_embeddings}, embedding_dim={self.embedding_dim}, "
+            f"dtype={self._packed_params.dtype}, qscheme={self.weight().qscheme()}"
+        )
+
+        return extra_repr_str
+
+    def set_weight(self, w: torch.Tensor) -> None:
+        self._packed_params.set_weight(w)
+
+    def weight(self):
+        return self._packed_params._weight()
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a quantized embedding module from a float module
+
+        Args:
+            mod (Module): a float module, either produced by torch.ao.quantization
+                          utilities or provided by user
+        """
+        if hasattr(mod, "weight_fake_quant"):
+            assert type(mod) == torch.ao.nn.qat.Embedding, (
+                "nnq."
+                + cls.__name__
+                + ".from_float "
+                + "with fake quant only works for "
+                + torch.ao.nn.qat.Embedding.__name__
+            )
+            weight_observer = mod.weight_fake_quant
+            activation_post_process = mod.activation_post_process
+        else:
+            assert type(mod) == nn.Embedding, (
+                "nnq."
+                + cls.__name__
+                + ".from_float only works for "
+                + nn.Embedding.__name__
+            )
+            assert hasattr(
+                mod, "qconfig"
+            ), "Embedding input float module must have qconfig defined"
+            from torch.ao.quantization import float_qparams_weight_only_qconfig
+
+            if mod.qconfig is not None and mod.qconfig.weight is not None:  # type: ignore[union-attr]
+                weight_observer = mod.qconfig.weight()  # type: ignore[union-attr, operator]
+            else:
+                weight_observer = float_qparams_weight_only_qconfig.weight()
+
+        dtype = weight_observer.dtype
+        is_float_qparams_qconfig = (
+            weight_observer.qscheme == torch.per_channel_affine_float_qparams
+        )
+        assert (
+            is_float_qparams_qconfig
+        ), "Embedding quantization is only supported with float_qparams_weight_only_qconfig."
+
+        assert (
+            dtype == torch.quint8 or dtype == torch.quint4x2
+        ), f"The only supported dtype for nnq.Embedding is torch.quint8 and torch.quint4x2, got {dtype}"
+
+        # Run the observer to calculate qparams.
+        weight_observer(mod.weight)
+        qweight = _quantize_weight(mod.weight.float(), weight_observer)
+
+        # Create quantized Embedding module and pass in the quantized weight
+        qembedding = Embedding(mod.num_embeddings, mod.embedding_dim)
+        qembedding.set_weight(qweight)
+        return qembedding
+
+    @classmethod
+    def from_reference(cls, ref_embedding):
+        qembedding = cls(
+            ref_embedding.num_embeddings,
+            ref_embedding.embedding_dim,
+            ref_embedding.padding_idx,
+            ref_embedding.max_norm,
+            ref_embedding.norm_type,
+            ref_embedding.scale_grad_by_freq,
+            ref_embedding.sparse,
+            ref_embedding.get_quantized_weight(),
+            ref_embedding.weight_dtype,
+        )
+        return qembedding
+
+
+class EmbeddingBag(Embedding):
+    r"""
+    A quantized EmbeddingBag module with quantized packed weights as inputs.
+    We adopt the same interface as `torch.nn.EmbeddingBag`, please see
+    https://pytorch.org/docs/stable/generated/torch.nn.EmbeddingBag.html for documentation.
+
+    Similar to :class:`~torch.nn.EmbeddingBag`, attributes will be randomly
+    initialized at module creation time and will be overwritten later
+
+    Attributes:
+        weight (Tensor): the non-learnable quantized weights of the module of
+                         shape :math:`(\text{num\_embeddings}, \text{embedding\_dim})`.
+
+    Examples::
+        >>> m = nn.quantized.EmbeddingBag(num_embeddings=10, embedding_dim=12, include_last_offset=True, mode='sum')
+        >>> indices = torch.tensor([9, 6, 5, 7, 8, 8, 9, 2, 8, 6, 6, 9, 1, 6, 8, 8, 3, 2, 3, 6, 3, 6, 5, 7, 0, 8, 4, 6, 5, 8, 2, 3])
+        >>> offsets = torch.tensor([0, 19, 20, 28, 28, 32])
+        >>> output = m(indices, offsets)
+        >>> print(output.size())
+        torch.Size([5, 12])
+
+    """
+    _version = 1
+
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+        max_norm: Optional[float] = None,
+        norm_type: float = 2.0,
+        scale_grad_by_freq: bool = False,
+        mode: str = "sum",
+        sparse: bool = False,
+        _weight: Optional[Tensor] = None,
+        include_last_offset: bool = False,
+        dtype=torch.quint8,
+    ) -> None:
+        super().__init__(num_embeddings, embedding_dim, _weight=_weight, dtype=dtype)
+
+        self.mode = mode
+        self.pruned_weights = False
+        self.include_last_offset = include_last_offset
+        self.dtype = dtype
+
+    def forward(
+        self,
+        indices: Tensor,
+        offsets: Optional[Tensor] = None,
+        per_sample_weights: Optional[Tensor] = None,
+        compressed_indices_mapping: Optional[Tensor] = None,
+    ) -> Tensor:
+        if self.dtype == torch.quint4x2:
+            return torch.ops.quantized.embedding_bag_4bit(
+                self._packed_params._packed_weight,
+                indices,
+                offsets,
+                False,
+                0,
+                self.pruned_weights,
+                per_sample_weights,
+                compressed_indices_mapping,
+                self.include_last_offset,
+            )
+        else:
+            return torch.ops.quantized.embedding_bag_byte(
+                self._packed_params._packed_weight,
+                indices,
+                offsets,
+                False,
+                0,
+                self.pruned_weights,
+                per_sample_weights,
+                compressed_indices_mapping,
+                self.include_last_offset,
+            )
+
+    def _get_name(self):
+        return "QuantizedEmbeddingBag"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a quantized embedding_bag module from a float module
+
+        Args:
+            mod (Module): a float module, either produced by torch.ao.quantization
+                          utilities or provided by user
+        """
+        if hasattr(mod, "weight_fake_quant"):
+            weight_observer = mod.weight_fake_quant
+        else:
+            assert type(mod) == nn.EmbeddingBag, (
+                "nnq."
+                + cls.__name__
+                + ".from_float only works for "
+                + nn.EmbeddingBag.__name__
+            )
+            assert hasattr(
+                mod, "qconfig"
+            ), "EmbeddingBag input float module must have qconfig defined"
+            from torch.ao.quantization.qconfig import float_qparams_weight_only_qconfig
+
+            if mod.qconfig is not None and mod.qconfig.weight is not None:  # type: ignore[union-attr]
+                weight_observer = mod.qconfig.weight()  # type: ignore[union-attr, operator]
+            else:
+                weight_observer = float_qparams_weight_only_qconfig.weight()
+
+        dtype = weight_observer.dtype
+        is_float_qparams_qconfig = (
+            weight_observer.qscheme == torch.per_channel_affine_float_qparams
+        )
+        assert (
+            is_float_qparams_qconfig
+        ), "EmbeddingBag quantization is only supported with float_qparams_weight_only_qconfig."
+
+        assert (
+            dtype == torch.quint8 or dtype == torch.quint4x2
+        ), f"The only supported dtype for nnq.EmbeddingBag is torch.quint8 and torch.quint4x2, got {dtype}"
+
+        # Run the observer to calculate qparams.
+        weight_observer(mod.weight)
+        qweight = _quantize_weight(mod.weight.float(), weight_observer)
+
+        # Create quantized EmbeddingBag module and pass in the quantized weight
+        qembedding_bag = EmbeddingBag(
+            mod.num_embeddings, mod.embedding_dim, dtype=dtype
+        )
+        qembedding_bag.set_weight(qweight)
+        return qembedding_bag
+
+    @classmethod
+    def from_reference(cls, ref_embedding_bag):
+        qembedding_bag = cls(
+            ref_embedding_bag.num_embeddings,
+            ref_embedding_bag.embedding_dim,
+            ref_embedding_bag.max_norm,
+            ref_embedding_bag.norm_type,
+            ref_embedding_bag.scale_grad_by_freq,
+            ref_embedding_bag.mode,
+            ref_embedding_bag.sparse,
+            ref_embedding_bag.get_quantized_weight(),
+            ref_embedding_bag.include_last_offset,
+            ref_embedding_bag.weight_dtype,
+        )
+        return qembedding_bag
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/functional_modules.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/functional_modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..45dc7fc0444c16882d2d7d10cf17c7b0d0943af0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/functional_modules.py
@@ -0,0 +1,299 @@
+# mypy: allow-untyped-defs
+from typing import List
+
+import torch
+from torch import Tensor
+from torch._ops import ops
+
+
+__all__ = ["FloatFunctional", "FXFloatFunctional", "QFunctional"]
+
+
+class FloatFunctional(torch.nn.Module):
+    r"""State collector class for float operations.
+
+    The instance of this class can be used instead of the ``torch.`` prefix for
+    some operations. See example usage below.
+
+    .. note::
+
+        This class does not provide a ``forward`` hook. Instead, you must use
+        one of the underlying functions (e.g. ``add``).
+
+    Examples::
+
+        >>> f_add = FloatFunctional()
+        >>> a = torch.tensor(3.0)
+        >>> b = torch.tensor(4.0)
+        >>> f_add.add(a, b)  # Equivalent to ``torch.add(a, b)``
+
+    Valid operation names:
+        - add
+        - cat
+        - mul
+        - add_relu
+        - add_scalar
+        - mul_scalar
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.activation_post_process = torch.nn.Identity()
+
+    def forward(self, x):
+        raise RuntimeError(
+            "FloatFunctional is not intended to use the "
+            + "'forward'. Please use the underlying operation"
+        )
+
+    r"""Operation equivalent to ``torch.add(Tensor, Tensor)``"""
+
+    def add(self, x: Tensor, y: Tensor) -> Tensor:
+        r = torch.add(x, y)
+        r = self.activation_post_process(r)
+        return r
+
+    r"""Operation equivalent to ``torch.add(Tensor, float)``"""
+
+    def add_scalar(self, x: Tensor, y: float) -> Tensor:
+        r = torch.add(x, y)
+        # Note: this operation is not observed because the observation is not
+        # needed for the quantized op.
+        return r
+
+    r"""Operation equivalent to ``torch.mul(Tensor, Tensor)``"""
+
+    def mul(self, x: Tensor, y: Tensor) -> Tensor:
+        r = torch.mul(x, y)
+        r = self.activation_post_process(r)
+        return r
+
+    r"""Operation equivalent to ``torch.mul(Tensor, float)``"""
+
+    def mul_scalar(self, x: Tensor, y: float) -> Tensor:
+        r = torch.mul(x, y)
+        # Note: this operation is not observed because the observation is not
+        # needed for the quantized op.
+        return r
+
+    r"""Operation equivalent to ``torch.cat``"""
+
+    def cat(self, x: List[Tensor], dim: int = 0) -> Tensor:
+        r = torch.cat(x, dim=dim)
+        r = self.activation_post_process(r)
+        return r
+
+    r"""Operation equivalent to ``relu(torch.add(x,y))``"""
+
+    def add_relu(self, x: Tensor, y: Tensor) -> Tensor:
+        r = torch.add(x, y)
+        r = torch.nn.functional.relu(r)
+        r = self.activation_post_process(r)
+        return r
+
+    r"""Operation equivalent to ``torch.matmul(Tensor, Tensor)``"""
+
+    def matmul(self, x: Tensor, y: Tensor) -> Tensor:
+        r = torch.matmul(x, y)
+        r = self.activation_post_process(r)
+        return r
+
+
+class FXFloatFunctional(torch.nn.Module):
+    r"""module to replace FloatFunctional module before FX graph mode quantization,
+    since activation_post_process will be inserted in top level module directly
+
+    Valid operation names:
+        - add
+        - cat
+        - mul
+        - add_relu
+        - add_scalar
+        - mul_scalar
+    """
+
+    def forward(self, x):
+        raise RuntimeError(
+            "FloatFunctional is not intended to use the "
+            + "'forward'. Please use the underlying operation"
+        )
+
+    r"""Operation equivalent to ``torch.add(Tensor, Tensor)``"""
+
+    def add(self, x: Tensor, y: Tensor) -> Tensor:
+        r = torch.add(x, y)
+        return r
+
+    r"""Operation equivalent to ``torch.add(Tensor, float)``"""
+
+    def add_scalar(self, x: Tensor, y: float) -> Tensor:
+        r = torch.add(x, y)
+        return r
+
+    r"""Operation equivalent to ``torch.mul(Tensor, Tensor)``"""
+
+    def mul(self, x: Tensor, y: Tensor) -> Tensor:
+        r = torch.mul(x, y)
+        return r
+
+    r"""Operation equivalent to ``torch.mul(Tensor, float)``"""
+
+    def mul_scalar(self, x: Tensor, y: float) -> Tensor:
+        r = torch.mul(x, y)
+        return r
+
+    r"""Operation equivalent to ``torch.cat``"""
+
+    def cat(self, x: List[Tensor], dim: int = 0) -> Tensor:
+        r = torch.cat(x, dim=dim)
+        return r
+
+    r"""Operation equivalent to ``relu(torch.add(x,y))``"""
+
+    def add_relu(self, x: Tensor, y: Tensor) -> Tensor:
+        r = torch.add(x, y)
+        r = torch.nn.functional.relu(r)
+        return r
+
+    r"""Operation equivalent to ``torch.matmul(Tensor, Tensor)``"""
+
+    def matmul(self, x: Tensor, y: Tensor) -> Tensor:
+        r = torch.matmul(x, y)
+        return r
+
+
+class QFunctional(torch.nn.Module):
+    r"""Wrapper class for quantized operations.
+
+    The instance of this class can be used instead of the
+    ``torch.ops.quantized`` prefix. See example usage below.
+
+    .. note::
+
+        This class does not provide a ``forward`` hook. Instead, you must use
+        one of the underlying functions (e.g. ``add``).
+
+    Examples::
+
+        >>> q_add = QFunctional()
+        >>> # xdoctest: +SKIP
+        >>> a = torch.quantize_per_tensor(torch.tensor(3.0), 1.0, 0, torch.qint32)
+        >>> b = torch.quantize_per_tensor(torch.tensor(4.0), 1.0, 0, torch.qint32)
+        >>> q_add.add(a, b)  # Equivalent to ``torch.ops.quantized.add(a, b, 1.0, 0)``
+
+    Valid operation names:
+        - add
+        - cat
+        - mul
+        - add_relu
+        - add_scalar
+        - mul_scalar
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.scale = 1.0
+        self.zero_point = 0
+        self.activation_post_process = torch.nn.Identity()
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "scale"] = torch.tensor(self.scale)
+        destination[prefix + "zero_point"] = torch.tensor(self.zero_point)
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        self.scale = float(state_dict.pop(prefix + "scale"))
+        self.zero_point = int(state_dict.pop(prefix + "zero_point"))
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    def _get_name(self):
+        return "QFunctional"
+
+    def extra_repr(self):
+        return f"scale={self.scale}, zero_point={self.zero_point}"
+
+    def forward(self, x):
+        raise RuntimeError(
+            "Functional is not intended to use the "
+            + "'forward'. Please use the underlying operation"
+        )
+
+    r"""Operation equivalent to ``torch.ops.quantized.add``"""
+
+    def add(self, x: Tensor, y: Tensor) -> Tensor:
+        r = ops.quantized.add(x, y, scale=self.scale, zero_point=self.zero_point)
+        r = self.activation_post_process(r)
+        return r
+
+    r"""Operation equivalent to ``torch.ops.quantized.add(Tensor, float)``"""
+
+    def add_scalar(self, x: Tensor, y: float) -> Tensor:
+        r = ops.quantized.add_scalar(x, y)
+        # Note: this operation is not observed because the observation is not
+        # needed for the quantized op.
+        return r
+
+    r"""Operation equivalent to ``torch.ops.quantized.mul(Tensor, Tensor)``"""
+
+    def mul(self, x: Tensor, y: Tensor) -> Tensor:
+        r = ops.quantized.mul(x, y, scale=self.scale, zero_point=self.zero_point)
+        r = self.activation_post_process(r)
+        return r
+
+    r"""Operation equivalent to ``torch.ops.quantized.mul(Tensor, float)``"""
+
+    def mul_scalar(self, x: Tensor, y: float) -> Tensor:
+        r = ops.quantized.mul_scalar(x, y)
+        # Note: this operation is not observed because the observation is not
+        # needed for the quantized op.
+        return r
+
+    r"""Operation equivalent to ``torch.ops.quantized.cat``"""
+
+    def cat(self, x: List[Tensor], dim: int = 0) -> Tensor:
+        r = ops.quantized.cat(x, scale=self.scale, zero_point=self.zero_point, dim=dim)
+        r = self.activation_post_process(r)
+        return r
+
+    r"""Operation equivalent to ``torch.ops.quantized.add_relu``"""
+
+    def add_relu(self, x: Tensor, y: Tensor) -> Tensor:
+        r = ops.quantized.add_relu(x, y, scale=self.scale, zero_point=self.zero_point)
+        r = self.activation_post_process(r)
+        return r
+
+    r"""Operation equivalent to ``torch.ops.quantized.matmul(Tensor, Tensor)``"""
+
+    def matmul(self, x: Tensor, y: Tensor) -> Tensor:
+        r = ops.quantized.matmul(x, y, scale=self.scale, zero_point=self.zero_point)
+        # Note: this operation is not observed because the observation is not
+        # needed for the quantized op.
+        return r
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        assert (
+            type(mod) == FloatFunctional
+        ), "QFunctional.from_float expects an instance of FloatFunctional"
+        scale, zero_point = mod.activation_post_process.calculate_qparams()  # type: ignore[operator]
+        new_mod = QFunctional()
+        new_mod.scale = float(scale)
+        new_mod.zero_point = int(zero_point)
+        return new_mod
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..73e0b57861ed9278d6bdb3100bd7c23fd269f7f7
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py
@@ -0,0 +1,358 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+from collections.abc import Iterable
+from typing import Optional
+
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.intrinsic.qat as nniqat
+import torch.nn as nn
+from torch.nn.utils.fusion import fuse_linear_bn_weights
+from torch.nn.utils.parametrize import type_before_parametrizations
+
+from .utils import _hide_packed_params_repr, _quantize_weight, WeightedQuantizedModule
+
+
+__all__ = ["LinearPackedParams", "Linear"]
+
+
+class LinearPackedParams(torch.nn.Module):
+    _version = 3
+
+    def __init__(self, dtype=torch.qint8):
+        super().__init__()
+        self.dtype = dtype
+        if self.dtype == torch.qint8:
+            wq = torch._empty_affine_quantized(
+                [1, 1], scale=1.0, zero_point=0, dtype=torch.qint8
+            )
+        elif self.dtype == torch.float16:
+            wq = torch.zeros([1, 1], dtype=torch.float)
+        self.set_weight_bias(wq, None)  # type: ignore[possibly-undefined]
+
+    @torch.jit.export
+    def set_weight_bias(
+        self, weight: torch.Tensor, bias: Optional[torch.Tensor]
+    ) -> None:
+        if self.dtype == torch.qint8:
+            self._packed_params = torch.ops.quantized.linear_prepack(weight, bias)
+        elif self.dtype == torch.float16:
+            self._packed_params = torch.ops.quantized.linear_prepack_fp16(weight, bias)
+        else:
+            raise RuntimeError("Unsupported dtype on dynamic quantized linear!")
+
+    @torch.jit.export
+    def _weight_bias(self):
+        if self.dtype == torch.qint8:
+            return torch.ops.quantized.linear_unpack(self._packed_params)
+        elif self.dtype == torch.float16:
+            return torch.ops.quantized.linear_unpack_fp16(self._packed_params)
+        else:
+            raise RuntimeError("Unsupported dtype on dynamic quantized linear!")
+
+    def forward(self, x):
+        return x
+
+    # Version 1
+    #   self
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #
+    # Version 2
+    #   self
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #   |--- dtype : torch.dtype
+    #
+    # Version 3
+    #   self
+    #   |--- _packed_params : (Tensor, Tensor) representing (weight, bias)
+    #                         of LinearPackedParams
+    #   |--- dtype : torch.dtype
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "dtype"] = self.dtype
+        destination[prefix + "_packed_params"] = self._weight_bias()
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        version = local_metadata.get("version", None)
+        if version is None or version < 2:
+            self.dtype = torch.qint8
+        else:
+            self.dtype = state_dict[prefix + "dtype"]
+            state_dict.pop(prefix + "dtype")
+
+        if version is None or version < 3:
+            self.set_weight_bias(
+                state_dict[prefix + "weight"], state_dict[prefix + "bias"]
+            )
+            state_dict.pop(prefix + "weight")
+            state_dict.pop(prefix + "bias")
+
+        if version == 3:
+            weight, bias = state_dict[prefix + "_packed_params"]
+            state_dict.pop(prefix + "_packed_params")
+            self.set_weight_bias(weight, bias)
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    def __repr__(self):
+        return self._weight_bias().__repr__()
+
+
+class Linear(WeightedQuantizedModule):
+    r"""
+    A quantized linear module with quantized tensor as inputs and outputs.
+    We adopt the same interface as `torch.nn.Linear`, please see
+    https://pytorch.org/docs/stable/nn.html#torch.nn.Linear for documentation.
+
+    Similar to :class:`~torch.nn.Linear`, attributes will be randomly
+    initialized at module creation time and will be overwritten later
+
+    Attributes:
+        weight (Tensor): the non-learnable quantized weights of the module of
+                         shape :math:`(\text{out\_features}, \text{in\_features})`.
+        bias (Tensor): the non-learnable bias of the module of shape :math:`(\text{out\_features})`.
+                If :attr:`bias` is ``True``, the values are initialized to zero.
+        scale: `scale` parameter of output Quantized Tensor, type: double
+        zero_point: `zero_point` parameter for output Quantized Tensor, type: long
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> m = nn.quantized.Linear(20, 30)
+        >>> input = torch.randn(128, 20)
+        >>> # xdoctest: +SKIP
+        >>> input = torch.quantize_per_tensor(input, 1.0, 0, torch.quint8)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 30])
+    """
+    _version = 3
+    _FLOAT_MODULE = (nn.Linear, nn.modules.linear.NonDynamicallyQuantizableLinear)
+
+    def __init__(self, in_features, out_features, bias_=True, dtype=torch.qint8):
+        super().__init__()
+        # We don't muck around with buffers or attributes or anything here
+        # to keep the module simple. *everything* is simply a Python attribute.
+        # Serialization logic is explicitly handled in the below serialization and
+        # deserialization modules
+        self.in_features = in_features
+        self.out_features = out_features
+        bias = None
+        if bias_:
+            bias = torch.zeros(out_features, dtype=torch.float)
+
+        if dtype == torch.qint8:
+            qweight = torch._empty_affine_quantized(
+                [out_features, in_features], scale=1, zero_point=0, dtype=torch.qint8
+            )
+        elif dtype == torch.float16:
+            qweight = torch.zeros([out_features, in_features], dtype=torch.float)
+        else:
+            raise RuntimeError("Unsupported dtype specified for quantized Linear!")
+
+        self._packed_params = LinearPackedParams(dtype)
+        self._packed_params.set_weight_bias(qweight, bias)
+        self.scale = 1.0
+        self.zero_point = 0
+
+    def _get_name(self):
+        return "QuantizedLinear"
+
+    def extra_repr(self):
+        return (
+            f"in_features={self.in_features}, out_features={self.out_features}, scale={self.scale}, "
+            f"zero_point={self.zero_point}, qscheme={self.weight().qscheme()}"
+        )
+
+    def __repr__(self):
+        return _hide_packed_params_repr(self, LinearPackedParams)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.ops.quantized.linear(
+            x, self._packed_params._packed_params, self.scale, self.zero_point
+        )
+
+    # ===== Serialization methods =====
+    # The special consideration here is that we have to unpack the weights into their
+    # regular QTensor form for serialization. Packed weights should not live
+    # outside the process in which they were created, rather they should be derived
+    # from the QTensor weight.
+    #
+    # Version 1
+    #   self
+    #   |--- scale : float
+    #   |--- zero_point : int
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #
+    # Version 2
+    #   self
+    #   |--- scale : float
+    #   |--- zero_point : int
+    #   |--- _packed_params : Module
+    #        |--- weight : Tensor
+    #        |--- bias : Tensor
+    #
+    # Version 3
+    #   self
+    #   |--- scale : float
+    #   |--- zero_point : int
+    #   |--- _packed_params : Module
+    #        |--- _packed_params : (Tensor, Tensor) representing weight, bias
+    #                              of LinearPackedParams C++ struct
+    #
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "scale"] = torch.tensor(self.scale)
+        destination[prefix + "zero_point"] = torch.tensor(self.zero_point)
+
+    # ===== Deserialization methods =====
+    # Counterpart to the serialization methods, we must pack the serialized QTensor
+    # weight into its packed format for use by the FBGEMM ops.
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        self.scale = float(state_dict[prefix + "scale"])
+        state_dict.pop(prefix + "scale")
+
+        self.zero_point = int(state_dict[prefix + "zero_point"])
+        state_dict.pop(prefix + "zero_point")
+
+        version = local_metadata.get("version", None)
+
+        if version is None or version == 1:
+            # We moved the parameters into a LinearPackedParameters submodule
+            weight = state_dict.pop(prefix + "weight")
+            bias = state_dict.pop(prefix + "bias")
+            state_dict.update(
+                {
+                    prefix + "_packed_params.weight": weight,
+                    prefix + "_packed_params.bias": bias,
+                }
+            )
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    # Function rather than property to make sure that JIT serialization doesn't
+    # register this as an attribute
+    def _weight_bias(self):
+        return self._packed_params._weight_bias()
+
+    def weight(self):
+        return self._weight_bias()[0]
+
+    def bias(self):
+        return self._weight_bias()[1]
+
+    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
+        self._packed_params.set_weight_bias(w, b)
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a quantized module from an observed float module
+
+        Args:
+            mod (Module): a float module, either produced by torch.ao.quantization
+                          utilities or provided by the user
+            use_precomputed_fake_quant (bool): if True, the module will reuse min/max
+                          values from the precomputed fake quant module.
+        """
+        if hasattr(mod, "weight_fake_quant"):
+            if type_before_parametrizations(mod) == nniqat.LinearBn1d:
+                mod.weight, mod.bias = fuse_linear_bn_weights(
+                    mod.weight,
+                    mod.bias,
+                    mod.bn.running_mean,
+                    mod.bn.running_var,
+                    mod.bn.eps,
+                    mod.bn.weight,
+                    mod.bn.bias,
+                )
+            weight_post_process = mod.weight_fake_quant
+            activation_post_process = mod.activation_post_process
+        else:
+            # This function does not participate in JIT, so it is OK to ignore
+            # the type mismatch in assignment. Also, mypy has an issue with
+            # iterables not being implemented, so we are ignoring those too.
+            if not isinstance(cls._FLOAT_MODULE, Iterable):
+                cls._FLOAT_MODULE = [cls._FLOAT_MODULE]  # type: ignore[assignment]
+            supported_modules = ", ".join([float_mod.__name__ for float_mod in cls._FLOAT_MODULE])  # type: ignore[attr-defined]
+            error_msg = f"nnq.{cls.__name__}.from_float only works for {supported_modules}, but got: {type(mod)}"
+            assert type_before_parametrizations(mod) in cls._FLOAT_MODULE, error_msg.format()  # type: ignore[attr-defined]
+            assert hasattr(
+                mod, "qconfig"
+            ), "Input float module must have qconfig defined"
+            activation_post_process = mod.activation_post_process
+            if type_before_parametrizations(mod) == nni.LinearReLU:
+                mod = mod[0]
+            weight_post_process = (
+                mod.qconfig.weight()
+                if not hasattr(mod, "weight_fake_quant")
+                else mod.weight_fake_quant
+            )
+
+        if not use_precomputed_fake_quant:
+            # Observer may not have been called yet
+            # Observer might have been called in the previous stage via PTQ algorithm e.g. AdaRound
+            weight_post_process(mod.weight)
+        dtype = weight_post_process.dtype
+        act_scale, act_zp = activation_post_process.calculate_qparams()
+        assert dtype == torch.qint8, "Weight observer must have dtype torch.qint8"
+        qweight = _quantize_weight(mod.weight.float(), weight_post_process)
+        qlinear = cls(mod.in_features, mod.out_features, dtype=dtype)
+        qlinear.set_weight_bias(qweight, mod.bias)
+        qlinear.scale = float(act_scale)
+        qlinear.zero_point = int(act_zp)
+        return qlinear
+
+    @classmethod
+    def from_reference(cls, ref_qlinear, output_scale, output_zero_point):
+        r"""Create a (fbgemm/qnnpack) quantized module from a reference quantized module
+
+        Args:
+            ref_qlinear (Module): a reference quantized linear module, either produced by torch.ao.quantization
+                          utilities or provided by the user
+            output_scale (float): scale for output Tensor
+            output_zero_point (int): zero point for output Tensor
+        """
+        qlinear = cls(ref_qlinear.in_features, ref_qlinear.out_features)
+        qweight = ref_qlinear.get_quantized_weight()
+        qlinear.set_weight_bias(qweight, ref_qlinear.bias)
+
+        qlinear.scale = float(output_scale)
+        qlinear.zero_point = int(output_zero_point)
+        return qlinear
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/normalization.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/normalization.py
new file mode 100644
index 0000000000000000000000000000000000000000..e025184bd4a20c75a51993aa999e71f2c11f8e63
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/normalization.py
@@ -0,0 +1,346 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+__all__ = [
+    "LayerNorm",
+    "GroupNorm",
+    "InstanceNorm1d",
+    "InstanceNorm2d",
+    "InstanceNorm3d",
+]
+
+
+class LayerNorm(torch.nn.LayerNorm):
+    r"""This is the quantized version of :class:`~torch.nn.LayerNorm`.
+
+    Additional args:
+        * **scale** - quantization scale of the output, type: double.
+        * **zero_point** - quantization zero point of the output, type: long.
+
+    """
+
+    def __init__(
+        self,
+        normalized_shape,
+        weight,
+        bias,
+        scale,
+        zero_point,
+        eps=1e-5,
+        elementwise_affine=True,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(
+            normalized_shape,
+            eps=eps,
+            elementwise_affine=elementwise_affine,
+            **factory_kwargs,
+        )
+        self.weight = weight
+        self.bias = bias
+        self.register_buffer("scale", torch.tensor(scale, **factory_kwargs))
+        self.register_buffer("zero_point", torch.tensor(zero_point, **factory_kwargs))
+
+    def forward(self, input):
+        return torch.ops.quantized.layer_norm(
+            input,
+            self.normalized_shape,
+            weight=self.weight,
+            bias=self.bias,
+            eps=self.eps,
+            output_scale=self.scale,
+            output_zero_point=self.zero_point,
+        )
+
+    def _get_name(self):
+        return "QuantizedLayerNorm"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        new_mod = cls(
+            mod.normalized_shape,
+            mod.weight,
+            mod.bias,
+            float(scale),
+            int(zero_point),
+            mod.eps,
+            mod.elementwise_affine,
+        )
+        return new_mod
+
+    @classmethod
+    def from_reference(cls, mod, scale, zero_point):
+        return cls(
+            mod.normalized_shape,
+            mod.weight,
+            mod.bias,
+            float(scale),
+            int(zero_point),
+            mod.eps,
+            mod.elementwise_affine,
+        )
+
+
+class GroupNorm(torch.nn.GroupNorm):
+    r"""This is the quantized version of :class:`~torch.nn.GroupNorm`.
+
+    Additional args:
+        * **scale** - quantization scale of the output, type: double.
+        * **zero_point** - quantization zero point of the output, type: long.
+
+    """
+    __constants__ = ["num_groups", "num_channels", "eps", "affine"]
+
+    def __init__(
+        self,
+        num_groups,
+        num_channels,
+        weight,
+        bias,
+        scale,
+        zero_point,
+        eps=1e-5,
+        affine=True,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(num_groups, num_channels, eps, affine, **factory_kwargs)
+        self.weight = weight
+        self.bias = bias
+        self.register_buffer("scale", torch.tensor(scale, **factory_kwargs))
+        self.register_buffer("zero_point", torch.tensor(zero_point, **factory_kwargs))
+
+    def forward(self, input):
+        return torch.ops.quantized.group_norm(
+            input,
+            self.num_groups,
+            self.weight,
+            self.bias,
+            self.eps,
+            self.scale,
+            self.zero_point,
+        )
+
+    def _get_name(self):
+        return "QuantizedGroupNorm"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        new_mod = cls(
+            mod.num_groups,
+            mod.num_channels,
+            mod.weight,
+            mod.bias,
+            float(scale),
+            int(zero_point),
+            mod.eps,
+            mod.affine,
+        )
+        return new_mod
+
+
+class InstanceNorm1d(torch.nn.InstanceNorm1d):
+    r"""This is the quantized version of :class:`~torch.nn.InstanceNorm1d`.
+
+    Additional args:
+        * **scale** - quantization scale of the output, type: double.
+        * **zero_point** - quantization zero point of the output, type: long.
+
+    """
+
+    def __init__(
+        self,
+        num_features,
+        weight,
+        bias,
+        scale,
+        zero_point,
+        eps=1e-5,
+        momentum=0.1,
+        affine=False,
+        track_running_stats=False,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(
+            num_features, eps, momentum, affine, track_running_stats, **factory_kwargs
+        )
+        self.weight = weight
+        self.bias = bias
+        self.register_buffer("scale", torch.tensor(scale, **factory_kwargs))
+        self.register_buffer("zero_point", torch.tensor(zero_point, **factory_kwargs))
+
+    def forward(self, input):
+        return torch.ops.quantized.instance_norm(
+            input, self.weight, self.bias, self.eps, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedInstanceNorm1d"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        new_mod = cls(
+            mod.num_features,
+            mod.weight,
+            mod.bias,
+            float(scale),
+            int(zero_point),
+            mod.eps,
+            mod.affine,
+        )
+        return new_mod
+
+    @classmethod
+    def from_reference(cls, mod, scale, zero_point):
+        return cls(
+            mod.num_features,
+            mod.weight,
+            mod.bias,
+            float(scale),
+            int(zero_point),
+            mod.eps,
+            mod.affine,
+        )
+
+
+class InstanceNorm2d(torch.nn.InstanceNorm2d):
+    r"""This is the quantized version of :class:`~torch.nn.InstanceNorm2d`.
+
+    Additional args:
+        * **scale** - quantization scale of the output, type: double.
+        * **zero_point** - quantization zero point of the output, type: long.
+
+    """
+
+    def __init__(
+        self,
+        num_features,
+        weight,
+        bias,
+        scale,
+        zero_point,
+        eps=1e-5,
+        momentum=0.1,
+        affine=False,
+        track_running_stats=False,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(
+            num_features, eps, momentum, affine, track_running_stats, **factory_kwargs
+        )
+        self.weight = weight
+        self.bias = bias
+        self.register_buffer("scale", torch.tensor(scale, **factory_kwargs))
+        self.register_buffer("zero_point", torch.tensor(zero_point, **factory_kwargs))
+
+    def forward(self, input):
+        return torch.ops.quantized.instance_norm(
+            input, self.weight, self.bias, self.eps, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedInstanceNorm2d"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        new_mod = cls(
+            mod.num_features,
+            mod.weight,
+            mod.bias,
+            float(scale),
+            int(zero_point),
+            mod.eps,
+            mod.affine,
+        )
+        return new_mod
+
+    @classmethod
+    def from_reference(cls, mod, scale, zero_point):
+        return cls(
+            mod.num_features,
+            mod.weight,
+            mod.bias,
+            float(scale),
+            int(zero_point),
+            mod.eps,
+            mod.affine,
+        )
+
+
+class InstanceNorm3d(torch.nn.InstanceNorm3d):
+    r"""This is the quantized version of :class:`~torch.nn.InstanceNorm3d`.
+
+    Additional args:
+        * **scale** - quantization scale of the output, type: double.
+        * **zero_point** - quantization zero point of the output, type: long.
+
+    """
+
+    def __init__(
+        self,
+        num_features,
+        weight,
+        bias,
+        scale,
+        zero_point,
+        eps=1e-5,
+        momentum=0.1,
+        affine=False,
+        track_running_stats=False,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(
+            num_features, eps, momentum, affine, track_running_stats, **factory_kwargs
+        )
+        self.weight = weight
+        self.bias = bias
+        self.register_buffer("scale", torch.tensor(scale, **factory_kwargs))
+        self.register_buffer("zero_point", torch.tensor(zero_point, **factory_kwargs))
+
+    def forward(self, input):
+        return torch.ops.quantized.instance_norm(
+            input, self.weight, self.bias, self.eps, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedInstanceNorm3d"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        new_mod = cls(
+            mod.num_features,
+            mod.weight,
+            mod.bias,
+            float(scale),
+            int(zero_point),
+            mod.eps,
+            mod.affine,
+        )
+        return new_mod
+
+    @classmethod
+    def from_reference(cls, mod, scale, zero_point):
+        return cls(
+            mod.num_features,
+            mod.weight,
+            mod.bias,
+            float(scale),
+            int(zero_point),
+            mod.eps,
+            mod.affine,
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/rnn.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/rnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..f555e79735b73d5a149abeb6e679824dec77e1d4
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/rnn.py
@@ -0,0 +1,57 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+__all__ = [
+    "LSTM",
+]
+
+
+class LSTM(torch.ao.nn.quantizable.LSTM):
+    r"""A quantized long short-term memory (LSTM).
+
+    For the description and the argument types, please, refer to :class:`~torch.nn.LSTM`
+
+    Attributes:
+        layers : instances of the `_LSTMLayer`
+
+    .. note::
+        To access the weights and biases, you need to access them per layer.
+        See examples in :class:`~torch.ao.nn.quantizable.LSTM`
+
+    Examples::
+        >>> # xdoctest: +SKIP
+        >>> custom_module_config = {
+        ...     'float_to_observed_custom_module_class': {
+        ...         nn.LSTM: nn.quantizable.LSTM,
+        ...     },
+        ...     'observed_to_quantized_custom_module_class': {
+        ...         nn.quantizable.LSTM: nn.quantized.LSTM,
+        ...     }
+        ... }
+        >>> tq.prepare(model, prepare_custom_module_class=custom_module_config)
+        >>> tq.convert(model, convert_custom_module_class=custom_module_config)
+    """
+    _FLOAT_MODULE = torch.ao.nn.quantizable.LSTM  # type: ignore[assignment]
+
+    def _get_name(self):
+        return "QuantizedLSTM"
+
+    @classmethod
+    def from_float(cls, *args, **kwargs):
+        # The whole flow is float -> observed -> quantized
+        # This class does observed -> quantized only
+        raise NotImplementedError(
+            "It looks like you are trying to convert a "
+            "non-observed LSTM module. Please, see "
+            "the examples on quantizable LSTMs."
+        )
+
+    @classmethod
+    def from_observed(cls, other):
+        assert type(other) == cls._FLOAT_MODULE  # type: ignore[has-type]
+        converted = torch.ao.quantization.convert(
+            other, inplace=False, remove_qconfig=True
+        )
+        converted.__class__ = cls
+        return converted
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..be59d496b8d07a3861b4420e25946e75e6eb0db7
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/utils.py
@@ -0,0 +1,144 @@
+# mypy: allow-untyped-defs
+import abc
+import collections
+import itertools
+
+import torch
+from torch.nn.modules.module import _addindent
+
+
+__all__ = [
+    "WeightedQuantizedModule",
+]
+
+
+class WeightedQuantizedModule(torch.nn.Module, metaclass=abc.ABCMeta):
+    """Wrapper for quantized modules than can be lowered from reference modules."""
+
+    @classmethod
+    @abc.abstractmethod
+    def from_reference(cls, ref_module, output_scale, output_zero_point):
+        raise NotImplementedError
+
+
+def _get_weight_observer(observer):
+    # FakeQuantize observer
+    if hasattr(observer, "activation_post_process"):
+        observer = observer.activation_post_process
+    # UniformQuantizationObserverBase observer
+    return observer
+
+
+def _needs_weight_clamping(observer, dtype):
+    observer = _get_weight_observer(observer)
+    if dtype in [torch.qint8, torch.quint8, torch.qint32]:
+        info = torch.iinfo(dtype)
+        return observer.quant_min > info.min or observer.quant_max < info.max
+    return False
+
+
+def _clamp_weights(qweight, observer, scale, zp):
+    if not _needs_weight_clamping(observer, qweight.dtype):
+        return qweight
+
+    observer = _get_weight_observer(observer)
+    min_, max_ = observer.quant_min, observer.quant_max
+
+    # Doing this because can't use torch.ops.quantized.clamp() with per_channel qscheme yet.
+    qw_int_max = torch.clone(qweight.int_repr()).fill_(max_)
+    qw_int_min = torch.clone(qweight.int_repr()).fill_(min_)
+    qw_int = torch.minimum(torch.maximum(qweight.int_repr(), qw_int_min), qw_int_max)
+
+    if observer.qscheme in [torch.per_tensor_symmetric, torch.per_tensor_affine]:
+        qweight = torch._make_per_tensor_quantized_tensor(
+            qw_int, scale.item(), zp.item()
+        )
+    elif observer.qscheme in [
+        torch.per_channel_symmetric,
+        torch.per_channel_affine,
+        torch.per_channel_affine_float_qparams,
+    ]:
+        qweight = torch._make_per_channel_quantized_tensor(
+            qw_int, scale, zp, axis=observer.ch_axis
+        )
+    else:
+        raise ValueError("Unexpected qscheme " + observer.qscheme)
+    return qweight
+
+
+def _quantize_weight(float_wt, observer):
+    wt_scale, wt_zp = observer.calculate_qparams()
+    if observer.qscheme in [torch.per_tensor_symmetric, torch.per_tensor_affine]:
+        qweight = torch.quantize_per_tensor(
+            float_wt, float(wt_scale), int(wt_zp), torch.qint8
+        )
+        qweight = _clamp_weights(qweight, observer, wt_scale, wt_zp)
+    elif observer.qscheme in [torch.per_channel_symmetric, torch.per_channel_affine]:
+        wt_axis = observer.ch_axis
+        qweight = torch.quantize_per_channel(
+            float_wt,
+            wt_scale.to(torch.double),
+            wt_zp.to(torch.int64),
+            wt_axis,
+            torch.qint8,
+        )
+        qweight = _clamp_weights(qweight, observer, wt_scale, wt_zp)
+    elif observer.qscheme in [torch.per_channel_affine_float_qparams]:
+        qweight = torch.quantize_per_channel(
+            float_wt,
+            wt_scale.to(torch.float),
+            wt_zp.to(torch.float),
+            observer.ch_axis,
+            observer.dtype,
+        )
+        qweight = _clamp_weights(qweight, observer, wt_scale, wt_zp)
+    else:
+        raise ValueError("Unexpected qscheme " + observer.qscheme)
+    return qweight
+
+
+def _ntuple_from_first(n):
+    """Converts the argument to a tuple of size n
+    with the first element repeated."""
+
+    def parse(x):
+        while isinstance(x, collections.abc.Sequence):
+            if len(x) == n:
+                break
+            x = x[0]
+        return tuple(itertools.repeat(x, n))
+
+    return parse
+
+
+def _hide_packed_params_repr(self, params):
+    # We don't want to show `PackedParams` children, hence custom
+    # `__repr__`. This is the same as nn.Module.__repr__, except the check
+    # for the `params module`.
+    extra_lines = []
+    extra_repr = self.extra_repr()
+    # empty string will be split into list ['']
+    if extra_repr:
+        extra_lines = extra_repr.split("\n")
+    child_lines = []
+    for key, module in self._modules.items():
+        if isinstance(module, params):
+            continue
+        mod_str = repr(module)
+        mod_str = _addindent(mod_str, 2)
+        child_lines.append("(" + key + "): " + mod_str)
+    lines = extra_lines + child_lines
+
+    main_str = self._get_name() + "("
+    if lines:
+        # simple one-liner info, which most builtin Modules will use
+        if len(extra_lines) == 1 and not child_lines:
+            main_str += extra_lines[0]
+        else:
+            main_str += "\n  " + "\n  ".join(lines) + "\n"
+
+    main_str += ")"
+    return main_str
+
+
+_pair_from_first = _ntuple_from_first(2)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1e15e9c1516d30f7ca9ee47b21b267533de75b6
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/__init__.py
@@ -0,0 +1,19 @@
+from .modules import *  # noqa: F403
+
+
+__all__ = [
+    "Linear",
+    "Conv1d",
+    "Conv2d",
+    "Conv3d",
+    "ConvTranspose1d",
+    "ConvTranspose2d",
+    "ConvTranspose3d",
+    "RNNCell",
+    "LSTMCell",
+    "GRUCell",
+    "LSTM",
+    "GRU",
+    "Embedding",
+    "EmbeddingBag",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..358013f6089cfc8d41e483a363eed4a3a4801963
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbe97c22f5a46a5eafc1432075fc57dd44c3aa8d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/__init__.py
@@ -0,0 +1,29 @@
+from .conv import (
+    Conv1d,
+    Conv2d,
+    Conv3d,
+    ConvTranspose1d,
+    ConvTranspose2d,
+    ConvTranspose3d,
+)
+from .linear import Linear
+from .rnn import GRU, GRUCell, LSTM, LSTMCell, RNNCell
+from .sparse import Embedding, EmbeddingBag
+
+
+__all__ = [
+    "Linear",
+    "Conv1d",
+    "Conv2d",
+    "Conv3d",
+    "ConvTranspose1d",
+    "ConvTranspose2d",
+    "ConvTranspose3d",
+    "RNNCell",
+    "LSTMCell",
+    "GRUCell",
+    "LSTM",
+    "GRU",
+    "Embedding",
+    "EmbeddingBag",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/__pycache__/conv.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/__pycache__/conv.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/conv.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/conv.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f5048f6e58e00af2e908e13bf3e62c7946992e8
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/conv.py
@@ -0,0 +1,511 @@
+# mypy: allow-untyped-defs
+from typing import Any, Dict, List, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.common_types import _size_1_t
+
+from .utils import ReferenceQuantizedModule
+
+
+__all__ = [
+    "Conv1d",
+    "Conv2d",
+    "Conv3d",
+    "ConvTranspose1d",
+    "ConvTranspose2d",
+    "ConvTranspose3d",
+]
+
+
+class _ConvNd(torch.nn.modules.conv._ConvNd, ReferenceQuantizedModule):
+    """A reference version of nn.quantized.Conv2d
+    we will not pack the parameters in this module, since weight packing is an
+    optimization for quantized backends supported in PyTorch (fbgemm/qnnpack),
+    this is useful when user want to use this module in other backends like Glow.
+    """
+
+    __annotations__ = {"bias": Optional[torch.Tensor]}
+    _IS_REFERENCE = True
+
+    @staticmethod
+    def from_float(cls, float_conv, weight_qparams):
+        qref_conv = cls(
+            float_conv.in_channels,
+            float_conv.out_channels,
+            float_conv.kernel_size,  # type: ignore[arg-type]
+            float_conv.stride,  # type: ignore[arg-type]
+            float_conv.padding,  # type: ignore[arg-type]
+            float_conv.dilation,  # type: ignore[arg-type]
+            float_conv.groups,
+            float_conv.bias is not None,  # type: ignore[arg-type]
+            float_conv.padding_mode,
+            device=float_conv.weight.device,
+            dtype=float_conv.weight.dtype,
+            weight_qparams=weight_qparams,
+        )
+        qref_conv.weight = torch.nn.Parameter(float_conv.weight.detach())
+        if float_conv.bias is not None:
+            qref_conv.bias = torch.nn.Parameter(float_conv.bias.detach())
+        return qref_conv
+
+
+class Conv1d(_ConvNd, nn.Conv1d):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: _size_1_t,
+        stride: _size_1_t = 1,
+        padding: _size_1_t = 0,
+        dilation: _size_1_t = 1,
+        groups: int = 1,
+        bias: bool = True,
+        padding_mode: str = "zeros",
+        device=None,
+        dtype=None,
+        weight_qparams: Optional[Dict[str, Any]] = None,
+    ):
+        nn.Conv1d.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            padding_mode,
+            device,
+            dtype,
+        )
+        self._init_weight_qparams(weight_qparams, device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        we have:
+        w(float) -- quant - dequant \
+        x(float) ------------- F.conv1d ---
+
+        In the full model, we will see
+        w(float) -- quant - *dequant \
+        x -- quant --- *dequant --  *F.conv1d --- *quant - dequant
+        and the backend should be able to fuse the ops with `*` into a quantized conv1d
+        """
+        weight_quant_dequant = self.get_weight()
+        result = F.conv1d(
+            x,
+            weight_quant_dequant,
+            self.bias,
+            self.stride,
+            self.padding,
+            self.dilation,
+            self.groups,
+        )
+        return result
+
+    def _get_name(self):
+        return "QuantizedConv1d(Reference)"
+
+    @classmethod
+    def from_float(cls, float_conv, weight_qparams):
+        return _ConvNd.from_float(cls, float_conv, weight_qparams)
+
+
+class Conv2d(_ConvNd, nn.Conv2d):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+        weight_qparams: Optional[Dict[str, Any]] = None,
+    ):
+        nn.Conv2d.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            padding_mode,
+            device,
+            dtype,
+        )
+        self._init_weight_qparams(weight_qparams, device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        we have:
+        w(float) -- quant - dequant \
+        x(float) ------------- F.conv2d ---
+
+        In the full model, we will see
+        w(float) -- quant - *dequant \
+        x -- quant --- *dequant --  *F.conv2d --- *quant - dequant
+        and the backend should be able to fuse the ops with `*` into a quantized conv2d
+        """
+        weight_quant_dequant = self.get_weight()
+        result = F.conv2d(
+            x,
+            weight_quant_dequant,
+            self.bias,
+            self.stride,
+            self.padding,
+            self.dilation,
+            self.groups,
+        )
+        return result
+
+    def _get_name(self):
+        return "QuantizedConv2d(Reference)"
+
+    @classmethod
+    def from_float(cls, float_conv, weight_qparams):
+        return _ConvNd.from_float(cls, float_conv, weight_qparams)
+
+
+class Conv3d(_ConvNd, nn.Conv3d):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+        weight_qparams: Optional[Dict[str, Any]] = None,
+    ):
+        nn.Conv3d.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            padding_mode,
+            device,
+            dtype,
+        )
+        self._init_weight_qparams(weight_qparams, device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        we have:
+        w(float) -- quant - dequant \
+        x(float) ------------- F.conv3d ---
+
+        In the full model, we will see
+        w(float) -- quant - *dequant \
+        x -- quant --- *dequant --  *F.conv3d --- *quant - dequant
+        and the backend should be able to fuse the ops with `*` into a quantized conv3d
+        """
+        weight_quant_dequant = self.get_weight()
+        result = F.conv3d(
+            x,
+            weight_quant_dequant,
+            self.bias,
+            self.stride,
+            self.padding,
+            self.dilation,
+            self.groups,
+        )
+        return result
+
+    def _get_name(self):
+        return "QuantizedConv3d(Reference)"
+
+    @classmethod
+    def from_float(cls, float_conv, weight_qparams):
+        return _ConvNd.from_float(cls, float_conv, weight_qparams)
+
+
+class _ConvTransposeNd(_ConvNd, torch.nn.modules.conv._ConvTransposeNd):
+    """A reference version of nn.quantized.ConvTranspose2d
+    we will not pack the parameters in this module, since weight packing is an
+    optimization for quantized backends supported in PyTorch (fbgemm/qnnpack),
+    this is useful when user want to use this module in other backends like Glow.
+    """
+
+    @staticmethod
+    def from_float(cls, float_conv, weight_qparams):
+        qref_conv = cls(
+            float_conv.in_channels,
+            float_conv.out_channels,
+            float_conv.kernel_size,  # type: ignore[arg-type]
+            float_conv.stride,  # type: ignore[arg-type]
+            float_conv.padding,  # type: ignore[arg-type]
+            float_conv.output_padding,  # type: ignore[arg-type]
+            float_conv.groups,
+            float_conv.bias is not None,  # type: ignore[arg-type]
+            float_conv.dilation,  # type: ignore[arg-type]
+            float_conv.padding_mode,
+            device=float_conv.weight.device,
+            dtype=float_conv.weight.dtype,
+            weight_qparams=weight_qparams,
+        )
+        qref_conv.weight = torch.nn.Parameter(float_conv.weight.detach())
+        if float_conv.bias is not None:
+            qref_conv.bias = torch.nn.Parameter(float_conv.bias.detach())
+        return qref_conv
+
+
+class ConvTranspose1d(_ConvTransposeNd, nn.ConvTranspose1d):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: _size_1_t,
+        stride: _size_1_t = 1,
+        padding: _size_1_t = 0,
+        output_padding: _size_1_t = 0,
+        groups: int = 1,
+        bias: bool = True,
+        dilation: _size_1_t = 1,
+        padding_mode: str = "zeros",
+        device=None,
+        dtype=None,
+        weight_qparams: Optional[Dict[str, Any]] = None,
+    ):
+        nn.ConvTranspose1d.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            output_padding,
+            groups,
+            bias,
+            dilation,
+            padding_mode,
+            device,
+            dtype,
+        )
+        self._init_weight_qparams(weight_qparams, device)
+
+    def forward(
+        self, x: torch.Tensor, output_size: Optional[List[int]] = None
+    ) -> torch.Tensor:
+        """
+        we have:
+        w(float) -- quant - dequant \
+        x(float) ------------- F.convTranspose1d ---
+        In the full model, we will see
+        w(float) -- quant - *dequant \
+        x -- quant --- *dequant --  *F.convTranspose1d --- *quant - dequant
+        and the backend should be able to fuse the ops with `*` into a quantized conv1d
+        """
+
+        assert isinstance(self.padding, tuple)
+        # One cannot replace List by Tuple or Sequence in "_output_padding" because
+        # TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.
+        output_padding = self._output_padding(
+            input,  # type: ignore[arg-type]
+            output_size,
+            self.stride,  # type: ignore[arg-type]
+            self.padding,  # type: ignore[arg-type]
+            self.kernel_size,  # type: ignore[arg-type]
+            self.dilation,  # type: ignore[arg-type]
+        )
+
+        weight_quant_dequant = self.get_weight()
+        result = F.conv_transpose1d(
+            x,
+            weight_quant_dequant,
+            self.bias,
+            self.stride,
+            self.padding,
+            output_padding,
+            self.groups,
+            self.dilation,
+        )
+        return result
+
+    def _get_name(self):
+        return "QuantizedConvTranspose1d(Reference)"
+
+    @classmethod
+    def from_float(cls, float_conv, weight_qparams):
+        return _ConvTransposeNd.from_float(cls, float_conv, weight_qparams)
+
+
+class ConvTranspose2d(_ConvTransposeNd, nn.ConvTranspose2d):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        output_padding=0,
+        groups=1,
+        bias=True,
+        dilation=1,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+        weight_qparams: Optional[Dict[str, Any]] = None,
+    ):
+        nn.ConvTranspose2d.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            output_padding,
+            groups,
+            bias,
+            dilation,
+            padding_mode,
+            device,
+            dtype,
+        )
+        self._init_weight_qparams(weight_qparams, device)
+
+    def forward(
+        self, x: torch.Tensor, output_size: Optional[List[int]] = None
+    ) -> torch.Tensor:
+        """
+        we have:
+        w(float) -- quant - dequant \
+        x(float) ------------- F.convTranspose2d ---
+        In the full model, we will see
+        w(float) -- quant - *dequant \
+        x -- quant --- *dequant --  *F.convTranspose2d --- *quant - dequant
+        and the backend should be able to fuse the ops with `*` into a quantized conv2d
+        """
+        assert isinstance(self.padding, tuple)
+        # One cannot replace List by Tuple or Sequence in "_output_padding" because
+        # TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.
+
+        output_padding = self._output_padding(
+            input,  # type: ignore[arg-type]
+            output_size,
+            self.stride,  # type: ignore[arg-type]
+            self.padding,  # type: ignore[arg-type]
+            self.kernel_size,  # type: ignore[arg-type]
+            self.dilation,  # type: ignore[arg-type]
+        )
+
+        weight_quant_dequant = self.get_weight()
+        result = F.conv_transpose2d(
+            x,
+            weight_quant_dequant,
+            self.bias,
+            self.stride,
+            self.padding,
+            output_padding,
+            self.groups,
+            self.dilation,
+        )
+
+        return result
+
+    def _get_name(self):
+        return "QuantizedConvTranspose2d(Reference)"
+
+    @classmethod
+    def from_float(cls, float_conv, weight_qparams):
+        return _ConvTransposeNd.from_float(cls, float_conv, weight_qparams)
+
+
+class ConvTranspose3d(_ConvTransposeNd, nn.ConvTranspose3d):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        output_padding=0,
+        groups=1,
+        bias=True,
+        dilation=1,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+        weight_qparams: Optional[Dict[str, Any]] = None,
+    ):
+        nn.ConvTranspose3d.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            output_padding,
+            groups,
+            bias,
+            dilation,
+            padding_mode,
+            device,
+            dtype,
+        )
+        self._init_weight_qparams(weight_qparams, device)
+
+    def forward(
+        self, x: torch.Tensor, output_size: Optional[List[int]] = None
+    ) -> torch.Tensor:
+        """
+        we have:
+        w(float) -- quant - dequant \
+        x(float) ------------- F.convTranspose3d ---
+        In the full model, we will see
+        w(float) -- quant - *dequant \
+        x -- quant --- *dequant --  *F.convTranspose3d --- *quant - dequant
+        and the backend should be able to fuse the ops with `*` into a quantized conv3d
+        """
+
+        assert isinstance(self.padding, tuple)
+        # One cannot replace List by Tuple or Sequence in "_output_padding" because
+        # TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.
+        output_padding = self._output_padding(
+            input,  # type: ignore[arg-type]
+            output_size,
+            self.stride,  # type: ignore[arg-type]
+            self.padding,  # type: ignore[arg-type]
+            self.kernel_size,  # type: ignore[arg-type]
+            self.dilation,  # type: ignore[arg-type]
+        )
+
+        weight_quant_dequant = self.get_weight()
+        result = F.conv_transpose3d(
+            x,
+            weight_quant_dequant,
+            self.bias,
+            self.stride,
+            self.padding,
+            output_padding,
+            self.groups,
+            self.dilation,
+        )
+        return result
+
+    def _get_name(self):
+        return "QuantizedConvTranspose3d(Reference)"
+
+    @classmethod
+    def from_float(cls, float_conv, weight_qparams):
+        return _ConvTransposeNd.from_float(cls, float_conv, weight_qparams)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/linear.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a090c583b6b82dd50390fb8b12201b98c3d8b44
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/linear.py
@@ -0,0 +1,68 @@
+# mypy: allow-untyped-defs
+from typing import Any, Dict, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .utils import ReferenceQuantizedModule
+
+
+__all__ = ["Linear"]
+
+
+class Linear(nn.Linear, ReferenceQuantizedModule):
+    """A reference quantized linear module that fits into the FX
+    Graph Mode Quantization workflow
+    activation will be floating point Tensor, we will store floating
+    point weight as well in the module, but in forward we'll quantize
+    and dequantize the weight before running the floating point functional
+    linear operator.
+    """
+
+    _IS_REFERENCE = True
+
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        bias_: bool = True,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+        weight_qparams: Optional[Dict[str, Any]] = None,
+    ):
+        super().__init__(in_features, out_features, bias_, device, dtype)
+        self._init_weight_qparams(weight_qparams, device)
+
+    def _get_name(self):
+        return "QuantizedLinear(Reference)"
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        we have:
+        w(float) -- quant - dequant \
+        x(float) ------------- F.linear ---
+
+        In the full model, we will see
+        w(float) -- quant - *dequant \
+        x -- quant --- *dequant --  *F.linear --- *quant - dequant
+        and the backend should be able to fuse the ops with `*` into a quantized linear
+        """
+        weight_quant_dequant = self.get_weight()
+        result = F.linear(x, weight_quant_dequant, self.bias)
+        return result
+
+    @classmethod
+    def from_float(cls, float_linear, weight_qparams):
+        qref_linear = Linear(
+            float_linear.in_features,
+            float_linear.out_features,
+            float_linear.bias is not None,
+            device=float_linear.weight.device,
+            dtype=float_linear.weight.dtype,
+            weight_qparams=weight_qparams,
+        )
+        qref_linear.weight = torch.nn.Parameter(float_linear.weight.detach())
+        if float_linear.bias is not None:
+            qref_linear.bias = torch.nn.Parameter(float_linear.bias.detach())
+        return qref_linear
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/rnn.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/rnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..9479e3e1a63f48bef1093b3294cd265d5082c3f2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/rnn.py
@@ -0,0 +1,845 @@
+# mypy: allow-untyped-defs
+from typing import Any, Dict, Optional, Tuple
+
+import torch
+import torch.nn as nn
+from torch import _VF, Tensor
+from torch.nn.utils.rnn import PackedSequence
+
+from .utils import _quantize_and_dequantize_weight, _quantize_weight
+
+
+__all__ = [
+    "RNNCellBase",
+    "RNNCell",
+    "LSTMCell",
+    "GRUCell",
+    "RNNBase",
+    "LSTM",
+    "GRU",
+    "get_quantized_weight",
+]
+
+
+def _apply_permutation(tensor: Tensor, permutation: Tensor, dim: int = 1) -> Tensor:
+    return tensor.index_select(dim, permutation)
+
+
+def _get_weight_and_quantization_params(module, wn):
+    weight = getattr(module, wn)
+    params = [weight]
+    for param_name in [
+        wn + n for n in ["_qscheme", "_dtype", "_scale", "_zero_point", "_axis_int"]
+    ]:
+        if hasattr(module, param_name):
+            param = getattr(module, param_name)
+        else:
+            param = None
+        params.append(param)
+    return params
+
+
+def get_quantized_weight(module, wn):
+    if not hasattr(module, wn):
+        return None
+    params = _get_weight_and_quantization_params(module, wn)
+    weight = _quantize_weight(*params)
+    return weight
+
+
+def _get_quantize_and_dequantized_weight(module, wn):
+    if not hasattr(module, wn):
+        return None
+    params = _get_weight_and_quantization_params(module, wn)
+    weight = _quantize_and_dequantize_weight(*params)
+    return weight
+
+
+class RNNCellBase(nn.RNNCellBase):
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int,
+        bias: bool,
+        num_chunks: int,
+        device=None,
+        dtype=None,
+        weight_qparams_dict=None,
+    ) -> None:
+        super().__init__(
+            input_size, hidden_size, bias, num_chunks, device=device, dtype=dtype
+        )
+        # TODO(jerryzh168): maybe make this arg a required arg
+        if weight_qparams_dict is None:
+            weight_qparams = {
+                "qscheme": torch.per_tensor_affine,
+                "dtype": torch.quint8,
+                "scale": 1.0,
+                "zero_point": 0,
+            }
+            weight_qparams_dict = {
+                "weight_ih": weight_qparams,
+                "weight_hh": weight_qparams,
+                "is_decomposed": False,
+            }
+        assert (
+            len(weight_qparams_dict) == 3
+        ), "Expected length for weight_qparams_dict to be 3 for QuantizedRNNCellBase(Reference)"
+        self._init_weight_qparams_dict(weight_qparams_dict, device)
+
+    def _init_weight_qparams_dict(self, weight_qparams_dict, device):
+        assert weight_qparams_dict is not None
+        self.is_decomposed = weight_qparams_dict["is_decomposed"]
+        for key, weight_qparams in weight_qparams_dict.items():
+            if key == "is_decomposed":
+                continue
+            # TODO: refactor the duplicated code to utils.py
+            weight_qscheme = weight_qparams["qscheme"]
+            weight_dtype = weight_qparams["dtype"]
+            setattr(self, key + "_qscheme", weight_qscheme)
+            setattr(self, key + "_dtype", weight_dtype)
+            assert weight_qscheme in [
+                None,
+                torch.per_tensor_affine,
+                torch.per_channel_affine,
+            ], Exception(
+                f"qscheme: {weight_qscheme} is not support in {self._get_name()}"
+            )
+            if weight_qscheme is not None:
+                scale = weight_qparams["scale"]
+                scale_tensor = (
+                    scale.clone().detach()
+                    if isinstance(scale, torch.Tensor)
+                    else torch.tensor(scale, dtype=torch.float, device=device)
+                )
+                self.register_buffer(key + "_scale", scale_tensor)
+                zp = weight_qparams["zero_point"]
+                zp_tensor = (
+                    zp.clone().detach()
+                    if isinstance(zp, torch.Tensor)
+                    else torch.tensor(zp, dtype=torch.int, device=device)
+                )
+                self.register_buffer(key + "_zero_point", zp_tensor)
+                if weight_qscheme == torch.per_channel_affine:
+                    axis = weight_qparams["axis"]
+                    axis_tensor = (
+                        axis.clone().detach()
+                        if isinstance(axis, torch.Tensor)
+                        else torch.tensor(axis, dtype=torch.int, device=device)
+                    )
+                    self.register_buffer(key + "_axis", axis_tensor)
+                else:
+                    # added for TorchScriptability, not used
+                    self.register_buffer(
+                        key + "_axis", torch.tensor(0, dtype=torch.int, device=device)
+                    )
+                setattr(self, key + "_axis_int", getattr(self, key + "_axis").item())
+
+    def _get_name(self):
+        return "QuantizedRNNCellBase(Reference)"
+
+    def get_quantized_weight_ih(self):
+        return get_quantized_weight(self, "weight_ih")
+
+    def get_quantized_weight_hh(self):
+        return get_quantized_weight(self, "weight_hh")
+
+    def get_weight_ih(self):
+        return _get_quantize_and_dequantized_weight(self, "weight_ih")
+
+    def get_weight_hh(self):
+        return _get_quantize_and_dequantized_weight(self, "weight_hh")
+
+
+class RNNCell(RNNCellBase):
+    """
+    We'll store weight_qparams for all the weights (weight_ih and weight_hh),
+    we need to pass in a `weight_qparams_dict` that maps from weight name,
+    e.g. weight_ih, to the weight_qparams for that weight
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int,
+        bias: bool = True,
+        nonlinearity: str = "tanh",
+        device=None,
+        dtype=None,
+        weight_qparams_dict: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        factory_kwargs = {
+            "device": device,
+            "dtype": dtype,
+            "weight_qparams_dict": weight_qparams_dict,
+        }
+        super().__init__(input_size, hidden_size, bias, num_chunks=1, **factory_kwargs)
+        self.nonlinearity = nonlinearity
+
+    def _get_name(self):
+        return "QuantizedRNNCell(Reference)"
+
+    # TODO: refactor nn.RNNCell to have a _forward that takes weight_ih and weight_hh as input
+    # and remove duplicated code, same for the other two Cell modules
+    def forward(self, input: Tensor, hx: Optional[Tensor] = None) -> Tensor:
+        assert input.dim() in (
+            1,
+            2,
+        ), f"RNNCell: Expected input to be 1-D or 2-D but received {input.dim()}-D tensor"
+        is_batched = input.dim() == 2
+        if not is_batched:
+            input = input.unsqueeze(0)
+
+        if hx is None:
+            hx = torch.zeros(
+                input.size(0), self.hidden_size, dtype=input.dtype, device=input.device
+            )
+        else:
+            hx = hx.unsqueeze(0) if not is_batched else hx
+
+        if self.nonlinearity == "tanh":
+            ret = _VF.rnn_tanh_cell(
+                input,
+                hx,
+                self.get_weight_ih(),
+                self.get_weight_hh(),
+                self.bias_ih,
+                self.bias_hh,
+            )
+        elif self.nonlinearity == "relu":
+            ret = _VF.rnn_relu_cell(
+                input,
+                hx,
+                self.get_weight_ih(),
+                self.get_weight_hh(),
+                self.bias_ih,
+                self.bias_hh,
+            )
+        else:
+            ret = input  # TODO: remove when jit supports exception flow
+            raise RuntimeError(f"Unknown nonlinearity: {self.nonlinearity}")
+
+        if not is_batched:
+            ret = ret.squeeze(0)
+
+        return ret
+
+    @classmethod
+    def from_float(cls, mod, weight_qparams_dict):
+        ref_mod = cls(
+            mod.input_size,
+            mod.hidden_size,
+            mod.bias,
+            mod.nonlinearity,
+            mod.weight_ih.device,
+            mod.weight_ih.dtype,
+            weight_qparams_dict,
+        )
+        ref_mod.weight_ih = mod.weight_ih
+        ref_mod.weight_hh = mod.weight_hh
+        ref_mod.bias_ih = mod.bias_ih
+        ref_mod.bias_hh = mod.bias_hh
+        return ref_mod
+
+
+class LSTMCell(RNNCellBase):
+    """
+    We'll store weight_qparams for all the weights (weight_ih and weight_hh),
+    we need to pass in a `weight_qparams_dict` that maps from weight name,
+    e.g. weight_ih, to the weight_qparams for that weight
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int,
+        bias: bool = True,
+        device=None,
+        dtype=None,
+        weight_qparams_dict: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        factory_kwargs = {
+            "device": device,
+            "dtype": dtype,
+            "weight_qparams_dict": weight_qparams_dict,
+        }
+        super().__init__(input_size, hidden_size, bias, num_chunks=4, **factory_kwargs)
+
+    def _get_name(self):
+        return "QuantizedLSTMCell(Reference)"
+
+    def forward(
+        self, input: Tensor, hx: Optional[Tuple[Tensor, Tensor]] = None
+    ) -> Tuple[Tensor, Tensor]:
+        assert input.dim() in (
+            1,
+            2,
+        ), f"LSTMCell: Expected input to be 1-D or 2-D but received {input.dim()}-D tensor"
+        is_batched = input.dim() == 2
+        if not is_batched:
+            input = input.unsqueeze(0)
+
+        if hx is None:
+            zeros = torch.zeros(
+                input.size(0), self.hidden_size, dtype=input.dtype, device=input.device
+            )
+            hx = (zeros, zeros)
+        else:
+            hx = (hx[0].unsqueeze(0), hx[1].unsqueeze(0)) if not is_batched else hx
+
+        ret = _VF.lstm_cell(
+            input,
+            hx,
+            self.get_weight_ih(),
+            self.get_weight_hh(),
+            self.bias_ih,
+            self.bias_hh,
+        )
+
+        if not is_batched:
+            ret = (ret[0].squeeze(0), ret[1].squeeze(0))
+        return ret
+
+    @classmethod
+    def from_float(cls, mod, weight_qparams_dict, use_precomputed_fake_quant=False):
+        ref_mod = cls(
+            mod.input_size,
+            mod.hidden_size,
+            mod.bias,
+            mod.weight_ih.device,
+            mod.weight_ih.dtype,
+            weight_qparams_dict,
+        )
+        ref_mod.weight_ih = mod.weight_ih
+        ref_mod.weight_hh = mod.weight_hh
+        ref_mod.bias_ih = mod.bias_ih
+        ref_mod.bias_hh = mod.bias_hh
+        return ref_mod
+
+
+class GRUCell(RNNCellBase):
+    """
+    We'll store weight_qparams for all the weights (weight_ih and weight_hh),
+    we need to pass in a `weight_qparams_dict` that maps from weight name,
+    e.g. weight_ih, to the weight_qparams for that weight
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int,
+        bias: bool = True,
+        device=None,
+        dtype=None,
+        weight_qparams_dict: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        factory_kwargs = {
+            "device": device,
+            "dtype": dtype,
+            "weight_qparams_dict": weight_qparams_dict,
+        }
+        super().__init__(input_size, hidden_size, bias, num_chunks=3, **factory_kwargs)
+
+    def _get_name(self):
+        return "QuantizedGRUCell(Reference)"
+
+    def forward(self, input: Tensor, hx: Optional[Tensor] = None) -> Tensor:
+        assert input.dim() in (
+            1,
+            2,
+        ), f"GRUCell: Expected input to be 1-D or 2-D but received {input.dim()}-D tensor"
+        is_batched = input.dim() == 2
+        if not is_batched:
+            input = input.unsqueeze(0)
+
+        if hx is None:
+            hx = torch.zeros(
+                input.size(0), self.hidden_size, dtype=input.dtype, device=input.device
+            )
+        else:
+            hx = hx.unsqueeze(0) if not is_batched else hx
+
+        ret = _VF.gru_cell(
+            input,
+            hx,
+            self.get_weight_ih(),
+            self.get_weight_hh(),
+            self.bias_ih,
+            self.bias_hh,
+        )
+
+        if not is_batched:
+            ret = ret.squeeze(0)
+
+        return ret
+
+    @classmethod
+    def from_float(cls, mod, weight_qparams_dict):
+        ref_mod = cls(
+            mod.input_size,
+            mod.hidden_size,
+            mod.bias,
+            mod.weight_ih.device,
+            mod.weight_ih.dtype,
+            weight_qparams_dict,
+        )
+        ref_mod.weight_ih = mod.weight_ih
+        ref_mod.weight_hh = mod.weight_hh
+        ref_mod.bias_ih = mod.bias_ih
+        ref_mod.bias_hh = mod.bias_hh
+        return ref_mod
+
+
+class RNNBase(nn.RNNBase):
+    def __init__(
+        self,
+        mode: str,
+        input_size: int,
+        hidden_size: int,
+        num_layers: int = 1,
+        bias: bool = True,
+        batch_first: bool = False,
+        dropout: float = 0.0,
+        bidirectional: bool = False,
+        proj_size: int = 0,
+        device=None,
+        dtype=None,
+        weight_qparams_dict: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        super().__init__(
+            mode,
+            input_size,
+            hidden_size,
+            num_layers,
+            bias,
+            batch_first,
+            dropout,
+            bidirectional,
+            proj_size,
+            device,
+            dtype,
+        )
+        # TODO(jerryzh168): maybe make this arg a required arg
+        if weight_qparams_dict is None:
+            weight_qparams = {
+                "qscheme": torch.per_tensor_affine,
+                "dtype": torch.quint8,
+                "scale": 1.0,
+                "zero_point": 0,
+            }
+            weight_qparams_dict = {"is_decomposed": False}  # type: ignore[dict-item]
+            for wn in self._flat_weights_names:
+                if wn.startswith("weight"):
+                    weight_qparams_dict[wn] = weight_qparams
+        self._init_weight_qparams_dict(weight_qparams_dict, device)
+
+    def _init_weight_qparams_dict(self, weight_qparams_dict, device):
+        self.is_decomposed = weight_qparams_dict["is_decomposed"]
+        for key, weight_qparams in weight_qparams_dict.items():
+            if key == "is_decomposed":
+                continue
+            weight_qscheme = weight_qparams["qscheme"]
+            weight_dtype = weight_qparams["dtype"]
+            setattr(self, key + "_qscheme", weight_qscheme)
+            setattr(self, key + "_dtype", weight_dtype)
+            assert weight_qscheme in [
+                None,
+                torch.per_tensor_affine,
+                torch.per_channel_affine,
+            ], Exception(
+                f"qscheme: {weight_qscheme} is not support in {self._get_name()}"
+            )
+            if weight_qscheme is not None:
+                self.register_buffer(
+                    key + "_scale",
+                    torch.tensor(
+                        weight_qparams["scale"], dtype=torch.float, device=device
+                    ),
+                )
+                self.register_buffer(
+                    key + "_zero_point",
+                    torch.tensor(
+                        weight_qparams["zero_point"], dtype=torch.int, device=device
+                    ),
+                )
+                if weight_qscheme == torch.per_channel_affine:
+                    self.register_buffer(
+                        key + "_axis",
+                        torch.tensor(
+                            weight_qparams["axis"], dtype=torch.int, device=device
+                        ),
+                    )
+                else:
+                    # added for TorchScriptability, not used
+                    self.register_buffer(
+                        key + "_axis", torch.tensor(0, dtype=torch.int, device=device)
+                    )
+                setattr(self, key + "_axis_int", getattr(self, key + "_axis").item())
+
+
+class LSTM(RNNBase):
+    """Reference Quantized LSTM Module
+    We'll store weight_qparams for all the weights in _flat_weights, we need to pass in
+    a `weight_qparams_dict` that maps from weight name, e.g. weight_ih_l0,
+    to the weight_qparams for that weight
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__("LSTM", *args, **kwargs)
+
+    # Same as above, see torch/nn/modules/module.py::_forward_unimplemented
+    def permute_hidden(  # type: ignore[override]
+        self,
+        hx: Tuple[Tensor, Tensor],
+        permutation: Optional[Tensor],
+    ) -> Tuple[Tensor, Tensor]:
+        if permutation is None:
+            return hx
+        return _apply_permutation(hx[0], permutation), _apply_permutation(
+            hx[1], permutation
+        )
+
+    def get_expected_cell_size(
+        self, input: Tensor, batch_sizes: Optional[Tensor]
+    ) -> Tuple[int, int, int]:
+        if batch_sizes is not None:
+            mini_batch = int(batch_sizes[0])
+        else:
+            mini_batch = input.size(0) if self.batch_first else input.size(1)
+        num_directions = 2 if self.bidirectional else 1
+        expected_hidden_size = (
+            self.num_layers * num_directions,
+            mini_batch,
+            self.hidden_size,
+        )
+        return expected_hidden_size
+
+    # In the future, we should prevent mypy from applying contravariance rules here.
+    # See torch/nn/modules/module.py::_forward_unimplemented
+    def check_forward_args(  # type: ignore[override]
+        self,
+        input: Tensor,
+        hidden: Tuple[Tensor, Tensor],
+        batch_sizes: Optional[Tensor],
+    ):
+        self.check_input(input, batch_sizes)
+        self.check_hidden_size(
+            hidden[0],
+            self.get_expected_hidden_size(input, batch_sizes),
+            "Expected hidden[0] size {}, got {}",
+        )
+        self.check_hidden_size(
+            hidden[1],
+            self.get_expected_cell_size(input, batch_sizes),
+            "Expected hidden[1] size {}, got {}",
+        )
+
+    def get_quantized_weight_bias_dict(self):
+        """dictionary from flat_weight_name to quantized weight or (unquantized) bias
+        e.g.
+        {
+          "weight_ih_l0": quantized_weight,
+          "bias_ih_l0": unquantized_bias,
+          ...
+        }
+        """
+        quantized_weight_bias_dict = {}
+        for wn in self._flat_weights_names:
+            if hasattr(self, wn):
+                if wn.startswith("weight"):
+                    weight_or_bias = get_quantized_weight(self, wn)
+                else:
+                    weight_or_bias = getattr(self, wn)
+            else:
+                weight_or_bias = None
+            quantized_weight_bias_dict[wn] = weight_or_bias
+        return quantized_weight_bias_dict
+
+    def get_flat_weights(self):
+        flat_weights = []
+        for wn in self._flat_weights_names:
+            if hasattr(self, wn):
+                weight = getattr(self, wn)
+                if wn.startswith("weight"):
+                    params = _get_weight_and_quantization_params(self, wn)
+                    weight = _quantize_and_dequantize_weight(*params)
+            else:
+                weight = None
+            flat_weights.append(weight)
+        return flat_weights
+
+    def forward(self, input, hx=None):  # noqa: F811
+        orig_input = input
+        # xxx: isinstance check needs to be in conditional for TorchScript to compile
+        batch_sizes = None
+        if isinstance(orig_input, PackedSequence):
+            input, batch_sizes, sorted_indices, unsorted_indices = input
+            max_batch_size = int(batch_sizes[0])
+        else:
+            batch_sizes = None
+            is_batched = input.dim() == 3
+            batch_dim = 0 if self.batch_first else 1
+            if not is_batched:
+                input = input.unsqueeze(batch_dim)
+            max_batch_size = input.size(0) if self.batch_first else input.size(1)
+            sorted_indices = None
+            unsorted_indices = None
+
+        if hx is None:
+            num_directions = 2 if self.bidirectional else 1
+            real_hidden_size = (
+                self.proj_size if self.proj_size > 0 else self.hidden_size
+            )
+            h_zeros = torch.zeros(
+                self.num_layers * num_directions,
+                max_batch_size,
+                real_hidden_size,
+                dtype=input.dtype,
+                device=input.device,
+            )
+            c_zeros = torch.zeros(
+                self.num_layers * num_directions,
+                max_batch_size,
+                self.hidden_size,
+                dtype=input.dtype,
+                device=input.device,
+            )
+            hx = (h_zeros, c_zeros)
+        else:
+            if batch_sizes is None:  # If not PackedSequence input.
+                if is_batched:  # type: ignore[possibly-undefined]
+                    if hx[0].dim() != 3 or hx[1].dim() != 3:
+                        msg = (
+                            "For batched 3-D input, hx and cx should "
+                            f"also be 3-D but got ({hx[0].dim()}-D, {hx[1].dim()}-D) tensors"
+                        )
+                        raise RuntimeError(msg)
+                else:
+                    if hx[0].dim() != 2 or hx[1].dim() != 2:
+                        msg = (
+                            "For unbatched 2-D input, hx and cx should "
+                            f"also be 2-D but got ({hx[0].dim()}-D, {hx[1].dim()}-D) tensors"
+                        )
+                        raise RuntimeError(msg)
+                    hx = (hx[0].unsqueeze(1), hx[1].unsqueeze(1))
+
+            # Each batch of the hidden state should match the input sequence that
+            # the user believes he/she is passing in.
+            hx = self.permute_hidden(hx, sorted_indices)
+
+        self.check_forward_args(input, hx, batch_sizes)
+        if batch_sizes is None:
+            result = _VF.lstm(
+                input,
+                hx,
+                self.get_flat_weights(),
+                self.bias,
+                self.num_layers,
+                self.dropout,
+                self.training,
+                self.bidirectional,
+                self.batch_first,
+            )
+        else:
+            result = _VF.lstm(
+                input,
+                batch_sizes,
+                hx,
+                self.get_flat_weights(),
+                self.bias,
+                self.num_layers,
+                self.dropout,
+                self.training,
+                self.bidirectional,
+            )
+        output = result[0]
+        hidden = result[1:]
+        # xxx: isinstance check needs to be in conditional for TorchScript to compile
+        if isinstance(orig_input, PackedSequence):
+            output_packed = PackedSequence(
+                output, batch_sizes, sorted_indices, unsorted_indices
+            )
+            return output_packed, self.permute_hidden(hidden, unsorted_indices)
+        else:
+            if not is_batched:  # type: ignore[possibly-undefined]
+                output = output.squeeze(batch_dim)  # type: ignore[possibly-undefined]
+                hidden = (hidden[0].squeeze(1), hidden[1].squeeze(1))
+            return output, self.permute_hidden(hidden, unsorted_indices)
+
+    def _get_name(self):
+        return "QuantizedLSTM(Reference)"
+
+    @classmethod
+    def from_float(cls, mod, weight_qparams_dict):
+        ref_mod = cls(
+            mod.input_size,
+            mod.hidden_size,
+            mod.num_layers,
+            mod.bias,
+            mod.batch_first,
+            mod.dropout,
+            mod.bidirectional,
+            weight_qparams_dict=weight_qparams_dict,
+        )
+        for wn in mod._flat_weights_names:
+            setattr(ref_mod, wn, getattr(mod, wn))
+        return ref_mod
+
+
+class GRU(RNNBase):
+    """Reference Quantized GRU Module
+    We'll store weight_qparams for all the weights in _flat_weights, we need to pass in
+    a `weight_qparams_dict` that maps from weight name, e.g. weight_ih_l0,
+    to the weight_qparams for that weight
+    """
+
+    def __init__(self, *args, **kwargs):
+        if "proj_size" in kwargs:
+            raise ValueError(
+                "proj_size argument is only supported for LSTM, not RNN or GRU"
+            )
+        super().__init__("GRU", *args, **kwargs)
+
+    def get_quantized_weight_bias_dict(self):
+        """dictionary from flat_weight_name to quantized weight or (unquantized) bias
+        e.g.
+        {
+          "weight_ih_l0": quantized_weight,
+          "bias_ih_l0": unquantized_bias,
+          ...
+        }
+        """
+        quantized_weight_bias_dict = {}
+        for wn in self._flat_weights_names:
+            if hasattr(self, wn):
+                if wn.startswith("weight"):
+                    weight_or_bias = get_quantized_weight(self, wn)
+                else:
+                    weight_or_bias = getattr(self, wn)
+            else:
+                weight_or_bias = None
+            quantized_weight_bias_dict[wn] = weight_or_bias
+        return quantized_weight_bias_dict
+
+    def get_flat_weights(self):
+        flat_weights = []
+        for wn in self._flat_weights_names:
+            if hasattr(self, wn):
+                weight = getattr(self, wn)
+                if wn.startswith("weight"):
+                    params = _get_weight_and_quantization_params(self, wn)
+                    weight = _quantize_and_dequantize_weight(*params)
+            else:
+                weight = None
+            flat_weights.append(weight)
+        return flat_weights
+
+    def forward(self, input, hx=None):  # noqa: F811
+        # Note: this is copied from the forward of GRU in https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/rnn.py
+        # only changed self._flat_weights to self.get_flat_weights()
+        # TODO: maybe we can try inheriting from that class and define get_flat_weights
+        # as a @property? this might interfere with TorchScript, if we remove that
+        # requirement in the future we should be able to do this
+        orig_input = input
+        # xxx: isinstance check needs to be in conditional for TorchScript to compile
+        if isinstance(orig_input, PackedSequence):
+            input, batch_sizes, sorted_indices, unsorted_indices = input
+            max_batch_size = int(batch_sizes[0])
+        else:
+            batch_sizes = None
+            assert input.dim() in (
+                2,
+                3,
+            ), f"GRU: Expected input to be 2-D or 3-D but received {input.dim()}-D tensor"
+            is_batched = input.dim() == 3
+            batch_dim = 0 if self.batch_first else 1
+            if not is_batched:
+                input = input.unsqueeze(batch_dim)
+                if hx is not None:
+                    if hx.dim() != 2:
+                        raise RuntimeError(
+                            f"For unbatched 2-D input, hx should also be 2-D but got {hx.dim()}-D tensor"
+                        )
+                    hx = hx.unsqueeze(1)
+            else:
+                if hx is not None and hx.dim() != 3:
+                    raise RuntimeError(
+                        f"For batched 3-D input, hx should also be 3-D but got {hx.dim()}-D tensor"
+                    )
+            max_batch_size = input.size(0) if self.batch_first else input.size(1)
+            sorted_indices = None
+            unsorted_indices = None
+
+        if hx is None:
+            num_directions = 2 if self.bidirectional else 1
+            hx = torch.zeros(
+                self.num_layers * num_directions,
+                max_batch_size,
+                self.hidden_size,
+                dtype=input.dtype,
+                device=input.device,
+            )
+        else:
+            # Each batch of the hidden state should match the input sequence that
+            # the user believes he/she is passing in.
+            hx = self.permute_hidden(hx, sorted_indices)
+
+        self.check_forward_args(input, hx, batch_sizes)
+        if batch_sizes is None:
+            result = _VF.gru(
+                input,
+                hx,
+                self.get_flat_weights(),
+                self.bias,
+                self.num_layers,
+                self.dropout,
+                self.training,
+                self.bidirectional,
+                self.batch_first,
+            )
+        else:
+            result = _VF.gru(
+                input,
+                batch_sizes,
+                hx,
+                self.get_flat_weights(),
+                self.bias,
+                self.num_layers,
+                self.dropout,
+                self.training,
+                self.bidirectional,
+            )
+        output = result[0]
+        hidden = result[1]
+
+        # xxx: isinstance check needs to be in conditional for TorchScript to compile
+        if isinstance(orig_input, PackedSequence):
+            output_packed = PackedSequence(
+                output, batch_sizes, sorted_indices, unsorted_indices
+            )
+            return output_packed, self.permute_hidden(hidden, unsorted_indices)
+        else:
+            if not is_batched:  # type: ignore[possibly-undefined]
+                output = output.squeeze(batch_dim)  # type: ignore[possibly-undefined]
+                hidden = hidden.squeeze(1)
+
+            return output, self.permute_hidden(hidden, unsorted_indices)
+
+    def _get_name(self):
+        return "QuantizedGRU(Reference)"
+
+    @classmethod
+    def from_float(cls, mod, weight_qparams_dict):
+        ref_mod = cls(
+            mod.input_size,
+            mod.hidden_size,
+            mod.num_layers,
+            mod.bias,
+            mod.batch_first,
+            mod.dropout,
+            mod.bidirectional,
+            weight_qparams_dict=weight_qparams_dict,
+        )
+        for wn in mod._flat_weights_names:
+            setattr(ref_mod, wn, getattr(mod, wn))
+        return ref_mod
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/sparse.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/sparse.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea2e7e1f16b4fb9d6a3644d297f6de299819c99a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/sparse.py
@@ -0,0 +1,162 @@
+# mypy: allow-untyped-defs
+from typing import Any, Dict, Optional
+
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+
+from .utils import ReferenceQuantizedModule
+
+
+__all__ = ["Embedding", "EmbeddingBag"]
+
+
+class Embedding(nn.Embedding, ReferenceQuantizedModule):
+    """A reference quantized Embedding module that fits into the
+    FX Graph Mode Quantization workflow, activation will be floating point Tensor,
+    we will store floating point weight as well in the module, but in forward we'll
+    quantize and dequantize the weight before running the floating point functional
+    embedding operator.
+    """
+
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+        padding_idx: Optional[int] = None,
+        max_norm: Optional[float] = None,
+        norm_type: float = 2.0,
+        scale_grad_by_freq: bool = False,
+        sparse: bool = False,
+        _weight: Optional[Tensor] = None,
+        device=None,
+        dtype=None,
+        weight_qparams: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        super().__init__(
+            num_embeddings,
+            embedding_dim,
+            padding_idx,
+            max_norm,
+            norm_type,
+            scale_grad_by_freq,
+            sparse,
+            _weight,
+            device,
+            dtype,
+        )
+        self._init_weight_qparams(weight_qparams, device)
+
+    def _get_name(self):
+        return "QuantizedEmbedding(Reference)"
+
+    def forward(self, input: Tensor) -> Tensor:
+        weight_quant_dequant = self.get_weight()
+        return F.embedding(
+            input,
+            weight_quant_dequant,
+            self.padding_idx,
+            self.max_norm,
+            self.norm_type,
+            self.scale_grad_by_freq,
+            self.sparse,
+        )
+
+    @classmethod
+    def from_float(cls, mod, weight_qparams):
+        return cls(
+            mod.num_embeddings,
+            mod.embedding_dim,
+            mod.padding_idx,
+            mod.max_norm,
+            mod.norm_type,
+            mod.scale_grad_by_freq,
+            mod.sparse,
+            mod.weight,
+            mod.weight.device,
+            mod.weight.dtype,
+            weight_qparams,
+        )
+
+
+class EmbeddingBag(nn.EmbeddingBag, ReferenceQuantizedModule):
+    """A reference quantized EmbeddingBag module that fits into the
+    FX Graph Mode Quantization workflow, activation will be floating point Tensor,
+    we will store floating point weight as well in the module, but in forward we'll
+    quantize and dequantize the weight before running the floating point functional
+    embedding operator.
+    """
+
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+        max_norm: Optional[float] = None,
+        norm_type: float = 2.0,
+        scale_grad_by_freq: bool = False,
+        mode: str = "mean",
+        sparse: bool = False,
+        _weight: Optional[Tensor] = None,
+        include_last_offset: bool = False,
+        padding_idx: Optional[int] = None,
+        device=None,
+        dtype=None,
+        weight_qparams: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        super().__init__(
+            num_embeddings,
+            embedding_dim,
+            max_norm,
+            norm_type,
+            scale_grad_by_freq,
+            mode,
+            sparse,
+            _weight,
+            include_last_offset,
+            padding_idx,
+            device,
+            dtype,
+        )
+        self._init_weight_qparams(weight_qparams, device)
+
+    def _get_name(self):
+        return "QuantizedEmbedding(Reference)"
+
+    def forward(
+        self,
+        input: Tensor,
+        offsets: Optional[Tensor] = None,
+        per_sample_weights: Optional[Tensor] = None,
+    ) -> Tensor:
+        weight_quant_dequant = self.get_weight()
+        return F.embedding_bag(
+            input,
+            weight_quant_dequant,
+            offsets,
+            self.max_norm,
+            self.norm_type,
+            self.scale_grad_by_freq,
+            self.mode,
+            self.sparse,
+            per_sample_weights,
+            self.include_last_offset,
+            self.padding_idx,
+        )
+
+    @classmethod
+    def from_float(cls, mod, weight_qparams, use_precomputed_fake_quant=False):
+        return cls(
+            mod.num_embeddings,
+            mod.embedding_dim,
+            mod.max_norm,
+            mod.norm_type,
+            mod.scale_grad_by_freq,
+            mod.mode,
+            mod.sparse,
+            mod.weight,
+            mod.include_last_offset,
+            mod.padding_idx,
+            mod.weight.device,
+            mod.weight.dtype,
+            weight_qparams,
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..85b183c267d557a9316087a9caca612b37e2f8ae
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/quantized/reference/modules/utils.py
@@ -0,0 +1,431 @@
+# mypy: allow-untyped-defs
+import typing
+
+import torch
+
+
+__all__ = [
+    "ReferenceQuantizedModule",
+]
+
+
+class ReferenceQuantizedModule(torch.nn.Module):
+    def _init_weight_qparams(self, weight_qparams, device):
+        if weight_qparams is None:
+            weight_qparams = {
+                "qscheme": torch.per_tensor_affine,
+                "dtype": torch.quint8,
+                "scale": 1.0,
+                "zero_point": 0,
+            }
+        self.weight_qscheme: torch.qscheme = weight_qparams["qscheme"]
+        self.weight_dtype = weight_qparams["dtype"]
+        assert self.weight_qscheme in [
+            None,
+            torch.per_tensor_affine,
+            torch.per_channel_affine,
+            torch.per_channel_affine_float_qparams,
+        ], f"qscheme: {self.weight_qscheme} is not support in reference quantized {self._get_name()}"
+        if self.weight_dtype in [
+            torch.quint8,
+            torch.qint8,
+            torch.quint4x2,
+            torch.qint32,
+        ]:
+            zero_point_dtype = (
+                weight_qparams["zero_point"].dtype
+                if isinstance(weight_qparams["zero_point"], torch.Tensor)
+                else torch.int
+            )
+            w_scale = weight_qparams["scale"]
+            w_scale_tensor = (
+                w_scale.clone().detach()
+                if isinstance(w_scale, torch.Tensor)
+                else torch.tensor(w_scale, dtype=torch.float, device=device)
+            )
+            self.register_buffer("weight_scale", w_scale_tensor)
+            w_zp = weight_qparams["zero_point"]
+            w_zp_tensor = (
+                w_zp.clone().detach()
+                if isinstance(w_zp, torch.Tensor)
+                else torch.tensor(w_zp, dtype=zero_point_dtype, device=device)
+            )
+            self.register_buffer("weight_zero_point", w_zp_tensor)
+            if self.weight_qscheme in [
+                torch.per_channel_affine,
+                torch.per_channel_affine_float_qparams,
+            ]:
+                w_axis = weight_qparams["axis"]
+                w_axis_tensor = (
+                    w_axis.clone().detach()
+                    if isinstance(w_axis, torch.Tensor)
+                    else torch.tensor(w_axis, dtype=torch.int, device=device)
+                )
+                self.register_buffer("weight_axis", w_axis_tensor)
+            else:
+                # added for TorchScriptability, not used
+                self.register_buffer(
+                    "weight_axis", torch.tensor(0, dtype=torch.int, device=device)
+                )
+        else:
+            # added for TorchScriptability, and for torch.float
+            self.register_buffer(
+                "weight_scale", torch.tensor(1.0, dtype=torch.float, device=device)
+            )
+            self.register_buffer(
+                "weight_zero_point", torch.tensor(0, dtype=torch.int, device=device)
+            )
+            self.register_buffer(
+                "weight_axis", torch.tensor(0, dtype=torch.int, device=device)
+            )
+        self.is_decomposed: bool = weight_qparams.get("is_decomposed", False)
+        # store weight_axis as weight_axis_int due to some constraints of torchdynamo.export
+        # for capturing `.item` operations
+        self.weight_axis_int: int = self.weight_axis.item()  # type: ignore[operator, assignment]
+        self.weight_quant_min: typing.Optional[int] = weight_qparams.get(
+            "quant_min", None
+        )
+        self.weight_quant_max: typing.Optional[int] = weight_qparams.get(
+            "quant_max", None
+        )
+
+    def get_weight(self):
+        """
+        Fake quantize (quantize and dequantize) the weight with
+        the quantization parameters for weight, this is used to
+        simulate the numerics for the quantized weight in a quantized
+        model
+        """
+        # suppress mypy warning
+        assert isinstance(self.weight_scale, torch.Tensor)
+        assert isinstance(self.weight_zero_point, torch.Tensor)
+        if self.is_decomposed:
+            return _quantize_and_dequantize_weight_decomposed(
+                self.weight,  # type: ignore[arg-type]
+                self.weight_qscheme,
+                self.weight_dtype,
+                self.weight_scale,
+                self.weight_zero_point,
+                self.weight_axis_int,
+                self.weight_quant_min,
+                self.weight_quant_max,
+            )
+        else:
+            return _quantize_and_dequantize_weight(
+                self.weight,  # type: ignore[arg-type]
+                self.weight_qscheme,
+                self.weight_dtype,
+                self.weight_scale,
+                self.weight_zero_point,
+                self.weight_axis_int,
+            )
+
+    def get_quantized_weight(self):
+        # suppress mypy warning
+        assert isinstance(self.weight_scale, torch.Tensor)
+        assert isinstance(self.weight_zero_point, torch.Tensor)
+        # assert isinstance(self.weight_axis, torch.Tensor)
+        if self.is_decomposed:
+            return _quantize_weight_decomposed(
+                self.weight,  # type: ignore[arg-type]
+                self.weight_qscheme,
+                self.weight_dtype,
+                self.weight_scale,
+                self.weight_zero_point,
+                self.weight_axis_int,
+                self.weight_quant_min,
+                self.weight_quant_max,
+            )
+        else:
+            return _quantize_weight(
+                self.weight,  # type: ignore[arg-type]
+                self.weight_qscheme,
+                self.weight_dtype,
+                self.weight_scale,
+                self.weight_zero_point,
+                self.weight_axis_int,
+            )
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        _save_weight_qparams(
+            destination,
+            prefix,
+            self.weight_qscheme,
+            self.weight_dtype,
+            self.weight_scale,
+            self.weight_zero_point,
+            self.weight_axis,
+        )
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        for key in _get_weight_qparam_keys(state_dict, prefix):
+            setattr(self, key, state_dict[prefix + key])
+            state_dict.pop(prefix + key)
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+
+def _quantize_weight_decomposed(
+    weight: torch.Tensor,
+    weight_qscheme: torch.qscheme,
+    weight_dtype: torch.dtype,
+    weight_scale: torch.Tensor,
+    weight_zero_point: torch.Tensor,
+    weight_axis: int,
+    weight_quant_min: typing.Optional[int],
+    weight_quant_max: typing.Optional[int],
+) -> torch.Tensor:
+    _DTYPE_TO_QVALUE_BOUNDS = {
+        torch.uint8: (0, 255),
+        torch.int8: (-128, 127),
+        torch.int32: (-(2**31), 2**31 - 1),
+    }
+    # TODO: add an util function for converting qdtype to dtype
+    _QDTYPE_TO_UNDERLYING_INT_REPR_DTYPE = {
+        torch.quint8: torch.uint8,
+        torch.qint8: torch.int8,
+        torch.qint32: torch.int32,
+    }
+    if weight_qscheme == torch.per_tensor_affine:
+        if weight_dtype in [torch.quint8, torch.qint8, torch.qint32]:
+            weight_dtype_ = _QDTYPE_TO_UNDERLYING_INT_REPR_DTYPE[weight_dtype]
+            if weight_quant_min is None or weight_quant_max is None:
+                weight_quant_min, weight_quant_max = _DTYPE_TO_QVALUE_BOUNDS[
+                    weight_dtype_
+                ]
+            weight = torch.ops.quantized_decomposed.quantize_per_tensor(
+                weight,
+                weight_scale,
+                weight_zero_point,
+                weight_quant_min,
+                weight_quant_max,
+                weight_dtype_,
+            )
+            return weight
+    elif weight_qscheme in [
+        torch.per_channel_affine,
+        torch.per_channel_affine_float_qparams,
+    ]:
+        # TODO: torch.quint4x2 is not supported
+        if weight_dtype in [torch.quint8, torch.qint8, torch.qint32]:
+            weight_dtype_ = _QDTYPE_TO_UNDERLYING_INT_REPR_DTYPE[weight_dtype]
+            if weight_quant_min is None or weight_quant_max is None:
+                weight_quant_min, weight_quant_max = _DTYPE_TO_QVALUE_BOUNDS[
+                    weight_dtype_
+                ]
+            weight = torch.ops.quantized_decomposed.quantize_per_channel(
+                weight,
+                weight_scale,
+                weight_zero_point,
+                weight_axis,
+                weight_quant_min,
+                weight_quant_max,
+                weight_dtype_,
+            )  # type: ignore[arg-type]
+            return weight
+    raise ValueError(f"Unsupported dtype and qscheme: {weight_dtype}, {weight_qscheme}")
+
+
+def _dequantize_weight_decomposed(
+    weight: torch.Tensor,
+    weight_qscheme: torch.qscheme,
+    weight_dtype: torch.dtype,
+    weight_scale: torch.Tensor,
+    weight_zero_point: torch.Tensor,
+    weight_axis: int,
+    weight_quant_min: typing.Optional[int],
+    weight_quant_max: typing.Optional[int],
+) -> torch.Tensor:
+    # TODO: get the quant_min and quant_max from activation_post_process
+    _DTYPE_TO_QVALUE_BOUNDS = {
+        torch.uint8: (0, 255),
+        torch.int8: (-128, 127),
+        torch.int32: (-(2**31), 2**31 - 1),
+    }
+    # TODO: add an util function for converting qdtype to dtype
+    _QDTYPE_TO_UNDERLYING_INT_REPR_DTYPE = {
+        torch.quint8: torch.uint8,
+        torch.qint8: torch.int8,
+        torch.qint32: torch.int32,
+    }
+    weight_dtype_ = _QDTYPE_TO_UNDERLYING_INT_REPR_DTYPE[weight_dtype]
+    if weight_quant_min is None or weight_quant_max is None:
+        weight_quant_min, weight_quant_max = _DTYPE_TO_QVALUE_BOUNDS[weight_dtype_]
+    if weight_qscheme == torch.per_tensor_affine:
+        if weight_dtype in [torch.quint8, torch.qint8, torch.qint32]:
+            weight = torch.ops.quantized_decomposed.dequantize_per_tensor(
+                weight,
+                weight_scale,
+                weight_zero_point,
+                weight_quant_min,
+                weight_quant_max,
+                weight_dtype_,
+            )
+            return weight
+    elif weight_qscheme in [
+        torch.per_channel_affine,
+        torch.per_channel_affine_float_qparams,
+    ]:
+        # TODO: torch.quint4x2 is not supported
+        if weight_dtype in [torch.quint8, torch.qint8, torch.qint32]:
+            weight = torch.ops.quantized_decomposed.dequantize_per_channel(
+                weight,
+                weight_scale,
+                weight_zero_point,
+                weight_axis,
+                weight_quant_min,
+                weight_quant_max,
+                weight_dtype_,
+            )  # type: ignore[arg-type]
+            return weight
+    raise ValueError(f"Unsupported dtype and qscheme: {weight_dtype}, {weight_qscheme}")
+
+
+def _quantize_weight(
+    weight: torch.Tensor,
+    weight_qscheme: torch.qscheme,
+    weight_dtype: torch.dtype,
+    weight_scale: torch.Tensor,
+    weight_zero_point: torch.Tensor,
+    weight_axis_int: int,
+) -> torch.Tensor:
+    if weight_dtype == torch.float16:
+        weight = weight.to(weight_dtype)
+        return weight
+
+    if weight_qscheme == torch.per_tensor_affine:
+        if weight_dtype in [torch.quint8, torch.qint8, torch.qint32]:
+            weight = torch.quantize_per_tensor(
+                weight, weight_scale, weight_zero_point, weight_dtype
+            )
+            return weight
+    elif weight_qscheme in [
+        torch.per_channel_affine,
+        torch.per_channel_affine_float_qparams,
+    ]:
+        if weight_dtype in [torch.quint8, torch.qint8, torch.quint4x2, torch.qint32]:
+            weight = torch.quantize_per_channel(
+                weight, weight_scale, weight_zero_point, weight_axis_int, weight_dtype
+            )  # type: ignore[arg-type]
+            return weight
+    raise ValueError(f"Unsupported dtype and qscheme: {weight_dtype}, {weight_qscheme}")
+
+
+def _quantize_and_dequantize_weight_decomposed(
+    weight: torch.Tensor,
+    weight_qscheme: torch.qscheme,
+    weight_dtype: torch.dtype,
+    weight_scale: torch.Tensor,
+    weight_zero_point: torch.Tensor,
+    weight_axis_int: int,
+    weight_quant_min: typing.Optional[int],
+    weight_quant_max: typing.Optional[int],
+) -> torch.Tensor:
+    """Quantize and then dequantize the weight based on
+    the quantization parameters
+    """
+    if weight_qscheme in [
+        torch.per_tensor_affine,
+        torch.per_channel_affine,
+        torch.per_channel_affine_float_qparams,
+    ]:
+        weight_quant = _quantize_weight_decomposed(
+            weight,
+            weight_qscheme,
+            weight_dtype,
+            weight_scale,
+            weight_zero_point,
+            weight_axis_int,
+            weight_quant_min,
+            weight_quant_max,
+        )
+        weight_dequant = _dequantize_weight_decomposed(
+            weight_quant,
+            weight_qscheme,
+            weight_dtype,
+            weight_scale,
+            weight_zero_point,
+            weight_axis_int,
+            weight_quant_min,
+            weight_quant_max,
+        )
+    else:
+        weight_dequant = weight
+    return weight_dequant
+
+
+def _quantize_and_dequantize_weight(
+    weight: torch.Tensor,
+    weight_qscheme: torch.qscheme,
+    weight_dtype: torch.dtype,
+    weight_scale: torch.Tensor,
+    weight_zero_point: torch.Tensor,
+    weight_axis_int: int,
+) -> torch.Tensor:
+    """Quantize and then dequantize the weight based on
+    the quantization parameters
+    """
+    if weight_qscheme in [
+        torch.per_tensor_affine,
+        torch.per_channel_affine,
+        torch.per_channel_affine_float_qparams,
+    ]:
+        weight_quant = _quantize_weight(
+            weight,
+            weight_qscheme,
+            weight_dtype,
+            weight_scale,
+            weight_zero_point,
+            weight_axis_int,
+        )
+        weight_dequant = weight_quant.dequantize()
+    else:
+        weight_dequant = weight
+    return weight_dequant
+
+
+def _save_weight_qparams(
+    destination,
+    prefix,
+    weight_qscheme,
+    weight_dtype,
+    weight_scale,
+    weight_zero_point,
+    weight_axis,
+):
+    destination[prefix + "weight_qscheme"] = weight_qscheme
+    destination[prefix + "weight_dtype"] = weight_dtype
+    if weight_qscheme is not None:
+        destination[prefix + "weight_scale"] = weight_scale
+        destination[prefix + "weight_zero_point"] = weight_zero_point
+        if weight_qscheme == torch.per_channel_affine:
+            destination[prefix + "weight_axis"] = weight_axis
+
+
+def _get_weight_qparam_keys(state_dict: typing.Dict[str, typing.Any], prefix: str):
+    keys = ["weight_qscheme", "weight_dtype"]
+    weight_qscheme = state_dict[prefix + "weight_qscheme"]
+    if weight_qscheme is not None:
+        keys.append("weight_scale")
+        keys.append("weight_zero_point")
+        if weight_qscheme == torch.quantize_per_channel:
+            keys.append("weight_axis")
+    return keys
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0fda5a58f2984ee05b0d167297b458f62c37fc59
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/__init__.py
@@ -0,0 +1 @@
+from . import quantized
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef66c90b0e8ecdbc7cd2cfb4c1cecf0bc38e8466
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/__init__.py
@@ -0,0 +1,10 @@
+from torch.ao.nn.sparse.quantized import dynamic
+
+from .linear import Linear, LinearPackedParams
+
+
+__all__ = [
+    "dynamic",
+    "Linear",
+    "LinearPackedParams",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/__pycache__/linear.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/__pycache__/linear.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/__pycache__/utils.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/__pycache__/utils.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/dynamic/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/dynamic/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..91ecfd8793dc08b96ed64f47f531724aa8a866d0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/dynamic/__init__.py
@@ -0,0 +1,6 @@
+from .linear import Linear
+
+
+__all__ = [
+    "Linear",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/dynamic/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/dynamic/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/dynamic/__pycache__/linear.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/dynamic/__pycache__/linear.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/dynamic/linear.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/dynamic/linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e76d96e83cf645cdf5c4d657894aaf57cee5986
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/dynamic/linear.py
@@ -0,0 +1,188 @@
+# mypy: allow-untyped-defs
+from typing import Optional
+
+import torch
+import torch.ao.nn.intrinsic as nni
+from torch.ao.nn.quantized.modules.utils import (
+    _hide_packed_params_repr,
+    _quantize_weight,
+)
+from torch.ao.nn.sparse.quantized import linear
+from torch.ao.nn.sparse.quantized.utils import LinearBlockSparsePattern
+
+
+__all__ = ["Linear"]
+
+
+class Linear(torch.nn.Module):
+    r"""
+    A dynamically quantized sparse linear module with float tensor as inputs and outputs.
+    """
+    _version = 1
+    _op_type = "sparse_dynamic"
+    _FLOAT_MODULE = torch.nn.Linear
+
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        row_block_size,
+        col_block_size,
+        bias=True,
+        dtype=torch.qint8,
+    ):
+        super().__init__()
+
+        if dtype != torch.qint8:
+            raise NotImplementedError(
+                "Only QINT8 is supported for Sparse Quantized Linear Dynamic"
+            )
+
+        self.in_features = in_features
+        self.out_features = out_features
+
+        if bias:
+            bias = torch.zeros(self.out_features, dtype=torch.float)
+        else:
+            bias = None
+
+        qweight = torch._empty_affine_quantized(
+            [out_features, in_features], scale=1, zero_point=0, dtype=torch.qint8
+        )
+        self._packed_params = linear.LinearPackedParams(
+            row_block_size=row_block_size, col_block_size=col_block_size, dtype=dtype
+        )
+        self._packed_params.set_weight_bias(
+            qweight, bias, row_block_size, col_block_size
+        )
+
+    def _get_name(self):
+        return "SparseQuantizedDynamicLinear"
+
+    def extra_repr(self):
+        return f"in_features={self.in_features}, out_features={self.out_features}, qscheme={self.weight().qscheme()}"
+
+    def __repr__(self):
+        return _hide_packed_params_repr(self, linear.LinearPackedParams)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.ops.sparse.qlinear_dynamic(x, self._packed_params._packed_params)
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "op_type"] = self._op_type
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        op_type = int(state_dict[prefix + "op_type"])
+        assert (
+            op_type == "sparse"
+        ), f"Cannot load from op_type [{op_type}], expecting [{self._op_type}]"
+        state_dict.pop(prefix + "op_type")
+
+        version = local_metadata.get("version", None)
+        assert version <= self._version
+
+        # Is this code valid? In old quantization it seemed to be used to load
+        # older model
+        weight = state_dict.pop(prefix + "weight")
+        bias = state_dict.pop(prefix + "bias")
+        state_dict.update(
+            {
+                prefix + "_packed_params.weight": weight,
+                prefix + "_packed_params.bias": bias,
+            }
+        )
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    def _weight_bias(self):
+        return self._packed_params._weight_bias()
+
+    def weight(self):
+        return self._weight_bias()[0]
+
+    def bias(self):
+        return self._weight_bias()[1]
+
+    def set_weight_bias(
+        self,
+        w: torch.Tensor,
+        b: Optional[torch.Tensor],
+        row_block_size: Optional[int],
+        col_block_size: Optional[int],
+    ) -> None:
+        assert row_block_size is not None and col_block_size is not None
+        self.out_features = w.shape[0]
+        self.in_features = w.shape[1]
+        self._packed_params.set_weight_bias(w, b, row_block_size, col_block_size)
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a quantized sparse dynamic module from a float module.
+
+        We only care about the convert at this stage, no need for observers just yet.
+        """
+        assert type(mod) == cls._FLOAT_MODULE, (
+            " nnq."
+            + cls.__name__
+            + ".from_float only works for "
+            + cls._FLOAT_MODULE.__name__
+        )
+        # TODO: Need to add options to qconfig to avoid the calibration.
+        # TODO: Add calibration for the sparsity
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        if type(mod) == nni.LinearReLU:
+            mod = mod[0]
+        if mod.qconfig is not None and mod.qconfig.weight is not None:
+            weight_observer = mod.qconfig.weight()
+        else:
+            # We have the circular import issues if we import the qconfig in the beginning of this file:
+            # https://github.com/pytorch/pytorch/pull/24231. The current workaround is to postpone the
+            # import until we need it.
+            from torch.ao.quantization.qconfig import default_dynamic_qconfig
+
+            weight_observer = default_dynamic_qconfig.weight()
+
+        # It is important to multiply by the mask BEFORE calling the `weight_observer`
+        # TODO (zaf): Mask might not be part of the qconfig (T83295194)
+        weight = mod.weight
+        if getattr(mod.qconfig, "mask", False):
+            weight = mod.qconfig.mask * mod.weight
+
+        weight_observer(weight)
+        dtype = weight_observer.dtype
+        assert dtype == torch.qint8, "Weight observer must have dtype torch.qint8"
+        w_sc, w_zp = weight_observer.calculate_qparams()
+        if isinstance(w_zp, torch.Tensor):
+            assert not torch.any(w_zp.bool()), "All weight zero points must map to 0"
+        else:
+            assert w_zp == 0, "Weight zero point must map to 0"
+        qweight = _quantize_weight(weight.float(), weight_observer)
+
+        row_block_size, col_block_size = LinearBlockSparsePattern.block_size()
+        qlinear = cls(
+            mod.in_features,
+            mod.out_features,
+            row_block_size,
+            col_block_size,
+            dtype=dtype,
+        )
+        qlinear.set_weight_bias(qweight, mod.bias, row_block_size, col_block_size)
+        return qlinear
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/linear.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..83a59ac8945242eec8f5b229655df1cb439bf8ae
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/linear.py
@@ -0,0 +1,273 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+from typing import Optional
+
+import torch
+from torch.ao.nn.quantized.modules.utils import (
+    _hide_packed_params_repr,
+    _quantize_weight,
+)
+
+
+__all__ = ["LinearPackedParams", "Linear"]
+
+
+# TODO (zaf): Inherit from `quantized.LinearPackedParams` (T83294430)
+class LinearPackedParams(torch.nn.Module):
+    _version = 1
+
+    def __init__(self, row_block_size=1, col_block_size=4, dtype=torch.qint8):
+        super().__init__()
+
+        if dtype != torch.qint8:
+            raise NotImplementedError("Linear prepacking only supports QINT8")
+        self.dtype = dtype
+        wq = torch._empty_affine_quantized(
+            [1, 1], scale=1.0, zero_point=0, dtype=torch.qint8
+        )
+        self.set_weight_bias(wq, None, row_block_size, col_block_size)
+
+    def _get_name(self):
+        return "SparseQuantizedLinearPackedParams"
+
+    @torch.jit.export
+    def set_weight_bias(
+        self,
+        weight: torch.Tensor,
+        bias: Optional[torch.Tensor],
+        row_block_size: Optional[int],
+        col_block_size: Optional[int],
+    ) -> None:
+        assert row_block_size is not None and col_block_size is not None
+        self._packed_params = torch.ops.sparse.qlinear_prepack(
+            weight, bias, row_block_size, col_block_size
+        )
+
+    @torch.jit.export
+    def _weight_bias(self):
+        (weight, bias, block_sizes) = torch.ops.sparse.qlinear_unpack(
+            self._packed_params
+        )
+        return (weight, bias, block_sizes[0], block_sizes[1])
+
+    def forward(self, x):
+        return x
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "dtype"] = self.dtype
+        destination[prefix + "_packed_params"] = self._weight_bias()
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        version = local_metadata.get("version", None)
+        assert version <= self._version
+
+        self.dtype = state_dict.pop(prefix + "dtype")
+        weight, bias, row_block_size, col_block_size = state_dict.pop(
+            prefix + "_packed_params"
+        )
+        self.set_weight_bias(weight, bias, row_block_size, col_block_size)
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    @torch.jit.export
+    def __getstate__(self):
+        return self._packed_params, self.training, self.dtype
+
+    @torch.jit.export
+    def __setstate__(self, state):
+        (self._packed_params, self.training, self.dtype) = state
+
+    def __repr__(self):
+        return self._weight_bias().__repr__()
+
+
+# TODO (zaf): Inherit from `quantized.Linear` (T83294430)
+class Linear(torch.nn.Module):
+    r"""
+    A quantized sparse linear module with quantized tensor as inputs and outputs.
+    """
+    _version = 1
+    _FLOAT_MODULE = torch.nn.Linear
+
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        row_block_size,
+        col_block_size,
+        bias=True,
+        dtype=torch.qint8,
+    ):
+        super().__init__()
+
+        if dtype != torch.qint8:
+            raise NotImplementedError(
+                "Only QINT8 is supported for Sparse Quantized Linear"
+            )
+
+        self.in_features = in_features
+        self.out_features = out_features
+
+        if bias:
+            bias = torch.zeros(self.out_features, dtype=torch.float)
+        else:
+            bias = None
+
+        qweight = torch._empty_affine_quantized(
+            [out_features, in_features], scale=1, zero_point=0, dtype=torch.qint8
+        )
+        self._packed_params = LinearPackedParams(
+            row_block_size=row_block_size, col_block_size=col_block_size, dtype=dtype
+        )
+        self._packed_params.set_weight_bias(
+            qweight, bias, row_block_size, col_block_size
+        )
+        self.scale = 1.0
+        self.zero_point = 0
+
+    @classmethod
+    def _get_name(cls):
+        return "SparseQuantizedLinear"
+
+    def extra_repr(self):
+        return (
+            f"in_features={self.in_features}, out_features={self.out_features}, scale={self.scale}, "
+            f"zero_point={self.zero_point}, qscheme={self.weight().qscheme()}"
+        )
+
+    def __repr__(self):
+        return _hide_packed_params_repr(self, LinearPackedParams)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.ops.sparse.qlinear(
+            x, self._packed_params._packed_params, self.scale, self.zero_point
+        )
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "scale"] = torch.tensor(self.scale)
+        destination[prefix + "zero_point"] = torch.tensor(self.zero_point)
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        self.scale = float(state_dict[prefix + "scale"])
+        state_dict.pop(prefix + "scale")
+
+        self.zero_point = int(state_dict[prefix + "zero_point"])
+        state_dict.pop(prefix + "zero_point")
+
+        op_type = int(state_dict[prefix + "op_type"])
+        state_dict.pop(prefix + "op_type")
+
+        version = local_metadata.get("version", None)
+        assert version <= self._version
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    def _weight_bias(self):
+        return self._packed_params._weight_bias()
+
+    def weight(self):
+        return self._weight_bias()[0]
+
+    def bias(self):
+        return self._weight_bias()[1]
+
+    def set_weight_bias(
+        self,
+        w: torch.Tensor,
+        b: Optional[torch.Tensor],
+        row_block_size: Optional[int],
+        col_block_size: Optional[int],
+    ) -> None:
+        assert row_block_size is not None and col_block_size is not None
+        self._packed_params.set_weight_bias(w, b, row_block_size, col_block_size)
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a quantized sparse module from a float module.
+
+        We only care about the convert at this stage, no need for observers just yet.
+
+        TODO(zaf): Need to add the sparse params to the qconfig
+        """
+        assert type(mod) == cls._FLOAT_MODULE, (
+            cls._get_name() + ".from_float only works for " + cls._FLOAT_MODULE.__name__
+        )
+        assert hasattr(mod, "sparse_params"), (
+            "Expecting the Linear to have `sparse_params`. Make sure you have provided arguments "
+            'in the `sparsifier.squash_mask(params_to_save=("sparse_block_shape",))` method.'
+        )
+        sparse_block_shape = mod.sparse_params.get("sparse_block_shape", None)  # type: ignore[operator, union-attr]
+        assert isinstance(sparse_block_shape, (tuple, list))
+        assert len(sparse_block_shape) == 2
+        # TODO: Need to add options to qconfig to avoid the calibration.
+        # TODO: Add calibration for the sparsity
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        activation_post_process = mod.activation_post_process
+        weight_post_process = mod.qconfig.weight()  # type: ignore[operator, union-attr]
+
+        # Assumption is that the weight is already sparsified by the
+        # `sparsifier.convert`
+        weight = mod.weight
+
+        weight_post_process(weight)
+        dtype = weight_post_process.dtype
+        act_scale, act_zp = activation_post_process.calculate_qparams()  # type: ignore[operator, union-attr]
+        assert dtype == torch.qint8, "Weight observer must have dtype torch.qint8"
+        w_sc, w_zp = weight_post_process.calculate_qparams()
+        if isinstance(w_zp, torch.Tensor):
+            assert not torch.any(w_zp.bool()), "All weight zero points must map to 0"
+        else:
+            assert w_zp == 0, "Weight zero point must map to 0"
+        qweight = _quantize_weight(weight.float(), weight_post_process)
+
+        row_block_size = mod.sparse_params["sparse_block_shape"][0]  # type: ignore[index]
+        col_block_size = mod.sparse_params["sparse_block_shape"][1]  # type: ignore[index]
+        qlinear = cls(
+            mod.in_features,
+            mod.out_features,
+            row_block_size,
+            col_block_size,
+            dtype=dtype,
+        )
+        qlinear.set_weight_bias(
+            qweight, mod.bias, row_block_size, col_block_size
+        )  # type: ignore[arg-type]
+        qlinear.scale = float(act_scale)
+        qlinear.zero_point = int(act_zp)
+        return qlinear
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a28e5742c9b6ccc0c6cc5c93b1a29bf331f26090
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/nn/sparse/quantized/utils.py
@@ -0,0 +1,56 @@
+# mypy: allow-untyped-defs
+import threading
+
+
+__all__ = ["LinearBlockSparsePattern"]
+
+
+def _is_valid_linear_block_sparse_pattern(row_block_size, col_block_size):
+    return (row_block_size == 1 and col_block_size == 4) or (
+        row_block_size == 8 and col_block_size == 1
+    )
+
+
+# This is a stop-gap measure as current flow does not allow module
+# specific block sparse pattern.
+# Infact there is no way to convey sparse pattern via module config
+# of quantization flow. Thus using the global context to convey
+# sparsity pattern.
+# Once the flow supports it, this should be removed.
+class LinearBlockSparsePattern:
+    rlock = threading.RLock()
+    row_block_size = 1
+    col_block_size = 4
+    prev_row_block_size = 1
+    prev_col_block_size = 4
+
+    def __init__(self, row_block_size=1, col_block_size=4):
+        assert _is_valid_linear_block_sparse_pattern(row_block_size, col_block_size)
+        LinearBlockSparsePattern.rlock.acquire()
+        LinearBlockSparsePattern.prev_row_block_size = (
+            LinearBlockSparsePattern.row_block_size
+        )
+        LinearBlockSparsePattern.prev_col_block_size = (
+            LinearBlockSparsePattern.col_block_size
+        )
+        LinearBlockSparsePattern.row_block_size = row_block_size
+        LinearBlockSparsePattern.col_block_size = col_block_size
+
+    def __enter__(self):
+        pass
+
+    def __exit__(self, exc_type, exc_value, backtrace):
+        LinearBlockSparsePattern.row_block_size = (
+            LinearBlockSparsePattern.prev_row_block_size
+        )
+        LinearBlockSparsePattern.col_block_size = (
+            LinearBlockSparsePattern.prev_col_block_size
+        )
+        LinearBlockSparsePattern.rlock.release()
+
+    @staticmethod
+    def block_size():
+        return (
+            LinearBlockSparsePattern.row_block_size,
+            LinearBlockSparsePattern.col_block_size,
+        )
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/_numeric_suite.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/_numeric_suite.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a80b4af6f47941a095b6912d7370328b540f2c2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/_numeric_suite.py
@@ -0,0 +1,563 @@
+# mypy: allow-untyped-defs
+from typing import Any, Callable, Dict, List, Optional, Set, Union
+
+import torch
+import torch.ao.nn.quantized as nnq
+import torch.ao.nn.quantized.dynamic as nnqd
+import torch.nn as nn
+from torch.ao.quantization import prepare
+from torch.ao.quantization.quantization_mappings import (
+    get_default_compare_output_module_list,
+)
+
+
+NON_LEAF_MODULE_TO_ADD_OBSERVER_ALLOW_LIST = {
+    nnqd.Linear,
+    nnq.Linear,
+    nnqd.LSTM,
+    nn.LSTM,
+}
+
+
+def _find_match(
+    str_list: Union[Dict[str, Any], List[str]],
+    key_str: str,
+    postfix: str,
+) -> Optional[str]:
+    split_str = key_str.split(".")
+    if split_str[-1] == postfix:
+        match_string = "".join(key_str.split(".")[0:-1])
+        for s2 in str_list:
+            pattern1 = "".join(s2.split(".")[0:-1])
+            pattern2 = "".join(s2.split(".")[0:-2])
+            if match_string == pattern1:
+                return s2
+            if match_string == pattern2:
+                return s2
+
+        # For matching "fc.weight" and "fc._packed_params._packed_params"
+        if postfix == "_packed_params":
+            match_string = "".join(key_str.split(".")[0:-2])
+            if len(match_string) == 0:
+                return None
+            for s2 in str_list:
+                pattern1 = "".join(s2.split(".")[0:-1])
+                pattern2 = "".join(s2.split(".")[0:-2])
+                if match_string == pattern1:
+                    return s2
+                if match_string == pattern2:
+                    return s2
+        return None
+    else:
+        return None
+
+
+def compare_weights(
+    float_dict: Dict[str, Any], quantized_dict: Dict[str, Any]
+) -> Dict[str, Dict[str, torch.Tensor]]:
+    r"""Compare the weights of the float module with its corresponding quantized
+    module. Return a dict with key corresponding to module names and each entry being
+    a dictionary with two keys 'float' and 'quantized', containing the float and
+    quantized weights. This dict can be used to compare and compute the quantization
+    error of the weights of float and quantized models.
+
+    Example usage::
+
+        wt_compare_dict = compare_weights(
+            float_model.state_dict(), qmodel.state_dict())
+        for key in wt_compare_dict:
+            print(
+                key,
+                compute_error(
+                    wt_compare_dict[key]['float'],
+                    wt_compare_dict[key]['quantized'].dequantize()
+                )
+            )
+
+    Args:
+        float_dict: state dict of the float model
+        quantized_dict: state dict of the quantized model
+
+    Return:
+        weight_dict: dict with key corresponding to module names and each entry being
+        a dictionary with two keys 'float' and 'quantized', containing the float and
+        quantized weights
+    """
+    torch._C._log_api_usage_once("quantization_api._numeric_suite.compare_weights")
+    weight_dict: Dict[str, Dict] = {}
+    for key in quantized_dict:
+        match_key = _find_match(float_dict, key, "weight")
+        if match_key is not None:
+            weight_dict[key] = {}
+            weight_dict[key]["float"] = float_dict[match_key]
+            weight_dict[key]["quantized"] = quantized_dict[key]
+            continue
+
+        # For matching "fc.weight" and "fc._packed_params._packed_params"
+        match_key = _find_match(float_dict, key, "_packed_params")
+        if match_key is not None:
+            weight_dict[key] = {}
+            weight_dict[key]["float"] = float_dict[match_key]
+            weight_dict[key]["quantized"] = quantized_dict[key][0]
+
+        # For LSTM
+        split_str = key.split(".")
+        if split_str[-1] == "param" and split_str[-3] == "_all_weight_values":
+            layer = split_str[-2]
+            module_name = ".".join(split_str[:-3])
+            float_weight_ih_key = module_name + ".weight_ih_l" + layer
+            float_weight_hh_key = module_name + ".weight_hh_l" + layer
+            if float_weight_ih_key in float_dict and float_weight_hh_key in float_dict:
+                weight_dict[key] = {}
+                weight_dict[key]["float"] = float_dict[float_weight_ih_key]
+                weight_dict[key]["quantized"] = (
+                    quantized_dict[key].__getstate__()[0][4][0].__getstate__()[0][0]
+                )
+                weight_dict[key]["float"] = float_dict[float_weight_hh_key]
+                weight_dict[key]["quantized"] = (
+                    quantized_dict[key].__getstate__()[0][4][1].__getstate__()[0][0]
+                )
+
+    return weight_dict
+
+
+def _get_logger_dict_helper(
+    mod: nn.Module,
+    target_dict: Dict[str, Any],
+    prefix: str = "",
+) -> None:
+    r"""This is the helper function for get_logger_dict
+
+    Args:
+        mod: module we want to save all logger stats
+        prefix: prefix for the current module
+        target_dict: the dictionary used to save all logger stats
+    """
+
+    def get_prefix(prefix):
+        return prefix if prefix == "" else prefix + "."
+
+    for name, child in mod.named_children():
+        if isinstance(child, Logger):
+            target_dict[get_prefix(prefix) + "stats"] = child.stats
+            break
+
+    for name, child in mod.named_children():
+        module_prefix = get_prefix(prefix) + name if prefix else name
+        _get_logger_dict_helper(child, target_dict, module_prefix)
+
+
+def get_logger_dict(mod: nn.Module, prefix: str = "") -> Dict[str, Dict]:
+    r"""Traverse the modules and save all logger stats into target dict.
+    This is mainly used for quantization accuracy debug.
+
+    Type of loggers supported:
+        ShadowLogger: used to log the outputs of the quantized module and its matching float shadow module,
+        OutputLogger: used to log the outputs of the modules
+
+    Args:
+        mod: module we want to save all logger stats
+        prefix: prefix for the current module
+
+    Return:
+        target_dict: the dictionary used to save all logger stats
+
+    """
+    torch._C._log_api_usage_once("quantization_api._numeric_suite.get_logger_dict")
+
+    target_dict: Dict[str, Dict] = {}
+    _get_logger_dict_helper(mod, target_dict, prefix)
+    return target_dict
+
+
+class Logger(nn.Module):
+    r"""Base class for stats logging"""
+
+    def __init__(self):
+        super().__init__()
+        self.stats = {}
+        # We only insert observer if the op is quantized with static quantization,
+        # which is identified by activation_observer.dtype == quint8.  This is needed
+        # when attaching Logger as observer for FX mode
+        self.dtype = torch.quint8
+
+    def forward(self, x):
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+
+
+class ShadowLogger(Logger):
+    r"""Class used in Shadow module to record the outputs of the original and
+    shadow modules.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.stats["float"] = []
+        self.stats["quantized"] = []
+
+    def forward(self, x, y):
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        if len(x) > 1:
+            x = x[0]
+        if len(y) > 1:
+            y = y[0]
+        self.stats["quantized"].append(x.detach())
+        self.stats["float"].append(y.detach())
+
+
+class OutputLogger(Logger):
+    r"""Class used to log the outputs of the module"""
+
+    def __init__(self):
+        super().__init__()
+        self.stats["tensor_val"] = []
+
+    def forward(self, x):
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        self.stats["tensor_val"].append(x)
+        return x
+
+
+def _convert_tuple_to_list(t: Any) -> Any:
+    return [_convert_tuple_to_list(x) for x in t] if type(t) is tuple else t
+
+
+def _dequantize_tensor_list(t: Any) -> Any:
+    return (
+        [_dequantize_tensor_list(x) for x in t]
+        if type(t) is list
+        else t.dequantize()
+        if t.is_quantized
+        else t
+    )
+
+
+class Shadow(nn.Module):
+    r"""Shadow module attaches the float module to its matching quantized module
+    as the shadow. Then it uses Logger module to process the outputs of both
+    modules.
+
+    Args:
+        q_module: module quantized from float_module that we want to shadow
+        float_module: float module used to shadow q_module
+        logger_cls: type of logger used to process the outputs of q_module and
+            float_module. ShadowLogger or custom loggers can be used.
+    """
+
+    def __init__(self, q_module, float_module, logger_cls):
+        super().__init__()
+        self.orig_module = q_module
+        self.shadow_module = float_module
+        self.dequant = nnq.DeQuantize()
+        self.logger = logger_cls()
+
+    def forward(self, *x) -> torch.Tensor:
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        xl = _convert_tuple_to_list(x)
+        output = self.orig_module(*xl)
+        xl_float = _dequantize_tensor_list(xl)
+        shadow_output = self.shadow_module(*xl_float)
+        self.logger(output, shadow_output)
+        return output
+
+    def add(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        output = self.orig_module.add(x, y)
+        x = x.dequantize()
+        y = y.dequantize()
+        shadow_output = self.shadow_module.add(x, y)
+        self.logger(output, shadow_output)
+        return output
+
+    def add_scalar(self, x: torch.Tensor, y: float) -> torch.Tensor:
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        output = self.orig_module.add_scalar(x, y)
+        x = x.dequantize()
+        shadow_output = self.shadow_module.add_scalar(x, y)
+        self.logger(output, shadow_output)
+        return output
+
+    def mul(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        output = self.orig_module.mul(x, y)
+        x = x.dequantize()
+        y = y.dequantize()
+        shadow_output = self.shadow_module.mul(x, y)
+        self.logger(output, shadow_output)
+        return output
+
+    def mul_scalar(self, x: torch.Tensor, y: float) -> torch.Tensor:
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        output = self.orig_module.mul_scalar(x, y)
+        x = x.dequantize()
+        shadow_output = self.shadow_module.mul_scalar(x, y)
+        self.logger(output, shadow_output)
+        return output
+
+    def cat(self, x: List[torch.Tensor], dim: int = 0) -> torch.Tensor:
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        output = self.orig_module.cat(x, dim)
+        x = [y.dequantize() for y in x]
+        shadow_output = self.shadow_module.cat(x, dim)
+        self.logger(output, shadow_output)
+        return output
+
+    def add_relu(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        output = self.orig_module.add_relu(x, y)
+        x = x.dequantize()
+        y = y.dequantize()
+        shadow_output = self.shadow_module.add_relu(x, y)
+        self.logger(output, shadow_output)
+        return output
+
+
+def prepare_model_with_stubs(
+    float_module: nn.Module,
+    q_module: nn.Module,
+    module_swap_list: Set[type],
+    logger_cls: Callable,
+) -> None:
+    r"""Prepare the model by attaching the float module to its matching quantized
+    module as the shadow if the float module type is in module_swap_list.
+
+    Example usage::
+
+        prepare_model_with_stubs(float_model, q_model, module_swap_list, Logger)
+        q_model(data)
+        ob_dict = get_logger_dict(q_model)
+
+    Args:
+        float_module: float module used to generate the q_module
+        q_module: module quantized from float_module
+        module_swap_list: list of float module types to attach the shadow
+        logger_cls: type of logger to be used in shadow module to process the outputs of
+            quantized module and its float shadow module
+    """
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite.prepare_model_with_stubs"
+    )
+
+    float_module_children = {}
+    for name, mod in float_module.named_children():
+        float_module_children[name] = mod
+
+    reassign = {}
+    for name, mod in q_module.named_children():
+        if name not in float_module_children:
+            continue
+
+        float_mod = float_module_children[name]
+
+        if type(float_mod) not in module_swap_list:
+            prepare_model_with_stubs(float_mod, mod, module_swap_list, logger_cls)
+
+        # Insert shadow module only if the module is not of the same type as
+        # the floating point module
+        if type(float_mod) in module_swap_list and not _is_identical_module_type(
+            mod, float_mod
+        ):
+            reassign[name] = Shadow(mod, float_mod, logger_cls)
+
+    for key, value in reassign.items():
+        q_module._modules[key] = value
+
+
+def _is_identical_module_type(mod1, mod2):
+    # Compare if two modules have the same dtype
+    mod1_module_types = [type(mod) for mod in mod1.modules()]
+    mod2_module_types = [type(mod) for mod in mod2.modules()]
+    return mod1_module_types == mod2_module_types
+
+
+def compare_model_stub(
+    float_model: nn.Module,
+    q_model: nn.Module,
+    module_swap_list: Set[type],
+    *data,
+    logger_cls=ShadowLogger,
+) -> Dict[str, Dict]:
+    r"""Compare quantized module in a model with its floating point counterpart,
+    feeding both of them the same input. Return a dict with key corresponding to
+    module names and each entry being a dictionary with two keys 'float' and
+    'quantized', containing the output tensors of quantized and its matching
+    float shadow module. This dict can be used to compare and compute the module
+    level quantization error.
+
+    This function first call prepare_model_with_stubs() to swap the quantized
+    module that we want to compare with the Shadow module, which takes quantized
+    module, corresponding float module and logger as input, and creates a forward
+    path inside to make the float module to shadow quantized module sharing the
+    same input. The logger can be customizable, default logger is ShadowLogger
+    and it will save the outputs of the quantized module and float module that
+    can be used to compute the module level quantization error.
+
+    Example usage::
+
+        module_swap_list = [torchvision.models.quantization.resnet.QuantizableBasicBlock]
+        ob_dict = compare_model_stub(float_model,qmodel,module_swap_list, data)
+        for key in ob_dict:
+            print(key, compute_error(ob_dict[key]['float'], ob_dict[key]['quantized'].dequantize()))
+
+    Args:
+        float_model: float model used to generate the q_model
+        q_model: model quantized from float_model
+        module_swap_list: list of float module types at which shadow modules will
+            be attached.
+        data: input data used to run the prepared q_model
+        logger_cls: type of logger to be used in shadow module to process the outputs of
+            quantized module and its float shadow module
+    """
+    torch._C._log_api_usage_once("quantization_api._numeric_suite.compare_model_stub")
+    prepare_model_with_stubs(float_model, q_model, module_swap_list, logger_cls)
+    q_model(*data)
+    ob_dict = get_logger_dict(q_model)
+    return ob_dict
+
+
+def get_matching_activations(
+    float_module: nn.Module,
+    q_module: nn.Module,
+) -> Dict[str, Dict[str, torch.Tensor]]:
+    r"""Find the matching activation between float and quantized modules.
+
+    Args:
+        float_module: float module used to generate the q_module
+        q_module: module quantized from float_module
+
+    Return:
+        act_dict: dict with key corresponding to quantized module names and each
+        entry being a dictionary with two keys 'float' and 'quantized', containing
+        the matching float and quantized activations
+    """
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite.get_matching_activations"
+    )
+    float_dict = get_logger_dict(float_module)
+    quantized_dict = get_logger_dict(q_module)
+    act_dict: Dict[str, Dict] = {}
+    for key in quantized_dict:
+        if len(quantized_dict[key]["tensor_val"]) == 0:
+            continue
+        match_key = _find_match(sorted(float_dict, reverse=True), key, "stats")
+        if match_key is not None:
+            act_dict[key] = {}
+            act_dict[key]["float"] = float_dict[match_key]["tensor_val"]
+            act_dict[key]["quantized"] = quantized_dict[key]["tensor_val"]
+    return act_dict
+
+
+def prepare_model_outputs(
+    float_module: nn.Module,
+    q_module: nn.Module,
+    logger_cls=OutputLogger,
+    allow_list=None,
+) -> None:
+    r"""Prepare the model by attaching the logger to both float module
+    and quantized module if they are in the allow_list.
+
+    Args:
+        float_module: float module used to generate the q_module
+        q_module: module quantized from float_module
+        logger_cls: type of logger to be attached to float_module and q_module
+        allow_list: list of module types to attach logger
+    """
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite.prepare_model_outputs"
+    )
+    if allow_list is None:
+        allow_list = get_default_compare_output_module_list()
+
+    qconfig_debug = torch.ao.quantization.QConfig(activation=logger_cls, weight=None)
+    float_module.qconfig = qconfig_debug  # type: ignore[assignment]
+    prepare(
+        float_module, inplace=True, allow_list=allow_list, prepare_custom_config_dict={}
+    )
+    q_module.qconfig = qconfig_debug  # type: ignore[assignment]
+    prepare(
+        q_module,
+        inplace=True,
+        allow_list=allow_list,
+        observer_non_leaf_module_list=NON_LEAF_MODULE_TO_ADD_OBSERVER_ALLOW_LIST,
+        prepare_custom_config_dict={},
+    )
+
+
+def compare_model_outputs(
+    float_model: nn.Module,
+    q_model: nn.Module,
+    *data,
+    logger_cls=OutputLogger,
+    allow_list=None,
+) -> Dict[str, Dict[str, torch.Tensor]]:
+    r"""Compare output activations between float and quantized models at
+    corresponding locations for the same input. Return a dict with key corresponding
+    to quantized module names and each entry being a dictionary with two keys
+    'float' and 'quantized', containing the activations of quantized model and
+    float model at matching locations. This dict can be used to compare and
+    compute the propagation quantization error.
+
+    Example usage::
+
+        act_compare_dict = compare_model_outputs(float_model, qmodel, data)
+        for key in act_compare_dict:
+            print(
+                key,
+                compute_error(
+                    act_compare_dict[key]['float'],
+                    act_compare_dict[key]['quantized'].dequantize()
+                )
+            )
+
+    Args:
+        float_model: float model used to generate the q_model
+        q_model: model quantized from float_model
+        data: input data used to run the prepared float_model and q_model
+        logger_cls: type of logger to be attached to float_module and q_module
+        allow_list: list of module types to attach logger
+
+    Return:
+        act_compare_dict: dict with key corresponding to quantized module names
+        and each entry being a dictionary with two keys 'float' and 'quantized',
+        containing the matching float and quantized activations
+    """
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite.compare_model_outputs"
+    )
+    if allow_list is None:
+        allow_list = get_default_compare_output_module_list()
+    prepare_model_outputs(float_model, q_model, logger_cls, allow_list)
+    float_model(*data)
+    q_model(*data)
+    act_compare_dict = get_matching_activations(float_model, q_model)
+    return act_compare_dict
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/_numeric_suite_fx.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/_numeric_suite_fx.py
new file mode 100644
index 0000000000000000000000000000000000000000..3cea146b8031fd314e95030f93384dfcdd2407df
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/_numeric_suite_fx.py
@@ -0,0 +1,1130 @@
+# mypy: allow-untyped-defs
+"""
+This module contains tooling to compare weights and activations
+across models. Example usage::
+
+    import copy
+    import torch
+    import torch.ao.quantization.quantize_fx as quantize_fx
+    import torch.ao.ns._numeric_suite_fx as ns
+
+    m = torch.nn.Sequential(torch.nn.Conv2d(1, 1, 1)).eval()
+    mp = quantize_fx.prepare_fx(m, {'': torch.ao.quantization.default_qconfig})
+    # We convert a copy because we need the original prepared model
+    # to be available for comparisons, and `quantize_fx.convert_fx` is inplace.
+    mq = quantize_fx.convert_fx(copy.deepcopy(mp))
+
+    #
+    # Comparing weights
+    #
+
+    # extract weight pairs
+    weight_comparison = ns.extract_weights('a', mp, 'b', mq)
+
+    # add SQNR for each comparison, inplace
+    ns.extend_logger_results_with_comparison(
+        weight_comparison, 'a', 'b', torch.ao.ns.fx.utils.compute_sqnr,
+        'sqnr')
+
+    # weight_comparison contains the weights from `mp` and `mq` stored
+    # in pairs, and can be used for further analysis.
+
+
+    #
+    # Comparing activations, with error propagation
+    #
+
+    # add loggers
+    mp_ns, mq_ns = ns.add_loggers(
+        'a', copy.deepcopy(mp),
+        'b', copy.deepcopy(mq),
+        ns.OutputLogger)
+
+    # send an example datum to capture intermediate activations
+    datum = torch.randn(1, 1, 1, 1)
+    mp_ns(datum)
+    mq_ns(datum)
+
+    # extract intermediate activations
+    act_comparison = ns.extract_logger_info(
+        mp_ns, mq_ns, ns.OutputLogger, 'b')
+
+    # add SQNR for each comparison, inplace
+    ns.extend_logger_results_with_comparison(
+        act_comparison, 'a', 'b', torch.ao.ns.fx.utils.compute_sqnr,
+        'sqnr')
+
+    # act_comparison contains the activations from `mp_ns` and `mq_ns` stored
+    # in pairs, and can be used for further analysis.
+
+    #
+    # Comparing activations, without error propagation
+    #
+
+    # create shadow model
+    mp_shadows_mq = ns.add_shadow_loggers(
+        'a', copy.deepcopy(mp),
+        'b', copy.deepcopy(mq),
+        ns.OutputLogger)
+
+    # send an example datum to capture intermediate activations
+    datum = torch.randn(1, 1, 1, 1)
+    mp_shadows_mq(datum)
+
+    # extract intermediate activations
+    shadow_act_comparison = ns.extract_shadow_logger_info(
+        mp_shadows_mq, ns.OutputLogger, 'b')
+
+    # add SQNR for each comparison, inplace
+    ns.extend_logger_results_with_comparison(
+        shadow_act_comparison, 'a', 'b', torch.ao.ns.fx.utils.compute_sqnr,
+        'sqnr')
+
+    # shadow_act_comparison contains the activations from `mp_ns` and `mq_ns` stored
+    # in pairs, and can be used for further analysis.
+
+"""
+
+import collections
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, TYPE_CHECKING
+
+import torch
+import torch.ao.quantization.quantize_fx as quantize_fx
+import torch.nn as nn
+from torch.ao.ns.fx.graph_matcher import (
+    get_matching_subgraph_pairs,
+    get_type_a_related_to_b,
+)
+from torch.ao.ns.fx.mappings import get_base_name_to_sets_of_related_ops
+from torch.ao.ns.fx.n_shadows_utils import (
+    _get_dedup_subgraphs,
+    create_add_loggers_graph,
+    create_n_transformed_and_logged_copies_of_subgraph,
+    create_results_comparison,
+    extract_weight_comparison,
+    group_results_by_subgraph,
+    OutputProp,
+    print_n_shadows_summary,
+    SHADOW_WRAPPER_NODE_NAME_PREFIX,
+)
+from torch.ao.ns.fx.qconfig_multi_mapping import QConfigMultiMapping
+from torch.ao.quantization import QConfigMapping
+from torch.ao.quantization.backend_config import BackendConfig
+from torch.ao.quantization.backend_config.utils import (
+    get_fusion_pattern_to_root_node_getter,
+)
+from torch.ao.quantization.fx.graph_module import _get_observed_graph_module_attr
+from torch.ao.quantization.fx.match_utils import _find_matches
+from torch.ao.quantization.fx.qconfig_mapping_utils import (
+    _generate_node_name_to_qconfig,
+)
+from torch.ao.quantization.fx.quantize_handler import _get_pattern_to_quantize_handlers
+from torch.fx import GraphModule
+from torch.fx.graph import Node
+
+from .fx.graph_passes import add_loggers_to_model, create_a_shadows_b
+from .fx.ns_types import NSNodeTargetType, NSResultsType, NSSingleResultValuesType
+from .fx.utils import (
+    get_target_type_str,
+    maybe_add_missing_fqns,
+    rekey_logger_info_on_node_name_of_model,
+)
+from .fx.weight_utils import extract_weight_from_node
+
+
+if TYPE_CHECKING:
+    from torch.ao.quantization.qconfig import QConfigAny
+
+RNNReturnType = Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]
+
+
+class OutputLogger(nn.Module):
+    """
+    Base class for capturing intermediate values.
+    """
+
+    stats: List[torch.Tensor]
+    stats_rnn: List[RNNReturnType]
+
+    # Mark as impure so that calls to it will not be removed during DCE.
+    _is_impure = True
+
+    def __init__(
+        self,
+        ref_node_name: str,
+        prev_node_name: str,
+        model_name: str,
+        ref_name: str,
+        prev_node_target_type: str,
+        ref_node_target_type: str,
+        results_type: str,
+        index_within_arg: int,
+        index_of_arg: int,
+        fqn: Optional[str],
+        qconfig_str: Optional[str] = "",
+    ):
+        super().__init__()
+        self.stats: List[torch.Tensor] = []
+        self.stats_rnn: List[RNNReturnType] = []
+
+        # name of the node which was responsible for adding this logger
+        # Note:
+        # - if we are logging node outputs, this is the same as prev_node_name
+        # - if we are logging node inputs, this is the name of the node
+        #   whose input this logger is logging.
+        #
+        # example, where logger1 is logging input of op1 and logger2 is logging
+        #    the output of op1:
+        #
+        #  x1 -> logger1 -> op1 -> logger2 -> x2
+        #
+        # in this example,
+        #   - logger1's prev_node_name is x1 and ref_node_name is op1
+        #   - logger2's prev_node_name is op1 and ref_node_name is op1
+        self.ref_node_name = ref_node_name
+        # name of the node whose output this Logger is capturing
+        self.prev_node_name = prev_node_name
+
+        # name of the model from which the node originated from
+        self.model_name = model_name
+        # reference name, used to match loggers from separate models
+        # to each other
+        self.ref_name = ref_name
+        # type of the target of the node whose output this logger is logging
+        self.prev_node_target_type = prev_node_target_type
+        # type of the target of the node which was responsible for adding this
+        # logger
+        self.ref_node_target_type = ref_node_target_type
+        # what kind of values are inside of stats
+        self.results_type = results_type
+        # index of this node within the arg of the input/output node
+        # for example, in cat([x1, x2, x3], dim=0), x2 would have index_within_arg == 1
+        self.index_within_arg = index_within_arg
+        # index of this node within the args of the input/output node
+        # for example, in add(x1, x2), x2 would have index_of_arg == 1
+        self.index_of_arg = index_of_arg
+        # fully qualified name
+        self.fqn = fqn
+        # if loggers are added before prepare_fx, but we do not want
+        # collect results of calibration, only results after convert_fx
+        # so, we add a flag to control whether this logger collects data
+        self.enabled = True
+        # string representation of qconfig
+        self.qconfig_str = qconfig_str
+        # this can be turned off to reduce memory usage during calibration
+        self.save_activations = True
+
+    # Note: cannot annotate the type of x because TorchScript does not support
+    #   the Union type.
+    def forward(self, x):
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        # TODO(future PR): consider designing this better, as the difference
+        # between these two flags is subtle and not obvious.
+        if not self.enabled:
+            return x
+        if not self.save_activations:
+            return x
+        # TODO(future PR): consider refactoring this to better reuse the parent
+        # class
+        if isinstance(x, torch.Tensor):
+            self.stats.append(x.detach())
+        elif isinstance(x, tuple) and len(x) == 2 and len(x[1]) == 2:
+            new_res = (x[0].detach(), (x[1][0].detach(), x[1][1].detach()))
+            self.stats_rnn.append(new_res)
+        return x
+
+    def __repr__(self):
+        clean_dict = {
+            k: v
+            for k, v in self.__dict__.items()
+            # skip nn.Module keys
+            if (k != "training") and not k.startswith("_")
+        }
+        return f"OutputLogger({clean_dict})"
+
+
+class OutputComparisonLogger(OutputLogger):
+    """
+    Same as OutputLogger, but also requires the original activation
+    in order to calculate the comparison at calibration time
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # TODO(future PR): make the comparison function configurable
+        self.comparison_fn = torch.ao.ns.fx.utils.compute_sqnr
+        self.comparison_fn_name = "sqnr"
+        # precalculated comparisons of logger output versus reference
+        self.comparisons = []
+        # precalculated comparisons function
+
+    def forward(self, x, x_ref):
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        if not self.enabled:
+            return x
+        assert isinstance(x, torch.Tensor), "non-tensor inputs not yet supported"
+        if self.save_activations:
+            # save the activation, for debugging
+            self.stats.append(x.detach())
+        # save the comparison
+        self.comparisons.append(self.comparison_fn(x, x_ref))
+        return x
+
+    def __repr__(self):
+        clean_dict = {
+            k: v
+            for k, v in self.__dict__.items()
+            # skip nn.Module keys
+            if (k != "training") and not k.startswith("_")
+        }
+        return f"OutputComparisonLogger({clean_dict})"
+
+
+class NSTracer(quantize_fx.QuantizationTracer):
+    """
+    Just like a regular FX quantization tracer, but treats observers and fake_quantize
+    modules as leaf modules.
+    """
+
+    def is_leaf_module(self, m: torch.nn.Module, module_qualified_name: str) -> bool:
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        if isinstance(m, torch.ao.quantization.ObserverBase):
+            return True
+        elif isinstance(m, torch.ao.quantization.FakeQuantizeBase):
+            return True
+        return super().is_leaf_module(m, module_qualified_name)
+
+
+def _extract_weights_one_model(
+    model_name: str,
+    model: GraphModule,
+    nodes_and_names_to_instrument: List[Tuple[Node, str]],
+    results: NSResultsType,
+    op_to_type_to_weight_extraction_fn: Optional[
+        Dict[str, Dict[Callable, Callable]]
+    ] = None,
+) -> None:
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite_fx._extract_weights_one_model"
+    )
+    for node, ref_name in nodes_and_names_to_instrument:
+        res_type = NSSingleResultValuesType.WEIGHT.value
+        extracted_weight = extract_weight_from_node(
+            node, model, op_to_type_to_weight_extraction_fn
+        )
+        if extracted_weight:
+            if ref_name not in results:
+                results[ref_name] = {res_type: {}}
+            results[ref_name][res_type][model_name] = [extracted_weight]
+
+
+def _extract_weights_impl(
+    model_name_a: str,
+    gm_a: GraphModule,
+    model_name_b: str,
+    gm_b: GraphModule,
+    base_name_to_sets_of_related_ops: Optional[Dict[str, Set[NSNodeTargetType]]] = None,
+    unmatchable_types_map: Optional[Dict[str, Set[NSNodeTargetType]]] = None,
+    op_to_type_to_weight_extraction_fn: Optional[
+        Dict[str, Dict[Callable, Callable]]
+    ] = None,
+) -> NSResultsType:
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite_fx._extract_weights_impl"
+    )
+    matched_subgraph_pairs = get_matching_subgraph_pairs(
+        gm_a, gm_b, base_name_to_sets_of_related_ops, unmatchable_types_map
+    )
+
+    # split the subgraph pairs into one data structure for each model
+    nodes_and_names_to_instrument_a: List[Tuple[Node, str]] = []
+    nodes_and_names_to_instrument_b: List[Tuple[Node, str]] = []
+    for match_name, match in matched_subgraph_pairs.items():
+        subgraph_a, subgraph_b = match
+        nodes_and_names_to_instrument_a.append((subgraph_a.base_op_node, match_name))
+        nodes_and_names_to_instrument_b.append((subgraph_b.base_op_node, match_name))
+
+    # populate the results, one model at a time
+    results: NSResultsType = {}
+    _extract_weights_one_model(
+        model_name_a,
+        gm_a,
+        nodes_and_names_to_instrument_a,
+        results,
+        op_to_type_to_weight_extraction_fn,
+    )
+    _extract_weights_one_model(
+        model_name_b,
+        gm_b,
+        nodes_and_names_to_instrument_b,
+        results,
+        op_to_type_to_weight_extraction_fn,
+    )
+
+    # fill in missing fqn entries
+    maybe_add_missing_fqns(results)
+
+    # rekey on names of nodes in gm_b
+    results = rekey_logger_info_on_node_name_of_model(results, model_name_b)
+
+    return results
+
+
+def extract_weights(
+    model_name_a: str,
+    model_a: nn.Module,
+    model_name_b: str,
+    model_b: nn.Module,
+    base_name_to_sets_of_related_ops: Optional[Dict[str, Set[NSNodeTargetType]]] = None,
+    unmatchable_types_map: Optional[Dict[str, Set[NSNodeTargetType]]] = None,
+    op_to_type_to_weight_extraction_fn: Optional[
+        Dict[str, Dict[Callable, Callable]]
+    ] = None,
+) -> NSResultsType:
+    """
+    Extract weights from model A and model B, and return a comparison.
+
+    Args:
+        model_name_a: string name of model A to use in results
+        model_a: model A
+        model_name_b: string name of model B to use in results
+        model_b: model B
+        base_name_to_sets_of_related_ops: optional override of subgraph base nodes, subject to change
+        unmatchable_types_map: optional override of unmatchable types, subject to change
+        op_to_type_to_weight_extraction_fn: optional override of function which extracts weight
+            from a type, subject to change
+
+    Return:
+        NSResultsType, containing the weight comparisons
+    """
+
+    torch._C._log_api_usage_once("quantization_api._numeric_suite_fx.extract_weights")
+    if base_name_to_sets_of_related_ops is None:
+        base_name_to_sets_of_related_ops = get_base_name_to_sets_of_related_ops()
+    type_a_related_to_b = get_type_a_related_to_b(base_name_to_sets_of_related_ops)
+
+    # TODO(future PR): expose these
+    skipped_module_names: List[str] = []
+    skipped_module_classes: List[Callable] = []
+    tracer_a = NSTracer(skipped_module_names, skipped_module_classes)
+    tracer_b = NSTracer(skipped_module_names, skipped_module_classes)
+    gm_a = GraphModule(model_a, tracer_a.trace(model_a))
+    maybe_model_a_node_name_to_scope = _get_observed_graph_module_attr(
+        model_a, "node_name_to_scope"
+    )
+    if maybe_model_a_node_name_to_scope is not None:
+        gm_a._node_name_to_scope = maybe_model_a_node_name_to_scope
+    gm_b = GraphModule(model_b, tracer_b.trace(model_b))
+    maybe_model_b_node_name_to_scope = _get_observed_graph_module_attr(
+        model_b, "node_name_to_scope"
+    )
+    if maybe_model_b_node_name_to_scope is not None:
+        gm_b._node_name_to_scope = maybe_model_b_node_name_to_scope
+    return _extract_weights_impl(
+        model_name_a,
+        gm_a,
+        model_name_b,
+        gm_b,
+        base_name_to_sets_of_related_ops,
+        unmatchable_types_map,
+        op_to_type_to_weight_extraction_fn,
+    )
+
+
+def _add_loggers_one_model(
+    model_name: str,
+    model: GraphModule,
+    nodes_and_names_to_instrument_inputs: List[Tuple[Node, str, str]],
+    nodes_and_names_to_instrument_outputs: List[Tuple[Node, str, str]],
+    logger_cls: Callable,
+) -> nn.Module:
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite_fx._add_loggers_one_model"
+    )
+
+    # TODO(future PR): do not observe nodes we do not care
+    #   about (both fp32, denylist, etc)
+    node_to_instrument_inputs_to_ref_name: Dict[Node, Tuple[str, str]] = {}
+    node_to_instrument_outputs_to_ref_name: Dict[Node, Tuple[str, str]] = {}
+    for node, ref_name, ref_node_type in nodes_and_names_to_instrument_inputs:
+        node_to_instrument_inputs_to_ref_name[node] = (ref_name, ref_node_type)
+    for node, ref_name, ref_node_type in nodes_and_names_to_instrument_outputs:
+        node_to_instrument_outputs_to_ref_name[node] = (ref_name, ref_node_type)
+
+    model = add_loggers_to_model(
+        model,
+        node_to_instrument_inputs_to_ref_name,
+        node_to_instrument_outputs_to_ref_name,
+        logger_cls,
+        model_name,
+    )
+    return model
+
+
+def _add_loggers_impl(
+    name_a: str,
+    gm_a: GraphModule,
+    name_b: str,
+    gm_b: GraphModule,
+    logger_cls: Callable,
+    should_log_inputs: bool,
+    base_name_to_sets_of_related_ops: Optional[Dict[str, Set[NSNodeTargetType]]] = None,
+    unmatchable_types_map: Optional[Dict[str, Set[NSNodeTargetType]]] = None,
+) -> Tuple[nn.Module, nn.Module]:
+    torch._C._log_api_usage_once("quantization_api._numeric_suite_fx._add_loggers_impl")
+    matched_subgraph_pairs = get_matching_subgraph_pairs(
+        gm_a, gm_b, base_name_to_sets_of_related_ops, unmatchable_types_map
+    )
+    nodes_and_names_to_instrument_inputs_a = []
+    nodes_and_names_to_instrument_inputs_b = []
+    nodes_and_names_to_instrument_outputs_a = []
+    nodes_and_names_to_instrument_outputs_b = []
+    for match_name, (subgraph_a, subgraph_b) in matched_subgraph_pairs.items():
+        ref_node_type_a = get_target_type_str(subgraph_a.base_op_node, gm_a)
+        ref_node_type_b = get_target_type_str(subgraph_b.base_op_node, gm_b)
+        # Note: for matching inputs we use start_node, such as observing
+        # the input of linear in linear-relu
+        if should_log_inputs:
+            nodes_and_names_to_instrument_inputs_a.append(
+                (subgraph_a.start_node, match_name, ref_node_type_a)
+            )
+            nodes_and_names_to_instrument_inputs_b.append(
+                (subgraph_b.start_node, match_name, ref_node_type_b)
+            )
+        # Note: for matching activations we always use end_node,
+        # such as observing the output of relu in linear-relu
+        nodes_and_names_to_instrument_outputs_a.append(
+            (subgraph_a.end_node, match_name, ref_node_type_a)
+        )
+        nodes_and_names_to_instrument_outputs_b.append(
+            (subgraph_b.end_node, match_name, ref_node_type_b)
+        )
+
+    new_model_a = _add_loggers_one_model(
+        name_a,
+        gm_a,
+        nodes_and_names_to_instrument_inputs_a,
+        nodes_and_names_to_instrument_outputs_a,
+        logger_cls,
+    )
+    new_model_b = _add_loggers_one_model(
+        name_b,
+        gm_b,
+        nodes_and_names_to_instrument_inputs_b,
+        nodes_and_names_to_instrument_outputs_b,
+        logger_cls,
+    )
+    return (new_model_a, new_model_b)
+
+
+def add_loggers(
+    name_a: str,
+    model_a: nn.Module,
+    name_b: str,
+    model_b: nn.Module,
+    logger_cls: Callable,
+    should_log_inputs: bool = False,
+    base_name_to_sets_of_related_ops: Optional[Dict[str, Set[NSNodeTargetType]]] = None,
+    unmatchable_types_map: Optional[Dict[str, Set[NSNodeTargetType]]] = None,
+) -> Tuple[nn.Module, nn.Module]:
+    """
+    Instrument model A and model B with loggers.
+
+    Args:
+        name_a: string name of model A to use in results
+        model_a: model A
+        name_b: string name of model B to use in results
+        model_b: model B
+        logger_cls: class of Logger to use
+        base_name_to_sets_of_related_ops: optional override of subgraph base nodes, subject to change
+        unmatchable_types_map: optional override of unmatchable types, subject to change
+
+    Return:
+        Returns a tuple of (model_a_with_loggers, model_b_with_loggers).  Modifies both models inplace.
+    """
+
+    torch._C._log_api_usage_once("quantization_api._numeric_suite_fx.add_loggers")
+    # TODO(future PR): expose these
+    skipped_module_names: List[str] = []
+    skipped_module_classes: List[Callable] = []
+    tracer_a = NSTracer(skipped_module_names, skipped_module_classes)
+    tracer_b = NSTracer(skipped_module_names, skipped_module_classes)
+    gm_a = GraphModule(model_a, tracer_a.trace(model_a))
+    maybe_model_a_node_name_to_scope = _get_observed_graph_module_attr(
+        model_a, "node_name_to_scope"
+    )
+    if maybe_model_a_node_name_to_scope is not None:
+        gm_a._node_name_to_scope = maybe_model_a_node_name_to_scope
+    gm_b = GraphModule(model_b, tracer_b.trace(model_b))
+    maybe_model_b_node_name_to_scope = _get_observed_graph_module_attr(
+        model_b, "node_name_to_scope"
+    )
+    if maybe_model_b_node_name_to_scope is not None:
+        gm_b._node_name_to_scope = maybe_model_b_node_name_to_scope
+    return _add_loggers_impl(
+        name_a,
+        gm_a,
+        name_b,
+        gm_b,
+        logger_cls,
+        should_log_inputs=should_log_inputs,
+        base_name_to_sets_of_related_ops=base_name_to_sets_of_related_ops,
+        unmatchable_types_map=unmatchable_types_map,
+    )
+
+
+def _extract_logger_info_one_model(
+    model: nn.Module,
+    results: NSResultsType,
+    logger_cls: Callable,
+) -> None:
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite_fx._extract_logger_info_one_model"
+    )
+    for gm_name, mod in model.named_modules():
+        # TODO(future PR): better check when scripted
+        is_logger = isinstance(mod, logger_cls) or (  # type: ignore[arg-type]
+            isinstance(mod, torch.jit.RecursiveScriptModule)
+            and mod.original_name == "OutputLogger"
+        )
+        if is_logger:
+            key = mod.ref_name
+            if key not in results:
+                results[key] = {}
+            assert (
+                mod.model_name not in results[key]
+            ), f"{mod.model_name} is already present in results"
+            if mod.results_type not in results[key]:
+                results[key][mod.results_type] = {}
+            if mod.model_name not in results[key][mod.results_type]:
+                results[key][mod.results_type][mod.model_name] = []
+            stats_to_use = mod.stats
+            if len(mod.stats_rnn) > 0:
+                stats_to_use = mod.stats_rnn
+            data = {
+                "type": mod.results_type,
+                "values": stats_to_use,
+                "ref_node_name": mod.ref_node_name,
+                "ref_node_target_type": mod.ref_node_target_type,
+                "prev_node_name": mod.prev_node_name,
+                "prev_node_target_type": mod.prev_node_target_type,
+                "index_within_arg": mod.index_within_arg,
+                "index_of_arg": mod.index_of_arg,
+                "fqn": mod.fqn,
+                "qconfig_str": mod.qconfig_str,
+            }
+            if hasattr(mod, "comparisons"):
+                data["comparisons"] = mod.comparisons
+                data["comparison_fn_name"] = mod.comparison_fn_name
+            else:
+                data["comparisons"] = []
+                data["comparison_fn_name"] = ""
+            results[key][mod.results_type][mod.model_name].append(data)
+            # ensure the list stays sorted
+            results[key][mod.results_type][mod.model_name].sort(
+                key=lambda res: f"{res['index_of_arg']}:{res['index_within_arg']}"
+            )
+
+
+# TODO(future PR): align on naming
+# this is equivalent of just the comparison extraction part of `ns.compare_model_outputs`
+def extract_logger_info(
+    model_a: nn.Module,
+    model_b: nn.Module,
+    logger_cls: Callable,
+    model_name_to_use_for_layer_names: str,
+) -> NSResultsType:
+    """
+    Traverse all loggers in `model_a` and `model_b`, and extract the logged
+    information.
+
+    Args:
+        model_a: model A
+        model_b: model B
+        logger_cls: class of Logger to use
+        model_name_to_use_for_layer_names: string name of model to use for
+          layer names in the output
+
+    Return:
+        NSResultsType, containing the logged comparisons
+    """
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite_fx.extract_logger_info"
+    )
+    results: NSResultsType = {}
+    for model in (model_a, model_b):
+        _extract_logger_info_one_model(model, results, logger_cls)
+    # fill in missing fqn entries
+    maybe_add_missing_fqns(results)
+    # rekey on the name of model b
+    results = rekey_logger_info_on_node_name_of_model(
+        results, model_name_to_use_for_layer_names
+    )
+    return results
+
+
+def _add_shadow_loggers_impl(
+    name_a: str,
+    gm_a: GraphModule,
+    name_b: str,
+    gm_b: GraphModule,
+    logger_cls: Callable,
+    should_log_inputs: bool,
+    base_name_to_sets_of_related_ops: Optional[Dict[str, Set[NSNodeTargetType]]] = None,
+    node_type_to_io_type_map: Optional[Dict[str, Set[NSNodeTargetType]]] = None,
+    unmatchable_types_map: Optional[Dict[str, Set[NSNodeTargetType]]] = None,
+) -> nn.Module:
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite_fx._add_shadow_loggers_impl"
+    )
+    matched_subgraph_pairs = get_matching_subgraph_pairs(
+        gm_a, gm_b, base_name_to_sets_of_related_ops, unmatchable_types_map
+    )
+    gm_a_shadows_b = create_a_shadows_b(
+        name_a,
+        gm_a,
+        name_b,
+        gm_b,
+        matched_subgraph_pairs,
+        logger_cls,
+        should_log_inputs=should_log_inputs,
+        node_type_to_io_type_map=node_type_to_io_type_map,
+    )
+    return gm_a_shadows_b
+
+
+def add_shadow_loggers(
+    name_a: str,
+    model_a: nn.Module,
+    name_b: str,
+    model_b: nn.Module,
+    logger_cls: Callable,
+    should_log_inputs: bool = False,
+    base_name_to_sets_of_related_ops: Optional[Dict[str, Set[NSNodeTargetType]]] = None,
+    node_type_to_io_type_map: Optional[Dict[str, Set[NSNodeTargetType]]] = None,
+    unmatchable_types_map: Optional[Dict[str, Set[NSNodeTargetType]]] = None,
+) -> nn.Module:
+    """
+    Instrument model A and model B with shadow loggers.
+
+    Args:
+        name_a: string name of model A to use in results
+        model_a: model A
+        name_b: string name of model B to use in results
+        model_b: model B
+        logger_cls: class of Logger to use
+        should_log_inputs: whether to log inputs
+        base_name_to_sets_of_related_ops: optional override of subgraph base nodes, subject to change
+        unmatchable_types_map: optional override of unmatchable types, subject to change
+    """
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite_fx.add_shadow_loggers"
+    )
+    # TODO(future PR): expose these
+    skipped_module_names: List[str] = []
+    skipped_module_classes: List[Callable] = []
+    tracer_a = NSTracer(skipped_module_names, skipped_module_classes)
+    tracer_b = NSTracer(skipped_module_names, skipped_module_classes)
+    gm_a = GraphModule(model_a, tracer_a.trace(model_a))
+    maybe_model_a_node_name_to_scope = _get_observed_graph_module_attr(
+        model_a, "node_name_to_scope"
+    )
+    if maybe_model_a_node_name_to_scope is not None:
+        gm_a._node_name_to_scope = maybe_model_a_node_name_to_scope
+    gm_b = GraphModule(model_b, tracer_b.trace(model_b))
+    maybe_model_b_node_name_to_scope = _get_observed_graph_module_attr(
+        model_b, "node_name_to_scope"
+    )
+    if maybe_model_b_node_name_to_scope is not None:
+        gm_b._node_name_to_scope = maybe_model_b_node_name_to_scope
+    return _add_shadow_loggers_impl(
+        name_a,
+        gm_a,
+        name_b,
+        gm_b,
+        logger_cls,
+        should_log_inputs=should_log_inputs,
+        base_name_to_sets_of_related_ops=base_name_to_sets_of_related_ops,
+        node_type_to_io_type_map=node_type_to_io_type_map,
+        unmatchable_types_map=unmatchable_types_map,
+    )
+
+
+def extract_shadow_logger_info(
+    model_a_shadows_b: nn.Module,
+    logger_cls: Callable,
+    model_name_to_use_for_layer_names: str,
+) -> NSResultsType:
+    """
+    Traverse all loggers in a shadow model, and extract the logged
+    information.
+
+    Args:
+        model_a_shadows_b: shadow model
+        logger_cls: class of Logger to use
+        model_name_to_use_for_layer_names: string name of model to use for
+          layer names in the output
+
+    Return:
+        NSResultsType, containing the logged comparisons
+    """
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite_fx.extract_shadow_logger_info"
+    )
+    results: NSResultsType = collections.defaultdict(dict)
+    _extract_logger_info_one_model(model_a_shadows_b, results, logger_cls)
+    # fill in missing fqn entries
+    maybe_add_missing_fqns(results)
+    # rekey on the name of model b
+    results = rekey_logger_info_on_node_name_of_model(
+        results, model_name_to_use_for_layer_names
+    )
+    return dict(results)
+
+
+def extend_logger_results_with_comparison(
+    results: NSResultsType,
+    model_name_1: str,
+    model_name_2: str,
+    comparison_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor],
+    comparison_name: str,
+) -> None:
+    """
+    Compares the logged values from `model_name_2` against the corresponding
+    values in `model_name_1`, using `comparison_fn`. Records the result
+    in `model_name_2`'s results under `comparison_name`. Modifies `results` inplace.
+
+    Args:
+        results: the result data structure from `extract_logger_info` or
+          `extract_shadow_logger_info`.
+        model_name_1: string name of model 1
+        model_name_2: string name of model 2
+        comparison_fn: function to compare two Tensors
+        comparison_name: string name of model to use for
+          layer names in the output
+    """
+    for results_type_to_results in results.values():
+        for model_name_to_results in results_type_to_results.values():
+            assert (
+                model_name_1 in model_name_to_results
+            ), f"{model_name_1} not found in results"
+            assert (
+                model_name_2 in model_name_to_results
+            ), f"{model_name_2} not found in results"
+
+            results_1 = model_name_to_results[model_name_1]
+            results_2 = model_name_to_results[model_name_2]
+
+            for result_2 in results_2:
+                index_within_arg_2 = result_2["index_within_arg"]
+                index_of_arg_2 = result_2["index_of_arg"]
+                # find corresponding result_1
+                result_1 = None
+                for cur_result_1 in results_1:
+                    index_within_arg_1 = cur_result_1["index_within_arg"]
+                    index_of_arg_1 = cur_result_1["index_of_arg"]
+                    if (index_within_arg_1 == index_within_arg_2) and (
+                        index_of_arg_1 == index_of_arg_2
+                    ):
+                        result_1 = cur_result_1
+                        break
+                assert result_1 is not None
+
+                values_1 = result_1["values"]
+                values_2 = result_2["values"]
+                result_2[comparison_name] = []
+                for value_1, value_2 in zip(values_1, values_2):
+                    comparison_result = comparison_fn(value_1, value_2)
+                    result_2[comparison_name].append(comparison_result)
+
+
+def prepare_n_shadows_model(
+    model: torch.nn.Module,
+    example_inputs: Any,
+    qconfig_multi_mapping: QConfigMultiMapping,
+    backend_config: BackendConfig,
+    custom_prepare_fn: Optional[Callable] = None,
+    custom_prepare_kwargs: Optional[Dict[str, Any]] = None,
+    custom_tracer: Any = None,
+) -> GraphModule:
+    """
+    Given a model with a graph with M ops such as
+
+
+      args_kwargs_m -> op_m -> output_m
+
+
+    And a set of N qconfigs for each op, creates a new model, with
+    each of the subgraph of `op_m` transformed into
+
+    .. code::
+
+           |---------> op_m_n -> log_m_n
+           |                     /
+      args_kwargs_m ---------> op_m -> log_m_0
+
+    Where op_m_n is op_m wrapped in a submodule and transformed with
+    qconfig_n, and its inner graph looks like
+
+    .. code::
+
+      args_m -------- op_m_prepared_with_qconfig_n -> out_m_n
+                  /
+      kwargs_m ---
+
+    This is useful for testing different quantization of multiple layers in
+    a single pass through the model.
+
+    High level TODOs for future PRs:
+    * figure out a better way to name the output structure
+    * return a results data structure instead of printing it out
+    * add examples to docblocks
+    """
+
+    if custom_tracer is None:
+        tracer = quantize_fx.QuantizationTracer([], [])
+    else:
+        tracer = custom_tracer
+    mt = torch.fx.GraphModule(model, tracer.trace(model))
+    # this is necessary to ensure logger FQNs get populated
+    mt._node_name_to_scope = tracer.node_name_to_scope  # type: ignore[assignment]
+
+    # run example input propagation, we need this to call prepare_fx on
+    # individual subgraphs
+    output_prop = OutputProp(mt)
+    output_prop.propagate(*example_inputs)
+
+    # Find the set of subgraphs in the original graph which we need to
+    # consider.
+    modules = dict(mt.named_modules(remove_duplicate=False))
+    patterns = _get_pattern_to_quantize_handlers(backend_config)
+    root_node_getter_mapping = get_fusion_pattern_to_root_node_getter(backend_config)
+    standalone_module_names: List[str] = []
+    standalone_module_classes: List[Type] = []
+    custom_module_classes: List[Type] = []
+    matches = _find_matches(
+        mt.graph,
+        modules,
+        patterns,
+        root_node_getter_mapping,
+        standalone_module_names,
+        standalone_module_classes,
+        custom_module_classes,
+    )
+    subgraphs_dedup: Dict[str, List[Node]] = _get_dedup_subgraphs(matches)
+
+    # generate node to qconfig for each subgraph
+    # TODO(future PR): deduplicate repeating entries
+    list_of_node_name_to_qconfig: List[Dict[str, QConfigAny]] = []
+    for qconfig_mapping in qconfig_multi_mapping.qconfig_mappings_list:
+        node_name_to_qconfig = _generate_node_name_to_qconfig(
+            mt, modules, mt.graph, qconfig_mapping, tracer.node_name_to_scope
+        )
+        list_of_node_name_to_qconfig.append(node_name_to_qconfig)
+
+    # For each region in the model, do the following:
+    #   For each qconfig for that region, do the following:
+    #     1. create a copy of the region wrapped in a module
+    #     2. pass original args, original kwargs, and expected output to module
+    #     3. add an output comparison logger and hook it up to compare
+    #        actual output to expected output
+    #     4. run `prepare_fx` on the module
+    for subgraph_idx, (match_name, nodes_in_this_subgraph) in enumerate(
+        subgraphs_dedup.items()
+    ):
+        create_n_transformed_and_logged_copies_of_subgraph(
+            mt,
+            subgraph_idx,
+            match_name,
+            nodes_in_this_subgraph,
+            qconfig_multi_mapping.qconfig_mappings_list,
+            list_of_node_name_to_qconfig,
+            custom_prepare_fn,
+            custom_prepare_kwargs,  # type: ignore[arg-type]
+        )
+
+    return mt
+
+
+# TODO(future PR): we should rethink the names of all the PNP APIs
+def _prepare_n_shadows_add_loggers_model(
+    model: torch.nn.Module,
+    example_inputs: Any,
+    qconfig_mapping: QConfigMapping,
+    backend_config: BackendConfig,
+) -> torch.nn.Module:
+    r"""
+    Note: this API is not recommended for wide usage, it is only
+    provided for customers who need to migrate from the `add_loggers`
+    API.
+
+    This creates a model which provides logging for the following
+    problem: if we quantize `model` with `qconfig_mapping` and feed
+    the same input through both models, log the comparisons of
+    corresponding intermediate layers.
+
+    The problem is solved with a single model.  Specifically, we
+    partition `model` into N subgraphs, create a copy of each relevant
+    subgraph, wrap it in a module, apply the quantization API to that
+    module, and hook up loggers to measure the comparisons.
+
+    Example starting graph:
+
+      x0 -> op0 -> x1 -> op1 -> x2
+
+    Example config: quantize op0 to int8, do nothing to op1.
+    The following graph will be created:
+
+    .. code::
+
+      x0_0 -> op0_0 -> x1_0 -> log -----> op1_0 -> x2_0 -> log
+       \                        \                           \       # noqa: W605
+         ---> op0_1 -> x1_1 ----> clog -> op1_0 -> x2_1 ----> clog
+
+    Where op0_0 is op0, op0_1 is op0 wrapped in a submodule and quantized
+    to int8, op1_0 is op1 (appearing in the graph twice), log is a logger,
+    and clog is a comparison logger.
+    """
+
+    tracer = quantize_fx.QuantizationTracer([], [])
+    mt = torch.fx.GraphModule(model, tracer.trace(model))
+    # this is necessary to ensure logger FQNs get populated
+    mt._node_name_to_scope = tracer.node_name_to_scope  # type: ignore[assignment]
+
+    # run example input propagation, we need this to call prepare_fx on
+    # individual subgraphs
+    output_prop = OutputProp(mt)
+    output_prop.propagate(*example_inputs)
+
+    # Find the set of subgraphs in the original graph which we need to
+    # consider.
+    modules = dict(mt.named_modules(remove_duplicate=False))
+    patterns = _get_pattern_to_quantize_handlers(backend_config)
+    root_node_getter_mapping = get_fusion_pattern_to_root_node_getter(backend_config)
+    standalone_module_names: List[str] = []
+    standalone_module_classes: List[Type] = []
+    custom_module_classes: List[Type] = []
+    matches = _find_matches(
+        mt.graph,
+        modules,
+        patterns,
+        root_node_getter_mapping,
+        standalone_module_names,
+        standalone_module_classes,
+        custom_module_classes,
+    )
+    subgraphs_dedup: Dict[str, List[Node]] = _get_dedup_subgraphs(matches)
+
+    # generate node to qconfig for each subgraph
+    node_name_to_qconfig = _generate_node_name_to_qconfig(
+        mt, modules, mt.graph, qconfig_mapping, tracer.node_name_to_scope
+    )
+
+    # Now, mutate the graph to be the add_loggers graph with propagation
+    # error.
+    create_add_loggers_graph(mt, subgraphs_dedup, qconfig_mapping, node_name_to_qconfig)
+
+    return mt
+
+
+# TODO(future PR): we should rethink the names of all the PNP APIs
+def _n_shadows_compare_weights(
+    model: torch.nn.Module,
+    example_inputs: Any,
+    qconfig_mapping: QConfigMapping,
+    backend_config: BackendConfig,
+) -> NSResultsType:
+    """
+    Note: this API is not recommended for wide usage, it is only
+    provided for customers who need to migrate from the `add_loggers`
+    API.
+    """
+    qconfig_multi_mapping = QConfigMultiMapping.from_list_qconfig_mapping(
+        [qconfig_mapping]
+    )
+    mp = prepare_n_shadows_model(
+        model, example_inputs, qconfig_multi_mapping, backend_config
+    )
+    # passing inputs through the model is necessary to populate
+    # observers which observe weights with real values
+    mp(*example_inputs)
+    mq = convert_n_shadows_model(mp)
+    weight_comparison = extract_weight_comparison(mq)
+    return weight_comparison
+
+
+# TODO(future PR): consider aligning API signature with other similar quantization
+# functions (enable_fake_quant, etc)
+def loggers_set_enabled(model: torch.nn.Module, enabled: bool) -> None:
+    """
+    Sets the `enabled` setting on a `model`'s loggers
+    """
+    for name, child in model.named_modules():
+        if isinstance(child, OutputLogger):
+            child.enabled = enabled
+
+
+# TODO(future PR): consider aligning API signature with other similar quantization
+# functions (enable_fake_quant, etc)
+def loggers_set_save_activations(
+    model: torch.nn.Module,
+    save_activations: bool,
+) -> None:
+    """
+    Sets the `save_activations` setting on a `model`'s loggers
+    """
+    for name, child in model.named_modules():
+        if isinstance(child, OutputLogger):
+            child.save_activations = save_activations
+
+
+def convert_n_shadows_model(
+    model: GraphModule,
+    custom_convert_fn: Optional[Callable] = None,
+    custom_convert_kwargs: Optional[Dict[str, Any]] = None,
+) -> GraphModule:
+    """
+    Given a model from `prepare_n_shadows_model`, runs `convert_fx`
+    on each shadow submodule.
+    """
+    for node in model.graph.nodes:
+        # TODO(future PR): consider matching in a safer way than
+        # node name string match
+        if node.name.startswith(SHADOW_WRAPPER_NODE_NAME_PREFIX):
+            orig_mod = getattr(model, node.name)
+            if custom_convert_fn is None:
+                converted_mod = torch.ao.quantization.quantize_fx.convert_fx(orig_mod)
+            else:
+                if custom_convert_kwargs is None:
+                    custom_convert_kwargs = {}
+                converted_mod = custom_convert_fn(orig_mod, **custom_convert_kwargs)
+            setattr(model, node.name, converted_mod)
+
+    return model
+
+
+def extract_results_n_shadows_model(model: torch.nn.Module) -> NSResultsType:
+    """
+    Extracts logger results from `model`.
+    """
+    results: NSResultsType = {}
+    _extract_logger_info_one_model(model, results, OutputLogger)
+    return results
+
+
+def print_comparisons_n_shadows_model(results: NSResultsType) -> None:
+    """
+    Prints a summary of extracted `results`.
+    """
+    results_grouped = group_results_by_subgraph(results)
+    results_comparison = create_results_comparison(results_grouped)
+    print_n_shadows_summary(results_comparison)
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--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/graph_matcher.py
@@ -0,0 +1,470 @@
+# mypy: allow-untyped-defs
+import collections
+import enum
+from typing import Any, Dict, List, Optional, Set, Tuple
+
+import torch
+from torch.ao.quantization import FakeQuantizeBase, ObserverBase
+from torch.ao.quantization.utils import getattr_from_fqn
+from torch.fx import GraphModule
+from torch.fx.graph import Graph, Node
+
+from .mappings import get_base_name_to_sets_of_related_ops, get_unmatchable_types_map
+from .ns_types import NSNodeTargetType, NSSubgraph
+from .pattern_utils import (
+    end_node_matches_reversed_fusion,
+    get_reversed_fusions,
+    get_type_a_related_to_b,
+)
+
+
+toq = torch.ops.quantized
+
+
+def _get_output_nodes(g: Graph) -> List[Node]:
+    return [n for n in g.nodes if n.op == "output"]
+
+
+class _NSGraphMatchableSubgraphsIterator:
+    """
+    Iterates through the graph of gm, starting with the output nodes
+    and continuing backwards.
+    1. Returns matchable subgraphs, in order. A subgraph is defined by
+       (start_node, end_node).
+    2. Skips over non-matchable subgraphs
+    """
+
+    def __init__(
+        self,
+        gm: GraphModule,
+        non_matchable_functions: Set[NSNodeTargetType],
+        non_matchable_modules: Set[NSNodeTargetType],
+        non_matchable_methods: Set[NSNodeTargetType],
+    ):
+        self.gm: GraphModule = gm
+        self.non_matchable_functions: Set[NSNodeTargetType] = non_matchable_functions
+        self.non_matchable_modules: Set[NSNodeTargetType] = non_matchable_modules
+        self.non_matchable_methods: Set[NSNodeTargetType] = non_matchable_methods
+        self.seen_nodes: Set[Node] = set()
+        self.stack: List[Node] = []
+        for start_node in _get_output_nodes(self.gm.graph):
+            self.stack.append(start_node)
+
+    def __iter__(self):
+        return self
+
+    def __next__(self) -> NSSubgraph:
+        """
+        Returns the next matchable subgraph.
+        """
+        while len(self.stack) > 0:
+            cur_end_node = self.stack.pop()
+            if cur_end_node in self.seen_nodes:
+                continue
+
+            # for subgraphs which are single nodes, start_node == end_node
+            # for subgraphs with more than one node, start node != end_node
+            cur_start_node = cur_end_node
+            # Subgraphs like linear-relu have the base node as the start node.
+            # Subgraphs like dequantize-linear-relu-to(torch.float16) have the
+            #   base node as the second node.
+            # The cur_base_op_node var will move to the actual node during
+            #   the fusion matching later in this code block.
+            cur_base_op_node = cur_end_node
+
+            # Check for potential fusions. For now, we are greedy
+            # and always skip all non-base nodes of a fusion.  For example,
+            # if we match linear-relu backwards, we will always skip the
+            # relu node and attempt to match the linear node.  This can
+            # be made configurable later if needed.
+            for _reverse_fusion_ops, base_op_idx in get_reversed_fusions():
+                is_match = end_node_matches_reversed_fusion(
+                    cur_end_node, _reverse_fusion_ops, self.gm, self.seen_nodes
+                )
+                if is_match:
+                    # navigate to the base node
+                    for rev_fusion_idx in range(len(_reverse_fusion_ops) - 1):
+                        self.seen_nodes.add(cur_start_node)
+                        # for now, assume that there are no other nodes
+                        # which need to be added to the stack
+                        cur_start_node = cur_start_node.args[0]  # type: ignore[assignment]
+                        # if the base op index matches the current node, set it
+                        rev_base_op_idx = len(_reverse_fusion_ops) - 2 - base_op_idx
+                        if rev_fusion_idx == rev_base_op_idx:
+                            cur_base_op_node = cur_start_node
+                    break
+
+            self.seen_nodes.add(cur_start_node)
+            # add args of previous nodes to stack
+            for arg in cur_start_node.all_input_nodes:
+                self._recursively_add_node_arg_to_stack(arg)
+
+            # skip unmatchable nodes
+            # note: this check is done on the start_node, i.e.
+            # if we are matching linear-relu in reverse, this would do the matchable
+            # check on the linear
+            if not self._is_matchable(cur_base_op_node):
+                continue
+
+            # If an observer or a fake_quant was not matched as a part of
+            # a pattern of multiple nodes, ignore it. One case where this is
+            # relevant is an observer on a graph input, which was added because
+            # it is necessary for the next node.
+            if cur_end_node.op == "call_module" and cur_start_node is cur_end_node:
+                maybe_obs = getattr_from_fqn(self.gm, cur_end_node.target)  # type: ignore[arg-type]
+                if isinstance(maybe_obs, (ObserverBase, FakeQuantizeBase)):
+                    continue
+
+            return NSSubgraph(
+                start_node=cur_start_node,
+                end_node=cur_end_node,
+                base_op_node=cur_base_op_node,
+            )
+
+        raise StopIteration
+
+    def _recursively_add_node_arg_to_stack(self, arg: Any) -> None:
+        """
+        Adds all of the nodes in this arg to the stack, properly navigating
+        through list, dicts and tuples.
+        """
+        if isinstance(arg, Node):
+            self.stack.append(arg)
+        elif (
+            isinstance(arg, torch.fx.immutable_collections.immutable_list)
+            or type(arg) is tuple
+        ):
+            for inner_arg in arg:
+                self._recursively_add_node_arg_to_stack(inner_arg)
+        elif isinstance(arg, torch.fx.immutable_collections.immutable_dict):
+            for value in arg.values():
+                self._recursively_add_node_arg_to_stack(value)
+
+    def _is_matchable(self, node: Node) -> bool:
+        if node.op == "call_function":
+            return node.target not in self.non_matchable_functions
+        elif node.op == "call_module":
+            assert isinstance(node.target, str)
+            target_mod = getattr_from_fqn(self.gm, node.target)
+            return not any(
+                isinstance(target_mod, t)  # type: ignore[arg-type]
+                for t in self.non_matchable_modules
+            )
+        elif node.op == "call_method":
+            return node.target not in self.non_matchable_methods
+        else:
+            return False
+
+
+class GraphMatchingException(Exception):
+    """
+    Exception raised when two graphs cannot be matched.
+    """
+
+
+class SubgraphTypeRelationship(enum.Enum):
+    # same type, known
+    # example: F.linear and F.linear, or nn.Conv2d and nn.Conv2d
+    EQUAL = enum.auto()
+    # same type, but the type is not known to Numerical Suite
+    # (user defined type, etc).
+    EQUAL_BUT_UKNOWN = enum.auto()
+    # known, same subgraph_relationship set, but not the same type
+    # example: F.linear and toq.linear
+    RELATED_BUT_NOT_EQUAL = enum.auto()
+    # not related
+    NOT_RELATED = enum.auto()
+
+
+def _get_subgraph_relationship_type(
+    subgraph_a: NSSubgraph,
+    subgraph_b: NSSubgraph,
+    gm_a: GraphModule,
+    gm_b: GraphModule,
+    type_a_related_to_b: Set[Tuple[NSNodeTargetType, NSNodeTargetType]],
+) -> SubgraphTypeRelationship:
+    node_a = subgraph_a.base_op_node
+    node_b = subgraph_b.base_op_node
+
+    # TODO(next): make this code handle matching by what is before the base op
+    if node_a.op != node_b.op:
+        if not (
+            node_a.op in ("call_function", "call_method")
+            and node_b.op in ("call_function", "call_method")
+        ):
+            return SubgraphTypeRelationship.NOT_RELATED
+
+    if node_a.op in ("call_function", "call_method"):
+        key = (node_a.target, node_b.target)
+
+        if key not in type_a_related_to_b:
+            if node_a.target == node_b.target:
+                return SubgraphTypeRelationship.EQUAL_BUT_UKNOWN
+            else:
+                return SubgraphTypeRelationship.NOT_RELATED
+        # after this point, we are dealing with known types
+
+        if node_a.target == node_b.target:
+            node_a_has_prev = subgraph_a.base_op_node == subgraph_a.start_node
+            node_b_has_prev = subgraph_b.base_op_node == subgraph_b.start_node
+            if node_a_has_prev and (not node_b_has_prev):
+                return SubgraphTypeRelationship.RELATED_BUT_NOT_EQUAL
+            elif (not node_a_has_prev) and node_b_has_prev:
+                return SubgraphTypeRelationship.RELATED_BUT_NOT_EQUAL
+            elif (not node_a_has_prev) and (not node_b_has_prev):
+                return SubgraphTypeRelationship.EQUAL
+            else:
+                # TODO(future PR): check for matches start_op_node and base_op_node
+                return SubgraphTypeRelationship.EQUAL
+
+        if key in type_a_related_to_b:
+            return SubgraphTypeRelationship.RELATED_BUT_NOT_EQUAL
+        else:
+            return SubgraphTypeRelationship.NOT_RELATED
+    elif node_a.op == "call_module":
+        assert (
+            subgraph_a.base_op_node == subgraph_a.start_node
+            and subgraph_b.base_op_node == subgraph_b.start_node
+        ), "Matching call_module patterns where base_op_node != start_node is not supported yet"
+        # for call_module, we need to look up the modules to do the type check
+        assert isinstance(node_a.target, str)
+        mod_a = getattr_from_fqn(gm_a, node_a.target)
+        assert isinstance(node_b.target, str)
+        mod_b = getattr_from_fqn(gm_b, node_b.target)
+
+        key = (type(mod_a), type(mod_b))
+
+        if key not in type_a_related_to_b:
+            if type(mod_a) == type(mod_b):
+                return SubgraphTypeRelationship.EQUAL_BUT_UKNOWN
+            else:
+                return SubgraphTypeRelationship.NOT_RELATED
+        elif type(mod_a) == type(mod_b):
+            return SubgraphTypeRelationship.EQUAL
+        else:
+            return SubgraphTypeRelationship.RELATED_BUT_NOT_EQUAL
+
+    return SubgraphTypeRelationship.NOT_RELATED
+
+
+def _get_name_for_subgraph(
+    subgraph_a: NSSubgraph,
+    gm_a: GraphModule,
+    base_name_to_sets_of_related_ops: Dict[str, Set[NSNodeTargetType]],
+    existing_names: Set[str],
+) -> str:
+    """
+    Returns a unique name for a subgraph. This name is based on two things:
+    1. the name of the set containing the underlying type of the base op in the
+       subgraph (i.e. 'torch.nn.functional.linear' if this is related to a linear op)
+    2. the number of previous subgraphs with related underlying type of the base op
+
+    For example, in the graph
+
+    linear0 -> relu0 -> linear1 -> relu1
+
+    The subgraphs are (linear0, relu0) and (linear1, relu1).  If we iterate
+    from the output node backwards, the name given to (linear1, relu1) will be
+    `base_op_torch.nn.functional.linear_0`, and the name given to (linear0, relu0)
+    will be `base_op_torch.nn.functional.linear_1`.
+
+    Why are we not just using the node name? Answer: because of two requirements:
+    A. fusions must be supported
+    B. some Numeric Suite APIs can be called without having all of the models in memory
+
+    For example, let's say we need to match nodes of
+
+    (1) ... -> linear0 -> relu0 -> ...
+
+    And
+
+    (2) ... -> linear_relu0 -> ...
+
+    Without being able to inspect them together. With the current naming scheme, if
+    we iterate through both of these graphs in the same order, and assuming the rest
+    of the graphs match, both of these subgraphs will get the same name without
+    (1) and (2) knowing anything about each other.
+    """
+    target_type = _get_node_target_type(subgraph_a.base_op_node, gm_a)
+    target_base_type = None
+    for base_name, sets_of_related_ops in base_name_to_sets_of_related_ops.items():
+        if target_type in sets_of_related_ops:
+            target_base_type = base_name
+    target_base_name = "base_op_" + str(target_base_type)
+    counter = 0
+    proposed_name = target_base_name + "_" + str(counter)
+    while proposed_name in existing_names:
+        counter += 1
+        proposed_name = target_base_name + "_" + str(counter)
+    existing_names.add(proposed_name)
+    return proposed_name
+
+
+def _get_node_target_type(node: Node, gm: GraphModule) -> Optional[NSNodeTargetType]:
+    if node.op in ("call_function", "call_method"):
+        return node.target
+    elif node.op == "call_module":
+        assert isinstance(node.target, str)
+        mod = getattr_from_fqn(gm, node.target)
+        return type(mod)
+    return None
+
+
+def get_matching_subgraph_pairs(
+    gm_a: GraphModule,
+    gm_b: GraphModule,
+    base_name_to_sets_of_related_ops: Optional[Dict[str, Set[NSNodeTargetType]]] = None,
+    unmatchable_types_map: Optional[Dict[str, Set[NSNodeTargetType]]] = None,
+) -> Dict[str, Tuple[NSSubgraph, NSSubgraph]]:
+    """
+    Matches matchable subgraphs of graph_a to graph_b.
+
+    For a node, "matchable" is defined as a node which is not an observer,
+    fake_quants, quant or dequant.
+
+    A subgraph can contain one or more nodes.  A subgraph is matchable if
+    at least one node inside of it is matchable.  Currently, all nodes in
+    a subgraph must be matchable (because we assume no observers will be
+    inserted in the middle of a fusion).
+
+    A subgraph is defined by (start_node, end_node).  We assume that only
+    start_node and end_node are linked with the surrounding graph, all other
+    nodes in a subgraph are self-contained.
+
+    A pair of nodes is "related" if both nodes represent the same mathematical
+    operation across different quantization flavors. For example,
+    `F.linear` and `torch.ops.quantized.linear` are related, and
+    `F.linear` and `torch.nn.Conv` are not related.
+
+    For each matchable pair of nodes node_a and node_b, they will match
+    if node_a and node_b are related.
+
+    For graphs A and B, they will match iff:
+    1. the number of matchable subgraphs in A and B is equivalent
+    2. when iterating through the matchable subgraphs of A and B in the same order, each
+       corresponding pair of base nodes is related.
+
+    This enables us to find the corresponding subgraphs between
+    graphs of related models.  For example, if we had two graphs such as:
+
+    graph_a: x0 -> conv_0 (type: nn.Conv2d) -> obs_0 -> x1
+             w  -/
+             b  -/
+
+    graph_b: x0 -> quant_0 -> qconv_0 (type: nnq.Conv2d) -> dequant_0 -> x1
+           packed_params_0 -/
+
+    This function will return the following result:
+    {
+        'conv_0': (  # the name of the node in graph_b
+          (conv_0, conv_0),  # (start_node_a, end_node_a)
+          (qconv_0, qconv_0),  # (start_node_b, end_node_b)
+        ),
+    }
+
+    Or, if we have a fusion pattern,
+
+    graph_a: x0 -> linear_0 -> relu_0 -> obs_0 -> x1
+             w  -/
+             b  -/
+
+    graph_b: x0 -> quant_0 -> linear_relu_0 -> dequant_0 -> x1
+           packed_params_0 -/
+
+    This function will return the following result:
+    {
+        'linear_relu_0': (  # the name of the node in graph_b
+          (linear_0, relu_0),  # (start_node_a, end_node_a)
+          (linear_relu_0, linear_relu_0),  # (start_node_b, end_node_b)
+        ),
+    }
+    """
+    if unmatchable_types_map is None:
+        unmatchable_types_map = get_unmatchable_types_map()
+    non_matchable_functions = unmatchable_types_map["funs_unmatchable"]
+    non_matchable_modules = unmatchable_types_map["mods_unmatchable"]
+    non_matchable_methods = unmatchable_types_map["meths_unmatchable"]
+
+    graph_a_iterator = _NSGraphMatchableSubgraphsIterator(
+        gm_a, non_matchable_functions, non_matchable_modules, non_matchable_methods
+    )
+    graph_b_iterator = _NSGraphMatchableSubgraphsIterator(
+        gm_b, non_matchable_functions, non_matchable_modules, non_matchable_methods
+    )
+    results = collections.OrderedDict()
+    if base_name_to_sets_of_related_ops is None:
+        base_name_to_sets_of_related_ops = get_base_name_to_sets_of_related_ops()
+    type_a_related_to_b = get_type_a_related_to_b(base_name_to_sets_of_related_ops)
+
+    existing_names_a: Set[str] = set()
+    existing_names_b: Set[str] = set()
+
+    while True:
+        # fetch the next subgraphs from a and b
+        cur_subgraph_a, cur_subgraph_b = None, None
+        try:
+            cur_subgraph_a = next(graph_a_iterator)
+        except StopIteration:
+            pass
+        try:
+            cur_subgraph_b = next(graph_b_iterator)
+        except StopIteration:
+            pass
+
+        # look up types of a and b for useful error messages
+        type_start_a, type_start_b = None, None
+        if cur_subgraph_a is not None:
+            type_start_a = _get_node_target_type(cur_subgraph_a.start_node, gm_a)
+        if cur_subgraph_b is not None:
+            type_start_b = _get_node_target_type(cur_subgraph_b.start_node, gm_b)
+
+        # check for results and determine what to do next
+        if cur_subgraph_a is not None and cur_subgraph_b is not None:
+            # both nodes were fetched, check for subgraph_relationship
+            # note: subgraph_relationship is checked on the start node, i.e.
+            # if a linear-relu pattern is checked, we would check for subgraph_relationship
+            # of the linear
+            subgraph_relationship = _get_subgraph_relationship_type(
+                cur_subgraph_a, cur_subgraph_b, gm_a, gm_b, type_a_related_to_b
+            )
+            if subgraph_relationship == SubgraphTypeRelationship.NOT_RELATED:
+                msg = f"""
+The subgraphs
+({cur_subgraph_a}, {type_start_a}) and
+({cur_subgraph_b}, {type_start_b})
+are not related. Please ensure that the two models you pass in have the same number
+of subgraphs, and each pair of subgraphs is related to each other."""
+                raise GraphMatchingException(msg)
+            elif subgraph_relationship == SubgraphTypeRelationship.EQUAL_BUT_UKNOWN:
+                # skip matching but unknown types
+                continue
+            key_name_a = _get_name_for_subgraph(
+                cur_subgraph_a, gm_a, base_name_to_sets_of_related_ops, existing_names_a
+            )
+            key_name_b = _get_name_for_subgraph(
+                cur_subgraph_b, gm_b, base_name_to_sets_of_related_ops, existing_names_b
+            )
+            assert (
+                key_name_a == key_name_b
+            ), f"Subgraph names {key_name_a} and {key_name_b} do not match"
+            results[key_name_a] = (cur_subgraph_a, cur_subgraph_b)
+            continue
+        elif cur_subgraph_a is None and cur_subgraph_b is None:
+            # we reached the end of both graphs
+            break
+        else:
+            # only one node was fetched, no match possible, throw error
+            msg = f"""
+Attempting to match
+({cur_subgraph_a}, {type_start_a}) and
+({cur_subgraph_b}, {type_start_b}),
+one of which is empty. Please ensure that the two models you pass in have the same number
+of subgraphs."""
+            raise GraphMatchingException(msg)
+
+    # The subgraph pairs are originally created by traversing the two graphs
+    # from the outputs to the inputs. Reverse the results to return the
+    # subgraphs in their order of execution.
+    results = collections.OrderedDict(reversed(list(results.items())))
+
+    return results
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/graph_passes.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/graph_passes.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd24a0edc2f8b5853bdfeb54dee94fb25175b0e9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/graph_passes.py
@@ -0,0 +1,1131 @@
+# mypy: allow-untyped-defs
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
+
+import torch
+from torch.ao.ns.fx.mappings import get_node_type_to_io_type_map
+from torch.ao.quantization.fx.utils import get_new_attr_name_with_prefix
+from torch.ao.quantization.observer import _is_activation_post_process
+from torch.fx import GraphModule, map_arg
+from torch.fx.graph import Graph, Node
+
+from .ns_types import NSNodeTargetType, NSSingleResultValuesType, NSSubgraph
+from .utils import (
+    get_arg_indices_of_inputs_to_log,
+    get_node_first_input_and_output_type,
+    get_node_input_qparams,
+    get_normalized_nth_input,
+    get_number_of_non_param_args,
+    get_target_type_str,
+    getattr_from_fqn,
+    NodeInputOrOutputType,
+    op_type_supports_shadowing,
+    return_first_non_observer_node,
+)
+
+
+def _maybe_get_fqn(node: Node, gm: GraphModule) -> Optional[str]:
+    fqn = None
+    if hasattr(gm, "_node_name_to_scope"):
+        # fqn on observers is not present, because they do not
+        # exist when the fqns are created during tracing. If this is
+        # an observer, get the fqn of the node being observed.
+        node_to_use_for_fqn = node
+        if node.op == "call_module":
+            assert isinstance(node.target, str)
+            module = getattr_from_fqn(gm, node.target)
+            if _is_activation_post_process(module):
+                node_to_use_for_fqn = get_normalized_nth_input(node, gm, 0)
+        fqn = gm._node_name_to_scope[node_to_use_for_fqn.name][0]  # type: ignore[index]
+    return fqn  # type: ignore[return-value]
+
+
+def _insert_logger_after_node(
+    node: Node,
+    gm: GraphModule,
+    logger_cls: Callable,
+    logger_node_name_suffix: str,
+    ref_node_name: str,
+    model_name: str,
+    ref_name: str,
+    ref_node_target_type: str,
+    results_type: str,
+    index_within_arg: int,
+    index_of_arg: int,
+    fqn: Optional[str],
+) -> Node:
+    """
+    Given a starting graph of
+
+    prev_node -> node -> next_node
+
+    This function creates a new logger_cls obj and adds it
+    after node, resulting in
+
+    prev_node -> node -> logger_obj -> next_node
+    """
+    # create new name
+    logger_node_name = get_new_attr_name_with_prefix(
+        node.name + logger_node_name_suffix
+    )(gm)
+    target_type = get_target_type_str(node, gm)
+    # create the logger object
+    logger_obj = logger_cls(
+        ref_node_name,
+        node.name,
+        model_name,
+        ref_name,
+        target_type,
+        ref_node_target_type,
+        results_type,
+        index_within_arg,
+        index_of_arg,
+        fqn,
+    )
+    # attach the logger object to the parent module
+    setattr(gm, logger_node_name, logger_obj)
+    logger_node = node.graph.create_node("call_module", logger_node_name, (node,), {})
+    return logger_node
+
+
+def add_loggers_to_model(
+    gm: GraphModule,
+    node_to_instrument_inputs_to_ref_node_name: Dict[Node, Tuple[str, str]],
+    node_to_instrument_outputs_to_ref_node_name: Dict[Node, Tuple[str, str]],
+    logger_cls: Callable,
+    model_name: str,
+) -> GraphModule:
+    """
+    Takes the graph of gm, adds loggers to the output
+    of each node in nodes_to_instrument. Returns a GraphModule with the new
+    graph.
+    """
+
+    new_graph = Graph()
+    env: Dict[str, Any] = {}
+    modules = dict(gm.named_modules())
+
+    def load_arg(a):
+        return map_arg(a, lambda node: env[node.name])
+
+    for node in gm.graph.nodes:
+        if node.op == "output":
+            new_graph.output(map_arg(get_normalized_nth_input(node, gm, 0), load_arg))
+            continue
+
+        if (node in node_to_instrument_inputs_to_ref_node_name) or (
+            node in node_to_instrument_outputs_to_ref_node_name
+        ):
+            fqn = _maybe_get_fqn(node, gm)
+
+            if node in node_to_instrument_inputs_to_ref_node_name:
+                ref_name, ref_node_type = node_to_instrument_inputs_to_ref_node_name[
+                    node
+                ]
+                # Ops such add and mul are special because either
+                # one or two of the first two arguments can be tensors,
+                # and if one argument is a tensor it can be first or
+                # second (x + 1 versus 1 + x).
+                arg_indices_to_log = get_arg_indices_of_inputs_to_log(node)
+                for node_arg_idx in arg_indices_to_log:
+                    node_arg = get_normalized_nth_input(node, gm, node_arg_idx)
+                    if type(node_arg) == Node:
+                        # create a single input logger
+                        prev_node = env[node_arg.name]
+                        env[node_arg.name] = _insert_logger_after_node(
+                            prev_node,
+                            gm,
+                            logger_cls,
+                            "_ns_logger_",
+                            node.name,
+                            model_name,
+                            ref_name,
+                            ref_node_type,
+                            NSSingleResultValuesType.NODE_INPUT.value,
+                            index_within_arg=0,
+                            index_of_arg=node_arg_idx,
+                            fqn=fqn,
+                        )
+                    elif (
+                        type(node_arg) == torch.fx.immutable_collections.immutable_list
+                    ):
+                        # create N input loggers, one for each node
+                        for arg_idx, arg in enumerate(node_arg):  # type: ignore[var-annotated, arg-type]
+                            prev_node = env[arg.name]
+                            env[prev_node.name] = _insert_logger_after_node(
+                                prev_node,
+                                gm,
+                                logger_cls,
+                                "_ns_logger_",
+                                node.name,
+                                model_name,
+                                ref_name,
+                                ref_node_type,
+                                NSSingleResultValuesType.NODE_INPUT.value,
+                                index_within_arg=arg_idx,
+                                index_of_arg=node_arg_idx,
+                                fqn=fqn,
+                            )
+                    else:
+                        pass
+
+            # ensure env is populated with base node
+            # Note: runs for both inputs and outputs
+            env[node.name] = new_graph.node_copy(node, load_arg)
+
+            if node in node_to_instrument_outputs_to_ref_node_name:
+                ref_name, ref_node_type = node_to_instrument_outputs_to_ref_node_name[
+                    node
+                ]
+                # add the logger after the base node
+                env[node.name] = _insert_logger_after_node(
+                    env[node.name],
+                    gm,
+                    logger_cls,
+                    "_ns_logger_",
+                    node.name,
+                    model_name,
+                    ref_name,
+                    ref_node_type,
+                    NSSingleResultValuesType.NODE_OUTPUT.value,
+                    index_within_arg=0,
+                    index_of_arg=0,
+                    fqn=fqn,
+                )
+
+        else:
+            env[node.name] = new_graph.node_copy(node, load_arg)
+
+    new_gm = GraphModule(gm, new_graph)
+    return new_gm
+
+
+def _insert_quantize_per_tensor_node(
+    prev_node_c: Node,
+    node_a: Node,
+    gm_b: GraphModule,
+    graph_c: Graph,
+    scale: Union[torch.Tensor, float],
+    zero_point: Union[torch.Tensor, int],
+    dtype_cast_name: str,
+) -> Node:
+    # copy scale
+    scale_node_name = get_new_attr_name_with_prefix(node_a.name + "_input_scale_")(gm_b)
+    setattr(gm_b, scale_node_name, scale)
+    scale_node = graph_c.create_node(
+        "get_attr", scale_node_name, (), {}, scale_node_name
+    )
+    # copy zero_point
+    zero_point_node_name = get_new_attr_name_with_prefix(
+        node_a.name + "_input_zero_point_"
+    )(gm_b)
+    setattr(gm_b, zero_point_node_name, zero_point)
+    zero_point_node = graph_c.create_node(
+        "get_attr", zero_point_node_name, (), {}, zero_point_node_name
+    )
+    # create the quantize_per_tensor call
+    return graph_c.create_node(
+        "call_function",
+        torch.quantize_per_tensor,
+        (prev_node_c, scale_node, zero_point_node, torch.quint8),
+        {},
+        dtype_cast_name,
+    )
+
+
+def _insert_dtype_cast_after_node(
+    node_a: Node,
+    node_c: Node,
+    prev_node_c: Union[Node, List[Node]],
+    gm_a: GraphModule,
+    gm_b: GraphModule,
+    graph_c: Graph,
+    node_name_prefix: str,
+    logger_cls: Callable,
+    node_type_to_io_type_map: Dict[str, Set[NSNodeTargetType]],
+) -> Union[Node, List[Node]]:
+    """
+    Given a starting graph C (derived from graph B) of
+
+    ... -> prev_node_c -> node_c -> ...
+
+    And a corresponding related node_a, inserts the correct dtype
+    cast node after prev_node_c to cast into the dtype expected
+    by node_a, resulting in:
+
+                          dtype_cast
+                        /
+    ... -> prev_node_c -> node_c -> ...
+
+    For example, if node_c is an int8 op and node_a is an fp32 op, this function
+    will insert a dequant.
+    """
+    dtype_cast_op = None
+    dtype_cast_mod_cls = None
+    dtype_cast_method = None
+    dtype_cast_method_dtype = None
+    dtype_cast_scale = None
+    dtype_cast_zero_point = None
+    node_input_type_a, _node_output_type_a = get_node_first_input_and_output_type(
+        node_a, gm_a, logger_cls, node_type_to_io_type_map
+    )
+    node_input_type_c, _node_output_type_c = get_node_first_input_and_output_type(
+        node_c, gm_b, logger_cls, node_type_to_io_type_map
+    )
+
+    if (
+        (
+            node_input_type_a == NodeInputOrOutputType.FP32
+            and node_input_type_c == NodeInputOrOutputType.INT8
+        )
+        or (
+            node_input_type_a == NodeInputOrOutputType.FP32
+            and node_input_type_c == NodeInputOrOutputType.FP16
+        )
+        or
+        # TODO(future PR): determine the actual dtype of node_c,
+        # the current code only works because dequantize works with
+        # multiple input dtypes.
+        (
+            node_input_type_a == NodeInputOrOutputType.FP32
+            and node_input_type_c == NodeInputOrOutputType.FP32_OR_INT8
+        )
+    ):
+        dtype_cast_op = torch.dequantize
+    elif (
+        node_input_type_a == node_input_type_c
+        and node_input_type_a != NodeInputOrOutputType.UNKNOWN
+    ):
+        dtype_cast_mod_cls = torch.nn.Identity
+    elif (
+        node_input_type_a == NodeInputOrOutputType.INT8
+        and node_input_type_c == NodeInputOrOutputType.FP32
+    ):
+        # int8 shadows fp32, the dtype cast needs to quantize to int8
+        # with the right qparams.
+        node_a_input_qparams = get_node_input_qparams(
+            node_a, gm_a, node_type_to_io_type_map
+        )
+        if node_a_input_qparams is not None:
+            dtype_cast_op = torch.quantize_per_tensor  # type: ignore[assignment]
+            dtype_cast_scale, dtype_cast_zero_point = node_a_input_qparams
+    elif (
+        node_input_type_a == NodeInputOrOutputType.FP16
+        and node_input_type_c == NodeInputOrOutputType.FP32
+    ):
+        dtype_cast_method = "to"
+        dtype_cast_method_dtype = torch.float16
+    else:
+        raise AssertionError(
+            f"dtype cast from {node_input_type_c} {node_c.format_node()} to "
+            + f"{node_input_type_a} {node_a.format_node()} needs to be implemented"
+        )
+
+    if isinstance(prev_node_c, Node):
+        new_dtype_cast_name = get_new_attr_name_with_prefix(node_name_prefix)(gm_b)
+        if dtype_cast_op:
+            if dtype_cast_scale is not None and dtype_cast_zero_point is not None:
+                return _insert_quantize_per_tensor_node(
+                    prev_node_c,
+                    node_a,
+                    gm_b,
+                    graph_c,
+                    dtype_cast_scale,
+                    dtype_cast_zero_point,
+                    new_dtype_cast_name,
+                )
+            else:
+                return graph_c.create_node(
+                    "call_function",
+                    dtype_cast_op,
+                    (prev_node_c,),
+                    {},
+                    new_dtype_cast_name,
+                )
+        elif dtype_cast_method:
+            return graph_c.create_node(
+                "call_method",
+                dtype_cast_method,
+                (prev_node_c, dtype_cast_method_dtype),
+                {},
+                new_dtype_cast_name,
+            )
+        else:
+            assert dtype_cast_mod_cls
+            dtype_cast_mod = dtype_cast_mod_cls()
+            setattr(gm_b, new_dtype_cast_name, dtype_cast_mod)
+            return graph_c.create_node(
+                "call_module",
+                new_dtype_cast_name,
+                (prev_node_c,),
+                {},
+                new_dtype_cast_name,
+            )
+    elif isinstance(prev_node_c, list):
+        results = []
+        for prev_node_c_inner in prev_node_c:
+            new_dtype_cast_name = get_new_attr_name_with_prefix(node_name_prefix)(gm_b)
+            if dtype_cast_op:
+                # TODO(future PR): add handling for quantize_per_tensor
+                new_dtype_cast_node = graph_c.create_node(
+                    "call_function",
+                    dtype_cast_op,
+                    (prev_node_c_inner,),
+                    {},
+                    new_dtype_cast_name,
+                )
+                results.append(new_dtype_cast_node)
+            else:
+                assert dtype_cast_mod_cls
+                dtype_cast_mod = dtype_cast_mod_cls()
+                setattr(gm_b, new_dtype_cast_name, dtype_cast_mod)
+                new_dtype_cast_node = graph_c.create_node(
+                    "call_module",
+                    new_dtype_cast_name,
+                    (prev_node_c_inner,),
+                    {},
+                    new_dtype_cast_name,
+                )
+                results.append(new_dtype_cast_node)
+        return results
+    else:
+        raise AssertionError(f"type f{type(prev_node_c)} is not handled")
+
+
+# TODO(future PR): look into using copy_node API instead
+def _copy_node_from_a_to_c(
+    node_a: Node,
+    gm_a: GraphModule,
+    gm_b: GraphModule,
+    graph_c: Graph,
+) -> Node:
+    """
+    Simple copy of node_a to graph_c.
+    """
+    if node_a.op == "get_attr":
+        node_a_copy_name = get_new_attr_name_with_prefix(node_a.name + "_shadow_copy_")(
+            gm_b
+        )
+        node_a_obj = getattr_from_fqn(gm_a, node_a.target)  # type: ignore[arg-type]
+        if torch.is_tensor(node_a_obj):
+            node_a_obj = node_a_obj.detach()
+        setattr(gm_b, node_a_copy_name, node_a_obj)
+        node_a_copy = graph_c.create_node(
+            node_a.op, node_a_copy_name, (), {}, node_a_copy_name
+        )
+        return node_a_copy
+    elif node_a.op == "call_method":
+        assert node_a.target in (
+            "dequantize",
+            "to",
+        ), f"target {node_a.target} is not implemented"
+        if node_a.target == "dequantize":
+            arg_copy = _copy_node_from_a_to_c(
+                get_normalized_nth_input(node_a, gm_a, 0), gm_a, gm_b, graph_c
+            )  # type: ignore[arg-type]
+            node_a_copy_name = get_new_attr_name_with_prefix(
+                node_a.name + "_shadow_copy_"
+            )(gm_b)
+            node_a_copy = graph_c.create_node(
+                node_a.op, node_a.target, (arg_copy,), {}, node_a_copy_name
+            )
+            return node_a_copy
+        else:  # to
+            arg_copy = _copy_node_from_a_to_c(
+                get_normalized_nth_input(node_a, gm_a, 0), gm_a, gm_b, graph_c
+            )  # type: ignore[arg-type]
+            node_a_copy_name = get_new_attr_name_with_prefix(
+                node_a.name + "_shadow_copy_"
+            )(gm_b)
+            node_a_copy = graph_c.create_node(
+                node_a.op,
+                node_a.target,
+                (arg_copy, get_normalized_nth_input(node_a, gm_a, 1)),
+                {},
+                node_a_copy_name,
+            )
+            return node_a_copy
+
+    else:
+        raise AssertionError(
+            f"handling of node {node_a.format_node()} with op {node_a.op} is not implemented"
+        )
+
+
+def _can_insert_copy_of_subgraph_a(
+    subgraph_a: NSSubgraph,
+    gm_a: GraphModule,
+    num_non_param_args_node_a: int,
+) -> bool:
+    """
+    This function returns `False` if the input subgraph cannot be copied by
+    `_insert_copy_of_subgraph_a_after_input_node_c`. This usually means
+    that there is a corner case logic for which copy is not yet implemented.
+    """
+    # populate the list of nodes we need to check
+    nodes = []
+    cur_node = subgraph_a.end_node
+    while cur_node != subgraph_a.start_node:
+        nodes.append(cur_node)
+        cur_node = get_normalized_nth_input(cur_node, gm_a, 0)  # type: ignore[assignment]
+    nodes.append(cur_node)
+    nodes.reverse()
+
+    def _can_insert(node_a_arg, gm_a):
+        if isinstance(node_a_arg, Node):
+            arg_a = return_first_non_observer_node(node_a_arg, gm_a)
+            if arg_a.op == "call_method":
+                return arg_a.target in ("dequantize", "to")
+            elif arg_a.op == "get_attr":
+                return True
+            else:
+                return False
+        elif isinstance(node_a_arg, (list, tuple)):
+            for el in node_a_arg:
+                if not isinstance(el, Node):
+                    return False
+        return True
+
+    # For each node, check if we handle the copy behavior. This follows the
+    # logic in `_insert_copy_of_subgraph_a_after_input_node_c`.
+    for node_a in nodes:
+        local_num_non_param_args_node_a = (
+            num_non_param_args_node_a if node_a is nodes[0] else 1
+        )
+
+        norm_args_kwargs = node_a.normalized_arguments(
+            gm_a, normalize_to_only_use_kwargs=True
+        )
+        if norm_args_kwargs is not None:
+            norm_args, norm_kwargs = norm_args_kwargs
+        else:
+            norm_args, norm_kwargs = node_a.args, node_a.kwargs
+
+        cur_idx = 0
+
+        while cur_idx < len(norm_args):
+            if cur_idx == 0:
+                pass
+            elif cur_idx == 1 and local_num_non_param_args_node_a == 2:
+                pass
+            else:
+                if not _can_insert(norm_args[cur_idx], gm_a):
+                    return False
+            cur_idx += 1
+
+        for kwarg_val in norm_kwargs.values():
+            # stitch the inputs from base graph
+            if cur_idx == 0:
+                pass
+            elif cur_idx == 1 and local_num_non_param_args_node_a == 2:
+                pass
+            else:
+                if not _can_insert(kwarg_val, gm_a):
+                    return False
+            cur_idx += 1
+
+    return True
+
+
+def _insert_copy_of_subgraph_a_after_input_node_c(
+    input_node_c: Union[Node, List[Node]],
+    input_node_c_2: Optional[Union[Node, List[Node]]],
+    subgraph_a: NSSubgraph,
+    gm_a: GraphModule,
+    gm_b: GraphModule,
+    node_name_prefix: str,
+) -> Node:
+    """
+    TODO(before land): real docblock
+    """
+    if isinstance(input_node_c, Node):
+        graph_c = input_node_c.graph
+    else:
+        assert isinstance(input_node_c, list)
+        graph_c = input_node_c[0].graph
+
+    # create a sequential list of the subgraphs' nodes from start to end,
+    # because we need to add the nodes to graph C in non-reverse order
+    nodes_of_a = [subgraph_a.end_node]
+    cur_node = subgraph_a.end_node
+    while cur_node != subgraph_a.start_node:
+        cur_node = get_normalized_nth_input(cur_node, gm_a, 0)  # type: ignore[assignment]
+        nodes_of_a.insert(0, cur_node)
+
+    # go through nodes of a in order, and insert them into the graph of c
+    # sequentially
+    cur_node_a = nodes_of_a[0]
+    cur_node_c = _insert_copy_of_node_a_after_input_node_c(
+        input_node_c, input_node_c_2, cur_node_a, gm_a, gm_b, node_name_prefix
+    )
+    for cur_idx_a in range(1, len(nodes_of_a)):
+        cur_node_a = nodes_of_a[cur_idx_a]
+        prev_node_c = cur_node_c  # previous added node is the input to next node
+        cur_node_c = _insert_copy_of_node_a_after_input_node_c(
+            prev_node_c,
+            # TODO(future PR): enable multiple inputs for nodes which are not at start of subgraph
+            None,
+            cur_node_a,
+            gm_a,
+            gm_b,
+            node_name_prefix,
+        )
+    # return the last inserted node
+    return cur_node_c
+
+
+def _insert_copy_of_node_a_after_input_node_c(
+    input_node_c: Union[Node, List[Node]],
+    input_node_c_2: Optional[Union[Node, List[Node]]],
+    node_a: Node,
+    gm_a: GraphModule,
+    gm_b: GraphModule,
+    node_name_prefix: str,
+) -> Node:
+    """
+    Assume that node_a from graph_a has
+      args (input, (input2)?, arg1, ...), and
+      kwargs {kw0: kwarg0, ...}
+
+    Note: input2 is optional. If it equals to None, we assume that the op
+    has a single non-param input.  If it is specified, we assume that the op
+    has two non-param inputs.
+
+    Copies the underlying values of arg1..argn and kwarg0..kwargn into gm_b,
+    and creates the corresponding nodes in graph_c. Note: observers are ignored,
+    so if an arg is an observer we navigate up until we find a non-observer parent.
+
+    If node_a is a call_module, points the module pointed to by node_a to gm_b.
+
+    Creates the copy of node_a in graph_c, with input as the first arg,
+    and all other args and kwargs pointing to the copies of the objects
+    in gm_b created above.
+
+    An example in pictures:
+
+    graph A:
+    ========
+
+    input -------------> node_a
+                         / / /
+    (input_2)?----------/ / /
+                         / /
+    weight -> weight_obs  /
+                         /
+    bias ----------------
+
+    graph C (derived from B):
+    =========================
+
+    input_node_c --> node_a_copy
+                     / / /
+    (input_node_c_2)? / /
+                     / /
+    weight_copy ----/ /
+                     /
+    bias_copy ------/
+    """
+    if isinstance(input_node_c, Node):
+        graph_c = input_node_c.graph
+    else:
+        assert isinstance(input_node_c, list)
+        graph_c = input_node_c[0].graph
+
+    norm_args_kwargs = node_a.normalized_arguments(
+        gm_a, normalize_to_only_use_kwargs=True
+    )
+    if norm_args_kwargs is not None:
+        norm_args, norm_kwargs = norm_args_kwargs
+    else:
+        norm_args, norm_kwargs = node_a.args, node_a.kwargs
+
+    new_args = []
+    new_kwargs = {}
+
+    def _copy_arg(arg):
+        # copy the other inputs from the other graph
+        if isinstance(arg, Node):
+            arg = return_first_non_observer_node(arg, gm_a)
+            arg = _copy_node_from_a_to_c(arg, gm_a, gm_b, graph_c)
+            return arg
+        elif isinstance(arg, (int, float, torch.dtype)):
+            return arg
+        elif isinstance(kwarg_val, (list, tuple)):
+            for el in kwarg_val:
+                assert not isinstance(
+                    el, Node
+                ), "handling of Node inside list is not implemented"
+            return arg
+        else:
+            raise AssertionError(
+                f"handling for kwarg of type {type(kwarg_val)} is not implemented"
+            )
+
+    cur_idx = 0
+
+    while cur_idx < len(norm_args):
+        if cur_idx == 0:
+            new_arg = input_node_c
+        elif cur_idx == 1 and input_node_c_2 is not None:
+            new_arg = input_node_c_2
+        else:
+            new_arg = _copy_arg(norm_args[cur_idx])
+        new_args.append(new_arg)
+        cur_idx += 1
+
+    for kwarg_name, kwarg_val in norm_kwargs.items():
+        # stitch the inputs from base graph
+        if cur_idx == 0:
+            new_kwargs[kwarg_name] = input_node_c
+        elif cur_idx == 1 and input_node_c_2 is not None:
+            new_kwargs[kwarg_name] = input_node_c_2
+        else:
+            new_kwargs[kwarg_name] = _copy_arg(kwarg_val)
+        cur_idx += 1
+
+    new_args = tuple(new_args)  # type: ignore[assignment]
+
+    node_a_shadows_c_name = get_new_attr_name_with_prefix(node_name_prefix)(gm_b)
+
+    if node_a.op == "call_module":
+        # if target is a module, we point to the module from gm_b
+        new_mod_copy_name = get_new_attr_name_with_prefix(node_name_prefix)(gm_b)
+        # fetch the corresponding module from gm_a
+        assert isinstance(node_a.target, str)
+        mod_a = getattr_from_fqn(gm_a, node_a.target)
+        setattr(gm_b, new_mod_copy_name, mod_a)
+        node_a_shadows_c = graph_c.create_node(
+            node_a.op, new_mod_copy_name, new_args, new_kwargs, node_a_shadows_c_name  # type: ignore[arg-type]
+        )
+        return node_a_shadows_c
+    else:
+        assert node_a.op in ("call_function", "call_method")
+        node_a_shadows_c = graph_c.create_node(
+            node_a.op, node_a.target, new_args, new_kwargs, node_a_shadows_c_name  # type: ignore[arg-type]
+        )
+        return node_a_shadows_c
+
+
+def create_a_shadows_b(
+    name_a: str,
+    gm_a: GraphModule,
+    name_b: str,
+    gm_b: GraphModule,
+    matched_subgraph_pairs: Dict[str, Tuple[NSSubgraph, NSSubgraph]],
+    logger_cls: Callable,
+    should_log_inputs: bool,
+    node_type_to_io_type_map: Optional[Dict[str, Set[NSNodeTargetType]]] = None,
+) -> GraphModule:
+    """
+    Creates a new GraphModule consisting of the graph of C, with the meaningful
+    nodes of A shadowing the corresponding nodes of B.  For example,
+
+    Graph A:
+    a0 -> op0_fp32 -> a1 -> op1_fp32 -> a2
+
+    Graph B:
+    b0 -> op0_int8 -> b1 -> op1_int8 -> b2
+
+    matched_node_pairs: {'op0': (op0_fp32, op0_int8), 'op1': (op1_fp32, op1_int8)}
+
+    Graph C (A shadows B):
+
+        / dequant0 -> op0_fp32 -> logger_a_0  / dequant_1 -> op1_fp32 -> logger_a_1
+       /                                     /
+    b0 -------------> op0_int8 -> logger_b_0 --------------> op1_int8 -> logger_b_1
+
+    In a nutshell, this function does the following for each node pair:
+    * copies the necessary attributes and modules from gm_a to gm_b,
+      keeping names unique
+    * adds a dtype cast op (dequant, quant, etc)
+    * adds a copy of node_a in gm_b's graph
+    * adds loggers to the outputs of node_a and node_b
+    """
+
+    if node_type_to_io_type_map is None:
+        node_type_to_io_type_map = get_node_type_to_io_type_map()
+
+    # graph_c is the graph created from copying the nodes of graph_b and inserting
+    # the shadows with the nodes copied from graph_a
+    graph_c = Graph()
+    env_c: Dict[str, Any] = {}
+    modules = dict(gm_b.named_modules())
+
+    def load_arg(a):
+        return map_arg(a, lambda node: env_c[node.name])
+
+    start_node_b_to_matched_subgraph_a_and_name = {}
+    end_node_b_to_matched_subgraph_a_and_name = {}
+    for match_name, match in matched_subgraph_pairs.items():
+        subgraph_a, subgraph_b = match
+        ref_node_type_a = get_target_type_str(subgraph_a.base_op_node, gm_a)
+        ref_node_type_b = get_target_type_str(subgraph_b.base_op_node, gm_b)
+        start_node_b_to_matched_subgraph_a_and_name[subgraph_b.start_node] = (
+            subgraph_a,
+            match_name,
+            ref_node_type_a,
+            ref_node_type_b,
+        )
+        end_node_b_to_matched_subgraph_a_and_name[subgraph_b.end_node] = (
+            subgraph_a,
+            match_name,
+            ref_node_type_a,
+            ref_node_type_b,
+        )
+
+    for node_b in gm_b.graph.nodes:
+        if node_b.op == "output":
+            graph_c.output(map_arg(node_b.args[0], load_arg))
+            continue
+
+        # calculate the flags to determine what to do with this node
+        node_b_is_start_node = node_b in start_node_b_to_matched_subgraph_a_and_name
+        node_b_is_end_node = node_b in end_node_b_to_matched_subgraph_a_and_name
+
+        if node_b_is_start_node or node_b_is_end_node:
+            if node_b_is_start_node:
+                (
+                    subgraph_a,
+                    ref_name,
+                    ref_node_type_a,
+                    ref_node_type_b,
+                ) = start_node_b_to_matched_subgraph_a_and_name[node_b]
+            else:
+                assert node_b_is_end_node
+                (
+                    subgraph_a,
+                    ref_name,
+                    ref_node_type_a,
+                    ref_node_type_b,
+                ) = end_node_b_to_matched_subgraph_a_and_name[node_b]
+
+            all_op_types_support_shadowing = op_type_supports_shadowing(
+                subgraph_a.start_node
+            ) and op_type_supports_shadowing(node_b)
+            if not all_op_types_support_shadowing:
+                print(
+                    f"skipping shadow loggers for node_b: {get_target_type_str(node_b, gm_b)}"
+                    + f", start_node_a: {get_target_type_str(subgraph_a.start_node, gm_a)}"
+                    + ", unsupported"
+                )
+                env_c[node_b.name] = graph_c.node_copy(node_b, load_arg)
+                continue
+
+            # For both start_node and end_node verify that we know how to do
+            # the dtype cast. If we do not, skip.
+            (
+                node_input_type_a,
+                node_output_type_a,
+            ) = get_node_first_input_and_output_type(
+                subgraph_a.start_node, gm_a, logger_cls, node_type_to_io_type_map
+            )
+            (
+                node_input_type_b,
+                node_output_type_b,
+            ) = get_node_first_input_and_output_type(
+                node_b, gm_b, logger_cls, node_type_to_io_type_map
+            )
+            node_io_types_known_a_and_b = (
+                node_input_type_a != NodeInputOrOutputType.UNKNOWN
+                and node_output_type_a != NodeInputOrOutputType.UNKNOWN
+                and node_input_type_b != NodeInputOrOutputType.UNKNOWN
+                and node_output_type_b != NodeInputOrOutputType.UNKNOWN
+            )
+            if not node_io_types_known_a_and_b:
+                print(
+                    f"skipping shadow loggers for node_b: {get_target_type_str(node_b, gm_b)}"
+                    + f", start_node_a: {get_target_type_str(subgraph_a.start_node, gm_a)}"
+                    + ", unknown dtype cast"
+                )
+                env_c[node_b.name] = graph_c.node_copy(node_b, load_arg)
+                continue
+
+            # If we are shadowing from fp32 to int8, we need to insert
+            # quantize_per_tensor call with qparams from the previous node.
+            # Only do this if we are able to infer these qparams from the graph.
+            if (
+                node_input_type_a == NodeInputOrOutputType.INT8
+                and node_input_type_b == NodeInputOrOutputType.FP32
+            ):
+                node_a_input_qparams = get_node_input_qparams(
+                    subgraph_a.start_node, gm_a, node_type_to_io_type_map
+                )
+                if not node_a_input_qparams:
+                    print(
+                        f"skipping shadow loggers for node_b: {get_target_type_str(node_b, gm_b)}"
+                        + f", start_node_a: {get_target_type_str(subgraph_a.start_node, gm_a)}"
+                        + ", unknown input qparams"
+                    )
+                    env_c[node_b.name] = graph_c.node_copy(node_b, load_arg)
+                    continue
+
+            num_non_param_args_node_a = get_number_of_non_param_args(
+                subgraph_a.start_node, gm_a
+            )
+            if not _can_insert_copy_of_subgraph_a(
+                subgraph_a, gm_a, num_non_param_args_node_a
+            ):
+                print(
+                    f"skipping shadow loggers for node_b: {get_target_type_str(node_b, gm_b)}"
+                    + f", start_node_a: {get_target_type_str(subgraph_a.start_node, gm_a)}"
+                    + ", unhandled logic in subgraph copy"
+                )
+                env_c[node_b.name] = graph_c.node_copy(node_b, load_arg)
+                continue
+
+            fqn_base_a = _maybe_get_fqn(subgraph_a.base_op_node, gm_a)
+            fqn_base_b = _maybe_get_fqn(subgraph_b.base_op_node, gm_b)  # type: ignore[possibly-undefined]
+
+            if node_b_is_start_node:
+                # if necessary, log the input of node_c
+                if should_log_inputs:
+                    prev_node_b = get_normalized_nth_input(node_b, gm_b, 0)
+                    if isinstance(prev_node_b, Node):
+                        prev_node_c = env_c[prev_node_b.name]
+                        env_c[prev_node_c.name] = _insert_logger_after_node(
+                            prev_node_c,
+                            gm_b,
+                            logger_cls,
+                            "_ns_logger_b_inp_",
+                            node_b.name,
+                            name_b,
+                            ref_name,
+                            ref_node_type_b,
+                            NSSingleResultValuesType.NODE_INPUT.value,
+                            index_within_arg=0,
+                            index_of_arg=0,
+                            fqn=fqn_base_b,
+                        )
+                    elif isinstance(prev_node_b, list):
+                        # first, save the prev_node instances, because they
+                        # will be overwritten in the env after the first logger
+                        # is added
+                        prev_node_c_list = [env_c[arg.name] for arg in prev_node_b]
+
+                        for arg_idx, arg in enumerate(prev_node_b):
+                            prev_node_c = prev_node_c_list[arg_idx]
+                            env_c[prev_node_c.name] = _insert_logger_after_node(
+                                prev_node_c,
+                                gm_b,
+                                logger_cls,
+                                "_ns_logger_b_inp_",
+                                node_b.name,
+                                name_b,
+                                ref_name,
+                                ref_node_type_b,
+                                NSSingleResultValuesType.NODE_INPUT.value,
+                                index_within_arg=arg_idx,
+                                index_of_arg=0,
+                                fqn=fqn_base_b,
+                            )
+                    else:
+                        # logging of inputs which are not lists is not supported yet
+                        raise AssertionError(
+                            f"type {type(prev_node_b)} is not handled yet"
+                        )
+                # subgraph so far:
+                #
+                # (prev_node_c)+ -> (logger_c_input)?
+
+            # Note: this if statement is always True, spelling it out to clarify code
+            # intent.
+            if node_b_is_start_node or node_b_is_end_node:
+                # ensure env_c is populated with base node
+                env_c[node_b.name] = graph_c.node_copy(node_b, load_arg)
+                node_c = env_c[node_b.name]
+
+                # after this point,
+                #
+                # node_a is the original node from graph_a, with parent module gm_a
+                # node_b is the original node from graph_b, with parent module gm_b
+                # node_c is the copy of node_b in graph_c
+                #
+                # subgraph so far:
+                #
+                # (prev_node_c)+ -> (logger_c_input)? -> node_start_c
+
+            if node_b_is_start_node:
+                # cast dtype from the dtype of node_c's input to the dtype of
+                # node_a's input (dequant, etc)
+                # prev_node_c = node_c.args[0]
+                prev_node_c = get_normalized_nth_input(node_c, gm_b, 0)  # type: ignore[possibly-undefined]
+                if should_log_inputs:
+                    # skip the input logger when inserting a dtype cast
+                    if isinstance(prev_node_c, Node):
+                        prev_node_c = get_normalized_nth_input(node_c, gm_b, 0)
+                    elif isinstance(prev_node_c, list):
+                        prev_node_c = [
+                            get_normalized_nth_input(arg, gm_b, 0)
+                            for arg in prev_node_c
+                        ]
+                dtype_cast_node = _insert_dtype_cast_after_node(
+                    subgraph_a.start_node,
+                    node_c,
+                    prev_node_c,
+                    gm_a,
+                    gm_b,
+                    graph_c,
+                    node_b.name + "_dtype_cast_",
+                    logger_cls,
+                    node_type_to_io_type_map,
+                )
+                # note: not inserting to env_c because all nodes which use the dtype
+                #   casts are copied from graph_a
+                #
+                # subgraph so far:
+                #
+                #           (dtype_cast_node)+
+                #                  /
+                # (prev_node_c)+ -> (logger_c_input)? -> node_start_c
+
+                # if input logging is enabled, log the input to the subgraph
+                if should_log_inputs:
+                    # TODO: explain this
+                    ref_node_name = ""
+                    if isinstance(dtype_cast_node, Node):
+                        dtype_cast_node = _insert_logger_after_node(
+                            dtype_cast_node,
+                            gm_b,
+                            logger_cls,
+                            "_ns_logger_a_inp_",
+                            ref_node_name,
+                            name_a,
+                            ref_name,
+                            ref_node_type_a,
+                            NSSingleResultValuesType.NODE_INPUT.value,
+                            index_within_arg=0,
+                            index_of_arg=0,
+                            fqn=fqn_base_a,
+                        )
+                        input_logger: Union[Node, List[Node]] = dtype_cast_node
+                    else:
+                        assert isinstance(dtype_cast_node, list)
+                        new_loggers = []
+                        for dtype_cast_idx, dtype_cast_node_inner in enumerate(
+                            dtype_cast_node
+                        ):
+                            dtype_cast_logger = _insert_logger_after_node(
+                                dtype_cast_node_inner,
+                                gm_b,
+                                logger_cls,
+                                "_ns_logger_a_inp_",
+                                ref_node_name,
+                                name_a,
+                                ref_name,
+                                ref_node_type_a,
+                                NSSingleResultValuesType.NODE_INPUT.value,
+                                index_within_arg=dtype_cast_idx,
+                                index_of_arg=0,
+                                fqn=fqn_base_a,
+                            )
+                            new_loggers.append(dtype_cast_logger)
+                        dtype_cast_node = new_loggers
+                        input_logger = dtype_cast_node
+                    # subgraph so far:
+                    #
+                    #       (dtype_cast_node)+ -> (logger_a_input)?
+                    #                  /
+                    # prev_node_c -> (logger_c_input)? -> node_start_c
+
+                # hook up the new mod_a copy to be in the graph, receiving the
+                # same inputs as mod_b does, with dtype cast to match a
+                # Some ops, such as LSTMs, have two non-param inputs. If we have
+                # such an op, pass the second param as well. Note: dtype casting
+                # for the second param is not implemented yet, it can be added
+                # later if there is a use case.
+                node_c_second_non_param_arg = None
+                num_non_param_args_node_a = get_number_of_non_param_args(
+                    subgraph_a.start_node, gm_a
+                )
+                if num_non_param_args_node_a == 2:
+                    # node_c_second_non_param_arg = node_c.args[1]
+                    node_c_second_non_param_arg = get_normalized_nth_input(
+                        node_c, gm_b, 1
+                    )
+                node_a_shadows_c = _insert_copy_of_subgraph_a_after_input_node_c(
+                    dtype_cast_node,
+                    node_c_second_non_param_arg,
+                    subgraph_a,
+                    gm_a,
+                    gm_b,
+                    node_c.name + "_shadow_copy_",
+                )
+                env_c[node_a_shadows_c.name] = node_a_shadows_c
+                # subgraph so far:
+                #
+                #       dtype_cast_node -> (logger_a_input)? -> subgraph_a_copy(args/kwargs not shown)
+                #                  /
+                # (prev_node_c)+ -> (logger_c_input)? -> node_start_c
+
+                if should_log_inputs:
+                    # When we created the input logger, we left the ref_node_name
+                    # as an empty string, because the subgraph copy did not exist
+                    # yet. Now that the subgraph copy exists, we modify this name
+                    # to its true value.
+                    # Note: the alternative to this is to create the input logger
+                    # after creating the subgraph, which is slightly more
+                    # complicated. This is the lesser of two evils.
+                    # input_logger = env_c[dtype_cast_node.name]
+                    # Find the first node in the subgraph
+                    cur_node = node_a_shadows_c
+                    while get_normalized_nth_input(cur_node, gm_b, 0) != input_logger:  # type: ignore[possibly-undefined]
+                        cur_node = get_normalized_nth_input(cur_node, gm_b, 0)  # type: ignore[assignment]
+                    if isinstance(input_logger, Node):
+                        input_logger_mod = getattr(gm_b, input_logger.name)
+                        input_logger_mod.ref_node_name = cur_node.name
+                    else:
+                        assert isinstance(input_logger, list)
+                        for input_logger_inner in input_logger:
+                            input_logger_mod = getattr(gm_b, input_logger_inner.name)
+                            input_logger_mod.ref_node_name = cur_node.name
+
+                # hook up a logger to the mod_a copy
+                env_c[node_a_shadows_c.name] = _insert_logger_after_node(
+                    env_c[node_a_shadows_c.name],
+                    gm_b,
+                    logger_cls,
+                    "_ns_logger_a_",
+                    node_a_shadows_c.name,
+                    name_a,
+                    ref_name,
+                    ref_node_type_a,
+                    NSSingleResultValuesType.NODE_OUTPUT.value,
+                    index_within_arg=0,
+                    index_of_arg=0,
+                    fqn=fqn_base_a,
+                )
+                # subgraph so far:
+                #
+                #       dtype_cast_node -> (logger_a_input)? -> subgraph_a_copy -> logger_a
+                #                  /
+                # (prev_node_c)+ -> (logger_c_input)? -> node_start_c
+
+            if node_b_is_end_node:
+                # hook up a logger to the mod_b copy
+                env_c[node_b.name] = _insert_logger_after_node(
+                    env_c[node_b.name],
+                    gm_b,
+                    logger_cls,
+                    "_ns_logger_b_",
+                    node_b.name,
+                    name_b,
+                    ref_name,
+                    ref_node_type_b,
+                    NSSingleResultValuesType.NODE_OUTPUT.value,
+                    index_within_arg=0,
+                    index_of_arg=0,
+                    fqn=fqn_base_b,
+                )
+                # subgraph so far:
+                #
+                #       dtype_cast_node -> (logger_a_input)? -> subgraph_a_copy -> logger_a
+                #                  /
+                # (prev_node_c+) -> (logger_c_input)? -> node_start_c -> ... -> node_end_c -> logger_c
+                #
+                # Note: node_start_c may be the same node as node_end_c, or they
+                # may have nodes inbetween.
+
+        else:
+            env_c[node_b.name] = graph_c.node_copy(node_b, load_arg)
+
+    gm_c = GraphModule(gm_b, graph_c)
+    return gm_c
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/mappings.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/mappings.py
new file mode 100644
index 0000000000000000000000000000000000000000..875af8c875b0a46630d92a05943960401aa2ade4
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/mappings.py
@@ -0,0 +1,759 @@
+import operator
+from typing import Callable, Dict, List, Optional, Set, Tuple
+
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.intrinsic.qat as nniqat
+import torch.ao.nn.intrinsic.quantized as nniq
+import torch.ao.nn.intrinsic.quantized.dynamic as nniqd
+import torch.ao.nn.qat as nnqat
+import torch.ao.nn.qat.dynamic as nnqatd
+import torch.ao.nn.quantized as nnq
+import torch.ao.nn.quantized.dynamic as nnqd
+import torch.ao.quantization.fx._lower_to_native_backend as _lower_to_native_backend
+import torch.ao.quantization.quantization_mappings as quantization_mappings
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.ao.quantization.backend_config import get_native_backend_config
+
+from .ns_types import NSNodeTargetType
+
+
+toq = torch.ops.quantized
+
+
+def get_base_name_to_sets_of_related_ops() -> Dict[str, Set[NSNodeTargetType]]:
+    # note: this set is modified below by items from backend_config
+    sets_of_related_ops: List[Set[NSNodeTargetType]] = [
+        # conv modules
+        {
+            nn.Conv1d,
+        },
+        {
+            nn.Conv2d,
+        },
+        {
+            nn.Conv3d,
+        },
+        # conv functionals
+        {
+            F.conv1d,
+        },
+        {
+            F.conv2d,
+        },
+        {
+            F.conv3d,
+        },
+        # linear modules
+        {
+            nn.Linear,
+        },
+        # linear functionals
+        {
+            F.linear,
+        },
+        # average pool
+        {
+            nn.AvgPool1d,
+            torch.avg_pool1d,
+        },
+        {
+            nn.AvgPool2d,
+            torch._C._nn.avg_pool2d,
+        },
+        {
+            nn.AvgPool3d,
+            torch._C._nn.avg_pool3d,
+        },
+        # adaptive average pool
+        {
+            nn.AdaptiveAvgPool1d,
+            F.adaptive_avg_pool1d,
+        },
+        {
+            nn.AdaptiveAvgPool2d,
+            F.adaptive_avg_pool2d,
+        },
+        {
+            nn.AdaptiveAvgPool3d,
+            F.adaptive_avg_pool3d,
+        },
+        # LSTM
+        {
+            nn.LSTM,
+        },
+        # add
+        {
+            torch.add,
+            operator.add,  # x + y
+        },
+        # cat
+        {
+            torch.cat,
+        },
+        # mul
+        {
+            torch.mul,
+            operator.mul,
+        },
+        # relu
+        {
+            F.relu,
+            nn.ReLU,
+            "relu",
+            "relu_",
+            torch.relu,
+        },
+        # maxpool
+        {
+            nn.MaxPool1d,
+            F.max_pool1d,
+        },
+        {
+            nn.MaxPool2d,
+            F.max_pool2d,
+        },
+        {
+            nn.MaxPool3d,
+            F.max_pool3d,
+        },
+        # sigmoid
+        {
+            torch.sigmoid,
+            "sigmoid",
+            "sigmoid_",
+            nn.Sigmoid,
+            F.sigmoid,
+        },
+        # BatchNorm
+        {
+            nn.BatchNorm2d,
+        },
+        {
+            nn.BatchNorm3d,
+        },
+        # ConvTranspose
+        {
+            nn.ConvTranspose1d,
+        },
+        {
+            nn.ConvTranspose2d,
+        },
+        {
+            nn.ConvTranspose3d,
+        },
+        # functional transposed conv
+        {
+            F.conv_transpose1d,
+        },
+        {
+            F.conv_transpose2d,
+        },
+        {
+            F.conv_transpose3d,
+        },
+        # ELU
+        {
+            nn.ELU,
+        },
+        # Embedding
+        {
+            nn.Embedding,
+        },
+        # EmbeddingBag
+        {
+            nn.EmbeddingBag,
+        },
+        # GroupNorm
+        {
+            nn.GroupNorm,
+        },
+        # Hardswish
+        {
+            nn.Hardswish,
+        },
+        # InstanceNorm
+        {
+            nn.InstanceNorm1d,
+        },
+        {
+            nn.InstanceNorm2d,
+        },
+        {
+            nn.InstanceNorm3d,
+        },
+        # LayerNorm
+        {
+            nn.LayerNorm,
+        },
+        # LeakyReLU
+        {
+            nn.LeakyReLU,
+        },
+        # ReLU6
+        {
+            nn.ReLU6,
+            F.relu6,
+        },
+        # F.elu
+        {
+            F.elu,
+        },
+        # F.hardswish
+        {
+            F.hardswish,
+        },
+        # F.group_norm
+        {
+            F.group_norm,
+        },
+        # F.instance_norm
+        {
+            F.instance_norm,
+        },
+        # F.layer_norm
+        {
+            F.layer_norm,
+        },
+        # F.leaky_relu
+        {
+            F.leaky_relu,
+        },
+        # F.silu
+        {
+            nn.SiLU,
+            F.silu,
+        },
+        # F.mish
+        {
+            nn.Mish,
+            F.mish,
+        },
+        # F.tanh
+        {
+            nn.Tanh,
+            F.tanh,
+            torch.tanh,
+            "tanh_",
+            "tanh",
+        },
+        # F.hardsigmoid
+        {
+            "hardsigmoid_",
+            "hardsigmoid",
+            F.hardsigmoid,
+            nn.Hardsigmoid,
+        },
+        # F.hardtanh
+        {
+            nn.Hardtanh,
+            F.hardtanh,
+            F.hardtanh_,
+        },
+        # floordiv
+        {
+            operator.floordiv,
+        },
+        # unsqueeze
+        {
+            torch.unsqueeze,
+        },
+        # stack
+        {
+            torch.stack,
+        },
+        # squeeze
+        {
+            torch.squeeze,
+        },
+        # sort
+        {
+            torch.sort,
+        },
+        # repeat_interleave
+        {
+            torch.repeat_interleave,
+        },
+        # min
+        {
+            torch.min,
+        },
+        # mean
+        {
+            torch.mean,
+        },
+        # max
+        {
+            torch.max,
+        },
+        # transpose
+        {
+            torch.transpose,
+        },
+        # flatten
+        {
+            torch.flatten,
+        },
+        # clamp
+        {
+            torch.clamp,
+        },
+        # chunk
+        {
+            torch.chunk,
+        },
+        # interpolate
+        {
+            torch.nn.functional.interpolate,
+        },
+        # dropout
+        {
+            nn.Dropout,
+        },
+        # F.dropout
+        {
+            F.dropout,
+        },
+        # matmul
+        {
+            torch.matmul,
+        },
+        # Softmax
+        {
+            nn.Softmax,
+        },
+        # PReLU
+        {
+            nn.PReLU,
+            nnq.PReLU,
+        },
+        # F.prelu
+        {
+            F.prelu,
+            toq.prelu,
+        },
+        # pixel shuffle
+        {
+            nn.PixelShuffle,
+        },
+        {
+            F.pixel_shuffle,
+        },
+        # pixel unshuffle
+        {
+            nn.PixelUnshuffle,
+        },
+        {
+            F.pixel_unshuffle,
+        },
+        # narrow
+        {
+            torch.narrow,
+        },
+    ]
+
+    # for each floating point op, add versions of the op added by
+    # backend_config
+    backend_config = get_native_backend_config()
+
+    new_connections: List[Tuple[Callable, Callable]] = [
+        # technical debt edge case
+        (nn.Linear, nn.modules.linear.NonDynamicallyQuantizableLinear),
+    ]
+
+    for pattern, config in backend_config._pattern_complex_format_to_config.items():
+        # pattern format: (c, (b, a))
+        first_element = pattern
+        # look from the end, because pattern is in reverse order
+        while isinstance(first_element, (list, tuple)):
+            first_element = first_element[-1]
+
+        if config.fused_module is not None:
+            # case 1: pattern fuses a pattern of ops into an op
+            # example: nn.Conv1d, nn.ReLU fused into nni.ConvReLU1d
+            new_connections.append((first_element, config.fused_module))
+
+        if config.qat_module is not None:
+            # case 2: pattern swaps a module into a QAT module
+            # example: nni.ConvReLU1d swapped into nniqat.ConvReLU1d
+            new_connections.append((first_element, config.qat_module))
+
+        if config.reference_quantized_module is not None:
+            # case 3: reference version of floating point module, such as
+            # nn.Conv2d and nnqr.Conv2d
+            new_connections.append((first_element, config.reference_quantized_module))
+
+    #
+    # Add reference module swaps from default lowering path
+    #
+
+    for source_to_target in (
+        _lower_to_native_backend.STATIC_LOWER_MODULE_MAP,
+        _lower_to_native_backend.DYNAMIC_LOWER_MODULE_MAP,
+        _lower_to_native_backend.WEIGHT_ONLY_LOWER_MODULE_MAP,
+        _lower_to_native_backend.SPECIAL_PATTERN_LOWER_MODULE_MAP,
+    ):
+        for source, target in source_to_target.items():  # type: ignore[attr-defined]
+            new_connections.append((source, target))
+
+    for source_to_double_target in (
+        _lower_to_native_backend.STATIC_LOWER_FUSED_MODULE_MAP,
+        _lower_to_native_backend.STATIC_LOWER_FUSED_MODULE_TWO_INPUTS_MAP,
+        _lower_to_native_backend.DYNAMIC_LOWER_FUSED_MODULE_MAP,
+    ):
+        for source, (target1, target2) in source_to_double_target.items():  # type: ignore[attr-defined]
+            new_connections.append((source, target1))
+            new_connections.append((source, target2))
+
+    #
+    # Add function swaps from default lowering path
+    #
+
+    for source, (
+        target1,
+        target2,
+    ) in _lower_to_native_backend.STATIC_LOWER_FUNCTIONAL_MAP.items():
+        new_connections.append((source, target1))
+        new_connections.append((source, target2))
+
+    for source_to_target in (
+        _lower_to_native_backend.QBIN_OP_MAPPING,
+        _lower_to_native_backend.QBIN_RELU_OP_MAPPING,
+        quantization_mappings.DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS,
+    ):
+        for source, target in source_to_target.items():
+            new_connections.append((source, target))
+
+    #
+    # Add other swaps, ideally in the future this could be removed
+    # after the lowering code stops using these.
+    #
+    for source_to_target in (
+        quantization_mappings.DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS,
+    ):
+        for source, target in source_to_target.items():
+            new_connections.append((source, target))
+
+    # add the new connections from backend_config
+    for item1, item2 in new_connections:
+        for set_of_related_ops in sets_of_related_ops:
+            if item1 in set_of_related_ops or item2 in set_of_related_ops:
+                set_of_related_ops.add(item1)
+                set_of_related_ops.add(item2)
+                break
+
+    base_name_to_sets_of_related_ops: Dict[str, Set[NSNodeTargetType]] = {}
+
+    counter = 0
+    for set_of_related_ops in sets_of_related_ops:
+        base_name = str(counter)
+        counter += 1
+        base_name_to_sets_of_related_ops[base_name] = set_of_related_ops
+
+    return base_name_to_sets_of_related_ops
+
+
+def get_base_name_for_op(
+    base_name_to_sets_of_related_ops: Dict[str, Set[NSNodeTargetType]],
+    op: NSNodeTargetType,
+) -> Optional[str]:
+    for base_name, set_of_related_ops in base_name_to_sets_of_related_ops.items():
+        if op in set_of_related_ops:
+            return base_name
+    return None
+
+
+def add_op_to_sets_of_related_ops(
+    base_name_to_sets_of_related_ops: Dict[str, Set[NSNodeTargetType]],
+    op: NSNodeTargetType,
+    related_op: Optional[NSNodeTargetType],
+) -> None:
+    if related_op is not None:
+        for set_of_related_ops in base_name_to_sets_of_related_ops.values():
+            if related_op in set_of_related_ops:
+                set_of_related_ops.add(op)
+                return
+        # if we got here, related_op was not found
+        raise AssertionError(f"{related_op} was not found")
+    else:
+        counter = 0
+        while str(counter) in base_name_to_sets_of_related_ops:
+            counter += 1
+        base_name_to_sets_of_related_ops[str(counter)] = {op}
+
+
+# TODO(future PR): clean this up
+def get_node_type_to_io_type_map() -> Dict[str, Set[NSNodeTargetType]]:
+    FUNS_IO_TYPE_FP32: Set[NSNodeTargetType] = {
+        F.linear,
+        F.conv1d,
+        F.conv2d,
+        F.conv3d,
+        torch.cat,
+        F.elu,
+        F.hardswish,
+        F.instance_norm,
+        F.layer_norm,
+        F.leaky_relu,
+        F.dropout,
+        F.silu,
+        F.mish,
+        operator.add,
+        torch.add,
+        operator.mul,
+        torch.mul,
+        torch.sum,
+        F.prelu,
+    }
+
+    FUNS_IO_TYPE_FP16: Set[NSNodeTargetType] = set()
+
+    FUNS_IO_TYPE_INT8: Set[NSNodeTargetType] = {
+        toq.linear,
+        toq.linear_relu,
+        toq.conv1d,
+        toq.conv1d_relu,
+        toq.conv2d,
+        toq.conv2d_relu,
+        toq.conv3d,
+        toq.conv3d_relu,
+        toq.cat,
+        toq.elu,
+        toq.hardswish,
+        toq.instance_norm,
+        toq.layer_norm,
+        toq.leaky_relu,
+        toq.dropout,
+        toq.prelu,
+        # TODO(future PR): implement shadowing for binary ops and
+        # uncomment below
+        # toq.add,
+        # toq.mul,
+    }
+
+    FUNS_IO_TYPE_FP32_OR_INT8: Set[NSNodeTargetType] = {
+        F.relu,
+        F.tanh,
+        torch.tanh,
+        F.sigmoid,
+        torch.sigmoid,
+        F.hardsigmoid,
+        operator.floordiv,
+        torch.adaptive_avg_pool1d,
+        F.adaptive_avg_pool2d,
+        F.adaptive_avg_pool3d,
+        F.dropout,
+        F.hardtanh,
+        F.hardtanh_,
+        F.interpolate,
+        F.max_pool1d,
+        F.max_pool2d,
+        F.max_pool3d,
+        F.relu6,
+        F.pixel_shuffle,
+        F.pixel_unshuffle,
+        torch.avg_pool1d,
+        torch._C._nn.avg_pool2d,
+        torch._C._nn.avg_pool3d,
+        torch.cat,
+        torch.chunk,
+        torch.clamp,
+        torch.flatten,
+        torch.transpose,
+        torch.max,
+        torch.mean,
+        torch.min,
+        torch.narrow,
+        torch.repeat_interleave,
+        torch.sort,
+        torch.squeeze,
+        torch.stack,
+        torch.unsqueeze,
+        operator.add,
+    }
+
+    MODS_IO_TYPE_FP32: Set[NSNodeTargetType] = {
+        nn.Linear,
+        nnqat.Linear,
+        nnqatd.Linear,
+        nnqd.Linear,
+        torch.nn.modules.linear.NonDynamicallyQuantizableLinear,
+        nn.Conv1d,
+        nn.Conv2d,
+        nn.Conv3d,
+        nnqat.Conv1d,
+        nnqat.Conv2d,
+        nnqat.Conv3d,
+        nnqat.Embedding,
+        nnqat.EmbeddingBag,
+        nn.LSTM,
+        # note: nnqd.Linear is an instance of nnq.Linear, so this
+        # check has to happen before the int8 module check
+        nnqd.LSTM,
+        nn.BatchNorm2d,
+        nn.BatchNorm3d,
+        nn.Dropout,
+        nn.ConvTranspose1d,
+        nn.ConvTranspose2d,
+        nn.ConvTranspose3d,
+        nn.ELU,
+        nn.GroupNorm,
+        nn.InstanceNorm1d,
+        nn.InstanceNorm2d,
+        nn.InstanceNorm3d,
+        nn.LayerNorm,
+        nn.Hardswish,
+        nn.LeakyReLU,
+        nn.ReLU6,
+        nn.SiLU,
+        nn.Mish,
+        nn.Softmax,
+        nn.PReLU,
+        nni.BNReLU2d,
+        nni.BNReLU3d,
+        nni.ConvReLU1d,
+        nni.ConvReLU2d,
+        nni.ConvReLU3d,
+        nni.LinearReLU,
+        nni.LinearBn1d,
+        nni.ConvBn1d,
+        nni.ConvBn2d,
+        nni.ConvBn3d,
+        nniqat.ConvBn1d,
+        nniqat.ConvBn2d,
+        nniqat.ConvBn3d,
+        nniqat.ConvBnReLU1d,
+        nniqat.ConvBnReLU2d,
+        nniqat.ConvBnReLU3d,
+        nniqat.ConvReLU1d,
+        nniqat.ConvReLU2d,
+        nniqat.ConvReLU3d,
+        nniqat.LinearReLU,
+        nniqat.LinearBn1d,
+        nniqd.LinearReLU,
+        nni.LinearLeakyReLU,
+        nni.LinearTanh,
+        nni.ConvAdd2d,
+        nni.ConvAddReLU2d,
+    }
+
+    MODS_IO_TYPE_INT8: Set[NSNodeTargetType] = {
+        nnq.Linear,
+        nnq.Conv1d,
+        nnq.Conv2d,
+        nnq.Conv3d,
+        nnq.BatchNorm2d,
+        nnq.BatchNorm3d,
+        nnq.Dropout,
+        nnq.ConvTranspose1d,
+        nnq.ConvTranspose2d,
+        nnq.ELU,
+        nnq.InstanceNorm1d,
+        nnq.InstanceNorm2d,
+        nnq.InstanceNorm3d,
+        nnq.LayerNorm,
+        nnq.Hardswish,
+        nnq.LeakyReLU,
+        nnq.Embedding,
+        nnq.EmbeddingBag,
+        nnq.Dropout,
+        nnq.Softmax,
+        nnq.PReLU,
+        nniq.BNReLU2d,
+        nniq.BNReLU3d,
+        nniq.ConvReLU1d,
+        nniq.ConvReLU2d,
+        nniq.ConvReLU3d,
+        nniq.LinearReLU,
+        nniq.LinearLeakyReLU,
+        nniq.LinearTanh,
+        nniq.ConvAdd2d,
+        nniq.ConvAddReLU2d,
+    }
+
+    MODS_IO_TYPE_FP32_OR_INT8: Set[NSNodeTargetType] = {
+        nn.ReLU,
+        nn.Tanh,
+        nn.Sigmoid,
+        nn.Hardsigmoid,
+        nn.AdaptiveAvgPool1d,
+        nn.AdaptiveAvgPool2d,
+        nn.AdaptiveAvgPool3d,
+        nn.AvgPool1d,
+        nn.AvgPool2d,
+        nn.AvgPool3d,
+        nn.Dropout,
+        nn.Hardtanh,
+        nn.Identity,
+        nn.MaxPool1d,
+        nn.MaxPool2d,
+        nn.MaxPool3d,
+        nn.PixelShuffle,
+        nn.PixelUnshuffle,
+        nn.ReLU6,
+    }
+
+    METHS_IO_TYPE_FP32_OR_INT8: Set[NSNodeTargetType] = {
+        "sigmoid_",
+        "sigmoid",
+        "tanh_",
+        "tanh",
+        "hardsigmoid_",
+        "hardsigmoid",
+        "relu_",
+        "relu",
+    }
+
+    return {
+        "funs_io_type_fp32": FUNS_IO_TYPE_FP32,
+        "funs_io_type_fp16": FUNS_IO_TYPE_FP16,
+        "funs_io_type_int8": FUNS_IO_TYPE_INT8,
+        "funs_io_type_fp32_or_int8": FUNS_IO_TYPE_FP32_OR_INT8,
+        "mods_io_type_fp32": MODS_IO_TYPE_FP32,
+        "mods_io_type_int8": MODS_IO_TYPE_INT8,
+        "mods_io_type_fp32_or_int8": MODS_IO_TYPE_FP32_OR_INT8,
+        "meths_io_type_fp32_or_int8": METHS_IO_TYPE_FP32_OR_INT8,
+    }
+
+
+def get_unmatchable_types_map() -> Dict[str, Set[NSNodeTargetType]]:
+    FUNS_UNMATCHABLE: Set[NSNodeTargetType] = {
+        torch.quantize_per_tensor,
+        operator.getitem,
+    }
+
+    MODS_UNMATCHABLE: Set[NSNodeTargetType] = {
+        nn.Identity,
+    }
+
+    METHS_UNMATCHABLE: Set[NSNodeTargetType] = {
+        "to",
+        "dequantize",
+        "reshape",
+        "view",
+        "unsqueeze_",
+        "unsqueeze",
+        "transpose",
+        "squeeze_",
+        "squeeze",
+        "size",
+        "shape",
+        "resize_",
+        "repeat_interleave",
+        "repeat",
+        "permute",
+        "numel",
+        "mean",
+        "detach_",
+        "detach",
+        "contiguous",
+        "clamp",
+        "chunk",
+    }
+
+    return {
+        "funs_unmatchable": FUNS_UNMATCHABLE,
+        "mods_unmatchable": MODS_UNMATCHABLE,
+        "meths_unmatchable": METHS_UNMATCHABLE,
+    }
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/n_shadows_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/n_shadows_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..0998fe78cdc01948a035a1a41657c58c71a61e36
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/n_shadows_utils.py
@@ -0,0 +1,1382 @@
+# mypy: allow-untyped-defs
+import collections
+import copy
+import operator
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple
+
+import torch
+import torch.fx
+from torch.ao.ns.fx.graph_passes import _maybe_get_fqn
+from torch.ao.ns.fx.ns_types import NSResultsType, NSSingleResultValuesType
+from torch.ao.ns.fx.utils import (  # TODO(future PR): make this work correctly for methods
+    get_normalized_nth_input,
+    get_target_type_str,
+)
+from torch.ao.quantization import QConfigMapping
+from torch.ao.quantization.fx.match_utils import _MatchResult
+from torch.ao.quantization.qconfig import QConfigAny
+from torch.ao.quantization.utils import getattr_from_fqn
+from torch.fx import Graph, GraphModule, Node
+from torch.utils._pytree import tree_map
+
+
+SHADOW_NODE_NAME_PREFIX = "shadow"
+SHADOW_WRAPPER_NODE_NAME_PREFIX = "shadow_wrapper"
+
+# TODO(future PR): reuse existing mapping instead of creating a new one
+BINARY_FUNCTIONS = {
+    torch.add,
+    torch.Tensor.add,
+    operator.add,
+    torch.mul,
+    torch.Tensor.mul,
+    operator.mul,
+}
+
+
+def _get_attr_name(subgraph_idx, subgraph_candidate_idx):
+    return f"{SHADOW_NODE_NAME_PREFIX}_{subgraph_idx}_{subgraph_candidate_idx}"
+
+
+def _get_attr_wrapper_name(subgraph_idx, subgraph_candidate_idx):
+    return f"{SHADOW_WRAPPER_NODE_NAME_PREFIX}_{subgraph_idx}_{subgraph_candidate_idx}"
+
+
+class OutputProp:
+    """
+    Output propagation (modeled from shape propagation).
+
+    Given a GraphModule and an example input, saves the output flowing
+    through each node on `node.traced_result`.
+
+    Code based on the example from
+    https://pytorch.org/docs/stable/fx.html#the-interpreter-pattern
+    """
+
+    def __init__(self, mod):
+        self.mod = mod
+        self.graph = mod.graph
+        self.modules = dict(self.mod.named_modules())
+
+    def propagate(self, *args):
+        args_iter = iter(args)
+        env: Dict[str, Node] = {}
+
+        def load_arg(a):
+            return torch.fx.graph.map_arg(a, lambda n: env[n.name])
+
+        def fetch_attr(target: str):
+            target_atoms = target.split(".")
+            attr_itr = self.mod
+            for i, atom in enumerate(target_atoms):
+                if not hasattr(attr_itr, atom):
+                    raise RuntimeError(
+                        f"Node referenced nonexistent target {'.'.join(target_atoms[:i])}"
+                    )
+                attr_itr = getattr(attr_itr, atom)
+            return attr_itr
+
+        for node in self.graph.nodes:
+            if node.op == "placeholder":
+                result = next(args_iter)
+            elif node.op == "get_attr":
+                result = fetch_attr(node.target)
+            elif node.op == "call_function":
+                result = node.target(*load_arg(node.args), **load_arg(node.kwargs))
+            elif node.op == "call_method":
+                self_obj, *args = load_arg(node.args)
+                kwargs = load_arg(node.kwargs)
+                result = getattr(self_obj, node.target)(*args, **kwargs)
+            elif node.op == "call_module":
+                result = self.modules[node.target](
+                    *load_arg(node.args), **load_arg(node.kwargs)
+                )
+
+            if isinstance(result, torch.Tensor):  # type: ignore[possibly-undefined]
+                node.traced_result = result
+
+            env[node.name] = result
+
+        return None
+
+
+def _get_dedup_subgraphs(matches: Dict[str, _MatchResult]) -> Dict[str, List[Node]]:
+    # the original matches variable is unique by node, make it unique by subgraph
+    # instead
+    seen_nodes = set()
+    subgraphs_dedup = {}
+
+    # Dict items are not reversible until Python 3.8, so we hack it
+    # to be compatible with previous Python versions
+    # TODO(future PR): try reversed(list(matches.items()))
+    matches_items_reversed: List[Tuple[str, _MatchResult]] = []
+    for name, cur_match in matches.items():
+        matches_items_reversed.insert(0, (name, cur_match))
+
+    # Note: the order is important.  `matches` currently provides the matches
+    # in reverse order.  We would like to process the matches in non-reverse
+    # order, so that we can create an intuitive naming scheme, such as
+    # naming the first op's submodules `shadow_0_0` through `shadow_0_(n-1)`
+    for name, cur_match in matches_items_reversed:  # type: ignore[call-overload]
+        was_seen = False
+        for node_or_tuple in cur_match[1]:
+            # Cur_match[1] has an unusual type. It says that it's a `List[Node]`,
+            # but it is really not. Furthermore, the contents of this field
+            # can change from match results of multiple nodes of the same pattern
+            #
+            # For example, for conv -> bn -> relu, we see
+            # match_results = {
+            #   'conv': (relu, [(bn, conv), relu], ...),
+            #   'bn': (relu, [(bn, conv), relu], ...),
+            #   'relu': (relu, [(bn, conv), relu], ...),
+            # }
+            #
+            # Ideally we should clean up the `find_matches` function to make
+            # this more intuitive. For the purposes of this prototype, we hack
+            # around it.
+
+            if isinstance(node_or_tuple, Node):
+                if node_or_tuple in seen_nodes:
+                    was_seen = True
+                seen_nodes.add(node_or_tuple)
+
+            else:
+                assert isinstance(node_or_tuple, tuple)
+                for node in node_or_tuple:
+                    assert isinstance(node, Node)
+                    if node in seen_nodes:
+                        was_seen = True
+                    seen_nodes.add(node)
+
+        if was_seen:
+            continue
+
+        # Start with the unusual type, convert it to [op_0, ..., op_n]
+        list_of_nodes = []
+
+        if len(cur_match[1]) == 1:
+            list_of_nodes = cur_match[1]
+        else:
+            assert len(cur_match[1]) == 2
+            # either (a, b), or ((a, b), c) or (c, (a, b))
+            # cannot make any assumptions on order, not clear what the
+            # _find_matches function is doing to populate this
+            # TODO(future PR): make this code less confusing,  see discussion
+            # in https://github.com/pytorch/pytorch/pull/80521/files#r975918836
+
+            def _order_nodes(node_a, node_b, node_c) -> List[Node]:
+                nodes = [node_a, node_b, node_c]
+                first_node = None
+                mid_node = None
+                last_node = None
+                for n in nodes:
+                    prev_n = n.args[0]
+                    next_n = next(iter(n.users))
+                    if prev_n not in nodes:
+                        first_node = n
+                    elif next_n not in nodes:
+                        last_node = n
+                    else:
+                        mid_node = n
+                assert (
+                    first_node is not None
+                    and mid_node is not None
+                    and last_node is not None
+                )
+                assert mid_node.args[0] is first_node
+                assert last_node.args[0] is mid_node
+                return [last_node, mid_node, first_node]
+
+            if isinstance(cur_match[1][0], Node) and isinstance(cur_match[1][1], Node):
+                # (a, b)
+                list_of_nodes = cur_match[1]
+            elif isinstance(cur_match[1][0], tuple):
+                # ((a, b), c)
+                node_a, node_b = cur_match[1][0]
+                node_c = cur_match[1][1]
+                list_of_nodes = _order_nodes(node_a, node_b, node_c)
+            elif isinstance(cur_match[1][1], tuple):
+                # (a, (b, c))
+                node_a, node_b = cur_match[1][1]
+                node_c = cur_match[1][0]
+                list_of_nodes = _order_nodes(node_a, node_b, node_c)
+
+        # [node_n, ..., node_0], note that the order is reversed
+        # to make it chronological for simple subgraphs
+        list_of_nodes.reverse()
+        subgraphs_dedup[name] = list_of_nodes
+
+    return subgraphs_dedup
+
+
+def _get_logger_for_subgraph(
+    model: GraphModule,
+    first_node: Node,
+    last_node: Node,
+    subgraph_idx: int,
+    subgraph_candidate_idx: int,
+    qconfig_str: str,
+    logger_cls: Callable,
+    fqn: Optional[str],
+) -> torch.nn.Module:
+    """
+    Given a model and a linear subgraph starting from `first_node` and
+    ending with `last_node`, creates a logger for the end of this
+    subgraph.
+    """
+    if fqn is None:
+        fqn = ""
+    logger_mod_orig = logger_cls(
+        first_node.name,  # ref_node_name
+        last_node.name,  # prev_node_name
+        f"subgraph_{subgraph_idx}_{subgraph_candidate_idx}",  # model_name
+        "model",  # ref_name
+        get_target_type_str(last_node, model),  # prev_node_target_type
+        get_target_type_str(first_node, model),  # ref_node_target_type
+        NSSingleResultValuesType.NODE_OUTPUT.value,  # results_type
+        0,  # index_within_arg
+        0,  # index_of_arg
+        fqn,  # fqn
+        qconfig_str,
+    )
+    # Usually we expect the user to add loggers, then calibrate, then convert,
+    # and then populate loggers.  This is why the loggers start disabled.
+    # TODO(future PR): reconsider the design to make this more intuitive.
+    logger_mod_orig.enabled = False
+    return logger_mod_orig
+
+
+def create_submodule_from_subgraph(
+    model: torch.nn.Module,
+    first_node: Node,
+    last_node: Node,
+) -> GraphModule:
+    """
+    Input: a model, and a linear subgraph within the model from first_node to
+      last_node.
+
+    Output: a new submodule containing a copy of the subgraph, with the inputs
+      to the first node becoming the inputs to the submodule, and all other
+      nodes in the subgraph being copied.
+
+    Example inputs:
+
+    `model`: a module with graph
+
+      x0 -> op1 -> x1 -> op2 -> x2
+             |
+            arg1
+
+    `first_node`: op1
+    `last_node`: op2
+
+    Example output: a new module with graph
+
+      input1 -> op1_copy -> x1 -> op2_copy -> output1
+                   |
+                  arg1
+    """
+
+    #
+    # create a blank GraphModule with an empty graph
+    #
+
+    class M(torch.nn.Module):
+        def forward(self, x):
+            pass
+
+    m = M()
+    gm = torch.fx.symbolic_trace(m)
+    g = gm.graph
+    for node in reversed(gm.graph.nodes):
+        g.erase_node(node)
+
+    #
+    # modify the graph to have a copy of our subgraph
+    #
+
+    cur_node_orig = first_node
+    cur_args_orig = cur_node_orig.args
+    cur_kwargs_orig = cur_node_orig.kwargs
+
+    cur_name_idx = 0
+
+    iteration_limit = 100
+    cur_iteration = 0
+
+    while True:
+        if cur_node_orig is first_node:
+            # we are at the first node, we need to set up graph inputs
+            # TODO(future): some graphs could have placeholders which are unrelated
+            # to the first node, need to handle this
+            cur_args_copy = []
+            cur_kwargs_copy = {}
+            seen_names: Set[str] = set()
+            old_name_to_new_node: Dict[str, Node] = {}
+
+            def _add_placeholder(
+                g: Graph, node: Node, seen_names, old_name_to_new_node
+            ):
+                # note: for graphs starting with patterns such as `y = x + x`, we
+                # need to ensure we do not add multiple placeholders with the
+                # same name
+                counter = 0
+                while node.name + "_" + str(counter) in seen_names:
+                    counter += 1
+                cur_name = node.name + "_" + str(counter)
+                seen_names.add(cur_name)
+                placeholder = g.placeholder(cur_name)
+                old_name_to_new_node[node.name] = placeholder
+                return placeholder
+
+            for arg in cur_node_orig.args:
+                if isinstance(arg, Node):
+                    p = _add_placeholder(g, arg, seen_names, old_name_to_new_node)
+                    cur_args_copy.append(p)
+                elif isinstance(arg, (list, tuple)):
+                    new_arg = []
+                    for inner_arg in arg:
+                        if isinstance(inner_arg, Node):
+                            new_arg.append(
+                                _add_placeholder(
+                                    g, inner_arg, seen_names, old_name_to_new_node
+                                )
+                            )
+                        else:
+                            new_arg.append(inner_arg)
+                    cur_args_copy.append(new_arg)
+                else:
+                    cur_args_copy.append(arg)
+
+            # TODO(future PR): handle non-normalized kwargs
+            for kwarg_name, kwarg in cur_node_orig.kwargs.items():
+                if isinstance(kwarg, Node):
+                    cur_kwargs_copy[kwarg_name] = _add_placeholder(
+                        g, kwarg, seen_names, old_name_to_new_node
+                    )
+                elif isinstance(kwarg, (list, tuple)):
+                    new_kwarg = []
+                    for inner_kwarg in kwarg:
+                        p = _add_placeholder(
+                            g, inner_kwarg, seen_names, old_name_to_new_node
+                        )
+                        new_kwarg.append(p)
+                    cur_kwargs_copy[kwarg_name] = new_kwarg
+                else:
+                    cur_kwargs_copy[kwarg_name] = kwarg
+
+            cur_args_copy = tuple(cur_args_copy)  # type: ignore[assignment]
+        else:
+            # we are not at first node, first arg is from the previous node,
+            # and all other args are copied
+
+            # the current implementation is simplistic and cannot handle
+            # ops with two or more arguments which need to be passed from
+            # the previous op, so we assert them out
+            assert cur_node_orig.target not in BINARY_FUNCTIONS
+
+            # at this point in the code, cur_node_copy is pointing to the copy
+            # of the previous node
+            # TODO(future PR): this is not handling complicated graphs correctly, need to
+            # look at actual relationships instead of assuming sequential graph
+            # TODO(future PR): this is ignoring kwargs, will need to support kwargs
+            # for any fusion pattern which has them for a node that is not the
+            # first node.
+            cur_args_copy = [cur_node_copy]  # type: ignore[has-type, possibly-undefined]  # noqa: F821
+
+            if len(cur_node_orig.args) > 1:
+                for arg in cur_node_orig.args[1:]:
+                    if isinstance(arg, torch.nn.Parameter):
+                        new_arg = arg.clone().detach()  # type: ignore[assignment]
+                        mod_name = f"mod_{cur_name_idx}"
+                        cur_name_idx += 1
+                        setattr(gm, mod_name, new_arg)
+                        new_arg_placeholder = gm.placeholder(mod_name)
+                        cur_args_copy.append(new_arg_placeholder)
+                    elif isinstance(arg, (float, int, torch.dtype)):
+                        cur_args_copy.append(arg)
+                    else:
+                        raise AssertionError(f"arg of type {type(arg)} not handled yet")
+            cur_args_copy = tuple(cur_args_copy)  # type: ignore[assignment]
+
+        # copy the node
+        if cur_node_orig.op == "call_module":
+            orig_mod = getattr_from_fqn(model, cur_node_orig.target)  # type: ignore[arg-type]
+            orig_mod_copy = copy.deepcopy(orig_mod)
+            mod_name = f"mod_{cur_name_idx}"
+            setattr(gm, mod_name, orig_mod_copy)
+            cur_name_idx += 1
+            cur_node_copy = g.call_module(mod_name, cur_args_copy, cur_kwargs_copy)  # type: ignore[possibly-undefined,arg-type]
+
+        elif cur_node_orig.op == "call_function":
+            cur_node_copy = g.call_function(
+                cur_node_orig.target,  # type: ignore[arg-type]
+                cur_args_copy,  # type: ignore[arg-type]
+                cur_kwargs_copy,  # type: ignore[possibly-undefined]
+            )
+
+        elif cur_node_orig.op == "call_method":
+            cur_node_copy = g.call_method(
+                cur_node_orig.target,  # type: ignore[arg-type]
+                cur_args_copy,  # type: ignore[arg-type]
+                cur_kwargs_copy,  # type: ignore[possibly-undefined]
+            )
+
+        else:
+            raise AssertionError(f"{cur_node_orig.op} not supported yet")
+
+        if cur_node_orig is last_node:
+            break
+
+        # go to next node
+        assert (
+            len(cur_node_orig.users.keys()) == 1
+        ), f"{cur_node_orig} has more than 1 users, not supported yet"
+        cur_node_orig = next(iter(cur_node_orig.users.keys()))
+        cur_args_orig = cur_node_orig.args
+        cur_kwargs_orig = cur_node_orig.kwargs
+
+        cur_iteration += 1
+        if cur_iteration > iteration_limit:
+            raise AssertionError("iteration limit exceeded")
+
+    # set up outputs
+    g.output(cur_node_copy)
+
+    gm.recompile()
+    return gm
+
+
+def create_one_transformed_and_logged_copy_of_subgraph(
+    mt: GraphModule,
+    subgraph_idx: int,
+    subgraph_candidate_idx: int,
+    first_node: Node,
+    last_node: Node,
+    fqn: Optional[str],
+    list_of_node_name_to_qconfig: List[Dict[str, QConfigAny]],
+    example_inputs: Any,
+    last_added_shadow_node_list: List[Optional[Node]],
+    custom_prepare_fn: Optional[Callable] = None,
+    custom_prepare_kwargs: Optional[Dict[str, Any]] = None,
+) -> None:
+    """
+    Given a subgraph in `mt` and a subgraph candidate idx, inserts the
+    subgraph candidate copy and instruments it with loggers.
+
+    If subgraph_candidate_idx is 0, this is the baseline fp32 subgraph and we just
+    add a logger to the end.
+
+    If subgraph_candidate_idx is not 0, we create a copy of the subgraph and
+    prepare it with `prepare_fx`.
+    """
+
+    # TODO(future PR): move logger classes to utils to remove circular dependency
+    from torch.ao.ns._numeric_suite_fx import OutputComparisonLogger, OutputLogger
+
+    if subgraph_candidate_idx == 0:
+        # idx = 0 is the floating point (original) version of the subgraph
+        # We keep the subgraph as is, and add a logger at the end
+
+        qconfig_str = ""
+        logger_mod_orig = _get_logger_for_subgraph(
+            mt,
+            first_node,
+            last_node,
+            subgraph_idx,
+            subgraph_candidate_idx,
+            qconfig_str,
+            OutputLogger,
+            fqn,
+        )
+
+        attr_name = _get_attr_name(subgraph_idx, subgraph_candidate_idx)
+        assert not hasattr(mt, attr_name)
+        setattr(mt, attr_name, logger_mod_orig)
+        with mt.graph.inserting_after(last_node):
+            new_node = mt.graph.call_module(attr_name, args=(last_node,), kwargs={})
+            last_added_shadow_node_list[0] = new_node
+
+    else:
+        # idx > 0 means we have a candidate qconfig to try, so we need
+        # to make a copy of the subgraph, feed it with the right inputs,
+        # and add a logger at the end
+
+        # get the qconfig
+        # subtract one because the first candidate is the floating point
+        # version of the subgraph
+        node_name_to_qconfig = list_of_node_name_to_qconfig[subgraph_candidate_idx - 1]
+        qconfig = node_name_to_qconfig[first_node.name]
+
+        # if no quantization is requested, skip
+        # TODO(future PR): deduplicate equivalent qconfigs that come from
+        #   different qconfig mapping objects
+        if qconfig is None:
+            return
+
+        qconfig_mapping = QConfigMapping().set_global(qconfig)
+
+        # create a copy of the submodule, wrapped in a separate module
+        orig_mod_copy_wrapped = create_submodule_from_subgraph(
+            mt, first_node, last_node
+        )
+
+        # add a call to prepare_fx on the wrapper module
+        if custom_prepare_fn is None:
+            orig_mod_copy_wrapped = torch.ao.quantization.quantize_fx.prepare_fx(
+                orig_mod_copy_wrapped, qconfig_mapping, example_inputs=example_inputs
+            )
+        else:
+            if custom_prepare_kwargs is None:
+                custom_prepare_kwargs = {}
+            for kwarg_name in [
+                "example_inputs",
+                "prepare_custom_config",
+                "qconfig_mapping",
+            ]:
+                assert (
+                    kwarg_name not in custom_prepare_kwargs
+                ), f"cannot specify {kwarg_name} in custom_prepare_kwargs"
+            prepare_kwargs: Dict[str, Any] = {
+                "example_inputs": example_inputs,
+                "qconfig_mapping": qconfig_mapping,
+            }
+            prepare_kwargs.update(custom_prepare_kwargs)
+            orig_mod_copy_wrapped = custom_prepare_fn(
+                orig_mod_copy_wrapped, **prepare_kwargs
+            )
+
+        # attach the wrapper to the model
+        attr_name = _get_attr_wrapper_name(subgraph_idx, subgraph_candidate_idx)
+        assert not hasattr(mt, attr_name)
+        setattr(mt, attr_name, orig_mod_copy_wrapped)
+
+        # add a call to the wrapper module from the parent graph
+        insert_after_node = last_added_shadow_node_list[0]
+        with mt.graph.inserting_after(insert_after_node):
+            # TODO(future PR): handle fusion patterns where non-first nodes
+            # need inputs
+
+            # pass in all node args and kwargs
+
+            new_args = []
+            for arg in first_node.args:
+                if isinstance(arg, Node):
+                    new_args.append(arg)
+                elif (
+                    isinstance(arg, (list, tuple))
+                    and len(arg)
+                    and isinstance(arg[0], Node)
+                ):
+                    for inner_arg in arg:
+                        if isinstance(inner_arg, Node):
+                            new_args.append(inner_arg)
+
+            new_kwargs = {}
+            for name, old_kwarg in first_node.kwargs.items():
+                if isinstance(old_kwarg, Node):
+                    new_kwargs[name] = old_kwarg
+                elif isinstance(old_kwarg, (list, tuple)) and len(old_kwarg):
+                    # TODO(future PR): clarify why we are adding kwargs to args
+                    new_args.extend(old_kwarg)
+
+            new_args = tuple(new_args)  # type: ignore[assignment]
+
+            new_node = mt.graph.call_module(attr_name, args=new_args, kwargs=new_kwargs)  # type: ignore[arg-type]
+
+        # add a logger to parent graph to observe the shadow wrapper
+        logger_mod_orig = _get_logger_for_subgraph(
+            mt,
+            first_node,
+            last_node,
+            subgraph_idx,
+            subgraph_candidate_idx,
+            str(qconfig),
+            OutputComparisonLogger,
+            fqn,
+        )
+
+        attr_name = _get_attr_name(subgraph_idx, subgraph_candidate_idx)
+        assert not hasattr(mt, attr_name)
+        setattr(mt, attr_name, logger_mod_orig)
+        with mt.graph.inserting_after(new_node):
+            logger = mt.graph.call_module(
+                attr_name, args=(new_node, last_node), kwargs={}
+            )
+            last_added_shadow_node_list[0] = logger
+
+    mt.recompile()
+
+
+def create_n_transformed_and_logged_copies_of_subgraph(
+    mt: GraphModule,
+    subgraph_idx: int,
+    match_name: str,
+    nodes_in_this_subgraph: List[Any],
+    qconfig_mappings: List[QConfigMapping],
+    list_of_node_name_to_qconfig: List[Dict[str, QConfigAny]],
+    custom_prepare_fn: Optional[Callable] = None,
+    custom_prepare_kwargs: Optional[Dict[str, Any]] = None,
+) -> None:
+    """
+    Given a model `mt` and a subgraph_idx, creates the needed copies
+    of the subgraph for all qconfigs, and instruments them with loggers.
+    """
+    # for now, assume that
+    # 1. the first node has one input
+    # 2. the last node has one output
+
+    # for now, ignore all subgraphs that contain non-nodes (tuples, etc)
+    # TODO(future PR): implement this
+    if any(not isinstance(node, Node) for node in nodes_in_this_subgraph):
+        return
+
+    first_node = nodes_in_this_subgraph[0]
+    last_node = nodes_in_this_subgraph[-1]
+    # We used output propagation to populate example values on each
+    # node. Use the example values from the previous node as the input
+    # to the current node.
+    prev_node = get_normalized_nth_input(first_node, mt, 0)
+    if isinstance(prev_node, list):
+        example_inputs = [x.traced_result for x in prev_node]
+    elif isinstance(prev_node, tuple):
+        example_inputs = (x.traced_result for x in prev_node)  # type: ignore[assignment]
+    else:
+        # currently some customer models do not have a traced_result in
+        # every node, so we have to guard for this case since we cannot
+        # quantize without an example input
+        # TODO(future PR): add a test case for this once we have an easy
+        # repro, see https://github.com/pytorch/pytorch/pull/80521/files#r975940489
+        # for additional context
+        if hasattr(prev_node, "traced_result"):
+            example_inputs = (prev_node.traced_result,)  # type: ignore[attr-defined, assignment]
+        else:
+            print(
+                "unable to get example input for node "
+                + f"{first_node.format_node()}, skipping"
+            )
+            return
+
+    # If there are no quantization configs for this subgraph, skip adding
+    # loggers. This reduces memory usage for models where not all layers are
+    # quantized.
+    # TODO(future): consider making this configurable
+    found_at_least_one_qconfig = False
+    for subgraph_candidate_idx in range(len(qconfig_mappings) + 1):
+        if subgraph_candidate_idx == 0:
+            # fp32 baseline does not need a qconfig
+            continue
+
+        # a. we have N shadows, so len(qconfig_mappings) is N
+        # b. we will have the fp32 layer + N shadows, so overall number of
+        #    (original_op) + (*shadows) will be N+1
+        # c. since `subgraph_candidate_idx` represents (b), we need
+        #    to subtract 1 to query from (a)
+        node_name_to_qconfig = list_of_node_name_to_qconfig[subgraph_candidate_idx - 1]
+        qconfig = node_name_to_qconfig[first_node.name]
+        if qconfig is not None:
+            found_at_least_one_qconfig = True
+            break
+    if not found_at_least_one_qconfig:
+        print(
+            "unable to find at least one qconfig for node "
+            + f"{first_node.format_node()}, skipping"
+        )
+        return
+
+    fqn = _maybe_get_fqn(first_node, mt)
+
+    # We want the results to contain the subgraphs in natural order,
+    # and the graph to also contain shadow wrappers and shadow loggers
+    # in natural order.
+    # If we just iterate in reverse, the graph will be in natural
+    # order but the eventual results will be in reverse order.
+    # So, we keep track of the last shadow logger we added and
+    # always insert after it.
+    last_added_shadow_node_list: List[Optional[Node]] = [None]
+    for subgraph_candidate_idx in range(len(qconfig_mappings) + 1):
+        create_one_transformed_and_logged_copy_of_subgraph(
+            mt,
+            subgraph_idx,
+            subgraph_candidate_idx,
+            first_node,
+            last_node,
+            fqn,
+            list_of_node_name_to_qconfig,
+            example_inputs,
+            last_added_shadow_node_list,
+            custom_prepare_fn,
+            custom_prepare_kwargs,
+        )
+
+
+def create_add_loggers_graph(
+    model: GraphModule,
+    subgraphs_dedup: Dict[str, List[Node]],
+    qconfig_mapping: QConfigMapping,
+    node_name_to_qconfig: Dict[str, QConfigAny],
+) -> None:
+    r"""
+    Given a model, a model graph partition (currently a set of matched
+    subgraphs) and instructions how to transform each subgraph
+    (currently quantizing it according to qconfig_mapping), modifies
+    the model graph to create an alternate path through the original graph,
+    with each of the subgraphs quantized.  This is useful to compare
+    propagation error of a transformation such as quantization.
+
+    For example, given layer op0 and op1, there are four cases when handling op1:
+    1. op0 and op1 quantized
+    2. op0 and op1 unquantized
+    3. op0 quantized, op1 unquantized
+    4. op0 unquantized, op1 quantized
+
+    Example input, case 1:
+
+    .. code::
+
+      x0_0 -> op0_0 -> x1_0 -> log -----> op1_0 -> x2_0 -> log
+       \                        \          \                 \       # noqa: W605
+         ---> op0_1 -> x1_1 ----> clog    op1_1 -> x2_1 ----> clog
+
+    Example output, case 1:
+
+    .. code::
+
+      x0_0 -> op0_0 -> x1_0 -> log -----> op1_0 -> x2_0 -> log
+       \                        \                           \        # noqa: W605
+         ---> op0_1 -> x1_1 ----> clog -> op1_1 -> x2_1 ----> clog
+
+    """
+    # TODO(future PR): move logger classes to utils to remove circular dependency
+    from torch.ao.ns._numeric_suite_fx import OutputComparisonLogger, OutputLogger
+
+    def _get_subgraph_containing_node(node, subgraphs_dedup):
+        for subgraph in subgraphs_dedup.values():
+            if node in subgraph:
+                return subgraph
+        return None
+
+    # First, we need to create shadow branches, going from
+    #
+    #   x0 -> op0 -> x1 -> ...
+    #
+    #
+    # to
+    #
+    #   x0 -> op0_0 -> x1_0 -> log -> ...
+    #    \                     \
+    #      -> op0_1 -> x1_1 -> clog
+    #
+    # Later, the outputs of each shadow will be rerouted to calculate
+    # propagation error.
+
+    # Note: we cannot iterate over matched subgraphs because some nodes
+    # may not be matched. So, we iterate over nodes in the graph, and
+    # associate them to matched subgraphs if possible.
+
+    nodes_to_skip = set()
+    # for each subgraph, save a mapping from first node of subgraph
+    # to first and last node of the shadow of this subgraph
+    orig_first_node_to_shadow_in_node = {}
+    orig_first_node_to_shadow_out_node = {}
+    # need to record original list because we will mutate the graph as we go
+    orig_nodes = list(model.graph.nodes)  # type: ignore[union-attr, arg-type]
+    cur_subgraph_idx = 0
+    for n in orig_nodes:
+        if n.op in ("placeholder", "get_attr", "output") or n in nodes_to_skip:
+            continue
+
+        maybe_subgraph = _get_subgraph_containing_node(n, subgraphs_dedup)
+        insert_submodule_copy = False
+        if maybe_subgraph is not None:
+            first_node, last_node = maybe_subgraph[0], maybe_subgraph[-1]
+            nodes_to_skip.update(maybe_subgraph)
+            qconfig = node_name_to_qconfig[first_node.name]
+            if qconfig is not None:
+                insert_submodule_copy = True
+        else:
+            first_node, last_node = n, n
+
+        if insert_submodule_copy:
+            match_name = first_node.name
+            create_n_transformed_and_logged_copies_of_subgraph(
+                model,
+                cur_subgraph_idx,
+                match_name,
+                maybe_subgraph,
+                [qconfig_mapping],
+                [node_name_to_qconfig],
+                None,
+                None,  # type: ignore[arg-type]
+            )
+            # find the created shadow module and record it so we
+            # can find it easily in step 2
+            expected_shadow_target = f"shadow_wrapper_{cur_subgraph_idx}_1"
+            new_shadow_mod = None
+            for maybe_shadow_mod in model.graph.nodes:
+                if (
+                    maybe_shadow_mod.op == "call_module"
+                    and maybe_shadow_mod.target == expected_shadow_target
+                ):
+                    new_shadow_mod = maybe_shadow_mod
+                    break
+            assert new_shadow_mod is not None
+            orig_first_node_to_shadow_in_node[first_node] = new_shadow_mod
+            orig_first_node_to_shadow_out_node[first_node] = new_shadow_mod
+
+        else:
+            # create a copy of the subgraph by only copying FX nodes
+            # but not copying any parameters, to minimize memory usage
+            subgraph_to_use = (
+                maybe_subgraph if maybe_subgraph is not None else [first_node]
+            )
+
+            # add a regular logger after last_node
+            qconfig_str = ""
+            subgraph_candidate_idx = 0
+            fqn = _maybe_get_fqn(first_node, model)
+            logger_mod_orig = _get_logger_for_subgraph(
+                model,
+                first_node,
+                last_node,
+                cur_subgraph_idx,
+                subgraph_candidate_idx,
+                qconfig_str,
+                OutputLogger,
+                fqn,
+            )
+            attr_name = _get_attr_name(cur_subgraph_idx, subgraph_candidate_idx)
+            assert not hasattr(model, attr_name)
+            setattr(model, attr_name, logger_mod_orig)
+            insertion_point = last_node
+            with model.graph.inserting_after(insertion_point):
+                logger = model.graph.call_module(
+                    attr_name, args=(last_node,), kwargs={}
+                )
+                insertion_point = logger
+
+            # create a copy of the subgraph
+            cur_node_orig = first_node
+            cur_node_copy = None
+            first_node_copy = None
+            while cur_node_orig in subgraph_to_use:
+                # TODO(future PR): make this support all possible args/kwargs
+                if cur_node_orig is first_node:
+                    new_args = cur_node_orig.args
+                    new_kwargs = cur_node_orig.kwargs
+                else:
+                    first_arg_for_copy = cur_node_copy
+                    new_args = (first_arg_for_copy, *cur_node_orig.args[1:])
+                    new_kwargs = cur_node_orig.kwargs
+                # make a copy of cur_node_orig
+                with model.graph.inserting_after(insertion_point):
+                    cur_node_copy = model.graph.create_node(
+                        cur_node_orig.op,
+                        cur_node_orig.target,
+                        new_args,
+                        new_kwargs,
+                        # cur_node_orig.name,  # TODO(future PR): set name explicitly
+                    )
+                    if first_node_copy is None:
+                        first_node_copy = cur_node_copy
+                # since now only linear subgraphs are supported, all nodes
+                # except the last one must have only one user
+                if cur_node_orig != last_node:
+                    assert len(cur_node_orig.users.keys()) == 1
+                cur_node_orig = next(iter(cur_node_orig.users.keys()))
+                assert not cur_node_orig.name.startswith(SHADOW_NODE_NAME_PREFIX)
+                insertion_point = cur_node_copy
+
+            # add a comparison logger after last_node's copy
+            subgraph_candidate_idx = 1
+            logger_mod_orig = _get_logger_for_subgraph(
+                model,
+                first_node,
+                last_node,
+                cur_subgraph_idx,
+                subgraph_candidate_idx,
+                qconfig_str,
+                OutputComparisonLogger,
+                fqn,
+            )
+            attr_name = _get_attr_name(cur_subgraph_idx, subgraph_candidate_idx)
+            assert not hasattr(model, attr_name)
+            setattr(model, attr_name, logger_mod_orig)
+            with model.graph.inserting_after(insertion_point):
+                logger = model.graph.call_module(
+                    attr_name, args=(cur_node_copy, last_node), kwargs={}
+                )
+
+            # save the final node so we can use it in step 2
+            orig_first_node_to_shadow_in_node[first_node] = first_node_copy
+            orig_first_node_to_shadow_out_node[first_node] = cur_node_copy
+
+        cur_subgraph_idx += 1
+
+    model.recompile()
+
+    # Now, we go from
+    #
+    #   x0 -> op0_0 -> x1_0 -> log -> x1 -> op1_0 -> ...
+    #    \                     \       \
+    #      -> op0_1 -> x1_1 -> clog      -> op1_1 -> ...
+    #
+    # to
+    #
+    #   x0 -> op0_0 -> x1_0 -> log --> x1_0 -> op1_0 -> ...
+    #    \                     \
+    #      -> op0_1 -> x1_1 -> clog -> x1_1 -> op1_1 -> ...
+    #
+    # sample values of key internal variables for the example above:
+    #
+    #   orig_first_node_to_shadow_in_node = {op0_0: op0_1, op1_0: op1_1}
+    #   orig_first_node_to_shadow_out_node = {op0_0: op0_1, op1_0: op1_1}
+    #
+    # note: for subgraphs with more than one node, in_node will be different
+    # compared to out_node
+
+    nodes_to_skip = set()
+    for n in orig_nodes:
+        if n.op in ("placeholder", "get_attr", "output") or n in nodes_to_skip:
+            continue
+
+        maybe_subgraph = _get_subgraph_containing_node(n, subgraphs_dedup)
+        if maybe_subgraph is not None:
+            first_node, last_node = maybe_subgraph[0], maybe_subgraph[-1]
+            nodes_to_skip.update(maybe_subgraph)
+        else:
+            first_node, last_node = n, n
+
+        def maybe_remap_node_to_shadow(node):
+            """
+            If unshadowed `node` has a shadow version, return that. If not,
+            return `node`.
+            """
+            if not isinstance(node, Node):
+                # handle scalars
+                return node
+
+            if node.op in ("placeholder", "get_attr"):
+                return node
+
+            # Find the shadowed version of this arg from the previous
+            # subgraph. For this, we need to:
+            # 1. navigate to the first node of the previous subgraph
+            # 2. get the output of the shadow wrapper which has (1) as an input
+
+            # For now, assume the arg is in matched subgraphs. In the
+            # future we may have to handle the case where this is not true.
+            prev_subgraph = _get_subgraph_containing_node(node, subgraphs_dedup)
+            if prev_subgraph is None:
+                prev_subgraph = [node]
+            prev_first_node = prev_subgraph[0]
+            prev_shadow_output = orig_first_node_to_shadow_out_node[prev_first_node]
+            return prev_shadow_output
+
+        cur_shadow_input = orig_first_node_to_shadow_in_node[first_node]
+        assert cur_shadow_input is not None
+        cur_shadow_input.args = tree_map(
+            maybe_remap_node_to_shadow, cur_shadow_input.args
+        )
+        cur_shadow_input.kwargs = tree_map(
+            maybe_remap_node_to_shadow, cur_shadow_input.kwargs
+        )
+
+        model.recompile()
+
+
+def _get_weight_info_from_shadow_wrapper(shadow_wrapper: torch.nn.Module):
+    # input: shadow wrapper module
+    # output if shadow wrapper module has a weighted op:
+    #   (quantize_fn, (quantize_fn_args))
+    # output if shadow wrapper module doesn't have a weighted op:
+    #   None
+
+    # For now, assume that the weight is the second input
+    # to the shadow module. If that changes, we can fix it later.
+    placeholders_seen = 0
+    for shadow_n in shadow_wrapper.graph.nodes:  # type: ignore[union-attr]
+        if shadow_n.op != "placeholder":
+            continue
+
+        placeholders_seen += 1
+        if placeholders_seen != 2:
+            continue
+
+        # the subgraph looks like
+        #
+        #   _input_scale_1 = self._input_scale_1
+        #   _input_zero_point_1 = self._input_zero_point_1
+        #   quantize_per_channel = torch.quantize_per_channel(
+        #       w2_0, _input_scale_1, _input_zero_point_1,
+        #       0, torch.qint8)
+        #
+        #  we have `w2_0`, and are navigating this subgraph
+        #  to get `_input_scale_1` and `_input_zero_point_1`
+
+        assert len(shadow_n.users) == 1
+        quant_node = next(iter(shadow_n.users.keys()))
+        new_args: Any = None
+        if quant_node.target == torch.quantize_per_channel:
+            _weight, scale_node, zp_node, axis, dtype = quant_node.args
+            scale_val = getattr_from_fqn(shadow_wrapper, scale_node.target)
+            zp_val = getattr_from_fqn(shadow_wrapper, zp_node.target)
+            new_args = (scale_val, zp_val, axis, dtype)
+        else:
+            assert quant_node.target == torch.quantize_per_tensor
+            _weight, scale_node, zp_node, dtype = quant_node.args
+            scale_val = getattr_from_fqn(shadow_wrapper, scale_node.target)
+            zp_val = getattr_from_fqn(shadow_wrapper, zp_node.target)
+            new_args = (scale_val, zp_val, dtype)
+        return (quant_node.target, new_args)
+
+    return None
+
+
+def extract_weight_comparison(m: GraphModule) -> NSResultsType:
+    # example graph:
+    #
+    #   w1 = self.w1
+    #   b1 = self.b1
+    #   linear = torch._C._nn.linear(x, w1, b1)
+    #   shadow_0_0 = self.shadow_0_0(linear)
+    #   shadow_wrapper_0_1 = self.shadow_wrapper_0_1(x, w1, b1)
+    #   shadow_0_1 = self.shadow_0_1(shadow_wrapper_0_1, linear)
+    #
+    # algorithm:
+    # 1. for each call_function node matching our allowlist:
+    # 2.   if corresponding shadow wrapper exists, extract the weight pair
+    #
+    # Note: this is not super robust, but that's ok because this is
+    # just for legacy customers who depend on the previous two-model version
+    # of this API. TBD if we need to make this robust.
+    # Note: modules are not supported, since existing customers only
+    # use functions.
+
+    # TODO(future PR): move this to config
+    weighted_ops = {
+        torch.nn.functional.linear,
+    }
+
+    results: NSResultsType = {"model": {NSSingleResultValuesType.WEIGHT.value: {}}}
+
+    for n in m.graph.nodes:  # type: ignore[union-attr]
+        if not (n.op == "call_function" and n.target in weighted_ops):
+            continue
+
+        # Check if we have a corresponding shadow wrapper
+        # TODO(future PR, if needed): support kwargs
+        # TODO(future PR, if needed): support multiple shadow users
+        first_arg = n.args[0]
+        shadow_wrapper_node = None
+        for user in first_arg.users:
+            # TODO(before land): fix string match
+            if user.op == "call_module" and user.target.startswith("shadow_wrapper"):
+                shadow_wrapper_node = user
+                break
+
+        if shadow_wrapper_node is None:
+            continue
+
+        shadow_wrapper = getattr_from_fqn(
+            m, shadow_wrapper_node.target
+        )  # type: ignore[arg-type]
+        weight_info = _get_weight_info_from_shadow_wrapper(shadow_wrapper)
+        if weight_info is None:
+            continue
+
+        # get weight
+        w_node = n.args[1]
+        w_obj = getattr_from_fqn(m, w_node.target).detach()
+
+        # get a quantized version of weight
+        quant_fn, quant_fn_args_except_first = weight_info
+        new_args = (w_obj, *quant_fn_args_except_first)
+        w_obj_q = quant_fn(*new_args)
+
+        # add a comparison
+        ref_node_name = n.name
+        prev_node_name = n.name
+        ref_node_type = get_target_type_str(n, m)
+        prev_node_type = ref_node_type
+        fqn = None
+        if hasattr(m, "_node_name_to_scope"):
+            fqn = m._node_name_to_scope[n.name][0]  # type: ignore[index]
+        comparison = torch.ao.ns.fx.utils.compute_sqnr(w_obj, w_obj_q)
+        result_fp32 = {
+            "res_type": NSSingleResultValuesType.WEIGHT.value,
+            "values": [w_obj],
+            "prev_node_name": prev_node_name,
+            "prev_node_target_type": prev_node_type,
+            "ref_node_name": ref_node_name,
+            "ref_node_target_type": ref_node_type,
+            "index_within_arg": 0,
+            "index_of_arg": 0,
+            "fqn": fqn,
+            "qconfig_str": "",
+            "comparisons": [comparison],
+            "comparison_fn_name": "sqnr",
+        }
+        result_q = {
+            "res_type": NSSingleResultValuesType.WEIGHT.value,
+            "values": [w_obj_q],
+            "prev_node_name": prev_node_name,
+            "prev_node_target_type": prev_node_type,
+            "ref_node_name": ref_node_name,
+            "ref_node_target_type": ref_node_type,
+            "index_within_arg": 0,
+            "index_of_arg": 0,
+            "fqn": fqn,
+            "qconfig_str": "",
+            "comparisons": [comparison],
+            "comparison_fn_name": "sqnr",
+        }
+
+        # go from subgraph_n_1 to subgraph_n_0
+        _1, _2, node_idx, _3 = shadow_wrapper_node.target.split("_")
+        name_fp32 = f"subgraph_{node_idx}_0"
+        name_q = f"subgraph_{node_idx}_1"
+
+        results["model"][NSSingleResultValuesType.WEIGHT.value][name_fp32] = [
+            result_fp32
+        ]
+        results["model"][NSSingleResultValuesType.WEIGHT.value][name_q] = [result_q]
+
+    return results
+
+
+# TODO(future PR): redesign this to make it easier to consume outputs
+def group_results_by_subgraph(results: NSResultsType) -> Any:
+    """
+    Creates a comparison of results
+
+    Input:
+
+    {
+      'model': {
+        'node_output': {
+          'subgraph_0_0': [
+            'values': [torch.tensor(...), ...], ...
+            'ref_node_name': ...,
+            'ref_node_target_type': ...,
+            'qconfig_str': ...,
+            'comparisons': [], ...
+            'comparison_fn_name': '',
+            'fqn': '...',
+          ],
+          'subgraph_0_1': [
+            'values': [torch.tensor(...), ...], ...
+            'ref_node_name': ...,
+            'ref_node_target_type': ...,
+            'qconfig_str': ...,
+            'comparisons': [torch.tensor(...), ...], ...
+            'comparison_fn_name': '...',
+            'fqn': '...',
+          ],
+          ...
+        },
+      },
+    }
+
+    Output:
+    {
+      'subgraph_0': {
+        '0': {
+          'ref_node_name': '...',
+          'ref_node_target_type': ...,
+          'values': [torch.tensor(...), ...],
+          'qconfig_str': None,
+          'comparisons': [torch.tensor(...), ...], ...
+          'comparison_fn_name': '...',
+          'fqn': '...',
+        },
+        '1': {
+          'ref_node_name': '...',
+          'ref_node_target_type': ...,
+          'values': [torch.tensor(...), ...],
+          'qconfig_str': '...',
+          'comparisons': [torch.tensor(...), ...], ...
+          'comparison_fn_name': '...',
+          'fqn': '...',
+        },
+      },
+    }
+
+    """
+    subgraph_name_to_subgraph_results: Any = collections.defaultdict(dict)
+
+    # node_output or weight
+    key_to_use = next(iter(results["model"].keys()))
+
+    for subgraph_name_with_idx, subgraph_candidate_results in results["model"][
+        key_to_use
+    ].items():
+        # convert from `subgraph_m_n` to `subgraph_m` and `n`
+        (
+            subgraph_str,
+            subgraph_idx,
+            subgraph_candidate_idx,
+        ) = subgraph_name_with_idx.split("_")
+        subgraph_name = f"{subgraph_str}_{subgraph_idx}"
+
+        subgraph_results = {
+            "ref_node_name": subgraph_candidate_results[0]["ref_node_name"],
+            "ref_node_target_type": subgraph_candidate_results[0][
+                "ref_node_target_type"
+            ],
+            "fqn": subgraph_candidate_results[0]["fqn"],
+            "values": subgraph_candidate_results[0]["values"],
+            "qconfig_str": subgraph_candidate_results[0]["qconfig_str"],
+            "comparisons": subgraph_candidate_results[0]["comparisons"],
+            "comparison_fn_name": subgraph_candidate_results[0]["comparison_fn_name"],
+        }
+
+        subgraph_name_to_subgraph_results[subgraph_name][
+            subgraph_candidate_idx
+        ] = subgraph_results
+
+    return dict(subgraph_name_to_subgraph_results)
+
+
+# TODO(future PR): redesign this to make it easier to consume outputs
+def create_results_comparison(
+    results_grouped,
+) -> Any:
+    """
+    Input:
+
+    {
+      'subgraph_0': {
+        '0': {
+          'ref_node_name': '...',
+          'ref_node_target_type': ...,
+          'values': [torch.tensor(...), ...],
+          'qconfig_str': '',
+          'comparisons': [],
+          'comparison_fn_name': '',
+          'fqn': '...',
+        },
+        '1': {
+          'ref_node_name': '...',
+          'ref_node_target_type': ...,
+          'values': [torch.tensor(...), ...],
+          'qconfig_str': '...',
+          'comparisons': [torch.tensor(...), ...],
+          'comparison_fn_name': 'sqnr',
+          'fqn': '...',
+        },
+      },
+    }
+
+    Output:
+    {
+      'subgraph_0': {
+        'ref_node_name': '...',
+        'ref_node_target_type': '...',
+        'fqn': '...',
+        'candidates': {
+          '1': {
+            'qconfig_str': ...,
+            'comparison_fn_name': 'sqnr',
+            'cmp_raw': [..., ...],
+            'cmp_mean': ...,
+          },
+          ...,
+        },
+      },
+    }
+    """
+
+    results_comparison = {}
+
+    for subgraph_name, subgraph_results in results_grouped.items():
+        candidates = {}
+        for subgraph_inner_name, subgraph_inner_result in subgraph_results.items():
+            # skip comparing baseline to baseline
+            if subgraph_inner_name == "0":
+                continue
+
+            # we expect the comparisons to be precalculated from
+            # calibration, so we just fetch them here
+            cmp_raw = subgraph_inner_result["comparisons"]
+            cmp_raw_tensor = torch.stack(cmp_raw)
+
+            candidates[subgraph_inner_name] = {
+                "qconfig_str": subgraph_inner_result["qconfig_str"],
+                "comparison_fn_name": subgraph_inner_result["comparison_fn_name"],
+                "cmp_raw": cmp_raw_tensor,
+                "cmp_mean": torch.mean(cmp_raw_tensor),
+            }
+
+        results_comparison[subgraph_name] = {
+            "ref_node_name": subgraph_results["0"]["ref_node_name"],
+            "ref_node_target_type": subgraph_results["0"]["ref_node_target_type"],
+            "fqn": subgraph_results["0"]["fqn"],
+            "candidates": candidates,
+        }
+
+    return results_comparison
+
+
+# TODO(future PR): redesign this to make it easier to consume outputs
+def print_n_shadows_summary(
+    results_comparison,
+) -> None:
+    """
+    Input:
+
+    {
+      'subgraph_0': {
+        'ref_node_name': 'linear1',
+        'ref_node_target_type': '...',
+        'fqn': '...',
+        'candidates': {
+          '1': {
+            'qconfig_str': ...,
+            'comparison_fn_name': ...,
+            'cmp_raw': [45.0, 55.0],
+            'cmp_mean': 50.0,
+          },
+          ...,
+        },
+      },
+    }
+
+    Prints:
+
+    node_name | node_type | fqn | 0    | 1    | ...
+    linear1   | ...       | ... | 45.0 | 50.0 | ...
+    """
+
+    try:
+        from tabulate import tabulate
+    except ImportError:
+        print(
+            "`print_tabular` relies on the library `tabulate`, "
+            "which could not be found on this machine. Run `pip "
+            "install tabulate` to install the library."
+        )
+        return
+
+    results = []
+    for subgraph_data in results_comparison.values():
+        mean_all_candidates = [
+            candidate["cmp_mean"]
+            for candidate_name, candidate in subgraph_data["candidates"].items()
+        ]
+
+        data_row = [
+            subgraph_data["ref_node_name"],
+            subgraph_data["ref_node_target_type"],
+            subgraph_data["fqn"],
+            *mean_all_candidates,
+        ]
+        results.append(data_row)
+
+    max_candidate_idx_len = -1
+    for data_row in results:
+        max_candidate_idx_len = max(max_candidate_idx_len, len(data_row[1]))
+    candidate_idx_headers = [str(x) for x in range(max_candidate_idx_len)]
+
+    headers = ["node_name", "node_type", "fqn", *candidate_idx_headers]
+    print(tabulate(results, headers=headers))
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/ns_types.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/ns_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..e55d985c84c7fcf9d0efa038f17ff9d4205bdbcd
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/ns_types.py
@@ -0,0 +1,65 @@
+import enum
+from typing import Any, Callable, Dict, List, NamedTuple, Union
+
+from torch.fx.graph import Node
+
+
+class NSSingleResultValuesType(str, enum.Enum):
+    WEIGHT = "weight"
+    NODE_OUTPUT = "node_output"
+    NODE_INPUT = "node_input"
+
+
+class NSSubgraph(NamedTuple):
+    start_node: Node
+    end_node: Node
+    base_op_node: Node
+
+
+# TODO(future PR): see if we can use typing_extensions's TypedDict instead
+# to properly type the various keys
+# {
+#   # one of NSSingleResultValuesType
+#   'type': 'weight',
+#   # the values of type specified above
+#   'values': [torch.tensor(...), ...],
+#   # name of the node directly before the logger
+#   'prev_node_name': 'linear1',
+#   # type of the underlying function or module
+#   'prev_node_target_type': torch.nn.functional.linear  # or torch.nn.Linear, etc
+#   # name of the node responsible for adding this logger
+#   # Note: this may differ from prev_node_name if we are logging inputs
+#   'ref_node_name': 'linear1',
+#   # index of this node within the arg of the input/output node
+#   # for example, in cat([x1, x2, x3], dim=0), x2 would have index_within_arg == 1
+#   'index_within_arg': 0,
+#   # index of this node within the args of the input/output node
+#   # for example, in add(x1, x2), x2 would have index_of_arg == 1
+#   'index_of_arg': 0,
+#   # precomputed comparisons of logger values to reference values
+#   'comparisons': [torch.tensor(...), ...]
+#   # name of function used for precomputed comparisons
+#   'comparison_fn_name': 'sqnr',
+#   # string representation of qconfig responsible for creating this logger
+#   'qconfig_str': 'QConfig(...)',
+# }
+NSSingleResultType = Dict[str, Any]
+
+# {
+#   'layer_name_1': {  # subgraph name
+#     'node_output': {  # results type (node_output, node_input, weight)
+#       'model_name_a':  # model name
+#          [NSSingleResultType, ...],  # results, ordered by index_within_arg
+#       'model_name_b':
+#          [NSSingleResultType, ...],
+#     },
+#   },
+# }
+#
+NSResultsType = Dict[str, Dict[str, Dict[str, List[NSSingleResultType]]]]
+
+# Defines the underlying target type of a node, for example:
+# `F.conv1d` for a `call_function` conv node
+# `nn.Conv1d` for a `call_module` node calling the forward of a `nn.Conv1d` module
+# `'sigmoid'` for a `call_method` node calling `x.sigmoid()`
+NSNodeTargetType = Union[Callable, str]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/pattern_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/pattern_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6d7606995744faf9155cf936cf4f4ebfa3b7cda
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/pattern_utils.py
@@ -0,0 +1,209 @@
+from typing import Any, Callable, Dict, List, Set, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.ao.quantization import FakeQuantizeBase, ObserverBase
+from torch.ao.quantization.backend_config import get_native_backend_config
+from torch.ao.quantization.fx.quantize_handler import _get_pattern_to_quantize_handlers
+from torch.ao.quantization.utils import getattr_from_fqn
+from torch.fx import GraphModule
+from torch.fx.graph import Node
+
+from .ns_types import NSNodeTargetType
+
+
+toq = torch.ops.quantized
+
+
+def get_type_a_related_to_b(
+    base_name_to_sets_of_related_ops: Dict[str, Set[NSNodeTargetType]],
+) -> Set[Tuple[NSNodeTargetType, NSNodeTargetType]]:
+    # TODO(future PR): allow customizations
+    # TODO(future PR): reuse existing quantization mappings
+    # TODO(future PR): add the rest of modules and ops here
+    type_a_related_to_b: Set[Tuple[NSNodeTargetType, NSNodeTargetType]] = set()
+
+    for s in base_name_to_sets_of_related_ops.values():
+        s_list = list(s)
+        # add every bidirectional pair
+        for idx_0 in range(0, len(s_list)):
+            for idx_1 in range(idx_0, len(s_list)):
+                type_a_related_to_b.add((s_list[idx_0], s_list[idx_1]))
+                type_a_related_to_b.add((s_list[idx_1], s_list[idx_0]))
+
+    return type_a_related_to_b
+
+
+NSFusionElType = Union[
+    Callable,  # call_function or call_module type, example: F.linear or nn.Conv2d
+    str,  # call_method name, example: "dequantize"
+    Tuple[
+        str, Any
+    ],  # call_method name and first argument, example: ("to", torch.float16)
+]
+NSFusionType = Union[
+    Tuple[NSFusionElType, NSFusionElType],
+    Tuple[NSFusionElType, NSFusionElType, NSFusionElType, NSFusionElType],
+]
+
+
+def get_reversed_fusions() -> List[Tuple[NSFusionType, int]]:
+    """
+    Set of potential fusions, in reverse order.  The order is reversed
+    to match how fusion patterns are defined in quantization code.
+
+    Fusion format:
+    ((fusion_op_0, fusion_op_1), base_op_idx)
+
+    Where base_op_idx is the idx of the op we should use to match other related
+    ops. Note: base_op_idx is specified in non-reverse order, i.e. a base_op_idx
+    of 0 represents the first op in regular (non-reverse) order, 1 represents the
+    second op, etc.
+    """
+    results: List[Tuple[NSFusionType, int]] = []
+
+    # Possible syntaxes:
+    # * single op: torch.nn.Conv2d
+    # * multiple ops: (torch.nn.ReLU, torch.nn.Conv2d)
+    # For fusions, we only care about patterns composed of multiple ops.
+    # TODO(future PR): allow customizations from default patterns.
+    all_quant_patterns = _get_pattern_to_quantize_handlers(get_native_backend_config())
+
+    default_base_op_idx = 0
+    for quant_pattern in all_quant_patterns.keys():
+        # TODO: this is a temporary hack to flatten the patterns from quantization so
+        # that it works with the ns matcher function, maybe we should use `_is_match`
+        # in torch.ao.quantization.fx.match_utils to match the patterns
+        if (
+            isinstance(quant_pattern, tuple)
+            and len(quant_pattern) == 2
+            and isinstance(quant_pattern[1], tuple)
+            and len(quant_pattern[1]) == 2
+        ):
+            # flatten the pattern with form (nn.ReLU, (nn.BatchNorm2d, nn.Conv2d))
+            quant_pattern = (quant_pattern[0], quant_pattern[1][0], quant_pattern[1][1])
+
+        # Only patterns of multiple ops are fusions, ignore
+        # patterns which contain a single ops (they get matched
+        # without caring about fusions).
+        if isinstance(quant_pattern, tuple):
+            results.append((quant_pattern, default_base_op_idx))  # type: ignore[arg-type]
+
+        # For each pattern, add additional patterns with observers and
+        # fake quants at the end.
+        # TODO(future PR): if needed, implement matching for a node
+        #   having multiple output observers.
+        for cls in (ObserverBase, FakeQuantizeBase):
+            if isinstance(quant_pattern, tuple):
+                new_pattern = (cls, *quant_pattern)
+            else:
+                new_pattern = (cls, quant_pattern)
+            results.append((new_pattern, default_base_op_idx))  # type: ignore[arg-type]
+
+    # After this point, results contains values such as
+    # [..., ((torch.nn.Relu, torch.nn.Conv2d), 0), ...]
+
+    # Patterns for matching fp16 emulation are not specified in the quantization
+    # fusion mappings.  For now, define them here.
+    fp16_em_base_op_idx = 1
+    patterns_to_add = [
+        # linear-relu fp16 emulation:
+        # fp16_to_fp32 -> linear -> relu -> fp32_to_fp16
+        (
+            (("to", torch.float16), F.relu, F.linear, "dequantize"),
+            fp16_em_base_op_idx,
+        ),
+        # Conv-BN fusion (this happens outside of quantization patterns,
+        # which is why it is defined separately here).
+        ((nn.BatchNorm1d, nn.Conv1d), default_base_op_idx),
+        ((nn.BatchNorm2d, nn.Conv2d), default_base_op_idx),
+        ((nn.BatchNorm3d, nn.Conv3d), default_base_op_idx),
+        ((nn.ReLU, nn.BatchNorm1d, nn.Conv1d), default_base_op_idx),
+        ((nn.ReLU, nn.BatchNorm2d, nn.Conv2d), default_base_op_idx),
+        ((nn.ReLU, nn.BatchNorm3d, nn.Conv3d), default_base_op_idx),
+    ]
+    for p in patterns_to_add:
+        results.append(p)  # type: ignore[arg-type]
+        results.append(((ObserverBase, *p[0]), p[1]))  # type: ignore[arg-type]
+        results.append(((FakeQuantizeBase, *p[0]), p[1]))  # type: ignore[arg-type]
+
+    return results
+
+
+def end_node_matches_reversed_fusion(
+    end_node: Node,
+    reversed_fusion: NSFusionType,
+    gm: GraphModule,
+    seen_nodes: Set[Node],
+) -> bool:
+    """
+    Returns true if a pattern ending with `end_node` matches
+    the fusion pattern.
+    """
+    cur_node = end_node
+    for fusion_idx in range(len(reversed_fusion)):
+        # each node can only belong to one matched pattern
+        if cur_node in seen_nodes:
+            return False
+
+        cur_fusion_el = reversed_fusion[fusion_idx]
+
+        if cur_node.op == "call_function":
+            fusion_el_is_fun = (not isinstance(cur_fusion_el, str)) and (
+                not isinstance(cur_fusion_el, type)
+            )
+            if fusion_el_is_fun:
+                if cur_node.target != cur_fusion_el:
+                    return False
+                if len(cur_node.args) > 0 and isinstance(cur_node.args[0], Node):
+                    cur_node = cur_node.args[0]
+                else:
+                    return False
+            else:
+                return False
+
+        elif cur_node.op == "call_module":
+            fusion_el_is_mod = isinstance(cur_fusion_el, type)
+            if fusion_el_is_mod:
+                assert isinstance(cur_node.target, str)
+                target_mod = getattr_from_fqn(gm, cur_node.target)
+                if not isinstance(cur_fusion_el, type):
+                    return False
+                if not isinstance(target_mod, cur_fusion_el):
+                    return False
+                if len(cur_node.args) > 0 and isinstance(cur_node.args[0], Node):
+                    cur_node = cur_node.args[0]
+                else:
+                    return False
+            else:
+                return False
+
+        elif cur_node.op == "call_method":
+            fusion_el_is_meth_with_second_arg = (
+                isinstance(cur_fusion_el, tuple) and len(cur_fusion_el) == 2
+            )
+            fusion_el_is_meth_without_args = isinstance(cur_fusion_el, str)
+            if fusion_el_is_meth_without_args or fusion_el_is_meth_with_second_arg:
+                if fusion_el_is_meth_without_args:
+                    if cur_node.target != cur_fusion_el:
+                        return False
+                else:
+                    assert isinstance(cur_fusion_el, tuple)
+                    if cur_node.target != cur_fusion_el[0]:
+                        return False
+                    elif len(cur_node.args) < 2:
+                        return False
+                    elif cur_node.args[1] != cur_fusion_el[1]:
+                        return False
+
+                if len(cur_node.args) > 0 and isinstance(cur_node.args[0], Node):
+                    cur_node = cur_node.args[0]
+                else:
+                    return False
+            else:
+                return False
+        else:
+            return False
+
+    return True
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/qconfig_multi_mapping.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/qconfig_multi_mapping.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cd4190110ffe3d277c421fe569484f3392f197e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/qconfig_multi_mapping.py
@@ -0,0 +1,249 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import copy
+from typing import Any, Callable, Dict, List, TYPE_CHECKING, Union
+
+import torch
+from torch.ao.quantization import QConfigMapping
+from torch.ao.quantization.qconfig_mapping import _QCONFIG_STYLE_ORDER
+
+
+if TYPE_CHECKING:
+    from torch.ao.quantization.qconfig import QConfigAny
+
+__all__ = ["QConfigMultiMapping"]
+
+_QCONFIG_STYLE_TO_METHOD: Dict[str, str] = {
+    "global_qconfig": "set_global",
+    "object_type_qconfigs": "set_object_type",
+    "module_name_regex_qconfigs": "set_module_name_regex",
+    "module_name_qconfigs": "set_module_name",
+    "module_name_object_type_order_qconfigs": "set_module_name_object_type_order",
+}
+
+
+def _remove_duplicates_and_none(qconfig_list: List[QConfigAny]) -> None:
+    to_remove = []
+    for index, cur_qconfig in enumerate(qconfig_list):
+        if cur_qconfig is None:
+            to_remove.append(index)
+            break
+        for checked_qconfig in qconfig_list[:index]:
+            if torch.ao.quantization.qconfig_equals(cur_qconfig, checked_qconfig):
+                to_remove.append(index)
+                break
+    for index in to_remove[::-1]:
+        qconfig_list.pop(index)
+
+
+class QConfigMultiMapping:
+    """
+    This class, used with the prepare_n_shadows_model API, stores a list of :class:`torch.ao.quantization.QConfigMapping`s
+    so that multiple QConfigs can be specified for each QConfig matching style.
+
+    The user can specify QConfigs using the following methods (in increasing match priority):
+
+        ``set_global`` : sets the global (default) QConfigs
+
+        ``set_object_type`` : sets the QConfigs for a given module type, function, or method name
+
+        ``set_module_name_regex`` : sets the QConfigs for modules matching the given regex string
+
+        ``set_module_name`` : sets the QConfigs for modules matching the given module name
+
+        ``set_module_name_object_type_order`` : sets the QConfigs for modules matching a combination
+        of the given module name, object type, and the index at which the module appears
+
+    Note: Usage of set methods is the same as in QConfigMapping except with a passed in list of QConfigs rather than a
+    single QConfig.
+
+    Example usage::
+
+        qconfig_mapping = QConfigMultiMapping()
+            .set_global([qconfig1, qconfig2])
+            .set_object_type(torch.nn.Linear, [qconfig2, qconfig3])
+            .set_object_type(torch.nn.ReLU, [qconfig1])
+            .set_module_name_regex("foo.*bar.*conv[0-9]+", [qconfig2])
+            .set_module_name_regex("foo.*", [qconfig1, qconfig2, qconfig3])
+            .set_module_name("module1", [None])
+            .set_module_name("module2", [qconfig2])
+            .set_module_name_object_type_order("foo.bar", torch.nn.functional.linear, 0, [qconfig3])
+
+    """
+
+    def __init__(self) -> None:
+        # initialize this with 1 QConfigMapping to avoid corner cases
+        self.qconfig_mappings_list: List[QConfigMapping] = [QConfigMapping()]
+
+    def _handle_list_size_mismatch(
+        self, qconfig_list: List[QConfigAny], style: str
+    ) -> None:
+        # this method handles cases where the size of qconfig_list does not match
+        # the size of qconfig_mappings_list.
+        # Issue: Consider a user inserting global_qconfig A and B first, then inserting
+        # qconfig C as an object_type_qconfig for conv ops. If we internally store
+        # 1 QConfigMapping with A and C and another with just B, then the
+        # second QConfigMapping will match B to conv ops (which is not wanted), since B is global.
+
+        # we avoid this by maintaining the invariant that if any QConfigMapping
+        # has a qconfig style+key with a qconfig in it, all QConfigMappings must
+        # have either a qconfig or None for that same style+key. In the above
+        # example, a None qconfig would prevent the unwanted match in the
+        # second QConfigMapping
+
+        if len(qconfig_list) > len(self.qconfig_mappings_list):
+            # Case: we have more qconfigs (in qconfig_list) than QConfigMappings
+
+            # Add new QConfigMappings (initialized so we maintain the `invariant`)
+
+            new_qconfig_mapping = QConfigMapping()
+            # searches other QConfigMappings for qconfig style+keys
+            # that need to be inserted as `None` into the new QConfigMapping
+            for qconfig_mapping in self.qconfig_mappings_list:
+                # global_qconfig has None by default
+                for check_style in _QCONFIG_STYLE_ORDER[1:]:
+                    qconfigs_dict = getattr(qconfig_mapping, check_style)
+                    target_qconfigs_dict = getattr(new_qconfig_mapping, check_style)
+                    for key in qconfigs_dict:
+                        target_qconfigs_dict[key] = None
+                break
+
+            # insert copies of this new QConfigMapping until all entires
+            # in qconfig_list can fit among the QConfigMappings
+            while len(qconfig_list) > len(self.qconfig_mappings_list):
+                self.qconfig_mappings_list.append(copy.deepcopy(new_qconfig_mapping))
+        else:
+            # Case: we have fewer qconfigs in qconfig_list than QConfigMappings
+
+            # pad qconfig_list with `None` until length is same
+            while len(qconfig_list) < len(self.qconfig_mappings_list):
+                qconfig_list.append(None)
+
+    # this function applies the insertion method across each QConfigMapping
+    def _insert_qconfig_list(
+        self,
+        style: str,
+        args: List[Union[str, int, Callable]],
+        qconfig_list: List[QConfigAny],
+    ) -> None:
+        # we remove duplicates and None to make the ordering of qconfigs
+        # deterministic upon insertion.
+        _remove_duplicates_and_none(qconfig_list)
+
+        self._handle_list_size_mismatch(qconfig_list, style)
+        method_name = _QCONFIG_STYLE_TO_METHOD[style]
+        for qconfig_mapping, qconfig in zip(self.qconfig_mappings_list, qconfig_list):
+            # uses QConfigMapping set method to insert qconfig
+            set_method = getattr(qconfig_mapping, method_name)
+            set_method(*args, qconfig)
+
+    def set_global(self, global_qconfig_list: List[QConfigAny]) -> QConfigMultiMapping:
+        """
+        Set global QConfigs
+        see :func:`~torch.ao.quantization.QConfigMapping.set_global()` for more info
+        """
+        self._insert_qconfig_list("global_qconfig", [], global_qconfig_list)
+        return self
+
+    def set_object_type(
+        self, object_type: Union[Callable, str], qconfig_list: List[QConfigAny]
+    ) -> QConfigMultiMapping:
+        """
+        Set object type QConfigs
+        see :func:`~torch.ao.quantization.QConfigMapping.set_object_type()` for more info
+        """
+        self._insert_qconfig_list("object_type_qconfigs", [object_type], qconfig_list)
+        return self
+
+    def set_module_name_regex(
+        self, module_name_regex: str, qconfig_list: List[QConfigAny]
+    ) -> QConfigMultiMapping:
+        """
+        Set module_name_regex QConfigs
+        see :func:`~torch.ao.quantization.QConfigMapping.set_module_name_regex()` for more info
+        """
+        self._insert_qconfig_list(
+            "module_name_regex_qconfigs", [module_name_regex], qconfig_list
+        )
+        return self
+
+    def set_module_name(
+        self, module_name: str, qconfig_list: List[QConfigAny]
+    ) -> QConfigMultiMapping:
+        """
+        Set module_name QConfigs
+        see :func:`~torch.ao.quantization.QConfigMapping.set_module_name()` for more info
+        """
+        self._insert_qconfig_list("module_name_qconfigs", [module_name], qconfig_list)
+        return self
+
+    def set_module_name_object_type_order(
+        self,
+        module_name: str,
+        object_type: Callable,
+        index: int,
+        qconfig_list: List[QConfigAny],
+    ) -> QConfigMultiMapping:
+        """
+        Set module_name QConfigs
+        see :func:`~torch.ao.quantization.QConfigMapping.set_module_name_object_type_order()` for more info
+        """
+        self._insert_qconfig_list(
+            "module_name_object_type_order_qconfigs",
+            [module_name, object_type, index],
+            qconfig_list,
+        )
+        return self
+
+    def __repr__(self):
+        return (
+            self.__class__.__name__
+            + " ["
+            + "".join(
+                f"\n{qconfig_mapping.__repr__()},"
+                for qconfig_mapping in self.qconfig_mappings_list
+            )
+            + "\n]"
+        )
+
+    @classmethod
+    def from_list_qconfig_mapping(
+        cls, qconfig_mapping_list: List[QConfigMapping]
+    ) -> QConfigMultiMapping:
+        """
+        Creates a QConfigMultiMapping from a list of QConfigMappings
+        """
+        new_qconfig_multi_mapping = cls()
+
+        new_qconfig_multi_mapping.qconfig_mappings_list = copy.deepcopy(
+            qconfig_mapping_list
+        )
+
+        # we need to avoid the issue described in _handle_list_size_mismatch,
+        # so we reinsert all the qconfigs using the QConfigMultiMapping
+        # set methods
+
+        # go through all qconfig styles
+        # note: global can be ignored since it is None by default
+        for style in _QCONFIG_STYLE_ORDER[1:]:
+            # gather all key+qconfigs for current style
+            # into qconfig_dict_list
+            qconfig_dict_list: Dict[Any, List[QConfigAny]] = {}
+            for qconfig_mapping in qconfig_mapping_list:
+                qconfig_dict = getattr(qconfig_mapping, style)
+                for key, qconfig in qconfig_dict.items():
+                    if key not in qconfig_dict_list:
+                        qconfig_dict_list[key] = []
+                    qconfig_dict_list[key].append(qconfig)
+
+            # reinsert all gathered key+qconfigs
+            set_method_name = _QCONFIG_STYLE_TO_METHOD[style]
+            set_method = getattr(new_qconfig_multi_mapping, set_method_name)
+            for key, qconfig_list in qconfig_dict_list.items():
+                if isinstance(key, tuple):
+                    set_method(*key, qconfig_list)
+                else:
+                    set_method(key, qconfig_list)
+
+        return new_qconfig_multi_mapping
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..03d9a63560ddbda853b3ed2bd1a7c6de1158a135
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/utils.py
@@ -0,0 +1,540 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import enum
+import operator
+from typing import Callable, Dict, List, Optional, Set, Tuple, Union
+
+import torch
+import torch.ao.nn.intrinsic.quantized as nniq
+import torch.ao.nn.quantized as nnq
+import torch.nn as nn
+from torch.ao.quantization import FakeQuantizeBase, ObserverBase
+from torch.ao.quantization.observer import _is_activation_post_process
+from torch.ao.quantization.utils import getattr_from_fqn
+from torch.fx import GraphModule
+from torch.fx.graph import Node
+
+from .ns_types import NSNodeTargetType, NSResultsType
+
+
+toq = torch.ops.quantized
+
+
+# TODO(future PR): consider deleting this enum and using the torch types
+# directly.  This might be tricky because it is not a one to one mapping.
+class NodeInputOrOutputType(enum.Enum):
+    FP32 = enum.auto()  # torch.float
+    INT8 = enum.auto()  # torch.qint8 or torch.quint8
+    FP16 = enum.auto()  # torch.float16
+    UNKNOWN = enum.auto()  # we cannot determine input/output dtype
+    # TODO(future PR): while these functions can support multiple dtypes,
+    #   for the purposes of numerical debugging we want to get the actual
+    #   dtype used in the model. We will likely need some kind of dtype
+    #   propagation to estimate this.
+    FP32_OR_INT8 = enum.auto()  # either torch.float or torch.quint8 or torch.qint8
+    # TODO(future PRs): dynamic quant, fake quant, etc
+
+
+def get_node_first_input_and_output_type(
+    node: Node,
+    gm: GraphModule,
+    logger_cls: Callable,
+    node_type_to_io_type_map: Dict[str, Set[NSNodeTargetType]],
+) -> Tuple[NodeInputOrOutputType, NodeInputOrOutputType]:
+    # TODO(future PR): clean this up
+    FUNS_IO_TYPE_FP32 = node_type_to_io_type_map["funs_io_type_fp32"]
+    FUNS_IO_TYPE_FP16 = node_type_to_io_type_map["funs_io_type_fp16"]
+    FUNS_IO_TYPE_INT8 = node_type_to_io_type_map["funs_io_type_int8"]
+    FUNS_IO_TYPE_FP32_OR_INT8 = node_type_to_io_type_map["funs_io_type_fp32_or_int8"]
+    MODS_IO_TYPE_FP32 = node_type_to_io_type_map["mods_io_type_fp32"]
+    MODS_IO_TYPE_INT8 = node_type_to_io_type_map["mods_io_type_int8"]
+    MODS_IO_TYPE_FP32_OR_INT8 = node_type_to_io_type_map["mods_io_type_fp32_or_int8"]
+    METHS_IO_TYPE_FP32_OR_INT8 = node_type_to_io_type_map["meths_io_type_fp32_or_int8"]
+
+    if node.op == "call_function":
+        if node.target in FUNS_IO_TYPE_FP32:
+            return (NodeInputOrOutputType.FP32, NodeInputOrOutputType.FP32)
+        if node.target in FUNS_IO_TYPE_FP16:
+            return (NodeInputOrOutputType.FP16, NodeInputOrOutputType.FP16)
+        elif node.target in FUNS_IO_TYPE_INT8:
+            return (NodeInputOrOutputType.INT8, NodeInputOrOutputType.INT8)
+        elif node.target in FUNS_IO_TYPE_FP32_OR_INT8:
+            first_arg = get_normalized_nth_input(node, gm, 0)
+            assert isinstance(first_arg, Node)
+            (
+                _prev_node_input_type,
+                prev_node_output_type,
+            ) = get_node_first_input_and_output_type(
+                first_arg, gm, logger_cls, node_type_to_io_type_map
+            )
+            return (prev_node_output_type, prev_node_output_type)
+        else:
+            return (NodeInputOrOutputType.UNKNOWN, NodeInputOrOutputType.UNKNOWN)
+
+    elif node.op == "call_module":
+        assert node.op == "call_module"
+        assert isinstance(node.target, str)
+        mod = getattr_from_fqn(gm, node.target)
+        is_known_fp32_or_int8_input_module = any(
+            isinstance(mod, target_type) for target_type in MODS_IO_TYPE_FP32_OR_INT8  # type: ignore[arg-type]
+        )
+        if (
+            isinstance(mod, (logger_cls, ObserverBase, FakeQuantizeBase))  # type: ignore[arg-type]
+            or is_known_fp32_or_int8_input_module
+        ):
+            # A logger or observer's input and output type is the output
+            # type of the preceding node.
+            first_arg = get_normalized_nth_input(node, gm, 0)
+            assert isinstance(first_arg, Node)
+            (
+                _prev_node_input_type,
+                prev_node_output_type,
+            ) = get_node_first_input_and_output_type(
+                first_arg, gm, logger_cls, node_type_to_io_type_map
+            )
+            return (prev_node_output_type, prev_node_output_type)
+        is_known_fp32_input_module = any(
+            isinstance(mod, target_type) for target_type in MODS_IO_TYPE_FP32  # type: ignore[arg-type]
+        )
+        is_known_int8_input_module = any(
+            isinstance(mod, target_type) for target_type in MODS_IO_TYPE_INT8  # type: ignore[arg-type]
+        )
+        if is_known_fp32_input_module:
+            return (NodeInputOrOutputType.FP32, NodeInputOrOutputType.FP32)
+        elif is_known_int8_input_module:
+            return (NodeInputOrOutputType.INT8, NodeInputOrOutputType.INT8)
+        else:
+            return (NodeInputOrOutputType.UNKNOWN, NodeInputOrOutputType.UNKNOWN)
+
+    elif node.op == "call_method":
+        if node.target == "dequantize":
+            # Dequantize is a special node because it allows multiple input types.
+            # So, we look up the output type of the previous node and return that
+            # as the input type of this node instance.
+            prev_node = get_normalized_nth_input(node, gm, 0)
+            assert isinstance(prev_node, Node)
+            (
+                _prev_node_input_type,
+                prev_node_output_type,
+            ) = get_node_first_input_and_output_type(
+                prev_node, gm, logger_cls, node_type_to_io_type_map
+            )
+            return (prev_node_output_type, NodeInputOrOutputType.FP32)
+
+        elif node.target == "to":
+            # to is a special node because it allows multiple input types.
+            # So, we look up the output type of the previous node and return that
+            # as the input type of this node instance. We also look up the target
+            # of to and return the correct output type.
+            prev_node = get_normalized_nth_input(node, gm, 0)
+            assert isinstance(prev_node, Node)
+            (
+                _prev_node_input_type,
+                prev_node_output_type,
+            ) = get_node_first_input_and_output_type(
+                prev_node, gm, logger_cls, node_type_to_io_type_map
+            )
+
+            cur_node_dtype_target = get_normalized_nth_input(node, gm, 1)
+            assert (
+                cur_node_dtype_target is torch.float16
+            ), f"{cur_node_dtype_target} handling needs to be added"
+
+            return (prev_node_output_type, NodeInputOrOutputType.FP16)
+
+        elif node.target in METHS_IO_TYPE_FP32_OR_INT8:
+            first_arg = get_normalized_nth_input(node, gm, 0)
+            assert isinstance(first_arg, Node)
+            (
+                _prev_node_input_type,
+                prev_node_output_type,
+            ) = get_node_first_input_and_output_type(
+                first_arg, gm, logger_cls, node_type_to_io_type_map
+            )
+            return (prev_node_output_type, prev_node_output_type)
+
+        return (NodeInputOrOutputType.UNKNOWN, NodeInputOrOutputType.UNKNOWN)
+    else:
+        return (NodeInputOrOutputType.UNKNOWN, NodeInputOrOutputType.UNKNOWN)
+
+
+def get_node_input_qparams(
+    node: Node,
+    gm: GraphModule,
+    node_type_to_io_type_map: Dict[str, Set[NSNodeTargetType]],
+) -> Optional[Tuple[Union[torch.Tensor, float], Union[torch.Tensor, int]]]:
+    """
+    Returns the qparams (scale, zero_point) of the first input to `node`,
+    if they can be inferred from the graph.
+    """
+    prev_node = get_normalized_nth_input(node, gm, 0)
+
+    if not isinstance(prev_node, Node):
+        return None
+
+    MODS_IO_TYPE_FP32_OR_INT8 = node_type_to_io_type_map["mods_io_type_fp32_or_int8"]
+
+    def _get_scale_zp_from_function_args(node, gm, scale_arg_idx, zp_arg_idx):
+        scale_node = get_normalized_nth_input(node, gm, scale_arg_idx)
+        zp_node = get_normalized_nth_input(node, gm, zp_arg_idx)
+        assert isinstance(scale_node, Node) and isinstance(scale_node.target, str)
+        assert isinstance(zp_node, Node) and isinstance(zp_node.target, str)
+        scale_obj = getattr_from_fqn(gm, scale_node.target)
+        zp_obj = getattr_from_fqn(gm, zp_node.target)
+        return (scale_obj, zp_obj)
+
+    if prev_node.op == "call_function":
+        # quantize - read the args directly
+        if prev_node.target == torch.quantize_per_tensor:
+            return _get_scale_zp_from_function_args(prev_node, gm, 1, 2)
+        elif prev_node.target in (toq.add, toq.add_relu, toq.mul, toq.mul_relu):
+            return _get_scale_zp_from_function_args(prev_node, gm, 2, 3)
+
+        return None
+        # TODO(future PR): handle more functionals
+        # TODO(future PR): handle functional ops which inherit qparams from input
+
+    elif prev_node.op == "call_module":
+        # get type of the module
+        assert isinstance(prev_node.target, str)
+        module_obj = getattr_from_fqn(gm, prev_node.target)
+        if isinstance(
+            module_obj,
+            (
+                nnq.Linear,
+                nnq.Conv1d,
+                nnq.Conv2d,
+                nniq.ConvReLU2d,
+                nnq.Conv3d,
+                nnq.BatchNorm2d,
+                nnq.BatchNorm3d,
+                nnq.ConvTranspose1d,
+                nnq.ConvTranspose2d,
+                nnq.ELU,
+                nnq.GroupNorm,
+                nnq.InstanceNorm1d,
+                nnq.InstanceNorm2d,
+                nnq.InstanceNorm3d,
+                nnq.LayerNorm,
+                nnq.Hardswish,
+                nnq.LeakyReLU,
+                nnq.ReLU6,
+                nniq.BNReLU2d,
+                nniq.BNReLU3d,
+                nniq.ConvReLU1d,
+                nniq.ConvReLU2d,
+                nniq.ConvReLU3d,
+                nniq.LinearReLU,
+            ),
+        ):
+            return (module_obj.scale, module_obj.zero_point)  # type: ignore[return-value]
+
+        is_known_fp32_or_int8_input_module = any(
+            isinstance(module_obj, target_type) for target_type in MODS_IO_TYPE_FP32_OR_INT8  # type: ignore[arg-type]
+        )
+        if is_known_fp32_or_int8_input_module:
+            return get_node_input_qparams(prev_node, gm, node_type_to_io_type_map)
+
+    return None
+
+
+def return_first_non_observer_node(
+    node: Node,
+    gm: GraphModule,
+) -> Node:
+    """
+    If node is not an observer, returns it.  If node is an observer,
+    navigates up the graph and returns the first parent which is not an
+    observer.  For example,
+
+    graph: (node_non_obs), node = node_non_obs : returns node_non_obs
+    graph: (node_non_obs -> obs0), node = obs0 : returns node_non_obs
+    graph: (node_non_obs -> obs0 -> fq0), node = fq0 : returns node_non_obs
+    """
+    if node.op == "call_module":
+        node_obj = getattr_from_fqn(gm, node.target)  # type: ignore[arg-type]
+        if _is_activation_post_process(node_obj):
+            assert len(node.args) == 1
+            assert isinstance(node.args[0], Node)
+            node = node.args[0]
+            # code duplication intended, not worth refactoring
+            assert isinstance(node.target, str)
+            node_obj = getattr_from_fqn(gm, node.target)
+            if _is_activation_post_process(node_obj):
+                assert len(node.args) == 1
+                assert isinstance(node.args[0], Node)
+                node = node.args[0]
+    return node
+
+
+def get_number_of_non_param_args(
+    node: Node,
+    gm: GraphModule,
+) -> int:
+    """
+    Assumes that all non-param args occur first. Returns the number of
+    non-param args expected for a node.  For example, for
+
+      F.linear(x, weight, bias)
+
+    Returns 1, because x is a non-param arg and weight and bias are params.
+    For
+
+      lstm_mod(x, hid)
+
+    Returns 2, because both x and hid are non-param args.
+    """
+    if node.op == "call_module":
+        node_obj = getattr_from_fqn(gm, node.target)  # type: ignore[arg-type]
+        if isinstance(node_obj, nn.LSTM):
+            return 2
+
+    # default is 1
+    return 1
+
+
+def get_arg_indices_of_inputs_to_log(node: Node) -> List[int]:
+    """
+    Returns the indices of args of the node which we should attach
+    loggers to, if input logging is enabled.
+
+    For example,
+    * for (x + y), returns [0, 1]
+    * for (1 + y), returns [1]
+    * for (x + 1), returns [0]
+    * for (linear(x, w, b)) returns [0]
+    * by default, returns [0]
+    """
+    if len(node.args) == 0:
+        return []
+    if node.op == "call_function" and (
+        # TODO(future PR): use relationship map instead of hardcoding
+        node.target in (torch.add, torch.ops.quantized.add, operator.add)
+        or node.target in (torch.mul, torch.ops.quantized.mul, operator.mul)
+    ):
+        result = []
+        for i in range(2):
+            if type(node.args[i]) == Node:
+                result.append(i)
+        return result
+    return [0]
+
+
+def get_target_type_str(node: Node, gm: GraphModule) -> str:
+    """
+    Returns a string representation of the type of the function or module
+    pointed to by this node, or '' for other node types.
+    """
+    target_type = ""
+    if node.op in ("call_function", "call_method"):
+        target_type = torch.typename(node.target)
+    elif node.op == "call_module":
+        assert isinstance(node.target, str)
+        target_mod = getattr_from_fqn(gm, node.target)
+        target_type = torch.typename(target_mod)
+    return target_type
+
+
+def rekey_logger_info_on_node_name_of_model(
+    results: NSResultsType,
+    model_name: str,
+) -> NSResultsType:
+    """
+    Rekeys the layer name of a results dictionary to use node names
+    from `model_name`.
+
+    For example, transforms
+
+        {'base_op_1_0': {'node_output': {'model_a':
+          [{'ref_node_name': 'linear1', ...}]}}}
+
+    into
+
+        {'linear1': {'node_output': {'model_a':
+          [{'ref_node_name': 'linear1', ...}]}}}
+
+    Note: we cannot use these node names directly because they are not
+    guaranteed to be consistent across models. This is why we extract
+    the results first and rekey afterwards.
+    """
+    new_results = {}
+    for old_layer_name, result_type_to_results in results.items():
+        new_layer_name = None
+        for model_name_to_results in result_type_to_results.values():
+            for cur_model_name, list_of_results in model_name_to_results.items():
+                if cur_model_name == model_name:
+                    assert len(list_of_results)
+                    new_layer_name = list_of_results[0]["ref_node_name"]
+                else:
+                    continue
+        if new_layer_name is not None:
+            new_results[new_layer_name] = result_type_to_results
+        else:
+            new_results[old_layer_name] = result_type_to_results
+    return new_results
+
+
+def maybe_add_missing_fqns(results: NSResultsType) -> None:
+    """
+    If `fqn` entries are filled in for one of the models in `results`, copies
+    them over to any models which do not have them filled out.
+
+    A common use case benefitting from this is comparing a model prepared by
+    quantization to a quantized model. In this case, the model prepared by
+    quantization would have `fqn` entries, and the quantized model would not.
+    """
+
+    # Check in the first result to find any model with fqn entries defined.
+    model_name_with_fqns = None
+    for result_type_to_results in results.values():
+        for model_name_to_results in result_type_to_results.values():
+            for model_name, model_results in model_name_to_results.items():
+                if len(model_results) > 0:
+                    if model_results[0]["fqn"] is not None:
+                        model_name_with_fqns = model_name
+                        break
+            break
+        break
+
+    if model_name_with_fqns:
+        for result_type_to_results in results.values():
+            for model_name_to_results in result_type_to_results.values():
+                ref_model_results = model_name_to_results[model_name_with_fqns]
+                for model_name, model_results in model_name_to_results.items():
+                    if model_name == model_name_with_fqns:
+                        continue
+                    for i in range(len(model_results)):
+                        fqn = ref_model_results[i]["fqn"]
+                        model_results[i]["fqn"] = fqn
+
+
+def maybe_dequantize_first_two_tensor_args_and_handle_tuples(f):
+    def inner(*args, **kwargs):
+        a0, a1, *a_other = args
+
+        if (isinstance(a0, tuple) and isinstance(a1, tuple)) or (
+            isinstance(a0, list) and isinstance(a1, list)
+        ):
+            results = []
+            for el0, el1 in zip(a0, a1):
+                new_args = (el0, el1, *a_other)
+                results.append(inner(*new_args, **kwargs))
+            return results
+
+        elif isinstance(a0, torch.Tensor) and isinstance(a1, torch.Tensor):
+            if a0.is_quantized:
+                a0 = a0.dequantize()
+            if a1.is_quantized:
+                a1 = a1.dequantize()
+
+        # for the purposes of this util, only handle floats
+        if a0.dtype != torch.float or a1.dtype != torch.float:
+            return None
+
+        new_args = (a0, a1, *a_other)
+        return f(*new_args, **kwargs)
+
+    return inner
+
+
+@maybe_dequantize_first_two_tensor_args_and_handle_tuples
+def compute_sqnr(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+    """
+    Computes the SQNR between `x` and `y`.
+
+    Args:
+        x: Tensor or tuple of tensors
+        y: Tensor or tuple of tensors
+
+    Return:
+        float or tuple of floats
+    """
+    Ps = torch.norm(x)
+    Pn = torch.norm(x - y)
+    return 20 * torch.log10(Ps / Pn)
+
+
+@maybe_dequantize_first_two_tensor_args_and_handle_tuples
+def compute_normalized_l2_error(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+    """
+    Computes the normalized L2 error between `x` and `y`.
+
+    Args:
+        x: Tensor or tuple of tensors
+        y: Tensor or tuple of tensors
+
+    Return:
+        float or tuple of floats
+    """
+    return torch.sqrt(((x - y) ** 2).sum() / (x**2).sum())
+
+
+@maybe_dequantize_first_two_tensor_args_and_handle_tuples
+def compute_cosine_similarity(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+    """
+    Computes the cosine similarity between `x` and `y`.
+
+    Args:
+        x: Tensor or tuple of tensors
+        y: Tensor or tuple of tensors
+
+    Return:
+        float or tuple of floats
+    """
+    # For convolutions, the shape of the quantized weight has one additional
+    # dimension compared to the shape of the fp32 weight. Match the shapes
+    # to enable cosine similarity comparison.
+    x = x.reshape(1, -1)
+    y = y.reshape(1, -1)
+    return torch.nn.functional.cosine_similarity(x, y)
+
+
+def op_type_supports_shadowing(node: Node) -> bool:
+    if node.op == "call_function":
+        if node.target in (
+            torch.add,
+            torch.mul,
+            operator.add,
+            operator.mul,
+            torch.cat,
+            torch.stack,
+        ):
+            # shadowing for ops with multiple tensor inputs is not implemented yet
+            return False
+    return True
+
+
+def get_normalized_nth_input(node: Node, gm: GraphModule, idx: int) -> Node:
+    """
+    Given a node, gets the n'th input to that node, normalizing
+    args and kwargs to the best of its ability.
+    """
+    try:
+        norm_args_and_kwargs = node.normalized_arguments(
+            gm, normalize_to_only_use_kwargs=True
+        )
+        if norm_args_and_kwargs is not None:
+            norm_args, norm_kwargs = norm_args_and_kwargs
+            assert len(norm_args) + len(norm_kwargs) > idx
+            if idx < len(norm_args):
+                return norm_args[idx]
+            else:
+                # note: in Python 3.7+ dicts are ordered
+                return list(norm_kwargs.values())[idx]
+        else:
+            assert len(node.args) + len(node.kwargs) > idx
+            if idx < len(node.args):
+                return node.args[idx]  # type: ignore[return-value]
+            else:
+                kwargs_idx = idx + len(node.args)
+                return list(node.kwargs.values())[kwargs_idx]  # type: ignore[return-value]
+    except RuntimeError:
+        # this RuntimeError happens when node argument normalization
+        # requires typehints to proceed, such as for torch.add where
+        # either the first, second or both arguments could be tensors
+        assert len(node.args) + len(node.kwargs) > idx
+        if idx < len(node.args):
+            return node.args[idx]  # type: ignore[return-value]
+        else:
+            kwargs_idx = idx + len(node.args)
+            return list(node.kwargs.values())[kwargs_idx]  # type: ignore[return-value]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/weight_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/weight_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..d695674b11597f356911a62b56516f5f22efb5dc
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/ns/fx/weight_utils.py
@@ -0,0 +1,280 @@
+from typing import Callable, Dict, List, Optional
+
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.intrinsic.qat as nniqat
+import torch.ao.nn.intrinsic.quantized as nniq
+import torch.ao.nn.qat as nnqat
+import torch.ao.nn.quantized as nnq
+import torch.ao.nn.quantized.dynamic as nnqd
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.fx import GraphModule
+from torch.fx.graph import Node
+
+from .ns_types import NSSingleResultType, NSSingleResultValuesType
+from .utils import get_target_type_str, getattr_from_fqn, return_first_non_observer_node
+
+
+toq = torch.ops.quantized
+
+
+def mod_weight_detach(mod: nn.Module) -> torch.Tensor:
+    return mod.weight.detach()  # type: ignore[operator]
+
+
+def mod_0_weight_detach(mod: nn.Module) -> torch.Tensor:
+    return mod[0].weight.detach()  # type: ignore[index]
+
+
+def mod_weight_bias_0(mod: nn.Module) -> torch.Tensor:
+    return mod._weight_bias()[0]  # type: ignore[operator]
+
+
+def get_lstm_weight(mod: nn.Module) -> List[torch.Tensor]:
+    res = []
+    for idx, param_name in enumerate(mod._flat_weights_names):  # type: ignore[arg-type]
+        if "weight_ih_l" in param_name or "weight_hh_l" in param_name:
+            param_value = mod._flat_weights[idx].detach()  # type: ignore[index]
+            res.append(param_value)
+    return res
+
+
+def get_qlstm_weight(mod: nn.Module) -> List[torch.Tensor]:
+    res = []
+    for weight_value in mod._all_weight_values:  # type: ignore[union-attr]
+        res.append(weight_value.param.__getstate__()[0][4][0].__getstate__()[0][0])
+        res.append(weight_value.param.__getstate__()[0][4][1].__getstate__()[0][0])
+    return res
+
+
+def get_conv_mod_weight(mod: nn.Module) -> torch.Tensor:
+    if isinstance(mod, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
+        return mod.weight.detach()
+    elif isinstance(mod, (nni.ConvReLU1d, nni.ConvReLU2d, nni.ConvReLU3d)):
+        return mod[0].weight.detach()
+    else:
+        return mod._weight_bias()[0]  # type: ignore[operator]
+
+
+def get_linear_mod_weight(mod: nn.Module) -> torch.Tensor:
+    if isinstance(mod, nn.Linear):
+        return mod.weight.detach()
+    elif isinstance(mod, nni.LinearReLU):
+        return mod[0].weight.detach()
+    else:
+        return mod._weight_bias()[0]  # type: ignore[operator]
+
+
+def get_lstm_mod_weights(mod: nn.Module) -> List[torch.Tensor]:
+    # TODO(future PR): make more generic, handle everything
+    if isinstance(mod, nn.LSTM):
+        res = []
+        for idx, param_name in enumerate(mod._flat_weights_names):
+            if "weight_ih_l" in param_name or "weight_hh_l" in param_name:
+                param_value = mod._flat_weights[idx].detach()
+                res.append(param_value)
+        return res
+    else:
+        assert isinstance(mod, nnqd.LSTM), f"type {type(mod)} not handled yet"
+        res = []
+        for weight_value in mod._all_weight_values:
+            res.append(weight_value.param.__getstate__()[0][4][0].__getstate__()[0][0])
+            res.append(weight_value.param.__getstate__()[0][4][1].__getstate__()[0][0])
+        return res
+
+
+def get_conv_fun_weight(node: Node, gm: GraphModule) -> torch.Tensor:
+    # traverse backwards from the weight arg, accounting for any observers
+    weight_arg_node = node.args[1]
+    assert isinstance(weight_arg_node, Node)
+    weight_node = return_first_non_observer_node(weight_arg_node, gm)
+    assert isinstance(weight_node, Node)
+    assert weight_node.op == "get_attr"
+    weight = getattr_from_fqn(gm, weight_node.target)  # type: ignore[arg-type]
+    return weight.detach()
+
+
+def get_qconv_fun_weight(node: Node, gm: GraphModule) -> torch.Tensor:
+    # qconv state is arg 1
+    qconv_state_node = node.args[1]
+    assert isinstance(qconv_state_node, Node)
+    assert qconv_state_node.op == "get_attr"
+    qconv_state_obj = getattr_from_fqn(gm, qconv_state_node.target)  # type: ignore[arg-type]
+    return qconv_state_obj.weight()
+
+
+def get_linear_fun_weight(node: Node, gm: GraphModule) -> torch.Tensor:
+    # traverse backwards from the weight arg, accounting for any observers
+    # supported patterns:
+    # weight -> obs -> linear
+    # weight -> to(torch.float16) -> dequantize -> linear
+    linear_second_arg = node.args[1]
+    assert isinstance(linear_second_arg, Node)
+
+    if linear_second_arg.op == "call_module":
+        # weight -> obs -> linear
+        weight_arg_node = node.args[1]
+        assert isinstance(weight_arg_node, Node)
+        weight_node = weight_arg_node.args[0]
+        assert isinstance(weight_node, Node)
+        assert weight_node.op == "get_attr"
+        weight = getattr_from_fqn(gm, weight_node.target)  # type: ignore[arg-type]
+        return weight.detach()
+    elif linear_second_arg.op == "call_method":
+        # weight -> to(torch.float16) -> dequantize -> linear
+        assert linear_second_arg.op == "call_method"
+        dequant_node = node.args[1]
+        assert isinstance(dequant_node, Node)
+        to_fp16_node = dequant_node.args[0]
+        assert isinstance(to_fp16_node, Node)
+        # extract the dtype, so we can cast to it before returning
+        target_dtype = to_fp16_node.args[1]
+        weight_node = to_fp16_node.args[0]
+        assert isinstance(weight_node, Node)
+        assert weight_node.op == "get_attr"
+        weight = getattr_from_fqn(gm, weight_node.target)  # type: ignore[arg-type]
+        # return the weight with fp16 cast
+        return weight.detach().to(target_dtype)
+    else:
+        assert linear_second_arg.op == "get_attr"
+        weight = getattr_from_fqn(gm, linear_second_arg.target)  # type: ignore[arg-type]
+        return weight.detach()
+
+
+def get_qlinear_fun_weight(node: Node, gm: GraphModule) -> torch.Tensor:
+    # packed weight is arg 1
+    packed_weight_node = node.args[1]
+    assert isinstance(packed_weight_node, Node)
+    assert packed_weight_node.op == "get_attr"
+    packed_weight = getattr_from_fqn(gm, packed_weight_node.target)  # type: ignore[arg-type]
+    # TODO(future PR): why does packed_weight.unpack() not work?
+    (weight, _bias), _name = packed_weight.__getstate__()
+    return weight
+
+
+def get_op_to_type_to_weight_extraction_fn() -> Dict[str, Dict[Callable, Callable]]:
+    op_to_type_to_weight_extraction_fn: Dict[str, Dict[Callable, Callable]] = {
+        "call_module": {
+            # Conv1d
+            nn.Conv1d: mod_weight_detach,
+            nni.ConvReLU1d: mod_0_weight_detach,
+            nnq.Conv1d: mod_weight_bias_0,
+            nnqat.Conv1d: mod_weight_detach,
+            nniqat.ConvBn1d: mod_weight_detach,
+            nniqat.ConvBnReLU1d: mod_weight_detach,
+            nniqat.ConvReLU1d: mod_weight_detach,
+            nniq.ConvReLU1d: mod_weight_bias_0,
+            # Conv2d
+            nn.Conv2d: mod_weight_detach,
+            nni.ConvReLU2d: mod_0_weight_detach,
+            nnq.Conv2d: mod_weight_bias_0,
+            nnqat.Conv2d: mod_weight_detach,
+            nniqat.ConvBn2d: mod_weight_detach,
+            nniqat.ConvBnReLU2d: mod_weight_detach,
+            nniqat.ConvReLU2d: mod_weight_detach,
+            nniq.ConvReLU2d: mod_weight_bias_0,
+            # Conv3d
+            nn.Conv3d: mod_weight_detach,
+            nni.ConvReLU3d: mod_0_weight_detach,
+            nnq.Conv3d: mod_weight_bias_0,
+            nnqat.Conv3d: mod_weight_detach,
+            nniqat.ConvBn3d: mod_weight_detach,
+            nniqat.ConvBnReLU3d: mod_weight_detach,
+            nniqat.ConvReLU3d: mod_weight_detach,
+            nniq.ConvReLU3d: mod_weight_bias_0,
+            # Linear
+            nn.Linear: mod_weight_detach,
+            nnq.Linear: mod_weight_bias_0,
+            nni.LinearReLU: mod_0_weight_detach,
+            nniq.LinearReLU: mod_weight_bias_0,
+            nnqat.Linear: mod_weight_detach,
+            nnqd.Linear: mod_weight_bias_0,
+            nniqat.LinearReLU: mod_weight_detach,
+            nniqat.LinearBn1d: mod_weight_detach,
+            nn.modules.linear.NonDynamicallyQuantizableLinear: mod_weight_detach,
+            # LSTM
+            nn.LSTM: get_lstm_weight,
+            nnqd.LSTM: get_qlstm_weight,
+        },
+        "call_function": {
+            # Conv
+            F.conv1d: get_conv_fun_weight,
+            F.conv2d: get_conv_fun_weight,
+            F.conv3d: get_conv_fun_weight,
+            toq.conv1d: get_qconv_fun_weight,
+            toq.conv2d: get_qconv_fun_weight,
+            toq.conv3d: get_qconv_fun_weight,
+            toq.conv1d_relu: get_qconv_fun_weight,
+            toq.conv2d_relu: get_qconv_fun_weight,
+            toq.conv3d_relu: get_qconv_fun_weight,
+            # Linear
+            F.linear: get_linear_fun_weight,
+            toq.linear: get_qlinear_fun_weight,
+            toq.linear_relu: get_qlinear_fun_weight,
+        },
+    }
+
+    return op_to_type_to_weight_extraction_fn
+
+
+def extract_weight_from_node(
+    node: Node,
+    gm: GraphModule,
+    op_to_type_to_weight_extraction_fn: Optional[
+        Dict[str, Dict[Callable, Callable]]
+    ] = None,
+) -> Optional[NSSingleResultType]:
+    res_type = NSSingleResultValuesType.WEIGHT.value
+
+    # Not all graphmodules have _node_name_to_scope, so only fill it
+    # out if it exists.
+    fqn = None
+    if hasattr(gm, "_node_name_to_scope"):
+        fqn = gm._node_name_to_scope[node.name][0]  # type: ignore[index]
+
+    if op_to_type_to_weight_extraction_fn is None:
+        op_to_type_to_weight_extraction_fn = get_op_to_type_to_weight_extraction_fn()
+
+    ref_node_type = get_target_type_str(node, gm)
+    # for extracting weights, these are always the same
+    prev_node_type = ref_node_type
+
+    if node.op == "call_function":
+        function_mapping = op_to_type_to_weight_extraction_fn["call_function"]
+        for target_fn_type, weight_extraction_fn in function_mapping.items():
+            if node.target == target_fn_type:
+                weight = weight_extraction_fn(node, gm)
+                return {
+                    "type": res_type,
+                    "values": [weight],
+                    "prev_node_name": node.name,
+                    "prev_node_target_type": prev_node_type,
+                    "ref_node_name": node.name,
+                    "ref_node_target_type": ref_node_type,
+                    "index_within_arg": 0,
+                    "index_of_arg": 0,
+                    "fqn": fqn,
+                }
+
+    elif node.op == "call_module":
+        # for call_module, we need to look up the modules to do the type check
+        assert isinstance(node.target, str)
+        mod = getattr_from_fqn(gm, node.target)
+        module_mapping = op_to_type_to_weight_extraction_fn["call_module"]
+        for target_mod_type, weight_extraction_fn in module_mapping.items():
+            if type(mod) == target_mod_type:
+                weight = weight_extraction_fn(mod)
+                return {
+                    "type": res_type,
+                    "values": [weight],
+                    "prev_node_name": node.name,
+                    "prev_node_target_type": prev_node_type,
+                    "ref_node_name": node.name,
+                    "ref_node_target_type": ref_node_type,
+                    "index_within_arg": 0,
+                    "index_of_arg": 0,
+                    "fqn": fqn,
+                }
+
+    return None
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..52fc301befd34642d51f1c27e07600a1f3ef26ff
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/__init__.py
@@ -0,0 +1,23 @@
+# Variables
+from ._mappings import (
+    get_dynamic_sparse_quantized_mapping,
+    get_static_sparse_quantized_mapping,
+)
+
+# Scheduler
+from .scheduler.base_scheduler import BaseScheduler
+from .scheduler.cubic_scheduler import CubicSL
+from .scheduler.lambda_scheduler import LambdaSL
+
+# Sparsifier
+from .sparsifier.base_sparsifier import BaseSparsifier
+from .sparsifier.nearly_diagonal_sparsifier import NearlyDiagonalSparsifier
+
+# Parametrizations
+from .sparsifier.utils import (
+    FakeSparsity,
+    fqn_to_module,
+    get_arg_info_from_tensor_fqn,
+    module_to_fqn,
+)
+from .sparsifier.weight_norm_sparsifier import WeightNormSparsifier
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py
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index 0000000000000000000000000000000000000000..8e109c9e0923cdcc295efdf8ec98aa218f4459a4
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py
@@ -0,0 +1,471 @@
+# mypy: allow-untyped-defs
+import copy
+import warnings
+from collections import defaultdict
+from typing import Any, Dict, List, Optional
+
+import torch
+from torch import nn
+from torch.ao.pruning.sparsifier.utils import fqn_to_module, module_to_fqn
+
+
+__all__ = ["ActivationSparsifier"]
+
+
+class ActivationSparsifier:
+    r"""
+    The Activation sparsifier class aims to sparsify/prune activations in a neural
+    network. The idea is to attach the sparsifier to a layer (or layers) and it
+    zeroes out the activations based on the mask_fn (or sparsification function)
+    input by the user.
+    The mask_fn is applied once all the inputs are aggregated and reduced i.e.
+    mask = mask_fn(reduce_fn(aggregate_fn(activations)))
+
+    Note::
+        The sparsification mask is computed on the input **before it goes through the attached layer**.
+
+    Args:
+        model (nn.Module):
+            The model whose layers will be sparsified. The layers that needs to be
+            sparsified should be added separately using the register_layer() function
+        aggregate_fn (Optional, Callable):
+            default aggregate_fn that is used if not specified while registering the layer.
+            specifies how inputs should be aggregated over time.
+            The aggregate_fn should usually take 2 torch tensors and return the aggregated tensor.
+            Example
+                def add_agg_fn(tensor1, tensor2):  return tensor1 + tensor2
+                reduce_fn (Optional, Callable):
+                    default reduce_fn that is used if not specified while registering the layer.
+                    reduce_fn will be called on the aggregated tensor i.e. the tensor obtained after
+                    calling agg_fn() on all inputs.
+                    Example
+                def mean_reduce_fn(agg_tensor):    return agg_tensor.mean(dim=0)
+                mask_fn (Optional, Callable):
+                    default mask_fn that is used to create the sparsification mask using the tensor obtained after
+                    calling the reduce_fn(). This is used by default if a custom one is passed in the
+                    register_layer().
+                    Note that the mask_fn() definition should contain the sparse arguments that is passed in sparse_config
+                    arguments.
+                features (Optional, list):
+                    default selected features to sparsify.
+                    If this is non-empty, then the mask_fn will be applied for each feature of the input.
+                    For example,
+                mask = [mask_fn(reduce_fn(aggregated_fn(input[feature])) for feature in features]
+                feature_dim (Optional, int):
+                    default dimension of input features. Again, features along this dim will be chosen
+                    for sparsification.
+                sparse_config (Dict):
+                    Default configuration for the mask_fn. This config will be passed
+                    with the mask_fn()
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> model = SomeModel()
+        >>> act_sparsifier = ActivationSparsifier(...)  # init activation sparsifier
+        >>> # Initialize aggregate_fn
+        >>> def agg_fn(x, y):
+        >>>     return x + y
+        >>>
+        >>> # Initialize reduce_fn
+        >>> def reduce_fn(x):
+        >>>     return torch.mean(x, dim=0)
+        >>>
+        >>> # Initialize mask_fn
+        >>> def mask_fn(data):
+        >>>     return torch.eye(data.shape).to(data.device)
+        >>>
+        >>>
+        >>> act_sparsifier.register_layer(model.some_layer, aggregate_fn=agg_fn, reduce_fn=reduce_fn, mask_fn=mask_fn)
+        >>>
+        >>> # start training process
+        >>> for _ in [...]:
+        >>>     # epoch starts
+        >>>         # model.forward(), compute_loss() and model.backwards()
+        >>>     # epoch ends
+        >>>     act_sparsifier.step()
+        >>> # end training process
+        >>> sparsifier.squash_mask()
+    """
+
+    def __init__(
+        self,
+        model: nn.Module,
+        aggregate_fn=None,
+        reduce_fn=None,
+        mask_fn=None,
+        features=None,
+        feature_dim=None,
+        **sparse_config,
+    ):
+        self.model = model
+        self.defaults: Dict[str, Any] = defaultdict()
+        self.defaults["sparse_config"] = sparse_config
+
+        # functions
+        self.defaults["aggregate_fn"] = aggregate_fn
+        self.defaults["reduce_fn"] = reduce_fn
+        self.defaults["mask_fn"] = mask_fn
+
+        # default feature and feature_dim
+        self.defaults["features"] = features
+        self.defaults["feature_dim"] = feature_dim
+
+        self.data_groups: Dict[str, Dict] = defaultdict(
+            dict
+        )  # contains all relevant info w.r.t each registered layer
+
+        self.state: Dict[str, Any] = defaultdict(dict)  # layer name -> mask
+
+    @staticmethod
+    def _safe_rail_checks(args):
+        """Makes sure that some of the functions and attributes are not passed incorrectly"""
+
+        # if features are not None, then feature_dim must not be None
+        features, feature_dim = args["features"], args["feature_dim"]
+        if features is not None:
+            assert feature_dim is not None, "need feature dim to select features"
+
+        # all the *_fns should be callable
+        fn_keys = ["aggregate_fn", "reduce_fn", "mask_fn"]
+        for key in fn_keys:
+            fn = args[key]
+            assert callable(fn), "function should be callable"
+
+    def _aggregate_hook(self, name):
+        """Returns hook that computes aggregate of activations passing through."""
+
+        # gather some data
+        feature_dim = self.data_groups[name]["feature_dim"]
+        features = self.data_groups[name]["features"]
+        agg_fn = self.data_groups[name]["aggregate_fn"]
+
+        def hook(module, input) -> None:
+            input_data = input[0]
+
+            data = self.data_groups[name].get("data")  # aggregated data
+            if features is None:
+                # no features associated, data should not be a list
+                if data is None:
+                    data = torch.zeros_like(input_data)
+                    self.state[name]["mask"] = torch.ones_like(input_data)
+                out_data = agg_fn(data, input_data)
+            else:
+                # data should be a list [aggregated over each feature only]
+                if data is None:
+                    out_data = [
+                        0 for _ in range(0, len(features))
+                    ]  # create one incase of 1st forward
+                    self.state[name]["mask"] = [0 for _ in range(0, len(features))]
+                else:
+                    out_data = data  # a list
+
+                # compute aggregate over each feature
+                for feature_idx in range(len(features)):
+                    # each feature is either a list or scalar, convert it to torch tensor
+                    feature_tensor = (
+                        torch.Tensor([features[feature_idx]])
+                        .long()
+                        .to(input_data.device)
+                    )
+                    data_feature = torch.index_select(
+                        input_data, feature_dim, feature_tensor
+                    )
+                    if data is None:
+                        curr_data = torch.zeros_like(data_feature)
+                        self.state[name]["mask"][feature_idx] = torch.ones_like(
+                            data_feature
+                        )
+                    else:
+                        curr_data = data[feature_idx]
+                    out_data[feature_idx] = agg_fn(curr_data, data_feature)
+            self.data_groups[name]["data"] = out_data
+
+        return hook
+
+    def register_layer(
+        self,
+        layer: nn.Module,
+        aggregate_fn=None,
+        reduce_fn=None,
+        mask_fn=None,
+        features=None,
+        feature_dim=None,
+        **sparse_config,
+    ):
+        r"""
+        Registers a layer for sparsification. The layer should be part of self.model.
+        Specifically, registers a pre-forward hook to the layer. The hook will apply the aggregate_fn
+        and store the aggregated activations that is input over each step.
+
+        Note::
+            - There is no need to pass in the name of the layer as it is automatically computed as per
+              the fqn convention.
+
+            - All the functions (fn) passed as argument will be called at a dim, feature level.
+        """
+        name = module_to_fqn(self.model, layer)
+        assert name is not None, "layer not found in the model"  # satisfy mypy
+
+        if name in self.data_groups:  # unregister layer if already present
+            warnings.warn(
+                "layer already attached to the sparsifier, deregistering the layer and registering with new config"
+            )
+            self.unregister_layer(name=name)
+
+        local_args = copy.deepcopy(self.defaults)
+        update_dict = {
+            "aggregate_fn": aggregate_fn,
+            "reduce_fn": reduce_fn,
+            "mask_fn": mask_fn,
+            "features": features,
+            "feature_dim": feature_dim,
+            "layer": layer,
+        }
+        local_args.update(
+            (arg, val) for arg, val in update_dict.items() if val is not None
+        )
+        local_args["sparse_config"].update(sparse_config)
+
+        self._safe_rail_checks(local_args)
+
+        self.data_groups[name] = local_args
+        agg_hook = layer.register_forward_pre_hook(self._aggregate_hook(name=name))
+
+        self.state[name][
+            "mask"
+        ] = None  # mask will be created when model forward is called.
+
+        # attach agg hook
+        self.data_groups[name]["hook"] = agg_hook
+
+        # for serialization purposes, we know whether aggregate_hook is attached
+        # or sparsify_hook()
+        self.data_groups[name]["hook_state"] = "aggregate"  # aggregate hook is attached
+
+    def get_mask(self, name: Optional[str] = None, layer: Optional[nn.Module] = None):
+        """
+        Returns mask associated to the layer.
+
+        The mask is
+            - a torch tensor is features for that layer is None.
+            - a list of torch tensors for each feature, otherwise
+
+        Note::
+            The shape of the mask is unknown until model.forward() is applied.
+            Hence, if get_mask() is called before model.forward(), an
+            error will be raised.
+        """
+        assert (
+            name is not None or layer is not None
+        ), "Need at least name or layer obj to retrieve mask"
+
+        if name is None:
+            assert layer is not None
+            name = module_to_fqn(self.model, layer)
+            assert name is not None, "layer not found in the specified model"
+
+        if name not in self.state:
+            raise ValueError("Error: layer with the given name not found")
+
+        mask = self.state[name].get("mask", None)
+
+        if mask is None:
+            raise ValueError(
+                "Error: shape unknown, call layer() routine at least once to infer mask"
+            )
+        return mask
+
+    def unregister_layer(self, name):
+        """Detaches the sparsifier from the layer"""
+
+        # detach any hooks attached
+        self.data_groups[name]["hook"].remove()
+
+        # pop from the state dict
+        self.state.pop(name)
+
+        # pop from the data groups
+        self.data_groups.pop(name)
+
+    def step(self):
+        """Internally calls the update_mask() function for each layer"""
+        with torch.no_grad():
+            for name, configs in self.data_groups.items():
+                data = configs["data"]
+                self.update_mask(name, data, configs)
+
+                self.data_groups[name].pop("data")  # reset the accumulated data
+
+    def update_mask(self, name, data, configs):
+        """
+        Called for each registered layer and does the following-
+            1. apply reduce_fn on the aggregated activations
+            2. use mask_fn to compute the sparsification mask
+
+        Note:
+            the reduce_fn and mask_fn is called for each feature, dim over the data
+        """
+        mask = self.get_mask(name)
+        sparse_config = configs["sparse_config"]
+        features = configs["features"]
+        reduce_fn = configs["reduce_fn"]
+        mask_fn = configs["mask_fn"]
+        if features is None:
+            data = reduce_fn(data)
+            mask.data = mask_fn(data, **sparse_config)
+        else:
+            for feature_idx in range(len(features)):
+                data_feature = reduce_fn(data[feature_idx])
+                mask[feature_idx].data = mask_fn(data_feature, **sparse_config)
+
+    def _sparsify_hook(self, name):
+        """Returns hook that applies sparsification mask to input entering the attached layer"""
+        mask = self.get_mask(name)
+        features = self.data_groups[name]["features"]
+        feature_dim = self.data_groups[name]["feature_dim"]
+
+        def hook(module, input):
+            input_data = input[0]
+            if features is None:
+                # apply to all the features
+                return input_data * mask
+            else:
+                # apply per feature, feature_dim
+                for feature_idx in range(0, len(features)):
+                    feature = (
+                        torch.Tensor([features[feature_idx]])
+                        .long()
+                        .to(input_data.device)
+                    )
+                    sparsified = (
+                        torch.index_select(input_data, feature_dim, feature)
+                        * mask[feature_idx]
+                    )
+                    input_data.index_copy_(feature_dim, feature, sparsified)
+                return input_data
+
+        return hook
+
+    def squash_mask(self, attach_sparsify_hook=True, **kwargs):
+        """
+        Unregisters aggregate hook that was applied earlier and registers sparsification hooks if
+        attach_sparsify_hook = True.
+        """
+        for name, configs in self.data_groups.items():
+            # unhook agg hook
+            configs["hook"].remove()
+            configs.pop("hook")
+            self.data_groups[name]["hook_state"] = "None"
+            if attach_sparsify_hook:
+                configs["hook"] = configs["layer"].register_forward_pre_hook(
+                    self._sparsify_hook(name)
+                )
+            configs[
+                "hook_state"
+            ] = "sparsify"  # signals that sparsify hook is now attached
+
+    def _get_serializable_data_groups(self):
+        """Exclude hook and layer from the config keys before serializing
+
+        TODO: Might have to treat functions (reduce_fn, mask_fn etc) in a different manner while serializing.
+              For time-being, functions are treated the same way as other attributes
+        """
+        data_groups: Dict[str, Any] = defaultdict()
+        for name, config in self.data_groups.items():
+            new_config = {
+                key: value
+                for key, value in config.items()
+                if key not in ["hook", "layer"]
+            }
+            data_groups[name] = new_config
+        return data_groups
+
+    def _convert_mask(self, states_dict, sparse_coo=True):
+        r"""Converts the mask to sparse coo or dense depending on the `sparse_coo` argument.
+        If `sparse_coo=True`, then the mask is stored as sparse coo else dense tensor
+        """
+        states = copy.deepcopy(states_dict)
+        for state in states.values():
+            if state["mask"] is not None:
+                if isinstance(state["mask"], List):
+                    for idx in range(len(state["mask"])):
+                        if sparse_coo:
+                            state["mask"][idx] = state["mask"][idx].to_sparse_coo()
+                        else:
+                            state["mask"][idx] = state["mask"][idx].to_dense()
+                else:
+                    if sparse_coo:
+                        state["mask"] = state["mask"].to_sparse_coo()
+                    else:
+                        state["mask"] = state["mask"].to_dense()
+        return states
+
+    def state_dict(self) -> Dict[str, Any]:
+        r"""Returns the state of the sparsifier as a :class:`dict`.
+
+        It contains:
+        * state - contains name -> mask mapping.
+        * data_groups - a dictionary containing all config information for each
+            layer
+        * defaults - the default config while creating the constructor
+        """
+        data_groups = self._get_serializable_data_groups()
+        state = self._convert_mask(self.state)
+        return {"state": state, "data_groups": data_groups, "defaults": self.defaults}
+
+    def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
+        r"""The load_state_dict() restores the state of the sparsifier based on the state_dict
+
+        Args:
+        * state_dict - the dictionary that to which the current sparsifier needs to be restored to
+        """
+        state = state_dict["state"]
+        data_groups, defaults = state_dict["data_groups"], state_dict["defaults"]
+
+        self.__set_state__(
+            {"state": state, "data_groups": data_groups, "defaults": defaults}
+        )
+
+    def __get_state__(self) -> Dict[str, Any]:
+        data_groups = self._get_serializable_data_groups()
+        state = self._convert_mask(self.state)
+        return {
+            "defaults": self.defaults,
+            "state": state,
+            "data_groups": data_groups,
+        }
+
+    def __set_state__(self, state: Dict[str, Any]) -> None:
+        state["state"] = self._convert_mask(
+            state["state"], sparse_coo=False
+        )  # convert mask to dense tensor
+        self.__dict__.update(state)
+
+        # need to attach layer and hook info into the data_groups
+        for name, config in self.data_groups.items():
+            # fetch layer
+            layer = fqn_to_module(self.model, name)
+            assert layer is not None  # satisfy mypy
+
+            # if agg_mode is True, then layer in aggregate mode
+            if "hook_state" in config and config["hook_state"] == "aggregate":
+                hook = layer.register_forward_pre_hook(self._aggregate_hook(name))
+
+            elif "hook_state" in config and config["hook_state"] == "sparsify":
+                hook = layer.register_forward_pre_hook(self._sparsify_hook(name))
+
+            config["layer"] = layer
+            config["hook"] = hook  # type: ignore[possibly-undefined]
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + " ("
+        for name, config in self.data_groups.items():
+            format_string += "\n"
+            format_string += "\tData Group\n"
+            format_string += f"\t    name: {name}\n"
+            for key in sorted(config.keys()):
+                if key in ["data", "hook", "reduce_fn", "mask_fn", "aggregate_fn"]:
+                    continue
+                format_string += f"\t    {key}: {config[key]}\n"
+        format_string += ")"
+        return format_string
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_scheduler/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_scheduler/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a7564fe408b36e5fb62eb4cb2272ef432095981
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_scheduler/__init__.py
@@ -0,0 +1,6 @@
+from .base_data_scheduler import BaseDataScheduler
+
+
+__all__ = [
+    "BaseDataScheduler",
+]
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f685661bd9fcbdf72775c20c8436567aadbfcd1
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py
@@ -0,0 +1,195 @@
+# mypy: allow-untyped-defs
+import abc
+import warnings
+import weakref
+from functools import wraps
+
+from torch.ao.pruning._experimental.data_sparsifier import BaseDataSparsifier
+
+
+__all__ = ["BaseDataScheduler"]
+
+
+class BaseDataScheduler:
+    r"""
+    The BaseDataScheduler is the abstract scheduler class specifically for the
+    BaseDataSparsifier class. This class controls a specific hyperparameter of
+    the sparsifier class and varies it across the training process (or across time).
+
+    Args:
+        data_sparsifier (instance of BaseDataSparsifier)
+            Implemented class data sparsifier class wherein the update_mask is implemented
+        schedule_param (str)
+            A specific hyperparameter of the passed sparsifier that needs to be scheduled/varied
+        last_epoch (int, default=-1)
+            This is specifically is passed when training needs to be resumed from a particular
+            point.
+        verbose (bool, default=False)
+            Verbosity of the BaseDataScheduler
+
+    The *get_hyperparam()* function needs to be implemented by the user.
+    """
+
+    def __init__(
+        self, data_sparsifier, schedule_param: str, last_epoch=-1, verbose=False
+    ):
+        # Attach sparsifier
+        if not isinstance(data_sparsifier, BaseDataSparsifier):
+            raise TypeError(
+                f"{type(data_sparsifier).__name__} is not an instance of torch.ao.pruning.BaseDataSparsifier"
+            )
+        self.data_sparsifier = data_sparsifier
+        self.schedule_param = schedule_param
+
+        # Initialize epoch and base hyper-params
+        self.base_param = {
+            name: config.get(schedule_param, None)
+            for name, config in self.data_sparsifier.data_groups.items()
+        }
+
+        self.last_epoch = last_epoch
+
+        # Following https://github.com/pytorch/pytorch/issues/20124
+        # We would like to ensure that `scheduler.step()` is called after
+        # `sparsifier.step()`
+        def with_counter(method):
+            if getattr(method, "_with_counter", False):
+                # `sparsifier.step()` has already been replaced, return.
+                return method
+
+            # Keep a weak reference to the sparsifier instance to prevent
+            # cyclic references.
+            instance_ref = weakref.ref(method.__self__)
+            # Get the unbound method for the same purpose.
+            func = method.__func__
+            cls = instance_ref().__class__
+            del method
+
+            @wraps(func)
+            def wrapper(*args, **kwargs):
+                instance = instance_ref()
+                instance._step_count += 1  # type: ignore[union-attr]
+                wrapped = func.__get__(instance, cls)
+                return wrapped(*args, **kwargs)
+
+            # Note that the returned function here is no longer a bound method,
+            # so attributes like `__func__` and `__self__` no longer exist.
+            wrapper._with_counter = True  # type: ignore[attr-defined]
+            return wrapper
+
+        self.data_sparsifier.step = with_counter(self.data_sparsifier.step)  # type: ignore[assignment]
+        self.data_sparsifier._step_count = 0  # type: ignore[attr-defined]
+        self._step_count: int = 0
+        self.verbose = verbose
+
+        # Housekeeping
+        self._get_sp_called_within_step: bool = False  # sp -> schedule parameter
+        self.step()
+
+    @abc.abstractmethod
+    def get_schedule_param(self):
+        r"""
+        Abstract method that needs to be implemented by the child class.
+        The expected return type should is a dictionary of name to schedule_param value
+        The returned values will be updated in sparsifier when the scheduler step() function
+        is called.
+
+        Example:
+            >>> def get_schedule_param(self):
+            ...     new_param = {}
+            ...     for name in self.sparsifier.data_groups.keys():
+            ...         new_param[name] = self.sparsifier.data_groups[name][self.schedule_param] * 0.5
+            ...     return new_param
+
+        When the step() function is called, the value in self.sparsifier.data_groups[name][self.schedule_param]
+        would be halved
+        """
+        raise NotImplementedError
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + " ("
+        format_string += "\n"
+        format_string += f"Data Sparsifier {self.data_sparsifier}\n"
+        format_string += f"    {self.schedule_param}: {self.base_param}\n"
+        format_string += ")"
+        return format_string
+
+    def state_dict(self):
+        """Returns the state of the scheduler as a :class:`dict`.
+
+        It contains an entry for every variable in self.__dict__ which
+        is not the sparsifier.
+
+        Note:
+            The scheduler class does not track the state of the data_sparsifier.
+            Make sure to store the state of the sparsifier before storing the
+            state of the scheduler
+        """
+        return {
+            key: value
+            for key, value in self.__dict__.items()
+            if key != "data_sparsifier"
+        }
+
+    def load_state_dict(self, state_dict):
+        """Loads the schedulers state.
+
+        Note:
+            Remember to restore the state of the data_sparsifier before the scheduler.
+
+        Args:
+            state_dict (dict): scheduler state. Should be an object returned
+                from a call to :meth:`state_dict`.
+        """
+        self.__dict__.update(state_dict)
+
+    def get_last_param(self):
+        return self._last_param
+
+    def step(self):
+        # Raise warning if trying to call scheduler step before the sparsifier.
+        # https://github.com/pytorch/pytorch/issues/20124
+        if self._step_count == 1:
+            if not hasattr(self.data_sparsifier.step, "_with_counter"):
+                warnings.warn(
+                    "Seems like `data_sparsifier.step()` has been overridden after sparsity scheduler "
+                    "initialization. Please, make sure to call `data_sparsifier.step()` before "
+                    "`scheduler.step()`.",
+                    UserWarning,
+                )
+
+            # Just check if there were two first scheduler.step() calls before sparsifier.step()
+            elif self.data_sparsifier._step_count < 1:  # type: ignore[attr-defined]
+                warnings.warn(
+                    "Detected call of `scheduler.step()` before `data_sparsifier.step()`. "
+                    "You have to make sure you run the data_sparsifier.step() BEFORE any "
+                    "calls to the scheduler.step().",
+                    UserWarning,
+                )
+        self._step_count += 1
+
+        class _enable_get_sp_call:
+            def __init__(self, o):
+                self.o = o
+
+            def __enter__(self):
+                self.o._get_sp_called_within_step = True
+                return self
+
+            def __exit__(self, type, value, traceback):
+                self.o._get_sp_called_within_step = False
+
+        with _enable_get_sp_call(self):
+            self.last_epoch += 1
+            updated_scheduler_params = self.get_schedule_param()
+
+        for name, param in updated_scheduler_params.items():
+            self.data_sparsifier.data_groups[name][self.schedule_param] = param
+            if self.verbose:
+                print(f"Adjusting {self.schedule_param} for group {name} to {param}")
+
+        self._last_param = {
+            name: config.get(self.schedule_param, None)
+            for name, config in self.data_sparsifier.data_groups.items()
+        }
+        self.data_sparsifier.enable_mask_update = True
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_sparsifier/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_sparsifier/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1b5b9b96ec96fffdb0b66e21686a927a0c41b4a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_sparsifier/__init__.py
@@ -0,0 +1,8 @@
+from .base_data_sparsifier import BaseDataSparsifier
+from .data_norm_sparsifier import DataNormSparsifier
+
+
+__all__ = [
+    "BaseDataSparsifier",
+    "DataNormSparsifier",
+]
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..b81ee658e553186968f7fe63236b870e04b37f9c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py
@@ -0,0 +1,331 @@
+# mypy: allow-untyped-defs
+import abc
+import copy
+import sys
+import warnings
+from collections import defaultdict
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+from torch import nn
+from torch.ao.pruning.sparsifier import base_sparsifier, utils
+from torch.nn.utils import parametrize
+
+
+if not sys.warnoptions:
+    # to suppress repeated warnings when being used in a training loop.
+    warnings.simplefilter("once")
+
+__all__ = ["BaseDataSparsifier"]
+
+EMBEDDING_TYPES = {
+    nn.Embedding,
+    nn.EmbeddingBag,
+}
+
+SUPPORTED_TYPES = {
+    torch.Tensor,
+    nn.Parameter,
+    *EMBEDDING_TYPES,
+}
+
+
+class _Container(nn.Module):
+    pass
+
+
+class BaseDataSparsifier(base_sparsifier.BaseSparsifier):
+    r"""
+    Base Data Sparsifier class for all Data sparsifiers.
+    The abstract class accepts raw torch tensors / embedding / embedding bags (refer to SUPPORTED_TYPES above)
+    to prepare for sparsification.
+    In this case, mask (and parametrizations) is owned by the class and not by the user.
+    Specifically, the container object inside the class maintains the mask and parametrizations of the input data
+
+    Args:
+        data_list (list of tuples)
+            list of (name, data) tuples to sparsify. Lookup SUPPORTED_TYPES
+            for type of data. Internally, a container module handles the data sparsification.
+
+        defaults (dict)
+            default configurations will be attached to the
+            configuration. Only the keys that don't exist in the `config` will
+            be updated.
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> data_list = [('tensor_1', torch.randn(3,3)), ('tensor_2', torch.randn(4,4))]
+        >>> defaults = {'sparsity_level': 0.7}
+        >>> sparsifier = DerivedDataSparsifier(data_list = data_list, **defaults) # Some sparsifier that inherits BaseDataSparsifier
+        >>> new_tensor_to_add = {'name': 'tensor_3', 'data': torch.randn(5,5), 'sparsity_level': 0.3}
+        >>> sparsifier.add_data(**new_tensor_to_add)
+        >>> # tensor_1 and tensor_2 will have sparsity_level of 0.7 but tensor_3 will have sparsity_level=0.3
+    """
+
+    def __init__(self, data_list: Optional[List[Tuple[str, Any]]] = None, **defaults):
+        super().__init__(defaults=defaults)
+
+        self._container = _Container()
+
+        self.data_groups: Dict[str, Dict] = defaultdict(dict)  # name -> {**config}
+        if data_list is not None:
+            # add data with default config here
+            [self.add_data(name, data, **self.defaults) for name, data in data_list]
+
+    def prepare(self):
+        raise NotImplementedError("this function is undefined for this class")
+
+    def _extract_weight(self, data):
+        # extract the weight parameter instead of underlying data
+        if type(data) in [torch.Tensor, nn.Parameter]:
+            return data
+        elif type(data) in EMBEDDING_TYPES:
+            return data.weight
+
+    def add_data(self, name: str, data, reuse_mask=True, **config):
+        r"""Configures and parametrizes the internal container model with name and data.
+
+        **Note**:
+            1. If the data with name already exists, it replaces the data.
+            2. While replacing, the old mask is reused when `reuse_mask=True`
+            3. If `reuse_mask=True`, then the replacing data needs to have the same shape as that of old data.
+            4. By default, the config of the replaced data is used as config for the replacing data, unless something
+               is specified in the config dictionary.
+        """
+        assert (
+            type(data) in SUPPORTED_TYPES
+        ), "specified data type not supported at the moment"
+        local_args = copy.deepcopy(self.defaults)
+        local_args.update(config)
+        weight = self._extract_weight(data)
+
+        # Bookkeeping in the container class
+        mask = local_args.get("mask", torch.ones_like(weight))
+        param_class = local_args.get("parametrization", utils.FakeSparsity)
+
+        if name in self.state:
+            # If the named data already exists - replace
+            warnings.warn(
+                "Replacing existing data of the same name. - Did you mean a different name?"
+            )
+
+            # reuse old config
+            old_args = self.data_groups[name]
+            local_args = copy.deepcopy(old_args)
+            local_args.update(config)
+
+            if reuse_mask:
+                current_data = self.get_data(name=name)
+                assert (
+                    weight.shape == current_data.shape
+                ), "to retain the old mask, the shape of the new data must be the same as the previous one"
+                mask = self.get_mask(
+                    name=name
+                )  # reuse mask instead of creating a new one
+
+            self._delete_data(name=name)
+
+        # parameter creates a deepcopy of the weight inside, so create a buffer
+        self._container.register_buffer(name=name, tensor=weight)
+        parametrize.register_parametrization(self._container, name, param_class(mask))
+        self.state[name]["mask"] = mask
+        self.data_groups[name] = local_args
+        return getattr(self._container, name)
+
+    def get_data(self, name: str, return_original: bool = True):
+        r"""Returns weight tensor (or data)
+        Args:
+            - name: name of the data to be returned
+            - return_original returns weight tensor without applying parametrization if True
+                else - returns the sparsified version (parametrized)
+        """
+        if name not in self.data_groups:
+            raise ValueError("data with specified name does not exist")
+
+        if return_original:
+            if not parametrize.is_parametrized(self._container, name):
+                raise ValueError("mask squashed - original mask value does not exist")
+            data = getattr(self._container.parametrizations, name).original
+            return data
+        else:
+            return getattr(self._container, name)
+
+    def _convert_mask(self, states, sparse_coo=True):
+        r"""Converts the mask to sparse coo or dense tensors depending on the `sparse_coo` argument."""
+        states = copy.deepcopy(states)
+        for state in states.values():
+            if sparse_coo:
+                state["mask"] = state["mask"].to_sparse_coo()
+            else:
+                state["mask"] = state["mask"].to_dense()
+
+        return states
+
+    def state_dict(self):
+        r"""Returns the state of the optimizer as a :class:`dict`.
+
+        It contains:
+        * state - contains name -> mask mapping.
+        * data_groups - a list containing all sparsity configuration groups
+            with the key name specifying the name of the data
+        * container_state_dict - the state dictionary of the internal
+            container model used for sparsification
+        """
+        state = self._convert_mask(self.state)
+        return {
+            "state": state,
+            "data_groups": self.data_groups,
+            "_container": self._container.state_dict(),
+        }
+
+    def _load_container_from_state(self, states, data_groups, container_state_dict):
+        r"""This restores the state of the container specifically based on the data present in state and data_groups
+        If the data was parametrized, then the data would be added to the container and then parametrized,
+        else it would just add the attribute the container.
+        """
+        for name, state in states.items():
+            config_name = data_groups.get(name, None)
+            if config_name is None:
+                raise RuntimeError(f"Error loading {name}")
+
+            # check if the data with such a name was parametrized, if so parametrize
+            # otherwise just set the attribute and continue
+            parametrized_name = f"parametrizations.{name}.original"
+            parametrized = False
+            data = container_state_dict.get(name, None)
+            if name in container_state_dict:
+                # the parametrization was probably removed for this
+                data = container_state_dict.get(name)
+
+            elif parametrized_name in container_state_dict:
+                # so the weight was parametrized
+                data = container_state_dict.get(parametrized_name)
+                parametrized = True
+
+            else:
+                raise RuntimeError(f"Error loading {name}")
+
+            self._container.register_buffer(name=name, tensor=data)
+
+            if parametrized:
+                # register parameter if parametrized
+                mask = state.get("mask", torch.ones_like(data))
+                param_class = data_groups.get(
+                    "parametrization", utils.FakeSparsity
+                )  # change once public_api for utils is fixed!
+                parametrize.register_parametrization(
+                    self._container, name, param_class(mask)
+                )
+
+    def load_state_dict(self, state_dict, strict=True):
+        r"""The load_state_dict() restores the state of the sparsifier based on the state_dict
+
+        Args:
+        * state_dict - the dictionary that to which the current sparsifier needs to be restored to
+        * strict - If True - the sparsifier is reset and is restored exactly to the state in state_dict.
+            If False - the current sparsifier is not reset before loading the state_dict i.e. data added
+            before loading the state_dict is not erased.
+        """
+        states = copy.deepcopy(state_dict["state"])
+        data_groups = copy.deepcopy(state_dict["data_groups"])
+        container_state_dict = copy.deepcopy(state_dict["_container"])
+
+        states = self._convert_mask(
+            states, sparse_coo=False
+        )  # convert sparse coo mask to dense
+        if strict:
+            # if strict load -> then reset container
+            self._container = _Container()
+
+        self._load_container_from_state(states, data_groups, container_state_dict)
+
+        if not strict:
+            states.update(self.state)
+            data_groups.update(self.data_groups)
+
+        self.__setstate__({"state": states, "data_groups": data_groups})
+
+    def __setstate__(self, state):
+        if "_container" in state:  # If container object is in state then load model
+            container_dict = state.pop("_container")
+            self._container = _Container()
+            state["state"] = self._convert_mask(
+                state["state"], sparse_coo=False
+            )  # convert sparse coo mask to dense
+            self._load_container_from_state(
+                state["state"], state["data_groups"], container_dict
+            )
+
+        self.__dict__.update(state)
+
+    def __getstate__(self):
+        state = self._convert_mask(self.state)
+        return {
+            "defaults": self.defaults,
+            "state": state,
+            "data_groups": self.data_groups,
+            "_container": self._container.state_dict(),
+        }
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + " ("
+        for name, sparse_args in self.data_groups.items():
+            format_string += "\n"
+            format_string += "\tData Group\n"
+            format_string += f"\t    name: {name}\n"
+            for key in sorted(sparse_args.keys()):
+                if key == "data":
+                    continue
+                format_string += f"\t    {key}: {sparse_args[key]}\n"
+        format_string += ")"
+        return format_string
+
+    def get_mask(self, name: str):
+        if name not in self.state:
+            raise ValueError("data with specified name does not exist")
+        return self.state[name]["mask"]
+
+    def squash_mask(self, *args, leave_parametrized=True, names=None, **kwargs):
+        r"""Squashes the sparse masks into the appropriate tensors. Also, accepts list of strings
+        to squash mask for. If none, squashes mask for all the keys
+        kwargs:
+            * names: list of strings to squash mask for
+            * sparsified: if true - applies the mask before squashing
+                          if false - does not apply the mask before squashing
+        """
+        if names is None:
+            names = list(self.data_groups.keys())
+        for name in names:
+            parametrize.remove_parametrizations(
+                self._container, name, leave_parametrized=leave_parametrized
+            )
+
+    def step(self):
+        if not self.enable_mask_update:
+            return
+        with torch.no_grad():
+            for name, config in self.data_groups.items():
+                # get non-sparsified data
+                data = self.get_data(name)
+                # need name for the mask otherwise can directly pass mask?
+                self.update_mask(name, data, **config)
+
+    @abc.abstractmethod
+    def update_mask(self, name, data, **kwargs):
+        pass
+
+    def _delete_data(self, name):
+        """Detaches some data from the sparsifier.
+
+        Args:
+            name (str)
+                Name of the data to be removed from the sparsifier
+
+        Note:
+            Currently private. Kind of used as a helper function when replacing data of the same name
+        """
+        self.squash_mask(
+            names=[name], leave_parametrized=False
+        )  # do not apply the mask while deleting
+        delattr(self._container, name)
+        self.state.pop(name)
+        self.data_groups.pop(name)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_sparsifier/data_norm_sparsifier.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_sparsifier/data_norm_sparsifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..34444fa79df0fe3ad17a8ee3e73259a0c3265737
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_sparsifier/data_norm_sparsifier.py
@@ -0,0 +1,203 @@
+# mypy: allow-untyped-defs
+import operator
+from functools import reduce
+from typing import Any, List, Optional, Tuple
+
+import torch
+from torch.nn import functional as F
+
+from .base_data_sparsifier import BaseDataSparsifier
+
+
+__all__ = ["DataNormSparsifier"]
+
+
+class DataNormSparsifier(BaseDataSparsifier):
+    r"""L1-Norm Sparsifier
+    This sparsifier computes the *L1-norm* of every sparse block and "zeroes-out" the
+    ones with the lowest norm. The level of sparsity defines how many of the
+    blocks is removed.
+    This sparsifier is controlled by three variables:
+    1. `sparsity_level` defines the number of *sparse blocks* that are zeroed-out
+    2. `sparse_block_shape` defines the shape of the sparse blocks. Note that
+        the sparse blocks originate at the zero-index of the tensor.
+    3. `zeros_per_block` is the number of zeros that we are expecting in each
+        sparse block. By default we assume that all elements within a block are
+        zeroed-out. However, setting this variable sets the target number of
+        zeros per block. The zeros within each block are chosen as the *smallest
+        absolute values*.
+    Args:
+        sparsity_level: The target level of sparsity
+        sparse_block_shape: The shape of a sparse block
+        zeros_per_block: Number of zeros in a sparse block
+    Note::
+        All arguments to the DataNormSparsifier constructor are "default"
+        arguments and could be overriden by the configuration provided in the
+        `add_data` step.
+    """
+
+    def __init__(
+        self,
+        data_list: Optional[List[Tuple[str, Any]]] = None,
+        sparsity_level: float = 0.5,
+        sparse_block_shape: Tuple[int, int] = (1, 4),
+        zeros_per_block: Optional[int] = None,
+        norm: str = "L1",
+    ):
+        if zeros_per_block is None:
+            zeros_per_block = reduce(operator.mul, sparse_block_shape)
+
+        assert norm in ["L1", "L2"], "only L1 and L2 norm supported at the moment"
+
+        defaults = {
+            "sparsity_level": sparsity_level,
+            "sparse_block_shape": sparse_block_shape,
+            "zeros_per_block": zeros_per_block,
+        }
+        self.norm = norm
+        super().__init__(data_list=data_list, **defaults)
+
+    def __get_scatter_folded_mask(
+        self, data, dim, indices, output_size, sparse_block_shape
+    ):
+        mask = torch.ones_like(data)
+        mask.scatter_(dim=dim, index=indices, value=0)  # zeroing out
+        mask = F.fold(
+            mask,
+            output_size=output_size,
+            kernel_size=sparse_block_shape,
+            stride=sparse_block_shape,
+        )
+        mask = mask.to(torch.int8)
+        return mask
+
+    def __get_block_level_mask(self, data, sparse_block_shape, zeros_per_block):
+        # Assume data is a squeezed tensor
+        height, width = data.shape[-2], data.shape[-1]
+        block_height, block_width = sparse_block_shape
+        values_per_block = block_height * block_width
+
+        # just return zeros if zeroing all elements in block
+        if values_per_block == zeros_per_block:
+            return torch.zeros_like(data, dtype=torch.int8)
+
+        # creating additional height and width to support padding
+        dh = (block_height - height % block_height) % block_height
+        dw = (block_width - width % block_width) % block_width
+
+        # create a new padded tensor like data (to match the block_shape)
+        padded_data = torch.ones(
+            height + dh, width + dw, dtype=data.dtype, device=data.device
+        )
+        padded_data = (
+            padded_data * torch.nan
+        )  # can also be replaced with 0 to stop the removal of edge data
+        padded_data[0:height, 0:width] = data
+        unfolded_data = F.unfold(
+            padded_data[None, None, :],
+            kernel_size=sparse_block_shape,
+            stride=sparse_block_shape,
+        )
+
+        _, sorted_idx = torch.sort(unfolded_data, dim=1)
+        sorted_idx = sorted_idx[
+            :, :zeros_per_block, :
+        ]  # zero out zeros_per_block number of elements
+
+        mask = self.__get_scatter_folded_mask(
+            data=unfolded_data,
+            dim=1,
+            indices=sorted_idx,
+            output_size=padded_data.shape,
+            sparse_block_shape=sparse_block_shape,
+        )
+
+        mask = (
+            mask.squeeze(0).squeeze(0)[:height, :width].contiguous()
+        )  # remove padding and make contiguous
+        return mask
+
+    def __get_data_level_mask(self, data, sparsity_level, sparse_block_shape):
+        height, width = data.shape[-2], data.shape[-1]
+        block_height, block_width = sparse_block_shape
+        dh = (block_height - height % block_height) % block_height
+        dw = (block_width - width % block_width) % block_width
+
+        data_norm = F.avg_pool2d(
+            data[None, None, :],
+            kernel_size=sparse_block_shape,
+            stride=sparse_block_shape,
+            ceil_mode=True,
+        )
+
+        values_per_block = reduce(operator.mul, sparse_block_shape)
+
+        data_norm = data_norm.flatten()
+        num_blocks = len(data_norm)
+
+        data_norm = data_norm.repeat(
+            1, values_per_block, 1
+        )  # get similar shape after unfold
+        _, sorted_idx = torch.sort(data_norm, dim=2)
+
+        threshold_idx = round(sparsity_level * num_blocks)  # number of blocks to remove
+        sorted_idx = sorted_idx[:, :, :threshold_idx]
+
+        mask = self.__get_scatter_folded_mask(
+            data=data_norm,
+            dim=2,
+            indices=sorted_idx,
+            output_size=(height + dh, width + dw),
+            sparse_block_shape=sparse_block_shape,
+        )
+
+        mask = mask.squeeze(0).squeeze(0)[
+            :height, :width
+        ]  # squeeze only the first 2 dimension
+        return mask
+
+    def update_mask(
+        self, name, data, sparsity_level, sparse_block_shape, zeros_per_block, **kwargs
+    ):
+        values_per_block = reduce(operator.mul, sparse_block_shape)
+        if zeros_per_block > values_per_block:
+            raise ValueError(
+                "Number of zeros per block cannot be more than "
+                "the total number of elements in that block."
+            )
+        if zeros_per_block < 0:
+            raise ValueError("Number of zeros per block should be positive.")
+
+        if self.norm == "L1":
+            data_norm = torch.abs(data).squeeze()  # absolute value based (L1)
+        else:
+            data_norm = (data * data).squeeze()  # square every element for L2
+
+        if len(data_norm.shape) > 2:  # only supports 2 dimensional data at the moment
+            raise ValueError("only supports 2-D at the moment")
+
+        elif len(data_norm.shape) == 1:  # in case the data is bias (or 1D)
+            data_norm = data_norm[None, :]
+
+        mask = self.get_mask(name)
+        if sparsity_level <= 0 or zeros_per_block == 0:
+            mask.data = torch.ones_like(mask)
+        elif sparsity_level >= 1.0 and (zeros_per_block == values_per_block):
+            mask.data = torch.zeros_like(mask)
+
+        # Fetch the high level mask that zeros out entire blocks
+        data_lvl_mask = self.__get_data_level_mask(
+            data=data_norm,
+            sparsity_level=sparsity_level,
+            sparse_block_shape=sparse_block_shape,
+        )
+
+        # Fetch block level mask that zeros out 'zeros_per_block' number of elements in every block
+        block_lvl_mask = self.__get_block_level_mask(
+            data=data_norm,
+            sparse_block_shape=sparse_block_shape,
+            zeros_per_block=zeros_per_block,
+        )
+
+        # zero out the entries inside those blocks whose block is sparsified
+        mask.data = torch.where(data_lvl_mask == 1, data_lvl_mask, block_lvl_mask)
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_sparsifier/lightning/callbacks/_data_sparstity_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_sparsifier/lightning/callbacks/_data_sparstity_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..50d5684961bc807d5ae1b02615ade168416c9b3d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_sparsifier/lightning/callbacks/_data_sparstity_utils.py
@@ -0,0 +1,44 @@
+# mypy: allow-untyped-defs
+import logging
+
+from torch.ao.pruning._experimental.data_sparsifier.base_data_sparsifier import (
+    SUPPORTED_TYPES,
+)
+
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+def _attach_model_to_data_sparsifier(module, data_sparsifier, config=None):
+    """Attaches a data sparsifier to all the layers of the module.
+    Essentially, loop over all the weight parameters in the module and
+    attach it to the data sparsifier.
+    Note::
+        The '.' in the layer names are replaced with '_' (refer to _get_valid_name() below)
+        before attaching to the sparsifier. This is because, the data
+        sparsifier uses a dummy model inside to store the weight parameters.
+    """
+    if config is None:
+        config = {}
+    for name, parameter in module.named_parameters():
+        if type(parameter) in SUPPORTED_TYPES:
+            valid_name = _get_valid_name(name)
+            # will be defaulted to default configs
+            data_sparsifier.add_data(
+                name=valid_name, data=parameter, **config.get(valid_name, {})
+            )
+
+
+def _get_valid_name(name):
+    return name.replace(".", "_")  # . is not allowed as a name
+
+
+def _log_sparsified_level(model, data_sparsifier) -> None:
+    # Show the level of sparsity AFTER step:
+    for name, parameter in model.named_parameters():
+        if type(parameter) not in SUPPORTED_TYPES:
+            continue
+        valid_name = _get_valid_name(name)
+        mask = data_sparsifier.get_mask(name=valid_name)
+        sparsity_level = 1.0 - mask.float().mean()
+        logger.info("Sparsity in layer %s = % .2%", name, sparsity_level)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_sparsifier/lightning/callbacks/data_sparsity.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_sparsifier/lightning/callbacks/data_sparsity.py
new file mode 100644
index 0000000000000000000000000000000000000000..07ca69e2bb8faa66994613f06cf1db2e662c0e87
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_sparsifier/lightning/callbacks/data_sparsity.py
@@ -0,0 +1,181 @@
+# mypy: allow-untyped-defs
+from collections import defaultdict
+from copy import deepcopy
+from typing import Any, Dict, Optional, TYPE_CHECKING
+
+import pytorch_lightning as pl  # type: ignore[import]
+
+from ._data_sparstity_utils import (
+    _attach_model_to_data_sparsifier,
+    _get_valid_name,
+    _log_sparsified_level,
+)
+
+
+if TYPE_CHECKING:
+    import torch
+
+
+class PostTrainingDataSparsity(pl.callbacks.Callback):
+    """Lightning callback that enables post-training sparsity.
+
+    This callback aims to sparsify the model inside lightning module after training.
+    **Note that the model is copied and then sparsified, so the existing model is not modified**
+
+    The sparsified model can be used for comparison and can be accessed using
+        .sparsified
+
+    Args:
+        data_sparsifier_class (some implemented class of BaseDataSparsifier)
+            The data sparsifier object of this class is created when the
+            training starts.
+            Note: Objects should not be passed in here as they are created
+            once the training completes.
+
+        data_sparsifier_args (Dict)
+            Dictionary of args to be passed to the data sparsifier.
+            Note: data_list arg should be ignored
+
+    Hooks implemented:
+        on_fit_end()
+            1. copies the model and attaches it to the sparsifier
+            2. sparsier step() is called
+            3. squashes the mask()
+    """
+
+    def __init__(self, data_sparsifier_class, data_sparsifier_args):
+        super().__init__()
+        self.data_sparsifier_class = data_sparsifier_class
+        self.data_sparsifier_args = data_sparsifier_args
+        self.data_sparsifier: Any = None
+        self.sparsified: Optional[torch.nn.Module] = None
+
+    def on_fit_end(self, trainer, pl_module) -> None:
+        self.sparsified = deepcopy(pl_module.model).eval()
+        self.data_sparsifier = self.data_sparsifier_class(**self.data_sparsifier_args)
+
+        _attach_model_to_data_sparsifier(self.sparsified, self.data_sparsifier)
+
+        self.data_sparsifier.step()
+
+        self.data_sparsifier.squash_mask()  # currently squashes params for all mask
+
+        _log_sparsified_level(self.sparsified, self.data_sparsifier)
+
+
+class TrainingAwareDataSparsity(pl.callbacks.Callback):
+    """Lightning callback that enables in-training sparsity.
+
+    This callback aims to sparsify the model inside lightning module during training.
+    **Note that the model is copied and then sparsified, so the existing model is not modified**
+
+    The sparsified model can be used for comparison and can be accessed using
+        .sparsified
+
+    Args:
+        data_sparsifier_class (some implemented class of BaseDataSparsifier)
+            The data sparsifier object of this class is created when the
+            training starts.
+            Note: Objects should not be passed in here as they are created
+            when the training starts.
+
+        data_sparsifier_args (Dict)
+            Dictionary of args to be passed to the data sparsifier.
+            Note: data_list arg should be ignored
+
+        data_scheduler_class (some implemented class of BaseDataScheduler)
+            The data scheduler of this class is created when the training starts
+            Note: Objects should not be passed in here as they are created
+            when the training starts.
+
+        data_scheduler_args(Dict)
+            Dictionary of args to be passed to the data scheduler.
+            **Note: data_sparsifier arg should be ignored as the recipe
+            creates and pass sparsifier object into the class**
+
+    Hooks implemented:
+        on_train_start()
+            Data sparsifier and scheduler objects are created.
+            Pytorch model attached to the sparsifier
+
+        on_train_epoch_start()
+            Loads the state_dict of the data sparsifier
+
+        on_train_epoch_end()
+            1. Copies the model and attaches it to the sparsifier
+            2. sparsifier step() and scheduler step()
+            3. Dump state_dict of the current sparsifier
+
+        on_train_end()
+            squash mask
+    """
+
+    def __init__(
+        self,
+        data_sparsifier_class,
+        data_sparsifier_args,
+        data_scheduler_class,
+        data_scheduler_args,
+    ):
+        super().__init__()
+        # data sparsifier objects
+        self.data_sparsifier_class = data_sparsifier_class
+        self.data_sparsifier_args = data_sparsifier_args
+
+        # scheduler objects
+        self.data_scheduler_class = data_scheduler_class
+        self.data_scheduler_args = data_scheduler_args
+
+        # fields
+        self.data_sparsifier: Any = None
+        self.data_scheduler: Any = None
+        self.sparsified: Optional[torch.nn.Module] = None
+
+        self.data_sparsifier_state_dict: Any = None
+
+    def on_train_start(self, trainer, pl_module) -> None:
+        # create sparsifier
+        self.data_sparsifier = self.data_sparsifier_class(**self.data_sparsifier_args)
+        self.sparsified = deepcopy(pl_module.model)
+
+        _attach_model_to_data_sparsifier(
+            self.sparsified, self.data_sparsifier
+        )  # just to populate the base_sl in the scheduler
+
+        # create scheduler
+        args = deepcopy(self.data_scheduler_args)
+        args["data_sparsifier"] = self.data_sparsifier
+        self.data_scheduler = self.data_scheduler_class(**args)
+
+    def on_train_epoch_start(self, trainer, pl_module):
+        if self.data_sparsifier_state_dict is None:
+            return  # probably first epoch
+
+        # load the existing config for each data
+        self.data_sparsifier.load_state_dict(self.data_sparsifier_state_dict)
+
+    def __create_config_based_on_state(self, pl_module):
+        config: Dict = defaultdict()
+        if self.data_sparsifier_state_dict is None:
+            return config
+        for name, _ in pl_module.model.named_parameters():
+            valid_name = _get_valid_name(name)
+            config[valid_name] = self.data_sparsifier.data_groups[valid_name]
+
+        return config
+
+    def on_train_epoch_end(self, trainer, pl_module):
+        self.sparsified = deepcopy(pl_module.model)
+        config = self.__create_config_based_on_state(pl_module)
+
+        # attach model to the data sparsifier
+        _attach_model_to_data_sparsifier(
+            self.sparsified, self.data_sparsifier, config=config
+        )
+        self.data_sparsifier.step()
+        self.data_scheduler.step()
+
+        self.data_sparsifier_state_dict = self.data_sparsifier.state_dict()
+
+    def on_train_end(self, trainer, pl_module):
+        self.data_sparsifier.squash_mask()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_sparsifier/quantization_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_sparsifier/quantization_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2905283a93b92c445cd22a1ef1a684ac869969c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/data_sparsifier/quantization_utils.py
@@ -0,0 +1,150 @@
+# mypy: allow-untyped-defs
+from typing import Dict, List, Optional
+
+import torch
+import torch.nn as nn
+from torch.ao.pruning.sparsifier.utils import fqn_to_module, module_to_fqn
+
+
+SUPPORTED_MODULES = {nn.Embedding, nn.EmbeddingBag}
+
+
+def _fetch_all_embeddings(model):
+    """Fetches Embedding and EmbeddingBag modules from the model"""
+    embedding_modules = []
+    stack = [model]
+    while stack:
+        module = stack.pop()
+        for _, child in module.named_children():
+            fqn_name = module_to_fqn(model, child)
+            if type(child) in SUPPORTED_MODULES:
+                embedding_modules.append((fqn_name, child))
+            else:
+                stack.append(child)
+    return embedding_modules
+
+
+def post_training_sparse_quantize(
+    model,
+    data_sparsifier_class,
+    sparsify_first=True,
+    select_embeddings: Optional[List[nn.Module]] = None,
+    **sparse_config,
+):
+    """Takes in a model and applies sparsification and quantization to only embeddings & embeddingbags.
+    The quantization step can happen before or after sparsification depending on the `sparsify_first` argument.
+
+    Args:
+        - model (nn.Module)
+            model whose embeddings needs to be sparsified
+        - data_sparsifier_class (type of data sparsifier)
+            Type of sparsification that needs to be applied to model
+        - sparsify_first (bool)
+            if true, sparsifies first and then quantizes
+            otherwise, quantizes first and then sparsifies.
+        - select_embeddings (List of Embedding modules)
+            List of embedding modules to in the model to be sparsified & quantized.
+            If None, all embedding modules with be sparsified
+        - sparse_config (Dict)
+            config that will be passed to the constructor of data sparsifier object.
+
+    Note:
+        1. When `sparsify_first=False`, quantization occurs first followed by sparsification.
+            - before sparsifying, the embedding layers are dequantized.
+            - scales and zero-points are saved
+            - embedding layers are sparsified and `squash_mask` is applied
+            - embedding weights are requantized using the saved scales and zero-points
+        2. When `sparsify_first=True`, sparsification occurs first followed by quantization.
+            - embeddings are sparsified first
+            - quantization is applied on the sparsified embeddings
+    """
+    data_sparsifier = data_sparsifier_class(**sparse_config)
+
+    # if select_embeddings is None, perform it on all embeddings
+    if select_embeddings is None:
+        embedding_modules = _fetch_all_embeddings(model)
+
+    else:
+        embedding_modules = []
+        assert isinstance(
+            select_embeddings, List
+        ), "the embedding_modules must be a list of embedding modules"
+        for emb in select_embeddings:
+            assert (
+                type(emb) in SUPPORTED_MODULES
+            ), "the embedding_modules list must be an embedding or embedding bags"
+            fqn_name = module_to_fqn(model, emb)
+            assert (
+                fqn_name is not None
+            ), "the embedding modules must be part of input model"
+            embedding_modules.append((fqn_name, emb))
+
+    if sparsify_first:
+        # sparsify
+        for name, emb_module in embedding_modules:
+            valid_name = name.replace(".", "_")
+            data_sparsifier.add_data(name=valid_name, data=emb_module)
+
+        data_sparsifier.step()
+        data_sparsifier.squash_mask()
+
+        # quantize
+        for _, emb_module in embedding_modules:
+            emb_module.qconfig = torch.ao.quantization.float_qparams_weight_only_qconfig
+
+        torch.ao.quantization.prepare(model, inplace=True)
+        torch.ao.quantization.convert(model, inplace=True)
+
+    else:
+        # quantize
+        for _, emb_module in embedding_modules:
+            emb_module.qconfig = torch.ao.quantization.float_qparams_weight_only_qconfig
+
+        torch.ao.quantization.prepare(model, inplace=True)
+        torch.ao.quantization.convert(model, inplace=True)
+
+        # retrieve scale & zero_points
+        quantize_params: Dict[str, Dict] = {
+            "scales": {},
+            "zero_points": {},
+            "dequant_weights": {},
+            "axis": {},
+            "dtype": {},
+        }
+
+        for name, _ in embedding_modules:
+            quantized_emb = fqn_to_module(model, name)
+            assert quantized_emb is not None  # satisfy mypy
+
+            quantized_weight = quantized_emb.weight()  # type: ignore[operator]
+            quantize_params["scales"][name] = quantized_weight.q_per_channel_scales()
+            quantize_params["zero_points"][
+                name
+            ] = quantized_weight.q_per_channel_zero_points()
+            quantize_params["dequant_weights"][name] = torch.dequantize(
+                quantized_weight
+            )
+            quantize_params["axis"][name] = quantized_weight.q_per_channel_axis()
+            quantize_params["dtype"][name] = quantized_weight.dtype
+
+            # attach data to sparsifier
+            data_sparsifier.add_data(
+                name=name.replace(".", "_"),
+                data=quantize_params["dequant_weights"][name],
+            )
+
+        data_sparsifier.step()
+        data_sparsifier.squash_mask()
+
+        for name, _ in embedding_modules:
+            quantized_emb = fqn_to_module(model, name)
+            assert quantized_emb is not None  # satisfy mypy
+            requantized_vector = torch.quantize_per_channel(
+                quantize_params["dequant_weights"][name],
+                scales=quantize_params["scales"][name],
+                zero_points=quantize_params["zero_points"][name],
+                dtype=quantize_params["dtype"][name],
+                axis=quantize_params["axis"][name],
+            )
+
+            quantized_emb.set_weight(requantized_vector)  # type: ignore[operator]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/FPGM_pruner.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/FPGM_pruner.py
new file mode 100644
index 0000000000000000000000000000000000000000..54177272ce4f2aa2ea18e49670a0d709cb2b1b0a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/FPGM_pruner.py
@@ -0,0 +1,95 @@
+# mypy: allow-untyped-defs
+from typing import Callable, Optional, Union
+
+import torch
+
+from .base_structured_sparsifier import BaseStructuredSparsifier
+
+
+__all__ = ["FPGMPruner"]
+
+
+class FPGMPruner(BaseStructuredSparsifier):
+    r"""Filter Pruning via Geometric Median (FPGM) Structured Pruner
+    This sparsifier prune fliter (row) in a tensor according to distances among filters according to
+    `Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration `_.
+
+    This sparsifier is controlled by three variables:
+    1. `sparsity_level` defines the number of filters (rows) that are zeroed-out.
+    2. `dist` defines the distance measurement type. Default: 3 (L2 distance).
+    Available options are: [1, 2, (custom callable distance function)].
+
+    Note::
+        Inputs should be a 4D convolutional tensor of shape (N, C, H, W).
+            - N: output channels size
+            - C: input channels size
+            - H: height of kernel
+            - W: width of kernel
+    """
+
+    def __init__(
+        self, sparsity_level: float = 0.5, dist: Optional[Union[Callable, int]] = None
+    ):
+        defaults = {
+            "sparsity_level": sparsity_level,
+        }
+
+        if dist is None:
+            dist = 2
+
+        if callable(dist):
+            self.dist_fn = dist
+        elif dist == 1:
+            self.dist_fn = lambda x: torch.cdist(x, x, p=1)
+        elif dist == 2:
+            self.dist_fn = lambda x: torch.cdist(x, x, p=2)
+        else:
+            raise NotImplementedError("Distance function is not yet implemented.")
+        super().__init__(defaults=defaults)
+
+    def _compute_distance(self, t):
+        r"""Compute distance across all entries in tensor `t` along all dimension
+        except for the one identified by dim.
+        Args:
+            t (torch.Tensor): tensor representing the parameter to prune
+        Returns:
+            distance (torch.Tensor): distance computed across filtters
+        """
+        dim = 0  # prune filter (row)
+
+        size = t.size(dim)
+        slc = [slice(None)] * t.dim()
+
+        # flatten the tensor along the dimension
+        t_flatten = [
+            t[tuple(slc[:dim] + [slice(i, i + 1)] + slc[dim + 1 :])].reshape(-1)
+            for i in range(size)
+        ]
+        t_flatten = torch.stack(t_flatten)
+
+        # distance measurement
+        dist_matrix = self.dist_fn(t_flatten)
+
+        # more similar with other filter indicates large in the sum of row
+        distance = torch.sum(torch.abs(dist_matrix), 1)
+
+        return distance
+
+    def update_mask(self, module, tensor_name, sparsity_level, **kwargs):
+        tensor_weight = getattr(module, tensor_name)
+        mask = getattr(module.parametrizations, tensor_name)[0].mask
+
+        if sparsity_level <= 0:
+            mask.data = torch.ones_like(mask).bool()
+        elif sparsity_level >= 1.0:
+            mask.data = torch.zeros_like(mask).bool()
+        else:
+            distance = self._compute_distance(tensor_weight)
+
+            tensor_size = tensor_weight.shape[0]  # prune filter (row)
+            nparams_toprune = round(sparsity_level * tensor_size)
+            nparams_toprune = min(
+                max(nparams_toprune, 0), tensor_size
+            )  # clamp to [0, tensor_size]
+            topk = torch.topk(distance, k=nparams_toprune, largest=False)
+            mask[topk.indices] = False
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a57db6a8d8cde9a89c7cbda4dff6f6075559b59b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/__init__.py
@@ -0,0 +1,5 @@
+from .base_structured_sparsifier import BaseStructuredSparsifier
+from .FPGM_pruner import FPGMPruner
+from .lstm_saliency_pruner import LSTMSaliencyPruner
+from .parametrization import BiasHook, FakeStructuredSparsity
+from .saliency_pruner import SaliencyPruner
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/base_structured_sparsifier.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/base_structured_sparsifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..d00520483e8c9f765c90a81e5752480ce92fa364
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/base_structured_sparsifier.py
@@ -0,0 +1,314 @@
+# mypy: allow-untyped-defs
+from itertools import chain
+from operator import getitem
+from typing import Callable, Dict, Optional, Set, Tuple, Type, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.ao.pruning.sparsifier.base_sparsifier import BaseSparsifier
+from torch.fx import symbolic_trace
+from torch.nn.utils import parametrize
+
+from .match_utils import apply_match, MatchAllNode
+from .parametrization import BiasHook, FakeStructuredSparsity, module_contains_param
+from .prune_functions import (
+    prune_conv2d,
+    prune_conv2d_activation_conv2d,
+    prune_conv2d_activation_pool_conv2d,
+    prune_conv2d_conv2d,
+    prune_conv2d_pool_activation_conv2d,
+    prune_conv2d_pool_flatten_linear,
+    prune_linear,
+    prune_linear_activation_linear,
+    prune_linear_linear,
+    prune_lstm_output_layernorm_linear,
+    prune_lstm_output_linear,
+)
+
+
+def _get_supported_structured_pruning_modules():
+    SUPPORTED_STRUCTURED_PRUNING_MODULES = {  # added to config if None given
+        nn.Linear,
+        nn.Conv2d,
+        nn.LSTM,
+    }
+    return SUPPORTED_STRUCTURED_PRUNING_MODULES
+
+
+def _get_supported_activation_functions():
+    SUPPORTED_ACTIVATION_FUNCTIONS = {
+        F.relu,
+        F.rrelu,
+        F.hardtanh,
+        F.relu6,
+        F.sigmoid,
+        F.hardsigmoid,
+        F.tanh,
+        F.silu,
+        F.mish,
+        F.hardswish,
+        F.elu,
+        F.celu,
+        F.selu,
+        F.hardshrink,
+        F.leaky_relu,
+        F.logsigmoid,
+        F.softplus,
+        F.prelu,
+        F.softsign,
+        F.tanhshrink,
+        F.gelu,
+    }
+    return SUPPORTED_ACTIVATION_FUNCTIONS
+
+
+def _get_supported_activation_modules():
+    SUPPORTED_ACTIVATION_MODULES = {
+        nn.ReLU,
+        nn.RReLU,
+        nn.Hardtanh,
+        nn.ReLU6,
+        nn.Sigmoid,
+        nn.Hardsigmoid,
+        nn.Tanh,
+        nn.SiLU,
+        nn.Mish,
+        nn.Hardswish,
+        nn.ELU,
+        nn.CELU,
+        nn.SELU,
+        nn.Hardshrink,
+        nn.LeakyReLU,
+        nn.LogSigmoid,
+        nn.Softplus,
+        nn.PReLU,
+        nn.Softsign,
+        nn.Tanhshrink,
+        nn.GELU,
+    }
+    return SUPPORTED_ACTIVATION_MODULES
+
+
+def _get_default_structured_pruning_patterns() -> (
+    Dict[
+        Tuple[Union[Type[nn.Module], Callable, MatchAllNode, str], ...],
+        Callable[..., None],
+    ]
+):
+    """
+    Returns the patterns for conv2d / linear conversion for each element in the activation functions/modules defined above.
+    """
+    patterns: Dict[
+        Tuple[Union[Type[nn.Module], Callable, MatchAllNode, str], ...],
+        Callable[..., None],
+    ] = {
+        # linear -> linear
+        (nn.Linear, "output"): prune_linear,
+        (nn.Linear, nn.Linear): prune_linear_linear,
+        # conv2d -> conv2d
+        (nn.Conv2d, "output"): prune_conv2d,
+        (nn.Conv2d, nn.Conv2d): prune_conv2d_conv2d,
+        # TODO LSTM Structured pruning does not support returned state currently.
+        # Should find a way to explicitly match getitem(0) instead of getitem.
+        # This will also require changing the pruning function.
+        # lstm -> getitem(0) -> linear
+        (nn.LSTM, getitem, nn.Linear): prune_lstm_output_linear,
+        # lstm -> getitem(0) -> layernorm -> linear
+        (nn.LSTM, getitem, nn.LayerNorm, nn.Linear): prune_lstm_output_layernorm_linear,
+    }
+
+    for activation in chain(
+        _get_supported_activation_functions(), _get_supported_activation_modules()
+    ):
+        patterns.update(
+            {
+                # linear -> activation -> linear
+                (nn.Linear, activation, nn.Linear): prune_linear_activation_linear,
+                # conv2d -> activation -> conv2d
+                (nn.Conv2d, activation, nn.Conv2d): prune_conv2d_activation_conv2d,
+                # conv2d -> activation -> pool -> conv2d
+                (
+                    nn.Conv2d,
+                    activation,
+                    nn.AvgPool2d,
+                    nn.Conv2d,
+                ): prune_conv2d_activation_pool_conv2d,
+                (
+                    nn.Conv2d,
+                    activation,
+                    F.avg_pool2d,
+                    nn.Conv2d,
+                ): prune_conv2d_activation_pool_conv2d,
+                (
+                    nn.Conv2d,
+                    activation,
+                    nn.MaxPool2d,
+                    nn.Conv2d,
+                ): prune_conv2d_activation_pool_conv2d,
+                (
+                    nn.Conv2d,
+                    activation,
+                    F.max_pool2d,
+                    nn.Conv2d,
+                ): prune_conv2d_activation_pool_conv2d,
+                # conv2d -> pool -> activation -> conv2d
+                (
+                    nn.Conv2d,
+                    nn.AvgPool2d,
+                    activation,
+                    nn.Conv2d,
+                ): prune_conv2d_pool_activation_conv2d,
+                (
+                    nn.Conv2d,
+                    F.avg_pool2d,
+                    activation,
+                    nn.Conv2d,
+                ): prune_conv2d_pool_activation_conv2d,
+                (
+                    nn.Conv2d,
+                    nn.MaxPool2d,
+                    activation,
+                    nn.Conv2d,
+                ): prune_conv2d_pool_activation_conv2d,
+                (
+                    nn.Conv2d,
+                    F.max_pool2d,
+                    activation,
+                    nn.Conv2d,
+                ): prune_conv2d_pool_activation_conv2d,
+                # conv2d -> adaptive pool -> flatten -> linear
+                (
+                    nn.Conv2d,
+                    nn.AdaptiveAvgPool2d,
+                    nn.Flatten,
+                    nn.Linear,
+                ): prune_conv2d_pool_flatten_linear,
+                (
+                    nn.Conv2d,
+                    nn.AdaptiveAvgPool2d,
+                    torch.flatten,
+                    nn.Linear,
+                ): prune_conv2d_pool_flatten_linear,
+                (
+                    nn.Conv2d,
+                    nn.AdaptiveMaxPool2d,
+                    nn.Flatten,
+                    nn.Linear,
+                ): prune_conv2d_pool_flatten_linear,
+                (
+                    nn.Conv2d,
+                    nn.AdaptiveMaxPool2d,
+                    torch.flatten,
+                    nn.Linear,
+                ): prune_conv2d_pool_flatten_linear,
+            }
+        )
+    return patterns
+
+
+class BaseStructuredSparsifier(BaseSparsifier):
+    r"""Base class for structured pruning.
+
+    Abstract methods that need to be implemented:
+        - update_mask: Function to compute a new mask for all keys in the
+            `groups` attribute.
+
+    Args:
+        - defaults [dict]: default configurations will be attached to the
+            configuration. Only the keys that don't exist in the `config` will
+            be updated.
+    """
+
+    def __init__(self, defaults, patterns=None):
+        super().__init__(defaults)
+        if patterns is None:
+            patterns = _get_default_structured_pruning_patterns()
+        self.patterns = patterns
+
+    def make_config_from_model(
+        self,
+        model: nn.Module,
+        SUPPORTED_MODULES: Optional[Set[Type]] = None,
+    ) -> None:
+        if SUPPORTED_MODULES is None:
+            SUPPORTED_MODULES = _get_supported_structured_pruning_modules()
+        super().make_config_from_model(model, SUPPORTED_MODULES=SUPPORTED_MODULES)
+
+    def _prepare(self, *args, **kwargs) -> None:
+        r"""This function will attach the FakeStructuredSparsity parameterizations
+        and BiasHooks at the appropriate points in the model.
+        """
+        for config in self.groups:
+            module = config["module"]
+            tensor_name = config["tensor_name"]
+            parametrization = config.get("parametrization", FakeStructuredSparsity)
+            tensor = getattr(module, tensor_name)
+
+            mask = config.get(
+                "mask",
+                torch.ones(tensor.shape[0], dtype=torch.bool, device=tensor.device),
+            )
+            self.state[config["tensor_fqn"]]["mask"] = mask
+            parametrize.register_parametrization(
+                module, tensor_name, parametrization(mask)
+            )
+
+            # if linear / conv, we add in bias hooks
+            if isinstance(module, (nn.Linear, nn.Conv2d)):
+                prune_bias = config.get("prune_bias", True)
+                if module.bias is not None:
+                    module.register_parameter(
+                        "_bias", nn.Parameter(module.bias.detach())
+                    )
+                    module.bias = None
+                    module.prune_bias = prune_bias
+
+                module.register_forward_hook(
+                    BiasHook(module.parametrizations.weight[0], prune_bias)
+                )
+
+    def prune(self) -> None:
+        r"""
+        This function will FX symbolically trace the model and then find instances of the patterns
+        defined in self.patterns (by default SUPPORTED_STRUCTURED_PRUNING_PATTERNS ).
+
+        For each pattern, it will apply to corresponding conversion function, which will modify the output
+        and input size expected by the modules within the pattern
+        """
+
+        self.traced = symbolic_trace(self.model)
+        modules = dict(self.traced.named_modules())
+
+        # Right now we check for matches simply by iterating across all the patterns
+        # if this is slow we can store patterns in a trie-structure and modify this code for faster lookup
+        for node in self.traced.graph.nodes:
+            for pattern, convert_fn in self.patterns.items():
+                matched = apply_match(modules, pattern, node, [])
+                if matched is None:
+                    continue
+
+                first_module = modules.get(node.target)
+                # check if first module exists and has appropriate parameterization, otherwise skip
+                if (
+                    first_module is not None
+                    and parametrize.is_parametrized(first_module)
+                    and module_contains_param(first_module, FakeStructuredSparsity)
+                ):
+                    convert_block = []
+                    for node in matched:
+                        if node.op == "call_module":
+                            convert_block.append(modules.get(node.target))
+                        elif node.op == "call_function":
+                            convert_block.append(node.target)
+                    convert_fn(*convert_block)
+
+        for module in self.traced.modules():
+            if module_contains_param(module, FakeStructuredSparsity):
+                raise Exception(  # noqa: TRY002
+                    f"Error: {module} still contains FakeStructuredSparsity parametrizations!"
+                )
+
+        self.traced.graph.lint()
+        self.traced.recompile()
+        return self.traced  # type: ignore[return-value]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/lstm_saliency_pruner.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/lstm_saliency_pruner.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8acbc5e458c65a83bd4d05608d46b43cdb94722
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/lstm_saliency_pruner.py
@@ -0,0 +1,53 @@
+# mypy: allow-untyped-defs
+from typing import cast
+
+import torch
+
+from .base_structured_sparsifier import BaseStructuredSparsifier, FakeStructuredSparsity
+
+
+class LSTMSaliencyPruner(BaseStructuredSparsifier):
+    """
+    Prune packed LSTM weights based on saliency.
+    For each layer {k} inside a LSTM, we have two packed weight matrices
+    - weight_ih_l{k}
+    - weight_hh_l{k}
+
+    These tensors pack the weights for the 4 linear layers together for efficiency.
+
+    [W_ii | W_if | W_ig | W_io]
+
+    Pruning this tensor directly will lead to weights being misassigned when unpacked.
+    To ensure that each packed linear layer is pruned the same amount:
+        1. We split the packed weight into the 4 constituent linear parts
+        2. Update the mask for each individual piece using saliency individually
+
+    This applies to both weight_ih_l{k} and weight_hh_l{k}.
+    """
+
+    def update_mask(self, module, tensor_name, **kwargs):
+        weights = getattr(module, tensor_name)
+
+        for p in getattr(module.parametrizations, tensor_name):
+            if isinstance(p, FakeStructuredSparsity):
+                mask = cast(torch.Tensor, p.mask)
+
+                # select weights based on magnitude
+                if weights.dim() <= 1:
+                    raise Exception(  # noqa: TRY002
+                        "Structured pruning can only be applied to a 2+dim weight tensor!"
+                    )
+                # take norm over all but first dim
+                dims = tuple(range(1, weights.dim()))
+                saliency = weights.norm(dim=dims, p=1)
+
+                # handle weights in 4 groups
+                split_size = len(mask) // 4
+                masks = torch.split(mask, split_size)
+                saliencies = torch.split(saliency, split_size)
+
+                for keep_mask, sal in zip(masks, saliencies):
+                    # mask smallest k values to be removed
+                    k = int(len(keep_mask) * kwargs["sparsity_level"])
+                    prune = sal.topk(k, largest=False, sorted=False).indices
+                    keep_mask.data[prune] = False  # modifies underlying p.mask directly
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/match_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/match_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2f80fbee1f182d24eaa1525b7c1d2e1545dd13a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/match_utils.py
@@ -0,0 +1,64 @@
+"""
+Contains utility functions to check if a pattern is in the graph and return the matching nodes
+"""
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+from torch.ao.quantization.utils import MatchAllNode
+from torch.fx import Node
+from torch.nn.utils import parametrize
+
+
+def _match(
+    modules: Dict[str, nn.ModuleDict],
+    node: Node,
+    current: Union[nn.Module, Any],
+) -> bool:
+    r"""
+    checks to see if a single node of a pattern matches
+    """
+    if isinstance(current, type) and issubclass(current, MatchAllNode):
+        return True
+    if not isinstance(node, Node):
+        return False
+    if isinstance(current, type) and issubclass(current, torch.nn.Module):
+        return (
+            node.op == "call_module"
+            and parametrize.type_before_parametrizations(modules[node.target])  # type: ignore[index]
+            == current
+        )
+    elif callable(current):
+        return node.op == "call_function" and node.target is current
+    elif isinstance(current, str):
+        return node.target == current
+    return False
+
+
+def apply_match(
+    modules: Dict[str, nn.ModuleDict],
+    pattern: Union[Tuple[Any], Any],
+    node: Node,
+    matched_node_pattern: List[Node],
+) -> Optional[List[Node]]:
+    r"""
+    This function will return the matched nodes if the pattern matches the node given
+    If there is no match, it will return None
+    """
+    if isinstance(pattern, tuple):
+        if len(pattern) == 1:
+            if _match(modules, node, pattern[0]):
+                return matched_node_pattern + [node]
+
+        first, *rest = pattern
+        if _match(modules, node, first):
+            if rest is None:
+                return matched_node_pattern + [node]
+
+            for user in node.users:
+                return apply_match(
+                    modules, tuple(rest), user, matched_node_pattern + [node]
+                )
+    elif _match(modules, node, pattern):
+        return [node]
+    return None
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/parametrization.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/parametrization.py
new file mode 100644
index 0000000000000000000000000000000000000000..58b3f7651caab971ff524c85e00d6448a77a932d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/parametrization.py
@@ -0,0 +1,59 @@
+# mypy: allow-untyped-defs
+import torch
+from torch import nn
+from torch.nn.utils.parametrize import is_parametrized
+
+
+def module_contains_param(module, parametrization):
+    if is_parametrized(module):
+        # see if any of the module tensors have a parametriztion attached that matches the one passed in
+        return any(
+            any(isinstance(param, parametrization) for param in param_list)
+            for key, param_list in module.parametrizations.items()
+        )
+    return False
+
+
+# Structured Pruning Parameterizations
+class FakeStructuredSparsity(nn.Module):
+    r"""
+    Parametrization for Structured Pruning. Like FakeSparsity, this should be attached to
+    the  'weight' or any other parameter that requires a mask.
+
+    Instead of an element-wise bool mask, this parameterization uses a row-wise bool mask.
+    """
+
+    def __init__(self, mask):
+        super().__init__()
+        self.register_buffer("mask", mask)
+
+    def forward(self, x):
+        assert isinstance(self.mask, torch.Tensor)
+        assert self.mask.shape[0] == x.shape[0]
+        shape = [1] * len(x.shape)
+        shape[0] = -1
+        return self.mask.reshape(shape) * x
+
+    def state_dict(self, *args, **kwargs):
+        # avoid double saving masks
+        return {}
+
+
+class BiasHook:
+    def __init__(self, parametrization, prune_bias):
+        self.param = parametrization
+        self.prune_bias = prune_bias
+
+    def __call__(self, module, input, output):
+        if getattr(module, "_bias", None) is not None:
+            bias = module._bias.data
+            if self.prune_bias:
+                bias[~self.param.mask] = 0
+
+            # reshape bias to broadcast over output dimensions
+            idx = [1] * len(output.shape)
+            idx[1] = -1
+            bias = bias.reshape(idx)
+
+            output += bias
+        return output
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/prune_functions.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/prune_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7c4619f232e0ba863252526e2ff974cd04fb116
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/prune_functions.py
@@ -0,0 +1,478 @@
+# mypy: allow-untyped-defs
+"""
+Collection of conversion functions for linear / conv2d structured pruning
+Also contains utilities for bias propagation
+"""
+from typing import Callable, cast, List, Optional, Tuple
+
+import torch
+from torch import nn, Tensor
+from torch.nn.utils import parametrize
+from torch.nn.utils.parametrize import ParametrizationList
+
+from .parametrization import BiasHook, FakeStructuredSparsity
+
+
+# BIAS PROPAGATION
+def _remove_bias_handles(module: nn.Module) -> None:
+    if hasattr(module, "_forward_hooks"):
+        bias_hooks: List[int] = []
+        for key, hook in module._forward_hooks.items():
+            if isinstance(hook, BiasHook):
+                bias_hooks.append(key)
+
+        for key in bias_hooks:
+            del module._forward_hooks[key]
+
+
+def _get_adjusted_next_layer_bias(
+    next_layer: nn.Module, pruned_biases: Tensor, mask: Tensor
+) -> nn.Parameter:
+    r"""Returns new adjusted bias for the second supported module"""
+    if parametrize.is_parametrized(next_layer):
+        # need to access original weight
+        parametrization_dict = cast(nn.ModuleDict, next_layer.parametrizations)
+        weight_parameterizations = cast(
+            ParametrizationList, parametrization_dict.weight
+        )
+        next_weight = weight_parameterizations.original
+    else:
+        next_weight = cast(Tensor, next_layer.weight)
+
+    scaling_weight = next_weight[:, ~mask]
+    if isinstance(next_layer, nn.Conv2d):  # checking for Conv2d
+        # Propagating first layer pruned biases and calculating the new second layer bias
+        # involves more steps since the Conv2d scaling weight has extra dimensions,
+        # so adding bias involves broadcasting, logically:
+        # for each channel k in range(oC):
+        #     scaled_biases = sum(first_bias[pruned_idx] @ next_weight[k, pruned_idx, :, :].T)
+        #     new_next_bias[k] = old_next_bias[k] + scaled_biases
+        scaling_product = torch.matmul(
+            pruned_biases.reshape(1, -1), torch.transpose(scaling_weight, 1, 2)
+        )
+        sum_range = list(range(len(scaling_product.shape)))[
+            1:
+        ]  # all but the first dimension
+        scaled_biases = torch.sum(scaling_product, sum_range)
+    elif isinstance(next_layer, nn.Linear):  # Linear
+        scaled_biases = torch.matmul(
+            pruned_biases, torch.transpose(scaling_weight, 0, 1)
+        )  # recall b2_new = b1 @ w2.T + b2
+    else:
+        raise NotImplementedError(f"Type {type(next_layer)} not supported yet.")
+
+    if (
+        parametrize.is_parametrized(next_layer)
+        and getattr(next_layer, "_bias", None) is not None
+    ):  # next_layer is parametrized & has original bias ._bias
+        adjusted_bias = nn.Parameter(scaled_biases + next_layer._bias)
+    elif (
+        not parametrize.is_parametrized(next_layer) and next_layer.bias is not None
+    ):  # next_layer not parametrized & has .bias
+        adjusted_bias = nn.Parameter(scaled_biases + next_layer.bias)
+    else:  # next_layer has no bias
+        adjusted_bias = nn.Parameter(scaled_biases)
+    return adjusted_bias
+
+
+def _prune_module_bias(module: nn.Module, mask: Tensor) -> None:
+    r"""Applies mask to given modules bias"""
+    # prune bias along with weights, discard pruned indices of bias
+    original_bias = cast(Tensor, getattr(module, "_bias", module.bias))
+    if original_bias is not None:
+        module.bias = nn.Parameter(original_bias[mask])
+
+    #  remove _bias parameter
+    if hasattr(module, "_bias"):
+        delattr(module, "_bias")
+
+
+def _propagate_module_bias(module: nn.Module, mask: Tensor) -> Optional[Tensor]:
+    r"""
+    In the case that we need to propagate biases, this function will return the biases we need
+    """
+    # set current module bias
+    if module.bias is not None:
+        module.bias = nn.Parameter(cast(Tensor, module.bias)[mask])
+    elif getattr(module, "_bias", None) is not None:
+        module.bias = nn.Parameter(cast(Tensor, module._bias)[mask])
+
+    # get pruned biases to propagate to subsequent layer
+    if getattr(module, "_bias", None) is not None:
+        pruned_biases = cast(Tensor, module._bias)[~mask]
+    else:
+        pruned_biases = None
+
+    if hasattr(module, "_bias"):
+        delattr(module, "_bias")
+
+    return pruned_biases
+
+
+# LINEAR
+def _prune_linear_helper(linear: nn.Linear) -> Tensor:
+    # expects linear to be a parameterized linear module
+    parametrization_dict = cast(nn.ModuleDict, linear.parametrizations)
+    weight_parameterizations = cast(ParametrizationList, parametrization_dict.weight)
+    for p in weight_parameterizations:
+        if isinstance(p, FakeStructuredSparsity):
+            mask = cast(Tensor, p.mask)
+
+    with torch.no_grad():
+        parametrize.remove_parametrizations(linear, "weight", leave_parametrized=True)
+        linear.weight = nn.Parameter(linear.weight[mask])  # type: ignore[possibly-undefined]
+    linear.out_features = linear.weight.shape[0]
+    _remove_bias_handles(linear)
+
+    return mask
+
+
+def prune_linear(linear: nn.Linear) -> None:
+    mask = _prune_linear_helper(linear)
+    if getattr(linear, "prune_bias", False):
+        _prune_module_bias(linear, mask)
+
+
+def prune_linear_linear(linear1: nn.Linear, linear2: nn.Linear) -> None:
+    prune_linear_activation_linear(linear1, None, linear2)
+
+
+def prune_linear_activation_linear(
+    linear1: nn.Linear,
+    activation: Optional[Callable[[Tensor], Tensor]],
+    linear2: nn.Linear,
+):
+    mask = _prune_linear_helper(linear1)
+    if getattr(linear1, "prune_bias", False):
+        _prune_module_bias(linear1, mask)
+    else:
+        pruned_biases = _propagate_module_bias(linear1, mask)
+        if pruned_biases is not None:
+            if activation:
+                pruned_biases = activation(pruned_biases)
+            linear2.bias = _get_adjusted_next_layer_bias(linear2, pruned_biases, mask)
+
+    with torch.no_grad():
+        if parametrize.is_parametrized(linear2):
+            parametrization_dict = cast(nn.ModuleDict, linear2.parametrizations)
+            weight_parameterizations = cast(
+                ParametrizationList, parametrization_dict.weight
+            )
+
+            weight_parameterizations.original = nn.Parameter(
+                weight_parameterizations.original[:, mask]
+            )
+            linear2.in_features = weight_parameterizations.original.shape[1]
+        else:
+            linear2.weight = nn.Parameter(linear2.weight[:, mask])
+            linear2.in_features = linear2.weight.shape[1]
+
+
+# CONV2D
+def _prune_conv2d_helper(conv2d: nn.Conv2d) -> Tensor:
+    parametrization_dict = cast(nn.ModuleDict, conv2d.parametrizations)
+    weight_parameterizations = cast(ParametrizationList, parametrization_dict.weight)
+    for p in weight_parameterizations:
+        if isinstance(p, FakeStructuredSparsity):
+            mask = cast(Tensor, p.mask)
+
+    with torch.no_grad():
+        parametrize.remove_parametrizations(conv2d, "weight", leave_parametrized=True)
+        conv2d.weight = nn.Parameter(conv2d.weight[mask])  # type: ignore[possibly-undefined]
+    conv2d.out_channels = conv2d.weight.shape[0]
+
+    _remove_bias_handles(conv2d)
+    return mask
+
+
+def prune_conv2d_padded(conv2d_1: nn.Conv2d) -> None:
+    parametrization_dict = cast(nn.ModuleDict, conv2d_1.parametrizations)
+    weight_parameterizations = cast(ParametrizationList, parametrization_dict.weight)
+    for p in weight_parameterizations:
+        if isinstance(p, FakeStructuredSparsity):
+            mask = cast(Tensor, p.mask)
+
+    with torch.no_grad():
+        parametrize.remove_parametrizations(conv2d_1, "weight", leave_parametrized=True)
+
+    if getattr(conv2d_1, "_bias", None) is not None:
+        if (
+            conv2d_1.bias is not None
+        ):  # conv2d_1 has original bias and bias propagated from previous layer
+            new_bias = torch.zeros(conv2d_1.bias.shape)
+            new_bias[mask] = conv2d_1.bias[mask]  # type: ignore[possibly-undefined]
+            # adjusted bias that to keep in conv2d_1
+            new_bias[~mask] = cast(Tensor, conv2d_1._bias)[~mask]
+            # pruned biases that are kept instead of propagated
+            conv2d_1.bias = nn.Parameter(new_bias)
+        else:  # conv2d_1 has only original bias
+            conv2d_1.bias = nn.Parameter(cast(Tensor, conv2d_1._bias))
+    else:
+        # no original bias, only propagated bias
+        if (
+            conv2d_1.bias is not None
+        ):  # conv2d_1 has bias propagated from previous layer
+            conv2d_1.bias.data[~mask] = 0  # type: ignore[possibly-undefined]
+
+    if hasattr(conv2d_1, "_bias"):
+        delattr(conv2d_1, "_bias")
+
+
+def prune_conv2d(conv2d: nn.Conv2d) -> None:
+    mask = _prune_conv2d_helper(conv2d)
+    if getattr(conv2d, "prune_bias", False):
+        _prune_module_bias(conv2d, mask)
+
+
+def prune_conv2d_conv2d(conv2d_1: nn.Conv2d, conv2d_2: nn.Conv2d) -> None:
+    prune_conv2d_activation_conv2d(conv2d_1, None, conv2d_2)
+
+
+def prune_conv2d_activation_conv2d(
+    conv2d_1: nn.Conv2d,
+    activation: Optional[Callable[[Tensor], Tensor]],
+    conv2d_2: nn.Conv2d,
+):
+    r"""
+    Fusion Pattern for conv2d -> some activation module / function -> conv2d layers
+    """
+    parametrization_dict = cast(nn.ModuleDict, conv2d_1.parametrizations)
+    weight_parameterizations = cast(ParametrizationList, parametrization_dict.weight)
+    for p in weight_parameterizations:
+        if isinstance(p, FakeStructuredSparsity):
+            mask = cast(Tensor, p.mask)
+
+    prune_bias = getattr(conv2d_1, "prune_bias", False)
+    if (
+        hasattr(conv2d_2, "padding")
+        and cast(Tuple[int], conv2d_2.padding) > (0, 0)
+        and (conv2d_1.bias is not None or getattr(conv2d_1, "_bias", None) is not None)
+    ):
+        prune_conv2d_padded(conv2d_1)
+    else:
+        mask = _prune_conv2d_helper(conv2d_1)
+        if prune_bias:
+            _prune_module_bias(conv2d_1, mask)
+        else:
+            pruned_biases = _propagate_module_bias(conv2d_1, mask)
+            if pruned_biases is not None:
+                if activation:
+                    pruned_biases = activation(pruned_biases)
+                conv2d_2.bias = _get_adjusted_next_layer_bias(
+                    conv2d_2, pruned_biases, mask
+                )
+
+        if (
+            not (
+                hasattr(conv2d_2, "padding")
+                and cast(Tuple[int], conv2d_2.padding) > (0, 0)
+            )
+            or conv2d_1.bias is None
+        ):
+            with torch.no_grad():
+                if parametrize.is_parametrized(conv2d_2):
+                    parametrization_dict = cast(
+                        nn.ModuleDict, conv2d_2.parametrizations
+                    )
+                    weight_parameterizations = cast(
+                        ParametrizationList, parametrization_dict.weight
+                    )
+                    weight_parameterizations.original = nn.Parameter(
+                        weight_parameterizations.original[:, mask]
+                    )
+                    conv2d_2.in_channels = weight_parameterizations.original.shape[1]
+                else:
+                    conv2d_2.weight = nn.Parameter(conv2d_2.weight[:, mask])
+                    conv2d_2.in_channels = conv2d_2.weight.shape[1]
+
+
+def prune_conv2d_pool_activation_conv2d(
+    c1: nn.Conv2d,
+    pool: nn.Module,
+    activation: Optional[Callable[[Tensor], Tensor]],
+    c2: nn.Conv2d,
+) -> None:
+    prune_conv2d_activation_conv2d(c1, activation, c2)
+
+
+def prune_conv2d_activation_pool_conv2d(
+    c1: nn.Conv2d,
+    activation: Optional[Callable[[Tensor], Tensor]],
+    pool: nn.Module,
+    c2: nn.Conv2d,
+) -> None:
+    prune_conv2d_activation_conv2d(c1, activation, c2)
+
+
+def prune_conv2d_pool_flatten_linear(
+    conv2d: nn.Conv2d,
+    pool: nn.Module,
+    flatten: Optional[Callable[[Tensor], Tensor]],
+    linear: nn.Linear,
+) -> None:
+    mask = _prune_conv2d_helper(conv2d)
+
+    # We map the pruned indices of the Conv2d output to the flattened indices of the Linear following the Flatten layer.
+    # we determine the flattening scale (h * w), and readjust `first_pruned_indices`
+    # (each idx maps to range idx * h * w to (idx+1) * h * w), `first_valid_indices`,
+    # and `pruned_biases` (repeat each bias by h * w).
+    if parametrize.is_parametrized(linear):
+        parametrization_dict = cast(nn.ModuleDict, linear.parametrizations)
+        weight_parameterizations = cast(
+            ParametrizationList, parametrization_dict.weight
+        )
+        linear_ic = weight_parameterizations.original.shape[1]
+    else:
+        linear_ic = linear.weight.shape[1]
+
+    conv2d_oc = len(mask)
+    assert (
+        linear_ic % conv2d_oc == 0
+    ), f"Flattening from dimensions {conv2d_oc} to {linear_ic} not supported"
+
+    flatten_scale = linear_ic // conv2d_oc
+    flattened_mask = torch.tensor(
+        [[val] * flatten_scale for val in mask], dtype=torch.bool, device=mask.device
+    ).flatten()
+
+    if getattr(conv2d, "prune_bias", False):
+        _prune_module_bias(conv2d, mask)
+    else:
+        pruned_biases = cast(Tensor, _propagate_module_bias(conv2d, mask))
+        flattened_pruned_biases = torch.tensor(
+            [[bias] * flatten_scale for bias in pruned_biases], device=mask.device
+        ).flatten()
+        linear.bias = _get_adjusted_next_layer_bias(
+            linear, flattened_pruned_biases, flattened_mask
+        )
+
+    with torch.no_grad():
+        if parametrize.is_parametrized(linear):
+            parametrization_dict = cast(nn.ModuleDict, linear.parametrizations)
+            weight_parameterizations = cast(
+                ParametrizationList, parametrization_dict.weight
+            )
+            weight_parameterizations.original = nn.Parameter(
+                weight_parameterizations.original[:, flattened_mask]
+            )
+            linear.in_features = weight_parameterizations.original.shape[1]
+        else:
+            linear.weight = nn.Parameter(linear.weight[:, flattened_mask])
+            linear.in_features = linear.weight.shape[1]
+
+
+def prune_lstm_output_linear(
+    lstm: nn.LSTM, getitem: Callable, linear: nn.Linear
+) -> None:
+    prune_lstm_output_layernorm_linear(lstm, getitem, None, linear)
+
+
+def prune_lstm_output_layernorm_linear(
+    lstm: nn.LSTM,
+    getitem: Callable,
+    layernorm: Optional[nn.LayerNorm],
+    linear: nn.Linear,
+) -> None:
+    for i in range(lstm.num_layers):
+        if parametrize.is_parametrized(lstm, f"weight_ih_l{i}"):
+            parametrization_dict = cast(nn.ModuleDict, lstm.parametrizations)
+            weight_parameterizations = cast(
+                ParametrizationList, parametrization_dict[f"weight_ih_l{i}"]
+            )
+            mask = weight_parameterizations[0].mask
+
+            with torch.no_grad():
+                parametrize.remove_parametrizations(
+                    lstm, f"weight_ih_l{i}", leave_parametrized=True
+                )
+                setattr(
+                    lstm,
+                    f"weight_ih_l{i}",
+                    nn.Parameter(getattr(lstm, f"weight_ih_l{i}")[mask]),
+                )
+                setattr(
+                    lstm,
+                    f"bias_ih_l{i}",
+                    nn.Parameter(getattr(lstm, f"bias_ih_l{i}")[mask]),
+                )
+
+        if parametrize.is_parametrized(lstm, f"weight_hh_l{i}"):
+            parametrization_dict = cast(nn.ModuleDict, lstm.parametrizations)
+            weight_parameterizations = cast(
+                ParametrizationList, parametrization_dict[f"weight_hh_l{i}"]
+            )
+            mask = weight_parameterizations[0].mask
+
+            with torch.no_grad():
+                parametrize.remove_parametrizations(
+                    lstm, f"weight_hh_l{i}", leave_parametrized=True
+                )
+                # splitting out hidden-hidden masks
+                W_hi, W_hf, W_hg, W_ho = torch.split(
+                    getattr(lstm, f"weight_hh_l{i}"), lstm.hidden_size
+                )
+                M_hi, M_hf, M_hg, M_ho = torch.split(mask, lstm.hidden_size)
+
+                # resize each individual weight separately
+                W_hi = W_hi[M_hi][:, M_hi]
+                W_hf = W_hf[M_hf][:, M_hf]
+                W_hg = W_hg[M_hg][:, M_hg]
+                W_ho = W_ho[M_ho][:, M_ho]
+
+                # concat, use this as new weight
+                new_weight = torch.cat((W_hi, W_hf, W_hg, W_ho))
+                setattr(lstm, f"weight_hh_l{i}", nn.Parameter(new_weight))
+                setattr(
+                    lstm,
+                    f"bias_hh_l{i}",
+                    nn.Parameter(getattr(lstm, f"bias_hh_l{i}")[mask]),
+                )
+
+            # If this is the final layer, then we need to prune linear layer columns
+            if i + 1 == lstm.num_layers:
+                lstm.hidden_size = int(M_hi.sum())
+                with torch.no_grad():
+                    if parametrize.is_parametrized(linear):
+                        parametrization_dict = cast(
+                            nn.ModuleDict, linear.parametrizations
+                        )
+                        weight_parameterizations = cast(
+                            ParametrizationList, parametrization_dict.weight
+                        )
+
+                        weight_parameterizations.original = nn.Parameter(
+                            weight_parameterizations.original[:, M_ho]
+                        )
+                        linear.in_features = weight_parameterizations.original.shape[1]
+                    else:
+                        linear.weight = nn.Parameter(linear.weight[:, M_ho])
+                        linear.in_features = linear.weight.shape[1]
+
+                    # if layernorm module, prune weight and bias
+                    if layernorm is not None:
+                        layernorm.normalized_shape = (linear.in_features,)
+                        layernorm.weight = nn.Parameter(layernorm.weight[M_ho])
+                        layernorm.bias = nn.Parameter(layernorm.bias[M_ho])
+
+            # otherwise need to prune the columns of the input of the next LSTM layer
+            else:
+                with torch.no_grad():
+                    if parametrize.is_parametrized(lstm, f"weight_ih_l{i + 1}"):
+                        parametrization_dict = cast(
+                            nn.ModuleDict, lstm.parametrizations
+                        )
+                        weight_parameterizations = cast(
+                            ParametrizationList,
+                            getattr(parametrization_dict, f"weight_ih_l{i + 1}"),
+                        )
+
+                        weight_parameterizations.original = nn.Parameter(
+                            weight_parameterizations.original[:, M_ho]
+                        )
+                    else:
+                        next_layer_weight = getattr(lstm, f"weight_ih_l{i + 1}")
+                        setattr(
+                            lstm,
+                            f"weight_ih_l{i + 1}",
+                            nn.Parameter(next_layer_weight[:, M_ho]),
+                        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/saliency_pruner.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/saliency_pruner.py
new file mode 100644
index 0000000000000000000000000000000000000000..a295b4622cc2d64714d4dab969a8923a6014a55d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_experimental/pruner/saliency_pruner.py
@@ -0,0 +1,32 @@
+# mypy: allow-untyped-defs
+from .base_structured_sparsifier import BaseStructuredSparsifier
+
+
+class SaliencyPruner(BaseStructuredSparsifier):
+    """
+    Prune rows based on the saliency (L1 norm) of each row.
+
+    This pruner works on N-Dimensional weight tensors.
+    For each row, we will calculate the saliency, whic is the sum the L1 norm of all weights in that row.
+    We expect that the resulting saliency vector has the same shape as our mask.
+    We then pick elements to remove until we reach the target sparsity_level.
+    """
+
+    def update_mask(self, module, tensor_name, **kwargs):
+        # tensor_name will give you the FQN, all other entries in sparse config is present in kwargs
+        weights = getattr(module, tensor_name)
+        mask = getattr(module.parametrizations, tensor_name)[0].mask
+
+        # use negative weights so we can use topk (we prune out the smallest)
+        if weights.dim() <= 1:
+            raise Exception(  # noqa: TRY002
+                "Structured pruning can only be applied to a 2+dim weight tensor!"
+            )
+        saliency = -weights.norm(dim=tuple(range(1, weights.dim())), p=1)
+        assert saliency.shape == mask.shape
+
+        num_to_pick = int(len(mask) * kwargs["sparsity_level"])
+        prune = saliency.topk(num_to_pick).indices
+
+        # Set the mask to be false for the rows we want to prune
+        mask.data[prune] = False
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_mappings.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_mappings.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fc2c4f10aef5585072f36116282a2048965197a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/_mappings.py
@@ -0,0 +1,23 @@
+# mypy: allow-untyped-defs
+__all__ = [
+    "get_static_sparse_quantized_mapping",
+    "get_dynamic_sparse_quantized_mapping",
+]
+
+
+def get_static_sparse_quantized_mapping():
+    import torch.ao.nn.sparse
+
+    _static_sparse_quantized_mapping = {
+        torch.nn.Linear: torch.ao.nn.sparse.quantized.Linear,
+    }
+    return _static_sparse_quantized_mapping
+
+
+def get_dynamic_sparse_quantized_mapping():
+    import torch.ao.nn.sparse
+
+    _dynamic_sparse_quantized_mapping = {
+        torch.nn.Linear: torch.ao.nn.sparse.quantized.dynamic.Linear,
+    }
+    return _dynamic_sparse_quantized_mapping
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/scheduler/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/scheduler/__init__.py
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/scheduler/base_scheduler.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/scheduler/base_scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..f602028d475ce7b60c64bb953e3794a927283c75
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/scheduler/base_scheduler.py
@@ -0,0 +1,170 @@
+# mypy: allow-untyped-defs
+
+import warnings
+import weakref
+from functools import wraps
+
+from torch.ao.pruning.sparsifier.base_sparsifier import BaseSparsifier
+
+
+__all__ = ["BaseScheduler"]
+
+
+class BaseScheduler:
+    def __init__(self, sparsifier, last_epoch=-1, verbose=False):
+        # Attach sparsifier
+        if not isinstance(sparsifier, BaseSparsifier):
+            raise TypeError(
+                f"{type(sparsifier).__name__} is not an instance of torch.ao.pruning.BaseSparsifier"
+            )
+        self.sparsifier = sparsifier
+
+        # Initialize epoch and base sparsity levels
+
+        self.base_sl = [group["sparsity_level"] for group in sparsifier.groups]
+        self.last_epoch = last_epoch
+
+        # Following https://github.com/pytorch/pytorch/issues/20124
+        # We would like to ensure that `scheduler.step()` is called after
+        # `sparsifier.step()`
+        def with_counter(method):
+            if getattr(method, "_with_counter", False):
+                # `sparsifier.step()` has already been replaced, return.
+                return method
+
+            # Keep a weak reference to the sparsifier instance to prevent
+            # cyclic references.
+            instance_ref = weakref.ref(method.__self__)
+            # Get the unbound method for the same purpose.
+            func = method.__func__
+            cls = instance_ref().__class__
+            del method
+
+            @wraps(func)
+            def wrapper(*args, **kwargs):
+                instance = instance_ref()
+                instance._step_count += 1  # type: ignore[union-attr]
+                wrapped = func.__get__(instance, cls)
+                return wrapped(*args, **kwargs)
+
+            # Note that the returned function here is no longer a bound method,
+            # so attributes like `__func__` and `__self__` no longer exist.
+            wrapper._with_counter = True  # type: ignore[attr-defined]
+            return wrapper
+
+        self.sparsifier.step = with_counter(self.sparsifier.step)  # type: ignore[assignment]
+        self.sparsifier._step_count = 0  # type: ignore[attr-defined]
+        self._step_count: int = 0
+        self.verbose = verbose
+
+        # Housekeeping
+        self._get_sl_called_within_step: bool = False
+
+        self.step()
+
+    def state_dict(self):
+        """Returns the state of the scheduler as a :class:`dict`.
+
+        It contains an entry for every variable in self.__dict__ which
+        is not the sparsifier.
+        """
+        return {
+            key: value for key, value in self.__dict__.items() if key != "sparsifier"
+        }
+
+    def load_state_dict(self, state_dict):
+        """Loads the schedulers state.
+
+        Args:
+            state_dict (dict): scheduler state. Should be an object returned
+                from a call to :meth:`state_dict`.
+        """
+        self.__dict__.update(state_dict)
+
+    def get_last_sl(self):
+        """Return last computed sparsity level by current scheduler."""
+        return self._last_sl
+
+    def get_sl(self):
+        # Compute sparsity level using chainable form of the scheduler
+        # Note: This method is not intended to be called directly, and is only
+        #       used by the ".step" method. Use .get_last_sl() instead.
+        if not self._get_sl_called_within_step:
+            warnings.warn(
+                "To get the last sparsity level computed by the scheduler, "
+                "please use `get_last_sl()`."
+            )
+        raise NotImplementedError
+
+    def print_sl(self, is_verbose, group, sl, epoch=None):
+        """Display the current sparsity level."""
+        if is_verbose:
+            if epoch is None:
+                print(f"Adjusting sparsity level of group {group} to {sl:.4e}.")
+            else:
+                print(
+                    f"Epoch {epoch:5d}: adjusting sparsity level of group {group} to {sl:.4e}."
+                )
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + " ("
+        format_string += "\n"
+        format_string += f"Sparsifier {self.sparsifier}\n"
+        format_string += f"    base_sl: {self.base_sl}\n"
+        format_string += ")"
+        return format_string
+
+    def step(self, epoch=None):
+        # Raise warning if trying to call scheduler step before the sparsifier.
+        # https://github.com/pytorch/pytorch/issues/20124
+        if self._step_count == 1:
+            if not hasattr(self.sparsifier.step, "_with_counter"):
+                warnings.warn(
+                    "Seems like `sparsifier.step()` has been overridden after sparsity scheduler "
+                    "initialization. Please, make sure to call `sparsifier.step()` before "
+                    "`scheduler.step()`.",
+                    UserWarning,
+                )
+
+            # Just check if there were two first scheduler.step() calls before sparsifier.step()
+            elif self.sparsifier._step_count < 1:  # type: ignore[attr-defined]
+                warnings.warn(
+                    "Detected call of `scheduler.step()` before `sparsifier.step()`. "
+                    "You have to make sure you run the sparsifier.step() BEFORE any "
+                    "calls to the scheduler.step().",
+                    UserWarning,
+                )
+        self._step_count += 1
+
+        class _enable_get_sl_call:
+            def __init__(self, o):
+                self.o = o
+
+            def __enter__(self):
+                self.o._get_sl_called_within_step = True
+                return self
+
+            def __exit__(self, type, value, traceback):
+                self.o._get_sl_called_within_step = False
+
+        with _enable_get_sl_call(self):
+            self.last_epoch += 1
+            values = self.get_sl()
+
+        for i, data in enumerate(zip(self.sparsifier.groups, values)):
+            param_group, sl = data
+            param_group["sparsity_level"] = sl
+            self.print_sl(self.verbose, i, sl, epoch)
+
+        self._last_sl = [group["sparsity_level"] for group in self.sparsifier.groups]
+        self.sparsifier.enable_mask_update = True
+
+    def _make_sure_a_list(self, var):
+        r"""Utility that extends it to the same length as the .groups, ensuring it is a list"""
+        n = len(self.sparsifier.groups)
+        if not isinstance(var, (list, tuple)):
+            return [var] * n
+        else:
+            if len(var) != n:
+                raise ValueError(f"Expected variable of length {n}, but got {len(var)}")
+            return list(var)  # We want the result to be in a list, not tuple
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/scheduler/cubic_scheduler.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/scheduler/cubic_scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..45985a8bbc524b3b1929b439ee100fef8ea23a1c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/scheduler/cubic_scheduler.py
@@ -0,0 +1,113 @@
+# mypy: allow-untyped-defs
+import warnings
+
+from .base_scheduler import BaseScheduler
+
+
+__all__ = ["CubicSL"]
+
+
+def _clamp(x, lo, hi):
+    return max(lo, min(hi, x))
+
+
+class CubicSL(BaseScheduler):
+    r"""Sets the sparsity level of each parameter group to the final sl
+    plus a given exponential function.
+
+    .. math::
+
+        s_i = s_f + (s_0 - s_f) \cdot \left( 1 - \frac{t - t_0}{n\Delta t} \right)^3
+
+    where :math:`s_i` is the sparsity at epoch :math:`t`, :math;`s_f` is the final
+    sparsity level, :math:`f(i)` is the function to be applied to the current epoch
+    :math:`t`, initial epoch :math:`t_0`, and final epoch :math:`t_f`.
+    :math:`\Delta t` is used to control how often the update of the sparsity level
+    happens. By default,
+
+    Args:
+        sparsifier (BaseSparsifier): Wrapped sparsifier.
+        init_sl (int, list): Initial level of sparsity
+        init_t (int, list): Initial step, when pruning starts
+        delta_t (int, list): Pruning frequency
+        total_t (int, list): Total number of pruning steps
+        initially_zero (bool, list): If True, sets the level of sparsity to 0
+            before init_t (:math:`t_0`). Otherwise, the sparsity level before
+            init_t (:math:`t_0`) is set to init_sl(:math:`s_0`)
+        last_epoch (int): The index of last epoch. Default: -1.
+        verbose (bool): If ``True``, prints a message to stdout for
+            each update. Default: ``False``.
+    """
+
+    def __init__(
+        self,
+        sparsifier,
+        init_sl=0.0,
+        init_t=0,
+        delta_t=10,
+        total_t=100,
+        initially_zero=False,
+        last_epoch=-1,
+        verbose=False,
+    ):
+        self.sparsifier = sparsifier
+
+        self.init_sl = self._make_sure_a_list(init_sl)
+        self.init_t = self._make_sure_a_list(init_t)
+        self.delta_t = self._make_sure_a_list(delta_t)
+        self.total_t = self._make_sure_a_list(total_t)
+
+        self.initially_zero = self._make_sure_a_list(initially_zero)
+
+        super().__init__(sparsifier, last_epoch, verbose)
+
+    @staticmethod
+    def sparsity_compute_fn(s_0, s_f, t, t_0, dt, n, initially_zero=False):
+        r""" "Computes the current level of sparsity.
+
+        Based on https://arxiv.org/pdf/1710.01878.pdf
+
+        Args:
+            s_0: Initial level of sparsity, :math:`s_i`
+            s_f: Target level of sparsity, :math:`s_f`
+            t: Current step, :math:`t`
+            t_0: Initial step, :math:`t_0`
+            dt: Pruning frequency, :math:`\Delta T`
+            n: Pruning steps, :math:`n`
+            initially_zero: Sets the level of sparsity to 0 before t_0.
+                If False, sets to s_0
+
+        Returns:
+            The sparsity level :math:`s_t` at the current step :math:`t`
+        """
+        if initially_zero and t < t_0:
+            return 0
+        s_t = s_f + (s_0 - s_f) * (1.0 - (t - t_0) / (dt * n)) ** 3
+        s_t = _clamp(s_t, s_0, s_f)
+        return s_t
+
+    def get_sl(self):
+        if not self._get_sl_called_within_step:
+            warnings.warn(
+                "To get the last sparsity level computed by the scheduler, "
+                "please use `get_last_sl()`."
+            )
+        return [
+            self.sparsity_compute_fn(
+                s_0=initial_sparsity,
+                s_f=final_sparsity,
+                t=self.last_epoch,
+                t_0=initial_epoch,
+                dt=delta_epoch,
+                n=interval_epochs,
+                initially_zero=initially_zero,
+            )
+            for initial_sparsity, final_sparsity, initial_epoch, delta_epoch, interval_epochs, initially_zero in zip(
+                self.init_sl,
+                self.base_sl,
+                self.init_t,
+                self.delta_t,
+                self.total_t,
+                self.initially_zero,
+            )
+        ]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..07e95b5248119ad96f6d3b9bd35ddd35f906a57a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py
@@ -0,0 +1,55 @@
+# mypy: allow-untyped-defs
+import warnings
+
+from .base_scheduler import BaseScheduler
+
+
+__all__ = ["LambdaSL"]
+
+
+class LambdaSL(BaseScheduler):
+    """Sets the sparsity level of each parameter group to the final sl
+    times a given function. When last_epoch=-1, sets initial sl as zero.
+    Args:
+        sparsifier (BaseSparsifier): Wrapped sparsifier.
+        sl_lambda (function or list): A function which computes a multiplicative
+            factor given an integer parameter epoch, or a list of such
+            functions, one for each group in sparsifier.param_groups.
+        last_epoch (int): The index of last epoch. Default: -1.
+        verbose (bool): If ``True``, prints a message to stdout for
+            each update. Default: ``False``.
+    Example:
+        >>> # Assuming sparsifier has two groups.
+        >>> lambda1 = lambda epoch: epoch // 30
+        >>> lambda2 = lambda epoch: 0.95 ** epoch
+        >>> # xdoctest: +SKIP
+        >>> scheduler = LambdaSL(sparsifier, sl_lambda=[lambda1, lambda2])
+        >>> for epoch in range(100):
+        >>>     train(...)
+        >>>     validate(...)
+        >>>     scheduler.step()
+    """
+
+    def __init__(self, sparsifier, sl_lambda, last_epoch=-1, verbose=False):
+        self.sparsifier = sparsifier
+
+        if not isinstance(sl_lambda, list) and not isinstance(sl_lambda, tuple):
+            self.sl_lambdas = [sl_lambda] * len(sparsifier.groups)
+        else:
+            if len(sl_lambda) != len(sparsifier.groups):
+                raise ValueError(
+                    f"Expected {len(sparsifier.groups)} lr_lambdas, but got {len(sl_lambda)}"
+                )
+            self.sl_lambdas = list(sl_lambda)
+        super().__init__(sparsifier, last_epoch, verbose)
+
+    def get_sl(self):
+        if not self._get_sl_called_within_step:
+            warnings.warn(
+                "To get the last sparsity level computed by the scheduler, "
+                "please use `get_last_sl()`."
+            )
+        return [
+            base_sl * lmbda(self.last_epoch)
+            for lmbda, base_sl in zip(self.sl_lambdas, self.base_sl)
+        ]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/sparsifier/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/sparsifier/__init__.py
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..3bae84d02f8fc95341864ccbbf3acd79ed3e4605
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py
@@ -0,0 +1,351 @@
+# mypy: allow-untyped-defs
+import abc
+import copy
+from collections import defaultdict
+from typing import Any, Dict, List, Optional, Set, Tuple, Type
+
+import torch
+from torch import nn
+from torch.nn.utils import parametrize
+from torch.nn.utils.parametrize import type_before_parametrizations
+
+from .utils import (
+    FakeSparsity,
+    get_arg_info_from_tensor_fqn,
+    module_contains_param,
+    module_to_fqn,
+    swap_module,
+)
+
+
+__all__ = ["BaseSparsifier"]
+
+SUPPORTED_MODULES = {nn.Linear}
+
+KEYS_NOT_IN_STATE_DICT = ["module", "module_fqn", "tensor_name"]
+
+
+# TODO update desc with new config args
+class BaseSparsifier(abc.ABC):
+    r"""Base class for all sparsifiers.
+
+    Abstract methods that need to be implemented:
+
+    - update_mask: Function to compute a new mask for all keys in the
+        `groups`.
+
+    Args:
+        - model [nn.Module]: model to configure. The model itself is not saved
+            but used for the state_dict saving / loading.
+        - config [list]: configuration elements should be a dict map that includes
+            `tensor_fqn` of tensors to sparsify
+        - defaults [dict]: default configurations will be attached to the
+            configuration. Only the keys that don't exist in the `config` will
+            be updated.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("Can't instantiate abstract class BaseSparsifier with abstract method update_mask")
+        >>> config = [{'tensor_fqn': 'layer1.weight', 'tensor_fqn': 'linear2.weight2', 'sparsity_level': 0.5}]
+        >>> defaults = {'sparsity_level': 0.7}
+        >>> # model.layer1.weight will have `sparsity_level` = 0.7 (getting default)
+        >>> sparsifier = BaseSparsifier(config, defaults)
+    """
+
+    def __init__(self, defaults: Optional[Dict[str, Any]] = None):
+        super().__init__()
+        self.defaults: Dict[str, Any] = defaults or {}
+
+        self.state: Dict[str, Dict] = defaultdict(dict)
+        self.groups: List[Dict[str, Any]] = []
+        self.enable_mask_update = True
+
+    def __getstate__(self) -> Dict[str, Any]:
+        return {
+            "defaults": self.defaults,
+            "state": self.state,
+            "groups": self.groups,
+        }
+
+    def __setstate__(self, state: Dict[str, Dict[str, Any]]) -> None:
+        self.__dict__.update(state)
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + " ("
+        for i, sparse_args in enumerate(self.groups):
+            module = sparse_args["module"]
+            format_string += "\n"
+            format_string += f"\tGroup {i}\n"
+            format_string += f"\t    module: {module}\n"
+            for key in sorted(sparse_args.keys()):
+                if key == "module":
+                    continue
+                format_string += f"\t    {key}: {sparse_args[key]}\n"
+        format_string += ")"
+        return format_string
+
+    def state_dict(self) -> Dict[str, Any]:
+        r"""Returns the state of the optimizer as a :class:`dict`.
+
+        It contains:
+        * state - current state of the sparsification.
+        * groups - a list containing all sparsity configuration groups
+            with the key 'tensor_fqn' specifying the path to the sparsified tensor within a model
+
+        TODO: Need a clean way of loading the state of the "prepared" module
+        """
+
+        groups: List[Dict[str, Any]] = [
+            dict(
+                filter(
+                    lambda key_value: key_value[0] not in KEYS_NOT_IN_STATE_DICT,
+                    mg.items(),
+                )
+            )
+            for mg in self.groups
+        ]
+
+        return {
+            "state": self.state,
+            "groups": groups,
+        }
+
+    def load_state_dict(self, state_dict: Dict[str, Any], strict: bool = True):
+        groups = copy.deepcopy(state_dict["groups"])
+        states = state_dict["state"]
+        for tensor_fqn, s in states.items():
+            arg_info = get_arg_info_from_tensor_fqn(self.model, tensor_fqn)
+            module = arg_info["module"]
+            tensor_name = arg_info["tensor_name"]
+            if strict and module is None:
+                raise RuntimeError(f"Error loading {tensor_fqn} into the model")
+
+            found = False
+            for p in module.parametrizations[tensor_name]:
+                if isinstance(p, FakeSparsity):
+                    found = True
+                    break
+            if not found:
+                p = FakeSparsity(torch.ones(getattr(module, tensor_name).shape))
+                parametrize.register_parametrization(module, tensor_name, p)
+            if s.get("mask", None) is not None:
+                mask = s.pop("mask")
+                p.mask = mask
+
+            for mg in groups:
+                if mg["tensor_fqn"] == tensor_fqn:
+                    mg.update(arg_info)
+        self.__setstate__({"state": states, "groups": groups})
+
+    def make_config_from_model(
+        self,
+        model: nn.Module,
+        SUPPORTED_MODULES: Set[Type] = SUPPORTED_MODULES,
+    ) -> None:
+        self.config = []
+        stack = [model]
+        while stack:
+            module = stack.pop()
+            for name, child in module.named_children():
+                if type(child) in SUPPORTED_MODULES:
+                    module_fqn = module_to_fqn(model, child)
+                    assert isinstance(module_fqn, str)  # for mypy
+                    self.config.append({"tensor_fqn": module_fqn + ".weight"})
+                else:
+                    stack.append(child)
+
+    def prepare(self, model, config):
+        r"""Prepares a model, by adding the parametrizations.
+
+        Note::
+
+            The model is modified inplace. If you need to preserve the original
+            model, use copy.deepcopy.
+        """
+        self.model = model  # TODO: Need to figure out how to load without this.
+        self.config = config
+
+        # If no config -- try getting all the supported layers
+        if self.config is None:
+            self.make_config_from_model(model)
+
+        # TODO: Remove the configuration by reference ('module')
+        for module_config in self.config:
+            assert isinstance(module_config, dict), (
+                "config elements should be dicts not modules i.e.:"
+                "[{`tensor_fqn`: `foo.bar.weight`}, {`tensor_fqn`: ... }, ...]"
+            )
+
+            assert isinstance(self.defaults, Dict)  # for mypy
+            local_args = copy.deepcopy(self.defaults)
+            local_args.update(module_config)
+
+            tensor_fqn = local_args.get("tensor_fqn", None)
+            assert tensor_fqn is not None, (
+                "tensor_fqn is a required argument in the sparsity config which"
+                "replaces previous `module` and [module]`fqn` arguments"
+            )
+
+            # populate all information from tensor_fqn
+            info_from_tensor_fqn = get_arg_info_from_tensor_fqn(model, tensor_fqn)
+
+            # check that whatever was put into local_args agrees with what was obtained
+            # from tensor_fqn
+            for key in info_from_tensor_fqn.keys():
+                if key in local_args:
+                    assert (
+                        info_from_tensor_fqn[key] == local_args[key]
+                        or (
+                            key == "tensor_fqn"
+                            and "." + info_from_tensor_fqn[key] == local_args[key]
+                        )
+                        # info_from_tensor_fqn will chop leading '.' from tensor_fqn so ignore that
+                    ), f"Given both `{key}` and `tensor_fqn` in the config, it is expected them to agree!"
+            local_args.update(info_from_tensor_fqn)
+            self.groups.append(local_args)
+        self._prepare()
+
+    def _prepare(self, *args, **kwargs):
+        r"""Adds mask parametrization to the layer weight"""
+        for config in self.groups:
+            module = config["module"]
+            tensor_name = config["tensor_name"]
+            parametrization = config.get("parametrization", FakeSparsity)
+            mask = config.get("mask", torch.ones_like(getattr(module, tensor_name)))
+            self.state[config["tensor_fqn"]]["mask"] = mask
+            parametrize.register_parametrization(
+                module, tensor_name, parametrization(mask)
+            )
+
+    def squash_mask(
+        self,
+        params_to_keep: Optional[Tuple[str, ...]] = None,
+        params_to_keep_per_layer: Optional[Dict[str, Tuple[str, ...]]] = None,
+        *args,
+        **kwargs,
+    ):
+        r"""Squashes the sparse masks into the appropriate tensors.
+
+        If either the `params_to_keep` or `params_to_keep_per_layer` is set,
+        the module will have a `sparse_params` dict attached to it.
+
+        Args:
+            params_to_keep: List of keys to save in the module or a dict
+                            representing the modules and keys that will have
+                            sparsity parameters saved
+            params_to_keep_per_layer: Dict to specify the params that should be
+                            saved for specific layers. The keys in the dict
+                            should be the module fqn, while the values should
+                            be a list of strings with the names of the variables
+                            to save in the `sparse_params`
+
+        Examples:
+            >>> # xdoctest: +SKIP("locals are undefined")
+            >>> # Don't save any sparse params
+            >>> sparsifier.squash_mask()
+            >>> hasattr(model.submodule1, 'sparse_params')
+            False
+
+            >>> # Keep sparse params per layer
+            >>> sparsifier.squash_mask(
+            ...     params_to_keep_per_layer={
+            ...         'submodule1.linear1': ('foo', 'bar'),
+            ...         'submodule2.linear42': ('baz',)
+            ...     })
+            >>> print(model.submodule1.linear1.sparse_params)
+            {'foo': 42, 'bar': 24}
+            >>> print(model.submodule2.linear42.sparse_params)
+            {'baz': 0.1}
+
+            >>> # Keep sparse params for all layers
+            >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar'))
+            >>> print(model.submodule1.linear1.sparse_params)
+            {'foo': 42, 'bar': 24}
+            >>> print(model.submodule2.linear42.sparse_params)
+            {'foo': 42, 'bar': 24}
+
+            >>> # Keep some sparse params for all layers, and specific ones for
+            >>> # some other layers
+            >>> sparsifier.squash_mask(
+            ...     params_to_keep=('foo', 'bar'),
+            ...     params_to_keep_per_layer={
+            ...         'submodule2.linear42': ('baz',)
+            ...     })
+            >>> print(model.submodule1.linear1.sparse_params)
+            {'foo': 42, 'bar': 24}
+            >>> print(model.submodule2.linear42.sparse_params)
+            {'foo': 42, 'bar': 24, 'baz': 0.1}
+        """
+        for config in self.groups:
+            module = config["module"]
+            tensor_name = config["tensor_name"]
+            parametrize.remove_parametrizations(
+                module, tensor_name, leave_parametrized=True
+            )
+            sparse_params = {}
+            if params_to_keep is not None:
+                global_params = {k: config[k] for k in params_to_keep}
+                sparse_params.update(global_params)
+            if params_to_keep_per_layer is not None:
+                params = params_to_keep_per_layer.get(config["module_fqn"], None)
+                if params is not None:
+                    per_layer_params = {k: config[k] for k in params}
+                    sparse_params.update(per_layer_params)
+            if sparse_params:
+                # TODO handle multiple tensor being quantized on a single module, where to store sparse_params?
+                module.sparse_params = sparse_params
+
+    def convert(
+        self,
+        module: nn.Module,
+        mapping: Optional[Dict[Type[nn.Module], Type[nn.Module]]] = None,
+        inplace: bool = False,
+        parameterization: Type[nn.Module] = FakeSparsity,
+    ):
+        r"""Converts submodules in input module to a different module according to `mapping`
+        by calling `from_dense` method on the target module class
+        Args:
+            module: input module
+            mapping: a dictionary that maps from source module type to target
+                module type, can be overwritten to allow swapping user defined
+                Modules
+            inplace: carry out model transformations in-place, the original module
+                is mutated
+        """
+        if mapping is None:
+            raise NotImplementedError("Need to auto generate mapping ")
+        if not inplace:
+            module = copy.deepcopy(module)
+
+        reassign = {}
+        for name, mod in module.named_children():
+            # leaf node
+            if (
+                module_contains_param(mod, parameterization)
+                and type_before_parametrizations(mod) in mapping
+            ):
+                reassign[name] = swap_module(mod, mapping)
+            else:
+                # recurse
+                reassign[name] = self.convert(
+                    mod,
+                    mapping=mapping,
+                    inplace=True,
+                    parameterization=parameterization,
+                )
+
+        for key, value in reassign.items():
+            module._modules[key] = value
+
+        return module
+
+    def step(self, use_path: bool = True) -> None:
+        if not self.enable_mask_update:
+            return
+        with torch.no_grad():
+            for config in self.groups:
+                self.update_mask(**config)
+
+    @abc.abstractmethod
+    def update_mask(self, module: nn.Module, tensor_name: str, **kwargs):
+        pass
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/sparsifier/nearly_diagonal_sparsifier.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/sparsifier/nearly_diagonal_sparsifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..47c567ec6d93ebcb4780829bd4a629b67d181b8c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/sparsifier/nearly_diagonal_sparsifier.py
@@ -0,0 +1,58 @@
+# mypy: allow-untyped-defs
+import torch
+
+from . import base_sparsifier
+
+
+class NearlyDiagonalSparsifier(base_sparsifier.BaseSparsifier):
+    r"""Nearly Diagonal Sparsifier
+
+    This sparsifier creates a nearly diagonal mask to be applied to the weight matrix.
+    Nearly Diagonal Matrix is a matrix that contains non-zero elements near the diagonal and the rest are zero.
+    An example of a nearly diagonal matrix with degree (or nearliness) 3 and 5 are follows respectively.
+    1 1 0 0       1 1 1 0
+    1 1 1 0       1 1 1 1
+    0 1 1 1       1 1 1 1
+    0 0 1 1       0 1 1 1
+    Note that a nearly diagonal matrix with degree 1 is just a matrix with main diagonal populated
+
+    This sparsifier is controlled by one variable:
+    1. `nearliness` defines the number of non-zero diagonal lines that are closest to the main diagonal.
+        Currently - supports only odd number
+
+    Note:
+        This can be accelerated (vectorized) once the Spdiagonal feature (PR: #78439) is landed or the banded matrix
+        feature is landed: https://stackoverflow.com/questions/52463972/generating-banded-matrices-using-numpy
+
+    Args:
+        nearliness: The degree of nearliness (default = 1)
+
+    """
+
+    def __init__(self, nearliness: int = 1):
+        defaults = {"nearliness": nearliness}
+        super().__init__(defaults=defaults)
+
+    def update_mask(self, module, tensor_name, nearliness, **kwargs):
+        mask = getattr(module.parametrizations, tensor_name)[0].mask
+        mask.data = torch.zeros_like(mask)
+        if nearliness <= 0:
+            return
+
+        tensor = getattr(module, tensor_name)
+        height, width = tensor.shape
+
+        if nearliness % 2 == 0:
+            raise ValueError("nearliness can only be an odd number")
+        dist_to_diagonal = nearliness // 2
+        # check
+        if dist_to_diagonal >= min(height, width):
+            raise ValueError(
+                "nearliness cannot be larger than the dimensions of tensor."
+            )
+
+        for row in range(0, height):
+            # Bounds of entries that needs to be set to 1
+            low = max(0, row - dist_to_diagonal)
+            high = min(width, row + dist_to_diagonal + 1)
+            mask[row, low:high].fill_(1)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/sparsifier/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/sparsifier/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..69c459c7779d9fd26f8312c212c5ee3f80dcfb66
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/sparsifier/utils.py
@@ -0,0 +1,138 @@
+# mypy: allow-untyped-defs
+from itertools import chain
+from typing import Any, Dict, Optional, Type
+
+from torch import nn
+from torch.nn.utils.parametrize import is_parametrized, type_before_parametrizations
+
+
+__all__ = [
+    "module_contains_param",
+    "swap_module",
+    "module_to_fqn",
+    "fqn_to_module",
+    "get_arg_info_from_tensor_fqn",
+    "FakeSparsity",
+]
+
+
+def module_contains_param(module: nn.Module, parametrization: Type[nn.Module]) -> bool:
+    if is_parametrized(module):
+        # see if any of the module tensors have a parametriztion attached that matches the one passed in
+        return any(
+            any(isinstance(param, parametrization) for param in param_list)
+            for key, param_list in module.parametrizations.items()  # type: ignore[union-attr,operator]
+        )
+    return False
+
+
+def swap_module(
+    mod: nn.Module, mapping: Dict[Type[nn.Module], Type[nn.Module]]
+) -> nn.Module:
+    r"""Swaps the module using from_dense according to the mapping passed in.
+    Args:
+        mod: input module
+        mapping: a dictionary that maps from nn module to sparse nn module
+    Return:
+        The corresponding sparse module of `mod` according to mapping, created using from_dense
+    """
+    if type_before_parametrizations(mod) in mapping:
+        sparse_mod = mapping[type_before_parametrizations(mod)]
+
+        # TODO Fix this typing, as Type[Module] has no attribute "from_dense"
+        new_mod = sparse_mod.from_dense(mod)  # type: ignore[attr-defined]
+
+        # Preserve module's pre forward hooks. They'll be called on quantized input
+        for pre_hook_fn in mod._forward_pre_hooks.values():
+            new_mod.register_forward_pre_hook(pre_hook_fn)
+        # Preserve module's post forward hooks except _observer_forward_hook
+        # After convert they'll work with quantized output
+        for hook_fn in mod._forward_hooks.values():
+            new_mod.register_forward_hook(hook_fn)
+
+        # respect device affinity when swapping modules
+        devices = {p.device for p in chain(mod.parameters(), mod.buffers())}
+        assert (
+            len(devices) <= 1
+        ), f"swap_module only works with cpu or single-device CUDA modules, but got devices {devices}"
+        device = next(iter(devices)) if len(devices) > 0 else None
+        if device:
+            new_mod.to(device)
+
+        return new_mod
+
+    else:
+        return mod
+
+
+def module_to_fqn(
+    model: nn.Module, module: nn.Module, prefix: str = ""
+) -> Optional[str]:
+    """
+    Returns the fqn for a module or None if module not a descendent of model.
+    """
+    if module is model:
+        return ""
+    for name, child in model.named_children():
+        fqn = module_to_fqn(child, module, ".")
+        if isinstance(fqn, str):
+            return prefix + name + fqn
+    return None
+
+
+def fqn_to_module(model: Optional[nn.Module], path: str) -> Optional[nn.Module]:
+    """
+    Given an fqn, returns the corresponding module or tensor or None if the fqn given by `path`
+    doesn't correspond to anything. Similar to model.get_submodule(path) but works for tensors.
+    """
+    if path != "":
+        for name in path.split("."):
+            model = getattr(model, name, None)
+    return model
+
+
+def get_arg_info_from_tensor_fqn(model: nn.Module, tensor_fqn: str) -> Dict[str, Any]:
+    """
+    Uses tensor_fqn to obtain a dict containing module_fqn, module and tensor_name
+    """
+    # string manip to split tensor_fqn into module_fqn and tensor_name
+    # if tensor_fqn is 'weight' then module_fqn and tensor_name are '' and 'weight'
+    # if tensor_fqn is 'linear.weight' then module_fqn and tensor_name are 'linear' and 'weight'
+    tensor_name = tensor_fqn.split(".")[-1]
+    module_fqn = tensor_fqn[: -len(tensor_name) - ("." in tensor_fqn)]
+
+    module = fqn_to_module(model, module_fqn)
+
+    return {
+        "module_fqn": module_fqn,
+        "module": module,
+        "tensor_name": tensor_name,
+        "tensor_fqn": tensor_fqn,
+    }
+
+
+# Parametrizations
+class FakeSparsity(nn.Module):
+    r"""Parametrization for the weights. Should be attached to the 'weight' or
+    any other parameter that requires a mask applied to it.
+
+    Note::
+
+        Once the mask is passed, the variable should not change the id. The
+        contents of the mask can change, but the mask reference itself should
+        not.
+    """
+
+    def __init__(self, mask):
+        super().__init__()
+        self.register_buffer("mask", mask)
+
+    def forward(self, x):
+        assert self.mask.shape == x.shape
+        return self.mask * x
+
+    def state_dict(self, *args, **kwargs):
+        # We don't want to let the parametrizations to save the mask.
+        # That way we make sure that the linear module doesn't store the masks
+        # alongside their parametrizations.
+        return {}
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/sparsifier/weight_norm_sparsifier.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/sparsifier/weight_norm_sparsifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c2eef0dd15c79d8ff91d9b1ca9be123cc8dd40d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/pruning/sparsifier/weight_norm_sparsifier.py
@@ -0,0 +1,248 @@
+# mypy: allow-untyped-defs
+import operator
+from functools import reduce
+from typing import Callable, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+
+from .base_sparsifier import BaseSparsifier
+
+
+__all__ = ["WeightNormSparsifier"]
+
+
+def _flat_idx_to_2d(idx, shape):
+    rows = idx // shape[1]
+    cols = idx % shape[1]
+    return rows, cols
+
+
+class WeightNormSparsifier(BaseSparsifier):
+    r"""Weight-Norm Sparsifier
+
+    This sparsifier computes the norm of every sparse block and "zeroes-out" the
+    ones with the lowest norm. The level of sparsity defines how many of the
+    blocks is removed.
+
+    This sparsifier is controlled by three variables:
+    1. `sparsity_level` defines the number of *sparse blocks* that are zeroed-out
+    2. `sparse_block_shape` defines the shape of the sparse blocks. Note that
+        the sparse blocks originate at the zero-index of the tensor.
+    3. `zeros_per_block` is the number of zeros that we are expecting in each
+        sparse block. By default we assume that all elements within a block are
+        zeroed-out. However, setting this variable sets the target number of
+        zeros per block. The zeros within each block are chosen as the *smallest
+        absolute values*.
+
+    Args:
+
+        sparsity_level: The target level of sparsity
+        sparse_block_shape: The shape of a sparse block (see note below)
+        zeros_per_block: Number of zeros in a sparse block
+        norm: Norm to use. Could be either `int` or a callable.
+            If `int`, only L1 and L2 are implemented.
+
+    Note::
+        The `sparse_block_shape` is tuple representing (block_ROWS, block_COLS),
+        irrespective of what the rows / cols mean in the data tensor. That means,
+        if you were to sparsify a weight tensor in the nn.Linear, which has a
+        weight shape `(Cout, Cin)`, the `block_ROWS` would refer to the output
+        channels, while the `block_COLS` would refer to the input channels.
+
+    Note::
+        All arguments to the WeightNormSparsifier constructor are "default"
+        arguments and could be overriden by the configuration provided in the
+        `prepare` step.
+    """
+
+    def __init__(
+        self,
+        sparsity_level: float = 0.5,
+        sparse_block_shape: Tuple[int, int] = (1, 4),
+        zeros_per_block: Optional[int] = None,
+        norm: Optional[Union[Callable, int]] = None,
+    ):
+        if zeros_per_block is None:
+            zeros_per_block = reduce(operator.mul, sparse_block_shape)
+        defaults = {
+            "sparsity_level": sparsity_level,
+            "sparse_block_shape": sparse_block_shape,
+            "zeros_per_block": zeros_per_block,
+        }
+        if norm is None:
+            norm = 2
+        if callable(norm):
+            self.norm_fn = norm
+        elif norm == 1:
+            self.norm_fn = lambda T: T.abs()
+        elif norm == 2:
+            self.norm_fn = lambda T: T * T
+        else:
+            raise NotImplementedError(f"L-{norm} is not yet implemented.")
+        super().__init__(defaults=defaults)
+
+    def _scatter_fold_block_mask(
+        self,
+        output_shape,
+        dim,
+        indices,
+        block_shape,
+        mask=None,
+        input_shape=None,
+        device=None,
+    ):
+        r"""Creates patches of size `block_shape` after scattering the indices."""
+        if mask is None:
+            assert input_shape is not None
+            mask = torch.ones(input_shape, device=device)
+        mask.scatter_(dim=dim, index=indices, value=0)
+        mask.data = F.fold(
+            mask, output_size=output_shape, kernel_size=block_shape, stride=block_shape
+        )
+        return mask
+
+    def _make_tensor_mask(
+        self, data, input_shape, sparsity_level, sparse_block_shape, mask=None
+    ):
+        r"""Creates a tensor-level mask.
+
+        Tensor-level mask is described as a mask, where the granularity of sparsification of the
+        smallest patch is the sparse_block_shape. That means, that for a given mask and a
+        sparse_block_shape, the smallest "patch" of zeros/ones could be the sparse_block_shape.
+
+        In this context, `sparsity_level` describes the fraction of sparse patches.
+        """
+        h, w = data.shape[-2:]
+        block_h, block_w = sparse_block_shape
+        dh = (block_h - h % block_h) % block_h
+        dw = (block_w - w % block_w) % block_w
+
+        if mask is None:
+            mask = torch.ones(h + dh, w + dw, device=data.device)
+
+        if sparsity_level >= 1.0:
+            mask.data = torch.zeros_like(mask)
+            return mask
+        elif sparsity_level <= 0.0:
+            mask.data = torch.ones_like(mask)
+            return mask
+
+        values_per_block = reduce(operator.mul, sparse_block_shape)
+        if values_per_block > 1:
+            # Reduce the data
+            data = F.avg_pool2d(
+                data[None, None, :],
+                kernel_size=sparse_block_shape,
+                stride=sparse_block_shape,
+                ceil_mode=True,
+            )
+        data = data.flatten()
+        num_blocks = len(data)
+
+        data = data.repeat(1, values_per_block, 1)
+
+        threshold_idx = int(round(sparsity_level * num_blocks))
+        threshold_idx = max(0, min(num_blocks - 1, threshold_idx))  # Sanity check
+        _, sorted_idx = torch.topk(data, k=threshold_idx, dim=2, largest=False)
+
+        # Temp reshape for mask
+        mask_reshape = mask.reshape(data.shape)  # data might be reshaped
+        self._scatter_fold_block_mask(
+            dim=2,
+            output_shape=(h + dh, w + dw),
+            indices=sorted_idx,
+            block_shape=sparse_block_shape,
+            mask=mask_reshape,
+        )
+        mask.data = mask_reshape.squeeze().reshape(mask.shape)[:h, :w].contiguous()
+        return mask
+
+    def _make_block_mask(self, data, sparse_block_shape, zeros_per_block, mask=None):
+        r"""Creates a block-level mask.
+
+        Block-level mask is described as a mask, where the granularity of sparsification of the
+        largest patch is the sparse_block_shape. That means that for a given mask and a
+        sparse_block_shape, the sparsity is computed only within a patch of a size sparse_block_shape.
+
+        In this context the `zeros_per_block` describes the number of zeroed-out elements within a patch.
+        """
+        h, w = data.shape[-2:]
+        block_h, block_w = sparse_block_shape
+        dh = (block_h - h % block_h) % block_h
+        dw = (block_w - w % block_w) % block_w
+        values_per_block = reduce(operator.mul, sparse_block_shape)
+
+        if mask is None:
+            mask = torch.ones((h + dh, w + dw), device=data.device)
+
+        if values_per_block == zeros_per_block:
+            # Everything should be sparsified
+            mask.data = torch.zeros_like(mask)
+            return mask
+
+        # create a new padded tensor like data (to match the block_shape)
+        padded_data = torch.ones(h + dh, w + dw, dtype=data.dtype, device=data.device)
+        padded_data.fill_(torch.nan)
+        padded_data[:h, :w] = data
+        unfolded_data = F.unfold(
+            padded_data[None, None, :],
+            kernel_size=sparse_block_shape,
+            stride=sparse_block_shape,
+        )
+
+        # Temp reshape for mask
+        mask_reshape = mask.reshape(unfolded_data.shape)
+        _, sorted_idx = torch.topk(
+            unfolded_data, k=zeros_per_block, dim=1, largest=False
+        )
+
+        self._scatter_fold_block_mask(
+            dim=1,
+            indices=sorted_idx,
+            output_shape=padded_data.shape,
+            block_shape=sparse_block_shape,
+            mask=mask_reshape,
+        )
+
+        mask.data = mask_reshape.squeeze().reshape(mask.shape).contiguous()
+        return mask
+
+    def update_mask(
+        self,
+        module,
+        tensor_name,
+        sparsity_level,
+        sparse_block_shape,
+        zeros_per_block,
+        **kwargs,
+    ):
+        values_per_block = reduce(operator.mul, sparse_block_shape)
+        if zeros_per_block > values_per_block:
+            raise ValueError(
+                "Number of zeros per block cannot be more than the total number of elements in that block."
+            )
+        if zeros_per_block < 0:
+            raise ValueError("Number of zeros per block should be positive.")
+
+        mask = getattr(module.parametrizations, tensor_name)[0].mask
+        if sparsity_level <= 0 or zeros_per_block == 0:
+            mask.data = torch.ones_like(mask)
+        elif sparsity_level >= 1.0 and (zeros_per_block == values_per_block):
+            mask.data = torch.zeros_like(mask)
+        else:
+            ww = self.norm_fn(getattr(module, tensor_name))
+            tensor_mask = self._make_tensor_mask(
+                data=ww,
+                input_shape=ww.shape,
+                sparsity_level=sparsity_level,
+                sparse_block_shape=sparse_block_shape,
+            )
+            if values_per_block != zeros_per_block:
+                block_mask = self._make_block_mask(
+                    data=ww,
+                    sparse_block_shape=sparse_block_shape,
+                    zeros_per_block=zeros_per_block,
+                )
+                tensor_mask = torch.logical_or(tensor_mask, block_mask)
+            mask.data = tensor_mask
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..840d2715c7717f3808031a3e715739ee3f57d7e5
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/__init__.py
@@ -0,0 +1,220 @@
+# mypy: allow-untyped-defs
+
+from typing import Callable, List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+
+from .fake_quantize import *  # noqa: F403
+from .fuse_modules import fuse_modules, fuse_modules_qat  # noqa: F403
+from .fuser_method_mappings import *  # noqa: F403
+from .observer import *  # noqa: F403
+from .pt2e._numeric_debugger import (  # noqa: F401
+    compare_results,
+    CUSTOM_KEY,
+    extract_results_from_loggers,
+    generate_numeric_debug_handle,
+    NUMERIC_DEBUG_HANDLE_KEY,
+    prepare_for_propagation_comparison,
+)
+from .pt2e.export_utils import (
+    _allow_exported_model_train_eval as allow_exported_model_train_eval,
+    _move_exported_model_to_eval as move_exported_model_to_eval,
+    _move_exported_model_to_train as move_exported_model_to_train,
+)
+from .qconfig import *  # noqa: F403
+from .qconfig_mapping import *  # noqa: F403
+from .quant_type import *  # noqa: F403
+from .quantization_mappings import *  # noqa: F403 # type: ignore[no-redef]
+from .quantize import *  # noqa: F403
+from .quantize_jit import *  # noqa: F403
+from .stubs import *  # noqa: F403
+
+
+# ensure __module__ is set correctly for public APIs
+ObserverOrFakeQuantize = Union[ObserverBase, FakeQuantizeBase]
+ObserverOrFakeQuantize.__module__ = "torch.ao.quantization"
+for _f in [
+    compare_results,
+    extract_results_from_loggers,
+    generate_numeric_debug_handle,
+    prepare_for_propagation_comparison,
+]:
+    _f.__module__ = "torch.ao.quantization"
+
+__all__ = [
+    "DeQuantStub",
+    "FakeQuantize",
+    "FakeQuantizeBase",
+    "FixedQParamsFakeQuantize",
+    "FixedQParamsObserver",
+    "FusedMovingAvgObsFakeQuantize",
+    "HistogramObserver",
+    "MatchAllNode",
+    "MinMaxObserver",
+    "MovingAverageMinMaxObserver",
+    "MovingAveragePerChannelMinMaxObserver",
+    "NoopObserver",
+    "ObserverBase",
+    "ObserverOrFakeQuantize",
+    "Pattern",
+    "PerChannelMinMaxObserver",
+    "PlaceholderObserver",
+    "QConfig",
+    "QConfigAny",
+    "QConfigDynamic",
+    "QConfigMapping",
+    "QuantStub",
+    "QuantType",
+    "QuantWrapper",
+    "RecordingObserver",
+    "ReuseInputObserver",
+    "UniformQuantizationObserverBase",
+    "add_quant_dequant",
+    "convert",
+    "convert_dynamic_jit",
+    "convert_jit",
+    "default_affine_fixed_qparams_fake_quant",
+    "default_affine_fixed_qparams_observer",
+    "default_debug_observer",
+    "default_dynamic_fake_quant",
+    "default_dynamic_quant_observer",
+    "default_embedding_fake_quant",
+    "default_embedding_fake_quant_4bit",
+    "default_eval_fn",
+    "default_fake_quant",
+    "default_fixed_qparams_range_0to1_fake_quant",
+    "default_fixed_qparams_range_0to1_observer",
+    "default_fixed_qparams_range_neg1to1_fake_quant",
+    "default_fixed_qparams_range_neg1to1_observer",
+    "default_float_qparams_observer",
+    "default_float_qparams_observer_4bit",
+    "default_fused_act_fake_quant",
+    "default_fused_per_channel_wt_fake_quant",
+    "default_fused_wt_fake_quant",
+    "default_histogram_fake_quant",
+    "default_histogram_observer",
+    "default_observer",
+    "default_per_channel_weight_fake_quant",
+    "default_per_channel_weight_observer",
+    "default_placeholder_observer",
+    "default_reuse_input_observer",
+    "default_symmetric_fixed_qparams_fake_quant",
+    "default_symmetric_fixed_qparams_observer",
+    "default_weight_fake_quant",
+    "default_weight_observer",
+    "disable_fake_quant",
+    "disable_observer",
+    "enable_fake_quant",
+    "enable_observer",
+    "fuse_conv_bn",
+    "fuse_conv_bn_jit",
+    "fuse_conv_bn_relu",
+    "fuse_convtranspose_bn",
+    "fuse_linear_bn",
+    "fuse_modules",
+    "fuse_modules_qat",
+    "fused_per_channel_wt_fake_quant_range_neg_127_to_127",
+    "fused_wt_fake_quant_range_neg_127_to_127",
+    "get_combined_dict",
+    "get_default_compare_output_module_list",
+    "get_default_custom_config_dict",
+    "get_default_dynamic_quant_module_mappings",
+    "get_default_dynamic_sparse_quant_module_mappings",
+    "get_default_float_to_quantized_operator_mappings",
+    "get_default_qat_module_mappings",
+    "get_default_qat_qconfig",
+    "get_default_qat_qconfig_dict",
+    "get_default_qat_qconfig_mapping",
+    "get_default_qconfig",
+    "get_default_qconfig_dict",
+    "get_default_qconfig_mapping",
+    "get_default_qconfig_propagation_list",
+    "get_default_static_quant_module_mappings",
+    "get_default_static_quant_reference_module_mappings",
+    "get_default_static_sparse_quant_module_mappings",
+    "get_dynamic_quant_module_class",
+    "get_embedding_qat_module_mappings",
+    "get_embedding_static_quant_module_mappings",
+    "get_fuser_method",
+    "get_fuser_method_new",
+    "get_observer_state_dict",
+    "get_quantized_operator",
+    "get_static_quant_module_class",
+    "load_observer_state_dict",
+    "move_exported_model_to_eval",
+    "move_exported_model_to_train",
+    "allow_exported_model_train_eval",
+    "no_observer_set",
+    "per_channel_weight_observer_range_neg_127_to_127",
+    "prepare",
+    "prepare_dynamic_jit",
+    "prepare_jit",
+    "prepare_qat",
+    "propagate_qconfig_",
+    "qconfig_equals",
+    "quantize",
+    "quantize_dynamic",
+    "quantize_dynamic_jit",
+    "quantize_jit",
+    "quantize_qat",
+    "script_qconfig",
+    "script_qconfig_dict",
+    "swap_module",
+    "weight_observer_range_neg_127_to_127",
+    "generate_numeric_debug_handle",
+    "CUSTOM_KEY",
+    "NUMERIC_DEBUG_HANDLE_KEY",
+    "prepare_for_propagation_comparison",
+    "extract_results_from_loggers",
+    "compare_results",
+]
+
+
+def default_eval_fn(model, calib_data):
+    r"""Define the default evaluation function.
+
+    Default evaluation function takes a torch.utils.data.Dataset or a list of
+    input Tensors and run the model on the dataset
+    """
+    for data, target in calib_data:
+        model(data)
+
+
+class _DerivedObserverOrFakeQuantize(ObserverBase):
+    r"""This observer is used to describe an observer whose quantization parameters
+    are derived from other observers
+    """
+
+    def __init__(
+        self,
+        dtype: torch.dtype,
+        obs_or_fqs: List[ObserverOrFakeQuantize],
+        derive_qparams_fn: Callable[
+            [List[ObserverOrFakeQuantize]], Tuple[Tensor, Tensor]
+        ],
+        quant_min: Optional[int] = None,
+        quant_max: Optional[int] = None,
+        qscheme: Optional[torch.qscheme] = None,
+        ch_axis: Optional[int] = None,
+    ):
+        super().__init__(dtype)
+        self.obs_or_fqs = obs_or_fqs
+        self.derive_qparams_fn = derive_qparams_fn
+        self.quant_min = quant_min
+        self.quant_max = quant_max
+        self.qscheme = qscheme
+        self.ch_axis = ch_axis
+
+        from .utils import is_per_channel
+
+        if is_per_channel(self.qscheme):
+            assert (
+                self.ch_axis is not None
+            ), "Must provide a valid ch_axis if qscheme is per channel"
+
+    def forward(self, x: Tensor) -> Tensor:
+        return x
+
+    def calculate_qparams(self):
+        return self.derive_qparams_fn(self.obs_or_fqs)
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/_correct_bias.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/_correct_bias.py
new file mode 100644
index 0000000000000000000000000000000000000000..4be37467de1818194e41c953edf2e29ddeb7e636
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/_correct_bias.py
@@ -0,0 +1,157 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.ao.nn.quantized as nnq
+import torch.ao.ns._numeric_suite as ns
+import torch.ao.quantization
+import torch.nn as nn
+
+
+__all__ = [
+    "get_module",
+    "parent_child_names",
+    "get_param",
+    "MeanShadowLogger",
+    "bias_correction",
+]
+
+_supported_modules = {nn.Linear, nn.Conv2d}
+_supported_modules_quantized = {nnq.Linear, nnq.Conv2d}
+
+
+def get_module(model, name):
+    """Given name of submodule, this function grabs the submodule from given model."""
+    return dict(model.named_modules())[name]
+
+
+def parent_child_names(name):
+    """Split full name of submodule into parent submodule's full name and submodule's name."""
+    split_name = name.rsplit(".", 1)
+    if len(split_name) == 1:
+        return "", split_name[0]
+    else:
+        return split_name[0], split_name[1]
+
+
+def get_param(module, attr):
+    """Get the parameter given a module and attribute.
+
+    Sometimes the weights/bias attribute gives you the raw tensor, but sometimes
+    gives a function that will give you the raw tensor, this function takes care of that logic
+    """
+    param = getattr(module, attr, None)
+    if callable(param):
+        return param()
+    else:
+        return param
+
+
+class MeanShadowLogger(ns.Logger):
+    """Mean Logger for a Shadow module.
+
+    A logger for a Shadow module whose purpose is to record the rolling mean
+    of the data passed to the floating point and quantized models
+    """
+
+    def __init__(self):
+        """Set up initial values for float and quantized stats, count, float sum, and quant sum."""
+        super().__init__()
+        self.stats["float"] = None
+        self.stats["quantized"] = None
+        self.count = 0
+        self.float_sum = None
+        self.quant_sum = None
+
+    def forward(self, x, y):
+        """Compute the average of quantized and floating-point data from modules.
+
+        The inputs x,y are output data from the quantized and floating-point modules.
+        x is for the quantized module, y is for the floating point module
+        """
+        if x.is_quantized:
+            x = x.dequantize()
+
+        self.count += 1
+        if self.stats["quantized"] is None:
+            self.stats["quantized"] = x
+            self.quant_sum = x
+        else:
+            self.quant_sum += x
+            self.stats["quantized"] = self.quant_sum / self.count
+
+        if self.stats["float"] is None:
+            self.stats["float"] = y
+            self.float_sum = y
+        else:
+            self.float_sum += y
+            self.stats["float"] = self.float_sum / self.count
+
+    def clear(self):
+        self.stats["float"] = None
+        self.stats["quantized"] = None
+        self.count = 0
+        self.float_sum = None
+        self.quant_sum = None
+
+
+def bias_correction(
+    float_model,
+    quantized_model,
+    img_data,
+    target_modules=_supported_modules_quantized,
+    neval_batches=None,
+):
+    """Perform bias correction on a module.
+
+    Using numeric suite shadow module, the expected output of the floating point and quantized modules
+    is recorded. Using that data the bias of supported modules is shifted to compensate for the drift caused
+    by quantization
+    Paper reference: https://arxiv.org/pdf/1906.04721.pdf (Section 4.2)
+
+    Args:
+        float_model: a trained model that serves as a reference to what bias correction should aim for
+        quantized_model: quantized form of float_model that bias correction is to applied to
+        img_data: calibration data to estimate the expected output (used to find quantization error)
+        target_modules: specifies what submodules in quantized_model need bias correction (can be extended to
+                unquantized submodules)
+        neval_batches: a cap to the number of batches you want to be used for estimating the expected output
+    """
+    ns.prepare_model_with_stubs(
+        float_model, quantized_model, _supported_modules, MeanShadowLogger
+    )
+
+    uncorrected_modules = {}
+    for name, submodule in quantized_model.named_modules():
+        if type(submodule) in target_modules:
+            uncorrected_modules[name] = submodule
+
+    for uncorrected_module in uncorrected_modules:
+        quantized_submodule = get_module(quantized_model, uncorrected_module)
+        bias = get_param(quantized_submodule, "bias")
+        if bias is not None:
+            count = 0
+            for data in img_data:
+                quantized_model(data[0])
+                count += 1
+                if count == neval_batches:
+                    break
+            ob_dict = ns.get_logger_dict(quantized_model)
+            parent_name, _ = parent_child_names(uncorrected_module)
+
+            float_data = ob_dict[parent_name + ".stats"]["float"]
+            quant_data = ob_dict[parent_name + ".stats"]["quantized"]
+
+            # math for expected_error
+            quantization_error = quant_data - float_data
+            dims = list(range(quantization_error.dim()))
+            # Note: we don't want to take the mean over the output channel dimension
+            dims.remove(1)
+            expected_error = torch.mean(quantization_error, dims)
+
+            updated_bias = bias.data - expected_error
+
+            bias.data = updated_bias
+
+            # Resets the data contained in the loggers
+            for name, submodule in quantized_model.named_modules():
+                if isinstance(submodule, MeanShadowLogger):
+                    submodule.clear()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/_equalize.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/_equalize.py
new file mode 100644
index 0000000000000000000000000000000000000000..08316f755552b70cd9d4c1945ecfb73ea4a555c2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/_equalize.py
@@ -0,0 +1,282 @@
+# mypy: allow-untyped-defs
+import copy
+from typing import Any, Dict
+
+import torch
+
+
+__all__ = [
+    "set_module_weight",
+    "set_module_bias",
+    "has_bias",
+    "get_module_weight",
+    "get_module_bias",
+    "max_over_ndim",
+    "min_over_ndim",
+    "channel_range",
+    "get_name_by_module",
+    "cross_layer_equalization",
+    "process_paired_modules_list_to_name",
+    "expand_groups_in_paired_modules_list",
+    "equalize",
+    "converged",
+]
+
+_supported_types = {torch.nn.Conv2d, torch.nn.Linear, torch.nn.Conv1d}
+_supported_intrinsic_types = {
+    torch.ao.nn.intrinsic.ConvReLU2d,
+    torch.ao.nn.intrinsic.LinearReLU,
+    torch.ao.nn.intrinsic.ConvReLU1d,
+}
+_all_supported_types = _supported_types.union(_supported_intrinsic_types)
+
+
+def set_module_weight(module, weight) -> None:
+    if type(module) in _supported_types:
+        module.weight = torch.nn.Parameter(weight)
+    else:
+        module[0].weight = torch.nn.Parameter(weight)
+
+
+def set_module_bias(module, bias) -> None:
+    if type(module) in _supported_types:
+        module.bias = torch.nn.Parameter(bias)
+    else:
+        module[0].bias = torch.nn.Parameter(bias)
+
+
+def has_bias(module) -> bool:
+    if type(module) in _supported_types:
+        return module.bias is not None
+    else:
+        return module[0].bias is not None
+
+
+def get_module_weight(module):
+    if type(module) in _supported_types:
+        return module.weight
+    else:
+        return module[0].weight
+
+
+def get_module_bias(module):
+    if type(module) in _supported_types:
+        return module.bias
+    else:
+        return module[0].bias
+
+
+def max_over_ndim(input, axis_list, keepdim=False):
+    """Apply 'torch.max' over the given axes."""
+    axis_list.sort(reverse=True)
+    for axis in axis_list:
+        input, _ = input.max(axis, keepdim)
+    return input
+
+
+def min_over_ndim(input, axis_list, keepdim=False):
+    """Apply 'torch.min' over the given axes."""
+    axis_list.sort(reverse=True)
+    for axis in axis_list:
+        input, _ = input.min(axis, keepdim)
+    return input
+
+
+def channel_range(input, axis=0):
+    """Find the range of weights associated with a specific channel."""
+    size_of_tensor_dim = input.ndim
+    axis_list = list(range(size_of_tensor_dim))
+    axis_list.remove(axis)
+
+    mins = min_over_ndim(input, axis_list)
+    maxs = max_over_ndim(input, axis_list)
+
+    assert mins.size(0) == input.size(
+        axis
+    ), "Dimensions of resultant channel range does not match size of requested axis"
+    return maxs - mins
+
+
+def get_name_by_module(model, module):
+    """Get the name of a module within a model.
+
+    Args:
+        model: a model (nn.module) that equalization is to be applied on
+        module: a module within the model
+
+    Returns:
+        name: the name of the module within the model
+    """
+    for name, m in model.named_modules():
+        if m is module:
+            return name
+    raise ValueError("module is not in the model")
+
+
+def cross_layer_equalization(module1, module2, output_axis=0, input_axis=1):
+    """Scale the range of Tensor1.output to equal Tensor2.input.
+
+    Given two adjacent tensors', the weights are scaled such that
+    the ranges of the first tensors' output channel are equal to the
+    ranges of the second tensors' input channel
+    """
+    if (
+        type(module1) not in _all_supported_types
+        or type(module2) not in _all_supported_types
+    ):
+        raise ValueError(
+            "module type not supported:", type(module1), " ", type(module2)
+        )
+
+    conv1_has_bias = has_bias(module1)
+    bias = None
+
+    weight1 = get_module_weight(module1)
+    weight2 = get_module_weight(module2)
+
+    if weight1.size(output_axis) != weight2.size(input_axis):
+        raise TypeError(
+            "Number of output channels of first arg do not match \
+        number input channels of second arg"
+        )
+
+    if conv1_has_bias:
+        bias = get_module_bias(module1)
+
+    weight1_range = channel_range(weight1, output_axis)
+    weight2_range = channel_range(weight2, input_axis)
+
+    # producing scaling factors to applied
+    weight2_range += 1e-9
+    scaling_factors = torch.sqrt(weight1_range / weight2_range)
+    inverse_scaling_factors = torch.reciprocal(scaling_factors)
+
+    if conv1_has_bias:
+        bias = bias * inverse_scaling_factors
+
+    # formatting the scaling (1D) tensors to be applied on the given argument tensors
+    # pads axis to (1D) tensors to then be broadcasted
+    size1 = [1] * weight1.ndim
+    size1[output_axis] = weight1.size(output_axis)
+    size2 = [1] * weight2.ndim
+    size2[input_axis] = weight2.size(input_axis)
+
+    scaling_factors = torch.reshape(scaling_factors, size2)
+    inverse_scaling_factors = torch.reshape(inverse_scaling_factors, size1)
+
+    weight1 = weight1 * inverse_scaling_factors
+    weight2 = weight2 * scaling_factors
+
+    set_module_weight(module1, weight1)
+    if conv1_has_bias:
+        set_module_bias(module1, bias)
+    set_module_weight(module2, weight2)
+
+
+def process_paired_modules_list_to_name(model, paired_modules_list):
+    """Processes a list of paired modules to a list of names of paired modules."""
+
+    for group in paired_modules_list:
+        for i, item in enumerate(group):
+            if isinstance(item, torch.nn.Module):
+                group[i] = get_name_by_module(model, item)
+            elif not isinstance(item, str):
+                raise TypeError("item must be a nn.Module or a string")
+    return paired_modules_list
+
+
+def expand_groups_in_paired_modules_list(paired_modules_list):
+    """Expands module pair groups larger than two into groups of two modules."""
+    new_list = []
+
+    for group in paired_modules_list:
+        if len(group) == 1:
+            raise ValueError("Group must have at least two modules")
+        elif len(group) == 2:
+            new_list.append(group)
+        elif len(group) > 2:
+            for i in range(len(group) - 1):
+                new_list.append([group[i], group[i + 1]])
+
+    return new_list
+
+
+def equalize(model, paired_modules_list, threshold=1e-4, inplace=True):
+    """Equalize modules until convergence is achieved.
+
+    Given a list of adjacent modules within a model, equalization will
+    be applied between each pair, this will repeated until convergence is achieved
+
+    Keeps a copy of the changing modules from the previous iteration, if the copies
+    are not that different than the current modules (determined by converged_test),
+    then the modules have converged enough that further equalizing is not necessary
+
+    Reference is section 4.1 of this paper https://arxiv.org/pdf/1906.04721.pdf
+
+    Args:
+        model: a model (nn.Module) that equalization is to be applied on
+            paired_modules_list (List(List[nn.module || str])): a list of lists
+            where each sublist is a pair of two submodules found in the model,
+            for each pair the two modules have to be adjacent in the model,
+            with only piece-wise-linear functions like a (P)ReLU or LeakyReLU in between
+            to get expected results.
+            The list can contain either modules, or names of modules in the model.
+            If you pass multiple modules in the same list, they will all be equalized together.
+            threshold (float): a number used by the converged function to determine what degree
+            of similarity between models is necessary for them to be called equivalent
+        inplace (bool): determines if function is inplace or not
+    """
+
+    paired_modules_list = process_paired_modules_list_to_name(
+        model, paired_modules_list
+    )
+
+    if not inplace:
+        model = copy.deepcopy(model)
+
+    paired_modules_list = expand_groups_in_paired_modules_list(paired_modules_list)
+
+    name_to_module: Dict[str, torch.nn.Module] = {}
+    previous_name_to_module: Dict[str, Any] = {}
+    name_set = {name for pair in paired_modules_list for name in pair}
+
+    for name, module in model.named_modules():
+        if name in name_set:
+            name_to_module[name] = module
+            previous_name_to_module[name] = None
+    while not converged(name_to_module, previous_name_to_module, threshold):
+        for pair in paired_modules_list:
+            previous_name_to_module[pair[0]] = copy.deepcopy(name_to_module[pair[0]])
+            previous_name_to_module[pair[1]] = copy.deepcopy(name_to_module[pair[1]])
+
+            cross_layer_equalization(name_to_module[pair[0]], name_to_module[pair[1]])
+
+    return model
+
+
+def converged(curr_modules, prev_modules, threshold=1e-4):
+    """Test whether modules are converged to a specified threshold.
+
+    Tests for the summed norm of the differences between each set of modules
+    being less than the given threshold
+
+    Takes two dictionaries mapping names to modules, the set of names for each dictionary
+    should be the same, looping over the set of names, for each name take the difference
+    between the associated modules in each dictionary
+
+    """
+    if curr_modules.keys() != prev_modules.keys():
+        raise ValueError(
+            "The keys to the given mappings must have the same set of names of modules"
+        )
+
+    summed_norms = torch.tensor(0.0)
+    if None in prev_modules.values():
+        return False
+    for name in curr_modules.keys():
+        curr_weight = get_module_weight(curr_modules[name])
+        prev_weight = get_module_weight(prev_modules[name])
+
+        difference = curr_weight.sub(prev_weight)
+        summed_norms += torch.norm(difference)
+    return bool(summed_norms < threshold)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/_learnable_fake_quantize.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/_learnable_fake_quantize.py
new file mode 100644
index 0000000000000000000000000000000000000000..81359800bc833fd792a8c9cf506924ab2d011693
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/_learnable_fake_quantize.py
@@ -0,0 +1,202 @@
+# mypy: allow-untyped-defs
+from typing import List
+
+import torch
+from torch.nn.parameter import Parameter
+
+
+__all__: List[str] = []
+
+
+class _LearnableFakeQuantize(torch.ao.quantization.FakeQuantizeBase):
+    r"""Generalized extension of the FakeQuantize module in fake_quantize.py.
+
+    This is an extension of the FakeQuantize module in fake_quantize.py, which
+    supports more generalized lower-bit quantization and supports learning of the scale
+    and zero point parameters through backpropagation.
+
+    In addition to the attributes in the original FakeQuantize module, the _LearnableFakeQuantize
+    module also includes the following attributes to support quantization parameter learning.
+
+    * :attr:`channel_len` defines the length of the channel when initializing scale and zero point
+      for the per channel case.
+
+    * :attr:`use_grad_scaling` defines the flag for whether the gradients for scale and zero point are
+      normalized by the constant, which is proportional to the square root of the number of
+      elements in the tensor. The related literature justifying the use of this particular constant
+      can be found here: https://openreview.net/pdf?id=rkgO66VKDS.
+
+    * :attr:`fake_quant_enabled` defines the flag for enabling fake quantization on the output.
+
+    * :attr:`static_enabled` defines the flag for using observer's static estimation for
+      scale and zero point.
+
+    * :attr:`learning_enabled` defines the flag for enabling backpropagation for scale and zero point.
+    """
+
+    def __init__(
+        self,
+        observer,
+        quant_min=0,
+        quant_max=255,
+        scale=1.0,
+        zero_point=0.0,
+        channel_len=-1,
+        use_grad_scaling=False,
+        **observer_kwargs,
+    ):
+        super().__init__()
+        assert quant_min < quant_max, "quant_min must be strictly less than quant_max."
+        self.quant_min = quant_min
+        self.quant_max = quant_max
+        # also pass quant_min and quant_max to observer
+        observer_kwargs["quant_min"] = quant_min
+        observer_kwargs["quant_max"] = quant_max
+        self.use_grad_scaling = use_grad_scaling
+        if channel_len == -1:
+            self.scale = Parameter(torch.tensor([scale]))
+            self.zero_point = Parameter(torch.tensor([zero_point]))
+        else:
+            assert (
+                isinstance(channel_len, int) and channel_len > 0
+            ), "Channel size must be a positive integer."
+            self.scale = Parameter(torch.tensor([scale] * channel_len))
+            self.zero_point = Parameter(torch.tensor([zero_point] * channel_len))
+
+        self.activation_post_process = observer(**observer_kwargs)
+        assert (
+            torch.iinfo(self.activation_post_process.dtype).min <= quant_min
+        ), "quant_min out of bound"
+        assert (
+            quant_max <= torch.iinfo(self.activation_post_process.dtype).max
+        ), "quant_max out of bound"
+        self.dtype = self.activation_post_process.dtype
+        self.qscheme = self.activation_post_process.qscheme
+        self.ch_axis = (
+            self.activation_post_process.ch_axis
+            if hasattr(self.activation_post_process, "ch_axis")
+            else -1
+        )
+        self.register_buffer("fake_quant_enabled", torch.tensor([1], dtype=torch.uint8))
+        self.register_buffer("static_enabled", torch.tensor([1], dtype=torch.uint8))
+        self.register_buffer("learning_enabled", torch.tensor([0], dtype=torch.uint8))
+
+        bitrange = torch.tensor(quant_max - quant_min + 1).double()
+        self.bitwidth = int(torch.log2(bitrange).item())
+        self.register_buffer("eps", torch.tensor([torch.finfo(torch.float32).eps]))
+
+    @torch.jit.export
+    def enable_param_learning(self):
+        r"""Enable parameter learning over static observer estimates.
+
+        Enables learning of quantization parameters and
+        disables static observer estimates. Forward path returns fake quantized X.
+        """
+        self.toggle_qparam_learning(enabled=True).toggle_fake_quant(
+            enabled=True
+        ).toggle_observer_update(enabled=False)
+        return self
+
+    @torch.jit.export
+    def enable_static_estimate(self):
+        """Enable static estimates of quantization parameters.
+
+        Enables static observer estimates and disables learning of
+        quantization parameters. Forward path returns fake quantized X.
+        """
+        self.toggle_qparam_learning(enabled=False).toggle_fake_quant(
+            enabled=True
+        ).toggle_observer_update(enabled=True)
+
+    @torch.jit.export
+    def enable_static_observation(self):
+        """Enable accumulation of data without updating quantization parameters.
+
+        Enables static observer accumulating data from input but doesn't
+        update the quantization parameters. Forward path returns the original X.
+        """
+        self.toggle_qparam_learning(enabled=False).toggle_fake_quant(
+            enabled=False
+        ).toggle_observer_update(enabled=True)
+
+    @torch.jit.export
+    def toggle_observer_update(self, enabled=True):
+        self.static_enabled[0] = int(enabled)  # type: ignore[operator]
+        return self
+
+    @torch.jit.export
+    def enable_observer(self, enabled=True):
+        self.toggle_observer_update(enabled)
+
+    @torch.jit.export
+    def toggle_qparam_learning(self, enabled=True):
+        self.learning_enabled[0] = int(enabled)  # type: ignore[operator]
+        self.scale.requires_grad = enabled
+        self.zero_point.requires_grad = enabled
+        return self
+
+    @torch.jit.export
+    def toggle_fake_quant(self, enabled=True):
+        self.fake_quant_enabled[0] = int(enabled)
+        return self
+
+    @torch.jit.export
+    def observe_quant_params(self):
+        print(f"_LearnableFakeQuantize Scale: {self.scale.detach()}")
+        print(f"_LearnableFakeQuantize Zero Point: {self.zero_point.detach()}")
+
+    @torch.jit.export
+    def calculate_qparams(self):
+        self.scale.data.clamp_(min=self.eps.item())  # type: ignore[operator]
+        scale = self.scale.detach()
+        zero_point = (
+            self.zero_point.detach()
+            .round()
+            .clamp(self.quant_min, self.quant_max)
+            .long()
+        )
+        return scale, zero_point
+
+    def forward(self, X):
+        if self.static_enabled[0] == 1:  # type: ignore[index]
+            self.activation_post_process(X.detach())
+            _scale, _zero_point = self.activation_post_process.calculate_qparams()
+            _scale = _scale.to(self.scale.device)
+            _zero_point = _zero_point.to(self.zero_point.device)
+            self.scale.data.copy_(_scale)
+            self.zero_point.data.copy_(_zero_point)
+        else:
+            self.scale.data.clamp_(min=self.eps.item())  # type: ignore[operator]
+
+        if self.fake_quant_enabled[0] == 1:
+            if self.qscheme in (
+                torch.per_channel_symmetric,
+                torch.per_tensor_symmetric,
+            ):
+                self.zero_point.data.zero_()
+
+            if self.use_grad_scaling:
+                grad_factor = 1.0 / (X.numel() * self.quant_max) ** 0.5
+            else:
+                grad_factor = 1.0
+            if self.qscheme in (torch.per_channel_symmetric, torch.per_channel_affine):
+                X = torch._fake_quantize_learnable_per_channel_affine(
+                    X,
+                    self.scale,
+                    self.zero_point,
+                    self.ch_axis,
+                    self.quant_min,
+                    self.quant_max,
+                    grad_factor,
+                )
+            else:
+                X = torch._fake_quantize_learnable_per_tensor_affine(
+                    X,
+                    self.scale,
+                    self.zero_point,
+                    self.quant_min,
+                    self.quant_max,
+                    grad_factor,
+                )
+
+        return X
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d452359d41c36dc719e6df932b6fb018ca6a36b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/__init__.py
@@ -0,0 +1,30 @@
+from .backend_config import (
+    BackendConfig,
+    BackendPatternConfig,
+    DTypeConfig,
+    DTypeWithConstraints,
+    ObservationType,
+)
+from .executorch import get_executorch_backend_config
+from .fbgemm import get_fbgemm_backend_config
+from .native import get_native_backend_config, get_native_backend_config_dict
+from .onednn import get_onednn_backend_config
+from .qnnpack import get_qnnpack_backend_config
+from .tensorrt import get_tensorrt_backend_config, get_tensorrt_backend_config_dict
+
+
+__all__ = [
+    "get_fbgemm_backend_config",
+    "get_native_backend_config",
+    "get_native_backend_config_dict",
+    "get_qnnpack_backend_config",
+    "get_tensorrt_backend_config",
+    "get_tensorrt_backend_config_dict",
+    "get_executorch_backend_config",
+    "BackendConfig",
+    "BackendPatternConfig",
+    "DTypeConfig",
+    "DTypeWithConstraints",
+    "ObservationType",
+    "get_onednn_backend_config",
+]
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/_common_operator_config_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/_common_operator_config_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c28c8795667d368911ed3b90f0abc13a13d7af72
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/_common_operator_config_utils.py
@@ -0,0 +1,795 @@
+# mypy: allow-untyped-defs
+import copy
+import operator
+from collections import namedtuple
+from typing import Callable, Dict, List, Union
+
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.intrinsic.qat as nniqat
+import torch.ao.nn.qat as nnqat
+import torch.ao.nn.quantized.reference as nnqr
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.ao.quantization.fuser_method_mappings import (
+    _sequential_wrapper2,
+    fuse_conv_bn,
+    fuse_conv_bn_relu,
+    fuse_convtranspose_bn,
+    fuse_linear_bn,
+)
+
+from .backend_config import (
+    BackendPatternConfig,
+    DTypeConfig,
+    DTypeWithConstraints,
+    ObservationType,
+)
+
+
+__all__: List[str] = []
+
+# TODO: rename to be more explicit, e.g. qat_conv_relu
+_ConvMetadata = namedtuple(
+    "_ConvMetadata",
+    [
+        "root",
+        "transpose",
+        "bn",
+        "reference",
+        "transpose_reference",
+        "fused_conv_relu",
+        "fused_conv_bn",
+        "fused_conv_bn_relu",
+        "qat",
+        "relu_qat",
+        "bn_qat",
+        "bn_relu_qat",
+        "func",
+        "func_transpose",
+    ],
+)
+_Conv1dMetadata = _ConvMetadata(
+    nn.Conv1d,
+    nn.ConvTranspose1d,
+    nn.BatchNorm1d,
+    nnqr.Conv1d,
+    nnqr.ConvTranspose1d,
+    nni.ConvReLU1d,
+    nni.ConvBn1d,
+    nni.ConvBnReLU1d,
+    nnqat.Conv1d,
+    nniqat.ConvReLU1d,
+    nniqat.ConvBn1d,
+    nniqat.ConvBnReLU1d,
+    F.conv1d,
+    F.conv_transpose1d,
+)
+_Conv2dMetadata = _ConvMetadata(
+    nn.Conv2d,
+    nn.ConvTranspose2d,
+    nn.BatchNorm2d,
+    nnqr.Conv2d,
+    nnqr.ConvTranspose2d,
+    nni.ConvReLU2d,
+    nni.ConvBn2d,
+    nni.ConvBnReLU2d,
+    nnqat.Conv2d,
+    nniqat.ConvReLU2d,
+    nniqat.ConvBn2d,
+    nniqat.ConvBnReLU2d,
+    F.conv2d,
+    F.conv_transpose2d,
+)
+_Conv3dMetadata = _ConvMetadata(
+    nn.Conv3d,
+    nn.ConvTranspose3d,
+    nn.BatchNorm3d,
+    nnqr.Conv3d,
+    nnqr.ConvTranspose3d,
+    nni.ConvReLU3d,
+    nni.ConvBn3d,
+    nni.ConvBnReLU3d,
+    nnqat.Conv3d,
+    nniqat.ConvReLU3d,
+    nniqat.ConvBn3d,
+    nniqat.ConvBnReLU3d,
+    F.conv3d,
+    F.conv_transpose3d,
+)
+
+# Add constraints for fixed qparams ops like sigmoid and tanh to ensure values
+# fall within the proper ranges, e.g. [0, 1] for sigmoid, [-1, 1] for tanh
+_FIXED_QPARAM_OP_0TO1_CONSTRAINTS = DTypeWithConstraints(
+    dtype=torch.quint8,
+    quant_min_lower_bound=0,
+    quant_max_upper_bound=255,
+    scale_exact_match=1.0 / 256.0,
+    zero_point_exact_match=0,
+)
+_FIXED_QPARAM_OP_NEG1TO1_CONSTRAINTS = DTypeWithConstraints(
+    dtype=torch.quint8,
+    quant_min_lower_bound=0,
+    quant_max_upper_bound=255,
+    scale_exact_match=2.0 / 256.0,
+    zero_point_exact_match=128,
+)
+_FIXED_QPARAMS_OP_TO_CONSTRAINTS: Dict[Union[Callable, str], DTypeWithConstraints] = {
+    torch.nn.Hardsigmoid: _FIXED_QPARAM_OP_0TO1_CONSTRAINTS,
+    torch.nn.functional.hardsigmoid: _FIXED_QPARAM_OP_0TO1_CONSTRAINTS,
+    "hardsigmoid": _FIXED_QPARAM_OP_0TO1_CONSTRAINTS,
+    "hardsigmoid_": _FIXED_QPARAM_OP_0TO1_CONSTRAINTS,
+    torch.nn.Sigmoid: _FIXED_QPARAM_OP_0TO1_CONSTRAINTS,
+    torch.sigmoid: _FIXED_QPARAM_OP_0TO1_CONSTRAINTS,
+    "sigmoid": _FIXED_QPARAM_OP_0TO1_CONSTRAINTS,
+    "sigmoid_": _FIXED_QPARAM_OP_0TO1_CONSTRAINTS,
+    torch.nn.Softmax: _FIXED_QPARAM_OP_0TO1_CONSTRAINTS,
+    torch.nn.Tanh: _FIXED_QPARAM_OP_NEG1TO1_CONSTRAINTS,
+    torch.tanh: _FIXED_QPARAM_OP_NEG1TO1_CONSTRAINTS,
+    "tanh": _FIXED_QPARAM_OP_NEG1TO1_CONSTRAINTS,
+    "tanh_": _FIXED_QPARAM_OP_NEG1TO1_CONSTRAINTS,
+}
+
+
+def _get_binary_op_configs(
+    dtype_configs: List[DTypeConfig],
+) -> List[BackendPatternConfig]:
+    binary_op_configs: List[BackendPatternConfig] = []
+    num_tensor_args_to_observation_type_mapping = {
+        # TODO: this is not used right now since we have extra check in prepare
+        # will need to change this to NO_OBSERVER later after we implemented
+        # Tensor dtype inference properly
+        0: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
+        1: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
+        2: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
+    }
+    for op_with_quantized_bop_scalar_variant in [
+        operator.add,
+        torch.add,
+        operator.mul,
+        torch.mul,
+    ]:
+        bop_patterns = [
+            (op_with_quantized_bop_scalar_variant, nn.ReLU),
+            (op_with_quantized_bop_scalar_variant, F.relu),
+            (op_with_quantized_bop_scalar_variant, torch.relu),
+            op_with_quantized_bop_scalar_variant,
+        ]
+        for bop_pattern in bop_patterns:
+            binary_op_configs.append(
+                BackendPatternConfig(bop_pattern)
+                .set_dtype_configs(dtype_configs)  # noqa: E131
+                ._set_num_tensor_args_to_observation_type(
+                    num_tensor_args_to_observation_type_mapping
+                )
+            )
+    # matmul
+    binary_op_configs.append(
+        BackendPatternConfig(torch.matmul).set_dtype_configs(
+            dtype_configs
+        )  # noqa: E131
+    )
+    return binary_op_configs
+
+
+def _get_linear_configs(dtype_configs: List[DTypeConfig]) -> List[BackendPatternConfig]:
+    """
+    Return all configs related to linear modules and ops.
+    """
+    observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+    linear_configs: List[BackendPatternConfig] = []
+
+    # (1) Single linear modules/functions
+    # -------------------------------------
+    # linear module
+    linear_configs.append(
+        BackendPatternConfig(torch.nn.Linear)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        .set_root_module(torch.nn.Linear)
+        .set_reference_quantized_module(nnqr.Linear)
+        .set_qat_module(nnqat.Linear)
+    )
+    # linear qat module
+    linear_configs.append(
+        BackendPatternConfig(nnqat.Linear)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        .set_root_module(torch.nn.Linear)
+        .set_reference_quantized_module(nnqr.Linear)
+    )
+    # functional linear
+    linear_configs.append(
+        BackendPatternConfig(torch.nn.functional.linear)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_input_type_to_index({"weight": 1, "bias": 2})
+    )
+
+    # (2) Linear + relu
+    # -------------------
+    # 2.1 linear module + relu fusion config
+    # linear relu, linear module + relu module
+    linear_configs.append(
+        BackendPatternConfig((torch.nn.Linear, torch.nn.ReLU))
+        .set_dtype_configs(dtype_configs)  # noqa: E131
+        .set_fuser_method(_sequential_wrapper2(nni.LinearReLU))
+        .set_fused_module(nni.LinearReLU)
+    )
+    # linear relu, linear module + functional relu
+    linear_configs.append(
+        BackendPatternConfig((torch.nn.Linear, torch.nn.functional.relu))
+        .set_dtype_configs(dtype_configs)  # noqa: E131
+        .set_fuser_method(_sequential_wrapper2(nni.LinearReLU))
+        .set_fused_module(nni.LinearReLU)
+    )
+
+    # 2.2 linear module + relu, fused module configs
+    # linear relu, fused module
+    linear_configs.append(
+        BackendPatternConfig(nni.LinearReLU)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        .set_root_module(torch.nn.Linear)
+        .set_reference_quantized_module(nnqr.Linear)
+        .set_qat_module(nniqat.LinearReLU)
+    )
+    # linear relu, qat fused module
+    linear_configs.append(
+        BackendPatternConfig(nniqat.LinearReLU)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        .set_root_module(torch.nn.Linear)
+        .set_reference_quantized_module(nnqr.Linear)
+    )
+    # 2.3 functional linear + relu configs
+    # linear relu, functional linear + relu module
+    linear_configs.append(
+        BackendPatternConfig((F.linear, torch.nn.ReLU))
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+    )
+    # linear relu, functional linear + functional relu
+    linear_configs.append(
+        BackendPatternConfig((F.linear, F.relu))
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+    )
+
+    # (3) Linear + batchnorm
+    # ------------------------
+    # 3.1 linear bn fusion
+    linear_configs.append(
+        BackendPatternConfig((nn.Linear, nn.BatchNorm1d))
+        .set_dtype_configs(dtype_configs)  # noqa: E131
+        .set_fuser_method(fuse_linear_bn)
+        .set_fused_module(nni.LinearBn1d)
+    )
+
+    # 3.2 linear bn fused
+    # linear bn, fused module
+    linear_configs.append(
+        BackendPatternConfig(nni.LinearBn1d)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        .set_root_module(torch.nn.Linear)
+        .set_reference_quantized_module(nnqr.Linear)
+        .set_qat_module(nniqat.LinearBn1d)
+    )
+    # linear bn, qat fused module
+    linear_configs.append(
+        BackendPatternConfig(nniqat.LinearBn1d)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        .set_root_module(torch.nn.Linear)
+        .set_reference_quantized_module(nnqr.Linear)
+    )
+    return linear_configs
+
+
+def _get_conv_configs(dtype_configs):
+    """
+    Return all configs related to conv modules and ops.
+    """
+    conv_configs = []
+    observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+    for convs in [_Conv1dMetadata, _Conv2dMetadata, _Conv3dMetadata]:
+        # (1) Single conv modules/functions
+        # -----------------------------------
+        # conv module
+        conv_configs.append(
+            BackendPatternConfig(convs.root)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+            .set_qat_module(convs.qat)
+        )
+        # conv qat module
+        conv_configs.append(
+            BackendPatternConfig(convs.qat)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+        )
+        # functional conv
+        conv_configs.append(
+            BackendPatternConfig(convs.func)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            ._set_input_type_to_index({"weight": 1, "bias": 2})
+        )
+
+        # (2) Conv + relu
+        # -----------------
+        # 2.1 conv module + relu fusion configs
+        # conv relu fusion, conv module + relu module
+        conv_configs.append(
+            BackendPatternConfig((convs.root, torch.nn.ReLU))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(_sequential_wrapper2(convs.fused_conv_relu))
+            .set_fused_module(convs.fused_conv_relu)
+        )
+        # conv relu fusion, conv module + functional relu
+        conv_configs.append(
+            BackendPatternConfig((convs.root, F.relu))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(_sequential_wrapper2(convs.fused_conv_relu))
+            .set_fused_module(convs.fused_conv_relu)
+        )
+        # 2.2 conv module + relu fused module configs
+        # conv relu, fused module
+        conv_configs.append(
+            BackendPatternConfig(convs.fused_conv_relu)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+            .set_qat_module(convs.relu_qat)
+        )
+        # conv relu, qat fused module
+        conv_configs.append(
+            BackendPatternConfig(convs.relu_qat)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+        )
+        # 2.3 functional conv + relu configs
+        # conv relu, functional conv + relu module
+        conv_configs.append(
+            BackendPatternConfig((convs.func, torch.nn.ReLU))
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+        )
+        # conv relu, functional conv + functional relu
+        conv_configs.append(
+            BackendPatternConfig((convs.func, F.relu))
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+        )
+
+        # fused conv relu
+        conv_configs.append(
+            BackendPatternConfig(convs.fused_conv_relu)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_qat_module(convs.relu_qat)
+        )
+
+        conv_configs.append(
+            BackendPatternConfig(convs.relu_qat)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+        )
+
+        # (3) Conv + batchnorm (+ relu)
+        # -------------------------------
+        # 3.1 conv bn fusion configs
+        # conv + bn fusion
+        conv_configs.append(
+            BackendPatternConfig((convs.root, convs.bn))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(fuse_conv_bn)
+            .set_fused_module(convs.fused_conv_bn)
+        )
+        # conv + bn + relu module fusion
+        conv_configs.append(
+            BackendPatternConfig((convs.root, convs.bn, nn.ReLU))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(fuse_conv_bn_relu)
+            .set_fused_module(convs.fused_conv_bn_relu)
+        )
+        # conv + bn + relu functional fusion
+        conv_configs.append(
+            BackendPatternConfig((convs.root, convs.bn, F.relu))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_root_module(convs.root)
+            .set_fuser_method(fuse_conv_bn_relu)
+            .set_fused_module(convs.fused_conv_bn_relu)
+        )
+        # TODO: we can add fusion for torch.relu as well
+
+        # 3.2 conv + bn (+ relu) fused module configs
+        # fused conv bn
+        conv_configs.append(
+            BackendPatternConfig(convs.fused_conv_bn)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_qat_module(convs.bn_qat)
+        )
+
+        # fused conv bn relu
+        conv_configs.append(
+            BackendPatternConfig(convs.fused_conv_bn_relu)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_qat_module(convs.bn_relu_qat)
+        )
+
+        # conv bn, qat fused module
+        conv_configs.append(
+            BackendPatternConfig(convs.bn_qat)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+        )
+        # conv bn relu, qat fused module
+        conv_configs.append(
+            BackendPatternConfig(convs.bn_relu_qat)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+        )
+
+        # (4) conv transpose and its fusion
+        # 4.1 conv transpose config
+        conv_configs.append(
+            BackendPatternConfig(convs.transpose)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_root_module(convs.transpose)
+            .set_reference_quantized_module(convs.transpose_reference)
+        )
+
+        # 4.2 conv transpose + bn fusion
+        conv_configs.append(
+            BackendPatternConfig((convs.transpose, convs.bn))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(fuse_convtranspose_bn)
+            .set_root_module(convs.transpose)
+            .set_reference_quantized_module(convs.transpose_reference)
+        )
+
+        # 4.3 functional conv transpose
+        conv_configs.append(
+            BackendPatternConfig(convs.func_transpose)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            ._set_input_type_to_index({"weight": 1, "bias": 2})
+        )
+
+    return conv_configs
+
+
+def _get_cat_config(dtype_configs: List[DTypeConfig]) -> BackendPatternConfig:
+    return (
+        BackendPatternConfig(torch.cat)
+        .set_observation_type(ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT)
+        .set_dtype_configs(dtype_configs)
+    )
+
+
+def _get_ln_configs(dtype_configs: List[DTypeConfig]) -> List[BackendPatternConfig]:
+    ln_configs = []
+    ln_configs.append(
+        BackendPatternConfig(torch.nn.LayerNorm)
+        .set_observation_type(
+            ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+        )  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+    )
+    ln_configs.append(
+        BackendPatternConfig(torch.nn.functional.layer_norm)
+        .set_observation_type(
+            ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+        )  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_input_type_to_index({"weight": 2, "bias": 3})
+    )
+    return ln_configs
+
+
+def _get_default_op_configs(
+    dtype_configs: List[DTypeConfig],
+) -> List[BackendPatternConfig]:
+    configs = []
+    default_ops = [
+        torch.nn.ELU,
+        torch.nn.LeakyReLU,
+        torch.nn.Hardswish,
+        torch.nn.InstanceNorm1d,
+        torch.nn.InstanceNorm2d,
+        torch.nn.InstanceNorm3d,
+        torch.nn.Dropout,
+        torch.nn.PReLU,
+        torch.nn.functional.elu,
+        torch.nn.functional.hardswish,
+        torch.nn.functional.leaky_relu,
+        torch.nn.functional.dropout,
+    ]
+    for op in default_ops:
+        configs.append(
+            BackendPatternConfig(op)
+            .set_observation_type(
+                ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+            )  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+        )
+
+    configs.append(
+        BackendPatternConfig(torch.nn.functional.group_norm)
+        .set_observation_type(
+            ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+        )  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_input_type_to_index({"weight": 2, "bias": 3})
+    )
+
+    configs.append(
+        BackendPatternConfig(torch.nn.functional.instance_norm)
+        .set_observation_type(
+            ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+        )  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_input_type_to_index({"weight": 3, "bias": 4})
+    )
+    return configs
+
+
+def _add_fixed_qparams_to_dtype_configs(
+    dtype_configs: List[DTypeConfig],
+    constraints: DTypeWithConstraints,
+) -> List[DTypeConfig]:
+    """
+    Return a copy of the list of DTypeConfigs where activations are subject to the specified
+    constraints required for fixed qparams ops.
+
+    If the data type doesn't match the one in the constraints, simply leave the corresponding
+    DTypeConfig unchanged.
+
+    If `scale_min_lower_bound` or `scale_max_upper_bound` is specified in the activations,
+    throw an exception since these settings are incompatible with fixed qparams ops.
+    """
+    new_dtype_configs = []
+    for dtype_config in dtype_configs:
+        dc = copy.deepcopy(dtype_config)
+        for orig_constraints in [
+            dc.input_dtype_with_constraints,
+            dc.output_dtype_with_constraints,
+        ]:
+            if orig_constraints.dtype != constraints.dtype:
+                continue
+            if orig_constraints.scale_min_lower_bound is not None:
+                raise ValueError(
+                    f"scale_min_lower_bound is invalid for fixed qparams ops: {dtype_config}"
+                )
+            if orig_constraints.scale_max_upper_bound is not None:
+                raise ValueError(
+                    f"scale_max_upper_bound is invalid for fixed qparams ops: {dtype_config}"
+                )
+            orig_constraints.quant_min_lower_bound = constraints.quant_min_lower_bound
+            orig_constraints.quant_max_upper_bound = constraints.quant_max_upper_bound
+            orig_constraints.scale_exact_match = constraints.scale_exact_match
+            orig_constraints.zero_point_exact_match = constraints.zero_point_exact_match
+        new_dtype_configs.append(dc)
+    return new_dtype_configs
+
+
+def _get_fixed_qparams_op_configs(
+    dtype_configs: List[DTypeConfig],
+) -> List[BackendPatternConfig]:
+    fixed_qparams_op_configs = []
+    for fixed_qparam_op, constraints in _FIXED_QPARAMS_OP_TO_CONSTRAINTS.items():
+        new_dtype_configs = _add_fixed_qparams_to_dtype_configs(
+            dtype_configs, constraints
+        )
+        fixed_qparams_op_configs.append(
+            BackendPatternConfig(fixed_qparam_op)
+            .set_observation_type(
+                ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+            )  # noqa: E131
+            .set_dtype_configs(new_dtype_configs)
+        )
+    return fixed_qparams_op_configs
+
+
+def _get_share_qparams_op_configs(dtype_configs):
+    """Get the operator config for the operators that works for both float and quantized input
+    if input is quantized, the output Tensor shares the same quantization parameter
+    with input.
+    Example operator: avgpool2d, reshape, transpose, maxpool2d
+    Example observed operator:
+    observer_0 - avgpool2d - observer_0 (same observer instance as input)
+    """
+
+    def _get_share_qprams_op_backend_config(op):
+        return (
+            BackendPatternConfig(op)
+            .set_observation_type(ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT)
+            .set_dtype_configs(dtype_configs)
+        )
+
+    share_qparams_ops = [
+        torch.nn.AdaptiveAvgPool1d,
+        torch.nn.AdaptiveAvgPool2d,
+        torch.nn.AdaptiveAvgPool3d,
+        torch.nn.AvgPool1d,
+        torch.nn.AvgPool2d,
+        torch.nn.AvgPool3d,
+        torch.nn.Hardtanh,
+        torch.nn.Identity,
+        torch.nn.MaxPool1d,
+        torch.nn.MaxPool2d,
+        torch.nn.MaxPool3d,
+        torch.nn.PixelShuffle,
+        torch.nn.PixelUnshuffle,
+        torch.nn.ReLU,
+        torch.nn.ReLU6,
+        torch.adaptive_avg_pool1d,
+        torch.nn.functional.adaptive_avg_pool2d,
+        torch.nn.functional.adaptive_avg_pool3d,
+        torch.nn.functional.hardtanh,
+        torch.nn.functional.hardtanh_,
+        torch.nn.functional.interpolate,
+        torch.nn.functional.max_pool1d,
+        torch.nn.functional.max_pool2d,
+        torch.nn.functional.max_pool3d,
+        torch.nn.functional.pixel_shuffle,
+        torch.nn.functional.pixel_unshuffle,
+        torch.nn.functional.relu,
+        torch.nn.functional.relu6,
+        torch.avg_pool1d,
+        torch._C._nn.avg_pool2d,
+        torch._C._nn.avg_pool3d,
+        torch.clamp,
+        torch.flatten,
+        torch.mean,
+        torch.narrow,
+        torch.repeat_interleave,
+        torch.transpose,
+        torch.squeeze,
+        torch.stack,
+        torch.unsqueeze,
+        operator.floordiv,
+        "contiguous",
+        "clamp",
+        "detach",
+        "detach_",
+        "mean",
+        "permute",
+        "repeat",
+        "repeat_interleave",
+        "reshape",
+        "resize_",
+        "relu",
+        "relu_",
+        "squeeze",
+        "squeeze_",
+        "transpose",
+        "unsqueeze",
+        "unsqueeze_",
+        "view",
+    ]
+    return [_get_share_qprams_op_backend_config(op) for op in share_qparams_ops]
+
+
+def _get_bn_configs(dtype_configs: List[DTypeConfig]) -> List[BackendPatternConfig]:
+    """Get configs related to batchnorm."""
+    bn_configs = []
+    bn_to_fused_bn = {
+        torch.nn.BatchNorm2d: nni.BNReLU2d,
+        torch.nn.BatchNorm3d: nni.BNReLU3d,
+    }
+    for bn in bn_to_fused_bn.keys():
+        fused_bn = bn_to_fused_bn[bn]
+        # bn module + relu module fusion config
+        bn_configs.append(
+            BackendPatternConfig((bn, nn.ReLU))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(_sequential_wrapper2(fused_bn))
+            .set_fused_module(fused_bn)
+        )
+        # bn module + F.relu fusion config
+        bn_configs.append(
+            BackendPatternConfig((bn, F.relu))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(_sequential_wrapper2(fused_bn))
+            .set_fused_module(fused_bn)
+        )
+        bn_configs.append(
+            BackendPatternConfig(bn)
+            .set_observation_type(
+                ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+            )  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+        )
+
+    # fused bn configs
+    for fused_bn in bn_to_fused_bn.values():
+        bn_configs.append(
+            BackendPatternConfig(fused_bn)
+            .set_observation_type(
+                ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+            )  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+        )
+    return bn_configs
+
+
+def _get_rnn_op_configs(dtype_configs: List[DTypeConfig]) -> List[BackendPatternConfig]:
+    rnn_op_configs = []
+    for rnn_op, ref_rnn_op in [
+        (nn.GRUCell, nnqr.GRUCell),
+        (nn.LSTMCell, nnqr.LSTMCell),
+        (nn.RNNCell, nnqr.RNNCell),
+        (nn.LSTM, nnqr.LSTM),
+        (nn.GRU, nnqr.GRU),
+    ]:
+        rnn_op_configs.append(
+            BackendPatternConfig(rnn_op)
+            .set_observation_type(
+                ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+            )  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(rnn_op)
+            .set_reference_quantized_module(ref_rnn_op)
+        )
+    return rnn_op_configs
+
+
+def _get_embedding_op_configs(
+    dtype_configs: List[DTypeConfig],
+) -> List[BackendPatternConfig]:
+    embedding_op_configs = []
+    for embedding_op, qat_embedding_op, ref_embedding_op in [
+        (nn.Embedding, nnqat.Embedding, nnqr.Embedding),
+        (nn.EmbeddingBag, nnqat.EmbeddingBag, nnqr.EmbeddingBag),
+    ]:
+        embedding_op_configs.append(
+            BackendPatternConfig(embedding_op)
+            .set_observation_type(
+                ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+            )  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_qat_module(qat_embedding_op)
+            .set_root_module(embedding_op)
+            .set_reference_quantized_module(ref_embedding_op)
+        )
+
+        # config for qat op
+        embedding_op_configs.append(
+            BackendPatternConfig(qat_embedding_op)
+            .set_observation_type(
+                ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+            )  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(embedding_op)
+            .set_reference_quantized_module(ref_embedding_op)
+        )
+    return embedding_op_configs
+
+
+def _get_tensor_info_op_configs(dtype_configs):
+    """
+    These ops work on tensors of different dtypes but return non-tensors
+    containing information about the input tensor.
+    """
+
+    def _get_config(op):
+        return (
+            BackendPatternConfig(op)
+            .set_observation_type(ObservationType.INPUT_OUTPUT_NOT_OBSERVED)
+            .set_dtype_configs(dtype_configs)
+        )
+
+    return [_get_config(op) for op in ("shape", "size")]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/_qnnpack_pt2e.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/_qnnpack_pt2e.py
new file mode 100644
index 0000000000000000000000000000000000000000..fdfeb4ab36ee4b5f4703d5ed0745d089b815e432
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/_qnnpack_pt2e.py
@@ -0,0 +1,182 @@
+# mypy: allow-untyped-defs
+import operator
+
+import torch
+from torch.ao.quantization.backend_config import (
+    BackendConfig,
+    BackendPatternConfig,
+    DTypeConfig,
+    ObservationType,
+)
+
+
+weighted_op_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+)
+from typing import List
+
+
+def get_linear_configs():
+    linear_configs = []
+    observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+    dtype_configs = [weighted_op_quint8_dtype_config]
+
+    # TODO: need to fix the way we insert observers for this pattern
+    # should be solved in the new fusion API
+    # reason that this doesn't work: the pattern is a bit complicated and we don't
+    # have a way to specify which input of the pattern we would like to observe
+    # pattern:
+    # bias input weight
+    # \     |    /
+    #  \    |   t
+    #   \   |  /
+    #    addmm
+    # we want to observe "weight" as weight, but there is not way to convey this
+    # information with current pattern language
+    #
+    # right now:
+    # original:
+    #         weight - t \
+    #         input  - addmm
+    # observed (no hack):
+    #      weight - t - observer \
+    #       input - observer - addmm
+    # target:
+    #      weight - observer - t \
+    #        input - observer - addmm
+
+    # def root_node_getter(node_pattern):
+    #     addmm, bias, act, weight = node_pattern
+    #     return addmm
+
+    # linear_configs.append(
+    #     BackendPatternConfig((torch.ops.aten.addmm.default, MatchAllNode, MatchAllNode, torch.ops.aten.t.default))
+    #     .set_observation_type(observation_type)  # noqa: E131
+    #     .set_dtype_configs(dtype_configs)
+    #     ._set_root_node_getter(root_node_getter))
+
+    linear_configs.append(
+        BackendPatternConfig(torch.ops.aten.addmm.default)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_input_type_to_index({"weight": 2, "bias": 0})
+    )
+    # linear is decomposed to `t - mm` if bias is not present
+    linear_configs.append(
+        BackendPatternConfig(torch.ops.aten.mm.default)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_input_type_to_index({"weight": 1})
+    )
+    return linear_configs
+
+
+def get_conv_configs():
+    conv_configs = []
+    observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+    dtype_configs = [weighted_op_quint8_dtype_config]
+    conv_configs.append(
+        BackendPatternConfig(torch.ops.aten.convolution.default)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_input_type_to_index({"weight": 1, "bias": 2})
+    )
+    conv_configs.append(
+        BackendPatternConfig(
+            (torch.ops.aten.convolution.default, torch.ops.aten.relu.default)
+        )
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_input_type_to_index({"weight": 1, "bias": 2})
+    )
+    # TODO: remove when functionalization is supported in PT2 mode
+    conv_configs.append(
+        BackendPatternConfig(
+            (torch.ops.aten.convolution.default, torch.ops.aten.relu_.default)
+        )
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_input_type_to_index({"weight": 1, "bias": 2})
+    )
+    return conv_configs
+
+
+def get_pooling_configs():
+    backend_pattern_configs = []
+    observation_type = ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT
+    dtype_configs = [weighted_op_quint8_dtype_config]
+
+    def root_node_getter(node_pattern):
+        getitem, maxpool, index = node_pattern
+        return maxpool
+
+    backend_pattern_configs.append(
+        BackendPatternConfig()
+        ._set_pattern_complex_format(
+            (operator.getitem, torch.ops.aten.max_pool2d_with_indices.default, 0)
+        )
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_root_node_getter(root_node_getter)
+    )
+
+    return backend_pattern_configs
+
+
+def get_relu_configs():
+    backend_pattern_configs = []
+    observation_type = ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT
+    dtype_configs = [weighted_op_quint8_dtype_config]
+    backend_pattern_configs.append(
+        BackendPatternConfig(torch.ops.aten.relu.default)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+    )
+    return backend_pattern_configs
+
+
+def get_binary_op_configs():
+    binary_op_configs: List[BackendPatternConfig] = []
+    dtype_configs = [weighted_op_quint8_dtype_config]
+    num_tensor_args_to_observation_type_mapping = {
+        # TODO: this is not used right now since we have extra check in prepare
+        # will need to change this to NO_OBSERVER later after we implemented
+        # Tensor dtype inference properly
+        0: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
+        1: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
+        2: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
+    }
+    for op_with_quantized_bop_scalar_variant in [
+        torch.ops.aten.add.Tensor,
+        torch.ops.aten.add_.Tensor,
+    ]:
+        bop_patterns = [
+            (op_with_quantized_bop_scalar_variant, torch.ops.aten.relu.default),
+            op_with_quantized_bop_scalar_variant,
+            # TODO: remove when functionalization is supported in pt2_mode
+            (op_with_quantized_bop_scalar_variant, torch.ops.aten.relu_.default),
+        ]
+        for bop_pattern in bop_patterns:
+            binary_op_configs.append(
+                BackendPatternConfig(bop_pattern)
+                .set_dtype_configs(dtype_configs)  # noqa: E131
+                ._set_num_tensor_args_to_observation_type(
+                    num_tensor_args_to_observation_type_mapping
+                )
+            )
+
+    return binary_op_configs
+
+
+def get_qnnpack_pt2e_backend_config():
+    return (
+        BackendConfig("qnnpack_pytorch_2.0_export")
+        .set_backend_pattern_configs(get_linear_configs())
+        .set_backend_pattern_configs(get_binary_op_configs())
+        .set_backend_pattern_configs(get_conv_configs())
+        .set_backend_pattern_configs(get_pooling_configs())
+        .set_backend_pattern_configs(get_relu_configs())
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/backend_config.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/backend_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..1051455da712b23d8bb914283aeff07a5e5bac91
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/backend_config.py
@@ -0,0 +1,749 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+from dataclasses import dataclass
+from enum import Enum
+from typing import Any, Callable, Dict, List, Optional, Type, TYPE_CHECKING, Union
+
+import torch
+
+
+if TYPE_CHECKING:
+    from torch.ao.quantization.utils import Pattern
+
+
+__all__ = [
+    "BackendConfig",
+    "BackendPatternConfig",
+    "DTypeConfig",
+    "DTypeWithConstraints",
+    "ObservationType",
+]
+
+
+# DTypeConfig dict keys
+INPUT_DTYPE_DICT_KEY = "input_dtype"
+OUTPUT_DTYPE_DICT_KEY = "output_dtype"
+WEIGHT_DTYPE_DICT_KEY = "weight_dtype"
+BIAS_DTYPE_DICT_KEY = "bias_dtype"
+IS_DYNAMIC_DICT_KEY = "is_dynamic"
+
+# BackendConfig dict keys
+NAME_DICT_KEY = "name"
+CONFIGS_DICT_KEY = "configs"
+
+# BackendPatternConfig dict keys
+PATTERN_DICT_KEY = "pattern"
+PATTERN_COMPLEX_FORMAT_DICT_KEY = "pattern_complex_format"
+OBSERVATION_TYPE_DICT_KEY = "observation_type"
+DTYPE_CONFIGS_DICT_KEY = "dtype_configs"
+ROOT_MODULE_DICT_KEY = "root_module"
+QAT_MODULE_DICT_KEY = "qat_module"
+REFERENCE_QUANTIZED_MODULE_DICT_KEY = "reference_quantized_module_for_root"
+FUSED_MODULE_DICT_KEY = "fused_module"
+FUSER_METHOD_DICT_KEY = "fuser_method"
+ROOT_NODE_GETTER_DICT_KEY = "root_node_getter"
+EXTRA_INPUTS_GETTER_DICT_KEY = "extra_inputs_getter"
+NUM_TENSOR_ARGS_TO_OBSERVATION_TYPE_DICT_KEY = "num_tensor_args_to_observation_type"
+INPUT_TYPE_TO_INDEX_DICT_KEY = "input_type_to_index"
+
+
+# TODO: maybe rename this to something that's not related to observer
+# e.g. QParamsType
+class ObservationType(Enum):
+    """An enum that represents different ways of how an operator/operator pattern
+    should be observed
+    """
+
+    OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT = 0
+    """this means input and output are observed with different observers, based
+    on qconfig.activation
+    example: conv, linear, softmax
+    """
+
+    OUTPUT_SHARE_OBSERVER_WITH_INPUT = 1
+    """this means the output will use the same observer instance as input, based
+    on qconfig.activation
+    example: torch.cat, maxpool
+    """
+
+    INPUT_OUTPUT_NOT_OBSERVED = 2
+    """this means the input and output are never observed
+    example: x.shape, x.size
+    """
+
+
+@dataclass
+class DTypeWithConstraints:
+    """
+    Config for specifying additional constraints for a given dtype, such as quantization
+    value ranges, scale value ranges, and fixed quantization params, to be used in
+    :class:`~torch.ao.quantization.backend_config.DTypeConfig`.
+
+    The constraints currently supported are:
+
+    * `quant_min_lower_bound` and `quant_max_upper_bound`: Lower and upper
+      bounds for the minimum and maximum quantized values respectively. If
+      the QConfig's `quant_min` and `quant_max` fall outside this range,
+      then the QConfig will be ignored.
+
+    * `scale_min_lower_bound` and `scale_max_upper_bound`: Lower and upper
+      bounds for the minimum and maximum scale values respectively. If the
+      QConfig's minimum scale value (currently exposed as `eps`) falls below
+      the lower bound, then the QConfig will be ignored. Note that the upper
+      bound is currently not enforced.
+
+    * `scale_exact_match` and `zero_point_exact_match`: Exact match requirements
+      for scale and zero point, to be used for operators with fixed quantization
+      parameters such as sigmoid and tanh. If the observer specified in the QConfig
+      is neither `FixedQParamsObserver` nor `FixedQParamsFakeQuantize`, or if
+      the quantization parameters don't match, then the QConfig will be ignored.
+    """
+
+    dtype: Optional[torch.dtype] = None
+    quant_min_lower_bound: Union[int, float, None] = None
+    quant_max_upper_bound: Union[int, float, None] = None
+    scale_min_lower_bound: Union[int, float, None] = None
+    scale_max_upper_bound: Union[int, float, None] = None
+    scale_exact_match: Optional[float] = None
+    zero_point_exact_match: Optional[int] = None
+
+
+@dataclass
+class DTypeConfig:
+    """
+    Config object that specifies the supported data types passed as arguments to
+    quantize ops in the reference model spec, for input and output activations,
+    weights, and biases.
+
+    For example, consider the following reference model:
+
+      quant1 - [dequant1 - fp32_linear - quant2] - dequant2
+
+    The pattern in the square brackets refers to the reference pattern of
+    statically quantized linear. Setting the input dtype as `torch.quint8`
+    in the DTypeConfig means we pass in `torch.quint8` as the dtype argument
+    to the first quantize op (quant1). Similarly, setting the output dtype as
+    `torch.quint8` means we pass in `torch.quint8` as the dtype argument to
+    the second quantize op (quant2).
+
+    Note that the dtype here does not refer to the interface dtypes of the
+    op. For example, the "input dtype" here is not the dtype of the input
+    tensor passed to the quantized linear op. Though it can still be the
+    same as the interface dtype, this is not always the case, e.g. the
+    interface dtype is fp32 in dynamic quantization but the "input dtype"
+    specified in the DTypeConfig would still be quint8. The semantics of
+    dtypes here are the same as the semantics of the dtypes specified in
+    the observers.
+
+    These dtypes are matched against the ones specified in the user's
+    QConfig. If there is a match, and the QConfig satisfies the constraints
+    specified in the DTypeConfig (if any), then we will quantize the given
+    pattern using this DTypeConfig. Otherwise, the QConfig is ignored and
+    the pattern will not be quantized.
+
+    Example usage::
+
+        >>> # xdoctest: +SKIP(failing)
+        >>> dtype_config1 = DTypeConfig(
+        ...     input_dtype=torch.quint8,
+        ...     output_dtype=torch.quint8,
+        ...     weight_dtype=torch.qint8,
+        ...     bias_dtype=torch.float)
+
+        >>> dtype_config2 = DTypeConfig(
+        ...     input_dtype=DTypeWithConstraints(
+        ...         dtype=torch.quint8,
+        ...         quant_min_lower_bound=0,
+        ...         quant_max_upper_bound=255,
+        ...     ),
+        ...     output_dtype=DTypeWithConstraints(
+        ...         dtype=torch.quint8,
+        ...         quant_min_lower_bound=0,
+        ...         quant_max_upper_bound=255,
+        ...     ),
+        ...     weight_dtype=DTypeWithConstraints(
+        ...         dtype=torch.qint8,
+        ...         quant_min_lower_bound=-128,
+        ...         quant_max_upper_bound=127,
+        ...     ),
+        ...     bias_dtype=torch.float)
+
+        >>> dtype_config1.input_dtype
+        torch.quint8
+
+        >>> dtype_config2.input_dtype
+        torch.quint8
+
+        >>> dtype_config2.input_dtype_with_constraints
+        DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, \
+scale_min_lower_bound=None, scale_max_upper_bound=None)
+    """
+
+    input_dtype_with_constraints: DTypeWithConstraints
+    output_dtype_with_constraints: DTypeWithConstraints
+    weight_dtype_with_constraints: DTypeWithConstraints
+    bias_dtype: Optional[torch.dtype]
+    is_dynamic: Optional[bool]
+
+    def __init__(
+        self,
+        input_dtype: Union[torch.dtype, DTypeWithConstraints, None] = None,
+        output_dtype: Union[torch.dtype, DTypeWithConstraints, None] = None,
+        weight_dtype: Union[torch.dtype, DTypeWithConstraints, None] = None,
+        bias_dtype: Optional[torch.dtype] = None,
+        is_dynamic: Optional[bool] = None,
+    ):
+        if isinstance(input_dtype, DTypeWithConstraints):
+            self.input_dtype_with_constraints = input_dtype
+        else:
+            self.input_dtype_with_constraints = DTypeWithConstraints(dtype=input_dtype)
+
+        if isinstance(output_dtype, DTypeWithConstraints):
+            self.output_dtype_with_constraints = output_dtype
+        else:
+            self.output_dtype_with_constraints = DTypeWithConstraints(
+                dtype=output_dtype
+            )
+
+        if isinstance(weight_dtype, DTypeWithConstraints):
+            self.weight_dtype_with_constraints = weight_dtype
+        else:
+            self.weight_dtype_with_constraints = DTypeWithConstraints(
+                dtype=weight_dtype
+            )
+
+        self.bias_dtype = bias_dtype
+        self.is_dynamic = is_dynamic
+
+    @property
+    def input_dtype(self) -> Optional[torch.dtype]:
+        return self.input_dtype_with_constraints.dtype
+
+    @property
+    def output_dtype(self) -> Optional[torch.dtype]:
+        return self.output_dtype_with_constraints.dtype
+
+    @property
+    def weight_dtype(self) -> Optional[torch.dtype]:
+        return self.weight_dtype_with_constraints.dtype
+
+    @classmethod
+    def from_dict(cls, dtype_config_dict: Dict[str, Any]) -> DTypeConfig:
+        """
+        Create a ``DTypeConfig`` from a dictionary with the following items (all optional):
+            "input_dtype": torch.dtype or ``DTypeWithConstraints``
+            "output_dtype": torch.dtype or ``DTypeWithConstraints``
+            "weight_dtype": torch.dtype or ``DTypeWithConstraints``
+            "bias_type": torch.dtype
+            "is_dynamic": bool
+        """
+        input_dtype = dtype_config_dict.get(INPUT_DTYPE_DICT_KEY, None)
+        if input_dtype is not None and not isinstance(
+            input_dtype, (torch.dtype, DTypeWithConstraints)
+        ):
+            raise ValueError(
+                "Expected input_dtype to be a torch.dtype or DTypeWithConstraints"
+            )
+        output_dtype = dtype_config_dict.get(OUTPUT_DTYPE_DICT_KEY, None)
+        if output_dtype is not None and not isinstance(
+            output_dtype, (torch.dtype, DTypeWithConstraints)
+        ):
+            raise ValueError(
+                "Expected output_dtype to be a torch.dtype or DTypeWithConstraints"
+            )
+        weight_dtype = dtype_config_dict.get(WEIGHT_DTYPE_DICT_KEY, None)
+        if weight_dtype is not None and not isinstance(
+            weight_dtype, (torch.dtype, DTypeWithConstraints)
+        ):
+            raise ValueError(
+                "Expected weight_dtype to be a torch.dtype or DTypeWithConstraints"
+            )
+        bias_dtype = dtype_config_dict.get(BIAS_DTYPE_DICT_KEY, None)
+        is_dynamic = dtype_config_dict.get(IS_DYNAMIC_DICT_KEY, None)
+        return cls(input_dtype, output_dtype, weight_dtype, bias_dtype, is_dynamic)
+
+    def to_dict(self) -> Dict[str, Any]:
+        """
+        Convert this ``DTypeConfig`` to a dictionary with the items described in
+        :func:`~torch.ao.quantization.backend_config.DTypeConfig.from_dict`.
+        """
+        dtype_config_dict: Dict[str, Any] = {}
+        if self.input_dtype is not None:
+            dtype_config_dict[INPUT_DTYPE_DICT_KEY] = self.input_dtype_with_constraints
+        if self.output_dtype is not None:
+            dtype_config_dict[
+                OUTPUT_DTYPE_DICT_KEY
+            ] = self.output_dtype_with_constraints
+        if self.weight_dtype is not None:
+            dtype_config_dict[
+                WEIGHT_DTYPE_DICT_KEY
+            ] = self.weight_dtype_with_constraints
+        if self.bias_dtype is not None:
+            dtype_config_dict[BIAS_DTYPE_DICT_KEY] = self.bias_dtype
+        if self.is_dynamic is not None:
+            dtype_config_dict[IS_DYNAMIC_DICT_KEY] = self.is_dynamic
+        return dtype_config_dict
+
+
+class BackendConfig:
+    # TODO: refer to NativeBackendConfig once that is implemented
+    """Config that defines the set of patterns that can be quantized on a given backend, and how reference
+    quantized models can be produced from these patterns.
+
+    A pattern in this context refers to a module, a functional, an operator, or a directed acyclic graph
+    of the above. Each pattern supported on the target backend can be individually configured through
+    :class:`~torch.ao.quantization.backend_config.BackendPatternConfig` in terms of:
+
+    (1) The supported input/output activation, weight, and bias data types
+
+    (2) How observers and quant/dequant ops are inserted in order to construct the reference pattern, and
+
+    (3) (Optionally) Fusion, QAT, and reference module mappings.
+
+    The format of the patterns is described in:
+    https://github.com/pytorch/pytorch/blob/master/torch/ao/quantization/backend_config/README.md
+
+    Example usage::
+
+        import torch
+        from torch.ao.quantization.backend_config import (
+            BackendConfig,
+            BackendPatternConfig,
+            DTypeConfig,
+            ObservationType,
+        )
+
+        weighted_int8_dtype_config = DTypeConfig(
+            input_dtype=torch.quint8,
+            output_dtype=torch.quint8,
+            weight_dtype=torch.qint8,
+            bias_dtype=torch.float)
+
+        def fuse_conv2d_relu(is_qat, conv, relu):
+            return torch.ao.nn.intrinsic.ConvReLU2d(conv, relu)
+
+        # For quantizing Linear
+        linear_config = BackendPatternConfig(torch.nn.Linear) \
+            .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT) \
+            .add_dtype_config(weighted_int8_dtype_config) \
+            .set_root_module(torch.nn.Linear) \
+            .set_qat_module(torch.ao.nn.qat.Linear) \
+            .set_reference_quantized_module(torch.ao.nn.quantized.reference.Linear)
+
+        # For fusing Conv2d + ReLU into ConvReLU2d
+        conv_relu_config = BackendPatternConfig((torch.nn.Conv2d, torch.nn.ReLU)) \
+            .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT) \
+            .add_dtype_config(weighted_int8_dtype_config) \
+            .set_fused_module(torch.ao.nn.intrinsic.ConvReLU2d) \
+            .set_fuser_method(fuse_conv2d_relu)
+
+        # For quantizing ConvReLU2d
+        fused_conv_relu_config = BackendPatternConfig(torch.ao.nn.intrinsic.ConvReLU2d) \
+            .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT) \
+            .add_dtype_config(weighted_int8_dtype_config) \
+            .set_root_module(torch.nn.Conv2d) \
+            .set_qat_module(torch.ao.nn.intrinsic.qat.ConvReLU2d) \
+            .set_reference_quantized_module(torch.ao.nn.quantized.reference.Conv2d)
+
+        backend_config = BackendConfig("my_backend") \
+            .set_backend_pattern_config(linear_config) \
+            .set_backend_pattern_config(conv_relu_config) \
+            .set_backend_pattern_config(fused_conv_relu_config)
+
+    """
+
+    def __init__(self, name: str = ""):
+        self.name = name
+        # Store all BackendPatternConfigs in a map to handle duplicates
+        # Note: the key in this map uses the complex reversed tuple format.
+        # This is intended only for internal use; users who wish to access
+        # the original patterns should go through `self.configs` instead.
+        self._pattern_complex_format_to_config: Dict[Pattern, BackendPatternConfig] = {}
+
+    def __repr__(self):
+        return f"BackendConfig({self.__dict__})"
+
+    def set_name(self, name: str) -> BackendConfig:
+        """
+        Set the name of the target backend.
+        """
+        self.name = name
+        return self
+
+    def set_backend_pattern_config(self, config: BackendPatternConfig) -> BackendConfig:
+        """
+        Set the config for an pattern that can be run on the target backend.
+        This overrides any existing config for the given pattern.
+        """
+        # Avoid circular dependencies
+        pattern_complex_format = torch.ao.quantization.backend_config.utils._get_pattern_in_reversed_nested_tuple_format(
+            config
+        )  # type: ignore[attr-defined]
+        self._pattern_complex_format_to_config[pattern_complex_format] = config
+        return self
+
+    def set_backend_pattern_configs(
+        self, configs: List[BackendPatternConfig]
+    ) -> BackendConfig:
+        """
+        Set the configs for patterns that can be run on the target backend.
+        This overrides any existing config for a given pattern if it was previously registered already.
+        """
+        for conf in configs:
+            self.set_backend_pattern_config(conf)
+        return self
+
+    @property
+    def configs(self) -> List[BackendPatternConfig]:
+        """
+        Return a copy of the list of configs set in this `BackendConfig`.
+        """
+        return list(self._pattern_complex_format_to_config.values())
+
+    @classmethod
+    def from_dict(cls, backend_config_dict: Dict[str, Any]) -> BackendConfig:
+        """
+        Create a ``BackendConfig`` from a dictionary with the following items:
+
+            "name": the name of the target backend
+
+            "configs": a list of dictionaries that each represents a `BackendPatternConfig`
+
+        """
+        conf = cls(backend_config_dict.get(NAME_DICT_KEY, ""))
+        for d in backend_config_dict.get(CONFIGS_DICT_KEY, []):
+            if isinstance(d, BackendPatternConfig):
+                conf.set_backend_pattern_config(d)
+            elif isinstance(d, Dict):
+                conf.set_backend_pattern_config(BackendPatternConfig.from_dict(d))
+            else:
+                raise ValueError(
+                    f"Expected backend_config_dict['{CONFIGS_DICT_KEY}'] to be a dictionary"
+                )
+        return conf
+
+    def to_dict(self) -> Dict[str, Any]:
+        """
+        Convert this ``BackendConfig`` to a dictionary with the items described in
+        :func:`~torch.ao.quantization.backend_config.BackendConfig.from_dict`.
+        """
+        return {
+            NAME_DICT_KEY: self.name,
+            CONFIGS_DICT_KEY: [c.to_dict() for c in self.configs],
+        }
+
+
+class BackendPatternConfig:
+    """
+    Config object that specifies quantization behavior for a given operator pattern.
+    For a detailed example usage, see :class:`~torch.ao.quantization.backend_config.BackendConfig`.
+    """
+
+    def __init__(self, pattern: Optional[Pattern] = None):
+        self.pattern: Optional[Pattern] = pattern
+        self.observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+        self.dtype_configs: List[DTypeConfig] = []
+        self.root_module: Optional[Type[torch.nn.Module]] = None
+        self.qat_module: Optional[Type[torch.nn.Module]] = None
+        self.reference_quantized_module: Optional[Type[torch.nn.Module]] = None
+        self.fused_module: Optional[Type[torch.nn.Module]] = None
+        self.fuser_method: Optional[Callable] = None
+
+        # Temporary/internal configs
+        self._root_node_getter: Optional[Callable] = None
+        self._extra_inputs_getter: Optional[Callable] = None
+        self._num_tensor_args_to_observation_type: Dict[int, ObservationType] = {}
+        self._input_type_to_index: Dict[str, int] = {}
+        self._pattern_complex_format: Optional[Pattern] = None
+
+    def __repr__(self):
+        dict_nonempty = {
+            k: v
+            for k, v in self.__dict__.items()
+            if (
+                (not isinstance(v, (list, dict)) and v is not None)
+                or (isinstance(v, (list, dict)) and len(v) > 0)
+            )
+        }
+        return f"BackendPatternConfig({dict_nonempty})"
+
+    def set_pattern(self, pattern: Pattern) -> BackendPatternConfig:
+        """
+        Set the pattern to configure.
+
+        The pattern can be a float module, functional operator, pytorch operator, or a tuple
+        combination of the above. Tuple patterns are treated as sequential patterns, and
+        currently only tuples of 2 or 3 elements are supported.
+        """
+        if self._pattern_complex_format is not None:
+            raise ValueError(
+                "Only one of 'pattern' or 'pattern_complex_format' can be set"
+            )
+        self.pattern = pattern
+        return self
+
+    def set_observation_type(
+        self, observation_type: ObservationType
+    ) -> BackendPatternConfig:
+        """
+        Set how observers should be inserted in the graph for this pattern.
+
+        Observation type here refers to how observers (or quant-dequant ops) will be placed
+        in the graph. This is used to produce the desired reference patterns understood by
+        the backend. Weighted ops such as linear and conv require different observers
+        (or quantization parameters passed to quantize ops in the reference model) for the
+        input and the output.
+
+        There are two observation types:
+
+            `OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT` (default): the output observer instance
+            will be different from the input. This is the most common observation type.
+
+            `OUTPUT_SHARE_OBSERVER_WITH_INPUT`: the output observer instance will be the
+            same as the input. This is useful for operators like `cat`.
+
+        Note: This will be renamed in the near future, since we will soon insert QuantDeQuantStubs
+        with observers (and fake quantizes) attached instead of observers themselves.
+        """
+        self.observation_type = observation_type
+        return self
+
+    def add_dtype_config(self, dtype_config: DTypeConfig) -> BackendPatternConfig:
+        """
+        Add a set of supported data types passed as arguments to quantize ops in the
+        reference model spec.
+        """
+        self.dtype_configs.append(dtype_config)
+        return self
+
+    def set_dtype_configs(
+        self, dtype_configs: List[DTypeConfig]
+    ) -> BackendPatternConfig:
+        """
+        Set the supported data types passed as arguments to quantize ops in the
+        reference model spec, overriding all previously registered data types.
+        """
+        self.dtype_configs = dtype_configs
+        return self
+
+    def set_root_module(
+        self, root_module: Type[torch.nn.Module]
+    ) -> BackendPatternConfig:
+        """
+        Set the module that represents the root for this pattern.
+
+        When we construct the reference quantized model during the convert phase,
+        the root modules (e.g. torch.nn.Linear for torch.ao.nn.intrinsic.LinearReLU)
+        will be swapped to the corresponding reference quantized modules (e.g.
+        torch.ao.nn.reference.quantized.Linear). This allows custom backends to
+        specify custom reference quantized module implementations to match the
+        numerics of their lowered operators. Since this is a one-to-one mapping,
+        both the root module and the reference quantized module must be specified
+        in the same BackendPatternConfig in order for the conversion to take place.
+        """
+        self.root_module = root_module
+        return self
+
+    def set_qat_module(self, qat_module: Type[torch.nn.Module]) -> BackendPatternConfig:
+        """
+        Set the module that represents the QAT implementation for this pattern.
+        """
+        self.qat_module = qat_module
+        return self
+
+    def set_reference_quantized_module(
+        self, reference_quantized_module: Type[torch.nn.Module]
+    ) -> BackendPatternConfig:
+        """
+        Set the module that represents the reference quantized implementation for
+        this pattern's root module.
+
+        For more detail, see :func:`~torch.ao.quantization.backend_config.BackendPatternConfig.set_root_module`.
+        """
+        self.reference_quantized_module = reference_quantized_module
+        return self
+
+    def set_fused_module(
+        self, fused_module: Type[torch.nn.Module]
+    ) -> BackendPatternConfig:
+        """
+        Set the module that represents the fused implementation for this pattern.
+        """
+        self.fused_module = fused_module
+        return self
+
+    def set_fuser_method(self, fuser_method: Callable) -> BackendPatternConfig:
+        """
+        Set the function that specifies how to fuse this BackendPatternConfig's pattern.
+
+        The first argument of this function should be `is_qat`, and the rest of the arguments
+        should be the items in the tuple pattern. The return value of this function should be
+        the resulting fused module.
+
+        For example, the fuser method for the pattern `(torch.nn.Linear, torch.nn.ReLU)` can be:
+
+            def fuse_linear_relu(is_qat, linear, relu):
+                return torch.ao.nn.intrinsic.LinearReLU(linear, relu)
+
+        For a more complicated example, see https://gist.github.com/jerryzh168/8bea7180a8ba3c279f2c9b050f2a69a6.
+        """
+        self.fuser_method = fuser_method
+        return self
+
+    def _set_root_node_getter(self, root_node_getter: Callable) -> BackendPatternConfig:
+        self._root_node_getter = root_node_getter
+        return self
+
+    def _set_extra_inputs_getter(
+        self, extra_inputs_getter: Callable
+    ) -> BackendPatternConfig:
+        self._extra_inputs_getter = extra_inputs_getter
+        return self
+
+    def _set_num_tensor_args_to_observation_type(
+        self, num_tensor_args_to_observation_type: Dict[int, ObservationType]
+    ) -> BackendPatternConfig:
+        self._num_tensor_args_to_observation_type = num_tensor_args_to_observation_type
+        return self
+
+    def _set_input_type_to_index(
+        self, input_type_to_index: Dict[str, int]
+    ) -> BackendPatternConfig:
+        self._input_type_to_index = input_type_to_index
+        return self
+
+    def _set_pattern_complex_format(self, pattern: Pattern) -> BackendPatternConfig:
+        """
+        Set the pattern to configure, using the reversed nested tuple format.
+
+        See the BackendConfig README for more detail:
+        https://github.com/pytorch/pytorch/blob/master/torch/ao/quantization/backend_config/README.md#advanced-pattern-specification
+        """
+        if self.pattern is not None:
+            raise ValueError(
+                "Only one of 'pattern' or 'pattern_complex_format' can be set"
+            )
+        self._pattern_complex_format = pattern
+        return self
+
+    @classmethod
+    def from_dict(
+        cls, backend_pattern_config_dict: Dict[str, Any]
+    ) -> BackendPatternConfig:
+        """
+        Create a ``BackendPatternConfig`` from a dictionary with the following items:
+
+            "pattern": the pattern being configured
+            "observation_type": the :class:`~torch.ao.quantization.backend_config.ObservationType` that specifies how
+            observers should be inserted for this pattern
+            "dtype_configs": a list of dictionaries that represents :class:`~torch.ao.quantization.backend_config.DTypeConfig` s
+            "root_module": a :class:`torch.nn.Module` that represents the root for this pattern
+            "qat_module": a :class:`torch.nn.Module` that represents the QAT implementation for this pattern
+            "reference_quantized_module": a :class:`torch.nn.Module` that represents the reference quantized
+            implementation for this pattern's root module.
+            "fused_module": a :class:`torch.nn.Module` that represents the fused implementation for this pattern
+            "fuser_method": a function that specifies how to fuse the pattern for this pattern
+            "pattern_complex_format": the pattern specified in the reversed nested tuple format (deprecated)
+
+        """
+
+        def _get_dtype_config(obj: Any) -> DTypeConfig:
+            """
+            Convert the given object into a ``DTypeConfig`` if possible, else throw an exception.
+            """
+            if isinstance(obj, DTypeConfig):
+                return obj
+            if isinstance(obj, Dict):
+                return DTypeConfig.from_dict(obj)
+            raise ValueError(
+                f"Expected a list of DTypeConfigs in "
+                f"backend_pattern_config_dict[\"{DTYPE_CONFIGS_DICT_KEY}\"], got '{type(obj)}'"
+            )
+
+        conf = cls()
+        if PATTERN_DICT_KEY in backend_pattern_config_dict:
+            conf.set_pattern(backend_pattern_config_dict[PATTERN_DICT_KEY])
+        if OBSERVATION_TYPE_DICT_KEY in backend_pattern_config_dict:
+            conf.set_observation_type(
+                backend_pattern_config_dict[OBSERVATION_TYPE_DICT_KEY]
+            )
+        for d in backend_pattern_config_dict.get(DTYPE_CONFIGS_DICT_KEY, []):
+            conf.add_dtype_config(_get_dtype_config(d))
+        conf.set_root_module(
+            backend_pattern_config_dict.get(ROOT_MODULE_DICT_KEY, None)
+        )
+        conf.set_qat_module(backend_pattern_config_dict.get(QAT_MODULE_DICT_KEY, None))
+        conf.set_reference_quantized_module(
+            backend_pattern_config_dict.get(REFERENCE_QUANTIZED_MODULE_DICT_KEY, None)
+        )
+        conf.set_fused_module(
+            backend_pattern_config_dict.get(FUSED_MODULE_DICT_KEY, None)
+        )
+        conf.set_fuser_method(
+            backend_pattern_config_dict.get(FUSER_METHOD_DICT_KEY, None)
+        )
+        conf._set_root_node_getter(
+            backend_pattern_config_dict.get(ROOT_NODE_GETTER_DICT_KEY, None)
+        )
+        conf._set_extra_inputs_getter(
+            backend_pattern_config_dict.get(EXTRA_INPUTS_GETTER_DICT_KEY, None)
+        )
+        conf._set_num_tensor_args_to_observation_type(
+            backend_pattern_config_dict.get(
+                NUM_TENSOR_ARGS_TO_OBSERVATION_TYPE_DICT_KEY, {}
+            )
+        )
+        conf._set_input_type_to_index(
+            backend_pattern_config_dict.get(INPUT_TYPE_TO_INDEX_DICT_KEY, {})
+        )
+        if PATTERN_COMPLEX_FORMAT_DICT_KEY in backend_pattern_config_dict:
+            conf._set_pattern_complex_format(
+                backend_pattern_config_dict[PATTERN_COMPLEX_FORMAT_DICT_KEY]
+            )
+        return conf
+
+    def to_dict(self) -> Dict[str, Any]:
+        """
+        Convert this ``BackendPatternConfig`` to a dictionary with the items described in
+        :func:`~torch.ao.quantization.backend_config.BackendPatternConfig.from_dict`.
+        """
+        backend_pattern_config_dict: Dict[str, Any] = {
+            OBSERVATION_TYPE_DICT_KEY: self.observation_type,
+            DTYPE_CONFIGS_DICT_KEY: [c.to_dict() for c in self.dtype_configs],
+        }
+        if self.pattern is not None:
+            backend_pattern_config_dict[PATTERN_DICT_KEY] = self.pattern
+        if self.root_module is not None:
+            backend_pattern_config_dict[ROOT_MODULE_DICT_KEY] = self.root_module
+        if self.qat_module is not None:
+            backend_pattern_config_dict[QAT_MODULE_DICT_KEY] = self.qat_module
+        if self.reference_quantized_module is not None:
+            backend_pattern_config_dict[
+                REFERENCE_QUANTIZED_MODULE_DICT_KEY
+            ] = self.reference_quantized_module
+        if self.fused_module is not None:
+            backend_pattern_config_dict[FUSED_MODULE_DICT_KEY] = self.fused_module
+        if self.fuser_method is not None:
+            backend_pattern_config_dict[FUSER_METHOD_DICT_KEY] = self.fuser_method
+        if self._root_node_getter is not None:
+            backend_pattern_config_dict[
+                ROOT_NODE_GETTER_DICT_KEY
+            ] = self._root_node_getter
+        if self._extra_inputs_getter is not None:
+            backend_pattern_config_dict[
+                EXTRA_INPUTS_GETTER_DICT_KEY
+            ] = self._extra_inputs_getter
+        if len(self._num_tensor_args_to_observation_type) > 0:
+            backend_pattern_config_dict[
+                NUM_TENSOR_ARGS_TO_OBSERVATION_TYPE_DICT_KEY
+            ] = self._num_tensor_args_to_observation_type
+        if len(self._input_type_to_index) > 0:
+            backend_pattern_config_dict[
+                INPUT_TYPE_TO_INDEX_DICT_KEY
+            ] = self._input_type_to_index
+        if self._pattern_complex_format is not None:
+            backend_pattern_config_dict[
+                PATTERN_COMPLEX_FORMAT_DICT_KEY
+            ] = self._pattern_complex_format
+        return backend_pattern_config_dict
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/executorch.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/executorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ad423c6d26539c54915ee286d9dacb48473a8ce
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/executorch.py
@@ -0,0 +1,500 @@
+# TODO: rename executorch to qnnpack_executorch since executorch is a general runtime
+# not a specific backend
+
+import operator
+from typing import List
+
+import torch
+import torch.ao.nn.qat as nnqat
+import torch.ao.nn.quantized.reference as nnqr
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.ao.quantization.fuser_method_mappings import (
+    _sequential_wrapper2,
+    fuse_conv_bn,
+    fuse_conv_bn_relu,
+)
+
+from ._common_operator_config_utils import _Conv2dMetadata
+from .backend_config import (
+    BackendConfig,
+    BackendPatternConfig,
+    DTypeConfig,
+    DTypeWithConstraints,
+    ObservationType,
+)
+from .qnnpack import (
+    qnnpack_default_op_qint8_symmetric_dtype_config,
+    qnnpack_weighted_op_qint8_symmetric_dtype_config,
+)
+
+
+__all__ = [
+    "get_executorch_backend_config",
+]
+
+
+# ===================
+# |  DTYPE CONFIGS  |
+# ===================
+
+executorch_weighted_op_int8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+)
+
+executorch_default_op_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+)
+
+executorch_default_dynamic_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.float,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+    is_dynamic=True,
+)
+
+executorch_act_qint8_scale_min_2_neg_12 = DTypeWithConstraints(
+    dtype=torch.qint8,
+    scale_min_lower_bound=2**-12,
+)
+
+executorch_weight_qint8_neg_127_to_127_scale_min_2_neg_12 = DTypeWithConstraints(
+    dtype=torch.qint8,
+    quant_min_lower_bound=-127,
+    quant_max_upper_bound=127,
+    scale_min_lower_bound=2**-12,
+)
+
+executorch_default_dynamic_qint8_dtype_config = DTypeConfig(
+    input_dtype=executorch_act_qint8_scale_min_2_neg_12,
+    output_dtype=torch.float,
+    weight_dtype=executorch_weight_qint8_neg_127_to_127_scale_min_2_neg_12,
+    bias_dtype=torch.float,
+    is_dynamic=True,
+)
+
+executorch_default_dynamic_float16_dtype_config = DTypeConfig(
+    input_dtype=torch.float16,
+    output_dtype=torch.float,
+    weight_dtype=torch.float16,
+    bias_dtype=torch.float,
+    is_dynamic=True,
+)
+
+executorch_weight_only_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.float,
+    output_dtype=torch.float,
+    weight_dtype=torch.quint8,
+)
+
+
+# =============================
+# |  BACKEND PATTERN CONFIGS  |
+# =============================
+
+
+def _get_linear_configs() -> List[BackendPatternConfig]:
+    """
+    Return all configs related to linear modules and ops.
+    """
+    observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+    dtype_configs = [
+        qnnpack_weighted_op_qint8_symmetric_dtype_config,
+        executorch_weighted_op_int8_dtype_config,
+        executorch_default_dynamic_quint8_dtype_config,
+        executorch_default_dynamic_qint8_dtype_config,
+        executorch_default_dynamic_float16_dtype_config,
+    ]
+    linear_configs: List[BackendPatternConfig] = []
+    # linear module
+    linear_configs.append(
+        BackendPatternConfig(torch.nn.Linear)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        .set_root_module(torch.nn.Linear)
+        .set_reference_quantized_module(nnqr.Linear)
+        .set_qat_module(nnqat.Linear)
+    )
+    # linear qat module
+    linear_configs.append(
+        BackendPatternConfig(nnqat.Linear)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        .set_root_module(torch.nn.Linear)
+        .set_reference_quantized_module(nnqr.Linear)
+    )
+    # functional linear
+    linear_configs.append(
+        BackendPatternConfig(torch.nn.functional.linear)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_input_type_to_index({"weight": 1, "bias": 2})
+    )
+    return linear_configs
+
+
+def _get_conv_configs() -> List[BackendPatternConfig]:
+    """
+    Return all configs related to conv modules and ops.
+    """
+    observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+    dtype_configs = [
+        qnnpack_weighted_op_qint8_symmetric_dtype_config,
+        executorch_weighted_op_int8_dtype_config,
+    ]
+    conv_configs = []
+    for convs in [_Conv2dMetadata]:
+        # (1) Single conv modules/functions
+        # -----------------------------------
+        # conv module
+        conv_configs.append(
+            BackendPatternConfig(convs.root)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+            .set_qat_module(convs.qat)
+        )
+        # conv qat module
+        conv_configs.append(
+            BackendPatternConfig(convs.qat)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+        )
+        # functional conv
+        conv_configs.append(
+            BackendPatternConfig(convs.func)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            ._set_input_type_to_index({"weight": 1, "bias": 2})
+        )
+
+        # (2) Conv + relu
+        # -----------------------------------
+        # conv module + relu module
+        conv_configs.append(
+            BackendPatternConfig((convs.root, nn.ReLU))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(_sequential_wrapper2(convs.fused_conv_relu))
+            .set_fused_module(convs.fused_conv_relu)
+        )
+        # conv module + functional relu
+        conv_configs.append(
+            BackendPatternConfig((convs.root, F.relu))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(_sequential_wrapper2(convs.fused_conv_relu))
+            .set_fused_module(convs.fused_conv_relu)
+        )
+        # fused conv relu module
+        conv_configs.append(
+            BackendPatternConfig(convs.fused_conv_relu)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+            .set_qat_module(convs.relu_qat)
+        )
+        # conv relu, qat fused module
+        conv_configs.append(
+            BackendPatternConfig(convs.relu_qat)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+        )
+        # functional conv + relu module
+        conv_configs.append(
+            BackendPatternConfig((convs.func, nn.ReLU))
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+        )
+        # functional conv + functional relu
+        conv_configs.append(
+            BackendPatternConfig((convs.func, F.relu))
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+        )
+        # fused conv relu
+        conv_configs.append(
+            BackendPatternConfig(convs.fused_conv_relu)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_qat_module(convs.relu_qat)
+        )
+
+        conv_configs.append(
+            BackendPatternConfig(convs.relu_qat)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+        )
+
+        # (3) Conv + batchnorm (+ relu)
+        # -------------------------------
+        # conv + batchnorm (+ relu)
+        conv_configs.append(
+            BackendPatternConfig((convs.root, convs.bn))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(fuse_conv_bn)
+            .set_fused_module(convs.fused_conv_bn)
+        )
+        # conv + bn + relu module fusion
+        conv_configs.append(
+            BackendPatternConfig((convs.root, convs.bn, nn.ReLU))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(fuse_conv_bn_relu)
+            .set_fused_module(convs.fused_conv_bn_relu)
+        )
+        # conv + bn + relu functional fusion
+        conv_configs.append(
+            BackendPatternConfig((convs.root, convs.bn, F.relu))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_root_module(convs.root)
+            .set_fuser_method(fuse_conv_bn_relu)
+            .set_fused_module(convs.fused_conv_bn_relu)
+        )
+        # TODO: we can add fusion for torch.relu as well
+        # 3.2 conv + bn (+ relu) fused module configs
+        # fused conv bn
+        conv_configs.append(
+            BackendPatternConfig(convs.fused_conv_bn)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_qat_module(convs.bn_qat)
+        )
+
+        # fused conv bn relu
+        conv_configs.append(
+            BackendPatternConfig(convs.fused_conv_bn_relu)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_qat_module(convs.bn_relu_qat)
+        )
+
+        # conv bn, qat fused module
+        conv_configs.append(
+            BackendPatternConfig(convs.bn_qat)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+        )
+        # conv bn relu, qat fused module
+        conv_configs.append(
+            BackendPatternConfig(convs.bn_relu_qat)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+        )
+    return conv_configs
+
+
+def _get_binary_ops_configs() -> List[BackendPatternConfig]:
+    """
+    Return all configs related to binary ops.
+    """
+    dtype_configs = [
+        qnnpack_default_op_qint8_symmetric_dtype_config,
+        executorch_weighted_op_int8_dtype_config,
+    ]
+    num_tensor_args_to_observation_type_mapping = {
+        # TODO: this is not used right now since we have extra check in prepare
+        # will need to change this to NO_OBSERVER later after we implemented
+        # Tensor dtype inference properly
+        0: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
+        1: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
+        2: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
+    }
+    binary_op_configs: List[BackendPatternConfig] = []
+    for op in [
+        operator.add,
+        torch.add,
+        operator.sub,
+        torch.sub,
+        operator.mul,
+        torch.mul,
+    ]:
+        bop_patterns = [
+            (op, torch.nn.ReLU),
+            (op, torch.nn.functional.relu),
+            (op, torch.relu),
+            op,
+        ]
+        for bop_pattern in bop_patterns:
+            binary_op_configs.append(
+                BackendPatternConfig(bop_pattern)
+                .set_dtype_configs(dtype_configs)  # noqa: E131
+                ._set_num_tensor_args_to_observation_type(
+                    num_tensor_args_to_observation_type_mapping
+                )
+            )
+    return binary_op_configs
+
+
+def _get_share_qparams_ops_configs() -> List[BackendPatternConfig]:
+    """
+    Return the operator configs for the operators that works for both float and quantized
+    input if input is quantized, the output Tensor shares the same quantization parameter
+    with input.
+
+    Example operator: avgpool2d, reshape, transpose, maxpool2d
+    Example observed operator:
+    observer_0 - avgpool2d - observer_0 (same observer instance as input)
+    """
+    observation_type = ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT
+    dtype_configs = [
+        qnnpack_default_op_qint8_symmetric_dtype_config,
+        executorch_default_op_quint8_dtype_config,
+    ]
+    share_qparams_ops = [
+        torch.nn.Flatten,
+        F.adaptive_avg_pool2d,
+        F.elu,
+        F.hardtanh,
+        F.max_pool2d,
+        F.pad,
+        F.relu,
+        F.relu6,
+        F.leaky_relu,
+        F.leaky_relu_,
+        torch.nn.AdaptiveAvgPool2d,
+        torch.nn.ConstantPad2d,
+        torch.nn.ELU,
+        torch.nn.MaxPool2d,
+        torch.nn.ReLU6,
+        torch.nn.Hardtanh,
+        torch.nn.LeakyReLU,
+        torch.clamp,
+        torch.flatten,
+        torch.mean,
+        torch.permute,
+        torch.permute_copy,
+        torch.squeeze,
+        "clamp",
+        "mean",
+        "permute",
+        "reshape",
+        "relu",
+        "relu_",
+        "squeeze",
+        "squeeze_",
+        "leaky_relu",
+    ]
+    share_qparams_op_configs: List[BackendPatternConfig] = []
+    for op in share_qparams_ops:
+        share_qparams_op_configs.append(
+            BackendPatternConfig(op)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+        )
+    return share_qparams_op_configs
+
+
+def _get_bn_configs() -> List[BackendPatternConfig]:
+    """
+    Return all configs related to batchnorm.
+    """
+    observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+    dtype_configs = [
+        qnnpack_default_op_qint8_symmetric_dtype_config,
+        executorch_default_op_quint8_dtype_config,
+    ]
+    bn_configs = []
+    bn_configs.append(
+        BackendPatternConfig(nn.BatchNorm2d)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+    )
+    return bn_configs
+
+
+def _get_cat_configs() -> List[BackendPatternConfig]:
+    dtype_configs = [
+        qnnpack_default_op_qint8_symmetric_dtype_config,
+        executorch_default_op_quint8_dtype_config,
+    ]
+    cat_configs = []
+    cat_configs.append(
+        BackendPatternConfig(torch.cat)
+        .set_observation_type(ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT)
+        .set_dtype_configs(dtype_configs)
+    )
+    cat_configs.append(
+        BackendPatternConfig(torch.concat)
+        .set_observation_type(ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT)
+        .set_dtype_configs(dtype_configs)
+    )
+    cat_configs.append(
+        BackendPatternConfig(torch.concatenate)
+        .set_observation_type(ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT)
+        .set_dtype_configs(dtype_configs)
+    )
+    return cat_configs
+
+
+def _get_embedding_op_configs() -> List[BackendPatternConfig]:
+    dtype_configs = [
+        executorch_weight_only_quint8_dtype_config,
+    ]
+    embedding_op_configs = []
+    for embedding_op, qat_embedding_op, ref_embedding_op in [
+        (nn.Embedding, nnqat.Embedding, nnqr.Embedding),
+        (nn.EmbeddingBag, nnqat.EmbeddingBag, nnqr.EmbeddingBag),
+    ]:
+        embedding_op_configs.append(
+            BackendPatternConfig(embedding_op)
+            .set_observation_type(
+                ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+            )  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_qat_module(qat_embedding_op)
+            .set_root_module(embedding_op)
+            .set_reference_quantized_module(ref_embedding_op)
+        )
+        # config for qat op
+        embedding_op_configs.append(
+            BackendPatternConfig(qat_embedding_op)
+            .set_observation_type(
+                ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+            )  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(embedding_op)
+            .set_reference_quantized_module(ref_embedding_op)
+        )
+
+        # config for functional embedding
+        embedding_op_configs.append(
+            BackendPatternConfig(torch.nn.functional.embedding)
+            .set_observation_type(
+                ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+            )  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            ._set_input_type_to_index({"weight": 1})
+        )
+    return embedding_op_configs
+
+
+# =====================
+# |  BACKEND CONFIGS  |
+# =====================
+
+
+def get_executorch_backend_config() -> BackendConfig:
+    """
+    Return the `BackendConfig` for backends PyTorch lowers to through the Executorch stack.
+    """
+    return (
+        BackendConfig("executorch")
+        .set_backend_pattern_configs(_get_linear_configs())
+        .set_backend_pattern_configs(_get_conv_configs())
+        .set_backend_pattern_configs(_get_binary_ops_configs())
+        .set_backend_pattern_configs(_get_share_qparams_ops_configs())
+        .set_backend_pattern_configs(_get_bn_configs())
+        .set_backend_pattern_configs(_get_cat_configs())
+        .set_backend_pattern_configs(_get_embedding_op_configs())
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/fbgemm.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/fbgemm.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d665f4fd030aba47c98ee692f0d9e7eca41cbc6
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/fbgemm.py
@@ -0,0 +1,129 @@
+import torch
+
+from ._common_operator_config_utils import (
+    _get_binary_op_configs,
+    _get_bn_configs,
+    _get_cat_config,
+    _get_conv_configs,
+    _get_default_op_configs,
+    _get_embedding_op_configs,
+    _get_fixed_qparams_op_configs,
+    _get_linear_configs,
+    _get_rnn_op_configs,
+    _get_share_qparams_op_configs,
+    _get_tensor_info_op_configs,
+)
+from .backend_config import BackendConfig, DTypeConfig
+
+
+__all__ = [
+    "get_fbgemm_backend_config",
+]
+
+# ===================
+# |  DTYPE CONFIGS  |
+# ===================
+
+# TODO: For now, these DTypeConfigs are identical to the ones defined in native.py
+# In the future, once we support specifying quant_min/quant_max and scale_min/scale_max,
+# these will diverge. In particular, for FBGEMM, we will restrict the activation quantized
+# values to within [0, 127].
+
+fbgemm_weighted_op_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+)
+
+fbgemm_default_op_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+)
+
+fbgemm_default_op_fp16_dtype_config = DTypeConfig(
+    input_dtype=torch.float16,
+    output_dtype=torch.float16,
+    weight_dtype=torch.float16,
+    bias_dtype=torch.float16,
+)
+
+fbgemm_default_dynamic_int8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.float,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+    is_dynamic=True,
+)
+
+fbgemm_default_dynamic_float16_dtype_config = DTypeConfig(
+    input_dtype=torch.float16,
+    output_dtype=torch.float,
+    weight_dtype=torch.float16,
+    bias_dtype=torch.float,
+    is_dynamic=True,
+)
+
+fbgemm_weight_only_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.float,
+    output_dtype=torch.float,
+    weight_dtype=torch.quint8,
+)
+
+fbgemm_weight_only_quint4x2_dtype_config = DTypeConfig(
+    input_dtype=torch.float,
+    output_dtype=torch.float,
+    weight_dtype=torch.quint4x2,
+)
+
+
+# =====================
+# |  BACKEND CONFIGS  |
+# =====================
+
+
+def get_fbgemm_backend_config() -> BackendConfig:
+    """
+    Return the `BackendConfig` for PyTorch's native FBGEMM backend.
+    """
+    conv_dtype_configs = [fbgemm_weighted_op_quint8_dtype_config]
+    linear_dtype_configs = [
+        fbgemm_weighted_op_quint8_dtype_config,
+        fbgemm_default_dynamic_int8_dtype_config,
+        fbgemm_default_dynamic_float16_dtype_config,
+    ]
+    binary_op_dtype_configs = [fbgemm_default_op_quint8_dtype_config]
+    default_op_dtype_configs = [fbgemm_default_op_quint8_dtype_config]
+    fixed_qparams_op_dtype_configs = [fbgemm_default_op_quint8_dtype_config]
+    share_qparams_op_dtype_configs = [fbgemm_default_op_quint8_dtype_config]
+    tensor_info_op_dtype_configs = [fbgemm_default_op_quint8_dtype_config]
+    rnn_op_dtype_configs = [
+        fbgemm_default_dynamic_int8_dtype_config,
+        fbgemm_default_dynamic_float16_dtype_config,
+    ]
+    embedding_op_dtype_configs = [
+        fbgemm_weight_only_quint8_dtype_config,
+        fbgemm_weight_only_quint4x2_dtype_config,
+    ]
+    return (
+        BackendConfig("fbgemm")
+        .set_backend_pattern_configs(_get_conv_configs(conv_dtype_configs))
+        .set_backend_pattern_configs(_get_linear_configs(linear_dtype_configs))
+        .set_backend_pattern_configs(_get_binary_op_configs(binary_op_dtype_configs))
+        .set_backend_pattern_config(_get_cat_config(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_default_op_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_fixed_qparams_op_configs(fixed_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_share_qparams_op_configs(share_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_tensor_info_op_configs(tensor_info_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(_get_bn_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_rnn_op_configs(rnn_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_embedding_op_configs(embedding_op_dtype_configs)
+        )
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/native.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/native.py
new file mode 100644
index 0000000000000000000000000000000000000000..a98d1a9a3d41b43b1c0ce55a2471d3342af71a55
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/native.py
@@ -0,0 +1,231 @@
+# mypy: allow-untyped-defs
+import torch
+
+from ._common_operator_config_utils import (
+    _get_binary_op_configs,
+    _get_bn_configs,
+    _get_cat_config,
+    _get_conv_configs,
+    _get_default_op_configs,
+    _get_embedding_op_configs,
+    _get_fixed_qparams_op_configs,
+    _get_linear_configs,
+    _get_ln_configs,
+    _get_rnn_op_configs,
+    _get_share_qparams_op_configs,
+    _get_tensor_info_op_configs,
+)
+from .backend_config import BackendConfig, DTypeConfig
+
+
+__all__ = [
+    "get_test_only_legacy_native_backend_config",
+    "default_op_quint8_dtype_config",
+    "default_op_fp16_dtype_config",
+    "default_dynamic_int8_dtype_config",
+    "default_dynamic_float16_dtype_config",
+    "input_output_only_quint8_dtype_config",
+    "weight_only_quint8_dtype_config",
+    "weight_only_quint4x2_dtype_config",
+    "get_native_backend_config",
+    "get_native_backend_config_dict",
+    "get_test_only_legacy_native_backend_config_dict",
+]
+
+# ===================
+# |  DTYPE CONFIGS  |
+# ===================
+
+# weighted op int8 dtype config
+# this is config for ops that has quantized weights, like linear, conv
+weighted_op_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+)
+
+default_op_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+)
+
+default_op_fp16_dtype_config = DTypeConfig(
+    input_dtype=torch.float16,
+    output_dtype=torch.float16,
+    weight_dtype=torch.float16,
+    bias_dtype=torch.float16,
+)
+
+default_dynamic_int8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.float,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+    # currently the dtype check is not yet enabled, so we provided the dtype_configs but
+    # it is not really used yet,
+    # we will enable it a bit later after we moved everything to backend_config_dict
+    is_dynamic=True,
+)
+
+default_dynamic_float16_dtype_config = DTypeConfig(
+    input_dtype=torch.float16,
+    output_dtype=torch.float,
+    weight_dtype=torch.float16,
+    bias_dtype=torch.float,
+    # currently the dtype check is not yet enabled, so we provided the dtype_configs but
+    # it is not really used yet,
+    # we will enable it a bit later after we moved everything to backend_config_dict
+    is_dynamic=True,
+)
+
+# Needed for LayerNorm and f.layer_norm, since currently the kernel only supports float weights
+input_output_only_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+    weight_dtype=torch.float,
+    bias_dtype=torch.float,
+)
+
+weight_only_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.float,
+    output_dtype=torch.float,
+    weight_dtype=torch.quint8,
+)
+
+weight_only_quint4x2_dtype_config = DTypeConfig(
+    input_dtype=torch.float,
+    output_dtype=torch.float,
+    weight_dtype=torch.quint4x2,
+)
+
+
+# =====================
+# |  BACKEND CONFIGS  |
+# =====================
+
+
+def get_test_only_legacy_native_backend_config() -> BackendConfig:
+    """
+    Return the `BackendConfig` for PyTorch Native backend (fbgemm/qnnpack) with various additional fp16 ops.
+    """
+    conv_dtype_configs = [weighted_op_quint8_dtype_config]
+    linear_dtype_configs = [
+        weighted_op_quint8_dtype_config,
+        default_dynamic_int8_dtype_config,
+        default_dynamic_float16_dtype_config,
+        default_op_fp16_dtype_config,
+    ]
+    binary_op_dtype_configs = [
+        default_op_quint8_dtype_config,
+        default_op_fp16_dtype_config,
+    ]
+    default_op_dtype_configs = [default_op_quint8_dtype_config]
+    fixed_qparams_op_dtype_configs = [
+        default_op_quint8_dtype_config,
+        default_op_fp16_dtype_config,
+    ]
+    share_qparams_op_dtype_configs = [
+        default_op_quint8_dtype_config,
+        default_op_fp16_dtype_config,
+    ]
+    tensor_info_op_dtype_configs = [
+        default_op_quint8_dtype_config,
+    ]
+    rnn_op_dtype_configs = [
+        default_dynamic_int8_dtype_config,
+        default_dynamic_float16_dtype_config,
+    ]
+    embedding_op_dtype_configs = [
+        weight_only_quint8_dtype_config,
+        weight_only_quint4x2_dtype_config,
+    ]
+    layer_norm_op_dtype_configs = [input_output_only_quint8_dtype_config]
+    return (
+        BackendConfig("_native_and_fp16")
+        .set_backend_pattern_configs(_get_conv_configs(conv_dtype_configs))
+        .set_backend_pattern_configs(_get_linear_configs(linear_dtype_configs))
+        .set_backend_pattern_configs(_get_binary_op_configs(binary_op_dtype_configs))
+        .set_backend_pattern_config(_get_cat_config(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_default_op_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_fixed_qparams_op_configs(fixed_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_share_qparams_op_configs(share_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_tensor_info_op_configs(tensor_info_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(_get_bn_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_ln_configs(layer_norm_op_dtype_configs))
+        .set_backend_pattern_configs(_get_rnn_op_configs(rnn_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_embedding_op_configs(embedding_op_dtype_configs)
+        )
+    )
+
+
+def get_native_backend_config() -> BackendConfig:
+    """
+    Return the `BackendConfig` for PyTorch Native backend (fbgemm/qnnpack).
+    """
+    # TODO: express this BackendConfig as a union of the FBGEMM and QNNPACK BackendConfigs
+    conv_dtype_configs = [weighted_op_quint8_dtype_config]
+    linear_dtype_configs = [
+        weighted_op_quint8_dtype_config,
+        default_dynamic_int8_dtype_config,
+        default_dynamic_float16_dtype_config,
+    ]
+    binary_op_dtype_configs = [default_op_quint8_dtype_config]
+    default_op_dtype_configs = [default_op_quint8_dtype_config]
+    fixed_qparams_op_dtype_configs = [default_op_quint8_dtype_config]
+    share_qparams_op_dtype_configs = [default_op_quint8_dtype_config]
+    tensor_info_op_dtype_configs = [default_op_quint8_dtype_config]
+    rnn_op_dtype_configs = [
+        default_dynamic_int8_dtype_config,
+        default_dynamic_float16_dtype_config,
+    ]
+    embedding_op_dtype_configs = [
+        weight_only_quint8_dtype_config,
+        weight_only_quint4x2_dtype_config,
+    ]
+    layer_norm_op_dtype_configs = [input_output_only_quint8_dtype_config]
+    return (
+        BackendConfig("native")
+        .set_backend_pattern_configs(_get_conv_configs(conv_dtype_configs))
+        .set_backend_pattern_configs(_get_linear_configs(linear_dtype_configs))
+        .set_backend_pattern_configs(_get_binary_op_configs(binary_op_dtype_configs))
+        .set_backend_pattern_config(_get_cat_config(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_default_op_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_fixed_qparams_op_configs(fixed_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_share_qparams_op_configs(share_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_tensor_info_op_configs(tensor_info_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(_get_bn_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_ln_configs(layer_norm_op_dtype_configs))
+        .set_backend_pattern_configs(_get_rnn_op_configs(rnn_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_embedding_op_configs(embedding_op_dtype_configs)
+        )
+    )
+
+
+def get_native_backend_config_dict():
+    """
+    Return the `BackendConfig` for PyTorch Native backend (fbgemm/qnnpack) in dictionary form.
+    """
+    return get_native_backend_config().to_dict()
+
+
+def get_test_only_legacy_native_backend_config_dict():
+    """
+    Return the `BackendConfig` for PyTorch Native backend (fbgemm/qnnpack) with various additional
+    fp16 ops in dictionary form.
+    """
+    return get_test_only_legacy_native_backend_config().to_dict()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/observation_type.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/observation_type.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/onednn.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/onednn.py
new file mode 100644
index 0000000000000000000000000000000000000000..75560092d2b70a9c6b2a4dcd1dbc69c1d31c1889
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/onednn.py
@@ -0,0 +1,652 @@
+# mypy: allow-untyped-defs
+import itertools
+import operator
+
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.quantized.reference as nnqr
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.ao.quantization.fuser_method_mappings import _sequential_wrapper2
+from torch.ao.quantization.utils import MatchAllNode
+
+from ._common_operator_config_utils import (
+    _get_binary_op_configs,
+    _get_bn_configs,
+    _get_cat_config,
+    _get_conv_configs,
+    _get_default_op_configs,
+    _get_embedding_op_configs,
+    _get_fixed_qparams_op_configs,
+    _get_linear_configs,
+    _get_ln_configs,
+    _get_rnn_op_configs,
+    _get_share_qparams_op_configs,
+)
+from .backend_config import (
+    BackendConfig,
+    BackendPatternConfig,
+    DTypeConfig,
+    ObservationType,
+)
+
+
+# ===================
+# |  DTYPE CONFIGS  |
+# ===================
+
+onednn_weighted_op_int8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+)
+
+onednn_op_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+)
+
+onednn_dynamic_int8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.float,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+    is_dynamic=True,
+)
+
+onednn_weight_only_qint8_dtype_config = DTypeConfig(
+    input_dtype=torch.float,
+    output_dtype=torch.float,
+    weight_dtype=torch.qint8,
+)
+
+onednn_input_output_only_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+    weight_dtype=torch.float,
+    bias_dtype=torch.float,
+)
+
+# ===================
+# |  FUSER METHODS  |
+# ===================
+
+
+def _fuse_linear_bn_leaky_relu(is_qat, linear, bn, leaky_relu):
+    r"""Given the linear, bn and leaky_relu modules, fuses them and returns the fused module
+    Args:
+        is_qat: a flag for whether we are using quantization aware training fusion
+                or post training quantization fusion
+        linear: Module instance of type Linear
+        bn: BatchNorm1d instance that needs to be fused with the linear layer
+        leaky_relu: LeakyReLU instance that needs to be fused with the linear layer
+    Examples::
+        >>> # xdoctest: +SKIP(failing)
+        >>> m1 = nn.Linear(20, 10)
+        >>> b1 = nn.BatchNorm1d(10)
+        >>> lr = nn.LeakyReLU(0.01)
+        >>> m2 = _fuse_linear_bn_leaky_relu(m1, b1, lr)
+    """
+    assert (
+        linear.training == bn.training and bn.training == leaky_relu.training
+    ), "Linear, BN and LeakyReLU all must be in the same mode (train or eval)."
+
+    if is_qat:
+        raise NotImplementedError(
+            f"Cannot fuse train modules: {(linear, bn, leaky_relu)}"
+        )
+    else:
+        map_to_fused_module_eval = {
+            nn.Linear: nni.LinearLeakyReLU,
+        }
+        fused_module = map_to_fused_module_eval.get(type(linear), None)
+        if fused_module is not None:
+            fused_linear = nn.utils.fusion.fuse_linear_bn_eval(linear, bn)
+            fm = fused_module(fused_linear, leaky_relu)
+            return fm
+        else:
+            raise NotImplementedError(
+                f"Cannot fuse eval modules: {(linear, bn, leaky_relu)}"
+            )
+
+
+# ======================
+# |  CONFIGS FOR CONV  |
+# ======================
+observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+
+conv_dtype_configs = [onednn_weighted_op_int8_dtype_config]
+conv_configs = _get_conv_configs(conv_dtype_configs)
+
+# (1) Conv2d + Add
+
+# conv2d   Y
+#   \   /
+#    add
+
+# include:
+# conv2d conv2d
+#   \   /
+#    add
+
+
+def _fuse_conv_add_left(is_qat, add, conv, _):
+    return nni.ConvAdd2d(conv, add)
+
+
+def _conv_add_root_node_getter_left(pattern):
+    _, conv, _ = pattern
+    return conv
+
+
+def _conv_add_extra_inputs_getter_left(pattern):
+    """get inputs pattern for extra inputs, inputs for root node
+    are assumed to be copied over from root node to the fused node
+    """
+    _, conv, extra_input = pattern
+    return [extra_input]
+
+
+# conv2d
+#  \
+#  bn   Y
+#   \   /
+#    add
+
+
+def _fuse_conv_bn_add_left(is_qat, add, bn_conv, _):
+    bn, conv = bn_conv
+    if is_qat:
+        raise NotImplementedError(f"Cannot fuse train modules: {(conv, bn, add)}")
+    else:
+        fused_conv = nn.utils.fusion.fuse_conv_bn_eval(conv, bn)
+        return nni.ConvAdd2d(fused_conv, add)
+
+
+def _conv_bn_add_root_node_getter_left(add_pattern):
+    _, bn_conv, _ = add_pattern
+    bn, conv = bn_conv
+    return conv
+
+
+def _conv_bn_add_extra_inputs_getter_left(add_pattern):
+    """get inputs pattern for extra inputs, inputs for root node
+    are assumed to be copied over from root node to the fused node
+    """
+    _, bn_conv, extra_input = add_pattern
+    bn, conv = bn_conv
+    return [extra_input]
+
+
+conv_add_left_optioins = itertools.product(
+    [True, False],  # with_bn
+    [torch.add, operator.add],  # add_op
+)
+
+for with_bn, add_op in conv_add_left_optioins:
+    if with_bn:
+        conv_configs.append(
+            BackendPatternConfig()
+            ._set_pattern_complex_format(
+                (add_op, (nn.BatchNorm2d, nn.Conv2d), MatchAllNode)
+            )  # noqa: E131
+            .set_observation_type(observation_type)
+            .set_dtype_configs(conv_dtype_configs)
+            .set_fuser_method(_fuse_conv_bn_add_left)
+            ._set_root_node_getter(_conv_bn_add_root_node_getter_left)
+            ._set_extra_inputs_getter(_conv_bn_add_extra_inputs_getter_left)
+            .set_fused_module(nni.ConvAdd2d)
+        )
+    else:
+        conv_configs.append(
+            BackendPatternConfig()
+            ._set_pattern_complex_format(
+                (add_op, nn.Conv2d, MatchAllNode)
+            )  # noqa: E131
+            .set_observation_type(observation_type)
+            .set_dtype_configs(conv_dtype_configs)
+            .set_fuser_method(_fuse_conv_add_left)
+            ._set_root_node_getter(_conv_add_root_node_getter_left)
+            ._set_extra_inputs_getter(_conv_add_extra_inputs_getter_left)
+            .set_fused_module(nni.ConvAdd2d)
+        )
+
+#  Y   conv2d
+#   \   /
+#    add
+
+
+def _fuse_conv_add_right(is_qat, add, _, conv):
+    return nni.ConvAdd2d(conv, add)
+
+
+def _conv_add_root_node_getter_right(pattern):
+    add, _, conv = pattern
+    return conv
+
+
+def _conv_add_extra_inputs_getter_right(pattern):
+    """get inputs pattern for extra inputs, inputs for root node
+    are assumed to be copied over from root node to the fused node
+    """
+    _, extra_input, conv = pattern
+    return [extra_input]
+
+
+#      conv2d
+#        /
+#  Y    bn
+#   \   /
+#    add
+
+
+def _fuse_conv_bn_add_right(is_qat, add, _, bn_conv):
+    bn, conv = bn_conv
+    if is_qat:
+        raise NotImplementedError(f"Cannot fuse train modules: {(conv, bn, add)}")
+    else:
+        fused_conv = nn.utils.fusion.fuse_conv_bn_eval(conv, bn)
+        return nni.ConvAdd2d(fused_conv, add)
+
+
+def _conv_bn_add_root_node_getter_right(pattern):
+    add, _, bn_conv = pattern
+    bn, conv = bn_conv
+    return conv
+
+
+def _conv_bn_add_extra_inputs_getter_right(pattern):
+    """get inputs pattern for extra inputs, inputs for root node
+    are assumed to be copied over from root node to the fused node
+    """
+    _, extra_input, bn_conv = pattern
+    bn, conv = bn_conv
+    return [extra_input]
+
+
+conv_add_optioins = itertools.product(
+    [True, False],  # with_bn
+    [torch.add, operator.add],  # add_op
+)
+
+for with_bn, add_op in conv_add_optioins:
+    if with_bn:
+        conv_configs.append(
+            BackendPatternConfig()
+            ._set_pattern_complex_format(
+                (add_op, MatchAllNode, (nn.BatchNorm2d, nn.Conv2d))
+            )  # noqa: E131
+            .set_observation_type(observation_type)
+            .set_dtype_configs(conv_dtype_configs)
+            .set_fuser_method(_fuse_conv_bn_add_right)
+            ._set_root_node_getter(_conv_bn_add_root_node_getter_right)
+            ._set_extra_inputs_getter(_conv_bn_add_extra_inputs_getter_right)
+            .set_fused_module(nni.ConvAdd2d)
+        )
+    else:
+        conv_configs.append(
+            BackendPatternConfig()
+            ._set_pattern_complex_format(
+                (add_op, MatchAllNode, nn.Conv2d)
+            )  # noqa: E131
+            .set_observation_type(observation_type)
+            .set_dtype_configs(conv_dtype_configs)
+            .set_fuser_method(_fuse_conv_add_right)
+            ._set_root_node_getter(_conv_add_root_node_getter_right)
+            ._set_extra_inputs_getter(_conv_add_extra_inputs_getter_right)
+            .set_fused_module(nni.ConvAdd2d)
+        )
+
+conv_configs.append(
+    BackendPatternConfig(nni.ConvAdd2d)
+    .set_observation_type(observation_type)  # noqa: E131
+    .set_dtype_configs(conv_dtype_configs)
+    .set_root_module(nn.Conv2d)
+    .set_reference_quantized_module(nnqr.Conv2d)
+)
+
+# (2) Conv2d + Add + Relu
+
+# conv2d Y
+#   \   /
+#    add
+#     \
+#     relu
+
+
+def _fuse_conv_add_relu_left(is_qat, relu, add_pattern):
+    add, conv, _ = add_pattern
+    return nni.ConvAddReLU2d(conv, add, relu)
+
+
+def _conv_add_relu_root_node_getter_left(pattern):
+    relu, add_pattern = pattern
+    _, conv, _ = add_pattern
+    return conv
+
+
+def _conv_add_relu_extra_inputs_getter_left(pattern):
+    """get inputs pattern for extra inputs, inputs for root node
+    are assumed to be copied over from root node to the fused node
+    """
+    relu, add_pattern = pattern
+    _, conv, extra_input = add_pattern
+    return [extra_input]
+
+
+# conv2d
+#  \
+#  bn   Y
+#   \   /
+#    add
+#     \
+#     relu
+
+
+def _fuse_conv_bn_add_relu_left(is_qat, relu, add_pattern):
+    add, bn_conv, _ = add_pattern
+    bn, conv = bn_conv
+    if is_qat:
+        raise NotImplementedError(f"Cannot fuse train modules: {(conv, bn, add, relu)}")
+    else:
+        fused_conv = nn.utils.fusion.fuse_conv_bn_eval(conv, bn)
+        return nni.ConvAddReLU2d(fused_conv, add, relu)
+
+
+def _conv_bn_add_relu_root_node_getter_left(pattern):
+    relu, add_pattern = pattern
+    _, bn_conv, _ = add_pattern
+    bn, conv = bn_conv
+    return conv
+
+
+def _conv_bn_add_relu_extra_inputs_getter_left(pattern):
+    """get inputs pattern for extra inputs, inputs for root node
+    are assumed to be copied over from root node to the fused node
+    """
+    relu, add_pattern = pattern
+    _, bn_conv, extra_input = add_pattern
+    bn, conv = bn_conv
+    return [extra_input]
+
+
+conv_add_relu_left_optioins = itertools.product(
+    [True, False],  # with_bn
+    [torch.add, operator.add],  # add_op
+)
+
+for with_bn, add_op in conv_add_relu_left_optioins:
+    if with_bn:
+        conv_configs.append(
+            BackendPatternConfig()
+            ._set_pattern_complex_format(
+                (nn.ReLU, (add_op, (nn.BatchNorm2d, nn.Conv2d), MatchAllNode))
+            )  # noqa: E131
+            .set_observation_type(observation_type)
+            .set_dtype_configs(conv_dtype_configs)
+            .set_fuser_method(_fuse_conv_bn_add_relu_left)
+            ._set_root_node_getter(_conv_bn_add_relu_root_node_getter_left)
+            ._set_extra_inputs_getter(_conv_bn_add_relu_extra_inputs_getter_left)
+            .set_fused_module(nni.ConvAddReLU2d)
+        )
+    else:
+        conv_configs.append(
+            BackendPatternConfig()
+            ._set_pattern_complex_format(
+                (nn.ReLU, (add_op, nn.Conv2d, MatchAllNode))
+            )  # noqa: E131
+            .set_observation_type(observation_type)
+            .set_dtype_configs(conv_dtype_configs)
+            .set_fuser_method(_fuse_conv_add_relu_left)
+            ._set_root_node_getter(_conv_add_relu_root_node_getter_left)
+            ._set_extra_inputs_getter(_conv_add_relu_extra_inputs_getter_left)
+            .set_fused_module(nni.ConvAddReLU2d)
+        )
+
+#  Y   conv2d
+#   \   /
+#    add
+#     \
+#     relu
+
+
+def _fuse_conv_add_relu_right(is_qat, relu, add_pattern):
+    add, _, conv = add_pattern
+    return nni.ConvAddReLU2d(conv, add, relu)
+
+
+def _conv_add_relu_root_node_getter_right(pattern):
+    relu, add_pattern = pattern
+    _, _, conv = add_pattern
+    return conv
+
+
+def _conv_add_relu_extra_inputs_getter_right(pattern):
+    """get inputs pattern for extra inputs, inputs for root node
+    are assumed to be copied over from root node to the fused node
+    """
+    relu, add_pattern = pattern
+    _, extra_input, conv = add_pattern
+    return [extra_input]
+
+
+#      conv2d
+#        /
+#  Y    bn
+#   \   /
+#    add
+#     \
+#     relu
+
+
+def _fuse_conv_bn_add_relu_right(is_qat, relu, add_pattern):
+    add, _, bn_conv = add_pattern
+    bn, conv = bn_conv
+    if is_qat:
+        raise NotImplementedError(f"Cannot fuse train modules: {(conv, bn, add, relu)}")
+    else:
+        fused_conv = nn.utils.fusion.fuse_conv_bn_eval(conv, bn)
+        return nni.ConvAddReLU2d(fused_conv, add, relu)
+
+
+def _conv_bn_add_relu_root_node_getter_right(pattern):
+    relu, add_pattern = pattern
+    _, _, bn_conv = add_pattern
+    bn, conv = bn_conv
+    return conv
+
+
+def _conv_bn_add_relu_extra_inputs_getter_right(pattern):
+    """get inputs pattern for extra inputs, inputs for root node
+    are assumed to be copied over from root node to the fused node
+    """
+    relu, add_pattern = pattern
+    _, extra_input, bn_conv = add_pattern
+    bn, conv = bn_conv
+    return [extra_input]
+
+
+conv_add_relu_optioins = itertools.product(
+    [True, False],  # with_bn
+    [torch.add, operator.add],  # add_op
+)
+
+for with_bn, add_op in conv_add_relu_optioins:
+    if with_bn:
+        conv_configs.append(
+            BackendPatternConfig()
+            ._set_pattern_complex_format(
+                (nn.ReLU, (add_op, MatchAllNode, (nn.BatchNorm2d, nn.Conv2d)))
+            )  # noqa: E131
+            .set_observation_type(observation_type)
+            .set_dtype_configs(conv_dtype_configs)
+            .set_fuser_method(_fuse_conv_bn_add_relu_right)
+            ._set_root_node_getter(_conv_bn_add_relu_root_node_getter_right)
+            ._set_extra_inputs_getter(_conv_bn_add_relu_extra_inputs_getter_right)
+            .set_fused_module(nni.ConvAddReLU2d)
+        )
+    else:
+        conv_configs.append(
+            BackendPatternConfig()
+            ._set_pattern_complex_format(
+                (nn.ReLU, (add_op, MatchAllNode, nn.Conv2d))
+            )  # noqa: E131
+            .set_observation_type(observation_type)
+            .set_dtype_configs(conv_dtype_configs)
+            .set_fuser_method(_fuse_conv_add_relu_right)
+            ._set_root_node_getter(_conv_add_relu_root_node_getter_right)
+            ._set_extra_inputs_getter(_conv_add_relu_extra_inputs_getter_right)
+            .set_fused_module(nni.ConvAddReLU2d)
+        )
+
+conv_configs.append(
+    BackendPatternConfig(nni.ConvAddReLU2d)
+    .set_observation_type(observation_type)  # noqa: E131
+    .set_dtype_configs(conv_dtype_configs)
+    .set_root_module(nn.Conv2d)
+    .set_reference_quantized_module(nnqr.Conv2d)
+)
+
+# ========================
+# |  CONFIGS FOR LINEAR  |
+# ========================
+
+linear_dtype_configs = [
+    onednn_weighted_op_int8_dtype_config,
+    onednn_dynamic_int8_dtype_config,
+]
+linear_configs = _get_linear_configs(linear_dtype_configs)
+
+
+def _add_eltwise_fusion_configs(
+    configs,
+    root_module,
+    root_op,
+    post_module,
+    post_op,
+    dtype_configs,
+    fuser_method,
+    fused_module,
+    observation_type,
+    ref_quant_module,
+):
+    # 1 base module + op module fusion config
+    configs.append(
+        BackendPatternConfig((root_module, post_module))
+        .set_dtype_configs(dtype_configs)  # noqa: E131
+        .set_fuser_method(fuser_method)
+        .set_fused_module(fused_module)
+    )
+    # base module + functional post op
+    configs.append(
+        BackendPatternConfig((root_module, post_op))
+        .set_dtype_configs(dtype_configs)  # noqa: E131
+        .set_fuser_method(fuser_method)
+        .set_fused_module(fused_module)
+    )
+
+    # 2 fused module configs
+    configs.append(
+        BackendPatternConfig(fused_module)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        .set_root_module(root_module)
+        .set_reference_quantized_module(ref_quant_module)
+    )
+
+    # 3 functional base op + post op configs
+    configs.append(
+        BackendPatternConfig((root_op, post_module))
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+    )
+    configs.append(
+        BackendPatternConfig((root_op, post_op))
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+    )
+
+
+# Configs for linear + leaky_relu fusion
+_add_eltwise_fusion_configs(
+    linear_configs,
+    nn.Linear,
+    F.linear,
+    nn.LeakyReLU,
+    F.leaky_relu,
+    linear_dtype_configs,
+    _sequential_wrapper2(nni.LinearLeakyReLU),
+    nni.LinearLeakyReLU,
+    observation_type,
+    nnqr.Linear,
+)
+
+# Configs for linear module + batchnorm + leaky_relu
+linear_configs.append(
+    BackendPatternConfig((nn.Linear, nn.BatchNorm1d, nn.LeakyReLU))
+    .set_dtype_configs(linear_dtype_configs)  # noqa: E131
+    .set_fuser_method(_fuse_linear_bn_leaky_relu)
+    .set_fused_module(nni.LinearLeakyReLU)
+)
+
+# Configs for linear + tanh fusion
+_add_eltwise_fusion_configs(
+    linear_configs,
+    nn.Linear,
+    F.linear,
+    nn.Tanh,
+    torch.tanh,
+    linear_dtype_configs,
+    _sequential_wrapper2(nni.LinearTanh),
+    nni.LinearTanh,
+    observation_type,
+    nnqr.Linear,
+)
+
+# ===========================
+# |  CONFIGS FOR OTHER OPS  |
+# ===========================
+
+binary_op_dtype_configs = [onednn_op_quint8_dtype_config]
+default_op_dtype_configs = [onednn_op_quint8_dtype_config]
+fixed_qparams_op_dtype_configs = [onednn_op_quint8_dtype_config]
+share_qparams_op_dtype_configs = [onednn_op_quint8_dtype_config]
+rnn_op_dtype_configs = [onednn_dynamic_int8_dtype_config]
+embedding_op_dtype_configs = [onednn_weight_only_qint8_dtype_config]
+layer_norm_op_dtype_configs = [onednn_input_output_only_quint8_dtype_config]
+
+# =====================
+# |  BACKEND CONFIGS  |
+# =====================
+
+
+def get_onednn_backend_config() -> BackendConfig:
+    """
+    Return the `BackendConfig` for PyTorch's native ONEDNN backend.
+    """
+    return (
+        BackendConfig("onednn")
+        .set_backend_pattern_configs(conv_configs)
+        .set_backend_pattern_configs(linear_configs)
+        .set_backend_pattern_configs(_get_binary_op_configs(binary_op_dtype_configs))
+        .set_backend_pattern_config(_get_cat_config(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_default_op_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_fixed_qparams_op_configs(fixed_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_share_qparams_op_configs(share_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(_get_bn_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_ln_configs(layer_norm_op_dtype_configs))
+        .set_backend_pattern_configs(_get_rnn_op_configs(rnn_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_embedding_op_configs(embedding_op_dtype_configs)
+        )
+    )
+
+
+__all__ = [
+    "get_onednn_backend_config",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/qnnpack.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/qnnpack.py
new file mode 100644
index 0000000000000000000000000000000000000000..841bac512a6549f39f757b9531591f1e47e72a83
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/qnnpack.py
@@ -0,0 +1,171 @@
+import torch
+
+from ._common_operator_config_utils import (
+    _get_binary_op_configs,
+    _get_bn_configs,
+    _get_cat_config,
+    _get_conv_configs,
+    _get_default_op_configs,
+    _get_embedding_op_configs,
+    _get_fixed_qparams_op_configs,
+    _get_linear_configs,
+    _get_rnn_op_configs,
+    _get_share_qparams_op_configs,
+)
+from .backend_config import BackendConfig, DTypeConfig, DTypeWithConstraints
+
+
+__all__ = [
+    "get_qnnpack_backend_config",
+]
+
+# ===================
+# |  DTYPE CONFIGS  |
+# ===================
+
+qnnpack_weighted_op_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+)
+
+qnnpack_default_op_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+)
+
+qnnpack_default_op_fp16_dtype_config = DTypeConfig(
+    input_dtype=torch.float16,
+    output_dtype=torch.float16,
+    weight_dtype=torch.float16,
+    bias_dtype=torch.float16,
+)
+
+qnnpack_default_dynamic_int8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.float,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+    is_dynamic=True,
+)
+
+qnnpack_default_dynamic_float16_dtype_config = DTypeConfig(
+    input_dtype=torch.float16,
+    output_dtype=torch.float,
+    weight_dtype=torch.float16,
+    bias_dtype=torch.float,
+    is_dynamic=True,
+)
+
+qnnpack_weight_only_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.float,
+    output_dtype=torch.float,
+    weight_dtype=torch.quint8,
+)
+
+qnnpack_weight_only_quint4x2_dtype_config = DTypeConfig(
+    input_dtype=torch.float,
+    output_dtype=torch.float,
+    weight_dtype=torch.quint4x2,
+)
+
+# xnnpack compatible dtype configs
+
+# We restrict scale values to be 2 ** -12 to ensure the
+# requantization scale never falls below the xnnpack lower
+# threshold. Additionally, for qint8 weight, we restrict
+# the quantization values to [-127, +127], excluding -128.
+# For more detail, refer to the description of
+# `default_symmetric_qnnpack_qconfig`.
+
+# TODO: add additional restriction on qscheme to ensure it
+# is either per_tensor_symmetric or per_channel_symmetric
+
+qnnpack_act_qint8_scale_min_2_neg_12 = DTypeWithConstraints(
+    dtype=torch.qint8,
+    scale_min_lower_bound=2**-12,
+)
+
+qnnpack_weight_qint8_neg_127_to_127_scale_min_2_neg_12 = DTypeWithConstraints(
+    dtype=torch.qint8,
+    quant_min_lower_bound=-127,
+    quant_max_upper_bound=127,
+    scale_min_lower_bound=2**-12,
+)
+
+qnnpack_weighted_op_qint8_symmetric_dtype_config = DTypeConfig(
+    input_dtype=qnnpack_act_qint8_scale_min_2_neg_12,
+    output_dtype=qnnpack_act_qint8_scale_min_2_neg_12,
+    weight_dtype=qnnpack_weight_qint8_neg_127_to_127_scale_min_2_neg_12,
+    bias_dtype=torch.float,
+)
+
+qnnpack_default_op_qint8_symmetric_dtype_config = DTypeConfig(
+    input_dtype=qnnpack_act_qint8_scale_min_2_neg_12,
+    output_dtype=qnnpack_act_qint8_scale_min_2_neg_12,
+)
+
+
+# =====================
+# |  BACKEND CONFIGS  |
+# =====================
+
+
+def get_qnnpack_backend_config() -> BackendConfig:
+    """
+    Return the `BackendConfig` for PyTorch's native QNNPACK backend.
+    """
+    conv_dtype_configs = [
+        qnnpack_weighted_op_qint8_symmetric_dtype_config,
+        qnnpack_weighted_op_quint8_dtype_config,
+    ]
+    linear_dtype_configs = [
+        qnnpack_weighted_op_qint8_symmetric_dtype_config,
+        qnnpack_weighted_op_quint8_dtype_config,
+        qnnpack_default_dynamic_int8_dtype_config,
+        qnnpack_default_dynamic_float16_dtype_config,
+    ]
+    binary_op_dtype_configs = [
+        qnnpack_default_op_qint8_symmetric_dtype_config,
+        qnnpack_default_op_quint8_dtype_config,
+    ]
+    default_op_dtype_configs = [
+        qnnpack_default_op_qint8_symmetric_dtype_config,
+        qnnpack_default_op_quint8_dtype_config,
+    ]
+    fixed_qparams_op_dtype_configs = [
+        qnnpack_default_op_qint8_symmetric_dtype_config,
+        qnnpack_default_op_quint8_dtype_config,
+    ]
+    share_qparams_op_dtype_configs = [
+        qnnpack_default_op_qint8_symmetric_dtype_config,
+        qnnpack_default_op_quint8_dtype_config,
+    ]
+    rnn_op_dtype_configs = [
+        qnnpack_default_dynamic_int8_dtype_config,
+        qnnpack_default_dynamic_float16_dtype_config,
+    ]
+    embedding_op_dtype_configs = [
+        qnnpack_weight_only_quint8_dtype_config,
+        qnnpack_weight_only_quint4x2_dtype_config,
+    ]
+    return (
+        BackendConfig("qnnpack")
+        .set_backend_pattern_configs(_get_conv_configs(conv_dtype_configs))
+        .set_backend_pattern_configs(_get_linear_configs(linear_dtype_configs))
+        .set_backend_pattern_configs(_get_binary_op_configs(binary_op_dtype_configs))
+        .set_backend_pattern_config(_get_cat_config(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_default_op_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_fixed_qparams_op_configs(fixed_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_share_qparams_op_configs(share_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(_get_bn_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_rnn_op_configs(rnn_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_embedding_op_configs(embedding_op_dtype_configs)
+        )
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/tensorrt.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/tensorrt.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0490e2071f4f2df59b4bb6eb2a1d7885b4aa036
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/tensorrt.py
@@ -0,0 +1,98 @@
+# mypy: allow-untyped-defs
+import torch
+
+from ._common_operator_config_utils import (
+    _get_binary_op_configs,
+    _get_conv_configs,
+    _get_linear_configs,
+    _get_share_qparams_op_configs,
+    _get_tensor_info_op_configs,
+)
+from .backend_config import (
+    BackendConfig,
+    BackendPatternConfig,
+    DTypeConfig,
+    ObservationType,
+)
+
+
+__all__ = [
+    "get_tensorrt_backend_config",
+    "get_tensorrt_backend_config_dict",
+]
+
+
+def get_tensorrt_backend_config() -> BackendConfig:
+    """
+    Return the `BackendConfig` for the TensorRT backend.
+    NOTE: Current api will change in the future, it's just to unblock experimentation for
+    new backends, please don't use it right now.
+    TODO: add a README when it's more stable
+    """
+    # dtype configs
+    weighted_op_qint8_dtype_config = DTypeConfig(
+        input_dtype=torch.qint8,
+        output_dtype=torch.qint8,
+        weight_dtype=torch.qint8,
+        bias_dtype=torch.float,
+    )
+    non_weighted_op_qint8_dtype_config = DTypeConfig(
+        input_dtype=torch.qint8,
+        output_dtype=torch.qint8,
+    )
+
+    addmm_config = (
+        BackendPatternConfig(torch.addmm)
+        .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT)
+        .add_dtype_config(weighted_op_qint8_dtype_config)
+        ._set_input_type_to_index(
+            {
+                "bias": 0,
+                "input": 1,
+                "weight": 2,
+            }
+        )
+    )
+    cat_config = (
+        BackendPatternConfig(torch.cat)
+        .set_observation_type(ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT)
+        .add_dtype_config(non_weighted_op_qint8_dtype_config)
+    )
+    conv_dtype_configs = [
+        weighted_op_qint8_dtype_config,
+    ]
+    linear_dtype_configs = [
+        weighted_op_qint8_dtype_config,
+    ]
+    binary_op_dtype_configs = [
+        weighted_op_qint8_dtype_config,
+    ]
+    share_qparams_op_dtype_configs = [
+        non_weighted_op_qint8_dtype_config,
+    ]
+    tensor_info_op_dtype_configs = [
+        non_weighted_op_qint8_dtype_config,
+    ]
+    # there might be things not supported in fx2trt, but it will error out
+    # during fx2trt conversion and can support them after that
+    return (
+        BackendConfig("tensorrt")
+        .set_backend_pattern_configs(_get_conv_configs(conv_dtype_configs))
+        .set_backend_pattern_config(addmm_config)
+        .set_backend_pattern_config(cat_config)
+        .set_backend_pattern_configs(_get_linear_configs(linear_dtype_configs))
+        .set_backend_pattern_configs(_get_binary_op_configs(binary_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_share_qparams_op_configs(share_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_tensor_info_op_configs(tensor_info_op_dtype_configs)
+        )
+    )
+
+
+def get_tensorrt_backend_config_dict():
+    """
+    Return the `BackendConfig` for the TensorRT backend in dictionary form.
+    """
+    return get_tensorrt_backend_config().to_dict()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..957816ab590ed1a33d8bd6fc56bee26a26be60f6
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/utils.py
@@ -0,0 +1,312 @@
+# mypy: allow-untyped-defs
+from typing import Any, Callable, Dict, List, Tuple, Type, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.ao.quantization.fuser_method_mappings import _reverse2, _reverse3
+from torch.ao.quantization.utils import Pattern
+
+from .backend_config import BackendConfig, BackendPatternConfig, DTypeConfig
+
+
+__all__ = [
+    "get_pattern_to_dtype_configs",
+    "get_qat_module_classes",
+    "get_fused_module_classes",
+    "get_pattern_to_input_type_to_index",
+    "get_root_module_to_quantized_reference_module",
+    "get_fuser_method_mapping",
+    "get_module_to_qat_module",
+    "get_fusion_pattern_to_root_node_getter",
+    "get_fusion_pattern_to_extra_inputs_getter",
+    "remove_boolean_dispatch_from_name",
+    "pattern_to_human_readable",
+    "entry_to_pretty_str",
+]
+
+
+def get_pattern_to_dtype_configs(
+    backend_config: BackendConfig,
+) -> Dict[Pattern, List[DTypeConfig]]:
+    pattern_to_dtype_configs: Dict[Pattern, List[DTypeConfig]] = {}
+    for pattern, config in backend_config._pattern_complex_format_to_config.items():
+        pattern_to_dtype_configs[pattern] = config.dtype_configs
+    return pattern_to_dtype_configs
+
+
+def get_qat_module_classes(backend_config: BackendConfig) -> Tuple[type, ...]:
+    qat_module_classes = []
+    for config in backend_config.configs:
+        if config.qat_module is not None:
+            qat_module_classes.append(config.qat_module)
+    return tuple(set(qat_module_classes))
+
+
+def get_fused_module_classes(backend_config: BackendConfig) -> Tuple[type, ...]:
+    fused_module_classes = []
+    for config in backend_config.configs:
+        if config.fused_module is not None:
+            fused_module_classes.append(config.fused_module)
+    return tuple(set(fused_module_classes))
+
+
+def get_pattern_to_input_type_to_index(
+    backend_config: BackendConfig,
+) -> Dict[Pattern, Dict[str, int]]:
+    pattern_to_input_type_to_index: Dict[Pattern, Dict[str, int]] = {}
+    for pattern, config in backend_config._pattern_complex_format_to_config.items():
+        pattern_to_input_type_to_index[pattern] = config._input_type_to_index
+    return pattern_to_input_type_to_index
+
+
+def get_root_module_to_quantized_reference_module(
+    backend_config: BackendConfig,
+) -> Dict[Type[torch.nn.Module], Type[torch.nn.Module]]:
+    mapping: Dict[Type[torch.nn.Module], Type[torch.nn.Module]] = {}
+    for config in backend_config.configs:
+        if (
+            config.root_module is not None
+            and config.reference_quantized_module is not None
+        ):
+            mapping[config.root_module] = config.reference_quantized_module
+    return mapping
+
+
+def get_fuser_method_mapping(
+    backend_config: BackendConfig,
+) -> Dict[Pattern, Union[nn.Sequential, Callable]]:
+    fuser_method_mapping: Dict[Pattern, Union[nn.Sequential, Callable]] = {}
+    for pattern, config in backend_config._pattern_complex_format_to_config.items():
+        if config.fuser_method is not None:
+            # Note: both the fuser method and the pattern are specified in forward order in the
+            # BackendConfig, but the internal pattern matching code uses the reversed nested tuple
+            # format, so we need to convert both to the internal format
+            fuser_method = _get_fuser_method_in_reversed_nested_tuple_format(config)
+            fuser_method_mapping[pattern] = fuser_method
+    return fuser_method_mapping
+
+
+def get_module_to_qat_module(
+    backend_config: BackendConfig,
+) -> Dict[Pattern, Type[torch.nn.Module]]:
+    module_to_qat_module: Dict[Pattern, Type[torch.nn.Module]] = {}
+    for pattern, config in backend_config._pattern_complex_format_to_config.items():
+        if config.qat_module is not None:
+            module_to_qat_module[pattern] = config.qat_module
+    return module_to_qat_module
+
+
+def get_fusion_pattern_to_root_node_getter(
+    backend_config: BackendConfig,
+) -> Dict[Pattern, Callable]:
+    """Get a map from fusion pattern to a function that returns the root node
+    from the fusion pattern, e.g. the most common one is:
+    def get_root_node(node_pattern):
+        while not isinstance(node_pattern[-1], Node):
+            node_pattern = node_pattern[-1]
+        return node_pattern[-1]
+    This can work for all patterns whose root node is the "last node" in the pattern,
+    e.g. (torch.add, MatchAllNode, (torch.ReLU, torch.Conv2d))
+    """
+    root_node_getter_mapping: Dict[Pattern, Callable] = {}
+    for pattern, config in backend_config._pattern_complex_format_to_config.items():
+        if config._root_node_getter is not None:
+            root_node_getter_mapping[pattern] = config._root_node_getter
+    return root_node_getter_mapping
+
+
+def get_fusion_pattern_to_extra_inputs_getter(
+    backend_config: BackendConfig,
+) -> Dict[Pattern, Callable]:
+    """Get a map from fusion pattern to a function that returns extra input nodes
+    from the fusion pattern, in the order required by the root node. This is optional,
+    if not specified, we will not copy over any extra inputs for the root node.
+    Example:
+    # Let's say we have the pattern (torch.add, MatchAllNode, (torch.nn.BatchNorm2d, torch.nn.Conv2d))
+    # and root node is torch.nn.Conv2d, and the node in MatchAllNode would be an extra
+    # argument to the fused module, we can unpack the pattern and return the node at
+    # MatchAllNode here
+    # we can implement extra_inputs_getter as follows:
+    def extra_inputs_getter(pattern) -> List[Any]:
+        add, extra_input, conv_pattern = pattern
+        return [extra_input]
+    """
+    extra_inputs_getter_mapping: Dict[Pattern, Callable] = {}
+    for pattern, config in backend_config._pattern_complex_format_to_config.items():
+        if config._extra_inputs_getter is not None:
+            extra_inputs_getter_mapping[pattern] = config._extra_inputs_getter
+    return extra_inputs_getter_mapping
+
+
+def remove_boolean_dispatch_from_name(p) -> Any:
+    """
+    Some ops have a default string representation such as
+    '.fn at 0x7ff1106bf280>',
+    this function replaces them with the hardcoded function names.
+    """
+    if p is F.fractional_max_pool2d:
+        return "torch.nn.functional.fractional_max_pool2d"
+    elif p is F.fractional_max_pool3d:
+        return "torch.nn.functional.fractional_max_pool3d"
+    elif p is F.max_pool1d:
+        return "torch.nn.functional.max_pool1d"
+    elif p is F.max_pool2d:
+        return "torch.nn.functional.max_pool2d"
+    elif p is F.max_pool3d:
+        return "torch.nn.functional.max_pool3d"
+    elif p is F.adaptive_max_pool1d:
+        return "torch.nn.functional.adaptive_max_pool1d"
+    elif p is F.adaptive_max_pool2d:
+        return "torch.nn.functional.adaptive_max_pool2d"
+    elif p is F.adaptive_max_pool3d:
+        return "torch.nn.functional.adaptive_max_pool3d"
+    assert "boolean_dispatch" not in str(p), (
+        f"{p} does not have a human readable representation in "
+        + "quantization documentation"
+    )
+    return p
+
+
+def pattern_to_human_readable(p) -> Any:
+    if isinstance(p, tuple):
+        # nested patterns, recurse
+        return tuple(pattern_to_human_readable(inner_p) for inner_p in p)
+    elif isinstance(p, str):
+        # method names are already human readable
+        return p
+    else:
+        p = remove_boolean_dispatch_from_name(p)
+        return p
+
+
+# TODO(future PR): move backend_config_dict to use dataclass and move this logic to
+# the corresponding __str__ function
+def entry_to_pretty_str(entry) -> str:
+    """
+    Given a backend_config_dict entry, returns a string with the human readable
+    representation of it.
+    """
+    s = "{\n"
+
+    # always output the pattern first
+    if "pattern" in entry:
+        pattern_str = pattern_to_human_readable(entry["pattern"])
+
+        s += f"  'pattern': {pattern_str},\n"
+
+    # custom output for dtype_configs to make it look nice
+    if "dtype_configs" in entry:
+        s += "  'dtype_configs': [\n"
+        for dtype_config in entry["dtype_configs"]:
+            s += "    {\n"
+            for k, v in dtype_config.items():
+                s += f"      '{k}': {v},\n"
+            s += "    },\n"
+        s += "  ],\n"
+
+    # custom output for num_tensor_args_to_observation_type to make it look nice
+    if "num_tensor_args_to_observation_type" in entry:
+        s += "  'num_tensor_args_to_observation_type': {\n"
+        for k, v in entry["num_tensor_args_to_observation_type"].items():
+            s += f"    {k}: {v},\n"
+        s += "  },\n"
+
+    # output all the other fields
+    custom_handled_fields = [
+        "pattern",
+        "dtype_configs",
+        "num_tensor_args_to_observation_type",
+    ]
+    for field_name in entry:
+        if field_name in custom_handled_fields:
+            continue
+        s += f"  '{field_name}': {entry[field_name]},\n"
+
+    s += "}"
+    return s
+
+
+def _get_pattern_in_reversed_nested_tuple_format(
+    config: BackendPatternConfig,
+) -> Pattern:
+    """
+    Return the pattern specified in the given config in the reversed nested tuple format
+    used internally in the quantization pattern matching code.
+
+    If the pattern is not a tuple, or the pattern is already specified in the reversed
+    nested tuple format, return the pattern as is. Otherwise:
+
+    For 2-tuples (a, b), return (b, a).
+    For 3-tuples (a, b, c), return (c, (b, a)).
+
+    For example:
+        * Given nn.Linear, return nn.Linear
+        * Given (nn.Linear, nn.ReLU), return (nn.ReLU, nn.Linear)
+        * Given (nn.Conv2d, nn.BatchNorm2d, nn.ReLU), return
+          (nn.ReLU, (nn.BatchNorm2d, nn.Conv2d))
+
+    For context, the reason why this is needed is the user-facing BackendConfig
+    API accepts the flat 2-or-3-tuple format in forward order. While this simple
+    format handles the vast majority of use cases, it does not handle the more
+    complex ones, and so the internal pattern matching code for quantization uses
+    the following, more general reversed nested tuple format instead:
+
+        operator = module_type | functional | torch op | native op | MatchAllNode
+        Pattern = (operator, Pattern, Pattern, ...) | operator
+
+    In the future, we expect to replace the above complex format with the one used
+    by the subgraph rewriter in torch.fx, so we don't have to maintain our own
+    complex pattern matching code. Then we won't need this helper function anymore.
+    """
+    if config._pattern_complex_format is not None:
+        return config._pattern_complex_format
+    if config.pattern is None:
+        raise ValueError(
+            "Either 'pattern' or 'pattern_complex_format' must be specified"
+        )
+    if not isinstance(config.pattern, tuple):
+        return config.pattern
+
+    # Pattern is specified in the simple tuple format, need to convert
+    if len(config.pattern) == 2:
+        (a, b) = config.pattern
+        return (b, a)
+    elif len(config.pattern) == 3:
+        (a, b, c) = config.pattern
+        return (c, (b, a))
+    else:
+        raise ValueError("Expected a tuple with 2 or 3 elements, got: ", config.pattern)
+
+
+def _get_fuser_method_in_reversed_nested_tuple_format(
+    config: BackendPatternConfig,
+) -> Callable:
+    """
+    Return the fuser method specified in the given config in the reversed nested
+    tuple format used internally in the quantization pattern matching code.
+
+    If pattern is specified in the reversed nested tuple format, we assume the
+    fuser method is also specified in this format and simply return it as is.
+    Otherwise, we convert the fuser method as follows:
+
+        * Given f(is_qat, conv, relu), return f'(is_qat, relu, conv)
+        * Given f(is_qat, conv, bn, relu), return f'(is_qat, relu, bn_conv),
+          where bn_conv is a 2-tuple (bn, conv)
+
+    The first argument of a fuser method is always `is_qat` and is not affected
+    in the conversion. We currently only support functions with 3 or 4 arguments.
+    """
+    assert config.fuser_method is not None
+    if config._pattern_complex_format is not None:
+        return config.fuser_method
+    if not isinstance(config.pattern, tuple):
+        raise ValueError("Expected pattern to be a tuple, got: ", config.pattern)
+
+    # Pattern is specified in the simple tuple format, need to convert
+    if len(config.pattern) == 2:
+        return _reverse2(config.fuser_method)
+    elif len(config.pattern) == 3:
+        return _reverse3(config.fuser_method)
+    else:
+        raise ValueError("Expected a tuple with 2 or 3 elements, got: ", config.pattern)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/x86.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/x86.py
new file mode 100644
index 0000000000000000000000000000000000000000..c64b56c981b391140f63038ac507b0708ee876f4
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/backend_config/x86.py
@@ -0,0 +1,126 @@
+import torch
+
+from ._common_operator_config_utils import (
+    _get_binary_op_configs,
+    _get_bn_configs,
+    _get_cat_config,
+    _get_conv_configs,
+    _get_default_op_configs,
+    _get_embedding_op_configs,
+    _get_fixed_qparams_op_configs,
+    _get_linear_configs,
+    _get_rnn_op_configs,
+    _get_share_qparams_op_configs,
+    _get_tensor_info_op_configs,
+)
+from .backend_config import BackendConfig, DTypeConfig
+
+
+__all__ = [
+    "get_x86_backend_config",
+]
+
+# ===================
+# |  DTYPE CONFIGS  |
+# ===================
+
+# X86 aligns with FBGEMM for now
+
+x86_weighted_op_int8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+)
+
+x86_default_op_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+)
+
+x86_default_op_fp16_dtype_config = DTypeConfig(
+    input_dtype=torch.float16,
+    output_dtype=torch.float16,
+    weight_dtype=torch.float16,
+    bias_dtype=torch.float16,
+)
+
+x86_default_dynamic_int8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.float,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+    is_dynamic=True,
+)
+
+x86_default_dynamic_float16_dtype_config = DTypeConfig(
+    input_dtype=torch.float16,
+    output_dtype=torch.float,
+    weight_dtype=torch.float16,
+    bias_dtype=torch.float,
+    is_dynamic=True,
+)
+
+x86_weight_only_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.float,
+    output_dtype=torch.float,
+    weight_dtype=torch.quint8,
+)
+
+x86_weight_only_quint4x2_dtype_config = DTypeConfig(
+    input_dtype=torch.float,
+    output_dtype=torch.float,
+    weight_dtype=torch.quint4x2,
+)
+
+
+# =====================
+# |  BACKEND CONFIGS  |
+# =====================
+
+
+def get_x86_backend_config() -> BackendConfig:
+    """
+    Return the `BackendConfig` for PyTorch's native x86 backend.
+    """
+    conv_dtype_configs = [x86_weighted_op_int8_dtype_config]
+    linear_dtype_configs = [
+        x86_weighted_op_int8_dtype_config,
+        x86_default_dynamic_int8_dtype_config,
+        x86_default_dynamic_float16_dtype_config,
+    ]
+    binary_op_dtype_configs = [x86_weighted_op_int8_dtype_config]
+    default_op_dtype_configs = [x86_default_op_quint8_dtype_config]
+    fixed_qparams_op_dtype_configs = [x86_weighted_op_int8_dtype_config]
+    share_qparams_op_dtype_configs = [x86_default_op_quint8_dtype_config]
+    tensor_info_op_dtype_configs = [x86_default_op_quint8_dtype_config]
+    rnn_op_dtype_configs = [
+        x86_default_dynamic_int8_dtype_config,
+        x86_default_dynamic_float16_dtype_config,
+    ]
+    embedding_op_dtype_configs = [
+        x86_weight_only_quint8_dtype_config,
+        x86_weight_only_quint4x2_dtype_config,
+    ]
+    return (
+        BackendConfig("x86")
+        .set_backend_pattern_configs(_get_conv_configs(conv_dtype_configs))
+        .set_backend_pattern_configs(_get_linear_configs(linear_dtype_configs))
+        .set_backend_pattern_configs(_get_binary_op_configs(binary_op_dtype_configs))
+        .set_backend_pattern_config(_get_cat_config(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_default_op_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_fixed_qparams_op_configs(fixed_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_share_qparams_op_configs(share_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_tensor_info_op_configs(tensor_info_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(_get_bn_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_rnn_op_configs(rnn_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_embedding_op_configs(embedding_op_dtype_configs)
+        )
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fake_quantize.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fake_quantize.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ef266ebe47f50369096ccb95b4a468c7490a73f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fake_quantize.py
@@ -0,0 +1,647 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+"""Implements modules  used to perform fake quantization."""
+
+import re
+from abc import ABC, abstractmethod
+from typing import Any, Tuple
+
+import torch
+from torch.ao.quantization.observer import (
+    _with_args,
+    default_fixed_qparams_range_0to1_observer,
+    default_fixed_qparams_range_neg1to1_observer,
+    FixedQParamsObserver,
+    HistogramObserver,
+    MovingAverageMinMaxObserver,
+    MovingAveragePerChannelMinMaxObserver,
+)
+from torch.nn import Module
+
+
+__all__ = [
+    "FakeQuantizeBase",
+    "FakeQuantize",
+    "FixedQParamsFakeQuantize",
+    "FusedMovingAvgObsFakeQuantize",
+    "disable_fake_quant",
+    "disable_observer",
+    "enable_fake_quant",
+    "enable_observer",
+    "default_fake_quant",
+    "default_weight_fake_quant",
+    "default_dynamic_fake_quant",
+    "default_fixed_qparams_range_neg1to1_fake_quant",
+    "default_fixed_qparams_range_0to1_fake_quant",
+    "default_symmetric_fixed_qparams_fake_quant",
+    "default_affine_fixed_qparams_fake_quant",
+    "default_per_channel_weight_fake_quant",
+    "default_embedding_fake_quant",
+    "default_embedding_fake_quant_4bit",
+    "default_histogram_fake_quant",
+    "default_fused_act_fake_quant",
+    "default_fused_wt_fake_quant",
+    "default_fused_per_channel_wt_fake_quant",
+    "fused_wt_fake_quant_range_neg_127_to_127",
+    "fused_per_channel_wt_fake_quant_range_neg_127_to_127",
+]
+
+
+def _is_per_channel(qscheme: "torch.qscheme") -> bool:
+    return qscheme in [
+        torch.per_channel_symmetric,
+        torch.per_channel_affine,
+        torch.per_channel_affine_float_qparams,
+    ]
+
+
+def _is_per_tensor(qscheme: "torch.qscheme") -> bool:
+    return qscheme in [torch.per_tensor_symmetric, torch.per_tensor_affine]
+
+
+def _is_symmetric_quant(qscheme: "torch.qscheme") -> bool:
+    return qscheme in [torch.per_tensor_symmetric, torch.per_channel_symmetric]
+
+
+def _is_float_qparams(qscheme: "torch.qscheme") -> bool:
+    return qscheme in [
+        torch.per_channel_affine_float_qparams,
+    ]
+
+
+class FakeQuantizeBase(ABC, Module):
+    r"""Base fake quantize module.
+
+    Base fake quantize module
+    Any fake quantize implementation should derive from this class.
+
+    Concrete fake quantize module should follow the same API. In forward, they will update
+    the statistics of the observed Tensor and fake quantize the input. They should also provide a
+    `calculate_qparams` function that computes the quantization parameters given
+    the collected statistics.
+
+    """
+
+    fake_quant_enabled: torch.Tensor
+    observer_enabled: torch.Tensor
+
+    def __init__(self) -> None:
+        """Set fake_quant_enabled and observer_enabled."""
+        super().__init__()
+        # fake_quant_enabled and observer_enabled are buffers to support their
+        # replication in DDP. Data type is uint8 because NCCL does not support
+        # bool tensors.
+        self.register_buffer("fake_quant_enabled", torch.tensor([1], dtype=torch.uint8))
+        self.register_buffer("observer_enabled", torch.tensor([1], dtype=torch.uint8))
+
+    @abstractmethod
+    def forward(self, x):
+        pass
+
+    @abstractmethod
+    def calculate_qparams(self, **kwargs):
+        pass
+
+    @torch.jit.export
+    def enable_fake_quant(self, enabled: bool = True) -> None:
+        self.fake_quant_enabled[0] = 1 if enabled else 0
+
+    @torch.jit.export
+    def disable_fake_quant(self):
+        self.enable_fake_quant(False)
+
+    @torch.jit.export
+    def enable_observer(self, enabled: bool = True) -> None:
+        self.observer_enabled[0] = 1 if enabled else 0
+
+    @torch.jit.export
+    def disable_observer(self):
+        self.enable_observer(False)
+
+    @classmethod
+    def with_args(cls, **kwargs):
+        fake_quant_constructor = _with_args(cls, **kwargs)
+        # need to assign the correct module to fake_quantize
+        # constructors to satisfy public v private requirements
+        fake_quant_constructor.__module__ = "torch.ao.quantization.fake_quantize"
+        return fake_quant_constructor
+
+
+class FakeQuantize(FakeQuantizeBase):
+    r"""Simulate the quantize and dequantize operations in training time.
+
+    The output of this module is given by::
+
+        x_out = (
+          clamp(round(x/scale + zero_point), quant_min, quant_max) - zero_point
+        ) * scale
+
+    * :attr:`is_dynamic` indicates whether the fake quantie is a placeholder for dynamic quantization
+      operators (choose_qparams -> q -> dq) or static quantization operators (q -> dq)
+
+    * :attr:`scale` defines the scale factor used for quantization.
+
+    * :attr:`zero_point` specifies the quantized value to which 0 in floating point maps to
+
+    * :attr:`fake_quant_enabled` controls the application of fake quantization on tensors, note that
+      statistics can still be updated.
+
+    * :attr:`observer_enabled` controls statistics collection on tensors
+
+    * :attr:`dtype` specifies the quantized dtype that is being emulated with fake-quantization,
+        allowable values are torch.qint8 and torch.quint8.
+
+    Args:
+
+        observer (module): Module for observing statistics on input tensors and calculating scale
+          and zero-point.
+        observer_kwargs (optional): Arguments for the observer module
+
+    Attributes:
+        activation_post_process (Module): User provided module that collects statistics on the input tensor and
+          provides a method to calculate scale and zero-point.
+
+    """
+
+    scale: torch.Tensor
+    zero_point: torch.Tensor
+
+    def __init__(
+        self,
+        observer=MovingAverageMinMaxObserver,
+        quant_min=None,
+        quant_max=None,
+        is_dynamic=False,
+        **observer_kwargs,
+    ):
+        super().__init__()
+        # Populate quant_min/quant_max to observer_kwargs if valid
+        if quant_min is not None and quant_max is not None:
+            assert (
+                quant_min <= quant_max
+            ), "quant_min must be less than or equal to quant_max"
+            dtype = observer_kwargs.get("dtype", torch.quint8)
+            if hasattr(observer, "p"):
+                # In case observer is _PartialWrapper, dtype can be stored in
+                # observer.p.keywords["dtype"]
+                dtype = getattr(getattr(observer, "p", {}), "keywords", {}).get(
+                    "dtype", dtype
+                )
+            assert torch.iinfo(dtype).min <= quant_min, "quant_min out of bound"
+            assert quant_max <= torch.iinfo(dtype).max, "quant_max out of bound"
+            observer_kwargs.update({"quant_min": quant_min, "quant_max": quant_max})
+        observer_kwargs["is_dynamic"] = is_dynamic
+        self.activation_post_process = observer(**observer_kwargs)
+        # TODO: keeping self.quant_min/max for BC; remove after a couple releases
+        # Users should use self.activation_post_process.quant_min
+        self.quant_min = self.activation_post_process.quant_min
+        self.quant_max = self.activation_post_process.quant_max
+        self.is_dynamic = self.activation_post_process.is_dynamic
+        if _is_float_qparams(self.activation_post_process.qscheme):
+            zero_point_dtype = torch.float
+        else:
+            zero_point_dtype = torch.int
+        self.register_buffer("scale", torch.tensor([1.0], dtype=torch.float))
+        self.register_buffer("zero_point", torch.tensor([0], dtype=zero_point_dtype))
+        self.dtype = self.activation_post_process.dtype
+        self.qscheme = self.activation_post_process.qscheme
+        self.ch_axis = (
+            self.activation_post_process.ch_axis
+            if hasattr(self.activation_post_process, "ch_axis")
+            else -1
+        )
+        assert _is_per_channel(self.qscheme) or _is_per_tensor(self.qscheme), (
+            "Only per channel and per tensor quantization are supported in fake quantize"
+            + " got qscheme: "
+            + str(self.qscheme)
+        )
+        self.is_per_channel = _is_per_channel(self.qscheme)
+
+    @torch.jit.export
+    def calculate_qparams(self):
+        return self.activation_post_process.calculate_qparams()
+
+    def forward(self, X):
+        if self.observer_enabled[0] == 1:
+            self.activation_post_process(X.detach())
+            _scale, _zero_point = self.calculate_qparams()
+            _scale, _zero_point = _scale.to(self.scale.device), _zero_point.to(
+                self.zero_point.device
+            )
+            if self.scale.shape != _scale.shape:
+                self.scale.resize_(_scale.shape)
+                self.zero_point.resize_(_zero_point.shape)
+            self.scale.copy_(_scale)
+            self.zero_point.copy_(_zero_point)
+
+        if self.fake_quant_enabled[0] == 1:
+            if self.is_per_channel:
+                X = torch.fake_quantize_per_channel_affine(
+                    X,
+                    self.scale,
+                    self.zero_point,
+                    self.ch_axis,
+                    self.activation_post_process.quant_min,
+                    self.activation_post_process.quant_max,
+                )
+            else:
+                X = torch.fake_quantize_per_tensor_affine(
+                    X,
+                    self.scale,
+                    self.zero_point,
+                    self.activation_post_process.quant_min,
+                    self.activation_post_process.quant_max,
+                )
+        return X
+
+    @torch.jit.export
+    def extra_repr(self):
+        return (
+            f"fake_quant_enabled={self.fake_quant_enabled}, observer_enabled={self.observer_enabled}, "
+            f"quant_min={self.activation_post_process.quant_min}, quant_max={self.activation_post_process.quant_max}, "
+            f"dtype={self.dtype}, qscheme={self.qscheme}, ch_axis={self.ch_axis}, "
+            f"scale={self.scale}, zero_point={self.zero_point}"
+        )
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        # We cannot currently register scalar values as buffers, so need to manually
+        # specify serialization here.
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "scale"] = self.scale
+        destination[prefix + "zero_point"] = self.zero_point
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        # Removing this function throws an error that the size of the loaded tensor does not match the original size
+        # i.e., These buffers start out with numel 0 and become numel 1 once they have their first forward pass.
+        local_state = ["scale", "zero_point"]
+        for name in local_state:
+            key = prefix + name
+            if key in state_dict:
+                val = state_dict[key]
+                # Custom handling to allow loading scale and zero_point
+                # of size N into uninitialized buffers of size 0. The
+                # buffers are resized here, and the values are copied in
+                # the default state_dict loading code of the parent.
+                if name == "scale":
+                    self.scale.resize_(val.shape)
+                else:
+                    assert name == "zero_point"
+                    self.zero_point.resize_(val.shape)
+                # For torchscript module we need to update the attributes here since we do not
+                # call the `_load_from_state_dict` function defined module.py
+                if torch.jit.is_scripting():
+                    if name == "scale":
+                        self.scale.copy_(val)
+                    else:
+                        assert name == "zero_point"
+                        self.zero_point.copy_(val)
+            elif strict:
+                missing_keys.append(key)
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            strict,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+
+class FixedQParamsFakeQuantize(FakeQuantize):
+    """Simulate quantize and dequantize in training time.
+
+    Simulate quantize and dequantize with fixed quantization
+    parameters in training time. Only per tensor quantization
+    is supported.
+    """
+
+    # TODO: rename observer to observer_ctr
+    def __init__(self, observer):
+        super().__init__(observer=observer)
+        assert (
+            type(self.activation_post_process) == FixedQParamsObserver
+        ), f"{self.__class__.__name__}'s observer must be a {FixedQParamsObserver.__name__}"
+        self._observer_ctr = observer
+        self.scale = self.activation_post_process.scale
+        self.zero_point = self.activation_post_process.zero_point
+        assert _is_per_tensor(self.qscheme), (
+            "Only per tensor quantization is supported"
+            + " FixedQParamsFakeQuantize module, got qscheme:"
+            + str(self.qscheme)
+        )
+
+    @torch.jit.export
+    def calculate_qparams(self):
+        return self.scale, self.zero_point
+
+    @torch.jit.export
+    def extra_repr(self):
+        """Define a string representation of the object's attributes."""
+        return (
+            f"fake_quant_enabled={self.fake_quant_enabled}, observer_enabled={self.observer_enabled}, "
+            f"scale={self.scale}, zero_point={self.zero_point}, "
+            f"dtype={self.dtype}, quant_min={self.activation_post_process.quant_min}, "
+            f"quant_max={self.activation_post_process.quant_max}, qscheme={self.qscheme}"
+        )
+
+
+class FusedMovingAvgObsFakeQuantize(FakeQuantize):
+    r"""Define a fused module to observe the tensor.
+
+    Fused module that is used to observe the input tensor (compute min/max), compute
+    scale/zero_point and fake_quantize the tensor.
+    This module uses calculation similar MovingAverageMinMaxObserver for the inputs,
+    to compute the min/max values in order to compute the scale/zero_point.
+    The qscheme input in the observer is used to differentiate between symmetric/affine
+    quantization scheme.
+
+    The output of this module is given by
+    x_out = (clamp(round(x/scale + zero_point), quant_min, quant_max)-zero_point)*scale
+
+    Similar to :class:`~torch.ao.quantization.FakeQuantize`, and accepts the same attributes as the
+    base class.
+
+    """
+
+    def __init__(
+        self,
+        observer: Any = MovingAverageMinMaxObserver,
+        quant_min: int = 0,
+        quant_max: int = 255,
+        **observer_kwargs: Any,
+    ) -> None:
+        super().__init__(observer, quant_min, quant_max, **observer_kwargs)
+        assert isinstance(
+            self.activation_post_process,
+            (MovingAverageMinMaxObserver, MovingAveragePerChannelMinMaxObserver),
+        ), "Fused observer+fake_quant module only works with MovingAverageMinMaxObserver"
+        self.register_buffer("fake_quant_enabled", torch.tensor([1], dtype=torch.long))
+        self.register_buffer("observer_enabled", torch.tensor([1], dtype=torch.long))
+        self.is_symmetric_quant = _is_symmetric_quant(
+            self.activation_post_process.qscheme
+        )
+
+    @torch.jit.export
+    def calculate_qparams(self) -> Tuple[torch.Tensor, torch.Tensor]:
+        return self.activation_post_process.calculate_qparams()
+
+    @torch.jit.export
+    def extra_repr(self) -> str:
+        return (
+            f"fake_quant_enabled={self.fake_quant_enabled}, observer_enabled={self.observer_enabled}, "
+            f"scale={self.scale}, zero_point={self.zero_point}, dtype={self.dtype}, "
+            f"quant_min={self.activation_post_process.quant_min}, quant_max={self.activation_post_process.quant_max}, "
+            f"qscheme={self.qscheme}, reduce_range={self.activation_post_process.reduce_range}"
+        )
+
+    def forward(self, X: torch.Tensor) -> torch.Tensor:
+        return torch.fused_moving_avg_obs_fake_quant(
+            X,
+            self.observer_enabled,
+            self.fake_quant_enabled,
+            self.activation_post_process.min_val,
+            self.activation_post_process.max_val,
+            self.scale,
+            self.zero_point,
+            self.activation_post_process.averaging_constant,
+            self.activation_post_process.quant_min,
+            self.activation_post_process.quant_max,
+            self.ch_axis,
+            self.is_per_channel,
+            self.is_symmetric_quant,
+        )
+
+
+default_fake_quant = FakeQuantize.with_args(
+    observer=MovingAverageMinMaxObserver,
+    quant_min=0,
+    quant_max=255,
+    dtype=torch.quint8,
+    qscheme=torch.per_tensor_affine,
+    reduce_range=True,
+)
+"""
+Default fake_quant for activations.
+"""
+
+default_weight_fake_quant = FakeQuantize.with_args(
+    observer=MovingAverageMinMaxObserver,
+    quant_min=-128,
+    quant_max=127,
+    dtype=torch.qint8,
+    qscheme=torch.per_tensor_symmetric,
+    reduce_range=False,
+)
+"""
+Default fake_quant for weights.
+Observer is memoryless since averaging_constant is 1.
+"""
+
+default_dynamic_fake_quant = FakeQuantize.with_args(
+    observer=MovingAverageMinMaxObserver,
+    quant_min=0,
+    quant_max=255,
+    is_dynamic=True,
+    dtype=torch.quint8,
+    averaging_constant=1,
+)
+"""
+Default dynamic fake_quant for activations.
+"""
+
+default_fixed_qparams_range_neg1to1_fake_quant = FixedQParamsFakeQuantize.with_args(
+    observer=default_fixed_qparams_range_neg1to1_observer
+)
+default_fixed_qparams_range_0to1_fake_quant = FixedQParamsFakeQuantize.with_args(
+    observer=default_fixed_qparams_range_0to1_observer
+)
+# TODO: the following 2 variables are kept for backwards compatibility; remove after a few releases
+default_symmetric_fixed_qparams_fake_quant = (
+    default_fixed_qparams_range_neg1to1_fake_quant
+)
+default_affine_fixed_qparams_fake_quant = default_fixed_qparams_range_0to1_fake_quant
+
+default_per_channel_weight_fake_quant = FakeQuantize.with_args(
+    observer=MovingAveragePerChannelMinMaxObserver,
+    quant_min=-128,
+    quant_max=127,
+    dtype=torch.qint8,
+    qscheme=torch.per_channel_symmetric,
+    reduce_range=False,
+    ch_axis=0,
+)
+"""
+Default fake_quant for per-channel weights.
+Observer is memoryless since averaging_constant is 1.
+"""
+default_embedding_fake_quant = FakeQuantize.with_args(
+    observer=MovingAveragePerChannelMinMaxObserver,
+    qscheme=torch.per_channel_affine_float_qparams,
+    dtype=torch.quint8,
+    quant_min=0,
+    quant_max=255,
+    ch_axis=0,
+    averaging_constant=1,
+)
+"""
+Default fake_quant for embeddings.
+Observer is memoryless since averaging_constant is 1.
+"""
+
+default_embedding_fake_quant_4bit = FakeQuantize.with_args(
+    observer=MovingAveragePerChannelMinMaxObserver,
+    qscheme=torch.per_channel_affine_float_qparams,
+    ch_axis=0,
+    dtype=torch.quint4x2,
+    averaging_constant=1,
+)
+
+default_histogram_fake_quant = FakeQuantize.with_args(
+    observer=HistogramObserver,
+    quant_min=0,
+    quant_max=255,
+    dtype=torch.quint8,
+    qscheme=torch.per_tensor_affine,
+    reduce_range=True,
+)
+"""
+Fake_quant for activations using a histogram..
+"""
+
+
+default_fused_act_fake_quant = FusedMovingAvgObsFakeQuantize.with_args(
+    observer=MovingAverageMinMaxObserver,
+    quant_min=0,
+    quant_max=255,
+    dtype=torch.quint8,
+)
+
+"""
+Fused version of `default_fake_quant`, with improved performance.
+"""
+
+
+default_fused_wt_fake_quant = FusedMovingAvgObsFakeQuantize.with_args(
+    observer=MovingAverageMinMaxObserver,
+    quant_min=-128,
+    quant_max=127,
+    dtype=torch.qint8,
+    qscheme=torch.per_tensor_symmetric,
+)
+"""
+Fused version of `default_weight_fake_quant`, with improved performance.
+"""
+
+default_fused_per_channel_wt_fake_quant = FusedMovingAvgObsFakeQuantize.with_args(
+    observer=MovingAveragePerChannelMinMaxObserver,
+    quant_min=-128,
+    quant_max=127,
+    dtype=torch.qint8,
+    qscheme=torch.per_channel_symmetric,
+)
+"""
+Fused version of `default_per_channel_weight_fake_quant`, with improved performance.
+"""
+
+fused_wt_fake_quant_range_neg_127_to_127 = FusedMovingAvgObsFakeQuantize.with_args(
+    observer=MovingAverageMinMaxObserver,
+    quant_min=-127,
+    quant_max=127,
+    dtype=torch.qint8,
+    qscheme=torch.per_tensor_symmetric,
+    eps=2**-12,
+)
+"""
+Fused version of `default_weight_fake_quant`, with the 8-bit values restricted to [-127, +127], excluding -128.
+"""
+
+fused_per_channel_wt_fake_quant_range_neg_127_to_127 = (
+    FusedMovingAvgObsFakeQuantize.with_args(
+        observer=MovingAveragePerChannelMinMaxObserver,
+        quant_min=-127,
+        quant_max=127,
+        dtype=torch.qint8,
+        qscheme=torch.per_channel_symmetric,
+        eps=2**-12,
+    )
+)
+
+"""
+Fused version of `default_per_channel_weight_fake_quant`, with the 8-bit values restricted to [-127, +127], excluding -128.
+"""
+
+
+def _is_fake_quant_script_module(mod):
+    """Return true if given mod is an instance of FakeQuantize script module."""
+    if isinstance(mod, torch.jit.RecursiveScriptModule):
+        # qualified name looks like '__torch__.torch.ao.quantization.fake_quantize.___torch_mangle_2.FakeQuantize'
+        suffix = mod._c.qualified_name.split(".", 1)[1]
+        name = re.sub(r"\.___torch_mangle_\d+", "", suffix)
+        return (
+            name == "torch.ao.quantization.fake_quantize.FakeQuantize"
+            or name
+            == "torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize"
+        )
+    return False
+
+
+def disable_fake_quant(mod):
+    """Disable fake quantization for the module.
+
+    Disable fake quantization for this module, if applicable. Example usage::
+
+      # model is any PyTorch model
+      model.apply(torch.ao.quantization.disable_fake_quant)
+
+    """
+    if isinstance(mod, FakeQuantizeBase) or _is_fake_quant_script_module(mod):
+        mod.disable_fake_quant()
+
+
+def enable_fake_quant(mod):
+    """Enable fake quantization for the module.
+
+    Enable fake quantization for this module, if applicable. Example usage::
+
+      # model is any PyTorch model
+      model.apply(torch.ao.quantization.enable_fake_quant)
+
+    """
+    if isinstance(mod, FakeQuantizeBase) or _is_fake_quant_script_module(mod):
+        mod.enable_fake_quant()
+
+
+def disable_observer(mod):
+    """Disable observation for this module.
+
+    Disable observation for this module, if applicable. Example usage::
+
+      # model is any PyTorch model
+      model.apply(torch.ao.quantization.disable_observer)
+
+    """
+    if isinstance(mod, FakeQuantizeBase) or _is_fake_quant_script_module(mod):
+        mod.disable_observer()
+
+
+def enable_observer(mod):
+    """Enable observation for this module.
+
+    Enable observation for this module, if applicable. Example usage::
+
+      # model is any PyTorch model
+      model.apply(torch.ao.quantization.enable_observer)
+
+    """
+    if isinstance(mod, FakeQuantizeBase) or _is_fake_quant_script_module(mod):
+        mod.enable_observer()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fuse_modules.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fuse_modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..c16e82b1e0aaa6fd962e487d82b9a11800b5c0f8
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fuse_modules.py
@@ -0,0 +1,218 @@
+# mypy: allow-untyped-defs
+import copy
+from typing import List, Optional
+
+import torch.nn as nn
+
+# for backward compatibility
+from torch.ao.quantization.fuser_method_mappings import (  # noqa: F401  # noqa: F401
+    fuse_conv_bn,
+    fuse_conv_bn_relu,
+    get_fuser_method,
+)
+from torch.nn.utils.parametrize import type_before_parametrizations
+
+
+__all__ = [
+    "fuse_known_modules",
+    "fuse_modules",
+    "fuse_modules_qat",
+]
+
+
+# Generalization of getattr
+def _get_module(model, submodule_key):
+    tokens = submodule_key.split(".")
+    cur_mod = model
+    for s in tokens:
+        cur_mod = getattr(cur_mod, s)
+    return cur_mod
+
+
+# Generalization of setattr
+def _set_module(model, submodule_key, module):
+    tokens = submodule_key.split(".")
+    sub_tokens = tokens[:-1]
+    cur_mod = model
+    for s in sub_tokens:
+        cur_mod = getattr(cur_mod, s)
+
+    setattr(cur_mod, tokens[-1], module)
+
+
+def fuse_known_modules(mod_list, is_qat, additional_fuser_method_mapping=None):
+    r"""Return a list of known fuse modules.
+
+    Returns a list of modules that fuses the operations specified
+     in the input module list.
+
+    Fuses only the following sequence of modules:
+    conv, bn
+    conv, bn, relu
+    conv, relu
+    linear, bn
+    linear, relu
+    For these sequences, the first element in the output module list performs
+    the fused operation. The rest of the elements are set to nn.Identity()
+    """
+    types = tuple(type_before_parametrizations(m) for m in mod_list)
+    fuser_method = get_fuser_method(types, additional_fuser_method_mapping)
+    if fuser_method is None:
+        raise NotImplementedError(f"Cannot fuse modules: {types}")
+    new_mod: List[Optional[nn.Module]] = [None] * len(mod_list)
+    fused = fuser_method(is_qat, *mod_list)
+    # NOTE: forward hooks not processed in the two following for loops will be lost after the fusion
+    # Move pre forward hooks of the base module to resulting fused module
+    for pre_hook_fn in mod_list[0]._forward_pre_hooks.values():
+        fused.register_forward_pre_hook(pre_hook_fn)
+    mod_list[0]._forward_pre_hooks.clear()
+    # Move post forward hooks of the last module to resulting fused module
+    for hook_fn in mod_list[-1]._forward_hooks.values():
+        fused.register_forward_hook(hook_fn)
+    mod_list[-1]._forward_hooks.clear()
+    new_mod[0] = fused
+
+    for i in range(1, len(mod_list)):
+        identity = nn.Identity()
+        identity.training = mod_list[0].training
+        new_mod[i] = identity
+
+    return new_mod
+
+
+def _fuse_modules_helper(
+    model,
+    modules_to_fuse,
+    is_qat,
+    fuser_func=fuse_known_modules,
+    fuse_custom_config_dict=None,
+):
+    if fuse_custom_config_dict is None:
+        fuse_custom_config_dict = {}
+    additional_fuser_method_mapping = fuse_custom_config_dict.get(
+        "additional_fuser_method_mapping", {}
+    )
+    mod_list = []
+    for item in modules_to_fuse:
+        mod_list.append(_get_module(model, item))
+
+    # Fuse list of modules
+    new_mod_list = fuser_func(mod_list, is_qat, additional_fuser_method_mapping)
+
+    # Replace original module list with fused module list
+    for i, item in enumerate(modules_to_fuse):
+        _set_module(model, item, new_mod_list[i])
+
+
+def _fuse_modules(
+    model,
+    modules_to_fuse,
+    is_qat,
+    inplace=False,
+    fuser_func=fuse_known_modules,
+    fuse_custom_config_dict=None,
+):
+    if not inplace:
+        model = copy.deepcopy(model)
+
+    if all(isinstance(module_element, str) for module_element in modules_to_fuse):
+        # Handle case of modules_to_fuse being a list
+        _fuse_modules_helper(
+            model, modules_to_fuse, is_qat, fuser_func, fuse_custom_config_dict
+        )
+    else:
+        # Handle case of modules_to_fuse being a list of lists
+        for module_list in modules_to_fuse:
+            _fuse_modules_helper(
+                model, module_list, is_qat, fuser_func, fuse_custom_config_dict
+            )
+    return model
+
+
+def fuse_modules(
+    model,
+    modules_to_fuse,
+    inplace=False,
+    fuser_func=fuse_known_modules,
+    fuse_custom_config_dict=None,
+):
+    r"""Fuse a list of modules into a single module.
+
+    Fuses only the following sequence of modules:
+    conv, bn
+    conv, bn, relu
+    conv, relu
+    linear, relu
+    bn, relu
+    All other sequences are left unchanged.
+    For these sequences, replaces the first item in the list
+    with the fused module, replacing the rest of the modules
+    with identity.
+
+    Args:
+        model: Model containing the modules to be fused
+        modules_to_fuse: list of list of module names to fuse. Can also be a list
+                         of strings if there is only a single list of modules to fuse.
+        inplace: bool specifying if fusion happens in place on the model, by default
+                 a new model is returned
+        fuser_func: Function that takes in a list of modules and outputs a list of fused modules
+                    of the same length. For example,
+                    fuser_func([convModule, BNModule]) returns the list [ConvBNModule, nn.Identity()]
+                    Defaults to torch.ao.quantization.fuse_known_modules
+        `fuse_custom_config_dict`: custom configuration for fusion
+
+    .. code-block:: python
+
+       # Example of fuse_custom_config_dict
+       fuse_custom_config_dict = {
+           # Additional fuser_method mapping
+           "additional_fuser_method_mapping": {
+               (torch.nn.Conv2d, torch.nn.BatchNorm2d): fuse_conv_bn
+           },
+       }
+
+    Returns:
+        model with fused modules. A new copy is created if inplace=True.
+
+    Examples::
+
+            >>> # xdoctest: +SKIP
+            >>> m = M().eval()
+            >>> # m is a module containing the sub-modules below
+            >>> modules_to_fuse = [ ['conv1', 'bn1', 'relu1'], ['submodule.conv', 'submodule.relu']]
+            >>> fused_m = torch.ao.quantization.fuse_modules(m, modules_to_fuse)
+            >>> output = fused_m(input)
+
+            >>> m = M().eval()
+            >>> # Alternately provide a single list of modules to fuse
+            >>> modules_to_fuse = ['conv1', 'bn1', 'relu1']
+            >>> fused_m = torch.ao.quantization.fuse_modules(m, modules_to_fuse)
+            >>> output = fused_m(input)
+
+    """
+    return _fuse_modules(
+        model,
+        modules_to_fuse,
+        is_qat=False,
+        inplace=inplace,
+        fuser_func=fuser_func,
+        fuse_custom_config_dict=fuse_custom_config_dict,
+    )
+
+
+def fuse_modules_qat(
+    model,
+    modules_to_fuse,
+    inplace=False,
+    fuser_func=fuse_known_modules,
+    fuse_custom_config_dict=None,
+):
+    """QAT version for `fuse_modules`."""
+    return _fuse_modules(
+        model,
+        modules_to_fuse,
+        is_qat=True,
+        inplace=inplace,
+        fuser_func=fuser_func,
+        fuse_custom_config_dict=fuse_custom_config_dict,
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fuser_method_mappings.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fuser_method_mappings.py
new file mode 100644
index 0000000000000000000000000000000000000000..40f305919fb36d3e0da31ab953f042840ee747a7
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fuser_method_mappings.py
@@ -0,0 +1,294 @@
+# mypy: allow-untyped-defs
+import itertools
+from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
+
+import torch.ao.nn.intrinsic as nni
+import torch.nn as nn
+from torch.ao.quantization.utils import get_combined_dict, MatchAllNode, Pattern
+
+
+__all__ = [
+    "fuse_conv_bn",
+    "fuse_conv_bn_relu",
+    "fuse_linear_bn",
+    "fuse_convtranspose_bn",
+    "get_fuser_method",
+    "get_fuser_method_new",
+]
+
+
+def fuse_conv_bn(is_qat, conv, bn):
+    r"""Return the fused the conv and bn modules.
+    Given the conv and bn modules, fuses them and returns the fused module
+
+    Args:
+        is_qat: a flag for whether we are using quantization aware training fusion
+        or post training quantization fusion
+        conv: Module instance of type conv2d/conv3d
+        bn: Spatial BN instance that needs to be fused with the conv
+
+    Examples::
+
+        >>> m1 = nn.Conv2d(10, 20, 3)
+        >>> b1 = nn.BatchNorm2d(20)
+        >>> # xdoctest: +SKIP
+        >>> m2 = fuse_conv_bn(m1, b1)
+    """
+    assert (
+        conv.training == bn.training
+    ), "Conv and BN both must be in the same mode (train or eval)."
+
+    fused_module_class_map = {
+        nn.Conv1d: nni.ConvBn1d,
+        nn.Conv2d: nni.ConvBn2d,
+        nn.Conv3d: nni.ConvBn3d,
+    }
+
+    if is_qat:
+        assert (
+            bn.num_features == conv.out_channels
+        ), "Output channel of Conv2d must match num_features of BatchNorm2d"
+        assert bn.affine, "Only support fusing BatchNorm2d with affine set to True"
+        assert (
+            bn.track_running_stats
+        ), "Only support fusing BatchNorm2d with tracking_running_stats set to True"
+        fused_module_class = fused_module_class_map.get((type(conv)), None)
+        if fused_module_class is not None:
+            return fused_module_class(conv, bn)
+        else:
+            raise NotImplementedError(f"Cannot fuse train modules: {(conv, bn)}")
+    else:
+        return nn.utils.fuse_conv_bn_eval(conv, bn)
+
+
+def fuse_conv_bn_relu(is_qat, conv, bn, relu):
+    r"""Return the fused conv and bv modules.
+
+    Given the conv and bn modules, fuses them and returns the fused module
+
+    Args:
+        is_qat: a flag for whether we are using quantization aware training fusion
+        or post training quantization fusion
+        conv: Module instance of type conv2d/conv3d
+        bn: Spatial BN instance that needs to be fused with the conv
+
+    Examples::
+
+        >>> m1 = nn.Conv2d(10, 20, 3)
+        >>> b1 = nn.BatchNorm2d(20)
+        >>> r1 = nn.ReLU(inplace=False)
+        >>> # xdoctest: +SKIP
+        >>> m2 = fuse_conv_bn_relu(m1, b1, r1)
+    """
+    assert (
+        conv.training == bn.training == relu.training
+    ), "Conv and BN both must be in the same mode (train or eval)."
+    fused_module: Optional[Type[nn.Sequential]] = None
+    if is_qat:
+        map_to_fused_module_train = {
+            nn.Conv1d: nni.ConvBnReLU1d,
+            nn.Conv2d: nni.ConvBnReLU2d,
+            nn.Conv3d: nni.ConvBnReLU3d,
+        }
+        assert (
+            bn.num_features == conv.out_channels
+        ), "Output channel of Conv must match num_features of BatchNorm"
+        assert bn.affine, "Only support fusing BatchNorm with affine set to True"
+        assert (
+            bn.track_running_stats
+        ), "Only support fusing BatchNorm with tracking_running_stats set to True"
+        fused_module = map_to_fused_module_train.get(type(conv), None)
+        if fused_module is not None:
+            return fused_module(conv, bn, relu)
+        else:
+            raise NotImplementedError(f"Cannot fuse train modules: {(conv, bn, relu)}")
+    else:
+        map_to_fused_module_eval = {
+            nn.Conv1d: nni.ConvReLU1d,
+            nn.Conv2d: nni.ConvReLU2d,
+            nn.Conv3d: nni.ConvReLU3d,
+        }
+        fused_module = map_to_fused_module_eval.get(type(conv), None)
+        if fused_module is not None:
+            fused_conv = nn.utils.fusion.fuse_conv_bn_eval(conv, bn)
+            return fused_module(fused_conv, relu)
+        else:
+            raise NotImplementedError(f"Cannot fuse eval modules: {(conv, bn, relu)}")
+
+
+def fuse_linear_bn(is_qat, linear, bn):
+    r"""Return the fused linear and bn modules.
+    Given the linear and bn modules, fuses them and returns the fused module
+
+    Args:
+        is_qat: a flag for whether we are using quantization aware training fusion
+        or post training quantization fusion
+        linear: Module instance of type Linear
+        bn: BatchNorm1d instance that needs to be fused with the linear layer
+
+    Examples::
+
+        >>> m1 = nn.Linear(20, 10)
+        >>> b1 = nn.BatchNorm1d(10)
+        >>> # xdoctest: +SKIP
+        >>> m2 = fuse_linear_bn(m1, b1)
+    """
+    assert (
+        linear.training == bn.training
+    ), "Linear and BN both must be in the same mode (train or eval)."
+
+    if is_qat:
+        assert (
+            bn.num_features == linear.out_features
+        ), "Output features of Linear must match num_features of BatchNorm1d"
+        assert bn.affine, "Only support fusing BatchNorm1d with affine set to True"
+        assert (
+            bn.track_running_stats
+        ), "Only support fusing BatchNorm1d with tracking_running_stats set to True"
+        return nni.LinearBn1d(linear, bn)
+    else:
+        return nn.utils.fusion.fuse_linear_bn_eval(linear, bn)
+
+
+def fuse_convtranspose_bn(is_qat, convt, bn):
+    r"""Return the fused ConvTranspose and bn modules.
+    Given ConvTranspose and bn modules, fuses them and returns the fused module
+
+    Args:
+        convt: Module instance of type ConvTransposeNd
+        bn: BatchNormNd instance that needs to be fused with the linear layer.
+            batch norm N should match the ConvTranspose N
+
+    Examples::
+
+        >>> m1 = nn.ConvTranspose2d(10, 20, 3)
+        >>> b1 = nn.BatchNorm2d(20)
+        >>> # xdoctest: +SKIP
+        >>> m2 = fuse_convtranspose_bn(m1, b1)
+    """
+    assert (
+        convt.training == bn.training
+    ), "ConvTranspose and BN both must be in the same mode (train or eval)."
+
+    if is_qat:
+        raise Exception(  # noqa: TRY002
+            "Fusing ConvTranspose+BatchNorm not yet supported in QAT."
+        )
+    else:
+        return nn.utils.fusion.fuse_conv_bn_eval(convt, bn, transpose=True)
+
+
+def _sequential_wrapper2(sequential):
+    """Return a sequential wrapped that for is_qat and two modules.
+    Given a sequential class for two modules, return a function that takes
+    is_qat, and then two modules as argument, that ignores the is_qat flag
+    and always returns the sequential that combines the two input modules
+    """
+
+    def fuser_method(is_qat, m1, m2):
+        return sequential(m1, m2)
+
+    return fuser_method
+
+
+_DEFAULT_OP_LIST_TO_FUSER_METHOD: Dict[Tuple, Union[nn.Sequential, Callable]] = {
+    (nn.Conv1d, nn.BatchNorm1d): fuse_conv_bn,
+    (nn.Conv1d, nn.BatchNorm1d, nn.ReLU): fuse_conv_bn_relu,
+    (nn.Conv2d, nn.BatchNorm2d): fuse_conv_bn,
+    (nn.Conv2d, nn.BatchNorm2d, nn.ReLU): fuse_conv_bn_relu,
+    (nn.Conv3d, nn.BatchNorm3d): fuse_conv_bn,
+    (nn.Conv3d, nn.BatchNorm3d, nn.ReLU): fuse_conv_bn_relu,
+    (nn.Conv1d, nn.ReLU): _sequential_wrapper2(nni.ConvReLU1d),
+    (nn.Conv2d, nn.ReLU): _sequential_wrapper2(nni.ConvReLU2d),
+    (nn.Conv3d, nn.ReLU): _sequential_wrapper2(nni.ConvReLU3d),
+    (nn.Linear, nn.BatchNorm1d): fuse_linear_bn,
+    (nn.Linear, nn.ReLU): _sequential_wrapper2(nni.LinearReLU),
+    (nn.BatchNorm2d, nn.ReLU): _sequential_wrapper2(nni.BNReLU2d),
+    (nn.BatchNorm3d, nn.ReLU): _sequential_wrapper2(nni.BNReLU3d),
+    (nn.ConvTranspose1d, nn.BatchNorm1d): fuse_convtranspose_bn,
+    (nn.ConvTranspose2d, nn.BatchNorm2d): fuse_convtranspose_bn,
+    (nn.ConvTranspose3d, nn.BatchNorm3d): fuse_convtranspose_bn,
+}
+
+
+def get_fuser_method(op_list, additional_fuser_method_mapping=None):
+    """Get fuser method for the given list of module types.
+
+    Get fuser method for the given list of module types,
+    return None if fuser method does not exist
+    """
+    if additional_fuser_method_mapping is None:
+        additional_fuser_method_mapping = {}
+    all_mappings = get_combined_dict(
+        _DEFAULT_OP_LIST_TO_FUSER_METHOD, additional_fuser_method_mapping
+    )
+    fuser_method = all_mappings.get(op_list, None)
+    assert fuser_method is not None, f"did not find fuser method for: {op_list} "
+    return fuser_method
+
+
+def _reverse2(f):
+    def reversed(is_qat, x, y):
+        return f(is_qat, y, x)
+
+    return reversed
+
+
+def _reverse3(f):
+    def reversed(is_qat, x, w):
+        y, z = w
+        return f(is_qat, z, y, x)
+
+    return reversed
+
+
+def _get_valid_patterns(op_pattern):
+    """Return a list of valid patterns generated from the op_pattern.
+
+    Returns a list of valid patterns generated from the op_pattern,
+    since MatchAllNode can match all types of nodes,
+    e.g. pattern (torch.nn.Conv2d, torch.add) should also be able to match keys like
+    (MatchAllNode, torch.add) and (torch.nn.Conv2d, MatchAllNode)
+
+    Example Input:
+    (torch.add, (torch.nn.ReLU, torch.nn.Conv2d))
+
+    Example Output:
+    [(torch.add, (torch.nn.ReLU, torch.nn.Conv2d)),
+     (torch.add, (torch.nn.ReLU, MatchAllNode)),
+     (torch.add, (MatchAllNode, torch.nn.Conv2d)),
+     (torch.add, (MatchAllNode, MatchAllNode)),
+     (MatchAllNode, (torch.nn.ReLU, torch.nn.Conv2d)),
+     (MatchAllNode, (torch.nn.ReLU, MatchAllNode)),
+     (MatchAllNode, (MatchAllNode, torch.nn.Conv2d)),
+     (MatchAllNode, (MatchAllNode, MatchAllNode)),
+    ]
+    """
+    result: List[Any]
+    if isinstance(op_pattern, (tuple, list)):
+        sub_combs = []
+        for sub_pattern in op_pattern:
+            sub_combs.append(_get_valid_patterns(sub_pattern))
+        result = list(itertools.product(*sub_combs))
+    else:
+        result = [op_pattern, MatchAllNode]
+    return result
+
+
+def get_fuser_method_new(
+    op_pattern: Pattern,
+    fuser_method_mapping: Dict[Pattern, Union[nn.Sequential, Callable]],
+):
+    """Get fuser method.
+
+    This will be made default after we deprecate the get_fuser_method
+    Would like to implement this first and have a separate PR for deprecation
+    """
+    op_patterns = _get_valid_patterns(op_pattern)
+    fuser_method = None
+    for op_pattern in op_patterns:
+        fuser_method = fuser_method_mapping.get(op_pattern, None)
+        if fuser_method is not None:
+            break
+    assert fuser_method is not None, f"did not find fuser method for: {op_pattern} "
+    return fuser_method
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..72d624ad7d6a3926c5d34afab3b7066928f9933d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/__init__.py
@@ -0,0 +1,3 @@
+from .convert import convert
+from .fuse import fuse
+from .prepare import prepare
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+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_decomposed.py
@@ -0,0 +1,1186 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import math
+from typing import Optional, Tuple
+
+import torch
+from torch._refs import _unsqueeze_multiple
+from torch.ao.quantization.utils import determine_qparams, validate_qmin_qmax
+from torch.library import impl, Library
+
+
+# Note: decomposed means decomposed quantized tensor, using decomposed so that the
+# name is not too long
+quantized_decomposed_lib = Library("quantized_decomposed", "DEF")
+
+_INTEGER_DTYPES = [torch.uint8, torch.int8, torch.int16, torch.int32]
+_FLOAT_DTYPES = [torch.float8_e5m2, torch.float8_e4m3fn]
+
+_DTYPE_TO_QVALUE_BOUNDS = {
+    k: (torch.iinfo(k).min, torch.iinfo(k).max) for k in _INTEGER_DTYPES
+}
+_DTYPE_TO_QVALUE_BOUNDS.update(
+    {k: (int(torch.finfo(k).min), int(torch.finfo(k).max)) for k in _FLOAT_DTYPES}
+)
+
+
+# Helper to check the passed in quant min and max are valid for the dtype
+def _quant_min_max_bounds_check(quant_min, quant_max, dtype):
+    if dtype not in _DTYPE_TO_QVALUE_BOUNDS:
+        raise ValueError(f"Unsupported dtype: {dtype}")
+    quant_min_lower_bound, quant_max_upper_bound = _DTYPE_TO_QVALUE_BOUNDS[dtype]
+
+    assert quant_min >= quant_min_lower_bound, (
+        "quant_min out of bound for dtype, "
+        f"quant_min_lower_bound: {quant_min_lower_bound} quant_min: {quant_min}"
+    )
+
+    assert quant_max <= quant_max_upper_bound, (
+        "quant_max out of bound for dtype, "
+        f"quant_max_upper_bound: {quant_max_upper_bound} quant_max: {quant_max}"
+    )
+
+
+quantized_decomposed_lib.define(
+    "quantize_per_tensor(Tensor input, float scale, int zero_point, "
+    "int quant_min, int quant_max, ScalarType dtype) -> Tensor"
+)
+
+
+@impl(quantized_decomposed_lib, "quantize_per_tensor", "CompositeExplicitAutograd")
+def quantize_per_tensor(
+    input: torch.Tensor,
+    scale: float,
+    zero_point: int,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    """Affine quantization for the Tensor using the same quantization parameters to map
+    from floating point to quantized values
+
+    Args:
+       input (torch.Tensor): original float32 or bfloat16 Tensor
+       scale (float): quantization parameter for affine quantization
+       zero_point (int): quantization parameter for affine quantization
+       quant_min (int): minimum quantized value for output Tensor
+       quant_max (int): maximum quantized value for output Tensor
+       dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor
+
+    Returns:
+       Tensor with requested dtype (e.g. torch.uint8), note the quantization parameters
+       are not stored in the Tensor, we are storing them in function arguments instead
+    """
+    if input.dtype in [torch.float16, torch.bfloat16]:
+        input = input.to(torch.float32)
+    assert (
+        input.dtype == torch.float32
+    ), f"Expecting input to have dtype torch.float32, but got dtype: {input.dtype}"
+    _quant_min_max_bounds_check(quant_min, quant_max, dtype)
+
+    inv_scale = 1.0 / scale
+    return torch.clamp(
+        torch.round(input * inv_scale) + zero_point, quant_min, quant_max
+    ).to(dtype)
+
+
+@impl(quantized_decomposed_lib, "quantize_per_tensor", "Meta")
+def quantize_per_tensor_meta(
+    input: torch.Tensor,
+    scale: float,
+    zero_point: int,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    if input.dtype in [torch.float16, torch.bfloat16]:
+        input = input.to(torch.float32)
+    assert (
+        input.dtype == torch.float32
+    ), f"Expecting input to have dtype torch.float32, but got dtype: {input.dtype}"
+    return torch.empty_like(input, dtype=dtype)
+
+
+quantized_decomposed_lib.define(
+    "quantize_per_tensor.tensor(Tensor input, Tensor scale, Tensor zero_point, "
+    "int quant_min, int quant_max, ScalarType dtype) -> Tensor"
+)
+
+
+@impl(
+    quantized_decomposed_lib, "quantize_per_tensor.tensor", "CompositeExplicitAutograd"
+)
+def quantize_per_tensor_tensor(
+    input: torch.Tensor,
+    scale: torch.Tensor,
+    zero_point: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    """Affine quantization for the Tensor using the same quantization parameters to map
+    from floating point to quantized values
+    Same as `quantize_per_tensor` but scale and zero_point are Scalar Tensor instead of
+    scalar values
+    """
+    assert (
+        zero_point.numel() == 1
+    ), f"Expecting zero_point tensor to be one element, but received : {zero_point.numel()}"
+    assert (
+        scale.numel() == 1
+    ), f"Expecting scale tensor to be one element, but received : {scale.numel()}"
+    return quantize_per_tensor(
+        input, scale.item(), zero_point.item(), quant_min, quant_max, dtype
+    )
+
+
+@impl(quantized_decomposed_lib, "quantize_per_tensor.tensor", "Meta")
+def quantize_per_tensor_tensor_meta(
+    input: torch.Tensor,
+    scale: torch.Tensor,
+    zero_point: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    if input.dtype in [torch.float16, torch.bfloat16]:
+        input = input.to(torch.float32)
+    assert (
+        zero_point.numel() == 1
+    ), f"Expecting zero_point tensor to be one element, but received : {zero_point.numel()}"
+    assert (
+        scale.numel() == 1
+    ), f"Expecting scale tensor to be one element, but received : {scale.numel()}"
+    assert (
+        input.dtype == torch.float32
+    ), f"Expecting input to have dtype torch.float32, but got dtype: {input.dtype}"
+    return torch.empty_like(input, dtype=dtype)
+
+
+# TODO: remove other variants and keep this one
+quantized_decomposed_lib.define(
+    "quantize_per_tensor.tensor2(Tensor input, Tensor scale, Tensor zero_point, "
+    "Tensor quant_min, Tensor quant_max, ScalarType dtype) -> Tensor"
+)
+
+
+@impl(
+    quantized_decomposed_lib, "quantize_per_tensor.tensor2", "CompositeExplicitAutograd"
+)
+def quantize_per_tensor_tensor2(
+    input: torch.Tensor,
+    scale: torch.Tensor,
+    zero_point: torch.Tensor,
+    quant_min: torch.Tensor,
+    quant_max: torch.Tensor,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    """Affine quantization for the Tensor using the same quantization parameters to map
+    from floating point to quantized values
+    Same as `quantize_per_tensor` but scale and zero_point are Scalar Tensor instead of
+    scalar values
+    """
+    assert (
+        zero_point.numel() == 1
+    ), f"Expecting zero_point tensor to be one element, but received : {zero_point.numel()}"
+    assert (
+        scale.numel() == 1
+    ), f"Expecting scale tensor to be one element, but received : {scale.numel()}"
+    return quantize_per_tensor(
+        input,
+        scale.item(),
+        zero_point.item(),
+        quant_min.item(),
+        quant_max.item(),
+        dtype,
+    )
+
+
+@impl(quantized_decomposed_lib, "quantize_per_tensor.tensor2", "Meta")
+def quantize_per_tensor_tensor2_meta(
+    input: torch.Tensor,
+    scale: torch.Tensor,
+    zero_point: torch.Tensor,
+    quant_min: torch.Tensor,
+    quant_max: torch.Tensor,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    return quantize_per_tensor_tensor_meta(
+        input, scale, zero_point, quant_min, quant_max, dtype
+    )
+
+
+# Note: quant_min/quant_max/dtype are not used in the operator, but for now it's kept in
+# the signature as metadata for the input Tensor, this might be useful for pattern
+# matching in the future
+# We will revisit this later if we found there are no use cases for it
+quantized_decomposed_lib.define(
+    "dequantize_per_tensor(Tensor input, float scale, int zero_point, "
+    "int quant_min, int quant_max, ScalarType dtype, *, ScalarType? out_dtype=None) -> Tensor"
+)
+
+
+@impl(quantized_decomposed_lib, "dequantize_per_tensor", "CompositeExplicitAutograd")
+def dequantize_per_tensor(
+    input: torch.Tensor,
+    scale: float,
+    zero_point: int,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    *,
+    out_dtype: Optional[torch.dtype] = None,
+) -> torch.Tensor:
+    """Affine dequantization for the Tensor using the same quantization parameters to map
+    from quantized values to floating point values
+
+    Args:
+       input (torch.Tensor): Tensor with dtype matching `dtype` argument,
+       e.g. (`torch.uint8`), it is a per tensor quantized Tensor if combined with
+       quantization parameters in the argument of this function (scale/zero_point)
+
+       scale (float): quantization parameter for affine quantization
+
+       zero_point (int): quantization parameter for affine quantization
+
+       quant_min (int): minimum quantized value for input Tensor (not used in computation,
+       reserved for pattern matching)
+
+       quant_max (int): maximum quantized value for input Tensor (not used in computation,
+       reserved for pattern matching)
+
+       dtype (torch.dtype): dtype for input Tensor (not used in computation,
+       reserved for pattern matching)
+
+       out_dtype (torch.dtype?): optional dtype for output Tensor
+
+    Returns:
+       dequantized float32 Tensor
+    """
+    assert (
+        input.dtype == dtype
+    ), f"Expecting input to have dtype: {dtype}, but got {input.dtype}"
+    if out_dtype is None:
+        out_dtype = torch.float32
+    if dtype in _DTYPE_TO_QVALUE_BOUNDS:
+        # TODO: investigate why
+        # (input - zero_point).to(torch.float32) * scale
+        # failed the test
+        return (input.to(out_dtype) - zero_point) * scale
+    else:
+        raise ValueError(f"Unsupported dtype in dequantize_per_tensor: {dtype}")
+
+
+@impl(quantized_decomposed_lib, "dequantize_per_tensor", "Meta")
+def dequantize_per_tensor_meta(
+    input: torch.Tensor,
+    scale: torch.Tensor,
+    zero_point: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    *,
+    out_dtype: Optional[torch.dtype] = None,
+) -> torch.Tensor:
+    if out_dtype is None:
+        out_dtype = torch.float32
+    return torch.empty_like(input, dtype=out_dtype)
+
+
+quantized_decomposed_lib.define(
+    "dequantize_per_tensor.tensor(Tensor input, Tensor scale, Tensor zero_point, "
+    "int quant_min, int quant_max, ScalarType dtype, *, ScalarType? out_dtype=None) -> Tensor"
+)
+
+
+@impl(
+    quantized_decomposed_lib,
+    "dequantize_per_tensor.tensor",
+    "CompositeExplicitAutograd",
+)
+def dequantize_per_tensor_tensor(
+    input: torch.Tensor,
+    scale: torch.Tensor,
+    zero_point: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    *,
+    out_dtype: Optional[torch.dtype] = None,
+) -> torch.Tensor:
+    """Affine dequantization for the Tensor using the same quantization parameters to map
+    from quantized values to floating point values
+    Same as `dequantize_per_tensor` but scale and zero_point are Scalar Tensor instead of
+    scalar values
+    """
+    assert (
+        zero_point.numel() == 1
+    ), f"Expecting zero_point tensor to be one element, but received : {zero_point.numel()}"
+    assert (
+        scale.numel() == 1
+    ), f"Expecting scale tensor to be one element, but received : {scale.numel()}"
+    return dequantize_per_tensor(
+        input,
+        scale.item(),
+        zero_point.item(),
+        quant_min,
+        quant_max,
+        dtype,
+        out_dtype=out_dtype,
+    )
+
+
+@impl(quantized_decomposed_lib, "dequantize_per_tensor.tensor", "Meta")
+def dequantize_per_tensor_tensor_meta(
+    input: torch.Tensor,
+    scale: torch.Tensor,
+    zero_point: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    *,
+    out_dtype: Optional[torch.dtype] = None,
+) -> torch.Tensor:
+    if out_dtype is None:
+        out_dtype = torch.float32
+    assert (
+        zero_point.numel() == 1
+    ), f"Expecting zero_point tensor to be one element, but received : {zero_point.numel()}"
+    assert (
+        scale.numel() == 1
+    ), f"Expecting scale tensor to be one element, but received : {scale.numel()}"
+    assert input.dtype == dtype, f"Expecting input to have dtype: {dtype}"
+    if dtype in _DTYPE_TO_QVALUE_BOUNDS:
+        return torch.empty_like(input, dtype=out_dtype)
+    else:
+        raise ValueError(f"Unsupported dtype in dequantize_per_tensor: {dtype}")
+
+
+# TODO: remove other variants and keep this one
+quantized_decomposed_lib.define(
+    "dequantize_per_tensor.tensor2(Tensor input, Tensor scale, Tensor zero_point, "
+    "Tensor quant_min, Tensor quant_max, ScalarType dtype, *, ScalarType? out_dtype=None) -> Tensor"
+)
+
+
+@impl(
+    quantized_decomposed_lib,
+    "dequantize_per_tensor.tensor2",
+    "CompositeExplicitAutograd",
+)
+def dequantize_per_tensor_tensor2(
+    input: torch.Tensor,
+    scale: torch.Tensor,
+    zero_point: torch.Tensor,
+    quant_min: torch.Tensor,
+    quant_max: torch.Tensor,
+    dtype: torch.dtype,
+    *,
+    out_dtype: Optional[torch.dtype] = None,
+) -> torch.Tensor:
+    """Affine dequantization for the Tensor using the same quantization parameters to map
+    from quantized values to floating point values
+    Same as `dequantize_per_tensor` but scale and zero_point are Scalar Tensor instead of
+    scalar values
+    """
+    assert (
+        zero_point.numel() == 1
+    ), f"Expecting zero_point tensor to be one element, but received : {zero_point.numel()}"
+    assert (
+        scale.numel() == 1
+    ), f"Expecting scale tensor to be one element, but received : {scale.numel()}"
+    return dequantize_per_tensor(
+        input,
+        scale.item(),
+        zero_point.item(),
+        quant_min.item(),
+        quant_max.item(),
+        dtype,
+        out_dtype=out_dtype,
+    )
+
+
+@impl(quantized_decomposed_lib, "dequantize_per_tensor.tensor2", "Meta")
+def dequantize_per_tensor_tensor2_meta(
+    input,
+    scale,
+    zero_point,
+    quant_min,
+    quant_max,
+    dtype,
+    *,
+    out_dtype: Optional[torch.dtype] = None,
+) -> torch.Tensor:
+    return dequantize_per_tensor_tensor_meta(
+        input, scale, zero_point, quant_min, quant_max, dtype, out_dtype=out_dtype
+    )
+
+
+quantized_decomposed_lib.define(
+    "choose_qparams.tensor(Tensor input, int quant_min, int quant_max, "
+    "float eps, ScalarType dtype) -> (Tensor, Tensor)"
+)
+
+
+@impl(quantized_decomposed_lib, "choose_qparams.tensor", "CompositeExplicitAutograd")
+def choose_qparams_tensor(
+    input: torch.Tensor, qmin: int, qmax: int, eps: float, dtype: torch.dtype
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Given an input Tensor, derive the per tensor affine quantization parameter
+    (scale and zero_point) for target quantized Tensor from the Tensor
+
+    Args:
+       input (torch.Tensor): floating point input Tensor
+       quant_min (int): minimum quantized value for target quantized Tensor
+       quant_max (int): maximum quantized value for target quantized Tensor
+       dtype (torch.dtype): dtype for target quantized Tensor
+
+    Returns:
+       scale (float): quantization parameter for the target quantized Tensor
+       zero_point (int): quantization parameter for the target quantized Tensor
+    """
+    assert input.dtype in [
+        torch.float32,
+        torch.float16,
+        torch.bfloat16,
+    ], f"Expecting input to have dtype torch.float32/16/b16, but got dtype: {input.dtype}"
+    assert (
+        dtype in _DTYPE_TO_QVALUE_BOUNDS
+    ), f"Expecting target dtype to be one of {_DTYPE_TO_QVALUE_BOUNDS.keys()}, but got: {dtype}"
+    validate_qmin_qmax(qmin, qmax)
+
+    min_val, max_val = torch.aminmax(input)
+
+    return determine_qparams(
+        min_val,
+        max_val,
+        qmin,
+        qmax,
+        dtype,
+        torch.Tensor([eps]),
+        has_customized_qrange=False,
+    )
+
+
+quantized_decomposed_lib.define(
+    "choose_qparams_symmetric.tensor(Tensor input, int quant_min, int quant_max, "
+    "float eps, ScalarType dtype) -> (Tensor, Tensor)"
+)
+
+
+@impl(
+    quantized_decomposed_lib,
+    "choose_qparams_symmetric.tensor",
+    "CompositeExplicitAutograd",
+)
+def choose_qparams_symmetric_tensor(
+    input: torch.Tensor, qmin: int, qmax: int, eps: float, dtype: torch.dtype
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Given an input Tensor, derive the per tensor affine quantization parameter
+    (scale and zero_point) for target quantized Tensor from the Tensor
+
+    Args:
+       input (torch.Tensor): floating point input Tensor
+       quant_min (int): minimum quantized value for target quantized Tensor
+       quant_max (int): maximum quantized value for target quantized Tensor
+       dtype (torch.dtype): dtype for target quantized Tensor
+
+    Returns:
+       scale (float): quantization parameter for the target quantized Tensor
+       zero_point (int): quantization parameter for the target quantized Tensor
+    """
+    assert input.dtype in [
+        torch.float32,
+        torch.float16,
+        torch.bfloat16,
+    ], f"Expecting input to have dtype torch.float32/16/b16, but got dtype: {input.dtype}"
+    assert (
+        dtype in _DTYPE_TO_QVALUE_BOUNDS
+    ), f"Expecting target dtype to be one of {_DTYPE_TO_QVALUE_BOUNDS.keys()}, but got: {dtype}"
+    validate_qmin_qmax(qmin, qmax)
+
+    min_val, max_val = torch.aminmax(input)
+    return determine_qparams(
+        min_val,
+        max_val,
+        qmin,
+        qmax,
+        dtype,
+        torch.Tensor([eps]),
+        has_customized_qrange=False,
+        qscheme=torch.per_tensor_symmetric,
+    )
+
+
+@impl(quantized_decomposed_lib, "choose_qparams.tensor", "Meta")
+def choose_qparams_tensor_meta(
+    input: torch.Tensor, quant_min: int, quant_max: int, eps: float, dtype: torch.dtype
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    assert input.dtype in [
+        torch.float32,
+        torch.float16,
+        torch.bfloat16,
+    ], f"Expecting input to have dtype torch.float32/16/b16, but got dtype: {input.dtype}"
+    assert (
+        quant_min < quant_max
+    ), f"Expecting quant_min to be smaller than quant_max but received min: \
+        {quant_min} max: {quant_max}"
+    return torch.empty(1, dtype=torch.double, device=input.device), torch.empty(
+        1, dtype=torch.int64, device=input.device
+    )
+
+
+@impl(quantized_decomposed_lib, "choose_qparams_symmetric.tensor", "Meta")
+def choose_qparams_symmetric_tensor_meta(
+    input: torch.Tensor, quant_min: int, quant_max: int, eps: float, dtype: torch.dtype
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    return torch.empty(1, dtype=torch.double, device=input.device), torch.empty(
+        1, dtype=torch.int64, device=input.device
+    )
+
+
+# Helper function used to implement per-channel quantization against any axis
+def _permute_to_axis_zero(x, axis):
+    new_axis_list = list(range(x.dim()))
+    new_axis_list[axis] = 0
+    new_axis_list[0] = axis
+    y = x.permute(tuple(new_axis_list))
+    return y, new_axis_list
+
+
+quantized_decomposed_lib.define(
+    "quantize_per_channel(Tensor input, Tensor scales, Tensor zero_points, int axis, "
+    "int quant_min, int quant_max, ScalarType dtype) -> Tensor"
+)
+
+
+@impl(quantized_decomposed_lib, "quantize_per_channel", "CompositeExplicitAutograd")
+def quantize_per_channel(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor,
+    axis: int,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    """Affine per channel quantization for the Tensor using the same quantization
+    parameters for each channel/axis to map from floating point to quantized values
+
+    Args:
+       input (torch.Tensor): original float32 or bfloat16 Tensor
+       scales (torch.Tensor): a list of scale quantization parameter for
+       affine quantization, one per channel
+       zero_point (torch.Tensor): a list of zero_point quantization parameter for
+       affine quantization, one per channel
+       quant_min (int): minimum quantized value for output Tensor
+       quant_max (int): maximum quantized value for output Tensor
+       dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor
+
+    Returns:
+       Tensor with requested dtype (e.g. torch.uint8), note the quantization parameters
+       are not stored in the Tensor, we are storing them in function arguments instead
+    """
+    if input.dtype in [torch.float16, torch.bfloat16]:
+        input = input.to(torch.float32)
+    assert (
+        input.dtype == torch.float32
+    ), f"Expecting input to have dtype torch.float32, but got dtype: {input.dtype}"
+    assert axis < input.dim(), f"Expecting axis to be < {input.dim()}"
+    _quant_min_max_bounds_check(quant_min, quant_max, dtype)
+    input, permute_axis_list = _permute_to_axis_zero(input, axis)
+
+    new_shape = [1] * input.dim()
+    new_shape[0] = scales.shape[0]
+    scales = scales.view(new_shape)
+    zero_points = zero_points.view(new_shape)
+
+    res = torch.clamp(
+        torch.round(input * (1.0 / scales)) + zero_points, quant_min, quant_max
+    )
+    out = res.permute(tuple(permute_axis_list))
+    return out.to(dtype)
+
+
+@impl(quantized_decomposed_lib, "quantize_per_channel", "Meta")
+def quantize_per_channel_meta(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor,
+    axis: int,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    if input.dtype in [torch.float16, torch.bfloat16]:
+        input = input.to(torch.float32)
+    assert (
+        input.dtype == torch.float32
+    ), f"Expecting input to have dtype torch.float32, but got dtype: {input.dtype}"
+    assert axis < input.dim(), f"Expecting axis to be < {input.dim()}"
+    _quant_min_max_bounds_check(quant_min, quant_max, dtype)
+    return torch.empty_like(input, dtype=dtype)
+
+
+# Note: quant_min/quant_max/dtype are not used in the operator, but for now it's kept in
+# the signature as metadata for the input Tensor, this might be useful for pattern
+# matching in the future
+# We will revisit this later if we found there are no use cases for it
+quantized_decomposed_lib.define(
+    "dequantize_per_channel(Tensor input, Tensor scales, Tensor? zero_points, int axis, "
+    "int quant_min, int quant_max, ScalarType dtype, *, ScalarType? out_dtype=None) -> Tensor"
+)
+
+
+@impl(quantized_decomposed_lib, "dequantize_per_channel", "CompositeExplicitAutograd")
+def dequantize_per_channel(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: Optional[torch.Tensor],
+    axis: int,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    *,
+    out_dtype: Optional[torch.dtype] = None,
+) -> torch.Tensor:
+    """Affine per channel dequantization for the Tensor using the same quantization
+    parameters for each channel/axis to map from quantized values to floating point values
+
+    Args:
+       input (torch.Tensor): Tensor with dtype matching `dtype` argument,
+       e.g. (`torch.uint8`), it is a per channel quantized Tensor if combined with
+       quantization parameter in the argument of this function (scales/zero_points/axis)
+
+       scales (torch.Tensor): a list of scale quantization parameter for
+       affine quantization, one per channel
+
+       zero_points (torch.Tensor): a list of zero_point quantization parameter for
+       affine quantization, one per channel
+
+       quant_min (int): minimum quantized value for output Tensor (not used in computation,
+       reserved for pattern matching)
+
+       quant_max (int): maximum quantized value for output Tensor (not used in computation,
+       reserved for pattern matching)
+
+       dtype (torch.dtype): requested dtype for output Tensor (not used in computation,
+       reserved for pattern matching)
+
+       out_dtype (torch.dtype?): optional dtype for output Tensor
+
+    Returns:
+       dequantized float32 Tensor
+    """
+    assert (
+        input.dtype == dtype
+    ), f"Expecting input to have dtype {dtype}, but got dtype: {input.dtype}"
+    if out_dtype is None:
+        out_dtype = torch.float32
+    assert axis < input.dim(), f"Expecting axis to be < {input.dim()}"
+    _quant_min_max_bounds_check(quant_min, quant_max, dtype)
+    input, permute_axis_list = _permute_to_axis_zero(input, axis)
+
+    new_shape = [1] * input.dim()
+    new_shape[0] = scales.shape[0]
+    scales = scales.view(new_shape)
+    if zero_points is not None:
+        res = (input - zero_points.view(new_shape)) * scales
+    else:
+        res = input * scales
+
+    res = res.to(out_dtype)
+
+    out = res.permute(tuple(permute_axis_list))
+    return out
+
+
+@impl(quantized_decomposed_lib, "dequantize_per_channel", "Meta")
+def dequantize_per_channel_meta(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: Optional[torch.Tensor],
+    axis: int,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    *,
+    out_dtype: Optional[torch.dtype] = None,
+) -> torch.Tensor:
+    assert (
+        input.dtype == dtype
+    ), f"Expecting input to have dtype {dtype}, but got dtype: {input.dtype}"
+    if out_dtype is None:
+        out_dtype = torch.float32
+    assert axis < input.dim(), f"Expecting axis to be < {input.dim()}"
+    _quant_min_max_bounds_check(quant_min, quant_max, dtype)
+    return torch.empty_like(input, dtype=out_dtype)
+
+
+quantized_decomposed_lib.define(
+    "choose_qparams_per_token(Tensor input, ScalarType dtype) -> (Tensor, Tensor)"
+)
+
+
+@impl(
+    quantized_decomposed_lib,
+    "choose_qparams_per_token",
+    "CompositeExplicitAutograd",
+)
+def choose_qparams_per_token(
+    input: torch.Tensor,
+    dtype: torch.dtype,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Choose quantization parameters for per token quantization. This means for a N dimension Tensor
+    (M1, M2, ...Mn, N), we calculate scales/zero_points for each N elements and quantize
+    every N elements with the same quantization parameter. The dimension for scales/zero_points
+    will be (M1 * M2 ... * Mn)
+
+    Args:
+       input (torch.Tensor): original float32/float16 Tensor
+       dtype (torch.dtype): dtype (e.g. torch.uint8) for input Tensor
+
+    Returns:
+        scales and zero_points, both float32 Tensors
+    """
+
+    scales = input.abs().amax(dim=-1, keepdim=True)
+    if scales.dtype == torch.float16:
+        scales = (
+            scales.float()
+        )  # want float scales to avoid overflows for fp16, (bf16 has wide enough range)
+    if dtype == torch.int8:
+        n_bits = 8
+        quant_max = 2 ** (n_bits - 1) - 1
+    else:
+        raise Exception(  # noqa: TRY002
+            f"unsupported dtype in choose_qparams_per_token: {dtype}"
+        )
+
+    scales = scales.clamp(min=1e-5).div(quant_max)
+    zero_points = torch.zeros_like(scales)
+    return scales, zero_points
+
+
+@impl(
+    quantized_decomposed_lib,
+    "choose_qparams_per_token",
+    "Meta",
+)
+def choose_qparams_per_token_meta(
+    input: torch.Tensor,
+    dtype: torch.dtype,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    size = (1, input.size(-1))
+    return torch.empty(size, dtype=torch.double, device=input.device), torch.empty(
+        size, dtype=torch.int64, device=input.device
+    )
+
+
+quantized_decomposed_lib.define(
+    "_choose_qparams_per_token_asymmetric_impl(Tensor input, ScalarType dtype) -> (Tensor, Tensor)"
+)
+
+
+@impl(
+    quantized_decomposed_lib,
+    "_choose_qparams_per_token_asymmetric_impl",
+    "CompositeImplicitAutograd",
+)
+def _choose_qparams_per_token_asymmetric_impl(
+    input: torch.Tensor,
+    dtype: torch.dtype,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Choose quantization parameters for per token quantization. This means for a N dimension Tensor
+    (M1, M2, ...Mn, N), we calculate scales/zero_points for each N elements and quantize
+    every N elements with the same quantization parameter. The dimension for scales/zero_points
+    will be (M1 * M2 ... * Mn)
+
+    Args:
+       input (torch.Tensor): original float32/float16 Tensor
+       dtype (torch.dtype): dtype (e.g. torch.uint8) for input Tensor
+
+    Returns:
+        scales and zero_points, both float32 Tensors
+    """
+    # Based on https://github.com/google/XNNPACK/blob/df156f0cf3db5a4576cc711123eeb54915f82ffc/src/xnnpack/quantization.h#L18
+    qmin, qmax = -128, 127
+    min_val = torch.amin(input, dim=-1, keepdim=True)
+    max_val = torch.amax(input, dim=-1, keepdim=True)
+    min_val_neg = torch.min(min_val, torch.zeros_like(min_val))
+    max_val_pos = torch.max(max_val, torch.zeros_like(max_val))
+    eps = torch.finfo(torch.float32).eps  # use xnnpack eps?
+
+    # scale
+    scale = (max_val_pos - min_val_neg) / float(qmax - qmin)
+    scale = scale.clamp(min=eps)
+
+    # zero point
+    descaled_min = min_val_neg / scale
+    descaled_max = max_val_pos / scale
+    zero_point_from_min_error = qmin + descaled_min
+    zero_point_from_max_error = qmax + descaled_max
+    zero_point = torch.where(
+        zero_point_from_min_error + zero_point_from_max_error > 0,
+        qmin - descaled_min,
+        qmax - descaled_max,
+    )
+    zero_point = torch.clamp(zero_point, qmin, qmax).round()
+
+    return scale.to(torch.float32), zero_point.to(torch.float32)
+
+
+quantized_decomposed_lib.define(
+    "choose_qparams_per_token_asymmetric(Tensor input, ScalarType dtype) -> (Tensor, Tensor)"
+)
+
+
+@impl(
+    quantized_decomposed_lib,
+    "choose_qparams_per_token_asymmetric",
+    "CompositeExplicitAutograd",
+)
+def choose_qparams_per_token_asymmetric(
+    input: torch.Tensor,
+    dtype: torch.dtype,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    return _choose_qparams_per_token_asymmetric_impl(input, dtype)
+
+
+@impl(
+    quantized_decomposed_lib,
+    "choose_qparams_per_token_asymmetric",
+    "Meta",
+)
+def choose_qparams_per_token_asymmetric_meta(
+    input: torch.Tensor,
+    dtype: torch.dtype,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    size = (1, input.size(-1))
+    return torch.empty(size, dtype=torch.double, device=input.device), torch.empty(
+        size, dtype=torch.int64, device=input.device
+    )
+
+
+def _per_token_quant_qparam_dim_check(input, scales, zero_points):
+    num_tokens = math.prod(list(input.size())[:-1])
+    assert (
+        num_tokens == scales.numel()
+    ), f"num_tokens: {num_tokens} scales: {scales.size()}"
+    assert (
+        num_tokens == zero_points.numel()
+    ), f"num_tokens: {num_tokens} zero_points: {zero_points.size()}"
+
+
+quantized_decomposed_lib.define(
+    "quantize_per_token(Tensor input, Tensor scales, Tensor zero_points, "
+    "int quant_min, int quant_max, ScalarType dtype) -> Tensor"
+)
+
+
+@impl(quantized_decomposed_lib, "quantize_per_token", "CompositeExplicitAutograd")
+def quantize_per_token(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+):
+    """Per token quantization for the Tensor using the quantization parameters to map
+    from floating point to quantized values. This means for a N dimension Tensor
+    (M1, M2, ...Mn, N), we calculate scales/zero_points for each N elements and quantize
+    every N elements with the same quantization parameter. The dimension for scales/zero_points
+    will be (M1 * M2 ... * Mn)
+
+    Args:
+       input (torch.Tensor): original float32 or bfloat16 Tensor
+       scales (float32 torch.Tensor): quantization parameter for per token affine quantization
+       zero_points (int32 torch.Tensor): quantization parameter for per token affine quantization
+       quant_min (int): minimum quantized value for output Tensor
+       quant_max (int): maximum quantized value for output Tensor
+       dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor
+
+    Returns:
+       Tensor with requested dtype (e.g. torch.uint8), note the quantization parameters
+       are not stored in the Tensor, we are storing them in function arguments instead
+    """
+    _quant_min_max_bounds_check(quant_min, quant_max, dtype)
+    _per_token_quant_qparam_dim_check(input, scales, zero_points)
+    input = (
+        input.mul(1.0 / scales)
+        .add(zero_points)
+        .round()
+        .clamp(quant_min, quant_max)
+        .to(dtype)
+    )
+    return input
+
+
+@impl(quantized_decomposed_lib, "quantize_per_token", "Meta")
+def quantize_per_token_meta(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+):
+    _quant_min_max_bounds_check(quant_min, quant_max, dtype)
+    return torch.empty_like(input, dtype=dtype)
+
+
+quantized_decomposed_lib.define(
+    "dequantize_per_token(Tensor input, Tensor scales, Tensor zero_points, "
+    "int quant_min, int quant_max, ScalarType dtype, ScalarType output_dtype) -> Tensor"
+)
+
+
+@impl(quantized_decomposed_lib, "dequantize_per_token", "CompositeExplicitAutograd")
+def dequantize_per_token(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    output_dtype: torch.dtype = torch.float32,
+):
+    """Per token dequantization for the Tensor using the quantization parameters to map
+    from floating point to quantized values. This means for a N dimension Tensor
+    (M1, M2, ...Mn, N), we calculate scales/zero_points for each N elements and quantize
+    every N elements with the same quantization parameter. The dimension for scales/zero_points
+    will be (M1 * M2 ... * Mn)
+
+    Args:
+       input (torch.Tensor): quantized Tensor (uint8, int8 etc.)
+       scales (float32 torch.Tensor): quantization parameter for per token affine quantization
+       zero_points (int32 torch.Tensor): quantization parameter for per token affine quantization
+       quant_min (int): minimum quantized value for input Tensor
+       quant_max (int): maximum quantized value for input Tensor
+       dtype (torch.dtype): dtype (e.g. torch.uint8) for input Tensor
+       output_dtype (torch.dtype): dtype (e.g. torch.float32) for output Tensor
+
+    Returns:
+       dequantized Tensor with dtype `output_dtype`
+    """
+    input = input - zero_points
+    input = input.to(output_dtype) * scales
+    return input
+
+
+@impl(quantized_decomposed_lib, "dequantize_per_token", "Meta")
+def dequantize_per_token_meta(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    output_dtype: torch.dtype = torch.float32,
+):
+    _quant_min_max_bounds_check(quant_min, quant_max, dtype)
+    # TODO: support fp16
+    return torch.empty_like(input, dtype=output_dtype)
+
+
+quantized_decomposed_lib.define(
+    "quantize_per_channel_group(Tensor input, Tensor scales, Tensor zero_points, int quant_min, "
+    "int quant_max, ScalarType dtype, int group_size) -> Tensor"
+)
+
+
+# TODO: dtype is ignored for now
+@impl(
+    quantized_decomposed_lib, "quantize_per_channel_group", "CompositeExplicitAutograd"
+)
+def quantize_per_channel_group(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    group_size=128,
+):
+    assert group_size > 1
+    # needed for GPTQ single column quantize
+    if group_size > input.shape[-1] and scales.shape[-1] == 1:
+        group_size = input.shape[-1]
+
+    assert input.shape[-1] % group_size == 0
+    assert input.dim() == 2
+
+    # TODO: check for dtype, currently we can't express torch.int4 so it's omitted
+    to_quant = input.reshape(-1, group_size)
+    assert torch.isnan(to_quant).sum() == 0
+
+    scales = scales.reshape(-1, 1)
+    zero_points = zero_points.reshape(-1, 1)
+
+    input_int8 = (
+        to_quant.mul(1.0 / scales)
+        .add(zero_points)
+        .round()
+        .clamp_(quant_min, quant_max)
+        .to(dtype)
+        .reshape_as(input)
+    )
+
+    return input_int8
+
+
+@impl(quantized_decomposed_lib, "quantize_per_channel_group", "Meta")
+def quantize_per_channel_group_meta(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    group_size=128,
+):
+    """Groupwise quantization within each channel for an 2-d Tensor using the quantization parameters
+    to map from floating point to quantized values. This means for each row of a 2-d Tensor
+    (M, N), we calculate scales/zero_points for each `group_size` elements
+    and quantize every `group_size` elements with the same quantization parameter.
+    The dimension for scales/zero_points will be (M * ceil(N, group_size),)
+
+    Args:
+       input (torch.Tensor): original float32 or bfloat16 Tensor
+       scales (float32 torch.Tensor): quantization parameter for per channel group affine quantization
+       zero_points (int32 torch.Tensor): quantization parameter for per channel group affine quantization
+       quant_min (int): minimum quantized value for output Tensor
+       quant_max (int): maximum quantized value for output Tensor
+       dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor
+
+    Returns:
+       Tensor with requested dtype (e.g. torch.uint8), note the quantization parameters
+       are not stored in the Tensor, we are storing them in function arguments instead
+    """
+    assert group_size > 1
+    # needed for GPTQ single column quantize
+    if group_size > input.shape[-1] and scales.shape[-1] == 1:
+        group_size = input.shape[-1]
+
+    assert input.shape[-1] % group_size == 0
+    assert input.dim() == 2
+    return torch.empty_like(input, dtype=dtype)
+
+
+quantized_decomposed_lib.define(
+    "dequantize_per_channel_group(Tensor input, Tensor scales, Tensor? zero_points, int quant_min, "
+    "int quant_max, ScalarType dtype, int group_size, ScalarType output_dtype) -> Tensor"
+)
+
+
+@impl(
+    quantized_decomposed_lib,
+    "dequantize_per_channel_group",
+    "CompositeExplicitAutograd",
+)
+def dequantize_per_channel_group(
+    w_int8: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: Optional[torch.Tensor],
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    group_size: int = 128,
+    output_dtype: torch.dtype = torch.float32,
+):
+    """Groupwise dequantization within each channel for an 2-d Tensor using the quantization parameters
+    to map from floating point to quantized values. This means for each row of a 2-d Tensor
+    (M, N), we calculate scales/zero_points for each `group_size` elements
+    and quantize every `group_size` elements with the same quantization parameter.
+    The dimension for scales/zero_points will be (M * ceil(N, group_size),)
+
+    Args:
+       input (torch.Tensor): quantized Tensor (uint8/int8 etc.)
+       scales (float32 torch.Tensor): quantization parameter for per channel group affine quantization
+       zero_points (int32 torch.Tensor): quantization parameter for per channel group affine quantization
+       quant_min (int): minimum quantized value for input Tensor
+       quant_max (int): maximum quantized value for input Tensor
+       dtype (torch.dtype): dtype (e.g. torch.uint8) for input Tensor
+       output_dtype (torch.dtype): dtype (e.g. torch.float32) for output Tensor
+
+    Returns:
+       dequantized Tensor with dtype `output_dtype`
+    """
+
+    assert group_size > 1
+    # needed for GPTQ single column dequantize
+    if group_size > w_int8.shape[-1] and scales.shape[-1] == 1:
+        group_size = w_int8.shape[-1]
+    assert w_int8.shape[-1] % group_size == 0
+    assert w_int8.dim() == 2
+
+    w_int8_grouped = w_int8.reshape(-1, group_size)
+    scales = scales.reshape(-1, 1)
+    if zero_points is not None:
+        zp = zero_points.reshape(-1, 1)
+    else:
+        zp = torch.zeros([], dtype=torch.int32, device=scales.device)
+    w_dq = w_int8_grouped.sub(zp).mul(scales).reshape_as(w_int8).to(output_dtype)
+    return w_dq
+
+
+quantized_decomposed_lib.define(
+    "fake_quant_per_channel(Tensor input, Tensor scales, Tensor zero_points, int axis, "
+    "int quant_min, int quant_max) -> Tensor"
+)
+
+
+class FakeQuantPerChannel(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input, scales, zero_points, axis, quant_min, quant_max):
+        if scales.dtype != torch.float32:
+            scales = scales.to(torch.float32)
+        if zero_points.dtype != torch.int32:
+            zero_points = zero_points.to(torch.int32)
+        assert (
+            input.dtype == torch.float32
+        ), f"Expecting input to have dtype torch.float32, but got dtype: {input.dtype}"
+        assert axis < input.dim(), f"Expecting axis to be < {input.dim()}"
+        broadcast_dims = list(range(0, axis)) + list(range(axis + 1, input.ndim))
+        unsqueeze_scales = _unsqueeze_multiple(scales, broadcast_dims)
+        unsqueeze_zero_points = _unsqueeze_multiple(zero_points, broadcast_dims)
+        temp = torch.round(input * (1.0 / unsqueeze_scales)) + unsqueeze_zero_points
+        out = (
+            torch.clamp(temp, quant_min, quant_max) - unsqueeze_zero_points
+        ) * unsqueeze_scales
+        mask = torch.logical_and((temp >= quant_min), (temp <= quant_max))
+
+        ctx.save_for_backward(mask)
+        return out
+
+    @staticmethod
+    def backward(ctx, gy):
+        (mask,) = ctx.saved_tensors
+        return gy * mask, None, None, None, None, None
+
+
+@impl(quantized_decomposed_lib, "fake_quant_per_channel", "Autograd")
+def fake_quant_per_channel(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor,
+    axis: int,
+    quant_min: int,
+    quant_max: int,
+) -> torch.Tensor:
+    return FakeQuantPerChannel.apply(
+        input, scales, zero_points, axis, quant_min, quant_max
+    )
+
+
+@impl(quantized_decomposed_lib, "fake_quant_per_channel", "Meta")
+def fake_quant_per_channel_meta(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor,
+    axis: int,
+    quant_min: int,
+    quant_max: int,
+) -> torch.Tensor:
+    return torch.empty_like(input)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_equalize.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_equalize.py
new file mode 100644
index 0000000000000000000000000000000000000000..1525a793868811b70c7b6378f4c6d4d0efa97186
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+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_equalize.py
@@ -0,0 +1,951 @@
+# mypy: allow-untyped-defs
+import operator
+import warnings
+from collections import namedtuple
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.ao.quantization.fx.graph_module import _get_observed_graph_module_attr
+from torch.ao.quantization.observer import (
+    _with_args,
+    ObserverBase,
+    PerChannelMinMaxObserver,
+)
+from torch.ao.quantization.utils import _parent_name, check_min_max_valid
+from torch.fx import GraphModule
+from torch.fx.graph import Node
+
+from .utils import (
+    get_new_attr_name_with_prefix,
+    maybe_get_next_module,
+    node_arg_is_weight,
+)
+
+
+CUSTOM_MODULE_SUPP_LIST: List[Any] = []
+
+
+def reshape_scale(scale: torch.Tensor, axis: int, input: torch.Tensor) -> torch.Tensor:
+    """Reshapes the scale so that we can multiply it to the input by the given axis."""
+    new_shape = [1] * input.ndim
+    new_shape[axis] = input.size(axis)
+    return scale.view(new_shape)
+
+
+qsheme_mapping_per_tensor_to_per_channel = {
+    torch.per_tensor_affine: torch.per_channel_affine,
+    torch.per_tensor_symmetric: torch.per_channel_symmetric,
+}
+
+
+class _InputEqualizationObserver(nn.Module):
+    r"""Observer for tracking the running min/max values of input columns, and
+    computing the quantization parameters for the overall min/max input values.
+
+    Args:
+        dtype: Quantized data type
+        qscheme: Quantization scheme
+        quant_min: Minimum quantization value. If unspecified, it will
+            follow the 8-bit setup.
+        quant_max: Maximum quantization value. If unspecified, it will
+            follow the 8-bit setup.
+
+    The running minimum/maximum :math:`x_\text{min/max}` are computed in the
+    same way as :class:`~torch.ao.quantization.observer.PerChannelMinMaxObserver`,
+    with the difference that the running min/max values are stored per column.
+    This observer is intended to be used along with a WeightEqualizationObserver
+    to calculate the equalization scale.
+    """
+
+    def __init__(
+        self,
+        dtype=torch.quint8,
+        qscheme=torch.per_tensor_affine,
+        quant_min=None,
+        quant_max=None,
+        factory_kwargs=None,
+    ) -> None:
+        super().__init__()
+
+        if qscheme not in {torch.per_tensor_affine, torch.per_tensor_symmetric}:
+            raise TypeError("Input qscheme must be per-tensor")
+
+        self.dtype = dtype
+        self.qscheme = qscheme
+
+        per_channel_qscheme = qsheme_mapping_per_tensor_to_per_channel[qscheme]
+        self.input_obs = PerChannelMinMaxObserver(
+            ch_axis=1,
+            dtype=dtype,
+            qscheme=per_channel_qscheme,
+            quant_min=quant_min,
+            quant_max=quant_max,
+            factory_kwargs=factory_kwargs,
+        )
+
+        self.equalization_scale = torch.tensor(1)
+        self.equalization_shape: List[int] = []
+
+    def forward(self, x_orig):
+        if not (x_orig.ndim >= 2 and x_orig.ndim <= 5):
+            raise ValueError(
+                "InputEqualizationObserver only supports Linear and Conv layers"
+            )
+
+        # Calculate the shape needed to reshape the equalization scale later (needed for Conv layers)
+        self.equalization_shape = [1] * x_orig.ndim
+        self.equalization_shape[1] = x_orig.size(1)
+
+        return self.input_obs(x_orig)
+
+    def get_input_minmax(self):
+        return (self.input_obs.min_val, self.input_obs.max_val)
+
+    def set_equalization_scale(self, equalization_scale):
+        # Reshape the equalization scale along axis=1 so that it can be
+        # multiplied with the input along axis=1
+        if equalization_scale.nelement() == 1 and equalization_scale == torch.tensor(1):
+            return
+        self.equalization_scale = torch.reshape(
+            equalization_scale, self.equalization_shape
+        )
+
+    def calculate_scaled_minmax(self):
+        r"""Returns the scaled min/max inputs"""
+        if (
+            self.equalization_scale.nelement() == 1
+            and self.equalization_scale == torch.tensor(1)
+        ):
+            warnings.warn(
+                "Must call calculate_equalization_scale before calling calculate_scaled_minmax. "
+                + "Will not scale the next quantization observer."
+            )
+            return None, None
+
+        # Calculate qparams for the scaled min/max inputs
+        # Scale the input by the equalization scale located at the same column
+        # index
+        (min_inputs, max_inputs) = self.get_input_minmax()
+        equalization_scale_reshaped = reshape_scale(
+            self.equalization_scale, 0, min_inputs
+        )
+        min_input_scaled = torch.min(torch.mul(min_inputs, equalization_scale_reshaped))
+        max_input_scaled = torch.max(torch.mul(max_inputs, equalization_scale_reshaped))
+
+        return min_input_scaled, max_input_scaled
+
+    with_args = classmethod(_with_args)
+
+
+class _WeightEqualizationObserver(nn.Module):
+    r"""Observer for tracking the running min/max values of weight columns and
+    rows, and computing the quantization parameters for the weight rows.
+
+    Args:
+        dtype: Quantized data type
+        qscheme: Quantization scheme
+        quant_min: Minimum quantization value. If unspecified, it will
+            follow the 8-bit setup.
+        quant_max: Maximum quantization value. If unspecified, it will
+            follow the 8-bit setup.
+
+    This observer is made up of 1 PerChannelMinMaxObserver `weight_col_obs` used
+    to record the running minimum and maximum of columns of incoming weight
+    tensors. This observer is intended to be used along with an
+    InputEqualizationObserver to calculate the equalization scale.
+
+    The running minimum/maximum :math:`w_\text{min/max}` are computed in the
+    same way as :class:`~torch.ao.quantization.observer.PerChannelMinMaxObserver`.
+    """
+
+    def __init__(
+        self,
+        dtype=torch.qint8,
+        qscheme=torch.per_tensor_affine,
+        quant_min=None,
+        quant_max=None,
+        factory_kwargs=None,
+    ) -> None:
+        super().__init__()
+
+        self.dtype = dtype
+        self.qscheme = qscheme
+        self.ch_axis = 1
+
+        per_channel_qscheme = qscheme
+        if qscheme in {torch.per_tensor_affine, torch.per_tensor_symmetric}:
+            per_channel_qscheme = qsheme_mapping_per_tensor_to_per_channel[qscheme]
+        self.weight_col_obs = PerChannelMinMaxObserver(
+            ch_axis=1,
+            dtype=dtype,
+            qscheme=per_channel_qscheme,
+            quant_min=quant_min,
+            quant_max=quant_max,
+            factory_kwargs=factory_kwargs,
+        )
+
+        self.equalization_scale = torch.tensor(1)
+
+    def forward(self, w_orig):
+        if not (w_orig.ndim >= 2 and w_orig.ndim <= 5):
+            raise ValueError(
+                "InputEqualizationObserver only supports Linear and Conv layers"
+            )
+
+        return self.weight_col_obs(w_orig)
+
+    def get_weight_col_minmax(self):
+        return (self.weight_col_obs.min_val, self.weight_col_obs.max_val)
+
+    def set_equalization_scale(self, equalization_scale):
+        self.equalization_scale = equalization_scale
+
+    with_args = classmethod(_with_args)
+
+
+def calculate_equalization_scale(
+    input_obs: _InputEqualizationObserver, weight_obs: _WeightEqualizationObserver
+) -> torch.Tensor:
+    r"""Calculates the equalization scale and sets the equalization_scale value
+    in the observers.
+
+    Args:
+        input_obs: Observer that tracks the ranges for the input columns
+        weight_obs: Observer that tracks the ranges for the weight columns
+    """
+
+    (min_inputs, max_inputs) = input_obs.get_input_minmax()
+    (min_weights, max_weights) = weight_obs.get_weight_col_minmax()
+
+    if not (
+        check_min_max_valid(min_inputs, max_inputs)
+        and check_min_max_valid(min_weights, max_weights)
+    ):
+        warnings.warn(
+            "Must run observer before calling calculate_equalization_scale. "
+            + "Returning default equalization scale torch.tensor(1)."
+        )
+        return torch.tensor(1)
+
+    if not (min_inputs.shape == min_weights.shape):
+        raise ValueError(
+            "Input and Weight must have the same column dimension. "
+            + f"Found {min_inputs.shape} and {min_weights.shape} shapes instead."
+        )
+
+    equalization_scale = torch.sqrt(
+        (max_weights - min_weights) / (max_inputs - min_inputs)
+    )
+    # Replace all 'inf', 'nan', 0's with 1s to prevent errors
+    equalization_scale[equalization_scale == 0.0] = 1
+    equalization_scale = torch.nan_to_num(equalization_scale, nan=1, posinf=1, neginf=1)
+    return equalization_scale
+
+
+class EqualizationQConfig(
+    namedtuple("EqualizationQConfig", ["input_activation", "weight"])
+):
+    """
+    Describes how to quantize a layer or a part of the network specifically for
+    input-weight equalization by providing settings (observer classes) for
+    inputs, outputs, and weights.
+
+    Note that EqualizationQConfig needs to contain observer **classes** (like
+    MinMaxObserver) or a callable that returns instances on invocation, not the
+    concrete observer instances themselves.
+    Quantization function will instantiate observers multiple times for each of
+    the layers.
+
+    Observer classes have usually reasonable default arguments, but they can be
+    overwritten with `with_args` method (that behaves like functools.partial):
+
+    my_qconfig = EqualizationQConfig(input_activation=_InputEqualizationObserver.with_args(dtype=torch.qint8),
+                                    weight=_WeightEqualizationObserver.with_args(dtype=torch.qint8))
+    """
+
+    def __new__(cls, input_activation=torch.nn.Identity, weight=torch.nn.Identity):
+        if isinstance(input_activation, nn.Module) or isinstance(weight, nn.Module):
+            raise ValueError(
+                "EqualizationQConfig received observer instance, please pass observer class instead. "
+                + "Use MyObserver.with_args(x=1) to override arguments to constructor if needed"
+            )
+        self = super().__new__(cls, input_activation, weight)
+        return self
+
+
+input_equalization_observer = _InputEqualizationObserver.with_args(
+    dtype=torch.quint8, qscheme=torch.per_tensor_symmetric
+)
+weight_equalization_observer = _WeightEqualizationObserver.with_args(
+    dtype=torch.qint8, qscheme=torch.per_channel_symmetric
+)
+default_equalization_qconfig = EqualizationQConfig(
+    input_activation=input_equalization_observer, weight=weight_equalization_observer
+)
+
+
+def fused_module_supports_equalization(module) -> bool:
+    """Checks if the fused node supports equalization."""
+    return type(module) in [
+        nni.LinearReLU,
+        nni.ConvReLU1d,
+        nni.ConvReLU2d,
+        nni.ConvReLU3d,
+    ]
+
+
+def nn_module_supports_equalization(module) -> bool:
+    """Checks if the torch.nn node supports equalization."""
+    return type(module) in [nn.Linear, nn.Conv1d, nn.Conv2d, nn.Conv3d]
+
+
+def custom_module_supports_equalization(module) -> bool:
+    """Checks if the custom node supports equalization."""
+    return type(module) in CUSTOM_MODULE_SUPP_LIST
+
+
+def node_supports_equalization(node: Node, modules) -> bool:
+    """Checks if the current node supports equalization
+    Currently we only support nn.Linear/F.Linear and nn.Conv/F.conv layers
+    """
+    if node.op == "call_module":
+        return (
+            nn_module_supports_equalization(modules[str(node.target)])
+            or fused_module_supports_equalization(modules[str(node.target)])
+            or custom_module_supports_equalization(modules[str(node.target)])
+        )
+    elif node.op == "call_function":
+        return node.target in [F.linear, F.conv1d, F.conv2d, F.conv3d]
+    return False
+
+
+def is_equalization_observer(observer: nn.Module) -> bool:
+    return isinstance(
+        observer, (_InputEqualizationObserver, _WeightEqualizationObserver)
+    )
+
+
+###############################################################################
+# Functions for equalization during convert                                   #
+###############################################################################
+
+
+def get_op_node_and_weight_eq_obs(
+    input_eq_obs_node: Node, model: GraphModule, modules: Dict[str, nn.Module]
+) -> Tuple[Optional[Node], Optional[_WeightEqualizationObserver]]:
+    """Gets the following weight equalization observer. There should always
+    exist a weight equalization observer after an input equalization observer.
+
+    Returns the operation node that follows the input equalization observer node
+    and the weight equalization observer
+    """
+
+    # Find the op node that comes directly after the input equalization observer
+    op_node = None
+    for user in input_eq_obs_node.users.keys():
+        if node_supports_equalization(user, modules):
+            op_node = user
+            break
+
+    assert op_node is not None
+    if op_node.op == "call_module":
+        # If the op_node is a nn.Linear layer, then it must have a
+        # WeightEqualizationObserver configuration
+        maybe_equalization_node_name_to_config = _get_observed_graph_module_attr(
+            model, "equalization_node_name_to_qconfig"
+        )
+        assert maybe_equalization_node_name_to_config is not None
+        equalization_node_name_to_qconfig: Dict[str, Any] = maybe_equalization_node_name_to_config  # type: ignore[assignment]
+        assert equalization_node_name_to_qconfig.get(op_node.name, None) is not None
+        weight_eq_obs = equalization_node_name_to_qconfig.get(
+            op_node.name, None
+        ).weight()
+
+        assert isinstance(weight_eq_obs, _WeightEqualizationObserver)
+        return op_node, weight_eq_obs
+
+    elif op_node.op == "call_function":
+        weight_node = maybe_get_weight_eq_obs_node(op_node, modules)
+        if weight_node is not None:
+            weight_eq_obs = modules[str(weight_node.target)]
+            assert isinstance(weight_eq_obs, _WeightEqualizationObserver)
+            return op_node, weight_eq_obs
+
+    return None, None
+
+
+def maybe_get_weight_eq_obs_node(
+    op_node: Node, modules: Dict[str, nn.Module]
+) -> Optional[Node]:
+    """Gets the weight equalization observer node if it exists."""
+    assert op_node.op == "call_function"
+    for node_arg in op_node.args:
+        if node_arg_is_weight(op_node, node_arg):
+            assert (
+                isinstance(node_arg, Node)
+                and node_arg.op == "call_module"
+                and isinstance(
+                    modules[str(node_arg.target)], _WeightEqualizationObserver
+                )
+            )
+            return node_arg
+    return None
+
+
+def maybe_get_next_input_eq_obs(
+    node: Node, modules: Dict[str, nn.Module]
+) -> Optional[_InputEqualizationObserver]:
+    """Gets the following input equalization observer if it exists.
+
+    For example, in the case of connecting linear layers:
+        x -> inp_obs1 -> eq_obs1 -> linear1 -> out_obs1 -> eq_obs2 -> linear2 -> out_obs2
+    If the node being passed in is the linear1 node, then we want to return eq_obs2,
+    the following equalization observer for linear2.
+
+    However, if there are no connecting layers:
+        x -> inp_obs1 -> eq_obs1 -> linear1 -> out_obs1 -> add
+    Then we want to return None.
+
+    In the case of an unfused linear-relu layer with a connecting linear layer:
+        linear1 -> relu -> out_obs1 -> eq_obs2 -> linear2 -> out_obs2
+    Since it is unfused, we want to skip over the relu layer and return eq_obs2,
+    the following equalization observer for linear2.
+    """
+
+    assert node_supports_equalization(node, modules)
+
+    # Locate the following nn.ReLU or F.relu node if it exists
+    maybe_relu_node = maybe_get_next_module(node, modules, nn.ReLU)
+    if maybe_relu_node is None:
+        maybe_relu_node = maybe_get_next_module(
+            node, modules, target_functional_type=F.relu
+        )
+
+    # Locate the following output observer if it exists.
+    # We will skip the relu node if it exists.
+    maybe_obs_node = (
+        maybe_get_next_module(node, modules, ObserverBase)
+        if maybe_relu_node is None
+        else maybe_get_next_module(maybe_relu_node, modules, ObserverBase)
+    )
+    if maybe_obs_node is None:
+        return None
+
+    maybe_eq_obs_node = maybe_get_next_module(
+        maybe_obs_node, modules, _InputEqualizationObserver
+    )
+    if maybe_eq_obs_node is None:
+        return None
+
+    maybe_eq_obs = modules[str(maybe_eq_obs_node)]
+    assert isinstance(maybe_eq_obs, _InputEqualizationObserver)
+    return maybe_eq_obs
+
+
+def maybe_get_next_equalization_scale(
+    node: Node, modules: Dict[str, nn.Module]
+) -> Optional[torch.Tensor]:
+    """If the next next node is an InputEqualizationObserver then we want to
+    return its equalization scale, else we return 1
+
+    This is used in the case where there are two connecting linear layers:
+        linear1 -> LinearOutObs -> InputEqObs -> linear2
+    In this case, the node given is linear1 and we want to locate the InputEqObs.
+    """
+    next_inp_eq_obs = maybe_get_next_input_eq_obs(node, modules)
+    if next_inp_eq_obs:
+        if (
+            next_inp_eq_obs.equalization_scale.nelement() == 1
+            and next_inp_eq_obs.equalization_scale == torch.tensor(1)
+        ):
+            return None
+        return next_inp_eq_obs.equalization_scale
+    return None
+
+
+def scale_input_observer(node: Node, modules: Dict[str, nn.Module]) -> None:
+    """Scales the following input quantization observer's min/max values by
+    updating the values with the scaled min/max values calculated by the input
+    equalization observer
+    """
+    input_eq_obs = modules[str(node.target)]
+    assert isinstance(input_eq_obs, _InputEqualizationObserver)
+
+    input_quant_obs_node = node.args[0]
+    assert isinstance(input_quant_obs_node, Node)
+
+    input_quant_obs = modules[str(input_quant_obs_node.target)]
+    if not isinstance(input_quant_obs, ObserverBase):
+        return
+
+    min_input_scaled, max_input_scaled = input_eq_obs.calculate_scaled_minmax()
+    if min_input_scaled is None and max_input_scaled is None:
+        return
+    input_quant_obs.min_val = min_input_scaled
+    input_quant_obs.max_val = max_input_scaled
+
+
+def scale_weight_node(
+    node: Node,
+    modules: Dict[str, nn.Module],
+    equalization_scale: torch.Tensor,
+    next_equalization_scale: Optional[torch.Tensor],
+) -> None:
+    """Scale the weights for input-weight equalization by multiplying the
+    weight by 1/equalization_scale and next_equalization_scale
+
+    Args:
+        node: Current node whose weights we want to scale
+        equalization_scale: Current node's calculated equalization scale
+        next_equalization_scale: Next node's calculated equalization scale if
+           the following node needs to be equalized, 1 otherwise
+    """
+    if equalization_scale is None:
+        return
+
+    if fused_module_supports_equalization(modules[str(node.target)]):
+        op_module = modules[str(node.target)][0]  # type: ignore[index]
+    else:
+        op_module = modules[str(node.target)]
+    assert nn_module_supports_equalization(
+        op_module
+    ) or custom_module_supports_equalization(op_module)
+
+    # Scale the weights for input-weight equalization
+    # If the following layer needs to be equalized then we will multiply its scale
+    weight = op_module.weight
+    assert isinstance(weight, torch.Tensor)
+
+    # Scale the weights by the reciprocal of the equalization scale
+    # Reshape the equalization scale so that we can multiply it to the weight along axis=1
+    equalization_scale_reshaped = reshape_scale(equalization_scale, 1, weight)
+    scaled_weight = torch.mul(weight, torch.reciprocal(equalization_scale_reshaped))
+
+    if next_equalization_scale is None:
+        op_module.weight = nn.Parameter(scaled_weight)
+        return
+
+    # Multiply the weights row wise by the next equalization scale
+    # Reshape the equalization scale so that we can multiply it to the weight along axis=0
+    next_equalization_scale_reshaped = reshape_scale(next_equalization_scale, 0, weight)
+    scaled_weight = torch.mul(scaled_weight, next_equalization_scale_reshaped)
+
+    op_module.weight = nn.Parameter(scaled_weight)
+
+    # Multiply the bias element wise by the next equalization scale
+    bias = op_module.bias
+    if bias is None:
+        return
+    assert isinstance(bias, torch.Tensor)
+
+    # Reshape the equalization scale so that we can multiply it element-wise to the bias
+    next_equalization_scale_reshaped = reshape_scale(next_equalization_scale, 0, bias)
+    scaled_bias = torch.mul(bias, next_equalization_scale_reshaped)
+    op_module.bias = nn.Parameter(scaled_bias)
+
+
+def scale_weight_functional(
+    op_node: Node,
+    model: GraphModule,
+    modules: Dict[str, nn.Module],
+    equalization_scale: torch.Tensor,
+    next_equalization_scale: Optional[torch.Tensor],
+) -> None:
+    """Scales the weight value for functional layers"""
+    if equalization_scale is None:
+        return
+
+    # From the given op_node, the path looks like:
+    #   get_attr(weight) -> weight_quant_obs -> weight_eq_obs -> op_node
+    # So we want to trace back from the op_node to get the equalization observer
+    # node, then the quantization observer node, and then finally the weight
+    # node which contains the weight values.
+
+    # Get the equalization observer node
+    weight_eq_obs_node = maybe_get_weight_eq_obs_node(op_node, modules)
+    if weight_eq_obs_node is None:
+        return
+
+    # Get the quantization observer node
+    weight_quant_obs_node = weight_eq_obs_node.args[0]
+    if weight_quant_obs_node is None:
+        return
+    assert isinstance(weight_quant_obs_node, Node) and isinstance(
+        modules[str(weight_quant_obs_node.target)], ObserverBase
+    )
+
+    # Get the get_attr(weight) node
+    weight_node = weight_quant_obs_node.args[0]
+    if weight_node is None:
+        return
+    assert isinstance(weight_node, Node) and weight_node.op == "get_attr"
+
+    weight_parent_name, weight_name = _parent_name(weight_node.target)
+    weight = getattr(modules[weight_parent_name], weight_name)
+
+    # Scale the weights for input-weight equalization
+    # If the following layer needs to be equalized then we will multiply its scale
+    # Reshape the equalization scale so that we can multiply it to the weight along axis=1
+    equalization_scale_reshaped = reshape_scale(equalization_scale, 1, weight)
+    scaled_weight = torch.mul(weight, torch.reciprocal(equalization_scale_reshaped))
+
+    if next_equalization_scale is None:
+        setattr(modules[weight_parent_name], weight_name, scaled_weight)
+        return
+
+    # Multiply the weights row wise by the next equalization scale
+    # Reshape the equalization scale so that we can multiply it to the weight along axis=1
+    next_equalization_scale_reshaped = reshape_scale(
+        next_equalization_scale, 0, scaled_weight
+    )
+    scaled_weight = torch.mul(scaled_weight, next_equalization_scale_reshaped)
+
+    setattr(modules[weight_parent_name], weight_name, scaled_weight)
+    assert torch.allclose(model.get_buffer(str(weight_node.target)), scaled_weight)
+
+    # Multiply the bias element wise by the next equalization scale
+    bias_node = None
+    for node in op_node.args:
+        # Find the node containing the weight values
+        if isinstance(node, Node) and node.op == "get_attr" and "bias" in node.name:
+            bias_node = node
+            break
+    if bias_node is None:
+        return
+
+    bias_parent_name, bias_name = _parent_name(bias_node.target)
+    bias = getattr(modules[bias_parent_name], bias_name)
+
+    # Reshape the equalization scale so that we can multiply it element-wise to the bias
+    next_equalization_scale_reshaped = reshape_scale(next_equalization_scale, 0, bias)
+    scaled_bias = torch.mul(bias, next_equalization_scale_reshaped)
+    setattr(modules[bias_parent_name], bias_name, scaled_bias)
+
+
+def clear_weight_quant_obs_node(op_node: Node, modules: Dict[str, nn.Module]) -> None:
+    """Given the operation node, we want find the corresponding quantization
+    observer and reset its min/max values
+    """
+    weight_eq_obs_node = maybe_get_weight_eq_obs_node(op_node, modules)
+    if weight_eq_obs_node is None:
+        return
+
+    weight_quant_obs_node = weight_eq_obs_node.args[0]
+    if weight_quant_obs_node is None:
+        return
+    assert isinstance(weight_quant_obs_node, Node)
+
+    weight_quant_obs = modules[str(weight_quant_obs_node.target)]
+    assert isinstance(modules[str(weight_quant_obs_node.target)], ObserverBase)
+    weight_quant_obs.reset_min_max_vals()  # type: ignore[operator]
+
+
+def remove_node(model: GraphModule, node: Node, prev_node: Node):
+    """Removes the given node from the model by replacing all of its users with
+    the given previous node
+    """
+    # For all of the current node's users, replace the current node with
+    # the input quantization observer node
+    orig_users = list(node.users.keys())
+    for user_node in orig_users:
+        user_node.replace_input_with(node, prev_node)
+
+    # Erase the InputEqualizationObserver node
+    model.graph.erase_node(node)
+
+
+def update_obs_for_equalization(
+    model: GraphModule, modules: Dict[str, nn.Module]
+) -> Dict[str, _WeightEqualizationObserver]:
+    """Update all of the observer's equalization scale. For each
+    InputEqualizationObserver, we will find the location of the next
+    WeightEqualizationObserver, create it, and calculate the equalization scale
+    based on the two observers.
+
+    We will then return a dictionary mapping operation node names to
+    the corresponding WeightEqualizationObservers for that operation.
+    """
+    weight_eq_obs_dict = {}
+    for node in model.graph.nodes:
+        if node.op == "call_module" and isinstance(
+            modules[node.target], _InputEqualizationObserver
+        ):
+            input_eq_obs = modules[node.target]
+            assert isinstance(input_eq_obs, _InputEqualizationObserver)
+            op_node, weight_eq_obs = get_op_node_and_weight_eq_obs(node, model, modules)
+
+            if op_node is None or weight_eq_obs is None:
+                continue
+
+            if op_node.op == "call_module":
+                # Calibrate the weight equalization observer since it has just
+                # been created
+                if fused_module_supports_equalization(modules[str(op_node.target)]):
+                    module = modules[str(op_node.target)][0]  # type: ignore[index]
+                    assert nn_module_supports_equalization(module)
+                    weight_eq_obs(module.weight)
+                else:
+                    weight_eq_obs(modules[str(op_node.target)].weight)
+
+            # Calculate and set the equalization scale values
+            equalization_scale = calculate_equalization_scale(
+                input_eq_obs, weight_eq_obs
+            )
+            input_eq_obs.set_equalization_scale(equalization_scale)
+            weight_eq_obs.set_equalization_scale(equalization_scale)
+
+            weight_eq_obs_dict[op_node.name] = weight_eq_obs
+
+    return weight_eq_obs_dict
+
+
+def convert_eq_obs(
+    model: GraphModule,
+    modules: Dict[str, nn.Module],
+    weight_eq_obs_dict: Dict[str, _WeightEqualizationObserver],
+) -> None:
+    """Converts the equalization operations and updates the other nodes in the
+    following way:
+        - Removes the input equalization observers and inserts a mul operator
+          along with an equalization scale node wherever applicable (we do not
+          want to insert a mul operator between connecting linear layers).
+        - Updates the input quantization observers with the scaled input min/max
+          values.
+        - Scales the weights by the current and next equalization scales.
+        - Removes the weight equalization observer node if it exists.
+
+    Before (after prepare):
+                                    weight values
+                                          |
+                                    WeightQuantObs
+                                          |
+                                      WeightEqObs
+                                          |
+        x -> InpQuantObs -> InpEqObs -> linear -> OutQuantObs
+
+    After this function:
+                                              scaled weight values
+                                                      |
+       equalization scale                       WeightQuantObs
+              |                                       |
+        x -> mul -> InpQuantObs (scaled min/max) -> linear -> OutQuantObs
+
+    After convert:
+       equalization scale                 scaled weight values
+              |                                    |
+        x -> mul -> quantize_per_tensor -> quantized::linear
+
+    Note that although the equalization observer appeared after the quantization
+    observer after prepare_fx, the mul node appears before the quantization node
+    after convert_fx. This is because placing the equalization observer after
+    the quantization observer in prepare_fx would allow us to keep the invariant
+    that the graph before the current node inserts its observers is not
+    modified.
+
+    Having the equalization observer before the quantization observer would also
+    cause some inconsistences between the ordering of the quantization and
+    equalization observers.
+    For example, a single linear layer would look like:
+        x -> InpEqObs1 -> InpQuantObs1 -> linear1 -> OutQuantObs1
+    But between two connected linear layers, it would look like:
+        linear1 -> OutQuantObs1 -> InpEqObs2 -> linear2 -> OutQuantObs2
+    """
+    for node in model.graph.nodes:
+        if node.op == "call_module" and isinstance(
+            modules[node.target], _InputEqualizationObserver
+        ):
+            inp_quant_obs_node = node.args[0]
+            prev_node = inp_quant_obs_node.args[0]
+
+            # If the previous node is a layer that needs to be equalized, then
+            # we will remove the current node because we do not need to add any
+            # equalization nodes between two layers that need to be equalized
+
+            # Before: linear1/relu (prev_node) -> output_quant_obs1 (inp_quant_obs_node) -> input_eq_obs2 (node) -> linear2
+            # After: linear1/relu (prev_node) -> output_quant_obs1 (inp_quant_obs_node) -> linear2
+            if (
+                node_supports_equalization(prev_node, modules)
+                or "relu" in prev_node.name
+            ):
+                remove_node(model, node, inp_quant_obs_node)
+                continue
+
+            # Update the following input quantization observer's min/max values
+            scale_input_observer(node, modules)
+
+            # Remove the InputEqualization node and add a mul operator before
+            # the quantization observer node that appears before the equalization node
+            # Before: x -> input_quant_obs -> input_eq_obs -> linear
+            # After: x -> mul -> input_quant_obs -> linear
+
+            # Create a node containing the equalization scale
+            with model.graph.inserting_before(inp_quant_obs_node):
+                get_new_eq_scale_name = get_new_attr_name_with_prefix(
+                    prev_node.name + "_equalization_scale"
+                )
+                name = get_new_eq_scale_name(modules)
+                setattr(model, name, modules[node.target].equalization_scale)
+                eq_scale_node = model.graph.create_node("get_attr", name)
+
+            # Create a node multiplying the input with the equalization scale
+            with model.graph.inserting_after(eq_scale_node):
+                inputs = (prev_node, eq_scale_node)
+                mul_node = model.graph.create_node("call_function", torch.mul, inputs)
+
+            # Set the mul nod to be the input_quant_obs_node's input instead of
+            # the previous node
+            inp_quant_obs_node.replace_input_with(prev_node, mul_node)
+            remove_node(model, node, inp_quant_obs_node)
+
+        elif weight_eq_obs_dict.get(node.name, None) is not None:
+            weight_eq_obs = weight_eq_obs_dict.get(node.name)
+            assert isinstance(weight_eq_obs, _WeightEqualizationObserver)
+            equalization_scale = weight_eq_obs.equalization_scale
+
+            if (
+                equalization_scale.nelement() == 1
+                and equalization_scale == torch.tensor(1)
+            ):
+                equalization_scale = None  # type: ignore[assignment]
+            maybe_next_equalization_scale = maybe_get_next_equalization_scale(
+                node, modules
+            )
+
+            # Scale the weight nodes
+            if node.op == "call_module":
+                scale_weight_node(
+                    node, modules, equalization_scale, maybe_next_equalization_scale
+                )
+            elif node.op == "call_function":
+                scale_weight_functional(
+                    node,
+                    model,
+                    modules,
+                    equalization_scale,
+                    maybe_next_equalization_scale,
+                )
+
+                weight_eq_obs_node = maybe_get_weight_eq_obs_node(node, modules)
+                if weight_eq_obs_node is None:
+                    return
+                assert isinstance(
+                    modules[str(weight_eq_obs_node.target)], _WeightEqualizationObserver
+                )
+
+                # Clear the quantization observer's min/max values so that they
+                # can get updated later based on the new scale values
+                clear_weight_quant_obs_node(node, modules)
+
+                # Erase the weight equalization observer node
+                prev_node = weight_eq_obs_node.args[0]
+                remove_node(model, weight_eq_obs_node, prev_node)
+            else:
+                raise ValueError(
+                    "Expected operation node to be 'call_module' or 'call_function"
+                    + f"Instead got node {node.name} as '{node.op}'."
+                )
+
+
+def _convert_equalization_ref(model: GraphModule):
+    """Reference function which applies changes needed for equalization, but
+    does not quantize the nodes
+    """
+    modules = dict(model.named_modules(remove_duplicate=False))
+
+    # Calculate the equalization scale, update the observers with the scaled
+    # inputs, and scale the weight
+    weight_eq_obs_dict = update_obs_for_equalization(model, modules)
+    convert_eq_obs(model, modules, weight_eq_obs_dict)
+
+    return GraphModule(model, model.graph)
+
+
+###############################################################################
+# Functions for running the equalized model on the Numeric Suite              #
+###############################################################################
+
+
+def get_layer_sqnr_dict(
+    model_a: nn.Module, model_b: nn.Module, x: torch.Tensor
+) -> Dict[str, float]:
+    """Runs the Numeric Suite on model_a and model_b and returns a dictionary
+    containing the SQNR between layers in model_a and model_b.
+
+    Note: In order to support equalized models, this function has a hacky fix in
+    which we do not match any torch.mul operators. This is because equalized
+    models contain extra mul operators to scale the input by the equalization
+    scale, but this edge case has not been resolved yet within the numeric suite code.
+
+    Args:
+        model_a: A float model
+        model_b: A quantized model
+        x: Inputs to use during calibration
+    """
+    import torch.ao.ns._numeric_suite_fx as ns
+    from torch.ao.ns.fx.mappings import get_unmatchable_types_map
+
+    unmatchable_types_map = get_unmatchable_types_map()
+    unmatchable_types_map["funs_unmatchable"].add(torch.mul)
+
+    model_a_ns, model_b_ns = ns.add_loggers(
+        "fp32",
+        model_a,
+        "int8",
+        model_b,
+        ns.OutputLogger,
+        unmatchable_types_map=unmatchable_types_map,
+    )
+
+    model_a_ns(x)
+    model_b_ns(x)
+
+    activation_comparison_dict = ns.extract_logger_info(
+        model_a_ns, model_b_ns, ns.OutputLogger, "int8"
+    )
+    ns.extend_logger_results_with_comparison(
+        activation_comparison_dict,
+        "fp32",
+        "int8",
+        torch.ao.ns.fx.utils.compute_sqnr,
+        "sqnr",
+    )
+
+    # Construct a dictionary mapping layer names to the SQNR values
+    layer_sqnr_dict = {}
+    for key in activation_comparison_dict:
+        layer = activation_comparison_dict[key]["node_output"]["int8"][0]["fqn"]
+        sqnr = activation_comparison_dict[key]["node_output"]["int8"][0]["sqnr"][0]
+        layer_sqnr_dict[layer] = sqnr
+
+    return layer_sqnr_dict
+
+
+def get_equalization_qconfig_dict(
+    layer_sqnr_dict: Dict[str, float], num_layers_to_equalize: int
+) -> Any:
+    """Given the layer to SQNR dictionary, find the layers with the highest
+    quantization errors, and return an equalization_qconfig_dict
+    specifying to only equalize those top layers.
+
+    Args:
+        layer_sqnr_dict: Dictionary mapping layer names to SQNR values (found
+            when comparing an equalized model against a float model)
+        num_layers_to_equalize: Number of layers with the highest quantization
+           errors to equalize
+    """
+
+    # Sort the layer_sqnr_dictionary values and get the layers with the lowest
+    # SQNR values (aka highest quantization errors)
+    layer_sqnr_sorted = sorted(layer_sqnr_dict.items(), key=operator.itemgetter(1))
+    layers_to_equalize = layer_sqnr_sorted[:num_layers_to_equalize]
+
+    # Constructs an equalization_qconfig_dict that specifies to only equalize
+    # the layers with the highest quantization errors
+    module_to_qconfig_list = [
+        (item[0], default_equalization_qconfig) for item in layers_to_equalize
+    ]
+    equalization_qconfig_dict = {"module_name": module_to_qconfig_list}
+    return equalization_qconfig_dict
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_lower_to_native_backend.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_lower_to_native_backend.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0b7b76a777d1b0ec4793fce98fcc3d70cf0402e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_lower_to_native_backend.py
@@ -0,0 +1,1323 @@
+# mypy: allow-untyped-defs
+import operator
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
+
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.intrinsic.quantized as nniq
+import torch.ao.nn.intrinsic.quantized.dynamic as nniqd
+import torch.ao.nn.quantized as nnq
+import torch.ao.nn.quantized.dynamic as nnqd
+import torch.ao.nn.quantized.reference as nnqr
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.ao.nn.quantized.modules.utils import WeightedQuantizedModule
+from torch.ao.quantization.qconfig import QConfigAny
+from torch.ao.quantization.quantization_mappings import get_quantized_operator
+from torch.ao.quantization.utils import _parent_name
+from torch.fx import GraphModule, map_arg, Node
+from torch.fx.graph import Graph
+
+from .utils import (
+    collect_producer_nodes,
+    create_node_from_old_node_preserve_meta,
+    get_linear_prepack_op_for_dtype,
+    get_new_attr_name_with_prefix,
+    get_qconv_prepack_op,
+    graph_module_from_producer_nodes,
+)
+
+
+QOP_TO_ARG_NAMES_TO_SKIP = {
+    torch._ops.ops.quantized.hardswish: ["inplace"],
+    torch._ops.ops.quantized.elu: ["inplace"],
+    torch._ops.ops.quantized.dropout: ["inplace"],
+    torch._ops.ops.quantized.instance_norm: [
+        "running_mean",
+        "running_var",
+        "use_input_stats",
+        "momentum",
+    ],
+}
+
+
+def _is_node_in_list(node, modules, func_list, method_list, module_type_list):
+    is_call_function = node.op == "call_function" and node.target in func_list
+    is_call_method = node.op == "call_method" and node.target in method_list
+    is_call_module = (
+        node.op == "call_module" and type(modules[str(node.target)]) in module_type_list
+    )
+    return is_call_function, is_call_method, is_call_module
+
+
+def is_fixed_qparams_node(node, modules):
+    func_list = [
+        torch.nn.functional.hardsigmoid,
+        torch.nn.functional.sigmoid,
+        torch.sigmoid,
+        torch.tanh,
+    ]
+    method_list = [
+        "hardsigmoid",
+        "hardsigmoid_",
+        "sigmoid",
+        "sigmoid_",
+        "tanh",
+        "tanh_",
+    ]
+    module_type_list = [
+        torch.nn.Hardsigmoid,
+        torch.nn.Sigmoid,
+        torch.nn.Tanh,
+        torch.nn.Softmax,
+    ]
+    return _is_node_in_list(node, modules, func_list, method_list, module_type_list)
+
+
+def is_default_node(node, modules):
+    func_list = [
+        torch.nn.functional.elu,
+        torch.nn.functional.hardswish,
+        torch.nn.functional.instance_norm,
+        torch.nn.functional.layer_norm,
+        torch.nn.functional.leaky_relu,
+        torch.nn.functional.dropout,
+    ]
+    method_list: List[Any] = []
+    module_type_list = [
+        nnqr.ConvTranspose1d,
+        nnqr.ConvTranspose2d,
+        nnqr.ConvTranspose3d,
+        torch.nn.ELU,
+        torch.nn.LeakyReLU,
+        torch.nn.Hardswish,
+        torch.nn.InstanceNorm1d,
+        torch.nn.InstanceNorm2d,
+        torch.nn.InstanceNorm3d,
+        torch.nn.LayerNorm,
+        torch.nn.Dropout,
+        torch.nn.PReLU,
+        torch.nn.BatchNorm2d,
+        torch.nn.BatchNorm3d,
+        torch.ao.nn.intrinsic.BNReLU2d,
+        torch.ao.nn.intrinsic.BNReLU3d,
+    ]
+    return _is_node_in_list(node, modules, func_list, method_list, module_type_list)
+
+
+def is_copy_node(node, modules):
+    func_list = [
+        torch.adaptive_avg_pool1d,
+        torch.nn.functional.adaptive_avg_pool2d,
+        torch.nn.functional.adaptive_avg_pool3d,
+        torch.nn.functional.hardtanh,
+        torch.nn.functional.hardtanh_,
+        torch.nn.functional.interpolate,
+        torch.nn.functional.max_pool1d,
+        torch.nn.functional.max_pool2d,
+        torch.nn.functional.max_pool3d,
+        torch.nn.functional.relu,
+        torch.nn.functional.relu6,
+        torch.avg_pool1d,
+        torch._C._nn.avg_pool2d,
+        torch._C._nn.avg_pool3d,
+        torch.clamp,
+        torch.flatten,
+        torch.mean,
+        operator.floordiv,
+        # F.channel_shuffle and torch.channel_shuffle are essentially the same thing
+        # so we only need to put one of them here
+        torch.channel_shuffle,
+    ]
+    method_list = [
+        "clamp",
+        "mean",
+        "relu",
+        "relu_",
+    ]
+    module_type_list = [
+        torch.nn.AdaptiveAvgPool1d,
+        torch.nn.AdaptiveAvgPool2d,
+        torch.nn.AdaptiveAvgPool3d,
+        torch.nn.AvgPool1d,
+        torch.nn.AvgPool2d,
+        torch.nn.AvgPool3d,
+        torch.nn.Hardtanh,
+        torch.nn.MaxPool1d,
+        torch.nn.MaxPool2d,
+        torch.nn.MaxPool3d,
+        torch.nn.ReLU,
+        torch.nn.ReLU6,
+        torch.nn.ChannelShuffle,
+    ]
+    return _is_node_in_list(node, modules, func_list, method_list, module_type_list)
+
+
+def is_general_tensor_shape_node(node, modules):
+    func_list = [
+        torch.narrow,
+        torch.transpose,
+        torch.repeat_interleave,
+        torch.squeeze,
+        torch.stack,
+        torch.unsqueeze,
+        torch.nn.functional.pixel_shuffle,
+        torch.nn.functional.pixel_unshuffle,
+    ]
+    method_list = [
+        "contiguous",
+        "detach",
+        "detach_",
+        "permute",
+        "repeat",
+        "repeat_interleave",
+        "reshape",
+        "resize_",
+        "shape",
+        "size",
+        "squeeze",
+        "squeeze_",
+        "transpose",
+        "unsqueeze",
+        "unsqueeze_",
+        "view",
+    ]
+    module_type_list = [
+        torch.nn.Identity,
+        torch.nn.PixelShuffle,
+        torch.nn.PixelUnshuffle,
+    ]
+    return _is_node_in_list(node, modules, func_list, method_list, module_type_list)
+
+
+def is_other_node(node, modules):
+    func_list = [
+        torch.cat,
+    ]
+    method_list: List[Any] = []
+    module_type_list: List[Any] = []
+    return _is_node_in_list(node, modules, func_list, method_list, module_type_list)
+
+
+def is_special_pattern_node(node, modules):
+    res_function, res_method, res_module = False, False, False
+    for checker in [
+        is_fixed_qparams_node,
+        is_default_node,
+        is_copy_node,
+        is_general_tensor_shape_node,
+        is_other_node,
+    ]:
+        is_call_function, is_call_method, is_call_module = checker(node, modules)
+        res_function = res_function or is_call_function
+        res_method = res_method or is_call_method
+        res_module = res_module or is_call_module
+    return res_function, res_method, res_module
+
+
+def is_dequantize_node(node):
+    return (
+        isinstance(node, Node)
+        and node.op == "call_method"
+        and node.target == "dequantize"
+    )
+
+
+def is_getattr_tensor_metadata_node(node):
+    return (
+        node.op == "call_function"
+        and node.target == getattr
+        and node.args[1] in ["shape"]
+    )
+
+
+def is_get_tensor_info_node(node):
+    return node.op == "call_method" and node.target in ["shape", "size"]
+
+
+def should_skip_lowering(op: torch.fx.node.Node, qconfig_map: Dict[str, QConfigAny]):
+    """
+    Return True if the op is configured with a None qconfig, False otherwise.
+    Note: maybe need to generalize this to also check for the dtype, and we
+    only lower when dtype matches, but right now fbgemm/qnnpack only support
+    a single dtype, so it is OK for now.
+    """
+    return op.name in qconfig_map and qconfig_map[op.name] is None
+
+
+# Mapping from reference module class to the replacement static quantized module class for lowering
+STATIC_LOWER_MODULE_MAP: Dict[Type[nn.Module], Type[WeightedQuantizedModule]] = {
+    nnqr.Linear: nnq.Linear,
+    nnqr.Conv1d: nnq.Conv1d,
+    nnqr.Conv2d: nnq.Conv2d,
+    nnqr.Conv3d: nnq.Conv3d,
+}
+
+# Mapping from reference module class to the replacement dynamic quantized module class for lowering
+DYNAMIC_LOWER_MODULE_MAP: Dict[Type[nn.Module], Type[nn.Module]] = {
+    nnqr.Linear: nnqd.Linear,
+    nnqr.GRUCell: nnqd.GRUCell,
+    nnqr.LSTMCell: nnqd.LSTMCell,
+    nnqr.RNNCell: nnqd.RNNCell,
+    nnqr.LSTM: nnqd.LSTM,
+    nnqr.GRU: nnqd.GRU,
+}
+
+# Mapping from reference module class to the replacement weight only quantized module class for lowering
+# TODO: correct the namespace for these modules
+WEIGHT_ONLY_LOWER_MODULE_MAP: Dict[Type[nn.Module], Type[nn.Module]] = {
+    nnqr.Embedding: nnq.Embedding,
+    nnqr.EmbeddingBag: nnq.EmbeddingBag,
+}
+
+# TODO: merge with STATIC_LOWER_MODULE_MAP after we merge
+# _lower_static_weighted_ref_module and special_pattern_replacement
+SPECIAL_PATTERN_LOWER_MODULE_MAP = {
+    nn.BatchNorm2d: nnq.BatchNorm2d,
+    nn.BatchNorm3d: nnq.BatchNorm3d,
+    nnqr.ConvTranspose1d: nnq.ConvTranspose1d,
+    nnqr.ConvTranspose2d: nnq.ConvTranspose2d,
+    nnqr.ConvTranspose3d: nnq.ConvTranspose3d,
+    nn.ELU: nnq.ELU,
+    nn.LeakyReLU: nnq.LeakyReLU,
+    nn.Hardswish: nnq.Hardswish,
+    nn.InstanceNorm1d: nnq.InstanceNorm1d,
+    nn.InstanceNorm2d: nnq.InstanceNorm2d,
+    nn.InstanceNorm3d: nnq.InstanceNorm3d,
+    nn.LayerNorm: nnq.LayerNorm,
+    nn.Dropout: nnq.Dropout,
+    nn.Softmax: nnq.Softmax,
+    nn.PReLU: nnq.PReLU,
+    nni.BNReLU2d: nniq.BNReLU2d,
+    nni.BNReLU3d: nniq.BNReLU3d,
+}
+
+# Mapping from fused module class to a 2-tuple of:
+#   1) The inner reference module class
+#   2) The replacement static quantized module class for lowering
+STATIC_LOWER_FUSED_MODULE_MAP: Dict[
+    Type[nn.Module], Tuple[Type[nn.Module], Type[WeightedQuantizedModule]]
+] = {
+    nni.LinearReLU: (nnqr.Linear, nniq.LinearReLU),
+    # TODO: LinearLeakyReLU is registered as global but it is only fused and
+    # lowered when ondnn's backend config is used. Maybe need to separate
+    # registration and lowering functions for different backends in the future.
+    nni.LinearLeakyReLU: (nnqr.Linear, nniq.LinearLeakyReLU),
+    nni.LinearTanh: (nnqr.Linear, nniq.LinearTanh),
+    nni.ConvReLU1d: (nnqr.Conv1d, nniq.ConvReLU1d),
+    nni.ConvReLU2d: (nnqr.Conv2d, nniq.ConvReLU2d),
+    nni.ConvReLU3d: (nnqr.Conv3d, nniq.ConvReLU3d),
+}
+
+# The difference between STATIC_LOWER_FUSED_MODULE_TWO_INPUTS_MAP and STATIC_LOWER_FUSED_MODULE_MAP:
+# The refer node inside STATIC_LOWER_FUSED_MODULE_TWO_INPUTS_MAP has 2 inputs.
+# Mapping from fused module class to a 2-tuple of:
+#   1) The inner reference module class
+#   2) The replacement static quantized module class for lowering
+STATIC_LOWER_FUSED_MODULE_TWO_INPUTS_MAP: Dict[
+    Type[nn.Module], Tuple[Type[nn.Module], Type[WeightedQuantizedModule]]
+] = {
+    nni.ConvAdd2d: (nnqr.Conv2d, nniq.ConvAdd2d),
+    nni.ConvAddReLU2d: (nnqr.Conv2d, nniq.ConvAddReLU2d),
+}
+
+# Mapping from fused module class to a 2-tuple of:
+#   1) The inner reference module class
+#   2) The replacement dynamic quantized module class for lowering
+DYNAMIC_LOWER_FUSED_MODULE_MAP: Dict[
+    Type[nn.Module], Tuple[Type[nn.Module], Type[nn.Module]]
+] = {
+    nni.LinearReLU: (nnqr.Linear, nniqd.LinearReLU),
+}
+
+# Mapping from a functional to lower to a 2-tuple of
+#   1) The quantized version of the op
+#   2) The quantized version of the op fused with relu, if it exists, else None
+STATIC_LOWER_FUNCTIONAL_MAP: Dict[Callable, Tuple[Callable, Optional[Callable]]] = {
+    F.linear: (torch.ops.quantized.linear, torch.ops.quantized.linear_relu),
+    F.conv1d: (torch.ops.quantized.conv1d, torch.ops.quantized.conv1d_relu),
+    F.conv2d: (torch.ops.quantized.conv2d, torch.ops.quantized.conv2d_relu),
+    F.conv3d: (torch.ops.quantized.conv3d, torch.ops.quantized.conv3d_relu),
+    F.conv_transpose1d: (torch.ops.quantized.conv_transpose1d, None),
+    F.conv_transpose2d: (torch.ops.quantized.conv_transpose2d, None),
+    F.conv_transpose3d: (torch.ops.quantized.conv_transpose3d, None),
+}
+
+WEIGHT_PREPACK_OPS: Set[Callable] = {
+    torch._ops.ops.quantized.linear_prepack,
+    torch._ops.ops.quantized.linear_prepack_fp16,
+    torch._ops.ops.quantized.conv1d_prepack,
+    torch._ops.ops.quantized.conv2d_prepack,
+    torch._ops.ops.quantized.conv3d_prepack,
+    torch.ops.quantized.conv_transpose1d_prepack,
+    torch.ops.quantized.conv_transpose2d_prepack,
+    torch.ops.quantized.conv_transpose3d_prepack,
+}
+
+# Mapping from a functional to a dictionary, where the key is a 2-tuple of
+# (input_activation_dtype, weight_dtype) and the value is a 2-tuple of
+#   1) The dynamically quantized version of the op
+#   2) The dynamically quantized version of the op fused with relu, if it exists, else None
+DYNAMIC_LOWER_FUNCTIONAL_MAP: Dict[
+    Callable, Dict[Tuple[torch.dtype, torch.dtype], Tuple[Callable, Optional[Callable]]]
+] = {
+    F.linear: {
+        (torch.quint8, torch.qint8): (
+            torch.ops.quantized.linear_dynamic,
+            torch.ops.quantized.linear_relu_dynamic,
+        ),
+        (torch.float16, torch.float16): (
+            torch.ops.quantized.linear_dynamic_fp16,
+            torch.ops.quantized.linear_relu_dynamic_fp16,
+        ),
+    },
+    # dynamic conv + relu is not available yet
+    F.conv1d: {
+        (torch.quint8, torch.qint8): (torch.ops.quantized.conv1d_dynamic, None),
+    },
+    F.conv2d: {
+        (torch.quint8, torch.qint8): (torch.ops.quantized.conv2d_dynamic, None),
+    },
+    F.conv3d: {
+        (torch.quint8, torch.qint8): (torch.ops.quantized.conv3d_dynamic, None),
+    },
+}
+
+CONV_FUNCTIONAL_OPS: Set[Callable] = {
+    F.conv1d,
+    F.conv2d,
+    F.conv3d,
+}
+
+CONV_TRANSPOSE_FUNCTIONAL_OPS: Set[Callable] = {
+    F.conv_transpose1d,
+    F.conv_transpose2d,
+    F.conv_transpose3d,
+}
+
+# TODO: add tests for lowering these ops
+QBIN_OP_MAPPING: Dict[Union[Callable, str], Callable] = {
+    operator.add: torch.ops.quantized.add,
+    torch.add: torch.ops.quantized.add,
+    operator.mul: torch.ops.quantized.mul,
+    operator.matmul: torch.ops.quantized.matmul,
+    torch.mul: torch.ops.quantized.mul,
+    torch.matmul: torch.ops.quantized.matmul,
+}
+QBIN_RELU_OP_MAPPING: Dict[Union[Callable, str], Callable] = {
+    operator.add: torch.ops.quantized.add_relu,
+    torch.add: torch.ops.quantized.add_relu,
+    operator.mul: torch.ops.quantized.mul_relu,
+    torch.mul: torch.ops.quantized.mul_relu,
+}
+
+
+def _save_packed_weight(self, destination, prefix, keep_vars):
+    for attr_name in dir(self):
+        if "_packed_weight" in attr_name and isinstance(
+            getattr(self, attr_name), torch._C.ScriptObject
+        ):  # type: ignore[attr-defined]
+            packed_weight = getattr(self, attr_name)
+            destination[prefix + attr_name] = packed_weight
+
+
+def _load_packed_weight(
+    self,
+    state_dict,
+    prefix,
+    local_metadata,
+    strict,
+    missing_keys,
+    unexpected_keys,
+    error_msgs,
+):
+    attrs_to_pop = []
+    for attr_name in state_dict:
+        if attr_name.startswith("_packed_weight") and isinstance(state_dict[attr_name], torch._C.ScriptObject):  # type: ignore[attr-defined] # noqa: B950
+            setattr(self, attr_name, state_dict[attr_name])
+            attrs_to_pop.append(attr_name)
+
+    # pop the packed param attributesn
+    for attr_name in attrs_to_pop:
+        state_dict.pop(attr_name)
+
+
+def fold_weight(
+    quantized_model: GraphModule, node_name_to_scope: Dict[str, Tuple[str, type]]
+) -> GraphModule:
+    """
+    Trace back from the weight node util we hit getattr, reconstruct the
+    graph module with the traced nodes and run the graph module to pack the
+    weight. then replace the original chain of ops with the packed weight.
+    """
+    packed_weights = {}
+    # map from folded node name to the prepacked weight name
+    folded_nodes = {}
+    # get packed weights
+    for node in quantized_model.graph.nodes:
+        if node.op == "call_function" and node.target in WEIGHT_PREPACK_OPS:
+            nodes_to_fold = collect_producer_nodes(node)
+            if nodes_to_fold is not None:
+                for node_to_fold in nodes_to_fold:
+                    folded_nodes[node_to_fold.name] = node
+
+                prepacking_module = graph_module_from_producer_nodes(
+                    quantized_model, nodes_to_fold
+                )
+                packed_weight = prepacking_module()
+                packed_weights[node.name] = packed_weight
+
+    # remove folded nodes and replace the prepacking node with getattr
+    folded_graph = Graph()
+    env: Dict[Any, Any] = {}
+
+    def load_arg(a):
+        return map_arg(a, lambda node: env[node.name])
+
+    for node in quantized_model.graph.nodes:
+        prepack_node = folded_nodes.get(node.name, None)
+        if prepack_node is node:
+            packed_weight = packed_weights[node.name]
+            # add a prepacked attribute to root
+            op_node = next(iter(prepack_node.users))
+            module_path, _ = node_name_to_scope[op_node.name]
+            get_new_packed_weight_name = get_new_attr_name_with_prefix(
+                module_path + "_packed_weight_"
+            )
+            packed_weight_name = get_new_packed_weight_name(quantized_model)
+            setattr(quantized_model, packed_weight_name, packed_weight)
+            # replace prepack node with a getattr node
+            env[node.name] = folded_graph.create_node(
+                "get_attr", packed_weight_name, (), {}
+            )
+        elif prepack_node is not None:
+            # remove the foled node
+            continue
+        else:
+            # copy other nodes
+            env[node.name] = folded_graph.node_copy(node, load_arg)
+
+    quantized_model = GraphModule(quantized_model, folded_graph)
+    quantized_model._register_state_dict_hook(_save_packed_weight)
+    quantized_model.register_load_state_dict_pre_hook(_load_packed_weight)
+    return quantized_model
+
+
+def _get_module(node: Node, modules: Dict[str, nn.Module]) -> Optional[nn.Module]:
+    """
+    Return the `torch.nn.Module` that corresponds to the specified node's target.
+    If no such node exists, return None.
+    """
+    if node.op == "call_module" and str(node.target) in modules:
+        return modules[str(node.target)]
+    else:
+        return None
+
+
+def _match_static_pattern(
+    node: Node,
+    modules: Dict[str, nn.Module],
+    qconfig_map: Dict[str, QConfigAny],
+    matching_modules_or_ops: List[Callable],
+    dequantize_node_arg_indices: List[int],
+) -> Union[Tuple[Node, Node, Node], Tuple[None, None, None]]:
+    """
+    Match the pattern (dequantize - ref node - quantize) against the node provided.
+
+    If there is a match, return a 3-tuple of:
+      1) q_node: the quantize node,
+      2) relu_node: a relu node wrapping the ref_node, and
+      3) ref_node: a reference module or functional node to replace with its quantized counterpart
+    Otherwise, if there is no match, return a 3-tuple of (None, None, None).
+
+    Parameters:
+      node: The `torch.fx.Node` to match against.
+      modules: A mapping from node names to modules in the model graph, used for module lookup.
+      qconfig_map: A mapping from node names to the qconfigs associated with the nodes.
+          If the corresponding qconfig for the reference node is None, then return no match.
+      matching_modules_or_ops: Either a list of functions or a list of `torch.nn.Module`s.
+          If the reference node is not in this list, then return no match.
+      dequantize_node_arg_indices: A list of indices in the reference node args where dequantize
+          nodes may be present. An empty list means skipping the check for dequantize nodes.
+    """
+    SKIP_LOWERING_VALUE = (None, None, None)
+
+    # Match quantize node
+    if node.op != "call_function" or node.target != torch.quantize_per_tensor:
+        return SKIP_LOWERING_VALUE
+    q_node = node
+    ref_node = q_node.args[0]
+    assert isinstance(ref_node, Node)
+
+    # Handle cases where the node is wrapped in a ReLU
+    if (ref_node.op == "call_function" and ref_node.target in (F.relu, torch.relu)) or (
+        ref_node.op == "call_module" and type(_get_module(ref_node, modules)) == nn.ReLU
+    ):
+        relu_node = ref_node
+        ref_node = relu_node.args[0]
+        assert isinstance(ref_node, Node)
+    else:
+        relu_node = None
+    if should_skip_lowering(ref_node, qconfig_map):
+        return SKIP_LOWERING_VALUE
+
+    # Match reference module or functional
+    if isinstance(matching_modules_or_ops[0], type) and issubclass(
+        matching_modules_or_ops[0], nn.Module
+    ):
+        expected_op = "call_module"
+        match_key = type(_get_module(ref_node, modules))
+    else:
+        expected_op = "call_function"
+        match_key = ref_node.target  # type: ignore[assignment]
+    if ref_node.op != expected_op or match_key not in matching_modules_or_ops:
+        return SKIP_LOWERING_VALUE
+
+    # Match dequantize node(s). Both of the following conditions must pass:
+    # (1) All `torch.fx.Node`s at the matching indices must be a dequantize node
+    # (2) There must be at least one dequantize node
+    matched_dequantize = False
+    for i in dequantize_node_arg_indices:
+        assert i < len(
+            ref_node.args
+        ), f"Dequantize index {i} exceeded reference node's arg length {len(ref_node.args)}"
+        arg = ref_node.args[i]
+        if is_dequantize_node(arg):
+            matched_dequantize = True
+        elif isinstance(arg, Node):
+            return SKIP_LOWERING_VALUE
+    if not matched_dequantize:
+        return SKIP_LOWERING_VALUE
+
+    return (q_node, relu_node, ref_node)  # type: ignore[return-value]
+
+
+def _match_static_pattern_with_two_inputs(
+    node: Node,
+    modules: Dict[str, nn.Module],
+    qconfig_map: Dict[str, QConfigAny],
+    matching_modules_or_ops: List[Callable],
+) -> Union[Tuple[Node, Node], Tuple[None, None]]:
+    """
+                      (dequantize \
+    Match the pattern (dequantize - ref node - quantize) against the node provided.
+
+    If there is a match, return a 2-tuple of:
+      1) q_node: the quantize node,
+      2) ref_node: a reference module or functional node to replace with its quantized counterpart
+    Otherwise, if there is no match, return a 2-tuple of (None, None).
+
+    Parameters:
+      node: The `torch.fx.Node` to match against.
+      modules: A mapping from node names to modules in the model graph, used for module lookup.
+      qconfig_map: A mapping from node names to the qconfigs associated with the nodes.
+          If the corresponding qconfig for the reference node is None, then return no match.
+      matching_modules_or_ops: Either a list of functions or a list of `torch.nn.Module`s.
+          If the reference node is not in this list, then return no match.
+    """
+    SKIP_LOWERING_VALUE = (None, None)
+
+    # Match quantize node
+    if node.op != "call_function" or node.target != torch.quantize_per_tensor:
+        return SKIP_LOWERING_VALUE
+    q_node = node
+    ref_node = q_node.args[0]
+    assert isinstance(ref_node, Node)
+
+    if should_skip_lowering(ref_node, qconfig_map):
+        return SKIP_LOWERING_VALUE
+
+    # Match reference module or functional
+    if isinstance(matching_modules_or_ops[0], type) and issubclass(
+        matching_modules_or_ops[0], nn.Module
+    ):
+        expected_op = "call_module"
+        match_key = type(_get_module(ref_node, modules))
+    else:
+        # This pass only support op of "call_module"
+        return SKIP_LOWERING_VALUE
+
+    if ref_node.op != expected_op or match_key not in matching_modules_or_ops:
+        return SKIP_LOWERING_VALUE
+
+    # Check ref_node has 2 input nodes, both are dq node.
+    if len(ref_node.args) != 2:
+        return SKIP_LOWERING_VALUE
+    for i in range(len(ref_node.args)):
+        arg = ref_node.args[i]
+        if not is_dequantize_node(arg):
+            return SKIP_LOWERING_VALUE
+
+    return (q_node, ref_node)
+
+
+def _lower_static_weighted_ref_module(
+    model: GraphModule, qconfig_map: Dict[str, QConfigAny]
+):
+    """
+    Traverse the graph and find dequantize - ref module - quantize patterns
+    and replace them with the quantized version of the ref module.
+    """
+    modules = dict(model.named_modules(remove_duplicate=False))
+    nodes = list(model.graph.nodes)
+    for n in model.graph.nodes:
+        # Step 0: Find nodes that match this pattern (dequantize - ref module - quantize)
+        matching_modules = list(STATIC_LOWER_MODULE_MAP.keys()) + list(
+            STATIC_LOWER_FUSED_MODULE_MAP.keys()
+        )
+        (q_node, relu_node, ref_node) = _match_static_pattern(
+            n, modules, qconfig_map, matching_modules, dequantize_node_arg_indices=[0]  # type: ignore[arg-type]
+        )
+        if q_node is None:
+            continue
+        assert ref_node is not None
+        (_, scale_node, zero_point_node, _) = q_node.args
+        ref_module = _get_module(ref_node, modules)
+        ref_class = type(ref_module)
+        assert isinstance(scale_node, Node)
+        assert isinstance(zero_point_node, Node)
+        assert issubclass(ref_class, nn.Module)
+
+        # Step 1: Change this pattern to use the corresponding quantized module
+        # For fused modules, we also check whether the inner module is a reference module
+        # If so, we replace the entire fused module with the corresponding quantized module
+        if ref_class in STATIC_LOWER_FUSED_MODULE_MAP:
+            inner_ref_class, q_class = STATIC_LOWER_FUSED_MODULE_MAP[ref_class]
+            if type(ref_module[0]) != inner_ref_class:  # type: ignore[index]
+                continue
+        else:
+            q_class = STATIC_LOWER_MODULE_MAP[ref_class]
+        output_scale = getattr(model, scale_node.target)  # type: ignore[arg-type]
+        output_zero_point = getattr(model, zero_point_node.target)  # type: ignore[arg-type]
+        q_module = q_class.from_reference(ref_module, output_scale, output_zero_point)
+        # replace reference module with quantized module
+        parent_name, module_name = _parent_name(ref_node.target)
+        setattr(modules[parent_name], module_name, q_module)
+
+        # Step 2: Reroute around dq_node, and remove q_node and its args
+        assert len(ref_node.args) == 1
+        dq_node = ref_node.args[0]
+        assert isinstance(dq_node, Node)
+        ref_node.replace_input_with(dq_node, dq_node.args[0])  # type: ignore[arg-type]
+        q_node.replace_all_uses_with(ref_node)
+        model.graph.erase_node(q_node)
+        model.graph.erase_node(scale_node)
+        model.graph.erase_node(zero_point_node)
+
+
+def _lower_static_weighted_ref_module_with_two_inputs(
+    model: GraphModule, qconfig_map: Dict[str, QConfigAny]
+):
+    """
+    Traverse the graph and find patterns
+    dequantize   dequantize
+       \\         //
+        ref module
+            \\
+          quantize
+    and replace them with the quantized version of the ref module.
+    """
+    modules = dict(model.named_modules(remove_duplicate=False))
+    nodes = list(model.graph.nodes)
+    for n in model.graph.nodes:
+        #                                            (dequantize \
+        # Step 0: Find nodes that match this pattern (dequantize - ref module - quantize)
+        matching_modules = list(STATIC_LOWER_FUSED_MODULE_TWO_INPUTS_MAP.keys())
+        (q_node, ref_node) = _match_static_pattern_with_two_inputs(
+            n, modules, qconfig_map, matching_modules  # type: ignore[arg-type]
+        )
+        if q_node is None:
+            continue
+        assert ref_node is not None
+        (_, scale_node, zero_point_node, _) = q_node.args
+        ref_module = _get_module(ref_node, modules)
+        ref_class = type(ref_module)
+        assert isinstance(scale_node, Node)
+        assert isinstance(zero_point_node, Node)
+        assert issubclass(ref_class, nn.Module)
+
+        # Step 1: Change this pattern to use the corresponding quantized module
+        # For fused modules, we also check whether the inner module is a reference module
+        # If so, we replace the entire fused module with the corresponding quantized module
+        if ref_class in STATIC_LOWER_FUSED_MODULE_TWO_INPUTS_MAP:
+            inner_ref_class, q_class = STATIC_LOWER_FUSED_MODULE_TWO_INPUTS_MAP[
+                ref_class
+            ]
+            if type(ref_module[0]) != inner_ref_class:  # type: ignore[index]
+                continue
+        else:
+            continue
+        output_scale = getattr(model, scale_node.target)  # type: ignore[arg-type]
+        output_zero_point = getattr(model, zero_point_node.target)  # type: ignore[arg-type]
+        q_module = q_class.from_reference(ref_module, output_scale, output_zero_point)
+        # replace reference module with quantized module
+        parent_name, module_name = _parent_name(ref_node.target)
+        setattr(modules[parent_name], module_name, q_module)
+
+        # Step 2: Reroute around dq_node, and remove q_node and its args
+        assert len(ref_node.args) == 2
+        for arg in ref_node.args:
+            if not is_dequantize_node(arg):
+                continue
+            dq_node = arg
+            assert isinstance(dq_node, Node)
+            ref_node.replace_input_with(dq_node, dq_node.args[0])  # type: ignore[arg-type]
+
+        q_node.replace_all_uses_with(ref_node)
+        model.graph.erase_node(q_node)
+        model.graph.erase_node(scale_node)
+        model.graph.erase_node(zero_point_node)
+
+
+def _lower_dynamic_weighted_ref_module(model: GraphModule):
+    """
+    Traverse the graph and find quantize_per_tensor_dynamic - dequantize - ref_module patterns
+    and replace them with the dynamically quantized version of the ref module.
+    """
+    named_modules = dict(model.named_modules(remove_duplicate=False))
+    for n in model.graph.nodes:
+        if n.op != "call_module" or type(named_modules[str(n.target)]) not in set(
+            DYNAMIC_LOWER_MODULE_MAP.keys()
+        ).union(set(DYNAMIC_LOWER_FUSED_MODULE_MAP.keys())):
+            continue
+        ref_node = n
+        dq_node = ref_node.args[0]
+        if dq_node.op != "call_method" or dq_node.target != "dequantize":
+            continue
+
+        input_dynamic_q_node = dq_node.args[0]
+
+        if (
+            input_dynamic_q_node.op != "call_function"
+            or input_dynamic_q_node.target != torch.quantize_per_tensor_dynamic
+        ):
+            continue
+
+        activation_dtype = input_dynamic_q_node.args[1]
+        is_fp16 = activation_dtype == torch.float16
+        is_int8 = activation_dtype in [torch.quint8, torch.qint8]
+        if not is_int8 and not is_fp16:
+            continue
+
+        ref_module = named_modules[str(ref_node.target)]
+        ref_class = type(ref_module)
+        if ref_class in DYNAMIC_LOWER_FUSED_MODULE_MAP:
+            inner_ref_class, q_class = DYNAMIC_LOWER_FUSED_MODULE_MAP[ref_class]
+            if type(ref_module[0]) != inner_ref_class:
+                continue
+        else:
+            q_class = DYNAMIC_LOWER_MODULE_MAP.get(ref_class)  # type: ignore[assignment]
+        # TODO: maybe define a WeightedDynamicallyQuantizedModule
+        q_module = q_class.from_reference(ref_module)  # type: ignore[attr-defined]
+
+        # replace reference module with dynamically quantized module
+        parent_name, module_name = _parent_name(ref_node.target)
+        setattr(named_modules[parent_name], module_name, q_module)
+        ref_node.replace_input_with(dq_node, input_dynamic_q_node.args[0])
+
+
+def _lower_weight_only_weighted_ref_module(model: GraphModule):
+    """
+    Traverse the graph and find ref_module patterns
+    and replace them with the weight only quantized version of the ref module.
+    """
+    named_modules = dict(model.named_modules(remove_duplicate=False))
+    for n in model.graph.nodes:
+        if n.op != "call_module" or type(named_modules[str(n.target)]) not in set(
+            WEIGHT_ONLY_LOWER_MODULE_MAP.keys()
+        ):
+            continue
+        ref_node = n
+        ref_module = named_modules[str(ref_node.target)]
+        ref_class = type(ref_module)
+        q_class = WEIGHT_ONLY_LOWER_MODULE_MAP.get(ref_class)
+        # TODO: WeightedQuantizedModule is currently assuming static quant apis
+        # with output_scale, output_zero_point in from_reference, we may want to
+        # relax that, or rename this
+        # TODO: maybe define a WeightedWeightOnlyQuantizedModule
+        q_module = q_class.from_reference(ref_module)  # type: ignore[union-attr]
+
+        # replace reference module with dynamically quantized module
+        parent_name, module_name = _parent_name(ref_node.target)
+        setattr(named_modules[parent_name], module_name, q_module)
+
+
+def _lower_static_weighted_ref_functional(
+    model: GraphModule, qconfig_map: Dict[str, QConfigAny]
+):
+    """
+    Traverse the graph and replace functional reference patterns with their quantized versions.
+    """
+    modules = dict(model.named_modules(remove_duplicate=False))
+    nodes = list(model.graph.nodes)
+    for n in model.graph.nodes:
+        # Step 0: Find nodes that match this pattern (dequantize - functional op - quantize)
+        matching_ops = list(STATIC_LOWER_FUNCTIONAL_MAP.keys())
+        (q_node, relu_node, func_node) = _match_static_pattern(
+            n, modules, qconfig_map, matching_ops, dequantize_node_arg_indices=[0, 1]
+        )
+        if q_node is None:
+            continue
+        assert func_node is not None
+        (_, output_scale_node, output_zp_node, _) = q_node.args
+        (input_dq_node, weight_dq_node, *remaining_func_args) = func_node.args
+        assert isinstance(output_zp_node, Node)
+        assert isinstance(input_dq_node, Node)
+        assert isinstance(weight_dq_node, Node)
+        quantized_weight = weight_dq_node.args[0]
+        assert isinstance(quantized_weight, Node)
+        if quantized_weight.op != "call_function" or quantized_weight.target not in (
+            torch.quantize_per_tensor,
+            torch.quantize_per_channel,
+        ):
+            continue
+
+        # Step 1: Replace quantized weights with packed weights, which will be folded later
+        # Use the right prepack op and prepare the corresponding args
+        # Linear prepack args: (quantized weights[, bias])
+        # Conv prepack args: (quantized weights[, bias, stride, padding, dilation, groups])
+        prepack_args = [quantized_weight] + remaining_func_args
+        if func_node.target == F.linear:
+            weight_dtype = quantized_weight.args[-1]
+            prepack_op = get_linear_prepack_op_for_dtype(weight_dtype)
+        elif func_node.target in CONV_FUNCTIONAL_OPS:
+            prepack_op = get_qconv_prepack_op(func_node.target)  # type: ignore[arg-type]
+            # For conv1d, the stride, padding, and dilation args may be ints,
+            # in which case we need to convert them to tuples
+            if func_node.target == F.conv1d:
+                for i in [2, 3, 4]:
+                    if len(prepack_args) > i and isinstance(prepack_args[i], int):
+                        prepack_args[i] = (prepack_args[i],)
+        elif func_node.target in CONV_TRANSPOSE_FUNCTIONAL_OPS:
+            prepack_op = get_qconv_prepack_op(func_node.target)  # type: ignore[arg-type]
+            # For conv_transpose1d, the stride, padding, and dilation args may be ints,
+            # in which case we need to convert them to tuples
+            if func_node.target == F.conv_transpose1d:
+                # Note prepack_args[5] is groups.
+                for i in [2, 3, 4, 6]:
+                    if len(prepack_args) > i and isinstance(prepack_args[i], int):
+                        prepack_args[i] = (prepack_args[i],)
+            # swap dilation and groups
+            # prepack op has arguments: {w, b, stride, padding, output_padding, dilation, groups}
+            # transposed conv op has arguments: {x, w, b, stride, padding, output_padding, groups, dilation}
+            if len(prepack_args) > 6:
+                prepack_args[5], prepack_args[6] = prepack_args[6], prepack_args[5]
+        else:
+            raise ValueError(f"Lowering is not supported for op '{func_node.target}'")
+        with model.graph.inserting_before(output_scale_node):  # type: ignore[arg-type]
+            # kwargs of the func node are needed for prepack op (i.e., quantized::linear_prepack)
+            # They are not needed for compute op (i.e., quantized::linear)
+            kwargs = func_node.kwargs
+            # F.linear uses 'bias' key for bias while qlinear_prepack uses 'B' for bias
+            if func_node.target == F.linear and "bias" in kwargs:
+                kwargs = kwargs.copy()
+                kwargs["B"] = kwargs["bias"]
+                del kwargs["bias"]
+            packed_weight = model.graph.create_node(
+                "call_function", prepack_op, tuple(prepack_args), kwargs
+            )
+
+        # Step 2: Replace reference pattern with the corresponding quantized op
+        (q_func, q_relu_func) = STATIC_LOWER_FUNCTIONAL_MAP[func_node.target]  # type: ignore[index]
+        # conv_transpose does not support fusion with relu yet. q_relu_func is None in such cases
+        if q_relu_func is not None:
+            func_node.target = q_relu_func if relu_node is not None else q_func
+        else:
+            func_node.target = q_func
+        func_node.args = (
+            input_dq_node.args[0],
+            packed_weight,
+            output_scale_node,
+            output_zp_node,
+        )
+        # kwargs for func_node has been moved to kwargs for prepack op
+        func_node.kwargs = {}
+        q_node.replace_all_uses_with(func_node)
+        # Move func_node after output_zp_node in the graph
+        output_zp_node.append(func_node)
+
+        # Clean up: Remove quantize node, and the relu node if it exists
+        model.graph.erase_node(q_node)
+        if relu_node is not None and q_relu_func is not None:
+            model.graph.erase_node(relu_node)
+
+
+def _lower_dynamic_weighted_ref_functional(
+    model: GraphModule, qconfig_map: Dict[str, QConfigAny]
+):
+    """
+    Traverse the graph and replace functional reference patterns with their dynamically
+    quantized versions.
+    Examples:
+    quantize_per_tensor_dynamic - dequantize - functional linear --> linear_dynamic
+    to(torch.float16) - dequantize - functional linear --> linear_dynamic_fp16
+    """
+    modules = dict(model.named_modules(remove_duplicate=False))
+    nodes = list(model.graph.nodes)
+    # we want to search in reserved order so that we can match the larger patterns first
+    # e.g. we want to match linear - relu before linear.
+    for n in reversed(model.graph.nodes):
+        # Step 0: Find nodes that match this pattern
+        # (quantize_per_tensor_dynamic - dequantize - dynamically quantized op)
+        # We search for the pattern backwards, starting with the quantize node
+        # Quantize node args: (func, scale, zp, dtype)
+        func_node = n
+        # Handle cases where the functional op is wrapped in a ReLU
+        if (
+            func_node.op == "call_function"
+            and func_node.target == F.relu
+            or func_node.op == "call_module"
+            and type(modules[str(func_node.target)]) == torch.nn.ReLU
+        ):
+            relu_node = func_node
+            func_node = relu_node.args[0]
+        else:
+            relu_node = None
+        if should_skip_lowering(func_node, qconfig_map):
+            continue
+        # Linear args: (dequantized inputs, dequantized weights[, bias])
+        # Conv args: (dequantized inputs, dequantized weights[, bias, stride, padding, dilation, groups])
+        if (
+            func_node.op != "call_function"
+            or func_node.target not in DYNAMIC_LOWER_FUNCTIONAL_MAP
+        ):
+            continue
+        (input_dq_node, weight_dq_node, *remaining_func_args) = func_node.args
+        if (
+            input_dq_node.op != "call_method"
+            or input_dq_node.target != "dequantize"
+            or weight_dq_node.op != "call_method"
+            or weight_dq_node.target != "dequantize"
+        ):
+            continue
+
+        input_dynamic_q_node = input_dq_node.args[0]
+
+        if (
+            input_dynamic_q_node.op != "call_function"
+            or input_dynamic_q_node.target != torch.quantize_per_tensor_dynamic
+        ):
+            continue
+
+        reduce_range_node = None
+        (pattern_input, activation_dtype, reduce_range_node) = input_dynamic_q_node.args
+        is_fp16 = activation_dtype == torch.float16
+        is_int8 = activation_dtype in [torch.quint8, torch.qint8]
+        if not is_int8 and not is_fp16:
+            continue
+
+        quantized_weight = weight_dq_node.args[0]
+        weight_dtype = quantized_weight.args[-1]
+
+        # Step 1: Try to select reference pattern with the corresponding quantized op
+        dynamic_quant_dtype_key = (activation_dtype, weight_dtype)
+        if (
+            dynamic_quant_dtype_key
+            not in DYNAMIC_LOWER_FUNCTIONAL_MAP[func_node.target]
+        ):
+            print(
+                f"Didn't find dtype combination {dynamic_quant_dtype_key} during "
+                f"dynamic quantized op lowering for {func_node.target}"
+            )
+            continue
+        (q_func, q_relu_func) = DYNAMIC_LOWER_FUNCTIONAL_MAP[func_node.target][
+            dynamic_quant_dtype_key
+        ]
+
+        if q_func is None or q_relu_func is None:
+            print(
+                "Didn't find corresponding quantized function or quantized relu function "
+                f"for {func_node.target}, {dynamic_quant_dtype_key}"
+            )
+            continue
+
+        # Step 2: Replace quantized weights with packed weights, which will be folded later
+        # Use the right prepack op and prepare the corresponding args
+        # Linear prepack args: (quantized weights[, bias])
+        # Conv prepack args: (quantized weights[, bias, stride, padding, dilation, groups])
+        prepack_args = [quantized_weight] + remaining_func_args
+        prepack_kwargs = {}
+        if func_node.target == F.linear:
+            prepack_op = get_linear_prepack_op_for_dtype(weight_dtype)
+            kwargs = func_node.kwargs.copy()
+            if "bias" in kwargs:
+                prepack_kwargs["B"] = kwargs["bias"]
+                del kwargs["bias"]
+                func_node.kwargs = kwargs
+        elif func_node.target in CONV_FUNCTIONAL_OPS:
+            prepack_op = get_qconv_prepack_op(func_node.target)
+            # For conv1d, the stride, padding, and dilation args may be ints,
+            # in which case we need to convert them to tuples
+            if func_node.target == F.conv1d:
+                for i in [2, 3, 4]:
+                    if len(prepack_args) > i and isinstance(prepack_args[i], int):
+                        prepack_args[i] = (prepack_args[i],)
+        else:
+            raise ValueError(f"Lowering is not supported for op '{func_node.target}'")
+        with model.graph.inserting_before(func_node):
+            packed_weight = model.graph.create_node(
+                "call_function", prepack_op, tuple(prepack_args), prepack_kwargs
+            )
+
+        # Step 3: Replace reference pattern with the corresponding quantized op
+        func_node.target = q_relu_func if relu_node is not None else q_func
+        if is_int8:
+            func_node.args = (pattern_input, packed_weight, reduce_range_node)
+        else:
+            func_node.args = (pattern_input, packed_weight)
+
+        if relu_node is not None:
+            relu_node.replace_all_uses_with(func_node)
+
+        # Step 4: Remove the relu node if it exists
+        if relu_node is not None:
+            model.graph.erase_node(relu_node)
+
+
+def _lower_quantized_binary_op(model: GraphModule, qconfig_map: Dict[str, QConfigAny]):
+    binary_ops_to_lower: List[Callable] = [
+        operator.add,
+        torch.add,
+        operator.mul,
+        torch.mul,
+        torch.matmul,
+    ]
+    modules = dict(model.named_modules(remove_duplicate=False))
+    for n in model.graph.nodes:
+        # Step 0: Find nodes that match this pattern (dequantize - ref module - quantize)
+        (q_node, relu_node, bop_node) = _match_static_pattern(
+            n,
+            modules,
+            qconfig_map,
+            binary_ops_to_lower,
+            dequantize_node_arg_indices=[0, 1],
+        )
+        if q_node is None:
+            continue
+        assert bop_node is not None
+        (_, scale_node, zero_point_node, _) = q_node.args
+
+        # Step 1: Remove dequant nodes
+        num_dq_nodes = 0
+        for arg in bop_node.args:
+            if not is_dequantize_node(arg):
+                continue
+            dq_node = arg
+            assert isinstance(dq_node, Node)
+            dn_input = dq_node.args[0]
+            bop_node.replace_input_with(dq_node, dn_input)  # type: ignore[arg-type]
+            num_dq_nodes += 1
+        assert num_dq_nodes > 0
+
+        # Step 2: Swap binary op to quantized binary op
+        assert bop_node.target in QBIN_OP_MAPPING
+        binop_to_qbinop = QBIN_OP_MAPPING if relu_node is None else QBIN_RELU_OP_MAPPING
+        qbin_op = binop_to_qbinop[bop_node.target]
+        # prepare the args for quantized binary op
+        # (x, y)
+        qop_node_args = list(bop_node.args)
+        # (x, y, scale, zero_point)
+        # add scale and zero_point arguments for Tensor - Tensor operation
+        if num_dq_nodes == 2:
+            qop_node_args.extend([scale_node, zero_point_node])
+        # insert a call to quantized binary op and remove the original binary op
+        with model.graph.inserting_after(q_node):
+            qop_node = create_node_from_old_node_preserve_meta(
+                model.graph,
+                ("call_function", qbin_op, tuple(qop_node_args), {}),
+                bop_node,
+            )
+            q_node.replace_all_uses_with(qop_node)
+
+        # Step 3: Remove quantize node, binary op node, and relu node if any
+        model.graph.erase_node(q_node)
+        if relu_node is not None:
+            model.graph.erase_node(relu_node)
+        model.graph.erase_node(bop_node)
+
+
+def special_pattern_replacement(model: GraphModule):
+    modules = dict(model.named_modules(remove_duplicate=False))
+    for n in model.graph.nodes:
+        q_node = n
+        is_quantize = q_node.target == torch.quantize_per_tensor
+        is_to_fp16 = (
+            q_node.op == "call_method"
+            and q_node.target == "to"
+            and len(q_node.args) == 2
+            and q_node.args[1] == torch.float16
+        )
+        if not (is_quantize or is_to_fp16):
+            continue
+        ref_node = q_node.args[0]
+        # get output scale/zero_point/dtype from the quantize node
+        # ref_node, scale_node, zero_point_node, dtype = q_node.args
+        # TODO: add safety checks that users for the ref_node and dq_node needs to be one
+        is_call_function, is_call_method, is_call_module = is_fixed_qparams_node(
+            ref_node, modules
+        )
+        if is_to_fp16 and (is_call_function or is_call_method or is_call_module):
+            # TODO: add a warning or error out here? (bc-breaking if error out)
+            # warnings.warn(
+            #     "Only reference patterns are currently supported for {dtype} dtype with {op} op"
+            #     "".format(dtype=dtypes, op=ref_node))
+            continue
+
+        is_call_function, is_call_method, is_call_module = is_default_node(
+            ref_node, modules
+        )
+        if is_to_fp16 and (is_call_function or is_call_method or is_call_module):
+            # TODO: add a warning or error out here? (bc-breaking if error out)
+            continue
+
+        # This check includes all supported ops
+        is_call_function, is_call_method, is_call_module = is_special_pattern_node(
+            ref_node, modules
+        )
+        if not (is_call_module or is_call_function or is_call_method):
+            continue
+        assert len(ref_node.args) > 0 or len(ref_node.kwargs) > 0
+        dq_node_or_nodes = (
+            ref_node.args[0]
+            if len(ref_node.args) > 0
+            else next(iter(ref_node.kwargs.values()))
+        )
+        assert isinstance(dq_node_or_nodes, (Node, tuple, list))
+        is_dequantize = False
+        if isinstance(dq_node_or_nodes, Node):
+            is_dequantize = (
+                dq_node_or_nodes.op == "call_method"
+                and dq_node_or_nodes.target == "dequantize"
+            )
+        elif isinstance(dq_node_or_nodes, (tuple, list)):
+            is_dequantize = all(
+                x.op == "call_method" and x.target == "dequantize"
+                for x in dq_node_or_nodes
+            )
+
+        if not is_dequantize:
+            continue
+
+        # TODO: enable we have patterns that needs to swap the modules
+        if is_call_module:
+            ref_module = modules[ref_node.target]
+            if type(ref_module) in SPECIAL_PATTERN_LOWER_MODULE_MAP and is_quantize:
+                qmodule_cls = SPECIAL_PATTERN_LOWER_MODULE_MAP.get(type(ref_module))
+                scale_node = q_node.args[1]
+                zero_point_node = q_node.args[2]
+                output_scale = getattr(model, scale_node.target)
+                output_zero_point = getattr(model, zero_point_node.target)
+
+                qmodule = qmodule_cls.from_reference(  # type:ignore[union-attr]
+                    ref_module, output_scale, output_zero_point
+                )
+                # replace reference module with quantized module
+                parent_name, module_name = _parent_name(ref_node.target)
+                setattr(modules[parent_name], module_name, qmodule)
+
+        # reroute around dq node:
+        dq_nodes: List[Node] = []
+        if isinstance(dq_node_or_nodes, Node):
+            dq_nodes = [dq_node_or_nodes]
+        elif isinstance(dq_node_or_nodes, (tuple, list)):
+            dq_nodes = list(dq_node_or_nodes)
+
+        for dq_node in dq_nodes:
+            dn_input = dq_node.args[0]
+            ref_node.replace_input_with(dq_node, dn_input)
+
+        # store q node args
+        qnode_qparams = list(q_node.args)[1:]
+        # replace uses of q node with input and remove q node
+        q_node_input = q_node.args[0]
+        q_node.replace_all_uses_with(q_node_input)
+        model.graph.erase_node(q_node)
+
+        is_call_function, is_call_method, is_call_module = is_default_node(
+            ref_node, modules
+        )
+        if is_call_function:
+            # pass scale/zer_point arguments from quantize_per_tensor to the default node operator
+            # insert an op after the zero_point node so that the scale/zero_point
+            # nodes are is available
+            qop = get_quantized_operator(ref_node.target)
+            args = list(ref_node.args)
+            kwargs = dict(ref_node.kwargs)
+            if qop in QOP_TO_ARG_NAMES_TO_SKIP:
+                args_to_skip = QOP_TO_ARG_NAMES_TO_SKIP[qop]
+                for arg in args_to_skip:
+                    if arg in kwargs:
+                        kwargs.pop(arg)
+            kwargs["output_scale"] = qnode_qparams[0]
+            kwargs["output_zero_point"] = qnode_qparams[1]
+            with model.graph.inserting_after(qnode_qparams[1]):
+                qop_node = create_node_from_old_node_preserve_meta(
+                    model.graph, ("call_function", qop, tuple(args), kwargs), ref_node
+                )
+                ref_node.replace_all_uses_with(qop_node)
+                model.graph.erase_node(ref_node)
+        else:
+            # remove scale/zero_point node for quantize node
+            for n in qnode_qparams:
+                if isinstance(n, Node):
+                    model.graph.erase_node(n)
+
+    return model
+
+
+def _lower_getattr_tensor_metadta_op(model: GraphModule):
+    """Modified the graph of the model inplace, to skip extra dequantize op before
+    the general tensor shape ops when possible
+    """
+    for n in model.graph.nodes:
+        if is_getattr_tensor_metadata_node(n):
+            maybe_dq = n.args[0]
+            if maybe_dq.op != "call_method" or maybe_dq.target != "dequantize":
+                continue
+            # skip the dequantize node
+            args = list(n.args)
+            args[0] = n.args[0].args[0]
+            n.args = tuple(args)
+
+
+def _lower_get_tensor_info_op(model: GraphModule):
+    """Modified the graph of the model inplace, to skip extra dequantize op before
+    the general tensor shape ops when possible
+    """
+    for n in model.graph.nodes:
+        if not is_get_tensor_info_node(n):
+            continue
+        maybe_dq = n.args[0]
+        if maybe_dq.op != "call_method" or maybe_dq.target != "dequantize":
+            continue
+        # skip the dequantize node
+        args = list(n.args)
+        args[0] = n.args[0].args[0]
+        n.args = tuple(args)
+
+
+def _lower_to_native_backend(
+    model: GraphModule,
+    qconfig_map: Dict[str, QConfigAny],
+    node_name_to_scope: Dict[str, Tuple[str, type]],
+) -> GraphModule:
+    """Lower a quantized reference model (with reference quantized operator patterns)
+    to the native backend in PyTorch (fbgemm/qnnpack), both backends shares the same
+    operator signature so they can be lowered with the same function
+    """
+    _lower_static_weighted_ref_module(model, qconfig_map)
+    _lower_static_weighted_ref_module_with_two_inputs(model, qconfig_map)
+    _lower_dynamic_weighted_ref_module(model)
+    _lower_weight_only_weighted_ref_module(model)
+    _lower_static_weighted_ref_functional(model, qconfig_map)
+    _lower_dynamic_weighted_ref_functional(model, qconfig_map)
+    _lower_quantized_binary_op(model, qconfig_map)
+    _lower_getattr_tensor_metadta_op(model)
+    _lower_get_tensor_info_op(model)
+    special_pattern_replacement(model)
+    model.graph.eliminate_dead_code()
+    model = fold_weight(model, node_name_to_scope)
+    model.graph.eliminate_dead_code()
+    model.recompile()
+    model.graph.lint()
+    return model
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_model_report/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_model_report/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_model_report/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_model_report/__pycache__/__init__.cpython-311.pyc
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+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_model_report/detector.py
@@ -0,0 +1,1732 @@
+# mypy: allow-untyped-defs
+from abc import ABC, abstractmethod
+from typing import Any, Callable, Dict, List, Set, Tuple
+
+import torch
+import torch.ao.nn.qat as nnqat
+import torch.nn as nn
+from torch.ao.quantization.fake_quantize import FakeQuantize
+from torch.ao.quantization.fx._equalize import (
+    default_equalization_qconfig,
+    EqualizationQConfig,
+)
+from torch.ao.quantization.fx._model_report.model_report_observer import (
+    ModelReportObserver,
+)
+from torch.ao.quantization.fx.graph_module import GraphModule
+from torch.ao.quantization.observer import (
+    _is_activation_post_process,
+    default_dynamic_quant_observer,
+    default_observer,
+    default_per_channel_weight_observer,
+    default_weight_observer,
+    ObserverBase,
+)
+from torch.ao.quantization.qconfig import (
+    _assert_valid_qconfig,
+    default_qconfig,
+    QConfig,
+)
+
+
+# Names for observer insert keys
+DETECTOR_TARGET_NODE_KEY = "target_node"
+DETECTOR_OBS_TO_INSERT_KEY = "observer_to_insert"
+DETECTOR_IS_POST_OBS_KEY = "is_post_observer"
+DETECTOR_OBS_ARGS_KEY = "observer_args"
+
+
+# Mapping related code
+class DetectorQConfigInfo:
+    r"""
+    This class contains the QConfig information for a single module.
+    The list of variables / values this contains can grow depending on the
+    extensibility of the qconfig mapping feature set but this currently includes:
+    - if activation observer is dynamic
+    - if weight observer is per channel
+
+
+    Args:
+        module_fqn (str): The fully qualified name (fqn) of the module that this
+            information contains info relevant to qconfig for
+    """
+
+    def __init__(self, module_fqn: str):
+        super().__init__()
+        self.module_fqn = module_fqn
+
+        # populate this section with all the variables we might find important
+        # change from none if your detector is actually using this
+        self.is_activation_dynamic = False
+        self.is_weight_per_channel = False
+
+        # equalization related options
+        self.is_equalization_recommended = False
+
+    def generate_quantization_qconfig(self, module: torch.nn.Module) -> QConfig:
+        r"""
+        Args:
+            module (torch.nn.Module) The module we are generating
+            the qconfig for
+
+        Returns the generated quantization QConfig according to what a valid configuration is
+        """
+        # Apply suggestions to new qconfig
+        module_qconfig = default_qconfig
+
+        # keep track of dynamic and per_channel recommendations
+        recommendations_list = []
+        # append as if a list of combinations
+        recommendations_list.append(
+            (self.is_activation_dynamic, self.is_weight_per_channel)
+        )
+        recommendations_list.append(
+            (self.is_activation_dynamic, False)
+        )  # only trying dynamic rec
+        recommendations_list.append(
+            (False, self.is_weight_per_channel)
+        )  # only trying dynamic
+
+        # now we try each of the combinations
+        for rec in recommendations_list:
+            # rec[0] -> dynamic recommended
+            # rec[1] -> per channel recommended
+            activation = default_dynamic_quant_observer if rec[0] else default_observer
+            weight = (
+                default_per_channel_weight_observer
+                if rec[1]
+                else default_weight_observer
+            )
+            test_config = QConfig(activation, weight)
+            try:
+                _assert_valid_qconfig(test_config, module)
+                module_qconfig = test_config
+                break
+            except AssertionError:
+                # if not a valid configuration, we move on to the next one in priority
+                continue
+
+        # return the QConfig chosen
+        return module_qconfig
+
+    def generate_equalization_qconfig(self) -> EqualizationQConfig:
+        r"""
+        This returns the equalization configuration for a module.
+
+        For now, it just returns the default, but as more equalization options become
+        possible, this method can get more fleshed out with more nuanced granularity.
+
+
+        Returns the generated equalization QConfig according to what a valid configuration is
+        """
+        # in this case, we just return default equalization config
+        # we know this is valid because only valid modules would even
+        # have this option
+        return default_equalization_qconfig
+
+
+# Adding base class for detectors
+class DetectorBase(ABC):
+    r"""Base Detector Module
+    Any detector class should derive from this class.
+
+    Concrete detectors should follow the same general API, which includes:
+    - A method to calculate and return observer insertion points
+        - Should return both the fqns and the Observer class to insert
+    - A method to return a report based on the detector
+        - Should return a str-based report and dict info in Tuple[str,Dict] format
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.detector_config_info = None
+
+    @abstractmethod
+    def determine_observer_insert_points(self, model) -> Dict:
+        r"""
+        Args
+            model (nn.Module or subclass): model to find observer insertion points
+
+        Returns a Dict mapping from unique observer fqns (where we want to insert them) to a Dict.
+            This dict maps string keys to detector specific information
+        """
+
+    @abstractmethod
+    def get_detector_name(self) -> str:
+        r"""Returns the name of the current detector"""
+
+    @abstractmethod
+    def get_qconfig_info(self, model) -> Dict[str, DetectorQConfigInfo]:
+        r"""Returns the DetectorQConfigInfo for each module_fqn relevant
+        Args
+            model (nn.Module or subclass): model to find observer insertion points
+
+        Returns a Dict mapping from unique observer fqns (where we want to insert them) to:
+            A DetectorQConfigInfo with the information to generate a QConfig for a specific module
+        """
+
+    def _get_targeting_node(
+        self, prepared_fx_model: GraphModule, target_fqn: str
+    ) -> torch.fx.node.Node:
+        r"""
+        Takes in a GraphModule and the target_fqn and finds the node whose target is this fqn.
+
+        If it's not found, it means it is most likely inside a fused layer
+            We just go one layer up in terms of the fqn we are searching for until we find parent node
+            If we get to empty string, then we know that it doesn't exist
+
+        The reason for the recursion is that if the model that we are looking for got fused,
+        we will have module fqn as e.g. x.linear.0 but the graph will only have a node for the fused module,
+        which would have fqn as x.linear so they will not match.
+        To handle this, if we don't match, we then take off the last bit of the fqn e.g. x.linear.0 -> x.linear,
+        or more generally foo.bar.baz -> foo.bar and search again, this will allow us to locate the correct module
+        even in cases with fusion
+
+        Args:
+            prepared_fx_model (GraphModule):  The prepared Fx GraphModule
+            target_fqn (str): The fqn of the layer we are trying to target
+
+        Returns the node object we are trying to add observers around
+        """
+        for node in prepared_fx_model.graph.nodes:
+            # if the node's target is our target, return it
+            if node.target == target_fqn:
+                return node
+
+        # getting here means node not found
+        # if no "." we are already at base and failed
+        parent_fqn_sep_index = target_fqn.rfind(".")
+        if parent_fqn_sep_index == -1:
+            raise ValueError("passed in target_fqn not found in graph's targets.")
+        else:
+            # recursively call it with parent fqn
+            return self._get_targeting_node(
+                prepared_fx_model, target_fqn[:parent_fqn_sep_index]
+            )
+
+    @abstractmethod
+    def generate_detector_report(self, model) -> Tuple[str, Dict[str, Any]]:
+        r"""
+        Args
+            model (nn.Module or subclass): model to find observer insertion points
+
+        Returns a Tuple of two elements:
+            Str: string report of the suggested improvements
+            Dict: contains useful data collected by the observer pertinent to this report
+        """
+
+
+class PerChannelDetector(DetectorBase):
+    r"""This class is used to detect if any Linear or Conv layers in a model utilize per_channel quantization.
+    Only Linear and Conv layers can use per_channel as of now so only these two are currently checked.
+
+    per_channel quantization can lead to major benefits in the form of accuracy.
+    Therefore, if the backend used by the user supports it, it is recommended to use
+
+    Args:
+        backend (str, optional): the backend the user wishes to use in production
+            Default value is current torch.backends.quantized.engine
+    """
+
+    # Keys for return dictionary
+    BACKEND_KEY = "backend"
+    PER_CHAN_SUPPORTED_KEY = "per_channel_quantization_supported"
+    PER_CHAN_USED_KEY = "per_channel_quantization_used"
+
+    # Default map for representing supported per channel quantization modules for different backends
+    DEFAULT_BACKEND_PER_CHANNEL_SUPPORTED_MODULES: Dict[str, Set[Any]] = {
+        "fbgemm": {
+            nn.Linear,
+            nn.Conv1d,
+            nn.Conv2d,
+            nn.Conv3d,
+            nnqat.Linear,
+            nnqat.Conv1d,
+            nnqat.Conv2d,
+            nnqat.Conv3d,
+        },
+        "qnnpack": {
+            nn.Linear,
+            nn.Conv1d,
+            nn.Conv2d,
+            nn.Conv3d,
+            nnqat.Linear,
+            nnqat.Conv1d,
+            nnqat.Conv2d,
+            nnqat.Conv3d,
+        },
+        "onednn": {
+            nn.Linear,
+            nn.Conv1d,
+            nn.Conv2d,
+            nn.Conv3d,
+            nnqat.Linear,
+            nnqat.Conv1d,
+            nnqat.Conv2d,
+            nnqat.Conv3d,
+        },
+        "x86": {
+            nn.Linear,
+            nn.Conv1d,
+            nn.Conv2d,
+            nn.Conv3d,
+            nnqat.Linear,
+            nnqat.Conv1d,
+            nnqat.Conv2d,
+            nnqat.Conv3d,
+        },
+    }
+
+    def __init__(self, backend: str = torch.backends.quantized.engine):
+        super().__init__()
+
+        # store the backend information
+        self.backend_chosen = backend
+        self.supported_modules = set()
+        if self.backend_chosen in self.DEFAULT_BACKEND_PER_CHANNEL_SUPPORTED_MODULES:
+            self.supported_modules = self.DEFAULT_BACKEND_PER_CHANNEL_SUPPORTED_MODULES[
+                self.backend_chosen
+            ]
+        else:
+            raise ValueError(
+                f"Not configured to work with {self.backend_chosen}. Try a different default backend"
+            )
+
+    def get_detector_name(self) -> str:
+        r"""returns the string name of this detector"""
+        return "per_channel_detector"
+
+    def get_qconfig_info(self, model) -> Dict[str, DetectorQConfigInfo]:
+        r"""Returns the DetectorQConfigInfo for each module_fqn relevant
+        Args
+            model (nn.Module or subclass): model to find observer insertion points
+
+        Returns a Dict mapping from unique observer fqns (where we want to insert them) to:
+            A DetectorQConfigInfo with the information to generate a QConfig for a specific module
+        """
+        # run the helper function to populate the dictionary
+        per_channel_info = self._detect_per_channel_helper(model)
+
+        # we actually have a qconfig info object we are populating
+        module_fqn_to_detector_qconfig_info = {}
+
+        for module_fqn in per_channel_info:
+            # create a detector info instance
+            detector_qconfig_info = DetectorQConfigInfo(module_fqn)
+
+            # see if per channel quantization is supported
+            per_chan_supported: bool = per_channel_info[module_fqn][
+                self.PER_CHAN_SUPPORTED_KEY
+            ]
+            detector_qconfig_info.is_weight_per_channel = per_chan_supported
+            module_fqn_to_detector_qconfig_info[module_fqn] = detector_qconfig_info
+
+        return module_fqn_to_detector_qconfig_info
+
+    def determine_observer_insert_points(self, model: nn.Module) -> Dict:
+        r"""
+        There is no observers inserted for the PerChannelDetector.
+
+        Returns an empty dictionary since no observers are added or needed
+        """
+        return {}
+
+    def _detect_per_channel_helper(self, model: nn.Module):
+        r"""
+        determines if per_channel quantization is supported in modules and submodules.
+
+        Returns a dictionary in the higher level _detect_per_channel function.
+        Each entry maps the fully-qualified-name to information on whether per_channel quantization.
+
+        Args:
+            model: The current module that is being checked to see if it is per_channel quantizable
+
+        Returns dictionary mapping fqns to if per_channel quantization is possible
+        """
+        # create dict we will return
+        per_channel_info: Dict = {}
+
+        # get the fully qualified name and check if in list of modules to include and list of modules to ignore
+        for fqn, module in model.named_modules():
+            is_in_include_list = any(
+                isinstance(module, x) for x in self.supported_modules
+            )
+
+            # check if the module per_channel is supported
+            # based on backend
+            per_channel_supported = False
+
+            if is_in_include_list:
+                per_channel_supported = True
+
+                # assert statement for MyPy
+                q_config_file = module.qconfig
+                assert isinstance(q_config_file, QConfig)
+
+                # this object should either be fake quant or observer
+                q_or_s_obj = module.qconfig.weight.p.func()
+                assert isinstance(q_or_s_obj, (FakeQuantize, ObserverBase))
+
+                per_channel_used = False  # will be true if found in qconfig
+
+                if hasattr(
+                    q_or_s_obj, "ch_axis"
+                ):  # then we know that per_channel quantization used
+                    # all fake quants have channel axis so need to check is_per_channel
+                    if isinstance(q_or_s_obj, FakeQuantize):
+                        if (
+                            hasattr(q_or_s_obj, "is_per_channel")
+                            and q_or_s_obj.is_per_channel
+                        ):
+                            per_channel_used = True
+                    elif isinstance(q_or_s_obj, ObserverBase):
+                        # should be an observer otherwise
+                        per_channel_used = True
+                    else:
+                        raise ValueError("Should be either observer or fake quant")
+
+                per_channel_info[fqn] = {
+                    self.PER_CHAN_SUPPORTED_KEY: per_channel_supported,
+                    self.PER_CHAN_USED_KEY: per_channel_used,
+                    self.BACKEND_KEY: self.backend_chosen,
+                }
+
+        return per_channel_info
+
+    def generate_detector_report(self, model: nn.Module) -> Tuple[str, Dict[str, Any]]:
+        r"""Checks if any Linear or Conv layers in the model utilize per_channel quantization.
+        Only Linear and Conv layers can use per_channel as of now so only these two are currently checked.
+
+        Looks at q_config format and backend to determine if per_channel can be utilized.
+        Uses the DEFAULT_BACKEND_PER_CHANNEL_SUPPORTED_MODULES structure to determine support
+
+        Args:
+            model: The prepared and calibrated model we want to check if using per_channel
+
+        Returns a tuple with two elements:
+            String report of potential actions to improve model (if per_channel quantization is available in backend)
+            Dictionary mapping per_channel quantizable elements to:
+                whether per_channel quantization is supported by the backend
+                if it is being utilized in the current model
+        """
+
+        # run the helper function to populate the dictionary
+        per_channel_info = self._detect_per_channel_helper(model)
+
+        # String to let the user know of further optimizations
+        further_optims_str = (
+            f"Further Optimizations for backend {self.backend_chosen}: \n"
+        )
+
+        optimizations_possible = False
+        for fqn in per_channel_info:
+            fqn_dict = per_channel_info[fqn]
+            if (
+                fqn_dict[self.PER_CHAN_SUPPORTED_KEY]
+                and not fqn_dict[self.PER_CHAN_USED_KEY]
+            ):
+                optimizations_possible = True
+                further_optims_str += (
+                    f"Module {fqn} can be configured to use per_channel quantization.\n"
+                )
+
+        if optimizations_possible:
+            further_optims_str += "To use per_channel quantization, make sure the qconfig has a per_channel weight observer."
+        else:
+            further_optims_str += "No further per_channel optimizations possible."
+
+        # return the string and the dictionary form of same information
+        return (further_optims_str, per_channel_info)
+
+
+class DynamicStaticDetector(DetectorBase):
+    r"""
+    Determines whether dynamic or static quantization is more appropriate for a given module.
+
+    Takes advantage of the ModelReportObserver that records range information.
+    Stationary distribution of data are strictly above tolerance level for the comparison statistic:
+
+        S = average_batch_activation_range/epoch_activation_range
+
+    Nonstationary distributions are below or at the tolerance level for this metric.
+
+    If the distribution of data right after the module is non-stationary, recommend dynamic quantization
+        Otherwise recommend static quantization
+
+    Args:
+        tolerance (float, optional): The threshold where S metric is stationary above and non-stationary otherwise. Default: 0.5
+    """
+    # names for the pre and post observers that are inserted
+    DEFAULT_PRE_OBSERVER_NAME = "model_report_pre_observer"
+    DEFAULT_POST_OBSERVER_NAME = "model_report_post_observer"
+
+    # naming conventions for stationary vs non-stationary data
+    STATIONARY_STR = "stationary"
+    NON_STATIONARY_STR = "non-stationary"
+
+    # naming for activation
+    INPUT_ACTIVATION_PREFIX = "input_activation_"
+    OUTPUT_ACTIVATION_PREFIX = "output_activation_"
+
+    # naming conventions for the keys of the return module info
+    TOLERANCE_KEY = "dynamic_static_tolerance"
+    DEFAULT_DYNAMIC_REC_KEY = "dynamic_recommended"
+    PRE_OBS_COMP_STAT_KEY = INPUT_ACTIVATION_PREFIX + "dynamic_static_comp_stat"
+    POST_OBS_COMP_STAT_KEY = OUTPUT_ACTIVATION_PREFIX + "dynamic_static_comp_stat"
+    PRE_OBS_DATA_DIST_KEY = (
+        INPUT_ACTIVATION_PREFIX + "dynamic_static_data_classification"
+    )
+    POST_OBS_DATA_DIST_KEY = (
+        OUTPUT_ACTIVATION_PREFIX + "dynamic_static_data_classification"
+    )
+    IS_CURRENTLY_SUPPORTED_KEY = "is_dynamic_supported"
+
+    # modules that are supported both dynamic and static for this report function
+    DEFAULT_DYNAMIC_STATIC_CHECK_SUPPORTED = {nn.Linear}
+
+    # modules that will be supported soon for both
+    DEFAULT_DYNAMIC_STATIC_FUTURE_SUPPORTED = {nn.Conv1d, nn.Conv2d, nn.Conv3d}
+
+    def __init__(self, tolerance=0.5):
+        super().__init__()
+
+        # set tolerance level and initialize a set to keep track of useful fqn locations
+        self.tolerance = tolerance
+        self.useful_observer_fqns: Set[str] = set()
+
+    def determine_observer_insert_points(
+        self, prepared_fx_model: GraphModule
+    ) -> Dict[str, Dict[str, Any]]:
+        r"""
+        Determines where observers need to be inserted for the Dynamic vs Static detector.
+        For this detector, we want to place observers on either side of linear layers in the model.
+
+        Currently inserts observers for:
+            linear layers
+
+        Args:
+            prepared_fx_model (GraphModule):  The prepared Fx GraphModule
+
+        Returns a Dict mapping from unique observer fqns (where we want to insert them) to a Dict with:
+            key "target_node" -> the node we are trying to observe with this observer (torch.fx.node.Node)
+            key "observer_to_insert" -> the observer we wish to insert (ObserverBase)
+            key "is_post_observer" -> True if this is meant to be a post-observer for target_node, False if pre-observer
+            key "observer_args" -> The arguments that are meant to be passed into the observer
+        """
+
+        # observer for this detector is ModelReportObserver
+        obs_ctr = ModelReportObserver
+
+        # return dict
+        obs_fqn_to_info: Dict[str, Dict[str, Any]] = {}
+
+        for fqn, module in prepared_fx_model.named_modules():
+            # make sure module is supported
+            if self._is_supported(module, insert=True):
+                # if it's a supported type, we want to get node and add observer insert locations
+                targeted_node = self._get_targeting_node(prepared_fx_model, fqn)
+
+                # add entry for pre-observer
+                pre_obs_fqn = fqn + "." + self.DEFAULT_PRE_OBSERVER_NAME
+
+                obs_fqn_to_info[pre_obs_fqn] = {
+                    DETECTOR_TARGET_NODE_KEY: targeted_node,
+                    DETECTOR_OBS_TO_INSERT_KEY: obs_ctr(),
+                    DETECTOR_IS_POST_OBS_KEY: False,
+                    DETECTOR_OBS_ARGS_KEY: targeted_node.args,
+                }
+
+                # add entry for post-observer
+                post_obs_fqn = fqn + "." + self.DEFAULT_POST_OBSERVER_NAME
+
+                obs_fqn_to_info[post_obs_fqn] = {
+                    DETECTOR_TARGET_NODE_KEY: targeted_node,
+                    DETECTOR_OBS_TO_INSERT_KEY: obs_ctr(),
+                    DETECTOR_IS_POST_OBS_KEY: True,
+                    DETECTOR_OBS_ARGS_KEY: (targeted_node,),
+                }
+
+        return obs_fqn_to_info
+
+    def get_detector_name(self) -> str:
+        r"""returns the string name of this detector"""
+        return "dynamic_vs_static_detector"
+
+    def get_qconfig_info(self, model) -> Dict[str, DetectorQConfigInfo]:
+        r"""Returns the DetectorQConfigInfo for each module_fqn relevant
+        Args
+            model (nn.Module or subclass): model to find observer insertion points
+
+        Returns a Dict mapping from unique observer fqns (where we want to insert them) to:
+            A DetectorQConfigInfo with the information to generate a QConfig for a specific module
+        """
+        # run the helper function to populate the dictionary
+        dynamic_static_info = self._generate_dict_info(model)
+
+        # we actually have a qconfig info object we are populating
+        module_fqn_to_detector_qconfig_info = {}
+
+        for module_fqn in dynamic_static_info:
+            # create a detector info instance
+            detector_qconfig_info = DetectorQConfigInfo(module_fqn)
+
+            # see if per channel quantization is supported
+            dynamic_static_recommended: bool = dynamic_static_info[module_fqn][
+                self.DEFAULT_DYNAMIC_REC_KEY
+            ]
+            detector_qconfig_info.is_activation_dynamic = dynamic_static_recommended
+            module_fqn_to_detector_qconfig_info[module_fqn] = detector_qconfig_info
+
+        return module_fqn_to_detector_qconfig_info
+
+    def _is_supported(self, module: nn.Module, insert: bool = False) -> bool:
+        r"""Returns whether the given module is supported for observers
+
+        Args
+            module: The module to check and ensure is supported
+            insert: True if this is check for observer insertion, false if for report gen
+
+        Returns True if the module is supported by observer, False otherwise
+        """
+        # check to see if module is of a supported type
+        is_supported_type = any(
+            isinstance(module, x) for x in self.DEFAULT_DYNAMIC_STATIC_CHECK_SUPPORTED
+        )
+
+        # check if it will be supported
+        future_supported_type = any(
+            isinstance(module, x) for x in self.DEFAULT_DYNAMIC_STATIC_FUTURE_SUPPORTED
+        )
+
+        # supported
+        supported = is_supported_type or future_supported_type
+
+        # this is check for observer insertion
+        if insert:
+            return supported
+        else:
+            # this is for report gen and we also need to check if it contains observers
+            has_obs = hasattr(module, self.DEFAULT_PRE_OBSERVER_NAME) and hasattr(
+                module, self.DEFAULT_POST_OBSERVER_NAME
+            )
+            return supported and has_obs
+
+    def _generate_dict_info(self, model: GraphModule) -> Dict[str, Any]:
+        r"""
+        Helper function for generate_detector_report that does the generation of the dictionary.
+        This process is done as specified in generate_detector_report documentation
+
+        Args:
+            model (GraphModule): The prepared and calibrated GraphModule with inserted ModelReportObservers
+
+        Returns a Dictionary mapping modules with ModelReportObservers around them to:
+                whether dynamic quantization is recommended
+                their S metric of input to module
+                whether input to module is stationary or non-stationary
+                their S metric of output of module
+                whether output of module is stationary or non-stationary
+                the tolerance level to decided whether input/output is stationary or non-stationary
+                whether it is currently supported or planned for the future
+        """
+        # store modules dynamic vs static information
+        module_dynamic_static_info = {}
+
+        # This for loop goes through the modules, and extracts all relevant information into module_dynamic_static_info
+        #   This information primary includes whether the data distributions around a supported module is stationary or not
+        #   Based on this, it is recorded whether dynamic or static quantization is recommended
+
+        # loop through all submodules included nested ones
+        for fqn, module in model.named_modules():
+            # if module is Linear has the ModelReportObserver attached to it
+            if self._is_supported(module):
+                # get pre and post observers for the module
+                pre_obs = getattr(module, self.DEFAULT_PRE_OBSERVER_NAME)
+                post_obs = getattr(module, self.DEFAULT_POST_OBSERVER_NAME)
+
+                # get the statistics for each module
+                pre_stat = pre_obs.get_batch_to_epoch_ratio()
+                post_stat = post_obs.get_batch_to_epoch_ratio()
+
+                # record module, pre and post stat, and whether to do dynamic or static based off it
+                # true if post observer data distribution is non-stationary, false if it's stationary
+                dynamic_recommended = post_stat <= self.tolerance
+
+                # specify the classifications for whether data distributions considered stationary or non-stationary
+                pre_obs_dist_classif = (
+                    self.STATIONARY_STR
+                    if pre_stat > self.tolerance
+                    else self.NON_STATIONARY_STR
+                )
+                post_obs_dist_classif = (
+                    self.STATIONARY_STR
+                    if post_stat > self.tolerance
+                    else self.NON_STATIONARY_STR
+                )
+
+                # check if current support or future support
+                is_supported_type = any(
+                    isinstance(module, x)
+                    for x in self.DEFAULT_DYNAMIC_STATIC_CHECK_SUPPORTED
+                )
+
+                # store the set of important information for this module
+                module_info = {
+                    self.TOLERANCE_KEY: self.tolerance,
+                    self.DEFAULT_DYNAMIC_REC_KEY: dynamic_recommended,
+                    self.PRE_OBS_COMP_STAT_KEY: pre_stat,
+                    self.PRE_OBS_DATA_DIST_KEY: pre_obs_dist_classif,
+                    self.POST_OBS_COMP_STAT_KEY: post_stat,
+                    self.POST_OBS_DATA_DIST_KEY: post_obs_dist_classif,
+                    self.IS_CURRENTLY_SUPPORTED_KEY: is_supported_type,
+                }
+
+                module_dynamic_static_info[fqn] = module_info
+
+        return module_dynamic_static_info
+
+    def generate_detector_report(
+        self, model: GraphModule
+    ) -> Tuple[str, Dict[str, Any]]:
+        r"""
+        Determines whether dynamic or static quantization is more appropriate for a given module.
+
+        Takes advantage of the ModelReportObserver that records range information.
+        Stationary distribution of data are strictly above tolerance level for the comparison statistic:
+
+            S = average_batch_activation_range/epoch_activation_range
+
+        Nonstationary distributions are below or at the tolerance level for this metric.
+
+        If the distribution of data right after the module is non-stationary, recommend dynamic quantization
+            Otherwise recommend static quantization
+
+        This will then generate suggestions for dynamic vs static quantization focused around Linear.
+
+        Args:
+            model (GraphModule): The prepared and calibrated GraphModule with inserted ModelReportObservers
+
+        Returns a tuple with two elements:
+            String report of of whether dynamic or static quantization is recommended for certain modules
+            Dictionary mapping modules with ModelReportObservers around them to:
+                whether dynamic quantization is recommended
+                their S metric of input to module
+                whether input to module is stationary or non-stationary
+                their S metric of output of module
+                whether output of module is stationary or non-stationary
+                the tolerance level to decided whether input/output is stationary or non-stationary
+                whether it is currently supported or planned for the future
+        """
+
+        # get the dictionary of the information to format the string report
+        module_dynamic_static_info = self._generate_dict_info(model)
+
+        dynamic_vs_static_string = "Dynamic vs. Static Quantization suggestions: \n"
+
+        modules_added: bool = False  # check to make sure at least 1 module added.
+
+        dynamic_benefit = (
+            " You will get more accurate results if you use dynamic quantization"
+        )
+        static_benefit = (
+            " You can increase model efficiency if you use static quantization"
+        )
+        future_support_str = (
+            ". This layer is not yet supported for dynamic quantization"
+        )
+        # This for loop goes through the information collected in module_dynamic_static_info and:
+        #   Populates the string based report with the information from module_dynamic_static_info
+        #   Compiles the complete report by appending relevant formatted strings
+
+        for module_fqn in module_dynamic_static_info.keys():
+            # there is at least 1 module for suggestion
+            modules_added = True
+            module_info = module_dynamic_static_info[module_fqn]
+            suggestion_string_template = (
+                "For module {} it is suggested to use {} quantization because {}.\n"
+            )
+
+            # decide what string formatting values will be
+            quantization_type = ""
+            quantization_reasoning = "the distribution of data before {} is {} and the distribution after is {}."
+
+            benefit_str = ""
+
+            # strings for if dynamic quantized per tensor is needed
+            recommend_per_tensor = (
+                ". We recommend to add a {} before this module if it is static."
+            )
+            rec_lay_to_add = "dynamic quantize per tensor layer"
+            dynamic_per_tensor_string = recommend_per_tensor.format(rec_lay_to_add)
+            dynamic_per_tensor_reasoning_string = " This is because the input to this module has a non-stationary distribution"
+
+            # start composing explanation
+            if module_info[self.DEFAULT_DYNAMIC_REC_KEY]:
+                quantization_type = "dynamic"
+                # check if currently supported or future supported
+                benefit_str = dynamic_benefit
+                if not module_info[self.IS_CURRENTLY_SUPPORTED_KEY]:
+                    benefit_str += future_support_str
+            else:
+                quantization_type = "static"
+                benefit_str = static_benefit
+
+            # now set the quantization explanation string
+            quantization_reasoning = (
+                quantization_reasoning.format(
+                    module_fqn,
+                    module_info[self.PRE_OBS_DATA_DIST_KEY],
+                    module_info[self.POST_OBS_DATA_DIST_KEY],
+                )
+                + benefit_str
+            )
+
+            # if we have a non-stationary input -> linear -> stationary we suggested static
+            # however, we want to also recommend they add a dynamic quantize per tensor right if this change is made
+            if (
+                module_info[self.PRE_OBS_DATA_DIST_KEY] == self.NON_STATIONARY_STR
+                and module_info[self.POST_OBS_DATA_DIST_KEY] == self.STATIONARY_STR
+            ):
+                quantization_reasoning = (
+                    quantization_reasoning
+                    + dynamic_per_tensor_string
+                    + dynamic_per_tensor_reasoning_string
+                )
+
+            # format the overall suggestion string with the specific inputs
+            module_suggestion_string = suggestion_string_template.format(
+                module_fqn, quantization_type, quantization_reasoning
+            )
+
+            # append to overall suggestion
+            dynamic_vs_static_string += module_suggestion_string
+
+        if not modules_added:
+            dynamic_vs_static_string += "No applicable layers for suggestions. Only linear and conv are valid.\n"
+
+        # return the string as well as the dictionary of information
+        return (dynamic_vs_static_string, module_dynamic_static_info)
+
+
+class InputWeightEqualizationDetector(DetectorBase):
+    r"""
+    Determines whether input-weight equalization can help improve quantization for certain modules.
+
+    Specifically, this list of modules includes:
+        linear
+        conv
+
+    Determines whether input-weight equalization is recommended based on the comp stat:
+        s_c = sqrt(w_c/W)/sqrt(i_c/I)
+        where:
+            w_c is range of weight for channel c, W is range of weight over all channels
+            i_c is range of input for channel c, I is range of input over all channels
+
+        if s_c >= threshold or <= 1 / threshold, recommends input-weight equalization
+
+    Args:
+        ratio_threshold (float): The threshold for s_c to determine if input-weight equalization is suggested
+            Should be between 0 and 1 (both non-inclusive)
+        ch_axis (int, optional): The channel axis being observed to determine input weight equalization
+            Default: 1
+
+    * :attr:`ratio_threshold`: The threshold for s_c to determine if input-weight equalization is suggested
+        Should be between 0 and 1
+
+    * :attr:`ch_axis`: The channel axis being observed to determine input weight equalization
+
+    * :attr:`SUPPORTED_MODULES`: This specifies the modules that are supported for input-weight equalization
+
+    * :attr:`DEFAULT_PRE_OBSERVER_NAME`: The name of the pre-observer to be inserted for this detector
+    """
+
+    SUPPORTED_MODULES: Set[Callable] = {
+        nn.Linear,
+        nn.Conv1d,
+        nn.Conv2d,
+        nn.Conv3d,
+        nnqat.Linear,
+        nnqat.Conv1d,
+        nnqat.Conv2d,
+        nnqat.Conv3d,
+    }
+
+    # names for the pre and post observers that are inserted
+    DEFAULT_PRE_OBSERVER_NAME: str = "model_report_pre_observer"
+
+    # weight / activation prefix for each of the below info
+    WEIGHT_PREFIX = "weight_"
+    ACTIVATION_PREFIX = "input_activation_"
+
+    # string names for keys of info dictionaries
+    PER_CHANNEL_MAX_KEY = "per_channel_max"
+    PER_CHANNEL_MIN_KEY = "per_channel_min"
+    GLOBAL_MAX_KEY = "global_max"
+    GLOBAL_MIN_KEY = "global_min"
+
+    # keys for return dict of recommendations
+    RECOMMENDED_KEY = "input_weight_equalization_recommended"
+    COMP_METRIC_KEY = "input_weight_channel_comparison_metrics"
+    THRESHOLD_KEY = "input_weight_threshold"
+    CHANNEL_KEY = "input_weight_channel_axis"
+
+    # default weight and info strings
+    WEIGHT_STR = "weight"
+    INPUT_STR = "input"
+
+    # default for what ratio we recommend input weight
+    DEFAULT_RECOMMEND_INPUT_WEIGHT_CHANNEL_RATIO = 0.4
+
+    def __init__(self, ratio_threshold: float, ch_axis: int = 1):
+        # ensure passed in inputs are valid
+        if ratio_threshold <= 0 or ratio_threshold >= 1:
+            raise ValueError("Make sure threshold is > 0 and < 1")
+
+        # initialize attributes based on args
+        self.ratio_threshold: float = ratio_threshold
+        self.ch_axis: int = ch_axis
+
+    def _is_supported(self, module: nn.Module, insert: bool = False) -> bool:
+        r"""Returns whether the given module is supported for observers
+
+        Args
+            module: The module to check and ensure is supported
+            insert: True if this is check for observer insertion, false if for report gen
+
+        Returns True if the module is supported by observer, False otherwise
+        """
+        # check to see if module is of a supported type
+        is_supported_type = any(type(module) is x for x in self.SUPPORTED_MODULES)
+
+        # this is check for observer insertion
+        if insert:
+            return is_supported_type
+        else:
+            # this is for report gen and we also need to check if it contains observers
+            has_obs = hasattr(module, self.DEFAULT_PRE_OBSERVER_NAME)
+            return is_supported_type and has_obs
+
+    def get_qconfig_info(self, model) -> Dict[str, DetectorQConfigInfo]:
+        r"""Returns the DetectorQConfigInfo for each module_fqn relevant
+        Args
+            model (nn.Module or subclass): model to find observer insertion points
+
+        Returns a Dict mapping from unique observer fqns (where we want to insert them) to:
+            A DetectorQConfigInfo with the information to generate a QConfig for a specific module
+        """
+        # run the helper function to populate the dictionary
+        # find the range of inputs
+        input_values: Dict[str, Dict] = self._extract_input_info(model)
+
+        # find the range of weights
+        weight_values: Dict[str, Dict] = self._extract_weight_info(model)
+
+        # calculate per_channel comparison statistic s_c
+        comp_stats: Dict[str, torch.Tensor] = self._generate_comparison_values(
+            input_values, weight_values
+        )
+
+        # generate the return dictionary
+        input_weight_equalization_info: Dict[str, Dict] = self._generate_dict_info(
+            input_values, weight_values, comp_stats
+        )
+
+        # we actually have a qconfig info object we are populating
+        module_fqn_to_detector_qconfig_info = {}
+
+        for module_fqn in input_weight_equalization_info:
+            # create a detector info instance
+            detector_qconfig_info = DetectorQConfigInfo(module_fqn)
+
+            # see if per channel quantization is supported
+            input_weight_recommended: bool = input_weight_equalization_info[module_fqn][
+                self.RECOMMENDED_KEY
+            ]
+            detector_qconfig_info.is_equalization_recommended = input_weight_recommended
+            module_fqn_to_detector_qconfig_info[module_fqn] = detector_qconfig_info
+
+        return module_fqn_to_detector_qconfig_info
+
+    def determine_observer_insert_points(
+        self, prepared_fx_model: GraphModule
+    ) -> Dict[str, Dict[str, Any]]:
+        r"""Determines where observers need to be inserted for the Input Weight Equalization Detector.
+        For this detector, we want to place observers in front of supported layers.
+
+        Currently inserts observers for:
+            linear layers
+            conv layers
+
+        Args:
+            prepared_fx_model (GraphModule):  The prepared Fx GraphModule
+
+        Returns a Dict mapping from unique observer fqns (where we want to insert them) to a Dict with:
+            key "target_node" -> the node we are trying to observe with this observer (torch.fx.node.Node)
+            key "observer_to_insert" -> the observer we wish to insert (ObserverBase)
+            key "is_post_observer" -> True if this is meant to be a post-observer for target_node, False if pre-observer
+            key "observer_args" -> The arguments that are meant to be passed into the observer
+        """
+
+        # observer for this detector is ModelReportObserver
+        obs_ctr = ModelReportObserver
+
+        # return dict
+        obs_fqn_to_info: Dict[str, Dict[str, Any]] = {}
+
+        for fqn, module in prepared_fx_model.named_modules():
+            # check to see if module is of a supported type
+            if self._is_supported(module, insert=True):
+                # if it's a supported type, we want to get node and add observer insert locations
+                targeted_node = self._get_targeting_node(prepared_fx_model, fqn)
+
+                # add entry for pre-observer
+                pre_obs_fqn = fqn + "." + self.DEFAULT_PRE_OBSERVER_NAME
+
+                obs_fqn_to_info[pre_obs_fqn] = {
+                    DETECTOR_TARGET_NODE_KEY: targeted_node,
+                    DETECTOR_OBS_TO_INSERT_KEY: obs_ctr(ch_axis=self.ch_axis),
+                    DETECTOR_IS_POST_OBS_KEY: False,
+                    DETECTOR_OBS_ARGS_KEY: targeted_node.args,
+                }
+
+        return obs_fqn_to_info
+
+    def get_detector_name(self) -> str:
+        r"""Returns the name of this detector"""
+        return "input_weight_equalization_detector"
+
+    def _extract_input_info(self, model: GraphModule) -> Dict[str, Dict]:
+        r"""
+        Takes in a calibrated GraphModule and then finds the relevant observers.
+        It then extracts the input information for each observer returns it
+
+        Args
+            model (GraphModule): The prepared and calibrated GraphModule with inserted ModelReportObservers
+
+        Returns a dict mapping relevant module fqns (str) to a dict with keys:
+            "input_activation_per_channel_max" : maps to the per_channel max values
+            "input_activation_per_channel_min" : maps to the per_channel min values
+            "input_activation_global_max" : maps to the global max recorded
+            "input_activation_global_min" : maps to the global min recorded
+        """
+
+        # return dictionary mapping observer fqns to desired info
+        input_info: Dict[str, Dict] = {}
+
+        for fqn, module in model.named_modules():
+            # if module is supported and it has a pre-observer
+            if self._is_supported(module):
+                # get pre observer for the module
+                pre_obs = getattr(module, self.DEFAULT_PRE_OBSERVER_NAME)
+
+                input_info[fqn] = {
+                    self.ACTIVATION_PREFIX + self.PER_CHANNEL_MAX_KEY: pre_obs.max_val,
+                    self.ACTIVATION_PREFIX + self.PER_CHANNEL_MIN_KEY: pre_obs.min_val,
+                    self.ACTIVATION_PREFIX + self.GLOBAL_MAX_KEY: max(pre_obs.max_val),
+                    self.ACTIVATION_PREFIX + self.GLOBAL_MIN_KEY: min(pre_obs.min_val),
+                }
+
+        return input_info
+
+    def _extract_weight_info(self, model: GraphModule) -> Dict[str, Dict]:
+        r"""
+        Takes in a calibrated GraphModule and then finds the relevant observers.
+        It then extracts the weight information for each layer an observer is attached to.
+
+        Args
+            model (GraphModule): The prepared and calibrated GraphModule with inserted ModelReportObservers
+
+        Returns a dict mapping module fqns (str) to a dict with keys:
+            "per_channel_max" : maps to the per_channel max values
+            "per_channel_min" : maps to the per_channel min values
+            "global_max" : maps to the global max recorded
+            "global_min" : maps to the global min recorded
+        """
+        # return dictionary mapping observer fqns to desired info
+        weight_info: Dict[str, Dict] = {}
+
+        for fqn, module in model.named_modules():
+            # if module is supported and it has a pre-observer
+            if self._is_supported(module):
+                # we don't need actual observer, just the module weights
+                # calculate min and max vals
+                device = module.weight.device
+                min_val: torch.Tensor = torch.tensor([float("inf")], device=device)
+                max_val: torch.Tensor = torch.tensor([float("-inf")], device=device)
+                x_copy = module.weight
+                x_dim = x_copy.size()
+
+                new_axis_list = [i for i in range(len(x_dim))]  # noqa: C416
+                new_axis_list[self.ch_axis] = 0
+                new_axis_list[0] = self.ch_axis
+                y = x_copy.permute(new_axis_list)
+
+                # Need to match dtype of min/max because the updates to buffers
+                # are done in place and types need to match for comparisons
+                y = y.to(min_val.dtype)
+                y = torch.flatten(y, start_dim=1)
+                if min_val.numel() == 0 or max_val.numel() == 0:
+                    min_val, max_val = torch.aminmax(y, dim=1)
+                else:
+                    min_val_cur, max_val_cur = torch.aminmax(y, dim=1)
+                    min_val = torch.min(min_val_cur, min_val)
+                    max_val = torch.max(max_val_cur, max_val)
+
+                weight_info[fqn] = {
+                    self.WEIGHT_PREFIX + self.PER_CHANNEL_MAX_KEY: max_val,
+                    self.WEIGHT_PREFIX + self.PER_CHANNEL_MIN_KEY: min_val,
+                    self.WEIGHT_PREFIX + self.GLOBAL_MAX_KEY: max(max_val),
+                    self.WEIGHT_PREFIX + self.GLOBAL_MIN_KEY: min(min_val),
+                }
+
+        return weight_info
+
+    def _calculate_range_ratio(
+        self, info_dict: Dict, info_str: str, module_fqn: str
+    ) -> torch.Tensor:
+        r"""
+        Takes in an info dict and calculates the s_c matrix.
+
+        Args:
+            info_dict (dict): A dictionary of either input or weight range info
+            info_str (str): A str describing whether currently looking at weight or input info
+                Either "weight" or "input"
+            module_fqn (str): The fqn of the module we are looking at
+
+        Returns a tensor of values, where each value is the s_c stat for a different channel
+        """
+        # calculate the ratios of the info
+        # get the prefix str
+        prefix_str = (
+            self.ACTIVATION_PREFIX if info_str == self.INPUT_STR else self.WEIGHT_PREFIX
+        )
+
+        per_channel_range = (
+            info_dict[prefix_str + self.PER_CHANNEL_MAX_KEY]
+            - info_dict[prefix_str + self.PER_CHANNEL_MIN_KEY]
+        )
+        global_range = (
+            info_dict[prefix_str + self.GLOBAL_MAX_KEY]
+            - info_dict[prefix_str + self.GLOBAL_MIN_KEY]
+        )
+
+        if global_range == 0:
+            range_zero_explanation = "We recommend removing this channel as it doesn't provide any useful information."
+            raise ValueError(
+                f"The range of the {info_str} data for module {module_fqn} is 0, "
+                f"which means you have a constant value channel. {range_zero_explanation}"
+            )
+
+        ratio = per_channel_range / global_range
+
+        return ratio
+
+    def _generate_comparison_values(
+        self, input_info: Dict, weight_info: Dict
+    ) -> Dict[str, torch.Tensor]:
+        r"""
+        Takes in the information on the min and max values of the inputs and weights and:
+            Calculates the comp stat for each channel: s_c = sqrt(w_c/W)/sqrt(i_c/I)
+
+        Args:
+            input_info (dict): A dict mapping each observer to input range information
+            weight_info (dict): A dict mapping each observer to weight range information
+
+        Returns a dict mapping relevant observer fqns (str) to a 1-D tensor.
+            Each value is a different s_c value for a different channel
+        """
+        # create return dictionary for each observer
+        module_fqn_to_channel: Dict[str, torch.Tensor] = {}
+
+        # for each module (both passed in dicts should have same keys)
+        for module_fqn in input_info:
+            # raise error if not in weight info
+            if module_fqn not in weight_info:
+                raise KeyError(
+                    f"Unable to find weight range stats for module {module_fqn}"
+                )
+
+            # calculate the ratios of the weight info and input info
+            weight_ratio = self._calculate_range_ratio(
+                weight_info[module_fqn], self.WEIGHT_STR, module_fqn
+            )
+            input_ratio = self._calculate_range_ratio(
+                input_info[module_fqn], self.INPUT_STR, module_fqn
+            )
+
+            # if mismatched size, because of grouping, we want to replicate weight enough times
+            weight_channels = len(weight_ratio)
+            input_channels = len(input_ratio)
+            if weight_channels != input_channels:
+                # we try to replicate
+                assert (
+                    input_channels % weight_channels == 0
+                ), "input channels should be divisible by weight channels."
+                # get replication factor
+                rep_factor: int = input_channels // weight_channels
+
+                # weight ratio is (n,), input ratio is (k,), we just repeat weight ratio k // n
+                weight_ratio = weight_ratio.repeat(rep_factor)
+
+            # calculate the s metric per channel
+            s = torch.sqrt(weight_ratio) / torch.sqrt(input_ratio)
+            module_fqn_to_channel[module_fqn] = s
+
+        # return compiled observer ratios
+        return module_fqn_to_channel
+
+    def _generate_dict_info(
+        self, input_info: Dict, weight_info: Dict, comp_stats: Dict
+    ) -> Dict[str, Dict]:
+        r"""
+        Helper function for generate_detector_report that does the generation of the dictionary.
+        This process is done as specified in generate_detector_report documentation
+
+        Args:
+            input_info (dict): A dict mapping each module to input range information
+            weight_info (dict): A dict mapping each module to weight range information
+            comp_stats (dict): A dict mapping each module to its corresponding comp stat
+
+        Returns a dictionary mapping each module with relevant ModelReportObservers around them to:
+            whether input weight equalization is recommended
+            their s_c metric compared to the threshold
+            the threshold used to make the recommendation
+            the channel used for recording data
+            the input channel range info
+            the weight channel range info
+        """
+        # store modules input weight equalization info
+        input_weight_equalization_info: Dict[str, Dict] = {}
+
+        # for each module we add separate set of suggestions
+        for module_fqn in input_info:
+            # get relevant info for this module
+            mod_input_info: Dict = input_info[module_fqn]
+            mod_weight_info: Dict = weight_info[module_fqn]
+            mod_comp_stat: Dict = comp_stats[module_fqn]
+
+            # decide if each channel should have input weight equalization or not
+            channel_rec_vals: list = []
+
+            for val in mod_comp_stat:
+                float_rep: float = val.item()
+
+                # decide if recommending input weight equalization
+                recommended: bool = (
+                    float_rep >= self.ratio_threshold
+                    and float_rep <= 1 / self.ratio_threshold
+                )
+                channel_rec_vals.append(recommended)
+
+            # build the return dict input
+            # also unpack input and weight dicts into it
+            input_weight_equalization_info[module_fqn] = {
+                self.RECOMMENDED_KEY: channel_rec_vals,
+                self.COMP_METRIC_KEY: mod_comp_stat,
+                self.THRESHOLD_KEY: self.ratio_threshold,
+                self.CHANNEL_KEY: self.ch_axis,
+                **mod_input_info,
+                **mod_weight_info,
+            }
+
+        # return our compiled info for each module
+        return input_weight_equalization_info
+
+    def generate_detector_report(
+        self, model: GraphModule
+    ) -> Tuple[str, Dict[str, Any]]:
+        r"""
+        Determines whether input weight equalization is appropriate for a given module.
+
+        Takes advantage of the ModelReport Observer which records per channel information of input range
+        It then uses the passed in weight info inconjunction to compute the desired ratio
+        Finally, it gives suggestions based on this information for each module of interest
+
+        Args:
+            model (GraphModule): The prepared and calibrated GraphModule with inserted ModelReportObservers
+
+        Returns a tuple with two elements:
+            String report of of whether input weight equalization is recommended for certain modules
+            Dictionary mapping modules of interest to:
+                whether input weight equalization is recommended
+                their s_c metric compared to the threshold
+                the threshold used to make the recommendation
+                the channel used for recording data
+                the input channel range info
+                the weight channel range info
+        """
+
+        # find the range of inputs
+        input_values: Dict[str, Dict] = self._extract_input_info(model)
+
+        # find the range of weights
+        weight_values: Dict[str, Dict] = self._extract_weight_info(model)
+
+        # calculate per_channel comparison statistic s_c
+        comp_stats: Dict[str, torch.Tensor] = self._generate_comparison_values(
+            input_values, weight_values
+        )
+
+        # generate the return dictionary
+        input_weight_equalization_info: Dict[str, Dict] = self._generate_dict_info(
+            input_values, weight_values, comp_stats
+        )
+
+        # now we can generate report based on this information
+        input_weight_string = "Input-Weight Equalization suggestions: \n"
+
+        # some strings to be formatted depending on module we are adding
+        module_suggestion_str = "For Module {} looked at with axis {}: \n"
+        channel_suggestion_str = (
+            "\tWe suggest {} input weight equalization because {}\n"
+        )
+        use_str = "to use"
+        no_use_str = "to not use"
+        input_weight_benefit_str = "{}/{} channels would benefit and we expect significant reduction in quantization error."
+        input_weight_non_benefit_reasoning = (
+            "{}/{} channels benefitting from input-weight equalization being applied."
+        )
+        input_weight_non_benefit_str = "we don't expect much improvement from input-weight equalization based on {}"
+
+        # added module check
+        added_module: bool = False
+
+        # compile the suggestion string
+        for module_fqn in input_weight_equalization_info:
+            # we added at least 1 module
+            added_module = True
+            # add the module level description
+            input_weight_string += module_suggestion_str.format(
+                module_fqn, self.ch_axis
+            )
+
+            mod_info: Dict[str, Any] = input_weight_equalization_info[module_fqn]
+
+            # gather info on how many channels would benefit from input weight and
+            recommendation_per_channel: torch.Tensor = mod_info[self.RECOMMENDED_KEY]
+            num_recs = sum(recommendation_per_channel)
+
+            if (
+                num_recs / len(recommendation_per_channel)
+                >= self.DEFAULT_RECOMMEND_INPUT_WEIGHT_CHANNEL_RATIO
+            ):
+                input_benefit_formatted = input_weight_benefit_str.format(
+                    num_recs, len(recommendation_per_channel)
+                )
+                channel_str = channel_suggestion_str.format(
+                    use_str, input_benefit_formatted
+                )
+                input_weight_string += channel_str
+            else:
+                non_benefit_reason_formatted = (
+                    input_weight_non_benefit_reasoning.format(
+                        num_recs, len(recommendation_per_channel)
+                    )
+                )
+                non_benefit_str = input_weight_non_benefit_str.format(
+                    non_benefit_reason_formatted
+                )
+                channel_str = channel_suggestion_str.format(no_use_str, non_benefit_str)
+                input_weight_string += channel_str
+
+        # if no modules looked at, amend return string
+        if not added_module:
+            input_weight_string += (
+                "No applicable layers for suggestions. Only linear and conv valid.\n"
+            )
+
+        # return a tuple with the string explanation and the compiled dict info
+        return (input_weight_string, input_weight_equalization_info)
+
+
+class OutlierDetector(DetectorBase):
+    r"""
+    Determines whether there are significant outliers in activation data around a certain layer.
+
+    This is ideally used in conjunction with information on stationary vs. non-stationary distribution:
+        If the data is stationary, and there are significant outliers, then we want to flag them
+        We want to do this on a per channel basis for detecting outliers
+
+    Determines whether activation data is flagged as outlier based on if data is stationary and:
+        p_r = avg(100th percentile / "reference_percentile"th percentile)
+        where:
+            p_r is average percentile ratio across all batches in the epoch
+            reference_percentile is a percentile values between 0 and 100 exclusive
+
+        if p_r is above some threshold, then we consider the activations to have significant outliers
+
+    Args:
+        ratio_threshold (float, optional): The threshold for p_r to determine if there are outliers in activations
+            Should be >= 1
+            Default: 3.5
+        reference_percentile (float, optional): The denominator to find the relative scale of the 100th percentile
+            Should be between 0 and 1
+            Default: 0.975
+        fraction_batches_used_threshold (float, optional): Threshold of fraction of batches per channel to determine outlier
+            If fraction is below this, we deem number of samples used to calculate outliers as insignificant and alert user
+            regardless of whether we detected outliers or not in channel to take a closer look at channel results
+            Should be between 0 and 1
+            Default: 0.95
+        ch_axis (int, optional): The channel axis being observed to determine input weight equalization
+            Default: 1
+
+    * :attr:`ratio_threshold`: The threshold for p_r to determine if there are outliers in activations
+        The p_r value (average ratio of 100th percentile/reference_percentile) is compared to ratio_threshold
+        If it is significantly greater, then we consider it an outlier
+        This threshold was calculated based on the ratio of the percentiles in a normal distribution
+        The calculations behind value choice: https://drive.google.com/file/d/1N2wdtXWI-kOH8S7HH4-PYB_NmqzZil4p/view?usp=sharing
+
+    * :attr:`reference_percentile`: The denominator of the top fraction to find the relative scale of the 100th percentile
+        Should be between 0 and 1
+        The calculations behind value choice: https://drive.google.com/file/d/1N2wdtXWI-kOH8S7HH4-PYB_NmqzZil4p/view?usp=sharing
+
+    * :attr:`fraction_batches_used_threshold`: The fraction of batches to determine outliers for each channel should be above this
+        Some batches may not be used because of 0-based errors, so this is to ensure a good amount of the total batches are used
+        Should be between 0 and 1
+
+    * :attr:`ch_axis`: The channel axis being observed to determine outliers
+
+    * :attr:`DEFAULT_PRE_OBSERVER_NAME`: The name of the pre-observer to be inserted for this detector
+    """
+
+    # names for the pre observers that are inserted
+    DEFAULT_PRE_OBSERVER_NAME: str = "model_report_pre_observer"
+
+    # pre activation prefix
+    INPUT_ACTIVATION_PREFIX = "input_activation_"
+
+    # names for dict keys
+    OUTLIER_KEY = "outliers_detected"
+    NUM_BATCHES_KEY = "outlier_detection_batches_used"
+    IS_SUFFICIENT_BATCHES_KEY = "outlier_detection_is_sufficient_batches"
+    COMP_METRIC_KEY = "outlier_detection_percentile_ratios"
+    RATIO_THRES_KEY = "outlier_detection_ratio_threshold"
+    REF_PERCENTILE_KEY = "outlier_detection_reference_percentile"
+    CHANNEL_AXIS_KEY = "outlier_detection_channel_axis"
+    MAX_VALS_KEY = INPUT_ACTIVATION_PREFIX + "per_channel_max"
+    CONSTANT_COUNTS_KEY = "constant_batch_counts"
+
+    def __init__(
+        self,
+        ratio_threshold: float = 3.5,
+        reference_percentile: float = 0.975,
+        fraction_batches_used_threshold: float = 0.95,
+        ch_axis: int = 1,
+    ):
+        # initialize the variables of interest
+        self.ratio_threshold = ratio_threshold
+
+        # make sure passed in percentile is valid
+        assert reference_percentile >= 0 and reference_percentile <= 1
+        assert (
+            fraction_batches_used_threshold >= 0
+            and fraction_batches_used_threshold <= 1
+        )
+        self.reference_percentile = reference_percentile
+        self.fraction_batches_used_threshold = fraction_batches_used_threshold
+        self.ch_axis = ch_axis
+
+    def get_detector_name(self) -> str:
+        r"""Returns the name of this detector"""
+        return "outlier_detector"
+
+    def _supports_insertion(self, module: nn.Module) -> bool:
+        r"""Returns whether the given module is supported for observers insertion
+
+        Any module that doesn't have children and isn't an observer itself is supported
+
+        Args
+            module: The module to check and ensure is supported
+
+        Returns True if the module is supported by observer, False otherwise
+        """
+        # case for insertion of module
+        # check if the module has any children and isn't observer
+        num_children = len(list(module.children()))
+        return num_children == 0 and not _is_activation_post_process(module)
+
+    def get_qconfig_info(self, model) -> Dict[str, DetectorQConfigInfo]:
+        r"""Returns the DetectorQConfigInfo for each module_fqn relevant
+        Args
+            model (nn.Module or subclass): model to find observer insertion points
+
+        Returns a Dict mapping from unique observer fqns (where we want to insert them) to:
+            A DetectorQConfigInfo with the information to generate a QConfig for a specific module
+        """
+        # currently doesn't do anything for outlier detector
+        return {}
+
+    def _supports_report_gen(self, module: nn.Module) -> bool:
+        r"""Returns whether the given module is supported for report generation
+
+        Any module that has a model report pre-observer is supported
+
+        Args
+            module: The module to check and ensure is supported
+
+        Returns True if the module is supported by observer, False otherwise
+        """
+        return hasattr(module, self.DEFAULT_PRE_OBSERVER_NAME)
+
+    def determine_observer_insert_points(
+        self, prepared_fx_model: GraphModule
+    ) -> Dict[str, Dict[str, Any]]:
+        r"""Determines where observers need to be inserted for the Outlier Detector.
+
+        For this detector, we want to place observers in front of supported layers.
+
+        Currently inserts observers for:
+            all layers that do not have children (leaf level layers)
+
+        Args:
+            prepared_fx_model (GraphModule):  The prepared Fx GraphModule
+
+        Returns a Dict mapping from unique observer fqns (where we want to insert them) to a Dict with:
+            key "target_node" -> the node we are trying to observe with this observer (torch.fx.node.Node)
+            key "observer_to_insert" -> the observer we wish to insert (ObserverBase)
+            key "is_post_observer" -> True if this is meant to be a post-observer for target_node, False if pre-observer
+            key "observer_args" -> The arguments that are meant to be passed into the observer
+        """
+        # observer for this detector is ModelReportObserver
+        obs_ctr = ModelReportObserver
+
+        # return dict
+        obs_fqn_to_info: Dict[str, Dict[str, Any]] = {}
+
+        for fqn, module in prepared_fx_model.named_modules():
+            # check to see if module is of a supported type
+            if self._supports_insertion(module):
+                # if it's a supported type, we want to get node and add observer insert locations
+                targeted_node = self._get_targeting_node(prepared_fx_model, fqn)
+
+                # add entry for pre-observer
+                pre_obs_fqn = fqn + "." + self.DEFAULT_PRE_OBSERVER_NAME
+
+                obs_fqn_to_info[pre_obs_fqn] = {
+                    DETECTOR_TARGET_NODE_KEY: targeted_node,
+                    DETECTOR_OBS_TO_INSERT_KEY: obs_ctr(
+                        ch_axis=self.ch_axis, comp_percentile=self.reference_percentile
+                    ),
+                    DETECTOR_IS_POST_OBS_KEY: False,
+                    DETECTOR_OBS_ARGS_KEY: targeted_node.args,
+                }
+
+        return obs_fqn_to_info
+
+    def _calculate_outlier_info(
+        self,
+        percentile_ratios: torch.Tensor,
+        counted_batches: torch.Tensor,
+        total_batches: int,
+    ) -> Dict[str, List[bool]]:
+        r"""
+        Gives info on whether the percentile ratios calculated would be considered outliers
+        Also gives information on whether the collected data is statistically significant to make this claim
+
+        Args:
+            percentile_ratios (torch.Tensor): The average percentile_ratios per channel calculated by the observer
+            counted_batches (torch.Tensor): The number of batches used for average calculation per tensor
+            total_batches (int): The total number of batches that passed through observer in this epoch
+
+        Returns a dictionary mapping:
+            "outliers_detected" : list of bools per channel that are true if it is considered an outlier
+            "is_sufficient_batches": if o_r was >= fraction_batches_used_threshold:
+                where o_r = counted_batches / total_batches
+        """
+        outlier_dict: Dict[str, List[bool]] = {
+            self.OUTLIER_KEY: [],
+            self.IS_SUFFICIENT_BATCHES_KEY: [],
+        }
+
+        # get both as flattened lists for easy mapping
+        ratios_list: List = percentile_ratios.tolist()
+        num_batches_list: List = counted_batches.tolist()
+
+        # calculate whether channels were statistically significant
+        significant_size = [
+            batch_size / total_batches >= self.fraction_batches_used_threshold
+            for batch_size in num_batches_list
+        ]
+        outlier_dict[self.IS_SUFFICIENT_BATCHES_KEY] = significant_size
+
+        # calculate for each channel whether it's an outlier or not based on ratio
+        outlier_detected = [ratio > self.ratio_threshold for ratio in ratios_list]
+        outlier_dict[self.OUTLIER_KEY] = outlier_detected
+
+        # return the dictionary with the two lists
+        return outlier_dict
+
+    def _generate_info_dict(self, model: GraphModule) -> Dict[str, Dict]:
+        r"""
+        Helper function for generate_detector_report that does the generation of the dictionary.
+        This process is done as specified in generate_detector_report documentation
+
+        Args:
+            model (GraphModule): The prepared and calibrated GraphModule with inserted ModelReportObservers
+
+        Returns a dict mapping relevant module fqns to:
+            whether there were outliers found in activation before
+            the number of batches used for each channel
+            whether fraction of applicable batches used is above fraction_batches_used_threshold
+            their p_r metric compared to the threshold
+            the threshold used to make the recommendation
+            the reference_percentile used to make the recommendation
+            the channel axis used to determine individual channels
+            the constant batch counts per channel
+            the per channel max values
+        """
+        # return dictionary mapping observer fqns to desired info
+        info_dict: Dict[str, Dict] = {}
+
+        for fqn, module in model.named_modules():
+            # if module is supported and it has a pre-observer
+            if self._supports_report_gen(module):
+                # get pre observer for the module
+                pre_obs: ModelReportObserver = getattr(
+                    module, self.DEFAULT_PRE_OBSERVER_NAME
+                )
+
+                # get the number of batches and calculated ratio thresholds
+                num_batches: torch.Tensor = pre_obs.percentile_batches_tracked
+                average_ratios: torch.Tensor = pre_obs.average_percentile_ratio
+                channel_batch_cnts: torch.Tensor = pre_obs.constant_channels
+                total_batches: int = pre_obs.num_batches_tracked
+
+                # also get the max values
+                max_vals: torch.Tensor = pre_obs.max_val
+
+                # we have to specifically modify how we are recording negative ratio for pre-relu layers
+                for index, ratio_val in enumerate(average_ratios):
+                    # check if we have a negative ratio
+                    # a ratio might be negative if we have a situation where the 100th percentile is
+                    # > 0 while the nth percentile is < 0, in which case this would not be detected
+                    # as an outlier. Since we care more about magnitude, we make it positive.
+                    if ratio_val.item() < 0:
+                        # first make it positive
+                        average_ratios[index] = -ratio_val
+
+                    if ratio_val.item() < 1:
+                        # if it's less than 1 we have the flip it as well
+                        average_ratios[index] = 1 / ratio_val
+
+                outlier_calcs = self._calculate_outlier_info(
+                    average_ratios, num_batches, total_batches
+                )
+
+                # calculate whether ratios were outliers
+                info_dict[fqn] = {
+                    self.CHANNEL_AXIS_KEY: self.ch_axis,
+                    self.REF_PERCENTILE_KEY: self.reference_percentile,
+                    self.RATIO_THRES_KEY: self.ratio_threshold,
+                    self.COMP_METRIC_KEY: average_ratios,
+                    self.NUM_BATCHES_KEY: num_batches,
+                    self.OUTLIER_KEY: outlier_calcs[self.OUTLIER_KEY],
+                    self.IS_SUFFICIENT_BATCHES_KEY: outlier_calcs[
+                        self.IS_SUFFICIENT_BATCHES_KEY
+                    ],
+                    self.CONSTANT_COUNTS_KEY: channel_batch_cnts,
+                    self.MAX_VALS_KEY: max_vals,
+                }
+
+        return info_dict
+
+    def generate_detector_report(
+        self, model: GraphModule
+    ) -> Tuple[str, Dict[str, Any]]:
+        r"""
+        Determines whether input weight equalization is appropriate for a given module.
+
+        Takes advantage of the ModelReport Observer which records the relevant percentile information
+
+        Args:
+            model (GraphModule): The prepared and calibrated GraphModule with inserted ModelReportObservers
+
+        Returns a tuple with two elements:
+            String report of of whether there are outliers in the activations around certain modules
+            Dictionary mapping modules of interest to:
+                whether there were outliers found in activation before
+                the number of batches used for each channel
+                whether fraction of applicable batches used is above fraction_batches_used_threshold
+                their p_r metric compared to the threshold
+                the threshold used to make the recommendation
+                the reference_percentile used to make the recommendation
+                the channel axis used to determine individual channels
+                the constant batch counts per channel
+                the per channel max values
+        """
+        # generate the information dictionary of outlier information
+        info_dict = self._generate_info_dict(model)
+
+        # now we can generate report based on this information
+        outlier_string = "Outlier detection report: \n"
+
+        # added module check
+        added_module: bool = False
+
+        # some strings to be formatted depending on module we are adding
+        module_suggestion_str = "For Module {} looked at with axis {}: \n"
+        channel_suggestion_str = "\tFor channel {}, we found outliers in the preceding activation data with {}.\n"
+        channel_max_value_str = "a max value across all batches of {}"
+        note_string = "Note: outlier detection is only reliable for {}. We recommend {} to ensure the most accurate results."
+        note_distribution = "stationary distributions"
+        note_rec = "running the static vs. dynamic detector to ensure activation data before modules above is stationary"
+
+        # suggestion for constant batch check since that can make it no outliers
+        constant_str = "\tFor channel {}, we found {} constant value batches. {}\n"
+        constant_suggestion = "We recommend taking a look at the dict and data to see how frequent this occurred and why."
+
+        # compile the suggestion string
+        for module_fqn in info_dict:
+            # get module specific info
+            mod_info: Dict[str, Any] = info_dict[module_fqn]
+            # check to see if we already added high level model desc
+            added_model_desc = False
+            # look at each individual channel and add a suggestion
+            for index, outlier_detected in enumerate(mod_info[self.OUTLIER_KEY]):
+                if outlier_detected:
+                    # we found at least 1 outlier
+                    if not added_model_desc:
+                        # add the module level description
+                        outlier_string += module_suggestion_str.format(
+                            module_fqn, self.ch_axis
+                        )
+                        added_model_desc = True
+
+                    # we mark that we found at least one outlier
+                    added_module = True
+                    max_value_found_str = channel_max_value_str.format(
+                        mod_info[self.MAX_VALS_KEY][index]
+                    )
+                    channel_str = channel_suggestion_str.format(
+                        index, max_value_found_str
+                    )
+                    outlier_string += channel_str
+
+                # also check if we found constant batch
+                if mod_info[self.CONSTANT_COUNTS_KEY][index] != 0:
+                    # make sure we add a module level highlight.
+                    if not added_model_desc:
+                        # add the module level description
+                        outlier_string += module_suggestion_str.format(
+                            module_fqn, self.ch_axis
+                        )
+                        added_model_desc = True
+
+                    constant_values_for_channel = mod_info[self.CONSTANT_COUNTS_KEY][
+                        index
+                    ]
+                    formatted_str = constant_str.format(
+                        index, constant_values_for_channel, constant_suggestion
+                    )
+                    outlier_string += formatted_str
+                    # we also added at least one thing to description
+                    added_module = True
+
+        # if found outlier, give suggestion, else give default response
+        if added_module:
+            # compose the note string
+            note_composed = note_string.format(note_distribution, note_rec)
+            outlier_string += note_composed
+        else:
+            outlier_string += "There were no outliers found in the activations.\n"
+
+        return (outlier_string, info_dict)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_model_report/model_report.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_model_report/model_report.py
new file mode 100644
index 0000000000000000000000000000000000000000..19c43f6d5240acd300ce9a0ca04b1eeca2ec22d4
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_model_report/model_report.py
@@ -0,0 +1,655 @@
+# mypy: allow-untyped-defs
+from collections import OrderedDict
+from typing import Any, Callable, Dict, Set, Tuple
+
+import torch
+from torch.ao.quantization.fx._equalize import EqualizationQConfig
+from torch.ao.quantization.fx._model_report.detector import (
+    DETECTOR_IS_POST_OBS_KEY,
+    DETECTOR_OBS_ARGS_KEY,
+    DETECTOR_OBS_TO_INSERT_KEY,
+    DETECTOR_TARGET_NODE_KEY,
+    DetectorBase,
+    DetectorQConfigInfo,
+)
+from torch.ao.quantization.fx._model_report.model_report_visualizer import (
+    ModelReportVisualizer,
+)
+from torch.ao.quantization.fx.graph_module import GraphModule
+from torch.ao.quantization.observer import ObserverBase
+from torch.ao.quantization.qconfig_mapping import QConfig, QConfigMapping
+
+
+class ModelReport:
+    r"""
+    The ModelReport class aims to provide users an easy way to diagnose issues that they run into
+    with their models. The class works with all traceable GraphModules to help diagnose issues,
+    though the requirements on the type of model more-so depends on the specific report the user
+    is trying to generate. With respect to the reports, the ModelReport class is initialized with
+    a set of Detector classes, each of which generate reports on quantization configuration
+    issues a use might have.
+
+    Currently supports generating reports on:
+    - Suggestions for per-channel vs. per-tensor quantization (nn.Module)
+    - Suggestions for dynamic vs static quantization for linear layers (Graph Modules)
+    - Suggestions for input-weight equalization for linear and conv layers (Graph Modules)
+    - Suggestions for outlier detection for all layers (Graph Modules)
+
+    The ModelReport class has the primary functionality of inserting observers (primarily the ModelReportObserver)
+    where needed for each detector to gather the information it needs, and then after callibration, the ModelReport
+    class compiles the report generated by each Detector class into a single report to return to the user. It also
+    has the capability to remove all the observers it inserted as well.
+
+    * :attr:`_model` The model we wish to generate the report for. Must be a traceable GraphModule
+
+    * :attr:`_desired_report_detectors` The set of Detectors representing desired reports from the ModelReport class
+        Make sure that these are all unique types of detectors [do not have more than 1 of the same class]
+
+    * :attr:`_desired_detector_names` The set of detector names of the _desired_report_detectors.
+        This set is generated by calling the get_detector_name() of each detector
+
+    * :attr:`_detector_name_to_observer_fqns` The mapping from each detector to fqns of observers of interest
+        The purpose of this is to keep track of what observers were inserted for each detector, so that they
+        can be removed at the end if desired
+
+    * :attr:`_prepared_flag` A boolean flag that keeps track of whether we have prepared the model or not
+        This is to ensure we only insert observers once with the ModelReport instance
+
+    * :attr:`_removed_observers` A boolean to track if we have removed observers already
+        The purpose is to ensure we don't attempt to remove observers twice with the same ModelReport
+        instance. This also allows the functionality where we can generate the report multiple times
+        as long as we haven't removed the observers yet.
+
+    Note:
+        This class was initially designed to work with the Fx Graph Mode workflow in mind. However,
+        full functionality is available as long as there is a traceable GraphModule that is being used.
+        One method to get a traceable GraphModule without going through the Fx workflow is to use
+        the QuantizationTracer class.
+
+    General Flow for Fx workflow:
+    1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects and model
+    2.) Prepare your model with prepare_fx
+    3.) Call model_report.prepare_detailed_calibration to add relevant observers
+    4.) Callibrate your model with data
+    5.) Call model_report.generate_report on your model to generate report and optionally remove added observers
+    Optional
+        6.) Call model_report.generate_visualizer to get a ModelReportVisualizer instance
+        7.) To help in parsing report information and debugging, view report info as a:
+            - Table
+            - Histogram
+            - Line plot
+    8.) Call model_report.generate_qconfigs to generate the qconfigs based on the report suggestions
+
+    Example (with QuantizationTracer):
+        >>> # xdoctest: +SKIP
+        >>> # get the necessary qconfig
+        >>> config = PrepareCustomConfig()
+        >>> skipped_module_names, skipped_module_classes = get_skipped_module_name_and_classes(config, False)
+
+        >>> # initialize our model and get GraphModule
+        >>> model = SomeModel()
+        >>> tracer = QuantizationTracer(skipped_module_names, skipped_module_classes)
+        >>> graph_module = GraphModule(model, tracer.trace(model))
+
+        >>> # get our set of detectors and ModelReport instance
+        >>> detector_set = set([DynamicStaticDetector(tolerance=0.5), InputWeightEqualizationDetector(ratio_threshold=0.7)])
+        >>> tracer_reporter = ModelReport(graph_module, tracer_detector_set)
+
+        >>> # now we insert the observers and callibrate the model
+        >>> tracer_model_with_observers = tracer_reporter.prepare_detailed_calibration()
+        >>> for i in range(num_callibration_batches):
+        >>>     example_input = get_callibration_input()
+        >>>     tracer_model_with_observers(example_input)
+
+        >>> # finally we generate the reports and optionally remove the observers we inserted
+        >>> reports = tracer_reporter.generate_model_report(remove_inserted_observers=True)
+
+        >>> # Optional: we can generate the qconfig mapping based on the suggestions
+        >>> qconfigs = model_report.generate_qconfig_mapping()
+
+        >>> # Optional: we can generate the equalization mapping based on the suggestions
+        >>> qconfigs = model_report.generate_equalization_mapping()
+
+        >>> # Optional: we get a ModelReportVisualizer instance to do any visualizations desired
+        >>> model_report_visualizer = tracer_reporter.generate_visualizer()
+
+    """
+
+    def __init__(self, model: GraphModule, desired_report_detectors: Set[DetectorBase]):
+        if len(desired_report_detectors) == 0:
+            raise ValueError("Should include at least 1 desired report")
+
+        # keep track of the model we wish to generate report for
+        self._model: GraphModule = model
+
+        # keep the reports private so they can't be modified
+        self._desired_report_detectors = desired_report_detectors
+        self._desired_detector_names = {
+            detector.get_detector_name() for detector in desired_report_detectors
+        }
+
+        # keep a mapping of desired reports to observers of interest
+        # this is to get the readings, and to remove them, can create a large set
+        # this set can then be used to traverse the graph and remove added observers
+        self._detector_name_to_observer_fqns: Dict[str, Set[str]] = {}
+
+        # initialize each report to have empty set of observers of interest
+        for desired_report in self._desired_detector_names:
+            self._detector_name_to_observer_fqns[desired_report] = set()
+
+        # flags to ensure that we can only prepare and remove observers once
+        self._prepared_flag = False
+        self._removed_observers = False
+
+        # store the reports that we generated for visualization purposes
+        # initially empty since no reports generated
+        self._generated_reports: Dict[str, Dict] = {}
+
+    def get_desired_reports_names(self) -> Set[str]:
+        """Returns a copy of the desired reports for viewing"""
+        return self._desired_detector_names.copy()
+
+    def get_observers_of_interest(self) -> Dict[str, Set[str]]:
+        """Returns a copy of the observers of interest for viewing"""
+        return self._detector_name_to_observer_fqns.copy()
+
+    def prepare_detailed_calibration(self) -> GraphModule:
+        r"""
+        Takes in a graph model and inserts the following observers:
+        - ModelReportObserver
+
+        Each observer is inserted based on the desired_reports into the relevant locations
+
+        Right now, each report in self._desired_detector_names has independent insertions
+            However, if a module already has a Observer of the same type, the insertion will not occur
+            This is because all of the same type of Observer collect same information, so redundant
+
+        Returns the same GraphModule with the observers inserted
+        """
+
+        # if already prepared once, cannot prepare again
+        if self._prepared_flag:
+            raise ValueError(
+                "Already ran preparing detailed callibration. Run the report generation next after callibration."
+            )
+
+        # loop through each detector, find where placements should be, and keep track
+        insert_observers_fqns: Dict[str, Any] = {}
+
+        for detector in self._desired_report_detectors:
+            # determine observer points for each detector
+            obs_fqn_to_info = detector.determine_observer_insert_points(self._model)
+            # map each insert point to the observer to use
+            insert_observers_fqns.update(obs_fqn_to_info)
+            # update the set of observers this report cares about
+            self._detector_name_to_observer_fqns[detector.get_detector_name()] = set(
+                obs_fqn_to_info.keys()
+            )
+
+        # now insert all the observers at their desired locations
+        for observer_fqn in insert_observers_fqns:
+            target_node = insert_observers_fqns[observer_fqn][DETECTOR_TARGET_NODE_KEY]
+            insert_obs = insert_observers_fqns[observer_fqn][DETECTOR_OBS_TO_INSERT_KEY]
+            insert_post = insert_observers_fqns[observer_fqn][DETECTOR_IS_POST_OBS_KEY]
+            observer_args = insert_observers_fqns[observer_fqn][DETECTOR_OBS_ARGS_KEY]
+            self._insert_observer_around_module(
+                observer_fqn, target_node, insert_obs, observer_args, insert_post
+            )
+
+        self._prepared_flag = True
+
+        return self._model
+
+    def _insert_observer_around_module(
+        self,
+        obs_fqn: str,
+        target_node: torch.fx.node.Node,
+        obs_to_insert: ObserverBase,
+        observer_args: Tuple,
+        insert_post: bool,
+    ):
+        r"""
+        Helper function that inserts the observer into both the graph structure and the module of the model
+
+        Args
+            node_fqn (str): The fully qualified name of the observer we want to insert
+            target_node (torch.fx.node.Node): The node in model we are inserting observers around
+            obs_to_insert (ObserverBase): The observer we are inserting around target_node
+            observer_args (Tuple): The arguments we want to pass into the observer
+            insert_post (bool): whether this is meant to be a post observer for this node
+        """
+        # if we are inserting post, then our target node is the next node
+        if insert_post:
+            target_node = target_node.next
+
+        with self._model.graph.inserting_before(target_node):
+            self._model.add_submodule(obs_fqn, obs_to_insert)
+            self._model.graph.create_node(
+                op="call_module", target=obs_fqn, args=observer_args
+            )
+
+        # recompile model after inserts are made
+        self._model.recompile()
+
+    def _get_node_from_fqn(self, node_fqn: str) -> torch.fx.node.Node:
+        r"""
+        Takes in a node fqn and returns the node based on the fqn
+
+        Args
+            node_fqn (str): The fully qualified name of the node we want to find in model
+
+        Returns the Node object of the given node_fqn otherwise returns None
+        """
+        node_to_return = None
+        for node in self._model.graph.nodes:
+            # if the target matches the fqn, it's the node we are looking for
+            if node.target == node_fqn:
+                node_to_return = node
+                break
+
+        if node_to_return is None:
+            raise ValueError("The node_fqn is was not found within the module.")
+
+        # assert for MyPy
+        assert isinstance(node_to_return, torch.fx.node.Node)
+
+        return node_to_return
+
+    def generate_model_report(
+        self, remove_inserted_observers: bool
+    ) -> Dict[str, Tuple[str, Dict]]:
+        r"""
+        Generates all the requested reports.
+
+        Note:
+            You should have callibrated the model with relevant data before calling this
+
+        The reports generated are specified by the desired_reports specified in desired_reports
+
+        Can optionally remove all the observers inserted by the ModelReport instance
+
+        Args:
+            remove_inserted_observers (bool): True to remove the observers inserted by this ModelReport instance
+
+        Returns a mapping of each desired report name to a tuple with:
+            The textual summary of that report information
+            A dictionary containing relevant statistics or information for that report
+
+        Note:
+            Throws exception if we try to generate report on model we already removed observers from
+            Throws exception if we try to generate report without preparing for callibration
+        """
+        # if we haven't prepped model for callibration, then we shouldn't generate report yet
+        if not self._prepared_flag:
+            raise Exception(  # noqa: TRY002
+                "Cannot generate report without preparing model for callibration"
+            )
+
+        # if we already removed the observers, we cannot generate report
+        if self._removed_observers:
+            raise Exception(  # noqa: TRY002
+                "Cannot generate report on model you already removed observers from"
+            )
+
+        # keep track of all the reports of interest and their outputs
+        reports_of_interest = {}
+
+        for detector in self._desired_report_detectors:
+            # generate the individual report for the detector
+            report_output = detector.generate_detector_report(self._model)
+            reports_of_interest[detector.get_detector_name()] = report_output
+
+        # if user wishes to remove inserted observers, go ahead and remove
+        if remove_inserted_observers:
+            self._removed_observers = True
+            # get the set of all Observers inserted by this instance of ModelReport
+            all_observers_of_interest: Set[str] = set()
+            for desired_report in self._detector_name_to_observer_fqns:
+                observers_of_interest = self._detector_name_to_observer_fqns[
+                    desired_report
+                ]
+                all_observers_of_interest.update(observers_of_interest)
+
+            # go through all_observers_of_interest and remove them from the graph and model
+            for observer_fqn in all_observers_of_interest:
+                # remove the observer from the model
+                self._model.delete_submodule(observer_fqn)
+
+                # remove the observer from the graph structure
+                node_obj = self._get_node_from_fqn(observer_fqn)
+
+                if node_obj:
+                    self._model.graph.erase_node(node_obj)
+                else:
+                    raise ValueError("Node no longer exists in GraphModule structure")
+
+            # remember to recompile the model
+            self._model.recompile()
+
+        # save the generated reports for visualization purposes
+        saved_reports: Dict[str, Dict] = {
+            report_name: report_tuple[1]
+            for report_name, report_tuple in reports_of_interest.items()
+        }
+
+        self._generated_reports = saved_reports
+
+        # return the reports of interest
+        return reports_of_interest
+
+    def _is_same_info_for_same_key(self, info_dict_a: Dict, info_dict_b: Dict) -> bool:
+        r"""
+        Takes in two dictionaries and ensures that any common keys between the two have the same
+        values.
+
+        Args:
+            info_dict_a (Dict): First dictionary we wish to compare
+            info_dict_b (Dict): Second dictionary we wish to compare
+
+        Returns True if all shared keys have same values, false otherwise
+        """
+        # get the set of keys for both
+        dict_a_keys: Set = set(info_dict_a.keys())
+        dict_b_keys: Set = set(info_dict_b.keys())
+
+        # get the insersection keys and check if same value for both dicts
+        intersecting_keys: Set = dict_a_keys.intersection(dict_b_keys)
+
+        for key in intersecting_keys:
+            dict_a_val = info_dict_a[key]
+            dict_b_val = info_dict_b[key]
+
+            # if it's a tensor we have to handle separately
+            if type(dict_a_val) == torch.Tensor:
+                # if dict_b_val not tensor, automatically false
+                if (
+                    type(dict_b_val) != torch.Tensor
+                    or sum(dict_a_val != dict_b_val) != 0
+                ):
+                    return False
+            else:
+                # for non-tensor vals
+                if dict_a_val != dict_b_val:
+                    return False
+
+        # if no non matching shared keys found, return true
+        return True
+
+    def _reformat_reports_for_visualizer(self) -> OrderedDict:
+        r"""
+        Takes the generated reports and reformats them into the format that is desired by the
+        ModelReportVisualizer
+
+        Returns an OrderedDict mapping module_fqns to their features
+        """
+        # we want to reorder and reformat the information so it is ordered in terms of order
+        # found in the model
+
+        # first create new dict with all modules as keys and features under respective module
+        module_fqns_to_features: Dict[str, Dict] = {}
+
+        for report_name in self._generated_reports:
+            # get mod -> feature dict and go through
+            module_info = self._generated_reports[report_name]
+
+            for module_fqn in module_info:
+                # check if already in our accumulation dict
+                if module_fqn in module_fqns_to_features:
+                    # we merge all the features together
+                    new_info: Dict = module_info[module_fqn]
+                    present_info: Dict = module_fqns_to_features[module_fqn]
+
+                    # merge them together into the new unioned dict
+                    # same features keys -> same info, so okay if override
+
+                    # do safety check to make sure shared keys have same info
+                    if self._is_same_info_for_same_key(new_info, present_info):
+                        module_fqns_to_features[module_fqn] = {
+                            **new_info,
+                            **present_info,
+                        }
+                    else:
+                        error_str = "You have the same key with different values across detectors. "
+                        error_str += "Someone incorrectly implemented a detector with conflicting keys to existing detectors."
+                        raise ValueError(error_str)
+                else:
+                    # we just set it
+                    module_fqns_to_features[module_fqn] = module_info[module_fqn]
+
+        # our ordered dict so that modules can be ordered in order of how they appear in model
+        features_by_module: OrderedDict[str, Dict] = OrderedDict()
+
+        # we loop through modules in graph in order
+        for fqn, module in self._model.named_modules():
+            # find that fqn in fqns_to_features
+            if fqn in module_fqns_to_features:
+                # add it to our ordered dict
+                features_by_module[fqn] = module_fqns_to_features[fqn]
+
+        # return the ordered dict of info we created
+        return features_by_module
+
+    def generate_visualizer(self) -> ModelReportVisualizer:
+        r"""
+        Generates a ModelReportVisualizer instance using the reports generated
+        by the generate_model_report() method.
+
+        Returns the generated ModelReportVisualizer instance initialized
+
+        Note:
+            Throws exception if attempt to get visualizers without generating report
+        """
+        # check if user has generated reports at least once
+        if len(self._generated_reports) == 0:
+            raise Exception(  # noqa: TRY002
+                "Unable to generate visualizers without first generating reports"
+            )
+
+        # get the ordered dict mapping modules to their full set of collected features / stats
+        module_fqns_to_features: OrderedDict = self._reformat_reports_for_visualizer()
+
+        # create and return ModelReportVisualizer instance
+        visualizer: ModelReportVisualizer = ModelReportVisualizer(
+            module_fqns_to_features
+        )
+
+        return visualizer
+
+    def _generate_qconfig_mapping_helper(
+        self,
+        detector_qconfig_info_combined: Dict[str, DetectorQConfigInfo],
+        generation_function: Callable,
+    ) -> QConfigMapping:
+        r"""
+        This helper takes in the compiled detector qconfig info that
+        has been compiled together and merges it into a QConfigMapping
+        """
+        # keep track of the qconfigmapping
+        qconfig_mapping = QConfigMapping()
+
+        # loop through each module / fqn and attempt to create QConfigMapping
+        for fqn, module in self._model.named_modules():
+            # if we have a qconfig info for this module
+            if fqn in detector_qconfig_info_combined:
+                qconfig_info_compiled = detector_qconfig_info_combined[fqn]
+
+                # now generate the qconfig and add it to the mapping
+                generated_qconfig = generation_function(qconfig_info_compiled, module)
+
+                # add to our config
+                qconfig_mapping.set_module_name(fqn, generated_qconfig)
+
+        # return compiled mapping
+        return qconfig_mapping
+
+    def _update_detector_quantizaiton_qconfig_info(
+        self, combined_info: DetectorQConfigInfo, new_info: DetectorQConfigInfo
+    ):
+        r"""
+        Takes in the old and new information and updates the combined information.
+
+        Args:
+            combined_info (DetectorQConfigInfo): The DetectorQConfigInfo we are compiling all of the information in
+            new_info (DetectorQConfigInfo): The DetectorQConfigInfo with the information we are trying to merge the new info
+                into it
+        """
+        combined_info.is_activation_dynamic = (
+            combined_info.is_activation_dynamic or new_info.is_activation_dynamic
+        )
+        combined_info.is_weight_per_channel = (
+            combined_info.is_weight_per_channel or new_info.is_weight_per_channel
+        )
+
+    def _update_detector_equalization_qconfig_info(
+        self, combined_info: DetectorQConfigInfo, new_info: DetectorQConfigInfo
+    ):
+        r"""
+        Takes in the old and new information and updates the combined information.
+
+        Args:
+            combined_info (DetectorQConfigInfo): The DetectorQConfigInfo we are compiling all of the information in
+            new_info (DetectorQConfigInfo): The DetectorQConfigInfo with the information we are trying to merge the new info
+                into it
+        """
+        is_equalization_recommended = (
+            combined_info.is_equalization_recommended
+            or new_info.is_equalization_recommended
+        )
+        combined_info.is_equalization_recommended = is_equalization_recommended
+
+    def _generate_module_fqn_to_detector_info_mapping(
+        self, update_qconfig_info_function: Callable
+    ) -> Dict[str, DetectorQConfigInfo]:
+        r"""
+        Generates a QConfigMapping based on the suggestions of the
+        ModelReport API. The generated mapping encompasses all the
+        different types of feedback from the different detectors
+        all into one place.
+
+        These configs are based on the suggestions provided by the ModelReport API
+        and can only be generated once the reports have been generated.
+
+        Args:
+            update_qconfig_info_function (Callable) takes in a function that takes in two DetectorQConfigInfo
+            and updates the one that is being compiled
+
+        Returns a Dict mapping module_fqns to DetectorQConfigInfo objects
+
+        Note:
+            Throws exception if we try to generate mapping on model we already removed observers from
+            Throws exception if we try to generate mapping without preparing for callibration
+        """
+        # if we haven't prepped model for callibration, then we shouldn't generate mapping yet
+        if not self._prepared_flag:
+            raise Exception(  # noqa: TRY002
+                "Cannot generate report without preparing model for callibration"
+            )
+
+        # if we already removed the observers, we cannot mapping
+        if self._removed_observers:
+            raise Exception(  # noqa: TRY002
+                "Cannot generate report on model you already removed observers from"
+            )
+
+        # keep track of qconfig info for each module across detectors
+        detector_qconfig_info_combined: Dict[str, DetectorQConfigInfo] = {}
+
+        for detector in self._desired_report_detectors:
+            # get the info from the detector
+            detector_info: Dict[str, DetectorQConfigInfo] = detector.get_qconfig_info(
+                self._model
+            )
+
+            # we go through the modules
+            for module_fqn in detector_info:
+                # see if we already have info on it
+                if module_fqn in detector_qconfig_info_combined:
+                    # we combine the current options with what is there
+                    current_options = detector_qconfig_info_combined[module_fqn]
+                    detector_options = detector_info[module_fqn]
+
+                    update_qconfig_info_function(current_options, detector_options)
+                else:
+                    # we just use this for now
+                    detector_qconfig_info_combined[module_fqn] = detector_info[
+                        module_fqn
+                    ]
+
+        return detector_qconfig_info_combined
+
+    def generate_qconfig_mapping(self) -> QConfigMapping:
+        r"""
+        Generates a QConfigMapping based on the suggestions of the
+        ModelReport API. The generated mapping encompasses all the
+        different types of feedback from the different detectors
+        all into one place.
+
+        These configs are based on the suggestions provided by the ModelReport API
+        and can only be generated once the reports have been generated.
+
+        Returns a QConfigMapping for the quantization configuration
+
+        Note:
+            Throws exception if we try to generate mapping on model we already removed observers from
+            Throws exception if we try to generate mapping without preparing for callibration
+        """
+        # get the mapping info
+        detector_qconfig_info_combined = (
+            self._generate_module_fqn_to_detector_info_mapping(
+                self._update_detector_quantizaiton_qconfig_info
+            )
+        )
+
+        # we will do a bit of processing and remove fqns that don't have input weight recommended
+
+        # now we generate the QConfig for each of the options
+        mapping: QConfigMapping = self._generate_qconfig_mapping_helper(
+            detector_qconfig_info_combined, self._quantization_config_generator
+        )
+
+        # return the generated mapping
+        return mapping
+
+    def _quantization_config_generator(
+        self, detector_qconfig_info: DetectorQConfigInfo, module: torch.nn.Module
+    ) -> QConfig:
+        r"""
+        Returns the quantization configuration generated by the DetectorQConfigInfo object
+        """
+        return detector_qconfig_info.generate_quantization_qconfig(module)
+
+    def _equalization_config_generator(
+        self, detector_qconfig_info: DetectorQConfigInfo, module: torch.nn.Module
+    ) -> EqualizationQConfig:
+        r"""
+        We ignore the module argument here, and only focus on thedetector_qconfig_info
+
+        Returns the equalization configuration generated by the DetectorQConfigInfo object
+        """
+        return detector_qconfig_info.generate_equalization_qconfig()
+
+    def generate_equalization_mapping(self) -> QConfigMapping:
+        r"""
+        Generates a QConfigMapping based on the suggestions of the
+        ModelReport API for equalization. The generated mapping encompasses all the
+        different types of feedback from the input-weight equalization detector.
+
+        These configs are based on the suggestions provided by the ModelReport API
+        and can only be generated once the reports have been generated.
+
+        Returns a QConfigMapping for the equalization configuration
+        """
+        # get the mapping info
+        detector_qconfig_info_combined = (
+            self._generate_module_fqn_to_detector_info_mapping(
+                self._update_detector_equalization_qconfig_info
+            )
+        )
+
+        # now we generate the QConfig for each of the options
+        mapping: QConfigMapping = self._generate_qconfig_mapping_helper(
+            detector_qconfig_info_combined, self._equalization_config_generator
+        )
+
+        # return the generated mapping
+        return mapping
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_model_report/model_report_observer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_model_report/model_report_observer.py
new file mode 100644
index 0000000000000000000000000000000000000000..db9c130606a8201d03f4adf9ca81694145e41718
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_model_report/model_report_observer.py
@@ -0,0 +1,285 @@
+# mypy: allow-untyped-defs
+import torch
+from torch.ao.quantization.observer import ObserverBase
+
+
+class ModelReportObserver(ObserverBase):
+    r"""This observer is used to record additional information regarding keeping track
+    of S = average_batch_activation_range/epoch_activation_range.
+
+    The purpose of this information is to prepare a report to present to users on whether
+    Dynamic or Static Quantization is more appropriate for their model given the general
+    distributions of their data.
+
+    Args:
+        ch_axis (int, optional): The channel axis for which the range and outlier stats are computed
+            Default: 1
+        comp_percentile (float, optional): The percentile to compare against 100 percentile to find outliers
+            Should be between 0 and 1 exclusive
+            Default: 0.9
+
+    * :attr:`num_batches_tracked` specifies number of batches passed through the observer
+
+    * :attr:`average_batch_activation_range` defines average across the ranges of each batch passed through
+
+    * :attr:`epoch_activation_min` defines the minimum value passed through the observer
+
+    * :attr:`epoch_activation_max` defines the maximum value passed through the observer
+
+    * :attr:`ch_axis` defines the channel being used to compute per channel min max stats
+
+    * :attr:`min_val` defines the per channel minimum values passed through
+
+    * :attr:`max_val` defines the per channel maximum values passed through
+
+    * :attr:`comp_percentile` defines comparison percentile to find outliers
+
+    * :attr:`average_percentile_ratio` defines the per channel average percentile ratios
+
+    * :attr:`percentile_batches_tracked` defines the number of percentile batches tracked for each channel
+
+    * :attr:`constant_channels` defines the number of batches that aren't constant channels per channel
+
+    Note: this tool is meant for FX Graph Mode Quantization
+    """
+
+    epoch_activation_min: torch.Tensor
+    epoch_activation_max: torch.Tensor
+    min_val: torch.Tensor
+    max_val: torch.Tensor
+    comp_percentile: torch.Tensor
+    average_percentile_ratio: torch.Tensor
+    percentile_batches_tracked: torch.Tensor
+    constant_channels: torch.Tensor
+
+    def __init__(self, ch_axis: int = 1, comp_percentile: float = 0.9):
+        super().__init__(torch.qint8)
+        self.num_batches_tracked = 0
+
+        # keep track of the min and mix of the range for average batch and epoch as a whole
+        self.average_batch_activation_range: torch.Tensor = torch.tensor(float(0))
+        self.register_buffer("epoch_activation_min", torch.tensor(float("inf")))
+        self.register_buffer("epoch_activation_max", torch.tensor(float("-inf")))
+
+        # keep track of per channel min max information using the given channel
+        self.ch_axis: int = ch_axis
+        self.register_buffer("min_val", torch.tensor([]))
+        self.register_buffer("max_val", torch.tensor([]))
+
+        # keep track of percentile ratio information per channel
+        self.register_buffer("comp_percentile", torch.tensor([comp_percentile]))
+        self.register_buffer("average_percentile_ratio", torch.tensor([]))
+        self.register_buffer("percentile_batches_tracked", torch.tensor([]))
+        self.register_buffer("constant_channels", torch.tensor([]))
+
+    def forward(self, x):
+        x_copy = x.detach()  # avoid keeping autograd tape
+        x_copy = x_copy.to(self.epoch_activation_min.dtype)
+
+        x_copy = self._calculate_range_stats(x_copy)
+        x_copy = self._calculate_min_max_stats(x_copy)
+        x_copy = self._calculate_percentile_stats(x_copy)
+
+        # return the passed in the value
+        return x
+
+    def _calculate_range_stats(self, x_copy):
+        r"""Calculates and stores range stats with forward values.
+
+        Args
+            x_copy: A copy of the forward data
+
+        Returns the passed in x_copy
+        """
+        # get the min, max values of the data
+        min_val_cur, max_val_cur = torch.aminmax(x_copy)
+
+        # calculate new epoch range values
+        epoch_min_val = torch.min(self.epoch_activation_min, min_val_cur)
+        epoch_max_val = torch.max(self.epoch_activation_max, max_val_cur)
+
+        self.epoch_activation_min.copy_(epoch_min_val)
+        self.epoch_activation_max.copy_(epoch_max_val)
+
+        # calculate the average batch activation range
+        current_batch_range = max_val_cur - min_val_cur
+        new_range = (
+            self.average_batch_activation_range * self.num_batches_tracked
+            + current_batch_range
+        ) / (self.num_batches_tracked + 1)
+
+        self.average_batch_activation_range = new_range
+        self.num_batches_tracked += 1  # new batch was processed
+
+        return x_copy
+
+    def _calculate_min_max_stats(self, x_copy):
+        r"""Calculates and stores the per_channel min, max stats with forward values.
+        Does calculation based on channel axis: self.ch_axis
+
+        Args
+            x_copy: A copy of the forward data
+
+        Returns the passed in x_copy
+        """
+        # get the current min and max vals
+        min_val = self.min_val
+        max_val = self.max_val
+        x_dim = x_copy.size()
+
+        new_axis_list = [i for i in range(len(x_dim))]  # noqa: C416
+        new_axis_list[self.ch_axis] = 0
+        new_axis_list[0] = self.ch_axis
+        y = x_copy.permute(new_axis_list)
+        # Need to match dtype of min/max because the updates to buffers
+        # are done in place and types need to match for comparisons
+        y = y.to(self.min_val.dtype)
+        y = torch.flatten(y, start_dim=1)
+        if min_val.numel() == 0 or max_val.numel() == 0:
+            min_val, max_val = torch.aminmax(y, dim=1)
+        else:
+            min_val_cur, max_val_cur = torch.aminmax(y, dim=1)
+            min_val = torch.min(min_val_cur, min_val)
+            max_val = torch.max(max_val_cur, max_val)
+
+        self.min_val.resize_(min_val.shape)
+        self.max_val.resize_(max_val.shape)
+        self.min_val.copy_(min_val)
+        self.max_val.copy_(max_val)
+
+        return x_copy
+
+    def _calculate_percentile_stats(self, x_copy):
+        r"""Calculates and stores the per_channel percentile stats with forward values.
+        Does calculation based on channel axis: self.ch_axis
+
+        Args
+            x_copy: A copy of the forward data
+
+        Returns the passed in x_copy
+        """
+        # get the dimension of the copy
+        x_dim = x_copy.size()
+
+        new_axis_list = [i for i in range(len(x_dim))]  # noqa: C416
+        new_axis_list[self.ch_axis] = 0
+        new_axis_list[0] = self.ch_axis
+        y = x_copy.permute(new_axis_list)
+        # Need to match dtype of min/max because the updates to buffers
+        # are done in place and types need to match for comparisons
+        y = y.to(self.min_val.dtype)
+        y = torch.flatten(y, start_dim=1)
+        y = y.to(dtype=self.min_val.dtype, device="cpu")
+
+        # find the percentile values along the axis
+        # we want both 100th percentile and comp_percentile
+        # we also want to find 0th quartile to see if we have constant channel
+        quantiles_list = [0, self.comp_percentile, 1.00]
+        quantiles_to_find = torch.tensor(quantiles_list, dtype=self.min_val.dtype)
+
+        # find the quantiles
+        desired_quantiles = torch.quantile(
+            y, quantiles_to_find, dim=self.ch_axis, interpolation="lower"
+        )
+        zero_quantile = desired_quantiles[0]
+        comp_quantile = desired_quantiles[1]
+        hundreth_quartile = desired_quantiles[2]
+
+        # if any of the channels have 0s, we ignore that channel for this calculation
+        any_non_zero_quantile_value: torch.Tensor = (
+            comp_quantile != torch.tensor([0])
+        ) | (hundreth_quartile != torch.tensor([0]))
+        any_non_zero_quantile_value = (
+            any_non_zero_quantile_value.int()
+        )  # transform boolean values to int values
+
+        # we also check if we have a constant channel
+        any_constant_channels: torch.Tensor = (
+            hundreth_quartile - zero_quantile
+        ) == torch.tensor([0])
+        any_constant_channels = (
+            any_constant_channels.int()
+        )  # transform boolean values to int values
+
+        # possibilities to get nan as an answer
+        #   will ignore any of these three cases with 0s and just not deal with them for now
+        # case (1) 0 in numerator: issue if 0 is largest, all negative, and rest are really negative
+        # case (2) 0 in denominator: is possible unless case 3, we just ignore
+        # case (3) 0 in both: not outlier, channel just kinda useless, ignore
+
+        # get the ratio and get rid of nan values
+        quantile_ratios = hundreth_quartile / comp_quantile
+        quantile_ratios = torch.nan_to_num(quantile_ratios)
+        # update averages, remembering to only update if didn't have zeros
+        ratio_if_not_zero = any_non_zero_quantile_value * quantile_ratios
+
+        # if num_batches and average_ratio are not initialized, we want to initialize them
+        if (
+            self.percentile_batches_tracked.shape[0] == 0
+            or self.average_percentile_ratio.shape[0] == 0
+        ):
+            self.percentile_batches_tracked = torch.zeros_like(
+                any_non_zero_quantile_value
+            )
+            self.average_percentile_ratio = torch.zeros_like(ratio_if_not_zero)
+
+        # also initialize the constant channel var if that is not initialized separately
+        if self.constant_channels.shape[0] == 0:
+            self.constant_channels = torch.zeros_like(any_constant_channels)
+
+        # get current num batches and average ratio
+        num_batches = self.percentile_batches_tracked
+        average_ratio = self.average_percentile_ratio
+
+        # calculate new_number of batches, new_ratios, and get rid of nans because of 0 size batches
+        new_number_of_batches: torch.Tensor = num_batches + any_non_zero_quantile_value
+        new_ratios: torch.Tensor = (
+            (average_ratio * num_batches) + ratio_if_not_zero
+        ) / new_number_of_batches
+        new_ratios = torch.nan_to_num(new_ratios)
+
+        # update the number of non-constant channels
+        new_constant_count: torch.Tensor = (
+            self.constant_channels + any_constant_channels
+        )
+
+        # update the values locally
+        self.percentile_batches_tracked.copy_(new_number_of_batches)
+        self.average_percentile_ratio.copy_(new_ratios)
+        self.constant_channels.copy_(new_constant_count)
+
+        return x_copy
+
+    @torch.jit.export
+    def get_batch_to_epoch_ratio(self):
+        epoch_activation_range = self.epoch_activation_max - self.epoch_activation_min
+
+        if epoch_activation_range == torch.tensor(float(0)):
+            raise ValueError("Range for Epoch is 0")
+        elif epoch_activation_range == torch.tensor(float("inf")):
+            raise ValueError(
+                "No data has been run through observer or infinity value present"
+            )
+        else:
+            return self.average_batch_activation_range / epoch_activation_range
+
+    @torch.jit.export
+    def reset_batch_and_epoch_values(self):
+        # set all the values back to their original defaults for a new epoch
+        # keep device
+        device = self.max_val.device
+        self.num_batches_tracked = 0
+        self.average_batch_activation_range = torch.tensor(float(0), device=device)
+        self.epoch_activation_min = torch.tensor(float("inf"), device=device)
+        self.epoch_activation_max = torch.tensor(float("-inf"), device=device)
+        self.min_val = torch.tensor([], device=device)
+        self.max_val = torch.tensor([], device=device)
+        self.average_percentile_ratio = torch.tensor([], device=device)
+        self.percentile_batches_tracked = torch.tensor([], device=device)
+        self.constant_channels = torch.tensor([], device=device)
+
+    @torch.jit.export
+    def calculate_qparams(self):
+        raise Exception(  # noqa: TRY002
+            "calculate_qparams should not be called for ModelReportObserver"
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..712b0669ee330d404dfc09a85fd0c002e7d6b9ab
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py
@@ -0,0 +1,713 @@
+# mypy: allow-untyped-defs
+from collections import OrderedDict as OrdDict
+from typing import Any, Dict, List, OrderedDict, Set, Tuple
+
+import torch
+
+
+# try to import tablate
+got_tabulate = True
+try:
+    from tabulate import tabulate
+except ImportError:
+    got_tabulate = False
+
+
+# var to see if we could import matplotlib
+got_matplotlib = True
+try:
+    import matplotlib.pyplot as plt
+except ImportError:
+    got_matplotlib = False
+
+
+class ModelReportVisualizer:
+    r"""
+    The ModelReportVisualizer class aims to provide users a way to visualize some of the statistics
+    that were generated by the ModelReport API. However, at a higher level, the class aims to provide
+    some level of visualization of statistics to PyTorch in order to make it easier to parse data and
+    diagnose any potential issues with data or a specific model. With respect to the visualizations,
+    the ModelReportVisualizer class currently supports several methods of visualizing data.
+
+    Supported Visualization Methods Include:
+    - Table format
+    - Plot format (line graph)
+    - Histogram format
+
+    For all of the existing visualization methods, there is the option to filter data based on:
+    - A module fqn prefix
+    - Feature [required for the plot and histogram]
+
+    * :attr:`generated_reports` The reports generated by the ModelReport class in the structure below
+        Ensure sure that features that are the same across different report contain the same name
+        Ensure that objects representing the same features are the same type / dimension (where applicable)
+
+    Note:
+        Currently, the ModelReportVisualizer class supports visualization of data generated by the
+        ModelReport class. However, this structure is extensible and should allow the visualization of
+        other information as long as the information is structured in the following general format:
+
+        Report Structure
+        -- module_fqn [module with attached detectors]
+            |
+            -- feature keys [not every detector extracts same information]
+                                    [same collected info has same keys, unless can be specific to detector]
+
+
+    The goal behind the class is that the generated visualizations can be used in conjunction with the generated
+    report for people to get a better understanding of issues and what the fix might be. It is also just to provide
+    a good visualization platform, since it might be hard to parse through the ModelReport returned dictionary as
+    that grows in size.
+
+    General Use Flow Expected
+    1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects
+    2.) Prepare your model with prepare_fx
+    3.) Call model_report.prepare_detailed_calibration on your model to add relevant observers
+    4.) Callibrate your model with data
+    5.) Call model_report.generate_report on your model to generate report and optionally remove added observers
+    6.) Use output of model_report.generate_report to initialize ModelReportVisualizer instance
+    7.) Use instance to view different views of data as desired, applying filters as needed
+        8.) Either see the super detailed information or just the actual printed or shown table / plot / histogram
+
+    """
+
+    # keys for table dict
+    TABLE_TENSOR_KEY = "tensor_level_info"
+    TABLE_CHANNEL_KEY = "channel_level_info"
+
+    # Constants for header vals
+    NUM_NON_FEATURE_TENSOR_HEADERS = 2
+    NUM_NON_FEATURE_CHANNEL_HEADERS = 3
+
+    # Constants for row index in header
+    CHANNEL_NUM_INDEX = 2
+
+    def __init__(self, generated_reports: OrderedDict[str, Any]):
+        r"""
+        Initializes the ModelReportVisualizer instance with the necessary reports.
+
+        Args:
+            generated_reports (Dict[str, Any]): The reports generated by the ModelReport class
+                can also be a dictionary generated in another manner, as long as format is same
+        """
+        self.generated_reports = generated_reports
+
+    def get_all_unique_module_fqns(self) -> Set[str]:
+        r"""
+        The purpose of this method is to provide a user the set of all module_fqns so that if
+        they wish to use some of the filtering capabilities of the ModelReportVisualizer class,
+        they don't need to manually parse the generated_reports dictionary to get this information.
+
+        Returns all the unique module fqns present in the reports the ModelReportVisualizer
+        instance was initialized with.
+        """
+        # returns the keys of the ordered dict
+        return set(self.generated_reports.keys())
+
+    def get_all_unique_feature_names(
+        self, plottable_features_only: bool = True
+    ) -> Set[str]:
+        r"""
+        The purpose of this method is to provide a user the set of all feature names so that if
+        they wish to use the filtering capabilities of the generate_table_view(), or use either of
+        the generate_plot_view() or generate_histogram_view(), they don't need to manually parse
+        the generated_reports dictionary to get this information.
+
+        Args:
+            plottable_features_only (bool): True if the user is only looking for plottable features,
+                False otherwise
+                plottable features are those that are tensor values
+                Default: True (only return those feature names that are plottable)
+
+        Returns all the unique module fqns present in the reports the ModelReportVisualizer
+        instance was initialized with.
+        """
+        unique_feature_names = set()
+        for module_fqn in self.generated_reports:
+            # get dict of the features
+            feature_dict: Dict[str, Any] = self.generated_reports[module_fqn]
+
+            # loop through features
+            for feature_name in feature_dict:
+                # if we need plottable, ensure type of val is tensor
+                if (
+                    not plottable_features_only
+                    or type(feature_dict[feature_name]) == torch.Tensor
+                ):
+                    unique_feature_names.add(feature_name)
+
+        # return our compiled set of unique feature names
+        return unique_feature_names
+
+    def _get_filtered_data(
+        self, feature_filter: str, module_fqn_filter: str
+    ) -> OrderedDict[str, Any]:
+        r"""
+        Filters the data and returns it in the same ordered dictionary format so the relevant views can be displayed.
+
+        Args:
+            feature_filter (str): The feature filter, if we want to filter the set of data to only include
+                a certain set of features that include feature_filter
+                If feature = "", then we do not filter based on any features
+            module_fqn_filter (str): The filter on prefix for the module fqn. All modules that have fqn with
+                this prefix will be included
+                If module_fqn_filter = "" we do not filter based on module fqn, and include all modules
+
+        First, the data is filtered based on module_fqn, and then filtered based on feature
+        Returns an OrderedDict (sorted in order of model) mapping:
+            module_fqns -> feature_names -> values
+        """
+        # create return dict
+        filtered_dict: OrderedDict[str, Any] = OrdDict()
+
+        for module_fqn in self.generated_reports:
+            # first filter based on module
+            if module_fqn_filter == "" or module_fqn_filter in module_fqn:
+                # create entry for module and loop through features
+                filtered_dict[module_fqn] = {}
+                module_reports = self.generated_reports[module_fqn]
+                for feature_name in module_reports:
+                    # check if filtering on features and do so if desired
+                    if feature_filter == "" or feature_filter in feature_name:
+                        filtered_dict[module_fqn][feature_name] = module_reports[
+                            feature_name
+                        ]
+
+        # we have populated the filtered dict, and must return it
+
+        return filtered_dict
+
+    def _generate_tensor_table(
+        self,
+        filtered_data: OrderedDict[str, Dict[str, Any]],
+        tensor_features: List[str],
+    ) -> Tuple[List, List]:
+        r"""
+        Takes in the filtered data and features list and generates the tensor headers and table
+
+        Currently meant to generate the headers and table for both the tensor information.
+
+        Args:
+            filtered_data (OrderedDict[str, Dict[str, Any]]): An OrderedDict (sorted in order of model) mapping:
+                module_fqns -> feature_names -> values
+            tensor_features (List[str]): A list of the tensor level features
+
+        Returns a tuple with:
+            A list of the headers of the tensor table
+            A list of lists containing the table information row by row
+            The 0th index row will contain the headers of the columns
+            The rest of the rows will contain data
+        """
+        # now we compose the tensor information table
+        tensor_table: List[List[Any]] = []
+        tensor_headers: List[str] = []
+
+        # append the table row to the table only if we have features
+        if len(tensor_features) > 0:
+            # now we add all the data
+            for index, module_fqn in enumerate(filtered_data):
+                # we make a new row for the tensor table
+                tensor_table_row = [index, module_fqn]
+                for feature in tensor_features:
+                    # we iterate in same order of added features
+
+                    if feature in filtered_data[module_fqn]:
+                        # add value if applicable to module
+                        feature_val = filtered_data[module_fqn][feature]
+                    else:
+                        # add that it is not applicable
+                        feature_val = "Not Applicable"
+
+                    # if it's a tensor we want to extract val
+                    if isinstance(feature_val, torch.Tensor):
+                        feature_val = feature_val.item()
+
+                    # we add to our list of values
+                    tensor_table_row.append(feature_val)
+
+                tensor_table.append(tensor_table_row)
+
+        # add row of headers of we actually have something, otherwise just empty
+        if len(tensor_table) != 0:
+            tensor_headers = ["idx", "layer_fqn"] + tensor_features
+
+        return (tensor_headers, tensor_table)
+
+    def _generate_channels_table(
+        self,
+        filtered_data: OrderedDict[str, Any],
+        channel_features: List[str],
+        num_channels: int,
+    ) -> Tuple[List, List]:
+        r"""
+        Takes in the filtered data and features list and generates the channels headers and table
+
+        Currently meant to generate the headers and table for both the channels information.
+
+        Args:
+            filtered_data (OrderedDict[str, Any]): An OrderedDict (sorted in order of model) mapping:
+                module_fqns -> feature_names -> values
+            channel_features (List[str]): A list of the channel level features
+            num_channels (int): Number of channels in the channel data
+
+        Returns a tuple with:
+            A list of the headers of the channel table
+            A list of lists containing the table information row by row
+            The 0th index row will contain the headers of the columns
+            The rest of the rows will contain data
+        """
+        # now we compose the table for the channel information table
+        channel_table: List[List[Any]] = []
+        channel_headers: List[str] = []
+
+        # counter to keep track of number of entries in
+        channel_table_entry_counter: int = 0
+
+        if len(channel_features) > 0:
+            # now we add all channel data
+            for module_fqn in filtered_data:
+                # we iterate over all channels
+                for channel in range(num_channels):
+                    # we make a new row for the channel
+                    new_channel_row = [channel_table_entry_counter, module_fqn, channel]
+                    for feature in channel_features:
+                        if feature in filtered_data[module_fqn]:
+                            # add value if applicable to module
+                            feature_val = filtered_data[module_fqn][feature][channel]
+                        else:
+                            # add that it is not applicable
+                            feature_val = "Not Applicable"
+
+                        # if it's a tensor we want to extract val
+                        if type(feature_val) is torch.Tensor:
+                            feature_val = feature_val.item()
+
+                        # add value to channel specific row
+                        new_channel_row.append(feature_val)
+
+                    # add to table and increment row index counter
+                    channel_table.append(new_channel_row)
+                    channel_table_entry_counter += 1
+
+        # add row of headers of we actually have something, otherwise just empty
+        if len(channel_table) != 0:
+            channel_headers = ["idx", "layer_fqn", "channel"] + channel_features
+
+        return (channel_headers, channel_table)
+
+    def generate_filtered_tables(
+        self, feature_filter: str = "", module_fqn_filter: str = ""
+    ) -> Dict[str, Tuple[List, List]]:
+        r"""
+        Takes in optional filter values and generates two tables with desired information.
+
+        The generated tables are presented in both a list-of-lists format
+
+        The reason for the two tables are that they handle different things:
+        1.) the first table handles all tensor level information
+        2.) the second table handles and displays all channel based information
+
+        The reasoning for this is that having all the info in one table can make it ambiguous which collected
+            statistics are global, and which are actually per-channel, so it's better to split it up into two
+            tables. This also makes the information much easier to digest given the plethora of statistics collected
+
+        Tensor table columns:
+            idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
+            ----  ---------  ---------   ---------   ---------        ---------
+
+        Per-Channel table columns:
+            idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
+            ----  ---------  -------  ---------   ---------   ---------        ---------
+
+        Args:
+            feature_filter (str, optional): Filters the features presented to only those that
+                contain this filter substring
+                Default = "", results in all the features being printed
+            module_fqn_filter (str, optional): Only includes modules that contains this string
+                Default = "", results in all the modules in the reports to be visible in the table
+
+        Returns a dictionary with two keys:
+            (Dict[str, Tuple[List, List]]) A dict containing two keys:
+            "tensor_level_info", "channel_level_info"
+                Each key maps to a tuple with:
+                    A list of the headers of each table
+                    A list of lists containing the table information row by row
+                    The 0th index row will contain the headers of the columns
+                    The rest of the rows will contain data
+
+        Example Use:
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> mod_report_visualizer.generate_filtered_tables(
+            ...     feature_filter = "per_channel_min",
+            ...     module_fqn_filter = "block1"
+            ... ) # generates table with per_channel_min info for all modules in block 1 of the model
+        """
+        # first get the filtered data
+        filtered_data: OrderedDict[str, Any] = self._get_filtered_data(
+            feature_filter, module_fqn_filter
+        )
+
+        # now we split into tensor and per-channel data
+        tensor_features: Set[str] = set()
+        channel_features: Set[str] = set()
+
+        # keep track of the number of channels we have
+        num_channels: int = 0
+
+        for module_fqn in filtered_data:
+            for feature_name in filtered_data[module_fqn]:
+                # get the data for that specific feature
+                feature_data = filtered_data[module_fqn][feature_name]
+
+                # check if not zero dim tensor
+                is_tensor: bool = isinstance(feature_data, torch.Tensor)
+                is_not_zero_dim: bool = is_tensor and len(feature_data.shape) != 0
+
+                if is_not_zero_dim or isinstance(feature_data, list):
+                    # works means per channel
+                    channel_features.add(feature_name)
+                    num_channels = len(feature_data)
+                else:
+                    # means is per-tensor
+                    tensor_features.add(feature_name)
+
+        # we make them lists for iteration purposes
+        tensor_features_list: List[str] = sorted(tensor_features)
+        channel_features_list: List[str] = sorted(channel_features)
+
+        # get the tensor info
+        tensor_headers, tensor_table = self._generate_tensor_table(
+            filtered_data, tensor_features_list
+        )
+
+        # get the channel info
+        channel_headers, channel_table = self._generate_channels_table(
+            filtered_data, channel_features_list, num_channels
+        )
+
+        # let's now create the dictionary to return
+        table_dict = {
+            self.TABLE_TENSOR_KEY: (tensor_headers, tensor_table),
+            self.TABLE_CHANNEL_KEY: (channel_headers, channel_table),
+        }
+
+        # return the two tables
+        return table_dict
+
+    def generate_table_visualization(
+        self, feature_filter: str = "", module_fqn_filter: str = ""
+    ):
+        r"""
+        Takes in optional filter values and prints out formatted tables of the information.
+
+        The reason for the two tables printed out instead of one large one are that they handle different things:
+        1.) the first table handles all tensor level information
+        2.) the second table handles and displays all channel based information
+
+        The reasoning for this is that having all the info in one table can make it ambiguous which collected
+            statistics are global, and which are actually per-channel, so it's better to split it up into two
+            tables. This also makes the information much easier to digest given the plethora of statistics collected
+
+        Tensor table columns:
+         idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
+        ----  ---------  ---------   ---------   ---------        ---------
+
+        Per-Channel table columns:
+
+         idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
+        ----  ---------  -------  ---------   ---------   ---------        ---------
+
+        Args:
+            feature_filter (str, optional): Filters the features presented to only those that
+                contain this filter substring
+                Default = "", results in all the features being printed
+            module_fqn_filter (str, optional): Only includes modules that contains this string
+                Default = "", results in all the modules in the reports to be visible in the table
+
+        Example Use:
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> mod_report_visualizer.generate_table_visualization(
+            ...     feature_filter = "per_channel_min",
+            ...     module_fqn_filter = "block1"
+            ... )
+            >>> # prints out neatly formatted table with per_channel_min info
+            >>> # for all modules in block 1 of the model
+        """
+        # see if we got tabulate
+        if not got_tabulate:
+            print("Make sure to install tabulate and try again.")
+            return None
+
+        # get the table dict and the specific tables of interest
+        table_dict = self.generate_filtered_tables(feature_filter, module_fqn_filter)
+        tensor_headers, tensor_table = table_dict[self.TABLE_TENSOR_KEY]
+        channel_headers, channel_table = table_dict[self.TABLE_CHANNEL_KEY]
+
+        # get the table string and print it out
+        # now we have populated the tables for each one
+        # let's create the strings to be returned
+        table_str = ""
+        # the tables will have some headers columns that are non-feature
+        # ex. table index, module name, channel index, etc.
+        # we want to look at header columns for features, that come after those headers
+        if len(tensor_headers) > self.NUM_NON_FEATURE_TENSOR_HEADERS:
+            # if we have at least one tensor level feature to be added we add tensor table
+            table_str += "Tensor Level Information \n"
+            table_str += tabulate(tensor_table, headers=tensor_headers)
+        if len(channel_headers) > self.NUM_NON_FEATURE_CHANNEL_HEADERS:
+            # if we have at least one channel level feature to be added we add tensor table
+            table_str += "\n\n Channel Level Information \n"
+            table_str += tabulate(channel_table, headers=channel_headers)
+
+        # if no features at all, let user know
+        if table_str == "":
+            table_str = "No data points to generate table with."
+
+        print(table_str)
+
+    def _get_plottable_data(
+        self, feature_filter: str, module_fqn_filter: str
+    ) -> Tuple[List, List[List], bool]:
+        r"""
+        Takes in the feature filters and module filters and outputs the x and y data for plotting
+
+        Args:
+            feature_filter (str): Filters the features presented to only those that
+                contain this filter substring
+            module_fqn_filter (str): Only includes modules that contains this string
+
+        Returns a tuple of three elements
+            The first is a list containing relevant x-axis data
+            The second is a list containing the corresponding y-axis data
+            If the data is per channel
+        """
+        # get the table dict and the specific tables of interest
+        table_dict = self.generate_filtered_tables(feature_filter, module_fqn_filter)
+        tensor_headers, tensor_table = table_dict[self.TABLE_TENSOR_KEY]
+        channel_headers, channel_table = table_dict[self.TABLE_CHANNEL_KEY]
+
+        # make sure it is only 1 feature that is being plotted
+        # get the number of features in each of these
+        tensor_info_features_count = (
+            len(tensor_headers) - ModelReportVisualizer.NUM_NON_FEATURE_TENSOR_HEADERS
+        )
+        channel_info_features_count = (
+            len(channel_headers) - ModelReportVisualizer.NUM_NON_FEATURE_CHANNEL_HEADERS
+        )
+
+        # see if valid tensor or channel plot
+        is_valid_per_tensor_plot: bool = tensor_info_features_count == 1
+        is_valid_per_channel_plot: bool = channel_info_features_count == 1
+
+        # offset should either be one of tensor or channel table or neither
+        feature_column_offset = ModelReportVisualizer.NUM_NON_FEATURE_TENSOR_HEADERS
+        table = tensor_table
+
+        # if a per_channel plot, we have different offset and table
+        if is_valid_per_channel_plot:
+            feature_column_offset = (
+                ModelReportVisualizer.NUM_NON_FEATURE_CHANNEL_HEADERS
+            )
+            table = channel_table
+
+        x_data: List = []
+        y_data: List[List] = []
+        # the feature will either be a tensor feature or channel feature
+        if is_valid_per_tensor_plot:
+            for table_row_num, row in enumerate(table):
+                # get x_value to append
+                x_val_to_append = table_row_num
+                # the index of the feature will the 0 + num non feature columns
+                tensor_feature_index = feature_column_offset
+                row_value = row[tensor_feature_index]
+                if not type(row_value) == str:
+                    x_data.append(x_val_to_append)
+                    y_data.append(row_value)
+        elif is_valid_per_channel_plot:
+            # gather the x_data and multiple y_data
+            # calculate the number of channels
+            num_channels: int = max(row[self.CHANNEL_NUM_INDEX] for row in table) + 1
+            for channel in range(num_channels):
+                y_data.append([])  # separate data list per channel
+
+            for table_row_num, row in enumerate(table):
+                # get x_value to append
+                x_val_to_append = table_row_num
+                current_channel = row[
+                    self.CHANNEL_NUM_INDEX
+                ]  # initially chose current channel
+                new_module_index: int = table_row_num // num_channels
+                x_val_to_append = new_module_index
+
+                # the index of the feature will the 0 + num non feature columns
+                tensor_feature_index = feature_column_offset
+                row_value = row[tensor_feature_index]
+                if not type(row_value) == str:
+                    # only append if new index we are appending
+                    if len(x_data) == 0 or x_data[-1] != x_val_to_append:
+                        x_data.append(x_val_to_append)
+
+                    # append value for that channel
+                    y_data[current_channel].append(row_value)
+        else:
+            # more than one feature was chosen
+            error_str = "Make sure to pick only a single feature with your filter to plot a graph."
+            error_str += " We recommend calling get_all_unique_feature_names() to find unique feature names."
+            error_str += " Pick one of those features to plot."
+            raise ValueError(error_str)
+
+        # return x, y values, and if data is per-channel
+        return (x_data, y_data, is_valid_per_channel_plot)
+
+    def generate_plot_visualization(
+        self, feature_filter: str, module_fqn_filter: str = ""
+    ):
+        r"""
+        Takes in a feature and optional module_filter and plots of the desired data.
+
+        For per channel features, it averages the value across the channels and plots a point
+        per module. The reason for this is that for models with hundreds of channels, it can
+        be hard to differentiate one channel line from another, and so the point of generating
+        a single average point per module is to give a sense of general trends that encourage
+        further deep dives.
+
+        Note:
+            Only features in the report that have tensor value data are plottable by this class
+            When the tensor information is plotted, it will plot:
+                idx as the x val, feature value as the y_val
+            When the channel information is plotted, it will plot:
+                the first idx of each module as the x val, feature value as the y_val [for each channel]
+                The reason for this is that we want to be able to compare values across the
+                channels for same layer, and it will be hard if values are staggered by idx
+                This means each module is represented by only 1 x value
+        Args:
+            feature_filter (str): Filters the features presented to only those that
+                contain this filter substring
+            module_fqn_filter (str, optional): Only includes modules that contains this string
+                Default = "", results in all the modules in the reports to be visible in the table
+
+        Example Use:
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> mod_report_visualizer.generate_plot_visualization(
+            ...     feature_filter = "per_channel_min",
+            ...     module_fqn_filter = "block1"
+            ... )
+            >>> # outputs line plot of per_channel_min information for all
+            >>> # modules in block1 of model each channel gets it's own line,
+            >>> # and it's plotted across the in-order modules on the x-axis
+        """
+        # checks if we have matplotlib and let's user know to install it if don't
+        if not got_matplotlib:
+            print("make sure to install matplotlib and try again.")
+            return None
+
+        # get the x and y data and if per channel
+        x_data, y_data, data_per_channel = self._get_plottable_data(
+            feature_filter, module_fqn_filter
+        )
+
+        # plot based on whether data is per channel or not
+        ax = plt.subplot()
+        ax.set_ylabel(feature_filter)
+        ax.set_title(feature_filter + " Plot")
+        plt.xticks(x_data)  # only show ticks for actual points
+
+        if data_per_channel:
+            ax.set_xlabel("First idx of module")
+            # set the legend as well
+            # plot a single line that is average of the channel values
+            num_modules = len(
+                y_data[0]
+            )  # all y_data have same length, so get num modules
+            num_channels = len(
+                y_data
+            )  # we want num channels to be able to calculate average later
+
+            avg_vals = [
+                sum(y_data[:][index]) / num_channels for index in range(num_modules)
+            ]
+
+            # plot the three things we measured
+            ax.plot(
+                x_data, avg_vals, label=f"Average Value Across {num_channels} Channels"
+            )
+            ax.legend(loc="upper right")
+        else:
+            ax.set_xlabel("idx")
+            ax.plot(x_data, y_data)
+
+        # actually show the plot
+        plt.show()
+
+    def generate_histogram_visualization(
+        self, feature_filter: str, module_fqn_filter: str = "", num_bins: int = 10
+    ):
+        r"""
+        Takes in a feature and optional module_filter and plots the histogram of desired data.
+
+        Note:
+            Only features in the report that have tensor value data can be viewed as a histogram
+            If you want to plot a histogram from all the channel values of a specific feature for
+                a specific model, make sure to specify both the model and the feature properly
+                in the filters and you should be able to see a distribution of the channel data
+
+        Args:
+            feature_filter (str, optional): Filters the features presented to only those that
+                contain this filter substring
+                Default = "", results in all the features being printed
+            module_fqn_filter (str, optional): Only includes modules that contains this string
+                Default = "", results in all the modules in the reports to be visible in the table
+            num_bins (int, optional): The number of bins to create the histogram with
+                Default = 10, the values will be split into 10 equal sized bins
+
+        Example Use:
+            >>> # xdoctest: +SKIP
+            >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization(
+            ...     feature_filter = "per_channel_min",
+            ...     module_fqn_filter = "block1"
+            ... )
+            # outputs histogram of per_channel_min information for all modules in block1 of model
+                information is gathered across all channels for all modules in block 1 for the
+                per_channel_min and is displayed in a histogram of equally sized bins
+        """
+        # checks if we have matplotlib and let's user know to install it if don't
+        if not got_matplotlib:
+            print("make sure to install matplotlib and try again.")
+            return None
+
+        # get the x and y data and if per channel
+        x_data, y_data, data_per_channel = self._get_plottable_data(
+            feature_filter, module_fqn_filter
+        )
+
+        # for histogram, we just care about plotting the y data
+        # plot based on whether data is per channel or not
+        ax = plt.subplot()
+        ax.set_xlabel(feature_filter)
+        ax.set_ylabel("Frequency")
+        ax.set_title(feature_filter + " Histogram")
+
+        if data_per_channel:
+            # set the legend as well
+            # combine all the data
+            all_data = []
+            for channel_info in y_data:
+                all_data.extend(channel_info)
+
+            val, bins, _ = plt.hist(
+                all_data,
+                bins=num_bins,
+                stacked=True,
+                rwidth=0.8,
+            )
+            plt.xticks(bins)
+        else:
+            val, bins, _ = plt.hist(
+                y_data,
+                bins=num_bins,
+                stacked=False,
+                rwidth=0.8,
+            )
+            plt.xticks(bins)
+
+        plt.show()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/convert.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..834873748453302897088034be689309bf66e06f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/convert.py
@@ -0,0 +1,1252 @@
+# mypy: ignore-errors
+
+import copy
+import operator
+import warnings
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
+
+import torch
+from torch.ao.quantization import CUSTOM_KEY, NUMERIC_DEBUG_HANDLE_KEY
+from torch.ao.quantization.backend_config import (
+    BackendConfig,
+    get_native_backend_config,
+)
+from torch.ao.quantization.backend_config.utils import (
+    get_fused_module_classes,
+    get_pattern_to_dtype_configs,
+    get_qat_module_classes,
+    get_root_module_to_quantized_reference_module,
+)
+from torch.ao.quantization.observer import _is_activation_post_process
+from torch.ao.quantization.qconfig import qconfig_equals, QConfigAny
+from torch.ao.quantization.qconfig_mapping import QConfigMapping
+from torch.ao.quantization.quant_type import QuantType
+from torch.ao.quantization.quantize import _remove_qconfig
+from torch.ao.quantization.stubs import DeQuantStub
+from torch.ao.quantization.utils import (
+    _parent_name,
+    activation_is_statically_quantized,
+    get_qparam_dict,
+    get_swapped_custom_module_class,
+    is_per_channel,
+    to_underlying_dtype,
+    weight_is_quantized,
+)
+from torch.fx import GraphModule
+from torch.fx.graph import Argument, Graph, Node
+from torch.nn.utils.parametrize import type_before_parametrizations
+
+# importing the lib so that the quantized_decomposed ops are registered
+from ._decomposed import quantized_decomposed_lib  # noqa: F401
+from ._equalize import convert_eq_obs, update_obs_for_equalization
+from .custom_config import ConvertCustomConfig, PrepareCustomConfig
+from .graph_module import _is_observed_module, _is_observed_standalone_module
+from .lower_to_fbgemm import lower_to_fbgemm
+from .qconfig_mapping_utils import (
+    _compare_prepare_convert_qconfig_mappings,
+    _generate_node_name_to_qconfig,
+    _is_qconfig_supported_by_dtype_configs,
+    _update_qconfig_for_fusion,
+    _update_qconfig_for_qat,
+)
+from .utils import (
+    _get_module,
+    _is_custom_module_lstm,
+    _is_custom_module_mha,
+    assert_and_get_unique_device,
+    collect_producer_nodes,
+    create_getattr_from_value,
+    get_custom_module_class_keys,
+    graph_module_from_producer_nodes,
+    node_arg_is_weight,
+)
+
+
+__all__ = [
+    "convert",
+    "convert_custom_module",
+    "convert_standalone_module",
+    "convert_weighted_module",
+]
+
+SUPPORTED_QDTYPES = [
+    torch.quint8,
+    torch.qint8,
+    torch.qint32,
+    torch.uint8,
+    torch.int8,
+    torch.int16,
+    torch.int32,
+    torch.float8_e5m2,
+    torch.float8_e4m3fn,
+]
+
+_QSCHEME_TO_CHOOSE_QPARAMS_OP = {
+    torch.per_tensor_affine: torch.ops.quantized_decomposed.choose_qparams.tensor,
+    torch.per_tensor_symmetric: torch.ops.quantized_decomposed.choose_qparams_symmetric.tensor,
+}
+
+
+def _replace_observer_with_quantize_dequantize_node_decomposed(
+    model: torch.fx.GraphModule,
+    node: Node,
+    modules: Dict[str, torch.nn.Module],
+    node_name_to_scope: Dict[str, Tuple[str, type]],
+    node_name_to_qconfig: Dict[str, QConfigAny],
+) -> None:
+    """Replace activation_post_process module call node with quantize and
+    dequantize node working with decomposed Tensor
+
+    Before:
+    ... -> observer_0(x) -> ...
+    After:
+    ... -> torch.ops.quantized_decomposed.quantize_per_tensor(x, ...) ->
+    torch.ops.quantized_decomposed.dequantize_per_tensor() -> ...
+
+    or quantize_per_channel and dequantize_per_channel
+    """
+    graph = model.graph
+    assert modules is not None
+    assert isinstance(node.target, str)
+    module_path, prefix = _get_module_path_and_prefix(
+        node, node_name_to_scope, node_name_to_qconfig
+    )
+    activation_post_process = modules[node.target]
+    if hasattr(activation_post_process, "convert"):
+        activation_post_process.convert(model, node)
+        return
+    # skip replacing observers to quant/dequant nodes if the qconfigs of all
+    # consumers and producers of this observer are None
+    skip_replacement = all(
+        _has_none_qconfig(n, node_name_to_qconfig)
+        for n in list(node.args) + list(node.users.keys())
+    )
+    if skip_replacement or not _is_conversion_supported(activation_post_process):
+        # didn't find corresponding quantize op and info for the activation_post_process
+        # so we just remove the observer
+        with graph.inserting_before(node):
+            node.replace_all_uses_with(node.args[0])
+            graph.erase_node(node)
+        return
+
+    # otherwise, we can convert the activation_post_process module call to quantize/dequantize node
+
+    # 1. extract the information from activation_post_process module for generating
+    # the quantize and dequantize operator
+    dtype = activation_post_process.dtype  # type: ignore[attr-defined]
+
+    is_dynamic = False
+    if hasattr(activation_post_process, "is_dynamic"):
+        is_dynamic = activation_post_process.is_dynamic  # type: ignore[assignment]
+
+    def add_dequantize_op_kwargs(dequantize_op, input_node):
+        dequantize_op_kwargs = {}
+        if "val" in input_node.meta:
+            dq_out_dtype = input_node.meta["val"].dtype
+            if dq_out_dtype != torch.float32:
+                dequantize_op_kwargs = {"out_dtype": dq_out_dtype}
+        return dequantize_op_kwargs
+
+    if dtype in SUPPORTED_QDTYPES and (not is_dynamic):
+        # TODO: probably should cleanup this condition check, it's hard
+        # to reason about this if and the following elif
+
+        # uint8/int8/int32 static quantization branch
+
+        # 1. extract information for inserting q/dq node from activation_post_process
+        node_type = "call_function"
+        quantize_op: Optional[Callable] = None
+        scale, zero_point = activation_post_process.calculate_qparams()  # type: ignore[attr-defined, operator]
+        if is_per_channel(activation_post_process.qscheme):  # type: ignore[attr-defined]
+            ch_axis = int(activation_post_process.ch_axis)  # type: ignore[attr-defined, arg-type]
+            quantize_op = torch.ops.quantized_decomposed.quantize_per_channel.default
+            dequantize_op = (
+                torch.ops.quantized_decomposed.dequantize_per_channel.default
+            )
+            quant_min = activation_post_process.quant_min
+            quant_max = activation_post_process.quant_max
+            dtype_ = to_underlying_dtype(dtype)
+            qparams = {
+                "_scale_": scale,
+                "_zero_point_": zero_point,
+                "_axis_": ch_axis,
+                "_quant_min_": quant_min,
+                "_quant_max_": quant_max,
+                "_dtype_": dtype_,
+            }
+        else:
+            quantize_op = torch.ops.quantized_decomposed.quantize_per_tensor.default
+            dequantize_op = torch.ops.quantized_decomposed.dequantize_per_tensor.default
+            scale = float(scale)
+            zero_point = int(zero_point)
+            quant_min = activation_post_process.quant_min  # type: ignore[attr-defined]
+            quant_max = activation_post_process.quant_max  # type: ignore[attr-defined]
+            dtype_ = to_underlying_dtype(dtype)
+            qparams = {
+                "_scale_": scale,
+                "_zero_point_": zero_point,
+                "_quant_min_": quant_min,
+                "_quant_max_": quant_max,
+                "_dtype_": dtype_,
+            }
+
+        # 2. replace activation_post_process node with quantize and dequantize
+        with graph.inserting_before(node):
+            input_node = node.args[0]
+            quantize_op_inputs = [input_node]
+            for key, value_or_node in qparams.items():
+                # TODO: we can add the information of whether a value needs to
+                # be registered as an attribute in qparams dict itself
+                if key in ["_scale_", "_zero_point_"] and (
+                    not isinstance(value_or_node, (float, int))
+                ):
+                    # For scale and zero_point values we register them as buffers in the root module.
+                    # However, note that when the values are not tensors, as in the case of
+                    # per_tensor quantization, they will be treated as literals.
+                    # However, registering them as a node seems to cause issue with dynamo
+                    # tracing where it may consider tensor overload as opposed to default.
+                    # With extra check of scale and zero_point being scalar, it makes
+                    # sure that the default overload can be used.
+                    # TODO: maybe need more complex attr name here
+                    qparam_node = create_getattr_from_value(
+                        model, graph, module_path + prefix + key, value_or_node
+                    )
+                    quantize_op_inputs.append(qparam_node)
+                else:
+                    # for qparams that are not scale/zero_point (like axis, dtype) we store them as literals in the graph.
+                    quantize_op_inputs.append(value_or_node)
+
+            quantized_node = graph.create_node(
+                node_type, quantize_op, tuple(quantize_op_inputs), {}
+            )
+            # use the same qparams from quantize op
+            dq_inputs = [quantized_node] + quantize_op_inputs[1:]
+            dequantized_node = graph.call_function(
+                dequantize_op,
+                tuple(dq_inputs),
+                add_dequantize_op_kwargs(dequantize_op, input_node),
+            )
+
+            node.replace_all_uses_with(dequantized_node)
+            # propagate numeric debug handle from observer/fake_quant node to dequantize node
+            if (
+                CUSTOM_KEY in node.meta
+                and NUMERIC_DEBUG_HANDLE_KEY in node.meta[CUSTOM_KEY]
+            ):
+                if CUSTOM_KEY not in dequantized_node.meta:
+                    dequantized_node.meta[CUSTOM_KEY] = {}
+                dequantized_node.meta[CUSTOM_KEY][NUMERIC_DEBUG_HANDLE_KEY] = node.meta[
+                    CUSTOM_KEY
+                ][NUMERIC_DEBUG_HANDLE_KEY]
+            graph.erase_node(node)
+    elif is_dynamic:
+        # uint8/int8/fp16 dynamic quantization
+
+        # 1. extract information for inserting q/dq node from activation_post_process
+        node_type = "call_function"
+        quantize_op = torch.ops.quantized_decomposed.quantize_per_tensor.tensor
+        # we only use choose_qparams for is_decomposed now,
+        # but we should probably align the non-decomposed path with this as well,
+        # and that can be done after we remove reduce_range flag
+        # 1. extract qparams from activation_post_process module
+        dtype_ = to_underlying_dtype(dtype)
+        assert dtype_ in [torch.uint8, torch.int8], (
+            "only uint8 and int8 are supported in reference flow for "
+            "dynamic quantization right now"
+        )
+        quant_min = activation_post_process.quant_min  # type: ignore[attr-defined]
+        quant_max = activation_post_process.quant_max  # type: ignore[attr-defined]
+        qscheme = getattr(activation_post_process, "qscheme", torch.per_tensor_affine)  # type: ignore[attr-defined]
+        eps = getattr(activation_post_process, "eps", torch.finfo(torch.float32).eps)  # type: ignore[attr-defined]
+        # note: scale and zero_point are missing for quantize_per_tensor op
+        # we'll need to get this from choose_qparams op, which we'll add after
+        # this step
+        qparams = {
+            "_quant_min_": quant_min,
+            "_quant_max_": quant_max,
+            "_eps_": eps,
+            "_dtype_": dtype_,
+        }
+
+        choose_qparams_op = _QSCHEME_TO_CHOOSE_QPARAMS_OP[qscheme]
+        # 2. insert choose_qparams op and update the qparams list
+        with graph.inserting_before(node):
+            input_node = node.args[0]
+            choose_qparams_op_inputs = [node.args[0]]
+            for key, value in qparams.items():
+                # we have quant_min, quant_max and dtype, all should be stored
+                # as literals
+                choose_qparams_op_inputs.append(value)
+            choose_qparams_node = graph.create_node(
+                "call_function", choose_qparams_op, tuple(choose_qparams_op_inputs), {}
+            )
+            # choose_qparms returns (scale, zero_point)
+            scale_node = graph.create_node(
+                "call_function", operator.getitem, (choose_qparams_node, 0), {}
+            )
+            zero_point_node = graph.create_node(
+                "call_function", operator.getitem, (choose_qparams_node, 1), {}
+            )
+            quant_min = qparams["_quant_min_"]
+            quant_max = qparams["_quant_max_"]
+            dtype = qparams["_dtype_"]
+            qparams = {
+                "_scale_": scale_node,
+                "_zero_point_": zero_point_node,
+                "_quant_min_": quant_min,
+                "_quant_max_": quant_max,
+                "_dtype_": dtype,
+            }
+
+        # 3. replace activation_post_process node to quantize and dequantize node
+        with graph.inserting_before(node):
+            input_node = node.args[0]
+            quantize_op_inputs = [input_node]
+            for key, value_or_node in qparams.items():
+                # TODO: we can add the information of whether a value needs to
+                # be registered as an attribute in qparams dict itself
+                if key in ["_scale_", "_zero_point_"]:
+                    # in this case we have a node in the graph since it's dynamically
+                    # computed from the input, with choose_qparams op
+                    qparam_node = value_or_node
+                    quantize_op_inputs.append(qparam_node)
+                else:
+                    # for qparams that are not scale/zero_point (like axis, dtype) we
+                    # store them as literals in the graph.
+                    quantize_op_inputs.append(value_or_node)
+
+            quantized_node = graph.create_node(
+                node_type, quantize_op, tuple(quantize_op_inputs), {}
+            )
+            # use the same qparams from quantize op
+            dq_inputs = [quantized_node] + quantize_op_inputs[1:]
+            # need to use the tensor variant of this op, since scale and zero_point
+            # from choose_qparam are Tensors, instead of float/int, this is to
+            # prevent these nodes being traced away by downstream systems
+            dequantize_op = torch.ops.quantized_decomposed.dequantize_per_tensor.tensor
+            dequantized_node = graph.call_function(
+                dequantize_op,
+                tuple(dq_inputs),
+                add_dequantize_op_kwargs(dequantize_op, input_node),
+            )
+
+            def remap_fn(x):
+                return dequantized_node if x is node else x
+
+            node.replace_all_uses_with(dequantized_node)
+            # propagate numeric debug handle from observer/fake_quant node to dequantize node
+            if NUMERIC_DEBUG_HANDLE_KEY in node.meta:
+                dequantized_node.meta[NUMERIC_DEBUG_HANDLE_KEY] = node.meta[
+                    NUMERIC_DEBUG_HANDLE_KEY
+                ]
+            graph.erase_node(node)
+    elif dtype == torch.float16:
+        raise NotImplementedError("decomposed to float16 op not implemented yet")
+
+    # should not reach since we have checks in the beginning to make sure the
+    # activation_post_process is supported
+
+
+def _replace_observer_with_quantize_dequantize_node(
+    model: torch.fx.GraphModule,
+    node: Node,
+    modules: Dict[str, torch.nn.Module],
+    node_name_to_scope: Dict[str, Tuple[str, type]],
+    node_name_to_qconfig: Dict[str, QConfigAny],
+) -> None:
+    """Replace activation_post_process module call node with quantize and
+    dequantize node
+
+    Before:
+    ... -> observer_0(x) -> ...
+    After:
+    ... -> torch.quantize_per_tensor(x, ...) -> x.dequantize() -> ...
+    """
+    assert modules is not None
+    assert isinstance(node.target, str)
+    graph = model.graph
+    module_path, prefix = _get_module_path_and_prefix(
+        node, node_name_to_scope, node_name_to_qconfig
+    )
+    activation_post_process = modules[node.target]
+    # skip replacing observers to quant/dequant nodes if the qconfigs of all
+    # consumers and producers of this observer are None
+    skip_replacement = all(
+        _has_none_qconfig(n, node_name_to_qconfig)
+        for n in list(node.args) + list(node.users.keys())
+    )
+    if skip_replacement or not _is_conversion_supported(activation_post_process):
+        # didn't find corresponding quantize op and info for the activation_post_process
+        # so we just remove the observer
+        with graph.inserting_before(node):
+            node.replace_all_uses_with(node.args[0])
+            graph.erase_node(node)
+        return
+
+    # otherwise, we can convert the activation_post_process module call to quantize/dequantize node
+    dtype = activation_post_process.dtype  # type: ignore[attr-defined]
+
+    is_dynamic = False
+    if hasattr(activation_post_process, "is_dynamic"):
+        is_dynamic = activation_post_process.is_dynamic  # type: ignore[attr-defined, assignment]
+
+    if dtype in [
+        torch.quint8,
+        torch.qint8,
+        torch.qint32,
+        torch.float8_e5m2,
+        torch.float8_e4m3fn,
+    ] and (not is_dynamic):
+        # TODO: probably should cleanup this condition check, it's hard
+        # to reason about this if and the following elif
+
+        # uint8/int8/int32 static quantization branch
+
+        # 1. extract the information from activation_post_process module for generating
+        # the quantize and dequantize operator
+        node_type = "call_function"
+        quantize_op: Optional[Callable] = None
+        scale, zero_point = activation_post_process.calculate_qparams()  # type: ignore[attr-defined, operator]
+        if is_per_channel(activation_post_process.qscheme):  # type: ignore[attr-defined]
+            ch_axis = int(activation_post_process.ch_axis)  # type: ignore[attr-defined, arg-type]
+            qparams = {
+                "_scale_": scale,
+                "_zero_point_": zero_point,
+                "_axis_": ch_axis,
+                "_dtype_": dtype,
+            }
+            quantize_op = torch.quantize_per_channel
+        else:
+            scale = float(scale)
+            zero_point = int(zero_point)
+            qparams = {"_scale_": scale, "_zero_point_": zero_point, "_dtype_": dtype}
+            quantize_op = torch.quantize_per_tensor
+
+        # 2. replace activation_post_process node with quantize and dequantize
+        with graph.inserting_before(node):
+            input_node = node.args[0]
+            quantize_op_inputs = [input_node]
+            for key, value_or_node in qparams.items():
+                # TODO: we can add the information of whether a value needs to
+                # be registered as an attribute in qparams dict itself
+                if key in ["_scale_", "_zero_point_"]:
+                    # For scale and zero_point values we register them as buffers in the root module.
+                    # TODO: maybe need more complex attr name here
+                    qparam_node = create_getattr_from_value(
+                        model, graph, module_path + prefix + key, value_or_node
+                    )
+                    quantize_op_inputs.append(qparam_node)
+                else:
+                    # for qparams that are not scale/zero_point (like axis, dtype) we store them as literals in the graph.
+                    quantize_op_inputs.append(value_or_node)
+
+            quantized_node = graph.create_node(
+                node_type, quantize_op, tuple(quantize_op_inputs), {}
+            )
+            dequantized_node = graph.call_method("dequantize", args=(quantized_node,))
+            node.replace_all_uses_with(dequantized_node)
+            graph.erase_node(node)
+    elif is_dynamic:
+        # uint8/int8/fp16 dynamic quantization branch
+
+        node_type = "call_function"
+        quantize_op = torch.quantize_per_tensor_dynamic
+        # TODO: get reduce range from observer
+        # reduce_range = activation_post_process.reduce_range
+        reduce_range = torch.backends.quantized.engine in ("fbgemm", "x86")
+        qparams = {"_dtype_": dtype, "_reduce_range_": reduce_range}
+
+        with graph.inserting_before(node):
+            input_node = node.args[0]
+            quantize_op_inputs = [input_node]
+            for key, value in qparams.items():
+                quantize_op_inputs.append(value)
+
+            quantized_node = graph.create_node(
+                node_type, quantize_op, tuple(quantize_op_inputs), {}
+            )
+            dequantized_node = graph.call_method("dequantize", args=(quantized_node,))
+            node.replace_all_uses_with(dequantized_node)
+            graph.erase_node(node)
+    elif dtype == torch.float16:
+        node_type = "call_method"
+        quantize_op = "to"  # type: ignore[assignment]
+        qparams = {"_dtype_": dtype}
+        with graph.inserting_before(node):
+            input_node = node.args[0]
+            quantize_op_inputs = [input_node]
+            for key, value in qparams.items():
+                # TODO: we can add the information of whether a value needs to
+                # be registered as an attribute in qparams dict itself
+                quantize_op_inputs.append(value)
+
+            quantized_node = graph.create_node(
+                node_type, quantize_op, tuple(quantize_op_inputs), {}
+            )
+            dequantized_node = graph.call_method("dequantize", args=(quantized_node,))
+            node.replace_all_uses_with(dequantized_node)
+            graph.erase_node(node)
+
+    # should not reach since we have checks in the beginning to make sure the
+    # activation_post_process is supported
+
+
+# this is a temporary hack for custom module, we may want to implement
+# this properly after the custom module class design is finalized
+# TODO: DeQuantStubs are currently inserted only after custom module LSTM, while observers are inserted
+# after all other custom modules. In the future, we should simply insert QuantStubs before and DeQuantStubs
+# after custom modules in general, and replace these with "quantize" and "dequantize" nodes respectively.
+def _replace_observer_or_dequant_stub_with_dequantize_node(
+    node: Node, graph: Graph
+) -> None:
+    call_custom_module_node = node.args[0]
+    assert isinstance(
+        call_custom_module_node, Node
+    ), f"Expecting the for call custom module node to be a Node, but got {call_custom_module_node}"
+    node.replace_all_uses_with(call_custom_module_node)
+    graph.erase_node(node)
+    _insert_dequantize_node(call_custom_module_node, graph)
+
+
+def _is_conversion_supported(activation_post_process: torch.nn.Module) -> bool:
+    dtype = activation_post_process.dtype  # type: ignore[attr-defined]
+
+    is_dynamic = False
+    if hasattr(activation_post_process, "is_dynamic"):
+        is_dynamic = activation_post_process.is_dynamic  # type: ignore[attr-defined, assignment]
+
+    return (
+        (dtype in SUPPORTED_QDTYPES and (not is_dynamic))
+        or is_dynamic  # type: ignore[return-value]
+        or dtype == torch.float16
+    )
+
+
+def _has_none_qconfig(
+    node: Argument, node_name_to_qconfig: Dict[str, QConfigAny]
+) -> bool:
+    """Check if a node has a qconfig of None, i.e. user requested to not quantize
+    the node
+    """
+    return (
+        isinstance(node, Node)
+        and node.name in node_name_to_qconfig
+        and node_name_to_qconfig[node.name] is None
+    )
+
+
+def _run_weight_observers(observed: GraphModule, backend_config: BackendConfig) -> None:
+    """Extract the subgraph that produces the weight for dynamic quant
+    or weight only quant node and run the subgraph to observe the weight.
+    Note that the observers of dynamic quant or weight only quant ops are
+    run during the convert step.
+    """
+    for node in observed.graph.nodes:
+        if node.op != "call_function":
+            continue
+        for node_arg in node.args:
+            # node_arg is weight
+            if node_arg and node_arg_is_weight(node, node_arg):
+                weight_observer_nodes = collect_producer_nodes(node_arg)
+                if weight_observer_nodes is None:
+                    continue
+                weight_observer_module = graph_module_from_producer_nodes(
+                    observed, weight_observer_nodes
+                )
+                # run the weight observer
+                weight_observer_module()
+
+
+def _maybe_recursive_remove_dequantize(arg: Any, node: Node, graph: Graph) -> None:
+    """If the arg is a dequantize Node, or a list/tuple/dict of dequantize Node,
+    we'll recursively remove the dequantize Node
+    """
+    if isinstance(arg, Node) and arg.op == "call_method" and arg.target == "dequantize":
+        quantize_node = arg.args[0]
+        # we only replace the specific use since dequantize could be used by other nodes
+        # as well
+        node.replace_input_with(arg, quantize_node)
+    elif isinstance(arg, (list, tuple)):
+        for arg_element in arg:
+            _maybe_recursive_remove_dequantize(arg_element, node, graph)
+    elif isinstance(arg, dict):
+        for arg_element in arg.values():
+            _maybe_recursive_remove_dequantize(arg_element, node, graph)
+    else:
+        warnings.warn(
+            f"Unsupported node type in recursive remove dequantize: {type(arg)}"
+        )
+
+
+def _get_module_path_and_prefix(
+    obs_node: Node,
+    node_name_to_scope: Dict[str, Tuple[str, type]],
+    node_name_to_qconfig: Dict[str, QConfigAny],
+) -> Tuple[str, str]:
+    """Given and observer node, get the `Scope` or the fully qualified name for
+    the submodule containing the observed node, also return a prefix of "_input"
+    when the observed node is an input of a F.linear op, and not the output of another
+    quantized op.
+    TODO: this logic is hacky, we should think about how to remove it or make it more
+    general
+    """
+    observed_node = obs_node.args[0]
+    # an observer can be inserted for both input of the next operator or output of the previous
+    # operator (they can be the same)
+    # this flag identifies if the observer is inserted only because the observed node is
+    # the input of the next operator
+    assert isinstance(
+        observed_node, Node
+    ), f"Expecting observed node to be a Node, but got {observed_node}"
+    is_input_observer_only = (
+        node_name_to_qconfig[observed_node.name] is None
+        if observed_node.name in node_name_to_qconfig
+        else None
+    )
+    if is_input_observer_only:
+        # if the quantize function is at the input of op, then we find the first user of the observer_node
+        # to get the path. If a linear call_function is in the user list, we return the first instance
+        # of linear node to get the FQN.
+        users = list(obs_node.users)
+        first_linear_use_or_first_use = users[0] if users else None
+        linear_node = None
+        for n in users:
+            if n.op == "call_function" and n.target == torch.nn.functional.linear:
+                linear_node = n
+                break
+        if linear_node:
+            first_linear_use_or_first_use = linear_node
+        prefix = "_input"
+    else:
+        # if the quantize function is at the output of the op, we use the observer input node to get the path
+        first_linear_use_or_first_use = observed_node
+        prefix = ""
+
+    if (
+        first_linear_use_or_first_use
+        and first_linear_use_or_first_use.name in node_name_to_scope
+    ):
+        module_path, _ = node_name_to_scope[first_linear_use_or_first_use.name]
+    else:
+        # TODO: it's not used, so actually we can skip quantization
+        # but this requires changing return type of quantize_node
+        # we can fix it later if needed
+        module_path = ""
+    return module_path, prefix
+
+
+def _insert_dequantize_node(node: Node, graph: Graph) -> None:
+    """Inserts dequantize node for `node` in `graph`"""
+    with graph.inserting_after(node):
+        dequantize_node = graph.call_method("dequantize", (node,))
+        for user_node in dict(node.users):
+            if user_node is not dequantize_node:
+                user_node.replace_input_with(node, dequantize_node)
+
+
+def _maybe_get_observer_for_node(
+    node: Node, modules: Dict[str, torch.nn.Module]
+) -> Optional[torch.nn.Module]:
+    """
+    If the node is observed, return the observer
+    instance. Otherwise, return None.
+    """
+    for maybe_obs_node in node.users.keys():
+        if maybe_obs_node.op == "call_module":
+            maybe_obs = modules[str(maybe_obs_node.target)]
+            if _is_activation_post_process(maybe_obs):
+                return maybe_obs
+    return None
+
+
+def convert_standalone_module(
+    node: Node,
+    modules: Dict[str, torch.nn.Module],
+    model: torch.fx.GraphModule,
+    is_reference: bool,
+    backend_config: Optional[BackendConfig],
+) -> None:
+    """Converts a observed standalone module to a quantized standalone module by calling
+    the fx convert api, currently using the same `is_reference` flag as parent, but we may
+    changing this behavior in the future (e.g. separating quantization and lowering for
+    standalone module as well)
+
+    Args:
+      - node: The call_module node of the observed standalone module
+      - modules: named_module of original model
+      - model: original model
+      - is_reference: a flag from parent provided by user to decide if we want to
+        produce a reference model or a fbgemm/qnnpack model
+      - backend_config: backend configuration of the target backend of quantization
+    """
+    # TODO: remove is_reference flag
+    if is_reference:
+        convert_fn = torch.ao.quantization.quantize_fx.convert_to_reference_fx
+    else:
+        convert_fn = torch.ao.quantization.quantize_fx.convert_fx  # type: ignore[attr-defined]
+    # We know that observed standalone module is a GraphModule since
+    # it's produced by us
+    observed_standalone_module: GraphModule = modules[str(node.target)]  # type: ignore[assignment]
+    sm_input_quantized_idxs = observed_standalone_module.meta[
+        "_observed_graph_module_attrs"
+    ].standalone_module_input_quantized_idxs
+    # remove the dequantize nodes for inputs
+    args = list(node.args)
+    for idx in range(len(args)):
+        if idx in sm_input_quantized_idxs:
+            arg = args[idx]
+            if arg.op == "call_method" and arg.target == "dequantize":  # type: ignore[union-attr]
+                quantize_node = arg.args[0]  # type: ignore[union-attr]
+                node.replace_input_with(arg, quantize_node)
+                if len(arg.users) == 0:  # type: ignore[union-attr]
+                    model.graph.erase_node(arg)
+    # add dequantize node for output
+    sm_output_quantized_idxs = observed_standalone_module.meta[
+        "_observed_graph_module_attrs"
+    ].standalone_module_output_quantized_idxs
+    if len(sm_output_quantized_idxs) > 0:
+        assert sm_output_quantized_idxs[0] == 0, "Currently only quantized"
+        "output idxs = [0] is supported"
+
+        # if it's non-empty, then it means the output is kept in quantized form
+        # we'll just add a dequantize node after this node
+        _insert_dequantize_node(node, model.graph)
+
+    # TODO: allow convert_custom_config to override backend_config
+    # for standalone module
+    quantized_standalone_module = convert_fn(
+        observed_standalone_module, backend_config=backend_config
+    )
+    parent_name, name = _parent_name(node.target)
+    # update the modules dict
+    setattr(modules[parent_name], name, quantized_standalone_module)
+    modules[str(node.target)] = quantized_standalone_module
+
+
+def convert_weighted_module(
+    node: Node,
+    modules: Dict[str, torch.nn.Module],
+    observed_node_names: Set[str],
+    node_name_to_qconfig: Dict[str, QConfigAny],
+    backend_config: BackendConfig,
+    is_decomposed: bool = False,
+    is_reference: bool = False,
+) -> None:
+    """Convert a weighted module to reference quantized module in the model
+    If the QConfig of a QAT module is not set, the module will still be converted to
+    a float module.
+
+    Args:
+      - node: The call_module node of the observed standalone module
+      - modules: named_module of original model
+      - observed_node_names: names for the set of observed fx node, we can skip
+        this conversion if the node is not observed
+    """
+    original_module = modules[str(node.target)]
+    qconfig: QConfigAny = original_module.qconfig  # type: ignore[assignment]
+    weight_post_process = None
+    qat_module_classes = get_qat_module_classes(backend_config)
+
+    if isinstance(original_module, qat_module_classes):
+        # Converting qat module to a float module, we need to attach
+        # weight fake_quant to the module, weight fake_quant is assumed to be run during
+        # QAT so we don't need to run it again here
+        weight_post_process = original_module.weight_fake_quant
+        original_module = original_module.to_float()  # type: ignore[operator]
+        # change qat module to float module
+        parent_name, name = _parent_name(node.target)
+        setattr(modules[parent_name], name, original_module)
+
+    is_observed = node.name in observed_node_names
+    # If a qconfig is not defined for this node, then skip converting to a reference module
+    if (
+        qconfig is None
+        or _has_none_qconfig(node, node_name_to_qconfig)
+        or not is_observed
+    ):
+        return
+
+    # skip converting to reference quantized module if the qconfig is not supported
+    pattern_to_dtype_configs = get_pattern_to_dtype_configs(backend_config)
+    dtype_configs = pattern_to_dtype_configs.get(type(original_module), [])
+    if not _is_qconfig_supported_by_dtype_configs(qconfig, dtype_configs):
+        return
+
+    # TODO: rename weight_is_statically_quantized to weight_is_int8_quantized
+    is_weight_quantized = weight_is_quantized(qconfig)
+
+    # the condition for swapping the module to reference quantized module is:
+    # weights need to be quantized
+    if not is_weight_quantized:
+        return
+
+    fused_module = None
+    float_module = original_module
+    # extract the individual float_module and fused module
+    if isinstance(original_module, torch.ao.nn.intrinsic._FusedModule):
+        fused_module = float_module
+        float_module = fused_module[0]  # type: ignore[index]
+
+    # TODO: move this to the reference quantized module
+    # weight_qparams or weight_qparams dict
+    wq_or_wq_dict = {"is_decomposed": is_decomposed}
+    if isinstance(float_module, torch.nn.RNNCellBase):
+        weight_post_process_ih = qconfig.weight()  # type: ignore[union-attr, operator]
+        weight_post_process_hh = qconfig.weight()  # type: ignore[union-attr, operator]
+        weight_post_process_ih(float_module.weight_ih)
+        weight_post_process_hh(float_module.weight_hh)
+        weight_qparams_ih = get_qparam_dict(weight_post_process_ih)
+        weight_qparams_hh = get_qparam_dict(weight_post_process_hh)
+        wq_or_wq_dict.update(
+            {
+                "weight_ih": weight_qparams_ih,
+                "weight_hh": weight_qparams_hh,
+            }
+        )
+    elif isinstance(float_module, (torch.nn.LSTM, torch.nn.GRU)):
+        # format for wq_or_wq_dict (flattened attributes):
+        # {"weight_ih_l0_scale": ..., "weight_ih_l0_qscheme": ..., ...}
+        for wn in float_module._flat_weights_names:
+            if hasattr(float_module, wn) and wn.startswith("weight"):
+                weight = getattr(float_module, wn)
+                weight_post_process = qconfig.weight()  # type: ignore[union-attr, operator]
+                if weight_post_process.dtype == torch.qint8:  # type: ignore[union-attr]
+                    weight_post_process(weight)  # type: ignore[operator, misc]
+                wq_or_wq_dict[wn] = get_qparam_dict(weight_post_process)
+    else:
+        # weight_post_process is None means the original module is not a QAT module
+        # we need to get weight_post_process from qconfig in this case
+        is_ptq = weight_post_process is None
+        if is_ptq:
+            weight_post_process = qconfig.weight()  # type: ignore[union-attr, operator]
+            device = assert_and_get_unique_device(float_module)
+            if device:
+                weight_post_process.to(device)
+
+        # Call weight observer/fake_quant at least once to ensure the scales and zero points
+        # have the right shapes. Note: there are two cases where we don't have to do this:
+        #
+        # (1) QAT: The model's forward method already calls the weight observer/fake_quant,
+        #     and this typically happens during training, so we don't need to do it here.
+        #
+        # (2) Non-reference (lowered) case: The quantized module's from_float method already
+        #     calls the weight observer/fake_quant, so we don't have to do it here.
+        #
+        # Currently we ignore both cases and call the weight observer/fake_quant here
+        # regardless, which is technically incorrect. For (1), this is mainly to preserve BC
+        # in test code, which may not always train before convert. In the future, we should
+        # break BC for these two cases. See https://github.com/pytorch/pytorch/issues/73941.
+        #
+        # For PT2, however, we don't need to preserve BC here, so we can skip this hack
+        # for QAT. We identify this case as (is_decomposed + is_reference + is_qat).
+        # Note that we still need it for PTQ in the PT2 flow since the model's forward
+        # method doesn't call the weight observer.
+        is_qat = not is_ptq
+        if not (is_decomposed and is_reference and is_qat):
+            weight_post_process(float_module.weight)  # type: ignore[operator]
+
+        wq_or_wq_dict.update(get_qparam_dict(weight_post_process))
+
+    # We use the same reference module for all modes of quantization: static, dynamic, weight_only
+    # root_module_to_quantized_reference_module: module mapping from root (floating point) module class
+    # to quantized reference module class, e.g. nn.Conv2d to nn.quantized._reference.Conv2d
+    root_module_to_quantized_reference_module = (
+        get_root_module_to_quantized_reference_module(backend_config)
+    )
+    ref_qmodule_cls = root_module_to_quantized_reference_module.get(
+        type_before_parametrizations(float_module), None
+    )
+    assert (
+        ref_qmodule_cls is not None
+    ), f"No reference quantized module class configured for {type_before_parametrizations(float_module)}"
+    ref_qmodule = ref_qmodule_cls.from_float(float_module, wq_or_wq_dict)  # type: ignore[attr-defined]
+    if fused_module is not None:
+        fused_module[0] = ref_qmodule  # type: ignore[operator]
+    else:
+        parent_name, name = _parent_name(node.target)
+        setattr(modules[parent_name], name, ref_qmodule)
+
+
+def _remove_previous_dequantize_in_custom_module(
+    node: Node, prev_node: Node, graph: Graph
+) -> None:
+    """
+    Given a custom module `node`, if the previous node is a dequantize, reroute the custom as follows:
+
+    Before: quantize - dequantize - custom_module
+    After: quantize - custom_module
+                 \\ - dequantize
+    """
+    # expecting the input node for a custom module node to be a Node
+    assert isinstance(
+        prev_node, Node
+    ), f"Expecting the argument for custom module node to be a Node, but got {prev_node}"
+    if prev_node.op == "call_method" and prev_node.target == "dequantize":
+        node.replace_input_with(prev_node, prev_node.args[0])
+        # Remove the dequantize node if it doesn't have other users
+        if len(prev_node.users) == 0:
+            graph.erase_node(prev_node)
+
+
+def convert_custom_module(
+    node: Node,
+    graph: Graph,
+    modules: Dict[str, torch.nn.Module],
+    custom_module_class_mapping: Dict[QuantType, Dict[Type, Type]],
+    statically_quantized_custom_module_nodes: Set[Node],
+) -> None:
+    """Converts an observed custom module to a quantized custom module based on
+    `custom_module_class_mapping`
+    For static quantization, we'll also remove the previous `dequantize` node and
+    attach the observer node for output to the module, the observer for the node
+    will be converted to a dequantize node instead of quantize-dequantize pairs
+    later in the graph. In the end we would have a quantized custom module that
+    has the same interface as a default quantized module in nn.quantized namespace,
+    i.e. quantized input and quantized output.
+
+    Args:
+      - node: The call_module node of the observed standalone module
+      - graph: The graph containing the node
+      - modules: named_module of original model
+      - custom_module_class_mapping: mapping from observed custom module class to
+        quantized custom module class, used to swap custom modules
+      - statically_quantized_custom_module_nodes: we'll add the custom module node
+        if we find it is statically quantized, this will be used later when converting
+        observers to quant/dequant node pairs, if the observed node is a statically
+        quantized custom module nodes, we'll convert the observer to a dequantize node,
+        this is to keep the interface the same as the default quantized module.
+        TODO: maybe we want to redesign this part to align with reference model design
+        as well, but there has been some discussions around the interface, so we can do
+        it later.
+    """
+    observed_custom_module = modules[str(node.target)]
+    maybe_obs = _maybe_get_observer_for_node(node, modules)
+    qconfig = observed_custom_module.qconfig
+    if activation_is_statically_quantized(qconfig):
+        statically_quantized_custom_module_nodes.add(node)
+        if _is_custom_module_lstm(node, modules):
+            # The inputs are tuples in the form (input, (hidden0, hidden1))
+            # Ensure all three input nodes are quantized
+            assert (
+                len(node.args) == 2
+                and isinstance(node.args[1], tuple)
+                and len(node.args[1]) == 2
+            )
+            (inputs, (hidden0, hidden1)) = node.args  # type: ignore[misc]
+            assert isinstance(inputs, Node)
+            assert isinstance(hidden0, Node)
+            assert isinstance(hidden1, Node)
+            _remove_previous_dequantize_in_custom_module(node, inputs, graph)
+            _remove_previous_dequantize_in_custom_module(node, hidden0, graph)
+            _remove_previous_dequantize_in_custom_module(node, hidden1, graph)
+        elif _is_custom_module_mha(node, modules):
+            # Inputs are in the form (query, key, value)
+            # TODO: This is the first step in enabling the full fx custom module
+            # quantization path for MultiheadAttention, and only covers the inputs
+            # to the module.
+            # Additional handling is yet to be implemented for the outputs, similar
+            # to LSTM custom module
+            assert len(node.args) == 3
+            query, key, value = node.args
+            assert isinstance(query, Node)
+            assert isinstance(key, Node)
+            assert isinstance(value, Node)
+            _remove_previous_dequantize_in_custom_module(node, query, graph)
+            _remove_previous_dequantize_in_custom_module(node, key, graph)
+            _remove_previous_dequantize_in_custom_module(node, value, graph)
+        else:
+            # remove the previous dequant node to ensure the inputs are quantized
+            arg = node.args[0]
+            assert isinstance(arg, Node)
+            _remove_previous_dequantize_in_custom_module(node, arg, graph)
+            # absorb the following observer into the module conversion
+            activation_post_process = _maybe_get_observer_for_node(node, modules)
+            assert activation_post_process is not None
+            observed_custom_module.activation_post_process = activation_post_process
+
+    # swap the observed custom module to quantized custom module
+    quantized_custom_module_class = get_swapped_custom_module_class(
+        observed_custom_module, custom_module_class_mapping, qconfig
+    )
+    quantized_custom_module = quantized_custom_module_class.from_observed(
+        observed_custom_module
+    )
+    parent_name, name = _parent_name(node.target)
+    setattr(modules[parent_name], name, quantized_custom_module)
+
+
+def convert(
+    model: GraphModule,
+    is_reference: bool = False,
+    convert_custom_config: Union[ConvertCustomConfig, Dict[str, Any], None] = None,
+    is_standalone_module: bool = False,
+    _remove_qconfig_flag: bool = True,
+    qconfig_mapping: Union[QConfigMapping, Dict[str, Any], None] = None,
+    backend_config: Union[BackendConfig, Dict[str, Any], None] = None,
+    is_decomposed: bool = False,
+) -> GraphModule:
+    """
+    We will convert an observed model (a module with observer calls) to a reference
+    quantized model, the rule is simple:
+    1. for each observer module call in the graph, we'll convert it to calls to
+       quantize and dequantize functions based on the observer instance
+    2. for weighted operations like linear/conv, we need to convert them to reference
+       quantized module, this requires us to know whether the dtype configured for the
+       weight is supported in the backend, this is done in prepare step and the result
+       is stored in observed_node_names, we can decide whether we need to swap the
+       module based on this set
+
+    Args:
+       * `is_standalone_module`: when this flag is True, it means we are quantizing
+       a submodule that is not inlined in parent module, and will be quantized
+       separately as one unit.
+
+       * `is_decomposed`: a boolean flag to indicate whether we want to use the
+        quantize operator for decomposed quantized tensor
+        (torch.ops.quantized_decomposed.quantize_per_tensor) or default/standalone
+        quantized tensor (torch.quantize_per_tensor)
+
+    Returns:
+         a quantized standalone module, whether input/output is quantized is
+         specified by prepare_custom_config, with
+         input_quantized_idxs, output_quantized_idxs, please
+         see docs for :func:`~torch.ao.quantization.prepare_fx` for details
+    """
+    if convert_custom_config is None:
+        convert_custom_config = ConvertCustomConfig()
+
+    if isinstance(convert_custom_config, dict):
+        warnings.warn(
+            "Passing a convert_custom_config_dict to convert is deprecated and will not be supported "
+            "in a future version. Please pass in a ConvertCustomConfig instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        convert_custom_config = ConvertCustomConfig.from_dict(convert_custom_config)
+
+    if isinstance(qconfig_mapping, dict):
+        warnings.warn(
+            "Passing a QConfig dictionary to convert is deprecated and will not be supported "
+            "in a future version. Please pass in a QConfigMapping instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        qconfig_mapping = (
+            QConfigMapping.from_dict(qconfig_mapping) if qconfig_mapping else None
+        )
+    qconfig_mapping = copy.deepcopy(qconfig_mapping)
+    assert qconfig_mapping is None or isinstance(qconfig_mapping, QConfigMapping)
+
+    if isinstance(backend_config, dict):
+        warnings.warn(
+            "Passing a backend_config_dict to prepare is deprecated and will not be supported "
+            "in a future version. Please pass in a BackendConfig instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        backend_config = BackendConfig.from_dict(backend_config)
+
+    if backend_config is None:
+        backend_config = get_native_backend_config()
+
+    assert _is_observed_module(model), "incoming model must be produced by prepare_fx"
+    observed_graph_module_attrs = model.meta["_observed_graph_module_attrs"]
+    node_name_to_scope: Dict[
+        str, Tuple[str, type]
+    ] = observed_graph_module_attrs.node_name_to_scope
+    prepare_custom_config: PrepareCustomConfig = (
+        observed_graph_module_attrs.prepare_custom_config
+    )
+    observed_node_names: Set[str] = observed_graph_module_attrs.observed_node_names
+    node_name_to_qconfig: Dict[str, QConfigAny] = observed_graph_module_attrs.node_name_to_qconfig  # type: ignore[assignment]
+
+    # mapping from fully qualified module name to module instance
+    # for example,
+    # {
+    #   '': Model(...),
+    #   'linear': Linear(...),
+    #   'linear.weight_fake_quant': PerChannelMinMaxObserver(...),
+    # }
+    # We use remove_duplicate=False here because torch.cat uses
+    # the same activation_post_process module instance but different names
+    modules = dict(model.named_modules(remove_duplicate=False))
+
+    # TODO refactor this code once we update the prepare logic to have additional information on
+    # which graph nodes have been observed and share that with convert to decide which observers to ignore.
+    if qconfig_mapping:
+        prepare_qconfig_mapping: QConfigMapping = observed_graph_module_attrs.qconfig_mapping  # type: ignore[assignment]
+        modules_copy = copy.deepcopy(modules)
+
+        if observed_graph_module_attrs.is_qat:
+            _update_qconfig_for_qat(qconfig_mapping, backend_config)
+        _update_qconfig_for_fusion(model, qconfig_mapping)
+
+        _compare_prepare_convert_qconfig_mappings(prepare_qconfig_mapping, qconfig_mapping)  # type: ignore[arg-type]
+        convert_node_name_to_qconfig = _generate_node_name_to_qconfig(
+            model, modules_copy, model.graph, qconfig_mapping, node_name_to_scope
+        )
+        # check the convert_node_name_to_qconfig generated and ensure that
+        # all the values either match what was set in prepare node_name_to_qconfig
+        # or are set to None in the convert_node_name_to_qconfig.
+        for k, v in node_name_to_qconfig.items():
+            assert (
+                k in convert_node_name_to_qconfig
+            ), f"Expected key {k} in convert node_name_to_qconfig"
+            if convert_node_name_to_qconfig[k] is not None:
+                assert qconfig_equals(v, convert_node_name_to_qconfig[k]), (
+                    f"Expected k {k} to have the same value in prepare and convert QConfigMappings, "
+                    f"but {v} was updated to {convert_node_name_to_qconfig[k]}"
+                )
+        node_name_to_qconfig = convert_node_name_to_qconfig
+
+    custom_module_classes = get_custom_module_class_keys(
+        convert_custom_config.observed_to_quantized_mapping
+    )
+    custom_module_class_mapping = convert_custom_config.observed_to_quantized_mapping
+
+    if observed_graph_module_attrs.equalization_node_name_to_qconfig is not None:
+        # If we want to do equalization then do the following:
+        # Calculate the equalization scale, update the observers with the scaled
+        # inputs, and scale the weight
+        weight_eq_obs_dict = update_obs_for_equalization(model, modules)
+        convert_eq_obs(model, modules, weight_eq_obs_dict)
+
+    # always run weight observers in the top level forward method
+    # for dynamic quant ops or weight only quant ops
+    _run_weight_observers(model, backend_config)
+
+    graph_inputs: List[str] = []
+    for node in model.graph.nodes:
+        if node.op == "placeholder":
+            graph_inputs.append(node.name)
+
+    # additional state to override inputs to be quantized, if specified
+    # by the user
+    placeholder_node_seen_cnt = 0
+    input_quantized_idxs: List[int] = prepare_custom_config.input_quantized_indexes
+    output_quantized_idxs: List[int] = prepare_custom_config.output_quantized_indexes
+
+    root_module_to_quantized_reference_module = (
+        get_root_module_to_quantized_reference_module(backend_config)
+    )
+    # convert tuples so that it can work with isinstance(module, tuple_of_classes)
+    root_module_classes = tuple(root_module_to_quantized_reference_module.keys())
+    qat_module_classes = get_qat_module_classes(backend_config)
+    fused_module_classes = get_fused_module_classes(backend_config)
+    statically_quantized_custom_module_nodes: Set[Node] = set()
+
+    for node in list(model.graph.nodes):
+        if node.op == "placeholder":
+            cur_placeholder_node_idx = placeholder_node_seen_cnt
+            placeholder_node_seen_cnt += 1
+            if cur_placeholder_node_idx in input_quantized_idxs:
+                # Inputs are assumed to be quantized if the user specified the
+                # input_quantized_idxs override.
+                # we need to dequantize the inputs since all operators took
+                # floating point inputs in reference quantized models
+                _insert_dequantize_node(node, model.graph)
+        elif node.op == "output":
+            # If the argument is empty we don't need to do anything
+            if len(output_quantized_idxs) == 0:
+                continue
+            # Result are kept quantized if the user specified the
+            # output_quantized_idxs override.
+            # Remove the dequantize operator for the node in the end if any
+            return_node = node
+            output = node.args[0]
+            # outputs can be Node, list, tuple, dict, other cases are not supported yet
+            if isinstance(output, (list, tuple)):
+                for idx in output_quantized_idxs:
+                    _maybe_recursive_remove_dequantize(
+                        output[idx], return_node, model.graph
+                    )
+            elif isinstance(output, (Node, dict)):
+                # we treat dict as a single argument currently, but it can be extended
+                # to support {"key": dtype} after we change output_quantized_idxs to
+                # dict
+                if 0 in output_quantized_idxs:
+                    _maybe_recursive_remove_dequantize(output, return_node, model.graph)
+            else:
+                warnings.warn(
+                    f"Unsupported node type for output_quantized_idxs: {type(output)}"
+                )
+        elif node.op == "call_module":
+            mod = _get_module(node, modules)
+            assert mod is not None
+            if _is_activation_post_process(mod):
+                observed_node = node.args[0]
+                if observed_node in statically_quantized_custom_module_nodes:
+                    _replace_observer_or_dequant_stub_with_dequantize_node(
+                        node, model.graph
+                    )
+                else:
+                    if is_decomposed:
+                        _replace_observer_with_quantize_dequantize_node_decomposed(
+                            model,
+                            node,
+                            modules,
+                            node_name_to_scope,
+                            node_name_to_qconfig,
+                        )
+                    else:
+                        _replace_observer_with_quantize_dequantize_node(
+                            model,
+                            node,
+                            modules,
+                            node_name_to_scope,
+                            node_name_to_qconfig,
+                        )
+            elif isinstance(mod, DeQuantStub):
+                _replace_observer_or_dequant_stub_with_dequantize_node(
+                    node, model.graph
+                )
+            elif _is_observed_standalone_module(mod):
+                convert_standalone_module(
+                    node, modules, model, is_reference, backend_config
+                )
+            # below this point `type_before_parametrizations` is used
+            # instead of `type` to handle situations with fx quant + sparsity
+            elif type_before_parametrizations(mod) in set(root_module_classes).union(
+                qat_module_classes
+            ).union(fused_module_classes):
+                # extra check for fused module classes to make sure they are fused module classes
+                # of target modules
+                if (
+                    type_before_parametrizations(mod) in fused_module_classes
+                    and type_before_parametrizations(mod[0]) not in root_module_classes
+                ):  # type: ignore[index]
+                    continue
+                convert_weighted_module(
+                    node,
+                    modules,
+                    observed_node_names,
+                    node_name_to_qconfig,
+                    backend_config,
+                    is_decomposed,
+                    is_reference,
+                )
+            elif type_before_parametrizations(mod) in custom_module_classes:
+                convert_custom_module(
+                    node,
+                    model.graph,
+                    modules,
+                    custom_module_class_mapping,
+                    statically_quantized_custom_module_nodes,
+                )
+
+    # remove deadcode after converting observers to quant/dequant ops
+    model.graph.eliminate_dead_code()
+    model = GraphModule(model, model.graph)
+
+    # TODO: maybe move this to quantize_fx.py
+    if not is_reference:
+        model = lower_to_fbgemm(model, node_name_to_qconfig, node_name_to_scope)
+
+    # TODO: this looks hacky, we want to check why we need this and see if we can
+    # remove this
+    # removes qconfig and activation_post_process modules
+    if _remove_qconfig_flag:
+        _remove_qconfig(model)
+    model.delete_all_unused_submodules()
+    model.meta.pop("_observed_graph_module_attrs", None)
+    return model
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/custom_config.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/custom_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb00c95fdee1d863abbd41499ab4c3069dd2f0eb
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/custom_config.py
@@ -0,0 +1,519 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Type
+
+from torch.ao.quantization import QConfigMapping
+from torch.ao.quantization.backend_config import BackendConfig
+from torch.ao.quantization.quant_type import (
+    _get_quant_type_to_str,
+    _quant_type_from_str,
+    QuantType,
+)
+
+
+__all__ = [
+    "ConvertCustomConfig",
+    "FuseCustomConfig",
+    "PrepareCustomConfig",
+    "StandaloneModuleConfigEntry",
+]
+
+
+# TODO: replace all usages with these constants
+STANDALONE_MODULE_NAME_DICT_KEY = "standalone_module_name"
+STANDALONE_MODULE_CLASS_DICT_KEY = "standalone_module_class"
+FLOAT_TO_OBSERVED_DICT_KEY = "float_to_observed_custom_module_class"
+OBSERVED_TO_QUANTIZED_DICT_KEY = "observed_to_quantized_custom_module_class"
+NON_TRACEABLE_MODULE_NAME_DICT_KEY = "non_traceable_module_name"
+NON_TRACEABLE_MODULE_CLASS_DICT_KEY = "non_traceable_module_class"
+INPUT_QUANTIZED_INDEXES_DICT_KEY = "input_quantized_idxs"
+OUTPUT_QUANTIZED_INDEXES_DICT_KEY = "output_quantized_idxs"
+PRESERVED_ATTRIBUTES_DICT_KEY = "preserved_attributes"
+
+
+@dataclass
+class StandaloneModuleConfigEntry:
+    # qconfig_mapping for the prepare function called in the submodule,
+    # None means use qconfig from parent qconfig_mapping
+    qconfig_mapping: Optional[QConfigMapping]
+    example_inputs: Tuple[Any, ...]
+    prepare_custom_config: Optional[PrepareCustomConfig]
+    backend_config: Optional[BackendConfig]
+
+
+class PrepareCustomConfig:
+    """
+    Custom configuration for :func:`~torch.ao.quantization.quantize_fx.prepare_fx` and
+    :func:`~torch.ao.quantization.quantize_fx.prepare_qat_fx`.
+
+    Example usage::
+
+        prepare_custom_config = PrepareCustomConfig() \
+            .set_standalone_module_name("module1", qconfig_mapping, example_inputs, \
+                child_prepare_custom_config, backend_config) \
+            .set_standalone_module_class(MyStandaloneModule, qconfig_mapping, example_inputs, \
+                child_prepare_custom_config, backend_config) \
+            .set_float_to_observed_mapping(FloatCustomModule, ObservedCustomModule) \
+            .set_non_traceable_module_names(["module2", "module3"]) \
+            .set_non_traceable_module_classes([NonTraceableModule1, NonTraceableModule2]) \
+            .set_input_quantized_indexes([0]) \
+            .set_output_quantized_indexes([0]) \
+            .set_preserved_attributes(["attr1", "attr2"])
+    """
+
+    def __init__(self) -> None:
+        self.standalone_module_names: Dict[str, StandaloneModuleConfigEntry] = {}
+        self.standalone_module_classes: Dict[Type, StandaloneModuleConfigEntry] = {}
+        self.float_to_observed_mapping: Dict[QuantType, Dict[Type, Type]] = {}
+        self.non_traceable_module_names: List[str] = []
+        self.non_traceable_module_classes: List[Type] = []
+        self.input_quantized_indexes: List[int] = []
+        self.output_quantized_indexes: List[int] = []
+        self.preserved_attributes: List[str] = []
+
+    def __repr__(self):
+        dict_nonempty = {k: v for k, v in self.__dict__.items() if len(v) > 0}
+        return f"PrepareCustomConfig({dict_nonempty})"
+
+    def set_standalone_module_name(
+        self,
+        module_name: str,
+        qconfig_mapping: Optional[QConfigMapping],
+        example_inputs: Tuple[Any, ...],
+        prepare_custom_config: Optional[PrepareCustomConfig],
+        backend_config: Optional[BackendConfig],
+    ) -> PrepareCustomConfig:
+        """
+        Set the configuration for running a standalone module identified by ``module_name``.
+
+        If ``qconfig_mapping`` is None, the parent ``qconfig_mapping`` will be used instead.
+        If ``prepare_custom_config`` is None, an empty ``PrepareCustomConfig`` will be used.
+        If ``backend_config`` is None, the parent ``backend_config`` will be used instead.
+        """
+        self.standalone_module_names[module_name] = StandaloneModuleConfigEntry(
+            qconfig_mapping, example_inputs, prepare_custom_config, backend_config
+        )
+        return self
+
+    def set_standalone_module_class(
+        self,
+        module_class: Type,
+        qconfig_mapping: Optional[QConfigMapping],
+        example_inputs: Tuple[Any, ...],
+        prepare_custom_config: Optional[PrepareCustomConfig],
+        backend_config: Optional[BackendConfig],
+    ) -> PrepareCustomConfig:
+        """
+        Set the configuration for running a standalone module identified by ``module_class``.
+
+        If ``qconfig_mapping`` is None, the parent ``qconfig_mapping`` will be used instead.
+        If ``prepare_custom_config`` is None, an empty ``PrepareCustomConfig`` will be used.
+        If ``backend_config`` is None, the parent ``backend_config`` will be used instead.
+        """
+        self.standalone_module_classes[module_class] = StandaloneModuleConfigEntry(
+            qconfig_mapping, example_inputs, prepare_custom_config, backend_config
+        )
+        return self
+
+    def set_float_to_observed_mapping(
+        self,
+        float_class: Type,
+        observed_class: Type,
+        quant_type: QuantType = QuantType.STATIC,
+    ) -> PrepareCustomConfig:
+        """
+        Set the mapping from a custom float module class to a custom observed module class.
+
+        The observed module class must have a ``from_float`` class method that converts the float module class
+        to the observed module class. This is currently only supported for static quantization.
+        """
+        if quant_type != QuantType.STATIC:
+            raise ValueError(
+                "set_float_to_observed_mapping is currently only supported for static quantization"
+            )
+        if quant_type not in self.float_to_observed_mapping:
+            self.float_to_observed_mapping[quant_type] = {}
+        self.float_to_observed_mapping[quant_type][float_class] = observed_class
+        return self
+
+    def set_non_traceable_module_names(
+        self, module_names: List[str]
+    ) -> PrepareCustomConfig:
+        """
+        Set the modules that are not symbolically traceable, identified by name.
+        """
+        self.non_traceable_module_names = module_names
+        return self
+
+    def set_non_traceable_module_classes(
+        self, module_classes: List[Type]
+    ) -> PrepareCustomConfig:
+        """
+        Set the modules that are not symbolically traceable, identified by class.
+        """
+        self.non_traceable_module_classes = module_classes
+        return self
+
+    def set_input_quantized_indexes(self, indexes: List[int]) -> PrepareCustomConfig:
+        """
+        Set the indexes of the inputs of the graph that should be quantized.
+        Inputs are otherwise assumed to be in fp32 by default instead.
+        """
+        self.input_quantized_indexes = indexes
+        return self
+
+    def set_output_quantized_indexes(self, indexes: List[int]) -> PrepareCustomConfig:
+        """
+        Set the indexes of the outputs of the graph that should be quantized.
+        Outputs are otherwise assumed to be in fp32 by default instead.
+        """
+        self.output_quantized_indexes = indexes
+        return self
+
+    def set_preserved_attributes(self, attributes: List[str]) -> PrepareCustomConfig:
+        """
+        Set the names of the attributes that will persist in the graph module even if they are not used in
+        the model's ``forward`` method.
+        """
+        self.preserved_attributes = attributes
+        return self
+
+    # TODO: remove this
+    @classmethod
+    def from_dict(
+        cls, prepare_custom_config_dict: Dict[str, Any]
+    ) -> PrepareCustomConfig:
+        """
+        Create a ``PrepareCustomConfig`` from a dictionary with the following items:
+
+            "standalone_module_name": a list of (module_name, qconfig_mapping, example_inputs,
+            child_prepare_custom_config, backend_config) tuples
+
+            "standalone_module_class" a list of (module_class, qconfig_mapping, example_inputs,
+            child_prepare_custom_config, backend_config) tuples
+
+            "float_to_observed_custom_module_class": a nested dictionary mapping from quantization
+            mode to an inner mapping from float module classes to observed module classes, e.g.
+            {"static": {FloatCustomModule: ObservedCustomModule}}
+
+            "non_traceable_module_name": a list of modules names that are not symbolically traceable
+            "non_traceable_module_class": a list of module classes that are not symbolically traceable
+            "input_quantized_idxs": a list of indexes of graph inputs that should be quantized
+            "output_quantized_idxs": a list of indexes of graph outputs that should be quantized
+            "preserved_attributes": a list of attributes that persist even if they are not used in ``forward``
+
+        This function is primarily for backward compatibility and may be removed in the future.
+        """
+
+        def _get_qconfig_mapping(obj: Any, dict_key: str) -> Optional[QConfigMapping]:
+            """
+            Convert the given object into a QConfigMapping if possible, else throw an exception.
+            """
+            if isinstance(obj, QConfigMapping) or obj is None:
+                return obj
+            if isinstance(obj, Dict):
+                return QConfigMapping.from_dict(obj)
+            raise ValueError(
+                f"Expected QConfigMapping in prepare_custom_config_dict[\"{dict_key}\"], got '{type(obj)}'"
+            )
+
+        def _get_prepare_custom_config(
+            obj: Any, dict_key: str
+        ) -> Optional[PrepareCustomConfig]:
+            """
+            Convert the given object into a PrepareCustomConfig if possible, else throw an exception.
+            """
+            if isinstance(obj, PrepareCustomConfig) or obj is None:
+                return obj
+            if isinstance(obj, Dict):
+                return PrepareCustomConfig.from_dict(obj)
+            raise ValueError(
+                f"Expected PrepareCustomConfig in prepare_custom_config_dict[\"{dict_key}\"], got '{type(obj)}'"
+            )
+
+        def _get_backend_config(obj: Any, dict_key: str) -> Optional[BackendConfig]:
+            """
+            Convert the given object into a BackendConfig if possible, else throw an exception.
+            """
+            if isinstance(obj, BackendConfig) or obj is None:
+                return obj
+            if isinstance(obj, Dict):
+                return BackendConfig.from_dict(obj)
+            raise ValueError(
+                f"Expected BackendConfig in prepare_custom_config_dict[\"{dict_key}\"], got '{type(obj)}'"
+            )
+
+        conf = cls()
+        for (
+            module_name,
+            qconfig_dict,
+            example_inputs,
+            _prepare_custom_config_dict,
+            backend_config_dict,
+        ) in prepare_custom_config_dict.get(STANDALONE_MODULE_NAME_DICT_KEY, []):
+            qconfig_mapping = _get_qconfig_mapping(
+                qconfig_dict, STANDALONE_MODULE_NAME_DICT_KEY
+            )
+            prepare_custom_config = _get_prepare_custom_config(
+                _prepare_custom_config_dict, STANDALONE_MODULE_NAME_DICT_KEY
+            )
+            backend_config = _get_backend_config(
+                backend_config_dict, STANDALONE_MODULE_NAME_DICT_KEY
+            )
+            conf.set_standalone_module_name(
+                module_name,
+                qconfig_mapping,
+                example_inputs,
+                prepare_custom_config,
+                backend_config,
+            )
+        for (
+            module_class,
+            qconfig_dict,
+            example_inputs,
+            _prepare_custom_config_dict,
+            backend_config_dict,
+        ) in prepare_custom_config_dict.get(STANDALONE_MODULE_CLASS_DICT_KEY, []):
+            qconfig_mapping = _get_qconfig_mapping(
+                qconfig_dict, STANDALONE_MODULE_CLASS_DICT_KEY
+            )
+            prepare_custom_config = _get_prepare_custom_config(
+                _prepare_custom_config_dict, STANDALONE_MODULE_CLASS_DICT_KEY
+            )
+            backend_config = _get_backend_config(
+                backend_config_dict, STANDALONE_MODULE_CLASS_DICT_KEY
+            )
+            conf.set_standalone_module_class(
+                module_class,
+                qconfig_mapping,
+                example_inputs,
+                prepare_custom_config,
+                backend_config,
+            )
+        for quant_type_name, custom_module_mapping in prepare_custom_config_dict.get(
+            FLOAT_TO_OBSERVED_DICT_KEY, {}
+        ).items():
+            quant_type = _quant_type_from_str(quant_type_name)
+            for float_class, observed_class in custom_module_mapping.items():
+                conf.set_float_to_observed_mapping(
+                    float_class, observed_class, quant_type
+                )
+        conf.set_non_traceable_module_names(
+            prepare_custom_config_dict.get(NON_TRACEABLE_MODULE_NAME_DICT_KEY, [])
+        )
+        conf.set_non_traceable_module_classes(
+            prepare_custom_config_dict.get(NON_TRACEABLE_MODULE_CLASS_DICT_KEY, [])
+        )
+        conf.set_input_quantized_indexes(
+            prepare_custom_config_dict.get(INPUT_QUANTIZED_INDEXES_DICT_KEY, [])
+        )
+        conf.set_output_quantized_indexes(
+            prepare_custom_config_dict.get(OUTPUT_QUANTIZED_INDEXES_DICT_KEY, [])
+        )
+        conf.set_preserved_attributes(
+            prepare_custom_config_dict.get(PRESERVED_ATTRIBUTES_DICT_KEY, [])
+        )
+        return conf
+
+    def to_dict(self) -> Dict[str, Any]:
+        """
+        Convert this ``PrepareCustomConfig`` to a dictionary with the items described in
+        :func:`~torch.ao.quantization.fx.custom_config.PrepareCustomConfig.from_dict`.
+        """
+
+        def _make_tuple(key: Any, e: StandaloneModuleConfigEntry):
+            qconfig_dict = e.qconfig_mapping.to_dict() if e.qconfig_mapping else None
+            prepare_custom_config_dict = (
+                e.prepare_custom_config.to_dict() if e.prepare_custom_config else None
+            )
+            return (
+                key,
+                qconfig_dict,
+                e.example_inputs,
+                prepare_custom_config_dict,
+                e.backend_config,
+            )
+
+        d: Dict[str, Any] = {}
+        for module_name, sm_config_entry in self.standalone_module_names.items():
+            if STANDALONE_MODULE_NAME_DICT_KEY not in d:
+                d[STANDALONE_MODULE_NAME_DICT_KEY] = []
+            d[STANDALONE_MODULE_NAME_DICT_KEY].append(
+                _make_tuple(module_name, sm_config_entry)
+            )
+        for module_class, sm_config_entry in self.standalone_module_classes.items():
+            if STANDALONE_MODULE_CLASS_DICT_KEY not in d:
+                d[STANDALONE_MODULE_CLASS_DICT_KEY] = []
+            d[STANDALONE_MODULE_CLASS_DICT_KEY].append(
+                _make_tuple(module_class, sm_config_entry)
+            )
+        for (
+            quant_type,
+            float_to_observed_mapping,
+        ) in self.float_to_observed_mapping.items():
+            if FLOAT_TO_OBSERVED_DICT_KEY not in d:
+                d[FLOAT_TO_OBSERVED_DICT_KEY] = {}
+            d[FLOAT_TO_OBSERVED_DICT_KEY][
+                _get_quant_type_to_str(quant_type)
+            ] = float_to_observed_mapping
+        if len(self.non_traceable_module_names) > 0:
+            d[NON_TRACEABLE_MODULE_NAME_DICT_KEY] = self.non_traceable_module_names
+        if len(self.non_traceable_module_classes) > 0:
+            d[NON_TRACEABLE_MODULE_CLASS_DICT_KEY] = self.non_traceable_module_classes
+        if len(self.input_quantized_indexes) > 0:
+            d[INPUT_QUANTIZED_INDEXES_DICT_KEY] = self.input_quantized_indexes
+        if len(self.output_quantized_indexes) > 0:
+            d[OUTPUT_QUANTIZED_INDEXES_DICT_KEY] = self.output_quantized_indexes
+        if len(self.preserved_attributes) > 0:
+            d[PRESERVED_ATTRIBUTES_DICT_KEY] = self.preserved_attributes
+        return d
+
+
+class ConvertCustomConfig:
+    """
+    Custom configuration for :func:`~torch.ao.quantization.quantize_fx.convert_fx`.
+
+    Example usage::
+
+        convert_custom_config = ConvertCustomConfig() \
+            .set_observed_to_quantized_mapping(ObservedCustomModule, QuantizedCustomModule) \
+            .set_preserved_attributes(["attr1", "attr2"])
+    """
+
+    def __init__(self) -> None:
+        self.observed_to_quantized_mapping: Dict[QuantType, Dict[Type, Type]] = {}
+        self.preserved_attributes: List[str] = []
+
+    def __repr__(self):
+        dict_nonempty = {k: v for k, v in self.__dict__.items() if len(v) > 0}
+        return f"ConvertCustomConfig({dict_nonempty})"
+
+    def set_observed_to_quantized_mapping(
+        self,
+        observed_class: Type,
+        quantized_class: Type,
+        quant_type: QuantType = QuantType.STATIC,
+    ) -> ConvertCustomConfig:
+        """
+        Set the mapping from a custom observed module class to a custom quantized module class.
+
+        The quantized module class must have a ``from_observed`` class method that converts the observed module class
+        to the quantized module class.
+        """
+        if quant_type not in self.observed_to_quantized_mapping:
+            self.observed_to_quantized_mapping[quant_type] = {}
+        self.observed_to_quantized_mapping[quant_type][observed_class] = quantized_class
+        return self
+
+    def set_preserved_attributes(self, attributes: List[str]) -> ConvertCustomConfig:
+        """
+        Set the names of the attributes that will persist in the graph module even if they are not used in
+        the model's ``forward`` method.
+        """
+        self.preserved_attributes = attributes
+        return self
+
+    # TODO: remove this
+    @classmethod
+    def from_dict(
+        cls, convert_custom_config_dict: Dict[str, Any]
+    ) -> ConvertCustomConfig:
+        """
+        Create a ``ConvertCustomConfig`` from a dictionary with the following items:
+
+            "observed_to_quantized_custom_module_class": a nested dictionary mapping from quantization
+            mode to an inner mapping from observed module classes to quantized module classes, e.g.::
+            {
+            "static": {FloatCustomModule: ObservedCustomModule},
+            "dynamic": {FloatCustomModule: ObservedCustomModule},
+            "weight_only": {FloatCustomModule: ObservedCustomModule}
+            }
+            "preserved_attributes": a list of attributes that persist even if they are not used in ``forward``
+
+        This function is primarily for backward compatibility and may be removed in the future.
+        """
+        conf = cls()
+        for quant_type_name, custom_module_mapping in convert_custom_config_dict.get(
+            OBSERVED_TO_QUANTIZED_DICT_KEY, {}
+        ).items():
+            quant_type = _quant_type_from_str(quant_type_name)
+            for observed_class, quantized_class in custom_module_mapping.items():
+                conf.set_observed_to_quantized_mapping(
+                    observed_class, quantized_class, quant_type
+                )
+        conf.set_preserved_attributes(
+            convert_custom_config_dict.get(PRESERVED_ATTRIBUTES_DICT_KEY, [])
+        )
+        return conf
+
+    def to_dict(self) -> Dict[str, Any]:
+        """
+        Convert this ``ConvertCustomConfig`` to a dictionary with the items described in
+        :func:`~torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict`.
+        """
+        d: Dict[str, Any] = {}
+        for (
+            quant_type,
+            observed_to_quantized_mapping,
+        ) in self.observed_to_quantized_mapping.items():
+            if OBSERVED_TO_QUANTIZED_DICT_KEY not in d:
+                d[OBSERVED_TO_QUANTIZED_DICT_KEY] = {}
+            d[OBSERVED_TO_QUANTIZED_DICT_KEY][
+                _get_quant_type_to_str(quant_type)
+            ] = observed_to_quantized_mapping
+        if len(self.preserved_attributes) > 0:
+            d[PRESERVED_ATTRIBUTES_DICT_KEY] = self.preserved_attributes
+        return d
+
+
+class FuseCustomConfig:
+    """
+    Custom configuration for :func:`~torch.ao.quantization.quantize_fx.fuse_fx`.
+
+    Example usage::
+
+        fuse_custom_config = FuseCustomConfig().set_preserved_attributes(["attr1", "attr2"])
+    """
+
+    def __init__(self) -> None:
+        self.preserved_attributes: List[str] = []
+
+    def __repr__(self):
+        dict_nonempty = {k: v for k, v in self.__dict__.items() if len(v) > 0}
+        return f"FuseCustomConfig({dict_nonempty})"
+
+    def set_preserved_attributes(self, attributes: List[str]) -> FuseCustomConfig:
+        """
+        Set the names of the attributes that will persist in the graph module even if they are not used in
+        the model's ``forward`` method.
+        """
+        self.preserved_attributes = attributes
+        return self
+
+    # TODO: remove this
+    @classmethod
+    def from_dict(cls, fuse_custom_config_dict: Dict[str, Any]) -> FuseCustomConfig:
+        """
+        Create a ``ConvertCustomConfig`` from a dictionary with the following items:
+
+            "preserved_attributes": a list of attributes that persist even if they are not used in ``forward``
+
+        This function is primarily for backward compatibility and may be removed in the future.
+        """
+        conf = cls()
+        conf.set_preserved_attributes(
+            fuse_custom_config_dict.get(PRESERVED_ATTRIBUTES_DICT_KEY, [])
+        )
+        return conf
+
+    def to_dict(self) -> Dict[str, Any]:
+        """
+        Convert this ``FuseCustomConfig`` to a dictionary with the items described in
+        :func:`~torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict`.
+        """
+        d: Dict[str, Any] = {}
+        if len(self.preserved_attributes) > 0:
+            d[PRESERVED_ATTRIBUTES_DICT_KEY] = self.preserved_attributes
+        return d
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/fuse.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/fuse.py
new file mode 100644
index 0000000000000000000000000000000000000000..23d013648e2433e614ec00c2a50a827650cae789
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/fuse.py
@@ -0,0 +1,191 @@
+# mypy: allow-untyped-defs
+import warnings
+from typing import Any, Callable, Dict, List, Tuple, Union
+
+from torch.ao.quantization.backend_config import (
+    BackendConfig,
+    get_native_backend_config,
+)
+from torch.ao.quantization.backend_config.utils import (
+    get_fuser_method_mapping,
+    get_fusion_pattern_to_extra_inputs_getter,
+    get_fusion_pattern_to_root_node_getter,
+)
+from torch.ao.quantization.utils import NodePattern, Pattern
+from torch.fx import GraphModule, map_arg, Node
+from torch.fx.graph import Graph
+
+from .custom_config import FuseCustomConfig
+from .fuse_handler import _get_fusion_pattern_to_fuse_handler_cls, FuseHandler
+from .match_utils import _is_match, MatchAllNode
+from .pattern_utils import _sorted_patterns_dict
+
+
+__all__ = [
+    "fuse",
+    # TODO: We should make this private in the future
+    # This is currently needed for test_public_bindings for some reason
+    "FuseHandler",
+]
+
+
+def fuse(
+    model: GraphModule,
+    is_qat: bool,
+    fuse_custom_config: Union[FuseCustomConfig, Dict[str, Any], None] = None,
+    backend_config: Union[BackendConfig, Dict[str, Any], None] = None,
+) -> GraphModule:
+    if fuse_custom_config is None:
+        fuse_custom_config = FuseCustomConfig()
+
+    if isinstance(fuse_custom_config, dict):
+        warnings.warn(
+            "Passing a fuse_custom_config_dict to fuse is deprecated and will not be supported "
+            "in a future version. Please pass in a FuseCustomConfig instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        fuse_custom_config = FuseCustomConfig.from_dict(fuse_custom_config)
+
+    if isinstance(backend_config, dict):
+        warnings.warn(
+            "Passing a backend_config_dict to prepare is deprecated and will not be supported "
+            "in a future version. Please pass in a BackendConfig instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        backend_config = BackendConfig.from_dict(backend_config)
+
+    named_modules = dict(model.named_modules())
+
+    if backend_config is None:
+        backend_config = get_native_backend_config()
+
+    fusion_pattern_to_fuse_handler_cls = _sorted_patterns_dict(
+        _get_fusion_pattern_to_fuse_handler_cls(backend_config)
+    )
+    fuser_method_mapping = get_fuser_method_mapping(backend_config)
+    fusion_pattern_to_root_node_getter = get_fusion_pattern_to_root_node_getter(
+        backend_config
+    )
+    fusion_pattern_to_extra_inputs_getter = get_fusion_pattern_to_extra_inputs_getter(
+        backend_config
+    )
+
+    # find fusion
+    fusion_pairs = _find_matches(model, model.graph, fusion_pattern_to_fuse_handler_cls)
+    # TODO: change this to inplace changes to graph, since we no longer construct
+    # new GraphModule anymore
+    fused_graph = Graph()
+    env: Dict[Any, Any] = {}
+
+    def load_arg(a):
+        return map_arg(a, lambda node: env[node.name])
+
+    def default_root_node_getter(node_pattern):
+        while not isinstance(node_pattern[-1], Node):
+            node_pattern = node_pattern[-1]
+        return node_pattern[-1]
+
+    for node in model.graph.nodes:
+        (
+            maybe_last_node,
+            pattern,
+            matched_node_pattern,
+            obj,
+            node_to_subpattern,
+        ) = fusion_pairs.get(node.name, (None, None, None, None, None))
+        # get the corresponding subpattern for the current node
+        if node_to_subpattern is not None:
+            node_subpattern = node_to_subpattern.get(node, None)
+        else:
+            node_subpattern = None
+        if maybe_last_node is node:
+            assert obj is not None
+            root_node_getter = fusion_pattern_to_root_node_getter.get(
+                pattern, default_root_node_getter
+            )
+            root_node = root_node_getter(matched_node_pattern)  # type: ignore[index]
+            extra_inputs_getter = fusion_pattern_to_extra_inputs_getter.get(
+                pattern, None
+            )
+            extra_inputs = []
+            if extra_inputs_getter is not None:
+                extra_inputs = extra_inputs_getter(matched_node_pattern)
+            # TODO: add validation that root_node is a module and has the same type
+            # as the root_module in the configuration
+            env[node.name] = obj.fuse(
+                load_arg,
+                named_modules,
+                fused_graph,
+                root_node,
+                extra_inputs,
+                matched_node_pattern,  # type: ignore[arg-type]
+                fuse_custom_config,
+                fuser_method_mapping,
+                is_qat,
+            )
+        elif maybe_last_node is None or node_subpattern is MatchAllNode:
+            env[node.name] = fused_graph.node_copy(node, load_arg)
+        # node matched in patterns and is not root is removed here
+
+    model = GraphModule(model, fused_graph)
+    return model
+
+
+def _find_matches(
+    root: GraphModule,
+    graph: Graph,
+    pattern_to_fuse_handler_cls: Dict[Pattern, Callable],
+) -> Dict[str, Tuple[Node, Pattern, NodePattern, FuseHandler, Dict[Node, Any]]]:
+    modules = dict(root.named_modules())
+    # node name -> (root_node, match_value)
+    match_map: Dict[
+        str, Tuple[Node, Pattern, NodePattern, FuseHandler, Dict[Node, Any]]
+    ] = {}
+    # a map from node to the matched subpattern
+    node_to_subpattern: Dict[Node, Any] = {}
+
+    # TODO: dedup with quantization matching function in match_utils.py
+    def apply_match(pattern, node, match, matched_node_pattern, node_to_subpattern):
+        if isinstance(pattern, tuple):
+            s, *args = pattern
+            current_node_pattern: List[Node] = []
+            apply_match(s, node, match, current_node_pattern, node_to_subpattern)
+            for subpattern, arg in zip(args, node.args):
+                apply_match(
+                    subpattern, arg, match, current_node_pattern, node_to_subpattern
+                )
+            matched_node_pattern.append(tuple(current_node_pattern))
+        else:
+            # the first pattern matches will take precedence
+            if node.name not in match_map:
+                matched_node_pattern.append(node)
+                # MatchAllNode here is actually MatchAllInputNode which should not
+                # be added to match_map
+                if pattern is not MatchAllNode:
+                    node_to_subpattern[node] = pattern
+                    root_node, pattern, handler = match
+                    match_map[node.name] = (
+                        root_node,
+                        pattern,
+                        matched_node_pattern,
+                        handler,
+                        node_to_subpattern,
+                    )
+
+    for node in reversed(graph.nodes):
+        if node.name not in match_map:
+            for pattern, fuse_handler_cls in pattern_to_fuse_handler_cls.items():
+                matched_node_pattern: List[Node] = []
+                if _is_match(modules, node, pattern):
+                    apply_match(
+                        pattern,
+                        node,
+                        (node, pattern, fuse_handler_cls(node)),
+                        matched_node_pattern,
+                        node_to_subpattern,
+                    )
+                    break
+
+    return match_map
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/fuse_handler.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/fuse_handler.py
new file mode 100644
index 0000000000000000000000000000000000000000..75e7f3cb9df503e2cfbf12d3d373d355ce82f88c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/fuse_handler.py
@@ -0,0 +1,132 @@
+# mypy: allow-untyped-defs
+from abc import ABC, abstractmethod
+from typing import Any, Callable, Dict, List, Union
+
+import torch
+from torch.ao.quantization.backend_config import BackendConfig
+from torch.ao.quantization.fuser_method_mappings import get_fuser_method_new
+from torch.ao.quantization.utils import _parent_name, NodePattern, Pattern
+from torch.fx.graph import Graph, Node
+from torch.nn.utils.parametrize import type_before_parametrizations
+
+from .custom_config import FuseCustomConfig
+from .match_utils import MatchAllNode
+
+
+__all__ = [
+    "DefaultFuseHandler",
+    "FuseHandler",
+]
+
+
+# ----------------------------
+# Fusion Pattern Registrations
+# ----------------------------
+
+
+# Base Pattern Handler
+class FuseHandler(ABC):
+    """Base handler class for the fusion patterns"""
+
+    @abstractmethod
+    def __init__(self, node: Node):
+        pass
+
+    @abstractmethod
+    def fuse(
+        self,
+        load_arg: Callable,
+        named_modules: Dict[str, torch.nn.Module],
+        fused_graph: Graph,
+        root_node: Node,
+        extra_inputs: List[Any],
+        matched_node_pattern: NodePattern,
+        fuse_custom_config: FuseCustomConfig,
+        fuser_method_mapping: Dict[Pattern, Union[torch.nn.Sequential, Callable]],
+        is_qat: bool,
+    ) -> Node:
+        pass
+
+
+class DefaultFuseHandler(FuseHandler):
+    def __init__(self, node: Node):
+        super().__init__(node)  # type:ignore[safe-super]
+
+    def fuse(
+        self,
+        load_arg: Callable,
+        named_modules: Dict[str, torch.nn.Module],
+        fused_graph: Graph,
+        root_node: Node,
+        extra_inputs: List[Any],
+        matched_node_pattern: NodePattern,
+        fuse_custom_config: FuseCustomConfig,
+        fuser_method_mapping: Dict[Pattern, Union[torch.nn.Sequential, Callable]],
+        is_qat: bool,
+    ) -> Node:
+        assert (
+            root_node.op == "call_module"
+        ), "Expecting module node to be a call_module Node"
+        root_module = named_modules[str(root_node.target)]
+
+        def get_modules(pattern):
+            """Given a node pattern, extract the corresponding modules
+            e.g. input: (relu_node, (bn_node, conv_node))
+                 output: (relu_module, (bn_module, conv_module))
+            """
+            if isinstance(pattern, (tuple, list)):
+                n, *args = pattern
+                modules: List[torch.nn.Module] = []
+                modules.append(get_modules(n))
+                for a in args:
+                    modules.append(get_modules(a))
+                return tuple(modules)
+            else:
+                n = pattern
+                if n.op == "call_module":
+                    return named_modules[n.target]
+                elif n.op == "call_function" and n.target == torch.nn.functional.relu:
+                    relu = torch.nn.ReLU()
+                    relu.training = root_module.training
+                    return relu
+                elif n.op == "call_function" or n.op == "call_method":
+                    return n.target
+                else:
+                    return MatchAllNode
+
+        # since relu can be used multiple times, we'll need to create a relu module for each match
+        matched_modules = get_modules(matched_node_pattern)
+
+        def get_matched_types(m):
+            if isinstance(m, tuple):
+                return tuple(map(get_matched_types, m))
+            if isinstance(m, torch.nn.Module):
+                return type_before_parametrizations(m)
+            return m
+
+        matched_module_types = get_matched_types(matched_modules)
+        module_parent_name, module_name = _parent_name(root_node.target)
+        fuser_method = get_fuser_method_new(matched_module_types, fuser_method_mapping)
+        # TODO: change the signature for fuser_method to take matched module patterns
+        # as input
+        fused_module = fuser_method(is_qat, *matched_modules)
+        setattr(named_modules[module_parent_name], module_name, fused_module)
+        extra_args = []
+        for input in extra_inputs:
+            extra_args.append(load_arg(input))
+        node = fused_graph.node_copy(root_node, load_arg)
+        args = list(node.args)
+        args.extend(extra_args)
+        node.args = tuple(args)
+        return node
+
+
+def _get_fusion_pattern_to_fuse_handler_cls(
+    backend_config: BackendConfig,
+) -> Dict[Pattern, Callable]:
+    fusion_pattern_to_fuse_handlers: Dict[Pattern, Callable] = {}
+    for pattern, config in backend_config._pattern_complex_format_to_config.items():
+        if config.fuser_method is not None:
+            # TODO: is this logic right?
+            fusion_pattern_to_fuse_handlers[pattern] = DefaultFuseHandler
+    return fusion_pattern_to_fuse_handlers
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/graph_module.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/graph_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..4037c1b1e01b893ee32fc424d33c45e030dd7006
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/graph_module.py
@@ -0,0 +1,203 @@
+# mypy: allow-untyped-defs
+import copy
+from typing import Any, Dict, Set, Union
+
+import torch
+from torch.fx import GraphModule
+from torch.fx.graph import Graph
+
+
+__all__ = [
+    "FusedGraphModule",
+    "ObservedGraphModule",
+    "ObservedStandaloneGraphModule",
+    "QuantizedGraphModule",
+]
+
+
+class FusedGraphModule(GraphModule):
+    def __init__(
+        self,
+        root: Union[torch.nn.Module, Dict[str, Any]],
+        graph: Graph,
+        preserved_attr_names: Set[str],
+    ):
+        self.preserved_attr_names = preserved_attr_names
+        preserved_attrs = {
+            attr: getattr(root, attr)
+            for attr in self.preserved_attr_names
+            if hasattr(root, attr)
+        }
+        super().__init__(root, graph)
+        for attr in preserved_attrs:
+            setattr(self, attr, preserved_attrs[attr])
+
+    # GraphModule does not copy attributes which are not in the __dict__
+    # of vanilla nn.Module.  So, we override __deepcopy__ in order
+    # to copy the quantization specific attributes correctly.
+    def __deepcopy__(self, memo):
+        fake_mod = torch.nn.Module()
+        fake_mod.__dict__ = copy.deepcopy(self.__dict__)
+        return FusedGraphModule(
+            fake_mod,
+            copy.deepcopy(self.graph),
+            copy.deepcopy(self.preserved_attr_names),
+        )
+
+
+class ObservedGraphModule(GraphModule):
+    def __init__(
+        self,
+        root: Union[torch.nn.Module, Dict[str, Any]],
+        graph: Graph,
+        preserved_attr_names: Set[str],
+    ):
+        self.preserved_attr_names = {
+            "_activation_post_process_map",
+            "_activation_post_process_indexes",
+            "_patterns",
+            "_node_name_to_qconfig",
+            "_prepare_custom_config",
+            "_equalization_node_name_to_qconfig",
+            "_node_name_to_scope",
+            "_qconfig_mapping",
+            "_is_qat",
+            "_observed_node_names",
+        }.union(preserved_attr_names)
+        preserved_attrs = {
+            attr: getattr(root, attr)
+            for attr in self.preserved_attr_names
+            if hasattr(root, attr)
+        }
+        super().__init__(root, graph)
+        for attr in preserved_attrs:
+            setattr(self, attr, preserved_attrs[attr])
+
+    # GraphModule does not copy attributes which are not in the __dict__
+    # of vanilla nn.Module.  So, we override __deepcopy__ in order
+    # to copy the quantization specific attributes correctly.
+    def __deepcopy__(self, memo):
+        fake_mod = torch.nn.Module()
+        fake_mod.__dict__ = copy.deepcopy(self.__dict__)
+        return ObservedGraphModule(
+            fake_mod,
+            copy.deepcopy(self.graph),
+            copy.deepcopy(self.preserved_attr_names),
+        )
+
+
+def _is_observed_module(module: Any) -> bool:
+    return hasattr(module, "meta") and "_observed_graph_module_attrs" in module.meta
+
+
+def _get_observed_graph_module_attr(
+    model: Union[torch.nn.Module, GraphModule], attr_name: str
+) -> Any:
+    if hasattr(model, "meta") and "_observed_graph_module_attrs" in model.meta:  # type: ignore[operator, index]
+        return getattr(model.meta["_observed_graph_module_attrs"], attr_name)  # type: ignore[index]
+    return None
+
+
+class ObservedStandaloneGraphModule(ObservedGraphModule):
+    def __init__(
+        self,
+        root: Union[torch.nn.Module, Dict[str, Any]],
+        graph: Graph,
+        preserved_attr_names: Set[str],
+    ):
+        preserved_attr_names = preserved_attr_names.union(
+            {
+                "_standalone_module_input_quantized_idxs",
+                "_standalone_module_output_quantized_idxs",
+            }
+        )
+        super().__init__(root, graph, preserved_attr_names)
+
+    def __deepcopy__(self, memo):
+        fake_mod = torch.nn.Module()
+        fake_mod.__dict__ = copy.deepcopy(self.__dict__)
+        return ObservedStandaloneGraphModule(
+            fake_mod,
+            copy.deepcopy(self.graph),
+            copy.deepcopy(self.preserved_attr_names),
+        )
+
+
+def _is_observed_standalone_module(module: Any) -> bool:
+    return (
+        _is_observed_module(module)
+        and module.meta["_observed_graph_module_attrs"].is_observed_standalone_module
+    )
+
+
+def _save_packed_weight(self, destination, prefix, keep_vars):
+    for attr_name in dir(self):
+        if "_packed_weight" in attr_name and isinstance(
+            getattr(self, attr_name), torch._C.ScriptObject
+        ):  # type: ignore[attr-defined]
+            packed_weight = getattr(self, attr_name)
+            destination[prefix + attr_name] = packed_weight
+
+
+class QuantizedGraphModule(GraphModule):
+    """This class is created to make sure PackedParams
+    (e.g. LinearPackedParams, Conv2dPackedParams) to appear in state_dict
+    so that we can serialize and deserialize quantized graph module with
+    torch.save(m.state_dict()) and m.load_state_dict(state_dict)
+    """
+
+    def __init__(
+        self,
+        root: Union[torch.nn.Module, Dict[str, Any]],
+        graph: Graph,
+        preserved_attr_names: Set[str],
+    ):
+        self.preserved_attr_names = preserved_attr_names
+        preserved_attrs = {
+            attr: getattr(root, attr)
+            for attr in self.preserved_attr_names
+            if hasattr(root, attr)
+        }
+        super().__init__(root, graph)
+        for attr in preserved_attrs:
+            setattr(self, attr, preserved_attrs[attr])
+        self._register_state_dict_hook(_save_packed_weight)
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        attrs_to_pop = []
+        for attr_name in state_dict:
+            if attr_name.startswith("_packed_weight") and isinstance(state_dict[attr_name], torch._C.ScriptObject):  # type: ignore[attr-defined] # noqa: B950
+                setattr(self, attr_name, state_dict[attr_name])
+                attrs_to_pop.append(attr_name)
+
+        # pop the packed param attributesn
+        for attr_name in attrs_to_pop:
+            state_dict.pop(attr_name)
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            strict,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    def __deepcopy__(self, memo):
+        fake_mod = torch.nn.Module()
+        fake_mod.__dict__ = copy.deepcopy(self.__dict__)
+        return QuantizedGraphModule(
+            fake_mod,
+            copy.deepcopy(self.graph),
+            copy.deepcopy(self.preserved_attr_names),
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/lower_to_fbgemm.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/lower_to_fbgemm.py
new file mode 100644
index 0000000000000000000000000000000000000000..b40d5cb4cca9b5f8eaed4f9c63f04b1d5ded76d6
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/lower_to_fbgemm.py
@@ -0,0 +1,20 @@
+from typing import Dict, Tuple
+
+from torch.ao.quantization.qconfig import QConfigAny
+from torch.fx import GraphModule
+
+from ._lower_to_native_backend import _lower_to_native_backend
+
+
+__all__ = ["lower_to_fbgemm"]
+
+
+def lower_to_fbgemm(
+    model: GraphModule,
+    qconfig_map: Dict[str, QConfigAny],
+    node_name_to_scope: Dict[str, Tuple[str, type]],
+) -> GraphModule:
+    """Lower a quantized reference model (with reference quantized operator patterns)
+    to fbgemm
+    """
+    return _lower_to_native_backend(model, qconfig_map, node_name_to_scope)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/lower_to_qnnpack.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/lower_to_qnnpack.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba31ee5d21862ba4123f6f14156e63d6c24c413b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/lower_to_qnnpack.py
@@ -0,0 +1,20 @@
+from typing import Dict, Tuple
+
+from torch.ao.quantization.qconfig import QConfigAny
+from torch.fx import GraphModule
+
+from ._lower_to_native_backend import _lower_to_native_backend
+
+
+__all__ = ["lower_to_qnnpack"]
+
+
+def lower_to_qnnpack(
+    model: GraphModule,
+    qconfig_map: Dict[str, QConfigAny],
+    node_name_to_scope: Dict[str, Tuple[str, type]],
+) -> GraphModule:
+    """Lower a quantized reference model (with reference quantized operator patterns)
+    to qnnpack
+    """
+    return _lower_to_native_backend(model, qconfig_map, node_name_to_scope)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/lstm_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/lstm_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9810ce5f9f372335444cf258193af0828fd035a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/lstm_utils.py
@@ -0,0 +1,202 @@
+import copy
+import operator
+from typing import Any, Callable, Optional, Tuple
+
+import torch
+from torch.ao.quantization import (
+    default_weight_fake_quant,
+    default_weight_observer,
+    FakeQuantizeBase,
+    QConfig,
+    QConfigMapping,
+)
+from torch.ao.quantization.backend_config import BackendConfig
+from torch.ao.quantization.observer import _PartialWrapper
+from torch.ao.quantization.quantize_fx import convert_to_reference_fx, prepare_fx
+
+
+# TODO: move all LSTM util functions from fx/utils.py to this file
+def _get_lstm_with_individually_observed_parts(
+    float_lstm: torch.nn.LSTM,
+    example_inputs: Tuple[Any, ...],
+    backend_config: Optional[BackendConfig] = None,
+    linear_output_obs_ctr: Optional[_PartialWrapper] = None,
+    sigmoid_obs_ctr: Optional[_PartialWrapper] = None,
+    tanh_obs_ctr: Optional[_PartialWrapper] = None,
+    cell_state_obs_ctr: Optional[_PartialWrapper] = None,
+    hidden_state_obs_ctr: Optional[_PartialWrapper] = None,
+) -> torch.ao.nn.quantizable.LSTM:
+    """
+    Return an observed `torch.ao.nn.quantizable.LSTM` created from a `torch.nn.LSTM`
+    with specific observers or fake quantizes assigned to the inner ops or submodules.
+
+    In both eager and FX graph mode quantization, `torch.ao.nn.quantizable.LSTM` is
+    used as an observed custom module, which is responsible for inserting its own
+    observers. By default, all inner ops inherit the parent custom module's QConfig.
+    Users who wish to override this behavior may extend `torch.ao.nn.quantizable.LSTM`
+    and use this helper function to customize the observer insertion logic.
+
+    This is meant to be used to convert a float module to an observed module in the
+    custom module flow.
+
+    Args:
+        `float_lstm`: The float LSTM module
+        `example_inputs`: example inputs for the forward function of the LSTM module
+        `backend_config`: BackendConfig to use to observe the LSTM module
+        `linear_output_obs_ctr`: observer or fake quantize for linear outputs Wx + b,
+            where W is the weight matrix, b is the bias, and x is either the inputs
+            or the hidden state from the previous layer (if any)
+        `sigmoid_obs_ctr`: observer or fake quantize for sigmoid activations
+        `tanh_obs_ctr`: observer or fake quantize for tanh activations
+        `cell_state_obs_ctr`: observer or fake quantize for the cell state
+        `hidden_state_obs_ctr`: observer or fake quantize for the hidden state and
+            the output
+
+    Return:
+        A `torch.ao.nn.quantizable.LSTM` with the specified observers or fake quantizes
+        assigned to the inner ops.
+    """
+
+    def make_qconfig(obs_ctr: _PartialWrapper) -> QConfig:
+        """
+        Make a QConfig with fixed qparams observers or fake quantizes.
+        """
+        if isinstance(obs_ctr(), FakeQuantizeBase):
+            weight = default_weight_fake_quant
+        else:
+            weight = default_weight_observer
+        return QConfig(activation=obs_ctr, weight=weight)
+
+    quantizable_lstm = torch.ao.nn.quantizable.LSTM(
+        float_lstm.input_size,
+        float_lstm.hidden_size,
+        float_lstm.num_layers,
+        float_lstm.bias,
+        float_lstm.batch_first,
+        float_lstm.dropout,
+        float_lstm.bidirectional,
+    )
+    quantizable_lstm.qconfig = float_lstm.qconfig
+
+    for idx in range(float_lstm.num_layers):
+        quantizable_lstm.layers[
+            idx
+        ] = torch.ao.nn.quantizable.modules.rnn._LSTMLayer.from_float(
+            float_lstm, idx, float_lstm.qconfig, batch_first=False
+        )
+
+    # Build QConfigMapping for the LSTM cell
+    # Note: FloatFunctional qconfigs will be configured separately below
+    cell_qm = QConfigMapping().set_global(float_lstm.qconfig)  # type: ignore[arg-type]
+    if sigmoid_obs_ctr is not None:
+        cell_qm.set_module_name("input_gate", make_qconfig(sigmoid_obs_ctr))
+        cell_qm.set_module_name("forget_gate", make_qconfig(sigmoid_obs_ctr))
+        cell_qm.set_module_name("output_gate", make_qconfig(sigmoid_obs_ctr))
+    if tanh_obs_ctr is not None:
+        cell_qm.set_module_name("cell_gate", make_qconfig(tanh_obs_ctr))
+
+    # Insert observers into each LSTM cell
+    # TODO: maybe make this work for layer_bw as well
+    for layer in quantizable_lstm.layers:
+        cell = layer.layer_fw.cell
+        cell = prepare_fx(cell, cell_qm, example_inputs, backend_config=backend_config)
+        # HACK: Manually replace the activation_post_process following these ops.
+        # This is needed for FloatFunctional ops because there is currently no way
+        # to configure these ops in FX graph mode quantization today. This is because
+        # the FloatFunctional modules simply disappear from the graph after tracing.
+        # In the future, we should rewrite quantizable LSTM without FloatFunctionals.
+        op_index_to_activation_post_process_ctr = {
+            (torch.add, 0): linear_output_obs_ctr,  # gates.add
+            (torch.mul, 0): cell_state_obs_ctr,  # fgate_cx.mul
+            (torch.mul, 1): cell_state_obs_ctr,  # igate_cgate.mul
+            (torch.add, 1): cell_state_obs_ctr,  # fgate_cx_igate_cgate.add
+            (torch.mul, 2): hidden_state_obs_ctr,  # ogate_cy.mul
+        }
+        add_count = 0
+        mul_count = 0
+        for node in cell.graph.nodes:
+            op_index: Optional[Tuple[Callable, int]] = None  # e.g. (torch.add, 1)
+            if node.target == torch.add:
+                op_index = (torch.add, add_count)
+                add_count += 1
+            elif node.target == torch.mul:
+                op_index = (torch.mul, mul_count)
+                mul_count += 1
+            else:
+                # Neither torch.add nor torch.mul
+                continue
+            if op_index not in op_index_to_activation_post_process_ctr:
+                continue
+            assert len(node.users) == 1
+            activation_post_process_name = next(iter(node.users.keys())).name
+            activation_post_process_ctr = op_index_to_activation_post_process_ctr[
+                op_index
+            ]
+            if activation_post_process_ctr is not None:
+                setattr(
+                    cell, activation_post_process_name, activation_post_process_ctr()
+                )
+        layer.layer_fw.cell = cell
+    return quantizable_lstm
+
+
+def _get_reference_quantized_lstm_module(
+    observed_lstm: torch.ao.nn.quantizable.LSTM,
+    backend_config: Optional[BackendConfig] = None,
+) -> torch.ao.nn.quantized.LSTM:
+    """
+    Return a `torch.ao.nn.quantized.LSTM` created from a `torch.ao.nn.quantizable.LSTM`
+    with observers or fake quantizes inserted through `prepare_fx`, e.g. from
+    `_get_lstm_with_individually_observed_parts`.
+
+    This is meant to be used to convert an observed module to a quantized module in the
+    custom module flow.
+
+    Args:
+        `observed_lstm`: a `torch.ao.nn.quantizable.LSTM` observed through `prepare_fx`
+        `backend_config`: BackendConfig to use to produce the reference quantized model
+
+    Return:
+        A reference `torch.ao.nn.quantized.LSTM` module.
+    """
+    quantized_lstm = torch.ao.nn.quantized.LSTM(
+        observed_lstm.input_size,
+        observed_lstm.hidden_size,
+        observed_lstm.num_layers,
+        observed_lstm.bias,
+        observed_lstm.batch_first,
+        observed_lstm.dropout,
+        observed_lstm.bidirectional,
+    )
+
+    for i, layer in enumerate(quantized_lstm.layers):
+        cell = copy.deepcopy(observed_lstm.layers.get_submodule(str(i)).layer_fw.cell)  # type: ignore[union-attr]
+        cell = convert_to_reference_fx(cell, backend_config=backend_config)  # type: ignore[arg-type]
+        assert isinstance(cell, torch.fx.GraphModule)
+        # HACK: Manually remove input quantize nodes and output dequantize nodes,
+        # since custom modules expect quint8 inputs and outputs for now. Note that
+        # this functionality is supposedly handled through PrepareCustomConfig's
+        # `set_input_quantized_indexes` and `set_output_quantized_indexes`, but that
+        # API doesn't currently handle tuple inputs and outputs, so we have to do
+        # this manually for now. In the future we should (1) relax the restriction
+        # on custom module input/output dtypes, and (2) expand support for complex
+        # input/output structures.
+        for node in cell.graph.nodes:
+            if node.target == torch.quantize_per_tensor:
+                arg = node.args[0]
+                # Remove quantize(x), quantize(hidden[0]), and quantize(hidden[1])
+                if arg.target == "x" or (
+                    arg.target == operator.getitem and arg.args[0].target == "hidden"
+                ):
+                    with cell.graph.inserting_before(node):
+                        node.replace_all_uses_with(arg)
+                        cell.graph.erase_node(node)
+            if node.target == "output":
+                # Remove all dequantize nodes in the output tuple
+                for arg in node.args[0]:
+                    with cell.graph.inserting_before(node):
+                        node.replace_input_with(arg, arg.args[0])
+        cell.graph.eliminate_dead_code()
+        cell.recompile()
+        layer.layer_fw.cell = cell
+    return quantized_lstm
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/match_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/match_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8bd31d0fef4c79e2bcc1569749e48ff825194c30
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/match_utils.py
@@ -0,0 +1,227 @@
+# mypy: allow-untyped-defs
+import sys
+from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Type
+
+import torch
+from torch.ao.quantization.qconfig import QConfigAny
+from torch.ao.quantization.utils import MatchAllNode, Pattern
+from torch.fx.graph import Graph, Node
+from torch.nn.utils.parametrize import type_before_parametrizations
+
+from .graph_module import _is_observed_standalone_module
+from .quantize_handler import QuantizeHandler
+
+
+__all__: List[str] = []
+
+# TODO(future PR): the 1st argument is typed as `List[Node]`, but a better type
+# would be a recursive `List[Union[Node, Tuple[Union[Node, ...]]]]`
+_MatchResult = Tuple[Node, List[Node], Optional[Pattern], QuantizeHandler]
+
+_MatchResultWithQConfig = Tuple[
+    Node, List[Node], Optional[Pattern], QuantizeHandler, QConfigAny
+]
+
+
+# Note: The order of patterns is important! match function will take whatever is matched first, so we'll
+# need to put the fusion patterns before single patterns. For example, add_relu should be registered come before relu.
+# decorators are applied in the reverse order we see. Also when we match the nodes in the graph with these patterns,
+# we'll start from the last node of the graph and traverse back.
+def _is_match(modules, node, pattern, max_uses=sys.maxsize):
+    """Matches a node in fx against a pattern"""
+    if isinstance(pattern, tuple):
+        self_match, *arg_matches = pattern
+        if self_match is getattr:
+            assert len(pattern) == 2, "Expecting getattr pattern to have two elements"
+            arg_matches = []
+    else:
+        self_match = pattern
+        arg_matches = []
+
+    if isinstance(self_match, type) and issubclass(self_match, MatchAllNode):
+        return True
+
+    if node == pattern:
+        return True
+
+    if not isinstance(node, Node) or len(node.users) > max_uses:
+        return False
+
+    if isinstance(self_match, type) and issubclass(self_match, torch.nn.Module):
+        if node.op != "call_module":
+            return False
+        if not type_before_parametrizations(modules[node.target]) == self_match:
+            return False
+    elif callable(self_match):
+        if node.op != "call_function" or node.target is not self_match:
+            return False
+        elif node.target is getattr:
+            if node.args[1] != pattern[1]:
+                return False
+    elif isinstance(self_match, str):
+        if node.op != "call_method" or node.target != self_match:
+            return False
+    elif node.target != self_match:
+        return False
+
+    if not arg_matches:
+        return True
+
+    if len(arg_matches) != len(node.args):
+        return False
+
+    return all(
+        _is_match(modules, node, arg_match, max_uses=1)
+        for node, arg_match in zip(node.args, arg_matches)
+    )
+
+
+def _find_matches(
+    graph: Graph,
+    modules: Dict[str, torch.nn.Module],
+    patterns: Dict[Pattern, QuantizeHandler],
+    root_node_getter_mapping: Dict[Pattern, Callable],
+    standalone_module_names: Optional[List[str]] = None,
+    standalone_module_classes: Optional[List[Type]] = None,
+    custom_module_classes: Optional[List[Any]] = None,
+) -> Dict[str, _MatchResult]:
+    """
+    Matches the nodes in the input graph to quantization patterns, and
+    outputs the information needed to quantize them in future steps.
+
+    Inputs:
+      - graph: an fx.Graph object
+      - modules: a mapping of fully qualified module name to instance,
+          for example, {'foo': ModuleFoo, ...}
+      - patterns: a mapping from a tuple of nodes in reverse order to
+          uninitialized QuantizeHandler subclass.
+
+    Outputs a map of
+      node_name ->
+        (node, matched_values, matched_pattern, QuantizeHandler instance,
+         qconfig)
+
+    For example, {
+      'relu_1': (relu_1, [relu_1], torch.nn.functional.relu,
+                 , QConfig(...)),
+      ...
+    }
+    """
+    if custom_module_classes is None:
+        custom_module_classes = []
+
+    if standalone_module_classes is None:
+        standalone_module_classes = []
+
+    if standalone_module_names is None:
+        standalone_module_names = []
+
+    match_map: Dict[str, _MatchResult] = {}
+    all_matched: Set[str] = set()
+
+    def _recursive_record_node_in_match_map(
+        last_node, match_map, node_pattern, matched_node_pattern, pattern, match_value
+    ):
+        if isinstance(node_pattern, Node):
+            match_map[node_pattern.name] = (
+                last_node,
+                matched_node_pattern,
+                pattern,
+                match_value,
+            )
+        elif not isinstance(node_pattern, Iterable):
+            return
+        else:
+            for n in node_pattern:
+                _recursive_record_node_in_match_map(
+                    last_node, match_map, n, matched_node_pattern, pattern, match_value
+                )
+
+    # TODO: 1. merge with fuse matcher 2. document the code
+    def record_match(pattern, node, last_node, matched_node_pattern, match_map):
+        if isinstance(pattern, tuple):
+            s, *args = pattern
+            is_single_arg = len(args) == 1
+            current_node_pattern: List[Node] = []
+            record_match(s, node, last_node, matched_node_pattern, match_map)
+            if pattern[0] is not getattr:
+                for subpattern, arg in zip(args, node.args):
+                    record_match(subpattern, arg, node, current_node_pattern, match_map)
+            if len(current_node_pattern) > 1:
+                # current_node_pattern is  the node pattern we get from matching
+                # the subpattern with arguments of the node
+                # we use is_single_arg to recover the original structure of the pattern
+                # if the original pattern has a single argument, we will have
+                # (original_op, (original_arg, ...))
+                # otherwise, we'll have a list of arguments
+                # (original_op, arg0, arg1, arg2, ...)
+                if is_single_arg:
+                    matched_node_pattern.append(tuple(current_node_pattern))
+                else:
+                    matched_node_pattern.extend(list(current_node_pattern))
+            else:
+                matched_node_pattern.append(current_node_pattern[0])
+        else:
+            matched_node_pattern.append(node)
+
+    for node in reversed(graph.nodes):
+        if node.name not in match_map and node.name not in all_matched:
+            for pattern, quantize_handler_cls in patterns.items():
+                root_node_getter = root_node_getter_mapping.get(pattern, None)
+                if _is_match(modules, node, pattern) and node.name not in match_map:
+                    matched_node_pattern: List[Node] = []
+                    record_match(pattern, node, node, matched_node_pattern, match_map)
+                    quantize_handler = quantize_handler_cls(  # type: ignore[operator]
+                        matched_node_pattern, modules, root_node_getter
+                    )
+                    last_node = node
+                    # record the match for all nodes in the pattern
+                    _recursive_record_node_in_match_map(
+                        last_node,
+                        match_map,
+                        # we need to record all nodes in the matched pattern in the match_map
+                        matched_node_pattern,
+                        # this is a part of the value corresponding to the node
+                        matched_node_pattern,
+                        pattern,
+                        quantize_handler,
+                    )
+                    break
+
+    # add custom module instances to the match result
+    assert modules is not None
+    for node in graph.nodes:
+        if (
+            node.op == "call_module"
+            and type(modules[node.target]) in custom_module_classes
+        ):
+            match_map[node.name] = (
+                node,
+                node,
+                None,
+                QuantizeHandler(node, modules, is_custom_module=True),
+            )
+
+    def is_standalone_module(node_target: str, modules: Dict[str, torch.nn.Module]):
+        assert modules is not None
+        return (
+            node_target in standalone_module_names
+            or type(modules[node_target])  # type: ignore[operator]
+            in standalone_module_classes  # type: ignore[operator]
+        )
+
+    # add standalone modules to the match
+    for node in graph.nodes:
+        if node.op == "call_module" and (
+            is_standalone_module(node.target, modules)
+            or _is_observed_standalone_module(modules[node.target])
+        ):
+            # add node to matched nodes
+            match_map[node.name] = (
+                node,
+                node,
+                None,
+                QuantizeHandler(node, modules, is_standalone_module=True),
+            )
+
+    return match_map
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/pattern_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/pattern_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..34594e081d7aaedc73f50a64097b0ed8db2aed79
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/pattern_utils.py
@@ -0,0 +1,112 @@
+# mypy: allow-untyped-defs
+import copy
+from collections import OrderedDict
+from typing import Any, Dict
+
+from torch.ao.quantization.fake_quantize import FixedQParamsFakeQuantize
+from torch.ao.quantization.observer import ObserverBase
+from torch.ao.quantization.utils import Pattern
+
+
+__all__ = [
+    "get_default_fusion_patterns",
+    "get_default_quant_patterns",
+    "get_default_output_activation_post_process_map",
+]
+
+# TODO(future PR): fix the typing on QuantizeHandler (currently a circular dependency)
+QuantizeHandler = Any
+
+# pattern for conv bn fusion
+_DEFAULT_FUSION_PATTERNS: Dict[Pattern, QuantizeHandler] = OrderedDict()
+
+
+def _register_fusion_pattern(pattern):
+    def insert(fn):
+        _DEFAULT_FUSION_PATTERNS[pattern] = fn
+        return fn
+
+    return insert
+
+
+def get_default_fusion_patterns() -> Dict[Pattern, QuantizeHandler]:
+    return copy.copy(_DEFAULT_FUSION_PATTERNS)
+
+
+_DEFAULT_QUANTIZATION_PATTERNS: Dict[Pattern, QuantizeHandler] = OrderedDict()
+
+# Mapping from pattern to activation_post_process(observer/fake_quant) constructor for output activation
+# e.g. pattern: torch.sigmoid,
+#      output_activation_post_process: default_fixed_qparams_range_0to1_fake_quant
+_DEFAULT_OUTPUT_FAKE_QUANTIZE_MAP: Dict[Pattern, QuantizeHandler] = {}
+_DEFAULT_OUTPUT_OBSERVER_MAP: Dict[Pattern, QuantizeHandler] = {}
+
+
+# Register pattern for both static quantization and qat
+def _register_quant_pattern(pattern, fixed_qparams_observer=None):
+    def insert(fn):
+        _DEFAULT_QUANTIZATION_PATTERNS[pattern] = fn
+        if fixed_qparams_observer is not None:
+            _DEFAULT_OUTPUT_FAKE_QUANTIZE_MAP[
+                pattern
+            ] = FixedQParamsFakeQuantize.with_args(observer=fixed_qparams_observer)
+            _DEFAULT_OUTPUT_OBSERVER_MAP[pattern] = fixed_qparams_observer
+        return fn
+
+    return insert
+
+
+# Get patterns for both static quantization and qat
+def get_default_quant_patterns() -> Dict[Pattern, QuantizeHandler]:
+    return copy.copy(_DEFAULT_QUANTIZATION_PATTERNS)
+
+
+# a map from pattern to output activation post process constructor
+# e.g. torch.sigmoid -> default_affine_fixed_qparam_fake_quant
+def get_default_output_activation_post_process_map(
+    is_training,
+) -> Dict[Pattern, ObserverBase]:
+    if is_training:
+        return copy.copy(_DEFAULT_OUTPUT_FAKE_QUANTIZE_MAP)
+    else:
+        return copy.copy(_DEFAULT_OUTPUT_OBSERVER_MAP)
+
+
+# Example use of register pattern function:
+# @_register_fusion_pattern(torch.nn.ReLU, (torch.nn.BatchNorm2d, torch.nn.Conv2d)))
+# class ConvOrLinearBNReLUFusion():
+#     def __init__(...):
+#         ...
+#
+
+
+def _sorted_patterns_dict(
+    patterns_dict: Dict[Pattern, QuantizeHandler]
+) -> Dict[Pattern, QuantizeHandler]:
+    """
+    Return a sorted version of the patterns dictionary such that longer patterns are matched first,
+    e.g. match (F.relu, F.linear) before F.relu.
+    This works for current use cases, but we may need to have a more clever way to sort
+    things to address more complex patterns
+    """
+
+    def get_len(pattern):
+        """this will calculate the length of the pattern by counting all the entries
+        in the pattern.
+        this will make sure (nn.ReLU, (nn.BatchNorm, nn.Conv2d)) comes before
+        (nn.BatchNorm, nn.Conv2d) so that we can match the former first
+        """
+        len = 0
+        if isinstance(pattern, tuple):
+            for item in pattern:
+                len += get_len(item)
+        else:
+            len += 1
+        return len
+
+    return OrderedDict(
+        sorted(
+            patterns_dict.items(),
+            key=lambda kv: -get_len(kv[0]) if isinstance(kv[0], tuple) else 1,
+        )
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/prepare.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/prepare.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b3073f5e312f103aec273342a63ffbaa6b8ff57
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/prepare.py
@@ -0,0 +1,2193 @@
+# mypy: allow-untyped-defs
+import copy
+import warnings
+from dataclasses import asdict
+from typing import Any, Dict, List, Optional, Set, Tuple, Type, Union
+
+import torch
+from torch._subclasses import FakeTensor
+from torch.ao.quantization import (
+    _DerivedObserverOrFakeQuantize,
+    FixedQParamsFakeQuantize,
+    FixedQParamsObserver,
+    ObserverBase,
+    ObserverOrFakeQuantize,
+    PlaceholderObserver,
+)
+from torch.ao.quantization.backend_config import (
+    BackendConfig,
+    DTypeConfig,
+    get_native_backend_config,
+)
+from torch.ao.quantization.backend_config.utils import (
+    get_fusion_pattern_to_root_node_getter,
+    get_module_to_qat_module,
+    get_pattern_to_dtype_configs,
+)
+from torch.ao.quantization.observer import _is_activation_post_process, _PartialWrapper
+from torch.ao.quantization.qconfig import _is_reuse_input_qconfig, QConfigAny
+from torch.ao.quantization.qconfig_mapping import QConfigMapping
+from torch.ao.quantization.quantize import convert, propagate_qconfig_
+from torch.ao.quantization.quantizer import (
+    DerivedQuantizationSpec,
+    EdgeOrNode,
+    FixedQParamsQuantizationSpec,
+    QuantizationSpec,
+    QuantizationSpecBase,
+    SharedQuantizationSpec,
+)
+from torch.ao.quantization.utils import (
+    _parent_name,
+    get_qconfig_dtypes,
+    get_swapped_custom_module_class,
+    NodePattern,
+    Pattern,
+)
+from torch.fx import GraphModule
+from torch.fx.graph import Graph, Node
+from torch.fx.node import Argument
+
+from ._equalize import is_equalization_observer, node_supports_equalization
+from .custom_config import PrepareCustomConfig, StandaloneModuleConfigEntry
+from .match_utils import _find_matches, _MatchResultWithQConfig
+from .pattern_utils import _sorted_patterns_dict
+from .qconfig_mapping_utils import (
+    _generate_node_name_to_qconfig,
+    _get_flattened_qconfig_dict,
+    _update_qconfig_for_fusion,
+    _update_qconfig_for_qat,
+)
+from .quantize_handler import (
+    _default_root_node_getter,
+    _get_pattern_to_quantize_handlers,
+    QuantizeHandler,
+)
+from .utils import (
+    _insert_dequant_stubs_for_custom_module_lstm_output,
+    _is_custom_module_lstm,
+    _maybe_get_custom_module_lstm_from_node_arg,
+    _qconfig_satisfies_dtype_config_constraints,
+    all_node_args_have_no_tensors,
+    assert_and_get_unique_device,
+    get_custom_module_class_keys,
+    get_new_attr_name_with_prefix,
+    get_non_observable_arg_indexes_and_types,
+    node_arg_is_bias,
+    node_arg_is_weight,
+    NON_QUANTIZABLE_WEIGHT_OPS,
+    ObservedGraphModuleAttrs,
+)
+
+
+__all__ = [
+    "insert_observers_for_model",
+    "prepare",
+    "propagate_dtypes_for_known_nodes",
+]
+
+
+# list of dtypes to not add observers to
+_DO_NOT_OBS_DTYPE_LIST = [int, float, torch.bool, None]
+_OBS_DTYPE_LIST = [
+    torch.quint8,
+    torch.qint8,
+    torch.qint32,
+    torch.float16,
+    torch.uint8,
+    torch.int8,
+    torch.int16,
+    torch.int32,
+    torch.float8_e5m2,
+    torch.float8_e4m3fn,
+]
+
+_DEFAULT_FP32_OBS_OR_FQ_CTR = PlaceholderObserver.with_args(dtype=torch.float)
+
+# note: the following default target dtype info dicts are temporary,
+# should be moved to the new programmable API class soon
+_DEFAULT_FP32_QCONFIG_FOR_TARGET_DTYPE_INFO = {
+    "input_act_obs_or_fq_ctr": torch.ao.quantization.qconfig._default_fp32_placeholder_qconfig.activation,
+    "output_act_obs_or_fq_ctr": torch.ao.quantization.qconfig._default_fp32_placeholder_qconfig.activation,
+}
+
+_DEFAULT_QUINT8_QCONFIG_FOR_TARGET_DTYPE_INFO = {
+    "input_act_obs_or_fq_ctr": torch.ao.quantization.qconfig._default_quint8_placeholder_qconfig.activation,
+    "output_act_obs_or_fq_ctr": torch.ao.quantization.qconfig._default_quint8_placeholder_qconfig.activation,
+}
+
+
+def _get_observer_kwargs(
+    quant_spec: Union[QuantizationSpec, FixedQParamsQuantizationSpec]
+):
+    kwargs_dict = asdict(quant_spec)
+    return copy.deepcopy(kwargs_dict)
+
+
+def _get_qspec_for_arg(
+    arg: Node,
+    input_qspec_map: Dict[Node, QuantizationSpecBase],
+    named_modules: Dict[str, torch.nn.Module],
+) -> Optional[QuantizationSpecBase]:
+    while _is_activation_post_process_node(arg, named_modules):
+        arg = arg.args[0]  # type: ignore[assignment]
+    return input_qspec_map.get(arg, None)
+
+
+def _create_obs_or_fq_from_qspec(
+    quantization_spec: Optional[QuantizationSpecBase],
+    obs_or_fq_map: Dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+):
+    """Create observer or fake quantize objects based on quantization spec
+
+    Args:
+       quantization_spec: used to store parameters to create the observer or fake quantizer
+       obs_or_fq_map: this is a map from edge/output to the corresponding observer/fake_quant
+       instance, it may be reused for different edge/output depending on configuration
+    """
+    if quantization_spec is None:
+        return None
+    if isinstance(quantization_spec, SharedQuantizationSpec):
+        edge_or_node = quantization_spec.edge_or_node
+        assert edge_or_node in obs_or_fq_map, (
+            "please make sure only refer to edge or node that has "
+            f"observer/fake_quant inserted: '{edge_or_node}' not in\n{obs_or_fq_map.keys()}"
+        )
+        return obs_or_fq_map[edge_or_node]
+    elif isinstance(quantization_spec, DerivedQuantizationSpec):
+        # can't use asdict, so not calling get_observer_kwargs here
+        kwargs = {
+            "dtype": quantization_spec.dtype,
+            "derive_qparams_fn": quantization_spec.derive_qparams_fn,
+            "quant_min": quantization_spec.quant_min,
+            "quant_max": quantization_spec.quant_max,
+            "qscheme": quantization_spec.qscheme,
+            "ch_axis": quantization_spec.ch_axis,
+        }
+        edge_or_nodes = quantization_spec.derived_from
+        obs_or_fqs = [obs_or_fq_map[k] for k in edge_or_nodes]
+        kwargs["obs_or_fqs"] = obs_or_fqs
+        return _DerivedObserverOrFakeQuantize.with_args(**kwargs)()
+    elif isinstance(quantization_spec, FixedQParamsQuantizationSpec):
+        kwargs = _get_observer_kwargs(quantization_spec)
+        observer_ctr = FixedQParamsObserver.with_args(**kwargs)
+        if is_qat:
+            return FixedQParamsFakeQuantize.with_args(observer=observer_ctr)()
+        else:
+            return observer_ctr()
+
+    assert isinstance(quantization_spec, QuantizationSpec)
+    observer_or_fake_quant_ctr = quantization_spec.observer_or_fake_quant_ctr
+    kwargs = _get_observer_kwargs(quantization_spec)
+    kwargs.pop("observer_or_fake_quant_ctr")
+    # we will remove is_dynamic from QuantizationSpec because
+    # it seems that dynamic range quantization
+    obs_or_fq_class = observer_or_fake_quant_ctr
+    if isinstance(observer_or_fake_quant_ctr, _PartialWrapper):
+        obs_or_fq_class = observer_or_fake_quant_ctr.p.func  # type: ignore[union-attr, assignment]
+    if "PerChannel" not in obs_or_fq_class.__name__:  # type: ignore[operator, union-attr]
+        kwargs.pop("ch_axis")
+    return observer_or_fake_quant_ctr.with_args(**kwargs)()
+
+
+def _needs_obs_or_fq(
+    prev_output_dtype: Any,
+    prev_output_is_dynamic: bool,
+    cur_target_dtype: Any,
+    cur_target_is_dynamic: bool,
+    reuse_input_obs_or_fq: bool,
+    is_zeroth_arg: bool = False,
+) -> bool:
+    """
+    note: we will treat "not specified" as torch.float for now
+    utility function that checks if we should insert an observer or fake quant node
+    base on the requested dtype for the nodes from user
+
+    is_zeroth_arg: we only dynamically quantize the first arg of the node right now
+      this should be removed when we enable configuring dynamic quantization
+      for a specific argument, this can be removed if we deprecate fx graph mode
+      quantization
+
+    """
+
+    # need to insert placeholder observer for dynamic quantization so that it can
+    # be converted to choose_qparams -> q -> dq in convert step
+    if cur_target_is_dynamic:
+        assert (
+            cur_target_dtype in _OBS_DTYPE_LIST
+        ), f"Expected cur_target_dtype to be torch.float, but got: {cur_target_dtype}"
+        assert prev_output_dtype not in _DO_NOT_OBS_DTYPE_LIST
+        return is_zeroth_arg
+    if reuse_input_obs_or_fq:
+        return False
+    # non dynamic quantization
+    if cur_target_dtype in _OBS_DTYPE_LIST:
+        return (
+            prev_output_dtype in _OBS_DTYPE_LIST + [torch.float]
+            and cur_target_dtype != prev_output_dtype
+        )
+
+    # lots of error checking are skipped here for now
+    return False
+
+
+def _is_activation_post_process_node(
+    node: Node, named_modules: Dict[str, torch.nn.Module]
+) -> bool:
+    return (
+        isinstance(node, torch.fx.Node)
+        and node.op == "call_module"
+        and _is_activation_post_process(named_modules[str(node.target)])
+    )
+
+
+def _get_dtype_and_is_dynamic(
+    obs_or_fq: Optional[ObserverOrFakeQuantize],
+) -> Tuple[Optional[torch.dtype], bool]:
+    """Given a constructor for observer or fake quant module, returns
+    a Tuple of dtype and is_dynamic
+    """
+    # TODO: instead of instantiating the instance, we can use inspect to get the default args
+    if obs_or_fq is None:
+        return None, False
+    else:
+        return obs_or_fq.dtype, getattr(obs_or_fq, "is_dynamic", False)  # type: ignore[return-value]
+
+
+def _is_input_arg_dtype_supported_by_backend(
+    arg: Argument,
+    node: Node,
+    qconfig: QConfigAny,
+    dtype_config: DTypeConfig,
+    backend_config: BackendConfig,
+) -> bool:
+    """Check if the configured qconfig for the argument
+    is supported by the backend or not
+    """
+    if isinstance(arg, (list, tuple)):
+        return all(
+            _is_input_arg_dtype_supported_by_backend(
+                a, node, qconfig, dtype_config, backend_config
+            )
+            for a in arg
+        )
+    if not isinstance(arg, Node):
+        return True
+    # TODO: support check for standalone module
+    is_weight = node_arg_is_weight(node, arg)
+    is_bias = node_arg_is_bias(node, arg)
+    is_activation = not is_weight and not is_bias
+    if is_activation:
+        input_act_obs_or_fq_ctr = node.meta["target_dtype_info"].get(
+            "input_act_obs_or_fq_ctr"
+        )
+        input_act_obs_or_fq = (
+            input_act_obs_or_fq_ctr() if input_act_obs_or_fq_ctr else None
+        )
+        qconfig_dtype, qconfig_is_dynamic = _get_dtype_and_is_dynamic(
+            input_act_obs_or_fq
+        )
+        # TODO(future PR): remove the cast to bool below after figuring
+        # out why backend_config has is_dynamic set to None in some cases.
+        return (dtype_config.input_dtype is None) or (
+            dtype_config.input_dtype == qconfig_dtype
+            and bool(dtype_config.is_dynamic) == bool(qconfig_is_dynamic)
+            and _qconfig_satisfies_dtype_config_constraints(
+                qconfig, dtype_config.input_dtype_with_constraints
+            )
+        )
+    elif is_weight:
+        # TODO: move dtype check into `_qconfig_satisfies_dtype_config_constraints` as well
+        weight_obs_or_fq_ctr = node.meta["target_dtype_info"].get(
+            "weight_obs_or_fq_ctr", None
+        )
+        weight_obs_or_fq = weight_obs_or_fq_ctr() if weight_obs_or_fq_ctr else None
+        qconfig_weight_dtype, _ = _get_dtype_and_is_dynamic(weight_obs_or_fq)
+        backend_config_weight_dtype = dtype_config.weight_dtype
+        dtype_matches = qconfig_weight_dtype == backend_config_weight_dtype
+        qconfig_satisfies_constraints = _qconfig_satisfies_dtype_config_constraints(
+            qconfig, dtype_config.weight_dtype_with_constraints, is_activation=False
+        )
+        return backend_config_weight_dtype is None or (
+            dtype_matches and qconfig_satisfies_constraints
+        )
+    else:  # bias
+        # TODO: move dtype check into `_qconfig_satisfies_dtype_config_constraints` as well
+        bias_obs_or_fq_ctr = node.meta["target_dtype_info"].get(
+            "bias_obs_or_fq_ctr", None
+        )
+        bias_obs_or_fq = bias_obs_or_fq_ctr() if bias_obs_or_fq_ctr else None
+        qconfig_bias_dtype, _ = _get_dtype_and_is_dynamic(bias_obs_or_fq)
+        backend_config_bias_dtype = dtype_config.bias_dtype
+        return (
+            backend_config_bias_dtype is None
+            or qconfig_bias_dtype == backend_config_bias_dtype
+        )
+
+
+def _is_output_dtype_supported_by_backend(
+    node: Node,
+    qconfig: QConfigAny,
+    dtype_config: DTypeConfig,
+) -> bool:
+    """Check if the configured qconfig for the output
+    is supported by the backend or not
+    """
+    # TODO: move dtype check into `_qconfig_satisfies_dtype_config_constraints` as well
+    backend_config_output_dtype = dtype_config.output_dtype
+    # TODO: we should check is_dynamic here as well, the code from _is_input_arg_dtype_supported_by_backend
+    # from input activation check can be reused here
+    qconfig_output_dtype = None
+    output_act_obs_or_fq_ctr = node.meta["target_dtype_info"].get(
+        "output_act_obs_or_fq_ctr", _DEFAULT_FP32_OBS_OR_FQ_CTR
+    )
+    output_act_obs_or_fq = (
+        output_act_obs_or_fq_ctr() if output_act_obs_or_fq_ctr else None
+    )
+    qconfig_output_dtype, qconfig_output_is_dynamic = _get_dtype_and_is_dynamic(
+        output_act_obs_or_fq
+    )
+    # TODO: this is a hack because we can only specify one activation_obs_or_fq for
+    # qconfig (qconfig.activation), and we are only supporting dynamically quantized
+    # linear op which has fp32 output dtype, this should be removed if we generalize
+    # the structure of qconfig in the future
+    if qconfig_output_is_dynamic:
+        qconfig_output_dtype = torch.float32
+    dtype_matches = qconfig_output_dtype == backend_config_output_dtype
+    qconfig_satisfies_constraints = _qconfig_satisfies_dtype_config_constraints(
+        qconfig, dtype_config.output_dtype_with_constraints
+    )
+    return backend_config_output_dtype is None or (
+        dtype_matches and qconfig_satisfies_constraints
+    )
+
+
+def _is_observer_in_same_graph(
+    node: Node,
+    named_modules: Dict[str, torch.nn.Module],
+    obs_or_fq_map: Dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat,
+):
+    """Check if observer in same graph
+    when the node output is not fp32 and input is 'placeholder'
+    the input is assumed to be quantized, so it is observed
+    in a different place rather than not observed.
+    """
+    node_output_dtype = _get_arg_target_dtype_as_output(
+        node, named_modules, obs_or_fq_map, is_qat
+    )
+    if len(node.args) > 0 and isinstance(node.args[0], Node):
+        if (
+            node_output_dtype in [torch.quint8, torch.uint8]
+            and node.args[0].op == "placeholder"
+        ):
+            return False
+    return True
+
+
+def _is_pattern_dtype_config_and_qconfig_supported_by_backend(
+    pattern: Optional[Pattern],
+    matched_node_pattern: Optional[List[Node]],
+    qconfig: QConfigAny,
+    backend_config: BackendConfig,
+) -> bool:
+    """Check if the dtype configuration of a pattern is supported by
+    the backend or not, and whether the qconfig satisfies constraints
+    specified in the corresponding dtype config.
+    """
+    if backend_config is None or pattern is None:
+        return True
+    assert matched_node_pattern is not None and len(matched_node_pattern) >= 1
+    pattern_to_dtype_configs = get_pattern_to_dtype_configs(backend_config)
+    dtype_configs: List[DTypeConfig] = pattern_to_dtype_configs.get(pattern, [])
+    pattern_to_root_node_getter = get_fusion_pattern_to_root_node_getter(backend_config)
+
+    root_node_getter = pattern_to_root_node_getter.get(
+        pattern, _default_root_node_getter
+    )
+    root_node = root_node_getter(matched_node_pattern)
+    input_node = root_node
+    output_node = matched_node_pattern[0]
+    for dtype_config in dtype_configs:
+        # check if arg dtype are supported
+        supported = True
+        for arg in list(input_node.args) + list(input_node.kwargs.values()):
+            supported = supported and _is_input_arg_dtype_supported_by_backend(
+                arg, input_node, qconfig, dtype_config, backend_config
+            )
+        # check if output dtype is supported
+        supported = supported and _is_output_dtype_supported_by_backend(
+            output_node, qconfig, dtype_config
+        )
+        if supported:
+            return True
+    return False
+
+
+def _get_standalone_module_configs(
+    node: Node,
+    named_modules: Dict[str, torch.nn.Module],
+    prepare_custom_config: PrepareCustomConfig,
+    parent_qconfig: QConfigAny,
+    parent_backend_config: Optional[BackendConfig],
+) -> Tuple[
+    QConfigMapping, Tuple[Any, ...], PrepareCustomConfig, Optional[BackendConfig]
+]:
+    """
+    Returns the standalone module QConfigMapping and PrepareCustomConfig
+    for `node`, assuming that the module pointed to by `node` is
+    a standalone modules.
+    """
+    module_name = str(node.target)
+    module_type = type(named_modules[module_name])  # type: ignore[index]
+    # name config has precedence over type config
+    config_entry = StandaloneModuleConfigEntry(None, (), None, None)
+    config_entry = prepare_custom_config.standalone_module_classes.get(
+        module_type, config_entry
+    )
+    config_entry = prepare_custom_config.standalone_module_names.get(
+        module_name, config_entry
+    )
+    # fallback to use parent module's qconfig if user didn't specify qconfig dict
+    qconfig_mapping = config_entry.qconfig_mapping or QConfigMapping().set_global(
+        parent_qconfig
+    )
+    example_inputs = config_entry.example_inputs
+    prepare_custom_config = config_entry.prepare_custom_config or PrepareCustomConfig()
+    backend_config = config_entry.backend_config or parent_backend_config
+    return (qconfig_mapping, example_inputs, prepare_custom_config, backend_config)
+
+
+def _qat_swap_modules(
+    root: torch.nn.Module, module_to_qat_module: Dict[Pattern, Type[torch.nn.Module]]
+) -> None:
+    convert(root, mapping=module_to_qat_module, inplace=True, remove_qconfig=False)
+
+
+def _add_matched_node_name_to_set(matched_node_pattern: NodePattern, s: Set[str]):
+    if isinstance(matched_node_pattern, Node):
+        s.add(matched_node_pattern.name)
+    elif isinstance(matched_node_pattern, (list, tuple)):
+        for maybe_node in matched_node_pattern:
+            _add_matched_node_name_to_set(maybe_node, s)
+
+
+def _insert_obs_or_fq(
+    node: Node,
+    obs_or_fq: ObserverOrFakeQuantize,
+    model: torch.nn.Module,
+    named_modules: Dict[str, torch.nn.Module],
+    graph: Graph,
+) -> Node:
+    """
+    Attaches `obs_or_fq` to `model`, and creates a node which calls
+    `obs_or_fq` on the output of `node`.
+
+    obs_or_fq: an instance of Observer or FakeQuantize module
+    """
+    model_device = assert_and_get_unique_device(model)
+    if model_device:
+        obs_or_fq.to(model_device)
+    # add obs_or_fq module as attribute
+    if is_equalization_observer(obs_or_fq):
+        prefix = node.name + "_equalization_process_"
+    else:
+        prefix = "activation_post_process_"
+    get_new_obs_or_fq_name = get_new_attr_name_with_prefix(prefix)
+    obs_or_fq_name = get_new_obs_or_fq_name(model)
+    setattr(model, obs_or_fq_name, obs_or_fq)
+    named_modules[obs_or_fq_name] = obs_or_fq
+    with graph.inserting_after(node):
+        new_obs = graph.create_node("call_module", obs_or_fq_name, (node,), {})
+    return new_obs
+
+
+def _set_target_dtype_info_for_matched_node_pattern(
+    matched_node_pattern: NodePattern,
+    last_node: Node,
+    qconfig: QConfigAny,
+    qhandler: Optional[QuantizeHandler],
+    backend_config: BackendConfig,
+    named_modules: Dict[str, torch.nn.Module],
+    cache_for_no_tensor_check: Dict[Node, bool],
+    processed_nodes: Set[Node],
+) -> None:
+    """Sets the target_dtype_info for each node in matched_node_pattern
+    Note: processed_nodes is used to ensure we only process each node once
+    """
+    if isinstance(matched_node_pattern, (list, tuple)):
+        for node_pattern in matched_node_pattern:
+            _set_target_dtype_info_for_matched_node_pattern(
+                node_pattern,
+                last_node,
+                qconfig,
+                qhandler,
+                backend_config,
+                named_modules,
+                cache_for_no_tensor_check,
+                processed_nodes,
+            )
+
+    # set target_dtype_info if matched_node_pattern is a Node
+    # other types of matched object, e.g. int, float literals, are ignored
+    elif isinstance(matched_node_pattern, Node):
+        # for pyre
+        assert isinstance(matched_node_pattern, Node)
+        node = matched_node_pattern
+        if node in processed_nodes:
+            return
+        processed_nodes.add(node)
+
+        if qconfig is None:
+            return
+        # TODO: refactor the following code in terms of apply a qconfig to a pattern
+        # e.g. for a pattern with op1 -> op2 -> op3, and qconfig = QConfig(input_act=obs0, output_act=obs1)
+        # we set the input_obs_or_fq_ctr for the arguments of op1 to based on qconfig.input_act,
+        # and set output_obs_or_fq_ctr based on qconfig.output_act
+        # this also requires we extend the structure of QConfig to support more fine
+        # grained configurations
+        target_dtype_info: Dict[str, Any] = _get_target_activation_dtype_for_node(
+            node,
+            qconfig,
+            qhandler,
+            named_modules,
+            backend_config,
+            cache_for_no_tensor_check,
+        )
+        node.meta["target_dtype_info"] = target_dtype_info
+
+
+def _get_target_activation_dtype_for_node(
+    node: Node,
+    qconfig: QConfigAny,
+    qhandler: Optional[QuantizeHandler],
+    named_modules: Dict[str, torch.nn.Module],
+    backend_config: BackendConfig,
+    cache_for_no_tensor_check: Dict[Node, bool],
+) -> Dict[str, Any]:
+    """
+    For each op attribute in the op's input activation, output activation,
+    weight, bias - returns the settings of dtype and is_dynamic we expect
+    for the `quantize` call in the reference model representation, or None
+    if there is no `quantize` call needed.
+
+    For example, if we have a node corresponding to `op0` in
+
+      x0 -> op0 -> x1
+
+    And we want a reference quantized representation to be
+
+      x0 -> quant_static -> dequant -> op0 -> quant_dynamic -> dequant -> x1
+
+    Then this function will return
+
+      {
+        "input_act_obs_or_fq_ctr": MinMaxObserver.with_args(dtype=torch.quint8, is_dynamic=False),
+        "output_act_obs_or_fq_ctr": MinMaxObserver.with_args(dtype=torch.quint8, is_dynamic=False),
+      }
+
+    TODO(future PR, if needed): explicitly spell out the non-Tensor
+    dtypes.
+    """
+    args_have_no_tensors = all_node_args_have_no_tensors(
+        node, named_modules, cache_for_no_tensor_check
+    )
+    if args_have_no_tensors:
+        return {
+            "input_act_obs_or_fq_ctr": None,
+            "output_act_obs_or_fq_ctr": None,
+        }
+    # get qconfig to determine the eventual dtype of this node
+    if qconfig is not None:
+        act_dtype, weight_dtype, input_act_is_dynamic = get_qconfig_dtypes(qconfig)
+
+        # Currently `QConfig` only has one `activation` field.
+        # For static quantization, it is reused for both input
+        # and output activation. For dynamic quantization, this
+        # field is currently only used for the input activation,
+        # with the output activation being in fp32.
+        # In the future this may change as we add more fields
+        # to the `QConfig` object.
+        output_act_dtype = act_dtype if (not input_act_is_dynamic) else torch.float
+
+        bias_dtype = (
+            torch.float16
+            if (
+                act_dtype == torch.float16
+                and weight_dtype == torch.float16
+                and (not input_act_is_dynamic)
+            )
+            else torch.float
+        )
+
+        is_general_tensor_value_op = (
+            qhandler is not None and qhandler.is_general_tensor_value_op()
+        )
+
+        _is_standalone_module = qhandler is not None and qhandler.is_standalone_module()
+
+        weight_index = None
+        if (
+            isinstance(node, Node)
+            and node.op == "call_function"
+            and node.target in backend_config._pattern_complex_format_to_config
+        ):
+            weight_index = backend_config._pattern_complex_format_to_config[
+                node.target
+            ]._input_type_to_index.get("weight")
+
+        bias_index = None
+        if (
+            isinstance(node, Node)
+            and node.op == "call_function"
+            and node.target in backend_config._pattern_complex_format_to_config
+        ):
+            bias_index = backend_config._pattern_complex_format_to_config[
+                node.target
+            ]._input_type_to_index.get("bias")
+
+        return {
+            "input_act_obs_or_fq_ctr": qconfig.activation,
+            "weight_obs_or_fq_ctr": qconfig.weight,
+            "bias_obs_or_fq_ctr": PlaceholderObserver.with_args(dtype=bias_dtype),
+            "weight_index": weight_index,
+            "bias_index": bias_index,
+            "output_act_obs_or_fq_ctr": qconfig.activation,
+            "reuse_input_obs_or_fq": _is_reuse_input_qconfig(qconfig),
+            "input_output_share_observers": is_general_tensor_value_op,
+            "_is_standalone_module": _is_standalone_module,
+        }
+    return copy.copy(_DEFAULT_FP32_QCONFIG_FOR_TARGET_DTYPE_INFO)
+
+
+def _get_output_act_obs_or_fq(
+    arg: Node,
+    named_modules: Dict[str, torch.nn.Module],
+    obs_or_fq_map: Dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+) -> ObserverOrFakeQuantize:
+    """Get the constructor for observer or fake quant object for
+    the argument in the original graph as the output of previous node,
+    skipping inserted observers
+
+    We are assuming that the observers are inserted correctly, and the dtype for
+    argument in quantized graph will match what is specified by the qconfig
+    """
+    assert isinstance(arg, Node)
+    if "quantization_annotation" in arg.meta:
+        return _create_obs_or_fq_from_qspec(
+            arg.meta["quantization_annotation"].output_qspec, obs_or_fq_map, is_qat
+        )
+
+    # Custom module LSTM output is a tuple that we broke down into the internal nodes in order
+    # to insert DeQuantStubs (see `_insert_dequant_stubs_for_custom_module_lstm_output`).
+    # Since we modified the graph in this case, we must trace back from the args through
+    # the specific nodes we added in order to reach the original LSTM node. Otherwise, we would
+    # not be able to accurately detect whether this node is a consumer of custom module LSTM.
+    custom_module_lstm_node = _maybe_get_custom_module_lstm_from_node_arg(
+        arg, named_modules
+    )
+    output_act_obs_or_fq_ctr = None
+    if custom_module_lstm_node is not None:
+        output_act_obs_or_fq_ctr = custom_module_lstm_node.meta["target_dtype_info"][
+            "output_act_obs_or_fq_ctr"
+        ]
+        output_act_obs_or_fq = (
+            output_act_obs_or_fq_ctr() if output_act_obs_or_fq_ctr else None
+        )
+    elif _is_activation_post_process_node(arg, named_modules):
+        observed_arg = arg.args[0]
+        assert isinstance(
+            observed_arg, Node
+        ), "Currently we only support observing Node"
+        if "quantization_annotation" in observed_arg.meta:
+            output_act_obs_or_fq = _create_obs_or_fq_from_qspec(
+                observed_arg.meta["quantization_annotation"].output_qspec,
+                obs_or_fq_map,
+                is_qat,
+            )
+        else:
+            assert "target_dtype_info" in observed_arg.meta
+            output_act_obs_or_fq_ctr = observed_arg.meta["target_dtype_info"][
+                "output_act_obs_or_fq_ctr"
+            ]
+            output_act_obs_or_fq = (
+                output_act_obs_or_fq_ctr() if output_act_obs_or_fq_ctr else None
+            )
+    else:
+        if "target_dtype_info" in arg.meta:
+            output_act_obs_or_fq_ctr = arg.meta["target_dtype_info"].get(
+                "output_act_obs_or_fq_ctr", _DEFAULT_FP32_OBS_OR_FQ_CTR
+            )
+        else:
+            output_act_obs_or_fq_ctr = _DEFAULT_FP32_OBS_OR_FQ_CTR
+        output_act_obs_or_fq = (
+            output_act_obs_or_fq_ctr() if output_act_obs_or_fq_ctr else None
+        )
+
+    return output_act_obs_or_fq
+
+
+def _get_arg_target_dtype_as_output(
+    arg: Node,
+    named_modules: Dict[str, torch.nn.Module],
+    obs_or_fq_map: Dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+) -> Optional[torch.dtype]:
+    arg_as_output_act_obs_or_fq = _get_output_act_obs_or_fq(
+        arg, named_modules, obs_or_fq_map, is_qat
+    )
+    arg_as_output_target_dtype, _ = _get_dtype_and_is_dynamic(
+        arg_as_output_act_obs_or_fq
+    )
+    return arg_as_output_target_dtype
+
+
+def _get_arg_as_input_act_obs_or_fq(
+    arg: Node,
+    node: Node,
+    named_modules: Dict[str, torch.nn.Module],
+    obs_or_fq_map: Dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+) -> Optional[ObserverOrFakeQuantize]:
+    """Get the observer or fake quant constructor for the Argument `arg`, as input
+    to Node `node`
+    """
+    assert isinstance(arg, Node)
+    # "input_qspec_map" is the more general design we'll use for pt2e path
+    # it is a map from input argument node to observer or fake quant constructor, for example
+    # for the following graph:
+    # x -> conv -> output
+    #
+    # we may annotate conv node like the following:
+    # conv.meta[...] = QuantizationAnnotation("input_qspec_map": {x: MinMaxObserver.with_args(dtype=torch.qint8)}, ...)
+    #
+    if "quantization_annotation" in node.meta:
+        input_qspec_map = node.meta["quantization_annotation"].input_qspec_map
+        input_arg_qspec = _get_qspec_for_arg(arg, input_qspec_map, named_modules)
+        if input_arg_qspec is None:
+            input_arg_obs_or_fq = _DEFAULT_FP32_OBS_OR_FQ_CTR()
+        else:
+            input_arg_obs_or_fq = _create_obs_or_fq_from_qspec(
+                input_arg_qspec, obs_or_fq_map, is_qat
+            )
+        return input_arg_obs_or_fq
+
+    # we can remove the following path in the future if fx graph mode quantization is
+    # no longer used
+    is_weight = node_arg_is_weight(node, arg)
+    is_bias = node_arg_is_bias(node, arg)
+    is_activation = not is_weight and not is_bias
+    obs_or_fq_ctr = None
+    if is_activation:
+        obs_or_fq_ctr = node.meta["target_dtype_info"].get(
+            "input_act_obs_or_fq_ctr", _DEFAULT_FP32_OBS_OR_FQ_CTR
+        )
+    elif is_weight:
+        if node.target not in NON_QUANTIZABLE_WEIGHT_OPS:
+            obs_or_fq_ctr = node.meta["target_dtype_info"].get(
+                "weight_obs_or_fq_ctr", _DEFAULT_FP32_OBS_OR_FQ_CTR
+            )
+    else:
+        obs_or_fq_ctr = node.meta["target_dtype_info"].get(
+            "bias_obs_or_fq_ctr", _DEFAULT_FP32_OBS_OR_FQ_CTR
+        )
+    return obs_or_fq_ctr() if obs_or_fq_ctr else None
+
+
+def _maybe_insert_input_observer_for_arg_or_kwarg(
+    node: Union[Node, Any],
+    arg: Argument,
+    qconfig: QConfigAny,
+    model: torch.nn.Module,
+    named_modules: Dict[str, torch.nn.Module],
+    graph: Graph,
+    qhandler: Optional[QuantizeHandler],
+    prepare_custom_config: PrepareCustomConfig,
+    obs_or_fq_map: Dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+    backend_config: Optional[BackendConfig] = None,
+) -> Argument:
+    """
+    Given a `node` and an `arg`, inserts an input observer between
+    `node` and `arg` if necessary.
+    """
+    # for ops such as torch.cat([x0, x1]),
+    # traverse through the list
+    if isinstance(arg, (list, tuple)):
+        new_arg_to_return = []
+        for inner_arg in arg:
+            new_inner_arg = _maybe_insert_input_observer_for_arg_or_kwarg(
+                node,
+                inner_arg,
+                qconfig,
+                model,
+                named_modules,
+                graph,
+                qhandler,
+                prepare_custom_config,
+                obs_or_fq_map,
+                is_qat,
+                backend_config,
+            )
+            new_arg_to_return.append(new_inner_arg)
+        return type(arg)(new_arg_to_return)
+
+    if not isinstance(arg, Node):
+        return arg
+    assert isinstance(arg, Node)
+    # default (no observer)
+    new_arg = arg
+
+    is_standalone_module = qhandler is not None and qhandler.is_standalone_module()
+    # TODO: move this to a separate function
+    if not is_standalone_module:
+        # Note: qconfig can be None in this branch this we are getting act/fq from
+        # node.meta now
+        # regular flow for most nodes, except standalone modules
+
+        if "quantization_annotation" in node.meta:
+            reuse_input_obs_or_fq = node.meta[
+                "quantization_annotation"
+            ]._reuse_input_obs_or_fq
+        else:
+            assert "target_dtype_info" in node.meta
+            # TODO: we are assuming "target_dtype_info" exists here, maybe
+            # a default value also need to be provided here
+            target_dtype_info = node.meta["target_dtype_info"]
+            # for nodes that doesn't have `reuse_input_obs_or_fq` configured,
+            # we'll default to False, this makes configuring this field optional for users
+            reuse_input_obs_or_fq = target_dtype_info.get(
+                "reuse_input_obs_or_fq", False
+            )
+        arg_as_input_act_obs_or_fq = _get_arg_as_input_act_obs_or_fq(
+            arg, node, named_modules, obs_or_fq_map, is_qat
+        )
+        (
+            arg_as_input_target_dtype,
+            arg_as_input_target_is_dynamic,
+        ) = _get_dtype_and_is_dynamic(arg_as_input_act_obs_or_fq)
+
+        arg_as_output_act_obs_or_fq = _get_output_act_obs_or_fq(
+            arg, named_modules, obs_or_fq_map, is_qat
+        )
+        (
+            arg_as_output_target_dtype,
+            arg_as_output_target_is_dynamic,
+        ) = _get_dtype_and_is_dynamic(arg_as_output_act_obs_or_fq)
+
+        needs_obs_or_fq = _needs_obs_or_fq(
+            arg_as_output_target_dtype,
+            arg_as_output_target_is_dynamic,
+            arg_as_input_target_dtype,
+            arg_as_input_target_is_dynamic,
+            reuse_input_obs_or_fq,
+            is_zeroth_arg=len(node.args) > 0 and arg is node.args[0],
+        )
+
+    else:
+        assert qconfig is not None
+        # custom flow for standalone modules
+        _, _, sm_prepare_custom_config, _ = _get_standalone_module_configs(
+            node, named_modules, prepare_custom_config, qconfig, backend_config
+        )
+        sm_input_quantized_idxs = sm_prepare_custom_config.input_quantized_indexes
+
+        # for args, this is set to the index of the current arg
+        # for kwargs, this is left at None
+        cur_input_idx = None
+        for arg_idx, arg_to_check in enumerate(node.args):
+            if arg_to_check is arg:
+                cur_input_idx = arg_idx
+                break
+
+        if cur_input_idx is None:
+            needs_obs_or_fq = False
+        else:
+            arg_as_output_target_dtype = _get_arg_target_dtype_as_output(
+                arg, named_modules, obs_or_fq_map, is_qat
+            )
+            arg_as_input_target_dtype = (
+                torch.quint8
+                if cur_input_idx in sm_input_quantized_idxs
+                else torch.float
+            )
+            needs_obs_or_fq = (
+                arg_as_output_target_dtype != arg_as_input_target_dtype
+            ) and (arg_as_input_target_dtype != torch.float)
+
+        act_post_process_ctr = qconfig.activation
+        arg_as_input_act_obs_or_fq = (
+            act_post_process_ctr() if act_post_process_ctr else None
+        )
+
+    if needs_obs_or_fq:
+        existing_obs_node = None
+
+        # Before using the new observer, check if an observer
+        # of the correct type already exists. If it does, use it.
+        # This prevents duplicate observer insertions if a node is
+        # used by multiple nodes.
+        # TODO: this is looking into how the value is used in the future
+        # we should remove this
+        # removing this means we insert one observer for each use, even if they
+        # have the same dtype, we can have an extra pass that removes the extra observers
+        for maybe_obs_node in arg.users.keys():
+            if maybe_obs_node.op == "call_module":
+                maybe_obs_mod = named_modules[maybe_obs_node.target]  # type: ignore[index]
+                if (
+                    type(maybe_obs_mod) == type(arg_as_input_act_obs_or_fq)
+                    and maybe_obs_mod.dtype
+                    == arg_as_input_target_dtype  # type: ignore[possibly-undefined]
+                ):
+                    arg_as_input_act_obs_or_fq = maybe_obs_mod  # type: ignore[assignment]
+                    existing_obs_node = maybe_obs_node
+                    break
+
+        assert arg_as_input_act_obs_or_fq is not None
+        obs_or_fq_map[(arg, node)] = arg_as_input_act_obs_or_fq
+        if existing_obs_node is None:
+            new_obs_node = _insert_obs_or_fq(
+                arg, arg_as_input_act_obs_or_fq, model, named_modules, graph
+            )
+            # override this arg to be the observed arg
+            new_arg = new_obs_node
+        else:
+            new_arg = existing_obs_node
+
+    return new_arg
+
+
+def _maybe_insert_input_observers_for_node(
+    node: Node,
+    qconfig: QConfigAny,
+    model: torch.nn.Module,
+    named_modules: Dict[str, torch.nn.Module],
+    graph: Graph,
+    qhandler: Optional[QuantizeHandler],
+    prepare_custom_config: PrepareCustomConfig,
+    obs_or_fq_map: Dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+    backend_config: Optional[BackendConfig] = None,
+) -> None:
+    """
+    If needed, inserts observers to the input args and kwargs of `node`.
+    Note: modifies `node` inplace.
+
+    For example, if cur_node needs an observer after prev_node, we change from
+
+      prev_node -> cur_node
+
+    To
+
+      prev_node -> obs -> cur_node
+
+    Note: backend_config only needed for standalone_module node
+    """
+    # Look through every input arg.  If that arg's target dtype does not
+    # match the current node's target dtype, insert an observer.
+    new_args = []
+    for arg in node.args:
+        new_arg = _maybe_insert_input_observer_for_arg_or_kwarg(
+            node,
+            arg,
+            qconfig,
+            model,
+            named_modules,
+            graph,
+            qhandler,
+            prepare_custom_config,
+            obs_or_fq_map,
+            is_qat,
+            backend_config,
+        )
+        new_args.append(new_arg)
+
+    new_kwargs = {}
+    for k, kwarg in node.kwargs.items():
+        new_kwarg = _maybe_insert_input_observer_for_arg_or_kwarg(
+            node,
+            kwarg,
+            qconfig,
+            model,
+            named_modules,
+            graph,
+            qhandler,
+            prepare_custom_config,
+            obs_or_fq_map,
+            is_qat,
+            backend_config,
+        )
+        new_kwargs[k] = new_kwarg
+
+    # assign the new args and kwargs to the node, inplace
+    node.args = tuple(new_args)
+    node.kwargs = new_kwargs
+
+
+def _maybe_insert_input_equalization_observers_for_node(
+    node: Node,
+    equalization_qconfig: Any,
+    model: torch.nn.Module,
+    named_modules: Dict[str, torch.nn.Module],
+    graph: Graph,
+    is_branch: bool,
+) -> None:
+    """
+    If `node` needs to be equalized, find the input/weight observers it needs in
+    `equalization_qconfig`, creates them, and inserts it into `graph`.
+
+    If `node` does not need an equalization observer, returns None.
+    """
+    if equalization_qconfig is None or not node_supports_equalization(
+        node, named_modules
+    ):
+        return
+
+    if is_branch:
+        warnings.warn(f"Cannot equalize {node} because it is part of a branch.")
+        return
+
+    new_args = []
+    for arg in node.args:
+        if not isinstance(arg, Node) or node_arg_is_bias(node, arg):
+            new_args.append(arg)
+            continue
+
+        is_weight = node_arg_is_weight(node, arg)
+
+        act_eq_process_ctr = (
+            equalization_qconfig.weight
+            if is_weight
+            else equalization_qconfig.input_activation
+        )
+
+        new_eq_obs_mod = act_eq_process_ctr()
+        new_eq_obs_node = _insert_obs_or_fq(
+            arg, new_eq_obs_mod, model, named_modules, graph
+        )
+
+        new_args.append(new_eq_obs_node)
+
+    # assign the new args and kwargs to the node, inplace
+    node.args = tuple(new_args)
+
+
+def _maybe_insert_output_observer_for_node(
+    node: Node,
+    model: torch.nn.Module,
+    named_modules: Dict[str, torch.nn.Module],
+    graph: Graph,
+    obs_or_fq_map: Dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+) -> Optional[Node]:
+    """
+    If `node` needs an output observer, creates it, inserts it into `graph`
+    and returns it.
+
+    If `node` does not need an output observer, returns None.
+
+    Note: inserting dynamic quantization ops for output is not supported in fx graph mode
+    quantization code path right now
+    """
+    assert node.op != "output", "observer insertion for outputs is handled elsewhere"
+
+    is_standalone_module = False
+    if "quantization_annotation" in node.meta:
+        output_act_obs_or_fq = _create_obs_or_fq_from_qspec(
+            node.meta["quantization_annotation"].output_qspec, obs_or_fq_map, is_qat
+        )
+    else:
+        assert "target_dtype_info" in node.meta
+        is_standalone_module = node.meta["target_dtype_info"].get(
+            "_is_standalone_module", False
+        )
+        output_act_obs_or_fq_ctr = node.meta["target_dtype_info"].get(
+            "output_act_obs_or_fq_ctr"
+        )
+        output_act_obs_or_fq = (
+            output_act_obs_or_fq_ctr() if output_act_obs_or_fq_ctr else None
+        )
+    target_dtype, target_is_dynamic = _get_dtype_and_is_dynamic(output_act_obs_or_fq)
+    # uncomment after we support reuse_input_obs_or_fq properly by having separate
+    # implemntations for this key instead of reusing the input_output_share_observers
+    # code
+    # reuse_input_obs_or_fq = node.meta["target_dtype_info"].get("reuse_input_obs_or_fq", False)
+    # for now we set this to False since reuse_input_obs_or_fq for
+    # the output of a node is implementation in the same code path as observer sharing,
+    # we should refactor this part to make it clearer in the future
+    # and we would be able to read this from config directly
+    reuse_input_obs_or_fq = False
+
+    # Note: prev_output_dtype = torch.float and prev_output_is_dynamic=False
+    # because the prev_output is the output of an fp32 op, althought technically
+    # we should get the dtype of the output from node.meta["val"] in the future
+    # if we deprecate fx graph mode quantization
+    needs_obs_or_fq = _needs_obs_or_fq(
+        torch.float, False, target_dtype, target_is_dynamic, reuse_input_obs_or_fq
+    )
+    # currently the activation in QConfig(activation=...,) is for both input
+    # and output, and when the activation is configured to be dynamic quantization
+    # e.g. PlaceholderObserver(dtype=torch.quint8, is_dynamic=True, ...), it means
+    # the input should by dynamically quantized, but output should not be quantized
+    #
+    # there is no way we can specify different observer/fq for input and output
+    # activation through QConfig today, this limitation is lifted in the
+    # quantizer/annotation API in pytorch 2.0 export quantization code path,
+    # but since this code is reused, annotating output to be dynamically quantized
+    # would not work either for that.
+    # we can change QConfig to support input/output activation if we want
+    # to remove the following check, or if we can deprecate fx graph mode quantization
+    if target_is_dynamic:
+        needs_obs_or_fq = False
+
+    # we never insert observers to output of standalone module, we assume
+    # if needed, they are inserted inside the standalone module
+    needs_obs_or_fq = needs_obs_or_fq and (not is_standalone_module)
+
+    if needs_obs_or_fq:
+        obs_or_fq_map[node] = output_act_obs_or_fq
+        return _insert_obs_or_fq(
+            node, output_act_obs_or_fq, model, named_modules, graph
+        )
+    else:
+        return None
+
+
+def _maybe_insert_observers_before_graph_output(
+    graph_output_node: Node,
+    model: torch.nn.Module,
+    named_modules: Dict[str, torch.nn.Module],
+    graph: Graph,
+    obs_or_fq_map: Dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+) -> None:
+    """
+    If the output needs to be quantized and there are any nodes
+    in the output which are not already observed, inserts observers
+    for those nodes.
+    """
+
+    def _recursive_maybe_replace_node_with_obs(
+        maybe_node: Argument,
+        model: torch.nn.Module,
+        named_modules: Dict[str, torch.nn.Module],
+        graph: Graph,
+    ) -> Argument:
+        """
+        Navigate an arbitrary data structure of lists, tuples, dicts.
+        For each container type, recurse on all inputs. Once any Node
+        is found, insert an observer if needed and do not recurse further.
+
+        For example, given a structure of
+
+          {'foo1': [[bar1]], 'foo2': {'foo3': [[[bar3]]]}}
+
+        we recurse down to bar1 and bar3, observe them if necessary,
+        and if we inserted an observer then replace the original node
+        with its observer.
+
+        Returns the data structure with all nodes needing observation being
+        replaced by their observers.
+        """
+        if isinstance(maybe_node, Node):
+            # check dtype of this node
+            arg_as_output_target_dtype = _get_arg_target_dtype_as_output(
+                maybe_node, named_modules, obs_or_fq_map, is_qat
+            )
+            observer_mod = None
+            arg_as_input_target_dtype = torch.float
+            if "target_dtype_info" in maybe_node.meta:
+                observer_cls = maybe_node.meta["target_dtype_info"].get(
+                    "input_act_obs_or_fq_ctr", None
+                )
+                if observer_cls is not None:
+                    observer_mod = observer_cls()
+                    arg_as_input_target_dtype = observer_mod.dtype
+            # TODO: this does not handle dynamic quantization yet
+            need_obs = (
+                arg_as_output_target_dtype != arg_as_input_target_dtype
+                and arg_as_input_target_dtype != torch.float
+            )
+            if need_obs:
+                assert observer_mod is not None
+                # insert observer
+                observer_node = _insert_obs_or_fq(
+                    maybe_node, observer_mod, model, named_modules, graph
+                )
+                return observer_node
+            else:
+                return maybe_node
+        elif isinstance(maybe_node, (list, tuple)):
+            results = []
+            for inner_node in maybe_node:
+                results.append(
+                    _recursive_maybe_replace_node_with_obs(
+                        inner_node, model, named_modules, graph
+                    )
+                )
+            if isinstance(maybe_node, list):
+                return results
+            else:
+                return tuple(results)
+        elif isinstance(maybe_node, dict):
+            results_dict = {}
+            for k, inner_v in maybe_node.items():
+                results_dict[k] = _recursive_maybe_replace_node_with_obs(
+                    inner_v, model, named_modules, graph
+                )
+            return results_dict
+        elif maybe_node is None:
+            return None
+        else:
+            raise Exception(  # noqa: TRY002
+                "Unhandled type for returned node:", maybe_node
+            )
+
+    new_args = []
+    for old_arg in graph_output_node.args:
+        new_args.append(
+            _recursive_maybe_replace_node_with_obs(old_arg, model, named_modules, graph)
+        )
+
+    graph_output_node.args = tuple(new_args)  # type: ignore[assignment]
+
+
+def _maybe_propagate_dtype_for_node(
+    node: Node,
+    target_dtype: Union[torch.dtype, type],
+    node_name_to_match_result_with_qconfig: Dict[str, _MatchResultWithQConfig],
+) -> None:
+    """
+    Assigns `target_dtype` to `node`, setting `is_dynamic` to False. If `node`
+    is a general tensor shape op, also call this function recursively on
+    the first argument, to propagate the dtype to the caller.
+    """
+    node.meta["target_dtype_info"]["input_act_obs_or_fq_ctr"] = None
+    node.meta["target_dtype_info"]["output_act_obs_or_fq_ctr"] = None
+    # if this is a copy node, propagate to first arg
+    (
+        root_node,
+        _,
+        pattern,
+        qhandler,
+        qconfig,
+    ) = node_name_to_match_result_with_qconfig.get(
+        node.name, (None, None, None, None, None)
+    )
+    # TODO: probably need to remove `is_general_tensor_value_op`
+    if qhandler is not None and qhandler.is_general_tensor_value_op():
+        prev_node = node.args[0]
+        if isinstance(prev_node, Node):
+            _maybe_propagate_dtype_for_node(
+                prev_node, target_dtype, node_name_to_match_result_with_qconfig
+            )
+
+
+def propagate_dtypes_for_known_nodes(
+    graph: Graph,
+    node_name_to_match_result_with_qconfig: Dict[str, _MatchResultWithQConfig],
+) -> None:
+    """
+    Currently we assume that inputs to the graph are either `torch.float` or
+    `torch.quint8`, which is not always correct. For ops such as
+    `x.masked_fill(mask, value)`, we know that the dtype of  `mask` is a
+    `BoolTensor`. Propagate this information throughout the graph.
+
+    Note: not all dtypes in the graph will be correct after this pass, but a
+    higher percentage of them will be correct. Hopefully in the future we can
+    replace this with a better way to reason about dtypes of tensors.
+    """
+    for node in graph.nodes:
+        non_observable_arg_dict = get_non_observable_arg_indexes_and_types(node)
+
+        for arg_type in non_observable_arg_dict:
+            non_observable_indices = non_observable_arg_dict[arg_type](node)
+
+            for index in non_observable_indices:
+                arg = node.args[index]
+
+                # when an argument is a tuple, it does not show up as another node so we need to go through
+                # all elements of the tuple manually
+                if isinstance(arg, (tuple, list)):
+                    arg_list = list(arg)
+                else:
+                    arg_list = [arg]
+
+                for cur_arg in arg_list:
+                    # hard coded arguments show up but aren't `Node` typed and do not need dtype propagated
+                    if isinstance(cur_arg, torch.fx.node.Node):
+                        _maybe_propagate_dtype_for_node(
+                            cur_arg, arg_type, node_name_to_match_result_with_qconfig
+                        )
+
+
+def _maybe_make_input_output_share_observers(
+    node: Node,
+    model: torch.nn.Module,
+    named_modules: Dict[str, torch.nn.Module],
+) -> bool:
+    """
+    Ensures that we share an observer
+    for all input arguments as well as the output argument. In detail, given
+    a graph of
+
+      x0 -> obs0 -> op -> x2
+                  /
+      x1 -> obs1 /
+
+    where node obs0 points to observer instance observer0,
+    obs1 points to observer1 and obs2 points to observer2, we make nodes obs1
+    and ob2 point to observer0.
+    Returns: whether the operation succeeded or not
+    """
+    first_arg = None
+    # find the first non-Tensor arg
+    for i in range(len(node.args)):
+        if isinstance(node.args[i], (Node, list, tuple)):
+            first_arg = node.args[i]
+            break
+
+    # if there is no non-Tensor arg, return directly
+    if first_arg is None:
+        return False
+
+    if isinstance(first_arg, (list, tuple)):
+        first_arg_arg = first_arg[0]
+    elif isinstance(first_arg, Node):
+        first_arg_arg = first_arg
+    else:
+        return False
+
+    # if we have a graph such as
+    #   observed_node -> non_observed_node -> cat
+    # we need to navigate up to the first observer
+    iteration_guard = 0
+    while not _is_activation_post_process_node(first_arg_arg, named_modules):
+        if not isinstance(first_arg_arg, Node):
+            return False
+        # did not find an activation_post_process for the op
+        if first_arg_arg.op == "placeholder":
+            return False
+        # trace back the args until we found the first Tensor/Node
+        trace_back_node = None
+        for i in range(len(first_arg_arg.args)):
+            trace_back_node = first_arg_arg.args[i]
+            if isinstance(trace_back_node, Node):
+                break
+        if trace_back_node is None:
+            return False
+        first_arg_arg = trace_back_node
+
+        iteration_guard += 1
+        if iteration_guard > 10000:
+            raise AssertionError("Unable to find observer of previous node")
+
+    assert isinstance(first_arg_arg, Node)
+    target_to_use = first_arg_arg.target
+    assert isinstance(target_to_use, str)
+    obs_mod_to_use = named_modules[target_to_use]
+
+    if isinstance(first_arg, (list, tuple)):
+        # set all other input observer nodes to use that module
+        for input_idx, input_arg in enumerate(first_arg):
+            if input_idx == 0:
+                continue
+            iteration_guard = 0
+            while not _is_activation_post_process_node(input_arg, named_modules):
+                # failed to trace back since no input arg for the current node
+                if len(input_arg.args) < 1:
+                    return False
+                input_arg = input_arg.args[0]
+                iteration_guard += 1
+                if iteration_guard > 10000:
+                    raise AssertionError("Unable to find observer of previous node")
+
+            parent_name, name = _parent_name(input_arg.target)
+            setattr(named_modules[parent_name], name, obs_mod_to_use)
+
+    # set the output observer node to use that module
+    for output_obs_node in node.users.keys():
+        assert _is_activation_post_process_node(output_obs_node, named_modules)
+        parent_name, name = _parent_name(output_obs_node.target)
+        setattr(named_modules[parent_name], name, obs_mod_to_use)
+
+    # TODO(future PR): delete the orphaned observer modules
+    return True
+
+
+def _remove_output_observer(
+    node: Node, model: torch.nn.Module, named_modules: Dict[str, torch.nn.Module]
+):
+    items = list(node.users.items())
+    for output_obs_node, _ in items:
+        assert _is_activation_post_process_node(output_obs_node, named_modules)
+        output_obs_node.replace_all_uses_with(node)
+        model.graph.erase_node(output_obs_node)  # type: ignore[union-attr, operator]
+
+
+def _swap_custom_module_to_observed(
+    node: Node,
+    qconfig: QConfigAny,
+    named_modules: Dict[str, torch.nn.Module],
+    prepare_custom_config: PrepareCustomConfig,
+):
+    custom_module = named_modules[node.target]  # type: ignore[index]
+    custom_module_class_mapping = prepare_custom_config.float_to_observed_mapping
+    observed_custom_module_class = get_swapped_custom_module_class(
+        custom_module, custom_module_class_mapping, qconfig
+    )
+    observed_custom_module = observed_custom_module_class.from_float(custom_module)
+    parent_name, name = _parent_name(node.target)
+    setattr(named_modules[parent_name], name, observed_custom_module)
+
+
+def insert_observers_for_model(
+    model: GraphModule,
+    node_name_to_match_result_with_qconfig: Dict[str, _MatchResultWithQConfig],
+    node_name_to_qconfig: Dict[str, QConfigAny],
+    prepare_custom_config: PrepareCustomConfig,
+    equalization_config_map: Dict[str, Any],
+    backend_config: BackendConfig,
+    observed_node_names: Set[str],
+    is_qat: bool,
+) -> Optional[Node]:
+    """
+    Inserts observers, using the following high level algorithm:
+
+    For each node in the graph:
+      1. determine the target dtype of this node in the quantized graph, and save
+           it for future steps
+      2. determine the target dtype or all args and kwargs of this node
+      3. if any arg or kwarg's target dtype does not match the current node's
+           dtype, insert an observer
+      4. if the current node needs an output observer, insert it
+
+    For example:
+
+    - starting graph:
+        x0 -> linear -> x1
+
+    - observed graph after processing x0:
+        x0(fp32)
+
+    - observed graph after processing linear:
+        x0(fp32) -> x0_obs0(int8) -> linear(int8) -> linear_obs0(int8)
+
+    - observed graph after processing x1:
+        x0(fp32) -> x0_obs0(int8) -> linear(int8) -> linear_obs0(int8) -> x1
+
+    After a node is processed, the naive observer placement is guaranteed to be
+    complete for that node and all of its predecessors. There can be future
+    passes which optimize the graph by deduplicating observers, etc.
+    """
+
+    # node.meta["target_dtype_info"] stores the target dtype information
+    # that's derived from qconfig for the Node, for example, if we have
+    # a conv2d node that has a qconfig
+    # qconfig = QConfig(activation=..., weight=...)
+    # # information for input and bias node omitted
+    # # for getattr node
+    # # weight = getattr(self, 'weight')
+    # weight.meta["target_dtype_info"] = {
+    #    'output_act_obs_or_fq_ctr': qconfig.weight,
+    # }
+    # # for conv2d node
+    # # conv2d = call_function[target=torch.nn.functional.conv2d](
+    # #            args=(input, weight, bias))
+    # conv2d.meta["target_dtype_info"] = {
+    #   'input_act_obs_or_fq_ctr': qconfig.activation
+    #   'weight_obs_or_fq_ctr': qconfig.weight,
+    #   'bias_obs_or_fq_ctr': PlaceholderObserver.with_args(dtype=torch.float32),
+    #   'output_act_obs_or_fq_ctr': qconfig.activation,
+    # }
+    #
+    cache_for_no_tensor_check: Dict[Node, bool] = {}
+
+    # first, populate the dtype map based only on qconfig and qhandler
+    # this assumes:
+    # graph inputs are fp32 by default, and int8 where overriden
+    # other nodes output dtype is specified by the qconfig
+    named_modules = dict(model.named_modules(remove_duplicate=False))
+
+    input_quantized_idxs: List[int] = prepare_custom_config.input_quantized_indexes
+    output_quantized_idxs: List[int] = prepare_custom_config.output_quantized_indexes
+    processed_nodes: Set[Node] = set()
+    # initialize target_dtype_info
+    for node in model.graph.nodes:
+        node.meta["target_dtype_info"] = copy.copy(
+            _DEFAULT_FP32_QCONFIG_FOR_TARGET_DTYPE_INFO
+        )
+
+    inputs_seen_counter = 0
+    outputs_seen_counter = 0
+    placeholder_node_to_input_index: Dict[Node, int] = {}
+    # TODO: we probably don't need this counter since each graph will only have
+    # one output node?
+    output_node_to_output_index: Dict[Node, int] = {}
+    for node in model.graph.nodes:
+        if node.op == "placeholder":
+            placeholder_node_to_input_index[node] = inputs_seen_counter
+            inputs_seen_counter += 1
+        if node.op == "output":
+            output_node_to_output_index[node] = outputs_seen_counter
+            outputs_seen_counter += 1
+
+    # Step 1, set the observer or fake quantize module constructor for each node in the
+    # matched_node_pattern
+
+    for match_res_with_qconfig in node_name_to_match_result_with_qconfig.values():
+        (
+            last_node,
+            matched_node_pattern,
+            pattern,
+            qhandler,
+            qconfig,
+        ) = match_res_with_qconfig
+        assert qhandler is not None
+        _set_target_dtype_info_for_matched_node_pattern(
+            matched_node_pattern,
+            last_node,
+            qconfig,
+            qhandler,
+            backend_config,
+            named_modules,
+            cache_for_no_tensor_check,
+            processed_nodes,
+        )
+
+    # Step 2. Special cases for some operators, we might be able to remove them
+    # in the future if we know dtype information of each node better
+
+    # Step 2.1. some settings are not based on patterns, we need to process each node
+    # instead
+    for node in model.graph.nodes:
+        if (
+            node.op == "placeholder"
+            and placeholder_node_to_input_index[node] in input_quantized_idxs
+        ):
+            # users are not supposed to call calculate_qparams on PlaceholderObserver, and
+            # this is OK because we are using this as a way to encode the dtypes of input
+            # tensor, we won't actually insert these observers in the graph and won't
+            # actually call calculate_qparams
+            node.meta["target_dtype_info"] = copy.copy(
+                _DEFAULT_QUINT8_QCONFIG_FOR_TARGET_DTYPE_INFO
+            )
+        elif node.op in ("call_module", "call_method", "call_function"):
+            args_have_no_tensors = all_node_args_have_no_tensors(
+                node, named_modules, cache_for_no_tensor_check
+            )
+            if args_have_no_tensors:
+                node.meta["target_dtype_info"] = {
+                    "input_act_obs_or_fq_ctr": None,
+                    "output_act_obs_or_fq_ctr": None,
+                }
+        elif (
+            node.op == "output"
+            and output_node_to_output_index[node] in output_quantized_idxs
+        ):
+            # TODO(future PR): update the output_quantized_idxs API to match
+            # arbitrary data structures. There is always a single output, and
+            # that output can have arbitrary nesting of values. List[int] is
+            # not the right data type for this.
+
+            # TODO(future PR): support more dtypes in model outputs, if necessary
+            node.meta["target_dtype_info"] = copy.copy(
+                _DEFAULT_QUINT8_QCONFIG_FOR_TARGET_DTYPE_INFO
+            )
+
+    # Step 2.2, for nodes with known input dtypes, propagate them throughout the
+    # graph. For example, if there is a call such as
+    #   x1 = x0.masked_fill(mask, 1)
+    # we propagate the type of mask to be torch.bool
+    propagate_dtypes_for_known_nodes(
+        model.graph, node_name_to_match_result_with_qconfig
+    )
+
+    # Step 3, check if the requested target_dtype_info is supported by backend or not
+    # if not, we'll reset the target_dtye_info to use the default (float Tensor)
+
+    # reset the counters and set of processed_nodes
+    processed_nodes: Set[Node] = set()
+    for match_res_with_qconfig in node_name_to_match_result_with_qconfig.values():
+        (
+            last_node,
+            matched_node_pattern,
+            pattern,
+            qhandler,
+            qconfig,
+        ) = match_res_with_qconfig
+        is_supported_by_backend = (
+            _is_pattern_dtype_config_and_qconfig_supported_by_backend(
+                pattern, matched_node_pattern, qconfig, backend_config
+            )
+        )
+        assert qhandler is not None
+
+        # get output_act_dtype so that we don't also reset the special typed nodes
+        # TODO: we might want to handle these more uniformly with the default path
+        # this can be improved if we can use node.meta["val"]
+        output_act_or_fq_ctr = node.meta["target_dtype_info"][
+            "output_act_obs_or_fq_ctr"
+        ]
+        output_act_or_fq = output_act_or_fq_ctr() if output_act_or_fq_ctr else None
+        output_act_dtype, _ = _get_dtype_and_is_dynamic(output_act_or_fq)
+        if not is_supported_by_backend and output_act_dtype not in [
+            None,
+            int,
+            float,
+            torch.bool,
+        ]:
+            # restore target_dtype_info to default if it is not supported by backend
+            _set_target_dtype_info_for_matched_node_pattern(
+                matched_node_pattern,
+                last_node,
+                torch.ao.quantization.qconfig._default_fp32_placeholder_qconfig,
+                None,
+                backend_config,
+                named_modules,
+                cache_for_no_tensor_check,
+                processed_nodes,
+            )
+
+    # After this point, the current node and all of its arguments
+    # have a target_dtype_info assigned. Now, we insert observers for inputs
+    # of this node (if needed for this node), and the output of this node
+    # (if needed for this node).
+
+    # Since we are mutating the graph as we go, we iterate over the original
+    # nodes before observer insertion, instead of model.graph.nodes.
+    nodes_before_observation = list(model.graph.nodes)
+
+    # Avoid duplicates custom module swaps for multiple nodes with same target.
+    custom_module_names_already_swapped: Set[str] = set()
+
+    # TODO: reuse placeholder_node_to_input_index and output_node_to_output_index
+    # reset inputs/outputs counters
+    inputs_seen_counter = 0
+    outputs_seen_counter = 0
+    results_node = None
+    obs_or_fq_map: Dict[EdgeOrNode, ObserverOrFakeQuantize] = {}
+
+    # TODO: change this to insert obs/fq by pattern instead of by node
+    for node in nodes_before_observation:
+        if node.op == "placeholder":
+            # if a graph input is in fp32, it does not need observation
+            # if a graph input is in int8, we assume the observation happens
+            #   outside of the graph, and no additional observation is needed
+            pass
+
+        elif node.op in ("call_module", "call_method", "call_function", "output"):
+            # check for matches
+            (
+                last_node,
+                matched_node_pattern,
+                pattern,
+                qhandler,
+                qconfig,
+            ) = node_name_to_match_result_with_qconfig.get(  # type: ignore[assignment]
+                node.name, (None, None, None, None, None)
+            )
+            equalization_qconfig = equalization_config_map.get(node.name, None)
+
+            this_node_dtype_info = node.meta["target_dtype_info"]
+            if "val" in node.meta:
+                output_is_a_tensor = this_node_dtype_info is not None and isinstance(
+                    node.meta["val"], FakeTensor
+                )
+            else:
+                output_is_a_tensor = this_node_dtype_info is not None
+
+            skip_inserting_observers = (
+                (qconfig is None) or not output_is_a_tensor
+            ) and (not node.op == "output")
+
+            # TODO: take a closer look to see if we can remove this check
+            # right now it is here because of `observed_node_names`, we are using
+            # it as an indicator for swapping the modules to reference modules in
+            # convert
+            is_supported_by_backend = (
+                _is_pattern_dtype_config_and_qconfig_supported_by_backend(
+                    pattern, matched_node_pattern, qconfig, backend_config
+                )
+            )
+
+            if not skip_inserting_observers and is_supported_by_backend:
+                named_modules = dict(model.named_modules(remove_duplicate=False))
+                if node.op != "output":
+                    assert matched_node_pattern is not None
+                    # add matched nodes to the observed node name set
+                    _add_matched_node_name_to_set(
+                        matched_node_pattern, observed_node_names
+                    )
+
+                    # This is currently only used for equalization.
+                    # Checks if the current node is in a branch in which the two
+                    # first layers are both being quantized.
+                    #
+                    # ex.       conv2
+                    #         /
+                    #      x -> conv1
+                    #
+                    # If this is the case, we will not apply equalization to the
+                    # initial two layers.
+                    is_quantized_branch = False
+                    if (
+                        len(node.args) > 0
+                        and isinstance(node.args[0], Node)
+                        and len(node.args[0].users) > 1
+                    ):
+                        for user in node.args[0].users:
+                            # Checks if there exists another user being quantized
+                            is_user_quantized = node_name_to_qconfig.get(
+                                user.name, None
+                            ) is not None or (
+                                user.op == "call_module"
+                                and isinstance(
+                                    named_modules[str(user.target)], ObserverBase
+                                )
+                            )
+                            if user != node and is_user_quantized:
+                                is_quantized_branch = True
+
+                    pattern_to_root_node_getter = (
+                        get_fusion_pattern_to_root_node_getter(backend_config)
+                    )
+                    root_node_getter = pattern_to_root_node_getter.get(
+                        pattern, _default_root_node_getter
+                    )
+                    root_node = root_node_getter(matched_node_pattern)
+                    is_input_node_of_the_pattern = node is root_node
+                    if is_input_node_of_the_pattern:
+                        # this modifies node inplace
+                        _maybe_insert_input_observers_for_node(
+                            node,
+                            qconfig,
+                            model,
+                            named_modules,
+                            model.graph,
+                            qhandler,
+                            prepare_custom_config,
+                            obs_or_fq_map,
+                            is_qat,
+                            backend_config,
+                        )
+
+                        # insert equalization input observers if needed
+                        _maybe_insert_input_equalization_observers_for_node(
+                            node,
+                            equalization_qconfig,
+                            model,
+                            named_modules,
+                            model.graph,
+                            is_quantized_branch,
+                        )
+
+                    is_last_node_of_pattern = node is last_node
+                    input_output_share_observers = node.meta["target_dtype_info"].get(
+                        "input_output_share_observers", False
+                    )
+                    reuse_input_obs_or_fq = node.meta["target_dtype_info"].get(
+                        "reuse_input_obs_or_fq", False
+                    )
+
+                    if is_last_node_of_pattern:
+                        if _is_custom_module_lstm(
+                            node, named_modules, qconfig, qhandler
+                        ):
+                            # Currently custom module outputs are assumed to be already quantized,
+                            # so we need to insert a DeQuantStub after the output. For custom module
+                            # LSTM specifically, the outputs are also a nested tuple, so we must first
+                            # break down the tuple to insert DeQuantStubs after the internal nodes.
+
+                            # TODO: This currently diverges from how custom modules are handled today,
+                            # where we insert observers after the output instead of DeQuantStubs, and
+                            # replace these observers with "dequantize" nodes during convert. Conceptually,
+                            # these output observers are the same as DeQuantStubs. In the future, we
+                            # should resolve this inconsistency by inserting DeQuantStubs for all custom
+                            # modules, not just for LSTM.
+                            _insert_dequant_stubs_for_custom_module_lstm_output(
+                                node, model, named_modules, model.graph
+                            )
+                            if node.target not in custom_module_names_already_swapped:
+                                custom_module_names_already_swapped.add(node.target)
+                                _swap_custom_module_to_observed(
+                                    node, qconfig, named_modules, prepare_custom_config
+                                )
+                        else:
+                            # this returns the new observer node if it was needed
+                            maybe_output_obs_node = (
+                                _maybe_insert_output_observer_for_node(
+                                    node,
+                                    model,
+                                    named_modules,
+                                    model.graph,
+                                    obs_or_fq_map,
+                                    is_qat,
+                                )
+                            )
+
+                            if maybe_output_obs_node is not None:
+                                # Update users of original node to use the output observer
+                                # instead. For example, change
+                                #
+                                #           next_node
+                                #          /
+                                #   cur_node -> obs
+                                #
+                                # to
+                                #
+                                #                 next_node
+                                #                 /
+                                #   cur_node -> obs
+                                #
+                                # We need to save orig users before updating uses because
+                                # the list of users will change as we update uses
+                                orig_users = list(node.users.keys())
+                                for user_node in orig_users:
+                                    if user_node is maybe_output_obs_node:
+                                        continue
+                                    user_node.replace_input_with(
+                                        node, maybe_output_obs_node
+                                    )
+
+                                _is_observer_in_same_graph_ = (
+                                    _is_observer_in_same_graph(
+                                        node, named_modules, obs_or_fq_map, is_qat
+                                    )
+                                )
+
+                                # for ops whose inputs and outputs share observer/fqs, we modify the graph
+                                # to make all inputs and outputs use the first input's
+                                # observer/fq
+                                if (
+                                    input_output_share_observers
+                                    and _is_observer_in_same_graph_
+                                ) or reuse_input_obs_or_fq:
+                                    if not _maybe_make_input_output_share_observers(
+                                        node, model, named_modules
+                                    ):
+                                        _remove_output_observer(
+                                            node, model, named_modules
+                                        )
+
+                                if qhandler is not None and qhandler.is_custom_module():
+                                    if (
+                                        node.target
+                                        not in custom_module_names_already_swapped
+                                    ):
+                                        custom_module_names_already_swapped.add(
+                                            node.target
+                                        )
+                                        _swap_custom_module_to_observed(
+                                            node,
+                                            qconfig,
+                                            named_modules,
+                                            prepare_custom_config,
+                                        )
+
+                else:  # output
+                    _maybe_insert_observers_before_graph_output(
+                        node, model, named_modules, model.graph, obs_or_fq_map, is_qat
+                    )
+
+        #
+        # After this point, the current node has input and output observers
+        # that it needs for itself inserted.
+        #
+
+        # increment the counters, so future inputs and outputs are assigned
+        # correct dtypes
+        if node.op == "placeholder":
+            inputs_seen_counter += 1
+        elif node.op == "output":
+            outputs_seen_counter += 1
+            results_node = node
+
+    return results_node
+
+
+def _run_prepare_fx_on_standalone_modules(
+    model: torch.nn.Module,
+    is_qat: bool,
+    named_modules: Dict[str, torch.nn.Module],
+    node_name_to_match_result_with_qconfig: Any,
+    prepare_custom_config: PrepareCustomConfig,
+    backend_config: BackendConfig,
+) -> None:
+    """
+    Runs prepare_fx on each standalone module. Note: this does
+    not modify the graph, it just replaces the unobserved modules with
+    their observed versions.
+    """
+    for (
+        root_node,
+        _,
+        pattern,
+        qhandler,
+        qconfig,
+    ) in node_name_to_match_result_with_qconfig.values():
+        if qhandler is None:
+            continue
+        elif not qhandler.is_standalone_module():
+            continue
+
+        (
+            sm_qconfig_mapping,
+            sm_example_inputs,
+            sm_prepare_custom_config,
+            sm_backend_config,
+        ) = _get_standalone_module_configs(
+            root_node, named_modules, prepare_custom_config, qconfig, backend_config
+        )
+
+        standalone_module = named_modules[root_node.target]
+        prepare = (
+            torch.ao.quantization.quantize_fx._prepare_standalone_module_fx
+        )  # type: ignore[attr-defined]
+        observed_standalone_module = prepare(
+            standalone_module,
+            sm_qconfig_mapping,
+            is_qat,
+            example_inputs=sm_example_inputs,
+            prepare_custom_config=sm_prepare_custom_config,
+            backend_config=sm_backend_config,
+        )
+        parent_name, name = _parent_name(root_node.target)
+        setattr(named_modules[parent_name], name, observed_standalone_module)
+        named_modules[root_node.target] = observed_standalone_module
+
+
+def _save_state(
+    observed: GraphModule,
+    node_name_to_qconfig: Dict[str, QConfigAny],
+    node_name_to_scope: Dict[str, Tuple[str, type]],
+    prepare_custom_config: PrepareCustomConfig,
+    equalization_node_name_to_qconfig: Dict[str, Any],
+    qconfig_mapping: QConfigMapping,
+    is_qat: bool,
+    observed_node_names: Set[str],
+) -> None:
+    observed.meta["_observed_graph_module_attrs"] = ObservedGraphModuleAttrs(
+        node_name_to_qconfig=node_name_to_qconfig,
+        node_name_to_scope=node_name_to_scope,
+        prepare_custom_config=prepare_custom_config,
+        equalization_node_name_to_qconfig=equalization_node_name_to_qconfig,
+        qconfig_mapping=qconfig_mapping,
+        is_qat=is_qat,
+        observed_node_names=observed_node_names,
+    )
+
+
+def prepare(
+    model: GraphModule,
+    qconfig_mapping: Union[QConfigMapping, Dict[str, Any]],
+    is_qat: bool,
+    node_name_to_scope: Dict[str, Tuple[str, type]],
+    example_inputs: Tuple[Any, ...],
+    prepare_custom_config: Union[PrepareCustomConfig, Dict[str, Any], None] = None,
+    _equalization_config: Union[QConfigMapping, Dict[str, Any], None] = None,
+    backend_config: Union[BackendConfig, Dict[str, Any], None] = None,
+    is_standalone_module: bool = False,
+) -> GraphModule:
+    """standalone_module means it a submodule that is not inlined in
+    parent module, and will be quantized separately as one unit.
+
+    How the standalone module is observed is specified by `input_quantized_idxs` and
+    `output_quantized_idxs` in the prepare_custom_config for the standalone module
+    Args:
+        node_name_to_scope: mapping from node name to the scope of the module which contains the node.
+        The scope is a tuple of fully qualified path of the module and the type of the module
+    Returns:
+        model(GraphModule): prepared standalone module
+        attributes related to standalone module
+        in model.meta["_observed_graph_module_attrs"]:
+            is_observed_standalone_module (bool): boolean value that shows whether the
+            current model is a observed standalone module or not
+            standalone_module_input_quantized_idxs(List[Int]): a list of
+                indexes for the graph input that is expected to be quantized,
+                same as input_quantized_idxs configuration provided
+                for the standalone module
+            standalone_module_output_quantized_idxs(List[Int]): a list of
+                indexs for the graph output that is quantized
+                same as input_quantized_idxs configuration provided
+                for the standalone module
+    """
+    if prepare_custom_config is None:
+        prepare_custom_config = PrepareCustomConfig()
+    if _equalization_config is None:
+        _equalization_config = QConfigMapping()
+
+    if isinstance(qconfig_mapping, dict):
+        warnings.warn(
+            "Passing a QConfig dictionary to prepare is deprecated and will not be supported "
+            "in a future version. Please pass in a QConfigMapping instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        qconfig_mapping = QConfigMapping.from_dict(qconfig_mapping)
+
+    if isinstance(_equalization_config, dict):
+        warnings.warn(
+            "Passing a QConfig dictionary to prepare for equalization is deprecated and will not "
+            "be supported in a future version. Please pass in a QConfigMapping instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        _equalization_config = QConfigMapping.from_dict(_equalization_config)
+
+    if isinstance(prepare_custom_config, dict):
+        warnings.warn(
+            "Passing a prepare_custom_config_dict to prepare is deprecated and will not be supported "
+            "in a future version. Please pass in a PrepareCustomConfig instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        prepare_custom_config = PrepareCustomConfig.from_dict(prepare_custom_config)
+
+    if isinstance(backend_config, dict):
+        warnings.warn(
+            "Passing a backend_config_dict to prepare is deprecated and will not be supported "
+            "in a future version. Please pass in a BackendConfig instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        backend_config = BackendConfig.from_dict(backend_config)
+
+    assert isinstance(qconfig_mapping, QConfigMapping)
+    assert isinstance(_equalization_config, QConfigMapping)
+    qconfig_mapping = copy.deepcopy(qconfig_mapping)
+    _equalization_config = copy.deepcopy(_equalization_config)
+
+    # mapping from a tuple of nodes in reverse order to uninitialized
+    #   QuantizeHandler subclass. For example,
+    # {
+    #   # match a single node
+    #   (:
+    #     ),
+    #   # match multiple nodes in reverse order
+    #   ((, ):
+    #     ),
+    # }
+
+    pattern_to_quantize_handler: Dict[Pattern, QuantizeHandler] = {}
+    if backend_config is None:
+        backend_config = get_native_backend_config()
+    pattern_to_quantize_handler = _get_pattern_to_quantize_handlers(backend_config)
+    pattern_to_quantize_handler = _sorted_patterns_dict(pattern_to_quantize_handler)
+
+    root_node_getter_mapping = get_fusion_pattern_to_root_node_getter(backend_config)
+
+    _update_qconfig_for_fusion(model, qconfig_mapping)
+    _update_qconfig_for_fusion(model, _equalization_config)
+    flattened_qconfig_dict = _get_flattened_qconfig_dict(qconfig_mapping)
+    # TODO: support regex as well
+    propagate_qconfig_(model, flattened_qconfig_dict, prepare_custom_config.to_dict())
+
+    if is_qat:
+        module_to_qat_module = get_module_to_qat_module(backend_config)
+        _qat_swap_modules(model, module_to_qat_module)
+        _update_qconfig_for_qat(qconfig_mapping, backend_config)
+
+    # mapping from fully qualified module name to module instance
+    # for example,
+    # {
+    #   '': Model(...),
+    #   'linear': Linear(...),
+    #   'linear.weight_fake_quant': PerChannelMinMaxObserver(...),
+    # }
+    named_modules = dict(model.named_modules(remove_duplicate=False))
+
+    # fill node_name_to_qconfig, a map from node name to qconfig, used in _find_matches
+    equalization_node_name_to_qconfig = _generate_node_name_to_qconfig(
+        model, named_modules, model.graph, _equalization_config, node_name_to_scope
+    )
+    node_name_to_qconfig = _generate_node_name_to_qconfig(
+        model, named_modules, model.graph, qconfig_mapping, node_name_to_scope
+    )
+
+    # match the patterns that will get quantized
+    standalone_module_names = list(prepare_custom_config.standalone_module_names.keys())
+    standalone_module_classes = list(
+        prepare_custom_config.standalone_module_classes.keys()
+    )
+
+    custom_module_classes = get_custom_module_class_keys(
+        prepare_custom_config.float_to_observed_mapping
+    )
+    matches_without_qconfig = _find_matches(
+        model.graph,
+        named_modules,
+        pattern_to_quantize_handler,
+        root_node_getter_mapping,
+        standalone_module_names,
+        standalone_module_classes,
+        custom_module_classes,
+    )
+
+    # map qconfig instances to matches
+    node_name_to_match_result_with_qconfig = {}
+    for node_name, match_without_qconfig in matches_without_qconfig.items():
+        match_with_qconfig = (*match_without_qconfig, node_name_to_qconfig[node_name])
+        node_name_to_match_result_with_qconfig[node_name] = match_with_qconfig
+
+    _run_prepare_fx_on_standalone_modules(
+        model,
+        is_qat,
+        named_modules,
+        node_name_to_match_result_with_qconfig,
+        prepare_custom_config,
+        backend_config,
+    )
+
+    # record names for the set of observed node, so that in convert step
+    # we know whether we need to convert a floating point module to reference
+    # quantized module or not
+    observed_node_names: Set[str] = set()
+
+    result_node = insert_observers_for_model(
+        model,
+        node_name_to_match_result_with_qconfig,
+        node_name_to_qconfig,
+        prepare_custom_config,
+        equalization_node_name_to_qconfig,
+        backend_config,
+        observed_node_names,
+        is_qat,
+    )
+    model = GraphModule(model, model.graph)
+
+    _save_state(
+        model,
+        node_name_to_qconfig,
+        node_name_to_scope,
+        prepare_custom_config,
+        equalization_node_name_to_qconfig,
+        qconfig_mapping,
+        is_qat,
+        observed_node_names,
+    )
+
+    if is_standalone_module:
+        assert result_node is not None
+        assert isinstance(result_node.args[0], Node), (
+            "standalone module only supports returning simple value currently"
+            "(not tuple, dict etc.)"
+        )
+        # these inputs are observed in parent
+        # converting List[int] to Tensor since module attribute is
+        # Union[Tensor, Module]
+        input_quantized_idxs: List[int] = prepare_custom_config.input_quantized_indexes
+        output_quantized_idxs: List[
+            int
+        ] = prepare_custom_config.output_quantized_indexes
+        observed_graph_module_attrs = model.meta["_observed_graph_module_attrs"]
+        # inplace modification
+        observed_graph_module_attrs.is_observed_standalone_module = True
+        observed_graph_module_attrs.standalone_module_input_quantized_idxs = (
+            input_quantized_idxs
+        )
+        observed_graph_module_attrs.standalone_module_output_quantized_idxs = (
+            output_quantized_idxs
+        )
+    return model
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/qconfig_mapping_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/qconfig_mapping_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9607204595295eb2bb9d5de487116d96d68fa3d8
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/qconfig_mapping_utils.py
@@ -0,0 +1,398 @@
+# mypy: allow-untyped-defs
+import re
+from collections import defaultdict, OrderedDict
+from typing import Any, Callable, Dict, List, Set, Tuple, Union
+
+import torch
+from torch.ao.nn.intrinsic import _FusedModule
+from torch.ao.quantization import QConfig
+from torch.ao.quantization.backend_config import BackendConfig, DTypeConfig
+from torch.ao.quantization.backend_config.utils import get_module_to_qat_module
+from torch.ao.quantization.observer import _is_activation_post_process
+from torch.ao.quantization.qconfig import (
+    _add_module_to_qconfig_obs_ctr,
+    qconfig_equals,
+    QConfigAny,
+)
+from torch.ao.quantization.qconfig_mapping import (
+    _MODULE_NAME_DICT_KEY,
+    _MODULE_NAME_REGEX_DICT_KEY,
+    _OBJECT_TYPE_DICT_KEY,
+    QConfigMapping,
+)
+from torch.ao.quantization.utils import _parent_name, get_qconfig_dtypes
+from torch.fx import GraphModule
+from torch.fx.graph import Graph
+
+
+__all__: List[str] = []
+
+
+def _maybe_adjust_qconfig_for_module_name_object_type_order(
+    qconfig_mapping: QConfigMapping,
+    cur_module_path: str,
+    cur_object_type: Callable,
+    cur_object_type_idx: int,
+    fallback_qconfig: QConfigAny,
+) -> QConfigAny:
+    for (
+        module_name,
+        object_type,
+        index,
+    ), qconfig in qconfig_mapping.module_name_object_type_order_qconfigs.items():
+        if (
+            (module_name == cur_module_path)
+            and (object_type == cur_object_type)
+            and (index == cur_object_type_idx)
+        ):
+            return qconfig
+    return fallback_qconfig
+
+
+def _update_qconfig_for_fusion(model: GraphModule, qconfig_mapping: QConfigMapping):
+    """
+    Update the QConfigMapping to account for fused modules such as LinearReLU.
+    This assumes the QConfigMapping's attributes have already been converted to OrderedDicts.
+    """
+    object_type_dict = qconfig_mapping.object_type_qconfigs
+    if len(object_type_dict) == 0:
+        return qconfig_mapping
+
+    modules = dict(model.named_modules())
+
+    for node in model.graph.nodes:
+        if node.op == "call_module" and node.target in modules:
+            maybe_fused_module = modules[str(node.target)]
+            if not isinstance(maybe_fused_module, _FusedModule):
+                continue
+
+            ops = list(maybe_fused_module._modules.values())
+            fused_qconfig = object_type_dict.get(type(ops[0]), None)
+
+            # Raise an error if the modules in the fused module have
+            # different qconfigs specified in the qconfig_dict
+            # TODO: currently it only works for modules,
+            # need to make this work for torch.nn.functional.relu
+            # TODO: currently it only works for object_type configurations,
+            # ideally it should work for different types of configurations,
+            # maybe we want to redesign this part
+            for op in ops[1:]:
+                if not qconfig_equals(
+                    object_type_dict.get(type(op), None), fused_qconfig
+                ):
+                    raise LookupError(
+                        "During fusion, we need to specify the same "
+                        + f"qconfigs for all module types in {type(maybe_fused_module)} "
+                        + f"offending type: {type(op)}"
+                    )
+
+            if fused_qconfig is not None:
+                object_type_dict[type(maybe_fused_module)] = fused_qconfig
+
+
+def _generate_node_name_to_qconfig(
+    root: torch.nn.Module,
+    modules: Dict[str, torch.nn.Module],
+    input_graph: Graph,
+    qconfig_mapping: QConfigMapping,
+    node_name_to_scope: Dict[str, Tuple[str, type]],
+) -> Dict[str, QConfigAny]:
+    global_qconfig = qconfig_mapping.global_qconfig
+    node_name_to_qconfig = {}
+
+    # example:
+    #
+    #   {'foo.bar': {F.linear: 0, F.conv2d: 1, ...}, ...}
+    #
+    # meaning in submodule 'foo.bar', we have seen 0 F.linear and
+    # 1 F.conv2d invocations so far.
+    submodule_to_object_type_to_cur_idx: Dict[str, Dict[Callable, int]] = defaultdict(
+        lambda: defaultdict(int)
+    )
+    for node in input_graph.nodes:
+        qconfig = None
+        if node.op == "get_attr":
+            module_name, _ = _parent_name(node.target)
+            qconfig = _maybe_adjust_qconfig_for_module_type_or_name(
+                qconfig_mapping, type(modules[module_name]), module_name, global_qconfig
+            )
+            qconfig_with_device_check = _add_module_to_qconfig_obs_ctr(
+                qconfig, modules.get(node.target, None)
+            )
+        elif node.op == "call_function":
+            # precedence: module_name_qconfig
+            # > function_qconfig > global_qconfig
+            # module_name takes precedence over function qconfig
+            function_qconfig = _get_object_type_qconfig(
+                qconfig_mapping, node.target, global_qconfig
+            )
+            module_path, module_type = node_name_to_scope[node.name]
+            qconfig = _maybe_adjust_qconfig_for_module_type_or_name(
+                qconfig_mapping, module_type, module_path, function_qconfig
+            )
+
+            cur_object_type_idx = submodule_to_object_type_to_cur_idx[module_path][
+                node.target
+            ]
+            submodule_to_object_type_to_cur_idx[module_path][node.target] += 1
+            qconfig = _maybe_adjust_qconfig_for_module_name_object_type_order(
+                qconfig_mapping, module_path, node.target, cur_object_type_idx, qconfig
+            )
+            qconfig_with_device_check = _add_module_to_qconfig_obs_ctr(
+                qconfig, modules.get(node.target, None)
+            )
+
+        elif node.op == "call_method":
+            module_path, module_type = node_name_to_scope[node.name]
+            # first use node.target (string) to get the qconfig
+            # this is to support configs like
+            # "object_type": [("reshape", qconfig)]
+            qconfig = _maybe_adjust_qconfig_for_module_type_or_name(
+                qconfig_mapping, node.target, module_path, global_qconfig
+            )
+            # if there is no special config for the method, we'll fall back to the
+            # config for the module that contains the call_method node
+            qconfig = _maybe_adjust_qconfig_for_module_type_or_name(
+                qconfig_mapping, module_type, module_path, qconfig
+            )
+            # currently call_method does not support modifying qconfig
+            # by order, we can add this later if it is needed.
+            qconfig_with_device_check = _add_module_to_qconfig_obs_ctr(
+                qconfig, modules.get(node.target, None)
+            )
+
+        elif node.op == "call_module":
+            # if the node is an observer, just continue - don't add it to the qconfig_map
+            if _is_activation_post_process(modules[node.target]):
+                continue
+            qconfig = _maybe_adjust_qconfig_for_module_type_or_name(
+                qconfig_mapping, type(modules[node.target]), node.target, global_qconfig
+            )
+
+            module_path, module_type = node_name_to_scope[node.name]
+            # Note: for call_module, the module_path is the current module's name.
+            # to meaningfully count invocations, we need to count them in the parent
+            # module.
+            parent_name, _ = _parent_name(module_path)
+            cur_object_type_idx = submodule_to_object_type_to_cur_idx[parent_name][
+                module_type
+            ]
+            submodule_to_object_type_to_cur_idx[parent_name][module_type] += 1
+            qconfig = _maybe_adjust_qconfig_for_module_name_object_type_order(
+                qconfig_mapping, parent_name, module_type, cur_object_type_idx, qconfig
+            )
+            qconfig_with_device_check = _add_module_to_qconfig_obs_ctr(
+                qconfig, modules.get(node.target, None)
+            )
+
+            # regex is not supported eager mode propagate_qconfig_, we'll
+            # need to set the qconfig explicitly here in case regex
+            # is used
+            modules[node.target].qconfig = qconfig_with_device_check
+        else:
+            qconfig_with_device_check = None
+
+        node_name_to_qconfig[node.name] = qconfig_with_device_check
+    return node_name_to_qconfig
+
+
+def _check_is_valid_config_dict(
+    config_dict: Any, allowed_keys: Set[str], dict_name: str
+) -> None:
+    r"""Checks if the given config_dict has the correct keys
+
+    Args:
+      `config_dict`: dictionary whose keys we want to check
+    """
+
+    for k in config_dict.keys():
+        if k not in allowed_keys:
+            raise ValueError(
+                "Expected "
+                + dict_name
+                + " to have the following keys: "
+                + str(allowed_keys)
+                + ". But found '"
+                + k
+                + "' instead."
+            )
+
+
+def _compare_prepare_convert_qconfig_mappings(
+    prepare_qconfig_mapping: QConfigMapping, convert_qconfig_mapping: QConfigMapping
+):
+    r"""Compare the qconfig_mapping passed in convert to the one from prepare and check the values
+
+    Args:
+      `prepare_qconfig_mapping`: configuration for prepare quantization step
+      `convert_qconfig_mapping`: configuration for convert quantization step
+    """
+    assert qconfig_equals(
+        prepare_qconfig_mapping.global_qconfig, convert_qconfig_mapping.global_qconfig
+    ), "Expected global qconfigs to be the same in the prepare and convert quantization configs"
+    prepare_dicts: List[OrderedDict] = [
+        prepare_qconfig_mapping.object_type_qconfigs,
+        prepare_qconfig_mapping.module_name_qconfigs,
+        prepare_qconfig_mapping.module_name_regex_qconfigs,
+    ]
+    convert_dicts: List[OrderedDict] = [
+        convert_qconfig_mapping.object_type_qconfigs,
+        convert_qconfig_mapping.module_name_qconfigs,
+        convert_qconfig_mapping.module_name_regex_qconfigs,
+    ]
+    dict_names = [
+        _OBJECT_TYPE_DICT_KEY,
+        _MODULE_NAME_DICT_KEY,
+        _MODULE_NAME_REGEX_DICT_KEY,
+    ]
+    for i in range(len(prepare_dicts)):
+        for name in prepare_dicts[i].keys():
+            assert (
+                name in convert_dicts[i]
+            ), f"Missing key {dict_names[i]} {name} in convert QConfigMapping \
+                when it was present in prepare"
+            assert convert_dicts[i][name] is None or qconfig_equals(
+                prepare_dicts[i][name], convert_dicts[i][name]
+            ), f"Expected convert QConfigMapping to have the same qconfig as prepare for key {dict_names[i]} {name}; \
+                prepare: {prepare_dicts[i][name]}; convert: {convert_dicts[i][name]}"
+
+
+def _is_qconfig_supported_by_dtype_configs(
+    qconfig: QConfig, dtype_configs: List[DTypeConfig]
+):
+    for dtype_config in dtype_configs:
+        is_dynamic = dtype_config.is_dynamic
+        if is_dynamic is None:
+            is_dynamic = False
+        input_dtype = dtype_config.input_dtype or torch.float
+        weight_dtype = dtype_config.weight_dtype or torch.float
+        bias_dtype = dtype_config.bias_dtype or torch.float
+        output_dtype = dtype_config.output_dtype or torch.float
+        (
+            qconfig_activation_dtype,
+            qconfig_weight_dtype,
+            qconfig_input_act_is_dynamic,
+        ) = get_qconfig_dtypes(qconfig)
+        qconfig_bias_dtype = (
+            torch.float16
+            if (
+                qconfig_activation_dtype == torch.float16
+                and qconfig_weight_dtype == torch.float16
+                and not is_dynamic
+            )
+            else torch.float
+        )
+
+        if is_dynamic:
+            is_match = (
+                qconfig_input_act_is_dynamic
+                and input_dtype == qconfig_activation_dtype
+                and output_dtype == torch.float
+                and weight_dtype == qconfig_weight_dtype
+            )
+        else:
+            is_match = (
+                input_dtype == qconfig_activation_dtype
+                and output_dtype == qconfig_activation_dtype
+                and weight_dtype == qconfig_weight_dtype
+                and bias_dtype == qconfig_bias_dtype
+            )
+        if is_match:
+            return True
+    return False
+
+
+def _get_object_type_qconfig(
+    qconfig_mapping: QConfigMapping,
+    object_type: Union[Callable, str],
+    fallback_qconfig: QConfigAny,
+) -> QConfigAny:
+    return qconfig_mapping.object_type_qconfigs.get(object_type, fallback_qconfig)
+
+
+def _get_module_name_regex_qconfig(qconfig_mapping, module_name, fallback_qconfig):
+    for regex_pattern, qconfig in qconfig_mapping.module_name_regex_qconfigs.items():
+        if re.match(regex_pattern, module_name):
+            # first match wins
+            return qconfig
+    return fallback_qconfig
+
+
+def _get_module_name_qconfig(qconfig_mapping, module_name, fallback_qconfig):
+    if module_name == "":
+        # module name qconfig not found
+        return fallback_qconfig
+    if module_name in qconfig_mapping.module_name_qconfigs:
+        return qconfig_mapping.module_name_qconfigs[module_name]
+    else:
+        parent, _ = _parent_name(module_name)
+        return _get_module_name_qconfig(qconfig_mapping, parent, fallback_qconfig)
+
+
+def _maybe_adjust_qconfig_for_module_type_or_name(
+    qconfig_mapping, module_type, module_name, global_qconfig
+):
+    # get qconfig for module_name,
+    # fallback to module_name_regex_qconfig, module_type_qconfig,
+    # global_qconfig if necessary
+    module_type_qconfig = _get_object_type_qconfig(
+        qconfig_mapping, module_type, global_qconfig
+    )
+    module_name_regex_qconfig = _get_module_name_regex_qconfig(
+        qconfig_mapping, module_name, module_type_qconfig
+    )
+    module_name_qconfig = _get_module_name_qconfig(
+        qconfig_mapping, module_name, module_name_regex_qconfig
+    )
+    return module_name_qconfig
+
+
+def _get_flattened_qconfig_dict(
+    qconfig_mapping: QConfigMapping,
+) -> Dict[Union[Callable, str], QConfigAny]:
+    """flatten the global, object_type and module_name qconfig
+    to the same qconfig_dict so that it can be used by
+    propagate_qconfig_ function.
+    "module_name_regex" is ignored for now since it's not supported
+    in propagate_qconfig_, but it can be fixed later.
+
+    For example:
+    Input: {
+      "": qconfig,
+      "object_type": [
+        (torch.add, qconfig)
+      ],
+      "module_name": [
+        ("conv", qconfig)
+      ]
+    }
+
+    Output: {
+      "": qconfig,
+      torch.add: qconfig,
+      "conv": qconfig
+    }
+    """
+    flattened: Dict[Union[Callable, str], QConfigAny] = {
+        "": qconfig_mapping.global_qconfig
+    }
+    for obj, qconfig in qconfig_mapping.object_type_qconfigs.items():
+        flattened[obj] = qconfig
+    for obj, qconfig in qconfig_mapping.module_name_qconfigs.items():
+        flattened[obj] = qconfig
+    return flattened
+
+
+def _update_qconfig_for_qat(
+    qconfig_mapping: QConfigMapping, backend_config: BackendConfig
+):
+    """
+    Update the qconfig_mapping to account for module swaps during QAT.
+    During QAT we perform a module swap on the nn.Module types to the corresponding nn.qat.modules types.
+    """
+    module_to_qat_module_class = get_module_to_qat_module(backend_config)
+    object_type_dict = qconfig_mapping.object_type_qconfigs
+    new_object_type_dict = object_type_dict.copy()
+    for k, v in new_object_type_dict.items():
+        if k in module_to_qat_module_class:
+            object_type_dict[module_to_qat_module_class[k]] = v
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/quantize_handler.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/quantize_handler.py
new file mode 100644
index 0000000000000000000000000000000000000000..c86529afa7303795f01db7e7448e4d2e3c12b29e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/quantize_handler.py
@@ -0,0 +1,225 @@
+# mypy: allow-untyped-defs
+from abc import ABC
+from typing import Callable, Dict, List, Optional, Type
+
+import torch
+from torch.ao.quantization.backend_config import (
+    BackendConfig,
+    DTypeConfig,
+    ObservationType,
+)
+from torch.ao.quantization.utils import NodePattern, Pattern, QuantizerCls
+from torch.fx.graph import Node
+
+from .utils import all_node_args_have_no_tensors
+
+
+__all__ = [
+    "QuantizeHandler",
+    "BinaryOpQuantizeHandler",
+    "CatQuantizeHandler",
+    "ConvReluQuantizeHandler",
+    "LinearReLUQuantizeHandler",
+    "BatchNormQuantizeHandler",
+    "EmbeddingQuantizeHandler",
+    "RNNDynamicQuantizeHandler",
+    "DefaultNodeQuantizeHandler",
+    "FixedQParamsOpQuantizeHandler",
+    "CopyNodeQuantizeHandler",
+    "GeneralTensorShapeOpQuantizeHandler",
+    "CustomModuleQuantizeHandler",
+    "StandaloneModuleQuantizeHandler",
+]
+
+
+def _default_root_node_getter(node_pattern):
+    if node_pattern is None:
+        return node_pattern
+    while not isinstance(node_pattern, Node):
+        node_pattern = node_pattern[-1]
+    return node_pattern
+
+
+# Base Pattern Handler
+class QuantizeHandler(ABC):  # noqa: B024
+    """Base handler class for the quantizer patterns"""
+
+    def __init__(
+        self,
+        node_pattern: NodePattern,
+        modules: Dict[str, torch.nn.Module],
+        root_node_getter: Optional[Callable] = None,
+        is_custom_module=False,
+        is_standalone_module=False,
+    ):
+        """Records pattern information in __init__, which will be used
+        in convert
+        """
+        self.node_pattern = node_pattern
+        self.modules = modules
+        if root_node_getter is None:
+            root_node_getter = _default_root_node_getter
+        self.root_node = root_node_getter(node_pattern)
+        self.is_custom_module_ = is_custom_module
+        self.is_standalone_module_ = is_standalone_module
+        self.num_tensor_args = 0
+        # determine how many of the first two args are Tensors (versus scalars)
+        # this distinguishes things like "x + y" from "x + 2" or "2 + x"
+        if isinstance(self.root_node, Node):
+            cache_for_no_tensor_check: Dict[Node, bool] = {}
+            for arg_idx in range(len(self.root_node.args)):
+                arg = self.root_node.args[arg_idx]
+                if isinstance(arg, Node) and (
+                    not all_node_args_have_no_tensors(
+                        arg, self.modules, cache_for_no_tensor_check
+                    )
+                ):
+                    self.num_tensor_args += 1
+
+    def is_general_tensor_value_op(self) -> bool:
+        """
+        Returns True if the operator works for both floating point and
+        quantized input, and does some computation based on the input Tensor,
+        or the ops that only re-arranges the Tensor values or query some metadata
+        about the Tensor
+        so we need to insert observer/fake_quant for the output of the
+        operator (same observer instance as input)
+        since the distribution of values is different for input and output
+        Tensors (for HistogramObserver) while they share the same quantization
+        parameters
+        Example operator: avgpool2d, reshape, transpose, maxpool2d
+        Example observed operator:
+        observer_0 - avgpool2d - observer_0 (same observer instance as input)
+        """
+        return False
+
+    def is_custom_module(self):
+        return self.is_custom_module_
+
+    def is_standalone_module(self):
+        return self.is_standalone_module_
+
+
+def _get_quantize_handler_cls(
+    observation_type: ObservationType,
+    dtype_configs: List[DTypeConfig],
+    num_tensor_args_to_observation_type: Dict[int, ObservationType],
+) -> Type[QuantizeHandler]:
+    """
+    Return a configurable QuantizeHandler that matches the given specifications from the backend.
+    """
+
+    class ConfigurableQuantizeHandler(QuantizeHandler):
+        def __init__(
+            self,
+            node_pattern: NodePattern,
+            modules: Dict[str, torch.nn.Module],
+            root_node_getter: Optional[Callable] = None,
+        ):
+            super().__init__(node_pattern, modules, root_node_getter)
+            if num_tensor_args_to_observation_type:
+                assert self.num_tensor_args in num_tensor_args_to_observation_type, (
+                    f"Must provide observation_type config for tensor number {self.num_tensor_args}"
+                    f" in num_tensor_args_to_observation_type for {node_pattern}"
+                )
+                self.observation_type = num_tensor_args_to_observation_type[
+                    self.num_tensor_args
+                ]
+            else:
+                self.observation_type = observation_type
+            self.dtype_configs = dtype_configs
+
+        def is_general_tensor_value_op(self) -> bool:
+            return (
+                self.observation_type
+                == ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT
+            )
+
+    return ConfigurableQuantizeHandler
+
+
+def _get_pattern_to_quantize_handlers(
+    backend_config: BackendConfig,
+) -> Dict[Pattern, QuantizerCls]:
+    """
+    Note: Quantize handler is just a holder for some check methods like
+    (should_insert_observer_for_output), maybe this can be a enum as well,
+    we can refactor this after we convert the path for fbgemm/qnnpack fully to the
+    new path, this is not exposed to backend developers
+    """
+    pattern_to_quantize_handlers = {}
+    for pattern, config in backend_config._pattern_complex_format_to_config.items():
+        observation_type = config.observation_type
+        dtype_configs = config.dtype_configs
+        num_tensor_args_to_observation_type = (
+            config._num_tensor_args_to_observation_type
+        )
+        pattern_to_quantize_handlers[pattern] = _get_quantize_handler_cls(
+            observation_type, dtype_configs, num_tensor_args_to_observation_type
+        )
+    return pattern_to_quantize_handlers
+
+
+# TODO: remove this class, this is still exposed in torch.ao.quantization
+# but we should be able to break bc
+class BinaryOpQuantizeHandler(QuantizeHandler):
+    pass
+
+
+class CatQuantizeHandler(QuantizeHandler):
+    pass
+
+
+# TODO: remove this class
+class ConvReluQuantizeHandler(QuantizeHandler):
+    pass
+
+
+# TODO: remove this class
+class LinearReLUQuantizeHandler(QuantizeHandler):
+    pass
+
+
+# TODO: remove this class
+class BatchNormQuantizeHandler(QuantizeHandler):
+    pass
+
+
+# TODO: remove this class
+class EmbeddingQuantizeHandler(QuantizeHandler):
+    pass
+
+
+# TODO: remove this class
+class RNNDynamicQuantizeHandler(QuantizeHandler):
+    pass
+
+
+# TODO: remove this class
+class DefaultNodeQuantizeHandler(QuantizeHandler):
+    """Common quantized op, first input and first output will be quantized"""
+
+
+# TODO: remove this class
+class FixedQParamsOpQuantizeHandler(QuantizeHandler):
+    pass
+
+
+# TODO: remove
+class CopyNodeQuantizeHandler(QuantizeHandler):
+    pass
+
+
+# TODO: remove
+class GeneralTensorShapeOpQuantizeHandler(QuantizeHandler):
+    pass
+
+
+# TODO: not used, can be removed after torch.ao.quantization namespace is deprecated
+class CustomModuleQuantizeHandler(QuantizeHandler):
+    pass
+
+
+# TODO: not used, can be removed after torch.ao.quantization namespace is deprecated
+class StandaloneModuleQuantizeHandler(QuantizeHandler):
+    pass
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/tracer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/tracer.py
new file mode 100644
index 0000000000000000000000000000000000000000..872f23e661e3623335a2971e49ed5f9b9c5d3feb
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/tracer.py
@@ -0,0 +1,48 @@
+from typing import Callable, List
+
+import torch
+from torch.ao.nn.intrinsic import _FusedModule
+from torch.fx._symbolic_trace import Tracer
+from torch.fx.proxy import Scope
+
+
+__all__ = [
+    "QuantizationTracer",
+]
+
+
+class ScopeContextManager(torch.fx.proxy.ScopeContextManager):
+    def __init__(
+        self, scope: Scope, current_module: torch.nn.Module, current_module_path: str
+    ):
+        super().__init__(scope, Scope(current_module_path, type(current_module)))
+
+
+class QuantizationTracer(Tracer):
+    def __init__(
+        self, skipped_module_names: List[str], skipped_module_classes: List[Callable]
+    ):
+        super().__init__()
+        self.skipped_module_names = skipped_module_names
+        self.skipped_module_classes = skipped_module_classes
+        # NB: initialized the module_type of top level module to None
+        # we are assuming people won't configure the model with the type of top level
+        # module here, since people can use "" for global config
+        # We can change this if there is a use case that configures
+        # qconfig using top level module type
+        self.scope = Scope("", None)
+        self.record_stack_traces = True
+
+    def is_leaf_module(self, m: torch.nn.Module, module_qualified_name: str) -> bool:
+        return (
+            (
+                (
+                    m.__module__.startswith("torch.nn")
+                    or m.__module__.startswith("torch.ao.nn")
+                )
+                and not isinstance(m, torch.nn.Sequential)
+            )
+            or module_qualified_name in self.skipped_module_names
+            or type(m) in self.skipped_module_classes
+            or isinstance(m, _FusedModule)
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..74dd8a44a59043abfc1b9a2adcfd7b596cfcf061
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/fx/utils.py
@@ -0,0 +1,959 @@
+# mypy: allow-untyped-defs
+import copy
+import operator
+import warnings
+from collections import namedtuple
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
+
+import torch
+import torch.nn as nn
+from torch.ao.quantization import QConfigAny, QuantType
+from torch.ao.quantization.backend_config import DTypeWithConstraints
+from torch.ao.quantization.fake_quantize import (
+    FakeQuantizeBase,
+    FixedQParamsFakeQuantize,
+)
+from torch.ao.quantization.observer import (
+    _is_activation_post_process,
+    FixedQParamsObserver,
+    ObserverBase,
+)
+from torch.ao.quantization.qconfig import (
+    float16_dynamic_qconfig,
+    float16_static_qconfig,
+    qconfig_equals,
+)
+from torch.ao.quantization.qconfig_mapping import QConfigMapping
+from torch.ao.quantization.stubs import DeQuantStub
+from torch.ao.quantization.utils import (
+    _assert_and_get_unique_device,
+    activation_is_statically_quantized,
+)
+from torch.fx import GraphModule, map_arg
+from torch.fx.graph import Graph, Node
+
+# importing the lib so that the quantized_decomposed ops are registered
+from ._decomposed import quantized_decomposed_lib  # noqa: F401
+from .custom_config import PrepareCustomConfig
+
+
+# TODO: revisit this list. Many helper methods shouldn't be public
+__all__ = [
+    "all_node_args_except_first",
+    "all_node_args_have_no_tensors",
+    "assert_and_get_unique_device",
+    "collect_producer_nodes",
+    "create_getattr_from_value",
+    "create_node_from_old_node_preserve_meta",
+    "EMPTY_ARG_DICT",
+    "get_custom_module_class_keys",
+    "get_linear_prepack_op_for_dtype",
+    "get_new_attr_name_with_prefix",
+    "get_non_observable_arg_indexes_and_types",
+    "get_qconv_prepack_op",
+    "get_skipped_module_name_and_classes",
+    "graph_module_from_producer_nodes",
+    "maybe_get_next_module",
+    "NodeInfo",
+    "node_arg_is_bias",
+    "node_arg_is_weight",
+    "NON_OBSERVABLE_ARG_DICT",
+    "NON_QUANTIZABLE_WEIGHT_OPS",
+    "return_arg_list",
+    "ObservedGraphModuleAttrs",
+]
+
+NON_QUANTIZABLE_WEIGHT_OPS = {
+    torch.nn.functional.layer_norm,
+    torch.nn.functional.group_norm,
+    torch.nn.functional.instance_norm,
+}
+
+
+@dataclass
+class ObservedGraphModuleAttrs:
+    node_name_to_qconfig: Dict[str, QConfigAny]
+    node_name_to_scope: Dict[str, Tuple[str, type]]
+    prepare_custom_config: PrepareCustomConfig
+    equalization_node_name_to_qconfig: Dict[str, Any]
+    qconfig_mapping: QConfigMapping
+    is_qat: bool
+    observed_node_names: Set[str]
+    is_observed_standalone_module: bool = False
+    standalone_module_input_quantized_idxs: Optional[List[int]] = None
+    standalone_module_output_quantized_idxs: Optional[List[int]] = None
+
+
+def node_arg_is_weight(node: Node, arg: Any) -> bool:
+    """Returns if node arg is weight"""
+    weight_index = None
+    if "target_dtype_info" in node.meta:
+        weight_index = node.meta["target_dtype_info"].get("weight_index", None)
+    if (
+        weight_index is not None
+        and weight_index < len(node.args)
+        and node.args[weight_index] is arg
+    ):
+        return True
+    return node.kwargs.get("weight") is arg
+
+
+def node_arg_is_bias(node: Node, arg: Any) -> bool:
+    """Returns if node arg is bias"""
+    bias_index = None
+    if "target_dtype_info" in node.meta:
+        bias_index = node.meta["target_dtype_info"].get("bias_index", None)
+    if (
+        bias_index is not None
+        and bias_index < len(node.args)
+        and node.args[bias_index] is arg
+    ):
+        return True
+    return node.kwargs.get("bias") is arg
+
+
+def get_custom_module_class_keys(
+    custom_module_mapping: Dict[QuantType, Dict[Type, Type]]
+) -> List[Any]:
+    r"""Get all the unique custom module keys in the custom config dict
+    e.g.
+    Input:
+    {
+        QuantType.STATIC: {
+            CustomModule1: ObservedCustomModule
+        },
+        QuantType.DYNAMIC: {
+            CustomModule2: DynamicObservedCustomModule
+        },
+        QuantType.WEIGHT_ONLY: {
+            CustomModule3: WeightOnlyObservedCustomModule
+        },
+    }
+
+    Output:
+    # extract the keys across all inner STATIC, DYNAMIC, and WEIGHT_ONLY dicts
+    [CustomModule1, CustomModule2, CustomModule3]
+    """
+    # using set to dedup
+    float_custom_module_classes: Set[Any] = set()
+    for quant_mode in [QuantType.STATIC, QuantType.DYNAMIC, QuantType.WEIGHT_ONLY]:
+        quant_mode_custom_module_config = custom_module_mapping.get(quant_mode, {})
+        quant_mode_custom_module_classes = set(quant_mode_custom_module_config.keys())
+        float_custom_module_classes |= quant_mode_custom_module_classes
+    return list(float_custom_module_classes)
+
+
+def get_linear_prepack_op_for_dtype(dtype):
+    if dtype == torch.float16:
+        return torch.ops.quantized.linear_prepack_fp16
+    elif dtype == torch.qint8:
+        return torch.ops.quantized.linear_prepack
+    else:
+        raise Exception("can't get linear prepack op for dtype:", dtype)  # noqa: TRY002
+
+
+def get_qconv_prepack_op(conv_op: Callable) -> Callable:
+    prepack_ops = {
+        torch.nn.functional.conv1d: torch.ops.quantized.conv1d_prepack,
+        torch.nn.functional.conv2d: torch.ops.quantized.conv2d_prepack,
+        torch.nn.functional.conv3d: torch.ops.quantized.conv3d_prepack,
+        torch.nn.functional.conv_transpose1d: torch.ops.quantized.conv_transpose1d_prepack,
+        torch.nn.functional.conv_transpose2d: torch.ops.quantized.conv_transpose2d_prepack,
+        torch.nn.functional.conv_transpose3d: torch.ops.quantized.conv_transpose3d_prepack,
+    }
+    prepack_op = prepack_ops.get(conv_op, None)
+    assert prepack_op, f"Didn't find prepack op for {conv_op}"
+    return prepack_op
+
+
+# Returns a function that can get a new attribute name for module with given
+# prefix, for example,
+# >> get_new_observer_name = get_new_attr_name_with_prefix('_observer')
+# >> new_name = get_new_observer_name(module)
+# new_name will be an unused attribute name on module, e.g. `_observer_1`
+def get_new_attr_name_with_prefix(prefix: str) -> Callable:
+    prefix = prefix.replace(".", "_")
+
+    def get_new_attr_name(module: torch.nn.Module):
+        def get_attr_name(i: int):
+            return prefix + str(i)
+
+        i = 0
+        attr_name = get_attr_name(i)
+        while hasattr(module, attr_name):
+            i += 1
+            attr_name = get_attr_name(i)
+        return attr_name
+
+    return get_new_attr_name
+
+
+def collect_producer_nodes(node: Node) -> Optional[List[Node]]:
+    r"""Starting from a target node, trace back until we hit inpu or
+    getattr node. This is used to extract the chain of operators
+    starting from getattr to the target node, for example
+    def forward(self, x):
+      observed = self.observer(self.weight)
+      return F.linear(x, observed)
+    collect_producer_nodes(observed) will either return a list of nodes that
+    produces the observed node or None if we can't extract a self contained
+    graph without free variables(inputs of the forward function).
+    """
+    nodes = [node]
+    frontier = [node]
+    while frontier:
+        node = frontier.pop()
+        all_args = list(node.args) + list(node.kwargs.values())
+        for arg in all_args:
+            if not isinstance(arg, Node):
+                continue
+            if arg.op == "placeholder":
+                # hit input, can't fold in this case
+                return None
+            nodes.append(arg)
+            if not (arg.op == "call_function" and arg.target == getattr):
+                frontier.append(arg)
+    return nodes
+
+
+def graph_module_from_producer_nodes(
+    root: GraphModule, producer_nodes: List[Node]
+) -> GraphModule:
+    r"""Construct a graph module from extracted producer nodes
+    from `collect_producer_nodes` function
+    Args:
+      root: the root module for the original graph
+      producer_nodes: a list of nodes we use to construct the graph
+    Return:
+      A graph module constructed from the producer nodes
+    """
+    assert len(producer_nodes) > 0, "list of producer nodes can not be empty"
+    # since we traced back from node to getattr
+    producer_nodes.reverse()
+    graph = Graph()
+    env: Dict[Any, Any] = {}
+
+    def load_arg(a):
+        return map_arg(a, lambda node: env[node])
+
+    for producer_node in producer_nodes:
+        env[producer_node] = graph.node_copy(producer_node, load_arg)
+    graph.output(load_arg(producer_nodes[-1]))
+    graph_module = GraphModule(root, graph)
+    return graph_module
+
+
+# TODO: delete
+def assert_and_get_unique_device(module: torch.nn.Module) -> Any:
+    """
+    Returns the unique device for a module, or None if no device is found.
+    Throws an error if multiple devices are detected.
+    """
+    return _assert_and_get_unique_device(module)
+
+
+def create_getattr_from_value(
+    module: torch.nn.Module, graph: Graph, prefix: str, value: Any
+) -> Node:
+    """
+    Given a value of any type, creates a getattr node corresponding to the value and
+    registers the value as a buffer to the module.
+    """
+    get_new_attr_name = get_new_attr_name_with_prefix(prefix)
+    attr_name = get_new_attr_name(module)
+    device = assert_and_get_unique_device(module)
+    new_value = (
+        value.clone().detach()
+        if isinstance(value, torch.Tensor)
+        else torch.tensor(value, device=device)
+    )
+    module.register_buffer(attr_name, new_value)
+    # Create get_attr with value
+    attr_node = graph.create_node("get_attr", attr_name)
+    return attr_node
+
+
+def all_node_args_have_no_tensors(
+    node: Node, modules: Dict[str, torch.nn.Module], cache: Dict[Node, bool]
+) -> bool:
+    """
+    If we know for sure that all of this node's args have no
+    tensors (are primitives), return True.  If we either
+    find a tensor or are not sure, return False. Note: this
+    function is not exact.
+    """
+    if cache and node in cache:
+        return cache[node]
+
+    result = False  # will be overwritten
+    if not isinstance(node, Node):
+        result = True
+    elif node.op == "placeholder":
+        result = False
+    elif node.op == "call_module":
+        assert isinstance(node.target, str)
+        if _is_activation_post_process(modules[node.target]):
+            result = all_node_args_have_no_tensors(node.args[0], modules, cache)  # type: ignore[arg-type]
+    elif node.op == "call_module":
+        result = False
+    elif node.op == "call_function" and node.target is operator.getitem:
+        result = all_node_args_have_no_tensors(node.args[0], modules, cache)  # type: ignore[arg-type]
+    elif node.op == "get_attr":
+        result = False
+    elif node.target is getattr and node.args[1] in ["ndim", "shape"]:
+        # x1 = x0.ndim
+        result = True
+    elif node.op == "call_method" and node.target == "size":
+        # x1 = x0.size(0)
+        result = True
+    else:
+        found_one_tensor = False
+        for arg in node.args:
+            if isinstance(arg, list):
+                for list_el in arg:
+                    if isinstance(list_el, Node):
+                        this_list_el_args_have_no_tensors = (
+                            all_node_args_have_no_tensors(list_el, modules, cache)
+                        )
+                        found_one_tensor = found_one_tensor or (
+                            not this_list_el_args_have_no_tensors
+                        )
+                        # If found_one_tensor is True, there is no point in
+                        # recursing further as the end result will always
+                        # be True.
+                        # TODO(future PR): remove this entire function  and
+                        # change to dtype inference without recursion.
+                        if found_one_tensor:
+                            result = not found_one_tensor
+                            if cache:
+                                cache[node] = result
+                            return result
+            elif isinstance(arg, int):
+                pass
+            else:
+                if isinstance(arg, Node):
+                    this_arg_args_have_no_tensors = all_node_args_have_no_tensors(
+                        arg, modules, cache
+                    )
+                    found_one_tensor = found_one_tensor or (
+                        not this_arg_args_have_no_tensors
+                    )
+                    # If found_one_tensor is True, there is no point in
+                    # recursing further as the end result will always
+                    # be True.
+                    # TODO(future PR): remove this entire function  and
+                    # change to dtype inference without recursion.
+                    if found_one_tensor:
+                        result = not found_one_tensor
+                        if cache:
+                            cache[node] = result
+                        return result
+                else:
+                    found_one_tensor = True
+            result = not found_one_tensor
+    if cache:
+        cache[node] = result
+    return result
+
+
+def all_node_args_except_first(node: Node) -> List[int]:
+    """
+    Returns all node arg indices after first
+    """
+    return list(range(1, len(node.args)))
+
+
+def return_arg_list(arg_indices: List[int]) -> Callable[[Node], List[int]]:
+    """
+    Constructs a function that takes a node as arg and returns the arg_indices
+    that are valid for node.args
+    """
+
+    def arg_indices_func(node: Node) -> List[int]:
+        return [i for i in arg_indices if i < len(node.args)]
+
+    return arg_indices_func
+
+
+NodeInfo = namedtuple("NodeInfo", "op target")
+
+# this dict identifies which indices of a node are non tensors
+# so that they can be propagated correctly since inserting observers
+# for them would cause errors
+
+NON_OBSERVABLE_ARG_DICT: Dict[
+    NodeInfo, Dict[Union[type, torch.dtype], Callable[[Node], List[int]]]
+] = {
+    NodeInfo("call_method", "masked_fill"): {
+        torch.bool: return_arg_list([1]),
+        float: return_arg_list([2]),
+    },
+    NodeInfo("call_method", "permute"): {int: all_node_args_except_first},
+    NodeInfo("call_method", "repeat"): {int: all_node_args_except_first},
+    NodeInfo("call_method", "reshape"): {int: all_node_args_except_first},
+    NodeInfo("call_method", "size"): {int: return_arg_list([1])},
+    NodeInfo("call_method", "transpose"): {int: all_node_args_except_first},
+    NodeInfo("call_method", torch.transpose): {int: all_node_args_except_first},
+    NodeInfo("call_method", "unsqueeze"): {int: return_arg_list([1])},
+    NodeInfo("call_method", "unsqueeze_"): {int: return_arg_list([1])},
+    NodeInfo("call_method", torch.unsqueeze): {int: return_arg_list([1])},
+    NodeInfo("call_method", "view"): {int: all_node_args_except_first},
+}
+
+EMPTY_ARG_DICT: Dict[Union[type, torch.dtype], Callable[[Node], List[int]]] = {}
+
+
+def get_non_observable_arg_indexes_and_types(
+    node: Node,
+) -> Dict[Union[type, torch.dtype], Callable[[Node], List[int]]]:
+    """
+    Returns a dict with of non float tensor types as keys and values which correspond to a
+    function to retrieve the list (which takes the node as an argument)
+    """
+    info = NodeInfo(node.op, node.target)
+
+    return NON_OBSERVABLE_ARG_DICT.get(info, EMPTY_ARG_DICT)
+
+
+def maybe_get_next_module(
+    node: Node,
+    modules: Dict[str, nn.Module],
+    target_module_type: Optional[Type[nn.Module]] = None,
+    target_functional_type: Any = None,
+) -> Optional[Node]:
+    """Gets the next module that matches what is needed in
+    is_target_module_type if it exists
+
+    Args:
+        node: The node whose users we want to look at
+        target_module_type: Module type that we want to check
+        target_functional_type: Functional type that we want to check
+    """
+
+    for user in node.users.keys():
+        if (
+            user.op == "call_module"
+            and target_module_type is not None
+            and isinstance(modules[str(user.target)], target_module_type)
+        ):
+            return user
+        elif (
+            user.op == "call_function"
+            and target_functional_type is not None
+            and user.target == target_functional_type
+        ):
+            return user
+
+    return None
+
+
+def create_node_from_old_node_preserve_meta(
+    quantized_graph: Graph,
+    create_node_args: Tuple[Any, ...],
+    old_node: Node,
+) -> Node:
+    """
+    Creates `new_node` and copies the necessary metadata to it from `old_node`.
+    """
+    new_node = quantized_graph.create_node(*create_node_args)
+    new_node.stack_trace = old_node.stack_trace
+    return new_node
+
+
+def get_skipped_module_name_and_classes(
+    prepare_custom_config: PrepareCustomConfig, is_standalone_module: bool
+) -> Tuple[List[str], List[Type[Any]]]:
+    skipped_module_names = copy.copy(prepare_custom_config.non_traceable_module_names)
+    skipped_module_classes = copy.copy(
+        prepare_custom_config.non_traceable_module_classes
+    )
+    if not is_standalone_module:
+        # standalone module and custom module config are applied in top level module
+        skipped_module_names += list(
+            prepare_custom_config.standalone_module_names.keys()
+        )
+        skipped_module_classes += list(
+            prepare_custom_config.standalone_module_classes.keys()
+        )
+        skipped_module_classes += get_custom_module_class_keys(
+            prepare_custom_config.float_to_observed_mapping
+        )
+
+    return skipped_module_names, skipped_module_classes
+
+
+def _is_custom_module_lstm(
+    node: Node,
+    named_modules: Dict[str, torch.nn.Module],
+    qconfig: QConfigAny = None,
+    # QuantizeHandler, but we cannot include the type here due to circular imports
+    qhandler: Optional[Any] = None,
+) -> bool:
+    """
+    Return whether this refers to the custom module LSTM flow.
+    """
+    mod = _get_module(node, named_modules)
+    if qconfig is not None and qhandler is not None:
+        assert isinstance(qhandler, torch.ao.quantization.fx.quantize_handler.QuantizeHandler)  # type: ignore[attr-defined]
+        return (
+            isinstance(mod, torch.nn.LSTM)
+            and activation_is_statically_quantized(qconfig)
+            and qhandler.is_custom_module()
+        )
+    else:
+        return isinstance(mod, torch.ao.nn.quantizable.LSTM)
+
+
+def _is_custom_module_mha(
+    node: Node,
+    named_modules: Dict[str, torch.nn.Module],
+    qconfig: QConfigAny = None,
+    # QuantizeHandler, but we cannot include the type here due to circular imports
+    qhandler: Optional[Any] = None,
+) -> bool:
+    """
+    Return whether this refers to the custom module MultiheadAttention flow.
+    """
+    mod = _get_module(node, named_modules)
+    if qconfig is not None and qhandler is not None:
+        assert isinstance(qhandler, torch.ao.quantization.fx.quantize_handler.QuantizeHandler)  # type: ignore[attr-defined]
+        return (
+            isinstance(mod, torch.nn.MultiheadAttention)
+            and activation_is_statically_quantized(qconfig)
+            and qhandler.is_custom_module()
+        )
+    else:
+        return isinstance(mod, torch.ao.nn.quantizable.MultiheadAttention)
+
+
+def _get_module(
+    node: Node, named_modules: Dict[str, torch.nn.Module]
+) -> Optional[torch.nn.Module]:
+    """
+    If `node` refers to a call_module node, return the module, else None.
+    """
+    if node.op == "call_module" and str(node.target) in named_modules:
+        return named_modules[str(node.target)]
+    else:
+        return None
+
+
+def _insert_dequant_stub(
+    node: Node,
+    model: torch.nn.Module,
+    named_modules: Dict[str, torch.nn.Module],
+    graph: Graph,
+) -> Node:
+    """
+    Attach a `DeQuantStub` to the model and create a node that calls this
+    `DeQuantStub` on the output of `node`, similar to how observers are inserted.
+    """
+    prefix = "dequant_stub_"
+    get_new_dequant_stub_name = get_new_attr_name_with_prefix(prefix)
+    dequant_stub_name = get_new_dequant_stub_name(model)
+    dequant_stub = DeQuantStub()
+    setattr(model, dequant_stub_name, dequant_stub)
+    named_modules[dequant_stub_name] = dequant_stub
+    with graph.inserting_after(node):
+        return graph.call_module(dequant_stub_name, (node,))
+
+
+def _insert_dequant_stubs_for_custom_module_lstm_output(
+    node: Node,
+    model: torch.nn.Module,
+    named_modules: Dict[str, torch.nn.Module],
+    graph: Graph,
+) -> Node:
+    """
+    Insert DeQuantStubs after each internal output node of custom module LSTM.
+
+    Custom module LSTM outputs are nested tuples of the structure (output, (hidden0, hidden1)),
+    Since we cannot dequantize a tuple as a whole, we must first break down the tuple into its
+    components through `getitem`. This function transforms the graph as follows:
+
+      (1) Split the LSTM node into (output, (hidden0, hidden1))
+      (2) Insert a DeQuantStub after each internal node
+      (3) Recombine the DeQuantStubs into the same structure as before
+      (4) Reroute all consumers of the original LSTM node and its sub-nodes
+          (e.g. lstm[0])
+
+    Before:
+                   lstm_output
+                        |
+                        v
+                  original_user(s)
+    After:
+                   lstm_output
+                  /           \\
+                 /  (getitem)  \\
+                /               \\
+               v                 v
+             output            hidden
+               |               /   \\
+         (DeQuantStub)        (getitem)
+               |             /       \\
+               v            v         v
+           output_dq     hidden0    hidden1
+               |            |         |
+               |    (DeQuantStub) (DeQuantStub)
+               |            |         |
+               |            v         v
+               |      hidden0_dq  hidden1_dq
+               |            \\       /
+               |              (tuple)
+               |              \\   /
+               |               v  v
+               |             hidden_dq
+               \\               /
+                \\   (tuple)   /
+                 v            v
+                 lstm_output_dq
+                       |
+                       v
+                original_user(s)
+
+    For step (4), reroute all users of the original LSTM node(s) as follows:
+      lstm_output -> lstm_output_dq
+      lstm_output[0] -> output_dq
+      lstm_output[1] -> hidden_dq
+      lstm_output[1][0] -> hidden0_dq
+      lstm_output[1][1] -> hidden1_dq
+
+    Return the node `lstm_output_dq`.
+    """
+    # (1) Split the LSTM node into (output, (hidden0, hidden1))
+    # (2) Insert a DeQuantStub after each internal node
+    with graph.inserting_after(node):
+        output = graph.call_function(operator.getitem, (node, 0))
+        output_dq = _insert_dequant_stub(output, model, named_modules, graph)
+    with graph.inserting_after(output_dq):
+        hidden = graph.call_function(operator.getitem, (node, 1))
+    with graph.inserting_after(hidden):
+        hidden0 = graph.call_function(operator.getitem, (hidden, 0))
+        hidden0_dq = _insert_dequant_stub(hidden0, model, named_modules, graph)
+    with graph.inserting_after(hidden0_dq):
+        hidden1 = graph.call_function(operator.getitem, (hidden, 1))
+        hidden1_dq = _insert_dequant_stub(hidden1, model, named_modules, graph)
+
+    # (3) Recombine the DeQuantStubs into the same structure as before
+    with graph.inserting_after(hidden1_dq):
+        hidden_dq = graph.call_function(tuple, ([hidden0_dq, hidden1_dq],))
+    with graph.inserting_after(hidden_dq):
+        lstm_output_dq = graph.call_function(tuple, ([output_dq, hidden_dq],))
+
+    # (4) Reroute all consumers of the original LSTM node and its sub-nodes
+    for user in list(node.users.keys()):
+        if user != output and user != hidden:
+            user.replace_input_with(node, lstm_output_dq)
+    # The getitem and tuple nodes we added here may interfere with reference quantized
+    # pattern matching, so we need to redirect the consumers of internal nodes to the
+    # corresponding nodes with DeQuantStubs (e.g. lstm_output_dq[0] -> output_dq) attached,
+    # in order to preserve reference patterns like "dequantize - consumer - quantize".
+    _reroute_tuple_getitem_pattern(graph)
+    return lstm_output_dq
+
+
+def _maybe_get_custom_module_lstm_from_node_arg(
+    arg: Node,
+    named_modules: Dict[str, torch.nn.Module],
+) -> Optional[Node]:
+    """
+    Given an argument of a node, if the argument refers to the path through which the node
+    is a consumer of custom module LSTM, return the custom module LSTM node, or None otherwise.
+
+    This is used to determine whether a node is a consumer of custom module LSTM, and, if so,
+    skip inserting input observers for this node. This is because custom module LSTM produces
+    quantized outputs, so inserting an input observer for the consumer of custom module LSTM
+    would unnecessarily quantize the outputs again.
+
+      lstm -> consumer
+
+    In practice, however, custom module LSTM outputs a tuple (output, (hidden0, hidden1)) with
+    DeQuantStubs attached to each internal node (see `_insert_dequant_stubs_for_custom_module_lstm_output`).
+    This tuple can be consumed in one of four ways:
+
+      lstm -> getitem -> DeQuantStub -> consumer                       # consume lstm[0]
+      lstm -> getitem -> getitem -> DeQuantStub -> tuple -> consumer   # consume lstm[1]
+      lstm -> getitem -> getitem -> DeQuantStub -> consumer            # consume lstm[1][0] or lstm[1][1]
+      lstm -> getitem -> DeQuantStub -> tuple -> consumer              # consume lstm
+
+    Thus, we must match against the above patterns instead of simply checking the parent node
+    to determine whether this node is a consumer of a custom module LSTM.
+    """
+
+    def match_dq(a):
+        return isinstance(_get_module(a, named_modules), DeQuantStub)
+
+    def match_lstm(a):
+        return _is_custom_module_lstm(a, named_modules)
+
+    def match_getitem(a):
+        return a.op == "call_function" and a.target == operator.getitem
+
+    def match_tuple(a):
+        return a.op == "call_function" and a.target == tuple
+
+    def _match_pattern(match_pattern: List[Callable]) -> Optional[Node]:
+        """
+        Traverse up the graph and match the args one by one.
+        If there is a match, return the last matched node, or None otherwise.
+        """
+        a = arg
+        for i, match in enumerate(match_pattern):
+            if not match(a):
+                return None
+            # Match next arg, for tuple the arg is a tuple of a list, e.g. ([dq_1, other_node],)
+            if i < len(match_pattern) - 1:
+                if match == match_tuple:
+                    a = a.args[0][0]  # type: ignore[assignment,index]
+                else:
+                    a = a.args[0]  # type: ignore[assignment]
+        return a
+
+    all_match_patterns = [
+        [match_dq, match_getitem, match_lstm],
+        [match_tuple, match_dq, match_getitem, match_getitem, match_lstm],
+        [match_dq, match_getitem, match_getitem, match_lstm],
+        [match_tuple, match_dq, match_getitem, match_lstm],
+    ]
+
+    for p in all_match_patterns:
+        matched_node = _match_pattern(p)
+        if matched_node is not None:
+            return matched_node
+    return None
+
+
+def _reroute_tuple_getitem_pattern(graph: Graph):
+    """
+    Search for patterns where N consecutive `tuple` call_function nodes are followed by
+    N consecutive `getitem` call_function nodes that are "reverses" of the `tuple` nodes.
+    If we find this pattern, reroute the consumers of the last `getitem` to skip these
+    N `tuple` and `getitem` nodes.
+
+    Before:
+
+        a   b     c
+        |   \\   /
+        \\   tuple
+         \\   /
+          tuple
+            |
+        getitem(1)
+            |
+        getitem(0)
+            |
+            d
+
+    After:
+
+        b
+        |
+        d
+    """
+
+    def find_patterns(
+        node: Node,
+        index_stack: List[int],
+        current_pattern: List[Node],
+        matched_patterns: List[List[Node]],
+        seen: Set[Tuple[Node, Tuple[int, ...]]],
+    ):
+        """
+        Traverse the graph recursively to match for the N-tuple - N-getitem patterns,
+        starting at the given node.
+
+        We use a stack to keep track of the expected `getitem` indices, since these are
+        reversed from the `tuple` indices. In the above example, the stack after
+        (b -> tuple -> tuple) will be [0, 1], which will be popped by getitem(1) first
+        and then by getitem(0).
+
+        TODO: traverse upwards from the output and handle the case when tuple is not a
+        separate node, e.g. graph.call_function(operator.getitem, args=(a, (b, c)))
+        """
+        if len(index_stack) == 0 and len(current_pattern) > 0:
+            matched_patterns.append(copy.copy(current_pattern))
+            current_pattern.clear()
+
+        # Avoid duplicating work
+        state = (node, tuple(index_stack))
+        if state in seen:
+            return
+        seen.add(state)
+
+        # Iterate through users of this node to find tuple/getitem nodes to match
+        for user in node.users:
+            if user.op == "call_function" and user.target == tuple:
+                for i, user_arg in enumerate(user.args[0]):  # type: ignore[arg-type]
+                    if user_arg == node:
+                        index_stack.append(i)
+                        current_pattern.append(user)
+                        find_patterns(
+                            user, index_stack, current_pattern, matched_patterns, seen
+                        )
+            elif user.op == "call_function" and user.target == operator.getitem:
+                if len(index_stack) > 0:
+                    if user.args[1] == index_stack[-1]:
+                        index_stack.pop()
+                        current_pattern.append(user)
+                        find_patterns(
+                            user, index_stack, current_pattern, matched_patterns, seen
+                        )
+        return matched_patterns
+
+    # Collect all matched patterns
+    matched_patterns: List[List[Node]] = []
+    seen: Set[Tuple[Node, Tuple[int, ...]]] = set()  # (node, index_stack)
+    for node in graph.nodes:
+        find_patterns(node, [], [], matched_patterns, seen)
+
+    # For each pattern, redirect all consumers of the last getitem node to the correct input
+    # of the first tuple node
+    for pattern in matched_patterns:
+        first_tuple = pattern[0]
+        last_getitem = pattern[-1]
+        assert first_tuple.op == "call_function" and first_tuple.target == tuple
+        assert (
+            last_getitem.op == "call_function"
+            and last_getitem.target == operator.getitem
+        )
+        last_getitem_index = last_getitem.args[1]
+        new_input = first_tuple.args[0][last_getitem_index]  # type: ignore[index]
+        for user in list(last_getitem.users.keys()):
+            user.replace_input_with(last_getitem, new_input)  # type: ignore[arg-type]
+
+
+def _get_observer_from_activation_post_process(
+    activation_post_process: Union[ObserverBase, FakeQuantizeBase],
+) -> ObserverBase:
+    """
+    If `activation_post_process` is an observer, return the observer.
+    If `activation_post_process` is a fake quantize, return the internal observer.
+    """
+    if isinstance(activation_post_process, ObserverBase):
+        return activation_post_process
+    else:
+        assert isinstance(activation_post_process, FakeQuantizeBase)
+        return activation_post_process.activation_post_process  # type: ignore[return-value]
+
+
+def _qconfig_satisfies_dtype_config_constraints(
+    qconfig: QConfigAny,
+    dtype_with_constraints: DTypeWithConstraints,
+    is_activation: bool = True,
+) -> bool:
+    """
+    Return whether `qconfig` satisfies the following constraints from the backend,
+    specified through the activation and weight DTypeWithConstraints.
+
+        1. QConfig specified a quantization range that falls within the backend's, if any
+        2. QConfig specified a min scale value that is >= the backend's, if any
+        3. QConfig specified a FixedQParamsObserver or FixedQParamsFakeQuantize that has
+           scale and zero point that match the backend's, if any
+
+    If `is_activation` is True, we check `qconfig.activation`, else we check `qconfig.weight`.
+    If `qconfig` or `dtype_with_constraints.dtype` is None, or the dtypes do not match, return True.
+    """
+
+    # TODO: log warnings only when the user enabled a debug flag
+    def _activation_post_process_satisfies_dtype_config_constraints(
+        activation_post_process: Union[ObserverBase, FakeQuantizeBase],
+        dtype_with_constraints: DTypeWithConstraints,
+        debug_string: str,
+    ) -> bool:
+        observer = _get_observer_from_activation_post_process(activation_post_process)
+        app_quant_min = getattr(observer, "quant_min", None)
+        app_quant_max = getattr(observer, "quant_max", None)
+        # TODO: for now, just use the existing eps value as scale_min. In the future, we should
+        # resolve the differences between the two, either by renaming eps or some other way
+        app_scale_min = getattr(observer, "eps", None)
+        backend_quant_min = dtype_with_constraints.quant_min_lower_bound
+        backend_quant_max = dtype_with_constraints.quant_max_upper_bound
+        backend_scale_min = dtype_with_constraints.scale_min_lower_bound
+        backend_scale_exact_match = dtype_with_constraints.scale_exact_match
+        backend_zero_point_exact_match = dtype_with_constraints.zero_point_exact_match
+        # check quantization ranges
+        if backend_quant_min is not None and backend_quant_max is not None:
+            if app_quant_min is None or app_quant_max is None:
+                warnings.warn(
+                    f"QConfig {debug_string} must specify 'quant_min' and 'quant_max', ignoring {qconfig}"
+                )
+                return False
+            elif app_quant_min < backend_quant_min or app_quant_max > backend_quant_max:
+                warnings.warn(
+                    f"QConfig {debug_string} quantization range must fall within the backend's:\n"
+                    f"QConfig range = ({app_quant_min}, {app_quant_max}), "
+                    f"BackendConfig range = ({backend_quant_min}, {backend_quant_max}), "
+                    f"ignoring {qconfig}"
+                )
+                return False
+        # check scale min
+        if backend_scale_min is not None:
+            if app_scale_min is None:
+                warnings.warn(
+                    f"QConfig {debug_string} must specify 'eps', ignoring {qconfig}"
+                )
+                return False
+            if app_scale_min < backend_scale_min:
+                warnings.warn(
+                    f"QConfig {debug_string} eps ({app_scale_min}) must be greater than or equal to "
+                    f"the backend's min scale value ({backend_scale_min}), ignoring {qconfig}"
+                )
+                return False
+        # check fixed scale and zero point
+        if (
+            backend_scale_exact_match is not None
+            and backend_zero_point_exact_match is not None
+        ):
+            # For tests only, accept the following qconfigs for now
+            # TODO: handle fp16 qconfigs properly
+            for accepted_qconfig in [float16_static_qconfig, float16_dynamic_qconfig]:
+                if qconfig_equals(qconfig, accepted_qconfig):
+                    return True
+            suggestion_str = (
+                "Please use torch.ao.quantization.get_default_qconfig_mapping or "
+                "torch.ao.quantization.get_default_qat_qconfig_mapping. Example:\n"
+                '    qconfig_mapping = get_default_qconfig_mapping("fbgemm")\n'
+                "    model = prepare_fx(model, qconfig_mapping, example_inputs)"
+            )
+            if not isinstance(
+                activation_post_process, FixedQParamsObserver
+            ) and not isinstance(activation_post_process, FixedQParamsFakeQuantize):
+                warnings.warn(
+                    f"QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize "
+                    f"for fixed qparams ops, ignoring {qconfig}.\n{suggestion_str}"
+                )
+                return False
+            if (
+                observer.scale != backend_scale_exact_match
+                or observer.zero_point != backend_zero_point_exact_match
+            ):
+                warnings.warn(
+                    f"QConfig fixed scale ({observer.scale}) and zero point ({observer.zero_point}) "
+                    f"do not match the backend's ({backend_scale_exact_match} and {backend_zero_point_exact_match}), "
+                    f"ignoring {qconfig}.\n{suggestion_str}"
+                )
+                return False
+        return True
+
+    if qconfig is None or dtype_with_constraints.dtype is None:
+        return True
+
+    activation_post_process_ctr = (
+        qconfig.activation if is_activation else qconfig.weight
+    )
+    debug_string = "activation" if is_activation else "weight"
+    satisfies_constraints = True
+    if activation_post_process_ctr is not None:
+        activation_post_process = activation_post_process_ctr()
+        assert _is_activation_post_process(activation_post_process)
+        # If dtypes don't match, don't check the activation_post_process and return True early
+        if activation_post_process.dtype != dtype_with_constraints.dtype:
+            return True
+        satisfies_constraints = (
+            _activation_post_process_satisfies_dtype_config_constraints(
+                activation_post_process, dtype_with_constraints, debug_string
+            )
+        )
+    return satisfies_constraints
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/observer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/observer.py
new file mode 100644
index 0000000000000000000000000000000000000000..e26f03027116e2ce1d9bfffcdd46d807537d1ebd
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/observer.py
@@ -0,0 +1,1765 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+"""
+This module implements observers which are used to collect statistics about
+the values observed during calibration (PTQ) or training (QAT).
+"""
+
+import re
+import warnings
+from abc import ABCMeta, abstractmethod
+from collections import OrderedDict
+from functools import partial
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+from torch.ao.quantization.utils import (
+    calculate_qmin_qmax,
+    check_min_max_valid,
+    is_per_channel,
+    is_per_tensor,
+    validate_qmin_qmax,
+)
+
+
+__all__ = [
+    "default_affine_fixed_qparams_observer",
+    "default_debug_observer",
+    "default_dynamic_quant_observer",
+    "default_fixed_qparams_range_0to1_observer",
+    "default_fixed_qparams_range_neg1to1_observer",
+    "default_float_qparams_observer",
+    "default_float_qparams_observer_4bit",
+    "default_histogram_observer",
+    "default_observer",
+    "default_per_channel_weight_observer",
+    "default_placeholder_observer",
+    "default_reuse_input_observer",
+    "default_symmetric_fixed_qparams_observer",
+    "default_weight_observer",
+    "get_observer_state_dict",
+    "load_observer_state_dict",
+    "per_channel_weight_observer_range_neg_127_to_127",
+    "weight_observer_range_neg_127_to_127",
+    "FixedQParamsObserver",
+    "HistogramObserver",
+    "MinMaxObserver",
+    "MovingAverageMinMaxObserver",
+    "MovingAveragePerChannelMinMaxObserver",
+    "NoopObserver",
+    "ObserverBase",
+    "PerChannelMinMaxObserver",
+    "PlaceholderObserver",
+    "RecordingObserver",
+    "ReuseInputObserver",
+    "UniformQuantizationObserverBase",
+]
+
+
+class _PartialWrapper:
+    def __init__(self, p):
+        self.p = p
+        self.callable_args = {}
+
+    def __call__(self, *args, **keywords):
+        # call each arg in callable_args and add them partial, then run with keywords
+        # skip if arg_name in keywords so its possible to overwrite
+        for arg_name in self.callable_args:
+            if arg_name not in keywords:
+                keywords = {**keywords, arg_name: self.callable_args[arg_name]()}
+        return self.p(*args, **keywords)
+
+    def __repr__(self):
+        return self.p.__repr__() + self.callable_args.__repr__()
+
+    def with_args(self, **kwargs):
+        return _with_args(self, **kwargs)
+
+    def with_callable_args(self, **kwargs):
+        result = _PartialWrapper(p=self.p)
+        result.callable_args = {**self.callable_args, **kwargs}
+        return result
+
+
+def _with_args(cls_or_self, **kwargs):
+    r"""Wrapper that allows creation of class factories.
+
+    This can be useful when there is a need to create classes with the same
+    constructor arguments, but different instances. Can be used in conjunction with
+    _callable_args
+
+    Example::
+
+        >>> # xdoctest: +SKIP("Undefined vars")
+        >>> Foo.with_args = classmethod(_with_args)
+        >>> foo_builder = Foo.with_args(a=3, b=4).with_args(answer=42)
+        >>> foo_instance1 = foo_builder()
+        >>> foo_instance2 = foo_builder()
+        >>> id(foo_instance1) == id(foo_instance2)
+        False
+    """
+    r = _PartialWrapper(partial(cls_or_self, **kwargs))
+    return r
+
+
+def _with_callable_args(cls_or_self, **kwargs):
+    r"""Wrapper that allows creation of class factories args that need to be
+    called at construction time.
+
+    This can be useful when there is a need to create classes with the same
+    constructor arguments, but different instances and those arguments should only
+    be calculated at construction time. Can be used in conjunction with _with_args
+
+    Example::
+
+        >>> # xdoctest: +SKIP("Undefined vars")
+        >>> Foo.with_callable_args = classmethod(_with_callable_args)
+        >>> Foo.with_args = classmethod(_with_args)
+        >>> foo_builder = Foo.with_callable_args(cur_time=get_time_func).with_args(name="dan")
+        >>> foo_instance1 = foo_builder()
+        >>> # wait 50
+        >>> foo_instance2 = foo_builder()
+        >>> id(foo_instance1.creation_time) == id(foo_instance2.creation_time)
+        False
+    """
+    r = _PartialWrapper(partial(cls_or_self))
+    return r.with_callable_args(**kwargs)
+
+
+ABC: Any = ABCMeta("ABC", (object,), {})  # compatible with Python 2 *and* 3:
+
+
+class ObserverBase(ABC, nn.Module):
+    r"""Base observer Module.
+    Any observer implementation should derive from this class.
+
+    Concrete observers should follow the same API. In forward, they will update
+    the statistics of the observed Tensor. And they should provide a
+    `calculate_qparams` function that computes the quantization parameters given
+    the collected statistics.
+
+    Args:
+        dtype: dtype argument to the `quantize` node needed to implement the
+               reference model spec.
+        is_dynamic: indicator for whether the observer is a placeholder for dynamic quantization
+        or static quantization
+    """
+
+    def __init__(self, dtype, is_dynamic=False):
+        super().__init__()
+        self.dtype = dtype
+        self.is_dynamic = is_dynamic
+
+    @abstractmethod
+    def forward(self, x):
+        pass
+
+    @abstractmethod
+    def calculate_qparams(self, **kwargs):
+        pass
+
+    with_args = classmethod(_with_args)
+    with_callable_args = classmethod(_with_callable_args)
+
+
+class UniformQuantizationObserverBase(ObserverBase):
+    r"""Common base for all observers using uniform quantization to calculate
+    scale and zero_point.
+
+    Args:
+        dtype: dtype argument to the `quantize` node needed to implement the
+               reference model spec.
+        qscheme: Quantization scheme to be used.
+        reduce_range: Reduces the range of the quantized data type by 1 bit.
+                      This is sometimes required to avoid instruction overflow.
+        quant_min: Minimum quantization value. If unspecified, it will follow the 8-bit setup.
+        quant_max: Maximum quantization value. If unspecified, it will follow the 8-bit setup.
+        eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`.
+
+    .. warning::
+
+        :attr:`dtype` can only take ``torch.qint8`` or ``torch.quint8``.
+               or `torch.int8` or `torch.uint8`
+
+    .. warning::
+
+        :attr:`qscheme` can only take one of the following options:
+
+        - ``torch.per_tensor_affine``
+        - ``torch.per_tensor_symmetric``
+        - ``torch.per_channel_affine``
+        - ``torch.per_channel_symmetric``
+    """
+
+    # Note: the version is shared by all observer types
+    #
+    # Version 1/None
+    #   self
+    #
+    # Version 2 (base class only, does not include child class buffers)
+    #   self
+    #   |--- eps : Tensor
+    #
+    # Version 3
+    #   for HistogramObserver only, changed the shape of uninitialized
+    #   min_val and max_val buffers from torch.Size([0]) to torch.Size([])
+    #   for PerChannelObservers, changed the name of the buffers from min_vals
+    #   to min_val and from max_vals to max_val.
+    _version = 3
+
+    eps: torch.Tensor
+
+    def __init__(
+        self,
+        dtype=torch.quint8,
+        qscheme=torch.per_tensor_affine,
+        reduce_range=False,
+        quant_min=None,
+        quant_max=None,
+        factory_kwargs=None,
+        eps=torch.finfo(torch.float32).eps,
+        is_dynamic=False,
+        **kwargs,
+    ) -> None:
+        factory_kwargs = torch.nn.factory_kwargs(factory_kwargs)
+        super().__init__(dtype=dtype, is_dynamic=is_dynamic, **kwargs)
+        self.qscheme = qscheme
+        if reduce_range:
+            warnings.warn(
+                "Please use quant_min and quant_max to specify the range for observers. \
+                    reduce_range will be deprecated in a future release of PyTorch."
+            )
+        self.reduce_range = reduce_range
+        self.register_buffer("eps", torch.tensor([eps], **factory_kwargs))
+        assert self.qscheme in (
+            torch.per_tensor_affine,
+            torch.per_tensor_symmetric,
+            torch.per_channel_affine,
+            torch.per_channel_symmetric,
+            torch.per_channel_affine_float_qparams,
+        ), "Default Observer only works for per_tensor_affine, \
+                per_tensor_symmetric, per_channel_affine, \
+                per_channel_symmetric and per_channel_float_qparams quantization scheme"
+
+        _ALLOWED_DTYPES = (
+            torch.qint8,
+            torch.quint8,
+            torch.quint4x2,
+            torch.qint32,
+            torch.int8,
+            torch.uint8,
+            torch.int16,
+            torch.int32,
+            torch.float8_e5m2,
+            torch.float8_e4m3fn,
+        )
+
+        assert (
+            self.dtype in _ALLOWED_DTYPES
+        ), f"Default Observer only works for {_ALLOWED_DTYPES} data type"
+        self.has_customized_qrange = (quant_min is not None) and (quant_max is not None)
+        if self.has_customized_qrange:
+            validate_qmin_qmax(quant_min, quant_max)
+        self.quant_min, self.quant_max = calculate_qmin_qmax(
+            quant_min,
+            quant_max,
+            self.has_customized_qrange,
+            self.dtype,
+            self.reduce_range,
+        )
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        version = local_metadata.get("version", None)
+
+        if version is None or version == 1:
+            # eps was moved to a buffer in version 2
+            eps = torch.tensor([torch.finfo(torch.float32).eps])
+            state_dict[prefix + "eps"] = eps
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            strict,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    @torch.jit.export
+    def _validate_qmin_qmax(self, quant_min: int, quant_max: int) -> None:
+        r"""Validates that the user-specified quantization range is properly initialized
+        and within the given bound supported by the observer dtype.
+
+        To accommodate lower-bit quantization with respect to the existing torch.qint8 and
+        torch.quint8 datatypes, the user can choose to use dynamic quantization range by passing
+        in a tuple of initial qmin and qmax values. One use case is these customized qmin and qmax
+        values are used to calculate static estimates of the scale and zero point for aggressive lower-bit
+        fake quantization. These estimates are compared against parameters learned through backpropagation.
+        The related literatures for scale and zero point via backpropagation are as follows:
+
+        Learned Step Size Quantization: https://openreview.net/pdf?id=rkgO66VKDS
+        Trained Quantization Thresholds: https://arxiv.org/pdf/1903.08066.pdf
+        """
+        # The variable names are prefixed with "initial" because their values (qmin and qmax) might be adjusted
+        # based on whether quantization range is reduced and the datatype (signed/unsigned) used by the observer.
+        assert (
+            quant_min <= 0 <= quant_max
+        ), "Used-specified quantization range must include 0."
+        assert (
+            quant_min < quant_max
+        ), "qmin must be strictly less than qmax for user-specified quantization range."
+
+    @torch.jit.export
+    def _calculate_qparams(
+        self, min_val: torch.Tensor, max_val: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        r"""Calculates the quantization parameters, given min and max
+        value tensors. Works for both per tensor and per channel cases
+
+        Args:
+            min_val: Minimum values per channel
+            max_val: Maximum values per channel
+
+        Returns:
+            scales: Scales tensor of shape (#channels,)
+            zero_points: Zero points tensor of shape (#channels,)
+        """
+        # Functionally equivalent to 'determine_qparams' in utils.py. Observers must be torchscriptable however and qscheme
+        # as far as I can tell is not allowed to passed as a parameter in torchscript functions. This makes refactoring observer
+        # to use this utility a massive pain and very gross. For now Im opting just to duplicate as this code
+        # seems unlikey to change (last update over 1 year ago) and when torchscript is fully deprecated we can refactor.
+        # TODO(jakeszwe, jerryzh168)
+        if not check_min_max_valid(min_val, max_val):
+            return torch.tensor([1.0], device=min_val.device.type), torch.tensor(
+                [0], device=min_val.device.type
+            )
+
+        quant_min, quant_max = self.quant_min, self.quant_max
+        min_val_neg = torch.min(min_val, torch.zeros_like(min_val))
+        max_val_pos = torch.max(max_val, torch.zeros_like(max_val))
+
+        device = min_val_neg.device
+        scale = torch.ones(min_val_neg.size(), dtype=torch.float32, device=device)
+        zero_point = torch.zeros(min_val_neg.size(), dtype=torch.int64, device=device)
+
+        if (
+            self.qscheme == torch.per_tensor_symmetric
+            or self.qscheme == torch.per_channel_symmetric
+        ):
+            max_val_pos = torch.max(-min_val_neg, max_val_pos)
+            scale = max_val_pos / (float(quant_max - quant_min) / 2)
+            scale = torch.max(scale, self.eps)
+            if self.dtype in [torch.quint8, torch.uint8]:
+                if self.has_customized_qrange:
+                    # When customized quantization range is used, down-rounded midpoint of the range is chosen.
+                    zero_point = zero_point.new_full(
+                        zero_point.size(), (quant_min + quant_max) // 2
+                    )
+                else:
+                    zero_point = zero_point.new_full(zero_point.size(), 128)
+        elif self.qscheme == torch.per_channel_affine_float_qparams:
+            scale = (max_val - min_val) / float(quant_max - quant_min)
+            scale = torch.where(scale > self.eps, scale, torch.ones_like(scale))
+            # We use the quantize function
+            # xq = Round(Xf * inv_scale + zero_point),
+            # setting zero_point to (-1 * min *inv_scale) we get
+            # Xq = Round((Xf - min) * inv_scale)
+            zero_point = -1 * min_val / scale
+        else:
+            scale = (max_val_pos - min_val_neg) / float(quant_max - quant_min)
+            scale = torch.max(scale, self.eps)
+            zero_point = quant_min - torch.round(min_val_neg / scale).to(torch.int)
+            zero_point = torch.clamp(zero_point, quant_min, quant_max)
+
+        # For scalar values, cast them to Tensors of size 1 to keep the shape
+        # consistent with default values in FakeQuantize.
+        if len(scale.shape) == 0:
+            # TODO: switch to scale.item() after adding JIT support
+            scale = torch.tensor([float(scale)], dtype=scale.dtype, device=device)
+        if len(zero_point.shape) == 0:
+            # TODO: switch to zero_point.item() after adding JIT support
+            zero_point = torch.tensor(
+                [int(zero_point)], dtype=zero_point.dtype, device=device
+            )
+            if self.qscheme == torch.per_channel_affine_float_qparams:
+                zero_point = torch.tensor(
+                    [float(zero_point)], dtype=zero_point.dtype, device=device
+                )
+
+        return scale, zero_point
+
+    @torch.jit.export
+    def reset_min_max_vals(self):
+        raise NotImplementedError("Cannot reset min/max values in the given observer.")
+
+
+# Originally, this class was called `_ObserverBase`.  Keeping the old name around
+# for backwards compatibility.
+# TODO(after v1.13): delete this
+_ObserverBase = UniformQuantizationObserverBase
+
+
+class MinMaxObserver(UniformQuantizationObserverBase):
+    r"""Observer module for computing the quantization parameters based on the
+    running min and max values.
+
+    This observer uses the tensor min/max statistics to compute the quantization
+    parameters. The module records the running minimum and maximum of incoming
+    tensors, and uses this statistic to compute the quantization parameters.
+
+    Args:
+        dtype: dtype argument to the `quantize` node needed to implement the
+               reference model spec.
+        qscheme: Quantization scheme to be used
+        reduce_range: Reduces the range of the quantized data type by 1 bit
+        quant_min: Minimum quantization value. If unspecified, it will follow the 8-bit setup.
+        quant_max: Maximum quantization value. If unspecified, it will follow the 8-bit setup.
+        eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`.
+
+    Given running min/max as :math:`x_\text{min}` and :math:`x_\text{max}`,
+    scale :math:`s` and zero point :math:`z` are computed as:
+
+    The running minimum/maximum :math:`x_\text{min/max}` is computed as:
+
+    .. math::
+
+        \begin{array}{ll}
+        x_\text{min} &= \begin{cases}
+            \min(X) & \text{if~}x_\text{min} = \text{None} \\
+            \min\left(x_\text{min}, \min(X)\right) & \text{otherwise}
+        \end{cases}\\
+        x_\text{max} &= \begin{cases}
+            \max(X) & \text{if~}x_\text{max} = \text{None} \\
+            \max\left(x_\text{max}, \max(X)\right) & \text{otherwise}
+        \end{cases}\\
+        \end{array}
+
+    where :math:`X` is the observed tensor.
+
+    The scale :math:`s` and zero point :math:`z` are then computed as:
+
+    .. math::
+
+        \begin{aligned}
+            \text{if Symmetric:}&\\
+            &s = 2 \max(|x_\text{min}|, x_\text{max}) /
+                \left( Q_\text{max} - Q_\text{min} \right) \\
+            &z = \begin{cases}
+                0 & \text{if dtype is qint8} \\
+                128 & \text{otherwise}
+            \end{cases}\\
+            \text{Otherwise:}&\\
+                &s = \left( x_\text{max} - x_\text{min}  \right ) /
+                    \left( Q_\text{max} - Q_\text{min} \right ) \\
+                &z = Q_\text{min} - \text{round}(x_\text{min} / s)
+        \end{aligned}
+
+    where :math:`Q_\text{min}` and :math:`Q_\text{max}` are the minimum and
+    maximum of the quantized data type.
+
+    .. warning:: :attr:`dtype` can only take ``torch.qint8`` or ``torch.quint8``.
+
+    .. note:: If the running minimum equals to the running maximum, the scale
+              and zero_point are set to 1.0 and 0.
+    """
+    min_val: torch.Tensor
+    max_val: torch.Tensor
+
+    def __init__(
+        self,
+        dtype=torch.quint8,
+        qscheme=torch.per_tensor_affine,
+        reduce_range=False,
+        quant_min=None,
+        quant_max=None,
+        factory_kwargs=None,
+        eps=torch.finfo(torch.float32).eps,
+        is_dynamic=False,
+        **kwargs,
+    ) -> None:
+        if not is_per_tensor(qscheme):
+            raise NotImplementedError(
+                "MinMaxObserver's qscheme only support torch.per_tensor_symmetric \
+                    and torch.per_tensor_affine."
+            )
+        # TODO: MinMaxObserver by itself doesn't support dynamic quantization, but
+        # if it's inherited by MovingAverageObserver, and averaging_constant is 1, it
+        # supports dynamic quantization, we may need to better error checking here
+
+        # For x86 quantized kernels, we need to ensure that the vpmaddubsw
+        # instruction does not overflow. We allow for a reduce_range argument to
+        # observers that reduces the quantized range to (0,127) or (-64, 63).
+        # For more details see aten/src/ATen/native/quantized/cpu/qconv.cpp
+        # This is not an optimal choice for non x86 backends as it loses a bit
+        # of precision for activations.
+        super().__init__(
+            dtype=dtype,
+            qscheme=qscheme,
+            reduce_range=reduce_range,
+            quant_min=quant_min,
+            quant_max=quant_max,
+            factory_kwargs=factory_kwargs,
+            eps=eps,
+            is_dynamic=is_dynamic,
+            **kwargs,
+        )
+        factory_kwargs = torch.nn.factory_kwargs(factory_kwargs)
+        self.register_buffer("min_val", torch.tensor(float("inf"), **factory_kwargs))
+        self.register_buffer("max_val", torch.tensor(float("-inf"), **factory_kwargs))
+        if (
+            self.qscheme == torch.per_tensor_symmetric
+            and self.reduce_range
+            and self.dtype == torch.quint8
+        ):
+            raise NotImplementedError(
+                "Cannot reduce range for symmetric \
+                                       quantization for quint8"
+            )
+
+    def forward(self, x_orig):
+        r"""Records the running minimum and maximum of ``x``."""
+        if x_orig.numel() == 0:
+            return x_orig
+        x = x_orig.detach()  # avoid keeping autograd tape
+        x = x.to(self.min_val.dtype)
+        min_val_cur, max_val_cur = torch.aminmax(x)
+        min_val = torch.min(min_val_cur, self.min_val)
+        max_val = torch.max(max_val_cur, self.max_val)
+        self.min_val.copy_(min_val)
+        self.max_val.copy_(max_val)
+        return x_orig
+
+    @torch.jit.export
+    def calculate_qparams(self):
+        r"""Calculates the quantization parameters."""
+        return self._calculate_qparams(self.min_val, self.max_val)
+
+    @torch.jit.export
+    def extra_repr(self):
+        return f"min_val={self.min_val}, max_val={self.max_val}"
+
+    @torch.jit.export
+    def reset_min_max_vals(self):
+        """Resets the min/max values."""
+        self.min_val.copy_(torch.tensor(float("inf")))
+        self.max_val.copy_(torch.tensor(float("-inf")))
+
+
+class MovingAverageMinMaxObserver(MinMaxObserver):
+    r"""Observer module for computing the quantization parameters based on the
+    moving average of the min and max values.
+
+    This observer computes the quantization parameters based on the moving
+    averages of minimums and maximums of the incoming tensors. The module
+    records the average minimum and maximum of incoming tensors, and uses this
+    statistic to compute the quantization parameters.
+
+    Args:
+        averaging_constant: Averaging constant for min/max.
+        dtype: dtype argument to the `quantize` node needed to implement the
+               reference model spec.
+        qscheme: Quantization scheme to be used
+        reduce_range: Reduces the range of the quantized data type by 1 bit
+        quant_min: Minimum quantization value. If unspecified, it will follow the 8-bit setup.
+        quant_max: Maximum quantization value. If unspecified, it will follow the 8-bit setup.
+        eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`.
+
+    The moving average min/max is computed as follows
+
+    .. math::
+
+        \begin{array}{ll}
+                x_\text{min} = \begin{cases}
+                    \min(X) & \text{if~}x_\text{min} = \text{None} \\
+                    (1 - c) x_\text{min} + c \min(X) & \text{otherwise}
+                \end{cases}\\
+                x_\text{max} = \begin{cases}
+                    \max(X) & \text{if~}x_\text{max} = \text{None} \\
+                    (1 - c) x_\text{max} + c \max(X) & \text{otherwise}
+                \end{cases}\\
+        \end{array}
+
+    where :math:`x_\text{min/max}` is the running average min/max, :math:`X` is
+    is the incoming tensor, and :math:`c` is the ``averaging_constant``.
+
+    The scale and zero point are then computed as in
+    :class:`~torch.ao.quantization.observer.MinMaxObserver`.
+
+    .. note:: Only works with ``torch.per_tensor_affine`` quantization scheme.
+
+    .. note:: If the running minimum equals to the running maximum, the scale
+              and zero_point are set to 1.0 and 0.
+    """
+
+    def __init__(
+        self,
+        averaging_constant=0.01,
+        dtype=torch.quint8,
+        qscheme=torch.per_tensor_affine,
+        reduce_range=False,
+        quant_min=None,
+        quant_max=None,
+        eps=torch.finfo(torch.float32).eps,
+        is_dynamic=False,
+        **kwargs,
+    ) -> None:
+        if not is_per_tensor(qscheme):
+            raise NotImplementedError(
+                f"MovingAverageMinMaxObserver's qscheme only support \
+                torch.per_tensor_symmetric and torch.per_tensor_affine. \
+                but got: {qscheme}"
+            )
+        self.averaging_constant = averaging_constant
+        if is_dynamic and self.averaging_constant != 1:
+            raise NotImplementedError(
+                "MovingAverageMinMaxObserver doesn't support dynamic quantization for "
+                f"averaging constant of {self.averaging_constant}"
+            )
+        super().__init__(
+            dtype=dtype,
+            qscheme=qscheme,
+            reduce_range=reduce_range,
+            quant_min=quant_min,
+            quant_max=quant_max,
+            eps=eps,
+            is_dynamic=is_dynamic,
+            **kwargs,
+        )
+
+    def forward(self, x_orig):
+        if x_orig.numel() == 0:
+            return x_orig
+        x = x_orig.detach()  # avoid keeping autograd tape
+        x = x.to(self.min_val.dtype)
+        min_val = self.min_val
+        max_val = self.max_val
+        if min_val == float("inf") and max_val == float("-inf"):
+            min_val, max_val = torch.aminmax(x)
+        else:
+            min_val_cur, max_val_cur = torch.aminmax(x)
+            min_val = min_val + self.averaging_constant * (min_val_cur - min_val)
+            max_val = max_val + self.averaging_constant * (max_val_cur - max_val)
+        self.min_val.copy_(min_val)
+        self.max_val.copy_(max_val)
+        return x_orig
+
+
+class PerChannelMinMaxObserver(UniformQuantizationObserverBase):
+    r"""Observer module for computing the quantization parameters based on the
+    running per channel min and max values.
+
+    This observer uses the tensor min/max statistics to compute the per channel
+    quantization parameters. The module records the running minimum and maximum
+    of incoming tensors, and uses this statistic to compute the quantization
+    parameters.
+
+    Args:
+        ch_axis: Channel axis
+        dtype: dtype argument to the `quantize` node needed to implement the
+               reference model spec.
+        qscheme: Quantization scheme to be used
+        reduce_range: Reduces the range of the quantized data type by 1 bit
+        quant_min: Minimum quantization value. If unspecified, it will follow the 8-bit setup.
+        quant_max: Maximum quantization value. If unspecified, it will follow the 8-bit setup.
+        eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`.
+
+    The quantization parameters are computed the same way as in
+    :class:`~torch.ao.quantization.observer.MinMaxObserver`, with the difference
+    that the running min/max values are stored per channel.
+    Scales and zero points are thus computed per channel as well.
+
+    .. note:: If the running minimum equals to the running maximum, the scales
+              and zero_points are set to 1.0 and 0.
+    """
+    min_val: torch.Tensor
+    max_val: torch.Tensor
+
+    def __init__(
+        self,
+        ch_axis=0,
+        dtype=torch.quint8,
+        qscheme=torch.per_channel_affine,
+        reduce_range=False,
+        quant_min=None,
+        quant_max=None,
+        factory_kwargs=None,
+        eps=torch.finfo(torch.float32).eps,
+        is_dynamic=False,
+        **kwargs,
+    ) -> None:
+        if not is_per_channel(qscheme):
+            raise NotImplementedError(
+                "PerChannelMinMaxObserver's qscheme only support \
+                    torch.per_channel_symmetric, torch.per_channel_affine and torch.per_channel_affine_float_qparams."
+            )
+        if is_dynamic:
+            raise NotImplementedError(
+                "PerChannelMinMaxObserver doesn't support dynamic quantization"
+            )
+        super().__init__(
+            dtype=dtype,
+            qscheme=qscheme,
+            reduce_range=reduce_range,
+            quant_min=quant_min,
+            quant_max=quant_max,
+            factory_kwargs=factory_kwargs,
+            eps=eps,
+            is_dynamic=is_dynamic,
+            **kwargs,
+        )
+        factory_kwargs = torch.nn.factory_kwargs(factory_kwargs)
+        self.ch_axis = ch_axis
+        self.register_buffer("min_val", torch.tensor([], **factory_kwargs))
+        self.register_buffer("max_val", torch.tensor([], **factory_kwargs))
+        if (
+            self.qscheme == torch.per_channel_symmetric
+            and self.reduce_range
+            and self.dtype == torch.quint8
+        ):
+            raise NotImplementedError(
+                "Cannot reduce range for symmetric quantization for quint8"
+            )
+
+    def forward(self, x_orig):
+        return self._forward(x_orig)
+
+    def _forward(self, x_orig):
+        if x_orig.numel() == 0:
+            return x_orig
+        x = x_orig.detach()  # avoid keeping autograd tape
+        min_val = self.min_val
+        max_val = self.max_val
+        x_dim = x.size()
+
+        new_axis_list = [i for i in range(len(x_dim))]  # noqa: C416
+        new_axis_list[self.ch_axis] = 0
+        new_axis_list[0] = self.ch_axis
+        y = x.permute(new_axis_list)
+        # Need to match dtype of min/max because the updates to buffers
+        # are done in place and types need to match for comparisons
+        y = y.to(self.min_val.dtype)
+        y = torch.flatten(y, start_dim=1)
+        if min_val.numel() == 0 or max_val.numel() == 0:
+            min_val, max_val = torch.aminmax(y, dim=1)
+        else:
+            min_val_cur, max_val_cur = torch.aminmax(y, dim=1)
+            min_val = torch.min(min_val_cur, min_val)
+            max_val = torch.max(max_val_cur, max_val)
+        self.min_val.resize_(min_val.shape)
+        self.max_val.resize_(max_val.shape)
+        self.min_val.copy_(min_val)
+        self.max_val.copy_(max_val)
+        return x_orig
+
+    @torch.jit.export
+    def calculate_qparams(self):
+        return self._calculate_qparams(self.min_val, self.max_val)
+
+    def extra_repr(self):
+        return f"min_val={self.min_val}, max_val={self.max_val}"
+
+    def _load_from_state_dict(
+        self,
+        state_dict: Dict[str, Any],
+        prefix: str,
+        local_metadata: Dict[str, torch.Tensor],
+        strict: bool,
+        missing_keys: List[str],
+        unexpected_keys: List[str],
+        error_msgs: List[str],
+    ):
+        version = local_metadata.get("version", None)
+        if version is not None and version < 3:
+            local_state = ["min_vals", "max_vals"]
+            expected_min_name = "min_vals"
+            expected_max_name = "max_vals"
+        else:
+            local_state = ["min_val", "max_val"]
+            expected_min_name = "min_val"
+            expected_max_name = "max_val"
+        for name in local_state:
+            key = prefix + name
+            if key in state_dict:
+                val = state_dict[key]
+                # Custom handling to allow loading min_val or max_val
+                # of size N into uninitialized buffers of size 0. The
+                # buffers are resized here, and the values are copied in
+                # the default state_dict loading code of the parent.
+                if name == expected_min_name:
+                    self.min_val.resize_(val.shape)
+                elif name == expected_max_name:
+                    self.max_val.resize_(val.shape)
+                else:
+                    warnings.warn(
+                        f"Observer load_from_state_dict got unexpected name {name}"
+                    )
+                # For torchscript module we need to update the attributes here since we do not
+                # call the `_load_from_state_dict` function defined module.py
+                if torch.jit.is_scripting():
+                    if name == expected_min_name:
+                        self.min_val.copy_(val)
+                    elif name == expected_max_name:
+                        self.max_val.copy_(val)
+                    else:
+                        warnings.warn(
+                            f"Observer load_from_state_dict got unexpected name {name}"
+                        )
+            elif strict:
+                missing_keys.append(key)
+
+        if not torch.jit.is_scripting():
+            super()._load_from_state_dict(
+                state_dict,
+                prefix,
+                local_metadata,
+                False,
+                missing_keys,
+                unexpected_keys,
+                error_msgs,
+            )
+
+    def _load_from_state_dict_script(
+        self,
+        state_dict: Dict[str, Any],
+        prefix: str,
+        local_metadata: Dict[str, torch.Tensor],
+        strict: bool,
+        missing_keys: List[str],
+        unexpected_keys: List[str],
+        error_msgs: List[str],
+    ):
+        self._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            strict,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    @torch.jit.export
+    def reset_min_max_vals(self):
+        """Resets the min/max values."""
+        # This used to be torch.ones but that does not work because
+        # JIT compiler can optimize it via common subexpression elimination
+        # in which case both min_val and max_val point to the same tensor.
+        self.min_val = torch.rand(
+            0,
+        )
+        self.max_val = torch.rand(
+            0,
+        )
+
+
+class MovingAveragePerChannelMinMaxObserver(PerChannelMinMaxObserver):
+    r"""Observer module for computing the quantization parameters based on the
+    running per channel min and max values.
+
+    This observer uses the tensor min/max statistics to compute the per channel
+    quantization parameters. The module records the running minimum and maximum
+    of incoming tensors, and uses this statistic to compute the quantization
+    parameters.
+
+    Args:
+        averaging_constant: Averaging constant for min/max.
+        ch_axis: Channel axis
+        dtype: Quantized data type
+        qscheme: Quantization scheme to be used
+        reduce_range: Reduces the range of the quantized data type by 1 bit
+        quant_min: Minimum quantization value. If unspecified, it will follow the 8-bit setup.
+        quant_max: Maximum quantization value. If unspecified, it will follow the 8-bit setup.
+        eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`.
+
+    The quantization parameters are computed the same way as in
+    :class:`~torch.ao.quantization.observer.MovingAverageMinMaxObserver`, with the
+    difference that the running min/max values are stored per channel.
+    Scales and zero points are thus computed per channel as well.
+
+    .. note:: If the running minimum equals to the running maximum, the scales
+              and zero_points are set to 1.0 and 0.
+    """
+
+    def __init__(
+        self,
+        averaging_constant=0.01,
+        ch_axis=0,
+        dtype=torch.quint8,
+        qscheme=torch.per_channel_affine,
+        reduce_range=False,
+        quant_min=None,
+        quant_max=None,
+        eps=torch.finfo(torch.float32).eps,
+        is_dynamic=False,
+        **kwargs,
+    ) -> None:
+        if not is_per_channel(qscheme):
+            raise NotImplementedError(
+                "MovingAveragePerChannelMinMaxObserver's qscheme only support \
+                    torch.per_channel_symmetric, torch.per_channel_affine and torch.per_channel_affine_float_qparams."
+            )
+        if is_dynamic:
+            raise NotImplementedError(
+                "MovingAveragePerChannelMinMaxObserver doesn't support dynamic quantization"
+            )
+        super().__init__(
+            ch_axis=ch_axis,
+            dtype=dtype,
+            qscheme=qscheme,
+            reduce_range=reduce_range,
+            quant_min=quant_min,
+            quant_max=quant_max,
+            eps=eps,
+            is_dynamic=is_dynamic,
+            **kwargs,
+        )
+        self.averaging_constant = averaging_constant
+
+    def forward(self, x_orig):
+        if x_orig.numel() == 0:
+            return x_orig
+        x = x_orig.detach()  # avoid keeping autograd tape
+        x = x.to(self.min_val.dtype)
+        min_val = self.min_val
+        max_val = self.max_val
+        x_dim = x.size()
+
+        new_axis_list = [i for i in range(len(x_dim))]  # noqa: C416
+        new_axis_list[self.ch_axis] = 0
+        new_axis_list[0] = self.ch_axis
+        y = x.permute(new_axis_list)
+        y = torch.flatten(y, start_dim=1)
+        if min_val.numel() == 0 or max_val.numel() == 0:
+            min_val, max_val = torch.aminmax(y, dim=1)
+        else:
+            min_val_cur, max_val_cur = torch.aminmax(y, dim=1)
+            min_val = min_val + self.averaging_constant * (min_val_cur - min_val)
+            max_val = max_val + self.averaging_constant * (max_val_cur - max_val)
+        self.min_val.resize_(min_val.shape)
+        self.max_val.resize_(max_val.shape)
+        self.min_val.copy_(min_val)
+        self.max_val.copy_(max_val)
+        return x_orig
+
+
+class HistogramObserver(UniformQuantizationObserverBase):
+    r"""
+    The module records the running histogram of tensor values along with
+    min/max values. ``calculate_qparams`` will calculate scale and zero_point.
+
+    Args:
+        bins: Number of bins to use for the histogram
+        dtype: dtype argument to the `quantize` node needed to implement the
+               reference model spec
+        qscheme: Quantization scheme to be used
+        reduce_range: Reduces the range of the quantized data type by 1 bit
+        eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`.
+
+    The scale and zero point are computed as follows:
+
+    1. Create the histogram of the incoming inputs.
+        The histogram is computed continuously, and the ranges per bin change
+        with every new tensor observed.
+    2. Search the distribution in the histogram for optimal min/max values.
+        The search for the min/max values ensures the minimization of the
+        quantization error with respect to the floating point model.
+    3. Compute the scale and zero point the same way as in the
+        :class:`~torch.ao.quantization.MinMaxObserver`
+    """
+    histogram: torch.Tensor
+    min_val: torch.Tensor
+    max_val: torch.Tensor
+
+    def __init__(
+        self,
+        bins: int = 2048,
+        dtype: torch.dtype = torch.quint8,
+        qscheme=torch.per_tensor_affine,
+        reduce_range=False,
+        quant_min=None,
+        quant_max=None,
+        factory_kwargs=None,
+        eps=torch.finfo(torch.float32).eps,
+        is_dynamic=False,
+        **kwargs,
+    ) -> None:
+        if not is_per_tensor(qscheme):
+            raise NotImplementedError(
+                "HistogramObserver's qscheme only support torch.per_tensor_symmetric \
+                    and torch.per_tensor_affine."
+            )
+        if is_dynamic:
+            raise NotImplementedError(
+                "HistogramObserver doesn't support dynamic quantization"
+            )
+        # bins: The number of bins used for histogram calculation.
+        super().__init__(
+            dtype=dtype,
+            qscheme=qscheme,
+            reduce_range=reduce_range,
+            quant_min=quant_min,
+            quant_max=quant_max,
+            factory_kwargs=factory_kwargs,
+            eps=eps,
+            is_dynamic=is_dynamic,
+            **kwargs,
+        )
+        factory_kwargs = torch.nn.factory_kwargs(factory_kwargs)
+        self.bins = bins
+        self.register_buffer("histogram", torch.zeros(self.bins, **factory_kwargs))
+        self.register_buffer("min_val", torch.tensor(float("inf"), **factory_kwargs))
+        self.register_buffer("max_val", torch.tensor(float("-inf"), **factory_kwargs))
+        self.dst_nbins = 2 ** torch.iinfo(self.dtype).bits
+        self.upsample_rate = (
+            16  # used to reduce quantization errors when upscaling histogram
+        )
+
+    def _get_norm(
+        self, delta_begin: torch.Tensor, delta_end: torch.Tensor, density: torch.Tensor
+    ) -> torch.Tensor:
+        r"""
+        Compute the norm of the values uniformaly distributed between
+        delta_begin and delta_end.
+        Currently only L2 norm is supported.
+
+        norm = density * (integral_{begin, end} x^2)
+             = density * (end^3 - begin^3) / 3
+        """
+        norm = (
+            delta_end * delta_end * delta_end - delta_begin * delta_begin * delta_begin
+        ) / 3
+        return density * norm
+
+    def _compute_quantization_error(self, next_start_bin: int, next_end_bin: int):
+        r"""
+        Compute the quantization error if we use start_bin to end_bin as the
+        min and max to do the quantization.
+        """
+        bin_width = (self.max_val.item() - self.min_val.item()) / self.bins
+
+        dst_bin_width = bin_width * (next_end_bin - next_start_bin + 1) / self.dst_nbins
+        if dst_bin_width == 0.0:
+            return 0.0
+
+        src_bin = torch.arange(self.bins, device=self.histogram.device)
+        # distances from the beginning of first dst_bin to the beginning and
+        # end of src_bin
+        src_bin_begin = (src_bin - next_start_bin) * bin_width
+        src_bin_end = src_bin_begin + bin_width
+
+        # which dst_bins the beginning and end of src_bin belong to?
+        dst_bin_of_begin = torch.clamp(
+            torch.div(src_bin_begin, dst_bin_width, rounding_mode="floor"),
+            0,
+            self.dst_nbins - 1,
+        )
+        dst_bin_of_begin_center = (dst_bin_of_begin + 0.5) * dst_bin_width
+
+        dst_bin_of_end = torch.clamp(
+            torch.div(src_bin_end, dst_bin_width, rounding_mode="floor"),
+            0,
+            self.dst_nbins - 1,
+        )
+        density = self.histogram / bin_width
+
+        norm = torch.zeros(self.bins, device=self.histogram.device)
+
+        delta_begin = src_bin_begin - dst_bin_of_begin_center
+        delta_end = dst_bin_width / 2
+        norm += self._get_norm(
+            delta_begin,
+            torch.ones(self.bins, device=self.histogram.device) * delta_end,
+            density,
+        )
+
+        norm += (dst_bin_of_end - dst_bin_of_begin - 1) * self._get_norm(
+            torch.tensor(-dst_bin_width / 2), torch.tensor(dst_bin_width / 2), density
+        )
+
+        dst_bin_of_end_center = dst_bin_of_end * dst_bin_width + dst_bin_width / 2
+
+        delta_begin = -dst_bin_width / 2
+        delta_end = src_bin_end - dst_bin_of_end_center
+        norm += self._get_norm(torch.tensor(delta_begin), delta_end, density)
+
+        return norm.sum().item()
+
+    def _non_linear_param_search(self) -> Tuple[torch.Tensor, torch.Tensor]:
+        r"""Non-linear parameter search.
+
+        An approximation for L2 error minimization for selecting min/max.
+        By selecting new min/max, we filter out outliers in input distribution.
+        This follows the implementation of NormMinimization::NonlinearQuantizationParamsSearch in
+        caffe2/quantization/server/norm_minimization.cc
+        """
+        assert self.histogram.size()[0] == self.bins, "bins mismatch"
+        bin_width = (self.max_val - self.min_val) / self.bins
+
+        # cumulative sum
+        total = torch.sum(self.histogram).item()
+        cSum = torch.cumsum(self.histogram, dim=0)
+
+        stepsize = 1e-5  # granularity
+        alpha = 0.0  # lower bound
+        beta = 1.0  # upper bound
+        start_bin = 0
+        end_bin = self.bins - 1
+        norm_min = float("inf")
+
+        while alpha < beta:
+            # Find the next step
+            next_alpha = alpha + stepsize
+            next_beta = beta - stepsize
+
+            # find the left and right bins between the quantile bounds
+            l = start_bin
+            r = end_bin
+            while l < end_bin and cSum[l] < next_alpha * total:
+                l = l + 1
+            while r > start_bin and cSum[r] > next_beta * total:
+                r = r - 1
+
+            # decide the next move
+            next_start_bin = start_bin
+            next_end_bin = end_bin
+            if (l - start_bin) > (end_bin - r):
+                # move the start bin
+                next_start_bin = l
+                alpha = next_alpha
+            else:
+                # move the end bin
+                next_end_bin = r
+                beta = next_beta
+
+            if next_start_bin == start_bin and next_end_bin == end_bin:
+                continue
+
+            # calculate the quantization error using next_start_bin and next_end_bin
+            norm = self._compute_quantization_error(next_start_bin, next_end_bin)
+
+            if norm > norm_min:
+                break
+            norm_min = norm
+            start_bin = next_start_bin
+            end_bin = next_end_bin
+
+        new_min = self.min_val + bin_width * start_bin
+        new_max = self.min_val + bin_width * (end_bin + 1)
+        return new_min, new_max
+
+    def _upscale_histogram(
+        self,
+        histogram: torch.Tensor,
+        orig_min: torch.Tensor,
+        orig_max: torch.Tensor,
+        update_min: torch.Tensor,
+        update_max: torch.Tensor,
+    ):
+        # this turns the histogram into a more fine-coarsed histogram to reduce
+        # bin quantization errors
+        histogram = histogram.repeat_interleave(self.upsample_rate) / self.upsample_rate
+        bin_size = (orig_max - orig_min) / (self.bins * self.upsample_rate)
+        mid_points_histogram = (
+            torch.linspace(
+                orig_min,
+                orig_max,
+                self.bins * self.upsample_rate + 1,
+                device=orig_min.device,
+            )[:-1].to(histogram.device)
+            + 0.5 * bin_size
+        )
+        boundaries_new_histogram = torch.linspace(
+            update_min, update_max, self.bins + 1, device=update_min.device
+        ).to(histogram.device)
+        # this maps the mid-poits of the histogram to the new histogram's space
+        bucket_assignments = (
+            torch.bucketize(mid_points_histogram, boundaries_new_histogram, right=True)
+            - 1
+        )
+        # this then maps the histogram mid-points in the new space, weighted by the original histogram's values
+        # this is just the old histogram in the new histogram's space
+
+        # In case due to numerical issues the values land higher/lower than the maximum/minimum
+        bucket_assignments[bucket_assignments >= self.bins] = self.bins - 1
+        bucket_assignments[bucket_assignments < 0] = 0
+
+        update_histogram = torch.bincount(
+            bucket_assignments, weights=histogram, minlength=self.bins
+        )
+        return update_histogram
+
+    def _combine_histograms(
+        self,
+        orig_hist: torch.Tensor,
+        orig_min: torch.Tensor,
+        orig_max: torch.Tensor,
+        update_hist: torch.Tensor,
+        update_min: torch.Tensor,
+        update_max: torch.Tensor,
+    ) -> torch.Tensor:
+        # If the new min and max are the same as the current min and max,
+        # we can just add the new histogram to the original histogram
+        if update_min == orig_min and update_max == orig_max:
+            return orig_hist + update_hist
+
+        # If the orig hist only has one value (i.e., the min and max are the same)
+        # we can just add it into new histogram
+        if orig_min == orig_max:
+            bin_value = torch.sum(update_hist)
+            transformed_orig_hist = (
+                torch.histc(orig_min, bins=self.bins, min=update_min, max=update_max)  # type: ignore[arg-type]
+                * bin_value
+            )
+            return transformed_orig_hist + update_hist
+
+        # We assume the update_hist is already in the target range, we will map the orig_max to it
+        assert update_min <= orig_min
+        assert update_max >= orig_max
+
+        # Now we need to turn the old_histogram, into the range of the new histogram
+        transformed_orig_hist = self._upscale_histogram(
+            orig_hist,
+            orig_min,
+            orig_max,
+            update_min,
+            update_max,
+        )
+
+        return update_hist + transformed_orig_hist
+
+    def reset_histogram(
+        self, x: torch.Tensor, min_val: torch.Tensor, max_val: torch.Tensor
+    ) -> None:
+        self.min_val.resize_(min_val.shape)
+        self.min_val.copy_(min_val)
+        self.max_val.resize_(max_val.shape)
+        self.max_val.copy_(max_val)
+        assert (
+            min_val.numel() == 1 and max_val.numel() == 1
+        ), "histogram min/max values must be scalar."
+        new_histogram = torch.histc(x, self.bins, min=min_val, max=max_val)  # type: ignore[arg-type]
+        self.histogram.detach_().resize_(new_histogram.shape)
+        self.histogram.copy_(new_histogram)
+
+    def forward(self, x_orig: torch.Tensor) -> torch.Tensor:  # pyre-ignore[14]
+        if x_orig.numel() == 0:
+            return x_orig
+        x = x_orig.detach()
+        x_min, x_max = torch.aminmax(x)
+        # want to ignore torch.inf since we don't actually
+        # want to make our quantization range infinite
+        # and in practice those values will be clamped
+        if x_min == -torch.inf or x_max == torch.inf:
+            warnings.warn("torch.inf detected in input tensor, ignoring input")
+            x = x[x.abs() != torch.inf]
+            if x.numel() == 0:
+                return x_orig
+            x_min, x_max = torch.aminmax(x)
+
+        current_min = self.min_val
+        current_max = self.max_val
+
+        is_uninitialized = self.min_val == float("inf") or self.max_val == float("-inf")
+        if is_uninitialized:
+            self.reset_histogram(x, x_min, x_max)
+        else:
+            update_min, update_max = x_min, x_max
+            new_min = torch.min(current_min, update_min)
+            new_max = torch.max(current_max, update_max)
+
+            # TODO: For some reason, this is required for it to pass torchscript test
+            # new_min and new_max should already have requires_grad set to False
+            new_min, new_max = new_min.detach(), new_max.detach()
+            update_histogram = torch.histc(
+                x, self.bins, min=new_min, max=new_max  # type: ignore[arg-type]
+            ).to(self.histogram.device)
+            if new_min == current_min and new_max == current_max:
+                combined_histogram = self.histogram + update_histogram
+                self.histogram.detach_().resize_(combined_histogram.shape)
+                self.histogram.copy_(combined_histogram)
+            else:
+                combined_histogram = self._combine_histograms(
+                    self.histogram,
+                    current_min,
+                    current_max,
+                    update_histogram,
+                    new_min,
+                    new_max,
+                )
+                self.histogram.detach_().resize_(combined_histogram.shape)
+                self.histogram.copy_(combined_histogram)
+                self.min_val.detach_().resize_(new_min.shape)
+                self.min_val.copy_(new_min)
+                self.max_val.detach_().resize_(new_max.shape)
+                self.max_val.copy_(new_max)
+
+        return x_orig
+
+    @torch.jit.export
+    def calculate_qparams(self):
+        is_uninitialized = self.min_val == float("inf") and self.max_val == float(
+            "-inf"
+        )
+        if is_uninitialized:
+            warnings.warn(
+                "must run observer before calling calculate_qparams.\
+                                    Returning default scale and zero point "
+            )
+            return torch.tensor([1.0], device=self.min_val.device.type), torch.tensor(
+                [0], device=self.min_val.device.type
+            )
+        assert self.bins == len(self.histogram), (
+            "The number of bins in histogram should be equal to the number of bins "
+            "supplied while making this observer"
+        )
+
+        new_min, new_max = self._non_linear_param_search()
+
+        return self._calculate_qparams(new_min, new_max)
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "min_val"] = self.min_val
+        destination[prefix + "max_val"] = self.max_val
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        version = local_metadata.get("version", None)
+
+        if version is None or version < 3:
+            # if min_val and max_val are not initialized, update their shape
+            # to account for the differences between v2 and v3
+            min_val_name, max_val_name = prefix + "min_val", prefix + "max_val"
+            if min_val_name in state_dict:
+                if state_dict[min_val_name].shape == torch.Size([0]):
+                    state_dict[min_val_name] = torch.tensor(float("inf"))
+            if max_val_name in state_dict:
+                if state_dict[max_val_name].shape == torch.Size([0]):
+                    state_dict[max_val_name] = torch.tensor(float("-inf"))
+
+        local_state = ["min_val", "max_val"]
+        for name in local_state:
+            key = prefix + name
+            if key in state_dict:
+                val = state_dict[key]
+                setattr(self, name, val)
+            elif strict:
+                missing_keys.append(key)
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            strict,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    def extra_repr(self):
+        return f"min_val={self.min_val}, max_val={self.max_val}"
+
+
+class FixedQParamsObserver(ObserverBase):
+    r"""
+    Observer that simulates quantize and dequantize with fixed
+    quantization parameters in training time. Only per tensor
+    quantization is supported.
+
+    Args:
+        `scale` (float): fixed scale for the observer
+        `zero_point` (int): fixed zero point for the observer
+        `dtype`, `qscheme`, `quant_min`, `quant_max`
+    """
+
+    scale: torch.Tensor
+    zero_point: torch.Tensor
+
+    def __init__(
+        self,
+        scale,
+        zero_point,
+        dtype=torch.quint8,
+        qscheme=torch.per_tensor_affine,
+        quant_min=0,
+        quant_max=255,
+        is_dynamic=False,
+        **kwargs,
+    ):
+        if is_dynamic:
+            raise NotImplementedError(
+                "FixedQParamsObserver doesn't support dynamic quantization"
+            )
+        super().__init__(dtype=dtype, is_dynamic=is_dynamic, **kwargs)
+        self.quant_min = quant_min
+        self.quant_max = quant_max
+        self.register_buffer("scale", torch.tensor([scale], dtype=torch.float))
+        self.register_buffer("zero_point", torch.tensor([zero_point], dtype=torch.int))
+        self.dtype = dtype
+        self.qscheme = qscheme
+
+    def forward(self, X):
+        return X
+
+    @torch.jit.export
+    def calculate_qparams(self):
+        return self.scale, self.zero_point
+
+
+class PlaceholderObserver(ObserverBase):
+    r"""
+    Observer that doesn't do anything and just passes its configuration to the
+    quantized module's ``.from_float()``.
+
+    Can be used for quantization to float16 which doesn't require determining
+    ranges.
+
+    Args:
+        dtype: dtype argument to the `quantize` node needed to implement the
+               reference model spec.
+        quant_min: minimum value in quantized domain (TODO: align behavior with other observers)
+        quant_max: maximum value in quantized domain
+        custom_op_name: (temporary) specify this observer for an operator that doesn't require any observation
+                        (Can be used in Graph Mode Passes for special case ops).
+        compute_dtype (deprecated): if set, marks the future quantize function to use
+                       dynamic quantization instead of static quantization.
+                       This field is deprecated, use `is_dynamic=True` instead.
+        is_dynamic: if True, the `quantize` function in the reference model
+                    representation taking stats from this observer instance will
+                    use dynamic quantization.
+    """
+
+    def __init__(
+        self,
+        dtype=torch.float32,
+        custom_op_name="",
+        compute_dtype=None,
+        quant_min=None,
+        quant_max=None,
+        qscheme=None,
+        eps=None,
+        is_dynamic=False,
+    ) -> None:
+        super().__init__(dtype=dtype, is_dynamic=is_dynamic)
+        if qscheme is None:
+            qscheme = torch.per_tensor_affine
+        if eps is None:
+            eps = torch.finfo(torch.float32).eps
+
+        # dtype of input of the target operator, e.g. for dynamic quantization
+        # ops, the dtype will be float32
+        self.dtype = dtype
+        self.qscheme = qscheme
+        self.quant_min = quant_min
+        self.quant_max = quant_max
+        self.eps = eps
+        self.custom_op = custom_op_name
+        # used for configuration of computation type for dynamic quantization
+        if compute_dtype:
+            is_dynamic = True
+            warnings.warn(
+                "Please use `is_dynamic` instead of `compute_dtype`. \
+                    `compute_dtype` will be deprecated in a future release \
+                    of PyTorch."
+            )
+
+    def forward(self, x):
+        return x
+
+    @torch.jit.export
+    def extra_repr(self):
+        return f"dtype={self.dtype}, is_dynamic={self.is_dynamic}"
+
+    @torch.jit.export
+    def calculate_qparams(self):
+        raise Exception(  # noqa: TRY002
+            "calculate_qparams should not be called for PlaceholderObserver"
+        )
+
+
+class RecordingObserver(ObserverBase):
+    r"""
+    The module is mainly for debug and records the tensor values during runtime.
+
+    Args:
+        dtype: Quantized data type
+        qscheme: Quantization scheme to be used
+        reduce_range: Reduces the range of the quantized data type by 1 bit
+    """
+    __annotations__ = {"tensor_val": List[Optional[torch.Tensor]]}
+
+    def __init__(self, dtype=torch.quint8):
+        super().__init__(dtype=dtype, is_dynamic=False)  # type: ignore[call-arg]
+        self.tensor_val = []
+
+    def forward(self, x):
+        self.tensor_val.append(x.clone())
+        return x
+
+    @torch.jit.export
+    def calculate_qparams(self):
+        raise Exception(  # noqa: TRY002
+            "calculate_qparams should not be called for RecordingObserver"
+        )
+
+    @torch.jit.export
+    def get_tensor_value(self):
+        return self.tensor_val
+
+
+class NoopObserver(ObserverBase):
+    r"""
+    Observer that doesn't do anything and just passes its configuration to the
+    quantized module's ``.from_float()``.
+
+    Primarily used for quantization to float16 which doesn't require determining
+    ranges.
+
+    Args:
+        dtype: Quantized data type
+        custom_op_name: (temporary) specify this observer for an operator that doesn't require any observation
+                        (Can be used in Graph Mode Passes for special case ops).
+    """
+
+    def __init__(self, dtype=torch.float16, custom_op_name="") -> None:
+        super().__init__(dtype=dtype, is_dynamic=False)
+        self.dtype = dtype
+        self.custom_op = custom_op_name
+
+    def forward(self, x):
+        return x
+
+    @torch.jit.export
+    def calculate_qparams(self):
+        raise Exception(  # noqa: TRY002
+            "calculate_qparams should not be called for NoopObserver"
+        )
+
+
+class ReuseInputObserver(ObserverBase):
+    r"""This observer is used when we want to reuse the observer from the operator
+    that produces the input Tensor, typically used for operators like reshape, e.g.
+    ```
+    x0 = ...
+    x1 = x0.reshape()
+    ```
+    if we configure x0 to be observed by some observer, let's say MinMaxObserver,
+    and reshape is configured with ReuseInputObserver, we'll reuse the observer instance
+    for x0 for x1 (output of reshape). If x0 is not observed, we also won't observe x1.
+
+    Note: this is only enabled in FX Graph Mode Quantization
+    """
+
+    def __init__(self) -> None:
+        super().__init__(torch.quint8, is_dynamic=False)
+
+    def forward(self, x):
+        return x
+
+    @torch.jit.export
+    def calculate_qparams(self):
+        raise Exception(  # noqa: TRY002
+            "calculate_qparams should not be called for ReuseInputObserver"
+        )
+
+
+def _is_observer_script_module(mod, obs_type_name):
+    """Returns true if given mod is an instance of Observer script module."""
+    if isinstance(mod, torch.jit.RecursiveScriptModule):
+        # qualified name looks like '__torch__.torch.ao.quantization.observer.___torch_mangle_2.MinMaxObserver'
+        suffix = mod._c.qualified_name.split(".", 1)[1]
+        name = re.sub(r"\.___torch_mangle_\d+", "", suffix)
+        return obs_type_name in name
+    return False
+
+
+def _is_activation_post_process(module):
+    return isinstance(
+        module,
+        (torch.ao.quantization.ObserverBase, torch.ao.quantization.FakeQuantizeBase),
+    ) or _is_observer_script_module(module, "quantization.observer")
+
+
+def _is_per_channel_script_obs_instance(module):
+    if isinstance(module, torch.jit.RecursiveScriptModule):
+        return _is_observer_script_module(
+            module, "quantization.observer.PerChannelMinMaxObserver"
+        ) or _is_observer_script_module(
+            module, "quantization.observer.MovingAveragePerChannelMinMaxObserver"
+        )
+    return False
+
+
+def get_observer_state_dict(mod):
+    r"""
+    Returns the state dict corresponding to the observer stats.
+    Traverse the model state_dict and extract out the stats.
+    """
+    od = OrderedDict()
+    if isinstance(mod, torch.jit.RecursiveScriptModule):
+        for k, v in mod.state_dict().items():
+            if "observer" in k:
+                od[k] = v
+    else:
+        # path for GraphModule and nn.Module (eager mode)
+        for k, v in mod.state_dict().items():
+            if "activation_post_process" in k:
+                od[k] = v
+    od._metadata = mod.state_dict()._metadata  # type: ignore[attr-defined]
+    return od
+
+
+def load_observer_state_dict(mod, obs_dict):
+    r"""
+    Given input model and a state_dict containing model observer stats,
+    load the stats back into the model. The observer state_dict can be saved
+    using torch.ao.quantization.get_observer_state_dict
+    """
+    missing_keys: List[str] = []
+    unexpected_keys: List[str] = []
+    for name, module in mod.named_modules():
+        prefix = name + "."
+        if _is_activation_post_process(module):
+            if _is_per_channel_script_obs_instance(module):
+                # For per-channel observers we need to call a custom load_from_state_dict to resize the tensor.
+                # However this is not called when the module is scripted and we end up calling the default one in module.py
+                module._load_from_state_dict_script(
+                    obs_dict, prefix, {}, True, missing_keys, unexpected_keys, []
+                )
+            else:
+                module._load_from_state_dict(
+                    obs_dict, prefix, {}, False, missing_keys, unexpected_keys, []
+                )
+    for k in missing_keys:
+        if "observer" in k or "activation_post_process" in k:
+            raise Exception(  # noqa: TRY002
+                f"Missing keys for observer {k} in state_dict"
+            )
+    for k in unexpected_keys:
+        if "observer" in k or "activation_post_process" in k:
+            raise Exception(  # noqa: TRY002
+                f"Unexpected keys for observer {k} in state_dict"
+            )
+
+
+# Restrict activations to be in the range (0,127)
+default_observer = MinMaxObserver.with_args(quant_min=0, quant_max=127)
+"""
+Default observer for static quantization, usually used for debugging.
+"""
+
+default_placeholder_observer = PlaceholderObserver
+"""
+Default placeholder observer, usually used for quantization to torch.float16.
+"""
+
+default_debug_observer = RecordingObserver
+"""
+Default debug-only observer.
+"""
+
+default_weight_observer = MinMaxObserver.with_args(
+    dtype=torch.qint8, qscheme=torch.per_tensor_symmetric
+)
+"""
+Default weight observer.
+"""
+
+weight_observer_range_neg_127_to_127 = MinMaxObserver.with_args(
+    dtype=torch.qint8,
+    qscheme=torch.per_tensor_symmetric,
+    quant_min=-127,
+    quant_max=127,
+    eps=2**-12,
+)
+"""
+Symmetric weight observer with the 8-bit values restricted to [-127, +127], excluding -128.
+"""
+
+default_histogram_observer = HistogramObserver.with_args(quant_min=0, quant_max=127)
+"""
+Default histogram observer, usually used for PTQ.
+"""
+
+default_per_channel_weight_observer = PerChannelMinMaxObserver.with_args(
+    dtype=torch.qint8, qscheme=torch.per_channel_symmetric
+)
+"""
+Default per-channel weight observer, usually used on backends where per-channel
+weight quantization is supported, such as `fbgemm`.
+"""
+
+per_channel_weight_observer_range_neg_127_to_127 = PerChannelMinMaxObserver.with_args(
+    dtype=torch.qint8,
+    qscheme=torch.per_channel_symmetric,
+    quant_min=-127,
+    quant_max=127,
+    eps=2**-12,
+)
+"""
+Per-channel, symmetric weight observer with the 8-bit values restricted to [-127, +127], excluding -128.
+"""
+
+default_dynamic_quant_observer = PlaceholderObserver.with_args(
+    dtype=torch.quint8,
+    quant_min=0,
+    quant_max=255,
+    is_dynamic=True,
+)
+"""
+Default observer for dynamic quantization.
+"""
+
+default_float_qparams_observer = PerChannelMinMaxObserver.with_args(
+    dtype=torch.quint8, qscheme=torch.per_channel_affine_float_qparams, ch_axis=0
+)
+"""
+Default observer for a floating point zero-point.
+"""
+
+default_float_qparams_observer_4bit = PerChannelMinMaxObserver.with_args(
+    dtype=torch.quint4x2, qscheme=torch.per_channel_affine_float_qparams, ch_axis=0
+)
+"""
+Default observer for a floating point zero-point and 4 bit activations.
+"""
+
+# TODO(future PR): remove these defaults and enforce activation functions
+# to explicitly specify their output range
+default_fixed_qparams_range_neg1to1_observer = FixedQParamsObserver.with_args(
+    scale=2.0 / 256.0, zero_point=128, dtype=torch.quint8, quant_min=0, quant_max=255
+)
+default_fixed_qparams_range_0to1_observer = FixedQParamsObserver.with_args(
+    scale=1.0 / 256.0, zero_point=0, dtype=torch.quint8, quant_min=0, quant_max=255
+)
+# TODO: the following 2 variables are kept for backwards compatibility; remove after a few releases
+default_symmetric_fixed_qparams_observer = default_fixed_qparams_range_neg1to1_observer
+default_affine_fixed_qparams_observer = default_fixed_qparams_range_0to1_observer
+
+"""
+Default observers for fixed qparams operations.
+"""
+
+default_reuse_input_observer = ReuseInputObserver
+"""
+Default observer for operators like reshape that reuses the observer of input to
+the operator
+"""
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/_numeric_debugger.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/_numeric_debugger.py
new file mode 100644
index 0000000000000000000000000000000000000000..fedcf470a18a1d35e9d72f723227de69305c3d6a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/_numeric_debugger.py
@@ -0,0 +1,235 @@
+import copy
+import logging
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Sequence, Tuple
+
+import torch
+from torch.ao.ns.fx.utils import compute_sqnr
+from torch.fx import GraphModule, Node
+from torch.nn import functional as F
+
+
+NUMERIC_DEBUG_HANDLE_KEY = "numeric_debug_handle"
+CUSTOM_KEY = "custom"
+
+log = logging.getLogger(__name__)
+
+
+def generate_numeric_debug_handle(graph_module: GraphModule) -> None:
+    """Attach numeric_debug_handle_id for all nodes in the model except for placeholder node
+    The graph nodes of input model is modified inplace.
+    """
+    unique_id = 0
+    for node in graph_module.graph.nodes:
+        if node.op in ["output", "placeholder"]:
+            continue
+
+        if CUSTOM_KEY not in node.meta:
+            node.meta[CUSTOM_KEY] = {}
+
+        if NUMERIC_DEBUG_HANDLE_KEY not in node.meta[CUSTOM_KEY]:
+            node.meta[CUSTOM_KEY][NUMERIC_DEBUG_HANDLE_KEY] = unique_id
+            unique_id += 1
+
+
+class OutputLogger(torch.nn.Module):
+    """
+    Base class for capturing output values for nodes in a GraphModule, it only captures
+    Tensor output currently, but we can extend it to work for other types of inputs later if needed
+    """
+
+    # Mark as impure so that calls to it will not be removed during DCE.
+    _is_impure = True
+
+    def __init__(
+        self,
+        debug_handle: int,
+        node_name: Optional[str] = None,
+        nn_module_stack: Optional[object] = None,
+    ) -> None:
+        super().__init__()
+        self.node_name = node_name
+        self.nn_module_stack = nn_module_stack
+        self.debug_handle = debug_handle
+        self.stats: List[torch.Tensor] = []
+
+    def forward(self, x: object) -> object:
+        if isinstance(x, torch.Tensor):
+            self.stats.append(x.detach())
+        return x
+
+    def __extra_repr__(self) -> str:
+        return (
+            f"debug_handle={self.debug_handle}, node_name={self.node_name}, "
+            "nn_module_stack={self.nn_module_stack}, num_stats={len(self.stats)})"
+        )
+
+
+def _insert_logger(model: GraphModule, node: Node, debug_handle: int) -> Node:
+    """For a given node, adds an OutputLogger that observes the output of that node,
+    and all its users use the OutputLogger output instead.
+    The OutputLogger will contain the debug_handle which can be used to compare
+    graphs after transforms"""
+
+    # to avoid circular dep
+    from torch.ao.quantization.fx.utils import get_new_attr_name_with_prefix
+
+    # add a logger after the node
+    with model.graph.inserting_after(node):
+        get_new_attr_name = get_new_attr_name_with_prefix(f"{node.name}_logger")
+        logger_name = get_new_attr_name(model)
+        setattr(
+            model,
+            logger_name,
+            OutputLogger(debug_handle, node.name, node.meta.get("nn_module_stack")),
+        )
+        logger_node = model.graph.call_module(logger_name, (node,), {})
+
+    orig_users = list(node.users.keys())
+    for user_node in orig_users:
+        if user_node is logger_node:
+            continue
+        user_node.replace_input_with(node, logger_node)
+
+    return logger_node
+
+
+def prepare_for_propagation_comparison(model: GraphModule) -> GraphModule:
+    """Add output loggers to node that has numeric_debug_handle
+
+    Args:
+        model (GraphModule): original model
+    Returns:
+        a model with output loggers for all nodes that has numeric_debug_handle_id
+    """
+    # don't change the original model
+    model = copy.deepcopy(model)
+    for n in model.graph.nodes:
+        if (
+            CUSTOM_KEY not in n.meta
+            or NUMERIC_DEBUG_HANDLE_KEY not in n.meta[CUSTOM_KEY]
+        ):
+            continue
+        numeric_debug_handle = n.meta[CUSTOM_KEY][NUMERIC_DEBUG_HANDLE_KEY]
+        _insert_logger(model, n, numeric_debug_handle)
+
+    model.recompile()
+    return model
+
+
+@dataclass(frozen=True)
+class QuantizationComparisonResult:
+    actual: torch.Tensor
+    ref: torch.Tensor
+
+    @property
+    def mse_loss(self) -> torch.Tensor:
+        return F.mse_loss(
+            self.actual.to(dtype=torch.float32), self.ref.to(dtype=torch.float32)
+        )
+
+    @property
+    def sqnr(self) -> torch.Tensor:
+        return compute_sqnr(
+            self.actual.to(dtype=torch.float32), self.ref.to(dtype=torch.float32)
+        )
+
+    def __repr__(self) -> str:
+        # Don't include the tensors themselves as they are quite large to print
+        # out.
+        return (
+            f"QuantizationComparisonResult(mse_loss={self.mse_loss}, sqnr={self.sqnr})"
+        )
+
+    def __post_init__(self) -> None:
+        if not isinstance(self.actual, torch.Tensor):
+            raise ValueError(
+                f"`self.actual` value must be a Tensor, got: {self.actual}"
+            )
+
+        if not isinstance(self.ref, torch.Tensor):
+            raise ValueError(f"`self.ref` value must be a Tensor, got: {self.ref}")
+
+
+@dataclass(frozen=True)
+class NodeAccuracySummary:
+    handle: int
+    actual_node_name: str
+    actual_module_stack: str
+    ref_node_name: str
+    ref_module_stack: str
+    results: Sequence[QuantizationComparisonResult]
+
+
+def _module_stack_to_str(module_stack: object) -> str:
+    """Simplifies the stack from ("mod", "mod.foo", "mod.foo.0", "mod.foo.0.linear")
+    to "mod.foo.0.linear"
+    """
+    if not isinstance(module_stack, dict):
+        return str(module_stack)
+    module_values_list = list(module_stack.values())
+    if len(module_values_list) > 0:
+        owning_module = module_values_list[-1][0]
+        return str(owning_module)
+    else:
+        return str(module_stack)
+
+
+def extract_results_from_loggers(
+    model: GraphModule,
+) -> Dict[int, Tuple[Optional[str], object, List[torch.Tensor]]]:
+    """For a given model, extract the tensors stats and related information for each debug handle.
+
+    Returns:
+        A dict is keyed by the debug_handle id and the values are a list of Tensors recorded
+        in loggers"""
+    # Results maps debug handle to a tensor list for each model being compared.
+    handles: Dict[int, Tuple[Optional[str], object, List[torch.Tensor]]] = {}
+    for _name, module in model.named_children():
+        if isinstance(module, OutputLogger) and len(module.stats) > 0:
+            handles[module.debug_handle] = (
+                module.node_name,
+                module.nn_module_stack,
+                module.stats,
+            )
+
+    return handles
+
+
+def compare_results(
+    ref_results: Dict[int, Tuple[str, object, List[torch.Tensor]]],
+    actual_results: Dict[int, Tuple[str, object, List[torch.Tensor]]],
+) -> Dict[int, NodeAccuracySummary]:
+    """Given two dict mapping from `debug_handle_id` (int) to list of tensors
+    return a map from `debug_handle_id` to `NodeAccuracySummary` that contains
+    comparison information like SQNR, MSE etc.
+
+    Args:
+        ref_results (Dict[int, Tuple[str, object, List[torch.Tensor]]]): reference results for each debug_handle_id
+        actual_results (Dict[int, Tuple[str, object, List[torch.Tensor]]]): actual results for each debug_handle_id
+
+    Returns:
+        Dict[int, NodeAccuracySummary]
+    """
+    comparisons = {}
+    for debug_handle, (ref_name, ref_stack, ref_stats) in ref_results.items():
+        if debug_handle not in actual_results:
+            log.debug(
+                "Cannot compare for handle %s because it wasn't found in the transformed model",
+                debug_handle,
+            )
+            continue
+        actual_name, actual_stack, actual_stats = actual_results[debug_handle]
+        comparisons[debug_handle] = NodeAccuracySummary(
+            handle=debug_handle,
+            actual_node_name=actual_name,
+            actual_module_stack=_module_stack_to_str(actual_stack),
+            ref_node_name=ref_name,
+            ref_module_stack=_module_stack_to_str(ref_stack),
+            results=[
+                QuantizationComparisonResult(actual=a, ref=b)
+                for a, b in zip(actual_stats, ref_stats)
+            ],
+        )
+
+    return comparisons
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/duplicate_dq_pass.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/duplicate_dq_pass.py
new file mode 100644
index 0000000000000000000000000000000000000000..fdfdc7f84acddfef4b682a5216be41e31ec1d52f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/duplicate_dq_pass.py
@@ -0,0 +1,82 @@
+# mypy: allow-untyped-defs
+import logging
+import operator
+
+import torch
+from torch.ao.quantization.pt2e.utils import (
+    _filter_sym_size_users,
+    _is_valid_annotation,
+)
+from torch.fx.node import map_arg
+from torch.fx.passes.infra.pass_base import PassBase, PassResult
+
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.WARNING)
+
+__all__ = ["DuplicateDQPass"]
+
+_QUANTIZE_OPS = [
+    torch.ops.quantized_decomposed.quantize_per_tensor.default,
+    torch.ops.quantized_decomposed.quantize_per_tensor.tensor,
+    torch.ops.quantized_decomposed.quantize_per_channel.default,
+]
+
+_DEQUANTIZE_OPS = [
+    torch.ops.quantized_decomposed.dequantize_per_tensor.default,
+    torch.ops.quantized_decomposed.dequantize_per_tensor.tensor,
+    torch.ops.quantized_decomposed.dequantize_per_channel.default,
+]
+
+
+def _maybe_duplicate_dq(
+    gm: torch.fx.GraphModule, dq_node: torch.fx.Node, user: torch.fx.Node
+):
+    annotation = user.meta.get("quantization_annotation", None)
+    if not _is_valid_annotation(annotation):
+        return
+    with gm.graph.inserting_after(dq_node):
+        new_node = gm.graph.node_copy(dq_node)
+
+        def maybe_replace_node(n: torch.fx.Node) -> torch.fx.Node:
+            if n == dq_node:
+                return new_node
+            else:
+                return n
+
+        new_args = map_arg(user.args, maybe_replace_node)
+        new_kwargs = map_arg(user.kwargs, maybe_replace_node)
+        user.args = new_args  # type: ignore[assignment]
+        user.kwargs = new_kwargs  # type: ignore[assignment]
+
+
+class DuplicateDQPass(PassBase):
+    def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
+        for node in graph_module.graph.nodes:
+            if node.op == "call_function" and node.target in _DEQUANTIZE_OPS:
+                dq_users = _filter_sym_size_users(node)
+                if len(dq_users) <= 1:
+                    continue
+                # Do not duplicate dq for dynamic quantization
+                # Pattern: choose_qparam - getitem - q - dq
+                q_node = node.args[0]
+                if q_node.op == "call_function" and q_node.target in _QUANTIZE_OPS:
+                    getitem_node = q_node.args[1]
+                    if (
+                        isinstance(getitem_node, torch.fx.node.Node)
+                        and getitem_node.op == "call_function"
+                        and getitem_node.target == operator.getitem
+                    ):
+                        choose_qparam_node = getitem_node.args[0]
+                        if (
+                            isinstance(choose_qparam_node, torch.fx.node.Node)
+                            and choose_qparam_node.op == "call_function"
+                            and choose_qparam_node.target
+                            == torch.ops.quantized_decomposed.choose_qparams.tensor
+                        ):
+                            continue
+                for user in dq_users:
+                    _maybe_duplicate_dq(graph_module, node, user)
+        graph_module.graph.eliminate_dead_code()
+        graph_module.recompile()
+        return PassResult(graph_module, True)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/export_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/export_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5fad1a9d7d299de567b697e39a7227de55aee798
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/export_utils.py
@@ -0,0 +1,222 @@
+# mypy: allow-untyped-defs
+import types
+
+import torch
+import torch.nn.functional as F
+from torch.ao.quantization.utils import _assert_and_get_unique_device
+
+
+__all__ = [
+    "model_is_exported",
+]
+
+
+class _WrapperModule(torch.nn.Module):
+    """Class to wrap a callable in an :class:`torch.nn.Module`. Use this if you
+    are trying to export a callable.
+    """
+
+    def __init__(self, fn):
+        super().__init__()
+        self.fn = fn
+
+    def forward(self, *args, **kwargs):
+        """Simple forward that just calls the ``fn`` provided to :meth:`WrapperModule.__init__`."""
+        return self.fn(*args, **kwargs)
+
+
+def model_is_exported(m: torch.nn.Module) -> bool:
+    """
+    Return True if the `torch.nn.Module` was exported, False otherwise
+    (e.g. if the model was FX symbolically traced or not traced at all).
+    """
+    return isinstance(m, torch.fx.GraphModule) and any(
+        "val" in n.meta for n in m.graph.nodes
+    )
+
+
+def _replace_dropout(m: torch.fx.GraphModule, train_to_eval: bool):
+    """
+    Switch dropout patterns in the model between train and eval modes.
+
+    Dropout has different behavior in train vs eval mode. For exported models,
+    however, calling `model.train()` or `model.eval()` does not automatically switch
+    the dropout behavior between the two modes, so here we need to rewrite the aten
+    dropout patterns manually to achieve the same effect.
+
+    See https://github.com/pytorch/pytorch/issues/103681.
+    """
+    # Avoid circular dependencies
+    from .utils import _get_aten_graph_module_for_pattern
+
+    # Needed to ensure subgraph matches are self-contained
+    m.graph.eliminate_dead_code()
+    m.recompile()
+
+    for inplace in [False, True]:
+
+        def dropout_train(x):
+            return F.dropout(x, p=0.5, training=True, inplace=inplace)
+
+        def dropout_eval(x):
+            return F.dropout(x, p=0.5, training=False, inplace=inplace)
+
+        example_inputs = (torch.randn(1),)
+        if train_to_eval:
+            match_pattern = _get_aten_graph_module_for_pattern(
+                _WrapperModule(dropout_train), example_inputs
+            )
+            replacement_pattern = _get_aten_graph_module_for_pattern(
+                _WrapperModule(dropout_eval), example_inputs
+            )
+        else:
+            match_pattern = _get_aten_graph_module_for_pattern(
+                _WrapperModule(dropout_eval), example_inputs
+            )
+            replacement_pattern = _get_aten_graph_module_for_pattern(
+                _WrapperModule(dropout_train), example_inputs
+            )
+
+        from torch.fx.subgraph_rewriter import replace_pattern_with_filters
+
+        replace_pattern_with_filters(
+            m,
+            match_pattern,
+            replacement_pattern,
+            match_filters=[],
+            ignore_literals=True,
+        )
+        m.recompile()
+
+
+def _replace_batchnorm(m: torch.fx.GraphModule, train_to_eval: bool):
+    """
+    Switch batchnorm patterns in the model between train and eval modes.
+
+    Batchnorm has different behavior in train vs eval mode. For exported models,
+    however, calling `model.train()` or `model.eval()` does not automatically switch
+    the batchnorm behavior between the two modes, so here we need to rewrite the aten
+    batchnorm patterns manually to achieve the same effect.
+    """
+    # TODO(Leslie): This function still fails to support custom momentum and eps value.
+    # Enable this support in future updates.
+
+    # Avoid circular dependencies
+    from .utils import _get_aten_graph_module_for_pattern
+
+    # Needed to ensure subgraph matches are self-contained
+    m.graph.eliminate_dead_code()
+    m.recompile()
+
+    def bn_train(
+        x: torch.Tensor,
+        bn_weight: torch.Tensor,
+        bn_bias: torch.Tensor,
+        bn_running_mean: torch.Tensor,
+        bn_running_var: torch.Tensor,
+    ):
+        return F.batch_norm(
+            x, bn_running_mean, bn_running_var, bn_weight, bn_bias, training=True
+        )
+
+    def bn_eval(
+        x: torch.Tensor,
+        bn_weight: torch.Tensor,
+        bn_bias: torch.Tensor,
+        bn_running_mean: torch.Tensor,
+        bn_running_var: torch.Tensor,
+    ):
+        return F.batch_norm(
+            x, bn_running_mean, bn_running_var, bn_weight, bn_bias, training=False
+        )
+
+    example_inputs = (
+        torch.randn(1, 1, 3, 3),  # x
+        torch.randn(1),  # bn_weight
+        torch.randn(1),  # bn_bias
+        torch.randn(1),  # bn_running_mean
+        torch.randn(1),  # bn_running_var
+    )
+
+    device = _assert_and_get_unique_device(m)
+    is_cuda = device is not None and device.type == "cuda"
+    bn_train_aten = _get_aten_graph_module_for_pattern(
+        _WrapperModule(bn_train),
+        example_inputs,
+        is_cuda,
+    )
+    bn_eval_aten = _get_aten_graph_module_for_pattern(
+        _WrapperModule(bn_eval),
+        example_inputs,
+        is_cuda,
+    )
+
+    if train_to_eval:
+        match_pattern = bn_train_aten
+        replacement_pattern = bn_eval_aten
+    else:
+        match_pattern = bn_eval_aten
+        replacement_pattern = bn_train_aten
+
+    from torch.fx.subgraph_rewriter import replace_pattern_with_filters
+
+    replace_pattern_with_filters(
+        m,
+        match_pattern,
+        replacement_pattern,
+        match_filters=[],
+        ignore_literals=True,
+    )
+    m.recompile()
+
+
+# TODO: expose these under this namespace?
+def _move_exported_model_to_eval(model: torch.fx.GraphModule):
+    """
+    Move an exported GraphModule to eval mode.
+
+    This is equivalent to model.eval() but only for certain special ops like dropout, batchnorm.
+    QAT users should call this before performing inference on the model.
+    """
+    _replace_dropout(model, train_to_eval=True)
+    _replace_batchnorm(model, train_to_eval=True)
+    return model
+
+
+def _move_exported_model_to_train(model: torch.fx.GraphModule):
+    """
+    Move an exported GraphModule to train mode.
+
+    This is equivalent to model.train() but only for certain special ops like dropout, batchnorm.
+    QAT users should call this before performing training on the model.
+    """
+    _replace_dropout(model, train_to_eval=False)
+    _replace_batchnorm(model, train_to_eval=False)
+    return model
+
+
+def _allow_exported_model_train_eval(model: torch.fx.GraphModule):
+    """
+    Allow users to call `model.train()` and `model.eval()` on an exported model,
+    but with the effect of changing behavior between the two modes limited to special
+    ops only, which are currently dropout and batchnorm.
+
+    Note: This does not achieve the same effect as what `model.train()` and `model.eval()`
+    does in eager models, but only provides an approximation. In particular, user code
+    branching on `training` flag will not function correctly in general because the branch
+    is already specialized at export time. Additionally, other ops beyond dropout and batchnorm
+    that have different train/eval behavior will also not be converted properly.
+    """
+
+    def _train(self, mode: bool = True):
+        if mode:
+            _move_exported_model_to_train(self)
+        else:
+            _move_exported_model_to_eval(self)
+
+    def _eval(self):
+        _move_exported_model_to_eval(self)
+
+    model.train = types.MethodType(_train, model)  # type: ignore[method-assign]
+    model.eval = types.MethodType(_eval, model)  # type: ignore[method-assign]
+    return model
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/graph_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/graph_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..34561416151f180fbcd13ea905d2be45e4ed3277
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/graph_utils.py
@@ -0,0 +1,115 @@
+# mypy: allow-untyped-defs
+import itertools
+import operator
+from typing import Any, Callable, List, Optional, OrderedDict, Set
+
+import torch
+from torch.fx import Node
+from torch.fx.passes.utils.source_matcher_utils import (
+    check_subgraphs_connected,
+    get_source_partitions,
+    SourcePartition,
+)
+
+
+__all__ = [
+    "find_sequential_partitions",
+    "get_equivalent_types",
+    "update_equivalent_types_dict",
+]
+
+_EQUIVALENT_TYPES: List[Set] = [
+    {torch.nn.Conv1d, torch.nn.functional.conv1d},
+    {torch.nn.Conv2d, torch.nn.functional.conv2d},
+    {torch.nn.AdaptiveAvgPool2d, torch.nn.functional.adaptive_avg_pool2d},
+    {torch.nn.ReLU, torch.nn.functional.relu, torch.nn.functional.relu_},
+    {torch.nn.BatchNorm2d, torch.nn.functional.batch_norm},
+    {torch.nn.Hardtanh, torch.nn.functional.hardtanh, torch.nn.functional.hardtanh_},
+    {torch.add, operator.add, operator.iadd, "add", "add_"},
+    {torch.mul, operator.mul, operator.imul, "mul", "mul_"},
+]
+
+
+def _create_equivalent_types_dict():
+    _DICT = {}
+    for values in _EQUIVALENT_TYPES:
+        for v in values:
+            _DICT[v] = list(values)
+    return _DICT
+
+
+_EQUIVALENT_TYPES_DICT = _create_equivalent_types_dict()
+
+
+def get_equivalent_types() -> List[Set]:
+    return _EQUIVALENT_TYPES
+
+
+def update_equivalent_types_dict(customized_equivalent_types=None):
+    """Help function for user who wants to customize the _EQUIVALENT_TYPES and _EQUIVALENT_TYPES_DICT.
+    When customized_equivalent_types passes in,
+    re-generate _EQUIVALENT_TYPES and _EQUIVALENT_TYPES_DICT.
+    """
+    if customized_equivalent_types is None:
+        raise ValueError("customized_equivalent_types should not be None")
+    global _EQUIVALENT_TYPES
+    global _EQUIVALENT_TYPES_DICT
+    _EQUIVALENT_TYPES = customized_equivalent_types
+    _EQUIVALENT_TYPES_DICT = _create_equivalent_types_dict()
+
+
+def _partitions_sequential(partitions: List[SourcePartition]):
+    prev_partition = None
+    for partition in partitions:
+        if prev_partition is not None and not check_subgraphs_connected(
+            prev_partition, partition
+        ):
+            return False
+        prev_partition = partition
+    return True
+
+
+def _get_matching_types(partition_type):
+    matching_types = [partition_type]
+    if partition_type in _EQUIVALENT_TYPES_DICT:
+        matching_types.extend(_EQUIVALENT_TYPES_DICT[partition_type])
+    return matching_types
+
+
+def _valid_type_sequence(partition_types: List[Any]):
+    partition_types_set = set()  # type: ignore[var-annotated]
+    for partition_type in partition_types:
+        matching_types = _get_matching_types(partition_type)
+        matching_types_set = set(matching_types)
+        if len(partition_types_set & matching_types_set) > 0:
+            return False
+        partition_types_set |= matching_types_set
+    return True
+
+
+def find_sequential_partitions(
+    gm: torch.fx.GraphModule,
+    partition_types: List[Any],
+    include_functional_equivalent=True,
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+):
+    if not _valid_type_sequence(partition_types):
+        raise ValueError(
+            f"Invalid partition types: {partition_types}. Each type in the sequence must be unique"
+        )
+
+    typed_partitions: OrderedDict[Any, List[SourcePartition]] = OrderedDict()
+    for partition_type in partition_types:
+        types_to_match = _get_matching_types(partition_type)
+        partitions = get_source_partitions(gm.graph, types_to_match, filter_fn)
+        typed_partitions[partition_type] = list(
+            itertools.chain.from_iterable(partitions.values())
+        )
+
+    typed_partitions_list = list(typed_partitions.values())
+    fusion_candidates = itertools.product(*typed_partitions_list)
+    fused_partitions = []
+    for candidate in fusion_candidates:
+        if _partitions_sequential(candidate):  # type: ignore[arg-type]
+            fused_partitions.append(candidate)
+    return fused_partitions
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/port_metadata_pass.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/port_metadata_pass.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0946d0075c995608da2ee5a251081c9e15ae1c4
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/port_metadata_pass.py
@@ -0,0 +1,215 @@
+# mypy: allow-untyped-defs
+import logging
+from typing import Optional
+
+import torch
+from torch._export.error import InternalError
+from torch.ao.quantization.pt2e.utils import (
+    _filter_sym_size_users,
+    _find_q_dq_node_for_user,
+    _is_valid_annotation,
+)
+from torch.ao.quantization.quantizer import QuantizationSpecBase
+from torch.fx.passes.infra.pass_base import PassBase, PassResult
+
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.ERROR)
+
+__all__ = ["PortNodeMetaForQDQ"]
+
+_METADATA_TO_PORT = [
+    "stack_trace",
+    "quantization_tag",
+]
+
+_QUANTIZE_OPS = [
+    torch.ops.quantized_decomposed.quantize_per_tensor.default,
+    torch.ops.quantized_decomposed.quantize_per_tensor.tensor,
+    torch.ops.quantized_decomposed.quantize_per_channel.default,
+]
+
+_DEQUANTIZE_OPS = [
+    torch.ops.quantized_decomposed.dequantize_per_tensor.default,
+    torch.ops.quantized_decomposed.dequantize_per_tensor.tensor,
+    torch.ops.quantized_decomposed.dequantize_per_channel.default,
+]
+
+_CHOOSE_QPARAMS_OPS = [
+    torch.ops.quantized_decomposed.choose_qparams.tensor,
+    torch.ops.quantized_decomposed.choose_qparams_symmetric.tensor,
+]
+
+
+def _add_metadata(to_node: torch.fx.Node, from_node: torch.fx.Node) -> None:
+    from_meta = from_node.meta
+    for meta_name in _METADATA_TO_PORT:
+        if meta_name in from_meta:
+            to_node.meta[meta_name] = from_meta[meta_name]
+
+
+def _has_quant_annotation(node: torch.fx.Node) -> bool:
+    return "quantization_annotation" in node.meta
+
+
+def _find_choose_qparams_node(node: torch.fx.Node) -> Optional[torch.fx.Node]:
+    # BFS to look for choose qparams
+    from collections import deque
+
+    queue = deque(list(node.users.keys()))
+    while len(queue):
+        n = queue.popleft()
+        if n.op == "output":
+            continue
+        if n.op == "call_function" and n.target in _CHOOSE_QPARAMS_OPS:
+            return n
+        for k in n.users.keys():
+            queue.append(k)
+    return None
+
+
+def _port_metadata_for_input_quant_nodes(
+    input_node: torch.fx.Node,
+    node: torch.fx.Node,
+    qspec: Optional[QuantizationSpecBase],
+):
+    if qspec is None:
+        return
+
+    is_dynamic_quant = getattr(qspec, "is_dynamic", None)
+    if is_dynamic_quant is not None and is_dynamic_quant is True:
+        choose_qparams_node = _find_choose_qparams_node(input_node)
+        if choose_qparams_node is None:
+            raise ValueError(f"No chose qparams node found for {node}")
+        choose_qparam_users = _filter_sym_size_users(choose_qparams_node)
+        if len(choose_qparam_users) != 2:
+            raise InternalError(f"Expecting exactly two user for {choose_qparams_node}")
+        scale_node = choose_qparam_users.pop()
+        dynamic_q_node = next(iter(scale_node.users.keys()))
+        dynamic_q_node_users = _filter_sym_size_users(dynamic_q_node)
+        if len(dynamic_q_node_users) > 1:
+            raise InternalError(f"Expecting single user for {dynamic_q_node}")
+        dynamic_dq_node = dynamic_q_node_users.pop()
+        _add_metadata(choose_qparams_node, node)
+        _add_metadata(dynamic_q_node, node)
+        _add_metadata(dynamic_dq_node, node)
+    else:
+        q_node, dq_node = _find_q_dq_node_for_user(input_node, node)
+        if q_node is None or dq_node is None:
+            return
+        # add metadata for all the node between q_node and get_attr node
+        # if the q_node can be traced back to get_attr node
+        q_to_get_attr_nodes = [q_node]
+        q_node_input = q_node.args[0]
+        while (
+            isinstance(q_node_input, torch.fx.Node)
+            and q_node_input.op == "call_function"
+            and q_node_input.target
+            in [
+                torch.ops.aten.flatten.using_ints,
+                torch.ops.aten.permute.default,
+                torch.ops.aten.permute_copy.default,
+                torch.ops.aten.slice_copy.Tensor,
+                torch.ops.aten.squeeze.dim,
+                torch.ops.aten.squeeze_copy.dim,
+                torch.ops.aten.transpose.Dimname,
+                torch.ops.aten.transpose.int,
+                torch.ops.aten.transpose_,
+                torch.ops.aten.view_copy.default,
+                torch.ops.aten.view.default,
+                torch.ops.aten._mkldnn_transpose,
+            ]
+        ):
+            q_to_get_attr_nodes.append(q_node_input)
+            q_node_input = q_node_input.args[0]
+        if isinstance(q_node_input, torch.fx.Node) and q_node_input.op == "get_attr":
+            for n in q_to_get_attr_nodes:
+                _add_metadata(n, q_node_input)
+        _add_metadata(dq_node, node)
+
+
+def _port_metadata_for_output_quant_nodes(
+    node: torch.fx.Node, qspec: Optional[QuantizationSpecBase]
+):
+    if qspec is None:
+        return
+
+    node_users = _filter_sym_size_users(node)
+    if len(node.users) == 0:
+        return
+    if len(node_users) != 1:
+        logger.warning(f"Expecting {node} to have single user")  # noqa: G004
+    q_node = node_users.pop()
+    if q_node.op != "call_function" or q_node.target not in _QUANTIZE_OPS:
+        logger.warning(
+            f"Expecting {node} user to be a quantized op but got {q_node}"  # noqa: G004
+        )  # noqa: G004
+        return
+
+    _add_metadata(q_node, node)
+
+
+class PortNodeMetaForQDQ(PassBase):
+    """
+    Port metadata for nodes added by quantization flow.
+    For static quant these are:
+    - quantizer_per_tensor.default, dequantize_per_tensor.default
+    - quantizer_per_channel.default, dequantize_per_channel.default
+    For dynamic quant these are:
+    - choose_qparams.tensor
+    - quantizer_per_tensor.tensor, dequantize_per_tensor.tensor
+    - quantizer_per_channel.default, dequantize_per_channel.default
+
+    Rules of porting metadata:
+    - Metadata to be ported:
+      - nn_module_stack
+      - stack_trace
+      - quantization_tag
+    - Metadata to NOT be ported:
+      - Everything else
+    - Rules:
+      - Statically quantized patterns:
+        - Dequantize nodes on the inputs to be quantized inherit metadata of the consumer node.
+        - Quantize nodes on the outputs inherit metadata of the producer node.
+        - Example 1:
+          - Original: [Conv -> AvgPool -> Linear]
+          - Quantized [Q-> DQ -> Conv -> Q -> DQ -> AvgPool -> Q -> DQ -> Linear -> Q -> DQ]
+          - Inner brackets specify which nodes Q/DQ inherit metdata from
+          - [Q-> [DQ -> Conv -> Q] -> [DQ -> AvgPool -> Q] -> [DQ -> Linear -> Q] -> DQ]
+          - Note first Q and last DQ do not inherit metadata from any nodes
+        - Example 2:
+          - Original: [Conv -> AvgPool -> Linear]
+          - AvgPool is not quantized
+          - Quantized [Q-> DQ -> Conv -> Q -> DQ -> AvgPool -> Q -> DQ -> Linear -> Q -> DQ]
+          - Inner brackets specify which nodes Q/DQ inherit metdata from
+          - [Q-> [DQ -> Conv -> Q] -> DQ -> [AvgPool] -> Q -> [DQ -> Linear -> Q] -> DQ]
+          - Note DQ and Q nodes around AvgPool do not inherit metadata from AvgPool because
+            AvgPool was not supposed to be quantized. Metadata porting relies on quantization_annotation
+            on the nodes (in this case AvgPool node) to conclude if the node or patter was
+            supposed to be quantized. And subsequntly decide if the preceding Q, if any, should
+            inherit metadata from AvgPool.
+      - Dynamically quantized patterns:
+        - Input that are dynamically quantized have choose_qparams, quantize and dequantize nodes
+        - For example, below linear is dynamically quantized while rest statically:
+          - Original: [Conv -> AvgPool -> Linear]
+          - Quantized [Q-> DQ -> Conv -> Q -> DQ -> AvgPool -> Q -> DQ -> choose_params -> Q -> DQ -> Linear]
+          - Quantized [Q-> [DQ -> Conv -> Q] -> [DQ -> AvgPool -> Q] -> DQ -> [choose_params -> Q -> DQ -> Linear]]
+          - Note first Q does not inherit metadata from any nodes
+    NB:
+    - The best place for porting metadata is during observer conversion to q/dq. This is because it precisely
+      knows which quantization spec is converted to q/dq and thus from where the metadata should be ported.
+      However, since FX and PT2E quant workflow are on a common code-base, this hurts readability quite a bit.
+      Doing it via a separate pass, helps readability of the code. Once we are able to refactor PT2E quant
+      code, this pass should like to be integrated in the refactored variant of "convert" step.
+    """
+
+    def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
+        for node in graph_module.graph.nodes:
+            annotation = node.meta.get("quantization_annotation", None)
+            if _is_valid_annotation(annotation):
+                input_qspec_map = node.meta["quantization_annotation"].input_qspec_map
+                output_qspec = node.meta["quantization_annotation"].output_qspec
+                for input_node, qspec in input_qspec_map.items():
+                    _port_metadata_for_input_quant_nodes(input_node, node, qspec)
+                _port_metadata_for_output_quant_nodes(node, output_qspec)
+        return PassResult(graph_module, True)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/prepare.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/prepare.py
new file mode 100644
index 0000000000000000000000000000000000000000..88ab2be9ff5a834cb7201dfcee06640bdf4c2fec
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/prepare.py
@@ -0,0 +1,569 @@
+# mypy: allow-untyped-defs
+from typing import Any, Dict, Optional, Tuple, Union
+
+import torch
+from torch._subclasses import FakeTensor
+from torch.ao.quantization import (
+    CUSTOM_KEY,
+    NUMERIC_DEBUG_HANDLE_KEY,
+    ObserverOrFakeQuantize,
+    QConfigMapping,
+)
+from torch.ao.quantization.fx.custom_config import PrepareCustomConfig
+from torch.ao.quantization.fx.prepare import (
+    _create_obs_or_fq_from_qspec,
+    _insert_obs_or_fq,
+    _is_activation_post_process_node,
+    _save_state,
+)
+from torch.ao.quantization.qconfig import QConfigAny
+from torch.ao.quantization.quantizer import (
+    EdgeOrNode,
+    QuantizationSpecBase,
+    SharedQuantizationSpec,
+)
+from torch.fx import Graph, GraphModule, Node
+from torch.fx.node import Argument
+
+
+# TODO: make pt2e folder private?
+__all__ = [
+    "prepare",
+]
+
+
+def _find_root_edge_or_node(
+    edge_or_node: EdgeOrNode, shared_with_map: Dict[EdgeOrNode, EdgeOrNode]
+) -> EdgeOrNode:
+    """Find the root node for the sharing tree
+    Args:
+        edge_or_node: edge/node that we want to find the root
+        shared_with_map: each edge/node points to the parent, the root node will points to itself
+
+    Returns:
+        root edge/node
+    """
+    parent = shared_with_map[edge_or_node]
+    if parent == edge_or_node:
+        return edge_or_node
+    root = _find_root_edge_or_node(parent, shared_with_map)
+    # path compression
+    shared_with_map[edge_or_node] = root
+    return root
+
+
+def _union(
+    parent: EdgeOrNode,
+    child: EdgeOrNode,
+    shared_with_map: Dict[EdgeOrNode, EdgeOrNode],
+) -> None:
+    """Merge the subtree for `child` with `parent`, the order is important here"""
+    root_parent = _find_root_edge_or_node(parent, shared_with_map)
+    root_child = _find_root_edge_or_node(child, shared_with_map)
+    # union the two trees by pointing the root of child to root of parent
+    shared_with_map[root_child] = root_parent
+
+
+def _update_shared_with(
+    child: EdgeOrNode,
+    qspec: QuantizationSpecBase,
+    shared_with_map: Dict[EdgeOrNode, EdgeOrNode],
+):
+    """Update the `shared_with_map` based on the qspec, this applies the `SharedQuantizationSpec`
+    configuration and established the relationship between `edge_or_node` with the edge/node that it
+    is pointing to, we'll use this information in the end to get the group id
+    """
+    if isinstance(qspec, SharedQuantizationSpec):
+        parent = qspec.edge_or_node
+        # we point from edge_or_node to the node that it is sharing_with, e.g.
+        # qspec for a = SharedQuantizationSpec(b) means `a` points to `b`
+        _union(parent, child, shared_with_map)
+
+
+def _unwrap_shared_qspec(
+    qspec: QuantizationSpecBase,
+    edge_or_node_to_qspec: Dict[EdgeOrNode, QuantizationSpecBase],
+    shared_with_map: Dict[EdgeOrNode, EdgeOrNode],
+) -> QuantizationSpecBase:
+    """Unwraps qspec to get the final root qspec (non SharedQuantizationSpec)
+    if qspec is SharedQuantizationSpec
+       (1). tries to find the root edge or node for the node that the qspec points to
+       (2). recursively find the root qspec based on the qspec for the root node
+    """
+    if isinstance(qspec, SharedQuantizationSpec):
+        sharing_with = qspec.edge_or_node
+        root = _find_root_edge_or_node(sharing_with, shared_with_map)
+        qspec = edge_or_node_to_qspec[root]
+        return _unwrap_shared_qspec(qspec, edge_or_node_to_qspec, shared_with_map)
+    return qspec
+
+
+def _has_same_dtype(qspec_a: QuantizationSpecBase, qspec_b: QuantizationSpecBase):
+    return (
+        hasattr(qspec_a, "dtype")
+        and hasattr(qspec_b, "dtype")
+        and qspec_a.dtype == qspec_b.dtype
+    )
+
+
+def _has_same_is_dynamic(qspec_a: QuantizationSpecBase, qspec_b: QuantizationSpecBase):
+    return (
+        hasattr(qspec_a, "is_dynamic")
+        and hasattr(qspec_b, "is_dynamic")
+        and qspec_a.is_dynamic == qspec_b.is_dynamic
+    )
+
+
+def _get_edge_or_node_to_qspec(
+    model: torch.fx.GraphModule,
+) -> Dict[EdgeOrNode, QuantizationSpecBase]:
+    """Get a map from EdgeOrNode to quantization spec based on annotations on the nodes"""
+    edge_or_node_to_qspec: Dict[EdgeOrNode, QuantizationSpecBase] = {}
+    for n in model.graph.nodes:
+        if hasattr(n, "meta") and "quantization_annotation" in n.meta:
+            qa = n.meta["quantization_annotation"]
+            for input_to_n, qspec in qa.input_qspec_map.items():
+                input_edge = (input_to_n, n)
+                edge_or_node_to_qspec[input_edge] = qspec
+            if qa.output_qspec is not None:
+                output_node = n
+                qspec = qa.output_qspec
+                edge_or_node_to_qspec[output_node] = qspec
+    return edge_or_node_to_qspec
+
+
+def _union_input_edge_with(
+    input_edge,
+    input_edge_root_qspec,
+    edge_or_node,
+    edge_or_node_to_qspec,
+    shared_with_map,
+):
+    """Union input edge with another edge or node, used in implicit sharing to point the current input
+    edge to other user edges of the producer node, or the output of producer node since these are
+    referring to the same Tensor
+    """
+    root_qspec = None
+    if edge_or_node in edge_or_node_to_qspec:
+        qspec = edge_or_node_to_qspec[edge_or_node]
+        root_qspec = _unwrap_shared_qspec(qspec, edge_or_node_to_qspec, shared_with_map)
+    # TODO: add assertions for types of root qspecs
+    if (
+        root_qspec is not None
+        and _has_same_dtype(root_qspec, input_edge_root_qspec)
+        and _has_same_is_dynamic(root_qspec, input_edge_root_qspec)
+    ):
+        # the input arg to the node should reuse the existing output observer for arg
+        # since dtype is the same (we may want to extend this to be a more strict check
+        # in the future)
+        # so we point from `input_edge` to `arg` (output of the argument)
+        _union(edge_or_node, input_edge, shared_with_map)
+
+
+def _get_edge_or_node_to_group_id(
+    edge_or_node_to_qspec: Dict[EdgeOrNode, QuantizationSpecBase]
+) -> Dict[EdgeOrNode, int]:
+    """Map from edge/node to the group ID, generated from quantization annotations,
+    edge/node with the same group ID should use the same observer/fake_quant instance
+
+    This is applying SharedQuantizationSpec configuration and map each edge/node to a group
+    There is another implicit sharing that's built in the quantization, when we have the following:
+       * op1 -> op2
+       * output of op1: int8_qspec
+       * (op1 -> op2) input edge: int8_qspec
+    we'll assume sharing between the output of op1 and input of (op1 -> op2) since these are the same Tensor.
+
+    Figuring out the correct group ID for all edge/node is a standard union find problem:
+    https://www.geeksforgeeks.org/introduction-to-disjoint-set-data-structure-or-union-find-algorithm/
+
+    Args:
+        edge_or_node_to_qspec: Dictionary from edge_or_node to the qspec, derived from annotations
+    Returns:
+        edge_or_node_to_group_id: Dictionary from edge_or_node to group_id (int), all edge or node that
+        belongs to the same group should have the same id
+
+    Example:
+        op2 -> cat1 -> cat2
+           op1 /        /
+                     op3
+        edge_or_node_to_qspec: {
+            op1: int8_qspec,
+            op2: int8_qspec,
+            (op1, cat1): int8_qspc,
+            (op2, cat1): SharedQuantizationSpec((op1, cat1)),
+            cat1: SharedQuantizationSpec((op1, cat1)),
+            (op3, cat2): int8_qspec,
+            (cat1, cat2): SharedQuantizationSpec((op3, cat2)),
+            cat2: SharedQuantizationSpec((op3, cat2)),
+        }
+
+        edge_or_node_to_group_id = _get_edge_or_node_to_group_id(edge_or_node_to_qspec)
+        edge_or_node_to_group_id: {
+            op1: 1,
+            op2: 1,
+            (op1, cat1): 1,
+            (op2, cat1): 1,
+            cat1: 1,
+            (op3, cat2): 1,
+            (cat1, cat2): 1,
+            cat2: 1,
+        }
+        # everything are in the same group because (cat1) and (cat1, cat2) are implicitly shared, which
+        # connects the two sharing group around cat1 and cat2 op due to transitive sharing
+    """
+    # means the observer of key should be shared with observer with value, by default it will
+    # be shared with itself
+    shared_with_map: Dict[EdgeOrNode, EdgeOrNode] = {
+        k: k for k in edge_or_node_to_qspec.keys()
+    }
+    for edge_or_node, qspec in edge_or_node_to_qspec.items():
+        if isinstance(edge_or_node, torch.fx.Node):
+            output_node = edge_or_node
+            _update_shared_with(output_node, qspec, shared_with_map)
+        else:
+            input_edge = edge_or_node
+            input_edge_root_qspec = _unwrap_shared_qspec(
+                qspec, edge_or_node_to_qspec, shared_with_map
+            )
+
+            assert isinstance(input_edge, tuple)
+            arg, n = input_edge
+            if n.meta["quantization_annotation"].allow_implicit_sharing:
+                # NOTE: the order is important here, we first share with other users and then share with previous
+                # output because the reverse order could cause circular dependency
+                # e.g node1 -> node2
+                #          \ -> node3
+                # when processing (node1, node2), if we first point (node1, node2) to node1
+                # Step 1. shared_map = {(node1, node2): node1}
+                # Step 2. after that, we point the (node1, node2) to its other user (node1, node3) ,
+                # which means shared_map = {(node1, node2): node1, node1: (node1, node3)}
+                # because we will point the root of (node1, node2) (in this case node1) to the root of (node1, node3)
+                # Step 3. and when we process (node1, node3), it can try to point to node1 as well, then we'll
+                # have a circular dependency
+                # the following order works around this issue, but this does not allow arbitrary configuration
+                # of sharing so it might break in a different case in the future, when it breaks
+                # quantizer writer can check the notes here to debug the issue
+
+                # sharing with other users of the producer node
+                # (arg, user)
+                if not isinstance(arg, Node) or not isinstance(n, Node):
+                    raise Exception(  # noqa: TRY002
+                        f"Expected input_edge to have type Tuple[Node, Node], but got: {arg, n}"
+                    )
+                for user in arg.users:
+                    if user is n:
+                        continue
+                    arg_to_user_edge = (arg, user)
+                    _union_input_edge_with(
+                        input_edge,
+                        input_edge_root_qspec,
+                        arg_to_user_edge,
+                        edge_or_node_to_qspec,
+                        shared_with_map,
+                    )
+
+                # sharing with output of producer node
+                _union_input_edge_with(
+                    input_edge,
+                    input_edge_root_qspec,
+                    arg,
+                    edge_or_node_to_qspec,
+                    shared_with_map,
+                )
+
+            _update_shared_with(input_edge, qspec, shared_with_map)
+
+    # now that we get the sharing relations between all edges and nodes, we can assingn group ids
+    cur_group_id = 0
+    edge_or_node_to_group_id: Dict[EdgeOrNode, int] = {}
+    for edge_or_node in shared_with_map.keys():
+        root = _find_root_edge_or_node(edge_or_node, shared_with_map)
+        if root not in edge_or_node_to_group_id:
+            edge_or_node_to_group_id[root] = cur_group_id
+            cur_group_id += 1
+        edge_or_node_to_group_id[edge_or_node] = edge_or_node_to_group_id[root]
+
+    return edge_or_node_to_group_id
+
+
+def _get_obs_or_fq_map(
+    edge_or_node_to_group_id: Dict[EdgeOrNode, int],
+    edge_or_node_to_qspec: Dict[EdgeOrNode, QuantizationSpecBase],
+    is_qat: bool,
+) -> Dict[EdgeOrNode, ObserverOrFakeQuantize]:
+    """Generates the EdgeOrNode to observer/fake_quant instances
+    Makes sure that for EdgeOrNode that has the same group_id should have the same observer or fake quant
+    instances
+    """
+    obs_or_fq_map: Dict[EdgeOrNode, ObserverOrFakeQuantize] = {}
+    group_id_to_obs_or_fq: Dict[int, ObserverOrFakeQuantize] = {}
+    for edge_or_node, qspec in edge_or_node_to_qspec.items():
+        group_id = edge_or_node_to_group_id[edge_or_node]
+        if group_id not in group_id_to_obs_or_fq:
+            # TODO: maybe edge_or_node_to_qspec should be edge_or_node_to_root_qspec, this will simplify
+            # the implementation for _create_obs_or_fq_from_qspec
+            group_id_to_obs_or_fq[group_id] = _create_obs_or_fq_from_qspec(
+                qspec, obs_or_fq_map, is_qat
+            )
+        obs_or_fq_map[edge_or_node] = group_id_to_obs_or_fq[group_id]
+    return obs_or_fq_map
+
+
+def _maybe_insert_input_observer_for_arg_or_kwarg(
+    node: Union[Node, Any],
+    arg: Argument,
+    qconfig: QConfigAny,
+    model: torch.nn.Module,
+    named_modules: Dict[str, torch.nn.Module],
+    obs_or_fq_map: Dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+) -> Argument:
+    """
+    Given a `node` and an `arg`, inserts an input observer between
+    `node` and `arg` if necessary.
+    """
+    # for ops such as torch.cat([x0, x1]),
+    # traverse through the list
+    if isinstance(arg, (list, tuple)):
+        new_arg_to_return = []
+        for inner_arg in arg:
+            new_inner_arg = _maybe_insert_input_observer_for_arg_or_kwarg(
+                node,
+                inner_arg,
+                qconfig,
+                model,
+                named_modules,
+                obs_or_fq_map,
+                is_qat,
+            )
+            new_arg_to_return.append(new_inner_arg)
+        return type(arg)(new_arg_to_return)
+
+    if not isinstance(arg, Node):
+        return arg
+    assert isinstance(arg, Node)
+    # default (no observer)
+    new_arg = arg
+
+    # find the original `arg` node to the current node, skipping inserted observer/fake_quant nodes
+    original_arg = arg
+    while _is_activation_post_process_node(original_arg, named_modules):
+        original_arg = original_arg.args[0]  # type: ignore[assignment]
+    assert isinstance(
+        original_arg, Node
+    ), f"expect original argument to be a Node, but got: {type(original_arg)}"
+
+    input_edge = (original_arg, node)
+    if input_edge not in obs_or_fq_map:
+        return new_arg
+    # input_edge needs to be observed
+    input_edge_obs_or_fq = obs_or_fq_map[input_edge]
+    if input_edge_obs_or_fq is None:
+        return new_arg
+
+    arg_as_output_obs_or_fq = obs_or_fq_map.get(original_arg, None)
+    # the arg is observed as the output and is using the same instance as the input_edge
+    # we'll reuse the inserted observer/fake_quant
+    if arg_as_output_obs_or_fq is not None and id(arg_as_output_obs_or_fq) == id(
+        input_edge_obs_or_fq
+    ):
+        return new_arg
+
+    # otherwise, we'll insert a new observer/fake_quant node
+
+    existing_obs_node = None
+    # skip inserting new observers if the same observer instance is inserted before for another user
+    # Example:
+    # conv1 -> obs1 -> existing_obs -> conv2
+    #             \ -> conv3
+    #
+    # instead of inserting new observers we will have:
+    # conv1 -> obs1 -> existing_obs -> conv2
+    #                            \ -> conv3
+    for maybe_obs_node in arg.users.keys():
+        if not _is_activation_post_process_node(maybe_obs_node, named_modules):
+            continue
+        maybe_obs_mod = named_modules[maybe_obs_node.target]  # type: ignore[index]
+        if id(maybe_obs_mod) == id(input_edge_obs_or_fq):
+            return maybe_obs_node
+
+    new_arg = _insert_obs_or_fq(
+        arg, input_edge_obs_or_fq, model, named_modules, model.graph
+    )
+    return new_arg
+
+
+def _maybe_insert_input_observers_for_node(
+    node: Node,
+    qconfig: QConfigAny,
+    model: torch.nn.Module,
+    named_modules: Dict[str, torch.nn.Module],
+    obs_or_fq_map: Dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+) -> None:
+    """
+    If needed, inserts observers to the input args and kwargs of `node`.
+    Note: modifies `node` inplace.
+
+    For example, if cur_node needs an observer after prev_node, we change from
+
+      prev_node -> cur_node
+
+    To
+
+      prev_node -> obs -> cur_node
+
+    """
+    # Look through every input arg.  If that arg's target dtype does not
+    # match the current node's target dtype, insert an observer.
+    new_args = []
+    for arg in node.args:
+        new_arg = _maybe_insert_input_observer_for_arg_or_kwarg(
+            node,
+            arg,
+            qconfig,
+            model,
+            named_modules,
+            obs_or_fq_map,
+            is_qat,
+        )
+        new_args.append(new_arg)
+
+    # Clone has a memory_format kwarg, zeros_like has a pin_memory kwarg, and
+    # gelu has a has an approximate kwarg that persist in exported graph.
+    # This is just a work around for these.
+    assert (
+        node.target == torch.ops.aten.clone.default
+        or node.target == torch.ops.aten.zeros_like.default
+        or node.target == torch.ops.aten.gelu.default
+        or len(node.kwargs) == 0
+    ), " expecting kwargs for aten op IR to be empty"
+
+    # assign the new args to the node, inplace
+    node.args = tuple(new_args)
+
+
+def _maybe_insert_output_observer_for_node(
+    node: Node,
+    model: torch.nn.Module,
+    named_modules: Dict[str, torch.nn.Module],
+    graph: Graph,
+    obs_or_fq_map: Dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+) -> Optional[Node]:
+    if node in obs_or_fq_map:
+        output_act_obs_or_fq = obs_or_fq_map[node]
+        new_output = _insert_obs_or_fq(
+            node, output_act_obs_or_fq, model, named_modules, graph
+        )
+        # propagate numeric debug handle from original node to observer/fake_quant node
+        if (
+            isinstance(node, Node)
+            and isinstance(new_output, Node)
+            and CUSTOM_KEY in node.meta
+            and NUMERIC_DEBUG_HANDLE_KEY in node.meta[CUSTOM_KEY]
+        ):
+            if CUSTOM_KEY not in new_output.meta:
+                new_output.meta[CUSTOM_KEY] = {}
+            new_output.meta[CUSTOM_KEY][NUMERIC_DEBUG_HANDLE_KEY] = node.meta[
+                CUSTOM_KEY
+            ][NUMERIC_DEBUG_HANDLE_KEY]
+        return new_output
+    return None
+
+
+def _maybe_insert_input_and_output_observers_for_node(
+    node: Node,
+    model: torch.fx.GraphModule,
+    obs_or_fq_map: Dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+):
+    this_node_quantization_annotation = (
+        node.meta["quantization_annotation"]
+        if "quantization_annotation" in node.meta
+        else None
+    )
+    if this_node_quantization_annotation is None:
+        return
+
+    named_modules = dict(model.named_modules(remove_duplicate=False))
+    _maybe_insert_input_observers_for_node(
+        node,
+        None,  # qconfig
+        model,
+        named_modules,
+        obs_or_fq_map,
+        is_qat,
+    )
+
+    output_is_a_tensor = "val" in node.meta and isinstance(node.meta["val"], FakeTensor)
+    if not output_is_a_tensor:
+        return
+
+    # this returns the new observer node if it was needed
+    maybe_output_obs_node = _maybe_insert_output_observer_for_node(
+        node, model, named_modules, model.graph, obs_or_fq_map, is_qat
+    )
+
+    if maybe_output_obs_node is None:
+        return
+    # Update users of original node to use the output observer
+    # instead. For example, change
+    #
+    #           next_node
+    #          /
+    #   cur_node -> obs
+    #
+    # to
+    #
+    #                 next_node
+    #                 /
+    #   cur_node -> obs
+    #
+    # We need to save orig users before updating uses because
+    # the list of users will change as we update uses
+    orig_users = list(node.users.keys())
+    for user_node in orig_users:
+        if user_node is maybe_output_obs_node:
+            continue
+        user_node.replace_input_with(node, maybe_output_obs_node)
+
+
+def prepare(
+    model: GraphModule,
+    node_name_to_scope: Dict[str, Tuple[str, type]],
+    is_qat: bool,
+) -> GraphModule:
+    # Since we are mutating the graph as we go, we iterate over the original
+    # nodes before observer insertion, instead of model.graph.nodes.
+    nodes_before_observation = list(model.graph.nodes)
+
+    # At the high level we construct a map from EdgeOrNode to a observer_or_fake_quant instance
+    # all edge/nodes that belongs to the same group will use the same instance
+    # and when we insert observers we'll just query this map to get the correct observer_or_fake_quant
+    # instance
+    edge_or_node_to_qspec = _get_edge_or_node_to_qspec(model)
+    edge_or_node_to_group_id = _get_edge_or_node_to_group_id(edge_or_node_to_qspec)
+    obs_or_fq_map = _get_obs_or_fq_map(
+        edge_or_node_to_group_id, edge_or_node_to_qspec, is_qat
+    )
+
+    for node in nodes_before_observation:
+        # TODO: simplify logic for inserting observers
+        _maybe_insert_input_and_output_observers_for_node(
+            node, model, obs_or_fq_map, is_qat
+        )
+
+    model = GraphModule(model, model.graph)
+
+    _save_state(
+        model,
+        {},  # node_name_to_qconfig
+        node_name_to_scope,
+        PrepareCustomConfig(),
+        {},  # equalization_node_name_to_qconfig
+        QConfigMapping(),
+        is_qat,
+        set(),  # observed_node_names
+    )
+    return model
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/qat_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/qat_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..da15db99a9f5b1eeba074fdcd7ccdef491037eeb
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/qat_utils.py
@@ -0,0 +1,962 @@
+# mypy: allow-untyped-defs
+import dataclasses
+import itertools
+import operator
+from typing import Any, Callable, Dict, List, Optional, Tuple, TYPE_CHECKING
+
+import torch
+import torch.nn.functional as F
+from torch.ao.quantization.fx._decomposed import quantized_decomposed_lib  # noqa: F401
+from torch.ao.quantization.pt2e.export_utils import _WrapperModule
+from torch.ao.quantization.quantizer import (
+    DerivedQuantizationSpec,
+    EdgeOrNode,
+    QuantizationSpecBase,
+    SharedQuantizationSpec,
+)
+from torch.fx import Graph, GraphModule, Node
+from torch.fx.subgraph_rewriter import replace_pattern_with_filters, ReplacedPatterns
+
+from .utils import (
+    _conv1d_bn_example_inputs,
+    _conv2d_bn_example_inputs,
+    _get_aten_graph_module_for_pattern,
+    _is_bn_node,
+    _is_conv_or_conv_transpose_node,
+    _is_conv_transpose_fn,
+    fold_bn_weights_into_conv_node,
+)
+
+
+if TYPE_CHECKING:
+    from torch.fx.passes.utils.matcher_with_name_node_map_utils import InternalMatch
+
+__all__ = []  # type: ignore[var-annotated]
+
+
+# Example inputs for quantized and folded conv-bn1d patterns used in convert
+_quantized_conv1d_bn_example_inputs = (
+    torch.randn(1, 1, 3),  # x
+    torch.randn(1, 1, 1),  # conv_weight
+    torch.randn(1),  # bn_weight
+    torch.randn(1),  # bn_bias
+    torch.randn(1),  # bn_running_mean
+    torch.randn(1),  # bn_running_var
+)
+
+# Example inputs for quantized and folded conv-bn2d patterns used in convert
+_quantized_conv2d_bn_example_inputs = (
+    torch.randn(1, 1, 3, 3),  # x
+    torch.randn(1, 1, 1, 1),  # conv_weight
+    torch.randn(1),  # bn_weight
+    torch.randn(1),  # bn_bias
+    torch.randn(1),  # bn_running_mean
+    torch.randn(1),  # bn_running_var
+)
+
+
+def _get_quantized_conv_bn_example_inputs_kwargs(
+    is_per_channel: bool,
+    has_bias: bool,
+    bias_is_quantized: bool,
+    is_cuda: bool,
+) -> Dict[str, Any]:
+    """
+    Optional example inputs for quantized and folded conv-bn patterns
+    used in convert, expressed as kwargs.
+    """
+    kwargs = {}
+    # Per tensor quantization uses literals to represent scale and zero
+    # point, so there is no need to include them here as kwargs
+    if is_per_channel:
+        kwargs["weight_scale"] = torch.tensor([1], dtype=torch.float)
+        kwargs["weight_zero_point"] = torch.tensor([0], dtype=torch.int)
+        if has_bias and bias_is_quantized:
+            kwargs["bias_scale"] = torch.tensor([1], dtype=torch.float)
+            kwargs["bias_zero_point"] = torch.tensor([0], dtype=torch.int)
+    if has_bias:
+        kwargs["conv_bias"] = torch.randn(1)
+    if is_cuda:
+        for k, v in kwargs.items():
+            if isinstance(v, torch.Tensor):
+                kwargs[k] = v.cuda()
+    return kwargs
+
+
+def _get_conv_bn_pattern(conv_fn: Callable) -> Callable:
+    def _conv_bn_pattern(
+        x: torch.Tensor,
+        conv_weight: torch.Tensor,
+        conv_bias: torch.Tensor,
+        bn_weight: torch.Tensor,
+        bn_bias: torch.Tensor,
+        bn_running_mean: torch.Tensor,
+        bn_running_var: torch.Tensor,
+    ) -> torch.Tensor:
+        x = conv_fn(x, conv_weight, conv_bias)
+        x = F.batch_norm(
+            x, bn_running_mean, bn_running_var, bn_weight, bn_bias, training=True
+        )
+        return x
+
+    return _WrapperModule(_conv_bn_pattern)
+
+
+# TODO: merge this with the `no_conv_bias` case
+def _get_qat_conv_bn_pattern(conv_fn: Callable) -> Callable:
+    def _qat_conv_bn_pattern(
+        x: torch.Tensor,
+        conv_weight: torch.Tensor,
+        conv_bias: torch.Tensor,
+        bn_weight: torch.Tensor,
+        bn_bias: torch.Tensor,
+        bn_running_mean: torch.Tensor,
+        bn_running_var: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Approximated method to fuse conv and bn. It requires only one forward pass.
+        conv_orig = conv / scale_factor where scale_factor = bn.weight / running_std.
+        This is based on `nniqat.ConvBn2d._forward_approximate`.
+        """
+        # TODO: allow setting eps
+        bn_eps = 1e-5
+        running_std = torch.sqrt(bn_running_var + bn_eps)
+        scale_factor = bn_weight / running_std
+        weight_shape = [1] * len(conv_weight.shape)
+        weight_in_channel_axis = 1 if _is_conv_transpose_fn(conv_fn) else 0
+        weight_shape[weight_in_channel_axis] = -1
+        bias_shape = [1] * len(conv_weight.shape)
+        bias_shape[1] = -1
+        scaled_weight = conv_weight * scale_factor.reshape(weight_shape)
+        zero_bias = torch.zeros_like(conv_bias, dtype=x.dtype)
+        x = conv_fn(x, scaled_weight, zero_bias)
+        x = x / scale_factor.reshape(bias_shape)
+        x = x + conv_bias.reshape(bias_shape)
+        x = F.batch_norm(
+            x,
+            bn_running_mean,
+            bn_running_var,
+            bn_weight,
+            bn_bias,
+            training=True,
+            eps=bn_eps,
+        )
+        return x
+
+    return _WrapperModule(_qat_conv_bn_pattern)
+
+
+def _get_qat_conv_bn_pattern_no_conv_bias(conv_fn: Callable) -> Callable:
+    def _qat_conv_bn_pattern_no_conv_bias(
+        x: torch.Tensor,
+        conv_weight: torch.Tensor,
+        # Not used, only for matching convenience
+        conv_bias: torch.Tensor,
+        bn_weight: torch.Tensor,
+        bn_bias: torch.Tensor,
+        bn_running_mean: torch.Tensor,
+        bn_running_var: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Same as `_get_qat_conv_bn_pattern`, but handles the case with no conv bias.
+        """
+        # TODO: allow setting eps
+        bn_eps = 1e-5
+        running_std = torch.sqrt(bn_running_var + bn_eps)
+        scale_factor = bn_weight / running_std
+        weight_shape = [1] * len(conv_weight.shape)
+        weight_in_channel_axis = 1 if _is_conv_transpose_fn(conv_fn) else 0
+        weight_shape[weight_in_channel_axis] = -1
+        bias_shape = [1] * len(conv_weight.shape)
+        bias_shape[1] = -1
+        scaled_weight = conv_weight * scale_factor.reshape(weight_shape)
+        x = conv_fn(x, scaled_weight, None)
+        x = x / scale_factor.reshape(bias_shape)
+        x = F.batch_norm(
+            x,
+            bn_running_mean,
+            bn_running_var,
+            bn_weight,
+            bn_bias,
+            training=True,
+            eps=bn_eps,
+        )
+        return x
+
+    return _WrapperModule(_qat_conv_bn_pattern_no_conv_bias)
+
+
+def _append_qdq(x, is_per_channel, is_bias, kwargs):
+    """
+    Helper function to append q-dq ops after `x`, using dummy values for the qparams
+    and qmin/qmax. We use dummy values here because we match with `ignore_literals=True`
+    and will manually replace these values after subgraph rewriting.
+
+    Return the dq node.
+    """
+    # Dummy args to be passed into q-dq ops
+    per_channel_axis = 0
+    scale_key = "bias_scale" if is_bias else "weight_scale"
+    zp_key = "bias_zero_point" if is_bias else "weight_zero_point"
+    scale = kwargs[scale_key] if is_per_channel else 1.0
+    zp = kwargs[zp_key] if is_per_channel else 0
+    qmin = -127
+    qmax = 127
+    dtype = torch.int8
+
+    qd = torch.ops.quantized_decomposed
+    if is_per_channel:
+        x = qd.quantize_per_channel(x, scale, zp, per_channel_axis, qmin, qmax, dtype)
+        x = qd.dequantize_per_channel(x, scale, zp, per_channel_axis, qmin, qmax, dtype)
+    else:
+        x = qd.quantize_per_tensor(x, scale, zp, qmin, qmax, dtype)
+        x = qd.dequantize_per_tensor(x, scale, zp, qmin, qmax, dtype)
+    return x
+
+
+def _get_quantized_qat_conv_bn_pattern(
+    is_per_channel: bool,
+    has_bias: bool,
+    bias_is_quantized: bool,
+    conv_fn: Callable,
+    bn_is_training: bool,
+) -> Callable:
+    """
+    Return the quantized version of QAT conv + BN pattern.
+    This is based on `nniqat.ConvBn2d._forward_approximate`,
+    used in QAT convert. We first match this pattern and replace
+    it with the normal [conv - bn] pattern, then fold the BN
+    weights into conv.
+    """
+    # TODO: allow setting eps
+    bn_eps = 1e-5
+
+    def _quantized_qat_conv_bn_pattern(
+        x: torch.Tensor,
+        conv_weight: torch.Tensor,
+        bn_weight: torch.Tensor,
+        bn_bias: torch.Tensor,
+        bn_running_mean: torch.Tensor,
+        bn_running_var: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        running_std = torch.sqrt(bn_running_var + bn_eps)
+        scale_factor = bn_weight / running_std
+        weight_shape = [1] * len(conv_weight.shape)
+        weight_shape[0] = -1
+        bias_shape = [1] * len(conv_weight.shape)
+        bias_shape[1] = -1
+        scaled_weight = conv_weight * scale_factor.reshape(weight_shape)
+        scaled_weight = _append_qdq(
+            scaled_weight,
+            is_per_channel,
+            is_bias=False,
+            kwargs=kwargs,
+        )
+        if has_bias:
+            zero_bias = torch.zeros_like(kwargs["conv_bias"], dtype=x.dtype)
+            if bias_is_quantized:
+                zero_bias = _append_qdq(
+                    zero_bias,
+                    is_per_channel,
+                    is_bias=True,
+                    kwargs=kwargs,
+                )
+            x = conv_fn(x, scaled_weight, zero_bias)
+        else:
+            x = conv_fn(x, scaled_weight, None)
+        x = x / scale_factor.reshape(bias_shape)
+        if has_bias:
+            x = x + kwargs["conv_bias"].reshape(bias_shape)
+        x = F.batch_norm(
+            x,
+            bn_running_mean,
+            bn_running_var,
+            bn_weight,
+            bn_bias,
+            training=bn_is_training,
+            eps=bn_eps,
+        )
+        return x
+
+    return _WrapperModule(_quantized_qat_conv_bn_pattern)
+
+
+def _get_folded_quantized_qat_conv_bn_pattern(
+    is_per_channel: bool,
+    has_bias: bool,
+    bias_is_quantized: bool,
+    conv_fn: Callable,
+    bn_is_training: bool,
+) -> Callable:
+    """
+    Quantized QAT conv - bn pattern with bn weights being folded into conv.
+    """
+    # TODO: allow setting eps
+    bn_eps = 1e-5
+
+    def _folded_quantized_qat_conv_bn_pattern(
+        x: torch.Tensor,
+        conv_weight: torch.Tensor,
+        bn_weight: torch.Tensor,
+        bn_bias: torch.Tensor,
+        bn_running_mean: torch.Tensor,
+        bn_running_var: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        conv_weight = _append_qdq(
+            conv_weight,
+            is_per_channel,
+            is_bias=False,
+            kwargs=kwargs,
+        )
+        if has_bias:
+            bias = kwargs["conv_bias"]
+            if bias_is_quantized:
+                bias = _append_qdq(
+                    bias,
+                    is_per_channel,
+                    is_bias=True,
+                    kwargs=kwargs,
+                )
+        else:
+            bias = None
+        x = conv_fn(x, conv_weight, bias)
+        x = F.batch_norm(
+            x,
+            bn_running_mean,
+            bn_running_var,
+            bn_weight,
+            bn_bias,
+            training=bn_is_training,
+            eps=bn_eps,
+        )
+        return x
+
+    return _WrapperModule(_folded_quantized_qat_conv_bn_pattern)
+
+
+def _has_conv_bias_filter(
+    match: "InternalMatch",
+    original_graph: Graph,
+    pattern_graph: Graph,
+) -> bool:
+    """
+    Match filter for the subgraph rewriter that returns True if the conv node in
+    the original graph has bias.
+    """
+    for n in match.nodes_map.values():
+        if _is_conv_or_conv_transpose_node(n):
+            return len(n.args) > 2 and n.args[2] is not None
+    raise ValueError("Could not find conv node in matched conv + bn pattern")
+
+
+def _no_conv_bias_filter(
+    match: "InternalMatch",
+    original_graph: Graph,
+    pattern_graph: Graph,
+) -> bool:
+    """
+    Match filter for the subgraph rewriter that returns True if the conv node in
+    the original graph does NOT have bias.
+    """
+    return not _has_conv_bias_filter(match, original_graph, pattern_graph)
+
+
+def _is_quantize(n: Node) -> bool:
+    return n.target in [
+        torch.ops.quantized_decomposed.quantize_per_tensor.default,
+        torch.ops.quantized_decomposed.quantize_per_tensor.tensor,
+        torch.ops.quantized_decomposed.quantize_per_channel.default,
+    ]
+
+
+def _is_dequantize(n: Node) -> bool:
+    return n.target in [
+        torch.ops.quantized_decomposed.dequantize_per_tensor.default,
+        torch.ops.quantized_decomposed.dequantize_per_tensor.tensor,
+        torch.ops.quantized_decomposed.dequantize_per_channel.default,
+    ]
+
+
+def _get_conv_bn_pattern_nodes(r: ReplacedPatterns) -> Dict[str, Tuple[Node, Node]]:
+    """
+    Helper function to extract the nodes in the conv-bn fusion pattern after
+    subgraph rewriting, in the form of a map:
+
+        {name: (original_node, replacement_node)}
+
+    The following names must exist in the map:
+
+        "conv", "conv_weight", "conv_input", "bn", "getitem"
+
+    The following names may exist in the map:
+
+        "conv_weight_q", "conv_weight_dq", "conv_bias",
+        "conv_bias_q", "conv_bias_dq"
+    """
+
+    def _get_nodes(nodes: List[Node]) -> Tuple[Node, Node, Optional[Node]]:
+        """
+        Return a 3-tuple of (conv_node, bn_node, getitem_node).
+        This asserts that the match contains exactly one of each node.
+        """
+        conv_node, bn_node, getitem_node = None, None, None
+        for n in nodes:
+            if n.op != "call_function":
+                continue
+            if _is_conv_or_conv_transpose_node(n):
+                assert conv_node is None
+                conv_node = n
+            if _is_bn_node(n):
+                assert bn_node is None
+                bn_node = n
+            if n.target == operator.getitem:
+                assert getitem_node is None
+                getitem_node = n
+        assert conv_node is not None
+        assert bn_node is not None
+        # getitem_node might be None in new training IR
+        return (conv_node, bn_node, getitem_node)
+
+    def _get_q_dq_nodes(n: Node) -> Tuple[Node, Node, Node]:
+        """
+        Return a 3-tuple of (orig_node, q_node, dq_node).
+        """
+        assert _is_dequantize(n)
+        q_node = n.args[0]
+        assert isinstance(q_node, Node)
+        assert _is_quantize(q_node)
+        orig_node = q_node.args[0]
+        assert isinstance(orig_node, Node)
+        return (orig_node, q_node, n)
+
+    original_nodes = list(_filter_nodes_map(r.nodes_map).values())
+    o_conv, o_bn, o_getitem = _get_nodes(original_nodes)
+    r_conv, r_bn, r_getitem = _get_nodes(r.replacements)
+
+    # Create the mapping from original node to replacement node
+    if o_getitem is None:
+        # getitem is None is new training IR
+        assert r_getitem is None
+        mapping = {
+            "conv": (o_conv, r_conv),
+            "bn": (o_bn, r_bn),
+        }
+    else:
+        # TODO: This branch is going through a deprecated branch and should be deleted soon,
+        # after capture_pre_autograd_graph fully migrate to training IR
+        # T199018392
+        assert r_getitem is not None
+        assert o_getitem is not None
+        mapping = {
+            "conv": (o_conv, r_conv),
+            "bn": (o_bn, r_bn),
+            "getitem": (o_getitem, r_getitem),
+        }
+
+    # Extract conv input and weight
+    # Note: here we extract the original nodes indirectly through the pattern nodes
+    # because the args of the original nodes are no longer available after replacement
+    (p_conv, _, _) = _get_nodes(list(r.nodes_map.keys()))
+    (p_conv_input, p_conv_weight, *_) = p_conv.args
+    (r_conv_input, r_conv_weight, *_) = r_conv.args
+    assert isinstance(p_conv_input, Node)
+    assert isinstance(p_conv_weight, Node)
+    assert isinstance(r_conv_input, Node)
+    assert isinstance(r_conv_weight, Node)
+    o_conv_input = r.nodes_map[p_conv_input]
+    o_conv_weight = r.nodes_map[p_conv_weight]
+
+    # If conv weight is quantized, extract the q - dq nodes
+    if _is_dequantize(p_conv_weight):
+        p_conv_weight, p_conv_weight_q, p_conv_weight_dq = _get_q_dq_nodes(
+            p_conv_weight
+        )
+        r_conv_weight, r_conv_weight_q, r_conv_weight_dq = _get_q_dq_nodes(
+            r_conv_weight
+        )
+        o_conv_weight = r.nodes_map[p_conv_weight]
+        o_conv_weight_q = r.nodes_map[p_conv_weight_q]
+        o_conv_weight_dq = r.nodes_map[p_conv_weight_dq]
+        mapping["conv_weight_q"] = (o_conv_weight_q, r_conv_weight_q)
+        mapping["conv_weight_dq"] = (o_conv_weight_dq, r_conv_weight_dq)
+    mapping["conv_input"] = (o_conv_input, r_conv_input)
+    mapping["conv_weight"] = (o_conv_weight, r_conv_weight)
+
+    # Extract conv bias
+    if len(p_conv.args) > 2 and len(r_conv.args) > 2:
+        p_conv_bias = p_conv.args[2]
+        r_conv_bias = r_conv.args[2]
+        assert isinstance(p_conv_bias, Node)
+        assert isinstance(r_conv_bias, Node)
+        o_conv_bias = r.nodes_map[p_conv_bias]
+
+        # If conv bias is quantized, extract the q - dq nodes
+        if _is_dequantize(p_conv_bias):
+            p_conv_bias, p_conv_bias_q, p_conv_bias_dq = _get_q_dq_nodes(p_conv_bias)
+            r_conv_bias, r_conv_bias_q, r_conv_bias_dq = _get_q_dq_nodes(r_conv_bias)
+            o_conv_bias = r.nodes_map[p_conv_bias]
+            o_conv_bias_q = r.nodes_map[p_conv_bias_q]
+            o_conv_bias_dq = r.nodes_map[p_conv_bias_dq]
+            mapping["conv_bias_q"] = (o_conv_bias_q, r_conv_bias_q)
+            mapping["conv_bias_dq"] = (o_conv_bias_dq, r_conv_bias_dq)
+        mapping["conv_bias"] = (o_conv_bias, r_conv_bias)
+    return mapping
+
+
+def _filter_nodes_map(nodes_map: Dict[Node, Node]) -> Dict[Node, Node]:
+    """
+    Return a filtered `nodes_map` returned from the subgraph rewriter.
+    The filtered `nodes_map` will contain only nodes that are actually
+    matched in the pattern, excluding None or placeholder nodes.
+    """
+    new_nodes_map: Dict[Node, Node] = {}
+    for pattern_node, graph_node in nodes_map.items():
+        # bias can be None
+        if graph_node is None:
+            continue
+        # skip pattern placeholder nodes
+        if pattern_node.op == "placeholder":
+            continue
+        new_nodes_map[pattern_node] = graph_node
+    return new_nodes_map
+
+
+# TODO: this is error prone, use the replace_literals_with_placeholders hack instead
+def _copy_over_literal_conv_args(original_node: Node, new_node: Node):
+    """
+    Copy over literal args in conv, such as stride and padding, from the matched node
+    in the original graph to its replacement in the new graph.
+
+    This is needed due to the following limitation in the subgraph rewriter when used
+    with dynamo export: literal (non-tensor) args are not supported in the match and
+    replacement patterns. This is because dynamo export automatically inlines these
+    literal args, making them dead placeholder nodes. In the future, we should check
+    if dynamo export can optionally disable this inlining, or if subgraph rewriter
+    can do the copying for us. See https://github.com/pytorch/pytorch/issues/100419.
+
+    Note: Unlike other tensor args like conv weights and biases, literal args are
+    preserved in the original nodes after replacement, so we can access them here.
+    """
+    assert _is_conv_or_conv_transpose_node(original_node)
+    assert _is_conv_or_conv_transpose_node(new_node)
+    # x, weight, bias, [stride, padding, dilation, transposed, output_padding, groups]
+    new_args = list(new_node.args)
+    if len(new_args) < 3:
+        # bias is optional, when it is not present, it means it is None
+        new_args.append(None)
+    new_node.args = tuple(new_args[:3]) + original_node.args[3:]
+
+
+def _update_conv_input_qspec_map_after_replacement(
+    original_node: Node, replacement_node: Node
+):
+    """
+    Update the `input_qspec_map` in the annotation after subgraph rewriting.
+
+    The original annotation referred to the nodes in the original graph,
+    so the keys in the `input_qspec_map` will need to be updated to reflect
+    the corresponding nodes in the replacement graph.
+    """
+    assert _is_conv_or_conv_transpose_node(original_node)
+    assert _is_conv_or_conv_transpose_node(replacement_node)
+    if "quantization_annotation" not in original_node.meta:
+        return
+    original_input_qspec_map = original_node.meta[
+        "quantization_annotation"
+    ].input_qspec_map
+    input_qspec_map = {}
+    # get the list of configs, it should be ordered as input, weight, bias
+    # note: this is really hacky, we need a better solution, hopefully
+    # in subgraph_rewriter, issue tracking the problem: https://github.com/pytorch/pytorch/issues/101820
+    all_configs = list(original_input_qspec_map.items())
+    # input activation
+    input_qspec_map[replacement_node.args[0]] = all_configs[0][1]
+    # weight
+    input_qspec_map[replacement_node.args[1]] = all_configs[1][1]
+    # bias
+    if len(replacement_node.args) > 2 and len(all_configs) > 2:
+        input_qspec_map[replacement_node.args[2]] = all_configs[2][1]
+    replacement_node.meta["quantization_annotation"].input_qspec_map = input_qspec_map
+
+
+def _update_special_qspecs_after_replacement(
+    node: Node,
+    original_to_replacement_node: Dict[Node, Node],
+):
+    """
+    Update the `SharedQuantizationSpec`s and `DerivedQuantizationSpec`s
+    used in `node`'s quantization annotation after subgraph rewriting.
+
+    The original annotation referred to the nodes in the original graph,
+    so the nodes used in these special quantization specs will need to
+    be updated to the corresponding nodes in the replacement graph.
+    """
+
+    def _get_new_edge_or_node(edge_or_node: EdgeOrNode):
+        if isinstance(edge_or_node, Node):
+            _node = edge_or_node
+            return original_to_replacement_node.get(_node, _node)
+        elif (
+            isinstance(edge_or_node, tuple)
+            and len(edge_or_node) == 2
+            and all(isinstance(x, Node) for x in edge_or_node)
+        ):
+            src, dest = edge_or_node
+            return (
+                original_to_replacement_node.get(src, src),
+                original_to_replacement_node.get(dest, dest),
+            )
+        else:
+            raise ValueError("unexpected type for edge_or_node: ", type(edge_or_node))
+
+    def _get_new_qspec(qspec: QuantizationSpecBase):
+        if isinstance(qspec, SharedQuantizationSpec):
+            new_edge_or_node = _get_new_edge_or_node(qspec.edge_or_node)
+            return SharedQuantizationSpec(new_edge_or_node)
+        elif isinstance(qspec, DerivedQuantizationSpec):
+            new_derived_from = [_get_new_edge_or_node(x) for x in qspec.derived_from]
+            return dataclasses.replace(qspec, derived_from=new_derived_from)
+        else:
+            return qspec
+
+    if "quantization_annotation" not in node.meta:
+        return
+    annotation = node.meta["quantization_annotation"]
+    for input_node, qspec in annotation.input_qspec_map.items():
+        annotation.input_qspec_map[input_node] = _get_new_qspec(qspec)
+    annotation.output_qspec = _get_new_qspec(annotation.output_qspec)
+
+
+def _fuse_conv_bn_qat(m: GraphModule) -> GraphModule:
+    has_bn = any(_is_bn_node(n) for n in m.graph.nodes)
+    if not has_bn:
+        return m
+    is_cuda_options = [True, False] if torch.cuda.is_available() else [False]
+    for is_cuda in is_cuda_options:
+        m = _fuse_conv_bn_qat_helper(
+            m, F.conv1d, _conv1d_bn_example_inputs, is_cuda=is_cuda
+        )
+        m = _fuse_conv_bn_qat_helper(
+            m, F.conv2d, _conv2d_bn_example_inputs, is_cuda=is_cuda
+        )
+        m = _fuse_conv_bn_qat_helper(
+            m, F.conv_transpose1d, _conv1d_bn_example_inputs, is_cuda=is_cuda
+        )
+        m = _fuse_conv_bn_qat_helper(
+            m, F.conv_transpose2d, _conv2d_bn_example_inputs, is_cuda=is_cuda
+        )
+    return m
+
+
+def _fuse_conv_bn_qat_helper(
+    m: GraphModule,
+    conv_fn: Callable,
+    example_inputs: Tuple[Any, ...],
+    is_cuda: bool,
+) -> GraphModule:
+    """
+    Given a graph of decomposed aten ops, replace the (conv + bn) pattern with
+    the fused QAT subgraph equivalent. The input graph should already be annotated.
+    The annotations in the original nodes will be preserved in the corresponding
+    nodes in the new subgraph.
+
+    Note: This also handles the (conv + bn + relu) pattern.
+    """
+    m.graph.eliminate_dead_code()
+    m.recompile()
+    conv_bn_pattern = _get_conv_bn_pattern(conv_fn)
+    match_pattern = _get_aten_graph_module_for_pattern(
+        conv_bn_pattern, example_inputs, is_cuda
+    )
+
+    # Step (1): Replace patterns with conv bias
+    #
+    # Here we do replacement separately for cases with and without conv bias, since
+    # the replacement patterns for these two cases are substantially different.
+    # TODO: use the public replace_pattern API once it also returns replacement nodes
+
+    qat_conv_bn_pattern = _get_qat_conv_bn_pattern(conv_fn)
+    replacement_pattern_with_conv_bias = _get_aten_graph_module_for_pattern(
+        qat_conv_bn_pattern,
+        example_inputs,
+        is_cuda,
+    )
+    replacements_with_conv_bias = replace_pattern_with_filters(
+        m,
+        match_pattern,
+        replacement_pattern_with_conv_bias,
+        match_filters=[_has_conv_bias_filter],
+        ignore_literals=True,
+    )
+    m.recompile()
+
+    # Step (2): Replace patterns without conv bias
+
+    qat_conv_bn_pattern_no_conv_bias = _get_qat_conv_bn_pattern_no_conv_bias(conv_fn)
+    replacement_pattern_no_conv_bias = _get_aten_graph_module_for_pattern(
+        qat_conv_bn_pattern_no_conv_bias,
+        example_inputs,
+        is_cuda,
+    )
+    replacements_no_conv_bias = replace_pattern_with_filters(
+        m,
+        match_pattern,
+        replacement_pattern_no_conv_bias,
+        match_filters=[_no_conv_bias_filter],
+        ignore_literals=True,
+    )
+    m.recompile()
+
+    # Step (3): Post processing
+    #
+    # Due to limited functionality in the subgraph rewriter, here we manually
+    # update the replacement graph as follows:
+    #
+    #   (a) Copy over metadata from original subgraph. This ensures the stack traces
+    #       and annotations are preserved in the new subgraph
+    #
+    #   (b) Copy over literal args for conv from the original subgraph
+    #       TODO: do this for literal args for batchnorm as well
+    #
+    #   (c) Update all references of the old nodes in the original subgraph to refer
+    #       to the corresponding nodes in the new subgraph in the annotations
+    #
+    # In the future, we should try to push as much of this functionality into the
+    # subgraph rewriter as possible, so we don't have to manually copy anything over.
+    # For more detail, see https://github.com/pytorch/pytorch/issues/100419.
+
+    all_original_to_replacement_nodes = {}
+    for r in replacements_with_conv_bias + replacements_no_conv_bias:
+        for original_node, replacement_node in _get_conv_bn_pattern_nodes(r).values():
+            # Step (3a): Copy over metadata for all nodes in [conv - bn - getitem]
+            replacement_node.meta = original_node.meta
+            if _is_conv_or_conv_transpose_node(original_node):
+                # Step (3b): Copy over conv literal args
+                _copy_over_literal_conv_args(original_node, replacement_node)
+                # Step (3c): Update old references in the conv node's input_qspec_map
+                _update_conv_input_qspec_map_after_replacement(
+                    original_node, replacement_node
+                )
+            all_original_to_replacement_nodes[original_node] = replacement_node
+
+    # Step (3c): Update old references in the special qspecs for all nodes in the graph
+    for n in m.graph.nodes:
+        _update_special_qspecs_after_replacement(n, all_original_to_replacement_nodes)
+
+    return m
+
+
+def _duplicate_dequantize_node(m: GraphModule):
+    """
+    Helper function to duplicate all dequantize nodes in the graph if the
+    node has more than one user. For example:
+
+    Before:
+      quantize -> dequantize -> a
+                          \\--> b
+                          \\--> c
+
+    After:
+      quantize -> dequantize_1 -> a
+            \\--> dequantize_2 -> b
+            \\--> dequantize_3 -> c
+
+    This is useful for subgraph rewriting. E.g. if we wish to match the
+    pattern [dequantize - a] above, subgraph matching would fail because
+    the dequantize node has users outside the matched portion of the graph.
+    Instead, we match [dequantize_1 - a], which is safe.
+    """
+    dq_op = torch.ops.quantized_decomposed.dequantize_per_tensor
+    for n in m.graph.nodes:
+        if n.op != "call_function" or n.target != dq_op or len(n.users) == 1:
+            continue
+        for user in list(n.users):
+            with m.graph.inserting_before(n):
+                new_node = m.graph.create_node("call_function", dq_op, n.args, n.kwargs)
+            user.replace_input_with(n, new_node)
+        m.graph.erase_node(n)
+    m.recompile()
+
+
+def _remove_extra_dequantize(m: GraphModule):
+    """
+    Removes duplicate dequant nodes in the graph, for an operator that has
+    multiple dequant nodes as a user, replace them with a single dequant node
+    that can be shared across all the uses. This should be seen as the "reverse"
+    of `_duplicate_dequantize_node`.
+    """
+    dq_op = torch.ops.quantized_decomposed.dequantize_per_tensor
+    for n in m.graph.nodes:
+        dq_users = [
+            user
+            for user in n.users
+            if user.op == "call_function" and user.target == dq_op
+        ]
+        if len(dq_users) > 1:
+            with m.graph.inserting_after(dq_users[0]):
+                new_node = m.graph.create_node(
+                    "call_function", dq_op, dq_users[0].args, {}
+                )
+            for dq_user in dq_users:
+                dq_user.replace_all_uses_with(new_node)
+                m.graph.erase_node(dq_user)
+    m.recompile()
+
+
+def _copy_over_q_dq_args(original_node: Node, replacement_node: Node):
+    """
+    Given a pair of quantize or dequantize nodes, copy over all literal args
+    from the original node to the replacement node.
+    """
+    # For quantize_per_tensor, scale and zp are literals and need to be copied
+    # For quantize_per_channel, scale and zp are get_attr nodes and should be skipped
+    assert original_node.target == replacement_node.target
+    if original_node.target in (
+        torch.ops.quantized_decomposed.quantize_per_tensor.default,
+        torch.ops.quantized_decomposed.dequantize_per_tensor.default,
+    ):
+        # Args: input, [scale, zp, qmin, qmax, dtype]
+        start_copy_arg_index = 1
+    elif original_node.target in (
+        torch.ops.quantized_decomposed.quantize_per_channel.default,
+        torch.ops.quantized_decomposed.dequantize_per_channel.default,
+    ):
+        # Args: input, scale, zp, [axis, qmin, qmax, dtype]
+        start_copy_arg_index = 3
+    else:
+        raise ValueError(
+            f"Expected quantize/dequantize nodes, got '{original_node.target}'"
+        )
+    replacement_node.args = (
+        replacement_node.args[:start_copy_arg_index]
+        + original_node.args[start_copy_arg_index:]
+    )
+
+
+def _fold_conv_bn_qat(m: GraphModule) -> GraphModule:
+    has_bn = any(_is_bn_node(n) for n in m.graph.nodes)
+    if not has_bn:
+        return m
+    is_cuda_options = [True, False] if torch.cuda.is_available() else [False]
+    for is_cuda in is_cuda_options:
+        m = _fold_conv_bn_qat_helper(
+            m, F.conv1d, _quantized_conv1d_bn_example_inputs, is_cuda=is_cuda
+        )
+        m = _fold_conv_bn_qat_helper(
+            m, F.conv2d, _quantized_conv2d_bn_example_inputs, is_cuda=is_cuda
+        )
+        m = _fold_conv_bn_qat_helper(
+            m, F.conv_transpose1d, _quantized_conv1d_bn_example_inputs, is_cuda=is_cuda
+        )
+        m = _fold_conv_bn_qat_helper(
+            m, F.conv_transpose2d, _quantized_conv2d_bn_example_inputs, is_cuda=is_cuda
+        )
+
+    # remove in place add from batchnorm tracking traning stats
+    for node in m.graph.nodes:
+        if (
+            node.target == torch.ops.aten.add_.Tensor
+            and node.args[0].op == "get_attr"
+            and node.args[1] == 1
+            and torch.nn.modules.batchnorm.BatchNorm2d
+            in [val[1] for val in node.meta["source_fn_stack"]]
+        ):
+            m.graph.erase_node(node)
+
+    m.graph.eliminate_dead_code()
+    m.recompile()
+
+    return m
+
+
+def _fold_conv_bn_qat_helper(
+    m: GraphModule,
+    conv_fn: Callable,
+    example_inputs: Tuple[Any, ...],
+    is_cuda: bool,
+) -> GraphModule:
+    """
+    Replace the quantized (conv + bn) pattern with conv with bn weights folded into the weights of conv.
+    """
+    m.graph.eliminate_dead_code()
+    m.recompile()
+    _duplicate_dequantize_node(m)
+
+    # Step (1): Replace QAT pattern with simple [conv - bn] pattern
+    replacements = []
+    replacement_options = itertools.product(
+        [True, False],  # is_per_channel
+        [True, False],  # has_bias
+        [True, False],  # bias_is_quantized
+        [True, False],  # bn_is_training
+    )
+    for (
+        is_per_channel,
+        has_bias,
+        bias_is_quantized,
+        bn_is_training,
+    ) in replacement_options:
+        # For the cases without bias, `bias_is_quantized` is irrelevant, so here we arbitrarily
+        # filter out one of the values for this flag to avoid having duplicate patterns
+        if not has_bias and bias_is_quantized:
+            continue
+        kwargs = _get_quantized_conv_bn_example_inputs_kwargs(
+            is_per_channel, has_bias, bias_is_quantized, is_cuda
+        )
+        match_pattern = _get_quantized_qat_conv_bn_pattern(
+            is_per_channel, has_bias, bias_is_quantized, conv_fn, bn_is_training
+        )
+        match_pattern = _get_aten_graph_module_for_pattern(
+            match_pattern, example_inputs, is_cuda, **kwargs
+        )
+        replacement_pattern = _get_folded_quantized_qat_conv_bn_pattern(
+            is_per_channel, has_bias, bias_is_quantized, conv_fn, bn_is_training
+        )
+        replacement_pattern = _get_aten_graph_module_for_pattern(
+            replacement_pattern, example_inputs, is_cuda, **kwargs
+        )
+        replacements.extend(
+            replace_pattern_with_filters(
+                m,
+                match_pattern,
+                replacement_pattern,
+                ignore_literals=True,
+            )
+        )
+    m.recompile()
+    _remove_extra_dequantize(m)
+
+    for r in replacements:
+        node_map = _get_conv_bn_pattern_nodes(r)
+
+        # Step (2): Copy over metadata from original subgraph
+        for original_node, replacement_node in node_map.values():
+            replacement_node.meta = original_node.meta
+
+        # Step (3): Copy over args for weight (and optionally bias) q - dq nodes
+        _copy_over_q_dq_args(*node_map["conv_weight_q"])
+        _copy_over_q_dq_args(*node_map["conv_weight_dq"])
+        if "conv_bias_q" in node_map:
+            assert "conv_bias_dq" in node_map
+            _copy_over_q_dq_args(*node_map["conv_bias_q"])
+            _copy_over_q_dq_args(*node_map["conv_bias_dq"])
+
+        # Step (4): Fold BN weights into conv
+        conv_bias = None
+        (_, conv_node) = node_map["conv"]
+        (_, bn_node) = node_map["bn"]
+        (_, conv_weight) = node_map["conv_weight"]
+        if "conv_bias" in node_map:
+            (_, conv_bias) = node_map["conv_bias"]
+        fold_bn_weights_into_conv_node(conv_node, conv_weight, conv_bias, bn_node, m)
+
+        # Copy over literal args for conv
+        for original_node in _filter_nodes_map(r.nodes_map).values():
+            if _is_conv_or_conv_transpose_node(original_node):
+                _copy_over_literal_conv_args(original_node, conv_node)
+
+    m.graph.eliminate_dead_code()
+    m.recompile()
+    return m
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/representation/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/representation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8876d439feb41929ca9b64f3f023db499eac007b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/representation/__init__.py
@@ -0,0 +1,6 @@
+from .rewrite import reference_representation_rewrite
+
+
+__all__ = [
+    "reference_representation_rewrite",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/representation/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/representation/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6b141c60dc05b527a123b818e5c0f3d155b7f2bd
Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/representation/__pycache__/__init__.cpython-311.pyc differ
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/representation/__pycache__/rewrite.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/representation/__pycache__/rewrite.cpython-311.pyc
new file mode 100644
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/representation/rewrite.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/representation/rewrite.py
new file mode 100644
index 0000000000000000000000000000000000000000..c040cd36ffd687a7a4dba74cabc66a41554db631
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/representation/rewrite.py
@@ -0,0 +1,827 @@
+# mypy: allow-untyped-defs
+from dataclasses import dataclass
+from functools import partial
+from typing import Any, Callable, Optional, Tuple
+
+import torch
+from torch._higher_order_ops.out_dtype import out_dtype
+from torch.ao.quantization.fx._decomposed import quantized_decomposed_lib  # noqa: F401
+from torch.ao.quantization.pt2e.export_utils import _WrapperModule
+from torch.ao.quantization.pt2e.utils import (
+    _get_aten_graph_module_for_pattern,
+    _replace_literals_with_existing_placeholders,
+    _replace_literals_with_new_placeholders,
+    remove_tensor_overload_for_qdq_ops,
+)
+from torch.fx import GraphModule
+from torch.fx.subgraph_rewriter import replace_pattern
+
+
+__all__ = [
+    "reference_representation_rewrite",
+]
+
+
+_QUANTIZED_LINEAR_EXAMPLE_INPUTS = (
+    torch.randint(-128, 127, (2, 5), dtype=torch.int8),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-128], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+    torch.randint(-128, 127, (5, 5), dtype=torch.int8),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-127], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+    torch.randn(1, dtype=torch.float),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-128], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+)
+
+
+def _qdq_quantized_linear(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    x_quant_min,
+    x_quant_max,
+    weight_i8,
+    weight_scale,
+    weight_zero_point,
+    weight_quant_min,
+    weight_quant_max,
+    bias_fp32,
+    out_scale,
+    out_zero_point,
+    out_quant_min,
+    out_quant_max,
+):
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        x_i8, x_scale, x_zero_point, x_quant_min, x_quant_max, torch.int8
+    )
+    weight_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        weight_i8,
+        weight_scale,
+        weight_zero_point,
+        weight_quant_min,
+        weight_quant_max,
+        torch.int8,
+    )
+    out_fp32 = torch.ops.aten.linear.default(x_fp32, weight_fp32, bias_fp32)
+    out_i8 = torch.ops.quantized_decomposed.quantize_per_tensor(
+        out_fp32, out_scale, out_zero_point, out_quant_min, out_quant_max, torch.int8
+    )
+    return out_i8
+
+
+def _reference_quantized_linear(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    x_quant_min,
+    x_quant_max,
+    weight_i8,
+    weight_scale,
+    weight_zero_point,
+    weight_quant_min,
+    weight_quant_max,
+    bias_fp32,
+    out_scale,
+    out_zero_point,
+    out_quant_min,
+    out_quant_max,
+):
+    # without using quant_min/max in clamp, the traced graph will not have quant_mi/max args.
+    # This results in failure to match the pattern.
+    # Therefore, we call a torch.ops.aten.clamp here
+    x_i8 = torch.ops.aten.clamp(x_i8, x_quant_min, x_quant_max)
+    weight_i8 = torch.ops.aten.clamp(weight_i8, weight_quant_min, weight_quant_max)
+
+    x_i16 = x_i8.to(torch.int16)
+    weight_i16 = weight_i8.to(torch.int16)
+    # always set bias to None so that the same representation can work for the case
+    # no matter if bias_scale == x_scale * weight_scale or not
+    acc_i32 = out_dtype(
+        torch.ops.aten.linear.default,
+        torch.int32,
+        x_i16 - x_zero_point,
+        weight_i16 - weight_zero_point,
+        None,
+    )
+    # TODO: change to mul.Scalar
+    # Note: we are quantizing bias with these scales without signal from user, but it might be OK
+    bias_scale = x_scale * weight_scale
+    bias_i32 = out_dtype(torch.ops.aten.div.Tensor, torch.int32, bias_fp32, bias_scale)
+    acc_i32 = acc_i32 + bias_i32
+    # TODO: change to mul.Scalar when we make x_scale/weight_scale etc. Scalar values
+    acc_i32 = (
+        out_dtype(
+            torch.ops.aten.mul.Tensor,
+            torch.int32,
+            acc_i32,
+            x_scale * weight_scale / out_scale,
+        )
+        + out_zero_point
+    )
+    out_i8 = torch.ops.aten.clamp(acc_i32, out_quant_min, out_quant_max).to(torch.int8)
+    return out_i8
+
+
+_DYNAMIC_QUANTIZED_LINEAR_EXAMPLE_INPUTS = (
+    torch.randn((2, 5), dtype=torch.float),
+    -128,
+    127,
+    torch.finfo(torch.float32).eps,
+    torch.randint(-128, 127, (5, 5), dtype=torch.int8),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-127], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+    torch.randn(1, dtype=torch.float),
+)
+
+
+def _qdq_dynamic_quantized_linear(
+    x_fp32,
+    x_quant_min,
+    x_quant_max,
+    x_eps,
+    weight_i8,
+    weight_scale,
+    weight_zero_point,
+    weight_quant_min,
+    weight_quant_max,
+    bias_fp32,
+):
+    x_scale, x_zero_point = torch.ops.quantized_decomposed.choose_qparams(
+        x_fp32, x_quant_min, x_quant_max, x_eps, torch.int8
+    )
+    x_i8 = torch.ops.quantized_decomposed.quantize_per_tensor(
+        x_fp32, x_scale, x_zero_point, x_quant_min, x_quant_max, torch.int8
+    )
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        x_i8, x_scale, x_zero_point, x_quant_min, x_quant_max, torch.int8
+    )
+    weight_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        weight_i8,
+        weight_scale,
+        weight_zero_point,
+        weight_quant_min,
+        weight_quant_max,
+        torch.int8,
+    )
+    out_fp32 = torch.ops.aten.linear.default(x_fp32, weight_fp32, bias_fp32)
+    return out_fp32
+
+
+def _reference_dynamic_quantized_linear(
+    x_fp32,
+    x_quant_min,
+    x_quant_max,
+    x_eps,
+    weight_i8,
+    weight_scale,
+    weight_zero_point,
+    weight_quant_min,
+    weight_quant_max,
+    bias_fp32,
+):
+    x_scale, x_zero_point = torch.ops.quantized_decomposed.choose_qparams(
+        x_fp32, x_quant_min, x_quant_max, x_eps, torch.int8
+    )
+    # decomposed representation for quantize_per_tensor
+    # TODO: use out_dtype(mul, ...) here when the op is ready
+    x_fp32 = x_fp32 / x_scale  # fp32
+    # round modes might be different here
+    # pytorch is rounding to even, which is also common for most of the backends
+    x_fp32 = torch.round(x_fp32)  # fp32
+    x_i32 = x_fp32.to(dtype=torch.int32)  # int32
+    x_i32 = x_i32 + x_zero_point  # int32
+    # clamp works for fp32, int32 and int8 dtypes
+    x_i32 = torch.clamp(x_i32, x_quant_min, x_quant_max)  # int32
+    x_i8 = x_i32.to(dtype=torch.int8)
+
+    weight_i8 = torch.ops.aten.clamp(weight_i8, weight_quant_min, weight_quant_max)
+
+    x_i16 = x_i8.to(torch.int16)
+    weight_i16 = weight_i8.to(torch.int16)
+    # always set bias to None so that the same representation can work for the case
+    # no matter if bias_scale == x_scale * weight_scale or not
+    acc_i32 = out_dtype(
+        torch.ops.aten.linear.default,
+        torch.int32,
+        x_i16 - x_zero_point,
+        weight_i16 - weight_zero_point,
+        None,
+    )
+    bias_scale = x_scale * weight_scale
+    bias_i32 = out_dtype(torch.ops.aten.div.Tensor, torch.int32, bias_fp32, bias_scale)
+    acc_i32 = acc_i32 + bias_i32
+    out_fp32 = acc_i32 * (x_scale * weight_scale)
+    return out_fp32
+
+
+_QUANTIZED_CONV2d_EXAMPLE_INPUTS = (
+    torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-128], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+    torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-127], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+    torch.randn(1, dtype=torch.float),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-128], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+)
+
+
+def _qdq_quantized_conv2d(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    x_quant_min,
+    x_quant_max,
+    weight_i8,
+    weight_scale,
+    weight_zero_point,
+    weight_quant_min,
+    weight_quant_max,
+    bias_fp32,
+    out_scale,
+    out_zero_point,
+    out_quant_min,
+    out_quant_max,
+):
+    stride = [1, 1]
+    padding = [0, 0]
+    dilation = [1, 1]
+    transposed = False
+    output_padding = [0, 0]
+    groups = 1
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        x_i8, x_scale, x_zero_point, x_quant_min, x_quant_max, torch.int8
+    )
+    weight_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        weight_i8,
+        weight_scale,
+        weight_zero_point,
+        weight_quant_min,
+        weight_quant_max,
+        torch.int8,
+    )
+    out_fp32 = torch.ops.aten.convolution.default(
+        x_fp32,
+        weight_fp32,
+        bias_fp32,
+        stride,
+        padding,
+        dilation,
+        transposed,
+        output_padding,
+        groups,
+    )
+    out_i8 = torch.ops.quantized_decomposed.quantize_per_tensor(
+        out_fp32, out_scale, out_zero_point, out_quant_min, out_quant_max, torch.int8
+    )
+    return out_i8
+
+
+def _reference_quantized_conv2d(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    x_quant_min,
+    x_quant_max,
+    weight_i8,
+    weight_scale,
+    weight_zero_point,
+    weight_quant_min,
+    weight_quant_max,
+    bias_fp32,
+    out_scale,
+    out_zero_point,
+    out_quant_min,
+    out_quant_max,
+):
+    stride = [1, 1]
+    padding = [0, 0]
+    dilation = [1, 1]
+    transposed = False
+    output_padding = [0, 0]
+    groups = 1
+    # without using quant_min/max in clamp, the traced graph will not have quant_mi/max args.
+    # This results in failure to match the pattern.
+    # Therefore, we call a torch.ops.aten.clamp here
+    x_i8 = torch.ops.aten.clamp(x_i8, x_quant_min, x_quant_max)
+    weight_i8 = torch.ops.aten.clamp(weight_i8, weight_quant_min, weight_quant_max)
+
+    x_i16 = x_i8.to(torch.int16)
+    weight_i16 = weight_i8.to(torch.int16)
+    # always set bias to None so that the same representation can work for the case
+    # no matter if bias_scale == x_scale * weight_scale or not
+    acc_i32 = out_dtype(
+        torch.ops.aten.convolution.default,
+        torch.int32,
+        x_i16 - x_zero_point,
+        weight_i16 - weight_zero_point,
+        None,
+        stride,
+        padding,
+        dilation,
+        transposed,
+        output_padding,
+        groups,
+    )
+    # Note: we are quantizing bias with these scales without signal from user, but it might be OK
+    bias_scale = x_scale * weight_scale
+    # bias quantization to int32 uses bias_scale = x_scale * weight_scale due to:
+    # Take linear calculation for example
+    # Out_(i, j)_fp32 = Sum_(over k)[X_(i, k)_fp32 * W_(i, k)_fp32] + bias_(i)_fp32
+    # Represent X, W fp32 as their dequant transforms
+    # A_fp32 = (A_q - A_zero_point)/A_scale
+    # Out_(i, j)_fp32 = Sum_(over k)[(X_(i, k)_fp32 - X_zp) * X_scale * (W_(i, k)_fp32 - W_zp) * W_scale] + bias_(i)_fp32
+    # Factor out X_scale and W_scale
+    # Out_(i, j)_fp32 = ((X_scale * W_scale) * Sum_(over k)[(X_(i, k)_fp32 - X_zp) * (W_(i, k)_fp32 - W_zp)]) + bias_(i)_fp32
+    # In order to addition of bias_(i)_fp32 inside, we must do
+    # Out_(i, j)_fp32 = (X_scale * W_scale) * (Sum_(over k)[(X_(i, k)_fp32 - X_zp) * (W_(i, k)_fp32 - W_zp)] + (1 / (X_scale * W_scale)) * bias_(i)_fp32)W_scale  # noqa: B950
+    # Note we had to multiply bias_fp32 qith X_scale * W_scale = bias_scale
+    # Thus bias quantization to int32 must be with X_scale * W_scale
+
+    bias_i32 = out_dtype(torch.ops.aten.div.Tensor, torch.int32, bias_fp32, bias_scale)
+    # Unsqueeze to match broadcast dims
+    # Unfortnuately I cannot do bias_i32.unsqueeze(0) due to literal matching nightmare
+    # in graph pattern replacement
+    bias_i32 = bias_i32.unsqueeze(-1)
+    bias_i32 = bias_i32.unsqueeze(-1)
+    acc_i32 = acc_i32 + bias_i32
+    # TODO: change to mul.Scalar when we make x_scale/weight_scale etc. Scalar values
+    acc_i32 = (
+        out_dtype(
+            torch.ops.aten.mul.Tensor,
+            torch.int32,
+            acc_i32,
+            x_scale * weight_scale / out_scale,
+        )
+        + out_zero_point
+    )
+    out_i8 = torch.ops.aten.clamp(acc_i32, out_quant_min, out_quant_max).to(torch.int8)
+    return out_i8
+
+
+_QUANTIZED_ADD_OR_ADD_RELU_EXAMPLE_INPUTS = (
+    torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-128], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+)
+
+
+def _qdq_quantized_add_relu(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    y_i8,
+    y_scale,
+    y_zero_point,
+    out_scale,
+    out_zero_point,
+    quant_min,
+    quant_max,
+):
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        x_i8, x_scale, x_zero_point, quant_min, quant_max, torch.int8
+    )
+    y_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        y_i8, y_scale, y_zero_point, quant_min, quant_max, torch.int8
+    )
+    out_fp32 = x_fp32 + y_fp32
+    out_fp32 = torch.ops.aten.relu(out_fp32)
+    out_i8 = torch.ops.quantized_decomposed.quantize_per_tensor(
+        out_fp32, out_scale, out_zero_point, quant_min, quant_max, torch.int8
+    )
+    return out_i8
+
+
+def _reference_quantized_add_relu(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    y_i8,
+    y_scale,
+    y_zero_point,
+    out_scale,
+    out_zero_point,
+    quant_min,
+    quant_max,
+):
+    """
+    See comments for `_reference_quantized_add` for more information on
+    how to derive the formula for out_i8 based on x_i8 and y_i8
+    """
+    x_i32 = x_i8.to(torch.int32)
+    y_i32 = y_i8.to(torch.int32)
+    # TODO: change this to mul.Scalar?
+    x_i32 = out_dtype(
+        torch.ops.aten.mul.Tensor,
+        torch.int32,
+        (x_i32 - x_zero_point),
+        (x_scale / out_scale),
+    )
+    y_i32 = out_dtype(
+        torch.ops.aten.mul.Tensor,
+        torch.int32,
+        (y_i32 - y_zero_point),
+        (y_scale / out_scale),
+    )
+    out_i32 = x_i32 + y_i32 + out_zero_point
+    # out_i32 = torch.ops.aten.clamp(out_i32, out_zero_point)
+    out_i8 = torch.ops.aten.clamp(out_i32, out_zero_point, quant_max).to(torch.int8)
+    return out_i8
+
+
+def _qdq_quantized_add(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    y_i8,
+    y_scale,
+    y_zero_point,
+    out_scale,
+    out_zero_point,
+    quant_min,
+    quant_max,
+):
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        x_i8, x_scale, x_zero_point, quant_min, quant_max, torch.int8
+    )
+    y_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        y_i8, y_scale, y_zero_point, quant_min, quant_max, torch.int8
+    )
+    out_fp32 = x_fp32 + y_fp32
+    out_i8 = torch.ops.quantized_decomposed.quantize_per_tensor(
+        out_fp32, out_scale, out_zero_point, quant_min, quant_max, torch.int8
+    )
+    return out_i8
+
+
+def _reference_quantized_add(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    y_i8,
+    y_scale,
+    y_zero_point,
+    out_scale,
+    out_zero_point,
+    quant_min,
+    quant_max,
+):
+    """
+        # How to Derive the formula for out_i8 based on x_i8 and y_i8
+        # (since quantized add takes x_i8, y_i8 and their quantization parameters, and produce an out_i8)
+
+        # out_i8 is quantized output, we can write down the formula for it first:
+    out_i8 = out_f32 / out_scale + out_zero_point           (1)
+
+        # then out_fp32 is computed from x_f32 + y_f32, and the x_fp32 and y_fp32 are the dequantized x_i8 and y_i8
+        out_f32 = x_f32 + y_f32           (2)
+        x_fp32 = (x_i8 - x_zero_point) * x_scale         (3)
+        y_fp32 = (y_i8 - y_zero_point) * y_scale         (4)
+
+        # applying the above fomula to the out_i8 equation we can get the following:
+        out_i8 = out_fp32 / out_scale + out_zero_point             # (1)
+           = (x_f32 + y_f32) / out_scale + out_zero_point      # applying (2) to substitute out_fp32 with x_fp32 + y_fp32
+           = ((x_i8 - x_zero_point) * x_scale + (y_i8 - y_zero_point) * y_scale) / out_scale + out_zero_point  # apply (3) and (4)
+    """
+    x_i32 = x_i8.to(torch.int32)
+    y_i32 = y_i8.to(torch.int32)
+    # TODO: use out_dtype op
+    x_i32 = torch.round((x_scale / out_scale) * (x_i32 - x_zero_point)).to(torch.int32)
+    y_i32 = torch.round((y_scale / out_scale) * (y_i32 - y_zero_point)).to(torch.int32)
+    out_i32 = x_i32 + y_i32 + out_zero_point
+    quant_min = -128
+    quant_max = 127
+    out_i8 = torch.ops.aten.clamp(out_i32, quant_min, quant_max).to(torch.int8)
+    return out_i8
+
+
+_QUANTIZED_MAX_POOL2D_EXAMPLE_INPUTS = (
+    torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-128], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-128], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+)
+
+
+def _qdq_quantized_max_pool2d(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    x_quant_min,
+    x_quant_max,
+    out_scale,
+    out_zero_point,
+    out_quant_min,
+    out_quant_max,
+):
+    kernel_size = 1
+    stride = 1
+    padding = 0
+    dilation = 1
+    ceil_mode = False
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        x_i8, x_scale, x_zero_point, x_quant_min, x_quant_max, torch.int8
+    )
+    out_fp32, _ = torch.ops.aten.max_pool2d_with_indices.default(
+        x_fp32, kernel_size, stride, padding, dilation, ceil_mode
+    )
+    out_i8 = torch.ops.quantized_decomposed.quantize_per_tensor(
+        out_fp32, out_scale, out_zero_point, out_quant_min, out_quant_max, torch.int8
+    )
+    return out_i8
+
+
+def _reference_quantized_max_pool2d(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    x_quant_min,
+    x_quant_max,
+    out_scale,
+    out_zero_point,
+    out_quant_min,
+    out_quant_max,
+):
+    kernel_size = 1
+    stride = 1
+    padding = 0
+    dilation = 1
+    ceil_mode = False
+    # to preserve x_quant_min, x_quant_max in the graph for pattern matching
+    x_i8 = torch.clamp(x_i8, x_quant_min, x_quant_max)
+    x_i32 = x_i8.to(torch.int32)
+    out_i32, _ = torch.ops.aten.max_pool2d_with_indices.default(
+        x_i32 - x_zero_point, kernel_size, stride, padding, dilation, ceil_mode
+    )
+    out_fp32 = out_i32 * (x_scale / out_scale) + out_zero_point
+    out_fp32 = torch.clamp(out_fp32, out_quant_min, out_quant_max)
+    out_i8 = out_fp32.to(torch.int8)
+    return out_i8
+
+
+_QUANTIZE_PER_TENSOR_INT8_EXAMPLE_INPUTS = (
+    torch.randn(1, 3, 3, 3, dtype=torch.float),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-128], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+)
+
+
+def _quantize_per_tensor_int8(x_fp32, scale, zero_point, quant_min, quant_max):
+    x = torch.ops.quantized_decomposed.quantize_per_tensor(
+        x_fp32, scale, zero_point, quant_min, quant_max, torch.int8
+    )
+    return x
+
+
+def _reference_quantize_per_tensor_int8(
+    x_fp32, scale, zero_point, quant_min, quant_max
+):
+    # TODO: use out_dtype(mul, ...) here when the op is ready
+    x = x_fp32 / scale  # fp32
+    # round modes might be different here
+    # pytorch is rounding to even, which is also common for most of the backends
+    x = torch.round(x)  # fp32
+    x = x.to(dtype=torch.int32)  # int32
+    x = x + zero_point  # int32
+    # clamp works for fp32, int32 and int8 dtypes
+    x = torch.clamp(x, quant_min, quant_max)  # int32
+    x = x.to(dtype=torch.int8)
+    return x
+
+
+_DEQUANTIZE_PER_TENSOR_INT8_EXAMPLE_INPUTS = (
+    torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-128], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+)
+
+
+def _dequantize_per_tensor_int8(x_i8, scale, zero_point, quant_min, quant_max):
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        x_i8, scale, zero_point, quant_min, quant_max, torch.int8
+    )
+    return x_fp32
+
+
+def _reference_dequantize_per_tensor_int8(
+    x_i8, scale, zero_point, quant_min, quant_max
+):
+    # without using quant_min/max in clamp, the traced graph will not have quant_mi/max args.
+    # This results in failure to match the pattern.
+    # Therefore, we call a torch.ops.aten.clamp here
+    x_i8 = torch.ops.aten.clamp(x_i8, quant_min, quant_max)
+    # TODO: use out_dtype op
+    # note: x_i8.to(torch.int32) does not work here
+    # TODO: debug the implementation later when torchdynamo time out issue is resolved
+    return ((x_i8.to(torch.float32) - zero_point) * scale).to(dtype=torch.float32)
+
+
+_QUANTIZE_PER_CHANNEL_INT8_EXAMPLE_INPUTS = (
+    torch.randn(1, 3, 3, 3, dtype=torch.float),
+    torch.randn(3, dtype=torch.float),
+    torch.zeros(3, dtype=torch.int),
+    1,
+    -128,
+    127,
+)
+
+
+def _quantize_per_channel_int8(
+    x_fp32, scales, zero_points, ch_axis, quant_min, quant_max
+):
+    out_i8 = torch.ops.quantized_decomposed.quantize_per_channel(
+        x_fp32, scales, zero_points, ch_axis, quant_min, quant_max, torch.int8
+    )
+    return out_i8
+
+
+def _reference_quantize_per_channel_int8(
+    x_fp32, scales, zero_points, ch_axis, quant_min, quant_max
+):
+    x_fp32 = torch.transpose(x_fp32, ch_axis, -1)
+    out_i32 = torch.ops.aten.clamp(
+        torch.round(x_fp32 / scales).to(torch.int32) + zero_points, quant_min, quant_max
+    )
+    out_i32 = torch.transpose(out_i32, ch_axis, -1)
+    return out_i32.to(torch.int8)
+
+
+_DEQUANTIZE_PER_CHANNEL_INT8_EXAMPLE_INPUTS = (
+    torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+    torch.randn(3, dtype=torch.float),
+    torch.zeros(3, dtype=torch.int),
+    1,
+    -128,
+    127,
+)
+
+
+def _dequantize_per_channel_int8(
+    x_i8, scales, zero_points, ch_axis, quant_min, quant_max
+):
+    # the following will be replaced as placeholders
+    out_fp32 = torch.ops.quantized_decomposed.dequantize_per_channel(
+        x_i8, scales, zero_points, ch_axis, quant_min, quant_max, torch.int8
+    )
+    return out_fp32
+
+
+def _reference_dequantize_per_channel_int8(
+    x_i8, scales, zero_points, ch_axis, quant_min, quant_max
+):
+    # the following will be replaced as placeholders
+    # in order to preserve the quant_min/quant_max args for pattern matching (e.g. matching for int4 quantized ops)
+    # we call a torch.ops.aten.clamp here
+    x_i8 = torch.ops.aten.clamp(x_i8, quant_min, quant_max)
+    x_i8 = torch.transpose(x_i8, ch_axis, -1)
+    x_i32 = x_i8.to(torch.int32)
+    out_fp32 = (x_i32 - zero_points).to(torch.float) * scales
+    out_fp32 = torch.transpose(out_fp32, ch_axis, -1)
+    return out_fp32
+
+
+def _replace_ph_qdq_per_channel_replacement(gm: torch.fx.GraphModule):
+    return _replace_literals_with_existing_placeholders(
+        gm, exclude_literals=[-1], literal_to_ph_idx={1: 3, -128: 4, 127: 5}
+    )
+
+
+@dataclass
+class _RewriteInfo:
+    """Data needed for rewrite, this includes example inputs, pattern and replacement functions
+    and post transformation functions for the exported pattern and replacement GraphModule
+    """
+
+    # example inputs used for exporting the pattern into GraphModule
+    example_inputs: Tuple[Any, ...]
+    pattern: Callable
+    replacement: Callable
+    # post transformation on the exported pattern and replacement GraphModule
+    pattern_post_trans: Optional[Callable[[GraphModule], GraphModule]] = None
+    replacement_post_trans: Optional[Callable[[GraphModule], GraphModule]] = None
+
+
+_REWRITE_INFO_LIST = [
+    _RewriteInfo(
+        _DYNAMIC_QUANTIZED_LINEAR_EXAMPLE_INPUTS,
+        _WrapperModule(_qdq_dynamic_quantized_linear),
+        _WrapperModule(_reference_dynamic_quantized_linear),
+        partial(
+            _replace_literals_with_existing_placeholders,
+            literal_to_ph_idx={-128: 1, 127: 2, torch.finfo(torch.float32).eps: 3},
+        ),
+        partial(
+            _replace_literals_with_existing_placeholders,
+            literal_to_ph_idx={-128: 1, 127: 2, torch.finfo(torch.float32).eps: 3},
+        ),
+    ),
+    _RewriteInfo(
+        _QUANTIZED_LINEAR_EXAMPLE_INPUTS,
+        _WrapperModule(_qdq_quantized_linear),
+        _WrapperModule(_reference_quantized_linear),
+        _replace_literals_with_new_placeholders,
+        _replace_literals_with_new_placeholders,
+    ),
+    _RewriteInfo(
+        _QUANTIZED_CONV2d_EXAMPLE_INPUTS,
+        _WrapperModule(_qdq_quantized_conv2d),
+        _WrapperModule(_reference_quantized_conv2d),
+        partial(_replace_literals_with_new_placeholders, exclude_literals=[-1]),
+        partial(_replace_literals_with_new_placeholders, exclude_literals=[-1]),
+    ),
+    _RewriteInfo(
+        _QUANTIZED_ADD_OR_ADD_RELU_EXAMPLE_INPUTS,
+        _WrapperModule(_qdq_quantized_add_relu),
+        _WrapperModule(_reference_quantized_add_relu),
+    ),
+    _RewriteInfo(
+        _QUANTIZED_ADD_OR_ADD_RELU_EXAMPLE_INPUTS,
+        _WrapperModule(_qdq_quantized_add),
+        _WrapperModule(_reference_quantized_add),
+    ),
+    _RewriteInfo(
+        _QUANTIZED_MAX_POOL2D_EXAMPLE_INPUTS,
+        _WrapperModule(_qdq_quantized_max_pool2d),
+        _WrapperModule(_reference_quantized_max_pool2d),
+        _replace_literals_with_new_placeholders,
+        _replace_literals_with_new_placeholders,
+    ),
+    _RewriteInfo(
+        _QUANTIZE_PER_TENSOR_INT8_EXAMPLE_INPUTS,
+        _WrapperModule(_quantize_per_tensor_int8),
+        _WrapperModule(_reference_quantize_per_tensor_int8),
+    ),
+    _RewriteInfo(
+        _DEQUANTIZE_PER_TENSOR_INT8_EXAMPLE_INPUTS,
+        _WrapperModule(_dequantize_per_tensor_int8),
+        _WrapperModule(_reference_dequantize_per_tensor_int8),
+    ),
+    _RewriteInfo(
+        _QUANTIZE_PER_CHANNEL_INT8_EXAMPLE_INPUTS,
+        _WrapperModule(_quantize_per_channel_int8),
+        _WrapperModule(_reference_quantize_per_channel_int8),
+        _replace_ph_qdq_per_channel_replacement,
+        _replace_ph_qdq_per_channel_replacement,
+    ),
+    _RewriteInfo(
+        _DEQUANTIZE_PER_CHANNEL_INT8_EXAMPLE_INPUTS,
+        _WrapperModule(_dequantize_per_channel_int8),
+        _WrapperModule(_reference_dequantize_per_channel_int8),
+        _replace_ph_qdq_per_channel_replacement,
+        _replace_ph_qdq_per_channel_replacement,
+    ),
+]
+
+
+def reference_representation_rewrite(model: GraphModule) -> GraphModule:
+    remove_tensor_overload_for_qdq_ops(model)
+    for rewrite_info in _REWRITE_INFO_LIST:
+        example_inputs = rewrite_info.example_inputs
+        pattern = rewrite_info.pattern
+        replacement = rewrite_info.replacement
+        pattern_post_trans = rewrite_info.pattern_post_trans
+        replacement_post_trans = rewrite_info.replacement_post_trans
+        pattern = _get_aten_graph_module_for_pattern(pattern, example_inputs)  # type: ignore[arg-type, assignment]
+        remove_tensor_overload_for_qdq_ops(pattern)  # type: ignore[arg-type]
+        replacement = _get_aten_graph_module_for_pattern(replacement, example_inputs)  # type: ignore[arg-type, assignment]
+        remove_tensor_overload_for_qdq_ops(replacement)  # type: ignore[arg-type]
+        if pattern_post_trans:
+            pattern = pattern_post_trans(pattern)
+        if replacement_post_trans:
+            replacement = replacement_post_trans(replacement)
+        pattern.recompile()  # type: ignore[attr-defined]
+        replacement.recompile()  # type: ignore[attr-defined]
+        matches = replace_pattern(model, pattern, replacement)
+    return model
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c54973407625808257fe435d41e5860a8aa9f2a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/pt2e/utils.py
@@ -0,0 +1,630 @@
+# mypy: allow-untyped-defs
+import operator
+import types
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+from torch._export import capture_pre_autograd_graph
+
+# Makes sure that quantized_decomposed ops are registered
+from torch.ao.quantization.fx._decomposed import quantized_decomposed_lib  # noqa: F401
+from torch.ao.quantization.quantizer import QuantizationAnnotation
+from torch.export.unflatten import _assign_attr, _AttrKind
+from torch.fx import GraphModule, Node
+from torch.nn.utils.fusion import fuse_conv_bn_weights
+from torch.utils._pytree import LeafSpec
+
+
+__all__ = [
+    "fold_bn_weights_into_conv_node",
+    "remove_tensor_overload_for_qdq_ops",
+]
+
+_QUANTIZE_OPS = [
+    torch.ops.quantized_decomposed.quantize_per_tensor.default,
+    torch.ops.quantized_decomposed.quantize_per_tensor.tensor,
+    torch.ops.quantized_decomposed.quantize_per_channel.default,
+]
+
+
+_DEQUANTIZE_OPS = [
+    torch.ops.quantized_decomposed.dequantize_per_tensor.default,
+    torch.ops.quantized_decomposed.dequantize_per_tensor.tensor,
+    torch.ops.quantized_decomposed.dequantize_per_channel.default,
+]
+
+# Example inputs for conv-bn1d patterns
+_conv1d_bn_example_inputs = (
+    torch.randn(1, 1, 3),  # x
+    torch.randn(1, 1, 1),  # conv_weight
+    torch.randn(1),  # conv_bias
+    torch.randn(1),  # bn_weight
+    torch.randn(1),  # bn_bias
+    torch.randn(1),  # bn_running_mean
+    torch.randn(1),  # bn_running_var
+)
+
+# Example inputs for conv-bn2d patterns
+_conv2d_bn_example_inputs = (
+    torch.randn(1, 1, 3, 3),  # x
+    torch.randn(1, 1, 1, 1),  # conv_weight
+    torch.randn(1),  # conv_bias
+    torch.randn(1),  # bn_weight
+    torch.randn(1),  # bn_bias
+    torch.randn(1),  # bn_running_mean
+    torch.randn(1),  # bn_running_var
+)
+
+
+def _is_connected(source: torch.fx.Node, dest: torch.fx.Node) -> bool:
+    """
+    Assuming dest is one of the ops inserted by quant workflow, this function
+    finds if source and dest are connected. Assumption is that only quant workflow
+    inserted ops exist between source and dest
+    """
+    quant_workflow_ops = _QUANTIZE_OPS + _DEQUANTIZE_OPS
+    quant_workflow_ops.append(torch.ops.quantized_decomposed.choose_qparams.tensor)
+    while dest.target in quant_workflow_ops:
+        if not isinstance(dest.args[0], torch.fx.Node):
+            raise ValueError(
+                f"expected arg[0] of quant workflow ops to be a node but found {dest.args[0]}"
+            )
+        dest = dest.args[0]
+    return dest == source
+
+
+def _find_q_dq_node_for_user(
+    produer: torch.fx.Node, user: torch.fx.Node
+) -> Tuple[Any, Any]:
+    """
+    Find q, dq pair corresponding to [producer -> q -> dq -> user]
+    Utils works by finding dq arg of user and ensuring it is connected to
+    producer
+    """
+    dq_node = None
+    for n in user.args:
+        if (
+            isinstance(n, torch.fx.Node)
+            and n.op == "call_function"
+            and n.target in _DEQUANTIZE_OPS
+        ):
+            if _is_connected(produer, n):
+                dq_node = n
+                break
+    if dq_node is None:
+        for n in user.kwargs:
+            if (
+                isinstance(n, torch.fx.Node)
+                and n.op == "call_function"
+                and n.target in _DEQUANTIZE_OPS
+            ):
+                if _is_connected(produer, n):
+                    dq_node = n
+                    break
+    if dq_node is None:
+        return (None, None)
+
+    q_node = None
+    if (
+        dq_node.args[0].op == "call_function"  # type: ignore[union-attr]
+        and dq_node.args[0].target in _QUANTIZE_OPS  # type: ignore[union-attr]
+    ):
+        q_node = dq_node.args[0]
+    return (q_node, dq_node)
+
+
+def _is_sym_size_node(node: Node):
+    return (
+        node.op == "call_function"
+        and node.target == torch.ops.aten.sym_size.default
+        or node.target == torch.ops.aten.sym_numel.default
+        or node.target == torch.ops.aten.sym_numel
+        or node.target == torch.ops.aten.sym_size
+    )
+
+
+def _filter_sym_size_users(node: torch.fx.Node) -> List[torch.fx.Node]:
+    node_users = list(filter((lambda x: (_is_sym_size_node(x) is False)), node.users))
+    return node_users
+
+
+def _is_valid_annotation(annotation: QuantizationAnnotation) -> bool:
+    if annotation is None:
+        return False
+    input_qspec_map = annotation.input_qspec_map
+    output_qspec = annotation.output_qspec
+    if len(input_qspec_map) == 0 and output_qspec is None:
+        return False
+    return True
+
+
+def _get_tensor_constant_from_node(node, m):
+    if node is None:
+        return None
+    assert node.op == "get_attr"
+    target_atoms = node.target.split(".")
+    attr_itr = m
+    for i, atom in enumerate(target_atoms):
+        if not hasattr(attr_itr, atom):
+            raise RuntimeError(
+                f"Node referenced nonexistent target {'.'.join(target_atoms[:i])}"
+            )
+        attr_itr = getattr(attr_itr, atom)
+    return attr_itr
+
+
+def _get_all_arguments(orig_args, orig_kwargs, args_schema):
+    all_args = []
+    for i, schema in enumerate(args_schema):
+        if schema.name in orig_kwargs:
+            all_args.append(orig_kwargs[schema.name])
+        elif not schema.kwarg_only and i < len(orig_args):
+            all_args.append(orig_args[i])
+        else:
+            all_args.append(schema.default_value)
+    return all_args
+
+
+def _is_supported_batch_norm_for_training(node: Node):
+    """
+    Return True if the given node refers to an aten batch norm op QAT supports.
+    """
+    supported_ops = [
+        torch.ops.aten.batch_norm.default,
+        torch.ops.aten._native_batch_norm_legit.default,
+        # Note: we won't need this op anymore after batch norm consolidation
+        # For now, we need to continue to support it because it gives better
+        # training numerics than `_native_batch_norm_legit`
+        torch.ops.aten.cudnn_batch_norm.default,
+        torch.ops.aten.miopen_batch_norm.default,
+    ]
+    return node.target in supported_ops
+
+
+# TODO: move this to torch/ao/quantization/utils.py
+def _is_conv_node(n: Node):
+    """
+    Return whether the node refers to an aten conv op.
+    """
+    return n.op == "call_function" and n.target in [
+        torch.ops.aten.conv1d.default,
+        torch.ops.aten.conv2d.default,
+    ]
+
+
+def _is_conv_transpose_node(n: Node):
+    """
+    Return whether the node refers to an aten conv_transpose op.
+    """
+    return n.op == "call_function" and n.target in [
+        torch.ops.aten.conv_transpose1d,
+        torch.ops.aten.conv_transpose1d.default,
+        torch.ops.aten.conv_transpose2d,
+        torch.ops.aten.conv_transpose2d.input,
+    ]
+
+
+def _is_conv_or_conv_transpose_node(n: Node):
+    """
+    Return whether the node refers to an aten conv or conv transpose op.
+    """
+    return _is_conv_node(n) or _is_conv_transpose_node(n)
+
+
+def _is_conv_transpose_fn(conv_fn: Callable):
+    return conv_fn in [F.conv_transpose1d, F.conv_transpose2d]
+
+
+def _is_bn_node(n: Node):
+    return (
+        _is_supported_batch_norm_for_training(n)
+        or n.target == torch.ops.aten._native_batch_norm_legit_no_training.default
+    )
+
+
+def fold_bn_weights_into_conv_node(
+    conv_node: Node,
+    conv_weight_node: Node,
+    conv_bias_node: Optional[Node],
+    bn_node: Node,
+    m: GraphModule,
+) -> None:
+    # conv args: input, weight, bias, stride, padding, dilation, ...
+    conv_w = _get_tensor_constant_from_node(conv_weight_node, m)
+    conv_b = _get_tensor_constant_from_node(conv_bias_node, m)
+    transpose = _is_conv_transpose_node(conv_node)
+
+    # eval bn args: input, weight, bias, running mean, running var, momentum, eps
+    # train bn args: input, weight, bias, running mean, running var, training, momentum, eps
+    bn_args_schema = bn_node.target._schema.arguments  # type: ignore[union-attr]
+    bn_args = _get_all_arguments(bn_node.args, bn_node.kwargs, bn_args_schema)
+    bn_w = _get_tensor_constant_from_node(bn_args[1], m)
+    bn_b = _get_tensor_constant_from_node(bn_args[2], m)
+    bn_rm = _get_tensor_constant_from_node(bn_args[3], m)
+    bn_rv = _get_tensor_constant_from_node(bn_args[4], m)
+    if bn_node.target == torch.ops.aten._native_batch_norm_legit_no_training.default:
+        eps_arg_index = 6
+    elif _is_supported_batch_norm_for_training(bn_node):
+        eps_arg_index = 7
+    else:
+        raise ValueError("BN node target is unexpected ", bn_node.target)
+    bn_eps = bn_args[eps_arg_index]
+
+    fused_weight, fused_bias = fuse_conv_bn_weights(
+        conv_w, conv_b, bn_rm, bn_rv, bn_eps, bn_w, bn_b, transpose=transpose
+    )
+
+    # update the weight and bias for conv
+    conv_args = list(conv_node.args)
+    # filling in the default bias argument
+    if len(conv_args) == 2:
+        conv_args.append(None)
+
+    # calling data since the fused_weight and fused_bias are nn.Parameter
+    weight_attr_name = conv_weight_node.target
+    assert isinstance(weight_attr_name, str)
+    _assign_attr(fused_weight, m, weight_attr_name, _AttrKind.PARAMETER)
+    if conv_bias_node is not None:
+        bias_attr_name = conv_bias_node.target
+        _assign_attr(fused_bias, m, str(bias_attr_name), _AttrKind.PARAMETER)
+    else:
+        bias_attr_name = weight_attr_name + "_bias"
+        _assign_attr(fused_bias, m, bias_attr_name, _AttrKind.PARAMETER)
+        with m.graph.inserting_before(conv_node):
+            get_bias_node = m.graph.get_attr(bias_attr_name)
+        # NOTE: here we assume the bias of conv is not quantized!
+        conv_args[2] = get_bias_node
+    conv_node.args = tuple(conv_args)
+
+    # native_batch_norm has 3 outputs, we expect getitem calls on the output
+    # and we want to replace the uses of getitem 0 with the output of conv
+    #
+    if bn_node.target == torch.ops.aten.batch_norm.default:
+        # With the new training ir, instead of batch_norm + getitem,
+        # we only have the batch_norm node.
+        #
+        # Before:
+        # conv -> bn -> users
+        # After:
+        # conv -> users
+        #       bn has no users now
+        bn_node.replace_all_uses_with(conv_node)
+    else:
+        # Before:
+        # conv -> bn - (first output) -> users1
+        #          \ - (second output) -> users2
+        #          \ - (third output) -> users3
+        # After:
+        # conv -> (first output) -> users1
+        #       bn -
+        #          \ - (second output) -> users2
+        #          \ - (third output) -> users3
+        # if users2 and users3 are empty then bn will be removed through dead code elimination
+        for user in bn_node.users:
+            if (
+                user.op != "call_function"
+                or user.target != operator.getitem
+                or user.args[1] != 0
+            ):
+                continue
+            user.replace_all_uses_with(conv_node)
+
+    # If the BN node does not have users, erase it from the graph
+    # Note: we need to do this manually because the model can still be in train
+    # mode at this point, in which case DCE won't erase the BN node automatically
+    # since the node refers to a mutating op. Here we still need to call DCE first
+    # to get rid of the unused getitem nodes that consume the BN node.
+    m.graph.eliminate_dead_code()
+    if len(bn_node.users) == 0:
+        m.graph.erase_node(bn_node)
+
+
+# fuse conv bn weights, inplace modification of the graph_module and graph
+def _fuse_conv_bn_(m: GraphModule) -> None:
+    has_bn = any(_is_bn_node(n) for n in m.graph.nodes)
+    if not has_bn:
+        return
+    for n in m.graph.nodes:
+        if n.op != "call_function" or n.target not in (
+            torch.ops.aten._native_batch_norm_legit_no_training.default,
+            torch.ops.aten.batch_norm.default,
+        ):
+            continue
+        bn_node = n
+        n = bn_node.args[0]
+        if not _is_conv_or_conv_transpose_node(n):
+            continue
+        conv_node = n
+        conv_weight_node = conv_node.args[1]
+        conv_bias_node = conv_node.args[2] if len(conv_node.args) > 2 else None
+        fold_bn_weights_into_conv_node(
+            conv_node, conv_weight_node, conv_bias_node, bn_node, m
+        )
+
+    m.graph.eliminate_dead_code()
+    m.recompile()
+
+
+def _get_node_name_to_scope(model: GraphModule) -> Dict[str, Tuple[str, type]]:
+    # TODO: move this information to fx node itself
+    node_name_to_scope: Dict[str, Tuple[str, type]] = {}
+    for n in model.graph.nodes:
+        nn_module_stack = n.meta.get("nn_module_stack", None)
+        current_scope = ("", type(None))
+        if nn_module_stack:
+            bt = list(nn_module_stack.values())[-1]
+            current_scope = (bt[0].split(".")[-1], bt[1])
+        node_name_to_scope[n.name] = current_scope
+    return node_name_to_scope
+
+
+def _get_aten_graph_module_for_pattern(
+    pattern: Callable,
+    example_inputs: Tuple[Any, ...],
+    is_cuda: bool = False,
+    **kwargs,
+) -> GraphModule:
+    """
+    Convert the pattern to an FX graph with decomposed aten ops.
+    """
+    if is_cuda:
+        example_inputs = tuple(
+            [x.cuda() if isinstance(x, torch.Tensor) else x for x in example_inputs]
+        )
+    aten_pattern = capture_pre_autograd_graph(
+        pattern,  # type: ignore[arg-type]
+        example_inputs,
+        kwargs,
+    )
+    aten_pattern.graph.eliminate_dead_code()
+    aten_pattern.recompile()
+
+    # ep.module() adds copy_ nodes for the mutated inputs.
+    # For patterns, it doesn't matter
+    for node in aten_pattern.graph.nodes:
+        if (
+            node.op == "call_function"
+            and node.target == torch.ops.aten.copy_.default
+            and len(node.users) == 0
+        ):
+            aten_pattern.graph.erase_node(node)
+
+    aten_pattern.graph.eliminate_dead_code()
+    aten_pattern.recompile()
+
+    return aten_pattern  # type: ignore[return-value]
+
+
+def remove_tensor_overload_for_qdq_ops(match_pattern: GraphModule) -> None:
+    """Remove .tensor overload for quantize/dequantize ops so that we can
+    use the match_pattern that we get from torchdynamo export to match the output of convert_pt2e
+    """
+    _MAP = {
+        torch.ops.quantized_decomposed.quantize_per_tensor.default: torch.ops.quantized_decomposed.quantize_per_tensor,
+        torch.ops.quantized_decomposed.dequantize_per_tensor.default: torch.ops.quantized_decomposed.dequantize_per_tensor,
+        torch.ops.quantized_decomposed.quantize_per_tensor.tensor: torch.ops.quantized_decomposed.quantize_per_tensor,
+        torch.ops.quantized_decomposed.dequantize_per_tensor.tensor: torch.ops.quantized_decomposed.dequantize_per_tensor,
+        torch.ops.quantized_decomposed.quantize_per_tensor.tensor2: torch.ops.quantized_decomposed.quantize_per_tensor,
+        torch.ops.quantized_decomposed.dequantize_per_tensor.tensor2: torch.ops.quantized_decomposed.dequantize_per_tensor,
+        torch.ops.quantized_decomposed.quantize_per_channel.default: torch.ops.quantized_decomposed.quantize_per_channel,
+        torch.ops.quantized_decomposed.dequantize_per_channel.default: torch.ops.quantized_decomposed.dequantize_per_channel,
+        torch.ops.aten.clamp.Tensor: torch.ops.aten.clamp,
+    }
+    for n in match_pattern.graph.nodes:
+        if n.op != "call_function":
+            continue
+        if n.target in _MAP:
+            n.target = _MAP[n.target]
+
+
+def _is_literal(arg):
+    if isinstance(arg, (int, float)):
+        return True
+    if isinstance(arg, (tuple, list)):
+        return all(map(_is_literal, arg))
+    return False
+
+
+def _replace_literals_with_new_placeholders(
+    gm: torch.fx.GraphModule,
+    merge_dup: bool = False,
+    exclude_literals: Optional[List[Any]] = None,
+):
+    """Replace the literals in the graph with placeholder nodes that's created on the fly while we
+    traverse the graph, so that the literal arguments in the graph can be matched and replaced
+
+    To use this, the pattern and replacement graph should have the exact same number of literal args
+    and they should be used in the exact same order in the pattern and replacement graph.
+
+    If the literal arguments are not used in the same order in pattern and replacement graph, please
+    use `_replace_literals_with_existing_placeholders` instead
+
+    Args:
+        `gm`: input GraphModule that we'll transform
+        `merge_dup`: boolean flag to indicate that if the same literal appears multiple times in
+         the graph, whether they should correspond to the same placeholder or not
+        `exclude_literals`: a list of literals that will not be replaced with placeholders
+
+    Example:
+
+    # 1. Original Graph
+    def pattern(self, x):
+        return x + 3
+
+    def replacement(self, x):
+        return x - 3
+
+    example_inputs = (torch.randn(1, 3, 3, 3),)
+    pattern_gm = _get_aten_graph_module_for_pattern(pattern, example_inputs)
+    replacement_gm = _get_aten_graph_module_for_pattern(pattern, example_inptus)
+
+    # 2. Before calling replace literals we'll see the following graph:
+    def pattern(self, x):
+        return x + 3
+
+    def replacement(self, x):
+        return x - 3
+
+    pattern_gm = _replace_literals_with_new_placeholders(pattern_gm)
+    replacement_gm = _replace_literals_with_new_placeholders(replacement_gm)
+
+    # 3. After replacing literals with new placeholder nodes
+
+    def pattern(self, x, new_ph):
+        return x + new_ph
+
+    def pattern(self, x, new_ph):
+        return x - new_ph
+
+    """
+    last_ph = None
+    cnt = 0
+    literal_to_ph: Dict[Union[float, bool, int, torch.dtype], Node] = {}
+    if exclude_literals is None:
+        exclude_literals = []
+
+    in_spec = gm._in_spec
+    args_spec = in_spec.children_specs[0]
+    for node in gm.graph.nodes:
+        if node.op == "placeholder":
+            last_ph = node
+            cnt += 1
+            continue
+        with gm.graph.inserting_after(last_ph):
+            new_args = []
+            for arg in node.args:
+                if _is_literal(arg) and arg not in exclude_literals:
+                    if merge_dup and arg in literal_to_ph:
+                        new_args.append(literal_to_ph[arg])
+                    else:
+                        ph_node = gm.graph.placeholder("arg" + str(cnt))
+                        new_args.append(ph_node)
+                        args_spec.children_specs.append(LeafSpec())
+                        cnt += 1
+                        if merge_dup:
+                            literal_to_ph[arg] = ph_node
+                else:
+                    new_args.append(arg)
+            new_args = tuple(new_args)
+
+        node.args = new_args
+
+    # Update `num_nodes`, `num_leaves`, `num_children`.
+    args_spec.__post_init__()
+    in_spec.__post_init__()
+    return gm
+
+
+def _replace_literals_with_existing_placeholders(
+    gm: torch.fx.GraphModule,
+    exclude_literals: Optional[List[Any]] = None,
+    literal_to_ph_idx: Optional[Dict[Union[float, int, bool, torch.dtype], int]] = None,
+):
+    """Replace the literals in the graph with **existing** placeholder nodes, so that the literal arguments
+    in the graph can be matched and replaced
+
+    To use this, all literal args in the graph should be unique and each of them should correspond
+    to exactly one placeholder node
+
+    # 1. Original Graph
+    def pattern(self, x_i8, scale, zero_point, quant_min, quant_max):
+        return torch.dequantize_per_tensor(x_i8, scale, zero_point, quant_min, quant_max)
+
+    def replacement(x_i8, scale, zero_point, quant_min, quant_max):
+        x_i8 = torch.clamp(x_i8, quant_min, quant_max)
+        return ((x_i8.to(torch.float32) - zero_point) * scale).to(dtype=torch.float32)
+
+    example_inputs = (
+        torch.randn(1, 3, 3, 3),
+        1.0,
+        0,
+        -128,
+        127,
+    )
+    pattern_gm = _get_aten_graph_module_for_pattern(pattern, example_inputs)
+    replacement_gm = _get_aten_graph_module_for_pattern(pattern, example_inptus)
+
+    # 2. Before calling replace literals we'll see the following graph:
+    def pattern(self, x_i8, scale, zero_point, quant_min, quant_max):
+        # scale/zero_point/quant_min/quant_max are burnt in since they are scalar values
+        return torch.dequantize_per_tensor(x_i8, 1.0, 0, -128, 127)
+
+    def replacement(x_i8, scale, zero_point, quant_min, quant_max):
+        # scale/zero_point/quant_min/quant_max are burnt in since they are scalar values
+        x_i8 = torch.clamp(x_i8, -128, 127)
+        return ((x_i8.to(torch.float32) - 0) * 1.0).to(dtype=torch.float32)
+
+    # Note that literal args appear in different order in pattern and replacement graph, so
+    # we can't use _replace_literals_with_new_placeholders
+
+    literal_to_ph_idx = {1.0: 1, 0: 2, -128: 3, 127: 4}
+    pattern_gm = _replace_literals_with_existing_placeholders(pattern_gm, literal_to_ph_idx)
+    replacement_gm = _replace_literals_with_existing_placeholders(replacement_gm, literal_to_ph_idx)
+
+    # 3. After replacing literals with existing placeholder nodes
+
+    def pattern(self, x_i8, scale, zero_point, quant_min, quant_max):
+        # scale/zero_point/quant_min/quant_max are burnt in since they are scalar values
+        return torch.dequantize_per_tensor(x_i8, scale, zero_point, quant_min, quant_max)
+
+    def replacement(x_i8, scale, zero_point, quant_min, quant_max):
+        # scale/zero_point/quant_min/quant_max are burnt in since they are scalar values
+        x_i8 = torch.clamp(x_i8, quant_min, quant_max)
+        return ((x_i8.to(torch.float32) - zero_point) * scale).to(dtype=torch.float32)
+    """
+    if exclude_literals is None:
+        exclude_literals = []
+
+    if literal_to_ph_idx is None:
+        literal_to_ph_idx = {}
+
+    phs = [node for node in gm.graph.nodes if node.op == "placeholder"]
+
+    for node in gm.graph.nodes:
+        if node.op != "call_function":
+            continue
+        new_args = []
+        for arg in node.args:
+            if (
+                _is_literal(arg)
+                and arg not in exclude_literals
+                and arg in literal_to_ph_idx
+            ):
+                ph_idx = literal_to_ph_idx[arg]
+                ph_node = phs[ph_idx]
+                new_args.append(ph_node)
+            else:
+                new_args.append(arg)
+        new_args = tuple(new_args)
+        node.args = new_args
+    return gm
+
+
+# TODO: Handle this in export itself and don't wrap the model in another GraphModule
+# in prepare and convert
+def _disallow_eval_train(model: GraphModule):
+    """
+    Disallow calling `model.train()` or `model.eval()` on the given GraphModule.
+    This is useful for exported models, where these methods don't actually behave as expected.
+    """
+    error_message = """
+        Calling train() or eval() is not supported for exported models.
+        Please call `torch.ao.quantization.move_exported_model_to_train(model)` (or eval) instead.
+
+        If you cannot replace the calls to `model.train()` and `model.eval()`, you may override
+        the behavior for these methods by calling `torch.ao.quantization.allow_exported_model_train_eval(model)`,
+        which does the above automatically for you. Note that this has limited effect on switching
+        behavior between train and eval modes, and should be used only for special ops such as dropout
+        and batchnorm.
+        """
+
+    def _train(self, mode: bool = True):
+        raise NotImplementedError(error_message)
+
+    def _eval(self, mode: bool = True):
+        raise NotImplementedError(error_message)
+
+    model.train = types.MethodType(_train, model)  # type: ignore[method-assign]
+    model.eval = types.MethodType(_eval, model)  # type: ignore[method-assign]
+    return model
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/qconfig.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/qconfig.py
new file mode 100644
index 0000000000000000000000000000000000000000..a867acbeb1cb86b74eefd8e327ed33bd3a747d73
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/qconfig.py
@@ -0,0 +1,696 @@
+# mypy: allow-untyped-defs
+import copy
+import warnings
+from collections import namedtuple
+from typing import Any, Optional, Type, Union
+from typing_extensions import deprecated
+
+import torch
+import torch.nn as nn
+from torch.ao.quantization.fake_quantize import (
+    default_dynamic_fake_quant,
+    default_embedding_fake_quant,
+    default_embedding_fake_quant_4bit,
+    default_fake_quant,
+    default_fused_act_fake_quant,
+    default_fused_per_channel_wt_fake_quant,
+    default_fused_wt_fake_quant,
+    default_per_channel_weight_fake_quant,
+    default_weight_fake_quant,
+    FakeQuantize,
+    FakeQuantizeBase,
+    fused_per_channel_wt_fake_quant_range_neg_127_to_127,
+    fused_wt_fake_quant_range_neg_127_to_127,
+    FusedMovingAvgObsFakeQuantize,
+)
+
+from .observer import (
+    _PartialWrapper,
+    default_debug_observer,
+    default_dynamic_quant_observer,
+    default_float_qparams_observer,
+    default_float_qparams_observer_4bit,
+    default_observer,
+    default_per_channel_weight_observer,
+    default_placeholder_observer,
+    default_reuse_input_observer,
+    default_weight_observer,
+    HistogramObserver,
+    MinMaxObserver,
+    MovingAverageMinMaxObserver,
+    NoopObserver,
+    ObserverBase,
+    per_channel_weight_observer_range_neg_127_to_127,
+    PlaceholderObserver,
+    ReuseInputObserver,
+    weight_observer_range_neg_127_to_127,
+)
+
+
+__all__ = [
+    "QConfig",
+    # TODO: deprecated, remove
+    "QConfigDynamic",
+    "default_qconfig",
+    "default_debug_qconfig",
+    "default_per_channel_qconfig",
+    "default_dynamic_qconfig",
+    "float16_dynamic_qconfig",
+    "float16_static_qconfig",
+    "per_channel_dynamic_qconfig",
+    "float_qparams_weight_only_qconfig",
+    "float_qparams_weight_only_qconfig_4bit",
+    "default_quint8_weight_qconfig",
+    "default_qat_qconfig",
+    "default_dynamic_qat_qconfig",
+    "default_weight_only_qconfig",
+    "default_activation_only_qconfig",
+    "default_qat_qconfig_v2",
+    "default_reuse_input_qconfig",
+    "default_symmetric_qnnpack_qconfig",
+    "default_per_channel_symmetric_qnnpack_qconfig",
+    "default_symmetric_qnnpack_qat_qconfig",
+    "default_per_channel_symmetric_qnnpack_qat_qconfig",
+    "default_embedding_qat_qconfig",
+    "default_embedding_qat_qconfig_4bit",
+    "get_default_qconfig",
+    "get_default_qat_qconfig",
+    "get_default_qconfig_dict",
+    "get_default_qat_qconfig_dict",
+    "QConfigAny",
+    "qconfig_equals",
+]
+
+
+class QConfig(namedtuple("QConfig", ["activation", "weight"])):
+    """
+    Describes how to quantize a layer or a part of the network by providing
+    settings (observer classes) for activations and weights respectively.
+
+
+    Note that QConfig needs to contain observer **classes** (like MinMaxObserver) or a callable that returns
+    instances on invocation, not the concrete observer instances themselves.
+    Quantization preparation function will instantiate observers multiple times for each of the layers.
+
+
+    Observer classes have usually reasonable default arguments, but they can be overwritten with `with_args`
+    method (that behaves like functools.partial)::
+
+      my_qconfig = QConfig(
+          activation=MinMaxObserver.with_args(dtype=torch.qint8),
+          weight=default_observer.with_args(dtype=torch.qint8))
+
+    """
+
+    def __new__(cls, activation, weight):
+        # catch common mistakes
+        if isinstance(activation, nn.Module) or isinstance(weight, nn.Module):
+            raise ValueError(
+                "QConfig received observer instance, please pass observer class instead. "
+                + "Use MyObserver.with_args(x=1) to override arguments to constructor if needed"
+            )
+        return super().__new__(cls, activation, weight)
+
+
+@deprecated(
+    "`QConfigDynamic` is going to be deprecated in PyTorch 1.12, please use `QConfig` instead",
+    category=FutureWarning,
+)
+class QConfigDynamic(namedtuple("QConfigDynamic", ["activation", "weight"])):
+    """
+    Describes how to dynamically quantize a layer or a part of the network by providing
+    settings (observer classes) for weights.
+
+    It's like QConfig, but for dynamic quantization.
+
+    Note that QConfigDynamic needs to contain observer **classes** (like MinMaxObserver) or a callable that returns
+    instances on invocation, not the concrete observer instances themselves.
+    Quantization function will instantiate observers multiple times for each of the layers.
+
+    Observer classes have usually reasonable default arguments, but they can be overwritten with `with_args`
+    method (that behaves like functools.partial)::
+
+      my_qconfig = QConfigDynamic(weight=default_observer.with_args(dtype=torch.qint8))
+    """
+
+    def __new__(cls, activation=torch.nn.Identity, weight=torch.nn.Identity):
+        # catch common mistakes
+        if isinstance(weight, nn.Module):
+            raise ValueError(
+                "QConfigDynamic received observer instance, please pass observer class instead. "
+                + "Use MyObserver.with_args(x=1) to override arguments to constructor if needed"
+            )
+        return super().__new__(cls, activation, weight)
+
+
+default_qconfig = QConfig(activation=default_observer, weight=default_weight_observer)
+"""
+Default qconfig configuration.
+"""
+
+default_debug_qconfig = QConfig(
+    weight=default_weight_observer, activation=default_debug_observer
+)
+"""
+Default qconfig configuration for debugging.
+"""
+
+default_per_channel_qconfig = QConfig(
+    activation=default_observer, weight=default_per_channel_weight_observer
+)
+"""
+Default qconfig configuration for per channel weight quantization.
+"""
+
+default_dynamic_qconfig = QConfig(
+    activation=default_dynamic_quant_observer, weight=default_weight_observer
+)
+"""
+Default dynamic qconfig.
+"""
+
+float16_dynamic_qconfig = QConfig(
+    activation=PlaceholderObserver.with_args(dtype=torch.float16, is_dynamic=True),
+    weight=PlaceholderObserver.with_args(dtype=torch.float16),
+)
+"""
+Dynamic qconfig with weights quantized to `torch.float16`.
+"""
+
+float16_static_qconfig = QConfig(
+    activation=PlaceholderObserver.with_args(dtype=torch.float16),
+    weight=PlaceholderObserver.with_args(dtype=torch.float16),
+)
+"""
+Dynamic qconfig with both activations and weights quantized to `torch.float16`.
+"""
+
+per_channel_dynamic_qconfig = QConfig(
+    activation=default_dynamic_quant_observer,
+    weight=default_per_channel_weight_observer,
+)
+"""
+Dynamic qconfig with weights quantized per channel.
+"""
+
+float_qparams_weight_only_qconfig = QConfig(
+    activation=default_placeholder_observer, weight=default_float_qparams_observer
+)
+"""
+Dynamic qconfig with weights quantized with a floating point zero_point.
+"""
+
+float_qparams_weight_only_qconfig_4bit = QConfig(
+    activation=default_placeholder_observer, weight=default_float_qparams_observer_4bit
+)
+
+default_qat_qconfig = QConfig(
+    activation=default_fake_quant, weight=default_weight_fake_quant
+)
+"""
+Default qconfig for QAT.
+"""
+
+default_dynamic_qat_qconfig = QConfig(
+    activation=default_dynamic_fake_quant, weight=default_weight_fake_quant
+)
+"""
+Default qconfig for dynamic QAT.
+"""
+
+default_weight_only_qconfig = QConfig(
+    activation=torch.nn.Identity, weight=default_weight_fake_quant
+)
+"""
+Default qconfig for quantizing weights only.
+"""
+
+default_activation_only_qconfig = QConfig(
+    activation=default_fake_quant, weight=torch.nn.Identity
+)
+"""
+Default qconfig for quantizing activations only.
+"""
+
+# QAT config that uses a fused observer + fake quant modules for optimized training performance.
+# to modify the activation/weight observers, the default entries in fake_quantize.py can be modified.
+default_qat_qconfig_v2 = QConfig(
+    activation=default_fused_act_fake_quant, weight=default_fused_wt_fake_quant
+)
+"""
+Fused version of `default_qat_config`, has performance benefits.
+"""
+
+default_reuse_input_qconfig = QConfig(
+    activation=default_reuse_input_observer, weight=NoopObserver
+)
+"""
+Default qconfig for operators that reuse the observers from input Tensor, e.g. reshape
+"""
+
+
+def get_default_qconfig(backend="x86", version=0):
+    """
+    Returns the default PTQ qconfig for the specified backend.
+
+    Args:
+      * `backend` (str): a string representing the target backend. Currently supports
+        `x86` (default), `fbgemm`, `qnnpack` and `onednn`.
+
+    Return:
+        qconfig
+    """
+    supported_backends = ["fbgemm", "x86", "qnnpack", "onednn"]
+    if backend not in supported_backends:
+        raise AssertionError(
+            "backend: "
+            + str(backend)
+            + f" not supported. backend must be one of {supported_backends}"
+        )
+
+    if version == 0:
+        if backend == "fbgemm":
+            qconfig = QConfig(
+                activation=HistogramObserver.with_args(reduce_range=True),
+                weight=default_per_channel_weight_observer,
+            )
+        elif backend == "qnnpack":
+            # TODO: make this compatible with xnnpack constraints
+            qconfig = QConfig(
+                activation=HistogramObserver.with_args(reduce_range=False),
+                weight=default_weight_observer,
+            )
+        elif backend == "onednn":
+            if not torch.cpu._is_vnni_supported():
+                warnings.warn(
+                    "Default qconfig of oneDNN backend with reduce_range of false may have accuracy issues "
+                    "on CPU without Vector Neural Network Instruction support."
+                )
+            qconfig = QConfig(
+                activation=HistogramObserver.with_args(reduce_range=False),
+                weight=default_per_channel_weight_observer,
+            )
+        elif backend == "x86":
+            qconfig = QConfig(
+                activation=HistogramObserver.with_args(reduce_range=True),
+                weight=default_per_channel_weight_observer,
+            )
+        else:
+            # won't reach
+            qconfig = default_qconfig
+    else:
+        raise AssertionError(
+            "Version number: "
+            + str(version)
+            + " in get_default_qconfig is not supported. Version number must be 0"
+        )
+
+    return qconfig
+
+
+"""
+Default, symmetric PTQ qconfig for the specified backend. And a per_channel
+variant of the same.
+
+Symmetric here applies to signed weights with zero point = 0, and additional
+value restrictions. The activations are also signed 8-bit integers with this
+qconfig.
+
+    * Once this change is merged [as of 3/17/22], with backend or qengine =
+    'qnnpack', some quantized operators with this symmetric qconfig may use
+    operators from xnnpack library.
+
+        ** Support to use xnnpack ops with `qnnpack` backed for asymmetric
+        qconfig (returned by get_default_qconfig()) is not available yet.
+
+    * This qconfig uses signed activations and weights. Weights have added
+    restrictions such as zero point is forced to be 0, making the weights
+    symmetric, hence the name. And the 8-bit quantized values are
+    restricting to to [-127, +127], excluding -128.
+
+    * xnnpack has a requantization scale value restriction, 0x1p-32 <=
+    requantization_scale < 256.0 where, `requantization_scale = (input_scale
+    * kernel_scale) / (output_scale)`. Using this eps (w/ assumed max value
+    of 256) is to prevent requantization_scale to go below xnnpack lower
+    threshold.
+"""
+default_symmetric_qnnpack_qconfig = QConfig(
+    activation=HistogramObserver.with_args(
+        dtype=torch.qint8, reduce_range=False, eps=2**-12
+    ),
+    weight=weight_observer_range_neg_127_to_127,
+)
+
+default_per_channel_symmetric_qnnpack_qconfig = QConfig(
+    activation=HistogramObserver.with_args(
+        dtype=torch.qint8, reduce_range=False, eps=2**-12
+    ),
+    weight=per_channel_weight_observer_range_neg_127_to_127,
+)
+
+default_embedding_qat_qconfig = QConfig(
+    activation=NoopObserver.with_args(dtype=torch.float32),
+    weight=default_embedding_fake_quant,
+)
+
+default_embedding_qat_qconfig_4bit = QConfig(
+    activation=NoopObserver.with_args(dtype=torch.float32),
+    weight=default_embedding_fake_quant_4bit,
+)
+
+default_quint8_weight_qconfig = QConfig(
+    activation=HistogramObserver, weight=MinMaxObserver
+)
+
+
+def get_default_qat_qconfig(backend="x86", version=1):
+    """
+    Returns the default QAT qconfig for the specified backend.
+
+    Args:
+      * `backend` (str): a string representing the target backend. Currently supports
+        `x86` (default), `fbgemm`, `qnnpack` and `onednn`.
+      * `version`: version, for backwards compatibility. Can be `None` or `1`.
+
+    Return:
+        qconfig
+    """
+    supported_backends = ["fbgemm", "x86", "qnnpack", "onednn"]
+    if backend not in supported_backends:
+        raise AssertionError(
+            "backend: "
+            + str(backend)
+            + f" not supported. backend must be one of {supported_backends}"
+        )
+
+    # Histogram observer is too slow for quantization aware training
+    if version == 0:
+        if backend == "fbgemm":
+            qconfig = QConfig(
+                activation=FakeQuantize.with_args(
+                    observer=MovingAverageMinMaxObserver,
+                    quant_min=0,
+                    quant_max=255,
+                    reduce_range=True,
+                ),
+                weight=default_per_channel_weight_fake_quant,
+            )
+        elif backend == "qnnpack":
+            qconfig = QConfig(
+                activation=FakeQuantize.with_args(
+                    observer=MovingAverageMinMaxObserver,
+                    quant_min=0,
+                    quant_max=255,
+                    reduce_range=False,
+                ),
+                weight=default_weight_fake_quant,
+            )
+        elif backend == "onednn":
+            qconfig = QConfig(
+                activation=FakeQuantize.with_args(
+                    observer=MovingAverageMinMaxObserver, quant_min=0, quant_max=255
+                ),
+                weight=default_per_channel_weight_fake_quant,
+            )
+        elif backend == "x86":
+            qconfig = QConfig(
+                activation=FakeQuantize.with_args(
+                    observer=MovingAverageMinMaxObserver,
+                    quant_min=0,
+                    quant_max=255,
+                    reduce_range=True,
+                ),
+                weight=default_per_channel_weight_fake_quant,
+            )
+        else:
+            qconfig = default_qat_qconfig
+    # Use the fused observe + fake_quant modules for doing QAT.
+    elif version == 1:
+        if backend == "fbgemm":
+            qconfig = QConfig(
+                activation=FusedMovingAvgObsFakeQuantize.with_args(
+                    observer=MovingAverageMinMaxObserver,
+                    quant_min=0,
+                    quant_max=255,
+                    reduce_range=True,
+                ),
+                weight=default_fused_per_channel_wt_fake_quant,
+            )
+        elif backend == "qnnpack":
+            # TODO: make this compatible with xnnpack constraints
+            qconfig = QConfig(
+                activation=FusedMovingAvgObsFakeQuantize.with_args(
+                    observer=MovingAverageMinMaxObserver,
+                    quant_min=0,
+                    quant_max=255,
+                    reduce_range=False,
+                ),
+                weight=default_fused_wt_fake_quant,
+            )
+        elif backend == "onednn":
+            qconfig = QConfig(
+                activation=FusedMovingAvgObsFakeQuantize.with_args(
+                    observer=MovingAverageMinMaxObserver, quant_min=0, quant_max=255
+                ),
+                weight=default_fused_per_channel_wt_fake_quant,
+            )
+        elif backend == "x86":
+            qconfig = QConfig(
+                activation=FusedMovingAvgObsFakeQuantize.with_args(
+                    observer=MovingAverageMinMaxObserver,
+                    quant_min=0,
+                    quant_max=255,
+                    reduce_range=True,
+                ),
+                weight=default_fused_per_channel_wt_fake_quant,
+            )
+        else:
+            qconfig = default_qat_qconfig_v2
+    else:
+        raise AssertionError(
+            "Version number: "
+            + str(version)
+            + "in get_default_qat_qconfig is not supported. Version number must be 0 or 1"
+        )
+
+    return qconfig
+
+
+"""
+Default symmetric QAT qconfig for qnnpack. And its per channel weight variant.
+"""
+default_symmetric_qnnpack_qat_qconfig = QConfig(
+    activation=FusedMovingAvgObsFakeQuantize.with_args(
+        observer=MovingAverageMinMaxObserver,
+        quant_min=-128,
+        quant_max=127,
+        dtype=torch.qint8,
+        reduce_range=False,
+        eps=2**-12,
+    ),
+    weight=fused_wt_fake_quant_range_neg_127_to_127,
+)
+
+default_per_channel_symmetric_qnnpack_qat_qconfig = QConfig(
+    activation=FusedMovingAvgObsFakeQuantize.with_args(
+        observer=MovingAverageMinMaxObserver,
+        quant_min=-128,
+        quant_max=127,
+        dtype=torch.qint8,
+        reduce_range=False,
+        eps=2**-12,
+    ),
+    weight=fused_per_channel_wt_fake_quant_range_neg_127_to_127,
+)
+
+_default_fp32_placeholder_qconfig = QConfig(
+    activation=PlaceholderObserver.with_args(dtype=torch.float32),
+    weight=PlaceholderObserver.with_args(dtype=torch.float32),
+)
+
+_default_quint8_placeholder_qconfig = QConfig(
+    activation=PlaceholderObserver.with_args(dtype=torch.quint8),
+    # operators using this qconfig doesn't have weights
+    weight=None,
+)
+
+
+@deprecated(
+    "`torch.ao.quantization.get_default_qconfig_dict` is deprecated and will be removed in "
+    "a future version. Please use `torch.ao.quantization.get_default_qconfig_mapping` instead.",
+    category=FutureWarning,
+)
+def get_default_qconfig_dict(backend="x86", version=0):
+    return torch.ao.quantization.get_default_qconfig_mapping(backend, version).to_dict()
+
+
+@deprecated(
+    "`torch.ao.quantization.get_default_qat_qconfig_dict` is deprecated and will be removed in "
+    "a future version. Please use `torch.ao.quantization.get_default_qat_qconfig_mapping` instead.",
+    category=FutureWarning,
+)
+def get_default_qat_qconfig_dict(backend="x86", version=1):
+    return torch.ao.quantization.get_default_qat_qconfig_mapping(
+        backend, version
+    ).to_dict()
+
+
+def _assert_valid_qconfig(qconfig: Optional[QConfig], mod: torch.nn.Module) -> None:
+    """
+    Verifies that this `qconfig` is valid.
+    """
+    if qconfig is None:
+        return
+    is_conv_transpose_mod = isinstance(
+        mod,
+        (torch.nn.ConvTranspose1d, torch.nn.ConvTranspose2d, torch.nn.ConvTranspose3d),
+    )
+    if is_conv_transpose_mod:
+        if qconfig.weight is None:
+            # for now, we assume that any qconfig for ConvTranspose without a weight is valid
+            return
+        example_observer = qconfig.weight()
+        is_per_channel = isinstance(
+            example_observer,
+            (
+                torch.ao.quantization.PerChannelMinMaxObserver,
+                torch.ao.quantization.MovingAveragePerChannelMinMaxObserver,
+            ),
+        )
+        assert (
+            not is_per_channel
+        ), "Per channel weight observer is not supported yet for ConvTranspose{n}d."
+
+
+QConfigAny = Optional[QConfig]
+QConfigAny.__module__ = "torch.ao.quantization.qconfig"
+
+
+def _add_module_to_qconfig_obs_ctr(
+    qconfig: QConfigAny, module: Optional[nn.Module]
+) -> Any:
+    r"""This is a helper function for use in quantization prepare that updates a qconfig so that
+    the constructors stored in the qconfig will create observers on the same device that
+    'module' is on. This is intended to be used when the qconfigs are propagated to each
+    module in order to avoid potential device alignment issues.
+
+    Args:
+        qconfig: QConfig with obs constructors stored in activation and weight
+        module: module which the qconfig is related to
+
+    Return:
+        qconfig: configured so that obs constructors set to construct on the same device as module
+    """
+
+    if module is None or qconfig is None or qconfig._fields != ("activation", "weight"):
+        return qconfig
+
+    def get_factory_kwargs_based_on_module_device():
+        assert isinstance(module, torch.nn.Module)
+        devices = {p.device for p in module.parameters()} | {
+            p.device for p in module.buffers()
+        }
+        device = next(iter(devices)) if len(devices) > 0 else None
+        return None if device is None else {"device": device}
+
+    def configure_constructor_to_put_obs_on_module_device(original_constructor):
+        try:
+            # check if constructor can accept factory_kwargs
+            check = original_constructor.with_args(factory_kwargs=None)
+            check()
+            return original_constructor.with_callable_args(
+                factory_kwargs=get_factory_kwargs_based_on_module_device
+            )
+        except AttributeError:  # qconfig doesn't have activation or weight
+            return original_constructor
+        except TypeError:  # the class doesn't accept factory_kwargs argument
+            return original_constructor
+
+    activation = configure_constructor_to_put_obs_on_module_device(qconfig.activation)
+    weight = configure_constructor_to_put_obs_on_module_device(qconfig.weight)
+
+    return QConfig(activation, weight)
+
+
+_ObserverOrFakeQuantizeConstructor = Union[
+    _PartialWrapper, Type[ObserverBase], Type[FakeQuantizeBase]
+]
+
+
+def _obs_or_fq_ctr_equals(
+    obs_or_fq1: _ObserverOrFakeQuantizeConstructor,
+    obs_or_fq2: _ObserverOrFakeQuantizeConstructor,
+):
+    if isinstance(obs_or_fq1, _PartialWrapper) and isinstance(
+        obs_or_fq2, _PartialWrapper
+    ):
+        return _partial_wrapper_equals(obs_or_fq1, obs_or_fq2)
+    return obs_or_fq1 == obs_or_fq2
+
+
+def _partial_wrapper_equals(obs_or_fq1: _PartialWrapper, obs_or_fq2: _PartialWrapper):
+    """
+    Return whether the two partial wrappers are equal,
+    """
+    # functools.partial has no __eq__ operator defined so '==' defaults to 'is'
+    obs_or_fq1_keywords = copy.copy(obs_or_fq1.p.keywords)
+    obs_or_fq2_keywords = copy.copy(obs_or_fq2.p.keywords)
+    keywords_equal = True
+    # compare observer constructor with _obs_or_fq_ctr_equals since direct compare would fail
+    if "observer" in obs_or_fq1_keywords and "observer" in obs_or_fq2_keywords:
+        keywords_equal = keywords_equal and _obs_or_fq_ctr_equals(
+            obs_or_fq1_keywords["observer"], obs_or_fq2_keywords["observer"]
+        )
+        obs_or_fq1_keywords.pop("observer")
+        obs_or_fq2_keywords.pop("observer")
+    keywords_equal = keywords_equal and obs_or_fq1_keywords == obs_or_fq2_keywords
+    return (
+        obs_or_fq1.p.func == obs_or_fq2.p.func
+        and obs_or_fq1.p.args == obs_or_fq2.p.args
+        and keywords_equal
+    )
+
+
+def qconfig_equals(q1: QConfigAny, q2: QConfigAny):
+    """
+    Returns `True` if `q1` equals `q2`, and `False` otherwise.
+    """
+    if q1 is None or q2 is None:
+        return q1 == q2
+    else:
+        assert q1 is not None and q2 is not None
+        try:
+            # Qconfig weight and activation can be either a partial wrapper,
+            # or an observer class. Special handling is required (above) for
+            # comparing partial wrappers.
+            activation_same = _obs_or_fq_ctr_equals(q1.activation, q2.activation)
+            weight_same = _obs_or_fq_ctr_equals(q1.weight, q2.weight)
+            return activation_same and weight_same
+        except AttributeError:
+            return q1 == q2
+
+
+def _activation_is_memoryless(qconfig: QConfig):
+    """
+    Return whether the observer for activations defined in the given QConfig is memoryless.
+    This means a MovingAverage observer with averaging constant equal to 1.
+    """
+
+    def _is_memoryless(observer):
+        return (
+            hasattr(observer, "averaging_constant") and observer.averaging_constant == 1
+        )
+
+    act = qconfig.activation()
+    if isinstance(act, FakeQuantizeBase) and hasattr(act, "activation_post_process"):
+        return _is_memoryless(act.activation_post_process)
+    else:
+        return _is_memoryless(act)
+
+
+def _is_reuse_input_qconfig(qconfig: Optional[QConfig]):
+    return (
+        qconfig is not None
+        and isinstance(qconfig.activation(), ReuseInputObserver)
+        and isinstance(qconfig.weight(), NoopObserver)
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/qconfig_mapping.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/qconfig_mapping.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c12be74ce6815ecba321b58530412bdd520a9b2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/qconfig_mapping.py
@@ -0,0 +1,381 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+from collections import OrderedDict
+from typing import Any, Callable, Dict, List, Tuple, Union
+
+import torch
+
+from .fake_quantize import default_weight_fake_quant, FixedQParamsFakeQuantize
+from .observer import (
+    _PartialWrapper,
+    default_fixed_qparams_range_0to1_observer,
+    default_fixed_qparams_range_neg1to1_observer,
+    default_placeholder_observer,
+    default_weight_observer,
+)
+from .qconfig import (
+    default_quint8_weight_qconfig,
+    default_reuse_input_qconfig,
+    default_symmetric_qnnpack_qat_qconfig,
+    default_symmetric_qnnpack_qconfig,
+    get_default_qat_qconfig,
+    get_default_qconfig,
+    QConfig,
+    QConfigAny,
+)
+
+
+__all__ = [
+    "get_default_qconfig_mapping",
+    "get_default_qat_qconfig_mapping",
+    "QConfigMapping",
+]
+
+
+# TODO: replace all usages with these constants
+_GLOBAL_DICT_KEY = ""
+_OBJECT_TYPE_DICT_KEY = "object_type"
+_MODULE_NAME_REGEX_DICT_KEY = "module_name_regex"
+_MODULE_NAME_DICT_KEY = "module_name"
+_MODULE_NAME_OBJECT_TYPE_ORDER_DICT_KEY = "module_name_object_type_order"
+
+# TODO: derive this map from the BackendConfig
+_FIXED_QPARAMS_OP_TO_OBSERVER: Dict[Union[Callable, str], _PartialWrapper] = {
+    torch.nn.Hardsigmoid: default_fixed_qparams_range_0to1_observer,
+    torch.nn.functional.hardsigmoid: default_fixed_qparams_range_0to1_observer,
+    "hardsigmoid": default_fixed_qparams_range_0to1_observer,
+    "hardsigmoid_": default_fixed_qparams_range_0to1_observer,
+    torch.nn.Sigmoid: default_fixed_qparams_range_0to1_observer,
+    torch.sigmoid: default_fixed_qparams_range_0to1_observer,
+    "sigmoid": default_fixed_qparams_range_0to1_observer,
+    "sigmoid_": default_fixed_qparams_range_0to1_observer,
+    torch.nn.Softmax: default_fixed_qparams_range_0to1_observer,
+    torch.nn.Tanh: default_fixed_qparams_range_neg1to1_observer,
+    torch.tanh: default_fixed_qparams_range_neg1to1_observer,
+    "tanh": default_fixed_qparams_range_neg1to1_observer,
+    "tanh_": default_fixed_qparams_range_neg1to1_observer,
+}
+
+
+def _get_default_qconfig_mapping(
+    is_qat: bool, backend: str, version: int
+) -> QConfigMapping:
+    """
+    Return the default QConfigMapping for the given quantization type and backend.
+    """
+    if is_qat:
+        qconfig = get_default_qat_qconfig(backend, version)
+    else:
+        qconfig = get_default_qconfig(backend, version)
+    default_weight = default_weight_fake_quant if is_qat else default_weight_observer
+
+    # default_per_channel_weight_observer is not currently compatible with fbgemm backend
+    # so we have to modify the weight observer to default_weight_observer or another
+    # per tensor supported observer.
+    # see https://github.com/pytorch/pytorch/issues/47535
+    if backend in ("fbgemm", "x86"):
+        qconfig_transpose = QConfig(
+            activation=qconfig.activation, weight=default_weight
+        )
+    else:
+        qconfig_transpose = qconfig
+
+    # currently layernorm only supports float weights
+    # we have to add this because otherwise there will be a extra quantize-dequantize pair
+    qconfig_layernorm = QConfig(
+        activation=qconfig.activation, weight=default_placeholder_observer
+    )
+
+    qconfig_mapping = (
+        QConfigMapping()
+        .set_global(qconfig)
+        .set_object_type("reshape", default_reuse_input_qconfig)
+        .set_object_type(torch.nn.ConvTranspose1d, qconfig_transpose)
+        .set_object_type(torch.nn.ConvTranspose2d, qconfig_transpose)
+        .set_object_type(torch.nn.ConvTranspose3d, qconfig_transpose)
+        .set_object_type(torch.nn.functional.conv_transpose1d, qconfig_transpose)
+        .set_object_type(torch.nn.functional.conv_transpose2d, qconfig_transpose)
+        .set_object_type(torch.nn.functional.conv_transpose3d, qconfig_transpose)
+        .set_object_type(torch.nn.functional.layer_norm, qconfig_layernorm)
+        .set_object_type(torch.nn.LayerNorm, qconfig_layernorm)
+        .set_object_type(torch.nn.PReLU, default_quint8_weight_qconfig)
+    )
+    # Use special observers for ops with fixed qparams
+    fixed_qparams_observer_to_qconfig: Dict[Any, QConfigAny] = {}
+    for fixed_qparams_op, observer in _FIXED_QPARAMS_OP_TO_OBSERVER.items():
+        if observer in fixed_qparams_observer_to_qconfig:
+            fixed_qparams_qconfig = fixed_qparams_observer_to_qconfig[observer]
+        else:
+            if is_qat:
+                activation = FixedQParamsFakeQuantize.with_args(observer=observer)
+            else:
+                activation = observer
+            fixed_qparams_qconfig = QConfig(
+                activation=activation, weight=default_weight
+            )
+            fixed_qparams_observer_to_qconfig[observer] = fixed_qparams_qconfig
+        qconfig_mapping.set_object_type(fixed_qparams_op, fixed_qparams_qconfig)
+
+    # TODO Currently it's required that separate ops in a fused op/module have the same qconfig.
+    #      Need to be able to support fusion of ops with different qconfigs
+
+    return qconfig_mapping
+
+
+def get_default_qconfig_mapping(backend="x86", version=0) -> QConfigMapping:
+    """
+    Return the default QConfigMapping for post training quantization.
+
+    Args:
+      * ``backend`` (str) : the quantization backend for the default qconfig mapping, should be
+         one of ["x86" (default), "fbgemm", "qnnpack", "onednn"]
+      * ``version`` (int) : the version for the default qconfig mapping
+    """
+    # TODO: add assert for backend choices
+    return _get_default_qconfig_mapping(False, backend, version)
+
+
+def get_default_qat_qconfig_mapping(backend="x86", version=1) -> QConfigMapping:
+    """
+    Return the default QConfigMapping for quantization aware training.
+
+    Args:
+      * ``backend`` (str) : the quantization backend for the default qconfig mapping, should be
+         one of ["x86" (default), "fbgemm", "qnnpack", "onednn"]
+      * ``version`` (int) : the version for the default qconfig mapping
+    """
+    return _get_default_qconfig_mapping(True, backend, version)
+
+
+def _get_symmetric_qnnpack_qconfig_mapping() -> QConfigMapping:
+    """
+    Return a QConfigMapping that uses `torch.ao.quantization.default_symmetric_qnnpack_qconfig`
+    as the default QConfig.
+    """
+    default_qconfig = default_symmetric_qnnpack_qconfig
+    return _get_default_qconfig_mapping_with_default_qconfig(
+        False, "qnnpack", default_qconfig
+    )
+
+
+def _get_symmetric_qnnpack_qat_qconfig_mapping() -> QConfigMapping:
+    """
+    Return a QConfigMapping that uses `torch.ao.quantization.default_symmetric_qnnpack_qat_qconfig`
+    as the default QConfig.
+    """
+    default_qconfig = default_symmetric_qnnpack_qat_qconfig
+    return _get_default_qconfig_mapping_with_default_qconfig(
+        True, "qnnpack", default_qconfig
+    )
+
+
+def _get_default_qconfig_mapping_with_default_qconfig(
+    is_qat: bool,
+    backend: str,
+    default_qconfig: QConfig,
+) -> QConfigMapping:
+    """
+    Return a QConfigMapping that uses the provided qconfig as the default QConfig.
+    """
+    if is_qat:
+        qconfig_mapping = get_default_qat_qconfig_mapping(backend)
+    else:
+        qconfig_mapping = get_default_qconfig_mapping(backend)
+    qconfig_mapping.set_global(default_qconfig)
+    for pattern in qconfig_mapping.object_type_qconfigs.keys():
+        if pattern not in _FIXED_QPARAMS_OP_TO_OBSERVER:
+            qconfig_mapping.set_object_type(pattern, default_qconfig)
+    return qconfig_mapping
+
+
+_QCONFIG_STYLE_ORDER: List[str] = [
+    "global_qconfig",
+    "object_type_qconfigs",
+    "module_name_regex_qconfigs",
+    "module_name_qconfigs",
+    "module_name_object_type_order_qconfigs",
+]
+
+
+class QConfigMapping:
+    """
+    Mapping from model ops to :class:`torch.ao.quantization.QConfig` s.
+
+    The user can specify QConfigs using the following methods (in increasing match priority):
+
+        ``set_global`` : sets the global (default) QConfig
+
+        ``set_object_type`` : sets the QConfig for a given module type, function, or method name
+
+        ``set_module_name_regex`` : sets the QConfig for modules matching the given regex string
+
+        ``set_module_name`` : sets the QConfig for modules matching the given module name
+
+        ``set_module_name_object_type_order`` : sets the QConfig for modules matching a combination
+        of the given module name, object type, and the index at which the module appears
+
+    Example usage::
+
+        qconfig_mapping = QConfigMapping()
+            .set_global(global_qconfig)
+            .set_object_type(torch.nn.Linear, qconfig1)
+            .set_object_type(torch.nn.ReLU, qconfig1)
+            .set_module_name_regex("foo.*bar.*conv[0-9]+", qconfig1)
+            .set_module_name_regex("foo.*", qconfig2)
+            .set_module_name("module1", qconfig1)
+            .set_module_name("module2", qconfig2)
+            .set_module_name_object_type_order("foo.bar", torch.nn.functional.linear, 0, qconfig3)
+
+    """
+
+    def __init__(self) -> None:
+        # In increasing match priority:
+        self.global_qconfig: QConfigAny = None
+        self.object_type_qconfigs: OrderedDict[
+            Union[Callable, str], QConfigAny
+        ] = OrderedDict()
+        self.module_name_regex_qconfigs: OrderedDict[str, QConfigAny] = OrderedDict()
+        self.module_name_qconfigs: OrderedDict[str, QConfigAny] = OrderedDict()
+        self.module_name_object_type_order_qconfigs: OrderedDict[
+            Tuple[str, Callable, int], QConfigAny
+        ] = OrderedDict()
+
+    def set_global(self, global_qconfig: QConfigAny) -> QConfigMapping:
+        """
+        Set the global (default) QConfig.
+        """
+        self.global_qconfig = global_qconfig
+        return self
+
+    def set_object_type(
+        self, object_type: Union[Callable, str], qconfig: QConfigAny
+    ) -> QConfigMapping:
+        """
+        Set the QConfig for a given module type, function, or method name.
+        If the QConfig for an existing object type was already set, the new QConfig will override the old one.
+        """
+        self.object_type_qconfigs[object_type] = qconfig
+        return self
+
+    def set_module_name_regex(
+        self, module_name_regex: str, qconfig: QConfigAny
+    ) -> QConfigMapping:
+        """
+        Set the QConfig for modules matching the given regex string.
+
+        Regexes will be matched in the order in which they are registered through this method.
+        Thus, the caller should register more specific patterns first, e.g.::
+
+            qconfig_mapping = QConfigMapping()
+                .set_module_name_regex("foo.*bar.*conv[0-9]+", qconfig1)
+                .set_module_name_regex("foo.*bar.*", qconfig2)
+                .set_module_name_regex("foo.*", qconfig3)
+
+        In this example, "foo.bar.conv0" would match qconfig1, "foo.bar.linear" would match qconfig2,
+        and "foo.baz.relu" would match qconfig3.
+
+        If the QConfig for an existing module name regex was already set, the new QConfig will override the
+        old one while preserving the order in which the regexes were originally registered.
+        """
+        self.module_name_regex_qconfigs[module_name_regex] = qconfig
+        return self
+
+    def set_module_name(self, module_name: str, qconfig: QConfigAny) -> QConfigMapping:
+        """
+        Set the QConfig for modules matching the given module name.
+        If the QConfig for an existing module name was already set, the new QConfig will override the old one.
+        """
+        self.module_name_qconfigs[module_name] = qconfig
+        return self
+
+    def set_module_name_object_type_order(
+        self, module_name: str, object_type: Callable, index: int, qconfig: QConfigAny
+    ) -> QConfigMapping:
+        """
+        Set the QConfig for modules matching a combination of the given module name, object type,
+        and the index at which the module appears.
+
+        If the QConfig for an existing (module name, object type, index)  was already set, the new QConfig
+        will override the old one.
+        """
+        self.module_name_object_type_order_qconfigs[
+            (module_name, object_type, index)
+        ] = qconfig
+        return self
+
+    def __repr__(self) -> str:
+        output = self.__class__.__name__ + " ("
+        for style_name in _QCONFIG_STYLE_ORDER:
+            output += f"\n {style_name}"
+            qconfigs = getattr(self, style_name)
+            if isinstance(qconfigs, OrderedDict) and len(qconfigs) > 0:
+                for key, qconfig in qconfigs.items():
+                    output += f"\n  {key}: {qconfig}"
+            else:
+                output += f"\n  {qconfigs}"
+        return output + "\n)"
+
+    # TODO: remove this
+    def to_dict(self) -> Dict[str, Any]:
+        """
+        Convert this ``QConfigMapping`` to a dictionary with the following keys:
+
+            "" (for global QConfig)
+
+            "object_type"
+
+            "module_name_regex"
+
+            "module_name"
+
+            "module_name_object_type_order"
+
+        The values of this dictionary are lists of tuples.
+        """
+        return {
+            _GLOBAL_DICT_KEY: self.global_qconfig,
+            _OBJECT_TYPE_DICT_KEY: list(self.object_type_qconfigs.items()),
+            _MODULE_NAME_REGEX_DICT_KEY: list(self.module_name_regex_qconfigs.items()),
+            _MODULE_NAME_DICT_KEY: list(self.module_name_qconfigs.items()),
+            _MODULE_NAME_OBJECT_TYPE_ORDER_DICT_KEY: [
+                (*k, v) for k, v in self.module_name_object_type_order_qconfigs.items()
+            ],
+        }
+
+    # TODO: remove this
+    @classmethod
+    def from_dict(cls, qconfig_dict: Dict[str, Any]) -> QConfigMapping:
+        """
+        Create a ``QConfigMapping`` from a dictionary with the following keys (all optional):
+
+            "" (for global QConfig)
+
+            "object_type"
+
+            "module_name_regex"
+
+            "module_name"
+
+            "module_name_object_type_order"
+
+        The values of this dictionary are expected to be lists of tuples.
+        """
+        conf = cls()
+        if _GLOBAL_DICT_KEY in qconfig_dict:
+            conf.set_global(qconfig_dict[_GLOBAL_DICT_KEY])
+        for object_type, qconfig in qconfig_dict.get(_OBJECT_TYPE_DICT_KEY, []):
+            conf.set_object_type(object_type, qconfig)
+        for module_name_regex, qconfig in qconfig_dict.get(
+            _MODULE_NAME_REGEX_DICT_KEY, []
+        ):
+            conf.set_module_name_regex(module_name_regex, qconfig)
+        for module_name, qconfig in qconfig_dict.get(_MODULE_NAME_DICT_KEY, []):
+            conf.set_module_name(module_name, qconfig)
+        for module_name, object_type, index, qconfig in qconfig_dict.get(
+            _MODULE_NAME_OBJECT_TYPE_ORDER_DICT_KEY, []
+        ):
+            conf.set_module_name_object_type_order(
+                module_name, object_type, index, qconfig
+            )
+        return conf
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quant_type.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quant_type.py
new file mode 100644
index 0000000000000000000000000000000000000000..18488d7f9ccba604ca8f1df7ea0ef4a88546d63e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quant_type.py
@@ -0,0 +1,35 @@
+import enum
+
+
+__all__ = [
+    "QuantType",
+]
+
+
+# Quantization type (dynamic quantization, static quantization).
+# Should match the c++ enum in quantization_type.h
+class QuantType(enum.IntEnum):
+    DYNAMIC = 0
+    STATIC = 1
+    QAT = 2
+    WEIGHT_ONLY = 3
+
+
+_quant_type_to_str = {
+    QuantType.STATIC: "static",
+    QuantType.DYNAMIC: "dynamic",
+    QuantType.QAT: "qat",
+    QuantType.WEIGHT_ONLY: "weight_only",
+}
+
+
+# TODO: make this private
+def _get_quant_type_to_str(quant_type: QuantType) -> str:
+    return _quant_type_to_str[quant_type]
+
+
+def _quant_type_from_str(name: str) -> QuantType:
+    for quant_type, s in _quant_type_to_str.items():
+        if name == s:
+            return quant_type
+    raise ValueError(f"Unknown QuantType name '{name}'")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantization_mappings.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantization_mappings.py
new file mode 100644
index 0000000000000000000000000000000000000000..abe803fc0f520f490cba3594fc836cea78cbeafb
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantization_mappings.py
@@ -0,0 +1,366 @@
+import copy
+from typing import Any, Callable, Dict, Optional, Set, Union
+
+import torch
+import torch.ao.nn as ao_nn
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.intrinsic.qat as nniqat
+import torch.ao.nn.intrinsic.quantized as nniq
+import torch.ao.nn.intrinsic.quantized.dynamic as nniqd
+import torch.ao.nn.qat as nnqat
+import torch.ao.nn.qat.dynamic as nnqatd
+import torch.ao.nn.quantized as nnq
+import torch.ao.nn.quantized.dynamic as nnqd
+import torch.ao.nn.quantized.reference as nnqr
+
+# Because `torch.ao.nn` uses lazy imports, we need to make
+# sure we import the contents explicitly here.
+import torch.ao.nn.sparse
+import torch.nn.functional as F
+from torch import nn
+from torch.ao.quantization.fake_quantize import (
+    default_fixed_qparams_range_0to1_fake_quant,
+    default_fixed_qparams_range_neg1to1_fake_quant,
+)
+from torch.ao.quantization.stubs import DeQuantStub, QuantStub
+from torch.ao.quantization.utils import get_combined_dict
+from torch.nn.utils.parametrize import type_before_parametrizations
+
+
+__all__ = [
+    "DEFAULT_REFERENCE_STATIC_QUANT_MODULE_MAPPINGS",
+    "DEFAULT_STATIC_QUANT_MODULE_MAPPINGS",
+    "DEFAULT_QAT_MODULE_MAPPINGS",
+    "DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS",
+    "DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS",
+    "DEFAULT_MODULE_TO_ACT_POST_PROCESS",
+    "DEFAULT_STATIC_SPARSE_QUANT_MODULE_MAPPINGS",
+    "DEFAULT_DYNAMIC_SPARSE_QUANT_MODULE_MAPPINGS",
+    "no_observer_set",
+    "get_default_static_quant_module_mappings",
+    "get_default_static_quant_reference_module_mappings",
+    "get_embedding_static_quant_module_mappings",
+    "get_default_static_sparse_quant_module_mappings",
+    "get_static_quant_module_class",
+    "get_dynamic_quant_module_class",
+    "get_default_qat_module_mappings",
+    "get_embedding_qat_module_mappings",
+    "get_default_dynamic_quant_module_mappings",
+    "get_default_dynamic_sparse_quant_module_mappings",
+    "get_default_qconfig_propagation_list",
+    "get_default_compare_output_module_list",
+    "get_default_float_to_quantized_operator_mappings",
+    "get_quantized_operator",
+]
+
+# Default map for swapping float module to reference quantized modules
+DEFAULT_REFERENCE_STATIC_QUANT_MODULE_MAPPINGS: Dict[Callable, Any] = {
+    QuantStub: nnq.Quantize,
+    DeQuantStub: nnq.DeQuantize,
+    nn.Linear: nnqr.Linear,
+    nn.Conv1d: nnqr.Conv1d,
+    nn.Conv2d: nnqr.Conv2d,
+    nn.Conv3d: nnqr.Conv3d,
+    nn.ConvTranspose1d: nnqr.ConvTranspose1d,
+    nn.ConvTranspose2d: nnqr.ConvTranspose2d,
+    nn.ConvTranspose3d: nnqr.ConvTranspose3d,
+    nn.Embedding: nnqr.Embedding,
+    nn.EmbeddingBag: nnqr.EmbeddingBag,
+    nn.GRUCell: nnqr.GRUCell,
+    nn.LSTMCell: nnqr.LSTMCell,
+    nn.RNNCell: nnqr.RNNCell,
+    nn.LSTM: nnqr.LSTM,
+}
+
+# Default map for swapping float module to quantized ones
+DEFAULT_STATIC_QUANT_MODULE_MAPPINGS: Dict[Callable, Any] = {
+    QuantStub: nnq.Quantize,
+    DeQuantStub: nnq.DeQuantize,
+    nn.BatchNorm2d: nnq.BatchNorm2d,
+    nn.BatchNorm3d: nnq.BatchNorm3d,
+    nn.Dropout: nnq.Dropout,
+    nn.Conv1d: nnq.Conv1d,
+    nn.Conv2d: nnq.Conv2d,
+    nn.Conv3d: nnq.Conv3d,
+    nn.ConvTranspose1d: nnq.ConvTranspose1d,
+    nn.ConvTranspose2d: nnq.ConvTranspose2d,
+    nn.ConvTranspose3d: nnq.ConvTranspose3d,
+    nn.ELU: nnq.ELU,
+    nn.Embedding: nnq.Embedding,
+    nn.EmbeddingBag: nnq.EmbeddingBag,
+    nn.GroupNorm: nnq.GroupNorm,
+    nn.Hardswish: nnq.Hardswish,
+    nn.InstanceNorm1d: nnq.InstanceNorm1d,
+    nn.InstanceNorm2d: nnq.InstanceNorm2d,
+    nn.InstanceNorm3d: nnq.InstanceNorm3d,
+    nn.LayerNorm: nnq.LayerNorm,
+    nn.LeakyReLU: nnq.LeakyReLU,
+    nn.modules.linear.NonDynamicallyQuantizableLinear: nnq.Linear,
+    nn.Linear: nnq.Linear,
+    nn.ReLU6: nnq.ReLU6,
+    nn.Dropout: nnq.Dropout,
+    nn.PReLU: nnq.PReLU,
+    # Wrapper Modules:
+    nnq.FloatFunctional: nnq.QFunctional,
+    # Intrinsic modules:
+    nni.BNReLU2d: nniq.BNReLU2d,
+    nni.BNReLU3d: nniq.BNReLU3d,
+    nni.ConvReLU1d: nniq.ConvReLU1d,
+    nni.ConvReLU2d: nniq.ConvReLU2d,
+    nni.ConvReLU3d: nniq.ConvReLU3d,
+    nni.ConvAdd2d: nniq.ConvAdd2d,
+    nni.ConvAddReLU2d: nniq.ConvAddReLU2d,
+    nni.LinearReLU: nniq.LinearReLU,
+    nni.LinearLeakyReLU: nniq.LinearLeakyReLU,
+    nni.LinearTanh: nniq.LinearTanh,
+    nniqat.ConvBn1d: nnq.Conv1d,
+    nniqat.ConvBn2d: nnq.Conv2d,
+    nniqat.ConvBn3d: nnq.Conv3d,
+    nniqat.ConvBnReLU1d: nniq.ConvReLU1d,
+    nniqat.ConvBnReLU2d: nniq.ConvReLU2d,
+    nniqat.ConvBnReLU3d: nniq.ConvReLU3d,
+    nniqat.ConvReLU2d: nniq.ConvReLU2d,
+    nniqat.ConvReLU3d: nniq.ConvReLU3d,
+    nniqat.LinearReLU: nniq.LinearReLU,
+    nniqat.LinearBn1d: nnq.Linear,
+    # QAT modules:
+    nnqat.Linear: nnq.Linear,
+    nnqat.Conv2d: nnq.Conv2d,
+    nnqat.Conv3d: nnq.Conv3d,
+}
+
+# Default map for swapping float module to qat modules
+DEFAULT_QAT_MODULE_MAPPINGS: Dict[Callable, Any] = {
+    nn.Conv2d: nnqat.Conv2d,
+    nn.Conv3d: nnqat.Conv3d,
+    nn.Linear: nnqat.Linear,
+    nn.modules.linear.NonDynamicallyQuantizableLinear: nnqat.Linear,
+    # Intrinsic modules:
+    nni.ConvBn1d: nniqat.ConvBn1d,
+    nni.ConvBn2d: nniqat.ConvBn2d,
+    nni.ConvBn3d: nniqat.ConvBn3d,
+    nni.ConvBnReLU1d: nniqat.ConvBnReLU1d,
+    nni.ConvBnReLU2d: nniqat.ConvBnReLU2d,
+    nni.ConvBnReLU3d: nniqat.ConvBnReLU3d,
+    nni.ConvReLU2d: nniqat.ConvReLU2d,
+    nni.ConvReLU3d: nniqat.ConvReLU3d,
+    nni.LinearReLU: nniqat.LinearReLU,
+    nni.LinearBn1d: nniqat.LinearBn1d,
+}
+
+# Default map for swapping dynamic modules
+DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS: Dict[Callable, Any] = {
+    nn.GRUCell: nnqd.GRUCell,
+    nn.Linear: nnqd.Linear,
+    nnqatd.Linear: nnqd.Linear,
+    nn.modules.linear.NonDynamicallyQuantizableLinear: nnqd.Linear,
+    nn.LSTM: nnqd.LSTM,
+    nn.GRU: nnqd.GRU,
+    nn.LSTMCell: nnqd.LSTMCell,
+    nn.RNNCell: nnqd.RNNCell,
+    nni.LinearReLU: nniqd.LinearReLU,
+    nn.EmbeddingBag: nnq.EmbeddingBag,
+    nn.Embedding: nnq.Embedding,
+    # Don't want to enable these by default because the numerical
+    # accuracy is poor compared to other dynamic ops
+    # nn.Conv1d: nnqd.Conv1d,
+    # nn.Conv2d: nnqd.Conv2d,
+    # nn.Conv3d: nnqd.Conv3d,
+    # nn.ConvTranspose1d: nnqd.ConvTranspose1d,
+    # nn.ConvTranspose2d: nnqd.ConvTranspose2d,
+    # nn.ConvTranspose3d: nnqd.ConvTranspose3d,
+}
+
+# Allowlist for propagating the qconfig
+_INCLUDE_QCONFIG_PROPAGATE_LIST: Set[Callable] = {
+    nn.Sequential,
+}
+
+# Default mapping from floating point function or torch ops to quantized ops
+# TODO: merge with default static mapping
+DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS: Dict[Union[Callable, str], Callable] = {
+    F.elu: torch.ops.quantized.elu,
+    F.hardswish: torch.ops.quantized.hardswish,
+    F.instance_norm: torch.ops.quantized.instance_norm,
+    F.layer_norm: torch.ops.quantized.layer_norm,
+    F.leaky_relu: torch.ops.quantized.leaky_relu,
+    F.dropout: torch.ops.quantized.dropout,
+}
+
+# mapping from module to output activation post process class
+DEFAULT_MODULE_TO_ACT_POST_PROCESS: Dict[Callable, Callable] = {
+    nn.Hardsigmoid: default_fixed_qparams_range_0to1_fake_quant,
+    nn.Sigmoid: default_fixed_qparams_range_0to1_fake_quant,
+    nn.Softmax: default_fixed_qparams_range_0to1_fake_quant,
+    nn.Tanh: default_fixed_qparams_range_neg1to1_fake_quant,
+}
+
+# Default map for swapping float module to static sparse quantized ones
+DEFAULT_STATIC_SPARSE_QUANT_MODULE_MAPPINGS: Dict[Callable, Any] = {
+    nn.Linear: ao_nn.sparse.quantized.Linear
+}
+
+# Default map for swapping float module to dynamic sparse quantized ones
+DEFAULT_DYNAMIC_SPARSE_QUANT_MODULE_MAPPINGS: Dict[Callable, Any] = {
+    nn.Linear: ao_nn.sparse.quantized.dynamic.Linear
+}
+
+
+def no_observer_set() -> Set[Any]:
+    r"""These modules cannot have observers inserted by default."""
+    no_observers = {nn.quantizable.LSTM, nn.quantizable.MultiheadAttention}
+    return no_observers
+
+
+def get_default_static_quant_module_mappings() -> Dict[Callable, Any]:
+    """Get module mapping for post training static quantization"""
+    return copy.deepcopy(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS)
+
+
+def get_default_static_quant_reference_module_mappings() -> Dict[Callable, Any]:
+    """Get reference module mapping for post training static quantization"""
+    return copy.deepcopy(DEFAULT_REFERENCE_STATIC_QUANT_MODULE_MAPPINGS)
+
+
+def get_embedding_static_quant_module_mappings() -> Dict[Callable, Any]:
+    """Get module mapping, including mapping for embedding QAT"""
+    mapping = copy.deepcopy(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS)
+    mapping[nnqat.EmbeddingBag] = nnq.EmbeddingBag
+    mapping[nnqat.Embedding] = nnq.Embedding
+    return mapping
+
+
+def get_default_static_sparse_quant_module_mappings() -> Dict[Callable, Any]:
+    """Get module mapping for post training static sparse quantization"""
+    return copy.deepcopy(DEFAULT_STATIC_SPARSE_QUANT_MODULE_MAPPINGS)
+
+
+def get_static_quant_module_class(
+    float_module_class: Callable,
+    additional_static_quant_mapping: Optional[Dict[Callable, Any]] = None,
+    is_reference: bool = False,
+) -> Any:
+    r"""n Get the statically quantized module class corresponding to
+    the floating point module class
+    """
+    if additional_static_quant_mapping is None:
+        additional_static_quant_mapping = {}
+    all_mappings = get_combined_dict(
+        DEFAULT_REFERENCE_STATIC_QUANT_MODULE_MAPPINGS
+        if is_reference
+        else DEFAULT_STATIC_QUANT_MODULE_MAPPINGS,
+        additional_static_quant_mapping,
+    )
+    static_quant_module_class = all_mappings.get(float_module_class, None)
+    assert static_quant_module_class is not None, (
+        f"Floating point module class {str(float_module_class)}"
+        + " does not have a corresponding quantized module class"
+    )
+    return copy.deepcopy(static_quant_module_class)
+
+
+def get_dynamic_quant_module_class(
+    float_module_class: Callable,
+    additional_dynamic_quant_mapping: Optional[Dict[Callable, Any]] = None,
+) -> Any:
+    r"""n Get the dynamically quantized module class corresponding to
+    the floating point module class
+    """
+    if additional_dynamic_quant_mapping is None:
+        additional_dynamic_quant_mapping = {}
+    all_mappings = get_combined_dict(
+        DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS, additional_dynamic_quant_mapping
+    )
+    dynamic_quant_module_class = all_mappings.get(float_module_class, None)
+    assert dynamic_quant_module_class is not None, (
+        f"Floating point module class {str(float_module_class)}"
+        + " does not have a corresponding quantized module class"
+    )
+    return copy.deepcopy(dynamic_quant_module_class)
+
+
+def get_default_qat_module_mappings() -> Dict[Callable, Any]:
+    """Get default module mapping for quantization aware training"""
+    return copy.deepcopy(DEFAULT_QAT_MODULE_MAPPINGS)
+
+
+def get_embedding_qat_module_mappings() -> Dict[Callable, Any]:
+    """Get module mapping for quantization aware training
+    This is includes default values in addition to
+    enabling qat for embeddings.
+    """
+    mapping = copy.deepcopy(DEFAULT_QAT_MODULE_MAPPINGS)
+    mapping[nn.EmbeddingBag] = nnqat.EmbeddingBag
+    mapping[nn.Embedding] = nnqat.Embedding
+    return mapping
+
+
+def get_default_dynamic_quant_module_mappings() -> Dict[Callable, Any]:
+    """Get module mapping for post training dynamic quantization"""
+    return DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS
+
+
+def get_default_dynamic_sparse_quant_module_mappings() -> Dict[Callable, Any]:
+    """Get module mapping for post training dynamic sparse quantization"""
+    return DEFAULT_DYNAMIC_SPARSE_QUANT_MODULE_MAPPINGS
+
+
+def get_default_qconfig_propagation_list() -> Set[Callable]:
+    """Get the default list of module types that we'll attach qconfig
+    attribute to in prepare
+    """
+    QCONFIG_PROPAGATE_MODULE_CLASS_LIST = (
+        set(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS.keys())
+        | set(DEFAULT_QAT_MODULE_MAPPINGS.keys())
+        | set(DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS.keys())
+        | _INCLUDE_QCONFIG_PROPAGATE_LIST
+    )
+    return copy.deepcopy(QCONFIG_PROPAGATE_MODULE_CLASS_LIST)
+
+
+def get_default_compare_output_module_list() -> Set[Callable]:
+    """Get list of module class types that we will record output
+    in numeric suite
+    """
+    NUMERIC_SUITE_COMPARE_MODEL_OUTPUT_MODULE_LIST = (
+        set(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS.values())
+        | set(DEFAULT_QAT_MODULE_MAPPINGS.values())
+        | set(DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS.values())
+        | set(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS.keys())
+        | set(DEFAULT_QAT_MODULE_MAPPINGS.keys())
+        | set(DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS.keys())
+        | _INCLUDE_QCONFIG_PROPAGATE_LIST
+    )
+    return copy.deepcopy(NUMERIC_SUITE_COMPARE_MODEL_OUTPUT_MODULE_LIST)
+
+
+def get_default_float_to_quantized_operator_mappings() -> (
+    Dict[Union[Callable, str], Callable]
+):
+    return copy.deepcopy(DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS)
+
+
+# TODO: merge with get_static_quant_module_class
+def get_quantized_operator(float_op: Union[Callable, str]) -> Callable:
+    """Get the quantized operator corresponding to the float operator"""
+    quantized_op = DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS.get(float_op, None)
+    assert (
+        quantized_op is not None
+    ), f"Operator {str(float_op)} does not have corresponding quantized op"
+    return quantized_op
+
+
+def _get_special_act_post_process(module: torch.nn.Module) -> Optional[Callable]:
+    r"""Get the special activation post process for `module`, this has
+    higher priority than the activation post process in `qconfig`
+    e.g.
+    input: torch.nn.Sigmoid
+    output: default_affine_fixed_qparam_fake_quant
+    """
+    return DEFAULT_MODULE_TO_ACT_POST_PROCESS.get(
+        type_before_parametrizations(module), None
+    )
+
+
+def _has_special_act_post_process(module: torch.nn.Module) -> bool:
+    return module.training and type(module) in DEFAULT_MODULE_TO_ACT_POST_PROCESS
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantize.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantize.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e04ffd71774f874dd1c458950022a0307abca62
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantize.py
@@ -0,0 +1,812 @@
+# mypy: allow-untyped-defs
+import copy
+import inspect
+import itertools
+import warnings
+
+import torch
+import torch.ao.nn.quantized as nnq
+import torch.nn as nn
+from torch.ao.nn.intrinsic import _FusedModule
+from torch.ao.quantization.observer import _is_activation_post_process
+from torch.ao.quantization.qconfig import (
+    _activation_is_memoryless,
+    _add_module_to_qconfig_obs_ctr,
+    default_dynamic_qconfig,
+    float16_dynamic_qconfig,
+    float_qparams_weight_only_qconfig,
+    float_qparams_weight_only_qconfig_4bit,
+)
+from torch.ao.quantization.quantization_mappings import (
+    _get_special_act_post_process,
+    _has_special_act_post_process,
+    get_default_dynamic_quant_module_mappings,
+    get_default_qat_module_mappings,
+    get_default_qconfig_propagation_list,
+    get_default_static_quant_module_mappings,
+    get_default_static_quant_reference_module_mappings,
+    no_observer_set,
+)
+from torch.ao.quantization.stubs import DeQuantStub, QuantWrapper
+from torch.nn.utils.parametrize import type_before_parametrizations
+
+from .utils import get_qparam_dict, has_no_children_ignoring_parametrizations
+
+
+__all__ = [
+    "get_default_custom_config_dict",
+    "propagate_qconfig_",
+    "add_quant_dequant",
+    "prepare",
+    "quantize",
+    "quantize_dynamic",
+    "prepare_qat",
+    "quantize_qat",
+    "convert",
+    "swap_module",
+]
+
+
+# TODO remove this once BC is no longer required to avoid a SEV
+is_activation_post_process = _is_activation_post_process
+
+
+_DEFAULT_CUSTOM_CONFIG_DICT = {
+    "float_to_observed_custom_module_class": {
+        nn.LSTM: nn.quantizable.LSTM,
+        nn.MultiheadAttention: nn.quantizable.MultiheadAttention,
+    },
+    "observed_to_quantized_custom_module_class": {
+        nn.quantizable.LSTM: nn.quantized.LSTM,
+        nn.quantizable.MultiheadAttention: nn.quantized.MultiheadAttention,
+    },
+}
+
+
+def get_default_custom_config_dict():
+    r"""Defines the default custom config dict."""
+    return _DEFAULT_CUSTOM_CONFIG_DICT
+
+
+def _propagate_qconfig_helper(
+    module,
+    qconfig_dict,
+    qconfig_parent=None,
+    prefix="",
+    prepare_custom_config_dict=None,
+):
+    r"""This is a helper function for `propagate_qconfig_`
+
+    Args:
+        module: input module
+        qconfig_dict: dictionary that maps from name of submodule to quantization
+                     configuration
+        qconfig_parent: quantization config of parent module, we will fallback to
+                       this config when there is no specified config for current
+                       module
+        prefix: corresponding prefix of the current module, used as key in
+                qconfig_dict
+        prepare_custom_config_dict: dictionary for custom handling of modules
+                                    see docs for :func:`~torch.ao.quantization.prepare_fx`
+
+    Return:
+        None, module is modified inplace with qconfig attached
+    """
+
+    module_qconfig = qconfig_dict.get(
+        type_before_parametrizations(module), qconfig_parent
+    )
+    module_qconfig = qconfig_dict.get(prefix, module_qconfig)
+    module_qconfig = getattr(module, "qconfig", module_qconfig)
+
+    torch.ao.quantization.qconfig._assert_valid_qconfig(module_qconfig, module)
+
+    qconfig_with_device_check = _add_module_to_qconfig_obs_ctr(module_qconfig, module)
+    module.qconfig = qconfig_with_device_check
+
+    for name, child in module.named_children():
+        module_prefix = prefix + "." + name if prefix else name
+        #  do no not propagate qconfig to child if child is non traceable
+        if prepare_custom_config_dict is None or not (
+            name in prepare_custom_config_dict.get("non_traceable_module_name", [])
+            or type(child)
+            in prepare_custom_config_dict.get("non_traceable_module_class", [])
+        ):
+            _propagate_qconfig_helper(
+                child, qconfig_dict, qconfig_with_device_check, module_prefix
+            )
+
+
+def propagate_qconfig_(module, qconfig_dict=None, prepare_custom_config_dict=None):
+    r"""Propagate qconfig through the module hierarchy and assign `qconfig`
+    attribute on each leaf module
+
+    Args:
+        module: input module
+        qconfig_dict: dictionary that maps from name or type of submodule to
+            quantization configuration, qconfig applies to all submodules of a
+            given module unless qconfig for the submodules are specified (when
+            the submodule already has qconfig attribute)
+        prepare_custom_config_dict: dictionary for custom handling of modules
+            see docs for :func:`~torch.ao.quantization.prepare_fx`
+
+    Return:
+        None, module is modified inplace with qconfig attached
+    """
+    if qconfig_dict is None:
+        qconfig_dict = {}
+    if prepare_custom_config_dict is None:
+        prepare_custom_config_dict = {}
+    _propagate_qconfig_helper(
+        module, qconfig_dict, prepare_custom_config_dict=prepare_custom_config_dict
+    )
+
+
+def _observer_forward_hook(self, input, output):
+    r"""Forward hook that calls observer on the output"""
+    return self.activation_post_process(output)
+
+
+def _observer_forward_pre_hook(self, input):
+    r"""Forward pre hook that calls observer on the output"""
+    return self.activation_post_process(input[0])
+
+
+def _register_activation_post_process_hook(module, pre_hook=False):
+    assert hasattr(
+        module, "activation_post_process"
+    ), "Expect activation_post_process attribute already attached to the module"
+    if pre_hook:
+        handle = module.register_forward_pre_hook(
+            _observer_forward_pre_hook, prepend=True
+        )
+    else:
+        handle = module.register_forward_hook(_observer_forward_hook, prepend=True)
+
+
+def _add_observer_(
+    module,
+    qconfig_propagation_list=None,
+    non_leaf_module_list=None,
+    device=None,
+    custom_module_class_mapping=None,
+):
+    r"""Add observer for the leaf child of the module.
+
+    This function insert observer module to all leaf child module that
+    has a valid qconfig attribute.
+
+    Args:
+        module: input module with qconfig attributes for all the leaf modules that we want to quantize
+        qconfig_propagation_list: a list of quantizable modules that will have observers added to them
+            if they are leaf nodes
+        device: parent device, if any
+        non_leaf_module_list: list of non-leaf modules we want to add observer
+
+    Return:
+        None, module is modified inplace with added observer modules and forward_hooks
+    """
+    if qconfig_propagation_list is None:
+        qconfig_propagation_list = get_default_qconfig_propagation_list()
+
+    if custom_module_class_mapping is None:
+        custom_module_class_mapping = {}
+
+    # respect device affinity when adding observers
+    if device is None:
+        devices = _get_unique_devices_(module)
+        assert (
+            len(devices) <= 1
+        ), f"_add_observer_ only works with cpu or single-device CUDA modules, but got devices {devices}"
+        device = next(iter(devices)) if len(devices) > 0 else None
+
+    def get_activation_post_process(qconfig, device, special_act_post_process=None):
+        activation = (
+            qconfig.activation()
+            if special_act_post_process is None
+            else special_act_post_process()
+        )
+        if device is not None:
+            activation.to(device)
+        return activation
+
+    def needs_observation(m):
+        return hasattr(m, "qconfig") and m.qconfig is not None
+
+    def insert_activation_post_process(m, special_act_post_process=None):
+        """Adds an activation post process module and register
+        a pre or post hook that calls the module
+        """
+        # We don't insert observer/fake_quantize for DeQuantStub
+        if needs_observation(m) and not isinstance(m, DeQuantStub):
+            # observer and hook will be gone after we swap the module
+            m.add_module(
+                "activation_post_process",
+                get_activation_post_process(
+                    m.qconfig, device, special_act_post_process
+                ),
+            )
+            # Register observer as the first entry in the hook list
+            # All post forward hooks are preserved and will be executed after the observer before convert
+            _register_activation_post_process_hook(
+                m, pre_hook=_activation_is_memoryless(m.qconfig)
+            )
+
+    for name, child in module.named_children():
+        # TODO remove Dropout special after codebase stable
+        if type_before_parametrizations(child) in [nn.Dropout]:
+            continue
+        elif issubclass(
+            type_before_parametrizations(child), (nnq.FloatFunctional, nnq.QFunctional)
+        ):
+            if needs_observation(child):
+                assert hasattr(
+                    child, "activation_post_process"
+                ), f"functional class {type_before_parametrizations(child)} has no pre-defined `activation_post_process`"
+                child.activation_post_process = get_activation_post_process(
+                    child.qconfig, device
+                )
+        elif isinstance(child, _FusedModule):
+            # activation_post_process are now added directly to nn.Sequential/_FusedModule
+            if needs_observation(child):
+                insert_activation_post_process(child)
+        elif (
+            non_leaf_module_list is not None
+            and type_before_parametrizations(child) in non_leaf_module_list
+        ):
+            if needs_observation(child):
+                insert_activation_post_process(child)
+        elif _has_special_act_post_process(child):
+            special_act_post_process = _get_special_act_post_process(child)
+            insert_activation_post_process(child, special_act_post_process)
+        elif (
+            needs_observation(child)
+            and type_before_parametrizations(child) in custom_module_class_mapping
+        ):
+            observed_child = custom_module_class_mapping[
+                type_before_parametrizations(child)
+            ].from_float(child)
+            setattr(module, name, observed_child)
+            # TODO: These are the modules that cannot be observed
+            #       Once there are more, we should move them to a separate list
+            if (
+                custom_module_class_mapping[type_before_parametrizations(child)]
+                not in no_observer_set()
+            ):
+                insert_activation_post_process(observed_child)
+        else:
+            _add_observer_(
+                child,
+                qconfig_propagation_list,
+                non_leaf_module_list,
+                device,
+                custom_module_class_mapping,
+            )
+
+    # Insert observers only for leaf nodes, note that this observer is for
+    # the output of the module, for input QuantStub will observe them
+    if (
+        has_no_children_ignoring_parametrizations(module)
+        and not isinstance(module, torch.nn.Sequential)
+        and type_before_parametrizations(module) in qconfig_propagation_list
+    ):
+        insert_activation_post_process(module)
+    # This is a special case for AdaRound eager mode
+    # AdaRound contains weight_fake_quant to be propagated from API to convert
+    # leaf node check with a number of children looks naive assumption that blocks
+    # Adding an exception case for AdaRound
+    if (
+        hasattr(module, "weight_fake_quant")
+        and not isinstance(module, torch.nn.Sequential)
+        and type_before_parametrizations(module) in qconfig_propagation_list
+    ):
+        insert_activation_post_process(module)
+
+
+def _get_unique_devices_(module):
+    return {p.device for p in module.parameters()} | {
+        p.device for p in module.buffers()
+    }
+
+
+def add_quant_dequant(module):
+    r"""Wrap the leaf child module in QuantWrapper if it has a valid qconfig
+    Note that this function will modify the children of module inplace and it
+    can return a new module which wraps the input module as well.
+
+    Args:
+        module: input module with qconfig attributes for all the leaf modules
+        that we want to quantize
+
+    Return:
+        Either the inplace modified module with submodules wrapped in
+        `QuantWrapper` based on qconfig or a new `QuantWrapper` module which
+        wraps the input module, the latter case only happens when the input
+        module is a leaf module and we want to quantize it.
+    """
+    if (
+        has_no_children_ignoring_parametrizations(module)
+        and hasattr(module, "qconfig")
+        and module.qconfig
+    ):
+        return QuantWrapper(module)
+
+    for name, child in module.named_children():
+        module._modules[name] = add_quant_dequant(child)
+    return module
+
+
+def prepare(
+    model,
+    inplace=False,
+    allow_list=None,
+    observer_non_leaf_module_list=None,
+    prepare_custom_config_dict=None,
+):
+    r"""Prepares a copy of the model for quantization calibration or quantization-aware training.
+
+    Quantization configuration should be assigned preemptively
+    to individual submodules in `.qconfig` attribute.
+
+    The model will be attached with observer or fake quant modules, and qconfig
+    will be propagated.
+
+    Args:
+        `model`: input model to be modified in-place
+        `inplace`: carry out model transformations in-place, the original module is mutated
+        `allow_list`: list of quantizable modules
+        `observer_non_leaf_module_list`: list of non-leaf modules we want to add observer
+        `prepare_custom_config_dict`: customization configuration dictionary for prepare function
+
+    .. code-block:: python
+
+       # Example of prepare_custom_config_dict:
+       prepare_custom_config_dict = {
+           # user will manually define the corresponding observed
+           # module class which has a from_float class method that converts
+           # float custom module to observed custom module
+           "float_to_observed_custom_module_class": {
+               CustomModule: ObservedCustomModule
+           }
+        }
+
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize.prepare")
+    if prepare_custom_config_dict is None:
+        prepare_custom_config_dict = get_default_custom_config_dict()
+    custom_module_class_mapping = prepare_custom_config_dict.get(
+        "float_to_observed_custom_module_class", {}
+    )
+
+    if not inplace:
+        model = copy.deepcopy(model)
+
+    # TODO: remove allow_list
+    qconfig_propagation_list = allow_list
+    if allow_list is None:
+        qconfig_propagation_list = get_default_qconfig_propagation_list()
+    propagate_qconfig_(model, qconfig_dict=None)
+
+    # sanity check common API misusage
+    if not any(hasattr(m, "qconfig") and m.qconfig for m in model.modules()):
+        warnings.warn(
+            "None of the submodule got qconfig applied. Make sure you "
+            "passed correct configuration through `qconfig_dict` or "
+            "by assigning the `.qconfig` attribute directly on submodules"
+        )
+
+    _add_observer_(
+        model,
+        qconfig_propagation_list,
+        observer_non_leaf_module_list,
+        custom_module_class_mapping=custom_module_class_mapping,
+    )
+    return model
+
+
+def _remove_activation_post_process(module):
+    # TODO: maybe we should change activation_post_process to _activation_post_process
+    # to prevent it from being used by user
+    if hasattr(module, "activation_post_process") and _is_activation_post_process(
+        module.activation_post_process
+    ):
+        delattr(module, "activation_post_process")
+
+    # remove activation_post_process pre and post hooks
+    def remove_hooks(pre_hook=False):
+        hook_map = module._forward_pre_hooks if pre_hook else module._forward_hooks
+        observer_hook = (
+            _observer_forward_pre_hook if pre_hook else _observer_forward_hook
+        )
+        handle_ids_to_remove = set()
+        for handle_id, hook_fn in hook_map.items():
+            if hook_fn is observer_hook:
+                handle_ids_to_remove.add(handle_id)
+        for handle_id in handle_ids_to_remove:
+            hook_map.pop(handle_id)
+
+    remove_hooks(pre_hook=True)
+    remove_hooks(pre_hook=False)
+
+
+# TODO: rename to something more general
+def _remove_qconfig(module):
+    r"""Clean up the qconfig left in the module so that new qconfig can be
+    propagated.
+
+    Args:
+        module: module to be cleaned up
+    """
+    for child in module.children():
+        _remove_qconfig(child)
+
+    if hasattr(module, "qconfig"):
+        del module.qconfig
+
+    _remove_activation_post_process(module)
+
+
+def quantize(model, run_fn, run_args, mapping=None, inplace=False):
+    r"""Quantize the input float model with post training static quantization.
+
+    First it will prepare the model for calibration, then it calls
+    `run_fn` which will run the calibration step, after that we will
+    convert the model to a quantized model.
+
+    Args:
+        model: input float model
+        run_fn: a calibration function for calibrating the prepared model
+        run_args: positional arguments for `run_fn`
+        inplace: carry out model transformations in-place, the original module is mutated
+        mapping: correspondence between original module types and quantized counterparts
+
+    Return:
+        Quantized model.
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize.quantize")
+    if mapping is None:
+        mapping = get_default_static_quant_module_mappings()
+    if not inplace:
+        model = copy.deepcopy(model)
+    model.eval()
+    prepare(model, inplace=True)
+    run_fn(model, *run_args)
+    convert(model, mapping, inplace=True)
+    return model
+
+
+def quantize_dynamic(
+    model, qconfig_spec=None, dtype=torch.qint8, mapping=None, inplace=False
+):
+    r"""Converts a float model to dynamic (i.e. weights-only) quantized model.
+
+    Replaces specified modules with dynamic weight-only quantized versions and output the quantized model.
+
+    For simplest usage provide `dtype` argument that can be float16 or qint8. Weight-only quantization
+    by default is performed for layers with large weights size - i.e. Linear and RNN variants.
+
+    Fine grained control is possible with `qconfig` and `mapping` that act similarly to `quantize()`.
+    If `qconfig` is provided, the `dtype` argument is ignored.
+
+    Args:
+        model: input model
+        qconfig_spec: Either:
+
+            - A dictionary that maps from name or type of submodule to quantization
+              configuration, qconfig applies to all submodules of a given
+              module unless qconfig for the submodules are specified (when the
+              submodule already has qconfig attribute). Entries in the dictionary
+              need to be QConfig instances.
+
+            - A set of types and/or submodule names to apply dynamic quantization to,
+              in which case the `dtype` argument is used to specify the bit-width
+
+        inplace: carry out model transformations in-place, the original module is mutated
+        mapping: maps type of a submodule to a type of corresponding dynamically quantized version
+            with which the submodule needs to be replaced
+
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize.quantize_dynamic")
+    if qconfig_spec is None:
+        if dtype == torch.qint8:
+            qconfig_spec = {
+                nn.Linear: default_dynamic_qconfig,
+                nn.LSTM: default_dynamic_qconfig,
+                nn.GRU: default_dynamic_qconfig,
+                nn.LSTMCell: default_dynamic_qconfig,
+                nn.RNNCell: default_dynamic_qconfig,
+                nn.GRUCell: default_dynamic_qconfig,
+            }
+        elif dtype == torch.float16:
+            qconfig_spec = {
+                nn.Linear: float16_dynamic_qconfig,
+                nn.LSTM: float16_dynamic_qconfig,
+                nn.GRU: float16_dynamic_qconfig,
+                nn.LSTMCell: float16_dynamic_qconfig,
+                nn.RNNCell: float16_dynamic_qconfig,
+                nn.GRUCell: float16_dynamic_qconfig,
+            }
+        elif dtype == torch.quint8:
+            qconfig_spec = {
+                nn.EmbeddingBag: float_qparams_weight_only_qconfig,
+                nn.Embedding: float_qparams_weight_only_qconfig,
+            }
+        elif dtype == torch.quint4x2:
+            qconfig_spec = {
+                nn.EmbeddingBag: float_qparams_weight_only_qconfig_4bit,
+            }
+        else:
+            raise ValueError(
+                f"Don't know how to quantize with default settings for {dtype}. Provide full qconfig please"
+            )
+    elif isinstance(qconfig_spec, set):
+        if dtype is torch.qint8:
+            default_qconfig = default_dynamic_qconfig
+        elif dtype is torch.float16:
+            default_qconfig = float16_dynamic_qconfig
+        elif dtype is torch.quint8:
+            default_qconfig = float_qparams_weight_only_qconfig
+        elif dtype is torch.quint4x2:
+            default_qconfig = float_qparams_weight_only_qconfig_4bit
+        else:
+            raise RuntimeError(
+                "Unknown dtype specified for quantize_dynamic: ", str(dtype)
+            )
+        qconfig_spec = dict(zip(qconfig_spec, itertools.repeat(default_qconfig)))
+
+    if mapping is None:
+        mapping = get_default_dynamic_quant_module_mappings()
+
+    if not inplace:
+        model = copy.deepcopy(model)
+    model.eval()
+    propagate_qconfig_(model, qconfig_spec)
+    convert(model, mapping, inplace=True)
+    return model
+
+
+def prepare_qat(model, mapping=None, inplace=False):
+    r"""
+    Prepares a copy of the model for quantization calibration or
+    quantization-aware training and converts it to quantized version.
+
+    Quantization configuration should be assigned preemptively
+    to individual submodules in `.qconfig` attribute.
+
+    Args:
+        model: input model to be modified in-place
+        mapping: dictionary that maps float modules to quantized modules to be
+                 replaced.
+        inplace: carry out model transformations in-place, the original module
+                 is mutated
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize.prepare_qat")
+    assert model.training, "prepare_qat only works on models in training mode"
+    if mapping is None:
+        mapping = get_default_qat_module_mappings()
+
+    if not inplace:
+        model = copy.deepcopy(model)
+
+    propagate_qconfig_(model, qconfig_dict=None)
+    convert(model, mapping=mapping, inplace=True, remove_qconfig=False)
+    prepare(model, observer_non_leaf_module_list=set(mapping.values()), inplace=True)
+    return model
+
+
+def quantize_qat(model, run_fn, run_args, inplace=False):
+    r"""Do quantization aware training and output a quantized model
+
+    Args:
+        model: input model
+        run_fn: a function for evaluating the prepared model, can be a
+                function that simply runs the prepared model or a training
+                loop
+        run_args: positional arguments for `run_fn`
+
+    Return:
+        Quantized model.
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize.quantize_qat")
+    if not inplace:
+        model = copy.deepcopy(model)
+    model.train()
+    prepare_qat(model, inplace=True)
+    run_fn(model, *run_args)
+    convert(model, inplace=True)
+    return model
+
+
+def convert(
+    module,
+    mapping=None,
+    inplace=False,
+    remove_qconfig=True,
+    is_reference=False,
+    convert_custom_config_dict=None,
+    use_precomputed_fake_quant=False,
+):
+    r"""Converts submodules in input module to a different module according to `mapping`
+    by calling `from_float` method on the target module class. And remove qconfig at the
+    end if remove_qconfig is set to True.
+
+    Args:
+        `module`: prepared and calibrated module
+        `mapping`: a dictionary that maps from source module type to target
+                   module type, can be overwritten to allow swapping user defined
+                   Modules
+        `inplace`: carry out model transformations in-place, the original module
+                   is mutated
+        `convert_custom_config_dict`: custom configuration dictionary for convert function
+        `use_precomputed_fake_quant`: a flag to enable use of precomputed fake quant
+
+    .. code-block:: python
+
+       # Example of convert_custom_config_dict:
+       convert_custom_config_dict = {
+           # user will manually define the corresponding quantized
+           # module class which has a from_observed class method that converts
+           # observed custom module to quantized custom module
+           "observed_to_quantized_custom_module_class": {
+               ObservedCustomModule: QuantizedCustomModule
+           }
+       }
+
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize.convert")
+    if not inplace:
+        module = copy.deepcopy(module)
+    _convert(
+        module,
+        mapping,
+        inplace=True,
+        is_reference=is_reference,
+        convert_custom_config_dict=convert_custom_config_dict,
+        use_precomputed_fake_quant=use_precomputed_fake_quant,
+    )
+    if remove_qconfig:
+        _remove_qconfig(module)
+    return module
+
+
+def _convert(
+    module,
+    mapping=None,
+    inplace=False,
+    is_reference=False,
+    convert_custom_config_dict=None,
+    use_precomputed_fake_quant=False,
+):
+    r"""Converts submodules in input module to a different module according to `mapping`
+    by calling `from_float` method on the target module class
+
+    Args:
+        module: input module
+        mapping: a dictionary that maps from source module type to target
+                 module type, can be overwritten to allow swapping user defined
+                 Modules
+        inplace: carry out model transformations in-place, the original module
+                 is mutated
+        is_reference: a flag to enable quantized reference module
+        use_precomputed_fake_quant: a flag to enable use of precomputed fake quant
+
+    """
+    if mapping is None:
+        mapping = (
+            get_default_static_quant_reference_module_mappings()
+            if is_reference
+            else get_default_static_quant_module_mappings()
+        )
+    if convert_custom_config_dict is None:
+        convert_custom_config_dict = get_default_custom_config_dict()
+    custom_module_class_mapping = convert_custom_config_dict.get(
+        "observed_to_quantized_custom_module_class", {}
+    )
+
+    if not inplace:
+        module = copy.deepcopy(module)
+    reassign = {}
+    for name, mod in module.named_children():
+        # both fused modules and observed custom modules are
+        # swapped as one unit
+        if (
+            not isinstance(mod, _FusedModule)
+            and type_before_parametrizations(mod) not in custom_module_class_mapping
+        ):
+            _convert(
+                mod,
+                mapping,
+                True,  # inplace
+                is_reference,
+                convert_custom_config_dict,
+                use_precomputed_fake_quant=use_precomputed_fake_quant,
+            )
+        reassign[name] = swap_module(
+            mod, mapping, custom_module_class_mapping, use_precomputed_fake_quant
+        )
+
+    for key, value in reassign.items():
+        module._modules[key] = value
+
+    return module
+
+
+def swap_module(
+    mod, mapping, custom_module_class_mapping, use_precomputed_fake_quant=False
+):
+    r"""Swaps the module if it has a quantized counterpart and it has an
+    `observer` attached.
+
+    Args:
+        mod: input module
+        mapping: a dictionary that maps from nn module to nnq module
+
+    Return:
+        The corresponding quantized module of `mod`
+    """
+    new_mod = mod
+    if hasattr(mod, "qconfig") and mod.qconfig is not None:
+        swapped = False
+        if type_before_parametrizations(mod) in custom_module_class_mapping:
+            new_mod = custom_module_class_mapping[
+                type_before_parametrizations(mod)
+            ].from_observed(mod)
+            swapped = True
+        elif type_before_parametrizations(mod) in mapping:
+            qmod = mapping[type_before_parametrizations(mod)]
+            if hasattr(qmod, "_IS_REFERENCE") and qmod._IS_REFERENCE:
+                assert mod.qconfig is not None
+                weight_post_process = mod.qconfig.weight()
+                weight_post_process(mod.weight)
+                weight_qparams = get_qparam_dict(weight_post_process)
+                new_mod = qmod.from_float(mod, weight_qparams)
+            else:
+                sig = inspect.signature(qmod.from_float)
+                if "use_precomputed_fake_quant" in sig.parameters:
+                    new_mod = qmod.from_float(
+                        mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+                    )
+                else:
+                    new_mod = qmod.from_float(mod)
+            swapped = True
+
+        if swapped:
+            # Preserve module's pre forward hooks. They'll be called on quantized input
+            for pre_hook_fn in mod._forward_pre_hooks.values():
+                new_mod.register_forward_pre_hook(pre_hook_fn)
+            # Preserve module's post forward hooks except _observer_forward_hook
+            # After convert they'll work with quantized output
+            for hook_fn in mod._forward_hooks.values():
+                if hook_fn is not _observer_forward_hook:
+                    new_mod.register_forward_hook(hook_fn)
+
+            # respect device affinity when swapping modules
+            devices = _get_unique_devices_(mod)
+            assert (
+                len(devices) <= 1
+            ), f"swap_module only works with cpu or single-device CUDA modules, but got devices {devices}"
+            device = next(iter(devices)) if len(devices) > 0 else None
+            if device:
+                new_mod.to(device)
+    return new_mod
+
+
+def _get_observer_dict(mod, target_dict, prefix=""):
+    r"""Traverse the modules and save all observers into dict.
+    This is mainly used for quantization accuracy debug
+    Args:
+        mod: the top module we want to save all observers
+        prefix: the prefix for the current module
+        target_dict: the dictionary used to save all the observers
+    """
+
+    def get_prefix(prefix):
+        return prefix if prefix == "" else prefix + "."
+
+    if hasattr(mod, "activation_post_process"):
+        target_dict[
+            get_prefix(prefix) + "activation_post_process"
+        ] = mod.activation_post_process
+    for name, child in mod.named_children():
+        module_prefix = get_prefix(prefix) + name if prefix else name
+        _get_observer_dict(child, target_dict, module_prefix)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantize_fx.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantize_fx.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd8f3e811a39ead00a810a7b6e29f2c1ec65f8a6
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantize_fx.py
@@ -0,0 +1,745 @@
+import copy
+import warnings
+from typing import Any, Dict, Optional, Tuple, Union
+
+import torch
+from torch.fx import GraphModule
+from torch.fx.graph_module import _USER_PRESERVED_ATTRIBUTES_KEY
+
+from .backend_config import BackendConfig, get_tensorrt_backend_config  # noqa: F401
+from .fx.convert import convert
+from .fx.custom_config import ConvertCustomConfig, FuseCustomConfig, PrepareCustomConfig
+from .fx.fuse import fuse  # noqa: F401
+from .fx.graph_module import ObservedGraphModule  # noqa: F401
+from .fx.prepare import prepare  # noqa: F401
+from .fx.tracer import QuantizationTracer, Scope, ScopeContextManager  # noqa: F401
+from .fx.utils import (  # noqa: F401
+    get_custom_module_class_keys,
+    get_skipped_module_name_and_classes,
+)
+from .qconfig_mapping import QConfigMapping
+
+
+def attach_preserved_attrs_to_model(
+    model: Union[GraphModule, torch.nn.Module],
+    preserved_attrs: Dict[str, Any],
+) -> None:
+    """Store preserved attributes to the model.meta so that it can be preserved during deepcopy"""
+    model.meta[_USER_PRESERVED_ATTRIBUTES_KEY] = copy.copy(preserved_attrs)  # type: ignore[operator, index, assignment]
+    # set the preserved attributes in the model so that user can call
+    # model.attr as they do before calling fx graph mode quantization
+    for attr_name, attr in model.meta[_USER_PRESERVED_ATTRIBUTES_KEY].items():  # type: ignore[index, union-attr]
+        setattr(model, attr_name, attr)
+
+
+def _check_is_graph_module(model: torch.nn.Module) -> None:
+    if not isinstance(model, GraphModule):
+        raise ValueError(
+            "input model must be a GraphModule, "
+            + "Got type:"
+            + str(type(model))
+            + " Please make "
+            + "sure to follow the tutorials."
+        )
+
+
+def _attach_meta_to_node_if_not_exist(model: GraphModule) -> None:
+    """Attach meta field to all nodes of the graph if it does not exist,
+    meta field is a field stores some meta information about the node, such
+    as dtype and shape information for output of the node, this only exists
+    if the program is captured by make_fx (used in quantize_pt2e flow), if
+    the program is captured by torch.fx symbolic tracing, this field may not exist,
+    so we add it here to avoid checking this all over the places
+    """
+    for node in model.graph.nodes:
+        if not hasattr(node, "meta"):
+            node.meta = {}
+
+
+def _swap_ff_with_fxff(model: torch.nn.Module) -> None:
+    r"""Swap FloatFunctional with FXFloatFunctional"""
+    modules_to_swap = []
+    for name, module in model.named_children():
+        if isinstance(module, torch.ao.nn.quantized.FloatFunctional):
+            modules_to_swap.append(name)
+        else:
+            _swap_ff_with_fxff(module)
+
+    for name in modules_to_swap:
+        del model._modules[name]
+        model._modules[name] = torch.ao.nn.quantized.FXFloatFunctional()
+
+
+def _fuse_fx(
+    model: GraphModule,
+    is_qat: bool,
+    fuse_custom_config: Union[FuseCustomConfig, Dict[str, Any], None] = None,
+    backend_config: Union[BackendConfig, Dict[str, Any], None] = None,
+) -> GraphModule:
+    r"""Internal helper function to fuse modules in preparation for quantization
+
+    Args:
+        model: GraphModule object from symbolic tracing (torch.fx.symbolic_trace)
+    """
+    _check_is_graph_module(model)
+    return fuse(
+        model, is_qat, fuse_custom_config, backend_config
+    )  # type: ignore[operator]
+
+
+def _prepare_fx(
+    model: torch.nn.Module,
+    qconfig_mapping: Union[QConfigMapping, Dict[str, Any]],
+    is_qat: bool,
+    example_inputs: Tuple[Any, ...],
+    prepare_custom_config: Union[PrepareCustomConfig, Dict[str, Any], None] = None,
+    _equalization_config: Optional[Union[QConfigMapping, Dict[str, Any]]] = None,
+    backend_config: Union[BackendConfig, Dict[str, Any], None] = None,
+    is_standalone_module: bool = False,
+) -> GraphModule:
+    r"""Internal helper function for prepare_fx
+        Args:
+          `model`, `qconfig_mapping`, `prepare_custom_config`, `_equalization_config`:
+          see docs for :func:`~torch.ao.quantization.prepare_fx`
+          `is_standalone_module`: a boolean flag indicates whether we are
+          quantizing a standalone module or not, a standalone module
+          is a submodule of the parent module that is not inlined in the
+    forward graph of the parent module,
+          the way we quantize standalone module is described in:
+          :func:`~torch.ao.quantization._prepare_standalone_module_fx`
+    """
+    if prepare_custom_config is None:
+        prepare_custom_config = PrepareCustomConfig()
+    if _equalization_config is None:
+        _equalization_config = QConfigMapping()
+
+    if isinstance(prepare_custom_config, dict):
+        warnings.warn(
+            "Passing a prepare_custom_config_dict to prepare is deprecated and will not be supported "
+            "in a future version. Please pass in a PrepareCustomConfig instead.",
+            FutureWarning,
+            stacklevel=3,
+        )
+        prepare_custom_config = PrepareCustomConfig.from_dict(prepare_custom_config)
+
+    # swap FloatFunctional with FXFloatFunctional
+    _swap_ff_with_fxff(model)
+
+    skipped_module_names, skipped_module_classes = get_skipped_module_name_and_classes(
+        prepare_custom_config, is_standalone_module
+    )
+    preserved_attr_names = prepare_custom_config.preserved_attributes
+    preserved_attrs = {
+        attr: getattr(model, attr)
+        for attr in preserved_attr_names
+        if hasattr(model, attr)
+    }
+    # symbolically trace the model
+    tracer = QuantizationTracer(skipped_module_names, skipped_module_classes)  # type: ignore[arg-type]
+    graph_module = GraphModule(model, tracer.trace(model))
+    _attach_meta_to_node_if_not_exist(graph_module)
+
+    fuse_custom_config = FuseCustomConfig().set_preserved_attributes(
+        prepare_custom_config.preserved_attributes
+    )
+    graph_module = _fuse_fx(graph_module, is_qat, fuse_custom_config, backend_config)
+    prepared = prepare(
+        graph_module,
+        qconfig_mapping,
+        is_qat,
+        tracer.node_name_to_scope,
+        example_inputs=example_inputs,
+        prepare_custom_config=prepare_custom_config,
+        _equalization_config=_equalization_config,
+        backend_config=backend_config,
+        is_standalone_module=is_standalone_module,
+    )  # type: ignore[operator]
+
+    attach_preserved_attrs_to_model(prepared, preserved_attrs)
+    return prepared
+
+
+def _prepare_standalone_module_fx(
+    model: torch.nn.Module,
+    qconfig_mapping: Union[QConfigMapping, Dict[str, Any]],
+    is_qat: bool,
+    example_inputs: Tuple[Any, ...],
+    prepare_custom_config: Union[PrepareCustomConfig, Dict[str, Any], None] = None,
+    backend_config: Union[BackendConfig, Dict[str, Any], None] = None,
+) -> GraphModule:
+    r"""[Internal use only] Prepare a standalone module, so that it can be used when quantizing the
+    parent module.
+    standalone_module means it a submodule that is not inlined in parent module,
+    and will be quantized separately as one unit.
+
+    How the standalone module is observed is specified by `input_quantized_idxs` and
+    `output_quantized_idxs` in the prepare_custom_config for the standalone module
+
+    Returns:
+
+        * model(GraphModule): prepared standalone module. It has these attributes in
+          model.meta:
+
+            * `standalone_module_input_quantized_idxs(List[Int])`: a list of
+              indexes for the graph input that is expected to be quantized,
+              same as input_quantized_idxs configuration provided
+              for the standalone module
+            * `standalone_module_output_quantized_idxs(List[Int])`: a list of
+              indexs for the graph output that is quantized
+              same as input_quantized_idxs configuration provided
+              for the standalone module
+
+    """
+    return _prepare_fx(
+        model,
+        qconfig_mapping,
+        is_qat,
+        example_inputs,
+        prepare_custom_config,
+        backend_config=backend_config,
+        is_standalone_module=True,
+    )
+
+
+def fuse_fx(
+    model: torch.nn.Module,
+    fuse_custom_config: Union[FuseCustomConfig, Dict[str, Any], None] = None,
+    backend_config: Union[BackendConfig, Dict[str, Any], None] = None,
+) -> GraphModule:
+    r"""Fuse modules like conv+bn, conv+bn+relu etc, model must be in eval mode.
+    Fusion rules are defined in torch.ao.quantization.fx.fusion_pattern.py
+
+    Args:
+
+        * `model` (torch.nn.Module): a torch.nn.Module model
+        * `fuse_custom_config` (FuseCustomConfig): custom configurations for fuse_fx.
+            See :class:`~torch.ao.quantization.fx.custom_config.FuseCustomConfig` for more details
+    Example::
+
+        from torch.ao.quantization import fuse_fx
+        m = Model().eval()
+        m = fuse_fx(m)
+
+    """
+    if fuse_custom_config is None:
+        fuse_custom_config = FuseCustomConfig()
+
+    if isinstance(fuse_custom_config, dict):
+        warnings.warn(
+            "Passing a fuse_custom_config_dict to fuse is deprecated and will not be supported "
+            "in a future version. Please pass in a FuseCustomConfig instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        fuse_custom_config = FuseCustomConfig.from_dict(fuse_custom_config)
+
+    torch._C._log_api_usage_once("quantization_api.quantize_fx.fuse_fx")
+    preserved_attr_names = fuse_custom_config.preserved_attributes
+    preserved_attrs = {
+        attr: getattr(model, attr)
+        for attr in preserved_attr_names
+        if hasattr(model, attr)
+    }
+
+    graph_module = torch.fx.symbolic_trace(model)
+    _attach_meta_to_node_if_not_exist(graph_module)
+    graph_module = _fuse_fx(graph_module, False, fuse_custom_config, backend_config)
+
+    attach_preserved_attrs_to_model(graph_module, preserved_attrs)
+    return graph_module
+
+
+def prepare_fx(
+    model: torch.nn.Module,
+    qconfig_mapping: Union[QConfigMapping, Dict[str, Any]],
+    example_inputs: Tuple[Any, ...],
+    prepare_custom_config: Union[PrepareCustomConfig, Dict[str, Any], None] = None,
+    _equalization_config: Optional[Union[QConfigMapping, Dict[str, Any]]] = None,
+    backend_config: Union[BackendConfig, Dict[str, Any], None] = None,
+) -> GraphModule:
+    r""" Prepare a model for post training quantization
+
+    Args:
+      * `model` (torch.nn.Module): torch.nn.Module model
+
+      * `qconfig_mapping` (QConfigMapping): QConfigMapping object to configure how a model is
+         quantized, see :class:`~torch.ao.quantization.qconfig_mapping.QConfigMapping`
+         for more details
+
+      * `example_inputs` (Tuple[Any, ...]): Example inputs for forward function of the model,
+         Tuple of positional args (keyword args can be passed as positional args as well)
+
+      * `prepare_custom_config` (PrepareCustomConfig): customization configuration for quantization tool.
+          See :class:`~torch.ao.quantization.fx.custom_config.PrepareCustomConfig` for more details
+
+      * `_equalization_config`: config for specifying how to perform equalization on the model
+
+      * `backend_config` (BackendConfig): config that specifies how operators are quantized
+         in a backend, this includes how the operators are observed,
+         supported fusion patterns, how quantize/dequantize ops are
+         inserted, supported dtypes etc. See :class:`~torch.ao.quantization.backend_config.BackendConfig` for more details
+
+    Return:
+      A GraphModule with observer (configured by qconfig_mapping), ready for calibration
+
+    Example::
+
+        import torch
+        from torch.ao.quantization import get_default_qconfig_mapping
+        from torch.ao.quantization.quantize_fx import prepare_fx
+
+        class Submodule(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.linear = torch.nn.Linear(5, 5)
+            def forward(self, x):
+                x = self.linear(x)
+                return x
+
+        class M(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.linear = torch.nn.Linear(5, 5)
+                self.sub = Submodule()
+
+            def forward(self, x):
+                x = self.linear(x)
+                x = self.sub(x) + x
+                return x
+
+        # initialize a floating point model
+        float_model = M().eval()
+
+        # define calibration function
+        def calibrate(model, data_loader):
+            model.eval()
+            with torch.no_grad():
+                for image, target in data_loader:
+                    model(image)
+
+        # qconfig is the configuration for how we insert observers for a particular
+        # operator
+        # qconfig = get_default_qconfig("fbgemm")
+        # Example of customizing qconfig:
+        # qconfig = torch.ao.quantization.QConfig(
+        #    activation=MinMaxObserver.with_args(dtype=torch.qint8),
+        #    weight=MinMaxObserver.with_args(dtype=torch.qint8))
+        # `activation` and `weight` are constructors of observer module
+
+        # qconfig_mapping is a collection of quantization configurations, user can
+        # set the qconfig for each operator (torch op calls, functional calls, module calls)
+        # in the model through qconfig_mapping
+        # the following call will get the qconfig_mapping that works best for models
+        # that target "fbgemm" backend
+        qconfig_mapping = get_default_qconfig_mapping("fbgemm")
+
+        # We can customize qconfig_mapping in different ways.
+        # e.g. set the global qconfig, which means we will use the same qconfig for
+        # all operators in the model, this can be overwritten by other settings
+        # qconfig_mapping = QConfigMapping().set_global(qconfig)
+        # e.g. quantize the linear submodule with a specific qconfig
+        # qconfig_mapping = QConfigMapping().set_module_name("linear", qconfig)
+        # e.g. quantize all nn.Linear modules with a specific qconfig
+        # qconfig_mapping = QConfigMapping().set_object_type(torch.nn.Linear, qconfig)
+        # for a more complete list, please see the docstring for :class:`torch.ao.quantization.QConfigMapping`
+        # argument
+
+        # example_inputs is a tuple of inputs, that is used to infer the type of the
+        # outputs in the model
+        # currently it's not used, but please make sure model(*example_inputs) runs
+        example_inputs = (torch.randn(1, 3, 224, 224),)
+
+        # TODO: add backend_config after we split the backend_config for fbgemm and qnnpack
+        # e.g. backend_config = get_default_backend_config("fbgemm")
+        # `prepare_fx` inserts observers in the model based on qconfig_mapping and
+        # backend_config. If the configuration for an operator in qconfig_mapping
+        # is supported in the backend_config (meaning it's supported by the target
+        # hardware), we'll insert observer modules according to the qconfig_mapping
+        # otherwise the configuration in qconfig_mapping will be ignored
+        #
+        # Example:
+        # in qconfig_mapping, user sets linear module to be quantized with quint8 for
+        # activation and qint8 for weight:
+        # qconfig = torch.ao.quantization.QConfig(
+        #     observer=MinMaxObserver.with_args(dtype=torch.quint8),
+        #     weight=MinMaxObserver.with-args(dtype=torch.qint8))
+        # Note: current qconfig api does not support setting output observer, but
+        # we may extend this to support these more fine grained control in the
+        # future
+        #
+        # qconfig_mapping = QConfigMapping().set_object_type(torch.nn.Linear, qconfig)
+        # in backend config, linear module also supports in this configuration:
+        # weighted_int8_dtype_config = DTypeConfig(
+        #   input_dtype=torch.quint8,
+        #   output_dtype=torch.quint8,
+        #   weight_dtype=torch.qint8,
+        #   bias_type=torch.float)
+
+        # linear_pattern_config = BackendPatternConfig(torch.nn.Linear) \
+        #    .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT) \
+        #    .add_dtype_config(weighted_int8_dtype_config) \
+        #    ...
+
+        # backend_config = BackendConfig().set_backend_pattern_config(linear_pattern_config)
+        # `prepare_fx` will check that the setting requested by suer in qconfig_mapping
+        # is supported by the backend_config and insert observers and fake quant modules
+        # in the model
+        prepared_model = prepare_fx(float_model, qconfig_mapping, example_inputs)
+        # Run calibration
+        calibrate(prepared_model, sample_inference_data)
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize_fx.prepare_fx")
+    return _prepare_fx(
+        model,
+        qconfig_mapping,
+        False,  # is_qat
+        example_inputs,
+        prepare_custom_config,
+        _equalization_config,
+        backend_config,
+    )
+
+
+def prepare_qat_fx(
+    model: torch.nn.Module,
+    qconfig_mapping: Union[QConfigMapping, Dict[str, Any]],
+    example_inputs: Tuple[Any, ...],
+    prepare_custom_config: Union[PrepareCustomConfig, Dict[str, Any], None] = None,
+    backend_config: Union[BackendConfig, Dict[str, Any], None] = None,
+) -> GraphModule:
+    r"""Prepare a model for quantization aware training
+
+    Args:
+      * `model` (torch.nn.Module): torch.nn.Module model
+      * `qconfig_mapping` (QConfigMapping): see :func:`~torch.ao.quantization.prepare_fx`
+      * `example_inputs` (Tuple[Any, ...]): see :func:`~torch.ao.quantization.prepare_fx`
+      * `prepare_custom_config` (PrepareCustomConfig): see :func:`~torch.ao.quantization.prepare_fx`
+      * `backend_config` (BackendConfig): see :func:`~torch.ao.quantization.prepare_fx`
+
+    Return:
+      A GraphModule with fake quant modules (configured by qconfig_mapping and backend_config), ready for
+      quantization aware training
+
+    Example::
+
+        import torch
+        from torch.ao.quantization import get_default_qat_qconfig_mapping
+        from torch.ao.quantization.quantize_fx import prepare_qat_fx
+
+        class Submodule(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.linear = torch.nn.Linear(5, 5)
+            def forward(self, x):
+                x = self.linear(x)
+                return x
+
+        class M(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.linear = torch.nn.Linear(5, 5)
+                self.sub = Submodule()
+
+            def forward(self, x):
+                x = self.linear(x)
+                x = self.sub(x) + x
+                return x
+
+        # initialize a floating point model
+        float_model = M().train()
+        # (optional, but preferred) load the weights from pretrained model
+        # float_model.load_weights(...)
+
+        # define the training loop for quantization aware training
+        def train_loop(model, train_data):
+            model.train()
+            for image, target in data_loader:
+                ...
+
+        # qconfig is the configuration for how we insert observers for a particular
+        # operator
+        # qconfig = get_default_qconfig("fbgemm")
+        # Example of customizing qconfig:
+        # qconfig = torch.ao.quantization.QConfig(
+        #    activation=FakeQuantize.with_args(observer=MinMaxObserver.with_args(dtype=torch.qint8)),
+        #    weight=FakeQuantize.with_args(observer=MinMaxObserver.with_args(dtype=torch.qint8)))
+        # `activation` and `weight` are constructors of observer module
+
+        # qconfig_mapping is a collection of quantization configurations, user can
+        # set the qconfig for each operator (torch op calls, functional calls, module calls)
+        # in the model through qconfig_mapping
+        # the following call will get the qconfig_mapping that works best for models
+        # that target "fbgemm" backend
+        qconfig_mapping = get_default_qat_qconfig("fbgemm")
+
+        # We can customize qconfig_mapping in different ways, please take a look at
+        # the docstring for :func:`~torch.ao.quantization.prepare_fx` for different ways
+        # to configure this
+
+        # example_inputs is a tuple of inputs, that is used to infer the type of the
+        # outputs in the model
+        # currently it's not used, but please make sure model(*example_inputs) runs
+        example_inputs = (torch.randn(1, 3, 224, 224),)
+
+        # TODO: add backend_config after we split the backend_config for fbgemm and qnnpack
+        # e.g. backend_config = get_default_backend_config("fbgemm")
+        # `prepare_qat_fx` inserts observers in the model based on qconfig_mapping and
+        # backend_config, if the configuration for an operator in qconfig_mapping
+        # is supported in the backend_config (meaning it's supported by the target
+        # hardware), we'll insert fake_quantize modules according to the qconfig_mapping
+        # otherwise the configuration in qconfig_mapping will be ignored
+        # see :func:`~torch.ao.quantization.prepare_fx` for a detailed explanation of
+        # how qconfig_mapping interacts with backend_config
+        prepared_model = prepare_qat_fx(float_model, qconfig_mapping, example_inputs)
+        # Run training
+        train_loop(prepared_model, train_loop)
+
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize_fx.prepare_qat_fx")
+    return _prepare_fx(
+        model,
+        qconfig_mapping,
+        True,  # is_qat
+        example_inputs,
+        prepare_custom_config,
+        backend_config=backend_config,
+    )
+
+
+def _convert_fx(
+    graph_module: GraphModule,
+    is_reference: bool,
+    convert_custom_config: Union[ConvertCustomConfig, Dict[str, Any], None] = None,
+    is_standalone_module: bool = False,
+    _remove_qconfig: bool = True,
+    qconfig_mapping: Union[QConfigMapping, Dict[str, Any], None] = None,
+    backend_config: Union[BackendConfig, Dict[str, Any], None] = None,
+    is_decomposed: bool = False,
+) -> GraphModule:
+    """`is_standalone_module`: see docs in :func:`~torch.ao.quantization.prepare_standalone_module_fx`"""
+    if convert_custom_config is None:
+        convert_custom_config = ConvertCustomConfig()
+
+    if isinstance(convert_custom_config, dict):
+        warnings.warn(
+            "Passing a convert_custom_config_dict to convert is deprecated and will not be supported "
+            "in a future version. Please pass in a ConvertCustomConfig instead.",
+            FutureWarning,
+            stacklevel=3,
+        )
+        convert_custom_config = ConvertCustomConfig.from_dict(convert_custom_config)
+
+    _check_is_graph_module(graph_module)
+    preserved_attr_names = convert_custom_config.preserved_attributes
+    preserved_attrs = {
+        attr: getattr(graph_module, attr)
+        for attr in preserved_attr_names
+        if hasattr(graph_module, attr)
+    }
+
+    quantized = convert(
+        graph_module,
+        is_reference,
+        convert_custom_config,
+        is_standalone_module,
+        _remove_qconfig_flag=_remove_qconfig,
+        qconfig_mapping=qconfig_mapping,
+        backend_config=backend_config,
+        is_decomposed=is_decomposed,
+    )
+
+    attach_preserved_attrs_to_model(quantized, preserved_attrs)
+    return quantized
+
+
+def convert_fx(
+    graph_module: GraphModule,
+    convert_custom_config: Union[ConvertCustomConfig, Dict[str, Any], None] = None,
+    _remove_qconfig: bool = True,
+    qconfig_mapping: Union[QConfigMapping, Dict[str, Any], None] = None,
+    backend_config: Union[BackendConfig, Dict[str, Any], None] = None,
+) -> GraphModule:
+    r"""Convert a calibrated or trained model to a quantized model
+
+    Args:
+        * `graph_module` (torch.fx.GraphModule): A prepared and calibrated/trained model (GraphModule)
+
+        * `convert_custom_config` (ConvertCustomConfig): custom configurations for convert function.
+            See :class:`~torch.ao.quantization.fx.custom_config.ConvertCustomConfig` for more details
+
+        * `_remove_qconfig` (bool): Option to remove the qconfig attributes in the model after convert.
+
+        * `qconfig_mapping` (QConfigMapping): config for specifying how to convert a model for quantization.
+
+           The keys must include the ones in the qconfig_mapping passed to `prepare_fx` or `prepare_qat_fx`,
+           with the same values or `None`. Additional keys can be specified with values set to `None`.
+
+          For each entry whose value is set to None, we skip quantizing that entry in the model::
+
+            qconfig_mapping = QConfigMapping
+                .set_global(qconfig_from_prepare)
+                .set_object_type(torch.nn.functional.add, None)  # skip quantizing torch.nn.functional.add
+                .set_object_type(torch.nn.functional.linear, qconfig_from_prepare)
+                .set_module_name("foo.bar", None)  # skip quantizing module "foo.bar"
+
+         * `backend_config` (BackendConfig): A configuration for the backend which describes how
+            operators should be quantized in the backend, this includes quantization
+            mode support (static/dynamic/weight_only), dtype support (quint8/qint8 etc.),
+            observer placement for each operators and fused operators.
+            See :class:`~torch.ao.quantization.backend_config.BackendConfig` for more details
+
+    Return:
+        A quantized model (torch.nn.Module)
+
+    Example::
+
+        # prepared_model: the model after prepare_fx/prepare_qat_fx and calibration/training
+        # convert_fx converts a calibrated/trained model to a quantized model for the
+        # target hardware, this includes converting the model first to a reference
+        # quantized model, and then lower the reference quantized model to a backend
+        # Currently, the supported backends are fbgemm (onednn), qnnpack (xnnpack) and
+        # they share the same set of quantized operators, so we are using the same
+        # lowering procedure
+        #
+        # backend_config defines the corresponding reference quantized module for
+        # the weighted modules in the model, e.g. nn.Linear
+        # TODO: add backend_config after we split the backend_config for fbgemm and qnnpack
+        # e.g. backend_config = get_default_backend_config("fbgemm")
+        quantized_model = convert_fx(prepared_model)
+
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize_fx.convert_fx")
+    return _convert_fx(
+        graph_module,
+        is_reference=False,
+        convert_custom_config=convert_custom_config,
+        _remove_qconfig=_remove_qconfig,
+        qconfig_mapping=qconfig_mapping,
+        backend_config=backend_config,
+    )
+
+
+def convert_to_reference_fx(
+    graph_module: GraphModule,
+    convert_custom_config: Union[ConvertCustomConfig, Dict[str, Any], None] = None,
+    _remove_qconfig: bool = True,
+    qconfig_mapping: Union[QConfigMapping, Dict[str, Any], None] = None,
+    backend_config: Union[BackendConfig, Dict[str, Any], None] = None,
+) -> GraphModule:
+    r"""Convert a calibrated or trained model to a reference quantized model,
+    see https://github.com/pytorch/rfcs/blob/master/RFC-0019-Extending-PyTorch-Quantization-to-Custom-Backends.md for more details,
+    reference quantized model is a standard representation of a quantized model provided
+    by FX Graph Mode Quantization, it can be further lowered to run on the target
+    hardware, like accelerators
+
+    Args:
+        * `graph_module` (GraphModule): A prepared and calibrated/trained model (GraphModule)
+
+        * `convert_custom_config` (ConvertCustomConfig): custom configurations for convert function.
+            See :func:`~torch.ao.quantization.quantize_fx.convert_fx` for more details.
+
+        * `_remove_qconfig` (bool): Option to remove the qconfig attributes in the model after convert.
+
+        * `qconfig_mapping` (QConfigMapping): config for specifying how to convert a model for quantization.
+            See :func:`~torch.ao.quantization.quantize_fx.convert_fx` for more details.
+
+         * `backend_config` (BackendConfig): A configuration for the backend which describes how
+            operators should be quantized in the backend. See
+            :func:`~torch.ao.quantization.quantize_fx.convert_fx` for more details.
+
+    Return:
+        A reference quantized model (GraphModule)
+
+    Example::
+
+        # prepared_model: the model after prepare_fx/prepare_qat_fx and calibration/training
+        # TODO: add backend_config after we split the backend_config for fbgemm and qnnpack
+        # e.g. backend_config = get_default_backend_config("fbgemm")
+        reference_quantized_model = convert_to_reference_fx(prepared_model)
+
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize_fx.convert_to_reference_fx")
+    return _convert_fx(
+        graph_module,
+        is_reference=True,
+        convert_custom_config=convert_custom_config,
+        _remove_qconfig=_remove_qconfig,
+        qconfig_mapping=qconfig_mapping,
+        backend_config=backend_config,
+    )
+
+
+def _convert_to_reference_decomposed_fx(
+    graph_module: GraphModule,
+    convert_custom_config: Union[ConvertCustomConfig, Dict[str, Any], None] = None,
+    qconfig_mapping: Union[QConfigMapping, Dict[str, Any], None] = None,
+    backend_config: Union[BackendConfig, Dict[str, Any], None] = None,
+) -> GraphModule:
+    r"""Convert a calibrated or trained model to a reference quantized model, with
+    decomposed representation for quantized Tensor
+    see https://github.com/pytorch/rfcs/blob/master/RFC-0019-Extending-PyTorch-Quantization-to-Custom-Backends.md for more details,
+    reference quantized model is a standard representation of a quantized model provided
+    by FX Graph Mode Quantization, it can be further lowered to run on the target
+    hardware, like accelerators
+
+    Note: this is not public API
+
+    Args:
+        * `graph_module` (GraphModule): A prepared and calibrated/trained model (GraphModule)
+
+        * `convert_custom_config` (ConvertCustomConfig): custom configurations for convert function.
+            See :func:`~torch.ao.quantization.quantize_fx.convert_fx` for more details.
+
+        * `_remove_qconfig` (bool): Option to remove the qconfig attributes in the model after convert.
+
+        * `qconfig_mapping` (QConfigMapping): config for specifying how to convert a model for quantization.
+            See :func:`~torch.ao.quantization.quantize_fx.convert_fx` for more details.
+
+         * `backend_config` (BackendConfig): A configuration for the backend which describes how
+            operators should be quantized in the backend. See
+            :func:`~torch.ao.quantization.quantize_fx.convert_fx` for more details.
+
+    Return:
+        A reference quantized model (GraphModule) with operators working with decomposed quantized Tensor
+
+    Example::
+
+        # prepared_model: the model after prepare_fx/prepare_qat_fx and calibration/training
+        # TODO: add backend_config after we split the backend_config for fbgemm and qnnpack
+        # e.g. backend_config = get_default_backend_config("fbgemm")
+        reference_quantized_model = _convert_to_reference_decomposed_fx(prepared_model)
+
+    """
+    torch._C._log_api_usage_once(
+        "quantization_api.quantize_fx._convert_to_reference_decomposed_fx"
+    )
+    return _convert_fx(
+        graph_module,
+        is_reference=True,
+        convert_custom_config=convert_custom_config,
+        _remove_qconfig=False,
+        qconfig_mapping=qconfig_mapping,
+        backend_config=backend_config,
+        is_decomposed=True,
+    )
+
+
+def _convert_standalone_module_fx(
+    graph_module: GraphModule,
+    is_reference: bool = False,
+    convert_custom_config: Union[ConvertCustomConfig, Dict[str, Any], None] = None,
+) -> GraphModule:
+    r"""[Internal use only] Convert a model produced by :func:`~torch.ao.quantization.prepare_standalone_module_fx`
+    and convert it to a quantized model
+
+    Returns a quantized standalone module, whether input/output is quantized is
+    specified by prepare_custom_config, with
+    input_quantized_idxs, output_quantized_idxs, please
+    see docs for prepare_fx for details
+    """
+    return _convert_fx(
+        graph_module,
+        is_reference,
+        convert_custom_config,
+        is_standalone_module=True,
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantize_jit.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantize_jit.py
new file mode 100644
index 0000000000000000000000000000000000000000..59e546458f8c05138e151bfd599e93e2294d7f75
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantize_jit.py
@@ -0,0 +1,415 @@
+# mypy: allow-untyped-defs
+
+import torch
+from torch.ao.quantization.qconfig import QConfig
+from torch.ao.quantization.quant_type import QuantType
+from torch.jit._recursive import wrap_cpp_module
+
+
+__all__ = [
+    "script_qconfig",
+    "script_qconfig_dict",
+    "fuse_conv_bn_jit",
+    "prepare_jit",
+    "prepare_dynamic_jit",
+    "convert_jit",
+    "convert_dynamic_jit",
+    "quantize_jit",
+    "quantize_dynamic_jit",
+]
+
+
+def _check_is_script_module(model):
+    if not isinstance(model, torch.jit.ScriptModule):
+        raise ValueError("input must be a script module, got: " + str(type(model)))
+
+
+def _check_forward_method(model):
+    if not model._c._has_method("forward"):
+        raise ValueError("input script module does not have forward method")
+
+
+def script_qconfig(qconfig):
+    r"""Instantiate the activation and weight observer modules and script
+    them, these observer module instances will be deepcopied during
+    prepare_jit step.
+    """
+    return QConfig(
+        activation=torch.jit.script(qconfig.activation())._c,
+        weight=torch.jit.script(qconfig.weight())._c,
+    )
+
+
+def script_qconfig_dict(qconfig_dict):
+    r"""Helper function used by `prepare_jit`.
+    Apply `script_qconfig` for all entries in `qconfig_dict` that is
+    not None.
+    """
+    return {k: script_qconfig(v) if v else None for k, v in qconfig_dict.items()}
+
+
+def fuse_conv_bn_jit(model, inplace=False):
+    r"""Fuse conv - bn module
+    Works for eval model only.
+
+    Args:
+        model: TorchScript model from scripting or tracing
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize_jit.fuse_conv_bn_jit")
+    model_c = model._c
+    model_c = torch._C._jit_pass_fold_convbn(model_c)
+    if inplace:
+        model._reconstruct(model_c)
+    else:
+        model = wrap_cpp_module(model_c)
+    return model
+
+
+def _prepare_jit(model, qconfig_dict, inplace=False, quant_type=QuantType.STATIC):
+    _check_is_script_module(model)
+    _check_forward_method(model)
+    if not all(isinstance(x, str) for x in qconfig_dict.keys()):
+        raise ValueError("qconfig_dict should only contain names(str) as keys.")
+    scripted_qconfig_dict = script_qconfig_dict(qconfig_dict)
+    model = fuse_conv_bn_jit(model, inplace)
+    model_c = torch._C._jit_pass_insert_observers(
+        model._c, "forward", scripted_qconfig_dict, inplace, quant_type
+    )
+    if inplace:
+        model._reconstruct(model_c)
+    else:
+        model = wrap_cpp_module(model_c)
+    return model
+
+
+def _prepare_ondevice_jit(
+    model,
+    qconfig_dict,
+    method_name="forward",
+    inplace=False,
+    quant_type=QuantType.STATIC,
+):
+    _check_is_script_module(model)
+    if not all(isinstance(x, str) for x in qconfig_dict.keys()):
+        raise ValueError("qconfig_dict should only contain names(str) as keys.")
+    scripted_qconfig_dict = script_qconfig_dict(qconfig_dict)
+    method_graph = model._c._get_method(method_name).graph
+    torch._C._jit_pass_inline(method_graph)
+    model = fuse_conv_bn_jit(model, inplace)
+    model_c = torch._C._jit_pass_insert_observer_method_for_ondevice_ptq(
+        model._c, method_name, scripted_qconfig_dict, inplace, quant_type
+    )
+    if inplace:
+        model._reconstruct(model_c)
+    else:
+        model = wrap_cpp_module(model_c)
+    return model
+
+
+def prepare_jit(model, qconfig_dict, inplace=False):
+    torch._C._log_api_usage_once("quantization_api.quantize_jit.prepare_jit")
+    return _prepare_jit(model, qconfig_dict, inplace, quant_type=QuantType.STATIC)
+
+
+def prepare_dynamic_jit(model, qconfig_dict, inplace=False):
+    torch._C._log_api_usage_once("quantization_api.quantize_jit.prepare_dynamic_jit")
+    return _prepare_jit(model, qconfig_dict, inplace, quant_type=QuantType.DYNAMIC)
+
+
+def _prepare_ondevice_dynamic_jit(
+    model, qconfig_dict, method_name="forward", inplace=False
+):
+    return _prepare_ondevice_jit(
+        model, qconfig_dict, method_name, inplace, quant_type=QuantType.DYNAMIC
+    )
+
+
+def _convert_jit(
+    model, inplace=False, debug=False, quant_type=QuantType.STATIC, preserved_attrs=None
+):
+    _check_is_script_module(model)
+    model.eval()
+    model_c = model._c
+    model_c = torch._C._jit_pass_insert_quant_dequant(
+        model_c, "forward", inplace, debug, quant_type
+    )
+    if not debug:
+        is_xpu = all(p.device.type == "xpu" for p in model.parameters())
+        if not is_xpu:
+            # Moving model parameters to CPU since quantized operators
+            # are only supported on CPU and XPU right now
+            model.cpu()
+        if preserved_attrs is None:
+            preserved_attrs = []
+        model_c = torch._C._jit_pass_quant_finalize(
+            model_c, quant_type, preserved_attrs
+        )
+    if inplace:
+        model._reconstruct(model_c)
+    else:
+        model = wrap_cpp_module(model_c)
+    torch._C._jit_pass_constant_propagation(model.graph)
+    torch._C._jit_pass_dce(model.graph)
+    return model
+
+
+def _convert_ondevice_jit(
+    model, method_name, inplace=False, debug=False, quant_type=QuantType.STATIC
+):
+    _check_is_script_module(model)
+    assert (
+        quant_type == QuantType.DYNAMIC
+    ), "This API, while should work for static quant, is only tested for dynamic quant."
+    assert not method_name.startswith(
+        "observe_"
+    ), "Pass in valid method to be quantized, e.g. forward"
+    observe_method_name = "observe_" + method_name
+    quantize_method_name = "quantize_" + method_name
+    model_c = model._c
+    model_c = torch._C._jit_pass_insert_quant_dequant_for_ondevice_ptq(
+        model._c, observe_method_name, inplace, debug, QuantType.DYNAMIC
+    )
+    model_c = torch._C._jit_pass_quant_finalize_for_ondevice_ptq(
+        model_c, QuantType.DYNAMIC, quantize_method_name
+    )
+    if inplace:
+        model._reconstruct(model_c)
+    else:
+        model = wrap_cpp_module(model_c)
+    return model
+
+
+def convert_jit(model, inplace=False, debug=False, preserved_attrs=None):
+    torch._C._log_api_usage_once("quantization_api.quantize_jit.convert_jit")
+    return _convert_jit(
+        model,
+        inplace,
+        debug,
+        quant_type=QuantType.STATIC,
+        preserved_attrs=preserved_attrs,
+    )
+
+
+def convert_dynamic_jit(model, inplace=False, debug=False, preserved_attrs=None):
+    torch._C._log_api_usage_once("quantization_api.quantize_jit.convert_dynamic_jit")
+    return _convert_jit(
+        model,
+        inplace,
+        debug,
+        quant_type=QuantType.DYNAMIC,
+        preserved_attrs=preserved_attrs,
+    )
+
+
+def _convert_ondevice_dynamic_jit(model, method_name, inplace=False, debug=False):
+    return _convert_ondevice_jit(
+        model, method_name, inplace, debug, quant_type=QuantType.DYNAMIC
+    )
+
+
+def _quantize_ondevice_dynamic_jit_impl(
+    model, qconfig_dict, method_name, inplace=False
+):
+    model = _prepare_ondevice_dynamic_jit(model, qconfig_dict, method_name, inplace)
+    model = _convert_ondevice_dynamic_jit(model, method_name, inplace)
+    return model
+
+
+def _quantize_jit(
+    model,
+    qconfig_dict,
+    run_fn=None,
+    run_args=None,
+    inplace=False,
+    debug=False,
+    quant_type=QuantType.STATIC,
+):
+    # Always do inplace convert because the Tensor is already
+    # copied in prepare_jit when inplace is False
+    if quant_type == QuantType.DYNAMIC:
+        model = prepare_dynamic_jit(model, qconfig_dict, inplace)
+        model = convert_dynamic_jit(model, True, debug)
+    else:
+        assert (
+            run_fn
+        ), "Must provide calibration function for post training static quantization"
+        assert (
+            run_args
+        ), "Must provide calibration dataset for post training static quantization"
+        model = prepare_jit(model, qconfig_dict, inplace)
+        run_fn(model, *run_args)
+        model = convert_jit(model, True, debug)
+
+    torch._C._jit_pass_constant_propagation(model.graph)
+    torch._C._jit_pass_dce(model.graph)
+    return model
+
+
+def quantize_jit(model, qconfig_dict, run_fn, run_args, inplace=False, debug=False):
+    r"""Quantize the input float TorchScript model with
+    post training static quantization.
+
+    First it will prepare the model for calibration, then it calls
+    `run_fn` which will run the calibration step, after that we will
+    convert the model to a quantized model.
+
+    Args:
+        `model`: input float TorchScript model
+        `qconfig_dict`: qconfig_dict is a dictionary with names of sub modules as key and
+        qconfig for that module as value, empty key means the qconfig will be applied
+        to whole model unless it's overwritten by more specific configurations, the
+        qconfig for each module is either found in the dictionary or fallback to
+         the qconfig of parent module.
+
+        Right now qconfig_dict is the only way to configure how the model is quantized,
+        and it is done in the granularity of module, that is, we only support one type
+        of qconfig for each torch.nn.Module, and the qconfig for sub module will
+        override the qconfig for parent module, empty string means global configuration.
+        `run_fn`: a calibration function for calibrating the prepared model
+        `run_args`: positional arguments for `run_fn`
+        `inplace`: carry out model transformations in-place, the original module is
+        mutated
+        `debug`: flag for producing a debug friendly model (preserve weight attribute)
+
+    Return:
+        Quantized TorchSciprt model.
+
+    Example:
+    ```python
+    import torch
+    from torch.ao.quantization import get_default_qconfig
+    from torch.ao.quantization import quantize_jit
+
+    ts_model = torch.jit.script(float_model.eval())  # or torch.jit.trace(float_model, input)
+    qconfig = get_default_qconfig('fbgemm')
+    def calibrate(model, data_loader):
+        model.eval()
+        with torch.no_grad():
+            for image, target in data_loader:
+                model(image)
+
+    quantized_model = quantize_jit(
+        ts_model,
+        {'': qconfig},
+        calibrate,
+        [data_loader_test])
+    ```
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize_jit.quantize_jit")
+    return _quantize_jit(
+        model,
+        qconfig_dict,
+        run_fn,
+        run_args,
+        inplace,
+        debug,
+        quant_type=QuantType.STATIC,
+    )
+
+
+def quantize_dynamic_jit(model, qconfig_dict, inplace=False, debug=False):
+    r"""Quantize the input float TorchScript model with
+    post training dynamic quantization.
+    Currently only qint8 quantization of torch.nn.Linear is supported.
+
+    Args:
+        `model`: input float TorchScript model
+        `qconfig_dict`: qconfig_dict is a dictionary with names of sub modules as key and
+        qconfig for that module as value, please see detailed
+        descriptions in :func:`~torch.ao.quantization.quantize_jit`
+        `inplace`: carry out model transformations in-place, the original module is
+        mutated
+        `debug`: flag for producing a debug friendly model (preserve weight attribute)
+
+    Return:
+        Quantized TorchSciprt model.
+
+    Example:
+    ```python
+    import torch
+    from torch.ao.quantization import per_channel_dynamic_qconfig
+    from torch.ao.quantization import quantize_dynamic_jit
+
+    ts_model = torch.jit.script(float_model.eval())  # or torch.jit.trace(float_model, input)
+    qconfig = get_default_qconfig('fbgemm')
+    def calibrate(model, data_loader):
+        model.eval()
+        with torch.no_grad():
+            for image, target in data_loader:
+                model(image)
+
+    quantized_model = quantize_dynamic_jit(
+        ts_model,
+        {'': qconfig},
+        calibrate,
+        [data_loader_test])
+    ```
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize_jit.quantize_dynamic_jit")
+    return _quantize_jit(
+        model, qconfig_dict, inplace=inplace, debug=debug, quant_type=QuantType.DYNAMIC
+    )
+
+
+def _quantize_ondevice_dynamic_jit(
+    model, qconfig_dict, method_name="forward", inplace=False
+):
+    r"""Prepares the input float TorchScript model with
+    *on-device* post training dynamic quantization.
+    Currently only qint8 quantization of torch.nn.Linear is supported.
+
+    Args:
+        `model`: input float TorchScript model
+        `qconfig_dict`: qconfig_dict is a dictionary with names of sub modules as key and
+        qconfig for that module as value, please see detailed
+        `method_name`: Name of the method within the model, to be prepared for quantization
+        descriptions in :func:`~torch.ao.quantization.quantize_jit`
+        `inplace`: carry out model transformations in-place, the original module is
+        mutated
+
+    Return:
+        TorchScript model that is ready for on device quantization.
+        This means that the returned
+        model has:
+        - Method is inlined.
+        - Model has observer modules inserted in the model.
+        - Model has packed params inserted in the model. However they are empty as in they dont
+          contain valid quantized weights.
+        - observe_ is added that observe the values to be quantized.
+        - reset_observers_ to reset observers.
+        - quantize_ is added to the model.
+          - This method extract scale, zero points.
+          - Quantizes observed weights.
+          - Creates packed params from it and update the attribute of the model with the new values
+            for the packed params.
+          - Reset the original fp32 weights with empty tensor using SetAttr.
+        - quantized_ is added to the model.
+          - This method uses quantized weights and quantized linear ops instead of fp32 op.
+          - This method should be used for inference post PTQ.
+        - Note that all method's signatures should be the same as method_name.
+
+        Later on device:
+        - Run reset_observers_
+        - Run observe_
+        - Run quantize_
+        - Now model can be saved and loaded later.
+        - Run model with quantized_
+
+    Example:
+    ```python
+    import torch
+    from torch.ao.quantization import per_channel_dynamic_qconfig
+    from torch.ao.quantization.quantize_jit import _quantize_ondevice_dynamic_jit
+
+    ts_model = torch.jit.script(float_model.eval())  # or torch.jit.trace(float_model, input)
+    qconfig = get_default_qconfig('fbgemm')
+    quant_ready_model = _quantize_ondevice_dynamic_jit(
+        ts_model,
+        {'': qconfig},
+        'forward',
+        True)
+    ```
+    """
+    return _quantize_ondevice_dynamic_jit_impl(
+        model, qconfig_dict, method_name, inplace=inplace
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantize_pt2e.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantize_pt2e.py
new file mode 100644
index 0000000000000000000000000000000000000000..5fc4eb7d1510b77d8f0e3932849fd249bdfc58c5
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantize_pt2e.py
@@ -0,0 +1,249 @@
+import torch
+from torch._export.passes.constant_folding import constant_fold
+from torch.ao.quantization.pt2e.duplicate_dq_pass import DuplicateDQPass
+from torch.ao.quantization.pt2e.port_metadata_pass import PortNodeMetaForQDQ
+from torch.ao.quantization.quantizer import (  # noqa: F401
+    DerivedQuantizationSpec,
+    FixedQParamsQuantizationSpec,
+    QuantizationAnnotation,
+    QuantizationSpec,
+    QuantizationSpecBase,
+    Quantizer,
+    SharedQuantizationSpec,
+)
+from torch.fx import GraphModule, Node
+from torch.fx.passes.infra.pass_manager import PassManager
+
+from .pt2e.prepare import prepare
+from .pt2e.qat_utils import _fold_conv_bn_qat, _fuse_conv_bn_qat
+from .pt2e.representation import reference_representation_rewrite
+from .pt2e.utils import _disallow_eval_train, _fuse_conv_bn_, _get_node_name_to_scope
+from .quantize_fx import _convert_to_reference_decomposed_fx
+
+
+__all__ = [
+    "prepare_pt2e",
+    "prepare_qat_pt2e",
+    "convert_pt2e",
+]
+
+
+def prepare_pt2e(
+    model: GraphModule,
+    quantizer: Quantizer,
+) -> GraphModule:
+    """Prepare a model for post training quantization
+
+    Args:
+      * `model` (torch.fx.GraphModule): a model captured by `torch.export` API
+        in the short term we are using `torch._export.capture_pre_autograd_graph`,
+        in the long term we'll migrate to some `torch.export` API
+      * `quantizer`: A backend specific quantizer that conveys how user want the
+        model to be quantized. Tutorial for how to write a quantizer can be found here:
+        https://pytorch.org/tutorials/prototype/pt2e_quantizer.html
+
+    Return:
+      A GraphModule with observer (based on quantizer annotation), ready for calibration
+
+    Example::
+
+        import torch
+        from torch.ao.quantization.quantize_pt2e import prepare_pt2e
+        from torch._export import capture_pre_autograd_graph
+        from torch.ao.quantization.quantizer import (
+            XNNPACKQuantizer,
+            get_symmetric_quantization_config,
+        )
+
+        class M(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.linear = torch.nn.Linear(5, 10)
+
+           def forward(self, x):
+               return self.linear(x)
+
+        # initialize a floating point model
+        float_model = M().eval()
+
+        # define calibration function
+        def calibrate(model, data_loader):
+            model.eval()
+            with torch.no_grad():
+                for image, target in data_loader:
+                    model(image)
+
+        # Step 1. program capture
+        # NOTE: this API will be updated to torch.export API in the future, but the captured
+        # result shoud mostly stay the same
+        m = capture_pre_autograd_graph(m, *example_inputs)
+        # we get a model with aten ops
+
+        # Step 2. quantization
+        # backend developer will write their own Quantizer and expose methods to allow
+        # users to express how they
+        # want the model to be quantized
+        quantizer = XNNPACKQuantizer().set_global(get_symmetric_quantization_config())
+        m = prepare_pt2e(m, quantizer)
+
+        # run calibration
+        # calibrate(m, sample_inference_data)
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize_pt2e.prepare_pt2e")
+    original_graph_meta = model.meta
+    node_name_to_scope = _get_node_name_to_scope(model)
+    # TODO: check qconfig_mapping to make sure conv and bn are both configured
+    # to be quantized before fusion
+    # TODO: (maybe) rewrite this with subgraph_rewriter
+    _fuse_conv_bn_(model)
+    model = quantizer.transform_for_annotation(model)
+    quantizer.annotate(model)
+    quantizer.validate(model)
+    model = prepare(model, node_name_to_scope, is_qat=False)
+    model.meta.update(original_graph_meta)
+    model = _disallow_eval_train(model)
+    return model
+
+
+def prepare_qat_pt2e(
+    model: GraphModule,
+    quantizer: Quantizer,
+) -> GraphModule:
+    """Prepare a model for quantization aware training
+
+    Args:
+      * `model` (torch.fx.GraphModule): see :func:`~torch.ao.quantization.quantize_pt2e.prepare_pt2e`
+      * `quantizer`: see :func:`~torch.ao.quantization.quantize_pt2e.prepare_pt2e`
+
+    Return:
+      A GraphModule with fake quant modules (based on quantizer annotation), ready for
+      quantization aware training
+
+    Example::
+        import torch
+        from torch.ao.quantization.quantize_pt2e import prepare_qat_pt2e
+        from torch._export import capture_pre_autograd_graph
+        from torch.ao.quantization.quantizer import (
+            XNNPACKQuantizer,
+            get_symmetric_quantization_config,
+        )
+
+        class M(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.linear = torch.nn.Linear(5, 10)
+
+           def forward(self, x):
+               return self.linear(x)
+
+        # initialize a floating point model
+        float_model = M().eval()
+
+        # define the training loop for quantization aware training
+        def train_loop(model, train_data):
+            model.train()
+            for image, target in data_loader:
+                ...
+
+        # Step 1. program capture
+        # NOTE: this API will be updated to torch.export API in the future, but the captured
+        # result shoud mostly stay the same
+        m = capture_pre_autograd_graph(m, *example_inputs)
+        # we get a model with aten ops
+
+        # Step 2. quantization
+        # backend developer will write their own Quantizer and expose methods to allow
+        # users to express how they
+        # want the model to be quantized
+        quantizer = XNNPACKQuantizer().set_global(get_symmetric_quantization_config())
+        m = prepare_qat_pt2e(m, quantizer)
+
+        # run quantization aware training
+        train_loop(prepared_model, train_loop)
+
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize_pt2e.prepare_qat_pt2e")
+    original_graph_meta = model.meta
+    node_name_to_scope = _get_node_name_to_scope(model)
+    model = quantizer.transform_for_annotation(model)
+    quantizer.annotate(model)
+    quantizer.validate(model)
+    # Perform fusion after annotate to avoid quantizing ops in the new
+    # subgraph that don't need to be quantized
+    # TODO: only fuse if conv and bn are both configured to be quantized
+    _fuse_conv_bn_qat(model)
+    model = prepare(model, node_name_to_scope, is_qat=True)
+    model.meta.update(original_graph_meta)
+    model = _disallow_eval_train(model)
+    return model
+
+
+_QUANT_OPS = [
+    torch.ops.quantized_decomposed.quantize_per_tensor.default,
+    torch.ops.quantized_decomposed.quantize_per_tensor.tensor,
+    torch.ops.quantized_decomposed.quantize_per_channel.default,
+]
+
+
+def _quant_node_constraint(n: Node) -> bool:
+    """If there is any pure ops between get_attr and quantize op they will be const propagated
+    e.g. get_attr(weight) -> transpose -> quantize -> dequantize*
+    (Note: dequantize op is not going to be constant propagated)
+
+    This filter is added because we don't want to constant fold the things that are not
+    related to quantization
+    """
+    return n.op == "call_function" and n.target in _QUANT_OPS
+
+
+def convert_pt2e(
+    model: GraphModule,
+    use_reference_representation: bool = False,
+    fold_quantize: bool = True,
+) -> GraphModule:
+    """Convert a calibrated/trained model to a quantized model
+
+    Args:
+      * `model` (torch.fx.GraphModule): calibrated/trained model
+      * `use_reference_representation` (bool): boolean flag to indicate whether to produce referece representation or not
+      * `fold_quantize` (bool): boolean flag for whether fold the quantize op or not
+
+    Returns:
+        quantized model, either in q/dq representation or reference representation
+
+    Example::
+
+        # prepared_model: the model produced by `prepare_pt2e`/`prepare_qat_pt2e` and calibration/training
+        # `convert_pt2e` produces a quantized model that represents quantized computation with
+        # quantize dequantize ops and fp32 ops by default.
+        # Please refer to
+        # https://pytorch.org/tutorials/prototype/pt2e_quant_ptq_static.html#convert-the-calibrated-model-to-a-quantized-model
+        # for detailed explanation of output quantized model
+        quantized_model = convert_pt2e(prepared_model)
+
+    """  # flake8: noqa
+    torch._C._log_api_usage_once("quantization_api.quantize_pt2e.convert_pt2e")
+    if not isinstance(use_reference_representation, bool):
+        raise ValueError(
+            "Unexpected argument type for `use_reference_representation`, "
+            f"please make sure you intend to pass argument {use_reference_representation} to convert_pt2e"
+        )
+    original_graph_meta = model.meta
+    model = _convert_to_reference_decomposed_fx(model)
+    model = _fold_conv_bn_qat(model)
+
+    pm = PassManager([DuplicateDQPass()])
+    model = pm(model).graph_module
+
+    pm = PassManager([PortNodeMetaForQDQ()])
+    model = pm(model).graph_module
+
+    if fold_quantize:
+        constant_fold(model, _quant_node_constraint)
+
+    if use_reference_representation:
+        model = reference_representation_rewrite(model)
+
+    model.meta.update(original_graph_meta)
+    model = _disallow_eval_train(model)
+    return model
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5cd5e8696d39781004960f47e6f44d3b1987ff4
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/__init__.py
@@ -0,0 +1,22 @@
+from .quantizer import (
+    DerivedQuantizationSpec,
+    EdgeOrNode,
+    FixedQParamsQuantizationSpec,
+    QuantizationAnnotation,
+    QuantizationSpec,
+    QuantizationSpecBase,
+    Quantizer,
+    SharedQuantizationSpec,
+)
+
+
+__all__ = [
+    "EdgeOrNode",
+    "Quantizer",
+    "QuantizationSpecBase",
+    "QuantizationSpec",
+    "FixedQParamsQuantizationSpec",
+    "SharedQuantizationSpec",
+    "DerivedQuantizationSpec",
+    "QuantizationAnnotation",
+]
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/composable_quantizer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/composable_quantizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..65fad4f7f92379adc381252fe9770b06e14a1e4c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/composable_quantizer.py
@@ -0,0 +1,79 @@
+from __future__ import annotations
+
+from typing import Dict, List, TYPE_CHECKING
+
+from .quantizer import QuantizationAnnotation, Quantizer
+
+
+if TYPE_CHECKING:
+    import torch
+    from torch.fx import Node
+
+__all__ = [
+    "ComposableQuantizer",
+]
+
+
+class ComposableQuantizer(Quantizer):
+    """
+    ComposableQuantizer allows users to combine more than one quantizer into a single quantizer.
+    This allows users to quantize a model with multiple quantizers. E.g., embedding quantization
+    maybe supported by one quantizer while linear layers and other ops might be supported by another
+    quantizer.
+
+    ComposableQuantizer is initialized with a list of `Quantizer` instances.
+    The order of the composition matters since that is the order in which the quantizers will be
+    applies.
+    Example:
+    ```
+    embedding_quantizer = EmbeddingQuantizer()
+    linear_quantizer = MyLinearQuantizer()
+    xnnpack_quantizer = XNNPackQuantizer() # to handle ops not quantized by previous two quantizers
+    composed_quantizer = ComposableQuantizer([embedding_quantizer, linear_quantizer, xnnpack_quantizer])
+    prepared_m = prepare_pt2e(model, composed_quantizer)
+    ```
+    """
+
+    def __init__(self, quantizers: List[Quantizer]):
+        super().__init__()
+        self.quantizers = quantizers
+        self._graph_annotations: Dict[Node, QuantizationAnnotation] = {}
+
+    def _record_and_validate_annotations(
+        self, gm: torch.fx.GraphModule, quantizer: Quantizer
+    ) -> None:
+        for n in gm.graph.nodes:
+            if "quantization_annotation" in n.meta:
+                # check if the annotation has been changed by
+                # comparing QuantizationAnnotation object id
+                if n in self._graph_annotations and (
+                    id(self._graph_annotations[n])
+                    != id(n.meta["quantization_annotation"])
+                ):
+                    raise RuntimeError(
+                        f"Quantizer {quantizer.__class__.__name__} has changed annotations on node {n}"
+                    )
+                else:
+                    self._graph_annotations[n] = n.meta["quantization_annotation"]
+            else:
+                if n in self._graph_annotations:
+                    raise RuntimeError(
+                        f"Quantizer {quantizer.__class__.__name__} has removed annotations on node {n}"
+                    )
+
+    def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+        """just handling global spec for now"""
+        for quantizer in self.quantizers:
+            quantizer.annotate(model)
+            self._record_and_validate_annotations(model, quantizer)
+        return model
+
+    def transform_for_annotation(
+        self, model: torch.fx.GraphModule
+    ) -> torch.fx.GraphModule:
+        for quantizer in self.quantizers:
+            model = quantizer.transform_for_annotation(model)
+        return model
+
+    def validate(self, model: torch.fx.GraphModule) -> None:
+        pass
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/embedding_quantizer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/embedding_quantizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..32ec3814637cba1650087ae800ece33b07c7cb50
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/embedding_quantizer.py
@@ -0,0 +1,98 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import copy
+from typing import List, Set
+
+import torch
+import torch.nn.functional as F
+from torch.ao.quantization.observer import PerChannelMinMaxObserver
+from torch.ao.quantization.quantizer.quantizer import (
+    QuantizationAnnotation,
+    QuantizationSpec,
+    Quantizer,
+)
+from torch.ao.quantization.quantizer.xnnpack_quantizer_utils import (
+    OperatorConfig,
+    OperatorPatternType,
+    QuantizationConfig,
+)
+
+
+__all__ = [
+    "get_embedding_operators_config",
+    "EmbeddingQuantizer",
+]
+
+
+def get_embedding_operators_config() -> OperatorConfig:
+    weight_quantization_spec = QuantizationSpec(
+        dtype=torch.uint8,
+        qscheme=torch.per_channel_affine_float_qparams,
+        ch_axis=0,
+        observer_or_fake_quant_ctr=PerChannelMinMaxObserver.with_args(eps=2**-12),
+    )
+    quantization_config = QuantizationConfig(None, None, weight_quantization_spec, None)
+    ops: List[OperatorPatternType] = [[torch.nn.Embedding]]
+    ops.append([F.embedding])
+    supported_config_and_operators = OperatorConfig(
+        config=quantization_config, operators=ops
+    )
+    return copy.deepcopy(supported_config_and_operators)
+
+
+class EmbeddingQuantizer(Quantizer):
+    def __init__(self) -> None:
+        super().__init__()
+
+    @classmethod
+    def get_supported_quantization_configs(cls) -> List[QuantizationConfig]:
+        op_configs: Set[QuantizationConfig] = {
+            spec for spec, _ in cls.get_supported_operators()
+        }
+        return list(op_configs)
+
+    @classmethod
+    def get_supported_operator_for_quantization_config(
+        cls, quantization_config: QuantizationConfig
+    ) -> List[OperatorPatternType]:
+        for config, ops in cls.get_supported_operators():
+            # note: this assumes each entry in cls.supported_spec_and_operators
+            # corresponds to one spec, e.g. we don't have
+            # [(spec1, op_list1), (spec1, op_list2), (spec2, op_list3)]
+            # where the first and second entry have the same spec but did not
+            # merge the op list
+            if config == quantization_config:
+                return ops
+        return []
+
+    def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+        """just handling global spec for now"""
+        self._annotate_embedding_ops(model.graph)
+        return model
+
+    def _annotate_embedding_ops(self, graph: torch.fx.Graph) -> None:
+        embedding_config: OperatorConfig = get_embedding_operators_config()
+        for node in graph.nodes:
+            # Keep node parsing based annotations instead of module partitioners
+            # just as an example of alternate ways of annotating
+            if (
+                node.op == "call_function"
+                and node.target == torch.ops.aten.embedding.default
+            ):
+                if embedding_config.config.weight is None:
+                    raise ValueError(
+                        "Embedding config must have a valid weight quantization spec."
+                    )
+                node.meta["quantization_annotation"] = QuantizationAnnotation(
+                    input_qspec_map={
+                        node.args[0]: embedding_config.config.weight,
+                    }
+                )
+
+    def validate(self, model: torch.fx.GraphModule) -> None:
+        pass
+
+    @classmethod
+    def get_supported_operators(cls) -> List[OperatorConfig]:
+        return [get_embedding_operators_config()]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/quantizer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/quantizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebe70dab00b51ce441d2fb42e615526834ae9b9d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/quantizer.py
@@ -0,0 +1,161 @@
+# mypy: allow-untyped-defs
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+from torch.ao.quantization import ObserverOrFakeQuantize
+from torch.ao.quantization.qconfig import _ObserverOrFakeQuantizeConstructor
+from torch.fx import Node
+
+
+__all__ = [
+    "Quantizer",
+    "QuantizationSpecBase",
+    "QuantizationSpec",
+    "FixedQParamsQuantizationSpec",
+    "EdgeOrNode",
+    "SharedQuantizationSpec",
+    "DerivedQuantizationSpec",
+    "QuantizationAnnotation",
+]
+
+
+class QuantizationSpecBase(ABC):  # noqa: B024
+    """Base class for different types of quantization specs that allows users to
+    specify how to quantize a Tensor (input/output of a Node) in the model
+    """
+
+
+@dataclass(eq=True, frozen=True)
+class QuantizationSpec(QuantizationSpecBase):
+    """Quantization spec for common operators that allows user to specify how to
+    quantize a Tensor, this includes dtype, quant_min, quant_max etc.
+    """
+
+    dtype: torch.dtype
+    # observer or fake_quantize constructor such as
+    # MinMaxObserver, PerChannelHistogramObserver etc.
+    # or we can attach some custom args to them
+    # e.g. MinMaxObserver.with_args(eps=eps)
+    observer_or_fake_quant_ctr: _ObserverOrFakeQuantizeConstructor
+    quant_min: Optional[int] = None
+    quant_max: Optional[int] = None
+    qscheme: Optional[torch.qscheme] = None
+    ch_axis: Optional[int] = None
+    is_dynamic: bool = False
+
+    def __post_init__(self):
+        # TODO: add init for quant_min/quant_max
+        # quant_min must be less than quant_max
+        if (
+            self.quant_min is not None
+            and self.quant_max is not None
+            and self.quant_min > self.quant_max
+        ):
+            raise ValueError(
+                f"quant_min {self.quant_min} must be <= quant_max {self.quant_max}."
+            )
+
+        # ch_axis must be less than the number of channels
+        # but no way to check here. Just check that it is not < 0.
+        if self.ch_axis is not None and self.ch_axis < 0:
+            raise ValueError("Ch_axis is < 0.")
+
+
+@dataclass(eq=True, frozen=True)
+class FixedQParamsQuantizationSpec(QuantizationSpecBase):
+    dtype: torch.dtype
+    scale: float
+    zero_point: int
+    quant_min: Optional[int] = None
+    quant_max: Optional[int] = None
+    qscheme: Optional[torch.qscheme] = None
+    is_dynamic: bool = False
+
+
+"""
+The way we refer to other points of quantization in the graph will be either
+an input edge or an output value
+input edge is the connection between input node and the node consuming the input, so it's a Tuple[Node, Node]
+output value is an fx Node
+"""
+EdgeOrNode = Union[Tuple[Node, Node], Node]
+EdgeOrNode.__module__ = "torch.ao.quantization.quantizer.quantizer"
+
+
+@dataclass(eq=True, frozen=True)
+class SharedQuantizationSpec(QuantizationSpecBase):
+    """
+    Quantization spec for the Tensors whose quantization parameters are shared with other Tensors
+    """
+
+    # the edge or node to share observer or fake quant instances with
+    edge_or_node: EdgeOrNode
+
+
+@dataclass(eq=True, frozen=True)
+class DerivedQuantizationSpec(QuantizationSpecBase):
+    """Quantization spec for the Tensors whose quantization parameters are derived from other Tensors"""
+
+    derived_from: List[EdgeOrNode]
+    derive_qparams_fn: Callable[[List[ObserverOrFakeQuantize]], Tuple[Tensor, Tensor]]
+    dtype: torch.dtype
+    quant_min: Optional[int] = None
+    quant_max: Optional[int] = None
+    qscheme: Optional[torch.qscheme] = None
+    ch_axis: Optional[int] = None
+    is_dynamic: bool = False
+
+
+@dataclass
+class QuantizationAnnotation:
+    """How are input arguemnt or output should be quantized,
+    expressed as QuantizationSpec, this corresponds to how a Tensor in the
+    operator Graph is observed (PTQ) or fake quantized (QAT)
+    """
+
+    # a map from torch.fx.Node to a type of QuantizationSpecBase
+    input_qspec_map: Dict[Node, Optional[QuantizationSpecBase]] = field(
+        default_factory=dict
+    )
+
+    # How the output of this node is quantized, expressed as QuantizationSpec
+    # TODO: change the value to QuantizationSpec in a separate PR
+    output_qspec: Optional[QuantizationSpecBase] = None
+
+    # For a Node: node1 and edge: (node1, node2), since they are observing the same
+    # Tensor, we may want to implicitly share observers, this flag allows people to
+    # turn off this behavior for the output of the node
+    allow_implicit_sharing: bool = True
+
+    # whether the node is annotated or not
+    _annotated: bool = False
+
+
+class Quantizer(ABC):
+    def transform_for_annotation(
+        self, model: torch.fx.GraphModule
+    ) -> torch.fx.GraphModule:
+        """Allows for user defined transforms to run before annotating the graph.
+        This allows quantizer to allow quantizing part of the model that are otherwise not quantizable.
+        For example quantizer can
+        a) decompose a compound operator like scaled dot product attention,
+        into bmm and softmax if quantizer knows how to quantize bmm/softmax but not sdpa
+        or b) transform scalars to tensor to allow quantizing scalares.
+
+        Note: this is an optional method
+        """
+        return model
+
+    # annotate nodes in the graph with observer or fake quant constructors
+    # to convey the desired way of quantization
+    @abstractmethod
+    def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+        pass
+
+    # validate the annotated graph is supported by the backend
+    @abstractmethod
+    def validate(self, model: torch.fx.GraphModule) -> None:
+        pass
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..23882dccd59c902c58f1ddf0e211321d605c6ab4
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/utils.py
@@ -0,0 +1,83 @@
+# mypy: allow-untyped-defs
+from typing import List
+
+from torch.ao.quantization.pt2e.utils import _is_sym_size_node
+from torch.ao.quantization.quantizer.quantizer import QuantizationAnnotation
+from torch.fx import Node
+
+
+def _annotate_input_qspec_map(node: Node, input_node: Node, qspec):
+    quantization_annotation = node.meta.get(
+        "quantization_annotation", QuantizationAnnotation()
+    )
+    if quantization_annotation.input_qspec_map is None:
+        quantization_annotation.input_qspec_map = {}
+    quantization_annotation.input_qspec_map[input_node] = qspec
+    node.meta["quantization_annotation"] = quantization_annotation
+
+
+def _annotate_output_qspec(node: Node, qspec):
+    quantization_annotation = node.meta.get(
+        "quantization_annotation", QuantizationAnnotation()
+    )
+    quantization_annotation.output_qspec = qspec
+    node.meta["quantization_annotation"] = quantization_annotation
+
+
+def _node_only_used_for_sym_size(node: Node, partition_nodes: List[Node]):
+    """
+    This utility is used to handle cases when dynami_shape=True tracing leads
+    to symint nodes in the pattern of linear module. In those cases, we need to
+    distinguish between the nodes that are in input for just extracting value of
+    some dimentions (and symint nodes) vs. the one that is activation.
+    For example:
+    graph(x, y, weight):
+       size_0 = torch.ops.aten.sym_size([x], [0])
+       size_1 = torch.ops.aten.sym_size([y], [1])
+       view_size = size_0 * size_1
+       size_3 = torch.ops.aten.sym_size([x], [2])
+       vie_out = torch.ops.aten.view(x, [view_size, size_3])
+       return mm(view_out, weight)
+    In the example above y node is not actual input. It exist only to extract size_1
+    """
+    if _is_sym_size_node(node):
+        return True
+
+    return all(
+        ((user not in partition_nodes) or _is_sym_size_node(user))
+        for user in node.users
+    )
+
+
+def _get_module_name_filter(module_name: str):
+    """Get the module_name_filter function for a given module name, the filter accepts
+    a node and checks if the node comes from a module that has certain module name
+
+    For example:
+        node: linear_op = call_function[...](...)  # comes from a module with name blocks.sub.linear1
+
+
+    >> module_name_filter = _get_module_name_filter("blocks.sub")
+    >> print(module_name_filter(node))
+    True  # the node is from "blocks.sub" based on the fully qualified name "blocks.sub.linear1"
+    """
+
+    def module_name_filter(n: Node) -> bool:
+        # example: {
+        #    'L__self___sub': ("L['self'].sub", ),
+        #    'L__self___sub_linear': ("L['self'].sub.linear", )
+        # }
+        # get_attr nodes doesn't have nn_module_stack?
+        nn_module_stack = n.meta.get("nn_module_stack", {})
+
+        def _normalize_path(n):
+            prefix = 0
+            # TODO This is non standard behavior and should be removed when we migrate off capture_pre_autograd_graph.
+            if n.startswith("L['self']."):
+                prefix = len("L['self'].")
+            return n[prefix:]
+
+        names = [_normalize_path(n) for n, _ in nn_module_stack.values()]
+        return module_name in names
+
+    return module_name_filter
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/x86_inductor_quantizer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/x86_inductor_quantizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..574af30a7159b962a5f83f9e72b89f7174f05632
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/x86_inductor_quantizer.py
@@ -0,0 +1,1625 @@
+# mypy: allow-untyped-defs
+import copy
+import functools
+import itertools
+import operator
+import warnings
+from dataclasses import dataclass
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Optional,
+    Sequence,
+    Set,
+    Tuple,
+    TYPE_CHECKING,
+    Union,
+)
+from typing_extensions import TypeAlias
+
+import torch
+import torch.nn.functional as F
+from torch.ao.quantization.fake_quantize import (
+    FakeQuantize,
+    FusedMovingAvgObsFakeQuantize,
+)
+from torch.ao.quantization.observer import (
+    HistogramObserver,
+    MovingAverageMinMaxObserver,
+    MovingAveragePerChannelMinMaxObserver,
+    PerChannelMinMaxObserver,
+    PlaceholderObserver,
+)
+from torch.ao.quantization.pt2e.graph_utils import find_sequential_partitions
+from torch.ao.quantization.quantizer.quantizer import (
+    QuantizationAnnotation,
+    QuantizationSpec,
+    Quantizer,
+    SharedQuantizationSpec,
+)
+from torch.ao.quantization.quantizer.utils import _get_module_name_filter
+from torch.ao.quantization.quantizer.xnnpack_quantizer_utils import (
+    get_bias_qspec,
+    get_input_act_qspec,
+    get_output_act_qspec,
+    get_weight_qspec,
+    OperatorConfig,
+    OperatorPatternType,
+    QuantizationConfig,
+)
+from torch.fx import Node
+from torch.fx.passes.utils.source_matcher_utils import (
+    get_source_partitions,
+    SourcePartition,
+)
+
+
+FilterFn: TypeAlias = Callable[[List[Node]], bool]
+
+
+if TYPE_CHECKING:
+    from torch.ao.quantization.qconfig import _ObserverOrFakeQuantizeConstructor
+
+__all__ = [
+    "X86InductorQuantizer",
+    "get_default_x86_inductor_quantization_config",
+]
+
+
+@dataclass
+class _X86InductorQuantizationAnnotation(QuantizationAnnotation):
+    # _is_output_of_quantized_pattern:
+    #  * Node as output node of a fusion pattern.
+    #  * The fusion pattern supports int8 data type.
+    #  * The fusion pattern has inputs annotated to insert observer.
+    #  * The quantization_config is not `None`.
+    _is_output_of_quantized_pattern: bool = False
+
+
+# Operators that:
+# 1. Operators are optimized to run with int8 when int8 input provided.
+# 2. Operators do not support int8 input and produce fp32 output.
+int8_in_int8_out_ops: Set = {
+    torch.ops.aten.max_pool2d.default,
+    torch.ops.aten.cat.default,
+    torch.ops.aten.avg_pool2d.default,
+    torch.ops.aten.adaptive_avg_pool2d.default,
+    torch.ops.aten.flatten.using_ints,
+}
+
+# Operators that support the int8 data type for quantization config propagation.
+# A superset of int8_in_int8_out_ops incorporating additional operators.
+propagation_quantizable_ops = int8_in_int8_out_ops
+
+# Operators support the int8 data type
+# and recipe is configured by default in X86InductorQuantizer.
+default_quantizable_ops = propagation_quantizable_ops | {
+    torch.ops.aten.conv2d.default,
+    torch.ops.aten.linear.default,
+}
+
+# A superset of default_quantizable_ops includes operators support the int8 data type
+# but not enabled by default recipe of X86InductorQuantizer.
+quantizable_ops = default_quantizable_ops | {
+    torch.ops.aten.matmul.default,
+}
+
+QUANT_ANNOTATION_KEY = "quantization_annotation"
+
+
+def _skip_annotate(nodes: List[Node], filter_fn: Optional[FilterFn] = None) -> bool:
+    """Determine whether to skip annotation for a list of nodes."""
+
+    # 1) Skip annotate if any node is already annotated
+    if _is_any_annotated(nodes):
+        return True
+
+    # 2) Proceed annotate if a) a filter function is provided
+    # and b) the given nodes list passes the filter function check.
+    if filter_fn and filter_fn(nodes):
+        return False
+
+    return True
+
+
+def _create_module_name_filter(module_name: str) -> FilterFn:
+    """Create a filter function for a given module name.
+
+    The filter function takes a list of nodes (as determined by the annotate function)
+    and return True if *all* nodes come from the specified module name, False otherwise.
+
+    For example:
+        linear_1: "f32[3, 10]" = torch.ops.aten.linear.default(...) # comes from a module with name `sub.linear1`
+        relu: "f32[3, 10]" = torch.ops.aten.relu.default(linear_1); # comes from a module with name `sub.relu1`
+
+    >> module_name_filter = _create_module_name_filter_inner("sub")
+    >> print(module_name_filter([relu, linear_1]))
+    # True  # These two nodes are determined by `_annotate_linear_unary` function and from "sub".
+    """
+
+    filter_fn = _get_module_name_filter(module_name)
+
+    def check_all_nodes_from_module(nodes: List[Node]) -> bool:
+        all_nodes_from_module_name: bool = all(filter_fn(n) for n in nodes)
+        return all_nodes_from_module_name
+
+    return check_all_nodes_from_module
+
+
+def _create_operator_type_filter(
+    operator_type: Callable,
+) -> FilterFn:
+    """Create a filter function for a given operator type.
+
+    The filter function takes a list of nodes and returns True if it contains
+    exactly one node with the specified operator type, False otherwise.
+
+    For example:
+        linear_1: "f32[3, 10]" = torch.ops.aten.linear.default(...) # comes from a module with name `sub.linear1`
+        relu: "f32[3, 10]" = torch.ops.aten.relu.default(linear_1); # comes from a module with name `sub.relu1`
+
+    >> operator_type_filter = _create_operator_type_filter(torch.ops.aten.linear.default)
+    >> print(operator_type_filter([relu, linear_1]))
+    # True  # These two nodes are determined by `_annotate_linear_unary` function and the second node is `linear`.
+    """
+
+    def operator_type_filter(nodes: List[Node]):
+        num_nodes_with_operator_type = sum(
+            node.target == operator_type for node in nodes
+        )
+        if num_nodes_with_operator_type > 1:
+            raise NotImplementedError(
+                f"Several nodes within a single pattern are {operator_type}."
+            )
+        return num_nodes_with_operator_type == 1
+
+    return operator_type_filter
+
+
+def _global_config_filter(nodes: List[Node]) -> bool:
+    """Filter function for global configuration.
+
+    This filter function takes a list of nodes and returns True if there is exactly one node
+    in the list that is a default quantizable operation, False otherwise.
+    """
+    num_nodes_in_default_quantizable_ops = sum(
+        node.target in default_quantizable_ops for node in nodes
+    )
+    if num_nodes_in_default_quantizable_ops > 1:
+        raise NotImplementedError(
+            "Several nodes within a single pattern are default quantizable operations."
+        )
+    return num_nodes_in_default_quantizable_ops == 1
+
+
+def _map_module_function_to_aten_operator_type():
+    module_function_to_aten_operator: Dict[Callable, torch._ops.OpOverloadPacket] = {}
+    map_list = (
+        ([torch.nn.Conv2d, F.conv2d], torch.ops.aten.conv2d.default),
+        ([torch.nn.Linear, F.linear], torch.ops.aten.linear.default),
+        ([torch.nn.MaxPool2d, F.max_pool2d], torch.ops.aten.max_pool2d.default),
+        (
+            [
+                torch.cat,
+            ],
+            torch.ops.aten.cat.default,
+        ),
+        ([torch.nn.AvgPool2d, F.avg_pool2d], torch.ops.aten.avg_pool2d.default),
+        (
+            [torch.nn.AdaptiveAvgPool2d, F.adaptive_avg_pool2d],
+            torch.ops.aten.adaptive_avg_pool2d.default,
+        ),
+        (
+            [
+                torch.flatten,
+            ],
+            torch.ops.aten.flatten.using_ints,
+        ),
+        (
+            [
+                torch.matmul,
+            ],
+            torch.ops.aten.matmul.default,
+        ),
+    )
+    for map_item in map_list:
+        module_function_to_aten_operator.update(dict.fromkeys(map_item[0], map_item[1]))  # type: ignore[call-overload]
+    return module_function_to_aten_operator
+
+
+def _mark_nodes_as_annotated(nodes: List[Node]):
+    for node in nodes:
+        if node is not None:
+            if QUANT_ANNOTATION_KEY not in node.meta:
+                node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation()
+            node.meta[QUANT_ANNOTATION_KEY]._annotated = True
+
+
+def _is_node_annotated(_node):
+    """
+    return True if the node is annotated, otherwise return False
+    """
+    return (
+        QUANT_ANNOTATION_KEY in _node.meta
+        and _node.meta[QUANT_ANNOTATION_KEY]._annotated
+    )
+
+
+def _is_any_annotated(nodes: List[Node]):
+    """
+    Given a list of nodes (that represents an operator pattern),
+    check if any of the node is annotated, return True if any of the node
+    is annotated, otherwise return False.
+    """
+    return any(_is_node_annotated(node) for node in nodes)
+
+
+def _is_all_annotated(nodes: List[Node]):
+    """
+    Given a list of nodes (that represents an operator pattern),
+    return True if all of the node is annotated, otherwise return False.
+    """
+    return all(_is_node_annotated(node) for node in nodes)
+
+
+def _is_quantized_op_pt2e(node: torch.fx.Node):
+    """
+    Used for pt2e flow to check if the node is a quantized node:
+    Case1: the node has been annotated as output node of a fusion pattern.
+    Case2: the node has been annotated as single quantized node.
+    """
+    if not _is_any_annotated([node]):
+        # The node has not been annotated, directly return False
+        return False
+    quantization_annotation = node.meta.get(QUANT_ANNOTATION_KEY, None)
+    assert isinstance(quantization_annotation, _X86InductorQuantizationAnnotation)
+    return quantization_annotation._is_output_of_quantized_pattern
+
+
+def _supported_quantized_operators() -> Dict[str, List[OperatorPatternType]]:
+    # TODO: Add more supported operators here.
+    supported_operators: Dict[str, List[OperatorPatternType]] = {
+        "conv2d": [
+            [torch.nn.Conv2d],
+            [F.conv2d],
+        ],
+    }
+
+    # Append Conv Optional(Add) Optioinal(ReLU)
+    conv_add_relu_options = itertools.product(
+        [torch.nn.Conv2d, F.conv2d],
+        [torch.add, operator.add, None],  # add
+        [torch.nn.ReLU, F.relu, None],  # relu
+    )
+    for conv_op, add_op, relu_op in conv_add_relu_options:
+        if add_op is None:
+            # Append Conv ReLU
+            supported_operators["conv2d"].append([conv_op, relu_op])  # type: ignore[list-item]
+        elif relu_op is None:
+            # Append Conv Add
+            supported_operators["conv2d"].append([conv_op, add_op])  # type: ignore[list-item]
+        else:
+            # Append Conv Add ReLU
+            supported_operators["conv2d"].append([conv_op, add_op, relu_op])  # type: ignore[list-item]
+
+    return copy.deepcopy(supported_operators)
+
+
+def _get_supported_x86_inductor_config_and_operators() -> List[OperatorConfig]:
+    supported_config_and_operators: List[OperatorConfig] = []
+    for quantization_config in [
+        get_default_x86_inductor_quantization_config(),
+    ]:
+        ops = _supported_quantized_operators()
+        for pattern_list in ops.values():
+            supported_config_and_operators.append(
+                OperatorConfig(quantization_config, pattern_list)
+            )
+    return copy.deepcopy(supported_config_and_operators)
+
+
+@functools.lru_cache
+def get_default_x86_inductor_quantization_config(
+    is_qat: bool = False,
+    is_dynamic: bool = False,
+):
+    extra_args: Dict[str, Any] = {"eps": 2**-12}
+    if is_qat:
+        if is_dynamic:
+            act_observer_or_fake_quant_ctr = FakeQuantize
+            dynamic_quant_observer = MovingAverageMinMaxObserver.with_args(
+                averaging_constant=1
+            )
+            extra_args["observer"] = dynamic_quant_observer
+        else:
+            act_observer_or_fake_quant_ctr = FusedMovingAvgObsFakeQuantize  # type: ignore[assignment]
+    else:
+        if is_dynamic:
+            act_observer_or_fake_quant_ctr = PlaceholderObserver  # type: ignore[assignment]
+        else:
+            act_observer_or_fake_quant_ctr = HistogramObserver  # type: ignore[assignment]
+
+    # Copy from x86 default qconfig from torch/ao/quantization/qconfig.py
+    act_quantization_spec = QuantizationSpec(
+        dtype=torch.uint8,
+        quant_min=0,
+        quant_max=255,  # reduce_range=False
+        qscheme=torch.per_tensor_affine,
+        is_dynamic=is_dynamic,
+        observer_or_fake_quant_ctr=act_observer_or_fake_quant_ctr.with_args(
+            **extra_args
+        ),
+    )
+
+    weight_observer_or_fake_quant_ctr: _ObserverOrFakeQuantizeConstructor = (
+        FusedMovingAvgObsFakeQuantize if is_qat else PerChannelMinMaxObserver
+    )
+
+    if is_qat:
+        # Only support per channel quant for now
+        extra_args["observer"] = MovingAveragePerChannelMinMaxObserver  # type: ignore[dict-item]
+    weight_quantization_spec = QuantizationSpec(
+        dtype=torch.int8,
+        quant_min=-128,
+        quant_max=127,
+        qscheme=torch.per_channel_symmetric,
+        ch_axis=0,  # 0 corresponding to weight shape = (oc, ic, kh, kw) of conv
+        is_dynamic=False,
+        observer_or_fake_quant_ctr=weight_observer_or_fake_quant_ctr.with_args(
+            **extra_args
+        ),
+    )
+    bias_quantization_spec = None  # will use placeholder observer by default
+    quantization_config = QuantizationConfig(
+        act_quantization_spec,
+        act_quantization_spec,
+        weight_quantization_spec,
+        bias_quantization_spec,
+        is_qat,
+    )
+    return quantization_config
+
+
+def _get_supported_config_and_operators() -> List[OperatorConfig]:
+    return _get_supported_x86_inductor_config_and_operators()
+
+
+def _annotate_nodes_not_quantize(nodes: Union[Node, List[Node]]) -> None:
+    """Annotate nodes to exclude them from quantization (their `quantization_config` is `None`)."""
+    if not isinstance(nodes, list):
+        nodes = [nodes]
+    for node in nodes:
+        node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+            _annotated=True
+        )
+
+
+def _config_checker(method: Callable) -> Callable:
+    @functools.wraps(method)
+    def wrapper(
+        quantizer: "X86InductorQuantizer",
+        name: Any,
+        quantization_config: Optional["QuantizationConfig"],
+    ) -> "X86InductorQuantizer":
+        if quantizer._need_skip_config(quantization_config):
+            warnings.warn(
+                f"Skip the quantization config for {name}.",
+            )
+            return quantizer
+        return method(quantizer, name, quantization_config)
+
+    return wrapper
+
+
+@dataclass
+class _CurrentQuantizationMode:
+    r"""Configuration defining the current quantization mode for the quantizer.
+
+    All possible current quantization modes are listed below:
+    ----------------------------------------------------------------------------------------------------------
+                |                                       dynamic_state
+     qat_state  |---------------------------------------------------------------------------------------------
+                |                           None                              |    True       |  False
+    ----------------------------------------------------------------------------------------------------------
+        None    | quantizer does not receive a non-None `quantization_config` | \             | \
+        False   | quantizer will not do QAT                                   | dynamic       | static
+        True    | quantizer will do QAT                                       | QAT + dynamic | QAT + static
+    """
+
+    qat_state: Optional[bool]
+    dynamic_state: Optional[bool]
+
+
+class X86InductorQuantizer(Quantizer):
+    supported_config_and_operators = _get_supported_config_and_operators()
+    module_function_to_aten_operator_type = _map_module_function_to_aten_operator_type()
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.global_config: Optional[QuantizationConfig] = None
+        self.operator_type_qconfig: Dict[
+            torch._ops.OpOverloadPacket, Optional[QuantizationConfig]
+        ] = {}
+        self.module_name_qconfig: Dict[str, Optional[QuantizationConfig]] = {}
+
+    @classmethod
+    def get_supported_quantization_configs(cls) -> List[QuantizationConfig]:
+        op_configs: Set[QuantizationConfig] = {
+            spec for spec, _ in cls.supported_config_and_operators
+        }
+        return list(op_configs)
+
+    @classmethod
+    def get_supported_operator_for_quantization_config(
+        cls, quantization_config: Optional[QuantizationConfig]
+    ) -> List[OperatorPatternType]:
+        if quantization_config is None:
+            all_ops = []
+            for _, ops in cls.supported_config_and_operators:
+                all_ops.extend(ops)
+            return all_ops
+
+        for config, ops in cls.supported_config_and_operators:
+            if config == quantization_config:
+                return ops
+        return []
+
+    def _get_current_quantization_mode(self) -> _CurrentQuantizationMode:
+        """Retrieves the current quantization mode based on all configurations."""
+        qat_state = None
+        dynamic_state = None
+
+        # As we use `_need_skip_config` to skip all invalid configurations,
+        # we can safely assume that the all existing non-None configurations
+        # have the same quantization mode.
+        for qconfig in (
+            list(self.module_name_qconfig.values())
+            + list(self.operator_type_qconfig.values())
+            + [self.global_config]
+        ):
+            if qconfig is not None:
+                # Query the `is_qat` state
+                if qat_state is None:
+                    qat_state = qconfig.is_qat
+                else:
+                    assert qat_state == qconfig.is_qat, (
+                        f"All non-None quantization configs should have the same `is_qat`,"
+                        f"but got {qat_state} and {qconfig.is_qat}."
+                    )
+                # Query the `is_dynamic` state
+                input_activation_spec = qconfig.input_activation
+                if input_activation_spec is not None:
+                    if dynamic_state is None:
+                        dynamic_state = input_activation_spec.is_dynamic
+                    else:
+                        assert dynamic_state == input_activation_spec.is_dynamic, (
+                            f"All non-None `input_activation_spec` should have the same `is_dynamic`,"
+                            f"but got {dynamic_state} and {input_activation_spec.is_dynamic}."
+                        )
+        return _CurrentQuantizationMode(
+            qat_state=qat_state, dynamic_state=dynamic_state
+        )
+
+    def _need_skip_config(
+        self, quantization_config: Optional[QuantizationConfig]
+    ) -> bool:
+        """Check if the provided quantization config is valid for X86InductorQuantizer.
+
+        Mixed static/dynamic configurations or mixed QAT/non-QAT configurations are not supported.
+        To avoid such a mix, we compare the incoming configuration with current configuration status.
+        Refer the `_CurrentQuantizationMode` definition for all possible modes.
+        """
+        if quantization_config is None:
+            return False
+
+        need_skip = False
+        current_mode = self._get_current_quantization_mode()
+        if (
+            current_mode.qat_state is not None
+            and current_mode.qat_state != quantization_config.is_qat
+        ):
+            warnings.warn("Mixed QAT and Non-QAT quantization config is not supported.")
+            need_skip = True
+        if current_mode.dynamic_state is not None:
+            input_activation_spec = quantization_config.input_activation
+            if (
+                input_activation_spec is not None
+                and current_mode.dynamic_state != input_activation_spec.is_dynamic
+            ):
+                warnings.warn(
+                    "Mixed dynamic and static quantization config is not supported."
+                )
+                need_skip = True
+        return need_skip
+
+    def set_global(self, quantization_config: QuantizationConfig):
+        if self._need_skip_config(quantization_config):
+            warnings.warn("Skip the global quantization config.")
+            return self
+        self.global_config = quantization_config
+        return self
+
+    def get_global_quantization_config(self):
+        if not isinstance(self.global_config, QuantizationConfig):
+            warnings.warn(
+                "The global_config for X86InductorQuantizer is currently invalid. \
+                Please ensure that you use set_global to establish the global quantization configuration."
+            )
+        return self.global_config
+
+    @_config_checker
+    def set_function_type_qconfig(
+        self,
+        function_type: Callable,
+        quantization_config: Optional[QuantizationConfig],
+    ) -> "X86InductorQuantizer":
+        if function_type in X86InductorQuantizer.module_function_to_aten_operator_type:
+            self._set_aten_operator_qconfig(
+                X86InductorQuantizer.module_function_to_aten_operator_type[
+                    function_type
+                ],
+                quantization_config,
+            )
+        else:
+            warnings.warn(
+                f"function: Unable to customize quantization config for {function_type} by X86InductorQuantizer."
+            )
+        return self
+
+    @_config_checker
+    def set_module_type_qconfig(
+        self,
+        module_type: torch.nn.Module,
+        quantization_config: Optional[QuantizationConfig],
+    ) -> "X86InductorQuantizer":
+        if module_type in X86InductorQuantizer.module_function_to_aten_operator_type:
+            self._set_aten_operator_qconfig(
+                X86InductorQuantizer.module_function_to_aten_operator_type[module_type],
+                quantization_config,
+            )
+        else:
+            warnings.warn(
+                f"Module: Unable to customize quantization config for {module_type} by X86InductorQuantizer."
+            )
+        return self
+
+    @_config_checker
+    def set_module_name_qconfig(
+        self, module_name: str, quantization_config: Optional[QuantizationConfig]
+    ):
+        """Set quantization_config for a submodule with name: `module_name`, for example:
+        quantizer.set_module_name_qconfig("blocks.sub"), it will quantize all supported operator/operator
+        patterns in the submodule with this module name with the given `quantization_config`
+
+        The supported operators include `quantizable_ops` and `propagation_quantizable_ops`.
+        """
+        self.module_name_qconfig[module_name] = quantization_config
+        return self
+
+    def _set_aten_operator_qconfig(
+        self,
+        operator_type: torch._ops.OpOverloadPacket,
+        quantization_config: Optional[QuantizationConfig],
+    ) -> "X86InductorQuantizer":
+        if operator_type in quantizable_ops:
+            self.operator_type_qconfig[operator_type] = quantization_config
+        else:
+            warnings.warn(
+                f"operator: Unable to quantize {operator} by X86InductorQuantizer."
+            )
+        return self
+
+    def _annotate_conv_node_helper(
+        self,
+        conv_node: torch.fx.Node,
+        annotate_output: bool,
+        quantization_config: Optional[QuantizationConfig],
+    ) -> None:
+        """Helper function to annotate the conv node"""
+        if quantization_config is None:
+            _annotate_nodes_not_quantize(conv_node)
+            return
+        input_qspec_map = {}
+        input_node = conv_node.args[0]
+        assert isinstance(input_node, Node)
+        input_qspec_map[input_node] = get_input_act_qspec(quantization_config)
+        weight_node = conv_node.args[1]
+        assert isinstance(weight_node, Node)
+        input_qspec_map[weight_node] = get_weight_qspec(quantization_config)
+        bias_node = None if len(conv_node.args) == 2 else conv_node.args[2]
+        if isinstance(bias_node, Node):
+            input_qspec_map[bias_node] = get_bias_qspec(quantization_config)
+        if annotate_output:
+            conv_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                input_qspec_map=input_qspec_map,
+                _annotated=True,
+                _is_output_of_quantized_pattern=True,
+            )
+        else:
+            conv_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                input_qspec_map=input_qspec_map,
+                _annotated=True,
+            )
+
+    def _annotate_linear_node_helper(
+        self,
+        linear_node: torch.fx.Node,
+        annotate_output: bool,
+        quantization_config: Optional[QuantizationConfig],
+    ) -> None:
+        """Helper function to annotate the linear node"""
+        if quantization_config is None:
+            _annotate_nodes_not_quantize(linear_node)
+            return
+        input_qspec_map = {}
+        assert linear_node.target in (torch.ops.aten.linear.default,)
+        has_bias = len(linear_node.args) == 3
+        input_index = 0
+        weight_index = 1
+        bias_index = 2
+
+        input_node = linear_node.args[input_index]
+        assert isinstance(input_node, Node)
+        input_qspec_map[input_node] = get_input_act_qspec(quantization_config)
+
+        weight_node = linear_node.args[weight_index]
+        assert isinstance(weight_node, Node)
+        input_qspec_map[weight_node] = get_weight_qspec(quantization_config)
+
+        bias_node = linear_node.args[bias_index] if has_bias else None
+        if isinstance(bias_node, Node):
+            input_qspec_map[bias_node] = get_bias_qspec(quantization_config)
+
+        if annotate_output:
+            linear_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                input_qspec_map=input_qspec_map,
+                _annotated=True,
+                _is_output_of_quantized_pattern=True,
+            )
+        else:
+            linear_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                input_qspec_map=input_qspec_map, _annotated=True
+            )
+
+    def _get_output_nodes_of_partitions(
+        self,
+        partition_list: List[SourcePartition],
+    ) -> List[torch.fx.Node]:
+        """Helper function to get the output node list from partition list"""
+        output_node_list = []
+        for partition in partition_list:
+            if len(partition.output_nodes) > 1:
+                raise ValueError("Input partition has more than one output node")
+            output_node = partition.output_nodes[0]
+            assert isinstance(output_node, Node)
+            output_node_list.append(output_node)
+        if len(output_node_list) != len(partition_list):
+            raise ValueError(
+                "length of output_node_list should equal to length of partition_list"
+            )
+        return output_node_list
+
+    def _get_input_idx_for_binary_node(
+        self,
+        conv_gemm_node: torch.fx.Node,
+        binary_node: torch.fx.Node,
+    ):
+        """Helper function to check conv_gemm and extra input node index
+        for binary node fused with conv_gemm.
+        """
+        conv_gemm_node_idx = None
+        extra_input_node_idx = None
+        if (binary_node.args[0].op == "call_function") and (  # type: ignore[union-attr]
+            binary_node.args[0] == conv_gemm_node
+        ):
+            conv_gemm_node_idx = 0
+            extra_input_node_idx = 1
+        elif (binary_node.args[1].op == "call_function") and (  # type: ignore[union-attr]
+            binary_node.args[1] == conv_gemm_node
+        ):
+            conv_gemm_node_idx = 1
+            extra_input_node_idx = 0
+        extra_input_node = binary_node.args[extra_input_node_idx]  # type: ignore[index]
+        assert isinstance(extra_input_node, Node)
+        return conv_gemm_node_idx, extra_input_node_idx
+
+    def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+        """Annotate the given model with quantization configurations.
+
+        Annotation contracts:
+        1. Annotate each node according to the user's qconfig in the following order:
+        `module_name_qconfig`, `operator_type_qconfig`, and `global_config`.
+        2. Avoid re-annotating nodes already annotated in prior stages. For example,
+        if `linear1` has been annotated by `module_name_qconfig`, it won't be annotated again
+        during the processing of the 'operator_type_qconfig' or 'global_config'.
+        3. For config is `None`, the node will be annotated with `_X86InductorQuantizationAnnotation(_annotated=True)`.
+
+        For each pair of (module_name_or_operator_type_or_global, qconfig), a filter function is created.
+        This filter function checks if the node is marked by current stage and not annotated by the previous stage.
+        """
+        for module_name, quantization_config in self.module_name_qconfig.items():
+            self._annotate_with_config(
+                model, quantization_config, _create_module_name_filter(module_name)
+            )
+
+        for operator_type, quantization_config in self.operator_type_qconfig.items():
+            self._annotate_with_config(
+                model, quantization_config, _create_operator_type_filter(operator_type)
+            )
+
+        if self.global_config:
+            self._annotate_with_config(
+                model,
+                self.global_config,
+                _global_config_filter,
+            )
+
+        # Once we've annotated the model with quantization configurations, we also need to annotate
+        # the output of quantizable operations. For example, if we annotated `maxpool2d` to quantize its inputs,
+        # we will quantize its output accordingly. This enables us to fuse the dq-operator-q into a quantized op.
+        # Refer to https://github.com/intel/intel-extension-for-pytorch/blob/
+        # 90d19323d96afc53fcc22ba5a7bb3fb07fdd6c1c/intel_extension_for_pytorch/quantization/_recipe.py#L487
+
+        self._annotate_output_for_int8_in_int8_out_pattern_entry(model)
+
+        return model
+
+    def _annotate_with_config(
+        self,
+        model: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: FilterFn,
+    ) -> None:
+        """Annotate the model with the given quantization configuration.
+
+        High-level description of quantization recipe for X86 Inductor Backend:
+        Step 1: Apply quantization recipe for fusion patterns of conv/linear to enable int8 data type actively.
+        Step 2: Propagate quantization annotation for patterns besides conv/linear. Go through the pattern in model
+        from start to the end. If a pattern supports computation with int8 data type and inputs connected to
+        quantized patterns, annotate its inputs as quantized pattern.
+        """
+
+        # Step1: Recipe of fusion patterns like conv/linear.
+        self._annotate_conv2d_fusion_pattern(model, quantization_config, filter_fn)
+        self._annotate_linear_fusion_pattern(model, quantization_config, filter_fn)
+        self._annotate_matmul(model, quantization_config, filter_fn)
+
+        # Step2: Recipe to propagate annotation for patterns beside conv/linear.
+        # Go through all the nodes from start to end.
+        # Recipe refer to https://github.com/intel/intel-extension-for-pytorch/blob/
+        # 90d19323d96afc53fcc22ba5a7bb3fb07fdd6c1c/intel_extension_for_pytorch/quantization/_recipe.py#L538
+
+        self._annotate_propagation_quantizable_pattern_entry(
+            model, quantization_config, filter_fn
+        )
+
+    def _annotate_qat_conv2d_fusion_pattern(
+        self,
+        model: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: Optional[FilterFn] = None,
+    ):
+        # Annotate QAT Specific patterns
+        self._annotate_qat_conv2d_bn_binary_unary(model, quantization_config, filter_fn)
+        self._annotate_qat_conv2d_bn_binary(model, quantization_config, filter_fn)
+        self._annotate_qat_conv2d_bn_unary(model, quantization_config, filter_fn)
+        self._annotate_qat_conv2d_bn(model, quantization_config, filter_fn)
+
+    def _annotate_qat_conv2d_bn_binary_unary(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: Optional[FilterFn] = None,
+    ) -> None:
+        fused_partitions = find_sequential_partitions(
+            gm, [torch.nn.Conv2d, torch.nn.BatchNorm2d, operator.add, torch.nn.ReLU]
+        )
+        for fused_partition in fused_partitions:
+            (
+                conv_partition,
+                bn_partition,
+                binary_partition,
+                unary_partition,
+            ) = fused_partition
+
+            (
+                conv_node,
+                bn_output_node,
+                binary_node,
+                unary_node,
+            ) = self._get_output_nodes_of_partitions(
+                [conv_partition, bn_partition, binary_partition, unary_partition]
+            )
+            if len(bn_output_node.users) != 1:
+                # Conv BN pattern should only has 1 user.
+                continue
+            (
+                bn_output_node_idx,
+                extra_input_node_idx,
+            ) = self._get_input_idx_for_binary_node(bn_output_node, binary_node)
+            if (bn_output_node_idx is None) or (extra_input_node_idx is None):
+                continue
+            if bn_output_node != binary_node.args[bn_output_node_idx]:
+                raise ValueError(f"{bn_output_node} doesn't match input of binary node")
+            extra_input_node = binary_node.args[extra_input_node_idx]
+
+            if (
+                conv_node.op != "call_function"
+                or conv_node.target != torch.ops.aten.conv2d.default
+            ):
+                continue
+
+            if _skip_annotate(
+                [unary_node, binary_node, bn_output_node, conv_node], filter_fn
+            ):
+                continue
+
+            self._annotate_conv_node_helper(conv_node, False, quantization_config)
+
+            if quantization_config is not None:
+                binary_node_input_qspec_map = {}
+                binary_node_input_qspec_map[extra_input_node] = get_input_act_qspec(
+                    quantization_config
+                )
+                binary_node.meta[
+                    QUANT_ANNOTATION_KEY
+                ] = _X86InductorQuantizationAnnotation(
+                    input_qspec_map=binary_node_input_qspec_map,
+                    _annotated=True,
+                )
+                unary_node.meta[
+                    QUANT_ANNOTATION_KEY
+                ] = _X86InductorQuantizationAnnotation(
+                    # TODO Remove the annotate of output in QAT when qat util support pattern matcher.
+                    output_qspec=get_output_act_qspec(quantization_config),  # type: ignore[arg-type]
+                    _annotated=True,
+                    _is_output_of_quantized_pattern=True,
+                )
+            else:
+                _annotate_nodes_not_quantize([binary_node, unary_node])
+            nodes_to_mark_annotated = list(conv_partition.nodes)
+            nodes_to_mark_annotated.extend(list(bn_partition.nodes))
+            nodes_to_mark_annotated.extend(list(binary_partition.nodes))
+            nodes_to_mark_annotated.extend(list(unary_partition.nodes))
+            _mark_nodes_as_annotated(nodes_to_mark_annotated)
+
+    def _annotate_qat_conv2d_bn_binary(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: Optional[FilterFn] = None,
+    ) -> None:
+        fused_partitions = find_sequential_partitions(
+            gm, [torch.nn.Conv2d, torch.nn.BatchNorm2d, operator.add]
+        )
+        for fused_partition in fused_partitions:
+            conv_partition, bn_partition, binary_partition = fused_partition
+            (
+                conv_node,
+                bn_output_node,
+                binary_node,
+            ) = self._get_output_nodes_of_partitions(
+                [conv_partition, bn_partition, binary_partition]
+            )
+            if len(bn_output_node.users) != 1:
+                # Conv BN pattern should only has 1 user.
+                continue
+            (
+                bn_output_node_idx,
+                extra_input_node_idx,
+            ) = self._get_input_idx_for_binary_node(bn_output_node, binary_node)
+            if (bn_output_node_idx is None) or (extra_input_node_idx is None):
+                continue
+            if bn_output_node != binary_node.args[bn_output_node_idx]:
+                raise ValueError(f"{bn_output_node} doesn't match input of binary node")
+
+            extra_input_node = binary_node.args[extra_input_node_idx]
+
+            if (
+                conv_node.op != "call_function"
+                or conv_node.target != torch.ops.aten.conv2d.default
+            ):
+                continue
+
+            if _skip_annotate([binary_node, bn_output_node, conv_node], filter_fn):
+                continue
+
+            self._annotate_conv_node_helper(conv_node, False, quantization_config)
+
+            if quantization_config is not None:
+                binary_node_input_qspec_map = {}
+                binary_node_input_qspec_map[extra_input_node] = get_input_act_qspec(
+                    quantization_config
+                )
+                binary_node.meta[
+                    QUANT_ANNOTATION_KEY
+                ] = _X86InductorQuantizationAnnotation(
+                    input_qspec_map=binary_node_input_qspec_map,
+                    # TODO Remove the annotate of output in QAT when qat util support pattern matcher.
+                    output_qspec=get_output_act_qspec(quantization_config),  # type: ignore[arg-type]
+                    _annotated=True,
+                    _is_output_of_quantized_pattern=True,
+                )
+            else:
+                _annotate_nodes_not_quantize(binary_node)
+            nodes_to_mark_annotated = list(conv_partition.nodes)
+            nodes_to_mark_annotated.extend(list(bn_partition.nodes))
+            nodes_to_mark_annotated.extend(list(binary_partition.nodes))
+            _mark_nodes_as_annotated(nodes_to_mark_annotated)
+
+    def _annotate_qat_conv2d_bn_unary(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: Optional[FilterFn] = None,
+    ) -> None:
+        fused_partitions = []
+        unary_patterns = [
+            [torch.nn.Conv2d, torch.nn.BatchNorm2d, torch.nn.ReLU],
+            [torch.nn.Conv2d, torch.nn.BatchNorm2d, torch.nn.Hardtanh],
+            [torch.nn.Conv2d, torch.nn.BatchNorm2d, torch.nn.Hardswish],
+            [torch.nn.Conv2d, torch.nn.BatchNorm2d, torch.nn.ReLU6],
+            [torch.nn.Conv2d, torch.nn.BatchNorm2d, torch.nn.SiLU],
+        ]
+        for unary_pattern in unary_patterns:
+            partitions = find_sequential_partitions(gm, unary_pattern)
+            if partitions:
+                # Extend the fused_partitions if partitions is not empty
+                fused_partitions.extend(partitions)
+
+        for fused_partition in fused_partitions:
+            conv_partition, bn_partition, unary_partition = fused_partition
+            (
+                conv_node,
+                bn_output_node,
+                unary_node,
+            ) = self._get_output_nodes_of_partitions(
+                [conv_partition, bn_partition, unary_partition]
+            )
+
+            if (
+                conv_node.op != "call_function"
+                or conv_node.target != torch.ops.aten.conv2d.default
+            ):
+                continue
+
+            if _skip_annotate([unary_node, bn_output_node, conv_node], filter_fn):
+                continue
+
+            self._annotate_conv_node_helper(conv_node, False, quantization_config)
+            if quantization_config is not None:
+                unary_node.meta[
+                    QUANT_ANNOTATION_KEY
+                ] = _X86InductorQuantizationAnnotation(
+                    # TODO Remove the annotate of output in QAT when qat util support pattern matcher.
+                    output_qspec=get_output_act_qspec(quantization_config),  # type: ignore[arg-type]
+                    _annotated=True,
+                    _is_output_of_quantized_pattern=True,
+                )
+            else:
+                _annotate_nodes_not_quantize(unary_node)
+            nodes_to_mark_annotated = list(conv_partition.nodes)
+            nodes_to_mark_annotated.extend(list(bn_partition.nodes))
+            nodes_to_mark_annotated.extend(list(unary_partition.nodes))
+            _mark_nodes_as_annotated(nodes_to_mark_annotated)
+
+    def _annotate_qat_conv2d_bn(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: Optional[FilterFn] = None,
+    ) -> None:
+        fused_partitions = find_sequential_partitions(
+            gm, [torch.nn.Conv2d, torch.nn.BatchNorm2d]
+        )
+        for fused_partition in fused_partitions:
+            conv_partition, bn_partition = fused_partition
+            conv_node, bn_output_node = self._get_output_nodes_of_partitions(
+                [conv_partition, bn_partition]
+            )
+
+            if (
+                conv_node.op != "call_function"
+                or conv_node.target != torch.ops.aten.conv2d.default
+            ):
+                continue
+
+            if _skip_annotate([bn_output_node, conv_node], filter_fn):
+                continue
+
+            self._annotate_conv_node_helper(conv_node, False, quantization_config)
+            if quantization_config is not None:
+                bn_output_node.meta[
+                    QUANT_ANNOTATION_KEY
+                ] = _X86InductorQuantizationAnnotation(
+                    # TODO Remove the annotate of output in QAT when qat util support pattern matcher.
+                    output_qspec=get_output_act_qspec(quantization_config),  # type: ignore[arg-type]
+                    _annotated=True,
+                    _is_output_of_quantized_pattern=True,
+                )
+            else:
+                _annotate_nodes_not_quantize(bn_output_node)
+            nodes_to_mark_annotated = list(conv_partition.nodes)
+            nodes_to_mark_annotated.extend(list(bn_partition.nodes))
+            _mark_nodes_as_annotated(nodes_to_mark_annotated)
+
+    def _annotate_conv2d_fusion_pattern(
+        self,
+        model: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: Optional[FilterFn] = None,
+    ):
+        if (quantization_config is None) or (quantization_config.is_qat):
+            # Annotate QAT specific pattern: mainly due to BN not folded in prepare_qat
+            self._annotate_qat_conv2d_fusion_pattern(
+                model, quantization_config, filter_fn
+            )
+        self._annotate_conv2d_binary_unary(model, quantization_config, filter_fn)
+        self._annotate_conv2d_binary(model, quantization_config, filter_fn)
+        self._annotate_conv2d_unary(model, quantization_config, filter_fn)
+        self._annotate_conv2d(model, quantization_config, filter_fn)
+
+    def _annotate_linear_fusion_pattern(
+        self,
+        model: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: Optional[FilterFn] = None,
+    ):
+        self._annotate_linear_binary_unary(model, quantization_config, filter_fn)
+        self._annotate_linear_unary(model, quantization_config, filter_fn)
+        self._annotate_linear(model, quantization_config, filter_fn)
+
+    def _annotate_matmul(
+        self,
+        model: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: Optional[FilterFn] = None,
+    ):
+        for node in model.graph.nodes:
+            if node.target != torch.ops.aten.matmul.default:
+                continue
+            if _skip_annotate([node], filter_fn):
+                continue
+
+            if quantization_config is None:
+                _annotate_nodes_not_quantize(node)
+                continue
+
+            input_qspec_map = {}
+            matmul_node = node
+            for input_node in matmul_node.args:
+                input_qspec_map[input_node] = get_input_act_qspec(quantization_config)
+            matmul_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                input_qspec_map=input_qspec_map,
+                _annotated=True,
+                _is_output_of_quantized_pattern=True,
+            )
+
+    def _annotate_conv2d_binary_unary(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: Optional[FilterFn] = None,
+    ) -> None:
+        # Conv2d + add + unary op
+        fused_partitions = find_sequential_partitions(
+            gm, [torch.nn.Conv2d, operator.add, torch.nn.ReLU]
+        )
+        for fused_partition in fused_partitions:
+            conv_partition, binary_partition, unary_partition = fused_partition
+            conv_node, binary_node, unary_node = self._get_output_nodes_of_partitions(
+                [conv_partition, binary_partition, unary_partition]
+            )
+            if len(conv_node.users) != 1:
+                # Conv Node should only has 1 user node
+                continue
+            conv_node_idx, extra_input_node_idx = self._get_input_idx_for_binary_node(
+                conv_node, binary_node
+            )
+            if (conv_node_idx is None) or (extra_input_node_idx is None):
+                continue
+            if conv_node != binary_node.args[conv_node_idx]:
+                raise ValueError(f"{conv_node} doesn't match input of binary node")
+            extra_input_node = binary_node.args[extra_input_node_idx]
+            if (
+                conv_node.op != "call_function"
+                or conv_node.target != torch.ops.aten.conv2d.default
+            ):
+                # No conv node found to be fused with add
+                continue
+            if _skip_annotate([unary_node, binary_node, conv_node], filter_fn):
+                continue
+
+            if quantization_config is None:
+                _annotate_nodes_not_quantize([conv_node, binary_node, unary_node])
+                continue
+
+            self._annotate_conv_node_helper(conv_node, False, quantization_config)
+            binary_node_input_qspec_map = {}
+            binary_node_input_qspec_map[extra_input_node] = get_input_act_qspec(
+                quantization_config
+            )
+            binary_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                input_qspec_map=binary_node_input_qspec_map,
+                _annotated=True,
+            )
+            unary_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                _annotated=True,
+                _is_output_of_quantized_pattern=True,
+            )
+
+    def _annotate_conv2d_binary(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: Optional[FilterFn] = None,
+    ) -> None:
+        # Conv2d + add
+        fused_partitions = find_sequential_partitions(
+            gm, [torch.nn.Conv2d, operator.add]
+        )
+        for fused_partition in fused_partitions:
+            conv_partition, binary_partition = fused_partition
+            conv_node, binary_node = self._get_output_nodes_of_partitions(
+                [conv_partition, binary_partition]
+            )
+            if len(conv_node.users) != 1:
+                # Conv Node should only has 1 user node
+                continue
+            conv_node_idx, extra_input_node_idx = self._get_input_idx_for_binary_node(
+                conv_node, binary_node
+            )
+            if (conv_node_idx is None) or (extra_input_node_idx is None):
+                continue
+            if conv_node != binary_node.args[conv_node_idx]:
+                raise ValueError(f"{conv_node} doesn't match input of binary node")
+            extra_input_node = binary_node.args[extra_input_node_idx]
+            assert isinstance(conv_node, Node)
+            if (
+                conv_node.op != "call_function"
+                or conv_node.target != torch.ops.aten.conv2d.default
+            ):
+                # No conv node found to be fused with add
+                continue
+            if _skip_annotate([binary_node, conv_node], filter_fn):
+                continue
+
+            if quantization_config is None:
+                _annotate_nodes_not_quantize([conv_node, binary_node])
+                continue
+
+            self._annotate_conv_node_helper(conv_node, False, quantization_config)
+            binary_node_input_qspec_map = {}
+            binary_node_input_qspec_map[extra_input_node] = get_input_act_qspec(
+                quantization_config
+            )
+            binary_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                input_qspec_map=binary_node_input_qspec_map,
+                _annotated=True,
+                _is_output_of_quantized_pattern=True,
+            )
+
+    def _annotate_conv2d_unary(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: Optional[FilterFn] = None,
+    ) -> None:
+        fused_partitions = []
+        unary_patterns = [
+            [torch.nn.Conv2d, torch.nn.ReLU],
+            [torch.nn.Conv2d, torch.nn.Hardtanh],
+            [torch.nn.Conv2d, torch.nn.Hardswish],
+            [torch.nn.Conv2d, torch.nn.ReLU6],
+            [torch.nn.Conv2d, torch.nn.SiLU],
+        ]
+        for unary_pattern in unary_patterns:
+            partitions = find_sequential_partitions(gm, unary_pattern)
+            if partitions:
+                # Extend the fused_partitions if partitions is not empty
+                fused_partitions.extend(partitions)
+
+        for fused_partition in fused_partitions:
+            conv_partition, unary_partition = fused_partition
+            conv_node, unary_node = self._get_output_nodes_of_partitions(
+                [conv_partition, unary_partition]
+            )
+            if (
+                conv_node.op != "call_function"
+                or conv_node.target != torch.ops.aten.conv2d.default
+            ):
+                continue
+            if _skip_annotate([unary_node, conv_node], filter_fn):
+                continue
+
+            if quantization_config is None:
+                _annotate_nodes_not_quantize([conv_node, unary_node])
+                continue
+
+            self._annotate_conv_node_helper(conv_node, False, quantization_config)
+            unary_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                _annotated=True,
+                _is_output_of_quantized_pattern=True,
+            )
+
+    def _annotate_conv2d(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: Optional[FilterFn] = None,
+    ) -> None:
+        conv_partitions = get_source_partitions(
+            gm.graph, [torch.nn.Conv2d, torch.nn.functional.conv2d]
+        )
+        conv_partitions = list(itertools.chain.from_iterable(conv_partitions.values()))
+        for conv_partition in conv_partitions:
+            if len(conv_partition.output_nodes) > 1:
+                raise ValueError("conv partition has more than one output node")
+            conv_node = conv_partition.output_nodes[0]
+            if (
+                conv_node.op != "call_function"
+                or conv_node.target != torch.ops.aten.conv2d.default
+            ):
+                raise ValueError(f"{conv_node} is not an aten conv2d operator")
+            # skip annotation if it is already annotated
+            if _skip_annotate([conv_node], filter_fn):
+                continue
+            self._annotate_conv_node_helper(conv_node, True, quantization_config)
+
+    def _annotate_maxpool2d(
+        self,
+        node: Node,
+        quantization_config: Optional[QuantizationConfig],
+    ) -> None:
+        if node.target is not torch.ops.aten.max_pool2d.default:
+            return
+        if quantization_config is None:
+            _annotate_nodes_not_quantize(node)
+            return
+
+        maxpool_node = node
+        if _is_any_annotated(
+            [
+                maxpool_node,
+            ]
+        ):
+            return
+
+        input_node = maxpool_node.args[0]
+        assert isinstance(input_node, Node)
+        input_qspec_map = {}
+        input_qspec_map[input_node] = get_input_act_qspec(quantization_config)
+        maxpool_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            _annotated=True,
+            _is_output_of_quantized_pattern=True,
+        )
+
+    def _annotate_cat(
+        self, node: Node, quantization_config: QuantizationConfig
+    ) -> None:
+        if quantization_config is None:
+            _annotate_nodes_not_quantize(node)
+            return
+        cat_node = node
+        input_nodes = cat_node.args[0]
+        assert isinstance(input_nodes, Sequence)
+        first_input_node = input_nodes[0]
+        input_qspec_map = {}
+        assert isinstance(first_input_node, Node)
+        assert isinstance(cat_node, Node)
+        input_qspec_map[first_input_node] = get_input_act_qspec(quantization_config)
+        share_qparams_with_input_act0_qspec = SharedQuantizationSpec(
+            (first_input_node, cat_node)
+        )
+
+        for input_node in input_nodes[1:]:
+            if input_node not in input_qspec_map:
+                # There has the case of cat same nodes: torch.cat([input0, input0], 1)
+                assert isinstance(input_node, Node)
+                input_qspec_map[input_node] = share_qparams_with_input_act0_qspec
+
+        cat_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            _annotated=True,
+            _is_output_of_quantized_pattern=True,
+        )
+
+    def _annotate_propagation_quantizable_pattern_entry(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: Optional[FilterFn] = None,
+    ):
+        for node in gm.graph.nodes:
+            self._annotate_propagation_quantizable_pattern(
+                node, quantization_config, filter_fn
+            )
+
+    def _annotate_propagation_quantizable_pattern(
+        self, node: Node, quantization_config, filter_fn
+    ) -> None:
+        # Propagate annotation to quantizable patterns.
+        if (
+            (node.target in propagation_quantizable_ops)
+            and (not _is_any_annotated([node]))
+            and (node.op == "call_function")
+        ):
+
+            def is_all_inputs_connected_to_quantized_op(input_nodes):
+                # Ensure all the inputs connect to fusion pattern or quantized node
+                for input_node in input_nodes:
+                    if not _is_quantized_op_pt2e(input_node):
+                        return False
+                return True
+
+            if _skip_annotate([node], filter_fn):
+                return
+
+            if quantization_config is None:
+                _annotate_nodes_not_quantize(node)
+                return
+
+            if node.target is torch.ops.aten.max_pool2d.default:
+                # Recipe of maxpool2d: check input arg[0] of maxpool2d is quantized or not
+                input_nodes_to_check = [node.all_input_nodes[0]]
+                if not is_all_inputs_connected_to_quantized_op(input_nodes_to_check):
+                    if quantization_config is not None:
+                        warnings.warn(
+                            f"The input of maxpool2d is not quantized, skip annotate maxpool2d with config {quantization_config}."
+                        )
+                    return
+
+                self._annotate_maxpool2d(node, quantization_config)
+                return
+            elif node.target is torch.ops.aten.cat.default:
+                input_nodes_to_check = node.all_input_nodes
+                if not is_all_inputs_connected_to_quantized_op(input_nodes_to_check):
+                    return
+                self._annotate_cat(node, quantization_config)
+            else:
+                input_node = node.all_input_nodes[0]
+                if not is_all_inputs_connected_to_quantized_op(
+                    [
+                        input_node,
+                    ]
+                ):
+                    return
+                input_qspec_map = {}
+                input_qspec_map[input_node] = get_input_act_qspec(quantization_config)
+                node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                    input_qspec_map=input_qspec_map,
+                    _annotated=True,
+                    _is_output_of_quantized_pattern=True,
+                )
+        return
+
+    def _annotate_output_share_observer_as_input(
+        self, input_node: Node, source_node: Node
+    ):
+        source_node_quantization_annotation = (
+            source_node.meta[QUANT_ANNOTATION_KEY]
+            if QUANT_ANNOTATION_KEY in source_node.meta
+            else None
+        )
+        if (
+            source_node_quantization_annotation
+            and source_node_quantization_annotation._is_output_of_quantized_pattern
+        ):
+            edge_or_node = (input_node, source_node)
+            source_node_quantization_annotation.output_qspec = SharedQuantizationSpec(
+                edge_or_node
+            )
+        return
+
+    def _annotate_output_for_int8_in_int8_out_pattern_entry(
+        self,
+        model: torch.fx.GraphModule,
+    ):
+        for node in model.graph.nodes:
+            self._annotate_output_for_int8_in_int8_out_pattern(node)
+
+    def _annotate_output_for_int8_in_int8_out_pattern(
+        self,
+        node: Node,
+    ) -> None:
+        r"""
+        Check and insert observer at output of node in int8_in_int8_out_ops if needed.
+        Recipe refers to https://github.com/intel/intel-extension-for-pytorch/blob/
+        90d19323d96afc53fcc22ba5a7bb3fb07fdd6c1c/intel_extension_for_pytorch/quantization/_utils.py#L495
+        """
+        edge_or_node: Tuple[Node, Node]
+        if (node.target in int8_in_int8_out_ops) and (_is_any_annotated([node])):
+            if node.target == torch.ops.aten.max_pool2d.default:
+                maxpool_node = node
+                if not _is_all_annotated(
+                    [
+                        maxpool_node,
+                    ]
+                ):
+                    return
+
+                # Get the quantization_annotation from getitem_node
+                maxpool_node_quantization_annotation = (
+                    maxpool_node.meta[QUANT_ANNOTATION_KEY]
+                    if QUANT_ANNOTATION_KEY in maxpool_node.meta
+                    else None
+                )
+                if (
+                    maxpool_node_quantization_annotation
+                    and maxpool_node_quantization_annotation._is_output_of_quantized_pattern
+                ):
+                    # Annotate the output_qspec of getitem_node
+                    input_act = maxpool_node.args[0]
+                    assert isinstance(input_act, Node)
+                    assert isinstance(maxpool_node, Node)
+                    edge_or_node = (input_act, maxpool_node)
+                    maxpool_node_quantization_annotation.output_qspec = (
+                        SharedQuantizationSpec(edge_or_node)
+                    )
+            else:
+                input_node = node.all_input_nodes[0]
+                self._annotate_output_share_observer_as_input(input_node, node)
+        return
+
+    def _annotate_linear(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: Optional[FilterFn] = None,
+    ) -> None:
+        linear_partitions = get_source_partitions(
+            gm.graph, [torch.nn.Linear, torch.nn.functional.linear]
+        )
+        linear_partitions = list(
+            itertools.chain.from_iterable(linear_partitions.values())
+        )
+        for partition in linear_partitions:
+            if len(partition.output_nodes) > 1:
+                raise ValueError(
+                    "Linear partition cannot have more than one output node"
+                )
+            linear_node = partition.output_nodes[0]
+            if linear_node.op != "call_function" or linear_node.target not in (
+                torch.ops.aten.linear.default,
+            ):
+                raise ValueError(f"{linear_node} is not an aten linear operator")
+            # skip annotation if it is already annotated
+            if _skip_annotate([linear_node], filter_fn):
+                continue
+            self._annotate_linear_node_helper(linear_node, True, quantization_config)
+
+    def _annotate_linear_unary(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: Optional[FilterFn] = None,
+    ) -> None:
+        postop_list = [
+            torch.nn.ReLU,
+            torch.nn.LeakyReLU,
+            torch.nn.Tanh,
+            torch.nn.GELU,
+        ]
+        fused_partitions: List[tuple] = []
+        for postop in postop_list:
+            fused_partitions = fused_partitions + find_sequential_partitions(
+                gm, [torch.nn.Linear, postop]
+            )
+        for fused_partition in fused_partitions:
+            linear_partition, unary_partition = fused_partition
+            linear_node, unary_node = self._get_output_nodes_of_partitions(
+                [linear_partition, unary_partition]
+            )
+            if linear_node.op != "call_function" or linear_node.target not in (
+                torch.ops.aten.linear.default,
+            ):
+                continue
+            if _skip_annotate([unary_node, linear_node], filter_fn):
+                continue
+
+            if quantization_config is None:
+                _annotate_nodes_not_quantize([linear_node, unary_node])
+                continue
+
+            self._annotate_linear_node_helper(linear_node, False, quantization_config)
+            unary_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                _annotated=True,
+                _is_output_of_quantized_pattern=True,
+            )
+
+    def _annotate_linear_binary_unary(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: Optional[FilterFn] = None,
+    ) -> None:
+        # linear + binary_op + (optional) unary op
+        binary_op_list = [operator.add]
+        unary_op_list = [torch.nn.ReLU, None]
+        combinations = itertools.product(binary_op_list, unary_op_list)
+        for binary_op, unary_op in combinations:
+            has_unary = unary_op is not None
+            seq_partition = [torch.nn.Linear, binary_op]
+            if has_unary:
+                seq_partition.append(unary_op)
+            fused_partitions = find_sequential_partitions(gm, seq_partition)
+            for fused_partition in fused_partitions:
+                unary_partition, unary_node = None, None
+                if has_unary:
+                    (
+                        linear_partition,
+                        binary_partition,
+                        unary_partition,
+                    ) = fused_partition
+                    (
+                        linear_node,
+                        binary_node,
+                        unary_node,
+                    ) = self._get_output_nodes_of_partitions(
+                        [linear_partition, binary_partition, unary_partition]
+                    )
+                else:
+                    linear_partition, binary_partition = fused_partition
+                    linear_node, binary_node = self._get_output_nodes_of_partitions(
+                        [linear_partition, binary_partition]
+                    )
+                if len(linear_node.users) != 1:
+                    # Linear Node should only has 1 user node
+                    continue
+                (
+                    linear_node_idx,
+                    extra_input_node_idx,
+                ) = self._get_input_idx_for_binary_node(linear_node, binary_node)
+                if (linear_node_idx is None) or (extra_input_node_idx is None):
+                    continue
+                if linear_node != binary_node.args[linear_node_idx]:
+                    raise ValueError(
+                        f"{linear_node} doesn't match input of binary node"
+                    )
+                assert isinstance(linear_node, Node)
+                if (
+                    linear_node.op != "call_function"
+                    or linear_node.target != torch.ops.aten.linear.default
+                ):
+                    # No linear node found to be fused with add
+                    continue
+                node_list = (
+                    [binary_node, linear_node]
+                    if unary_node is None
+                    else [unary_node, binary_node, linear_node]
+                )
+                if _skip_annotate(node_list, filter_fn):
+                    continue
+
+                if quantization_config is None:
+                    _annotate_nodes_not_quantize(node_list)
+                    continue
+
+                self._annotate_linear_node_helper(
+                    linear_node, False, quantization_config
+                )
+                # We don't insert q-dq before the binary input node due to accuracy issues
+                binary_node.meta[
+                    QUANT_ANNOTATION_KEY
+                ] = _X86InductorQuantizationAnnotation(
+                    input_qspec_map={},
+                    _annotated=True,
+                    _is_output_of_quantized_pattern=(not has_unary),
+                )
+                if unary_node is not None:
+                    unary_node.meta[
+                        QUANT_ANNOTATION_KEY
+                    ] = _X86InductorQuantizationAnnotation(
+                        _annotated=True,
+                        _is_output_of_quantized_pattern=True,
+                    )
+
+    def validate(self, model: torch.fx.GraphModule) -> None:
+        pass
+
+    @classmethod
+    def get_supported_operators(cls) -> List[OperatorConfig]:
+        return cls.supported_config_and_operators
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/xnnpack_quantizer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/xnnpack_quantizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..078e48e42e272cd8ad3a9c8a252dabc5f3bedb0b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/xnnpack_quantizer.py
@@ -0,0 +1,436 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import copy
+import functools
+from typing import Any, Callable, Dict, List, Optional, Set, TYPE_CHECKING
+
+import torch
+import torch._dynamo as torchdynamo
+import torch.nn.functional as F
+from torch.ao.quantization.fake_quantize import (
+    FakeQuantize,
+    FusedMovingAvgObsFakeQuantize,
+)
+from torch.ao.quantization.observer import (
+    HistogramObserver,
+    MinMaxObserver,
+    MovingAverageMinMaxObserver,
+    MovingAveragePerChannelMinMaxObserver,
+    PerChannelMinMaxObserver,
+    PlaceholderObserver,
+)
+from torch.ao.quantization.quantizer import QuantizationSpec, Quantizer
+from torch.ao.quantization.quantizer.utils import _get_module_name_filter
+from torch.ao.quantization.quantizer.xnnpack_quantizer_utils import (
+    _convert_scalars_to_attrs,
+    OP_TO_ANNOTATOR,
+    OperatorConfig,
+    OperatorPatternType,
+    propagate_annotation,
+    QuantizationConfig,
+)
+
+
+if TYPE_CHECKING:
+    from torch.ao.quantization.qconfig import _ObserverOrFakeQuantizeConstructor
+    from torch.fx import Node
+
+
+__all__ = [
+    "XNNPACKQuantizer",
+    "get_symmetric_quantization_config",
+]
+
+
+def _get_dynamo_graph(function: Callable, inputs) -> torch.fx.Graph:
+    gm, _ = torchdynamo.export(function, aten_graph=True)(*inputs)
+    gm.graph.eliminate_dead_code()
+    return gm.graph
+
+
+def _get_linear_patterns(input_size: List[int]):
+    in_channels = input_size[-1]
+    out_channels = 8  # hard coding but this should not matter
+    weight = torch.ones((out_channels, in_channels))
+    bias = torch.ones((out_channels,))
+    act = torch.ones(input_size)
+
+    def linear_op(act, weight, bias=None):
+        return F.linear(act, weight, bias)
+
+    pattern_w_bias = _get_dynamo_graph(linear_op, (act, weight, bias))
+    pattern_wo_bias = _get_dynamo_graph(linear_op, (act, weight))
+    return [pattern_w_bias, pattern_wo_bias]
+
+
+def _supported_symmetric_quantized_operators() -> Dict[str, List[OperatorPatternType]]:
+    supported_operators: Dict[str, List[OperatorPatternType]] = {
+        # Both conv and linear should be able to handle relu + hardtanh fusion since
+        # those are clamp ops
+        "conv2d": [
+            [torch.nn.Conv2d, torch.nn.ReLU],
+            [torch.nn.Conv2d, F.relu],
+            [F.conv2d, torch.nn.ReLU],
+            [F.conv2d, F.relu],
+        ],
+        "linear": [[torch.nn.Linear], [F.linear]],
+        "add": [[torch.add]],
+        "adaptive_avg_pool2d": [
+            [torch.nn.AdaptiveAvgPool2d],
+            [F.adaptive_avg_pool2d],
+        ],
+    }
+    return copy.deepcopy(supported_operators)
+
+
+def _get_supported_symmetric_config_and_operators() -> List[OperatorConfig]:
+    supported_config_and_operators: List[OperatorConfig] = []
+    for quantization_config in [
+        get_symmetric_quantization_config(),
+        get_symmetric_quantization_config(is_qat=True),
+        get_symmetric_quantization_config(is_per_channel=True),
+        get_symmetric_quantization_config(is_per_channel=True, is_qat=True),
+    ]:
+        ops = _supported_symmetric_quantized_operators()
+        for pattern_list in ops.values():
+            supported_config_and_operators.append(
+                OperatorConfig(quantization_config, pattern_list)
+            )
+    return copy.deepcopy(supported_config_and_operators)
+
+
+@functools.lru_cache
+def get_symmetric_quantization_config(
+    is_per_channel: bool = False,
+    is_qat: bool = False,
+    is_dynamic: bool = False,
+    act_qmin: int = -128,
+    act_qmax: int = 127,
+    weight_qmin: int = -127,
+    weight_qmax: int = 127,
+):
+    extra_args: Dict[str, Any] = {"eps": 2**-12}
+    if is_qat:
+        if is_dynamic:
+            act_observer_or_fake_quant_ctr = FakeQuantize
+            dynamic_quant_observer = MovingAverageMinMaxObserver.with_args(
+                averaging_constant=1
+            )
+            extra_args["observer"] = dynamic_quant_observer
+        else:
+            act_observer_or_fake_quant_ctr = FusedMovingAvgObsFakeQuantize  # type: ignore[assignment]
+    else:
+        if is_dynamic:
+            act_observer_or_fake_quant_ctr = PlaceholderObserver  # type: ignore[assignment]
+        else:
+            act_observer_or_fake_quant_ctr = HistogramObserver  # type: ignore[assignment]
+
+    act_quantization_spec = QuantizationSpec(
+        dtype=torch.int8,
+        quant_min=act_qmin,
+        quant_max=act_qmax,
+        qscheme=torch.per_tensor_affine,
+        is_dynamic=is_dynamic,
+        observer_or_fake_quant_ctr=act_observer_or_fake_quant_ctr.with_args(
+            **extra_args,
+        ),
+    )
+    weight_qscheme = (
+        torch.per_channel_symmetric if is_per_channel else torch.per_tensor_symmetric
+    )
+    weight_observer_or_fake_quant_ctr: _ObserverOrFakeQuantizeConstructor = (
+        MinMaxObserver
+    )
+    if is_qat:
+        # TODO: qat + per channel?
+        weight_observer_or_fake_quant_ctr = FusedMovingAvgObsFakeQuantize
+    elif is_per_channel:
+        weight_observer_or_fake_quant_ctr = PerChannelMinMaxObserver
+
+    extra_args: Dict[str, Any] = {"eps": 2**-12}
+    if is_qat:
+        if weight_qscheme == torch.per_tensor_symmetric:
+            extra_args["observer"] = MovingAverageMinMaxObserver
+        else:
+            extra_args["observer"] = MovingAveragePerChannelMinMaxObserver  # type: ignore[dict-item]
+    weight_quantization_spec = QuantizationSpec(
+        dtype=torch.int8,
+        quant_min=weight_qmin,
+        quant_max=weight_qmax,
+        qscheme=weight_qscheme,
+        ch_axis=0,
+        is_dynamic=False,
+        observer_or_fake_quant_ctr=weight_observer_or_fake_quant_ctr.with_args(
+            **extra_args
+        ),
+    )
+
+    bias_quantization_spec = None
+    if is_dynamic:
+        quantization_config = QuantizationConfig(
+            act_quantization_spec,
+            None,
+            weight_quantization_spec,
+            bias_quantization_spec,
+            is_qat,
+        )
+    else:
+        quantization_config = QuantizationConfig(
+            act_quantization_spec,
+            act_quantization_spec,
+            weight_quantization_spec,
+            bias_quantization_spec,
+            is_qat,
+        )
+    return quantization_config
+
+
+def _get_supported_config_and_operators() -> List[OperatorConfig]:
+    return _get_supported_symmetric_config_and_operators()
+
+
+def _get_module_type_filter(tp: Callable):
+    """Get the module_type_filter function for a given module type, the filter accepts
+    a node and checks if the node comes from a module that has certain module type
+
+    For example:
+        node: linear_op = call_function[...](...)  # comes from a module with type Block -> Sub -> Linear
+
+
+    >> module_type_filter = _get_module_type_filter(Sub)  # submodule with type `Sub`, under the `Block` submodule
+    >> print(module_type_filter(node))
+    True  # the node is from the submodule `Sub` (same for `Block` and `Linear` as well)
+    """
+
+    tp_str = tp.__module__ + "." + tp.__qualname__
+
+    def module_type_filter(n: Node) -> bool:
+        # example: {
+        #     'L__self___sub': ("L['self'].sub", ),
+        #     'L__self___sub_linear': ("L['self'].sub.linear", )
+        # }
+        nn_module_stack = n.meta.get("nn_module_stack", {})
+        types = []
+        for _, t in nn_module_stack.values():
+            # export() returns str, but older APIs (e.g. capture_pre_autograd_graph)
+            # return type. Handle both cases.
+            if isinstance(t, type):
+                t = t.__module__ + "." + t.__qualname__
+            types.append(t)
+        return tp_str in types
+
+    return module_type_filter
+
+
+def _get_not_module_type_or_name_filter(
+    tp_list: List[Callable], module_name_list: List[str]
+) -> Callable[[Node], bool]:
+    module_type_filters = [_get_module_type_filter(tp) for tp in tp_list]
+    module_name_list_filters = [_get_module_name_filter(m) for m in module_name_list]
+
+    def not_module_type_or_name_filter(n: Node) -> bool:
+        return not any(f(n) for f in module_type_filters + module_name_list_filters)
+
+    return not_module_type_or_name_filter
+
+
+class XNNPACKQuantizer(Quantizer):
+    supported_config_and_operators = _get_supported_config_and_operators()
+    STATIC_QAT_ONLY_OPS = [
+        "conv_bn_relu",
+        "conv_bn",
+        "conv_transpose_bn_relu",
+        "conv_transpose_bn",
+    ]
+
+    # static quantization ops (both PTQ and QAT)
+    # Preserve the order that fusions come before singular ops
+    STATIC_OPS = [
+        "linear_relu",
+        "linear",
+        "conv_relu",
+        "conv",
+        "conv_transpose_relu",
+        "adaptive_avg_pool2d",
+        # TODO: move this to BoltNNQuantizer?
+        "gru_io_only",
+        "add_relu",
+        "add",
+        "mul_relu",
+        "mul",
+        "cat",
+    ]
+
+    DYNAMIC_OPS = [
+        "linear",
+    ]
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.global_config: Optional[QuantizationConfig] = None
+        self.operator_type_config: Dict[
+            torch._ops.OpOverloadPacket, Optional[QuantizationConfig]
+        ] = {}
+        self.module_type_config: Dict[Callable, Optional[QuantizationConfig]] = {}
+        self.module_name_config: Dict[str, Optional[QuantizationConfig]] = {}
+
+    @classmethod
+    def get_supported_quantization_configs(cls) -> List[QuantizationConfig]:
+        op_configs: Set[QuantizationConfig] = {
+            spec for spec, _ in cls.supported_config_and_operators
+        }
+        return list(op_configs)
+
+    @classmethod
+    def get_supported_operator_for_quantization_config(
+        cls, quantization_config: Optional[QuantizationConfig]
+    ) -> List[OperatorPatternType]:
+        if quantization_config is None:
+            all_ops = []
+            for _, ops in cls.supported_config_and_operators:
+                all_ops.extend(ops)
+            return all_ops
+
+        for config, ops in cls.supported_config_and_operators:
+            # note: this assumes each entry in cls.supported_spec_and_operators
+            # corresponds to one spec, e.g. we don't have
+            # [(spec1, op_list1), (spec1, op_list2), (spec2, op_list3)]
+            # where the first and second entry have the same spec but did not
+            # merge the op list
+            if config == quantization_config:
+                return ops
+        return []
+
+    def set_global(self, quantization_config: QuantizationConfig) -> XNNPACKQuantizer:
+        self.global_config = quantization_config
+        return self
+
+    def set_operator_type(
+        self,
+        operator_type: torch._ops.OpOverloadPacket,
+        quantization_config: QuantizationConfig,
+    ) -> XNNPACKQuantizer:
+        self.operator_type_config[operator_type] = quantization_config
+        return self
+
+    def set_module_type(
+        self, module_type: Callable, quantization_config: QuantizationConfig
+    ):
+        """Set quantization_config for a submodule with type: `module_type`, for example:
+        quantizer.set_module_name(Sub) or quantizer.set_module_name(nn.Linear), it will quantize all supported operator/operator
+        patterns in the submodule with this module type with the given `quantization_config`
+        """
+        self.module_type_config[module_type] = quantization_config
+        return self
+
+    def set_module_name(
+        self, module_name: str, quantization_config: Optional[QuantizationConfig]
+    ):
+        """Set quantization_config for a submodule with name: `module_name`, for example:
+        quantizer.set_module_name("blocks.sub"), it will quantize all supported operator/operator
+        patterns in the submodule with this module name with the given `quantization_config`
+        """
+        assert (
+            quantization_config is not None
+        ), " quantization_config == None is not supported yet"
+        self.module_name_config[module_name] = quantization_config
+        return self
+
+    def transform_for_annotation(
+        self, model: torch.fx.GraphModule
+    ) -> torch.fx.GraphModule:
+        """Transforms scalar values to tensor attributes"""
+        return _convert_scalars_to_attrs(model)
+
+    def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+        """just handling global spec for now"""
+        # hacked for handling dynamic linear quant. will fix later.
+        if self.global_config and self.global_config.input_activation.is_dynamic:  # type: ignore[union-attr]
+            model = self._annotate_for_dynamic_quantization_config(model)
+        else:
+            model = self._annotate_for_static_quantization_config(model)
+        propagate_annotation(model)
+        return model
+
+    def _annotate_all_static_patterns(
+        self,
+        model: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: Optional[Callable[[Node], bool]] = None,
+    ) -> torch.fx.GraphModule:
+        # TODO: implement the support for None to be canceling out previous annotations
+        if quantization_config is None:
+            return model
+
+        if quantization_config.is_qat:
+            for op in self.STATIC_QAT_ONLY_OPS:
+                OP_TO_ANNOTATOR[op](model, quantization_config, filter_fn)
+        for op in self.STATIC_OPS:
+            OP_TO_ANNOTATOR[op](model, quantization_config, filter_fn)
+        return model
+
+    def _annotate_all_dynamic_patterns(
+        self,
+        model: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: Optional[Callable[[Node], bool]] = None,
+    ) -> torch.fx.GraphModule:
+        # TODO: implement the support for None to be canceling out previous annotations
+        if quantization_config is None:
+            return model
+
+        for op in self.DYNAMIC_OPS:
+            OP_TO_ANNOTATOR[op](model, quantization_config, filter_fn)
+        return model
+
+    def _annotate_for_static_quantization_config(
+        self, model: torch.fx.GraphModule
+    ) -> torch.fx.GraphModule:
+        module_name_list = list(self.module_name_config.keys())
+        for module_name, config in self.module_name_config.items():
+            self._annotate_all_static_patterns(
+                model, config, _get_module_name_filter(module_name)
+            )
+
+        tp_list = list(self.module_type_config.keys())
+        for module_type, config in self.module_type_config.items():
+            self._annotate_all_static_patterns(
+                model, config, _get_module_type_filter(module_type)
+            )
+
+        self._annotate_all_static_patterns(
+            model,
+            self.global_config,
+            _get_not_module_type_or_name_filter(tp_list, module_name_list),
+        )
+        return model
+
+    def _annotate_for_dynamic_quantization_config(
+        self, model: torch.fx.GraphModule
+    ) -> torch.fx.GraphModule:
+        module_name_list = list(self.module_name_config.keys())
+        for module_name, config in self.module_name_config.items():
+            self._annotate_all_dynamic_patterns(
+                model, config, _get_module_name_filter(module_name)
+            )
+
+        tp_list = list(self.module_type_config.keys())
+        for module_type, config in self.module_type_config.items():
+            self._annotate_all_dynamic_patterns(
+                model, config, _get_module_type_filter(module_type)
+            )
+
+        self._annotate_all_dynamic_patterns(
+            model,
+            self.global_config,
+            _get_not_module_type_or_name_filter(tp_list, module_name_list),
+        )
+        return model
+
+    def validate(self, model: torch.fx.GraphModule) -> None:
+        pass
+
+    @classmethod
+    def get_supported_operators(cls) -> List[OperatorConfig]:
+        return cls.supported_config_and_operators
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/xnnpack_quantizer_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/xnnpack_quantizer_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6be02db18e2f91f992aa4fcaac0b42ae216783d5
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/quantizer/xnnpack_quantizer_utils.py
@@ -0,0 +1,1085 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import itertools
+from dataclasses import dataclass
+from typing import Callable, Dict, List, NamedTuple, Optional
+
+import torch
+import torch.nn.functional as F
+from torch._subclasses import FakeTensor
+from torch.ao.quantization.fx.utils import get_new_attr_name_with_prefix
+from torch.ao.quantization.pt2e.export_utils import _WrapperModule
+from torch.ao.quantization.pt2e.utils import (
+    _conv1d_bn_example_inputs,
+    _conv2d_bn_example_inputs,
+    _get_aten_graph_module_for_pattern,
+    _is_conv_node,
+    _is_conv_transpose_node,
+)
+from torch.ao.quantization.quantizer import (
+    QuantizationAnnotation,
+    QuantizationSpec,
+    QuantizationSpecBase,
+    SharedQuantizationSpec,
+)
+from torch.ao.quantization.quantizer.utils import (
+    _annotate_input_qspec_map,
+    _annotate_output_qspec,
+)
+from torch.fx import Node
+from torch.fx.passes.utils.matcher_with_name_node_map_utils import (
+    SubgraphMatcherWithNameNodeMap,
+)
+from torch.fx.passes.utils.source_matcher_utils import get_source_partitions
+
+
+__all__ = [
+    "OperatorConfig",
+    "OperatorPatternType",
+    "QuantizationConfig",
+    "get_input_act_qspec",
+    "get_output_act_qspec",
+    "get_weight_qspec",
+    "get_bias_qspec",
+    "OP_TO_ANNOTATOR",
+    "propagate_annotation",
+]
+
+
+# In the absence of better name, just winging it with QuantizationConfig
+@dataclass(eq=True, frozen=True)
+class QuantizationConfig:
+    input_activation: Optional[QuantizationSpec]
+    output_activation: Optional[QuantizationSpec]
+    weight: Optional[QuantizationSpec]
+    bias: Optional[QuantizationSpec]
+    # TODO: remove, since we can use observer_or_fake_quant_ctr to express this
+    is_qat: bool = False
+
+
+OperatorPatternType = List[Callable]
+OperatorPatternType.__module__ = (
+    "torch.ao.quantization.quantizer.xnnpack_quantizer_utils"
+)
+
+AnnotatorType = Callable[
+    [
+        torch.fx.GraphModule,
+        Optional[QuantizationConfig],
+        Optional[Callable[[Node], bool]],
+    ],
+    Optional[List[List[Node]]],
+]
+OP_TO_ANNOTATOR: Dict[str, AnnotatorType] = {}
+
+
+def register_annotator(op: str):
+    def decorator(annotator: AnnotatorType):
+        OP_TO_ANNOTATOR[op] = annotator
+
+    return decorator
+
+
+class OperatorConfig(NamedTuple):
+    # fix List[str] with List[List[Union[nn.Module, FunctionType, BuiltinFunctionType]]]
+    # Basically we are mapping a quantization config to some list of patterns.
+    # a pattern is defined as a list of nn module, function or builtin function names
+    # e.g. [nn.Conv2d, torch.relu, torch.add]
+    # We have not resolved whether fusion can be considered internal details of the
+    # quantizer hence it does not need communication to user.
+    # Note this pattern is not really informative since it does not really
+    # tell us the graph structure resulting from the list of ops.
+    config: QuantizationConfig
+    operators: List[OperatorPatternType]
+
+
+def _is_annotated(nodes: List[Node]):
+    """
+    Given a list of nodes (that represents an operator pattern),
+    check if any of the node is annotated, return True if any of the node
+    is annotated, otherwise return False
+    """
+    annotated = False
+    for node in nodes:
+        annotated = annotated or (
+            "quantization_annotation" in node.meta
+            and node.meta["quantization_annotation"]._annotated
+        )
+    return annotated
+
+
+def _mark_nodes_as_annotated(nodes: List[Node]):
+    for node in nodes:
+        if node is not None:
+            if "quantization_annotation" not in node.meta:
+                node.meta["quantization_annotation"] = QuantizationAnnotation()
+            node.meta["quantization_annotation"]._annotated = True
+
+
+def get_input_act_qspec(quantization_config: Optional[QuantizationConfig]):
+    if quantization_config is None:
+        return None
+    if quantization_config.input_activation is None:
+        return None
+    quantization_spec: QuantizationSpec = quantization_config.input_activation
+    assert quantization_spec.qscheme in [
+        torch.per_tensor_affine,
+        torch.per_tensor_symmetric,
+    ]
+    return quantization_spec
+
+
+def get_output_act_qspec(quantization_config: Optional[QuantizationConfig]):
+    if quantization_config is None:
+        return None
+    if quantization_config.output_activation is None:
+        return None
+    quantization_spec: QuantizationSpec = quantization_config.output_activation
+    assert quantization_spec.qscheme in [
+        torch.per_tensor_affine,
+        torch.per_tensor_symmetric,
+    ]
+    return quantization_spec
+
+
+def get_weight_qspec(quantization_config: Optional[QuantizationConfig]):
+    if quantization_config is None:
+        return None
+    assert quantization_config is not None
+    if quantization_config.weight is None:
+        return None
+    quantization_spec: QuantizationSpec = quantization_config.weight
+    if quantization_spec.qscheme not in [
+        torch.per_tensor_symmetric,
+        torch.per_channel_symmetric,
+    ]:
+        raise ValueError(
+            f"Unsupported quantization_spec {quantization_spec} for weight"
+        )
+    return quantization_spec
+
+
+def get_bias_qspec(quantization_config: Optional[QuantizationConfig]):
+    if quantization_config is None:
+        return None
+    assert quantization_config is not None
+    if quantization_config.bias is None:
+        return None
+    quantization_spec: QuantizationSpec = quantization_config.bias
+    assert (
+        quantization_spec.dtype == torch.float
+    ), "Only float dtype for bias is supported for bias right now"
+    return quantization_spec
+
+
+@register_annotator("linear")
+def _annotate_linear(
+    gm: torch.fx.GraphModule,
+    quantization_config: Optional[QuantizationConfig],
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+) -> Optional[List[List[Node]]]:
+    annotated_partitions = []
+    input_act_qspec = get_input_act_qspec(quantization_config)
+    output_act_qspec = get_output_act_qspec(quantization_config)
+    weight_qspec = get_weight_qspec(quantization_config)
+    bias_qspec = get_bias_qspec(quantization_config)
+    for node in gm.graph.nodes:
+        if node.op != "call_function" or node.target != torch.ops.aten.linear.default:
+            continue
+        if filter_fn and not filter_fn(node):
+            continue
+        act_node = node.args[0]
+        weight_node = node.args[1]
+        bias_node = None
+        if len(node.args) > 2:
+            bias_node = node.args[2]
+
+        if _is_annotated([node]) is False:  # type: ignore[list-item]
+            _annotate_input_qspec_map(
+                node,
+                act_node,
+                input_act_qspec,
+            )
+            _annotate_input_qspec_map(
+                node,
+                weight_node,
+                weight_qspec,
+            )
+            nodes_to_mark_annotated = [node, weight_node]
+            if bias_node:
+                _annotate_input_qspec_map(
+                    node,
+                    bias_node,
+                    bias_qspec,
+                )
+                nodes_to_mark_annotated.append(bias_node)
+            _annotate_output_qspec(node, output_act_qspec)
+            _mark_nodes_as_annotated(nodes_to_mark_annotated)
+            annotated_partitions.append(nodes_to_mark_annotated)
+
+    return annotated_partitions
+
+
+@register_annotator("linear_relu")
+def _annotate_linear_relu(
+    gm: torch.fx.GraphModule,
+    quantization_config: Optional[QuantizationConfig],
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+) -> Optional[List[List[Node]]]:
+    annotated_partitions = []
+    input_act_qspec = get_input_act_qspec(quantization_config)
+    output_act_qspec = get_output_act_qspec(quantization_config)
+    weight_qspec = get_weight_qspec(quantization_config)
+    bias_qspec = get_bias_qspec(quantization_config)
+    for node in gm.graph.nodes:
+        if node.op != "call_function" or node.target not in [
+            torch.ops.aten.relu.default,
+            torch.ops.aten.relu_.default,
+        ]:
+            continue
+        relu_node = node
+        maybe_linear_node = node.args[0]
+        if (
+            not isinstance(maybe_linear_node, Node)
+            or maybe_linear_node.op != "call_function"
+            or maybe_linear_node.target != torch.ops.aten.linear.default
+        ):
+            continue
+
+        linear_node = maybe_linear_node
+        input_qspec_map = {}
+        input_act = linear_node.args[0]
+        assert isinstance(input_act, Node)
+        input_qspec_map[input_act] = input_act_qspec
+
+        weight = linear_node.args[1]
+        assert isinstance(weight, Node)
+        input_qspec_map[weight] = weight_qspec
+
+        # adding weight node to the partition as well
+        partition = [relu_node, linear_node, weight]
+        bias = linear_node.args[2] if len(linear_node.args) > 2 else None
+        if isinstance(bias, Node):
+            input_qspec_map[bias] = bias_qspec
+            partition.append(bias)
+
+        if _is_annotated(partition):
+            continue
+
+        if filter_fn and any(not filter_fn(n) for n in partition):
+            continue
+
+        linear_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            _annotated=True,
+        )
+        relu_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            output_qspec=output_act_qspec,
+            _annotated=True,
+        )
+        _mark_nodes_as_annotated(partition)
+        annotated_partitions.append(partition)
+    return annotated_partitions
+
+
+@register_annotator("conv")
+def _annotate_conv(
+    gm: torch.fx.GraphModule,
+    quantization_config: Optional[QuantizationConfig],
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+) -> Optional[List[List[Node]]]:
+    annotated_partitions = []
+    for n in gm.graph.nodes:
+        if n.op != "call_function" or n.target not in [
+            torch.ops.aten.conv1d.default,
+            torch.ops.aten.conv2d.default,
+        ]:
+            continue
+        conv_node = n
+
+        input_qspec_map = {}
+        input_act = conv_node.args[0]
+        assert isinstance(input_act, Node)
+        input_qspec_map[input_act] = get_input_act_qspec(quantization_config)
+
+        weight = conv_node.args[1]
+        assert isinstance(weight, Node)
+        input_qspec_map[weight] = get_weight_qspec(quantization_config)
+
+        # adding weight node to the partition as well
+        partition = [conv_node, conv_node.args[1]]
+
+        bias = conv_node.args[2] if len(conv_node.args) > 2 else None
+        if isinstance(bias, Node):
+            input_qspec_map[bias] = get_bias_qspec(quantization_config)
+            partition.append(bias)
+
+        if _is_annotated(partition):
+            continue
+
+        if filter_fn and any(not filter_fn(n) for n in partition):
+            continue
+
+        conv_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            output_qspec=get_output_act_qspec(quantization_config),
+            _annotated=True,
+        )
+        _mark_nodes_as_annotated(partition)
+        annotated_partitions.append(partition)
+    return annotated_partitions
+
+
+def _do_annotate_conv_relu(
+    gm: torch.fx.GraphModule,
+    quantization_config: Optional[QuantizationConfig],
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+    is_conv_transpose: bool = False,
+):
+    annotated_partitions = []
+    for n in gm.graph.nodes:
+        if n.op != "call_function" or n.target not in [
+            torch.ops.aten.relu.default,
+            torch.ops.aten.relu_.default,
+        ]:
+            continue
+        relu_node = n
+        maybe_conv_node = n.args[0]
+
+        is_conv_node = _is_conv_transpose_node if is_conv_transpose else _is_conv_node
+        if not isinstance(maybe_conv_node, Node) or not is_conv_node(maybe_conv_node):
+            continue
+        conv_node = maybe_conv_node
+
+        input_qspec_map = {}
+        input_act = conv_node.args[0]
+        assert isinstance(input_act, Node)
+        input_qspec_map[input_act] = get_input_act_qspec(quantization_config)
+
+        weight = conv_node.args[1]
+        assert isinstance(weight, Node)
+        input_qspec_map[weight] = get_weight_qspec(quantization_config)
+
+        # adding weight node to the partition as well
+        partition = [relu_node, conv_node, conv_node.args[1]]
+        bias = conv_node.args[2] if len(conv_node.args) > 2 else None
+        if isinstance(bias, Node):
+            input_qspec_map[bias] = get_bias_qspec(quantization_config)
+            partition.append(bias)
+
+        if _is_annotated(partition):
+            continue
+
+        if filter_fn and any(not filter_fn(n) for n in partition):
+            continue
+
+        conv_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map, _annotated=True
+        )
+        relu_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            output_qspec=get_output_act_qspec(quantization_config),  # type: ignore[arg-type]
+            _annotated=True,
+        )
+        _mark_nodes_as_annotated(partition)
+        annotated_partitions.append(partition)
+    return annotated_partitions
+
+
+@register_annotator("conv_relu")
+def _annotate_conv_relu(
+    gm: torch.fx.GraphModule,
+    quantization_config: Optional[QuantizationConfig],
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+) -> Optional[List[List[Node]]]:
+    return _do_annotate_conv_relu(
+        gm, quantization_config, filter_fn, is_conv_transpose=False
+    )
+
+
+@register_annotator("conv_transpose_relu")
+def _annotate_conv_transpose_relu(
+    gm: torch.fx.GraphModule,
+    quantization_config: Optional[QuantizationConfig],
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+) -> Optional[List[List[Node]]]:
+    return _do_annotate_conv_relu(
+        gm, quantization_config, filter_fn, is_conv_transpose=True
+    )
+
+
+@register_annotator("conv_bn")
+def _annotate_conv_bn(
+    gm: torch.fx.GraphModule,
+    quantization_config: Optional[QuantizationConfig],
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+) -> Optional[List[List[Node]]]:
+    """
+    Find conv + batchnorm parititions
+    Note: This is only used for QAT. In PTQ, batchnorm should already be fused into the conv.
+    """
+    return _do_annotate_conv_bn(gm, quantization_config, filter_fn, has_relu=False)
+
+
+@register_annotator("conv_bn_relu")
+def _annotate_conv_bn_relu(
+    gm: torch.fx.GraphModule,
+    quantization_config: Optional[QuantizationConfig],
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+) -> Optional[List[List[Node]]]:
+    """
+    Find conv + batchnorm + relu parititions
+    Note: This is only used for QAT. In PTQ, batchnorm should already be fused into the conv.
+    """
+    return _do_annotate_conv_bn(gm, quantization_config, filter_fn, has_relu=True)
+
+
+@register_annotator("conv_transpose_bn")
+def _annotate_conv_transpose_bn(
+    gm: torch.fx.GraphModule,
+    quantization_config: Optional[QuantizationConfig],
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+) -> Optional[List[List[Node]]]:
+    """
+    Find conv_transpose + batchnorm parititions
+    Note: This is only used for QAT. In PTQ, batchnorm should already be fused into the conv.
+    """
+    return _do_annotate_conv_bn(
+        gm, quantization_config, filter_fn, has_relu=False, is_conv_transpose=True
+    )
+
+
+@register_annotator("conv_transpose_bn_relu")
+def _annotate_conv_transpose_bn_relu(
+    gm: torch.fx.GraphModule,
+    quantization_config: Optional[QuantizationConfig],
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+) -> Optional[List[List[Node]]]:
+    """
+    Find conv_transpose + batchnorm + relu parititions
+    Note: This is only used for QAT. In PTQ, batchnorm should already be fused into the conv.
+    """
+    return _do_annotate_conv_bn(
+        gm, quantization_config, filter_fn, has_relu=True, is_conv_transpose=True
+    )
+
+
+def _do_annotate_conv_bn(
+    gm: torch.fx.GraphModule,
+    quantization_config: Optional[QuantizationConfig],
+    filter_fn: Optional[Callable[[Node], bool]],
+    has_relu: bool,
+    is_conv_transpose: bool = False,
+) -> List[List[Node]]:
+    """
+    Given a function that takes in a `conv_fn` and returns a conv-bn[-relu] pattern,
+    return a list of annotated partitions.
+
+    The output of the pattern must include a dictionary from string name to node
+    for the following names: "input", "conv", "weight", "bias", and "output".
+    """
+
+    def get_pattern(conv_fn: Callable, relu_is_inplace: bool):
+        def _conv_bn(x, conv_weight, conv_bias, bn_weight, bn_bias, bn_rm, bn_rv):
+            conv = conv_fn(x, conv_weight, conv_bias)
+            bn = F.batch_norm(conv, bn_rm, bn_rv, bn_weight, bn_bias, training=True)
+            if has_relu:
+                output = F.relu_(bn) if relu_is_inplace else F.relu(bn)
+            else:
+                output = bn
+            return output, {
+                "input": x,
+                "conv": conv,
+                "weight": conv_weight,
+                "bias": conv_bias,
+                "output": output,
+            }
+
+        return _WrapperModule(_conv_bn)
+
+    # Needed for matching, otherwise the matches gets filtered out due to unused
+    # nodes returned by batch norm
+    gm.graph.eliminate_dead_code()
+    gm.recompile()
+
+    matches = []
+    if is_conv_transpose:
+        combinations = [
+            (F.conv_transpose1d, _conv1d_bn_example_inputs),
+            (F.conv_transpose2d, _conv2d_bn_example_inputs),
+        ]
+    else:
+        combinations = [
+            (F.conv1d, _conv1d_bn_example_inputs),  # type: ignore[list-item]
+            (F.conv2d, _conv2d_bn_example_inputs),  # type: ignore[list-item]
+        ]
+
+    # Add `is_cuda` and `relu_is_inplace` dimensions
+    combinations = itertools.product(  # type: ignore[assignment]
+        combinations,
+        [True, False] if torch.cuda.is_available() else [False],  # is_cuda
+        [True, False] if has_relu else [False],  # relu_is_inplace
+    )
+
+    # Match against all conv dimensions and cuda variants
+    for (conv_fn, example_inputs), is_cuda, relu_is_inplace in combinations:  # type: ignore[misc]
+        pattern = get_pattern(conv_fn, relu_is_inplace)  # type: ignore[has-type]
+        pattern = _get_aten_graph_module_for_pattern(pattern, example_inputs, is_cuda)  # type: ignore[has-type]
+        pattern.graph.eliminate_dead_code()
+        pattern.recompile()
+        matcher = SubgraphMatcherWithNameNodeMap(pattern, ignore_literals=True)
+        matches.extend(matcher.match(gm.graph))
+
+    # Annotate nodes returned in the matches
+    annotated_partitions = []
+    for match in matches:
+        name_node_map = match.name_node_map
+        input_node = name_node_map["input"]
+        conv_node = name_node_map["conv"]
+        weight_node = name_node_map["weight"]
+        bias_node = name_node_map["bias"]
+        output_node = name_node_map["output"]
+
+        # TODO: annotate the uses of input, weight, and bias separately instead
+        # of assuming they come from a single conv node. This is not possible today
+        # because input may have multiple users, and we can't rely on the conv node
+        # always being the first user. This was the case in models with skip
+        # connections like resnet18
+
+        # Validate conv args
+        if conv_node.args[0] is not input_node:
+            raise ValueError("Conv arg did not contain input node ", input_node)
+        if conv_node.args[1] is not weight_node:
+            raise ValueError("Conv arg did not contain weight node ", weight_node)
+        if len(conv_node.args) > 2 and conv_node.args[2] is not bias_node:
+            raise ValueError("Conv arg did not contain bias node ", bias_node)
+
+        # Skip if the partition is already annotated or is filtered out by the user
+        partition = [conv_node, weight_node]
+        if bias_node is not None:
+            partition.append(bias_node)
+        if _is_annotated(partition):
+            continue
+        if filter_fn and any(not filter_fn(n) for n in partition):
+            continue
+
+        # Annotate conv inputs and pattern output
+        input_qspec_map = {}
+        input_qspec_map[input_node] = get_input_act_qspec(quantization_config)
+        input_qspec_map[weight_node] = get_weight_qspec(quantization_config)
+        if bias_node is not None:
+            input_qspec_map[bias_node] = get_bias_qspec(quantization_config)
+        conv_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            _annotated=True,
+        )
+        output_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            output_qspec=get_output_act_qspec(quantization_config),  # type: ignore[arg-type]
+            _annotated=True,
+        )
+        _mark_nodes_as_annotated(partition)
+        annotated_partitions.append(partition)
+    return annotated_partitions
+
+
+@register_annotator("gru_io_only")
+def _annotate_gru_io_only(
+    gm: torch.fx.GraphModule,
+    quantization_config: Optional[QuantizationConfig],
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+) -> Optional[List[List[Node]]]:
+    gru_partitions = get_source_partitions(gm.graph, [torch.nn.GRU], filter_fn)
+    gru_partitions = list(itertools.chain.from_iterable(gru_partitions.values()))
+    annotated_partitions = []
+    for gru_partition in gru_partitions:
+        annotated_partitions.append(gru_partition.nodes)
+        output_nodes = gru_partition.output_nodes
+        input_nodes = gru_partition.input_nodes
+        # skip annotation if it is already annotated
+        if _is_annotated(input_nodes + output_nodes):
+            continue
+        # inside each GRU partition, we should be able to annotate each linear
+        # subgraph
+        input_qspec_map: Dict[Node, QuantizationSpecBase] = {}
+        input_act = input_nodes[0]
+        input_act_user = next(iter(input_act.users.keys()))
+        assert isinstance(input_act, Node)
+        assert isinstance(input_act_user, Node)
+        input_act_user.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map={
+                input_act: get_input_act_qspec(quantization_config),
+            },
+            _annotated=True,
+        )
+
+        hidden_state = input_nodes[1]
+        hidden_state_user = next(iter(hidden_state.users.keys()))
+        assert isinstance(hidden_state, Node)
+        assert isinstance(hidden_state_user, Node)
+        hidden_state_user.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map={
+                hidden_state: get_input_act_qspec(quantization_config),
+            },
+            _annotated=True,
+        )
+
+        assert len(output_nodes) == 2, "expecting GRU to have two outputs"
+        for output in output_nodes:
+            output.meta["quantization_annotation"] = QuantizationAnnotation(
+                output_qspec=get_output_act_qspec(quantization_config),
+                _annotated=True,
+            )
+        nodes_to_mark_annotated = list(gru_partition.nodes)
+        _mark_nodes_as_annotated(nodes_to_mark_annotated)
+    return annotated_partitions
+
+
+@register_annotator("adaptive_avg_pool2d")
+def _annotate_adaptive_avg_pool2d(
+    gm: torch.fx.GraphModule,
+    quantization_config: Optional[QuantizationConfig],
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+) -> Optional[List[List[Node]]]:
+    """Always annotate adaptive_avg_pool2d op"""
+    module_partitions = get_source_partitions(
+        gm.graph, [torch.nn.AdaptiveAvgPool2d, F.adaptive_avg_pool2d], filter_fn
+    )
+    partitions = list(itertools.chain.from_iterable(module_partitions.values()))
+    annotated_partitions = []
+    for partition in partitions:
+        pool_node = partition.output_nodes[0]
+        if (
+            pool_node.op != "call_function"
+            or pool_node.target != torch.ops.aten.adaptive_avg_pool2d.default
+        ):
+            raise ValueError(f"{pool_node} is not an aten adaptive_avg_pool2d operator")
+
+        if _is_annotated([pool_node]):
+            continue
+
+        annotated_partitions.append(partition.nodes)
+        input_act = pool_node.args[0]
+        assert isinstance(input_act, Node)
+
+        # only annotate input output sharing operator
+        # when the output of the input node is annotated
+        if (
+            "quantization_annotation" not in input_act.meta
+            or not input_act.meta["quantization_annotation"]._annotated
+            or input_act.meta["quantization_annotation"].output_qspec is None
+        ):
+            input_act_qspec = get_input_act_qspec(quantization_config)
+        else:
+            input_act_qspec = SharedQuantizationSpec(input_act)
+
+        # output sharing with input
+        output_act_qspec = SharedQuantizationSpec((input_act, pool_node))
+        pool_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map={
+                input_act: input_act_qspec,
+            },
+            output_qspec=output_act_qspec,
+            _annotated=True,
+        )
+    return annotated_partitions
+
+
+def _is_input_large_scalar(node: Node, gm: torch.fx.GraphModule):
+    """Check if input is a large scalar value. So that we can skip quantization for the node
+    since histc op (in HistogramObserver) only works for values up to certain upper bound
+    """
+    if node.op == "get_attr":
+        qualified_name = str(node.target)
+        module_path, _, name = qualified_name.rpartition(".")
+        submod = gm.get_submodule(module_path)
+        tensor = getattr(submod, name)
+        # torch.histc works until this upper bound
+        HISTC_UPPER_BOUND = 3.4028235e15
+        return tensor.numel() == 1 and abs(tensor.item()) > HISTC_UPPER_BOUND
+    return False
+
+
+def _is_input_non_float_tensor(node: Node):
+    """Check if the input is not a float tensor, so that we can skip quantization for the node
+    since observers only works with float Tensors
+    """
+    if "val" not in node.meta or not isinstance(node.meta["val"], FakeTensor):
+        return True
+    return node.meta["val"].dtype != torch.float32
+
+
+@register_annotator("add_relu")
+def _annotate_add_relu(
+    gm: torch.fx.GraphModule,
+    quantization_config: Optional[QuantizationConfig],
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+) -> Optional[List[List[Node]]]:
+    annotated_partitions = []
+    for node in gm.graph.nodes:
+        if node.op != "call_function" or node.target not in [
+            torch.ops.aten.relu.default,
+            torch.ops.aten.relu_.default,
+        ]:
+            continue
+        relu_node = node
+        maybe_add = node.args[0]
+        if (
+            not isinstance(maybe_add, Node)
+            or maybe_add.op != "call_function"
+            or maybe_add.target
+            not in [
+                torch.ops.aten.add.Tensor,
+                torch.ops.aten.add_.Tensor,
+            ]
+        ):
+            continue
+
+        add_node = maybe_add
+        partition = [relu_node, add_node]
+
+        if _is_annotated(partition):
+            continue
+
+        if filter_fn and any(not filter_fn(n) for n in partition):
+            continue
+
+        input_act_qspec = get_input_act_qspec(quantization_config)
+        output_act_qspec = get_output_act_qspec(quantization_config)
+
+        input_qspec_map = {}
+        input_act0 = add_node.args[0]
+        if isinstance(input_act0, Node):
+            if _is_input_large_scalar(input_act0, gm):
+                continue
+            if _is_input_non_float_tensor(input_act0):
+                continue
+            partition.append(input_act0)
+            input_qspec_map[input_act0] = input_act_qspec
+
+        input_act1 = add_node.args[1]
+        if isinstance(input_act1, Node):
+            if _is_input_large_scalar(input_act1, gm):
+                continue
+            if _is_input_non_float_tensor(input_act1):
+                continue
+            partition.append(input_act1)
+            input_qspec_map[input_act1] = input_act_qspec
+
+        add_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            _annotated=True,
+        )
+        relu_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            output_qspec=output_act_qspec,
+            _annotated=True,
+        )
+        annotated_partitions.append(partition)
+    return annotated_partitions
+
+
+@register_annotator("add")
+def _annotate_add(
+    gm: torch.fx.GraphModule,
+    quantization_config: Optional[QuantizationConfig],
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+) -> Optional[List[List[Node]]]:
+    annotated_partitions = []
+    for node in gm.graph.nodes:
+        if node.op != "call_function" or node.target not in [
+            torch.ops.aten.add.Tensor,
+            torch.ops.aten.add_.Tensor,
+        ]:
+            continue
+        add_node = node
+        partition = [add_node]
+
+        if _is_annotated(partition):
+            continue
+
+        if filter_fn and any(not filter_fn(n) for n in partition):
+            continue
+
+        input_act_qspec = get_input_act_qspec(quantization_config)
+        output_act_qspec = get_output_act_qspec(quantization_config)
+
+        input_qspec_map = {}
+        input_act0 = add_node.args[0]
+        if isinstance(input_act0, Node):
+            if _is_input_large_scalar(input_act0, gm):
+                continue
+            if _is_input_non_float_tensor(input_act0):
+                continue
+            input_qspec_map[input_act0] = input_act_qspec
+            partition.append(input_act0)
+
+        input_act1 = add_node.args[1]
+        if isinstance(input_act1, Node):
+            if _is_input_large_scalar(input_act1, gm):
+                continue
+            if _is_input_non_float_tensor(input_act1):
+                continue
+            input_qspec_map[input_act1] = input_act_qspec
+            partition.append(input_act1)
+
+        add_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            output_qspec=output_act_qspec,
+            _annotated=True,
+        )
+        annotated_partitions.append(partition)
+    return annotated_partitions
+
+
+@register_annotator("mul_relu")
+def _annotate_mul_relu(
+    gm: torch.fx.GraphModule,
+    quantization_config: Optional[QuantizationConfig],
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+) -> Optional[List[List[Node]]]:
+    annotated_partitions = []
+    for node in gm.graph.nodes:
+        if node.op != "call_function" or node.target not in [
+            torch.ops.aten.relu.default,
+            torch.ops.aten.relu_.default,
+        ]:
+            continue
+        relu_node = node
+        maybe_mul = node.args[0]
+        if (
+            not isinstance(maybe_mul, Node)
+            or maybe_mul.op != "call_function"
+            or maybe_mul.target
+            not in [
+                torch.ops.aten.mul.Tensor,
+                torch.ops.aten.mul_.Tensor,
+            ]
+        ):
+            continue
+
+        mul_node = maybe_mul
+        partition = [relu_node, mul_node]
+
+        if _is_annotated(partition):
+            continue
+
+        if filter_fn and any(not filter_fn(n) for n in partition):
+            continue
+
+        input_act_qspec = get_input_act_qspec(quantization_config)
+        output_act_qspec = get_output_act_qspec(quantization_config)
+
+        input_qspec_map = {}
+        input_act0 = mul_node.args[0]
+        if isinstance(input_act0, Node):
+            if _is_input_large_scalar(input_act0, gm):
+                continue
+            if _is_input_non_float_tensor(input_act0):
+                continue
+            partition.append(input_act0)
+            input_qspec_map[input_act0] = input_act_qspec
+
+        input_act1 = mul_node.args[1]
+        if isinstance(input_act1, Node):
+            if _is_input_large_scalar(input_act1, gm):
+                continue
+            if _is_input_non_float_tensor(input_act1):
+                continue
+            partition.append(input_act1)
+            input_qspec_map[input_act1] = input_act_qspec
+
+        mul_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            _annotated=True,
+        )
+        relu_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            output_qspec=output_act_qspec,
+            _annotated=True,
+        )
+        annotated_partitions.append(partition)
+    return annotated_partitions
+
+
+@register_annotator("mul")
+def _annotate_mul(
+    gm: torch.fx.GraphModule,
+    quantization_config: Optional[QuantizationConfig],
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+) -> Optional[List[List[Node]]]:
+    annotated_partitions = []
+    for node in gm.graph.nodes:
+        if node.op != "call_function" or node.target not in [
+            torch.ops.aten.mul.Tensor,
+            torch.ops.aten.mul_.Tensor,
+        ]:
+            continue
+
+        mul_node = node
+        partition = [mul_node]
+        if _is_annotated(partition):
+            continue
+
+        if filter_fn and any(not filter_fn(n) for n in partition):
+            continue
+
+        input_act_qspec = get_input_act_qspec(quantization_config)
+        output_act_qspec = get_output_act_qspec(quantization_config)
+
+        input_qspec_map = {}
+        input_act0 = mul_node.args[0]
+        if isinstance(input_act0, Node):
+            if _is_input_large_scalar(input_act0, gm):
+                continue
+            if _is_input_non_float_tensor(input_act0):
+                continue
+            input_qspec_map[input_act0] = input_act_qspec
+            partition.append(input_act0)
+
+        input_act1 = mul_node.args[1]
+        if isinstance(input_act1, Node):
+            if _is_input_large_scalar(input_act1, gm):
+                continue
+            if _is_input_non_float_tensor(input_act1):
+                continue
+            input_qspec_map[input_act1] = input_act_qspec
+            partition.append(input_act0)
+
+        mul_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            output_qspec=output_act_qspec,
+            _annotated=True,
+        )
+        annotated_partitions.append(partition)
+    return annotated_partitions
+
+
+# TODO: remove Optional in return type, fix annotated_partitions logic
+@register_annotator("cat")
+def _annotate_cat(
+    gm: torch.fx.GraphModule,
+    quantization_config: Optional[QuantizationConfig],
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+) -> Optional[List[List[Node]]]:
+    cat_partitions = get_source_partitions(gm.graph, [torch.cat], filter_fn)
+    cat_partitions = list(itertools.chain.from_iterable(cat_partitions.values()))
+    annotated_partitions = []
+    for cat_partition in cat_partitions:
+        cat_node = cat_partition.output_nodes[0]
+        if _is_annotated([cat_node]):
+            continue
+
+        if cat_node.target != torch.ops.aten.cat.default:
+            # TODO: change this to AnnotationException
+            raise Exception(  # noqa: TRY002
+                f"Expected cat node: torch.ops.aten.cat.default, but found {cat_node.target}"
+                " please check if you are calling the correct capture API"
+            )
+
+        annotated_partitions.append(cat_partition.nodes)
+
+        input_act_qspec = get_input_act_qspec(quantization_config)
+        inputs = cat_node.args[0]
+
+        input_qspec_map = {}
+        input_act0 = inputs[0]  # type: ignore[index]
+        if isinstance(input_act0, Node):
+            input_qspec_map[input_act0] = input_act_qspec
+
+        shared_with_input0_qspec = SharedQuantizationSpec((input_act0, cat_node))  # type: ignore[arg-type]
+        for input_act in inputs[1:]:  # type: ignore[index]
+            input_qspec_map[input_act] = shared_with_input0_qspec  # type: ignore[index]
+
+        output_act_qspec = shared_with_input0_qspec
+
+        cat_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            output_qspec=output_act_qspec,
+            _annotated=True,
+        )
+    return annotated_partitions
+
+
+def _is_share_obs_or_fq_op(op: Callable) -> bool:
+    return op in [
+        torch.ops.aten.hardtanh.default,
+        torch.ops.aten.hardtanh_.default,
+        torch.ops.aten.max_pool2d.default,
+        torch.ops.aten.mean.default,
+        torch.ops.aten.mean.dim,
+        torch.ops.aten.permute.default,
+        torch.ops.aten.permute_copy.default,
+        torch.ops.aten.squeeze.dim,
+        torch.ops.aten.squeeze_copy.dim,
+        # TODO: remove?
+        torch.ops.aten.adaptive_avg_pool2d.default,
+        torch.ops.aten.view_copy.default,
+        torch.ops.aten.view.default,
+        torch.ops.aten.slice_copy.Tensor,
+        torch.ops.aten.flatten.using_ints,
+    ]
+
+
+def propagate_annotation(model: torch.fx.GraphModule) -> None:
+    for n in model.graph.nodes:
+        if n.op != "call_function" or not _is_share_obs_or_fq_op(n.target):
+            continue
+
+        prev_node = n.args[0]
+        if not isinstance(prev_node, Node):
+            continue
+
+        quantization_annotation = prev_node.meta.get("quantization_annotation", None)
+        if not quantization_annotation:
+            continue
+
+        output_qspec = quantization_annotation.output_qspec
+        if not output_qspec:
+            continue
+
+        # make sure current node is not annotated
+        if (
+            "quantization_annotation" in n.meta
+            and n.meta["quantization_annotation"]._annotated
+        ):
+            continue
+
+        shared_qspec = SharedQuantizationSpec(prev_node)
+        # propagate the previous output_qspec to the current node
+        n.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map={
+                prev_node: shared_qspec,
+            },
+            output_qspec=shared_qspec,
+            _annotated=True,
+        )
+
+
+# TODO: make the list of ops customizable
+def _convert_scalars_to_attrs(model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+    for n in model.graph.nodes:
+        if n.op != "call_function" or n.target not in [
+            torch.ops.aten.add.Tensor,
+            torch.ops.aten.mul.Tensor,
+        ]:
+            continue
+        args = list(n.args)
+        new_args = []
+        for i in range(len(args)):
+            if isinstance(args[i], torch.fx.Node):
+                new_args.append(args[i])
+                continue
+            prefix = "_tensor_constant_"
+            get_new_attr_name = get_new_attr_name_with_prefix(prefix)
+            tensor_constant_name = get_new_attr_name(model)
+            float_tensor = torch.tensor(float(args[i]))
+            model.register_buffer(tensor_constant_name, float_tensor)
+            fake_mode = n.meta["val"].fake_mode
+            with model.graph.inserting_before(n):
+                get_attr_node = model.graph.create_node(
+                    "get_attr", tensor_constant_name, (), {}
+                )
+                get_attr_node.meta["val"] = fake_mode.from_tensor(
+                    float_tensor, static_shapes=True
+                )
+                new_args.append(get_attr_node)
+        n.args = tuple(new_args)
+    model.recompile()
+    return model
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/stubs.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/stubs.py
new file mode 100644
index 0000000000000000000000000000000000000000..916d7de35c902eb696b321cd6ef8ff1c56320757
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/stubs.py
@@ -0,0 +1,68 @@
+# mypy: allow-untyped-defs
+
+from torch import nn
+
+
+class QuantStub(nn.Module):
+    r"""Quantize stub module, before calibration, this is same as an observer,
+    it will be swapped as `nnq.Quantize` in `convert`.
+
+    Args:
+        qconfig: quantization configuration for the tensor,
+            if qconfig is not provided, we will get qconfig from parent modules
+    """
+
+    def __init__(self, qconfig=None):
+        super().__init__()
+        if qconfig:
+            self.qconfig = qconfig
+
+    def forward(self, x):
+        return x
+
+
+class DeQuantStub(nn.Module):
+    r"""Dequantize stub module, before calibration, this is same as identity,
+    this will be swapped as `nnq.DeQuantize` in `convert`.
+
+    Args:
+        qconfig: quantization configuration for the tensor,
+            if qconfig is not provided, we will get qconfig from parent modules
+    """
+
+    def __init__(self, qconfig=None):
+        super().__init__()
+        if qconfig:
+            self.qconfig = qconfig
+
+    def forward(self, x):
+        return x
+
+
+class QuantWrapper(nn.Module):
+    r"""A wrapper class that wraps the input module, adds QuantStub and
+    DeQuantStub and surround the call to module with call to quant and dequant
+    modules.
+
+    This is used by the `quantization` utility functions to add the quant and
+    dequant modules, before `convert` function `QuantStub` will just be observer,
+    it observes the input tensor, after `convert`, `QuantStub`
+    will be swapped to `nnq.Quantize` which does actual quantization. Similarly
+    for `DeQuantStub`.
+    """
+    quant: QuantStub
+    dequant: DeQuantStub
+    module: nn.Module
+
+    def __init__(self, module):
+        super().__init__()
+        qconfig = getattr(module, "qconfig", None)
+        self.add_module("quant", QuantStub(qconfig))
+        self.add_module("dequant", DeQuantStub(qconfig))
+        self.add_module("module", module)
+        self.train(module.training)
+
+    def forward(self, X):
+        X = self.quant(X)
+        X = self.module(X)
+        return self.dequant(X)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..d243932a1ef82d08a330b8ce3e7b4b9b4e3c92bd
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/ao/quantization/utils.py
@@ -0,0 +1,816 @@
+# mypy: allow-untyped-defs
+"""
+Utils shared by different modes of quantization (eager/graph)
+"""
+import functools
+import warnings
+from collections import OrderedDict
+from inspect import getfullargspec, signature
+from typing import Any, Callable, Dict, Optional, Tuple, Union
+
+import torch
+from torch.ao.quantization.quant_type import QuantType
+from torch.fx import Node
+from torch.nn.utils.parametrize import is_parametrized
+
+
+NodePattern = Union[Tuple[Node, Node], Tuple[Node, Tuple[Node, Node]], Any]
+NodePattern.__module__ = "torch.ao.quantization.utils"
+
+# This is the Quantizer class instance from torch/quantization/fx/quantize.py.
+# Define separately to prevent circular imports.
+# TODO(future PR): improve this.
+# make this public once fixed (can't be public as is because setting the module directly
+# doesn't work)
+QuantizerCls = Any
+
+# Type for fusion patterns, it can be more complicated than the following actually,
+# see pattern.md for docs
+# TODO: not sure if typing supports recursive data types
+Pattern = Union[
+    Callable, Tuple[Callable, Callable], Tuple[Callable, Tuple[Callable, Callable]], Any
+]
+Pattern.__module__ = "torch.ao.quantization.utils"
+
+
+# TODO: maybe rename this to MatchInputNode
+class MatchAllNode:
+    """A node pattern that matches all nodes, used in defining
+    fusion patterns in FX Graph Mode Quantization
+    """
+
+
+module_type_list = {
+    torch.nn.ReLU,
+    torch.nn.ReLU6,
+    torch.nn.AdaptiveAvgPool1d,
+    torch.nn.AdaptiveAvgPool2d,
+    torch.nn.AdaptiveAvgPool3d,
+    torch.nn.AvgPool1d,
+    torch.nn.AvgPool2d,
+    torch.nn.AvgPool3d,
+    torch.nn.MaxPool1d,
+    torch.nn.MaxPool2d,
+    torch.nn.MaxPool3d,
+    torch.nn.Identity,
+    torch.nn.Hardsigmoid,
+    torch.nn.Sigmoid,
+    torch.nn.Tanh,
+}
+func_list = {
+    torch.nn.functional.adaptive_avg_pool1d,
+    torch.nn.functional.adaptive_avg_pool2d,
+    torch.nn.functional.adaptive_avg_pool3d,
+    torch.nn.functional.elu,
+    torch.nn.functional.hardswish,
+    torch.nn.functional.instance_norm,
+    torch.nn.functional.layer_norm,
+    torch.nn.functional.leaky_relu,
+    torch.nn.functional.silu,
+    torch.nn.functional.mish,
+    torch.nn.functional.dropout,
+    torch.nn.functional.max_pool1d,
+    torch.nn.functional.max_pool2d,
+    torch.nn.functional.max_pool3d,
+    torch.nn.functional.relu,
+    torch.nn.functional.hardtanh,
+    torch.nn.functional.hardtanh_,
+    torch.nn.functional.hardsigmoid,
+    torch.nn.functional.sigmoid,
+    torch.transpose,
+    torch.repeat_interleave,
+    torch.sigmoid,
+    torch.squeeze,
+    torch.stack,
+    torch.sum,
+    torch.tanh,
+    torch.unsqueeze,
+    torch.cat,
+}
+method_list = {
+    torch.mean,
+    "relu",
+    "relu_",
+    "contiguous",
+    "detach",
+    "detach_",
+    "hardsigmoid",
+    "hardsigmoid_",
+    "permute",
+    "repeat",
+    "repeat_interleave",
+    "reshape",
+    "resize_",
+    "shape",
+    "sigmoid",
+    "sigmoid_",
+    "size",
+    "squeeze",
+    "squeeze_",
+    "tanh",
+    "tanh_",
+    "transpose",
+    "unsqueeze",
+    "unsqueeze_",
+    "view",
+}
+
+
+# TODO: not used now, remove
+def check_node(node, modules):
+    # TODO: reuse is_fixed_qparam_node after we move this function to _lower_to_native_backend.py
+    is_call_function = node.op == "call_function" and node.target in func_list
+    is_call_method = node.op == "call_method" and node.target in method_list
+    is_call_module = (
+        node.op == "call_module" and type(modules[str(node.target)]) in module_type_list
+    )
+    return is_call_function, is_call_method, is_call_module
+
+
+def get_combined_dict(default_dict, additional_dict):
+    """
+    Combines two dictionaries.
+
+    This function takes two dictionaries as input and returns a new dictionary
+    that contains all the key-value pairs from both input dictionaries.
+    If there are any duplicate keys in the `additional_dict`, the values
+    from the `additional_dict` will overwrite those in the `default_dict`.
+    Args:
+        default_dict (dict): The main dictionary that will be used as the base
+        additional_dict (dict): The dictionary used to update `default_dict`
+
+    Returns:
+        dict: The resulting dictionary
+    Example:
+        >>> x = dict(a=1, b=1)
+        >>> y = dict(b=2, c=3)
+        >>> get_combined_dict(x, y)
+        {'a': 1, 'b': 2, 'c': 3}
+    """
+    d = default_dict.copy()
+    d.update(additional_dict)
+    return d
+
+
+def is_per_tensor(qscheme):
+    return qscheme == torch.per_tensor_affine or qscheme == torch.per_tensor_symmetric
+
+
+def is_per_channel(qscheme):
+    return qscheme in [
+        torch.per_channel_affine,
+        torch.per_channel_affine_float_qparams,
+        torch.per_channel_symmetric,
+    ]
+
+
+def getattr_from_fqn(obj: Any, fqn: str) -> Any:
+    """
+    Given an obj and a fqn such as "foo.bar.baz", returns gm.foo.bar.baz.
+    """
+    return functools.reduce(getattr, fqn.split("."), obj)
+
+
+def to_underlying_dtype(qdtype):
+    DTYPE_MAPPING = {
+        torch.quint8: torch.uint8,
+        torch.qint8: torch.int8,
+        torch.qint32: torch.int32,
+        torch.quint4x2: torch.uint8,
+        torch.quint2x4: torch.uint8,
+        torch.uint8: torch.uint8,
+        torch.int8: torch.int8,
+        torch.int16: torch.int16,
+        torch.int32: torch.int32,
+        torch.float8_e5m2: torch.float8_e5m2,
+        torch.float8_e4m3fn: torch.float8_e4m3fn,
+    }
+    assert qdtype in DTYPE_MAPPING, "Unsupported dtype: " + str(qdtype)
+    return DTYPE_MAPPING[qdtype]
+
+
+def get_qparam_dict(observer_or_fake_quant):
+    from torch.ao.quantization.observer import PlaceholderObserver
+
+    qscheme = getattr(observer_or_fake_quant, "qscheme", None)
+    dtype = observer_or_fake_quant.dtype
+    qparams = {"qscheme": qscheme, "dtype": dtype}
+
+    if not qscheme or isinstance(observer_or_fake_quant, PlaceholderObserver):
+        return {"qscheme": None, "dtype": dtype}
+
+    if is_per_tensor(qscheme):
+        qscheme = torch.per_tensor_affine
+    elif is_per_channel(qscheme):
+        # change symmetric to affine since we do not have symmetric
+        # quantized Tensor
+        if qscheme == torch.per_channel_symmetric:
+            qscheme = torch.per_channel_affine
+        qparams["axis"] = observer_or_fake_quant.ch_axis
+    else:
+        raise RuntimeError(f"Unrecognized qscheme: {qscheme}")
+    # update qscheme, since we don't have symmetric quant qscheme
+    # in quantized Tensor
+    qparams["qscheme"] = qscheme
+
+    scale, zero_point = observer_or_fake_quant.calculate_qparams()
+    qparams["scale"] = scale
+    qparams["zero_point"] = zero_point
+
+    if hasattr(observer_or_fake_quant, "quant_min"):
+        qparams["quant_min"] = observer_or_fake_quant.quant_min
+    if hasattr(observer_or_fake_quant, "quant_max"):
+        qparams["quant_max"] = observer_or_fake_quant.quant_max
+
+    return qparams
+
+
+def get_swapped_custom_module_class(
+    custom_module, custom_module_class_mapping, qconfig
+):
+    """Get the observed/quantized custom module class that we need
+    to swap `custom_module` to
+    Input:
+        custom_module: input, can be an instance of either a float or observed custom module
+        custom_module_class_mapping: the float to observed or observed to quantized custom module class mapping
+        qconfig: qconfig configured for the custom module
+
+    Output:
+        corresponding observed/quantized custom module class for input custom module instance
+    """
+    quant_type = get_quant_type(qconfig)
+    class_mapping = custom_module_class_mapping.get(quant_type, {})
+    assert type(custom_module) in class_mapping, (
+        "did not find corresponding observed "
+        f"module class for {type(custom_module)} in mapping: {class_mapping}"
+    )
+    return class_mapping[type(custom_module)]
+
+
+def activation_dtype(qconfig):
+    assert qconfig is not None
+    activation = qconfig.activation()
+    return activation.dtype
+
+
+def weight_dtype(qconfig):
+    assert qconfig is not None
+    weight = qconfig.weight()
+    return weight.dtype
+
+
+def activation_is_statically_quantized(qconfig):
+    """Given a qconfig, decide if the activation needs to be
+    quantized or not, this includes quantizing to quint8, qint8 and qint32 and float16
+    """
+    return activation_dtype(qconfig) in [
+        torch.quint8,
+        torch.qint8,
+        torch.qint32,
+        torch.float16,
+        torch.uint8,
+        torch.int8,
+        torch.int16,
+        torch.int32,
+        torch.float8_e5m2,
+        torch.float8_e4m3fn,
+    ] and (not activation_is_dynamically_quantized(qconfig))
+
+
+def activation_is_dynamically_quantized(qconfig):
+    """Given a qconfig, decide if the activation needs to be
+    dynamically quantized or not, this includes dynamically quantizing to
+    quint8, qint8 and float16
+    """
+    activation_dtype, _, activation_is_dynamic = get_qconfig_dtypes(qconfig)
+    return activation_is_dynamic
+
+
+def activation_is_int8_quantized(qconfig):
+    """Given a qconfig, decide if the activation needs to be
+    quantized to int8 or not, this includes quantizing to quint8, qint8
+    """
+    return activation_dtype(qconfig) in [
+        torch.quint8,
+        torch.qint8,
+        torch.uint8,
+        torch.int8,
+    ]
+
+
+def activation_is_int32_quantized(qconfig):
+    """Given a qconfig, decide if the activation needs to be
+    quantized to int32 or not
+    """
+    return activation_dtype(qconfig) in [torch.qint32, torch.int32]
+
+
+def weight_is_quantized(qconfig):
+    """Given a qconfig, decide if the weight needs to be
+    quantized or not
+    """
+    return weight_dtype(qconfig) in [
+        torch.quint8,
+        torch.qint8,
+        torch.float16,
+        torch.quint4x2,
+        torch.uint8,
+        torch.int8,
+        torch.int16,
+        torch.int32,
+        torch.float8_e5m2,
+        torch.float8_e4m3fn,
+    ]
+
+
+def weight_is_statically_quantized(qconfig):
+    """Given a qconfig, decide if the weight needs to be statically
+    quantized or not
+    """
+    return weight_dtype(qconfig) in [torch.quint8, torch.qint8, torch.uint8, torch.int8]
+
+
+def op_is_int8_dynamically_quantized(qconfig) -> bool:
+    """Given a qconfig, returns True if this op is using int8 dynamic
+    quantization
+    """
+    activation_dtype, weight_dtype, activation_is_dynamic = get_qconfig_dtypes(qconfig)
+    return (
+        activation_dtype in [torch.quint8, torch.uint8]
+        and
+        # for now, the lines below assume fbgemm or qnnpack
+        weight_dtype in [torch.qint8, torch.int8]
+        and activation_is_dynamic
+    )
+
+
+def get_qconfig_dtypes(qconfig):
+    r"""returns the qconfig tuple for qconfig:
+    (activation_dtype, weight_dtype, activation_is_dynamic)
+    """
+    assert qconfig is not None
+    activation = qconfig.activation()
+    weight = qconfig.weight()
+    act_is_dynamic = getattr(activation, "is_dynamic", False)
+    return (activation.dtype, weight.dtype, act_is_dynamic)
+
+
+def get_quant_type(qconfig):
+    assert qconfig is not None
+    activation = qconfig.activation()
+    weight = qconfig.weight()
+    static_dtypes = [
+        torch.quint8,
+        torch.qint8,
+        torch.quint4x2,
+        torch.qint32,
+        torch.uint8,
+        torch.int8,
+        torch.int16,
+        torch.int32,
+        torch.float8_e5m2,
+        torch.float8_e4m3fn,
+    ]
+    if weight.dtype in static_dtypes:
+        if hasattr(activation, "is_dynamic") and activation.is_dynamic:
+            return QuantType.DYNAMIC
+        elif activation.dtype in static_dtypes:
+            return QuantType.STATIC
+        else:
+            return QuantType.WEIGHT_ONLY
+
+    if weight.dtype == torch.float16:
+        if hasattr(activation, "is_dynamic") and activation.is_dynamic:
+            return QuantType.DYNAMIC
+        elif activation.dtype == torch.float16:
+            return QuantType.STATIC
+
+    raise Exception(  # noqa: TRY002
+        f"Unrecognized dtype combination in get_quant_type: activation({activation.dtype}),"
+        f"weight({weight.dtype})"
+    )
+
+
+def check_min_max_valid(min_val: torch.Tensor, max_val: torch.Tensor) -> bool:
+    """Checks if the given minimum and maximum values are valid, meaning that
+    they exist and the min value is less than the max value.
+    """
+    if min_val.numel() == 0 or max_val.numel() == 0:
+        warnings.warn(
+            "must run observer before calling calculate_qparams. "
+            + "Returning default values."
+        )
+        return False
+
+    if min_val.dim() == 0 or max_val.dim() == 0:
+        if min_val == float("inf") and max_val == float("-inf"):
+            warnings.warn(
+                "must run observer before calling calculate_qparams. "
+                + "Returning default values."
+            )
+
+            return False
+
+        assert min_val <= max_val, f"min {min_val} should be less than max {max_val}"
+    else:
+        assert torch.all(
+            min_val <= max_val
+        ), f"min {min_val} should be less than max {max_val}"
+
+    return True
+
+
+def calculate_qmin_qmax(
+    quant_min: int,
+    quant_max: int,
+    has_customized_qrange: bool,
+    dtype: torch.dtype,
+    reduce_range: bool,
+) -> Tuple[int, int]:
+    r"""Calculates actual qmin and qmax based on the quantization range,
+    observer datatype and if range is reduced.
+    """
+    # TODO(jerryzh): Figure out why custom quant_min/quant_max are still adjusted.
+    if has_customized_qrange:
+        # This initialization here is to be resolve TorchScript compilation issues and allow
+        # using of refinement to decouple initial_qmin and initial_qmax from quantization range.
+        # The actual values of initial_qmin and initial_qmax will be reset below.
+        if dtype in [torch.qint32, torch.int32]:
+            initial_quant_min, initial_quant_max = 0, 2**32 - 1
+        else:
+            initial_quant_min, initial_quant_max = 0, 255
+        # The following assignment of self.qmin and self.qmax to the local variables and the if check refine the
+        # attribute from Optional valid integers for use, based on TorchScript's requirements.
+        custom_quant_min, custom_quant_max = quant_min, quant_max
+        if custom_quant_min is not None and custom_quant_max is not None:
+            initial_quant_min, initial_quant_max = (
+                custom_quant_min,
+                custom_quant_max,
+            )
+
+        qrange_len = initial_quant_max - initial_quant_min + 1
+        if dtype in [torch.qint8, torch.int8]:
+            assert (
+                0 < qrange_len <= 256
+            ), "quantization range should be positive and not exceed the maximum bit range (=256)."
+        elif dtype in [torch.qint32, torch.int32]:
+            assert (
+                0 < qrange_len <= 2**32
+            ), "quantization range should be positive and not exceed the maximum bit range (=4294967296)."
+        if reduce_range:
+            quant_min, quant_max = quant_min // 2, quant_max // 2
+    else:
+        # Fallback onto default 8-bit qmin and qmax calculation if dynamic range is not used.
+        if dtype in [torch.qint8, torch.int8]:
+            if reduce_range:
+                quant_min, quant_max = -64, 63
+            else:
+                quant_min, quant_max = -128, 127
+        elif dtype in [torch.quint8, torch.uint8]:
+            if reduce_range:
+                quant_min, quant_max = 0, 127
+            else:
+                quant_min, quant_max = 0, 255
+        elif dtype in [torch.qint32, torch.int32]:
+            quant_min, quant_max = -1 * (2**31), (2**31) - 1
+        else:
+            quant_min, quant_max = 0, 15
+    return quant_min, quant_max
+
+
+def _parent_name(target):
+    """
+    Turn 'foo.bar' into ['foo', 'bar']
+    """
+    r = target.rsplit(".", 1)
+    if len(r) == 1:
+        return "", r[0]
+    else:
+        return r[0], r[1]
+
+
+def has_no_children_ignoring_parametrizations(module):
+    """
+    Checks if module._modules is empty or
+    if module is a parametrization, checks that module._modules only has
+    the 'parametrizations' module
+    """
+    if len(module._modules) == 0:
+        return True
+    elif is_parametrized(module):
+        return len(module._modules) == 1 and "parametrizations" in module._modules
+    else:
+        return False
+
+
+def _get_path_of_module(
+    root: torch.nn.Module, submodule: torch.nn.Module
+) -> Optional[str]:
+    """Get the path (fully qualified name) of a submodule
+
+    Example::
+
+    >> class M(torch.nn.Module):
+           def __init__(self) -> None:
+               self.linear = torch.nn.Linear(5, 5)
+           def forward(self, x):
+               return self.linear(x)
+
+    >> m = M()
+    >> l = m.linear
+    >> _get_path_of_module(m, l)
+    "linear"
+    """
+    for n, p in root.named_modules():
+        if submodule is p:
+            return n
+    return None
+
+
+def _get_signature_locals(f: Callable, loc: Dict[str, Any]) -> Dict[str, Any]:
+    """Get local keyword arguments
+
+    Example::
+
+    >> def f(self, a, b=9):
+           pass
+    >> loc = {"a": 6, "c": 7}
+    >> _get_signature_locals(f, loc)
+    {"a": 6}
+    """
+    return {k: v for k, v in loc.items() if k in signature(f).parameters}
+
+
+def _get_default_kwargs(f: Callable) -> "OrderedDict[str, Any]":
+    """Get all default keyword arguments from function signature
+
+    Example::
+
+    >> def f(self, a, b=9):
+           pass
+    >> _get_default_kwargs(f)
+    {"b": 9}
+    """
+    kwargs = {}
+    for name, param in signature(f).parameters.items():
+        if param.default is not param.empty:
+            kwargs[name] = param.default
+        elif param.kind is param.VAR_POSITIONAL:
+            kwargs[name] = ()
+        elif param.kind is param.VAR_KEYWORD:
+            kwargs[name] = {}
+    return OrderedDict(kwargs)
+
+
+def _normalize_kwargs(func: Callable, loc: Dict[str, Any]) -> "OrderedDict[str, Any]":
+    """Given a function and local function arguments, normalize the keyword
+    arguments by filling in default arguments from function signature
+
+    Example::
+
+    >> def f(self, key1=3, key2=3):
+           pass
+    >> loc = {"key2": 6}
+    >> _normalize_kwargs(f, loc)
+    {"key1": 3, "key2": 6}
+    """
+    default_kwargs = _get_default_kwargs(func)
+    local_kwargs = _get_signature_locals(func, loc)
+    normalized_kwargs = default_kwargs.copy()
+    for attr, val in local_kwargs.items():
+        if attr in normalized_kwargs:
+            # override the default keyword arguments
+            normalized_kwargs[attr] = val
+    return normalized_kwargs
+
+
+def validate_qmin_qmax(quant_min: int, quant_max: int) -> None:
+    r"""Validates that the user-specified quantization range is properly initialized
+    and within the given bound supported by the observer dtype.
+
+    To accommodate lower-bit quantization with respect to the existing torch.qint8 and
+    torch.quint8 datatypes, the user can choose to use dynamic quantization range by passing
+    in a tuple of initial qmin and qmax values. One use case is these customized qmin and qmax
+    values are used to calculate static estimates of the scale and zero point for aggressive lower-bit
+    fake quantization. These estimates are compared against parameters learned through backpropagation.
+    The related literatures for scale and zero point via backpropagation are as follows:
+
+    Learned Step Size Quantization: https://openreview.net/pdf?id=rkgO66VKDS
+    Trained Quantization Thresholds: https://arxiv.org/pdf/1903.08066.pdf
+    """
+    # The variable names are prefixed with "initial" because their values (qmin and qmax) might be adjusted
+    # based on whether quantization range is reduced and the datatype (signed/unsigned) used by the observer.
+    assert (
+        quant_min <= 0 <= quant_max
+    ), "Used-specified quantization range must include 0."
+    assert (
+        quant_min < quant_max
+    ), "qmin must be strictly less than qmax for user-specified quantization range."
+
+
+# Functionally equivalent to '_calculate_qparams' in observer.py. Observers must be torchscriptable however and qscheme
+# as far as I can tell is not allowed to passed as a parameter in torchscript functions. This makes refactoring observer
+# to use this utility a massive pain and very gross. For now Im opting just to duplicate as this code seems unlikey to change
+# (last update over 1 year ago) and when torchscript is fully deprecated we can refactor. TODO(jakeszwe, jerryzh168)
+def determine_qparams(
+    min_val: torch.Tensor,
+    max_val: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    eps: torch.Tensor,
+    has_customized_qrange: bool,
+    qscheme: torch.qscheme = torch.per_tensor_affine,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    r"""Calculates the quantization parameters, given min and max
+    value tensors. Works for both per tensor and per channel cases
+
+    Args:
+        min_val: Minimum values per channel
+        max_val: Maximum values per channel
+
+    Returns:
+        scales: Scales tensor of shape (#channels,)
+        zero_points: Zero points tensor of shape (#channels,)
+    """
+    if not check_min_max_valid(min_val, max_val):
+        return torch.tensor([1.0], device=min_val.device.type), torch.tensor(
+            [0], device=min_val.device.type
+        )
+
+    min_val_neg = torch.min(min_val, torch.zeros_like(min_val))
+    max_val_pos = torch.max(max_val, torch.zeros_like(max_val))
+
+    device = min_val_neg.device
+    scale = torch.ones(min_val_neg.size(), dtype=torch.double, device=device)
+    zero_point = torch.zeros(min_val_neg.size(), dtype=torch.int64, device=device)
+
+    if qscheme == torch.per_tensor_symmetric or qscheme == torch.per_channel_symmetric:
+        max_val_pos = torch.max(-min_val_neg, max_val_pos)
+        scale = max_val_pos / (float(quant_max - quant_min) / 2)
+        scale = torch.max(scale, eps)
+        if dtype in [torch.uint8, torch.quint8]:
+            if has_customized_qrange:
+                # When customized quantization range is used, down-rounded midpoint of the range is chosen.
+                zero_point = zero_point.new_full(
+                    zero_point.size(), (quant_min + quant_max) // 2
+                )
+            else:
+                zero_point = zero_point.new_full(zero_point.size(), 128)
+    elif qscheme == torch.per_channel_affine_float_qparams:
+        scale = (max_val - min_val) / float(quant_max - quant_min)
+        scale = torch.where(scale > eps, scale, torch.ones_like(scale))
+        # We use the quantize function
+        # xq = Round(Xf * inv_scale + zero_point),
+        # setting zero_point to (-1 * min *inv_scale) we get
+        # Xq = Round((Xf - min) * inv_scale)
+        zero_point = -1 * min_val / scale
+    else:
+        scale = (max_val_pos - min_val_neg) / float(quant_max - quant_min)
+        scale = torch.max(scale, eps)
+        zero_point = quant_min - torch.round(min_val_neg / scale).to(torch.int)
+        zero_point = torch.clamp(zero_point, quant_min, quant_max)
+
+    # For scalar values, cast them to Tensors of size 1 to keep the shape
+    # consistent with default values in FakeQuantize.
+    if len(scale.shape) == 0:
+        # TODO: switch to scale.item() after adding JIT support
+        scale = torch.tensor([float(scale)], dtype=scale.dtype, device=device)
+    if len(zero_point.shape) == 0:
+        # TODO: switch to zero_point.item() after adding JIT support
+        zero_point = torch.tensor(
+            [int(zero_point)], dtype=zero_point.dtype, device=device
+        )
+        if qscheme == torch.per_channel_affine_float_qparams:
+            zero_point = torch.tensor(
+                [float(zero_point)], dtype=zero_point.dtype, device=device
+            )
+
+    return scale.to(torch.double), zero_point.to(torch.int64)
+
+
+def _get_num_pos_args(f: Callable) -> int:
+    """Get number of positional args for a function
+
+    Example::
+
+    >> def f(self, key1=3, key2=3):
+           pass
+    >> _get_num_pos_args(f)
+    3
+    """
+    return len(getfullargspec(f).args)
+
+
+def get_fqn_to_example_inputs(
+    model: torch.nn.Module, example_inputs: Tuple[Any, ...]
+) -> Dict[str, Tuple[Any, ...]]:
+    """Given a model and its example inputs, return a dictionary from
+    fully qualified name of submodules to example_inputs for that submodule,
+    e.g. {"linear1": (tensor1,), "linear2": (tensor2,), "sub": (tensor3,),
+          "sub.linear1": (tensor4,), ...}
+
+    Used to make quantizing submodules easier now that FX Graph Mode Quantization requires
+    example inputs.
+
+    Also works for keyword arguments with default values, we would flatten keyword
+    arguments as positional arguments and fill in the missing keyword args with default
+    values, e.g. if we have a forward function:
+    def forward(self, x, key1=3, key2=3):
+        ...
+
+    and we call it with self.submodule(x, key2=6)
+    we'll get example_inputs: (x, 3, 6)
+
+    user can also override `key1` with positional arguments as well:
+    for self.submodule(x, 5, key2=6)
+    we'll get: (x, 5, 6)
+
+    variable positional arguments and variable positional keyword arguments in forward
+    function are not supported currently, so please make sure no submodules is using
+    them.
+    """
+    root = model
+    fqn_to_example_inputs = {}
+
+    def _patched_module_call(self, *args, **kwargs):
+        submodule_example_inputs = list(args).copy()
+        normalized_kwargs = _normalize_kwargs(self.forward, kwargs)
+        # minus 1 to skipping counting `self`
+        num_args = _get_num_pos_args(self.forward) - 1
+        num_to_pop = num_args - len(submodule_example_inputs)
+        while num_to_pop and normalized_kwargs:
+            normalized_kwargs.popitem(last=False)
+            num_to_pop -= 1
+        submodule_example_inputs.extend(normalized_kwargs.values())
+        submodule_example_inputs_tuple = tuple(submodule_example_inputs)
+        fqn = _get_path_of_module(root, self)
+        if fqn is not None:
+            fqn_to_example_inputs[fqn] = submodule_example_inputs_tuple
+        return orig_module_call(self, *args, **kwargs)
+
+    orig_module_call = torch.nn.Module.__call__
+    torch.nn.Module.__call__ = _patched_module_call  # type: ignore[method-assign]
+    try:
+        model(*example_inputs)
+    finally:
+        # restore the module call even if there is an exception
+        torch.nn.Module.__call__ = orig_module_call  # type: ignore[method-assign]
+    return fqn_to_example_inputs
+
+
+def _assert_and_get_unique_device(module: torch.nn.Module) -> Any:
+    """
+    Returns the unique device for a module, or None if no device is found.
+    Throws an error if multiple devices are detected.
+    """
+    devices = {p.device for p in module.parameters()} | {
+        p.device for p in module.buffers()
+    }
+    """
+    As a temp workaround for AIMP HHC publish we added CPU check.remove it later. T163614564
+    """
+    if {torch.device("cpu"), torch.device("meta")} == devices:
+        warnings.warn(
+            "Both 'meta' and 'cpu' are present in the list of devices. Module can have one device. We Select 'cpu'."
+        )
+        devices = {torch.device("cpu")}
+    ""
+    assert len(devices) <= 1, (
+        "prepare only works with cpu or single-device CUDA modules, "
+        f"but got devices {devices}"
+    )
+    device = next(iter(devices)) if len(devices) > 0 else None
+    return device
+
+
+__all__ = [
+    "NodePattern",
+    "Pattern",
+    "MatchAllNode",
+    "check_node",
+    "get_combined_dict",
+    "is_per_tensor",
+    "is_per_channel",
+    "getattr_from_fqn",
+    "get_qparam_dict",
+    "get_swapped_custom_module_class",
+    "activation_dtype",
+    "weight_dtype",
+    "activation_is_statically_quantized",
+    "activation_is_dynamically_quantized",
+    "activation_is_int8_quantized",
+    "activation_is_int32_quantized",
+    "weight_is_quantized",
+    "weight_is_statically_quantized",
+    "op_is_int8_dynamically_quantized",
+    "get_qconfig_dtypes",
+    "get_quant_type",
+    "check_min_max_valid",
+    "calculate_qmin_qmax",
+    "has_no_children_ignoring_parametrizations",
+    "get_fqn_to_example_inputs",
+    "to_underlying_dtype",
+    "determine_qparams",
+    "validate_qmin_qmax",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f51456ef15ce8ef7da0b3e8d8057d2c185247ac6
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/__init__.py
@@ -0,0 +1,599 @@
+# mypy: allow-untyped-defs
+"""
+``torch.autograd`` provides classes and functions implementing automatic differentiation of arbitrary scalar valued functions.
+
+It requires minimal changes to the existing code - you only need to declare :class:`Tensor` s
+for which gradients should be computed with the ``requires_grad=True`` keyword.
+As of now, we only support autograd for floating point :class:`Tensor` types (
+half, float, double and bfloat16) and complex :class:`Tensor` types (cfloat, cdouble).
+"""
+
+import warnings
+from typing import cast, List, Optional, Sequence, Tuple, Union
+
+import torch
+from torch import _vmap_internals
+from torch.overrides import handle_torch_function, has_torch_function, is_tensor_like
+from torch.types import _size, _TensorOrTensors, _TensorOrTensorsOrGradEdge
+
+from . import forward_ad, functional, graph
+from .anomaly_mode import detect_anomaly, set_detect_anomaly
+from .function import Function, NestedIOFunction
+from .grad_mode import (
+    _force_original_view_tracking,
+    _unsafe_preserve_version_counter,
+    enable_grad,
+    inference_mode,
+    no_grad,
+    set_grad_enabled,
+    set_multithreading_enabled,
+)
+from .gradcheck import gradcheck, gradgradcheck
+from .graph import _engine_run_backward
+from .variable import Variable
+
+
+__all__ = [
+    "Variable",
+    "Function",
+    "backward",
+    "grad_mode",
+    "NestedIOFunction",
+    "detect_anomaly",
+    "enable_grad",
+    "grad",
+    "gradcheck",
+    "gradgradcheck",
+    "inference_mode",
+    "no_grad",
+    "set_detect_anomaly",
+    "set_grad_enabled",
+    "set_multithreading_enabled",
+    "variable",
+]
+
+_OptionalTensor = Optional[torch.Tensor]
+_ShapeorNestedShape = Union[_size, Sequence[_size], torch.Tensor]
+
+
+def _calculate_shape(
+    output: Union[torch.Tensor, graph.GradientEdge],
+    grad: torch.Tensor,
+    is_grads_batched: bool,
+) -> Tuple[_ShapeorNestedShape, _ShapeorNestedShape]:
+    # is_same_size ensures that both tensors are either nested or non nested
+    # circular import
+    from torch.nested._internal.nested_tensor import NestedTensor
+
+    if isinstance(output, graph.GradientEdge):
+        # We have already checked that we are not a C++ NestedTensor
+        if is_grads_batched:
+            raise RuntimeError("Batched grads are not supported with GradientEdge")
+        out_metadata = output.node._input_metadata[output.output_nr]
+        return torch.Size(out_metadata.shape), grad.shape
+
+    if output.is_nested and not isinstance(output, NestedTensor):
+        if is_grads_batched:
+            raise RuntimeError("Batched grads are not supported with Nested Tensor.")
+        out_shape = output._nested_tensor_size()
+        grad_shape = grad._nested_tensor_size()
+
+        return out_shape, grad_shape
+
+    reg_out_shape = output.shape
+    reg_grad_shape = grad.shape if not is_grads_batched else grad.shape[1:]
+    return reg_out_shape, reg_grad_shape
+
+
+def _make_grads(
+    outputs: Union[Sequence[torch.Tensor], Sequence[graph.GradientEdge]],
+    grads: Sequence[_OptionalTensor],
+    is_grads_batched: bool,
+) -> Tuple[_OptionalTensor, ...]:
+    new_grads: List[_OptionalTensor] = []
+    for out, grad in zip(outputs, grads):
+        out = cast(Union[torch.Tensor, graph.GradientEdge], out)
+        out_size = None
+        out_device = None
+
+        if isinstance(out, graph.GradientEdge):
+            out_metadata = out.node._input_metadata[out.output_nr]
+            out_size = torch.Size(out_metadata.shape)
+            out_dtype = out_metadata.dtype
+            out_device = out_metadata.device
+            out_is_nested = out_metadata.is_nested_tensor
+            if out_metadata.is_cpp_nested_tensor:
+                raise RuntimeError(
+                    "C++ NestedTensor are not supported with GradientEdge"
+                )
+            out_is_cpp_nested = False
+        else:
+            # circular import
+            from torch.nested._internal.nested_tensor import NestedTensor
+
+            assert isinstance(out, torch.Tensor)
+            out_dtype = out.dtype
+            out_is_nested = out.is_nested
+            out_is_cpp_nested = out_is_nested and not isinstance(out, NestedTensor)
+            if not out_is_cpp_nested:
+                out_size = out.shape
+
+        if isinstance(grad, torch.Tensor):
+            from torch.fx.experimental.symbolic_shapes import expect_true, sym_eq
+
+            first_grad = grad if not is_grads_batched else grad[0]
+
+            # TODO: We can remove this conditional once we uniformly use
+            # singleton int to represent jagged dimension, so that size() call
+            # on nested tensor works.
+            if out_is_cpp_nested:
+                assert isinstance(out, torch.Tensor)
+                shape_matches = torch.is_same_size(out, first_grad)
+            else:
+                # We need to do a regular size check, without going through
+                # the operator, to be able to handle unbacked symints
+                # (expect_true ensures we can deal with unbacked)
+                assert out_size is not None
+                shape_matches = expect_true(sym_eq(out_size, first_grad.size()))
+
+            if not shape_matches:
+                out = cast(Union[torch.Tensor, graph.GradientEdge], out)
+                out_shape, grad_shape = _calculate_shape(
+                    out, first_grad, is_grads_batched
+                )
+                if is_grads_batched:
+                    raise RuntimeError(
+                        "If `is_grads_batched=True`, we interpret the first "
+                        "dimension of each grad_output as the batch dimension. "
+                        "The sizes of the remaining dimensions are expected to match "
+                        "the shape of corresponding output, but a mismatch "
+                        "was detected: grad_output["
+                        + str(grads.index(grad))
+                        + "] has a shape of "
+                        + str(grad_shape)
+                        + " and output["
+                        + str(outputs.index(out))
+                        + "] has a shape of "
+                        + str(out_shape)
+                        + ". "
+                        "If you only want some tensors in `grad_output` to be considered "
+                        "batched, consider using vmap."
+                    )
+                else:
+                    raise RuntimeError(
+                        "Mismatch in shape: grad_output["
+                        + str(grads.index(grad))
+                        + "] has a shape of "
+                        + str(grad_shape)
+                        + " and output["
+                        + str(outputs.index(out))
+                        + "] has a shape of "
+                        + str(out_shape)
+                        + "."
+                    )
+            if out_dtype.is_complex != grad.dtype.is_complex:
+                raise RuntimeError(
+                    "For complex Tensors, both grad_output and output"
+                    " are required to have the same dtype."
+                    " Mismatch in dtype: grad_output["
+                    + str(grads.index(grad))
+                    + "] has a dtype of "
+                    + str(grad.dtype)
+                    + " and output["
+                    + str(outputs.index(out))
+                    + "] has a dtype of "
+                    + str(out_dtype)
+                    + "."
+                )
+            new_grads.append(grad)
+        elif grad is None:
+            if isinstance(out, graph.GradientEdge) or out.requires_grad:  # type: ignore[attr-defined]
+                if isinstance(out, graph.GradientEdge):
+                    assert out_size is not None
+                    out_numel_is_1 = all(o == 1 for o in out_size)
+                else:
+                    assert isinstance(out, torch.Tensor)
+                    out_numel_is_1 = out.numel() == 1
+                if not out_numel_is_1:
+                    raise RuntimeError(
+                        "grad can be implicitly created only for scalar outputs"
+                    )
+                if not out_dtype.is_floating_point:
+                    msg = (
+                        "grad can be implicitly created only for real scalar outputs"
+                        f" but got {out_dtype}"
+                    )
+                    raise RuntimeError(msg)
+                if isinstance(out, graph.GradientEdge):
+                    assert out_size is not None
+                    assert out_device is not None
+                    new_grads.append(
+                        torch.ones(
+                            out_size,
+                            dtype=out_dtype,
+                            device=out_device,
+                        )
+                    )
+                else:
+                    assert isinstance(out, torch.Tensor)
+                    new_grads.append(
+                        torch.ones_like(out, memory_format=torch.preserve_format)
+                    )
+            else:
+                new_grads.append(None)
+        else:
+            raise TypeError(
+                "gradients can be either Tensors or None, but got "
+                + type(grad).__name__
+            )
+    return tuple(new_grads)
+
+
+def _tensor_or_tensors_to_tuple(
+    tensors: Optional[_TensorOrTensors], length: int
+) -> Tuple[_OptionalTensor, ...]:
+    if tensors is None:
+        return (None,) * length
+    if isinstance(tensors, torch.Tensor):
+        return (tensors,)
+    return tuple(tensors)
+
+
+def backward(
+    tensors: _TensorOrTensors,
+    grad_tensors: Optional[_TensorOrTensors] = None,
+    retain_graph: Optional[bool] = None,
+    create_graph: bool = False,
+    grad_variables: Optional[_TensorOrTensors] = None,
+    inputs: Optional[_TensorOrTensorsOrGradEdge] = None,
+) -> None:
+    r"""Compute the sum of gradients of given tensors with respect to graph leaves.
+
+    The graph is differentiated using the chain rule. If any of ``tensors``
+    are non-scalar (i.e. their data has more than one element) and require
+    gradient, then the Jacobian-vector product would be computed, in this
+    case the function additionally requires specifying ``grad_tensors``.
+    It should be a sequence of matching length, that contains the "vector"
+    in the Jacobian-vector product, usually the gradient of the differentiated
+    function w.r.t. corresponding tensors (``None`` is an acceptable value for
+    all tensors that don't need gradient tensors).
+
+    This function accumulates gradients in the leaves - you might need to zero
+    ``.grad`` attributes or set them to ``None`` before calling it.
+    See :ref:`Default gradient layouts`
+    for details on the memory layout of accumulated gradients.
+
+    .. note::
+        Using this method with ``create_graph=True`` will create a reference cycle
+        between the parameter and its gradient which can cause a memory leak.
+        We recommend using ``autograd.grad`` when creating the graph to avoid this.
+        If you have to use this function, make sure to reset the ``.grad`` fields of your
+        parameters to ``None`` after use to break the cycle and avoid the leak.
+
+    .. note::
+
+        If you run any forward ops, create ``grad_tensors``, and/or call ``backward``
+        in a user-specified CUDA stream context, see
+        :ref:`Stream semantics of backward passes`.
+
+    .. note::
+
+        When ``inputs`` are provided and a given input is not a leaf,
+        the current implementation will call its grad_fn (even though it is not strictly needed to get this gradients).
+        It is an implementation detail on which the user should not rely.
+        See https://github.com/pytorch/pytorch/pull/60521#issuecomment-867061780 for more details.
+
+    Args:
+        tensors (Sequence[Tensor] or Tensor): Tensors of which the derivative will be
+            computed.
+        grad_tensors (Sequence[Tensor or None] or Tensor, optional): The "vector" in
+            the Jacobian-vector product, usually gradients w.r.t. each element of
+            corresponding tensors. None values can be specified for scalar Tensors or
+            ones that don't require grad. If a None value would be acceptable for all
+            grad_tensors, then this argument is optional.
+        retain_graph (bool, optional): If ``False``, the graph used to compute the grad
+            will be freed. Note that in nearly all cases setting this option to ``True``
+            is not needed and often can be worked around in a much more efficient
+            way. Defaults to the value of ``create_graph``.
+        create_graph (bool, optional): If ``True``, graph of the derivative will
+            be constructed, allowing to compute higher order derivative products.
+            Defaults to ``False``.
+        inputs (Sequence[Tensor] or Tensor or Sequence[GradientEdge], optional): Inputs w.r.t. which the gradient
+            be will accumulated into ``.grad``. All other Tensors will be ignored. If
+            not provided, the gradient is accumulated into all the leaf Tensors that
+            were used to compute the :attr:`tensors`.
+    """
+    if torch._C._are_functorch_transforms_active():
+        raise RuntimeError(
+            "backward() called inside a functorch transform. This is not "
+            "supported, please use functorch.grad or functorch.vjp instead "
+            "or call backward() outside of functorch transforms."
+        )
+
+    if grad_variables is not None:
+        warnings.warn(
+            "`grad_variables` is deprecated. Use `grad_tensors` instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        if grad_tensors is None:
+            grad_tensors = grad_variables
+        else:
+            raise RuntimeError(
+                "`grad_tensors` and `grad_variables` (deprecated) "
+                "arguments both passed to `backward()`. Please only "
+                "use `grad_tensors`."
+            )
+    if inputs is not None and len(inputs) == 0:
+        raise RuntimeError("`inputs` argument to `backward()` cannot be empty.")
+
+    tensors = (tensors,) if isinstance(tensors, torch.Tensor) else tuple(tensors)
+    inputs = (
+        (inputs,)
+        if isinstance(inputs, (torch.Tensor, graph.GradientEdge))
+        else tuple(inputs)
+        if inputs is not None
+        else ()
+    )
+
+    grad_tensors_ = _tensor_or_tensors_to_tuple(grad_tensors, len(tensors))
+    grad_tensors_ = _make_grads(tensors, grad_tensors_, is_grads_batched=False)
+    if retain_graph is None:
+        retain_graph = create_graph
+
+    # The reason we repeat the same comment below is that
+    # some Python versions print out the first line of a multi-line function
+    # calls in the traceback and some print out the last line
+    _engine_run_backward(
+        tensors,
+        grad_tensors_,
+        retain_graph,
+        create_graph,
+        inputs,
+        allow_unreachable=True,
+        accumulate_grad=True,
+    )
+
+
+def grad(
+    outputs: _TensorOrTensorsOrGradEdge,
+    inputs: _TensorOrTensorsOrGradEdge,
+    grad_outputs: Optional[_TensorOrTensors] = None,
+    retain_graph: Optional[bool] = None,
+    create_graph: bool = False,
+    only_inputs: bool = True,
+    allow_unused: Optional[bool] = None,
+    is_grads_batched: bool = False,
+    materialize_grads: bool = False,
+) -> Tuple[torch.Tensor, ...]:
+    r"""Compute and return the sum of gradients of outputs with respect to the inputs.
+
+    ``grad_outputs`` should be a sequence of length matching ``output``
+    containing the "vector" in vector-Jacobian product, usually the pre-computed
+    gradients w.r.t. each of the outputs. If an output doesn't require_grad,
+    then the gradient can be ``None``).
+
+    .. note::
+
+        If you run any forward ops, create ``grad_outputs``, and/or call ``grad``
+        in a user-specified CUDA stream context, see
+        :ref:`Stream semantics of backward passes`.
+
+    .. note::
+
+        ``only_inputs`` argument is deprecated and is ignored now (defaults to ``True``).
+        To accumulate gradient for other parts of the graph, please use
+        ``torch.autograd.backward``.
+
+    Args:
+        outputs (sequence of Tensor or GradientEdge): outputs of the differentiated function.
+        inputs (sequence of Tensor or GradientEdge): Inputs w.r.t. which the gradient will be
+            returned (and not accumulated into ``.grad``).
+        grad_outputs (sequence of Tensor): The "vector" in the vector-Jacobian product.
+            Usually gradients w.r.t. each output. None values can be specified for scalar
+            Tensors or ones that don't require grad. If a None value would be acceptable
+            for all grad_tensors, then this argument is optional. Default: None.
+        retain_graph (bool, optional): If ``False``, the graph used to compute the grad
+            will be freed. Note that in nearly all cases setting this option to ``True``
+            is not needed and often can be worked around in a much more efficient
+            way. Defaults to the value of ``create_graph``.
+        create_graph (bool, optional): If ``True``, graph of the derivative will
+            be constructed, allowing to compute higher order derivative products.
+            Default: ``False``.
+        allow_unused (Optional[bool], optional): If ``False``, specifying inputs
+            that were not used when computing outputs (and therefore their grad is
+            always zero) is an error. Defaults to the value of ``materialize_grads``.
+        is_grads_batched (bool, optional): If ``True``, the first dimension of each
+            tensor in ``grad_outputs`` will be interpreted as the batch dimension.
+            Instead of computing a single vector-Jacobian product, we compute a
+            batch of vector-Jacobian products for each "vector" in the batch.
+            We use the vmap prototype feature as the backend to vectorize calls
+            to the autograd engine so that this computation can be performed in a
+            single call. This should lead to performance improvements when compared
+            to manually looping and performing backward multiple times. Note that
+            due to this feature being experimental, there may be performance
+            cliffs. Please use ``torch._C._debug_only_display_vmap_fallback_warnings(True)``
+            to show any performance warnings and file an issue on github if warnings exist
+            for your use case. Defaults to ``False``.
+        materialize_grads (bool, optional): If ``True``, set the gradient for unused inputs
+            to zero instead of None. This is useful when computing higher-order derivatives.
+            If ``materialize_grads`` is ``True`` and ``allow_unused`` is ``False``, an error
+            will be raised. Defaults to ``False``.
+
+    """
+    if materialize_grads and allow_unused is False:
+        raise ValueError(
+            "Expected allow_unused to be True or not passed when materialize_grads=True, "
+            "but got: allow_unused=False."
+        )
+    if allow_unused is None:
+        allow_unused = materialize_grads
+    if is_tensor_like(outputs) or isinstance(outputs, graph.GradientEdge):
+        outputs = cast(
+            Union[Sequence[torch.Tensor], Sequence[graph.GradientEdge]], (outputs,)
+        )
+    else:
+        outputs = tuple(outputs)
+    if is_tensor_like(inputs) or isinstance(inputs, graph.GradientEdge):
+        inputs = cast(_TensorOrTensorsOrGradEdge, (inputs,))
+    else:
+        inputs = tuple(inputs)
+    t_outputs = tuple(i for i in outputs if is_tensor_like(i))
+    t_inputs = tuple(i for i in inputs if is_tensor_like(i))
+    overridable_args = t_outputs + t_inputs
+    if has_torch_function(overridable_args):
+        return handle_torch_function(
+            grad,
+            overridable_args,
+            outputs,
+            inputs,
+            grad_outputs=grad_outputs,
+            retain_graph=retain_graph,
+            create_graph=create_graph,
+            only_inputs=only_inputs,
+            allow_unused=allow_unused,
+            is_grads_batched=is_grads_batched,
+            materialize_grads=materialize_grads,
+        )
+
+    if not only_inputs:
+        warnings.warn(
+            "only_inputs argument is deprecated and is ignored now "
+            "(defaults to True). To accumulate gradient for other "
+            "parts of the graph, please use torch.autograd.backward.",
+            FutureWarning,
+            stacklevel=2,
+        )
+
+    grad_outputs_ = _tensor_or_tensors_to_tuple(grad_outputs, len(outputs))
+    grad_outputs_ = _make_grads(
+        outputs, grad_outputs_, is_grads_batched=is_grads_batched
+    )
+
+    if retain_graph is None:
+        retain_graph = create_graph
+
+    # The reason we repeat the same comment several times below is because
+    # some Python versions print out the first line of multi-line function
+    # calls in the traceback and some print out the last line
+    if is_grads_batched:
+
+        def vjp(gO):
+            return _engine_run_backward(
+                outputs,
+                gO,
+                retain_graph,
+                create_graph,
+                inputs,
+                allow_unused,
+                accumulate_grad=False,
+            )
+
+        result = _vmap_internals._vmap(vjp, 0, 0, allow_none_pass_through=True)(
+            grad_outputs_
+        )
+    else:
+        result = _engine_run_backward(
+            outputs,
+            grad_outputs_,
+            retain_graph,
+            create_graph,
+            inputs,
+            allow_unused,
+            accumulate_grad=False,
+        )
+    if materialize_grads:
+        if any(
+            result[i] is None and not is_tensor_like(inputs[i])
+            for i in range(len(inputs))
+        ):
+            raise RuntimeError(
+                "materialize_grads cannot be used when the given input is a GradientEdge"
+            )
+        result = tuple(
+            output
+            if output is not None
+            else torch.zeros_like(input, requires_grad=True)
+            for (output, input) in zip(result, inputs)
+        )
+    return result
+
+
+# This function applies in case of gradient checkpointing for memory
+# optimization. Currently, gradient checkpointing is supported only if the
+# execution engine is invoked through torch.autograd.backward() and its
+# inputs argument is not passed. It is not supported for torch.autograd.grad().
+# This is because if inputs are specified, the gradient won't be calculated for
+# anything else e.g. model parameters like weights, bias etc.
+#
+# This function returns whether the checkpointing is valid i.e. torch.autograd.backward
+# or not i.e. torch.autograd.grad. The implementation works by maintaining a thread
+# local variable in torch/csrc/autograd/engine.cpp which looks at the NodeTask
+# in the stack and before a NodeTask is executed in evaluate_function, it
+# checks for whether reentrant backwards is imperative or not.
+# See https://github.com/pytorch/pytorch/pull/4594 for more discussion/context
+def _is_checkpoint_valid():
+    return Variable._execution_engine.is_checkpoint_valid()
+
+
+def variable(*args, **kwargs):  # noqa: D103
+    raise RuntimeError(
+        "torch.autograd.variable(...) is deprecated, use torch.tensor(...) instead"
+    )
+
+
+# Monkey patching variable.Variable to fix FX codegen. FX generates a call by roughly doing
+# f"{fn.__module__}.{fn.__name__}(...). This yields torch.autograd.variable.Variable(...) in the
+# output of an FX graph.  Unfortunately the module name torch.autograd.variable is shadowed by the
+# deprecated function - variable(...).
+variable.Variable = Variable  # type: ignore[attr-defined]
+
+if not torch._C._autograd_init():
+    raise RuntimeError("autograd initialization failed")
+
+# Import all native method/classes
+from torch._C._autograd import (
+    _add_metadata_json,
+    _disable_profiler,
+    _disable_profiler_legacy,
+    _enable_profiler,
+    _enable_profiler_legacy,
+    _enable_record_function,
+    _get_sequence_nr,
+    _kineto_step,
+    _KinetoEvent,
+    _pop_saved_tensors_default_hooks,
+    _prepare_profiler,
+    _profiler_enabled,
+    _ProfilerResult,
+    _push_saved_tensors_default_hooks,
+    _record_function_with_args_enter,
+    _record_function_with_args_exit,
+    _set_empty_test_observer,
+    _supported_activities,
+    _toggle_collection_dynamic,
+    DeviceType,
+    kineto_available,
+    ProfilerEvent,
+    SavedTensor,
+)
+from torch._C._profiler import ProfilerActivity, ProfilerConfig, ProfilerState
+
+from . import profiler
+
+
+def _register_py_tensor_class_for_device(device, cls):
+    if not isinstance(cls, type):
+        raise RuntimeError("cls isn't a typeinfo object")
+    torch._C._register_py_class_for_device(device, cls)
+
+
+is_multithreading_enabled = torch._C._is_multithreading_enabled
+torch._C._add_docstr(
+    is_multithreading_enabled, "Returns True if multithreading is currently enabled."
+)
+
+is_view_replay_enabled = torch._C._is_view_replay_enabled
+torch._C._add_docstr(
+    is_view_replay_enabled, "Returns True if view-replay is currently enabled."
+)
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/_functions/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/_functions/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4170fad3eeac788dcb36b6ae1ddbee1b44dc25a1
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/_functions/__init__.py
@@ -0,0 +1 @@
+from .tensor import *  # noqa: F403
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/_functions/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/_functions/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/_functions/tensor.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/_functions/tensor.py
new file mode 100644
index 0000000000000000000000000000000000000000..a37c8bf58593bb4f57d307f290070990469a7aba
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/_functions/tensor.py
@@ -0,0 +1,65 @@
+# mypy: allow-untyped-defs
+import operator
+from functools import reduce
+from typing_extensions import deprecated
+
+import torch
+import torch._utils
+from torch.autograd.function import Function
+
+
+class Type(Function):
+    @staticmethod
+    @deprecated(
+        "`torch.autograd._functions.Type` is deprecated as of PyTorch 2.1, "
+        "please use `torch.tensor.to(dtype=dtype)` instead.",
+        category=FutureWarning,
+    )
+    def forward(ctx, i, dest_type):
+        ctx.input_type = type(i)
+        ctx.input_device = -1 if not i.is_cuda else i.get_device()
+        return i.type(dest_type)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        if ctx.input_device == -1:
+            return grad_output.type(ctx.input_type), None
+        else:
+            with torch.cuda.device(ctx.input_device):
+                return grad_output.type(ctx.input_type), None
+
+
+# TODO: deprecate this
+class Resize(Function):
+    @staticmethod
+    def forward(ctx, tensor, sizes):
+        ctx.sizes = sizes
+        ctx.numel = reduce(operator.mul, sizes, 1)
+        if tensor.numel() != ctx.numel:
+            raise RuntimeError(
+                (
+                    "requested resize to {} ({} elements in total), "
+                    "but the given tensor has a size of {} ({} elements). "
+                    "autograd's resize can only change the shape of a given "
+                    "tensor, while preserving the number of elements. "
+                ).format(
+                    "x".join(map(str, sizes)),
+                    ctx.numel,
+                    "x".join(map(str, tensor.size())),
+                    tensor.numel(),
+                )
+            )
+        ctx.input_sizes = tensor.size()
+        if tensor.is_quantized:
+            tensor.copy_(tensor)
+            return tensor.contiguous().view(*sizes)
+        if tensor.is_contiguous():
+            result = tensor.new(tensor).contiguous().view(*sizes)
+            return result
+        else:
+            return tensor.contiguous().view(*sizes)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        assert grad_output.numel() == ctx.numel
+        return grad_output.contiguous().view(ctx.input_sizes), None
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/_functions/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/_functions/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..56baae4aae3b05926a57a32853eb240bc30aa423
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/_functions/utils.py
@@ -0,0 +1,63 @@
+# mypy: allow-untyped-defs
+import operator
+from functools import reduce
+
+
+def maybe_view(tensor, size, check_same_size=True):
+    if check_same_size and tensor.size() == size:
+        return tensor
+    return tensor.contiguous().view(size)
+
+
+def maybe_unexpand(tensor, old_size, check_same_size=True):
+    if check_same_size and tensor.size() == old_size:
+        return tensor
+    num_unsqueezed = tensor.dim() - len(old_size)
+    expanded_dims = [
+        dim
+        for dim, (expanded, original) in enumerate(
+            zip(tensor.size()[num_unsqueezed:], old_size)
+        )
+        if expanded != original
+    ]
+
+    for _ in range(num_unsqueezed):
+        tensor = tensor.sum(0, keepdim=False)
+    for dim in expanded_dims:
+        tensor = tensor.sum(dim, keepdim=True)
+    return tensor
+
+
+# Check whether the op enable broadcasting, and whether it is supported by ONNX.
+# If dims1 and dims2 are different, then broadcast is True.
+# We always assume the combination of dims1 and dims2 is broadcastable.
+# The following types of broadcasting are supported in ONNX:
+#     1) Only one element in dims2, such as dims2 = [1, 1]
+#     2) dims2 is suffix of dims1, such as dims1 = [2, 3, 4], and dims2 = [3, 4]
+# Details can be found here: https://github.com/onnx/onnx/blob/master/docs/Operators.md#Gemm
+def check_onnx_broadcast(dims1, dims2):
+    broadcast = False
+    supported = True
+    len1 = len(dims1)
+    len2 = len(dims2)
+    numel1 = reduce(operator.mul, dims1)
+    numel2 = reduce(operator.mul, dims2)
+    if len1 < len2:
+        broadcast = True
+        if numel2 != 1:
+            supported = False
+    elif len1 > len2:
+        broadcast = True
+        if numel2 != 1 and dims1[len1 - len2 :] != dims2:
+            supported = False
+    else:
+        if dims1 != dims2:
+            broadcast = True
+            if numel2 != 1:
+                supported = False
+
+    if not supported:
+        raise ValueError(
+            f"Numpy style broadcasting is not supported in ONNX. Input dims are: {dims1}, {dims2}"
+        )
+    return broadcast
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/anomaly_mode.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/anomaly_mode.py
new file mode 100644
index 0000000000000000000000000000000000000000..511d27df077fab88b68365c1ea9360e0b2755195
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/anomaly_mode.py
@@ -0,0 +1,121 @@
+# mypy: allow-untyped-defs
+r"""Autograd anomaly mode."""
+import warnings
+
+import torch
+
+
+__all__ = ["detect_anomaly", "set_detect_anomaly"]
+
+
+class detect_anomaly:
+    r"""Context-manager that enable anomaly detection for the autograd engine.
+
+    This does two things:
+
+    - Running the forward pass with detection enabled will allow the backward
+      pass to print the traceback of the forward operation that created the failing
+      backward function.
+    - If ``check_nan`` is ``True``, any backward computation that generate "nan"
+      value will raise an error. Default ``True``.
+
+    .. warning::
+        This mode should be enabled only for debugging as the different tests
+        will slow down your program execution.
+
+    Example:
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_ANOMALY)
+        >>> import torch
+        >>> from torch import autograd
+        >>> class MyFunc(autograd.Function):
+        ...     @staticmethod
+        ...     def forward(ctx, inp):
+        ...         return inp.clone()
+        ...     @staticmethod
+        ...     def backward(ctx, gO):
+        ...         # Error during the backward pass
+        ...         raise RuntimeError("Some error in backward")
+        ...         return gO.clone()
+        >>> def run_fn(a):
+        ...     out = MyFunc.apply(a)
+        ...     return out.sum()
+        >>> inp = torch.rand(10, 10, requires_grad=True)
+        >>> out = run_fn(inp)
+        >>> out.backward()
+            Traceback (most recent call last):
+              File "", line 1, in 
+              File "/your/pytorch/install/torch/_tensor.py", line 93, in backward
+                torch.autograd.backward(self, gradient, retain_graph, create_graph)
+              File "/your/pytorch/install/torch/autograd/__init__.py", line 90, in backward
+                allow_unreachable=True)  # allow_unreachable flag
+              File "/your/pytorch/install/torch/autograd/function.py", line 76, in apply
+                return self._forward_cls.backward(self, *args)
+              File "", line 8, in backward
+            RuntimeError: Some error in backward
+        >>> with autograd.detect_anomaly():
+        ...     inp = torch.rand(10, 10, requires_grad=True)
+        ...     out = run_fn(inp)
+        ...     out.backward()
+            Traceback of forward call that caused the error:
+              File "tmp.py", line 53, in 
+                out = run_fn(inp)
+              File "tmp.py", line 44, in run_fn
+                out = MyFunc.apply(a)
+            Traceback (most recent call last):
+              File "", line 4, in 
+              File "/your/pytorch/install/torch/_tensor.py", line 93, in backward
+                torch.autograd.backward(self, gradient, retain_graph, create_graph)
+              File "/your/pytorch/install/torch/autograd/__init__.py", line 90, in backward
+                allow_unreachable=True)  # allow_unreachable flag
+              File "/your/pytorch/install/torch/autograd/function.py", line 76, in apply
+                return self._forward_cls.backward(self, *args)
+              File "", line 8, in backward
+            RuntimeError: Some error in backward
+
+    """
+
+    def __init__(self, check_nan=True) -> None:  # noqa: D107
+        self.prev = torch.is_anomaly_enabled()
+        self.check_nan = check_nan
+        self.prev_check_nan = torch.is_anomaly_check_nan_enabled()
+        warnings.warn(
+            "Anomaly Detection has been enabled. "
+            "This mode will increase the runtime "
+            "and should only be enabled for debugging.",
+            stacklevel=2,
+        )
+
+    def __enter__(self) -> None:  # noqa: D105
+        torch.set_anomaly_enabled(True, self.check_nan)
+
+    def __exit__(self, *args: object) -> None:  # noqa: D105
+        torch.set_anomaly_enabled(self.prev, self.prev_check_nan)
+
+
+class set_detect_anomaly:
+    r"""Context-manager that sets the anomaly detection for the autograd engine on or off.
+
+    ``set_detect_anomaly`` will enable or disable the autograd anomaly detection
+    based on its argument :attr:`mode`.
+    It can be used as a context-manager or as a function.
+
+    See ``detect_anomaly`` above for details of the anomaly detection behaviour.
+
+    Args:
+        mode (bool): Flag whether to enable anomaly detection (``True``),
+                     or disable (``False``).
+        check_nan (bool): Flag whether to raise an error when the backward
+                          generate "nan"
+
+    """
+
+    def __init__(self, mode: bool, check_nan: bool = True) -> None:  # noqa: D107
+        self.prev = torch.is_anomaly_enabled()
+        self.prev_check_nan = torch.is_anomaly_check_nan_enabled()
+        torch.set_anomaly_enabled(mode, check_nan)
+
+    def __enter__(self) -> None:  # noqa: D105
+        pass
+
+    def __exit__(self, *args: object) -> None:  # noqa: D105
+        torch.set_anomaly_enabled(self.prev, self.prev_check_nan)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/forward_ad.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/forward_ad.py
new file mode 100644
index 0000000000000000000000000000000000000000..42652386529621a0a82b2841b9d3eff25dbbe546
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/forward_ad.py
@@ -0,0 +1,231 @@
+# mypy: allow-untyped-defs
+import os
+from collections import namedtuple
+from typing import Any
+
+import torch
+
+from .grad_mode import _DecoratorContextManager
+
+
+__all__ = [
+    "UnpackedDualTensor",
+    "enter_dual_level",
+    "exit_dual_level",
+    "make_dual",
+    "unpack_dual",
+    "dual_level",
+]
+
+# Global variable used to make the python API simpler to use
+_current_level = -1
+
+
+def enter_dual_level():
+    r"""Enter a new forward grad level.
+
+    This level can be used to make and unpack dual Tensors to compute
+    forward gradients.
+
+    This function also updates the current level that is used by default
+    by the other functions in this API.
+    """
+    global _current_level
+    new_level = torch._C._enter_dual_level()
+    if new_level != _current_level + 1:
+        raise RuntimeError(
+            "Entering a new forward AD level but the current level "
+            "is not valid. Make sure you did not modified it directly."
+        )
+    _current_level = new_level
+    return new_level
+
+
+def exit_dual_level(*, level=None):
+    r"""Exit a forward grad level.
+
+    This function deletes all the gradients associated with this
+    level. Only deleting the latest entered level is allowed.
+
+    This function also updates the current level that is used by default
+    by the other functions in this API.
+    """
+    global _current_level
+    if level is None:
+        level = _current_level
+    if level != _current_level:
+        raise RuntimeError(
+            "Trying to exit a forward AD level that was not the last one "
+            "that was created. This is not supported."
+        )
+    torch._C._exit_dual_level(level=level)
+    _current_level = level - 1
+
+
+def _maybe_load_decompositions():
+    if os.environ.get("PYTORCH_JIT", "1") == "1" and __debug__:
+        from torch._decomp import decompositions_for_jvp  # noqa: F401
+
+
+def make_dual(tensor, tangent, *, level=None):
+    r"""Associate a tensor value with its tangent to create a "dual tensor" for forward AD gradient computation.
+
+    The result is a new tensor aliased to :attr:`tensor` with :attr:`tangent` embedded
+    as an attribute as-is if it has the same storage layout or copied otherwise.
+    The tangent attribute can be recovered with :func:`unpack_dual`.
+
+    This function is backward differentiable.
+
+    Given a function `f` whose jacobian is `J`, it allows one to compute the Jacobian-vector product (`jvp`)
+    between `J` and a given vector `v` as follows.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("Undefined variables")
+        >>> with dual_level():
+        ...     inp = make_dual(x, v)
+        ...     out = f(inp)
+        ...     y, jvp = unpack_dual(out)
+
+    Please see the `forward-mode AD tutorial `__
+    for detailed steps on how to use this API.
+
+    """
+    # See NOTE: [forward-mode AD decompositions mechanism]
+    #
+    # Import from torch._decomp import decompositions_for_jvp to register
+    # decompositions for jvp to the jit registry
+    #
+    # FIXME: We specify that __debug__ must be True because
+    # if python is run with -OO or -O flags (i.e., __debug__ is False), we encounter the
+    # following error:
+    #
+    # Return value was annotated as having type Tuple[NoneType, NoneType] but is actually of
+    # type Tuple[Tensor, Tensor]:
+    #   File ".../torch/_decomp/__init__.py", line 1585
+    #     else:
+    #         buffer = z
+    #     return min - torch.log1p(z), buffer
+    #     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
+    _maybe_load_decompositions()
+
+    if level is None:
+        level = _current_level
+
+    if level < 0:
+        raise RuntimeError(
+            "Trying to create a dual Tensor for forward AD but no level "
+            "exists, make sure to enter_dual_level() first."
+        )
+    if not (tensor.is_floating_point() or tensor.is_complex()):
+        raise ValueError(
+            f"Expected primal to be floating point or complex, but got: {tensor.dtype}"
+        )
+    if not (tangent.is_floating_point() or tangent.is_complex()):
+        raise ValueError(
+            f"Expected tangent to be floating point or complex, but got: {tangent.dtype}"
+        )
+
+    return torch._VF._make_dual(tensor, tangent, level=level)
+
+
+_UnpackedDualTensor = namedtuple("_UnpackedDualTensor", ["primal", "tangent"])
+
+
+class UnpackedDualTensor(_UnpackedDualTensor):
+    r"""Namedtuple returned by :func:`unpack_dual` containing the primal and tangent components of the dual tensor.
+
+    See :func:`unpack_dual` for more details.
+
+    """
+
+
+def unpack_dual(tensor, *, level=None):
+    r"""Unpack a "dual tensor" to get both its Tensor value and its forward AD gradient.
+
+    The result is a namedtuple ``(primal, tangent)`` where ``primal`` is a view of
+    :attr:`tensor`'s primal and ``tangent`` is :attr:`tensor`'s tangent as-is.
+    Neither of these tensors can be dual tensor of level :attr:`level`.
+
+    This function is backward differentiable.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("Undefined variables")
+        >>> with dual_level():
+        ...     inp = make_dual(x, x_t)
+        ...     out = f(inp)
+        ...     y, jvp = unpack_dual(out)
+        ...     jvp = unpack_dual(out).tangent
+
+    Please see the `forward-mode AD tutorial `__
+    for detailed steps on how to use this API.
+    """
+    if level is None:
+        level = _current_level
+
+    if level < 0:
+        return UnpackedDualTensor(tensor, None)
+
+    primal, dual = torch._VF._unpack_dual(tensor, level=level)
+
+    return UnpackedDualTensor(primal, dual)
+
+
+class dual_level(_DecoratorContextManager):
+    r"""Context-manager for forward AD, where all forward AD computation must occur within the ``dual_level`` context.
+
+    .. Note::
+
+        The ``dual_level`` context appropriately enters and exit the dual level to
+        controls the current forward AD level, which is used by default by the other
+        functions in this API.
+
+        We currently don't plan to support nested ``dual_level`` contexts, however, so
+        only a single forward AD level is supported. To compute higher-order
+        forward grads, one can use :func:`torch.func.jvp`.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("Undefined variables")
+        >>> x = torch.tensor([1])
+        >>> x_t = torch.tensor([1])
+        >>> with dual_level():
+        ...     inp = make_dual(x, x_t)
+        ...     # Do computations with inp
+        ...     out = your_fn(inp)
+        ...     _, grad = unpack_dual(out)
+        >>> grad is None
+        False
+        >>> # After exiting the level, the grad is deleted
+        >>> _, grad_after = unpack_dual(out)
+        >>> grad is None
+        True
+
+    Please see the `forward-mode AD tutorial `__
+    for detailed steps on how to use this API.
+    """
+
+    def __enter__(self):
+        return enter_dual_level()
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        exit_dual_level()
+
+
+# Private helper functions
+_is_fwd_grad_enabled = torch._C._is_fwd_grad_enabled
+
+
+# Private helper function to enable or disable fwd grad.
+# If you're a user and want to use this, please file an issue to discuss the use case.
+class _set_fwd_grad_enabled(_DecoratorContextManager):
+    def __init__(self, mode: bool) -> None:
+        self.prev = _is_fwd_grad_enabled()
+        torch._C._set_fwd_grad_enabled(mode)
+
+    def __enter__(self) -> None:
+        pass
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        torch._C._set_fwd_grad_enabled(self.prev)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/function.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/function.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc72702a780359a1035b1732ed1738a9a0d8a689
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/function.py
@@ -0,0 +1,844 @@
+# mypy: allow-untyped-defs
+import functools
+import inspect
+import itertools
+import warnings
+from collections import OrderedDict
+from typing import Any, List, Optional, Tuple
+from typing_extensions import deprecated
+
+import torch
+import torch._C as _C
+import torch._functorch as _functorch
+import torch.utils.hooks as hooks
+from torch._C import _functions
+from torch._functorch.autograd_function import custom_function_call
+
+
+__all__ = [
+    "FunctionCtx",
+    "BackwardCFunction",
+    "FunctionMeta",
+    "Function",
+    "once_differentiable",
+    "InplaceFunction",
+    "NestedIOFunction",
+]
+
+# Unique id provider for each class inheriting from Function
+# This is incremented in FunctionMeta during class definition
+AUTOGRAD_FUNCTION_COUNTER = itertools.count()
+
+
+# Formerly known as: _ContextMethodMixin
+class FunctionCtx:
+    def save_for_backward(self, *tensors: torch.Tensor):
+        r"""Save given tensors for a future call to :func:`~Function.backward`.
+
+        ``save_for_backward`` should be called at most once, in either the
+        :func:`setup_context` or :func:`forward` methods, and only with tensors.
+
+        All tensors intended to be used in the backward pass should be saved
+        with ``save_for_backward`` (as opposed to directly on ``ctx``) to prevent
+        incorrect gradients and memory leaks, and enable the application of saved
+        tensor hooks. See :class:`torch.autograd.graph.saved_tensors_hooks`.
+
+        Note that if intermediary tensors, tensors that are neither inputs
+        nor outputs of :func:`forward`, are saved for backward, your custom Function
+        may not support double backward.
+        Custom Functions that do not support double backward should decorate their
+        :func:`backward` method with ``@once_differentiable`` so that performing
+        double backward raises an error. If you'd like to support double backward,
+        you can either recompute intermediaries based on the inputs during backward
+        or return the intermediaries as the outputs of the custom Function. See the
+        `double backward tutorial `_
+        for more details.
+
+        In :func:`backward`, saved tensors can be accessed through the :attr:`saved_tensors`
+        attribute. Before returning them to the user, a check is made to ensure
+        they weren't used in any in-place operation that modified their content.
+
+        Arguments can also be ``None``. This is a no-op.
+
+        See :ref:`extending-autograd` for more details on how to use this method.
+
+        Example::
+            >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+            >>> class Func(Function):
+            >>>     @staticmethod
+            >>>     def forward(ctx, x: torch.Tensor, y: torch.Tensor, z: int):
+            >>>         w = x * z
+            >>>         out = x * y + y * z + w * y
+            >>>         ctx.save_for_backward(x, y, w, out)
+            >>>         ctx.z = z  # z is not a tensor
+            >>>         return out
+            >>>
+            >>>     @staticmethod
+            >>>     @once_differentiable
+            >>>     def backward(ctx, grad_out):
+            >>>         x, y, w, out = ctx.saved_tensors
+            >>>         z = ctx.z
+            >>>         gx = grad_out * (y + y * z)
+            >>>         gy = grad_out * (x + z + w)
+            >>>         gz = None
+            >>>         return gx, gy, gz
+            >>>
+            >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double)
+            >>> b = torch.tensor(2., requires_grad=True, dtype=torch.double)
+            >>> c = 4
+            >>> d = Func.apply(a, b, c)
+
+        """
+        self.to_save = tensors
+
+    def save_for_forward(self, *tensors: torch.Tensor):
+        r"""Save given tensors for a future call to :func:`~Function.jvp`.
+
+        ``save_for_forward`` should be called at most once, in either the
+        :func:`setup_context` or :func:`forward` methods, and all arguments
+        should be tensors.
+
+        In :func:`jvp`, saved objects can be accessed through the :attr:`saved_tensors`
+        attribute.
+
+        Arguments can also be ``None``. This is a no-op.
+
+        See :ref:`extending-autograd` for more details on how to use this method.
+
+        Example::
+            >>> # xdoctest: +SKIP
+            >>> class Func(torch.autograd.Function):
+            >>>     @staticmethod
+            >>>     def forward(ctx, x: torch.Tensor, y: torch.Tensor, z: int):
+            >>>         ctx.save_for_backward(x, y)
+            >>>         ctx.save_for_forward(x, y)
+            >>>         ctx.z = z
+            >>>         return x * y * z
+            >>>
+            >>>     @staticmethod
+            >>>     def jvp(ctx, x_t, y_t, _):
+            >>>         x, y = ctx.saved_tensors
+            >>>         z = ctx.z
+            >>>         return z * (y * x_t + x * y_t)
+            >>>
+            >>>     @staticmethod
+            >>>     def vjp(ctx, grad_out):
+            >>>         x, y = ctx.saved_tensors
+            >>>         z = ctx.z
+            >>>         return z * grad_out * y, z * grad_out * x, None
+            >>>
+            >>>     a = torch.tensor(1., requires_grad=True, dtype=torch.double)
+            >>>     t = torch.tensor(1., dtype=torch.double)
+            >>>     b = torch.tensor(2., requires_grad=True, dtype=torch.double)
+            >>>     c = 4
+            >>>
+            >>>     with fwAD.dual_level():
+            >>>         a_dual = fwAD.make_dual(a, t)
+            >>>         d = Func.apply(a_dual, b, c)
+
+        """
+        for tensor in tensors:
+            assert isinstance(tensor, torch.Tensor) or tensor is None, (
+                "save_for_forward expects all arguments to be tensors; you should "
+                "save non-tensors as attributes on ctx."
+            )
+
+        self.saved_for_forward = tensors
+
+    def mark_dirty(self, *args: torch.Tensor):
+        r"""Mark given tensors as modified in an in-place operation.
+
+        This should be called at most once, in either the :func:`setup_context`
+        or :func:`forward` methods, and all arguments should be inputs.
+
+        Every tensor that's been modified in-place in a call to :func:`forward`
+        should be given to this function, to ensure correctness of our checks.
+        It doesn't matter whether the function is called before or after
+        modification.
+
+        Examples::
+            >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+            >>> class Inplace(Function):
+            >>>     @staticmethod
+            >>>     def forward(ctx, x):
+            >>>         x_npy = x.numpy() # x_npy shares storage with x
+            >>>         x_npy += 1
+            >>>         ctx.mark_dirty(x)
+            >>>         return x
+            >>>
+            >>>     @staticmethod
+            >>>     @once_differentiable
+            >>>     def backward(ctx, grad_output):
+            >>>         return grad_output
+            >>>
+            >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double).clone()
+            >>> b = a * a
+            >>> Inplace.apply(a)  # This would lead to wrong gradients!
+            >>>                   # but the engine would not know unless we mark_dirty
+            >>> # xdoctest: +SKIP
+            >>> b.backward() # RuntimeError: one of the variables needed for gradient
+            >>>              # computation has been modified by an inplace operation
+
+        """
+        self.dirty_tensors = args
+
+    @deprecated(
+        "`mark_shared_storage` is deprecated. "
+        "Tensors with shared storages are automatically tracked. "
+        "Note that calls to `set_()` are not tracked",
+        category=FutureWarning,
+    )
+    def mark_shared_storage(self, *pairs):
+        pass
+
+    def mark_non_differentiable(self, *args: torch.Tensor):
+        r"""Mark outputs as non-differentiable.
+
+        This should be called at most once, in either the :func:`setup_context`
+        or :func:`forward` methods, and all arguments should be tensor outputs.
+
+        This will mark outputs as not requiring gradients, increasing the
+        efficiency of backward computation. You still need to accept a gradient
+        for each output in :meth:`~Function.backward`, but it's always going to
+        be a zero tensor with the same shape as the shape of a corresponding
+        output.
+
+        This is used e.g. for indices returned from a sort. See example::
+            >>> class Func(Function):
+            >>>     @staticmethod
+            >>>     def forward(ctx, x):
+            >>>         sorted, idx = x.sort()
+            >>>         ctx.mark_non_differentiable(idx)
+            >>>         ctx.save_for_backward(x, idx)
+            >>>         return sorted, idx
+            >>>
+            >>>     @staticmethod
+            >>>     @once_differentiable
+            >>>     def backward(ctx, g1, g2):  # still need to accept g2
+            >>>         x, idx = ctx.saved_tensors
+            >>>         grad_input = torch.zeros_like(x)
+            >>>         grad_input.index_add_(0, idx, g1)
+            >>>         return grad_input
+
+        """
+        self.non_differentiable = args
+
+    def set_materialize_grads(self, value: bool):
+        r"""Set whether to materialize grad tensors. Default is ``True``.
+
+        This should be called only from either the :func:`setup_context` or
+        :func:`forward` methods.
+
+        If ``True``, undefined grad tensors will be expanded to tensors full of zeros
+        prior to calling the :func:`backward` and :func:`jvp` methods.
+
+        Example::
+            >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+            >>> class SimpleFunc(Function):
+            >>>     @staticmethod
+            >>>     def forward(ctx, x):
+            >>>         return x.clone(), x.clone()
+            >>>
+            >>>     @staticmethod
+            >>>     @once_differentiable
+            >>>     def backward(ctx, g1, g2):
+            >>>         return g1 + g2  # No check for None necessary
+            >>>
+            >>> # We modify SimpleFunc to handle non-materialized grad outputs
+            >>> class Func(Function):
+            >>>     @staticmethod
+            >>>     def forward(ctx, x):
+            >>>         ctx.set_materialize_grads(False)
+            >>>         ctx.save_for_backward(x)
+            >>>         return x.clone(), x.clone()
+            >>>
+            >>>     @staticmethod
+            >>>     @once_differentiable
+            >>>     def backward(ctx, g1, g2):
+            >>>         x, = ctx.saved_tensors
+            >>>         grad_input = torch.zeros_like(x)
+            >>>         if g1 is not None:  # We must check for None now
+            >>>             grad_input += g1
+            >>>         if g2 is not None:
+            >>>             grad_input += g2
+            >>>         return grad_input
+            >>>
+            >>> a = torch.tensor(1., requires_grad=True)
+            >>> b, _ = Func.apply(a)  # induces g2 to be undefined
+
+        """
+        self.materialize_grads = value
+
+
+# DO NOT USE: This is only defined to be able to load old serialized models
+_ContextMethodMixin = FunctionCtx
+
+
+class _HookMixin:
+    @staticmethod
+    def _register_hook(backward_hooks, hook):
+        if backward_hooks is None:
+            backward_hooks = OrderedDict()
+        handle = hooks.RemovableHandle(backward_hooks)
+        backward_hooks[handle.id] = hook
+        return backward_hooks, handle
+
+
+class BackwardCFunction(_C._FunctionBase, FunctionCtx, _HookMixin):
+    r"""
+    This class is used for internal autograd work. Do not use.
+    """
+
+    def apply(self, *args):
+        r"""
+        Apply method used when executing this Node during the backward
+        """
+        # _forward_cls is defined by derived class
+        # The user should define either backward or vjp but never both.
+        backward_fn = self._forward_cls.backward  # type: ignore[attr-defined]
+        vjp_fn = self._forward_cls.vjp  # type: ignore[attr-defined]
+        if backward_fn is not Function.backward and vjp_fn is not Function.vjp:
+            raise RuntimeError(
+                "Implementing both 'backward' and 'vjp' for a custom "
+                "Function is not allowed. You should only implement one "
+                "of them."
+            )
+        user_fn = vjp_fn if vjp_fn is not Function.vjp else backward_fn
+        return user_fn(self, *args)
+
+    def apply_jvp(self, *args):
+        r"""
+        Apply method used when executing forward mode AD during the forward
+        """
+        # _forward_cls is defined by derived class
+        return self._forward_cls.jvp(self, *args)  # type: ignore[attr-defined]
+
+    def _compiled_autograd_key(self):
+        return self._forward_cls._compiled_autograd_key(self)  # type: ignore[attr-defined]
+
+
+class FunctionMeta(type):
+    """Function metaclass.
+
+    This metaclass sets up the following properties:
+        _backward_cls: The Function class corresponding to the differentiated
+            version of this function (which is generated on the fly by this
+            metaclass).
+    """
+
+    def __init__(cls, name, bases, attrs):
+        backward_fn = type(
+            name + "Backward", (BackwardCFunction,), {"_forward_cls": cls}
+        )
+        backward_fn._autograd_function_id = next(AUTOGRAD_FUNCTION_COUNTER)  # type: ignore[attr-defined]
+        backward_fn._compiled_autograd_should_lift = attrs.get(  # type: ignore[attr-defined]
+            "_compiled_autograd_should_lift", True
+        )
+        cls._backward_cls = backward_fn
+
+        super().__init__(name, bases, attrs)
+
+
+class _SingleLevelFunction(
+    _C._FunctionBase, FunctionCtx, _HookMixin, metaclass=FunctionMeta
+):
+    @staticmethod
+    def forward(*args: Any, **kwargs: Any) -> Any:
+        r"""Define the forward of the custom autograd Function.
+
+        This function is to be overridden by all subclasses.
+        There are two ways to define forward:
+
+        Usage 1 (Combined forward and ctx)::
+
+            @staticmethod
+            def forward(ctx: Any, *args: Any, **kwargs: Any) -> Any:
+                pass
+
+        - It must accept a context ctx as the first argument, followed by any
+          number of arguments (tensors or other types).
+        - See :ref:`combining-forward-context` for more details
+
+        Usage 2 (Separate forward and ctx)::
+
+            @staticmethod
+            def forward(*args: Any, **kwargs: Any) -> Any:
+                pass
+
+            @staticmethod
+            def setup_context(ctx: Any, inputs: Tuple[Any, ...], output: Any) -> None:
+                pass
+
+        - The forward no longer accepts a ctx argument.
+        - Instead, you must also override the :meth:`torch.autograd.Function.setup_context`
+          staticmethod to handle setting up the ``ctx`` object.
+          ``output`` is the output of the forward, ``inputs`` are a Tuple of inputs
+          to the forward.
+        - See :ref:`extending-autograd` for more details
+
+        The context can be used to store arbitrary data that can be then
+        retrieved during the backward pass. Tensors should not be stored
+        directly on `ctx` (though this is not currently enforced for
+        backward compatibility). Instead, tensors should be saved either with
+        :func:`ctx.save_for_backward` if they are intended to be used in
+        ``backward`` (equivalently, ``vjp``) or :func:`ctx.save_for_forward`
+        if they are intended to be used for in ``jvp``.
+        """
+        raise NotImplementedError(
+            "You must implement the forward function for custom autograd.Function."
+        )
+
+    @staticmethod
+    def setup_context(ctx: Any, inputs: Tuple[Any, ...], output: Any) -> Any:
+        r"""There are two ways to define the forward pass of an autograd.Function.
+
+        Either:
+
+        1. Override forward with the signature ``forward(ctx, *args, **kwargs)``.
+           ``setup_context`` is not overridden. Setting up the ctx for backward
+           happens inside the ``forward``.
+        2. Override forward with the signature ``forward(*args, **kwargs)`` and
+           override ``setup_context``. Setting up the ctx for backward happens
+           inside ``setup_context`` (as opposed to inside the ``forward``)
+
+        See :meth:`torch.autograd.Function.forward` and :ref:`extending-autograd` for more details.
+        """
+        raise NotImplementedError("setup_context is not implemented.")
+
+    @staticmethod
+    def backward(ctx: Any, *grad_outputs: Any) -> Any:
+        r"""Define a formula for differentiating the operation with backward mode automatic differentiation.
+
+        This function is to be overridden by all subclasses.
+        (Defining this function is equivalent to defining the ``vjp`` function.)
+
+        It must accept a context :attr:`ctx` as the first argument, followed by
+        as many outputs as the :func:`forward` returned (None will be passed in
+        for non tensor outputs of the forward function),
+        and it should return as many tensors, as there were inputs to
+        :func:`forward`. Each argument is the gradient w.r.t the given output,
+        and each returned value should be the gradient w.r.t. the
+        corresponding input. If an input is not a Tensor or is a Tensor not
+        requiring grads, you can just pass None as a gradient for that input.
+
+        The context can be used to retrieve tensors saved during the forward
+        pass. It also has an attribute :attr:`ctx.needs_input_grad` as a tuple
+        of booleans representing whether each input needs gradient. E.g.,
+        :func:`backward` will have ``ctx.needs_input_grad[0] = True`` if the
+        first input to :func:`forward` needs gradient computed w.r.t. the
+        output.
+        """
+        raise NotImplementedError(
+            "You must implement either the backward or vjp method for "
+            "your custom autograd.Function to use it with backward "
+            "mode AD."
+        )
+
+    # vjp and backward are alias of each other
+    vjp = backward
+
+    @staticmethod
+    def jvp(ctx: Any, *grad_inputs: Any) -> Any:
+        r"""Define a formula for differentiating the operation with forward mode automatic differentiation.
+
+        This function is to be overridden by all subclasses.
+        It must accept a context :attr:`ctx` as the first argument, followed by
+        as many inputs as the :func:`forward` got (None will be passed in
+        for non tensor inputs of the forward function),
+        and it should return as many tensors as there were outputs to
+        :func:`forward`. Each argument is the gradient w.r.t the given input,
+        and each returned value should be the gradient w.r.t. the
+        corresponding output. If an output is not a Tensor or the function is not
+        differentiable with respect to that output, you can just pass None as a
+        gradient for that input.
+
+        You can use the :attr:`ctx` object to pass any value from the forward to this
+        functions.
+        """
+        raise NotImplementedError(
+            "You must implement the jvp function for custom "
+            "autograd.Function to use it with forward mode AD."
+        )
+
+
+class Function(_SingleLevelFunction):
+    r"""Base class to create custom `autograd.Function`.
+
+    To create a custom `autograd.Function`, subclass this class and implement
+    the :meth:`forward` and :meth:`backward` static methods. Then, to use your custom
+    op in the forward pass, call the class method ``apply``. Do not call
+    :meth:`forward` directly.
+
+    To ensure correctness and best performance, make sure you are calling the
+    correct methods on ``ctx`` and validating your backward function using
+    :func:`torch.autograd.gradcheck`.
+
+    See :ref:`extending-autograd` for more details on how to use this class.
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> class Exp(Function):
+        >>>     @staticmethod
+        >>>     def forward(ctx, i):
+        >>>         result = i.exp()
+        >>>         ctx.save_for_backward(result)
+        >>>         return result
+        >>>
+        >>>     @staticmethod
+        >>>     def backward(ctx, grad_output):
+        >>>         result, = ctx.saved_tensors
+        >>>         return grad_output * result
+        >>>
+        >>> # Use it by calling the apply method:
+        >>> # xdoctest: +SKIP
+        >>> output = Exp.apply(input)
+    """
+
+    def __init__(self, *args, **kwargs):
+        warnings.warn(
+            f"{self.__class__} should not be instantiated. Methods on autograd functions"
+            "are all static, so you should invoke them on the class itself. "
+            "Instantiating an autograd function will raise an "
+            "error in a future version of PyTorch.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+
+    def __call__(self, *args, **kwargs):
+        raise RuntimeError(
+            "Legacy autograd function with non-static forward method is deprecated. "
+            "Please use new-style autograd function with static forward method. "
+            "(Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)"
+        )
+
+    """
+    Bool that specifies if PyTorch should attempt to autogenerate
+    :func:`torch.vmap` support for this autograd.Function. You may set this to
+    True only if this autograd.Function's forward, backward, and jvp (if they
+    exist) are written using PyTorch operations; otherwise, please override
+    :meth:`torch.autograd.Function.vmap` to add support for :func:`torch.vmap`.
+
+    Please see :ref:`func-autograd-function` for more details.
+    """
+    generate_vmap_rule = False
+
+    @staticmethod
+    def vmap(info, in_dims, *args):
+        r"""Define the behavior for this autograd.Function underneath :func:`torch.vmap`.
+
+        For a :func:`torch.autograd.Function` to support
+        :func:`torch.vmap`, you must either override this static method, or set
+        ``generate_vmap_rule`` to ``True`` (you may not do both).
+
+        If you choose to override this staticmethod: it must accept
+
+        - an ``info`` object as the first argument. ``info.batch_size``
+          specifies the size of the dimension being vmapped over,
+          while ``info.randomness`` is the randomness option passed to
+          :func:`torch.vmap`.
+        - an ``in_dims`` tuple as the second argument.
+          For each arg in ``args``, ``in_dims`` has a corresponding
+          ``Optional[int]``. It is ``None`` if the arg is not a Tensor or if
+          the arg is not being vmapped over, otherwise, it is an integer
+          specifying what dimension of the Tensor is being vmapped over.
+        - ``*args``, which is the same as the args to :meth:`~Function.forward`.
+
+        The return of the vmap staticmethod is a tuple of ``(output, out_dims)``.
+        Similar to ``in_dims``, ``out_dims`` should be of the same structure as
+        ``output`` and contain one ``out_dim`` per output that specifies if the
+        output has the vmapped dimension and what index it is in.
+
+        Please see :ref:`func-autograd-function` for more details.
+        """
+        raise NotImplementedError(
+            "To use autograd.Function with vmap, you must either override the "
+            "vmap staticmethod or set generate_vmap_rule=True."
+        )
+
+    @classmethod
+    def apply(cls, *args, **kwargs):
+        def bind_default_args(func, *args, **kwargs):
+            signature = inspect.signature(func)
+            bound_args = signature.bind(*args, **kwargs)
+            bound_args.apply_defaults()
+
+            return bound_args.args
+
+        is_setup_ctx_defined = _is_setup_context_defined(cls.setup_context)
+        if is_setup_ctx_defined:
+            args = bind_default_args(cls.forward, *args, **kwargs)
+
+        if not torch._C._are_functorch_transforms_active():
+            # See NOTE: [functorch vjp and autograd interaction]
+            args = _functorch.utils.unwrap_dead_wrappers(args)
+            return super().apply(*args, **kwargs)  # type: ignore[misc]
+
+        if not is_setup_ctx_defined:
+            raise RuntimeError(
+                "In order to use an autograd.Function with functorch transforms "
+                "(vmap, grad, jvp, jacrev, ...), it must override the setup_context "
+                "staticmethod. For more details, please see "
+                "https://pytorch.org/docs/main/notes/extending.func.html"
+            )
+
+        return custom_function_call(cls, *args, **kwargs)
+
+    @staticmethod
+    def _compiled_autograd_key(ctx):
+        return (ctx._autograd_function_id,)
+
+
+def _is_setup_context_defined(fn):
+    return fn != _SingleLevelFunction.setup_context
+
+
+def once_differentiable(fn):
+    @functools.wraps(fn)
+    def wrapper(ctx, *args):
+        with torch.no_grad():
+            outputs = fn(ctx, *args)
+
+        if not torch.is_grad_enabled():
+            return outputs
+
+        # If any of the inputs have requires_grad=True, we force the outputs
+        # to have requires_grad=True but point to a grad_fn which throws an
+        # error message during (double) back-propagation.
+        # XXX: this is only an approximation of requires_grad - there's no way
+        # to figure out if fn didn't use ctx.saved_tensors and as a result
+        # some Tensors might require grad, even if no args do.
+        # Unfortunately, this leads to unexpected error messages ("no nodes
+        # require computing gradients"), but I don't have a better idea.
+        # These functions would raise an error in backward anyway.
+        requires_grad = any(
+            isinstance(arg, torch.Tensor) and arg.requires_grad for arg in args
+        )
+        if not requires_grad:
+            return outputs
+
+        if not isinstance(outputs, tuple):
+            outputs = (outputs,)
+
+        err_fn = _functions.DelayedError(
+            b"trying to differentiate twice a function that was marked "
+            b"with @once_differentiable",
+            len(outputs),
+        )
+
+        # Create aliases of each output that has requires_grad=True. We need
+        # at least one of the inputs to err_fn to require grad so that the
+        # output will have a grad_fn.
+        def fake_requires_grad(var):
+            if var is not None:
+                var = var.detach()
+                var.requires_grad = True
+            return var
+
+        return err_fn(*[fake_requires_grad(v) for v in outputs])
+
+    return wrapper
+
+
+class InplaceFunction(Function):
+    r"""
+    This class is here only for backward compatibility reasons.
+    Use :class:`Function` instead of this for any new use case.
+    """
+
+    def __init__(self, inplace=False):
+        super().__init__()
+        self.inplace = inplace
+
+
+def _nested_map(condition, fn, condition_msg=None):
+    def _map(obj):
+        if condition(obj):
+            return fn(obj)
+        elif obj is None:
+            return None
+        elif isinstance(obj, (list, tuple)):
+            mapped = (_map(x) for x in obj)
+            if hasattr(obj, "_fields"):
+                # obj is namedtuple
+                return type(obj)(*mapped)
+            return type(obj)(mapped)
+        elif isinstance(obj, dict):
+            return {x: _map(obj[x]) for x in obj}
+        else:
+            raise ValueError(
+                "Auto nesting doesn't know how to process "
+                "an input object of type "
+                + torch.typename(obj)
+                + (
+                    ". Accepted types: " + condition_msg + ", or lists/tuples of them"
+                    if condition_msg
+                    else ""
+                )
+            )
+
+    return _map
+
+
+def _jit_unwrap_structured(obj):
+    if hasattr(obj, "_jit_unwrap"):
+        return obj._jit_unwrap()
+    return obj
+
+
+def _iter_filter(condition, allow_unknown=False, condition_msg=None, conversion=None):
+    def _iter(obj):
+        if conversion is not None:
+            obj = conversion(obj)
+        if condition(obj):
+            yield obj
+        elif obj is None:
+            return
+        elif isinstance(obj, (list, tuple)):
+            for o in obj:
+                yield from _iter(o)
+        elif isinstance(obj, dict):
+            # We only accept primitive key types, so we needn't inspect them
+            for o in obj.values():
+                yield from _iter(o)
+        elif allow_unknown:
+            yield obj
+        else:
+            raise ValueError(
+                "Auto nesting doesn't know how to process "
+                "an input object of type "
+                + torch.typename(obj)
+                + (
+                    ". Accepted types: " + condition_msg + ", or lists/tuples of them"
+                    if condition_msg
+                    else ""
+                )
+            )
+
+    return _iter
+
+
+def _unflatten(input, proto):
+    # unflatten a list or tuple input into a nested list/tuple structure
+    # specified by proto
+    def unflatten_helper(input, proto):
+        res: List[Optional[torch.Tensor]] = []
+        if hasattr(proto, "_jit_wrap"):
+            return proto._jit_wrap(input)
+        if not isinstance(proto, (list, tuple)):
+            return input[0], input[1:]
+        for e in proto:
+            if e is None:
+                res.append(e)
+            else:
+                res_e, input = unflatten_helper(input, e)
+                res.append(res_e)
+        return type(proto)(res), input
+
+    return unflatten_helper(input, proto)[0]
+
+
+_iter_jit_values = _iter_filter(
+    lambda o: o is None or isinstance(o, torch._C.Value),
+    condition_msg="jit's Values or None",
+)
+_iter_tensors = _iter_filter(
+    lambda x: isinstance(x, torch.Tensor),
+    condition_msg="Tensors",
+    conversion=_jit_unwrap_structured,
+)
+_iter_tensors_permissive = _iter_filter(
+    lambda x: isinstance(x, torch.Tensor),
+    allow_unknown=True,
+    condition_msg="Tensors (permissive)",
+)
+_iter_None_tensors = _iter_filter(
+    lambda o: o is None or isinstance(o, torch.Tensor), condition_msg="Tensors or None"
+)
+_map_tensor_data = _nested_map(
+    lambda x: isinstance(x, torch.Tensor), lambda o: o.data, condition_msg="Tensors"
+)
+
+
+class NestedIOFunction(Function):
+    r"""
+    This class is here only for backward compatibility reasons.
+    Use :class:`Function` instead of this for any new use case.
+    """
+    # The 'type: ignore' statements are needed here because these functions are declared as '@staticmethod' in the
+    # superclass (Function) but are instance methods here, which mypy reports as incompatible.
+
+    def _do_forward(self, *input):
+        self._nested_input = input
+        flat_input = tuple(_iter_tensors(input))
+        flat_output = super()._do_forward(*flat_input)  # type: ignore[misc]
+        nested_output = self._nested_output
+        nested_tensors = _unflatten(flat_output, self._nested_output)
+        return nested_tensors
+
+    def _do_backward(self, gradients, retain_variables):
+        self.retain_variables = retain_variables
+        result = super()._do_backward(gradients, retain_variables)  # type: ignore[misc]
+        if not retain_variables:
+            del self._nested_output
+            del self._to_save_nested
+        return result
+
+    def backward(self, *gradients: Any) -> Any:  # type: ignore[override]
+        r"""
+        Shared backward utility.
+        """
+        nested_gradients = _unflatten(gradients, self._nested_output)
+        result = self.backward_extended(*nested_gradients)  # type: ignore[func-returns-value]
+        return tuple(_iter_None_tensors(result))
+
+    __call__ = _do_forward
+
+    def forward(self, *args: Any) -> Any:  # type: ignore[override]
+        r"""
+        Shared forward utility.
+        """
+        nested_tensors = _map_tensor_data(self._nested_input)
+        result = self.forward_extended(*nested_tensors)  # type: ignore[func-returns-value]
+        del self._nested_input
+        self._nested_output = result
+        return tuple(_iter_tensors(result))
+
+    def save_for_backward(self, *args: Any) -> None:
+        r"""
+        See :meth:`Function.save_for_backward`.
+        """
+        self.to_save = tuple(_iter_tensors(args))
+        self._to_save_nested = args
+
+    @property
+    def saved_tensors(self):
+        r"""
+        See :meth:`Function.saved_tensors`.
+        """
+        flat_tensors = super().saved_tensors  # type: ignore[misc]
+        return _unflatten(flat_tensors, self._to_save_nested)
+
+    def mark_dirty(self, *args: Any, **kwargs: Any) -> None:
+        r"""
+        See :meth:`Function.mark_dirty`.
+        """
+        self.dirty_tensors = tuple(_iter_tensors((args, kwargs)))
+
+    def mark_non_differentiable(self, *args: Any, **kwargs: Any) -> None:
+        r"""
+        See :meth:`Function.mark_non_differentiable`.
+        """
+        self.non_differentiable = tuple(_iter_tensors((args, kwargs)))
+
+    def forward_extended(self, *input: Any) -> None:
+        r"""
+        User defined forward.
+        """
+        raise NotImplementedError
+
+    def backward_extended(self, *grad_output: Any) -> None:
+        r"""
+        User defined backward.
+        """
+        raise NotImplementedError
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/functional.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/functional.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2b3fdc8652262dafa4311e304e7daef89b63670
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/functional.py
@@ -0,0 +1,1185 @@
+# mypy: allow-untyped-defs
+from typing import List, Tuple
+
+import torch
+from torch._vmap_internals import _vmap
+
+from . import forward_ad as fwAD
+
+
+__all__ = ["vjp", "jvp", "jacobian", "hessian", "hvp", "vhp"]
+
+# Utility functions
+
+
+def _as_tuple_nocheck(x):
+    if isinstance(x, tuple):
+        return x
+    elif isinstance(x, list):
+        return tuple(x)
+    else:
+        return (x,)
+
+
+def _as_tuple(inp, arg_name=None, fn_name=None):
+    # Ensures that inp is a tuple of Tensors
+    # Returns whether or not the original inp was a tuple and the tupled version of the input
+    if arg_name is None and fn_name is None:
+        return _as_tuple_nocheck(inp)
+
+    is_inp_tuple = True
+    if not isinstance(inp, tuple):
+        inp = (inp,)
+        is_inp_tuple = False
+
+    for i, el in enumerate(inp):
+        if not isinstance(el, torch.Tensor):
+            if is_inp_tuple:
+                raise TypeError(
+                    f"The {arg_name} given to {fn_name} must be either a Tensor or a tuple of Tensors but the"
+                    f" value at index {i} has type {type(el)}."
+                )
+            else:
+                raise TypeError(
+                    f"The {arg_name} given to {fn_name} must be either a Tensor or a tuple of Tensors but the"
+                    f" given {arg_name} has type {type(el)}."
+                )
+
+    return is_inp_tuple, inp
+
+
+def _tuple_postprocess(res, to_unpack):
+    # Unpacks a potentially nested tuple of Tensors
+    # to_unpack should be a single boolean or a tuple of two booleans.
+    # It is used to:
+    # - invert _as_tuple when res should match the inp given to _as_tuple
+    # - optionally remove nesting of two tuples created by multiple calls to _as_tuple
+    if isinstance(to_unpack, tuple):
+        assert len(to_unpack) == 2
+        if not to_unpack[1]:
+            res = tuple(el[0] for el in res)
+        if not to_unpack[0]:
+            res = res[0]
+    else:
+        if not to_unpack:
+            res = res[0]
+    return res
+
+
+def _grad_preprocess(inputs, create_graph, need_graph):
+    # Preprocess the inputs to make sure they require gradient
+    # inputs is a tuple of Tensors to preprocess
+    # create_graph specifies if the user wants gradients to flow back to the Tensors in inputs
+    # need_graph specifies if we internally want gradients to flow back to the Tensors in res
+    # Note that we *always* create a new Tensor object to be able to see the difference between
+    # inputs given as arguments and the same Tensors automatically captured by the user function.
+    # Check this issue for more details on how that can happen: https://github.com/pytorch/pytorch/issues/32576
+    res = []
+    for inp in inputs:
+        if create_graph and inp.requires_grad:
+            # Create at least a new Tensor object in a differentiable way
+            if not inp.is_sparse:
+                # Use .view_as() to get a shallow copy
+                res.append(inp.view_as(inp))
+            else:
+                # We cannot use view for sparse Tensors so we clone
+                res.append(inp.clone())
+        else:
+            res.append(inp.detach().requires_grad_(need_graph))
+    return tuple(res)
+
+
+def _grad_postprocess(inputs, create_graph):
+    # Postprocess the generated Tensors to avoid returning Tensors with history when the user did not
+    # request it.
+    if isinstance(inputs[0], torch.Tensor):
+        if not create_graph:
+            return tuple(inp.detach() for inp in inputs)
+        else:
+            return inputs
+    else:
+        return tuple(_grad_postprocess(inp, create_graph) for inp in inputs)
+
+
+def _validate_v(v, other, is_other_tuple):
+    # This assumes that other is the correct shape, and v should match
+    # Both are assumed to be tuples of Tensors
+    if len(other) != len(v):
+        if is_other_tuple:
+            raise RuntimeError(
+                f"v is a tuple of invalid length: should be {len(other)} but got {len(v)}."
+            )
+        else:
+            raise RuntimeError("The given v should contain a single Tensor.")
+
+    for idx, (el_v, el_other) in enumerate(zip(v, other)):
+        if el_v.size() != el_other.size():
+            prepend = ""
+            if is_other_tuple:
+                prepend = f"Entry {idx} in "
+            raise RuntimeError(
+                f"{prepend}v has invalid size: should be {el_other.size()} but got {el_v.size()}."
+            )
+
+
+def _check_requires_grad(inputs, input_type, strict):
+    # Used to make all the necessary checks to raise nice errors in strict mode.
+    if not strict:
+        return
+
+    if input_type not in ["outputs", "grad_inputs", "jacobian", "hessian"]:
+        raise RuntimeError("Invalid input_type to _check_requires_grad")
+    for i, inp in enumerate(inputs):
+        if inp is None:
+            # This can only be reached for grad_inputs.
+            raise RuntimeError(
+                f"The output of the user-provided function is independent of input {i}."
+                " This is not allowed in strict mode."
+            )
+        if not inp.requires_grad:
+            if input_type == "hessian":
+                raise RuntimeError(
+                    f"The hessian of the user-provided function with respect to input {i}"
+                    " is independent of the input. This is not allowed in strict mode."
+                    " You should ensure that your function is thrice differentiable and that"
+                    " the hessian depends on the inputs."
+                )
+            elif input_type == "jacobian":
+                raise RuntimeError(
+                    "While computing the hessian, found that the jacobian of the user-provided"
+                    f" function with respect to input {i} is independent of the input. This is not"
+                    " allowed in strict mode. You should ensure that your function is twice"
+                    " differentiable and that the jacobian depends on the inputs (this would be"
+                    " violated by a linear function for example)."
+                )
+            elif input_type == "grad_inputs":
+                raise RuntimeError(
+                    f"The gradient with respect to input {i} is independent of the inputs of the"
+                    " user-provided function. This is not allowed in strict mode."
+                )
+            else:
+                raise RuntimeError(
+                    f"Output {i} of the user-provided function does not require gradients."
+                    " The outputs must be computed in a differentiable manner from the input"
+                    " when running in strict mode."
+                )
+
+
+def _autograd_grad(
+    outputs,
+    inputs,
+    grad_outputs=None,
+    create_graph=False,
+    retain_graph=None,
+    is_grads_batched=False,
+):
+    # Version of autograd.grad that accepts `None` in outputs and do not compute gradients for them.
+    # This has the extra constraint that inputs has to be a tuple
+    assert isinstance(outputs, tuple)
+    if grad_outputs is None:
+        grad_outputs = (None,) * len(outputs)
+    assert isinstance(grad_outputs, tuple)
+    assert len(outputs) == len(grad_outputs)
+
+    new_outputs: Tuple[torch.Tensor, ...] = ()
+    new_grad_outputs: Tuple[torch.Tensor, ...] = ()
+    for out, grad_out in zip(outputs, grad_outputs):
+        if out is not None and out.requires_grad:
+            new_outputs += (out,)
+            new_grad_outputs += (grad_out,)
+
+    if len(new_outputs) == 0:
+        # No differentiable output, we don't need to call the autograd engine
+        return (None,) * len(inputs)
+    else:
+        return torch.autograd.grad(
+            new_outputs,
+            inputs,
+            new_grad_outputs,
+            allow_unused=True,
+            create_graph=create_graph,
+            retain_graph=retain_graph,
+            is_grads_batched=is_grads_batched,
+        )
+
+
+def _fill_in_zeros(grads, refs, strict, create_graph, stage):
+    # Used to detect None in the grads and depending on the flags, either replace them
+    # with Tensors full of 0s of the appropriate size based on the refs or raise an error.
+    # strict and create graph allow us to detect when it is appropriate to raise an error
+    # stage gives us information of which backward call we consider to give good error message
+    if stage not in ["back", "back_trick", "double_back", "double_back_trick"]:
+        raise RuntimeError(f"Invalid stage argument '{stage}' to _fill_in_zeros")
+
+    res: Tuple[torch.Tensor, ...] = ()
+    for i, grads_i in enumerate(grads):
+        if grads_i is None:
+            if strict:
+                if stage == "back":
+                    raise RuntimeError(
+                        "The output of the user-provided function is independent of "
+                        f"input {i}. This is not allowed in strict mode."
+                    )
+                elif stage == "back_trick":
+                    raise RuntimeError(
+                        f"The gradient with respect to the input is independent of entry {i}"
+                        " in the grad_outputs when using the double backward trick to compute"
+                        " forward mode gradients. This is not allowed in strict mode."
+                    )
+                elif stage == "double_back":
+                    raise RuntimeError(
+                        "The jacobian of the user-provided function is independent of "
+                        f"input {i}. This is not allowed in strict mode."
+                    )
+                else:
+                    raise RuntimeError(
+                        "The hessian of the user-provided function is independent of "
+                        f"entry {i} in the grad_jacobian. This is not allowed in strict "
+                        "mode as it prevents from using the double backward trick to "
+                        "replace forward mode AD."
+                    )
+
+            grads_i = torch.zeros_like(refs[i])
+        else:
+            if strict and create_graph and not grads_i.requires_grad:
+                if "double" not in stage:
+                    raise RuntimeError(
+                        "The jacobian of the user-provided function is independent of "
+                        f"input {i}. This is not allowed in strict mode when create_graph=True."
+                    )
+                else:
+                    raise RuntimeError(
+                        "The hessian of the user-provided function is independent of "
+                        f"input {i}. This is not allowed in strict mode when create_graph=True."
+                    )
+
+        res += (grads_i,)
+
+    return res
+
+
+# Public API
+
+
+def vjp(func, inputs, v=None, create_graph=False, strict=False):
+    r"""Compute the dot product between a vector ``v`` and the Jacobian of the given function at the point given by the inputs.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a tuple of Tensors or a Tensor.
+        inputs (tuple of Tensors or Tensor): inputs to the function ``func``.
+        v (tuple of Tensors or Tensor): The vector for which the vector
+            Jacobian product is computed.  Must be the same size as the output
+            of ``func``. This argument is optional when the output of ``func``
+            contains a single element and (if it is not provided) will be set
+            as a Tensor containing a single ``1``.
+        create_graph (bool, optional): If ``True``, both the output and result
+            will be computed in a differentiable way. Note that when ``strict``
+            is ``False``, the result can not require gradients or be
+            disconnected from the inputs.  Defaults to ``False``.
+        strict (bool, optional): If ``True``, an error will be raised when we
+            detect that there exists an input such that all the outputs are
+            independent of it. If ``False``, we return a Tensor of zeros as the
+            vjp for said inputs, which is the expected mathematical value.
+            Defaults to ``False``.
+
+    Returns:
+        output (tuple): tuple with:
+            func_output (tuple of Tensors or Tensor): output of ``func(inputs)``
+
+            vjp (tuple of Tensors or Tensor): result of the dot product with
+            the same shape as the inputs.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def exp_reducer(x):
+        ...     return x.exp().sum(dim=1)
+        >>> inputs = torch.rand(4, 4)
+        >>> v = torch.ones(4)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> vjp(exp_reducer, inputs, v)
+        (tensor([5.7817, 7.2458, 5.7830, 6.7782]),
+         tensor([[1.4458, 1.3962, 1.3042, 1.6354],
+                [2.1288, 1.0652, 1.5483, 2.5035],
+                [2.2046, 1.1292, 1.1432, 1.3059],
+                [1.3225, 1.6652, 1.7753, 2.0152]]))
+
+        >>> vjp(exp_reducer, inputs, v, create_graph=True)
+        (tensor([5.7817, 7.2458, 5.7830, 6.7782], grad_fn=),
+         tensor([[1.4458, 1.3962, 1.3042, 1.6354],
+                [2.1288, 1.0652, 1.5483, 2.5035],
+                [2.2046, 1.1292, 1.1432, 1.3059],
+                [1.3225, 1.6652, 1.7753, 2.0152]], grad_fn=))
+
+        >>> def adder(x, y):
+        ...     return 2 * x + 3 * y
+        >>> inputs = (torch.rand(2), torch.rand(2))
+        >>> v = torch.ones(2)
+        >>> vjp(adder, inputs, v)
+        (tensor([2.4225, 2.3340]),
+         (tensor([2., 2.]), tensor([3., 3.])))
+    """
+    with torch.enable_grad():
+        is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "vjp")
+        inputs = _grad_preprocess(inputs, create_graph=create_graph, need_graph=True)
+
+        outputs = func(*inputs)
+        is_outputs_tuple, outputs = _as_tuple(
+            outputs, "outputs of the user-provided function", "vjp"
+        )
+        _check_requires_grad(outputs, "outputs", strict=strict)
+
+        if v is not None:
+            _, v = _as_tuple(v, "v", "vjp")
+            v = _grad_preprocess(v, create_graph=create_graph, need_graph=False)
+            _validate_v(v, outputs, is_outputs_tuple)
+        else:
+            if len(outputs) != 1 or outputs[0].nelement() != 1:
+                raise RuntimeError(
+                    "The vector v can only be None if the "
+                    "user-provided function returns "
+                    "a single Tensor with a single element."
+                )
+
+    enable_grad = True if create_graph else torch.is_grad_enabled()
+    with torch.set_grad_enabled(enable_grad):
+        grad_res = _autograd_grad(outputs, inputs, v, create_graph=create_graph)
+        vjp = _fill_in_zeros(grad_res, inputs, strict, create_graph, "back")
+
+    # Cleanup objects and return them to the user
+    outputs = _grad_postprocess(outputs, create_graph)
+    vjp = _grad_postprocess(vjp, create_graph)
+
+    return _tuple_postprocess(outputs, is_outputs_tuple), _tuple_postprocess(
+        vjp, is_inputs_tuple
+    )
+
+
+def jvp(func, inputs, v=None, create_graph=False, strict=False):
+    r"""Compute the dot product between the Jacobian of the given function at the point given by the inputs and a vector ``v``.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a tuple of Tensors or a Tensor.
+        inputs (tuple of Tensors or Tensor): inputs to the function ``func``.
+        v (tuple of Tensors or Tensor): The vector for which the Jacobian
+            vector product is computed. Must be the same size as the input of
+            ``func``. This argument is optional when the input to ``func``
+            contains a single element and (if it is not provided) will be set
+            as a Tensor containing a single ``1``.
+        create_graph (bool, optional): If ``True``, both the output and result
+            will be computed in a differentiable way. Note that when ``strict``
+            is ``False``, the result can not require gradients or be
+            disconnected from the inputs.  Defaults to ``False``.
+        strict (bool, optional): If ``True``, an error will be raised when we
+            detect that there exists an input such that all the outputs are
+            independent of it. If ``False``, we return a Tensor of zeros as the
+            jvp for said inputs, which is the expected mathematical value.
+            Defaults to ``False``.
+
+    Returns:
+        output (tuple): tuple with:
+            func_output (tuple of Tensors or Tensor): output of ``func(inputs)``
+
+            jvp (tuple of Tensors or Tensor): result of the dot product with
+            the same shape as the output.
+
+    Note:
+        ``autograd.functional.jvp`` computes the jvp by using the backward of
+        the backward (sometimes called the double backwards trick). This is not
+        the most performant way of computing the jvp. Please consider using
+        :func:`torch.func.jvp` or the
+        :ref:`low-level forward-mode AD API ` instead.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def exp_reducer(x):
+        ...     return x.exp().sum(dim=1)
+        >>> inputs = torch.rand(4, 4)
+        >>> v = torch.ones(4, 4)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> jvp(exp_reducer, inputs, v)
+        (tensor([6.3090, 4.6742, 7.9114, 8.2106]),
+         tensor([6.3090, 4.6742, 7.9114, 8.2106]))
+
+        >>> jvp(exp_reducer, inputs, v, create_graph=True)
+        (tensor([6.3090, 4.6742, 7.9114, 8.2106], grad_fn=),
+         tensor([6.3090, 4.6742, 7.9114, 8.2106], grad_fn=))
+
+        >>> def adder(x, y):
+        ...     return 2 * x + 3 * y
+        >>> inputs = (torch.rand(2), torch.rand(2))
+        >>> v = (torch.ones(2), torch.ones(2))
+        >>> jvp(adder, inputs, v)
+        (tensor([2.2399, 2.5005]),
+         tensor([5., 5.]))
+
+    """
+    with torch.enable_grad():
+        is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "jvp")
+        inputs = _grad_preprocess(inputs, create_graph=create_graph, need_graph=True)
+
+        if v is not None:
+            _, v = _as_tuple(v, "v", "jvp")
+            v = _grad_preprocess(v, create_graph=create_graph, need_graph=False)
+            _validate_v(v, inputs, is_inputs_tuple)
+        else:
+            if len(inputs) != 1 or inputs[0].nelement() != 1:
+                raise RuntimeError(
+                    "The vector v can only be None if the input to "
+                    "the user-provided function is a single Tensor "
+                    "with a single element."
+                )
+
+        outputs = func(*inputs)
+        is_outputs_tuple, outputs = _as_tuple(
+            outputs, "outputs of the user-provided function", "jvp"
+        )
+        _check_requires_grad(outputs, "outputs", strict=strict)
+        # The backward is linear so the value of grad_outputs is not important as
+        # it won't appear in the double backward graph. We only need to ensure that
+        # it does not contain inf or nan.
+        grad_outputs = tuple(
+            torch.zeros_like(out, requires_grad=True) for out in outputs
+        )
+
+        grad_inputs = _autograd_grad(outputs, inputs, grad_outputs, create_graph=True)
+        _check_requires_grad(grad_inputs, "grad_inputs", strict=strict)
+
+    if create_graph:
+        with torch.enable_grad():
+            grad_res = _autograd_grad(
+                grad_inputs, grad_outputs, v, create_graph=create_graph
+            )
+            jvp = _fill_in_zeros(grad_res, outputs, strict, create_graph, "back_trick")
+    else:
+        grad_res = _autograd_grad(
+            grad_inputs, grad_outputs, v, create_graph=create_graph
+        )
+        jvp = _fill_in_zeros(grad_res, outputs, strict, create_graph, "back_trick")
+
+    # Cleanup objects and return them to the user
+    outputs = _grad_postprocess(outputs, create_graph)
+    jvp = _grad_postprocess(jvp, create_graph)
+
+    return _tuple_postprocess(outputs, is_outputs_tuple), _tuple_postprocess(
+        jvp, is_outputs_tuple
+    )
+
+
+def _construct_standard_basis_for(
+    tensors: Tuple[torch.Tensor, ...], tensor_numels: Tuple[int, ...]
+) -> Tuple[torch.Tensor, ...]:
+    # This function:
+    # - constructs a N=sum(tensor_numels) standard basis. i.e. an NxN identity matrix.
+    # - Splits the identity matrix into chunks with each chunk size determined by `tensor_numels`.
+    # - Each chunk corresponds to one tensor. The chunk has the same dtype and
+    #   device as the tensor
+    #
+    # For example, with tensor_numels = [1, 2, 1], this function returns:
+    # ( tensor([[1],     tensor([[0, 0],      tensor([[0],
+    #           [0],             [1, 0],              [0],
+    #           [0],             [0, 1],              [0],
+    #           [0]])  ,         [0, 0]])  ,          [1]])  )
+    #
+    # Precondition: tensor_numels == tuple(tensor.numel() for tensor in tensors)
+    # Precondition: tensors always has at least one element.
+    #
+    # See NOTE: [Computing jacobian with vmap and grad for multiple tensors]
+    # for context behind this function. All the pre-conditions are guarded for
+    # in torch.autograd.functional.jacobian.
+    assert len(tensors) == len(tensor_numels)
+    assert len(tensors) > 0
+    total_numel = sum(tensor_numels)
+    chunks = tuple(
+        tensor.new_zeros(total_numel, tensor_numel)
+        for tensor, tensor_numel in zip(tensors, tensor_numels)
+    )
+    diag_start_idx = 0
+    for chunk, numel in zip(chunks, tensor_numels):
+        chunk.diagonal(diag_start_idx).fill_(1)
+        diag_start_idx -= numel
+    return chunks
+
+
+def _jacfwd(func, inputs, strict=False, vectorize=False):
+    if strict:
+        raise RuntimeError(
+            "torch.autograd.functional.jacobian: `strict=True` "
+            'and `strategy="forward-mode"` are not supported together (yet). '
+            "Please either set `strict=False` or "
+            '`strategy="reverse-mode"`.'
+        )
+    is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "jacobian")
+    output_info = []
+
+    if vectorize:
+        # See NOTE: [Computing jacobian with vmap and grad for multiple outputs]
+        input_numels = tuple(input.numel() for input in inputs)
+
+        # Step 1: Prepare tangents
+        tangents = _construct_standard_basis_for(inputs, input_numels)
+
+        # Step 2: Compute vmap over computation with dual tensors
+        def jvp(tangents):
+            with fwAD.dual_level():
+                dual_inputs = tuple(
+                    fwAD.make_dual(input, tangent.view_as(input))
+                    for input, tangent in zip(inputs, tangents)
+                )
+                _is_outputs_tuple, dual_outputs = _as_tuple(
+                    func(*dual_inputs), "outputs"
+                )
+                output_info.append(_is_outputs_tuple)
+                jv = []
+                primal_outs = []
+                for dual_out in dual_outputs:
+                    primal, tangent = fwAD.unpack_dual(dual_out)
+                    primal_outs.append(primal)
+                    if tangent is not None:
+                        jv.append(tangent)
+                    else:
+                        jv.append(torch.zeros_like(primal))
+                output_info.append(primal_outs)
+                return tuple(jv)
+
+        outputs_before_split = _vmap(jvp)(tangents)
+        is_outputs_tuple, outputs = output_info
+        # Step 3: for each of the output tangents, split along dim 0
+        jacobian_input_output = []
+        for jac_output_i, output_i in zip(outputs_before_split, outputs):
+            jacobian_output_i_output = []
+            for jac, input_j in zip(jac_output_i.split(input_numels, dim=0), inputs):
+                # We need to transpose the Jacobian because in forward AD, the
+                # batch dimension represents that of the inputs
+                jacobian_input_i_output_j = jac.permute(*range(1, jac.ndim), 0).reshape(
+                    (*output_i.shape, *input_j.shape)
+                )  # noqa: C409
+
+                jacobian_output_i_output.append(jacobian_input_i_output_j)
+            jacobian_input_output.append(jacobian_output_i_output)
+
+        # Omit [Step 4] because everything is already transposed w/ forward AD
+        return _tuple_postprocess(
+            jacobian_input_output, (is_outputs_tuple, is_inputs_tuple)
+        )
+    else:
+        raise NotImplementedError(
+            "Computing Jacobian using forward-AD or forward-over-reverse Hessian is"
+            "only implemented for `vectorize=True`."
+        )
+
+
+def jacobian(
+    func,
+    inputs,
+    create_graph=False,
+    strict=False,
+    vectorize=False,
+    strategy="reverse-mode",
+):
+    r"""Compute the Jacobian of a given function.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a tuple of Tensors or a Tensor.
+        inputs (tuple of Tensors or Tensor): inputs to the function ``func``.
+        create_graph (bool, optional): If ``True``, the Jacobian will be
+            computed in a differentiable manner. Note that when ``strict`` is
+            ``False``, the result can not require gradients or be disconnected
+            from the inputs.  Defaults to ``False``.
+        strict (bool, optional): If ``True``, an error will be raised when we
+            detect that there exists an input such that all the outputs are
+            independent of it. If ``False``, we return a Tensor of zeros as the
+            jacobian for said inputs, which is the expected mathematical value.
+            Defaults to ``False``.
+        vectorize (bool, optional): This feature is experimental.
+            Please consider using :func:`torch.func.jacrev` or
+            :func:`torch.func.jacfwd` instead if you are looking for something
+            less experimental and more performant.
+            When computing the jacobian, usually we invoke
+            ``autograd.grad`` once per row of the jacobian. If this flag is
+            ``True``, we perform only a single ``autograd.grad`` call with
+            ``batched_grad=True`` which uses the vmap prototype feature.
+            Though this should lead to performance improvements in many cases,
+            because this feature is still experimental, there may be performance
+            cliffs. See :func:`torch.autograd.grad`'s ``batched_grad`` parameter for
+            more information.
+        strategy (str, optional): Set to ``"forward-mode"`` or ``"reverse-mode"`` to
+            determine whether the Jacobian will be computed with forward or reverse
+            mode AD. Currently, ``"forward-mode"`` requires ``vectorized=True``.
+            Defaults to ``"reverse-mode"``. If ``func`` has more outputs than
+            inputs, ``"forward-mode"`` tends to be more performant. Otherwise,
+            prefer to use ``"reverse-mode"``.
+
+    Returns:
+        Jacobian (Tensor or nested tuple of Tensors): if there is a single
+        input and output, this will be a single Tensor containing the
+        Jacobian for the linearized inputs and output. If one of the two is
+        a tuple, then the Jacobian will be a tuple of Tensors. If both of
+        them are tuples, then the Jacobian will be a tuple of tuple of
+        Tensors where ``Jacobian[i][j]`` will contain the Jacobian of the
+        ``i``\th output and ``j``\th input and will have as size the
+        concatenation of the sizes of the corresponding output and the
+        corresponding input and will have same dtype and device as the
+        corresponding input. If strategy is ``forward-mode``, the dtype will be
+        that of the output; otherwise, the input.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def exp_reducer(x):
+        ...     return x.exp().sum(dim=1)
+        >>> inputs = torch.rand(2, 2)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> jacobian(exp_reducer, inputs)
+        tensor([[[1.4917, 2.4352],
+                 [0.0000, 0.0000]],
+                [[0.0000, 0.0000],
+                 [2.4369, 2.3799]]])
+
+        >>> jacobian(exp_reducer, inputs, create_graph=True)
+        tensor([[[1.4917, 2.4352],
+                 [0.0000, 0.0000]],
+                [[0.0000, 0.0000],
+                 [2.4369, 2.3799]]], grad_fn=)
+
+        >>> def exp_adder(x, y):
+        ...     return 2 * x.exp() + 3 * y
+        >>> inputs = (torch.rand(2), torch.rand(2))
+        >>> jacobian(exp_adder, inputs)
+        (tensor([[2.8052, 0.0000],
+                [0.0000, 3.3963]]),
+         tensor([[3., 0.],
+                 [0., 3.]]))
+    """
+    assert strategy in ("forward-mode", "reverse-mode"), (
+        'Expected strategy to be either "forward-mode" or "reverse-mode". Hint: If your '
+        'function has more outputs than inputs, "forward-mode" tends to be more performant. '
+        'Otherwise, prefer to use "reverse-mode".'
+    )
+    if strategy == "forward-mode":
+        if create_graph:
+            raise NotImplementedError(
+                "torch.autograd.functional.jacobian: `create_graph=True` "
+                'and `strategy="forward-mode"` are not supported together (yet). '
+                "Please either set `create_graph=False` or "
+                '`strategy="reverse-mode"`.'
+            )
+        return _jacfwd(func, inputs, strict, vectorize)
+
+    with torch.enable_grad():
+        is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "jacobian")
+        inputs = _grad_preprocess(inputs, create_graph=create_graph, need_graph=True)
+
+        outputs = func(*inputs)
+        is_outputs_tuple, outputs = _as_tuple(
+            outputs, "outputs of the user-provided function", "jacobian"
+        )
+        _check_requires_grad(outputs, "outputs", strict=strict)
+
+        if vectorize:
+            if strict:
+                raise RuntimeError(
+                    "torch.autograd.functional.jacobian: `strict=True` "
+                    "and `vectorized=True` are not supported together. "
+                    "Please either set `strict=False` or "
+                    "`vectorize=False`."
+                )
+            # NOTE: [Computing jacobian with vmap and grad for multiple outputs]
+            #
+            # Let's consider f(x) = (x**2, x.sum()) and let x = torch.randn(3).
+            # It turns out we can compute the jacobian of this function with a single
+            # call to autograd.grad by using vmap over the correct grad_outputs.
+            #
+            # Firstly, one way to compute the jacobian is to stack x**2 and x.sum()
+            # into a 4D vector. E.g., use g(x) = torch.stack([x**2, x.sum()])
+            #
+            # To get the first row of the jacobian, we call
+            # >>> autograd.grad(g(x), x, grad_outputs=torch.tensor([1, 0, 0, 0]))
+            # To get the 2nd row of the jacobian, we call
+            # >>> autograd.grad(g(x), x, grad_outputs=torch.tensor([0, 1, 0, 0]))
+            # and so on.
+            #
+            # Using vmap, we can vectorize all 4 of these computations into one by
+            # passing the standard basis for R^4 as the grad_output.
+            # vmap(partial(autograd.grad, g(x), x))(torch.eye(4)).
+            #
+            # Now, how do we compute the jacobian *without stacking the output*?
+            # We can just split the standard basis across the outputs. So to
+            # compute the jacobian of f(x), we'd use
+            # >>> autograd.grad(f(x), x, grad_outputs=_construct_standard_basis_for(...))
+            # The grad_outputs looks like the following:
+            # ( torch.tensor([[1, 0, 0],
+            #                 [0, 1, 0],
+            #                 [0, 0, 1],
+            #                 [0, 0, 0]]),
+            #   torch.tensor([[0],
+            #                 [0],
+            #                 [0],
+            #                 [1]]) )
+            #
+            # But we're not done yet!
+            # >>> vmap(partial(autograd.grad(f(x), x, grad_outputs=...)))
+            # returns a Tensor of shape [4, 3]. We have to remember to split the
+            # jacobian of shape [4, 3] into two:
+            # - one of shape [3, 3] for the first output
+            # - one of shape [   3] for the second output
+
+            # Step 1: Construct grad_outputs by splitting the standard basis
+            output_numels = tuple(output.numel() for output in outputs)
+            grad_outputs = _construct_standard_basis_for(outputs, output_numels)
+            flat_outputs = tuple(output.reshape(-1) for output in outputs)
+
+            # Step 2: Call vmap + autograd.grad
+            def vjp(grad_output):
+                vj = list(
+                    _autograd_grad(
+                        flat_outputs,
+                        inputs,
+                        grad_output,
+                        create_graph=create_graph,
+                        is_grads_batched=True,
+                    )
+                )
+                for el_idx, vj_el in enumerate(vj):
+                    if vj_el is not None:
+                        continue
+                    vj[el_idx] = torch.zeros_like(inputs[el_idx]).expand(
+                        (sum(output_numels),) + inputs[el_idx].shape
+                    )
+                return tuple(vj)
+
+            jacobians_of_flat_output = vjp(grad_outputs)
+
+            # Step 3: The returned jacobian is one big tensor per input. In this step,
+            # we split each Tensor by output.
+            jacobian_input_output = []
+            for jac_input_i, input_i in zip(jacobians_of_flat_output, inputs):
+                jacobian_input_i_output = []
+                for jac, output_j in zip(
+                    jac_input_i.split(output_numels, dim=0), outputs
+                ):
+                    jacobian_input_i_output_j = jac.view(output_j.shape + input_i.shape)
+                    jacobian_input_i_output.append(jacobian_input_i_output_j)
+                jacobian_input_output.append(jacobian_input_i_output)
+
+            # Step 4: Right now, `jacobian` is a List[List[Tensor]].
+            # The outer List corresponds to the number of inputs,
+            # the inner List corresponds to the number of outputs.
+            # We need to exchange the order of these and convert to tuples
+            # before returning.
+            jacobian_output_input = tuple(zip(*jacobian_input_output))
+
+            jacobian_output_input = _grad_postprocess(
+                jacobian_output_input, create_graph
+            )
+            return _tuple_postprocess(
+                jacobian_output_input, (is_outputs_tuple, is_inputs_tuple)
+            )
+
+        jacobian: Tuple[torch.Tensor, ...] = ()
+
+        for i, out in enumerate(outputs):
+            # mypy complains that expression and variable have different types due to the empty list
+            jac_i: Tuple[List[torch.Tensor]] = tuple([] for _ in range(len(inputs)))  # type: ignore[assignment]
+            for j in range(out.nelement()):
+                vj = _autograd_grad(
+                    (out.reshape(-1)[j],),
+                    inputs,
+                    retain_graph=True,
+                    create_graph=create_graph,
+                )
+
+                for el_idx, (jac_i_el, vj_el, inp_el) in enumerate(
+                    zip(jac_i, vj, inputs)
+                ):
+                    if vj_el is not None:
+                        if strict and create_graph and not vj_el.requires_grad:
+                            msg = (
+                                "The jacobian of the user-provided function is "
+                                f"independent of input {i}. This is not allowed in "
+                                "strict mode when create_graph=True."
+                            )
+                            raise RuntimeError(msg)
+                        jac_i_el.append(vj_el)
+                    else:
+                        if strict:
+                            msg = (
+                                f"Output {i} of the user-provided function is "
+                                f"independent of input {el_idx}. This is not allowed in "
+                                "strict mode."
+                            )
+                            raise RuntimeError(msg)
+                        jac_i_el.append(torch.zeros_like(inp_el))
+
+            jacobian += (
+                tuple(
+                    torch.stack(jac_i_el, dim=0).view(
+                        out.size() + inputs[el_idx].size()  # type: ignore[operator]
+                    )
+                    for (el_idx, jac_i_el) in enumerate(jac_i)
+                ),
+            )
+
+        jacobian = _grad_postprocess(jacobian, create_graph)
+
+        return _tuple_postprocess(jacobian, (is_outputs_tuple, is_inputs_tuple))
+
+
+def hessian(
+    func,
+    inputs,
+    create_graph=False,
+    strict=False,
+    vectorize=False,
+    outer_jacobian_strategy="reverse-mode",
+):
+    r"""Compute the Hessian of a given scalar function.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a Tensor with a single element.
+        inputs (tuple of Tensors or Tensor): inputs to the function ``func``.
+        create_graph (bool, optional): If ``True``, the Hessian will be computed in
+            a differentiable manner. Note that when ``strict`` is ``False``, the result can not
+            require gradients or be disconnected from the inputs.
+            Defaults to ``False``.
+        strict (bool, optional): If ``True``, an error will be raised when we detect that there exists an input
+            such that all the outputs are independent of it. If ``False``, we return a Tensor of zeros as the
+            hessian for said inputs, which is the expected mathematical value.
+            Defaults to ``False``.
+        vectorize (bool, optional): This feature is experimental.
+            Please consider using :func:`torch.func.hessian`
+            instead if you are looking for something less experimental and more performant.
+            When computing the hessian, usually we invoke
+            ``autograd.grad`` once per row of the hessian. If this flag is
+            ``True``, we use the vmap prototype feature as the backend to
+            vectorize calls to ``autograd.grad`` so we only invoke it once
+            instead of once per row. This should lead to performance
+            improvements in many use cases, however, due to this feature
+            being incomplete, there may be performance cliffs. Please
+            use `torch._C._debug_only_display_vmap_fallback_warnings(True)`
+            to show any performance warnings and file us issues if
+            warnings exist for your use case. Defaults to ``False``.
+        outer_jacobian_strategy (str, optional): The Hessian is computed by
+            computing the Jacobian of a Jacobian. The inner Jacobian is always
+            computed in reverse-mode AD. Setting strategy to ``"forward-mode"``
+            or ``"reverse-mode"`` determines whether the outer Jacobian will be
+            computed with forward or reverse mode AD. Currently, computing the outer
+            Jacobian in ``"forward-mode"`` requires ``vectorized=True``. Defaults
+            to ``"reverse-mode"``.
+
+    Returns:
+        Hessian (Tensor or a tuple of tuple of Tensors): if there is a single input,
+        this will be a single Tensor containing the Hessian for the input.
+        If it is a tuple, then the Hessian will be a tuple of tuples where
+        ``Hessian[i][j]`` will contain the Hessian of the ``i``\th input
+        and ``j``\th input with size the sum of the size of the ``i``\th input plus
+        the size of the ``j``\th input. ``Hessian[i][j]`` will have the same
+        dtype and device as the corresponding ``i``\th input.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def pow_reducer(x):
+        ...     return x.pow(3).sum()
+        >>> inputs = torch.rand(2, 2)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> hessian(pow_reducer, inputs)
+        tensor([[[[5.2265, 0.0000],
+                  [0.0000, 0.0000]],
+                 [[0.0000, 4.8221],
+                  [0.0000, 0.0000]]],
+                [[[0.0000, 0.0000],
+                  [1.9456, 0.0000]],
+                 [[0.0000, 0.0000],
+                  [0.0000, 3.2550]]]])
+
+        >>> hessian(pow_reducer, inputs, create_graph=True)
+        tensor([[[[5.2265, 0.0000],
+                  [0.0000, 0.0000]],
+                 [[0.0000, 4.8221],
+                  [0.0000, 0.0000]]],
+                [[[0.0000, 0.0000],
+                  [1.9456, 0.0000]],
+                 [[0.0000, 0.0000],
+                  [0.0000, 3.2550]]]], grad_fn=)
+
+
+        >>> def pow_adder_reducer(x, y):
+        ...     return (2 * x.pow(2) + 3 * y.pow(2)).sum()
+        >>> inputs = (torch.rand(2), torch.rand(2))
+        >>> hessian(pow_adder_reducer, inputs)
+        ((tensor([[4., 0.],
+                  [0., 4.]]),
+          tensor([[0., 0.],
+                  [0., 0.]])),
+         (tensor([[0., 0.],
+                  [0., 0.]]),
+          tensor([[6., 0.],
+                  [0., 6.]])))
+    """
+    is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "hessian")
+    assert outer_jacobian_strategy in (
+        "forward-mode",
+        "reverse-mode",
+    ), 'Expected strategy to be either "forward-mode" or "reverse-mode".'
+
+    def ensure_single_output_function(*inp):
+        out = func(*inp)
+        is_out_tuple, t_out = _as_tuple(
+            out, "outputs of the user-provided function", "hessian"
+        )
+        _check_requires_grad(t_out, "outputs", strict=strict)
+
+        if is_out_tuple or not isinstance(out, torch.Tensor):
+            raise RuntimeError(
+                "The function given to hessian should return a single Tensor"
+            )
+
+        if out.nelement() != 1:
+            raise RuntimeError(
+                "The Tensor returned by the function given to hessian should contain a single element"
+            )
+
+        return out.squeeze()
+
+    def jac_func(*inp):
+        if outer_jacobian_strategy == "forward-mode":
+            # _grad_preprocess requires create_graph=True and input to require_grad
+            # or else the input will be detached
+            inp = tuple(t.requires_grad_(True) for t in inp)
+        jac = jacobian(ensure_single_output_function, inp, create_graph=True)
+        _check_requires_grad(jac, "jacobian", strict=strict)
+        return jac
+
+    res = jacobian(
+        jac_func,
+        inputs,
+        create_graph=create_graph,
+        strict=strict,
+        vectorize=vectorize,
+        strategy=outer_jacobian_strategy,
+    )
+    return _tuple_postprocess(res, (is_inputs_tuple, is_inputs_tuple))
+
+
+def vhp(func, inputs, v=None, create_graph=False, strict=False):
+    r"""Compute the dot product between vector ``v`` and Hessian of a  given scalar function at a specified point.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a Tensor with a single element.
+        inputs (tuple of Tensors or Tensor): inputs to the function ``func``.
+        v (tuple of Tensors or Tensor): The vector for which the vector Hessian
+            product is computed. Must be the same size as the input of
+            ``func``. This argument is optional when ``func``'s input contains
+            a single element and (if it is not provided) will be set as a
+            Tensor containing a single ``1``.
+        create_graph (bool, optional): If ``True``, both the output and result
+            will be computed in a differentiable way. Note that when ``strict``
+            is ``False``, the result can not require gradients or be
+            disconnected from the inputs.
+            Defaults to ``False``.
+        strict (bool, optional): If ``True``, an error will be raised when we
+            detect that there exists an input such that all the outputs are
+            independent of it. If ``False``, we return a Tensor of zeros as the
+            vhp for said inputs, which is the expected mathematical value.
+            Defaults to ``False``.
+
+    Returns:
+        output (tuple): tuple with:
+            func_output (tuple of Tensors or Tensor): output of ``func(inputs)``
+
+            vhp (tuple of Tensors or Tensor): result of the dot product with the
+            same shape as the inputs.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def pow_reducer(x):
+        ...     return x.pow(3).sum()
+        >>> inputs = torch.rand(2, 2)
+        >>> v = torch.ones(2, 2)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> vhp(pow_reducer, inputs, v)
+        (tensor(0.5591),
+         tensor([[1.0689, 1.2431],
+                 [3.0989, 4.4456]]))
+        >>> vhp(pow_reducer, inputs, v, create_graph=True)
+        (tensor(0.5591, grad_fn=),
+         tensor([[1.0689, 1.2431],
+                 [3.0989, 4.4456]], grad_fn=))
+        >>> def pow_adder_reducer(x, y):
+        ...     return (2 * x.pow(2) + 3 * y.pow(2)).sum()
+        >>> inputs = (torch.rand(2), torch.rand(2))
+        >>> v = (torch.zeros(2), torch.ones(2))
+        >>> vhp(pow_adder_reducer, inputs, v)
+        (tensor(4.8053),
+         (tensor([0., 0.]),
+          tensor([6., 6.])))
+    """
+    with torch.enable_grad():
+        is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "vhp")
+        inputs = _grad_preprocess(inputs, create_graph=create_graph, need_graph=True)
+
+        if v is not None:
+            _, v = _as_tuple(v, "v", "vhp")
+            v = _grad_preprocess(v, create_graph=create_graph, need_graph=False)
+            _validate_v(v, inputs, is_inputs_tuple)
+        else:
+            if len(inputs) != 1 or inputs[0].nelement() != 1:
+                raise RuntimeError(
+                    "The vector v can only be None if the input to the user-provided function "
+                    "is a single Tensor with a single element."
+                )
+        outputs = func(*inputs)
+        is_outputs_tuple, outputs = _as_tuple(
+            outputs, "outputs of the user-provided function", "vhp"
+        )
+        _check_requires_grad(outputs, "outputs", strict=strict)
+
+        if is_outputs_tuple or not isinstance(outputs[0], torch.Tensor):
+            raise RuntimeError(
+                "The function given to vhp should return a single Tensor"
+            )
+
+        if outputs[0].nelement() != 1:
+            raise RuntimeError(
+                "The Tensor returned by the function given to vhp should contain a single element"
+            )
+
+        jac = _autograd_grad(outputs, inputs, create_graph=True)
+        _check_requires_grad(jac, "jacobian", strict=strict)
+
+    enable_grad = True if create_graph else torch.is_grad_enabled()
+    with torch.set_grad_enabled(enable_grad):
+        grad_res = _autograd_grad(jac, inputs, v, create_graph=create_graph)
+        vhp = _fill_in_zeros(grad_res, inputs, strict, create_graph, "double_back")
+
+    outputs = _grad_postprocess(outputs, create_graph)
+    vhp = _grad_postprocess(vhp, create_graph)
+
+    return _tuple_postprocess(outputs, is_outputs_tuple), _tuple_postprocess(
+        vhp, is_inputs_tuple
+    )
+
+
+def hvp(func, inputs, v=None, create_graph=False, strict=False):
+    r"""Compute the dot product between the scalar function's Hessian and a vector ``v`` at a specified point.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a Tensor with a single element.
+        inputs (tuple of Tensors or Tensor): inputs to the function ``func``.
+        v (tuple of Tensors or Tensor): The vector for which the Hessian vector
+            product is computed. Must be the same size as the input of
+            ``func``. This argument is optional when ``func``'s input contains
+            a single element and (if it is not provided) will be set as a
+            Tensor containing a single ``1``.
+        create_graph (bool, optional): If ``True``, both the output and result will be
+            computed in a differentiable way. Note that when ``strict`` is
+            ``False``, the result can not require gradients or be disconnected
+            from the inputs.  Defaults to ``False``.
+        strict (bool, optional): If ``True``, an error will be raised when we
+            detect that there exists an input such that all the outputs are
+            independent of it. If ``False``, we return a Tensor of zeros as the
+            hvp for said inputs, which is the expected mathematical value.
+            Defaults to ``False``.
+    Returns:
+        output (tuple): tuple with:
+            func_output (tuple of Tensors or Tensor): output of ``func(inputs)``
+
+            hvp (tuple of Tensors or Tensor): result of the dot product with
+            the same shape as the inputs.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def pow_reducer(x):
+        ...     return x.pow(3).sum()
+        >>> inputs = torch.rand(2, 2)
+        >>> v = torch.ones(2, 2)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> hvp(pow_reducer, inputs, v)
+        (tensor(0.1448),
+         tensor([[2.0239, 1.6456],
+                 [2.4988, 1.4310]]))
+
+        >>> hvp(pow_reducer, inputs, v, create_graph=True)
+        (tensor(0.1448, grad_fn=),
+         tensor([[2.0239, 1.6456],
+                 [2.4988, 1.4310]], grad_fn=))
+
+
+        >>> def pow_adder_reducer(x, y):
+        ...     return (2 * x.pow(2) + 3 * y.pow(2)).sum()
+        >>> inputs = (torch.rand(2), torch.rand(2))
+        >>> v = (torch.zeros(2), torch.ones(2))
+        >>> hvp(pow_adder_reducer, inputs, v)
+        (tensor(2.3030),
+         (tensor([0., 0.]),
+          tensor([6., 6.])))
+
+    Note:
+
+        This function is significantly slower than `vhp` due to backward mode AD constraints.
+        If your functions is twice continuously differentiable, then hvp = vhp.t(). So if you
+        know that your function satisfies this condition, you should use vhp instead that is
+        much faster with the current implementation.
+
+    """
+    with torch.enable_grad():
+        is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "hvp")
+        inputs = _grad_preprocess(inputs, create_graph=create_graph, need_graph=True)
+
+        if v is not None:
+            _, v = _as_tuple(v, "v", "hvp")
+            v = _grad_preprocess(v, create_graph=create_graph, need_graph=False)
+            _validate_v(v, inputs, is_inputs_tuple)
+        else:
+            if len(inputs) != 1 or inputs[0].nelement() != 1:
+                raise RuntimeError(
+                    "The vector v can only be None if the input to the user-provided function "
+                    "is a single Tensor with a single element."
+                )
+        outputs = func(*inputs)
+        is_outputs_tuple, outputs = _as_tuple(
+            outputs, "outputs of the user-provided function", "hvp"
+        )
+        _check_requires_grad(outputs, "outputs", strict=strict)
+
+        if is_outputs_tuple or not isinstance(outputs[0], torch.Tensor):
+            raise RuntimeError(
+                "The function given to hvp should return a single Tensor"
+            )
+
+        if outputs[0].nelement() != 1:
+            raise RuntimeError(
+                "The Tensor returned by the function given to hvp should contain a single element"
+            )
+
+        jac = _autograd_grad(outputs, inputs, create_graph=True)
+        _check_requires_grad(jac, "jacobian", strict=strict)
+
+        grad_jac = tuple(torch.zeros_like(inp, requires_grad=True) for inp in inputs)
+
+        double_back = _autograd_grad(jac, inputs, grad_jac, create_graph=True)
+        _check_requires_grad(jac, "hessian", strict=strict)
+
+    enable_grad = True if create_graph else torch.is_grad_enabled()
+    with torch.set_grad_enabled(enable_grad):
+        grad_res = _autograd_grad(double_back, grad_jac, v, create_graph=create_graph)
+        hvp = _fill_in_zeros(
+            grad_res, inputs, strict, create_graph, "double_back_trick"
+        )
+
+    outputs = _grad_postprocess(outputs, create_graph)
+    hvp = _grad_postprocess(hvp, create_graph)
+
+    return _tuple_postprocess(outputs, is_outputs_tuple), _tuple_postprocess(
+        hvp, is_inputs_tuple
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/grad_mode.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/grad_mode.py
new file mode 100644
index 0000000000000000000000000000000000000000..3cc7593cf6a5a1dd59a5dd35a1279128ecf4b560
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/grad_mode.py
@@ -0,0 +1,397 @@
+# mypy: allow-untyped-defs
+from typing import Any
+
+import torch
+from torch.utils._contextlib import (
+    _DecoratorContextManager,
+    _NoParamDecoratorContextManager,
+    F,
+)
+
+
+__all__ = [
+    "no_grad",
+    "enable_grad",
+    "set_grad_enabled",
+    "inference_mode",
+    "set_multithreading_enabled",
+]
+
+
+class no_grad(_NoParamDecoratorContextManager):
+    r"""Context-manager that disables gradient calculation.
+
+    Disabling gradient calculation is useful for inference, when you are sure
+    that you will not call :meth:`Tensor.backward()`. It will reduce memory
+    consumption for computations that would otherwise have `requires_grad=True`.
+
+    In this mode, the result of every computation will have
+    `requires_grad=False`, even when the inputs have `requires_grad=True`.
+    There is an exception! All factory functions, or functions that create
+    a new Tensor and take a requires_grad kwarg, will NOT be affected by
+    this mode.
+
+    This context manager is thread local; it will not affect computation
+    in other threads.
+
+    Also functions as a decorator.
+
+    .. note::
+        No-grad is one of several mechanisms that can enable or
+        disable gradients locally see :ref:`locally-disable-grad-doc` for
+        more information on how they compare.
+
+    .. note::
+        This API does not apply to :ref:`forward-mode AD `.
+        If you want to disable forward AD for a computation, you can unpack
+        your dual tensors.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> x = torch.tensor([1.], requires_grad=True)
+        >>> with torch.no_grad():
+        ...     y = x * 2
+        >>> y.requires_grad
+        False
+        >>> @torch.no_grad()
+        ... def doubler(x):
+        ...     return x * 2
+        >>> z = doubler(x)
+        >>> z.requires_grad
+        False
+        >>> @torch.no_grad()
+        ... def tripler(x):
+        ...     return x * 3
+        >>> z = tripler(x)
+        >>> z.requires_grad
+        False
+        >>> # factory function exception
+        >>> with torch.no_grad():
+        ...     a = torch.nn.Parameter(torch.rand(10))
+        >>> a.requires_grad
+        True
+    """
+
+    def __init__(self) -> None:
+        if not torch._jit_internal.is_scripting():
+            super().__init__()
+        self.prev = False
+
+    def __enter__(self) -> None:
+        self.prev = torch.is_grad_enabled()
+        torch.set_grad_enabled(False)
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        torch.set_grad_enabled(self.prev)
+
+
+class enable_grad(_NoParamDecoratorContextManager):
+    r"""Context-manager that enables gradient calculation.
+
+    Enables gradient calculation, if it has been disabled via :class:`~no_grad`
+    or :class:`~set_grad_enabled`.
+
+    This context manager is thread local; it will not affect computation
+    in other threads.
+
+    Also functions as a decorator.
+
+    .. note::
+        enable_grad is one of several mechanisms that can enable or
+        disable gradients locally see :ref:`locally-disable-grad-doc` for
+        more information on how they compare.
+
+    .. note::
+        This API does not apply to :ref:`forward-mode AD `.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> x = torch.tensor([1.], requires_grad=True)
+        >>> with torch.no_grad():
+        ...     with torch.enable_grad():
+        ...         y = x * 2
+        >>> y.requires_grad
+        True
+        >>> y.backward()
+        >>> x.grad
+        tensor([2.])
+        >>> @torch.enable_grad()
+        ... def doubler(x):
+        ...     return x * 2
+        >>> with torch.no_grad():
+        ...     z = doubler(x)
+        >>> z.requires_grad
+        True
+        >>> @torch.enable_grad()
+        ... def tripler(x):
+        ...     return x * 3
+        >>> with torch.no_grad():
+        ...     z = tripler(x)
+        >>> z.requires_grad
+        True
+
+    """
+
+    def __enter__(self) -> None:
+        self.prev = torch.is_grad_enabled()
+        torch._C._set_grad_enabled(True)
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        torch._C._set_grad_enabled(self.prev)
+
+
+class set_grad_enabled(_DecoratorContextManager):
+    r"""Context-manager that sets gradient calculation on or off.
+
+    ``set_grad_enabled`` will enable or disable grads based on its argument :attr:`mode`.
+    It can be used as a context-manager or as a function.
+
+    This context manager is thread local; it will not affect computation
+    in other threads.
+
+    Args:
+        mode (bool): Flag whether to enable grad (``True``), or disable
+                     (``False``). This can be used to conditionally enable
+                     gradients.
+
+    .. note::
+        set_grad_enabled is one of several mechanisms that can enable or
+        disable gradients locally see :ref:`locally-disable-grad-doc` for
+        more information on how they compare.
+
+    .. note::
+        This API does not apply to :ref:`forward-mode AD `.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> x = torch.tensor([1.], requires_grad=True)
+        >>> is_train = False
+        >>> with torch.set_grad_enabled(is_train):
+        ...     y = x * 2
+        >>> y.requires_grad
+        False
+        >>> _ = torch.set_grad_enabled(True)
+        >>> y = x * 2
+        >>> y.requires_grad
+        True
+        >>> _ = torch.set_grad_enabled(False)
+        >>> y = x * 2
+        >>> y.requires_grad
+        False
+
+    """
+
+    def __init__(self, mode: bool) -> None:
+        self.prev = torch.is_grad_enabled()
+        self.mode = mode
+        torch._C._set_grad_enabled(mode)
+
+    def __call__(self, orig_func: F) -> F:
+        torch._C._set_grad_enabled(self.prev)
+        return super().__call__(orig_func)
+
+    def __enter__(self) -> None:
+        torch._C._set_grad_enabled(self.mode)
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        torch._C._set_grad_enabled(self.prev)
+
+    def clone(self) -> "set_grad_enabled":
+        r"""
+        Create a copy of this class
+        """
+        return self.__class__(self.mode)
+
+
+class inference_mode(_DecoratorContextManager):
+    r"""Context-manager that enables or disables inference mode.
+
+    InferenceMode is a context manager analogous to :class:`~no_grad`
+    to be used when you are certain your operations will have no interactions
+    with autograd (e.g., model training). Code run under this mode gets better
+    performance by disabling view tracking and version counter bumps. Note that
+    unlike some other mechanisms that locally enable or disable grad,
+    entering inference_mode also disables to :ref:`forward-mode AD `.
+
+    This context manager is thread local; it will not affect computation
+    in other threads.
+
+    Also functions as a decorator.
+
+    .. note::
+        Inference mode is one of several mechanisms that can enable or
+        disable gradients locally see :ref:`locally-disable-grad-doc` for
+        more information on how they compare.
+
+    Args:
+        mode (bool or function): Either a boolean flag whether to enable or
+            disable inference mode or a Python function to decorate with
+            inference mode enabled
+
+    Example::
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> import torch
+        >>> x = torch.ones(1, 2, 3, requires_grad=True)
+        >>> with torch.inference_mode():
+        ...     y = x * x
+        >>> y.requires_grad
+        False
+        >>> # xdoctest: +SKIP("want string isnt quite right")
+        >>> y._version
+        Traceback (most recent call last):
+        File "", line 1, in 
+        RuntimeError: Inference tensors do not track version counter.
+        >>> @torch.inference_mode()
+        ... def func(x):
+        ...     return x * x
+        >>> out = func(x)
+        >>> out.requires_grad
+        False
+        >>> @torch.inference_mode()
+        ... def doubler(x):
+        ...     return x * 2
+        >>> out = doubler(x)
+        >>> out.requires_grad
+        False
+
+    """
+
+    def __init__(self, mode: bool = True) -> None:
+        if not torch._jit_internal.is_scripting():
+            super().__init__()
+        self.mode = mode
+
+    def __new__(cls, mode=True):
+        if isinstance(mode, bool):
+            return super().__new__(cls)
+        return cls()(mode)
+
+    def __enter__(self) -> None:
+        self._inference_mode_context = torch._C._InferenceMode(self.mode)
+        self._inference_mode_context.__enter__()
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        self._inference_mode_context.__exit__(exc_type, exc_value, traceback)
+
+    def clone(self) -> "inference_mode":
+        r"""
+        Create a copy of this class
+        """
+        return self.__class__(self.mode)
+
+
+def _enter_inference_mode(mode):
+    mode_context = torch._C._InferenceMode(mode)
+    mode_context.__enter__()
+    return mode_context
+
+
+def _exit_inference_mode(mode):
+    mode.__exit__(None, None, None)
+
+
+class set_multithreading_enabled(_DecoratorContextManager):
+    r"""Context-manager that sets multithreaded backwards on or off.
+
+    ``set_multithreading_enabled`` will enable or disable multithreaded backwards based on its argument :attr:`mode`.
+    It can be used as a context-manager or as a function.
+
+    This context manager is thread local; it will not affect computation
+    in other threads.
+
+    Args:
+        mode (bool): Flag whether to enable multithreaded backwards (``True``), or disable
+                     (``False``).
+
+    .. note::
+        This API does not apply to :ref:`forward-mode AD `.
+
+    """
+
+    def __init__(self, mode: bool) -> None:
+        self.prev = torch._C._is_multithreading_enabled()
+        torch._C._set_multithreading_enabled(mode)
+        self.mode = mode
+
+    def __enter__(self) -> None:
+        pass
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        torch._C._set_multithreading_enabled(self.prev)
+
+    def clone(self) -> "set_multithreading_enabled":
+        r"""
+        Create a copy of this class
+        """
+        return self.__class__(self.mode)
+
+
+class _force_original_view_tracking(_DecoratorContextManager):
+    r"""Context-manager that sets whether or not to always enable view-replay in autograd.
+
+    ``set_view_replay_enabled`` will enable or disable view-replay based on its argument :attr:`mode`.
+    It can be used as a context-manager or as a function.
+
+    This context manager is thread local; it will not affect computation
+    in other threads.
+
+    When a tensor view is mutated, the autograd engine needs to decide whether or not
+    to regenerate the "updated view" by either replaying the chain of views from the updated base,
+    or with a single call to as_strided.
+
+    If set_view_replay_enabled is set to True, then autograd will always use view replay.
+    Otherwise, it will fall back to its existing logic.
+
+    Args:
+        mode (bool): Flag whether to enable view-replay (``True``), or disable
+                     (``False``).
+
+    """
+
+    def __init__(self, mode: bool) -> None:
+        self.prev = torch._C._is_view_replay_enabled()
+        torch._C._set_view_replay_enabled(mode)
+        self.mode = mode
+
+    def __enter__(self) -> None:
+        pass
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        torch._C._set_view_replay_enabled(self.prev)
+
+    def clone(self):
+        return self.__class__(self.mode)
+
+
+class _unsafe_preserve_version_counter(_DecoratorContextManager):
+    r"""DO NOT USE THIS UNLESS YOU KNOW EXACTLY WHAT YOU'RE DOING.
+
+    This context manager can lead to arbitrary silent-correctness issues in any other part of your code
+    (even the ones not touched directly by the context manager)!
+
+    Ordinarily, autograd will track mutations to tensors by incrementing it's `._version` attribute.
+    This is generally important for correctness, as for example, mutating a tensor that autograd has saved
+    for the backwards pass can result in incorrect gradients, and autograd uses the version counter to detect
+    and error out in this situation.
+
+    However, there are rare instances where it might be useful to hide mutations from autograd. For example:
+    if a tensor is very large, and you'd like to free its memory by storing it elsewhere, and re-populate
+    the tensor right before it is needed by autograd.
+
+    Args:
+        tensor (torch.Tensor): the tensor in question, that you would like to preserve the version counter of.
+
+    .. note::
+        This API does not apply to :ref:`forward-mode AD `.
+
+    """
+
+    def __init__(self, tensor: torch.Tensor) -> None:
+        self.tensor = tensor
+        self.prev_version = tensor._version
+
+    def __enter__(self) -> None:
+        pass
+
+    def __exit__(self, *args) -> None:
+        torch._C._autograd._unsafe_set_version_counter(self.tensor, self.prev_version)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/gradcheck.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/gradcheck.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8e0bacb214d7de6b542b867fc832fd226302ecc
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/gradcheck.py
@@ -0,0 +1,2269 @@
+# mypy: allow-untyped-defs
+import collections
+import functools
+import warnings
+from itertools import product
+from typing import Callable, Dict, Iterable, List, Optional, Tuple, Union
+from typing_extensions import deprecated
+
+import torch
+import torch.testing
+from torch._vmap_internals import _vmap, vmap
+from torch.overrides import is_tensor_like
+from torch.types import _TensorOrTensors
+
+
+# Note: `get_*_jacobian` functions are added here even though we didn't intend to make them public
+# since they have been exposed from before we added `__all__`  and we already maintain BC for them
+# We should eventually deprecate them and remove them from `__all__`
+__all__ = [
+    "gradcheck",
+    "gradgradcheck",
+    "GradcheckError",
+    "get_numerical_jacobian",
+    "get_analytical_jacobian",
+    "get_numerical_jacobian_wrt_specific_input",
+]
+
+
+class GradcheckError(RuntimeError):
+    r"""Error raised by :func:`gradcheck` and :func:`gradgradcheck`."""
+
+
+def _is_sparse_compressed_tensor(obj: torch.Tensor):
+    return obj.layout in {
+        torch.sparse_csr,
+        torch.sparse_csc,
+        torch.sparse_bsr,
+        torch.sparse_bsc,
+    }
+
+
+def _is_sparse_any_tensor(obj: torch.Tensor):
+    return _is_sparse_compressed_tensor(obj) or obj.layout is torch.sparse_coo
+
+
+def _is_float_or_complex_tensor(obj):
+    return is_tensor_like(obj) and (obj.is_floating_point() or obj.is_complex())
+
+
+def _allocate_jacobians_with_inputs(
+    input_tensors: Tuple, numel_output
+) -> Tuple[torch.Tensor, ...]:
+    # Makes zero-filled tensors from inputs. If `numel_output` is not None, for
+    # each tensor in `input_tensors`, returns a new zero-filled tensor with height
+    # of `t.numel` and width of `numel_output`. Otherwise, for each tensor, returns
+    # a 1-d tensor with size `(t.numel,)`. Each new tensor will be strided and have
+    # the same dtype and device as those of the corresponding input.
+    out: List[torch.Tensor] = []
+    for t in input_tensors:
+        if _is_float_or_complex_tensor(t) and t.requires_grad:
+            out.append(t.new_zeros((t.numel(), numel_output), layout=torch.strided))
+    return tuple(out)
+
+
+def _allocate_jacobians_with_outputs(
+    output_tensors: Tuple, numel_input, dtype=None, device=None
+) -> Tuple[torch.Tensor, ...]:
+    # Makes zero-filled tensors from outputs. If `dim` is not None, for each tensor
+    # in `output_tensors`, returns a new zero-filled tensor with height of `dim` and
+    # width of `t.numel`. Otherwise, for each tensor, returns a 1-d tensor with size
+    # (t.numel,).
+    out: List[torch.Tensor] = []
+    options = {"dtype": dtype, "device": device, "layout": torch.strided}
+    for t in output_tensors:
+        if _is_float_or_complex_tensor(t):
+            out.append(t.new_zeros((numel_input, t.numel()), **options))
+    return tuple(out)
+
+
+def _iter_tensors(
+    x: Union[torch.Tensor, Iterable[torch.Tensor]], only_requiring_grad: bool = False
+) -> Iterable[torch.Tensor]:
+    if is_tensor_like(x):
+        # mypy doesn't narrow type of `x` to torch.Tensor
+        if x.requires_grad or not only_requiring_grad:  # type: ignore[union-attr]
+            yield x  # type: ignore[misc]
+    elif isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
+        for elem in x:
+            yield from _iter_tensors(elem, only_requiring_grad)
+
+
+def _densify(x):
+    # return a copy of sparse x with all unspecified elements
+    # "replaced" with zero-valued elements
+    if isinstance(x, (list, tuple)):
+        return type(x)(map(_densify, x))
+    elif not is_tensor_like(x) or x.layout in {torch.strided, torch._mkldnn}:  # type: ignore[attr-defined] # no attr _mkldnn
+        return x
+    elif x.layout is torch.sparse_coo:
+        device = x.device
+        indices_dtype = x._indices().dtype
+        tmp = torch.ones(x.shape[: x.sparse_dim()], dtype=torch.int8, device=device)
+        indices = tmp.nonzero().t().to(dtype=indices_dtype)
+        values = torch.zeros(
+            (tmp.numel(), *x.shape[x.sparse_dim() :]), dtype=x.dtype, device=device
+        )
+        x_coalesced = x.detach().coalesce()
+        if x_coalesced.numel() > 0:
+            stride = tmp.stride()
+            flat_indices = (
+                x_coalesced.indices()
+                .mul(
+                    torch.tensor(stride, dtype=indices_dtype, device=device).unsqueeze(
+                        1
+                    )
+                )
+                .sum(0)
+            )
+            values[flat_indices] = x_coalesced.values()
+        return (
+            torch.sparse_coo_tensor(indices, values, x.shape)
+            ._coalesced_(True)
+            .requires_grad_(x.requires_grad)
+        )
+    elif _is_sparse_compressed_tensor(x):
+        blocksize = (
+            x.values().shape[1:3]
+            if x.layout in {torch.sparse_bsr, torch.sparse_bsc}
+            else None
+        )
+        compressed_indices = (
+            x.crow_indices()
+            if x.layout in {torch.sparse_csr, torch.sparse_bsr}
+            else x.ccol_indices()
+        )
+        # We'll use intermediate sparse COO for simplicity
+        r = _densify(x.detach().to_sparse(layout=torch.sparse_coo)).to_sparse(
+            layout=x.layout, blocksize=blocksize
+        )
+        # Check that all elements are specified also after `to_sparse` op:
+        dense_numel = r.values().numel() // max(1, r.values().shape[0])
+        batch_numel = compressed_indices.numel() // compressed_indices.shape[-1]
+        sparse_numel = r.numel() // max(1, dense_numel * batch_numel)
+        if sparse_numel != r._nnz():
+            raise AssertionError(
+                f"{x.layout} densify failed: expected nnz={sparse_numel} but got {r._nnz()}"
+            )
+        return r.requires_grad_(x.requires_grad)
+    elif _is_sparse_any_tensor(x):
+        raise NotImplementedError(x.layout)
+    return x
+
+
+def _iter_tensor(x_tensor):
+    # (Only used for slow gradcheck) Returns a generator that yields the following
+    # elements at each iteration:
+    #  1) a tensor: the same tensor is returned across all iterations. The tensor
+    #     is not the same as the original x_tensor as given as input - it is
+    #     prepared so that it can be modified in-place. Depending on whether the
+    #     input tensor is strided, sparse, or dense, the returned tensor may or may
+    #     not share storage with x_tensor.
+    #  2) a tuple of indices that can be used with advanced indexing (yielded in
+    #     dictionary order)
+    #  3) flattened index that will be used to index into the Jacobian tensor
+    #
+    # For a tensor t with size (2, 2), _iter_tensor yields:
+    #     `x, (0, 0), 0`, `x, (0, 1), 1`, `x, (1, 0), 2`, `x, (1, 1), 3`
+    #
+    # where x is the t.data of the original tensor. Perturbing the entry of x
+    # at index (1, 1) yields the 3rd column of the overall Jacobian matrix.
+    if _is_sparse_any_tensor(x_tensor):
+
+        def get_stride(size):
+            dim = len(size)
+            tmp = 1
+            stride = [0] * dim
+            for i in reversed(range(dim)):
+                stride[i] = tmp
+                tmp *= size[i]
+            return stride
+
+        x_nnz = x_tensor._nnz()
+        x_size = list(x_tensor.size())
+        if x_tensor.layout is torch.sparse_coo:
+            x_indices = x_tensor._indices().t()
+            x_values = x_tensor._values()
+        elif x_tensor.layout is torch.sparse_csr:
+            x_indices = torch._convert_indices_from_csr_to_coo(
+                x_tensor.crow_indices(), x_tensor.col_indices()
+            ).t()
+            x_values = x_tensor.values()
+        elif x_tensor.layout is torch.sparse_csc:
+            x_indices = torch._convert_indices_from_csr_to_coo(
+                x_tensor.ccol_indices(), x_tensor.row_indices(), transpose=True
+            ).t()
+            x_values = x_tensor.values()
+        elif x_tensor.layout is torch.sparse_bsr:
+            x_block_values = x_tensor.values()
+            x_blocksize = x_block_values.size()[1:3]
+            x_indices = (
+                torch._convert_indices_from_csr_to_coo(
+                    x_tensor.crow_indices(), x_tensor.col_indices()
+                )
+                .repeat_interleave(x_blocksize[0] * x_blocksize[1], 1)
+                .mul_(torch.tensor(x_blocksize, device=x_tensor.device).reshape(2, 1))
+                .add_(
+                    torch.stack(
+                        torch.where(torch.ones(x_blocksize, device=x_tensor.device))
+                    ).repeat(1, x_nnz)
+                )
+                .t()
+            )
+            x_values = x_block_values.flatten(0, 2)
+            x_nnz = x_values.size(0)
+        elif x_tensor.layout is torch.sparse_bsc:
+            x_block_values = x_tensor.values()
+            x_blocksize = x_block_values.size()[1:3]
+            x_indices = (
+                torch._convert_indices_from_csr_to_coo(
+                    x_tensor.ccol_indices(), x_tensor.row_indices(), transpose=True
+                )
+                .repeat_interleave(x_blocksize[0] * x_blocksize[1], 1)
+                .mul_(torch.tensor(x_blocksize, device=x_tensor.device).reshape(2, 1))
+                .add_(
+                    torch.stack(
+                        torch.where(torch.ones(x_blocksize, device=x_tensor.device))
+                    ).repeat(1, x_nnz)
+                )
+                .t()
+            )
+            x_values = x_block_values.flatten(0, 2)
+            x_nnz = x_values.size(0)
+        else:
+            raise NotImplementedError(f"_iter_tensor for {x_tensor.layout} input")
+        x_stride = get_stride(x_size)
+        # Use .data here to get around the version check
+        x_values = x_values.data
+        for i in range(x_nnz):
+            x_value = x_values[i]
+            for x_idx in product(*[range(m) for m in x_values.size()[1:]]):
+                indices = x_indices[i].tolist() + list(x_idx)
+                d_idx = sum(indices[k] * x_stride[k] for k in range(len(x_size)))
+                yield x_value, x_idx, d_idx
+    elif x_tensor.layout == torch._mkldnn:  # type: ignore[attr-defined]
+        for d_idx, x_idx in enumerate(product(*[range(m) for m in x_tensor.size()])):
+            # this is really inefficient, but without indexing implemented, there's
+            # not really a better way than converting back and forth
+            x_tensor_dense = x_tensor.to_dense()
+            yield x_tensor_dense, x_idx, d_idx
+    else:
+        # Use .data here to get around the version check
+        x_tensor = x_tensor.data
+        for d_idx, x_idx in enumerate(product(*[range(m) for m in x_tensor.size()])):
+            yield x_tensor, x_idx, d_idx
+
+
+def _get_numerical_jacobian(
+    fn, inputs, outputs=None, target=None, eps=1e-3, is_forward_ad=False
+) -> List[Tuple[torch.Tensor, ...]]:
+    """Compute the numerical Jacobian of `fn(inputs)` with respect to `target`.
+
+    If not specified, targets are the input. Returns M * N Jacobians where N is the
+    number of tensors in target that require grad and M is the number of non-integral
+    outputs.
+
+    Args:
+        fn: the function to compute the jacobian for
+        inputs: inputs to `fn`
+        outputs: provide precomputed outputs to avoid one extra invocation of fn
+        target: the Tensors wrt whom Jacobians are calculated (default=`inputs`)
+        eps: the magnitude of the perturbation during finite differencing
+             (default=`1e-3`)
+        is_forward_ad: if this numerical jacobian is computed to be checked wrt
+                       forward AD gradients (this is used for error checking only)
+
+    Returns:
+        A list of M N-tuples of tensors
+
+    Note that `target` may not even be part of `input` to `fn`, so please be
+    **very careful** in this to not clone `target`.
+    """
+    jacobians: List[Tuple[torch.Tensor, ...]] = []
+    if outputs is None:
+        outputs = _as_tuple(fn(*_as_tuple(inputs)))
+    if not is_forward_ad and any(o.is_complex() for o in outputs):
+        raise ValueError(
+            "Expected output to be non-complex. get_numerical_jacobian no "
+            "longer supports functions that return complex outputs."
+        )
+    if target is None:
+        target = inputs
+    inp_indices = [
+        i for i, a in enumerate(target) if is_tensor_like(a) and a.requires_grad
+    ]
+    for i, (inp, inp_idx) in enumerate(zip(_iter_tensors(target, True), inp_indices)):
+        jacobians += [
+            get_numerical_jacobian_wrt_specific_input(
+                fn,
+                inp_idx,
+                inputs,
+                outputs,
+                eps,
+                input=inp,
+                is_forward_ad=is_forward_ad,
+            )
+        ]
+    return jacobians
+
+
+@deprecated(
+    "`get_numerical_jacobian` was part of PyTorch's private API and not "
+    "meant to be exposed. We are deprecating it and it will be removed "
+    "in a future version of PyTorch. If you have a specific use for "
+    "this or feature request for this to be a stable API, please file "
+    "us an issue at https://github.com/pytorch/pytorch/issues/new",
+    category=FutureWarning,
+)
+def get_numerical_jacobian(fn, inputs, target=None, eps=1e-3, grad_out=1.0):
+    """Compute the numerical Jacobian for a given fn and its inputs.
+
+    This is a Deprecated API.
+
+    Args:
+        fn: the function to compute the Jacobian for (must take inputs as a tuple)
+        input: input to `fn`
+        target: the Tensors wrt whom Jacobians are calculated (default=`input`)
+        eps: the magnitude of the perturbation during finite differencing
+             (default=`1e-3`)
+
+    Returns:
+        A list of Jacobians of `fn` (restricted to its first output) with respect to
+        each input or target, if provided.
+
+    Note that `target` may not even be part of `input` to `fn`, so please be
+    **very careful** in this to not clone `target`.
+    """
+    if (
+        grad_out != 1.0
+    ):  # grad_out param is only kept for backward compatibility reasons
+        raise ValueError(
+            "Expected grad_out to be 1.0. get_numerical_jacobian no longer "
+            "supports values of grad_out != 1.0."
+        )
+
+    def fn_pack_inps(*inps):
+        return fn(inps)
+
+    jacobians = _get_numerical_jacobian(fn_pack_inps, inputs, None, target, eps)
+
+    return tuple(jacobian_for_each_output[0] for jacobian_for_each_output in jacobians)
+
+
+def _compute_numerical_gradient(fn, entry, v, norm_v, nbhd_checks_fn):
+    # Computes numerical directional derivative as finite difference
+    # of function `fn` at input `entry`, perturbed by vector `v`.
+    if _is_sparse_compressed_tensor(entry):
+        # sparse compressed tensors don't implement sub/add/copy_
+        # yet. However, in non-masked semantics context entry and v
+        # have the same sparse indices ...
+        assert entry.layout == v.layout, (entry.layout, v.layout)
+        assert entry._nnz() == v._nnz(), (entry._nnz(), v._nnz(), entry.shape)
+        # ... the finite differencing can be performed on values only:
+        entry = entry.values()
+        v = v.values()
+        # we'll detach to avoid backward computations that sparse
+        # tensors have limited support for.
+        entry = entry.detach()
+
+    orig = entry.clone()
+    entry.copy_(orig - v)
+    outa = fn()
+    entry.copy_(orig + v)
+    outb = fn()
+    entry.copy_(orig)
+
+    def compute(a, b):
+        nbhd_checks_fn(a, b)
+        ret = (b - a) / (2 * norm_v)  # use central difference approx
+        return ret.detach().reshape(-1)
+
+    return tuple(compute(a, b) for (a, b) in zip(outa, outb))
+
+
+def _compute_numerical_jvps_wrt_specific_input(
+    jvp_fn, delta, input_is_complex, is_forward_ad=False
+) -> List[torch.Tensor]:
+    # Computing the jacobian only works for real delta
+    # For details on the algorithm used here, refer:
+    # Section 3.5.3 https://arxiv.org/pdf/1701.00392.pdf
+    # s = fn(z) where z = x for real valued input
+    # and z = x + yj for complex valued input
+    jvps: List[torch.Tensor] = []
+    ds_dx_tup = jvp_fn(delta[0] if isinstance(delta, tuple) else delta)
+
+    if input_is_complex:  # C -> R
+        ds_dy_tup = (
+            jvp_fn(delta[1] * 1j) if isinstance(delta, tuple) else jvp_fn(delta * 1j)
+        )
+        for ds_dx, ds_dy in zip(ds_dx_tup, ds_dy_tup):
+            assert not ds_dx.is_complex()
+            # conjugate wirtinger derivative
+            conj_w_d = ds_dx + ds_dy * 1j
+            jvps.append(conj_w_d)
+    else:
+        for ds_dx in ds_dx_tup:  # R -> R or (R -> C for the forward AD case)
+            assert is_forward_ad or not ds_dx.is_complex()
+            jvps.append(ds_dx)
+    return jvps
+
+
+def _combine_jacobian_cols(
+    jacobians_cols: Dict[int, List[torch.Tensor]], outputs, input, numel
+) -> Tuple[torch.Tensor, ...]:
+    # jacobian_cols maps column_idx -> output_idx -> single column of jacobian Tensor
+    # we return a list that maps output_idx -> full jacobian Tensor
+    jacobians = _allocate_jacobians_with_outputs(
+        outputs, numel, dtype=input.dtype if input.dtype.is_complex else None
+    )
+    for i, jacobian in enumerate(jacobians):
+        for k, v in jacobians_cols.items():
+            jacobian[k] = v[i]
+    return jacobians
+
+
+def _prepare_input(
+    input: torch.Tensor, maybe_perturbed_input: Optional[torch.Tensor], fast_mode=False
+) -> torch.Tensor:
+    # Prepares the inputs to be passed into the function while including the new
+    # modified input.
+    if input.layout == torch._mkldnn:  # type: ignore[attr-defined] # no attr _mkldnn
+        # Convert back to mkldnn
+        if maybe_perturbed_input is not None:
+            return maybe_perturbed_input.to_mkldnn()
+        else:
+            return input
+    elif _is_sparse_any_tensor(input):
+        if fast_mode and maybe_perturbed_input is not None:
+            # entry is already a "cloned" version of the original tensor
+            # thus changes to entry are not reflected in the input
+            return maybe_perturbed_input
+        else:
+            return input
+    else:
+        # We cannot use entry (input.data) if we want gradgrad to work because
+        # fn (in the gradgrad case) needs to compute grad wrt input
+        return input
+
+
+def _check_outputs_same_dtype_and_shape(output1, output2, eps, idx=None) -> None:
+    # Check that the returned outputs don't have different dtype or shape when you
+    # perturb the input
+    on_index = "on index {idx} " if idx is not None else ""
+    assert output1.shape == output2.shape, (
+        f"Expected `func` to return outputs with the same shape"
+        f" when inputs are perturbed {on_index}by {eps}, but got:"
+        f" shapes {output1.shape} and {output2.shape}."
+    )
+    assert output1.dtype == output2.dtype, (
+        f"Expected `func` to return outputs with the same dtype"
+        f" when inputs are perturbed {on_index}by {eps}, but got:"
+        f" dtypes {output1.dtype} and {output2.dtype}."
+    )
+
+
+def get_numerical_jacobian_wrt_specific_input(
+    fn, input_idx, inputs, outputs, eps, input=None, is_forward_ad=False
+) -> Tuple[torch.Tensor, ...]:
+    # Computes the numerical jacobians wrt to a single input. Returns N jacobian
+    # tensors, where N is the number of outputs. We use a dictionary for
+    # jacobian_cols because indices aren't necessarily consecutive for sparse inputs
+    # When we perturb only a single element of the input tensor at a time, the jvp
+    # is equivalent to a single col of the Jacobian matrix of fn.
+    jacobian_cols: Dict[int, List[torch.Tensor]] = {}
+    input = inputs[input_idx] if input is None else input
+    assert input.requires_grad
+    for x, idx, d_idx in _iter_tensor(input):
+        wrapped_fn = _with_prepare_inputs(fn, inputs, input_idx, x)
+        input_to_perturb = x[idx]
+        nbhd_checks_fn = functools.partial(
+            _check_outputs_same_dtype_and_shape, idx=idx, eps=eps
+        )
+        jvp_fn = _get_numerical_jvp_fn(
+            wrapped_fn, input_to_perturb, eps, nbhd_checks_fn
+        )
+        jacobian_cols[d_idx] = _compute_numerical_jvps_wrt_specific_input(
+            jvp_fn, eps, x.is_complex(), is_forward_ad
+        )
+    return _combine_jacobian_cols(jacobian_cols, outputs, input, input.numel())
+
+
+def _get_analytical_jacobian_forward_ad(
+    fn, inputs, outputs, *, check_grad_dtypes=False, all_u=None
+) -> Tuple[Tuple[torch.Tensor, ...], ...]:
+    """Compute the analytical Jacobian using forward mode AD of `fn(inputs)` using forward mode AD with respect to `target`.
+
+    Return N * M Jacobians where N is the number of tensors in target that require grad and
+    M is the number of non-integral outputs.
+    Contrary to other functions here, this function requires "inputs" to actually be used by the function.
+    The computed value is expected to be wrong if the function captures the inputs by side effect instead of
+    using the passed ones (many torch.nn tests do this).
+
+    Args:
+        fn: the function to compute the jacobian for
+        inputs: inputs to `fn`
+        outputs: provide precomputed outputs to avoid one extra invocation of fn
+        check_grad_dtypes: if True, will check that the gradient dtype are valid
+        all_u (optional): if provided, the Jacobian will be right multiplied with this vector
+
+    Returns:
+        A tuple of M N-tuples of tensors
+    """
+    # To avoid early import issues
+    fwAD = torch.autograd.forward_ad
+
+    tensor_inputs = tuple(i for i in inputs if is_tensor_like(i) and i.requires_grad)
+
+    if any(i.is_complex() for i in tensor_inputs):
+        raise ValueError(
+            "Expected inputs to be non-complex for _get_analytical_jacobian_forward_ad."
+        )
+
+    if all_u:
+        jacobians = tuple(
+            _allocate_jacobians_with_outputs(outputs, 1) for i in tensor_inputs
+        )
+    else:
+        jacobians = tuple(
+            _allocate_jacobians_with_outputs(outputs, i.numel()) for i in tensor_inputs
+        )
+
+    with fwAD.dual_level():
+        fw_grads = []
+        dual_inputs = []
+        for i, inp in enumerate(inputs):
+            if is_tensor_like(inp) and inp.requires_grad:
+                if inp.layout == torch._mkldnn:  # type: ignore[attr-defined]
+                    raise ValueError(
+                        "MKLDNN inputs are not support for forward AD gradcheck."
+                    )
+
+                inp = fwAD.make_dual(inp.detach(), torch.zeros_like(inp))
+                # If inp is a differentiable view, the dual might not be the tangent given to
+                # make_dual, so read it explicitly from the dual tensor
+                fw_grads.append(fwAD.unpack_dual(inp)[1])
+            dual_inputs.append(inp)
+
+        if all_u:
+            # Do the full reduction in one pass
+            # To be consistent with numerical evaluation, we actually compute one reduction per input
+            for i, (fw_grad, u) in enumerate(zip(fw_grads, all_u)):
+                fw_grad.copy_(u.view_as(fw_grad))
+                raw_outputs = _as_tuple(fn(*dual_inputs))
+                dual_outputs = filter(_is_float_or_complex_tensor, raw_outputs)
+                for index_o, d_o in enumerate(dual_outputs):
+                    val, res = fwAD.unpack_dual(d_o)
+                    if (
+                        check_grad_dtypes
+                        and res is not None
+                        and val.is_complex() != res.is_complex()
+                    ):
+                        raise GradcheckError("Forward AD gradient has dtype mismatch.")
+
+                    # Remove extra dimension of size 1 corresponding to the reduced input
+                    jacobians[i][index_o].squeeze_(0)
+                    if res is None:
+                        jacobians[i][index_o].zero_()
+                    else:
+                        jacobians[i][index_o].copy_(res.reshape(-1))
+                fw_grad.zero_()
+        else:
+            # Reconstruct the full Jacobian column by column
+            for i, fw_grad in enumerate(fw_grads):
+                for lin_idx, grad_idx in enumerate(
+                    product(*[range(m) for m in fw_grad.size()])
+                ):
+                    fw_grad[grad_idx] = 1.0
+                    raw_outputs = _as_tuple(fn(*dual_inputs))
+                    dual_outputs = filter(_is_float_or_complex_tensor, raw_outputs)
+                    for index_o, d_o in enumerate(dual_outputs):
+                        val, res = fwAD.unpack_dual(d_o)
+                        if (
+                            check_grad_dtypes
+                            and res is not None
+                            and val.is_complex() != res.is_complex()
+                        ):
+                            raise GradcheckError(
+                                "Forward AD gradient has dtype mismatch."
+                            )
+
+                        if res is None:
+                            jacobians[i][index_o][lin_idx].zero_()
+                        else:
+                            jacobians[i][index_o][lin_idx].copy_(res.reshape(-1))
+                    fw_grad[grad_idx] = 0.0
+
+    return jacobians
+
+
+def _get_input_to_perturb(input):
+    # Prepare the input so that it can be modified in-place and do certain
+    # operations that require the tensor to have strides. If fast_mode=False,
+    # _iter_tensor would handle the below cases:
+    if input.layout == torch._mkldnn:  # type: ignore[attr-defined] # no attr _mkldnn
+        # Convert to dense so we can perform operations that require strided tensors
+        input_to_perturb = input.to_dense()
+    elif _is_sparse_any_tensor(input):
+        # Clone because input may require grad, and copy_ calls resize_,
+        # which is not allowed for .data
+        input_to_perturb = input.clone()
+    else:
+        input_to_perturb = input.data
+    return input_to_perturb
+
+
+def _with_prepare_inputs(fn, inputs, input_idx, input_to_perturb, fast_mode=False):
+    # Wraps `fn` so that its inputs are already supplied
+    def wrapped_fn():
+        inp = tuple(
+            _prepare_input(a, input_to_perturb if i == input_idx else None, fast_mode)
+            if is_tensor_like(a)
+            else a
+            for i, a in enumerate(_as_tuple(inputs))
+        )
+        return tuple(a.clone() for a in _as_tuple(fn(*inp)))
+
+    return wrapped_fn
+
+
+def _get_numerical_jvp_fn(wrapped_fn, input_to_perturb, eps, nbhd_checks_fn):
+    # Wraps jvp_fn so that certain arguments are already supplied
+    def jvp_fn(delta):
+        return _compute_numerical_gradient(
+            wrapped_fn, input_to_perturb, delta, eps, nbhd_checks_fn
+        )
+
+    return jvp_fn
+
+
+def _reshape_tensor_or_tuple(u, shape):
+    # We don't need to reshape when input corresponding to u is sparse
+    if isinstance(u, tuple):
+        if not _is_sparse_any_tensor(u[0]):
+            return (u[0].reshape(shape), u[1].reshape(shape))
+    else:
+        if not _is_sparse_any_tensor(u):
+            return u.reshape(shape)
+    return u
+
+
+def _mul_tensor_or_tuple(u, k):
+    if isinstance(u, tuple):
+        return (k * u[0], k * u[1])
+    else:
+        return k * u
+
+
+def _get_numerical_jvp_wrt_specific_input(
+    fn, input_idx, inputs, u, eps, is_forward_ad=False
+) -> List[torch.Tensor]:
+    input = inputs[input_idx]
+    input_to_perturb = _get_input_to_perturb(input)
+    wrapped_fn = _with_prepare_inputs(fn, inputs, input_idx, input_to_perturb, True)
+    nbhd_checks_fn = functools.partial(_check_outputs_same_dtype_and_shape, eps=eps)
+    jvp_fn = _get_numerical_jvp_fn(wrapped_fn, input_to_perturb, eps, nbhd_checks_fn)
+    u = _reshape_tensor_or_tuple(u, input_to_perturb.shape)
+    u = _mul_tensor_or_tuple(u, eps)
+    return _compute_numerical_jvps_wrt_specific_input(
+        jvp_fn, u, input.is_complex(), is_forward_ad
+    )
+
+
+def _get_numerical_vJu(
+    fn, inputs, inp_indices, func_out, all_u, all_v, eps, is_forward_ad
+):
+    # Note that all_v can also be None, in that case, this function only computes Ju.
+    reduced_jacobians: List[List[torch.Tensor]] = []
+    for i, (inp_idx, u) in enumerate(zip(inp_indices, all_u)):
+        all_Ju = _get_numerical_jvp_wrt_specific_input(
+            fn, inp_idx, inputs, u, eps, is_forward_ad
+        )
+        # Filter out the Ju for non floating point outputs
+        filtered_Ju = []
+        func_out = _as_tuple(func_out)
+        assert len(all_Ju) == len(func_out)
+        for Ju, output in zip(all_Ju, func_out):
+            if _is_float_or_complex_tensor(output):
+                filtered_Ju.append(Ju)
+            else:
+                # TODO: handle the other Ju
+                pass
+        if all_v is not None:
+            jacobian_scalars: List[torch.Tensor] = []
+            for v, Ju in zip(all_v, filtered_Ju):
+                jacobian_scalars.append(_dot_with_type_promotion(v, Ju))
+            reduced_jacobians.append(jacobian_scalars)
+        else:
+            reduced_jacobians.append(filtered_Ju)
+    return reduced_jacobians
+
+
+def _check_jacobians_equal(j1, j2, atol):
+    # Check whether the max difference between two Jacobian tensors are within some
+    # tolerance `atol`.
+    for j1_x, j2_x in zip(j1, j2):
+        if j1_x.numel() != 0 and (j1_x - j2_x).abs().max() > atol:
+            return False
+    return True
+
+
+def _stack_and_check_tensors(
+    list_of_list_of_tensors, inputs, numel_outputs
+) -> Tuple[Tuple[torch.Tensor, ...], bool, bool]:
+    # For the ith tensor in the inner list checks whether it has the same size and
+    # dtype as the ith differentiable input.
+    out_jacobians = _allocate_jacobians_with_inputs(inputs, numel_outputs)
+    diff_input_list = list(_iter_tensors(inputs, True))
+    correct_grad_sizes = True
+    correct_grad_types = True
+    for i, tensor_list in enumerate(list_of_list_of_tensors):
+        inp = diff_input_list[i]
+        out_jacobian = out_jacobians[i]
+        for j, tensor in enumerate(tensor_list):
+            if tensor is not None and tensor.size() != inp.size():
+                correct_grad_sizes = False
+            elif tensor is not None and tensor.dtype != inp.dtype:
+                correct_grad_types = False
+            if tensor is None:
+                out_jacobian[:, j].zero_()
+            else:
+                dense = (
+                    tensor.to_dense() if not tensor.layout == torch.strided else tensor
+                )
+                assert out_jacobian[:, j].numel() == dense.numel()
+                out_jacobian[:, j] = dense.reshape(-1)
+    return out_jacobians, correct_grad_sizes, correct_grad_types
+
+
+FAILED_NONDET_MSG = """\n
+NOTE: If your op relies on non-deterministic operations i.e., it is listed here:
+https://pytorch.org/docs/stable/generated/torch.use_deterministic_algorithms.html
+this failure might be expected.
+
+If you are adding a new operator, please file an issue and then use one of the
+workarounds. The workaround depends on how your test invokes gradcheck/gradgradcheck.
+If the test
+- manually invokes gradcheck/gradgradcheck, then call gradcheck/gradgradcheck
+  with `nondet_tol=` as a keyword argument.
+- is OpInfo-based (e.g., in test_ops_gradients.py), then modify the OpInfo for the test
+  to have `gradcheck_nondet_tol=`.
+- is a Module test (e.g., in common_nn.py), then modify the corresponding
+  module_test entry to have `gradcheck_nondet_tol=`
+"""
+
+
+def _check_analytical_jacobian_attributes(
+    inputs, output, nondet_tol, check_grad_dtypes, fast_mode=False, v=None
+) -> Tuple[torch.Tensor, ...]:
+    # This is used by both fast and slow mode:
+    #  - For slow mode, vjps[i][j] is the jth row of the Jacobian wrt the ith
+    #    input.
+    #  - For fast mode, vjps[i][0] is a linear combination of the rows
+    #    of the Jacobian wrt the ith input
+    diff_input_list = list(_iter_tensors(inputs, True))
+
+    def vjp_fn(grad_output):
+        return torch.autograd.grad(
+            output, diff_input_list, grad_output, retain_graph=True, allow_unused=True
+        )
+
+    # Compute everything twice to check for nondeterminism (which we call reentrancy)
+    if fast_mode:
+        vjps1 = _get_analytical_vjps_wrt_specific_output(vjp_fn, output.clone(), v)
+        vjps2 = _get_analytical_vjps_wrt_specific_output(vjp_fn, output.clone(), v)
+    else:
+        vjps1 = _compute_analytical_jacobian_rows(vjp_fn, output.clone())
+        vjps2 = _compute_analytical_jacobian_rows(vjp_fn, output.clone())
+
+    output_numel = output.numel() if not fast_mode else 1
+    jacobians1, types_ok, sizes_ok = _stack_and_check_tensors(
+        vjps1, inputs, output_numel
+    )
+    jacobians2, _, _ = _stack_and_check_tensors(vjps2, inputs, output_numel)
+    reentrant = _check_jacobians_equal(jacobians1, jacobians2, nondet_tol)
+
+    if not types_ok and check_grad_dtypes:
+        raise GradcheckError("Gradient has dtype mismatch")
+    if not sizes_ok:
+        raise GradcheckError("Analytical gradient has incorrect size")
+    if not reentrant:
+        raise GradcheckError(
+            "Backward is not reentrant, i.e., running backward with "
+            "same input and grad_output multiple times gives different values, "
+            "although analytical gradient matches numerical gradient."
+            f"The tolerance for nondeterminism was {nondet_tol}." + FAILED_NONDET_MSG
+        )
+    return jacobians1
+
+
+def _get_analytical_vJu_backward_mode(
+    inputs, outputs, nondet_tol, check_grad_dtypes, all_v, all_u
+):
+    reduced_jacobians: List[List[torch.Tensor]] = []
+    for output, v in zip(outputs, all_v):
+        all_vJ = _check_analytical_jacobian_attributes(
+            inputs, output, nondet_tol, check_grad_dtypes, fast_mode=True, v=v
+        )
+        jacobian_scalars: List[torch.Tensor] = []
+        for vJ, u in zip(all_vJ, all_u):
+            # Why do we need squeeze here? vJ is a 2-d tensor so that we can reuse
+            # the error checking logic from slow mode
+            vJ = vJ.T.squeeze(0)
+            if vJ.is_complex():  # C -> R
+                tv = torch.view_as_real(vJ.resolve_conj())
+                tr = tv.select(-1, 0)
+                ti = tv.select(-1, 1)
+                jacobian_scalars.append(tr.dot(u[0]) + 1j * ti.dot(u[1]))
+            else:  # R -> R
+                jacobian_scalars.append(vJ.dot(u))
+        reduced_jacobians.append(jacobian_scalars)
+    return reduced_jacobians
+
+
+@deprecated(
+    "`get_analytical_jacobian` was part of PyTorch's private API and not "
+    "meant to be exposed. We are deprecating it and it will be removed "
+    "in a future version of PyTorch. If you have a specific use for "
+    "this or feature request for this to be a stable API, please file "
+    "us an issue at https://github.com/pytorch/pytorch/issues/new",
+    category=FutureWarning,
+)
+def get_analytical_jacobian(inputs, output, nondet_tol=0.0, grad_out=1.0):
+    # Replicates the behavior of the old get_analytical_jacobian before the refactor
+    # This shares much of its code with _check_analytical_jacobian_attributes
+    if (
+        grad_out != 1.0
+    ):  # grad_out param is only kept for backward compatibility reasons
+        raise ValueError(
+            "Expected grad_out to be 1.0. get_analytical_jacobian no longer "
+            "supports values of grad_out != 1.0."
+        )
+    if output.is_complex():
+        raise ValueError(
+            "Expected output to be non-complex. get_analytical_jacobian no "
+            "longer supports functions that return complex outputs."
+        )
+    diff_input_list = list(_iter_tensors(inputs, True))
+
+    def vjp_fn(grad_output):
+        return torch.autograd.grad(
+            output, diff_input_list, grad_output, retain_graph=True, allow_unused=True
+        )
+
+    # Compute everything twice to check for nondeterminism (which we call reentrancy)
+    vjps1 = _compute_analytical_jacobian_rows(vjp_fn, output.clone())
+    vjps2 = _compute_analytical_jacobian_rows(vjp_fn, output.clone())
+
+    output_numel = output.numel()
+    jacobians1, types_ok, sizes_ok = _stack_and_check_tensors(
+        vjps1, inputs, output_numel
+    )
+    jacobians2, _, _ = _stack_and_check_tensors(vjps2, inputs, output_numel)
+    reentrant = _check_jacobians_equal(jacobians1, jacobians2, nondet_tol)
+
+    return jacobians1, reentrant, sizes_ok, types_ok
+
+
+def _get_analytical_jacobian(inputs, outputs, input_idx, output_idx):
+    # Computes the analytical Jacobian in slow mode for a single input-output pair.
+    # Forgoes performing checks on dtype, shape, and reentrancy.
+    jacobians = _check_analytical_jacobian_attributes(
+        inputs, outputs[output_idx], nondet_tol=float("inf"), check_grad_dtypes=False
+    )
+    return jacobians[input_idx]
+
+
+def _compute_analytical_jacobian_rows(
+    vjp_fn, sample_output
+) -> List[List[Optional[torch.Tensor]]]:
+    # Computes Jacobian row-by-row by projecting `vjp_fn` = v^T J on standard basis
+    # vectors: vjp_fn(e) = e^T J is a corresponding row of the Jacobian.
+    # NB: this function does not assume vjp_fn(v) to return tensors with the same
+    # number of elements for different v. This is checked when we later combine the
+    # rows into a single tensor.
+    grad_out_base = torch.zeros_like(
+        sample_output, memory_format=torch.legacy_contiguous_format
+    )
+    flat_grad_out = grad_out_base.view(-1)
+    # jacobians_rows[i][j] is the Jacobian jth row for the ith input
+    jacobians_rows: List[List[Optional[torch.Tensor]]] = []
+    for j in range(flat_grad_out.numel()):
+        flat_grad_out.zero_()
+        flat_grad_out[j] = 1.0  # projection for jth row of Jacobian
+        grad_inputs = vjp_fn(grad_out_base)
+        for i, d_x in enumerate(grad_inputs):
+            if j == 0:
+                jacobians_rows.append([])
+            jacobians_rows[i] += [
+                d_x.clone() if isinstance(d_x, torch.Tensor) else None
+            ]
+    return jacobians_rows
+
+
+def _get_analytical_vjps_wrt_specific_output(
+    vjp_fn, sample_output, v
+) -> List[List[Optional[torch.Tensor]]]:
+    vjps: List[List[Optional[torch.Tensor]]] = []
+    grad_inputs = vjp_fn(v.reshape(sample_output.shape))
+    for vjp in grad_inputs:
+        vjps.append([vjp.clone() if isinstance(vjp, torch.Tensor) else None])
+    return vjps
+
+
+def _check_inputs(tupled_inputs) -> bool:
+    # Make sure that gradients are saved for at least one input
+    any_input_requiring_grad = False
+    for idx, inp in enumerate(tupled_inputs):
+        if is_tensor_like(inp) and inp.requires_grad:
+            if not (inp.dtype == torch.float64 or inp.dtype == torch.complex128):
+                warnings.warn(
+                    f"Input #{idx} requires gradient and "
+                    "is not a double precision floating point or complex. "
+                    "This check will likely fail if all the inputs are "
+                    "not of double precision floating point or complex. "
+                )
+            if inp.is_sparse:
+                content = inp._values()
+            elif _is_sparse_compressed_tensor(inp):
+                content = inp.values()
+            else:
+                content = inp
+            # TODO: To cover more problematic cases, replace stride = 0 check with
+            # "any overlap in memory" once we have a proper function to check it.
+            if content.layout is not torch._mkldnn:  # type: ignore[attr-defined]
+                if not all(
+                    st > 0 or sz <= 1
+                    for st, sz in zip(content.stride(), content.size())
+                ):
+                    raise RuntimeError(
+                        f"The {idx}th input has a dimension with stride 0. gradcheck only "
+                        "supports inputs that are non-overlapping to be able to "
+                        "compute the numerical gradients correctly. You should call "
+                        ".contiguous on the input before passing it to gradcheck."
+                    )
+            any_input_requiring_grad = True
+
+    if not any_input_requiring_grad:
+        raise ValueError(
+            "gradcheck expects at least one input tensor to require gradient, "
+            "but none of the them have requires_grad=True."
+        )
+    return True
+
+
+def _check_outputs(outputs) -> None:
+    if any(_is_sparse_any_tensor(t) for t in outputs if isinstance(t, torch.Tensor)):
+        # it is easier to call to_dense() on the sparse output than
+        # to modify analytical jacobian
+        raise ValueError(
+            "Sparse output is not supported at gradcheck yet. "
+            "Please call to_dense(masked_grad=...) on the output of fn for gradcheck."
+        )
+    if any(t.layout == torch._mkldnn for t in outputs if isinstance(t, torch.Tensor)):  # type: ignore[attr-defined]
+        raise ValueError(
+            "MKLDNN output is not supported at gradcheck yet. "
+            "Please call to_dense(masked_grad=...) on the output of fn for gradcheck."
+        )
+
+
+def _check_no_differentiable_outputs(
+    func, inputs, func_out, eps, *, is_forward_ad
+) -> bool:
+    # When there are no differentiable outputs, numerical gradient for a function is
+    # expected to be zero.
+    jacobians_all_inputs_outputs = _get_numerical_jacobian(
+        func, inputs, func_out, eps=eps, is_forward_ad=is_forward_ad
+    )
+    for jacobians_all_outputs_and_fixed_input in jacobians_all_inputs_outputs:
+        for jacobian in jacobians_all_outputs_and_fixed_input:
+            if torch.ne(jacobian, 0).sum() > 0:
+                raise GradcheckError(
+                    "Numerical gradient for function expected to be zero"
+                )
+    return True
+
+
+def _check_no_differentiable_outputs_fast(
+    func, func_out, all_inputs, inputs_indices, all_u, eps, nondet_tol
+):
+    for inp_idx, u in zip(inputs_indices, all_u):
+        jvps = _get_numerical_jvp_wrt_specific_input(func, inp_idx, all_inputs, u, eps)
+        for jvp in jvps:
+            if jvp.numel() == 0:
+                continue
+            if (jvp - torch.zeros_like(jvp)).abs().max() > nondet_tol:
+                raise GradcheckError(
+                    "Numerical gradient for function expected to be zero"
+                )
+    return True
+
+
+FAILED_BATCHED_GRAD_MSG = """
+gradcheck or gradgradcheck failed while testing batched gradient computation.
+This could have been invoked in a number of ways (via a test that calls
+gradcheck/gradgradcheck directly or via an autogenerated test).
+
+If you are adding a new operator, please file an issue and then use one of the
+workarounds. The workaround depends on how your test invokes gradcheck/gradgradcheck.
+If the test
+- manually invokes gradcheck/gradgradcheck, then call gradcheck/gradgradcheck
+  with `check_batched_grad=False` as a keyword argument.
+- is OpInfo-based (e.g., in test_ops_gradients.py), then modify the OpInfo for the test
+  to have `check_batched_grad=False` and/or `check_batched_gradgrad=False`.
+
+If you're modifying an existing operator that supports batched grad computation,
+or wish to make a new operator work with batched grad computation, please read
+the following.
+
+To compute batched grads (e.g., jacobians, hessians), we vmap over the backward
+computation. The most common failure case is if there is a 'vmap-incompatible
+operation' in the backward pass. Please see
+NOTE: [How to write vmap-compatible backward formulas]
+in the codebase for an explanation of how to fix this.
+""".strip()
+
+FAILED_BATCHED_GRAD_MSG_FWD_AD = """
+gradcheck failed while testing batched gradient computation with forward-mode AD.
+This test is enabled automatically when both `check_batched_grad=True`
+and `check_forward_ad=True`, but can be disabled in the following ways
+dependong on how the test was invoked (via a test that calls gradcheck
+directly or via an autogenerated test).
+
+If you are adding a new operator, please file an issue and then use one of the
+workarounds. The workaround depends on how your test invokes gradcheck/gradgradcheck.
+If the test
+- manually invokes gradcheck/gradgradcheck, then call gradcheck/gradgradcheck
+  with `check_batched_forward_grad=False` as a keyword argument.
+- is OpInfo-based (e.g., in test_ops_gradients.py), then modify the OpInfo for the test
+  to have `check_batched_forward_grad=False`
+"""
+
+
+def _get_failed_batched_grad_test_msg(
+    output_idx, input_idx, res, exp, is_forward_ad=False
+):
+    return f"""
+For output {output_idx} and input {input_idx}:
+
+{FAILED_BATCHED_GRAD_MSG_FWD_AD if is_forward_ad else FAILED_BATCHED_GRAD_MSG}
+
+Got:
+{res}
+
+Expected:
+{exp}
+""".strip()
+
+
+def _test_batched_grad_forward_ad(func, inputs) -> bool:
+    fwAD = torch.autograd.forward_ad  # To avoid early import issues (do we need this?)
+    assert isinstance(inputs, tuple)
+
+    for input_idx, current_input in enumerate(inputs):
+        if not (is_tensor_like(current_input) and current_input.requires_grad):
+            continue
+
+        def jvp(tangent: torch.Tensor):
+            with fwAD.dual_level():
+                dual = fwAD.make_dual(current_input.detach(), tangent)
+                inputs_with_dual = tuple(
+                    dual
+                    if idx == input_idx
+                    else (inp.detach() if is_tensor_like(inp) else inp)
+                    for idx, inp in enumerate(inputs)
+                )
+                dual_outputs = _as_tuple(func(*inputs_with_dual))
+                ret = []
+                for dual_output in dual_outputs:
+                    if dual_output is None:
+                        continue
+                    primal_out, tangent_out = fwAD.unpack_dual(dual_output)
+                    if tangent_out is not None:
+                        ret.append(tangent_out)
+                    else:
+                        ret.append(
+                            torch.zeros(
+                                [], dtype=primal_out.dtype, device=primal_out.device
+                            ).expand(primal_out.shape)
+                        )
+                return tuple(ret)
+
+        if not _is_float_or_complex_tensor(current_input):
+            continue
+
+        tangents = [torch.randn_like(current_input) for _ in range(2)]
+        expected = [jvp(t) for t in tangents]
+        expected = [torch.stack(shards) for shards in zip(*expected)]
+
+        try:
+            result = _vmap(jvp)(torch.stack(tangents))
+        except RuntimeError as ex:
+            # Rethrow to provide a better error message
+            raise GradcheckError(
+                f"While computing batched gradients, got: {ex}\n\n{FAILED_BATCHED_GRAD_MSG_FWD_AD}"
+            ) from ex
+
+        for input_idx, (res, exp) in enumerate(zip(result, expected)):
+            if torch.allclose(res, exp):
+                continue
+            raise GradcheckError(
+                _get_failed_batched_grad_test_msg(
+                    input_idx, input_idx, res, exp, is_forward_ad=True
+                )
+            )
+    return True
+
+
+def _test_batched_grad(input, output, output_idx) -> bool:
+    # NB: _test_batched_grad compares two autograd.grad invocations with a single
+    # vmap(autograd.grad) invocation. It's not exactly a "gradcheck" in the
+    # sense that we're not comparing an analytical jacobian with a numeric one,
+    # but it is morally similar (we could have computed a full analytic jac
+    # via vmap, but that is potentially slow)
+    diff_input_list = list(_iter_tensors(input, True))
+    grad = functools.partial(
+        torch.autograd.grad,
+        output,
+        diff_input_list,
+        retain_graph=True,
+        allow_unused=True,
+    )
+
+    def vjp(v):
+        results = grad(v)
+        results = tuple(
+            grad
+            if grad is not None
+            else torch.zeros([], dtype=inp.dtype, device=inp.device).expand(inp.shape)
+            for grad, inp in zip(results, diff_input_list)
+        )
+        return results
+
+    grad_outputs = [torch.randn_like(output) for _ in range(2)]
+
+    expected = [vjp(gO) for gO in grad_outputs]
+    expected = [torch.stack(shards) for shards in zip(*expected)]
+
+    # Squash warnings since these are expected to happen in most cases
+    # NB: this doesn't work for CUDA tests: https://github.com/pytorch/pytorch/issues/50209
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", message="There is a performance drop")
+        warnings.filterwarnings("ignore", message="Please use torch.vmap")
+        try:
+            result = vmap(vjp)(torch.stack(grad_outputs))
+        except RuntimeError as ex:
+            # It's OK that we're not raising the error at the correct callsite.
+            # That's because the callsite is always going to inside the Python
+            # autograd.grad instead of the C++ traceback of what line in the
+            # backward formula
+            raise GradcheckError(
+                f"While computing batched gradients, got: {ex}\n\n{FAILED_BATCHED_GRAD_MSG}"
+            ) from ex
+
+    for input_idx, (res, exp) in enumerate(zip(result, expected)):
+        if torch.allclose(res, exp):
+            continue
+        raise GradcheckError(
+            _get_failed_batched_grad_test_msg(output_idx, input_idx, res, exp)
+        )
+    return True
+
+
+def _test_backward_mul_by_grad_output(outputs, inputs, masked) -> bool:
+    # Tests that backward is multiplied by grad_output
+    diff_input_list: List[torch.Tensor] = list(_iter_tensors(inputs, True))
+    if not diff_input_list:
+        raise GradcheckError("no Tensors requiring grad found in input")
+    grads_input = torch.autograd.grad(
+        outputs,
+        diff_input_list,
+        [
+            torch.zeros_like(o, memory_format=torch.legacy_contiguous_format)
+            for o in outputs
+        ],
+        allow_unused=True,
+    )
+    for gi, di in zip(grads_input, diff_input_list):
+        if gi is None:
+            continue
+        if isinstance(gi, torch.Tensor) and gi.layout != torch.strided:
+            if gi.layout != di.layout:
+                raise GradcheckError(
+                    "grad is incorrect layout ("
+                    + str(gi.layout)
+                    + " is not "
+                    + str(di.layout)
+                    + ")"
+                )
+            if _is_sparse_any_tensor(gi):
+                sparse_kind = str(gi.layout).replace("torch.", "").replace("_coo", "")
+                if gi.sparse_dim() != di.sparse_dim():
+                    raise GradcheckError(
+                        f"grad is {sparse_kind} tensor, but has incorrect sparse_dim"
+                        f" {gi.sparse_dim()}, expected {di.sparse_dim()}"
+                    )
+                if gi.dense_dim() != di.dense_dim():
+                    raise GradcheckError(
+                        f"grad is {sparse_kind} tensor, but has incorrect dense_dim"
+                        f" {gi.dense_dim()}, expected {di.dense_dim()}"
+                    )
+            gi = gi.to_dense()
+            di = di.to_dense()
+        if masked:
+            if not torch.allclose(gi, torch.zeros_like(gi)):
+                raise GradcheckError("backward not multiplied by grad_output")
+        elif not gi.eq(0).all():
+            raise GradcheckError("backward not multiplied by grad_output")
+        if gi.dtype != di.dtype:
+            raise GradcheckError("grad is incorrect type")
+        if gi.device != di.device:
+            raise GradcheckError("grad is incorrect device")
+        if gi.size() != di.size():
+            raise GradcheckError("grad is incorrect size")
+    return True
+
+
+def _test_undefined_forward_mode(func, outputs, inputs):
+    fwAD = torch.autograd.forward_ad
+
+    inp_tensors_idx, inp_tensors = _get_inp_tensors(inputs)
+    all_v, all_u, all_u_dense = _make_vectors(inp_tensors, outputs, use_forward_ad=True)
+
+    tensor_inputs = tuple(i for i in inputs if is_tensor_like(i) and i.requires_grad)
+
+    with fwAD.dual_level():
+        fw_grads = []
+        dual_inputs = []
+        tensor_indices = set()
+        for i, inp in enumerate(inputs):
+            if is_tensor_like(inp) and inp.requires_grad:
+                if inp.layout == torch._mkldnn:  # type: ignore[attr-defined]
+                    raise ValueError(
+                        "MKLDNN inputs are not support for forward AD gradcheck."
+                    )
+
+                inp = fwAD.make_dual(inp.detach(), torch.zeros_like(inp))
+                # If inp is a differentiable view, the dual might not be the tangent given to
+                # make_dual, so read it explicitly from the dual tensor
+                fw_grads.append(fwAD.unpack_dual(inp)[1])
+                tensor_indices.add(i)
+            dual_inputs.append(inp)
+
+        for i, (fw_grad, u) in enumerate(zip(fw_grads, all_u)):
+            fw_grad.copy_(u.view_as(fw_grad))
+
+        for idx, inp in enumerate(inputs):
+            if idx not in tensor_indices:
+                continue
+            dual_inp_obj = dual_inputs[idx]
+
+            # case 1 (Materialized Zero Tensor Tangent)
+            dual_inputs[idx] = fwAD.make_dual(inp.detach(), torch.zeros_like(inp))
+            raw_outputs = _as_tuple(func(*dual_inputs))
+            dual_outputs1 = filter(_is_float_or_complex_tensor, raw_outputs)
+
+            # case 2 (Efficient Zero Tensor Tangent since we don't make a dual object and pass a regular tensor)
+            dual_inputs[idx] = inp.detach()
+            raw_outputs = _as_tuple(func(*dual_inputs))
+            dual_outputs2 = filter(_is_float_or_complex_tensor, raw_outputs)
+
+            # reset
+            dual_inputs[idx] = dual_inp_obj
+
+            for index_o, (d_o1, d_o2) in enumerate(zip(dual_outputs1, dual_outputs2)):
+                val1, res1 = fwAD.unpack_dual(d_o1)
+                val2, res2 = fwAD.unpack_dual(d_o2)
+
+                if not (res1 is None or res2 is None):
+                    if not torch.allclose(res1, res2):
+                        raise GradcheckError(
+                            "Mismatch in tangent values for output with index: ",
+                            index_o,
+                            " when input: ",
+                            inp,
+                            " has an undefined tangent value. ",
+                            " Got: ",
+                            res1,
+                            " but expected: ",
+                            res2,
+                        )
+    return True
+
+
+def _test_undefined_backward_mode(func, outputs, inputs) -> bool:
+    diff_input_list: List[torch.Tensor] = list(_iter_tensors(inputs, True))
+    if not diff_input_list:
+        raise GradcheckError("no Tensors requiring grad found in input")
+
+    def warn_bc_breaking():
+        warnings.warn(
+            "Backwards compatibility: New undefined gradient support checking "
+            "feature is enabled by default, but it may break existing callers "
+            "of this function. If this is true for you, you can call this "
+            'function with "check_undefined_grad=False" to disable the feature'
+        )
+
+    def check_undefined_grad_support(output_to_check):
+        grads_output = [
+            torch.zeros_like(o, memory_format=torch.legacy_contiguous_format)
+            for o in output_to_check
+        ]
+        try:
+            grads_input = torch.autograd.grad(
+                output_to_check, diff_input_list, grads_output, allow_unused=True
+            )
+        except RuntimeError as e:
+            warn_bc_breaking()
+            raise GradcheckError(
+                "Expected backward function to handle undefined output grads. "
+                'Please look at "Notes about undefined output gradients" in '
+                '"tools/autograd/derivatives.yaml"'
+            ) from e
+
+        for gi, i in zip(grads_input, diff_input_list):
+            if (gi is not None) and (not gi.eq(0).all()):
+                warn_bc_breaking()
+                raise GradcheckError(
+                    "Expected all input grads to be undefined or zero when all output grads are undefined "
+                    'or zero. Please look at "Notes about undefined output gradients" in '
+                    '"tools/autograd/derivatives.yaml"'
+                )
+        return True
+
+    # All backward functions must work properly if all output grads are undefined
+    outputs_to_check = [
+        [
+            torch._C._functions.UndefinedGrad()(o)
+            for o in _differentiable_outputs(func(*inputs))
+            # This check filters out Tensor-likes that aren't instances of Tensor.
+            if isinstance(o, torch.Tensor)
+        ]
+    ]
+
+    # If there are multiple output grads, we should be able to undef one at a time without error
+    if len(outputs_to_check[0]) > 1:
+        for undef_grad_idx in range(len(outputs)):
+            output_to_check = _differentiable_outputs(func(*inputs))
+            outputs_to_check.append(
+                [
+                    torch._C._functions.UndefinedGrad()(o)
+                    if idx == undef_grad_idx
+                    else o
+                    for idx, o in enumerate(output_to_check)
+                ]
+            )
+
+    return all(check_undefined_grad_support(output) for output in outputs_to_check)
+
+
+def _as_tuple(x):
+    if isinstance(x, tuple):
+        return x
+    elif isinstance(x, list):
+        return tuple(x)
+    else:
+        return (x,)
+
+
+def _differentiable_outputs(x):
+    return tuple(o for o in _as_tuple(x) if o.requires_grad)
+
+
+def _get_notallclose_msg(
+    analytical,
+    numerical,
+    output_idx,
+    input_idx,
+    complex_indices,
+    test_imag=False,
+    is_forward_ad=False,
+) -> str:
+    out_is_complex = (
+        (not is_forward_ad) and complex_indices and output_idx in complex_indices
+    )
+    inp_is_complex = is_forward_ad and complex_indices and input_idx in complex_indices
+    part = "imaginary" if test_imag else "real"
+    element = "inputs" if is_forward_ad else "outputs"
+    prefix = (
+        ""
+        if not (out_is_complex or inp_is_complex)
+        else f"While considering the {part} part of complex {element} only, "
+    )
+    mode = "computed with forward mode " if is_forward_ad else ""
+    return (
+        prefix + "Jacobian %smismatch for output %d with respect to input %d,\n"
+        "numerical:%s\nanalytical:%s\n"
+        % (mode, output_idx, input_idx, numerical, analytical)
+    )
+
+
+def _transpose(matrix_of_tensors):
+    # returns list of tuples
+    return list(zip(*matrix_of_tensors))
+
+
+def _real_and_imag_output(fn):
+    # returns new functions real(fn), and imag(fn) where real(fn) and imag(fn) behave the same as
+    # the original fn, except torch.real or torch.imag are applied to the complex outputs
+    def apply_to_c_outs(fn, fn_to_apply):
+        def wrapped_fn(*inputs):
+            outs = _as_tuple(fn(*inputs))
+            return tuple(fn_to_apply(o) if o.is_complex() else o for o in outs)
+
+        return wrapped_fn
+
+    return apply_to_c_outs(fn, torch.real), apply_to_c_outs(fn, torch.imag)
+
+
+def _real_and_imag_input(fn, complex_inp_indices, tupled_inputs):
+    # returns new functions that take real inputs instead of complex inputs as
+    # (x, y) -> fn(x + y * 1j). And it computes: inp -> fn(inp + y * 1j) and inp -> fn(x + inp * 1j).
+    # In each case, the other part is considered constant.
+    # We do not use 0 for the constant here to make sure we always call the user function with a valid input.
+    def apply_to_c_inps(fn, fn_to_apply):
+        def wrapped_fn(*inputs):
+            new_inputs = list(inputs)
+            for should_be_complex in complex_inp_indices:
+                new_inputs[should_be_complex] = fn_to_apply(
+                    new_inputs[should_be_complex], tupled_inputs[should_be_complex]
+                )
+            return _as_tuple(fn(*new_inputs))
+
+        return wrapped_fn
+
+    real_fn = apply_to_c_inps(fn, lambda inp, orig: inp + orig.imag * 1j)
+    imag_fn = apply_to_c_inps(fn, lambda inp, orig: orig.real + inp * 1j)
+    return real_fn, imag_fn
+
+
+def _gradcheck_real_imag(
+    gradcheck_fn,
+    func,
+    func_out,
+    tupled_inputs,
+    outputs,
+    eps,
+    rtol,
+    atol,
+    check_grad_dtypes,
+    check_forward_ad,
+    check_backward_ad,
+    nondet_tol,
+    check_undefined_grad,
+):
+    complex_out_indices = [i for i, o in enumerate(outputs) if o.is_complex()]
+    has_any_complex_output = any(o.is_complex() for o in _as_tuple(func_out))
+    if check_backward_ad:
+        if has_any_complex_output:
+            real_fn, imag_fn = _real_and_imag_output(func)
+
+            imag_func_out = imag_fn(*tupled_inputs)
+            imag_outputs = _differentiable_outputs(imag_func_out)
+            gradcheck_fn(
+                imag_fn,
+                imag_func_out,
+                tupled_inputs,
+                imag_outputs,
+                eps,
+                rtol,
+                atol,
+                check_grad_dtypes,
+                nondet_tol,
+                complex_indices=complex_out_indices,
+                test_imag=True,
+            )
+
+            real_func_out = real_fn(*tupled_inputs)
+            real_outputs = _differentiable_outputs(real_func_out)
+            gradcheck_fn(
+                real_fn,
+                real_func_out,
+                tupled_inputs,
+                real_outputs,
+                eps,
+                rtol,
+                atol,
+                check_grad_dtypes,
+                nondet_tol,
+                complex_indices=complex_out_indices,
+            )
+        else:
+            gradcheck_fn(
+                func,
+                func_out,
+                tupled_inputs,
+                outputs,
+                eps,
+                rtol,
+                atol,
+                check_grad_dtypes,
+                nondet_tol,
+            )
+
+    if check_forward_ad:
+        complex_inp_indices = [
+            i
+            for i, inp in enumerate(tupled_inputs)
+            if is_tensor_like(inp) and inp.is_complex()
+        ]
+        if complex_inp_indices:
+            real_fn, imag_fn = _real_and_imag_input(
+                func, complex_inp_indices, tupled_inputs
+            )
+
+            imag_inputs = [
+                inp.imag if is_tensor_like(inp) and inp.is_complex() else inp
+                for inp in tupled_inputs
+            ]
+            imag_func_out = imag_fn(*imag_inputs)
+            diff_imag_func_out = _differentiable_outputs(imag_func_out)
+            gradcheck_fn(
+                imag_fn,
+                imag_func_out,
+                imag_inputs,
+                diff_imag_func_out,
+                eps,
+                rtol,
+                atol,
+                check_grad_dtypes,
+                nondet_tol,
+                complex_indices=complex_inp_indices,
+                test_imag=True,
+                use_forward_ad=True,
+            )
+
+            real_inputs = [
+                inp.real if is_tensor_like(inp) and inp.is_complex() else inp
+                for inp in tupled_inputs
+            ]
+            real_func_out = real_fn(*real_inputs)
+            diff_real_func_out = _differentiable_outputs(real_func_out)
+            gradcheck_fn(
+                real_fn,
+                real_func_out,
+                real_inputs,
+                diff_real_func_out,
+                eps,
+                rtol,
+                atol,
+                check_grad_dtypes,
+                nondet_tol,
+                complex_indices=complex_inp_indices,
+                use_forward_ad=True,
+            )
+            if check_undefined_grad:
+                _test_undefined_forward_mode(imag_fn, imag_func_out, imag_inputs)
+                _test_undefined_forward_mode(real_fn, real_func_out, real_inputs)
+        else:
+            gradcheck_fn(
+                func,
+                func_out,
+                tupled_inputs,
+                outputs,
+                eps,
+                rtol,
+                atol,
+                check_grad_dtypes,
+                nondet_tol,
+                use_forward_ad=True,
+            )
+            if check_undefined_grad:
+                _test_undefined_forward_mode(func, outputs, tupled_inputs)
+
+
+def _slow_gradcheck(
+    func,
+    func_out,
+    tupled_inputs,
+    outputs,
+    eps,
+    rtol,
+    atol,
+    check_grad_dtypes,
+    nondet_tol,
+    *,
+    use_forward_ad=False,
+    complex_indices=None,
+    test_imag=False,
+    masked=False,
+):
+    func_out = _as_tuple(func_out)
+    if not outputs:
+        return _check_no_differentiable_outputs(
+            func, tupled_inputs, func_out, eps=eps, is_forward_ad=use_forward_ad
+        )
+    tupled_inputs_numerical = tupled_inputs if masked else _densify(tupled_inputs)
+
+    numerical = _transpose(
+        _get_numerical_jacobian(
+            func,
+            tupled_inputs_numerical,
+            func_out,
+            eps=eps,
+            is_forward_ad=use_forward_ad,
+        )
+    )
+    # Note: [numerical vs analytical output length]
+    # The numerical path returns jacobian quantity for all outputs, even if requires_grad of that
+    # output is False. This behavior is necessary for _check_no_differentiable_outputs to work.
+    numerical = [nj for o, nj in zip(func_out, numerical) if o.requires_grad]
+    if use_forward_ad:
+        analytical_forward = _get_analytical_jacobian_forward_ad(
+            func, tupled_inputs, func_out, check_grad_dtypes=check_grad_dtypes
+        )
+
+        for i, n_per_out in enumerate(numerical):
+            for j, n in enumerate(n_per_out):
+                a = analytical_forward[j][i]
+                if not _allclose_with_type_promotion(a, n.to(a.device), rtol, atol):
+                    raise GradcheckError(
+                        _get_notallclose_msg(
+                            a, n, i, j, complex_indices, test_imag, is_forward_ad=True
+                        )
+                    )
+    else:
+        for i, o in enumerate(outputs):
+            analytical = _check_analytical_jacobian_attributes(
+                tupled_inputs, o, nondet_tol, check_grad_dtypes
+            )
+
+            for j, (a, n) in enumerate(zip(analytical, numerical[i])):
+                if not _allclose_with_type_promotion(a, n.to(a.device), rtol, atol):
+                    raise GradcheckError(
+                        _get_notallclose_msg(a, n, i, j, complex_indices, test_imag)
+                    )
+
+    return True
+
+
+def _dot_with_type_promotion(u, v):
+    assert u.dim() == 1 and v.dim() == 1
+    return (u * v).sum()
+
+
+def _allclose_with_type_promotion(a, b, rtol, atol):
+    promoted_type = torch.promote_types(a.dtype, b.dtype)
+    a = a.to(dtype=promoted_type)
+    b = b.to(dtype=promoted_type)
+    return torch.allclose(a, b, rtol, atol)
+
+
+def _to_real_dtype(dtype):
+    if dtype == torch.complex128:
+        return torch.float64
+    elif dtype == torch.complex64:
+        return torch.float32
+    else:
+        return dtype
+
+
+def _vec_from_tensor(x, generator, downcast_complex=False):
+    # Create a random vector with the same number of elements as x and the same
+    # dtype/device. If x is complex and downcast_complex is False, we create a
+    # complex tensor with only real component.
+    if x.layout == torch.sparse_coo:
+        # For sparse, create a random sparse vec with random values in the same
+        # indices. Make sure size is set so that it isn't inferred to be smaller.
+        x_values = x._values()
+        dtype = _to_real_dtype(x.dtype) if downcast_complex else x.dtype
+        values = (
+            torch.rand(x_values.numel(), generator=generator)
+            .to(dtype=dtype, device=x.device)
+            .view(x_values.shape)
+        )
+        values /= values.norm()
+        vec = torch.sparse_coo_tensor(x._indices(), values, x.size(), device=x.device)
+    elif _is_sparse_compressed_tensor(x):
+        if x.layout in {torch.sparse_csr, torch.sparse_bsr}:
+            compressed_indices, plain_indices = x.crow_indices(), x.col_indices()
+        else:
+            compressed_indices, plain_indices = x.ccol_indices(), x.row_indices()
+        x_values = x.values()
+        dtype = _to_real_dtype(x.dtype) if downcast_complex else x.dtype
+        values = (
+            torch.rand(x_values.numel(), generator=generator)
+            .to(dtype=dtype, device=x.device)
+            .view(x_values.shape)
+        )
+        values /= values.norm()
+        vec = torch.sparse_compressed_tensor(
+            compressed_indices,
+            plain_indices,
+            values,
+            x.size(),
+            layout=x.layout,
+            device=x.device,
+        )
+    else:
+        dtype = _to_real_dtype(x.dtype) if downcast_complex else x.dtype
+        vec = torch.rand(x.numel(), generator=generator).to(
+            dtype=dtype, device=x.device
+        )
+        vec /= vec.norm()
+    return vec
+
+
+def _get_inp_tensors(tupled_inputs):
+    inp_idx_tup = [
+        (i, t)
+        for i, t in enumerate(tupled_inputs)
+        if is_tensor_like(t) and t.requires_grad
+    ]
+    return [tup[0] for tup in inp_idx_tup], [tup[1] for tup in inp_idx_tup]
+
+
+def _adjusted_atol(atol, u, v):
+    # In slow gradcheck, we compare A and B element-wise, i.e., for some a, b we
+    # allow: |a - b| < atol + rtol * b. But since we now compare q1 = v^T A u and
+    # q2 = v^T B u, we must allow |q1 - q2| < v^T E u + rtol * v^T B u, where E is
+    # the correctly sized matrix in which each entry is atol.
+    #
+    # We see that atol needs to be scaled by v^T M u (where M is an all-ones M x N
+    # matrix): v^T M u = \sum_{i} \sum_{j} u_i * v_j = (\sum_{i} u_i)(\sum_{i} v_i)
+    # TODO: properly handle case when u is tuple instead of only taking first element
+    u = u[0] if isinstance(u, tuple) else u
+    sum_u = u.sum()
+    sum_v = 1.0 if v is None else v.sum()
+    return atol * float(sum_u) * float(sum_v)
+
+
+FAST_FAIL_SLOW_OK_MSG = """
+Fast gradcheck failed but element-wise differences are small. This means that the
+test might've passed in slow_mode!
+
+If you are adding a new operator, please file an issue and then use one of the
+workarounds. The workaround depends on how your test invokes gradcheck/gradgradcheck:
+
+If the test
+- manually invokes gradcheck/gradgradcheck, then call gradcheck/gradgradcheck
+  with `fast_mode=False` as a keyword argument.
+- is OpInfo-based (e.g., in test_ops_gradients.py), then modify the OpInfo for the test
+  to have `gradcheck_fast_mode=False`
+- is a Module test (e.g., in common_nn.py), then modify the corresponding
+  module_test entry to have `gradcheck_fast_mode=False`
+""".strip()
+
+
+def _run_slow_mode_and_get_error(
+    func, tupled_inputs, outputs, input_idx, output_idx, rtol, atol, eps, is_forward_ad
+):
+    # Compute jacobians in slow mode for better error message
+    slow_numerical = _get_numerical_jacobian(
+        func, tupled_inputs, outputs, eps=eps, is_forward_ad=is_forward_ad
+    )[input_idx][output_idx]
+    if is_forward_ad:
+
+        def new_fn(inp):
+            new_inputs = list(tupled_inputs)
+            new_inputs[input_idx] = inp
+            return _as_tuple(func(*new_inputs))[output_idx]
+
+        slow_analytical = _get_analytical_jacobian_forward_ad(
+            new_fn, (tupled_inputs[input_idx],), (outputs[output_idx],)
+        )[0][0]
+    else:
+        slow_analytical = _get_analytical_jacobian(
+            tupled_inputs, outputs, input_idx, output_idx
+        )
+
+    # Assume jacobians are non-empty and have the same shape
+    slow_max_diff = (slow_numerical - slow_analytical).abs().max()
+
+    slow_allclose = torch.allclose(slow_analytical, slow_numerical, rtol, atol)
+    msg = (
+        "\nThe above quantities relating the numerical and analytical jacobians are computed \n"
+        "in fast mode. See: https://github.com/pytorch/pytorch/issues/53876 for more background \n"
+        "about fast mode. Below, we recompute numerical and analytical jacobians in slow mode:\n\n"
+        f"Numerical:\n {slow_numerical}\n"
+        f"Analytical:\n{slow_analytical}\n\n"
+        f"The max per-element difference (slow mode) is: {slow_max_diff}.\n"
+    )
+    if slow_allclose:
+        # Slow gradcheck would've passed!
+        msg += FAST_FAIL_SLOW_OK_MSG
+    return msg
+
+
+def _to_flat_dense_if_sparse(tensor):
+    if _is_sparse_any_tensor(tensor):
+        return tensor.to_dense().reshape(-1)
+    else:
+        return tensor
+
+
+def _make_vectors(inp_tensors, outputs, *, use_forward_ad):
+    # Use our own generator to avoid messing with the user's RNG state
+    g_cpu = torch.Generator()
+
+    def _vec_from_tensor_cpu(*args):
+        # Default allocate all tensors on CPU, so they are on the same device as the generator
+        # even if the user specified a default device
+        with torch.device("cpu"):
+            return _vec_from_tensor(*args)
+
+    all_u = []
+    all_u_dense = []
+    for inp in inp_tensors:
+        ur = _vec_from_tensor_cpu(inp, g_cpu, True)
+        ur_dense = _to_flat_dense_if_sparse(ur)
+        if inp.is_complex():
+            ui = _vec_from_tensor_cpu(inp, g_cpu, True)
+            all_u.append((ur, ui))
+            ui_dense = _to_flat_dense_if_sparse(ui)
+            all_u_dense.append((ur_dense, ui_dense))
+        else:
+            all_u.append(ur)
+            all_u_dense.append(ur_dense)
+    all_v = (
+        None
+        if use_forward_ad
+        else [_vec_from_tensor_cpu(out, g_cpu) for out in outputs]
+    )
+    return all_v, all_u, all_u_dense
+
+
+def _check_analytical_numerical_equal(
+    all_analytical,
+    all_numerical,
+    complex_indices,
+    tupled_inputs,
+    outputs,
+    func,
+    all_v,
+    all_u,
+    rtol,
+    atol,
+    eps,
+    test_imag,
+    *,
+    is_forward_ad=False,
+):
+    for i, all_numerical_for_input_i in enumerate(all_numerical):
+        for j, n in enumerate(all_numerical_for_input_i):
+            # Forward AD generates the transpose of what this function expects
+            if is_forward_ad:
+                a = all_analytical[i][j]
+            else:
+                a = all_analytical[j][i]
+            n = n.to(device=a.device)
+            updated_atol = _adjusted_atol(atol, all_u[i], all_v[j] if all_v else None)
+            if not _allclose_with_type_promotion(a, n.to(a.device), rtol, updated_atol):
+                jacobians_str = _run_slow_mode_and_get_error(
+                    func, tupled_inputs, outputs, i, j, rtol, atol, eps, is_forward_ad
+                )
+                raise GradcheckError(
+                    _get_notallclose_msg(
+                        a, n, j, i, complex_indices, test_imag, is_forward_ad
+                    )
+                    + jacobians_str
+                )
+
+
+def _fast_gradcheck(
+    func,
+    func_out,
+    inputs,
+    outputs,
+    eps,
+    rtol,
+    atol,
+    check_grad_dtypes,
+    nondet_tol,
+    *,
+    use_forward_ad=False,
+    complex_indices=None,
+    test_imag=False,
+    masked=False,
+):
+    # See https://github.com/pytorch/pytorch/issues/53876 for details
+    inp_tensors_idx, inp_tensors = _get_inp_tensors(inputs)
+    # Backward mode computes v^T * J (VJP)
+    # Since we computed J * u (JVP) through finite difference method, we perform an equality check
+    # between VJP * u, v * JVP
+    # ----
+    # Forward mode computes J * u (JVP)
+    # Since we already compute JVP through finite difference method,
+    # we don't need v for correctness check here as asserted below
+    all_v, all_u, all_u_dense = _make_vectors(
+        inp_tensors, outputs, use_forward_ad=use_forward_ad
+    )
+
+    inputs_numerical, all_u_numerical, all_v_numerical = (
+        (inputs, all_u, all_v) if masked else _densify((inputs, all_u, all_v))
+    )
+
+    numerical_vJu = _get_numerical_vJu(
+        func,
+        inputs_numerical,
+        inp_tensors_idx,
+        func_out,
+        all_u_numerical,
+        all_v_numerical,
+        eps,
+        is_forward_ad=use_forward_ad,
+    )
+    # TODO: replicate https://github.com/pytorch/pytorch/pull/77743 for fast gradcheck as well
+    if use_forward_ad:
+        assert all_v is None
+        analytical_vJu = _get_analytical_jacobian_forward_ad(
+            func,
+            inputs,
+            _as_tuple(func_out),
+            all_u=all_u,
+            check_grad_dtypes=check_grad_dtypes,
+        )
+    else:
+        if not outputs:
+            _check_no_differentiable_outputs_fast(
+                func, func_out, inputs, inp_tensors_idx, all_u, eps, nondet_tol
+            )
+
+        analytical_vJu = _get_analytical_vJu_backward_mode(
+            inputs, outputs, nondet_tol, check_grad_dtypes, all_v, all_u_dense
+        )
+
+    _check_analytical_numerical_equal(
+        analytical_vJu,
+        numerical_vJu,
+        complex_indices,
+        inputs,
+        outputs,
+        func,
+        all_v,
+        all_u,
+        rtol,
+        atol,
+        eps,
+        test_imag,
+        is_forward_ad=use_forward_ad,
+    )
+
+    return True
+
+
+# Note [VarArg of Tensors]
+# ~~~~~~~~~~~~~~~~~~~~~~~~
+# 'func' accepts a vararg of tensors, which isn't expressable in the type system at the moment.
+# If https://mypy.readthedocs.io/en/latest/additional_features.html?highlight=callable#extended-callable-types is accepted,
+# the '...' first argument of Callable can be replaced with VarArg(Tensor).
+# For now, we permit any input.
+def gradcheck(
+    func: Callable[..., Union[_TensorOrTensors]],  # See Note [VarArg of Tensors]
+    inputs: _TensorOrTensors,
+    *,
+    eps: float = 1e-6,
+    atol: float = 1e-5,
+    rtol: float = 1e-3,
+    raise_exception: bool = True,
+    nondet_tol: float = 0.0,
+    check_undefined_grad: bool = True,
+    check_grad_dtypes: bool = False,
+    check_batched_grad: bool = False,
+    check_batched_forward_grad: bool = False,
+    check_forward_ad: bool = False,
+    check_backward_ad: bool = True,
+    fast_mode: bool = False,
+    masked: Optional[bool] = None,
+) -> bool:  # noqa: D400,D205
+    r"""Check gradients computed via small finite differences against analytical
+    gradients wrt tensors in :attr:`inputs` that are of floating point or complex type
+    and with ``requires_grad=True``.
+
+    The check between numerical and analytical gradients uses :func:`~torch.allclose`.
+
+    For most of the complex functions we consider for optimization purposes, no notion of
+    Jacobian exists. Instead, gradcheck verifies if the numerical and analytical values of
+    the Wirtinger and Conjugate Wirtinger derivatives are consistent. Because the gradient
+    computation is done under the assumption that the overall function has a real-valued
+    output, we treat functions with complex output in a special way. For these functions,
+    gradcheck is applied to two real-valued functions corresponding to taking the real
+    components of the complex outputs for the first, and taking the imaginary components
+    of the complex outputs for the second. For more details, check out
+    :ref:`complex_autograd-doc`.
+
+    .. note::
+        The default values are designed for :attr:`input` of double precision.
+        This check will likely fail if :attr:`input` is of less precision, e.g.,
+        ``FloatTensor``.
+
+    .. note::
+        Gradcheck may fail when evaluated on non-differentiable points
+        because the numerically computed gradients via finite differencing may differ
+        those computed analytically (not necessarily because either is incorrect).
+        For more context, see :ref:`non-differentiable-func-grad`.
+
+    .. warning::
+       If any checked tensor in :attr:`input` has overlapping memory, i.e.,
+       different indices pointing to the same memory address (e.g., from
+       :func:`torch.expand`), this check will likely fail because the numerical
+       gradients computed by point perturbation at such indices will change
+       values at all other indices that share the same memory address.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a Tensor or a tuple of Tensors
+        inputs (tuple of Tensor or Tensor): inputs to the function
+        eps (float, optional): perturbation for finite differences
+        atol (float, optional): absolute tolerance
+        rtol (float, optional): relative tolerance
+        raise_exception (bool, optional): indicating whether to raise an exception if
+            the check fails. The exception gives more information about the
+            exact nature of the failure. This is helpful when debugging gradchecks.
+        nondet_tol (float, optional): tolerance for non-determinism. When running
+            identical inputs through the differentiation, the results must either match
+            exactly (default, 0.0) or be within this tolerance.
+        check_undefined_grad (bool, optional): if ``True``, check if undefined output grads
+            are supported and treated as zeros, for ``Tensor`` outputs.
+        check_batched_grad (bool, optional): if ``True``, check if we can compute
+            batched gradients using prototype vmap support. Defaults to False.
+        check_batched_forward_grad (bool, optional): if ``True``, checks if we can compute
+            batched forward gradients using forward ad and prototype vmap support. Defaults to ``False``.
+        check_forward_ad (bool, optional): if ``True``, check that the gradients computed with forward
+            mode AD match the numerical ones. Defaults to ``False``.
+        check_backward_ad (bool, optional): if ``False``, do not perform any checks that rely on
+            backward mode AD to be implemented. Defaults to ``True``.
+        fast_mode (bool, optional): Fast mode for gradcheck and gradgradcheck is currently only
+            implemented for R to R functions. If none of the inputs and outputs are complex
+            a faster implementation of gradcheck that no longer computes the entire jacobian
+            is run; otherwise, we fall back to the slow implementation.
+        masked (bool, optional): if ``True``, the gradients of unspecified elements of
+            sparse tensors are ignored. Defaults to ``False``.
+    Returns:
+        ``True`` if all differences satisfy allclose condition
+
+    """
+    assert (
+        check_forward_ad or check_backward_ad
+    ), "Expected at least one of check_forward_ad or check_backward_ad to be True"
+    assert not (
+        check_batched_grad and not check_backward_ad
+    ), "Setting check_batched_grad=True requires check_backward_ad to be True"
+    assert not (
+        check_batched_forward_grad and not check_forward_ad
+    ), "Setting check_batched_forward_grad=True requires check_forward_ad to be True"
+    args = locals().copy()
+    args.pop("raise_exception")
+    if not raise_exception:
+        try:
+            return _gradcheck_helper(**args)
+        except GradcheckError as e:
+            return False
+    else:
+        return _gradcheck_helper(**args)
+
+
+def _gradcheck_helper(
+    func,
+    inputs,
+    eps,
+    atol,
+    rtol,
+    nondet_tol,
+    check_undefined_grad,
+    check_grad_dtypes,
+    check_batched_grad,
+    check_batched_forward_grad,
+    check_forward_ad,
+    check_backward_ad,
+    fast_mode,
+    masked,
+):
+    tupled_inputs = _as_tuple(inputs)
+    _check_inputs(tupled_inputs)
+
+    func_out = func(*tupled_inputs)
+    outputs = _differentiable_outputs(func_out)
+    _check_outputs(outputs)
+
+    gradcheck_fn = functools.partial(
+        _fast_gradcheck if fast_mode else _slow_gradcheck, masked=masked
+    )
+    _gradcheck_real_imag(
+        gradcheck_fn,
+        func,
+        func_out,
+        tupled_inputs,
+        outputs,
+        eps,
+        rtol,
+        atol,
+        check_grad_dtypes,
+        check_forward_ad=check_forward_ad,
+        check_backward_ad=check_backward_ad,
+        nondet_tol=nondet_tol,
+        check_undefined_grad=check_undefined_grad,
+    )
+
+    if check_batched_forward_grad:
+        _test_batched_grad_forward_ad(func, tupled_inputs)
+
+    # Short circuit because remaining tests rely on backward AD to be implemented
+    if not check_backward_ad:
+        return True
+
+    for i, o in enumerate(outputs):
+        if check_batched_grad:
+            _test_batched_grad(tupled_inputs, o, i)
+
+    _test_backward_mul_by_grad_output(outputs, tupled_inputs, masked)
+
+    if check_undefined_grad and check_backward_ad:
+        _test_undefined_backward_mode(func, outputs, tupled_inputs)
+    return True
+
+
+def gradgradcheck(
+    func: Callable[..., _TensorOrTensors],  # See Note [VarArg of Tensors]
+    inputs: _TensorOrTensors,
+    grad_outputs: Optional[_TensorOrTensors] = None,
+    *,
+    eps: float = 1e-6,
+    atol: float = 1e-5,
+    rtol: float = 1e-3,
+    gen_non_contig_grad_outputs: bool = False,
+    raise_exception: bool = True,
+    nondet_tol: float = 0.0,
+    check_undefined_grad: bool = True,
+    check_grad_dtypes: bool = False,
+    check_batched_grad: bool = False,
+    check_fwd_over_rev: bool = False,
+    check_rev_over_rev: bool = True,
+    fast_mode: bool = False,
+    masked: bool = False,
+) -> bool:  # noqa: D400,D205
+    r"""Check gradients of gradients computed via small finite differences
+    against analytical gradients wrt tensors in :attr:`inputs` and
+    :attr:`grad_outputs` that are of floating point or complex type and with
+    ``requires_grad=True``.
+
+    This function checks that backpropagating through the gradients computed
+    to the given :attr:`grad_outputs` are correct.
+
+    The check between numerical and analytical gradients uses :func:`~torch.allclose`.
+
+    .. note::
+        The default values are designed for :attr:`input` and
+        :attr:`grad_outputs` of double precision. This check will likely fail if
+        they are of less precision, e.g., ``FloatTensor``.
+
+    .. warning::
+       If any checked tensor in :attr:`input` and :attr:`grad_outputs` has
+       overlapping memory, i.e., different indices pointing to the same memory
+       address (e.g., from :func:`torch.expand`), this check will likely fail
+       because the numerical gradients computed by point perturbation at such
+       indices will change values at all other indices that share the same
+       memory address.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a Tensor or a tuple of Tensors
+        inputs (tuple of Tensor or Tensor): inputs to the function
+        grad_outputs (tuple of Tensor or Tensor, optional): The gradients with
+            respect to the function's outputs.
+        eps (float, optional): perturbation for finite differences
+        atol (float, optional): absolute tolerance
+        rtol (float, optional): relative tolerance
+        gen_non_contig_grad_outputs (bool, optional): if :attr:`grad_outputs` is
+            ``None`` and :attr:`gen_non_contig_grad_outputs` is ``True``, the
+            randomly generated gradient outputs are made to be noncontiguous
+        raise_exception (bool, optional): indicating whether to raise an exception if
+            the check fails. The exception gives more information about the
+            exact nature of the failure. This is helpful when debugging gradchecks.
+        nondet_tol (float, optional): tolerance for non-determinism. When running
+            identical inputs through the differentiation, the results must either match
+            exactly (default, 0.0) or be within this tolerance. Note that a small amount
+            of nondeterminism in the gradient will lead to larger inaccuracies in
+            the second derivative.
+        check_undefined_grad (bool, optional): if True, check if undefined output grads
+            are supported and treated as zeros
+        check_batched_grad (bool, optional): if True, check if we can compute
+            batched gradients using prototype vmap support. Defaults to False.
+        fast_mode (bool, optional): if True, run a faster implementation of gradgradcheck that
+            no longer computes the entire jacobian.
+        masked (bool, optional): if True, the gradients of unspecified elements of
+            sparse tensors are ignored (default, False).
+    Returns:
+        True if all differences satisfy allclose condition
+    """
+    assert (
+        check_fwd_over_rev or check_rev_over_rev
+    ), "Expected at least one of check_fwd_over_rev or check_rev_over_rev to be True"
+    assert not (
+        check_undefined_grad and not check_rev_over_rev
+    ), "Setting check_undefined_grad=True requires check_rev_over_rev to be True"
+    assert not (
+        check_batched_grad and not check_rev_over_rev
+    ), "Setting check_batched_grad=True requires check_rev_over_rev to be True"
+    # TODO: do we want to test this too?
+    # assert not (check_batched_forward_grad and not check_fwd_over_rev), (
+    #     "Setting check_batched_forward_grad=True requires check_fwd_over_rev to be True")
+    tupled_inputs = _as_tuple(inputs)
+
+    if grad_outputs is None:
+        # If grad_outputs is not specified, create random Tensors of the same shape, type, and device as the outputs
+
+        outputs = _differentiable_outputs(func(*tupled_inputs))
+        tupled_grad_outputs = tuple(
+            torch.testing.make_tensor(
+                x.shape,
+                dtype=x.dtype
+                if x.is_floating_point() or x.is_complex()
+                else torch.double,
+                device=x.device,
+                low=-1,
+                high=1,
+                requires_grad=True,
+                noncontiguous=gen_non_contig_grad_outputs,
+            )
+            for x in outputs
+        )
+    else:
+        tupled_grad_outputs = _as_tuple(grad_outputs)
+
+    num_outputs = len(tupled_grad_outputs)
+
+    # NB: We need to save the requires_grad information about the inputs here because gradcheck detaches inputs
+    #     before running forward mode AD
+    diff_input_args_indices = {
+        i for i, x in enumerate(tupled_inputs) if is_tensor_like(x) and x.requires_grad
+    }
+    diff_grad_output_indices = {
+        i for i, x in enumerate(tupled_grad_outputs) if x.requires_grad
+    }
+
+    def new_func(*args):
+        # Restore the requires_grad information
+        input_args = tuple(
+            x.requires_grad_() if i in diff_input_args_indices else x
+            for i, x in enumerate(args[:-num_outputs])
+        )
+        outputs = _differentiable_outputs(func(*input_args))
+        grad_outputs = tuple(
+            x.requires_grad_() if i in diff_grad_output_indices else x
+            for i, x in enumerate(args[-num_outputs:])
+        )
+        diff_input_args = tuple(
+            x for i, x in enumerate(input_args) if i in diff_input_args_indices
+        )
+        grad_inputs = torch.autograd.grad(
+            outputs, diff_input_args, grad_outputs, create_graph=True, allow_unused=True
+        )
+        grad_inputs = tuple(g for g in grad_inputs if g is not None)
+        return grad_inputs
+
+    return gradcheck(
+        new_func,
+        tupled_inputs + tupled_grad_outputs,
+        eps=eps,
+        atol=atol,
+        rtol=rtol,
+        raise_exception=raise_exception,
+        nondet_tol=nondet_tol,
+        check_undefined_grad=check_undefined_grad,
+        check_grad_dtypes=check_grad_dtypes,
+        check_batched_grad=check_batched_grad,
+        fast_mode=fast_mode,
+        check_forward_ad=check_fwd_over_rev,
+        check_backward_ad=check_rev_over_rev,
+        masked=masked,
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/graph.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..4792ced32b2703af47539c5f3c86bb9fd36d0694
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/graph.py
@@ -0,0 +1,830 @@
+import abc
+import contextlib
+import functools
+import logging
+import threading
+from collections import defaultdict, deque
+from typing import (
+    Any,
+    Callable,
+    cast,
+    Deque,
+    Dict,
+    Generator,
+    Iterable,
+    Iterator,
+    List,
+    Literal,
+    MutableMapping,
+    NamedTuple,
+    Optional,
+    Sequence,
+    Set,
+    Tuple,
+    TYPE_CHECKING,
+    Union,
+)
+from typing_extensions import TypeAlias
+from weakref import WeakKeyDictionary, WeakValueDictionary
+
+import torch
+from torch.autograd.variable import Variable
+from torch.utils._python_dispatch import TorchDispatchMode
+from torch.utils.hooks import RemovableHandle
+
+
+if TYPE_CHECKING:
+    from torch._ops import OpOverload
+
+
+__all__ = [
+    "saved_tensors_hooks",
+    "save_on_cpu",
+    "disable_saved_tensors_hooks",
+    "register_multi_grad_hook",
+    "allow_mutation_on_saved_tensors",
+    "Node",
+    "GradientEdge",
+    "get_gradient_edge",
+    "increment_version",
+]
+
+
+log = logging.getLogger(__name__)
+
+
+class Node(abc.ABC):
+    @abc.abstractmethod
+    def name(self) -> str:
+        r"""Return the name.
+
+        Example::
+
+            >>> import torch
+            >>> a = torch.tensor([0., 0., 0.], requires_grad=True)
+            >>> b = a.clone()
+            >>> assert isinstance(b.grad_fn, torch.autograd.graph.Node)
+            >>> print(b.grad_fn.name())
+            CloneBackward0
+        """
+        raise NotImplementedError
+
+    @property
+    @abc.abstractmethod
+    def next_functions(self) -> Tuple[Tuple[Optional["Node"], int], ...]:
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def metadata(self) -> dict:
+        r"""Return the metadata."""
+        raise NotImplementedError
+
+    @property
+    @abc.abstractmethod
+    def _input_metadata(self) -> List[Any]:
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def _register_hook_dict(self, tensor: torch.Tensor) -> None:
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def register_hook(self, fn: Callable[..., Any]) -> RemovableHandle:
+        r"""Register a backward hook.
+
+        The hook will be called every time a gradient with respect to the
+        Node is computed. The hook should have the following signature::
+
+            hook(grad_inputs: Tuple[Tensor], grad_outputs: Tuple[Tensor]) -> Tuple[Tensor] or None
+
+
+        The hook should not modify its argument, but it can optionally return
+        a new gradient which will be used in place of :attr:`grad_inputs`.
+
+        This function returns a handle with a method ``handle.remove()``
+        that removes the hook from the module.
+
+        .. note::
+            See :ref:`backward-hooks-execution` for more information on how when this hook
+            is executed, and how its execution is ordered relative to other hooks.
+
+        .. note::
+            In the rare case where the hook is registered while the Node has already
+            begun execution, there is no longer any guarantee on :attr:`grad_outputs`
+            content (it might be as usual or empty depending on other factors). The
+            hook can still optionally return a new gradient to be used in place of
+            :attr:`grad_inputs` independent of :attr:`grad_outputs`.
+
+        Example::
+
+            >>> import torch
+            >>> a = torch.tensor([0., 0., 0.], requires_grad=True)
+            >>> b = a.clone()
+            >>> assert isinstance(b.grad_fn, torch.autograd.graph.Node)
+            >>> handle = b.grad_fn.register_hook(lambda gI, gO: (gO[0] * 2,))
+            >>> b.sum().backward(retain_graph=True)
+            >>> print(a.grad)
+            tensor([2., 2., 2.])
+            >>> handle.remove() # Removes the hook
+            >>> a.grad = None
+            >>> b.sum().backward(retain_graph=True)
+            >>> print(a.grad)
+            tensor([1., 1., 1.])
+        """
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def register_prehook(self, fn: Callable[..., Any]) -> RemovableHandle:
+        r"""Register a backward pre-hook.
+
+        The hook will be called every time a gradient with respect to the
+        Node is computed. The hook should have the following signature::
+
+            hook(grad_outputs: Tuple[Tensor]) -> Tuple[Tensor] or None
+
+        The hook should not modify its argument, but it can optionally return
+        a new gradient which will be used in place of :attr:`grad_outputs`.
+
+        This function returns a handle with a method ``handle.remove()``
+        that removes the hook from the module.
+
+        .. note::
+            See :ref:`backward-hooks-execution` for more information on how when this hook
+            is executed, and how its execution is ordered relative to other hooks.
+
+        Example::
+
+            >>> a = torch.tensor([0., 0., 0.], requires_grad=True)
+            >>> b = a.clone()
+            >>> assert isinstance(b.grad_fn, torch.autograd.graph.Node)
+            >>> handle = b.grad_fn.register_prehook(lambda gI: (gI[0] * 2,))
+            >>> b.sum().backward(retain_graph=True)
+            >>> print(a.grad)
+            tensor([2., 2., 2.])
+            >>> handle.remove()
+            >>> a.grad = None
+            >>> b.sum().backward(retain_graph=True)
+            >>> print(a.grad)
+            tensor([1., 1., 1.])
+        """
+        raise NotImplementedError
+
+    @classmethod
+    def __subclasshook__(cls, subclass: type) -> bool:
+        if cls is Node and (
+            (
+                subclass is not None
+                and subclass is getattr(torch._C._functions, subclass.__name__, None)
+            )
+            or issubclass(subclass, torch.autograd.function.BackwardCFunction)
+        ):
+            return True
+        return NotImplemented
+
+
+def _get_grad_fn_or_grad_acc(t: Union[torch.Tensor, "GradientEdge"]) -> Node:
+    if isinstance(t, GradientEdge):
+        return t.node
+    if t.requires_grad and t.grad_fn is None:
+        with torch.enable_grad():
+            node = t.view_as(t).grad_fn.next_functions[0][0]  # type: ignore[union-attr]
+    else:
+        node = t.grad_fn
+    assert node is not None
+    return node
+
+
+class GradientEdge(NamedTuple):
+    """Object representing a given gradient edge within the autograd graph.
+
+    To get the gradient edge where a given Tensor gradient will be computed,
+    you can do ``edge = autograd.graph.get_gradient_edge(tensor)``.
+    """
+
+    node: Node
+    output_nr: int
+
+
+def get_gradient_edge(tensor: torch.Tensor) -> GradientEdge:
+    """Get the gradient edge for computing the gradient of the given Tensor.
+
+    In particular, it is equivalent to call
+    ``g = autograd.grad(loss, input)`` and ``g = autograd.grad(loss, get_gradient_edge(input))``.
+    """
+    if not tensor.requires_grad:
+        raise RuntimeError(
+            "It is not possible to get the gradient edge for a Tensor "
+            "that does not require gradients",
+        )
+    grad_fn = _get_grad_fn_or_grad_acc(tensor)
+
+    # Note that output_nr default to 0 which is the right value
+    # for the AccumulateGrad node.
+    return GradientEdge(grad_fn, tensor.output_nr)
+
+
+def increment_version(tensor: Union[torch.Tensor, Iterable[torch.Tensor]]) -> None:
+    """Update autograd metadata tracking whether the given Tensor was modified in place.
+
+    This is to enable more accurate error checking within the autograd engine.
+    It is already done automatically by PyTorch functions and within custom Function
+    when mark_dirty() is called appropriately so you only need to call this explicitly
+    if you are doing inplace operation on the Tensor data in a way that Pytorch doesn't
+    know about. For example a custom kernel that reads the Tensor data_ptr and modifies
+    the memory inplace based on this pointer. Can accept either a tensor, or a list of tensors.
+
+    Note that incrementing the version counter multiple times for a single inplace operation
+    is not problematic.
+
+    Note that if you pass in tensor constructed under torch.inference_mode(),
+    we will not bump its version counter (because your tensor does not have one).
+    """
+    if isinstance(tensor, torch.Tensor):
+        tensor = (tensor,)
+    torch._C._increment_version(tensor)
+
+
+class saved_tensors_hooks:
+    """Context-manager that sets a pair of pack / unpack hooks for saved tensors.
+
+    Use this context-manager to define how intermediary results of an operation
+    should be packed before saving, and unpacked on retrieval.
+
+    In that context, the ``pack_hook`` function will be called everytime an
+    operation saves a tensor for backward (this includes intermediary results
+    saved using
+    :func:`~torch.autograd.function._ContextMethodMixin.save_for_backward` but
+    also those recorded by a PyTorch-defined operation). The output of
+    ``pack_hook`` is then stored in the computation graph instead of the
+    original tensor.
+
+    The ``unpack_hook`` is called when the saved tensor needs to be accessed,
+    namely when executing :func:`torch.Tensor.backward()` or
+    :func:`torch.autograd.grad()`. It takes as argument the *packed* object
+    returned by ``pack_hook`` and should return a tensor which has the same
+    content as the original tensor (passed as input to the corresponding
+    ``pack_hook``).
+
+    The hooks should have the following signatures:
+
+        pack_hook(tensor: Tensor) -> Any
+
+        unpack_hook(Any) -> Tensor
+
+    where the return value of ``pack_hook`` is a valid input to ``unpack_hook``.
+
+    In general, you want ``unpack_hook(pack_hook(t))`` to be equal to ``t`` in terms
+    of value, size, dtype and device.
+
+    Example::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def pack_hook(x):
+        ...     print("Packing", x)
+        ...     return x
+        >>>
+        >>> def unpack_hook(x):
+        ...     print("Unpacking", x)
+        ...     return x
+        >>>
+        >>> a = torch.ones(5, requires_grad=True)
+        >>> b = torch.ones(5, requires_grad=True) * 2
+        >>> with torch.autograd.graph.saved_tensors_hooks(pack_hook, unpack_hook):
+        ...     y = a * b
+        Packing tensor([1., 1., 1., 1., 1.], requires_grad=True)
+        Packing tensor([2., 2., 2., 2., 2.], grad_fn=)
+        >>> y.sum().backward()
+        Unpacking tensor([1., 1., 1., 1., 1.], requires_grad=True)
+        Unpacking tensor([2., 2., 2., 2., 2.], grad_fn=)
+
+    .. warning ::
+        Performing an inplace operation on the input to either hooks may lead
+        to undefined behavior.
+
+    .. warning ::
+        Only one pair of hooks is allowed at a time. When recursively nesting this
+        context-manager, only the inner-most pair of hooks will be applied.
+    """
+
+    def __init__(
+        self,
+        pack_hook: Callable[[torch.Tensor], Any],
+        unpack_hook: Callable[[Any], torch.Tensor],
+    ) -> None:
+        self.pack_hook = pack_hook
+        self.unpack_hook = unpack_hook
+
+    def __enter__(self) -> None:
+        torch._C._autograd._push_saved_tensors_default_hooks(
+            self.pack_hook, self.unpack_hook
+        )
+
+    def __exit__(self, *args: object) -> None:
+        torch._C._autograd._pop_saved_tensors_default_hooks()
+
+
+class save_on_cpu(saved_tensors_hooks):
+    """Context manager under which tensors saved by the forward pass will be stored on cpu, then retrieved for backward.
+
+    When performing operations within this context manager, intermediary
+    results saved in the graph during the forward pass will be moved to CPU,
+    then copied back to the original device when needed for the backward pass.
+    If the graph was already on CPU, no tensor copy is performed.
+
+    Use this context-manager to trade compute for GPU memory usage (e.g.
+    when your model doesn't fit in GPU memory during training).
+
+    Args:
+        pin_memory (bool): If ``True`` tensors will be saved to CPU pinned memory
+                           during packing and copied to GPU asynchronously during unpacking.
+                           Defaults to ``False``.
+                           Also see :ref:`cuda-memory-pinning`.
+
+
+    Example::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> a = torch.randn(5, requires_grad=True, device="cuda")
+        >>> b = torch.randn(5, requires_grad=True, device="cuda")
+        >>> c = torch.randn(5, requires_grad=True, device="cuda")
+        >>>
+        >>> def f(a, b, c):
+        ...     prod_1 = a * b           # a and b are saved on GPU
+        ...     with torch.autograd.graph.save_on_cpu():
+        ...         prod_2 = prod_1 * c  # prod_1 and c are saved on CPU
+        ...     y = prod_2 * a           # prod_2 and a are saved on GPU
+        ...     return y
+        >>>
+        >>> y = f(a, b, c)
+        >>> del a, b, c  # for illustration only
+        >>> # the content of a, b, and prod_2 are still alive on GPU
+        >>> # the content of prod_1 and c only live on CPU
+        >>> y.sum().backward()  # all CPU tensors are moved back to GPU, for backward
+        >>> # all intermediary tensors are released (deleted) after the call to backward
+    """
+
+    def __init__(self, pin_memory: bool = False, device_type: str = "cuda") -> None:
+        device_module = getattr(torch, device_type, torch.cuda)
+
+        def pack_to_cpu(tensor: torch.Tensor) -> Tuple[torch.device, torch.Tensor]:
+            if not pin_memory:
+                return (tensor.device, tensor.cpu())
+            packed = torch.empty(
+                tensor.size(),
+                dtype=tensor.dtype,
+                layout=tensor.layout,
+                pin_memory=(device_module.is_available() and not tensor.is_sparse),
+            )
+            packed.copy_(tensor)
+            return (tensor.device, packed)
+
+        def unpack_from_cpu(packed: Tuple[torch.device, torch.Tensor]) -> torch.Tensor:
+            device, tensor = packed
+            return tensor.to(device, non_blocking=pin_memory)
+
+        super().__init__(pack_to_cpu, unpack_from_cpu)
+
+
+@contextlib.contextmanager
+def disable_saved_tensors_hooks(error_message: str) -> Generator[None, None, None]:
+    """Context-manager that disables the saved tensors default hooks feature.
+
+    Useful for if you are creating a feature that does not work with saved
+    tensors default hooks.
+
+    Args:
+        error_message (str): When saved tensors default hooks are used when they
+                             have been are disabled, a RuntimeError with this
+                             error message gets raised.
+
+    Example::
+
+        >>> # xdoctest: +SKIP(failing)
+        >>> message = "saved tensors default hooks are disabled"
+        >>> with torch.autograd.graph.disable_saved_tensors_hooks(message):
+        ...     # Raises RuntimeError: saved tensors default hooks are disabled
+        ...     with torch.autograd.graph.save_on_cpu():
+        ...         pass
+    """
+    maybe_prev_message = None
+    try:
+        maybe_prev_message = (
+            torch._C._autograd._saved_tensors_hooks_get_disabled_error_message()
+        )
+        torch._C._autograd._saved_tensors_hooks_disable(error_message)
+        yield
+    finally:
+        # See NOTE: [disabled_error_message invariant]
+        if maybe_prev_message is None:
+            torch._C._autograd._saved_tensors_hooks_enable()
+        else:
+            torch._C._autograd._saved_tensors_hooks_disable(maybe_prev_message)
+
+
+class _MultiHandle(RemovableHandle):
+    handles: Tuple[RemovableHandle, ...]
+
+    def __init__(self, handles: Tuple[RemovableHandle, ...]) -> None:
+        self.handles = handles
+
+    def remove(self) -> None:
+        for handle in self.handles:
+            handle.remove()
+
+    def __getstate__(self) -> Tuple[RemovableHandle, ...]:
+        return self.handles
+
+    def __setstate__(self, state: Tuple[RemovableHandle, ...]) -> None:
+        self.handles = state
+
+
+def register_multi_grad_hook(
+    tensors: Sequence[torch.Tensor],
+    fn: Union[
+        Callable[[Sequence[Optional[torch.Tensor]]], None],
+        Callable[[torch.Tensor], None],
+    ],
+    *,
+    mode: Literal["all", "any"] = "all",
+) -> RemovableHandle:
+    r"""Register a multi-grad backward hook.
+
+    There are two supported modes: ``"all"`` and ``"any"``.
+
+    Under the ``"all"`` mode, the hook will be called after gradients with respect to every tensor in
+    :attr:`tensors` have been computed. If a tensor is in :attr:`tensors` but
+    is not part of the graph, or if a tensor is not needed to compute the gradients
+    for any ``inputs`` specified for the current ``.backward()`` or ``.grad()`` call,
+    this tensor will be ignored and the hook will not wait for its gradient to be
+    computed.
+
+    After every non-ignored tensor's gradient has been computed, :attr:`fn` will be
+    called with those gradients. ``None`` will be passed for tensors that did not
+    have their gradients computed.
+
+    Under the ``"any"`` mode, the hook will be called after the first gradient
+    with respect to a tensor in :attr:`tensors` has been computed. The hook
+    will be called with that gradient as its argument.
+
+    The hook should not modify its arguments.
+
+    This function returns a handle with a method ``handle.remove()`` that removes the hook.
+
+    .. note::
+        See :ref:`backward-hooks-execution` for more information on how when this hook
+        is executed, and how its execution is ordered relative to other hooks.
+
+    Example::
+
+        >>> import torch
+        >>>
+        >>> a = torch.rand(2, 3, requires_grad=True)
+        >>> b = torch.rand(2, 3, requires_grad=True)
+        >>> c = a * b
+        >>> d = a * b
+        >>>
+        >>> def fn(grads):
+        ...     print([g is not None for g in grads])
+        ...
+        >>> torch.autograd.graph.register_multi_grad_hook((a, b, c, d), fn)
+        >>>
+        >>> c.sum().backward(retain_graph=True)
+        [True, True, True, False]
+        >>> c.sum().backward(inputs=(a,), retain_graph=True)
+        [True, False, True, False]
+        >>>
+    """
+    supported_modes = ("all", "any")
+    lock = threading.Lock()
+
+    if mode not in supported_modes:
+        raise ValueError(f"Expects mode to be one of {supported_modes} but got {mode}")
+
+    if mode == "all":
+        count: Dict[int, int] = {}
+        nb_calls = None
+        buffer: Dict[int, List[Optional[torch.Tensor]]] = {}
+
+        grad_fns = list(map(_get_grad_fn_or_grad_acc, tensors))
+        len_tensors = len(tensors)
+
+        def get_inner_hook(idx: int) -> Callable[[torch.Tensor], None]:
+            def inner_hook(grad: torch.Tensor) -> None:
+                nonlocal count, nb_calls, buffer, fn
+                id = torch._C._current_graph_task_id()
+                assert (
+                    id != -1
+                ), "expected this hook to be called inside a backward call"
+                count[id] = count.get(id, 0)
+                buffer[id] = buffer.get(id, [None] * len_tensors)
+
+                with lock:
+                    curr_count, count[id] = count[id], count[id] + 1
+
+                    if curr_count == 0:
+                        # On the first call, compute the actual nb_calls and buffer
+                        nb_calls = sum(
+                            map(torch._C._will_engine_execute_node, grad_fns)
+                        )
+
+                buffer[id][idx] = grad
+
+                assert nb_calls is not None
+                if curr_count == nb_calls - 1:
+                    fn = cast(Callable[[Sequence[Optional[torch.Tensor]]], None], fn)
+                    fn(buffer[id])
+                    del count[id]
+                    del buffer[id]
+
+            return inner_hook
+
+        handles = tuple(
+            t.register_hook(get_inner_hook(i)) for i, t in enumerate(tensors)
+        )
+    elif mode == "any":
+        fn = cast(Callable[[torch.Tensor], None], fn)
+        ran_hook: Dict[int, bool] = defaultdict(bool)
+
+        @functools.wraps(fn)
+        def wrapped_fn(grad: torch.Tensor) -> None:
+            nonlocal ran_hook
+            id = torch._C._current_graph_task_id()
+            assert id != -1, "expected this hook to be called inside a backward call"
+            with lock:
+                prev, ran_hook[id] = ran_hook[id], True
+            if prev:
+                return
+            fn(grad)
+
+        handles = tuple(
+            tensor.register_hook(wrapped_fn)
+            for tensor in tensors
+            if tensor.requires_grad
+        )
+
+    return _MultiHandle(handles)  # type: ignore[possibly-undefined]
+
+
+# NOTE [Allow mutation on tensors saved for backward]
+#
+# 1. Tensor gets saved for backward
+#    - remember the python object id and the version of the tensor
+#    - remember aliasing information (data_ptr of base + version)
+#    - save the original so we control its lifetime
+# 2. Any time a tensor gets in-placed
+#    - for each tensor aliased to it:
+#      - check using its object id and version to see if it has been saved
+#      - if it has been saved, clone it
+#      - delete the reference to the original
+# 3. during backward
+#    - if the clone exists, the tensor must've been modified in-place
+_allow_mutation_on_saved_tensors_enabled: bool = False
+
+
+_TID: TypeAlias = Tuple[int, int, int]
+_SID: TypeAlias = Tuple[int, int]
+
+
+def _get_tid(tensor: torch.Tensor) -> _TID:
+    # FIXME: This is almost definitely a bug.
+    if isinstance(
+        tensor,
+        (
+            torch._subclasses.fake_tensor.FakeTensor,
+            torch._subclasses.functional_tensor.FunctionalTensor,
+        ),
+    ):
+        data_ptr = 0
+    else:
+        data_ptr = tensor.data_ptr()
+    return (id(tensor), data_ptr, tensor._version)
+
+
+def _get_sid(tensor: torch.Tensor) -> _SID:
+    # FIXME: This is almost definitely a bug.
+    if isinstance(
+        tensor,
+        (
+            torch._subclasses.fake_tensor.FakeTensor,
+            torch._subclasses.functional_tensor.FunctionalTensor,
+        ),
+    ):
+        data_ptr = 0
+    else:
+        data_ptr = tensor.data_ptr()
+    return (data_ptr, tensor._version)
+
+
+class _Handle:
+    pass
+
+
+class _swap_with_cloned(saved_tensors_hooks):
+    def __init__(self, ctx: "_AllowMutationOnSavedContext") -> None:
+        def pack_hook(tensor: torch.Tensor) -> _Handle:
+            tid = _get_tid(tensor)
+            sid = _get_sid(tensor)
+            # Tensors saved for backward have an entry in _tid_to_weakhandle
+            handle: Optional[_Handle] = None
+
+            # Save aliasing information
+            ctx.sid_to_tid[sid].add(tid)
+
+            # NB: The same tensor (of the same version) can be saved multiple times
+            if tid not in ctx.tid_to_weakhandle:
+                handle = _Handle()
+                ctx.tid_to_weakhandle[tid] = handle
+                ctx.original[handle] = tensor
+            else:
+                # Store an additional strong reference to the handle
+                handle = ctx.tid_to_weakhandle[tid]
+            return handle
+
+        def unpack_hook(handle: _Handle) -> torch.Tensor:
+            error_msg = (
+                "Trying to backward outside of the 'allow_mutation_on_saved_tensors' context"
+                "in which the graph was originally recorded."
+            )
+            assert _allow_mutation_on_saved_tensors_enabled, error_msg
+            if handle in ctx.cloned:
+                res = ctx.cloned[handle]
+            else:
+                assert handle in ctx.original, error_msg
+                res = ctx.original[handle]
+            return res
+
+        super().__init__(pack_hook, unpack_hook)
+
+
+class _CloneArgBeforeMutateMode(TorchDispatchMode):
+    def __init__(self, ctx: "_AllowMutationOnSavedContext") -> None:
+        self.ctx = ctx
+
+    def __torch_dispatch__(
+        self,
+        func: "OpOverload",
+        types: Iterable[type],
+        args: Tuple[Any, ...] = (),
+        kwargs: Optional[Dict[Any, Any]] = None,
+    ) -> Any:
+        kwargs = kwargs or {}
+
+        for idx, arg in enumerate(func._schema.arguments):
+            if arg.alias_info is not None and arg.alias_info.is_write:
+                t = kwargs["out"] if arg.is_out else args[idx]
+                tid = _get_tid(t)
+                sid = _get_sid(t)
+                ctx = self.ctx
+                if sid in ctx.sid_to_tid:
+                    for tid in ctx.sid_to_tid[sid]:
+                        if tid not in ctx.tid_to_weakhandle:
+                            # We know that if tid is in sid_to_tid, then it must also be in
+                            # tid_to_weakhandle. However, it is possible for the tensor to be
+                            # saved at one point, but cleared by backward before it is modified
+                            # in-place. Consider the following example:
+                            #
+                            # >>> a = torch.randn(2, 3, requires_grad=True).clone()
+                            # >>> out = (a**2).sum()
+                            # >>> out.backward()
+                            # >>> a.sin_()
+                            continue
+                        handle = ctx.tid_to_weakhandle[tid]
+                        if handle in ctx.cloned:
+                            # The same exact tensor has been cloned already
+                            continue
+                        ctx.cloned[handle] = ctx.original[handle].clone()
+                        del ctx.original[handle]
+
+        return func(*args, **kwargs)
+
+
+class _AllowMutationOnSavedContext:
+    def __init__(self) -> None:
+        self.cloned: MutableMapping[_Handle, torch.Tensor] = WeakKeyDictionary()
+        self.original: MutableMapping[_Handle, torch.Tensor] = WeakKeyDictionary()
+        self.tid_to_weakhandle: MutableMapping[_TID, _Handle] = WeakValueDictionary()
+        self.sid_to_tid: Dict[_SID, Set[_TID]] = defaultdict(set)
+
+    def clear(self) -> None:
+        self.cloned.clear()
+        self.original.clear()
+        self.tid_to_weakhandle.clear()
+        self.sid_to_tid.clear()
+
+
+@contextlib.contextmanager
+def allow_mutation_on_saved_tensors() -> (
+    Generator[_AllowMutationOnSavedContext, None, None]
+):
+    """Context manager under which mutating tensors saved for backward is allowed.
+
+    Under this context manager, tensors saved for backward are cloned on mutation,
+    so the original version can still be used during backward. Normally, mutating a tensor
+    saved for backward will result in an error raised when it's used during backward.
+
+    To ensure the correct behavior, both the forward and backward should be run under
+    the same context manager.
+
+    Returns:
+        An _AllowMutationOnSavedContext object storing the state managed by this
+        context manager. This object can be useful for debugging purposes. The state
+        managed by the context manager is automatically cleared upon exiting.
+
+    Example::
+
+        >>> import torch
+        >>> with torch.autograd.graph.allow_mutation_on_saved_tensors():
+        ...     # forward
+        ...     a = torch.ones(2, 3, requires_grad=True)
+        ...     b = a.clone()
+        ...     out = (b**2).sum()
+        ...     b.sin_()
+        ...     # backward
+        ...     out.sum().backward()
+        ...
+        tensor([[0.8415, 0.8415, 0.8415],
+                [0.8415, 0.8415, 0.8415]], grad_fn=)
+    """
+    global _allow_mutation_on_saved_tensors_enabled
+
+    ctx = _AllowMutationOnSavedContext()
+
+    with _swap_with_cloned(ctx), _CloneArgBeforeMutateMode(ctx):
+        try:
+            if _allow_mutation_on_saved_tensors_enabled:
+                raise RuntimeError(
+                    "allow_mutation_on_saved_tensors contexts cannot be nested"
+                )
+            _allow_mutation_on_saved_tensors_enabled = True
+            yield ctx
+        finally:
+            ctx.clear()
+            _allow_mutation_on_saved_tensors_enabled = False
+
+
+def _register_logging_hooks_on_whole_graph(
+    t_outputs: Sequence[Union[torch.Tensor, GradientEdge]],
+) -> Callable[[], None]:
+    grad_fns = list(map(_get_grad_fn_or_grad_acc, t_outputs))
+
+    def iter_graph(roots: List[Node]) -> Iterator[Node]:
+        if not roots:
+            return
+        seen: Set[Node] = set()
+        q: Deque[Node] = deque()
+        for node in roots:
+            if node is not None:
+                seen.add(node)
+                q.append(node)
+
+        while q:
+            node = q.popleft()
+            for fn, _ in node.next_functions:
+                if fn in seen or fn is None:
+                    continue
+                seen.add(fn)
+                q.append(fn)
+
+            yield node
+
+    def fmt(t: Optional[torch.Tensor]) -> str:
+        # Avoid circular import
+        from torch.testing._internal.common_utils import dtype_abbrs
+
+        if t is None:
+            return "None"
+        return f"{dtype_abbrs[t.dtype]}[{', '.join(map(str, t.shape))}]"
+
+    def prehook(grad_outputs: Sequence[Optional[torch.Tensor]]) -> None:
+        node = torch._C._current_autograd_node()
+        grad_outputs_str = f"[{','.join(fmt(t) for t in grad_outputs)}]"
+        log_str = f"Executing: {node} with grad_outputs: {grad_outputs_str}"
+        log.debug(log_str)
+
+    handles = []
+    for node in iter_graph(grad_fns):
+        handles.append(node.register_prehook(prehook))
+
+    def unregister_hooks() -> None:
+        for handle in handles:
+            handle.remove()
+
+    return unregister_hooks
+
+
+def _engine_run_backward(
+    t_outputs: Sequence[Union[torch.Tensor, GradientEdge]],
+    *args: Any,
+    **kwargs: Any,
+) -> Tuple[torch.Tensor, ...]:
+    attach_logging_hooks = log.getEffectiveLevel() <= logging.DEBUG
+    if attach_logging_hooks:
+        unregister_hooks = _register_logging_hooks_on_whole_graph(t_outputs)
+    try:
+        return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+            t_outputs, *args, **kwargs
+        )  # Calls into the C++ engine to run the backward pass
+    finally:
+        if attach_logging_hooks:
+            unregister_hooks()  # type: ignore[possibly-undefined]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/profiler.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/profiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..207ad6d272f9400d8667ddb28faf49cc54a623d0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/profiler.py
@@ -0,0 +1,1170 @@
+# mypy: allow-untyped-defs
+from collections import defaultdict
+from dataclasses import dataclass
+from time import perf_counter_ns
+from typing import Any, Dict, Iterable, List, Optional
+from warnings import warn
+
+import torch
+import torch.cuda
+from torch._C import _get_privateuse1_backend_name
+from torch._C._profiler import _ExperimentalConfig
+from torch.autograd import (
+    _disable_profiler,
+    _enable_profiler,
+    _kineto_step,
+    _prepare_profiler,
+    _ProfilerResult,
+    _supported_activities,
+    _toggle_collection_dynamic,
+    DeviceType,
+    kineto_available,
+    ProfilerActivity,
+    ProfilerConfig,
+    ProfilerState,
+)
+from torch.autograd.profiler_util import (
+    _filter_name,
+    _filter_stack_entry,
+    _rewrite_name,
+    EventList,
+    FunctionEvent,
+    MEMORY_EVENT_NAME,
+    MemRecordsAcc,
+    OUT_OF_MEMORY_EVENT_NAME,
+)
+from torch.futures import Future
+
+
+__all__ = [
+    "profile",
+    "record_function",
+    "emit_itt",
+    "emit_nvtx",
+    "load_nvprof",
+    "EnforceUnique",
+    "parse_nvprof_trace",
+    "KinetoStepTracker",
+    "EventList",
+    "FunctionEvent",
+    "MemRecordsAcc",
+]
+
+try:
+    # Available in Python >= 3.2
+    from contextlib import ContextDecorator as _ContextDecorator
+except ImportError:
+    import functools
+
+    class _ContextDecorator:  # type: ignore[no-redef]
+        def __enter__(self):
+            raise NotImplementedError
+
+        def __exit__(self, exc_type, exc_val, exc_tb):
+            raise NotImplementedError
+
+        def __call__(self, func):
+            @functools.wraps(func)
+            def wrapped(*args, **kwargs):
+                with self:
+                    return func(*args, **kwargs)
+
+            return wrapped
+
+
+# global python state - whether profiler is currently enabled
+# useful for fast python checks to reduce latency
+_is_profiler_enabled: bool = False
+
+
+def _set_is_profiler_enabled(enable: bool):
+    global _is_profiler_enabled
+    _is_profiler_enabled = enable
+
+
+def _run_on_profiler_start():
+    _set_is_profiler_enabled(True)
+
+
+def _run_on_profiler_stop():
+    _set_is_profiler_enabled(False)
+
+
+@dataclass
+class _ProfilerStats:
+    "Profiler timing and stats used by developers to catch issues/regressions"
+    profiling_window_duration_sec: float = 0
+    number_of_events: int = 0
+    profiler_prepare_call_duration_us: int = 0
+    profiler_enable_call_duration_us: int = 0
+    profiler_disable_call_duration_us: int = 0
+    parse_kineto_call_duration_us: int = 0
+    function_events_build_tree_call_duration_us: int = 0
+
+
+class profile:
+    """Context manager that manages autograd profiler state and holds a summary of results.
+
+    Under the hood it just records events of functions being executed in C++ and
+    exposes those events to Python. You can wrap any code into it and it will
+    only report runtime of PyTorch functions.
+    Note: profiler is thread local and is automatically propagated into the async tasks
+
+    Args:
+        enabled (bool, optional): Setting this to False makes this context manager a no-op.
+
+        use_cuda (bool, optional): Enables timing of CUDA events as well
+            using the cudaEvent API. (will be deprecated)
+
+        use_device (str, optional): Enables timing of device events.
+            Adds approximately 4us of overhead to each tensor operation when use cuda.
+            The valid devices options are 'cuda', 'xpu', 'mtia' and 'privateuseone'.
+
+        record_shapes (bool, optional): If shapes recording is set, information
+            about input dimensions will be collected. This allows one to see which
+            dimensions have been used under the hood and further group by them
+            using prof.key_averages(group_by_input_shape=True). Please note that
+            shape recording might skew your profiling data. It is recommended to
+            use separate runs with and without shape recording to validate the timing.
+            Most likely the skew will be negligible for bottom most events (in a case
+            of nested function calls). But for higher level functions the total
+            self cpu time might be artificially increased because of the shape
+            collection.
+
+        with_flops (bool, optional): If with_flops is set, the profiler will estimate
+            the FLOPs (floating point operations) value using the operator's input shape.
+            This allows one to estimate the hardware performance. Currently,
+            this option only works for the matrix multiplication and 2D convolution operators.
+
+        profile_memory (bool, optional): track tensor memory allocation/deallocation.
+
+        with_stack (bool, optional): record source information (file and line number) for the ops.
+
+        with_modules (bool): record module hierarchy (including function names)
+            corresponding to the callstack of the op. e.g. If module A's forward call's
+            module B's forward which contains an aten::add op,
+            then aten::add's module hierarchy is A.B
+            Note that this support exist, at the moment, only for TorchScript models
+            and not eager mode models.
+
+        use_kineto (bool, optional): experimental, enable profiling with Kineto profiler.
+
+        use_cpu (bool, optional): profile CPU events; setting to ``False`` requires
+            ``use_kineto=True`` and can be used to lower the overhead for GPU-only profiling.
+
+        experimental_config (_ExperimentalConfig) : A set of experimental options
+            used by profiler libraries like Kineto. Note, backward compatibility is not guaranteed.
+
+        acc_events (bool): Enable the accumulation of FunctionEvents across multiple profiling cycles
+
+
+    .. warning:
+        Enabling memory profiling or source attribution incurs additional profiler
+        overhead
+
+    .. warning:
+        This context managers should not be called recursively, i.e. no nested
+        instances are allowed
+
+    .. warning:
+        Due to some CUDA multiprocessing limitations (multiprocessing-cuda-note_),
+        one cannot use the profiler with ``use_device = 'cuda'`` to benchmark
+        DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading,
+        please use ``use_device = None`` or ``num_workers = 0``.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER)
+        >>> x = torch.randn((1, 1), requires_grad=True)
+        >>> with torch.autograd.profiler.profile() as prof:
+        >>>     for _ in range(100):  # any normal python code, really!
+        >>>         y = x ** 2
+        >>>         y.backward()
+        >>> # NOTE: some columns were removed for brevity
+        >>> print(prof.key_averages().table(sort_by="self_cpu_time_total"))
+        -----------------------------------  ---------------  ---------------  ---------------
+        Name                                 Self CPU total   CPU time avg     Number of Calls
+        -----------------------------------  ---------------  ---------------  ---------------
+        mul                                  32.048ms         32.048ms         200
+        pow                                  27.041ms         27.041ms         200
+        PowBackward0                         9.727ms          55.483ms         100
+        torch::autograd::AccumulateGrad      9.148ms          9.148ms          100
+        torch::autograd::GraphRoot           691.816us        691.816us        100
+        -----------------------------------  ---------------  ---------------  ---------------
+
+    """
+
+    def __init__(
+        self,
+        enabled=True,
+        *,
+        use_cuda=False,  # Deprecated
+        use_device=None,
+        record_shapes=False,
+        with_flops=False,
+        profile_memory=False,
+        with_stack=False,
+        with_modules=False,
+        use_kineto=False,
+        use_cpu=True,
+        experimental_config=None,
+        acc_events=False,
+    ):
+        self.enabled: bool = enabled
+        if not self.enabled:
+            return
+        self.use_cuda = use_cuda
+        if self.use_cuda:
+            warn(
+                "The attribute `use_cuda` will be deprecated soon, "
+                "please use ``use_device = 'cuda'`` instead.",
+                FutureWarning,
+                stacklevel=2,
+            )
+            self.use_device: Optional[str] = "cuda"
+        else:
+            self.use_device = use_device
+        # TODO Consider changing _function_events into data structure with size cap
+        self._function_events: Optional[EventList] = None
+        self._old_function_events: Optional[EventList] = None
+        # Function event processing is done lazily
+        self._needs_processing = False
+        self.entered = False
+        self.record_shapes = record_shapes
+        self.with_flops = with_flops
+        self.record_shapes |= self.with_flops
+        self.profile_memory = profile_memory
+        self.with_stack = with_stack
+        self.with_modules = with_modules
+        self.use_cpu = use_cpu
+        self.acc_events = acc_events
+        if experimental_config is None:
+            experimental_config = _ExperimentalConfig()
+        self.experimental_config = experimental_config
+        self.kineto_results: Optional[_ProfilerResult] = None
+        self.profiling_start_time_ns = 0
+        self.profiling_end_time_ns = 0
+        self._stats = _ProfilerStats()
+
+        if not self.use_cpu:
+            assert (
+                use_kineto
+            ), "Device-only events supported only with Kineto (use_kineto=True)"
+
+        if self.use_device is not None:
+            VALID_DEVICE_OPTIONS = ["cuda", "xpu", "mtia"]
+            if _get_privateuse1_backend_name() != "privateuseone":
+                VALID_DEVICE_OPTIONS.append(_get_privateuse1_backend_name())
+            if self.use_device not in VALID_DEVICE_OPTIONS:
+                warn(f"The {self.use_device} is not a valid device option.")
+                self.use_device = None
+
+            if self.use_device == "cuda" and not torch.cuda.is_available():
+                warn("CUDA is not available, disabling CUDA profiling")
+                self.use_cuda = False
+                self.use_device = None
+
+            if self.use_device == "xpu" and not torch.xpu.is_available():
+                warn("XPU is not available, disabling XPU profiling")
+                self.use_device = None
+
+        self.kineto_activities = set()
+        if self.use_cpu:
+            self.kineto_activities.add(ProfilerActivity.CPU)
+
+        self.profiler_kind = ProfilerState.KINETO
+        if self.use_device == "cuda":
+            if not use_kineto or ProfilerActivity.CUDA not in _supported_activities():
+                assert self.use_cpu, "Legacy CUDA profiling requires use_cpu=True"
+                self.profiler_kind = ProfilerState.KINETO_GPU_FALLBACK
+            else:
+                self.kineto_activities.add(ProfilerActivity.CUDA)
+        elif self.use_device == "xpu":
+            assert (
+                use_kineto and ProfilerActivity.XPU in _supported_activities()
+            ), "Legacy XPU profiling is not supported. Requires use_kineto=True on XPU devices."
+            self.kineto_activities.add(ProfilerActivity.XPU)
+        elif self.use_device == "mtia":
+            assert (
+                use_kineto and ProfilerActivity.MTIA in _supported_activities()
+            ), "Legacy MTIA profiling is not supported. Requires use_kineto=True on MTIA devices."
+            self.kineto_activities.add(ProfilerActivity.MTIA)
+        elif self.use_device is not None and self.use_device != "privateuseone":
+            if (
+                not use_kineto
+                or ProfilerActivity.PrivateUse1 not in _supported_activities()
+            ):
+                assert (
+                    self.use_cpu
+                ), "Legacy custombackend profiling requires use_cpu=True"
+                self.profiler_kind = ProfilerState.KINETO_PRIVATEUSE1_FALLBACK
+            else:
+                self.kineto_activities.add(ProfilerActivity.PrivateUse1)
+
+        assert (
+            len(self.kineto_activities) > 0
+        ), "No activities specified for the profiler"
+
+    def config(self):
+        return ProfilerConfig(
+            self.profiler_kind,
+            self.record_shapes,
+            self.profile_memory,
+            self.with_stack,
+            self.with_flops,
+            self.with_modules,
+            self.experimental_config,
+        )
+
+    def __enter__(self):
+        if not self.enabled:
+            return
+        if self.entered:
+            raise RuntimeError("Profiler context manager is not reentrant")
+        self._prepare_trace()
+        self._start_trace()
+        return self
+
+    def _prepare_trace(self):
+        self.entered = True
+        t0 = perf_counter_ns()
+        _prepare_profiler(self.config(), self.kineto_activities)
+        t1 = perf_counter_ns()
+        self._stats.profiler_prepare_call_duration_us = int((t1 - t0) / 1000)
+
+    def _start_trace(self):
+        self.entered = True
+        _run_on_profiler_start()
+        t0 = perf_counter_ns()
+        _enable_profiler(self.config(), self.kineto_activities)
+        t1 = perf_counter_ns()
+        self._stats.profiler_enable_call_duration_us = int((t1 - t0) / 1000)
+        self.profiling_start_time_ns = t1
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if not self.enabled:
+            return
+        if self.use_device and hasattr(torch, self.use_device):
+            device_module = getattr(torch, self.use_device)
+            if hasattr(device_module, "synchronize"):
+                device_module.synchronize()
+
+        if self._function_events and self.acc_events:
+            self._old_function_events = self._function_events
+        self._function_events = None
+        self._needs_processing = True
+
+        t0 = perf_counter_ns()
+
+        self.kineto_results = _disable_profiler()
+        t1 = perf_counter_ns()
+        self._stats.profiler_disable_call_duration_us = int((t1 - t0) / 1000)
+        self.profiling_end_time_ns = t0
+
+        _run_on_profiler_stop()
+
+        self._stats.profiling_window_duration_sec = (
+            (self.profiling_end_time_ns - self.profiling_start_time_ns) * 1.0 / 1e9
+        )
+
+        # If we plan to accumulate events we should post process the function events
+        # right away to retain the state across mulitple start/stop calls
+        if self.acc_events:
+            self._ensure_function_events()
+        return False
+
+    def __repr__(self):
+        if self._needs_processing:
+            self._ensure_function_events()
+        if self._function_events is None:
+            return ""
+        return repr(self._function_events)
+
+    def __str__(self):
+        if self._needs_processing:
+            self._ensure_function_events()
+        if self._function_events is None:
+            return ""
+        return str(self._function_events)
+
+    def _ensure_function_events(self):
+        """Process function events lazily if required"""
+        if self._function_events is not None:
+            return
+        self._needs_processing = False
+
+        t0 = perf_counter_ns()
+        parsed_results = []
+        if self.kineto_results:
+            parsed_results = self._parse_kineto_results(self.kineto_results)
+        t1 = perf_counter_ns()
+        self._stats.parse_kineto_call_duration_us = int((t1 - t0) / 1000)
+
+        self._function_events = EventList(
+            parsed_results,
+            use_device=self.use_device,
+            profile_memory=self.profile_memory,
+            with_flops=self.with_flops,
+        )
+        t0 = perf_counter_ns()
+        self._function_events._build_tree()
+        t1 = perf_counter_ns()
+        self._stats.function_events_build_tree_call_duration_us = int((t1 - t0) / 1000)
+        self._stats.number_of_events = len(self._function_events)
+
+        if self._old_function_events and self.acc_events:
+            for evt in self._old_function_events:
+                self._function_events.append(evt)
+            self._old_function_events = None
+
+        if self._function_events is None:
+            raise RuntimeError("Profiler didn't finish running")
+
+    @property
+    def function_events(self):
+        if self._function_events is None or self._needs_processing:
+            self._ensure_function_events()
+        return self._function_events
+
+    def table(
+        self,
+        sort_by=None,
+        row_limit=100,
+        max_src_column_width=75,
+        max_name_column_width=55,
+        max_shapes_column_width=80,
+        header=None,
+        top_level_events_only=False,
+    ):
+        self._ensure_function_events()
+        assert self._function_events is not None
+        return self._function_events.table(
+            sort_by=sort_by,
+            row_limit=row_limit,
+            max_src_column_width=max_src_column_width,
+            max_name_column_width=max_name_column_width,
+            max_shapes_column_width=max_shapes_column_width,
+            header=header,
+            top_level_events_only=top_level_events_only,
+        )
+
+    table.__doc__ = EventList.table.__doc__
+
+    def export_chrome_trace(self, path):
+        """
+        Exports the collected trace in Chrome JSON format. If kineto is enabled, only
+        last cycle in schedule is exported.
+        """
+        if kineto_available():
+            self.kineto_results.save(path)  # type: ignore[union-attr]
+        else:
+            self._ensure_function_events()
+            return self._function_events.export_chrome_trace(path)  # type: ignore[union-attr]
+
+    export_chrome_trace.__doc__ = EventList.export_chrome_trace.__doc__
+
+    def export_stacks(self, path: str, metric: str = "self_cpu_time_total"):
+        self._ensure_function_events()
+        assert self._function_events is not None, "Expected profiling results"
+        assert self.with_stack, "export_stacks() requires with_stack=True"
+        return self._function_events.export_stacks(path, metric)
+
+    def toggle_collection_dynamic(
+        self, enabled: bool, activities: Iterable[ProfilerActivity]
+    ):
+        """
+        Toggles the collection of activities for the current profiler instance.
+        """
+        return _toggle_collection_dynamic(enabled, set(activities))
+
+    def key_averages(self, group_by_input_shape=False, group_by_stack_n=0):
+        self._ensure_function_events()
+        assert self._function_events is not None, "Expected profiling results"
+        return self._function_events.key_averages(
+            group_by_input_shape, group_by_stack_n
+        )
+
+    key_averages.__doc__ = EventList.key_averages.__doc__
+
+    def total_average(self):
+        self._ensure_function_events()
+        assert self._function_events is not None, "Expected profiling results"
+        return self._function_events.total_average()
+
+    total_average.__doc__ = EventList.total_average.__doc__
+
+    @property
+    def self_cpu_time_total(self):
+        """Returns total time spent on CPU.
+
+        The total time is a sum of all self times across all the events.
+        """
+        self._ensure_function_events()
+        assert self._function_events is not None
+        return self._function_events.self_cpu_time_total
+
+    def _parse_kineto_results(self, result: _ProfilerResult):
+        # result.events() has most of the events - PyTorch op-level and device-level events
+
+        trace_start_ns = result.trace_start_ns()
+        mem_records = [
+            [evt, False] for evt in result.events() if evt.name() == MEMORY_EVENT_NAME
+        ]
+        oom_records = [
+            evt for evt in result.events() if evt.name() == OUT_OF_MEMORY_EVENT_NAME
+        ]
+        mem_records_acc = MemRecordsAcc(mem_records)
+
+        def _cpu_memory_usage(mem_record):
+            return (
+                mem_record.nbytes()
+                if mem_record.device_type()
+                in [DeviceType.CPU, DeviceType.MKLDNN, DeviceType.IDEEP]
+                else 0
+            )
+
+        def _device_memory_usage(mem_record):
+            return (
+                mem_record.nbytes()
+                if mem_record.device_type()
+                in [DeviceType.CUDA, DeviceType.PrivateUse1, DeviceType.HIP]
+                else 0
+            )
+
+        # Create and return FunctionEvent list, which contains all function events
+        # Here 2 function events are created:
+        # all_function_events contains all events associated with each kineto event from result
+        all_function_events = []
+        # frontend_function_events contains the events in aten or torch frontend level,
+        # whose correlation id is 0
+        frontend_function_events = []
+        device_corr_map: Dict[int, List[FunctionEvent]] = {}
+        max_evt_id = 0
+        for kineto_event in result.events():
+            if _filter_name(kineto_event.name()):
+                continue
+            rel_start_ns = kineto_event.start_ns() - trace_start_ns
+            rel_end_ns = kineto_event.end_ns() - trace_start_ns
+            abs_end_ns = kineto_event.end_ns()
+
+            cpu_memory_usage = 0
+            device_memory_usage = 0
+            if kineto_event.device_type() == DeviceType.CPU:
+                # find the corresponding memory allocation events
+                for mem_record in mem_records_acc.in_interval(
+                    kineto_event.start_ns() / 1000, abs_end_ns / 1000
+                ):
+                    cpu_memory_usage += _cpu_memory_usage(mem_record[0])
+                    device_memory_usage += _device_memory_usage(mem_record[0])
+                    mem_record[1] = True
+
+            is_async = kineto_event.is_async() or (
+                kineto_event.start_thread_id() != kineto_event.end_thread_id()
+            )
+
+            fe = FunctionEvent(
+                id=kineto_event.correlation_id(),
+                name=_rewrite_name(name=kineto_event.name(), with_wildcard=True),
+                trace_name=_rewrite_name(name=kineto_event.name(), with_wildcard=False),
+                thread=kineto_event.start_thread_id(),
+                start_us=rel_start_ns / 1000,
+                end_us=rel_end_ns / 1000,
+                fwd_thread=kineto_event.fwd_thread_id(),
+                input_shapes=kineto_event.shapes(),
+                concrete_inputs=kineto_event.concrete_inputs(),
+                kwinputs=kineto_event.kwinputs(),
+                stack=[
+                    entry
+                    for entry in kineto_event.stack()
+                    if _filter_stack_entry(entry)
+                ],
+                scope=kineto_event.scope(),
+                use_device=self.use_device,
+                cpu_memory_usage=cpu_memory_usage,
+                device_memory_usage=device_memory_usage,
+                is_async=is_async,
+                sequence_nr=kineto_event.sequence_nr(),
+                device_type=kineto_event.device_type(),
+                device_index=kineto_event.device_index(),
+                device_resource_id=kineto_event.device_resource_id(),
+                flops=kineto_event.flops(),
+                is_user_annotation=kineto_event.is_user_annotation(),
+            )
+            max_evt_id = max(max_evt_id, fe.id)
+            if fe.device_type == DeviceType.CPU and not fe.is_async:
+                if self.use_device == "privateuseone":
+                    privateuse1_time = kineto_event.privateuse1_elapsed_us()
+                    if privateuse1_time > 0:
+                        fe.append_kernel(fe.name, fe.device_index, privateuse1_time)
+                        fe.is_legacy = True
+                elif self.use_device == "cuda":
+                    # Check if we have CUDA time as a fallback
+                    cuda_time = kineto_event.cuda_elapsed_us()
+                    if cuda_time > 0:
+                        fe.append_kernel(fe.name, fe.device_index, cuda_time)
+                        fe.is_legacy = True
+            all_function_events.append(fe)
+            corr_id = kineto_event.linked_correlation_id()
+            if corr_id > 0:
+                if corr_id not in device_corr_map:
+                    device_corr_map[corr_id] = []
+                device_corr_map[corr_id].append(fe)
+            elif corr_id == 0:
+                frontend_function_events.append(fe)
+            else:
+                raise RuntimeError(
+                    f"Got negative correlation id {corr_id} in profiler post processing"
+                )
+
+        # associate device kernels and device runtime (CPU) with CPU events
+        for fe in frontend_function_events:
+            if (
+                fe.device_type == DeviceType.CPU
+                and not fe.is_async
+                and fe.id in device_corr_map
+            ):
+                for f_evt in device_corr_map[fe.id]:
+                    if (
+                        f_evt.device_type == DeviceType.CUDA
+                        or f_evt.device_type == DeviceType.PrivateUse1
+                    ):
+                        fe.append_kernel(
+                            f_evt.name,
+                            f_evt.device_index,
+                            f_evt.time_range.end - f_evt.time_range.start,
+                        )
+                    elif f_evt.device_type == DeviceType.CPU:
+                        # make sure that 'thread' of a CPU Kineto (e.g. Device Runtime) event is associated
+                        # with the 'thread' of the corresponding linked PyTorch event to properly track
+                        # parents and children
+                        f_evt.thread = fe.thread
+
+        def createFunctionEventForMemoryEvents(evt):
+            rel_start_ns = evt.start_ns() - trace_start_ns
+            fe = FunctionEvent(
+                id=max_evt_id,
+                name=evt.name(),
+                trace_name=None,  # not outputting in the trace
+                thread=evt.start_thread_id(),
+                start_us=rel_start_ns / 1000,
+                end_us=rel_start_ns / 1000,  # no duration
+                fwd_thread=evt.start_thread_id(),
+                input_shapes=[],
+                stack=[],
+                scope=0,  # RecordScope::FUNCTION
+                use_device=self.use_device,
+                cpu_memory_usage=_cpu_memory_usage(evt),
+                device_memory_usage=_device_memory_usage(evt),
+                is_async=False,
+                sequence_nr=-1,
+                device_type=DeviceType.CPU,
+                device_index=0,
+            )
+            return fe
+
+        # output top-level memory events
+        for mem_record in mem_records:
+            if not mem_record[1]:
+                max_evt_id += 1
+                fe = createFunctionEventForMemoryEvents(mem_record[0])
+                all_function_events.append(fe)
+
+        for oom_record in oom_records:
+            max_evt_id += 1
+            fe = createFunctionEventForMemoryEvents(oom_record)
+            all_function_events.append(fe)
+
+        all_function_events.sort(
+            key=lambda evt: [evt.time_range.start, -evt.time_range.end]
+        )
+        return all_function_events
+
+
+class record_function(_ContextDecorator):
+    """Context manager/function decorator that adds a label to a code block/function when running autograd profiler.
+    Label will only appear if CPU activity tracing is enabled.
+
+    It is useful when tracing the code profile.
+
+    Args:
+        name (str): Label assigned to the block of code.
+        node_id (int): ID of node, for distributed profiling. Unset in
+        non-distributed cases.
+
+    Example:
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER)
+        >>> x = torch.randn((1, 1), requires_grad=True)
+        >>> with torch.autograd.profiler.profile() as prof:
+        ...     y = x ** 2
+        ...     with torch.autograd.profiler.record_function("label-z"): # label the block
+        ...         z = y ** 3
+        ...     y.backward()
+        ...
+        >>> # xdoctest: +IGNORE_WANT
+        >>> # NOTE: some columns were removed for brevity
+        >>> print(prof.key_averages().table(sort_by="self_cpu_time_total"))
+        -----------------------------------  ---------------  ---------------  ---------------
+        Name                                 Self CPU total %  CPU time avg     Number of Calls
+        -----------------------------------  ---------------  ---------------  ---------------
+        pow                                  60.77%           47.470us         3
+        mul                                  21.73%           25.465us         2
+        PowBackward0                         12.03%           121.891us        1
+        torch::autograd::AccumulateGrad      2.70%            6.324us          1
+        label-z                              2.13%            12.421us         1
+        torch::autograd::GraphRoot           0.64%            1.503us          1
+        -----------------------------------  ---------------  ---------------  ---------------
+        Self CPU time total: 234.344us
+        CUDA time total: 0.000us
+
+    """
+
+    def __init__(self, name: str, args: Optional[str] = None):
+        self.name: str = name
+        self.args: Optional[str] = args
+        # Whether or not we should run record function's end callbacks when exiting.
+        self.run_callbacks_on_exit: bool = True
+        # TODO: TorchScript ignores standard type annotation here
+        # self.record: Optional["torch.classes.profiler._RecordFunction"] = None
+        self.record = torch.jit.annotate(
+            Optional["torch.classes.profiler._RecordFunction"], None
+        )
+
+    def __enter__(self):
+        self.record = torch.ops.profiler._record_function_enter_new(
+            self.name, self.args
+        )
+        return self
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any):
+        if not self.run_callbacks_on_exit:
+            return
+
+        # Local variable is needed by TorchScript to refine Optional[T] to T
+        record = self.record
+        assert record is not None
+
+        # TODO: Too slow with __torch_function__ handling enabled
+        # See https://github.com/pytorch/pytorch/issues/76410
+        if not torch.jit.is_scripting():
+            with torch._C.DisableTorchFunctionSubclass():
+                torch.ops.profiler._record_function_exit._RecordFunction(record)
+        else:
+            torch.ops.profiler._record_function_exit(record)
+
+    def _call_end_callbacks_on_future(self, fut: Future[Any]) -> Future[Any]:
+        """Use for profiling async calls that return a future.
+
+        Calling this function will extend recording beyond this scope, until the future is
+        satisfied. It is useful for profiling the end to end time of asynchronous calls.
+        This function should only be called once to attach the callback onto the future, and
+        will throw if called multiple times.
+
+        Args:
+            fut: (torch._C.Future): future for which to schedule
+            callback for.
+
+        Returns:
+            A future that completes with the value of the passed in future when
+            the profiling callbacks have ran.
+
+        """
+        # Throw if we have already attached a callback onto the future.
+        if not self.run_callbacks_on_exit:
+            raise RuntimeError("_call_end_callbacks_on_future can only be called once.")
+
+        # We are scheduling to run this RecordFunction's end callbacks when the
+        # passed in future completes, so don't run end callbacks on exit.
+        self.run_callbacks_on_exit = False
+
+        # Local variable is needed by TorchScript to refine Optional[T] to T
+        record = self.record
+        assert record is not None
+
+        # TODO: Too slow with __torch_function__ handling enabled
+        # See https://github.com/pytorch/pytorch/issues/76410
+        if not torch.jit.is_scripting():
+            with torch._C.DisableTorchFunctionSubclass():
+                profiled_future = (
+                    torch.ops.profiler._call_end_callbacks_on_jit_fut._RecordFunction(
+                        record, fut
+                    )
+                )
+        else:
+            profiled_future = torch.ops.profiler._call_end_callbacks_on_jit_fut(
+                record, fut
+            )
+        return profiled_future
+
+
+class emit_itt:
+    """Context manager that makes every autograd operation emit an ITT range.
+
+    It is useful when running the program under Intel(R) VTune Profiler::
+
+        vtune <--vtune-flags> 
+
+    The Instrumentation and Tracing Technology (ITT) API enables your application to generate and
+    control the collection of trace data during its execution across different Intel tools.
+    This context manager is to annotate Intel(R) VTune Profiling trace. With help of this context manager,
+    you will be able to see labled ranges in Intel(R) VTune Profiler GUI.
+
+    .. warning:
+        This context manager should not be called recursively, i.e. at most one
+        instance should be enabled at any given time.
+
+    Args:
+        enabled (bool, optional): Setting ``enabled=False`` makes this context manager a no-op.
+            Default: ``True``.
+        record_shapes (bool, optional): If ``record_shapes=True``, the itt range wrapping
+            each autograd op will append information about the sizes of Tensor arguments received
+            by that op, in the following format:
+            ``[[arg0.size(0), arg0.size(1), ...], [arg1.size(0), arg1.size(1), ...], ...]``
+            Non-tensor arguments will be represented by ``[]``.
+            Arguments will be listed in the order they are received by the backend op.
+            Please note that this order may not match the order in which those arguments were passed
+            on the Python side.  Also note that shape recording may increase the overhead of itt range creation.
+            Default: ``False``
+
+    Example:
+        >>> # xdoctest: +SKIP("Undefined variables")
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER)
+        >>> with torch.autograd.profiler.emit_itt():
+        ...     model(x)
+
+    """
+
+    def __init__(self, enabled=True, record_shapes=False):
+        self.enabled = enabled
+        self.entered = False
+        self.record_shapes = record_shapes
+
+    def __enter__(self):
+        if not self.enabled:
+            return
+        if self.entered:
+            raise RuntimeError("ITT annotation context manager is not reentrant")
+        self.entered = True
+        _run_on_profiler_start()
+        _enable_profiler(
+            ProfilerConfig(
+                ProfilerState.ITT,
+                self.record_shapes,
+                False,
+                False,
+                False,
+                False,
+                _ExperimentalConfig(),
+            ),
+            set(),
+        )
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if not self.enabled:
+            return
+        _disable_profiler()
+        _run_on_profiler_stop()
+        return False
+
+
+class emit_nvtx:
+    """Context manager that makes every autograd operation emit an NVTX range.
+
+    It is useful when running the program under nvprof::
+
+        nvprof --profile-from-start off -o trace_name.prof -- 
+
+    Unfortunately, there's no way to force nvprof to flush the data it collected
+    to disk, so for CUDA profiling one has to use this context manager to annotate
+    nvprof traces and wait for the process to exit before inspecting them.
+    Then, either NVIDIA Visual Profiler (nvvp) can be used to visualize the timeline, or
+    :func:`torch.autograd.profiler.load_nvprof` can load the results for inspection
+    e.g. in Python REPL.
+
+    .. warning:
+        This context manager should not be called recursively, i.e. at most one
+        instance should be enabled at any given time.
+
+    Args:
+        enabled (bool, optional): Setting ``enabled=False`` makes this context manager a no-op.
+            Default: ``True``.
+        record_shapes (bool, optional): If ``record_shapes=True``, the nvtx range wrapping
+            each autograd op will append information about the sizes of Tensor arguments received
+            by that op, in the following format:
+            ``[[arg0.size(0), arg0.size(1), ...], [arg1.size(0), arg1.size(1), ...], ...]``
+            Non-tensor arguments will be represented by ``[]``.
+            Arguments will be listed in the order they are received by the backend op.
+            Please note that this order may not match the order in which those arguments were passed
+            on the Python side.  Also note that shape recording may increase the overhead of nvtx range creation.
+            Default: ``False``
+
+    Example:
+        >>> # xdoctest: +SKIP("undefined variables")
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER)
+        >>> with torch.cuda.profiler.profile():
+        ...     model(x)  # Warmup CUDA memory allocator and profiler
+        ...     with torch.autograd.profiler.emit_nvtx():
+        ...         model(x)
+
+    **Forward-backward correlation**
+
+    When viewing a profile created using :class:`emit_nvtx` in the Nvidia Visual Profiler,
+    correlating each backward-pass op with the corresponding forward-pass op can be difficult.
+    To ease this task, :class:`emit_nvtx` appends sequence number information to the ranges it
+    generates.
+
+    During the forward pass, each function range is decorated with ``seq=``.  ``seq`` is a running
+    counter, incremented each time a new backward Function object is created and stashed for backward.
+    Thus, the ``seq=`` annotation associated with each forward function range tells you that
+    if a backward Function object is created by this forward function,
+    the backward object will receive sequence number N.
+    During the backward pass, the top-level range wrapping each C++ backward Function's
+    ``apply()`` call is decorated with ``stashed seq=``.  ``M`` is the sequence number that
+    the backward object was created with.  By comparing ``stashed seq`` numbers in backward with ``seq``
+    numbers in forward, you can track down which forward op created each backward Function.
+
+    Any functions executed during the backward pass are also decorated with ``seq=``.  During
+    default backward (with ``create_graph=False``) this information is irrelevant, and in fact,
+    ``N`` may simply be 0 for all such functions.  Only the top-level ranges associated with
+    backward Function objects' ``apply()`` methods are useful, as a way to correlate these Function
+    objects with the earlier forward pass.
+
+    **Double-backward**
+
+    If, on the other hand, a backward pass with ``create_graph=True`` is underway (in other words,
+    if you are setting up for a double-backward), each function's execution during backward
+    is given a nonzero, useful ``seq=``.  Those functions may themselves create Function objects
+    to be executed later during double-backward, just as the original functions in the forward pass did.
+    The relationship between backward and double-backward is conceptually the same as the relationship
+    between forward and backward: The functions still emit current-sequence-number-tagged ranges,
+    the Function objects they create still stash those sequence numbers, and during the eventual
+    double-backward, the Function objects' ``apply()`` ranges are still tagged with ``stashed seq``
+    numbers, which can be compared to `seq` numbers from the backward pass.
+
+    .. warning:
+        The sequence number is thread-local, and some forward functions don't create an associated
+        backward Function object (instead delegating that to sub-functions further down the call chain).
+        For these reasons, the correspondence of stashed sequence numbers in
+        backward Function ``apply()`` ranges with `seq` numbers in forward-pass ranges is
+        not guaranteed to be 1 to 1.  The sequence numbers alone may not be enough to fully
+        disambiguate which forward function created which
+        backward Function object.  You may need to make a judgment based on analytic knowledge of what
+        the expected correspondence should be.
+    """
+
+    def __init__(self, enabled=True, record_shapes=False):
+        self.enabled = enabled
+        self.entered = False
+        self.record_shapes = record_shapes
+
+    def __enter__(self):
+        if not self.enabled:
+            return
+        if self.entered:
+            raise RuntimeError("NVTX annotation context manager is not reentrant")
+        self.entered = True
+        torch.cuda.synchronize()
+        _run_on_profiler_start()
+        _enable_profiler(
+            ProfilerConfig(
+                ProfilerState.NVTX,
+                self.record_shapes,
+                False,
+                False,
+                False,
+                False,
+                _ExperimentalConfig(),
+            ),
+            set(),
+        )
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if not self.enabled:
+            return
+        torch.cuda.synchronize()
+        _disable_profiler()
+        _run_on_profiler_stop()
+        return False
+
+
+def load_nvprof(path):
+    """Open an nvprof trace file and parses autograd annotations.
+
+    Args:
+        path (str): path to nvprof trace
+    """
+    return EventList(parse_nvprof_trace(path))
+
+
+class EnforceUnique:
+    """Raises an error if a key is seen more than once."""
+
+    def __init__(self):
+        self.seen = set()
+
+    def see(self, *key):
+        r"""
+        Observe a key and raise an error if it is seen multiple times.
+        """
+        if key in self.seen:
+            raise RuntimeError("duplicate key: " + str(key))
+        self.seen.add(key)
+
+
+def parse_nvprof_trace(path):
+    import sqlite3
+
+    conn = sqlite3.connect(path)
+    conn.row_factory = sqlite3.Row
+
+    # Parse strings table
+    strings = {}
+    for r in conn.execute("SELECT _id_ as id, value FROM StringTable"):
+        strings[r["id"]] = torch._C._demangle(r["value"])
+
+    # First, find all functions and create FunctionEvents for them
+    marker_query = """
+    SELECT
+        start.id AS marker_id, start.name, start.timestamp AS start_time, end.timestamp AS end_time
+    FROM
+        CUPTI_ACTIVITY_KIND_MARKER AS start INNER JOIN CUPTI_ACTIVITY_KIND_MARKER AS end
+        ON start.id = end.id
+    WHERE
+        start.name != 0 AND end.name = 0
+    """
+    functions = []
+    functions_map = {}
+    unique = EnforceUnique()
+    for row in conn.execute(marker_query):
+        unique.see(row["marker_id"])
+        evt = FunctionEvent(
+            id=row["marker_id"],
+            node_id=0,  # missing a node_id when calling FunctionEvent. This is just to ensure
+            # that pytorch doesn't crash when creating a FunctionEvent() object
+            name=strings[row["name"]],
+            start_us=row["start_time"],
+            end_us=row["end_time"],
+            thread=0,
+        )  # TODO: find in sqlite database
+        functions.append(evt)
+        functions_map[evt.id] = evt
+
+    # Now, correlate all kernels with FunctionEvents
+    kernel_query = """
+    SELECT
+        start.id AS marker_id, start.name, start.timestamp, end.timestamp,
+        runtime._id_ AS runtime_id, runtime.cbid, runtime.start AS runtime_start, runtime.end AS runtime_end,
+        kernel.start AS kernel_start, kernel.end AS kernel_end, kernel.name AS kernel_name
+    FROM
+        CUPTI_ACTIVITY_KIND_MARKER AS start
+        INNER JOIN CUPTI_ACTIVITY_KIND_MARKER AS end
+            ON start.id = end.id
+        INNER JOIN CUPTI_ACTIVITY_KIND_RUNTIME as runtime
+            ON (start.timestamp < runtime.start AND runtime.end < end.timestamp)
+        INNER JOIN CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL AS kernel
+            ON kernel.correlationId = runtime.correlationId
+    """
+    unique = EnforceUnique()
+    for row in conn.execute(kernel_query):
+        unique.see(row["marker_id"], row["runtime_id"])
+        # 211 is cudaKernelLaunch for cuda >= 9.2
+        assert row["cbid"] == 211
+        evt = functions_map[row["marker_id"]]
+        evt.append_kernel(
+            row["kernel_name"], 0, row["kernel_end"] - row["kernel_start"]
+        )
+
+    functions.sort(key=lambda evt: evt.time_range.start)
+    return functions
+
+
+class KinetoStepTracker:
+    """Provides an abstraction for incrementing the step count globally.
+
+    Previously, we only had one place to mark that a step() has occurred
+    in the program via pytorch profiler step(). We will now add step hooks
+    in the Optimizer class https://github.com/pytorch/pytorch/issues/88446
+
+    - This could mean programs that already call profiler.step() every
+      iteration can end up double incrementing step count.
+    - If a model uses multiple optimizers we can also have double or more
+      counting of the step.
+
+    We fix this by adding a layer of abstraction before calling step()
+    to the kineto library. The idea is to maintain steps per requester in a dict:
+
+    .. code-block::
+
+        {
+           "ProfilerStep": 100,  # triggered by profiler step() call
+           "Optimizer1Step": 100,   # Optimizer 1 or 2 are just examples, could be SGD, Adam etc
+           "Optimizer2Step": 100,
+        }
+
+    To figure out the global step count just take the max of dict values (100).
+
+    If one of the count increments the max will go up.
+
+    .. code-block::
+
+        {
+           "ProfilerStep": 100,
+           "Optimizer1Step": 101,   # Optimizer1 got incremented first say
+           "Optimizer2Step": 100,
+        }
+
+    Then global step count is 101
+    We only call the kineto step() function when global count increments.
+
+    NOTE: Please do not use the KinetoStepTracker in modules beside the Optimizer
+    for now. The result could be incorrect increments of the step count.
+    """
+
+    _current_step = 0
+    _step_dict: Dict[str, int] = defaultdict(int)
+
+    @classmethod
+    def init_step_count(cls, requester: str):
+        r"""
+        Initialize for a given requester.
+        """
+        cls._step_dict[requester] = cls._current_step
+
+    @classmethod
+    def erase_step_count(cls, requester: str) -> bool:
+        r"""
+        Remove a given requester.
+        """
+        return cls._step_dict.pop(requester, None) is not None
+
+    @classmethod
+    def increment_step(cls, requester: str) -> int:
+        """Increments the step count for the requester.
+
+        Additionally if the max over all step counts has incremented then
+        trigger the _kineto_step() returns global step count
+        """
+        if requester not in cls._step_dict:
+            cls.init_step_count(requester)
+        cls._step_dict[requester] += 1
+
+        new_step = max(cls._step_dict.values())
+        if new_step > cls._current_step:
+            delta = new_step - cls._current_step
+            if delta > 1:
+                warn(
+                    "Profiler step count has increased more than 1 - "
+                    f"current_step = {cls._current_step} step dict =  {cls._step_dict}"
+                )
+            for _ in range(0, delta):
+                _kineto_step()
+            cls._current_step = new_step
+        return cls._current_step
+
+    @classmethod
+    def current_step(cls) -> int:
+        r"""
+        Get the latest step for any requester
+        """
+        return cls._current_step
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/profiler_legacy.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/profiler_legacy.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f147b1b45629e1f8aad6c6edd42600f7de1fd52
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/profiler_legacy.py
@@ -0,0 +1,314 @@
+# mypy: allow-untyped-defs
+import itertools
+import warnings
+from typing_extensions import deprecated
+
+import torch
+import torch.cuda
+from torch.autograd import (
+    _disable_profiler_legacy,
+    _enable_profiler_legacy,
+    DeviceType,
+    ProfilerConfig,
+    ProfilerState,
+)
+from torch.autograd.profiler_util import (
+    _filter_name,
+    _filter_stack_entry,
+    _rewrite_name,
+    EventList,
+    FunctionEvent,
+    MEMORY_EVENT_NAME,
+)
+
+
+__all__ = ["profile"]
+
+
+@deprecated(
+    "`torch.autograd.profiler_legacy.profile` is deprecated and will be removed in a future release. "
+    "Please use `torch.profiler` instead.",
+    category=None,  # TODO: change to `FutureWarning`
+)
+class profile:
+    """DEPRECATED: use torch.profiler instead."""
+
+    def __init__(
+        self,
+        enabled=True,
+        *,
+        use_cuda=False,
+        record_shapes=False,
+        with_flops=False,
+        profile_memory=False,
+        with_stack=False,
+        with_modules=False,
+    ):
+        self.enabled: bool = enabled
+        if not self.enabled:
+            return
+        self.use_cuda = use_cuda
+        self.function_events = None
+        self.entered = False
+        self.record_shapes = record_shapes
+        self.with_flops = with_flops
+        self.record_shapes |= self.with_flops
+        self.profile_memory = profile_memory
+        self.with_stack = with_stack
+        self.with_modules = with_modules
+
+        if self.use_cuda and not torch.cuda.is_available():
+            warnings.warn(
+                "CUDA is not available, disabling CUDA profiling",
+                stacklevel=2,
+            )
+            self.use_cuda = False
+
+        if self.use_cuda:
+            self.profiler_kind = ProfilerState.CUDA
+        else:
+            self.profiler_kind = ProfilerState.CPU
+
+    def config(self):
+        return ProfilerConfig(
+            self.profiler_kind,
+            self.record_shapes,
+            self.profile_memory,
+            self.with_stack,
+            self.with_flops,
+            self.with_modules,
+            # avoid exposing _ExperimentalConfig this in legacy public API
+            torch._C._profiler._ExperimentalConfig(),
+        )
+
+    def __enter__(self):
+        if not self.enabled:
+            return
+        if self.entered:
+            raise RuntimeError("Profiler context manager is not reentrant")
+        self.entered = True
+        self._start_trace()
+        return self
+
+    def _start_trace(self):
+        _enable_profiler_legacy(self.config())
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if not self.enabled:
+            return
+        if self.use_cuda:
+            torch.cuda.synchronize()
+
+        records = _disable_profiler_legacy()
+        parsed_results = _parse_legacy_records(records)
+        self.function_events = EventList(
+            parsed_results,
+            use_device="cuda" if self.use_cuda else None,
+            profile_memory=self.profile_memory,
+            with_flops=self.with_flops,
+        )
+        self.function_events._build_tree()
+        return False
+
+    def __repr__(self):
+        if self.function_events is None:
+            return ""
+        return repr(self.function_events)
+
+    def __str__(self):
+        if self.function_events is None:
+            return ""
+        return str(self.function_events)
+
+    def _check_finish(self):
+        if self.function_events is None:
+            raise RuntimeError("Profiler didn't finish running")
+
+    def table(
+        self,
+        sort_by=None,
+        row_limit=100,
+        max_src_column_width=75,
+        max_name_column_width=55,
+        max_shapes_column_width=80,
+        header=None,
+        top_level_events_only=False,
+    ):
+        self._check_finish()
+        assert self.function_events is not None
+        return self.function_events.table(
+            sort_by=sort_by,
+            row_limit=row_limit,
+            max_src_column_width=max_src_column_width,
+            max_name_column_width=max_name_column_width,
+            max_shapes_column_width=max_shapes_column_width,
+            header=header,
+            top_level_events_only=top_level_events_only,
+        )
+
+    table.__doc__ = EventList.table.__doc__
+
+    def export_chrome_trace(self, path):
+        self._check_finish()
+        assert self.function_events is not None
+        return self.function_events.export_chrome_trace(path)
+
+    export_chrome_trace.__doc__ = EventList.export_chrome_trace.__doc__
+
+    def export_stacks(self, path: str, metric: str = "self_cpu_time_total"):
+        self._check_finish()
+        assert self.function_events is not None, "Expected profiling results"
+        assert self.with_stack, "export_stacks() requires with_stack=True"
+        return self.function_events.export_stacks(path, metric)
+
+    def key_averages(self, group_by_input_shape=False, group_by_stack_n=0):
+        self._check_finish()
+        assert self.function_events is not None, "Expected profiling results"
+        return self.function_events.key_averages(group_by_input_shape, group_by_stack_n)
+
+    key_averages.__doc__ = EventList.key_averages.__doc__
+
+    def total_average(self):
+        self._check_finish()
+        assert self.function_events is not None, "Expected profiling results"
+        return self.function_events.total_average()
+
+    total_average.__doc__ = EventList.total_average.__doc__
+
+    @property
+    def self_cpu_time_total(self):
+        """Return CPU time as the sum of self times across all events."""
+        self._check_finish()
+        assert self.function_events is not None
+        return self.function_events.self_cpu_time_total
+
+
+def _parse_legacy_records(thread_records):
+    def _get_record_key(record):
+        """Return a tuple for correlating start and end records in `_parse_legacy_records`."""
+        return (record.handle(), record.node_id())
+
+    next_id = 0
+    start_record = None
+    functions = []
+    record_stack = []
+
+    # '__start_profile' is not guaranteed to be first, so we must find it here
+    for record in itertools.chain.from_iterable(thread_records):
+        name = record.name()
+        if start_record is None and name == "__start_profile":
+            start_record = record
+
+    assert start_record is not None and not start_record.is_remote()
+
+    for thread_record_list in thread_records:
+        # accumulated memory allocations per handle
+        cpu_memory_allocs = {}
+        cuda_memory_allocs = {}
+        # ranges per handle
+        range_starts = {}
+
+        filtered_handles = set()
+        prev_record = None
+        for record in thread_record_list:
+            record_key = _get_record_key(record)
+            if _filter_name(record.name()) or record_key in filtered_handles:
+                filtered_handles.add(record_key)
+                continue
+
+            if record.kind() == "push":
+                # workaround to reduce double logging from operator
+                # wrappers and redispatch
+                if prev_record is not None:
+                    duplicate = (
+                        prev_record.name() == record.name()
+                        and prev_record.kind() == record.kind()
+                        and prev_record.node_id() == record.node_id()
+                    )
+                    if duplicate:
+                        filtered_handles.add(record_key)
+                        continue
+
+                range_starts[record_key] = record
+                cpu_memory_allocs[record_key] = 0
+                cuda_memory_allocs[record_key] = 0
+            elif record.kind() == "pop":
+                assert (
+                    record_key in range_starts
+                ), f"""Expected record with key {record_key} to exist in range_starts.
+                    This means that the pop event did not have a corresponding push."""
+
+                start = range_starts[record_key]
+
+                cpu_memory_usage = cpu_memory_allocs[record_key]
+                cuda_memory_usage = cuda_memory_allocs[record_key]
+                is_async = start.is_async() or (start.thread_id() != record.thread_id())
+                is_remote_event = record.is_remote()
+                start_flops = start.flops()
+
+                fe = FunctionEvent(
+                    id=record.handle(),
+                    node_id=record.node_id(),
+                    name=_rewrite_name(name=start.name(), with_wildcard=True),
+                    trace_name=_rewrite_name(name=start.name(), with_wildcard=False),
+                    thread=start.thread_id(),
+                    start_us=start_record.cpu_elapsed_us(start),
+                    end_us=start_record.cpu_elapsed_us(record),
+                    fwd_thread=start.fwd_thread_id(),
+                    input_shapes=start.shapes(),
+                    stack=[
+                        entry for entry in start.stack() if _filter_stack_entry(entry)
+                    ],
+                    scope=start.scope(),
+                    use_device="cuda" if start.has_cuda() else None,
+                    cpu_memory_usage=cpu_memory_usage,
+                    device_memory_usage=cuda_memory_usage,
+                    is_async=is_async,
+                    is_remote=is_remote_event,
+                    sequence_nr=start.sequence_nr(),
+                    device_type=DeviceType.CPU,
+                    is_legacy=True,
+                    flops=start_flops,
+                )
+                # note: async events have only cpu total time
+                if not is_async and start.has_cuda():
+                    duration = start.cuda_elapsed_us(record)
+                    if duration > 0:
+                        fe.append_kernel(start.name(), start.device(), duration)
+                functions.append(fe)
+                del range_starts[record_key]
+                del cpu_memory_allocs[record_key]
+                del cuda_memory_allocs[record_key]
+            elif record.kind() == "memory_alloc":
+                num_open_handles_cpu = len(cpu_memory_allocs)
+                num_open_handles_cuda = len(cuda_memory_allocs)
+                assert num_open_handles_cpu == num_open_handles_cuda
+                for handle in cpu_memory_allocs.keys():
+                    cpu_memory_allocs[handle] += record.cpu_memory_usage()
+                for handle in cuda_memory_allocs.keys():
+                    cuda_memory_allocs[handle] += record.cuda_memory_usage()
+                if num_open_handles_cpu == 0:
+                    # output event as a top-level memory event
+                    fe = FunctionEvent(
+                        id=0,
+                        name=MEMORY_EVENT_NAME,
+                        trace_name=None,
+                        thread=0,
+                        start_us=0,
+                        end_us=0,
+                        stack=[],
+                        cpu_memory_usage=record.cpu_memory_usage(),
+                        device_memory_usage=record.cuda_memory_usage(),
+                        is_legacy=True,
+                    )
+                    functions.append(fe)
+            prev_record = record
+
+    # Sort functions by start time then by end time ascending.
+    # This ensures that--in the case of nested events which
+    # have the same start time (which may happen due to the
+    # granularity of the given clock tick)--we always show
+    # the outermost nested call first. This adds stability
+    # in how FunctionEvents appear
+    functions.sort(key=lambda evt: [evt.time_range.start, -evt.time_range.end])
+    return functions
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/profiler_util.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/profiler_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..3957c044cbbca475444267aaf2891e3a87a043fd
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/profiler_util.py
@@ -0,0 +1,1110 @@
+# mypy: allow-untyped-defs
+import bisect
+import itertools
+import math
+from collections import defaultdict, namedtuple
+from operator import attrgetter
+from typing import Any, Dict, List, Optional, Tuple
+from typing_extensions import deprecated
+
+import torch
+from torch.autograd import DeviceType
+
+
+__all__ = [
+    "EventList",
+    "FormattedTimesMixin",
+    "Interval",
+    "Kernel",
+    "FunctionEvent",
+    "FunctionEventAvg",
+    "StringTable",
+    "MemRecordsAcc",
+]
+
+
+class EventList(list):
+    """A list of Events (for pretty printing)."""
+
+    def __init__(self, *args, **kwargs):
+        use_device = kwargs.pop("use_device", None)
+        profile_memory = kwargs.pop("profile_memory", False)
+        with_flops = kwargs.pop("with_flops", False)
+        super().__init__(*args, **kwargs)
+        self._use_device = use_device
+        self._profile_memory = profile_memory
+        self._tree_built = False
+        self._with_flops = with_flops
+
+    def _build_tree(self):
+        self._populate_cpu_children()
+        self._remove_dup_nodes()
+        self._set_backward_stacktraces()
+        self._tree_built = True
+
+    def __str__(self):
+        return self.table()
+
+    def _remove_dup_nodes(self):
+        while True:
+            to_delete = set()
+            for idx in range(len(self)):
+                if (
+                    self[idx].cpu_parent is not None
+                    and self[idx].cpu_parent.name == self[idx].name
+                    and len(self[idx].cpu_parent.cpu_children) == 1
+                ):
+                    self[idx].cpu_parent.cpu_children = self[idx].cpu_children
+                    self[idx].cpu_parent.kernels = self[idx].kernels  # lift kernels up
+                    for ch in self[idx].cpu_children:
+                        ch.cpu_parent = self[idx].cpu_parent
+                    to_delete.add(idx)
+            if len(to_delete) == 0:
+                break
+            new_evts = [ev for ind, ev in enumerate(self) if ind not in to_delete]
+            self.clear()
+            self.extend(new_evts)
+
+    def _populate_cpu_children(self):
+        """Populate child events into each underlying FunctionEvent object.
+
+        One event is a child of another if [s1, e1) is inside [s2, e2). Where
+        s1 and e1 would be start and end of the child event's interval. And
+        s2 and e2 start and end of the parent event's interval
+
+        Example: In event list [[0, 10], [1, 3], [3, 4]] would have make [0, 10]
+        be a parent of two other intervals.
+
+        If for any reason two intervals intersect only partially, this function
+        will not record a parent child relationship between then.
+        """
+        # Some events can be async (i.e. start and end on different threads),
+        # since it's generally undefined how to attribute children ranges to
+        # async ranges, we do not use them when calculating nested ranges and stats
+        sync_events = [
+            evt
+            for evt in self
+            if not evt.is_async and evt.device_type == DeviceType.CPU
+        ]
+        events = sorted(
+            sync_events,
+            key=attrgetter("thread"),
+        )
+        # Group by both thread and node_id, so that events that happen to have
+        # the same thread_id but are from different nodes aren't incorrectly
+        # grouped together.
+        threads = itertools.groupby(
+            events, key=lambda event: (event.thread, event.node_id)
+        )
+
+        # For each thread we keep a stack of current nested parents.
+        # We maintain the invariant that each interval is a subset of all other
+        # intervals lower in the stack.
+        #
+        # First we sort the intervals by their start time. Then we iterate over them.
+        # Every time we see a new interval we remove several parents from
+        # the top until we restore the invariant. Then parent child relationship
+        # if recorded if the stack is not empty.
+        # Finally we add new interval to the list
+        #
+        # Algorithm has O(N * log(N)) complexity where N is number of
+        # intervals
+        for thread_id, thread_events in threads:
+            thread_events_ = sorted(
+                thread_events,
+                key=lambda event: [event.time_range.start, -event.time_range.end],
+            )
+            current_events: List[FunctionEvent] = []
+            cur_end = 0
+            for event in thread_events_:
+                while len(current_events) > 0:
+                    parent = current_events[-1]
+                    if (
+                        event.time_range.start >= parent.time_range.end
+                        or event.time_range.end > parent.time_range.end
+                    ):
+                        # this can't be a parent
+                        current_events.pop()
+                    else:
+                        parent.append_cpu_child(event)
+                        assert (
+                            event.cpu_parent is None
+                        ), f"There is already a CPU parent event for {event.key}"
+                        event.set_cpu_parent(parent)
+                        break
+
+                current_events.append(event)
+
+    def _set_backward_stacktraces(self):
+        def bw_parent(evt):
+            if evt is None:
+                return None
+            elif evt.scope == 1:  # BACKWARD_FUNCTION
+                return evt
+            else:
+                return bw_parent(evt.cpu_parent)
+
+        fwd_stacks = {}
+        for evt in self:
+            if bw_parent(evt) is None and evt.stack is not None:
+                t = (evt.sequence_nr, evt.thread)
+                if t not in fwd_stacks:
+                    fwd_stacks[t] = evt.stack
+
+        for evt in self:
+            p = bw_parent(evt)
+            if p is not None:
+                assert p.fwd_thread is not None
+                t = (p.sequence_nr, p.fwd_thread)
+                if t in fwd_stacks:
+                    evt.stack = fwd_stacks[t]
+                else:
+                    evt.stack = []
+
+    @property
+    def self_cpu_time_total(self):
+        return sum(event.self_cpu_time_total for event in self)
+
+    def table(
+        self,
+        sort_by=None,
+        row_limit=100,
+        max_src_column_width=75,
+        max_name_column_width=55,
+        max_shapes_column_width=80,
+        header=None,
+        top_level_events_only=False,
+    ):
+        """Print an EventList as a nicely formatted table.
+
+        Args:
+            sort_by (str, optional): Attribute used to sort entries. By default
+                they are printed in the same order as they were registered.
+                Valid keys include: ``cpu_time``, ``cuda_time``, ``xpu_time``,
+                ``cpu_time_total``, ``cuda_time_total``, ``xpu_time_total``,
+                ``cpu_memory_usage``, ``cuda_memory_usage``, ``xpu_memory_usage``,
+                ``self_cpu_memory_usage``, ``self_cuda_memory_usage``,
+                ``self_xpu_memory_usage``, ``count``.
+            top_level_events_only(bool, optional): Boolean flag to determine the
+                selection of events to display. If true, the profiler will only
+                display events at top level like top-level invocation of python
+                `lstm`, python `add` or other functions, nested events like low-level
+                cpu/cuda/xpu ops events are omitted for profiler result readability.
+
+        Returns:
+            A string containing the table.
+        """
+        return _build_table(
+            self,
+            sort_by=sort_by,
+            row_limit=row_limit,
+            max_src_column_width=max_src_column_width,
+            max_name_column_width=max_name_column_width,
+            max_shapes_column_width=max_shapes_column_width,
+            header=header,
+            profile_memory=self._profile_memory,
+            with_flops=self._with_flops,
+            top_level_events_only=top_level_events_only,
+        )
+
+    def export_chrome_trace(self, path):
+        """Export an EventList as a Chrome tracing tools file.
+
+        The checkpoint can be later loaded and inspected under ``chrome://tracing`` URL.
+
+        Args:
+            path (str): Path where the trace will be written.
+        """
+        import os
+
+        device_name = "cuda" if not self._use_device else self._use_device
+        with open(path, "w") as f:
+            chrome_events = []
+            next_id = 0
+            # Use file IO over using json.dump since JSON dumping is very slow and
+            # this technique is proven to give a 4x speedup.
+            f.write("[")
+            for evt in self:
+                if evt.trace_name is None:
+                    continue
+                f.write(
+                    '{{"name": "{}", '
+                    '"ph": "X", '
+                    '"ts": {}, '
+                    '"dur": {}, '
+                    '"tid": {}, '
+                    '"pid": "CPU functions", '
+                    '"args": {{}}}}, '.format(
+                        evt.trace_name,
+                        evt.time_range.start,
+                        evt.time_range.elapsed_us(),
+                        evt.thread
+                        if not evt.is_remote
+                        else f'" node_id:{evt.node_id}, thread_id:{evt.thread} "',
+                    )
+                )
+                for k in evt.kernels:
+                    # 's' and 'f' draw Flow arrows from
+                    # the CPU launch to the GPU kernel
+                    f.write(
+                        f'{{"name": "{evt.trace_name}", '
+                        '"ph": "s", '
+                        f'"ts": {evt.time_range.start}, '
+                        f'"tid": {evt.thread}, '
+                        '"pid": "CPU functions", '
+                        f'"id": {next_id}, '
+                        f'"cat": "cpu_to_{device_name}", '
+                        '"args": {}}, '
+                    )
+                    # Note: use torch.profiler to get device kernel trace
+                    next_id += 1
+            if len(self) > 0:
+                # remove trailing whitespace and comma
+                f.seek(f.tell() - 2, os.SEEK_SET)
+                f.truncate()
+            f.write("]")
+
+    def supported_export_stacks_metrics(self):
+        return [
+            "self_cpu_time_total",
+            "self_cuda_time_total",
+            "self_xpu_time_total",
+            "self_privateuse1_time_total",
+        ]
+
+    def export_stacks(self, path: str, metric: str):
+        if metric not in self.supported_export_stacks_metrics():
+            raise ValueError(
+                "metric should be one of: "
+                + str(self.supported_export_stacks_metrics())
+            )
+        translate_table = str.maketrans(" ;\t\n", "____")
+        with open(path, "w") as f:
+            for evt in self:
+                if evt.stack and len(evt.stack) > 0:
+                    metric_value = getattr(
+                        evt,
+                        metric.replace("cuda", "device")
+                        .replace("xpu", "device")
+                        .replace("privateuse1", "device"),
+                    )
+                    if int(metric_value) > 0:
+                        stack_str = ""
+                        for entry in reversed(evt.stack):
+                            stack_str += entry.translate(translate_table)
+                            stack_str += ";"
+                        stack_str = stack_str[:-1] + " " + str(int(metric_value))
+                        f.write(stack_str + "\n")
+
+    def key_averages(self, group_by_input_shapes=False, group_by_stack_n=0):
+        """Averages all function events over their keys.
+
+        Args:
+            group_by_input_shapes: group entries by
+                (event name, input shapes) rather than just event name.
+                This is useful to see which input shapes contribute to the runtime
+                the most and may help with size-specific optimizations or
+                choosing the best candidates for quantization (aka fitting a roof line)
+
+            group_by_stack_n: group by top n stack trace entries
+
+        Returns:
+            An EventList containing FunctionEventAvg objects.
+        """
+        assert self._tree_built
+        stats: Dict[Tuple[str, ...], FunctionEventAvg] = defaultdict(FunctionEventAvg)
+
+        def get_key(event, group_by_input_shapes, group_by_stack_n) -> Tuple[str, ...]:
+            key = [
+                str(event.key),
+                str(event.node_id),
+                str(event.device_type),
+                str(event.is_legacy),
+                str(event.is_user_annotation),
+            ]
+            if group_by_input_shapes:
+                key.append(str(event.input_shapes))
+            if group_by_stack_n > 0:
+                key += event.stack[:group_by_stack_n]
+            return tuple(key)
+
+        for evt in self:
+            stats[get_key(evt, group_by_input_shapes, group_by_stack_n)].add(evt)
+
+        avg_list = EventList(
+            stats.values(),
+            use_device=self._use_device,
+            profile_memory=self._profile_memory,
+            with_flops=self._with_flops,
+        )
+        for evt in avg_list:
+            evt.stack = evt.stack[:group_by_stack_n]
+            if not group_by_input_shapes:
+                evt.input_shapes = ""
+        return avg_list
+
+    def total_average(self):
+        """Averages all events.
+
+        Returns:
+            A FunctionEventAvg object.
+        """
+        total_stat = FunctionEventAvg()
+        for evt in self:
+            total_stat += evt
+            total_stat.key = None
+        total_stat.key = "Total"
+        return total_stat
+
+
+def _format_time(time_us):
+    """Define how to format time in FunctionEvent."""
+    US_IN_SECOND = 1000.0 * 1000.0
+    US_IN_MS = 1000.0
+    if time_us >= US_IN_SECOND:
+        return f"{time_us / US_IN_SECOND:.3f}s"
+    if time_us >= US_IN_MS:
+        return f"{time_us / US_IN_MS:.3f}ms"
+    return f"{time_us:.3f}us"
+
+
+def _format_time_share(time_us, total_time_us):
+    """Define how to format time in FunctionEvent."""
+    if total_time_us == 0:
+        assert time_us == 0, f"Expected time_us == 0 but got {time_us}"
+        return "NaN"
+    return f"{time_us * 100.0 / total_time_us:.2f}%"
+
+
+def _format_memory(nbytes):
+    """Return a formatted memory size string."""
+    KB = 1024
+    MB = 1024 * KB
+    GB = 1024 * MB
+    if abs(nbytes) >= GB:
+        return f"{nbytes * 1.0 / GB:.2f} Gb"
+    elif abs(nbytes) >= MB:
+        return f"{nbytes * 1.0 / MB:.2f} Mb"
+    elif abs(nbytes) >= KB:
+        return f"{nbytes * 1.0 / KB:.2f} Kb"
+    else:
+        return str(nbytes) + " b"
+
+
+def _attr_formatter(name):
+    return property(lambda self: _format_time(getattr(self, name)))
+
+
+class FormattedTimesMixin:
+    """Helpers for FunctionEvent and FunctionEventAvg.
+
+    The subclass should define `*_time_total` and `count` attributes.
+    """
+
+    cpu_time_str = _attr_formatter("cpu_time")
+    device_time_str = _attr_formatter("device_time")
+    cpu_time_total_str = _attr_formatter("cpu_time_total")
+    device_time_total_str = _attr_formatter("device_time_total")
+    self_cpu_time_total_str = _attr_formatter("self_cpu_time_total")
+    self_device_time_total_str = _attr_formatter("self_device_time_total")
+
+    @property
+    def cpu_time(self):
+        return 0.0 if self.count == 0 else 1.0 * self.cpu_time_total / self.count  # type: ignore[attr-defined]
+
+    @property
+    def device_time(self):
+        return 0.0 if self.count == 0 else 1.0 * self.device_time_total / self.count  # type: ignore[attr-defined]
+
+    @property
+    @deprecated(
+        "`cuda_time` is deprecated, please use `device_time` instead.",
+        category=FutureWarning,
+    )
+    def cuda_time(self):  # To be deprecated
+        return self.device_time
+
+
+class Interval:
+    def __init__(self, start, end):
+        self.start = start
+        self.end = end
+
+    def elapsed_us(self):
+        r"""
+        Returns the length of the interval
+        """
+        return self.end - self.start
+
+
+Kernel = namedtuple("Kernel", ["name", "device", "duration"])
+
+
+class FunctionEvent(FormattedTimesMixin):
+    """Profiling information about a single function."""
+
+    def __init__(
+        self,
+        id,
+        name,
+        thread,
+        start_us,
+        end_us,
+        fwd_thread=None,
+        input_shapes=None,
+        stack=None,
+        scope=0,
+        use_device=None,
+        cpu_memory_usage=0,
+        device_memory_usage=0,
+        is_async=False,
+        is_remote=False,
+        sequence_nr=-1,
+        node_id=-1,
+        device_type=DeviceType.CPU,
+        device_index=0,
+        device_resource_id=None,
+        is_legacy=False,
+        flops=None,
+        trace_name=None,
+        concrete_inputs=None,
+        kwinputs=None,
+        is_user_annotation=False,
+    ):
+        self.id: int = id
+        self.node_id: int = node_id
+        self.name: str = name
+        self.trace_name: str = trace_name
+        self.time_range: Interval = Interval(start_us, end_us)
+        self.thread: int = thread
+        self.fwd_thread: Optional[int] = fwd_thread
+        self.kernels: List[Kernel] = []
+        self.count: int = 1
+        self.cpu_children: List[FunctionEvent] = []
+        self.cpu_parent: Optional[FunctionEvent] = None
+        self.input_shapes: Tuple[int, ...] = input_shapes
+        self.concrete_inputs: List[Any] = concrete_inputs
+        self.kwinputs: Dict[str, Any] = kwinputs
+        self.stack: List = stack
+        self.scope: int = scope
+        self.use_device: Optional[str] = use_device
+        self.cpu_memory_usage: int = cpu_memory_usage
+        self.device_memory_usage: int = device_memory_usage
+        self.is_async: bool = is_async
+        self.is_remote: bool = is_remote
+        self.sequence_nr: int = sequence_nr
+        self.device_type: DeviceType = device_type
+        self.device_index: int = device_index
+        self.device_resource_id: int = (
+            thread if device_resource_id is None else device_resource_id
+        )
+        self.is_legacy: bool = is_legacy
+        self.flops: Optional[int] = flops
+        self.is_user_annotation: Optional[bool] = is_user_annotation
+        self.self_cpu_percent = -1
+        self.total_cpu_percent = -1
+        self.total_device_percent = -1
+
+    def append_kernel(self, name, device, duration):
+        assert self.device_type == DeviceType.CPU
+        self.kernels.append(Kernel(name, device, duration))
+
+    def append_cpu_child(self, child):
+        """Append a CPU child of type FunctionEvent.
+
+        One is supposed to append only direct children to the event to have
+        correct self cpu time being reported.
+        """
+        assert self.device_type == DeviceType.CPU
+        assert isinstance(child, FunctionEvent)
+        assert child.device_type == DeviceType.CPU
+        self.cpu_children.append(child)
+
+    def set_cpu_parent(self, parent):
+        """Set the immediate CPU parent of type FunctionEvent.
+
+        One profiling FunctionEvent should have only one CPU parent such that
+        the child's range interval is completely inside the parent's. We use
+        this connection to determine the event is from top-level op or not.
+        """
+        assert self.device_type == DeviceType.CPU
+        assert isinstance(parent, FunctionEvent)
+        assert parent.device_type == DeviceType.CPU
+        self.cpu_parent = parent
+
+    # Note: async events don't have children, are not used when computing 'self'
+    # metrics of other events, have only total cpu time
+    @property
+    def self_cpu_memory_usage(self):
+        if self.is_async or self.device_type != DeviceType.CPU:
+            return 0
+        return self.cpu_memory_usage - sum(
+            child.cpu_memory_usage for child in self.cpu_children
+        )
+
+    @property
+    def self_device_memory_usage(self):
+        if self.is_async or self.device_type != DeviceType.CPU:
+            return 0
+        return self.device_memory_usage - sum(
+            child.device_memory_usage for child in self.cpu_children
+        )
+
+    @property
+    @deprecated(
+        "`self_cuda_memory_usage` is deprecated. Use `self_device_memory_usage` instead.",
+        category=FutureWarning,
+    )
+    def self_cuda_memory_usage(self):  # To be deprecated
+        return self.self_device_memory_usage
+
+    @property
+    def cpu_time_total(self):
+        if self.device_type == DeviceType.CPU:
+            return self.time_range.elapsed_us()
+        else:
+            return 0
+
+    @property
+    def self_cpu_time_total(self):
+        if self.is_async or self.device_type != DeviceType.CPU:
+            return 0
+        return self.cpu_time_total - sum(
+            child.cpu_time_total for child in self.cpu_children
+        )
+
+    @property
+    def device_time_total(self):
+        if self.is_async or not self.use_device:
+            return 0
+        if self.device_type == DeviceType.CPU:
+            if not self.is_legacy:
+                # account for the kernels in the children ops
+                return sum(kinfo.duration for kinfo in self.kernels) + sum(
+                    ch.device_time_total for ch in self.cpu_children
+                )
+            else:
+                # each legacy cpu events has a single (fake) kernel
+                return sum(kinfo.duration for kinfo in self.kernels)
+        else:
+            assert self.device_type in [
+                DeviceType.CUDA,
+                DeviceType.PrivateUse1,
+                DeviceType.MTIA,
+            ]
+            return self.time_range.elapsed_us()
+
+    @property
+    @deprecated(
+        "`cuda_time_total` is deprecated. Use `device_time_total` instead.",
+        category=FutureWarning,
+    )
+    def cuda_time_total(self):  # To be deprecated
+        return self.device_time_total
+
+    @property
+    def self_device_time_total(self):
+        if self.is_async or not self.use_device:
+            return 0
+        if self.device_type == DeviceType.CPU:
+            return self.device_time_total - sum(
+                child.device_time_total for child in self.cpu_children
+            )
+        else:
+            assert self.device_type in [
+                DeviceType.CUDA,
+                DeviceType.PrivateUse1,
+                DeviceType.MTIA,
+            ]
+            return self.device_time_total
+
+    @property
+    @deprecated(
+        "`self_cuda_time_total` is deprecated. Use `self_device_time_total` instead.",
+        category=FutureWarning,
+    )
+    def self_cuda_time_total(self):  # To be deprecated
+        return self.self_device_time_total
+
+    @property
+    def key(self):
+        return self.name
+
+    def __repr__(self):
+        device_name = self.use_device
+        device_time = self.device_time_str
+        device_memory_usage = self.device_memory_usage
+        return (
+            f""
+        )
+
+
+class FunctionEventAvg(FormattedTimesMixin):
+    """Used to average stats over multiple FunctionEvent objects."""
+
+    def __init__(self) -> None:
+        self.key: Optional[str] = None
+        self.count: int = 0
+        self.node_id: int = 0
+        self.is_async: bool = False
+        self.is_remote: bool = False
+        self.use_device: Optional[str] = None
+        self.cpu_time_total: int = 0
+        self.device_time_total: int = 0
+        self.self_cpu_time_total: int = 0
+        self.self_device_time_total: int = 0
+        self.input_shapes: Optional[List[List[int]]] = None
+        self.stack: Optional[List] = None
+        self.scope: Optional[int] = None
+        self.cpu_memory_usage: int = 0
+        self.device_memory_usage: int = 0
+        self.self_cpu_memory_usage: int = 0
+        self.self_device_memory_usage: int = 0
+        self.cpu_children: Optional[List[FunctionEvent]] = None
+        self.cpu_parent: Optional[FunctionEvent] = None
+        self.device_type: DeviceType = DeviceType.CPU
+        self.is_legacy: bool = False
+        self.flops: int = 0
+
+    def add(self, other):
+        if self.key is None:
+            # First function being recorded as part of FunctionEventAvg, propagate
+            # fields.
+            self.key = other.key
+            self.node_id = other.node_id
+            self.is_async = other.is_async
+            self.is_remote = other.is_remote
+            self.cpu_parent = other.cpu_parent
+            self.cpu_children = other.cpu_children
+
+            self.input_shapes = other.input_shapes
+            self.stack = other.stack
+            self.scope = other.scope
+            self.device_type = other.device_type
+            self.is_legacy = other.is_legacy
+            self.use_device = other.use_device
+            self.is_user_annotation = other.is_user_annotation
+
+        assert isinstance(other, (FunctionEvent, FunctionEventAvg))
+        assert other.key == self.key
+        self.cpu_time_total += other.cpu_time_total
+        self.device_time_total += other.device_time_total
+        self.self_cpu_time_total += other.self_cpu_time_total
+        self.self_device_time_total += other.self_device_time_total
+        self.cpu_memory_usage += other.cpu_memory_usage
+        self.device_memory_usage += other.device_memory_usage
+        self.self_cpu_memory_usage += other.self_cpu_memory_usage
+        self.self_device_memory_usage += other.self_device_memory_usage
+        self.count += other.count
+        if self.flops is None:
+            self.flops = other.flops
+        elif other.flops is not None:
+            self.flops += other.flops
+        return self
+
+    def __iadd__(self, other):
+        return self.add(other)
+
+    def __repr__(self):
+        device_name = "cuda" if not self.use_device else self.use_device
+        self_device_time = self.self_device_time_total_str
+        device_time = self.device_time_str
+        device_memory = self.device_memory_usage
+        return (
+            f""
+        )
+
+
+class StringTable(defaultdict):
+    def __missing__(self, key):
+        # manage cases like 't' (demangled to 'unsigned short') separately,
+        # for now simply check the length to avoid unexpected results for
+        # the short sequences
+        self[key] = torch._C._demangle(key) if len(key) > 1 else key
+        return self[key]
+
+
+class MemRecordsAcc:
+    """Acceleration structure for accessing mem_records in interval."""
+
+    def __init__(self, mem_records):
+        self._mem_records = mem_records
+        self._start_nses: List[int] = []
+        self._indices: List[int] = []
+        if len(mem_records) > 0:
+            tmp = sorted([(r[0].start_ns(), i) for i, r in enumerate(mem_records)])
+            self._start_nses, self._indices = zip(*tmp)  # type: ignore[assignment]
+
+    def in_interval(self, start_us, end_us):
+        r"""
+        Return all records in the given interval
+        To maintain backward compatibility, convert us to ns in function
+        """
+        start_idx = bisect.bisect_left(self._start_nses, start_us * 1000)
+        end_idx = bisect.bisect_right(self._start_nses, end_us * 1000)
+        for i in range(start_idx, end_idx):
+            yield self._mem_records[self._indices[i]]
+
+
+def _filter_stack_entry(entry):
+    filtered_entries = [
+        ("autograd/__init__", "_make_grads"),
+        ("autograd/__init__", "backward"),
+        ("torch/tensor", "backward"),
+        ("_internal/common_utils", "prof_callable"),
+        ("_internal/common_utils", "prof_func_call"),
+        ("_internal/common_utils", "prof_meth_call"),
+    ]
+    return all(not (f[0] in entry and f[1] in entry) for f in filtered_entries)
+
+
+MEMORY_EVENT_NAME = "[memory]"
+OUT_OF_MEMORY_EVENT_NAME = "[OutOfMemory]"
+
+
+def _filter_name(name):
+    # ignoring the following utility ops
+    filtered_out_names = [
+        MEMORY_EVENT_NAME,  # used only for the top-level memory events
+        OUT_OF_MEMORY_EVENT_NAME,
+        "profiler::_record_function_enter",
+        "profiler::_record_function_enter_new",
+        "profiler::_record_function_exit",
+        "aten::is_leaf",
+        "aten::output_nr",
+        "aten::_version",
+    ]
+    return name in filtered_out_names
+
+
+# Demangles and optionally rewrites the provided event name,
+# with_wildcard - whether to replace certain numbered event names
+# with a wildcard name to aggregate them together in the profiler table
+# output
+def _rewrite_name(name, with_wildcard=False):
+    string_table = StringTable()
+    name = string_table[name]
+    if with_wildcard:
+        if name.startswith("ProfilerStep#"):
+            name = "ProfilerStep*"
+    return name
+
+
+def _build_table(
+    events,
+    sort_by=None,
+    header=None,
+    row_limit=100,
+    max_src_column_width=75,
+    max_name_column_width=55,
+    max_shapes_column_width=80,
+    with_flops=False,
+    profile_memory=False,
+    top_level_events_only=False,
+):
+    """Print a summary of events (which can be a list of FunctionEvent or FunctionEventAvg)."""
+    if len(events) == 0:
+        return ""
+
+    has_device_time = any(event.self_device_time_total > 0 for event in events)
+    has_device_mem = any(event.self_device_memory_usage > 0 for event in events)
+    use_device = events[0].use_device
+    # Running on PrivateUse1 device with profiler but not enable
+    # ProfilerActivity.PrivateUse1 can also catch privateuse1 memory usage.
+    # Here only need to check has_privateuse1_time if not use_device.
+    if not use_device and has_device_time:
+        raise RuntimeError("use_device is None, but there is device performance data.")
+
+    has_input_shapes = any(
+        (event.input_shapes is not None and len(event.input_shapes) > 0)
+        for event in events
+    )
+
+    if sort_by is not None:
+        events = EventList(
+            sorted(
+                events,
+                key=lambda evt: getattr(
+                    evt,
+                    sort_by.replace("cuda", "device")
+                    .replace("xpu", "device")
+                    .replace("privateuse1", "device"),
+                ),
+                reverse=True,
+            ),
+            use_device=use_device,
+            profile_memory=profile_memory,
+            with_flops=with_flops,
+        )
+
+    name_column_width = max(len(evt.key) for evt in events) + 4
+    if max_name_column_width is not None:
+        name_column_width = min(name_column_width, max_name_column_width)
+
+    shapes_column_width = max(len(str(evt.input_shapes)) for evt in events) + 4
+    if max_shapes_column_width is not None:
+        shapes_column_width = min(shapes_column_width, max_shapes_column_width)
+
+    DEFAULT_COLUMN_WIDTH = 12
+    flops_column_width = DEFAULT_COLUMN_WIDTH
+
+    src_column_width = None
+    stacks = []
+    for evt in events:
+        if evt.stack is not None and len(evt.stack) > 0:
+            stacks.append(evt.stack)
+    has_stack = len(stacks) > 0
+    if has_stack:
+        src_column_width = (
+            max(max(len(entry) for entry in stack) for stack in stacks) + 4
+        )
+        if max_src_column_width is not None:
+            src_column_width = min(src_column_width, max_src_column_width)
+
+    headers = [
+        "Name",
+        "Self CPU %",
+        "Self CPU",
+        "CPU total %",
+        "CPU total",
+        "CPU time avg",
+    ]
+    device_name = use_device.upper() if use_device is not None else "None"
+    if has_device_time:
+        headers.extend(
+            [
+                f"Self {device_name}",
+                f"Self {device_name} %",
+                f"{device_name} total",
+                f"{device_name} time avg",
+            ]
+        )
+    if profile_memory:
+        headers.extend(
+            [
+                "CPU Mem",
+                "Self CPU Mem",
+            ]
+        )
+        if use_device and has_device_mem:
+            headers.extend(
+                [
+                    f"{device_name} Mem",
+                    f"Self {device_name} Mem",
+                ]
+            )
+    headers.append("# of Calls")
+    # Only append Node ID if any event has a valid (>= 0) Node ID
+    append_node_id = any(evt.node_id != -1 for evt in events)
+    if append_node_id:
+        headers.append("Node ID")
+
+    # Have to use a list because nonlocal is Py3 only...
+    SPACING_SIZE = 2
+    row_format_lst = [""]
+    header_sep_lst = [""]
+    line_length_lst = [-SPACING_SIZE]
+
+    def add_column(padding, text_dir=">"):
+        row_format_lst[0] += (
+            "{: " + text_dir + str(padding) + "}" + (" " * SPACING_SIZE)
+        )
+        header_sep_lst[0] += "-" * padding + (" " * SPACING_SIZE)
+        line_length_lst[0] += padding + SPACING_SIZE
+
+    def auto_scale_flops(flops):
+        flop_headers = [
+            "FLOPs",
+            "KFLOPs",
+            "MFLOPs",
+            "GFLOPs",
+            "TFLOPs",
+            "PFLOPs",
+        ]
+        assert flops > 0
+        log_flops = max(0, min(math.log10(flops) / 3, float(len(flop_headers) - 1)))
+        assert log_flops >= 0 and log_flops < len(flop_headers)
+        return (pow(10, (math.floor(log_flops) * -3.0)), flop_headers[int(log_flops)])
+
+    add_column(name_column_width)
+    for _ in headers[1:]:
+        add_column(DEFAULT_COLUMN_WIDTH)
+
+    if has_input_shapes:
+        headers.append("Input Shapes")
+        add_column(shapes_column_width)
+
+    if has_stack:
+        headers.append("Source Location")
+        add_column(src_column_width, text_dir="<")
+
+    if with_flops:
+        # Auto-scaling of flops header
+        raw_flops = []
+        for evt in events:
+            if evt.flops > 0:
+                raw_flops.append(evt.flops)
+        if len(raw_flops) != 0:
+            (flops_scale, flops_header) = auto_scale_flops(min(raw_flops))
+            headers.append(f"Total {flops_header}")
+            add_column(flops_column_width)
+        else:
+            with_flops = False  # can't find any valid flops
+
+    row_format = row_format_lst[0]
+    header_sep = header_sep_lst[0]
+    line_length = line_length_lst[0]
+    add_column = None  # type: ignore[assignment]
+
+    # Have to use a list because nonlocal is Py3 only...
+    result = []
+
+    def append(s):
+        result.append(s)
+        result.append("\n")  # Yes, newline after the end as well
+
+    sum_self_cpu_time_total = 0
+    sum_self_device_time_total = 0
+    for evt in events:
+        sum_self_cpu_time_total += evt.self_cpu_time_total
+        if evt.device_type == DeviceType.CPU and evt.is_legacy:
+            # in legacy profiler, kernel info is stored in cpu events
+            sum_self_device_time_total += evt.self_device_time_total
+        elif (
+            evt.device_type
+            in [
+                DeviceType.CUDA,
+                DeviceType.PrivateUse1,
+                DeviceType.MTIA,
+            ]
+            and not evt.is_user_annotation
+        ):
+            # in kineto profiler, there're events with the correct device type (e.g. CUDA)
+            sum_self_device_time_total += evt.self_device_time_total
+
+    # Actual printing
+    if header is not None:
+        append("=" * line_length)
+        append(header)
+    if top_level_events_only:
+        append("=" * line_length)
+        append("This report only display top-level ops statistics")
+    append(header_sep)
+    append(row_format.format(*headers))
+
+    append(header_sep)
+
+    def trim_path(path, src_column_width):
+        if len(path) > src_column_width:
+            offset = len(path) - src_column_width
+            path = path[offset:]
+            if len(path) > 3:
+                path = "..." + path[3:]
+        return path
+
+    event_limit = 0
+    for evt in events:
+        if event_limit == row_limit:
+            break
+        if top_level_events_only and evt.cpu_parent is not None:
+            continue
+        else:
+            event_limit += 1
+        name = evt.key
+        if max_name_column_width is not None and len(name) >= max_name_column_width - 3:
+            name = name[: (max_name_column_width - 3)] + "..."
+        evt.self_cpu_percent = _format_time_share(
+            evt.self_cpu_time_total, sum_self_cpu_time_total
+        )
+        evt.total_cpu_percent = (
+            _format_time_share(evt.cpu_time_total, sum_self_cpu_time_total)
+            if not evt.is_async
+            else 0
+        )
+        row_values = [
+            name,
+            # Self CPU total %, 0 for async events.
+            evt.self_cpu_percent,
+            evt.self_cpu_time_total_str,  # Self CPU total
+            # CPU total %, 0 for async events.
+            evt.total_cpu_percent,
+            evt.cpu_time_total_str,  # CPU total
+            evt.cpu_time_str,  # CPU time avg
+        ]
+        if has_device_time:
+            evt.total_device_percent = _format_time_share(
+                evt.self_device_time_total, sum_self_device_time_total
+            )
+            row_values.extend(
+                [
+                    evt.self_device_time_total_str,
+                    # device time total %
+                    evt.total_device_percent,
+                    evt.device_time_total_str,
+                    evt.device_time_str,  # device time avg
+                ]
+            )
+        if profile_memory:
+            row_values.extend(
+                [
+                    # CPU Mem Total
+                    _format_memory(evt.cpu_memory_usage),
+                    # Self CPU Mem Total
+                    _format_memory(evt.self_cpu_memory_usage),
+                ]
+            )
+            if use_device and has_device_mem:
+                row_values.extend(
+                    [
+                        # Device Mem Total
+                        _format_memory(evt.device_memory_usage),
+                        # Self Device Mem Total
+                        _format_memory(evt.self_device_memory_usage),
+                    ]
+                )
+        row_values.append(
+            evt.count,  # Number of calls
+        )
+
+        if append_node_id:
+            row_values.append(evt.node_id)
+        if has_input_shapes:
+            row_values.append(str(evt.input_shapes)[:shapes_column_width])
+        if with_flops:
+            if evt.flops <= 0:
+                row_values.append("--")
+            else:
+                row_values.append(f"{evt.flops * flops_scale:8.3f}")  # type: ignore[possibly-undefined]
+        if has_stack:
+            src_field = ""
+            if len(evt.stack) > 0:
+                src_field = trim_path(evt.stack[0], src_column_width)
+            row_values.append(src_field)
+        append(row_format.format(*row_values))
+
+        if has_stack:
+            empty_headers = [""] * (len(headers) - 1)
+            for entry in evt.stack[1:]:
+                append(
+                    row_format.format(
+                        *(empty_headers + [trim_path(entry, src_column_width)])
+                    )
+                )
+            empty_headers.append("")
+            append(row_format.format(*empty_headers))
+
+    append(header_sep)
+    append(f"Self CPU time total: {_format_time(sum_self_cpu_time_total)}")
+    if has_device_time:
+        append(
+            f"Self {use_device.upper() if use_device is not None else 'None'} "
+            f"time total: {_format_time(sum_self_device_time_total)}"
+        )
+    return "".join(result)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/variable.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/variable.py
new file mode 100644
index 0000000000000000000000000000000000000000..84b504a9c82c7ab855df9ba58d934fa92d936253
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/autograd/variable.py
@@ -0,0 +1,15 @@
+# mypy: allow-untyped-defs
+import torch
+from torch._C import _ImperativeEngine as ImperativeEngine
+
+
+__all__ = ["VariableMeta", "Variable"]
+
+
+class VariableMeta(type):
+    def __instancecheck__(cls, other):
+        return isinstance(other, torch.Tensor)
+
+
+class Variable(torch._C._LegacyVariableBase, metaclass=VariableMeta):  # type: ignore[misc]
+    _execution_engine = ImperativeEngine()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3017351888698ec23062348212b0d1884b4f5734
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/__init__.py
@@ -0,0 +1,72 @@
+# mypy: allow-untyped-defs
+import types
+from contextlib import contextmanager
+
+
+# The idea for this parameter is that we forbid bare assignment
+# to torch.backends..enabled and friends when running our
+# test suite, where it's very easy to forget to undo the change
+# later.
+__allow_nonbracketed_mutation_flag = True
+
+
+def disable_global_flags():
+    global __allow_nonbracketed_mutation_flag
+    __allow_nonbracketed_mutation_flag = False
+
+
+def flags_frozen():
+    return not __allow_nonbracketed_mutation_flag
+
+
+@contextmanager
+def __allow_nonbracketed_mutation():
+    global __allow_nonbracketed_mutation_flag
+    old = __allow_nonbracketed_mutation_flag
+    __allow_nonbracketed_mutation_flag = True
+    try:
+        yield
+    finally:
+        __allow_nonbracketed_mutation_flag = old
+
+
+class ContextProp:
+    def __init__(self, getter, setter):
+        self.getter = getter
+        self.setter = setter
+
+    def __get__(self, obj, objtype):
+        return self.getter()
+
+    def __set__(self, obj, val):
+        if not flags_frozen():
+            self.setter(val)
+        else:
+            raise RuntimeError(
+                f"not allowed to set {obj.__name__} flags "
+                "after disable_global_flags; please use flags() context manager instead"
+            )
+
+
+class PropModule(types.ModuleType):
+    def __init__(self, m, name):
+        super().__init__(name)
+        self.m = m
+
+    def __getattr__(self, attr):
+        return self.m.__getattribute__(attr)
+
+
+from torch.backends import (
+    cpu as cpu,
+    cuda as cuda,
+    cudnn as cudnn,
+    cusparselt as cusparselt,
+    mha as mha,
+    mkl as mkl,
+    mkldnn as mkldnn,
+    mps as mps,
+    nnpack as nnpack,
+    openmp as openmp,
+    quantized as quantized,
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/_coreml/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/_coreml/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/_coreml/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/_coreml/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/_coreml/__pycache__/preprocess.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/_coreml/__pycache__/preprocess.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/_coreml/preprocess.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/_coreml/preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f098bddf27e1728f41360299c4ad9ffebdbd052
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/_coreml/preprocess.py
@@ -0,0 +1,148 @@
+# mypy: allow-untyped-defs
+import hashlib
+import json
+from typing import Dict, Tuple
+
+import coremltools as ct  # type: ignore[import]
+from coremltools.converters.mil.input_types import TensorType  # type: ignore[import]
+from coremltools.converters.mil.mil import types  # type: ignore[import]
+from coremltools.models.neural_network import quantization_utils  # type: ignore[import]
+
+import torch
+
+
+CT_METADATA_VERSION = "com.github.apple.coremltools.version"
+CT_METADATA_SOURCE = "com.github.apple.coremltools.source"
+
+
+class ScalarType:
+    Float = 0
+    Double = 1
+    Int = 2
+    Long = 3
+    Undefined = 4
+
+
+# Supported Tensor types in coremltools:
+# https://github.com/apple/coremltools/blob/main/coremltools/converters/mil/frontend/torch/converter.py#L28
+torch_to_mil_types = {
+    ScalarType.Float: types.fp32,
+    ScalarType.Double: types.fp64,
+    ScalarType.Int: types.int32,
+    ScalarType.Long: types.int64,
+}
+
+
+class CoreMLComputeUnit:
+    CPU = "cpuOnly"
+    CPUAndGPU = "cpuAndGPU"
+    ALL = "all"
+
+
+class CoreMLQuantizationMode:
+    LINEAR = "linear"
+    LINEAR_SYMMETRIC = "linear_symmetric"
+    NONE = "none"
+
+
+def TensorSpec(shape, dtype=ScalarType.Float):
+    return (shape, dtype)
+
+
+def CompileSpec(
+    inputs,
+    outputs,
+    backend=CoreMLComputeUnit.CPU,
+    allow_low_precision=True,
+    quantization_mode=CoreMLQuantizationMode.NONE,
+    mlmodel_export_path=None,
+):
+    return (
+        inputs,
+        outputs,
+        backend,
+        allow_low_precision,
+        quantization_mode,
+        mlmodel_export_path,
+    )
+
+
+def _check_enumerated_shape(shape):
+    for s in shape:
+        if not isinstance(s, (list, tuple)):
+            return False
+    return True
+
+
+def _convert_to_mil_type(shape, dtype, name: str):
+    mil_shape = shape
+    if _check_enumerated_shape(shape):
+        mil_shape = ct.EnumeratedShapes(shape)
+    ml_type = TensorType(shape=mil_shape, dtype=torch_to_mil_types[dtype])
+    ml_type.name = name
+    return ml_type
+
+
+def preprocess(script_module: torch._C.ScriptObject, compile_spec: Dict[str, Tuple]):
+    spec = compile_spec["forward"]
+    (
+        input_specs,
+        output_specs,
+        backend,
+        allow_low_precision,
+        quantization_mode,
+        mlmodel_export_path,
+    ) = spec
+    mil_inputs = []
+    inputs = []
+    for index, input in enumerate(input_specs):
+        shape, dtype = input
+        name = "input_" + str(index)
+        inputs.append([name, str(dtype), str(shape)])
+        ml_type = _convert_to_mil_type(shape, dtype, name)
+        mil_inputs.append(ml_type)
+    model = torch.jit.RecursiveScriptModule._construct(script_module, lambda x: None)
+    mlmodel = ct.convert(model, inputs=mil_inputs)
+
+    if quantization_mode != CoreMLQuantizationMode.NONE:
+        quant_model_spec = quantization_utils.quantize_weights(
+            mlmodel, nbits=8, quantization_mode=quantization_mode
+        )
+        mlmodel = ct.models.MLModel(quant_model_spec)
+
+    spec = mlmodel.get_spec()
+    assert len(spec.description.output) == len(output_specs)  # type: ignore[attr-defined]
+    outputs = []
+    for index, output in enumerate(output_specs):
+        shape, dtype = output
+        name = spec.description.output[index].name  # type: ignore[attr-defined]
+        outputs.append([name, str(dtype), str(shape)])
+    mlmodel = ct.models.model.MLModel(spec)
+    print(mlmodel)
+
+    if mlmodel_export_path is not None:
+        print(f"Saving CoreML .mlmodel file to {mlmodel_export_path}")
+        mlmodel.save(mlmodel_export_path)
+
+    config = {
+        "spec_ver": str(spec.specificationVersion),  # type: ignore[attr-defined]
+        "backend": backend,
+        "allow_low_precision": str(allow_low_precision),
+    }
+    metadata = {
+        "coremltool_ver": mlmodel.user_defined_metadata[CT_METADATA_VERSION],
+        "torch_ver": mlmodel.user_defined_metadata[CT_METADATA_SOURCE],
+    }
+    coreml_compile_spec = {
+        "inputs": inputs,
+        "outputs": outputs,
+        "config": config,
+        "metadata": metadata,
+    }
+    mlmodel = spec.SerializeToString()  # type: ignore[attr-defined]
+
+    return {
+        "model": mlmodel,
+        "hash": str(hashlib.sha256(mlmodel).hexdigest()),
+        "extra": json.dumps(coreml_compile_spec),
+    }
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/_nnapi/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/_nnapi/__init__.py
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index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/_nnapi/__pycache__/prepare.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/_nnapi/__pycache__/prepare.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..538ffaa83c084227a07ded502b31045c6bcef476
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/_nnapi/prepare.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/_nnapi/prepare.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c04217b7d3cdde9f3f9b62ec1c2ff4f367cfdcd
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/_nnapi/prepare.py
@@ -0,0 +1,199 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+from typing import List, Optional
+
+import torch
+from torch.backends._nnapi.serializer import _NnapiSerializer
+
+
+ANEURALNETWORKS_PREFER_LOW_POWER = 0
+ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER = 1
+ANEURALNETWORKS_PREFER_SUSTAINED_SPEED = 2
+
+
+class NnapiModule(torch.nn.Module):
+    """Torch Module that wraps an NNAPI Compilation.
+
+    This module handles preparing the weights, initializing the
+    NNAPI TorchBind object, and adjusting the memory formats
+    of all inputs and outputs.
+    """
+
+    # _nnapi.Compilation is defined
+    comp: Optional[torch.classes._nnapi.Compilation]  # type: ignore[name-defined]
+    weights: List[torch.Tensor]
+    out_templates: List[torch.Tensor]
+
+    def __init__(
+        self,
+        shape_compute_module: torch.nn.Module,
+        ser_model: torch.Tensor,
+        weights: List[torch.Tensor],
+        inp_mem_fmts: List[int],
+        out_mem_fmts: List[int],
+        compilation_preference: int,
+        relax_f32_to_f16: bool,
+    ):
+        super().__init__()
+        self.shape_compute_module = shape_compute_module
+        self.ser_model = ser_model
+        self.weights = weights
+        self.inp_mem_fmts = inp_mem_fmts
+        self.out_mem_fmts = out_mem_fmts
+        self.out_templates = []
+        self.comp = None
+        self.compilation_preference = compilation_preference
+        self.relax_f32_to_f16 = relax_f32_to_f16
+
+    @torch.jit.export
+    def init(self, args: List[torch.Tensor]):
+        assert self.comp is None
+        self.out_templates = self.shape_compute_module.prepare(self.ser_model, args)  # type: ignore[operator]
+        self.weights = [w.contiguous() for w in self.weights]
+        comp = torch.classes._nnapi.Compilation()
+        comp.init2(
+            self.ser_model,
+            self.weights,
+            self.compilation_preference,
+            self.relax_f32_to_f16,
+        )
+
+        self.comp = comp
+
+    def forward(self, args: List[torch.Tensor]) -> List[torch.Tensor]:
+        if self.comp is None:
+            self.init(args)
+        comp = self.comp
+        assert comp is not None
+        outs = [torch.empty_like(out) for out in self.out_templates]
+
+        assert len(args) == len(self.inp_mem_fmts)
+        fixed_args = []
+        for idx in range(len(args)):
+            fmt = self.inp_mem_fmts[idx]
+            # These constants match the values in DimOrder in serializer.py
+            # TODO: See if it's possible to use those directly.
+            if fmt == 0:
+                fixed_args.append(args[idx].contiguous())
+            elif fmt == 1:
+                fixed_args.append(args[idx].permute(0, 2, 3, 1).contiguous())
+            else:
+                raise ValueError("Invalid mem_fmt")
+        comp.run(fixed_args, outs)
+        assert len(outs) == len(self.out_mem_fmts)
+        for idx in range(len(self.out_templates)):
+            fmt = self.out_mem_fmts[idx]
+            # These constants match the values in DimOrder in serializer.py
+            # TODO: See if it's possible to use those directly.
+            if fmt in (0, 2):
+                pass
+            elif fmt == 1:
+                outs[idx] = outs[idx].permute(0, 3, 1, 2)
+            else:
+                raise ValueError("Invalid mem_fmt")
+        return outs
+
+
+def convert_model_to_nnapi(
+    model,
+    inputs,
+    serializer=None,
+    return_shapes=None,
+    use_int16_for_qint16=False,
+    compilation_preference=ANEURALNETWORKS_PREFER_SUSTAINED_SPEED,
+    relax_f32_to_f16=False,
+):
+    (
+        shape_compute_module,
+        ser_model_tensor,
+        used_weights,
+        inp_mem_fmts,
+        out_mem_fmts,
+        retval_count,
+    ) = process_for_nnapi(
+        model, inputs, serializer, return_shapes, use_int16_for_qint16
+    )
+
+    nnapi_model = NnapiModule(
+        shape_compute_module,
+        ser_model_tensor,
+        used_weights,
+        inp_mem_fmts,
+        out_mem_fmts,
+        compilation_preference,
+        relax_f32_to_f16,
+    )
+
+    class NnapiInterfaceWrapper(torch.nn.Module):
+        """NNAPI list-ifying and de-list-ifying wrapper.
+
+        NNAPI always expects a list of inputs and provides a list of outputs.
+        This module allows us to accept inputs as separate arguments.
+        It returns results as either a single tensor or tuple,
+        matching the original module.
+        """
+
+        def __init__(self, mod):
+            super().__init__()
+            self.mod = mod
+
+    wrapper_model_py = NnapiInterfaceWrapper(nnapi_model)
+    wrapper_model = torch.jit.script(wrapper_model_py)
+    # TODO: Maybe make these names match the original.
+    arg_list = ", ".join(f"arg_{idx}" for idx in range(len(inputs)))
+    if retval_count < 0:
+        ret_expr = "retvals[0]"
+    else:
+        ret_expr = "".join(f"retvals[{idx}], " for idx in range(retval_count))
+    wrapper_model.define(
+        f"def forward(self, {arg_list}):\n"
+        f"    retvals = self.mod([{arg_list}])\n"
+        f"    return {ret_expr}\n"
+    )
+    return wrapper_model
+
+
+def process_for_nnapi(
+    model, inputs, serializer=None, return_shapes=None, use_int16_for_qint16=False
+):
+    model = torch.jit.freeze(model)
+
+    if isinstance(inputs, torch.Tensor):
+        inputs = [inputs]
+
+    serializer = serializer or _NnapiSerializer(
+        config=None, use_int16_for_qint16=use_int16_for_qint16
+    )
+    (
+        ser_model,
+        used_weights,
+        inp_mem_fmts,
+        out_mem_fmts,
+        shape_compute_lines,
+        retval_count,
+    ) = serializer.serialize_model(model, inputs, return_shapes)
+    ser_model_tensor = torch.tensor(ser_model, dtype=torch.int32)
+
+    # We have to create a new class here every time this function is called
+    # because module.define adds a method to the *class*, not the instance.
+    class ShapeComputeModule(torch.nn.Module):
+        """Code-gen-ed module for tensor shape computation.
+
+        module.prepare will mutate ser_model according to the computed operand
+        shapes, based on the shapes of args.  Returns a list of output templates.
+        """
+
+    shape_compute_module = torch.jit.script(ShapeComputeModule())
+    real_shape_compute_lines = [
+        "def prepare(self, ser_model: torch.Tensor, args: List[torch.Tensor]) -> List[torch.Tensor]:\n",
+    ] + [f"    {line}\n" for line in shape_compute_lines]
+    shape_compute_module.define("".join(real_shape_compute_lines))
+
+    return (
+        shape_compute_module,
+        ser_model_tensor,
+        used_weights,
+        inp_mem_fmts,
+        out_mem_fmts,
+        retval_count,
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/_nnapi/serializer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/_nnapi/serializer.py
new file mode 100644
index 0000000000000000000000000000000000000000..34bcc42f89278c41050e1a05821005e8d7e53351
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/_nnapi/serializer.py
@@ -0,0 +1,2229 @@
+# mypy: allow-untyped-defs
+import array
+import enum
+import functools
+import logging
+import operator
+import struct
+import sys
+from typing import List, NamedTuple, Optional, Tuple
+
+import torch
+
+
+# TODO: Add type annotations
+# TODO: Check tensor types for ops
+
+
+LOG = logging.getLogger("nnapi_serialize")
+
+
+class NNAPI_OperandCode:
+    FLOAT32 = 0
+    INT32 = 1
+    UINT32 = 2
+    TENSOR_FLOAT32 = 3
+    TENSOR_INT32 = 4
+    TENSOR_QUANT8_ASYMM = 5
+    BOOL = 6
+    TENSOR_QUANT16_SYMM = 7
+    TENSOR_FLOAT16 = 8
+    TENSOR_BOOL8 = 9
+    FLOAT16 = 10
+    TENSOR_QUANT8_SYMM_PER_CHANNEL = 11
+    TENSOR_QUANT16_ASYMM = 12
+
+
+class NNAPI_OperationCode:
+    ADD = 0
+    AVERAGE_POOL_2D = 1
+    CONCATENATION = 2
+    CONV_2D = 3
+    DEPTHWISE_CONV_2D = 4
+    DEPTH_TO_SPACE = 5
+    DEQUANTIZE = 6
+    EMBEDDING_LOOKUP = 7
+    FLOOR = 8
+    FULLY_CONNECTED = 9
+    HASHTABLE_LOOKUP = 10
+    L2_NORMALIZATION = 11
+    L2_POOL_2D = 12
+    LOCAL_RESPONSE_NORMALIZATION = 13
+    LOGISTIC = 14
+    LSH_PROJECTION = 15
+    LSTM = 16
+    MAX_POOL_2D = 17
+    MUL = 18
+    RELU = 19
+    RELU1 = 20
+    RELU6 = 21
+    RESHAPE = 22
+    RESIZE_BILINEAR = 23
+    RNN = 24
+    SOFTMAX = 25
+    SPACE_TO_DEPTH = 26
+    SVDF = 27
+    TANH = 28
+    BATCH_TO_SPACE_ND = 29
+    DIV = 30
+    MEAN = 31
+    PAD = 32
+    SPACE_TO_BATCH_ND = 33
+    SQUEEZE = 34
+    STRIDED_SLICE = 35
+    SUB = 36
+    TRANSPOSE = 37
+    ABS = 38
+    ARGMAX = 39
+    ARGMIN = 40
+    AXIS_ALIGNED_BBOX_TRANSFORM = 41
+    BIDIRECTIONAL_SEQUENCE_LSTM = 42
+    BIDIRECTIONAL_SEQUENCE_RNN = 43
+    BOX_WITH_NMS_LIMIT = 44
+    CAST = 45
+    CHANNEL_SHUFFLE = 46
+    DETECTION_POSTPROCESSING = 47
+    EQUAL = 48
+    EXP = 49
+    EXPAND_DIMS = 50
+    GATHER = 51
+    GENERATE_PROPOSALS = 52
+    GREATER = 53
+    GREATER_EQUAL = 54
+    GROUPED_CONV_2D = 55
+    HEATMAP_MAX_KEYPOINT = 56
+    INSTANCE_NORMALIZATION = 57
+    LESS = 58
+    LESS_EQUAL = 59
+    LOG = 60
+    LOGICAL_AND = 61
+    LOGICAL_NOT = 62
+    LOGICAL_OR = 63
+    LOG_SOFTMAX = 64
+    MAXIMUM = 65
+    MINIMUM = 66
+    NEG = 67
+    NOT_EQUAL = 68
+    PAD_V2 = 69
+    POW = 70
+    PRELU = 71
+    QUANTIZE = 72
+    QUANTIZED_16BIT_LSTM = 73
+    RANDOM_MULTINOMIAL = 74
+    REDUCE_ALL = 75
+    REDUCE_ANY = 76
+    REDUCE_MAX = 77
+    REDUCE_MIN = 78
+    REDUCE_PROD = 79
+    REDUCE_SUM = 80
+    ROI_ALIGN = 81
+    ROI_POOLING = 82
+    RSQRT = 83
+    SELECT = 84
+    SIN = 85
+    SLICE = 86
+    SPLIT = 87
+    SQRT = 88
+    TILE = 89
+    TOPK_V2 = 90
+    TRANSPOSE_CONV_2D = 91
+    UNIDIRECTIONAL_SEQUENCE_LSTM = 92
+    UNIDIRECTIONAL_SEQUENCE_RNN = 93
+    RESIZE_NEAREST_NEIGHBOR = 94
+
+
+class NNAPI_FuseCode:
+    FUSED_NONE = 0
+    FUSED_RELU = 1
+    FUSED_RELU1 = 2
+    FUSED_RELU6 = 3
+
+
+class OperandValueSourceType:
+    IMMEDIATE = 0
+    NUMBERED_BUFFER = 2
+    NUMBERED_MEMORY = 3
+
+
+# Scalar types that appear explicitly in models.
+# These must be kept in sync with
+# AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS.
+# TODO: Expose these directly to Python to avoid maintaining this list.
+class TorchScalarTypes(enum.Enum):
+    QUINT8 = 13
+
+
+def approx_equal(lhs, rhs, tolerance=1e-6):
+    return abs(lhs - rhs) <= tolerance * min(lhs, rhs)
+
+
+def tensor_size(op_type, dims):
+    ITEM_SIZES = {
+        NNAPI_OperandCode.TENSOR_FLOAT32: 4,
+        NNAPI_OperandCode.TENSOR_INT32: 4,
+        NNAPI_OperandCode.TENSOR_QUANT8_ASYMM: 1,
+        NNAPI_OperandCode.TENSOR_QUANT16_SYMM: 2,
+        NNAPI_OperandCode.TENSOR_QUANT16_ASYMM: 2,
+    }
+    size = ITEM_SIZES[op_type]
+    for d in dims:
+        size *= d
+    return size
+
+
+def change_element(tup, index, value):
+    ls = list(tup)
+    ls[index] = value
+    return tuple(ls)
+
+
+class ConvPoolArgs2d(NamedTuple):
+    """Configuration arguments for a convolution."""
+
+    kernel_h: int
+    kernel_w: int
+    stride_h: int
+    stride_w: int
+    pad_t: int
+    pad_b: int
+    pad_l: int
+    pad_r: int
+    dilation_h: int
+    dilation_w: int
+    group: int
+
+
+class DimOrder(enum.Enum):
+    PRESUMED_CONTIGUOUS = 0
+    CHANNELS_LAST = 1
+    SCALAR_OR_VECTOR = 2
+    UNKNOWN_CONSTANT = 999
+
+
+class Operand(NamedTuple):
+    """Represenation of an NNAPI operand."""
+
+    # NNAPI operand type.  One of NNAPI_OperandCode.
+    # TODO: Make this an enum.
+    op_type: int
+
+    # This is always the PyTorch shape, which is NCHW for feature maps.
+    # The actual NNAPI operand might have a transposed shape.
+    # we use 0 for load time dynamic shapes & -1 for runtime dynamic shapes
+    shape: Tuple[int, ...]
+
+    # Specifies how the shape of the operand that we define in NNAPI
+    # relates to the shape we track above.
+    # - PRESUMED_CONTIGUOUS: physical NNAPI operand will exactly match
+    #   the shape of the PyTorch tensor.
+    # - CHANNELS_LAST: The PyTorch tensor is expected to be NCHW, and
+    #   the NNAPI operand will be represented explicitly as NHWC.
+    dim_order: DimOrder
+
+    # Quantization params
+    scale: float
+    zero_point: int
+
+    def use_nchw(self):
+        if self.dim_order is DimOrder.PRESUMED_CONTIGUOUS:
+            return True
+        if self.dim_order is DimOrder.CHANNELS_LAST:
+            return False
+        raise Exception("Unknown dim order")  # noqa: TRY002
+
+
+def broadcast_shapes(shape1, shape2):
+    assert len(shape1) > 0
+    assert len(shape2) > 0
+    s1 = list(shape1)
+    s2 = list(shape2)
+    # TODO: Support non-equal-rank broadcast where semantics match.
+    # This can be tricky for NHWC tensors because dimension orders
+    # don't match between PT and NNAPI, even though semantics match.
+    if len(s1) > len(s2):
+        # s2 = [1] * (len(s1) - len(s2)) + s2
+        raise Exception(  # noqa: TRY002
+            "Non-equal-rank broadcast is not supported yet."
+        )  # noqa: TRY002
+    if len(s2) > len(s1):
+        # s3 = [1] * (len(s2) - len(s1)) + s1
+        raise Exception(  # noqa: TRY002
+            "Non-equal-rank broadcast is not supported yet."
+        )  # noqa: TRY002
+    ret = []
+    for d1, d2 in zip(s1, s2):
+        if d1 == 1:
+            ret.append(d2)
+        elif d2 == 1:
+            ret.append(d1)
+        elif d1 == d2:
+            ret.append(d1)
+        else:
+            raise Exception(  # noqa: TRY002
+                f"Cannot broadcast shapes: {shape1} and {shape2}"
+            )  # noqa: TRY002
+    return tuple(ret)
+
+
+def get_conv_pool_shape(image_shape, args, out_ch, transpose):
+    batch, in_c, in_h, in_w = image_shape
+
+    # TODO: Handle dilation
+    if args.dilation_h != 1 or args.dilation_w != 1:
+        raise Exception("Dilation not supported yet.")  # noqa: TRY002
+
+    if transpose:
+        out_h = (in_h - 1) * args.stride_h + args.kernel_h - args.pad_t - args.pad_b
+        out_w = (in_w - 1) * args.stride_w + args.kernel_w - args.pad_l - args.pad_l
+    else:
+        out_h = (in_h - args.kernel_h + args.pad_t + args.pad_b) // args.stride_h + 1
+        out_w = (in_w - args.kernel_w + args.pad_l + args.pad_r) // args.stride_w + 1
+
+    # Handle variable-sized tensors.
+    if in_h == 0:
+        out_h = 0
+    if in_w == 0:
+        out_w = 0
+
+    out_shape = (batch, out_ch, out_h, out_w)
+    return out_shape
+
+
+def fix_shape(shape, dim_order):
+    # Return the actual shape that an operand should have in NNAPI,
+    # given a PyTorch shape and dimension order.  This is where we
+    # convert from PyTorch's "always NCHW" shape to explicit NHWC.
+    if dim_order is DimOrder.PRESUMED_CONTIGUOUS:
+        return shape
+    if dim_order is DimOrder.CHANNELS_LAST:
+        return tuple([shape[0]] + list(shape[2:]) + [shape[1]])
+    if dim_order is DimOrder.SCALAR_OR_VECTOR:
+        assert len(shape) == 0 or len(shape) == 1
+        return shape
+    if dim_order is DimOrder.UNKNOWN_CONSTANT:
+        # XXX think this through
+        return shape
+    raise Exception(f"Bad dim_order: {dim_order!r}.")  # noqa: TRY002
+
+
+def reverse_map_dim(dim_order, d):
+    # Return the original PyTorch dimension position for a given dimension.
+    # d should be the dimension that NNAPI will see.
+    # reverse_map_dim(PRESUMED_CONTIGUOUS, x) == x
+    # reverse_map_dim(CHANNELS_LAST, 3) == 1
+    if dim_order in (DimOrder.PRESUMED_CONTIGUOUS, DimOrder.SCALAR_OR_VECTOR):
+        return d
+    assert dim_order is DimOrder.CHANNELS_LAST
+    return [0, 2, 3, 1][d]
+
+
+def flex_name(op_id, dim):
+    # Return the local variable name for the computed flexible size
+    # for a given op and dimension.
+    return f"s_{op_id}_{dim}"
+
+
+class _NnapiSerializer:
+    def __init__(self, config, use_int16_for_qint16=False):
+        self.operands = []
+        self.values = []
+        self.operations = []
+        self.value_data = []
+        self.operation_args = []
+        self.inputs = []
+        self.outputs = []
+        self.flexible_shape_computation_lines = []
+
+        self.modules = {}
+        self.constants = {}
+        self.tensor_sequences = {}
+        self.jitval_operand_map = {}
+        self.cached_immediates = {}
+        self.used_weights = []
+        self.weight_offset = 0
+        self.use_int16_for_qint16 = use_int16_for_qint16
+
+        if config is None:
+            config = {}
+
+    def get_next_operand_id(self):
+        return len(self.operands)
+
+    # Add a tensor operand corresponding to a JIT Value.
+    # Returns the NNAPI operand ID.  Can be looked up later with
+    # get_tensor_operand_by_jitval.
+    def add_tensor_operand(self, jitval, oper):
+        assert isinstance(oper, Operand)
+        if jitval in self.jitval_operand_map:
+            raise Exception(f"Duplicate tensor: {jitval!r}")  # noqa: TRY002
+
+        operand_id = self.get_next_operand_id()
+        self.operands.append(oper)
+        self.jitval_operand_map[jitval] = operand_id
+        return operand_id
+
+    # Add a tensor operand that does not correspond to a JIT Value.
+    # Useful for cases where multiple NNAPI operands are required
+    # to implement one JIT IR node.  Returns the NNAPI operand ID.
+    def add_anonymous_tensor_operand(self, oper):
+        assert isinstance(oper, Operand)
+        operand_id = self.get_next_operand_id()
+        self.operands.append(oper)
+        return operand_id
+
+    def torch_tensor_to_operand(self, tensor, dim_order):
+        dtype = str(tensor.dtype).replace("torch.", "")
+        scale = 0.0
+        zero_point = 0
+        if dtype == "float32":
+            op_type = NNAPI_OperandCode.TENSOR_FLOAT32
+        elif dtype == "int32":
+            op_type = NNAPI_OperandCode.TENSOR_INT32
+        elif dtype == "quint8":
+            op_type = NNAPI_OperandCode.TENSOR_QUANT8_ASYMM
+            scale = tensor.q_scale()
+            zero_point = tensor.q_zero_point()
+        elif dtype == "qint32":
+            op_type = NNAPI_OperandCode.TENSOR_INT32
+            scale = tensor.q_scale()
+            zero_point = tensor.q_zero_point()
+            assert zero_point == 0
+        elif dtype == "int16":
+            if self.use_int16_for_qint16:
+                nnapi_dtype = getattr(tensor, "nnapi_dtype", None)
+                op_codes = (
+                    NNAPI_OperandCode.TENSOR_QUANT16_SYMM,
+                    NNAPI_OperandCode.TENSOR_QUANT16_ASYMM,
+                )
+                if nnapi_dtype in op_codes:
+                    op_type = nnapi_dtype
+                    scale = tensor.nnapi_scale
+                    zero_point = tensor.nnapi_zero_point
+                else:
+                    raise Exception(  # noqa: TRY002
+                        f"`nnapi_type` needs to be one of {op_codes} for `int16`"
+                    )
+            else:
+                raise Exception(  # noqa: TRY002
+                    "`int16` isn't supported. If you're trying to represent NNAPI"
+                    " qint16 with Pytorch int16, set `use_int16_for_qint16 = True`"
+                )
+        else:
+            raise Exception(  # noqa: TRY002
+                f"Can't handle input with dtype '{tensor.dtype}'"
+            )  # noqa: TRY002
+        return Operand(
+            shape=tuple(tensor.shape),
+            op_type=op_type,
+            dim_order=dim_order,
+            scale=scale,
+            zero_point=zero_point,
+        )
+
+    def add_tensor_operand_for_input(self, arg_idx, jitval, tensor):
+        dim_order = (
+            DimOrder.CHANNELS_LAST
+            if getattr(tensor, "nnapi_nhwc", False)
+            else DimOrder.PRESUMED_CONTIGUOUS
+        )
+        toper = self.torch_tensor_to_operand(tensor, dim_order)
+        operand_id = self.add_tensor_operand(jitval, toper)
+        self.inputs.append(operand_id)
+        for dim, size in enumerate(tensor.shape):
+            if size == 0:
+                self.compute_operand_shape(
+                    operand_id, dim, f"args[{arg_idx}].shape[{dim}]"
+                )
+        return operand_id
+
+    def add_tensor_operand_for_weight(
+        self, tensor, dim_order=DimOrder.UNKNOWN_CONSTANT
+    ):
+        toper = self.torch_tensor_to_operand(tensor, dim_order)
+        operand_id = len(self.operands)
+        self.operands.append(toper)
+        tsize = tensor_size(toper.op_type, toper.shape)
+        psize = ((tsize - 1) | 0x3) + 1
+        self.values.append((operand_id, OperandValueSourceType.NUMBERED_BUFFER))
+        buf_num = len(self.used_weights)
+        offset = 0
+        self.value_data.append(struct.pack("iii", buf_num, offset, tsize))
+        # For NHWC NNAPI op, lay out data in the same dim order by permuting torch tensor
+        if dim_order == DimOrder.CHANNELS_LAST:
+            tensor = tensor.permute(0, 2, 3, 1)
+        self.used_weights.append(tensor)
+        return operand_id
+
+    def add_immediate_operand(self, code, value, dims):
+        assert isinstance(dims, tuple)
+        cache_key = (code, value)
+        if cache_key not in self.cached_immediates:
+            operand_id = len(self.operands)
+            self.operands.append(Operand(code, dims, DimOrder.SCALAR_OR_VECTOR, 0.0, 0))
+            self.values.append((operand_id, OperandValueSourceType.IMMEDIATE))
+            self.value_data.append(value)
+            self.cached_immediates[cache_key] = operand_id
+        return self.cached_immediates[cache_key]
+
+    def add_immediate_int_scalar(self, value):
+        return self.add_immediate_operand(
+            NNAPI_OperandCode.INT32, struct.pack("i", value), ()
+        )
+
+    def add_immediate_float_scalar(self, value):
+        return self.add_immediate_operand(
+            NNAPI_OperandCode.FLOAT32, struct.pack("f", value), ()
+        )
+
+    def add_immediate_bool_scalar(self, value):
+        return self.add_immediate_operand(
+            NNAPI_OperandCode.BOOL, b"\x01" if value else b"\x00", ()
+        )
+
+    def add_immediate_int_vector(self, value):
+        return self.add_immediate_operand(
+            NNAPI_OperandCode.TENSOR_INT32,
+            array.array("i", value).tobytes(),
+            (len(value),),
+        )
+
+    def has_operand_for_jitval(self, jitval):
+        return jitval in self.jitval_operand_map
+
+    def get_tensor_operand_by_jitval(self, jitval):
+        operand_id = self.jitval_operand_map[jitval]
+        return (operand_id, self.operands[operand_id])
+
+    def get_tensor_operand_by_jitval_fixed_size(self, jitval):
+        op_id, oper = self.get_tensor_operand_by_jitval(jitval)
+        for s in oper.shape:
+            if s == 0:
+                # TODO: Improve this error message, possibly after converting
+                # many callsites to support flexible size.
+                raise Exception(  # noqa: TRY002
+                    "Flexible size is not supported for this operand."
+                )  # noqa: TRY002
+            if s < 0:
+                # runtime flex
+                LOG.warning("Operand %s has runtime flex shape", oper)
+        return op_id, oper
+
+    def get_tensor_operand_or_constant(
+        self, jitval, dim_order=DimOrder.PRESUMED_CONTIGUOUS
+    ):
+        operand_id = self.jitval_operand_map.get(jitval)
+        if operand_id is None:
+            _, value = self.get_constant_value(jitval, "TensorType")
+            operand_id = self.add_tensor_operand_for_weight(value, dim_order)
+        return (operand_id, self.operands[operand_id])
+
+    def get_tensor_operand_for_weight(self, jitval):
+        _, value = self.get_constant_value(jitval, "TensorType")
+        operand_id = self.add_tensor_operand_for_weight(value)
+        return (operand_id, self.operands[operand_id])
+
+    def add_operation(self, opcode, inputs, outputs):
+        self.operations.append((opcode, len(inputs), len(outputs)))
+        self.operation_args.extend(inputs + outputs)
+
+    def add_tensor_sequence(self, jitval, values):
+        assert jitval not in self.tensor_sequences
+        self.tensor_sequences[jitval] = values
+
+    def add_constant_value(self, jitval, ctype, value):
+        assert jitval not in self.constants
+        self.constants[jitval] = (ctype, value)
+
+    def get_constant_value(self, jitval, typekind=None):
+        record = self.constants.get(jitval)
+        if record is None:
+            raise Exception(  # noqa: TRY002
+                f"Could not find constant value for '{jitval!r}'."
+            )  # noqa: TRY002
+        ctype, _ = record
+        if typekind is not None and ctype.kind() != typekind:
+            raise Exception(  # noqa: TRY002
+                f"Expected constant value of type {typekind}, but got {ctype.kind()} for value '{jitval!r}'"
+            )
+        return record
+
+    def operand_to_template_torchscript(self, op_id, oper, shape=None):
+        """Return a TorchScript expression to build a template for a given operand."""
+        if shape is None:
+            shape = oper.shape
+        else:
+            assert len(shape) == len(oper.shape)
+
+        shape_parts = ["("]
+        for d, s in enumerate(shape):
+            if s > 0:
+                # Fixed shape dimension: just add the value.
+                shape_parts.append(str(s))
+            elif s == 0:
+                # Load time flexible shape dimension: it should have been computed in a variable.
+                shape_parts.append(flex_name(op_id, d))
+            elif s == -1:
+                # Runtime flexible shape
+                shape_parts.append("0")
+            else:
+                raise Exception(  # noqa: TRY002
+                    "Unknown dim value, dimensions should be >= -1"
+                )  # noqa: TRY002
+            shape_parts.append(",")
+        shape_parts.append(")")
+        shape_code = "".join(shape_parts)
+        if oper.op_type == NNAPI_OperandCode.TENSOR_FLOAT32:
+            return f"torch.zeros({shape_code}, dtype=torch.float32)"
+        elif oper.op_type == NNAPI_OperandCode.TENSOR_INT32:
+            return f"torch.zeros({shape_code}, dtype=torch.int32)"
+        elif oper.op_type == NNAPI_OperandCode.TENSOR_QUANT8_ASYMM:
+            return (
+                f"torch.quantize_per_tensor("
+                f"torch.zeros(1), scale={oper.scale}, zero_point={oper.zero_point}, dtype=torch.quint8)"
+                f".expand({shape_code}).contiguous()"
+            )
+        elif oper.op_type in (
+            NNAPI_OperandCode.TENSOR_QUANT16_ASYMM,
+            NNAPI_OperandCode.TENSOR_QUANT16_SYMM,
+        ):
+            if self.use_int16_for_qint16:
+                return f"torch.zeros({shape_code}, dtype=torch.int16)"
+            else:
+                raise Exception(  # noqa: TRY002
+                    "`int16` isn't supported. If you're trying to represent NNAPI"
+                    " qint16 with Pytorch int16, set `use_int16_for_qint16 = True`"
+                )
+
+        raise Exception(  # noqa: TRY002
+            f"Unsupported output operand type: {oper.op_type}"
+        )  # noqa: TRY002
+
+    def forward_operand_shape(self, out_op_id, out_dim, in_op_id, in_dim):
+        self.compute_operand_shape(out_op_id, out_dim, flex_name(in_op_id, in_dim))
+
+    def compute_operand_shape(self, op_id, dim, expr):
+        self.flexible_shape_computation_lines.append(
+            f"{flex_name(op_id, dim)} = {expr}"
+        )
+
+    def transpose_to_nhwc(self, in_id, oper):
+        if oper.shape[2:] != (1, 1):
+            raise Exception(  # noqa: TRY002
+                "Automatic transpose only supported for H,W == 1,1"
+            )  # noqa: TRY002
+
+        out_oper = oper._replace(dim_order=DimOrder.CHANNELS_LAST)
+
+        inputs = [None] * 2
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_int_vector([0, 2, 3, 1])
+
+        outputs = [None] * 1
+        outputs[0] = self.add_anonymous_tensor_operand(out_oper)
+
+        self.add_operation(NNAPI_OperationCode.TRANSPOSE, inputs, outputs)
+
+        return outputs[0], out_oper
+
+    # Transpose inputs as necessary to allow broadcasting.
+    def transpose_for_broadcast(self, in0_id, in0_oper, in1_id, in1_oper):
+        if in0_oper.dim_order == in1_oper.dim_order:
+            return in0_id, in0_oper, in1_id, in1_oper
+
+        # Assume NHWC is preferred if there is a mismatch.
+        orders = (in0_oper.dim_order, in1_oper.dim_order)
+        if orders == (DimOrder.PRESUMED_CONTIGUOUS, DimOrder.CHANNELS_LAST):
+            return self.transpose_to_nhwc(in0_id, in0_oper) + (in1_id, in1_oper)
+        if orders == (DimOrder.CHANNELS_LAST, DimOrder.PRESUMED_CONTIGUOUS):
+            return (in0_id, in0_oper) + self.transpose_to_nhwc(in1_id, in1_oper)
+
+        raise Exception(  # noqa: TRY002
+            f"Automatic transpose not supported for dim_orders: {in0_oper.dim_order!r}, {in1_oper.dim_order!r}"
+        )
+
+    def get_size_arg(self, jitval):
+        ctype, value = self.get_constant_value(jitval)
+        if ctype.kind() == "ListType":
+            assert ctype.getElementType().kind() == "IntType"
+            return value
+        raise Exception(  # noqa: TRY002
+            f"Can't handle size arg of type '{ctype!r}' for '{jitval!r}'"
+        )  # noqa: TRY002
+
+    def get_conv_pool_args_2d_from_pack(self, kernel_size, packed_config):
+        pc = [i.item() for i in packed_config]
+        assert pc[0] == 2
+        strides = [pc[1], pc[2]]
+        paddings = [pc[3], pc[4]]
+        dilations = [pc[5], pc[6]]
+        output_padding = [pc[7], pc[8]]
+        group_num = pc[9]
+
+        assert len(pc) == 11
+        assert output_padding == [0, 0]
+
+        return self.get_conv_pool_args_2d_common(
+            kernel_size, strides, paddings, dilations, group_num
+        )
+
+    def get_conv_pool_args_2d_from_jit(
+        self, kernel_size, stride, padding, dilation=None, group=None
+    ):
+        strides = self.get_size_arg(stride)
+        paddings = self.get_size_arg(padding)
+        if dilation is None:
+            dilations = [1, 1]
+        else:
+            dilations = self.get_size_arg(dilation)
+        if group is not None:
+            _, group_num = self.get_constant_value(group, "IntType")
+        else:
+            group_num = None
+        return self.get_conv_pool_args_2d_common(
+            kernel_size, strides, paddings, dilations, group_num
+        )
+
+    def get_conv_pool_args_2d_common(
+        self, kernel_size, strides, paddings, dilations, group_num
+    ):
+        kernels = list(kernel_size)
+
+        assert len(kernels) == 2
+        assert len(strides) == 2
+        assert len(paddings) == 2
+        assert len(dilations) == 2
+
+        # NNAPI uses 4 values for padding.
+        ph, pw = paddings
+        real_paddings = [ph, ph, pw, pw]
+
+        return ConvPoolArgs2d(
+            *(kernels + strides + real_paddings + dilations + [group_num])
+        )
+
+    def serialize_model(self, model, inputs, return_shapes=None):
+        self.add_immediate_bool_scalar(False)
+        self.add_immediate_bool_scalar(True)
+
+        inp_dim_orders = []
+        out_dim_orders = []
+
+        self_jitval = next(model.graph.inputs())
+        self.add_constant_value(self_jitval, self_jitval.type(), model)
+
+        for arg_idx, (input_value, input_tensor) in enumerate(
+            zip(list(model.graph.inputs())[1:], inputs)
+        ):
+            op_id = self.add_tensor_operand_for_input(
+                arg_idx, input_value, input_tensor
+            )
+            inp_dim_orders.append(self.operands[op_id].dim_order.value)
+
+        for idx, node in enumerate(model.graph.nodes()):
+            LOG.debug("Processing node #%d: %r", idx, node)
+            self.add_node(node)
+
+        retn = model.graph.return_node()
+        assert retn.inputsSize() == 1
+        assert retn.outputsSize() == 0
+        retn_input = retn.inputsAt(0)
+        template_return_lines = ["return ["]
+        if retn_input.type().kind() == "TensorType":
+            return_values = [retn_input]
+            retval_count = -1
+        elif retn_input.type().kind() == "TupleType":
+            return_values = self.tensor_sequences[retn_input]
+            retval_count = len(return_values)
+        else:
+            raise Exception(  # noqa: TRY002
+                f"Unsupported return type: {retn_input.type()}"
+            )  # noqa: TRY002
+
+        if return_shapes is not None:
+            assert len(return_shapes) == len(return_values)
+        for i, v in enumerate(return_values):
+            op_id = self.jitval_operand_map[v]
+            self.outputs.append(op_id)
+            out_dim_orders.append(self.operands[op_id].dim_order.value)
+            shape = return_shapes[i] if return_shapes else None
+            template_return_lines.append(
+                self.operand_to_template_torchscript(op_id, self.operands[op_id], shape)
+                + ","
+            )
+        template_return_lines.append("]")
+
+        model = []
+
+        version = 1
+        header = struct.pack(
+            "iiiiii",
+            version,
+            len(self.operands),
+            len(self.values),
+            len(self.operations),
+            len(self.inputs),
+            len(self.outputs),
+        )
+        model.append(header)
+
+        serialized_values, serialized_value_data = self.serialize_values()
+
+        model.extend(
+            struct.pack("iifi", t, len(d), s, z) for (t, d, _m, s, z) in self.operands
+        )
+        model.extend(serialized_values)
+        model.extend(struct.pack("iii", *x) for x in self.operations)
+
+        # Compact the model so we can get its length so far.
+        model = [b"".join(model)]
+        model_offset = len(model[0])
+        # Model offset is the index into the model (in 32-bit words, not bytes)
+        # of the next dimension we're about to serialize.  If it's 0,
+        # generate code to mutate it before passing to NNAPI.
+        assert model_offset % 4 == 0
+        model_offset = int(model_offset / 4)
+
+        for op_id, (_, dims, dim_order, _, _) in enumerate(self.operands):
+            shape = fix_shape(dims, dim_order)
+            for d, s in enumerate(shape):
+                if s == 0:
+                    pt_d = reverse_map_dim(dim_order, d)
+                    self.flexible_shape_computation_lines.append(
+                        f"ser_model[{model_offset}] = {flex_name(op_id, pt_d)}"
+                    )
+                model_offset += 1
+
+            # convert runtime flex shape from -1 to 0
+            shape = tuple(d if d != -1 else 0 for d in shape)
+            model.append(self.serialize_ints(shape))
+
+        model.extend(serialized_value_data)
+        model.append(self.serialize_ints(self.operation_args))
+        model.append(self.serialize_ints(self.inputs))
+        model.append(self.serialize_ints(self.outputs))
+
+        self.flexible_shape_computation_lines.extend(template_return_lines)
+
+        return (
+            array.array("i", b"".join(model)),
+            self.used_weights,
+            inp_dim_orders,
+            out_dim_orders,
+            self.flexible_shape_computation_lines,
+            retval_count,
+        )
+
+    def serialize_values(self):
+        serialized_values = []
+        serialized_value_data = []
+        assert len(self.values) == len(self.value_data)
+        for (op_index, source_type), data in zip(self.values, self.value_data):
+            source_length = len(data)
+
+            # Pad with 0 bytes out to a multiple of 4 for alignment.
+            physical_length = ((source_length - 1) | 0x3) + 1
+            padded_data = data + (b"\0" * (physical_length - source_length))
+
+            serialized_values.append(
+                struct.pack("iii", op_index, source_type, source_length)
+            )
+            serialized_value_data.append(padded_data)
+
+        return serialized_values, serialized_value_data
+
+    @staticmethod
+    def serialize_ints(ints):
+        return array.array("i", ints).tobytes()
+
+    ADDER_MAP = {
+        "prim::GetAttr": lambda self, node: self.add_getattr(node),
+        "prim::Constant": lambda self, node: self.add_constant_node(node),
+        "prim::ListConstruct": lambda self, node: self.add_list_construct(node),
+        "prim::TupleConstruct": lambda self, node: self.add_tuple_construct(node),
+        "aten::unsqueeze": lambda self, node: self.add_unsqueeze(node),
+        "aten::to": lambda self, node: self.add_to(node),
+        "aten::detach": lambda self, node: self._identity(node),
+        "aten::reshape": lambda self, node: self.add_reshape(node),
+        "aten::flatten": lambda self, node: self.add_flatten(node),
+        "aten::slice": lambda self, node: self.add_slice(node),
+        "aten::size": lambda self, node: self.add_size(node),
+        "aten::cat": lambda self, node: self.add_cat(node),
+        "aten::mean": lambda self, node: self.add_mean(node),
+        "aten::quantize_per_tensor": lambda self, node: self.add_quantize(node),
+        "aten::dequantize": lambda self, node: self.add_dequantize(node),
+        "aten::add": lambda self, node: self.add_add_sub_op(
+            node, NNAPI_OperationCode.ADD, NNAPI_FuseCode.FUSED_NONE
+        ),
+        "aten::sub": lambda self, node: self.add_add_sub_op(
+            node, NNAPI_OperationCode.SUB, NNAPI_FuseCode.FUSED_NONE
+        ),
+        "aten::mul": lambda self, node: self.add_pointwise_simple_binary_broadcast_op(
+            node, NNAPI_OperationCode.MUL, NNAPI_FuseCode.FUSED_NONE
+        ),
+        "aten::div": lambda self, node: self.add_pointwise_simple_binary_broadcast_op(
+            node, NNAPI_OperationCode.DIV, NNAPI_FuseCode.FUSED_NONE
+        ),
+        "aten::relu": lambda self, node: self.add_pointwise_simple_unary_op(
+            node, NNAPI_OperationCode.RELU
+        ),
+        "aten::sigmoid": lambda self, node: self.add_pointwise_simple_unary_op(
+            node, NNAPI_OperationCode.LOGISTIC
+        ),
+        "aten::softmax": lambda self, node: self.add_softmax(node),
+        "aten::hardtanh": lambda self, node: self.add_hardtanh(node),
+        "aten::avg_pool2d": lambda self, node: self.add_avg_pool2d(node),
+        "aten::max_pool2d": lambda self, node: self.add_pool2d_node(
+            node, NNAPI_OperationCode.MAX_POOL_2D
+        ),
+        "aten::adaptive_avg_pool2d": lambda self, node: self.add_adaptive_avg_pool2d(
+            node
+        ),
+        "aten::upsample_nearest2d": lambda self, node: self.add_upsample_nearest2d(
+            node
+        ),
+        "aten::prelu": lambda self, node: self.add_prelu_op(node),
+        "aten::addmm": lambda self, node: self.add_addmm(node),
+        "aten::linear": lambda self, node: self.add_linear(node),
+        "aten::_convolution": lambda self, node: self.add_conv_underscore(node),
+        "aten::conv2d": lambda self, node: self.add_conv2d(node),
+        "aten::log_softmax": lambda self, node: self.add_log_softmax(node),
+        "quantized::linear": lambda self, node: self.add_qlinear(node),
+        "quantized::conv2d": lambda self, node: self.add_qconv2d(
+            node, NNAPI_FuseCode.FUSED_NONE
+        ),
+        "quantized::conv2d_relu": lambda self, node: self.add_qconv2d(
+            node, NNAPI_FuseCode.FUSED_RELU
+        ),
+        "quantized::conv_transpose2d": lambda self, node: self.add_qconv2d(
+            node, NNAPI_FuseCode.FUSED_NONE, transpose=True
+        ),
+        "quantized::add": lambda self, node: self.add_qadd(
+            node, NNAPI_OperationCode.ADD, NNAPI_FuseCode.FUSED_NONE
+        ),
+        "quantized::add_relu": lambda self, node: self.add_qadd(
+            node, NNAPI_OperationCode.ADD, NNAPI_FuseCode.FUSED_RELU
+        ),
+        "quantized::mul": lambda self, node: self.add_qadd(
+            node, NNAPI_OperationCode.MUL, NNAPI_FuseCode.FUSED_NONE
+        ),
+    }
+
+    def add_node(self, node):
+        adder = self.ADDER_MAP.get(node.kind())
+        if not adder:
+            raise Exception(  # noqa: TRY002
+                f"Unsupported node kind ({node.kind()!r}) in node {node!r}"
+            )  # noqa: TRY002
+        adder(self, node)
+
+    def _identity(self, node):
+        in_id, in_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+        jitval = node.outputsAt(0)
+        self.jitval_operand_map[jitval] = in_id
+
+    def add_getattr(self, node):
+        assert node.inputsSize() == 1
+        assert node.outputsSize() == 1
+        obj_ctype, obj = self.get_constant_value(node.inputsAt(0))
+        assert str(obj_ctype).startswith("__torch__.")
+        name = node.s("name")
+        value = getattr(obj, name)
+        output = node.outputsAt(0)
+        ctype = output.type()
+        self.add_constant_value(output, ctype, value)
+
+    def add_constant_node(self, node):
+        assert node.inputsSize() == 0
+        assert node.outputsSize() == 1
+        output = node.outputsAt(0)
+        ctype = output.type()
+        value = output.toIValue()
+        self.add_constant_value(output, ctype, value)
+
+    def add_list_construct(self, node):
+        assert node.outputsSize() == 1
+        output = node.outputsAt(0)
+        ctype = output.type()
+        const_vals: Optional[List] = []
+        tensors: Optional[List] = []
+        for inp in node.inputs():
+            if const_vals is not None and inp in self.constants:
+                _, val = self.get_constant_value(inp)
+                const_vals.append(val)
+            else:
+                const_vals = None
+            if tensors is not None and inp.type().kind() == "TensorType":
+                tensors.append(inp)
+            else:
+                tensors = None
+
+        if const_vals is not None:
+            # NOTE: Now that TorchScript supports list constants,
+            # this code path might not be used anymore.
+            self.add_constant_value(output, ctype, const_vals)
+        if tensors is not None:
+            self.add_tensor_sequence(output, tensors)
+        if const_vals is None and tensors is None:
+            raise Exception(  # noqa: TRY002
+                f"Unable to handle ListConstruct node.  Neither all constants nor all tensors. {node!r}"
+            )
+
+    def add_tuple_construct(self, node):
+        assert node.outputsSize() == 1
+        output = node.outputsAt(0)
+        values = list(node.inputs())
+        self.add_tensor_sequence(output, values)
+
+    def add_unsqueeze(self, node):
+        assert node.inputsSize() == 2
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+
+        _, dim = self.get_constant_value(node.inputsAt(1), "IntType")
+        assert in_oper.dim_order == DimOrder.PRESUMED_CONTIGUOUS
+
+        real_dim = dim if dim >= 0 else dim + len(in_oper.shape) + 1
+        out_shape_list = list(in_oper.shape)
+        out_shape_list.insert(real_dim, 1)
+        out_shape = tuple(out_shape_list)
+        out_oper = in_oper._replace(shape=out_shape)
+
+        inputs = [None] * 2
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_int_scalar(dim)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        self.add_operation(NNAPI_OperationCode.EXPAND_DIMS, inputs, outputs)
+
+    def add_to(self, node):
+        # Handle to("cpu") / to("gpu") case
+        self._identity(node)
+
+    def add_reshape(self, node):
+        assert node.inputsSize() == 2
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+
+        shape_ctype, shape = self.get_constant_value(node.inputsAt(1))
+        assert shape_ctype.kind() == "ListType"
+        assert shape_ctype.getElementType().kind() == "IntType"
+        is_trivial_reshape = len(shape) == 2 and shape[1] == -1
+
+        if in_oper.dim_order != DimOrder.PRESUMED_CONTIGUOUS and not is_trivial_reshape:
+            raise Exception(  # noqa: TRY002
+                "Currently, reshape is only supported on NHWC tensors if the target size is [X, -1]."
+            )
+
+        # Bit of a hack here.  Use a real tensor to infer the output shape.
+        out_shape = torch.zeros(1).expand(in_oper.shape).reshape(shape).shape
+        out_oper = in_oper._replace(
+            shape=out_shape, dim_order=DimOrder.PRESUMED_CONTIGUOUS
+        )
+
+        inputs = [None] * 2
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_int_vector(shape)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        self.add_operation(NNAPI_OperationCode.RESHAPE, inputs, outputs)
+
+    def add_flatten(self, node):
+        assert node.inputsSize() == 3
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+
+        start_ctype, start_dim = self.get_constant_value(node.inputsAt(1), "IntType")
+        end_ctype, end_dim = self.get_constant_value(node.inputsAt(2), "IntType")
+
+        # channels last with channels == 1 or (height & width both 1)
+        is_trivial_flatten = len(in_oper.shape) == 4 and (
+            in_oper.shape[1] == 1 or (in_oper.shape[2] == 1 and in_oper.shape[3] == 1)
+        )
+        if in_oper.dim_order != DimOrder.PRESUMED_CONTIGUOUS and not is_trivial_flatten:
+            raise Exception(  # noqa: TRY002
+                "Currently, flatten is not supported on NHWC tensors unless C=1 or H=W=1"
+            )
+
+        if start_dim < 0:
+            start_dim += len(in_oper.shape)
+        if end_dim < 0:
+            end_dim += len(in_oper.shape)
+
+        out_shape = (
+            in_oper.shape[:start_dim]
+            + (functools.reduce(operator.mul, in_oper.shape[start_dim : end_dim + 1]),)
+            + in_oper.shape[end_dim + 1 :]
+        )
+
+        if any(dim == 0 for dim in in_oper.shape[start_dim : end_dim + 1]):
+            raise Exception(  # noqa: TRY002
+                "Flattening flexible dims is not supported yet"
+            )  # noqa: TRY002
+        non_flattened_dims = in_oper.shape[:start_dim] + in_oper.shape[end_dim + 1 :]
+        if non_flattened_dims.count(0) > 1:
+            raise Exception("Only 1 dim can be flexible")  # noqa: TRY002
+
+        out_oper = in_oper._replace(
+            shape=out_shape, dim_order=DimOrder.PRESUMED_CONTIGUOUS
+        )
+        out_id = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        for idx, dim in enumerate(out_shape):
+            if dim == 0:
+                self.forward_operand_shape(out_id, idx, in_id, in_oper.shape.index(0))
+
+        inputs_1 = tuple(dim if dim != 0 else -1 for dim in out_shape)
+        inputs = [None] * 2
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_int_vector(inputs_1)
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.RESHAPE, inputs, outputs)
+
+    def add_slice(self, node):
+        assert node.inputsSize() == 5
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+        _, dim_value = self.get_constant_value(node.inputsAt(1))
+        _, start_value = self.get_constant_value(node.inputsAt(2))
+        _, stop_value = self.get_constant_value(node.inputsAt(3))
+        _, step_value = self.get_constant_value(node.inputsAt(4))
+
+        if start_value is None:
+            start_value = 0
+        if stop_value is None:
+            stop_value = sys.maxsize
+
+        if start_value < 0:
+            start_value += in_oper.shape[dim_value]
+        elif start_value == sys.maxsize:
+            start_value = 0
+
+        if start_value == 0 and stop_value == sys.maxsize:
+            self._identity(node)
+            return
+
+        if in_oper.shape[dim_value] == 0:
+            raise Exception("Unable to slice with flexible shape")  # noqa: TRY002
+
+        if stop_value < 0:
+            stop_value += in_oper.shape[dim_value]
+        elif stop_value == sys.maxsize:
+            stop_value = in_oper.shape[dim_value]
+
+        if start_value >= stop_value:
+            raise Exception(  # noqa: TRY002
+                "Slice start value should be less than stop value"
+            )  # noqa: TRY002
+
+        out_len = (stop_value - start_value) // step_value
+        out_shape = tuple(
+            out_len if i == dim_value else dim for i, dim in enumerate(in_oper.shape)
+        )
+        out_id = self.add_tensor_operand(
+            node.outputsAt(0), in_oper._replace(shape=out_shape)
+        )
+
+        # flex inputs
+        end_mask = 0
+        for idx, dim in enumerate(out_shape):
+            if dim == 0:
+                self.forward_operand_shape(out_id, idx, in_id, idx)
+                end_mask |= 1 << idx
+
+        inputs = [None] * 7
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_int_vector(
+            [start_value if i == dim_value else 0 for i in range(len(in_oper.shape))]
+        )
+        inputs[2] = self.add_immediate_int_vector(
+            [
+                stop_value if i == dim_value else dim
+                for i, dim in enumerate(in_oper.shape)
+            ]
+        )
+        inputs[3] = self.add_immediate_int_vector(
+            [step_value if i == dim_value else 1 for i in range(len(in_oper.shape))]
+        )
+        inputs[4] = self.add_immediate_int_scalar(0)  # begin mask
+        inputs[5] = self.add_immediate_int_scalar(end_mask)
+        inputs[6] = self.add_immediate_int_scalar(0)  # shrink axis mas
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.STRIDED_SLICE, inputs, outputs)
+
+    def add_size(self, node):
+        assert node.inputsSize() == 2
+        assert node.outputsSize() == 1
+
+        _, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+        _, value = self.constants[node.inputsAt(1)]
+        res = in_oper.shape[value]
+        output = node.outputsAt(0)
+        self.add_constant_value(output, output.type(), res)
+
+    def add_cat(self, node):
+        assert node.inputsSize() == 2
+        assert node.outputsSize() == 1
+
+        tensors = self.tensor_sequences[node.inputsAt(0)]
+        _, dim = self.get_constant_value(node.inputsAt(1), "IntType")
+
+        assert len(tensors) > 0
+        in_ids = []
+        out_oper = None
+        out_dim_size = 0
+        for inp in tensors:
+            in_id, in_oper = self.get_tensor_operand_by_jitval(inp)
+            if out_oper is None:
+                out_shape = change_element(in_oper.shape, dim, -1)
+                out_oper = in_oper._replace(shape=out_shape)
+            assert in_oper.op_type == out_oper.op_type
+            assert in_oper.dim_order == out_oper.dim_order
+            assert change_element(in_oper.shape, dim, -1) == change_element(
+                out_oper.shape, dim, -1
+            )
+            # TODO: Possibly check scale and zero point.
+            in_ids.append(in_id)
+            # TODO: Possibly support variable-sized inputs.
+            out_dim_size += in_oper.shape[dim]
+
+        assert out_oper is not None
+        out_oper = out_oper._replace(
+            shape=change_element(out_oper.shape, dim, out_dim_size)
+        )
+
+        if in_oper.dim_order == DimOrder.CHANNELS_LAST:  # type: ignore[possibly-undefined]
+            assert len(out_oper.shape) == 4
+            nnapi_dim = [0, 3, 1, 2][dim]
+        else:
+            nnapi_dim = dim
+
+        out_id = self.add_tensor_operand(node.outputsAt(0), out_oper)
+        for idx, d in enumerate(out_oper.shape):
+            if d == 0:
+                if idx == dim:
+                    shape = " + ".join(flex_name(ip_id, dim) for ip_id in in_ids)
+                    self.compute_operand_shape(out_id, idx, shape)
+                else:
+                    self.forward_operand_shape(out_id, idx, in_ids[0], idx)
+
+        inputs = in_ids + [self.add_immediate_int_scalar(nnapi_dim)]
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.CONCATENATION, inputs, outputs)
+
+    def add_mean(self, node):
+        assert node.inputsSize() == 4
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+        dim_ctype, dim = self.get_constant_value(node.inputsAt(1))
+        assert dim_ctype.kind() == "ListType"
+        assert dim_ctype.getElementType().kind() == "IntType"
+        _, keep_dim = self.get_constant_value(node.inputsAt(2), "BoolType")
+        # Expect None for dtype
+        self.get_constant_value(node.inputsAt(3), "NoneType")
+
+        if in_oper.dim_order == DimOrder.CHANNELS_LAST:
+            assert len(in_oper.shape) == 4
+            nnapi_dim = [[0, 3, 1, 2][d] for d in dim]
+        else:
+            nnapi_dim = dim
+
+        collapsed_dims = set()
+        for d in dim:
+            if d < 0:
+                d += len(in_oper.shape)
+            collapsed_dims.add(d)
+
+        if in_oper.dim_order == DimOrder.CHANNELS_LAST and not keep_dim:
+            assert collapsed_dims.issuperset({2, 3})
+            out_dim_order = DimOrder.PRESUMED_CONTIGUOUS
+        else:
+            out_dim_order = in_oper.dim_order
+
+        out_shape = []
+        for i, s in enumerate(in_oper.shape):
+            if i not in collapsed_dims:
+                out_shape.append(s)
+            elif keep_dim:
+                out_shape.append(1)
+
+        out_oper = in_oper._replace(shape=out_shape, dim_order=out_dim_order)
+
+        inputs = [None] * 3
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_int_vector(nnapi_dim)
+        inputs[2] = self.add_immediate_int_scalar(keep_dim)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        self.add_operation(NNAPI_OperationCode.MEAN, inputs, outputs)
+
+    def add_quantize(self, node):
+        assert node.inputsSize() == 4
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+        if in_oper.dim_order != DimOrder.CHANNELS_LAST:
+            raise Exception(  # noqa: TRY002
+                "Most hardware backends prefer NHWC quantized tensors.  "
+                "Try setting `t.nnapi_nhwc = True` on your tensor inputs.  "
+            )
+        _, scale = self.get_constant_value(node.inputsAt(1), "FloatType")
+        _, zero_point = self.get_constant_value(node.inputsAt(2), "IntType")
+        _, scalar_type = self.get_constant_value(node.inputsAt(3), "IntType")
+        if scalar_type != TorchScalarTypes.QUINT8.value:
+            raise Exception(  # noqa: TRY002
+                "PyTorch NNAPI export only supports quantized tensors "
+                "with the quint8 dtype."
+            )
+        op_type = NNAPI_OperandCode.TENSOR_QUANT8_ASYMM
+
+        out_oper = in_oper._replace(
+            op_type=op_type,
+            scale=scale,
+            zero_point=zero_point,
+        )
+
+        inputs = [None] * 1
+        inputs[0] = in_id
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        self.add_operation(NNAPI_OperationCode.QUANTIZE, inputs, outputs)
+
+    def add_dequantize(self, node):
+        assert node.inputsSize() == 1
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+        out_oper = in_oper._replace(
+            op_type=NNAPI_OperandCode.TENSOR_FLOAT32,
+            scale=0.0,
+            zero_point=0,
+        )
+
+        inputs = [None] * 1
+        inputs[0] = in_id
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        self.add_operation(NNAPI_OperationCode.DEQUANTIZE, inputs, outputs)
+
+    def add_pointwise_simple_unary_op(self, node, opcode):
+        assert node.inputsSize() == 1
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+
+        out_oper = in_oper
+        if opcode == NNAPI_OperationCode.LOGISTIC:
+            # NNAPI docs: For ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the scale
+            # must be 1.f / 256 and the zeroPoint must be 0.
+            # https://fburl.com/h52stoog
+            if in_oper.op_type == NNAPI_OperandCode.TENSOR_QUANT8_ASYMM:
+                out_oper = in_oper._replace(zero_point=0, scale=1.0 / 256)
+
+        out_id = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        for idx, dim in enumerate(in_oper.shape):
+            if dim == 0:
+                self.forward_operand_shape(out_id, idx, in_id, idx)
+
+        inputs = [None] * 1
+        inputs[0] = in_id
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(opcode, inputs, outputs)
+
+    def _do_add_binary(self, node, opcode, fuse_code, *, qparams=None):  # noqa: D401
+        """Helper for pointwise binary broadcast ops with superfluous extra args."""
+        assert node.outputsSize() == 1
+
+        assert node.inputsAt(0).type().kind() == "TensorType"
+        assert node.inputsAt(1).type().kind() == "TensorType"
+
+        if self.has_operand_for_jitval(node.inputsAt(0)):
+            in0_id, in0_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+            in1_id, in1_oper = self.get_tensor_operand_or_constant(
+                node.inputsAt(1), in0_oper.dim_order
+            )
+        elif self.has_operand_for_jitval(node.inputsAt(1)):
+            in1_id, in1_oper = self.get_tensor_operand_by_jitval(node.inputsAt(1))
+            in0_id, in0_oper = self.get_tensor_operand_or_constant(
+                node.inputsAt(0), in1_oper.dim_order
+            )
+        else:
+            raise Exception(  # noqa: TRY002
+                f"Can't do a NNAPI binary op: {opcode} on two constants"
+            )  # noqa: TRY002
+
+        assert in0_oper.op_type == in1_oper.op_type
+        in0_id, in0_oper, in1_id, in1_oper = self.transpose_for_broadcast(
+            in0_id, in0_oper, in1_id, in1_oper
+        )
+        # NOTE: PyTorch and NNAPI have the same broadcast semantics.
+        out_shape = broadcast_shapes(in0_oper.shape, in1_oper.shape)
+        out_oper = in0_oper._replace(shape=out_shape)
+        if qparams is not None:
+            scale, zp = qparams
+            out_oper = out_oper._replace(scale=scale, zero_point=zp)
+
+        out_id = self.add_tensor_operand(node.outputsAt(0), out_oper)
+        for idx, (d0, d1) in enumerate(zip(in0_oper.shape, in1_oper.shape)):
+            if d0 == 1 and d1 == 0:
+                self.forward_operand_shape(out_id, idx, in1_id, idx)
+            elif d0 == 0 and d1 == 1:
+                self.forward_operand_shape(out_id, idx, in0_id, idx)
+            elif d0 == 0 and d1 == 0:
+                self.flexible_shape_computation_lines.append(
+                    f"assert {flex_name(in0_id, idx)} == {flex_name(in1_id, idx)}"
+                )
+                self.forward_operand_shape(out_id, idx, in0_id, idx)
+
+        inputs = [None] * 3
+        inputs[0] = in0_id
+        inputs[1] = in1_id
+        inputs[2] = self.add_immediate_int_scalar(fuse_code)
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(opcode, inputs, outputs)
+
+    def add_pointwise_simple_binary_broadcast_op(self, node, opcode, fuse_code):
+        assert node.inputsSize() == 2
+        self._do_add_binary(node, opcode, fuse_code)
+
+    def add_add_sub_op(self, node, opcode, fuse_code):
+        assert node.inputsSize() == 3
+
+        _, alpha = self.get_constant_value(node.inputsAt(2), "IntType")
+        if alpha != 1:
+            raise Exception(  # noqa: TRY002
+                "NNAPI does not support add/sub with alpha."
+            )  # noqa: TRY002
+
+        self._do_add_binary(node, opcode, fuse_code)
+
+    def add_qadd(self, node, opcode, fuse_code):
+        assert node.inputsSize() == 4
+
+        _, scale = self.get_constant_value(node.inputsAt(2), "FloatType")
+        _, zero_point = self.get_constant_value(node.inputsAt(3), "IntType")
+
+        self._do_add_binary(node, opcode, fuse_code, qparams=(scale, zero_point))
+
+    def add_softmax(self, node):
+        assert node.inputsSize() == 3
+        in_id, in_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+
+        _, softmax_dim = self.get_constant_value(node.inputsAt(1), "IntType")
+
+        out_id = self.add_tensor_operand(node.outputsAt(0), in_oper)
+        for dim, size in enumerate(in_oper.shape):
+            if size == 0:
+                self.forward_operand_shape(out_id, dim, in_id, dim)
+
+        inputs = [None] * 3
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_float_scalar(
+            1.0
+        )  # positive scaling factor of exponent, beta
+        inputs[2] = self.add_immediate_int_scalar(softmax_dim)
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.SOFTMAX, inputs, outputs)
+
+    def add_hardtanh(self, node):
+        assert node.inputsSize() == 3
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+        _, min_val = self.get_constant_value(node.inputsAt(1), "FloatType")
+        _, max_val = self.get_constant_value(node.inputsAt(2), "FloatType")
+
+        op_map = {
+            (-1, 1): NNAPI_OperationCode.RELU1,
+            (0, 6): NNAPI_OperationCode.RELU6,  # noqa: E201
+        }
+
+        opcode = op_map.get((min_val, max_val))
+        if opcode is None:
+            raise Exception(  # noqa: TRY002
+                "NNAPI only supports hardtanh with args (-1, 1) or (0, 6)."
+            )  # noqa: TRY002
+
+        inputs = [None] * 1
+        inputs[0] = in_id
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), in_oper)
+
+        self.add_operation(opcode, inputs, outputs)
+
+    def add_prelu_op(self, node):
+        assert node.inputsSize() == 2
+        assert node.outputsSize() == 1
+
+        assert node.inputsAt(0).type().kind() == "TensorType"
+        assert node.inputsAt(1).type().kind() == "TensorType"
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+        w_id, w_oper = self.get_tensor_operand_for_weight(node.inputsAt(1))
+        assert len(w_oper.shape) == 1
+        assert w_oper.shape[0] > 0
+        if w_oper.shape[0] > 1:
+            if in_oper.use_nchw():
+                # TODO: Support this by adding trailing 1 dims.
+                raise Exception(  # noqa: TRY002
+                    "Per-channel PReLU only supports channels_last right now."
+                )
+
+        out_id = self.add_tensor_operand(node.outputsAt(0), in_oper)
+        for dim, size in enumerate(in_oper.shape):
+            if size > 0:
+                pass
+            elif dim <= 1:
+                raise Exception(  # noqa: TRY002
+                    "PReLU requires fixed size for dim 0 and dim 1."
+                )  # noqa: TRY002
+            else:
+                self.forward_operand_shape(out_id, dim, in_id, dim)
+
+        inputs = [None] * 2
+        inputs[0] = in_id
+        inputs[1] = w_id
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.PRELU, inputs, outputs)
+
+    def add_pool2d_node(self, node, opcode):
+        assert node.inputsSize() == 6
+        assert node.outputsSize() == 1
+        image, kernel, stride, padding, dilation, ceil_mode = node.inputs()
+
+        stride = stride or kernel
+
+        # TODO: Validate ceil_mode semantics.
+
+        args = self.get_conv_pool_args_2d_from_jit(
+            self.get_size_arg(kernel), stride, padding, dilation
+        )
+        if args.dilation_h != 1 or args.dilation_w != 1:
+            raise Exception("NNAPI does not support dilated pooling.")  # noqa: TRY002
+
+        image_id, image_oper = self.get_tensor_operand_by_jitval_fixed_size(image)
+        assert len(image_oper.shape) == 4
+
+        out_shape = get_conv_pool_shape(
+            image_oper.shape, args, image_oper.shape[1], False
+        )
+        use_nchw = image_oper.use_nchw()
+
+        inputs = [None] * 11
+        inputs[0] = image_id
+        inputs[1] = self.add_immediate_int_scalar(args.pad_l)
+        inputs[2] = self.add_immediate_int_scalar(args.pad_r)
+        inputs[3] = self.add_immediate_int_scalar(args.pad_t)
+        inputs[4] = self.add_immediate_int_scalar(args.pad_b)
+        inputs[5] = self.add_immediate_int_scalar(args.stride_w)
+        inputs[6] = self.add_immediate_int_scalar(args.stride_h)
+        inputs[7] = self.add_immediate_int_scalar(args.kernel_w)
+        inputs[8] = self.add_immediate_int_scalar(args.kernel_h)
+        inputs[9] = self.add_immediate_int_scalar(NNAPI_FuseCode.FUSED_NONE)
+        inputs[10] = self.add_immediate_bool_scalar(use_nchw)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(
+            node.outputsAt(0), image_oper._replace(shape=out_shape)
+        )
+
+        self.add_operation(opcode, inputs, outputs)
+
+    def add_avg_pool2d(self, node):
+        assert node.inputsSize() == 7
+        assert node.outputsSize() == 1
+        (
+            image,
+            kernel,
+            stride,
+            padding,
+            ceil_mode,
+            count_include_pad,
+            divisor_override,
+        ) = node.inputs()
+
+        _, count_include_pad_value = self.get_constant_value(count_include_pad)
+        _, divisor_override_value = self.get_constant_value(divisor_override)
+        if not count_include_pad_value or divisor_override_value:
+            raise Exception(  # noqa: TRY002
+                "NNAPI doesn't support count_include_pad=False or divisor_override"
+            )
+
+        args = self.get_conv_pool_args_2d_from_jit(
+            self.get_size_arg(kernel), stride, padding
+        )
+
+        image_id, image_oper = self.get_tensor_operand_by_jitval(image)
+        assert len(image_oper.shape) == 4
+
+        out_shape = get_conv_pool_shape(
+            image_oper.shape, args, image_oper.shape[1], False
+        )
+        use_nchw = image_oper.use_nchw()
+
+        inputs = [None] * 11
+        inputs[0] = image_id
+        inputs[1] = self.add_immediate_int_scalar(args.pad_l)
+        inputs[2] = self.add_immediate_int_scalar(args.pad_r)
+        inputs[3] = self.add_immediate_int_scalar(args.pad_t)
+        inputs[4] = self.add_immediate_int_scalar(args.pad_b)
+        inputs[5] = self.add_immediate_int_scalar(args.stride_w)
+        inputs[6] = self.add_immediate_int_scalar(args.stride_h)
+        inputs[7] = self.add_immediate_int_scalar(args.kernel_w)
+        inputs[8] = self.add_immediate_int_scalar(args.kernel_h)
+        inputs[9] = self.add_immediate_int_scalar(NNAPI_FuseCode.FUSED_NONE)
+        inputs[10] = self.add_immediate_bool_scalar(use_nchw)
+
+        outputs = [None] * 1
+        out_id = self.add_tensor_operand(
+            node.outputsAt(0), image_oper._replace(shape=out_shape)
+        )
+        self._handle_conv_pool_flexible_input(out_id, image, args, False)
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.AVERAGE_POOL_2D, inputs, outputs)
+
+    def add_adaptive_avg_pool2d(self, node):
+        assert node.inputsSize() == 2
+        assert node.outputsSize() == 1
+
+        image_id, image_oper = self.get_tensor_operand_by_jitval_fixed_size(
+            node.inputsAt(0)
+        )
+        assert len(image_oper.shape) == 4
+
+        size_ctype, size_arg = self.get_constant_value(node.inputsAt(1))
+        assert size_ctype.kind() == "ListType"
+        assert size_ctype.getElementType().kind() == "IntType"
+        if size_arg != [1, 1]:
+            raise Exception(  # noqa: TRY002
+                "NNAPI only supports adaptive_avg_pool2d with output size (1, 1)."
+            )
+
+        out_shape = image_oper.shape[0:2] + tuple(size_arg)
+        use_nchw = image_oper.use_nchw()
+
+        inputs = [None] * 11
+        inputs[0] = image_id
+        inputs[1] = self.add_immediate_int_scalar(0)
+        inputs[2] = self.add_immediate_int_scalar(0)
+        inputs[3] = self.add_immediate_int_scalar(0)
+        inputs[4] = self.add_immediate_int_scalar(0)
+        inputs[5] = self.add_immediate_int_scalar(1)
+        inputs[6] = self.add_immediate_int_scalar(1)
+        inputs[7] = self.add_immediate_int_scalar(image_oper.shape[3])
+        inputs[8] = self.add_immediate_int_scalar(image_oper.shape[2])
+        inputs[9] = self.add_immediate_int_scalar(NNAPI_FuseCode.FUSED_NONE)
+        inputs[10] = self.add_immediate_bool_scalar(use_nchw)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(
+            node.outputsAt(0), image_oper._replace(shape=out_shape)
+        )
+
+        self.add_operation(NNAPI_OperationCode.AVERAGE_POOL_2D, inputs, outputs)
+
+    def add_upsample_nearest2d(self, node):
+        assert node.inputsSize() == 3 or node.inputsSize() == 4
+        assert node.outputsSize() == 1
+        if node.inputsSize() == 3:
+            image, size_jit, scale_jit = node.inputs()
+        else:
+            image, size_jit, scale_h_jit, scale_w_jit = node.inputs()
+        size_ctype, size_arg = self.get_constant_value(size_jit)
+
+        if node.inputsSize() == 3:
+            scale_ctype, scale_arg = self.get_constant_value(scale_jit)  # type: ignore[possibly-undefined]
+        else:
+            scale_h_ctype, scale_h_arg = self.get_constant_value(scale_h_jit)  # type: ignore[possibly-undefined]
+            scale_w_ctype, scale_w_arg = self.get_constant_value(scale_w_jit)  # type: ignore[possibly-undefined]
+
+            # The only way for the 4-argument overload of upsample_nearest2d to
+            # have been added to the graph without error is if the scale_h and
+            # scale_w arguments are None
+            assert scale_h_ctype.kind() == "NoneType"
+            assert scale_w_ctype.kind() == "NoneType"
+
+            scale_ctype = scale_h_ctype
+            scale_arg = scale_h_arg
+
+        image_id, image_oper = self.get_tensor_operand_by_jitval(image)
+        assert len(image_oper.shape) == 4
+
+        if size_ctype.kind() != "NoneType" and scale_ctype.kind() != "NoneType":
+            raise Exception("Size and scale cannot both be non-None.")  # noqa: TRY002
+        elif size_ctype.kind() != "NoneType":
+            assert size_ctype.kind() == "ListType"
+            assert size_ctype.getElementType().kind() == "IntType"
+            assert scale_ctype.kind() == "NoneType"
+            assert scale_arg is None
+            assert isinstance(size_arg, list)
+            assert size_arg
+            assert all(isinstance(val, int) for val in size_arg)
+            if len(size_arg) == 1:
+                size_arg = size_arg * 2
+            assert len(size_arg) == 2
+            out_h = size_arg[0]
+            out_w = size_arg[1]
+            arg_h = self.add_immediate_int_scalar(out_h)
+            arg_w = self.add_immediate_int_scalar(out_w)
+        elif scale_ctype.kind() != "NoneType":
+            assert scale_ctype.kind() == "ListType"
+            assert scale_ctype.getElementType().kind() == "FloatType"
+            assert size_ctype.kind() == "NoneType"
+            assert size_arg is None
+            assert isinstance(scale_arg, list)
+            assert scale_arg
+            assert all(isinstance(val, float) for val in scale_arg)
+            if len(scale_arg) == 1:
+                scale_arg = scale_arg * 2
+            assert len(scale_arg) == 2
+            out_h = int(scale_arg[0] * image_oper.shape[2])
+            out_w = int(scale_arg[1] * image_oper.shape[3])
+            arg_h = self.add_immediate_float_scalar(scale_arg[0])
+            arg_w = self.add_immediate_float_scalar(scale_arg[1])
+        else:
+            raise Exception("Size and scale cannot both be None.")  # noqa: TRY002
+
+        out_shape = (image_oper.shape[0], image_oper.shape[1], out_h, out_w)
+        use_nchw = image_oper.use_nchw()
+        out_id = self.add_tensor_operand(
+            node.outputsAt(0), image_oper._replace(shape=out_shape)
+        )
+
+        if image_oper.shape[0] == 0 or image_oper.shape[1] == 0:
+            raise Exception("Flexible batch or channels not supported")  # noqa: TRY002
+
+        # Handle variable input size
+        for dim in (2, 3):  # h, w indices
+            if image_oper.shape[dim] == 0:
+                if size_ctype.kind() != "NoneType":
+                    self.compute_operand_shape(out_id, dim, size_arg[dim - 2])
+                elif scale_ctype.kind() != "NoneType":
+                    self.compute_operand_shape(
+                        out_id,
+                        dim,
+                        f"int({scale_arg[dim - 2]} * {flex_name(image_id, dim)})",
+                    )
+                else:
+                    raise Exception(  # noqa: TRY002
+                        "Size and scale cannot both be None."
+                    )  # noqa: TRY002
+
+        inputs = [None] * 4
+        inputs[0] = image_id
+        inputs[1] = arg_w
+        inputs[2] = arg_h
+        inputs[3] = self.add_immediate_bool_scalar(use_nchw)
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.RESIZE_NEAREST_NEIGHBOR, inputs, outputs)
+
+    def add_addmm(self, node):
+        assert node.inputsSize() == 5
+        assert node.outputsSize() == 1
+        jit_bias, jit_input, jit_weight, jit_beta, jit_alpha = node.inputs()
+
+        for jitval in (jit_beta, jit_alpha):
+            scale_ctype, scale_value = self.get_constant_value(jitval)
+            assert scale_ctype.kind() in ("IntType", "FloatType")
+            if scale_value != 1:
+                raise Exception(  # noqa: TRY002
+                    "NNAPI Fully-Connected does not support alpha and beta."
+                )
+
+        self.add_addmm_or_linear(node, True, jit_input, jit_weight, jit_bias)
+
+    def add_linear(self, node):
+        assert node.inputsSize() == 3
+        assert node.outputsSize() == 1
+        jit_input, jit_weight, jit_bias = node.inputs()
+
+        self.add_addmm_or_linear(node, False, jit_input, jit_weight, jit_bias)
+
+    def add_addmm_or_linear(
+        self, node, transpose_weight, jit_input, jit_weight, jit_bias
+    ):
+        input_id, input_oper = self.get_tensor_operand_by_jitval(jit_input)
+        bias_id, bias_oper = self.get_tensor_operand_for_weight(jit_bias)
+
+        assert len(input_oper.shape) == 2
+        assert len(bias_oper.shape) == 1
+
+        # TODO: Transform at load time to share weights with CPU model.
+        _, weight_tensor = self.get_constant_value(jit_weight, "TensorType")
+        assert len(weight_tensor.shape) == 2
+        if transpose_weight:
+            nnapi_weight_tensor = weight_tensor.t().contiguous()
+        else:
+            nnapi_weight_tensor = weight_tensor.contiguous()
+        weight_id = self.add_tensor_operand_for_weight(nnapi_weight_tensor)
+        weight_oper = self.operands[weight_id]
+
+        out_shape = (input_oper.shape[0], weight_oper.shape[0])
+        out_id = self.add_tensor_operand(
+            node.outputsAt(0), input_oper._replace(shape=out_shape)
+        )
+
+        if input_oper.shape[0] == 0:
+            self.forward_operand_shape(out_id, 0, input_id, 0)
+
+        inputs = [None] * 4
+        inputs[0] = input_id
+        inputs[1] = weight_id
+        inputs[2] = bias_id
+        inputs[3] = self.add_immediate_int_scalar(NNAPI_FuseCode.FUSED_NONE)
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.FULLY_CONNECTED, inputs, outputs)
+
+    def add_qlinear(self, node):
+        assert node.inputsSize() == 4
+        assert node.outputsSize() == 1
+        (
+            jit_input,
+            jit_packed_weight,
+            jit_scale,
+            jit_zero_point,
+        ) = node.inputs()
+
+        input_id, input_oper = self.get_tensor_operand_by_jitval_fixed_size(jit_input)
+        # TODO: Support automatic reshape
+        assert len(input_oper.shape) == 2
+
+        _, out_scale = self.get_constant_value(jit_scale, "FloatType")
+        _, out_zero_point = self.get_constant_value(jit_zero_point, "IntType")
+        weight_ctype, packed_weight = self.get_constant_value(jit_packed_weight)
+        assert weight_ctype.name() == "LinearPackedParamsBase"
+        raw_weight, raw_bias = packed_weight.__getstate__()[0]
+        assert raw_bias is not None
+
+        assert len(raw_weight.shape) == 2
+        assert len(raw_bias.shape) == 1
+        assert raw_bias.shape[0] == raw_weight.shape[0]
+        assert raw_weight.shape[1] == input_oper.shape[1]
+
+        assert raw_weight.qscheme() == torch.per_tensor_affine
+        if raw_weight.dtype == torch.quint8:
+            unsigned_weight = raw_weight
+        else:
+            assert raw_weight.dtype == torch.qint8
+            unsigned_weight = torch._make_per_tensor_quantized_tensor(
+                (raw_weight.int_repr().int() + 128).to(torch.uint8),
+                scale=raw_weight.q_scale(),
+                zero_point=raw_weight.q_zero_point() + 128,
+            )
+        weight_scale = unsigned_weight.q_scale()
+        bias_scale = input_oper.scale * weight_scale
+        int_bias = torch.quantize_per_tensor(raw_bias, bias_scale, 0, torch.qint32)
+        bias_id = self.add_tensor_operand_for_weight(int_bias)
+
+        multiplier = input_oper.scale * weight_scale / out_scale
+        assert multiplier > 0
+        if multiplier >= 1:
+            raise Exception(  # noqa: TRY002
+                "Quantized convolution multiplier is greater than 1.  "
+                "This is supported by NNAPI, but not by most hardware backends.  "
+                "Try training a model without quantization-aware training.  "
+            )
+
+        # TODO: Transform at load time to share weights with CPU model.
+        nnapi_weight_tensor = unsigned_weight.contiguous()
+        weight_id = self.add_tensor_operand_for_weight(nnapi_weight_tensor)
+        weight_oper = self.operands[weight_id]
+
+        out_shape = (input_oper.shape[0], weight_oper.shape[0])
+        out_oper = input_oper._replace(
+            shape=out_shape,
+            scale=out_scale,
+            zero_point=out_zero_point,
+        )
+
+        inputs = [None] * 4
+        inputs[0] = input_id
+        inputs[1] = weight_id
+        inputs[2] = bias_id
+        inputs[3] = self.add_immediate_int_scalar(NNAPI_FuseCode.FUSED_NONE)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        self.add_operation(NNAPI_OperationCode.FULLY_CONNECTED, inputs, outputs)
+
+    def get_optional_bias(self, jit_bias, weight_tensor, transpose=False):
+        ctype, value = self.get_constant_value(jit_bias)
+        if ctype.kind() == "NoneType":
+            bias_idx = 1 if transpose else 0
+            nnapi_bias_tensor = torch.zeros(
+                weight_tensor.size()[bias_idx], dtype=weight_tensor.dtype
+            )
+            bias_id = self.add_tensor_operand_for_weight(nnapi_bias_tensor)
+            bias_oper = self.operands[bias_id]
+            return bias_id, bias_oper
+        else:
+            return self.get_tensor_operand_for_weight(jit_bias)
+
+    def add_conv2d(self, node):
+        assert node.inputsSize() == 7
+        assert node.outputsSize() == 1
+
+        (
+            jit_image,
+            jit_weight,
+            jit_bias,
+            jit_stride,
+            jit_pad,
+            jit_dilation,
+            jit_groups,
+        ) = node.inputs()
+
+        _, weight_tensor = self.get_constant_value(jit_weight, "TensorType")
+        bias_id, bias_oper = self.get_optional_bias(jit_bias, weight_tensor)
+        args = self.get_conv_pool_args_2d_from_jit(
+            weight_tensor.shape[2:4], jit_stride, jit_pad, jit_dilation, jit_groups
+        )
+
+        return self.add_conv2d_common(
+            node.outputsAt(0),
+            0.0,
+            0,
+            jit_image,
+            weight_tensor,
+            bias_id,
+            args,
+            False,  # transpose
+            NNAPI_FuseCode.FUSED_NONE,
+        )
+
+    def add_conv_underscore(self, node):
+        assert node.inputsSize() == 13
+        assert node.outputsSize() == 1
+
+        (
+            jit_image,
+            jit_weight,
+            jit_bias,
+            jit_stride,
+            jit_pad,
+            jit_dilation,
+            jit_transpose,
+            _,
+            jit_groups,
+            _,
+            _,
+            _,
+            _,
+        ) = node.inputs()
+
+        _, weight_tensor = self.get_constant_value(jit_weight, "TensorType")
+        _, transpose = self.get_constant_value(jit_transpose)
+        bias_id, bias_oper = self.get_optional_bias(jit_bias, weight_tensor, transpose)
+        args = self.get_conv_pool_args_2d_from_jit(
+            weight_tensor.shape[2:4], jit_stride, jit_pad, jit_dilation, jit_groups
+        )
+
+        return self.add_conv2d_common(
+            node.outputsAt(0),
+            0.0,
+            0,
+            jit_image,
+            weight_tensor,
+            bias_id,
+            args,
+            transpose,
+            NNAPI_FuseCode.FUSED_NONE,
+        )
+
+    def add_log_softmax(self, node):
+        assert node.inputsSize() == 3
+        assert node.outputsSize() == 1
+
+        (jit_input, jit_dim, jit_half_to_float) = node.inputs()
+        input_id, input_oper = self.get_tensor_operand_by_jitval_fixed_size(jit_input)
+        _, dim = self.get_constant_value(jit_dim, "IntType")
+
+        out_shape = input_oper.shape
+
+        inputs = [None] * 3
+        inputs[0] = input_id
+        # specifying 1 as the scaling factor for the exponent, beta
+        inputs[1] = self.add_immediate_float_scalar(1)
+        inputs[2] = self.add_immediate_int_scalar(dim)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(
+            node.outputsAt(0), input_oper._replace(shape=out_shape)
+        )
+        self.add_operation(NNAPI_OperationCode.LOG_SOFTMAX, inputs, outputs)
+
+    def add_qconv2d(self, node, fuse_code, transpose=False):
+        assert node.inputsSize() == 4
+        assert node.outputsSize() == 1
+
+        (
+            jit_image,
+            jit_packed_weight,
+            jit_scale,
+            jit_zero_point,
+        ) = node.inputs()
+
+        _, out_scale = self.get_constant_value(jit_scale, "FloatType")
+        _, out_zero_point = self.get_constant_value(jit_zero_point, "IntType")
+        weight_ctype, packed_weight = self.get_constant_value(jit_packed_weight)
+        assert weight_ctype.name() == "Conv2dPackedParamsBase"
+        (
+            pack_version,
+            tensors,
+            opt_tensors,
+        ) = packed_weight.__getstate__()[0]
+        assert pack_version == "2"
+        packed_config, raw_weight = tensors
+        (raw_bias,) = opt_tensors
+        assert raw_bias is not None
+        args = self.get_conv_pool_args_2d_from_pack(
+            raw_weight.shape[2:4], packed_config
+        )
+
+        assert raw_weight.qscheme() == torch.per_tensor_affine
+        if raw_weight.dtype == torch.quint8:
+            unsigned_weight = raw_weight
+        else:
+            assert raw_weight.dtype == torch.qint8
+            unsigned_weight = torch._make_per_tensor_quantized_tensor(
+                (raw_weight.int_repr().int() + 128).to(torch.uint8),
+                scale=raw_weight.q_scale(),
+                zero_point=raw_weight.q_zero_point() + 128,
+            )
+        weight_scale = unsigned_weight.q_scale()
+        _, image_oper = self.get_tensor_operand_by_jitval(jit_image)
+        bias_scale = image_oper.scale * weight_scale
+        int_bias = torch.quantize_per_tensor(raw_bias, bias_scale, 0, torch.qint32)
+        bias_id = self.add_tensor_operand_for_weight(int_bias)
+
+        multiplier = image_oper.scale * weight_scale / out_scale
+        assert multiplier > 0
+        if multiplier >= 1:
+            raise Exception(  # noqa: TRY002
+                "Quantized convolution multiplier is greater than 1.  "
+                "This is supported by NNAPI, but not by most hardware backends.  "
+                "Try training a model without quantization-aware training.  "
+            )
+
+        return self.add_conv2d_common(
+            node.outputsAt(0),
+            out_scale,
+            out_zero_point,
+            jit_image,
+            unsigned_weight,
+            bias_id,
+            args,
+            transpose,
+            fuse_code,
+        )
+
+    def add_conv2d_common(
+        self,
+        jit_out,
+        out_scale,
+        out_zero_point,
+        jit_image,
+        weight_tensor,
+        bias_id,
+        args,
+        transpose,
+        fuse_code,
+    ):
+        image_id, image_oper = self.get_tensor_operand_by_jitval(jit_image)
+        in_c = image_oper.shape[1]
+
+        if args.group == 1:
+            # Full convolution
+            depthwise = False
+            if transpose:
+                weight_permutation = (1, 2, 3, 0)
+            else:
+                weight_permutation = (0, 2, 3, 1)
+        elif args.group == in_c:
+            # Depthwise convolution
+            depthwise = True
+            weight_permutation = (1, 2, 3, 0)
+        else:
+            raise Exception("Group convolution not supported yet.")  # noqa: TRY002
+
+        # TODO: Transform at load time to share weights with CPU model.
+        nnapi_weight_tensor = weight_tensor.permute(*weight_permutation).contiguous()
+        weight_id = self.add_tensor_operand_for_weight(nnapi_weight_tensor)
+        weight_oper = self.operands[weight_id]
+
+        bias_oper = self.operands[bias_id]
+
+        if image_oper.op_type == NNAPI_OperandCode.TENSOR_FLOAT32:
+            assert weight_oper.op_type == NNAPI_OperandCode.TENSOR_FLOAT32
+            assert bias_oper.op_type == NNAPI_OperandCode.TENSOR_FLOAT32
+        elif image_oper.op_type == NNAPI_OperandCode.TENSOR_QUANT8_ASYMM:
+            assert weight_oper.op_type == NNAPI_OperandCode.TENSOR_QUANT8_ASYMM
+            assert bias_oper.op_type == NNAPI_OperandCode.TENSOR_INT32
+            assert approx_equal(image_oper.scale * weight_oper.scale, bias_oper.scale)
+            assert bias_oper.zero_point == 0
+        else:
+            raise Exception(  # noqa: TRY002
+                f"Unsupported input type for conv2d: {image_oper.op_type}"
+            )  # noqa: TRY002
+
+        assert len(image_oper.shape) == 4
+        assert len(weight_oper.shape) == 4
+        assert len(bias_oper.shape) == 1
+
+        if depthwise:
+            # Depthwise convolution
+            one, kern_h, kern_w, out_c = weight_oper.shape
+            assert one == 1
+            assert out_c % in_c == 0
+            channel_multiplier = out_c // in_c
+            assert channel_multiplier == 1  # Don't support multiplier
+            assert out_c == in_c
+        else:
+            # Full convolution
+            out_c, kern_h, kern_w, kern_d = weight_oper.shape
+            assert kern_d == in_c
+
+        assert out_c == bias_oper.shape[0]
+
+        use_nchw = image_oper.use_nchw()
+
+        if depthwise:
+            num_args = 12
+            opcode = NNAPI_OperationCode.DEPTHWISE_CONV_2D
+        else:
+            num_args = 11
+            if transpose:
+                opcode = NNAPI_OperationCode.TRANSPOSE_CONV_2D
+            else:
+                opcode = NNAPI_OperationCode.CONV_2D
+
+        inputs = [None] * num_args
+        inputs[0] = image_id
+        inputs[1] = weight_id
+        inputs[2] = bias_id
+        inputs[3] = self.add_immediate_int_scalar(args.pad_l)
+        inputs[4] = self.add_immediate_int_scalar(args.pad_r)
+        inputs[5] = self.add_immediate_int_scalar(args.pad_t)
+        inputs[6] = self.add_immediate_int_scalar(args.pad_b)
+        inputs[7] = self.add_immediate_int_scalar(args.stride_w)
+        inputs[8] = self.add_immediate_int_scalar(args.stride_h)
+        if depthwise:
+            inputs[9] = self.add_immediate_int_scalar(1)
+            inputs[10] = self.add_immediate_int_scalar(fuse_code)
+            inputs[11] = self.add_immediate_bool_scalar(use_nchw)
+        else:
+            inputs[9] = self.add_immediate_int_scalar(fuse_code)
+            inputs[10] = self.add_immediate_bool_scalar(use_nchw)
+
+        outputs = [None] * 1
+        out_shape = get_conv_pool_shape(image_oper.shape, args, out_c, transpose)
+        out_oper = image_oper._replace(
+            shape=out_shape,
+            scale=out_scale,
+            zero_point=out_zero_point,
+        )
+        out_id = self.add_tensor_operand(jit_out, out_oper)
+        self._handle_conv_pool_flexible_input(out_id, jit_image, args, transpose)
+
+        outputs[0] = out_id
+        self.add_operation(opcode, inputs, outputs)
+
+    def _handle_conv_pool_flexible_input(self, out_id, jit_image, args, transpose):
+        image_id, image_oper = self.get_tensor_operand_by_jitval(jit_image)
+        batch, in_ch, in_h, in_w = image_oper.shape
+
+        if batch == 0:
+            self.forward_operand_shape(out_id, 0, image_id, 0)
+        if in_ch == 0:
+            raise Exception("Input channels can't be flexible")  # noqa: TRY002
+        # H & W
+        if transpose:
+            if in_h == 0:
+                self.compute_operand_shape(
+                    out_id,
+                    2,
+                    f"({flex_name(image_id, 2)} - 1) * {args.stride_h} + {args.kernel_h} - {args.pad_t} - {args.pad_b}",
+                )
+            if in_w == 0:
+                self.compute_operand_shape(
+                    out_id,
+                    3,
+                    f"({flex_name(image_id, 3)} - 1) * {args.stride_w} + {args.kernel_w} - {args.pad_l} - {args.pad_r}",
+                )
+        else:
+            if in_h == 0:
+                self.compute_operand_shape(
+                    out_id,
+                    2,
+                    f"({flex_name(image_id, 2)} - {args.kernel_h} + {args.pad_t} + {args.pad_b}) // {args.stride_h} + 1",
+                )
+            if in_w == 0:
+                self.compute_operand_shape(
+                    out_id,
+                    3,
+                    f"({flex_name(image_id, 3)} - {args.kernel_w} + {args.pad_l} + {args.pad_r}) // {args.stride_w} + 1",
+                )
+
+
+def serialize_model(
+    module, inputs, *, config=None, return_shapes=None, use_int16_for_qint16=False
+):
+    """Convert to NNAPI and serialize torchscript module.
+
+    Parameters:
+        module: Torchscript module to convert
+        inputs: Tensors used to specify input details for NNAPI
+        config (optional): Optional config to attach to module
+        return_shapes (optional): Specify shape of outputs if
+            your module uses runtime flexible shapes to set output
+            buffer size for NNAPI
+        use_int16_for_qint16 (optional): Use Pytorch int16 to represent NNAPI qint16 values
+    """
+    return _NnapiSerializer(config, use_int16_for_qint16).serialize_model(
+        module, inputs, return_shapes
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cpu/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cpu/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc8a174626bf17ef8c31c29ea2112162d9b70284
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cpu/__init__.py
@@ -0,0 +1,20 @@
+import torch
+
+
+__all__ = [
+    "get_cpu_capability",
+]
+
+
+def get_cpu_capability() -> str:
+    r"""Return cpu capability as a string value.
+
+    Possible values:
+    - "DEFAULT"
+    - "VSX"
+    - "Z VECTOR"
+    - "NO AVX"
+    - "AVX2"
+    - "AVX512"
+    """
+    return torch._C._get_cpu_capability()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cpu/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cpu/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ce83ecde14e47b09924d1774da00793a4297d69d
Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cpu/__pycache__/__init__.cpython-311.pyc differ
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cuda/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cuda/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d49f22b2415a82adf12fe6c493cecd47871bb5b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cuda/__init__.py
@@ -0,0 +1,478 @@
+# mypy: allow-untyped-defs
+import contextlib
+from typing import Union
+from typing_extensions import deprecated
+
+import torch
+
+
+__all__ = [
+    "is_built",
+    "cuFFTPlanCacheAttrContextProp",
+    "cuFFTPlanCache",
+    "cuFFTPlanCacheManager",
+    "cuBLASModule",
+    "preferred_linalg_library",
+    "preferred_blas_library",
+    "cufft_plan_cache",
+    "matmul",
+    "SDPAParams",
+    "enable_cudnn_sdp",
+    "cudnn_sdp_enabled",
+    "enable_flash_sdp",
+    "flash_sdp_enabled",
+    "enable_mem_efficient_sdp",
+    "mem_efficient_sdp_enabled",
+    "math_sdp_enabled",
+    "enable_math_sdp",
+    "allow_fp16_bf16_reduction_math_sdp",
+    "fp16_bf16_reduction_math_sdp_allowed",
+    "is_flash_attention_available",
+    "can_use_flash_attention",
+    "can_use_efficient_attention",
+    "can_use_cudnn_attention",
+    "sdp_kernel",
+]
+
+
+def is_built():
+    r"""
+    Return whether PyTorch is built with CUDA support.
+
+    Note that this doesn't necessarily mean CUDA is available; just that if this PyTorch
+    binary were run on a machine with working CUDA drivers and devices, we would be able to use it.
+    """
+    return torch._C._has_cuda
+
+
+class cuFFTPlanCacheAttrContextProp:
+    # Like regular ContextProp, but uses the `.device_index` attribute from the
+    # calling object as the first argument to the getter and setter.
+    def __init__(self, getter, setter):
+        self.getter = getter
+        self.setter = setter
+
+    def __get__(self, obj, objtype):
+        return self.getter(obj.device_index)
+
+    def __set__(self, obj, val):
+        if isinstance(self.setter, str):
+            raise RuntimeError(self.setter)
+        self.setter(obj.device_index, val)
+
+
+class cuFFTPlanCache:
+    r"""
+    Represent a specific plan cache for a specific `device_index`.
+
+    The attributes `size` and `max_size`, and method `clear`, can fetch and/ or
+    change properties of the C++ cuFFT plan cache.
+    """
+
+    def __init__(self, device_index):
+        self.device_index = device_index
+
+    size = cuFFTPlanCacheAttrContextProp(
+        torch._cufft_get_plan_cache_size,
+        ".size is a read-only property showing the number of plans currently in the "
+        "cache. To change the cache capacity, set cufft_plan_cache.max_size.",
+    )
+
+    max_size = cuFFTPlanCacheAttrContextProp(
+        torch._cufft_get_plan_cache_max_size, torch._cufft_set_plan_cache_max_size
+    )
+
+    def clear(self):
+        return torch._cufft_clear_plan_cache(self.device_index)
+
+
+class cuFFTPlanCacheManager:
+    r"""
+    Represent all cuFFT plan caches, return the cuFFTPlanCache for a given device when indexed.
+
+    Finally, this object, when used directly as a `cuFFTPlanCache` object (e.g.,
+    setting the `.max_size`) attribute, the current device's cuFFT plan cache is
+    used.
+    """
+
+    __initialized = False
+
+    def __init__(self):
+        self.caches = []
+        self.__initialized = True
+
+    def __getitem__(self, device):
+        index = torch.cuda._utils._get_device_index(device)
+        if index < 0 or index >= torch.cuda.device_count():
+            raise RuntimeError(
+                f"cufft_plan_cache: expected 0 <= device index < {torch.cuda.device_count()}, but got "
+                f"device with index {index}"
+            )
+        if len(self.caches) == 0:
+            self.caches.extend(
+                cuFFTPlanCache(index) for index in range(torch.cuda.device_count())
+            )
+        return self.caches[index]
+
+    def __getattr__(self, name):
+        return getattr(self[torch.cuda.current_device()], name)
+
+    def __setattr__(self, name, value):
+        if self.__initialized:
+            return setattr(self[torch.cuda.current_device()], name, value)
+        else:
+            return super().__setattr__(name, value)
+
+
+class cuBLASModule:
+    def __getattr__(self, name):
+        if name == "allow_tf32":
+            return torch._C._get_cublas_allow_tf32()
+        elif name == "allow_fp16_reduced_precision_reduction":
+            return torch._C._get_cublas_allow_fp16_reduced_precision_reduction()
+        elif name == "allow_bf16_reduced_precision_reduction":
+            return torch._C._get_cublas_allow_bf16_reduced_precision_reduction()
+        raise AttributeError("Unknown attribute " + name)
+
+    def __setattr__(self, name, value):
+        if name == "allow_tf32":
+            return torch._C._set_cublas_allow_tf32(value)
+        elif name == "allow_fp16_reduced_precision_reduction":
+            return torch._C._set_cublas_allow_fp16_reduced_precision_reduction(value)
+        elif name == "allow_bf16_reduced_precision_reduction":
+            return torch._C._set_cublas_allow_bf16_reduced_precision_reduction(value)
+        raise AttributeError("Unknown attribute " + name)
+
+
+_LinalgBackends = {
+    "default": torch._C._LinalgBackend.Default,
+    "cusolver": torch._C._LinalgBackend.Cusolver,
+    "magma": torch._C._LinalgBackend.Magma,
+}
+_LinalgBackends_str = ", ".join(_LinalgBackends.keys())
+
+
+def preferred_linalg_library(
+    backend: Union[None, str, torch._C._LinalgBackend] = None
+) -> torch._C._LinalgBackend:
+    r"""
+    Override the heuristic PyTorch uses to choose between cuSOLVER and MAGMA for CUDA linear algebra operations.
+
+    .. warning:: This flag is experimental and subject to change.
+
+    When PyTorch runs a CUDA linear algebra operation it often uses the cuSOLVER or MAGMA libraries,
+    and if both are available it decides which to use with a heuristic.
+    This flag (a :class:`str`) allows overriding those heuristics.
+
+    * If `"cusolver"` is set then cuSOLVER will be used wherever possible.
+    * If `"magma"` is set then MAGMA will be used wherever possible.
+    * If `"default"` (the default) is set then heuristics will be used to pick between
+      cuSOLVER and MAGMA if both are available.
+    * When no input is given, this function returns the currently preferred library.
+    * User may use the environment variable TORCH_LINALG_PREFER_CUSOLVER=1 to set the preferred library to cuSOLVER
+      globally.
+      This flag only sets the initial value of the preferred library and the preferred library
+      may still be overridden by this function call later in your script.
+
+    Note: When a library is preferred other libraries may still be used if the preferred library
+    doesn't implement the operation(s) called.
+    This flag may achieve better performance if PyTorch's heuristic library selection is incorrect
+    for your application's inputs.
+
+    Currently supported linalg operators:
+
+    * :func:`torch.linalg.inv`
+    * :func:`torch.linalg.inv_ex`
+    * :func:`torch.linalg.cholesky`
+    * :func:`torch.linalg.cholesky_ex`
+    * :func:`torch.cholesky_solve`
+    * :func:`torch.cholesky_inverse`
+    * :func:`torch.linalg.lu_factor`
+    * :func:`torch.linalg.lu`
+    * :func:`torch.linalg.lu_solve`
+    * :func:`torch.linalg.qr`
+    * :func:`torch.linalg.eigh`
+    * :func:`torch.linalg.eighvals`
+    * :func:`torch.linalg.svd`
+    * :func:`torch.linalg.svdvals`
+    """
+    if backend is None:
+        pass
+    elif isinstance(backend, str):
+        if backend not in _LinalgBackends:
+            raise RuntimeError(
+                "Unknown input value. " f"Choose from: {_LinalgBackends_str}."
+            )
+        torch._C._set_linalg_preferred_backend(_LinalgBackends[backend])
+    elif isinstance(backend, torch._C._LinalgBackend):
+        torch._C._set_linalg_preferred_backend(backend)
+    else:
+        raise RuntimeError("Unknown input value type.")
+
+    return torch._C._get_linalg_preferred_backend()
+
+
+_BlasBackends = {
+    "cublas": torch._C._BlasBackend.Cublas,
+    "cublaslt": torch._C._BlasBackend.Cublaslt,
+    "hipblaslt": torch._C._BlasBackend.Cublaslt,  # alias
+}
+_BlasBackends_str = ", ".join(_BlasBackends.keys())
+
+
+def preferred_blas_library(
+    backend: Union[None, str, torch._C._BlasBackend] = None
+) -> torch._C._BlasBackend:
+    r"""
+    Override the library PyTorch uses for BLAS operations. Choose between cuBLAS and cuBLASLt.
+
+    .. warning:: This flag is experimental and subject to change.
+
+    When PyTorch runs a CUDA BLAS operation it defaults to cuBLAS even if both cuBLAS and cuBLASLt are available.
+    For PyTorch built for ROCm, hipBLAS and hipBLASLt may offer different performance.
+    This flag (a :class:`str`) allows overriding which BLAS library to use.
+
+    * If `"cublas"` is set then cuBLAS will be used wherever possible.
+    * If `"cublaslt"` is set then cuBLASLt will be used wherever possible.
+    * When no input is given, this function returns the currently preferred library.
+    * User may use the environment variable TORCH_BLAS_PREFER_CUBLASLT=1 to set the preferred library to cuBLASLt
+      globally.
+      This flag only sets the initial value of the preferred library and the preferred library
+      may still be overridden by this function call later in your script.
+
+    Note: When a library is preferred other libraries may still be used if the preferred library
+    doesn't implement the operation(s) called.
+    This flag may achieve better performance if PyTorch's library selection is incorrect
+    for your application's inputs.
+
+    """
+    if backend is None:
+        pass
+    elif isinstance(backend, str):
+        if backend not in _BlasBackends:
+            raise RuntimeError(
+                "Unknown input value. " f"Choose from: {_BlasBackends_str}."
+            )
+        torch._C._set_blas_preferred_backend(_BlasBackends[backend])
+    elif isinstance(backend, torch._C._BlasBackend):
+        torch._C._set_blas_preferred_backend(backend)
+    else:
+        raise RuntimeError("Unknown input value type.")
+
+    return torch._C._get_blas_preferred_backend()
+
+
+from torch._C import _SDPAParams as SDPAParams, _SDPBackend as SDPBackend
+
+
+# Set the __module__ attribute
+SDPAParams.__module__ = "torch.backends.cuda"
+SDPAParams.__name__ = "SDPAParams"
+
+
+def flash_sdp_enabled():
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Returns whether flash scaled dot product attention is enabled or not.
+    """
+    return torch._C._get_flash_sdp_enabled()
+
+
+def enable_flash_sdp(enabled: bool):
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Enables or disables flash scaled dot product attention.
+    """
+    torch._C._set_sdp_use_flash(enabled)
+
+
+def mem_efficient_sdp_enabled():
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Returns whether memory efficient scaled dot product attention is enabled or not.
+    """
+    return torch._C._get_mem_efficient_sdp_enabled()
+
+
+def enable_mem_efficient_sdp(enabled: bool):
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Enables or disables memory efficient scaled dot product attention.
+    """
+    torch._C._set_sdp_use_mem_efficient(enabled)
+
+
+def math_sdp_enabled():
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Returns whether math scaled dot product attention is enabled or not.
+    """
+    return torch._C._get_math_sdp_enabled()
+
+
+def enable_math_sdp(enabled: bool):
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Enables or disables math scaled dot product attention.
+    """
+    torch._C._set_sdp_use_math(enabled)
+
+
+def allow_fp16_bf16_reduction_math_sdp(enabled: bool):
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Enables or disables fp16/bf16 reduction in math scaled dot product attention.
+    """
+    torch._C._set_math_sdp_allow_fp16_bf16_reduction(enabled)
+
+
+def fp16_bf16_reduction_math_sdp_allowed():
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Returns whether fp16/bf16 reduction in math scaled dot product attention is enabled or not.
+    """
+    return torch._C._get_math_sdp_allow_fp16_bf16_reduction()
+
+
+def is_flash_attention_available() -> bool:
+    r"""Check if PyTorch was built with FlashAttention for scaled_dot_product_attention.
+
+    Returns:
+        True if FlashAttention is built and available; otherwise, False.
+
+    Note:
+        This function is dependent on a CUDA-enabled build of PyTorch. It will return False
+        in non-CUDA environments.
+    """
+    return torch._C._is_flash_attention_available()
+
+
+def can_use_flash_attention(params: SDPAParams, debug: bool = False) -> bool:
+    r"""Check if FlashAttention can be utilized in scaled_dot_product_attention.
+
+    Args:
+        params: An instance of SDPAParams containing the tensors for query,
+                key, value, an optional attention mask, dropout rate, and
+                a flag indicating if the attention is causal.
+        debug: Whether to logging.warn debug information as to why FlashAttention could not be run.
+            Defaults to False.
+
+    Returns:
+        True if FlashAttention can be used with the given parameters; otherwise, False.
+
+    Note:
+        This function is dependent on a CUDA-enabled build of PyTorch. It will return False
+        in non-CUDA environments.
+    """
+    return torch._C._can_use_flash_attention(params, debug)
+
+
+def can_use_efficient_attention(params: SDPAParams, debug: bool = False) -> bool:
+    r"""Check if efficient_attention can be utilized in scaled_dot_product_attention.
+
+    Args:
+        params: An instance of SDPAParams containing the tensors for query,
+                key, value, an optional attention mask, dropout rate, and
+                a flag indicating if the attention is causal.
+        debug: Whether to logging.warn with information as to why efficient_attention could not be run.
+            Defaults to False.
+
+    Returns:
+        True if efficient_attention can be used with the given parameters; otherwise, False.
+
+    Note:
+        This function is dependent on a CUDA-enabled build of PyTorch. It will return False
+        in non-CUDA environments.
+    """
+    return torch._C._can_use_mem_efficient_attention(params, debug)
+
+
+def can_use_cudnn_attention(params: SDPAParams, debug: bool = False) -> bool:
+    r"""Check if cudnn_attention can be utilized in scaled_dot_product_attention.
+
+    Args:
+        params: An instance of SDPAParams containing the tensors for query,
+                key, value, an optional attention mask, dropout rate, and
+                a flag indicating if the attention is causal.
+        debug: Whether to logging.warn with information as to why cuDNN attention could not be run.
+            Defaults to False.
+
+    Returns:
+        True if cuDNN can be used with the given parameters; otherwise, False.
+
+    Note:
+        This function is dependent on a CUDA-enabled build of PyTorch. It will return False
+        in non-CUDA environments.
+    """
+    return torch._C._can_use_cudnn_attention(params, debug)
+
+
+def cudnn_sdp_enabled():
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Returns whether cuDNN scaled dot product attention is enabled or not.
+    """
+    return torch._C._get_cudnn_sdp_enabled()
+
+
+def enable_cudnn_sdp(enabled: bool):
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Enables or disables cuDNN scaled dot product attention.
+    """
+    torch._C._set_sdp_use_cudnn(enabled)
+
+
+@contextlib.contextmanager
+@deprecated(
+    (
+        "`torch.backends.cuda.sdp_kernel()` is deprecated. "
+        "In the future, this context manager will be removed. "
+        "Please see `torch.nn.attention.sdpa_kernel()` for the new context manager, "
+        "with updated signature."
+    ),
+    category=FutureWarning,
+)
+def sdp_kernel(
+    enable_flash: bool = True,
+    enable_math: bool = True,
+    enable_mem_efficient: bool = True,
+    enable_cudnn: bool = True,
+):
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    This context manager can be used to temporarily enable or disable any of the three backends for scaled dot product attention.
+    Upon exiting the context manager, the previous state of the flags will be restored.
+    """
+    from torch.nn.attention import sdpa_kernel
+
+    backend_list = []
+    if enable_flash:
+        backend_list.append(SDPBackend.FLASH_ATTENTION)
+    if enable_mem_efficient:
+        backend_list.append(SDPBackend.EFFICIENT_ATTENTION)
+    if enable_math:
+        backend_list.append(SDPBackend.MATH)
+    if enable_cudnn:
+        backend_list.append(SDPBackend.CUDNN_ATTENTION)
+
+    with sdpa_kernel(backend_list) as context:
+        try:
+            yield context
+        finally:
+            pass
+
+
+cufft_plan_cache = cuFFTPlanCacheManager()
+matmul = cuBLASModule()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cuda/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cuda/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..663a13374a4228b15549aaa983ddcb2e7d42128e
Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cuda/__pycache__/__init__.cpython-311.pyc differ
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cudnn/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cudnn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ba6f9e3b40bdfd8194dc9be97f542910ffb4632
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cudnn/__init__.py
@@ -0,0 +1,208 @@
+# mypy: allow-untyped-defs
+import os
+import sys
+import warnings
+from contextlib import contextmanager
+from typing import Optional
+
+import torch
+from torch.backends import __allow_nonbracketed_mutation, ContextProp, PropModule
+
+
+try:
+    from torch._C import _cudnn
+except ImportError:
+    _cudnn = None  # type: ignore[assignment]
+
+# Write:
+#
+#   torch.backends.cudnn.enabled = False
+#
+# to globally disable CuDNN/MIOpen
+
+__cudnn_version: Optional[int] = None
+
+if _cudnn is not None:
+
+    def _init():
+        global __cudnn_version
+        if __cudnn_version is None:
+            __cudnn_version = _cudnn.getVersionInt()
+            runtime_version = _cudnn.getRuntimeVersion()
+            compile_version = _cudnn.getCompileVersion()
+            runtime_major, runtime_minor, _ = runtime_version
+            compile_major, compile_minor, _ = compile_version
+            # Different major versions are always incompatible
+            # Starting with cuDNN 7, minor versions are backwards-compatible
+            # Not sure about MIOpen (ROCm), so always do a strict check
+            if runtime_major != compile_major:
+                cudnn_compatible = False
+            elif runtime_major < 7 or not _cudnn.is_cuda:
+                cudnn_compatible = runtime_minor == compile_minor
+            else:
+                cudnn_compatible = runtime_minor >= compile_minor
+            if not cudnn_compatible:
+                if os.environ.get("PYTORCH_SKIP_CUDNN_COMPATIBILITY_CHECK", "0") == "1":
+                    return True
+                base_error_msg = (
+                    f"cuDNN version incompatibility: "
+                    f"PyTorch was compiled  against {compile_version} "
+                    f"but found runtime version {runtime_version}. "
+                    f"PyTorch already comes bundled with cuDNN. "
+                    f"One option to resolving this error is to ensure PyTorch "
+                    f"can find the bundled cuDNN. "
+                )
+
+                if "LD_LIBRARY_PATH" in os.environ:
+                    ld_library_path = os.environ.get("LD_LIBRARY_PATH", "")
+                    if any(
+                        substring in ld_library_path for substring in ["cuda", "cudnn"]
+                    ):
+                        raise RuntimeError(
+                            f"{base_error_msg}"
+                            f"Looks like your LD_LIBRARY_PATH contains incompatible version of cudnn. "
+                            f"Please either remove it from the path or install cudnn {compile_version}"
+                        )
+                    else:
+                        raise RuntimeError(
+                            f"{base_error_msg}"
+                            f"one possibility is that there is a "
+                            f"conflicting cuDNN in LD_LIBRARY_PATH."
+                        )
+                else:
+                    raise RuntimeError(base_error_msg)
+
+        return True
+
+else:
+
+    def _init():
+        return False
+
+
+def version():
+    """Return the version of cuDNN."""
+    if not _init():
+        return None
+    return __cudnn_version
+
+
+CUDNN_TENSOR_DTYPES = {
+    torch.half,
+    torch.float,
+    torch.double,
+}
+
+
+def is_available():
+    r"""Return a bool indicating if CUDNN is currently available."""
+    return torch._C._has_cudnn
+
+
+def is_acceptable(tensor):
+    if not torch._C._get_cudnn_enabled():
+        return False
+    if tensor.device.type != "cuda" or tensor.dtype not in CUDNN_TENSOR_DTYPES:
+        return False
+    if not is_available():
+        warnings.warn(
+            "PyTorch was compiled without cuDNN/MIOpen support. To use cuDNN/MIOpen, rebuild "
+            "PyTorch making sure the library is visible to the build system."
+        )
+        return False
+    if not _init():
+        warnings.warn(
+            "cuDNN/MIOpen library not found. Check your {libpath}".format(
+                libpath={"darwin": "DYLD_LIBRARY_PATH", "win32": "PATH"}.get(
+                    sys.platform, "LD_LIBRARY_PATH"
+                )
+            )
+        )
+        return False
+    return True
+
+
+def set_flags(
+    _enabled=None,
+    _benchmark=None,
+    _benchmark_limit=None,
+    _deterministic=None,
+    _allow_tf32=None,
+):
+    orig_flags = (
+        torch._C._get_cudnn_enabled(),
+        torch._C._get_cudnn_benchmark(),
+        None if not is_available() else torch._C._cuda_get_cudnn_benchmark_limit(),
+        torch._C._get_cudnn_deterministic(),
+        torch._C._get_cudnn_allow_tf32(),
+    )
+    if _enabled is not None:
+        torch._C._set_cudnn_enabled(_enabled)
+    if _benchmark is not None:
+        torch._C._set_cudnn_benchmark(_benchmark)
+    if _benchmark_limit is not None and is_available():
+        torch._C._cuda_set_cudnn_benchmark_limit(_benchmark_limit)
+    if _deterministic is not None:
+        torch._C._set_cudnn_deterministic(_deterministic)
+    if _allow_tf32 is not None:
+        torch._C._set_cudnn_allow_tf32(_allow_tf32)
+    return orig_flags
+
+
+@contextmanager
+def flags(
+    enabled=False,
+    benchmark=False,
+    benchmark_limit=10,
+    deterministic=False,
+    allow_tf32=True,
+):
+    with __allow_nonbracketed_mutation():
+        orig_flags = set_flags(
+            enabled, benchmark, benchmark_limit, deterministic, allow_tf32
+        )
+    try:
+        yield
+    finally:
+        # recover the previous values
+        with __allow_nonbracketed_mutation():
+            set_flags(*orig_flags)
+
+
+# The magic here is to allow us to intercept code like this:
+#
+#   torch.backends..enabled = True
+
+
+class CudnnModule(PropModule):
+    def __init__(self, m, name):
+        super().__init__(m, name)
+
+    enabled = ContextProp(torch._C._get_cudnn_enabled, torch._C._set_cudnn_enabled)
+    deterministic = ContextProp(
+        torch._C._get_cudnn_deterministic, torch._C._set_cudnn_deterministic
+    )
+    benchmark = ContextProp(
+        torch._C._get_cudnn_benchmark, torch._C._set_cudnn_benchmark
+    )
+    benchmark_limit = None
+    if is_available():
+        benchmark_limit = ContextProp(
+            torch._C._cuda_get_cudnn_benchmark_limit,
+            torch._C._cuda_set_cudnn_benchmark_limit,
+        )
+    allow_tf32 = ContextProp(
+        torch._C._get_cudnn_allow_tf32, torch._C._set_cudnn_allow_tf32
+    )
+
+
+# This is the sys.modules replacement trick, see
+# https://stackoverflow.com/questions/2447353/getattr-on-a-module/7668273#7668273
+sys.modules[__name__] = CudnnModule(sys.modules[__name__], __name__)
+
+# Add type annotation for the replaced module
+enabled: bool
+deterministic: bool
+benchmark: bool
+allow_tf32: bool
+benchmark_limit: int
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cudnn/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cudnn/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cudnn/__pycache__/rnn.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cudnn/__pycache__/rnn.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cudnn/rnn.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cudnn/rnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b253e19054027e88c3ead9e536d0fa0cd0da3cc
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cudnn/rnn.py
@@ -0,0 +1,64 @@
+# mypy: allow-untyped-defs
+import torch.cuda
+
+
+try:
+    from torch._C import _cudnn
+except ImportError:
+    # Uses of all the functions below should be guarded by torch.backends.cudnn.is_available(),
+    # so it's safe to not emit any checks here.
+    _cudnn = None  # type: ignore[assignment]
+
+
+def get_cudnn_mode(mode):
+    if mode == "RNN_RELU":
+        return int(_cudnn.RNNMode.rnn_relu)
+    elif mode == "RNN_TANH":
+        return int(_cudnn.RNNMode.rnn_tanh)
+    elif mode == "LSTM":
+        return int(_cudnn.RNNMode.lstm)
+    elif mode == "GRU":
+        return int(_cudnn.RNNMode.gru)
+    else:
+        raise Exception(f"Unknown mode: {mode}")  # noqa: TRY002
+
+
+# NB: We don't actually need this class anymore (in fact, we could serialize the
+# dropout state for even better reproducibility), but it is kept for backwards
+# compatibility for old models.
+class Unserializable:
+    def __init__(self, inner):
+        self.inner = inner
+
+    def get(self):
+        return self.inner
+
+    def __getstate__(self):
+        # Note: can't return {}, because python2 won't call __setstate__
+        # if the value evaluates to False
+        return ""
+
+    def __setstate__(self, state):
+        self.inner = None
+
+
+def init_dropout_state(dropout, train, dropout_seed, dropout_state):
+    dropout_desc_name = "desc_" + str(torch.cuda.current_device())
+    dropout_p = dropout if train else 0
+    if (dropout_desc_name not in dropout_state) or (
+        dropout_state[dropout_desc_name].get() is None
+    ):
+        if dropout_p == 0:
+            dropout_state[dropout_desc_name] = Unserializable(None)
+        else:
+            dropout_state[dropout_desc_name] = Unserializable(
+                torch._cudnn_init_dropout_state(  # type: ignore[call-arg]
+                    dropout_p,
+                    train,
+                    dropout_seed,
+                    self_ty=torch.uint8,
+                    device=torch.device("cuda"),
+                )
+            )
+    dropout_ts = dropout_state[dropout_desc_name].get()
+    return dropout_ts
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cusparselt/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cusparselt/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0edddbe482421038e7f69a5631f9432e91575ba1
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cusparselt/__init__.py
@@ -0,0 +1,42 @@
+# mypy: allow-untyped-defs
+from typing import Optional
+
+import torch
+
+
+__all__ = [
+    "version",
+    "is_available",
+]
+
+try:
+    from torch._C import _cusparselt
+except ImportError:
+    _cusparselt = None  # type: ignore[assignment]
+
+__cusparselt_version: Optional[int] = None
+
+if _cusparselt is not None:
+
+    def _init():
+        global __cusparselt_version
+        if __cusparselt_version is None:
+            __cusparselt_version = _cusparselt.getVersionInt()
+        return True
+
+else:
+
+    def _init():
+        return False
+
+
+def version() -> Optional[int]:
+    """Return the version of cuSPARSELt"""
+    if not _init():
+        return None
+    return __cusparselt_version
+
+
+def is_available() -> bool:
+    r"""Return a bool indicating if cuSPARSELt is currently available."""
+    return torch._C._has_cusparselt
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cusparselt/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/cusparselt/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/mha/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/mha/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1ce7a5706dd6f65a0acff5971050f5f73c50074
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/mha/__init__.py
@@ -0,0 +1,25 @@
+# Config options to enable/disable C++ kernel for nn.functional.MHA
+# and nn.TransformerEncoder
+import torch
+
+
+_is_fastpath_enabled: bool = True
+
+
+def get_fastpath_enabled() -> bool:
+    """Returns whether fast path for TransformerEncoder and MultiHeadAttention
+    is enabled, or ``True`` if jit is scripting.
+
+    ..note:
+        The fastpath might not be run even if ``get_fastpath_enabled`` returns
+        ``True`` unless all conditions on inputs are met.
+    """
+    if not torch.jit.is_scripting():
+        return _is_fastpath_enabled
+    return True
+
+
+def set_fastpath_enabled(value: bool) -> None:
+    """Sets whether fast path is enabled"""
+    global _is_fastpath_enabled
+    _is_fastpath_enabled = value
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/mha/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/mha/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/mkl/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/mkl/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f96d692ae02190457b1e38cceadb62d95ab698a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/mkl/__init__.py
@@ -0,0 +1,57 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+def is_available():
+    r"""Return whether PyTorch is built with MKL support."""
+    return torch._C.has_mkl
+
+
+VERBOSE_OFF = 0
+VERBOSE_ON = 1
+
+
+class verbose:
+    """
+    On-demand oneMKL verbosing functionality.
+
+    To make it easier to debug performance issues, oneMKL can dump verbose
+    messages containing execution information like duration while executing
+    the kernel. The verbosing functionality can be invoked via an environment
+    variable named `MKL_VERBOSE`. However, this methodology dumps messages in
+    all steps. Those are a large amount of verbose messages. Moreover, for
+    investigating the performance issues, generally taking verbose messages
+    for one single iteration is enough. This on-demand verbosing functionality
+    makes it possible to control scope for verbose message dumping. In the
+    following example, verbose messages will be dumped out for the second
+    inference only.
+
+    .. highlight:: python
+    .. code-block:: python
+
+        import torch
+        model(data)
+        with torch.backends.mkl.verbose(torch.backends.mkl.VERBOSE_ON):
+            model(data)
+
+    Args:
+        level: Verbose level
+            - ``VERBOSE_OFF``: Disable verbosing
+            - ``VERBOSE_ON``:  Enable verbosing
+    """
+
+    def __init__(self, enable):
+        self.enable = enable
+
+    def __enter__(self):
+        if self.enable == VERBOSE_OFF:
+            return
+        st = torch._C._verbose.mkl_set_verbose(self.enable)
+        assert (
+            st
+        ), "Failed to set MKL into verbose mode. Please consider to disable this verbose scope."
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        torch._C._verbose.mkl_set_verbose(VERBOSE_OFF)
+        return False
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/mkl/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/mkl/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/mkldnn/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/mkldnn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d4b9c49519b107dac133c331073bb61865896f6
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/mkldnn/__init__.py
@@ -0,0 +1,100 @@
+# mypy: allow-untyped-defs
+import sys
+from contextlib import contextmanager
+from typing import TYPE_CHECKING
+
+import torch
+from torch.backends import __allow_nonbracketed_mutation, ContextProp, PropModule
+
+
+def is_available():
+    r"""Return whether PyTorch is built with MKL-DNN support."""
+    return torch._C._has_mkldnn
+
+
+VERBOSE_OFF = 0
+VERBOSE_ON = 1
+VERBOSE_ON_CREATION = 2
+
+
+class verbose:
+    """
+    On-demand oneDNN (former MKL-DNN) verbosing functionality.
+
+    To make it easier to debug performance issues, oneDNN can dump verbose
+    messages containing information like kernel size, input data size and
+    execution duration while executing the kernel. The verbosing functionality
+    can be invoked via an environment variable named `DNNL_VERBOSE`. However,
+    this methodology dumps messages in all steps. Those are a large amount of
+    verbose messages. Moreover, for investigating the performance issues,
+    generally taking verbose messages for one single iteration is enough.
+    This on-demand verbosing functionality makes it possible to control scope
+    for verbose message dumping. In the following example, verbose messages
+    will be dumped out for the second inference only.
+
+    .. highlight:: python
+    .. code-block:: python
+
+        import torch
+        model(data)
+        with torch.backends.mkldnn.verbose(torch.backends.mkldnn.VERBOSE_ON):
+            model(data)
+
+    Args:
+        level: Verbose level
+            - ``VERBOSE_OFF``: Disable verbosing
+            - ``VERBOSE_ON``:  Enable verbosing
+            - ``VERBOSE_ON_CREATION``: Enable verbosing, including oneDNN kernel creation
+    """
+
+    def __init__(self, level):
+        self.level = level
+
+    def __enter__(self):
+        if self.level == VERBOSE_OFF:
+            return
+        st = torch._C._verbose.mkldnn_set_verbose(self.level)
+        assert (
+            st
+        ), "Failed to set MKLDNN into verbose mode. Please consider to disable this verbose scope."
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        torch._C._verbose.mkldnn_set_verbose(VERBOSE_OFF)
+        return False
+
+
+def set_flags(_enabled, _deterministic=None):
+    orig_flags = (torch._C._get_mkldnn_enabled(), torch._C._get_mkldnn_deterministic())
+    torch._C._set_mkldnn_enabled(_enabled)
+    if _deterministic is not None:
+        torch._C._set_mkldnn_deterministic(_deterministic)
+    return orig_flags
+
+
+@contextmanager
+def flags(enabled=False, deterministic=False):
+    with __allow_nonbracketed_mutation():
+        orig_flags = set_flags(enabled, deterministic)
+    try:
+        yield
+    finally:
+        with __allow_nonbracketed_mutation():
+            set_flags(*orig_flags)
+
+
+class MkldnnModule(PropModule):
+    def __init__(self, m, name):
+        super().__init__(m, name)
+
+    enabled = ContextProp(torch._C._get_mkldnn_enabled, torch._C._set_mkldnn_enabled)
+    deterministic = ContextProp(
+        torch._C._get_mkldnn_deterministic, torch._C._set_mkldnn_deterministic
+    )
+
+
+if TYPE_CHECKING:
+    enabled: ContextProp
+    deterministic: ContextProp
+
+sys.modules[__name__] = MkldnnModule(sys.modules[__name__], __name__)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/mkldnn/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/mkldnn/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/mps/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/mps/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3b934a6ced4c3b5e7be9b798c2c54f13906bd4f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/mps/__init__.py
@@ -0,0 +1,55 @@
+# mypy: allow-untyped-defs
+from functools import lru_cache as _lru_cache
+from typing import Optional, TYPE_CHECKING
+
+import torch
+from torch.library import Library as _Library
+
+
+__all__ = ["is_built", "is_available", "is_macos13_or_newer", "is_macos_or_newer"]
+
+
+def is_built() -> bool:
+    r"""Return whether PyTorch is built with MPS support.
+
+    Note that this doesn't necessarily mean MPS is available; just that
+    if this PyTorch binary were run a machine with working MPS drivers
+    and devices, we would be able to use it.
+    """
+    return torch._C._has_mps
+
+
+@_lru_cache
+def is_available() -> bool:
+    r"""Return a bool indicating if MPS is currently available."""
+    return torch._C._mps_is_available()
+
+
+@_lru_cache
+def is_macos_or_newer(major: int, minor: int) -> bool:
+    r"""Return a bool indicating whether MPS is running on given MacOS or newer."""
+    return torch._C._mps_is_on_macos_or_newer(major, minor)
+
+
+@_lru_cache
+def is_macos13_or_newer(minor: int = 0) -> bool:
+    r"""Return a bool indicating whether MPS is running on MacOS 13 or newer."""
+    return torch._C._mps_is_on_macos_or_newer(13, minor)
+
+
+_lib: Optional[_Library] = None
+
+
+def _init():
+    r"""Register prims as implementation of var_mean and group_norm."""
+    global _lib
+
+    if _lib is not None or not is_built():
+        return
+
+    from torch._decomp.decompositions import native_group_norm_backward
+    from torch._refs import native_group_norm
+
+    _lib = _Library("aten", "IMPL")  # noqa: TOR901
+    _lib.impl("native_group_norm", native_group_norm, "MPS")
+    _lib.impl("native_group_norm_backward", native_group_norm_backward, "MPS")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/mps/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/mps/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/nnpack/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/nnpack/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d8a72f3cda9b0da16702c0d7c6fe92ae8f3f153
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/nnpack/__init__.py
@@ -0,0 +1,32 @@
+# mypy: allow-untyped-defs
+from contextlib import contextmanager
+
+import torch
+from torch.backends import __allow_nonbracketed_mutation, ContextProp, PropModule
+
+
+__all__ = ["is_available", "flags", "set_flags"]
+
+
+def is_available():
+    r"""Return whether PyTorch is built with NNPACK support."""
+    return torch._nnpack_available()
+
+
+def set_flags(_enabled):
+    r"""Set if nnpack is enabled globally"""
+    orig_flags = (torch._C._get_nnpack_enabled(),)
+    torch._C._set_nnpack_enabled(_enabled)
+    return orig_flags
+
+
+@contextmanager
+def flags(enabled=False):
+    r"""Context manager for setting if nnpack is enabled globally"""
+    with __allow_nonbracketed_mutation():
+        orig_flags = set_flags(enabled)
+    try:
+        yield
+    finally:
+        with __allow_nonbracketed_mutation():
+            set_flags(orig_flags[0])
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/nnpack/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/nnpack/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/openmp/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/openmp/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..aff8d46cd4ac2d9ff49942542d99ac2afbb85896
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/openmp/__init__.py
@@ -0,0 +1,7 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+def is_available():
+    r"""Return whether PyTorch is built with OpenMP support."""
+    return torch._C.has_openmp
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/openmp/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/openmp/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/opt_einsum/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/opt_einsum/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac63fa4bcf44035c11be305f686d17a3b102909d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/opt_einsum/__init__.py
@@ -0,0 +1,112 @@
+# mypy: allow-untyped-defs
+import sys
+import warnings
+from contextlib import contextmanager
+from functools import lru_cache as _lru_cache
+from typing import Any
+
+from torch.backends import __allow_nonbracketed_mutation, ContextProp, PropModule
+
+
+try:
+    import opt_einsum as _opt_einsum  # type: ignore[import]
+except ImportError:
+    _opt_einsum = None
+
+
+@_lru_cache
+def is_available() -> bool:
+    r"""Return a bool indicating if opt_einsum is currently available."""
+    return _opt_einsum is not None
+
+
+def get_opt_einsum() -> Any:
+    r"""Return the opt_einsum package if opt_einsum is currently available, else None."""
+    return _opt_einsum
+
+
+def _set_enabled(_enabled: bool) -> None:
+    if not is_available() and _enabled:
+        raise ValueError(
+            f"opt_einsum is not available, so setting `enabled` to {_enabled} will not reap "
+            "the benefits of calculating an optimal path for einsum. torch.einsum will "
+            "fall back to contracting from left to right. To enable this optimal path "
+            "calculation, please install opt-einsum."
+        )
+    global enabled
+    enabled = _enabled
+
+
+def _get_enabled() -> bool:
+    return enabled
+
+
+def _set_strategy(_strategy: str) -> None:
+    if not is_available():
+        raise ValueError(
+            f"opt_einsum is not available, so setting `strategy` to {_strategy} will not be meaningful. "
+            "torch.einsum will bypass path calculation and simply contract from left to right. "
+            "Please install opt_einsum or unset `strategy`."
+        )
+    if not enabled:
+        raise ValueError(
+            f"opt_einsum is not enabled, so setting a `strategy` to {_strategy} will not be meaningful. "
+            "torch.einsum will bypass path calculation and simply contract from left to right. "
+            "Please set `enabled` to `True` as well or unset `strategy`."
+        )
+    if _strategy not in ["auto", "greedy", "optimal"]:
+        raise ValueError(
+            f"`strategy` must be one of the following: [auto, greedy, optimal] but is {_strategy}"
+        )
+    global strategy
+    strategy = _strategy
+
+
+def _get_strategy() -> str:
+    return strategy
+
+
+def set_flags(_enabled=None, _strategy=None):
+    orig_flags = (enabled, None if not is_available() else strategy)
+    if _enabled is not None:
+        _set_enabled(_enabled)
+    if _strategy is not None:
+        _set_strategy(_strategy)
+    return orig_flags
+
+
+@contextmanager
+def flags(enabled=None, strategy=None):
+    with __allow_nonbracketed_mutation():
+        orig_flags = set_flags(enabled, strategy)
+    try:
+        yield
+    finally:
+        # recover the previous values
+        with __allow_nonbracketed_mutation():
+            set_flags(*orig_flags)
+
+
+# The magic here is to allow us to intercept code like this:
+#
+#   torch.backends.opt_einsum.enabled = True
+
+
+class OptEinsumModule(PropModule):
+    def __init__(self, m, name):
+        super().__init__(m, name)
+
+    global enabled
+    enabled = ContextProp(_get_enabled, _set_enabled)
+    global strategy
+    strategy = None
+    if is_available():
+        strategy = ContextProp(_get_strategy, _set_strategy)
+
+
+# This is the sys.modules replacement trick, see
+# https://stackoverflow.com/questions/2447353/getattr-on-a-module/7668273#7668273
+sys.modules[__name__] = OptEinsumModule(sys.modules[__name__], __name__)
+
+enabled = True if is_available() else False
+strategy = "auto" if is_available() else None
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/opt_einsum/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/opt_einsum/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/quantized/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/quantized/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3cb795dd39fcb2ab44bdbc0a4cb070df251616a2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/quantized/__init__.py
@@ -0,0 +1,66 @@
+# mypy: allow-untyped-defs
+import sys
+import types
+from typing import List
+
+import torch
+
+
+# This function should correspond to the enums present in c10/core/QEngine.h
+def _get_qengine_id(qengine: str) -> int:
+    if qengine == "none" or qengine == "" or qengine is None:
+        ret = 0
+    elif qengine == "fbgemm":
+        ret = 1
+    elif qengine == "qnnpack":
+        ret = 2
+    elif qengine == "onednn":
+        ret = 3
+    elif qengine == "x86":
+        ret = 4
+    else:
+        ret = -1
+        raise RuntimeError(f"{qengine} is not a valid value for quantized engine")
+    return ret
+
+
+# This function should correspond to the enums present in c10/core/QEngine.h
+def _get_qengine_str(qengine: int) -> str:
+    all_engines = {0: "none", 1: "fbgemm", 2: "qnnpack", 3: "onednn", 4: "x86"}
+    return all_engines.get(qengine, "*undefined")
+
+
+class _QEngineProp:
+    def __get__(self, obj, objtype) -> str:
+        return _get_qengine_str(torch._C._get_qengine())
+
+    def __set__(self, obj, val: str) -> None:
+        torch._C._set_qengine(_get_qengine_id(val))
+
+
+class _SupportedQEnginesProp:
+    def __get__(self, obj, objtype) -> List[str]:
+        qengines = torch._C._supported_qengines()
+        return [_get_qengine_str(qe) for qe in qengines]
+
+    def __set__(self, obj, val) -> None:
+        raise RuntimeError("Assignment not supported")
+
+
+class QuantizedEngine(types.ModuleType):
+    def __init__(self, m, name):
+        super().__init__(name)
+        self.m = m
+
+    def __getattr__(self, attr):
+        return self.m.__getattribute__(attr)
+
+    engine = _QEngineProp()
+    supported_engines = _SupportedQEnginesProp()
+
+
+# This is the sys.modules replacement trick, see
+# https://stackoverflow.com/questions/2447353/getattr-on-a-module/7668273#7668273
+sys.modules[__name__] = QuantizedEngine(sys.modules[__name__], __name__)
+engine: str
+supported_engines: List[str]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/quantized/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/quantized/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/xeon/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/xeon/__init__.py
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/xeon/__pycache__/run_cpu.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/xeon/__pycache__/run_cpu.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/xeon/run_cpu.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/xeon/run_cpu.py
new file mode 100644
index 0000000000000000000000000000000000000000..a701d10eb5601f353edf38164f4c4b8b7e6deb3e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/xeon/run_cpu.py
@@ -0,0 +1,943 @@
+# mypy: allow-untyped-defs
+"""
+This is a script for launching PyTorch inference on Intel(R) Xeon(R) Scalable Processors with optimal configurations.
+
+Single instance inference, multi-instance inference are enabled.
+
+Note: term "instance" here doesn't refer to a cloud instance. This script is executed as a single process. It invokes
+multiple "instances" which are formed from multiple threads for each. "instance" is kind of group of threads in this
+context.
+
+Illustrated as below:
+
+::
+
+    +-----------------------------+----------------------+-------+
+    |            process          |        thread        | core  |
+    +=============================+======================+=======+
+    | torch.backends.xeon.run_cpu | instance 0: thread 0 |   0   |
+    |                             |             thread 1 |   1   |
+    |                             +----------------------+-------+
+    |                             | instance 1: thread 0 |   2   |
+    |                             |             thread 1 |   3   |
+    |                             +----------------------+-------+
+    |                             | ...                  |  ...  |
+    |                             +----------------------+-------+
+    |                             | instance N: thread 0 |   M   |
+    |                             |             thread 1 |  M+1  |
+    +-----------------------------+----------------------+-------+
+
+To get the peak performance on Intel(R) Xeon(R) Scalable Processors, the script optimizes the configuration of thread and memory
+management. For thread management, the script configures thread affinity and the preload of Intel OMP library.
+For memory management, it configures NUMA binding and preload optimized memory allocation library (e.g. tcmalloc, jemalloc).
+
+Environment variables that will be set by this script:
+
++------------------+-------------------------------------------------------------------------------------------------+
+| Environ Variable |                                             Value                                               |
++==================+=================================================================================================+
+|    LD_PRELOAD    | Depending on knobs you set, /libiomp5.so, /libjemalloc.so, /libtcmalloc.so might |
+|                  | be appended to LD_PRELOAD.                                                                      |
++------------------+-------------------------------------------------------------------------------------------------+
+|   KMP_AFFINITY   | If libiomp5.so is preloaded, KMP_AFFINITY could be set to "granularity=fine,compact,1,0".       |
++------------------+-------------------------------------------------------------------------------------------------+
+|   KMP_BLOCKTIME  | If libiomp5.so is preloaded, KMP_BLOCKTIME is set to "1".                                       |
++------------------+-------------------------------------------------------------------------------------------------+
+|  OMP_NUM_THREADS | value of ncores_per_instance                                                                    |
++------------------+-------------------------------------------------------------------------------------------------+
+|    MALLOC_CONF   | If libjemalloc.so is preloaded, MALLOC_CONF will be set to                                      |
+|                  | "oversize_threshold:1,background_thread:true,metadata_thp:auto".                                |
++------------------+-------------------------------------------------------------------------------------------------+
+
+*Note*: This script respects environment variables set preliminarily. I.e. If you set the environment variables
+mentioned above before running the script, the script will not overwrite the values in the script.
+
+How to use this module:
+~~~~~~~~~~~~~~~~~~~~~~~
+
+Single instance inference
+-------------------------
+
+1. Run single-instance inference on a single node with all CPU nodes.
+
+::
+
+   python -m torch.backends.xeon.run_cpu --throughput-mode script.py args
+
+2. Run single-instance inference on a single CPU node.
+
+::
+
+   python -m torch.backends.xeon.run_cpu --node-id 1 script.py args
+
+Multi-instance inference
+------------------------
+
+1. Multi-instance
+   By default this tool runs one process per node. If you want to set the instance numbers and core per instance,
+   --ninstances and  --ncores-per-instance should be set.
+
+::
+
+   python -m torch.backends.xeon.run_cpu -- python_script args
+
+   eg: on an Intel(R) Xeon(R) Scalable Processor with 14 instance, 4 cores per instance
+
+::
+
+   python -m torch.backends.xeon.run_cpu --ninstances 14 --ncores-per-instance 4 python_script args
+
+2. Run single-instance inference among multiple instances.
+   By default, runs all ninstances. If you want to independently run a single instance among ninstances, specify rank.
+
+   eg: run 0th instance on an Intel(R) Xeon(R) Scalable Processor with 2 instance (i.e., numactl -C 0-27)
+
+::
+
+   python -m torch.backends.xeon.run_cpu --ninstances 2 --rank 0 python_script args
+
+   eg: run 1st instance on an Intel(R) Xeon(R) Scalable Processor with 2 instance (i.e., numactl -C 28-55)
+
+::
+
+   python -m torch.backends.xeon.run_cpu --ninstances 2 --rank 1 python_script args
+
+   eg: run 0th instance on an Intel(R) Xeon(R) Scalable Processor with 2 instance, 2 cores per instance,
+   first four cores (i.e., numactl -C 0-1)
+
+::
+
+   python -m torch.backends.xeon.run_cpu --core-list "0, 1, 2, 3" --ninstances 2 --ncores-per-instance 2
+   --rank 0 python_script args
+
+3. To look up what optional arguments this module offers:
+
+::
+
+    python -m torch.backends.xeon.run_cpu --help
+
+Memory allocator
+----------------
+
+"--enable-tcmalloc" and "--enable-jemalloc" can be used to enable different memory allcator.
+
+"""
+
+import glob
+import logging
+import os
+import platform
+import re
+import subprocess
+import sys
+from argparse import ArgumentParser, RawTextHelpFormatter, REMAINDER
+from os.path import expanduser
+from typing import Dict, List
+
+from torch.distributed.elastic.multiprocessing import (
+    DefaultLogsSpecs as _DefaultLogsSpecs,
+    start_processes,
+    Std,
+)
+
+
+format_str = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+logging.basicConfig(level=logging.INFO, format=format_str)
+logger = logging.getLogger(__name__)
+
+
+class _CPUinfo:
+    """Get CPU information, such as cores list and NUMA information."""
+
+    def __init__(self, test_input=""):
+        self.cpuinfo = []
+        if platform.system() in ["Windows", "Darwin"]:
+            raise RuntimeError(f"{platform.system()} is not supported!!!")
+        elif platform.system() == "Linux":
+            # Sample output of: `lscpu --parse=CPU,Core,Socket,Node`
+            #
+            # # The following is the parsable format, which can be fed to other
+            # # programs. Each different item in every column has an unique ID
+            # # starting from zero.
+            # # CPU,Core,Socket,Node
+            # 0,0,0,0
+            # 1,1,0,0
+            # ...
+            if test_input == "":
+                lscpu_cmd = ["lscpu", "--parse=CPU,Core,Socket,Node"]
+                lscpu_info = subprocess.check_output(
+                    lscpu_cmd, universal_newlines=True
+                ).split("\n")
+            else:
+                lscpu_info = test_input.split("\n")
+
+            # Get information about  cpu, core, socket and node
+            for line in lscpu_info:
+                pattern = r"^([\d]+,[\d]+,[\d]+,[\d]?)"
+                regex_out = re.search(pattern, line)
+                if regex_out:
+                    self.cpuinfo.append(regex_out.group(1).strip().split(","))
+
+            # physical cores := core column in lscpu output
+            #  logical cores :=  cPU column in lscpu output
+            self.node_nums = int(max(line[3] for line in self.cpuinfo)) + 1
+            self.node_physical_cores: List[List[int]] = []  # node_id is index
+            self.node_logical_cores: List[List[int]] = []  # node_id is index
+            self.physical_core_node_map = {}  # physical core to numa node id
+            self.logical_core_node_map = {}  # logical core to numa node id
+
+            for node_id in range(self.node_nums):
+                cur_node_physical_core = []
+                cur_node_logical_core = []
+                for cpuinfo in self.cpuinfo:
+                    nid = cpuinfo[3] if cpuinfo[3] != "" else "0"
+                    if node_id == int(nid):
+                        if int(cpuinfo[1]) not in cur_node_physical_core:
+                            cur_node_physical_core.append(int(cpuinfo[1]))
+                            self.physical_core_node_map[int(cpuinfo[1])] = int(node_id)
+                        cur_node_logical_core.append(int(cpuinfo[0]))
+                        self.logical_core_node_map[int(cpuinfo[0])] = int(node_id)
+                self.node_physical_cores.append(cur_node_physical_core)
+                self.node_logical_cores.append(cur_node_logical_core)
+
+    def _physical_core_nums(self):
+        return len(self.node_physical_cores) * len(self.node_physical_cores[0])
+
+    def _logical_core_nums(self):
+        return len(self.node_logical_cores) * len(self.node_logical_cores[0])
+
+    def get_node_physical_cores(self, node_id):
+        if node_id < 0 or node_id > self.node_nums - 1:
+            raise ValueError(
+                f"Invalid node id: {node_id}. Valid node ids: {list(range(len(self.node_physical_cores)))}"
+            )
+        return self.node_physical_cores[node_id]
+
+    def get_node_logical_cores(self, node_id):
+        if node_id < 0 or node_id > self.node_nums - 1:
+            raise ValueError(
+                f"Invalid node id: {node_id}. Valid node ids: {list(range(len(self.node_physical_cores)))}"
+            )
+        return self.node_logical_cores[node_id]
+
+    def get_all_physical_cores(self):
+        all_cores = []
+        for cores in self.node_physical_cores:
+            all_cores.extend(cores)
+        return all_cores
+
+    def get_all_logical_cores(self):
+        all_cores = []
+        for cores in self.node_logical_cores:
+            all_cores.extend(cores)
+        return all_cores
+
+    def numa_aware_check(self, core_list):
+        """
+        Check whether all cores in core_list are in the same NUMA node.
+
+        Cross NUMA will reduce performance.
+        We strongly advice to not use cores on different nodes.
+        """
+        cores_numa_map = self.logical_core_node_map
+        numa_ids = []
+        for core in core_list:
+            numa_id = cores_numa_map[core]
+            if numa_id not in numa_ids:
+                numa_ids.append(numa_id)
+        if len(numa_ids) > 1:
+            logger.warning(
+                "Numa Aware: cores:%s on different NUMA nodes:%s. To avoid \
+this behavior, please use --ncores-per-instance knob to make sure number of cores is divisible by --ncores-per-\
+instance. Alternatively, please use --skip-cross-node-cores knob.",
+                str(core_list),
+                str(numa_ids),
+            )
+        if len(numa_ids) == 0:
+            raise RuntimeError(
+                "invalid number of NUMA nodes; please make sure numa_ids >= 1"
+            )
+        return numa_ids
+
+
+class _Launcher:
+    r"""Class for launcher."""
+
+    msg_lib_notfound = f"Unable to find the {{0}} library file lib{{1}}.so in $CONDA_PREFIX/lib or $VIRTUAL_ENV/lib \
+or /.local/lib/ or /usr/local/lib/ or /usr/local/lib64/ or /usr/lib or /usr/lib64 or \
+{expanduser('~')}/.local/lib/ so the LD_PRELOAD environment variable will not be set."
+
+    def __init__(self) -> None:
+        self.cpuinfo = _CPUinfo()
+
+    def add_lib_preload(self, lib_type):
+        """Enable TCMalloc/JeMalloc/intel OpenMP."""
+        library_paths = []
+        if "CONDA_PREFIX" in os.environ:
+            library_paths.append(f"{os.environ['CONDA_PREFIX']}/lib")
+        if "VIRTUAL_ENV" in os.environ:
+            library_paths.append(f"{os.environ['VIRTUAL_ENV']}/lib")
+
+        library_paths += [
+            f"{expanduser('~')}/.local/lib",
+            "/usr/local/lib",
+            "/usr/local/lib64",
+            "/usr/lib",
+            "/usr/lib64",
+        ]
+
+        lib_find = False
+        lib_set = False
+        for item in os.getenv("LD_PRELOAD", "").split(":"):
+            if item.endswith(f"lib{lib_type}.so"):
+                lib_set = True
+                break
+        if not lib_set:
+            for lib_path in library_paths:
+                library_file = os.path.join(lib_path, f"lib{lib_type}.so")
+                matches = glob.glob(library_file)
+                if len(matches) > 0:
+                    ld_preloads = [f"{matches[0]}", os.getenv("LD_PRELOAD", "")]
+                    os.environ["LD_PRELOAD"] = os.pathsep.join(
+                        [p.strip(os.pathsep) for p in ld_preloads if p]
+                    )
+                    lib_find = True
+                    break
+        return lib_set or lib_find
+
+    def is_numactl_available(self):
+        numactl_available = False
+        try:
+            cmd = ["numactl", "-C", "0", "-m", "0", "hostname"]
+            r = subprocess.run(
+                cmd,
+                env=os.environ,
+                stdout=subprocess.DEVNULL,
+                stderr=subprocess.DEVNULL,
+                check=False,
+            )
+            if r.returncode == 0:
+                numactl_available = True
+        except Exception:
+            pass
+        return numactl_available
+
+    def set_memory_allocator(
+        self, enable_tcmalloc=True, enable_jemalloc=False, use_default_allocator=False
+    ):
+        """
+        Enable TCMalloc/JeMalloc with LD_PRELOAD and set configuration for JeMalloc.
+
+        By default, PTMalloc will be used for PyTorch, but TCMalloc and JeMalloc can get better
+        memory reuse and reduce page fault to improve performance.
+        """
+        if enable_tcmalloc and enable_jemalloc:
+            raise RuntimeError(
+                "Unable to enable TCMalloc and JEMalloc at the same time."
+            )
+
+        if enable_tcmalloc:
+            find_tc = self.add_lib_preload(lib_type="tcmalloc")
+            if not find_tc:
+                msg = f'{self.msg_lib_notfound} you can use "conda install -c conda-forge gperftools" to install {{0}}'
+                logger.warning(msg.format("TCmalloc", "tcmalloc"))  # noqa: G001
+            else:
+                logger.info("Use TCMalloc memory allocator")
+
+        elif enable_jemalloc:
+            find_je = self.add_lib_preload(lib_type="jemalloc")
+            if not find_je:
+                msg = f'{self.msg_lib_notfound} you can use "conda install -c conda-forge jemalloc" to install {{0}}'
+                logger.warning(msg.format("Jemalloc", "jemalloc"))  # noqa: G001
+            else:
+                logger.info("Use JeMalloc memory allocator")
+                self.set_env(
+                    "MALLOC_CONF",
+                    "oversize_threshold:1,background_thread:true,metadata_thp:auto",
+                )
+
+        elif use_default_allocator:
+            pass
+
+        else:
+            find_tc = self.add_lib_preload(lib_type="tcmalloc")
+            if find_tc:
+                logger.info("Use TCMalloc memory allocator")
+                return
+            find_je = self.add_lib_preload(lib_type="jemalloc")
+            if find_je:
+                logger.info("Use JeMalloc memory allocator")
+                return
+            logger.warning(
+                """Neither TCMalloc nor JeMalloc is found in $CONDA_PREFIX/lib or $VIRTUAL_ENV/lib
+                            or /.local/lib/ or /usr/local/lib/ or /usr/local/lib64/ or /usr/lib or /usr/lib64 or
+                           %s/.local/lib/ so the LD_PRELOAD environment variable will not be set.
+                           This may drop the performance""",
+                expanduser("~"),
+            )
+
+    def log_env_var(self, env_var_name=""):
+        if env_var_name in os.environ:
+            logger.info("%s=%s", env_var_name, os.environ[env_var_name])
+
+    def set_env(self, env_name, env_value):
+        if not env_value:
+            logger.warning("%s is None", env_name)
+        if env_name not in os.environ:
+            os.environ[env_name] = env_value
+        elif os.environ[env_name] != env_value:
+            logger.warning(
+                "Overriding value with the one set in environment variable: %s. \
+Value applied: %s. Value ignored: %s",
+                env_name,
+                os.environ[env_name],
+                env_value,
+            )
+        self.log_env_var(env_name)
+
+    # set_kmp_affinity is used to control whether to set KMP_AFFINITY or not.
+    # In scenario that use all cores on all nodes, including logical cores, setting KMP_AFFINITY disables logical cores.
+    # In this case, KMP_AFFINITY should not be set.
+    def set_multi_thread_and_allocator(
+        self,
+        ncores_per_instance,
+        disable_iomp=False,
+        set_kmp_affinity=True,
+        enable_tcmalloc=True,
+        enable_jemalloc=False,
+        use_default_allocator=False,
+    ):
+        """
+        Set multi-thread configuration and enable Intel openMP and TCMalloc/JeMalloc.
+
+        By default, GNU openMP and PTMalloc are used in PyTorch. but Intel openMP and TCMalloc/JeMalloc are better alternatives
+        to get performance benefit.
+        """
+        self.set_memory_allocator(
+            enable_tcmalloc, enable_jemalloc, use_default_allocator
+        )
+        self.set_env("OMP_NUM_THREADS", str(ncores_per_instance))
+        if not disable_iomp:
+            find_iomp = self.add_lib_preload(lib_type="iomp5")
+            if not find_iomp:
+                msg = f'{self.msg_lib_notfound} you can use "conda install mkl" to install {{0}}'
+                logger.warning(msg.format("iomp", "iomp5"))  # noqa: G001
+            else:
+                logger.info("Using Intel OpenMP")
+                if set_kmp_affinity:
+                    self.set_env("KMP_AFFINITY", "granularity=fine,compact,1,0")
+                self.set_env("KMP_BLOCKTIME", "1")
+        self.log_env_var("LD_PRELOAD")
+
+    r"""
+     Launcher for single instance and multi-instance
+     """
+
+    def launch(self, args):
+        cores = []
+        set_kmp_affinity = True
+        enable_taskset = False
+        if args.core_list:  # user specify what cores will be used by params
+            cores = [int(x) for x in args.core_list.split(",")]
+            if args.ncores_per_instance == -1:
+                raise RuntimeError(
+                    'please specify the "--ncores-per-instance" if you have pass the --core-list params'
+                )
+            elif (
+                args.ninstances > 1
+                and args.ncores_per_instance * args.ninstances < len(cores)
+            ):
+                logger.warning(
+                    "only first %s cores will be used, \
+but you specify %s cores in core_list",
+                    args.ncores_per_instance * args.ninstances,
+                    len(cores),
+                )
+            else:
+                args.ninstances = len(cores) // args.ncores_per_instance
+
+        else:
+            if args.use_logical_core:
+                if args.node_id != -1:
+                    cores = self.cpuinfo.get_node_logical_cores(args.node_id)
+                else:
+                    cores = self.cpuinfo.get_all_logical_cores()
+                    # When using all cores on all nodes, including logical cores,
+                    # setting KMP_AFFINITY disables logical cores. Thus, KMP_AFFINITY should not be set.
+                    set_kmp_affinity = False
+            else:
+                if args.node_id != -1:
+                    cores = self.cpuinfo.get_node_physical_cores(args.node_id)
+                else:
+                    cores = self.cpuinfo.get_all_physical_cores()
+            if (
+                not args.multi_instance
+                and args.ninstances == -1
+                and args.ncores_per_instance == -1
+            ):
+                args.ninstances = 1
+                args.ncores_per_instance = len(cores)
+            elif (
+                args.multi_instance
+                and args.ninstances == -1
+                and args.ncores_per_instance == -1
+            ):
+                args.throughput_mode = True
+            elif args.ncores_per_instance == -1 and args.ninstances != -1:
+                if args.ninstances > len(cores):
+                    raise RuntimeError(
+                        f"there are {len(cores)} total cores but you specify {args.ninstances} ninstances; \
+please make sure ninstances <= total_cores)"
+                    )
+                else:
+                    args.ncores_per_instance = len(cores) // args.ninstances
+            elif args.ncores_per_instance != -1 and args.ninstances == -1:
+                if not args.skip_cross_node_cores:
+                    args.ninstances = len(cores) // args.ncores_per_instance
+                else:
+                    ncore_per_node = len(self.cpuinfo.node_physical_cores[0])
+                    num_leftover_cores = ncore_per_node % args.ncores_per_instance
+                    if args.ncores_per_instance > ncore_per_node:
+                        # too many ncores_per_instance to skip cross-node cores
+                        logger.warning(
+                            "there are %s core(s) per socket, but you specify %s ncores_per_instance and \
+skip_cross_node_cores. Please make sure --ncores-per-instance < core(s) per \
+socket",
+                            ncore_per_node,
+                            args.ncores_per_instance,
+                        )
+                        sys.exit(-1)
+                    elif num_leftover_cores == 0:
+                        # aren't any cross-node cores
+                        logger.info(
+                            "--skip-cross-node-cores is set, but there are no cross-node cores."
+                        )
+                        args.ninstances = len(cores) // args.ncores_per_instance
+                    else:
+                        # skip cross-node cores
+                        if args.ninstances != -1:
+                            logger.warning(
+                                "--skip-cross-node-cores is exclusive to --ninstances. --ninstances \
+won't take effect even if it is set explicitly."
+                            )
+
+                        i = 1
+                        leftover_cores = set()
+                        while ncore_per_node * i <= len(cores):
+                            leftover_cores.update(
+                                cores[
+                                    ncore_per_node * i
+                                    - num_leftover_cores : ncore_per_node * i
+                                ]
+                            )
+                            i += 1
+                        cores = list(set(cores) - leftover_cores)
+                        assert len(cores) % args.ncores_per_instance == 0
+                        args.ninstances = len(cores) // args.ncores_per_instance
+            else:
+                if args.ninstances * args.ncores_per_instance > len(cores):
+                    raise RuntimeError(
+                        "Please make sure ninstances * ncores_per_instance <= total_cores"
+                    )
+            if args.latency_mode:
+                logger.warning(
+                    "--latency-mode is exclusive to --ninstances, --ncores-per-instance, --node-id and \
+--use-logical-core. They won't take effect even they are set explicitly."
+                )
+                args.ncores_per_instance = 4
+                cores = self.cpuinfo.get_all_physical_cores()
+                args.ninstances = len(cores) // args.ncores_per_instance
+
+            if args.throughput_mode:
+                logger.warning(
+                    "--throughput-mode is exclusive to --ninstances, --ncores-per-instance, --node-id and \
+--use-logical-core. They won't take effect even they are set explicitly."
+                )
+                args.ninstances = self.cpuinfo.node_nums
+                cores = self.cpuinfo.get_all_physical_cores()
+                args.ncores_per_instance = len(cores) // args.ninstances
+
+        if args.ninstances > 1 and args.rank != -1:
+            logger.info(
+                "assigning %s cores for instance %s",
+                args.ncores_per_instance,
+                args.rank,
+            )
+
+        if not args.disable_numactl:
+            numactl_available = self.is_numactl_available()
+            if not numactl_available:
+                if not args.disable_taskset:
+                    logger.warning(
+                        "Core binding with numactl is not available. Disabling numactl and using taskset instead. \
+                    This may affect performance in multi-socket system; please use numactl if memory binding is needed."
+                    )
+                    args.disable_numactl = True
+                    enable_taskset = True
+                else:
+                    logger.warning(
+                        "Core binding with numactl is not available, and --disable_taskset is set. \
+                    Please unset --disable_taskset to use taskset instead of numactl."
+                    )
+                    sys.exit(-1)
+
+        if not args.disable_taskset:
+            enable_taskset = True
+
+        self.set_multi_thread_and_allocator(
+            args.ncores_per_instance,
+            args.disable_iomp,
+            set_kmp_affinity,
+            args.enable_tcmalloc,
+            args.enable_jemalloc,
+            args.use_default_allocator,
+        )
+        entrypoint = ""
+        launch_args = {}
+        launch_envs: Dict[int, Dict] = {}
+        launch_tee = {}
+        # check whether is launched from torchrun with --nproc-per-node 
+        local_size = int(os.environ.get("LOCAL_WORLD_SIZE", 1))
+        local_rank = int(os.environ.get("LOCAL_RANK", 0))
+        for i in range(args.ninstances):
+            cmd = []
+            cur_process_cores = ""
+            if not args.disable_numactl or enable_taskset:
+                if not args.disable_numactl:
+                    cmd = ["numactl"]
+                elif enable_taskset:
+                    cmd = ["taskset"]
+                cores = sorted(cores)
+                if (
+                    args.rank == -1
+                ):  # sequentially assign ncores_per_instance to ninstances
+                    core_list = cores[
+                        i
+                        * args.ncores_per_instance : (i + 1)
+                        * args.ncores_per_instance
+                    ]
+                else:  # assign ncores_per_instance from rank
+                    core_list = cores[
+                        args.rank
+                        * args.ncores_per_instance : (args.rank + 1)
+                        * args.ncores_per_instance
+                    ]
+
+                core_ranges: List[Dict] = []
+                if local_size > 1:
+                    total_num_cores = len(core_list)
+                    cores_per_rank = total_num_cores // local_size
+                    assert (
+                        cores_per_rank >= 1
+                    ), "At least one core needs to be assigned to each rank"
+                    core_list = core_list[
+                        cores_per_rank * local_rank : cores_per_rank * (local_rank + 1)
+                    ]
+                for core in core_list:
+                    if len(core_ranges) == 0:
+                        range_elem = {"start": core, "end": core}
+                        core_ranges.append(range_elem)
+                    else:
+                        if core - core_ranges[-1]["end"] == 1:
+                            core_ranges[-1]["end"] = core
+                        else:
+                            range_elem = {"start": core, "end": core}
+                            core_ranges.append(range_elem)
+                for r in core_ranges:
+                    cur_process_cores = f"{cur_process_cores}{r['start']}-{r['end']},"
+                cur_process_cores = cur_process_cores[:-1]
+                if not args.disable_numactl:
+                    numa_params = f"-C {cur_process_cores} "
+                    numa_ids = ",".join(
+                        [
+                            str(numa_id)
+                            for numa_id in self.cpuinfo.numa_aware_check(core_list)
+                        ]
+                    )
+                    numa_params += f"-m {numa_ids}"
+                    cmd.extend(numa_params.split())
+                elif enable_taskset:
+                    taskset_params = f"-c {cur_process_cores} "
+                    cmd.extend(taskset_params.split())
+            with_python = not args.no_python
+            if with_python:
+                cmd.append(sys.executable)
+                cmd.append("-u")
+            if args.module:
+                cmd.append("-m")
+            cmd.append(args.program)
+            cmd.extend(args.program_args)
+            cmd_s = " ".join(cmd)
+            logger.info(cmd_s)
+            if entrypoint == "":
+                entrypoint = cmd[0]
+            del cmd[0]
+            launch_args[i] = tuple(cmd)
+            launch_envs[i] = {}
+            launch_tee[i] = Std.ALL
+
+            if args.rank != -1:  # launches single instance, rank, only
+                break
+
+        ctx = start_processes(
+            name=args.log_file_prefix,
+            entrypoint=entrypoint,
+            args=launch_args,
+            envs=launch_envs,
+            logs_specs=_DefaultLogsSpecs(log_dir=args.log_path, tee=launch_tee),
+        )
+        ctx.wait()
+
+
+def _add_memory_allocator_params(parser):
+    group = parser.add_argument_group("Memory Allocator Parameters")
+    # allocator control
+    group.add_argument(
+        "--enable-tcmalloc",
+        "--enable_tcmalloc",
+        action="store_true",
+        default=False,
+        help="Enable tcmalloc allocator",
+    )
+    group.add_argument(
+        "--enable-jemalloc",
+        "--enable_jemalloc",
+        action="store_true",
+        default=False,
+        help="Enable jemalloc allocator",
+    )
+    group.add_argument(
+        "--use-default-allocator",
+        "--use_default_allocator",
+        action="store_true",
+        default=False,
+        help="Use default memory allocator",
+    )
+
+
+def _add_multi_instance_params(parser):
+    group = parser.add_argument_group("Multi-instance Parameters")
+    # multi-instance control
+    group.add_argument(
+        "--ncores-per-instance",
+        "--ncores_per_instance",
+        metavar="\b",
+        default=-1,
+        type=int,
+        help="Cores per instance",
+    )
+    group.add_argument(
+        "--ninstances",
+        metavar="\b",
+        default=-1,
+        type=int,
+        help="For multi-instance, you should give the cores number you used for per instance.",
+    )
+    group.add_argument(
+        "--skip-cross-node-cores",
+        "--skip_cross_node_cores",
+        action="store_true",
+        default=False,
+        help="If specified --ncores-per-instance, skips cross-node cores.",
+    )
+    group.add_argument(
+        "--rank",
+        metavar="\b",
+        default="-1",
+        type=int,
+        help="Specify instance index to assign ncores_per_instance for rank; \
+otherwise ncores_per_instance will be assigned sequentially to ninstances. Please refer to \
+https://github.com/intel/intel-extension-for-pytorch/blob/master/docs/tutorials/performance_tuning/launch_script.md",
+    )
+    group.add_argument(
+        "--latency-mode",
+        "--latency_mode",
+        action="store_true",
+        default=False,
+        help="By default 4 core per instance and use all physical cores",
+    )
+    group.add_argument(
+        "--throughput-mode",
+        "--throughput_mode",
+        action="store_true",
+        default=False,
+        help="By default one instance per node and use all physical cores",
+    )
+    group.add_argument(
+        "--node-id",
+        "--node_id",
+        metavar="\b",
+        default=-1,
+        type=int,
+        help="node id for multi-instance, by default all nodes will be used",
+    )
+    group.add_argument(
+        "--use-logical-core",
+        "--use_logical_core",
+        action="store_true",
+        default=False,
+        help="Whether only use physical cores",
+    )
+    group.add_argument(
+        "--disable-numactl",
+        "--disable_numactl",
+        action="store_true",
+        default=False,
+        help="Disable numactl",
+    )
+    group.add_argument(
+        "--disable-taskset",
+        "--disable_taskset",
+        action="store_true",
+        default=False,
+        help="Disable taskset",
+    )
+    group.add_argument(
+        "--core-list",
+        "--core_list",
+        metavar="\b",
+        default=None,
+        type=str,
+        help='Specify the core list as "core_id, core_id, ....", otherwise, all the cores will be used.',
+    )
+    group.add_argument(
+        "--log-path",
+        "--log_path",
+        metavar="\b",
+        default="",
+        type=str,
+        help="The log file directory. Default path is "
+        ", which means disable logging to files.",
+    )
+    group.add_argument(
+        "--log-file-prefix",
+        "--log_file_prefix",
+        metavar="\b",
+        default="run",
+        type=str,
+        help="log file prefix",
+    )
+
+
+def _add_kmp_iomp_params(parser):
+    group = parser.add_argument_group("IOMP Parameters")
+    group.add_argument(
+        "--disable-iomp",
+        "--disable_iomp",
+        action="store_true",
+        default=False,
+        help="By default, we use Intel OpenMP and libiomp5.so will be add to LD_PRELOAD",
+    )
+
+
+def create_args(parser=None):
+    """
+    Parse the command line options.
+
+    @retval ArgumentParser
+    """
+    parser.add_argument(
+        "--multi-instance",
+        "--multi_instance",
+        action="store_true",
+        default=False,
+        help="Enable multi-instance, by default one instance per node",
+    )
+
+    parser.add_argument(
+        "-m",
+        "--module",
+        default=False,
+        action="store_true",
+        help="Changes each process to interpret the launch script "
+        "as a python module, executing with the same behavior as"
+        '"python -m".',
+    )
+
+    parser.add_argument(
+        "--no-python",
+        "--no_python",
+        default=False,
+        action="store_true",
+        help='Do not prepend the --program script with "python" - just exec '
+        "it directly. Useful when the script is not a Python script.",
+    )
+
+    _add_memory_allocator_params(parser)
+    _add_kmp_iomp_params(parser)
+
+    _add_multi_instance_params(parser)
+    # positional
+    parser.add_argument(
+        "program",
+        type=str,
+        help="The full path to the program/script to be launched. "
+        "followed by all the arguments for the script",
+    )
+
+    # rest from the training program
+    parser.add_argument("program_args", nargs=REMAINDER)
+
+
+def main(args):
+    env_before = set(os.environ.keys())
+    if platform.system() in ["Windows", "Darwin"]:
+        raise RuntimeError(f"{platform.system()} is not supported!!!")
+
+    if args.log_path:
+        os.makedirs(args.log_path, exist_ok=True)
+    else:
+        args.log_path = os.devnull
+
+    if args.latency_mode and args.throughput_mode:
+        raise RuntimeError(
+            "Either args.latency_mode or args.throughput_mode should be set"
+        )
+
+    if not args.no_python and not args.program.endswith(".py"):
+        raise RuntimeError(
+            'For non Python script, you should use "--no-python" parameter.'
+        )
+
+    # Verify LD_PRELOAD
+    if "LD_PRELOAD" in os.environ:
+        lst_valid = []
+        tmp_ldpreload = os.environ["LD_PRELOAD"]
+        for item in tmp_ldpreload.split(":"):
+            matches = glob.glob(item)
+            if len(matches) > 0:
+                lst_valid.append(item)
+            else:
+                logger.warning("%s doesn't exist. Removing it from LD_PRELOAD.", item)
+        if len(lst_valid) > 0:
+            os.environ["LD_PRELOAD"] = ":".join(lst_valid)
+        else:
+            os.environ["LD_PRELOAD"] = ""
+
+    launcher = _Launcher()
+    launcher.launch(args)
+    for x in sorted(set(os.environ.keys()) - env_before):
+        logger.debug("%s=%s", x, os.environ[x])
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser(
+        description="This is a script for launching PyTorch inference on Intel(R) Xeon(R) Scalable "
+        "Processors with optimal configurations. Single instance inference, "
+        "multi-instance inference are enable. To get the peak performance on Intel(R) "
+        "Xeon(R) Scalable Processors, the script optimizes the configuration "
+        "of thread and memory management. For thread management, the script configures thread "
+        "affinity and the preload of Intel OMP library. For memory management, it configures "
+        "NUMA binding and preload optimized memory allocation library (e.g. tcmalloc, jemalloc) "
+        "\n################################# Basic usage ############################# \n"
+        "\n 1. single instance\n"
+        "\n   >>> python -m torch.backends.xeon.run_cpu python_script args \n"
+        "\n2. multi-instance \n"
+        "\n   >>> python -m torch.backends.xeon.run_cpu --ninstances xxx "
+        "--ncores-per-instance xx python_script args\n"
+        "\n############################################################################# \n",
+        formatter_class=RawTextHelpFormatter,
+    )
+    create_args(parser)
+    args = parser.parse_args()
+    main(args)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/xnnpack/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/xnnpack/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..31e69876927d01878a9d1cb836d72fd14adf95e9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/xnnpack/__init__.py
@@ -0,0 +1,29 @@
+# mypy: allow-untyped-defs
+import sys
+import types
+
+import torch
+
+
+class _XNNPACKEnabled:
+    def __get__(self, obj, objtype):
+        return torch._C._is_xnnpack_enabled()
+
+    def __set__(self, obj, val):
+        raise RuntimeError("Assignment not supported")
+
+
+class XNNPACKEngine(types.ModuleType):
+    def __init__(self, m, name):
+        super().__init__(name)
+        self.m = m
+
+    def __getattr__(self, attr):
+        return self.m.__getattribute__(attr)
+
+    enabled = _XNNPACKEnabled()
+
+
+# This is the sys.modules replacement trick, see
+# https://stackoverflow.com/questions/2447353/getattr-on-a-module/7668273#7668273
+sys.modules[__name__] = XNNPACKEngine(sys.modules[__name__], __name__)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/xnnpack/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/xnnpack/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..572caa911a5e876953ed26fa1ea361b7bca21f0b
Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/backends/xnnpack/__pycache__/__init__.cpython-311.pyc differ
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/bin/torch_shm_manager b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/bin/torch_shm_manager
new file mode 100644
index 0000000000000000000000000000000000000000..acc130cc35a758fc0ae9d1bcef3a663340c6b459
Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/bin/torch_shm_manager differ
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/compiler/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/compiler/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7da8e911b83b22378ed07ff4368bf49d14d389cd
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/compiler/__init__.py
@@ -0,0 +1,329 @@
+# mypy: allow-untyped-defs
+from typing import Any, Callable, List, TypeVar
+
+import torch
+
+
+__all__ = [
+    "compile",
+    "assume_constant_result",
+    "reset",
+    "allow_in_graph",
+    "substitute_in_graph",
+    "list_backends",
+    "disable",
+    "cudagraph_mark_step_begin",
+    "wrap_numpy",
+    "is_compiling",
+    "is_dynamo_compiling",
+]
+
+
+_F = TypeVar("_F", bound=Callable[..., Any])
+
+
+def compile(*args, **kwargs):
+    """
+    See :func:`torch.compile` for details on the arguments for this function.
+    """
+    return torch.compile(*args, **kwargs)
+
+
+def reset() -> None:
+    """
+    This function clears all compilation caches and restores the system to its initial state.
+    It is recommended to call this function, especially after using operations like `torch.compile(...)`
+    to ensure a clean state before another unrelated compilation
+    """
+    import torch._dynamo
+
+    torch._dynamo.reset()
+
+
+def allow_in_graph(fn):
+    """
+    Tells the compiler frontend (Dynamo) to skip symbolic introspection of the function
+    and instead directly write it to the graph when encountered.
+
+    If you are using :func:`torch.compile` (with backend="inductor" (the default)), or
+    :func:`torch.export.export`, and trying to black-box a Python function throughout
+    all tracing, do not use this API.
+    Instead, please create a custom operator (see :ref:`custom-ops-landing-page`)
+
+    .. warning::
+
+        If you're a typical torch.compile user (e.g. you're applying torch.compile to
+        a model to make it run faster), you probably don't want to use this function.
+        :func:`allow_in_graph` is a footgun because it skips the compiler frontend
+        (Dynamo) that is responsible for doing safety checks (graph breaks, handling
+        closures, etc). Incorrect usage will lead to difficult-to-debug silent
+        incorrectness issues.
+
+    Given a Python function with no allow_in_graph decorator, regular execution
+    of torch.compile traces through the function. :func:`allow_in_graph` changes
+    it so that the frontend does not trace inside the function, but the compiler
+    backend still traces through it. Compare this to custom operators, which
+    treats a function as a black box throughout the torch.compile stack. The following
+    table compares these mechanisms.
+
+    +------------------------+-----------------------+--------------------------------+
+    | Mechanism              | Frontend (Dynamo)     | Backend (AOTAutograd+Inductor) |
+    +========================+=======================+================================+
+    | no decorator           | trace inside          | trace inside                   |
+    +------------------------+-----------------------+--------------------------------+
+    | allow_in_graph         | opaque callable       | trace inside                   |
+    +------------------------+-----------------------+--------------------------------+
+    | custom op              | opaque callable       | opaque callable                |
+    +------------------------+-----------------------+--------------------------------+
+
+    One common use case for :func:`allow_in_graph()` is as an escape hatch for the compiler
+    frontend: if you know the function works w.r.t. to the downstream components of the
+    compilation stack (AOTAutograd and Inductor) but there is a Dynamo bug that prevents it from
+    symbolically introspecting the function properly (or if your code is in C/C++ and
+    therefore cannot be introspected with Dynamo), then one can decorate said function
+    with :func:`allow_in_graph` to bypass Dynamo.
+
+    We require that ``fn`` adhere to the following restrictions. Failure to adhere
+    results in undefined behavior:
+
+    - The inputs to ``fn`` must be Proxy-able types in the FX graph. Valid types include:
+      Tensor/int/bool/float/None/List[Tensor?]/List[int?]/List[float?]
+      Tuple[Tensor?, ...]/Tuple[int?, ...]/Tuple[float?, ...]/torch.dtype/torch.device
+    - The outputs to ``fn`` must be Proxy-able types in the FX graph (see previous bullet)
+    - all Tensors used inside of ``fn`` must be passed directly as inputs to ``fn``
+      (as opposed to being captured variables).
+
+    Args:
+        fn: A callable representing the function to be included in the graph.
+            If ``fn`` is a list or tuple of callables it recursively applies
+            :func:`allow_in_graph()` to each function and returns a new list or
+            tuple containing the modified functions.
+
+    Example::
+
+        torch.compiler.allow_in_graph(my_custom_function)
+
+        @torch.compile(...)
+        def fn(a):
+            x = torch.add(x, 1)
+            x = my_custom_function(x)
+            x = torch.add(x, 1)
+            return x
+
+        fn(...)
+
+    Will capture a single graph containing ``my_custom_function()``.
+
+    """
+    import torch._dynamo
+
+    return torch._dynamo.allow_in_graph(fn)
+
+
+def substitute_in_graph(
+    original_fn: _F,
+    *,
+    can_constant_fold_through: bool = False,
+    skip_signature_check: bool = False,
+) -> Callable[[_F], _F]:
+    """
+    Register a polyfill handler for a function, usually a C function from the C extension, to be
+    used in place of the original function when inlining the original function in the graph.
+
+    .. note::
+
+        The polyfill handler is only used when inlining the original function. It is not used when
+        the original function is called directly. In the eager mode, the decorated function calls
+        the performant C function rather than the polyfill handler.
+
+    The polyfill handler is a function that will be called in place of the original function when
+    inlining the original function. The polyfill handler should have the same signature and the same
+    behavior as the original function.
+
+    Args:
+        original_fn (callable): The original function, usually a C function, to register a polyfill
+            handler for.
+        can_constant_fold_through (bool, optional): Whether the polyfill handler can be constant
+            folded through. That is, if the polyfill handler is a pure function and its arguments
+            are constant, the result of the polyfill handler can be constant folded during the
+            compilation. Defaults to ``False``.
+        skip_signature_check (bool, optional): Whether to skip the signature check between the
+            original function and the polyfill handler. Defaults to ``False``.
+
+    Returns:
+        A decorator that registers the polyfill handler for the original function.
+
+    Example::
+
+        >>> import operator
+        >>> operator.indexOf([1, 2, 3, 4, 5], 3)
+        2
+        >>> torch.compile(operator.indexOf, fullgraph=True)([1, 2, 3, 4, 5], 3)
+        ... # xdoctest: +SKIP("Long tracebacks")
+        Traceback (most recent call last):
+        ...
+        torch._dynamo.exc.Unsupported: ...
+
+        >>> @torch.compiler.substitute_in_graph(operator.indexOf)
+        ... def indexOf(a, b, /):
+        ...     for i, item in enumerate(a):
+        ...         if item is b or item == b:
+        ...             return i
+        ...     raise ValueError("sequence.index(x): x not in sequence")
+        >>>
+        >>> torch.compile(operator.indexOf, fullgraph=True)([1, 2, 3, 4, 5], 3)
+        2
+    """
+    import torch._dynamo
+
+    return torch._dynamo.substitute_in_graph(
+        original_fn,
+        can_constant_fold_through=can_constant_fold_through,
+        skip_signature_check=skip_signature_check,
+    )
+
+
+def list_backends(exclude_tags=("debug", "experimental")) -> List[str]:
+    """
+    Return valid strings that can be passed to `torch.compile(..., backend="name")`.
+
+    Args:
+        exclude_tags(optional): A tuple of strings representing tags to exclude.
+    """
+    import torch._dynamo
+
+    return torch._dynamo.list_backends(exclude_tags)
+
+
+def assume_constant_result(fn):
+    """
+    This function is used to mark a function `fn` as having a constant result.
+    This allows the compiler to optimize away your function
+    Returns The same function `fn`
+
+    Args:
+        fn: The function to be marked as having a constant result.
+
+    .. warning::
+        `assume_constant_result` can if invalid cause safety and soundness issues, :func:`torch.compile`
+        will not attempt to validate whether the constant assumption is true or not
+
+    """
+    import torch._dynamo
+
+    return torch._dynamo.assume_constant_result(fn)
+
+
+def disable(fn=None, recursive=True):
+    """
+    This function provides both a decorator and a context manager to disable compilation on a function
+    It also provides the option of recursively disabling called functions
+
+    Args:
+        fn (optional): The function to disable
+        recursive (optional): A boolean value indicating whether the disabling should be recursive.
+    """
+    import torch._dynamo
+
+    return torch._dynamo.disable(fn, recursive)
+
+
+def cudagraph_mark_step_begin():
+    """
+    Indicates that a new iteration of inference or training is about to begin.
+
+    CUDA Graphs will free tensors of a prior iteration. A new iteration is started on each invocation of
+    torch.compile, so long as there is not a pending backward that has not been called.
+
+    If that heuristic is wrong, such as in the following example, manually mark it with this api.
+
+    .. code-block:: python
+
+        @torch.compile(mode="reduce-overhead")
+        def rand_foo():
+            return torch.rand([4], device="cuda")
+
+        for _ in range(5):
+            torch.compiler.cudagraph_mark_step_begin()
+            rand_foo() + rand_foo()
+
+    For more details, see `torch.compiler_cudagraph_trees `__
+    """
+    from torch._inductor import cudagraph_trees
+
+    cudagraph_trees.mark_step_begin()
+
+
+def wrap_numpy(fn):
+    r"""Decorator that turns a function from ``np.ndarray``s to ``np.ndarray``s into a function
+    from ``torch.Tensor``s to ``torch.Tensor``s.
+
+    It is designed to be used with :func:`torch.compile` with ``fullgraph=True``. It allows to
+    compile a NumPy function as if it were a PyTorch function. This allows you to run NumPy code
+    on CUDA or compute its gradients.
+
+    .. note::
+
+        This decorator does not work without :func:`torch.compile`.
+
+    Example::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
+        >>> # Compile a NumPy function as a Tensor -> Tensor function
+        >>> @torch.compile(fullgraph=True)
+        >>> @torch.compiler.wrap_numpy
+        >>> def fn(a: np.ndarray):
+        >>>     return np.sum(a * a)
+        >>> # Execute the NumPy function using Tensors on CUDA and compute the gradients
+        >>> x = torch.arange(6, dtype=torch.float32, device="cuda", requires_grad=True)
+        >>> out = fn(x)
+        >>> out.backward()
+        >>> print(x.grad)
+        tensor([ 0.,  2.,  4.,  6.,  8., 10.], device='cuda:0')
+    """
+    from torch._dynamo.external_utils import wrap_numpy as wrap
+
+    return wrap(fn)
+
+
+_is_compiling_flag: bool = False
+
+
+def is_compiling() -> bool:
+    """
+    Indicates whether a graph is executed/traced as part of torch.compile() or torch.export().
+
+    Note that there are 2 other related flags that should deprecated eventually:
+      * torch._dynamo.external_utils.is_compiling()
+      * torch._utils.is_compiling()
+
+    Example::
+
+        >>> def forward(self, x):
+        >>>     if not torch.compiler.is_compiling():
+        >>>        pass # ...logic that is not needed in a compiled/traced graph...
+        >>>
+        >>>     # ...rest of the function...
+    """
+    if torch.jit.is_scripting():
+        return False
+    else:
+        return _is_compiling_flag
+
+
+def is_dynamo_compiling() -> bool:
+    """
+    Indicates whether a graph is traced via TorchDynamo.
+
+    It's stricter than is_compiling() flag, as it would only be set to True when
+    TorchDynamo is used.
+
+    Example::
+
+        >>> def forward(self, x):
+        >>>     if not torch.compiler.is_dynamo_compiling():
+        >>>        pass # ...logic that is not needed in a TorchDynamo-traced graph...
+        >>>
+        >>>     # ...rest of the function...
+    """
+    return False
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new file mode 100644
index 0000000000000000000000000000000000000000..ed1445dd7bce648bc4ac80a2782d72cf0faba2e0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/contrib/_tensorboard_vis.py
@@ -0,0 +1,143 @@
+# mypy: allow-untyped-defs
+import time
+from collections import defaultdict
+from functools import partial
+from typing import DefaultDict
+
+import torch
+
+
+# Unfortunately it doesn't seem as if there was any way to get TensorBoard to do
+# anything without having TF installed, and so this file has a hard dependency on it
+# as well. It really is a debugging tool, so it doesn't matter.
+try:
+    from tensorflow.core.util import event_pb2
+    from tensorflow.core.framework import graph_pb2
+    from tensorflow.python.summary.writer.writer import FileWriter
+except ImportError:
+    raise ImportError("TensorBoard visualization of GraphExecutors requires having "
+                      "TensorFlow installed") from None
+
+
+def dump_tensorboard_summary(graph_executor, logdir):
+    with FileWriter(logdir) as w:
+        pb_graph = visualize(graph_executor)
+        evt = event_pb2.Event(wall_time=time.time(), graph_def=pb_graph.SerializeToString())
+        w.add_event(evt)
+
+
+def visualize(graph, name_prefix='', pb_graph=None, executors_it=None):
+    """Visualizes an independent graph, or a graph executor."""
+    value_map = {}
+    pb_graph = pb_graph or graph_pb2.GraphDef()
+
+    if isinstance(graph, torch._C.GraphExecutorState):
+        visualize_graph_executor(graph, name_prefix, pb_graph,
+                                 partial(visualize, pb_graph=pb_graph))
+        return pb_graph
+
+    # Set up an input node
+    input_node = pb_graph.node.add(op='input', name=name_prefix + 'input')
+    for i, value in enumerate(graph.param_node().outputs()):
+        value_map[value.unique()] = name_prefix + 'input:' + str(i)
+
+    visualize_rec(graph, value_map, name_prefix, pb_graph, executors_it)
+
+    # Gather all outputs
+    return_node = pb_graph.node.add(op='output', name=name_prefix + 'output')
+    for value in graph.return_node().inputs():
+        return_node.input.append(value_map[value.unique()])
+
+    return pb_graph
+
+
+def visualize_graph_executor(state, name_prefix, pb_graph, inline_graph):
+    """Append the state of a given GraphExecutor to the graph protobuf.
+
+    Args:
+        state (GraphExecutor or GraphExecutorState): GraphExecutor to display.
+        name_prefix (str): Name prefix of the containing subgraph.
+        pb_graph (GraphDef): graph to append to.
+        inline_graph (Callable): a function that handles setting up a value_map,
+            so that some graphs in here can be inlined. This is necessary, because
+            this will simply be `visualize` for the top-level GraphExecutor,
+            or `inline_graph` for all nested ones.
+
+            The signature should look like (Graph, name_prefix) -> ().
+            It will be called exactly once.
+
+    The strategy is to embed all different configurations as independent subgraphs,
+    while inlining the original graph as the one that actually produces the values.
+    """
+    if state.autograd_fallback_graph is not None:
+        visualize(graph=state.autograd_fallback_graph,
+                  name_prefix=name_prefix + 'autograd_fallback/',
+                  pb_graph=pb_graph,
+                  executors_it=iter(state.autograd_fallback.executors()))
+
+    for i, (arg_spec, plan) in enumerate(state.execution_plans.items()):
+        subgraph_name = name_prefix + f'plan{i}/'
+
+        # Create a disconnected node that will keep information regarding the input
+        # types of this trace. This is unfortunately a bit too verbose to be included
+        # in the subgraph name.
+        input_kinds = pb_graph.node.add(op='INPUT_KIND', name=subgraph_name)
+        input_kinds.attr['inputs'].s = repr(arg_spec).encode('ascii')
+
+        visualize(plan.graph, subgraph_name, pb_graph, iter(plan.code.executors()))
+
+        # Show gradient as an independent subgraph of this plan
+        if plan.grad_executor is not None:
+            grad_subgraph_name = subgraph_name + 'grad/'
+            visualize(plan.grad_executor, grad_subgraph_name, pb_graph)
+
+    return inline_graph(state.graph, name_prefix + 'original/')
+
+
+def visualize_rec(graph, value_map, name_prefix, pb_graph, executors_it=None):
+    """Recursive part of visualize (basically skips setting up the input and output nodes)."""
+    def inline_graph(subgraph, name, node):
+        rec_value_map = {inp.unique(): value_map[val.unique()]
+                         for inp, val in zip(subgraph.inputs(), node.inputs())}
+        visualize_rec(graph=subgraph,
+                      value_map=rec_value_map,
+                      name_prefix=name,
+                      pb_graph=pb_graph)
+        for out, val in zip(subgraph.outputs(), node.outputs()):
+            value_map[val.unique()] = rec_value_map[out.unique()]
+
+    op_id_counter: DefaultDict[str, int] = defaultdict(int)
+
+    def name_for(node):
+        kind = node.kind()[node.kind().index('::') + 2:]
+        op_id_counter[kind] += 1
+        return kind, name_prefix + kind + '_' + str(op_id_counter[kind])
+
+    def add_fusion_group(node):
+        op, name = name_for(node)
+        inline_graph(node.g('Subgraph'), name + '/', node)
+
+    def add_graph_executor(node):
+        op, name = name_for(node)
+        if executors_it is None:
+            add_node(node)
+        else:
+            ge = next(executors_it)
+            visualize_graph_executor(ge, name + '/', pb_graph,
+                                     partial(inline_graph, node=node))
+
+    def add_node(node):
+        if node.kind() == 'prim::FusionGroup':
+            return add_fusion_group(node)
+        elif node.kind() == 'prim::GraphExecutor':
+            return add_graph_executor(node)
+        op, name = name_for(node)
+        pb_node = pb_graph.node.add(op=op, name=name)
+        for value in node.inputs():
+            pb_node.input.append(value_map[value.unique()])
+        # TODO: handle attrs
+        for i, value in enumerate(node.outputs()):
+            value_map[value.unique()] = name + ':' + str(i)
+
+    for node in graph.nodes():
+        add_node(node)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cpu/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cpu/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8443e0447aa25a62cf920233c3f266b5871a5a84
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cpu/__init__.py
@@ -0,0 +1,184 @@
+# mypy: allow-untyped-defs
+r"""
+This package implements abstractions found in ``torch.cuda``
+to facilitate writing device-agnostic code.
+"""
+
+from contextlib import AbstractContextManager
+from typing import Any, Optional, Union
+
+import torch
+
+from .. import device as _device
+from . import amp
+
+
+__all__ = [
+    "is_available",
+    "synchronize",
+    "current_device",
+    "current_stream",
+    "stream",
+    "set_device",
+    "device_count",
+    "Stream",
+    "StreamContext",
+    "Event",
+]
+
+_device_t = Union[_device, str, int, None]
+
+
+def _is_avx2_supported() -> bool:
+    r"""Returns a bool indicating if CPU supports AVX2."""
+    return torch._C._cpu._is_avx2_supported()
+
+
+def _is_avx512_supported() -> bool:
+    r"""Returns a bool indicating if CPU supports AVX512."""
+    return torch._C._cpu._is_avx512_supported()
+
+
+def _is_avx512_bf16_supported() -> bool:
+    r"""Returns a bool indicating if CPU supports AVX512_BF16."""
+    return torch._C._cpu._is_avx512_bf16_supported()
+
+
+def _is_vnni_supported() -> bool:
+    r"""Returns a bool indicating if CPU supports VNNI."""
+    # Note: Currently, it only checks avx512_vnni, will add the support of avx2_vnni later.
+    return torch._C._cpu._is_avx512_vnni_supported()
+
+
+def _is_amx_tile_supported() -> bool:
+    r"""Returns a bool indicating if CPU supports AMX_TILE."""
+    return torch._C._cpu._is_amx_tile_supported()
+
+
+def _init_amx() -> bool:
+    r"""Initializes AMX instructions."""
+    return torch._C._cpu._init_amx()
+
+
+def is_available() -> bool:
+    r"""Returns a bool indicating if CPU is currently available.
+
+    N.B. This function only exists to facilitate device-agnostic code
+
+    """
+    return True
+
+
+def synchronize(device: _device_t = None) -> None:
+    r"""Waits for all kernels in all streams on the CPU device to complete.
+
+    Args:
+        device (torch.device or int, optional): ignored, there's only one CPU device.
+
+    N.B. This function only exists to facilitate device-agnostic code.
+    """
+
+
+class Stream:
+    """
+    N.B. This class only exists to facilitate device-agnostic code
+    """
+
+    def __init__(self, priority: int = -1) -> None:
+        pass
+
+    def wait_stream(self, stream) -> None:
+        pass
+
+
+class Event:
+    def query(self) -> bool:
+        return True
+
+    def record(self, stream=None) -> None:
+        pass
+
+    def synchronize(self) -> None:
+        pass
+
+    def wait(self, stream=None) -> None:
+        pass
+
+
+_default_cpu_stream = Stream()
+_current_stream = _default_cpu_stream
+
+
+def current_stream(device: _device_t = None) -> Stream:
+    r"""Returns the currently selected :class:`Stream` for a given device.
+
+    Args:
+        device (torch.device or int, optional): Ignored.
+
+    N.B. This function only exists to facilitate device-agnostic code
+
+    """
+    return _current_stream
+
+
+class StreamContext(AbstractContextManager):
+    r"""Context-manager that selects a given stream.
+
+    N.B. This class only exists to facilitate device-agnostic code
+
+    """
+
+    cur_stream: Optional[Stream]
+
+    def __init__(self, stream):
+        self.stream = stream
+        self.prev_stream = _default_cpu_stream
+
+    def __enter__(self):
+        cur_stream = self.stream
+        if cur_stream is None:
+            return
+
+        global _current_stream
+        self.prev_stream = _current_stream
+        _current_stream = cur_stream
+
+    def __exit__(self, type: Any, value: Any, traceback: Any) -> None:
+        cur_stream = self.stream
+        if cur_stream is None:
+            return
+
+        global _current_stream
+        _current_stream = self.prev_stream
+
+
+def stream(stream: Stream) -> AbstractContextManager:
+    r"""Wrapper around the Context-manager StreamContext that
+    selects a given stream.
+
+    N.B. This function only exists to facilitate device-agnostic code
+    """
+    return StreamContext(stream)
+
+
+def device_count() -> int:
+    r"""Returns number of CPU devices (not cores). Always 1.
+
+    N.B. This function only exists to facilitate device-agnostic code
+    """
+    return 1
+
+
+def set_device(device: _device_t) -> None:
+    r"""Sets the current device, in CPU we do nothing.
+
+    N.B. This function only exists to facilitate device-agnostic code
+    """
+
+
+def current_device() -> str:
+    r"""Returns current device for cpu. Always 'cpu'.
+
+    N.B. This function only exists to facilitate device-agnostic code
+    """
+    return "cpu"
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d7d77dcd4134a8011937b544a3624dd13fe3c9a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/__init__.py
@@ -0,0 +1,1661 @@
+# mypy: allow-untyped-defs
+r"""
+This package adds support for CUDA tensor types.
+
+It implements the same function as CPU tensors, but they utilize
+GPUs for computation.
+
+It is lazily initialized, so you can always import it, and use
+:func:`is_available()` to determine if your system supports CUDA.
+
+:ref:`cuda-semantics` has more details about working with CUDA.
+"""
+
+import importlib
+import os
+import threading
+import traceback
+import warnings
+from functools import lru_cache
+from typing import Any, Callable, cast, List, Optional, Tuple, Union
+
+import torch
+import torch._C
+from torch import device as _device
+from torch._utils import _dummy_type, _LazySeedTracker, classproperty
+from torch.types import Device
+
+from . import gds
+from ._utils import _get_device_index
+from .graphs import (
+    CUDAGraph,
+    graph,
+    graph_pool_handle,
+    is_current_stream_capturing,
+    make_graphed_callables,
+)
+from .streams import Event, ExternalStream, Stream
+
+
+try:
+    from torch._C import _cudart  # type: ignore[attr-defined]
+except ImportError:
+    _cudart = None
+
+_initialized = False
+_tls = threading.local()
+_initialization_lock = threading.Lock()
+_queued_calls: List[
+    Tuple[Callable[[], None], List[str]]
+] = []  # don't invoke these until initialization occurs
+_is_in_bad_fork = getattr(torch._C, "_cuda_isInBadFork", lambda: False)
+_device_t = Union[_device, str, int, None]
+
+_HAS_PYNVML = False
+_PYNVML_ERR = None
+try:
+    from torch import version as _version
+
+    try:
+        if not _version.hip:
+            import pynvml  # type: ignore[import]
+        else:
+            import amdsmi  # type: ignore[import]
+
+        _HAS_PYNVML = True
+    except ModuleNotFoundError:
+        pass
+    finally:
+        del _version
+except ImportError as err:
+    _PYNVML_ERR = err  # sometimes a lib is installed but the import fails for some other reason, so we log the error for later
+
+_lazy_seed_tracker = _LazySeedTracker()
+
+# Define dummy _CudaDeviceProperties type if PyTorch was compiled without CUDA
+if hasattr(torch._C, "_CudaDeviceProperties"):
+    _CudaDeviceProperties = torch._C._CudaDeviceProperties
+else:
+    _CudaDeviceProperties = _dummy_type("_CudaDeviceProperties")  # type: ignore[assignment, misc]
+
+if hasattr(torch._C, "_cuda_exchangeDevice"):
+    _exchange_device = torch._C._cuda_exchangeDevice
+else:
+
+    def _exchange_device(device: int) -> int:
+        if device < 0:
+            return -1
+        raise RuntimeError("PyTorch was compiled without CUDA support")
+
+
+if hasattr(torch._C, "_cuda_maybeExchangeDevice"):
+    _maybe_exchange_device = torch._C._cuda_maybeExchangeDevice
+else:
+
+    def _maybe_exchange_device(device: int) -> int:
+        if device < 0:
+            return -1
+        raise RuntimeError("PyTorch was compiled without CUDA support")
+
+
+has_half: bool = True
+has_magma: bool = torch._C._has_magma
+
+default_generators: Tuple[torch._C.Generator] = ()  # type: ignore[assignment]
+
+
+def _is_compiled() -> bool:
+    r"""Return true if compile with CUDA support."""
+    return hasattr(torch._C, "_cuda_getDeviceCount")
+
+
+def _nvml_based_avail() -> bool:
+    return os.getenv("PYTORCH_NVML_BASED_CUDA_CHECK") == "1"
+
+
+def is_available() -> bool:
+    r"""Return a bool indicating if CUDA is currently available."""
+    if not _is_compiled():
+        return False
+    if _nvml_based_avail():
+        # The user has set an env variable to request this availability check that attempts to avoid fork poisoning by
+        # using NVML at the cost of a weaker CUDA availability assessment. Note that if NVML discovery/initialization
+        # fails, this assessment falls back to the default CUDA Runtime API assessment (`cudaGetDeviceCount`)
+        return device_count() > 0
+    else:
+        # The default availability inspection never throws and returns 0 if the driver is missing or can't
+        # be initialized. This uses the CUDA Runtime API `cudaGetDeviceCount` which in turn initializes the CUDA Driver
+        # API via `cuInit`
+        return torch._C._cuda_getDeviceCount() > 0
+
+
+def is_bf16_supported(including_emulation: bool = True):
+    r"""Return a bool indicating if the current CUDA/ROCm device supports dtype bfloat16."""
+    # Check for ROCm, if true return true, no ROCM_VERSION check required,
+    # since it is supported on AMD GPU archs.
+    if torch.version.hip:
+        return True
+
+    # If CUDA is not available, than it does not support bf16 either
+    if not is_available():
+        return False
+
+    device = torch.cuda.current_device()
+
+    # Check for CUDA version and device compute capability.
+    # This is a fast way to check for it.
+    cuda_version = torch.version.cuda
+    if (
+        cuda_version is not None
+        and int(cuda_version.split(".")[0]) >= 11
+        and torch.cuda.get_device_properties(device).major >= 8
+    ):
+        return True
+
+    if not including_emulation:
+        return False
+
+    # Finally try to create a bfloat16 device.
+    return _check_bf16_tensor_supported(device)
+
+
+@lru_cache(maxsize=16)
+def _check_bf16_tensor_supported(device: _device_t):
+    try:
+        torch.tensor([1.0], dtype=torch.bfloat16, device=device)
+        return True
+    except Exception:
+        return False
+
+
+def _sleep(cycles):
+    torch._C._cuda_sleep(cycles)
+
+
+def _extract_arch_version(arch_string: str):
+    """Extracts the architecture string from a CUDA version"""
+    base = arch_string.split("_")[1]
+    if base.endswith("a"):
+        base = base[:-1]
+    return int(base)
+
+
+def _check_capability():
+    incorrect_binary_warn = """
+    Found GPU%d %s which requires CUDA_VERSION >= %d to
+     work properly, but your PyTorch was compiled
+     with CUDA_VERSION %d. Please install the correct PyTorch binary
+     using instructions from https://pytorch.org
+    """
+
+    old_gpu_warn = """
+    Found GPU%d %s which is of cuda capability %d.%d.
+    PyTorch no longer supports this GPU because it is too old.
+    The minimum cuda capability supported by this library is %d.%d.
+    """
+
+    if torch.version.cuda is not None:  # on ROCm we don't want this check
+        CUDA_VERSION = torch._C._cuda_getCompiledVersion()
+        for d in range(device_count()):
+            capability = get_device_capability(d)
+            major = capability[0]
+            minor = capability[1]
+            name = get_device_name(d)
+            current_arch = major * 10 + minor
+            min_arch = min(
+                (_extract_arch_version(arch) for arch in torch.cuda.get_arch_list()),
+                default=35,
+            )
+            if current_arch < min_arch:
+                warnings.warn(
+                    old_gpu_warn
+                    % (d, name, major, minor, min_arch // 10, min_arch % 10)
+                )
+
+
+def _check_cubins():
+    incompatible_device_warn = """
+{} with CUDA capability sm_{} is not compatible with the current PyTorch installation.
+The current PyTorch install supports CUDA capabilities {}.
+If you want to use the {} GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/
+"""
+    if torch.version.cuda is None:  # on ROCm we don't want this check
+        return
+    arch_list = get_arch_list()
+    if len(arch_list) == 0:
+        return
+    supported_sm = [_extract_arch_version(arch) for arch in arch_list if "sm_" in arch]
+    for idx in range(device_count()):
+        cap_major, cap_minor = get_device_capability(idx)
+        # NVIDIA GPU compute architectures are backward compatible within major version
+        supported = any(sm // 10 == cap_major for sm in supported_sm)
+        if not supported:
+            device_name = get_device_name(idx)
+            capability = cap_major * 10 + cap_minor
+            warnings.warn(
+                incompatible_device_warn.format(
+                    device_name, capability, " ".join(arch_list), device_name
+                )
+            )
+
+
+def is_initialized():
+    r"""Return whether PyTorch's CUDA state has been initialized."""
+    return _initialized and not _is_in_bad_fork()
+
+
+def _lazy_call(callable, **kwargs):
+    if is_initialized():
+        callable()
+    else:
+        # TODO(torch_deploy): this accesses linecache, which attempts to read the
+        # file system to get traceback info. Patch linecache or do something
+        # else here if this ends up being important.
+        global _lazy_seed_tracker
+        if kwargs.get("seed_all", False):
+            _lazy_seed_tracker.queue_seed_all(callable, traceback.format_stack())
+        elif kwargs.get("seed", False):
+            _lazy_seed_tracker.queue_seed(callable, traceback.format_stack())
+        else:
+            # Don't store the actual traceback to avoid memory cycle
+            _queued_calls.append((callable, traceback.format_stack()))
+
+
+_lazy_call(_check_capability)
+_lazy_call(_check_cubins)
+
+
+class DeferredCudaCallError(Exception):
+    pass
+
+
+OutOfMemoryError = torch._C.OutOfMemoryError
+
+
+def init():
+    r"""Initialize PyTorch's CUDA state.
+
+    You may need to call this explicitly if you are interacting with
+    PyTorch via its C API, as Python bindings for CUDA functionality
+    will not be available until this initialization takes place.
+    Ordinary users should not need this, as all of PyTorch's CUDA methods
+    automatically initialize CUDA state on-demand.
+
+    Does nothing if the CUDA state is already initialized.
+    """
+    _lazy_init()
+
+
+def _lazy_init():
+    global _initialized, _queued_calls
+    if is_initialized() or hasattr(_tls, "is_initializing"):
+        return
+    with _initialization_lock:
+        # We be double-checked locking, boys!  This is OK because
+        # the above test was GIL protected anyway.  The inner test
+        # is for when a thread blocked on some other thread which was
+        # doing the initialization; when they get the lock, they will
+        # find there is nothing left to do.
+        if is_initialized():
+            return
+        # It is important to prevent other threads from entering _lazy_init
+        # immediately, while we are still guaranteed to have the GIL, because some
+        # of the C calls we make below will release the GIL
+        if _is_in_bad_fork():
+            raise RuntimeError(
+                "Cannot re-initialize CUDA in forked subprocess. To use CUDA with "
+                "multiprocessing, you must use the 'spawn' start method"
+            )
+        if not hasattr(torch._C, "_cuda_getDeviceCount"):
+            raise AssertionError("Torch not compiled with CUDA enabled")
+        if _cudart is None:
+            raise AssertionError(
+                "libcudart functions unavailable. It looks like you have a broken build?"
+            )
+        # This function throws if there's a driver initialization error, no GPUs
+        # are found or any other error occurs
+        if "CUDA_MODULE_LOADING" not in os.environ:
+            os.environ["CUDA_MODULE_LOADING"] = "LAZY"
+        torch._C._cuda_init()
+        # Some of the queued calls may reentrantly call _lazy_init();
+        # we need to just return without initializing in that case.
+        # However, we must not let any *other* threads in!
+        _tls.is_initializing = True
+
+        for calls in _lazy_seed_tracker.get_calls():
+            if calls:
+                _queued_calls.append(calls)
+
+        try:
+            for queued_call, orig_traceback in _queued_calls:
+                try:
+                    queued_call()
+                except Exception as e:
+                    msg = (
+                        f"CUDA call failed lazily at initialization with error: {str(e)}\n\n"
+                        f"CUDA call was originally invoked at:\n\n{''.join(orig_traceback)}"
+                    )
+                    raise DeferredCudaCallError(msg) from e
+        finally:
+            delattr(_tls, "is_initializing")
+        _initialized = True
+
+
+def cudart():
+    r"""Retrieves the CUDA runtime API module.
+
+
+    This function initializes the CUDA runtime environment if it is not already
+    initialized and returns the CUDA runtime API module (_cudart). The CUDA
+    runtime API module provides access to various CUDA runtime functions.
+
+    Args:
+        ``None``
+
+    Returns:
+        module: The CUDA runtime API module (_cudart).
+
+    Raises:
+        RuntimeError: If CUDA cannot be re-initialized in a forked subprocess.
+        AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable.
+
+    Example of CUDA operations with profiling:
+        >>> import torch
+        >>> from torch.cuda import cudart, check_error
+        >>> import os
+        >>>
+        >>> os.environ['CUDA_PROFILE'] = '1'
+        >>>
+        >>> def perform_cuda_operations_with_streams():
+        >>>     stream = torch.cuda.Stream()
+        >>>     with torch.cuda.stream(stream):
+        >>>         x = torch.randn(100, 100, device='cuda')
+        >>>         y = torch.randn(100, 100, device='cuda')
+        >>>         z = torch.mul(x, y)
+        >>>     return z
+        >>>
+        >>> torch.cuda.synchronize()
+        >>> print("====== Start nsys profiling ======")
+        >>> check_error(cudart().cudaProfilerStart())
+        >>> with torch.autograd.profiler.emit_nvtx():
+        >>>     result = perform_cuda_operations_with_streams()
+        >>>     print("CUDA operations completed.")
+        >>> check_error(torch.cuda.cudart().cudaProfilerStop())
+        >>> print("====== End nsys profiling ======")
+
+    To run this example and save the profiling information, execute:
+        >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py
+
+    This command profiles the CUDA operations in the provided script and saves
+    the profiling information to a file named `trace_name.prof`.
+    The `--profile-from-start off` option ensures that profiling starts only
+    after the `cudaProfilerStart` call in the script.
+    The `--csv` and `--print-summary` options format the profiling output as a
+    CSV file and print a summary, respectively.
+    The `-o` option specifies the output file name, and the `-f` option forces the
+    overwrite of the output file if it already exists.
+    """
+    _lazy_init()
+    return _cudart
+
+
+class cudaStatus:
+    SUCCESS: int = 0
+    ERROR_NOT_READY: int = 34
+
+
+class CudaError(RuntimeError):
+    def __init__(self, code: int) -> None:
+        msg = _cudart.cudaGetErrorString(_cudart.cudaError(code))
+        super().__init__(f"{msg} ({code})")
+
+
+def check_error(res: int) -> None:
+    if res != _cudart.cudaError.success:
+        raise CudaError(res)
+
+
+class _DeviceGuard:
+    def __init__(self, index: int):
+        self.idx = index
+        self.prev_idx = -1
+
+    def __enter__(self):
+        self.prev_idx = torch.cuda._exchange_device(self.idx)
+
+    def __exit__(self, type: Any, value: Any, traceback: Any):
+        self.idx = torch.cuda._maybe_exchange_device(self.prev_idx)
+        return False
+
+
+class device:
+    r"""Context-manager that changes the selected device.
+
+    Args:
+        device (torch.device or int): device index to select. It's a no-op if
+            this argument is a negative integer or ``None``.
+    """
+
+    def __init__(self, device: Any):
+        self.idx = _get_device_index(device, optional=True)
+        self.prev_idx = -1
+
+    def __enter__(self):
+        self.prev_idx = torch.cuda._exchange_device(self.idx)
+
+    def __exit__(self, type: Any, value: Any, traceback: Any):
+        self.idx = torch.cuda._maybe_exchange_device(self.prev_idx)
+        return False
+
+
+class device_of(device):
+    r"""Context-manager that changes the current device to that of given object.
+
+    You can use both tensors and storages as arguments. If a given object is
+    not allocated on a GPU, this is a no-op.
+
+    Args:
+        obj (Tensor or Storage): object allocated on the selected device.
+    """
+
+    def __init__(self, obj):
+        idx = obj.get_device() if obj.is_cuda else -1
+        super().__init__(idx)
+
+
+def set_device(device: _device_t) -> None:
+    r"""Set the current device.
+
+    Usage of this function is discouraged in favor of :any:`device`. In most
+    cases it's better to use ``CUDA_VISIBLE_DEVICES`` environmental variable.
+
+    Args:
+        device (torch.device or int): selected device. This function is a no-op
+            if this argument is negative.
+    """
+    device = _get_device_index(device)
+    if device >= 0:
+        torch._C._cuda_setDevice(device)
+
+
+def get_device_name(device: Optional[_device_t] = None) -> str:
+    r"""Get the name of a device.
+
+    Args:
+        device (torch.device or int or str, optional): device for which to return the
+            name. This function is a no-op if this argument is a negative
+            integer. It uses the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    Returns:
+        str: the name of the device
+    """
+    return get_device_properties(device).name
+
+
+def get_device_capability(device: Optional[_device_t] = None) -> Tuple[int, int]:
+    r"""Get the cuda capability of a device.
+
+    Args:
+        device (torch.device or int or str, optional): device for which to return the
+            device capability. This function is a no-op if this argument is
+            a negative integer. It uses the current device, given by
+            :func:`~torch.cuda.current_device`, if :attr:`device` is ``None``
+            (default).
+
+    Returns:
+        tuple(int, int): the major and minor cuda capability of the device
+    """
+    prop = get_device_properties(device)
+    return prop.major, prop.minor
+
+
+def get_device_properties(device: _device_t) -> _CudaDeviceProperties:
+    r"""Get the properties of a device.
+
+    Args:
+        device (torch.device or int or str): device for which to return the
+            properties of the device.
+
+    Returns:
+        _CudaDeviceProperties: the properties of the device
+    """
+    _lazy_init()  # will define _get_device_properties
+    device = _get_device_index(device, optional=True)
+    if device < 0 or device >= device_count():
+        raise AssertionError("Invalid device id")
+    return _get_device_properties(device)  # type: ignore[name-defined]
+
+
+def can_device_access_peer(device: _device_t, peer_device: _device_t) -> bool:
+    r"""Check if peer access between two devices is possible."""
+    _lazy_init()
+    device = _get_device_index(device, optional=True)
+    peer_device = _get_device_index(peer_device)
+    if device < 0 or device >= device_count():
+        raise AssertionError("Invalid device id")
+    if peer_device < 0 or peer_device >= device_count():
+        raise AssertionError("Invalid peer device id")
+    return torch._C._cuda_canDeviceAccessPeer(device, peer_device)
+
+
+class StreamContext:
+    r"""Context-manager that selects a given stream.
+
+    All CUDA kernels queued within its context will be enqueued on a selected
+    stream.
+
+    Args:
+        Stream (Stream): selected stream. This manager is a no-op if it's
+            ``None``.
+    .. note:: Streams are per-device.
+    """
+    cur_stream: Optional["torch.cuda.Stream"]
+
+    def __init__(self, stream: Optional["torch.cuda.Stream"]):
+        self.stream = stream
+        self.idx = _get_device_index(None, True)
+        if not torch.jit.is_scripting():
+            if self.idx is None:
+                self.idx = -1
+
+        self.src_prev_stream = (
+            None if not torch.jit.is_scripting() else torch.cuda.default_stream(None)
+        )
+        self.dst_prev_stream = (
+            None if not torch.jit.is_scripting() else torch.cuda.default_stream(None)
+        )
+
+    def __enter__(self):
+        # Local cur_stream variable for type refinement
+        cur_stream = self.stream
+        # Return if stream is None or CUDA device not available
+        if cur_stream is None or self.idx == -1:
+            return
+        self.src_prev_stream = torch.cuda.current_stream(None)
+
+        # If the stream is not on the current device, then
+        # set the current stream on the device
+        if self.src_prev_stream.device != cur_stream.device:
+            with device(cur_stream.device):
+                self.dst_prev_stream = torch.cuda.current_stream(cur_stream.device)
+        torch.cuda.set_stream(cur_stream)
+
+    def __exit__(self, type: Any, value: Any, traceback: Any):
+        # Local cur_stream variable for type refinement
+        cur_stream = self.stream
+        # If stream is None or no CUDA device available, return
+        if cur_stream is None or self.idx == -1:
+            return
+
+        # Reset the stream on the original device
+        # and destination device
+        if self.src_prev_stream.device != cur_stream.device:  # type: ignore[union-attr]
+            torch.cuda.set_stream(self.dst_prev_stream)  # type: ignore[arg-type]
+        torch.cuda.set_stream(self.src_prev_stream)  # type: ignore[arg-type]
+
+
+def stream(stream: Optional["torch.cuda.Stream"]) -> StreamContext:
+    r"""Wrap around the Context-manager StreamContext that selects a given stream.
+
+    Arguments:
+        stream (Stream): selected stream. This manager is a no-op if it's
+            ``None``.
+    ..Note:: In eager mode stream is of type Stream class while in JIT it is
+    an object of the custom class ``torch.classes.cuda.Stream``.
+    """
+    return StreamContext(stream)
+
+
+def _set_stream_by_id(stream_id, device_index, device_type):
+    r"""set stream specified by the stream id, device index and
+        device type
+
+    Args: stream_id (int): stream id in stream pool
+          device_index (int): device index in topo
+          device_type (int): enum device type
+    """
+    torch._C._cuda_setStream(
+        stream_id=stream_id,
+        device_index=device_index,
+        device_type=device_type,
+    )
+
+
+def set_stream(stream: Stream):
+    r"""Set the current stream.This is a wrapper API to set the stream.
+        Usage of this function is discouraged in favor of the ``stream``
+        context manager.
+
+    Args:
+        stream (Stream): selected stream. This function is a no-op
+            if this argument is ``None``.
+    """
+    if stream is None:
+        return
+    _set_stream_by_id(
+        stream_id=stream.stream_id,
+        device_index=stream.device_index,
+        device_type=stream.device_type,
+    )
+
+
+def _parse_visible_devices() -> Union[List[int], List[str]]:
+    r"""Parse CUDA_VISIBLE_DEVICES environment variable."""
+    var = os.getenv("CUDA_VISIBLE_DEVICES")
+
+    if torch.version.hip:
+        hip_devices = os.getenv("HIP_VISIBLE_DEVICES")
+        if hip_devices is not None:
+            var = hip_devices
+
+    if var is None:
+        return list(range(64))
+
+    def _strtoul(s: str) -> int:
+        """Return -1 or positive integer sequence string starts with."""
+        if not s:
+            return -1
+        for idx, c in enumerate(s):
+            if not (c.isdigit() or (idx == 0 and c in "+-")):
+                break
+            if idx + 1 == len(s):
+                idx += 1
+        return int(s[:idx]) if idx > 0 else -1
+
+    def parse_list_with_prefix(lst: str, prefix: str) -> List[str]:
+        rcs: List[str] = []
+        for elem in lst.split(","):
+            # Repeated id results in empty set
+            if elem in rcs:
+                return cast(List[str], [])
+            # Anything other but prefix is ignored
+            if not elem.startswith(prefix):
+                break
+            rcs.append(elem)
+        return rcs
+
+    if var.startswith("GPU-"):
+        return parse_list_with_prefix(var, "GPU-")
+    if var.startswith("MIG-"):
+        return parse_list_with_prefix(var, "MIG-")
+    # CUDA_VISIBLE_DEVICES uses something like strtoul
+    # which makes `1gpu2,2ampere` is equivalent to `1,2`
+    rc: List[int] = []
+    for elem in var.split(","):
+        x = _strtoul(elem.strip())
+        # Repeated ordinal results in empty set
+        if x in rc:
+            return cast(List[int], [])
+        # Negative value aborts the sequence
+        if x < 0:
+            break
+        rc.append(x)
+    return rc
+
+
+def _raw_device_count_amdsmi() -> int:
+    if not _HAS_PYNVML:  # If amdsmi is not available
+        return -1
+    try:
+        amdsmi.amdsmi_init()
+    except amdsmi.AmdSmiException as e:
+        warnings.warn(f"Can't initialize amdsmi - Error code: {e.err_code}")
+        return -1
+    socket_handles = amdsmi.amdsmi_get_processor_handles()
+    return len(socket_handles)
+
+
+def _raw_device_count_nvml() -> int:
+    r"""Return number of devices as reported by NVML or negative value if NVML discovery/initialization failed."""
+    from ctypes import byref, c_int, CDLL
+
+    nvml_h = CDLL("libnvidia-ml.so.1")
+    rc = nvml_h.nvmlInit()
+    if rc != 0:
+        warnings.warn("Can't initialize NVML")
+        return -1
+    dev_count = c_int(-1)
+    rc = nvml_h.nvmlDeviceGetCount_v2(byref(dev_count))
+    if rc != 0:
+        warnings.warn("Can't get nvml device count")
+        return -1
+    del nvml_h
+    return dev_count.value
+
+
+def _raw_device_uuid_amdsmi() -> Optional[List[str]]:
+    from ctypes import byref, c_int, c_void_p, CDLL, create_string_buffer
+
+    if not _HAS_PYNVML:  # If amdsmi is not available
+        return None
+    try:
+        amdsmi.amdsmi_init()
+    except amdsmi.AmdSmiException:
+        warnings.warn("Can't initialize amdsmi")
+        return None
+    try:
+        socket_handles = amdsmi.amdsmi_get_processor_handles()
+        dev_count = len(socket_handles)
+    except amdsmi.AmdSmiException:
+        warnings.warn("Can't get amdsmi device count")
+        return None
+    uuids: List[str] = []
+    for idx in range(dev_count):
+        try:
+            handler = amdsmi.amdsmi_get_processor_handles()[idx]
+        except amdsmi.AmdSmiException:
+            warnings.warn("Cannot get amd device handler")
+            return None
+        try:
+            uuid = amdsmi.amdsmi_get_gpu_device_uuid(handler)
+        except amdsmi.AmdSmiException:
+            warnings.warn("Cannot get uuid for amd device")
+            return None
+        uuids.append(str(uuid))
+    return uuids
+
+
+def _raw_device_uuid_nvml() -> Optional[List[str]]:
+    r"""Return list of device UUID as reported by NVML or None if NVM discovery/initialization failed."""
+    from ctypes import byref, c_int, c_void_p, CDLL, create_string_buffer
+
+    nvml_h = CDLL("libnvidia-ml.so.1")
+    rc = nvml_h.nvmlInit()
+    if rc != 0:
+        warnings.warn("Can't initialize NVML")
+        return None
+    dev_count = c_int(-1)
+    rc = nvml_h.nvmlDeviceGetCount_v2(byref(dev_count))
+    if rc != 0:
+        warnings.warn("Can't get nvml device count")
+        return None
+    uuids: List[str] = []
+    for idx in range(dev_count.value):
+        dev_id = c_void_p()
+        rc = nvml_h.nvmlDeviceGetHandleByIndex_v2(idx, byref(dev_id))
+        if rc != 0:
+            warnings.warn("Can't get device handle")
+            return None
+        buf_len = 96
+        buf = create_string_buffer(buf_len)
+        rc = nvml_h.nvmlDeviceGetUUID(dev_id, buf, buf_len)
+        if rc != 0:
+            warnings.warn("Can't get device UUID")
+            return None
+        uuids.append(buf.raw.decode("ascii").strip("\0"))
+    del nvml_h
+    return uuids
+
+
+def _transform_uuid_to_ordinals(candidates: List[str], uuids: List[str]) -> List[int]:
+    r"""Given the set of partial uuids and list of known uuids builds a set of ordinals excluding ambiguous partials IDs."""
+
+    def uuid_to_orinal(candidate: str, uuids: List[str]) -> int:
+        best_match = -1
+        for idx, uuid in enumerate(uuids):
+            if not uuid.startswith(candidate):
+                continue
+            # Ambiguous candidate
+            if best_match != -1:
+                return -1
+            best_match = idx
+        return best_match
+
+    rc: List[int] = []
+    for candidate in candidates:
+        idx = uuid_to_orinal(candidate, uuids)
+        # First invalid ordinal stops parsing
+        if idx < 0:
+            break
+        # Duplicates result in empty set
+        if idx in rc:
+            return cast(List[int], [])
+        rc.append(idx)
+    return rc
+
+
+def _device_count_amdsmi() -> int:
+    visible_devices = _parse_visible_devices()
+    if not visible_devices:
+        return 0
+    try:
+        if type(visible_devices[0]) is str:
+            return -1
+        else:
+            raw_cnt = _raw_device_count_amdsmi()
+            if raw_cnt <= 0:
+                return raw_cnt
+            # Trim the list up to a maximum available device
+            for idx, val in enumerate(visible_devices):
+                if cast(int, val) >= raw_cnt:
+                    return idx
+    except OSError:
+        return -1
+    except AttributeError:
+        return -1
+    return len(visible_devices)
+
+
+def _device_count_nvml() -> int:
+    r"""Return number of devices as reported by NVML taking CUDA_VISIBLE_DEVICES into account.
+
+    Negative value is returned if NVML discovery or initialization has failed.
+    """
+    visible_devices = _parse_visible_devices()
+    if not visible_devices:
+        return 0
+    try:
+        if type(visible_devices[0]) is str:
+            # Skip MIG parsing
+            if visible_devices[0].startswith("MIG-"):
+                return -1
+            uuids = _raw_device_uuid_nvml()
+            if uuids is None:
+                return -1
+            visible_devices = _transform_uuid_to_ordinals(
+                cast(List[str], visible_devices), uuids
+            )
+        else:
+            raw_cnt = _raw_device_count_nvml()
+            if raw_cnt <= 0:
+                return raw_cnt
+            # Trim the list up to a maximum available device
+            for idx, val in enumerate(visible_devices):
+                if cast(int, val) >= raw_cnt:
+                    return idx
+    except OSError:
+        return -1
+    except AttributeError:
+        return -1
+    return len(visible_devices)
+
+
+def _get_nvml_device_index(device: Optional[Union[int, Device]]) -> int:
+    r"""Return the NVML index of the device, taking CUDA_VISIBLE_DEVICES into account."""
+    idx = _get_device_index(device, optional=True)
+    visible_devices = _parse_visible_devices()
+    if type(visible_devices[0]) is str:
+        uuids = _raw_device_uuid_nvml()
+        if uuids is None:
+            raise RuntimeError("Can't get device UUIDs")
+        visible_devices = _transform_uuid_to_ordinals(
+            cast(List[str], visible_devices), uuids
+        )
+    visible_devices = cast(List[int], visible_devices)
+    if idx < 0 or idx >= len(visible_devices):
+        raise RuntimeError(
+            f"device {idx} is not visible (CUDA_VISIBLE_DEVICES={visible_devices})"
+        )
+    return visible_devices[idx]
+
+
+_cached_device_count: Optional[int] = None
+
+
+def device_count() -> int:
+    r"""Return the number of GPUs available."""
+    global _cached_device_count
+    if not _is_compiled():
+        return 0
+    if _cached_device_count is not None:
+        return _cached_device_count
+    # bypass _device_count_nvml() if rocm (not supported)
+    nvml_count = _device_count_amdsmi() if torch.version.hip else _device_count_nvml()
+    r = torch._C._cuda_getDeviceCount() if nvml_count < 0 else nvml_count
+    # NB: Do not cache the device count prior to CUDA initialization, because
+    # the number of devices can change due to changes to CUDA_VISIBLE_DEVICES
+    # setting prior to CUDA initialization.
+    if _initialized:
+        _cached_device_count = r
+    return r
+
+
+def get_arch_list() -> List[str]:
+    r"""Return list CUDA architectures this library was compiled for."""
+    if not is_available():
+        return []
+    arch_flags = torch._C._cuda_getArchFlags()
+    if arch_flags is None:
+        return []
+    return arch_flags.split()
+
+
+def get_gencode_flags() -> str:
+    r"""Return NVCC gencode flags this library was compiled with."""
+    arch_list = get_arch_list()
+    if len(arch_list) == 0:
+        return ""
+    arch_list_ = [arch.split("_") for arch in arch_list]
+    return " ".join(
+        [
+            f"-gencode compute=compute_{arch},code={kind}_{arch}"
+            for (kind, arch) in arch_list_
+        ]
+    )
+
+
+def current_device() -> int:
+    r"""Return the index of a currently selected device."""
+    _lazy_init()
+    return torch._C._cuda_getDevice()
+
+
+def synchronize(device: _device_t = None) -> None:
+    r"""Wait for all kernels in all streams on a CUDA device to complete.
+
+    Args:
+        device (torch.device or int, optional): device for which to synchronize.
+            It uses the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+    """
+    _lazy_init()
+    with torch.cuda.device(device):
+        return torch._C._cuda_synchronize()
+
+
+def ipc_collect():
+    r"""Force collects GPU memory after it has been released by CUDA IPC.
+
+    .. note::
+        Checks if any sent CUDA tensors could be cleaned from the memory. Force
+        closes shared memory file used for reference counting if there is no
+        active counters. Useful when the producer process stopped actively sending
+        tensors and want to release unused memory.
+    """
+    _lazy_init()
+    return torch._C._cuda_ipc_collect()
+
+
+def current_stream(device: Optional[_device_t] = None) -> Stream:
+    r"""Return the currently selected :class:`Stream` for a given device.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            the currently selected :class:`Stream` for the current device, given
+            by :func:`~torch.cuda.current_device`, if :attr:`device` is ``None``
+            (default).
+    """
+    _lazy_init()
+    streamdata = torch._C._cuda_getCurrentStream(
+        _get_device_index(device, optional=True)
+    )
+    return Stream(
+        stream_id=streamdata[0], device_index=streamdata[1], device_type=streamdata[2]
+    )
+
+
+def default_stream(device: Optional[_device_t] = None) -> Stream:
+    r"""Return the default :class:`Stream` for a given device.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            the default :class:`Stream` for the current device, given by
+            :func:`~torch.cuda.current_device`, if :attr:`device` is ``None``
+            (default).
+    """
+    _lazy_init()
+    streamdata = torch._C._cuda_getDefaultStream(
+        _get_device_index(device, optional=True)
+    )
+    return Stream(
+        stream_id=streamdata[0], device_index=streamdata[1], device_type=streamdata[2]
+    )
+
+
+def current_blas_handle():
+    r"""Return cublasHandle_t pointer to current cuBLAS handle"""
+    _lazy_init()
+    return torch._C._cuda_getCurrentBlasHandle()
+
+
+def set_sync_debug_mode(debug_mode: Union[int, str]) -> None:
+    r"""Set the debug mode for cuda synchronizing operations.
+
+    Args:
+        debug_mode(str or int): if "default" or 0, don't error or warn on synchronizing operations,
+            if "warn" or 1, warn on synchronizing operations, if "error" or 2, error out synchronizing operations.
+
+    Warning:
+        This is an experimental feature, and not all synchronizing operations will trigger warning or error. In
+        particular, operations in torch.distributed and torch.sparse namespaces are not covered yet.
+    """
+    _lazy_init()
+    if isinstance(debug_mode, str):
+        if debug_mode == "default":
+            debug_mode = 0
+        elif debug_mode == "warn":
+            debug_mode = 1
+        elif debug_mode == "error":
+            debug_mode = 2
+        else:
+            raise RuntimeError(
+                "invalid value of debug_mode, expected one of `default`, `warn`, `error`"
+            )
+
+    torch._C._cuda_set_sync_debug_mode(debug_mode)
+
+
+def get_sync_debug_mode() -> int:
+    r"""Return current value of debug mode for cuda synchronizing operations."""
+    _lazy_init()
+    return torch._C._cuda_get_sync_debug_mode()
+
+
+def _get_pynvml_handler(device: Optional[Union[Device, int]] = None):
+    if not _HAS_PYNVML:
+        raise ModuleNotFoundError(
+            "pynvml does not seem to be installed or it can't be imported."
+        ) from _PYNVML_ERR
+    from pynvml import NVMLError_DriverNotLoaded
+
+    try:
+        pynvml.nvmlInit()
+    except NVMLError_DriverNotLoaded as e:
+        raise RuntimeError("cuda driver can't be loaded, is cuda enabled?") from e
+
+    device = _get_nvml_device_index(device)
+    handle = pynvml.nvmlDeviceGetHandleByIndex(device)
+    return handle
+
+
+def _get_amdsmi_handler(device: Optional[Union[Device, int]] = None):
+    if not _HAS_PYNVML:
+        raise ModuleNotFoundError(
+            "amdsmi does not seem to be installed or it can't be imported."
+        ) from _PYNVML_ERR
+    try:
+        amdsmi.amdsmi_init()
+    except amdsmi.AmdSmiException as e:
+        raise RuntimeError(
+            "amdsmi driver can't be loaded, requires >=ROCm5.6 installation"
+        ) from e
+    device = _get_amdsmi_device_index(device)
+    handle = amdsmi.amdsmi_get_processor_handles()[device]
+    return handle
+
+
+def _get_amdsmi_device_index(device: Optional[Union[int, Device]]) -> int:
+    r"""Return the amdsmi index of the device, taking visible_devices into account."""
+    idx = _get_device_index(device, optional=True)
+    visible_devices = _parse_visible_devices()
+    if type(visible_devices[0]) is str:
+        raise RuntimeError("HIP_VISIBLE_DEVICES should be indices and not strings")
+    idx_map = dict(enumerate(cast(List[int], visible_devices)))
+    if idx not in idx_map:
+        raise RuntimeError(
+            f"device {idx} is not visible (HIP_VISIBLE_DEVICES={visible_devices})"
+        )
+    return idx_map[idx]
+
+
+def _get_amdsmi_memory_usage(device: Optional[Union[Device, int]] = None) -> int:
+    handle = _get_amdsmi_handler()
+    device = _get_amdsmi_device_index(device)
+    return amdsmi.amdsmi_get_gpu_vram_usage(handle)["vram_used"]
+
+
+def _get_amdsmi_utilization(device: Optional[Union[Device, int]] = None) -> int:
+    handle = _get_amdsmi_handler()
+    device = _get_amdsmi_device_index(device)
+    handle = amdsmi.amdsmi_get_processor_handles()[device]
+    return amdsmi.amdsmi_get_gpu_activity(handle)["gfx_activity"]
+
+
+def _get_amdsmi_temperature(device: Optional[Union[Device, int]] = None) -> int:
+    handle = _get_amdsmi_handler(device)
+    return amdsmi.amdsmi_get_temp_metric(
+        handle,
+        amdsmi.AmdSmiTemperatureType.JUNCTION,
+        amdsmi.AmdSmiTemperatureMetric.CURRENT,
+    )
+
+
+def _get_amdsmi_power_draw(device: Optional[Union[Device, int]] = None) -> int:
+    handle = _get_amdsmi_handler(device)
+    socket_power = amdsmi.amdsmi_get_power_info(handle)["average_socket_power"]
+    if socket_power != "N/A":
+        return socket_power
+    else:
+        return amdsmi.amdsmi_get_power_info(handle)["current_socket_power"]
+
+
+def _get_amdsmi_clock_rate(device: Optional[Union[Device, int]] = None) -> int:
+    handle = _get_amdsmi_handler(device)
+    clock_info = amdsmi.amdsmi_get_clock_info(handle, amdsmi.AmdSmiClkType.GFX)
+    if "cur_clk" in clock_info:  # ROCm 6.2 deprecation
+        return clock_info["cur_clk"]
+    else:
+        return clock_info["clk"]
+
+
+def memory_usage(device: Optional[Union[Device, int]] = None) -> int:
+    r"""Return the percent of time over the past sample period during which global (device)
+    memory was being read or written as given by `nvidia-smi`.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    Warning: Each sample period may be between 1 second and 1/6 second,
+    depending on the product being queried.
+    """
+    if not torch.version.hip:
+        handle = _get_pynvml_handler()
+        device = _get_nvml_device_index(device)
+        handle = pynvml.nvmlDeviceGetHandleByIndex(device)
+        return pynvml.nvmlDeviceGetUtilizationRates(handle).memory
+    else:
+        return _get_amdsmi_memory_usage(device)
+
+
+def utilization(device: Optional[Union[Device, int]] = None) -> int:
+    r"""Return the percent of time over the past sample period during which one or
+    more kernels was executing on the GPU as given by `nvidia-smi`.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    Warning: Each sample period may be between 1 second and 1/6 second,
+    depending on the product being queried.
+    """
+    if not torch.version.hip:
+        handle = _get_pynvml_handler(device)
+        device = _get_nvml_device_index(device)
+        handle = pynvml.nvmlDeviceGetHandleByIndex(device)
+        return pynvml.nvmlDeviceGetUtilizationRates(handle).gpu
+    else:
+        return _get_amdsmi_utilization(device)
+
+
+def temperature(device: Optional[Union[Device, int]] = None) -> int:
+    r"""Return the average temperature of the GPU sensor in Degrees C (Centigrades).
+
+    The average temperature is computed based on past sample period as given by `nvidia-smi`.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    Warning: Each sample period may be between 1 second and 1/6 second,
+    depending on the product being queried.
+    """
+    if not torch.version.hip:
+        handle = _get_pynvml_handler(device)
+        # 0 refers to the temperature sensor for the GPU die.
+        return pynvml.nvmlDeviceGetTemperature(handle, 0)
+    else:
+        return _get_amdsmi_temperature(device)
+
+
+def power_draw(device: Optional[Union[Device, int]] = None) -> int:
+    r"""Return the average power draw of the GPU sensor in mW (MilliWatts)
+        over the past sample period as given by `nvidia-smi` for Fermi or newer fully supported devices.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    Warning: Each sample period may be between 1 second and 1/6 second,
+    depending on the product being queried.
+    """
+    if not torch.version.hip:
+        handle = _get_pynvml_handler(device)
+        return pynvml.nvmlDeviceGetPowerUsage(handle)
+    else:
+        return _get_amdsmi_power_draw(device)
+
+
+def clock_rate(device: Optional[Union[Device, int]] = None) -> int:
+    r"""Return the clock speed of the GPU SM in Hz Hertz over the past sample period as given by `nvidia-smi`.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    Warning: Each sample period may be between 1 second and 1/6 second,
+    depending on the product being queried.
+    """
+    if not torch.version.hip:
+        handle = _get_pynvml_handler(device)
+        return pynvml.nvmlDeviceGetClockInfo(handle, 1)
+    else:
+        return _get_amdsmi_clock_rate(device)
+
+
+def _get_device(device: Union[int, str, torch.device]) -> torch.device:
+    r"""Return the torch.device type object from the passed in device.
+
+    Args:
+        device (torch.device or int): selected device.
+    """
+    if isinstance(device, str):
+        device = torch.device(device)
+    elif isinstance(device, int):
+        device = torch.device("cuda", device)
+    return device
+
+
+def _get_generator(device: torch.device) -> torch._C.Generator:
+    r"""Return the CUDA Generator object for the given device.
+
+    Args:
+        device (torch.device): selected device.
+    """
+    idx = device.index
+    if idx is None:
+        idx = current_device()
+    return torch.cuda.default_generators[idx]
+
+
+def _set_rng_state_offset(
+    offset: int, device: Union[int, str, torch.device] = "cuda"
+) -> None:
+    r"""Set the random number generator state offset of the specified GPU.
+
+    Args:
+        offset (int): The desired offset
+        device (torch.device or int, optional): The device to set the RNG state.
+            Default: ``'cuda'`` (i.e., ``torch.device('cuda')``, the current CUDA device).
+    """
+    final_device = _get_device(device)
+
+    def cb():
+        default_generator = _get_generator(final_device)
+        default_generator.set_offset(offset)
+
+    _lazy_call(cb)
+
+
+def _get_rng_state_offset(device: Union[int, str, torch.device] = "cuda") -> int:
+    r"""Return the random number generator state offset of the specified GPU.
+
+    Args:
+        device (torch.device or int, optional): The device to return the RNG state offset of.
+            Default: ``'cuda'`` (i.e., ``torch.device('cuda')``, the current CUDA device).
+
+    .. warning::
+        This function eagerly initializes CUDA.
+    """
+    _lazy_init()
+    final_device = _get_device(device)
+    default_generator = _get_generator(final_device)
+    return default_generator.get_offset()
+
+
+from .memory import *  # noqa: F403
+from .random import *  # noqa: F403
+
+
+################################################################################
+# Define Storage and Tensor classes
+################################################################################
+
+
+@staticmethod  # type: ignore[misc]
+def _lazy_new(cls, *args, **kwargs):
+    _lazy_init()
+    # We may need to call lazy init again if we are a forked child
+    # del _CudaBase.__new__
+    return super(_CudaBase, cls).__new__(cls, *args, **kwargs)
+
+
+class _CudaBase:
+    is_cuda = True
+    is_sparse = False
+
+    def type(self, *args, **kwargs):
+        # We could use a Protocol here to tell mypy that self has `get_device` method
+        # but it is only available in the typing module on Python >= 3.8
+        # or on typing_extensions module on Python >= 3.6
+        with device(self.get_device()):  # type: ignore[attr-defined]
+            return super().type(*args, **kwargs)  # type: ignore[misc]
+
+    __new__ = _lazy_new
+
+
+from torch.storage import _LegacyStorage, _warn_typed_storage_removal
+
+
+class _CudaLegacyStorage(_LegacyStorage):
+    @classmethod
+    def from_buffer(cls, *args, **kwargs):
+        _warn_typed_storage_removal()
+        raise RuntimeError("from_buffer: Not available for CUDA storage")
+
+    @classmethod
+    def _new_with_weak_ptr(cls, *args, **kwargs):
+        raise RuntimeError("_new_with_weak_ptr: Not available for CUDA storage")
+
+    @classmethod
+    def _new_shared_filename(cls, manager, obj, size, *, device=None, dtype=None):
+        raise RuntimeError("_new_shared_filename: Not available for CUDA storage")
+
+
+class ByteStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.uint8
+
+
+class DoubleStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.double
+
+
+class FloatStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.float
+
+
+class HalfStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.half
+
+
+class LongStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.long
+
+
+class IntStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.int
+
+
+class ShortStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.short
+
+
+class CharStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.int8
+
+
+class BoolStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.bool
+
+
+class BFloat16Storage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.bfloat16
+
+
+class ComplexDoubleStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.cdouble
+
+
+class ComplexFloatStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.cfloat
+
+
+del _LegacyStorage
+del _CudaLegacyStorage
+
+torch._storage_classes.add(DoubleStorage)
+torch._storage_classes.add(FloatStorage)
+torch._storage_classes.add(LongStorage)
+torch._storage_classes.add(IntStorage)
+torch._storage_classes.add(ShortStorage)
+torch._storage_classes.add(CharStorage)
+torch._storage_classes.add(ByteStorage)
+torch._storage_classes.add(HalfStorage)
+torch._storage_classes.add(BoolStorage)
+torch._storage_classes.add(BFloat16Storage)
+torch._storage_classes.add(ComplexDoubleStorage)
+torch._storage_classes.add(ComplexFloatStorage)
+
+
+class _WrappedTritonKernel:
+    """Just a simple wrapper to store some metadata for testing purposes."""
+
+    def __init__(self, kernel):
+        self.kernel = kernel
+        self.kernel_invoked = False
+
+    def __call__(self, *args, **kwargs):
+        res = self.kernel(*args, **kwargs)
+        self.kernel_invoked = True
+        return res
+
+
+def _register_triton_kernels():
+    if torch._running_with_deploy():
+        return
+
+    @_WrappedTritonKernel
+    def kernel_impl(*args, **kwargs):
+        from torch.sparse._triton_ops import bsr_dense_mm
+
+        return bsr_dense_mm(*args, skip_checks=True, **kwargs)
+
+    @_WrappedTritonKernel
+    def addmm_kernel_impl(*args, **kwargs):
+        from torch.sparse._triton_ops import bsr_dense_addmm
+
+        return bsr_dense_addmm(*args, skip_checks=True, **kwargs)
+
+    has_triton = importlib.util.find_spec("triton") is not None
+    if has_triton:
+        torch._TritonLibrary.registerOp(
+            "_triton_bsr_dense_mm_out",
+            "_triton_bsr_dense_mm_out(Tensor bsr, Tensor dense, *, Tensor(a!) out) -> Tensor(a!)",
+            kernel_impl,
+            "SparseCsrCUDA",
+        )
+
+        torch._TritonLibrary.registerOp(
+            "_triton_bsr_dense_addmm_out",
+            (
+                "_triton_bsr_dense_addmm_out(Tensor input, Tensor bsr, Tensor dense,"
+                " *, Scalar beta, Scalar alpha, Tensor(a!) out) -> Tensor(a!)"
+            ),
+            addmm_kernel_impl,
+            "SparseCsrCUDA",
+        )
+
+
+_lazy_call(_register_triton_kernels)
+
+
+from . import amp, jiterator, nvtx, profiler, sparse, tunable
+
+
+__all__ = [
+    # Typed storage and tensors
+    "BFloat16Storage",
+    "BFloat16Tensor",
+    "BoolStorage",
+    "BoolTensor",
+    "ByteStorage",
+    "ByteTensor",
+    "CharStorage",
+    "CharTensor",
+    "ComplexDoubleStorage",
+    "ComplexFloatStorage",
+    "DoubleStorage",
+    "DoubleTensor",
+    "FloatStorage",
+    "FloatTensor",
+    "HalfStorage",
+    "HalfTensor",
+    "IntStorage",
+    "IntTensor",
+    "LongStorage",
+    "LongTensor",
+    "ShortStorage",
+    "ShortTensor",
+    "CUDAGraph",
+    "CudaError",
+    "DeferredCudaCallError",
+    "Event",
+    "ExternalStream",
+    "Stream",
+    "StreamContext",
+    "amp",
+    "caching_allocator_alloc",
+    "caching_allocator_delete",
+    "can_device_access_peer",
+    "check_error",
+    "cudaStatus",
+    "cudart",
+    "current_blas_handle",
+    "current_device",
+    "current_stream",
+    "default_generators",
+    "default_stream",
+    "device",
+    "device_count",
+    "device_of",
+    "empty_cache",
+    "get_allocator_backend",
+    "CUDAPluggableAllocator",
+    "change_current_allocator",
+    "get_arch_list",
+    "get_device_capability",
+    "get_device_name",
+    "get_device_properties",
+    "get_gencode_flags",
+    "get_rng_state",
+    "get_rng_state_all",
+    "get_sync_debug_mode",
+    "graph",
+    "graph_pool_handle",
+    "graphs",
+    "has_half",
+    "has_magma",
+    "init",
+    "initial_seed",
+    "ipc_collect",
+    "is_available",
+    "is_bf16_supported",
+    "is_current_stream_capturing",
+    "is_initialized",
+    "jiterator",
+    "list_gpu_processes",
+    "make_graphed_callables",
+    "manual_seed",
+    "manual_seed_all",
+    "max_memory_allocated",
+    "max_memory_cached",
+    "max_memory_reserved",
+    "mem_get_info",
+    "memory",
+    "memory_allocated",
+    "memory_cached",
+    "memory_reserved",
+    "memory_snapshot",
+    "memory_stats",
+    "memory_stats_as_nested_dict",
+    "memory_summary",
+    "memory_usage",
+    "MemPool",
+    "MemPoolContext",
+    "use_mem_pool",
+    "temperature",
+    "power_draw",
+    "clock_rate",
+    "nccl",
+    "nvtx",
+    "profiler",
+    "random",
+    "reset_accumulated_memory_stats",
+    "reset_max_memory_allocated",
+    "reset_max_memory_cached",
+    "reset_peak_memory_stats",
+    "seed",
+    "seed_all",
+    "set_device",
+    "set_per_process_memory_fraction",
+    "set_rng_state",
+    "set_rng_state_all",
+    "set_stream",
+    "set_sync_debug_mode",
+    "sparse",
+    "stream",
+    "streams",
+    "synchronize",
+    "tunable",
+    "utilization",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/_gpu_trace.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/_gpu_trace.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a738b002d773118eb649e0ce34e608df3227655
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/_gpu_trace.py
@@ -0,0 +1,75 @@
+from typing import Callable
+
+from torch._utils import CallbackRegistry
+
+
+EventCreationCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "CUDA event creation"
+)
+EventDeletionCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "CUDA event deletion"
+)
+EventRecordCallbacks: "CallbackRegistry[int, int]" = CallbackRegistry(
+    "CUDA event record"
+)
+EventWaitCallbacks: "CallbackRegistry[int, int]" = CallbackRegistry(
+    "CUDA event wait"
+)
+MemoryAllocationCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "CUDA memory allocation"
+)
+MemoryDeallocationCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "CUDA memory deallocation"
+)
+StreamCreationCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "CUDA stream creation"
+)
+DeviceSynchronizationCallbacks: "CallbackRegistry[[]]" = CallbackRegistry(
+    "CUDA device synchronization"
+)
+StreamSynchronizationCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "CUDA stream synchronization"
+)
+EventSynchronizationCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "CUDA event synchronization"
+)
+
+
+def register_callback_for_event_creation(cb: Callable[[int], None]) -> None:
+    EventCreationCallbacks.add_callback(cb)
+
+
+def register_callback_for_event_deletion(cb: Callable[[int], None]) -> None:
+    EventDeletionCallbacks.add_callback(cb)
+
+
+def register_callback_for_event_record(cb: Callable[[int, int], None]) -> None:
+    EventRecordCallbacks.add_callback(cb)
+
+
+def register_callback_for_event_wait(cb: Callable[[int, int], None]) -> None:
+    EventWaitCallbacks.add_callback(cb)
+
+
+def register_callback_for_memory_allocation(cb: Callable[[int], None]) -> None:
+    MemoryAllocationCallbacks.add_callback(cb)
+
+
+def register_callback_for_memory_deallocation(cb: Callable[[int], None]) -> None:
+    MemoryDeallocationCallbacks.add_callback(cb)
+
+
+def register_callback_for_stream_creation(cb: Callable[[int], None]) -> None:
+    StreamCreationCallbacks.add_callback(cb)
+
+
+def register_callback_for_device_synchronization(cb: Callable[[], None]) -> None:
+    DeviceSynchronizationCallbacks.add_callback(cb)
+
+
+def register_callback_for_stream_synchronization(cb: Callable[[int], None]) -> None:
+    StreamSynchronizationCallbacks.add_callback(cb)
+
+
+def register_callback_for_event_synchronization(cb: Callable[[int], None]) -> None:
+    EventSynchronizationCallbacks.add_callback(cb)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/_memory_viz.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/_memory_viz.py
new file mode 100644
index 0000000000000000000000000000000000000000..2047ec4efb28fa31c768b96d65208c694865a587
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/_memory_viz.py
@@ -0,0 +1,632 @@
+# mypy: allow-untyped-defs
+import pickle
+import sys
+import os
+import io
+import subprocess
+import json
+from functools import lru_cache
+from typing import Any
+from itertools import groupby
+import base64
+import warnings
+import operator
+
+cache = lru_cache(None)
+
+__all__ = ["format_flamegraph", "segments", "memory", "compare"]
+
+def _frame_fmt(f, full_filename=False):
+    i = f['line']
+    fname = f['filename']
+    if not full_filename:
+        fname = fname.split('/')[-1]
+    func = f['name']
+    return f'{fname}:{i}:{func}'
+
+@cache
+def _frame_filter(name, filename):
+    omit_functions = [
+        "unwind::unwind",
+        "CapturedTraceback::gather",
+        "gather_with_cpp",
+        "_start",
+        "__libc_start_main",
+        "PyEval_",
+        "PyObject_",
+        "PyFunction_",
+    ]
+    omit_filenames = [
+        "core/boxing",
+        "/Register",
+        "/Redispatch",
+        "pythonrun.c",
+        "Modules/main.c",
+        "Objects/call.c",
+        "Objects/methodobject.c",
+        "pycore_ceval.h",
+        "ceval.c",
+        "cpython/abstract.h",
+    ]
+    for of in omit_functions:
+        if of in name:
+            return False
+    for of in omit_filenames:
+        if of in filename:
+            return False
+    return True
+
+def _frames_fmt(frames, full_filename=False, reverse=False):
+    if reverse:
+        frames = reversed(frames)
+    return [_frame_fmt(f, full_filename) for f in frames if _frame_filter(f['name'], f['filename'])]
+
+def _block_extra_legacy(b):
+    if 'history' in b:
+        frames = b['history'][0].get('frames', [])
+        real_size = b['history'][0]['real_size']
+    else:
+        real_size = b.get('requested_size', b['size'])
+        frames = []
+    return frames, real_size
+
+def _block_extra(b):
+    if 'frames' not in b:
+        # old snapshot format made it more complicated to get frames/allocated size
+        return _block_extra_legacy(b)
+    return b['frames'], b['requested_size']
+
+def format_flamegraph(flamegraph_lines, flamegraph_script=None):
+    if flamegraph_script is None:
+        flamegraph_script = f'/tmp/{os.getuid()}_flamegraph.pl'
+    if not os.path.exists(flamegraph_script):
+        import urllib.request
+        print(f"Downloading flamegraph.pl to: {flamegraph_script}")
+        urllib.request.urlretrieve(
+            'https://raw.githubusercontent.com/brendangregg/FlameGraph/master/flamegraph.pl', flamegraph_script)
+        subprocess.check_call(['chmod', '+x', flamegraph_script])
+    args = [flamegraph_script, '--countname', 'bytes']
+    p = subprocess.Popen(args, stdin=subprocess.PIPE, stdout=subprocess.PIPE, encoding='utf-8')
+    assert p.stdin is not None
+    assert p.stdout is not None
+    p.stdin.write(flamegraph_lines)
+    p.stdin.close()
+    result = p.stdout.read()
+    p.stdout.close()
+    p.wait()
+    assert p.wait() == 0
+    return result
+
+def _write_blocks(f, prefix, blocks):
+    def frames_fragment(frames):
+        if not frames:
+            return ""
+        return ';'.join(_frames_fmt(frames, reverse=True))
+    for b in blocks:
+        if 'history' not in b:
+            frames, accounted_for_size = _block_extra(b)
+            f.write(f'{prefix};{b["state"]};{frames_fragment(frames)} {accounted_for_size}\n')
+        else:
+            accounted_for_size = 0
+            for h in b['history']:
+                sz = h['real_size']
+                accounted_for_size += sz
+                if 'frames' in h:
+                    frames = h['frames']
+                    f.write(f'{prefix};{b["state"]};{frames_fragment(frames)} {sz}\n')
+                else:
+                    f.write(f'{prefix};{b["state"]}; {sz}\n')
+        gaps = b['size'] - accounted_for_size
+        if gaps:
+            f.write(f'{prefix};{b["state"]}; {gaps}\n')
+
+def segments(snapshot, format_flamegraph=format_flamegraph):
+    f = io.StringIO()
+    for seg in snapshot['segments']:
+        prefix = f'stream_{seg["stream"]};seg_{seg["address"]}'
+        _write_blocks(f, prefix, seg['blocks'])
+    return format_flamegraph(f.getvalue())
+
+def memory(snapshot, format_flamegraph=format_flamegraph):
+    f = io.StringIO()
+    for seg in snapshot['segments']:
+        prefix = f'stream_{seg["stream"]}'
+        _write_blocks(f, prefix, seg['blocks'])
+    return format_flamegraph(f.getvalue())
+
+def compare(before, after, format_flamegraph=format_flamegraph):
+    def _seg_key(seg):
+        return (seg['address'], seg['total_size'])
+
+    def _seg_info(seg):
+        return f'stream_{seg["stream"]};seg_{seg["address"]}'
+
+    f = io.StringIO()
+
+    before_segs = {_seg_key(seg) for seg in before}
+    after_segs = {_seg_key(seg) for seg in after}
+
+    print(f'only_before = {[a for a, _ in (before_segs - after_segs)]}')
+    print(f'only_after = {[a for a, _ in (after_segs - before_segs)]}')
+
+    for seg in before:
+        if _seg_key(seg) not in after_segs:
+            _write_blocks(f, f'only_before;{_seg_info(seg)}', seg['blocks'])
+
+    for seg in after:
+        if _seg_key(seg) not in before_segs:
+            _write_blocks(f, f'only_after;{_seg_info(seg)}', seg['blocks'])
+
+    return format_flamegraph(f.getvalue())
+
+def _format_size(num):
+    # https://stackoverflow.com/questions/1094841/get-human-readable-version-of-file-size
+    for unit in ["", "Ki", "Mi", "Gi", "Ti", "Pi", "Ei", "Zi"]:
+        if abs(num) < 1024.0:
+            return f"{num:3.1f}{unit}B"
+        num /= 1024.0
+    return f"{num:.1f}YiB"
+
+class Bytes:
+    def __init__(self, value):
+        self.value = value
+
+    def __add__(self, rhs):
+        return Bytes(self.value + rhs)
+
+    def __repr__(self):
+        return _format_size(self.value)
+
+def calc_active(seg):
+    return sum(b['size'] for b in seg['blocks'] if b['state'] == 'active_allocated')
+
+def _report_free(free_external, free_internal):
+    total = free_external + free_internal
+    suffix = ''
+    if total != 0:
+        pct = (free_internal / total) * 100
+        suffix = f' ({pct:.1f}% internal)'
+    return f'{Bytes(total)}{suffix}'
+
+PAGE_SIZE = 1024 * 1024 * 20
+legend = f"""\
+
+Legend:
+    [a     ] - a segment in the allocator
+     ^-- a page {Bytes(PAGE_SIZE)} of memory in the segment
+    a-z: pages filled with a single block's content
+    ' ': page is completely free
+    *: page if completely full with multiple blocks
+    0-9: page is partially full with tensors of multiple blocks (9 == 90% full)
+    (X% internal) - of the free memory, X% is free because we rounded the size of the allocation.
+"""
+
+def segsum(data):
+    r"""Visually reports how the allocator has filled its segments.
+
+    This printout can help debug fragmentation issues since free fragments
+    will appear as gaps in this printout.  The amount of free space is reported
+    for each segment.
+    We distinguish between internal free memory which occurs because the
+    allocator rounds the allocation size, and external free memory, which are
+    the gaps between allocations in a segment.
+    Args:
+        data: snapshot dictionary created from _snapshot()
+    """
+    segments = []
+    out = io.StringIO()
+    out.write(f"Summary of segments >= {Bytes(PAGE_SIZE)} in size\n")
+    total_reserved = 0
+    total_allocated = 0
+    free_external = 0
+    free_internal = 0
+    for seg in sorted(data['segments'], key=lambda x: (x['total_size'], calc_active(x))):
+        total_reserved += seg['total_size']
+
+        seg_free_external = 0
+        seg_free_internal = 0
+        seg_allocated = 0
+        all_ranges = []
+        boffset = 0
+        for b in seg['blocks']:
+            active = b['state'] == 'active_allocated'
+            if active:
+                _, allocated_size = _block_extra(b)
+                all_ranges.append((boffset, allocated_size, True))
+                seg_allocated += allocated_size
+                seg_free_internal += b['size'] - allocated_size
+            else:
+                seg_free_external += b['size']
+
+            boffset += b['size']
+
+        total_allocated += seg_allocated
+        free_external += seg_free_external
+        free_internal += seg_free_internal
+
+        nseg = (seg['total_size'] - 1) // PAGE_SIZE + 1
+        occupied = [' ' for _ in range(nseg)]
+        frac = [0.0 for _ in range(nseg)]
+        active_size = 0
+        for i, (start_, size, active) in enumerate(all_ranges):
+            active_size += size
+            finish_ = (start_ + size)
+            start = start_ // PAGE_SIZE
+            finish = (finish_ - 1) // PAGE_SIZE + 1
+            m = chr(ord('a' if active else 'A') + (i % 26))
+            for j in range(start, finish):
+                s = max(start_, j * PAGE_SIZE)
+                e = min(finish_, (j + 1) * PAGE_SIZE)
+                frac[j] += (e - s) / PAGE_SIZE
+                if occupied[j] != ' ':
+                    occupied[j] = '0123456789*'[int(frac[j] * 10)]
+                else:
+                    occupied[j] = m
+        stream = '' if seg['stream'] == 0 else f', stream_{seg["stream"]}'
+        body = ''.join(occupied)
+        assert seg_free_external + seg_free_internal + seg_allocated == seg['total_size']
+        stream = f' stream_{seg["stream"]}' if seg['stream'] != 0 else ''
+        if seg['total_size'] >= PAGE_SIZE:
+            out.write(f'[{body}] {Bytes(seg["total_size"])} allocated, '
+                      f'{_report_free(seg_free_external, seg_free_internal)} free{stream}\n')
+    out.write(f'segments: {len(data["segments"])}\n')
+    out.write(f'total_reserved: {Bytes(total_reserved)}\n')
+    out.write(f'total_allocated: {Bytes(total_allocated)}\n')
+    internal_external = f' ({Bytes(free_internal)} internal + {Bytes(free_external)} external)' if free_internal else ''
+    out.write(f'total_free: {_report_free(free_external, free_internal)}\n')
+    out.write(legend)
+    assert free_internal + free_external + total_allocated == total_reserved
+    return out.getvalue()
+
+def trace(data):
+    out = io.StringIO()
+
+    def format(entries):
+        segment_intervals : list = []
+        segment_addr_to_name = {}
+        allocation_addr_to_name = {}
+
+        free_names : list = []
+        next_name = 0
+
+        def _name():
+            nonlocal next_name
+            if free_names:
+                return free_names.pop()
+            r, m = next_name // 26, next_name % 26
+            next_name += 1
+            return f'{chr(ord("a") + m)}{"" if r == 0 else r}'
+
+        def find_segment(addr):
+            for name, saddr, size in segment_intervals:
+                if addr >= saddr and addr < saddr + size:
+                    return name, saddr
+            for i, seg in enumerate(data['segments']):
+                saddr = seg['address']
+                size = seg['allocated_size']
+                if addr >= saddr and addr < saddr + size:
+                    return f'seg_{i}', saddr
+            return None, None
+        count = 0
+        out.write(f'{len(entries)} entries\n')
+
+
+        total_reserved = 0
+        for seg in data['segments']:
+            total_reserved += seg['total_size']
+
+        for count, e in enumerate(entries):
+            if e['action'] == 'alloc':
+                addr, size = e['addr'], e['size']
+                n = _name()
+                seg_name, seg_addr = find_segment(addr)
+                if seg_name is None:
+                    seg_name = "MEM"
+                    offset = addr
+                else:
+                    offset = addr - seg_addr
+                out.write(f'{n} = {seg_name}[{offset}:{Bytes(size)}]\n')
+                allocation_addr_to_name[addr] = (n, size, count)
+                count += size
+            elif e['action'] == 'free_requested':
+                addr, size = e['addr'], e['size']
+                name, _, _ = allocation_addr_to_name.get(addr, (addr, None, None))
+                out.write(f'del {name} # {Bytes(size)}\n')
+            elif e['action'] == 'free_completed':
+                addr, size = e['addr'], e['size']
+                count -= size
+                name, _, _ = allocation_addr_to_name.get(addr, (addr, None, None))
+                out.write(f'# free completed for {name} {Bytes(size)}\n')
+                if name in allocation_addr_to_name:
+                    free_names.append(name)
+                    del allocation_addr_to_name[name]
+            elif e['action'] == 'segment_alloc':
+                addr, size = e['addr'], e['size']
+                name = _name()
+                out.write(f'{name} = cudaMalloc({addr}, {Bytes(size)})\n')
+                segment_intervals.append((name, addr, size))
+                segment_addr_to_name[addr] = name
+            elif e['action'] == 'segment_free':
+                addr, size = e['addr'], e['size']
+                name = segment_addr_to_name.get(addr, addr)
+                out.write(f'cudaFree({name}) # {Bytes(size)}\n')
+                if name in segment_addr_to_name:
+                    free_names.append(name)
+                    del segment_addr_to_name[name]
+            elif e['action'] == 'oom':
+                size = e['size']
+                free = e['device_free']
+                out.write(f'raise OutOfMemoryError # {Bytes(size)} requested, {Bytes(free)} free in CUDA\n')
+            else:
+                out.write(f'{e}\n')
+        out.write(f"TOTAL MEM: {Bytes(count)}")
+    for i, d in enumerate(data['device_traces']):
+        if d:
+            out.write(f'Device {i} ----------------\n')
+            format(d)
+    return out.getvalue()
+
+
+_memory_viz_template = r"""
+
+
+
+
+
+
+
+"""
+
+def _format_viz(data, viz_kind, device):
+    if device is not None:
+        warnings.warn(
+            'device argument is deprecated, plots now contain all device',
+            FutureWarning,
+            stacklevel=3,
+        )
+    buffer = pickle.dumps(data)
+    buffer += b'\x00' * (3 - len(buffer) % 3)
+    # Encode the buffer with base64
+    encoded_buffer = base64.b64encode(buffer).decode('utf-8')
+
+    json_format = json.dumps([{"name": 'snapshot.pickle', "base64": encoded_buffer}])
+    return _memory_viz_template.replace('$VIZ_KIND', repr(viz_kind)) \
+                               .replace('$SNAPSHOT', json_format)
+
+def trace_plot(data, device=None, plot_segments=False):
+    """Generate a visualization over time of the memory usage recorded by the trace as an html file.
+
+    Args:
+        data: Memory snapshot as generated from torch.cuda.memory._snapshot()
+        device (torch.device, optional): Generate the trace for this device, needed if multiple devices have allocations.
+        plot_segments (bool, optional): Plots memory returned from cudaMalloc, rather than individual allocations.
+                                        Defaults to False.
+
+    Returns:
+        str: HTML of visualization
+    """
+    return _format_viz(data, 'Active Memory Timeline' if not plot_segments else 'Active Cached Memory Timeline', device)
+
+
+def _profile_to_snapshot(profile):
+    import torch
+    from torch.profiler._memory_profiler import Action, TensorKey
+    from torch._C._profiler import _EventType
+    memory_profile = profile._memory_profile()
+
+    allocation_stacks = {}
+    for event in memory_profile._op_tree.sorted_nodes:
+        if event.tag == _EventType.Allocation:
+            parent = event.parent
+            python_parents = []
+            while parent:
+                if parent.tag in (_EventType.PyCall, _EventType.PyCCall):
+                    python_parents.append(parent)
+                parent = parent.parent
+            key = TensorKey.from_allocation(event.extra_fields)
+
+            # Corner case: If allocation doesn't have an ID (can't prove it was used as a Tensor)
+            #              key will be None. I should add some way to identify these, I just haven't yet.
+            if key and event.extra_fields.alloc_size > 0:
+                allocation_stacks[key] = python_parents
+
+
+    device_count = torch.cuda.device_count()
+    snapshot = {
+        'device_traces': [[] for _ in range(device_count + 1)],
+        'segments': [{'device': device,
+                      'address': None,
+                      'total_size': 0,
+                      'stream': 0,
+                      'blocks': []} for device in range(device_count + 1)]
+    }
+
+    def to_device(device):
+        if device.type == 'cuda':
+            return device.index
+        else:
+            return device_count
+
+    def allocate(size, tensor_key, version, during_trace=True):
+        device = to_device(tensor_key.device)
+        addr = tensor_key.storage.ptr
+
+        seg = snapshot['segments'][device]  # type: ignore[index]
+        if seg['address'] is None or seg['address'] > addr:
+            seg['address'] = addr
+        seg['total_size'] = max(seg['total_size'], addr + size)  # record max addr for now, we will make it the size later
+        category = memory_profile._categories.get(tensor_key, version)
+        category = category.name.lower() if category is not None else "unknown"
+        stack = allocation_stacks.get(tensor_key, ())
+        stack = [{'filename': 'none', 'line': 0, 'name': p.name} for p in stack]
+        r = {'action': 'alloc', 'addr': addr, 'size': size, 'stream': 0, 'frames': stack, 'category': category}
+        if during_trace:
+            snapshot['device_traces'][device].append(r)  # type: ignore[index]
+        return r
+
+    def free(alloc, device):
+        for e in ('free_requested', 'free_completed'):
+            snapshot['device_traces'][device].append({'action': e,  # type: ignore[index]
+                                                      'addr': alloc['addr'],
+                                                      'size': alloc['size'],
+                                                      'stream': 0,
+                                                      'frames': alloc['frames']})
+
+    kv_to_elem = {}
+
+
+
+    # create the device trace
+    for time, action, (tensor_key, version), size in memory_profile.timeline:
+        if not isinstance(tensor_key, TensorKey):
+            continue
+        if action == Action.CREATE:
+            kv_to_elem[(tensor_key, version)] = allocate(size, tensor_key, version)
+        elif action == Action.DESTROY:
+            free(kv_to_elem.pop((tensor_key, version)), to_device(tensor_key.device))
+        elif action == Action.INCREMENT_VERSION:
+            free(kv_to_elem.pop((tensor_key, version)), to_device(tensor_key.device))
+            kv_to_elem[(tensor_key, version + 1)] = allocate(size, tensor_key, version + 1)
+        elif action == Action.PREEXISTING:
+            kv_to_elem[(tensor_key, version)] = allocate(size, tensor_key, version, during_trace=False)
+
+
+    # create the final snapshot state
+    blocks_at_end = [(to_device(tensor_key.device), event['addr'], event['size'], event['frames'])
+                     for (tensor_key, version), event in kv_to_elem.items()]
+    for device, blocks in groupby(sorted(blocks_at_end), key=operator.itemgetter(0)):
+        seg = snapshot['segments'][device]  # type: ignore[index]
+        last_addr = seg['address']
+        for _, addr, size, frames in blocks:
+            if last_addr < addr:
+                seg['blocks'].append({'size': addr - last_addr, 'state': 'inactive'})
+            seg['blocks'].append({'size': size, 'state': 'active_allocated', 'requested_size': size, 'frames': frames})
+            last_addr = addr + size
+        if last_addr < seg['total_size']:
+            seg['blocks'].append({'size': seg['total_size'] - last_addr, 'state': 'inactive'})
+
+    snapshot['segments'] = [seg for seg in snapshot['segments'] if seg['blocks']]  # type: ignore[attr-defined]
+    for seg in snapshot['segments']:  # type: ignore[attr-defined, name-defined, no-redef]
+        seg['total_size'] -= seg['address']
+        if not seg['blocks']:
+            seg['blocks'].append({'size': seg['total_size'], 'state': 'inactive'})
+
+    return snapshot
+
+def profile_plot(profile, device=None):
+    """Generate a visualization over time of the memory usage recorded by kineto memory profiling as an html file.
+
+    Args:
+        profile: profile as generated by `torch.profiler.profile(profile_memory=True)`
+        device (torch.device, optional): Generate the trace for this device, needed if multiple devices have allocations.
+
+    Returns:
+        str: HTML of visualization
+    """
+    snapshot = _profile_to_snapshot(profile)
+    return _format_viz(snapshot, 'Active Memory Timeline', device)
+
+
+def segment_plot(data: Any, device=None):
+    return _format_viz(data, 'Allocator State History', device)
+
+if __name__ == "__main__":
+    import os.path
+    thedir = os.path.realpath(os.path.dirname(__file__))
+    if thedir in sys.path:
+        # otherwise we find cuda/random.py as random...
+        sys.path.remove(thedir)
+    import argparse
+
+    fn_name = 'torch.cuda.memory._snapshot()'
+    pickled = f'pickled memory statistics from {fn_name}'
+    parser = argparse.ArgumentParser(description=f'Visualize memory dumps produced by {fn_name}')
+
+    subparsers = parser.add_subparsers(dest='action')
+
+    def _output(p):
+        p.add_argument('-o', '--output', default='output.svg', help='flamegraph svg (default: output.svg)')
+
+    description = 'Prints overall allocation statistics and a visualization of how the allocators segments are currently filled.'
+    stats_a = subparsers.add_parser('stats', description=description)
+    stats_a.add_argument('input', help=pickled)
+
+    description = 'Prints buffer of the most recent allocation events embedded in the snapshot in a Pythonic style.'
+    trace_a = subparsers.add_parser('trace', description=description)
+    trace_a.add_argument('input', help=pickled)
+
+    description = 'Generate a flamegraph that visualizes what memory is stored in each allocator segment (aka block)'
+    segments_a = subparsers.add_parser('segments', description=description)
+    segments_a.add_argument('input', help=pickled)
+    _output(segments_a)
+
+    description = "Generate a flamegraph the program locations contributing to CUDA memory usage."
+    memory_a = subparsers.add_parser('memory', description=description)
+    memory_a.add_argument('input', help=pickled)
+    _output(memory_a)
+
+    description = 'Generate a flamegraph that shows segments (aka blocks) that have been added ' \
+        'or removed between two different memorys snapshots.'
+    compare_a = subparsers.add_parser('compare', description=description)
+    compare_a.add_argument('before', help=pickled)
+    compare_a.add_argument('after', help=pickled)
+    _output(compare_a)
+
+    plots = (
+        ("trace_plot", "Generate a visualization over time of the memory usage recorded by the trace as an html file."),
+        ("segment_plot", "Visualize how allocations are packed into allocator segments at each point in a trace as an html file.")
+    )
+    for cmd, description in plots:
+        trace_plot_a = subparsers.add_parser(cmd, description=description)
+        trace_plot_a.add_argument('input', help=pickled)
+        help = 'visualize trace from this device (default: chooses the only device with trace info or errors)'
+        trace_plot_a.add_argument('-d', '--device', type=int, default=None, help=help)
+        help = 'path to save the visualization(default: output.html)'
+        trace_plot_a.add_argument('-o', '--output', default='output.html', help=help)
+        if cmd == "trace_plot":
+            help = 'visualize change to segments rather than individual allocations'
+            trace_plot_a.add_argument('-s', '--segments', action='store_true', help=help)
+
+
+    args = parser.parse_args()
+
+    def _read(name):
+        if name == '-':
+            f = sys.stdin.buffer
+        else:
+            f = open(name, 'rb')
+        data = pickle.load(f)
+        if isinstance(data, list):  # segments only...
+            data = {'segments': data, 'traces': []}
+        return data
+
+    def _write(name, data):
+        with open(name, 'w') as f:
+            f.write(data)
+
+    if args.action == 'segments':
+        data = _read(args.input)
+        _write(args.output, segments(data))
+    elif args.action == 'memory':
+        data = _read(args.input)
+        _write(args.output, memory(data))
+    elif args.action == 'stats':
+        data = _read(args.input)
+        print(segsum(data))
+    elif args.action == 'trace':
+        data = _read(args.input)
+        print(trace(data))
+    elif args.action == 'compare':
+        before = _read(args.before)
+        after = _read(args.after)
+        _write(args.output, compare(before, after))
+    elif args.action == 'trace_plot':
+        data = _read(args.input)
+        _write(args.output, trace_plot(data, device=args.device, plot_segments=args.segments))
+    elif args.action == 'segment_plot':
+        data = _read(args.input)
+        _write(args.output, segment_plot(data, device=args.device))
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/_sanitizer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/_sanitizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab034850858782ae0547c3d5308b24fd376c5fc9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/_sanitizer.py
@@ -0,0 +1,621 @@
+# mypy: allow-untyped-defs
+r"""
+This module introduces CUDA Sanitizer, a tool for detecting synchronization errors between kernels ran on different streams.
+
+It stores information on accesses to tensors to determine if they are synchronized
+or not. When enabled in a python program and a possible data race is detected, a
+detailed warning will be printed and the program will exit.
+
+It can be enabled either by importing this module and calling
+:func:`enable_cuda_sanitizer()` or by exporting the ``TORCH_CUDA_SANITIZER``
+environment variable.
+"""
+
+import enum
+import functools
+import inspect
+import io
+import logging
+import sys
+import textwrap
+import traceback
+from dataclasses import dataclass, field
+from typing import Any, Dict, Iterator, List, Optional, Set, Tuple, TypeVar
+
+import torch
+import torch.cuda._gpu_trace as gpu_trace
+from torch.utils import _pytree as pytree
+from torch.utils._python_dispatch import TorchDispatchMode
+
+
+DEFAULT_STREAM_ID = 0
+
+TK = TypeVar("TK")
+TVa = TypeVar("TVa")
+TVb = TypeVar("TVb")
+
+DataPtr = int
+StreamId = int
+EventId = int
+SeqNum = int
+
+logger = logging.getLogger(__name__)
+
+
+class AccessType(enum.Enum):
+    READ = enum.auto()
+    WRITE = enum.auto()
+
+    def __str__(self):
+        return "reading from" if self is AccessType.READ else "writing to"
+
+
+@dataclass
+class Access:
+    r"""Stores information about a single access to a tensor by a kernel.
+
+    Args:
+        type: either AccessType.READ or AccessType.Write.
+        seq_num: the sequential number of the kernel performing the access.
+        stream: the stream id of the stream executing the kernel.
+        operator: the schema of the launched kernel, which lists the
+            arguments and return type.
+        aliases: the arguments in the schema this access corresponds to.
+        is_output: Whether the tensor was an output of the kernel.
+        stack_trace: the stack summary object captured during access.
+    """
+
+    type: AccessType
+    seq_num: SeqNum
+    stream: StreamId
+    operator: str
+    aliases: List[str]
+    is_output: bool
+    stack_trace: traceback.StackSummary
+
+
+class SynchronizationError(Exception):
+    """Base class for errors detected by CUDA Sanitizer."""
+
+
+class UnsynchronizedAccessError(SynchronizationError):
+    """Stores information about two unsynchronized accesses to one data pointer."""
+
+    def __init__(
+        self,
+        data_ptr: DataPtr,
+        allocation_stack_trace: Optional[traceback.StackSummary],
+        current_access: Access,
+        previous_access: Access,
+    ):
+        self.data_ptr = data_ptr
+        self.allocation_stack_trace = allocation_stack_trace
+        self.current_access = current_access
+        self.previous_access = previous_access
+
+    def __str__(self):
+        def format_access(access: Access):
+            message.write(f"{access.operator}\n{access.type}")
+            if access.aliases:
+                message.write(" argument(s) " + ", ".join(access.aliases))
+                if access.is_output:
+                    message.write(", and to")
+            if access.is_output:
+                message.write(" the output")
+            message.write(
+                f"\nWith stack trace:\n{''.join(access.stack_trace.format())}\n"
+            )
+
+        with io.StringIO() as message:
+            message.write(
+                textwrap.dedent(
+                    f"""\
+                    ============================
+                    CSAN detected a possible data race on tensor with data pointer {self.data_ptr}
+                    Access by stream {self.current_access.stream} during kernel:
+                    """
+                )
+            )
+            format_access(self.current_access)
+
+            message.write(
+                f"Previous access by stream {self.previous_access.stream} during kernel:\n"
+            )
+            format_access(self.previous_access)
+
+            if self.allocation_stack_trace:
+                message.write(
+                    "Tensor was allocated with stack trace:\n"
+                    f"{''.join(self.allocation_stack_trace.format())}"
+                )
+            else:
+                message.write("Trace for tensor allocation not found.")
+            return message.getvalue()
+
+
+class CUDASanitizerErrors(Exception):
+    """Wrapper class for errors reported by CUDA Sanitizer."""
+
+    def __init__(self, errors: List[SynchronizationError]):
+        self.errors = errors
+
+    def __str__(self):
+        return f"detected {len(self.errors)} errors"
+
+
+@dataclass
+class TensorInfo:
+    r"""Stores information about a single tensor and recent accesses to it.
+
+    Args:
+        allocation_stack_trace: the stack summary object captured during tensor
+            allocation. Can be ``None`` if the allocation wasn't caught by CSAN.
+        reads: list of read accesses to the tensor that were performed since
+            the last write.
+        write: the last write access to the tensor.
+    """
+
+    allocation_stack_trace: Optional[traceback.StackSummary]
+    reads: List[Access] = field(default_factory=list)
+    write: Optional[Access] = None
+
+
+class _TensorsAccessed:
+    def __init__(self) -> None:
+        self.accesses: Dict[DataPtr, TensorInfo] = {}
+
+    def ensure_tensor_exists(self, data_ptr: DataPtr) -> None:
+        if data_ptr not in self.accesses:
+            logger.info(
+                "Found tensor with pointer: %s, but no matching tensor "
+                "allocation in the trace. Backfilling the trace now. "
+                "Perhaps the sanitizer was enabled after some torch operations?",
+                data_ptr,
+            )
+            self.create_tensor(data_ptr, None)
+
+    def ensure_tensor_does_not_exist(self, data_ptr: DataPtr) -> None:
+        if data_ptr in self.accesses:
+            logger.info(
+                "Found duplicate tensor allocation in the trace for tensor with "
+                "pointer: %s. Assuming the trace for tensor deallocation "
+                "wasn't caught and backfilling it now. "
+                "Perhaps the sanitizer was enabled after some torch operations?",
+                data_ptr,
+            )
+            self.delete_tensor(data_ptr)
+
+    def create_tensor(
+        self, data_ptr: DataPtr, stack_trace: Optional[traceback.StackSummary]
+    ) -> None:
+        self.accesses[data_ptr] = TensorInfo(stack_trace)
+
+    def delete_tensor(self, data_ptr: DataPtr) -> None:
+        del self.accesses[data_ptr]
+
+    def were_there_reads_since_last_write(self, data_ptr: DataPtr) -> bool:
+        return True if self.accesses[data_ptr].reads else False
+
+    def get_allocation_stack_trace(
+        self, data_ptr: DataPtr
+    ) -> Optional[traceback.StackSummary]:
+        return self.accesses[data_ptr].allocation_stack_trace
+
+    def get_write(self, data_ptr: DataPtr) -> Optional[Access]:
+        return self.accesses[data_ptr].write
+
+    def get_reads(self, data_ptr: DataPtr) -> List[Access]:
+        return self.accesses[data_ptr].reads
+
+    def add_read(self, data_ptr: DataPtr, access: Access) -> None:
+        self.accesses[data_ptr].reads.append(access)
+
+    def set_write(self, data_ptr: DataPtr, access: Access) -> None:
+        self.accesses[data_ptr].write = access
+        self.accesses[data_ptr].reads = []
+
+
+class StreamSynchronizations:
+    def __init__(self) -> None:
+        self.current_sync_states: Dict[StreamId, Dict[StreamId, SeqNum]] = {}
+        self.recorded_sync_states: Dict[EventId, Dict[StreamId, SeqNum]] = {}
+        self.host_sync_state: Dict[StreamId, SeqNum] = {}
+        self.create_stream(DEFAULT_STREAM_ID)
+
+    def _ensure_stream_exists(self, stream: StreamId) -> None:
+        if stream not in self.current_sync_states:
+            logger.info(
+                "Found Stream with id: %s, but no matching stream "
+                "creation in the trace. Backfilling the trace now. "
+                "Perhaps the sanitizer was enabled after some torch operations?",
+                stream,
+            )
+            self.create_stream(stream)
+
+    def _ensure_event_exists(self, event: EventId) -> None:
+        if event not in self.recorded_sync_states:
+            logger.info(
+                "Found Event with id: %s, but no matching event "
+                "creation in the trace. Backfilling the trace now. "
+                "Perhaps the sanitizer was enabled after some torch operations?",
+                event,
+            )
+            self.create_event(event)
+
+    def _ensure_event_does_not_exist(self, event: EventId) -> None:
+        if event in self.recorded_sync_states:
+            logger.info(
+                "Found duplicate event creation in the trace for event with "
+                "id: %s. Assuming the trace for event deletion wasn't caught "
+                "and backfilling it now. "
+                "Perhaps the sanitizer was enabled after some torch operations?",
+                event,
+            )
+            self.delete_event(event)
+
+    def create_stream(self, stream: StreamId) -> None:
+        if stream in self.current_sync_states:
+            logger.info(
+                "Found duplicate Stream creation in the trace for Stream with "
+                "id: %s. PyTorch Streams are only created once, so this "
+                "trace entry is ignored.",
+                stream,
+            )
+        else:
+            self.host_sync_state[stream] = 0
+            self.current_sync_states[stream] = self.host_sync_state.copy()
+
+    def create_event(self, event: EventId) -> None:
+        self._ensure_event_does_not_exist(event)
+        self.recorded_sync_states[event] = {}
+
+    def delete_event(self, event: EventId) -> None:
+        self._ensure_event_exists(event)
+        del self.recorded_sync_states[event]
+
+    def update_seq_num(self, stream: StreamId, seq_num: SeqNum) -> None:
+        self._ensure_stream_exists(stream)
+        self.current_sync_states[stream][stream] = seq_num
+
+    def record_state(self, event: EventId, stream: StreamId) -> None:
+        self._ensure_event_exists(event)
+        self._ensure_stream_exists(stream)
+        self.recorded_sync_states[event] = self.current_sync_states[stream].copy()
+
+    def _state_wait_for_other(
+        self, state: Dict[StreamId, SeqNum], other: Dict[StreamId, SeqNum]
+    ) -> None:
+        for stream, seq_num in other.items():
+            state[stream] = max(state.get(stream, -1), seq_num)
+
+    def stream_wait_for_event(self, stream: StreamId, event: EventId) -> None:
+        self._ensure_stream_exists(stream)
+        self._ensure_event_exists(event)
+        self._state_wait_for_other(
+            self.current_sync_states[stream], self.recorded_sync_states[event]
+        )
+
+    def all_streams_wait_for_event(self, event: EventId) -> None:
+        self._ensure_event_exists(event)
+        for stream in self.current_sync_states.keys():
+            self.stream_wait_for_event(stream, event)
+
+        self._state_wait_for_other(
+            self.host_sync_state, self.recorded_sync_states[event]
+        )
+
+    def all_streams_wait_for_stream(self, stream: StreamId) -> None:
+        self._ensure_stream_exists(stream)
+        for state in self.current_sync_states.values():
+            self._state_wait_for_other(state, self.current_sync_states[stream])
+
+        self._state_wait_for_other(
+            self.host_sync_state, self.current_sync_states[stream]
+        )
+
+    def sync_all_streams(self) -> None:
+        for stream, state in self.current_sync_states.items():
+            self.host_sync_state[stream] = state[stream]
+
+        for state in self.current_sync_states.values():
+            self._state_wait_for_other(state, self.host_sync_state)
+
+    def is_ordered_after(
+        self, current_stream: StreamId, seq_num: SeqNum, other_stream: StreamId
+    ) -> bool:
+        self._ensure_stream_exists(current_stream)
+        self._ensure_stream_exists(other_stream)
+        return seq_num <= self.current_sync_states[current_stream].get(other_stream, -1)
+
+
+class EventHandler:
+    """Analyzes CSAN trace for synchronization errors.
+
+    Stores information on each stream's synchronizations with other streams as well
+    as tensor accesses to determine whether a given kernel launch might cause a
+    data race.
+    """
+
+    def __init__(self) -> None:
+        self.tensors_accessed = _TensorsAccessed()
+        self.syncs = StreamSynchronizations()
+        self.seq_num: SeqNum = 0
+
+    def _handle_kernel_launch(
+        self,
+        stream: StreamId,
+        read_only: Set[DataPtr],
+        read_write: Set[DataPtr],
+        outputs: Set[DataPtr],
+        operator: str,
+        tensor_aliases: Dict[int, List[str]],
+    ) -> List[SynchronizationError]:
+        def check_conflict(
+            data_ptr: DataPtr, current_access: Access, previous_access: Optional[Access]
+        ) -> None:
+            if previous_access is None:
+                return
+            if not self.syncs.is_ordered_after(
+                current_access.stream, previous_access.seq_num, previous_access.stream
+            ):
+                error_list.append(
+                    UnsynchronizedAccessError(
+                        data_ptr,
+                        self.tensors_accessed.get_allocation_stack_trace(data_ptr),
+                        current_access,
+                        previous_access,
+                    )
+                )
+
+        error_list: List[SynchronizationError] = []
+        self.seq_num += 1
+        self.syncs.update_seq_num(stream, self.seq_num)
+        stack_trace = traceback.StackSummary.extract(
+            traceback.walk_stack(inspect.currentframe()), lookup_lines=False
+        )
+        # The stack trace generated in this way is in the inverse order, so it must be
+        # reversed.
+        stack_trace.reverse()
+
+        for data_ptr in read_only:
+            self.tensors_accessed.ensure_tensor_exists(data_ptr)
+            current_access = Access(
+                AccessType.READ,
+                self.seq_num,
+                stream,
+                operator,
+                tensor_aliases[data_ptr],
+                data_ptr in outputs,
+                stack_trace,
+            )
+            check_conflict(
+                data_ptr, current_access, self.tensors_accessed.get_write(data_ptr)
+            )
+            self.tensors_accessed.add_read(data_ptr, current_access)
+
+        for data_ptr in read_write:
+            self.tensors_accessed.ensure_tensor_exists(data_ptr)
+            current_access = Access(
+                AccessType.WRITE,
+                self.seq_num,
+                stream,
+                operator,
+                tensor_aliases[data_ptr],
+                data_ptr in outputs,
+                stack_trace,
+            )
+            if self.tensors_accessed.were_there_reads_since_last_write(data_ptr):
+                for previous_access in self.tensors_accessed.get_reads(data_ptr):
+                    check_conflict(data_ptr, current_access, previous_access)
+            else:
+                check_conflict(
+                    data_ptr, current_access, self.tensors_accessed.get_write(data_ptr)
+                )
+            self.tensors_accessed.set_write(data_ptr, current_access)
+
+        return error_list
+
+    def _handle_event_creation(self, event: EventId) -> None:
+        self.syncs.create_event(event)
+
+    def _handle_event_deletion(self, event: EventId) -> None:
+        self.syncs.delete_event(event)
+
+    def _handle_event_record(self, event: EventId, stream: StreamId) -> None:
+        self.syncs.record_state(event, stream)
+
+    def _handle_event_wait(self, event: EventId, stream: StreamId) -> None:
+        self.syncs.stream_wait_for_event(stream, event)
+
+    def _handle_memory_allocation(self, data_ptr: DataPtr) -> None:
+        self.tensors_accessed.ensure_tensor_does_not_exist(data_ptr)
+        stack_trace = traceback.StackSummary.extract(
+            traceback.walk_stack(inspect.currentframe()), lookup_lines=False
+        )
+        # The stack trace generated in this way is in the inverse order, so it must be
+        # reversed.
+        stack_trace.reverse()
+        self.tensors_accessed.create_tensor(
+            data_ptr,
+            stack_trace,
+        )
+
+    def _handle_memory_deallocation(self, data_ptr: DataPtr) -> None:
+        self.tensors_accessed.ensure_tensor_exists(data_ptr)
+        self.tensors_accessed.delete_tensor(data_ptr)
+
+    def _handle_stream_creation(self, stream: StreamId) -> None:
+        self.syncs.create_stream(stream)
+
+    def _handle_device_synchronization(self) -> None:
+        self.syncs.sync_all_streams()
+
+    def _handle_stream_synchronization(self, stream: StreamId) -> None:
+        self.syncs.all_streams_wait_for_stream(stream)
+
+    def _handle_event_synchronization(self, event: EventId) -> None:
+        self.syncs.all_streams_wait_for_event(event)
+
+
+def zip_by_key(a: Dict[TK, TVa], b: Dict[TK, TVb]) -> Iterator[Tuple[TK, TVa, TVb]]:
+    for arg, value in a.items():
+        if arg in b:
+            yield arg, value, b[arg]
+
+
+def zip_arguments(
+    schema: torch.FunctionSchema, args: Tuple[Any, ...], kwargs: Dict[str, Any]
+) -> Iterator[Tuple[torch.Argument, Any]]:
+    schema_args = schema.arguments[: len(args)]
+    schema_kwargs = {arg.name: arg for arg in schema.arguments[len(args) :]}
+
+    yield from zip(schema_args, args)
+
+    for _, argument, value in zip_by_key(schema_kwargs, kwargs):
+        yield (argument, value)
+
+
+class ArgumentHandler:
+    def __init__(self) -> None:
+        self.dataptrs_read: Set[DataPtr] = set()
+        self.dataptrs_written: Set[DataPtr] = set()
+        self.tensor_aliases: Dict[DataPtr, List[str]] = {}
+        self.outputs: Set[DataPtr] = set()
+
+    def _handle_argument(
+        self,
+        value: Any,
+        is_write: bool,
+        name: Optional[str] = None,
+        is_output: bool = False,
+    ) -> None:
+        if isinstance(value, torch.Tensor) and value.is_cuda:
+            data_ptr = value.data_ptr()
+            if is_write:
+                self.dataptrs_written.add(data_ptr)
+            else:
+                self.dataptrs_read.add(data_ptr)
+
+            self.tensor_aliases.setdefault(data_ptr, [])
+            if name is not None:
+                self.tensor_aliases[data_ptr].append(name)
+            if is_output:
+                self.outputs.add(data_ptr)
+
+    def parse_inputs(
+        self,
+        schema: torch.FunctionSchema,
+        args: Tuple[Any, ...],
+        kwargs: Dict[str, Any],
+    ) -> None:
+        for argument, value in zip_arguments(schema, args, kwargs):
+            is_write = argument.alias_info is not None and argument.alias_info.is_write
+            pytree.tree_map_(
+                functools.partial(
+                    self._handle_argument, is_write=is_write, name=argument.name
+                ),
+                value,
+            )
+
+    def parse_outputs(self, outputs: Any) -> None:
+        pytree.tree_map_(
+            functools.partial(self._handle_argument, is_write=True, is_output=True),
+            outputs,
+        )
+
+
+class CUDASanitizerDispatchMode(TorchDispatchMode):
+    def __init__(self) -> None:
+        self.event_handler = EventHandler()
+        torch._C._activate_gpu_trace()
+        gpu_trace.register_callback_for_event_creation(
+            self.event_handler._handle_event_creation
+        )
+        gpu_trace.register_callback_for_event_deletion(
+            self.event_handler._handle_event_deletion
+        )
+        gpu_trace.register_callback_for_event_record(
+            self.event_handler._handle_event_record
+        )
+        gpu_trace.register_callback_for_event_wait(
+            self.event_handler._handle_event_wait
+        )
+        gpu_trace.register_callback_for_memory_allocation(
+            self.event_handler._handle_memory_allocation
+        )
+        gpu_trace.register_callback_for_memory_deallocation(
+            self.event_handler._handle_memory_deallocation
+        )
+        gpu_trace.register_callback_for_stream_creation(
+            self.event_handler._handle_stream_creation
+        )
+        gpu_trace.register_callback_for_device_synchronization(
+            self.event_handler._handle_device_synchronization
+        )
+        gpu_trace.register_callback_for_stream_synchronization(
+            self.event_handler._handle_stream_synchronization
+        )
+        gpu_trace.register_callback_for_event_synchronization(
+            self.event_handler._handle_event_synchronization
+        )
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        if kwargs is None:
+            kwargs = {}
+
+        argument_handler = ArgumentHandler()
+        argument_handler.parse_inputs(func._schema, args, kwargs)
+
+        outputs = func(*args, **kwargs)
+
+        argument_handler.parse_outputs(outputs)
+        errors = self.event_handler._handle_kernel_launch(
+            torch.cuda.current_stream().cuda_stream,
+            argument_handler.dataptrs_read - argument_handler.dataptrs_written,
+            argument_handler.dataptrs_written,
+            argument_handler.outputs,
+            func._schema,
+            argument_handler.tensor_aliases,
+        )
+        if errors:
+            for error in errors:
+                print(error, file=sys.stderr)
+            raise CUDASanitizerErrors(errors)
+
+        return outputs
+
+
+class CUDASanitizer:
+    """Manages the lifetime of a CUDASanitizer dispatch mode object.
+
+    The CUDASanitizer class wraps the entering/exiting functions of the dispatch mode
+    context manager in the enable function/destructor, respectively. This is to
+    explicitly set the lifetime of the dispatch mode object to that of the application.
+    This approach was deemed more elegant than using the atexit module.
+    """
+
+    def __init__(self) -> None:
+        self.dispatch = CUDASanitizerDispatchMode()
+        self.enabled = False
+
+    def enable(self):
+        self.dispatch.__enter__()
+        self.enabled = True
+
+    def __del__(self):
+        if self.enabled:
+            self.dispatch.__exit__(None, None, None)
+
+
+def enable_cuda_sanitizer():
+    """Enable CUDA Sanitizer.
+
+    The sanitizer will begin to analyze low-level CUDA calls invoked by torch functions
+    for synchronization errors. All data races found will be printed to the standard
+    error output along with stack traces of suspected causes. For best results, the
+    sanitizer should be enabled at the very beginning of the program.
+    """
+    cuda_sanitizer.enable()
+
+
+cuda_sanitizer = CUDASanitizer()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d0ee8830bd68c23115e7788bb7e1a0c220b1882
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/_utils.py
@@ -0,0 +1,38 @@
+from typing import Any
+
+import torch
+
+# The _get_device_index has been moved to torch.utils._get_device_index
+from torch._utils import _get_device_index as _torch_get_device_index
+
+
+def _get_device_index(
+    device: Any, optional: bool = False, allow_cpu: bool = False
+) -> int:
+    r"""Get the device index from :attr:`device`, which can be a torch.device object, a Python integer, or ``None``.
+
+    If :attr:`device` is a torch.device object, returns the device index if it
+    is a CUDA device. Note that for a CUDA device without a specified index,
+    i.e., ``torch.device('cuda')``, this will return the current default CUDA
+    device if :attr:`optional` is ``True``. If :attr:`allow_cpu` is ``True``,
+    CPU devices will be accepted and ``-1`` will be returned in this case.
+
+    If :attr:`device` is a Python integer, it is returned as is.
+
+    If :attr:`device` is ``None``, this will return the current default CUDA
+    device if :attr:`optional` is ``True``.
+    """
+    if isinstance(device, int):
+        return device
+    if isinstance(device, str):
+        device = torch.device(device)
+    if isinstance(device, torch.device):
+        if allow_cpu:
+            if device.type not in ["cuda", "cpu"]:
+                raise ValueError(f"Expected a cuda or cpu device, but got: {device}")
+        elif device.type != "cuda":
+            raise ValueError(f"Expected a cuda device, but got: {device}")
+    if not torch.jit.is_scripting():
+        if isinstance(device, torch.cuda.device):
+            return device.idx
+    return _torch_get_device_index(device, optional, allow_cpu)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/comm.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/comm.py
new file mode 100644
index 0000000000000000000000000000000000000000..2915de5d090fd18c82540beedb9971a0b7b6cc3e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/comm.py
@@ -0,0 +1,19 @@
+# The functions here have been moved to torch.nn.parallel.comm
+from torch.nn.parallel.comm import (
+    broadcast,
+    broadcast_coalesced,
+    gather,
+    reduce_add,
+    reduce_add_coalesced,
+    scatter,
+)
+
+
+__all__ = [
+    "broadcast",
+    "broadcast_coalesced",
+    "reduce_add",
+    "reduce_add_coalesced",
+    "scatter",
+    "gather",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/error.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/error.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/gds.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/gds.py
new file mode 100644
index 0000000000000000000000000000000000000000..7cd5b8824103d2ebf4e217793f7d42923bb3c2a0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/gds.py
@@ -0,0 +1,129 @@
+import os
+import sys
+from typing import Callable, List, Optional
+
+import torch
+from torch.types import Storage
+
+
+__all__: List[str] = []
+
+
+def _dummy_fn(name: str) -> Callable:
+    def fn(*args, **kwargs):  # type: ignore[no-untyped-def]
+        raise RuntimeError(f"torch._C.{name} is not supported on this platform")
+
+    return fn
+
+
+if not hasattr(torch._C, "_gds_register_buffer"):
+    assert not hasattr(torch._C, "_gds_deregister_buffer")
+    assert not hasattr(torch._C, "_gds_register_handle")
+    assert not hasattr(torch._C, "_gds_deregister_handle")
+    assert not hasattr(torch._C, "_gds_load_storage")
+    assert not hasattr(torch._C, "_gds_save_storage")
+    # Define functions
+    torch._C.__dict__["_gds_register_buffer"] = _dummy_fn("_gds_register_buffer")
+    torch._C.__dict__["_gds_deregister_buffer"] = _dummy_fn("_gds_deregister_buffer")
+    torch._C.__dict__["_gds_register_handle"] = _dummy_fn("_gds_register_handle")
+    torch._C.__dict__["_gds_deregister_handle"] = _dummy_fn("_gds_deregister_handle")
+    torch._C.__dict__["_gds_load_storage"] = _dummy_fn("_gds_load_storage")
+    torch._C.__dict__["_gds_save_storage"] = _dummy_fn("_gds_save_storage")
+
+
+def _gds_register_buffer(s: Storage) -> None:
+    """Registers a buffer.
+
+    Args:
+        s (Storage): Buffer to register.
+    """
+    torch._C._gds_register_buffer(s)
+
+
+def _gds_deregister_buffer(s: Storage) -> None:
+    """Registers a buffer.
+
+    Args:
+        s (Storage): Buffer to register.
+    """
+    torch._C._gds_deregister_buffer(s)
+
+
+class _GdsFile:
+    r"""Wrapper around cuFile.
+
+    cuFile is a file-like interface to the GPUDirect Storage (GDS) API.
+
+    Args:
+        filename (str): Name of the file to open.
+        flags (int): Flags to pass to ``os.open`` when opening the file. ``os.O_DIRECT`` will
+            be added automatically.
+
+    .. _CUDA GPUDirect Storage Documentation:
+        https://docs.nvidia.com/gpudirect-storage/api-reference-guide/index.html#cufile-io-api
+    """
+
+    def __init__(self, filename: str, flags: int):
+        if sys.platform == "win32":
+            raise RuntimeError("GdsFile is not supported on this platform.")
+        self.filename = filename
+        self.flags = flags
+        self.fd = os.open(filename, flags | os.O_DIRECT)
+        self.handle: Optional[int] = None
+        self.register_handle()
+
+    def __del__(self) -> None:
+        if self.handle is not None:
+            self.deregister_handle()
+        os.close(self.fd)
+
+    def register_handle(self) -> None:
+        """Registers file descriptor to cuFile Driver.
+
+        This is a wrapper around ``cuFileHandleRegister``.
+        """
+        assert (
+            self.handle is None
+        ), "Cannot register a handle that is already registered."
+        self.handle = torch._C._gds_register_handle(self.fd)
+
+    def deregister_handle(self) -> None:
+        """Deregisters file descriptor from cuFile Driver.
+
+        This is a wrapper around ``cuFileHandleDeregister``.
+        """
+        assert (
+            self.handle is not None
+        ), "Cannot deregister a handle that is not registered."
+        torch._C._gds_deregister_handle(self.handle)
+        self.handle = None
+
+    def load_storage(self, storage: Storage, offset: int = 0) -> None:
+        """Loads data from the file into the storage.
+
+        This is a wrapper around ``cuFileRead``. ``storage.nbytes()`` of data
+        will be loaded from the file at ``offset`` into the storage.
+
+        Args:
+            storage (Storage): Storage to load data into.
+            offset (int, optional): Offset into the file to start loading from. (Default: 0)
+        """
+        assert (
+            self.handle is not None
+        ), "Cannot load data from a file that is not registered."
+        torch._C._gds_load_storage(self.handle, storage, offset)
+
+    def save_storage(self, storage: Storage, offset: int = 0) -> None:
+        """Saves data from the storage into the file.
+
+        This is a wrapper around ``cuFileWrite``. All bytes of the storage
+        will be written to the file at ``offset``.
+
+        Args:
+            storage (Storage): Storage to save data from.
+            offset (int, optional): Offset into the file to start saving to. (Default: 0)
+        """
+        assert (
+            self.handle is not None
+        ), "Cannot save data to a file that is not registered."
+        torch._C._gds_save_storage(self.handle, storage, offset)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/graphs.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/graphs.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5de9f73df726cc2d6e0fd5bee2bd178dcf6aa89
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/graphs.py
@@ -0,0 +1,491 @@
+# mypy: allow-untyped-defs
+import gc
+import typing
+
+import torch
+
+from .._utils import _dummy_type
+
+
+if not hasattr(torch._C, "_CudaStreamBase"):
+    # Define dummy base classes
+    torch._C.__dict__["_CUDAGraph"] = _dummy_type("_CUDAGraph")
+    torch._C.__dict__["_graph_pool_handle"] = _dummy_type("_graph_pool_handle")
+    torch._C.__dict__["_cuda_isCurrentStreamCapturing"] = _dummy_type(
+        "_cuda_isCurrentStreamCapturing"
+    )
+
+from torch._C import (  # noqa: F401
+    _cuda_isCurrentStreamCapturing,
+    _CUDAGraph,
+    _graph_pool_handle,
+)
+
+
+def is_current_stream_capturing():
+    r"""Return True if CUDA graph capture is underway on the current CUDA stream, False otherwise.
+
+    If a CUDA context does not exist on the current device, returns False without initializing the context.
+    """
+    return _cuda_isCurrentStreamCapturing()
+
+
+# Python shim helps Sphinx process docstrings more reliably.
+def graph_pool_handle():
+    r"""Return an opaque token representing the id of a graph memory pool.
+
+    See :ref:`Graph memory management`.
+
+    .. warning::
+        This API is in beta and may change in future releases.
+    """
+    return _graph_pool_handle()
+
+
+# Python shim helps Sphinx process docstrings more reliably.
+class CUDAGraph(torch._C._CUDAGraph):
+    r"""Wrapper around a CUDA graph.
+
+    .. warning::
+        This API is in beta and may change in future releases.
+    """
+
+    def __new__(cls):
+        return super().__new__(cls)
+
+    def capture_begin(self, pool=None, capture_error_mode="global"):
+        r"""Begin capturing CUDA work on the current stream.
+
+        Typically, you shouldn't call ``capture_begin`` yourself.
+        Use :class:`~torch.cuda.graph` or :func:`~torch.cuda.make_graphed_callables`,
+        which call ``capture_begin`` internally.
+
+        Arguments:
+            pool (optional): Token (returned by :func:`~torch.cuda.graph_pool_handle` or
+                :meth:`other_Graph_instance.pool()`) that hints this graph may share memory
+                with the indicated pool.  See :ref:`Graph memory management`.
+            capture_error_mode (str, optional): specifies the cudaStreamCaptureMode for the graph capture stream.
+                Can be "global", "thread_local" or "relaxed". During cuda graph capture, some actions, such as cudaMalloc,
+                may be unsafe. "global" will error on actions in other threads, "thread_local" will only error for
+                actions in the current thread, and "relaxed" will not error on these actions. Do NOT change this setting
+                unless you're familiar with `cudaStreamCaptureMode `_
+        """  # noqa: B950
+        super().capture_begin(pool=pool, capture_error_mode=capture_error_mode)
+
+    def capture_end(self):
+        r"""End CUDA graph capture on the current stream.
+
+        After ``capture_end``, ``replay`` may be called on this instance.
+
+        Typically, you shouldn't call ``capture_end`` yourself.
+        Use :class:`~torch.cuda.graph` or :func:`~torch.cuda.make_graphed_callables`,
+        which call ``capture_end`` internally.
+        """
+        super().capture_end()
+
+    def replay(self):
+        r"""Replay the CUDA work captured by this graph."""
+        super().replay()
+
+    def reset(self):
+        r"""Delete the graph currently held by this instance."""
+        super().reset()
+
+    def pool(self):
+        r"""Return an opaque token representing the id of this graph's memory pool.
+
+        This id can optionally be passed to another graph's ``capture_begin``,
+        which hints the other graph may share the same memory pool.
+        """
+        return super().pool()
+
+    def enable_debug_mode(self):
+        r"""Enable debugging mode for CUDAGraph.debug_dump."""
+        return super().enable_debug_mode()
+
+    def debug_dump(self, debug_path):
+        r"""
+        Arguments:
+            debug_path (required): Path to dump the graph to.
+
+        Calls a debugging function to dump the graph if the debugging is
+        enabled via CUDAGraph.enable_debug_mode()
+        """
+        return super().debug_dump(debug_path)
+
+
+class graph:
+    r"""Context-manager that captures CUDA work into a :class:`torch.cuda.CUDAGraph` object for later replay.
+
+    See :ref:`CUDA Graphs ` for a general introduction,
+    detailed use, and constraints.
+
+    Arguments:
+        cuda_graph (torch.cuda.CUDAGraph): Graph object used for capture.
+        pool (optional): Opaque token (returned by a call to :func:`~torch.cuda.graph_pool_handle()` or
+            :meth:`other_Graph_instance.pool()`) hinting this graph's capture
+            may share memory from the specified pool. See :ref:`Graph memory management`.
+        stream (torch.cuda.Stream, optional): If supplied, will be set as the current stream in the context.
+            If not supplied, ``graph`` sets its own internal side stream as the current stream in the context.
+        capture_error_mode (str, optional): specifies the cudaStreamCaptureMode for the graph capture stream.
+            Can be "global", "thread_local" or "relaxed". During cuda graph capture, some actions, such as cudaMalloc,
+            may be unsafe. "global" will error on actions in other threads, "thread_local" will only error for
+            actions in the current thread, and "relaxed" will not error on actions. Do NOT change this setting
+            unless you're familiar with `cudaStreamCaptureMode `_
+
+    .. note::
+        For effective memory sharing, if you pass a ``pool`` used by a previous capture and the previous capture
+        used an explicit ``stream`` argument, you should pass the same ``stream`` argument to this capture.
+
+    .. warning::
+        This API is in beta and may change in future releases.
+
+    .. _cudaStreamCaptureMode:
+        https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html#group__CUDART__STREAM_1g9d0535d93a214cbf126835257b16ba85
+    """  # noqa: B950
+
+    default_capture_stream: typing.Optional["torch.cuda.Stream"] = None
+
+    def __init__(
+        self,
+        cuda_graph,
+        pool=None,
+        stream=None,
+        capture_error_mode: str = "global",
+    ):
+        # Lazy-init of default_capture_stream helps avoid circular-import errors.
+        # Not thread safe, but graphs already have the general (explicitly documented)
+        # restriction that only one capture may be underway at a time in the process.
+        if self.__class__.default_capture_stream is None:
+            self.__class__.default_capture_stream = torch.cuda.Stream()
+
+        self.pool = () if pool is None else (pool,)
+        self.capture_stream = (
+            stream if stream is not None else self.__class__.default_capture_stream
+        )
+        assert self.capture_stream is not None
+        self.stream_ctx = torch.cuda.stream(self.capture_stream)
+        self.cuda_graph = cuda_graph
+        self.capture_error_mode = capture_error_mode
+
+    def __enter__(self):
+        # Free as much memory as we can for the graph
+        torch.cuda.synchronize()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+        # Stackoverflow seems comfortable with this pattern
+        # https://stackoverflow.com/questions/26635684/calling-enter-and-exit-manually#39172487
+        self.stream_ctx.__enter__()
+
+        self.cuda_graph.capture_begin(
+            *self.pool, capture_error_mode=self.capture_error_mode
+        )
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.cuda_graph.capture_end()
+        self.stream_ctx.__exit__(exc_type, exc_value, traceback)
+        # returning None should propagate exceptions from either capture_end or stream_ctx.__exit__()
+
+
+def make_graphed_callables(
+    callables, sample_args, num_warmup_iters=3, allow_unused_input=False, pool=None
+):
+    r"""Accept callables (functions or :class:`nn.Module`\ s) and returns graphed versions.
+
+    Each graphed callable's forward pass runs its source callable's
+    forward CUDA work as a CUDA graph inside a single autograd node.
+
+    The graphed callable's forward pass also appends
+    a backward node to the autograd graph. During backward, this node runs the
+    callable's backward work as a CUDA graph.
+
+    Therefore, each graphed callable should be a drop-in replacement for its source callable
+    in an autograd-enabled training loop.
+
+    See :ref:`Partial-network capture` for detailed use and constraints.
+
+    If you pass a tuple of several callables, their captures will use the same memory pool.
+    See :ref:`Graph memory management` for when this is appropriate.
+
+    Arguments:
+        callables (torch.nn.Module or Python function, or tuple of these): Callable or callables to graph.
+            See :ref:`Graph memory management` for when passing a tuple of callables
+            is appropriate.  If you pass a tuple of callables, their order in the tuple must be the same order
+            they'll run in the live workload.
+        sample_args (tuple of Tensors, or tuple of tuples of Tensors): Samples args for each callable.
+            If a single callable was passed, ``sample_args`` must be a single tuple of argument Tensors.
+            If a tuple of callables was passed, ``sample_args`` must be tuple of tuples of argument Tensors.
+        num_warmup_iters (int): The number of warmup iterations. Currently, ``DataDistributedParallel`` needs
+            11 iterations for warm up. Default: ``3``.
+        allow_unused_input (bool): If False, specifying inputs that were not used when computing outputs
+            (and therefore their grad is always zero) is an error. Defaults to False.
+        pool (optional): Token (returned by :func:`~torch.cuda.graph_pool_handle` or
+            :meth:`other_Graph_instance.pool()`) that hints this graph may share memory
+            with the indicated pool.  See :ref:`Graph memory management`.
+    .. note::
+        The ``requires_grad`` state of each Tensor in ``sample_args`` must match the state
+        that's expected for the corresponding real input in the training loop.
+
+    .. warning::
+        This API is in beta and may change in future releases.
+
+    .. warning::
+        ``sample_args`` for each callable must contain only Tensors. Other types are not allowed.
+
+    .. warning::
+        Returned callables do not support higher order differentiation (e.g., double backward).
+
+    .. warning::
+        In any :class:`~torch.nn.Module` passed to :func:`~make_graphed_callables`, only parameters
+        may be trainable. Buffers must have ``requires_grad=False``.
+
+    .. warning::
+        After you pass a :class:`torch.nn.Module` through :func:`~make_graphed_callables`,
+        you may not add or remove any of that Module's parameters or buffers.
+
+    .. warning::
+        :class:`torch.nn.Module`\s passed to :func:`~torch.cuda.make_graphed_callables` must not have module hooks
+        registered on them at the time they are passed. However, registering hooks on modules *after* passing them
+        through :func:`~torch.cuda.make_graphed_callables` is allowed.
+
+    .. warning::
+        When running a graphed callable, you must pass its arguments in the same order and format
+        they appeared in that callable's ``sample_args``.
+
+    .. warning::
+        The automatic mixed precision is supported in :func:`~torch.cuda.make_graphed_callables` only with disabled
+        caching. The context manager `torch.cuda.amp.autocast()` must have `cache_enabled=False`.
+    """
+    if torch.is_autocast_enabled() and torch.is_autocast_cache_enabled():
+        raise RuntimeError(
+            "make_graphed_callables does not support the autocast caching. Please set `cache_enabled=False`."
+        )
+
+    just_one_callable = False
+
+    if not isinstance(callables, tuple):
+        just_one_callable = True
+        callables = (callables,)
+        sample_args = (sample_args,)
+
+    flatten_sample_args = []
+
+    for c, args in zip(callables, sample_args):
+        if isinstance(c, torch.nn.Module):
+            assert (
+                len(c._backward_hooks) == 0
+                and len(c._forward_hooks) == 0
+                and len(c._forward_pre_hooks) == 0
+            ), (
+                "Modules must not have hooks registered at the time they are passed. However, registering hooks "
+                + "on modules after passing them through make_graphed_callables is allowed."
+            )
+            assert all(b.requires_grad is False for b in c.buffers()), (
+                "In any :class:`~torch.nn.Module` passed to "
+                + ":func:`~make_graphed_callables`, only parameters may be trainable. All buffers must have "
+                + "``requires_grad=False``."
+            )
+        flatten_arg = torch.utils._pytree.arg_tree_leaves(*args)
+        flatten_sample_args.append(tuple(flatten_arg))
+        assert all(isinstance(arg, torch.Tensor) for arg in flatten_arg), (
+            "In the beta API, sample_args "
+            + "for each callable must contain only Tensors. Other types are not allowed."
+        )
+
+    # If a callable is an nn.Module, its graph's full input surface is the args the user explicitly
+    # passes to forward (ie, its sample_args) AND the module's parameter attributes.
+    per_callable_len_user_args = [len(args) for args in flatten_sample_args]
+    per_callable_module_params = [
+        tuple(c.parameters()) if isinstance(c, torch.nn.Module) else ()
+        for c in callables
+    ]
+    per_callable_static_input_surfaces = [
+        flatten_sample_args[i] + per_callable_module_params[i]
+        for i in range(len(callables))
+    ]
+
+    fwd_graphs = [torch.cuda.CUDAGraph() for _ in range(len(callables))]
+    bwd_graphs = [torch.cuda.CUDAGraph() for _ in range(len(callables))]
+
+    mempool = graph_pool_handle() if pool is None else pool
+
+    # Warmup
+    # Hopefully prevents cudnn benchmarking and other lazy-initialization cuda work
+    # from ending up in any captures.
+    torch.cuda.synchronize()
+    with torch.cuda.stream(torch.cuda.Stream()):
+        for func, args, static_input_surface in zip(
+            callables, sample_args, per_callable_static_input_surfaces
+        ):
+            grad_inputs, outputs, outputs_grad = None, None, None
+            for _ in range(num_warmup_iters):
+                outputs = torch.utils._pytree.tree_leaves(func(*args))
+                outputs_grad = tuple(o for o in outputs if o.requires_grad)
+                if len(outputs_grad) > 0:
+                    grad_inputs = torch.autograd.grad(
+                        outputs=outputs_grad,
+                        inputs=tuple(
+                            i for i in static_input_surface if i.requires_grad
+                        ),
+                        grad_outputs=tuple(
+                            torch.empty_like(o) for o in outputs if o.requires_grad
+                        ),
+                        only_inputs=True,
+                        allow_unused=allow_unused_input,
+                    )
+            for v in [outputs, outputs_grad, grad_inputs]:
+                del v
+
+    torch.cuda.synchronize()
+
+    # All captures here share a mempool. To avoid replays corrupting each other's memory,
+    # the safest approach is to capture all passes in the same order they'll run:
+    # fwd 1, fwd 2, ... fwd N, then bwd N, bwd N-1, ... bwd 1.
+
+    # Capture forward graphs
+    per_callable_static_outputs = []
+    per_callable_output_unflatten_spec = []
+    for func, args, fwd_graph in zip(callables, sample_args, fwd_graphs):
+        with torch.cuda.graph(fwd_graph, pool=mempool):
+            outputs = func(*args)
+
+        flatten_outputs, spec = torch.utils._pytree.tree_flatten(outputs)
+        per_callable_static_outputs.append(tuple(flatten_outputs))
+        per_callable_output_unflatten_spec.append(spec)
+
+    # Capture backward graphs in reverse order
+    per_callable_static_grad_outputs = []
+    per_callable_static_grad_inputs = []
+    for static_input_surface, static_outputs, bwd_graph, module_params in zip(
+        reversed(per_callable_static_input_surfaces),
+        reversed(per_callable_static_outputs),
+        reversed(bwd_graphs),
+        reversed(per_callable_module_params),
+    ):
+        # For now, assumes all static_outputs require grad
+        # assert all(o.requires_grad for o in static_outputs), "Outputs of graphed callables must require grad."
+        static_grad_outputs = tuple(
+            torch.empty_like(o) if o.requires_grad else None for o in static_outputs
+        )
+
+        outputs_grad = tuple(o for o in static_outputs if o.requires_grad)
+        grad_inputs = None
+        if len(outputs_grad) > 0:
+            with torch.cuda.graph(bwd_graph, pool=mempool):
+                grad_inputs = torch.autograd.grad(
+                    outputs=outputs_grad,
+                    inputs=tuple(i for i in static_input_surface if i.requires_grad),
+                    grad_outputs=tuple(o for o in static_grad_outputs if o is not None),
+                    only_inputs=True,
+                    allow_unused=allow_unused_input,
+                )
+
+        # Constructs a tuple suitable for returning from Graphed.backward:
+        # Pads out the actually-needed grads with Nones in gradient slots for inputs that don't require grad.
+        # I couldn't think of a slick one-liner for this pattern.
+        static_grad_inputs = []
+        grad_idx = 0
+        for arg in static_input_surface:
+            if arg.requires_grad and grad_inputs is not None:
+                static_grad_inputs.append(grad_inputs[grad_idx])
+                grad_idx += 1
+            else:
+                static_grad_inputs.append(None)  # type: ignore[arg-type]
+        static_grad_inputs = tuple(static_grad_inputs)  # type: ignore[assignment]
+
+        per_callable_static_grad_outputs.append(static_grad_outputs)
+        per_callable_static_grad_inputs.append(static_grad_inputs)
+
+    # Reverses the most recent two lists
+    per_callable_static_grad_outputs.reverse()
+    per_callable_static_grad_inputs.reverse()
+    # Now for every per_callable list, per_callable_*[i] holds the stuff for the ith callable.
+
+    def make_graphed_autograd_function(
+        fwd_graph,
+        bwd_graph,
+        module_params,
+        len_user_args,
+        output_unflatten_spec,
+        static_input_surface,
+        static_outputs,
+        static_grad_outputs,
+        static_grad_inputs,
+    ):
+        class Graphed(torch.autograd.Function):
+            @staticmethod
+            def forward(ctx, *inputs):
+                # At this stage, only the user args may (potentially) be new tensors.
+                for i in range(len_user_args):
+                    if static_input_surface[i].data_ptr() != inputs[i].data_ptr():
+                        static_input_surface[i].copy_(inputs[i])
+                fwd_graph.replay()
+                assert isinstance(static_outputs, tuple)
+                return tuple(o.detach() for o in static_outputs)
+
+            @staticmethod
+            @torch.autograd.function.once_differentiable
+            def backward(ctx, *grads):
+                assert len(grads) == len(static_grad_outputs)
+                for g, grad in zip(static_grad_outputs, grads):
+                    if g is not None:
+                        # don't copy if autograd gods have been kind and the
+                        # incoming grad is already in the right place
+                        if g.data_ptr() != grad.data_ptr():
+                            g.copy_(grad)
+                bwd_graph.replay()
+
+                # Input args that didn't require grad expect a None gradient.
+                assert isinstance(static_grad_inputs, tuple)
+                return tuple(
+                    b.detach() if b is not None else b for b in static_grad_inputs
+                )
+
+        def functionalized(*user_args):
+            # Runs the autograd function with inputs == all inputs to the graph that might require grad
+            # (explicit user args + module parameters)
+            # Assumes module params didn't change since capture.
+            flatten_user_args = torch.utils._pytree.arg_tree_leaves(*user_args)
+            out = Graphed.apply(*(tuple(flatten_user_args) + module_params))
+            return torch.utils._pytree.tree_unflatten(out, output_unflatten_spec)
+
+        return functionalized
+
+    # Put together the final graphed callables
+    ret = []
+    for i, func in enumerate(callables):
+        graphed = make_graphed_autograd_function(
+            fwd_graphs[i],
+            bwd_graphs[i],
+            per_callable_module_params[i],
+            per_callable_len_user_args[i],
+            per_callable_output_unflatten_spec[i],
+            per_callable_static_input_surfaces[i],
+            per_callable_static_outputs[i],
+            per_callable_static_grad_outputs[i],
+            per_callable_static_grad_inputs[i],
+        )
+
+        if isinstance(func, torch.nn.Module):
+
+            def make_graphed_forward(func, graph_training_state, graphed, orig_fwd):
+                def new_fwd(*user_args):
+                    # If the module's training-or-eval state matches what we graphed,
+                    # run the graph, otherwise run the original forward method
+                    if func.training == graph_training_state:
+                        return graphed(*user_args)
+                    else:
+                        return orig_fwd(*user_args)
+
+                return new_fwd
+
+            func.forward = make_graphed_forward(func, func.training, graphed, func.forward)  # type: ignore[assignment]
+            ret.append(func)
+        else:
+            ret.append(graphed)
+
+    if just_one_callable:
+        return ret[0]
+
+    return tuple(ret)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/jiterator.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/jiterator.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f1e6393bac3d8424d501b975cd0b0bd958b6828
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/jiterator.py
@@ -0,0 +1,187 @@
+# mypy: allow-untyped-defs
+import re
+from typing import Callable, List
+
+import torch
+from torch import Tensor
+
+
+__all__: List[str] = []
+
+
+class _CodeParser:
+    def __init__(self, code_string: str):
+        optional_ws = r"\s*"
+        required_ws = r"\s+"
+        template_params = r"(?P\<.+\>)"
+        return_type = r"(?P\w+)"
+        function_name = r"(?P\w+)"
+        function_params = r"(?P\(.+\))"
+        function_body = r"(?P\{.+\})"
+
+        pattern = (
+            optional_ws
+            + "template"
+            + optional_ws
+            + template_params
+            + optional_ws
+            + return_type
+            + required_ws
+            + function_name
+            + optional_ws
+            + function_params
+            + optional_ws
+            + function_body
+            + optional_ws
+        )
+
+        result = re.match(
+            pattern, code_string, re.DOTALL
+        )  # DOTALL for matching multiline
+
+        if result is None:
+            raise Exception(  # noqa: TRY002
+                f"Couldn't parse code, please check correctness:\n {code_string}"
+            )
+
+        self.template_params = result["template_params"]
+        self.return_type = result["return_type"]
+        self.function_name = result["function_name"]
+        self.function_params = result["function_params"]
+        self.function_body = result["function_body"]
+
+
+class _JittedFunction:
+    def __init__(
+        self, code_string: str, return_by_ref: bool, num_outputs: int, **kwargs
+    ):
+        self.code_string = code_string
+
+        assert (
+            return_by_ref or num_outputs == 1
+        ), "Return by value only works for single output. "
+        self.return_by_ref = return_by_ref
+        self.num_outputs = num_outputs
+
+        parsed_code = _CodeParser(code_string)
+        self.kernel_name = parsed_code.function_name
+
+        self.kwargs_dict = kwargs
+        self.is_cuda_available = torch.cuda.is_available()
+
+    def __call__(self, *tensors: Tensor, **kwargs):
+        # Jiterator follow torch.cuda's lazy initialization behavior
+        # Defer checking cuda's availability at the function invocation time
+        assert (
+            self.is_cuda_available
+        ), "Jiterator is only supported on CUDA and ROCm GPUs, none are available."
+
+        assert len(tensors) <= 8, "jiterator only supports up to 8 tensor inputs."
+
+        expanded_kwargs = self.kwargs_dict.copy()
+        for key, value in kwargs.items():
+            if key in self.kwargs_dict:
+                expanded_kwargs[key] = value
+            else:
+                raise KeyError(f"{key} is not declared in function definition")
+
+        return torch._C._cuda_jiterator_compile_and_launch_kernel(
+            self.code_string,
+            self.kernel_name,
+            self.return_by_ref,
+            self.num_outputs,
+            tensors,
+            expanded_kwargs,
+        )
+
+
+def _create_jit_fn(code_string: str, **kwargs) -> Callable:
+    """
+    Create a jiterator-generated cuda kernel for an elementwise op.
+
+    The code string has to be a valid CUDA function that describes the computation for a single element. The code
+    string has to follow the c++ template pattern, as shown in the example below. This function will be inlined
+    into elementwise kernel template, and compiled on the fly. Compiled kernel will be cached in memory, as well as
+    local temp dir.
+
+    Jiterator-generated kernels accepts noncontiguous tensors, and supports broadcasting and type promotion.
+
+    Args:
+        code_string (str): CUDA code string to be compiled by jiterator. The entry functor must return by value.
+        kwargs (Dict, optional): Keyword arguments for generated function
+
+    Example::
+
+        code_string = "template  T my_kernel(T x, T y, T alpha) { return -x + alpha * y; }"
+        jitted_fn = create_jit_fn(code_string, alpha=1.0)
+        a = torch.rand(3, device='cuda')
+        b = torch.rand(3, device='cuda')
+        # invoke jitted function like a regular python function
+        result = jitted_fn(a, b, alpha=3.14)
+
+    code_string also allows multiple function definitions, and the last function will be treated as the entry function.
+
+    Example::
+
+        code_string = "template  T util_fn(T x, T y) { return ::sin(x) + ::cos(y); }"
+        code_string += "template  T my_kernel(T x, T y, T val) { return ::min(val, util_fn(x, y)); }"
+        jitted_fn = create_jit_fn(code_string, val=0.0)
+        a = torch.rand(3, device='cuda')
+        b = torch.rand(3, device='cuda')
+        # invoke jitted function like a regular python function
+        result = jitted_fn(a, b)  # using default val=0.0
+
+    Jiterator can be used together with python registration to override an operator's cuda kernel.
+    Following example is overriding gelu's cuda kernel with relu.
+
+    Example::
+
+        code_string = "template  T my_gelu(T a) { return a > 0 ? a : 0; }"
+        my_gelu = create_jit_fn(code_string)
+        my_lib = torch.library.Library("aten", "IMPL")
+        my_lib.impl('aten::gelu', my_gelu, "CUDA")
+        # torch.nn.GELU and torch.nn.function.gelu are now overridden
+        a = torch.rand(3, device='cuda')
+        torch.allclose(torch.nn.functional.gelu(a), torch.nn.functional.relu(a))
+
+    .. warning::
+        This API is in beta and may change in future releases.
+
+    .. warning::
+        This API only supports up to 8 inputs and 1 output
+
+    .. warning::
+        All input tensors must live in CUDA device
+    """
+    return _JittedFunction(code_string, return_by_ref=False, num_outputs=1, **kwargs)
+
+
+def _create_multi_output_jit_fn(
+    code_string: str, num_outputs: int, **kwargs
+) -> Callable:
+    """
+    Create a jiterator-generated cuda kernel for an elementwise op that supports returning one or more outputs.
+
+    Args:
+        code_string (str): CUDA code string to be compiled by jiterator. The entry functor must return value by reference.
+        num_outputs(int): number of outputs return by the kernel
+        kwargs (Dict, optional): Keyword arguments for generated function
+
+    Example::
+
+        code_string = "template  void my_kernel(T x, T y, T alpha, T& out) { out = -x + alpha * y; }"
+        jitted_fn = create_jit_fn(code_string, alpha=1.0)
+        a = torch.rand(3, device='cuda')
+        b = torch.rand(3, device='cuda')
+        # invoke jitted function like a regular python function
+        result = jitted_fn(a, b, alpha=3.14)
+
+    .. warning::
+        This API is in beta and may change in future releases.
+
+    .. warning::
+        This API only supports up to 8 inputs and 8 outputs
+    """
+    return _JittedFunction(
+        code_string, return_by_ref=True, num_outputs=num_outputs, **kwargs
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/memory.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/memory.py
new file mode 100644
index 0000000000000000000000000000000000000000..af2c8d480c8345430ddc082a19b1a1f5b771364f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/memory.py
@@ -0,0 +1,1041 @@
+# mypy: allow-untyped-defs
+r"""This package adds support for device memory management implemented in CUDA."""
+
+import collections
+import contextlib
+import ctypes
+import pickle
+import sys
+import warnings
+from inspect import signature
+from typing import Any, Dict, Optional, Tuple, Union
+from typing_extensions import deprecated
+
+import torch
+from torch import _C
+from torch._utils import _dummy_type
+from torch.types import Device
+
+from . import (
+    _get_amdsmi_device_index,
+    _get_device_index,
+    _get_nvml_device_index,
+    _lazy_init,
+    is_initialized,
+)
+from ._memory_viz import memory as _memory, segments as _segments
+
+
+__all__ = [
+    "caching_allocator_alloc",
+    "caching_allocator_delete",
+    "set_per_process_memory_fraction",
+    "empty_cache",
+    "memory_stats",
+    "memory_stats_as_nested_dict",
+    "reset_accumulated_memory_stats",
+    "reset_peak_memory_stats",
+    "reset_max_memory_allocated",
+    "reset_max_memory_cached",
+    "memory_allocated",
+    "max_memory_allocated",
+    "memory_reserved",
+    "max_memory_reserved",
+    "memory_cached",
+    "max_memory_cached",
+    "memory_snapshot",
+    "memory_summary",
+    "list_gpu_processes",
+    "mem_get_info",
+    "get_allocator_backend",
+    "CUDAPluggableAllocator",
+    "change_current_allocator",
+    "MemPool",
+    "MemPoolContext",
+    "use_mem_pool",
+]
+
+
+if not hasattr(torch._C, "_cuda_CUDAAllocator"):
+    # Define dummy base classes
+    torch._C.__dict__["_cuda_CUDAAllocator"] = _dummy_type("_cuda_CUDAAllocator")
+
+
+if not hasattr(torch._C, "_MemPool"):
+    # Define dummy base classes
+    torch._C.__dict__["_MemPool"] = _dummy_type("_MemPool")
+    torch._C.__dict__["_MemPoolContext"] = _dummy_type("_MemPoolContext")
+    torch._C.__dict__["_cuda_beginAllocateToPool"] = _dummy_type(
+        "_cuda_beginAllocateToPool"
+    )
+    torch._C.__dict__["_cuda_endAllocateCurrentStreamToPool"] = _dummy_type(
+        "_cuda_endAllocateCurrentStreamToPool"
+    )
+
+from torch._C import (  # noqa: F401
+    _cuda_beginAllocateToPool,
+    _cuda_CUDAAllocator,
+    _cuda_endAllocateCurrentStreamToPool,
+    _MemPool,
+    _MemPoolContext,
+)
+
+
+def _host_allocator():
+    _lazy_init()
+    return torch._C._cuda_cudaHostAllocator()
+
+
+@contextlib.contextmanager
+def _free_mutex():
+    torch._C._cuda_lock_mutex()
+    try:
+        yield
+    finally:
+        torch._C._cuda_unlock_mutex()
+
+
+def caching_allocator_alloc(size, device: Union[Device, int] = None, stream=None):
+    r"""Perform a memory allocation using the CUDA memory allocator.
+
+    Memory is allocated for a given device and a stream, this
+    function is intended to be used for interoperability with other
+    frameworks. Allocated memory is released through
+    :func:`~torch.cuda.caching_allocator_delete`.
+
+    Args:
+        size (int): number of bytes to be allocated.
+        device (torch.device or int, optional): selected device. If it is
+            ``None`` the default CUDA device is used.
+        stream (torch.cuda.Stream or int, optional): selected stream. If is ``None`` then
+            the default stream for the selected device is used.
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    if device is None:
+        device = torch.cuda.current_device()
+    device = _get_device_index(device)
+    if stream is None:
+        stream = torch.cuda.current_stream(device)
+    if isinstance(stream, torch.cuda.streams.Stream):
+        stream = stream.cuda_stream
+    if not isinstance(stream, int):
+        raise TypeError(
+            "Invalid type for stream argument, must be "
+            "`torch.cuda.Stream` or `int` representing a pointer "
+            "to a existing stream"
+        )
+    with torch.cuda.device(device):
+        return torch._C._cuda_cudaCachingAllocator_raw_alloc(size, stream)
+
+
+def caching_allocator_delete(mem_ptr):
+    r"""Delete memory allocated using the CUDA memory allocator.
+
+    Memory allocated with :func:`~torch.cuda.caching_allocator_alloc`.
+    is freed here. The associated device and stream are tracked inside
+    the allocator.
+
+    Args:
+        mem_ptr (int): memory address to be freed by the allocator.
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    torch._C._cuda_cudaCachingAllocator_raw_delete(mem_ptr)
+
+
+def set_per_process_memory_fraction(
+    fraction, device: Union[Device, int] = None
+) -> None:
+    r"""Set memory fraction for a process.
+
+    The fraction is used to limit an caching allocator to allocated memory on a CUDA device.
+    The allowed value equals the total visible memory multiplied fraction.
+    If trying to allocate more than the allowed value in a process, will raise an out of
+    memory error in allocator.
+
+    Args:
+        fraction(float): Range: 0~1. Allowed memory equals total_memory * fraction.
+        device (torch.device or int, optional): selected device. If it is
+            ``None`` the default CUDA device is used.
+    .. note::
+        In general, the total available free memory is less than the total capacity.
+    """
+    _lazy_init()
+    if device is None:
+        device = torch.cuda.current_device()
+    device = _get_device_index(device)
+    if not isinstance(fraction, float):
+        raise TypeError("Invalid type for fraction argument, must be `float`")
+    if fraction < 0 or fraction > 1:
+        raise ValueError(f"Invalid fraction value: {fraction}. Allowed range: 0~1")
+
+    torch._C._cuda_setMemoryFraction(fraction, device)
+
+
+def empty_cache() -> None:
+    r"""Release all unoccupied cached memory currently held by the caching
+    allocator so that those can be used in other GPU application and visible in
+    `nvidia-smi`.
+
+    .. note::
+        :func:`~torch.cuda.empty_cache` doesn't increase the amount of GPU
+        memory available for PyTorch. However, it may help reduce fragmentation
+        of GPU memory in certain cases. See :ref:`cuda-memory-management` for
+        more details about GPU memory management.
+    """
+    if is_initialized():
+        torch._C._cuda_emptyCache()
+
+
+def memory_stats(device: Union[Device, int] = None) -> Dict[str, Any]:
+    r"""Return a dictionary of CUDA memory allocator statistics for a given device.
+
+    The return value of this function is a dictionary of statistics, each of
+    which is a non-negative integer.
+
+    Core statistics:
+
+    - ``"allocated.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      number of allocation requests received by the memory allocator.
+    - ``"allocated_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      amount of allocated memory.
+    - ``"segment.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      number of reserved segments from ``cudaMalloc()``.
+    - ``"reserved_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      amount of reserved memory.
+    - ``"active.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      number of active memory blocks.
+    - ``"active_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      amount of active memory.
+    - ``"inactive_split.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      number of inactive, non-releasable memory blocks.
+    - ``"inactive_split_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      amount of inactive, non-releasable memory.
+
+    For these core statistics, values are broken down as follows.
+
+    Pool type:
+
+    - ``all``: combined statistics across all memory pools.
+    - ``large_pool``: statistics for the large allocation pool
+      (as of October 2019, for size >= 1MB allocations).
+    - ``small_pool``: statistics for the small allocation pool
+      (as of October 2019, for size < 1MB allocations).
+
+    Metric type:
+
+    - ``current``: current value of this metric.
+    - ``peak``: maximum value of this metric.
+    - ``allocated``: historical total increase in this metric.
+    - ``freed``: historical total decrease in this metric.
+
+    In addition to the core statistics, we also provide some simple event
+    counters:
+
+    - ``"num_alloc_retries"``: number of failed ``cudaMalloc`` calls that
+      result in a cache flush and retry.
+    - ``"num_ooms"``: number of out-of-memory errors thrown.
+    - ``"num_sync_all_streams"``: number of ``synchronize_and_free_events`` calls.
+    - ``"num_device_alloc"``: number of CUDA allocation calls. This includes both
+      cuMemMap and cudaMalloc.
+    - ``"num_device_free"``: number of CUDA free calls. This includes both cuMemUnmap
+      and cudaFree.
+
+    The caching allocator can be configured via ENV to not split blocks larger than a
+    defined size (see Memory Management section of the Cuda Semantics documentation).
+    This helps avoid memory fragmentation but may have a performance
+    penalty. Additional outputs to assist with tuning and evaluating impact:
+
+    - ``"max_split_size"``: blocks above this size will not be split.
+    - ``"oversize_allocations.{current,peak,allocated,freed}"``:
+      number of over-size allocation requests received by the memory allocator.
+    - ``"oversize_segments.{current,peak,allocated,freed}"``:
+      number of over-size reserved segments from ``cudaMalloc()``.
+
+    The caching allocator can be configured via ENV to round memory allocations in order
+    to reduce fragmentation. Sometimes the overhead from rounding can be higher than
+    the fragmentation it helps reduce. The following stat can be used to check if
+    rounding adds too much overhead:
+
+    - ``"requested_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      memory requested by client code, compare this with allocated_bytes to check if
+      allocation rounding adds too much overhead.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistics for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+
+    .. note::
+        With :ref:`backend:cudaMallocAsync`, some stats are not
+        meaningful, and are always reported as zero.
+    """
+    result = []
+
+    def _recurse_add_to_result(prefix, obj):
+        if isinstance(obj, dict):
+            if len(prefix) > 0:
+                prefix += "."
+            for k, v in obj.items():
+                _recurse_add_to_result(prefix + k, v)
+        else:
+            result.append((prefix, obj))
+
+    stats = memory_stats_as_nested_dict(device=device)
+    _recurse_add_to_result("", stats)
+    result.sort()
+
+    return collections.OrderedDict(result)
+
+
+def memory_stats_as_nested_dict(device: Union[Device, int] = None) -> Dict[str, Any]:
+    r"""Return the result of :func:`~torch.cuda.memory_stats` as a nested dictionary."""
+    if not is_initialized():
+        return {}
+    device = _get_device_index(device, optional=True)
+    return torch._C._cuda_memoryStats(device)
+
+
+def reset_accumulated_memory_stats(device: Union[Device, int] = None) -> None:
+    r"""Reset the "accumulated" (historical) stats tracked by the CUDA memory allocator.
+
+    See :func:`~torch.cuda.memory_stats` for details. Accumulated stats correspond to
+    the `"allocated"` and `"freed"` keys in each individual stat dict, as well as
+    `"num_alloc_retries"` and `"num_ooms"`.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    device = _get_device_index(device, optional=True)
+    return torch._C._cuda_resetAccumulatedMemoryStats(device)
+
+
+def reset_peak_memory_stats(device: Union[Device, int] = None) -> None:
+    r"""Reset the "peak" stats tracked by the CUDA memory allocator.
+
+    See :func:`~torch.cuda.memory_stats` for details. Peak stats correspond to the
+    `"peak"` key in each individual stat dict.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    device = _get_device_index(device, optional=True)
+    return torch._C._cuda_resetPeakMemoryStats(device)
+
+
+def reset_max_memory_allocated(device: Union[Device, int] = None) -> None:
+    r"""Reset the starting point in tracking maximum GPU memory occupied by tensors for a given device.
+
+    See :func:`~torch.cuda.max_memory_allocated` for details.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    .. warning::
+        This function now calls :func:`~torch.cuda.reset_peak_memory_stats`, which resets
+        /all/ peak memory stats.
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    warnings.warn(
+        "torch.cuda.reset_max_memory_allocated now calls torch.cuda.reset_peak_memory_stats, "
+        "which resets /all/ peak memory stats.",
+        FutureWarning,
+    )
+    return reset_peak_memory_stats(device=device)
+
+
+def reset_max_memory_cached(device: Union[Device, int] = None) -> None:
+    r"""Reset the starting point in tracking maximum GPU memory managed by the caching allocator for a given device.
+
+    See :func:`~torch.cuda.max_memory_cached` for details.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    .. warning::
+        This function now calls :func:`~torch.cuda.reset_peak_memory_stats`, which resets
+        /all/ peak memory stats.
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    warnings.warn(
+        "torch.cuda.reset_max_memory_cached now calls torch.cuda.reset_peak_memory_stats, "
+        "which resets /all/ peak memory stats.",
+        FutureWarning,
+    )
+    return reset_peak_memory_stats(device=device)
+
+
+def memory_allocated(device: Union[Device, int] = None) -> int:
+    r"""Return the current GPU memory occupied by tensors in bytes for a given device.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    .. note::
+        This is likely less than the amount shown in `nvidia-smi` since some
+        unused memory can be held by the caching allocator and some context
+        needs to be created on GPU. See :ref:`cuda-memory-management` for more
+        details about GPU memory management.
+    """
+    return memory_stats(device=device).get("allocated_bytes.all.current", 0)
+
+
+def max_memory_allocated(device: Union[Device, int] = None) -> int:
+    r"""Return the maximum GPU memory occupied by tensors in bytes for a given device.
+
+    By default, this returns the peak allocated memory since the beginning of
+    this program. :func:`~torch.cuda.reset_peak_memory_stats` can be used to
+    reset the starting point in tracking this metric. For example, these two
+    functions can measure the peak allocated memory usage of each iteration in a
+    training loop.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    return memory_stats(device=device).get("allocated_bytes.all.peak", 0)
+
+
+def memory_reserved(device: Union[Device, int] = None) -> int:
+    r"""Return the current GPU memory managed by the caching allocator in bytes for a given device.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    return memory_stats(device=device).get("reserved_bytes.all.current", 0)
+
+
+def max_memory_reserved(device: Union[Device, int] = None) -> int:
+    r"""Return the maximum GPU memory managed by the caching allocator in bytes for a given device.
+
+    By default, this returns the peak cached memory since the beginning of this
+    program. :func:`~torch.cuda.reset_peak_memory_stats` can be used to reset
+    the starting point in tracking this metric. For example, these two functions
+    can measure the peak cached memory amount of each iteration in a training
+    loop.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    return memory_stats(device=device).get("reserved_bytes.all.peak", 0)
+
+
+@deprecated(
+    "`torch.cuda.memory_cached` has been renamed to `torch.cuda.memory_reserved`",
+    category=FutureWarning,
+)
+def memory_cached(device: Union[Device, int] = None) -> int:
+    r"""Deprecated; see :func:`~torch.cuda.memory_reserved`."""
+    return memory_reserved(device=device)
+
+
+@deprecated(
+    "`torch.cuda.max_memory_cached` has been renamed to `torch.cuda.max_memory_reserved`",
+    category=FutureWarning,
+)
+def max_memory_cached(device: Union[Device, int] = None) -> int:
+    r"""Deprecated; see :func:`~torch.cuda.max_memory_reserved`."""
+    return max_memory_reserved(device=device)
+
+
+def memory_snapshot():
+    r"""Return a snapshot of the CUDA memory allocator state across all devices.
+
+    Interpreting the output of this function requires familiarity with the
+    memory allocator internals.
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    return torch._C._cuda_memorySnapshot()["segments"]
+
+
+def memory_summary(device: Union[Device, int] = None, abbreviated: bool = False) -> str:
+    r"""Return a human-readable printout of the current memory allocator statistics for a given device.
+
+    This can be useful to display periodically during training, or when
+    handling out-of-memory exceptions.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            printout for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+        abbreviated (bool, optional): whether to return an abbreviated summary
+            (default: False).
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    device = _get_device_index(device, optional=True)
+    stats = memory_stats(device=device)
+
+    def _format_size(sz, pref_sz):
+        prefixes = ["B  ", "KiB", "MiB", "GiB", "TiB", "PiB"]
+        prefix = prefixes[0]
+        for new_prefix in prefixes[1:]:
+            if pref_sz < 768 * 1024:
+                break
+            prefix = new_prefix
+            sz //= 1024
+            pref_sz /= 1024
+        return f"{sz:6d} {prefix}"
+
+    def _format_count(cnt, pref_cnt):
+        prefixes = [" ", "K", "M"]
+        prefix = prefixes[0]
+        for new_prefix in prefixes[1:]:
+            if pref_cnt < 750 * 1000:
+                break
+            prefix = new_prefix
+            cnt //= 1000
+            pref_cnt /= 1000
+        return f"{cnt:7d} {prefix} "
+
+    metrics_to_display = [
+        ("allocated_bytes", "Allocated memory", _format_size),
+        ("active_bytes", "Active memory", _format_size),
+        ("requested_bytes", "Requested memory", _format_size),
+        ("reserved_bytes", "GPU reserved memory", _format_size),
+        ("inactive_split_bytes", "Non-releasable memory", _format_size),
+        ("allocation", "Allocations", _format_count),
+        ("active", "Active allocs", _format_count),
+        ("segment", "GPU reserved segments", _format_count),
+        ("inactive_split", "Non-releasable allocs", _format_count),
+    ]
+
+    lines = []
+    lines.append("=" * 75)
+    lines.append(" {_:16} PyTorch CUDA memory summary, device ID {device:<17d} ")
+    lines.append("-" * 75)
+    lines.append(
+        "  {_:9} CUDA OOMs: {num_ooms:<12d} | {_:6} cudaMalloc retries: {num_alloc_retries:<8d}  "
+    )
+    lines.append("=" * 75)
+    lines.append(
+        "        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  "
+    )
+
+    for metric_key, metric_name, formatter in metrics_to_display:
+        lines.append("-" * 75)
+        submetrics = [("all", metric_name)]
+        if not abbreviated:
+            submetrics.append(("large_pool", "      from large pool"))
+            submetrics.append(("small_pool", "      from small pool"))
+
+        current_prefval, peak_prefval, allocated_prefval, freed_prefval = (
+            None,
+            None,
+            None,
+            None,
+        )
+
+        for submetric_key, submetric_name in submetrics:
+            prefix = metric_key + "." + submetric_key + "."
+
+            current = stats[prefix + "current"]
+            peak = stats[prefix + "peak"]
+            allocated = stats[prefix + "allocated"]
+            freed = stats[prefix + "freed"]
+
+            if current_prefval is None:
+                current_prefval = current
+                peak_prefval = peak
+                allocated_prefval = allocated
+                freed_prefval = freed
+
+            lines.append(
+                f" {submetric_name:<21} | {formatter(current, current_prefval)} | {formatter(peak, peak_prefval)} | "
+                f"{formatter(allocated, allocated_prefval)} | {formatter(freed, freed_prefval)} ",
+            )
+
+    metrics_to_display = [
+        ("oversize_allocations", "Oversize allocations", _format_count),
+        ("oversize_segments", "Oversize GPU segments", _format_count),
+    ]
+
+    for metric_key, metric_name, formatter in metrics_to_display:
+        lines.append("-" * 75)
+
+        prefix = metric_key + "."
+
+        current = stats[prefix + "current"]
+        peak = stats[prefix + "peak"]
+        allocated = stats[prefix + "allocated"]
+        freed = stats[prefix + "freed"]
+
+        lines.append(
+            f" {metric_name:<21} | {formatter(current, current)} | {formatter(peak, peak)} | "
+            f"{formatter(allocated, allocated)} | {formatter(freed, freed)} ",
+        )
+
+    lines.append("=" * 75)
+
+    fmt_dict = {"_": "", "device": device}
+    for k, v in stats.items():
+        fmt_dict[k.replace(".", "-")] = v
+    return "|" + "|\n|".join(lines).format(**fmt_dict) + "|\n"
+
+
+def list_gpu_processes(device: Union[Device, int] = None) -> str:
+    r"""Return a human-readable printout of the running processes and their GPU memory use for a given device.
+
+    This can be useful to display periodically during training, or when
+    handling out-of-memory exceptions.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            printout for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+    """
+    if not torch.version.hip:
+        try:
+            import pynvml  # type: ignore[import]
+        except ModuleNotFoundError:
+            return "pynvml module not found, please install pynvml"
+        from pynvml import NVMLError_DriverNotLoaded
+
+        try:
+            pynvml.nvmlInit()
+        except NVMLError_DriverNotLoaded:
+            return "cuda driver can't be loaded, is cuda enabled?"
+
+        device = _get_nvml_device_index(device)
+        handle = pynvml.nvmlDeviceGetHandleByIndex(device)
+        procs = pynvml.nvmlDeviceGetComputeRunningProcesses(handle)
+    else:
+        try:
+            import amdsmi  # type: ignore[import]
+        except ModuleNotFoundError:
+            return "amdsmi module not found, please install amdsmi"
+        try:
+            amdsmi.amdsmi_init()  # type: ignore[attr-defined]
+        except amdsmi.AmdSmiException:  # type: ignore[attr-defined]
+            return "amdsmi driver can't be loaded, is ROCm installed?"
+
+        device = _get_amdsmi_device_index(device)
+
+        try:
+            handle = amdsmi.amdsmi_get_processor_handles()[device]  # type: ignore[attr-defined]
+            procs = amdsmi.amdsmi_get_gpu_process_list(handle)  # type: ignore[attr-defined]
+        except amdsmi.AmdSmiException:  # type: ignore[attr-defined]
+            return "amdsmi cannot list processes from other users"
+
+    lines = []
+    lines.append(f"GPU:{device}")
+    if len(procs) == 0:
+        lines.append("no processes are running")
+    for p in procs:
+        if not torch.version.hip:
+            mem = p.usedGpuMemory / (1024 * 1024)
+            pid = p.pid
+        else:
+            try:
+                proc_info = amdsmi.amdsmi_get_gpu_process_info(handle, p)  # type: ignore[possibly-undefined]
+            except AttributeError:
+                # https://github.com/ROCm/amdsmi/commit/c551c3caedbd903ba828e7fdffa5b56d475a15e7
+                # is a BC-breaking change that removes amdsmi_get_gpu_process_info API from amdsmi
+                proc_info = p
+            mem = proc_info["memory_usage"]["vram_mem"] / (1024 * 1024)
+            pid = proc_info["pid"]
+        lines.append(f"process {pid:>10d} uses {mem:>12.3f} MB GPU memory")
+    return "\n".join(lines)
+
+
+def mem_get_info(device: Union[Device, int] = None) -> Tuple[int, int]:
+    r"""Return the global free and total GPU memory for a given device using cudaMemGetInfo.
+
+    Args:
+        device (torch.device or int or str, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default) or if the device index is not specified.
+
+    .. note::
+        See :ref:`cuda-memory-management` for more
+        details about GPU memory management.
+    """
+    if device is None:
+        device = torch.cuda.current_device()
+    # optional=True allows `device = torch.device('cuda')` for which device.index is None
+    device = _get_device_index(device, optional=True)
+    return torch.cuda.cudart().cudaMemGetInfo(device)
+
+
+def _record_memory_history_legacy(
+    enabled: bool,
+    record_context=True,
+    trace_alloc_max_entries=1,
+    trace_alloc_record_context=False,
+    device: Union[Device, int] = None,
+    record_context_cpp=False,
+):
+    _C._cuda_record_memory_history_legacy(
+        enabled,
+        record_context,
+        trace_alloc_max_entries,
+        trace_alloc_record_context,
+        record_context_cpp,
+    )
+
+
+def _record_memory_history(enabled="all", *args, **kwargs):
+    """Enable recording of stack traces associated with memory
+    allocations, so you can tell what allocated any piece of memory in
+    :func:`torch.cuda.memory._snapshot()`.
+
+    In addition too keeping stack traces with each current allocation and free,
+    this will also enable recording of a history of all alloc/free events.
+
+    Use :func:`torch.cuda.memory._snapshot()` to retrieve this information,
+    and the tools in `_memory_viz.py` to visualize snapshots.
+
+    The Python trace collection is fast (2us per trace), so you may consider
+    enabling this on production jobs if you anticipate ever having to debug
+    memory issues.
+
+    C++ trace collection is also fast (~50ns/frame), which for many typical programs
+    works out to ~2us per trace, but can vary depending on stack depth.
+
+    Args:
+        enabled (Literal[None, "state", "all"], optional):
+            `None`, disable recording memory history.
+            `"state"`, keep information for currenly allocated memory.
+            `"all"`, additionally keep a history of all alloc/free calls.
+            Defaults to "all".
+        context (Literal[None, "state", "alloc", "all"], optional):
+            `None`, Do not record any tracebacks.
+            `"state"`, Record tracebacks for currently allocated memory.
+            `"alloc"`, additionally keep tracebacks for alloc calls.
+            `"all"`, additionally keep tracebacks for free calls.
+            Defaults to "all".
+        stacks (Literal["python", "all"], optional):
+            `"python"`, include Python, TorchScript, and inductor frames in tracebacks
+            `"all"`, additionally include C++ frames
+            Defaults to "all".
+        max_entries (int, optional): Keep a maximum of `max_entries`
+            alloc/free events in the recorded history recorded.
+    """
+    if isinstance(enabled, bool):
+        return _record_memory_history_legacy(enabled, *args, **kwargs)
+    else:
+        return _record_memory_history_impl(enabled, *args, **kwargs)
+
+
+def _record_memory_history_impl(
+    enabled: Optional[str] = "all",
+    context: Optional[str] = "all",
+    stacks: str = "all",
+    max_entries: int = sys.maxsize,
+    device: Union[Device, int] = None,
+):
+    _C._cuda_record_memory_history(enabled, context, stacks, max_entries)
+
+
+_record_memory_history.__signature__ = signature(_record_memory_history_impl)  # type: ignore[attr-defined]
+
+
+def _snapshot(device: Union[Device, int] = None):
+    """Save a snapshot of CUDA memory state at the time it was called.
+
+    The state is represented as a dictionary with the following structure.
+
+    .. code-block:: python
+
+        class Snapshot(TypedDict):
+            segments : List[Segment]
+            device_traces: List[List[TraceEntry]]
+
+        class Segment(TypedDict):
+            # Segments are memory returned from a cudaMalloc call.
+            # The size of reserved memory is the sum of all Segments.
+            # Segments are cached and reused for future allocations.
+            # If the reuse is smaller than the segment, the segment
+            # is split into more then one Block.
+            # empty_cache() frees Segments that are entirely inactive.
+            address: int
+            total_size: int #  cudaMalloc'd size of segment
+            stream: int
+            segment_type: Literal['small', 'large'] # 'large' (>1MB)
+            allocated_size: int # size of memory in use
+            active_size: int # size of memory in use or in active_awaiting_free state
+            blocks : List[Block]
+
+        class Block(TypedDict):
+            # A piece of memory returned from the allocator, or
+            # current cached but inactive.
+            size: int
+            requested_size: int # size requested during malloc, may be smaller than
+                                # size due to rounding
+            address: int
+            state: Literal['active_allocated', # used by a tensor
+                        'active_awaiting_free', # waiting for another stream to finish using
+                                                # this, then it will become free
+                        'inactive',] # free for reuse
+            frames: List[Frame] # stack trace from where the allocation occurred
+
+        class Frame(TypedDict):
+                filename: str
+                line: int
+                name: str
+
+        class TraceEntry(TypedDict):
+            # When `torch.cuda.memory._record_memory_history()` is enabled,
+            # the snapshot will contain TraceEntry objects that record each
+            # action the allocator took.
+            action: Literal[
+            'alloc'  # memory allocated
+            'free_requested', # the allocated received a call to free memory
+            'free_completed', # the memory that was requested to be freed is now
+                            # able to be used in future allocation calls
+            'segment_alloc', # the caching allocator ask cudaMalloc for more memory
+                            # and added it as a segment in its cache
+            'segment_free',  # the caching allocator called cudaFree to return memory
+                            # to cuda possibly trying free up memory to
+                            # allocate more segments or because empty_caches was called
+            'oom',          # the allocator threw an OOM exception. 'size' is
+                            # the requested number of bytes that did not succeed
+            'snapshot'      # the allocator generated a memory snapshot
+                            # useful to coorelate a previously taken
+                            # snapshot with this trace
+            ]
+            addr: int # not present for OOM
+            frames: List[Frame]
+            size: int
+            stream: int
+            device_free: int # only present for OOM, the amount of
+                            # memory cuda still reports to be free
+
+    Returns:
+        The Snapshot dictionary object
+    """
+    return _C._cuda_memorySnapshot()
+
+
+def _dump_snapshot(filename="dump_snapshot.pickle"):
+    """
+    Save a pickled version of the `torch.memory._snapshot()` dictionary to a file.
+
+    This file can be opened by the interactive snapshot viewer at pytorch.org/memory_viz
+
+    Args:
+        filename (str, optional): Name of the file to create. Defaults to "dump_snapshot.pickle".
+    """
+    s = _snapshot()
+    with open(filename, "wb") as f:
+        pickle.dump(s, f)
+
+
+def _save_segment_usage(filename="output.svg", snapshot=None):
+    if snapshot is None:
+        snapshot = _snapshot()
+    with open(filename, "w") as f:
+        f.write(_segments(snapshot))
+
+
+def _save_memory_usage(filename="output.svg", snapshot=None):
+    if snapshot is None:
+        snapshot = _snapshot()
+    with open(filename, "w") as f:
+        f.write(_memory(snapshot))
+
+
+def _set_allocator_settings(env: str):
+    return torch._C._cuda_cudaCachingAllocator_set_allocator_settings(env)
+
+
+def get_allocator_backend() -> str:
+    r"""Return a string describing the active allocator backend as set by
+    ``PYTORCH_CUDA_ALLOC_CONF``. Currently available backends are
+    ``native`` (PyTorch's native caching allocator) and `cudaMallocAsync``
+    (CUDA's built-in asynchronous allocator).
+
+    .. note::
+        See :ref:`cuda-memory-management` for details on choosing the allocator backend.
+    """
+    return torch._C._cuda_getAllocatorBackend()
+
+
+class _CUDAAllocator:
+    r"""Wrapper over internal CUDA memory allocators."""
+
+    def __init__(self, allocator: torch._C._cuda_CUDAAllocator):
+        self._allocator = allocator
+
+    def allocator(self):
+        return self._allocator
+
+
+class CUDAPluggableAllocator(_CUDAAllocator):
+    r"""CUDA memory allocator loaded from a so file."""
+
+    def __init__(self, path_to_so_file: str, alloc_fn_name: str, free_fn_name: str):
+        r"""Memory allocators are compiled in .so files and loaded dynamically using ctypes.
+
+        To change the active allocator use the :func:`torch.memory.cuda.change_current_allocator` function.
+
+        Args:
+            path_to_so_file(str): Path in the filesystem to the `.so` file containing
+                the allocator functions
+            alloc_fn_name(str): Name of the function to perform the memory allocation
+                in the so file. The signature must be:
+                void* alloc_fn_name(ssize_t size, int device, cudaStream_t stream);
+            free_fn_name(str): Name of the function to perform the memory release
+                in the so file. The signature must be:
+                void free_fn_name(void* ptr, size_t size, cudaStream_t stream);
+
+        .. warning::
+            This is currently supported only in unix OSs
+
+        .. note::
+            See :ref:`cuda-memory-management` for details on creating and using a custom allocator
+        """
+        allocator = ctypes.CDLL(path_to_so_file)
+        alloc_fn = ctypes.cast(getattr(allocator, alloc_fn_name), ctypes.c_void_p).value
+        free_fn = ctypes.cast(getattr(allocator, free_fn_name), ctypes.c_void_p).value
+        assert alloc_fn is not None
+        assert free_fn is not None
+        self._allocator = torch._C._cuda_customAllocator(alloc_fn, free_fn)
+
+
+def change_current_allocator(allocator: _CUDAAllocator) -> None:
+    r"""Change the currently used memory allocator to be the one provided.
+
+    If the current allocator has already been used/initialized, this function will error.
+
+
+    Args:
+        allocator (torch.cuda.memory._CUDAAllocator): allocator to be set as the active one.
+    .. note::
+        See :ref:`cuda-memory-management` for details on creating and using a custom allocator
+    """
+    torch._C._cuda_changeCurrentAllocator(allocator.allocator())
+
+
+def _get_current_allocator() -> _CUDAAllocator:
+    r"""Return the allocator being currently used.
+
+    .. note::
+        See :ref:`cuda-memory-management` for details on creating and using a custom allocator
+    """
+    return _CUDAAllocator(torch._C._cuda_getAllocator())
+
+
+class MemPool(_MemPool):
+    r"""MemPool represents a pool of memory in a caching allocator. Currently,
+    it's just the ID of the pool object maintained in the CUDACachingAllocator.
+
+    Args:
+        allocator(torch._C._cuda_CUDAAllocator, optional): a
+            torch._C._cuda_CUDAAllocator object that can be used to
+            define how memory gets allocated in the pool. If :attr:`allocator`
+            is ``None`` (default), memory allocation follows the default/
+            current configuration of the CUDACachingAllocator.
+
+    """
+
+    def __init__(self, allocator: Optional[_cuda_CUDAAllocator] = None):
+        super().__init__(allocator, True)
+
+    @property
+    def id(self) -> Tuple[int, int]:
+        r"""Returns the ID of this pool as a tuple of two ints."""
+        return super().id
+
+    @property
+    def allocator(self) -> Optional[_cuda_CUDAAllocator]:
+        r"""Returns the allocator this MemPool routes allocations to"""
+        return super().allocator
+
+
+class MemPoolContext(_MemPoolContext):
+    r"""MemPoolContext holds the currently active pool and stashes the previous
+    pool. On deletion it makes the previous pool active.
+
+    Args:
+        pool(torch.cuda.MemPool): a MemPool object to be made active so that
+        allocations route to this pool.
+
+    """
+
+    def __init__(self, pool: MemPool):
+        super().__init__(pool)
+
+    @staticmethod
+    def active_pool() -> Optional[_MemPool]:
+        r"""Returns the active MemPool"""
+        return _MemPoolContext.active_pool()
+
+
+@contextlib.contextmanager
+def use_mem_pool(pool: MemPool, device: Union[Device, int] = None):
+    r"""A context manager that routes allocations to a given pool.
+
+    Args:
+        pool(torch.cuda.MemPool): a MemPool object to be made active so that
+            allocations route to this pool.
+        device (torch.device or int, optional): selected device. Uses MemPool on
+            the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    """
+    ctx = MemPoolContext(pool)
+    device_index = (
+        torch.cuda.current_device() if device is None else _get_device_index(device)
+    )
+    _cuda_beginAllocateToPool(device_index, pool.id)
+    try:
+        yield
+    finally:
+        _cuda_endAllocateCurrentStreamToPool(device_index, pool.id)
+        del ctx
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/nccl.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/nccl.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c28443c9e29fba7edd34924006573497feb2d6e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/nccl.py
@@ -0,0 +1,151 @@
+# mypy: allow-untyped-defs
+import collections
+import warnings
+from typing import Optional, Sequence, Union
+
+import torch.cuda
+
+
+__all__ = ["all_reduce", "reduce", "broadcast", "all_gather", "reduce_scatter"]
+
+SUM = 0  # ncclRedOp_t
+
+
+def is_available(tensors):
+    if not hasattr(torch._C, "_nccl_all_reduce"):
+        warnings.warn("PyTorch is not compiled with NCCL support")
+        return False
+
+    devices = set()
+    for tensor in tensors:
+        if tensor.is_sparse:
+            return False
+        if not tensor.is_contiguous():
+            return False
+        if not tensor.is_cuda:
+            return False
+        device = tensor.get_device()
+        if device in devices:
+            return False
+        devices.add(device)
+
+    return True
+
+
+def version():
+    """
+    Returns the version of the NCCL.
+
+
+    This function returns a tuple containing the major, minor, and patch version numbers of the NCCL.
+    The suffix is also included in the tuple if a version suffix exists.
+    Returns:
+        tuple: The version information of the NCCL.
+    """
+    ver = torch._C._nccl_version()
+    major = ver >> 32
+    minor = (ver >> 16) & 65535
+    patch = ver & 65535
+    suffix = torch._C._nccl_version_suffix().decode("utf-8")
+    if suffix == "":
+        return (major, minor, patch)
+    else:
+        return (major, minor, patch, suffix)
+
+
+def unique_id():
+    return torch._C._nccl_unique_id()
+
+
+def init_rank(num_ranks, uid, rank):
+    return torch._C._nccl_init_rank(num_ranks, uid, rank)
+
+
+def _check_sequence_type(inputs: Union[torch.Tensor, Sequence[torch.Tensor]]) -> None:
+    if not isinstance(inputs, collections.abc.Container) or isinstance(
+        inputs, torch.Tensor
+    ):
+        raise TypeError("Inputs should be a collection of tensors")
+
+
+def all_reduce(inputs, outputs=None, op=SUM, streams=None, comms=None):
+    _check_sequence_type(inputs)
+    if outputs is None:
+        outputs = inputs
+    _check_sequence_type(outputs)
+    torch._C._nccl_all_reduce(inputs, outputs, op, streams, comms)
+
+
+# `output` used to be `outputs`, taking in a list of tensors. So we have two
+# arguments for BC reasons.
+def reduce(
+    inputs: Sequence[torch.Tensor],
+    output: Optional[Union[torch.Tensor, Sequence[torch.Tensor]]] = None,
+    root: int = 0,
+    op: int = SUM,
+    streams: Optional[Sequence[torch.cuda.Stream]] = None,
+    comms=None,
+    *,
+    outputs: Optional[Sequence[torch.Tensor]] = None,
+) -> None:
+    _check_sequence_type(inputs)
+    _output: torch.Tensor
+    if outputs is not None:
+        if output is not None:
+            raise ValueError(
+                "'output' and 'outputs' can not be both specified. 'outputs' is deprecated in "
+                "favor of 'output', taking in a single output tensor. The signature of reduce is: "
+                "reduce(inputs, output=None, root=0, op=SUM, streams=None, comms=None)."
+            )
+        else:
+            warnings.warn(
+                "`nccl.reduce` with an output tensor list is deprecated. "
+                "Please specify a single output tensor with argument 'output' instead instead.",
+                FutureWarning,
+                stacklevel=2,
+            )
+            _output = outputs[root]
+    elif not isinstance(output, torch.Tensor) and isinstance(
+        output, collections.abc.Sequence
+    ):
+        # User called old API with positional arguments of list of output tensors.
+        warnings.warn(
+            "nccl.reduce with an output tensor list is deprecated. "
+            "Please specify a single output tensor.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        _output = output[root]
+    else:
+        _output = inputs[root] if output is None else output
+    torch._C._nccl_reduce(inputs, _output, root, op, streams, comms)
+
+
+def broadcast(
+    inputs: Sequence[torch.Tensor], root: int = 0, streams=None, comms=None
+) -> None:
+    _check_sequence_type(inputs)
+    torch._C._nccl_broadcast(inputs, root, streams, comms)
+
+
+def all_gather(
+    inputs: Sequence[torch.Tensor],
+    outputs: Sequence[torch.Tensor],
+    streams=None,
+    comms=None,
+) -> None:
+    _check_sequence_type(inputs)
+    _check_sequence_type(outputs)
+    torch._C._nccl_all_gather(inputs, outputs, streams, comms)
+
+
+def reduce_scatter(
+    inputs: Sequence[torch.Tensor],
+    outputs: Sequence[torch.Tensor],
+    op: int = SUM,
+    streams=None,
+    comms=None,
+) -> None:
+    _check_sequence_type(inputs)
+    _check_sequence_type(outputs)
+    torch._C._nccl_reduce_scatter(inputs, outputs, op, streams, comms)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/nvtx.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/nvtx.py
new file mode 100644
index 0000000000000000000000000000000000000000..a74633da032280c3437504efa7b538d0c1e20312
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/nvtx.py
@@ -0,0 +1,93 @@
+# mypy: allow-untyped-defs
+r"""This package adds support for NVIDIA Tools Extension (NVTX) used in profiling."""
+
+from contextlib import contextmanager
+
+
+try:
+    from torch._C import _nvtx
+except ImportError:
+
+    class _NVTXStub:
+        @staticmethod
+        def _fail(*args, **kwargs):
+            raise RuntimeError(
+                "NVTX functions not installed. Are you sure you have a CUDA build?"
+            )
+
+        rangePushA = _fail
+        rangePop = _fail
+        markA = _fail
+
+    _nvtx = _NVTXStub()  # type: ignore[assignment]
+
+__all__ = ["range_push", "range_pop", "range_start", "range_end", "mark", "range"]
+
+
+def range_push(msg):
+    """
+    Push a range onto a stack of nested range span.  Returns zero-based depth of the range that is started.
+
+    Args:
+        msg (str): ASCII message to associate with range
+    """
+    return _nvtx.rangePushA(msg)
+
+
+def range_pop():
+    """Pop a range off of a stack of nested range spans.  Returns the  zero-based depth of the range that is ended."""
+    return _nvtx.rangePop()
+
+
+def range_start(msg) -> int:
+    """
+    Mark the start of a range with string message. It returns an unique handle
+    for this range to pass to the corresponding call to rangeEnd().
+
+    A key difference between this and range_push/range_pop is that the
+    range_start/range_end version supports range across threads (start on one
+    thread and end on another thread).
+
+    Returns: A range handle (uint64_t) that can be passed to range_end().
+
+    Args:
+        msg (str): ASCII message to associate with the range.
+    """
+    return _nvtx.rangeStartA(msg)
+
+
+def range_end(range_id) -> None:
+    """
+    Mark the end of a range for a given range_id.
+
+    Args:
+        range_id (int): an unique handle for the start range.
+    """
+    _nvtx.rangeEnd(range_id)
+
+
+def mark(msg):
+    """
+    Describe an instantaneous event that occurred at some point.
+
+    Args:
+        msg (str): ASCII message to associate with the event.
+    """
+    return _nvtx.markA(msg)
+
+
+@contextmanager
+def range(msg, *args, **kwargs):
+    """
+    Context manager / decorator that pushes an NVTX range at the beginning
+    of its scope, and pops it at the end. If extra arguments are given,
+    they are passed as arguments to msg.format().
+
+    Args:
+        msg (str): message to associate with the range
+    """
+    range_push(msg.format(*args, **kwargs))
+    try:
+        yield
+    finally:
+        range_pop()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/profiler.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/profiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae0674f4a4a1d9d3f20a759301606c8236b09828
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/profiler.py
@@ -0,0 +1,86 @@
+# mypy: allow-untyped-defs
+import contextlib
+import tempfile
+
+import torch
+
+from . import check_error, cudart
+
+
+__all__ = ["init", "start", "stop", "profile"]
+
+DEFAULT_FLAGS = [
+    "gpustarttimestamp",
+    "gpuendtimestamp",
+    "gridsize3d",
+    "threadblocksize",
+    "streamid",
+    "enableonstart 0",
+    "conckerneltrace",
+]
+
+
+def init(output_file, flags=None, output_mode="key_value"):
+    rt = cudart()
+    if not hasattr(rt, "cudaOutputMode"):
+        raise AssertionError("HIP does not support profiler initialization!")
+    if (
+        hasattr(torch.version, "cuda")
+        and torch.version.cuda is not None
+        and int(torch.version.cuda.split(".")[0]) >= 12
+    ):
+        # Check https://github.com/pytorch/pytorch/pull/91118
+        # cudaProfilerInitialize is no longer needed after CUDA 12
+        raise AssertionError("CUDA12+ does not need profiler initialization!")
+    flags = DEFAULT_FLAGS if flags is None else flags
+    if output_mode == "key_value":
+        output_mode_enum = rt.cudaOutputMode.KeyValuePair
+    elif output_mode == "csv":
+        output_mode_enum = rt.cudaOutputMode.CSV
+    else:
+        raise RuntimeError(
+            "supported CUDA profiler output modes are: key_value and csv"
+        )
+    with tempfile.NamedTemporaryFile(delete=True) as f:
+        f.write(b"\n".join(f.encode("ascii") for f in flags))
+        f.flush()
+        check_error(rt.cudaProfilerInitialize(f.name, output_file, output_mode_enum))
+
+
+def start():
+    r"""Starts cuda profiler data collection.
+
+    .. warning::
+        Raises CudaError in case of it is unable to start the profiler.
+    """
+    check_error(cudart().cudaProfilerStart())
+
+
+def stop():
+    r"""Stops cuda profiler data collection.
+
+    .. warning::
+        Raises CudaError in case of it is unable to stop the profiler.
+    """
+    check_error(cudart().cudaProfilerStop())
+
+
+@contextlib.contextmanager
+def profile():
+    """
+    Enable profiling.
+
+    Context Manager to enabling profile collection by the active profiling tool from CUDA backend.
+    Example:
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
+        >>> import torch
+        >>> model = torch.nn.Linear(20, 30).cuda()
+        >>> inputs = torch.randn(128, 20).cuda()
+        >>> with torch.cuda.profiler.profile() as prof:
+        ...     model(inputs)
+    """
+    try:
+        start()
+        yield
+    finally:
+        stop()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/random.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/random.py
new file mode 100644
index 0000000000000000000000000000000000000000..088e8398f64266e1473edba35f5cf0226787ac08
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/random.py
@@ -0,0 +1,182 @@
+# mypy: allow-untyped-defs
+from typing import Iterable, List, Union
+
+import torch
+from torch import Tensor
+
+from . import _lazy_call, _lazy_init, current_device, device_count
+
+
+__all__ = [
+    "get_rng_state",
+    "get_rng_state_all",
+    "set_rng_state",
+    "set_rng_state_all",
+    "manual_seed",
+    "manual_seed_all",
+    "seed",
+    "seed_all",
+    "initial_seed",
+]
+
+
+def get_rng_state(device: Union[int, str, torch.device] = "cuda") -> Tensor:
+    r"""Return the random number generator state of the specified GPU as a ByteTensor.
+
+    Args:
+        device (torch.device or int, optional): The device to return the RNG state of.
+            Default: ``'cuda'`` (i.e., ``torch.device('cuda')``, the current CUDA device).
+
+    .. warning::
+        This function eagerly initializes CUDA.
+    """
+    _lazy_init()
+    if isinstance(device, str):
+        device = torch.device(device)
+    elif isinstance(device, int):
+        device = torch.device("cuda", device)
+    idx = device.index
+    if idx is None:
+        idx = current_device()
+    default_generator = torch.cuda.default_generators[idx]
+    return default_generator.get_state()
+
+
+def get_rng_state_all() -> List[Tensor]:
+    r"""Return a list of ByteTensor representing the random number states of all devices."""
+    results = []
+    for i in range(device_count()):
+        results.append(get_rng_state(i))
+    return results
+
+
+def set_rng_state(
+    new_state: Tensor, device: Union[int, str, torch.device] = "cuda"
+) -> None:
+    r"""Set the random number generator state of the specified GPU.
+
+    Args:
+        new_state (torch.ByteTensor): The desired state
+        device (torch.device or int, optional): The device to set the RNG state.
+            Default: ``'cuda'`` (i.e., ``torch.device('cuda')``, the current CUDA device).
+    """
+    with torch._C._DisableFuncTorch():
+        new_state_copy = new_state.clone(memory_format=torch.contiguous_format)
+    if isinstance(device, str):
+        device = torch.device(device)
+    elif isinstance(device, int):
+        device = torch.device("cuda", device)
+
+    def cb():
+        idx = device.index
+        if idx is None:
+            idx = current_device()
+        default_generator = torch.cuda.default_generators[idx]
+        default_generator.set_state(new_state_copy)
+
+    _lazy_call(cb)
+
+
+def set_rng_state_all(new_states: Iterable[Tensor]) -> None:
+    r"""Set the random number generator state of all devices.
+
+    Args:
+        new_states (Iterable of torch.ByteTensor): The desired state for each device.
+    """
+    for i, state in enumerate(new_states):
+        set_rng_state(state, i)
+
+
+def manual_seed(seed: int) -> None:
+    r"""Set the seed for generating random numbers for the current GPU.
+
+    It's safe to call this function if CUDA is not available; in that
+    case, it is silently ignored.
+
+    Args:
+        seed (int): The desired seed.
+
+    .. warning::
+        If you are working with a multi-GPU model, this function is insufficient
+        to get determinism.  To seed all GPUs, use :func:`manual_seed_all`.
+    """
+    seed = int(seed)
+
+    def cb():
+        idx = current_device()
+        default_generator = torch.cuda.default_generators[idx]
+        default_generator.manual_seed(seed)
+
+    _lazy_call(cb, seed=True)
+
+
+def manual_seed_all(seed: int) -> None:
+    r"""Set the seed for generating random numbers on all GPUs.
+
+    It's safe to call this function if CUDA is not available; in that
+    case, it is silently ignored.
+
+    Args:
+        seed (int): The desired seed.
+    """
+    seed = int(seed)
+
+    def cb():
+        for i in range(device_count()):
+            default_generator = torch.cuda.default_generators[i]
+            default_generator.manual_seed(seed)
+
+    _lazy_call(cb, seed_all=True)
+
+
+def seed() -> None:
+    r"""Set the seed for generating random numbers to a random number for the current GPU.
+
+    It's safe to call this function if CUDA is not available; in that
+    case, it is silently ignored.
+
+    .. warning::
+        If you are working with a multi-GPU model, this function will only initialize
+        the seed on one GPU.  To initialize all GPUs, use :func:`seed_all`.
+    """
+
+    def cb():
+        idx = current_device()
+        default_generator = torch.cuda.default_generators[idx]
+        default_generator.seed()
+
+    _lazy_call(cb)
+
+
+def seed_all() -> None:
+    r"""Set the seed for generating random numbers to a random number on all GPUs.
+
+    It's safe to call this function if CUDA is not available; in that
+    case, it is silently ignored.
+    """
+
+    def cb():
+        random_seed = 0
+        seeded = False
+        for i in range(device_count()):
+            default_generator = torch.cuda.default_generators[i]
+            if not seeded:
+                default_generator.seed()
+                random_seed = default_generator.initial_seed()
+                seeded = True
+            else:
+                default_generator.manual_seed(random_seed)
+
+    _lazy_call(cb)
+
+
+def initial_seed() -> int:
+    r"""Return the current random seed of the current GPU.
+
+    .. warning::
+        This function eagerly initializes CUDA.
+    """
+    _lazy_init()
+    idx = current_device()
+    default_generator = torch.cuda.default_generators[idx]
+    return default_generator.initial_seed()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/sparse.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/sparse.py
new file mode 100644
index 0000000000000000000000000000000000000000..f37a34118d2d8f73437dee54337a666df1b99a09
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/sparse.py
@@ -0,0 +1 @@
+# The Tensor classes are added to this module by python_tensor.cpp
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/streams.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/streams.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ee6eb68d6892bc749847826c3c0873d900bbb9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/streams.py
@@ -0,0 +1,242 @@
+# mypy: allow-untyped-defs
+import ctypes
+
+import torch
+from torch._streambase import _EventBase, _StreamBase
+from torch._utils import _dummy_type
+
+
+if not hasattr(torch._C, "_CudaStreamBase"):
+    # Define dummy base classes
+    torch._C.__dict__["_CudaStreamBase"] = _dummy_type("_CudaStreamBase")
+    torch._C.__dict__["_CudaEventBase"] = _dummy_type("_CudaEventBase")
+
+
+class Stream(torch._C._CudaStreamBase, _StreamBase):
+    r"""Wrapper around a CUDA stream.
+
+    A CUDA stream is a linear sequence of execution that belongs to a specific
+    device, independent from other streams.  See :ref:`cuda-semantics` for
+    details.
+
+    Args:
+        device(torch.device or int, optional): a device on which to allocate
+            the stream. If :attr:`device` is ``None`` (default) or a negative
+            integer, this will use the current device.
+        priority(int, optional): priority of the stream, should be 0 or
+            negative, where negative numbers indicate higher priority. By default,
+            streams have priority 0.
+
+    """
+
+    def __new__(cls, device=None, priority=0, **kwargs):
+        # setting device manager is expensive, so we avoid it unless necessary
+        if device is None or ("stream_id" in kwargs and "device_index" in kwargs):
+            return super().__new__(cls, priority=priority, **kwargs)
+        else:
+            with torch.cuda.device(device):
+                return super().__new__(cls, priority=priority, **kwargs)
+
+    def wait_event(self, event) -> None:
+        r"""Make all future work submitted to the stream wait for an event.
+
+        Args:
+            event (torch.cuda.Event): an event to wait for.
+
+        .. note:: This is a wrapper around ``cudaStreamWaitEvent()``: see
+           `CUDA Stream documentation`_ for more info.
+
+           This function returns without waiting for :attr:`event`: only future
+           operations are affected.
+
+        .. _CUDA Stream documentation:
+           https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html
+        """
+        event.wait(self)
+
+    def wait_stream(self, stream) -> None:
+        r"""Synchronize with another stream.
+
+        All future work submitted to this stream will wait until all kernels
+        submitted to a given stream at the time of call complete.
+
+        Args:
+            stream (Stream): a stream to synchronize.
+
+        .. note:: This function returns without waiting for currently enqueued
+           kernels in :attr:`stream`: only future operations are affected.
+        """
+        self.wait_event(stream.record_event())
+
+    def record_event(self, event=None):
+        r"""Record an event.
+
+        Args:
+            event (torch.cuda.Event, optional): event to record. If not given, a new one
+                will be allocated.
+
+        Returns:
+            Recorded event.
+        """
+        if event is None:
+            event = Event()
+        event.record(self)
+        return event
+
+    def query(self) -> bool:
+        r"""Check if all the work submitted has been completed.
+
+        Returns:
+            A boolean indicating if all kernels in this stream are completed.
+        """
+        return super().query()
+
+    def synchronize(self) -> None:
+        r"""Wait for all the kernels in this stream to complete.
+
+        .. note:: This is a wrapper around ``cudaStreamSynchronize()``: see
+           `CUDA Stream documentation`_ for more info.
+        """
+        super().synchronize()
+
+    @property
+    def _as_parameter_(self):
+        return ctypes.c_void_p(self.cuda_stream)
+
+    def __eq__(self, o) -> bool:
+        if isinstance(o, Stream):
+            return super().__eq__(o)
+        return False
+
+    def __hash__(self):
+        return hash((self.cuda_stream, self.device))
+
+    def __repr__(self):
+        return f""
+
+
+class ExternalStream(Stream):
+    r"""Wrapper around an externally allocated CUDA stream.
+
+    This class is used to wrap streams allocated in other libraries in order
+    to facilitate data exchange and multi-library interactions.
+
+    .. note:: This class doesn't manage the stream life-cycle, it is the user
+       responsibility to keep the referenced stream alive while this class is
+       being used.
+
+    Args:
+        stream_ptr(int): Integer representation of the `cudaStream_t` value.
+            allocated externally.
+        device(torch.device or int, optional): the device where the stream
+            was originally allocated. If device is specified incorrectly,
+            subsequent launches using this stream may fail.
+    """
+
+    def __new__(cls, stream_ptr, device=None, **kwargs):
+        with torch.cuda.device(device):
+            return super().__new__(cls, stream_ptr=stream_ptr, **kwargs)
+
+
+class Event(torch._C._CudaEventBase, _EventBase):
+    r"""Wrapper around a CUDA event.
+
+    CUDA events are synchronization markers that can be used to monitor the
+    device's progress, to accurately measure timing, and to synchronize CUDA
+    streams.
+
+    The underlying CUDA events are lazily initialized when the event is first
+    recorded or exported to another process. After creation, only streams on the
+    same device may record the event. However, streams on any device can wait on
+    the event.
+
+    Args:
+        enable_timing (bool, optional): indicates if the event should measure time
+            (default: ``False``)
+        blocking (bool, optional): if ``True``, :meth:`wait` will be blocking (default: ``False``)
+        interprocess (bool): if ``True``, the event can be shared between processes
+            (default: ``False``)
+
+    .. _CUDA Event Documentation:
+       https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__EVENT.html
+    """
+
+    def __new__(cls, enable_timing=False, blocking=False, interprocess=False):
+        return super().__new__(
+            cls,
+            enable_timing=enable_timing,
+            blocking=blocking,
+            interprocess=interprocess,
+        )
+
+    @classmethod
+    def from_ipc_handle(cls, device, handle):
+        r"""Reconstruct an event from an IPC handle on the given device."""
+        return super().from_ipc_handle(device, handle)
+
+    def record(self, stream=None):
+        r"""Record the event in a given stream.
+
+        Uses ``torch.cuda.current_stream()`` if no stream is specified. The
+        stream's device must match the event's device.
+        """
+        if stream is None:
+            stream = torch.cuda.current_stream()
+        super().record(stream)
+
+    def wait(self, stream=None) -> None:
+        r"""Make all future work submitted to the given stream wait for this event.
+
+        Use ``torch.cuda.current_stream()`` if no stream is specified.
+
+        .. note:: This is a wrapper around ``cudaStreamWaitEvent()``: see
+            `CUDA Event documentation`_ for more info.
+        """
+        if stream is None:
+            stream = torch.cuda.current_stream()
+        super().wait(stream)
+
+    def query(self):
+        r"""Check if all work currently captured by event has completed.
+
+        Returns:
+            A boolean indicating if all work currently captured by event has
+            completed.
+        """
+        return super().query()
+
+    def elapsed_time(self, end_event):
+        r"""Return the time elapsed.
+
+        Time reported in milliseconds after the event was recorded and
+        before the end_event was recorded.
+        """
+        return super().elapsed_time(end_event)
+
+    def synchronize(self) -> None:
+        r"""Wait for the event to complete.
+
+        Waits until the completion of all work currently captured in this event.
+        This prevents the CPU thread from proceeding until the event completes.
+
+         .. note:: This is a wrapper around ``cudaEventSynchronize()``: see
+            `CUDA Event documentation`_ for more info.
+        """
+        super().synchronize()
+
+    def ipc_handle(self):
+        r"""Return an IPC handle of this event.
+
+        If not recorded yet, the event will use the current device.
+        """
+        return super().ipc_handle()
+
+    @property
+    def _as_parameter_(self):
+        return ctypes.c_void_p(self.cuda_event)
+
+    def __repr__(self) -> str:
+        if self.cuda_event:
+            return f""
+        else:
+            return ""
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/tunable.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/tunable.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b387102b43dcf3abd1a99c1d63f43cdddc36a4a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/cuda/tunable.py
@@ -0,0 +1,242 @@
+r"""
+This module exposes a TunableOp interface.
+
+Some operations, such as GEMMs, could be implemented using more than one library
+or more than one technique. For example, a GEMM could be implemented for CUDA or
+ROCm using either the blas or blasLt libraries. Further, ROCm's rocblas and
+hipblaslt libraries allow the user to query for all possible algorithms and then
+choose one. How does one know which implementation is the fastest and should be
+chosen? That's what TunableOp provides.
+
+Enabling TunableOp and Tuning Separately
+========================================
+
+The TunableOp feature is enabled separately from enabling the tuning phase
+itself. Enabling TunableOp means that PyTorch will replace any standard
+operators with their Tunable implementations. Any call to a TunableOp first
+checks whether it has already been tuned for the given operator inputs. If so,
+it will immediately call the tuned operation; no further tuning will take place
+even when the tuning setting is enabled. Instead if no tuning result is found,
+and tuning is enabled, the TunableOp will benchmark every registered
+implementation of that operator for the given set of inputs and select the
+fastest.
+
+File Input and Output
+=====================
+
+The first time any TunableOp is invoked, the internal database of tuned
+operations will be prepared by attempting to read the results from the given
+file. The default filename is 'tunableop_results.csv'. To support tuning when
+multiple GPUs are used across multiple processes, the GPU device ordinal is
+automatically inserted into the filename to avoid multiple processes overwriting
+the same file.
+
+If tuning is enabled and new tunings are discovered during the course of your
+workload, it will also write out to this same filename with all tunings, both
+the ones it read in at startup as well as the new ones found at runtime. This
+can be used, for example, to build up a tunings file across many workloads by
+reusing the same file. The output file is automatically created when the
+application terminates. This behavior can be controlled by the C++ and Python
+APIs but not the environment variables.
+
+Assuming you specified a filename, you'll end up with a CSV file with contents
+like so::
+
+  Validator,PT_VERSION,2.2.0
+  Validator,ROCM_VERSION,6.0.0.0-12969-1544e39
+  Validator,HIPBLASLT_VERSION,0.6.0-a9c5cc7
+  Validator,ROCBLAS_VERSION,4.0.0-72e57364-dirty
+  GemmTunableOp_float_NT,nt_25088_4096_64,1219,1.262
+  GemmTunableOp_float_NT,nt_4096_4096_64,1216,0.033
+
+Note the "Validator" lines. If you change a library verison, or ROCm version, or
+PyTorch version, TunableOp will detect this and reject the tunings file because
+the prior tunings are likely affected by other software changes.
+
+The remaining lines are the tuned solutions for each TunableOp encountered
+during your execution. Each line consists of 4 comma-separated fields: operator
+name, operator parameters, solution name, and average execution time. The
+execution time is an optional field. The CSV file can be edited, but with
+caution. For example, the solution name (field 3) can be changed to "Default"
+and it will fall back to the original PyTorch untuned implementation. Or, in the
+case of ROCm's hipBLAS or hipBLASLt libraries, if you know the specific solution
+index you can override the solution that TunableOp selected by replacing the
+value. The operator name and parameters (fields 1 and 2) are internally named
+and should not be modified. In the case of GemmTunableOp, field 1 indicates the
+datatype and whether the inputs are transposed (T) or not (N) and field 2
+indicates the M, N, K input shapes.
+
+There is an option to enable verbose output but it is only recommended for
+debugging purposes. This will produce a lot of diagnostic messages but may be
+useful to see if TunableOp is being used at all. Otherwise, TunableOp is
+completely silent, besides file output, unless there is a warning or error
+during its use. The verbose option is only available by setting the environment
+variable PYTORCH_TUNABLEOP_VEROBSE=1.
+
+A Note on Tuning Behavior
+=========================
+
+Tuning an operator consists of iterating through the list or registered
+implementations and profiling each one. The profile is established by running a
+single implementation in a loop multiple times and taking the average execution
+time.
+
+By default, each possible solution for a given operator will be run for either
+100 iterations or as many iterations that can be run within 30ms, whichever is
+smaller, and its average execution will be calculated. The fastest solution
+among all that were successfully profiled will be chosen. A profile might fail
+if the given solution doesn't achieve the same accuracy as the default
+implementation or if the solution returns an error code.
+
+Current Tunable Operators
+=========================
+
+TunableGemm for ROCm
+--------------------
+
+Currently only a TunableGemm for ROCm is implemented. Note that CUDA builds of
+PyTorch will function correctly when using TunableOp but the only solution
+available to CUDA builds is the 'Default' implementation i.e. the original
+cuBLAS default, now called through TunableOp. Any call to at::cuda::blas::gemm()
+or ::bgemm() will be routed through TunableOp when enabled. Calling gemm() for a
+given set of input arguments (transa, transb, m, n, k) will attempt to use the
+fastest available implementation across both rocblas and hipblaslt.
+
+Tuning Context
+==============
+
+The behavior of TunableOp is currently manipulated through environment
+variables, the C++ interface of at::cuda::tunable::getTuningContext(), or the
+torch.cuda.tunable python interfaces that wrap the C++ TuningContext. The
+environment variables take precedence over any setting you manipulate using the
+C++ or Python APIs.
+
+"""
+from typing import Optional, Tuple
+
+import torch
+
+
+__all__ = [
+    "enable",
+    "is_enabled",
+    "tuning_enable",
+    "tuning_is_enabled",
+    "set_max_tuning_duration",
+    "get_max_tuning_duration",
+    "set_max_tuning_iterations",
+    "get_max_tuning_iterations",
+    "set_filename",
+    "get_filename",
+    "get_results",
+    "get_validators",
+    "write_file_on_exit",
+    "write_file",
+    "read_file",
+]
+
+
+def enable(val: bool = True) -> None:
+    r"""This is the big on/off switch for all TunableOp implementations."""
+    torch._C._cuda_tunableop_enable(val)  # type: ignore[attr-defined]
+
+
+def is_enabled() -> bool:
+    r"""Returns whether the TunableOp feature is enabled."""
+    return torch._C._cuda_tunableop_is_enabled()  # type: ignore[attr-defined]
+
+
+def tuning_enable(val: bool = True) -> None:
+    r"""Enable tuning of TunableOp implementations.
+
+    When enabled, if a tuned entry isn't found, run the tuning step and record
+    the entry.
+    """
+    torch._C._cuda_tunableop_tuning_enable(val)  # type: ignore[attr-defined]
+
+
+def tuning_is_enabled() -> bool:
+    r"""Returns whether TunableOp implementations can be tuned."""
+    return torch._C._cuda_tunableop_tuning_is_enabled()  # type: ignore[attr-defined]
+
+
+def set_max_tuning_duration(duration: int) -> None:
+    r"""Set max time in milliseconds to spend tuning a given solution.
+
+    If both max tuning duration and iterations are set, the smaller of the two
+    will be honored. At minimum 1 tuning iteration will always be run.
+    """
+    torch._C._cuda_tunableop_set_max_tuning_duration(duration)  # type: ignore[attr-defined]
+
+
+def get_max_tuning_duration() -> int:
+    r"""Get max time to spend tuning a given solution."""
+    return torch._C._cuda_tunableop_get_max_tuning_duration()  # type: ignore[attr-defined]
+
+
+def set_max_tuning_iterations(iterations: int) -> None:
+    r"""Set max number of iterations to spend tuning a given solution.
+
+    If both max tuning duration and iterations are set, the smaller of the two
+    will be honored. At minimum 1 tuning iteration will always be run.
+    """
+    torch._C._cuda_tunableop_set_max_tuning_iterations(iterations)  # type: ignore[attr-defined]
+
+
+def get_max_tuning_iterations() -> int:
+    r"""Get max iterations to spend tuning a given solution."""
+    return torch._C._cuda_tunableop_get_max_tuning_iterations()  # type: ignore[attr-defined]
+
+
+def set_filename(filename: str, insert_device_ordinal: bool = False) -> None:
+    r"""Set the filename to use for input/output of tuning results.
+
+    If :attr:`insert_device_ordinal` is ``True`` then the current device ordinal
+    will be added to the given filename automatically. This can be used in a
+    1-process-per-gpu cenario to ensure all processes write to a separate file.
+    """
+    torch._C._cuda_tunableop_set_filename(filename, insert_device_ordinal)  # type: ignore[attr-defined]
+
+
+def get_filename() -> str:
+    r"""Get the results filename."""
+    return torch._C._cuda_tunableop_get_filename()  # type: ignore[attr-defined]
+
+
+def get_results() -> Tuple[str, str, str, float]:
+    r"""Return all TunableOp results."""
+    return torch._C._cuda_tunableop_get_results()  # type: ignore[attr-defined]
+
+
+def get_validators() -> Tuple[str, str]:
+    r"""Return the TunableOp validators."""
+    return torch._C._cuda_tunableop_get_validators()  # type: ignore[attr-defined]
+
+
+def write_file_on_exit(val: bool) -> None:
+    r"""During Tuning Context destruction, write file to disk.
+
+    This is useful as a final flush of your results to disk if your application
+    terminates as result of normal operation or an error. Manual flushing of
+    your results can be achieved by manually calling ``write_file()``."""
+    torch._C._cuda_tunableop_write_file_on_exit(val)  # type: ignore[attr-defined]
+
+
+def write_file(filename: Optional[str] = None) -> bool:
+    r"""Write results to a CSV file.
+
+    If :attr:`filename` is not given, ``get_filename()`` is called.
+    """
+    if filename is None:
+        filename = get_filename()
+    return torch._C._cuda_tunableop_write_file(filename)  # type: ignore[attr-defined]
+
+
+def read_file(filename: Optional[str] = None) -> bool:
+    r"""Read results from a TunableOp CSV file.
+
+    If :attr:`filename` is not given, ``get_filename()`` is called.
+    """
+    if filename is None:
+        filename = get_filename()
+    return torch._C._cuda_tunableop_read_file(filename)  # type: ignore[attr-defined]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..181fab713febe834bd42bbd0881e61dec15938db
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/__init__.py
@@ -0,0 +1,157 @@
+# mypy: allow-untyped-defs
+import logging
+import pdb
+import sys
+import traceback
+import typing
+
+import torch
+
+
+log = logging.getLogger(__name__)
+
+
+def is_available() -> bool:
+    """
+    Return ``True`` if the distributed package is available.
+
+    Otherwise,
+    ``torch.distributed`` does not expose any other APIs. Currently,
+    ``torch.distributed`` is available on Linux, MacOS and Windows. Set
+    ``USE_DISTRIBUTED=1`` to enable it when building PyTorch from source.
+    Currently, the default value is ``USE_DISTRIBUTED=1`` for Linux and Windows,
+    ``USE_DISTRIBUTED=0`` for MacOS.
+    """
+    return hasattr(torch._C, "_c10d_init")
+
+
+if is_available() and not torch._C._c10d_init():
+    raise RuntimeError("Failed to initialize torch.distributed")
+
+# Custom Runtime Errors thrown from the distributed package
+DistError = torch._C._DistError
+DistBackendError = torch._C._DistBackendError
+DistNetworkError = torch._C._DistNetworkError
+DistStoreError = torch._C._DistStoreError
+
+if is_available():
+    from torch._C._distributed_c10d import (
+        _broadcast_coalesced,
+        _compute_bucket_assignment_by_size,
+        _ControlCollectives,
+        _DEFAULT_FIRST_BUCKET_BYTES,
+        _make_nccl_premul_sum,
+        _register_builtin_comm_hook,
+        _register_comm_hook,
+        _StoreCollectives,
+        _test_python_store,
+        _verify_params_across_processes,
+        Backend as _Backend,
+        BuiltinCommHookType,
+        DebugLevel,
+        FileStore,
+        get_debug_level,
+        GradBucket,
+        Logger,
+        PrefixStore,
+        ProcessGroup as ProcessGroup,
+        Reducer,
+        set_debug_level,
+        set_debug_level_from_env,
+        Store,
+        TCPStore,
+        Work as _Work,
+    )
+
+    class _DistributedPdb(pdb.Pdb):
+        """
+        Supports using PDB from inside a multiprocessing child process.
+
+        Usage:
+        _DistributedPdb().set_trace()
+        """
+
+        def interaction(self, *args, **kwargs):
+            _stdin = sys.stdin
+            try:
+                sys.stdin = open("/dev/stdin")
+                pdb.Pdb.interaction(self, *args, **kwargs)
+            finally:
+                sys.stdin = _stdin
+
+    _breakpoint_cache: typing.Dict[int, typing.Any] = {}
+
+    def breakpoint(rank: int = 0, skip: int = 0):
+        """
+        Set a breakpoint, but only on a single rank.  All other ranks will wait for you to be
+        done with the breakpoint before continuing.
+
+        Args:
+            rank (int): Which rank to break on.  Default: ``0``
+            skip (int): Skip the first ``skip`` calls to this breakpoint. Default: ``0``.
+        """
+        if skip > 0:
+            key = hash(str(traceback.format_exc()))
+            counter = _breakpoint_cache.get(key, 0) + 1
+            _breakpoint_cache[key] = counter
+            if counter <= skip:
+                log.warning("Skip the breakpoint, counter=%d", counter)
+                return
+
+        if get_rank() == rank:
+            pdb = _DistributedPdb()
+            pdb.message(
+                "\n!!! ATTENTION !!!\n\n"
+                f"Type 'up' to get to the frame that called dist.breakpoint(rank={rank})\n"
+            )
+            pdb.set_trace()
+        # If Meta/Python keys are in the TLS, we want to make sure that we ignore them
+        # and hit the (default) CPU/CUDA implementation of barrier.
+        meta_in_tls = torch._C._meta_in_tls_dispatch_include()
+        guard = torch._C._DisableTorchDispatch()  # type: ignore[attr-defined]
+        torch._C._set_meta_in_tls_dispatch_include(False)
+        try:
+            barrier()
+        finally:
+            torch._C._set_meta_in_tls_dispatch_include(meta_in_tls)
+            del guard
+
+    if sys.platform != "win32":
+        from torch._C._distributed_c10d import HashStore
+
+    from .device_mesh import DeviceMesh, init_device_mesh
+
+    # Variables prefixed with underscore are not auto imported
+    # See the comment in `distributed_c10d.py` above `_backend` on why we expose
+    # this.
+    from .distributed_c10d import *  # noqa: F403
+    from .distributed_c10d import (
+        _all_gather_base,
+        _coalescing_manager,
+        _CoalescingManager,
+        _create_process_group_wrapper,
+        _get_process_group_name,
+        _rank_not_in_group,
+        _reduce_scatter_base,
+        get_node_local_rank,
+    )
+    from .remote_device import _remote_device
+    from .rendezvous import (
+        _create_store_from_options,
+        register_rendezvous_handler,
+        rendezvous,
+    )
+
+    set_debug_level_from_env()
+
+else:
+    # This stub is sufficient to get
+    #   python test/test_public_bindings.py -k test_correct_module_names
+    # working even when USE_DISTRIBUTED=0.  Feel free to add more
+    # stubs as necessary.
+    # We cannot define stubs directly because they confuse pyre
+
+    class _ProcessGroupStub:
+        pass
+
+    sys.modules["torch.distributed"].ProcessGroup = _ProcessGroupStub  # type: ignore[attr-defined]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/_checkpointable.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/_checkpointable.py
new file mode 100644
index 0000000000000000000000000000000000000000..409d1f8a473bb6b55933b5306c6bbfac03eaf19a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/_checkpointable.py
@@ -0,0 +1,38 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from typing import Any, Protocol, runtime_checkable
+
+import torch
+
+
+@runtime_checkable
+class _Checkpointable(Protocol):  # noqa: PYI046
+    """
+    Interface for checkpointable objects.
+    Implemented as a protocol, implicit subtyping is supported so subclasses do not need to inherit this explicitly.
+    This is to allow arbitrary objects/tensor subclasses to hook into DCP seamlessly through implementing the interface.
+    """
+
+    def __create_write_items__(self, fqn: str, object: Any):
+        """
+        Return a list of WriteItems based on object's contents.
+        """
+        raise NotImplementedError(
+            "_Checkpointable._create_write_items is not implemented"
+        )
+
+    def __create_chunk_list__(self):
+        """
+        Return a list of `ChunkStorageMetadata` based on object's contents.
+        """
+        raise NotImplementedError(
+            "_Checkpointable._create_chunk_list is not implemented"
+        )
+
+    def __get_tensor_shard__(self, index) -> torch.Tensor:
+        """
+        Return a 'torch.Tensor' shard based on 'MetadataIndex'.
+        """
+        raise NotImplementedError(
+            "_Checkpointable._get_tensor_shard is not implemented"
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/_composable_state.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/_composable_state.py
new file mode 100644
index 0000000000000000000000000000000000000000..f50da98f8c63e22a294c5739c172339904cbccab
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/_composable_state.py
@@ -0,0 +1,37 @@
+from typing import cast, Dict, Optional
+
+import torch.nn as nn
+
+
+class _State:
+    pass
+
+
+_module_state_mapping: Dict[nn.Module, _State] = {}
+
+
+def _insert_module_state(module: nn.Module, state: _State) -> None:
+    global _module_state_mapping
+    assert module not in _module_state_mapping, f"Inserting {module} more than once."
+    _module_state_mapping[module] = state
+
+
+def _get_module_state(module: nn.Module) -> Optional[_State]:
+    """
+    Return the ``_State`` in ``model``.
+
+    Given a ``module``, this API finds out if the module is also a ``_State``
+    instance or if the module is managed by a composable API. If the module
+    is also a ``_State``, ``module`` will be casted to ``_State` and returned.
+    If it is managed by a composable API, the corresponding ``_State`` will
+    be returned.
+    """
+    global _module_state_mapping
+    if isinstance(module, _State):
+        return cast(_State, module)
+    else:
+        # https://github.com/pytorch/pytorch/issues/107054
+        if module in _module_state_mapping:
+            return _module_state_mapping[module]
+        else:
+            return None
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/_functional_collectives.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/_functional_collectives.py
new file mode 100644
index 0000000000000000000000000000000000000000..77b962bf3df47c996e87afec70c0efda5a9fabd3
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/_functional_collectives.py
@@ -0,0 +1,1150 @@
+# mypy: allow-untyped-defs
+import sys
+import warnings
+from typing import cast, List, Optional, Tuple, TYPE_CHECKING, Union
+
+import torch
+import torch.distributed as dist
+import torch.distributed.distributed_c10d as c10d
+from torch.distributed.device_mesh import DeviceMesh
+from torch.fx.experimental.proxy_tensor import get_proxy_mode
+
+from . import _functional_collectives_impl as fun_col_impl
+
+
+try:
+    from torch.utils._cxx_pytree import tree_map_only
+except ImportError:
+    from torch.utils._pytree import tree_map_only  # type: ignore[no-redef]
+
+
+if torch._running_with_deploy():
+
+    def is_torchdynamo_compiling():
+        """Can't import torchdynamo in torchdeploy builds currently."""
+        return False
+
+else:
+    try:
+        from torch.compiler import is_dynamo_compiling as is_torchdynamo_compiling
+    except Exception:
+        warnings.warn(
+            "Unable to import torchdynamo util `is_torchdynamo_compiling`, so won't support torchdynamo correctly"
+        )
+
+        def is_torchdynamo_compiling():
+            return False
+
+
+"""
+New traceable, functional collectives.
+RFC: https://github.com/pytorch/pytorch/issues/93173
+
+  compiler: trace these ops with plain-old-data schemas, then choose how to lower them.
+  eager: execute these 'functional' ops which in eager return AsyncCollectiveTensor subclasses,
+         automatically calling .wait() on underlying/hidden async 'work' obj only when fed to
+         a downstream op.
+
+Issues:
+* Where should these ops live? Couldn't `import torch` if putting these ops in existing torch.distributed files
+* Proper support for eager requires inplace ops. We should explore having it as an option for the API.
+"""
+
+"""
+Functional collectives are asynchronous only and we perform implicit stream synchronization
+on behalf of the user.
+
+We use AsyncCollectiveTensor to wrap the result tensor of a collective and it lets us witness
+first usage of the tensor and insert cross stream sync at the right place.
+
+The above are the easy bits, the hard one is how we match the Work object returned by
+c10d and the tensor AsyncCollectiveTensor wraps. We alloc the tensor inside the collective
+op implementation (see ``clone()`` call in ``_all_reduce``) and then it's handled by the
+dispatcher which might call other implementations that are allowed to change the returned
+tensor - even return a tensor with a different shape (see ``torch.vmap``).
+
+This means the caller of our ops receives a Tensor that is not guaranteed to be the same
+allocated by our implementations and that makes pairing The AsyncTensor to the original
+tensor a lot harder. This pairing is needed so we can lookup the Work object to use.
+
+Originally, we tried WeakKeyDictionary to map from Tensor to Work, but because Tensor's
+identity is not stable across dispatch, the op caller would end up with a different Tensor
+instance that would not match any in the dictionary.
+
+With Tensor identity out of the question, we decided use the tensor data pointer, which
+should be stable across all the Tensor changes done during dispatch.
+
+We have a dictionary of tensor::data_ptr -> Work that we insert right after we call into c10d.
+
+We use this dictionary when AsyncCollectiveTensor is used to invoke Work::wait()
+
+Finally, we setup a finalizer against the tensor wrapper to observe it getting collected so we
+can clean up stale entries in the dictionary.
+
+To eliminate the possibility of races we have a global version counter that is used by the finalizer.
+
+As a wise man said once: Don't cross the streams (https://www.youtube.com/watch?v=wyKQe_i9yyo)
+
+"""
+
+"""
+Functional collectives can accept any of these types to describe the ranks participating in collectives.
+
+The different types will be desugared to a canonical format
+"""
+RANK_TYPES = Union[
+    List[int],
+    List[List[int]],
+    dist.ProcessGroup,
+    DeviceMesh,
+    Tuple["dist.tensor.DeviceMesh", int],
+    str,
+]
+
+
+"""
+User facing APIs for functional collectives
+-------------------------------------------
+
+These apis are called by user code and expected to work both in eager execution and compilation,
+but there are significant differences to how the two modes are implemented underneath.
+
+Eager execution is 'optimized' using a tensor subclass that schedules the synchronization (via wait_tensor() op)
+just before the tensor is first used.  Compiled tracing currently relies on the compiler to perform this optimization,
+and cannot yet correctly trace the AsyncTensor wrapper class.  In the future, these paths may be unified
+if sufficient subclass support is added in dynamo.
+
+Example: all_reduce is an entrypoint API, and other collectives follow a similar pattern.
+
+Here's how it works under torch.compile/dynamo:
+all_reduce(...)
+  |--> _expand_group(...)               - desugars processgroup into canonical/traceable format
+  |--> c10d_functional.all_reduce(...)  - dynamo captures this op call, doesn't trace deeper
+  |--> _maybe_wrap_tensor(...)          - wait_tensor() op is immediately called, no AsyncTensor subclass needed
+
+And under eager execution:
+all_reduce(...)
+  |--> _expand_group(...)               - same as above, but less critical for eager
+  |--> c10d_functional.all_reduce(...)  - dispatches to real kernel OR records op in trace
+  |--> _maybe_wrap_tensor(...)          - AsyncTensor wrapper applied to returned tensor,
+                                          which issues wait_tensor() at the time of first use
+"""
+
+
+def wait_tensor(tensor):
+    """
+    Wait on a tensor returned by the collectives ops.
+
+    Waiting follows device semantics, which means blocking on CPU and synchronizing streams on CUDA.
+    """
+    return torch.ops._c10d_functional.wait_tensor(tensor)  # type: ignore[attr-defined]
+
+
+def broadcast(self: torch.Tensor, src: int, group: RANK_TYPES, tag: str = ""):
+    """
+    Broadcasts the tensor to all processes in the given process group.
+
+    Args:
+        src (int): Source rank
+        group (ProcessGroup or List[int]): The process group to work on.
+        tag (str, optional): A unique identifier for the collective. Default: empty string
+    """
+    group_name = _resolve_group_name(group, tag)
+    tensor = torch.ops._c10d_functional.broadcast(self, src, group_name)
+    return _maybe_wrap_tensor(tensor)
+
+
+def all_reduce(self: torch.Tensor, reduceOp: str, group: RANK_TYPES, tag: str = ""):
+    """
+    Reduces the tensor data across all machines in such a way that all get
+    the final result.
+
+    The input tensor is left unmodified.
+
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    group_name = _resolve_group_name(group, tag)
+    tensor = torch.ops._c10d_functional.all_reduce(self, reduceOp.lower(), group_name)
+    return _maybe_wrap_tensor(tensor)
+
+
+def all_gather_tensor(
+    self: torch.Tensor,
+    gather_dim: int,
+    group: RANK_TYPES,
+    tag: str = "",
+):
+    """
+    Gather tensor data across from all machines and concatenate over ``gather_dim``.
+
+    Note that it currently only supports gather_dim = 0.
+
+    The input tensor is left unmodified.
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    assert self.is_contiguous()
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+    tensor = torch.ops._c10d_functional.all_gather_into_tensor(
+        self, group_size, group_name
+    )
+    res = _maybe_wrap_tensor(tensor)
+    # TODO this should be done inside AsyncCollectiveTensor to delay the wait() call
+    if gather_dim != 0:
+        # torch.cat access the data so we already need to wait here, first do wait
+        # and then chunk + cat avoid us going through ACT dispatching logic again
+        if isinstance(res, AsyncCollectiveTensor):
+            res = res.wait()  # type: ignore[attr-defined]
+        res = torch.cat(torch.chunk(res, group_size, dim=0), dim=gather_dim)
+    return res
+
+
+def all_gather_tensor_autograd(
+    self: torch.Tensor,
+    gather_dim: int,
+    group: RANK_TYPES,
+    tag: str = "",
+):
+    """
+    Gather tensor data across from all machines and concatenate over ``gather_dim``.
+
+    Note that it currently only supports gather_dim = 0.
+
+    This function is the same as all_gather_tensor but will propagate the
+    backwards gradient across workers.
+
+    See all_gather_tensor for more details on usage.
+    """
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+
+    tensor = torch.ops._c10d_functional_autograd.all_gather_into_tensor(
+        self, group_size, group_name
+    )
+    res = _FromTorchTensor.apply(tensor)
+    # TODO this should be done inside AsyncCollectiveTensor to delay the wait() call
+    if gather_dim != 0:
+        # torch.cat access the data so we already need to wait here, first do wait
+        # and then chunk + cat avoid us going through ACT dispatching logic again
+        if isinstance(res, AsyncCollectiveTensor):
+            res = res.wait()  # type: ignore[attr-defined]
+        res = torch.cat(torch.chunk(res, group_size, dim=0), dim=gather_dim)
+    return res
+
+
+def reduce_scatter_tensor(
+    self: torch.Tensor,
+    reduceOp: str,
+    scatter_dim: int,
+    group: RANK_TYPES,
+    tag: str = "",
+):
+    """
+    Reduces the tensor data across all machines in such a way that all get
+    the final result, then scatter the results to corresponding ranks.
+
+
+    The input tensor is left unmodified.
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+
+    assert (
+        self.size(scatter_dim) % group_size == 0
+    ), f"input dimension 0 ({self.size(0)} must be a multiple of group_size {group_size}"
+    if scatter_dim != 0:
+        tensor_list = torch.chunk(self, group_size, dim=scatter_dim)
+        self = torch.cat(tensor_list)
+
+    tensor = torch.ops._c10d_functional.reduce_scatter_tensor(
+        self,
+        reduceOp.lower(),
+        group_size,
+        group_name,  # type: ignore[possibly-undefined]
+    )
+    res = _maybe_wrap_tensor(tensor)
+    return res
+
+
+def reduce_scatter_tensor_autograd(
+    self: torch.Tensor,
+    reduceOp: str,
+    scatter_dim: int,
+    group: RANK_TYPES,
+    tag: str = "",
+):
+    """
+    Reduces the tensor data across all machines in such a way that all get
+    the final result, then scatter the results to corresponding ranks.
+
+    This function is the same as reduce_scatter_tensor but will propagate the
+    backwards gradient across workers.
+
+    Currently only the "sum" reduceOp is supported.
+
+    See reduce_scatter_tensor for more details on usage.
+    """
+
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+
+    assert (
+        self.size(scatter_dim) % group_size == 0
+    ), f"input dimension 0 ({self.size(0)} must be a multiple of group_size {group_size}"
+    if scatter_dim != 0:
+        tensor_list = torch.chunk(self, group_size, dim=scatter_dim)
+        self = torch.cat(tensor_list)
+
+    tensor = torch.ops._c10d_functional_autograd.reduce_scatter_tensor(
+        self,
+        reduceOp.lower(),
+        group_size,
+        group_name,  # type: ignore[possibly-undefined]
+    )
+    res = _FromTorchTensor.apply(tensor)
+    return res
+
+
+def all_reduce_coalesced(
+    self: List[torch.Tensor], reduceOp: str, group: RANK_TYPES, tag: str = ""
+) -> List[torch.Tensor]:
+    """
+    Reduces a list of tensors across all machines in such a way that all get
+    the final result.
+
+    The all tensors in the input list are left unmodified.
+
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    group_name = _resolve_group_name(group, tag)
+    tensor_list = torch.ops._c10d_functional.all_reduce_coalesced(  # type: ignore[attr-defined]
+        self,
+        reduceOp.lower(),
+        group_name,
+    )
+    return list(map(_maybe_wrap_tensor, tensor_list))
+
+
+def all_gather_into_tensor_coalesced(
+    self: List[torch.Tensor], group: RANK_TYPES, tag: str = ""
+) -> List[torch.Tensor]:
+    """
+    Gather a list of tensors across from all machines.
+
+    Note that it currently only supports gather_dim = 0.
+
+    The input tensor is left unmodified.
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+    tensor_list = torch.ops._c10d_functional.all_gather_into_tensor_coalesced(  # type: ignore[attr-defined]
+        self,
+        group_size,
+        group_name,
+    )
+    return list(map(_maybe_wrap_tensor, tensor_list))
+
+
+def reduce_scatter_tensor_coalesced(
+    inputs: List[torch.Tensor],
+    reduceOp: str,
+    scatter_dim: List[int],
+    group: RANK_TYPES,
+    tag: str = "",
+) -> List[torch.Tensor]:
+    """
+    Reduces a list of tensors across all machines in such a way that all get
+    the final result, then scatter the results to corresponding ranks.
+
+    The input tensors are left unmodified.
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+
+    assert len(scatter_dim) == len(inputs)
+    for idx, (dim, tensor) in enumerate(zip(scatter_dim, inputs)):
+        assert (
+            tensor.size(dim) % group_size == 0
+        ), f"input dimension {dim} ({tensor.size(dim)} must be a multiple of group_size {group_size} for tensor at index {idx}"
+        if dim != 0:
+            tensor_list = torch.chunk(tensor, group_size, dim=dim)
+            inputs[idx] = torch.cat(tensor_list)
+
+    tensor_list = torch.ops._c10d_functional.reduce_scatter_tensor_coalesced(  # type: ignore[attr-defined]
+        inputs,
+        reduceOp.lower(),
+        group_size,
+        group_name,  # type: ignore[possibly-undefined]
+    )
+
+    return list(map(_maybe_wrap_tensor, tensor_list))
+
+
+# This is a bit unsafe: it checks if the first argument in the schema reports as a non-mutable alias.
+# Today, this maps 1:1 with "aten ops that are views".
+def _is_view_op(tgt):
+    assert isinstance(tgt, torch._ops.OpOverload)
+    schema = tgt._schema
+    if len(schema.arguments) > 0:
+        first_arg = schema.arguments[0]
+        # check if op is a view
+        return first_arg.alias_info is not None and not first_arg.alias_info.is_write
+
+
+def all_to_all_single(
+    self: torch.Tensor,
+    output_split_sizes: Optional[List[int]],
+    input_split_sizes: Optional[List[int]],
+    group: RANK_TYPES,
+    tag: str = "",
+) -> torch.Tensor:
+    """
+    Each process splits input tensor and then scatters the split list
+    to all processes in a group. Then concatenate the received tensors from all
+    the processes in the group and return single output tensor.
+
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    if output_split_sizes is not None:
+        assert all(
+            isinstance(size, (int, torch.SymInt)) for size in output_split_sizes
+        ), output_split_sizes
+    if input_split_sizes is not None:
+        assert all(
+            isinstance(size, (int, torch.SymInt)) for size in input_split_sizes
+        ), input_split_sizes
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+    if output_split_sizes is None or input_split_sizes is None:
+        assert output_split_sizes is None and input_split_sizes is None, (
+            "output_split_sizes and input_split_sizes must either be "
+            "specified together or both set to None"
+        )
+        output_split_sizes = [self.shape[0] // group_size] * group_size
+        input_split_sizes = output_split_sizes
+    tensor = torch.ops._c10d_functional.all_to_all_single(  # type: ignore[attr-defined]
+        self,
+        output_split_sizes,
+        input_split_sizes,
+        group_name,
+    )
+    return _maybe_wrap_tensor(tensor)
+
+
+def all_to_all_single_autograd(
+    self: torch.Tensor,
+    output_split_sizes: Optional[List[int]],
+    input_split_sizes: Optional[List[int]],
+    group: RANK_TYPES,
+    tag: str = "",
+) -> torch.Tensor:
+    """
+    Same as all_to_all_single but supports autograd.
+    """
+    if output_split_sizes is not None:
+        assert all(
+            isinstance(size, (int, torch.SymInt)) for size in output_split_sizes
+        ), output_split_sizes
+    if input_split_sizes is not None:
+        assert all(
+            isinstance(size, (int, torch.SymInt)) for size in input_split_sizes
+        ), input_split_sizes
+
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+    if output_split_sizes is None or input_split_sizes is None:
+        assert output_split_sizes is None and input_split_sizes is None, (
+            "output_split_sizes and input_split_sizes must either be "
+            "specified together or both set to None"
+        )
+        output_split_sizes = [self.shape[0] // group_size] * group_size
+        input_split_sizes = output_split_sizes
+    tensor = torch.ops._c10d_functional_autograd.all_to_all_single(  # type: ignore[attr-defined]
+        self,
+        output_split_sizes,
+        input_split_sizes,
+        group_name,
+    )
+    return _FromTorchTensor.apply(tensor)
+
+
+def permute_tensor(
+    self: torch.Tensor,
+    src_dst: List[int],
+    group: RANK_TYPES,
+    tag: str = "",
+) -> torch.Tensor:
+    """
+    Permutes the elements of the tensor according to the given source/destination pairs. `src_dst` should
+    be defined such that src_dst[m] == n means m sends to n.
+
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one
+    """
+    t, rankset, group_size = _expand_group(group, tag)
+    local_pg = c10d._find_or_create_pg_by_ranks_and_tag(t, rankset, group_size)
+
+    output_split_sizes = [0] * group_size
+    input_split_sizes = [0] * group_size
+    for src, dst in enumerate(src_dst):
+        if src == dist.get_rank(local_pg):
+            input_split_sizes[dst] = self.numel()
+        if dst == dist.get_rank(local_pg):
+            output_split_sizes[src] = self.numel()
+
+    return all_to_all_single(self, output_split_sizes, input_split_sizes, group, tag)
+
+
+class AsyncCollectiveTensor(torch.Tensor):
+    r"""
+    A Tensor wrapper subclass that is used to trigger a call to wait
+    prior to first use of the underlying tensor.
+    Use it inside functional collective pytorch wrappers like the following:
+    def functional_collective(self, group, tag):
+        tag, rankset, group_size = _expand_group(group, tag)
+        tensor = torch.ops.c10d_functional.{collective}(self, tag, rankset, group_size)
+        return _maybe_wrap_tensor(tensor)
+    """
+    elem: torch.Tensor
+    completed: bool
+
+    __slots__ = ["elem", "completed"]
+
+    @staticmethod
+    def __new__(cls, elem: torch.Tensor):
+        r = torch.Tensor._make_wrapper_subclass(  # type: ignore[attr-defined]
+            cls,
+            elem.size(),
+            strides=elem.stride(),
+            storage_offset=elem.storage_offset(),
+            dtype=elem.dtype,
+            layout=elem.layout,
+            device=elem.device,
+            requires_grad=elem.requires_grad,
+        )
+        r.elem = elem
+        r.completed = False
+        return r
+
+    def __tensor_flatten__(self):
+        return ["elem"], None
+
+    def tolist(self):
+        return self.trigger_wait().tolist()
+
+    @staticmethod
+    def __tensor_unflatten__(inner_tensors, meta, outer_size, outer_stride):
+        assert meta is None
+        elem = inner_tensors["elem"]
+        return AsyncCollectiveTensor(elem)
+
+    def __repr__(self):
+        return f"AsyncCollectiveTensor({self.trigger_wait()})"
+
+    def trigger_wait(self):
+        if not self.completed:
+            out = wait_tensor(self.elem)
+            self.completed = True
+            return out
+        else:
+            return self.elem
+
+    def wait(self) -> torch.Tensor:
+        return wait_tensor(self.elem)
+
+    def _get_acs_underlying_tensor(self):
+        """This method enables  _functional_collectives_impl to test if a tensor is an ACS"""
+        return self.elem
+
+    @classmethod
+    def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
+        if func == torch.ops.aten.view.default:
+            # Fast handle aten.view as a lot of view related op goes to aten.view
+            # eventually, this avoids pytree slowdown
+            res = func(args[0].elem, args[1])
+            wrapper_res = AsyncCollectiveTensor(res)
+            return wrapper_res
+
+        is_view_op = _is_view_op(func)
+
+        def unwrap(e: AsyncCollectiveTensor):
+            # wait_tensor is idepotent and will do stream sync only once
+            if not is_view_op:
+                return e.trigger_wait()
+            return e.elem
+
+        def wrap(e: torch.Tensor):
+            # wait_tensor is idepotent and will do stream sync only once
+            assert not isinstance(e, AsyncCollectiveTensor)
+            res = AsyncCollectiveTensor(e)
+            return res
+
+        unwrapped_args = tree_map_only(AsyncCollectiveTensor, unwrap, args)
+        unwrapped_kwargs = tree_map_only(AsyncCollectiveTensor, unwrap, kwargs)
+
+        # we don't wrap the result as it doesn't need to be waited on.
+        out = func(*unwrapped_args, **unwrapped_kwargs)
+
+        # View ops dont require a sync, so we should re-wrap the outputs.
+        if is_view_op:
+            out = tree_map_only(torch.Tensor, wrap, out)
+
+        return out
+
+    def numpy(self):
+        return self.wait().numpy()
+
+
+"""
+Utils and infrastructure for tracing support
+"""
+
+
+def _expand_group(group: RANK_TYPES, tag: str = "") -> Tuple[str, List[int], int]:
+    """
+    _expand_group desugars the different RANK_TYPES types into a canonical format that is traceable.
+
+    By having this be part of the explicit eager codepath, we avoid having to specialize behavior inside
+    torchdynamo and can still interoperate with processgroup objects or other untraceable forms.
+    """
+    # had to define this hack _inside_ expand_group to avoid
+    # graph_break [('torch.* op returned non-Tensor int
+    # caused by 'cast_*` functions being treated as 'torch.*' ops (iiuc)
+    if TYPE_CHECKING:
+
+        def cast_listlistint(x):
+            return cast(List[List[int]], x)
+
+        def cast_listint(x):
+            return cast(List[int], x)
+
+    else:
+        # fake cast op for use at runtime since dynamo doesn't support real cast
+        # also, dynamo didn't like encountering 'typing' objects ()
+        # NotImplementedError: argument of type: 
+        def cast_listlistint(x):
+            return x
+
+        def cast_listint(x):
+            return x
+
+    rankset: List[int]
+    if isinstance(group, list):
+        if isinstance(group[0], list):
+            nested_list = cast_listlistint(group)
+            rankset = []
+            group_size = -1
+            for rs in nested_list:
+                rankset.extend(rs)
+                if group_size != -1 and group_size != len(rs):
+                    raise ValueError(
+                        f"group sizes must be identical found {group_size} and {len(rs)}"
+                    )
+                group_size = len(rs)
+        else:
+            rankset = cast_listint(group)
+            group_size = len(rankset)
+    elif isinstance(group, dist.ProcessGroup):
+        rankset = dist.get_process_group_ranks(group)
+        group_size = len(rankset)
+        tag = tag or c10d._get_group_tag(group)
+    elif isinstance(group, DeviceMesh):
+        assert (
+            group.ndim == 1
+        ), "Only 1D mesh is supported, pass in (DeviceMesh, int) together if mesh > 1D"
+        # TODO: it should run collective in the whole mesh instead of dim 0
+        tag, rankset, _ = group._dim_group_infos[0]
+        group_size = len(rankset)
+    elif isinstance(group, tuple):
+        if (
+            len(group) == 2
+            and isinstance(group[0], DeviceMesh)
+            and isinstance(group[1], int)
+        ):
+            dmesh = group[0]
+            dim = group[1]
+            tag, rankset, _ = dmesh._dim_group_infos[dim]
+            group_size = len(rankset)
+        else:
+            raise ValueError("Invalid tuple for group must be (DeviceMesh, int)")
+    else:
+        raise ValueError(
+            "Invalid type for group, must be one of List, Processgroup, DeviceMesh or (DeviceMesh, int)."
+        )
+
+    return (tag, rankset, group_size)
+
+
+def _resolve_group_name(group: RANK_TYPES, tag: str = "") -> str:
+    """
+    Given group in RANK_TYPES, return the group name.
+    """
+    # `tag` will be deprecated. See details in:
+    # https://github.com/pytorch/pytorch/issues/93173#issuecomment-1907095208
+    if isinstance(group, dist.ProcessGroup):
+        return group.group_name
+    elif isinstance(group, str):
+        return group
+    elif isinstance(group, DeviceMesh):
+        assert (
+            group.ndim == 1
+        ), "Only 1D mesh is supported, pass in (DeviceMesh, int) together if mesh > 1D"
+        return group._dim_group_infos[0][2]
+    elif isinstance(group, tuple):
+        if (
+            len(group) == 2
+            and isinstance(group[0], DeviceMesh)
+            and isinstance(group[1], int)
+        ):
+            dmesh = group[0]
+            dim = group[1]
+            return dmesh._dim_group_infos[dim][2]
+        else:
+            raise ValueError("Invalid tuple for group must be (DeviceMesh, int)")
+    elif isinstance(group, list):
+        if not is_torchdynamo_compiling():
+            warnings.warn(
+                "The combination of ranks + tag as process group "
+                "identifier has been deprecated. Please switch to "
+                "using ProcessGroup, DeviceMesh, or group name instead.",
+                FutureWarning,
+                stacklevel=3,
+            )
+        return c10d._resolve_group_name_by_ranks_and_tag(cast(List[int], group), tag)
+    else:
+        raise ValueError(f"Unsupported group type: {type(group)}, {group}")
+
+
+class _FromTorchTensor(torch.autograd.Function):
+    """
+    _FromTorchTensor allows autograd to propagate from a normal Tensor to an
+    AsyncCollectiveTensor.
+    """
+
+    @staticmethod
+    def forward(  # type: ignore[override]
+        ctx,  # pyre-ignore[2]: Parameter must be annotated.
+        input: torch.Tensor,
+    ) -> torch.Tensor:
+        return _maybe_wrap_tensor(input)
+
+    @staticmethod
+    def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor:  # type: ignore[override]
+        return grad_output
+
+
+def _are_we_tracing() -> bool:
+    if is_torchdynamo_compiling():
+        return True
+    # If functionalization is turned on, we are almost definitely compiling/tracing.
+    # (In particular, AOTAutograd traces a model once with functionalization on
+    #  but proxy tracing turned of, so this is how we detect it).
+    if (
+        torch._C._get_dispatch_mode(torch._C._TorchDispatchModeKey.FUNCTIONAL)
+        is not None
+    ):
+        return True
+    return get_proxy_mode() is not None
+
+
+def _maybe_wrap_tensor(self) -> torch.Tensor:
+    if _are_we_tracing():
+        return wait_tensor(self)
+    res = AsyncCollectiveTensor(self)
+    return cast(torch.Tensor, res)
+
+
+def _all_gather_into_tensor_coalesced_meta(self, tag, rankset, group_size):
+    def mk_out_tensor(shard):
+        out_size = list(shard.size())
+        out_size[0] *= group_size
+        out_tensor = shard.new_empty(out_size)
+        return out_tensor
+
+    return [mk_out_tensor(t) for t in self]
+
+
+# We now register meta kernels to deal with tracing
+def _broadcast_meta(self, *args):
+    return torch.empty_like(self)
+
+
+def _all_reduce_meta(self, *args):
+    return torch.empty_like(self)
+
+
+def _wait_tensor_meta(self, *args):
+    return torch.empty_like(self)
+
+
+def _all_gather_into_tensor_meta(shard, tag, rankset, group_size):
+    out_size = list(shard.size())
+    out_size[0] *= group_size
+    return shard.new_empty(out_size)
+
+
+def _reduce_scatter_tensor_meta(input, reduce_op, tag, rankset, group_size):
+    out_size = list(input.size())
+    out_size[0] //= group_size
+    return input.new_empty(out_size)
+
+
+def _all_reduce_coalesced_meta(self, *args):
+    return [torch.empty_like(t) for t in self]
+
+
+def _all_reduce__meta(inp, *args):
+    return inp
+
+
+def _broadcast__meta(inp, *args):
+    return inp
+
+
+def _all_reduce_coalesced__meta(inputs, *args):
+    return inputs
+
+
+def _reduce_scatter_tensor_coalesced_meta(inputs, reduceOp, tag, rankset, group_size):
+    def mk_out_tensor(input):
+        out_size = list(input.size())
+        out_size[0] //= group_size
+        out_tensor = input.new_empty(out_size)
+        return out_tensor
+
+    return [mk_out_tensor(t) for t in inputs]
+
+
+# NB: We often say all_to_all has dynamic output size, but this is not
+# technically true: instead, what typically happens is you manually
+# communicate the output_split_sizes ahead of time (which is dynamic),
+# but then you pass those sizes explicitly, and the all to all itself
+# isn't dynamic, it just follows the specified output splits
+def _all_to_all_single_meta(
+    input, output_split_sizes, input_split_sizes, *args, **kwargs
+):
+    if output_split_sizes is None:
+        return input.new_empty(input.size())
+    else:
+        for s in output_split_sizes:
+            torch._check_is_size(s)
+        out_size = list(input.size())
+        out_size[0] = sum(output_split_sizes)
+        return input.new_empty(out_size)
+
+
+def _all_gather_into_tensor_out_native_meta(input, group_size, group_name, *, out):
+    shape = list(input.size())
+    shape[0] *= group_size
+    return input.new_empty(shape)
+
+
+def _all_gather_into_tensor_native_meta(input, group_size, group_name):
+    shape = list(input.size())
+    shape[0] *= group_size
+    return input.new_empty(shape)
+
+
+def _all_gather_into_tensor_coalesced_native_meta(inputs, group_size, group_name):
+    return [
+        _all_gather_into_tensor_native_meta(input, group_size, group_name)
+        for input in inputs
+    ]
+
+
+def _reduce_scatter_tensor_native_meta(inp, reduce_op, group_size, group_name):
+    shape = list(inp.size())
+    shape[0] //= group_size
+    return inp.new_empty(shape)
+
+
+def _reduce_scatter_tensor_coalesced_native_meta(
+    inputs, reduce_op, group_size, group_name
+):
+    return [
+        _reduce_scatter_tensor_native_meta(inp, reduce_op, group_size, group_name)
+        for inp in inputs
+    ]
+
+
+if not torch._running_with_deploy():
+    # Library MUST be defined at module scope or it doesn't work
+    # Creating a "DEF" Library always crashes torch::deploy so we create our
+    # Library instances here guarded against running inside it
+    lib_impl = torch.library.Library("_c10d_functional", "IMPL")
+    lib_impl.impl("all_reduce", _all_reduce_meta, "Meta")
+    lib_impl.impl("all_reduce_", _all_reduce__meta, "Meta")
+    lib_impl.impl("all_reduce_coalesced", _all_reduce_coalesced_meta, "Meta")
+    lib_impl.impl("all_reduce_coalesced_", _all_reduce_coalesced__meta, "Meta")
+    lib_impl.impl("wait_tensor", _wait_tensor_meta, "Meta")
+    lib_impl.impl(
+        "all_gather_into_tensor_out", _all_gather_into_tensor_out_native_meta, "Meta"
+    )
+    lib_impl.impl("all_gather_into_tensor", _all_gather_into_tensor_native_meta, "Meta")
+    lib_impl.impl(
+        "all_gather_into_tensor_coalesced",
+        _all_gather_into_tensor_coalesced_native_meta,
+        "Meta",
+    )
+    lib_impl.impl("reduce_scatter_tensor", _reduce_scatter_tensor_native_meta, "Meta")
+    lib_impl.impl(
+        "reduce_scatter_tensor_coalesced",
+        _reduce_scatter_tensor_coalesced_native_meta,
+        "Meta",
+    )
+    lib_impl.impl("all_to_all_single", _all_to_all_single_meta, "Meta")
+    lib_impl.impl("broadcast", _broadcast_meta, "Meta")
+    lib_impl.impl("broadcast_", _broadcast__meta, "Meta")
+
+    # mark these ops has side effect so that they won't be removed by DCE
+    torch.fx.node.has_side_effect(torch.ops._c10d_functional.wait_tensor.default)
+    torch.fx.node.has_side_effect(torch.ops._c10d_functional.wait_tensor)
+
+    # Register legacy ops for backward compatibility
+    # TODO(yifu): remove these in functional collective beta release
+    legacy_lib = torch.library.Library("c10d_functional", "DEF")
+    legacy_lib_impl = torch.library.Library("c10d_functional", "IMPL")
+    ops_defs = [
+        "broadcast(Tensor self, int src, str tag, int[] ranks, int group_size) -> Tensor",
+        "all_reduce(Tensor self, str reduceOp, str tag, int[] ranks, int group_size) -> Tensor",
+        "all_reduce_coalesced(Tensor[] self, str reduceOp, str tag, int[] ranks, int group_size) -> Tensor[]",
+        "wait_tensor(Tensor self) -> Tensor",
+        "all_gather_into_tensor(Tensor shard, str tag, int[] ranks, int group_size) -> Tensor",
+        "all_gather_into_tensor_coalesced(Tensor[] input, str tag, int[] ranks, int group_size) -> Tensor[]",
+        "reduce_scatter_tensor(Tensor input, str reduceOp, str tag, int[] ranks, int group_size) -> Tensor",
+        "reduce_scatter_tensor_coalesced(Tensor[] inputs, str reduceOp, str tag, int[] ranks, int group_size) -> Tensor[]",
+        "all_to_all_single(Tensor input, SymInt[]? output_split_sizes, SymInt[]? input_split_sizes, str tag, int[] ranks, int group_size) -> Tensor",  # noqa: B950
+    ]
+
+    my_module = sys.modules[__name__]
+    for op_def in ops_defs:
+        op_name = op_def[0 : op_def.index("(")]
+        backend_impl = getattr(fun_col_impl, f"_{op_name}")
+        legacy_lib.define(op_def, tags=torch.Tag.pt2_compliant_tag)
+        legacy_lib_impl.impl(op_name, backend_impl, "CompositeImplicitAutograd")
+
+else:
+    warnings.warn(
+        "PyTorch Distributed functional collectives do not work with torch::deploy."
+    )
+
+
+"""
+Dynamo Remappings allow seamless translation from non-functional collectives of supportable form into
+functional collective calls followed by inplace copy ops, allowing them to be traced into a functional graph.
+
+We implement this by writing a decomposition and teaching dynamo how to associate it to a corresponding op via
+the mapping dict below.
+
+These schemas intentionally match torch.distributed.distributed_c10d.* ops that we are trying to remap from
+"""
+
+
+def all_gather_tensor_inplace(
+    output_tensor: torch.Tensor,
+    input_tensor: torch.Tensor,
+    group,  # TODO add a type,
+    async_op: bool = False,
+    tag: str = "",
+    gather_dim: int = 0,
+):
+    assert (
+        not async_op
+    ), "Can't remap async version of inplace op to functional collective"
+
+    group = group or dist.group.WORLD
+    assert group is not None
+
+    return output_tensor.copy_(all_gather_tensor(input_tensor, gather_dim, group, tag))
+
+
+def reduce_scatter_tensor_inplace(
+    output: torch.Tensor,
+    input: torch.Tensor,
+    op: str = "sum",  # TODO type is actually c10d ReduceOp. is this ok?
+    group=None,  # TODO add a type
+    async_op: bool = False,
+    scatter_dim: int = 0,
+    tag: str = "",
+):
+    assert (
+        not async_op
+    ), "Can't remap async version of inplace op to functional collective"
+
+    group = group or dist.group.WORLD
+    assert group is not None
+
+    return output.copy_(reduce_scatter_tensor(input, op, scatter_dim, group, tag))
+
+
+REDUCE_OP_TO_STR = {
+    dist.ReduceOp.SUM: "sum",
+    dist.ReduceOp.AVG: "avg",
+    dist.ReduceOp.PRODUCT: "product",
+    dist.ReduceOp.MIN: "min",
+    dist.ReduceOp.MAX: "max",
+    dist.ReduceOp.BAND: "band",
+    dist.ReduceOp.BOR: "bor",
+    dist.ReduceOp.BXOR: "bxor",
+}
+
+
+def all_reduce_inplace(
+    tensor: torch.Tensor,
+    op: str = "sum",
+    group=None,
+    async_op: bool = False,
+    tag: str = "",
+):
+    assert (
+        not async_op
+    ), "Can't remap async version of inplace op to functional collective"
+
+    group = group or dist.group.WORLD
+    assert group is not None
+
+    return tensor.copy_(all_reduce(tensor, op, group, tag))
+
+
+def all_to_all_inplace(
+    output: torch.Tensor,
+    input: torch.Tensor,
+    output_split_sizes=None,
+    input_split_sizes=None,
+    group=None,
+    async_op=False,
+    tag: str = "",
+):
+    assert (
+        not async_op
+    ), "Can't remap async version of inplace op to functional collective"
+
+    group = group or dist.group.WORLD
+    assert group is not None
+
+    return output.copy_(
+        all_to_all_single(
+            input,
+            output_split_sizes,
+            input_split_sizes,
+            group,
+            tag,
+        )
+    )
+
+
+def all_gather_inplace(
+    tensor_list: List[torch.Tensor],
+    tensor: torch.Tensor,
+    group=None,
+    async_op=False,
+    tag: str = "",
+):
+    assert (
+        not async_op
+    ), "Can't remap async version of inplace op to functional collective"
+    assert all(
+        t.size(0) == tensor.size(0) for t in tensor_list
+    ), "Remapping variable size all_gather is not yet supported"
+
+    group = group or dist.group.WORLD
+    assert group is not None
+
+    output = all_gather_tensor(tensor, 0, group, tag)
+
+    # Use aten.slice instead of aten.split because the latter causes
+    # tensor.shape(0) to be unnecessarily baked in when it's a SymInt.
+    output_splits = []
+    offset = 0
+    for t in tensor_list:
+        output_splits.append(output[offset : offset + t.size(0)])
+        offset += t.size(0)
+    for dst, src in zip(tensor_list, output_splits):
+        dst.copy_(src)
+    return tensor_list
+
+
+from torch.distributed.distributed_c10d import (
+    _all_gather_base as legacy_all_gather_base,
+    _reduce_scatter_base as legacy_reduce_scatter_base,
+    all_gather as legacy_all_gather,
+    all_gather_into_tensor as legacy_allgather,
+    all_reduce as legacy_allreduce,
+    all_to_all_single as legacy_all_to_all_single,
+    reduce_scatter_tensor as legacy_reducescatter,
+)
+
+
+# This dict should contain sets of functions that dynamo is allowed to remap.
+# Functions in this set should accept the same args/kwargs 1:1 as their mapping.
+traceable_collective_remaps = {
+    legacy_allgather: all_gather_tensor_inplace,
+    legacy_reducescatter: reduce_scatter_tensor_inplace,
+    legacy_allreduce: all_reduce_inplace,
+    legacy_all_to_all_single: all_to_all_inplace,
+    legacy_all_gather: all_gather_inplace,
+    legacy_reduce_scatter_base: reduce_scatter_tensor_inplace,
+    legacy_all_gather_base: all_gather_tensor_inplace,
+}
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/_functional_collectives_impl.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/_functional_collectives_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bd193d662bd6de80e87707455d0d1916410bf84
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/_functional_collectives_impl.py
@@ -0,0 +1,117 @@
+# mypy: allow-untyped-defs
+from typing import List, Optional
+
+import torch
+import torch.distributed.distributed_c10d as c10d
+
+
+"""
+This file contains the op impls for the legacy (c10d_functional) functional collectives.
+These impls simply call into the native (_c10d_functional) functional collectives.
+"""
+
+
+def _broadcast(input, src, tag, ranks, group_size):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.broadcast(
+        input,
+        src,
+        group_name,
+    )
+
+
+def _all_reduce(input, reduce_op, tag, ranks, group_size):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.all_reduce(
+        input,
+        reduce_op,
+        group_name,
+    )
+
+
+def _all_reduce_coalesced(inputs, reduce_op, tag, ranks, group_size):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.all_reduce_coalesced(
+        inputs,
+        reduce_op,
+        group_name,
+    )
+
+
+def _all_gather_into_tensor(input, tag, ranks, group_size):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.all_gather_into_tensor(
+        input,
+        group_size,
+        group_name,
+    )
+
+
+def _all_gather_into_tensor_coalesced(input, tag, ranks, group_size):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.all_gather_into_tensor_coalesced(
+        input,
+        group_size,
+        group_name,
+    )
+
+
+def _reduce_scatter_tensor(
+    input: torch.Tensor,
+    reduce_op: str,
+    tag: str,
+    ranks: List[int],
+    group_size: int,
+):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.reduce_scatter_tensor(
+        input,
+        reduce_op,
+        group_size,
+        group_name,
+    )
+
+
+def _reduce_scatter_tensor_coalesced(
+    inputs: List[torch.Tensor],
+    reduce_op: str,
+    tag: str,
+    ranks: List[int],
+    group_size: int,
+):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.reduce_scatter_tensor_coalesced(
+        inputs,
+        reduce_op,
+        group_size,
+        group_name,
+    )
+
+
+def _all_to_all_single(
+    input: torch.Tensor,
+    output_split_sizes: Optional[List[int]],
+    input_split_sizes: Optional[List[int]],
+    tag: str,
+    ranks: List[int],
+    group_size: int,
+):
+    if output_split_sizes is None or input_split_sizes is None:
+        assert output_split_sizes is None and input_split_sizes is None, (
+            "output_split_sizes and input_split_sizes must either be "
+            "specified together or both set to None"
+        )
+        output_split_sizes = [input.shape[0] // group_size] * group_size
+        input_split_sizes = output_split_sizes
+
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.all_to_all_single(
+        input,
+        output_split_sizes,
+        input_split_sizes,
+        group_name,
+    )
+
+
+def _wait_tensor(tensor: torch.Tensor) -> torch.Tensor:
+    return torch.ops._c10d_functional.wait_tensor(tensor)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/_state_dict_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/_state_dict_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c3a021c0a318796b89cb7021f95b8371762e847
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/_state_dict_utils.py
@@ -0,0 +1,753 @@
+# mypy: allow-untyped-defs
+import copy
+import io
+import math
+import weakref
+from typing import (
+    Any,
+    Callable,
+    cast,
+    Dict,
+    List,
+    Mapping,
+    MutableMapping,
+    NamedTuple,
+    Optional,
+    Tuple,
+    TYPE_CHECKING,
+    Union,
+)
+
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch.distributed._functional_collectives import AsyncCollectiveTensor
+
+
+if dist.is_available() or TYPE_CHECKING:
+    from torch.distributed import distributed_c10d
+    from torch.distributed._shard.sharded_tensor import ShardedTensor
+    from torch.distributed.tensor import distribute_tensor, DTensor, Replicate
+    from torch.distributed.tensor._utils import compute_local_shape_and_global_offset
+
+
+def _identity_func(
+    obj: torch.Tensor,
+    pg: Optional[dist.ProcessGroup],
+    device: Optional[torch.device],
+    companion_obj: Any,
+) -> torch.Tensor:
+    return obj
+
+
+def _all_gather_sharded_tensor(
+    sharded_tensor: "ShardedTensor",
+    pg: Optional[dist.ProcessGroup] = None,
+    device: Optional[torch.device] = None,
+) -> torch.Tensor:
+    if pg is None:
+        pg = distributed_c10d._get_default_group()
+    world_size = dist.get_world_size(pg)
+    shards = sharded_tensor.local_shards()
+    dim_0_size = sharded_tensor.size()[0]  # type: ignore[index]
+    tensor_numel = sharded_tensor.size().numel()  # type: ignore[union-attr]
+    chunk_size = math.ceil(dim_0_size / world_size) * tensor_numel // dim_0_size
+    pg_device = (
+        distributed_c10d._get_pg_default_device(pg) if device is None else device
+    )
+    if shards:
+        local_tensor = shards[0].tensor.flatten()
+        if local_tensor.device.type != pg_device.type:
+            local_tensor = local_tensor.to(pg_device)
+        num_padding = chunk_size - local_tensor.numel()
+        if num_padding > 0:
+            local_tensor = F.pad(local_tensor, [0, num_padding])
+    else:
+        local_tensor = torch.zeros(
+            chunk_size, dtype=sharded_tensor.dtype, device=pg_device
+        )
+
+    tensor = torch.empty(
+        chunk_size * world_size,
+        dtype=local_tensor.dtype,
+        device=pg_device,
+    )
+    dist.all_gather_into_tensor(tensor, local_tensor, group=pg)
+
+    tensor = tensor.narrow(0, 0, tensor_numel).reshape(sharded_tensor.size())
+    return tensor
+
+
+class CompanionMismatch(Exception):
+    ...
+
+
+def _iterate_state_dict(
+    iter_object: Any,
+    sharded_tensor_func: Callable,
+    dtensor_func: Callable,
+    tensor_func: Callable,
+    *,
+    pg: Optional[dist.ProcessGroup] = None,
+    device: Optional[torch.device] = None,
+    cpu_offload: bool = False,
+    companion_obj: Any = None,
+    ranks_only: Tuple[int, ...] = (),
+    type_check: bool = True,
+    non_blocking: bool = True,
+) -> Dict[str, Any]:
+    """Iterate through the state dict, applying the given functions to each tensor type.
+
+    Args:
+        iter_object (Any): the target state_dict.
+        sharded_tensor_func (Callable): the function to apply to ShardedTensor
+        dtensor_func (Callable): the function to apply to DTensor
+        tensor_func (Callable): the function to apply to Tensor
+        pg (Optional[dist.ProcessGroup]): process group passed to tensor functions
+        device (Optional[torch.device]): device passed to tensor functions
+        cpu_offload (bool): whether to offload the tensors to CPU memory. This option is ignored
+            if a companion_obj is supplied.
+        companion_obj (Any): A companion object to the state dict. If this object
+            is supplied, we attempt to copy the tensor to the companion object.
+        ranks_only (Tuple[int, ...]): if this tuple is empty, all ranks will
+            have the same state_dicts. Otherwise only ranks that in ``ranks_only``
+            have the same state_dicts. Other ranks will get empty state_dicts.
+        type_check (bool): check if the instance data type is a supported type
+            that can be saved by DCP.  The current supported data types are
+            torch.Tensor, DTensor, int, float, str, list, dict, None.
+        non_blocking (bool): whether to use non-blocking copy when copying to the companion object.
+    """
+    # TODO: should we use pytree?
+    cpu_device = torch.device("cpu")
+    if isinstance(iter_object, ShardedTensor):
+        ret = sharded_tensor_func(iter_object, pg, device, companion_obj)
+    elif isinstance(iter_object, DTensor):
+        ret = dtensor_func(iter_object, pg, device, companion_obj)
+    elif isinstance(iter_object, torch.Tensor):
+        ret = tensor_func(iter_object, pg, device, companion_obj)
+    elif (
+        isinstance(iter_object, (int, float, str, bytes, io.BytesIO))
+        or iter_object is None
+    ):
+        ret = iter_object
+    elif isinstance(iter_object, dict):
+        if companion_obj is not None and (
+            not isinstance(companion_obj, dict)
+            or set(companion_obj.keys()) != set(iter_object.keys())
+        ):
+            msg = (
+                ""
+                if isinstance(companion_obj, dict)
+                else f"{set(companion_obj.keys())=} {set(iter_object.keys())=}"
+            )
+            raise CompanionMismatch(msg)
+
+        ret = {
+            key: _iterate_state_dict(
+                value,
+                sharded_tensor_func,
+                dtensor_func,
+                tensor_func,
+                pg=pg,
+                device=device,
+                cpu_offload=cpu_offload,
+                companion_obj=companion_obj[key] if companion_obj is not None else None,
+                ranks_only=ranks_only,
+                type_check=type_check,
+                non_blocking=non_blocking,
+            )
+            for key, value in iter_object.items()
+        }
+    elif isinstance(iter_object, (list, tuple)):
+        if companion_obj is not None and (
+            not isinstance(companion_obj, (list, tuple))
+            or len(companion_obj) != len(iter_object)
+        ):
+            raise CompanionMismatch
+
+        ret = [
+            _iterate_state_dict(
+                v,
+                sharded_tensor_func,
+                dtensor_func,
+                tensor_func,
+                pg=pg,
+                device=device,
+                cpu_offload=cpu_offload,
+                companion_obj=companion_obj[idx] if companion_obj is not None else None,
+                ranks_only=ranks_only,
+                type_check=type_check,
+                non_blocking=non_blocking,
+            )
+            for idx, v in enumerate(iter_object)
+        ]
+        if isinstance(iter_object, tuple):
+            ret = tuple(ret)
+    elif not type_check:
+        ret = copy.deepcopy(iter_object)
+    else:
+        raise ValueError(f"Unexpected value type {type(iter_object)}")
+
+    if not ranks_only or dist.get_rank(pg) in ranks_only:
+        if isinstance(ret, torch.Tensor):
+            if cpu_offload and companion_obj is None:
+                ret = ret.to(cpu_device)
+
+            if companion_obj is not None:
+                # TODO: support DTensor
+                companion_obj.copy_(ret, non_blocking=non_blocking)
+                ret = companion_obj
+    else:
+        ret = {} if isinstance(ret, dict) else None
+
+    return ret
+
+
+def _gather_state_dict(
+    state_dict: Dict[str, Any],
+    *,
+    pg: Optional[dist.ProcessGroup] = None,
+    device: Optional[torch.device] = None,
+    cpu_offload: bool = False,
+    ranks_only: Tuple[int, ...] = (),
+    type_check: bool = True,
+) -> Dict[str, Any]:
+    """
+    Given a state_dict, this API gathers all the ShardedTensors or DTensors in
+    the state_dict.
+
+
+    Args:
+        state_dict (Dict[str, Any]): the target sharded state_dict.
+        pg (Optional[dist.ProcessGroup]): the process group that is used to
+            gather ShardedTensor. Note that gathering a DTensor will use
+            the DeviceMesh. So this argument will be ignored when gathering a
+            DTensor.
+        device: (Optional[torch.device]): the device that is used to
+            perform allgather for ShardedTensor. Note that gathering a DTensor
+            will use the DeviceMesh. So this argument will be ignored when
+            gathering a DTensor.
+        cpu_offload (bool): whether to offload the tensors to CPU memory. The
+            default value is False.
+        ranks_only: (Tuple[int, ...]): if this tuple is empty, all ranks will
+            have the same state_dicts. Otherwise only ranks that in ``ranks_only``
+            have the same state_dicts. Other ranks will get empty state_dicts.
+        type_check: (bool): check if the instance data type is a supported type
+            that can be saved by DCP.  The current supported data types are
+            torch.Tensor, DTensor, int, float, str, list, dict, None.
+
+    Returns:
+        The gathered state dictionary.
+    """
+
+    def sharded_tensor_func(value, pg, device, companion_obj):
+        # ShardedTensor does not seem to record the original device type.
+        # So if the tensor is moved to CPU, we won't know the original type.
+        # As a result, we have to rely on the user to tell us the correct one.
+        cpu_device = torch.device("cpu")
+        output_tensor = _all_gather_sharded_tensor(value, pg, device)
+        local_shard_device = (
+            value.local_shards()[0].tensor.device
+            if value.local_shards()
+            else cpu_device
+        )
+        if output_tensor.device != local_shard_device:
+            value = output_tensor.to(local_shard_device)
+        else:
+            value = output_tensor
+        return value
+
+    def dtensor_func(value, pg, device, companion_obj):
+        if value.device != value.device_mesh.device_type:
+            value = value.to(value.device_mesh.device_type)
+        # FSDP all_gather: [Shard(0)] -> [Replicate()]
+        # HSDP all_gather: [Replicate(), Shard(0)] -> [Replicate(), Replicate()]
+        # 2D FSDP + TP all_gather:
+        # - [Shard(0), Shard(n)] -> [Replicate(), Replicate()]
+        # - [Shard(0), Replicate()] -> [Replicate(), Replicate()]
+        placements = [Replicate() for _ in value.placements]
+        value = value.redistribute(
+            device_mesh=value.device_mesh,
+            placements=placements,
+        )
+        # Call `wait()` to force the tensor to be synchronous with respect
+        # to the main stream.
+        # See the discussion in https://github.com/pytorch/pytorch/pull/117799.
+        value = value.to_local()
+        if isinstance(value, AsyncCollectiveTensor):
+            value = value.wait()
+        return value
+
+    return _iterate_state_dict(
+        state_dict,
+        sharded_tensor_func,
+        dtensor_func,
+        _identity_func,
+        pg=pg,
+        device=device,
+        cpu_offload=cpu_offload,
+        ranks_only=ranks_only,
+        type_check=type_check,
+    )
+
+
+def _offload_state_dict_to_cpu(
+    state_dict: Dict[str, Any],
+    *,
+    ranks_only: Tuple[int, ...] = (),
+    type_check: bool = True,
+) -> Dict[str, Any]:
+    """
+    Given a state_dict, this API offload all the tensors to CPU memory.
+
+    Args:
+        state_dict (Dict[str, Any]): the target state_dict.
+        pg (Optional[dist.ProcessGroup]): the process group that is used to
+            gather ShardedTensor. Note that gathering a DTensor will use
+            the DeviceMesh. So this argument will be ignored when gathering a
+            DTensor.
+        ranks_only: (Tuple[int, ...]): if this tuple is empty, all ranks will
+            have the same state_dicts. Otherwise only ranks that in ``ranks_only``
+            have the same state_dicts. Other ranks will get empty state_dicts.
+        type_check: (bool): check if the instance data type is a supported type
+            that can be saved by DCP.  The current supported data types are
+            torch.Tensor, DTensor, int, float, str, list, dict, None.
+
+    Returns:
+        The gathered state dictionary.
+    """
+
+    ret = _iterate_state_dict(
+        state_dict,
+        _identity_func,
+        _identity_func,
+        _identity_func,
+        pg=None,
+        device=None,
+        cpu_offload=True,
+        ranks_only=ranks_only,
+        type_check=type_check,
+    )
+    return ret
+
+
+def _copy_state_dict(
+    state_dict: Dict[str, Any],
+    copy_state_dict: Dict[str, Any],
+    non_blocking: bool = False,
+    type_check: bool = True,
+) -> Dict[str, Any]:
+    """
+    Copies all tensors in a given state dict into a different state_dict with the
+    same structure. Additionally, a copied state dict with the same value references
+    is returned. Editing the keys on this state dict will not affect the
+    passed in copy_state_dict (but the value references are the same).
+
+    .. warning::
+        It is expected by this function that state_dict and copy_state_dict share
+        the same structure and data types.
+
+    .. warning::
+        The current supported data types are
+            torch.Tensor, DTensor, int, float, str, list, dict, None.
+
+    Args:
+        state_dict (Dict[str, Any]): the target state_dict.
+        copy_state_dict (Dict[str, Any]):
+            The state dict we are copying into. This state_dict must have exactly
+             the same structure as the source `state_dict`.
+        non_blocking: (bool): Whether copy ops should be performed asynchronously
+        type_check (bool): check if the instance data type is a supported type
+            that can be saved by DCP. The current supported data types are
+            torch.Tensor, DTensor, int, float, str, list, dict, None.
+
+    Returns:
+        State Dict copy
+    """
+
+    return _iterate_state_dict(
+        state_dict,
+        _identity_func,
+        _identity_func,
+        _identity_func,
+        pg=None,
+        device=None,
+        cpu_offload=False,
+        ranks_only=(),
+        companion_obj=copy_state_dict,
+        type_check=type_check,
+        non_blocking=non_blocking,
+    )
+
+
+def _create_cpu_state_dict(
+    state_dict: Dict[str, Any], pin_memory: bool = False, share_memory: bool = False
+) -> Dict[str, Any]:
+    """
+    Given a state_dict, create another state_dict with the same structure and elements.
+    However, all tensors in the returned state_dict are new tensors on CPU. These
+    tensors can be placed on pin_memory or share_memory based on the provided arguments.
+
+    .. warning::
+        Setting both `pin_memory` and `share_memory` to True significantly increases the
+        latency of this method because of the nuances which require us to register memory
+        as pinned directly as opposed to relying on the pin_memory cache allocator. This
+        option should only be used for long lived tensors which are required to be shared.
+        This is not the case as long as at least one of `pin_memory` or `share_memory` is
+         set to False.
+
+    """
+
+    def tensor_func(
+        obj: torch.Tensor,
+        pg: Optional[dist.ProcessGroup],
+        device: Optional[torch.device],
+        _: Any,
+    ) -> torch.Tensor:
+        if len(obj.size()) == 0:
+            return torch.tensor(0, dtype=obj.dtype)
+
+        if share_memory:
+            t = torch.empty(*tuple(obj.size()), dtype=obj.dtype)
+            t = t.share_memory_()
+            if pin_memory:
+
+                def unpin_memory(t):
+                    succ = int(torch.cuda.cudart().cudaHostUnregister(t.data_ptr()))
+                    assert (
+                        succ == 0
+                    ), f"Unpinning shared memory failed with error-code: {succ}"
+
+                weakref.finalize(t, unpin_memory, t)
+                succ = int(
+                    torch.cuda.cudart().cudaHostRegister(
+                        t.data_ptr(),
+                        t.numel() * t.element_size(),
+                        1,  # lines up with 'cudaHostRegisterPortable'
+                    )
+                )
+                assert (
+                    succ == 0
+                ), f"Pinning shared memory failed with error-code: {succ}"
+            return t
+        elif pin_memory:
+            return torch.empty(*tuple(obj.size()), dtype=obj.dtype).pin_memory()
+        else:
+            return torch.empty(*tuple(obj.size()), dtype=obj.dtype)
+
+    ret = _iterate_state_dict(
+        state_dict,
+        _identity_func,
+        _identity_func,
+        tensor_func,
+        pg=None,
+        device=None,
+        cpu_offload=False,
+        ranks_only=(),
+        type_check=False,
+    )
+    return ret
+
+
+def _check_state_dict_similarity(
+    state_dict: Dict[str, Any],
+    compared_state_dict: Dict[str, Any],
+) -> bool:
+    """
+    Given two state_dicts, check if the structures are the same. And
+    if a [key, tensor] pair exist in one state_dict there must be
+    the a corresponding pait, [key, other_tensor], in the other state_dict,
+    where tensor and other_tensor have the same size and dtype.
+
+    Return the check result.
+    """
+
+    def tensor_func(
+        obj: torch.Tensor,
+        pg: Optional[dist.ProcessGroup],
+        device: Optional[torch.device],
+        companion_obj: Any,
+    ) -> torch.Tensor:
+        if companion_obj.dtype != obj.dtype or companion_obj.size() != obj.size():
+            raise CompanionMismatch
+        return obj
+
+    try:
+        _iterate_state_dict(
+            state_dict,
+            _identity_func,
+            _identity_func,
+            tensor_func,
+            pg=None,
+            device=None,
+            cpu_offload=False,
+            ranks_only=(),
+            companion_obj=compared_state_dict,
+            type_check=False,
+        )
+    except CompanionMismatch:
+        return False
+
+    return True
+
+
+class _TensorInfo(NamedTuple):
+    size: torch.Size
+    dtype: torch.dtype
+
+
+def _broadcast_tensors(
+    full_state_dict: Dict[str, Any],
+    local_state_dict: Dict[str, Any],
+    keys: List[str],
+    device: torch.device,
+    pg: Optional[dist.ProcessGroup] = None,
+) -> None:
+    tensors = []
+    for key in keys:
+        if dist.get_rank() == 0:
+            full_state = full_state_dict[key]
+            assert isinstance(full_state, torch.Tensor)
+            full_tensor = full_state.detach().to(device)
+        else:
+            tensor_info = full_state_dict[key]
+            full_tensor = torch.empty(
+                size=tensor_info.size,
+                device=device,
+                dtype=tensor_info.dtype,
+            )
+
+        tensors.append(full_tensor)
+        local_state = local_state_dict.get(key, None)
+        if local_state is None:
+            continue
+        elif isinstance(local_state, DTensor):
+            local_state_dict[key] = (local_state, full_tensor)
+        else:
+            local_state_dict[key] = full_tensor
+
+    if pg is None:
+        pg = dist.distributed_c10d._get_default_group()
+
+    if len(tensors) > 1:
+        dist._broadcast_coalesced(pg, tensors, 500, 0)
+    else:
+        dist.broadcast(tensors[0], src=0, group=pg)
+
+    _distribute_tensors(local_state_dict, keys, device, pg)
+
+
+def _distribute_tensors(
+    local_state_dict: Dict[str, Any],
+    keys: List[str],
+    device: torch.device,
+    pg: Optional[dist.ProcessGroup] = None,
+) -> None:
+    if pg is None:
+        pg = dist.distributed_c10d._get_default_group()
+    for key in keys:
+        _local_state = local_state_dict.get(key, None)
+        if _local_state is None or torch.is_tensor(_local_state):
+            continue
+
+        local_state = _local_state[0]
+        full_tensor = _local_state[1]
+
+        shape, offset = compute_local_shape_and_global_offset(
+            full_tensor.shape, local_state.device_mesh, local_state.placements
+        )
+        slices = [slice(offset[i], shape[i] + offset[i]) for i in range(len(shape))]
+        local_tensor = full_tensor[slices]
+        # TODO: currently, we cannot handle strided sharding if the dp dimension is not even. For example,
+        # one of the case that is not yet supported is when placements = (Shard(0), _StridedShard(0, sf=2)).
+        local_state_dict[key] = DTensor.from_local(
+            local_tensor,
+            local_state.device_mesh,
+            local_state.placements,
+            shape=local_state.shape,
+            stride=local_state.stride(),
+        )
+
+
+def _broadcast_state_dict(
+    full_state_dict: Dict[str, Any],
+    local_state_dict: Dict[str, Any],
+    device: torch.device,
+    pg: Optional[dist.ProcessGroup] = None,
+    strict: bool = False,
+) -> None:
+    # Broadcast from rank0's `full_state_dict` to all ranks' `local_state_dict`.
+    # If strict is True, any keys in `local_state_dict` but not in `full_state_dict`
+    # will be removed from `local_state_dict`.
+    ret = {}
+    if dist.get_rank() == 0:
+        for key, value in full_state_dict.items():
+            if not torch.is_tensor(value):
+                ret[key] = value
+            elif value.dim() == 0:
+                ret[key] = value.cpu()
+            else:
+                ret[key] = _TensorInfo(value.size(), value.dtype)
+
+    broadcast_list = [ret]
+    dist.broadcast_object_list(broadcast_list, src=0, group=pg)
+    ret = broadcast_list[0]
+
+    # Gather values
+    keys = []
+    local_state_dict_keys = set(local_state_dict.keys())
+    global_keys = set()
+    for key, value in ret.items():
+        global_keys.add(key)
+        if not isinstance(value, _TensorInfo):
+            if key in local_state_dict:
+                local_state_dict[key] = value
+            continue
+
+        if dist.get_rank() == 0:
+            ret[key] = full_state_dict[key]
+
+        keys.append(key)
+        # Broadcast every tensor to avoid OOM for now.
+        if len(keys) >= 1:
+            _broadcast_tensors(ret, local_state_dict, keys, device, pg)
+            keys.clear()
+
+    if strict:
+        if missing_keys := (local_state_dict_keys - global_keys):
+            for key in missing_keys:
+                local_state_dict.pop(key)
+
+    if keys:
+        _broadcast_tensors(ret, local_state_dict, keys, device, pg)
+
+
+def _distribute_state_dict(
+    full_state_dict: Dict[str, Any],
+    local_state_dict: Dict[str, Any],
+    device: torch.device,
+    pg: Optional[dist.ProcessGroup] = None,
+) -> None:
+    # Full_state_dict = True, broadcast_from_rank0 = False here. Each rank has
+    # full_state_dict. Skip the broadcast in ``_broadcast_state_dict`` and
+    # distribute tensors in each rank
+    for key, value in full_state_dict.items():
+        if key not in full_state_dict:
+            continue
+        if not torch.is_tensor(value):
+            local_state_dict[key] = value
+        elif value.dim() == 0:
+            local_state_dict[key] = value.cpu()
+        else:
+            assert isinstance(value, torch.Tensor)
+            local_state = local_state_dict.get(key, None)
+            if local_state is None:
+                continue
+            elif isinstance(local_state, DTensor):
+                local_state_dict[key] = distribute_tensor(
+                    value.detach().to(device),
+                    local_state.device_mesh,
+                    local_state.placements,
+                )
+            else:
+                local_state_dict[key] = value.detach().to(device)
+
+
+# These APIs are from torch.distributed.checkpoint.
+# TODO: We should consolidate the code here as some not all modules can depend on
+# DCP.
+PATH_ITEM = Union[str, int]
+OBJ_PATH = Tuple[PATH_ITEM, ...]
+FLATTEN_MAPPING = Dict[str, OBJ_PATH]
+STATE_DICT_TYPE = Dict[str, Any]
+CONTAINER_TYPE = MutableMapping[PATH_ITEM, Any]
+
+
+def _traverse_state_dict(
+    state_dict: STATE_DICT_TYPE,
+    visitor: Callable[[OBJ_PATH, Any], None],
+) -> None:
+    """
+    Invoke ``visitor`` for each value recursively in ``state_dict``.
+    Mapping, list, and tuple will be flattened and other value types are treated
+    as the terminal values and will invoke ``visitor``.
+    """
+
+    def _traverse_obj(path: OBJ_PATH, value: Any) -> None:
+        if isinstance(value, Mapping):
+            for k, v in value.items():
+                _traverse_obj(path + (str(k),), v)
+        elif isinstance(value, (list, tuple)):
+            for i, v in enumerate(value):
+                _traverse_obj(path + (i,), v)
+        else:
+            visitor(path, value)
+
+    for key, value in state_dict.items():
+        _traverse_obj((str(key),), value)
+
+
+def _flatten_state_dict(
+    state_dict: STATE_DICT_TYPE,
+) -> Tuple[STATE_DICT_TYPE, FLATTEN_MAPPING]:
+    """
+    Flatten ``state_dict`` made of nested dicts and lists into a top level dictionary.
+
+    Use ``unflatten_state_dict`` to revert this process.
+    Returns:
+        A tuple with the flatten state_dict and a mapping from original to new state_dict.
+    N.B. The new keys are derived from the object paths, joined by dot.
+        For example: ``{ 'a': {'b':...}}`` results in the key `a.b`.
+    """
+    flattened: STATE_DICT_TYPE = {}
+    mappings: FLATTEN_MAPPING = {}
+
+    def flat_copy(path: OBJ_PATH, value: Any) -> None:
+        new_fqn = ".".join(map(str, path))
+        if new_fqn in flattened:
+            raise ValueError(f"duplicated flatten key {new_fqn}")
+        flattened[new_fqn] = value
+        mappings[new_fqn] = path
+
+    _traverse_state_dict(state_dict, flat_copy)
+    return flattened, mappings
+
+
+def _set_element(root_dict: STATE_DICT_TYPE, path: OBJ_PATH, value: Any) -> None:
+    """Set ``value`` in ``root_dict`` along the ``path`` object path."""
+    cur_container = cast(CONTAINER_TYPE, root_dict)
+
+    def extend_list(lst: List[Any], idx: int) -> None:
+        while len(lst) <= idx:
+            lst.append(None)
+
+    for i in range(1, len(path)):
+        prev_key = path[i - 1]
+        key = path[i]
+        def_val: Union[CONTAINER_TYPE, List[Any]] = {} if type(key) == str else []
+
+        if isinstance(cur_container, Mapping):
+            cur_container = cast(
+                CONTAINER_TYPE, cur_container.setdefault(prev_key, def_val)
+            )
+        else:
+            extend_list(cur_container, prev_key)
+            if cur_container[prev_key] is None:
+                cur_container[prev_key] = def_val
+            cur_container = cur_container[prev_key]
+
+    key = path[-1]
+    if type(key) == int:
+        extend_list(cast(List[Any], cur_container), key)
+
+    cur_container[key] = value
+
+
+def _unflatten_state_dict(
+    state_dict: STATE_DICT_TYPE, mapping: FLATTEN_MAPPING
+) -> STATE_DICT_TYPE:
+    """Restore the original nested state_dict according to ``mapping`` and the flattened ``state_dict``."""
+    nested: STATE_DICT_TYPE = {}
+    for key, value in state_dict.items():
+        _set_element(nested, mapping[key], value)
+    return nested
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/argparse_util.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/argparse_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..c475eebf21273abb53ab99e3edcbdef18e9f0c8f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/argparse_util.py
@@ -0,0 +1,104 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+from argparse import Action
+
+
+class env(Action):
+    """
+    Get argument values from ``PET_{dest}`` before defaulting to the given ``default`` value.
+
+    For flags (e.g. ``--standalone``)
+    use ``check_env`` instead.
+
+    .. note:: when multiple option strings are specified, ``dest`` is
+              the longest option string (e.g. for ``"-f", "--foo"``
+              the env var to set is ``PET_FOO`` not ``PET_F``)
+
+    Example:
+    ::
+
+     parser.add_argument("-f", "--foo", action=env, default="bar")
+
+     ./program                                      -> args.foo="bar"
+     ./program -f baz                               -> args.foo="baz"
+     ./program --foo baz                            -> args.foo="baz"
+     PET_FOO="env_bar" ./program -f baz    -> args.foo="baz"
+     PET_FOO="env_bar" ./program --foo baz -> args.foo="baz"
+     PET_FOO="env_bar" ./program           -> args.foo="env_bar"
+
+     parser.add_argument("-f", "--foo", action=env, required=True)
+
+     ./program                                      -> fails
+     ./program -f baz                               -> args.foo="baz"
+     PET_FOO="env_bar" ./program           -> args.foo="env_bar"
+     PET_FOO="env_bar" ./program -f baz    -> args.foo="baz"
+    """
+
+    def __init__(self, dest, default=None, required=False, **kwargs) -> None:
+        env_name = f"PET_{dest.upper()}"
+        default = os.environ.get(env_name, default)
+
+        # ``required`` means that it NEEDS to be present  in the command-line args
+        # rather than "this option requires a value (either set explicitly or default"
+        # so if we found default then we don't "require" it to be in the command-line
+        # so set it to False
+        if default:
+            required = False
+
+        super().__init__(dest=dest, default=default, required=required, **kwargs)
+
+    def __call__(self, parser, namespace, values, option_string=None):
+        setattr(namespace, self.dest, values)
+
+
+class check_env(Action):
+    """
+    Check whether the env var ``PET_{dest}`` exists before defaulting to the given ``default`` value.
+
+    Equivalent to
+    ``store_true`` argparse built-in action except that the argument can
+    be omitted from the commandline if the env var is present and has a
+    non-zero value.
+
+    .. note:: it is redundant to pass ``default=True`` for arguments
+              that use this action because a flag should be ``True``
+              when present and ``False`` otherwise.
+
+    Example:
+    ::
+
+     parser.add_argument("--verbose", action=check_env)
+
+     ./program                                  -> args.verbose=False
+     ./program --verbose                        -> args.verbose=True
+     PET_VERBOSE=1 ./program           -> args.verbose=True
+     PET_VERBOSE=0 ./program           -> args.verbose=False
+     PET_VERBOSE=0 ./program --verbose -> args.verbose=True
+
+    Anti-pattern (don't do this):
+
+    ::
+
+     parser.add_argument("--verbose", action=check_env, default=True)
+
+     ./program                                  -> args.verbose=True
+     ./program --verbose                        -> args.verbose=True
+     PET_VERBOSE=1 ./program           -> args.verbose=True
+     PET_VERBOSE=0 ./program           -> args.verbose=False
+
+    """
+
+    def __init__(self, dest, default=False, **kwargs) -> None:
+        env_name = f"PET_{dest.upper()}"
+        default = bool(int(os.environ.get(env_name, "1" if default else "0")))
+        super().__init__(dest=dest, const=True, default=default, nargs=0, **kwargs)
+
+    def __call__(self, parser, namespace, values, option_string=None):
+        setattr(namespace, self.dest, self.const)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/c10d_logger.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/c10d_logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..162cb62f992fdc14248e6d2b6e318e4701de4002
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/c10d_logger.py
@@ -0,0 +1,106 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import functools
+import logging
+import time
+from typing import Any, Callable, Dict, List, Tuple, TypeVar
+from typing_extensions import ParamSpec
+
+import torch
+import torch.distributed as dist
+from torch.distributed.logging_handlers import _log_handlers
+
+
+__all__: List[str] = []
+
+_DEFAULT_DESTINATION = "default"
+
+
+def _get_or_create_logger(destination: str = _DEFAULT_DESTINATION) -> logging.Logger:
+    logging_handler, log_handler_name = _get_logging_handler(destination)
+    logger = logging.getLogger(f"c10d-{log_handler_name}")
+    logger.setLevel(logging.DEBUG)
+    formatter = logging.Formatter(
+        "%(asctime)s %(filename)s:%(lineno)s %(levelname)s p:%(processName)s t:%(threadName)s: %(message)s"
+    )
+    logging_handler.setFormatter(formatter)
+    logger.propagate = False
+    logger.addHandler(logging_handler)
+    return logger
+
+
+def _get_logging_handler(
+    destination: str = _DEFAULT_DESTINATION,
+) -> Tuple[logging.Handler, str]:
+    log_handler = _log_handlers[destination]
+    log_handler_name = f"{type(log_handler).__name__}-{destination}"
+    return (log_handler, log_handler_name)
+
+
+global _c10d_logger
+_c10d_logger = _get_or_create_logger()
+
+
+def _get_msg_dict(func_name, *args, **kwargs) -> Dict[str, Any]:
+    if dist.is_initialized():
+        group = kwargs.get("group") or kwargs.get("process_group")
+        msg_dict = {
+            "func_name": f"{func_name}",
+            "args": f"{args}, {kwargs}",
+            "pg_name": f"{dist._get_process_group_name(kwargs.get('pg'))}",  # type: ignore[arg-type]
+            "backend": f"{dist.get_backend(group)}",
+            "world_size": f"{dist.get_world_size()}",
+            "group_size": f"{dist.get_world_size(group)}",
+            "global_rank": f"{dist.get_rank()}",
+            "local_rank": f"{dist.get_rank(group)}",
+        }
+        if msg_dict["backend"] == "nccl":
+            nccl_version = torch.cuda.nccl.version()
+            msg_dict["nccl_version"] = ".".join(str(v) for v in nccl_version)
+    else:
+        msg_dict = {
+            "func_name": f"{func_name}",
+            "args": f"{args}, {kwargs}",
+        }
+    return msg_dict
+
+
+_T = TypeVar("_T")
+_P = ParamSpec("_P")
+
+
+def _exception_logger(func: Callable[_P, _T]) -> Callable[_P, _T]:
+    @functools.wraps(func)
+    def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _T:
+        try:
+            return func(*args, **kwargs)
+        except Exception as error:
+            msg_dict = _get_msg_dict(func.__name__, *args, **kwargs)
+            msg_dict["error"] = f"{error}"
+            _c10d_logger.debug(msg_dict)
+            raise
+
+    return wrapper
+
+
+def _time_logger(func: Callable[_P, _T]) -> Callable[_P, _T]:
+    @functools.wraps(func)
+    def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _T:
+        t1 = time.time_ns()
+        func_return = func(*args, **kwargs)
+        time_spent = time.time_ns() - t1
+
+        msg_dict = _get_msg_dict(func.__name__, *args, **kwargs)
+        msg_dict["time_spent"] = f"{time_spent}ns"
+        _c10d_logger.debug(msg_dict)
+
+        return func_return
+
+    return wrapper
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/collective_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/collective_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..78199e7a26f22cee28564fb66fd8bbdbc8a1defa
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/collective_utils.py
@@ -0,0 +1,217 @@
+#!/usr/bin/env python3
+
+
+"""
+A set of primitive functions for performing collective ops.
+
+Each should also handle single rank scenario.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Callable, cast, Generic, List, Optional, Tuple, TypeVar, Union
+
+import torch.distributed as dist
+
+
+T = TypeVar("T")
+
+
+@dataclass
+class SyncPayload(Generic[T]):
+    stage_name: Optional[str]
+    success: bool
+    payload: T
+    exception: Optional[Exception] = None
+
+
+def broadcast(
+    data_or_fn: Union[T, Callable[[], T]],
+    *,
+    success: bool = True,
+    stage_name: Optional[str] = None,
+    rank: int = 0,
+    pg: Optional[dist.ProcessGroup] = None,
+) -> T:
+    """
+    Broadcasts the data payload from rank 0 to all other ranks.
+    Or if a function is passed, execute it in rank 0 and broadcast result to all other ranks.
+
+    Can be used to broadcast a failure signal to stop all ranks.
+
+    If the function raises an exception, all ranks will raise.
+
+    Args:
+        data_or_fn: the data to broadcast or function to execute and broadcast result.
+        success: False to stop all ranks.
+        stage_name: the name of the logical stage for synchronization and debugging
+        rank: rank to broadcast data or execute function and broadcast resutls.
+        pg: the process group for sync
+    Throws:
+        RuntimeError from original exception trace
+    Returns:
+        the value after synchronization
+
+    Example usage:
+    >> id = broadcast(data_or_fn=allocate_id, rank=0, pg=ext_pg.my_pg)
+    """
+
+    if not success and data_or_fn is not None:
+        raise AssertionError(
+            "Data or Function is expected to be None if not successful"
+        )
+
+    payload: Optional[T] = None
+    exception: Optional[Exception] = None
+    # if no pg is passed then execute if rank is 0
+    if (pg is None and rank == 0) or (pg is not None and pg.rank() == rank):
+        # determine if it is an executable function or data payload only
+        if callable(data_or_fn):
+            try:
+                payload = data_or_fn()
+            except Exception as e:
+                success = False
+                exception = e
+        else:
+            payload = data_or_fn
+
+    # broadcast the exception type if any to all ranks for failure categorization
+    sync_obj = SyncPayload(
+        stage_name=stage_name,
+        success=success,
+        payload=payload,
+        exception=exception,
+    )
+
+    if pg is not None:
+        broadcast_list = [sync_obj]
+        dist.broadcast_object_list(broadcast_list, src=rank, group=pg)
+        assert len(broadcast_list) == 1
+        sync_obj = broadcast_list[0]
+
+    # failure in any rank will trigger a throw in every rank.
+    if not sync_obj.success:
+        error_msg = f"Rank {rank} failed"
+        if stage_name is not None:
+            error_msg += f": stage {sync_obj.stage_name}"
+        if sync_obj.exception is not None:
+            error_msg += f": exception {sync_obj.exception}"
+        raise RuntimeError(error_msg) from sync_obj.exception
+
+    return cast(T, sync_obj.payload)
+
+
+def all_gather(
+    data_or_fn: Union[T, Callable[[], T]],
+    stage_name: Optional[str] = None,
+    pg: Optional[dist.ProcessGroup] = None,
+) -> List[T]:
+    """
+    A simple all_gather primitive with basic synchronization guard logic,
+    by checking payload from all ranks has the same stage name.
+
+    Args:
+        data_or_fn: the data to be all gathered across ranks or function to be executed
+        stage_name: the sync stage name for out-of-sync protection
+        pg: the process group for sync
+    Throws:
+        RuntimeError from original exception trace
+    Returns:
+        a list of synced data from all ranks
+
+    Example usage:
+    >> all_ids = all_gather(data_or_fn=allocate_id, pg=ext_pg.my_pg)
+    """
+    payload: Optional[T] = None
+    exception: Optional[Exception] = None
+    success = True
+    # determine if it is an executable function or data payload only
+    if callable(data_or_fn):
+        try:
+            payload = data_or_fn()
+        except Exception as e:
+            success = False
+            exception = e
+    else:
+        payload = data_or_fn
+
+    sync_obj = SyncPayload(
+        stage_name=stage_name,
+        success=success,
+        payload=payload,
+        exception=exception,
+    )
+
+    if pg is not None:
+        # List of success/failure across all ranks.
+        total_list = [None] * dist.get_world_size(pg)
+        all_gather_object_enforce_type(pg, total_list, sync_obj)
+        # Each rank will throw RuntimeError in case of failure on any rank.
+        stage_name = cast(SyncPayload[T], total_list[0]).stage_name
+        exception_list: List[Tuple[int, Exception]] = []
+        ret_list: List[T] = []
+        error_msg: str = ""
+
+        for i, sp in enumerate(cast(List[SyncPayload[T]], total_list)):
+            if sp.stage_name != stage_name:
+                error_msg += (
+                    f"Unexpected stage name received from rank {i}: {sp.stage_name} "
+                )
+                continue
+            if not sp.success and sp.exception is not None:
+                exception_list.append((i, sp.exception))
+                continue
+            ret_list.append(sp.payload)
+
+        if len(exception_list) > 0:
+            raise RuntimeError(  # type: ignore[misc]
+                error_msg, exception_list
+            ) from exception_list[0]
+        return ret_list
+    else:
+        if not sync_obj.success:
+            raise RuntimeError(
+                f"all_gather failed with exception {sync_obj.exception}",
+            ) from sync_obj.exception
+        return [sync_obj.payload]  # type: ignore[list-item]
+
+
+# Note: use Any for typing for now so users can pass in
+# either a list of None or target type placeholders
+# otherwise pyre would complain
+def all_gather_object_enforce_type(
+    pg: dist.ProcessGroup,
+    # pyre-fixme[2]: Parameter must have a type that does not contain `Any`
+    object_list: List[Any],
+    # pyre-fixme[2]: Parameter must have a type other than `Any`
+    obj: Any,
+    # pyre-fixme[2]: Parameter must have a type that does not contain `Any`
+    type_checker: Callable[[Any, Any], bool] = lambda x, y: type(x) == type(y),
+) -> None:
+    """
+    Similar to plain all_gather_object but with additional type checking
+    AFTER gather is done to ensure basic consistency.
+    If check does not pass, all ranks will fail with exception.
+
+    This is generally to prevent conditional logic leading to
+    unexpected messages being received. This is considered fatal code error,
+    but due to logic stacks this might happen implicitly in practice.
+
+    The default check does not check sub type (considered different)
+    or covariance (considered same) but users can pass in custom checker
+    if more complicated check is needed.
+    """
+    dist.all_gather_object(object_list, obj, group=pg)
+
+    # conservative check
+    list_len = len(object_list)
+    if list_len == 0:
+        return
+    first_obj = object_list[0]
+    for i in range(1, list_len):
+        if not type_checker(first_obj, object_list[i]):
+            raise TypeError(
+                f"Object type at index {i} is {type(object_list[i])}, "
+                f"while first object type is {type(first_obj)}"
+            )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/constants.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3754043644b8cc96feee06a4fc1cc9fa824b648
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/constants.py
@@ -0,0 +1,26 @@
+from datetime import timedelta
+from typing import Optional
+
+from torch._C._distributed_c10d import _DEFAULT_PG_TIMEOUT
+
+
+__all__ = ["default_pg_timeout", "default_pg_nccl_timeout"]
+
+# Default process group wide timeout, if applicable.
+# This only applies to the non-nccl backends
+# To make an attempt at backwards compatibility with THD, we use an
+# extraordinarily high default timeout, given that THD did not have timeouts.
+default_pg_timeout: timedelta = _DEFAULT_PG_TIMEOUT
+# Separate timeout for PGNCCL mainly becuase it's always been that way in the C++ layer, but until recently
+# there was one default that applied across all backends in the python layer.
+# Later, we could consider merging them back together at the c++ layer if we can align on a same value.
+# (only if TORCH_NCCL_BLOCKING_WAIT or TORCH_NCCL_ASYNC_ERROR_HANDLING is set to 1).
+
+try:
+    from torch._C._distributed_c10d import _DEFAULT_PG_NCCL_TIMEOUT
+
+    default_pg_nccl_timeout: Optional[timedelta] = _DEFAULT_PG_NCCL_TIMEOUT
+except ImportError:
+    # if C++ NCCL support is not compiled, we don't have access to the default nccl value.
+    # if anyone is actually trying to use nccl in this state, it should error.
+    default_pg_nccl_timeout = None
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/device_mesh.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/device_mesh.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8d33ab7ebfbea58b56b9e86b92934f9c1ff9fa9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/device_mesh.py
@@ -0,0 +1,953 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import logging
+import math
+import threading
+from functools import reduce
+from itertools import chain
+from typing import Dict, List, Optional, Tuple, TYPE_CHECKING, Union
+
+import torch
+from torch.distributed import is_available
+from torch.utils._typing_utils import not_none
+
+
+__all__ = ["init_device_mesh", "DeviceMesh"]
+
+
+if not is_available():
+    import sys
+
+    # We need to create the stubs when distributed is not available.
+    # Otherwise, we would fail the doc tests (```./.ci/pytorch/docs-test.sh```),
+    # since it would try to import ``torch.distributed.device_mesh`` or
+    # ``torch.distributed.init_device_mesh`` but cannot find them.
+
+    class _DeviceMeshStub:
+        pass
+
+    def _init_device_mesh_stub():
+        pass
+
+    sys.modules["torch.distributed.device_mesh"].DeviceMesh = _DeviceMeshStub  # type: ignore[attr-defined]
+    sys.modules[
+        "torch.distributed.device_mesh"
+    ].init_device_mesh = _init_device_mesh_stub  # type: ignore[attr-defined]
+
+
+else:
+    from torch.distributed.distributed_c10d import (
+        _find_pg_by_ranks_and_tag,
+        _get_default_group,
+        _get_group_tag,
+        get_backend,
+        get_process_group_ranks,
+        get_rank,
+        get_world_size,
+        init_process_group,
+        is_initialized,
+        new_group,
+        ProcessGroup,
+    )
+
+    logger = logging.getLogger(__name__)
+
+    # only import numpy typing when type checking
+    if TYPE_CHECKING:
+        try:
+            from numpy.typing import ArrayLike
+        except ImportError:
+            logger.warning(
+                "DeviceMesh requires numpy >= 1.21 to be installed for type checking"
+            )
+
+    class _MeshEnv(threading.local):
+        def __init__(self) -> None:
+            self.mesh_stack: List[DeviceMesh] = []
+            self.child_to_root_mapping: Dict[DeviceMesh, DeviceMesh] = {}
+            self.mesh_dim_group_options: Dict[
+                int, Tuple[str, Optional[ProcessGroup.Options]]
+            ] = {}
+            self.root_to_flatten_mapping: Dict[DeviceMesh, Dict[str, DeviceMesh]] = {}
+            # Record flatten mesh name to its mesh dim index in root mesh.
+            self.flatten_name_to_root_dims: Dict[
+                DeviceMesh, Dict[str, Tuple[int, ...]]
+            ] = {}
+
+        def get_current_mesh(self) -> "DeviceMesh":
+            if len(self.mesh_stack) == 0:
+                raise RuntimeError("No device mesh is currently active!")
+            return self.mesh_stack[-1]
+
+        def create_sub_mesh(
+            self,
+            device_mesh: "DeviceMesh",
+            submesh_dim_names: Tuple[str, ...],
+            submesh_dims: List[Tuple[int, ...]],
+        ) -> "DeviceMesh":
+            # Get the submesh dim size from the submesh_dims.
+            # For example, if we have a 3D mesh with mesh_shape (2, 2, 2) mesh_dim_names ("dp", "cp", "tp") and we want
+            # to slice out mesh["dp_cp"], then submesh_dims = [(0, 1), (2,)] and submesh_dim_size = [2 * 2, 2] = [4, 2].
+            # If we want to slice out mesh["dp", "cp"], then submesh_dims = [(0,), (1,)] and submesh_dim_size = [2, 2].
+            slice_dim_size = [
+                reduce(
+                    lambda x, y: device_mesh.mesh.size(x) * device_mesh.mesh.size(y),
+                    mesh_dim,
+                )
+                if len(mesh_dim) > 1
+                else device_mesh.mesh.size(mesh_dim[0])
+                for mesh_dim in submesh_dims
+            ]
+
+            mesh_tensor = device_mesh.mesh
+            # slice_dim_idx could be differnt from submesh_dims, as we may need to flatten out some dims.
+            slice_dim_idx = []
+            slice_dim_group_info = []
+            # keep track of the number of dims that have been flattened so we can get the correct slice_dim_idx in the
+            # flattened mesh tensor.
+            num_dims_flatten = 0
+            for mesh_dim_indices, mesh_dim_name in zip(submesh_dims, submesh_dim_names):
+                # Currently, this only allows slicing out a contiguous flattened dim.
+                # TODO: we need to handle reconstructing a non-contiguous flattened dim.
+                if len(mesh_dim_indices) > 1:
+                    # We need to move the start_dim and end_dim to the left if some dims are already flattened.
+                    mesh_tensor = mesh_tensor.flatten(
+                        start_dim=mesh_dim_indices[0] - num_dims_flatten,
+                        end_dim=mesh_dim_indices[-1] - num_dims_flatten,
+                    )
+                    # If some dims are already flattened, we need to adjust the slice_dim_idx accordingly.
+                    # For example, if the submesh_dims = [(0, 1), (2,), (3, 4)] with 0-1 flattened and 3-4 flattened,
+                    # then the final slice_dim_idx should be [0, 1, 2].
+                    slice_dim_idx.append(mesh_dim_indices[0] - num_dims_flatten)
+                    num_dims_flatten += len(mesh_dim_indices) - 1
+                    slice_dim_group_info.append(
+                        self.root_to_flatten_mapping[device_mesh][
+                            mesh_dim_name
+                        ]._dim_group_infos[0]
+                    )
+                else:
+                    slice_dim_idx.append(mesh_dim_indices[0] - num_dims_flatten)
+                    slice_dim_group_info.append(
+                        device_mesh._dim_group_infos[mesh_dim_indices[0]]
+                    )
+
+            # mesh_tensor has already been flattened if needed. So mesh_tensor.ndim <= device_mesh.mesh.ndim now.
+            mesh_dims_remained_idx = list(range(mesh_tensor.ndim))
+            for idx in slice_dim_idx:
+                mesh_dims_remained_idx.remove(idx)
+
+            # pg_ranks_by_dim is the size of [number of local ranks of the outermost submesh dimension, *slice_dim_idx]
+            # This means on each local rank of the outermost slice mesh dim, we have a tensor of submesh size with
+            # the pg ranks of the submesh. From this, we can extract the submesh mesh tensor contains the current rank.
+            pg_ranks_by_dim = mesh_tensor.permute(
+                *mesh_dims_remained_idx, *slice_dim_idx
+            ).reshape(-1, *slice_dim_size)
+
+            cur_rank = device_mesh.get_rank()
+            for mesh_nd in pg_ranks_by_dim:
+                submesh = DeviceMesh(
+                    device_mesh.device_type,
+                    mesh_nd,
+                    mesh_dim_names=submesh_dim_names,
+                    _init_backend=False,
+                )
+                if cur_rank in mesh_nd:
+                    res_submesh = submesh
+
+            res_submesh._dim_group_infos = slice_dim_group_info  # type: ignore[possibly-undefined]
+            self.child_to_root_mapping[res_submesh] = device_mesh
+
+            return res_submesh
+
+        def create_flatten_mesh(
+            self, device_mesh: "DeviceMesh", mesh_dim_name: Optional[str] = None
+        ) -> "DeviceMesh":
+            root_mesh = _mesh_resources.get_root_mesh(device_mesh)
+
+            flatten_dims_in_root = [
+                not_none(root_mesh.mesh_dim_names).index(flattened_mesh_dim_name)
+                for flattened_mesh_dim_name in not_none(device_mesh.mesh_dim_names)
+            ]
+
+            if not mesh_dim_name:
+                mesh_dim_name = "_".join(
+                    [
+                        not_none(root_mesh.mesh_dim_names)[dim]
+                        for dim in flatten_dims_in_root
+                    ]
+                )
+
+            # Check whether the mesh_dim_name for flattened mesh is valid.
+            self.flatten_name_to_root_dims.setdefault(root_mesh, {})
+            invalid_dim_names = chain(
+                *list(not_none(root_mesh.mesh_dim_names)),
+                *self.flatten_name_to_root_dims[root_mesh].keys(),
+            )
+            if mesh_dim_name in invalid_dim_names:
+                raise RuntimeError(
+                    f"{mesh_dim_name} already exists for submesh of the {root_mesh}. ",
+                    f"The mesh_dim_names of submesh and flattened mesh are {invalid_dim_names}. "
+                    f"Please specify another valid mesh_dim_name.",
+                )
+
+            # Quick return if the flatten mesh has been created before.
+            # TODO: If we decide to restrict flatten initialization once, we should remove
+            # this check and throw an error if the flatten mesh is already created before.
+            if (
+                root_mesh in self.root_to_flatten_mapping
+                and mesh_dim_name in self.root_to_flatten_mapping[root_mesh]
+            ):
+                return self.root_to_flatten_mapping[root_mesh][mesh_dim_name]
+
+            flattened_mesh_dim_size = math.prod(device_mesh.mesh.size())
+
+            remained_dims_in_root = list(range(root_mesh.mesh.ndim))
+            for flatten_dim_in_root in flatten_dims_in_root:
+                remained_dims_in_root.remove(flatten_dim_in_root)
+
+            pg_ranks_by_dim = root_mesh.mesh.permute(
+                *remained_dims_in_root, *flatten_dims_in_root
+            ).reshape(-1, flattened_mesh_dim_size)
+
+            cur_rank = root_mesh.get_rank()
+            for mesh_nd in pg_ranks_by_dim:
+                # need to init backend here since the flattened pg doesn't exist in root mesh.
+                flattened_mesh = DeviceMesh(
+                    root_mesh.device_type,
+                    mesh_nd,
+                    mesh_dim_names=(mesh_dim_name,),
+                )
+                if cur_rank in mesh_nd:
+                    res_flattened_mesh = flattened_mesh
+            self.child_to_root_mapping[res_flattened_mesh] = root_mesh  # type: ignore[possibly-undefined]
+            self.root_to_flatten_mapping.setdefault(root_mesh, {})[mesh_dim_name] = res_flattened_mesh  # type: ignore[possibly-undefined]
+            self.flatten_name_to_root_dims[root_mesh][mesh_dim_name] = tuple(flatten_dims_in_root)  # type: ignore[possibly-undefined]
+
+            return res_flattened_mesh
+
+        def get_root_mesh(self, device_mesh: "DeviceMesh") -> "DeviceMesh":
+            # If a mesh could not be found in the child_to_root_mapping, it is a root mesh itself.
+            # A root mesh is not created through slicing.
+            # We considers the root mesh of a root mesh is itself.
+            root_mesh = self.child_to_root_mapping.get(device_mesh, None)
+            return device_mesh if not root_mesh else root_mesh
+
+        def get_root_mesh_dim(self, device_mesh: "DeviceMesh") -> Optional[int]:
+            """
+            Returns the index of the mesh dim in the root mesh.
+            The device_mesh passed in needs to be sliced out from the root mesh
+            or submesh of the root mesh.
+            """
+            root_mesh = self.get_root_mesh(device_mesh)
+            child_mesh_dim_names = device_mesh.mesh_dim_names
+            if root_mesh and child_mesh_dim_names:
+                assert (
+                    len(child_mesh_dim_names) == 1
+                ), "The submesh can only be a 1D mesh."
+                child_mesh_dim_name = child_mesh_dim_names[0]
+                return self.get_mesh_dim_by_name(root_mesh, child_mesh_dim_name)
+            return None
+
+        @staticmethod
+        def num_devices_per_host(device_type: str) -> int:
+            return _get_device_handle(device_type).device_count()
+
+        @staticmethod
+        def num_hosts(device_type: str) -> int:
+            # ProcessGroup can't tell us this info so we have to infer it, assume
+            # homogeneous hardware for now
+            return get_world_size() // _MeshEnv.num_devices_per_host(device_type)
+
+        def get_mesh_dim_by_name(
+            self, device_mesh: "DeviceMesh", mesh_dim_name: str
+        ) -> int:
+            if (
+                device_mesh.mesh_dim_names is None
+                or len(device_mesh.mesh_dim_names) == 0
+            ):
+                raise KeyError(
+                    "No `mesh_dim_names` found.",
+                )
+            if mesh_dim_name not in device_mesh.mesh_dim_names:
+                raise KeyError(
+                    f"Mesh dimension '{mesh_dim_name}' does not exist.",
+                    f"Available mesh dimensions are: mesh_dim_names={device_mesh.mesh_dim_names}",
+                )
+            return not_none(device_mesh.mesh_dim_names.index(mesh_dim_name))
+
+        def _set_mesh_dim_group_options(
+            self,
+            dim: int,
+            backend: str,
+            pg_options: Optional[ProcessGroup.Options] = None,
+        ) -> None:
+            self.mesh_dim_group_options[dim] = (backend, pg_options)
+
+        def _get_slice_mesh_dims(
+            self, device_mesh, mesh_dim_names
+        ) -> List[Tuple[int, ...]]:
+            """
+            Validate whether the mesh_dim_names is valid for slicing the given device_mesh.
+            If valid, return dim indexes of the slice mesh in the device mesh.
+            """
+            if device_mesh != self.get_root_mesh(device_mesh):
+                raise RuntimeError("Cannot create a submesh from a submesh.")
+
+            # The slice mesh_dim_names should consist either the device_mesh's mesh_dim_names
+            # or its flattened mesh's mesh_dim_names.
+            self.flatten_name_to_root_dims.setdefault(device_mesh, {})
+            flatten_name_to_root_dims = self.flatten_name_to_root_dims[device_mesh]
+            valid_mesh_dim_names = [
+                *device_mesh.mesh_dim_names,
+                *flatten_name_to_root_dims,
+            ]
+
+            if not all(
+                mesh_dim_name in valid_mesh_dim_names
+                for mesh_dim_name in mesh_dim_names
+            ):
+                raise KeyError(
+                    f"Invalid mesh_dim_names {mesh_dim_names} specified. "
+                    f"Valid mesh_dim_names are {valid_mesh_dim_names}."
+                )
+
+            # Validate the order of the slice mesh dim indices.
+            # This needs to be in ascending order.
+            curr_idx = -1
+            slice_mesh_dims = []
+            for mesh_dim_name in mesh_dim_names:
+                if mesh_dim_name in flatten_name_to_root_dims:
+                    mesh_indices = flatten_name_to_root_dims[mesh_dim_name]
+                    # TODO: this doesn't allow non-contiguous slicing with flatten dim yet. next_idx
+                    # should be mesh_indices[0] once we support non-contiguous slicing with flatten dim.
+                    next_idx = mesh_indices[-1]
+                    slice_mesh_dims.append(mesh_indices)
+                else:
+                    next_idx = device_mesh.mesh_dim_names.index(mesh_dim_name)
+                    slice_mesh_dims.append((next_idx,))
+                if next_idx <= curr_idx:
+                    raise KeyError(
+                        f"Invalid mesh_dim_names {mesh_dim_names} specified. ",
+                        f"Found mesh dim indices to slice: {slice_mesh_dims}. ",
+                        "Mesh dim indices should be in ascending order.",
+                    )
+                curr_idx = next_idx
+
+            return slice_mesh_dims
+
+        def _get_all_submeshes(
+            self, device_mesh: "DeviceMesh", mesh_dim_name: str
+        ) -> List["DeviceMesh"]:
+            """
+            Return all the submeshes of a given mesh dimension of the device mesh.
+            """
+            mesh_dim = self.get_mesh_dim_by_name(device_mesh, mesh_dim_name)
+            pg_ranks_by_dim = device_mesh.mesh.swapdims(-1, mesh_dim).reshape(
+                -1, device_mesh.mesh.size(mesh_dim)
+            )
+
+            cur_rank = device_mesh.get_rank()
+            res_submeshes = []
+            for mesh_1d in pg_ranks_by_dim:
+                submesh = DeviceMesh(
+                    device_mesh.device_type,
+                    mesh_1d,
+                    mesh_dim_names=(mesh_dim_name,),
+                    _init_backend=False,
+                )
+                submesh._dim_group_infos = (
+                    [device_mesh._dim_group_infos[mesh_dim]]
+                    if cur_rank in mesh_1d
+                    else []
+                )
+                res_submeshes.append(submesh)
+
+            return res_submeshes
+
+    _mesh_resources: _MeshEnv = _MeshEnv()
+
+    def _get_device_handle(device_type: str = "cuda"):
+        """
+        Get the module corresponding to the device_type which is cuda or cuda-like device.
+        For example, when the device_type is cuda, the module `torch.cuda` is returned.
+        Return None when there is no corresponding module for device_type, otherwise
+        return the corresponding module.
+        """
+        return getattr(torch, device_type, None)
+
+    class DeviceMesh:
+        """
+        DeviceMesh represents a mesh of devices, where layout of devices could be
+        represented as a n-d dimension array, and each value of the n-d dimensional
+        array is the global id of the default process group ranks.
+
+        DeviceMesh could be used to describe the layout of devices across the cluster,
+        and serves as a proxy for communication among the device lists within the cluster.
+
+        DeviceMesh can be used as a context manager.
+
+        .. note::
+            DeviceMesh follows SPMD programming model, which means the same PyTorch Python program
+            is running on all processes/ranks in the cluster. Therefore, users need to make sure the
+            `mesh` array (which describes the layout of devices) should be identical across all ranks.
+            Inconsistent `mesh` will lead to silent hang.
+
+        Args:
+            device_type (str): The device type of the mesh. Currently supports: "cpu", "cuda/cuda-like".
+            mesh (ndarray): A multi-dimensional array or an integer tensor describing the layout
+                of devices, where the IDs are global IDs of the default process group.
+
+        Returns:
+            DeviceMesh: A :class:`DeviceMesh` object representing the device layout.
+
+        The following program runs on each process/rank in an SPMD manner. In this example, we have 2
+        hosts with 4 GPUs each.
+        A reduction over the first dimension of mesh will reduce across
+        columns (0, 4), .. and (3, 7), a reduction over the second dimension
+        of mesh reduces across rows (0, 1, 2, 3) and (4, 5, 6, 7).
+
+        Example::
+            >>> # xdoctest: +SKIP("no rank")
+            >>> from torch.distributed.device_mesh import DeviceMesh
+            >>>
+            >>> # Initialize device mesh as (2, 4) to represent the topology
+            >>> # of cross-host(dim 0), and within-host (dim 1).
+            >>> mesh = DeviceMesh(device_type="cuda", mesh=[[0, 1, 2, 3],[4, 5, 6, 7]])
+        """
+
+        device_type: str
+        mesh: torch.Tensor
+        mesh_dim_names: Optional[Tuple[str, ...]]
+
+        def __init__(
+            self,
+            device_type: str,
+            mesh: Union[torch.Tensor, "ArrayLike"],
+            *,
+            mesh_dim_names: Optional[Tuple[str, ...]] = None,
+            _init_backend: bool = True,
+        ) -> None:
+            self.device_type = device_type
+            if isinstance(mesh, torch.Tensor) and mesh.device.type != "cpu":
+                raise ValueError(f"`mesh` must be a CPU tensor, got {mesh}")
+            self.mesh = (
+                mesh.detach().to(dtype=torch.int)
+                if isinstance(mesh, torch.Tensor)
+                else torch.tensor(mesh, device="cpu", dtype=torch.int)
+            )
+            self.mesh_dim_names = tuple(mesh_dim_names) if mesh_dim_names else None
+
+            # private field to pre-generate DeviceMesh's hash
+            self._flatten_mesh_list = tuple(self.mesh.flatten().tolist())
+            self._thread_id = None
+
+            # Skip process group initialization if xla device or init backend is False
+            # TODO(yeounoh) implement DeviceMesh backend and register XLA backend.
+            if device_type != "xla":
+                # always try to create default (world) pg, even if it is not initialized
+                # already. The world pg is used for device mesh identity (rank) on each
+                # process (we need to know if the current global rank is in the mesh or not).
+                if _init_backend:
+                    self._get_or_create_default_group()
+                    self._init_process_groups()
+
+                if is_initialized() and get_backend() == "threaded":
+                    self._thread_id = threading.get_ident()
+
+                # calculate the coordinates of the current global rank on the mesh
+                rank_coords = (self.mesh == get_rank()).nonzero()
+                assert rank_coords.size(0) in (0, 1)
+                self._coordinate_on_dim: Optional[List[int]] = (
+                    rank_coords[0].tolist() if rank_coords.size(0) > 0 else None
+                )
+
+        def _get_or_create_default_group(self):
+            default_initialized = is_initialized()
+            if not default_initialized:
+                init_process_group()
+
+            world_size = get_world_size()
+            if self.mesh.numel() > world_size:
+                raise RuntimeError(
+                    f"Mesh should not be bigger than default world size {world_size}, but found {self.mesh.numel()} ranks!"
+                )
+
+            device_handle = _get_device_handle(self.device_type)
+            # TODO: if user want to pass pg_options, offer a way to do it
+            if not default_initialized and device_handle:
+                # automatically set the current cuda/cuda-like device base on num of gpu devices available in each host
+                # NOTE: This device selection would only work for homogeneous hardware.
+                num_devices_per_host = device_handle.device_count()
+                if (
+                    world_size > num_devices_per_host
+                    and world_size % num_devices_per_host != 0
+                ):
+                    raise RuntimeError(
+                        f"DeviceMesh only support homogeneous hardware, but found "
+                        f"{world_size} ranks and {num_devices_per_host} {self.device_type} devices!"
+                    )
+                device_handle.set_device(get_rank() % num_devices_per_host)
+
+            return _get_default_group()
+
+        def _init_process_groups(self):
+            # tag/ranks/group_name associated with each mesh dimension, each
+            # mesh dimension should have one sub-group per rank
+            #
+            # TODO(yifu): remove tag and ranks once we fully migrate to native
+            # functional collectives. See details in:
+            # https://github.com/pytorch/pytorch/issues/93173#issuecomment-1907095208
+            dim_group_infos: List[Tuple[str, List[int], str]] = []
+
+            if self.mesh.ndim == 1 and self.mesh.numel() == get_world_size():
+                # Append the default pg to the first dim groups only if the default pg is compatible with `self.device_type`.
+                # Otherwise, create new pg.
+                default_group = _get_default_group()
+                ranks = list(range(get_world_size()))
+                dim_group = (
+                    new_group(backend="cpu:gloo,cuda:nccl", ranks=ranks)
+                    if torch.cuda.is_available()
+                    and get_backend(default_group) == "gloo"
+                    else default_group
+                )
+                dim_group_infos.append(
+                    (
+                        _get_group_tag(dim_group),
+                        ranks,
+                        dim_group.group_name,
+                    )
+                )
+            else:
+                # create sub pgs base on the mesh argument specified
+                for dim in range(self.mesh.ndim):
+                    # swap the current dim to the last dim
+                    # then reshape to flatten out other dims
+                    pg_ranks_by_dim = self.mesh.swapdims(-1, dim).reshape(
+                        -1, self.mesh.size(dim)
+                    )
+                    # multi-dim mesh, create subgroups by looping over the pg_ranks
+                    # for each dim and append the groups
+                    for dim_mesh in pg_ranks_by_dim:
+                        subgroup_ranks = dim_mesh.tolist()
+
+                        # Respect dim group options specified via _MeshEnv.set_dim_group_options().
+                        # Inherit from the parent group if no options are specified for the group.
+                        if dim in _mesh_resources.mesh_dim_group_options:
+                            (
+                                backend,
+                                pg_options,
+                            ) = _mesh_resources.mesh_dim_group_options[dim]
+                        else:
+                            backend, pg_options = None, None
+
+                        # We temporarily revert the re-use subgroup, since it breaks two internal tests.
+                        # Temporarily reverting to resolve test timeout while root-causing.
+                        # TODO: Add two tests to cover internal tests scenarios and re-enable reuse subgroup if exists.
+                        dim_group = new_group(
+                            ranks=subgroup_ranks,
+                            backend=backend,
+                            pg_options=pg_options,
+                        )
+
+                        # only add to dim_groups if the current rank in the subgroup
+                        if self.get_rank() in subgroup_ranks:
+                            if len(dim_group_infos) > dim:
+                                raise RuntimeError(
+                                    f"Each device mesh dimension should get only one process group, but got {self.get_rank()} "
+                                    f"in {subgroup_ranks}!"
+                                )
+                            dim_group_infos.append(
+                                (
+                                    _get_group_tag(not_none(dim_group)),
+                                    subgroup_ranks,
+                                    dim_group.group_name,
+                                )
+                            )
+            self._dim_group_infos = dim_group_infos
+
+        def __enter__(self) -> "DeviceMesh":
+            # set this mesh as the current mesh in mesh env
+            _mesh_resources.mesh_stack.append(self)
+            return self
+
+        # pyre-fixme[2]: Parameter must be annotated.
+        def __exit__(self, exc_type, exc_value, exc_traceback) -> None:
+            # pop this mesh from mesh env
+            _mesh_resources.mesh_stack.pop()
+
+        def __repr__(self) -> str:
+            device_mesh_repr = (
+                f"DeviceMesh('{self.device_type}', {self.mesh.tolist()})"
+                if not self.mesh_dim_names
+                else f"DeviceMesh('{self.device_type}', {self.mesh.tolist()}, mesh_dim_names={self.mesh_dim_names})"
+            )
+            return device_mesh_repr
+
+        def __hash__(self):
+            # lazily compute hash
+            self._hash = getattr(self, "_hash", None)
+            if not self._hash:
+                self._hash = hash(
+                    (
+                        self._flatten_mesh_list,
+                        self.mesh.shape,
+                        self.device_type,
+                        self.mesh_dim_names,
+                        self._thread_id,
+                    )
+                )
+            return self._hash
+
+        def __eq__(self, other: object) -> bool:
+            if not isinstance(other, DeviceMesh):
+                return False
+            if id(self) == id(other):
+                return True
+            else:
+                return (
+                    self._flatten_mesh_list == other._flatten_mesh_list
+                    and self.mesh.shape == other.mesh.shape
+                    and self.device_type == other.device_type
+                    and self.mesh_dim_names == other.mesh_dim_names
+                    and self._thread_id == other._thread_id
+                )
+
+        def __getitem__(
+            self, mesh_dim_names: Union[str, Tuple[str, ...]]
+        ) -> "DeviceMesh":
+            """
+            Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh.
+            The submesh created consists of the dimensions and the communicators indicated by
+            ``mesh_dim_names``
+
+            Args:
+                mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the
+                mesh dimension of the DeviceMesh to create the submesh for.
+            Returns:
+                A :class:`DeviceMesh` object
+
+            The following program runs on each process/rank in an SPMD manner in a world size of 8.
+            In the first example:
+                Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]).
+                Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of  DeviceMesh:([4, 5, 6, 7]).
+                Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of  DeviceMesh:([0, 4]).
+                Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of  DeviceMesh:([1, 5]).
+                Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of  DeviceMesh:([2, 6]).
+                Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of  DeviceMesh:([3, 7]).
+
+            In the second example:
+                Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]).
+                Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]).
+                Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]).
+                Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]).
+
+            Example::
+                >>> # xdoctest: +SKIP("no rank")
+                >>> from torch.distributed.device_mesh import DeviceMesh
+                >>>
+                >>> # Initialize a 2D device mesh as (2, 4) to represent the topology
+                >>> # of cross-host(dim 0), and within-host (dim 1).
+                >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))
+                >>> tp_mesh = mesh_2d["tp"]
+                >>> dp_mesh = mesh_2d["dp"]
+                >>>
+                >>> # Initialize a 3D mesh.
+                >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp"))
+                >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh.
+                >>> dp_cp_mesh = mesh_3d["dp", "cp"]
+                >>> cp_dp_mesh = mesh_3d["cp", "dp"]
+            """
+            if not self.mesh_dim_names:
+                raise RuntimeError("Cannot slice a DeviceMesh without mesh_dim_names!")
+
+            mesh_dim_names = (
+                (mesh_dim_names,) if isinstance(mesh_dim_names, str) else mesh_dim_names
+            )
+
+            if mesh_dim_names == self.mesh_dim_names:
+                return self
+            else:
+                slice_mesh_dims = _mesh_resources._get_slice_mesh_dims(
+                    self, mesh_dim_names
+                )
+                submesh = _mesh_resources.create_sub_mesh(
+                    self, mesh_dim_names, slice_mesh_dims
+                )
+                return submesh
+
+        def get_group(self, mesh_dim: Optional[Union[int, str]] = None) -> ProcessGroup:
+            """
+            Returns the single ProcessGroup specified by mesh_dim, or, if mesh_dim is not specified and the
+            DeviceMesh is 1-dimensional, returns the only ProcessGroup in the mesh.
+
+            Args:
+                mesh_dim (str/int, optional): it can be the name of the mesh dimension or the index
+                of the mesh dimension. Default is None.
+
+            Returns:
+                A :class:`ProcessGroup` object.
+            """
+            if not hasattr(self, "_dim_group_infos"):
+                raise RuntimeError("DeviceMesh process groups not initialized!")
+
+            if self.mesh.ndim > 1 and mesh_dim is None:
+                raise RuntimeError(
+                    f"Found the DeviceMesh have {self.mesh.ndim} dimensions",
+                    "Optional kwarg `mesh_dim` needs to be specified when device_mesh.ndim > 1.",
+                    "If you want to get the list of all the ProcessGroups in the DeviceMesh,"
+                    "please use `get_all_groups()` instead.",
+                )
+
+            # Quick return if the current device_mesh is a 1D mesh.
+            if self.mesh.ndim == 1 and mesh_dim is None:
+                return not_none(
+                    _find_pg_by_ranks_and_tag(*self._dim_group_infos[0][:2])  # type: ignore[index]
+                )
+
+            root_mesh = _mesh_resources.get_root_mesh(self)
+            root_to_flatten_mapping = _mesh_resources.root_to_flatten_mapping.get(
+                root_mesh, None
+            )
+            if root_to_flatten_mapping and mesh_dim in root_to_flatten_mapping.keys():
+                dim_group_infos = root_to_flatten_mapping[mesh_dim]._dim_group_infos[0][:2]  # type: ignore[index]
+                return not_none(_find_pg_by_ranks_and_tag(*dim_group_infos))
+            else:
+                mesh_dim = (
+                    _mesh_resources.get_mesh_dim_by_name(self, mesh_dim)
+                    if isinstance(mesh_dim, str)
+                    else mesh_dim
+                )
+                return not_none(
+                    _find_pg_by_ranks_and_tag(*self._dim_group_infos[mesh_dim][:2])  # type: ignore[index]
+                )
+
+        def get_all_groups(self) -> List[ProcessGroup]:
+            """
+            Returns a list of ProcessGroups for all mesh dimensions.
+
+            Returns:
+                A list of :class:`ProcessGroup` object.
+            """
+            return [self.get_group(i) for i in range(self.mesh.ndim)]
+
+        @staticmethod
+        def from_group(
+            group: Union[ProcessGroup, List[ProcessGroup]],
+            device_type: str,
+            mesh: Optional[Union[torch.Tensor, "ArrayLike"]] = None,
+            *,
+            mesh_dim_names: Optional[Tuple[str, ...]] = None,
+        ) -> "DeviceMesh":
+            """
+            Constructs a :class:`DeviceMesh` with ``device_type`` from an
+            existing :class:`ProcessGroup`.
+
+            The constructed device mesh has number of dimensions equal to the
+            number of groups passed. If more than one group is passed, then the
+            ``mesh`` argument is required.
+            """
+            if isinstance(group, ProcessGroup):
+                group_ranks = get_process_group_ranks(group)
+                if (
+                    isinstance(mesh, torch.Tensor) and mesh.tolist() != group_ranks
+                ) or (mesh is not None and mesh != group_ranks):
+                    raise ValueError(
+                        f"Invalid mesh {str(mesh)} for ProcessGroup with ranks {group_ranks}"
+                    )
+                mesh = torch.tensor(group_ranks, device="cpu", dtype=torch.int)
+                device_mesh = DeviceMesh(
+                    device_type,
+                    mesh,
+                    mesh_dim_names=mesh_dim_names,
+                    _init_backend=False,
+                )
+                device_mesh._dim_group_infos = [
+                    (_get_group_tag(group), group_ranks, group.group_name)
+                ]
+                return device_mesh
+            groups = list(group)
+            if len(groups) == 0:
+                raise ValueError("Expects at least one ProcessGroup to be passed")
+            if mesh is None:
+                raise ValueError("Must pass mesh if passing multiple ProcessGroups")
+            mesh = (
+                mesh.detach().to(dtype=torch.int, device="cpu")
+                if isinstance(mesh, torch.Tensor)
+                else torch.tensor(mesh, device="cpu", dtype=torch.int)
+            )
+            if mesh.ndim != len(groups):
+                raise ValueError(
+                    "Expects mesh with ndim equal to number of ProcessGroups but got "
+                    f"mesh {mesh.tolist()} and {len(groups)} ProcessGroups"
+                )
+            device_mesh = DeviceMesh(
+                device_type, mesh, mesh_dim_names=mesh_dim_names, _init_backend=False
+            )
+            device_mesh._dim_group_infos = [
+                (
+                    _get_group_tag(group),
+                    get_process_group_ranks(group),
+                    group.group_name,
+                )
+                for group in groups
+            ]
+            return device_mesh
+
+        def size(self, mesh_dim: Optional[int] = None) -> int:
+            return self.mesh.numel() if mesh_dim is None else self.mesh.size(mesh_dim)
+
+        @property
+        def ndim(self) -> int:
+            return self.mesh.ndim
+
+        @property
+        def shape(self) -> Tuple[int, ...]:
+            return tuple(self.mesh.shape)
+
+        def get_rank(self) -> int:
+            """
+            Returns the current global rank.
+            """
+            return get_rank()
+
+        def get_local_rank(self, mesh_dim: Optional[Union[int, str]] = None) -> int:
+            """
+            Returns the local rank of the given mesh_dim of the DeviceMesh.
+
+            Args:
+                mesh_dim (str/int, optional): it can be the name of the mesh dimension or the index
+                of the mesh dimension. Default is None.
+
+            Returns:
+                An integer denotes the local rank.
+
+            The following program runs on each process/rank in an SPMD manner. In this example, we have 2
+            hosts with 4 GPUs each.
+            Calling mesh_2d.get_local_rank(mesh_dim=0) on rank 0, 1, 2, 3 would return 0.
+            Calling mesh_2d.get_local_rank(mesh_dim=0) on rank 4, 5, 6, 7 would return 1.
+            Calling mesh_2d.get_local_rank(mesh_dim=1) on rank 0, 4 would return 0.
+            Calling mesh_2d.get_local_rank(mesh_dim=1) on rank 1, 5 would return 1.
+            Calling mesh_2d.get_local_rank(mesh_dim=1) on rank 2, 6 would return 2.
+            Calling mesh_2d.get_local_rank(mesh_dim=1) on rank 3, 7 would return 3.
+
+            Example::
+                >>> # xdoctest: +SKIP("no rank")
+                >>> from torch.distributed.device_mesh import DeviceMesh
+                >>>
+                >>> # Initialize device mesh as (2, 4) to represent the topology
+                >>> # of cross-host(dim 0), and within-host (dim 1).
+                >>> mesh = DeviceMesh(device_type="cuda", mesh=[[0, 1, 2, 3],[4, 5, 6, 7]])
+            """
+            if self.ndim > 1 and mesh_dim is None:
+                raise RuntimeError(
+                    f"Found the DeviceMesh have {self.mesh.ndim} dimensions",
+                    "Optional kwarg `mesh_dim` needs to be specified when device_mesh.ndim > 1.",
+                )
+            elif mesh_dim is None:
+                mesh_dim = 0
+
+            mesh_dim_group = not_none(self.get_group(mesh_dim))
+            assert isinstance(
+                mesh_dim_group, ProcessGroup
+            ), "We expect ProcessGroup before calling `get_rank`!"
+            return not_none(get_rank(mesh_dim_group))
+
+        def get_coordinate(self) -> Optional[List[int]]:
+            """
+            Return the relative indices of this rank relative to all
+            dimensions of the mesh. If this rank is not part of the mesh, return None.
+            """
+            return self._coordinate_on_dim if self._coordinate_on_dim else None
+
+        def _flatten(self, mesh_dim_name: Optional[str] = None) -> "DeviceMesh":
+            """
+            Returns a 1D DeviceMesh by flattening the current DeviceMesh.
+
+            If no mesh_dim_name is provided, the default is a string concatentaing the mesh_dim_names of the
+            given submesh with each mesh_dim_name separated by "_". For example, if we have a 3D mesh
+            DeviceMesh([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], mesh_dim_names=("dp", "cp", "tp")), calling
+            mesh_3d["dp", "cp"]._flatten() will create a 1D submesh DeviceMesh([0, 1, 2, 3], mesh_dim_names=("dp_cp",))
+            on rank 0, 1, 2, 3 and a 1D submesh DeviceMesh([4, 5, 6, 7], mesh_dim_names=("dp_cp",)) on rank 4, 5, 6, 7.
+
+            After the flattened dimension is created, to access the flattened dimesnion in mesh_3d, one can use the
+            existing slicing method to obtain the flattened mesh through calling mesh_3d["dp_cp"].
+            """
+            if not self.mesh_dim_names:
+                raise RuntimeError(
+                    "Cannot flatten a DeviceMesh without mesh_dim_names!"
+                )
+
+            return _mesh_resources.create_flatten_mesh(self, mesh_dim_name)
+
+    def init_device_mesh(
+        device_type: str,
+        mesh_shape: Tuple[int, ...],
+        *,
+        mesh_dim_names: Optional[Tuple[str, ...]] = None,
+    ) -> DeviceMesh:
+        """
+        Initializes a `DeviceMesh` based on `device_type`, `mesh_shape`, and `mesh_dim_names` parameters.
+
+        This creates a DeviceMesh with an n-dimensional array layout, where `n` is the length of `mesh_shape`.
+        If `mesh_dim_names` is provided, each dimension is labeled as `mesh_dim_names[i]`.
+
+        .. note::
+            `init_device_mesh` follows SPMD programming model, meaning the same PyTorch Python program
+            runs on all processes/ranks in the cluster. Ensure `mesh_shape` (the dimensions of the nD array
+            describing device layout) is identical across all ranks. Inconsistent `mesh_shape` may lead to hanging.
+
+        .. note::
+            If no process group is found, init_device_mesh will initialize distributed process group/groups
+            required for distributed communications behind the scene.
+
+        Args:
+            device_type (str): The device type of the mesh. Currently supports: "cpu", "cuda/cuda-like".
+                Passing in a device type with a GPU index, such as "cuda:0", is not allowed.
+            mesh_shape (Tuple[int]): A tuple defining the dimensions of the multi-dimensional array
+                describing the layout of devices.
+            mesh_dim_names (Tuple[str], optional): A tuple of mesh dimension names to assign to each dimension
+                of the multi-dimensional array describing the layout of devices. Its length must match the length
+                of `mesh_shape`. Each string in `mesh_dim_names` must be unique.
+
+        Returns:
+            DeviceMesh: A :class:`DeviceMesh` object representing the device layout.
+
+        Example::
+            >>> # xdoctest: +SKIP("no rank")
+            >>> from torch.distributed.device_mesh import init_device_mesh
+            >>>
+            >>> mesh_1d = init_device_mesh("cuda", mesh_shape=(8,))
+            >>> mesh_2d = init_device_mesh("cuda", mesh_shape=(2, 8), mesh_dim_names=("dp", "tp"))
+
+        """
+        if mesh_dim_names is not None:
+            if len(set(mesh_dim_names)) != len(mesh_dim_names):
+                raise RuntimeError(
+                    "Each mesh_dim_name must be unique.",
+                    f"Found repeated mesh_dim_name in mesh_dim_names {mesh_dim_names}",
+                )
+
+            if len(mesh_shape) != len(mesh_dim_names):
+                raise RuntimeError(
+                    "mesh_shape and mesh_dim_names should have same length!",
+                    f"Found len(mesh_dim_names): {len(mesh_dim_names)} and len(mesh_shape):{len(mesh_shape)}.",
+                )
+
+        # assume valid device types are all letters
+        if device_type and not device_type.isalpha():
+            raise RuntimeError(
+                f"Device type with GPU index is not supported but got {device_type}. ",
+                "If you maintained a 'torch.device' object, it's recommended to pass in 'device.type'.",
+            )
+
+        # Always initialize the mesh's tensor on CPU, regardless of what the
+        # external device type has been set to be (e.g. meta)
+        with torch.device("cpu"):
+            mesh = torch.arange(math.prod(mesh_shape), dtype=torch.int).view(mesh_shape)
+        device_mesh = DeviceMesh(
+            device_type=device_type,
+            mesh=mesh,
+            mesh_dim_names=mesh_dim_names,
+        )
+
+        return device_mesh
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/distributed_c10d.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/distributed_c10d.py
new file mode 100644
index 0000000000000000000000000000000000000000..45e096985143a372932f523a46cf869e7f862310
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/distributed_c10d.py
@@ -0,0 +1,4983 @@
+# mypy: allow-untyped-defs
+"""Distributed Collective Communication (c10d)."""
+
+import collections.abc
+import contextlib
+import hashlib
+import io
+import itertools
+import logging
+import os
+import pickle
+import sys
+import time
+import warnings
+from collections import namedtuple
+from datetime import timedelta
+from typing import Any, Callable, Dict, List, Optional, Tuple, TYPE_CHECKING, Union
+from typing_extensions import deprecated
+
+import torch
+from torch._C import _DistStoreError as DistStoreError
+from torch._C._distributed_c10d import (
+    _DistributedBackendOptions,
+    _register_process_group,
+    _resolve_process_group,
+    _unregister_all_process_groups,
+    _unregister_process_group,
+    AllgatherOptions,
+    AllreduceCoalescedOptions,
+    AllreduceOptions,
+    AllToAllOptions,
+    BarrierOptions,
+    BroadcastOptions,
+    DebugLevel,
+    GatherOptions,
+    get_debug_level,
+    PrefixStore,
+    ProcessGroup,
+    ReduceOp,
+    ReduceOptions,
+    ReduceScatterOptions,
+    ScatterOptions,
+    Store,
+    Work,
+)
+from torch._utils_internal import set_pytorch_distributed_envs_from_justknobs
+from torch.utils._typing_utils import not_none
+
+from .c10d_logger import _exception_logger, _time_logger
+from .constants import default_pg_nccl_timeout, default_pg_timeout
+from .rendezvous import register_rendezvous_handler, rendezvous  # noqa: F401
+
+
+__all__ = [
+    "Backend",
+    "BackendConfig",
+    "GroupMember",
+    "P2POp",
+    "all_gather",
+    "all_gather_coalesced",
+    "all_gather_object",
+    "all_reduce",
+    "all_reduce_coalesced",
+    "all_to_all",
+    "all_to_all_single",
+    "barrier",
+    "batch_isend_irecv",
+    "broadcast",
+    "send_object_list",
+    "recv_object_list",
+    "broadcast_object_list",
+    "destroy_process_group",
+    "gather",
+    "gather_object",
+    "get_backend_config",
+    "get_backend",
+    "get_rank",
+    "get_world_size",
+    "get_pg_count",
+    "group",
+    "init_process_group",
+    "irecv",
+    "is_gloo_available",
+    "is_initialized",
+    "is_mpi_available",
+    "is_backend_available",
+    "is_nccl_available",
+    "is_torchelastic_launched",
+    "is_ucc_available",
+    "isend",
+    "monitored_barrier",
+    "new_group",
+    "new_subgroups",
+    "new_subgroups_by_enumeration",
+    "recv",
+    "reduce",
+    "reduce_scatter",
+    "scatter",
+    "scatter_object_list",
+    "send",
+    "supports_complex",
+    "AllreduceCoalescedOptions",
+    "AllreduceOptions",
+    "AllToAllOptions",
+    "BarrierOptions",
+    "BroadcastOptions",
+    "GatherOptions",
+    "PrefixStore",
+    "ProcessGroup",
+    "ReduceOp",
+    "ReduceOptions",
+    "ReduceScatterOptions",
+    "ScatterOptions",
+    "Store",
+    "DebugLevel",
+    "get_debug_level",
+    "Work",
+    "default_pg_timeout",
+    "get_group_rank",
+    "get_global_rank",
+    "get_process_group_ranks",
+    "reduce_op",
+    "all_gather_into_tensor",
+    "reduce_scatter_tensor",
+    "get_node_local_rank",
+    "split_group",
+]
+
+_MPI_AVAILABLE = True
+_NCCL_AVAILABLE = True
+_GLOO_AVAILABLE = True
+_UCC_AVAILABLE = True
+
+_pickler = pickle.Pickler
+_unpickler = pickle.Unpickler
+
+
+# Change __module__ of all imported types from torch._C._distributed_c10d that are public
+def _export_c_types() -> None:
+    _public_types_to_change_module = [
+        AllreduceCoalescedOptions,
+        AllreduceOptions,
+        AllToAllOptions,
+        BarrierOptions,
+        BroadcastOptions,
+        GatherOptions,
+        PrefixStore,
+        ProcessGroup,
+        ReduceOp,
+        ReduceOptions,
+        ReduceScatterOptions,
+        ScatterOptions,
+        Store,
+        DebugLevel,
+        get_debug_level,
+        Work,
+    ]
+    for type in _public_types_to_change_module:
+        type.__module__ = "torch.distributed.distributed_c10d"
+
+
+_export_c_types()
+
+try:
+    from torch._C._distributed_c10d import ProcessGroupMPI
+
+    ProcessGroupMPI.__module__ = "torch.distributed.distributed_c10d"
+    __all__ += ["ProcessGroupMPI"]
+except ImportError:
+    _MPI_AVAILABLE = False
+
+try:
+    from torch._C._distributed_c10d import ProcessGroupNCCL
+
+    ProcessGroupNCCL.__module__ = "torch.distributed.distributed_c10d"
+    __all__ += ["ProcessGroupNCCL"]
+except ImportError:
+    _NCCL_AVAILABLE = False
+
+try:
+    from torch._C._distributed_c10d import _ProcessGroupWrapper, ProcessGroupGloo
+
+    ProcessGroupGloo.__module__ = "torch.distributed.distributed_c10d"
+    __all__ += ["ProcessGroupGloo"]
+except ImportError:
+    _GLOO_AVAILABLE = False
+
+try:
+    from torch._C._distributed_c10d import ProcessGroupUCC
+
+    ProcessGroupUCC.__module__ = "torch.distributed.distributed_c10d"
+    __all__ += ["ProcessGroupUCC"]
+except ImportError:
+    _UCC_AVAILABLE = False
+
+logger = logging.getLogger(__name__)
+
+PG_WRAPPER_STORE_PREFIX = "pg_wrapper"
+
+
+# Some reduce ops are not supported by complex numbers and will result in an error.
+# We currently provide complex support to the distributed API by viewing
+# complex tensors as real (torch.view_as_real), meaning that calling
+# these unsupported ops will return garbage values rather than error out.
+# (e.g. max(2+3i, 3+2i) = 3+3i)
+# We'd like calls to unsupported ops to error out accordingly,
+# rather than returning garbage values.
+def supports_complex(reduceOp: ReduceOp) -> bool:
+    """Return true if reduce ops is supported. False otherwise."""
+    denyList = [
+        ReduceOp.MAX,
+        ReduceOp.MIN,
+        ReduceOp.PRODUCT,
+        ReduceOp.BAND,
+        ReduceOp.BOR,
+        ReduceOp.BXOR,
+    ]
+    return reduceOp not in denyList
+
+
+class Backend(str):
+    """
+    An enum-like class for backends.
+
+    Available backends: GLOO, NCCL, UCC, MPI, and other registered backends.
+
+    The values of this class are lowercase strings, e.g., ``"gloo"``. They can
+    be accessed as attributes, e.g., ``Backend.NCCL``.
+
+    This class can be directly called to parse the string, e.g.,
+    ``Backend(backend_str)`` will check if ``backend_str`` is valid, and
+    return the parsed lowercase string if so. It also accepts uppercase strings,
+    e.g., ``Backend("GLOO")`` returns ``"gloo"``.
+
+    .. note:: The entry ``Backend.UNDEFINED`` is present but only used as
+              initial value of some fields. Users should neither use it directly
+              nor assume its existence.
+    """
+
+    UNDEFINED = "undefined"
+    GLOO = "gloo"
+    NCCL = "nccl"
+    UCC = "ucc"
+    MPI = "mpi"
+
+    _BackendPlugin = namedtuple("_BackendPlugin", ["creator_fn", "extended_api"])
+
+    _plugins: Dict[str, _BackendPlugin] = {}
+
+    backend_list = [UNDEFINED, GLOO, NCCL, UCC, MPI]
+
+    default_device_backend_map: Dict[str, str] = {
+        "cpu": GLOO,
+        "cuda": NCCL,
+    }
+
+    backend_capability: Dict[str, List[str]] = {
+        GLOO: ["cpu", "cuda"],
+        NCCL: ["cuda"],
+        UCC: ["cpu", "cuda"],
+        MPI: ["cpu", "cuda"],
+    }
+
+    backend_type_map: Dict[str, ProcessGroup.BackendType] = {
+        UNDEFINED: ProcessGroup.BackendType.UNDEFINED,
+        GLOO: ProcessGroup.BackendType.GLOO,
+        NCCL: ProcessGroup.BackendType.NCCL,
+        UCC: ProcessGroup.BackendType.UCC,
+    }
+
+    def __new__(cls, name: str):
+        """Create and return a new instance of the class."""
+        if not isinstance(name, str):
+            raise ValueError("Backend constructor parameter must be string-ish")
+        value = getattr(Backend, name.upper(), Backend.UNDEFINED)
+
+        if value == Backend.UNDEFINED:
+            value = name.lower()
+        return value
+
+    @classmethod
+    def register_backend(
+        cls,
+        name,
+        func,
+        extended_api=False,
+        devices: Optional[Union[str, List[str]]] = None,
+    ) -> None:
+        """
+        Register a new backend with the given name and instantiating function.
+
+        This class method is used by 3rd party ``ProcessGroup`` extension to
+        register new backends.
+
+        Args:
+            name (str): Backend name of the ``ProcessGroup`` extension. It
+                        should match the one in ``init_process_group()``.
+            func (function): Function handler that instantiates the backend.
+                             The function should be implemented in the backend
+                             extension and takes four arguments, including
+                             ``store``, ``rank``, ``world_size``, and ``timeout``.
+            extended_api (bool, optional): Whether the backend supports extended argument structure.
+                                           Default: ``False``. If set to ``True``, the backend
+                                           will get an instance of ``c10d::DistributedBackendOptions``, and
+                                           a process group options object as defined by the backend implementation.
+            device (str or list of str, optional): device type this backend
+                            supports, e.g. "cpu", "cuda", etc. If `None`,
+                            assuming both "cpu" and "cuda"
+
+        .. note:: This support of 3rd party backend is experimental and subject to change.
+
+        """
+        # Allow UCC plugin if Pytorch is not built with native support.
+        # TODO: remove this exception once UCC plugin is fully deprecated.
+        if name != Backend.UCC or (name == Backend.UCC and is_ucc_available()):
+            assert not hasattr(
+                Backend, name.upper()
+            ), f"{name.upper()} c10d backend already exist"
+        assert (
+            name.upper() not in Backend._plugins
+        ), f"{name.upper()} c10d backend creator function already exist"
+
+        setattr(Backend, name.upper(), name.lower())
+        Backend.backend_list.append(name.lower())
+        if devices is not None:
+            for device in devices:
+                if device != "cpu" and device != "cuda":
+                    Backend.default_device_backend_map[device] = name.lower()
+        Backend.backend_type_map[name.lower()] = ProcessGroup.BackendType.CUSTOM
+
+        # Update device capability matrix in Backend class
+        if devices is None:
+            # This is more of a backward support for groups like `threaded`:
+            # assume default devices "cpu" and "cuda", but warn
+            warnings.warn(
+                f"Device capability of {name} unspecified, assuming `cpu` and "
+                "`cuda`. Please specify it via the `devices` argument of "
+                "`register_backend`."
+            )
+            Backend.backend_capability[name.lower()] = ["cpu", "cuda"]
+        elif isinstance(devices, str):
+            # Single device string specified. Simply convert to list.
+            Backend.backend_capability[name.lower()] = [devices]
+        else:
+            Backend.backend_capability[name.lower()] = devices
+
+        Backend._plugins[name.upper()] = Backend._BackendPlugin(func, extended_api)
+
+
+class BackendConfig:
+    """Backend configuration class."""
+
+    def __init__(self, backend: Backend):
+        """Init."""
+        self.device_backend_map: Dict[str, Backend] = {}
+        backend = str(backend)
+
+        if backend == Backend.UNDEFINED:
+            # default config when backend is not specified
+            # supported since PyTorch 2.0
+            for device, default_backend in Backend.default_device_backend_map.items():
+                if is_backend_available(default_backend):
+                    if (
+                        default_backend == Backend.NCCL
+                        and not torch.cuda.is_available()
+                    ):
+                        continue
+                    self.device_backend_map[device] = Backend(default_backend)
+        elif backend.lower() in Backend.backend_list:
+            # Cases for when backend is a single string (without device types)
+            # e.g. "nccl", "gloo", "ucc", "mpi"
+            supported_devices = Backend.backend_capability[backend.lower()]
+            backend_val = Backend(backend)
+            self.device_backend_map = dict.fromkeys(supported_devices, backend_val)
+        elif ":" in backend.lower():
+            # Backend specified in "device:backend" format
+            # make sure the backend string is in the correct format
+            # "{device_type1}:{backend1},{device_type2}:{backend2}"
+            # e.g. "cpu:gloo,cuda:nccl"
+            backend_str_error_message = f"""The custom backend string argument is invalid: {backend}.
+                Custom backend string is an experimental feature where the backend string must be in the format:
+                ":,:...". e.g. 'cpu:gloo,cuda:nccl'"""
+
+            # parse the backend string and populate the device_backend_map
+            for device_backend_pair_str in backend.lower().split(","):
+                device_backend_pair = device_backend_pair_str.split(":")
+                if len(device_backend_pair) != 2:
+                    raise ValueError(
+                        f"Invalid device:backend pairing: \
+                                     {device_backend_pair_str}. {backend_str_error_message}"
+                    )
+                device, backend = device_backend_pair
+                if device in self.device_backend_map:
+                    raise ValueError(
+                        f"Duplicate device type {device} \
+                                     in backend string: {backend}. {backend_str_error_message}"
+                    )
+                self.device_backend_map[device] = Backend(backend)
+        else:
+            # User specified a single backend name whose device capability is
+            # unknown, assuming it can support the default devices of PyTorch
+            # (cpu and cuda)
+            warnings.warn(
+                f"Device capability of {backend} unknown, assuming `cpu` and "
+                "`cuda`. You can specify it in `device:backend` format in "
+                "`init_process_group` call."
+            )
+            backend_val = Backend(backend)
+            self.device_backend_map = {
+                "cpu": backend_val,
+                "cuda": backend_val,
+                "xpu": backend_val,
+            }
+
+        logger.info("Using backend config: %s", self.device_backend_map)
+
+    def __repr__(self):
+        """Return all the device:backend pairs separated by commas."""
+        return ",".join(
+            f"{device}:{backend}" for device, backend in self.device_backend_map.items()
+        )
+
+    def get_device_backend_map(self) -> Dict[str, Backend]:
+        """Return backend map of the device."""
+        return self.device_backend_map
+
+
+class _reduce_op:
+    r"""
+    Deprecated enum-like class.
+
+    For reduction operations: ``SUM``, ``PRODUCT``, ``MIN``, and ``MAX``.
+
+    :class:`~torch.distributed.ReduceOp` is recommended to use instead.
+    """
+
+    def __init__(self) -> None:
+        # __members__ is a dict storing key-value pairs for enum classes
+        for k, v in ReduceOp.RedOpType.__members__.items():
+            setattr(self, k, v)
+        self.__members__ = ReduceOp.RedOpType.__members__
+
+    @deprecated(
+        "`torch.distributed.reduce_op` is deprecated, "
+        "please use `torch.distributed.ReduceOp` instead",
+        category=FutureWarning,
+    )
+    def __getattribute__(self, key):
+        return object.__getattribute__(self, key)
+
+
+reduce_op = _reduce_op()
+
+
+class P2POp:
+    """
+    A class to build point-to-point operations for ``batch_isend_irecv``.
+
+    This class builds the type of P2P operation, communication buffer, peer rank,
+    Process Group, and tag. Instances of this class will be passed to
+    ``batch_isend_irecv`` for point-to-point communications.
+
+    Args:
+        op (Callable): A function to send data to or receive data from a peer process.
+            The type of ``op`` is either ``torch.distributed.isend`` or
+            ``torch.distributed.irecv``.
+        tensor (Tensor): Tensor to send or receive.
+        peer (int): Destination or source rank.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        tag (int, optional): Tag to match send with recv.
+    """
+
+    def __init__(
+        self,
+        op: Callable,
+        tensor: torch.Tensor,
+        peer: int,
+        group: Optional[ProcessGroup] = None,
+        tag: int = 0,
+    ):
+        """Init."""
+        self.op = op
+        self.tensor = tensor
+        self.peer = peer
+        self.group = group
+        self.tag = tag
+
+    def __new__(
+        cls,
+        op: Callable,
+        tensor: torch.Tensor,
+        peer: int,
+        group: Optional[ProcessGroup] = None,
+        tag: int = 0,
+    ):
+        """Create and return a new instance of the class."""
+        _check_op(op)
+        _check_single_tensor(tensor, "tensor")
+        return object.__new__(cls)
+
+    def __repr__(self):
+        my_group_rank = get_rank(self.group)
+        peer_group_rank = (
+            get_group_rank(self.group, self.peer) if self.group else self.peer
+        )
+        op_name = self.op.__name__
+        group_name = self.group.group_name if self.group else "default_pg"
+        if "send" in op_name:
+            s = my_group_rank
+            d = peer_group_rank
+        elif "recv" in op_name:
+            s = peer_group_rank
+            d = my_group_rank
+        else:
+            return super().__repr__()
+
+        return f"P2POp({op_name} pg={group_name}, s={s}, d={d},  {self.tensor.shape}, {self.tensor.dtype})"
+
+
+class _CollOp:
+    """
+    A class to capture collective operations.
+
+    Args:
+        op (Callable): A collective function, e.g. ``torch.distributed.all_reduce``.
+        tensor (Tensor): Tensor to operate on.
+        dst_tensor (Tensor, optional): Provided when source and destinaton tensors are not the same.
+        redop (ReduceOp, optional): reduce operation.
+        root (int, optional): root of broadcast or reduce.
+    """
+
+    def __init__(
+        self,
+        op: Callable,
+        tensor: torch.Tensor,
+        dst_tensor: Optional[torch.Tensor] = None,
+        redop: Optional[ReduceOp] = None,
+        root: Optional[int] = None,
+    ):
+        self.op = op
+        self.tensor = tensor
+        self.dst_tensor = dst_tensor
+        self.redop = redop
+        self.root = root
+
+
+# DO NOT USE THESE FIELDS DIRECTLY.
+# Use them through the _world object to make sure the _world override mechanism
+_pg_map: Dict[ProcessGroup, Tuple[str, Store]] = {}
+_pg_names: Dict[ProcessGroup, str] = {}
+_pg_group_ranks: Dict[ProcessGroup, Dict[int, int]] = {}
+# For a pg, it is a map from ProcessGroup to BackendConfig
+_pg_backend_config: Dict[ProcessGroup, str] = {}
+_group_count = 0
+_tags_to_pg: Dict[str, List[ProcessGroup]] = {}
+_pg_to_tag: Dict[ProcessGroup, str] = {}
+_backend: Optional[str] = None
+
+
+class _World:
+    """
+    Container class for c10d process group state.
+
+    This is used during registration and lookup of PG state.
+
+    .. warning:: This is an experimental API intended to expose the inner workings
+       of c10d and is subject to change..
+    """
+
+    def __init__(self) -> None:
+        self._default_pg = None
+        self._pg_coalesce_state: Dict[ProcessGroup, List[_CollOp]] = {}
+        self._pg_default_device: Dict[ProcessGroup, torch.device] = {}
+
+    @property
+    def default_pg(self) -> Optional[ProcessGroup]:
+        """
+        Process group that includes all ranks of the cluster.
+
+        This default ProcessGroup is used by c10d APIs when a ProcessGroup is needed
+        but None is provided.
+        """
+        return self._default_pg
+
+    @default_pg.setter
+    def default_pg(self, value) -> None:
+        self._default_pg = value
+
+    @property
+    def pg_map(self) -> Dict[ProcessGroup, Tuple[str, Store]]:
+        """
+        Provide Mapping from ProcessGroup to backend name and store.
+
+        For NCCL and GLOO pg, it is a map from ProcessGroup to (Backend, Store)
+        For MPI pg, it is a map from ProcessGroup to (Backend, None)
+
+        TODO don't expose the map, expose fine grained ops
+        """
+        global _pg_map
+        return _pg_map
+
+    @property
+    def pg_names(self) -> Dict[ProcessGroup, str]:
+        """
+        Process group's names, map from ProcessGroup to str.
+
+        TODO don't expose the map, expose fine grained ops
+        """
+        global _pg_names
+        return _pg_names
+
+    @property
+    def pg_group_ranks(self) -> Dict[ProcessGroup, Dict[int, int]]:
+        """
+        Process group's global rank to local rank mapping.
+
+        TODO don't expose the map, expose fine grained ops
+        """
+        global _pg_group_ranks
+        return _pg_group_ranks
+
+    @property
+    def pg_backend_config(self) -> Dict[ProcessGroup, str]:
+        """
+        Process group's backend config.
+
+        TODO don't expose the map, expose fine grained ops
+        """
+        global _pg_backend_config
+        return _pg_backend_config
+
+    @property
+    def group_count(self) -> int:
+        """
+        Process group count for default naming.
+
+        TODO don't expose group_count, use something else instead
+        """
+        global _group_count
+        return _group_count
+
+    @group_count.setter
+    def group_count(self, value: int) -> None:
+        """Use to compute the name of ProcessGroups when using global synchronization."""
+        global _group_count
+        _group_count = value
+
+    @property
+    def tags_to_pg(self) -> Dict[str, List[ProcessGroup]]:
+        global _tags_to_pg
+        return _tags_to_pg
+
+    @property
+    def pg_to_tag(self) -> Dict[ProcessGroup, str]:
+        global _pg_to_tag
+        return _pg_to_tag
+
+    @property
+    def pg_coalesce_state(self) -> Dict[ProcessGroup, List[_CollOp]]:
+        return self._pg_coalesce_state
+
+    @property
+    def pg_default_device(self) -> Dict[ProcessGroup, torch.device]:
+        return self._pg_default_device
+
+    @property
+    def pg_config_info(self) -> List[Dict[str, Any]]:
+        """
+        Return a list of dict with process groups and backends.
+
+        Along with their unique IDs and configurations (types and ranks).
+        """
+        config_info: List[Dict[str, Any]] = []
+        default_pg_size = _get_group_size(None)
+        for pg in self.pg_map.keys():
+            ranks = self.pg_group_ranks[pg]
+            config_info.append(
+                {
+                    "pg_name": self.pg_names[pg],
+                    "pg_desc": pg.group_desc,
+                    "backend_config": self.pg_backend_config[pg],
+                    "ranks": list(ranks.keys())
+                    if len(ranks) != default_pg_size
+                    else [],  # 'ranks' is an empty list when all ranks are involved in a pg
+                    "group_size": len(ranks),
+                    "group_count": self.group_count,
+                }
+            )
+        return config_info
+
+
+_world = _World()
+"""Holds the singleton instance of ``_World`` used by c10. Experimental extension point to override it"""
+
+
+class _WorldMeta(type):
+    """
+    Meta class of ``group`` and ``GroupMember``.
+
+    Allows them to have the class property ``WORLD``.
+    """
+
+    # Points to the default PG once initialized.
+    @property
+    def WORLD(cls) -> Optional[ProcessGroup]:
+        return _world.default_pg
+
+    @WORLD.setter
+    def WORLD(cls, pg: Optional[ProcessGroup]):
+        _world.default_pg = pg
+
+
+class group(metaclass=_WorldMeta):
+    """Group class. Placeholder."""
+
+
+class GroupMember(metaclass=_WorldMeta):
+    """Group member class."""
+
+    NON_GROUP_MEMBER = -100
+
+
+def _get_default_timeout(backend: Backend) -> timedelta:
+    # see note on nccl vs other backend timeout (constants.py)
+    if backend == Backend.NCCL:
+        if not isinstance(default_pg_nccl_timeout, timedelta):
+            # TODO moco benchmark on CPU initializes pgnccl backend today, triggered this assert in CI before it was
+            # changed to be a warning.  We should fix the moco model.
+            warnings.warn(
+                "Attempted to get default timeout for nccl backend, but NCCL support is not compiled"
+            )
+            return default_pg_timeout
+        return default_pg_nccl_timeout
+    else:
+        return default_pg_timeout
+
+
+def _check_valid_timeout(timeout: Any) -> None:
+    if not isinstance(timeout, timedelta):
+        raise TypeError(
+            f"Expected timeout argument to be of type datetime.timedelta, got {timeout}"
+        )
+
+
+# Default process group state
+_default_pg_init_method: Optional[str] = None
+
+STORE_BASED_BARRIER_PREFIX = "store_based_barrier_key"
+
+
+def _get_pg_default_device(group: Optional[ProcessGroup] = None) -> torch.device:
+    """
+    Return the device to use with ``group`` for control flow usage (object collectives, barrier).
+
+    There are selection rules:
+        1. If user specifies exactly one backend in ``init_process_group`` call:
+            use that backend
+        2. Else if user specifies multiple "device:backend" pairs in init_process_group:
+            If "cpu" is among those pairs, use "cpu" (because the object is in cpu memory);
+            Otherwise, use the first backend (sort of a random pick).
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+
+    Returns:
+        torch.device: The device to use with ``group``.
+
+    """
+    group = group or _get_default_group()
+    if group in _world.pg_default_device:
+        # Previously searched and cached; just return
+        return _world.pg_default_device[group]
+
+    if not isinstance(group, ProcessGroup):
+        # Provide backward compatibility to cases where `group` passed in is
+        # actually a Backend (like `ProcessGroupGloo`) rather than a
+        # `ProcessGroup` in PT 2.0 sense
+        warnings.warn(
+            f"You are using a Backend {type(group)} as a ProcessGroup. "
+            "This usage is deprecated since PyTorch 2.0. Please use a public API "
+            "of PyTorch Distributed instead.",
+            FutureWarning,
+            stacklevel=3,
+        )
+        # Most users create Gloo with private API for object collectives
+        _world.pg_default_device[group] = torch.device("cpu")
+        return _world.pg_default_device[group]
+
+    """
+    ``group._device_types`` is a property pybind that returns the devices
+    ("cpu", "cuda", etc) supported by ``group``. Can be multiple if the
+    ``group`` supports multiple devices.
+    """
+    devices = group._device_types
+
+    if len(devices) == 1:
+        # User fixed exactly one backend in `init_process_group`
+        _world.pg_default_device[group] = devices[0]
+    elif len(devices) == 0:
+        # No backend has been registered with this PG (maybe because no
+        # collective has been run?) We pick cpu as the default and hopefully
+        # this would lazily init Gloo or other available cpu backend.
+        _world.pg_default_device[group] = torch.device("cpu")
+    elif torch.device("cpu") in devices:
+        # There are multiple backends in this PG and cpu is among them.
+        # cpu is preferred as the object is in cpu memory. No need for device
+        # copy.
+        _world.pg_default_device[group] = torch.device("cpu")
+    else:
+        # No cpu in the backend list. Randomly pick the first backend
+        _world.pg_default_device[group] = devices[0]
+
+    logger.info(
+        "Using device %s for object " "collectives.", _world.pg_default_device[group]
+    )
+    return _world.pg_default_device[group]
+
+
+@_time_logger
+def _store_based_barrier(
+    rank,
+    store,
+    group_name,
+    rendezvous_count,
+    timeout,
+    logging_interval=timedelta(seconds=10),
+) -> None:
+    """
+    Store based barrier for synchronizing processes.
+
+    Barrier based on store which is used for synchronizing processes after
+    ``init_process_group`` or ``new_group``. Intended to be used only with
+    those two methods and is not a generic alternative to ``barrier()``.
+    """
+    store_key = f"{STORE_BASED_BARRIER_PREFIX}:{group_name}"
+    store.add(store_key, 1)
+    logger.debug("Added key: %s to store for rank: %s", store_key, rank)
+
+    # Now wait for all workers to check in with the store.
+    world_size = rendezvous_count
+    worker_count = store.add(store_key, 0)
+
+    last_worker_key = f"{store_key}:last_worker"
+    if worker_count == world_size:
+        store.set(last_worker_key, "1")
+
+    # adjust the timeout to be at least 10secs + 1sec per thousand ranks to reduce the odds of timeout
+    # this value was empirically found while scale testing.
+    logging_interval = max(logging_interval, timedelta(seconds=10 + world_size / 1000))
+
+    start = time.time()
+    while True:
+        try:
+            # This will throw an exception after the logging_interval in which we print out
+            # the status of the group or time out officially, throwing runtime error
+            store.wait([last_worker_key], logging_interval)
+            break
+        except RuntimeError as e:
+            worker_count = store.add(store_key, 0)
+            # Print status periodically to keep track.
+            logger.debug(
+                "Waiting in store based barrier to initialize process group for "
+                "rank: %s, key: %s (world_size=%s, num_workers_joined=%s, timeout=%s error=%s)",
+                rank,
+                store_key,
+                world_size,
+                worker_count,
+                timeout,
+                e,
+            )
+
+            if timedelta(seconds=(time.time() - start)) > timeout:
+                raise DistStoreError(  # noqa: B904
+                    "Timed out initializing process group in store based barrier on "
+                    f"rank {rank}, for key: {store_key} (world_size={world_size}, "
+                    f"num_workers_joined={worker_count}, timeout={timeout} error={e})"
+                )
+
+    logger.info(
+        "Rank %s: Completed store-based barrier for key:%s with %s nodes.",
+        rank,
+        store_key,
+        world_size,
+    )
+
+
+def _rank_not_in_group(group: Optional[ProcessGroup]) -> bool:
+    """Check if the current process's rank is not in a given group."""
+    if group is None:
+        return False
+    return group == GroupMember.NON_GROUP_MEMBER
+
+
+def _warn_not_in_group(op_name) -> None:
+    global_rank = -1 if GroupMember.WORLD is None else GroupMember.WORLD.rank()
+    warnings.warn(
+        f"Running {op_name} on global rank {global_rank} which does not "
+        "belong to the given group."
+    )
+
+
+def get_group_rank(group: ProcessGroup, global_rank: int) -> int:
+    """
+    Translate a global rank into a group rank.
+
+    ``global_rank`` must be part of ``group`` otherwise this raises RuntimeError.
+
+    Args:
+        group (ProcessGroup): ProcessGroup to find the relative rank.
+        global_rank (int): Global rank to query.
+
+    Returns:
+        Group rank of ``global_rank`` relative to ``group``
+
+    N.B. calling this function on the default process group returns identity
+    """
+    if group is GroupMember.WORLD:
+        return global_rank
+    if group not in _world.pg_group_ranks:
+        raise ValueError(
+            f"Group {group} is not registered, please create group with torch.distributed.new_group API"
+        )
+    group_ranks = _world.pg_group_ranks[group]
+    if global_rank not in group_ranks:
+        raise ValueError(f"Global rank {global_rank} is not part of group {group}")
+
+    return group_ranks[global_rank]
+
+
+def get_global_rank(group: ProcessGroup, group_rank: int) -> int:
+    """
+    Translate a group rank into a global rank.
+
+    ``group_rank`` must be part of `group` otherwise this raises RuntimeError.
+
+    Args:
+        group (ProcessGroup): ProcessGroup to find the global rank from.
+        group_rank (int): Group rank to query.
+
+    Returns:
+        Global rank of ``group_rank`` relative to ``group``
+
+    N.B. calling this function on the default process group returns identity
+    """
+    if group is GroupMember.WORLD:
+        return group_rank
+    if group not in _world.pg_group_ranks:
+        raise ValueError(
+            f"Group {group} is not registered, please create group with torch.distributed.new_group API"
+        )
+    for rank, grp_rank in _world.pg_group_ranks[group].items():
+        if grp_rank == group_rank:
+            return rank
+    raise ValueError(f"Group rank {group_rank} is not part of group {group}")
+
+
+# TODO: remove this once the ecosystem moves away from it.
+@deprecated(
+    "`torch.distributed.distributed_c10d._get_global_rank` is deprecated, "
+    "please use `torch.distributed.distributed_c10d.get_global_rank` instead",
+    category=FutureWarning,
+)
+def _get_global_rank(group, rank) -> int:
+    """Use get_global_rank as this method is deprecated."""
+    return get_global_rank(group, rank)
+
+
+def get_process_group_ranks(group: ProcessGroup) -> List[int]:
+    """
+    Get all ranks associated with ``group``.
+
+    Args:
+        group (ProcessGroup): ProcessGroup to get all ranks from.
+
+    Returns:
+        List of global ranks ordered by group rank.
+    """
+    return list(_world.pg_group_ranks[group].keys())
+
+
+def _get_group_size(group) -> int:
+    """Get a given group's world size."""
+    if group is GroupMember.WORLD or group is None:
+        default_pg = _get_default_group()
+        return default_pg.size()
+    return group.size()
+
+
+def _get_group_size_by_name(group_name: str) -> int:
+    group = _resolve_process_group(group_name)
+    return group.size()
+
+
+def _resolve_group_name_by_ranks_and_tag(ranks: List[int], tag: str) -> str:
+    # TODO(yifu): remove this function once ranks + tag is not a supported
+    # identifier for process group for functional collectives.
+    group = _find_pg_by_ranks_and_tag(tag, ranks)
+    if group is None:
+        raise ValueError("")
+    return group.group_name
+
+
+def _check_single_tensor(param, param_name) -> None:
+    """Check that the parameter ``param_name`` is a single tensor."""
+    if not isinstance(param, torch.Tensor):
+        raise TypeError(
+            f"""Invalid function argument. Expected parameter `{param_name}` of type torch.Tensor
+             but got {type(param)} instead."""
+        )
+
+
+def _check_tensor_list(param, param_name) -> None:
+    """Check that the parameter ``param_name`` is a list of tensors."""
+    if not isinstance(param, list):
+        raise TypeError(
+            f"""Invalid function argument. Expected parameter `{param_name}` of type List[torch.Tensor]
+             but got {type(param)} instead."""
+        )
+    elif not all(isinstance(p, torch.Tensor) for p in param):
+        raise TypeError(
+            f"""Invalid function argument. Expected parameter `{param_name}` of type List[torch.Tensor]
+             but got {type(param)} with elements of type {[type(p) for p in param]}."""
+        )
+
+
+def _as_iterable(obj) -> collections.abc.Iterable:
+    return obj if isinstance(obj, list) else (obj,)
+
+
+def _ensure_all_tensors_same_dtype(*tensors) -> None:
+    last_dtype = None
+    for tensor in itertools.chain.from_iterable(map(_as_iterable, tensors)):
+        tensor_dtype = tensor.dtype
+        # Mixing complex and its element type is allowed
+        if tensor_dtype.is_complex:
+            tensor_dtype = (
+                torch.float32 if tensor_dtype == torch.complex64 else torch.complex128
+            )
+
+        if last_dtype is None:
+            last_dtype = tensor_dtype
+        else:
+            if last_dtype != tensor_dtype:
+                raise ValueError(
+                    "Invalid usage of tensors with different dtypes"
+                    f"Found {last_dtype} and  {tensor.dtype}"
+                )
+
+
+def _check_op(op) -> None:
+    """Check that the ``op`` is either isend or irecv."""
+    if op not in [isend, irecv]:
+        raise ValueError(
+            "Invalid ``op``. Expected ``op`` "
+            "to be of type ``torch.distributed.isend`` or "
+            "``torch.distributed.irecv``."
+        )
+
+
+def _check_p2p_op_list(p2p_op_list) -> None:
+    """
+    Check that the ``p2p_op_list`` is a list of P2POp instances.
+
+    Also, check that all ops use the same group.
+    """
+    if not isinstance(p2p_op_list, list) or not all(
+        isinstance(p2p_op, P2POp) for p2p_op in p2p_op_list
+    ):
+        raise ValueError(
+            "Invalid ``p2p_op_list``. Each op is expected to "
+            "to be of type ``torch.distributed.P2POp``."
+        )
+
+    group = p2p_op_list[0].group
+    if not all(group == p2p_op.group for p2p_op in p2p_op_list):
+        raise ValueError("All ops need to use the same group.")
+
+
+def is_mpi_available() -> bool:
+    """Check if the MPI backend is available."""
+    return _MPI_AVAILABLE
+
+
+def is_nccl_available() -> bool:
+    """Check if the NCCL backend is available."""
+    return _NCCL_AVAILABLE
+
+
+def is_gloo_available() -> bool:
+    """Check if the Gloo backend is available."""
+    return _GLOO_AVAILABLE
+
+
+def is_ucc_available() -> bool:
+    """Check if the UCC backend is available."""
+    return _UCC_AVAILABLE
+
+
+def is_backend_available(backend: str) -> bool:
+    """
+    Check backend availability.
+
+    Checks if the given backend is available and supports the built-in backends or
+    third-party backends through function ``Backend.register_backend``.
+
+    Args:
+        backend (str): Backend name.
+    Returns:
+        bool: Returns true if the backend is available otherwise false.
+    """
+    # If the backend has an ``is_backend_available`` function, return the result of that function directly
+    available_func = getattr(torch.distributed, f"is_{backend.lower()}_available", None)
+    if available_func:
+        return available_func()
+
+    return backend.lower() in Backend.backend_list
+
+
+def is_initialized() -> bool:
+    """Check if the default process group has been initialized."""
+    return GroupMember.WORLD is not None
+
+
+def is_torchelastic_launched() -> bool:
+    """
+    Check whether this process was launched with ``torch.distributed.elastic`` (aka torchelastic).
+
+    The existence of ``TORCHELASTIC_RUN_ID`` environment
+    variable is used as a proxy to determine whether the current process
+    was launched with torchelastic. This is a reasonable proxy since
+    ``TORCHELASTIC_RUN_ID`` maps to the rendezvous id which is always a
+    non-null value indicating the job id for peer discovery purposes..
+    """
+    return os.getenv("TORCHELASTIC_RUN_ID") is not None
+
+
+def _is_barrier_after_init() -> int:
+    # Environment variable to control whether process group should perform a
+    # barrier after its init. Default value is 0, i.e. no barrier. If you
+    # experience issue with this setting, you may set
+    # `TORCH_DIST_INIT_BARRIER=1` to add the barrier.
+    return int(os.getenv("TORCH_DIST_INIT_BARRIER", "0"))
+
+
+def _get_default_group() -> ProcessGroup:
+    """Get the default process group created by init_process_group."""
+    if not is_initialized():
+        raise ValueError(
+            "Default process group has not been initialized, "
+            "please make sure to call init_process_group."
+        )
+    if TYPE_CHECKING:
+        return not_none(GroupMember.WORLD)
+    else:
+        return GroupMember.WORLD
+
+
+def _get_default_store() -> Store:
+    """Get the default store created by init_process_group."""
+    if not is_initialized():
+        raise ValueError(
+            "Default process group has not been initialized, "
+            "please make sure to call init_process_group."
+        )
+    default_pg = _get_default_group()
+    _, default_store = _world.pg_map[default_pg]
+    return default_store
+
+
+def _update_default_pg(pg) -> None:
+    _world.default_pg = pg
+    rank = pg.rank() if pg is not None and pg != GroupMember.NON_GROUP_MEMBER else -1
+    torch._C._distributed_c10d._set_global_rank(rank)
+
+
+def get_backend_config(group: Optional[ProcessGroup] = None) -> str:
+    """
+    Return the backend configuration of the given process group.
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. The
+            default is the general main process group. If another specific group
+            is specified, the calling process must be part of :attr:`group`.
+
+    Returns:
+        The backend configuration of the given process group as a lower case string.
+
+    """
+    pg = group or _get_default_group()
+    if _rank_not_in_group(pg):
+        raise ValueError("Invalid process group specified")
+    backend_config = _world.pg_backend_config.get(pg)
+    return str(not_none(backend_config))
+
+
+def get_backend(group: Optional[ProcessGroup] = None) -> Backend:
+    """
+    Return the backend of the given process group.
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. The
+            default is the general main process group. If another specific group
+            is specified, the calling process must be part of :attr:`group`.
+
+    Returns:
+        The backend of the given process group as a lower case string.
+
+    """
+    pg = group or _get_default_group()
+    if _rank_not_in_group(pg):
+        raise ValueError("Invalid process group specified")
+    pg_store = _world.pg_map[pg] if pg in _world.pg_map else None
+    return Backend(not_none(pg_store)[0])
+
+
+def _get_process_group_uid(pg: ProcessGroup) -> int:
+    backend = None
+    try:
+        backend = pg._get_backend(torch.device("cuda"))
+    except RuntimeError:
+        pass
+    if is_nccl_available() and isinstance(backend, ProcessGroupNCCL):
+        return backend.uid
+    return -1
+
+
+def _get_pg_config(group: Optional[ProcessGroup] = None) -> Dict[str, Any]:
+    """
+    Return the pg configuration of the given process group.
+
+    """
+    pg = group or _get_default_group()
+    return {
+        "pg_name": _get_process_group_name(pg),
+        "pg_desc": pg.group_desc,
+        "backend_config": get_backend_config(pg),
+        "pg_size": _get_group_size(pg),
+        "ranks": get_process_group_ranks(pg),
+    }
+
+
+def _get_all_pg_configs() -> List[Dict[str, Any]]:
+    """
+    Return the pg configuration of all the process groups.
+
+    """
+    config_info: List[Dict[str, Any]] = []
+    for pg in _world.pg_map.keys():
+        config_info.append(_get_pg_config(pg))
+    return config_info
+
+
+def get_pg_count() -> int:
+    """
+    Return the number of process groups.
+
+    """
+    return _world.group_count
+
+
+def get_node_local_rank(fallback_rank: Optional[int] = None) -> int:
+    """
+    Return the local rank of the current process relative to the node.
+
+    Semantically, this is a useful concept for mapping processes to devices.
+    For example, on a node with 8 accelerator you could use the node local rank to decide
+    which accelerator device to bind the process to.
+
+    In practice, the actual assignment of node local ranks is handled by the process launcher outside of pytorch,
+    and communicated via the `LOCAL_RANK` environment variable.
+
+    Torchrun will automatically populate `LOCAL_RANK`, but other launchers may not.  If `LOCAL_RANK` is unspecified,
+    this API will fall back to the provided kwarg 'fallback_rank' if specified, otherwise it will raise an error. The
+    intent is to allow writing an application that runs either in single or multi device contexts without error.
+
+    """
+    if "LOCAL_RANK" in os.environ:
+        return int(os.environ["LOCAL_RANK"])
+    elif fallback_rank is not None:
+        return int(fallback_rank)
+    raise RuntimeError(
+        "LOCAL_RANK is not in the environment. Consider passing fallback_rank to allow `get_node_local_rank` to work, "
+        "assuming you are not running in a multi-device context and want the code to run locally instead."
+    )
+
+
+def _add_ephemeral_timeout_for_all_pgs(timeout: timedelta) -> None:
+    """
+    This API adds an ephemeral timeout extension for all PGs locally
+    on one rank. The timeout gets reset when the first collective issued
+    after API called finished.
+    NOTE: We only support to set timeout for cuda backends for now.
+    NOTE: While this feature
+    provides flexibility in specific scenarios, it introduces statefulness
+    to timeout setting. Therefore, it is advisable to use this API sparingly
+    and consider alternative approaches, such as directly setting the timeout
+    or utilizing a barrier collective (one can set any timeout to the barrier),
+    whenever feasible.
+
+    Args:
+        timeout (timedelta): The delta of timeout to extend.
+
+    Returns:
+        None.
+    """
+    for pg in _world.pg_map.keys():
+        devices = pg._device_types
+        if torch.device("cuda") in devices:
+            backend = pg._get_backend(torch.device("cuda"))
+            if is_nccl_available() and isinstance(backend, ProcessGroupNCCL):
+                backend._add_ephemeral_timeout(timeout)
+
+
+def _set_pg_timeout(timeout: timedelta, group: Optional[ProcessGroup] = None) -> None:
+    """
+    Set the timeout for the given process group when users want to use a different timeout instead of
+    default values.
+
+    Args:
+        timeout (timedelta): Timeout for operations executed against the process group which
+            users want to set. Default value is 10 minutes for NCCL and 30 minutes for other backends.
+            This is the duration after which collectives will be aborted asynchronously and the process will crash.
+            This is done since CUDA execution is async and it is no longer safe to continue executing user code since
+            failed async NCCL operations might result in subsequent CUDA operations running on corrupted data.
+            When TORCH_NCCL_BLOCKING_WAIT is set, the process will block and wait for this timeout.
+
+        group (ProcessGroup, optional): The process group to work on. The
+            default is the general main process group. If another specific group
+            is specified, the calling process must be part of :attr:`group`.
+
+    Returns:
+        None
+    """
+    if group is None:
+        group = _get_default_group()
+    if _rank_not_in_group(group):
+        raise ValueError("Invalid process group specified")
+    assert isinstance(group, ProcessGroup)
+    devices = group._device_types
+    backends = set()
+    if torch.device("cpu") in devices and is_gloo_available():
+        backend = group._get_backend(torch.device("cpu"))
+        if isinstance(backend, ProcessGroupGloo):
+            backends.add(backend)
+    if torch.device("cuda") in devices:
+        backend = group._get_backend(torch.device("cuda"))
+        if is_nccl_available() and isinstance(backend, ProcessGroupNCCL):
+            backends.add(backend)  # type: ignore[arg-type]
+        elif is_gloo_available() and isinstance(backend, ProcessGroupGloo):
+            backends.add(backend)  # type: ignore[arg-type]
+    if len(backends) == 0:
+        warnings.warn("Set timeout is now only supported for either nccl or gloo.")
+    for backend in backends:
+        backend._set_default_timeout(timeout)
+
+
+@_exception_logger
+@_time_logger
+def init_process_group(
+    backend: Optional[str] = None,
+    init_method: Optional[str] = None,
+    timeout: Optional[timedelta] = None,
+    world_size: int = -1,
+    rank: int = -1,
+    store: Optional[Store] = None,
+    group_name: str = "",
+    pg_options: Optional[Any] = None,
+    device_id: Optional[torch.device] = None,
+) -> None:
+    """
+    Initialize the default distributed process group.
+
+    This will also initialize the distributed package.
+
+    There are 2 main ways to initialize a process group:
+        1. Specify ``store``, ``rank``, and ``world_size`` explicitly.
+        2. Specify ``init_method`` (a URL string) which indicates where/how
+           to discover peers. Optionally specify ``rank`` and ``world_size``,
+           or encode all required parameters in the URL and omit them.
+
+    If neither is specified, ``init_method`` is assumed to be "env://".
+
+
+    Args:
+        backend (str or Backend, optional): The backend to use. Depending on
+            build-time configurations, valid values include ``mpi``, ``gloo``,
+            ``nccl``, and ``ucc``. If the backend is not provided, then both a ``gloo``
+            and ``nccl`` backend will be created, see notes below for how multiple
+            backends are managed. This field can be given as a lowercase string
+            (e.g., ``"gloo"``), which can also be accessed via
+            :class:`Backend` attributes (e.g., ``Backend.GLOO``). If using
+            multiple processes per machine with ``nccl`` backend, each process
+            must have exclusive access to every GPU it uses, as sharing GPUs
+            between processes can result in deadlocks. ``ucc`` backend is
+            experimental.
+        init_method (str, optional): URL specifying how to initialize the
+                                     process group. Default is "env://" if no
+                                     ``init_method`` or ``store`` is specified.
+                                     Mutually exclusive with ``store``.
+        world_size (int, optional): Number of processes participating in
+                                    the job. Required if ``store`` is specified.
+        rank (int, optional): Rank of the current process (it should be a
+                              number between 0 and ``world_size``-1).
+                              Required if ``store`` is specified.
+        store(Store, optional): Key/value store accessible to all workers, used
+                                to exchange connection/address information.
+                                Mutually exclusive with ``init_method``.
+        timeout (timedelta, optional): Timeout for operations executed against
+            the process group. Default value is 10 minutes for NCCL and 30 minutes for other backends.
+            This is the duration after which collectives will be aborted asynchronously and the process will crash.
+            This is done since CUDA execution is async and it is no longer safe to continue executing user code since
+            failed async NCCL operations might result in subsequent CUDA operations running on corrupted data.
+            When TORCH_NCCL_BLOCKING_WAIT is set, the process will block and wait for this timeout.
+
+        group_name (str, optional, deprecated): Group name. This argument is ignored
+        pg_options (ProcessGroupOptions, optional): process group options
+            specifying what additional options need to be passed in during
+            the construction of specific process groups. As of now, the only
+            options we support is ``ProcessGroupNCCL.Options`` for the ``nccl``
+            backend, ``is_high_priority_stream`` can be specified so that
+            the nccl backend can pick up high priority cuda streams when
+            there're compute kernels waiting. For other availble options to config nccl,
+            See https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/api/types.html#ncclconfig-t
+        device_id (torch.device, optional): a single, specific device
+            to "bind" this process to, allowing for backend-specific
+            optimizations.  Currently this has two effects, only under
+            NCCL: the communicator is immediately formed (calling
+            ``ncclCommInit*`` immediately rather than the normal lazy
+            call) and sub-groups will use ``ncclCommSplit`` when
+            possible to avoid unnecessary overhead of group creation. If you
+            want to know NCCL initialization error early, you can also use this
+            field.
+
+    .. note:: To enable ``backend == Backend.MPI``, PyTorch needs to be built from source
+        on a system that supports MPI.
+
+    .. note:: Support for multiple backends is experimental. Currently when no backend is
+        specified, both ``gloo`` and ``nccl`` backends will be created. The ``gloo`` backend
+        will be used for collectives with CPU tensors and the ``nccl`` backend will be used
+        for collectives with CUDA tensors. A custom backend can be specified by passing in
+        a string with format ":,:", e.g.
+        "cpu:gloo,cuda:custom_backend".
+
+    """
+
+    global _world
+
+    global _backend
+    global _default_pg_init_method
+
+    if GroupMember.WORLD is not None:
+        raise ValueError("trying to initialize the default process group twice!")
+
+    set_pytorch_distributed_envs_from_justknobs()
+
+    # Depending on the import order, some trace_rules functions may be evaluated
+    # during the import phase. In such a case, these functions may not correctly
+    # add the distributed related rules due to import circular dependency.
+    # We need to clear the lru_cache during the runtime to ensure the correctness
+    # of these trace_rules.
+    #
+    # Since this API must be called before all distributed code being compiled,
+    # clearing the cache here should be safe.
+    if "torch._dynamo" in sys.modules:
+        torch._dynamo.trace_rules.clear_lru_cache()
+
+    assert (store is None) or (
+        init_method is None
+    ), "Cannot specify both init_method and store."
+
+    if store is not None:
+        assert world_size > 0, "world_size must be positive if using store"
+        assert rank >= 0, "rank must be non-negative if using store"
+    elif init_method is None:
+        init_method = "env://"
+
+    if backend:
+        backend = Backend(backend)
+    else:
+        backend = Backend("undefined")
+
+    if timeout is None:
+        timeout = _get_default_timeout(backend)
+
+    _check_valid_timeout(timeout)
+
+    """
+    Group name is not visible to users unless they access
+    internals of c10d. This means we can ignore the value
+    they provide as it not exposed in a public way.
+    """
+    group_name = _process_group_name([], use_hashed_name=False)
+    if backend == Backend.MPI:
+        if world_size != -1 or rank != -1:
+            warnings.warn(
+                f"For MPI backend, world_size ({world_size}) and rank ({rank}) "
+                "are ignored since they are assigned by the "
+                "MPI runtime."
+            )
+
+        default_pg, _ = _new_process_group_helper(
+            -1,
+            -1,
+            [],
+            backend,
+            None,
+            group_name,
+            timeout=timeout,
+            group_desc="default_pg",
+        )
+        _update_default_pg(default_pg)
+    else:
+        # backward compatible API
+        if store is None:
+            rendezvous_iterator = rendezvous(
+                not_none(init_method), rank, world_size, timeout=timeout
+            )
+            store, rank, world_size = next(rendezvous_iterator)
+            store.set_timeout(timeout)
+
+            # Use a PrefixStore to avoid accidental overrides of keys used by
+            # different systems (e.g. RPC) in case the store is multi-tenant.
+            store = PrefixStore("default_pg", store)
+
+        default_pg, _ = _new_process_group_helper(
+            world_size,
+            rank,
+            [],
+            backend,
+            store,
+            group_name,
+            pg_options=pg_options,
+            timeout=timeout,
+            device_id=device_id,
+            group_desc="default_pg",
+        )
+        _update_default_pg(default_pg)
+
+    _world.pg_group_ranks[GroupMember.WORLD] = {i: i for i in range(GroupMember.WORLD.size())}  # type: ignore[attr-defined, index]
+    _backend = _world.pg_map[not_none(GroupMember.WORLD)][0]
+    _default_pg_init_method = init_method
+
+    old_hook = sys.excepthook
+    excepthook_prefix = f"[rank{get_rank()}]"
+
+    def _distributed_excepthook(*args):
+        old_stderr = sys.stderr
+        sys.stderr = buf = io.StringIO()
+        try:
+            old_hook(*args)
+        finally:
+            sys.stderr = old_stderr
+        msg = buf.getvalue()
+        msg = "\n".join(
+            f"{excepthook_prefix}: {s}" if s != "" else "" for s in msg.split("\n")
+        )
+        sys.stderr.write(msg)
+        sys.stderr.flush()
+
+    sys.excepthook = _distributed_excepthook
+
+    if _is_barrier_after_init() == 1:
+        # barrier at the end to ensure that once we return from this method, all
+        # process groups including global variables (if any) are updated
+        # correctly on all ranks.
+        # Update 04/2023: for large-scale runs, this barrier (esp. store-based
+        # barrier) may be costly and/or unscalable. Also, in a lot of cases,
+        # these barriers may be unnecessary, as proven by a green CI after
+        # removal. An environment variable `TORCH_DIST_INIT_BARRIER` has been
+        # added which enables this barrier only when set to 1.
+        logger.debug(
+            "Performing barrier after ProcessGroup initialization since "
+            "TORCH_DIST_INIT_BARRIER = 1"
+        )
+        if backend == Backend.MPI:
+            # MPI backend doesn't use store.
+            barrier()
+        else:
+            # Use store based barrier here since barrier() used a bunch of
+            # default devices and messes up NCCL internal state.
+            _store_based_barrier(rank, store, group_name, world_size, timeout)
+
+
+def _get_split_source(pg):
+    split_from = None
+    if pg.bound_device_id:
+        split_from = pg._get_backend(pg.bound_device_id)
+    elif pg is _world.default_pg:
+        try:
+            split_from = pg._get_backend(torch.device("cuda"))
+        except RuntimeError:
+            # no cuda device associated with this backend
+            pass
+
+    if not split_from or not split_from.supports_splitting:
+        return None
+
+    # If necessary, find a backend to split from by peeling process
+    # group wrappers from our potentially wrapped process group.
+    while _GLOO_AVAILABLE and isinstance(split_from, _ProcessGroupWrapper):
+        split_from = split_from.wrapped_pg
+
+    return split_from
+
+
+def _shutdown_backend(pg):
+    """
+    Try to shut down the backend of a process group.
+    Currently, only ProcessGroupNCCL backend is supported.
+    No op for other backends.
+    """
+    backend = None
+    try:
+        backend = pg._get_backend(torch.device("cuda"))
+    except RuntimeError:
+        pass
+    if is_nccl_available() and isinstance(backend, ProcessGroupNCCL):
+        # explictly call shutdown to ensure that NCCL resources are released
+        backend._shutdown()
+
+
+def _new_process_group_helper(
+    group_size,
+    group_rank,
+    global_ranks_in_group,
+    backend,
+    store,
+    group_name,
+    pg_options=None,
+    timeout=None,
+    pg_tag=None,
+    device_id=None,
+    group_desc=None,
+):
+    """
+    Create a new distributed process group.
+
+    This function must be called by ALL processes in the global group, even if
+    the calling process is not part of the newly created group. In that case,
+    this function returns GroupMember.NON_GROUP_MEMBER.
+
+    This function is called with ``global_ranks_in_group == []`` for the default group.
+    """
+    global _world
+
+    if group_name in _world.pg_names.values():
+        raise ValueError(
+            "The specified group name has already been "
+            "created, please use a different group name"
+        )
+
+    if device_id is not None and (device_id.index is None or device_id.type != "cuda"):
+        raise ValueError(
+            "init_process_group device_id parameter must be a cuda device with an "
+            "id, e.g. cuda:0, not just cuda or cpu"
+        )
+
+    # Note: _new_process_group_helper is only called from init_process_group, which always provides a timeout value
+    _check_valid_timeout(timeout)
+
+    if pg_tag not in [None, ""]:
+        # creating with the same tag and rank set results in the same underlying PG
+        existing_group = _find_pg_by_ranks_and_tag(pg_tag, global_ranks_in_group)
+        if existing_group:
+            _, prefix_store = _world.pg_map[existing_group]
+            return existing_group, prefix_store
+
+    group_desc = "undefined" if group_desc is None else group_desc
+
+    # The list of group ranks is empty if we're creating the default group.
+    is_default_group = len(global_ranks_in_group) == 0
+
+    # nccl and potentially other backends allow creation of
+    # communicators based on pre-existing ones, which can save
+    # initialization time.  Due to lazy initialization of
+    # communicators in some backends, we have to be careful and only
+    # split when we *know* the backends already are connected _on all
+    # ranks_.  We can only know this if the group we are making is the
+    # entire world or if we have bound a device id to the world (which
+    # causes early connection initialization).
+    if is_initialized() and (
+        len(global_ranks_in_group) == _get_default_group().size()
+        or _get_default_group().bound_device_id
+    ):
+        split_from = _get_split_source(_get_default_group())
+    else:
+        split_from = None
+
+    # If this is a subgroup (which means group_ranks is specified),
+    # we check if the current process is a member of the new group.
+    if not is_default_group:
+        global_rank = _get_default_group().rank()
+        if global_rank not in global_ranks_in_group:
+            # If we are using `ncclCommSplit` (or similar split from
+            # other APIs) to create the communicator, we will need to
+            # call `ncclCommSplit` on *all* ranks in this new group's
+            # parent group, even those not in the new group.  This is
+            # a requirement of the NCCL API as otherwise we would get
+            # out of sync.
+            if split_from:
+                split_from.perform_nocolor_split(_get_default_group().bound_device_id)
+            return GroupMember.NON_GROUP_MEMBER, None
+
+    prefix_store = PrefixStore(f"{group_name}/", store)
+    base_pg_options = ProcessGroup.Options(backend=str(backend))
+    base_pg_options._timeout = timeout
+    pg: ProcessGroup = ProcessGroup(
+        prefix_store, group_rank, group_size, base_pg_options
+    )
+    if device_id:
+        pg.bound_device_id = device_id
+    backend_config = BackendConfig(backend)
+    backend_class: torch._C._distributed_c10d.Backend
+    for device, backend_str in backend_config.get_device_backend_map().items():
+        # Use the group name as prefix in the default store, such that
+        # a single store can be reused by multiple groups.
+        backend_prefix_store = PrefixStore(f"{device}/", prefix_store)
+
+        if backend_str == Backend.MPI:
+            if not is_mpi_available():
+                raise RuntimeError(
+                    "Distributed package doesn't have MPI built in."
+                    " MPI is only included if you build PyTorch from"
+                    " source on a host that has MPI installed."
+                )
+            backend_class = ProcessGroupMPI.create(global_ranks_in_group)
+            backend_type = ProcessGroup.BackendType.MPI
+            if not backend_class:
+                return GroupMember.NON_GROUP_MEMBER, None
+            # create new process group with accurate rank and size
+            if pg.rank() == -1 and pg.size() == -1:
+                pg = ProcessGroup(
+                    backend_prefix_store,
+                    backend_class.rank(),
+                    backend_class.size(),
+                    base_pg_options,
+                )
+        elif backend_str == Backend.GLOO:
+            # TODO: remove this check after lazy initialization is supported
+            # if pg_options is not None:
+            #     raise RuntimeError("GLOO options not supported")
+            backend_class = ProcessGroupGloo(
+                backend_prefix_store, group_rank, group_size, timeout=timeout
+            )
+            backend_type = ProcessGroup.BackendType.GLOO
+        elif backend_str == Backend.NCCL:
+            if not is_nccl_available():
+                raise RuntimeError("Distributed package doesn't have NCCL built in")
+            if pg_options is not None:
+                assert isinstance(
+                    pg_options, ProcessGroupNCCL.Options
+                ), "Expected pg_options argument to be of type ProcessGroupNCCL.Options"
+                if pg_options._timeout != timeout:
+                    warnings.warn(
+                        "pg_options._timeout was specified, "
+                        "but timeout kwarg has a default value that will always override it. "
+                    )
+            else:
+                # default pg_options for NCCL
+                pg_options = ProcessGroupNCCL.Options()
+                pg_options.is_high_priority_stream = False
+            pg_options._timeout = timeout
+
+            if split_from:
+                pg_options.split_from = split_from
+                pg_options.split_color = _process_group_color(global_ranks_in_group)
+            pg_options.global_ranks_in_group = global_ranks_in_group
+            pg_options.group_name = group_name
+            backend_class = ProcessGroupNCCL(
+                backend_prefix_store, group_rank, group_size, pg_options
+            )
+            backend_type = ProcessGroup.BackendType.NCCL
+        elif backend_str == Backend.UCC and is_ucc_available():
+            # TODO: once UCC plugin is fully deprecated, remove
+            # is_ucc_available() from above elif-condition and raise
+            # RuntimeError if is_ucc_available() returns false.
+
+            backend_class = ProcessGroupUCC(
+                backend_prefix_store, group_rank, group_size, timeout=timeout
+            )
+            backend_type = ProcessGroup.BackendType.UCC
+        else:
+            assert (
+                backend_str.upper() in Backend._plugins
+            ), f"Unknown c10d backend type {backend_str.upper()}"
+
+            backend_plugin = Backend._plugins[backend_str.upper()]
+            creator_fn = backend_plugin.creator_fn
+            extended_api = backend_plugin.extended_api
+            backend_type = ProcessGroup.BackendType.CUSTOM
+
+            if not extended_api:
+                backend_class = creator_fn(
+                    backend_prefix_store, group_rank, group_size, timeout
+                )
+            else:
+                dist_backend_opts = _DistributedBackendOptions()
+                dist_backend_opts.store = backend_prefix_store
+                dist_backend_opts.group_rank = group_rank
+                dist_backend_opts.group_size = group_size
+                dist_backend_opts.timeout = timeout
+                dist_backend_opts.group_id = group_name
+                dist_backend_opts.global_ranks_in_group = global_ranks_in_group
+
+                backend_class = creator_fn(dist_backend_opts, pg_options)
+
+        # Set sequence numbers for gloo and nccl backends.
+        if backend_str == Backend.GLOO:
+            assert isinstance(backend_class, ProcessGroupGloo)
+            backend_class._set_sequence_number_for_group()
+        elif backend_str == Backend.NCCL:
+            assert isinstance(backend_class, ProcessGroupNCCL)
+            backend_class._set_sequence_number_for_group()
+
+        # If the type is a subclass of ProcessGroup then return this process group immediately
+        # TODO: This defaults to the old behavior for PythonProcessGroups which overwrites the
+        # ProcessGroup instance
+        if issubclass(type(backend_class), ProcessGroup):
+            pg = backend_class  # type: ignore[assignment]
+            break
+
+        # Process group wrapper initialization for supported PGs when TORCH_DISTRIBUTED_DEBUG is set
+        if (
+            backend_str in [Backend.GLOO, Backend.NCCL, Backend.UCC]
+            or backend_str.upper() in Backend._plugins
+        ):
+            # In debug mode and if GLOO is available, wrap in a wrapper PG that
+            # enables enhanced collective checking for debuggability.
+            if get_debug_level() == DebugLevel.DETAIL:
+                if not _GLOO_AVAILABLE:
+                    logger.info(
+                        """TORCH_DISTRIBUTED_DEBUG was set to DETAIL, but
+                                GLOO is not available. Build with Gloo to
+                                create a wrapper process group in debug mode
+                                to aid collective desynchronization debugging."""
+                    )
+                else:
+                    backend_class = _create_process_group_wrapper(
+                        wrapped_pg=backend_class,
+                        store_prefix=group_name,
+                        store=backend_prefix_store,
+                        rank=group_rank,
+                        world_size=group_size,
+                        timeout=timeout,
+                    )
+
+        # register only a single backend when all get_device_backend_map values are the same
+        if len(set(backend_config.get_device_backend_map().values())) == 1:
+            for device in backend_config.get_device_backend_map().keys():
+                pg._register_backend(torch.device(device), backend_type, backend_class)
+
+            # break out of outer loop to not create any more backends
+            break
+
+        pg._register_backend(torch.device(device), backend_type, backend_class)
+
+    # set group_name and group_dsec to backend
+    assert group_name is not None
+    assert group_desc is not None
+    pg._set_group_name(group_name)
+    pg._set_group_desc(group_desc)
+
+    if device_id and pg._get_backend(device_id).supports_splitting:
+        eager_backend = pg._get_backend(device_id)
+        eager_backend.eager_connect_single_device(device_id)
+
+    # update global state
+    _world.pg_map[pg] = (backend, prefix_store)
+    _world.pg_names[pg] = group_name
+    _register_process_group(group_name, pg)
+
+    _world.pg_backend_config[pg] = str(backend_config)
+    # "" is the default tag for user PGs
+    if pg_tag in [None, ""]:
+        pg_tag = f"ptd:{group_name}"
+        _world.tags_to_pg.setdefault("", []).append(pg)
+    else:
+        pg_tag = f"user:{pg_tag}"
+
+    _world.tags_to_pg.setdefault(pg_tag, []).append(pg)
+    _world.pg_to_tag[pg] = pg_tag
+    return pg, prefix_store
+
+
+def destroy_process_group(group: Optional[ProcessGroup] = None):
+    """
+    Destroy a given process group, and deinitialize the distributed package.
+
+    Args:
+        group (ProcessGroup, optional): The process group to be destroyed, if
+                                        group.WORLD is given, all process
+                                        groups including the default one will
+                                        be destroyed.
+    """
+    global _world
+
+    if group == GroupMember.NON_GROUP_MEMBER:
+        return
+
+    if group is None:
+        pg = GroupMember.WORLD
+    else:
+        pg = group
+
+    assert pg is not None
+    if _world.pg_map.get(pg, None) is None:
+        raise ValueError("Invalid process group specified")
+
+    # When users register Python onCompletion hooks, those hooks will run on a
+    # different thread than the main thread. Today, the ProcessGroup dtor does
+    # wait for that thread. However, the dtor might finish after the Python
+    # Interpreter exits. After that grabbing the GIL for the Python hook will crash.
+    # We can either revive the interpreter when running hooks or keep the main one
+    # alive until all works and hooks are done. The current implementation does the
+    # latter. Therefore, we explicitly call _wait_for_pending_works() here to wait
+    # for the pending hooks to finish.
+    if pg.name().lower() == "nccl" and pg._has_hooks():
+        pg._wait_for_pending_works()
+
+    if group is None or group == GroupMember.WORLD:
+        # shutdown all backends in the order of pg names. shutting down in order because
+        # ncclCommAbort() was a 'collective' call in some versions of NCCL.
+        for pg_to_shutdown in sorted(
+            _world.pg_names, key=lambda x: _world.pg_names[x], reverse=True
+        ):
+            _shutdown_backend(pg_to_shutdown)
+
+        _update_default_pg(None)
+        _world.pg_map.clear()
+        _world.pg_names.clear()
+        _world.pg_group_ranks.clear()
+        _world.pg_backend_config.clear()
+        _world.pg_to_tag.clear()
+        _world.tags_to_pg.clear()
+        _world.pg_coalesce_state.clear()
+        _world.pg_default_device.clear()
+        _unregister_all_process_groups()
+
+        # when process group doesn't have an explicit name (only WORLD (default)
+        # process group can have an explicit name), we use global _world.group_count
+        # to generate the name. We need to reset the counter on destruction to
+        # allow consistent value to be generated when we re-create process
+        # groups after some trainers recover from failure
+        #
+        # We only reset this when WORLD is being destroyed because if this
+        # process group is in good state, we aren't dealing with failures.
+        _world.group_count = 0
+    else:
+        _shutdown_backend(pg)
+        del _world.pg_map[pg]
+        del _world.pg_names[pg]
+        del _world.pg_group_ranks[pg]
+        del _world.pg_backend_config[pg]
+        if pg in _world.pg_default_device:
+            del _world.pg_default_device[pg]
+        if pg in _world.pg_coalesce_state.keys():
+            warnings.warn(
+                "Some coalesced collectives haven't been launched when "
+                "ProcessGroup is destroyed. They will be cleaned."
+            )
+            del _world.pg_coalesce_state[pg]
+
+        tag = _world.pg_to_tag.get(pg)
+        del _world.pg_to_tag[pg]
+        if tag is not None:
+            try:
+                _world.tags_to_pg[tag].remove(pg)
+                if tag.startswith("ptd:"):
+                    _world.tags_to_pg[""].remove(pg)
+            except Exception:
+                pass
+        _unregister_process_group(pg.group_name)
+
+
+def get_rank(group: Optional[ProcessGroup] = None) -> int:
+    """
+    Return the rank of the current process in the provided ``group``, default otherwise.
+
+    Rank is a unique identifier assigned to each process within a distributed
+    process group. They are always consecutive integers ranging from 0 to
+    ``world_size``.
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+
+    Returns:
+        The rank of the process group
+        -1, if not part of the group
+
+    """
+    if _rank_not_in_group(group):
+        return -1
+
+    default_pg = _get_default_group()
+    if group is None or group is GroupMember.WORLD:
+        return default_pg.rank()
+
+    return get_group_rank(group, default_pg.rank())
+
+
+def get_world_size(group: Optional[ProcessGroup] = None) -> int:
+    """
+    Return the number of processes in the current process group.
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+
+    Returns:
+        The world size of the process group
+        -1, if not part of the group
+
+    """
+    if _rank_not_in_group(group):
+        return -1
+
+    return _get_group_size(group)
+
+
+def isend(
+    tensor: torch.Tensor, dst: int, group: Optional[ProcessGroup] = None, tag: int = 0
+) -> Optional[Work]:
+    """
+    Send a tensor asynchronously.
+
+    .. warning::
+        Modifying ``tensor`` before the request completes causes undefined
+        behavior.
+
+    .. warning::
+        ``tag`` is not supported with the NCCL backend.
+
+    Args:
+        tensor (Tensor): Tensor to send.
+        dst (int): Destination rank on global process group (regardless of ``group`` argument)
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        tag (int, optional): Tag to match send with remote recv
+
+    Returns:
+        A distributed request object.
+        None, if not part of the group
+
+    """
+    _check_single_tensor(tensor, "tensor")
+    if _rank_not_in_group(group):
+        _warn_not_in_group("isend")
+        return None
+
+    if tensor.is_complex():
+        tensor = torch.view_as_real(tensor)
+
+    if group is None or group is GroupMember.WORLD:
+        pg = _get_default_group()
+    else:
+        pg = group
+        dst = get_group_rank(pg, dst)
+
+    return pg.send([tensor], dst, tag)
+
+
+def irecv(
+    tensor: torch.Tensor,
+    src: Optional[int] = None,
+    group: Optional[ProcessGroup] = None,
+    tag: int = 0,
+) -> Optional[Work]:
+    """
+    Receives a tensor asynchronously.
+
+    .. warning::
+        ``tag`` is not supported with the NCCL backend.
+
+    Args:
+        tensor (Tensor): Tensor to fill with received data.
+        src (int, optional): Source rank on global process group (regardless of ``group`` argument).
+            Will receive from any process if unspecified.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        tag (int, optional): Tag to match recv with remote send
+
+    Returns:
+        A distributed request object.
+        None, if not part of the group
+
+    """
+    _check_single_tensor(tensor, "tensor")
+    if _rank_not_in_group(group):
+        _warn_not_in_group("irecv")
+        return None
+
+    if tensor.is_complex():
+        tensor = torch.view_as_real(tensor)
+
+    if group is None or group is GroupMember.WORLD:
+        pg = _get_default_group()
+    else:
+        pg = group
+
+    if src is None:
+        return pg.recv_anysource([tensor], tag)
+    else:
+        if pg is GroupMember.WORLD:
+            return pg.recv([tensor], src, tag)
+        else:
+            group_src_rank = get_group_rank(pg, src)
+            return pg.recv([tensor], group_src_rank, tag)
+
+
+@_exception_logger
+def send(
+    tensor: torch.Tensor, dst: int, group: Optional[ProcessGroup] = None, tag: int = 0
+) -> None:
+    """
+    Send a tensor synchronously.
+
+    .. warning::
+        ``tag`` is not supported with the NCCL backend.
+
+    Args:
+        tensor (Tensor): Tensor to send.
+        dst (int): Destination rank on global process group (regardless of ``group`` argument).
+            Destination rank should not be the same as the rank of the current process.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        tag (int, optional): Tag to match send with remote recv
+
+    """
+    if get_rank() == dst:
+        raise ValueError(
+            "Invalid destination rank: destination rank should not be the same as "
+            "the rank of the current process."
+        )
+
+    _check_single_tensor(tensor, "tensor")
+    if _rank_not_in_group(group):
+        _warn_not_in_group("send")
+        return None
+
+    if tensor.is_complex():
+        tensor = torch.view_as_real(tensor)
+
+    if group is None or group is GroupMember.WORLD:
+        default_pg = _get_default_group()
+        default_pg.send([tensor], dst, tag).wait()
+    else:
+        group_dst_rank = get_group_rank(group, dst)
+        group.send([tensor], group_dst_rank, tag).wait()
+
+
+@_exception_logger
+def recv(
+    tensor: torch.Tensor,
+    src: Optional[int] = None,
+    group: Optional[ProcessGroup] = None,
+    tag: int = 0,
+) -> int:
+    """
+    Receives a tensor synchronously.
+
+    .. warning::
+        ``tag`` is not supported with the NCCL backend.
+
+    Args:
+        tensor (Tensor): Tensor to fill with received data.
+        src (int, optional): Source rank on global process group (regardless of ``group`` argument).
+            Will receive from any process if unspecified.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        tag (int, optional): Tag to match recv with remote send
+
+    Returns:
+        Sender rank
+        -1, if not part of the group
+
+    """
+    _check_single_tensor(tensor, "tensor")
+    if _rank_not_in_group(group):
+        _warn_not_in_group("recv")
+        return -1
+
+    if tensor.is_complex():
+        tensor = torch.view_as_real(tensor)
+
+    pg = group or _get_default_group()
+
+    if src is None:
+        work = pg.recv_anysource([tensor], tag)
+        work.wait()
+        src_rank = work._source_rank()
+        if group is None or group is GroupMember.WORLD:
+            return src_rank
+        else:
+            return get_global_rank(pg, src_rank)
+    else:
+        if group is None or group is GroupMember.WORLD:
+            pg.recv([tensor], src, tag).wait()
+        else:
+            group_src_rank = get_group_rank(pg, src)
+            pg.recv([tensor], group_src_rank, tag).wait()
+        return src
+
+
+class _IllegalWork(Work):
+    def __getattribute__(self, name):
+        if name in [
+            "is_success",
+            "exception",
+            "wait",
+            "source_rank",
+            "_source_rank",
+            "result",
+            "synchronize",
+        ]:
+            raise ValueError(f"Illegal to call {name} on IllegalWork object")
+
+
+class _CoalescingManager:
+    def __init__(self) -> None:
+        self.works: List[Work] = []
+
+    def append(self, work: Work):
+        if work:
+            self.works.append(work)
+
+    def wait(self):
+        for work in self.works:
+            work.wait()
+
+
+@contextlib.contextmanager
+def _coalescing_manager(
+    group: Optional[ProcessGroup] = None,
+    device: Optional[torch.device] = None,
+    async_ops: Optional[bool] = False,
+):
+    """
+    Context manager used to coalesce collectives or P2P operations when possible.
+
+    Args:
+        group (`ProcessGroup`, optional): The process group to work on. If None,
+            the default process group will be used.
+        device (`torch.device`, optional): Default is None, set to a device if
+            there isn't a `**_coalesced` implementation by the backend.
+        async_ops (`bool`, optional): whether the coalesced ops are async ops.
+
+    Examples:
+        >>> # xdoctest: +SKIP("no rank")
+        >>> # Synchronous ops
+        >>> with _coalescing_manager():
+        >>>     for i in range(num_colls):
+        >>>         dist.all_reduce(tensors[i])
+        >>> # Asynchronous ops
+        >>> with _coalescing_manager(async_ops=True) as cm:
+        >>>     for i in range(num_colls):
+        >>>         dist.all_reduce(tensors[i])
+        >>> cm.wait()
+
+    .. warning::
+       :func:`_coalescing_manager` currently do not support coalescing
+       all-reduces with different reduce operators, e.g.  `ReduceOp.SUM` mixed
+       with `ReduceOp.PRODUCT`.
+    """
+    group = group or _get_default_group()
+    op_list = _world.pg_coalesce_state.setdefault(group, [])
+    if op_list:
+        raise ValueError(
+            "ProcessGroup has non-empty op list at the start of coalescing"
+        )
+    if device:
+        group._start_coalescing(device)
+    cm = _CoalescingManager()
+    yield cm
+    op_list = _world.pg_coalesce_state.pop(group)
+    if op_list:
+        # Collectives supporting "Fast Path" coalescing are captured.
+        # See implementation in corresponding collective APIs.
+        # Currently supported:
+        # - coalesced `all_reduce`
+        # - coalesced `all_gather_into_tensor`
+        # - coalesced `reduce_scatter_tensor`
+        op0 = op_list[0].op
+        if op0 == all_reduce:
+            tensors = []
+            for op in op_list:
+                tensors.append(op.tensor)
+            all_reduce_opts = AllreduceCoalescedOptions()
+            all_reduce_opts.reduceOp = not_none(op_list[0].redop)
+            work = group.allreduce_coalesced(tensors, all_reduce_opts)
+        elif op0 == all_gather_into_tensor:
+            inputs = []
+            outputs = []
+            for op in op_list:
+                inputs.append(op.tensor)
+                outputs.append(not_none(op.dst_tensor))
+            work = group.allgather_into_tensor_coalesced(outputs, inputs)
+        elif op0 == reduce_scatter_tensor:
+            inputs = []
+            outputs = []
+            for op in op_list:
+                inputs.append(op.tensor)
+                outputs.append(not_none(op.dst_tensor))
+            reduce_opts = ReduceScatterOptions()
+            reduce_opts.reduceOp = not_none(op_list[0].redop)
+            work = group.reduce_scatter_tensor_coalesced(outputs, inputs, reduce_opts)
+        else:
+            raise AssertionError(
+                f"Coalescing manager does not support fast-path coalescing of {op0}, "
+                f"yet {op0} is still recorded in op list. This is an internal error of c10d."
+            )
+
+    if device:
+        # Old style of letting each coll inside the context manager to call into C++ counterpart via python binding
+        work = group._end_coalescing(device)
+
+    if async_ops:
+        cm.append(work)  # type: ignore[possibly-undefined]
+    else:
+        work.wait()  # type: ignore[possibly-undefined]
+
+
+def batch_isend_irecv(p2p_op_list):
+    """
+    Send or Receive a batch of tensors asynchronously and return a list of requests.
+
+    Process each of the operations in ``p2p_op_list`` and return the corresponding
+    requests. NCCL, Gloo, and UCC backend are currently supported.
+
+    Args:
+        p2p_op_list: A list of point-to-point operations(type of each operator is
+            ``torch.distributed.P2POp``). The order of the isend/irecv in the list
+            matters and it needs to match with corresponding isend/irecv on the
+            remote end.
+
+    Returns:
+        A list of distributed request objects returned by calling the corresponding
+        op in the op_list.
+
+    Examples:
+        >>> # xdoctest: +SKIP("no rank")
+        >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank
+        >>> recv_tensor = torch.randn(2, dtype=torch.float32)
+        >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1)%world_size)
+        >>> recv_op = dist.P2POp(dist.irecv, recv_tensor, (rank - 1 + world_size)%world_size)
+        >>> reqs = batch_isend_irecv([send_op, recv_op])
+        >>> for req in reqs:
+        >>>     req.wait()
+        >>> recv_tensor
+        tensor([2, 3])     # Rank 0
+        tensor([0, 1])     # Rank 1
+
+    .. note:: Note that when this API is used with the NCCL PG backend, users must set
+        the current GPU device with `torch.cuda.set_device`, otherwise it will
+        lead to unexpected hang issues.
+
+        In addition, if this API is the first collective call in the ``group``
+        passed to ``dist.P2POp``, all ranks of the ``group`` must participate in
+        this API call; otherwise, the behavior is undefined. If this API call is
+        not the first collective call in the ``group``, batched P2P operations
+        involving only a subset of ranks of the ``group`` are allowed.
+    """
+    _check_p2p_op_list(p2p_op_list)
+    group = p2p_op_list[0].group
+    device = p2p_op_list[0].tensor.device
+    if device.type == "cuda":
+        # NCCL style coalescing
+        with _coalescing_manager(group, device, async_ops=True) as cm:
+            for p2p_op in p2p_op_list:
+                p2p_op.op(p2p_op.tensor, p2p_op.peer, p2p_op.group, p2p_op.tag)
+        return cm.works
+    else:
+        # Backward support for Gloo
+        reqs = []
+        for p2p_op in p2p_op_list:
+            work = p2p_op.op(p2p_op.tensor, p2p_op.peer, p2p_op.group, p2p_op.tag)
+            if work:
+                reqs.append(work)
+        return reqs
+
+
+@_exception_logger
+def broadcast(tensor, src, group=None, async_op=False):
+    """
+    Broadcasts the tensor to the whole group.
+
+    ``tensor`` must have the same number of elements in all processes
+    participating in the collective.
+
+    Args:
+        tensor (Tensor): Data to be sent if ``src`` is the rank of current
+            process, and tensor to be used to save received data otherwise.
+        src (int): Source rank on global process group (regardless of ``group`` argument).
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    """
+    _check_single_tensor(tensor, "tensor")
+    if _rank_not_in_group(group):
+        _warn_not_in_group("broadcast")
+        return
+
+    opts = BroadcastOptions()
+    opts.rootRank = src
+    opts.rootTensor = 0
+    opts.asyncOp = async_op
+
+    if group is None or group is GroupMember.WORLD:
+        default_pg = _get_default_group()
+        work = default_pg.broadcast([tensor], opts)
+    else:
+        group_src_rank = get_group_rank(group, src)
+        opts.rootRank = group_src_rank
+        work = group.broadcast([tensor], opts)
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+@_exception_logger
+def all_reduce(tensor, op=ReduceOp.SUM, group=None, async_op=False):
+    """
+    Reduces the tensor data across all machines in a way that all get the final result.
+
+    After the call ``tensor`` is going to be bitwise identical in all processes.
+
+    Complex tensors are supported.
+
+    Args:
+        tensor (Tensor): Input and output of the collective. The function
+            operates in-place.
+        op (optional): One of the values from
+            ``torch.distributed.ReduceOp``
+            enum.  Specifies an operation used for element-wise reductions.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    Examples:
+        >>> # xdoctest: +SKIP("no rank")
+        >>> # All tensors below are of torch.int64 type.
+        >>> # We have 2 process groups, 2 ranks.
+        >>> device = torch.device(f'cuda:{rank}')
+        >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
+        >>> tensor
+        tensor([1, 2], device='cuda:0') # Rank 0
+        tensor([3, 4], device='cuda:1') # Rank 1
+        >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
+        >>> tensor
+        tensor([4, 6], device='cuda:0') # Rank 0
+        tensor([4, 6], device='cuda:1') # Rank 1
+
+        >>> # All tensors below are of torch.cfloat type.
+        >>> # We have 2 process groups, 2 ranks.
+        >>> tensor = torch.tensor([1+1j, 2+2j], dtype=torch.cfloat, device=device) + 2 * rank * (1+1j)
+        >>> tensor
+        tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0
+        tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1
+        >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
+        >>> tensor
+        tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0
+        tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1
+
+    """
+    _check_single_tensor(tensor, "tensor")
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_reduce")
+        return
+
+    if tensor.is_complex():
+        if not supports_complex(op):
+            raise ValueError(f"all_reduce does not support {op} on complex tensors")
+        tensor = torch.view_as_real(tensor)
+
+    opts = AllreduceOptions()
+    opts.reduceOp = op
+    if group is None:
+        group = _get_default_group()
+
+    if group in _world.pg_coalesce_state.keys():
+        # We are in coalescing context, do not issue single operation, just append a collective representation
+        coll = _CollOp(all_reduce, tensor, None, op, None)
+        _world.pg_coalesce_state[group].append(coll)
+        if async_op:
+            return _IllegalWork()
+        else:
+            return None
+
+    work = group.allreduce([tensor], opts)
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+@_exception_logger
+@deprecated(
+    "`torch.distributed.all_reduce_coalesced` will be deprecated. If you must "
+    "use it, please revisit our documentation later at "
+    "https://pytorch.org/docs/main/distributed.html#collective-functions",
+    category=FutureWarning,
+)
+def all_reduce_coalesced(tensors, op=ReduceOp.SUM, group=None, async_op=False):
+    """
+    WARNING: at this time individual shape checking is not implemented across nodes.
+
+    For example, if the rank 0 node passes [torch.rand(4), torch.rand(2)] and the
+    rank 1 node passes [torch.rand(2), torch.rand(2), torch.rand(2)], the allreduce
+    operation will proceed without complaint and return erroneous outputs. This lack
+    of shape checking results in significant performance improvements but users of this
+    function should take extra care to ensure that each node passes in tensors whose
+    shapes match across nodes.
+
+    Reduces each tensor in tensors (residing on the same device) across all machines
+    in such a way that all get the final result.
+
+    After the call each tensor in tensors is going to bitwise identical
+    in all processes.
+
+    Complex tensors are supported.
+
+    Args:
+        tensors (Union[List[Tensor], Tensor]): Input and output of the collective.
+            The function operates in-place.
+        op (Optional[ReduceOp]): One of the values from
+            ``torch.distributed.ReduceOp`` enum. Specifies an operation used for
+            element-wise reductions.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (Optional[bool]): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group.
+
+    """
+    if isinstance(tensors, torch.Tensor):
+        tensors = [tensors]
+    _check_tensor_list(tensors, "tensor")
+    _ensure_all_tensors_same_dtype(tensors)
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_reduce_coalesced")
+        return
+
+    if any(t.is_complex() for t in tensors) and not supports_complex(op):
+        raise ValueError(f"all_reduce does not support {op} on complex tensors")
+
+    tensors = [t if not t.is_complex() else torch.view_as_real(t) for t in tensors]
+
+    opts = AllreduceCoalescedOptions()
+    opts.reduceOp = op
+    group = group or _get_default_group()
+    work = group.allreduce_coalesced(tensors, opts)
+
+    if async_op:
+        return work.get_future()
+    else:
+        work.wait()
+
+
+@_exception_logger
+def reduce(tensor, dst, op=ReduceOp.SUM, group=None, async_op=False):
+    """
+    Reduces the tensor data across all machines.
+
+    Only the process with rank ``dst`` is going to receive the final result.
+
+    Args:
+        tensor (Tensor): Input and output of the collective. The function
+            operates in-place.
+        dst (int): Destination rank on global process group (regardless of ``group`` argument)
+        op (optional): One of the values from
+            ``torch.distributed.ReduceOp``
+            enum.  Specifies an operation used for element-wise reductions.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    """
+    _check_single_tensor(tensor, "tensor")
+    if _rank_not_in_group(group):
+        _warn_not_in_group("reduce")
+        return
+
+    opts = ReduceOptions()
+    opts.reduceOp = op
+    opts.rootRank = dst
+
+    if group is None or group is GroupMember.WORLD:
+        default_pg = _get_default_group()
+        work = default_pg.reduce([tensor], opts)
+    else:
+        group_dst_rank = get_group_rank(group, dst)
+        opts.rootRank = group_dst_rank
+        work = group.reduce([tensor], opts)
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+def _object_to_tensor(obj, device, group):
+    f = io.BytesIO()
+    _pickler(f).dump(obj)
+    byte_storage = torch.ByteStorage._from_buffer(f.getvalue())  # type: ignore[attr-defined]
+    # Do not replace `torch.ByteTensor` or `torch.LongTensor` with torch.tensor and specifying dtype.
+    # Otherwise, it will casue 100X slowdown.
+    # See: https://github.com/pytorch/pytorch/issues/65696
+    byte_tensor = torch.ByteTensor(byte_storage).to(device)
+    if get_debug_level() == DebugLevel.DETAIL and is_nccl_available():
+        backend = get_backend(group)
+        if backend == Backend.NCCL:
+            hash = torch._C._distributed_c10d._hash_tensors([byte_tensor])
+            logger.warning(
+                "_object_to_tensor size: %s hash value: %s", byte_tensor.numel(), hash
+            )
+    local_size = torch.LongTensor([byte_tensor.numel()]).to(device)
+    return byte_tensor, local_size
+
+
+def _tensor_to_object(tensor, tensor_size, group):
+    if get_debug_level() == DebugLevel.DETAIL and is_nccl_available():
+        backend = get_backend(group)
+        if backend == Backend.NCCL:
+            hash = torch._C._distributed_c10d._hash_tensors([tensor])
+            logger.warning(
+                "_tensor_to_object size: %s hash value: %s", tensor.numel(), hash
+            )
+    tensor = tensor.cpu()
+    buf = tensor.numpy().tobytes()[:tensor_size]
+    return _unpickler(io.BytesIO(buf)).load()
+
+
+@_exception_logger
+def all_gather_object(object_list, obj, group=None):
+    """
+    Gathers picklable objects from the whole group into a list.
+
+    Similar to :func:`all_gather`, but Python objects can be passed in.
+    Note that the object must be picklable in order to be gathered.
+
+    Args:
+        object_list (list[Any]): Output list. It should be correctly sized as the
+            size of the group for this collective and will contain the output.
+        obj (Any): Pickable Python object to be broadcast from current process.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Default is ``None``.
+
+    Returns:
+        None. If the calling rank is part of this group, the output of the
+        collective will be populated into the input ``object_list``. If the
+        calling rank is not part of the group, the passed in ``object_list`` will
+        be unmodified.
+
+    .. note:: Note that this API differs slightly from the :func:`all_gather`
+        collective since it does not provide an ``async_op`` handle and thus
+        will be a blocking call.
+
+    .. note:: For NCCL-based processed groups, internal tensor representations
+        of objects must be moved to the GPU device before communication takes
+        place. In this case, the device used is given by
+        ``torch.cuda.current_device()`` and it is the user's responsiblity to
+        ensure that this is set so that each rank has an individual GPU, via
+        ``torch.cuda.set_device()``.
+
+    .. warning::
+        :func:`all_gather_object` uses ``pickle`` module implicitly, which is
+        known to be insecure. It is possible to construct malicious pickle data
+        which will execute arbitrary code during unpickling. Only call this
+        function with data you trust.
+
+    .. warning::
+        Calling :func:`all_gather_object` with GPU tensors is not well supported
+        and inefficient as it incurs GPU -> CPU transfer since tensors would be
+        pickled. Please consider using :func:`all_gather` instead.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> # Assumes world_size of 3.
+        >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object
+        >>> output = [None for _ in gather_objects]
+        >>> dist.all_gather_object(output, gather_objects[dist.get_rank()])
+        >>> output
+        ['foo', 12, {1: 2}]
+    """
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_gather_object")
+        return
+
+    current_device = _get_pg_default_device(group)
+    input_tensor, local_size = _object_to_tensor(obj, current_device, group)
+
+    # Gather all local sizes. This is so that we can find the max size, and index
+    # until the correct size when deserializing the tensors.
+    group_size = get_world_size(group=group)
+    object_sizes_tensor = torch.zeros(
+        group_size, dtype=torch.long, device=current_device
+    )
+    object_size_list = [
+        object_sizes_tensor[i].unsqueeze(dim=0) for i in range(group_size)
+    ]
+    # Allgather tensor sizes
+    all_gather(object_size_list, local_size, group=group)
+    max_object_size = int(max(object_size_list).item())  # type: ignore[type-var]
+    # Resize tensor to max size across all ranks.
+    input_tensor.resize_(max_object_size)
+    coalesced_output_tensor = torch.empty(
+        max_object_size * group_size, dtype=torch.uint8, device=current_device
+    )
+    # Output tensors are nonoverlapping views of coalesced_output_tensor
+    output_tensors = [
+        coalesced_output_tensor[max_object_size * i : max_object_size * (i + 1)]
+        for i in range(group_size)
+    ]
+    all_gather(output_tensors, input_tensor, group=group)
+    # Deserialize outputs back to object.
+    for i, tensor in enumerate(output_tensors):
+        tensor = tensor.type(torch.uint8)
+        tensor_size = object_size_list[i]
+        object_list[i] = _tensor_to_object(tensor, tensor_size, group)
+
+
+@_exception_logger
+def gather_object(obj, object_gather_list=None, dst=0, group=None):
+    """
+    Gathers picklable objects from the whole group in a single process.
+
+    Similar to :func:`gather`, but Python objects can be passed in. Note that the
+    object must be picklable in order to be gathered.
+
+    Args:
+        obj (Any): Input object. Must be picklable.
+        object_gather_list (list[Any]): Output list. On the ``dst`` rank, it
+            should be correctly sized as the size of the group for this
+            collective and will contain the output. Must be ``None`` on non-dst
+            ranks. (default is ``None``)
+        dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). (default is 0)
+        group: (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Default is ``None``.
+
+    Returns:
+        None. On the ``dst`` rank, ``object_gather_list`` will contain the
+        output of the collective.
+
+    .. note:: Note that this API differs slightly from the gather collective
+        since it does not provide an async_op handle and thus will be a blocking
+        call.
+
+    .. note:: For NCCL-based processed groups, internal tensor representations
+        of objects must be moved to the GPU device before communication takes
+        place. In this case, the device used is given by
+        ``torch.cuda.current_device()`` and it is the user's responsiblity to
+        ensure that this is set so that each rank has an individual GPU, via
+        ``torch.cuda.set_device()``.
+
+    .. warning::
+        :func:`gather_object` uses ``pickle`` module implicitly, which is
+        known to be insecure. It is possible to construct malicious pickle data
+        which will execute arbitrary code during unpickling. Only call this
+        function with data you trust.
+
+    .. warning::
+        Calling :func:`gather_object` with GPU tensors is not well supported
+        and inefficient as it incurs GPU -> CPU transfer since tensors would be
+        pickled. Please consider using :func:`gather` instead.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> # Assumes world_size of 3.
+        >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object
+        >>> output = [None for _ in gather_objects]
+        >>> dist.gather_object(
+        ...     gather_objects[dist.get_rank()],
+        ...     output if dist.get_rank() == 0 else None,
+        ...     dst=0
+        ... )
+        >>> # On rank 0
+        >>> output
+        ['foo', 12, {1: 2}]
+    """
+    if _rank_not_in_group(group):
+        _warn_not_in_group("gather_object")
+        return
+
+    # Ensure object_gather_list is specified appropriately.
+    my_rank = get_rank()
+    _validate_output_list_for_rank(my_rank, dst, object_gather_list)
+    current_device = _get_pg_default_device(group)
+    input_tensor, local_size = _object_to_tensor(obj, current_device, group)
+
+    # Gather all local sizes. This is so that we can find the max size, and index
+    # until the correct size when deserializing the tensors.
+    group_size = get_world_size(group=group)
+    object_sizes_tensor = torch.zeros(
+        group_size, dtype=torch.long, device=current_device
+    )
+    object_size_list = [
+        object_sizes_tensor[i].unsqueeze(dim=0) for i in range(group_size)
+    ]
+    # Allgather tensor sizes. An all-gather is needed here despite this being a
+    # gather, since each rank needs to broadcast a tensor of the same (maximal)
+    # size.
+    all_gather(object_size_list, local_size, group=group)
+    max_object_size = int(max(object_size_list).item())  # type: ignore[type-var]
+    # Resize tensor to max size across all ranks.
+    input_tensor.resize_(max_object_size)
+    # Avoid populating output tensors if the result won't be gathered on this rank.
+    if my_rank == dst:
+        coalesced_output_tensor = torch.empty(
+            max_object_size * group_size, dtype=torch.uint8, device=current_device
+        )
+        # Output tensors are nonoverlapping views of coalesced_output_tensor
+        output_tensors = [
+            coalesced_output_tensor[max_object_size * i : max_object_size * (i + 1)]
+            for i in range(group_size)
+        ]
+    # All ranks call gather with equal-sized tensors.
+    gather(
+        input_tensor,
+        gather_list=output_tensors if my_rank == dst else None,  # type: ignore[possibly-undefined]
+        dst=dst,
+        group=group,
+    )
+    if my_rank != dst:
+        return
+    for i, tensor in enumerate(output_tensors):
+        tensor = tensor.type(torch.uint8)
+        tensor_size = object_size_list[i]
+        object_gather_list[i] = _tensor_to_object(tensor, tensor_size, group)
+
+
+@_exception_logger
+def send_object_list(object_list, dst, group=None, device=None):
+    """
+    Sends picklable objects in ``object_list`` synchronously.
+
+    Similar to :func:`send`, but Python objects can be passed in.
+    Note that all objects in ``object_list`` must be picklable in order to be
+    sent.
+
+    Args:
+        object_list (List[Any]): List of input objects to sent.
+            Each object must be picklable. Receiver must provide lists of equal sizes.
+        dst (int): Destination rank to send ``object_list`` to.
+            Destination rank is based on global process group (regardless of ``group`` argument)
+        group: (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Default is ``None``.
+        device (``torch.device``, optional): If not None, the objects are
+            serialized and converted to tensors which are moved to the
+            ``device`` before sending. Default is ``None``.
+
+    Returns:
+        ``None``.
+
+    .. note:: For NCCL-based process groups, internal tensor representations
+        of objects must be moved to the GPU device before communication takes
+        place. In this case, the device used is given by
+        ``torch.cuda.current_device()`` and it is the user's responsibility to
+        ensure that this is set so that each rank has an individual GPU, via
+        ``torch.cuda.set_device()``.
+
+    .. warning::
+        :func:`send_object_list` uses ``pickle`` module implicitly, which
+        is known to be insecure. It is possible to construct malicious pickle
+        data which will execute arbitrary code during unpickling. Only call this
+        function with data you trust.
+
+    .. warning::
+        Calling :func:`send_object_list` with GPU tensors is not well supported
+        and inefficient as it incurs GPU -> CPU transfer since tensors would be
+        pickled. Please consider using :func:`send` instead.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> # Assumes backend is not NCCL
+        >>> device = torch.device("cpu")
+        >>> if dist.get_rank() == 0:
+        >>>     # Assumes world_size of 2.
+        >>>     objects = ["foo", 12, {1: 2}] # any picklable object
+        >>>     dist.send_object_list(objects, dst=1, device=device)
+        >>> else:
+        >>>     objects = [None, None, None]
+        >>>     dist.recv_object_list(objects, src=0, device=device)
+        >>> objects
+        ['foo', 12, {1: 2}]
+    """
+    if get_rank() == dst:
+        raise ValueError(
+            "Invalid destination rank: destination rank should not be the same as "
+            "the rank of the current process."
+        )
+
+    if _rank_not_in_group(group):
+        _warn_not_in_group("send_object_list")
+        return
+
+    # Current device selection.
+    # To preserve backwards compatibility, ``device`` is default to ``None``
+    # in which case we run current logic of device selection, i.e.
+    # ``current_device`` is CUDA if backend is NCCL otherwise CPU device. In the
+    # case it is not ``None`` we move the size and object tensors to be
+    # sent to this device.
+    current_device = device or _get_pg_default_device(group)
+    # Serialize object_list elements to tensors on src rank.
+    tensor_list, size_list = zip(
+        *[_object_to_tensor(obj, current_device, group) for obj in object_list]
+    )
+    object_sizes_tensor = torch.cat(size_list)
+
+    # Send object sizes
+    send(object_sizes_tensor, dst=dst, group=group)
+
+    # Concatenate and send serialized object tensors
+    # Note: torch.cat will do an extra memory copy to the current device, if the tensor_list
+    # has only one element, we can skip the copy.
+    if len(tensor_list) == 1:  # type: ignore[possibly-undefined]
+        object_tensor = tensor_list[0]
+    else:
+        object_tensor = torch.cat(tensor_list)
+
+    send(object_tensor, dst=dst, group=group)
+
+
+@_exception_logger
+def recv_object_list(object_list, src=None, group=None, device=None):
+    """
+    Receives picklable objects in ``object_list`` synchronously.
+
+    Similar to :func:`recv`, but can receive Python objects.
+
+    Args:
+        object_list (List[Any]): List of objects to receive into.
+            Must provide a list of sizes equal to the size of the list being sent.
+        src (int, optional): Source rank from which to recv ``object_list``.
+            Source rank is based on global process group (regardless of ``group`` argument)
+            Will receive from any rank if set to None. Default is ``None``.
+        group: (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Default is ``None``.
+        device (``torch.device``, optional): If not None, receives on this device.
+            Default is ``None``.
+
+    Returns:
+        Sender rank. -1 if rank is not part of the group. If rank is part of the group,
+        ``object_list`` will contain the sent objects from ``src`` rank.
+
+    .. note:: For NCCL-based process groups, internal tensor representations
+        of objects must be moved to the GPU device before communication takes
+        place. In this case, the device used is given by
+        ``torch.cuda.current_device()`` and it is the user's responsibility to
+        ensure that this is set so that each rank has an individual GPU, via
+        ``torch.cuda.set_device()``.
+
+    .. warning::
+        :func:`recv_object_list` uses ``pickle`` module implicitly, which
+        is known to be insecure. It is possible to construct malicious pickle
+        data which will execute arbitrary code during unpickling. Only call this
+        function with data you trust.
+
+    .. warning::
+        Calling :func:`recv_object_list` with GPU tensors is not well supported
+        and inefficient as it incurs GPU -> CPU transfer since tensors would be
+        pickled. Please consider using :func:`recv` instead.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> # Assumes backend is not NCCL
+        >>> device = torch.device("cpu")
+        >>> if dist.get_rank() == 0:
+        >>>     # Assumes world_size of 2.
+        >>>     objects = ["foo", 12, {1: 2}] # any picklable object
+        >>>     dist.send_object_list(objects, dst=1, device=device)
+        >>> else:
+        >>>     objects = [None, None, None]
+        >>>     dist.recv_object_list(objects, src=0, device=device)
+        >>> objects
+        ['foo', 12, {1: 2}]
+    """
+    if _rank_not_in_group(group):
+        _warn_not_in_group("recv_object_list")
+        return -1
+
+    # Current device selection.
+    # To preserve backwards compatibility, ``device`` is default to ``None``
+    # in which case we run current logic of device selection, i.e.
+    # ``current_device`` is CUDA if backend is NCCL otherwise CPU device. In the
+    # case it is not ``None`` we move the size and object tensors to be
+    # received to this device.
+    current_device = device or _get_pg_default_device(group)
+    object_sizes_tensor = torch.empty(
+        len(object_list), dtype=torch.long, device=current_device
+    )
+
+    # Receive object sizes
+    rank_sizes = recv(object_sizes_tensor, src=src, group=group)
+
+    # Tensor to receive serialized objects into.
+    object_tensor = torch.empty(  # type: ignore[call-overload]
+        torch.sum(object_sizes_tensor).item(),  # type: ignore[arg-type]
+        dtype=torch.uint8,
+        device=current_device,
+    )
+
+    rank_objects = recv(object_tensor, src=src, group=group)
+    assert (
+        rank_sizes == rank_objects
+    ), "Mismatch in return ranks for object sizes and objects."
+    # Deserialize objects using their stored sizes.
+    offset = 0
+    for i, obj_size in enumerate(object_sizes_tensor):
+        obj_view = object_tensor[offset : offset + obj_size]
+        obj_view = obj_view.type(torch.uint8)
+        offset += obj_size
+        object_list[i] = _tensor_to_object(obj_view, obj_size, group)
+    return rank_objects
+
+
+@_exception_logger
+def broadcast_object_list(object_list, src=0, group=None, device=None):
+    """
+    Broadcasts picklable objects in ``object_list`` to the whole group.
+
+    Similar to :func:`broadcast`, but Python objects can be passed in.
+    Note that all objects in ``object_list`` must be picklable in order to be
+    broadcasted.
+
+    Args:
+        object_list (List[Any]): List of input objects to broadcast.
+            Each object must be picklable. Only objects on the ``src`` rank will
+            be broadcast, but each rank must provide lists of equal sizes.
+        src (int): Source rank from which to broadcast ``object_list``.
+            Source rank is based on global process group (regardless of ``group`` argument)
+        group: (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Default is ``None``.
+        device (``torch.device``, optional): If not None, the objects are
+            serialized and converted to tensors which are moved to the
+            ``device`` before broadcasting. Default is ``None``.
+
+    Returns:
+        ``None``. If rank is part of the group, ``object_list`` will contain the
+        broadcasted objects from ``src`` rank.
+
+    .. note:: For NCCL-based process groups, internal tensor representations
+        of objects must be moved to the GPU device before communication takes
+        place. In this case, the device used is given by
+        ``torch.cuda.current_device()`` and it is the user's responsibility to
+        ensure that this is set so that each rank has an individual GPU, via
+        ``torch.cuda.set_device()``.
+
+    .. note:: Note that this API differs slightly from the :func:`broadcast`
+        collective since it does not provide an ``async_op`` handle and thus
+        will be a blocking call.
+
+    .. warning::
+        :func:`broadcast_object_list` uses ``pickle`` module implicitly, which
+        is known to be insecure. It is possible to construct malicious pickle
+        data which will execute arbitrary code during unpickling. Only call this
+        function with data you trust.
+
+    .. warning::
+        Calling :func:`broadcast_object_list` with GPU tensors is not well supported
+        and inefficient as it incurs GPU -> CPU transfer since tensors would be
+        pickled. Please consider using :func:`broadcast` instead.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> if dist.get_rank() == 0:
+        >>>     # Assumes world_size of 3.
+        >>>     objects = ["foo", 12, {1: 2}] # any picklable object
+        >>> else:
+        >>>     objects = [None, None, None]
+        >>> # Assumes backend is not NCCL
+        >>> device = torch.device("cpu")
+        >>> dist.broadcast_object_list(objects, src=0, device=device)
+        >>> objects
+        ['foo', 12, {1: 2}]
+    """
+    if _rank_not_in_group(group):
+        _warn_not_in_group("broadcast_object_list")
+        return
+
+    # Current device selection.
+    # To preserve backwards compatibility, ``device`` is default to ``None``
+    # in which case we run current logic of device selection, i.e.
+    # ``current_device`` is CUDA if backend is NCCL otherwise CPU device. In the
+    # case it is not ``None`` we move the size and object tensors to be
+    # broadcasted to this device.
+    current_device = device or _get_pg_default_device(group)
+    my_rank = get_rank()
+    # Serialize object_list elements to tensors on src rank.
+    if my_rank == src:
+        tensor_list, size_list = zip(
+            *[_object_to_tensor(obj, current_device, group) for obj in object_list]
+        )
+        object_sizes_tensor = torch.cat(size_list)
+    else:
+        object_sizes_tensor = torch.empty(
+            len(object_list), dtype=torch.long, device=current_device
+        )
+
+    # Broadcast object sizes
+    broadcast(object_sizes_tensor, src=src, group=group)
+
+    # Concatenate and broadcast serialized object tensors
+    # Note: torch.cat will do an extra memory copy to the current device, if the tensor_list
+    # has only one element, we can skip the copy.
+    if my_rank == src:
+        if len(tensor_list) == 1:  # type: ignore[possibly-undefined]
+            object_tensor = tensor_list[0]
+        else:
+            object_tensor = torch.cat(tensor_list)
+    else:
+        object_tensor = torch.empty(  # type: ignore[call-overload]
+            torch.sum(object_sizes_tensor).item(),  # type: ignore[arg-type]
+            dtype=torch.uint8,
+            device=current_device,
+        )
+
+    broadcast(object_tensor, src=src, group=group)
+    # Deserialize objects using their stored sizes.
+    offset = 0
+    if my_rank != src:
+        for i, obj_size in enumerate(object_sizes_tensor):
+            obj_view = object_tensor[offset : offset + obj_size]
+            obj_view = obj_view.type(torch.uint8)
+            offset += obj_size
+            object_list[i] = _tensor_to_object(obj_view, obj_size, group)
+
+
+@_exception_logger
+def scatter_object_list(
+    scatter_object_output_list, scatter_object_input_list, src=0, group=None
+):
+    """
+    Scatters picklable objects in ``scatter_object_input_list`` to the whole group.
+
+    Similar to :func:`scatter`, but Python objects can be passed in. On
+    each rank, the scattered object will be stored as the first element of
+    ``scatter_object_output_list``. Note that all objects in
+    ``scatter_object_input_list`` must be picklable in order to be scattered.
+
+    Args:
+        scatter_object_output_list (List[Any]): Non-empty list whose first
+            element will store the object scattered to this rank.
+        scatter_object_input_list (List[Any]): List of input objects to scatter.
+            Each object must be picklable. Only objects on the ``src`` rank will
+            be scattered, and the argument can be ``None`` for non-src ranks.
+        src (int): Source rank from which to scatter ``scatter_object_input_list``.
+            Source rank is based on global process group (regardless of ``group`` argument).
+        group: (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Default is ``None``.
+
+    Returns:
+        ``None``. If rank is part of the group, ``scatter_object_output_list``
+        will have its first element set to the scattered object for this rank.
+
+    .. note:: Note that this API differs slightly from the scatter collective
+        since it does not provide an ``async_op`` handle and thus will be a
+        blocking call.
+
+    .. warning::
+        :func:`scatter_object_list` uses ``pickle`` module implicitly, which
+        is known to be insecure. It is possible to construct malicious pickle
+        data which will execute arbitrary code during unpickling. Only call this
+        function with data you trust.
+
+    .. warning::
+        Calling :func:`scatter_object_list` with GPU tensors is not well supported
+        and inefficient as it incurs GPU -> CPU transfer since tensors would be
+        pickled. Please consider using :func:`scatter` instead.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> if dist.get_rank() == 0:
+        >>>     # Assumes world_size of 3.
+        >>>     objects = ["foo", 12, {1: 2}] # any picklable object
+        >>> else:
+        >>>     # Can be any list on non-src ranks, elements are not used.
+        >>>     objects = [None, None, None]
+        >>> output_list = [None]
+        >>> dist.scatter_object_list(output_list, objects, src=0)
+        >>> # Rank i gets objects[i]. For example, on rank 2:
+        >>> output_list
+        [{1: 2}]
+    """
+    if _rank_not_in_group(group):
+        _warn_not_in_group("scatter_object_list")
+        return
+
+    if (
+        not isinstance(scatter_object_output_list, list)
+        or len(scatter_object_output_list) < 1
+    ):
+        raise ValueError(
+            "Expected argument scatter_object_output_list to be a list of size at least 1."
+        )
+
+    my_rank = get_rank()
+    pg_device = _get_pg_default_device(group)
+    if my_rank == src:
+        tensor_list, tensor_sizes = zip(
+            *[
+                _object_to_tensor(obj, pg_device, group)
+                for obj in scatter_object_input_list
+            ]
+        )
+        tensor_list, tensor_sizes = list(tensor_list), list(tensor_sizes)
+
+    # Src rank broadcasts the maximum tensor size. This is because all ranks are
+    # expected to call into scatter() with equal-sized tensors.
+    if my_rank == src:
+        max_tensor_size = max(tensor_sizes)  # type: ignore[possibly-undefined]
+        for tensor in tensor_list:  # type: ignore[possibly-undefined]
+            tensor.resize_(max_tensor_size)
+    else:
+        max_tensor_size = torch.tensor([0], dtype=torch.long, device=pg_device)
+    broadcast(max_tensor_size, src=src, group=group)
+
+    # Scatter actual serialized objects
+    output_tensor = torch.empty(
+        max_tensor_size.item(), dtype=torch.uint8, device=pg_device
+    )
+    scatter(
+        output_tensor,
+        scatter_list=None if my_rank != src else tensor_list,  # type: ignore[possibly-undefined]
+        src=src,
+        group=group,
+    )
+
+    # Scatter per-object sizes to trim tensors when deserializing back to object
+    obj_tensor_size = torch.tensor([0], dtype=torch.long, device=pg_device)
+    scatter(
+        obj_tensor_size,
+        scatter_list=None if my_rank != src else tensor_sizes,  # type: ignore[possibly-undefined]
+        src=src,
+        group=group,
+    )
+
+    # Deserialize back to object
+    scatter_object_output_list[0] = _tensor_to_object(
+        output_tensor, obj_tensor_size, group
+    )
+
+
+@_exception_logger
+def all_gather(tensor_list, tensor, group=None, async_op=False):
+    """
+    Gathers tensors from the whole group in a list.
+
+    Complex and uneven sized tensors are supported.
+
+    Args:
+        tensor_list (list[Tensor]): Output list. It should contain
+            correctly-sized tensors to be used for output of the collective.
+            Uneven sized tensors are supported.
+        tensor (Tensor): Tensor to be broadcast from current process.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    Examples:
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # All tensors below are of torch.int64 dtype.
+        >>> # We have 2 process groups, 2 ranks.
+        >>> device = torch.device(f'cuda:{rank}')
+        >>> tensor_list = [torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2)]
+        >>> tensor_list
+        [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0
+        [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1
+        >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
+        >>> tensor
+        tensor([1, 2], device='cuda:0') # Rank 0
+        tensor([3, 4], device='cuda:1') # Rank 1
+        >>> dist.all_gather(tensor_list, tensor)
+        >>> tensor_list
+        [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0
+        [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1
+
+        >>> # All tensors below are of torch.cfloat dtype.
+        >>> # We have 2 process groups, 2 ranks.
+        >>> tensor_list = [torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2)]
+        >>> tensor_list
+        [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0
+        [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1
+        >>> tensor = torch.tensor([1+1j, 2+2j], dtype=torch.cfloat, device=device) + 2 * rank * (1+1j)
+        >>> tensor
+        tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0
+        tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1
+        >>> dist.all_gather(tensor_list, tensor)
+        >>> tensor_list
+        [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0
+        [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1
+
+    """
+    _check_tensor_list(tensor_list, "tensor_list")
+    _check_single_tensor(tensor, "tensor")
+    _ensure_all_tensors_same_dtype(tensor_list, tensor)
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_gather")
+        return
+
+    tensor_list = [
+        t if not t.is_complex() else torch.view_as_real(t) for t in tensor_list
+    ]
+    tensor = tensor if not tensor.is_complex() else torch.view_as_real(tensor)
+
+    group = group or _get_default_group()
+    work = group.allgather([tensor_list], [tensor])
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+@_exception_logger
+def all_gather_into_tensor(output_tensor, input_tensor, group=None, async_op=False):
+    """
+    Gather tensors from all ranks and put them in a single output tensor.
+
+    This function requires all tensors to be the same size on each process.
+
+    Args:
+        output_tensor (Tensor): Output tensor to accommodate tensor elements
+            from all ranks. It must be correctly sized to have one of the
+            following forms:
+            (i) a concatenation of all the input tensors along the primary
+            dimension; for definition of "concatenation", see ``torch.cat()``;
+            (ii) a stack of all the input tensors along the primary dimension;
+            for definition of "stack", see ``torch.stack()``.
+            Examples below may better explain the supported output forms.
+        input_tensor (Tensor): Tensor to be gathered from current rank.
+            Different from the ``all_gather`` API, the input tensors in this
+            API must have the same size across all ranks.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    Examples:
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # All tensors below are of torch.int64 dtype and on CUDA devices.
+        >>> # We have two ranks.
+        >>> device = torch.device(f'cuda:{rank}')
+        >>> tensor_in = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
+        >>> tensor_in
+        tensor([1, 2], device='cuda:0') # Rank 0
+        tensor([3, 4], device='cuda:1') # Rank 1
+        >>> # Output in concatenation form
+        >>> tensor_out = torch.zeros(world_size * 2, dtype=torch.int64, device=device)
+        >>> dist.all_gather_into_tensor(tensor_out, tensor_in)
+        >>> tensor_out
+        tensor([1, 2, 3, 4], device='cuda:0') # Rank 0
+        tensor([1, 2, 3, 4], device='cuda:1') # Rank 1
+        >>> # Output in stack form
+        >>> tensor_out2 = torch.zeros(world_size, 2, dtype=torch.int64, device=device)
+        >>> dist.all_gather_into_tensor(tensor_out2, tensor_in)
+        >>> tensor_out2
+        tensor([[1, 2],
+                [3, 4]], device='cuda:0') # Rank 0
+        tensor([[1, 2],
+                [3, 4]], device='cuda:1') # Rank 1
+
+    .. warning::
+        The Gloo backend does not support this API.
+
+    """
+    _check_single_tensor(input_tensor, "input_tensor")
+    _check_single_tensor(output_tensor, "output_tensor")
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_gather_into_tensor")
+        return
+
+    output_tensor = (
+        output_tensor
+        if not output_tensor.is_complex()
+        else torch.view_as_real(output_tensor)
+    )
+    input_tensor = (
+        input_tensor
+        if not input_tensor.is_complex()
+        else torch.view_as_real(input_tensor)
+    )
+
+    opts = AllgatherOptions()
+    opts.asyncOp = async_op
+
+    group = group or _get_default_group()
+
+    if group in _world.pg_coalesce_state.keys():
+        # We are in coalescing context, do not issue single operation, just append a collective representation
+        coll = _CollOp(all_gather_into_tensor, input_tensor, output_tensor)
+        _world.pg_coalesce_state[group].append(coll)
+        if async_op:
+            return _IllegalWork()
+        else:
+            return None
+
+    work = group._allgather_base(output_tensor, input_tensor, opts)
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+@_exception_logger
+@deprecated(
+    "`torch.distributed._all_gather_base` is a private function and will be deprecated. "
+    "Please use `torch.distributed.all_gather_into_tensor` instead.",
+    category=FutureWarning,
+)
+def _all_gather_base(output_tensor, input_tensor, group=None, async_op=False):
+    """
+    Single tensor all gather. Gathers a single tensor from all ranks, and puts them in a single output tensor.
+
+    Args:
+        output_tensor (Tensor): Output tensor. It should contain
+            correctly-sized tensors to be used for output of the collective.
+        input_tensor (Tensor): Tensor to be broadcast from current process.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    .. warning::
+        `_all_gather_base` is a private function. Users should use
+        `all_gather_into_tensor` instead.
+
+    """
+    return all_gather_into_tensor(output_tensor, input_tensor, group, async_op)
+
+
+@_exception_logger
+@deprecated(
+    "`torch.distributed.all_gather_coalesced` will be deprecated. If you must use it, "
+    "please revisit our documentation later at "
+    "https://pytorch.org/docs/main/distributed.html#collective-functions",
+    category=FutureWarning,
+)
+def all_gather_coalesced(
+    output_tensor_lists, input_tensor_list, group=None, async_op=False
+):
+    """
+    Gathers input tensors from the whole group in a list in a coalesced manner.
+
+    Complex tensors are supported.
+
+    Args:
+        output_tensor_lists (list[list[Tensor]]): Output list. It should contain
+            correctly-sized tensors to be used for output of the collective.
+        input_tensor_list (list[Tensor]): Tensors to be broadcast from
+            current process. At least one tensor has to be non empty.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    Example:
+        we have 2 process groups, 2 ranks.
+        rank 0 passes:
+            input_tensor_list = [[[1, 1], [1, 1]], [2], [3, 3]]
+            output_tensor_lists =
+               [[[[-1, -1], [-1, -1]], [-1], [-1, -1]],
+                [[[-1, -1], [-1, -1]], [-1], [-1, -1]]]
+        rank 1 passes:
+            input_tensor_list = [[[3, 3], [3, 3]], [5], [1, 1]]
+            output_tensor_lists =
+               [[[[-1, -1], [-1, -1]], [-1], [-1, -1]],
+                [[[-1, -1], [-1, -1]], [-1], [-1, -1]]]
+        both rank 0 and 1 get:
+            output_tensor_lists =
+               [[[1, 1], [1, 1]], [2], [3, 3]],
+                [[3, 3], [3, 3]], [5], [1, 1]]].
+
+    WARNING: at this time individual shape checking is not implemented across nodes.
+    For example, if the rank 0 node passes [torch.rand(4), torch.rand(2)] and the
+    rank 1 node passes [torch.rand(2), torch.rand(2), torch.rand(2)], the
+    all_gather_coalesced operation will proceed without complaint and return
+    erroneous outputs. This lack of shape checking results in significant
+    performance improvements but users of this function should take extra care
+    to ensure that each node passes in tensors whose shapes match across nodes.
+    """
+    # We only check basic compatibility with C++ params here, C++ code will
+    # do shape and type checking.
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_gather_coalesced")
+        return
+    _check_tensor_list(input_tensor_list, "input_tensor_list")
+    _ensure_all_tensors_same_dtype(input_tensor_list)
+    if not isinstance(output_tensor_lists, list):
+        raise TypeError(
+            "Invalid function argument: output_tensor_lists should be a list"
+        )
+    for output_tensor_list in output_tensor_lists:
+        _check_tensor_list(output_tensor_list, "output_tensor_lists")
+        _ensure_all_tensors_same_dtype(output_tensor_list)
+
+    output_tensor_lists = [
+        [t if not t.is_complex() else torch.view_as_real(t) for t in l]
+        for l in output_tensor_lists
+    ]
+    input_tensor_list = [
+        t if not t.is_complex() else torch.view_as_real(t) for t in input_tensor_list
+    ]
+
+    group = group or _get_default_group()
+    work = group.allgather_coalesced(output_tensor_lists, input_tensor_list)
+
+    if async_op:
+        return work.get_future()
+    else:
+        work.wait()
+
+
+def _validate_output_list_for_rank(my_rank, dst, gather_list):
+    if dst == my_rank:
+        if not gather_list:
+            raise ValueError(
+                "Argument ``gather_list`` must be specified on destination rank."
+            )
+    elif gather_list:
+        raise ValueError(
+            "Argument ``gather_list`` must NOT be specified "
+            "on non-destination ranks."
+        )
+
+
+@_exception_logger
+def gather(tensor, gather_list=None, dst=0, group=None, async_op=False):
+    """
+    Gathers a list of tensors in a single process.
+
+    This function requires all tensors to be the same size on each process.
+
+    Args:
+        tensor (Tensor): Input tensor.
+        gather_list (list[Tensor], optional): List of appropriately,
+            same-sized tensors to use for gathered data
+            (default is None, must be specified on the destination rank)
+        dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). (default is 0)
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    """
+    _check_single_tensor(tensor, "tensor")
+
+    # Parameter ``gather_list`` may be left unspecified on non-dst ranks.
+    if gather_list:
+        _check_tensor_list(gather_list, "gather_list")
+    else:
+        gather_list = []
+    _ensure_all_tensors_same_dtype(tensor, gather_list)
+
+    if _rank_not_in_group(group):
+        _warn_not_in_group("gather")
+        return
+
+    my_rank = get_rank()
+    _validate_output_list_for_rank(my_rank, dst, gather_list)
+    output_tensors = [gather_list] if dst == my_rank else []
+    input_tensors = [tensor]
+
+    opts = GatherOptions()
+    opts.rootRank = dst
+
+    if group is None or group is GroupMember.WORLD:
+        default_pg = _get_default_group()
+        work = default_pg.gather(output_tensors, input_tensors, opts)
+    else:
+        group_dst_rank = get_group_rank(group, dst)
+        opts.rootRank = group_dst_rank
+        work = group.gather(output_tensors, input_tensors, opts)
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+@_exception_logger
+def scatter(tensor, scatter_list=None, src=0, group=None, async_op=False):
+    """
+    Scatters a list of tensors to all processes in a group.
+
+    Each process will receive exactly one tensor and store its data in the
+    ``tensor`` argument.
+
+    Complex tensors are supported.
+
+    Args:
+        tensor (Tensor): Output tensor.
+        scatter_list (list[Tensor]): List of tensors to scatter (default is
+            None, must be specified on the source rank)
+        src (int): Source rank on global process group (regardless of ``group`` argument).
+            Default is 0
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    .. note:: Note that all Tensors in scatter_list must have the same size.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> tensor_size = 2
+        >>> t_ones = torch.ones(tensor_size)
+        >>> t_fives = torch.ones(tensor_size) * 5
+        >>> output_tensor = torch.zeros(tensor_size)
+        >>> if dist.get_rank() == 0:
+        >>>     # Assumes world_size of 2.
+        >>>     # Only tensors, all of which must be the same size.
+        >>>     scatter_list = [t_ones, t_fives]
+        >>> else:
+        >>>     scatter_list = None
+        >>> dist.scatter(output_tensor, scatter_list, src=0)
+        >>> # Rank i gets scatter_list[i]. For example, on rank 1:
+        >>> output_tensor
+        tensor([5., 5.])
+
+    """
+    _check_single_tensor(tensor, "tensor")
+
+    # Parameter ``scatter_list`` may be left unspecified on non-src ranks.
+    if scatter_list:
+        _check_tensor_list(scatter_list, "scatter_list")
+    else:
+        scatter_list = []
+    _ensure_all_tensors_same_dtype(tensor, scatter_list)
+
+    if _rank_not_in_group(group):
+        _warn_not_in_group("scatter")
+        return
+    scatter_list = [
+        t if not t.is_complex() else torch.view_as_real(t) for t in scatter_list
+    ]
+    tensor = tensor if not tensor.is_complex() else torch.view_as_real(tensor)
+
+    my_rank = get_rank()
+    if src == my_rank:
+        if not scatter_list:
+            raise ValueError(
+                "Argument ``scatter_list`` must be specified on source rank."
+            )
+        input_tensors = [scatter_list]
+        output_tensors = [tensor]
+    else:
+        if scatter_list:
+            raise ValueError(
+                "Argument ``scatter_list`` must NOT be specified "
+                "on non-source ranks."
+            )
+        input_tensors = []
+        output_tensors = [tensor]
+
+    opts = ScatterOptions()
+    opts.rootRank = src
+    opts.asyncOp = async_op
+
+    if group is None or group is GroupMember.WORLD:
+        default_pg = _get_default_group()
+        work = default_pg.scatter(output_tensors, input_tensors, opts)
+    else:
+        group_src_rank = get_group_rank(group, src)
+        opts.rootRank = group_src_rank
+        work = group.scatter(output_tensors, input_tensors, opts)
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+@_exception_logger
+def reduce_scatter(output, input_list, op=ReduceOp.SUM, group=None, async_op=False):
+    """
+    Reduces, then scatters a list of tensors to all processes in a group.
+
+    Args:
+        output (Tensor): Output tensor.
+        input_list (list[Tensor]): List of tensors to reduce and scatter.
+        op (optional): One of the values from
+            ``torch.distributed.ReduceOp``
+            enum.  Specifies an operation used for element-wise reductions.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group.
+
+    """
+    _check_single_tensor(output, "output")
+    _check_tensor_list(input_list, "input_list")
+    _ensure_all_tensors_same_dtype(output, input_list)
+    if _rank_not_in_group(group):
+        _warn_not_in_group("reduce_scatter")
+        return
+
+    opts = ReduceScatterOptions()
+    opts.reduceOp = op
+
+    group = group or _get_default_group()
+    work = group.reduce_scatter([output], [input_list], opts)
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+@_exception_logger
+def reduce_scatter_tensor(output, input, op=ReduceOp.SUM, group=None, async_op=False):
+    """
+    Reduces, then scatters a tensor to all ranks in a group.
+
+    Args:
+        output (Tensor): Output tensor. It should have the same size across all
+            ranks.
+        input (Tensor): Input tensor to be reduced and scattered. Its size
+            should be output tensor size times the world size. The input tensor
+            can have one of the following shapes:
+            (i) a concatenation of the output tensors along the primary
+            dimension, or
+            (ii) a stack of the output tensors along the primary dimension.
+            For definition of "concatenation", see ``torch.cat()``.
+            For definition of "stack", see ``torch.stack()``.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group.
+
+    Examples:
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # All tensors below are of torch.int64 dtype and on CUDA devices.
+        >>> # We have two ranks.
+        >>> device = torch.device(f'cuda:{rank}')
+        >>> tensor_out = torch.zeros(2, dtype=torch.int64, device=device)
+        >>> # Input in concatenation form
+        >>> tensor_in = torch.arange(world_size * 2, dtype=torch.int64, device=device)
+        >>> tensor_in
+        tensor([0, 1, 2, 3], device='cuda:0') # Rank 0
+        tensor([0, 1, 2, 3], device='cuda:1') # Rank 1
+        >>> dist.reduce_scatter_tensor(tensor_out, tensor_in)
+        >>> tensor_out
+        tensor([0, 2], device='cuda:0') # Rank 0
+        tensor([4, 6], device='cuda:1') # Rank 1
+        >>> # Input in stack form
+        >>> tensor_in = torch.reshape(tensor_in, (world_size, 2))
+        >>> tensor_in
+        tensor([[0, 1],
+                [2, 3]], device='cuda:0') # Rank 0
+        tensor([[0, 1],
+                [2, 3]], device='cuda:1') # Rank 1
+        >>> dist.reduce_scatter_tensor(tensor_out, tensor_in)
+        >>> tensor_out
+        tensor([0, 2], device='cuda:0') # Rank 0
+        tensor([4, 6], device='cuda:1') # Rank 1
+
+    .. warning::
+        The Gloo backend does not support this API.
+
+    """
+    _check_single_tensor(output, "output")
+    _check_single_tensor(input, "input")
+
+    if _rank_not_in_group(group):
+        _warn_not_in_group("reduce_scatter_tensor")
+        return
+
+    opts = ReduceScatterOptions()
+    opts.reduceOp = op
+    opts.asyncOp = async_op
+
+    group = group or _get_default_group()
+
+    # Check if we are in coalescing context
+    # If we are, do not issue single operation, just append a collective representation
+    if group in _world.pg_coalesce_state.keys():
+        coll = _CollOp(reduce_scatter_tensor, input, output, op, None)
+        _world.pg_coalesce_state[group].append(coll)
+        if async_op:
+            return _IllegalWork()
+        else:
+            return None
+
+    work = group._reduce_scatter_base(output, input, opts)
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+@deprecated(
+    "`torch.distributed._reduce_scatter_base` is a private function and will be deprecated. "
+    "Please use `torch.distributed.reduce_scatter_tensor` instead.",
+    category=FutureWarning,
+)
+def _reduce_scatter_base(output, input, op=ReduceOp.SUM, group=None, async_op=False):
+    """
+    Reduces, then scatters a flattened tensor to all processes in a group.
+
+    Args:
+        output (Tensor): Output tensor.
+        input (Tensor): Input tensor that is of size output tensor size times world size
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group.
+
+    .. warning::
+        `_reduce_scatter_base` is a private function. Users should use
+        `reduce_scatter_tensor` instead.
+
+    """
+    return reduce_scatter_tensor(output, input, op, group, async_op)
+
+
+@_exception_logger
+def all_to_all_single(
+    output,
+    input,
+    output_split_sizes=None,
+    input_split_sizes=None,
+    group=None,
+    async_op=False,
+):
+    """
+    Split input tensor and then scatter the split list to all processes in a group.
+
+    Later the received tensors are concatenated from all the processes in the group
+    and returned as a single output tensor.
+
+    Complex tensors are supported.
+
+    Args:
+        output (Tensor): Gathered concatenated output tensor.
+        input (Tensor): Input tensor to scatter.
+        output_split_sizes: (list[Int], optional): Output split sizes for dim 0
+            if specified None or empty, dim 0 of ``output`` tensor must divide
+            equally by ``world_size``.
+        input_split_sizes: (list[Int], optional): Input split sizes for dim 0
+            if specified None or empty, dim 0 of ``input`` tensor must divide
+            equally by ``world_size``.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group.
+
+    .. warning::
+        `all_to_all_single` is experimental and subject to change.
+
+    Examples:
+        >>> # xdoctest: +SKIP("Undefined rank")
+        >>> input = torch.arange(4) + rank * 4
+        >>> input
+        tensor([0, 1, 2, 3])     # Rank 0
+        tensor([4, 5, 6, 7])     # Rank 1
+        tensor([8, 9, 10, 11])   # Rank 2
+        tensor([12, 13, 14, 15]) # Rank 3
+        >>> output = torch.empty([4], dtype=torch.int64)
+        >>> dist.all_to_all_single(output, input)
+        >>> output
+        tensor([0, 4, 8, 12])    # Rank 0
+        tensor([1, 5, 9, 13])    # Rank 1
+        tensor([2, 6, 10, 14])   # Rank 2
+        tensor([3, 7, 11, 15])   # Rank 3
+
+        >>> # Essentially, it is similar to following operation:
+        >>> scatter_list = list(input.chunk(world_size))
+        >>> gather_list  = list(output.chunk(world_size))
+        >>> for i in range(world_size):
+        >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i)
+
+        >>> # Another example with uneven split
+        >>> input
+        tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0
+        tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1
+        tensor([20, 21, 22, 23, 24])                                     # Rank 2
+        tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3
+        >>> input_splits
+        [2, 2, 1, 1]                                                     # Rank 0
+        [3, 2, 2, 2]                                                     # Rank 1
+        [2, 1, 1, 1]                                                     # Rank 2
+        [2, 2, 2, 1]                                                     # Rank 3
+        >>> output_splits
+        [2, 3, 2, 2]                                                     # Rank 0
+        [2, 2, 1, 2]                                                     # Rank 1
+        [1, 2, 1, 2]                                                     # Rank 2
+        [1, 2, 1, 1]                                                     # Rank 3
+        >>> output = ...
+        >>> dist.all_to_all_single(output, input, output_splits, input_splits)
+        >>> output
+        tensor([ 0,  1, 10, 11, 12, 20, 21, 30, 31])                     # Rank 0
+        tensor([ 2,  3, 13, 14, 22, 32, 33])                             # Rank 1
+        tensor([ 4, 15, 16, 23, 34, 35])                                 # Rank 2
+        tensor([ 5, 17, 18, 24, 36])                                     # Rank 3
+
+
+        >>> # Another example with tensors of torch.cfloat type.
+        >>> input = torch.tensor([1+1j, 2+2j, 3+3j, 4+4j], dtype=torch.cfloat) + 4 * rank * (1+1j)
+        >>> input
+        tensor([1+1j, 2+2j, 3+3j, 4+4j])                                # Rank 0
+        tensor([5+5j, 6+6j, 7+7j, 8+8j])                                # Rank 1
+        tensor([9+9j, 10+10j, 11+11j, 12+12j])                          # Rank 2
+        tensor([13+13j, 14+14j, 15+15j, 16+16j])                        # Rank 3
+        >>> output = torch.empty([4], dtype=torch.int64)
+        >>> dist.all_to_all_single(output, input)
+        >>> output
+        tensor([1+1j, 5+5j, 9+9j, 13+13j])                              # Rank 0
+        tensor([2+2j, 6+6j, 10+10j, 14+14j])                            # Rank 1
+        tensor([3+3j, 7+7j, 11+11j, 15+15j])                            # Rank 2
+        tensor([4+4j, 8+8j, 12+12j, 16+16j])                            # Rank 3
+    """
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_to_all_single")
+        return
+
+    opts = AllToAllOptions()
+    _check_single_tensor(output, "output")
+    _check_single_tensor(input, "input")
+    _ensure_all_tensors_same_dtype(output, input)
+
+    if input.is_complex():
+        input = torch.view_as_real(input)
+    if output.is_complex():
+        output = torch.view_as_real(output)
+
+    output_split_sizes = [] if output_split_sizes is None else output_split_sizes
+    input_split_sizes = [] if input_split_sizes is None else input_split_sizes
+
+    group = group or _get_default_group()
+    work = group.alltoall_base(
+        output, input, output_split_sizes, input_split_sizes, opts
+    )
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+@_exception_logger
+def all_to_all(output_tensor_list, input_tensor_list, group=None, async_op=False):
+    """
+    Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list.
+
+    Complex tensors are supported.
+
+    Args:
+        output_tensor_list (list[Tensor]): List of tensors to be gathered one
+            per rank.
+        input_tensor_list (list[Tensor]): List of tensors to scatter one per rank.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group.
+
+    .. warning::
+        `all_to_all` is experimental and subject to change.
+
+    Examples:
+        >>> # xdoctest: +SKIP("Undefined rank")
+        >>> input = torch.arange(4) + rank * 4
+        >>> input = list(input.chunk(4))
+        >>> input
+        [tensor([0]), tensor([1]), tensor([2]), tensor([3])]     # Rank 0
+        [tensor([4]), tensor([5]), tensor([6]), tensor([7])]     # Rank 1
+        [tensor([8]), tensor([9]), tensor([10]), tensor([11])]   # Rank 2
+        [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3
+        >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
+        >>> dist.all_to_all(output, input)
+        >>> output
+        [tensor([0]), tensor([4]), tensor([8]), tensor([12])]    # Rank 0
+        [tensor([1]), tensor([5]), tensor([9]), tensor([13])]    # Rank 1
+        [tensor([2]), tensor([6]), tensor([10]), tensor([14])]   # Rank 2
+        [tensor([3]), tensor([7]), tensor([11]), tensor([15])]   # Rank 3
+
+        >>> # Essentially, it is similar to following operation:
+        >>> scatter_list = input
+        >>> gather_list  = output
+        >>> for i in range(world_size):
+        >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i)
+
+        >>> input
+        tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0
+        tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1
+        tensor([20, 21, 22, 23, 24])                                     # Rank 2
+        tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3
+        >>> input_splits
+        [2, 2, 1, 1]                                                     # Rank 0
+        [3, 2, 2, 2]                                                     # Rank 1
+        [2, 1, 1, 1]                                                     # Rank 2
+        [2, 2, 2, 1]                                                     # Rank 3
+        >>> output_splits
+        [2, 3, 2, 2]                                                     # Rank 0
+        [2, 2, 1, 2]                                                     # Rank 1
+        [1, 2, 1, 2]                                                     # Rank 2
+        [1, 2, 1, 1]                                                     # Rank 3
+        >>> input = list(input.split(input_splits))
+        >>> input
+        [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])]                   # Rank 0
+        [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1
+        [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])]                 # Rank 2
+        [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])]         # Rank 3
+        >>> output = ...
+        >>> dist.all_to_all(output, input)
+        >>> output
+        [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])]   # Rank 0
+        [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])]           # Rank 1
+        [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])]              # Rank 2
+        [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])]                  # Rank 3
+
+        >>> # Another example with tensors of torch.cfloat type.
+        >>> input = torch.tensor([1+1j, 2+2j, 3+3j, 4+4j], dtype=torch.cfloat) + 4 * rank * (1+1j)
+        >>> input = list(input.chunk(4))
+        >>> input
+        [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])]            # Rank 0
+        [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])]            # Rank 1
+        [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])]      # Rank 2
+        [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])]    # Rank 3
+        >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
+        >>> dist.all_to_all(output, input)
+        >>> output
+        [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])]          # Rank 0
+        [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])]        # Rank 1
+        [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])]        # Rank 2
+        [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])]        # Rank 3
+
+    """
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_to_all")
+        return
+
+    opts = AllToAllOptions()
+    _check_tensor_list(output_tensor_list, "output_tensor_list")
+    _check_tensor_list(input_tensor_list, "input_tensor_list")
+    _ensure_all_tensors_same_dtype(output_tensor_list, input_tensor_list)
+
+    input_tensor_list = [
+        t if not t.is_complex() else torch.view_as_real(t) for t in input_tensor_list
+    ]
+    output_tensor_list = [
+        t if not t.is_complex() else torch.view_as_real(t) for t in output_tensor_list
+    ]
+
+    group = group or _get_default_group()
+    work = group.alltoall(output_tensor_list, input_tensor_list, opts)
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+@_exception_logger
+def barrier(group=GroupMember.WORLD, async_op=False, device_ids=None):
+    """
+    Synchronize all processes.
+
+    This collective blocks processes until the whole group enters this function,
+    if async_op is False, or if async work handle is called on wait().
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+        device_ids ([int], optional): List of device/GPU ids.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    .. note:: `ProcessGroupNCCL` now relies on stream synchronization instead of
+              device synchronization to block the CPU. Thus, please do not assume that
+              `barrier()` would perform a device synchronization.
+    """
+    if _rank_not_in_group(group):
+        _warn_not_in_group("barrier")
+        return
+
+    opts = BarrierOptions()
+    opts.device = _get_pg_default_device(group)
+    if device_ids is not None:
+        if isinstance(device_ids, list):
+            opts.device_ids = device_ids
+        else:
+            raise TypeError(
+                "Invalid function argument: device_ids type should be List[int]"
+            )
+
+    group = group or _get_default_group()
+    work = group.barrier(opts=opts)
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+def monitored_barrier(group=GroupMember.WORLD, timeout=None, wait_all_ranks=False):
+    """
+    Synchronize processes similar to ``torch.distributed.barrier``, but consider a configurable timeout.
+
+    It is able to report ranks that did not pass this barrier within the provided timeout.
+    Specifically, for non-zero ranks, will block until a send/recv is processed from rank 0.
+    Rank 0 will block until all send /recv from other ranks are processed, and will report
+    failures for ranks that failed to respond in time. Note that if one rank does not reach the
+    monitored_barrier (for example due to a hang), all other ranks would fail in monitored_barrier.
+
+    This collective will block all processes/ranks in the group, until the
+    whole group exits the function successfully, making it useful for debugging
+    and synchronizing. However, it can have a performance impact and should only
+    be used for debugging or scenarios that require full synchronization points
+    on the host-side. For debugging purposes, this barrier can be inserted
+    before the application's collective calls to check if any ranks are
+    desynchronized.
+
+    .. note:: Note that this collective is only supported with the GLOO backend.
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. If
+            ``None``, the default process group will be used.
+        timeout (datetime.timedelta, optional): Timeout for monitored_barrier.
+            If ``None``, the default process group timeout will be used.
+        wait_all_ranks (bool, optional): Whether to collect all failed ranks or
+            not. By default, this is ``False`` and ``monitored_barrier`` on rank 0
+            will throw on the first failed rank it encounters in order to fail
+            fast. By setting ``wait_all_ranks=True`` ``monitored_barrier`` will
+            collect all failed ranks and throw an error containing information
+            about all failed ranks.
+
+    Returns:
+        ``None``.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> if dist.get_rank() != 1:
+        >>>     dist.monitored_barrier() # Raises exception indicating that
+        >>> # rank 1 did not call into monitored_barrier.
+        >>> # Example with wait_all_ranks=True
+        >>> if dist.get_rank() == 0:
+        >>>     dist.monitored_barrier(wait_all_ranks=True) # Raises exception
+        >>> # indicating that ranks 1, 2, ... world_size - 1 did not call into
+        >>> # monitored_barrier.
+    """
+    # Need to call rank not in group before using the group, otherwise
+    # "Invalid process group" error is raised.
+    if _rank_not_in_group(group):
+        _warn_not_in_group("monitored_barrier")
+        return
+
+    if get_backend(group) != Backend.GLOO:
+        raise ValueError("monitored_barrier is only implemented for GLOO backend.")
+
+    if timeout is None:
+        timeout = _get_default_timeout(get_backend(group))
+    elif isinstance(timeout, float):
+        # TODO(whc) aparently some existing test case for monitored_barrier passes in a timeout in float format?
+        warnings.warn(
+            "Please specify timeout arg as a timedelta. "
+            f"Converting current value of {timeout} assuming it represents seconds",
+        )
+        timeout = timedelta(seconds=timeout)
+
+    _check_valid_timeout(timeout)
+
+    group_to_use = _get_default_group() if group is None else group
+    return group_to_use.monitored_barrier(timeout, wait_all_ranks=wait_all_ranks)
+
+
+def _create_process_group_wrapper(
+    wrapped_pg: torch._C._distributed_c10d.Backend,
+    store_prefix: str,
+    store: Store,
+    rank: int,
+    world_size: int,
+    timeout: timedelta = default_pg_timeout,
+):
+    assert _GLOO_AVAILABLE, "ProcessGroupWrapper unsupported without GLOO backend."
+
+    # (whc) this appears to be just for the gloo backend? if so, `default_pg_timeout` is appropriate...
+
+    # Create a separate prefix store for the helper process group.
+    prefix = f"{PG_WRAPPER_STORE_PREFIX}:{store_prefix}"
+    store = PrefixStore(prefix, store)
+    helper_pg = ProcessGroupGloo(store, rank, world_size, timeout=timeout)
+    # Wrap the underlying pg with ProcessGroupWrapper.
+    wrapped_pg = _ProcessGroupWrapper(wrapped_pg, helper_pg)
+    return wrapped_pg
+
+
+# helper function for deterministically hashing a list of ranks
+def _hash_ranks(ranks: List[int]):
+    return hashlib.sha1(bytes("_".join(map(str, ranks)), "utf-8")).hexdigest()
+
+
+# Takes a list of ranks and computes an integer color
+def _process_group_color(ranks: List[int]) -> int:
+    # Convert our hash to an int, but avoid negative numbers by shifting a bit.
+    return int(_hash_ranks(ranks), 16) % (sys.maxsize >> 1)
+
+
+def _process_group_name(ranks, use_hashed_name):
+    global _world
+    if use_hashed_name:
+        pg_name = _hash_ranks(ranks)
+        while pg_name in _world.pg_names.values():
+            pg_name = hashlib.sha1(bytes(pg_name + "_", "utf-8")).hexdigest()
+    else:
+        pg_name = str(_world.group_count)
+        _world.group_count += 1
+    return pg_name
+
+
+def _get_backend_from_str(backend: Optional[str] = None) -> Backend:
+    # Default to the same backend as the global process group
+    #  if backend is not specified.
+    if not backend:
+        backend = get_backend(_get_default_group())
+    return Backend(backend)
+
+
+def _is_safe_to_split() -> bool:
+    """
+    Checks if it is safe to split the any process group in the world.
+    This is only safe if the default pg has a bound device id, otherwise
+    users must be aware that a pg is only splittable after the first collective is
+    issued.
+    """
+    return False if _get_default_group().bound_device_id is None else True
+
+
+@_time_logger
+def split_group(
+    parent_pg: Optional[ProcessGroup] = None,
+    split_ranks: Optional[list] = None,
+    timeout: Optional[timedelta] = None,
+    pg_options: Optional[Any] = None,
+    group_desc: Optional[str] = None,
+) -> Optional[ProcessGroup]:
+    """
+    Create a new process group splitted from the given parent process group.
+
+    warning:: This is an experimental API and only the ``NCCL`` backend supports this API.
+    Other backends will raise an error.
+    Users of this API must gurantee that all ranks in the parent group enter this API call,
+    and the split of the sub groups is the same accross all ranks in the parent group.
+
+    Args:
+        parent_pg (ProcessGroup, optional): The parent process group. If None,
+            the default process group will be used. Users need to gurantee that
+            the parent group is fully initialized (e.g, communicators are initialized)
+        split_ranks (list[list[int]]): the split ranks, which is a list of list of ranks.
+            Users need to make sure the validity of the split ranks such that one
+            split (represented by one inner list of ints) does not overlap with any other split.
+            Note that the ranks in each split is the group rank (instead of global rank)
+            in the parent pg. For example, if the parent group has 4 ranks, and split_ranks can be
+            [[0, 1], [2, 3]]. Note [[0,1]] is also a valid split, in which case ranks 2, 3 would
+            return a non-group member.
+        timeout (timedelta, optional): see `init_process_group` for details and default value.
+        pg_options (ProcessGroupOptions, optional): only ProcessGroupNCCLOptions is supported now.
+            specifying what additional options need to be passed in during
+            the construction of specific process groups. i.e.``is_high_priority_stream``
+            can be specified so that process group can pick up high priority cuda streams.
+            For other availble options to config nccl,
+            See https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/api/types.html#ncclconfig-t
+        group_desc (str, optional): a string to describe the process group.
+
+    Returns:
+        ProcessGroup if the current rank is within one split/subgroup given by split_ranks,
+        or None if the current rank is not part of any split_ranks`.
+
+    """
+    # check inputs
+    if split_ranks is None:
+        raise ValueError("split_ranks cannot be None")
+
+    global _world
+    default_pg = _get_default_group()
+    device_id = default_pg.bound_device_id
+    if not device_id:
+        raise RuntimeError(
+            "No device associated with the default pg, not safe to split any process groups"
+        )
+    default_backend, default_store = _world.pg_map[default_pg]
+    global_rank = default_pg.rank()
+    global_world_size = default_pg.size()
+
+    if not parent_pg:
+        parent_pg = default_pg
+    if parent_pg not in _world.pg_group_ranks:
+        raise ValueError(f"Group {parent_pg} is not registered")
+
+    parent_global_to_group_ranks = _world.pg_group_ranks[parent_pg]
+    parent_group_to_global_ranks = {
+        group_rank: global_rank
+        for global_rank, group_rank in parent_global_to_group_ranks.items()
+    }
+
+    if global_rank not in parent_global_to_group_ranks:
+        raise ValueError(
+            f"Global rank {global_rank} is not part of the parent group {parent_pg}"
+        )
+
+    parent_group_rank = parent_global_to_group_ranks[global_rank]
+    parent_backend = parent_pg._get_backend(torch.device("cuda"))
+
+    # if the parent backend does not support splitting, raise error
+    # currently this API only support NCCL backend
+    if (
+        not parent_backend
+        or not parent_backend.supports_splitting
+        or not isinstance(parent_backend, ProcessGroupNCCL)
+    ):
+        raise RuntimeError(
+            "No backend for the parent process group or its backend does not support splitting"
+        )
+
+    # set the group_desc before the color or no_cloor split
+    group_desc = (
+        f"{parent_pg.group_desc}:split:{parent_backend.comm_split_count()}"
+        if group_desc is None
+        else group_desc
+    )
+
+    parent_backend_str, _ = _world.pg_map[parent_pg]
+    # same type of backend as the parent process group
+    backend = Backend(parent_backend_str)
+    backend_config = BackendConfig(backend)
+
+    if pg_options is not None:
+        assert isinstance(
+            pg_options, ProcessGroupNCCL.Options
+        ), "Expected pg_options argument to be of type ProcessGroupNCCL.Options"
+    else:
+        # default pg_options same as the parent process group
+        pg_options = parent_backend.options
+
+    # this timeout defaulting/validation is used for all the new_groups/new_subgroups variants,
+    # which may just pass their timeout value (or None)
+    if timeout is None:
+        timeout = _get_default_timeout(backend)
+    _check_valid_timeout(timeout)
+
+    # find my group of ranks and my group local rank in split_ranks
+    my_group = None
+    group_rank = -1
+
+    for split_group in split_ranks:
+        if len(split_group) == 0:
+            raise ValueError("the split group cannot be empty")
+        if len(split_group) > global_world_size:
+            raise ValueError(
+                "the split group's size should be less or equal to the world_size set by init_process_group"
+            )
+        if len(split_group) != len(set(split_group)):
+            raise ValueError("the split group cannot have duplicate ranks")
+        split_group = sorted(split_group)
+        if parent_group_rank in split_group:
+            my_group = split_group
+            group_rank = split_group.index(parent_group_rank)
+            break
+    # if my rank does not belong to any sub group,
+    # no_color split should be called
+    if my_group is None or group_rank == -1:
+        parent_backend.perform_nocolor_split(device_id)
+        return None
+
+    group_name = _process_group_name(my_group, use_hashed_name=False)
+    global_ranks_in_my_group = [parent_group_to_global_ranks[rank] for rank in my_group]
+
+    prefix_store = PrefixStore(f"{group_name}/", default_store)
+    base_pg_options = ProcessGroup.Options(backend=str(backend))
+    base_pg_options._timeout = timeout
+    pg: ProcessGroup = ProcessGroup(
+        prefix_store, group_rank, len(my_group), base_pg_options
+    )
+    pg.bound_device_id = device_id
+
+    pg_options._timeout = timeout
+    pg_options.split_from = parent_backend
+    pg_options.split_color = _process_group_color(my_group)
+    pg_options.global_ranks_in_group = global_ranks_in_my_group
+    pg_options.group_name = group_name
+    backend_class = ProcessGroupNCCL(
+        prefix_store, group_rank, len(my_group), pg_options
+    )
+    backend_type = ProcessGroup.BackendType.NCCL
+    backend_class._set_sequence_number_for_group()
+
+    pg._register_backend(torch.device("cuda"), backend_type, backend_class)
+
+    # set group_name and group_desc to backend
+    assert group_name is not None
+    assert group_desc is not None
+    pg._set_group_name(group_name)
+    pg._set_group_desc(group_desc)
+
+    # always eagerly initialize the backend in split_group
+    eager_backend = pg._get_backend(device_id)
+    eager_backend.eager_connect_single_device(device_id)
+
+    # update global state
+    _world.pg_map[pg] = (backend, prefix_store)
+    _world.pg_names[pg] = group_name
+    _register_process_group(group_name, pg)
+    _world.pg_backend_config[pg] = str(backend_config)
+    pg_tag = f"ptd:{group_name}"
+    _world.tags_to_pg.setdefault(pg_tag, []).append(pg)
+    _world.pg_to_tag[pg] = pg_tag
+
+    # Create the global rank to group rank mapping
+    _world.pg_group_ranks[pg] = {
+        global_rank: group_rank
+        for group_rank, global_rank in enumerate(global_ranks_in_my_group)
+    }
+
+    return pg
+
+
+@_time_logger
+def new_group(
+    ranks=None,
+    timeout=None,
+    backend=None,
+    pg_options=None,
+    use_local_synchronization=False,
+    group_desc=None,
+):
+    """
+    Create a new distributed group.
+
+    This function requires that all processes in the main group (i.e. all
+    processes that are part of the distributed job) enter this function, even
+    if they are not going to be members of the group. Additionally, groups
+    should be created in the same order in all processes.
+
+    .. warning::
+        Safe concurrent usage:
+        When using multiple process groups with the ``NCCL`` backend, the user
+        must ensure a globally consistent execution order of collectives across
+        ranks.
+
+        If multiple threads within a process issue collectives, explicit
+        synchronization is necessary to ensure consistent ordering.
+
+        When using async variants of torch.distributed communication APIs,
+        a work object is returned and the communication kernel is
+        enqueued on a separate CUDA stream, allowing overlap of communication
+        and computation. Once one or more async ops have been issued on one process
+        group, they must be synchronized with other cuda streams by calling `work.wait()`
+        before using another process group.
+
+        See `Using multiple NCCL communicators concurrently `_ for more details.
+
+    Args:
+        ranks (list[int]): List of ranks of group members. If ``None``, will be
+            set to all ranks. Default is ``None``.
+        timeout (timedelta, optional): see `init_process_group` for details and default value.
+        backend (str or Backend, optional): The backend to use. Depending on
+            build-time configurations, valid values are ``gloo`` and ``nccl``.
+            By default uses the same backend as the global group. This field
+            should be given as a lowercase string (e.g., ``"gloo"``), which can
+            also be accessed via :class:`Backend` attributes (e.g.,
+            ``Backend.GLOO``). If ``None`` is passed in, the backend
+            corresponding to the default process group will be used. Default is
+            ``None``.
+        pg_options (ProcessGroupOptions, optional): process group options
+            specifying what additional options need to be passed in during
+            the construction of specific process groups. i.e. for the ``nccl``
+            backend, ``is_high_priority_stream`` can be specified so that
+            process group can pick up high priority cuda streams. For other availble options to config nccl,
+            See https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/api/types.html#ncclconfig-t
+        use_local_synchronization (bool, optional): perform a group-local
+            barrier at the end of the process group creation. This is different
+            in that non-member ranks don't need to call into API and don't
+            join the barrier.
+        group_desc (str, optional): a string to describe the process group.
+
+    Returns:
+        A handle of distributed group that can be given to collective calls or
+        GroupMember.NON_GROUP_MEMBER if the rank is not part of ``ranks``.
+
+    N.B. use_local_synchronization doesn't work with MPI.
+
+    N.B. While use_local_synchronization=True can be significantly faster with larger
+    clusters and small process groups, care must be taken since it changes cluster behavior
+    as non-member ranks don't join the group barrier().
+
+    N.B. use_local_synchronization=True can lead to deadlocks when each rank creates
+    multiple overlaping process groups. To avoid that, make sure all ranks follow the
+    same global creation order.
+    """
+    return _new_group_with_tag(
+        ranks,
+        timeout,
+        backend,
+        pg_options,
+        None,
+        use_local_synchronization=use_local_synchronization,
+        group_desc=group_desc,
+    )
+
+
+def _new_group_with_tag(
+    ranks=None,
+    timeout=None,
+    backend=None,
+    pg_options=None,
+    pg_tag=None,
+    use_local_synchronization=False,
+    group_desc=None,
+):
+    """
+    Variant of ``new_group`` that exposes tag creation.
+
+    :: N.B. The mechanism is experimental and tied to the functional collectives effort, see
+    ``torch.distributed._functional_collectives`` for reference on how to use it.
+    """
+    global _world
+
+    default_pg = _get_default_group()
+    device_id = default_pg.bound_device_id
+    default_backend, default_store = _world.pg_map[default_pg]
+    global_rank = default_pg.rank()
+    global_world_size = default_pg.size()
+
+    # Default to the same backend as the global process group
+    # if the backend is not specified.
+    if not backend:
+        backend = default_backend
+    backend = Backend(backend)
+
+    # this timeout defaulting/validation is used for all the new_groups/new_subgroups variants,
+    # which may just pass their timeout value (or None)
+    if timeout is None:
+        timeout = _get_default_timeout(backend)
+    _check_valid_timeout(timeout)
+
+    if use_local_synchronization:
+        # MPI backend doesn't have have a way for us to perform a partial sync
+        if backend == Backend.MPI:
+            raise ValueError(
+                "MPI backend doesn't support use_local_synchronization=True"
+            )
+        if ranks is not None and get_rank() not in ranks:
+            return None
+
+    # checks the input ranks
+    if ranks is not None:
+        ranks = sorted(ranks)
+        group_world_size = len(ranks)
+        if group_world_size > global_world_size:
+            raise ValueError(
+                "the new group's world size should be less or "
+                "equal to the world size set by "
+                "init_process_group"
+            )
+        # check ranks' sanity
+        for rank in ranks:
+            if rank < 0 or rank >= global_world_size:
+                raise ValueError(
+                    "The new group's rank should be within "
+                    "the world_size set by init_process_group"
+                )
+        if global_rank in ranks:
+            group_rank = ranks.index(global_rank)
+        else:
+            group_rank = None
+    else:
+        ranks = list(range(global_world_size))
+        group_world_size = global_world_size
+        group_rank = global_rank
+
+    group_name = _process_group_name(ranks, use_hashed_name=use_local_synchronization)
+
+    pg, pg_store = _new_process_group_helper(
+        group_world_size,
+        group_rank,
+        ranks,
+        backend,
+        default_store,
+        group_name,
+        pg_options=pg_options,
+        timeout=timeout,
+        pg_tag=pg_tag,
+        device_id=device_id,
+        group_desc=group_desc,
+    )
+
+    # Create the global rank to group rank mapping
+    _world.pg_group_ranks[pg] = {
+        global_rank: group_rank for group_rank, global_rank in enumerate(ranks)
+    }
+
+    if _is_barrier_after_init() == 1:
+        # barrier at the end to ensure that once we return from this method, all
+        # process groups including global variables (if any) are updated
+        # correctly on all ranks.
+        # Update 04/2023: for large-scale runs, this barrier (esp. store-based
+        # barrier) may be costly and/or unscalable. Also, in a lot of cases,
+        # these barriers may be unnecessary, as proven by a green CI after
+        # removal. An environment variable `TORCH_DIST_INIT_BARRIER` has been
+        # added which enables this barrier only when set to 1.
+        logger.info(
+            "Performing barrier after ProcessGroup initialization since "
+            "TORCH_DIST_INIT_BARRIER = 1"
+        )
+        if backend == Backend.MPI:
+            # MPI doesn't have store.
+            barrier()
+        else:
+            barrier_store = pg_store if use_local_synchronization else default_store
+            world_size = len(ranks) if use_local_synchronization else get_world_size()
+            # Use store based barrier here since barrier() used a bunch of
+            # default devices and messes up NCCL internal state.
+            _store_based_barrier(
+                global_rank, barrier_store, group_name, world_size, timeout
+            )
+
+    return pg
+
+
+def new_subgroups(
+    group_size=None,
+    group=None,
+    timeout=None,
+    backend=None,
+    pg_options=None,
+    group_desc=None,
+):
+    """
+    Create subgroups of equal size.
+
+    By default, it creates intra-machine subgroups,
+    where each of which contains all the ranks of a machine, based on the assumption
+    that each machine has the same number of devices.
+
+    This is a convenience API that calls ``new_group`` to generate multiple subgroups.
+    It requires that all processes in the main group (i.e. all
+    processes that are part of the distributed job) enter this function, even
+    if they are not going to be members of the group.
+
+    .. warning::
+        If ``group_size`` is passed in, the world size must be divisible by ``group_size``.
+        If no ``group_size`` is passed in, it believe that you are creating a group based
+        on CUDA and determining the group size by number of CUDA devices, and if not all
+        the machines have the same number of devices, the subgroup division will be
+        different across nodes and can cause unexpected behaviors. Therefore, if you are
+        creating a subgroup that does not depend on CUDA (such as Gloo on CPU), please
+        pass in ``group_size`` correctly.
+
+    .. warning::
+        See warning `Safe concurrent usage` for `new_group` API for important details about
+        using multiple process groups concurrently in a safe manner.
+
+    Args:
+        group_size (int, optional): The size of each subgroup. If ``None``,
+            the default subgroup size is equal to the number of devices on each machine,
+            based on the assumption that each machine has exactly the same
+            number of devices. Default is ``None``.
+        timeout (timedelta, optional): see `init_process_group` for details and default value.
+        backend (str or Backend, optional): The backend to use. Depending on
+            build-time configurations, valid values are ``gloo`` and ``nccl``.
+            By default uses the same backend as the global group. This field
+            should be given as a lowercase string (e.g., ``"gloo"``), which can
+            also be accessed via :class:`Backend` attributes (e.g.,
+            ``Backend.GLOO``). If ``None`` is passed in, the backend
+            corresponding to the default process group will be used. Default is
+            ``None``.
+        pg_options (ProcessGroupOptions, optional): process group options
+            specifying what additional options need to be passed in during
+            the construction of specific process groups. i.e. for the ``nccl``
+            backend, ``is_high_priority_stream`` can be specified so that
+            process group can pick up high priority cuda streams.
+        group_desc (str, optional): A string describing the group. Each subgroup will
+            inherit its group_desc
+
+    Returns:
+        The subgroup containing the current rank, and all the subgroups used for cleanup.
+
+    Examples:
+        >>> # Create intra-machine subgroups.
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> cur_subgroup, subgroups = dist.new_subgroups()
+        >>> # Allreduce within the machine.
+        >>> rank = dist.get_rank()
+        >>> tensor = torch.ones(1, device=rank) * rank
+        >>> dist.all_reduce(tensor, group=cur_subgroup)
+        >>> tensor
+        tensor([28])  # Assume 8 CUDA devices per machine.  28 is sum(range(8)).
+        >>> # Cleanup.
+        >>> for subgroup in subgroups:
+        >>>     dist.destroy_process_group(subgroup)
+    """
+    if group_size is None:
+        if not torch.cuda.is_available():
+            raise ValueError(
+                "Default group size only takes effect when CUDA is available."
+                "If your subgroup using a backend that does not depend on CUDA,"
+                "please pass in 'group_size' correctly."
+            )
+        group_size = torch.cuda.device_count()
+    if group_size <= 0:
+        raise ValueError(f"The arg 'group_size' ({group_size}) must be positive")
+
+    world_size = get_world_size()
+    if world_size < group_size:
+        raise ValueError(
+            f"The arg 'group_size' ({group_size}) must not exceed the world size ({world_size})"
+        )
+    if world_size % group_size != 0:
+        raise ValueError("The world size must be divisible by 'group_size'")
+
+    subgroups = []
+    cur_subgroup = None
+
+    for subgroup_id in range(world_size // group_size):
+        start_rank = subgroup_id * group_size
+        end_rank = start_rank + group_size
+        ranks_in_subgroup = list(range(start_rank, end_rank))
+        subgroup = new_group(
+            ranks=ranks_in_subgroup,
+            timeout=timeout,
+            backend=backend,
+            pg_options=pg_options,
+            group_desc=group_desc,
+        )
+        subgroups.append(subgroup)
+
+        rank = get_rank()
+        if rank in ranks_in_subgroup:
+            cur_subgroup = subgroup
+            logger.info("Rank %s is assigned to subgroup %s", rank, ranks_in_subgroup)
+
+    return cur_subgroup, subgroups
+
+
+def new_subgroups_by_enumeration(
+    ranks_per_subgroup_list,
+    timeout=None,
+    backend=None,
+    pg_options=None,
+    group_desc=None,
+):
+    """
+    Create subgroups by dividing the global world.
+
+    The division is specified by a nested list of ranks. The subgroups cannot have
+    overlap, and some ranks may not have to be in any subgroup.
+
+    This is a convenience API that calls ``new_group`` to generate multiple subgroups.
+    It requires that all processes in the main group (i.e. all
+    processes that are part of the distributed job) enter this function, even
+    if they are not going to be members of the group.
+
+    .. warning::
+        See warning `Safe concurrent usage` for `new_group` API for important details about
+        using multiple process groups concurrently in a safe manner.
+
+    Args:
+        ranks_per_subgroup_list (list[list[int]]): A nested list of ranks of
+            group members.
+        timeout (timedelta, optional): see `init_process_group` for details and default value.
+        backend (str or Backend, optional): The backend to use. Depending on
+             build-time configurations, valid values are ``gloo`` and ``nccl``.
+             By default uses the same backend as the global group. This field
+             should be given as a lowercase string (e.g., ``"gloo"``), which can
+             also be accessed via :class:`Backend` attributes (e.g.,
+             ``Backend.GLOO``). If ``None`` is passed in, the backend
+             corresponding to the default process group will be used. Default is
+             ``None``.
+        pg_options (ProcessGroupOptions, optional): process group options
+            specifying what additional options need to be passed in during
+            the construction of specific process groups. i.e. for the ``nccl``
+            backend, ``is_high_priority_stream`` can be specified so that
+            process group can pick up high priority cuda streams.
+        group_desc (str, optional): A string describing the group. Each subgroup will
+            inherit its group_desc.
+
+    Returns:
+        The subgroup containing the current rank, and all the subgroups used for cleanup.
+
+    Examples:
+        >>> # Create two subgroups, where each has 2 processes.
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> cur_subgroup, subgroups = dist.new_subgroups(ranks=[[0, 2], [1, 3]])
+        >>> rank = dist.get_rank()
+        >>> tensor = torch.ones(1, device=rank) * rank
+        >>> dist.all_reduce(tensor, group=cur_subgroup)
+        >>> tensor
+        tensor([2])     # Subgroup 0: ranks 0 and 2
+        tensor([4])     # Subgroup 1: ranks 1 and 3
+    """
+    if ranks_per_subgroup_list is None or len(ranks_per_subgroup_list) == 0:
+        raise ValueError("The arg 'ranks_per_subgroup_list' cannot be empty")
+
+    subgroups = []
+    cur_subgroup = None
+    # Create a mapping from rank to subgroup to check if there is any subgroup overlap.
+    rank_to_ranks_dict = {}  # type: ignore[var-annotated]
+    for ranks in ranks_per_subgroup_list:
+        subgroup = new_group(
+            ranks=ranks,
+            timeout=timeout,
+            backend=backend,
+            pg_options=pg_options,
+            group_desc=group_desc,
+        )
+        subgroups.append(subgroup)
+        my_rank = get_rank()
+        for rank in ranks:
+            if rank in rank_to_ranks_dict:
+                raise ValueError(
+                    f"Rank {rank} has appeared in both subgroup {rank_to_ranks_dict[rank]} and {ranks}"
+                )
+            rank_to_ranks_dict[rank] = ranks
+            if my_rank == rank:
+                cur_subgroup = subgroup
+                logger.info("Rank %s is assigned to subgroup %s", rank, ranks)
+
+    return cur_subgroup, subgroups
+
+
+def _find_pg_by_ranks_and_tag(tag: str, ranks: List[int]) -> Optional[ProcessGroup]:
+    if len(tag) > 0 and not tag.startswith("ptd:") and not tag.startswith("user:"):
+        tag = f"user:{tag}"
+
+    for group in _world.tags_to_pg.get(tag, []):
+        if group.size() != len(ranks):
+            continue
+
+        group_ranks = get_process_group_ranks(group)
+        good = all(r in group_ranks for r in ranks)
+        if good:
+            return group
+    return None
+
+
+def _find_or_create_pg_by_ranks_and_tag(
+    tag: str, ranks: List[int], stride: int
+) -> ProcessGroup:
+    assert (
+        len(ranks) % stride == 0
+    ), f"Ranks length ({len(ranks)}) must be divisible by stride ({stride})"
+
+    my_rank = get_rank()
+    my_ranks = None
+
+    if stride == len(ranks):
+        my_ranks = ranks.copy()
+        assert my_rank in my_ranks, "rankset doesn't include the current node"
+    else:
+        for i in range(0, len(ranks), stride):
+            rank_set = ranks[i : i + stride]
+            if my_rank in rank_set:
+                my_ranks = rank_set
+        assert my_ranks is not None, "rankset doesn't include the current node"
+
+    my_ranks = sorted(my_ranks)
+
+    pg = _find_pg_by_ranks_and_tag(tag, my_ranks)
+    if pg is not None:
+        return pg
+    if tag == "":
+        raise ValueError("Cannot automatically create PG with empty tag")
+    # TODO copy settings and timeout from default PG
+    return _new_group_with_tag(my_ranks, pg_tag=tag)
+
+
+def _get_group_tag(pg: ProcessGroup) -> str:
+    """Return the tag associated with ``pg``."""
+    tag = _world.pg_to_tag[pg]
+    if tag.startswith("user:"):
+        tag = tag[5:]
+    return tag
+
+
+def _get_process_group_name(pg: ProcessGroup) -> str:
+    return _world.pg_names.get(pg, "None")
+
+
+def _get_process_group_store(pg: ProcessGroup) -> Store:
+    return _world.pg_map[pg][1]
+
+
+# This ops are not friendly to TorchDynamo. So, we decide to disallow these ops
+# in FX graph, allowing them to run them on eager, with torch.compile.
+dynamo_unsupported_distributed_c10d_ops = [
+    recv,
+    all_gather_object,
+    all_gather_coalesced,
+    all_to_all_single,
+    all_reduce,
+    gather_object,
+    all_to_all,
+    all_reduce_coalesced,
+    gather,
+    send_object_list,
+    recv_object_list,
+    broadcast_object_list,
+    barrier,
+    scatter,
+    scatter_object_list,
+    reduce,
+    all_gather,
+    reduce_scatter,
+    all_gather_into_tensor,
+    broadcast,
+    reduce_scatter_tensor,
+    send,
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/launch.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/launch.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9e35c36db7fbc585351128b9bbd712924e809af
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/launch.py
@@ -0,0 +1,208 @@
+# mypy: allow-untyped-defs
+r"""
+Module ``torch.distributed.launch``.
+
+``torch.distributed.launch`` is a module that spawns up multiple distributed
+training processes on each of the training nodes.
+
+.. warning::
+
+    This module is going to be deprecated in favor of :ref:`torchrun `.
+
+The utility can be used for single-node distributed training, in which one or
+more processes per node will be spawned. The utility can be used for either
+CPU training or GPU training. If the utility is used for GPU training,
+each distributed process will be operating on a single GPU. This can achieve
+well-improved single-node training performance. It can also be used in
+multi-node distributed training, by spawning up multiple processes on each node
+for well-improved multi-node distributed training performance as well.
+This will especially be beneficial for systems with multiple Infiniband
+interfaces that have direct-GPU support, since all of them can be utilized for
+aggregated communication bandwidth.
+
+In both cases of single-node distributed training or multi-node distributed
+training, this utility will launch the given number of processes per node
+(``--nproc-per-node``). If used for GPU training, this number needs to be less
+or equal to the number of GPUs on the current system (``nproc_per_node``),
+and each process will be operating on a single GPU from *GPU 0 to
+GPU (nproc_per_node - 1)*.
+
+**How to use this module:**
+
+1. Single-Node multi-process distributed training
+
+::
+
+    python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
+               YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other
+               arguments of your training script)
+
+2. Multi-Node multi-process distributed training: (e.g. two nodes)
+
+
+Node 1: *(IP: 192.168.1.1, and has a free port: 1234)*
+
+::
+
+    python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
+               --nnodes=2 --node-rank=0 --master-addr="192.168.1.1"
+               --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
+               and all other arguments of your training script)
+
+Node 2:
+
+::
+
+    python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
+               --nnodes=2 --node-rank=1 --master-addr="192.168.1.1"
+               --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
+               and all other arguments of your training script)
+
+3. To look up what optional arguments this module offers:
+
+::
+
+    python -m torch.distributed.launch --help
+
+
+**Important Notices:**
+
+1. This utility and multi-process distributed (single-node or
+multi-node) GPU training currently only achieves the best performance using
+the NCCL distributed backend. Thus NCCL backend is the recommended backend to
+use for GPU training.
+
+2. In your training program, you must parse the command-line argument:
+``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module.
+If your training program uses GPUs, you should ensure that your code only
+runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by:
+
+Parsing the local_rank argument
+
+::
+
+    >>> # xdoctest: +SKIP
+    >>> import argparse
+    >>> parser = argparse.ArgumentParser()
+    >>> parser.add_argument("--local-rank", "--local_rank", type=int)
+    >>> args = parser.parse_args()
+
+Set your device to local rank using either
+
+::
+
+    >>> torch.cuda.set_device(args.local_rank)  # before your code runs
+
+or
+
+::
+
+    >>> with torch.cuda.device(args.local_rank):
+    >>>    # your code to run
+    >>>    ...
+
+.. versionchanged:: 2.0.0
+
+    The launcher will passes the ``--local-rank=`` argument to your script.
+    From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the
+    previously used underscored ``--local_rank``.
+
+    For backward compatibility, it may be necessary for users to handle both
+    cases in their argument parsing code. This means including both ``"--local-rank"``
+    and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is
+    provided, the launcher will trigger an error: "error: unrecognized arguments:
+    --local-rank=". For training code that only supports PyTorch 2.0.0+,
+    including ``"--local-rank"`` should be sufficient.
+
+3. In your training program, you are supposed to call the following function
+at the beginning to start the distributed backend. It is strongly recommended
+that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work,
+but ``env://`` is the one that is officially supported by this module.
+
+::
+
+    >>> torch.distributed.init_process_group(backend='YOUR BACKEND',
+    >>>                                      init_method='env://')
+
+4. In your training program, you can either use regular distributed functions
+or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your
+training program uses GPUs for training and you would like to use
+:func:`torch.nn.parallel.DistributedDataParallel` module,
+here is how to configure it.
+
+::
+
+    >>> model = torch.nn.parallel.DistributedDataParallel(model,
+    >>>                                                   device_ids=[args.local_rank],
+    >>>                                                   output_device=args.local_rank)
+
+Please ensure that ``device_ids`` argument is set to be the only GPU device id
+that your code will be operating on. This is generally the local rank of the
+process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``,
+and ``output_device`` needs to be ``args.local_rank`` in order to use this
+utility
+
+5. Another way to pass ``local_rank`` to the subprocesses via environment variable
+``LOCAL_RANK``. This behavior is enabled when you launch the script with
+``--use-env=True``. You must adjust the subprocess example above to replace
+``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher
+will not pass ``--local-rank`` when you specify this flag.
+
+.. warning::
+
+    ``local_rank`` is NOT globally unique: it is only unique per process
+    on a machine.  Thus, don't use it to decide if you should, e.g.,
+    write to a networked filesystem.  See
+    https://github.com/pytorch/pytorch/issues/12042 for an example of
+    how things can go wrong if you don't do this correctly.
+
+
+
+"""
+
+from typing_extensions import deprecated as _deprecated
+
+from torch.distributed.run import get_args_parser, run
+
+
+def parse_args(args):
+    parser = get_args_parser()
+    parser.add_argument(
+        "--use-env",
+        "--use_env",
+        default=False,
+        action="store_true",
+        help="Use environment variable to pass "
+        "'local rank'. For legacy reasons, the default value is False. "
+        "If set to True, the script will not pass "
+        "--local-rank as argument, and will instead set LOCAL_RANK.",
+    )
+    return parser.parse_args(args)
+
+
+def launch(args):
+    if args.no_python and not args.use_env:
+        raise ValueError(
+            "When using the '--no-python' flag,"
+            " you must also set the '--use-env' flag."
+        )
+    run(args)
+
+
+@_deprecated(
+    "The module torch.distributed.launch is deprecated\n"
+    "and will be removed in future. Use torchrun.\n"
+    "Note that --use-env is set by default in torchrun.\n"
+    "If your script expects `--local-rank` argument to be set, please\n"
+    "change it to read from `os.environ['LOCAL_RANK']` instead. See \n"
+    "https://pytorch.org/docs/stable/distributed.html#launch-utility for \n"
+    "further instructions\n",
+    category=FutureWarning,
+)
+def main(args=None):
+    args = parse_args(args)
+    launch(args)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/logging_handlers.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/logging_handlers.py
new file mode 100644
index 0000000000000000000000000000000000000000..021ad100f06a89fa944d7e6dba18f1a9558d88f5
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/logging_handlers.py
@@ -0,0 +1,17 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+from typing import Dict, List
+
+
+__all__: List[str] = []
+
+_log_handlers: Dict[str, logging.Handler] = {
+    "default": logging.NullHandler(),
+}
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/remote_device.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/remote_device.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab5215e2f83a71e56c6f638bd1453341da671d37
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/remote_device.py
@@ -0,0 +1,120 @@
+# mypy: allow-untyped-defs
+from typing import Optional, Union
+
+import torch
+
+
+class _remote_device:
+    """
+    Represents a device on a remote worker.
+
+    Args:
+        remote_device (str or torch.device): Represents a device on a remote worker.
+            The string format should be one of the following:
+
+                1. "/", where the device field can be parsed as torch.device type.
+                   E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0".
+                   In addition, the device field can be optional and the default value is "cpu".
+                2. "rank:/", where  is the rank of the
+                   process and device can be parsed as torch.device type.
+                   E.g., "rank:0/cpu", "rank:0", "rank:0/cuda:0"
+                3.  and  are optional and formats like "cpu"
+                    and "cuda:1", just represent local devices.
+    """
+
+    def __init__(self, remote_device: Union[str, torch.device]):
+        PARSE_ERROR = (
+            f"Could not parse remote_device: {remote_device}. The valid format is "
+            "'/' or 'rank:/' or ''"
+        )
+        self._worker_name = None
+        self._rank = None
+        self._device: Optional[Union[str, int, torch.device]] = None
+
+        if isinstance(remote_device, torch.device):
+            self._device = remote_device
+        elif isinstance(remote_device, str):
+            fields = remote_device.split("/")
+            if len(fields) == 2:
+                self._worker_name, self._device = fields
+            elif len(fields) == 1:
+                # Check if this is a valid device.
+                if _remote_device._is_valid_local_device(fields[0]):
+                    self._device = fields[0]
+                else:
+                    self._worker_name = fields[0]
+                    self._device = "cpu"
+            else:
+                raise ValueError(PARSE_ERROR)
+        else:
+            raise TypeError(f"Invalid type for remote_device: {type(remote_device)}")
+
+        # Do some basic sanity check (no empty string)
+        if self._worker_name is not None and not self._worker_name:
+            raise ValueError(PARSE_ERROR)
+
+        # Validate the device.
+        self._device = torch.device(self._device)
+
+        # Check for rank based format.
+        if self._worker_name is not None:
+            fields = self._worker_name.split(":")
+            if len(fields) == 2:
+                # rank:/device format, extract rank
+                if fields[0] == "rank" and fields[1].isdigit():
+                    self._rank = int(fields[1])  # type: ignore[assignment]
+                    self._worker_name = None
+                else:
+                    raise ValueError(PARSE_ERROR)
+            elif len(fields) > 2:
+                raise ValueError(PARSE_ERROR)
+
+    @staticmethod
+    def _is_valid_local_device(device):
+        # Check for torch.device
+        try:
+            torch.device(device)
+            return True
+        except Exception:
+            return False
+
+    def worker_name(self) -> Optional[str]:
+        """Return the name of remote worker representing the remote device and ``None`` if no worker name is available."""
+        return self._worker_name
+
+    def rank(self) -> Optional[int]:
+        """
+        Returns the rank of remote worker representing the remote device.
+        Returns ``None`` if no rank is available.
+        """
+        return self._rank
+
+    def device(self) -> torch.device:
+        """Return the local device on the remote worker."""
+        return self._device  # type: ignore[return-value]
+
+    def __repr__(self):
+        if self._device is not None:
+            if self._worker_name is not None:
+                return f"{self._worker_name}/{self._device}"
+            elif self._rank is not None:
+                return f"rank:{self._rank}/{self._device}"
+            else:
+                return str(self._device)
+        else:
+            if self._worker_name is not None:
+                return f"{self._worker_name}"
+            elif self._rank is not None:
+                return f"{self._rank}"
+            else:
+                raise RuntimeError("Invalid state!")
+
+    def __eq__(self, other):
+        return isinstance(other, _remote_device) and (
+            self._worker_name == other._worker_name
+            and self._device == other._device
+            and self._rank == other._rank
+        )
+
+    def __hash__(self):
+        return hash(self._worker_name) ^ hash(self._device) ^ hash(self._rank)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/rendezvous.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/rendezvous.py
new file mode 100644
index 0000000000000000000000000000000000000000..a944a75271b0d6ab0c9ccfbc4a9911e81f89aa94
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/rendezvous.py
@@ -0,0 +1,281 @@
+# mypy: allow-untyped-defs
+try:
+    from urllib.parse import urlparse, urlunparse
+except ImportError as e:
+    raise ImportError(
+        "urllib cannot be found, urlparse from python2 is no longer supported."
+    ) from e
+
+import numbers
+import os
+import sys
+from datetime import timedelta
+from typing import Callable, Dict, Iterator, Optional, Tuple
+
+from torch.distributed import FileStore, PrefixStore, Store, TCPStore
+
+from .constants import default_pg_timeout
+
+
+_rendezvous_handlers: Dict[str, Callable[..., Iterator[Tuple[Store, int, int]]]] = {}
+
+__all__ = ["register_rendezvous_handler", "rendezvous"]
+
+
+def register_rendezvous_handler(scheme, handler):
+    """
+    Register a new rendezvous handler.
+
+    Before we can run collective algorithms, participating processes
+    need to find each other and exchange information to be able to
+    communicate. We call this process rendezvous.
+
+    The outcome of the rendezvous process is a triplet containing a
+    shared key/value store, the rank of the process, and the total
+    number of participating processes.
+
+    If none of the bundled rendezvous methods apply to your execution
+    environment you can opt to register your own rendezvous handler.
+    Pick a unique name and use the URL scheme to identify it when
+    calling the `rendezvous()` function.
+
+    Args:
+        scheme (str): URL scheme to identify your rendezvous handler.
+        handler (function): Handler that is invoked when the
+            `rendezvous()` function is called with a URL that uses
+            the corresponding scheme. It must be a generator function
+            that yields the triplet.
+    """
+    global _rendezvous_handlers
+    if scheme in _rendezvous_handlers:
+        raise RuntimeError(f"Rendezvous handler for {scheme}:// already registered")
+    _rendezvous_handlers[scheme] = handler
+
+
+# Query will have format "rank=0&world_size=1" and is
+# converted into {"rank": 0, "world_size": 1}
+def _query_to_dict(query: str) -> Dict[str, str]:
+    return {
+        pair[0]: pair[1]
+        for pair in (pair.split("=") for pair in filter(None, query.split("&")))
+    }
+
+
+def _get_use_libuv_from_query_dict(query_dict: Dict[str, str]) -> bool:
+    # libuv is the default backend for TCPStore. To enable the non-libuv backend,
+    # user can explicitly specify ``use_libuv=0`` in the URL parameter.
+    return query_dict.get("use_libuv", os.environ.get("USE_LIBUV", "1")) == "1"
+
+
+def _rendezvous_helper(url: str, rank: int, world_size_opt: Optional[int], **kwargs):
+    result = urlparse(url)
+    if world_size_opt is None:
+        world_size = -1
+        if result.scheme == "env":
+            rank = int(os.environ.get("RANK", rank))
+            # If the world_size env variable is not present then it is a dynamic group
+            world_size = int(os.environ.get("WORLD_SIZE", world_size))
+    else:
+        world_size = world_size_opt
+    if rank != -1 or world_size != -1 or world_size_opt is None:
+        query_dict = _query_to_dict(result.query)
+        assert (
+            "rank" not in query_dict and "world_size" not in query_dict
+        ), f"The url: {url} has node-specific arguments(rank, world_size) already."
+        if rank != -1:
+            query_dict["rank"] = str(rank)
+        if world_size != -1 or world_size_opt is None:
+            query_dict["world_size"] = str(world_size)
+        result = result._replace(
+            query=f"{'&'.join([f'{k}={v}' for k, v in query_dict.items()])}"
+        )
+        url = urlunparse(result)
+
+    if result.scheme not in _rendezvous_handlers:
+        raise RuntimeError(f"No rendezvous handler for {result.scheme}://")
+    return _rendezvous_handlers[result.scheme](url, **kwargs)
+
+
+def rendezvous(url: str, rank: int = -1, world_size: int = -1, **kwargs):
+    if not isinstance(url, (str, bytes)):
+        raise RuntimeError(f"`url` must be a string. {type(url)}: {url}")
+
+    if not isinstance(rank, numbers.Integral):
+        raise RuntimeError(f"`rank` must be an integer. {rank}")
+
+    if not isinstance(world_size, numbers.Integral):
+        raise RuntimeError(f"`world_size` must be an integer. {world_size}")
+
+    return _rendezvous_helper(url, rank, world_size, **kwargs)
+
+
+def _create_store_from_options(backend_options, rank):
+    store, _, _ = next(_rendezvous_helper(backend_options.init_method, rank, None))
+    return store
+
+
+def _rendezvous_error(msg):
+    return ValueError("Error initializing torch.distributed using " + msg)
+
+
+def _file_rendezvous_handler(url: str, **kwargs):
+    def _error(msg):
+        return _rendezvous_error("file:// rendezvous: " + msg)
+
+    result = urlparse(url)
+    path = result.path
+    if sys.platform == "win32":
+        import urllib.request
+
+        full_path = result.netloc + result.path
+        path = urllib.request.url2pathname(full_path)
+        if path:
+            # Normalizing an empty string produces ".", which is not expected.
+            path = os.path.normpath(path)
+
+    if not path:
+        raise _error("path missing")
+    query_dict = _query_to_dict(result.query)
+    if "rank" not in query_dict:
+        raise _error("rank parameter missing")
+    if "world_size" not in query_dict:
+        raise _error("world size parameter missing")
+
+    rank = int(query_dict["rank"])
+    world_size = int(query_dict["world_size"])
+    store = FileStore(path, world_size)
+    yield (store, rank, world_size)
+
+    # If this configuration is invalidated, there is nothing we can do about it
+    raise RuntimeError("Unable to perform rerendezvous using file:// method")
+
+
+def _torchelastic_use_agent_store() -> bool:
+    return os.environ.get("TORCHELASTIC_USE_AGENT_STORE", None) == str(True)
+
+
+def _create_c10d_store(
+    hostname, port, rank, world_size, timeout, use_libuv=True
+) -> Store:
+    """
+    Smartly creates a c10d Store object on ``rank`` based on whether we need to re-use agent store.
+
+    The TCPStore server is assumed to be hosted
+    on ``hostname:port``.
+
+    By default, the TCPStore server uses the asynchronous implementation
+    ``LibUVStoreDaemon`` which utilizes libuv.
+
+    If ``torchelastic_use_agent_store()`` is ``True``, then it is assumed that
+    the agent leader (node rank 0) hosts the TCPStore server (for which the
+    endpoint is specified by the given ``hostname:port``). Hence
+    ALL ranks will create and return a TCPStore client (e.g. ``start_daemon=False``).
+
+    If ``torchelastic_use_agent_store()`` is ``False``, then rank 0 will host
+    the TCPStore (with multi-tenancy) and it is assumed that rank 0's hostname
+    and port are correctly passed via ``hostname`` and ``port``. All
+    non-zero ranks will create and return a TCPStore client.
+    """
+    # check if port is uint16_t
+    if not 0 <= port < 2**16:
+        raise ValueError(f"port must have value from 0 to 65535 but was {port}.")
+
+    if _torchelastic_use_agent_store():
+        attempt = os.environ["TORCHELASTIC_RESTART_COUNT"]
+        tcp_store = TCPStore(hostname, port, world_size, False, timeout)
+        return PrefixStore(f"/worker/attempt_{attempt}", tcp_store)
+    else:
+        start_daemon = rank == 0
+        return TCPStore(
+            hostname,
+            port,
+            world_size,
+            start_daemon,
+            timeout,
+            multi_tenant=True,
+            use_libuv=use_libuv,
+        )
+
+
+def _tcp_rendezvous_handler(
+    url: str, timeout: timedelta = default_pg_timeout, **kwargs
+):
+    def _error(msg):
+        return _rendezvous_error("tcp:// rendezvous: " + msg)
+
+    result = urlparse(url)
+    if not result.port:
+        raise _error("port number missing")
+    query_dict = _query_to_dict(result.query)
+    if "rank" not in query_dict:
+        raise _error("rank parameter missing")
+    if "world_size" not in query_dict:
+        raise _error("world size parameter missing")
+
+    rank = int(query_dict["rank"])
+    world_size = int(query_dict["world_size"])
+    use_libuv = _get_use_libuv_from_query_dict(query_dict)
+
+    assert result.hostname is not None
+
+    store = _create_c10d_store(
+        result.hostname, result.port, rank, world_size, timeout, use_libuv
+    )
+
+    yield (store, rank, world_size)
+
+    # If this configuration is invalidated, there is nothing we can do about it
+    raise RuntimeError("Unable to perform re-rendezvous using tcp:// method")
+
+
+def _env_rendezvous_handler(
+    url: str, timeout: timedelta = default_pg_timeout, **kwargs
+):
+    def _error(msg):
+        return _rendezvous_error("env:// rendezvous: " + msg)
+
+    def _env_error(var):
+        return _error(f"environment variable {var} expected, but not set")
+
+    def _get_env_or_raise(env_var: str) -> str:
+        env_val = os.environ.get(env_var, None)
+        if not env_val:
+            raise _env_error(env_var)
+        else:
+            return env_val
+
+    result = urlparse(url)
+    query_dict = _query_to_dict(result.query)
+
+    rank: int
+    world_size: int
+    master_port: int
+    master_addr: str
+
+    if "rank" in query_dict:
+        rank = int(query_dict["rank"])
+    else:
+        rank = int(_get_env_or_raise("RANK"))
+
+    if "world_size" in query_dict:
+        world_size = int(query_dict["world_size"])
+    else:
+        world_size = int(_get_env_or_raise("WORLD_SIZE"))
+
+    master_addr = _get_env_or_raise("MASTER_ADDR")
+    master_port = int(_get_env_or_raise("MASTER_PORT"))
+    use_libuv = _get_use_libuv_from_query_dict(query_dict)
+
+    store = _create_c10d_store(
+        master_addr, master_port, rank, world_size, timeout, use_libuv
+    )
+
+    yield (store, rank, world_size)
+
+    # If this configuration is invalidated, there is nothing we can do about it
+    raise RuntimeError("Unable to perform re-rendezvous using env:// method")
+
+
+register_rendezvous_handler("tcp", _tcp_rendezvous_handler)
+register_rendezvous_handler("env", _env_rendezvous_handler)
+register_rendezvous_handler("file", _file_rendezvous_handler)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/run.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..d899ad8f1ff86629d81706b3815e9b47df5fe79d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/run.py
@@ -0,0 +1,923 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Superset of ``torch.distributed.launch``.
+
+``torchrun`` provides a superset of the functionality as ``torch.distributed.launch``
+with the following additional functionalities:
+
+1. Worker failures are handled gracefully by restarting all workers.
+
+2. Worker ``RANK`` and ``WORLD_SIZE`` are assigned automatically.
+
+3. Number of nodes is allowed to change between minimum and maximum sizes (elasticity).
+
+.. note:: ``torchrun`` is a python
+          `console script `_
+          to the main module
+          `torch.distributed.run `_
+          declared in the ``entry_points`` configuration in
+          `setup.py `_.
+          It is equivalent to invoking ``python -m torch.distributed.run``.
+
+
+Transitioning from torch.distributed.launch to torchrun
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+``torchrun`` supports the same arguments as ``torch.distributed.launch`` **except**
+for ``--use-env`` which is now deprecated. To migrate from ``torch.distributed.launch``
+to ``torchrun`` follow these steps:
+
+1.  If your training script is already reading ``local_rank`` from the ``LOCAL_RANK`` environment variable.
+    Then you need simply omit the ``--use-env`` flag, e.g.:
+
+    +--------------------------------------------------------------------+--------------------------------------------+
+    |         ``torch.distributed.launch``                               |                ``torchrun``                |
+    +====================================================================+============================================+
+    |                                                                    |                                            |
+    | .. code-block:: shell-session                                      | .. code-block:: shell-session              |
+    |                                                                    |                                            |
+    |    $ python -m torch.distributed.launch --use-env train_script.py  |    $ torchrun train_script.py              |
+    |                                                                    |                                            |
+    +--------------------------------------------------------------------+--------------------------------------------+
+
+2.  If your training script reads local rank from a ``--local-rank`` cmd argument.
+    Change your training script to read from the ``LOCAL_RANK`` environment variable as
+    demonstrated by the following code snippet:
+
+    +-------------------------------------------------------+----------------------------------------------------+
+    |         ``torch.distributed.launch``                  |                    ``torchrun``                    |
+    +=======================================================+====================================================+
+    |                                                       |                                                    |
+    | .. code-block:: python                                | .. code-block:: python                             |
+    |                                                       |                                                    |
+    |                                                       |                                                    |
+    |    import argparse                                    |     import os                                      |
+    |    parser = argparse.ArgumentParser()                 |     local_rank = int(os.environ["LOCAL_RANK"])     |
+    |    parser.add_argument("--local-rank", type=int)      |                                                    |
+    |    args = parser.parse_args()                         |                                                    |
+    |                                                       |                                                    |
+    |    local_rank = args.local_rank                       |                                                    |
+    |                                                       |                                                    |
+    +-------------------------------------------------------+----------------------------------------------------+
+
+.. versionchanged:: 2.0.0
+
+    The launcher will pass the ``--local-rank=`` argument to your script.
+    From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the
+    previously used underscored ``--local_rank``.
+
+    For backward compatibility, it may be necessary for users to handle both
+    cases in their argument parsing code. This means including both ``"--local-rank"``
+    and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is
+    provided, the launcher will trigger an error: "error: unrecognized arguments:
+    --local-rank=". For training code that only supports PyTorch 2.0.0+,
+    including ``"--local-rank"`` should be sufficient.
+
+    ::
+
+        >>> # xdoctest: +SKIP
+        >>> import argparse
+        >>> parser = argparse.ArgumentParser()
+        >>> parser.add_argument("--local-rank", "--local_rank", type=int)
+        >>> args = parser.parse_args()
+
+The aformentioned changes suffice to migrate from ``torch.distributed.launch`` to ``torchrun``.
+To take advantage of new features such as elasticity, fault-tolerance, and error reporting of ``torchrun``
+please refer to:
+
+* :ref:`elastic_train_script` for more information on authoring training scripts that are ``torchrun`` compliant.
+* the rest of this page for more information on the features of ``torchrun``.
+
+
+Usage
+--------
+
+Single-node multi-worker
+++++++++++++++++++++++++++++++
+
+::
+
+    torchrun
+        --standalone
+        --nnodes=1
+        --nproc-per-node=$NUM_TRAINERS
+        YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
+
+Stacked single-node multi-worker
++++++++++++++++++++++++++++++++++++
+
+To run multiple instances (separate jobs) of single-node, multi-worker on the
+same host, we need to make sure that each instance (job) is
+setup on different ports to avoid port conflicts (or worse, two jobs being merged
+as a single job). To do this you have to run with ``--rdzv-backend=c10d``
+and specify a different port by setting ``--rdzv-endpoint=localhost:$PORT_k``.
+For ``--nodes=1``, its often convenient to let ``torchrun`` pick a free random
+port automatically instead of manually assigning different ports for each run.
+
+::
+
+    torchrun
+        --rdzv-backend=c10d
+        --rdzv-endpoint=localhost:0
+        --nnodes=1
+        --nproc-per-node=$NUM_TRAINERS
+        YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
+
+
+Fault tolerant (fixed sized number of workers, no elasticity, tolerates 3 failures)
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+::
+
+    torchrun
+        --nnodes=$NUM_NODES
+        --nproc-per-node=$NUM_TRAINERS
+        --max-restarts=3
+        --rdzv-id=$JOB_ID
+        --rdzv-backend=c10d
+        --rdzv-endpoint=$HOST_NODE_ADDR
+        YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
+
+``HOST_NODE_ADDR``, in form [:] (e.g. node1.example.com:29400), specifies the node and
+the port on which the C10d rendezvous backend should be instantiated and hosted. It can be any
+node in your training cluster, but ideally you should pick a node that has a high bandwidth.
+
+.. note::
+   If no port number is specified ``HOST_NODE_ADDR`` defaults to 29400.
+
+Elastic (``min=1``, ``max=4``, tolerates up to 3 membership changes or failures)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+::
+
+    torchrun
+        --nnodes=1:4
+        --nproc-per-node=$NUM_TRAINERS
+        --max-restarts=3
+        --rdzv-id=$JOB_ID
+        --rdzv-backend=c10d
+        --rdzv-endpoint=$HOST_NODE_ADDR
+        YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
+
+``HOST_NODE_ADDR``, in form [:] (e.g. node1.example.com:29400), specifies the node and
+the port on which the C10d rendezvous backend should be instantiated and hosted. It can be any
+node in your training cluster, but ideally you should pick a node that has a high bandwidth.
+
+.. note::
+   If no port number is specified ``HOST_NODE_ADDR`` defaults to 29400.
+
+Note on rendezvous backend
+------------------------------
+
+For multi-node training you need to specify:
+
+1. ``--rdzv-id``: A unique job id (shared by all nodes participating in the job)
+2. ``--rdzv-backend``: An implementation of
+   :py:class:`torch.distributed.elastic.rendezvous.RendezvousHandler`
+3. ``--rdzv-endpoint``: The endpoint where the rendezvous backend is running; usually in form
+   ``host:port``.
+
+Currently ``c10d`` (recommended), ``etcd-v2``, and ``etcd`` (legacy)  rendezvous backends are
+supported out of the box. To use ``etcd-v2`` or ``etcd``, setup an etcd server with the ``v2`` api
+enabled (e.g. ``--enable-v2``).
+
+.. warning::
+   ``etcd-v2`` and ``etcd`` rendezvous use etcd API v2. You MUST enable the v2 API on the etcd
+   server. Our tests use etcd v3.4.3.
+
+.. warning::
+   For etcd-based rendezvous we recommend using ``etcd-v2`` over ``etcd`` which is functionally
+   equivalent, but uses a revised implementation. ``etcd`` is in maintenance mode and will be
+   removed in a future version.
+
+Definitions
+--------------
+
+1. ``Node`` - A physical instance or a container; maps to the unit that the job manager works with.
+
+2. ``Worker`` - A worker in the context of distributed training.
+
+3. ``WorkerGroup`` - The set of workers that execute the same function (e.g. trainers).
+
+4. ``LocalWorkerGroup`` - A subset of the workers in the worker group running on the same node.
+
+5. ``RANK`` - The rank of the worker within a worker group.
+
+6. ``WORLD_SIZE`` - The total number of workers in a worker group.
+
+7. ``LOCAL_RANK`` - The rank of the worker within a local worker group.
+
+8. ``LOCAL_WORLD_SIZE`` - The size of the local worker group.
+
+9. ``rdzv_id`` - A user-defined id that uniquely identifies the worker group for a job. This id is
+   used by each node to join as a member of a particular worker group.
+
+9. ``rdzv_backend`` - The backend of the rendezvous (e.g. ``c10d``). This is typically a strongly
+   consistent key-value store.
+
+10. ``rdzv_endpoint`` - The rendezvous backend endpoint; usually in form ``:``.
+
+A ``Node`` runs ``LOCAL_WORLD_SIZE`` workers which comprise a ``LocalWorkerGroup``. The union of
+all ``LocalWorkerGroups`` in the nodes in the job comprise the ``WorkerGroup``.
+
+Environment Variables
+----------------------
+
+The following environment variables are made available to you in your script:
+
+1. ``LOCAL_RANK`` -  The local rank.
+
+2. ``RANK`` -  The global rank.
+
+3. ``GROUP_RANK`` - The rank of the worker group. A number between 0 and ``max_nnodes``. When
+   running a single worker group per node, this is the rank of the node.
+
+4. ``ROLE_RANK`` -  The rank of the worker across all the workers that have the same role. The role
+   of the worker is specified in the ``WorkerSpec``.
+
+5. ``LOCAL_WORLD_SIZE`` - The local world size (e.g. number of workers running locally); equals to
+   ``--nproc-per-node`` specified on ``torchrun``.
+
+6. ``WORLD_SIZE`` - The world size (total number of workers in the job).
+
+7. ``ROLE_WORLD_SIZE`` - The total number of workers that was launched with the same role specified
+   in ``WorkerSpec``.
+
+8. ``MASTER_ADDR`` - The FQDN of the host that is running worker with rank 0; used to initialize
+   the Torch Distributed backend.
+
+9. ``MASTER_PORT`` - The port on the ``MASTER_ADDR`` that can be used to host the C10d TCP store.
+
+10. ``TORCHELASTIC_RESTART_COUNT`` - The number of worker group restarts so far.
+
+11. ``TORCHELASTIC_MAX_RESTARTS`` - The configured maximum number of restarts.
+
+12. ``TORCHELASTIC_RUN_ID`` - Equal to the rendezvous ``run_id`` (e.g. unique job id).
+
+13. ``PYTHON_EXEC`` - System executable override. If provided, the python user script will
+    use the value of ``PYTHON_EXEC`` as executable. The `sys.executable` is used by default.
+
+Deployment
+------------
+
+1. (Not needed for the C10d backend) Start the rendezvous backend server and get the endpoint (to be
+   passed as ``--rdzv-endpoint`` to the launcher script)
+
+2. Single-node multi-worker: Start the launcher on the host to start the agent process which
+   creates and monitors a local worker group.
+
+3. Multi-node multi-worker: Start the launcher with the same arguments on all the nodes
+   participating in training.
+
+When using a job/cluster manager the entry point command to the multi-node job should be this
+launcher.
+
+Failure Modes
+---------------
+
+1. Worker failure: For a training job with ``n`` workers, if ``k<=n`` workers fail all workers
+   are stopped and restarted up to ``max_restarts``.
+
+2. Agent failure: An agent failure results in a local worker group failure. It is up to the job
+   manager to fail the entire job (gang semantics) or attempt to replace the node. Both behaviors
+   are supported by the agent.
+
+3. Node failure: Same as agent failure.
+
+Membership Changes
+--------------------
+
+1. Node departure (scale-down): The agent is notified of the departure, all existing workers are
+   stopped, a new ``WorkerGroup`` is formed, and all workers are started with a new ``RANK`` and
+   ``WORLD_SIZE``.
+
+2. Node arrival (scale-up): The new node is admitted to the job, all existing workers are stopped,
+   a new ``WorkerGroup`` is formed, and all workers are started with a new ``RANK`` and
+   ``WORLD_SIZE``.
+
+Important Notices
+--------------------
+
+1. This utility and multi-process distributed (single-node or
+   multi-node) GPU training currently only achieves the best performance using
+   the NCCL distributed backend. Thus NCCL backend is the recommended backend to
+   use for GPU training.
+
+2. The environment variables necessary to initialize a Torch process group are provided to you by
+   this module, no need for you to pass ``RANK`` manually.  To initialize a process group in your
+   training script, simply run:
+
+::
+
+ >>> # xdoctest: +SKIP("stub")
+ >>> import torch.distributed as dist
+ >>> dist.init_process_group(backend="gloo|nccl")
+
+3. In your training program, you can either use regular distributed functions
+   or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your
+   training program uses GPUs for training and you would like to use
+   :func:`torch.nn.parallel.DistributedDataParallel` module,
+   here is how to configure it.
+
+::
+
+    local_rank = int(os.environ["LOCAL_RANK"])
+    model = torch.nn.parallel.DistributedDataParallel(model,
+                                                      device_ids=[local_rank],
+                                                      output_device=local_rank)
+
+Please ensure that ``device_ids`` argument is set to be the only GPU device id
+that your code will be operating on. This is generally the local rank of the
+process. In other words, the ``device_ids`` needs to be ``[int(os.environ("LOCAL_RANK"))]``,
+and ``output_device`` needs to be ``int(os.environ("LOCAL_RANK"))`` in order to use this
+utility
+
+
+4. On failures or membership changes ALL surviving workers are killed immediately. Make sure to
+   checkpoint your progress. The frequency of checkpoints should depend on your job's tolerance
+   for lost work.
+
+5. This module only supports homogeneous ``LOCAL_WORLD_SIZE``. That is, it is assumed that all
+   nodes run the same number of local workers (per role).
+
+6. ``RANK`` is NOT stable. Between restarts, the local workers on a node can be assigned a
+   different range of ranks than before. NEVER hard code any assumptions about the stable-ness of
+   ranks or some correlation between ``RANK`` and ``LOCAL_RANK``.
+
+7. When using elasticity (``min_size!=max_size``) DO NOT hard code assumptions about
+   ``WORLD_SIZE`` as the world size can change as nodes are allowed to leave and join.
+
+8. It is recommended for your script to have the following structure:
+
+::
+
+  def main():
+    load_checkpoint(checkpoint_path)
+    initialize()
+    train()
+
+  def train():
+    for batch in iter(dataset):
+      train_step(batch)
+
+      if should_checkpoint:
+        save_checkpoint(checkpoint_path)
+
+9. (Recommended) On worker errors, this tool will summarize the details of the error
+   (e.g. time, rank, host, pid, traceback, etc). On each node, the first error (by timestamp)
+   is heuristically reported as the "Root Cause" error. To get tracebacks as part of this
+   error summary print out, you must decorate your main entrypoint function in your
+   training script as shown in the example below. If not decorated, then the summary
+   will not include the traceback of the exception and will only contain the exitcode.
+   For details on torchelastic error handling see: https://pytorch.org/docs/stable/elastic/errors.html
+
+::
+
+  from torch.distributed.elastic.multiprocessing.errors import record
+
+  @record
+  def main():
+      # do train
+      pass
+
+  if __name__ == "__main__":
+      main()
+
+"""
+import logging
+import os
+import sys
+import uuid
+from argparse import ArgumentParser, REMAINDER
+from importlib import metadata
+from typing import Callable, List, Optional, Set, Tuple, Type, Union
+
+import torch
+from torch.distributed.argparse_util import check_env, env
+from torch.distributed.elastic.multiprocessing import DefaultLogsSpecs, LogsSpecs, Std
+from torch.distributed.elastic.multiprocessing.errors import record
+from torch.distributed.elastic.rendezvous.utils import _parse_rendezvous_config
+from torch.distributed.elastic.utils import macros
+from torch.distributed.elastic.utils.logging import get_logger
+from torch.distributed.launcher.api import elastic_launch, LaunchConfig
+from torch.utils.backend_registration import _get_custom_mod_func
+
+
+logger = get_logger(__name__)
+
+
+def get_args_parser() -> ArgumentParser:
+    """Parse the command line options."""
+    parser = ArgumentParser(description="Torch Distributed Elastic Training Launcher")
+
+    #
+    # Worker/node size related arguments.
+    #
+
+    parser.add_argument(
+        "--nnodes",
+        action=env,
+        type=str,
+        default="1:1",
+        help="Number of nodes, or the range of nodes in form :.",
+    )
+    parser.add_argument(
+        "--nproc-per-node",
+        "--nproc_per_node",
+        action=env,
+        type=str,
+        default="1",
+        help="Number of workers per node; supported values: [auto, cpu, gpu, int].",
+    )
+
+    #
+    # Rendezvous related arguments
+    #
+
+    parser.add_argument(
+        "--rdzv-backend",
+        "--rdzv_backend",
+        action=env,
+        type=str,
+        default="static",
+        help="Rendezvous backend.",
+    )
+    parser.add_argument(
+        "--rdzv-endpoint",
+        "--rdzv_endpoint",
+        action=env,
+        type=str,
+        default="",
+        help="Rendezvous backend endpoint; usually in form :.",
+    )
+    parser.add_argument(
+        "--rdzv-id",
+        "--rdzv_id",
+        action=env,
+        type=str,
+        default="none",
+        help="User-defined group id.",
+    )
+    parser.add_argument(
+        "--rdzv-conf",
+        "--rdzv_conf",
+        action=env,
+        type=str,
+        default="",
+        help="Additional rendezvous configuration (=,=,...).",
+    )
+    parser.add_argument(
+        "--standalone",
+        action=check_env,
+        help="Start a local standalone rendezvous backend that is represented by a C10d TCP store "
+        "on a free port. Useful when launching single-node, multi-worker job. If specified "
+        "--rdzv-backend, --rdzv-endpoint, --rdzv-id are auto-assigned and any explicitly set values "
+        "are ignored.",
+    )
+
+    #
+    # User-code launch related arguments.
+    #
+
+    parser.add_argument(
+        "--max-restarts",
+        "--max_restarts",
+        action=env,
+        type=int,
+        default=0,
+        help="Maximum number of worker group restarts before failing.",
+    )
+    parser.add_argument(
+        "--monitor-interval",
+        "--monitor_interval",
+        action=env,
+        type=float,
+        default=0.1,
+        help="Interval, in seconds, to monitor the state of workers.",
+    )
+    parser.add_argument(
+        "--start-method",
+        "--start_method",
+        action=env,
+        type=str,
+        default="spawn",
+        choices=["spawn", "fork", "forkserver"],
+        help="Multiprocessing start method to use when creating workers.",
+    )
+    parser.add_argument(
+        "--role",
+        action=env,
+        type=str,
+        default="default",
+        help="User-defined role for the workers.",
+    )
+    parser.add_argument(
+        "-m",
+        "--module",
+        action=check_env,
+        help="Change each process to interpret the launch script as a Python module, executing "
+        "with the same behavior as 'python -m'.",
+    )
+    parser.add_argument(
+        "--no-python",
+        "--no_python",
+        action=check_env,
+        help="Skip prepending the training script with 'python' - just execute it directly. Useful "
+        "when the script is not a Python script.",
+    )
+
+    parser.add_argument(
+        "--run-path",
+        "--run_path",
+        action=check_env,
+        help="Run the training script with runpy.run_path in the same interpreter."
+        " Script must be provided as an abs path (e.g. /abs/path/script.py)."
+        " Takes precedence over --no-python.",
+    )
+    parser.add_argument(
+        "--log-dir",
+        "--log_dir",
+        action=env,
+        type=str,
+        default=None,
+        help="Base directory to use for log files (e.g. /var/log/torch/elastic). The same "
+        "directory is re-used for multiple runs (a unique job-level sub-directory is created with "
+        "rdzv_id as the prefix).",
+    )
+    parser.add_argument(
+        "-r",
+        "--redirects",
+        action=env,
+        type=str,
+        default="0",
+        help="Redirect std streams into a log file in the log directory (e.g. [-r 3] redirects "
+        "both stdout+stderr for all workers, [-r 0:1,1:2] redirects stdout for local rank 0 and "
+        "stderr for local rank 1).",
+    )
+    parser.add_argument(
+        "-t",
+        "--tee",
+        action=env,
+        type=str,
+        default="0",
+        help="Tee std streams into a log file and also to console (see --redirects for format).",
+    )
+
+    parser.add_argument(
+        "--local-ranks-filter",
+        "--local_ranks_filter",
+        action=env,
+        type=str,
+        default="",
+        help="Only show logs from specified ranks in console (e.g. [--local_ranks_filter=0,1,2] will "
+        "only show logs from rank 0, 1 and 2). This will only apply to stdout and stderr, not to"
+        "log files saved via --redirect or --tee",
+    )
+
+    #
+    # Backwards compatible parameters with caffe2.distributed.launch.
+    #
+
+    parser.add_argument(
+        "--node-rank",
+        "--node_rank",
+        type=int,
+        action=env,
+        default=0,
+        help="Rank of the node for multi-node distributed training.",
+    )
+    parser.add_argument(
+        "--master-addr",
+        "--master_addr",
+        default="127.0.0.1",
+        type=str,
+        action=env,
+        help="Address of the master node (rank 0) that only used for static rendezvous. It should "
+        "be either the IP address or the hostname of rank 0. For single node multi-proc training "
+        "the --master-addr can simply be 127.0.0.1; IPv6 should have the pattern "
+        "`[0:0:0:0:0:0:0:1]`.",
+    )
+    parser.add_argument(
+        "--master-port",
+        "--master_port",
+        default=29500,
+        type=int,
+        action=env,
+        help="Port on the master node (rank 0) to be used for communication during distributed "
+        "training. It is only used for static rendezvous.",
+    )
+    parser.add_argument(
+        "--local-addr",
+        "--local_addr",
+        default=None,
+        type=str,
+        action=env,
+        help="Address of the local node. If specified, will use the given address for connection. "
+        "Else, will look up the local node address instead. Else, it will be default to local "
+        "machine's FQDN.",
+    )
+
+    parser.add_argument(
+        "--logs-specs",
+        "--logs_specs",
+        default=None,
+        type=str,
+        help="torchrun.logs_specs group entrypoint name, value must be type of LogsSpecs. "
+        "Can be used to override custom logging behavior.",
+    )
+
+    #
+    # Positional arguments.
+    #
+
+    parser.add_argument(
+        "training_script",
+        type=str,
+        help="Full path to the (single GPU) training program/script to be launched in parallel, "
+        "followed by all the arguments for the training script.",
+    )
+
+    # Rest from the training program.
+    parser.add_argument("training_script_args", nargs=REMAINDER)
+
+    return parser
+
+
+def parse_args(args):
+    parser = get_args_parser()
+    return parser.parse_args(args)
+
+
+def parse_min_max_nnodes(nnodes: str):
+    arr = nnodes.split(":")
+
+    if len(arr) == 1:
+        min_nodes = max_nodes = int(arr[0])
+    elif len(arr) == 2:
+        min_nodes = int(arr[0])
+        max_nodes = int(arr[1])
+    else:
+        raise RuntimeError(f'nnodes={nnodes} is not in "MIN:MAX" format')  # noqa: E231
+
+    return min_nodes, max_nodes
+
+
+def determine_local_world_size(nproc_per_node: str):
+    try:
+        logging.info("Using nproc_per_node=%s.", nproc_per_node)
+        return int(nproc_per_node)
+    except ValueError as e:
+        if nproc_per_node == "cpu":
+            num_proc = os.cpu_count()
+            device_type = "cpu"
+        elif nproc_per_node == "gpu":
+            if not torch.cuda.is_available():
+                raise ValueError("Cuda is not available.") from e
+            device_type = "gpu"
+            num_proc = torch.cuda.device_count()
+        elif nproc_per_node == torch._C._get_privateuse1_backend_name():
+            if not _get_custom_mod_func("is_available")():
+                raise ValueError(f"{nproc_per_node} is not available.") from e
+            device_type = nproc_per_node
+            num_proc = _get_custom_mod_func("device_count")()
+        elif nproc_per_node == "auto":
+            if torch.cuda.is_available():
+                num_proc = torch.cuda.device_count()
+                device_type = "gpu"
+            elif (
+                hasattr(torch, torch._C._get_privateuse1_backend_name())
+                and _get_custom_mod_func("is_available")()
+            ):
+                num_proc = _get_custom_mod_func("device_count")()
+                device_type = torch._C._get_privateuse1_backend_name()
+            else:
+                num_proc = os.cpu_count()
+                device_type = "cpu"
+        else:
+            raise ValueError(
+                f"Unsupported nproc_per_node value: {nproc_per_node}"
+            ) from e
+
+        logger.info(
+            "Using nproc_per_node=%s, setting nproc_per_node to %s since the instance has %s %s",
+            nproc_per_node,
+            num_proc,
+            num_proc,
+            device_type,
+        )
+        return num_proc
+
+
+def get_rdzv_endpoint(args):
+    if args.rdzv_backend == "static" and not args.rdzv_endpoint:
+        return f"{args.master_addr}:{args.master_port}"  # noqa: E231
+    return args.rdzv_endpoint
+
+
+def get_use_env(args) -> bool:
+    """
+    Retrieve ``use_env`` from the args.
+
+    ``use_env`` is a legacy argument, if ``use_env`` is False, the
+    ``--node-rank`` argument will be transferred to all worker processes.
+    ``use_env`` is only used by the ``torch.distributed.launch`` and will
+    be deprecated in future releases.
+    """
+    if not hasattr(args, "use_env"):
+        return True
+    return args.use_env
+
+
+def _get_logs_specs_class(logs_specs_name: Optional[str]) -> Type[LogsSpecs]:
+    """
+    Attemps to load `torchrun.logs_spec` entrypoint with key of `logs_specs_name` param.
+    Provides plugin mechanism to provide custom implementation of LogsSpecs.
+
+    Returns `DefaultLogsSpecs` when logs_spec_name is None.
+    Raises ValueError when entrypoint for `logs_spec_name` can't be found in entrypoints.
+    """
+    logs_specs_cls = None
+    if logs_specs_name is not None:
+        eps = metadata.entry_points()
+        if hasattr(eps, "select"):  # >= 3.10
+            group = eps.select(group="torchrun.logs_specs")
+            if group.select(name=logs_specs_name):
+                logs_specs_cls = group[logs_specs_name].load()
+
+        elif specs := eps.get("torchrun.logs_specs"):  # < 3.10
+            if entrypoint_list := [ep for ep in specs if ep.name == logs_specs_name]:
+                logs_specs_cls = entrypoint_list[0].load()
+
+        if logs_specs_cls is None:
+            raise ValueError(
+                f"Could not find entrypoint under 'torchrun.logs_specs[{logs_specs_name}]' key"
+            )
+
+        logging.info(
+            "Using logs_spec '%s' mapped to %s", logs_specs_name, str(logs_specs_cls)
+        )
+    else:
+        logs_specs_cls = DefaultLogsSpecs
+
+    return logs_specs_cls
+
+
+def config_from_args(args) -> Tuple[LaunchConfig, Union[Callable, str], List[str]]:
+    # If ``args`` not passed, defaults to ``sys.argv[:1]``
+    min_nodes, max_nodes = parse_min_max_nnodes(args.nnodes)
+    assert 0 < min_nodes <= max_nodes
+    assert args.max_restarts >= 0
+
+    if (
+        hasattr(args, "master_addr")
+        and args.rdzv_backend != "static"
+        and not args.rdzv_endpoint
+    ):
+        logger.warning(
+            "master_addr is only used for static rdzv_backend and when rdzv_endpoint "
+            "is not specified."
+        )
+
+    nproc_per_node = determine_local_world_size(args.nproc_per_node)
+    if "OMP_NUM_THREADS" not in os.environ and nproc_per_node > 1:
+        omp_num_threads = 1
+        logger.warning(
+            "\n*****************************************\n"
+            "Setting OMP_NUM_THREADS environment variable for each process to be "
+            "%s in default, to avoid your system being overloaded, "
+            "please further tune the variable for optimal performance in "
+            "your application as needed. \n"
+            "*****************************************",
+            omp_num_threads,
+        )
+        # This env variable will be passed down to the subprocesses
+        os.environ["OMP_NUM_THREADS"] = str(omp_num_threads)
+
+    log_line_prefix_template = os.getenv("TORCHELASTIC_LOG_LINE_PREFIX_TEMPLATE")
+
+    rdzv_configs = _parse_rendezvous_config(args.rdzv_conf)
+
+    if args.rdzv_backend == "static":
+        rdzv_configs["rank"] = args.node_rank
+
+    rdzv_endpoint = get_rdzv_endpoint(args)
+
+    ranks: Optional[Set[int]] = None
+    if args.local_ranks_filter:
+        try:
+            ranks = set(map(int, args.local_ranks_filter.split(",")))
+            assert ranks
+        except Exception as e:
+            raise ValueError(
+                "--local_ranks_filter must be a comma-separated list of integers e.g. --local_ranks_filter=0,1,2"
+            ) from e
+
+    logs_specs_cls: Type[LogsSpecs] = _get_logs_specs_class(args.logs_specs)
+    logs_specs = logs_specs_cls(
+        log_dir=args.log_dir,
+        redirects=Std.from_str(args.redirects),
+        tee=Std.from_str(args.tee),
+        local_ranks_filter=ranks,
+    )
+
+    config = LaunchConfig(
+        min_nodes=min_nodes,
+        max_nodes=max_nodes,
+        nproc_per_node=nproc_per_node,
+        run_id=args.rdzv_id,
+        role=args.role,
+        rdzv_endpoint=rdzv_endpoint,
+        rdzv_backend=args.rdzv_backend,
+        rdzv_configs=rdzv_configs,
+        max_restarts=args.max_restarts,
+        monitor_interval=args.monitor_interval,
+        start_method=args.start_method,
+        log_line_prefix_template=log_line_prefix_template,
+        local_addr=args.local_addr,
+        logs_specs=logs_specs,
+    )
+
+    with_python = not args.no_python
+    cmd: Union[Callable, str]
+    cmd_args = []
+    use_env = get_use_env(args)
+    if args.run_path:
+        cmd = run_script_path
+        cmd_args.append(args.training_script)
+    else:
+        if with_python:
+            cmd = os.getenv("PYTHON_EXEC", sys.executable)
+            cmd_args.append("-u")
+            if args.module:
+                cmd_args.append("-m")
+            cmd_args.append(args.training_script)
+        else:
+            if args.module:
+                raise ValueError(
+                    "Don't use both the '--no-python' flag"
+                    " and the '--module' flag at the same time."
+                )
+            cmd = args.training_script
+    if not use_env:
+        cmd_args.append(f"--local-rank={macros.local_rank}")
+    cmd_args.extend(args.training_script_args)
+
+    return config, cmd, cmd_args
+
+
+def run_script_path(training_script: str, *training_script_args: str):
+    """
+    Run the provided `training_script` from within this interpreter.
+
+    Usage: `script_as_function("/abs/path/to/script.py", "--arg1", "val1")`
+    """
+    import runpy
+    import sys
+
+    sys.argv = [training_script] + [*training_script_args]
+    runpy.run_path(sys.argv[0], run_name="__main__")
+
+
+def run(args):
+    torch.multiprocessing._set_thread_name("pt_elastic")
+
+    if args.standalone:
+        args.rdzv_backend = "c10d"
+        args.rdzv_endpoint = "localhost:0"
+        args.rdzv_id = str(uuid.uuid4())
+        logger.info(
+            "\n**************************************\n"
+            "Rendezvous info:\n"
+            "--rdzv-backend=%s "
+            "--rdzv-endpoint=%s "
+            "--rdzv-id=%s\n"
+            "**************************************\n",
+            args.rdzv_backend,
+            args.rdzv_endpoint,
+            args.rdzv_id,
+        )
+
+    config, cmd, cmd_args = config_from_args(args)
+    elastic_launch(
+        config=config,
+        entrypoint=cmd,
+    )(*cmd_args)
+
+
+@record
+def main(args=None):
+    args = parse_args(args)
+    run(args)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..faacd059f899e132889719af889783faed12aeac
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributed/utils.py
@@ -0,0 +1,383 @@
+# mypy: allow-untyped-defs
+import dataclasses
+import traceback
+from typing import (
+    Any,
+    Callable,
+    Container,
+    Dict,
+    List,
+    Optional,
+    OrderedDict,
+    overload,
+    Set,
+    Tuple,
+    TypeVar,
+)
+
+import torch
+import torch.distributed as dist
+from torch import nn
+from torch.nn.parallel._functions import _get_stream
+from torch.nn.parallel.scatter_gather import _is_namedtuple
+from torch.nn.utils.rnn import PackedSequence
+
+
+__all__ = []  # type: ignore[var-annotated]
+
+
+def _pack_kwargs(*args: Any, **kwargs: Any) -> Tuple[Tuple[Any, ...], Tuple[str, ...]]:
+    """
+    Turn argument list into separate key list and value list (unpack_kwargs does the opposite).
+
+    Inspiration: https://github.com/facebookresearch/fairscale/blob/eeb6684/fairscale/internal/containers.py#L70
+    Usage::
+
+        kwarg_keys, flat_args = pack_kwargs(1, 2, a=3, b=4)
+        assert kwarg_keys == ("a", "b")
+        assert flat_args == (1, 2, 3, 4)
+        args, kwargs = unpack_kwargs(kwarg_keys, flat_args)
+        assert args == (1, 2)
+        assert kwargs == {"a": 3, "b": 4}
+    Returns:
+        Tuple[Tuple[Any, ...], Tuple[str, ...]]: The first tuple element gives
+        gives both positional args and kwarg values, where the positional args
+        proceed kwarg values and kwarg values are ordered consistently with the
+        kwarg keys. The second tuple element gives the kwarg keys.
+        The second tuple element's length is at most the first tuple element's length.
+    """
+    kwarg_keys: List[str] = []
+    flat_args: List[Any] = list(args)
+    for k, v in kwargs.items():
+        kwarg_keys.append(k)
+        flat_args.append(v)
+
+    return tuple(flat_args), tuple(kwarg_keys)
+
+
+def _cast_forward_inputs(
+    dtype: Optional[torch.dtype],
+    *args: Any,
+    **kwargs: Any,
+) -> Tuple[Any, Any]:
+    """
+    Cast floating point tensors in ``args`` and ``kwargs`` to ``input_dtype``.
+
+    This respects the existing ``requires_grad`` on the tensors.
+    """
+    if dtype is None:
+        return args, kwargs
+
+    def cast_fn(x: torch.Tensor) -> torch.Tensor:
+        if not torch.is_floating_point(x) or x.dtype == dtype:
+            return x
+        return x.to(dtype)
+
+    return (_apply_to_tensors(cast_fn, args), _apply_to_tensors(cast_fn, kwargs))
+
+
+def _unpack_kwargs(
+    flat_args: Tuple[Any, ...], kwarg_keys: Tuple[str, ...]
+) -> Tuple[Tuple[Any, ...], Dict[str, Any]]:
+    """See _pack_kwargs."""
+    assert len(kwarg_keys) <= len(
+        flat_args
+    ), f"too many keys {len(kwarg_keys)} vs. {len(flat_args)}"
+    if len(kwarg_keys) == 0:
+        return flat_args, {}
+    args = flat_args[: -len(kwarg_keys)]
+    kwargs = dict(zip(kwarg_keys, flat_args[-len(kwarg_keys) :]))
+    return args, kwargs
+
+
+S = TypeVar("S", dict, list, tuple)
+T = TypeVar("T", torch.Tensor, PackedSequence)
+
+
+@overload
+def _recursive_to(
+    inputs: S, target_device: torch.device, use_side_stream_for_tensor_copies: bool
+) -> List[S]:
+    ...
+
+
+@overload
+def _recursive_to(
+    inputs: T, target_device: torch.device, use_side_stream_for_tensor_copies: bool
+) -> Tuple[T]:
+    ...
+
+
+def _recursive_to(inputs, target_device, use_side_stream_for_tensor_copies):
+    r"""Recursively moves input to the target_device."""
+
+    def to_map(obj):
+        if isinstance(obj, (torch.Tensor, PackedSequence)):
+            device = obj.data.device if isinstance(obj, PackedSequence) else obj.device
+            if device == target_device:
+                return (obj,)
+            if not use_side_stream_for_tensor_copies:
+                return (obj.to(target_device),)
+            else:
+                # If the custom module is not registered to torch, stream is not used for acceleration
+                device_mod = getattr(torch, device.type, None)
+                if device.type == "cpu" or device_mod is None:
+                    return (obj.to(target_device),)
+                # Perform CPU -> target_device copies in a background stream. This code is
+                # motivated from similar logic in torch/nn/parallel/_functions.py
+                stream = _get_stream(target_device)
+                with device_mod.stream(stream):
+                    output = obj.to(target_device)
+                # synchronize with the copy stream
+                with device_mod.device(target_device.index):
+                    current_stream = device_mod.current_stream()
+                    # Sync the current stream with the copy stream
+                    current_stream.wait_stream(stream)
+                    # Ensure tensor memory is not reused until work on
+                    # main stream is complete
+                    if isinstance(obj, PackedSequence):
+                        output.data.record_stream(current_stream)  # type: ignore[arg-type]
+                    else:
+                        assert isinstance(output, torch.Tensor)
+                        output.record_stream(current_stream)  # type: ignore[arg-type]
+                return (output,)
+        if _is_namedtuple(obj):
+            return [type(obj)(*args) for args in zip(*map(to_map, obj))]
+        if isinstance(obj, tuple) and len(obj) > 0:
+            return list(zip(*map(to_map, obj)))
+        if isinstance(obj, list) and len(obj) > 0:
+            return [list(i) for i in zip(*map(to_map, obj))]
+        if isinstance(obj, dict) and len(obj) > 0:
+            return [type(obj)(i) for i in zip(*map(to_map, obj.items()))]
+        return [obj]
+
+    # Avoid reference cycle
+    try:
+        res = to_map(inputs)
+    finally:
+        to_map = None  # type: ignore[assignment]
+    return res
+
+
+def _p_assert(cond: Any, s: str, raise_assertion_error: bool = True) -> None:
+    """Alternate to ``assert`` when in the backward context to print the error message ``s`` since otherwise, it is swallowed."""
+    if not cond:
+        print(s)
+        traceback.print_stack()
+        if raise_assertion_error:
+            raise AssertionError(s)
+
+
+def _alloc_storage(tensor: torch.Tensor, size: torch.Size) -> None:
+    """
+    Allocate storage for ``tensor`` with the given size.
+
+    Returns:
+        bool: ``True`` if this method allocated storage and ``False`` if the
+        storage was already allocated.
+    """
+    with torch.no_grad():
+        if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+            already_allocated = tensor._typed_storage()._size() == size.numel()
+            if not already_allocated:
+                tensor_storage_size = tensor._typed_storage()._size()
+                _p_assert(
+                    tensor_storage_size == 0,
+                    "Tensor storage should have been resized to be 0 but got PLACEHOLDEr",
+                )
+                tensor._typed_storage()._resize_(size.numel())
+
+
+def _free_storage(tensor: torch.Tensor):
+    """
+    Frees the underlying storage of ``tensor``.
+
+    Returns:
+        bool: ``True`` if the method freed the storage and ``False`` if the
+        storage was already freed.
+    """
+    with torch.no_grad():
+        if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+            already_freed = tensor._typed_storage()._size() == 0
+            if not already_freed:
+                _p_assert(
+                    tensor.storage_offset() == 0,
+                    "Freeing a tensor's storage is unsafe when it is not the sole occupant\n"
+                    f"storage offset: {tensor.storage_offset()}\n"
+                    f"storage size: {tensor._typed_storage()._size()}\n"
+                    f"tensor shape: {tensor.shape}",
+                )
+                tensor._typed_storage()._resize_(0)
+
+
+Q = TypeVar("Q")
+R = TypeVar("R", dict, list, tuple, set, OrderedDict, PackedSequence, Any)
+
+
+@overload
+def _apply_to_tensors(fn: Callable[[torch.Tensor], Q], container: torch.Tensor) -> Q:
+    ...
+
+
+@overload
+def _apply_to_tensors(fn: Callable[[torch.Tensor], Any], container: R) -> R:
+    ...
+
+
+def _apply_to_tensors(fn, container):
+    """Recursively apply to all tensor in different kinds of container types."""
+
+    def apply(x):
+        if isinstance(x, torch.Tensor):
+            return fn(x)
+        elif hasattr(x, "__dataclass_fields__"):
+            dc = dataclasses.replace(x)
+            changes = {
+                f.name: apply(getattr(dc, f.name)) for f in dataclasses.fields(dc)
+            }
+            return dataclasses.replace(dc, **changes)
+        elif isinstance(x, OrderedDict):
+            od = x.__class__()
+            for key, value in x.items():
+                od[key] = apply(value)
+            return od
+        elif isinstance(x, PackedSequence):
+            apply(x.data)
+            return x
+        elif isinstance(x, dict):
+            return {key: apply(value) for key, value in x.items()}
+        elif _is_namedtuple(x):
+            res = (apply(el) for el in x)
+            return type(x)(*res)
+        elif isinstance(x, (list, tuple, set)):
+            return type(x)(apply(el) for el in x)
+        else:
+            return x
+
+    return apply(container)
+
+
+def _to_kwargs(
+    inputs: Tuple[Any, ...],
+    kwargs: Optional[Dict[str, Any]],
+    target_device: torch.device,
+    use_side_stream_for_tensor_copies: bool,
+) -> Tuple[Tuple[Any, ...], Tuple[Dict[str, Any], ...]]:
+    moved_inputs = (
+        _recursive_to(inputs, target_device, use_side_stream_for_tensor_copies)
+        if inputs
+        else []
+    )
+    moved_kwargs = (
+        _recursive_to(kwargs, target_device, use_side_stream_for_tensor_copies)
+        if kwargs
+        else []
+    )
+    if len(moved_inputs) < len(moved_kwargs):
+        moved_inputs.extend([() for _ in range(len(moved_kwargs) - len(inputs))])
+    elif len(moved_kwargs) < len(moved_inputs):
+        moved_kwargs.extend([{} for _ in range(len(moved_inputs) - len(moved_kwargs))])
+    return tuple(moved_inputs), tuple(moved_kwargs)
+
+
+def _verify_param_shape_across_processes(
+    process_group: dist.ProcessGroup,
+    tensors: List[torch.Tensor],
+    logger: Optional["dist.Logger"] = None,
+):
+    return dist._verify_params_across_processes(process_group, tensors, logger)
+
+
+def _sync_module_states(
+    module: nn.Module,
+    process_group: dist.ProcessGroup,
+    broadcast_bucket_size: int,
+    src: int,
+    params_and_buffers_to_ignore: Container[str],
+    broadcast_buffers: bool = True,
+) -> None:
+    """
+    Sync ``module``'s parameters and buffers state.
+
+    Syncs ``module``'s parameters and buffers state so that all ranks contain
+    the same module state across all ranks. Note that this API assumes that all
+    parameter shapes are consistent before running the synchronization. This can
+    be checked with ``_verify_param_shape_across_processes``.
+    """
+    module_states: List[torch.Tensor] = []
+    for name, param in module.named_parameters():
+        if name not in params_and_buffers_to_ignore:
+            module_states.append(param.detach())
+
+    if broadcast_buffers:
+        for name, buffer in module.named_buffers():
+            if name not in params_and_buffers_to_ignore:
+                module_states.append(buffer.detach())
+
+    _sync_params_and_buffers(process_group, module_states, broadcast_bucket_size, src)
+
+
+def _sync_params_and_buffers(
+    process_group: dist.ProcessGroup,
+    module_states: List[torch.Tensor],
+    broadcast_bucket_size: int,
+    src: int,
+) -> None:
+    """Synchronize ``module_states`` (list of tensors) across all processes by broadcasting them from rank 0."""
+    if len(module_states) > 0:
+        dist._broadcast_coalesced(
+            process_group, module_states, broadcast_bucket_size, src
+        )
+
+
+def _replace_by_prefix(
+    state_dict: Dict[str, Any],
+    old_prefix: str,
+    new_prefix: str,
+) -> None:
+    """
+    Replace all keys that match a given old_prefix with a new_prefix (in-place).
+
+    Usage::
+
+        state_dict = {"layer.xyz": torch.tensor(1)}
+        replace_by_prefix_(state_dict, "layer.", "module.layer.")
+        assert state_dict == {"module.layer.xyz": torch.tensor(1)}
+    """
+    if old_prefix == new_prefix:
+        raise ValueError("old_prefix and new_prefix must be distinct")
+    for key in list(state_dict.keys()):
+        if not key.startswith(old_prefix):
+            continue
+        new_key = new_prefix + key[len(old_prefix) :]
+        state_dict[new_key] = state_dict[key]
+        del state_dict[key]
+
+
+def _data_ptr_allocated(tensor: torch.Tensor) -> bool:
+    return tensor.untyped_storage().data_ptr() > 0
+
+
+def _get_root_modules(modules: List[nn.Module]) -> List[nn.Module]:
+    """
+    Returns the modules in ``modules`` that are root modules (i.e.
+    parent-less) with respect to the set ``modules``. In other words, these
+    are the modules in ``modules`` that are the not child of any other
+    module in ``modules``.
+    """
+    root_modules: List[nn.Module] = []
+    module_to_modules: Dict[nn.Module, Set[nn.Module]] = {
+        module: set(module.modules()) for module in modules
+    }
+    for candidate_module in modules:
+        is_root_module = True
+        for module, _modules in module_to_modules.items():
+            is_child_module = (
+                candidate_module is not module and candidate_module in _modules
+            )
+            if is_child_module:
+                is_root_module = False
+                break
+        if is_root_module:
+            root_modules.append(candidate_module)
+    return root_modules
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbc2775468d540113beae24a21219778e55f4dad
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/__init__.py
@@ -0,0 +1,172 @@
+r"""
+The ``distributions`` package contains parameterizable probability distributions
+and sampling functions. This allows the construction of stochastic computation
+graphs and stochastic gradient estimators for optimization. This package
+generally follows the design of the `TensorFlow Distributions`_ package.
+
+.. _`TensorFlow Distributions`:
+    https://arxiv.org/abs/1711.10604
+
+It is not possible to directly backpropagate through random samples. However,
+there are two main methods for creating surrogate functions that can be
+backpropagated through. These are the score function estimator/likelihood ratio
+estimator/REINFORCE and the pathwise derivative estimator. REINFORCE is commonly
+seen as the basis for policy gradient methods in reinforcement learning, and the
+pathwise derivative estimator is commonly seen in the reparameterization trick
+in variational autoencoders. Whilst the score function only requires the value
+of samples :math:`f(x)`, the pathwise derivative requires the derivative
+:math:`f'(x)`. The next sections discuss these two in a reinforcement learning
+example. For more details see
+`Gradient Estimation Using Stochastic Computation Graphs`_ .
+
+.. _`Gradient Estimation Using Stochastic Computation Graphs`:
+     https://arxiv.org/abs/1506.05254
+
+Score function
+^^^^^^^^^^^^^^
+
+When the probability density function is differentiable with respect to its
+parameters, we only need :meth:`~torch.distributions.Distribution.sample` and
+:meth:`~torch.distributions.Distribution.log_prob` to implement REINFORCE:
+
+.. math::
+
+    \Delta\theta  = \alpha r \frac{\partial\log p(a|\pi^\theta(s))}{\partial\theta}
+
+where :math:`\theta` are the parameters, :math:`\alpha` is the learning rate,
+:math:`r` is the reward and :math:`p(a|\pi^\theta(s))` is the probability of
+taking action :math:`a` in state :math:`s` given policy :math:`\pi^\theta`.
+
+In practice we would sample an action from the output of a network, apply this
+action in an environment, and then use ``log_prob`` to construct an equivalent
+loss function. Note that we use a negative because optimizers use gradient
+descent, whilst the rule above assumes gradient ascent. With a categorical
+policy, the code for implementing REINFORCE would be as follows::
+
+    probs = policy_network(state)
+    # Note that this is equivalent to what used to be called multinomial
+    m = Categorical(probs)
+    action = m.sample()
+    next_state, reward = env.step(action)
+    loss = -m.log_prob(action) * reward
+    loss.backward()
+
+Pathwise derivative
+^^^^^^^^^^^^^^^^^^^
+
+The other way to implement these stochastic/policy gradients would be to use the
+reparameterization trick from the
+:meth:`~torch.distributions.Distribution.rsample` method, where the
+parameterized random variable can be constructed via a parameterized
+deterministic function of a parameter-free random variable. The reparameterized
+sample therefore becomes differentiable. The code for implementing the pathwise
+derivative would be as follows::
+
+    params = policy_network(state)
+    m = Normal(*params)
+    # Any distribution with .has_rsample == True could work based on the application
+    action = m.rsample()
+    next_state, reward = env.step(action)  # Assuming that reward is differentiable
+    loss = -reward
+    loss.backward()
+"""
+
+from . import transforms
+from .bernoulli import Bernoulli
+from .beta import Beta
+from .binomial import Binomial
+from .categorical import Categorical
+from .cauchy import Cauchy
+from .chi2 import Chi2
+from .constraint_registry import biject_to, transform_to
+from .continuous_bernoulli import ContinuousBernoulli
+from .dirichlet import Dirichlet
+from .distribution import Distribution
+from .exp_family import ExponentialFamily
+from .exponential import Exponential
+from .fishersnedecor import FisherSnedecor
+from .gamma import Gamma
+from .geometric import Geometric
+from .gumbel import Gumbel
+from .half_cauchy import HalfCauchy
+from .half_normal import HalfNormal
+from .independent import Independent
+from .inverse_gamma import InverseGamma
+from .kl import _add_kl_info, kl_divergence, register_kl
+from .kumaraswamy import Kumaraswamy
+from .laplace import Laplace
+from .lkj_cholesky import LKJCholesky
+from .log_normal import LogNormal
+from .logistic_normal import LogisticNormal
+from .lowrank_multivariate_normal import LowRankMultivariateNormal
+from .mixture_same_family import MixtureSameFamily
+from .multinomial import Multinomial
+from .multivariate_normal import MultivariateNormal
+from .negative_binomial import NegativeBinomial
+from .normal import Normal
+from .one_hot_categorical import OneHotCategorical, OneHotCategoricalStraightThrough
+from .pareto import Pareto
+from .poisson import Poisson
+from .relaxed_bernoulli import RelaxedBernoulli
+from .relaxed_categorical import RelaxedOneHotCategorical
+from .studentT import StudentT
+from .transformed_distribution import TransformedDistribution
+from .transforms import *  # noqa: F403
+from .uniform import Uniform
+from .von_mises import VonMises
+from .weibull import Weibull
+from .wishart import Wishart
+
+
+_add_kl_info()
+del _add_kl_info
+
+__all__ = [
+    "Bernoulli",
+    "Beta",
+    "Binomial",
+    "Categorical",
+    "Cauchy",
+    "Chi2",
+    "ContinuousBernoulli",
+    "Dirichlet",
+    "Distribution",
+    "Exponential",
+    "ExponentialFamily",
+    "FisherSnedecor",
+    "Gamma",
+    "Geometric",
+    "Gumbel",
+    "HalfCauchy",
+    "HalfNormal",
+    "Independent",
+    "InverseGamma",
+    "Kumaraswamy",
+    "LKJCholesky",
+    "Laplace",
+    "LogNormal",
+    "LogisticNormal",
+    "LowRankMultivariateNormal",
+    "MixtureSameFamily",
+    "Multinomial",
+    "MultivariateNormal",
+    "NegativeBinomial",
+    "Normal",
+    "OneHotCategorical",
+    "OneHotCategoricalStraightThrough",
+    "Pareto",
+    "RelaxedBernoulli",
+    "RelaxedOneHotCategorical",
+    "StudentT",
+    "Poisson",
+    "Uniform",
+    "VonMises",
+    "Weibull",
+    "Wishart",
+    "TransformedDistribution",
+    "biject_to",
+    "kl_divergence",
+    "register_kl",
+    "transform_to",
+]
+__all__.extend(transforms.__all__)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/bernoulli.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/bernoulli.py
new file mode 100644
index 0000000000000000000000000000000000000000..8bfcb500b4eb505ffcb43aad9fb124881935e71c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/bernoulli.py
@@ -0,0 +1,132 @@
+# mypy: allow-untyped-defs
+from numbers import Number
+
+import torch
+from torch import nan
+from torch.distributions import constraints
+from torch.distributions.exp_family import ExponentialFamily
+from torch.distributions.utils import (
+    broadcast_all,
+    lazy_property,
+    logits_to_probs,
+    probs_to_logits,
+)
+from torch.nn.functional import binary_cross_entropy_with_logits
+
+
+__all__ = ["Bernoulli"]
+
+
+class Bernoulli(ExponentialFamily):
+    r"""
+    Creates a Bernoulli distribution parameterized by :attr:`probs`
+    or :attr:`logits` (but not both).
+
+    Samples are binary (0 or 1). They take the value `1` with probability `p`
+    and `0` with probability `1 - p`.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = Bernoulli(torch.tensor([0.3]))
+        >>> m.sample()  # 30% chance 1; 70% chance 0
+        tensor([ 0.])
+
+    Args:
+        probs (Number, Tensor): the probability of sampling `1`
+        logits (Number, Tensor): the log-odds of sampling `1`
+    """
+    arg_constraints = {"probs": constraints.unit_interval, "logits": constraints.real}
+    support = constraints.boolean
+    has_enumerate_support = True
+    _mean_carrier_measure = 0
+
+    def __init__(self, probs=None, logits=None, validate_args=None):
+        if (probs is None) == (logits is None):
+            raise ValueError(
+                "Either `probs` or `logits` must be specified, but not both."
+            )
+        if probs is not None:
+            is_scalar = isinstance(probs, Number)
+            (self.probs,) = broadcast_all(probs)
+        else:
+            is_scalar = isinstance(logits, Number)
+            (self.logits,) = broadcast_all(logits)
+        self._param = self.probs if probs is not None else self.logits
+        if is_scalar:
+            batch_shape = torch.Size()
+        else:
+            batch_shape = self._param.size()
+        super().__init__(batch_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Bernoulli, _instance)
+        batch_shape = torch.Size(batch_shape)
+        if "probs" in self.__dict__:
+            new.probs = self.probs.expand(batch_shape)
+            new._param = new.probs
+        if "logits" in self.__dict__:
+            new.logits = self.logits.expand(batch_shape)
+            new._param = new.logits
+        super(Bernoulli, new).__init__(batch_shape, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    def _new(self, *args, **kwargs):
+        return self._param.new(*args, **kwargs)
+
+    @property
+    def mean(self):
+        return self.probs
+
+    @property
+    def mode(self):
+        mode = (self.probs >= 0.5).to(self.probs)
+        mode[self.probs == 0.5] = nan
+        return mode
+
+    @property
+    def variance(self):
+        return self.probs * (1 - self.probs)
+
+    @lazy_property
+    def logits(self):
+        return probs_to_logits(self.probs, is_binary=True)
+
+    @lazy_property
+    def probs(self):
+        return logits_to_probs(self.logits, is_binary=True)
+
+    @property
+    def param_shape(self):
+        return self._param.size()
+
+    def sample(self, sample_shape=torch.Size()):
+        shape = self._extended_shape(sample_shape)
+        with torch.no_grad():
+            return torch.bernoulli(self.probs.expand(shape))
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        logits, value = broadcast_all(self.logits, value)
+        return -binary_cross_entropy_with_logits(logits, value, reduction="none")
+
+    def entropy(self):
+        return binary_cross_entropy_with_logits(
+            self.logits, self.probs, reduction="none"
+        )
+
+    def enumerate_support(self, expand=True):
+        values = torch.arange(2, dtype=self._param.dtype, device=self._param.device)
+        values = values.view((-1,) + (1,) * len(self._batch_shape))
+        if expand:
+            values = values.expand((-1,) + self._batch_shape)
+        return values
+
+    @property
+    def _natural_params(self):
+        return (torch.logit(self.probs),)
+
+    def _log_normalizer(self, x):
+        return torch.log1p(torch.exp(x))
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/beta.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/beta.py
new file mode 100644
index 0000000000000000000000000000000000000000..f660d80326e3a9f8e8fd648de5df21f57a9f398d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/beta.py
@@ -0,0 +1,110 @@
+# mypy: allow-untyped-defs
+from numbers import Number, Real
+
+import torch
+from torch.distributions import constraints
+from torch.distributions.dirichlet import Dirichlet
+from torch.distributions.exp_family import ExponentialFamily
+from torch.distributions.utils import broadcast_all
+from torch.types import _size
+
+
+__all__ = ["Beta"]
+
+
+class Beta(ExponentialFamily):
+    r"""
+    Beta distribution parameterized by :attr:`concentration1` and :attr:`concentration0`.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = Beta(torch.tensor([0.5]), torch.tensor([0.5]))
+        >>> m.sample()  # Beta distributed with concentration concentration1 and concentration0
+        tensor([ 0.1046])
+
+    Args:
+        concentration1 (float or Tensor): 1st concentration parameter of the distribution
+            (often referred to as alpha)
+        concentration0 (float or Tensor): 2nd concentration parameter of the distribution
+            (often referred to as beta)
+    """
+    arg_constraints = {
+        "concentration1": constraints.positive,
+        "concentration0": constraints.positive,
+    }
+    support = constraints.unit_interval
+    has_rsample = True
+
+    def __init__(self, concentration1, concentration0, validate_args=None):
+        if isinstance(concentration1, Real) and isinstance(concentration0, Real):
+            concentration1_concentration0 = torch.tensor(
+                [float(concentration1), float(concentration0)]
+            )
+        else:
+            concentration1, concentration0 = broadcast_all(
+                concentration1, concentration0
+            )
+            concentration1_concentration0 = torch.stack(
+                [concentration1, concentration0], -1
+            )
+        self._dirichlet = Dirichlet(
+            concentration1_concentration0, validate_args=validate_args
+        )
+        super().__init__(self._dirichlet._batch_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Beta, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new._dirichlet = self._dirichlet.expand(batch_shape)
+        super(Beta, new).__init__(batch_shape, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    @property
+    def mean(self):
+        return self.concentration1 / (self.concentration1 + self.concentration0)
+
+    @property
+    def mode(self):
+        return self._dirichlet.mode[..., 0]
+
+    @property
+    def variance(self):
+        total = self.concentration1 + self.concentration0
+        return self.concentration1 * self.concentration0 / (total.pow(2) * (total + 1))
+
+    def rsample(self, sample_shape: _size = ()) -> torch.Tensor:
+        return self._dirichlet.rsample(sample_shape).select(-1, 0)
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        heads_tails = torch.stack([value, 1.0 - value], -1)
+        return self._dirichlet.log_prob(heads_tails)
+
+    def entropy(self):
+        return self._dirichlet.entropy()
+
+    @property
+    def concentration1(self):
+        result = self._dirichlet.concentration[..., 0]
+        if isinstance(result, Number):
+            return torch.tensor([result])
+        else:
+            return result
+
+    @property
+    def concentration0(self):
+        result = self._dirichlet.concentration[..., 1]
+        if isinstance(result, Number):
+            return torch.tensor([result])
+        else:
+            return result
+
+    @property
+    def _natural_params(self):
+        return (self.concentration1, self.concentration0)
+
+    def _log_normalizer(self, x, y):
+        return torch.lgamma(x) + torch.lgamma(y) - torch.lgamma(x + y)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/binomial.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/binomial.py
new file mode 100644
index 0000000000000000000000000000000000000000..18b267ea27fd9b93dbda2b9a809c3a135ca3f1f0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/binomial.py
@@ -0,0 +1,167 @@
+# mypy: allow-untyped-defs
+import torch
+from torch.distributions import constraints
+from torch.distributions.distribution import Distribution
+from torch.distributions.utils import (
+    broadcast_all,
+    lazy_property,
+    logits_to_probs,
+    probs_to_logits,
+)
+
+
+__all__ = ["Binomial"]
+
+
+def _clamp_by_zero(x):
+    # works like clamp(x, min=0) but has grad at 0 is 0.5
+    return (x.clamp(min=0) + x - x.clamp(max=0)) / 2
+
+
+class Binomial(Distribution):
+    r"""
+    Creates a Binomial distribution parameterized by :attr:`total_count` and
+    either :attr:`probs` or :attr:`logits` (but not both). :attr:`total_count` must be
+    broadcastable with :attr:`probs`/:attr:`logits`.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = Binomial(100, torch.tensor([0 , .2, .8, 1]))
+        >>> x = m.sample()
+        tensor([   0.,   22.,   71.,  100.])
+
+        >>> m = Binomial(torch.tensor([[5.], [10.]]), torch.tensor([0.5, 0.8]))
+        >>> x = m.sample()
+        tensor([[ 4.,  5.],
+                [ 7.,  6.]])
+
+    Args:
+        total_count (int or Tensor): number of Bernoulli trials
+        probs (Tensor): Event probabilities
+        logits (Tensor): Event log-odds
+    """
+    arg_constraints = {
+        "total_count": constraints.nonnegative_integer,
+        "probs": constraints.unit_interval,
+        "logits": constraints.real,
+    }
+    has_enumerate_support = True
+
+    def __init__(self, total_count=1, probs=None, logits=None, validate_args=None):
+        if (probs is None) == (logits is None):
+            raise ValueError(
+                "Either `probs` or `logits` must be specified, but not both."
+            )
+        if probs is not None:
+            (
+                self.total_count,
+                self.probs,
+            ) = broadcast_all(total_count, probs)
+            self.total_count = self.total_count.type_as(self.probs)
+        else:
+            (
+                self.total_count,
+                self.logits,
+            ) = broadcast_all(total_count, logits)
+            self.total_count = self.total_count.type_as(self.logits)
+
+        self._param = self.probs if probs is not None else self.logits
+        batch_shape = self._param.size()
+        super().__init__(batch_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Binomial, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new.total_count = self.total_count.expand(batch_shape)
+        if "probs" in self.__dict__:
+            new.probs = self.probs.expand(batch_shape)
+            new._param = new.probs
+        if "logits" in self.__dict__:
+            new.logits = self.logits.expand(batch_shape)
+            new._param = new.logits
+        super(Binomial, new).__init__(batch_shape, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    def _new(self, *args, **kwargs):
+        return self._param.new(*args, **kwargs)
+
+    @constraints.dependent_property(is_discrete=True, event_dim=0)
+    def support(self):
+        return constraints.integer_interval(0, self.total_count)
+
+    @property
+    def mean(self):
+        return self.total_count * self.probs
+
+    @property
+    def mode(self):
+        return ((self.total_count + 1) * self.probs).floor().clamp(max=self.total_count)
+
+    @property
+    def variance(self):
+        return self.total_count * self.probs * (1 - self.probs)
+
+    @lazy_property
+    def logits(self):
+        return probs_to_logits(self.probs, is_binary=True)
+
+    @lazy_property
+    def probs(self):
+        return logits_to_probs(self.logits, is_binary=True)
+
+    @property
+    def param_shape(self):
+        return self._param.size()
+
+    def sample(self, sample_shape=torch.Size()):
+        shape = self._extended_shape(sample_shape)
+        with torch.no_grad():
+            return torch.binomial(
+                self.total_count.expand(shape), self.probs.expand(shape)
+            )
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        log_factorial_n = torch.lgamma(self.total_count + 1)
+        log_factorial_k = torch.lgamma(value + 1)
+        log_factorial_nmk = torch.lgamma(self.total_count - value + 1)
+        # k * log(p) + (n - k) * log(1 - p) = k * (log(p) - log(1 - p)) + n * log(1 - p)
+        #     (case logit < 0)              = k * logit - n * log1p(e^logit)
+        #     (case logit > 0)              = k * logit - n * (log(p) - log(1 - p)) + n * log(p)
+        #                                   = k * logit - n * logit - n * log1p(e^-logit)
+        #     (merge two cases)             = k * logit - n * max(logit, 0) - n * log1p(e^-|logit|)
+        normalize_term = (
+            self.total_count * _clamp_by_zero(self.logits)
+            + self.total_count * torch.log1p(torch.exp(-torch.abs(self.logits)))
+            - log_factorial_n
+        )
+        return (
+            value * self.logits - log_factorial_k - log_factorial_nmk - normalize_term
+        )
+
+    def entropy(self):
+        total_count = int(self.total_count.max())
+        if not self.total_count.min() == total_count:
+            raise NotImplementedError(
+                "Inhomogeneous total count not supported by `entropy`."
+            )
+
+        log_prob = self.log_prob(self.enumerate_support(False))
+        return -(torch.exp(log_prob) * log_prob).sum(0)
+
+    def enumerate_support(self, expand=True):
+        total_count = int(self.total_count.max())
+        if not self.total_count.min() == total_count:
+            raise NotImplementedError(
+                "Inhomogeneous total count not supported by `enumerate_support`."
+            )
+        values = torch.arange(
+            1 + total_count, dtype=self._param.dtype, device=self._param.device
+        )
+        values = values.view((-1,) + (1,) * len(self._batch_shape))
+        if expand:
+            values = values.expand((-1,) + self._batch_shape)
+        return values
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/categorical.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/categorical.py
new file mode 100644
index 0000000000000000000000000000000000000000..717cf74ba7e56831a174ab4fc68bc8d23d554e84
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/categorical.py
@@ -0,0 +1,157 @@
+# mypy: allow-untyped-defs
+import torch
+from torch import nan
+from torch.distributions import constraints
+from torch.distributions.distribution import Distribution
+from torch.distributions.utils import lazy_property, logits_to_probs, probs_to_logits
+
+
+__all__ = ["Categorical"]
+
+
+class Categorical(Distribution):
+    r"""
+    Creates a categorical distribution parameterized by either :attr:`probs` or
+    :attr:`logits` (but not both).
+
+    .. note::
+        It is equivalent to the distribution that :func:`torch.multinomial`
+        samples from.
+
+    Samples are integers from :math:`\{0, \ldots, K-1\}` where `K` is ``probs.size(-1)``.
+
+    If `probs` is 1-dimensional with length-`K`, each element is the relative probability
+    of sampling the class at that index.
+
+    If `probs` is N-dimensional, the first N-1 dimensions are treated as a batch of
+    relative probability vectors.
+
+    .. note:: The `probs` argument must be non-negative, finite and have a non-zero sum,
+              and it will be normalized to sum to 1 along the last dimension. :attr:`probs`
+              will return this normalized value.
+              The `logits` argument will be interpreted as unnormalized log probabilities
+              and can therefore be any real number. It will likewise be normalized so that
+              the resulting probabilities sum to 1 along the last dimension. :attr:`logits`
+              will return this normalized value.
+
+    See also: :func:`torch.multinomial`
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = Categorical(torch.tensor([ 0.25, 0.25, 0.25, 0.25 ]))
+        >>> m.sample()  # equal probability of 0, 1, 2, 3
+        tensor(3)
+
+    Args:
+        probs (Tensor): event probabilities
+        logits (Tensor): event log probabilities (unnormalized)
+    """
+    arg_constraints = {"probs": constraints.simplex, "logits": constraints.real_vector}
+    has_enumerate_support = True
+
+    def __init__(self, probs=None, logits=None, validate_args=None):
+        if (probs is None) == (logits is None):
+            raise ValueError(
+                "Either `probs` or `logits` must be specified, but not both."
+            )
+        if probs is not None:
+            if probs.dim() < 1:
+                raise ValueError("`probs` parameter must be at least one-dimensional.")
+            self.probs = probs / probs.sum(-1, keepdim=True)
+        else:
+            if logits.dim() < 1:
+                raise ValueError("`logits` parameter must be at least one-dimensional.")
+            # Normalize
+            self.logits = logits - logits.logsumexp(dim=-1, keepdim=True)
+        self._param = self.probs if probs is not None else self.logits
+        self._num_events = self._param.size()[-1]
+        batch_shape = (
+            self._param.size()[:-1] if self._param.ndimension() > 1 else torch.Size()
+        )
+        super().__init__(batch_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Categorical, _instance)
+        batch_shape = torch.Size(batch_shape)
+        param_shape = batch_shape + torch.Size((self._num_events,))
+        if "probs" in self.__dict__:
+            new.probs = self.probs.expand(param_shape)
+            new._param = new.probs
+        if "logits" in self.__dict__:
+            new.logits = self.logits.expand(param_shape)
+            new._param = new.logits
+        new._num_events = self._num_events
+        super(Categorical, new).__init__(batch_shape, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    def _new(self, *args, **kwargs):
+        return self._param.new(*args, **kwargs)
+
+    @constraints.dependent_property(is_discrete=True, event_dim=0)
+    def support(self):
+        return constraints.integer_interval(0, self._num_events - 1)
+
+    @lazy_property
+    def logits(self):
+        return probs_to_logits(self.probs)
+
+    @lazy_property
+    def probs(self):
+        return logits_to_probs(self.logits)
+
+    @property
+    def param_shape(self):
+        return self._param.size()
+
+    @property
+    def mean(self):
+        return torch.full(
+            self._extended_shape(),
+            nan,
+            dtype=self.probs.dtype,
+            device=self.probs.device,
+        )
+
+    @property
+    def mode(self):
+        return self.probs.argmax(axis=-1)
+
+    @property
+    def variance(self):
+        return torch.full(
+            self._extended_shape(),
+            nan,
+            dtype=self.probs.dtype,
+            device=self.probs.device,
+        )
+
+    def sample(self, sample_shape=torch.Size()):
+        if not isinstance(sample_shape, torch.Size):
+            sample_shape = torch.Size(sample_shape)
+        probs_2d = self.probs.reshape(-1, self._num_events)
+        samples_2d = torch.multinomial(probs_2d, sample_shape.numel(), True).T
+        return samples_2d.reshape(self._extended_shape(sample_shape))
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        value = value.long().unsqueeze(-1)
+        value, log_pmf = torch.broadcast_tensors(value, self.logits)
+        value = value[..., :1]
+        return log_pmf.gather(-1, value).squeeze(-1)
+
+    def entropy(self):
+        min_real = torch.finfo(self.logits.dtype).min
+        logits = torch.clamp(self.logits, min=min_real)
+        p_log_p = logits * self.probs
+        return -p_log_p.sum(-1)
+
+    def enumerate_support(self, expand=True):
+        num_events = self._num_events
+        values = torch.arange(num_events, dtype=torch.long, device=self._param.device)
+        values = values.view((-1,) + (1,) * len(self._batch_shape))
+        if expand:
+            values = values.expand((-1,) + self._batch_shape)
+        return values
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/cauchy.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/cauchy.py
new file mode 100644
index 0000000000000000000000000000000000000000..436cc727baa10b9f95d2eaf0647bce5032709afc
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/cauchy.py
@@ -0,0 +1,93 @@
+# mypy: allow-untyped-defs
+import math
+from numbers import Number
+
+import torch
+from torch import inf, nan
+from torch.distributions import constraints
+from torch.distributions.distribution import Distribution
+from torch.distributions.utils import broadcast_all
+from torch.types import _size
+
+
+__all__ = ["Cauchy"]
+
+
+class Cauchy(Distribution):
+    r"""
+    Samples from a Cauchy (Lorentz) distribution. The distribution of the ratio of
+    independent normally distributed random variables with means `0` follows a
+    Cauchy distribution.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = Cauchy(torch.tensor([0.0]), torch.tensor([1.0]))
+        >>> m.sample()  # sample from a Cauchy distribution with loc=0 and scale=1
+        tensor([ 2.3214])
+
+    Args:
+        loc (float or Tensor): mode or median of the distribution.
+        scale (float or Tensor): half width at half maximum.
+    """
+    arg_constraints = {"loc": constraints.real, "scale": constraints.positive}
+    support = constraints.real
+    has_rsample = True
+
+    def __init__(self, loc, scale, validate_args=None):
+        self.loc, self.scale = broadcast_all(loc, scale)
+        if isinstance(loc, Number) and isinstance(scale, Number):
+            batch_shape = torch.Size()
+        else:
+            batch_shape = self.loc.size()
+        super().__init__(batch_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Cauchy, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new.loc = self.loc.expand(batch_shape)
+        new.scale = self.scale.expand(batch_shape)
+        super(Cauchy, new).__init__(batch_shape, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    @property
+    def mean(self):
+        return torch.full(
+            self._extended_shape(), nan, dtype=self.loc.dtype, device=self.loc.device
+        )
+
+    @property
+    def mode(self):
+        return self.loc
+
+    @property
+    def variance(self):
+        return torch.full(
+            self._extended_shape(), inf, dtype=self.loc.dtype, device=self.loc.device
+        )
+
+    def rsample(self, sample_shape: _size = torch.Size()) -> torch.Tensor:
+        shape = self._extended_shape(sample_shape)
+        eps = self.loc.new(shape).cauchy_()
+        return self.loc + eps * self.scale
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        return (
+            -math.log(math.pi)
+            - self.scale.log()
+            - (((value - self.loc) / self.scale) ** 2).log1p()
+        )
+
+    def cdf(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        return torch.atan((value - self.loc) / self.scale) / math.pi + 0.5
+
+    def icdf(self, value):
+        return torch.tan(math.pi * (value - 0.5)) * self.scale + self.loc
+
+    def entropy(self):
+        return math.log(4 * math.pi) + self.scale.log()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/chi2.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/chi2.py
new file mode 100644
index 0000000000000000000000000000000000000000..a44035b897edc133a928c41945c823203cbb30f8
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/chi2.py
@@ -0,0 +1,35 @@
+# mypy: allow-untyped-defs
+from torch.distributions import constraints
+from torch.distributions.gamma import Gamma
+
+
+__all__ = ["Chi2"]
+
+
+class Chi2(Gamma):
+    r"""
+    Creates a Chi-squared distribution parameterized by shape parameter :attr:`df`.
+    This is exactly equivalent to ``Gamma(alpha=0.5*df, beta=0.5)``
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = Chi2(torch.tensor([1.0]))
+        >>> m.sample()  # Chi2 distributed with shape df=1
+        tensor([ 0.1046])
+
+    Args:
+        df (float or Tensor): shape parameter of the distribution
+    """
+    arg_constraints = {"df": constraints.positive}
+
+    def __init__(self, df, validate_args=None):
+        super().__init__(0.5 * df, 0.5, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Chi2, _instance)
+        return super().expand(batch_shape, new)
+
+    @property
+    def df(self):
+        return self.concentration * 2
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/constraint_registry.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/constraint_registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce73b1a4df2eb1fe1ebf38865a061cdb99f14efc
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/constraint_registry.py
@@ -0,0 +1,294 @@
+# mypy: allow-untyped-defs
+r"""
+PyTorch provides two global :class:`ConstraintRegistry` objects that link
+:class:`~torch.distributions.constraints.Constraint` objects to
+:class:`~torch.distributions.transforms.Transform` objects. These objects both
+input constraints and return transforms, but they have different guarantees on
+bijectivity.
+
+1. ``biject_to(constraint)`` looks up a bijective
+   :class:`~torch.distributions.transforms.Transform` from ``constraints.real``
+   to the given ``constraint``. The returned transform is guaranteed to have
+   ``.bijective = True`` and should implement ``.log_abs_det_jacobian()``.
+2. ``transform_to(constraint)`` looks up a not-necessarily bijective
+   :class:`~torch.distributions.transforms.Transform` from ``constraints.real``
+   to the given ``constraint``. The returned transform is not guaranteed to
+   implement ``.log_abs_det_jacobian()``.
+
+The ``transform_to()`` registry is useful for performing unconstrained
+optimization on constrained parameters of probability distributions, which are
+indicated by each distribution's ``.arg_constraints`` dict. These transforms often
+overparameterize a space in order to avoid rotation; they are thus more
+suitable for coordinate-wise optimization algorithms like Adam::
+
+    loc = torch.zeros(100, requires_grad=True)
+    unconstrained = torch.zeros(100, requires_grad=True)
+    scale = transform_to(Normal.arg_constraints['scale'])(unconstrained)
+    loss = -Normal(loc, scale).log_prob(data).sum()
+
+The ``biject_to()`` registry is useful for Hamiltonian Monte Carlo, where
+samples from a probability distribution with constrained ``.support`` are
+propagated in an unconstrained space, and algorithms are typically rotation
+invariant.::
+
+    dist = Exponential(rate)
+    unconstrained = torch.zeros(100, requires_grad=True)
+    sample = biject_to(dist.support)(unconstrained)
+    potential_energy = -dist.log_prob(sample).sum()
+
+.. note::
+
+    An example where ``transform_to`` and ``biject_to`` differ is
+    ``constraints.simplex``: ``transform_to(constraints.simplex)`` returns a
+    :class:`~torch.distributions.transforms.SoftmaxTransform` that simply
+    exponentiates and normalizes its inputs; this is a cheap and mostly
+    coordinate-wise operation appropriate for algorithms like SVI. In
+    contrast, ``biject_to(constraints.simplex)`` returns a
+    :class:`~torch.distributions.transforms.StickBreakingTransform` that
+    bijects its input down to a one-fewer-dimensional space; this a more
+    expensive less numerically stable transform but is needed for algorithms
+    like HMC.
+
+The ``biject_to`` and ``transform_to`` objects can be extended by user-defined
+constraints and transforms using their ``.register()`` method either as a
+function on singleton constraints::
+
+    transform_to.register(my_constraint, my_transform)
+
+or as a decorator on parameterized constraints::
+
+    @transform_to.register(MyConstraintClass)
+    def my_factory(constraint):
+        assert isinstance(constraint, MyConstraintClass)
+        return MyTransform(constraint.param1, constraint.param2)
+
+You can create your own registry by creating a new :class:`ConstraintRegistry`
+object.
+"""
+
+import numbers
+
+from torch.distributions import constraints, transforms
+
+
+__all__ = [
+    "ConstraintRegistry",
+    "biject_to",
+    "transform_to",
+]
+
+
+class ConstraintRegistry:
+    """
+    Registry to link constraints to transforms.
+    """
+
+    def __init__(self):
+        self._registry = {}
+        super().__init__()
+
+    def register(self, constraint, factory=None):
+        """
+        Registers a :class:`~torch.distributions.constraints.Constraint`
+        subclass in this registry. Usage::
+
+            @my_registry.register(MyConstraintClass)
+            def construct_transform(constraint):
+                assert isinstance(constraint, MyConstraint)
+                return MyTransform(constraint.arg_constraints)
+
+        Args:
+            constraint (subclass of :class:`~torch.distributions.constraints.Constraint`):
+                A subclass of :class:`~torch.distributions.constraints.Constraint`, or
+                a singleton object of the desired class.
+            factory (Callable): A callable that inputs a constraint object and returns
+                a  :class:`~torch.distributions.transforms.Transform` object.
+        """
+        # Support use as decorator.
+        if factory is None:
+            return lambda factory: self.register(constraint, factory)
+
+        # Support calling on singleton instances.
+        if isinstance(constraint, constraints.Constraint):
+            constraint = type(constraint)
+
+        if not isinstance(constraint, type) or not issubclass(
+            constraint, constraints.Constraint
+        ):
+            raise TypeError(
+                f"Expected constraint to be either a Constraint subclass or instance, but got {constraint}"
+            )
+
+        self._registry[constraint] = factory
+        return factory
+
+    def __call__(self, constraint):
+        """
+        Looks up a transform to constrained space, given a constraint object.
+        Usage::
+
+            constraint = Normal.arg_constraints['scale']
+            scale = transform_to(constraint)(torch.zeros(1))  # constrained
+            u = transform_to(constraint).inv(scale)           # unconstrained
+
+        Args:
+            constraint (:class:`~torch.distributions.constraints.Constraint`):
+                A constraint object.
+
+        Returns:
+            A :class:`~torch.distributions.transforms.Transform` object.
+
+        Raises:
+            `NotImplementedError` if no transform has been registered.
+        """
+        # Look up by Constraint subclass.
+        try:
+            factory = self._registry[type(constraint)]
+        except KeyError:
+            raise NotImplementedError(
+                f"Cannot transform {type(constraint).__name__} constraints"
+            ) from None
+        return factory(constraint)
+
+
+biject_to = ConstraintRegistry()
+transform_to = ConstraintRegistry()
+
+
+################################################################################
+# Registration Table
+################################################################################
+
+
+@biject_to.register(constraints.real)
+@transform_to.register(constraints.real)
+def _transform_to_real(constraint):
+    return transforms.identity_transform
+
+
+@biject_to.register(constraints.independent)
+def _biject_to_independent(constraint):
+    base_transform = biject_to(constraint.base_constraint)
+    return transforms.IndependentTransform(
+        base_transform, constraint.reinterpreted_batch_ndims
+    )
+
+
+@transform_to.register(constraints.independent)
+def _transform_to_independent(constraint):
+    base_transform = transform_to(constraint.base_constraint)
+    return transforms.IndependentTransform(
+        base_transform, constraint.reinterpreted_batch_ndims
+    )
+
+
+@biject_to.register(constraints.positive)
+@biject_to.register(constraints.nonnegative)
+@transform_to.register(constraints.positive)
+@transform_to.register(constraints.nonnegative)
+def _transform_to_positive(constraint):
+    return transforms.ExpTransform()
+
+
+@biject_to.register(constraints.greater_than)
+@biject_to.register(constraints.greater_than_eq)
+@transform_to.register(constraints.greater_than)
+@transform_to.register(constraints.greater_than_eq)
+def _transform_to_greater_than(constraint):
+    return transforms.ComposeTransform(
+        [
+            transforms.ExpTransform(),
+            transforms.AffineTransform(constraint.lower_bound, 1),
+        ]
+    )
+
+
+@biject_to.register(constraints.less_than)
+@transform_to.register(constraints.less_than)
+def _transform_to_less_than(constraint):
+    return transforms.ComposeTransform(
+        [
+            transforms.ExpTransform(),
+            transforms.AffineTransform(constraint.upper_bound, -1),
+        ]
+    )
+
+
+@biject_to.register(constraints.interval)
+@biject_to.register(constraints.half_open_interval)
+@transform_to.register(constraints.interval)
+@transform_to.register(constraints.half_open_interval)
+def _transform_to_interval(constraint):
+    # Handle the special case of the unit interval.
+    lower_is_0 = (
+        isinstance(constraint.lower_bound, numbers.Number)
+        and constraint.lower_bound == 0
+    )
+    upper_is_1 = (
+        isinstance(constraint.upper_bound, numbers.Number)
+        and constraint.upper_bound == 1
+    )
+    if lower_is_0 and upper_is_1:
+        return transforms.SigmoidTransform()
+
+    loc = constraint.lower_bound
+    scale = constraint.upper_bound - constraint.lower_bound
+    return transforms.ComposeTransform(
+        [transforms.SigmoidTransform(), transforms.AffineTransform(loc, scale)]
+    )
+
+
+@biject_to.register(constraints.simplex)
+def _biject_to_simplex(constraint):
+    return transforms.StickBreakingTransform()
+
+
+@transform_to.register(constraints.simplex)
+def _transform_to_simplex(constraint):
+    return transforms.SoftmaxTransform()
+
+
+# TODO define a bijection for LowerCholeskyTransform
+@transform_to.register(constraints.lower_cholesky)
+def _transform_to_lower_cholesky(constraint):
+    return transforms.LowerCholeskyTransform()
+
+
+@transform_to.register(constraints.positive_definite)
+@transform_to.register(constraints.positive_semidefinite)
+def _transform_to_positive_definite(constraint):
+    return transforms.PositiveDefiniteTransform()
+
+
+@biject_to.register(constraints.corr_cholesky)
+@transform_to.register(constraints.corr_cholesky)
+def _transform_to_corr_cholesky(constraint):
+    return transforms.CorrCholeskyTransform()
+
+
+@biject_to.register(constraints.cat)
+def _biject_to_cat(constraint):
+    return transforms.CatTransform(
+        [biject_to(c) for c in constraint.cseq], constraint.dim, constraint.lengths
+    )
+
+
+@transform_to.register(constraints.cat)
+def _transform_to_cat(constraint):
+    return transforms.CatTransform(
+        [transform_to(c) for c in constraint.cseq], constraint.dim, constraint.lengths
+    )
+
+
+@biject_to.register(constraints.stack)
+def _biject_to_stack(constraint):
+    return transforms.StackTransform(
+        [biject_to(c) for c in constraint.cseq], constraint.dim
+    )
+
+
+@transform_to.register(constraints.stack)
+def _transform_to_stack(constraint):
+    return transforms.StackTransform(
+        [transform_to(c) for c in constraint.cseq], constraint.dim
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/constraints.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/constraints.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c510bd32abc62cbe3be05eeeb591965c3db7861
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/constraints.py
@@ -0,0 +1,681 @@
+# mypy: allow-untyped-defs
+r"""
+The following constraints are implemented:
+
+- ``constraints.boolean``
+- ``constraints.cat``
+- ``constraints.corr_cholesky``
+- ``constraints.dependent``
+- ``constraints.greater_than(lower_bound)``
+- ``constraints.greater_than_eq(lower_bound)``
+- ``constraints.independent(constraint, reinterpreted_batch_ndims)``
+- ``constraints.integer_interval(lower_bound, upper_bound)``
+- ``constraints.interval(lower_bound, upper_bound)``
+- ``constraints.less_than(upper_bound)``
+- ``constraints.lower_cholesky``
+- ``constraints.lower_triangular``
+- ``constraints.multinomial``
+- ``constraints.nonnegative``
+- ``constraints.nonnegative_integer``
+- ``constraints.one_hot``
+- ``constraints.positive_integer``
+- ``constraints.positive``
+- ``constraints.positive_semidefinite``
+- ``constraints.positive_definite``
+- ``constraints.real_vector``
+- ``constraints.real``
+- ``constraints.simplex``
+- ``constraints.symmetric``
+- ``constraints.stack``
+- ``constraints.square``
+- ``constraints.symmetric``
+- ``constraints.unit_interval``
+"""
+
+import torch
+
+
+__all__ = [
+    "Constraint",
+    "boolean",
+    "cat",
+    "corr_cholesky",
+    "dependent",
+    "dependent_property",
+    "greater_than",
+    "greater_than_eq",
+    "independent",
+    "integer_interval",
+    "interval",
+    "half_open_interval",
+    "is_dependent",
+    "less_than",
+    "lower_cholesky",
+    "lower_triangular",
+    "multinomial",
+    "nonnegative",
+    "nonnegative_integer",
+    "one_hot",
+    "positive",
+    "positive_semidefinite",
+    "positive_definite",
+    "positive_integer",
+    "real",
+    "real_vector",
+    "simplex",
+    "square",
+    "stack",
+    "symmetric",
+    "unit_interval",
+]
+
+
+class Constraint:
+    """
+    Abstract base class for constraints.
+
+    A constraint object represents a region over which a variable is valid,
+    e.g. within which a variable can be optimized.
+
+    Attributes:
+        is_discrete (bool): Whether constrained space is discrete.
+            Defaults to False.
+        event_dim (int): Number of rightmost dimensions that together define
+            an event. The :meth:`check` method will remove this many dimensions
+            when computing validity.
+    """
+
+    is_discrete = False  # Default to continuous.
+    event_dim = 0  # Default to univariate.
+
+    def check(self, value):
+        """
+        Returns a byte tensor of ``sample_shape + batch_shape`` indicating
+        whether each event in value satisfies this constraint.
+        """
+        raise NotImplementedError
+
+    def __repr__(self):
+        return self.__class__.__name__[1:] + "()"
+
+
+class _Dependent(Constraint):
+    """
+    Placeholder for variables whose support depends on other variables.
+    These variables obey no simple coordinate-wise constraints.
+
+    Args:
+        is_discrete (bool): Optional value of ``.is_discrete`` in case this
+            can be computed statically. If not provided, access to the
+            ``.is_discrete`` attribute will raise a NotImplementedError.
+        event_dim (int): Optional value of ``.event_dim`` in case this
+            can be computed statically. If not provided, access to the
+            ``.event_dim`` attribute will raise a NotImplementedError.
+    """
+
+    def __init__(self, *, is_discrete=NotImplemented, event_dim=NotImplemented):
+        self._is_discrete = is_discrete
+        self._event_dim = event_dim
+        super().__init__()
+
+    @property
+    def is_discrete(self):
+        if self._is_discrete is NotImplemented:
+            raise NotImplementedError(".is_discrete cannot be determined statically")
+        return self._is_discrete
+
+    @property
+    def event_dim(self):
+        if self._event_dim is NotImplemented:
+            raise NotImplementedError(".event_dim cannot be determined statically")
+        return self._event_dim
+
+    def __call__(self, *, is_discrete=NotImplemented, event_dim=NotImplemented):
+        """
+        Support for syntax to customize static attributes::
+
+            constraints.dependent(is_discrete=True, event_dim=1)
+        """
+        if is_discrete is NotImplemented:
+            is_discrete = self._is_discrete
+        if event_dim is NotImplemented:
+            event_dim = self._event_dim
+        return _Dependent(is_discrete=is_discrete, event_dim=event_dim)
+
+    def check(self, x):
+        raise ValueError("Cannot determine validity of dependent constraint")
+
+
+def is_dependent(constraint):
+    """
+    Checks if ``constraint`` is a ``_Dependent`` object.
+
+    Args:
+        constraint : A ``Constraint`` object.
+
+    Returns:
+        ``bool``: True if ``constraint`` can be refined to the type ``_Dependent``, False otherwise.
+
+    Examples:
+        >>> import torch
+        >>> from torch.distributions import Bernoulli
+        >>> from torch.distributions.constraints import is_dependent
+
+        >>> dist = Bernoulli(probs = torch.tensor([0.6], requires_grad=True))
+        >>> constraint1 = dist.arg_constraints["probs"]
+        >>> constraint2 = dist.arg_constraints["logits"]
+
+        >>> for constraint in [constraint1, constraint2]:
+        >>>     if is_dependent(constraint):
+        >>>         continue
+    """
+    return isinstance(constraint, _Dependent)
+
+
+class _DependentProperty(property, _Dependent):
+    """
+    Decorator that extends @property to act like a `Dependent` constraint when
+    called on a class and act like a property when called on an object.
+
+    Example::
+
+        class Uniform(Distribution):
+            def __init__(self, low, high):
+                self.low = low
+                self.high = high
+            @constraints.dependent_property(is_discrete=False, event_dim=0)
+            def support(self):
+                return constraints.interval(self.low, self.high)
+
+    Args:
+        fn (Callable): The function to be decorated.
+        is_discrete (bool): Optional value of ``.is_discrete`` in case this
+            can be computed statically. If not provided, access to the
+            ``.is_discrete`` attribute will raise a NotImplementedError.
+        event_dim (int): Optional value of ``.event_dim`` in case this
+            can be computed statically. If not provided, access to the
+            ``.event_dim`` attribute will raise a NotImplementedError.
+    """
+
+    def __init__(
+        self, fn=None, *, is_discrete=NotImplemented, event_dim=NotImplemented
+    ):
+        super().__init__(fn)
+        self._is_discrete = is_discrete
+        self._event_dim = event_dim
+
+    def __call__(self, fn):
+        """
+        Support for syntax to customize static attributes::
+
+            @constraints.dependent_property(is_discrete=True, event_dim=1)
+            def support(self):
+                ...
+        """
+        return _DependentProperty(
+            fn, is_discrete=self._is_discrete, event_dim=self._event_dim
+        )
+
+
+class _IndependentConstraint(Constraint):
+    """
+    Wraps a constraint by aggregating over ``reinterpreted_batch_ndims``-many
+    dims in :meth:`check`, so that an event is valid only if all its
+    independent entries are valid.
+    """
+
+    def __init__(self, base_constraint, reinterpreted_batch_ndims):
+        assert isinstance(base_constraint, Constraint)
+        assert isinstance(reinterpreted_batch_ndims, int)
+        assert reinterpreted_batch_ndims >= 0
+        self.base_constraint = base_constraint
+        self.reinterpreted_batch_ndims = reinterpreted_batch_ndims
+        super().__init__()
+
+    @property
+    def is_discrete(self):
+        return self.base_constraint.is_discrete
+
+    @property
+    def event_dim(self):
+        return self.base_constraint.event_dim + self.reinterpreted_batch_ndims
+
+    def check(self, value):
+        result = self.base_constraint.check(value)
+        if result.dim() < self.reinterpreted_batch_ndims:
+            expected = self.base_constraint.event_dim + self.reinterpreted_batch_ndims
+            raise ValueError(
+                f"Expected value.dim() >= {expected} but got {value.dim()}"
+            )
+        result = result.reshape(
+            result.shape[: result.dim() - self.reinterpreted_batch_ndims] + (-1,)
+        )
+        result = result.all(-1)
+        return result
+
+    def __repr__(self):
+        return f"{self.__class__.__name__[1:]}({repr(self.base_constraint)}, {self.reinterpreted_batch_ndims})"
+
+
+class _Boolean(Constraint):
+    """
+    Constrain to the two values `{0, 1}`.
+    """
+
+    is_discrete = True
+
+    def check(self, value):
+        return (value == 0) | (value == 1)
+
+
+class _OneHot(Constraint):
+    """
+    Constrain to one-hot vectors.
+    """
+
+    is_discrete = True
+    event_dim = 1
+
+    def check(self, value):
+        is_boolean = (value == 0) | (value == 1)
+        is_normalized = value.sum(-1).eq(1)
+        return is_boolean.all(-1) & is_normalized
+
+
+class _IntegerInterval(Constraint):
+    """
+    Constrain to an integer interval `[lower_bound, upper_bound]`.
+    """
+
+    is_discrete = True
+
+    def __init__(self, lower_bound, upper_bound):
+        self.lower_bound = lower_bound
+        self.upper_bound = upper_bound
+        super().__init__()
+
+    def check(self, value):
+        return (
+            (value % 1 == 0) & (self.lower_bound <= value) & (value <= self.upper_bound)
+        )
+
+    def __repr__(self):
+        fmt_string = self.__class__.__name__[1:]
+        fmt_string += (
+            f"(lower_bound={self.lower_bound}, upper_bound={self.upper_bound})"
+        )
+        return fmt_string
+
+
+class _IntegerLessThan(Constraint):
+    """
+    Constrain to an integer interval `(-inf, upper_bound]`.
+    """
+
+    is_discrete = True
+
+    def __init__(self, upper_bound):
+        self.upper_bound = upper_bound
+        super().__init__()
+
+    def check(self, value):
+        return (value % 1 == 0) & (value <= self.upper_bound)
+
+    def __repr__(self):
+        fmt_string = self.__class__.__name__[1:]
+        fmt_string += f"(upper_bound={self.upper_bound})"
+        return fmt_string
+
+
+class _IntegerGreaterThan(Constraint):
+    """
+    Constrain to an integer interval `[lower_bound, inf)`.
+    """
+
+    is_discrete = True
+
+    def __init__(self, lower_bound):
+        self.lower_bound = lower_bound
+        super().__init__()
+
+    def check(self, value):
+        return (value % 1 == 0) & (value >= self.lower_bound)
+
+    def __repr__(self):
+        fmt_string = self.__class__.__name__[1:]
+        fmt_string += f"(lower_bound={self.lower_bound})"
+        return fmt_string
+
+
+class _Real(Constraint):
+    """
+    Trivially constrain to the extended real line `[-inf, inf]`.
+    """
+
+    def check(self, value):
+        return value == value  # False for NANs.
+
+
+class _GreaterThan(Constraint):
+    """
+    Constrain to a real half line `(lower_bound, inf]`.
+    """
+
+    def __init__(self, lower_bound):
+        self.lower_bound = lower_bound
+        super().__init__()
+
+    def check(self, value):
+        return self.lower_bound < value
+
+    def __repr__(self):
+        fmt_string = self.__class__.__name__[1:]
+        fmt_string += f"(lower_bound={self.lower_bound})"
+        return fmt_string
+
+
+class _GreaterThanEq(Constraint):
+    """
+    Constrain to a real half line `[lower_bound, inf)`.
+    """
+
+    def __init__(self, lower_bound):
+        self.lower_bound = lower_bound
+        super().__init__()
+
+    def check(self, value):
+        return self.lower_bound <= value
+
+    def __repr__(self):
+        fmt_string = self.__class__.__name__[1:]
+        fmt_string += f"(lower_bound={self.lower_bound})"
+        return fmt_string
+
+
+class _LessThan(Constraint):
+    """
+    Constrain to a real half line `[-inf, upper_bound)`.
+    """
+
+    def __init__(self, upper_bound):
+        self.upper_bound = upper_bound
+        super().__init__()
+
+    def check(self, value):
+        return value < self.upper_bound
+
+    def __repr__(self):
+        fmt_string = self.__class__.__name__[1:]
+        fmt_string += f"(upper_bound={self.upper_bound})"
+        return fmt_string
+
+
+class _Interval(Constraint):
+    """
+    Constrain to a real interval `[lower_bound, upper_bound]`.
+    """
+
+    def __init__(self, lower_bound, upper_bound):
+        self.lower_bound = lower_bound
+        self.upper_bound = upper_bound
+        super().__init__()
+
+    def check(self, value):
+        return (self.lower_bound <= value) & (value <= self.upper_bound)
+
+    def __repr__(self):
+        fmt_string = self.__class__.__name__[1:]
+        fmt_string += (
+            f"(lower_bound={self.lower_bound}, upper_bound={self.upper_bound})"
+        )
+        return fmt_string
+
+
+class _HalfOpenInterval(Constraint):
+    """
+    Constrain to a real interval `[lower_bound, upper_bound)`.
+    """
+
+    def __init__(self, lower_bound, upper_bound):
+        self.lower_bound = lower_bound
+        self.upper_bound = upper_bound
+        super().__init__()
+
+    def check(self, value):
+        return (self.lower_bound <= value) & (value < self.upper_bound)
+
+    def __repr__(self):
+        fmt_string = self.__class__.__name__[1:]
+        fmt_string += (
+            f"(lower_bound={self.lower_bound}, upper_bound={self.upper_bound})"
+        )
+        return fmt_string
+
+
+class _Simplex(Constraint):
+    """
+    Constrain to the unit simplex in the innermost (rightmost) dimension.
+    Specifically: `x >= 0` and `x.sum(-1) == 1`.
+    """
+
+    event_dim = 1
+
+    def check(self, value):
+        return torch.all(value >= 0, dim=-1) & ((value.sum(-1) - 1).abs() < 1e-6)
+
+
+class _Multinomial(Constraint):
+    """
+    Constrain to nonnegative integer values summing to at most an upper bound.
+
+    Note due to limitations of the Multinomial distribution, this currently
+    checks the weaker condition ``value.sum(-1) <= upper_bound``. In the future
+    this may be strengthened to ``value.sum(-1) == upper_bound``.
+    """
+
+    is_discrete = True
+    event_dim = 1
+
+    def __init__(self, upper_bound):
+        self.upper_bound = upper_bound
+
+    def check(self, x):
+        return (x >= 0).all(dim=-1) & (x.sum(dim=-1) <= self.upper_bound)
+
+
+class _LowerTriangular(Constraint):
+    """
+    Constrain to lower-triangular square matrices.
+    """
+
+    event_dim = 2
+
+    def check(self, value):
+        value_tril = value.tril()
+        return (value_tril == value).view(value.shape[:-2] + (-1,)).min(-1)[0]
+
+
+class _LowerCholesky(Constraint):
+    """
+    Constrain to lower-triangular square matrices with positive diagonals.
+    """
+
+    event_dim = 2
+
+    def check(self, value):
+        value_tril = value.tril()
+        lower_triangular = (
+            (value_tril == value).view(value.shape[:-2] + (-1,)).min(-1)[0]
+        )
+
+        positive_diagonal = (value.diagonal(dim1=-2, dim2=-1) > 0).min(-1)[0]
+        return lower_triangular & positive_diagonal
+
+
+class _CorrCholesky(Constraint):
+    """
+    Constrain to lower-triangular square matrices with positive diagonals and each
+    row vector being of unit length.
+    """
+
+    event_dim = 2
+
+    def check(self, value):
+        tol = (
+            torch.finfo(value.dtype).eps * value.size(-1) * 10
+        )  # 10 is an adjustable fudge factor
+        row_norm = torch.linalg.norm(value.detach(), dim=-1)
+        unit_row_norm = (row_norm - 1.0).abs().le(tol).all(dim=-1)
+        return _LowerCholesky().check(value) & unit_row_norm
+
+
+class _Square(Constraint):
+    """
+    Constrain to square matrices.
+    """
+
+    event_dim = 2
+
+    def check(self, value):
+        return torch.full(
+            size=value.shape[:-2],
+            fill_value=(value.shape[-2] == value.shape[-1]),
+            dtype=torch.bool,
+            device=value.device,
+        )
+
+
+class _Symmetric(_Square):
+    """
+    Constrain to Symmetric square matrices.
+    """
+
+    def check(self, value):
+        square_check = super().check(value)
+        if not square_check.all():
+            return square_check
+        return torch.isclose(value, value.mT, atol=1e-6).all(-2).all(-1)
+
+
+class _PositiveSemidefinite(_Symmetric):
+    """
+    Constrain to positive-semidefinite matrices.
+    """
+
+    def check(self, value):
+        sym_check = super().check(value)
+        if not sym_check.all():
+            return sym_check
+        return torch.linalg.eigvalsh(value).ge(0).all(-1)
+
+
+class _PositiveDefinite(_Symmetric):
+    """
+    Constrain to positive-definite matrices.
+    """
+
+    def check(self, value):
+        sym_check = super().check(value)
+        if not sym_check.all():
+            return sym_check
+        return torch.linalg.cholesky_ex(value).info.eq(0)
+
+
+class _Cat(Constraint):
+    """
+    Constraint functor that applies a sequence of constraints
+    `cseq` at the submatrices at dimension `dim`,
+    each of size `lengths[dim]`, in a way compatible with :func:`torch.cat`.
+    """
+
+    def __init__(self, cseq, dim=0, lengths=None):
+        assert all(isinstance(c, Constraint) for c in cseq)
+        self.cseq = list(cseq)
+        if lengths is None:
+            lengths = [1] * len(self.cseq)
+        self.lengths = list(lengths)
+        assert len(self.lengths) == len(self.cseq)
+        self.dim = dim
+        super().__init__()
+
+    @property
+    def is_discrete(self):
+        return any(c.is_discrete for c in self.cseq)
+
+    @property
+    def event_dim(self):
+        return max(c.event_dim for c in self.cseq)
+
+    def check(self, value):
+        assert -value.dim() <= self.dim < value.dim()
+        checks = []
+        start = 0
+        for constr, length in zip(self.cseq, self.lengths):
+            v = value.narrow(self.dim, start, length)
+            checks.append(constr.check(v))
+            start = start + length  # avoid += for jit compat
+        return torch.cat(checks, self.dim)
+
+
+class _Stack(Constraint):
+    """
+    Constraint functor that applies a sequence of constraints
+    `cseq` at the submatrices at dimension `dim`,
+    in a way compatible with :func:`torch.stack`.
+    """
+
+    def __init__(self, cseq, dim=0):
+        assert all(isinstance(c, Constraint) for c in cseq)
+        self.cseq = list(cseq)
+        self.dim = dim
+        super().__init__()
+
+    @property
+    def is_discrete(self):
+        return any(c.is_discrete for c in self.cseq)
+
+    @property
+    def event_dim(self):
+        dim = max(c.event_dim for c in self.cseq)
+        if self.dim + dim < 0:
+            dim += 1
+        return dim
+
+    def check(self, value):
+        assert -value.dim() <= self.dim < value.dim()
+        vs = [value.select(self.dim, i) for i in range(value.size(self.dim))]
+        return torch.stack(
+            [constr.check(v) for v, constr in zip(vs, self.cseq)], self.dim
+        )
+
+
+# Public interface.
+dependent = _Dependent()
+dependent_property = _DependentProperty
+independent = _IndependentConstraint
+boolean = _Boolean()
+one_hot = _OneHot()
+nonnegative_integer = _IntegerGreaterThan(0)
+positive_integer = _IntegerGreaterThan(1)
+integer_interval = _IntegerInterval
+real = _Real()
+real_vector = independent(real, 1)
+positive = _GreaterThan(0.0)
+nonnegative = _GreaterThanEq(0.0)
+greater_than = _GreaterThan
+greater_than_eq = _GreaterThanEq
+less_than = _LessThan
+multinomial = _Multinomial
+unit_interval = _Interval(0.0, 1.0)
+interval = _Interval
+half_open_interval = _HalfOpenInterval
+simplex = _Simplex()
+lower_triangular = _LowerTriangular()
+lower_cholesky = _LowerCholesky()
+corr_cholesky = _CorrCholesky()
+square = _Square()
+symmetric = _Symmetric()
+positive_semidefinite = _PositiveSemidefinite()
+positive_definite = _PositiveDefinite()
+cat = _Cat
+stack = _Stack
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/continuous_bernoulli.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/continuous_bernoulli.py
new file mode 100644
index 0000000000000000000000000000000000000000..0eb49f951f1c06b35438cdb4022a6b9003857df2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/continuous_bernoulli.py
@@ -0,0 +1,238 @@
+# mypy: allow-untyped-defs
+import math
+from numbers import Number
+
+import torch
+from torch.distributions import constraints
+from torch.distributions.exp_family import ExponentialFamily
+from torch.distributions.utils import (
+    broadcast_all,
+    clamp_probs,
+    lazy_property,
+    logits_to_probs,
+    probs_to_logits,
+)
+from torch.nn.functional import binary_cross_entropy_with_logits
+from torch.types import _size
+
+
+__all__ = ["ContinuousBernoulli"]
+
+
+class ContinuousBernoulli(ExponentialFamily):
+    r"""
+    Creates a continuous Bernoulli distribution parameterized by :attr:`probs`
+    or :attr:`logits` (but not both).
+
+    The distribution is supported in [0, 1] and parameterized by 'probs' (in
+    (0,1)) or 'logits' (real-valued). Note that, unlike the Bernoulli, 'probs'
+    does not correspond to a probability and 'logits' does not correspond to
+    log-odds, but the same names are used due to the similarity with the
+    Bernoulli. See [1] for more details.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = ContinuousBernoulli(torch.tensor([0.3]))
+        >>> m.sample()
+        tensor([ 0.2538])
+
+    Args:
+        probs (Number, Tensor): (0,1) valued parameters
+        logits (Number, Tensor): real valued parameters whose sigmoid matches 'probs'
+
+    [1] The continuous Bernoulli: fixing a pervasive error in variational
+    autoencoders, Loaiza-Ganem G and Cunningham JP, NeurIPS 2019.
+    https://arxiv.org/abs/1907.06845
+    """
+    arg_constraints = {"probs": constraints.unit_interval, "logits": constraints.real}
+    support = constraints.unit_interval
+    _mean_carrier_measure = 0
+    has_rsample = True
+
+    def __init__(
+        self, probs=None, logits=None, lims=(0.499, 0.501), validate_args=None
+    ):
+        if (probs is None) == (logits is None):
+            raise ValueError(
+                "Either `probs` or `logits` must be specified, but not both."
+            )
+        if probs is not None:
+            is_scalar = isinstance(probs, Number)
+            (self.probs,) = broadcast_all(probs)
+            # validate 'probs' here if necessary as it is later clamped for numerical stability
+            # close to 0 and 1, later on; otherwise the clamped 'probs' would always pass
+            if validate_args is not None:
+                if not self.arg_constraints["probs"].check(self.probs).all():
+                    raise ValueError("The parameter probs has invalid values")
+            self.probs = clamp_probs(self.probs)
+        else:
+            is_scalar = isinstance(logits, Number)
+            (self.logits,) = broadcast_all(logits)
+        self._param = self.probs if probs is not None else self.logits
+        if is_scalar:
+            batch_shape = torch.Size()
+        else:
+            batch_shape = self._param.size()
+        self._lims = lims
+        super().__init__(batch_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(ContinuousBernoulli, _instance)
+        new._lims = self._lims
+        batch_shape = torch.Size(batch_shape)
+        if "probs" in self.__dict__:
+            new.probs = self.probs.expand(batch_shape)
+            new._param = new.probs
+        if "logits" in self.__dict__:
+            new.logits = self.logits.expand(batch_shape)
+            new._param = new.logits
+        super(ContinuousBernoulli, new).__init__(batch_shape, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    def _new(self, *args, **kwargs):
+        return self._param.new(*args, **kwargs)
+
+    def _outside_unstable_region(self):
+        return torch.max(
+            torch.le(self.probs, self._lims[0]), torch.gt(self.probs, self._lims[1])
+        )
+
+    def _cut_probs(self):
+        return torch.where(
+            self._outside_unstable_region(),
+            self.probs,
+            self._lims[0] * torch.ones_like(self.probs),
+        )
+
+    def _cont_bern_log_norm(self):
+        """computes the log normalizing constant as a function of the 'probs' parameter"""
+        cut_probs = self._cut_probs()
+        cut_probs_below_half = torch.where(
+            torch.le(cut_probs, 0.5), cut_probs, torch.zeros_like(cut_probs)
+        )
+        cut_probs_above_half = torch.where(
+            torch.ge(cut_probs, 0.5), cut_probs, torch.ones_like(cut_probs)
+        )
+        log_norm = torch.log(
+            torch.abs(torch.log1p(-cut_probs) - torch.log(cut_probs))
+        ) - torch.where(
+            torch.le(cut_probs, 0.5),
+            torch.log1p(-2.0 * cut_probs_below_half),
+            torch.log(2.0 * cut_probs_above_half - 1.0),
+        )
+        x = torch.pow(self.probs - 0.5, 2)
+        taylor = math.log(2.0) + (4.0 / 3.0 + 104.0 / 45.0 * x) * x
+        return torch.where(self._outside_unstable_region(), log_norm, taylor)
+
+    @property
+    def mean(self):
+        cut_probs = self._cut_probs()
+        mus = cut_probs / (2.0 * cut_probs - 1.0) + 1.0 / (
+            torch.log1p(-cut_probs) - torch.log(cut_probs)
+        )
+        x = self.probs - 0.5
+        taylor = 0.5 + (1.0 / 3.0 + 16.0 / 45.0 * torch.pow(x, 2)) * x
+        return torch.where(self._outside_unstable_region(), mus, taylor)
+
+    @property
+    def stddev(self):
+        return torch.sqrt(self.variance)
+
+    @property
+    def variance(self):
+        cut_probs = self._cut_probs()
+        vars = cut_probs * (cut_probs - 1.0) / torch.pow(
+            1.0 - 2.0 * cut_probs, 2
+        ) + 1.0 / torch.pow(torch.log1p(-cut_probs) - torch.log(cut_probs), 2)
+        x = torch.pow(self.probs - 0.5, 2)
+        taylor = 1.0 / 12.0 - (1.0 / 15.0 - 128.0 / 945.0 * x) * x
+        return torch.where(self._outside_unstable_region(), vars, taylor)
+
+    @lazy_property
+    def logits(self):
+        return probs_to_logits(self.probs, is_binary=True)
+
+    @lazy_property
+    def probs(self):
+        return clamp_probs(logits_to_probs(self.logits, is_binary=True))
+
+    @property
+    def param_shape(self):
+        return self._param.size()
+
+    def sample(self, sample_shape=torch.Size()):
+        shape = self._extended_shape(sample_shape)
+        u = torch.rand(shape, dtype=self.probs.dtype, device=self.probs.device)
+        with torch.no_grad():
+            return self.icdf(u)
+
+    def rsample(self, sample_shape: _size = torch.Size()) -> torch.Tensor:
+        shape = self._extended_shape(sample_shape)
+        u = torch.rand(shape, dtype=self.probs.dtype, device=self.probs.device)
+        return self.icdf(u)
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        logits, value = broadcast_all(self.logits, value)
+        return (
+            -binary_cross_entropy_with_logits(logits, value, reduction="none")
+            + self._cont_bern_log_norm()
+        )
+
+    def cdf(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        cut_probs = self._cut_probs()
+        cdfs = (
+            torch.pow(cut_probs, value) * torch.pow(1.0 - cut_probs, 1.0 - value)
+            + cut_probs
+            - 1.0
+        ) / (2.0 * cut_probs - 1.0)
+        unbounded_cdfs = torch.where(self._outside_unstable_region(), cdfs, value)
+        return torch.where(
+            torch.le(value, 0.0),
+            torch.zeros_like(value),
+            torch.where(torch.ge(value, 1.0), torch.ones_like(value), unbounded_cdfs),
+        )
+
+    def icdf(self, value):
+        cut_probs = self._cut_probs()
+        return torch.where(
+            self._outside_unstable_region(),
+            (
+                torch.log1p(-cut_probs + value * (2.0 * cut_probs - 1.0))
+                - torch.log1p(-cut_probs)
+            )
+            / (torch.log(cut_probs) - torch.log1p(-cut_probs)),
+            value,
+        )
+
+    def entropy(self):
+        log_probs0 = torch.log1p(-self.probs)
+        log_probs1 = torch.log(self.probs)
+        return (
+            self.mean * (log_probs0 - log_probs1)
+            - self._cont_bern_log_norm()
+            - log_probs0
+        )
+
+    @property
+    def _natural_params(self):
+        return (self.logits,)
+
+    def _log_normalizer(self, x):
+        """computes the log normalizing constant as a function of the natural parameter"""
+        out_unst_reg = torch.max(
+            torch.le(x, self._lims[0] - 0.5), torch.gt(x, self._lims[1] - 0.5)
+        )
+        cut_nat_params = torch.where(
+            out_unst_reg, x, (self._lims[0] - 0.5) * torch.ones_like(x)
+        )
+        log_norm = torch.log(torch.abs(torch.exp(cut_nat_params) - 1.0)) - torch.log(
+            torch.abs(cut_nat_params)
+        )
+        taylor = 0.5 * x + torch.pow(x, 2) / 24.0 - torch.pow(x, 4) / 2880.0
+        return torch.where(out_unst_reg, log_norm, taylor)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/dirichlet.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/dirichlet.py
new file mode 100644
index 0000000000000000000000000000000000000000..25e7bb9cd7c2383409de8ed10fd911e69f3b23c7
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/dirichlet.py
@@ -0,0 +1,126 @@
+# mypy: allow-untyped-defs
+import torch
+from torch.autograd import Function
+from torch.autograd.function import once_differentiable
+from torch.distributions import constraints
+from torch.distributions.exp_family import ExponentialFamily
+from torch.types import _size
+
+
+__all__ = ["Dirichlet"]
+
+
+# This helper is exposed for testing.
+def _Dirichlet_backward(x, concentration, grad_output):
+    total = concentration.sum(-1, True).expand_as(concentration)
+    grad = torch._dirichlet_grad(x, concentration, total)
+    return grad * (grad_output - (x * grad_output).sum(-1, True))
+
+
+class _Dirichlet(Function):
+    @staticmethod
+    def forward(ctx, concentration):
+        x = torch._sample_dirichlet(concentration)
+        ctx.save_for_backward(x, concentration)
+        return x
+
+    @staticmethod
+    @once_differentiable
+    def backward(ctx, grad_output):
+        x, concentration = ctx.saved_tensors
+        return _Dirichlet_backward(x, concentration, grad_output)
+
+
+class Dirichlet(ExponentialFamily):
+    r"""
+    Creates a Dirichlet distribution parameterized by concentration :attr:`concentration`.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = Dirichlet(torch.tensor([0.5, 0.5]))
+        >>> m.sample()  # Dirichlet distributed with concentration [0.5, 0.5]
+        tensor([ 0.1046,  0.8954])
+
+    Args:
+        concentration (Tensor): concentration parameter of the distribution
+            (often referred to as alpha)
+    """
+    arg_constraints = {
+        "concentration": constraints.independent(constraints.positive, 1)
+    }
+    support = constraints.simplex
+    has_rsample = True
+
+    def __init__(self, concentration, validate_args=None):
+        if concentration.dim() < 1:
+            raise ValueError(
+                "`concentration` parameter must be at least one-dimensional."
+            )
+        self.concentration = concentration
+        batch_shape, event_shape = concentration.shape[:-1], concentration.shape[-1:]
+        super().__init__(batch_shape, event_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Dirichlet, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new.concentration = self.concentration.expand(batch_shape + self.event_shape)
+        super(Dirichlet, new).__init__(
+            batch_shape, self.event_shape, validate_args=False
+        )
+        new._validate_args = self._validate_args
+        return new
+
+    def rsample(self, sample_shape: _size = ()) -> torch.Tensor:
+        shape = self._extended_shape(sample_shape)
+        concentration = self.concentration.expand(shape)
+        return _Dirichlet.apply(concentration)
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        return (
+            torch.xlogy(self.concentration - 1.0, value).sum(-1)
+            + torch.lgamma(self.concentration.sum(-1))
+            - torch.lgamma(self.concentration).sum(-1)
+        )
+
+    @property
+    def mean(self):
+        return self.concentration / self.concentration.sum(-1, True)
+
+    @property
+    def mode(self):
+        concentrationm1 = (self.concentration - 1).clamp(min=0.0)
+        mode = concentrationm1 / concentrationm1.sum(-1, True)
+        mask = (self.concentration < 1).all(axis=-1)
+        mode[mask] = torch.nn.functional.one_hot(
+            mode[mask].argmax(axis=-1), concentrationm1.shape[-1]
+        ).to(mode)
+        return mode
+
+    @property
+    def variance(self):
+        con0 = self.concentration.sum(-1, True)
+        return (
+            self.concentration
+            * (con0 - self.concentration)
+            / (con0.pow(2) * (con0 + 1))
+        )
+
+    def entropy(self):
+        k = self.concentration.size(-1)
+        a0 = self.concentration.sum(-1)
+        return (
+            torch.lgamma(self.concentration).sum(-1)
+            - torch.lgamma(a0)
+            - (k - a0) * torch.digamma(a0)
+            - ((self.concentration - 1.0) * torch.digamma(self.concentration)).sum(-1)
+        )
+
+    @property
+    def _natural_params(self):
+        return (self.concentration,)
+
+    def _log_normalizer(self, x):
+        return x.lgamma().sum(-1) - torch.lgamma(x.sum(-1))
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/distribution.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/distribution.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c3bdf9e85cd52aad69ef9342574604059e2e651
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/distribution.py
@@ -0,0 +1,340 @@
+# mypy: allow-untyped-defs
+import warnings
+from typing import Any, Dict, Optional
+from typing_extensions import deprecated
+
+import torch
+from torch.distributions import constraints
+from torch.distributions.utils import lazy_property
+from torch.types import _size
+
+
+__all__ = ["Distribution"]
+
+
+class Distribution:
+    r"""
+    Distribution is the abstract base class for probability distributions.
+    """
+
+    has_rsample = False
+    has_enumerate_support = False
+    _validate_args = __debug__
+
+    @staticmethod
+    def set_default_validate_args(value: bool) -> None:
+        """
+        Sets whether validation is enabled or disabled.
+
+        The default behavior mimics Python's ``assert`` statement: validation
+        is on by default, but is disabled if Python is run in optimized mode
+        (via ``python -O``). Validation may be expensive, so you may want to
+        disable it once a model is working.
+
+        Args:
+            value (bool): Whether to enable validation.
+        """
+        if value not in [True, False]:
+            raise ValueError
+        Distribution._validate_args = value
+
+    def __init__(
+        self,
+        batch_shape: torch.Size = torch.Size(),
+        event_shape: torch.Size = torch.Size(),
+        validate_args: Optional[bool] = None,
+    ):
+        self._batch_shape = batch_shape
+        self._event_shape = event_shape
+        if validate_args is not None:
+            self._validate_args = validate_args
+        if self._validate_args:
+            try:
+                arg_constraints = self.arg_constraints
+            except NotImplementedError:
+                arg_constraints = {}
+                warnings.warn(
+                    f"{self.__class__} does not define `arg_constraints`. "
+                    + "Please set `arg_constraints = {}` or initialize the distribution "
+                    + "with `validate_args=False` to turn off validation."
+                )
+            for param, constraint in arg_constraints.items():
+                if constraints.is_dependent(constraint):
+                    continue  # skip constraints that cannot be checked
+                if param not in self.__dict__ and isinstance(
+                    getattr(type(self), param), lazy_property
+                ):
+                    continue  # skip checking lazily-constructed args
+                value = getattr(self, param)
+                valid = constraint.check(value)
+                if not valid.all():
+                    raise ValueError(
+                        f"Expected parameter {param} "
+                        f"({type(value).__name__} of shape {tuple(value.shape)}) "
+                        f"of distribution {repr(self)} "
+                        f"to satisfy the constraint {repr(constraint)}, "
+                        f"but found invalid values:\n{value}"
+                    )
+        super().__init__()
+
+    def expand(self, batch_shape: _size, _instance=None):
+        """
+        Returns a new distribution instance (or populates an existing instance
+        provided by a derived class) with batch dimensions expanded to
+        `batch_shape`. This method calls :class:`~torch.Tensor.expand` on
+        the distribution's parameters. As such, this does not allocate new
+        memory for the expanded distribution instance. Additionally,
+        this does not repeat any args checking or parameter broadcasting in
+        `__init__.py`, when an instance is first created.
+
+        Args:
+            batch_shape (torch.Size): the desired expanded size.
+            _instance: new instance provided by subclasses that
+                need to override `.expand`.
+
+        Returns:
+            New distribution instance with batch dimensions expanded to
+            `batch_size`.
+        """
+        raise NotImplementedError
+
+    @property
+    def batch_shape(self) -> torch.Size:
+        """
+        Returns the shape over which parameters are batched.
+        """
+        return self._batch_shape
+
+    @property
+    def event_shape(self) -> torch.Size:
+        """
+        Returns the shape of a single sample (without batching).
+        """
+        return self._event_shape
+
+    @property
+    def arg_constraints(self) -> Dict[str, constraints.Constraint]:
+        """
+        Returns a dictionary from argument names to
+        :class:`~torch.distributions.constraints.Constraint` objects that
+        should be satisfied by each argument of this distribution. Args that
+        are not tensors need not appear in this dict.
+        """
+        raise NotImplementedError
+
+    @property
+    def support(self) -> Optional[Any]:
+        """
+        Returns a :class:`~torch.distributions.constraints.Constraint` object
+        representing this distribution's support.
+        """
+        raise NotImplementedError
+
+    @property
+    def mean(self) -> torch.Tensor:
+        """
+        Returns the mean of the distribution.
+        """
+        raise NotImplementedError
+
+    @property
+    def mode(self) -> torch.Tensor:
+        """
+        Returns the mode of the distribution.
+        """
+        raise NotImplementedError(f"{self.__class__} does not implement mode")
+
+    @property
+    def variance(self) -> torch.Tensor:
+        """
+        Returns the variance of the distribution.
+        """
+        raise NotImplementedError
+
+    @property
+    def stddev(self) -> torch.Tensor:
+        """
+        Returns the standard deviation of the distribution.
+        """
+        return self.variance.sqrt()
+
+    def sample(self, sample_shape: _size = torch.Size()) -> torch.Tensor:
+        """
+        Generates a sample_shape shaped sample or sample_shape shaped batch of
+        samples if the distribution parameters are batched.
+        """
+        with torch.no_grad():
+            return self.rsample(sample_shape)
+
+    def rsample(self, sample_shape: _size = torch.Size()) -> torch.Tensor:
+        """
+        Generates a sample_shape shaped reparameterized sample or sample_shape
+        shaped batch of reparameterized samples if the distribution parameters
+        are batched.
+        """
+        raise NotImplementedError
+
+    @deprecated(
+        "`sample_n(n)` will be deprecated. Use `sample((n,))` instead.",
+        category=FutureWarning,
+    )
+    def sample_n(self, n: int) -> torch.Tensor:
+        """
+        Generates n samples or n batches of samples if the distribution
+        parameters are batched.
+        """
+        return self.sample(torch.Size((n,)))
+
+    def log_prob(self, value: torch.Tensor) -> torch.Tensor:
+        """
+        Returns the log of the probability density/mass function evaluated at
+        `value`.
+
+        Args:
+            value (Tensor):
+        """
+        raise NotImplementedError
+
+    def cdf(self, value: torch.Tensor) -> torch.Tensor:
+        """
+        Returns the cumulative density/mass function evaluated at
+        `value`.
+
+        Args:
+            value (Tensor):
+        """
+        raise NotImplementedError
+
+    def icdf(self, value: torch.Tensor) -> torch.Tensor:
+        """
+        Returns the inverse cumulative density/mass function evaluated at
+        `value`.
+
+        Args:
+            value (Tensor):
+        """
+        raise NotImplementedError
+
+    def enumerate_support(self, expand: bool = True) -> torch.Tensor:
+        """
+        Returns tensor containing all values supported by a discrete
+        distribution. The result will enumerate over dimension 0, so the shape
+        of the result will be `(cardinality,) + batch_shape + event_shape`
+        (where `event_shape = ()` for univariate distributions).
+
+        Note that this enumerates over all batched tensors in lock-step
+        `[[0, 0], [1, 1], ...]`. With `expand=False`, enumeration happens
+        along dim 0, but with the remaining batch dimensions being
+        singleton dimensions, `[[0], [1], ..`.
+
+        To iterate over the full Cartesian product use
+        `itertools.product(m.enumerate_support())`.
+
+        Args:
+            expand (bool): whether to expand the support over the
+                batch dims to match the distribution's `batch_shape`.
+
+        Returns:
+            Tensor iterating over dimension 0.
+        """
+        raise NotImplementedError
+
+    def entropy(self) -> torch.Tensor:
+        """
+        Returns entropy of distribution, batched over batch_shape.
+
+        Returns:
+            Tensor of shape batch_shape.
+        """
+        raise NotImplementedError
+
+    def perplexity(self) -> torch.Tensor:
+        """
+        Returns perplexity of distribution, batched over batch_shape.
+
+        Returns:
+            Tensor of shape batch_shape.
+        """
+        return torch.exp(self.entropy())
+
+    def _extended_shape(self, sample_shape: _size = torch.Size()) -> torch.Size:
+        """
+        Returns the size of the sample returned by the distribution, given
+        a `sample_shape`. Note, that the batch and event shapes of a distribution
+        instance are fixed at the time of construction. If this is empty, the
+        returned shape is upcast to (1,).
+
+        Args:
+            sample_shape (torch.Size): the size of the sample to be drawn.
+        """
+        if not isinstance(sample_shape, torch.Size):
+            sample_shape = torch.Size(sample_shape)
+        return torch.Size(sample_shape + self._batch_shape + self._event_shape)
+
+    def _validate_sample(self, value: torch.Tensor) -> None:
+        """
+        Argument validation for distribution methods such as `log_prob`,
+        `cdf` and `icdf`. The rightmost dimensions of a value to be
+        scored via these methods must agree with the distribution's batch
+        and event shapes.
+
+        Args:
+            value (Tensor): the tensor whose log probability is to be
+                computed by the `log_prob` method.
+        Raises
+            ValueError: when the rightmost dimensions of `value` do not match the
+                distribution's batch and event shapes.
+        """
+        if not isinstance(value, torch.Tensor):
+            raise ValueError("The value argument to log_prob must be a Tensor")
+
+        event_dim_start = len(value.size()) - len(self._event_shape)
+        if value.size()[event_dim_start:] != self._event_shape:
+            raise ValueError(
+                f"The right-most size of value must match event_shape: {value.size()} vs {self._event_shape}."
+            )
+
+        actual_shape = value.size()
+        expected_shape = self._batch_shape + self._event_shape
+        for i, j in zip(reversed(actual_shape), reversed(expected_shape)):
+            if i != 1 and j != 1 and i != j:
+                raise ValueError(
+                    f"Value is not broadcastable with batch_shape+event_shape: {actual_shape} vs {expected_shape}."
+                )
+        try:
+            support = self.support
+        except NotImplementedError:
+            warnings.warn(
+                f"{self.__class__} does not define `support` to enable "
+                + "sample validation. Please initialize the distribution with "
+                + "`validate_args=False` to turn off validation."
+            )
+            return
+        assert support is not None
+        valid = support.check(value)
+        if not valid.all():
+            raise ValueError(
+                "Expected value argument "
+                f"({type(value).__name__} of shape {tuple(value.shape)}) "
+                f"to be within the support ({repr(support)}) "
+                f"of the distribution {repr(self)}, "
+                f"but found invalid values:\n{value}"
+            )
+
+    def _get_checked_instance(self, cls, _instance=None):
+        if _instance is None and type(self).__init__ != cls.__init__:
+            raise NotImplementedError(
+                f"Subclass {self.__class__.__name__} of {cls.__name__} that defines a custom __init__ method "
+                "must also define a custom .expand() method."
+            )
+        return self.__new__(type(self)) if _instance is None else _instance
+
+    def __repr__(self) -> str:
+        param_names = [k for k, _ in self.arg_constraints.items() if k in self.__dict__]
+        args_string = ", ".join(
+            [
+                f"{p}: {self.__dict__[p] if self.__dict__[p].numel() == 1 else self.__dict__[p].size()}"
+                for p in param_names
+            ]
+        )
+        return self.__class__.__name__ + "(" + args_string + ")"
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/exp_family.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/exp_family.py
new file mode 100644
index 0000000000000000000000000000000000000000..33234c47b10282856a94c0b29860db68ac715b18
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/exp_family.py
@@ -0,0 +1,64 @@
+# mypy: allow-untyped-defs
+import torch
+from torch.distributions.distribution import Distribution
+
+
+__all__ = ["ExponentialFamily"]
+
+
+class ExponentialFamily(Distribution):
+    r"""
+    ExponentialFamily is the abstract base class for probability distributions belonging to an
+    exponential family, whose probability mass/density function has the form is defined below
+
+    .. math::
+
+        p_{F}(x; \theta) = \exp(\langle t(x), \theta\rangle - F(\theta) + k(x))
+
+    where :math:`\theta` denotes the natural parameters, :math:`t(x)` denotes the sufficient statistic,
+    :math:`F(\theta)` is the log normalizer function for a given family and :math:`k(x)` is the carrier
+    measure.
+
+    Note:
+        This class is an intermediary between the `Distribution` class and distributions which belong
+        to an exponential family mainly to check the correctness of the `.entropy()` and analytic KL
+        divergence methods. We use this class to compute the entropy and KL divergence using the AD
+        framework and Bregman divergences (courtesy of: Frank Nielsen and Richard Nock, Entropies and
+        Cross-entropies of Exponential Families).
+    """
+
+    @property
+    def _natural_params(self):
+        """
+        Abstract method for natural parameters. Returns a tuple of Tensors based
+        on the distribution
+        """
+        raise NotImplementedError
+
+    def _log_normalizer(self, *natural_params):
+        """
+        Abstract method for log normalizer function. Returns a log normalizer based on
+        the distribution and input
+        """
+        raise NotImplementedError
+
+    @property
+    def _mean_carrier_measure(self):
+        """
+        Abstract method for expected carrier measure, which is required for computing
+        entropy.
+        """
+        raise NotImplementedError
+
+    def entropy(self):
+        """
+        Method to compute the entropy using Bregman divergence of the log normalizer.
+        """
+        result = -self._mean_carrier_measure
+        nparams = [p.detach().requires_grad_() for p in self._natural_params]
+        lg_normal = self._log_normalizer(*nparams)
+        gradients = torch.autograd.grad(lg_normal.sum(), nparams, create_graph=True)
+        result += lg_normal
+        for np, g in zip(nparams, gradients):
+            result -= (np * g).reshape(self._batch_shape + (-1,)).sum(-1)
+        return result
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/exponential.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/exponential.py
new file mode 100644
index 0000000000000000000000000000000000000000..02e349c11b9cb204279c3f40d80e2e8596b073f2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/exponential.py
@@ -0,0 +1,87 @@
+# mypy: allow-untyped-defs
+from numbers import Number
+
+import torch
+from torch.distributions import constraints
+from torch.distributions.exp_family import ExponentialFamily
+from torch.distributions.utils import broadcast_all
+from torch.types import _size
+
+
+__all__ = ["Exponential"]
+
+
+class Exponential(ExponentialFamily):
+    r"""
+    Creates a Exponential distribution parameterized by :attr:`rate`.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = Exponential(torch.tensor([1.0]))
+        >>> m.sample()  # Exponential distributed with rate=1
+        tensor([ 0.1046])
+
+    Args:
+        rate (float or Tensor): rate = 1 / scale of the distribution
+    """
+    arg_constraints = {"rate": constraints.positive}
+    support = constraints.nonnegative
+    has_rsample = True
+    _mean_carrier_measure = 0
+
+    @property
+    def mean(self):
+        return self.rate.reciprocal()
+
+    @property
+    def mode(self):
+        return torch.zeros_like(self.rate)
+
+    @property
+    def stddev(self):
+        return self.rate.reciprocal()
+
+    @property
+    def variance(self):
+        return self.rate.pow(-2)
+
+    def __init__(self, rate, validate_args=None):
+        (self.rate,) = broadcast_all(rate)
+        batch_shape = torch.Size() if isinstance(rate, Number) else self.rate.size()
+        super().__init__(batch_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Exponential, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new.rate = self.rate.expand(batch_shape)
+        super(Exponential, new).__init__(batch_shape, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    def rsample(self, sample_shape: _size = torch.Size()) -> torch.Tensor:
+        shape = self._extended_shape(sample_shape)
+        return self.rate.new(shape).exponential_() / self.rate
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        return self.rate.log() - self.rate * value
+
+    def cdf(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        return 1 - torch.exp(-self.rate * value)
+
+    def icdf(self, value):
+        return -torch.log1p(-value) / self.rate
+
+    def entropy(self):
+        return 1.0 - torch.log(self.rate)
+
+    @property
+    def _natural_params(self):
+        return (-self.rate,)
+
+    def _log_normalizer(self, x):
+        return -torch.log(-x)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/fishersnedecor.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/fishersnedecor.py
new file mode 100644
index 0000000000000000000000000000000000000000..824a77ad7835fd536c16ba71359f50f0e4cc323c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/fishersnedecor.py
@@ -0,0 +1,101 @@
+# mypy: allow-untyped-defs
+from numbers import Number
+
+import torch
+from torch import nan
+from torch.distributions import constraints
+from torch.distributions.distribution import Distribution
+from torch.distributions.gamma import Gamma
+from torch.distributions.utils import broadcast_all
+from torch.types import _size
+
+
+__all__ = ["FisherSnedecor"]
+
+
+class FisherSnedecor(Distribution):
+    r"""
+    Creates a Fisher-Snedecor distribution parameterized by :attr:`df1` and :attr:`df2`.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = FisherSnedecor(torch.tensor([1.0]), torch.tensor([2.0]))
+        >>> m.sample()  # Fisher-Snedecor-distributed with df1=1 and df2=2
+        tensor([ 0.2453])
+
+    Args:
+        df1 (float or Tensor): degrees of freedom parameter 1
+        df2 (float or Tensor): degrees of freedom parameter 2
+    """
+    arg_constraints = {"df1": constraints.positive, "df2": constraints.positive}
+    support = constraints.positive
+    has_rsample = True
+
+    def __init__(self, df1, df2, validate_args=None):
+        self.df1, self.df2 = broadcast_all(df1, df2)
+        self._gamma1 = Gamma(self.df1 * 0.5, self.df1)
+        self._gamma2 = Gamma(self.df2 * 0.5, self.df2)
+
+        if isinstance(df1, Number) and isinstance(df2, Number):
+            batch_shape = torch.Size()
+        else:
+            batch_shape = self.df1.size()
+        super().__init__(batch_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(FisherSnedecor, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new.df1 = self.df1.expand(batch_shape)
+        new.df2 = self.df2.expand(batch_shape)
+        new._gamma1 = self._gamma1.expand(batch_shape)
+        new._gamma2 = self._gamma2.expand(batch_shape)
+        super(FisherSnedecor, new).__init__(batch_shape, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    @property
+    def mean(self):
+        df2 = self.df2.clone(memory_format=torch.contiguous_format)
+        df2[df2 <= 2] = nan
+        return df2 / (df2 - 2)
+
+    @property
+    def mode(self):
+        mode = (self.df1 - 2) / self.df1 * self.df2 / (self.df2 + 2)
+        mode[self.df1 <= 2] = nan
+        return mode
+
+    @property
+    def variance(self):
+        df2 = self.df2.clone(memory_format=torch.contiguous_format)
+        df2[df2 <= 4] = nan
+        return (
+            2
+            * df2.pow(2)
+            * (self.df1 + df2 - 2)
+            / (self.df1 * (df2 - 2).pow(2) * (df2 - 4))
+        )
+
+    def rsample(self, sample_shape: _size = torch.Size(())) -> torch.Tensor:
+        shape = self._extended_shape(sample_shape)
+        #   X1 ~ Gamma(df1 / 2, 1 / df1), X2 ~ Gamma(df2 / 2, 1 / df2)
+        #   Y = df2 * df1 * X1 / (df1 * df2 * X2) = X1 / X2 ~ F(df1, df2)
+        X1 = self._gamma1.rsample(sample_shape).view(shape)
+        X2 = self._gamma2.rsample(sample_shape).view(shape)
+        tiny = torch.finfo(X2.dtype).tiny
+        X2.clamp_(min=tiny)
+        Y = X1 / X2
+        Y.clamp_(min=tiny)
+        return Y
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        ct1 = self.df1 * 0.5
+        ct2 = self.df2 * 0.5
+        ct3 = self.df1 / self.df2
+        t1 = (ct1 + ct2).lgamma() - ct1.lgamma() - ct2.lgamma()
+        t2 = ct1 * ct3.log() + (ct1 - 1) * torch.log(value)
+        t3 = (ct1 + ct2) * torch.log1p(ct3 * value)
+        return t1 + t2 - t3
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/gamma.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/gamma.py
new file mode 100644
index 0000000000000000000000000000000000000000..97631683d53b2432a974508dfd1daf3868171553
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/gamma.py
@@ -0,0 +1,111 @@
+# mypy: allow-untyped-defs
+from numbers import Number
+
+import torch
+from torch.distributions import constraints
+from torch.distributions.exp_family import ExponentialFamily
+from torch.distributions.utils import broadcast_all
+from torch.types import _size
+
+
+__all__ = ["Gamma"]
+
+
+def _standard_gamma(concentration):
+    return torch._standard_gamma(concentration)
+
+
+class Gamma(ExponentialFamily):
+    r"""
+    Creates a Gamma distribution parameterized by shape :attr:`concentration` and :attr:`rate`.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = Gamma(torch.tensor([1.0]), torch.tensor([1.0]))
+        >>> m.sample()  # Gamma distributed with concentration=1 and rate=1
+        tensor([ 0.1046])
+
+    Args:
+        concentration (float or Tensor): shape parameter of the distribution
+            (often referred to as alpha)
+        rate (float or Tensor): rate = 1 / scale of the distribution
+            (often referred to as beta)
+    """
+    arg_constraints = {
+        "concentration": constraints.positive,
+        "rate": constraints.positive,
+    }
+    support = constraints.nonnegative
+    has_rsample = True
+    _mean_carrier_measure = 0
+
+    @property
+    def mean(self):
+        return self.concentration / self.rate
+
+    @property
+    def mode(self):
+        return ((self.concentration - 1) / self.rate).clamp(min=0)
+
+    @property
+    def variance(self):
+        return self.concentration / self.rate.pow(2)
+
+    def __init__(self, concentration, rate, validate_args=None):
+        self.concentration, self.rate = broadcast_all(concentration, rate)
+        if isinstance(concentration, Number) and isinstance(rate, Number):
+            batch_shape = torch.Size()
+        else:
+            batch_shape = self.concentration.size()
+        super().__init__(batch_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Gamma, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new.concentration = self.concentration.expand(batch_shape)
+        new.rate = self.rate.expand(batch_shape)
+        super(Gamma, new).__init__(batch_shape, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    def rsample(self, sample_shape: _size = torch.Size()) -> torch.Tensor:
+        shape = self._extended_shape(sample_shape)
+        value = _standard_gamma(self.concentration.expand(shape)) / self.rate.expand(
+            shape
+        )
+        value.detach().clamp_(
+            min=torch.finfo(value.dtype).tiny
+        )  # do not record in autograd graph
+        return value
+
+    def log_prob(self, value):
+        value = torch.as_tensor(value, dtype=self.rate.dtype, device=self.rate.device)
+        if self._validate_args:
+            self._validate_sample(value)
+        return (
+            torch.xlogy(self.concentration, self.rate)
+            + torch.xlogy(self.concentration - 1, value)
+            - self.rate * value
+            - torch.lgamma(self.concentration)
+        )
+
+    def entropy(self):
+        return (
+            self.concentration
+            - torch.log(self.rate)
+            + torch.lgamma(self.concentration)
+            + (1.0 - self.concentration) * torch.digamma(self.concentration)
+        )
+
+    @property
+    def _natural_params(self):
+        return (self.concentration - 1, -self.rate)
+
+    def _log_normalizer(self, x, y):
+        return torch.lgamma(x + 1) + (x + 1) * torch.log(-y.reciprocal())
+
+    def cdf(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        return torch.special.gammainc(self.concentration, self.rate * value)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/geometric.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/geometric.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5f17802411082a55abe8ecc77c83746f11c9e47
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/geometric.py
@@ -0,0 +1,130 @@
+# mypy: allow-untyped-defs
+from numbers import Number
+
+import torch
+from torch.distributions import constraints
+from torch.distributions.distribution import Distribution
+from torch.distributions.utils import (
+    broadcast_all,
+    lazy_property,
+    logits_to_probs,
+    probs_to_logits,
+)
+from torch.nn.functional import binary_cross_entropy_with_logits
+
+
+__all__ = ["Geometric"]
+
+
+class Geometric(Distribution):
+    r"""
+    Creates a Geometric distribution parameterized by :attr:`probs`,
+    where :attr:`probs` is the probability of success of Bernoulli trials.
+
+    .. math::
+
+        P(X=k) = (1-p)^{k} p, k = 0, 1, ...
+
+    .. note::
+        :func:`torch.distributions.geometric.Geometric` :math:`(k+1)`-th trial is the first success
+        hence draws samples in :math:`\{0, 1, \ldots\}`, whereas
+        :func:`torch.Tensor.geometric_` `k`-th trial is the first success hence draws samples in :math:`\{1, 2, \ldots\}`.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = Geometric(torch.tensor([0.3]))
+        >>> m.sample()  # underlying Bernoulli has 30% chance 1; 70% chance 0
+        tensor([ 2.])
+
+    Args:
+        probs (Number, Tensor): the probability of sampling `1`. Must be in range (0, 1]
+        logits (Number, Tensor): the log-odds of sampling `1`.
+    """
+    arg_constraints = {"probs": constraints.unit_interval, "logits": constraints.real}
+    support = constraints.nonnegative_integer
+
+    def __init__(self, probs=None, logits=None, validate_args=None):
+        if (probs is None) == (logits is None):
+            raise ValueError(
+                "Either `probs` or `logits` must be specified, but not both."
+            )
+        if probs is not None:
+            (self.probs,) = broadcast_all(probs)
+        else:
+            (self.logits,) = broadcast_all(logits)
+        probs_or_logits = probs if probs is not None else logits
+        if isinstance(probs_or_logits, Number):
+            batch_shape = torch.Size()
+        else:
+            batch_shape = probs_or_logits.size()
+        super().__init__(batch_shape, validate_args=validate_args)
+        if self._validate_args and probs is not None:
+            # Add an extra check beyond unit_interval
+            value = self.probs
+            valid = value > 0
+            if not valid.all():
+                invalid_value = value.data[~valid]
+                raise ValueError(
+                    "Expected parameter probs "
+                    f"({type(value).__name__} of shape {tuple(value.shape)}) "
+                    f"of distribution {repr(self)} "
+                    f"to be positive but found invalid values:\n{invalid_value}"
+                )
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Geometric, _instance)
+        batch_shape = torch.Size(batch_shape)
+        if "probs" in self.__dict__:
+            new.probs = self.probs.expand(batch_shape)
+        if "logits" in self.__dict__:
+            new.logits = self.logits.expand(batch_shape)
+        super(Geometric, new).__init__(batch_shape, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    @property
+    def mean(self):
+        return 1.0 / self.probs - 1.0
+
+    @property
+    def mode(self):
+        return torch.zeros_like(self.probs)
+
+    @property
+    def variance(self):
+        return (1.0 / self.probs - 1.0) / self.probs
+
+    @lazy_property
+    def logits(self):
+        return probs_to_logits(self.probs, is_binary=True)
+
+    @lazy_property
+    def probs(self):
+        return logits_to_probs(self.logits, is_binary=True)
+
+    def sample(self, sample_shape=torch.Size()):
+        shape = self._extended_shape(sample_shape)
+        tiny = torch.finfo(self.probs.dtype).tiny
+        with torch.no_grad():
+            if torch._C._get_tracing_state():
+                # [JIT WORKAROUND] lack of support for .uniform_()
+                u = torch.rand(shape, dtype=self.probs.dtype, device=self.probs.device)
+                u = u.clamp(min=tiny)
+            else:
+                u = self.probs.new(shape).uniform_(tiny, 1)
+            return (u.log() / (-self.probs).log1p()).floor()
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        value, probs = broadcast_all(value, self.probs)
+        probs = probs.clone(memory_format=torch.contiguous_format)
+        probs[(probs == 1) & (value == 0)] = 0
+        return value * (-probs).log1p() + self.probs.log()
+
+    def entropy(self):
+        return (
+            binary_cross_entropy_with_logits(self.logits, self.probs, reduction="none")
+            / self.probs
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/gumbel.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/gumbel.py
new file mode 100644
index 0000000000000000000000000000000000000000..782aec9b350a52865f7b5fb0ac400640df0c5fe1
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/gumbel.py
@@ -0,0 +1,83 @@
+# mypy: allow-untyped-defs
+import math
+from numbers import Number
+
+import torch
+from torch.distributions import constraints
+from torch.distributions.transformed_distribution import TransformedDistribution
+from torch.distributions.transforms import AffineTransform, ExpTransform
+from torch.distributions.uniform import Uniform
+from torch.distributions.utils import broadcast_all, euler_constant
+
+
+__all__ = ["Gumbel"]
+
+
+class Gumbel(TransformedDistribution):
+    r"""
+    Samples from a Gumbel Distribution.
+
+    Examples::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = Gumbel(torch.tensor([1.0]), torch.tensor([2.0]))
+        >>> m.sample()  # sample from Gumbel distribution with loc=1, scale=2
+        tensor([ 1.0124])
+
+    Args:
+        loc (float or Tensor): Location parameter of the distribution
+        scale (float or Tensor): Scale parameter of the distribution
+    """
+    arg_constraints = {"loc": constraints.real, "scale": constraints.positive}
+    support = constraints.real
+
+    def __init__(self, loc, scale, validate_args=None):
+        self.loc, self.scale = broadcast_all(loc, scale)
+        finfo = torch.finfo(self.loc.dtype)
+        if isinstance(loc, Number) and isinstance(scale, Number):
+            base_dist = Uniform(finfo.tiny, 1 - finfo.eps, validate_args=validate_args)
+        else:
+            base_dist = Uniform(
+                torch.full_like(self.loc, finfo.tiny),
+                torch.full_like(self.loc, 1 - finfo.eps),
+                validate_args=validate_args,
+            )
+        transforms = [
+            ExpTransform().inv,
+            AffineTransform(loc=0, scale=-torch.ones_like(self.scale)),
+            ExpTransform().inv,
+            AffineTransform(loc=loc, scale=-self.scale),
+        ]
+        super().__init__(base_dist, transforms, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Gumbel, _instance)
+        new.loc = self.loc.expand(batch_shape)
+        new.scale = self.scale.expand(batch_shape)
+        return super().expand(batch_shape, _instance=new)
+
+    # Explicitly defining the log probability function for Gumbel due to precision issues
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        y = (self.loc - value) / self.scale
+        return (y - y.exp()) - self.scale.log()
+
+    @property
+    def mean(self):
+        return self.loc + self.scale * euler_constant
+
+    @property
+    def mode(self):
+        return self.loc
+
+    @property
+    def stddev(self):
+        return (math.pi / math.sqrt(6)) * self.scale
+
+    @property
+    def variance(self):
+        return self.stddev.pow(2)
+
+    def entropy(self):
+        return self.scale.log() + (1 + euler_constant)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/half_cauchy.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/half_cauchy.py
new file mode 100644
index 0000000000000000000000000000000000000000..17f48c45cf271ef2c7b4d42ccebf79c4397be09e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/half_cauchy.py
@@ -0,0 +1,84 @@
+# mypy: allow-untyped-defs
+import math
+
+import torch
+from torch import inf
+from torch.distributions import constraints
+from torch.distributions.cauchy import Cauchy
+from torch.distributions.transformed_distribution import TransformedDistribution
+from torch.distributions.transforms import AbsTransform
+
+
+__all__ = ["HalfCauchy"]
+
+
+class HalfCauchy(TransformedDistribution):
+    r"""
+    Creates a half-Cauchy distribution parameterized by `scale` where::
+
+        X ~ Cauchy(0, scale)
+        Y = |X| ~ HalfCauchy(scale)
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = HalfCauchy(torch.tensor([1.0]))
+        >>> m.sample()  # half-cauchy distributed with scale=1
+        tensor([ 2.3214])
+
+    Args:
+        scale (float or Tensor): scale of the full Cauchy distribution
+    """
+    arg_constraints = {"scale": constraints.positive}
+    support = constraints.nonnegative
+    has_rsample = True
+
+    def __init__(self, scale, validate_args=None):
+        base_dist = Cauchy(0, scale, validate_args=False)
+        super().__init__(base_dist, AbsTransform(), validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(HalfCauchy, _instance)
+        return super().expand(batch_shape, _instance=new)
+
+    @property
+    def scale(self):
+        return self.base_dist.scale
+
+    @property
+    def mean(self):
+        return torch.full(
+            self._extended_shape(),
+            math.inf,
+            dtype=self.scale.dtype,
+            device=self.scale.device,
+        )
+
+    @property
+    def mode(self):
+        return torch.zeros_like(self.scale)
+
+    @property
+    def variance(self):
+        return self.base_dist.variance
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        value = torch.as_tensor(
+            value, dtype=self.base_dist.scale.dtype, device=self.base_dist.scale.device
+        )
+        log_prob = self.base_dist.log_prob(value) + math.log(2)
+        log_prob = torch.where(value >= 0, log_prob, -inf)
+        return log_prob
+
+    def cdf(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        return 2 * self.base_dist.cdf(value) - 1
+
+    def icdf(self, prob):
+        return self.base_dist.icdf((prob + 1) / 2)
+
+    def entropy(self):
+        return self.base_dist.entropy() - math.log(2)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/half_normal.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/half_normal.py
new file mode 100644
index 0000000000000000000000000000000000000000..4031c34bbb578e633580167b7cdbaefdb6c7e2ac
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/half_normal.py
@@ -0,0 +1,76 @@
+# mypy: allow-untyped-defs
+import math
+
+import torch
+from torch import inf
+from torch.distributions import constraints
+from torch.distributions.normal import Normal
+from torch.distributions.transformed_distribution import TransformedDistribution
+from torch.distributions.transforms import AbsTransform
+
+
+__all__ = ["HalfNormal"]
+
+
+class HalfNormal(TransformedDistribution):
+    r"""
+    Creates a half-normal distribution parameterized by `scale` where::
+
+        X ~ Normal(0, scale)
+        Y = |X| ~ HalfNormal(scale)
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = HalfNormal(torch.tensor([1.0]))
+        >>> m.sample()  # half-normal distributed with scale=1
+        tensor([ 0.1046])
+
+    Args:
+        scale (float or Tensor): scale of the full Normal distribution
+    """
+    arg_constraints = {"scale": constraints.positive}
+    support = constraints.nonnegative
+    has_rsample = True
+
+    def __init__(self, scale, validate_args=None):
+        base_dist = Normal(0, scale, validate_args=False)
+        super().__init__(base_dist, AbsTransform(), validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(HalfNormal, _instance)
+        return super().expand(batch_shape, _instance=new)
+
+    @property
+    def scale(self):
+        return self.base_dist.scale
+
+    @property
+    def mean(self):
+        return self.scale * math.sqrt(2 / math.pi)
+
+    @property
+    def mode(self):
+        return torch.zeros_like(self.scale)
+
+    @property
+    def variance(self):
+        return self.scale.pow(2) * (1 - 2 / math.pi)
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        log_prob = self.base_dist.log_prob(value) + math.log(2)
+        log_prob = torch.where(value >= 0, log_prob, -inf)
+        return log_prob
+
+    def cdf(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        return 2 * self.base_dist.cdf(value) - 1
+
+    def icdf(self, prob):
+        return self.base_dist.icdf((prob + 1) / 2)
+
+    def entropy(self):
+        return self.base_dist.entropy() - math.log(2)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/independent.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/independent.py
new file mode 100644
index 0000000000000000000000000000000000000000..edf740138ef8743ae4833930bfd703eb90197672
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/independent.py
@@ -0,0 +1,128 @@
+# mypy: allow-untyped-defs
+from typing import Dict
+
+import torch
+from torch.distributions import constraints
+from torch.distributions.distribution import Distribution
+from torch.distributions.utils import _sum_rightmost
+from torch.types import _size
+
+
+__all__ = ["Independent"]
+
+
+class Independent(Distribution):
+    r"""
+    Reinterprets some of the batch dims of a distribution as event dims.
+
+    This is mainly useful for changing the shape of the result of
+    :meth:`log_prob`. For example to create a diagonal Normal distribution with
+    the same shape as a Multivariate Normal distribution (so they are
+    interchangeable), you can::
+
+        >>> from torch.distributions.multivariate_normal import MultivariateNormal
+        >>> from torch.distributions.normal import Normal
+        >>> loc = torch.zeros(3)
+        >>> scale = torch.ones(3)
+        >>> mvn = MultivariateNormal(loc, scale_tril=torch.diag(scale))
+        >>> [mvn.batch_shape, mvn.event_shape]
+        [torch.Size([]), torch.Size([3])]
+        >>> normal = Normal(loc, scale)
+        >>> [normal.batch_shape, normal.event_shape]
+        [torch.Size([3]), torch.Size([])]
+        >>> diagn = Independent(normal, 1)
+        >>> [diagn.batch_shape, diagn.event_shape]
+        [torch.Size([]), torch.Size([3])]
+
+    Args:
+        base_distribution (torch.distributions.distribution.Distribution): a
+            base distribution
+        reinterpreted_batch_ndims (int): the number of batch dims to
+            reinterpret as event dims
+    """
+    arg_constraints: Dict[str, constraints.Constraint] = {}
+
+    def __init__(
+        self, base_distribution, reinterpreted_batch_ndims, validate_args=None
+    ):
+        if reinterpreted_batch_ndims > len(base_distribution.batch_shape):
+            raise ValueError(
+                "Expected reinterpreted_batch_ndims <= len(base_distribution.batch_shape), "
+                f"actual {reinterpreted_batch_ndims} vs {len(base_distribution.batch_shape)}"
+            )
+        shape = base_distribution.batch_shape + base_distribution.event_shape
+        event_dim = reinterpreted_batch_ndims + len(base_distribution.event_shape)
+        batch_shape = shape[: len(shape) - event_dim]
+        event_shape = shape[len(shape) - event_dim :]
+        self.base_dist = base_distribution
+        self.reinterpreted_batch_ndims = reinterpreted_batch_ndims
+        super().__init__(batch_shape, event_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Independent, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new.base_dist = self.base_dist.expand(
+            batch_shape + self.event_shape[: self.reinterpreted_batch_ndims]
+        )
+        new.reinterpreted_batch_ndims = self.reinterpreted_batch_ndims
+        super(Independent, new).__init__(
+            batch_shape, self.event_shape, validate_args=False
+        )
+        new._validate_args = self._validate_args
+        return new
+
+    @property
+    def has_rsample(self):
+        return self.base_dist.has_rsample
+
+    @property
+    def has_enumerate_support(self):
+        if self.reinterpreted_batch_ndims > 0:
+            return False
+        return self.base_dist.has_enumerate_support
+
+    @constraints.dependent_property
+    def support(self):
+        result = self.base_dist.support
+        if self.reinterpreted_batch_ndims:
+            result = constraints.independent(result, self.reinterpreted_batch_ndims)
+        return result
+
+    @property
+    def mean(self):
+        return self.base_dist.mean
+
+    @property
+    def mode(self):
+        return self.base_dist.mode
+
+    @property
+    def variance(self):
+        return self.base_dist.variance
+
+    def sample(self, sample_shape=torch.Size()):
+        return self.base_dist.sample(sample_shape)
+
+    def rsample(self, sample_shape: _size = torch.Size()) -> torch.Tensor:
+        return self.base_dist.rsample(sample_shape)
+
+    def log_prob(self, value):
+        log_prob = self.base_dist.log_prob(value)
+        return _sum_rightmost(log_prob, self.reinterpreted_batch_ndims)
+
+    def entropy(self):
+        entropy = self.base_dist.entropy()
+        return _sum_rightmost(entropy, self.reinterpreted_batch_ndims)
+
+    def enumerate_support(self, expand=True):
+        if self.reinterpreted_batch_ndims > 0:
+            raise NotImplementedError(
+                "Enumeration over cartesian product is not implemented"
+            )
+        return self.base_dist.enumerate_support(expand=expand)
+
+    def __repr__(self):
+        return (
+            self.__class__.__name__
+            + f"({self.base_dist}, {self.reinterpreted_batch_ndims})"
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/inverse_gamma.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/inverse_gamma.py
new file mode 100644
index 0000000000000000000000000000000000000000..cff64d0a9e4956c6c2267dd10f921bb9e4fa6f4c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/inverse_gamma.py
@@ -0,0 +1,81 @@
+# mypy: allow-untyped-defs
+import torch
+from torch.distributions import constraints
+from torch.distributions.gamma import Gamma
+from torch.distributions.transformed_distribution import TransformedDistribution
+from torch.distributions.transforms import PowerTransform
+
+
+__all__ = ["InverseGamma"]
+
+
+class InverseGamma(TransformedDistribution):
+    r"""
+    Creates an inverse gamma distribution parameterized by :attr:`concentration` and :attr:`rate`
+    where::
+
+        X ~ Gamma(concentration, rate)
+        Y = 1 / X ~ InverseGamma(concentration, rate)
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> m = InverseGamma(torch.tensor([2.0]), torch.tensor([3.0]))
+        >>> m.sample()
+        tensor([ 1.2953])
+
+    Args:
+        concentration (float or Tensor): shape parameter of the distribution
+            (often referred to as alpha)
+        rate (float or Tensor): rate = 1 / scale of the distribution
+            (often referred to as beta)
+    """
+    arg_constraints = {
+        "concentration": constraints.positive,
+        "rate": constraints.positive,
+    }
+    support = constraints.positive
+    has_rsample = True
+
+    def __init__(self, concentration, rate, validate_args=None):
+        base_dist = Gamma(concentration, rate, validate_args=validate_args)
+        neg_one = -base_dist.rate.new_ones(())
+        super().__init__(
+            base_dist, PowerTransform(neg_one), validate_args=validate_args
+        )
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(InverseGamma, _instance)
+        return super().expand(batch_shape, _instance=new)
+
+    @property
+    def concentration(self):
+        return self.base_dist.concentration
+
+    @property
+    def rate(self):
+        return self.base_dist.rate
+
+    @property
+    def mean(self):
+        result = self.rate / (self.concentration - 1)
+        return torch.where(self.concentration > 1, result, torch.inf)
+
+    @property
+    def mode(self):
+        return self.rate / (self.concentration + 1)
+
+    @property
+    def variance(self):
+        result = self.rate.square() / (
+            (self.concentration - 1).square() * (self.concentration - 2)
+        )
+        return torch.where(self.concentration > 2, result, torch.inf)
+
+    def entropy(self):
+        return (
+            self.concentration
+            + self.rate.log()
+            + self.concentration.lgamma()
+            - (1 + self.concentration) * self.concentration.digamma()
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/kl.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/kl.py
new file mode 100644
index 0000000000000000000000000000000000000000..c94c711cc8b364c68daf5d34830b4756aaced93c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/kl.py
@@ -0,0 +1,972 @@
+# mypy: allow-untyped-defs
+import math
+import warnings
+from functools import total_ordering
+from typing import Callable, Dict, Tuple, Type
+
+import torch
+from torch import inf
+
+from .bernoulli import Bernoulli
+from .beta import Beta
+from .binomial import Binomial
+from .categorical import Categorical
+from .cauchy import Cauchy
+from .continuous_bernoulli import ContinuousBernoulli
+from .dirichlet import Dirichlet
+from .distribution import Distribution
+from .exp_family import ExponentialFamily
+from .exponential import Exponential
+from .gamma import Gamma
+from .geometric import Geometric
+from .gumbel import Gumbel
+from .half_normal import HalfNormal
+from .independent import Independent
+from .laplace import Laplace
+from .lowrank_multivariate_normal import (
+    _batch_lowrank_logdet,
+    _batch_lowrank_mahalanobis,
+    LowRankMultivariateNormal,
+)
+from .multivariate_normal import _batch_mahalanobis, MultivariateNormal
+from .normal import Normal
+from .one_hot_categorical import OneHotCategorical
+from .pareto import Pareto
+from .poisson import Poisson
+from .transformed_distribution import TransformedDistribution
+from .uniform import Uniform
+from .utils import _sum_rightmost, euler_constant as _euler_gamma
+
+
+_KL_REGISTRY: Dict[
+    Tuple[Type, Type], Callable
+] = {}  # Source of truth mapping a few general (type, type) pairs to functions.
+_KL_MEMOIZE: Dict[
+    Tuple[Type, Type], Callable
+] = {}  # Memoized version mapping many specific (type, type) pairs to functions.
+
+__all__ = ["register_kl", "kl_divergence"]
+
+
+def register_kl(type_p, type_q):
+    """
+    Decorator to register a pairwise function with :meth:`kl_divergence`.
+    Usage::
+
+        @register_kl(Normal, Normal)
+        def kl_normal_normal(p, q):
+            # insert implementation here
+
+    Lookup returns the most specific (type,type) match ordered by subclass. If
+    the match is ambiguous, a `RuntimeWarning` is raised. For example to
+    resolve the ambiguous situation::
+
+        @register_kl(BaseP, DerivedQ)
+        def kl_version1(p, q): ...
+        @register_kl(DerivedP, BaseQ)
+        def kl_version2(p, q): ...
+
+    you should register a third most-specific implementation, e.g.::
+
+        register_kl(DerivedP, DerivedQ)(kl_version1)  # Break the tie.
+
+    Args:
+        type_p (type): A subclass of :class:`~torch.distributions.Distribution`.
+        type_q (type): A subclass of :class:`~torch.distributions.Distribution`.
+    """
+    if not isinstance(type_p, type) and issubclass(type_p, Distribution):
+        raise TypeError(
+            f"Expected type_p to be a Distribution subclass but got {type_p}"
+        )
+    if not isinstance(type_q, type) and issubclass(type_q, Distribution):
+        raise TypeError(
+            f"Expected type_q to be a Distribution subclass but got {type_q}"
+        )
+
+    def decorator(fun):
+        _KL_REGISTRY[type_p, type_q] = fun
+        _KL_MEMOIZE.clear()  # reset since lookup order may have changed
+        return fun
+
+    return decorator
+
+
+@total_ordering
+class _Match:
+    __slots__ = ["types"]
+
+    def __init__(self, *types):
+        self.types = types
+
+    def __eq__(self, other):
+        return self.types == other.types
+
+    def __le__(self, other):
+        for x, y in zip(self.types, other.types):
+            if not issubclass(x, y):
+                return False
+            if x is not y:
+                break
+        return True
+
+
+def _dispatch_kl(type_p, type_q):
+    """
+    Find the most specific approximate match, assuming single inheritance.
+    """
+    matches = [
+        (super_p, super_q)
+        for super_p, super_q in _KL_REGISTRY
+        if issubclass(type_p, super_p) and issubclass(type_q, super_q)
+    ]
+    if not matches:
+        return NotImplemented
+    # Check that the left- and right- lexicographic orders agree.
+    # mypy isn't smart enough to know that _Match implements __lt__
+    # see: https://github.com/python/typing/issues/760#issuecomment-710670503
+    left_p, left_q = min(_Match(*m) for m in matches).types  # type: ignore[type-var]
+    right_q, right_p = min(_Match(*reversed(m)) for m in matches).types  # type: ignore[type-var]
+    left_fun = _KL_REGISTRY[left_p, left_q]
+    right_fun = _KL_REGISTRY[right_p, right_q]
+    if left_fun is not right_fun:
+        warnings.warn(
+            f"Ambiguous kl_divergence({type_p.__name__}, {type_q.__name__}). "
+            f"Please register_kl({left_p.__name__}, {right_q.__name__})",
+            RuntimeWarning,
+        )
+    return left_fun
+
+
+def _infinite_like(tensor):
+    """
+    Helper function for obtaining infinite KL Divergence throughout
+    """
+    return torch.full_like(tensor, inf)
+
+
+def _x_log_x(tensor):
+    """
+    Utility function for calculating x log x
+    """
+    return tensor * tensor.log()
+
+
+def _batch_trace_XXT(bmat):
+    """
+    Utility function for calculating the trace of XX^{T} with X having arbitrary trailing batch dimensions
+    """
+    n = bmat.size(-1)
+    m = bmat.size(-2)
+    flat_trace = bmat.reshape(-1, m * n).pow(2).sum(-1)
+    return flat_trace.reshape(bmat.shape[:-2])
+
+
+def kl_divergence(p: Distribution, q: Distribution) -> torch.Tensor:
+    r"""
+    Compute Kullback-Leibler divergence :math:`KL(p \| q)` between two distributions.
+
+    .. math::
+
+        KL(p \| q) = \int p(x) \log\frac {p(x)} {q(x)} \,dx
+
+    Args:
+        p (Distribution): A :class:`~torch.distributions.Distribution` object.
+        q (Distribution): A :class:`~torch.distributions.Distribution` object.
+
+    Returns:
+        Tensor: A batch of KL divergences of shape `batch_shape`.
+
+    Raises:
+        NotImplementedError: If the distribution types have not been registered via
+            :meth:`register_kl`.
+    """
+    try:
+        fun = _KL_MEMOIZE[type(p), type(q)]
+    except KeyError:
+        fun = _dispatch_kl(type(p), type(q))
+        _KL_MEMOIZE[type(p), type(q)] = fun
+    if fun is NotImplemented:
+        raise NotImplementedError(
+            f"No KL(p || q) is implemented for p type {p.__class__.__name__} and q type {q.__class__.__name__}"
+        )
+    return fun(p, q)
+
+
+################################################################################
+# KL Divergence Implementations
+################################################################################
+
+# Same distributions
+
+
+@register_kl(Bernoulli, Bernoulli)
+def _kl_bernoulli_bernoulli(p, q):
+    t1 = p.probs * (
+        torch.nn.functional.softplus(-q.logits)
+        - torch.nn.functional.softplus(-p.logits)
+    )
+    t1[q.probs == 0] = inf
+    t1[p.probs == 0] = 0
+    t2 = (1 - p.probs) * (
+        torch.nn.functional.softplus(q.logits) - torch.nn.functional.softplus(p.logits)
+    )
+    t2[q.probs == 1] = inf
+    t2[p.probs == 1] = 0
+    return t1 + t2
+
+
+@register_kl(Beta, Beta)
+def _kl_beta_beta(p, q):
+    sum_params_p = p.concentration1 + p.concentration0
+    sum_params_q = q.concentration1 + q.concentration0
+    t1 = q.concentration1.lgamma() + q.concentration0.lgamma() + (sum_params_p).lgamma()
+    t2 = p.concentration1.lgamma() + p.concentration0.lgamma() + (sum_params_q).lgamma()
+    t3 = (p.concentration1 - q.concentration1) * torch.digamma(p.concentration1)
+    t4 = (p.concentration0 - q.concentration0) * torch.digamma(p.concentration0)
+    t5 = (sum_params_q - sum_params_p) * torch.digamma(sum_params_p)
+    return t1 - t2 + t3 + t4 + t5
+
+
+@register_kl(Binomial, Binomial)
+def _kl_binomial_binomial(p, q):
+    # from https://math.stackexchange.com/questions/2214993/
+    # kullback-leibler-divergence-for-binomial-distributions-p-and-q
+    if (p.total_count < q.total_count).any():
+        raise NotImplementedError(
+            "KL between Binomials where q.total_count > p.total_count is not implemented"
+        )
+    kl = p.total_count * (
+        p.probs * (p.logits - q.logits) + (-p.probs).log1p() - (-q.probs).log1p()
+    )
+    inf_idxs = p.total_count > q.total_count
+    kl[inf_idxs] = _infinite_like(kl[inf_idxs])
+    return kl
+
+
+@register_kl(Categorical, Categorical)
+def _kl_categorical_categorical(p, q):
+    t = p.probs * (p.logits - q.logits)
+    t[(q.probs == 0).expand_as(t)] = inf
+    t[(p.probs == 0).expand_as(t)] = 0
+    return t.sum(-1)
+
+
+@register_kl(ContinuousBernoulli, ContinuousBernoulli)
+def _kl_continuous_bernoulli_continuous_bernoulli(p, q):
+    t1 = p.mean * (p.logits - q.logits)
+    t2 = p._cont_bern_log_norm() + torch.log1p(-p.probs)
+    t3 = -q._cont_bern_log_norm() - torch.log1p(-q.probs)
+    return t1 + t2 + t3
+
+
+@register_kl(Dirichlet, Dirichlet)
+def _kl_dirichlet_dirichlet(p, q):
+    # From http://bariskurt.com/kullback-leibler-divergence-between-two-dirichlet-and-beta-distributions/
+    sum_p_concentration = p.concentration.sum(-1)
+    sum_q_concentration = q.concentration.sum(-1)
+    t1 = sum_p_concentration.lgamma() - sum_q_concentration.lgamma()
+    t2 = (p.concentration.lgamma() - q.concentration.lgamma()).sum(-1)
+    t3 = p.concentration - q.concentration
+    t4 = p.concentration.digamma() - sum_p_concentration.digamma().unsqueeze(-1)
+    return t1 - t2 + (t3 * t4).sum(-1)
+
+
+@register_kl(Exponential, Exponential)
+def _kl_exponential_exponential(p, q):
+    rate_ratio = q.rate / p.rate
+    t1 = -rate_ratio.log()
+    return t1 + rate_ratio - 1
+
+
+@register_kl(ExponentialFamily, ExponentialFamily)
+def _kl_expfamily_expfamily(p, q):
+    if not type(p) == type(q):
+        raise NotImplementedError(
+            "The cross KL-divergence between different exponential families cannot \
+                            be computed using Bregman divergences"
+        )
+    p_nparams = [np.detach().requires_grad_() for np in p._natural_params]
+    q_nparams = q._natural_params
+    lg_normal = p._log_normalizer(*p_nparams)
+    gradients = torch.autograd.grad(lg_normal.sum(), p_nparams, create_graph=True)
+    result = q._log_normalizer(*q_nparams) - lg_normal
+    for pnp, qnp, g in zip(p_nparams, q_nparams, gradients):
+        term = (qnp - pnp) * g
+        result -= _sum_rightmost(term, len(q.event_shape))
+    return result
+
+
+@register_kl(Gamma, Gamma)
+def _kl_gamma_gamma(p, q):
+    t1 = q.concentration * (p.rate / q.rate).log()
+    t2 = torch.lgamma(q.concentration) - torch.lgamma(p.concentration)
+    t3 = (p.concentration - q.concentration) * torch.digamma(p.concentration)
+    t4 = (q.rate - p.rate) * (p.concentration / p.rate)
+    return t1 + t2 + t3 + t4
+
+
+@register_kl(Gumbel, Gumbel)
+def _kl_gumbel_gumbel(p, q):
+    ct1 = p.scale / q.scale
+    ct2 = q.loc / q.scale
+    ct3 = p.loc / q.scale
+    t1 = -ct1.log() - ct2 + ct3
+    t2 = ct1 * _euler_gamma
+    t3 = torch.exp(ct2 + (1 + ct1).lgamma() - ct3)
+    return t1 + t2 + t3 - (1 + _euler_gamma)
+
+
+@register_kl(Geometric, Geometric)
+def _kl_geometric_geometric(p, q):
+    return -p.entropy() - torch.log1p(-q.probs) / p.probs - q.logits
+
+
+@register_kl(HalfNormal, HalfNormal)
+def _kl_halfnormal_halfnormal(p, q):
+    return _kl_normal_normal(p.base_dist, q.base_dist)
+
+
+@register_kl(Laplace, Laplace)
+def _kl_laplace_laplace(p, q):
+    # From http://www.mast.queensu.ca/~communications/Papers/gil-msc11.pdf
+    scale_ratio = p.scale / q.scale
+    loc_abs_diff = (p.loc - q.loc).abs()
+    t1 = -scale_ratio.log()
+    t2 = loc_abs_diff / q.scale
+    t3 = scale_ratio * torch.exp(-loc_abs_diff / p.scale)
+    return t1 + t2 + t3 - 1
+
+
+@register_kl(LowRankMultivariateNormal, LowRankMultivariateNormal)
+def _kl_lowrankmultivariatenormal_lowrankmultivariatenormal(p, q):
+    if p.event_shape != q.event_shape:
+        raise ValueError(
+            "KL-divergence between two Low Rank Multivariate Normals with\
+                          different event shapes cannot be computed"
+        )
+
+    term1 = _batch_lowrank_logdet(
+        q._unbroadcasted_cov_factor, q._unbroadcasted_cov_diag, q._capacitance_tril
+    ) - _batch_lowrank_logdet(
+        p._unbroadcasted_cov_factor, p._unbroadcasted_cov_diag, p._capacitance_tril
+    )
+    term3 = _batch_lowrank_mahalanobis(
+        q._unbroadcasted_cov_factor,
+        q._unbroadcasted_cov_diag,
+        q.loc - p.loc,
+        q._capacitance_tril,
+    )
+    # Expands term2 according to
+    # inv(qcov) @ pcov = [inv(qD) - inv(qD) @ qW @ inv(qC) @ qW.T @ inv(qD)] @ (pW @ pW.T + pD)
+    #                  = [inv(qD) - A.T @ A] @ (pD + pW @ pW.T)
+    qWt_qDinv = q._unbroadcasted_cov_factor.mT / q._unbroadcasted_cov_diag.unsqueeze(-2)
+    A = torch.linalg.solve_triangular(q._capacitance_tril, qWt_qDinv, upper=False)
+    term21 = (p._unbroadcasted_cov_diag / q._unbroadcasted_cov_diag).sum(-1)
+    term22 = _batch_trace_XXT(
+        p._unbroadcasted_cov_factor * q._unbroadcasted_cov_diag.rsqrt().unsqueeze(-1)
+    )
+    term23 = _batch_trace_XXT(A * p._unbroadcasted_cov_diag.sqrt().unsqueeze(-2))
+    term24 = _batch_trace_XXT(A.matmul(p._unbroadcasted_cov_factor))
+    term2 = term21 + term22 - term23 - term24
+    return 0.5 * (term1 + term2 + term3 - p.event_shape[0])
+
+
+@register_kl(MultivariateNormal, LowRankMultivariateNormal)
+def _kl_multivariatenormal_lowrankmultivariatenormal(p, q):
+    if p.event_shape != q.event_shape:
+        raise ValueError(
+            "KL-divergence between two (Low Rank) Multivariate Normals with\
+                          different event shapes cannot be computed"
+        )
+
+    term1 = _batch_lowrank_logdet(
+        q._unbroadcasted_cov_factor, q._unbroadcasted_cov_diag, q._capacitance_tril
+    ) - 2 * p._unbroadcasted_scale_tril.diagonal(dim1=-2, dim2=-1).log().sum(-1)
+    term3 = _batch_lowrank_mahalanobis(
+        q._unbroadcasted_cov_factor,
+        q._unbroadcasted_cov_diag,
+        q.loc - p.loc,
+        q._capacitance_tril,
+    )
+    # Expands term2 according to
+    # inv(qcov) @ pcov = [inv(qD) - inv(qD) @ qW @ inv(qC) @ qW.T @ inv(qD)] @ p_tril @ p_tril.T
+    #                  = [inv(qD) - A.T @ A] @ p_tril @ p_tril.T
+    qWt_qDinv = q._unbroadcasted_cov_factor.mT / q._unbroadcasted_cov_diag.unsqueeze(-2)
+    A = torch.linalg.solve_triangular(q._capacitance_tril, qWt_qDinv, upper=False)
+    term21 = _batch_trace_XXT(
+        p._unbroadcasted_scale_tril * q._unbroadcasted_cov_diag.rsqrt().unsqueeze(-1)
+    )
+    term22 = _batch_trace_XXT(A.matmul(p._unbroadcasted_scale_tril))
+    term2 = term21 - term22
+    return 0.5 * (term1 + term2 + term3 - p.event_shape[0])
+
+
+@register_kl(LowRankMultivariateNormal, MultivariateNormal)
+def _kl_lowrankmultivariatenormal_multivariatenormal(p, q):
+    if p.event_shape != q.event_shape:
+        raise ValueError(
+            "KL-divergence between two (Low Rank) Multivariate Normals with\
+                          different event shapes cannot be computed"
+        )
+
+    term1 = 2 * q._unbroadcasted_scale_tril.diagonal(dim1=-2, dim2=-1).log().sum(
+        -1
+    ) - _batch_lowrank_logdet(
+        p._unbroadcasted_cov_factor, p._unbroadcasted_cov_diag, p._capacitance_tril
+    )
+    term3 = _batch_mahalanobis(q._unbroadcasted_scale_tril, (q.loc - p.loc))
+    # Expands term2 according to
+    # inv(qcov) @ pcov = inv(q_tril @ q_tril.T) @ (pW @ pW.T + pD)
+    combined_batch_shape = torch._C._infer_size(
+        q._unbroadcasted_scale_tril.shape[:-2], p._unbroadcasted_cov_factor.shape[:-2]
+    )
+    n = p.event_shape[0]
+    q_scale_tril = q._unbroadcasted_scale_tril.expand(combined_batch_shape + (n, n))
+    p_cov_factor = p._unbroadcasted_cov_factor.expand(
+        combined_batch_shape + (n, p.cov_factor.size(-1))
+    )
+    p_cov_diag = torch.diag_embed(p._unbroadcasted_cov_diag.sqrt()).expand(
+        combined_batch_shape + (n, n)
+    )
+    term21 = _batch_trace_XXT(
+        torch.linalg.solve_triangular(q_scale_tril, p_cov_factor, upper=False)
+    )
+    term22 = _batch_trace_XXT(
+        torch.linalg.solve_triangular(q_scale_tril, p_cov_diag, upper=False)
+    )
+    term2 = term21 + term22
+    return 0.5 * (term1 + term2 + term3 - p.event_shape[0])
+
+
+@register_kl(MultivariateNormal, MultivariateNormal)
+def _kl_multivariatenormal_multivariatenormal(p, q):
+    # From https://en.wikipedia.org/wiki/Multivariate_normal_distribution#Kullback%E2%80%93Leibler_divergence
+    if p.event_shape != q.event_shape:
+        raise ValueError(
+            "KL-divergence between two Multivariate Normals with\
+                          different event shapes cannot be computed"
+        )
+
+    half_term1 = q._unbroadcasted_scale_tril.diagonal(dim1=-2, dim2=-1).log().sum(
+        -1
+    ) - p._unbroadcasted_scale_tril.diagonal(dim1=-2, dim2=-1).log().sum(-1)
+    combined_batch_shape = torch._C._infer_size(
+        q._unbroadcasted_scale_tril.shape[:-2], p._unbroadcasted_scale_tril.shape[:-2]
+    )
+    n = p.event_shape[0]
+    q_scale_tril = q._unbroadcasted_scale_tril.expand(combined_batch_shape + (n, n))
+    p_scale_tril = p._unbroadcasted_scale_tril.expand(combined_batch_shape + (n, n))
+    term2 = _batch_trace_XXT(
+        torch.linalg.solve_triangular(q_scale_tril, p_scale_tril, upper=False)
+    )
+    term3 = _batch_mahalanobis(q._unbroadcasted_scale_tril, (q.loc - p.loc))
+    return half_term1 + 0.5 * (term2 + term3 - n)
+
+
+@register_kl(Normal, Normal)
+def _kl_normal_normal(p, q):
+    var_ratio = (p.scale / q.scale).pow(2)
+    t1 = ((p.loc - q.loc) / q.scale).pow(2)
+    return 0.5 * (var_ratio + t1 - 1 - var_ratio.log())
+
+
+@register_kl(OneHotCategorical, OneHotCategorical)
+def _kl_onehotcategorical_onehotcategorical(p, q):
+    return _kl_categorical_categorical(p._categorical, q._categorical)
+
+
+@register_kl(Pareto, Pareto)
+def _kl_pareto_pareto(p, q):
+    # From http://www.mast.queensu.ca/~communications/Papers/gil-msc11.pdf
+    scale_ratio = p.scale / q.scale
+    alpha_ratio = q.alpha / p.alpha
+    t1 = q.alpha * scale_ratio.log()
+    t2 = -alpha_ratio.log()
+    result = t1 + t2 + alpha_ratio - 1
+    result[p.support.lower_bound < q.support.lower_bound] = inf
+    return result
+
+
+@register_kl(Poisson, Poisson)
+def _kl_poisson_poisson(p, q):
+    return p.rate * (p.rate.log() - q.rate.log()) - (p.rate - q.rate)
+
+
+@register_kl(TransformedDistribution, TransformedDistribution)
+def _kl_transformed_transformed(p, q):
+    if p.transforms != q.transforms:
+        raise NotImplementedError
+    if p.event_shape != q.event_shape:
+        raise NotImplementedError
+    return kl_divergence(p.base_dist, q.base_dist)
+
+
+@register_kl(Uniform, Uniform)
+def _kl_uniform_uniform(p, q):
+    result = ((q.high - q.low) / (p.high - p.low)).log()
+    result[(q.low > p.low) | (q.high < p.high)] = inf
+    return result
+
+
+# Different distributions
+@register_kl(Bernoulli, Poisson)
+def _kl_bernoulli_poisson(p, q):
+    return -p.entropy() - (p.probs * q.rate.log() - q.rate)
+
+
+@register_kl(Beta, ContinuousBernoulli)
+def _kl_beta_continuous_bernoulli(p, q):
+    return (
+        -p.entropy()
+        - p.mean * q.logits
+        - torch.log1p(-q.probs)
+        - q._cont_bern_log_norm()
+    )
+
+
+@register_kl(Beta, Pareto)
+def _kl_beta_infinity(p, q):
+    return _infinite_like(p.concentration1)
+
+
+@register_kl(Beta, Exponential)
+def _kl_beta_exponential(p, q):
+    return (
+        -p.entropy()
+        - q.rate.log()
+        + q.rate * (p.concentration1 / (p.concentration1 + p.concentration0))
+    )
+
+
+@register_kl(Beta, Gamma)
+def _kl_beta_gamma(p, q):
+    t1 = -p.entropy()
+    t2 = q.concentration.lgamma() - q.concentration * q.rate.log()
+    t3 = (q.concentration - 1) * (
+        p.concentration1.digamma() - (p.concentration1 + p.concentration0).digamma()
+    )
+    t4 = q.rate * p.concentration1 / (p.concentration1 + p.concentration0)
+    return t1 + t2 - t3 + t4
+
+
+# TODO: Add Beta-Laplace KL Divergence
+
+
+@register_kl(Beta, Normal)
+def _kl_beta_normal(p, q):
+    E_beta = p.concentration1 / (p.concentration1 + p.concentration0)
+    var_normal = q.scale.pow(2)
+    t1 = -p.entropy()
+    t2 = 0.5 * (var_normal * 2 * math.pi).log()
+    t3 = (
+        E_beta * (1 - E_beta) / (p.concentration1 + p.concentration0 + 1)
+        + E_beta.pow(2)
+    ) * 0.5
+    t4 = q.loc * E_beta
+    t5 = q.loc.pow(2) * 0.5
+    return t1 + t2 + (t3 - t4 + t5) / var_normal
+
+
+@register_kl(Beta, Uniform)
+def _kl_beta_uniform(p, q):
+    result = -p.entropy() + (q.high - q.low).log()
+    result[(q.low > p.support.lower_bound) | (q.high < p.support.upper_bound)] = inf
+    return result
+
+
+# Note that the KL between a ContinuousBernoulli and Beta has no closed form
+
+
+@register_kl(ContinuousBernoulli, Pareto)
+def _kl_continuous_bernoulli_infinity(p, q):
+    return _infinite_like(p.probs)
+
+
+@register_kl(ContinuousBernoulli, Exponential)
+def _kl_continuous_bernoulli_exponential(p, q):
+    return -p.entropy() - torch.log(q.rate) + q.rate * p.mean
+
+
+# Note that the KL between a ContinuousBernoulli and Gamma has no closed form
+# TODO: Add ContinuousBernoulli-Laplace KL Divergence
+
+
+@register_kl(ContinuousBernoulli, Normal)
+def _kl_continuous_bernoulli_normal(p, q):
+    t1 = -p.entropy()
+    t2 = 0.5 * (math.log(2.0 * math.pi) + torch.square(q.loc / q.scale)) + torch.log(
+        q.scale
+    )
+    t3 = (p.variance + torch.square(p.mean) - 2.0 * q.loc * p.mean) / (
+        2.0 * torch.square(q.scale)
+    )
+    return t1 + t2 + t3
+
+
+@register_kl(ContinuousBernoulli, Uniform)
+def _kl_continuous_bernoulli_uniform(p, q):
+    result = -p.entropy() + (q.high - q.low).log()
+    return torch.where(
+        torch.max(
+            torch.ge(q.low, p.support.lower_bound),
+            torch.le(q.high, p.support.upper_bound),
+        ),
+        torch.ones_like(result) * inf,
+        result,
+    )
+
+
+@register_kl(Exponential, Beta)
+@register_kl(Exponential, ContinuousBernoulli)
+@register_kl(Exponential, Pareto)
+@register_kl(Exponential, Uniform)
+def _kl_exponential_infinity(p, q):
+    return _infinite_like(p.rate)
+
+
+@register_kl(Exponential, Gamma)
+def _kl_exponential_gamma(p, q):
+    ratio = q.rate / p.rate
+    t1 = -q.concentration * torch.log(ratio)
+    return (
+        t1
+        + ratio
+        + q.concentration.lgamma()
+        + q.concentration * _euler_gamma
+        - (1 + _euler_gamma)
+    )
+
+
+@register_kl(Exponential, Gumbel)
+def _kl_exponential_gumbel(p, q):
+    scale_rate_prod = p.rate * q.scale
+    loc_scale_ratio = q.loc / q.scale
+    t1 = scale_rate_prod.log() - 1
+    t2 = torch.exp(loc_scale_ratio) * scale_rate_prod / (scale_rate_prod + 1)
+    t3 = scale_rate_prod.reciprocal()
+    return t1 - loc_scale_ratio + t2 + t3
+
+
+# TODO: Add Exponential-Laplace KL Divergence
+
+
+@register_kl(Exponential, Normal)
+def _kl_exponential_normal(p, q):
+    var_normal = q.scale.pow(2)
+    rate_sqr = p.rate.pow(2)
+    t1 = 0.5 * torch.log(rate_sqr * var_normal * 2 * math.pi)
+    t2 = rate_sqr.reciprocal()
+    t3 = q.loc / p.rate
+    t4 = q.loc.pow(2) * 0.5
+    return t1 - 1 + (t2 - t3 + t4) / var_normal
+
+
+@register_kl(Gamma, Beta)
+@register_kl(Gamma, ContinuousBernoulli)
+@register_kl(Gamma, Pareto)
+@register_kl(Gamma, Uniform)
+def _kl_gamma_infinity(p, q):
+    return _infinite_like(p.concentration)
+
+
+@register_kl(Gamma, Exponential)
+def _kl_gamma_exponential(p, q):
+    return -p.entropy() - q.rate.log() + q.rate * p.concentration / p.rate
+
+
+@register_kl(Gamma, Gumbel)
+def _kl_gamma_gumbel(p, q):
+    beta_scale_prod = p.rate * q.scale
+    loc_scale_ratio = q.loc / q.scale
+    t1 = (
+        (p.concentration - 1) * p.concentration.digamma()
+        - p.concentration.lgamma()
+        - p.concentration
+    )
+    t2 = beta_scale_prod.log() + p.concentration / beta_scale_prod
+    t3 = (
+        torch.exp(loc_scale_ratio)
+        * (1 + beta_scale_prod.reciprocal()).pow(-p.concentration)
+        - loc_scale_ratio
+    )
+    return t1 + t2 + t3
+
+
+# TODO: Add Gamma-Laplace KL Divergence
+
+
+@register_kl(Gamma, Normal)
+def _kl_gamma_normal(p, q):
+    var_normal = q.scale.pow(2)
+    beta_sqr = p.rate.pow(2)
+    t1 = (
+        0.5 * torch.log(beta_sqr * var_normal * 2 * math.pi)
+        - p.concentration
+        - p.concentration.lgamma()
+    )
+    t2 = 0.5 * (p.concentration.pow(2) + p.concentration) / beta_sqr
+    t3 = q.loc * p.concentration / p.rate
+    t4 = 0.5 * q.loc.pow(2)
+    return (
+        t1
+        + (p.concentration - 1) * p.concentration.digamma()
+        + (t2 - t3 + t4) / var_normal
+    )
+
+
+@register_kl(Gumbel, Beta)
+@register_kl(Gumbel, ContinuousBernoulli)
+@register_kl(Gumbel, Exponential)
+@register_kl(Gumbel, Gamma)
+@register_kl(Gumbel, Pareto)
+@register_kl(Gumbel, Uniform)
+def _kl_gumbel_infinity(p, q):
+    return _infinite_like(p.loc)
+
+
+# TODO: Add Gumbel-Laplace KL Divergence
+
+
+@register_kl(Gumbel, Normal)
+def _kl_gumbel_normal(p, q):
+    param_ratio = p.scale / q.scale
+    t1 = (param_ratio / math.sqrt(2 * math.pi)).log()
+    t2 = (math.pi * param_ratio * 0.5).pow(2) / 3
+    t3 = ((p.loc + p.scale * _euler_gamma - q.loc) / q.scale).pow(2) * 0.5
+    return -t1 + t2 + t3 - (_euler_gamma + 1)
+
+
+@register_kl(Laplace, Beta)
+@register_kl(Laplace, ContinuousBernoulli)
+@register_kl(Laplace, Exponential)
+@register_kl(Laplace, Gamma)
+@register_kl(Laplace, Pareto)
+@register_kl(Laplace, Uniform)
+def _kl_laplace_infinity(p, q):
+    return _infinite_like(p.loc)
+
+
+@register_kl(Laplace, Normal)
+def _kl_laplace_normal(p, q):
+    var_normal = q.scale.pow(2)
+    scale_sqr_var_ratio = p.scale.pow(2) / var_normal
+    t1 = 0.5 * torch.log(2 * scale_sqr_var_ratio / math.pi)
+    t2 = 0.5 * p.loc.pow(2)
+    t3 = p.loc * q.loc
+    t4 = 0.5 * q.loc.pow(2)
+    return -t1 + scale_sqr_var_ratio + (t2 - t3 + t4) / var_normal - 1
+
+
+@register_kl(Normal, Beta)
+@register_kl(Normal, ContinuousBernoulli)
+@register_kl(Normal, Exponential)
+@register_kl(Normal, Gamma)
+@register_kl(Normal, Pareto)
+@register_kl(Normal, Uniform)
+def _kl_normal_infinity(p, q):
+    return _infinite_like(p.loc)
+
+
+@register_kl(Normal, Gumbel)
+def _kl_normal_gumbel(p, q):
+    mean_scale_ratio = p.loc / q.scale
+    var_scale_sqr_ratio = (p.scale / q.scale).pow(2)
+    loc_scale_ratio = q.loc / q.scale
+    t1 = var_scale_sqr_ratio.log() * 0.5
+    t2 = mean_scale_ratio - loc_scale_ratio
+    t3 = torch.exp(-mean_scale_ratio + 0.5 * var_scale_sqr_ratio + loc_scale_ratio)
+    return -t1 + t2 + t3 - (0.5 * (1 + math.log(2 * math.pi)))
+
+
+@register_kl(Normal, Laplace)
+def _kl_normal_laplace(p, q):
+    loc_diff = p.loc - q.loc
+    scale_ratio = p.scale / q.scale
+    loc_diff_scale_ratio = loc_diff / p.scale
+    t1 = torch.log(scale_ratio)
+    t2 = (
+        math.sqrt(2 / math.pi) * p.scale * torch.exp(-0.5 * loc_diff_scale_ratio.pow(2))
+    )
+    t3 = loc_diff * torch.erf(math.sqrt(0.5) * loc_diff_scale_ratio)
+    return -t1 + (t2 + t3) / q.scale - (0.5 * (1 + math.log(0.5 * math.pi)))
+
+
+@register_kl(Pareto, Beta)
+@register_kl(Pareto, ContinuousBernoulli)
+@register_kl(Pareto, Uniform)
+def _kl_pareto_infinity(p, q):
+    return _infinite_like(p.scale)
+
+
+@register_kl(Pareto, Exponential)
+def _kl_pareto_exponential(p, q):
+    scale_rate_prod = p.scale * q.rate
+    t1 = (p.alpha / scale_rate_prod).log()
+    t2 = p.alpha.reciprocal()
+    t3 = p.alpha * scale_rate_prod / (p.alpha - 1)
+    result = t1 - t2 + t3 - 1
+    result[p.alpha <= 1] = inf
+    return result
+
+
+@register_kl(Pareto, Gamma)
+def _kl_pareto_gamma(p, q):
+    common_term = p.scale.log() + p.alpha.reciprocal()
+    t1 = p.alpha.log() - common_term
+    t2 = q.concentration.lgamma() - q.concentration * q.rate.log()
+    t3 = (1 - q.concentration) * common_term
+    t4 = q.rate * p.alpha * p.scale / (p.alpha - 1)
+    result = t1 + t2 + t3 + t4 - 1
+    result[p.alpha <= 1] = inf
+    return result
+
+
+# TODO: Add Pareto-Laplace KL Divergence
+
+
+@register_kl(Pareto, Normal)
+def _kl_pareto_normal(p, q):
+    var_normal = 2 * q.scale.pow(2)
+    common_term = p.scale / (p.alpha - 1)
+    t1 = (math.sqrt(2 * math.pi) * q.scale * p.alpha / p.scale).log()
+    t2 = p.alpha.reciprocal()
+    t3 = p.alpha * common_term.pow(2) / (p.alpha - 2)
+    t4 = (p.alpha * common_term - q.loc).pow(2)
+    result = t1 - t2 + (t3 + t4) / var_normal - 1
+    result[p.alpha <= 2] = inf
+    return result
+
+
+@register_kl(Poisson, Bernoulli)
+@register_kl(Poisson, Binomial)
+def _kl_poisson_infinity(p, q):
+    return _infinite_like(p.rate)
+
+
+@register_kl(Uniform, Beta)
+def _kl_uniform_beta(p, q):
+    common_term = p.high - p.low
+    t1 = torch.log(common_term)
+    t2 = (
+        (q.concentration1 - 1)
+        * (_x_log_x(p.high) - _x_log_x(p.low) - common_term)
+        / common_term
+    )
+    t3 = (
+        (q.concentration0 - 1)
+        * (_x_log_x(1 - p.high) - _x_log_x(1 - p.low) + common_term)
+        / common_term
+    )
+    t4 = (
+        q.concentration1.lgamma()
+        + q.concentration0.lgamma()
+        - (q.concentration1 + q.concentration0).lgamma()
+    )
+    result = t3 + t4 - t1 - t2
+    result[(p.high > q.support.upper_bound) | (p.low < q.support.lower_bound)] = inf
+    return result
+
+
+@register_kl(Uniform, ContinuousBernoulli)
+def _kl_uniform_continuous_bernoulli(p, q):
+    result = (
+        -p.entropy()
+        - p.mean * q.logits
+        - torch.log1p(-q.probs)
+        - q._cont_bern_log_norm()
+    )
+    return torch.where(
+        torch.max(
+            torch.ge(p.high, q.support.upper_bound),
+            torch.le(p.low, q.support.lower_bound),
+        ),
+        torch.ones_like(result) * inf,
+        result,
+    )
+
+
+@register_kl(Uniform, Exponential)
+def _kl_uniform_exponetial(p, q):
+    result = q.rate * (p.high + p.low) / 2 - ((p.high - p.low) * q.rate).log()
+    result[p.low < q.support.lower_bound] = inf
+    return result
+
+
+@register_kl(Uniform, Gamma)
+def _kl_uniform_gamma(p, q):
+    common_term = p.high - p.low
+    t1 = common_term.log()
+    t2 = q.concentration.lgamma() - q.concentration * q.rate.log()
+    t3 = (
+        (1 - q.concentration)
+        * (_x_log_x(p.high) - _x_log_x(p.low) - common_term)
+        / common_term
+    )
+    t4 = q.rate * (p.high + p.low) / 2
+    result = -t1 + t2 + t3 + t4
+    result[p.low < q.support.lower_bound] = inf
+    return result
+
+
+@register_kl(Uniform, Gumbel)
+def _kl_uniform_gumbel(p, q):
+    common_term = q.scale / (p.high - p.low)
+    high_loc_diff = (p.high - q.loc) / q.scale
+    low_loc_diff = (p.low - q.loc) / q.scale
+    t1 = common_term.log() + 0.5 * (high_loc_diff + low_loc_diff)
+    t2 = common_term * (torch.exp(-high_loc_diff) - torch.exp(-low_loc_diff))
+    return t1 - t2
+
+
+# TODO: Uniform-Laplace KL Divergence
+
+
+@register_kl(Uniform, Normal)
+def _kl_uniform_normal(p, q):
+    common_term = p.high - p.low
+    t1 = (math.sqrt(math.pi * 2) * q.scale / common_term).log()
+    t2 = (common_term).pow(2) / 12
+    t3 = ((p.high + p.low - 2 * q.loc) / 2).pow(2)
+    return t1 + 0.5 * (t2 + t3) / q.scale.pow(2)
+
+
+@register_kl(Uniform, Pareto)
+def _kl_uniform_pareto(p, q):
+    support_uniform = p.high - p.low
+    t1 = (q.alpha * q.scale.pow(q.alpha) * (support_uniform)).log()
+    t2 = (_x_log_x(p.high) - _x_log_x(p.low) - support_uniform) / support_uniform
+    result = t2 * (q.alpha + 1) - t1
+    result[p.low < q.support.lower_bound] = inf
+    return result
+
+
+@register_kl(Independent, Independent)
+def _kl_independent_independent(p, q):
+    if p.reinterpreted_batch_ndims != q.reinterpreted_batch_ndims:
+        raise NotImplementedError
+    result = kl_divergence(p.base_dist, q.base_dist)
+    return _sum_rightmost(result, p.reinterpreted_batch_ndims)
+
+
+@register_kl(Cauchy, Cauchy)
+def _kl_cauchy_cauchy(p, q):
+    # From https://arxiv.org/abs/1905.10965
+    t1 = ((p.scale + q.scale).pow(2) + (p.loc - q.loc).pow(2)).log()
+    t2 = (4 * p.scale * q.scale).log()
+    return t1 - t2
+
+
+def _add_kl_info():
+    """Appends a list of implemented KL functions to the doc for kl_divergence."""
+    rows = [
+        "KL divergence is currently implemented for the following distribution pairs:"
+    ]
+    for p, q in sorted(
+        _KL_REGISTRY, key=lambda p_q: (p_q[0].__name__, p_q[1].__name__)
+    ):
+        rows.append(
+            f"* :class:`~torch.distributions.{p.__name__}` and :class:`~torch.distributions.{q.__name__}`"
+        )
+    kl_info = "\n\t".join(rows)
+    if kl_divergence.__doc__:
+        kl_divergence.__doc__ += kl_info  # type: ignore[operator]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/kumaraswamy.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/kumaraswamy.py
new file mode 100644
index 0000000000000000000000000000000000000000..367e5d52e44a29cf00d1edb4317ac8248d1e9e7a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/kumaraswamy.py
@@ -0,0 +1,99 @@
+# mypy: allow-untyped-defs
+import torch
+from torch import nan
+from torch.distributions import constraints
+from torch.distributions.transformed_distribution import TransformedDistribution
+from torch.distributions.transforms import AffineTransform, PowerTransform
+from torch.distributions.uniform import Uniform
+from torch.distributions.utils import broadcast_all, euler_constant
+
+
+__all__ = ["Kumaraswamy"]
+
+
+def _moments(a, b, n):
+    """
+    Computes nth moment of Kumaraswamy using using torch.lgamma
+    """
+    arg1 = 1 + n / a
+    log_value = torch.lgamma(arg1) + torch.lgamma(b) - torch.lgamma(arg1 + b)
+    return b * torch.exp(log_value)
+
+
+class Kumaraswamy(TransformedDistribution):
+    r"""
+    Samples from a Kumaraswamy distribution.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = Kumaraswamy(torch.tensor([1.0]), torch.tensor([1.0]))
+        >>> m.sample()  # sample from a Kumaraswamy distribution with concentration alpha=1 and beta=1
+        tensor([ 0.1729])
+
+    Args:
+        concentration1 (float or Tensor): 1st concentration parameter of the distribution
+            (often referred to as alpha)
+        concentration0 (float or Tensor): 2nd concentration parameter of the distribution
+            (often referred to as beta)
+    """
+    arg_constraints = {
+        "concentration1": constraints.positive,
+        "concentration0": constraints.positive,
+    }
+    support = constraints.unit_interval
+    has_rsample = True
+
+    def __init__(self, concentration1, concentration0, validate_args=None):
+        self.concentration1, self.concentration0 = broadcast_all(
+            concentration1, concentration0
+        )
+        finfo = torch.finfo(self.concentration0.dtype)
+        base_dist = Uniform(
+            torch.full_like(self.concentration0, 0),
+            torch.full_like(self.concentration0, 1),
+            validate_args=validate_args,
+        )
+        transforms = [
+            PowerTransform(exponent=self.concentration0.reciprocal()),
+            AffineTransform(loc=1.0, scale=-1.0),
+            PowerTransform(exponent=self.concentration1.reciprocal()),
+        ]
+        super().__init__(base_dist, transforms, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Kumaraswamy, _instance)
+        new.concentration1 = self.concentration1.expand(batch_shape)
+        new.concentration0 = self.concentration0.expand(batch_shape)
+        return super().expand(batch_shape, _instance=new)
+
+    @property
+    def mean(self):
+        return _moments(self.concentration1, self.concentration0, 1)
+
+    @property
+    def mode(self):
+        # Evaluate in log-space for numerical stability.
+        log_mode = (
+            self.concentration0.reciprocal() * (-self.concentration0).log1p()
+            - (-self.concentration0 * self.concentration1).log1p()
+        )
+        log_mode[(self.concentration0 < 1) | (self.concentration1 < 1)] = nan
+        return log_mode.exp()
+
+    @property
+    def variance(self):
+        return _moments(self.concentration1, self.concentration0, 2) - torch.pow(
+            self.mean, 2
+        )
+
+    def entropy(self):
+        t1 = 1 - self.concentration1.reciprocal()
+        t0 = 1 - self.concentration0.reciprocal()
+        H0 = torch.digamma(self.concentration0 + 1) + euler_constant
+        return (
+            t0
+            + t1 * H0
+            - torch.log(self.concentration1)
+            - torch.log(self.concentration0)
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/laplace.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/laplace.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4d33f2638289201a4dd2c580503790ac90256d4
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/laplace.py
@@ -0,0 +1,97 @@
+# mypy: allow-untyped-defs
+from numbers import Number
+
+import torch
+from torch.distributions import constraints
+from torch.distributions.distribution import Distribution
+from torch.distributions.utils import broadcast_all
+from torch.types import _size
+
+
+__all__ = ["Laplace"]
+
+
+class Laplace(Distribution):
+    r"""
+    Creates a Laplace distribution parameterized by :attr:`loc` and :attr:`scale`.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = Laplace(torch.tensor([0.0]), torch.tensor([1.0]))
+        >>> m.sample()  # Laplace distributed with loc=0, scale=1
+        tensor([ 0.1046])
+
+    Args:
+        loc (float or Tensor): mean of the distribution
+        scale (float or Tensor): scale of the distribution
+    """
+    arg_constraints = {"loc": constraints.real, "scale": constraints.positive}
+    support = constraints.real
+    has_rsample = True
+
+    @property
+    def mean(self):
+        return self.loc
+
+    @property
+    def mode(self):
+        return self.loc
+
+    @property
+    def variance(self):
+        return 2 * self.scale.pow(2)
+
+    @property
+    def stddev(self):
+        return (2**0.5) * self.scale
+
+    def __init__(self, loc, scale, validate_args=None):
+        self.loc, self.scale = broadcast_all(loc, scale)
+        if isinstance(loc, Number) and isinstance(scale, Number):
+            batch_shape = torch.Size()
+        else:
+            batch_shape = self.loc.size()
+        super().__init__(batch_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Laplace, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new.loc = self.loc.expand(batch_shape)
+        new.scale = self.scale.expand(batch_shape)
+        super(Laplace, new).__init__(batch_shape, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    def rsample(self, sample_shape: _size = torch.Size()) -> torch.Tensor:
+        shape = self._extended_shape(sample_shape)
+        finfo = torch.finfo(self.loc.dtype)
+        if torch._C._get_tracing_state():
+            # [JIT WORKAROUND] lack of support for .uniform_()
+            u = torch.rand(shape, dtype=self.loc.dtype, device=self.loc.device) * 2 - 1
+            return self.loc - self.scale * u.sign() * torch.log1p(
+                -u.abs().clamp(min=finfo.tiny)
+            )
+        u = self.loc.new(shape).uniform_(finfo.eps - 1, 1)
+        # TODO: If we ever implement tensor.nextafter, below is what we want ideally.
+        # u = self.loc.new(shape).uniform_(self.loc.nextafter(-.5, 0), .5)
+        return self.loc - self.scale * u.sign() * torch.log1p(-u.abs())
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        return -torch.log(2 * self.scale) - torch.abs(value - self.loc) / self.scale
+
+    def cdf(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        return 0.5 - 0.5 * (value - self.loc).sign() * torch.expm1(
+            -(value - self.loc).abs() / self.scale
+        )
+
+    def icdf(self, value):
+        term = value - 0.5
+        return self.loc - self.scale * (term).sign() * torch.log1p(-2 * term.abs())
+
+    def entropy(self):
+        return 1 + torch.log(2 * self.scale)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/lkj_cholesky.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/lkj_cholesky.py
new file mode 100644
index 0000000000000000000000000000000000000000..479568bdd428d6e36a78ee8a532f9cd6c35c7ef8
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/lkj_cholesky.py
@@ -0,0 +1,144 @@
+# mypy: allow-untyped-defs
+"""
+This closely follows the implementation in NumPyro (https://github.com/pyro-ppl/numpyro).
+
+Original copyright notice:
+
+# Copyright: Contributors to the Pyro project.
+# SPDX-License-Identifier: Apache-2.0
+"""
+
+import math
+
+import torch
+from torch.distributions import Beta, constraints
+from torch.distributions.distribution import Distribution
+from torch.distributions.utils import broadcast_all
+
+
+__all__ = ["LKJCholesky"]
+
+
+class LKJCholesky(Distribution):
+    r"""
+    LKJ distribution for lower Cholesky factor of correlation matrices.
+    The distribution is controlled by ``concentration`` parameter :math:`\eta`
+    to make the probability of the correlation matrix :math:`M` generated from
+    a Cholesky factor proportional to :math:`\det(M)^{\eta - 1}`. Because of that,
+    when ``concentration == 1``, we have a uniform distribution over Cholesky
+    factors of correlation matrices::
+
+        L ~ LKJCholesky(dim, concentration)
+        X = L @ L' ~ LKJCorr(dim, concentration)
+
+    Note that this distribution samples the
+    Cholesky factor of correlation matrices and not the correlation matrices
+    themselves and thereby differs slightly from the derivations in [1] for
+    the `LKJCorr` distribution. For sampling, this uses the Onion method from
+    [1] Section 3.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> l = LKJCholesky(3, 0.5)
+        >>> l.sample()  # l @ l.T is a sample of a correlation 3x3 matrix
+        tensor([[ 1.0000,  0.0000,  0.0000],
+                [ 0.3516,  0.9361,  0.0000],
+                [-0.1899,  0.4748,  0.8593]])
+
+    Args:
+        dimension (dim): dimension of the matrices
+        concentration (float or Tensor): concentration/shape parameter of the
+            distribution (often referred to as eta)
+
+    **References**
+
+    [1] `Generating random correlation matrices based on vines and extended onion method` (2009),
+    Daniel Lewandowski, Dorota Kurowicka, Harry Joe.
+    Journal of Multivariate Analysis. 100. 10.1016/j.jmva.2009.04.008
+    """
+    arg_constraints = {"concentration": constraints.positive}
+    support = constraints.corr_cholesky
+
+    def __init__(self, dim, concentration=1.0, validate_args=None):
+        if dim < 2:
+            raise ValueError(
+                f"Expected dim to be an integer greater than or equal to 2. Found dim={dim}."
+            )
+        self.dim = dim
+        (self.concentration,) = broadcast_all(concentration)
+        batch_shape = self.concentration.size()
+        event_shape = torch.Size((dim, dim))
+        # This is used to draw vectorized samples from the beta distribution in Sec. 3.2 of [1].
+        marginal_conc = self.concentration + 0.5 * (self.dim - 2)
+        offset = torch.arange(
+            self.dim - 1,
+            dtype=self.concentration.dtype,
+            device=self.concentration.device,
+        )
+        offset = torch.cat([offset.new_zeros((1,)), offset])
+        beta_conc1 = offset + 0.5
+        beta_conc0 = marginal_conc.unsqueeze(-1) - 0.5 * offset
+        self._beta = Beta(beta_conc1, beta_conc0)
+        super().__init__(batch_shape, event_shape, validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(LKJCholesky, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new.dim = self.dim
+        new.concentration = self.concentration.expand(batch_shape)
+        new._beta = self._beta.expand(batch_shape + (self.dim,))
+        super(LKJCholesky, new).__init__(
+            batch_shape, self.event_shape, validate_args=False
+        )
+        new._validate_args = self._validate_args
+        return new
+
+    def sample(self, sample_shape=torch.Size()):
+        # This uses the Onion method, but there are a few differences from [1] Sec. 3.2:
+        # - This vectorizes the for loop and also works for heterogeneous eta.
+        # - Same algorithm generalizes to n=1.
+        # - The procedure is simplified since we are sampling the cholesky factor of
+        #   the correlation matrix instead of the correlation matrix itself. As such,
+        #   we only need to generate `w`.
+        y = self._beta.sample(sample_shape).unsqueeze(-1)
+        u_normal = torch.randn(
+            self._extended_shape(sample_shape), dtype=y.dtype, device=y.device
+        ).tril(-1)
+        u_hypersphere = u_normal / u_normal.norm(dim=-1, keepdim=True)
+        # Replace NaNs in first row
+        u_hypersphere[..., 0, :].fill_(0.0)
+        w = torch.sqrt(y) * u_hypersphere
+        # Fill diagonal elements; clamp for numerical stability
+        eps = torch.finfo(w.dtype).tiny
+        diag_elems = torch.clamp(1 - torch.sum(w**2, dim=-1), min=eps).sqrt()
+        w += torch.diag_embed(diag_elems)
+        return w
+
+    def log_prob(self, value):
+        # See: https://mc-stan.org/docs/2_25/functions-reference/cholesky-lkj-correlation-distribution.html
+        # The probability of a correlation matrix is proportional to
+        #   determinant ** (concentration - 1) = prod(L_ii ^ 2(concentration - 1))
+        # Additionally, the Jacobian of the transformation from Cholesky factor to
+        # correlation matrix is:
+        #   prod(L_ii ^ (D - i))
+        # So the probability of a Cholesky factor is propotional to
+        #   prod(L_ii ^ (2 * concentration - 2 + D - i)) = prod(L_ii ^ order_i)
+        # with order_i = 2 * concentration - 2 + D - i
+        if self._validate_args:
+            self._validate_sample(value)
+        diag_elems = value.diagonal(dim1=-1, dim2=-2)[..., 1:]
+        order = torch.arange(2, self.dim + 1, device=self.concentration.device)
+        order = 2 * (self.concentration - 1).unsqueeze(-1) + self.dim - order
+        unnormalized_log_pdf = torch.sum(order * diag_elems.log(), dim=-1)
+        # Compute normalization constant (page 1999 of [1])
+        dm1 = self.dim - 1
+        alpha = self.concentration + 0.5 * dm1
+        denominator = torch.lgamma(alpha) * dm1
+        numerator = torch.mvlgamma(alpha - 0.5, dm1)
+        # pi_constant in [1] is D * (D - 1) / 4 * log(pi)
+        # pi_constant in multigammaln is (D - 1) * (D - 2) / 4 * log(pi)
+        # hence, we need to add a pi_constant = (D - 1) * log(pi) / 2
+        pi_constant = 0.5 * dm1 * math.log(math.pi)
+        normalize_term = pi_constant + numerator - denominator
+        return unnormalized_log_pdf - normalize_term
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/log_normal.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/log_normal.py
new file mode 100644
index 0000000000000000000000000000000000000000..d40d21b9ef4a6a28fd89656c9d3cc66d370492fa
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/log_normal.py
@@ -0,0 +1,64 @@
+# mypy: allow-untyped-defs
+from torch.distributions import constraints
+from torch.distributions.normal import Normal
+from torch.distributions.transformed_distribution import TransformedDistribution
+from torch.distributions.transforms import ExpTransform
+
+
+__all__ = ["LogNormal"]
+
+
+class LogNormal(TransformedDistribution):
+    r"""
+    Creates a log-normal distribution parameterized by
+    :attr:`loc` and :attr:`scale` where::
+
+        X ~ Normal(loc, scale)
+        Y = exp(X) ~ LogNormal(loc, scale)
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = LogNormal(torch.tensor([0.0]), torch.tensor([1.0]))
+        >>> m.sample()  # log-normal distributed with mean=0 and stddev=1
+        tensor([ 0.1046])
+
+    Args:
+        loc (float or Tensor): mean of log of distribution
+        scale (float or Tensor): standard deviation of log of the distribution
+    """
+    arg_constraints = {"loc": constraints.real, "scale": constraints.positive}
+    support = constraints.positive
+    has_rsample = True
+
+    def __init__(self, loc, scale, validate_args=None):
+        base_dist = Normal(loc, scale, validate_args=validate_args)
+        super().__init__(base_dist, ExpTransform(), validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(LogNormal, _instance)
+        return super().expand(batch_shape, _instance=new)
+
+    @property
+    def loc(self):
+        return self.base_dist.loc
+
+    @property
+    def scale(self):
+        return self.base_dist.scale
+
+    @property
+    def mean(self):
+        return (self.loc + self.scale.pow(2) / 2).exp()
+
+    @property
+    def mode(self):
+        return (self.loc - self.scale.square()).exp()
+
+    @property
+    def variance(self):
+        scale_sq = self.scale.pow(2)
+        return scale_sq.expm1() * (2 * self.loc + scale_sq).exp()
+
+    def entropy(self):
+        return self.base_dist.entropy() + self.loc
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/logistic_normal.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/logistic_normal.py
new file mode 100644
index 0000000000000000000000000000000000000000..466afe50f48e0fafe4dda73550748c331e0d0adc
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/logistic_normal.py
@@ -0,0 +1,56 @@
+# mypy: allow-untyped-defs
+from torch.distributions import constraints
+from torch.distributions.normal import Normal
+from torch.distributions.transformed_distribution import TransformedDistribution
+from torch.distributions.transforms import StickBreakingTransform
+
+
+__all__ = ["LogisticNormal"]
+
+
+class LogisticNormal(TransformedDistribution):
+    r"""
+    Creates a logistic-normal distribution parameterized by :attr:`loc` and :attr:`scale`
+    that define the base `Normal` distribution transformed with the
+    `StickBreakingTransform` such that::
+
+        X ~ LogisticNormal(loc, scale)
+        Y = log(X / (1 - X.cumsum(-1)))[..., :-1] ~ Normal(loc, scale)
+
+    Args:
+        loc (float or Tensor): mean of the base distribution
+        scale (float or Tensor): standard deviation of the base distribution
+
+    Example::
+
+        >>> # logistic-normal distributed with mean=(0, 0, 0) and stddev=(1, 1, 1)
+        >>> # of the base Normal distribution
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = LogisticNormal(torch.tensor([0.0] * 3), torch.tensor([1.0] * 3))
+        >>> m.sample()
+        tensor([ 0.7653,  0.0341,  0.0579,  0.1427])
+
+    """
+    arg_constraints = {"loc": constraints.real, "scale": constraints.positive}
+    support = constraints.simplex
+    has_rsample = True
+
+    def __init__(self, loc, scale, validate_args=None):
+        base_dist = Normal(loc, scale, validate_args=validate_args)
+        if not base_dist.batch_shape:
+            base_dist = base_dist.expand([1])
+        super().__init__(
+            base_dist, StickBreakingTransform(), validate_args=validate_args
+        )
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(LogisticNormal, _instance)
+        return super().expand(batch_shape, _instance=new)
+
+    @property
+    def loc(self):
+        return self.base_dist.base_dist.loc
+
+    @property
+    def scale(self):
+        return self.base_dist.base_dist.scale
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/lowrank_multivariate_normal.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/lowrank_multivariate_normal.py
new file mode 100644
index 0000000000000000000000000000000000000000..22dea3ca6bda217f8ad3de15ed771bd8cb1696a3
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/lowrank_multivariate_normal.py
@@ -0,0 +1,240 @@
+# mypy: allow-untyped-defs
+import math
+
+import torch
+from torch.distributions import constraints
+from torch.distributions.distribution import Distribution
+from torch.distributions.multivariate_normal import _batch_mahalanobis, _batch_mv
+from torch.distributions.utils import _standard_normal, lazy_property
+from torch.types import _size
+
+
+__all__ = ["LowRankMultivariateNormal"]
+
+
+def _batch_capacitance_tril(W, D):
+    r"""
+    Computes Cholesky of :math:`I + W.T @ inv(D) @ W` for a batch of matrices :math:`W`
+    and a batch of vectors :math:`D`.
+    """
+    m = W.size(-1)
+    Wt_Dinv = W.mT / D.unsqueeze(-2)
+    K = torch.matmul(Wt_Dinv, W).contiguous()
+    K.view(-1, m * m)[:, :: m + 1] += 1  # add identity matrix to K
+    return torch.linalg.cholesky(K)
+
+
+def _batch_lowrank_logdet(W, D, capacitance_tril):
+    r"""
+    Uses "matrix determinant lemma"::
+        log|W @ W.T + D| = log|C| + log|D|,
+    where :math:`C` is the capacitance matrix :math:`I + W.T @ inv(D) @ W`, to compute
+    the log determinant.
+    """
+    return 2 * capacitance_tril.diagonal(dim1=-2, dim2=-1).log().sum(-1) + D.log().sum(
+        -1
+    )
+
+
+def _batch_lowrank_mahalanobis(W, D, x, capacitance_tril):
+    r"""
+    Uses "Woodbury matrix identity"::
+        inv(W @ W.T + D) = inv(D) - inv(D) @ W @ inv(C) @ W.T @ inv(D),
+    where :math:`C` is the capacitance matrix :math:`I + W.T @ inv(D) @ W`, to compute the squared
+    Mahalanobis distance :math:`x.T @ inv(W @ W.T + D) @ x`.
+    """
+    Wt_Dinv = W.mT / D.unsqueeze(-2)
+    Wt_Dinv_x = _batch_mv(Wt_Dinv, x)
+    mahalanobis_term1 = (x.pow(2) / D).sum(-1)
+    mahalanobis_term2 = _batch_mahalanobis(capacitance_tril, Wt_Dinv_x)
+    return mahalanobis_term1 - mahalanobis_term2
+
+
+class LowRankMultivariateNormal(Distribution):
+    r"""
+    Creates a multivariate normal distribution with covariance matrix having a low-rank form
+    parameterized by :attr:`cov_factor` and :attr:`cov_diag`::
+
+        covariance_matrix = cov_factor @ cov_factor.T + cov_diag
+
+    Example:
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = LowRankMultivariateNormal(torch.zeros(2), torch.tensor([[1.], [0.]]), torch.ones(2))
+        >>> m.sample()  # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]`
+        tensor([-0.2102, -0.5429])
+
+    Args:
+        loc (Tensor): mean of the distribution with shape `batch_shape + event_shape`
+        cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape
+            `batch_shape + event_shape + (rank,)`
+        cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape
+            `batch_shape + event_shape`
+
+    Note:
+        The computation for determinant and inverse of covariance matrix is avoided when
+        `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity
+        `_ and
+        `matrix determinant lemma `_.
+        Thanks to these formulas, we just need to compute the determinant and inverse of
+        the small size "capacitance" matrix::
+
+            capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor
+    """
+    arg_constraints = {
+        "loc": constraints.real_vector,
+        "cov_factor": constraints.independent(constraints.real, 2),
+        "cov_diag": constraints.independent(constraints.positive, 1),
+    }
+    support = constraints.real_vector
+    has_rsample = True
+
+    def __init__(self, loc, cov_factor, cov_diag, validate_args=None):
+        if loc.dim() < 1:
+            raise ValueError("loc must be at least one-dimensional.")
+        event_shape = loc.shape[-1:]
+        if cov_factor.dim() < 2:
+            raise ValueError(
+                "cov_factor must be at least two-dimensional, "
+                "with optional leading batch dimensions"
+            )
+        if cov_factor.shape[-2:-1] != event_shape:
+            raise ValueError(
+                f"cov_factor must be a batch of matrices with shape {event_shape[0]} x m"
+            )
+        if cov_diag.shape[-1:] != event_shape:
+            raise ValueError(
+                f"cov_diag must be a batch of vectors with shape {event_shape}"
+            )
+
+        loc_ = loc.unsqueeze(-1)
+        cov_diag_ = cov_diag.unsqueeze(-1)
+        try:
+            loc_, self.cov_factor, cov_diag_ = torch.broadcast_tensors(
+                loc_, cov_factor, cov_diag_
+            )
+        except RuntimeError as e:
+            raise ValueError(
+                f"Incompatible batch shapes: loc {loc.shape}, cov_factor {cov_factor.shape}, cov_diag {cov_diag.shape}"
+            ) from e
+        self.loc = loc_[..., 0]
+        self.cov_diag = cov_diag_[..., 0]
+        batch_shape = self.loc.shape[:-1]
+
+        self._unbroadcasted_cov_factor = cov_factor
+        self._unbroadcasted_cov_diag = cov_diag
+        self._capacitance_tril = _batch_capacitance_tril(cov_factor, cov_diag)
+        super().__init__(batch_shape, event_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(LowRankMultivariateNormal, _instance)
+        batch_shape = torch.Size(batch_shape)
+        loc_shape = batch_shape + self.event_shape
+        new.loc = self.loc.expand(loc_shape)
+        new.cov_diag = self.cov_diag.expand(loc_shape)
+        new.cov_factor = self.cov_factor.expand(loc_shape + self.cov_factor.shape[-1:])
+        new._unbroadcasted_cov_factor = self._unbroadcasted_cov_factor
+        new._unbroadcasted_cov_diag = self._unbroadcasted_cov_diag
+        new._capacitance_tril = self._capacitance_tril
+        super(LowRankMultivariateNormal, new).__init__(
+            batch_shape, self.event_shape, validate_args=False
+        )
+        new._validate_args = self._validate_args
+        return new
+
+    @property
+    def mean(self):
+        return self.loc
+
+    @property
+    def mode(self):
+        return self.loc
+
+    @lazy_property
+    def variance(self):
+        return (
+            self._unbroadcasted_cov_factor.pow(2).sum(-1) + self._unbroadcasted_cov_diag
+        ).expand(self._batch_shape + self._event_shape)
+
+    @lazy_property
+    def scale_tril(self):
+        # The following identity is used to increase the numerically computation stability
+        # for Cholesky decomposition (see http://www.gaussianprocess.org/gpml/, Section 3.4.3):
+        #     W @ W.T + D = D1/2 @ (I + D-1/2 @ W @ W.T @ D-1/2) @ D1/2
+        # The matrix "I + D-1/2 @ W @ W.T @ D-1/2" has eigenvalues bounded from below by 1,
+        # hence it is well-conditioned and safe to take Cholesky decomposition.
+        n = self._event_shape[0]
+        cov_diag_sqrt_unsqueeze = self._unbroadcasted_cov_diag.sqrt().unsqueeze(-1)
+        Dinvsqrt_W = self._unbroadcasted_cov_factor / cov_diag_sqrt_unsqueeze
+        K = torch.matmul(Dinvsqrt_W, Dinvsqrt_W.mT).contiguous()
+        K.view(-1, n * n)[:, :: n + 1] += 1  # add identity matrix to K
+        scale_tril = cov_diag_sqrt_unsqueeze * torch.linalg.cholesky(K)
+        return scale_tril.expand(
+            self._batch_shape + self._event_shape + self._event_shape
+        )
+
+    @lazy_property
+    def covariance_matrix(self):
+        covariance_matrix = torch.matmul(
+            self._unbroadcasted_cov_factor, self._unbroadcasted_cov_factor.mT
+        ) + torch.diag_embed(self._unbroadcasted_cov_diag)
+        return covariance_matrix.expand(
+            self._batch_shape + self._event_shape + self._event_shape
+        )
+
+    @lazy_property
+    def precision_matrix(self):
+        # We use "Woodbury matrix identity" to take advantage of low rank form::
+        #     inv(W @ W.T + D) = inv(D) - inv(D) @ W @ inv(C) @ W.T @ inv(D)
+        # where :math:`C` is the capacitance matrix.
+        Wt_Dinv = (
+            self._unbroadcasted_cov_factor.mT
+            / self._unbroadcasted_cov_diag.unsqueeze(-2)
+        )
+        A = torch.linalg.solve_triangular(self._capacitance_tril, Wt_Dinv, upper=False)
+        precision_matrix = (
+            torch.diag_embed(self._unbroadcasted_cov_diag.reciprocal()) - A.mT @ A
+        )
+        return precision_matrix.expand(
+            self._batch_shape + self._event_shape + self._event_shape
+        )
+
+    def rsample(self, sample_shape: _size = torch.Size()) -> torch.Tensor:
+        shape = self._extended_shape(sample_shape)
+        W_shape = shape[:-1] + self.cov_factor.shape[-1:]
+        eps_W = _standard_normal(W_shape, dtype=self.loc.dtype, device=self.loc.device)
+        eps_D = _standard_normal(shape, dtype=self.loc.dtype, device=self.loc.device)
+        return (
+            self.loc
+            + _batch_mv(self._unbroadcasted_cov_factor, eps_W)
+            + self._unbroadcasted_cov_diag.sqrt() * eps_D
+        )
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        diff = value - self.loc
+        M = _batch_lowrank_mahalanobis(
+            self._unbroadcasted_cov_factor,
+            self._unbroadcasted_cov_diag,
+            diff,
+            self._capacitance_tril,
+        )
+        log_det = _batch_lowrank_logdet(
+            self._unbroadcasted_cov_factor,
+            self._unbroadcasted_cov_diag,
+            self._capacitance_tril,
+        )
+        return -0.5 * (self._event_shape[0] * math.log(2 * math.pi) + log_det + M)
+
+    def entropy(self):
+        log_det = _batch_lowrank_logdet(
+            self._unbroadcasted_cov_factor,
+            self._unbroadcasted_cov_diag,
+            self._capacitance_tril,
+        )
+        H = 0.5 * (self._event_shape[0] * (1.0 + math.log(2 * math.pi)) + log_det)
+        if len(self._batch_shape) == 0:
+            return H
+        else:
+            return H.expand(self._batch_shape)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/mixture_same_family.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/mixture_same_family.py
new file mode 100644
index 0000000000000000000000000000000000000000..99e0362cead32b12a0145977cad9d46bd1fe588b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/mixture_same_family.py
@@ -0,0 +1,216 @@
+# mypy: allow-untyped-defs
+from typing import Dict
+
+import torch
+from torch.distributions import Categorical, constraints
+from torch.distributions.distribution import Distribution
+
+
+__all__ = ["MixtureSameFamily"]
+
+
+class MixtureSameFamily(Distribution):
+    r"""
+    The `MixtureSameFamily` distribution implements a (batch of) mixture
+    distribution where all component are from different parameterizations of
+    the same distribution type. It is parameterized by a `Categorical`
+    "selecting distribution" (over `k` component) and a component
+    distribution, i.e., a `Distribution` with a rightmost batch shape
+    (equal to `[k]`) which indexes each (batch of) component.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP("undefined vars")
+        >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally
+        >>> # weighted normal distributions
+        >>> mix = D.Categorical(torch.ones(5,))
+        >>> comp = D.Normal(torch.randn(5,), torch.rand(5,))
+        >>> gmm = MixtureSameFamily(mix, comp)
+
+        >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally
+        >>> # weighted bivariate normal distributions
+        >>> mix = D.Categorical(torch.ones(5,))
+        >>> comp = D.Independent(D.Normal(
+        ...          torch.randn(5,2), torch.rand(5,2)), 1)
+        >>> gmm = MixtureSameFamily(mix, comp)
+
+        >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each
+        >>> # consisting of 5 random weighted bivariate normal distributions
+        >>> mix = D.Categorical(torch.rand(3,5))
+        >>> comp = D.Independent(D.Normal(
+        ...         torch.randn(3,5,2), torch.rand(3,5,2)), 1)
+        >>> gmm = MixtureSameFamily(mix, comp)
+
+    Args:
+        mixture_distribution: `torch.distributions.Categorical`-like
+            instance. Manages the probability of selecting component.
+            The number of categories must match the rightmost batch
+            dimension of the `component_distribution`. Must have either
+            scalar `batch_shape` or `batch_shape` matching
+            `component_distribution.batch_shape[:-1]`
+        component_distribution: `torch.distributions.Distribution`-like
+            instance. Right-most batch dimension indexes component.
+    """
+    arg_constraints: Dict[str, constraints.Constraint] = {}
+    has_rsample = False
+
+    def __init__(
+        self, mixture_distribution, component_distribution, validate_args=None
+    ):
+        self._mixture_distribution = mixture_distribution
+        self._component_distribution = component_distribution
+
+        if not isinstance(self._mixture_distribution, Categorical):
+            raise ValueError(
+                " The Mixture distribution needs to be an "
+                " instance of torch.distributions.Categorical"
+            )
+
+        if not isinstance(self._component_distribution, Distribution):
+            raise ValueError(
+                "The Component distribution need to be an "
+                "instance of torch.distributions.Distribution"
+            )
+
+        # Check that batch size matches
+        mdbs = self._mixture_distribution.batch_shape
+        cdbs = self._component_distribution.batch_shape[:-1]
+        for size1, size2 in zip(reversed(mdbs), reversed(cdbs)):
+            if size1 != 1 and size2 != 1 and size1 != size2:
+                raise ValueError(
+                    f"`mixture_distribution.batch_shape` ({mdbs}) is not "
+                    "compatible with `component_distribution."
+                    f"batch_shape`({cdbs})"
+                )
+
+        # Check that the number of mixture component matches
+        km = self._mixture_distribution.logits.shape[-1]
+        kc = self._component_distribution.batch_shape[-1]
+        if km is not None and kc is not None and km != kc:
+            raise ValueError(
+                f"`mixture_distribution component` ({km}) does not"
+                " equal `component_distribution.batch_shape[-1]`"
+                f" ({kc})"
+            )
+        self._num_component = km
+
+        event_shape = self._component_distribution.event_shape
+        self._event_ndims = len(event_shape)
+        super().__init__(
+            batch_shape=cdbs, event_shape=event_shape, validate_args=validate_args
+        )
+
+    def expand(self, batch_shape, _instance=None):
+        batch_shape = torch.Size(batch_shape)
+        batch_shape_comp = batch_shape + (self._num_component,)
+        new = self._get_checked_instance(MixtureSameFamily, _instance)
+        new._component_distribution = self._component_distribution.expand(
+            batch_shape_comp
+        )
+        new._mixture_distribution = self._mixture_distribution.expand(batch_shape)
+        new._num_component = self._num_component
+        new._event_ndims = self._event_ndims
+        event_shape = new._component_distribution.event_shape
+        super(MixtureSameFamily, new).__init__(
+            batch_shape=batch_shape, event_shape=event_shape, validate_args=False
+        )
+        new._validate_args = self._validate_args
+        return new
+
+    @constraints.dependent_property
+    def support(self):
+        # FIXME this may have the wrong shape when support contains batched
+        # parameters
+        return self._component_distribution.support
+
+    @property
+    def mixture_distribution(self):
+        return self._mixture_distribution
+
+    @property
+    def component_distribution(self):
+        return self._component_distribution
+
+    @property
+    def mean(self):
+        probs = self._pad_mixture_dimensions(self.mixture_distribution.probs)
+        return torch.sum(
+            probs * self.component_distribution.mean, dim=-1 - self._event_ndims
+        )  # [B, E]
+
+    @property
+    def variance(self):
+        # Law of total variance: Var(Y) = E[Var(Y|X)] + Var(E[Y|X])
+        probs = self._pad_mixture_dimensions(self.mixture_distribution.probs)
+        mean_cond_var = torch.sum(
+            probs * self.component_distribution.variance, dim=-1 - self._event_ndims
+        )
+        var_cond_mean = torch.sum(
+            probs * (self.component_distribution.mean - self._pad(self.mean)).pow(2.0),
+            dim=-1 - self._event_ndims,
+        )
+        return mean_cond_var + var_cond_mean
+
+    def cdf(self, x):
+        x = self._pad(x)
+        cdf_x = self.component_distribution.cdf(x)
+        mix_prob = self.mixture_distribution.probs
+
+        return torch.sum(cdf_x * mix_prob, dim=-1)
+
+    def log_prob(self, x):
+        if self._validate_args:
+            self._validate_sample(x)
+        x = self._pad(x)
+        log_prob_x = self.component_distribution.log_prob(x)  # [S, B, k]
+        log_mix_prob = torch.log_softmax(
+            self.mixture_distribution.logits, dim=-1
+        )  # [B, k]
+        return torch.logsumexp(log_prob_x + log_mix_prob, dim=-1)  # [S, B]
+
+    def sample(self, sample_shape=torch.Size()):
+        with torch.no_grad():
+            sample_len = len(sample_shape)
+            batch_len = len(self.batch_shape)
+            gather_dim = sample_len + batch_len
+            es = self.event_shape
+
+            # mixture samples [n, B]
+            mix_sample = self.mixture_distribution.sample(sample_shape)
+            mix_shape = mix_sample.shape
+
+            # component samples [n, B, k, E]
+            comp_samples = self.component_distribution.sample(sample_shape)
+
+            # Gather along the k dimension
+            mix_sample_r = mix_sample.reshape(
+                mix_shape + torch.Size([1] * (len(es) + 1))
+            )
+            mix_sample_r = mix_sample_r.repeat(
+                torch.Size([1] * len(mix_shape)) + torch.Size([1]) + es
+            )
+
+            samples = torch.gather(comp_samples, gather_dim, mix_sample_r)
+            return samples.squeeze(gather_dim)
+
+    def _pad(self, x):
+        return x.unsqueeze(-1 - self._event_ndims)
+
+    def _pad_mixture_dimensions(self, x):
+        dist_batch_ndims = len(self.batch_shape)
+        cat_batch_ndims = len(self.mixture_distribution.batch_shape)
+        pad_ndims = 0 if cat_batch_ndims == 1 else dist_batch_ndims - cat_batch_ndims
+        xs = x.shape
+        x = x.reshape(
+            xs[:-1]
+            + torch.Size(pad_ndims * [1])
+            + xs[-1:]
+            + torch.Size(self._event_ndims * [1])
+        )
+        return x
+
+    def __repr__(self):
+        args_string = (
+            f"\n  {self.mixture_distribution},\n  {self.component_distribution}"
+        )
+        return "MixtureSameFamily" + "(" + args_string + ")"
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/multinomial.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/multinomial.py
new file mode 100644
index 0000000000000000000000000000000000000000..12295a80e1855ceaf39aadd79f6df025ad7882ad
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/multinomial.py
@@ -0,0 +1,137 @@
+# mypy: allow-untyped-defs
+import torch
+from torch import inf
+from torch.distributions import Categorical, constraints
+from torch.distributions.binomial import Binomial
+from torch.distributions.distribution import Distribution
+from torch.distributions.utils import broadcast_all
+
+
+__all__ = ["Multinomial"]
+
+
+class Multinomial(Distribution):
+    r"""
+    Creates a Multinomial distribution parameterized by :attr:`total_count` and
+    either :attr:`probs` or :attr:`logits` (but not both). The innermost dimension of
+    :attr:`probs` indexes over categories. All other dimensions index over batches.
+
+    Note that :attr:`total_count` need not be specified if only :meth:`log_prob` is
+    called (see example below)
+
+    .. note:: The `probs` argument must be non-negative, finite and have a non-zero sum,
+              and it will be normalized to sum to 1 along the last dimension. :attr:`probs`
+              will return this normalized value.
+              The `logits` argument will be interpreted as unnormalized log probabilities
+              and can therefore be any real number. It will likewise be normalized so that
+              the resulting probabilities sum to 1 along the last dimension. :attr:`logits`
+              will return this normalized value.
+
+    -   :meth:`sample` requires a single shared `total_count` for all
+        parameters and samples.
+    -   :meth:`log_prob` allows different `total_count` for each parameter and
+        sample.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("FIXME: found invalid values")
+        >>> m = Multinomial(100, torch.tensor([ 1., 1., 1., 1.]))
+        >>> x = m.sample()  # equal probability of 0, 1, 2, 3
+        tensor([ 21.,  24.,  30.,  25.])
+
+        >>> Multinomial(probs=torch.tensor([1., 1., 1., 1.])).log_prob(x)
+        tensor([-4.1338])
+
+    Args:
+        total_count (int): number of trials
+        probs (Tensor): event probabilities
+        logits (Tensor): event log probabilities (unnormalized)
+    """
+    arg_constraints = {"probs": constraints.simplex, "logits": constraints.real_vector}
+    total_count: int
+
+    @property
+    def mean(self):
+        return self.probs * self.total_count
+
+    @property
+    def variance(self):
+        return self.total_count * self.probs * (1 - self.probs)
+
+    def __init__(self, total_count=1, probs=None, logits=None, validate_args=None):
+        if not isinstance(total_count, int):
+            raise NotImplementedError("inhomogeneous total_count is not supported")
+        self.total_count = total_count
+        self._categorical = Categorical(probs=probs, logits=logits)
+        self._binomial = Binomial(total_count=total_count, probs=self.probs)
+        batch_shape = self._categorical.batch_shape
+        event_shape = self._categorical.param_shape[-1:]
+        super().__init__(batch_shape, event_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Multinomial, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new.total_count = self.total_count
+        new._categorical = self._categorical.expand(batch_shape)
+        super(Multinomial, new).__init__(
+            batch_shape, self.event_shape, validate_args=False
+        )
+        new._validate_args = self._validate_args
+        return new
+
+    def _new(self, *args, **kwargs):
+        return self._categorical._new(*args, **kwargs)
+
+    @constraints.dependent_property(is_discrete=True, event_dim=1)
+    def support(self):
+        return constraints.multinomial(self.total_count)
+
+    @property
+    def logits(self):
+        return self._categorical.logits
+
+    @property
+    def probs(self):
+        return self._categorical.probs
+
+    @property
+    def param_shape(self):
+        return self._categorical.param_shape
+
+    def sample(self, sample_shape=torch.Size()):
+        sample_shape = torch.Size(sample_shape)
+        samples = self._categorical.sample(
+            torch.Size((self.total_count,)) + sample_shape
+        )
+        # samples.shape is (total_count, sample_shape, batch_shape), need to change it to
+        # (sample_shape, batch_shape, total_count)
+        shifted_idx = list(range(samples.dim()))
+        shifted_idx.append(shifted_idx.pop(0))
+        samples = samples.permute(*shifted_idx)
+        counts = samples.new(self._extended_shape(sample_shape)).zero_()
+        counts.scatter_add_(-1, samples, torch.ones_like(samples))
+        return counts.type_as(self.probs)
+
+    def entropy(self):
+        n = torch.tensor(self.total_count)
+
+        cat_entropy = self._categorical.entropy()
+        term1 = n * cat_entropy - torch.lgamma(n + 1)
+
+        support = self._binomial.enumerate_support(expand=False)[1:]
+        binomial_probs = torch.exp(self._binomial.log_prob(support))
+        weights = torch.lgamma(support + 1)
+        term2 = (binomial_probs * weights).sum([0, -1])
+
+        return term1 + term2
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        logits, value = broadcast_all(self.logits, value)
+        logits = logits.clone(memory_format=torch.contiguous_format)
+        log_factorial_n = torch.lgamma(value.sum(-1) + 1)
+        log_factorial_xs = torch.lgamma(value + 1).sum(-1)
+        logits[(value == 0) & (logits == -inf)] = 0
+        log_powers = (logits * value).sum(-1)
+        return log_factorial_n - log_factorial_xs + log_powers
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/multivariate_normal.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/multivariate_normal.py
new file mode 100644
index 0000000000000000000000000000000000000000..bece6d0606a8c93af31d78a3be1e5623bf127a2f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/multivariate_normal.py
@@ -0,0 +1,265 @@
+# mypy: allow-untyped-defs
+import math
+
+import torch
+from torch.distributions import constraints
+from torch.distributions.distribution import Distribution
+from torch.distributions.utils import _standard_normal, lazy_property
+from torch.types import _size
+
+
+__all__ = ["MultivariateNormal"]
+
+
+def _batch_mv(bmat, bvec):
+    r"""
+    Performs a batched matrix-vector product, with compatible but different batch shapes.
+
+    This function takes as input `bmat`, containing :math:`n \times n` matrices, and
+    `bvec`, containing length :math:`n` vectors.
+
+    Both `bmat` and `bvec` may have any number of leading dimensions, which correspond
+    to a batch shape. They are not necessarily assumed to have the same batch shape,
+    just ones which can be broadcasted.
+    """
+    return torch.matmul(bmat, bvec.unsqueeze(-1)).squeeze(-1)
+
+
+def _batch_mahalanobis(bL, bx):
+    r"""
+    Computes the squared Mahalanobis distance :math:`\mathbf{x}^\top\mathbf{M}^{-1}\mathbf{x}`
+    for a factored :math:`\mathbf{M} = \mathbf{L}\mathbf{L}^\top`.
+
+    Accepts batches for both bL and bx. They are not necessarily assumed to have the same batch
+    shape, but `bL` one should be able to broadcasted to `bx` one.
+    """
+    n = bx.size(-1)
+    bx_batch_shape = bx.shape[:-1]
+
+    # Assume that bL.shape = (i, 1, n, n), bx.shape = (..., i, j, n),
+    # we are going to make bx have shape (..., 1, j,  i, 1, n) to apply batched tri.solve
+    bx_batch_dims = len(bx_batch_shape)
+    bL_batch_dims = bL.dim() - 2
+    outer_batch_dims = bx_batch_dims - bL_batch_dims
+    old_batch_dims = outer_batch_dims + bL_batch_dims
+    new_batch_dims = outer_batch_dims + 2 * bL_batch_dims
+    # Reshape bx with the shape (..., 1, i, j, 1, n)
+    bx_new_shape = bx.shape[:outer_batch_dims]
+    for sL, sx in zip(bL.shape[:-2], bx.shape[outer_batch_dims:-1]):
+        bx_new_shape += (sx // sL, sL)
+    bx_new_shape += (n,)
+    bx = bx.reshape(bx_new_shape)
+    # Permute bx to make it have shape (..., 1, j, i, 1, n)
+    permute_dims = (
+        list(range(outer_batch_dims))
+        + list(range(outer_batch_dims, new_batch_dims, 2))
+        + list(range(outer_batch_dims + 1, new_batch_dims, 2))
+        + [new_batch_dims]
+    )
+    bx = bx.permute(permute_dims)
+
+    flat_L = bL.reshape(-1, n, n)  # shape = b x n x n
+    flat_x = bx.reshape(-1, flat_L.size(0), n)  # shape = c x b x n
+    flat_x_swap = flat_x.permute(1, 2, 0)  # shape = b x n x c
+    M_swap = (
+        torch.linalg.solve_triangular(flat_L, flat_x_swap, upper=False).pow(2).sum(-2)
+    )  # shape = b x c
+    M = M_swap.t()  # shape = c x b
+
+    # Now we revert the above reshape and permute operators.
+    permuted_M = M.reshape(bx.shape[:-1])  # shape = (..., 1, j, i, 1)
+    permute_inv_dims = list(range(outer_batch_dims))
+    for i in range(bL_batch_dims):
+        permute_inv_dims += [outer_batch_dims + i, old_batch_dims + i]
+    reshaped_M = permuted_M.permute(permute_inv_dims)  # shape = (..., 1, i, j, 1)
+    return reshaped_M.reshape(bx_batch_shape)
+
+
+def _precision_to_scale_tril(P):
+    # Ref: https://nbviewer.jupyter.org/gist/fehiepsi/5ef8e09e61604f10607380467eb82006#Precision-to-scale_tril
+    Lf = torch.linalg.cholesky(torch.flip(P, (-2, -1)))
+    L_inv = torch.transpose(torch.flip(Lf, (-2, -1)), -2, -1)
+    Id = torch.eye(P.shape[-1], dtype=P.dtype, device=P.device)
+    L = torch.linalg.solve_triangular(L_inv, Id, upper=False)
+    return L
+
+
+class MultivariateNormal(Distribution):
+    r"""
+    Creates a multivariate normal (also called Gaussian) distribution
+    parameterized by a mean vector and a covariance matrix.
+
+    The multivariate normal distribution can be parameterized either
+    in terms of a positive definite covariance matrix :math:`\mathbf{\Sigma}`
+    or a positive definite precision matrix :math:`\mathbf{\Sigma}^{-1}`
+    or a lower-triangular matrix :math:`\mathbf{L}` with positive-valued
+    diagonal entries, such that
+    :math:`\mathbf{\Sigma} = \mathbf{L}\mathbf{L}^\top`. This triangular matrix
+    can be obtained via e.g. Cholesky decomposition of the covariance.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = MultivariateNormal(torch.zeros(2), torch.eye(2))
+        >>> m.sample()  # normally distributed with mean=`[0,0]` and covariance_matrix=`I`
+        tensor([-0.2102, -0.5429])
+
+    Args:
+        loc (Tensor): mean of the distribution
+        covariance_matrix (Tensor): positive-definite covariance matrix
+        precision_matrix (Tensor): positive-definite precision matrix
+        scale_tril (Tensor): lower-triangular factor of covariance, with positive-valued diagonal
+
+    Note:
+        Only one of :attr:`covariance_matrix` or :attr:`precision_matrix` or
+        :attr:`scale_tril` can be specified.
+
+        Using :attr:`scale_tril` will be more efficient: all computations internally
+        are based on :attr:`scale_tril`. If :attr:`covariance_matrix` or
+        :attr:`precision_matrix` is passed instead, it is only used to compute
+        the corresponding lower triangular matrices using a Cholesky decomposition.
+    """
+    arg_constraints = {
+        "loc": constraints.real_vector,
+        "covariance_matrix": constraints.positive_definite,
+        "precision_matrix": constraints.positive_definite,
+        "scale_tril": constraints.lower_cholesky,
+    }
+    support = constraints.real_vector
+    has_rsample = True
+
+    def __init__(
+        self,
+        loc,
+        covariance_matrix=None,
+        precision_matrix=None,
+        scale_tril=None,
+        validate_args=None,
+    ):
+        if loc.dim() < 1:
+            raise ValueError("loc must be at least one-dimensional.")
+        if (covariance_matrix is not None) + (scale_tril is not None) + (
+            precision_matrix is not None
+        ) != 1:
+            raise ValueError(
+                "Exactly one of covariance_matrix or precision_matrix or scale_tril may be specified."
+            )
+
+        if scale_tril is not None:
+            if scale_tril.dim() < 2:
+                raise ValueError(
+                    "scale_tril matrix must be at least two-dimensional, "
+                    "with optional leading batch dimensions"
+                )
+            batch_shape = torch.broadcast_shapes(scale_tril.shape[:-2], loc.shape[:-1])
+            self.scale_tril = scale_tril.expand(batch_shape + (-1, -1))
+        elif covariance_matrix is not None:
+            if covariance_matrix.dim() < 2:
+                raise ValueError(
+                    "covariance_matrix must be at least two-dimensional, "
+                    "with optional leading batch dimensions"
+                )
+            batch_shape = torch.broadcast_shapes(
+                covariance_matrix.shape[:-2], loc.shape[:-1]
+            )
+            self.covariance_matrix = covariance_matrix.expand(batch_shape + (-1, -1))
+        else:
+            if precision_matrix.dim() < 2:
+                raise ValueError(
+                    "precision_matrix must be at least two-dimensional, "
+                    "with optional leading batch dimensions"
+                )
+            batch_shape = torch.broadcast_shapes(
+                precision_matrix.shape[:-2], loc.shape[:-1]
+            )
+            self.precision_matrix = precision_matrix.expand(batch_shape + (-1, -1))
+        self.loc = loc.expand(batch_shape + (-1,))
+
+        event_shape = self.loc.shape[-1:]
+        super().__init__(batch_shape, event_shape, validate_args=validate_args)
+
+        if scale_tril is not None:
+            self._unbroadcasted_scale_tril = scale_tril
+        elif covariance_matrix is not None:
+            self._unbroadcasted_scale_tril = torch.linalg.cholesky(covariance_matrix)
+        else:  # precision_matrix is not None
+            self._unbroadcasted_scale_tril = _precision_to_scale_tril(precision_matrix)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(MultivariateNormal, _instance)
+        batch_shape = torch.Size(batch_shape)
+        loc_shape = batch_shape + self.event_shape
+        cov_shape = batch_shape + self.event_shape + self.event_shape
+        new.loc = self.loc.expand(loc_shape)
+        new._unbroadcasted_scale_tril = self._unbroadcasted_scale_tril
+        if "covariance_matrix" in self.__dict__:
+            new.covariance_matrix = self.covariance_matrix.expand(cov_shape)
+        if "scale_tril" in self.__dict__:
+            new.scale_tril = self.scale_tril.expand(cov_shape)
+        if "precision_matrix" in self.__dict__:
+            new.precision_matrix = self.precision_matrix.expand(cov_shape)
+        super(MultivariateNormal, new).__init__(
+            batch_shape, self.event_shape, validate_args=False
+        )
+        new._validate_args = self._validate_args
+        return new
+
+    @lazy_property
+    def scale_tril(self):
+        return self._unbroadcasted_scale_tril.expand(
+            self._batch_shape + self._event_shape + self._event_shape
+        )
+
+    @lazy_property
+    def covariance_matrix(self):
+        return torch.matmul(
+            self._unbroadcasted_scale_tril, self._unbroadcasted_scale_tril.mT
+        ).expand(self._batch_shape + self._event_shape + self._event_shape)
+
+    @lazy_property
+    def precision_matrix(self):
+        return torch.cholesky_inverse(self._unbroadcasted_scale_tril).expand(
+            self._batch_shape + self._event_shape + self._event_shape
+        )
+
+    @property
+    def mean(self):
+        return self.loc
+
+    @property
+    def mode(self):
+        return self.loc
+
+    @property
+    def variance(self):
+        return (
+            self._unbroadcasted_scale_tril.pow(2)
+            .sum(-1)
+            .expand(self._batch_shape + self._event_shape)
+        )
+
+    def rsample(self, sample_shape: _size = torch.Size()) -> torch.Tensor:
+        shape = self._extended_shape(sample_shape)
+        eps = _standard_normal(shape, dtype=self.loc.dtype, device=self.loc.device)
+        return self.loc + _batch_mv(self._unbroadcasted_scale_tril, eps)
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        diff = value - self.loc
+        M = _batch_mahalanobis(self._unbroadcasted_scale_tril, diff)
+        half_log_det = (
+            self._unbroadcasted_scale_tril.diagonal(dim1=-2, dim2=-1).log().sum(-1)
+        )
+        return -0.5 * (self._event_shape[0] * math.log(2 * math.pi) + M) - half_log_det
+
+    def entropy(self):
+        half_log_det = (
+            self._unbroadcasted_scale_tril.diagonal(dim1=-2, dim2=-1).log().sum(-1)
+        )
+        H = 0.5 * self._event_shape[0] * (1.0 + math.log(2 * math.pi)) + half_log_det
+        if len(self._batch_shape) == 0:
+            return H
+        else:
+            return H.expand(self._batch_shape)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/negative_binomial.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/negative_binomial.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3e1a66639ba101a35c7deca67a7567e938a1608
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/negative_binomial.py
@@ -0,0 +1,135 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.nn.functional as F
+from torch.distributions import constraints
+from torch.distributions.distribution import Distribution
+from torch.distributions.utils import (
+    broadcast_all,
+    lazy_property,
+    logits_to_probs,
+    probs_to_logits,
+)
+
+
+__all__ = ["NegativeBinomial"]
+
+
+class NegativeBinomial(Distribution):
+    r"""
+    Creates a Negative Binomial distribution, i.e. distribution
+    of the number of successful independent and identical Bernoulli trials
+    before :attr:`total_count` failures are achieved. The probability
+    of success of each Bernoulli trial is :attr:`probs`.
+
+    Args:
+        total_count (float or Tensor): non-negative number of negative Bernoulli
+            trials to stop, although the distribution is still valid for real
+            valued count
+        probs (Tensor): Event probabilities of success in the half open interval [0, 1)
+        logits (Tensor): Event log-odds for probabilities of success
+    """
+    arg_constraints = {
+        "total_count": constraints.greater_than_eq(0),
+        "probs": constraints.half_open_interval(0.0, 1.0),
+        "logits": constraints.real,
+    }
+    support = constraints.nonnegative_integer
+
+    def __init__(self, total_count, probs=None, logits=None, validate_args=None):
+        if (probs is None) == (logits is None):
+            raise ValueError(
+                "Either `probs` or `logits` must be specified, but not both."
+            )
+        if probs is not None:
+            (
+                self.total_count,
+                self.probs,
+            ) = broadcast_all(total_count, probs)
+            self.total_count = self.total_count.type_as(self.probs)
+        else:
+            (
+                self.total_count,
+                self.logits,
+            ) = broadcast_all(total_count, logits)
+            self.total_count = self.total_count.type_as(self.logits)
+
+        self._param = self.probs if probs is not None else self.logits
+        batch_shape = self._param.size()
+        super().__init__(batch_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(NegativeBinomial, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new.total_count = self.total_count.expand(batch_shape)
+        if "probs" in self.__dict__:
+            new.probs = self.probs.expand(batch_shape)
+            new._param = new.probs
+        if "logits" in self.__dict__:
+            new.logits = self.logits.expand(batch_shape)
+            new._param = new.logits
+        super(NegativeBinomial, new).__init__(batch_shape, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    def _new(self, *args, **kwargs):
+        return self._param.new(*args, **kwargs)
+
+    @property
+    def mean(self):
+        return self.total_count * torch.exp(self.logits)
+
+    @property
+    def mode(self):
+        return ((self.total_count - 1) * self.logits.exp()).floor().clamp(min=0.0)
+
+    @property
+    def variance(self):
+        return self.mean / torch.sigmoid(-self.logits)
+
+    @lazy_property
+    def logits(self):
+        return probs_to_logits(self.probs, is_binary=True)
+
+    @lazy_property
+    def probs(self):
+        return logits_to_probs(self.logits, is_binary=True)
+
+    @property
+    def param_shape(self):
+        return self._param.size()
+
+    @lazy_property
+    def _gamma(self):
+        # Note we avoid validating because self.total_count can be zero.
+        return torch.distributions.Gamma(
+            concentration=self.total_count,
+            rate=torch.exp(-self.logits),
+            validate_args=False,
+        )
+
+    def sample(self, sample_shape=torch.Size()):
+        with torch.no_grad():
+            rate = self._gamma.sample(sample_shape=sample_shape)
+            return torch.poisson(rate)
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+
+        log_unnormalized_prob = self.total_count * F.logsigmoid(
+            -self.logits
+        ) + value * F.logsigmoid(self.logits)
+
+        log_normalization = (
+            -torch.lgamma(self.total_count + value)
+            + torch.lgamma(1.0 + value)
+            + torch.lgamma(self.total_count)
+        )
+        # The case self.total_count == 0 and value == 0 has probability 1 but
+        # lgamma(0) is infinite. Handle this case separately using a function
+        # that does not modify tensors in place to allow Jit compilation.
+        log_normalization = log_normalization.masked_fill(
+            self.total_count + value == 0.0, 0.0
+        )
+
+        return log_unnormalized_prob - log_normalization
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/normal.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/normal.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c6f9b7170855d615eb2ee94902e5b40a6facefc
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/normal.py
@@ -0,0 +1,112 @@
+# mypy: allow-untyped-defs
+import math
+from numbers import Number, Real
+
+import torch
+from torch.distributions import constraints
+from torch.distributions.exp_family import ExponentialFamily
+from torch.distributions.utils import _standard_normal, broadcast_all
+from torch.types import _size
+
+
+__all__ = ["Normal"]
+
+
+class Normal(ExponentialFamily):
+    r"""
+    Creates a normal (also called Gaussian) distribution parameterized by
+    :attr:`loc` and :attr:`scale`.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = Normal(torch.tensor([0.0]), torch.tensor([1.0]))
+        >>> m.sample()  # normally distributed with loc=0 and scale=1
+        tensor([ 0.1046])
+
+    Args:
+        loc (float or Tensor): mean of the distribution (often referred to as mu)
+        scale (float or Tensor): standard deviation of the distribution
+            (often referred to as sigma)
+    """
+    arg_constraints = {"loc": constraints.real, "scale": constraints.positive}
+    support = constraints.real
+    has_rsample = True
+    _mean_carrier_measure = 0
+
+    @property
+    def mean(self):
+        return self.loc
+
+    @property
+    def mode(self):
+        return self.loc
+
+    @property
+    def stddev(self):
+        return self.scale
+
+    @property
+    def variance(self):
+        return self.stddev.pow(2)
+
+    def __init__(self, loc, scale, validate_args=None):
+        self.loc, self.scale = broadcast_all(loc, scale)
+        if isinstance(loc, Number) and isinstance(scale, Number):
+            batch_shape = torch.Size()
+        else:
+            batch_shape = self.loc.size()
+        super().__init__(batch_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Normal, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new.loc = self.loc.expand(batch_shape)
+        new.scale = self.scale.expand(batch_shape)
+        super(Normal, new).__init__(batch_shape, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    def sample(self, sample_shape=torch.Size()):
+        shape = self._extended_shape(sample_shape)
+        with torch.no_grad():
+            return torch.normal(self.loc.expand(shape), self.scale.expand(shape))
+
+    def rsample(self, sample_shape: _size = torch.Size()) -> torch.Tensor:
+        shape = self._extended_shape(sample_shape)
+        eps = _standard_normal(shape, dtype=self.loc.dtype, device=self.loc.device)
+        return self.loc + eps * self.scale
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        # compute the variance
+        var = self.scale**2
+        log_scale = (
+            math.log(self.scale) if isinstance(self.scale, Real) else self.scale.log()
+        )
+        return (
+            -((value - self.loc) ** 2) / (2 * var)
+            - log_scale
+            - math.log(math.sqrt(2 * math.pi))
+        )
+
+    def cdf(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        return 0.5 * (
+            1 + torch.erf((value - self.loc) * self.scale.reciprocal() / math.sqrt(2))
+        )
+
+    def icdf(self, value):
+        return self.loc + self.scale * torch.erfinv(2 * value - 1) * math.sqrt(2)
+
+    def entropy(self):
+        return 0.5 + 0.5 * math.log(2 * math.pi) + torch.log(self.scale)
+
+    @property
+    def _natural_params(self):
+        return (self.loc / self.scale.pow(2), -0.5 * self.scale.pow(2).reciprocal())
+
+    def _log_normalizer(self, x, y):
+        return -0.25 * x.pow(2) / y + 0.5 * torch.log(-math.pi / y)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/one_hot_categorical.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/one_hot_categorical.py
new file mode 100644
index 0000000000000000000000000000000000000000..76cf6137b0c9df0e805b6e5e69992da09da77d1c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/one_hot_categorical.py
@@ -0,0 +1,132 @@
+# mypy: allow-untyped-defs
+import torch
+from torch.distributions import constraints
+from torch.distributions.categorical import Categorical
+from torch.distributions.distribution import Distribution
+from torch.types import _size
+
+
+__all__ = ["OneHotCategorical", "OneHotCategoricalStraightThrough"]
+
+
+class OneHotCategorical(Distribution):
+    r"""
+    Creates a one-hot categorical distribution parameterized by :attr:`probs` or
+    :attr:`logits`.
+
+    Samples are one-hot coded vectors of size ``probs.size(-1)``.
+
+    .. note:: The `probs` argument must be non-negative, finite and have a non-zero sum,
+              and it will be normalized to sum to 1 along the last dimension. :attr:`probs`
+              will return this normalized value.
+              The `logits` argument will be interpreted as unnormalized log probabilities
+              and can therefore be any real number. It will likewise be normalized so that
+              the resulting probabilities sum to 1 along the last dimension. :attr:`logits`
+              will return this normalized value.
+
+    See also: :func:`torch.distributions.Categorical` for specifications of
+    :attr:`probs` and :attr:`logits`.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = OneHotCategorical(torch.tensor([ 0.25, 0.25, 0.25, 0.25 ]))
+        >>> m.sample()  # equal probability of 0, 1, 2, 3
+        tensor([ 0.,  0.,  0.,  1.])
+
+    Args:
+        probs (Tensor): event probabilities
+        logits (Tensor): event log probabilities (unnormalized)
+    """
+    arg_constraints = {"probs": constraints.simplex, "logits": constraints.real_vector}
+    support = constraints.one_hot
+    has_enumerate_support = True
+
+    def __init__(self, probs=None, logits=None, validate_args=None):
+        self._categorical = Categorical(probs, logits)
+        batch_shape = self._categorical.batch_shape
+        event_shape = self._categorical.param_shape[-1:]
+        super().__init__(batch_shape, event_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(OneHotCategorical, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new._categorical = self._categorical.expand(batch_shape)
+        super(OneHotCategorical, new).__init__(
+            batch_shape, self.event_shape, validate_args=False
+        )
+        new._validate_args = self._validate_args
+        return new
+
+    def _new(self, *args, **kwargs):
+        return self._categorical._new(*args, **kwargs)
+
+    @property
+    def _param(self):
+        return self._categorical._param
+
+    @property
+    def probs(self):
+        return self._categorical.probs
+
+    @property
+    def logits(self):
+        return self._categorical.logits
+
+    @property
+    def mean(self):
+        return self._categorical.probs
+
+    @property
+    def mode(self):
+        probs = self._categorical.probs
+        mode = probs.argmax(axis=-1)
+        return torch.nn.functional.one_hot(mode, num_classes=probs.shape[-1]).to(probs)
+
+    @property
+    def variance(self):
+        return self._categorical.probs * (1 - self._categorical.probs)
+
+    @property
+    def param_shape(self):
+        return self._categorical.param_shape
+
+    def sample(self, sample_shape=torch.Size()):
+        sample_shape = torch.Size(sample_shape)
+        probs = self._categorical.probs
+        num_events = self._categorical._num_events
+        indices = self._categorical.sample(sample_shape)
+        return torch.nn.functional.one_hot(indices, num_events).to(probs)
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        indices = value.max(-1)[1]
+        return self._categorical.log_prob(indices)
+
+    def entropy(self):
+        return self._categorical.entropy()
+
+    def enumerate_support(self, expand=True):
+        n = self.event_shape[0]
+        values = torch.eye(n, dtype=self._param.dtype, device=self._param.device)
+        values = values.view((n,) + (1,) * len(self.batch_shape) + (n,))
+        if expand:
+            values = values.expand((n,) + self.batch_shape + (n,))
+        return values
+
+
+class OneHotCategoricalStraightThrough(OneHotCategorical):
+    r"""
+    Creates a reparameterizable :class:`OneHotCategorical` distribution based on the straight-
+    through gradient estimator from [1].
+
+    [1] Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
+    (Bengio et al., 2013)
+    """
+    has_rsample = True
+
+    def rsample(self, sample_shape: _size = torch.Size()) -> torch.Tensor:
+        samples = self.sample(sample_shape)
+        probs = self._categorical.probs  # cached via @lazy_property
+        return samples + (probs - probs.detach())
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/pareto.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/pareto.py
new file mode 100644
index 0000000000000000000000000000000000000000..798330d7bca7a8e497a306606f3d722d3ffbd4f3
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/pareto.py
@@ -0,0 +1,62 @@
+# mypy: allow-untyped-defs
+from torch.distributions import constraints
+from torch.distributions.exponential import Exponential
+from torch.distributions.transformed_distribution import TransformedDistribution
+from torch.distributions.transforms import AffineTransform, ExpTransform
+from torch.distributions.utils import broadcast_all
+
+
+__all__ = ["Pareto"]
+
+
+class Pareto(TransformedDistribution):
+    r"""
+    Samples from a Pareto Type 1 distribution.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = Pareto(torch.tensor([1.0]), torch.tensor([1.0]))
+        >>> m.sample()  # sample from a Pareto distribution with scale=1 and alpha=1
+        tensor([ 1.5623])
+
+    Args:
+        scale (float or Tensor): Scale parameter of the distribution
+        alpha (float or Tensor): Shape parameter of the distribution
+    """
+    arg_constraints = {"alpha": constraints.positive, "scale": constraints.positive}
+
+    def __init__(self, scale, alpha, validate_args=None):
+        self.scale, self.alpha = broadcast_all(scale, alpha)
+        base_dist = Exponential(self.alpha, validate_args=validate_args)
+        transforms = [ExpTransform(), AffineTransform(loc=0, scale=self.scale)]
+        super().__init__(base_dist, transforms, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Pareto, _instance)
+        new.scale = self.scale.expand(batch_shape)
+        new.alpha = self.alpha.expand(batch_shape)
+        return super().expand(batch_shape, _instance=new)
+
+    @property
+    def mean(self):
+        # mean is inf for alpha <= 1
+        a = self.alpha.clamp(min=1)
+        return a * self.scale / (a - 1)
+
+    @property
+    def mode(self):
+        return self.scale
+
+    @property
+    def variance(self):
+        # var is inf for alpha <= 2
+        a = self.alpha.clamp(min=2)
+        return self.scale.pow(2) * a / ((a - 1).pow(2) * (a - 2))
+
+    @constraints.dependent_property(is_discrete=False, event_dim=0)
+    def support(self):
+        return constraints.greater_than_eq(self.scale)
+
+    def entropy(self):
+        return (self.scale / self.alpha).log() + (1 + self.alpha.reciprocal())
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/poisson.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/poisson.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f386e9361fdd9da310568b79238127fe2caa5e7
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/poisson.py
@@ -0,0 +1,79 @@
+# mypy: allow-untyped-defs
+from numbers import Number
+
+import torch
+from torch.distributions import constraints
+from torch.distributions.exp_family import ExponentialFamily
+from torch.distributions.utils import broadcast_all
+
+
+__all__ = ["Poisson"]
+
+
+class Poisson(ExponentialFamily):
+    r"""
+    Creates a Poisson distribution parameterized by :attr:`rate`, the rate parameter.
+
+    Samples are nonnegative integers, with a pmf given by
+
+    .. math::
+      \mathrm{rate}^k \frac{e^{-\mathrm{rate}}}{k!}
+
+    Example::
+
+        >>> # xdoctest: +SKIP("poisson_cpu not implemented for 'Long'")
+        >>> m = Poisson(torch.tensor([4]))
+        >>> m.sample()
+        tensor([ 3.])
+
+    Args:
+        rate (Number, Tensor): the rate parameter
+    """
+    arg_constraints = {"rate": constraints.nonnegative}
+    support = constraints.nonnegative_integer
+
+    @property
+    def mean(self):
+        return self.rate
+
+    @property
+    def mode(self):
+        return self.rate.floor()
+
+    @property
+    def variance(self):
+        return self.rate
+
+    def __init__(self, rate, validate_args=None):
+        (self.rate,) = broadcast_all(rate)
+        if isinstance(rate, Number):
+            batch_shape = torch.Size()
+        else:
+            batch_shape = self.rate.size()
+        super().__init__(batch_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Poisson, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new.rate = self.rate.expand(batch_shape)
+        super(Poisson, new).__init__(batch_shape, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    def sample(self, sample_shape=torch.Size()):
+        shape = self._extended_shape(sample_shape)
+        with torch.no_grad():
+            return torch.poisson(self.rate.expand(shape))
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        rate, value = broadcast_all(self.rate, value)
+        return value.xlogy(rate) - rate - (value + 1).lgamma()
+
+    @property
+    def _natural_params(self):
+        return (torch.log(self.rate),)
+
+    def _log_normalizer(self, x):
+        return torch.exp(x)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/relaxed_bernoulli.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/relaxed_bernoulli.py
new file mode 100644
index 0000000000000000000000000000000000000000..04f70519e805cdfc47f9546f8b318456a46c62c9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/relaxed_bernoulli.py
@@ -0,0 +1,152 @@
+# mypy: allow-untyped-defs
+from numbers import Number
+
+import torch
+from torch.distributions import constraints
+from torch.distributions.distribution import Distribution
+from torch.distributions.transformed_distribution import TransformedDistribution
+from torch.distributions.transforms import SigmoidTransform
+from torch.distributions.utils import (
+    broadcast_all,
+    clamp_probs,
+    lazy_property,
+    logits_to_probs,
+    probs_to_logits,
+)
+from torch.types import _size
+
+
+__all__ = ["LogitRelaxedBernoulli", "RelaxedBernoulli"]
+
+
+class LogitRelaxedBernoulli(Distribution):
+    r"""
+    Creates a LogitRelaxedBernoulli distribution parameterized by :attr:`probs`
+    or :attr:`logits` (but not both), which is the logit of a RelaxedBernoulli
+    distribution.
+
+    Samples are logits of values in (0, 1). See [1] for more details.
+
+    Args:
+        temperature (Tensor): relaxation temperature
+        probs (Number, Tensor): the probability of sampling `1`
+        logits (Number, Tensor): the log-odds of sampling `1`
+
+    [1] The Concrete Distribution: A Continuous Relaxation of Discrete Random
+    Variables (Maddison et al., 2017)
+
+    [2] Categorical Reparametrization with Gumbel-Softmax
+    (Jang et al., 2017)
+    """
+    arg_constraints = {"probs": constraints.unit_interval, "logits": constraints.real}
+    support = constraints.real
+
+    def __init__(self, temperature, probs=None, logits=None, validate_args=None):
+        self.temperature = temperature
+        if (probs is None) == (logits is None):
+            raise ValueError(
+                "Either `probs` or `logits` must be specified, but not both."
+            )
+        if probs is not None:
+            is_scalar = isinstance(probs, Number)
+            (self.probs,) = broadcast_all(probs)
+        else:
+            is_scalar = isinstance(logits, Number)
+            (self.logits,) = broadcast_all(logits)
+        self._param = self.probs if probs is not None else self.logits
+        if is_scalar:
+            batch_shape = torch.Size()
+        else:
+            batch_shape = self._param.size()
+        super().__init__(batch_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(LogitRelaxedBernoulli, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new.temperature = self.temperature
+        if "probs" in self.__dict__:
+            new.probs = self.probs.expand(batch_shape)
+            new._param = new.probs
+        if "logits" in self.__dict__:
+            new.logits = self.logits.expand(batch_shape)
+            new._param = new.logits
+        super(LogitRelaxedBernoulli, new).__init__(batch_shape, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    def _new(self, *args, **kwargs):
+        return self._param.new(*args, **kwargs)
+
+    @lazy_property
+    def logits(self):
+        return probs_to_logits(self.probs, is_binary=True)
+
+    @lazy_property
+    def probs(self):
+        return logits_to_probs(self.logits, is_binary=True)
+
+    @property
+    def param_shape(self):
+        return self._param.size()
+
+    def rsample(self, sample_shape: _size = torch.Size()) -> torch.Tensor:
+        shape = self._extended_shape(sample_shape)
+        probs = clamp_probs(self.probs.expand(shape))
+        uniforms = clamp_probs(
+            torch.rand(shape, dtype=probs.dtype, device=probs.device)
+        )
+        return (
+            uniforms.log() - (-uniforms).log1p() + probs.log() - (-probs).log1p()
+        ) / self.temperature
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        logits, value = broadcast_all(self.logits, value)
+        diff = logits - value.mul(self.temperature)
+        return self.temperature.log() + diff - 2 * diff.exp().log1p()
+
+
+class RelaxedBernoulli(TransformedDistribution):
+    r"""
+    Creates a RelaxedBernoulli distribution, parametrized by
+    :attr:`temperature`, and either :attr:`probs` or :attr:`logits`
+    (but not both). This is a relaxed version of the `Bernoulli` distribution,
+    so the values are in (0, 1), and has reparametrizable samples.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = RelaxedBernoulli(torch.tensor([2.2]),
+        ...                      torch.tensor([0.1, 0.2, 0.3, 0.99]))
+        >>> m.sample()
+        tensor([ 0.2951,  0.3442,  0.8918,  0.9021])
+
+    Args:
+        temperature (Tensor): relaxation temperature
+        probs (Number, Tensor): the probability of sampling `1`
+        logits (Number, Tensor): the log-odds of sampling `1`
+    """
+    arg_constraints = {"probs": constraints.unit_interval, "logits": constraints.real}
+    support = constraints.unit_interval
+    has_rsample = True
+
+    def __init__(self, temperature, probs=None, logits=None, validate_args=None):
+        base_dist = LogitRelaxedBernoulli(temperature, probs, logits)
+        super().__init__(base_dist, SigmoidTransform(), validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(RelaxedBernoulli, _instance)
+        return super().expand(batch_shape, _instance=new)
+
+    @property
+    def temperature(self):
+        return self.base_dist.temperature
+
+    @property
+    def logits(self):
+        return self.base_dist.logits
+
+    @property
+    def probs(self):
+        return self.base_dist.probs
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/relaxed_categorical.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/relaxed_categorical.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f9b027f1a500c8bd91bbf07e76fe746b9866d55
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/relaxed_categorical.py
@@ -0,0 +1,142 @@
+# mypy: allow-untyped-defs
+import torch
+from torch.distributions import constraints
+from torch.distributions.categorical import Categorical
+from torch.distributions.distribution import Distribution
+from torch.distributions.transformed_distribution import TransformedDistribution
+from torch.distributions.transforms import ExpTransform
+from torch.distributions.utils import broadcast_all, clamp_probs
+from torch.types import _size
+
+
+__all__ = ["ExpRelaxedCategorical", "RelaxedOneHotCategorical"]
+
+
+class ExpRelaxedCategorical(Distribution):
+    r"""
+    Creates a ExpRelaxedCategorical parameterized by
+    :attr:`temperature`, and either :attr:`probs` or :attr:`logits` (but not both).
+    Returns the log of a point in the simplex. Based on the interface to
+    :class:`OneHotCategorical`.
+
+    Implementation based on [1].
+
+    See also: :func:`torch.distributions.OneHotCategorical`
+
+    Args:
+        temperature (Tensor): relaxation temperature
+        probs (Tensor): event probabilities
+        logits (Tensor): unnormalized log probability for each event
+
+    [1] The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
+    (Maddison et al., 2017)
+
+    [2] Categorical Reparametrization with Gumbel-Softmax
+    (Jang et al., 2017)
+    """
+    arg_constraints = {"probs": constraints.simplex, "logits": constraints.real_vector}
+    support = (
+        constraints.real_vector
+    )  # The true support is actually a submanifold of this.
+    has_rsample = True
+
+    def __init__(self, temperature, probs=None, logits=None, validate_args=None):
+        self._categorical = Categorical(probs, logits)
+        self.temperature = temperature
+        batch_shape = self._categorical.batch_shape
+        event_shape = self._categorical.param_shape[-1:]
+        super().__init__(batch_shape, event_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(ExpRelaxedCategorical, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new.temperature = self.temperature
+        new._categorical = self._categorical.expand(batch_shape)
+        super(ExpRelaxedCategorical, new).__init__(
+            batch_shape, self.event_shape, validate_args=False
+        )
+        new._validate_args = self._validate_args
+        return new
+
+    def _new(self, *args, **kwargs):
+        return self._categorical._new(*args, **kwargs)
+
+    @property
+    def param_shape(self):
+        return self._categorical.param_shape
+
+    @property
+    def logits(self):
+        return self._categorical.logits
+
+    @property
+    def probs(self):
+        return self._categorical.probs
+
+    def rsample(self, sample_shape: _size = torch.Size()) -> torch.Tensor:
+        shape = self._extended_shape(sample_shape)
+        uniforms = clamp_probs(
+            torch.rand(shape, dtype=self.logits.dtype, device=self.logits.device)
+        )
+        gumbels = -((-(uniforms.log())).log())
+        scores = (self.logits + gumbels) / self.temperature
+        return scores - scores.logsumexp(dim=-1, keepdim=True)
+
+    def log_prob(self, value):
+        K = self._categorical._num_events
+        if self._validate_args:
+            self._validate_sample(value)
+        logits, value = broadcast_all(self.logits, value)
+        log_scale = torch.full_like(
+            self.temperature, float(K)
+        ).lgamma() - self.temperature.log().mul(-(K - 1))
+        score = logits - value.mul(self.temperature)
+        score = (score - score.logsumexp(dim=-1, keepdim=True)).sum(-1)
+        return score + log_scale
+
+
+class RelaxedOneHotCategorical(TransformedDistribution):
+    r"""
+    Creates a RelaxedOneHotCategorical distribution parametrized by
+    :attr:`temperature`, and either :attr:`probs` or :attr:`logits`.
+    This is a relaxed version of the :class:`OneHotCategorical` distribution, so
+    its samples are on simplex, and are reparametrizable.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]),
+        ...                              torch.tensor([0.1, 0.2, 0.3, 0.4]))
+        >>> m.sample()
+        tensor([ 0.1294,  0.2324,  0.3859,  0.2523])
+
+    Args:
+        temperature (Tensor): relaxation temperature
+        probs (Tensor): event probabilities
+        logits (Tensor): unnormalized log probability for each event
+    """
+    arg_constraints = {"probs": constraints.simplex, "logits": constraints.real_vector}
+    support = constraints.simplex
+    has_rsample = True
+
+    def __init__(self, temperature, probs=None, logits=None, validate_args=None):
+        base_dist = ExpRelaxedCategorical(
+            temperature, probs, logits, validate_args=validate_args
+        )
+        super().__init__(base_dist, ExpTransform(), validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(RelaxedOneHotCategorical, _instance)
+        return super().expand(batch_shape, _instance=new)
+
+    @property
+    def temperature(self):
+        return self.base_dist.temperature
+
+    @property
+    def logits(self):
+        return self.base_dist.logits
+
+    @property
+    def probs(self):
+        return self.base_dist.probs
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/studentT.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/studentT.py
new file mode 100644
index 0000000000000000000000000000000000000000..50b2b995bd501dcf0c09d6b4f35aa1910f1a3182
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/studentT.py
@@ -0,0 +1,119 @@
+# mypy: allow-untyped-defs
+import math
+
+import torch
+from torch import inf, nan
+from torch.distributions import Chi2, constraints
+from torch.distributions.distribution import Distribution
+from torch.distributions.utils import _standard_normal, broadcast_all
+from torch.types import _size
+
+
+__all__ = ["StudentT"]
+
+
+class StudentT(Distribution):
+    r"""
+    Creates a Student's t-distribution parameterized by degree of
+    freedom :attr:`df`, mean :attr:`loc` and scale :attr:`scale`.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = StudentT(torch.tensor([2.0]))
+        >>> m.sample()  # Student's t-distributed with degrees of freedom=2
+        tensor([ 0.1046])
+
+    Args:
+        df (float or Tensor): degrees of freedom
+        loc (float or Tensor): mean of the distribution
+        scale (float or Tensor): scale of the distribution
+    """
+    arg_constraints = {
+        "df": constraints.positive,
+        "loc": constraints.real,
+        "scale": constraints.positive,
+    }
+    support = constraints.real
+    has_rsample = True
+
+    @property
+    def mean(self):
+        m = self.loc.clone(memory_format=torch.contiguous_format)
+        m[self.df <= 1] = nan
+        return m
+
+    @property
+    def mode(self):
+        return self.loc
+
+    @property
+    def variance(self):
+        m = self.df.clone(memory_format=torch.contiguous_format)
+        m[self.df > 2] = (
+            self.scale[self.df > 2].pow(2)
+            * self.df[self.df > 2]
+            / (self.df[self.df > 2] - 2)
+        )
+        m[(self.df <= 2) & (self.df > 1)] = inf
+        m[self.df <= 1] = nan
+        return m
+
+    def __init__(self, df, loc=0.0, scale=1.0, validate_args=None):
+        self.df, self.loc, self.scale = broadcast_all(df, loc, scale)
+        self._chi2 = Chi2(self.df)
+        batch_shape = self.df.size()
+        super().__init__(batch_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(StudentT, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new.df = self.df.expand(batch_shape)
+        new.loc = self.loc.expand(batch_shape)
+        new.scale = self.scale.expand(batch_shape)
+        new._chi2 = self._chi2.expand(batch_shape)
+        super(StudentT, new).__init__(batch_shape, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    def rsample(self, sample_shape: _size = torch.Size()) -> torch.Tensor:
+        # NOTE: This does not agree with scipy implementation as much as other distributions.
+        # (see https://github.com/fritzo/notebooks/blob/master/debug-student-t.ipynb). Using DoubleTensor
+        # parameters seems to help.
+
+        #   X ~ Normal(0, 1)
+        #   Z ~ Chi2(df)
+        #   Y = X / sqrt(Z / df) ~ StudentT(df)
+        shape = self._extended_shape(sample_shape)
+        X = _standard_normal(shape, dtype=self.df.dtype, device=self.df.device)
+        Z = self._chi2.rsample(sample_shape)
+        Y = X * torch.rsqrt(Z / self.df)
+        return self.loc + self.scale * Y
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        y = (value - self.loc) / self.scale
+        Z = (
+            self.scale.log()
+            + 0.5 * self.df.log()
+            + 0.5 * math.log(math.pi)
+            + torch.lgamma(0.5 * self.df)
+            - torch.lgamma(0.5 * (self.df + 1.0))
+        )
+        return -0.5 * (self.df + 1.0) * torch.log1p(y**2.0 / self.df) - Z
+
+    def entropy(self):
+        lbeta = (
+            torch.lgamma(0.5 * self.df)
+            + math.lgamma(0.5)
+            - torch.lgamma(0.5 * (self.df + 1))
+        )
+        return (
+            self.scale.log()
+            + 0.5
+            * (self.df + 1)
+            * (torch.digamma(0.5 * (self.df + 1)) - torch.digamma(0.5 * self.df))
+            + 0.5 * self.df.log()
+            + lbeta
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/transformed_distribution.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/transformed_distribution.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9450accea23057e01aeb7bb9d33e468124f7499
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/transformed_distribution.py
@@ -0,0 +1,216 @@
+# mypy: allow-untyped-defs
+from typing import Dict
+
+import torch
+from torch.distributions import constraints
+from torch.distributions.distribution import Distribution
+from torch.distributions.independent import Independent
+from torch.distributions.transforms import ComposeTransform, Transform
+from torch.distributions.utils import _sum_rightmost
+from torch.types import _size
+
+
+__all__ = ["TransformedDistribution"]
+
+
+class TransformedDistribution(Distribution):
+    r"""
+    Extension of the Distribution class, which applies a sequence of Transforms
+    to a base distribution.  Let f be the composition of transforms applied::
+
+        X ~ BaseDistribution
+        Y = f(X) ~ TransformedDistribution(BaseDistribution, f)
+        log p(Y) = log p(X) + log |det (dX/dY)|
+
+    Note that the ``.event_shape`` of a :class:`TransformedDistribution` is the
+    maximum shape of its base distribution and its transforms, since transforms
+    can introduce correlations among events.
+
+    An example for the usage of :class:`TransformedDistribution` would be::
+
+        # Building a Logistic Distribution
+        # X ~ Uniform(0, 1)
+        # f = a + b * logit(X)
+        # Y ~ f(X) ~ Logistic(a, b)
+        base_distribution = Uniform(0, 1)
+        transforms = [SigmoidTransform().inv, AffineTransform(loc=a, scale=b)]
+        logistic = TransformedDistribution(base_distribution, transforms)
+
+    For more examples, please look at the implementations of
+    :class:`~torch.distributions.gumbel.Gumbel`,
+    :class:`~torch.distributions.half_cauchy.HalfCauchy`,
+    :class:`~torch.distributions.half_normal.HalfNormal`,
+    :class:`~torch.distributions.log_normal.LogNormal`,
+    :class:`~torch.distributions.pareto.Pareto`,
+    :class:`~torch.distributions.weibull.Weibull`,
+    :class:`~torch.distributions.relaxed_bernoulli.RelaxedBernoulli` and
+    :class:`~torch.distributions.relaxed_categorical.RelaxedOneHotCategorical`
+    """
+    arg_constraints: Dict[str, constraints.Constraint] = {}
+
+    def __init__(self, base_distribution, transforms, validate_args=None):
+        if isinstance(transforms, Transform):
+            self.transforms = [
+                transforms,
+            ]
+        elif isinstance(transforms, list):
+            if not all(isinstance(t, Transform) for t in transforms):
+                raise ValueError(
+                    "transforms must be a Transform or a list of Transforms"
+                )
+            self.transforms = transforms
+        else:
+            raise ValueError(
+                f"transforms must be a Transform or list, but was {transforms}"
+            )
+
+        # Reshape base_distribution according to transforms.
+        base_shape = base_distribution.batch_shape + base_distribution.event_shape
+        base_event_dim = len(base_distribution.event_shape)
+        transform = ComposeTransform(self.transforms)
+        if len(base_shape) < transform.domain.event_dim:
+            raise ValueError(
+                f"base_distribution needs to have shape with size at least {transform.domain.event_dim}, but got {base_shape}."
+            )
+        forward_shape = transform.forward_shape(base_shape)
+        expanded_base_shape = transform.inverse_shape(forward_shape)
+        if base_shape != expanded_base_shape:
+            base_batch_shape = expanded_base_shape[
+                : len(expanded_base_shape) - base_event_dim
+            ]
+            base_distribution = base_distribution.expand(base_batch_shape)
+        reinterpreted_batch_ndims = transform.domain.event_dim - base_event_dim
+        if reinterpreted_batch_ndims > 0:
+            base_distribution = Independent(
+                base_distribution, reinterpreted_batch_ndims
+            )
+        self.base_dist = base_distribution
+
+        # Compute shapes.
+        transform_change_in_event_dim = (
+            transform.codomain.event_dim - transform.domain.event_dim
+        )
+        event_dim = max(
+            transform.codomain.event_dim,  # the transform is coupled
+            base_event_dim + transform_change_in_event_dim,  # the base dist is coupled
+        )
+        assert len(forward_shape) >= event_dim
+        cut = len(forward_shape) - event_dim
+        batch_shape = forward_shape[:cut]
+        event_shape = forward_shape[cut:]
+        super().__init__(batch_shape, event_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(TransformedDistribution, _instance)
+        batch_shape = torch.Size(batch_shape)
+        shape = batch_shape + self.event_shape
+        for t in reversed(self.transforms):
+            shape = t.inverse_shape(shape)
+        base_batch_shape = shape[: len(shape) - len(self.base_dist.event_shape)]
+        new.base_dist = self.base_dist.expand(base_batch_shape)
+        new.transforms = self.transforms
+        super(TransformedDistribution, new).__init__(
+            batch_shape, self.event_shape, validate_args=False
+        )
+        new._validate_args = self._validate_args
+        return new
+
+    @constraints.dependent_property(is_discrete=False)
+    def support(self):
+        if not self.transforms:
+            return self.base_dist.support
+        support = self.transforms[-1].codomain
+        if len(self.event_shape) > support.event_dim:
+            support = constraints.independent(
+                support, len(self.event_shape) - support.event_dim
+            )
+        return support
+
+    @property
+    def has_rsample(self):
+        return self.base_dist.has_rsample
+
+    def sample(self, sample_shape=torch.Size()):
+        """
+        Generates a sample_shape shaped sample or sample_shape shaped batch of
+        samples if the distribution parameters are batched. Samples first from
+        base distribution and applies `transform()` for every transform in the
+        list.
+        """
+        with torch.no_grad():
+            x = self.base_dist.sample(sample_shape)
+            for transform in self.transforms:
+                x = transform(x)
+            return x
+
+    def rsample(self, sample_shape: _size = torch.Size()) -> torch.Tensor:
+        """
+        Generates a sample_shape shaped reparameterized sample or sample_shape
+        shaped batch of reparameterized samples if the distribution parameters
+        are batched. Samples first from base distribution and applies
+        `transform()` for every transform in the list.
+        """
+        x = self.base_dist.rsample(sample_shape)
+        for transform in self.transforms:
+            x = transform(x)
+        return x
+
+    def log_prob(self, value):
+        """
+        Scores the sample by inverting the transform(s) and computing the score
+        using the score of the base distribution and the log abs det jacobian.
+        """
+        if self._validate_args:
+            self._validate_sample(value)
+        event_dim = len(self.event_shape)
+        log_prob = 0.0
+        y = value
+        for transform in reversed(self.transforms):
+            x = transform.inv(y)
+            event_dim += transform.domain.event_dim - transform.codomain.event_dim
+            log_prob = log_prob - _sum_rightmost(
+                transform.log_abs_det_jacobian(x, y),
+                event_dim - transform.domain.event_dim,
+            )
+            y = x
+
+        log_prob = log_prob + _sum_rightmost(
+            self.base_dist.log_prob(y), event_dim - len(self.base_dist.event_shape)
+        )
+        return log_prob
+
+    def _monotonize_cdf(self, value):
+        """
+        This conditionally flips ``value -> 1-value`` to ensure :meth:`cdf` is
+        monotone increasing.
+        """
+        sign = 1
+        for transform in self.transforms:
+            sign = sign * transform.sign
+        if isinstance(sign, int) and sign == 1:
+            return value
+        return sign * (value - 0.5) + 0.5
+
+    def cdf(self, value):
+        """
+        Computes the cumulative distribution function by inverting the
+        transform(s) and computing the score of the base distribution.
+        """
+        for transform in self.transforms[::-1]:
+            value = transform.inv(value)
+        if self._validate_args:
+            self.base_dist._validate_sample(value)
+        value = self.base_dist.cdf(value)
+        value = self._monotonize_cdf(value)
+        return value
+
+    def icdf(self, value):
+        """
+        Computes the inverse cumulative distribution function using
+        transform(s) and computing the score of the base distribution.
+        """
+        value = self._monotonize_cdf(value)
+        value = self.base_dist.icdf(value)
+        for transform in self.transforms:
+            value = transform(value)
+        return value
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/transforms.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1d42983b00db6a27054f54b1d16f9519a3c1ff0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/transforms.py
@@ -0,0 +1,1247 @@
+# mypy: allow-untyped-defs
+import functools
+import math
+import numbers
+import operator
+import weakref
+from typing import List
+
+import torch
+import torch.nn.functional as F
+from torch.distributions import constraints
+from torch.distributions.utils import (
+    _sum_rightmost,
+    broadcast_all,
+    lazy_property,
+    tril_matrix_to_vec,
+    vec_to_tril_matrix,
+)
+from torch.nn.functional import pad, softplus
+
+
+__all__ = [
+    "AbsTransform",
+    "AffineTransform",
+    "CatTransform",
+    "ComposeTransform",
+    "CorrCholeskyTransform",
+    "CumulativeDistributionTransform",
+    "ExpTransform",
+    "IndependentTransform",
+    "LowerCholeskyTransform",
+    "PositiveDefiniteTransform",
+    "PowerTransform",
+    "ReshapeTransform",
+    "SigmoidTransform",
+    "SoftplusTransform",
+    "TanhTransform",
+    "SoftmaxTransform",
+    "StackTransform",
+    "StickBreakingTransform",
+    "Transform",
+    "identity_transform",
+]
+
+
+class Transform:
+    """
+    Abstract class for invertable transformations with computable log
+    det jacobians. They are primarily used in
+    :class:`torch.distributions.TransformedDistribution`.
+
+    Caching is useful for transforms whose inverses are either expensive or
+    numerically unstable. Note that care must be taken with memoized values
+    since the autograd graph may be reversed. For example while the following
+    works with or without caching::
+
+        y = t(x)
+        t.log_abs_det_jacobian(x, y).backward()  # x will receive gradients.
+
+    However the following will error when caching due to dependency reversal::
+
+        y = t(x)
+        z = t.inv(y)
+        grad(z.sum(), [y])  # error because z is x
+
+    Derived classes should implement one or both of :meth:`_call` or
+    :meth:`_inverse`. Derived classes that set `bijective=True` should also
+    implement :meth:`log_abs_det_jacobian`.
+
+    Args:
+        cache_size (int): Size of cache. If zero, no caching is done. If one,
+            the latest single value is cached. Only 0 and 1 are supported.
+
+    Attributes:
+        domain (:class:`~torch.distributions.constraints.Constraint`):
+            The constraint representing valid inputs to this transform.
+        codomain (:class:`~torch.distributions.constraints.Constraint`):
+            The constraint representing valid outputs to this transform
+            which are inputs to the inverse transform.
+        bijective (bool): Whether this transform is bijective. A transform
+            ``t`` is bijective iff ``t.inv(t(x)) == x`` and
+            ``t(t.inv(y)) == y`` for every ``x`` in the domain and ``y`` in
+            the codomain. Transforms that are not bijective should at least
+            maintain the weaker pseudoinverse properties
+            ``t(t.inv(t(x)) == t(x)`` and ``t.inv(t(t.inv(y))) == t.inv(y)``.
+        sign (int or Tensor): For bijective univariate transforms, this
+            should be +1 or -1 depending on whether transform is monotone
+            increasing or decreasing.
+    """
+
+    bijective = False
+    domain: constraints.Constraint
+    codomain: constraints.Constraint
+
+    def __init__(self, cache_size=0):
+        self._cache_size = cache_size
+        self._inv = None
+        if cache_size == 0:
+            pass  # default behavior
+        elif cache_size == 1:
+            self._cached_x_y = None, None
+        else:
+            raise ValueError("cache_size must be 0 or 1")
+        super().__init__()
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["_inv"] = None
+        return state
+
+    @property
+    def event_dim(self):
+        if self.domain.event_dim == self.codomain.event_dim:
+            return self.domain.event_dim
+        raise ValueError("Please use either .domain.event_dim or .codomain.event_dim")
+
+    @property
+    def inv(self):
+        """
+        Returns the inverse :class:`Transform` of this transform.
+        This should satisfy ``t.inv.inv is t``.
+        """
+        inv = None
+        if self._inv is not None:
+            inv = self._inv()
+        if inv is None:
+            inv = _InverseTransform(self)
+            self._inv = weakref.ref(inv)
+        return inv
+
+    @property
+    def sign(self):
+        """
+        Returns the sign of the determinant of the Jacobian, if applicable.
+        In general this only makes sense for bijective transforms.
+        """
+        raise NotImplementedError
+
+    def with_cache(self, cache_size=1):
+        if self._cache_size == cache_size:
+            return self
+        if type(self).__init__ is Transform.__init__:
+            return type(self)(cache_size=cache_size)
+        raise NotImplementedError(f"{type(self)}.with_cache is not implemented")
+
+    def __eq__(self, other):
+        return self is other
+
+    def __ne__(self, other):
+        # Necessary for Python2
+        return not self.__eq__(other)
+
+    def __call__(self, x):
+        """
+        Computes the transform `x => y`.
+        """
+        if self._cache_size == 0:
+            return self._call(x)
+        x_old, y_old = self._cached_x_y
+        if x is x_old:
+            return y_old
+        y = self._call(x)
+        self._cached_x_y = x, y
+        return y
+
+    def _inv_call(self, y):
+        """
+        Inverts the transform `y => x`.
+        """
+        if self._cache_size == 0:
+            return self._inverse(y)
+        x_old, y_old = self._cached_x_y
+        if y is y_old:
+            return x_old
+        x = self._inverse(y)
+        self._cached_x_y = x, y
+        return x
+
+    def _call(self, x):
+        """
+        Abstract method to compute forward transformation.
+        """
+        raise NotImplementedError
+
+    def _inverse(self, y):
+        """
+        Abstract method to compute inverse transformation.
+        """
+        raise NotImplementedError
+
+    def log_abs_det_jacobian(self, x, y):
+        """
+        Computes the log det jacobian `log |dy/dx|` given input and output.
+        """
+        raise NotImplementedError
+
+    def __repr__(self):
+        return self.__class__.__name__ + "()"
+
+    def forward_shape(self, shape):
+        """
+        Infers the shape of the forward computation, given the input shape.
+        Defaults to preserving shape.
+        """
+        return shape
+
+    def inverse_shape(self, shape):
+        """
+        Infers the shapes of the inverse computation, given the output shape.
+        Defaults to preserving shape.
+        """
+        return shape
+
+
+class _InverseTransform(Transform):
+    """
+    Inverts a single :class:`Transform`.
+    This class is private; please instead use the ``Transform.inv`` property.
+    """
+
+    def __init__(self, transform: Transform):
+        super().__init__(cache_size=transform._cache_size)
+        self._inv: Transform = transform
+
+    @constraints.dependent_property(is_discrete=False)
+    def domain(self):
+        assert self._inv is not None
+        return self._inv.codomain
+
+    @constraints.dependent_property(is_discrete=False)
+    def codomain(self):
+        assert self._inv is not None
+        return self._inv.domain
+
+    @property
+    def bijective(self):
+        assert self._inv is not None
+        return self._inv.bijective
+
+    @property
+    def sign(self):
+        assert self._inv is not None
+        return self._inv.sign
+
+    @property
+    def inv(self):
+        return self._inv
+
+    def with_cache(self, cache_size=1):
+        assert self._inv is not None
+        return self.inv.with_cache(cache_size).inv
+
+    def __eq__(self, other):
+        if not isinstance(other, _InverseTransform):
+            return False
+        assert self._inv is not None
+        return self._inv == other._inv
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({repr(self._inv)})"
+
+    def __call__(self, x):
+        assert self._inv is not None
+        return self._inv._inv_call(x)
+
+    def log_abs_det_jacobian(self, x, y):
+        assert self._inv is not None
+        return -self._inv.log_abs_det_jacobian(y, x)
+
+    def forward_shape(self, shape):
+        return self._inv.inverse_shape(shape)
+
+    def inverse_shape(self, shape):
+        return self._inv.forward_shape(shape)
+
+
+class ComposeTransform(Transform):
+    """
+    Composes multiple transforms in a chain.
+    The transforms being composed are responsible for caching.
+
+    Args:
+        parts (list of :class:`Transform`): A list of transforms to compose.
+        cache_size (int): Size of cache. If zero, no caching is done. If one,
+            the latest single value is cached. Only 0 and 1 are supported.
+    """
+
+    def __init__(self, parts: List[Transform], cache_size=0):
+        if cache_size:
+            parts = [part.with_cache(cache_size) for part in parts]
+        super().__init__(cache_size=cache_size)
+        self.parts = parts
+
+    def __eq__(self, other):
+        if not isinstance(other, ComposeTransform):
+            return False
+        return self.parts == other.parts
+
+    @constraints.dependent_property(is_discrete=False)
+    def domain(self):
+        if not self.parts:
+            return constraints.real
+        domain = self.parts[0].domain
+        # Adjust event_dim to be maximum among all parts.
+        event_dim = self.parts[-1].codomain.event_dim
+        for part in reversed(self.parts):
+            event_dim += part.domain.event_dim - part.codomain.event_dim
+            event_dim = max(event_dim, part.domain.event_dim)
+        assert event_dim >= domain.event_dim
+        if event_dim > domain.event_dim:
+            domain = constraints.independent(domain, event_dim - domain.event_dim)
+        return domain
+
+    @constraints.dependent_property(is_discrete=False)
+    def codomain(self):
+        if not self.parts:
+            return constraints.real
+        codomain = self.parts[-1].codomain
+        # Adjust event_dim to be maximum among all parts.
+        event_dim = self.parts[0].domain.event_dim
+        for part in self.parts:
+            event_dim += part.codomain.event_dim - part.domain.event_dim
+            event_dim = max(event_dim, part.codomain.event_dim)
+        assert event_dim >= codomain.event_dim
+        if event_dim > codomain.event_dim:
+            codomain = constraints.independent(codomain, event_dim - codomain.event_dim)
+        return codomain
+
+    @lazy_property
+    def bijective(self):
+        return all(p.bijective for p in self.parts)
+
+    @lazy_property
+    def sign(self):
+        sign = 1
+        for p in self.parts:
+            sign = sign * p.sign
+        return sign
+
+    @property
+    def inv(self):
+        inv = None
+        if self._inv is not None:
+            inv = self._inv()
+        if inv is None:
+            inv = ComposeTransform([p.inv for p in reversed(self.parts)])
+            self._inv = weakref.ref(inv)
+            inv._inv = weakref.ref(self)
+        return inv
+
+    def with_cache(self, cache_size=1):
+        if self._cache_size == cache_size:
+            return self
+        return ComposeTransform(self.parts, cache_size=cache_size)
+
+    def __call__(self, x):
+        for part in self.parts:
+            x = part(x)
+        return x
+
+    def log_abs_det_jacobian(self, x, y):
+        if not self.parts:
+            return torch.zeros_like(x)
+
+        # Compute intermediates. This will be free if parts[:-1] are all cached.
+        xs = [x]
+        for part in self.parts[:-1]:
+            xs.append(part(xs[-1]))
+        xs.append(y)
+
+        terms = []
+        event_dim = self.domain.event_dim
+        for part, x, y in zip(self.parts, xs[:-1], xs[1:]):
+            terms.append(
+                _sum_rightmost(
+                    part.log_abs_det_jacobian(x, y), event_dim - part.domain.event_dim
+                )
+            )
+            event_dim += part.codomain.event_dim - part.domain.event_dim
+        return functools.reduce(operator.add, terms)
+
+    def forward_shape(self, shape):
+        for part in self.parts:
+            shape = part.forward_shape(shape)
+        return shape
+
+    def inverse_shape(self, shape):
+        for part in reversed(self.parts):
+            shape = part.inverse_shape(shape)
+        return shape
+
+    def __repr__(self):
+        fmt_string = self.__class__.__name__ + "(\n    "
+        fmt_string += ",\n    ".join([p.__repr__() for p in self.parts])
+        fmt_string += "\n)"
+        return fmt_string
+
+
+identity_transform = ComposeTransform([])
+
+
+class IndependentTransform(Transform):
+    """
+    Wrapper around another transform to treat
+    ``reinterpreted_batch_ndims``-many extra of the right most dimensions as
+    dependent. This has no effect on the forward or backward transforms, but
+    does sum out ``reinterpreted_batch_ndims``-many of the rightmost dimensions
+    in :meth:`log_abs_det_jacobian`.
+
+    Args:
+        base_transform (:class:`Transform`): A base transform.
+        reinterpreted_batch_ndims (int): The number of extra rightmost
+            dimensions to treat as dependent.
+    """
+
+    def __init__(self, base_transform, reinterpreted_batch_ndims, cache_size=0):
+        super().__init__(cache_size=cache_size)
+        self.base_transform = base_transform.with_cache(cache_size)
+        self.reinterpreted_batch_ndims = reinterpreted_batch_ndims
+
+    def with_cache(self, cache_size=1):
+        if self._cache_size == cache_size:
+            return self
+        return IndependentTransform(
+            self.base_transform, self.reinterpreted_batch_ndims, cache_size=cache_size
+        )
+
+    @constraints.dependent_property(is_discrete=False)
+    def domain(self):
+        return constraints.independent(
+            self.base_transform.domain, self.reinterpreted_batch_ndims
+        )
+
+    @constraints.dependent_property(is_discrete=False)
+    def codomain(self):
+        return constraints.independent(
+            self.base_transform.codomain, self.reinterpreted_batch_ndims
+        )
+
+    @property
+    def bijective(self):
+        return self.base_transform.bijective
+
+    @property
+    def sign(self):
+        return self.base_transform.sign
+
+    def _call(self, x):
+        if x.dim() < self.domain.event_dim:
+            raise ValueError("Too few dimensions on input")
+        return self.base_transform(x)
+
+    def _inverse(self, y):
+        if y.dim() < self.codomain.event_dim:
+            raise ValueError("Too few dimensions on input")
+        return self.base_transform.inv(y)
+
+    def log_abs_det_jacobian(self, x, y):
+        result = self.base_transform.log_abs_det_jacobian(x, y)
+        result = _sum_rightmost(result, self.reinterpreted_batch_ndims)
+        return result
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({repr(self.base_transform)}, {self.reinterpreted_batch_ndims})"
+
+    def forward_shape(self, shape):
+        return self.base_transform.forward_shape(shape)
+
+    def inverse_shape(self, shape):
+        return self.base_transform.inverse_shape(shape)
+
+
+class ReshapeTransform(Transform):
+    """
+    Unit Jacobian transform to reshape the rightmost part of a tensor.
+
+    Note that ``in_shape`` and ``out_shape`` must have the same number of
+    elements, just as for :meth:`torch.Tensor.reshape`.
+
+    Arguments:
+        in_shape (torch.Size): The input event shape.
+        out_shape (torch.Size): The output event shape.
+    """
+
+    bijective = True
+
+    def __init__(self, in_shape, out_shape, cache_size=0):
+        self.in_shape = torch.Size(in_shape)
+        self.out_shape = torch.Size(out_shape)
+        if self.in_shape.numel() != self.out_shape.numel():
+            raise ValueError("in_shape, out_shape have different numbers of elements")
+        super().__init__(cache_size=cache_size)
+
+    @constraints.dependent_property
+    def domain(self):
+        return constraints.independent(constraints.real, len(self.in_shape))
+
+    @constraints.dependent_property
+    def codomain(self):
+        return constraints.independent(constraints.real, len(self.out_shape))
+
+    def with_cache(self, cache_size=1):
+        if self._cache_size == cache_size:
+            return self
+        return ReshapeTransform(self.in_shape, self.out_shape, cache_size=cache_size)
+
+    def _call(self, x):
+        batch_shape = x.shape[: x.dim() - len(self.in_shape)]
+        return x.reshape(batch_shape + self.out_shape)
+
+    def _inverse(self, y):
+        batch_shape = y.shape[: y.dim() - len(self.out_shape)]
+        return y.reshape(batch_shape + self.in_shape)
+
+    def log_abs_det_jacobian(self, x, y):
+        batch_shape = x.shape[: x.dim() - len(self.in_shape)]
+        return x.new_zeros(batch_shape)
+
+    def forward_shape(self, shape):
+        if len(shape) < len(self.in_shape):
+            raise ValueError("Too few dimensions on input")
+        cut = len(shape) - len(self.in_shape)
+        if shape[cut:] != self.in_shape:
+            raise ValueError(
+                f"Shape mismatch: expected {shape[cut:]} but got {self.in_shape}"
+            )
+        return shape[:cut] + self.out_shape
+
+    def inverse_shape(self, shape):
+        if len(shape) < len(self.out_shape):
+            raise ValueError("Too few dimensions on input")
+        cut = len(shape) - len(self.out_shape)
+        if shape[cut:] != self.out_shape:
+            raise ValueError(
+                f"Shape mismatch: expected {shape[cut:]} but got {self.out_shape}"
+            )
+        return shape[:cut] + self.in_shape
+
+
+class ExpTransform(Transform):
+    r"""
+    Transform via the mapping :math:`y = \exp(x)`.
+    """
+    domain = constraints.real
+    codomain = constraints.positive
+    bijective = True
+    sign = +1
+
+    def __eq__(self, other):
+        return isinstance(other, ExpTransform)
+
+    def _call(self, x):
+        return x.exp()
+
+    def _inverse(self, y):
+        return y.log()
+
+    def log_abs_det_jacobian(self, x, y):
+        return x
+
+
+class PowerTransform(Transform):
+    r"""
+    Transform via the mapping :math:`y = x^{\text{exponent}}`.
+    """
+    domain = constraints.positive
+    codomain = constraints.positive
+    bijective = True
+
+    def __init__(self, exponent, cache_size=0):
+        super().__init__(cache_size=cache_size)
+        (self.exponent,) = broadcast_all(exponent)
+
+    def with_cache(self, cache_size=1):
+        if self._cache_size == cache_size:
+            return self
+        return PowerTransform(self.exponent, cache_size=cache_size)
+
+    @lazy_property
+    def sign(self):
+        return self.exponent.sign()
+
+    def __eq__(self, other):
+        if not isinstance(other, PowerTransform):
+            return False
+        return self.exponent.eq(other.exponent).all().item()
+
+    def _call(self, x):
+        return x.pow(self.exponent)
+
+    def _inverse(self, y):
+        return y.pow(1 / self.exponent)
+
+    def log_abs_det_jacobian(self, x, y):
+        return (self.exponent * y / x).abs().log()
+
+    def forward_shape(self, shape):
+        return torch.broadcast_shapes(shape, getattr(self.exponent, "shape", ()))
+
+    def inverse_shape(self, shape):
+        return torch.broadcast_shapes(shape, getattr(self.exponent, "shape", ()))
+
+
+def _clipped_sigmoid(x):
+    finfo = torch.finfo(x.dtype)
+    return torch.clamp(torch.sigmoid(x), min=finfo.tiny, max=1.0 - finfo.eps)
+
+
+class SigmoidTransform(Transform):
+    r"""
+    Transform via the mapping :math:`y = \frac{1}{1 + \exp(-x)}` and :math:`x = \text{logit}(y)`.
+    """
+    domain = constraints.real
+    codomain = constraints.unit_interval
+    bijective = True
+    sign = +1
+
+    def __eq__(self, other):
+        return isinstance(other, SigmoidTransform)
+
+    def _call(self, x):
+        return _clipped_sigmoid(x)
+
+    def _inverse(self, y):
+        finfo = torch.finfo(y.dtype)
+        y = y.clamp(min=finfo.tiny, max=1.0 - finfo.eps)
+        return y.log() - (-y).log1p()
+
+    def log_abs_det_jacobian(self, x, y):
+        return -F.softplus(-x) - F.softplus(x)
+
+
+class SoftplusTransform(Transform):
+    r"""
+    Transform via the mapping :math:`\text{Softplus}(x) = \log(1 + \exp(x))`.
+    The implementation reverts to the linear function when :math:`x > 20`.
+    """
+    domain = constraints.real
+    codomain = constraints.positive
+    bijective = True
+    sign = +1
+
+    def __eq__(self, other):
+        return isinstance(other, SoftplusTransform)
+
+    def _call(self, x):
+        return softplus(x)
+
+    def _inverse(self, y):
+        return (-y).expm1().neg().log() + y
+
+    def log_abs_det_jacobian(self, x, y):
+        return -softplus(-x)
+
+
+class TanhTransform(Transform):
+    r"""
+    Transform via the mapping :math:`y = \tanh(x)`.
+
+    It is equivalent to
+    ```
+    ComposeTransform([AffineTransform(0., 2.), SigmoidTransform(), AffineTransform(-1., 2.)])
+    ```
+    However this might not be numerically stable, thus it is recommended to use `TanhTransform`
+    instead.
+
+    Note that one should use `cache_size=1` when it comes to `NaN/Inf` values.
+
+    """
+    domain = constraints.real
+    codomain = constraints.interval(-1.0, 1.0)
+    bijective = True
+    sign = +1
+
+    def __eq__(self, other):
+        return isinstance(other, TanhTransform)
+
+    def _call(self, x):
+        return x.tanh()
+
+    def _inverse(self, y):
+        # We do not clamp to the boundary here as it may degrade the performance of certain algorithms.
+        # one should use `cache_size=1` instead
+        return torch.atanh(y)
+
+    def log_abs_det_jacobian(self, x, y):
+        # We use a formula that is more numerically stable, see details in the following link
+        # https://github.com/tensorflow/probability/blob/master/tensorflow_probability/python/bijectors/tanh.py#L69-L80
+        return 2.0 * (math.log(2.0) - x - softplus(-2.0 * x))
+
+
+class AbsTransform(Transform):
+    r"""
+    Transform via the mapping :math:`y = |x|`.
+    """
+    domain = constraints.real
+    codomain = constraints.positive
+
+    def __eq__(self, other):
+        return isinstance(other, AbsTransform)
+
+    def _call(self, x):
+        return x.abs()
+
+    def _inverse(self, y):
+        return y
+
+
+class AffineTransform(Transform):
+    r"""
+    Transform via the pointwise affine mapping :math:`y = \text{loc} + \text{scale} \times x`.
+
+    Args:
+        loc (Tensor or float): Location parameter.
+        scale (Tensor or float): Scale parameter.
+        event_dim (int): Optional size of `event_shape`. This should be zero
+            for univariate random variables, 1 for distributions over vectors,
+            2 for distributions over matrices, etc.
+    """
+    bijective = True
+
+    def __init__(self, loc, scale, event_dim=0, cache_size=0):
+        super().__init__(cache_size=cache_size)
+        self.loc = loc
+        self.scale = scale
+        self._event_dim = event_dim
+
+    @property
+    def event_dim(self):
+        return self._event_dim
+
+    @constraints.dependent_property(is_discrete=False)
+    def domain(self):
+        if self.event_dim == 0:
+            return constraints.real
+        return constraints.independent(constraints.real, self.event_dim)
+
+    @constraints.dependent_property(is_discrete=False)
+    def codomain(self):
+        if self.event_dim == 0:
+            return constraints.real
+        return constraints.independent(constraints.real, self.event_dim)
+
+    def with_cache(self, cache_size=1):
+        if self._cache_size == cache_size:
+            return self
+        return AffineTransform(
+            self.loc, self.scale, self.event_dim, cache_size=cache_size
+        )
+
+    def __eq__(self, other):
+        if not isinstance(other, AffineTransform):
+            return False
+
+        if isinstance(self.loc, numbers.Number) and isinstance(
+            other.loc, numbers.Number
+        ):
+            if self.loc != other.loc:
+                return False
+        else:
+            if not (self.loc == other.loc).all().item():
+                return False
+
+        if isinstance(self.scale, numbers.Number) and isinstance(
+            other.scale, numbers.Number
+        ):
+            if self.scale != other.scale:
+                return False
+        else:
+            if not (self.scale == other.scale).all().item():
+                return False
+
+        return True
+
+    @property
+    def sign(self):
+        if isinstance(self.scale, numbers.Real):
+            return 1 if float(self.scale) > 0 else -1 if float(self.scale) < 0 else 0
+        return self.scale.sign()
+
+    def _call(self, x):
+        return self.loc + self.scale * x
+
+    def _inverse(self, y):
+        return (y - self.loc) / self.scale
+
+    def log_abs_det_jacobian(self, x, y):
+        shape = x.shape
+        scale = self.scale
+        if isinstance(scale, numbers.Real):
+            result = torch.full_like(x, math.log(abs(scale)))
+        else:
+            result = torch.abs(scale).log()
+        if self.event_dim:
+            result_size = result.size()[: -self.event_dim] + (-1,)
+            result = result.view(result_size).sum(-1)
+            shape = shape[: -self.event_dim]
+        return result.expand(shape)
+
+    def forward_shape(self, shape):
+        return torch.broadcast_shapes(
+            shape, getattr(self.loc, "shape", ()), getattr(self.scale, "shape", ())
+        )
+
+    def inverse_shape(self, shape):
+        return torch.broadcast_shapes(
+            shape, getattr(self.loc, "shape", ()), getattr(self.scale, "shape", ())
+        )
+
+
+class CorrCholeskyTransform(Transform):
+    r"""
+    Transforms an uncontrained real vector :math:`x` with length :math:`D*(D-1)/2` into the
+    Cholesky factor of a D-dimension correlation matrix. This Cholesky factor is a lower
+    triangular matrix with positive diagonals and unit Euclidean norm for each row.
+    The transform is processed as follows:
+
+        1. First we convert x into a lower triangular matrix in row order.
+        2. For each row :math:`X_i` of the lower triangular part, we apply a *signed* version of
+           class :class:`StickBreakingTransform` to transform :math:`X_i` into a
+           unit Euclidean length vector using the following steps:
+           - Scales into the interval :math:`(-1, 1)` domain: :math:`r_i = \tanh(X_i)`.
+           - Transforms into an unsigned domain: :math:`z_i = r_i^2`.
+           - Applies :math:`s_i = StickBreakingTransform(z_i)`.
+           - Transforms back into signed domain: :math:`y_i = sign(r_i) * \sqrt{s_i}`.
+    """
+    domain = constraints.real_vector
+    codomain = constraints.corr_cholesky
+    bijective = True
+
+    def _call(self, x):
+        x = torch.tanh(x)
+        eps = torch.finfo(x.dtype).eps
+        x = x.clamp(min=-1 + eps, max=1 - eps)
+        r = vec_to_tril_matrix(x, diag=-1)
+        # apply stick-breaking on the squared values
+        # Note that y = sign(r) * sqrt(z * z1m_cumprod)
+        #             = (sign(r) * sqrt(z)) * sqrt(z1m_cumprod) = r * sqrt(z1m_cumprod)
+        z = r**2
+        z1m_cumprod_sqrt = (1 - z).sqrt().cumprod(-1)
+        # Diagonal elements must be 1.
+        r = r + torch.eye(r.shape[-1], dtype=r.dtype, device=r.device)
+        y = r * pad(z1m_cumprod_sqrt[..., :-1], [1, 0], value=1)
+        return y
+
+    def _inverse(self, y):
+        # inverse stick-breaking
+        # See: https://mc-stan.org/docs/2_18/reference-manual/cholesky-factors-of-correlation-matrices-1.html
+        y_cumsum = 1 - torch.cumsum(y * y, dim=-1)
+        y_cumsum_shifted = pad(y_cumsum[..., :-1], [1, 0], value=1)
+        y_vec = tril_matrix_to_vec(y, diag=-1)
+        y_cumsum_vec = tril_matrix_to_vec(y_cumsum_shifted, diag=-1)
+        t = y_vec / (y_cumsum_vec).sqrt()
+        # inverse of tanh
+        x = (t.log1p() - t.neg().log1p()) / 2
+        return x
+
+    def log_abs_det_jacobian(self, x, y, intermediates=None):
+        # Because domain and codomain are two spaces with different dimensions, determinant of
+        # Jacobian is not well-defined. We return `log_abs_det_jacobian` of `x` and the
+        # flattened lower triangular part of `y`.
+
+        # See: https://mc-stan.org/docs/2_18/reference-manual/cholesky-factors-of-correlation-matrices-1.html
+        y1m_cumsum = 1 - (y * y).cumsum(dim=-1)
+        # by taking diagonal=-2, we don't need to shift z_cumprod to the right
+        # also works for 2 x 2 matrix
+        y1m_cumsum_tril = tril_matrix_to_vec(y1m_cumsum, diag=-2)
+        stick_breaking_logdet = 0.5 * (y1m_cumsum_tril).log().sum(-1)
+        tanh_logdet = -2 * (x + softplus(-2 * x) - math.log(2.0)).sum(dim=-1)
+        return stick_breaking_logdet + tanh_logdet
+
+    def forward_shape(self, shape):
+        # Reshape from (..., N) to (..., D, D).
+        if len(shape) < 1:
+            raise ValueError("Too few dimensions on input")
+        N = shape[-1]
+        D = round((0.25 + 2 * N) ** 0.5 + 0.5)
+        if D * (D - 1) // 2 != N:
+            raise ValueError("Input is not a flattend lower-diagonal number")
+        return shape[:-1] + (D, D)
+
+    def inverse_shape(self, shape):
+        # Reshape from (..., D, D) to (..., N).
+        if len(shape) < 2:
+            raise ValueError("Too few dimensions on input")
+        if shape[-2] != shape[-1]:
+            raise ValueError("Input is not square")
+        D = shape[-1]
+        N = D * (D - 1) // 2
+        return shape[:-2] + (N,)
+
+
+class SoftmaxTransform(Transform):
+    r"""
+    Transform from unconstrained space to the simplex via :math:`y = \exp(x)` then
+    normalizing.
+
+    This is not bijective and cannot be used for HMC. However this acts mostly
+    coordinate-wise (except for the final normalization), and thus is
+    appropriate for coordinate-wise optimization algorithms.
+    """
+    domain = constraints.real_vector
+    codomain = constraints.simplex
+
+    def __eq__(self, other):
+        return isinstance(other, SoftmaxTransform)
+
+    def _call(self, x):
+        logprobs = x
+        probs = (logprobs - logprobs.max(-1, True)[0]).exp()
+        return probs / probs.sum(-1, True)
+
+    def _inverse(self, y):
+        probs = y
+        return probs.log()
+
+    def forward_shape(self, shape):
+        if len(shape) < 1:
+            raise ValueError("Too few dimensions on input")
+        return shape
+
+    def inverse_shape(self, shape):
+        if len(shape) < 1:
+            raise ValueError("Too few dimensions on input")
+        return shape
+
+
+class StickBreakingTransform(Transform):
+    """
+    Transform from unconstrained space to the simplex of one additional
+    dimension via a stick-breaking process.
+
+    This transform arises as an iterated sigmoid transform in a stick-breaking
+    construction of the `Dirichlet` distribution: the first logit is
+    transformed via sigmoid to the first probability and the probability of
+    everything else, and then the process recurses.
+
+    This is bijective and appropriate for use in HMC; however it mixes
+    coordinates together and is less appropriate for optimization.
+    """
+
+    domain = constraints.real_vector
+    codomain = constraints.simplex
+    bijective = True
+
+    def __eq__(self, other):
+        return isinstance(other, StickBreakingTransform)
+
+    def _call(self, x):
+        offset = x.shape[-1] + 1 - x.new_ones(x.shape[-1]).cumsum(-1)
+        z = _clipped_sigmoid(x - offset.log())
+        z_cumprod = (1 - z).cumprod(-1)
+        y = pad(z, [0, 1], value=1) * pad(z_cumprod, [1, 0], value=1)
+        return y
+
+    def _inverse(self, y):
+        y_crop = y[..., :-1]
+        offset = y.shape[-1] - y.new_ones(y_crop.shape[-1]).cumsum(-1)
+        sf = 1 - y_crop.cumsum(-1)
+        # we clamp to make sure that sf is positive which sometimes does not
+        # happen when y[-1] ~ 0 or y[:-1].sum() ~ 1
+        sf = torch.clamp(sf, min=torch.finfo(y.dtype).tiny)
+        x = y_crop.log() - sf.log() + offset.log()
+        return x
+
+    def log_abs_det_jacobian(self, x, y):
+        offset = x.shape[-1] + 1 - x.new_ones(x.shape[-1]).cumsum(-1)
+        x = x - offset.log()
+        # use the identity 1 - sigmoid(x) = exp(-x) * sigmoid(x)
+        detJ = (-x + F.logsigmoid(x) + y[..., :-1].log()).sum(-1)
+        return detJ
+
+    def forward_shape(self, shape):
+        if len(shape) < 1:
+            raise ValueError("Too few dimensions on input")
+        return shape[:-1] + (shape[-1] + 1,)
+
+    def inverse_shape(self, shape):
+        if len(shape) < 1:
+            raise ValueError("Too few dimensions on input")
+        return shape[:-1] + (shape[-1] - 1,)
+
+
+class LowerCholeskyTransform(Transform):
+    """
+    Transform from unconstrained matrices to lower-triangular matrices with
+    nonnegative diagonal entries.
+
+    This is useful for parameterizing positive definite matrices in terms of
+    their Cholesky factorization.
+    """
+
+    domain = constraints.independent(constraints.real, 2)
+    codomain = constraints.lower_cholesky
+
+    def __eq__(self, other):
+        return isinstance(other, LowerCholeskyTransform)
+
+    def _call(self, x):
+        return x.tril(-1) + x.diagonal(dim1=-2, dim2=-1).exp().diag_embed()
+
+    def _inverse(self, y):
+        return y.tril(-1) + y.diagonal(dim1=-2, dim2=-1).log().diag_embed()
+
+
+class PositiveDefiniteTransform(Transform):
+    """
+    Transform from unconstrained matrices to positive-definite matrices.
+    """
+
+    domain = constraints.independent(constraints.real, 2)
+    codomain = constraints.positive_definite  # type: ignore[assignment]
+
+    def __eq__(self, other):
+        return isinstance(other, PositiveDefiniteTransform)
+
+    def _call(self, x):
+        x = LowerCholeskyTransform()(x)
+        return x @ x.mT
+
+    def _inverse(self, y):
+        y = torch.linalg.cholesky(y)
+        return LowerCholeskyTransform().inv(y)
+
+
+class CatTransform(Transform):
+    """
+    Transform functor that applies a sequence of transforms `tseq`
+    component-wise to each submatrix at `dim`, of length `lengths[dim]`,
+    in a way compatible with :func:`torch.cat`.
+
+    Example::
+
+       x0 = torch.cat([torch.range(1, 10), torch.range(1, 10)], dim=0)
+       x = torch.cat([x0, x0], dim=0)
+       t0 = CatTransform([ExpTransform(), identity_transform], dim=0, lengths=[10, 10])
+       t = CatTransform([t0, t0], dim=0, lengths=[20, 20])
+       y = t(x)
+    """
+
+    transforms: List[Transform]
+
+    def __init__(self, tseq, dim=0, lengths=None, cache_size=0):
+        assert all(isinstance(t, Transform) for t in tseq)
+        if cache_size:
+            tseq = [t.with_cache(cache_size) for t in tseq]
+        super().__init__(cache_size=cache_size)
+        self.transforms = list(tseq)
+        if lengths is None:
+            lengths = [1] * len(self.transforms)
+        self.lengths = list(lengths)
+        assert len(self.lengths) == len(self.transforms)
+        self.dim = dim
+
+    @lazy_property
+    def event_dim(self):
+        return max(t.event_dim for t in self.transforms)
+
+    @lazy_property
+    def length(self):
+        return sum(self.lengths)
+
+    def with_cache(self, cache_size=1):
+        if self._cache_size == cache_size:
+            return self
+        return CatTransform(self.transforms, self.dim, self.lengths, cache_size)
+
+    def _call(self, x):
+        assert -x.dim() <= self.dim < x.dim()
+        assert x.size(self.dim) == self.length
+        yslices = []
+        start = 0
+        for trans, length in zip(self.transforms, self.lengths):
+            xslice = x.narrow(self.dim, start, length)
+            yslices.append(trans(xslice))
+            start = start + length  # avoid += for jit compat
+        return torch.cat(yslices, dim=self.dim)
+
+    def _inverse(self, y):
+        assert -y.dim() <= self.dim < y.dim()
+        assert y.size(self.dim) == self.length
+        xslices = []
+        start = 0
+        for trans, length in zip(self.transforms, self.lengths):
+            yslice = y.narrow(self.dim, start, length)
+            xslices.append(trans.inv(yslice))
+            start = start + length  # avoid += for jit compat
+        return torch.cat(xslices, dim=self.dim)
+
+    def log_abs_det_jacobian(self, x, y):
+        assert -x.dim() <= self.dim < x.dim()
+        assert x.size(self.dim) == self.length
+        assert -y.dim() <= self.dim < y.dim()
+        assert y.size(self.dim) == self.length
+        logdetjacs = []
+        start = 0
+        for trans, length in zip(self.transforms, self.lengths):
+            xslice = x.narrow(self.dim, start, length)
+            yslice = y.narrow(self.dim, start, length)
+            logdetjac = trans.log_abs_det_jacobian(xslice, yslice)
+            if trans.event_dim < self.event_dim:
+                logdetjac = _sum_rightmost(logdetjac, self.event_dim - trans.event_dim)
+            logdetjacs.append(logdetjac)
+            start = start + length  # avoid += for jit compat
+        # Decide whether to concatenate or sum.
+        dim = self.dim
+        if dim >= 0:
+            dim = dim - x.dim()
+        dim = dim + self.event_dim
+        if dim < 0:
+            return torch.cat(logdetjacs, dim=dim)
+        else:
+            return sum(logdetjacs)
+
+    @property
+    def bijective(self):
+        return all(t.bijective for t in self.transforms)
+
+    @constraints.dependent_property
+    def domain(self):
+        return constraints.cat(
+            [t.domain for t in self.transforms], self.dim, self.lengths
+        )
+
+    @constraints.dependent_property
+    def codomain(self):
+        return constraints.cat(
+            [t.codomain for t in self.transforms], self.dim, self.lengths
+        )
+
+
+class StackTransform(Transform):
+    """
+    Transform functor that applies a sequence of transforms `tseq`
+    component-wise to each submatrix at `dim`
+    in a way compatible with :func:`torch.stack`.
+
+    Example::
+
+       x = torch.stack([torch.range(1, 10), torch.range(1, 10)], dim=1)
+       t = StackTransform([ExpTransform(), identity_transform], dim=1)
+       y = t(x)
+    """
+
+    transforms: List[Transform]
+
+    def __init__(self, tseq, dim=0, cache_size=0):
+        assert all(isinstance(t, Transform) for t in tseq)
+        if cache_size:
+            tseq = [t.with_cache(cache_size) for t in tseq]
+        super().__init__(cache_size=cache_size)
+        self.transforms = list(tseq)
+        self.dim = dim
+
+    def with_cache(self, cache_size=1):
+        if self._cache_size == cache_size:
+            return self
+        return StackTransform(self.transforms, self.dim, cache_size)
+
+    def _slice(self, z):
+        return [z.select(self.dim, i) for i in range(z.size(self.dim))]
+
+    def _call(self, x):
+        assert -x.dim() <= self.dim < x.dim()
+        assert x.size(self.dim) == len(self.transforms)
+        yslices = []
+        for xslice, trans in zip(self._slice(x), self.transforms):
+            yslices.append(trans(xslice))
+        return torch.stack(yslices, dim=self.dim)
+
+    def _inverse(self, y):
+        assert -y.dim() <= self.dim < y.dim()
+        assert y.size(self.dim) == len(self.transforms)
+        xslices = []
+        for yslice, trans in zip(self._slice(y), self.transforms):
+            xslices.append(trans.inv(yslice))
+        return torch.stack(xslices, dim=self.dim)
+
+    def log_abs_det_jacobian(self, x, y):
+        assert -x.dim() <= self.dim < x.dim()
+        assert x.size(self.dim) == len(self.transforms)
+        assert -y.dim() <= self.dim < y.dim()
+        assert y.size(self.dim) == len(self.transforms)
+        logdetjacs = []
+        yslices = self._slice(y)
+        xslices = self._slice(x)
+        for xslice, yslice, trans in zip(xslices, yslices, self.transforms):
+            logdetjacs.append(trans.log_abs_det_jacobian(xslice, yslice))
+        return torch.stack(logdetjacs, dim=self.dim)
+
+    @property
+    def bijective(self):
+        return all(t.bijective for t in self.transforms)
+
+    @constraints.dependent_property
+    def domain(self):
+        return constraints.stack([t.domain for t in self.transforms], self.dim)
+
+    @constraints.dependent_property
+    def codomain(self):
+        return constraints.stack([t.codomain for t in self.transforms], self.dim)
+
+
+class CumulativeDistributionTransform(Transform):
+    """
+    Transform via the cumulative distribution function of a probability distribution.
+
+    Args:
+        distribution (Distribution): Distribution whose cumulative distribution function to use for
+            the transformation.
+
+    Example::
+
+        # Construct a Gaussian copula from a multivariate normal.
+        base_dist = MultivariateNormal(
+            loc=torch.zeros(2),
+            scale_tril=LKJCholesky(2).sample(),
+        )
+        transform = CumulativeDistributionTransform(Normal(0, 1))
+        copula = TransformedDistribution(base_dist, [transform])
+    """
+
+    bijective = True
+    codomain = constraints.unit_interval
+    sign = +1
+
+    def __init__(self, distribution, cache_size=0):
+        super().__init__(cache_size=cache_size)
+        self.distribution = distribution
+
+    @property
+    def domain(self):
+        return self.distribution.support
+
+    def _call(self, x):
+        return self.distribution.cdf(x)
+
+    def _inverse(self, y):
+        return self.distribution.icdf(y)
+
+    def log_abs_det_jacobian(self, x, y):
+        return self.distribution.log_prob(x)
+
+    def with_cache(self, cache_size=1):
+        if self._cache_size == cache_size:
+            return self
+        return CumulativeDistributionTransform(self.distribution, cache_size=cache_size)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/uniform.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/uniform.py
new file mode 100644
index 0000000000000000000000000000000000000000..0fe3678a319c406a1aabcacefa465391cd94e615
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/uniform.py
@@ -0,0 +1,102 @@
+# mypy: allow-untyped-defs
+from numbers import Number
+
+import torch
+from torch import nan
+from torch.distributions import constraints
+from torch.distributions.distribution import Distribution
+from torch.distributions.utils import broadcast_all
+from torch.types import _size
+
+
+__all__ = ["Uniform"]
+
+
+class Uniform(Distribution):
+    r"""
+    Generates uniformly distributed random samples from the half-open interval
+    ``[low, high)``.
+
+    Example::
+
+        >>> m = Uniform(torch.tensor([0.0]), torch.tensor([5.0]))
+        >>> m.sample()  # uniformly distributed in the range [0.0, 5.0)
+        >>> # xdoctest: +SKIP
+        tensor([ 2.3418])
+
+    Args:
+        low (float or Tensor): lower range (inclusive).
+        high (float or Tensor): upper range (exclusive).
+    """
+    # TODO allow (loc,scale) parameterization to allow independent constraints.
+    arg_constraints = {
+        "low": constraints.dependent(is_discrete=False, event_dim=0),
+        "high": constraints.dependent(is_discrete=False, event_dim=0),
+    }
+    has_rsample = True
+
+    @property
+    def mean(self):
+        return (self.high + self.low) / 2
+
+    @property
+    def mode(self):
+        return nan * self.high
+
+    @property
+    def stddev(self):
+        return (self.high - self.low) / 12**0.5
+
+    @property
+    def variance(self):
+        return (self.high - self.low).pow(2) / 12
+
+    def __init__(self, low, high, validate_args=None):
+        self.low, self.high = broadcast_all(low, high)
+
+        if isinstance(low, Number) and isinstance(high, Number):
+            batch_shape = torch.Size()
+        else:
+            batch_shape = self.low.size()
+        super().__init__(batch_shape, validate_args=validate_args)
+
+        if self._validate_args and not torch.lt(self.low, self.high).all():
+            raise ValueError("Uniform is not defined when low>= high")
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Uniform, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new.low = self.low.expand(batch_shape)
+        new.high = self.high.expand(batch_shape)
+        super(Uniform, new).__init__(batch_shape, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    @constraints.dependent_property(is_discrete=False, event_dim=0)
+    def support(self):
+        return constraints.interval(self.low, self.high)
+
+    def rsample(self, sample_shape: _size = torch.Size()) -> torch.Tensor:
+        shape = self._extended_shape(sample_shape)
+        rand = torch.rand(shape, dtype=self.low.dtype, device=self.low.device)
+        return self.low + rand * (self.high - self.low)
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        lb = self.low.le(value).type_as(self.low)
+        ub = self.high.gt(value).type_as(self.low)
+        return torch.log(lb.mul(ub)) - torch.log(self.high - self.low)
+
+    def cdf(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        result = (value - self.low) / (self.high - self.low)
+        return result.clamp(min=0, max=1)
+
+    def icdf(self, value):
+        result = value * (self.high - self.low) + self.low
+        return result
+
+    def entropy(self):
+        return torch.log(self.high - self.low)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..90c6d98a11ab1bb9c6571aa48293333b0cc3c28e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/utils.py
@@ -0,0 +1,200 @@
+# mypy: allow-untyped-defs
+from functools import update_wrapper
+from numbers import Number
+from typing import Any, Dict
+
+import torch
+import torch.nn.functional as F
+from torch.overrides import is_tensor_like
+
+
+euler_constant = 0.57721566490153286060  # Euler Mascheroni Constant
+
+__all__ = [
+    "broadcast_all",
+    "logits_to_probs",
+    "clamp_probs",
+    "probs_to_logits",
+    "lazy_property",
+    "tril_matrix_to_vec",
+    "vec_to_tril_matrix",
+]
+
+
+def broadcast_all(*values):
+    r"""
+    Given a list of values (possibly containing numbers), returns a list where each
+    value is broadcasted based on the following rules:
+      - `torch.*Tensor` instances are broadcasted as per :ref:`_broadcasting-semantics`.
+      - numbers.Number instances (scalars) are upcast to tensors having
+        the same size and type as the first tensor passed to `values`.  If all the
+        values are scalars, then they are upcasted to scalar Tensors.
+
+    Args:
+        values (list of `numbers.Number`, `torch.*Tensor` or objects implementing __torch_function__)
+
+    Raises:
+        ValueError: if any of the values is not a `numbers.Number` instance,
+            a `torch.*Tensor` instance, or an instance implementing __torch_function__
+    """
+    if not all(is_tensor_like(v) or isinstance(v, Number) for v in values):
+        raise ValueError(
+            "Input arguments must all be instances of numbers.Number, "
+            "torch.Tensor or objects implementing __torch_function__."
+        )
+    if not all(is_tensor_like(v) for v in values):
+        options: Dict[str, Any] = dict(dtype=torch.get_default_dtype())
+        for value in values:
+            if isinstance(value, torch.Tensor):
+                options = dict(dtype=value.dtype, device=value.device)
+                break
+        new_values = [
+            v if is_tensor_like(v) else torch.tensor(v, **options) for v in values
+        ]
+        return torch.broadcast_tensors(*new_values)
+    return torch.broadcast_tensors(*values)
+
+
+def _standard_normal(shape, dtype, device):
+    if torch._C._get_tracing_state():
+        # [JIT WORKAROUND] lack of support for .normal_()
+        return torch.normal(
+            torch.zeros(shape, dtype=dtype, device=device),
+            torch.ones(shape, dtype=dtype, device=device),
+        )
+    return torch.empty(shape, dtype=dtype, device=device).normal_()
+
+
+def _sum_rightmost(value, dim):
+    r"""
+    Sum out ``dim`` many rightmost dimensions of a given tensor.
+
+    Args:
+        value (Tensor): A tensor of ``.dim()`` at least ``dim``.
+        dim (int): The number of rightmost dims to sum out.
+    """
+    if dim == 0:
+        return value
+    required_shape = value.shape[:-dim] + (-1,)
+    return value.reshape(required_shape).sum(-1)
+
+
+def logits_to_probs(logits, is_binary=False):
+    r"""
+    Converts a tensor of logits into probabilities. Note that for the
+    binary case, each value denotes log odds, whereas for the
+    multi-dimensional case, the values along the last dimension denote
+    the log probabilities (possibly unnormalized) of the events.
+    """
+    if is_binary:
+        return torch.sigmoid(logits)
+    return F.softmax(logits, dim=-1)
+
+
+def clamp_probs(probs):
+    """Clamps the probabilities to be in the open interval `(0, 1)`.
+
+    The probabilities would be clamped between `eps` and `1 - eps`,
+    and `eps` would be the smallest representable positive number for the input data type.
+
+    Args:
+        probs (Tensor): A tensor of probabilities.
+
+    Returns:
+        Tensor: The clamped probabilities.
+
+    Examples:
+        >>> probs = torch.tensor([0.0, 0.5, 1.0])
+        >>> clamp_probs(probs)
+        tensor([1.1921e-07, 5.0000e-01, 1.0000e+00])
+
+        >>> probs = torch.tensor([0.0, 0.5, 1.0], dtype=torch.float64)
+        >>> clamp_probs(probs)
+        tensor([2.2204e-16, 5.0000e-01, 1.0000e+00], dtype=torch.float64)
+
+    """
+    eps = torch.finfo(probs.dtype).eps
+    return probs.clamp(min=eps, max=1 - eps)
+
+
+def probs_to_logits(probs, is_binary=False):
+    r"""
+    Converts a tensor of probabilities into logits. For the binary case,
+    this denotes the probability of occurrence of the event indexed by `1`.
+    For the multi-dimensional case, the values along the last dimension
+    denote the probabilities of occurrence of each of the events.
+    """
+    ps_clamped = clamp_probs(probs)
+    if is_binary:
+        return torch.log(ps_clamped) - torch.log1p(-ps_clamped)
+    return torch.log(ps_clamped)
+
+
+class lazy_property:
+    r"""
+    Used as a decorator for lazy loading of class attributes. This uses a
+    non-data descriptor that calls the wrapped method to compute the property on
+    first call; thereafter replacing the wrapped method into an instance
+    attribute.
+    """
+
+    def __init__(self, wrapped):
+        self.wrapped = wrapped
+        update_wrapper(self, wrapped)  # type:ignore[arg-type]
+
+    def __get__(self, instance, obj_type=None):
+        if instance is None:
+            return _lazy_property_and_property(self.wrapped)
+        with torch.enable_grad():
+            value = self.wrapped(instance)
+        setattr(instance, self.wrapped.__name__, value)
+        return value
+
+
+class _lazy_property_and_property(lazy_property, property):
+    """We want lazy properties to look like multiple things.
+
+    * property when Sphinx autodoc looks
+    * lazy_property when Distribution validate_args looks
+    """
+
+    def __init__(self, wrapped):
+        property.__init__(self, wrapped)
+
+
+def tril_matrix_to_vec(mat: torch.Tensor, diag: int = 0) -> torch.Tensor:
+    r"""
+    Convert a `D x D` matrix or a batch of matrices into a (batched) vector
+    which comprises of lower triangular elements from the matrix in row order.
+    """
+    n = mat.shape[-1]
+    if not torch._C._get_tracing_state() and (diag < -n or diag >= n):
+        raise ValueError(f"diag ({diag}) provided is outside [{-n}, {n-1}].")
+    arange = torch.arange(n, device=mat.device)
+    tril_mask = arange < arange.view(-1, 1) + (diag + 1)
+    vec = mat[..., tril_mask]
+    return vec
+
+
+def vec_to_tril_matrix(vec: torch.Tensor, diag: int = 0) -> torch.Tensor:
+    r"""
+    Convert a vector or a batch of vectors into a batched `D x D`
+    lower triangular matrix containing elements from the vector in row order.
+    """
+    # +ve root of D**2 + (1+2*diag)*D - |diag| * (diag+1) - 2*vec.shape[-1] = 0
+    n = (
+        -(1 + 2 * diag)
+        + ((1 + 2 * diag) ** 2 + 8 * vec.shape[-1] + 4 * abs(diag) * (diag + 1)) ** 0.5
+    ) / 2
+    eps = torch.finfo(vec.dtype).eps
+    if not torch._C._get_tracing_state() and (round(n) - n > eps):
+        raise ValueError(
+            f"The size of last dimension is {vec.shape[-1]} which cannot be expressed as "
+            + "the lower triangular part of a square D x D matrix."
+        )
+    n = round(n.item()) if isinstance(n, torch.Tensor) else round(n)
+    mat = vec.new_zeros(vec.shape[:-1] + torch.Size((n, n)))
+    arange = torch.arange(n, device=vec.device)
+    tril_mask = arange < arange.view(-1, 1) + (diag + 1)
+    mat[..., tril_mask] = vec
+    return mat
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/von_mises.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/von_mises.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd8fa87f2619a476d85cab44accac728d7b8fb2a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/von_mises.py
@@ -0,0 +1,211 @@
+# mypy: allow-untyped-defs
+import math
+
+import torch
+import torch.jit
+from torch.distributions import constraints
+from torch.distributions.distribution import Distribution
+from torch.distributions.utils import broadcast_all, lazy_property
+
+
+__all__ = ["VonMises"]
+
+
+def _eval_poly(y, coef):
+    coef = list(coef)
+    result = coef.pop()
+    while coef:
+        result = coef.pop() + y * result
+    return result
+
+
+_I0_COEF_SMALL = [
+    1.0,
+    3.5156229,
+    3.0899424,
+    1.2067492,
+    0.2659732,
+    0.360768e-1,
+    0.45813e-2,
+]
+_I0_COEF_LARGE = [
+    0.39894228,
+    0.1328592e-1,
+    0.225319e-2,
+    -0.157565e-2,
+    0.916281e-2,
+    -0.2057706e-1,
+    0.2635537e-1,
+    -0.1647633e-1,
+    0.392377e-2,
+]
+_I1_COEF_SMALL = [
+    0.5,
+    0.87890594,
+    0.51498869,
+    0.15084934,
+    0.2658733e-1,
+    0.301532e-2,
+    0.32411e-3,
+]
+_I1_COEF_LARGE = [
+    0.39894228,
+    -0.3988024e-1,
+    -0.362018e-2,
+    0.163801e-2,
+    -0.1031555e-1,
+    0.2282967e-1,
+    -0.2895312e-1,
+    0.1787654e-1,
+    -0.420059e-2,
+]
+
+_COEF_SMALL = [_I0_COEF_SMALL, _I1_COEF_SMALL]
+_COEF_LARGE = [_I0_COEF_LARGE, _I1_COEF_LARGE]
+
+
+def _log_modified_bessel_fn(x, order=0):
+    """
+    Returns ``log(I_order(x))`` for ``x > 0``,
+    where `order` is either 0 or 1.
+    """
+    assert order == 0 or order == 1
+
+    # compute small solution
+    y = x / 3.75
+    y = y * y
+    small = _eval_poly(y, _COEF_SMALL[order])
+    if order == 1:
+        small = x.abs() * small
+    small = small.log()
+
+    # compute large solution
+    y = 3.75 / x
+    large = x - 0.5 * x.log() + _eval_poly(y, _COEF_LARGE[order]).log()
+
+    result = torch.where(x < 3.75, small, large)
+    return result
+
+
+@torch.jit.script_if_tracing
+def _rejection_sample(loc, concentration, proposal_r, x):
+    done = torch.zeros(x.shape, dtype=torch.bool, device=loc.device)
+    while not done.all():
+        u = torch.rand((3,) + x.shape, dtype=loc.dtype, device=loc.device)
+        u1, u2, u3 = u.unbind()
+        z = torch.cos(math.pi * u1)
+        f = (1 + proposal_r * z) / (proposal_r + z)
+        c = concentration * (proposal_r - f)
+        accept = ((c * (2 - c) - u2) > 0) | ((c / u2).log() + 1 - c >= 0)
+        if accept.any():
+            x = torch.where(accept, (u3 - 0.5).sign() * f.acos(), x)
+            done = done | accept
+    return (x + math.pi + loc) % (2 * math.pi) - math.pi
+
+
+class VonMises(Distribution):
+    """
+    A circular von Mises distribution.
+
+    This implementation uses polar coordinates. The ``loc`` and ``value`` args
+    can be any real number (to facilitate unconstrained optimization), but are
+    interpreted as angles modulo 2 pi.
+
+    Example::
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = VonMises(torch.tensor([1.0]), torch.tensor([1.0]))
+        >>> m.sample()  # von Mises distributed with loc=1 and concentration=1
+        tensor([1.9777])
+
+    :param torch.Tensor loc: an angle in radians.
+    :param torch.Tensor concentration: concentration parameter
+    """
+
+    arg_constraints = {"loc": constraints.real, "concentration": constraints.positive}
+    support = constraints.real
+    has_rsample = False
+
+    def __init__(self, loc, concentration, validate_args=None):
+        self.loc, self.concentration = broadcast_all(loc, concentration)
+        batch_shape = self.loc.shape
+        event_shape = torch.Size()
+        super().__init__(batch_shape, event_shape, validate_args)
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        log_prob = self.concentration * torch.cos(value - self.loc)
+        log_prob = (
+            log_prob
+            - math.log(2 * math.pi)
+            - _log_modified_bessel_fn(self.concentration, order=0)
+        )
+        return log_prob
+
+    @lazy_property
+    def _loc(self):
+        return self.loc.to(torch.double)
+
+    @lazy_property
+    def _concentration(self):
+        return self.concentration.to(torch.double)
+
+    @lazy_property
+    def _proposal_r(self):
+        kappa = self._concentration
+        tau = 1 + (1 + 4 * kappa**2).sqrt()
+        rho = (tau - (2 * tau).sqrt()) / (2 * kappa)
+        _proposal_r = (1 + rho**2) / (2 * rho)
+        # second order Taylor expansion around 0 for small kappa
+        _proposal_r_taylor = 1 / kappa + kappa
+        return torch.where(kappa < 1e-5, _proposal_r_taylor, _proposal_r)
+
+    @torch.no_grad()
+    def sample(self, sample_shape=torch.Size()):
+        """
+        The sampling algorithm for the von Mises distribution is based on the
+        following paper: D.J. Best and N.I. Fisher, "Efficient simulation of the
+        von Mises distribution." Applied Statistics (1979): 152-157.
+
+        Sampling is always done in double precision internally to avoid a hang
+        in _rejection_sample() for small values of the concentration, which
+        starts to happen for single precision around 1e-4 (see issue #88443).
+        """
+        shape = self._extended_shape(sample_shape)
+        x = torch.empty(shape, dtype=self._loc.dtype, device=self.loc.device)
+        return _rejection_sample(
+            self._loc, self._concentration, self._proposal_r, x
+        ).to(self.loc.dtype)
+
+    def expand(self, batch_shape):
+        try:
+            return super().expand(batch_shape)
+        except NotImplementedError:
+            validate_args = self.__dict__.get("_validate_args")
+            loc = self.loc.expand(batch_shape)
+            concentration = self.concentration.expand(batch_shape)
+            return type(self)(loc, concentration, validate_args=validate_args)
+
+    @property
+    def mean(self):
+        """
+        The provided mean is the circular one.
+        """
+        return self.loc
+
+    @property
+    def mode(self):
+        return self.loc
+
+    @lazy_property
+    def variance(self):
+        """
+        The provided variance is the circular one.
+        """
+        return (
+            1
+            - (
+                _log_modified_bessel_fn(self.concentration, order=1)
+                - _log_modified_bessel_fn(self.concentration, order=0)
+            ).exp()
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/weibull.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/weibull.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1a5af16968413ebd16bed2d5f94f827294ff93a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/weibull.py
@@ -0,0 +1,85 @@
+# mypy: allow-untyped-defs
+import torch
+from torch.distributions import constraints
+from torch.distributions.exponential import Exponential
+from torch.distributions.gumbel import euler_constant
+from torch.distributions.transformed_distribution import TransformedDistribution
+from torch.distributions.transforms import AffineTransform, PowerTransform
+from torch.distributions.utils import broadcast_all
+
+
+__all__ = ["Weibull"]
+
+
+class Weibull(TransformedDistribution):
+    r"""
+    Samples from a two-parameter Weibull distribution.
+
+    Example:
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = Weibull(torch.tensor([1.0]), torch.tensor([1.0]))
+        >>> m.sample()  # sample from a Weibull distribution with scale=1, concentration=1
+        tensor([ 0.4784])
+
+    Args:
+        scale (float or Tensor): Scale parameter of distribution (lambda).
+        concentration (float or Tensor): Concentration parameter of distribution (k/shape).
+    """
+    arg_constraints = {
+        "scale": constraints.positive,
+        "concentration": constraints.positive,
+    }
+    support = constraints.positive
+
+    def __init__(self, scale, concentration, validate_args=None):
+        self.scale, self.concentration = broadcast_all(scale, concentration)
+        self.concentration_reciprocal = self.concentration.reciprocal()
+        base_dist = Exponential(
+            torch.ones_like(self.scale), validate_args=validate_args
+        )
+        transforms = [
+            PowerTransform(exponent=self.concentration_reciprocal),
+            AffineTransform(loc=0, scale=self.scale),
+        ]
+        super().__init__(base_dist, transforms, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Weibull, _instance)
+        new.scale = self.scale.expand(batch_shape)
+        new.concentration = self.concentration.expand(batch_shape)
+        new.concentration_reciprocal = new.concentration.reciprocal()
+        base_dist = self.base_dist.expand(batch_shape)
+        transforms = [
+            PowerTransform(exponent=new.concentration_reciprocal),
+            AffineTransform(loc=0, scale=new.scale),
+        ]
+        super(Weibull, new).__init__(base_dist, transforms, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    @property
+    def mean(self):
+        return self.scale * torch.exp(torch.lgamma(1 + self.concentration_reciprocal))
+
+    @property
+    def mode(self):
+        return (
+            self.scale
+            * ((self.concentration - 1) / self.concentration)
+            ** self.concentration.reciprocal()
+        )
+
+    @property
+    def variance(self):
+        return self.scale.pow(2) * (
+            torch.exp(torch.lgamma(1 + 2 * self.concentration_reciprocal))
+            - torch.exp(2 * torch.lgamma(1 + self.concentration_reciprocal))
+        )
+
+    def entropy(self):
+        return (
+            euler_constant * (1 - self.concentration_reciprocal)
+            + torch.log(self.scale * self.concentration_reciprocal)
+            + 1
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/wishart.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/wishart.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0a00f0cc6db06412b13d3d9157eb9d8bade1eaf
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/distributions/wishart.py
@@ -0,0 +1,339 @@
+# mypy: allow-untyped-defs
+import math
+import warnings
+from numbers import Number
+from typing import Optional, Union
+
+import torch
+from torch import nan
+from torch.distributions import constraints
+from torch.distributions.exp_family import ExponentialFamily
+from torch.distributions.multivariate_normal import _precision_to_scale_tril
+from torch.distributions.utils import lazy_property
+from torch.types import _size
+
+
+__all__ = ["Wishart"]
+
+_log_2 = math.log(2)
+
+
+def _mvdigamma(x: torch.Tensor, p: int) -> torch.Tensor:
+    assert x.gt((p - 1) / 2).all(), "Wrong domain for multivariate digamma function."
+    return torch.digamma(
+        x.unsqueeze(-1)
+        - torch.arange(p, dtype=x.dtype, device=x.device).div(2).expand(x.shape + (-1,))
+    ).sum(-1)
+
+
+def _clamp_above_eps(x: torch.Tensor) -> torch.Tensor:
+    # We assume positive input for this function
+    return x.clamp(min=torch.finfo(x.dtype).eps)
+
+
+class Wishart(ExponentialFamily):
+    r"""
+    Creates a Wishart distribution parameterized by a symmetric positive definite matrix :math:`\Sigma`,
+    or its Cholesky decomposition :math:`\mathbf{\Sigma} = \mathbf{L}\mathbf{L}^\top`
+
+    Example:
+        >>> # xdoctest: +SKIP("FIXME: scale_tril must be at least two-dimensional")
+        >>> m = Wishart(torch.Tensor([2]), covariance_matrix=torch.eye(2))
+        >>> m.sample()  # Wishart distributed with mean=`df * I` and
+        >>>             # variance(x_ij)=`df` for i != j and variance(x_ij)=`2 * df` for i == j
+
+    Args:
+        df (float or Tensor): real-valued parameter larger than the (dimension of Square matrix) - 1
+        covariance_matrix (Tensor): positive-definite covariance matrix
+        precision_matrix (Tensor): positive-definite precision matrix
+        scale_tril (Tensor): lower-triangular factor of covariance, with positive-valued diagonal
+    Note:
+        Only one of :attr:`covariance_matrix` or :attr:`precision_matrix` or
+        :attr:`scale_tril` can be specified.
+        Using :attr:`scale_tril` will be more efficient: all computations internally
+        are based on :attr:`scale_tril`. If :attr:`covariance_matrix` or
+        :attr:`precision_matrix` is passed instead, it is only used to compute
+        the corresponding lower triangular matrices using a Cholesky decomposition.
+        'torch.distributions.LKJCholesky' is a restricted Wishart distribution.[1]
+
+    **References**
+
+    [1] Wang, Z., Wu, Y. and Chu, H., 2018. `On equivalence of the LKJ distribution and the restricted Wishart distribution`.
+    [2] Sawyer, S., 2007. `Wishart Distributions and Inverse-Wishart Sampling`.
+    [3] Anderson, T. W., 2003. `An Introduction to Multivariate Statistical Analysis (3rd ed.)`.
+    [4] Odell, P. L. & Feiveson, A. H., 1966. `A Numerical Procedure to Generate a SampleCovariance Matrix`. JASA, 61(313):199-203.
+    [5] Ku, Y.-C. & Bloomfield, P., 2010. `Generating Random Wishart Matrices with Fractional Degrees of Freedom in OX`.
+    """
+    arg_constraints = {
+        "covariance_matrix": constraints.positive_definite,
+        "precision_matrix": constraints.positive_definite,
+        "scale_tril": constraints.lower_cholesky,
+        "df": constraints.greater_than(0),
+    }
+    support = constraints.positive_definite
+    has_rsample = True
+    _mean_carrier_measure = 0
+
+    def __init__(
+        self,
+        df: Union[torch.Tensor, Number],
+        covariance_matrix: Optional[torch.Tensor] = None,
+        precision_matrix: Optional[torch.Tensor] = None,
+        scale_tril: Optional[torch.Tensor] = None,
+        validate_args=None,
+    ):
+        assert (covariance_matrix is not None) + (scale_tril is not None) + (
+            precision_matrix is not None
+        ) == 1, "Exactly one of covariance_matrix or precision_matrix or scale_tril may be specified."
+
+        param = next(
+            p
+            for p in (covariance_matrix, precision_matrix, scale_tril)
+            if p is not None
+        )
+
+        if param.dim() < 2:
+            raise ValueError(
+                "scale_tril must be at least two-dimensional, with optional leading batch dimensions"
+            )
+
+        if isinstance(df, Number):
+            batch_shape = torch.Size(param.shape[:-2])
+            self.df = torch.tensor(df, dtype=param.dtype, device=param.device)
+        else:
+            batch_shape = torch.broadcast_shapes(param.shape[:-2], df.shape)
+            self.df = df.expand(batch_shape)
+        event_shape = param.shape[-2:]
+
+        if self.df.le(event_shape[-1] - 1).any():
+            raise ValueError(
+                f"Value of df={df} expected to be greater than ndim - 1 = {event_shape[-1]-1}."
+            )
+
+        if scale_tril is not None:
+            self.scale_tril = param.expand(batch_shape + (-1, -1))
+        elif covariance_matrix is not None:
+            self.covariance_matrix = param.expand(batch_shape + (-1, -1))
+        elif precision_matrix is not None:
+            self.precision_matrix = param.expand(batch_shape + (-1, -1))
+
+        self.arg_constraints["df"] = constraints.greater_than(event_shape[-1] - 1)
+        if self.df.lt(event_shape[-1]).any():
+            warnings.warn(
+                "Low df values detected. Singular samples are highly likely to occur for ndim - 1 < df < ndim."
+            )
+
+        super().__init__(batch_shape, event_shape, validate_args=validate_args)
+        self._batch_dims = [-(x + 1) for x in range(len(self._batch_shape))]
+
+        if scale_tril is not None:
+            self._unbroadcasted_scale_tril = scale_tril
+        elif covariance_matrix is not None:
+            self._unbroadcasted_scale_tril = torch.linalg.cholesky(covariance_matrix)
+        else:  # precision_matrix is not None
+            self._unbroadcasted_scale_tril = _precision_to_scale_tril(precision_matrix)
+
+        # Chi2 distribution is needed for Bartlett decomposition sampling
+        self._dist_chi2 = torch.distributions.chi2.Chi2(
+            df=(
+                self.df.unsqueeze(-1)
+                - torch.arange(
+                    self._event_shape[-1],
+                    dtype=self._unbroadcasted_scale_tril.dtype,
+                    device=self._unbroadcasted_scale_tril.device,
+                ).expand(batch_shape + (-1,))
+            )
+        )
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Wishart, _instance)
+        batch_shape = torch.Size(batch_shape)
+        cov_shape = batch_shape + self.event_shape
+        new._unbroadcasted_scale_tril = self._unbroadcasted_scale_tril.expand(cov_shape)
+        new.df = self.df.expand(batch_shape)
+
+        new._batch_dims = [-(x + 1) for x in range(len(batch_shape))]
+
+        if "covariance_matrix" in self.__dict__:
+            new.covariance_matrix = self.covariance_matrix.expand(cov_shape)
+        if "scale_tril" in self.__dict__:
+            new.scale_tril = self.scale_tril.expand(cov_shape)
+        if "precision_matrix" in self.__dict__:
+            new.precision_matrix = self.precision_matrix.expand(cov_shape)
+
+        # Chi2 distribution is needed for Bartlett decomposition sampling
+        new._dist_chi2 = torch.distributions.chi2.Chi2(
+            df=(
+                new.df.unsqueeze(-1)
+                - torch.arange(
+                    self.event_shape[-1],
+                    dtype=new._unbroadcasted_scale_tril.dtype,
+                    device=new._unbroadcasted_scale_tril.device,
+                ).expand(batch_shape + (-1,))
+            )
+        )
+
+        super(Wishart, new).__init__(batch_shape, self.event_shape, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    @lazy_property
+    def scale_tril(self):
+        return self._unbroadcasted_scale_tril.expand(
+            self._batch_shape + self._event_shape
+        )
+
+    @lazy_property
+    def covariance_matrix(self):
+        return (
+            self._unbroadcasted_scale_tril
+            @ self._unbroadcasted_scale_tril.transpose(-2, -1)
+        ).expand(self._batch_shape + self._event_shape)
+
+    @lazy_property
+    def precision_matrix(self):
+        identity = torch.eye(
+            self._event_shape[-1],
+            device=self._unbroadcasted_scale_tril.device,
+            dtype=self._unbroadcasted_scale_tril.dtype,
+        )
+        return torch.cholesky_solve(identity, self._unbroadcasted_scale_tril).expand(
+            self._batch_shape + self._event_shape
+        )
+
+    @property
+    def mean(self):
+        return self.df.view(self._batch_shape + (1, 1)) * self.covariance_matrix
+
+    @property
+    def mode(self):
+        factor = self.df - self.covariance_matrix.shape[-1] - 1
+        factor[factor <= 0] = nan
+        return factor.view(self._batch_shape + (1, 1)) * self.covariance_matrix
+
+    @property
+    def variance(self):
+        V = self.covariance_matrix  # has shape (batch_shape x event_shape)
+        diag_V = V.diagonal(dim1=-2, dim2=-1)
+        return self.df.view(self._batch_shape + (1, 1)) * (
+            V.pow(2) + torch.einsum("...i,...j->...ij", diag_V, diag_V)
+        )
+
+    def _bartlett_sampling(self, sample_shape=torch.Size()):
+        p = self._event_shape[-1]  # has singleton shape
+
+        # Implemented Sampling using Bartlett decomposition
+        noise = _clamp_above_eps(
+            self._dist_chi2.rsample(sample_shape).sqrt()
+        ).diag_embed(dim1=-2, dim2=-1)
+
+        i, j = torch.tril_indices(p, p, offset=-1)
+        noise[..., i, j] = torch.randn(
+            torch.Size(sample_shape) + self._batch_shape + (int(p * (p - 1) / 2),),
+            dtype=noise.dtype,
+            device=noise.device,
+        )
+        chol = self._unbroadcasted_scale_tril @ noise
+        return chol @ chol.transpose(-2, -1)
+
+    def rsample(
+        self, sample_shape: _size = torch.Size(), max_try_correction=None
+    ) -> torch.Tensor:
+        r"""
+        .. warning::
+            In some cases, sampling algorithm based on Bartlett decomposition may return singular matrix samples.
+            Several tries to correct singular samples are performed by default, but it may end up returning
+            singular matrix samples. Singular samples may return `-inf` values in `.log_prob()`.
+            In those cases, the user should validate the samples and either fix the value of `df`
+            or adjust `max_try_correction` value for argument in `.rsample` accordingly.
+        """
+
+        if max_try_correction is None:
+            max_try_correction = 3 if torch._C._get_tracing_state() else 10
+
+        sample_shape = torch.Size(sample_shape)
+        sample = self._bartlett_sampling(sample_shape)
+
+        # Below part is to improve numerical stability temporally and should be removed in the future
+        is_singular = self.support.check(sample)
+        if self._batch_shape:
+            is_singular = is_singular.amax(self._batch_dims)
+
+        if torch._C._get_tracing_state():
+            # Less optimized version for JIT
+            for _ in range(max_try_correction):
+                sample_new = self._bartlett_sampling(sample_shape)
+                sample = torch.where(is_singular, sample_new, sample)
+
+                is_singular = ~self.support.check(sample)
+                if self._batch_shape:
+                    is_singular = is_singular.amax(self._batch_dims)
+
+        else:
+            # More optimized version with data-dependent control flow.
+            if is_singular.any():
+                warnings.warn("Singular sample detected.")
+
+                for _ in range(max_try_correction):
+                    sample_new = self._bartlett_sampling(is_singular[is_singular].shape)
+                    sample[is_singular] = sample_new
+
+                    is_singular_new = ~self.support.check(sample_new)
+                    if self._batch_shape:
+                        is_singular_new = is_singular_new.amax(self._batch_dims)
+                    is_singular[is_singular.clone()] = is_singular_new
+
+                    if not is_singular.any():
+                        break
+
+        return sample
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        nu = self.df  # has shape (batch_shape)
+        p = self._event_shape[-1]  # has singleton shape
+        return (
+            -nu
+            * (
+                p * _log_2 / 2
+                + self._unbroadcasted_scale_tril.diagonal(dim1=-2, dim2=-1)
+                .log()
+                .sum(-1)
+            )
+            - torch.mvlgamma(nu / 2, p=p)
+            + (nu - p - 1) / 2 * torch.linalg.slogdet(value).logabsdet
+            - torch.cholesky_solve(value, self._unbroadcasted_scale_tril)
+            .diagonal(dim1=-2, dim2=-1)
+            .sum(dim=-1)
+            / 2
+        )
+
+    def entropy(self):
+        nu = self.df  # has shape (batch_shape)
+        p = self._event_shape[-1]  # has singleton shape
+        V = self.covariance_matrix  # has shape (batch_shape x event_shape)
+        return (
+            (p + 1)
+            * (
+                p * _log_2 / 2
+                + self._unbroadcasted_scale_tril.diagonal(dim1=-2, dim2=-1)
+                .log()
+                .sum(-1)
+            )
+            + torch.mvlgamma(nu / 2, p=p)
+            - (nu - p - 1) / 2 * _mvdigamma(nu / 2, p=p)
+            + nu * p / 2
+        )
+
+    @property
+    def _natural_params(self):
+        nu = self.df  # has shape (batch_shape)
+        p = self._event_shape[-1]  # has singleton shape
+        return -self.precision_matrix / 2, (nu - p - 1) / 2
+
+    def _log_normalizer(self, x, y):
+        p = self._event_shape[-1]
+        return (y + (p + 1) / 2) * (
+            -torch.linalg.slogdet(-2 * x).logabsdet + _log_2 * p
+        ) + torch.mvlgamma(y + (p + 1) / 2, p=p)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..367b3f127a16bba26b6e4191abc00220918c0093
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/__init__.py
@@ -0,0 +1,518 @@
+import builtins
+import copy
+import dataclasses
+import inspect
+import io
+import os
+import sys
+import typing
+import warnings
+import zipfile
+from enum import auto, Enum
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Iterator,
+    List,
+    Optional,
+    Tuple,
+    Type,
+    TYPE_CHECKING,
+    Union,
+)
+
+import torch
+import torch.utils._pytree as pytree
+from torch.fx._compatibility import compatibility
+from torch.fx.passes.infra.pass_base import PassResult
+from torch.fx.passes.infra.pass_manager import PassManager
+from torch.utils._pytree import (
+    FlattenFunc,
+    FromDumpableContextFn,
+    ToDumpableContextFn,
+    UnflattenFunc,
+)
+
+
+if TYPE_CHECKING:
+    # Import the following modules during type checking to enable code intelligence features,
+    # Do not import unconditionally, as they import sympy and importing sympy is very slow
+    from torch.fx.experimental.symbolic_shapes import StrictMinMaxConstraint
+
+
+__all__ = [
+    "Constraint",
+    "Dim",
+    "ExportBackwardSignature",
+    "ExportGraphSignature",
+    "ExportedProgram",
+    "ModuleCallEntry",
+    "ModuleCallSignature",
+    "dims",
+    "export",
+    "export_for_training",
+    "load",
+    "register_dataclass",
+    "save",
+    "unflatten",
+    "FlatArgsAdapter",
+    "UnflattenedModule",
+]
+
+
+from .dynamic_shapes import Constraint, Dim, dims, ShapesCollection
+from .exported_program import ExportedProgram, ModuleCallEntry, ModuleCallSignature
+from .graph_signature import ExportBackwardSignature, ExportGraphSignature
+from .unflatten import FlatArgsAdapter, unflatten, UnflattenedModule
+
+
+PassType = Callable[[torch.fx.GraphModule], Optional[PassResult]]
+
+
+def export_for_training(
+    mod: torch.nn.Module,
+    args: Tuple[Any, ...],
+    kwargs: Optional[Dict[str, Any]] = None,
+    *,
+    dynamic_shapes: Optional[Union[Dict[str, Any], Tuple[Any], List[Any]]] = None,
+    strict: bool = True,
+    preserve_module_call_signature: Tuple[str, ...] = (),
+) -> ExportedProgram:
+    """
+    :func:`export_for_training` takes any nn.Module along with example inputs, and produces a traced graph representing
+    only the Tensor computation of the function in an Ahead-of-Time (AOT) fashion,
+    which can subsequently be executed with different inputs or serialized. The
+    traced graph (1) produces normalized operators in the all ATen operator set
+    (as well as any user-specified custom operators), (2) has eliminated all Python control
+    flow and data structures (with certain exceptions), and (3) records the set of
+    shape constraints needed to show that this normalization and control-flow elimination
+    is sound for future inputs. This API is intended for PT2 quantization training use cases
+    and will soon be the default IR of torch.export.export in the near future.
+
+    **Soundness Guarantee**
+
+    See :func:`export()` docstring for more details.
+
+    Args:
+        mod: We will trace the forward method of this module.
+
+        args: Example positional inputs.
+
+        kwargs: Optional example keyword inputs.
+
+        dynamic_shapes:
+         An optional argument where the type should either be:
+         1) a dict from argument names of ``f`` to their dynamic shape specifications,
+         2) a tuple that specifies dynamic shape specifications for each input in original order.
+         If you are specifying dynamism on keyword args, you will need to pass them in the order that
+         is defined in the original function signature.
+
+         The dynamic shape of a tensor argument can be specified as either
+         (1) a dict from dynamic dimension indices to :func:`Dim` types, where it is
+         not required to include static dimension indices in this dict, but when they are,
+         they should be mapped to None; or (2) a tuple / list of :func:`Dim` types or None,
+         where the :func:`Dim` types correspond to dynamic dimensions, and static dimensions
+         are denoted by None. Arguments that are dicts or tuples / lists of tensors are
+         recursively specified by using mappings or sequences of contained specifications.
+
+        strict: When enabled (default), the export function will trace the program through
+         TorchDynamo which will ensure the soundness of the resulting graph. Otherwise, the
+         exported program will not validate the implicit assumptions baked into the graph and
+         may cause behavior divergence between the original model and the exported one. This is
+         useful when users need to workaround bugs in the tracer, or simply want incrementally
+         enable safety in their models. Note that this does not affect the resulting IR spec
+         to be different and the model will be serialized in the same way regardless of what value
+         is passed here.
+         WARNING: This option is experimental and use this at your own risk.
+
+    Returns:
+        An :class:`ExportedProgram` containing the traced callable.
+
+    **Acceptable input/output types**
+
+    Acceptable types of inputs (for ``args`` and ``kwargs``) and outputs include:
+
+    - Primitive types, i.e. ``torch.Tensor``, ``int``, ``float``, ``bool`` and ``str``.
+    - Dataclasses, but they must be registered by calling :func:`register_dataclass` first.
+    - (Nested) Data structures comprising of ``dict``, ``list``, ``tuple``, ``namedtuple`` and
+      ``OrderedDict`` containing all above types.
+
+    """
+    from ._trace import _export_for_training
+
+    if not isinstance(mod, torch.nn.Module):
+        raise ValueError(
+            f"Expected `mod` to be an instance of `torch.nn.Module`, got {type(mod)}."
+        )
+    if isinstance(mod, torch.jit.ScriptModule):
+        raise ValueError(
+            "Exporting a ScriptModule is not supported. "
+            "Maybe try converting your ScriptModule to an ExportedProgram "
+            "using `TS2EPConverter(mod, args, kwargs).convert()` instead."
+        )
+    return _export_for_training(
+        mod,
+        args,
+        kwargs,
+        dynamic_shapes,
+        strict=strict,
+        preserve_module_call_signature=preserve_module_call_signature,
+    )
+
+
+def export(
+    mod: torch.nn.Module,
+    args: Tuple[Any, ...],
+    kwargs: Optional[Dict[str, Any]] = None,
+    *,
+    dynamic_shapes: Optional[Union[Dict[str, Any], Tuple[Any], List[Any]]] = None,
+    strict: bool = True,
+    preserve_module_call_signature: Tuple[str, ...] = (),
+) -> ExportedProgram:
+    """
+    :func:`export` takes an arbitrary Python callable (an nn.Module, a function or
+    a method) along with example inputs, and produces a traced graph representing
+    only the Tensor computation of the function in an Ahead-of-Time (AOT) fashion,
+    which can subsequently be executed with different inputs or serialized.  The
+    traced graph (1) produces normalized operators in the functional ATen operator set
+    (as well as any user-specified custom operators), (2) has eliminated all Python control
+    flow and data structures (with certain exceptions), and (3) records the set of
+    shape constraints needed to show that this normalization and control-flow elimination
+    is sound for future inputs.
+
+    **Soundness Guarantee**
+
+    While tracing, :func:`export()` takes note of shape-related assumptions
+    made by the user program and the underlying PyTorch operator kernels.
+    The output :class:`ExportedProgram` is considered valid only when these
+    assumptions hold true.
+
+    Tracing makes assumptions on the shapes (not values) of input tensors.
+    Such assumptions must be validated at graph capture time for :func:`export`
+    to succeed. Specifically:
+
+    - Assumptions on static shapes of input tensors are automatically validated without additional effort.
+    - Assumptions on dynamic shape of input tensors require explicit specification
+      by using the :func:`Dim` API to construct dynamic dimensions and by associating
+      them with example inputs through the ``dynamic_shapes`` argument.
+
+    If any assumption can not be validated, a fatal error will be raised. When that happens,
+    the error message will include suggested fixes to the specification that are needed
+    to validate the assumptions. For example :func:`export` might suggest the
+    following fix to the definition of a dynamic dimension ``dim0_x``, say appearing in the
+    shape associated with input ``x``, that was previously defined as ``Dim("dim0_x")``::
+
+        dim = Dim("dim0_x", max=5)
+
+    This example means the generated code requires dimension 0 of input ``x`` to be less
+    than or equal to 5 to be valid. You can inspect the suggested fixes to dynamic dimension
+    definitions and then copy them verbatim into your code without needing to change the
+    ``dynamic_shapes`` argument to your :func:`export` call.
+
+    Args:
+        mod: We will trace the forward method of this module.
+
+        args: Example positional inputs.
+
+        kwargs: Optional example keyword inputs.
+
+        dynamic_shapes:
+         An optional argument where the type should either be:
+         1) a dict from argument names of ``f`` to their dynamic shape specifications,
+         2) a tuple that specifies dynamic shape specifications for each input in original order.
+         If you are specifying dynamism on keyword args, you will need to pass them in the order that
+         is defined in the original function signature.
+
+         The dynamic shape of a tensor argument can be specified as either
+         (1) a dict from dynamic dimension indices to :func:`Dim` types, where it is
+         not required to include static dimension indices in this dict, but when they are,
+         they should be mapped to None; or (2) a tuple / list of :func:`Dim` types or None,
+         where the :func:`Dim` types correspond to dynamic dimensions, and static dimensions
+         are denoted by None. Arguments that are dicts or tuples / lists of tensors are
+         recursively specified by using mappings or sequences of contained specifications.
+
+        strict: When enabled (default), the export function will trace the program through
+         TorchDynamo which will ensure the soundness of the resulting graph. Otherwise, the
+         exported program will not validate the implicit assumptions baked into the graph and
+         may cause behavior divergence between the original model and the exported one. This is
+         useful when users need to workaround bugs in the tracer, or simply want incrementally
+         enable safety in their models. Note that this does not affect the resulting IR spec
+         to be different and the model will be serialized in the same way regardless of what value
+         is passed here.
+         WARNING: This option is experimental and use this at your own risk.
+
+    Returns:
+        An :class:`ExportedProgram` containing the traced callable.
+
+    **Acceptable input/output types**
+
+    Acceptable types of inputs (for ``args`` and ``kwargs``) and outputs include:
+
+    - Primitive types, i.e. ``torch.Tensor``, ``int``, ``float``, ``bool`` and ``str``.
+    - Dataclasses, but they must be registered by calling :func:`register_dataclass` first.
+    - (Nested) Data structures comprising of ``dict``, ``list``, ``tuple``, ``namedtuple`` and
+      ``OrderedDict`` containing all above types.
+
+    """
+    from ._trace import _export
+
+    if not isinstance(mod, torch.nn.Module):
+        raise ValueError(
+            f"Expected `mod` to be an instance of `torch.nn.Module`, got {type(mod)}."
+        )
+    if isinstance(mod, torch.jit.ScriptModule):
+        raise ValueError(
+            "Exporting a ScriptModule is not supported. "
+            "Maybe try converting your ScriptModule to an ExportedProgram "
+            "using `TS2EPConverter(mod, args, kwargs).convert()` instead."
+        )
+    return _export(
+        mod,
+        args,
+        kwargs,
+        dynamic_shapes,
+        strict=strict,
+        preserve_module_call_signature=preserve_module_call_signature,
+        pre_dispatch=True,
+    )
+
+
+def save(
+    ep: ExportedProgram,
+    f: Union[str, os.PathLike, io.BytesIO],
+    *,
+    extra_files: Optional[Dict[str, Any]] = None,
+    opset_version: Optional[Dict[str, int]] = None,
+) -> None:
+    """
+
+    .. warning::
+        Under active development, saved files may not be usable in newer versions
+        of PyTorch.
+
+    Saves an :class:`ExportedProgram` to a file-like object. It can then be
+    loaded using the Python API :func:`torch.export.load `.
+
+    Args:
+        ep (ExportedProgram): The exported program to save.
+
+        f (Union[str, os.PathLike, io.BytesIO): A file-like object (has to
+         implement write and flush) or a string containing a file name.
+
+        extra_files (Optional[Dict[str, Any]]): Map from filename to contents
+         which will be stored as part of f.
+
+        opset_version (Optional[Dict[str, int]]): A map of opset names
+         to the version of this opset
+
+
+    Example::
+
+        import torch
+        import io
+
+        class MyModule(torch.nn.Module):
+            def forward(self, x):
+                return x + 10
+
+        ep = torch.export.export(MyModule(), (torch.randn(5),))
+
+        # Save to file
+        torch.export.save(ep, 'exported_program.pt2')
+
+        # Save to io.BytesIO buffer
+        buffer = io.BytesIO()
+        torch.export.save(ep, buffer)
+
+        # Save with extra files
+        extra_files = {'foo.txt': b'bar'.decode('utf-8')}
+        torch.export.save(ep, 'exported_program.pt2', extra_files=extra_files)
+
+    """
+    if not isinstance(ep, ExportedProgram):
+        raise TypeError(
+            f"The 'ep' parameter must be an instance of 'ExportedProgram', got '{type(ep).__name__}' instead."
+        )
+
+    from torch._export.serde.schema import SCHEMA_VERSION
+    from torch._export.serde.serialize import serialize, SerializedArtifact
+
+    artifact: SerializedArtifact = serialize(ep, opset_version)
+
+    if isinstance(f, (str, os.PathLike)):
+        f = os.fspath(f)
+
+    with zipfile.ZipFile(f, "w") as zipf:
+        # Save every field in the SerializedArtifact to a file.
+        assert isinstance(artifact.exported_program, bytes)
+        zipf.writestr("serialized_exported_program.json", artifact.exported_program)
+        zipf.writestr("serialized_state_dict.pt", artifact.state_dict)
+        zipf.writestr("serialized_constants.pt", artifact.constants)
+        zipf.writestr("serialized_example_inputs.pt", artifact.example_inputs)
+
+        zipf.writestr("version", ".".join(map(str, SCHEMA_VERSION)))
+
+        # Add extra files if provided
+        if extra_files:
+            for extra_file_name, content in extra_files.items():
+                encoded_content = content.encode("utf-8")
+                zipf.writestr(f"extra_files/{extra_file_name}", encoded_content)
+
+
+def load(
+    f: Union[str, os.PathLike, io.BytesIO],
+    *,
+    extra_files: Optional[Dict[str, Any]] = None,
+    expected_opset_version: Optional[Dict[str, int]] = None,
+) -> ExportedProgram:
+    """
+
+    .. warning::
+        Under active development, saved files may not be usable in newer versions
+        of PyTorch.
+
+    Loads an :class:`ExportedProgram` previously saved with
+    :func:`torch.export.save `.
+
+    Args:
+        ep (ExportedProgram): The exported program to save.
+
+        f (Union[str, os.PathLike, io.BytesIO): A file-like object (has to
+         implement write and flush) or a string containing a file name.
+
+        extra_files (Optional[Dict[str, Any]]): The extra filenames given in
+         this map would be loaded and their content would be stored in the
+         provided map.
+
+        expected_opset_version (Optional[Dict[str, int]]): A map of opset names
+         to expected opset versions
+
+    Returns:
+        An :class:`ExportedProgram` object
+
+    Example::
+
+        import torch
+        import io
+
+        # Load ExportedProgram from file
+        ep = torch.export.load('exported_program.pt2')
+
+        # Load ExportedProgram from io.BytesIO object
+        with open('exported_program.pt2', 'rb') as f:
+            buffer = io.BytesIO(f.read())
+        buffer.seek(0)
+        ep = torch.export.load(buffer)
+
+        # Load with extra files.
+        extra_files = {'foo.txt': ''}  # values will be replaced with data
+        ep = torch.export.load('exported_program.pt2', extra_files=extra_files)
+        print(extra_files['foo.txt'])
+        print(ep(torch.randn(5)))
+    """
+    if isinstance(f, (str, os.PathLike)):
+        f = os.fspath(f)
+
+    extra_files = extra_files or {}
+
+    with zipfile.ZipFile(f, "r") as zipf:
+        # Check the version
+        version = zipf.read("version").decode().split(".")
+        from torch._export.serde.schema import SCHEMA_VERSION
+
+        assert len(version) == len(SCHEMA_VERSION)
+        if version[0] != str(SCHEMA_VERSION[0]):
+            raise RuntimeError(
+                f"Serialized version {version} does not match our current "
+                f"schema version {SCHEMA_VERSION}."
+            )
+
+        from torch._export.serde.serialize import deserialize, SerializedArtifact
+
+        # Load serialized_ep and serialized_state_dict from the zip file
+
+        serialized_exported_program: Optional[bytes] = None
+        serialized_state_dict: Optional[bytes] = None
+        serialized_constants: Optional[bytes] = None
+        serialized_example_inputs: Optional[bytes] = None
+
+        for file_info in zipf.infolist():
+            file_content = zipf.read(file_info.filename)
+
+            if file_info.filename == "serialized_exported_program.json":
+                serialized_exported_program = file_content
+            elif file_info.filename == "serialized_state_dict.json":
+                warnings.warn("This version of file is deprecated")
+                serialized_state_dict = file_content
+            elif file_info.filename == "serialized_constants.json":
+                warnings.warn("This version of file is deprecated")
+                serialized_constants = file_content
+            elif file_info.filename == "serialized_state_dict.pt":
+                serialized_state_dict = file_content
+            elif file_info.filename == "serialized_constants.pt":
+                serialized_constants = file_content
+            elif file_info.filename == "serialized_example_inputs.pt":
+                serialized_example_inputs = file_content
+            elif file_info.filename.startswith("extra_files"):
+                filename = file_info.filename.split("/", 1)[1]
+                extra_files[filename] = file_content.decode("utf-8")
+
+        assert serialized_exported_program is not None
+        assert serialized_state_dict is not None
+        assert serialized_constants is not None
+        assert serialized_example_inputs is not None
+        artifact: SerializedArtifact = SerializedArtifact(
+            serialized_exported_program,
+            serialized_state_dict,
+            serialized_constants,
+            serialized_example_inputs,
+        )
+
+        # Deserialize ExportedProgram
+        ep = deserialize(artifact, expected_opset_version)
+
+        return ep
+
+
+def register_dataclass(
+    cls: Type[Any],
+    *,
+    serialized_type_name: Optional[str] = None,
+) -> None:
+    """
+    Registers a dataclass as a valid input/output type for :func:`torch.export.export`.
+
+    Args:
+        cls: the dataclass type to register
+        serialized_type_name: The serialized name for the dataclass. This is
+        required if you want to serialize the pytree TreeSpec containing this
+        dataclass.
+
+    Example::
+
+        @dataclass
+        class InputDataClass:
+            feature: torch.Tensor
+            bias: int
+
+        class OutputDataClass:
+            res: torch.Tensor
+
+        torch.export.register_dataclass(InputDataClass)
+        torch.export.register_dataclass(OutputDataClass)
+
+        def fn(o: InputDataClass) -> torch.Tensor:
+            res = res=o.feature + o.bias
+            return OutputDataClass(res=res)
+
+        ep = torch.export.export(fn, (InputDataClass(torch.ones(2, 2), 1), ))
+        print(ep)
+
+    """
+
+    from torch._export.utils import register_dataclass_as_pytree_node
+
+    return register_dataclass_as_pytree_node(
+        cls, serialized_type_name=serialized_type_name
+    )
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/_remove_auto_functionalized_pass.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/_remove_auto_functionalized_pass.py
new file mode 100644
index 0000000000000000000000000000000000000000..683e89c3d14910b3f753a9eba7093ba53bc631a4
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/_remove_auto_functionalized_pass.py
@@ -0,0 +1,52 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import torch
+from torch._higher_order_ops.auto_functionalize import (
+    auto_functionalized,
+    auto_functionalized_v2,
+)
+from torch._inductor.fx_passes.post_grad import decompose_auto_functionalized
+from torch.export import ExportedProgram
+
+
+def remove_self_clone(graph: torch.fx.Graph):
+    for node in graph.nodes:
+        if node.target == torch.ops.aten.copy_.default and node.args[0] == node.args[1]:
+            node.replace_all_uses_with(node.args[0])
+            graph.erase_node(node)
+
+
+def unsafe_remove_auto_functionalized_pass(
+    ep: ExportedProgram,
+) -> ExportedProgram:
+    """
+    This pass removes an instances of the higher order op 'auto_functionalized',
+    and modifies the calling EP inplace to have the original mutator op.
+    This pass doesn't perform safety checks to make sure that this inplace mutation is safe.
+    """
+
+    with ep.graph_module._set_replace_hook(ep.graph_signature.get_replace_hook()):
+        for module in ep.graph_module.modules():
+            if not isinstance(module, torch.fx.GraphModule):
+                continue
+            for node in ep.graph.nodes:
+                if (
+                    node.op == "call_function" and node.target is auto_functionalized
+                ) or (
+                    node.op == "call_function" and node.target is auto_functionalized_v2
+                ):
+                    func = node.args[0]
+                    assert isinstance(func, torch._ops.OpOverload)
+                    # re-inplace everything
+                    node.meta["only_clone_these_tensors"] = []
+            decompose_auto_functionalized(ep.graph)
+            remove_self_clone(ep.graph)
+            ep.graph.eliminate_dead_code()
+
+    return ep
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/_remove_effect_tokens_pass.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/_remove_effect_tokens_pass.py
new file mode 100644
index 0000000000000000000000000000000000000000..84adbf3663781124d3112a575895a0b4c051dc25
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/_remove_effect_tokens_pass.py
@@ -0,0 +1,161 @@
+# mypy: allow-untyped-defs
+import operator
+from typing import List
+
+import torch
+from torch._higher_order_ops.effects import _get_schema, with_effects
+
+from .exported_program import ExportedProgram
+from .graph_signature import (
+    CustomObjArgument,
+    InputKind,
+    InputSpec,
+    OutputKind,
+    OutputSpec,
+    TokenArgument,
+)
+
+
+def _remove_effect_tokens_from_graph_helper(
+    ep, num_tokens, input_token_names, output_token_names
+):
+    inputs_to_lifted_custom_objs = ep.graph_signature.inputs_to_lifted_custom_objs
+
+    output_node = None
+    with_effect_nodes: List[torch.fx.Node] = []
+
+    # Output node need to check its args agianst output_token_names (collected from output_spec)
+    # Therefore, we only need to find the top-levele output node
+    output_node = next(reversed(ep.graph_module.graph.find_nodes(op="output")))
+    for module in ep.graph_module.modules():
+        if not isinstance(module, torch.fx.GraphModule):
+            continue
+
+        for node in module.graph.nodes:
+            if not (node.op == "call_function" and node.target is with_effects):
+                continue
+
+            with_effect_nodes.append(node)
+
+    # Remove tokens from outputs
+    assert output_node is not None
+    output_args = output_node.args[0]
+    assert len(output_args) >= num_tokens
+    out_token_nodes = output_args[:num_tokens]
+    output_node.args = (tuple(output_args[num_tokens:]),)
+    for out_token in out_token_nodes:
+        assert out_token.name in output_token_names
+        out_token.users.clear()
+        ep.graph.erase_node(out_token)
+
+    # Replace with_effects(token, func, args) with just func(args)
+    for node in reversed(with_effect_nodes):
+        func = node.args[1]
+        assert isinstance(func, (torch._ops.OpOverload, torch._ops.HigherOrderOperator))
+
+        if func == torch.ops.higher_order.call_torchbind:
+            custom_obj_meta = node.args[2].meta["val"]
+            assert isinstance(custom_obj_meta, CustomObjArgument)
+            if custom_obj_meta.fake_val:
+                custom_obj = custom_obj_meta.fake_val
+            elif node.args[2].name in inputs_to_lifted_custom_objs:
+                custom_obj = ep.constants[
+                    inputs_to_lifted_custom_objs[node.args[2].name]
+                ]
+            else:
+                raise RuntimeError(f"Unable to find custom obj for node {node}")
+            schema = _get_schema(func, (custom_obj,) + node.args[3:])
+        else:
+            schema = _get_schema(func, node.args[2:])
+
+        with ep.graph.inserting_before(node):
+            new_node = ep.graph.call_function(func, node.args[2:], node.kwargs)
+        for k, v in node.meta.items():
+            new_node.meta[k] = v
+
+        node.replace_all_uses_with(new_node)
+
+        # Update user getitem nodes
+        for user in list(new_node.users.keys()):
+            assert user.target == operator.getitem
+            # getitem(with_effects, 0) == token
+            if user.args[1] == 0:
+                ep.graph.erase_node(user)
+
+        if len(schema.returns) == 1:
+            # If the function has 1 return then it will just directly return the
+            # result -- we don't need a getitem. So we can replace all the
+            # getitem(with_effects, 1) with just the note itself.
+            for user in list(new_node.users.keys()):
+                assert user.args[1] == 1
+                user.replace_all_uses_with(new_node)
+
+            new_node.meta["val"] = node.meta["val"][1]
+        elif len(schema.returns) > 1:
+            # If the function has more than 1 return then since we got rid of
+            # the 1st return value (the token), we need to bump all the other
+            # getitem calls by 1 down
+            for user in list(new_node.users.keys()):
+                assert user.args[1] >= 1
+                user.args = (user.args[0], user.args[1] - 1)
+
+            new_node.meta["val"] = node.meta["val"][1:]
+        else:
+            assert len(schema.returns) == 0
+            assert len(new_node.users) == 0
+            new_node.meta["val"] = None
+
+        ep.graph.erase_node(node)
+
+    # Remove tokens from inputs
+    placeholders = [node for node in ep.graph.nodes if node.op == "placeholder"]
+    assert len(placeholders) >= num_tokens
+    inp_token_nodes = placeholders[:num_tokens]
+    for inp_token in inp_token_nodes:
+        assert inp_token.name in input_token_names
+        ep.graph.erase_node(inp_token)
+
+    ep.graph.eliminate_dead_code()
+
+
+def _remove_effect_tokens(ep: ExportedProgram) -> ExportedProgram:
+    """
+    Removes the existance of tokens from the exported program, including:
+    - Removes the input and output tokens
+    - Replaces with_effects(token, func, args) with just func(args)
+
+    This function does an inplace modification on the given ExportedProgram.
+    """
+    num_tokens: int = 0
+    input_token_names: List[str] = []
+    new_input_specs: List[InputSpec] = []
+    for inp in ep.graph_signature.input_specs:
+        if inp.kind == InputKind.TOKEN:
+            num_tokens += 1
+            assert isinstance(inp.arg, TokenArgument)
+            input_token_names.append(inp.arg.name)
+        else:
+            new_input_specs.append(inp)
+
+    num_out_tokens: int = 0
+    new_output_specs: List[OutputSpec] = []
+    output_token_names: List[OutputSpec] = []
+    for out in ep.graph_signature.output_specs:
+        if out.kind == OutputKind.TOKEN:
+            num_out_tokens += 1
+            output_token_names.append(out.arg.name)
+        else:
+            new_output_specs.append(out)
+
+    # Update graph signature
+    ep.graph_signature.input_specs = new_input_specs
+    ep.graph_signature.output_specs = new_output_specs
+
+    assert num_tokens == num_out_tokens
+
+    with ep.graph_module._set_replace_hook(ep.graph_signature.get_replace_hook()):
+        _remove_effect_tokens_from_graph_helper(
+            ep, num_tokens, input_token_names, output_token_names
+        )
+
+    return ep
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/_safeguard.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/_safeguard.py
new file mode 100644
index 0000000000000000000000000000000000000000..76f22f369c566a97062fc60696ad7972dc2b260c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/_safeguard.py
@@ -0,0 +1,44 @@
+# mypy: allow-untyped-defs
+import torch
+from torch.fx.experimental.proxy_tensor import ProxyTorchDispatchMode
+from torch.overrides import TorchFunctionMode
+
+
+class AutogradStateOpsFailSafeguard(TorchFunctionMode):
+    """
+    Detect grad state ops during exporting the graph and fail the process by
+    raising an error, to avoid unexpected behavior. Those grad mode ops could be:
+    `torch.no_grad`
+    `torch.enable_grad`
+    `torch.set_grad_enabled`
+
+    Export with predispatch mode is exempted.
+    """
+
+    def __torch_function__(self, func, types, args=(), kwargs=None):
+        kwargs = kwargs or {}
+        unsupported_grad_mode_ops = [
+            torch._C._set_grad_enabled,
+        ]
+        # It's only enabled while tracing, by confirming the torch dispatch mode is
+        # any active PROXY. This is to allow the autograd ops out of tracing.
+        current_state = torch._C.is_grad_enabled()
+        if func in unsupported_grad_mode_ops:
+            assert len(args) == 1
+            changed_state = args[0]
+            mode = torch._C._get_dispatch_mode(torch._C._TorchDispatchModeKey.PROXY)
+            # Intend to check if it's not the pre_dispatch mode. It's allowed to use
+            # autograd ops in pre_dispatch mode, e.g. `torch.no_grad`
+            if (
+                mode
+                and isinstance(mode, ProxyTorchDispatchMode)
+                and not mode.pre_dispatch
+                and changed_state != current_state
+            ):
+                raise RuntimeError(
+                    f"Encountered autograd state manager op {func} trying to change global autograd state "
+                    "while exporting. This is unsafe because we don't capture this op in torch.export "
+                    "today, hence we can't reflect the user intention soundly. You can fix this by "
+                    "adding a torch.no_grad() context around the export call."
+                )
+        return func(*args, **kwargs)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/_trace.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/_trace.py
new file mode 100644
index 0000000000000000000000000000000000000000..abd2b5405fb937371501a06261c5a2fabef29cfb
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/_trace.py
@@ -0,0 +1,1943 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import dataclasses
+import functools
+import inspect
+import logging
+import re
+import time
+import warnings
+from contextlib import contextmanager, nullcontext
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
+
+import torch
+import torch._dynamo
+import torch.fx
+import torch.utils._pytree as pytree
+from torch._dispatch.python import enable_python_dispatcher
+from torch._dynamo.exc import UserError, UserErrorType
+from torch._export.db.logging import (
+    exportdb_error_message,
+    get_class_if_classified_error,
+)
+from torch._export.non_strict_utils import (
+    _fakify_script_objects,
+    _gather_constant_attrs,
+    _NonStrictTorchFunctionHandler,
+    make_constraints,
+    make_fake_inputs,
+    produce_guards_and_solve_constraints,
+)
+from torch._export.passes._node_metadata_hook import (
+    _node_metadata_hook,
+    _set_node_metadata_hook,
+)
+from torch._export.passes.collect_tracepoints_pass import CollectTracepointsPass
+from torch._export.passes.lift_constants_pass import (
+    ConstantAttrMap,
+    lift_constants_pass,
+    rewrite_script_object_meta,
+)
+from torch._export.utils import (
+    _collect_param_buffer_metadata,
+    _get_shape_env_from_gm,
+    _populate_param_buffer_metadata_to_new_gm,
+    placeholder_naming_pass,
+    placeholder_prefixes,
+)
+from torch._export.verifier import SpecViolationError
+from torch._export.wrappers import _wrap_submodules
+from torch._functorch._aot_autograd.input_output_analysis import (
+    _graph_input_names,
+    _graph_output_names,
+)
+from torch._functorch._aot_autograd.traced_function_transforms import (
+    create_functional_call,
+)
+from torch._functorch._aot_autograd.utils import create_tree_flattened_fn
+from torch._functorch.aot_autograd import aot_export_module
+from torch._guards import detect_fake_mode
+from torch._library.fake_class_registry import FakeScriptObject
+from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
+from torch._utils_internal import log_export_usage
+from torch.export.dynamic_shapes import (
+    _check_dynamic_shapes,
+    _combine_args,
+    _transform_shapes_for_default_dynamic,
+)
+from torch.export.exported_program import OutputKind
+from torch.fx._utils import first_call_function_nn_module_stack
+from torch.fx.experimental.proxy_tensor import make_fx
+from torch.fx.experimental.symbolic_shapes import (
+    ConstraintViolationError,
+    free_unbacked_symbols,
+    GuardOnDataDependentSymNode,
+    ShapeEnv,
+)
+from torch.fx.graph import _PyTreeCodeGen, _PyTreeInfo
+from torch.fx.passes.runtime_assert import insert_deferred_runtime_asserts
+from torch.utils._pytree import TreeSpec
+from torch.utils._sympy.value_ranges import ValueRangeError
+
+from ._safeguard import AutogradStateOpsFailSafeguard
+from .exported_program import (
+    _disable_prexisiting_fake_mode,
+    ExportedProgram,
+    InputKind,
+    ModuleCallEntry,
+    ModuleCallSignature,
+)
+from .graph_signature import _convert_to_export_graph_signature, ExportGraphSignature
+
+
+log = logging.getLogger(__name__)
+
+
+@dataclasses.dataclass
+class ExportDynamoConfig:
+    """
+    Manage Export-specific configurations of Dynamo.
+    """
+
+    allow_rnn: bool = True
+    reorderable_logging_functions: Set[Callable] = dataclasses.field(
+        default_factory=set
+    )
+    # Emit runtime asserts after AOTAutograd instead.
+    # This isn't really necessary, and isn't much more efficient since the runtime asserts pass does CSE,
+    # but if we want to reason more about what guards/runtime asserts to emit,
+    # this makes it a bit cleaner to do from the export side. Also no real point in running this twice.
+    do_not_emit_runtime_asserts = True
+
+
+@dataclasses.dataclass
+class ATenExportArtifact:
+    gm: torch.fx.GraphModule
+    sig: ExportGraphSignature
+    constants: Dict[
+        str,
+        Union[
+            torch.Tensor,
+            FakeScriptObject,
+            torch.ScriptObject,
+        ],
+    ]
+
+
+@dataclasses.dataclass(frozen=True)
+class ExportArtifact:
+    aten: ATenExportArtifact
+    out_spec: TreeSpec
+    fake_mode: FakeTensorMode
+    module_call_specs: Dict[str, Dict[str, pytree.TreeSpec]]
+
+
+DEFAULT_EXPORT_DYNAMO_CONFIG = ExportDynamoConfig()
+DEFAULT_EXPORT_DYNAMO_CONFIG.reorderable_logging_functions = {
+    logging.critical,
+    logging.debug,
+    logging.error,
+    logging.exception,
+    logging.info,
+    logging.log,
+    logging.warning,
+    print,
+    warnings.warn,
+}
+
+
+@contextmanager
+def _ignore_backend_decomps():
+    orig_mkldnn_flag = torch.backends.mkldnn.set_flags(False)
+    orig_nnpack_flag = torch.backends.nnpack.set_flags(False)
+    try:
+        yield
+    finally:
+        torch.backends.mkldnn.set_flags(*orig_mkldnn_flag)
+        torch.backends.nnpack.set_flags(*orig_nnpack_flag)
+
+
+def _fixup_key(x):
+    return "L__self__" + _strip_root(x)
+
+
+def _strip_root(x):
+    if isinstance(x, str) and x.startswith("_export_root"):
+        stripped = x[len("_export_root") :]
+        return stripped[1:] if stripped.startswith(".") else stripped
+    return x
+
+
+def _rewrite_tracepoint_node(gm: torch.fx.GraphModule):
+    """
+    In-place modifiy input graph module by replacing the export tracepoint with a new node
+    that has the same target and args, but with the _export_root stripped from path.
+    """
+    for node in gm.graph.nodes:
+        if node.target == torch.ops.higher_order._export_tracepoint:
+            if "path" in node.kwargs:
+                path = _strip_root(node.kwargs["path"])
+                with gm.graph.inserting_before(node):
+                    new_node = gm.graph.create_node(
+                        "call_function",
+                        torch.ops.higher_order._export_tracepoint,
+                        args=node.args,
+                        kwargs={
+                            "path": path,
+                            "kind": node.kwargs["kind"],
+                        },
+                    )
+                    new_node.meta = node.meta
+                    node.replace_all_uses_with(new_node)
+                    gm.graph.erase_node(node)
+
+
+def _extract_fake_inputs(gm, args, kwargs):
+    """
+    Given a graph module, extract fakified input tensors from the metadata of
+    its placeholders, and map them to the structure of given args and kwargs.
+    Also return the fake mode used to fakify those inputs.
+    """
+
+    fake_inps: List[torch.Tensor] = []
+    fake_vals: List[torch.Tensor] = []
+    for node in gm.graph.nodes:
+        if node.op == "placeholder" and "val" in node.meta:
+            fake_val = node.meta["val"]
+            if fake_val is not None and isinstance(fake_val, torch.Tensor):
+                fake_inps.append(fake_val)
+        elif "example_value" in node.meta:
+            fake_val = node.meta["example_value"]
+            if fake_val is not None and isinstance(fake_val, torch.Tensor):
+                fake_vals.append(fake_val)
+
+    if detected_fake_mode := detect_fake_mode(fake_inps + fake_vals):
+        fake_mode = detected_fake_mode
+    else:
+        fake_mode = FakeTensorMode(shape_env=ShapeEnv(), export=True)
+
+    count = 0
+
+    def lookup_fake(x):
+        nonlocal count
+        val = fake_inps[count]
+        count += 1
+        return val
+
+    fake_args = pytree.tree_map_only(torch.Tensor, lookup_fake, args)
+    fake_kwargs = pytree.tree_map_only(torch.Tensor, lookup_fake, kwargs)
+
+    return fake_args, fake_kwargs, fake_mode
+
+
+def _replace_param_buffer_names(param_buffer_table, sig):
+    for spec in sig.input_specs:
+        if spec.kind in (
+            InputKind.PARAMETER,
+            InputKind.BUFFER,
+        ):
+            spec.target = param_buffer_table[spec.target]
+    for spec in sig.output_specs:
+        if spec.kind in (
+            OutputKind.BUFFER_MUTATION,
+            OutputKind.GRADIENT_TO_PARAMETER,
+        ):
+            spec.target = param_buffer_table[spec.target]
+
+
+def _convert_to_positional_args(orig_arg_names, args, kwargs):
+    assert len(orig_arg_names) == len(args) + len(kwargs), (
+        f"Total number of arg names is expected to be {len(orig_arg_names)} "
+        f"but got {len(args)} positional args, {len(kwargs)} kwargs."
+    )
+    reordered_kwargs = [kwargs[kw_name] for kw_name in orig_arg_names[len(args) :]]
+    return (
+        *args,
+        *reordered_kwargs,
+    )
+
+
+def _normalize_nn_module_stack(gm_torch_level, root_cls):
+    # Append a root module to every nn_module_stack.
+    root = "L['self']"
+    root_key = re.sub(r"[^a-zA-Z0-9]", "_", root)
+    for gm in gm_torch_level.modules():
+        if not isinstance(gm, torch.fx.GraphModule):
+            continue
+        for node in gm.graph.nodes:
+            if node.op in ["placeholder", "output"]:
+                continue
+            add_root = True
+            if nn_module_stack := node.meta.get("nn_module_stack", {}):
+                path, ty = next(iter(nn_module_stack.values()))
+                # After deserializing the class `ty` might not exist anymore so
+                # it could be a string
+                if inspect.isclass(ty) and issubclass(ty, torch.nn.Module):
+                    # TODO Figure out why sometimes we have root sometimes we don't.
+                    if path == root and ty is root_cls:
+                        add_root = False
+                else:
+                    assert isinstance(ty, str)
+            if add_root:
+
+                def normalize_path(path):
+                    try:
+                        parts = []
+
+                        class Path:
+                            def __getattr__(self, name):
+                                parts.append(name)
+                                return self
+
+                            def __getitem__(self, idx):
+                                parts.append(str(idx))
+                                return self
+
+                        eval(path, {"L": {"self": Path()}})
+                        return ".".join(parts)
+                    except Exception:  # TODO(zhxchen17) Remove this.
+                        return path
+
+                nn_module_stack = {
+                    root_key: (root, root_cls.__module__ + "." + root_cls.__qualname__),
+                    **nn_module_stack,
+                }
+                node.meta["nn_module_stack"] = {
+                    key: (normalize_path(path), ty)
+                    for key, (path, ty) in nn_module_stack.items()
+                }
+
+
+def _get_param_buffer_mapping(
+    original_module: torch.nn.Module,
+    traced_module: torch.nn.Module,
+) -> Dict[str, str]:
+    """
+    Returns a mapping of parameter/buffer names from the new module to the
+    original model. This is to help with restoring the FQN for parameter/buffers
+    of a traced module to what the original module contains.
+    """
+
+    param_lookup: Dict[int, str] = {}
+    buffer_lookup: Dict[int, str] = {}
+    for name, param in original_module.named_parameters(remove_duplicate=False):
+        param_lookup[id(param)] = name
+    for name, buffer in original_module.named_buffers(remove_duplicate=False):
+        buffer_lookup[id(buffer)] = name
+
+    param_buffer_table: Dict[str, str] = {}
+    for dynamo_name, dynamo_param in traced_module.named_parameters(
+        remove_duplicate=False
+    ):
+        assert dynamo_name not in param_buffer_table
+        if id(dynamo_param) in param_lookup:
+            param_buffer_table[dynamo_name] = param_lookup[id(dynamo_param)]
+
+    for dynamo_name, dynamo_buffer in traced_module.named_buffers(
+        remove_duplicate=False
+    ):
+        assert dynamo_name not in param_buffer_table
+        if id(dynamo_buffer) in buffer_lookup:
+            param_buffer_table[dynamo_name] = buffer_lookup[id(dynamo_buffer)]
+
+    return param_buffer_table
+
+
+def _preserve_requires_grad_pass(
+    gm: torch.fx.GraphModule,
+    sig: ExportGraphSignature,
+    fake_params_buffers: Dict[str, torch.Tensor],
+    constants: Dict[str, Union[torch.Tensor, FakeScriptObject, torch.ScriptObject]],
+    flat_fake_args: List[Any],
+):
+    placeholders = [node for node in gm.graph.nodes if node.op == "placeholder"]
+    assert len(sig.input_specs) == len(placeholders)
+    i = 0
+    for node, spec in zip(placeholders, sig.input_specs):
+        if spec.kind in (
+            InputKind.PARAMETER,
+            InputKind.BUFFER,
+        ):
+            assert spec.target is not None
+            node.meta["val"].requires_grad = fake_params_buffers[
+                spec.target
+            ].requires_grad
+        elif spec.kind == InputKind.USER_INPUT:
+            fake_arg = flat_fake_args[i]
+            if isinstance(fake_arg, torch.Tensor):
+                node.meta["val"].requires_grad = fake_arg.requires_grad
+            i += 1
+        elif spec.kind == InputKind.CONSTANT_TENSOR:
+            assert spec.target is not None
+            constant = constants[spec.target]
+            if isinstance(constant, torch.Tensor):
+                # If the tensor is not leaf, it should already have a correct requires grad field
+                if node.meta["val"].is_leaf:
+                    node.meta["val"].requires_grad = constant.requires_grad
+                else:
+                    assert node.meta["val"].requires_grad == constant.requires_grad
+        elif spec.kind in (InputKind.CUSTOM_OBJ, InputKind.TOKEN):
+            continue
+        else:
+            raise AssertionError(spec.kind)
+
+
+def _remap_constants(
+    orig_constant_attrs: ConstantAttrMap,
+    graph_signature: ExportGraphSignature,
+    constants: Dict[str, Union[torch.Tensor, FakeScriptObject, torch.ScriptObject]],
+) -> None:
+    """Rewrite the graph signature and constants table to use the FQN from the original module."""
+    remap_table: Dict[str, List[str]] = {}
+    for name, value in constants.items():
+        if value in orig_constant_attrs:
+            remap_table[name] = orig_constant_attrs[value]
+
+    for spec in graph_signature.input_specs:
+        if spec.kind in (
+            InputKind.CONSTANT_TENSOR,
+            InputKind.CUSTOM_OBJ,
+        ):
+            orig_target = spec.target
+            assert orig_target is not None
+            targets = remap_table.get(orig_target, [orig_target])
+            spec.target = targets[0]
+
+            constant = constants[orig_target]
+            del constants[orig_target]
+            for target in targets:
+                constants[target] = constant
+
+
+def _rename_constants_nodes(
+    gm: torch.fx.GraphModule,
+    graph_signature: ExportGraphSignature,
+) -> None:
+    """
+    For strict mode, rename constants nodes that were previously annotated as buffers.
+    """
+    # handle name collisions with existing constants
+    node_names = {node.name for node in gm.graph.nodes}
+
+    def rename_constant(name):
+        if name in node_names:
+            n = 1
+            while (dup_name := f"{name}_{n}") in node_names:
+                n += 1
+            name = dup_name
+        node_names.add(name)
+        return name
+
+    # use input specs to map names from buffers to constants
+    buffer_prefix = placeholder_prefixes[InputKind.BUFFER]
+    const_prefix = placeholder_prefixes[InputKind.CONSTANT_TENSOR]
+    buffer_to_constant = {}
+    for spec in graph_signature.input_specs:
+        if spec.kind == InputKind.CONSTANT_TENSOR and not spec.arg.name.startswith(
+            const_prefix
+        ):
+            if spec.arg.name.startswith(buffer_prefix):  # map from buffer to constants
+                c_name = rename_constant(
+                    const_prefix + spec.arg.name[len(buffer_prefix) :]
+                )
+            else:  # lifted constant
+                c_name = rename_constant(const_prefix + spec.arg.name)
+            buffer_to_constant[spec.arg.name] = c_name
+            spec.arg.name = c_name
+    for spec in graph_signature.output_specs:
+        if spec.arg.name in buffer_to_constant:
+            spec.arg.name = buffer_to_constant[spec.arg.name]
+
+    # Rename constants nodes for all modules
+    for mod in gm.modules():
+        if not isinstance(mod, torch.fx.GraphModule):
+            continue
+        for node in mod.graph.nodes:
+            if node.name in buffer_to_constant:
+                node.name = node.target = buffer_to_constant[node.name]
+        mod.recompile()
+
+
+def _restore_state_dict(
+    original_module: torch.nn.Module, traced_module: torch.fx.GraphModule
+) -> None:
+    """
+    Restores the state dict of the traced module to that of the original module.
+    """
+    param_buffer_table = _get_param_buffer_mapping(original_module, traced_module)
+    # Since the graph module is flattened (no module heirarchy), we
+    # need to noramlize the module by replacing "." with "_". If we
+    # don't, it will try to save the weight to a submodule which no
+    # longer exists.
+    for name, fqn in param_buffer_table.items():
+        param_buffer_table[name] = fqn.replace(".", "_")
+
+    # Replace state dict attr names with the fqn
+    for name, fqn in param_buffer_table.items():
+        if not hasattr(traced_module, name):
+            continue
+
+        attr = getattr(traced_module, name)
+        if isinstance(attr, torch.Tensor) and not isinstance(attr, torch.nn.Parameter):
+            traced_module.register_buffer(fqn, attr)
+        else:
+            setattr(traced_module, fqn, attr)
+        delattr(traced_module, name)
+
+    # Replace graph getattr nodes with the correct name
+    for node in traced_module.graph.nodes:
+        if node.op == "get_attr":
+            attr_name = node.target
+            if attr_name in param_buffer_table:
+                node.target = param_buffer_table[attr_name]
+
+    traced_module.recompile()
+
+
+def _get_module_hierarchy(mod: torch.nn.Module) -> Dict[str, str]:
+    return {
+        name: type(m).__name__ for name, m in mod.named_modules(remove_duplicate=False)
+    }
+
+
+def _make_module_call_graph(
+    module_hierarchy: Dict[str, str],
+    in_spec: TreeSpec,
+    out_spec: TreeSpec,
+    module_call_signatures: Dict[str, ModuleCallSignature],
+) -> List[ModuleCallEntry]:
+    ret = [
+        ModuleCallEntry(fqn=fqn, signature=module_call_signatures.get(fqn))
+        for fqn in module_hierarchy
+    ]
+    assert ret[0].fqn == ""
+    ret[0].signature = ModuleCallSignature(
+        inputs=[], outputs=[], in_spec=in_spec, out_spec=out_spec
+    )
+    return ret
+
+
+def _export_to_torch_ir(
+    f: Callable,
+    args: Tuple[Any, ...],
+    kwargs: Optional[Dict[str, Any]] = None,
+    dynamic_shapes: Optional[Union[Dict[str, Any], Tuple[Any], List[Any]]] = None,
+    *,
+    preserve_module_call_signature: Tuple[str, ...] = (),
+    disable_constraint_solver: bool = False,
+    allow_complex_guards_as_runtime_asserts: bool = False,
+    restore_fqn: bool = True,
+    _log_export_usage: bool = True,
+    same_signature: bool = True,
+) -> torch.fx.GraphModule:
+    """
+    Traces either an nn.Module's forward function or just a callable with PyTorch
+    operations inside and produce a torch.fx.GraphModule in torch IR.
+    """
+
+    if _log_export_usage:
+        log_export_usage(event="export.private_api", flags={"_export_to_torch_ir"})
+
+    if not isinstance(args, tuple):
+        raise UserError(
+            UserErrorType.INVALID_INPUT,
+            f"Expecting `args` to be a tuple of example positional inputs, got {type(args)}",
+        )
+
+    kwargs = kwargs or {}
+    combined_args = _combine_args(f, args, kwargs)
+    _check_dynamic_shapes(combined_args, dynamic_shapes)
+    transformed_dynamic_shapes = _transform_shapes_for_default_dynamic(
+        combined_args, dynamic_shapes
+    )
+
+    with torch._dynamo.config.patch(dataclasses.asdict(DEFAULT_EXPORT_DYNAMO_CONFIG)):
+        try:
+            module_call_specs: Dict[str, Dict[str, pytree.TreeSpec]] = {}
+            with _wrap_submodules(
+                f, preserve_module_call_signature, module_call_specs
+            ), _ignore_backend_decomps():
+                gm_torch_level, _ = torch._dynamo.export(
+                    f,
+                    dynamic_shapes=transformed_dynamic_shapes,  # type: ignore[arg-type]
+                    tracing_mode="symbolic",
+                    disable_constraint_solver=disable_constraint_solver,
+                    # currently the following 2 flags are tied together for export purposes,
+                    # but untangle for sake of dynamo export api
+                    prefer_deferred_runtime_asserts_over_guards=True,
+                    allow_complex_guards_as_runtime_asserts=allow_complex_guards_as_runtime_asserts,
+                    _log_export_usage=_log_export_usage,
+                    same_signature=same_signature,
+                )(
+                    *args,
+                    **kwargs,
+                )
+        except (ConstraintViolationError, ValueRangeError) as e:
+            raise UserError(UserErrorType.CONSTRAINT_VIOLATION, str(e))  # noqa: B904
+        except GuardOnDataDependentSymNode as e:
+            raise UserError(  # noqa: B904
+                UserErrorType.ANTI_PATTERN,
+                f"Consider annotating your code using torch._check*(). {str(e)}",
+                case_name="constrain_as_size_example",
+            )
+
+    gm_torch_level.meta["module_call_specs"] = module_call_specs
+
+    if isinstance(f, torch.nn.Module) and restore_fqn:
+        _restore_state_dict(f, gm_torch_level)
+
+    return gm_torch_level
+
+
+def _export_to_aten_ir(
+    mod: torch.nn.Module,
+    fake_args,
+    fake_kwargs,
+    fake_params_buffers,
+    constant_attrs: ConstantAttrMap,
+    produce_guards_callback=None,
+    *,
+    transform=lambda x: x,  # TODO(zhxchen17) Revisit if this is needed later.
+    pre_dispatch=False,
+    decomp_table=None,
+    _check_autograd_state=True,
+    _is_torch_jit_trace=False,
+) -> ATenExportArtifact:
+    # [NOTE] If the user is exporting under training mode, we want to detect if there is any
+    # state change in the autograd global state and error. If the user is exporting under inference
+    # mode, we don't care. At predispatch level, we don't care about the state change.
+    is_grad_enabled = torch._C.is_grad_enabled()
+    grad_safe_guard = nullcontext()
+    # export_to_aten_ir is called when we decompose the ep into inference IR
+    # In that setting, we actually shouldn't check the state change as at this point,
+    # because the intention is specalizing to inference.
+    if _check_autograd_state:
+        if not pre_dispatch and is_grad_enabled:
+            grad_safe_guard = AutogradStateOpsFailSafeguard()  # type: ignore[assignment]
+
+    @contextmanager
+    def _compiling_state_context():
+        old_value = torch.compiler._is_compiling_flag
+        try:
+            torch.compiler._is_compiling_flag = True
+            yield
+        finally:
+            torch.compiler._is_compiling_flag = old_value
+
+    # This _reparametrize_module makes sure inputs and module.params/buffers have the same fake_mode,
+    # otherwise aot_export_module will error out because it sees a mix of fake_modes.
+    # And we want aot_export_module to use the fake_tensor mode in dynamo to keep the pipeline easy to reason about.
+    with torch.nn.utils.stateless._reparametrize_module(
+        mod,
+        fake_params_buffers,
+        tie_weights=True,
+        strict=True,
+        stack_weights=True,
+    ), grad_safe_guard, _ignore_backend_decomps(), _compiling_state_context():  # type: ignore[attr-defined]
+        gm, graph_signature = transform(aot_export_module)(
+            mod,
+            fake_args,
+            trace_joint=False,
+            pre_dispatch=pre_dispatch,
+            decompositions=decomp_table,
+            kwargs=fake_kwargs,
+        )
+
+    def _maybe_fixup_gm_and_output_node_meta(old_gm, new_gm):
+        if isinstance(old_gm, torch.fx.GraphModule):
+            if hasattr(old_gm, "meta"):
+                new_gm.meta.update(old_gm.meta)
+            old_output_node = list(old_gm.graph.nodes)[-1]
+            new_output_node = list(new_gm.graph.nodes)[-1]
+            assert old_output_node.op == "output" and new_output_node.op == "output"
+            # make sure we don't override any meta
+            assert len(new_output_node.meta) == 0
+            new_output_node.meta.update(old_output_node.meta)
+
+    # TODO unfortunately preserving graph-level metadata and output node's meta
+    # is not working well with aot_export. So we manually copy it.
+    # (The node-level meta is addressed above.)
+    _maybe_fixup_gm_and_output_node_meta(mod, gm)
+
+    # Run produce guards before we handle runtime asserts.
+    # This means we run the export solver before the runtime asserts pass.
+    # Right now this doesn't mean much - the export solver is only there for suggested fixes,
+    # and we won't even get to constraint solving if that's needed.
+    # But if in future we want to control what runtime asserts are emitted for export,
+    # or rely on produce_guards + solver for some simplification on runtime asserts, this probably makes sense.
+    if produce_guards_callback:
+        try:
+            produce_guards_callback(gm)
+        except (ConstraintViolationError, ValueRangeError) as e:
+            raise UserError(UserErrorType.CONSTRAINT_VIOLATION, str(e))  # noqa: B904
+
+    # Run runtime asserts pass before creating input/output specs, since size-related CSE/DCE might affect output signature.
+    # Overwrite output specs afterwards.
+    flat_fake_args = pytree.tree_leaves((fake_args, fake_kwargs))
+    if not torch._dynamo.config.do_not_emit_runtime_asserts:
+        stack_trace = (
+            'File "torch/fx/passes/runtime_assert.py", line 24, '
+            "in insert_deferred_runtime_asserts"
+        )
+        with _set_node_metadata_hook(
+            gm, functools.partial(_node_metadata_hook, stack_trace=stack_trace)
+        ):
+            shape_env = _get_shape_env_from_gm(gm)
+            if shape_env:
+                insert_deferred_runtime_asserts(
+                    gm,
+                    shape_env,
+                    f"exported program: {first_call_function_nn_module_stack(gm.graph)}",
+                    export=True,
+                )
+
+    # update output specs
+    gm.recompile()
+    graph_signature.user_outputs = _graph_output_names(gm)
+
+    # NOTE: aot_export adds symint metadata for placeholders with int values;
+    # since these become specialized, we replace such metadata with the original values
+    index = 0
+    total_non_user_inputs = (
+        len(graph_signature.parameters)
+        + len(graph_signature.buffers)
+        + len(graph_signature.input_tokens)
+    )
+    for node in gm.graph.nodes:
+        if node.op == "placeholder":
+            if index >= total_non_user_inputs:
+                user_arg = flat_fake_args[index - total_non_user_inputs]
+                if not isinstance(user_arg, torch.Tensor):
+                    node.meta["val"] = user_arg
+            index += 1
+
+    export_graph_signature = _convert_to_export_graph_signature(
+        graph_signature, gm, _get_non_persistent_buffers(mod)
+    )
+
+    constants = rewrite_script_object_meta(gm)
+    constants.update(lift_constants_pass(gm, export_graph_signature, constant_attrs))
+
+    if pre_dispatch:
+        from torch._export.passes.replace_autocast_with_hop_pass import (
+            replace_autocast_with_hop_pass,
+        )
+        from torch._export.passes.replace_set_grad_with_hop_pass import (
+            replace_set_grad_with_hop_pass,
+        )
+
+        # Note: replace_set_grad_with_hop_pass need to be after lift_constant_pass because
+        # a getattr of a constant tensor doesn't have meta["val"] until after lift_constant_pass.
+        # If replace_set_grad_with_hop_pass is before lift_constant_pass,
+        # and the constant_tensor is passed as input of the set grad hop, the placeholder's
+        # meta["val"] will be None and fails our verifier for placeholder.
+        gm, export_graph_signature = replace_set_grad_with_hop_pass(
+            gm, export_graph_signature
+        )
+
+        gm, export_graph_signature = replace_autocast_with_hop_pass(
+            gm, export_graph_signature
+        )
+
+    # Remove nn_module_stack, stack_trace metadata from all placeholders/inputs nodes.
+    for _mod in gm.modules():
+        if not isinstance(_mod, torch.fx.GraphModule):
+            continue
+        for node in _mod.graph.nodes:
+            if node.op in ["placeholder", "output"]:
+                node.meta.pop("nn_module_stack", None)
+                node.meta.pop("stack_trace", None)
+
+    # Prettify names for placeholder nodes.
+    placeholder_naming_pass(
+        gm,
+        export_graph_signature,
+        mod,
+        fake_args,
+        fake_kwargs,
+        fake_params_buffers,
+        constants,
+    )
+
+    _preserve_requires_grad_pass(
+        gm, export_graph_signature, fake_params_buffers, constants, flat_fake_args
+    )
+
+    return ATenExportArtifact(
+        gm,
+        export_graph_signature,
+        constants,
+    )
+
+
+def _fakify_params_buffers(
+    fake_mode: FakeTensorMode,
+    mod: torch.nn.Module,
+) -> Dict[str, Union[torch.Tensor, torch.nn.Parameter]]:
+    params_buffers = {
+        **dict(mod.named_parameters(remove_duplicate=False)),
+        **dict(mod.named_buffers(remove_duplicate=False)),
+    }
+
+    faked_params_buffers = {}
+    memo: Dict[int, FakeTensor] = {}
+    for key, value in params_buffers.items():
+        if id(value) in memo:
+            fake_tensor = memo[id(value)]
+        else:
+            fake_tensor = fake_mode.from_tensor(value, static_shapes=True)
+            memo[id(value)] = fake_tensor
+        faked_params_buffers[key] = fake_tensor
+    return faked_params_buffers  # type: ignore[return-value]
+
+
+def _get_forward_arg_names(
+    mod: torch.nn.Module,
+    args: Tuple[Any, ...],
+    kwargs: Optional[Dict[str, Any]] = None,
+) -> List[str]:
+    """
+    Gets the argument names to forward that are used, for restoring the
+    original signature when unlifting the exported program module.
+    - Positional args: retain the original argument names, and enumerate
+        *args as args_0, args_1, ...
+    - Keyword args: retain the original kwarg names in the order specified
+        by the user. This order seems to matter for the current state of
+        export lifted modules.
+    """
+    sig = inspect.signature(mod.forward)
+    _args = sig.bind_partial(*args).arguments
+
+    names: List[str] = []
+    for name, value in _args.items():
+        # handle variable number of positional args
+        if sig.parameters[name].kind == inspect._ParameterKind.VAR_POSITIONAL:
+            names.extend([f"{name}_{i}" for i, _ in enumerate(value)])
+        else:
+            names.append(name)
+    # order of kwargs matters for input spec
+    if kwargs:
+        names.extend([kwarg for kwarg, _ in kwargs.items()])
+
+    return names
+
+
+def _get_non_persistent_buffers(mod: torch.nn.Module) -> Set[str]:
+    """
+    Returns set of non-persistent buffers in a module and its submodules.
+    """
+    result = set()
+    for name, m in mod.named_modules():
+        for b in m._non_persistent_buffers_set:
+            result.add(f"{name}.{b}" if name else b)
+    return result
+
+
+def _rewrite_dynamo_tensor_constants(
+    orig_mod_buffers: Set[torch.Tensor],
+    traced_mod_buffers: Dict[str, torch.Tensor],
+    graph_signature: ExportGraphSignature,
+    constants: Dict[str, Union[torch.Tensor, FakeScriptObject, torch.ScriptObject]],
+):
+    """
+    Dynamo erroneously marks tensor attributes on modules as buffers.
+    Rewrite them to be tensor constants.
+    """
+    for spec in graph_signature.input_specs:
+        if spec.kind == InputKind.BUFFER:
+            assert spec.target is not None
+            value = traced_mod_buffers[spec.target]
+            if value not in orig_mod_buffers:
+                # This was a tensor constant erroneously marked as a buffer.
+                # Convert it into a constant in the graph signature, and add its
+                # value to the constants table.
+                spec.kind = InputKind.CONSTANT_TENSOR
+                constants[spec.target] = value  # type: ignore[arg-type]
+
+
+def _move_non_persistent_buffers_to_tensor_constants(
+    orig_mod: torch.nn.Module,
+    graph_signature: ExportGraphSignature,
+    constants: Dict[str, Union[torch.Tensor, FakeScriptObject, torch.ScriptObject]],
+):
+    """
+    Moves non-persistent buffers to tensor constants.
+    """
+    for spec in graph_signature.input_specs:
+        if spec.kind == InputKind.BUFFER and not spec.persistent:
+            assert spec.target is not None
+            assert spec.target not in constants
+            constants[spec.target] = orig_mod.get_buffer(spec.target)  # type: ignore[arg-type]
+
+
+def _verify_nn_module_stack(graph_module: torch.fx.GraphModule) -> None:
+    """
+    Perform nn_module_stack checks on the graph.
+    Current constraints:
+        For the top level graph:
+        - populated for 'call_function', 'get_attr'
+        - None for 'placeholder', 'output'
+        For submodule graphs:
+        - None for 'placeholder', output'
+
+    TODO(pianpwk): make this a consistent node-level check once nn_module_stack is populated for cond submodules.
+    """
+    # Check top-level graph for all nodes, all graphs for placeholder & output nodes
+    for i, mod in enumerate([graph_module] + list(graph_module.modules())):
+        if not isinstance(mod, torch.fx.GraphModule):
+            continue
+        for node in mod.graph.nodes:
+            if node.op in ["call_function", "get_attr"]:
+                if i == 0:
+                    if (
+                        nn_module_stack := node.meta.get("nn_module_stack", None)
+                    ) is None:
+                        raise SpecViolationError(
+                            f"Node {node} of type {node.op} is missing nn_module_stack metadata"
+                        )
+                    if not all(
+                        isinstance(k, str)
+                        and isinstance(v, tuple)
+                        and len(v) == 2
+                        and all(isinstance(x, str) for x in v)
+                        for k, v in nn_module_stack.items()
+                    ):
+                        raise SpecViolationError(
+                            f"Node {node} of type {node.op} has incorrect nn_module_stack metadata format"
+                            f"expected Dict[str, Tuple[str, str]], but got {nn_module_stack}"
+                        )
+            elif node.op in ["placeholder", "output"]:
+                if node.meta.get("nn_module_stack", None):
+                    raise SpecViolationError(
+                        f"Node {node} of type {node.op} contains nn_module_stack metadata, this should be None"
+                    )
+
+
+def _verify_stack_trace(graph_module: torch.fx.GraphModule) -> None:
+    """
+    Perform stack trace checks on the graph.
+    Constraints:
+        - None or non-empty str for 'call_function', 'get_attr'
+        - None for 'placeholder', 'output'
+    """
+    for i, mod in enumerate([graph_module] + list(graph_module.modules())):
+        if not isinstance(mod, torch.fx.GraphModule):
+            continue
+        for node in graph_module.graph.nodes:
+            stack_trace = node.meta.get("stack_trace", None)
+            if node.op in ["call_function", "get_attr"]:
+                if not (stack_trace is None or isinstance(stack_trace, str)):
+                    raise SpecViolationError(
+                        f"Node {node} of type {node.op} has invalid stack_trace metadata, "
+                        f"expected a string or None but instead found: {stack_trace}"
+                    )
+            elif node.op in ["placeholder", "output"]:
+                if stack_trace:
+                    raise SpecViolationError(
+                        f"Node {node} of type {node.op} contains stack_trace metadata, "
+                        f"expected None but instead found: {stack_trace}"
+                    )
+
+
+def _verify_placeholder_names(gm: torch.fx.GraphModule, sig: ExportGraphSignature):
+    """
+    Performs a sanity check on the placeholder node names.
+    - User input nodes: no restrictions, should match the original forward() signature
+    - Params/buffers/constants/custom_obj/token nodes: should start with prefixes defined in 
+    """
+    name_to_kind = {spec.arg.name: spec.kind for spec in sig.input_specs}
+    for mod in gm.modules():
+        if not isinstance(mod, torch.fx.GraphModule):
+            continue
+        for node in mod.graph.nodes:
+            if node.op == "placeholder":
+                if node.name not in name_to_kind:
+                    continue
+                node_kind = name_to_kind[node.name]
+                prefix = placeholder_prefixes[node_kind]
+                if not node.name.startswith(prefix):
+                    raise SpecViolationError(
+                        f"Placeholder node name {node.name} does not follow spec for {node_kind}, name should have prefix: {prefix}"
+                    )
+
+
+def get_ep_stats(ep: ExportedProgram) -> Dict[str, Any]:
+    op_count = 0
+    op_set = set()
+    for m in ep.graph_module.modules():
+        if not isinstance(m, torch.fx.GraphModule):
+            continue
+        for node in m.graph.nodes:
+            if node.op != "call_function":
+                continue
+            op_count += 1
+            assert hasattr(node.target, "__module__")
+            assert hasattr(node.target, "__name__")
+            op_set.add(f"{node.target.__module__}.{node.target.__name__}")
+    return {"op_count": op_count, "op_set": op_set}
+
+
+_EXPORT_FLAGS: Optional[Set[str]] = None
+_EXPORT_MODULE_HIERARCHY: Optional[Dict[str, str]] = None
+
+
+def _log_export_wrapper(fn):
+    @functools.wraps(fn)
+    def wrapper(*args, **kwargs):
+        global _EXPORT_FLAGS, _EXPORT_MODULE_HIERARCHY
+        try:
+            start = time.time()
+            ep = fn(*args, **kwargs)
+            end = time.time()
+            log_export_usage(
+                event="export.time",
+                metrics=end - start,
+                flags=_EXPORT_FLAGS,
+                **get_ep_stats(ep),
+            )
+        except Exception as e:
+            t = type(e)
+            error_type = t.__module__ + "." + t.__qualname__
+            case_name = get_class_if_classified_error(e)
+            if case_name is not None:
+                log.error(exportdb_error_message(case_name))
+                log_export_usage(
+                    event="export.error.classified",
+                    type=error_type,
+                    message=str(e),
+                    flags=_EXPORT_FLAGS,
+                )
+            else:
+                log_export_usage(
+                    event="export.error.unclassified",
+                    type=error_type,
+                    message=str(e),
+                    flags=_EXPORT_FLAGS,
+                )
+            raise e
+        finally:
+            _EXPORT_FLAGS = None
+            _EXPORT_MODULE_HIERARCHY = None
+
+        return ep
+
+    return wrapper
+
+
+def _process_jit_trace_inputs_for_export(example_inputs, example_kwarg_inputs):
+    if not isinstance(example_inputs, (tuple, list, dict)):
+        example_inputs = (example_inputs,)
+
+    elif isinstance(example_inputs, list):
+        example_inputs = tuple(example_inputs)
+
+    elif (
+        isinstance(example_inputs, (torch.Tensor, dict))
+        and example_kwarg_inputs is None
+    ):
+        example_inputs = (example_inputs,)
+
+    if example_kwarg_inputs is None:
+        example_kwarg_inputs = {}
+    return example_inputs, example_kwarg_inputs
+
+
+def _process_export_inputs(mod, args, kwargs, dynamic_shapes):
+    original_state_dict = mod.state_dict(keep_vars=True)
+
+    if not isinstance(args, tuple):
+        raise UserError(
+            UserErrorType.INVALID_INPUT,
+            f"Expecting `args` to be a tuple of example positional inputs, got {type(args)}",
+        )
+    kwargs = kwargs if kwargs is not None else {}
+    _, original_in_spec = pytree.tree_flatten((args, kwargs))
+
+    if isinstance(dynamic_shapes, torch.export.ShapesCollection):
+        dynamic_shapes = dynamic_shapes.dynamic_shapes(mod, args, kwargs)
+
+    return args, kwargs, original_in_spec, original_state_dict, dynamic_shapes
+
+
+def _get_module_call_graph(
+    export_artifact: ExportArtifact,
+    original_in_spec: TreeSpec,
+    preserve_module_call_signature: Tuple[str, ...],
+    strict_mode_export: bool,
+):
+    """
+    In-place modify the graph module in export_artifact, remove _export_tracepoint nodes and
+    return module_call_graph.
+    """
+    gm: torch.fx.GraphModule = export_artifact.aten.gm
+    export_graph_signature: ExportGraphSignature = export_artifact.aten.sig
+    module_call_specs: Dict[
+        str, Dict[str, TreeSpec]
+    ] = export_artifact.module_call_specs
+    out_spec: TreeSpec = export_artifact.out_spec
+
+    # Make module signatures.
+    module_call_signatures = {}
+    for fqn, specs in module_call_specs.items():
+        mod_fqn = _strip_root(fqn) if not strict_mode_export else fqn
+        module_call_signatures[mod_fqn] = ModuleCallSignature(
+            inputs=[], outputs=[], **specs
+        )
+
+    if len(preserve_module_call_signature) > 0:
+        if not strict_mode_export:
+            _rewrite_tracepoint_node(gm)
+        res = CollectTracepointsPass(module_call_signatures, export_graph_signature)(gm)
+        assert res is not None
+        gm = res.graph_module
+
+    assert _EXPORT_MODULE_HIERARCHY is not None
+    module_call_graph = _make_module_call_graph(
+        _EXPORT_MODULE_HIERARCHY,
+        original_in_spec,
+        out_spec,
+        module_call_signatures,
+    )
+    return gm, module_call_graph
+
+
+def _get_range_constraints(
+    export_artifact: ExportArtifact, combined_args: Dict[str, Any], dynamic_shapes
+):
+    gm: torch.fx.GraphModule = export_artifact.aten.gm
+    export_graph_signature: ExportGraphSignature = export_artifact.aten.sig
+    fake_mode: FakeTensorMode = export_artifact.fake_mode
+    num_lifted = next(
+        (
+            i
+            for i, s in enumerate(export_graph_signature.input_specs)
+            if s.kind == InputKind.USER_INPUT
+        ),
+        len(export_graph_signature.input_specs),
+    )
+    range_constraints = make_constraints(
+        fake_mode,
+        gm,
+        combined_args,
+        dynamic_shapes,
+        num_lifted,
+    )
+    return range_constraints
+
+
+def _get_inline_constraints(fake_mode: FakeTensorMode):
+    assert fake_mode.shape_env is not None
+    return {
+        k: v
+        for k, v in fake_mode.shape_env.var_to_range.items()
+        if free_unbacked_symbols(k)
+    }
+
+
+@contextmanager
+def patch_forward(obj: torch.nn.Module, new_method):
+    """Helper method to make it easier to cleanly torch.export() a method on a
+    module that is not `forward`.
+    """
+    # Save the original method
+    original_method = obj.forward
+
+    # Patch the method
+    obj.forward = new_method.__get__(obj, obj.__class__)
+
+    try:
+        yield
+    finally:
+        # Restore the original method
+        obj.forward = original_method
+
+
+@contextmanager
+def _temp_disable_texpr_fuser():
+    original_state = torch._C._jit_texpr_fuser_enabled()
+    torch._C._jit_set_texpr_fuser_enabled(False)
+    try:
+        yield
+    finally:
+        torch._C._jit_set_texpr_fuser_enabled(original_state)
+
+
+class _WrapperModule(torch.nn.Module):
+    def __init__(self, f):
+        super().__init__()
+        self.f = f
+
+    def forward(self, *args, **kwargs):
+        return self.f(*args, **kwargs)
+
+
+def _convert_ts_to_export_experimental(traced_callable, args, kwargs=None):
+    with _temp_disable_texpr_fuser():
+        from torch.jit._trace import TopLevelTracedModule
+
+        export_args, export_kwargs = _process_jit_trace_inputs_for_export(args, kwargs)
+
+        if isinstance(traced_callable, (TopLevelTracedModule, torch._C.ScriptModule)):  # type: ignore[operator]
+            return _export(
+                traced_callable,
+                export_args,
+                export_kwargs,
+                strict=False,
+                _is_torch_jit_trace=True,
+            ).module()
+
+        elif isinstance(traced_callable, torch.ScriptMethod) and isinstance(
+            traced_callable.owner(), (torch._C.ScriptModule, torch.nn.Module)  # type: ignore[operator]
+        ):
+            with patch_forward(traced_callable.owner(), traced_callable):  # type: ignore[operator]
+                return _export(
+                    traced_callable.owner(),  # type: ignore[operator]
+                    export_args,
+                    export_kwargs,
+                    strict=False,
+                    _is_torch_jit_trace=True,
+                ).module()
+
+        else:
+            return _export(
+                _WrapperModule(traced_callable),
+                export_args,
+                export_kwargs,
+                strict=False,
+                _is_torch_jit_trace=True,
+            ).module()
+
+
+def _strict_export(
+    mod: torch.nn.Module,
+    args: Tuple[Any, ...],
+    kwargs: Dict[str, Any],
+    dynamic_shapes: Optional[Union[Dict[str, Any], Tuple[Any], List[Any]]],
+    preserve_module_call_signature: Tuple[str, ...],
+    pre_dispatch: bool,
+    original_state_dict: Dict[str, Any],
+    orig_in_spec: TreeSpec,
+    allow_complex_guards_as_runtime_asserts: bool,
+    _is_torch_jit_trace: bool,
+) -> ExportArtifact:
+    lower_to_aten = functools.partial(_export_to_aten_ir, pre_dispatch=pre_dispatch)
+    return _strict_export_lower_to_aten_ir(
+        mod=mod,
+        args=args,
+        kwargs=kwargs,
+        dynamic_shapes=dynamic_shapes,
+        preserve_module_call_signature=preserve_module_call_signature,
+        pre_dispatch=pre_dispatch,
+        original_state_dict=original_state_dict,
+        orig_in_spec=orig_in_spec,
+        allow_complex_guards_as_runtime_asserts=allow_complex_guards_as_runtime_asserts,
+        _is_torch_jit_trace=_is_torch_jit_trace,
+        lower_to_aten_callback=lower_to_aten,
+    )
+
+
+def _strict_export_lower_to_aten_ir(
+    mod: torch.nn.Module,
+    args: Tuple[Any, ...],
+    kwargs: Dict[str, Any],
+    dynamic_shapes: Optional[Union[Dict[str, Any], Tuple[Any], List[Any]]],
+    preserve_module_call_signature: Tuple[str, ...],
+    pre_dispatch: bool,
+    original_state_dict: Dict[str, Any],
+    orig_in_spec: TreeSpec,
+    allow_complex_guards_as_runtime_asserts: bool,
+    _is_torch_jit_trace: bool,
+    lower_to_aten_callback: Callable,
+) -> ExportArtifact:
+    gm_torch_level = _export_to_torch_ir(
+        mod,
+        args,
+        kwargs,
+        dynamic_shapes,
+        preserve_module_call_signature=preserve_module_call_signature,
+        restore_fqn=False,  # don't need to restore because we will do it later
+        allow_complex_guards_as_runtime_asserts=allow_complex_guards_as_runtime_asserts,
+        _log_export_usage=False,
+    )
+
+    # We detect the fake_mode by looking at gm_torch_level's placeholders, this is the fake_mode created in dynamo.
+    (
+        fake_args,
+        fake_kwargs,
+        dynamo_fake_mode,
+    ) = _extract_fake_inputs(gm_torch_level, args, kwargs)
+
+    fake_params_buffers = _fakify_params_buffers(dynamo_fake_mode, gm_torch_level)
+
+    # First, we want to pass through the graph to try populating
+    # val field for getattr if there is anything missing.
+    # This can happen when quantization adds extra params and forgets
+    # to update "val"
+    for node in gm_torch_level.graph.nodes:
+        if node.op == "get_attr" and "val" not in node.meta:
+            attr = getattr(gm_torch_level, node.target)
+            # Checks if it is not a HigherOrderOp branch or a module
+            if not isinstance(attr, torch.nn.Module):
+                assert (
+                    dynamo_fake_mode is not None
+                ), "Cannot find dynamo_fake_mode. This could be due to the exported graph module have no placeholders."
+                node.meta["val"] = dynamo_fake_mode.from_tensor(
+                    attr, static_shapes=True
+                )
+
+    # Fix the graph output signature to be tuple if scalar
+    out_spec = orig_out_spec = gm_torch_level._out_spec
+
+    # Used to get rid of lint type error.
+    assert out_spec is not None
+    assert orig_out_spec is not None
+
+    # aot_export expect the return type to always be a tuple.
+    if out_spec.type not in (list, tuple):
+        out_spec = pytree.TreeSpec(tuple, None, [out_spec])
+
+    orig_arg_names = gm_torch_level.graph._codegen.pytree_info.orig_args  # type: ignore[attr-defined]
+
+    gm_torch_level.graph._codegen = _PyTreeCodeGen(
+        _PyTreeInfo(
+            orig_arg_names,
+            gm_torch_level._in_spec,
+            out_spec,
+        )
+    )
+    gm_torch_level.recompile()
+
+    _normalize_nn_module_stack(gm_torch_level, type(mod))
+
+    params_buffers_to_node_meta = _collect_param_buffer_metadata(gm_torch_level)
+
+    # When aot_export lifts the params, we lose metadata (e.g. source_fn_stack, stack_trace)
+    # from the param nodes as they are treated as fresh inputs
+    # Therefore, we manually extract them before calling into aot_export
+    # params_buffers_to_node_meta = _collect_param_buffer_metadata(gm_torch_level)
+
+    constant_attrs = _gather_constant_attrs(mod)
+    param_buffer_table: Dict[str, str] = _get_param_buffer_mapping(mod, gm_torch_level)
+
+    # Dynamo does not track which buffers were registered as non-persistent. This info
+    # is available in the original module, so we transfer it to the traced module. Also,
+    # since we didn't restore original param/buffer names yet, we must use traced names.
+    non_persistent_buffers = _get_non_persistent_buffers(mod)
+    reverse_name_lookup = {orig: traced for traced, orig in param_buffer_table.items()}
+    gm_torch_level._non_persistent_buffers_set = {
+        reverse_name_lookup[name]
+        for name in non_persistent_buffers
+        if name in reverse_name_lookup
+    }
+    with dynamo_fake_mode:
+        aten_export_artifact = lower_to_aten_callback(
+            gm_torch_level,
+            # NOTE: graph module expects only positional args
+            _convert_to_positional_args(orig_arg_names, fake_args, fake_kwargs),
+            {},
+            fake_params_buffers,
+            constant_attrs,
+        )
+
+    # Decompose for readability.
+    gm = aten_export_artifact.gm
+    export_graph_signature = aten_export_artifact.sig
+    constants = aten_export_artifact.constants
+
+    _populate_param_buffer_metadata_to_new_gm(
+        params_buffers_to_node_meta, gm, export_graph_signature
+    )
+
+    # Do some cleanups on the graph module to restore the state dict to the
+    # expected form. Each of these steps should probably get fixed upstream.
+    # 1. Remove tensor constants that were added as buffers.
+    _rewrite_dynamo_tensor_constants(
+        orig_mod_buffers=set(mod.buffers()),
+        traced_mod_buffers=dict(gm_torch_level.named_buffers()),
+        graph_signature=export_graph_signature,
+        constants=constants,
+    )
+    # 2. Restore FQN of param/buffers
+    _replace_param_buffer_names(param_buffer_table, export_graph_signature)
+
+    # 3. Move non-persistent buffers to tensor constants
+    _move_non_persistent_buffers_to_tensor_constants(
+        mod, export_graph_signature, constants
+    )
+
+    # 4. Rewrite constants to have the same FQN as the original module.
+    _remap_constants(constant_attrs, export_graph_signature, constants)
+
+    # 5. Rename constants nodes in graph module from buffers to constants
+    _rename_constants_nodes(gm, export_graph_signature)
+
+    return ExportArtifact(
+        aten=aten_export_artifact,
+        out_spec=orig_out_spec,
+        fake_mode=dynamo_fake_mode,
+        module_call_specs=gm_torch_level.meta["module_call_specs"],
+    )
+
+
+def _export_to_aten_ir_make_fx(
+    mod: torch.nn.Module,
+    fake_args,
+    fake_kwargs,
+    fake_params_buffers,
+    constant_attrs: ConstantAttrMap,
+    produce_guards_callback=None,
+    transform=lambda x: x,
+) -> ATenExportArtifact:
+    @contextmanager
+    def _compiling_state_context():
+        old_value = torch.compiler._is_compiling_flag
+        try:
+            torch.compiler._is_compiling_flag = True
+            yield
+        finally:
+            torch.compiler._is_compiling_flag = old_value
+
+    def _make_fx_helper(mod, args, kwargs, **flags):
+        from torch._functorch._aot_autograd.schemas import GraphSignature
+
+        kwargs = kwargs or {}
+
+        named_parameters = dict(mod.named_parameters(remove_duplicate=False))
+        named_buffers = dict(mod.named_buffers(remove_duplicate=False))
+
+        params_and_buffers = {**named_parameters, **named_buffers}
+        params_and_buffers_flat, params_spec = pytree.tree_flatten(params_and_buffers)
+        params_and_buffers_flat = tuple(params_and_buffers_flat)
+
+        param_len = len(named_parameters)
+        buffer_len = len(named_buffers)
+        params_len = len(params_and_buffers)
+
+        functional_call = create_functional_call(
+            mod, params_spec, params_len, store_orig_mod=True
+        )
+
+        params_buffers_args: List[Any] = []
+        params_buffers_args.extend(params_and_buffers_flat)
+        params_buffers_args.extend(args)
+
+        flat_fn, out_spec = create_tree_flattened_fn(
+            functional_call, params_buffers_args, kwargs
+        )
+        flat_args, in_spec = pytree.tree_flatten((params_buffers_args, kwargs))
+
+        @functools.wraps(flat_fn)
+        def wrapped_fn(*args):
+            return tuple(flat_fn(*args))
+
+        with enable_python_dispatcher():
+            gm = make_fx(
+                wrapped_fn,
+                record_module_stack=True,
+                pre_dispatch=True,
+            )(*flat_args)
+            gm.graph.eliminate_dead_code()
+
+        # create graph signature
+        input_names = _graph_input_names(gm)
+        output_names = _graph_output_names(gm)
+        sig = GraphSignature(
+            parameters=list(named_parameters),
+            buffers=list(named_buffers),
+            user_inputs=input_names[params_len:],
+            user_outputs=output_names,
+            inputs_to_parameters=dict(zip(input_names[0:param_len], named_parameters)),
+            inputs_to_buffers=dict(
+                zip(input_names[param_len : param_len + buffer_len], named_buffers)
+            ),
+            buffers_to_mutate={},
+            user_inputs_to_mutate={},
+            in_spec=in_spec,
+            out_spec=out_spec,  # type: ignore[arg-type]
+            backward_signature=None,
+            input_tokens=[],
+            output_tokens=[],
+        )
+        return gm, sig
+
+    # This _reparametrize_module makes sure inputs and module.params/buffers have the same fake_mode,
+    # otherwise aot_export_module will error out because it sees a mix of fake_modes.
+    # And we want aot_export_module to use the fake_tensor mode in dynamo to keep the pipeline easy to reason about.
+    with torch.nn.utils.stateless._reparametrize_module(
+        mod,
+        fake_params_buffers,
+        tie_weights=True,
+        strict=True,
+        stack_weights=True,
+    ), _ignore_backend_decomps(), _compiling_state_context():  # type: ignore[attr-defined]
+        param_len = len(dict(mod.named_parameters(remove_duplicate=False)))
+        buffer_len = len(dict(mod.named_buffers(remove_duplicate=False)))
+        params_len = param_len + buffer_len
+
+        gm, graph_signature = transform(_make_fx_helper)(
+            mod,
+            fake_args,
+            trace_joint=False,
+            kwargs=fake_kwargs,
+        )
+
+        if isinstance(mod, torch.fx.GraphModule) and hasattr(mod, "meta"):
+            gm.meta.update(mod.meta)
+
+    flat_args = pytree.tree_leaves((fake_args, fake_kwargs))
+    index = 0
+    for node in gm.graph.nodes:
+        if node.op == "placeholder":
+            if index >= params_len:
+                user_arg = flat_args[index - params_len]
+                if not isinstance(user_arg, torch.Tensor):
+                    node.meta["val"] = user_arg
+            index += 1
+
+    export_graph_signature = _convert_to_export_graph_signature(
+        graph_signature, gm, _get_non_persistent_buffers(mod)
+    )
+
+    # See comment in _export_to_aten_ir()
+    if produce_guards_callback:
+        try:
+            produce_guards_callback(gm)
+        except (ConstraintViolationError, ValueRangeError) as e:
+            raise UserError(UserErrorType.CONSTRAINT_VIOLATION, str(e))  # noqa: B904
+
+    fake_mode = detect_fake_mode(flat_args)
+
+    if not torch._dynamo.config.do_not_emit_runtime_asserts:
+        stack_trace = (
+            'File "torch/fx/passes/runtime_assert.py", line 24, '
+            "in insert_deferred_runtime_asserts"
+        )
+        with _set_node_metadata_hook(
+            gm, functools.partial(_node_metadata_hook, stack_trace=stack_trace)
+        ):
+            insert_deferred_runtime_asserts(
+                gm,
+                fake_mode.shape_env,
+                f"exported program: {first_call_function_nn_module_stack(gm.graph)}",
+                export=True,
+            )
+
+    # Remove nn_module_stack, stack_trace metadata from all placeholders/inputs nodes.
+    for _mod in gm.modules():
+        if not isinstance(_mod, torch.fx.GraphModule):
+            continue
+        for node in _mod.graph.nodes:
+            if node.op in ["placeholder", "output"]:
+                node.meta.pop("nn_module_stack", None)
+                node.meta.pop("stack_trace", None)
+
+    constants = rewrite_script_object_meta(gm)
+    constants.update(lift_constants_pass(gm, export_graph_signature, constant_attrs))
+
+    _preserve_requires_grad_pass(
+        gm, export_graph_signature, fake_params_buffers, constants, flat_args
+    )
+
+    # Prettify names for placeholder nodes.
+    placeholder_naming_pass(
+        gm,
+        export_graph_signature,
+        mod,
+        fake_args,
+        fake_kwargs,
+        fake_params_buffers,
+        constants,
+    )
+
+    return ATenExportArtifact(
+        gm,
+        export_graph_signature,
+        constants,
+    )
+
+
+def _non_strict_export(
+    mod: torch.nn.Module,
+    args: Tuple[Any, ...],
+    kwargs: Dict[str, Any],
+    dynamic_shapes: Optional[Union[Dict[str, Any], Tuple[Any], List[Any]]],
+    preserve_module_call_signature: Tuple[str, ...],
+    pre_dispatch: bool,
+    original_state_dict: Dict[str, Any],
+    orig_in_spec: TreeSpec,
+    allow_complex_guards_as_runtime_asserts: bool,
+    _is_torch_jit_trace: bool,
+    dispatch_tracing_mode: str = "aot_export",
+) -> ExportArtifact:
+    """
+    ``dispatch_tracing_mode`` can be either "make_fx” or “aot_export”, corresponding to
+    _export_to_aten_ir_make_fx and _export_to_aten_ir, respectively.
+    """
+    assert dispatch_tracing_mode in ["make_fx", "aot_export"]
+    out_spec: Optional[TreeSpec] = None
+
+    module_call_specs: Dict[str, Dict[str, pytree.TreeSpec]] = {}
+
+    def _tuplify_outputs(aot_export):
+        def _aot_export_non_strict(mod, args, kwargs=None, **flags):
+            kwargs = kwargs or {}
+
+            class Wrapper(torch.nn.Module):
+                def __init__(self, mod):
+                    super().__init__()
+                    self._export_root = mod
+
+                def forward(self, *args, **kwargs):
+                    nonlocal out_spec
+                    if isinstance(self._export_root, torch.fx.GraphModule):
+                        with torch.fx.traceback.preserve_node_meta():
+                            tree_out = torch.fx.Interpreter(self._export_root).run(
+                                *args, **kwargs
+                            )
+                    else:
+                        tree_out = self._export_root(*args, **kwargs)
+                    flat_outs, out_spec = pytree.tree_flatten(tree_out)
+                    return tuple(flat_outs)
+
+            wrapped_mod = Wrapper(mod)
+            # Patch export_root to the signatures so that wrapper module correctly populates the
+            # in/out spec
+            new_preserved_call_signatures = [
+                "_export_root." + i for i in preserve_module_call_signature
+            ]
+            with _wrap_submodules(
+                wrapped_mod, new_preserved_call_signatures, module_call_specs
+            ):
+                gm, sig = aot_export(wrapped_mod, args, kwargs=kwargs, **flags)
+                log.debug("Exported program from AOTAutograd:\n%s", gm)
+
+            sig.parameters = pytree.tree_map(_strip_root, sig.parameters)
+            sig.buffers = pytree.tree_map(_strip_root, sig.buffers)
+            sig.inputs_to_buffers = pytree.tree_map(_strip_root, sig.inputs_to_buffers)
+            sig.inputs_to_parameters = pytree.tree_map(
+                _strip_root, sig.inputs_to_parameters
+            )
+            sig.buffers_to_mutate = pytree.tree_map(_strip_root, sig.buffers_to_mutate)
+
+            for node in gm.graph.nodes:
+                if "nn_module_stack" in node.meta:
+                    nn_module_stack = node.meta["nn_module_stack"]
+                    node.meta["nn_module_stack"] = {
+                        _fixup_key(key): val
+                        for key, val in pytree.tree_map(
+                            _strip_root, nn_module_stack
+                        ).items()
+                    }
+
+            return gm, sig
+
+        return _aot_export_non_strict
+
+    (
+        fake_mode,
+        fake_args,
+        fake_kwargs,
+        equalities_inputs,
+        original_signature,
+        transformed_dynamic_shapes,
+    ) = make_fake_inputs(
+        mod,
+        args,
+        kwargs,
+        dynamic_shapes,
+        _is_torch_jit_trace=_is_torch_jit_trace,
+        allow_complex_guards_as_runtime_asserts=allow_complex_guards_as_runtime_asserts,  # for shape env initialization
+    )
+
+    fake_params_buffers = _fakify_params_buffers(fake_mode, mod)
+
+    def _produce_guards_callback(gm):
+        return produce_guards_and_solve_constraints(
+            fake_mode=fake_mode,
+            gm=gm,
+            dynamic_shapes=transformed_dynamic_shapes,
+            equalities_inputs=equalities_inputs,
+            original_signature=original_signature,
+            _is_torch_jit_trace=_is_torch_jit_trace,
+        )
+
+    with fake_mode, _NonStrictTorchFunctionHandler(), torch._dynamo.config.patch(
+        assume_static_by_default=False
+    ):
+        with _fakify_script_objects(mod, fake_args, fake_kwargs, fake_mode) as (
+            patched_mod,
+            new_fake_args,
+            new_fake_kwargs,
+            new_fake_constant_attrs,
+            map_fake_to_real,
+        ):
+            _to_aten_func = (
+                _export_to_aten_ir_make_fx
+                if dispatch_tracing_mode == "make_fx"
+                else functools.partial(
+                    _export_to_aten_ir,
+                    pre_dispatch=pre_dispatch,
+                    _is_torch_jit_trace=_is_torch_jit_trace,
+                )
+            )
+            aten_export_artifact = _to_aten_func(  # type: ignore[operator]
+                patched_mod,
+                new_fake_args,
+                new_fake_kwargs,
+                fake_params_buffers,
+                new_fake_constant_attrs,
+                produce_guards_callback=_produce_guards_callback,
+                transform=_tuplify_outputs,
+            )
+            # aten_export_artifact.constants contains only fake script objects, we need to map them back
+            aten_export_artifact.constants = {
+                fqn: map_fake_to_real[obj] if isinstance(obj, FakeScriptObject) else obj
+                for fqn, obj in aten_export_artifact.constants.items()
+            }
+
+    _move_non_persistent_buffers_to_tensor_constants(
+        mod, aten_export_artifact.sig, aten_export_artifact.constants
+    )
+
+    assert out_spec is not None
+
+    return ExportArtifact(
+        aten=aten_export_artifact,
+        out_spec=out_spec,
+        fake_mode=fake_mode,
+        module_call_specs=module_call_specs,
+    )
+
+
+# TODO (tmanlaibaatar) We need to preserve aten.to here somehow
+@_log_export_wrapper
+@_disable_prexisiting_fake_mode
+def _export_for_training(
+    mod: torch.nn.Module,
+    args: Tuple[Any, ...],
+    kwargs: Optional[Dict[str, Any]] = None,
+    dynamic_shapes: Optional[Union[Dict[str, Any], Tuple[Any], List[Any]]] = None,
+    *,
+    strict: bool = True,
+    preserve_module_call_signature: Tuple[str, ...] = (),
+) -> ExportedProgram:
+    global _EXPORT_MODULE_HIERARCHY
+    _EXPORT_MODULE_HIERARCHY = _get_module_hierarchy(mod)
+
+    (
+        args,
+        kwargs,
+        orig_in_spec,
+        original_state_dict,
+        dynamic_shapes,
+    ) = _process_export_inputs(mod, args, kwargs, dynamic_shapes)
+
+    export_func = (
+        functools.partial(
+            _strict_export_lower_to_aten_ir,
+            lower_to_aten_callback=_export_to_aten_ir_make_fx,
+        )
+        if strict
+        else functools.partial(
+            _non_strict_export,
+            dispatch_tracing_mode="make_fx",
+        )
+    )
+    export_artifact = export_func(  # type: ignore[operator]
+        mod=mod,
+        args=args,
+        kwargs=kwargs,
+        dynamic_shapes=dynamic_shapes,
+        preserve_module_call_signature=preserve_module_call_signature,
+        pre_dispatch=False,
+        original_state_dict=original_state_dict,
+        orig_in_spec=orig_in_spec,
+        allow_complex_guards_as_runtime_asserts=False,
+        _is_torch_jit_trace=False,
+    )
+
+    export_graph_signature = export_artifact.aten.sig
+
+    forward_arg_names = _get_forward_arg_names(mod, args, kwargs)
+    inline_constraints = _get_inline_constraints(export_artifact.fake_mode)
+    # The unbacked symint symbols are updated in aot_export
+    # so we serialize them here instead of inside dynamo.
+    # Note: _get_range_constraints depends on "inline_constraints" to be set.
+    export_artifact.aten.gm.meta["inline_constraints"] = inline_constraints
+    range_constraints = _get_range_constraints(
+        export_artifact,
+        _combine_args(mod, args, kwargs, _is_torch_jit_trace=False),
+        dynamic_shapes,
+    )
+    # The returned the gm is in-place modified
+    gm, module_call_graph = _get_module_call_graph(
+        export_artifact, orig_in_spec, preserve_module_call_signature, strict
+    )
+
+    # Add forward args metadata.
+    gm.meta["forward_arg_names"] = forward_arg_names
+
+    _verify_nn_module_stack(gm)
+    _verify_stack_trace(gm)
+    _verify_placeholder_names(gm, export_graph_signature)
+
+    from torch._export.verifier import TrainingIRVerifier
+
+    exported_program = ExportedProgram(
+        root=gm,
+        graph=gm.graph,
+        graph_signature=export_graph_signature,
+        state_dict=original_state_dict,
+        range_constraints=range_constraints,
+        module_call_graph=module_call_graph,
+        example_inputs=(args, kwargs),
+        constants=export_artifact.aten.constants,
+        verifiers=[TrainingIRVerifier],
+    )
+
+    return exported_program
+
+
+@_log_export_wrapper
+@_disable_prexisiting_fake_mode
+def _export(
+    mod: torch.nn.Module,
+    args: Tuple[Any, ...],
+    kwargs: Optional[Dict[str, Any]] = None,
+    dynamic_shapes: Optional[Union[Dict[str, Any], Tuple[Any], List[Any]]] = None,
+    *,
+    strict: bool = True,
+    preserve_module_call_signature: Tuple[str, ...] = (),
+    pre_dispatch: bool = False,
+    allow_complex_guards_as_runtime_asserts: bool = False,
+    _is_torch_jit_trace: bool = False,
+) -> ExportedProgram:
+    """
+    Traces either an nn.Module's forward function or just a callable with PyTorch
+    operations inside and produce a ExportedProgram.
+
+    Args:
+        f: the `nn.Module` to trace.
+
+        args: example positional inputs.
+
+        kwargs: optional example keyword inputs.
+
+        dynamic_shapes:
+         An optional argument where the type should either be:
+         1) a dict from argument names of ``f`` to their dynamic shape specifications,
+         2) a tuple that specifies dynamic shape specifications for each input in original order.
+         If you are specifying dynamism on keyword args, you will need to pass them in the order that
+         is defined in the original function signature.
+
+         The dynamic shape of a tensor argument can be specified as either
+         (1) a dict from dynamic dimension indices to :func:`Dim` types, where it is
+         not required to include static dimension indices in this dict, but when they are,
+         they should be mapped to None; or (2) a tuple / list of :func:`Dim` types or None,
+         where the :func:`Dim` types correspond to dynamic dimensions, and static dimensions
+         are denoted by None. Arguments that are dicts or tuples / lists of tensors are
+         recursively specified by using mappings or sequences of contained specifications.
+
+        preserve_module_call_signature: A list of submodule paths for which the original
+            calling conventions are preserved as metadata.
+
+        allow_complex_guards_as_runtime_asserts:
+         With the current dynamic shapes language for dims and derived dims, we can run into constraints
+         that are not expressible with the language. For example, flattening a matrix and adding to a vector,
+         both fully dynamic (i.e. x.reshape([-1]) + y) emits a guard s0 * s1 = s2, which is not expressible.
+         By default, we either raise a constraint violation error or specialize to static values.
+         If this flag is set to True, we avoid erroring out and instead allow complex constraints to exist as runtime
+         assertions in the graph. The sympy interpreter (torch/utils/_sympy/interp.py) will produce the math ops
+         required to compute and assert the value of the guard (e.g. sym_size_int, eq, _assert_scalar).
+         Additionally, if TORCH_DYNAMO_DO_NOT_EMIT_RUNTIME_ASSERTS=1 is specified, we will allow complex constraints
+         while not emitting runtime asserts, returning a cleaner graph with lesser guarantees around dynamic shapes.
+
+    Returns:
+        An ExportedProgram containing the traced method.
+    """
+
+    global _EXPORT_FLAGS, _EXPORT_MODULE_HIERARCHY
+    _EXPORT_MODULE_HIERARCHY = _get_module_hierarchy(mod)
+
+    flags = set()
+    flags.add("strict" if strict else "non_strict")
+    flags.add("pre_dispatch" if pre_dispatch else "aot_dispatch")
+    _EXPORT_FLAGS = flags
+
+    log_export_usage(event="export.enter", flags=_EXPORT_FLAGS)
+
+    (
+        args,
+        kwargs,
+        original_in_spec,
+        original_state_dict,
+        dynamic_shapes,
+    ) = _process_export_inputs(mod, args, kwargs, dynamic_shapes)
+
+    # Call the appropriate export function based on the strictness of tracing.
+    export_func = _strict_export if strict else _non_strict_export
+
+    export_artifact = export_func(  # type: ignore[operator]
+        mod,
+        args,
+        kwargs,
+        dynamic_shapes,
+        preserve_module_call_signature,
+        pre_dispatch,
+        original_state_dict,
+        original_in_spec,
+        allow_complex_guards_as_runtime_asserts,
+        _is_torch_jit_trace,
+    )
+    export_graph_signature: ExportGraphSignature = export_artifact.aten.sig
+
+    forward_arg_names = (
+        _get_forward_arg_names(mod, args, kwargs) if not _is_torch_jit_trace else None
+    )
+    inline_constraints = _get_inline_constraints(export_artifact.fake_mode)
+    # The unbacked symint symbols are updated in aot_export
+    # so we serialize them here instead of inside dynamo.
+    # Note: this step must be before _get_range_constraints.
+    export_artifact.aten.gm.meta["inline_constraints"] = inline_constraints
+    range_constraints = _get_range_constraints(
+        export_artifact,
+        _combine_args(mod, args, kwargs, _is_torch_jit_trace=_is_torch_jit_trace),
+        dynamic_shapes,
+    )
+    gm, module_call_graph = _get_module_call_graph(
+        export_artifact, original_in_spec, preserve_module_call_signature, strict
+    )
+
+    # Add forward args metadata.
+    gm.meta["forward_arg_names"] = forward_arg_names
+
+    _verify_nn_module_stack(gm)
+    _verify_stack_trace(gm)
+    if not _is_torch_jit_trace:
+        _verify_placeholder_names(gm, export_graph_signature)
+
+    # Remove Proxy because they cannot be deepcopied or pickled.
+    torch._export.utils.remove_proxy_from_state_dict(original_state_dict, in_place=True)
+
+    from torch._export.verifier import Verifier
+
+    if (
+        isinstance(mod, torch.fx.GraphModule)
+        and hasattr(mod, "meta")
+        and "custom" in mod.meta
+    ):
+        gm.meta.update({"custom": mod.meta["custom"]})
+
+    exported_program = ExportedProgram(
+        root=gm,
+        graph=gm.graph,
+        graph_signature=export_graph_signature,
+        state_dict=original_state_dict,
+        range_constraints=range_constraints,
+        module_call_graph=module_call_graph,
+        example_inputs=(args, kwargs),
+        constants=export_artifact.aten.constants,
+        verifiers=[Verifier],
+    )
+
+    return exported_program
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/_tree_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/_tree_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1615ebd5f586ce5216c65d40162a74ffb7bc5d1
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/_tree_utils.py
@@ -0,0 +1,64 @@
+from typing import Any, Callable, Dict, Optional
+
+from torch.utils._pytree import Context, TreeSpec
+
+
+def reorder_kwargs(user_kwargs: Dict[str, Any], spec: TreeSpec) -> Dict[str, Any]:
+    """Reorder user-provided kwargs to match the order in `spec`. `spec` is
+    expected to be the in_spec of an exported program, i.e. the spec that
+    results from flattening `(args, kwargs)`.
+
+    We need this to provide consistent input ordering, such so that users can
+    pass in foo(a=a, b=b) OR foo(b=b, a=a) and receive the same result.
+    """
+    # Make sure that the spec is actually shaped like (args, kwargs)
+    assert spec.type is tuple
+    assert spec.num_children == 2
+    kwargs_spec = spec.children_specs[1]
+    assert kwargs_spec.type is dict
+
+    if set(user_kwargs) != set(kwargs_spec.context):
+        raise ValueError(
+            f"kwarg key mismatch: "
+            f"Got {list(user_kwargs)} but expected {kwargs_spec.context}"
+        )
+
+    reordered_kwargs = {}
+    for kw in kwargs_spec.context:
+        reordered_kwargs[kw] = user_kwargs[kw]
+
+    return reordered_kwargs
+
+
+def is_equivalent(
+    spec1: TreeSpec,
+    spec2: TreeSpec,
+    equivalence_fn: Callable[[Optional[type], Context, Optional[type], Context], bool],
+) -> bool:
+    """Customizable equivalence check for two TreeSpecs.
+
+    Arguments:
+        spec1: The first TreeSpec to compare
+        spec2: The second TreeSpec to compare
+        equivalence_fn: A function to determine the equivalence of two
+            TreeSpecs by examining their types and contexts. It will be called like:
+
+                equivalence_fn(spec1.type, spec1.context, spec2.type, spec2.context)
+
+            This function will be applied recursively to all children.
+
+    Returns:
+        True if the two TreeSpecs are equivalent, False otherwise.
+    """
+    if not equivalence_fn(spec1.type, spec1.context, spec2.type, spec2.context):
+        return False
+
+    # Recurse on children
+    if len(spec1.children_specs) != len(spec2.children_specs):
+        return False
+
+    for child_spec1, child_spec2 in zip(spec1.children_specs, spec2.children_specs):
+        if not is_equivalent(child_spec1, child_spec2, equivalence_fn):
+            return False
+
+    return True
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/_unlift.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/_unlift.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad48372e2d9aef93e3e079135f1bd4af73904201
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/_unlift.py
@@ -0,0 +1,361 @@
+# mypy: allow-untyped-defs
+import copy
+import warnings
+from itertools import chain
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+import torch.utils._pytree as pytree
+from torch._export.utils import _check_input_constraints_for_graph
+from torch.export.unflatten import _assign_attr, _AttrKind, _recursive_getattr
+from torch.fx.graph import _PyTreeCodeGen, _PyTreeInfo
+
+from ._remove_effect_tokens_pass import _remove_effect_tokens
+from .exported_program import (
+    ExportedProgram,
+    ExportGraphSignature,
+    InputKind,
+    OutputKind,
+)
+
+
+@torch._dynamo.disable
+def _check_input_constraints_pre_hook(self, *args, **kwargs):
+    flat_args_with_path, received_spec = pytree.tree_flatten_with_path(args)
+
+    if received_spec != self._in_spec:
+        raise ValueError(  # noqa: B904
+            "Trying to flatten user inputs with exported input tree spec: \n"
+            f"{self._in_spec}\n"
+            "but actually got inputs with tree spec of: \n"
+            f"{received_spec}"
+        )
+
+    return _check_input_constraints_for_graph(
+        [node for node in self.graph.nodes if node.op == "placeholder"],
+        flat_args_with_path,
+        self.range_constraints,
+    )
+
+
+def _unlift_inputs_as_getattr(
+    gm: torch.fx.GraphModule,
+    lifted_inputs: List[Optional[str]],
+) -> Tuple[Dict[str, torch.fx.Node], Dict[str, torch.fx.Node]]:
+    """
+    Unlift inputs referring to params/buffers/constants as getattr nodes in the
+    graph
+    """
+    unlifted_name_to_node = {}
+    input_name_to_node = {}
+
+    placeholder_nodes = [node for node in gm.graph.nodes if node.op == "placeholder"]
+    assert len(lifted_inputs) == len(placeholder_nodes)
+    for input_node, lifted_node in zip(placeholder_nodes, lifted_inputs):
+        if lifted_node is None:
+            input_name_to_node[input_node.name] = input_node
+
+        else:
+            with gm.graph.inserting_after(input_node):
+                getattr_node = gm.graph.get_attr(lifted_node)
+                input_node.replace_all_uses_with(getattr_node)
+                metadata = input_node.meta
+                gm.graph.erase_node(input_node)
+                getattr_node.meta = metadata
+                unlifted_name_to_node[lifted_node] = getattr_node
+
+    return unlifted_name_to_node, input_name_to_node
+
+
+def _insert_copy_for_mutations(
+    gm: torch.fx.GraphModule,
+    mutated_outputs: List[Optional[str]],
+    unlifted_name_to_node: Dict[str, torch.fx.Node],
+    input_name_to_node: Dict[str, torch.fx.Node],
+) -> None:
+    """
+    Find the all the buffers and inputs that were mutated and insert copy_
+    operators to reflect mutations.
+    """
+    output_node = None
+    for node in gm.graph.nodes:
+        if node.op == "output":
+            output_node = node
+            break
+    assert output_node is not None
+    outputs = pytree.tree_flatten(output_node.args)[0]
+    assert len(outputs) == len(mutated_outputs)
+
+    user_output_nodes = []
+    return_nodes_to_copy = {}
+    for return_node, mutated_node_name in zip(outputs, mutated_outputs):
+        if mutated_node_name is None:
+            user_output_nodes.append(return_node)
+            continue
+
+        if mutated_node_name in unlifted_name_to_node:
+            mutated_node = unlifted_name_to_node[mutated_node_name]
+        elif mutated_node_name in input_name_to_node:
+            mutated_node = input_name_to_node[mutated_node_name]
+        else:
+            raise RuntimeError(
+                f"Could not find {mutated_node_name} in either buffer or input nodes"
+            )
+
+        with gm.graph.inserting_before(output_node):
+            copy_node = gm.graph.call_function(
+                torch.ops.aten.copy_.default, (mutated_node, return_node)
+            )
+            return_nodes_to_copy[return_node] = copy_node
+
+    output_args = [
+        return_nodes_to_copy[node] if node in return_nodes_to_copy else node
+        for node in user_output_nodes
+    ]
+    with gm.graph.inserting_before(output_node):
+        # Only return user outputs
+        new_output = gm.graph.output(tuple(output_args))
+        new_output.meta.update(output_node.meta)
+        output_node.replace_all_uses_with(new_output)
+        gm.graph.erase_node(output_node)
+
+
+def _get_codegen(
+    in_spec: pytree.TreeSpec,
+    out_spec: Optional[pytree.TreeSpec],
+    forward_arg_names: Optional[List[str]] = None,
+) -> _PyTreeCodeGen:
+    """
+    Create the codegen for the graph module based on the in/out specs
+    """
+    if forward_arg_names:
+        names = forward_arg_names
+    else:
+        if (
+            in_spec.type == tuple
+            and in_spec.num_children == 2
+            and in_spec.children_specs[0].type == tuple
+            and in_spec.children_specs[1].type == dict
+        ):
+            # if in_spec contains the args (tuple) and kwargs (dict)
+            names = [f"arg_{i}" for i in range(in_spec.children_specs[0].num_children)]
+            # add kwarg names
+            names.extend(in_spec.children_specs[1].context)
+        else:
+            names = [f"arg_{i}" for i in range(in_spec.num_children)]
+
+    return _PyTreeCodeGen(
+        _PyTreeInfo(
+            names,
+            in_spec,
+            out_spec,
+        )
+    )
+
+
+def _unlift(
+    gm: torch.fx.GraphModule,
+    lifted_inputs: List[Optional[str]],
+    mutated_outputs: List[Optional[str]],
+    in_spec: pytree.TreeSpec,
+    out_spec: Optional[pytree.TreeSpec],
+    state_dict: Dict[str, Any],
+    constants: Dict[str, Any],
+    forward_arg_names: Optional[List[str]] = None,
+):
+    """
+    Args:
+        lifted_inputs: A list matching the graph module's input nodes. For
+        an input node that is referring to a lifted parameter/buffer, this
+        list will contain the fqn the corresponding attribute. Otherwise, this
+        list will contain None. This is used to unlift the lifted parameters as
+        get_attr nodes.
+
+        mutated_outputs: A list matching the graph module's output nodes. For
+        an output node that is referring to a mutated buffer or user input, this
+        list will contain the name of the corresponding buffer or user input
+        that needs to be mutated. Otherwise, this list will contain None. This
+        is used to re-insert an inplace copy_ operator to copy the mutated
+        values back to the original node.
+    """
+    unlifted_name_to_node, input_name_to_node = _unlift_inputs_as_getattr(
+        gm, lifted_inputs
+    )
+    _insert_copy_for_mutations(
+        gm, mutated_outputs, unlifted_name_to_node, input_name_to_node
+    )
+    gm.graph._codegen = _get_codegen(in_spec, out_spec, forward_arg_names)
+    gm.graph.lint()
+    gm.recompile()
+    return gm
+
+
+def _register_attrs_to_new_gm(
+    new_gm: torch.fx.GraphModule,
+    graph_signature: ExportGraphSignature,
+    state_dict: Dict[str, Any],
+    constants: Dict[str, Any],
+) -> None:
+    non_persistent_buffers = set(graph_signature.non_persistent_buffers)
+    for name in graph_signature.buffers:
+        if name in non_persistent_buffers:
+            persistent = False
+            value = constants[name]
+        else:
+            persistent = True
+            value = state_dict[name]
+        _assign_attr(
+            value, new_gm, name, attr_kind=_AttrKind.BUFFER, persistent=persistent
+        )
+    for name in graph_signature.parameters:
+        value = state_dict[name]
+        _assign_attr(
+            value,
+            new_gm,
+            name,
+            attr_kind=_AttrKind.PARAMETER,
+        )
+
+    for name in chain(
+        graph_signature.lifted_custom_objs, graph_signature.lifted_tensor_constants
+    ):
+        value = constants[name]
+        _assign_attr(
+            value,
+            new_gm,
+            name,
+            attr_kind=_AttrKind.CONSTANT,
+        )
+
+
+class _StatefulGraphModuleFactory(type):
+    """
+    Metaclass that ensures a private constructor for _StatefulGraphModule
+    """
+
+    def __call__(cls, *args, **kwargs):
+        raise TypeError(
+            f"{cls.__module__}.{cls.__qualname__} has no public constructor. "
+        )
+
+    def _create(cls, root, graph, range_constraints=None):
+        return super().__call__(
+            root,
+            graph,
+            range_constraints=range_constraints,
+        )
+
+
+class _StatefulGraphModule(torch.fx.GraphModule, metaclass=_StatefulGraphModuleFactory):
+    def __init__(self, root, graph, range_constraints=None):
+        super().__init__(root, graph)
+        # Need to fix up non-persistent buffers.
+        self.range_constraints = range_constraints or []
+
+
+def _create_stateful_graph_module(
+    plain_graph_module: torch.fx.GraphModule,
+    range_constraints,
+    # TODO(suo) this should not be optional, but is since we still ahve
+    # capture_pre_autograd_graph grr
+    graph_signature: Optional[ExportGraphSignature] = None,
+):
+    stateful_gm = _StatefulGraphModule._create(
+        plain_graph_module,
+        plain_graph_module.graph,
+        range_constraints=range_constraints,
+    )
+
+    stateful_gm.register_forward_pre_hook(
+        _check_input_constraints_pre_hook, with_kwargs=True
+    )
+
+    if graph_signature is None:
+        return stateful_gm
+
+    # Fix up lifted tensor constants.
+    # fx.GraphModule() constructor silently turns a constant attribute of plain_graph_module
+    # into a buffer in stateful_gm and creates an inconsistency with graph_signature.
+    # We fix this by de-registering these buffers in lifted_tensor_constants
+    # and call _assign_attr(attr_kind=CONSTANT) to register them as constants.
+    for constant_fqn in graph_signature.lifted_tensor_constants:
+        # Sometimes, the constant can require gradient, this is probably a bug in user code,
+        # e.g. `self.const = torch.randn(2, 2, requires_grad=True)`.
+        # We call detach on the constant_val since they're tensor contants and we don't need to
+        # compute their gradients anyway.
+        # Users should properly register it as parameter if they want it to require gradient.
+        buffer = stateful_gm.get_buffer(constant_fqn)
+        if buffer.requires_grad:
+            warnings.warn(
+                f"A model attribute `{constant_fqn}` requires gradient. "
+                f"but it's not properly registered as a parameter. "
+                f"torch.export will detach it and treat it as a constant tensor "
+                f"but please register it as parameter instead."
+            )
+            buffer = buffer.detach()
+        *prefix, field = constant_fqn.rsplit(".")
+        submod = _recursive_getattr(stateful_gm, prefix)
+        delattr(submod, field)
+        _assign_attr(buffer, stateful_gm, constant_fqn, attr_kind=_AttrKind.CONSTANT)
+
+    # Fix up non-persistent buffers. torch.fx does not distinguish between
+    # persistent and non-persistent buffers, so we must restore that distinction
+    # here.
+    for buffer in graph_signature.non_persistent_buffers:
+        _assign_attr(
+            plain_graph_module.get_buffer(buffer),
+            stateful_gm,
+            buffer,
+            attr_kind=_AttrKind.BUFFER,
+            persistent=False,
+        )
+
+    return stateful_gm
+
+
+def _unlift_exported_program_lifted_states(ep: ExportedProgram) -> torch.nn.Module:
+    ep = _remove_effect_tokens(ep)
+    new_gm = torch.fx.GraphModule(ep.graph_module, copy.deepcopy(ep.graph))
+    _register_attrs_to_new_gm(new_gm, ep.graph_signature, ep.state_dict, ep.constants)
+    forward_arg_names = ep.graph_module.meta.get("forward_arg_names")
+
+    lifted_inputs: List[Optional[str]] = [
+        (
+            in_spec.target
+            if in_spec.kind
+            in (
+                InputKind.BUFFER,
+                InputKind.CONSTANT_TENSOR,
+                InputKind.PARAMETER,
+                InputKind.CUSTOM_OBJ,
+            )
+            else None
+        )
+        for in_spec in ep.graph_signature.input_specs
+    ]
+
+    mutated_outputs: List[Optional[str]] = [
+        (
+            out_spec.target
+            if out_spec.kind
+            in (OutputKind.BUFFER_MUTATION, OutputKind.USER_INPUT_MUTATION)
+            else None
+        )
+        for out_spec in ep.graph_signature.output_specs
+    ]
+
+    new_gm = _unlift(
+        new_gm,
+        lifted_inputs,
+        mutated_outputs,
+        ep.call_spec.in_spec,
+        ep.call_spec.out_spec,
+        ep.state_dict,
+        ep.constants,
+        forward_arg_names=forward_arg_names,
+    )
+    unlift_gm = _create_stateful_graph_module(
+        new_gm, ep.range_constraints, ep.graph_signature
+    )
+    unlift_gm.meta.update(ep.graph_module.meta)
+    return unlift_gm
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/custom_obj.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/custom_obj.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e7f2080a4ee705a2621386c9b69a089d507544a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/custom_obj.py
@@ -0,0 +1,16 @@
+from dataclasses import dataclass
+
+
+__all__ = ["ScriptObjectMeta"]
+
+
+@dataclass
+class ScriptObjectMeta:
+    """
+    Metadata which is stored on nodes representing ScriptObjects.
+    """
+
+    # Key into constants table to retrieve the real ScriptObject.
+    constant_name: str
+
+    class_fqn: str
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/dynamic_shapes.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/dynamic_shapes.py
new file mode 100644
index 0000000000000000000000000000000000000000..18fc1e73f0cf05fd4be077834ed4702d78097136
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/dynamic_shapes.py
@@ -0,0 +1,1220 @@
+# mypy: allow-untyped-defs
+import dataclasses
+import inspect
+import logging
+import sys
+from collections import defaultdict
+from enum import auto, Enum
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, TYPE_CHECKING, Union
+
+import torch
+from torch.utils._pytree import (
+    _get_node_type,
+    BUILTIN_TYPES,
+    keystr,
+    LeafSpec,
+    MappingKey,
+    SequenceKey,
+    SUPPORTED_NODES,
+    tree_flatten,
+    tree_map_with_path,
+)
+
+from .exported_program import ExportedProgram
+
+
+if TYPE_CHECKING:
+    from sympy import Symbol
+
+    from torch._guards import Source
+    from torch.fx.experimental.symbolic_shapes import ShapeEnv, StrictMinMaxConstraint
+
+__all__ = [
+    "Constraint",
+    "Dim",
+    "dims",
+    "refine_dynamic_shapes_from_suggested_fixes",
+]
+
+
+log = logging.getLogger(__name__)
+
+
+class _DimHint(Enum):
+    """
+    Enum for dynamic shape hints.
+    - AUTO means automatic inference of shape (static or dynamic).
+    - STATIC means static shape (always specialized).
+    """
+
+    AUTO = auto()
+    STATIC = auto()
+
+
+class _Dim(type):
+    """
+    Metaclass for :func:`Dim` types.
+    """
+
+    @staticmethod
+    def readable(name, min_, max_):
+        from torch.utils._sympy.numbers import int_oo
+
+        if min_ == 2:
+            min_ = None
+        if max_ == int_oo:
+            max_ = None
+        if min_ is None and max_ is None:
+            return f"Dim('{name}')"
+        if min_ is None:
+            return f"Dim('{name}', max={max_})"
+        if max_ is None:
+            return f"Dim('{name}', min={min_})"
+        return f"Dim('{name}', min={min_}, max={max_})"
+
+    def __add__(cls, other):
+        # e.g., dim + 1
+        if type(other) is not int:
+            raise NotImplementedError(
+                f"Attempted to add {other} to {cls.__name__}, where an integer was expected. "
+                "(Only increasing linear operations with integer coefficients are supported.)"
+            )
+        return cls._derive(lambda x: x + other)
+
+    def __radd__(cls, other):
+        return cls + other
+
+    def __sub__(cls, other):
+        # e.g., dim - 1
+        if type(other) is not int:
+            raise NotImplementedError(
+                f"Attempted to subtract {other} from {cls.__name__}, where an integer was expected. "
+                "(Only increasing linear operations with integer coefficients are supported.)"
+            )
+        return cls._derive(lambda x: x - other)
+
+    def __rsub__(cls, other):
+        raise NotImplementedError(
+            f"Attempted to negate {cls.__name__}. "
+            "(Only increasing linear operations with integer coefficients are supported.)"
+        )
+
+    def __mul__(cls, other):
+        # e.g., dim * 2
+        if type(other) is not int or other <= 0:
+            raise NotImplementedError(
+                f"Attempted to multiply {other} with {cls.__name__}, where a positive integer was expected. "
+                "(Only increasing linear operations with integer coefficients are supported.)"
+            )
+        return cls._derive(lambda x: x * other)
+
+    def __rmul__(cls, other):
+        return cls * other
+
+    def _derived_name(cls, fn):
+        from sympy import sympify
+
+        return str(fn(sympify(cls.__name__)))
+
+    def _derive(cls, fn):
+        return _DerivedDim(cls._derived_name(fn), (int,), {"root": cls, "fn": fn})
+
+
+class _StaticDim(_Dim):
+    """
+    Meta class for static :func:`Dim` types.
+
+    This class is only for setting and checking static dim constraints,
+    and the user should never interact with it.
+    """
+
+    @property
+    def min(self):
+        return self.value  # type: ignore[attr-defined]
+
+    @property
+    def max(self):
+        return self.value  # type: ignore[attr-defined]
+
+
+class _DerivedDim(_Dim):
+    """
+    Metaclass for derived :func:`Dim` types.
+
+    Currently we only support increasing linear expressions with integer coefficients.
+    In other words, a derived Dim can always be written in the form Ax + B, where
+    x is a regular Dim (i.e., non-derived Dim), A and B are integers, and A is positive.
+    (In particular, the latter ensures that x < y => Ax + B < Ay + B.)
+    These restrictions on the form of derived Dims makes the metatheory simpler: e.g.,
+    it simplifies computing ranges for derived Dims, solving for underlying regular Dims,
+    deciding equalities between derived Dims, and so on.
+
+    The function lambda x: Ax + B is expressed by `fn`, where x is a normal Dim, `root`.
+    The range of a derived Dim is computed by mapping `fn` over the range of its `root`.
+    """
+
+    @property
+    def min(self):
+        # assume that self.fn is an increasing function
+        # TODO(avik): use sympy value range analysis instead?
+        from sympy import Integer
+
+        from torch.utils._sympy.numbers import int_oo
+
+        if self.root.min is -int_oo:  # type: ignore[attr-defined]
+            return -int_oo  # fn not needed cuz increasing
+
+        _min_symint = self.fn(Integer(self.root.min))  # type: ignore[attr-defined]
+        root = self.root  # type: ignore[attr-defined]
+        assert _min_symint >= 0, (
+            f"Expected derived min value of {self.__name__} to be >= 0. "
+            f"Please specify an appropriate min value for {root.__name__} "
+            f"(currently {root.min})."
+        )
+        return int(_min_symint)
+
+    @property
+    def max(self):
+        # assume that self.fn is an increasing function
+        # TODO(avik): use sympy value range analysis instead?
+        from sympy import Integer
+
+        from torch.utils._sympy.numbers import int_oo
+
+        if self.root.max is int_oo:  # type: ignore[attr-defined]
+            return int_oo  # fn not needed cuz increasing
+
+        _max_symint = self.fn(Integer(self.root.max))  # type: ignore[attr-defined]
+        root = self.root  # type: ignore[attr-defined]
+        assert _max_symint <= sys.maxsize - 1, (
+            f"Expected derived max value of {self.__name__} to be <= {sys.maxsize - 1}. "
+            f"Please specify an appropriate max value for {root.__name__} "
+            f"(currently {root.max})."
+        )
+        return int(_max_symint)
+
+    def _derive(self, fn):
+        # We support nesting, e.g., 2*dim + 1.
+        # This is implemented by composing operations on the same root.
+        # As a consequence, roots are always regular Dims (i.e., not derived Dims).
+        return _DerivedDim(
+            self._derived_name(fn),
+            (int,),
+            {"root": self.root, "fn": lambda x: fn(self.fn(x))},  # type: ignore[attr-defined]
+        )
+
+
+def Dim(name: str, *, min: Optional[int] = None, max: Optional[int] = None):
+    """
+    :func:`Dim` constructs a type analogous to a named symbolic integer with a range.
+    It can be used to describe multiple possible values of a dynamic tensor dimension.
+    Note that different dynamic dimensions of the same tensor, or of different tensors,
+    can be described by the same type.
+
+    Args:
+        name (str): Human-readable name for debugging.
+        min (Optional[int]): Minimum possible value of given symbol (inclusive)
+        max (Optional[int]): Maximum possible value of given symbol (inclusive)
+
+    Returns:
+        A type that can be used in dynamic shape specifications for tensors.
+    """
+
+    from torch.utils._sympy.numbers import int_oo
+
+    _min = 0 if min is None else min
+    _max = int_oo if max is None else max
+    assert _max > _min, f"Cannot create Dim with inconsistent min={min}, max={max}"
+    assert name.isidentifier(), f"Dim name must be a valid identifier, got {name}"
+    dim = _Dim(name, (int,), {"min": _min, "max": _max})
+    dim.__module__ = getattr(
+        inspect.getmodule(inspect.stack()[1][0]), "__name__", "__main__"
+    )
+    return dim
+
+
+Dim.AUTO = _DimHint.AUTO  # type: ignore[attr-defined]
+Dim.STATIC = _DimHint.STATIC  # type: ignore[attr-defined]
+
+
+def dims(*names: str, min: Optional[int] = None, max: Optional[int] = None):
+    """
+    Util to create multiple :func:`Dim` types.
+    """
+    return tuple(Dim(name, min=min, max=max) for name in names)
+
+
+@dataclasses.dataclass
+class _ConstraintTarget:
+    """
+    This represents input tensor dimensions.
+    """
+
+    t_id: int
+    dim: int
+
+
+@dataclasses.dataclass
+class _Constraint(_ConstraintTarget):
+    """
+    This represents a Dim describing a constraint target.
+
+    `name` is the name of the Dim.
+    `constraint_range` contains the min/max bounds of the Dim.
+    """
+
+    name: str
+    constraint_range: "StrictMinMaxConstraint"
+
+    def _clone_with_range(self, lower=0, upper=None):
+        # Import sympy locally
+        from torch.fx.experimental.symbolic_shapes import StrictMinMaxConstraint
+        from torch.utils._sympy.numbers import int_oo
+        from torch.utils._sympy.value_ranges import ValueRanges
+
+        if upper is None:
+            upper = int_oo
+
+        constraint_range = StrictMinMaxConstraint(
+            vr=self.constraint_range.vr & ValueRanges(lower=lower, upper=upper),
+            warn_only=False,
+        )
+        return _Constraint(
+            self.t_id,
+            self.dim,
+            self.name,
+            constraint_range,
+        )
+
+    def __ge__(self, lower):
+        return self._clone_with_range(lower=lower)
+
+    def __gt__(self, lower):
+        return self._clone_with_range(lower=lower + 1)
+
+    def __le__(self, upper):
+        return self._clone_with_range(upper=upper)
+
+    def __lt__(self, upper):
+        return self._clone_with_range(upper=upper - 1)
+
+    def __bool__(self):
+        # NOTE(avik): We do not support compound expressions like a <= x <= b.
+        # This is because Python implicitly desugars them into bool(a <= x) and bool(x <= b),
+        # and moreover, enforces that any overload of __bool__ must return True or False.
+        # FWIW, sympy also raises TypeError in this case.
+        raise TypeError(
+            "Cannot determine truth value of _Constraint. "
+            "If you are trying to combine _Constraint's with logical connectives, "
+            "you can specify them separately instead."
+        )
+
+    @property
+    def serializable_spec(self):
+        # We need a serialization compatible format of the constraint so that it
+        # can be savedin the graph module w/o breaking the module serialization.
+        # The saved constraints will be used directly for the post-exporting pass
+        # that converts constraints to runtime assertion. The saved constraints
+        # will not be saved in the serialized module.
+        # TODO: A better way is needed. Currently we use 't_id' to map the constraint,
+        # which is not reliable
+        return {
+            "t_id": self.t_id,
+            "dim": self.dim,
+            "min": self.constraint_range.vr.lower,
+            "max": self.constraint_range.vr.upper,
+        }
+
+
+@dataclasses.dataclass
+class _PhantomRoot:
+    """
+    This represents the root of a derived Dim where the root does not directly
+    specify the shape of any input dimension, but the derived Dim does.
+
+    e.g., the input shapes 2*dim and dim + 1 are related via a "phantom" dim.
+
+    The fields `name`, `constraint_range`, and `val` carried by a phantom root
+    help create a symbol for it. Any derived dims with this phantom root are
+    backed by expressions over this symbol.
+    """
+
+    name: str
+    constraint_range: "StrictMinMaxConstraint"
+    val: int
+
+
+@dataclasses.dataclass
+class _DerivedConstraint(_ConstraintTarget):
+    """
+    This represents a derived Dim, whose root is either a regular constraint target
+    (which directly specifies the shape of some input dimension) or a phantom root
+    (which does so indirectly).
+
+    It can be thought of as a subclass of `_Constraint`, except that it does not
+    support <, <=, >, >= operations.
+    """
+
+    name: str
+    constraint_range: "StrictMinMaxConstraint"
+    root: Union[_ConstraintTarget, _PhantomRoot]
+    fn: Callable
+
+    @property
+    def serializable_spec(self):
+        # same as _Constraint.serializable_spec
+        return {
+            "t_id": self.t_id,
+            "dim": self.dim,
+            "min": self.constraint_range.vr.lower,
+            "max": self.constraint_range.vr.upper,
+        }
+
+
+Constraint = Union[_Constraint, _DerivedConstraint]
+
+
+def _process_equalities(
+    constraint: Constraint,
+    get_sources: Callable[[int, int], List["Source"]],
+    shape_env: "ShapeEnv",
+    names: Dict[str, Tuple[int, int]],
+    source_pairs: List[Tuple["Source", "Source"]],
+    derived_equalities: List[Tuple["Source", Union["Source", "Symbol"], Callable]],
+    phantom_symbols: Dict[str, "Symbol"],
+):
+    """
+    Updates `source_pairs`, `derived_equalities`, and `phantom_symbols` (which become
+    fields of `EqualityConstraint`) based on a given input `constraint`.
+    """
+
+    sources = get_sources(constraint.t_id, constraint.dim)
+    if not sources:  # empty sources due to unused shapes
+        return
+
+    source, *other_sources = sources
+    # When t.size()[dim] maps to src0, src1, ..., srcN, we add
+    # constraints that make src0 "equal" to src1, ..., srcN.
+    source_pairs.extend((source, other_source) for other_source in other_sources)
+    if not isinstance(constraint, _DerivedConstraint):
+        if constraint.name in names:
+            shared_t_id, shared_dim = names[constraint.name]
+            other_sources = get_sources(shared_t_id, shared_dim)
+            source_pairs.extend(
+                (source, other_source) for other_source in other_sources
+            )
+        else:
+            names[constraint.name] = (constraint.t_id, constraint.dim)
+    else:
+        # branch based on the root of the _DerivedConstraint
+        if not isinstance(constraint.root, _PhantomRoot):
+            # either root points to an input source
+            root = get_sources(constraint.root.t_id, constraint.root.dim)[0]  # type: ignore[assignment]
+        else:
+            # or root points to a phantom symbol
+            if constraint.root.name in phantom_symbols:
+                root = phantom_symbols[constraint.root.name]  # type: ignore[assignment]
+            else:
+                # create a phantom symbol in the shape env based on the _PhantomRoot
+                root = shape_env.create_symbol(
+                    val=constraint.root.val,
+                    source=torch._dynamo.source.ConstantSource(constraint.root.name),
+                    dynamic_dim=torch.fx.experimental.symbolic_shapes.DimDynamic.DYNAMIC,
+                    constraint_dim=constraint.root.constraint_range,
+                )
+                phantom_symbols[constraint.root.name] = root  # type: ignore[assignment]
+
+        fn = constraint.fn
+        # A derived equality (source, root, fn) informally corresponds to source = fn(root).
+        # Here source describes an input and root might describe another input or a phantom symbol.
+        derived_equalities.append((source, root, fn))
+
+
+def _tree_map_with_path(
+    func: Callable[..., Any],
+    tree: Any,
+    *dynamic_shapes: Any,
+    tree_name: Optional[str] = None,
+) -> Any:
+    """
+    Customized tree_map for mapping pytrees to dynamic_shapes.
+
+    For built-in types (e.g., standard collections) this behaves exactly like tree_map.
+
+    OTOH for a user-defined class C registered with pytree, we cannot assume that a C
+    containing tensors can be mapped to a C containing dynamic shapes (i.e., C may not
+    be a polymorphic container). In that case we use the flattened form of C instead.
+    Thus a C(**tensors) that flattens to (**tensors) will map to (**dynamic_shapes).
+
+    Args:
+        func: function to apply to each (int, float, str, bool, None, torch.Tensor)
+        tree: input pytree
+        dynamic_shapes: zero or more (typically one) dynamic_shapes to match
+
+    Returns:
+        output pytree mapping func to each (int, float, str, bool, None, torch.Tensor)
+    """
+
+    def is_leaf(t):
+        # BUILTIN_TYPES is a subset of SUPPORTED_NODES, the latter being all types
+        # registered with pytree. Types *not* in BUILTIN_TYPES include primitive types
+        # (int, float, str, bool, None, torch.Tensor), which are not in SUPPORTED_NODES,
+        # as well as user-defined classes registered with pytree, which are.
+        return _get_node_type(t) not in BUILTIN_TYPES
+
+    def f(path, t, *dynamic_shapes):
+        typ = _get_node_type(t)
+        # typ is not in BUILTIN_TYPES
+        if typ in SUPPORTED_NODES:
+            # thus typ is a user-defined class registered with pytree,
+            # in which case flatten and recurse
+            return tree_map_with_path(
+                f,
+                SUPPORTED_NODES[typ].flatten_fn(t)[0],
+                *dynamic_shapes,
+                is_leaf=is_leaf,
+            )
+        else:
+            return func(path, t, *dynamic_shapes)
+
+    try:
+        return tree_map_with_path(f, tree, *dynamic_shapes, is_leaf=is_leaf)
+    except ValueError as e:
+        if "mismatch" in e.args[0]:
+            # When PyTree finds a structural mismatch between tree and dynamic_shapes,
+            # the error message is unfortunately quite horrible. Let's fix that.
+            assert dynamic_shapes, "Cannot be a mismatch if there is no dynamic_shapes"
+            assert tree_name, "Must provide a tree_name when there might be a mismatch"
+
+            def _key(type_, context, i):
+                # derive a PyTree key given the type, context, and child # of a TreeSpec
+                if type_ is dict:
+                    return MappingKey(context[i])
+                if type_ in (list, tuple):
+                    assert context is None
+                    return SequenceKey(i)
+                raise AssertionError(f"Did not expect type {type_}")
+
+            def raise_mismatch_error(msg):
+                from torch._dynamo.exc import UserError, UserErrorType
+
+                raise UserError(
+                    UserErrorType.INVALID_INPUT,
+                    f"Detected mismatch between the structure of `{tree_name}` and `dynamic_shapes`: {msg}",
+                    case_name="dynamic_shapes_validation",
+                )
+
+            def _compare(tree, dynamic_shapes, path):
+                # raise an error at the point where tree and dynamic_shapes differ,
+                # including the path to that point and the reason for the difference
+                rendered_path = keystr(path)
+                if isinstance(tree, LeafSpec):
+                    return
+                if isinstance(dynamic_shapes, LeafSpec):
+                    raise_mismatch_error(
+                        f"`{tree_name}{rendered_path}` is a {tree.type}, "
+                        f"but `dynamic_shapes{rendered_path}` is not"
+                    )
+                if tree.type != dynamic_shapes.type:
+                    raise_mismatch_error(
+                        f"`{tree_name}{rendered_path}` is a {tree.type}, "
+                        f"but `dynamic_shapes{rendered_path}` is a {dynamic_shapes.type}"
+                    )
+                if len(tree.children_specs) != len(dynamic_shapes.children_specs):
+                    raise_mismatch_error(
+                        f"`{tree_name}{rendered_path}` has {len(tree.children_specs)} elements, "
+                        f"but `dynamic_shapes{rendered_path}` has {len(dynamic_shapes.children_specs)} elements"
+                    )
+                if tree.type is dict:
+                    # context, children could be out of order
+                    if sorted(tree.context) != sorted(dynamic_shapes.context):
+                        raise_mismatch_error(
+                            f"`{tree_name}{rendered_path}` has keys {tree.context}, "
+                            f"but `dynamic_shapes{rendered_path}` has keys {dynamic_shapes.context}"
+                        )
+                    _remap = dict(
+                        zip(dynamic_shapes.context, dynamic_shapes.children_specs)
+                    )
+                    dynamic_shapes_children_specs = [_remap[k] for k in tree.context]
+                else:
+                    dynamic_shapes_children_specs = dynamic_shapes.children_specs
+                for i, (tree_, dynamic_shapes_) in enumerate(
+                    zip(tree.children_specs, dynamic_shapes_children_specs)
+                ):
+                    _compare(
+                        tree_,
+                        dynamic_shapes_,
+                        path + [_key(tree.type, tree.context, i)],
+                    )
+
+            _, tree_spec = tree_flatten(tree, is_leaf=is_leaf)
+            for other_tree in dynamic_shapes:
+                _, other_tree_spec = tree_flatten(other_tree, is_leaf)
+                _compare(tree_spec, other_tree_spec, [])
+        raise
+
+
+def _combine_args(f, args, kwargs, _is_torch_jit_trace=False) -> Dict[str, Any]:
+    # combine args and kwargs following the signature of f, as it happens
+    # in the body of f when called with *args, **kwargs
+    if isinstance(f, ExportedProgram):
+        f = f.module()
+    if not _is_torch_jit_trace:
+        signature = (
+            inspect.signature(f.forward)
+            if isinstance(f, torch.nn.Module)
+            else inspect.signature(f)
+        )
+        kwargs = kwargs if kwargs is not None else {}
+        return signature.bind(*args, **kwargs).arguments
+    return args
+
+
+class ShapesCollection:
+    """
+    Builder for dynamic_shapes.
+    Used to assign dynamic shape specifications to tensors that appear in inputs.
+
+    Example::
+        args = ({"x": tensor_x, "others": [tensor_y, tensor_z]})
+
+        dim = torch.export.Dim(...)
+        dynamic_shapes = torch.export.ShapesCollection()
+        dynamic_shapes[tensor_x] = (dim, dim + 1, 8)
+        dynamic_shapes[tensor_y] = {0: dim * 2}
+        # This is equivalent to the following (now auto-generated):
+        # dynamic_shapes = {"x": (dim, dim + 1, 8), "others": [{0: dim * 2}, None]}
+
+        torch.export(..., args, dynamic_shapes=dynamic_shapes)
+    """
+
+    def __init__(self):
+        self._shapes = {}
+
+    def __setitem__(self, t, shape):
+        assert isinstance(
+            t, torch.Tensor
+        ), f"Cannot assign shape to non-tensor type {type(t)}"
+        # TODO(avik): check that shape is indeed a Shape
+        t_id = id(t)
+        if t_id in self._shapes:
+            _shape = self._shapes[t_id]
+            assert (
+                shape == _shape
+            ), f"Shapes assigned to tensor do not match: expected {_shape}, got {shape}"
+        else:
+            self._shapes[id(t)] = shape
+
+    def __getitem__(self, t):
+        t_id = id(t)
+        if t_id in self._shapes:
+            return self._shapes[t_id]
+        else:
+            return None
+
+    def __len__(self):
+        return len(self._shapes)
+
+    def dynamic_shapes(self, m, args, kwargs=None):
+        """
+        Generate dynamic_shapes.
+        """
+
+        t_ids = set()
+
+        def find_shape(path, t):
+            t_id = id(t)
+            if t_id in self._shapes:
+                t_ids.add(t_id)
+                return self._shapes[t_id]
+            else:
+                return None
+
+        combined_args = _combine_args(m, args, kwargs)
+        dynamic_shapes = _tree_map_with_path(find_shape, combined_args)
+        if any(t_id not in t_ids for t_id in self._shapes):
+            raise ValueError(
+                "Some tensors that were assigned shapes were not found in args. "
+                "Maybe such tensors were copied when passing them as args? "
+                "Maybe such tensors are contained in classes that were not registered with pytree?"
+            )
+        return dynamic_shapes
+
+
+def _warn_on_None_dynamic_shape_dimension():
+    msg = (
+        "Using None as a dynamic shape dimension is deprecated. "
+        "Please use Dim.STATIC instead"
+    )
+    # TODO(avik): raise an error in the future
+    log.warning(msg)
+
+
+def _check_dynamic_shapes(
+    combined_args: Dict[str, Any],
+    dynamic_shapes: Union[Dict[str, Any], Tuple[Any], List[Any], None],
+):
+    """
+    Checks the dynamic_shapes specification for correctness,
+    using combined args + kwargs as reference for inputs structure.
+    """
+    from torch._dynamo.exc import UserError, UserErrorType
+    from torch._export.non_strict_utils import _flatten_dynamic_shapes
+
+    if dynamic_shapes is None or len(dynamic_shapes) == 0:
+        return
+    if isinstance(dynamic_shapes, (tuple, list)):
+        combined_args = type(dynamic_shapes)(combined_args.values())  # type: ignore[assignment, misc]
+
+    bounds: Dict[str, Tuple[int, int]] = {}
+
+    def check_same_bounds(dim):
+        if dim.__name__ in bounds:
+            min_, max_ = bounds[dim.__name__]
+            if dim.min != min_ or dim.max != max_:
+                this_ = _Dim.readable(dim.__name__, min_, max_)
+                that_ = _Dim.readable(dim.__name__, dim.min, dim.max)
+                raise UserError(
+                    UserErrorType.INVALID_INPUT,
+                    f"Found different definitions {this_} and {that_} "
+                    f"for the same symbolic dimension {dim}!",
+                )
+        else:
+            bounds[dim.__name__] = (dim.min, dim.max)
+
+    def check_symbols(path, tensor, shape):
+        if isinstance(shape, dict):
+            for i, dim in shape.items():
+                if isinstance(dim, _Dim):
+                    check_same_bounds(dim)
+                elif dim is None:
+                    _warn_on_None_dynamic_shape_dimension()
+                elif not (isinstance(dim, (int, _DimHint))):
+                    raise UserError(
+                        UserErrorType.INVALID_INPUT,
+                        f"Unexpected dimension mapped to index {i} in input tensor shape {shape} "
+                        f"specified at `dynamic_shapes{keystr(path)}` "
+                        f"(expected None, an int, a Dim, Dim.AUTO, or Dim.STATIC, but got {dim} instead)",
+                        case_name="dynamic_shapes_validation",
+                    )
+        elif isinstance(shape, (tuple, list)):
+            for i, dim in enumerate(shape):
+                if isinstance(dim, _Dim):
+                    check_same_bounds(dim)
+                elif dim is None:
+                    _warn_on_None_dynamic_shape_dimension()
+                elif not (isinstance(dim, (int, _DimHint))):
+                    raise UserError(
+                        UserErrorType.INVALID_INPUT,
+                        f"Unexpected dimension #{i} in input tensor shape {shape} "
+                        f"specified at `dynamic_shapes{keystr(path)}` "
+                        f"(expected None, an int, a Dim, Dim.AUTO, or Dim.STATIC, but got {dim} instead)",
+                        case_name="dynamic_shapes_validation",
+                    )
+        elif shape is not None:
+            raise UserError(
+                UserErrorType.INVALID_INPUT,
+                f"Unexpected input tensor shape {shape} specified at `dynamic_shapes{keystr(path)}` "
+                f"(expected either a list/tuple of dimensions, or a dict mapping indices to dimensions,"
+                f" where each dimension is an int, a Dim, Dim.AUTO, or Dim.STATIC)",
+                case_name="dynamic_shapes_validation",
+            )
+
+    assert isinstance(dynamic_shapes, (dict, tuple, list))
+    if isinstance(dynamic_shapes, dict):
+        got_keys = list(dynamic_shapes.keys())
+        expected_arg_names = list(combined_args.keys())
+        if sorted(got_keys) != sorted(expected_arg_names):
+            msg = (
+                f"When `dynamic_shapes` is specified as a dict, its top-level keys "
+                f"must be the arg names {expected_arg_names} of `inputs`, but "
+                f"here they are {got_keys}. "
+            )
+            if (
+                len(combined_args) == 1
+                and expected_arg_names[0] not in got_keys
+                and isinstance(combined_args[expected_arg_names[0]], dict)
+            ):
+                msg += (
+                    "Since here `inputs` is a list/tuple enclosing a single dict, "
+                    "maybe you just forgot to enclose `dynamic_shapes` in a list/tuple?"
+                )
+            else:
+                msg += (
+                    "Alternatively, you could also ignore arg names entirely "
+                    "and specify `dynamic_shapes` as a list/tuple matching `inputs`."
+                )
+            raise UserError(
+                UserErrorType.INVALID_INPUT, msg, case_name="dynamic_shapes_validation"
+            )
+
+    def check_shape(path, t, dynamic_shape):
+        if isinstance(t, torch.Tensor):
+            check_symbols(path, t, dynamic_shape)
+        else:
+            if dynamic_shape is not None:
+                rendered_path = keystr(path)
+                raise UserError(
+                    UserErrorType.INVALID_INPUT,
+                    f"Cannot associate shape {dynamic_shape} specified at `dynamic_shapes{rendered_path}` "
+                    f"to non-tensor type {type(t)} at `inputs{rendered_path}` (expected None)",
+                    case_name="dynamic_shapes_validation",
+                )
+
+    _tree_map_with_path(check_shape, combined_args, dynamic_shapes, tree_name="inputs")
+
+    # raise user warning if both Dim.AUTO & Dims are specified in dynamic_shapes
+    flat_dynamic_shapes = _flatten_dynamic_shapes(combined_args, dynamic_shapes)
+    flatter_dynamic_shapes, _ = tree_flatten(flat_dynamic_shapes)
+    if any(isinstance(s, _Dim) for s in flatter_dynamic_shapes) and any(
+        s == _DimHint.AUTO for s in flatter_dynamic_shapes
+    ):
+        raise UserError(
+            UserErrorType.INVALID_INPUT,
+            "Specifying both `Dim.AUTO` and `Dim` or `DerivedDim` in `dynamic_shapes` is not well supported at the moment, "
+            "and can easily lead to constraint violation errors or obscure errors in torch.export. Dim/DerivedDims "
+            "expect all equal or related dimensions to be specified, and does not yet compose well with `Dim.AUTO`. "
+            "We suggest using `Dim.AUTO` mixed with `None` for auto-dynamic + static shapes, plus torch._check(dim >= min), "
+            "torch._check(dim <= max) calls in your program to specify min/max ranges, or `Dim`/`DerivedDim` mixed with `None` "
+            "if you want to assert on the exact specification of your program's dynamic shapes behavior.",
+            case_name="dynamic_shapes_validation",
+        )
+
+
+def _transform_shapes_for_default_dynamic(
+    combined_args: Dict[str, Any],
+    dynamic_shapes: Union[Dict[str, Any], Tuple[Any], List[Any], None],
+) -> Union[Dict[str, Any], Tuple[Any], List[Any], None]:
+    """
+    In the long run this might not be needed, but this exists because export.export() and _dynamo.export()
+    historically have different semantics for how dynamic_shapes are specified, but go through the same
+    process of producing constraints, and now both use assume_static_by_default=False.
+
+    For _dynamo.export(), the semantics for dynamic_shapes are:
+    - None: dynamic, allocated a symbol
+    - Dim/DerivedDim: a strict assertion on the min/max range for this symbol, and require a specification
+      for all dims governed by this symbol (i.e. relations, equality, linear relations, etc.)
+
+    For export.export(), historically dynamism for unspecified dims has been undesirable, so the semantics are:
+    - Dim.AUTO: dynamic, allocated a symbol
+    - None/unspecified/Dim.STATIC: static
+    - Dim/DerivedDims: also a strict assertion
+
+    To allow both APIs to follow the same process for producing constraints, this function converts dynamic_shapes
+    for export.export() to be compatible with _process_dynamic_shapes() and assume_static_by_default=False, turning them
+    into essentially what they'd look like for _dynamo.export().
+
+    An example conversion might look like, for a 3-d input tensor:
+
+        input spec: {
+            0: Dim.AUTO,
+            1: None,  # or Dim.STATIC
+            2: Dim("dx"),
+        }
+        output spec: {
+            0: None,  # None: dynamic by default
+            1: 32,  # explicitly provide static shape
+            2: Dim("dx"),  # remains the same
+        }
+    """
+
+    def _tree_map_helper(tree, val):
+        """
+        If the user generally specifies dynamic_shapes=None for a pytree input,
+        we'd like to convert this into a tree of Nones following the input spec,
+        so we can explicitly specify static dims for all tensor dimensions.
+        Non-builtin types for pytree (e.g. custom dataclasses) creates some difficulty,
+        in which case the correct format is a list containing specs for each child attribute.
+        """
+        if (node_type := _get_node_type(tree)) not in SUPPORTED_NODES:  # is_leaf
+            return val
+        flatten_fn = SUPPORTED_NODES[node_type].flatten_fn
+        child_pytrees, context = flatten_fn(tree)  # flatten from whatever original type
+        unflatten_fn = SUPPORTED_NODES[
+            node_type if node_type in BUILTIN_TYPES else list
+        ].unflatten_fn
+        children = [_tree_map_helper(child, val) for child in child_pytrees]
+        return unflatten_fn(
+            children, context
+        )  # unflatten into original type, or list if not built-in type
+
+    if (
+        dynamic_shapes is None or len(dynamic_shapes) == 0
+    ):  # create pytree structure of static dim
+        dynamic_shapes = _tree_map_helper(combined_args, None)
+    if isinstance(dynamic_shapes, (tuple, list)):
+        combined_args = type(dynamic_shapes)(combined_args.values())  # type: ignore[assignment, misc]
+
+    def transform_shapes(path, tensor, shape):
+        def _marked_dynamic(tensor, i):
+            # TODO(pianpwk): deprecate mark_dynamic() usage for export
+            return i in getattr(tensor, "_dynamo_dynamic_indices", set())
+
+        out: Union[None, List[Any], Dict[int, Any]] = None
+        if isinstance(shape, dict):
+            out = {}
+            for i, val in enumerate(tensor.shape):
+                dim = shape.get(i, _DimHint.STATIC)
+                if _marked_dynamic(tensor, i) or dim == _DimHint.AUTO:
+                    # don't have to specify anything if dynamic
+                    # None also works, since assume_static_by_default=False
+                    if dim == _DimHint.AUTO:
+                        torch._dynamo.maybe_mark_dynamic(tensor, i)  # avoid duck sizing
+                    continue
+                elif isinstance(dim, _Dim):
+                    out[i] = dim
+                elif isinstance(dim, int):
+                    # important that this is dim and not val,
+                    # so we can raise error if user-specified dim != val
+                    out[i] = dim
+                elif dim is None:
+                    _warn_on_None_dynamic_shape_dimension()
+                    out[i] = val
+                else:
+                    # make explicitly static
+                    assert dim == _DimHint.STATIC
+                    out[i] = val
+        elif isinstance(shape, (tuple, list)):
+            out = []
+            for i, val in enumerate(tensor.shape):
+                dim = shape[i]
+                if _marked_dynamic(tensor, i) or dim == _DimHint.AUTO:
+                    if dim == _DimHint.AUTO:
+                        torch._dynamo.maybe_mark_dynamic(tensor, i)  # avoid duck sizing
+                    out.append(None)
+                elif isinstance(dim, _Dim):
+                    out.append(dim)
+                elif isinstance(dim, int):
+                    out.append(dim)
+                elif dim is None:
+                    _warn_on_None_dynamic_shape_dimension()
+                    out.append(val)
+                else:
+                    assert dim == _DimHint.STATIC
+                    out.append(val)
+            out = type(shape)(out)  # type: ignore[assignment]
+        else:
+            assert shape is None
+            if isinstance(tensor, torch.Tensor):
+                out = []
+                for i, val in enumerate(tensor.shape):
+                    out.append(None if _marked_dynamic(tensor, i) else val)
+                out = out or None
+            else:
+                out = None
+        return out
+
+    def transform_shape(path, t, dynamic_shape):
+        if isinstance(t, torch.Tensor):
+            return transform_shapes(path, t, dynamic_shape)
+
+    result = _tree_map_with_path(
+        transform_shape, combined_args, dynamic_shapes, tree_name="inputs"
+    )
+    return result
+
+
+def _process_dynamic_shapes(
+    combined_args: Dict[str, Any],
+    dynamic_shapes: Union[Dict[str, Any], Tuple[Any], List[Any], None],
+) -> List[Constraint]:
+    """
+    Reads the dynamic_shapes specification and produces a list of constraints.
+    """
+    from torch._dynamo.exc import UserError, UserErrorType
+
+    if dynamic_shapes is None or len(dynamic_shapes) == 0:
+        # we run with dynamic by default, so no need to produce constraints
+        return []
+    if isinstance(dynamic_shapes, (tuple, list)):
+        combined_args = type(dynamic_shapes)(combined_args.values())  # type: ignore[assignment, misc]
+
+    # map of Dim names representing input shape dimensions to constraints on them
+    symbols: Dict[str, List[Constraint]] = defaultdict(list)
+    # track roots that do not directly represent input shape dimensions
+    phantom_roots: Dict[str, _PhantomRoot] = {}
+    derived_constraints_with_phantom_root: List[_DerivedConstraint] = []
+
+    def to_constraint(dim, tensor, i):
+        import sympy
+
+        from torch.fx.experimental.symbolic_shapes import StrictMinMaxConstraint
+        from torch.utils._sympy.solve import try_solve
+        from torch.utils._sympy.value_ranges import ValueRanges
+
+        def root_value():
+            # given tensor.shape[i] is the value of dim = fn(root),
+            # find the value of root
+            symbol = sympy.Symbol(dim.root.__name__, integer=True)
+            expr = dim.fn(symbol)
+            solution = try_solve(sympy.Eq(expr, tensor.shape[i]), symbol)
+            if solution is not None:
+                return int(solution[1])  # type: ignore[call-overload]
+            else:
+                raise UserError(  # noqa: B904
+                    UserErrorType.CONSTRAINT_VIOLATION,
+                    f"Expected shape[{i}] = {tensor.shape[i]} of input Tensor to be "
+                    f"of the form {expr}, where {symbol} is an integer",
+                )
+
+        if isinstance(dim, _DerivedDim):
+            # generate a _DerivedConstraint where the root is:
+            # - either a _ConstraintTarget (if dim.root directly describes an input shape)
+            # - or a _PhantomRoot (otherwise)
+            dim_root = dim.root  # type: ignore[attr-defined]
+            if dim_root.__name__ in symbols:
+                # root represents an input shape dimension
+                root_constraint = symbols[dim_root.__name__][0]
+                root = _ConstraintTarget(
+                    root_constraint.t_id,
+                    root_constraint.dim,
+                )
+            elif dim_root.__name__ not in phantom_roots:
+                # create a phantom root
+                root = _PhantomRoot(  # type: ignore[assignment]
+                    name=dim_root.__name__,
+                    constraint_range=StrictMinMaxConstraint(
+                        vr=ValueRanges(lower=dim_root.min, upper=dim_root.max),
+                        warn_only=False,
+                    ),
+                    val=root_value(),
+                )
+                phantom_roots[dim_root.__name__] = root  # type: ignore[assignment]
+            else:
+                root = phantom_roots[dim_root.__name__]  # type: ignore[assignment]
+            constraint = _DerivedConstraint(
+                id(tensor),
+                i,
+                dim.__name__,
+                StrictMinMaxConstraint(
+                    vr=ValueRanges(lower=dim.min, upper=dim.max),
+                    warn_only=False,
+                ),
+                root,
+                dim.fn,  # type: ignore[attr-defined]
+            )
+            if isinstance(root, _PhantomRoot):
+                # NOTE(avik): since we have not processed all inputs yet, we may replace this
+                # with a root that does represent an input shape dimension later (see below)
+                derived_constraints_with_phantom_root.append(constraint)
+        elif isinstance(dim, _StaticDim):
+            constraint = _Constraint(  # type: ignore[assignment]
+                id(tensor),
+                i,
+                dim.__name__,
+                StrictMinMaxConstraint(
+                    vr=ValueRanges(lower=dim.value, upper=dim.value), warn_only=False  # type: ignore[attr-defined]
+                ),
+            )
+        else:
+            constraint = _Constraint(  # type: ignore[assignment]
+                id(tensor),
+                i,
+                dim.__name__,
+                StrictMinMaxConstraint(
+                    vr=ValueRanges(lower=dim.min, upper=dim.max), warn_only=False  # type: ignore[attr-defined]
+                ),
+            )
+        return constraint
+
+    def update_symbols(path, tensor, shape):
+        def _create_static_dim(tensor, i, value):
+            return _StaticDim(str(value), (int,), {"value": value})
+
+        if isinstance(shape, dict):
+            for i, dim in shape.items():
+                if isinstance(dim, (int, _Dim)):
+                    if isinstance(dim, int):
+                        dim = _create_static_dim(tensor, i, dim)
+                    constraint = to_constraint(dim, tensor, i)
+                    symbols[dim.__name__].append(constraint)
+        elif isinstance(shape, (tuple, list)):
+            for i, dim in enumerate(shape):
+                if isinstance(dim, (int, _Dim)):
+                    if isinstance(dim, int):
+                        dim = _create_static_dim(tensor, i, dim)
+                    constraint = to_constraint(dim, tensor, i)
+                    symbols[dim.__name__].append(constraint)
+
+    def assoc_shape(path, t, dynamic_shape):
+        if isinstance(t, torch.Tensor):
+            update_symbols(path, t, dynamic_shape)
+
+    _tree_map_with_path(assoc_shape, combined_args, dynamic_shapes, tree_name="inputs")
+
+    constraints = []
+    for derived_constraint_with_phantom_root in derived_constraints_with_phantom_root:
+        phantom_root_name = derived_constraint_with_phantom_root.root.name  # type: ignore[union-attr]
+        if phantom_root_name in symbols:
+            # We found an input shape dimension corresponding to this name, so we
+            # do not need a phantom symbol for it after all.
+            # NOTE(avik): Overall we want to maintain the invariant that roots that
+            # are phantom symbols are really "phantom," i.e., they cannot be represented
+            # by any input source. This is important when we are deciding derived equalities,
+            # since we can focus our attention exclusively on input sources: deciding
+            # derived equalities involving phantom symbols are, in comparison, trivial.
+            derived_constraint_with_phantom_root.root = symbols[phantom_root_name][0]
+
+    for dynamic_dims in symbols.values():
+        constraints.extend(dynamic_dims)
+
+    return constraints  # type: ignore[return-value]
+
+
+def _get_dim_name_mapping(
+    dynamic_shapes: Union[Dict[str, Any], Tuple[Any], List[Any], None]
+):
+    name_to_dim = {}
+    for dim in tree_flatten(
+        dynamic_shapes,
+        is_leaf=lambda x: isinstance(x, _Dim),
+    )[0]:
+        if dim is None:
+            # NOTE: this must denote a non-Tensor or automatic at this point.
+            continue
+        if isinstance(dim, int):
+            continue
+        assert isinstance(dim, _Dim)  # dim hints should have boiled away
+        name_to_dim[dim.__name__] = dim
+        if isinstance(dim, _DerivedDim):
+            name_to_dim[dim.root.__name__] = dim.root  # type: ignore[attr-defined]
+    return name_to_dim
+
+
+def refine_dynamic_shapes_from_suggested_fixes(
+    msg: str,
+    dynamic_shapes: Union[Dict[str, Any], Tuple[Any], List[Any]],
+) -> Union[Dict[str, Any], Tuple[Any], List[Any]]:
+    """
+    For working with export's dynamic shapes suggested fixes, and/or automatic dynamic shapes.
+    Refines the given dynamic shapes spec, given a ConstraintViolation error message and the original dynamic shapes.
+
+    For most cases behavior is straightforward - i.e. for suggested fixes that specialize or refine a Dim's range,
+    or fixes that suggest a derived relation, the new dynamic shapes spec will be updated as such.
+
+    e.g.
+    Suggested fixes:
+
+        dim = Dim('dim', min=3, max=6) -> this just refines the dim's range
+        dim = 4 -> this specializes to a constant
+        dy = dx + 1 -> dy was specified as an independent dim, but is actually tied to dx with this relation
+
+    However, suggested fixes associated with derived dims can be more complicated.
+    For example, if a suggested fix is provided for a root dim, the new derived dim value is evaluated based on the root.
+
+    e.g.
+    dx = Dim('dx')
+    dy = dx + 2
+    dynamic_shapes = {"x": (dx,), "y": (dy,)}
+
+    Suggested fixes:
+
+        dx = 4  # specialization will lead to dy also specializing = 6
+        dx = Dim('dx', max=6)  # dy now has max = 8
+
+    Derived dims suggested fixes can also be used to express divisibility constraints.
+    This involves creating new root dims that aren't tied to a particular input shape.
+    In this case the root dims won't appear directly in the new spec, but as a root of
+    one of the dims.
+
+    e.g.
+    Suggested fixes:
+
+        _dx = Dim('_dx', max=1024)  # this won't appear in the return result, but dx will
+        dx = 4*_dx  # dx is now divisible by 4, with a max value of 4096
+    """
+
+    import re
+
+    import sympy
+
+    from torch._dynamo.exc import UserError, UserErrorType
+    from torch.fx.experimental.symbolic_shapes import _is_supported_equivalence
+
+    try:
+        shape_fixes_msg = msg.split("Suggested fixes:")[1].strip()
+    except Exception as exc:
+        raise UserError(
+            UserErrorType.INVALID_INPUT,
+            "Suggested fixes not found in error message given to refine_dynamic_shapes_from_suggested_fixes()",
+        ) from exc
+
+    # build shape_fixes dictionary
+    shape_fixes = {}
+    for fix in shape_fixes_msg.split("\n"):
+        fix = fix.strip()
+        if match := re.match(r"(.*) = Dim\('(.*)'.*\)", fix):
+            name = match.group(1)
+            _min, _max = None, None
+            if match_min := re.match(r".* = Dim\('.*', min\=([0-9]+).*\)", fix):
+                _min = int(match_min.group(1))
+            if match_max := re.match(r".* = Dim\('.*'.*max\=([0-9]+)\)", fix):
+                _max = int(match_max.group(1))
+            shape_fixes[name] = Dim(name, min=_min, max=_max)
+        else:
+            name, expr = fix.split(" = ")
+            expr = sympy.sympify(expr)
+            if isinstance(expr, sympy.Number):
+                # static, integer
+                shape_fixes[name] = int(expr)  # type: ignore[assignment]
+            else:
+                # relation or derived dim
+                shape_fixes[name] = expr
+
+    name_to_dim = _get_dim_name_mapping(dynamic_shapes)
+
+    # track derived dim roots
+    roots: Set[str] = set()
+    for k, c in shape_fixes.items():
+        assert isinstance(c, (int, _Dim, _DerivedDim, sympy.Expr))
+        if isinstance(c, sympy.Expr):  # check dim/derived dim expression
+            assert _is_supported_equivalence(c)
+            shape_fixes[k] = c
+            roots.add(str(next(iter(c.free_symbols))))
+        if isinstance(c, _DerivedDim):
+            roots.add(c.root.__name__)  # type: ignore[attr-defined]
+
+    # check keys are existing dims or new roots
+    for k, c in shape_fixes.items():
+        assert k in name_to_dim or k in roots
+
+    # cache so we don't produce multiple derived dim objects
+    derived_dim_cache: Dict[str, _DerivedDim] = {}
+
+    def apply_fixes(path, dim, dummy):
+        if dim is None or isinstance(dim, int):  # not dynamic
+            return dim
+        elif dim.__name__ in shape_fixes:  # directly fix
+            fix = shape_fixes[dim.__name__]
+            if isinstance(fix, sympy.Expr):  # now derived or related
+                if str(fix) in derived_dim_cache:
+                    return derived_dim_cache[str(fix)]
+                else:
+                    symbol = next(iter(fix.free_symbols))
+                    # try to locate symbol
+                    if symbol.name in shape_fixes:  # type: ignore[attr-defined]
+                        root = shape_fixes[symbol.name]  # type: ignore[attr-defined]
+                    else:
+                        assert symbol.name in name_to_dim  # type: ignore[attr-defined]
+                        root = name_to_dim[symbol.name]  # type: ignore[attr-defined]
+                    # figure out value of fix
+                    modulus, remainder = sympy.polys.polytools.div(fix, symbol)
+                    dim = root
+                    if modulus != 1:
+                        dim = int(modulus) * dim
+                    if remainder != 0:
+                        dim = dim + int(remainder)
+                    derived_dim_cache[str(fix)] = dim
+                    return dim
+            else:
+                return fix
+        elif isinstance(dim, _DerivedDim) and dim.root.__name__ in shape_fixes:  # type: ignore[attr-defined]
+            if dim.__name__ in derived_dim_cache:
+                return derived_dim_cache[dim.__name__]
+            else:  # evaluate new derived value based on root
+                _dim = dim.fn(shape_fixes[dim.root.__name__])  # type: ignore[attr-defined]
+                derived_dim_cache[dim.__name__] = _dim
+                return _dim
+        return dim  # unchanged dim
+
+    return _tree_map_with_path(apply_fixes, dynamic_shapes, dynamic_shapes)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/experimental/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c501a5b995358379b17911c391095448d8768738
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/experimental/__init__.py
@@ -0,0 +1,67 @@
+import copy
+import typing
+
+import torch
+from torch.export.exported_program import _decompose_exported_program
+
+
+def _copy_graph_module_and_signature(
+    ep: torch.fx.GraphModule,
+) -> typing.Tuple[
+    torch.fx.GraphModule, torch.export.graph_signature.ExportGraphSignature
+]:
+    # copy.deepcopy lets the objects override __deepcopy__ methods with graph_copy() and node_copy(),
+    # and this can break placeholder names in some particular cases.
+    # For example, node copying will avoid Python keywords like 'input', suffixing and renaming to 'input_1'.
+    # So we manually overwrite placeholder names by reading the old graph.
+    gm = copy.deepcopy(ep.graph_module)
+    new_graph_signature = copy.deepcopy(ep.graph_signature)
+
+    # iterate over old/new graph modules
+    for old_gm, new_gm in zip(ep.graph_module.modules(), gm.modules()):
+        old_phs = [node for node in old_gm.graph.nodes if node.op == "placeholder"]
+        new_phs = [node for node in new_gm.graph.nodes if node.op == "placeholder"]
+        # iterate over placeholders
+        assert len(old_phs) == len(new_phs)
+        for old_node, new_node in zip(old_phs, new_phs):
+            new_node.name = old_node.name
+
+    return gm, new_graph_signature
+
+
+def _remove_detach_pass(
+    gm: torch.fx.GraphModule, sig: torch.export.graph_signature.ExportGraphSignature
+) -> None:
+    with gm._set_replace_hook(sig.get_replace_hook()):
+        for node in list(reversed(gm.graph.nodes)):
+            if node.op != "call_function":
+                continue
+            if (
+                node.target == torch.ops.aten.detach.default
+                and len(node.users) == 1
+                and next(iter(node.users)).target == torch.ops.aten.detach.default
+            ):
+                next(iter(node.users)).replace_all_uses_with(node)
+
+    gm.graph.eliminate_dead_code()
+    gm.recompile()
+
+
+def _export_forward_backward(
+    ep: torch.export.ExportedProgram, joint_loss_index: int = 0
+) -> torch.export.ExportedProgram:
+    """
+    WARNING: This API is highly unstable and will be subject to change in the future.
+    """
+    from torch._decomp import core_aten_decompositions
+
+    ep = _decompose_exported_program(
+        ep,
+        decomp_table=core_aten_decompositions(),
+        _preserve_ops=(),  # type: ignore[arg-type]
+        joint_loss_index=joint_loss_index,
+    )
+    gm, new_graph_signature = _copy_graph_module_and_signature(ep)
+    _remove_detach_pass(gm, new_graph_signature)
+
+    return ep._update(gm, new_graph_signature)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/experimental/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/experimental/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..94d85a31ad4a3e9c059546fe24e82773621ea793
Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/experimental/__pycache__/__init__.cpython-311.pyc differ
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/exported_program.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/exported_program.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9c71bdc91deca8a34516d12929f97bc89b9efa1
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/exported_program.py
@@ -0,0 +1,1202 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import contextlib
+import copy
+import dataclasses
+import functools
+import operator
+import types
+import warnings
+from collections import namedtuple
+from contextlib import contextmanager
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    final,
+    Iterator,
+    List,
+    Optional,
+    Tuple,
+    Type,
+    TYPE_CHECKING,
+    Union,
+)
+
+from torch._higher_order_ops.utils import autograd_not_implemented
+from torch._library.fake_class_registry import FakeScriptObject
+from torch.fx._utils import first_call_function_nn_module_stack
+from torch.fx.graph import _PyTreeCodeGen, _PyTreeInfo
+from torch.fx.immutable_collections import immutable_dict, immutable_list
+from torch.fx.passes.runtime_assert import insert_deferred_runtime_asserts
+
+
+if TYPE_CHECKING:
+    # Import the following modules during type checking to enable code intelligence features,
+    # such as auto-completion in tools like pylance, even when these modules are not explicitly
+    # imported in user code.
+
+    import sympy
+
+    from torch.utils._sympy.value_ranges import ValueRanges
+
+import torch
+import torch.utils._pytree as pytree
+from torch._export.utils import (
+    _collect_and_set_constant_attrs,
+    _collect_param_buffer_metadata,
+    _detect_fake_mode_from_gm,
+    _name_hoo_subgraph_placeholders,
+    _overwrite_signature_for_non_persistent_buffers,
+    _populate_param_buffer_metadata_to_new_gm,
+    _rename_without_collisions,
+)
+from torch._export.verifier import Verifier
+from torch._guards import detect_fake_mode
+from torch._subclasses.fake_tensor import unset_fake_temporarily
+from torch._subclasses.functional_tensor import FunctionalTensor
+from torch.export._tree_utils import is_equivalent, reorder_kwargs
+from torch.fx._compatibility import compatibility
+from torch.fx.passes.infra.pass_base import PassResult
+from torch.fx.passes.infra.pass_manager import PassManager
+
+from .graph_signature import (  # noqa: F401
+    ArgumentSpec,
+    ConstantArgument,
+    CustomObjArgument,
+    ExportGraphSignature,
+    InputKind,
+    InputSpec,
+    OutputKind,
+    OutputSpec,
+    SymIntArgument,
+    TensorArgument,
+    TokenArgument,
+)
+
+
+__all__ = [
+    "ExportedProgram",
+    "ModuleCallEntry",
+    "ModuleCallSignature",
+]
+
+
+PassType = Callable[[torch.fx.GraphModule], Optional[PassResult]]
+
+
+@dataclasses.dataclass
+class ModuleCallSignature:
+    inputs: List[ArgumentSpec]
+    outputs: List[ArgumentSpec]
+    in_spec: pytree.TreeSpec
+    out_spec: pytree.TreeSpec
+
+    def replace_all_uses_with(self, original_node, new_node):
+        for i in self.inputs:
+            if i.name == original_node.name:
+                i.name = new_node.name
+        for o in self.outputs:
+            if o.name == original_node.name:
+                o.name = new_node.name
+
+
+@dataclasses.dataclass
+class ModuleCallEntry:
+    fqn: str
+    signature: Optional[ModuleCallSignature] = None
+
+
+def _disable_prexisiting_fake_mode(fn):
+    @functools.wraps(fn)
+    def wrapper(*args, **kwargs):
+        with unset_fake_temporarily():
+            return fn(*args, **kwargs)
+
+    return wrapper
+
+
+def _fx_collection_equivalence_fn(
+    spec1_type: Optional[type],
+    spec1_context: pytree.Context,
+    spec2_type: Optional[type],
+    spec2_context: pytree.Context,
+) -> bool:
+    """Treat containers and their immutable variants as the same type. Otherwise
+    compare as normal.
+    """
+    if spec1_type is None or spec2_type is None:
+        return spec1_type is spec2_type and spec1_context == spec2_context
+
+    if issubclass(spec1_type, (dict, immutable_dict)) and issubclass(
+        spec2_type, (dict, immutable_dict)
+    ):
+        return spec1_context == spec2_context
+
+    if issubclass(spec1_type, (list, immutable_list)) and issubclass(
+        spec2_type, (list, immutable_list)
+    ):
+        return spec1_context == spec2_context
+
+    return spec1_type is spec2_type and spec1_context == spec2_context
+
+
+def _register_cia_to_meta(*args, **kwargs):
+    kernel = kwargs["kernel"]
+    del kwargs["kernel"]
+
+    assert torch._C._dispatch_has_kernel_for_dispatch_key(
+        kernel.name(), torch._C.DispatchKey.CompositeImplicitAutograd
+    )
+
+    return kernel._op_dk(
+        torch._C.DispatchKey.CompositeImplicitAutograd, *args, **kwargs
+    )
+
+
+# This list is compiled from DispatchKey.cpp.
+# The idea is that we use these keys to override
+# CIA decomp in export
+_AUTOGRAD_ALIAS_BACKEND_KEYS_TO_OVERRIDE = [
+    torch._C.DispatchKey.AutogradCPU,
+    torch._C.DispatchKey.AutogradCUDA,
+    torch._C.DispatchKey.AutogradMeta,
+    torch._C.DispatchKey.AutogradXLA,
+    torch._C.DispatchKey.AutogradLazy,
+    torch._C.DispatchKey.AutogradIPU,
+    torch._C.DispatchKey.AutogradXPU,
+    torch._C.DispatchKey.AutogradMPS,
+    torch._C.DispatchKey.AutogradHPU,
+    torch._C.DispatchKey.AutogradPrivateUse1,
+    torch._C.DispatchKey.AutogradPrivateUse2,
+    torch._C.DispatchKey.AutogradPrivateUse3,
+]
+
+
+@contextmanager
+def _override_composite_implicit_decomp(ops_to_preserve, decomp_table, safe=True):
+    # This function overrides CompositeImplicitAutograd decomp for
+    # functional composite ops that user specified. Ideally we want to not-decompose
+    # ALL composite ops but today's C++ functinalization relies on
+    # the fact that it is working with the opset after decomp is run.
+    # Hence we can only do it for functional ops. One caveat is that
+    # there are some composite ops that lie about their schema (claimed to be
+    # functional but not really aka dropout), for these cases, we just decompose.
+
+    # When safe=False, we will assume that ops_to_preserve can be mutating/aliasing
+    # and their usual decompositions need to be shadowed rather than overridden.
+    # Thus we will avoid asserting that they are valid to preserve, and will not
+    # replace their CompositeImplicitAutograd kernels with NotImplemented.
+    # The only current users of this mode are variants of aten::to that we will
+    # replace with aten::_to_copy in FunctionalTensorMode.__torch_dispatch__.
+
+    saved_tables = {}
+    patched_ops = set()
+    removed_decomps = {}
+    for op_overload in ops_to_preserve:
+        # Our strategy for deciding if we can preserve CIA is following:
+        # 1. The op should be known statically that it is functional
+        # 2. If it is maybe aliasing, we decompose because we must know if an op
+        #    is mutating or aliasing.
+        # TODO (tmanlaibaatar) make this utility function and share it with functional_tensor
+        # decomp part. (https://github.com/pytorch/pytorch/issues/129431)
+        def assert_valid_to_preserve(op_overload):
+            if op_overload in FunctionalTensor.maybe_aliasing_or_mutating_ops:
+                raise RuntimeError(
+                    f"We can't detect {op_overload} as a functional op statically, so we can't preserve it"
+                )
+            if op_overload in FunctionalTensor.metadata_fns:
+                raise RuntimeError(
+                    f"{op_overload} is a metadata query function, "
+                    "it will be preserved implicitly in our tracing system. "
+                    "Please file an issue on github if you see otherwise"
+                )
+
+            alias_info = len(
+                [i for i in op_overload._schema.arguments if i.alias_info is not None]
+            )
+
+            is_mutating_or_aliasing = alias_info != 0 or op_overload._schema.is_mutable
+
+            if is_mutating_or_aliasing:
+                raise RuntimeError(
+                    f"{op_overload} is a mutating/aliasing op, we can't preserve it as is"
+                )
+
+            if not torch._C._dispatch_has_kernel(op_overload.name()):
+                raise RuntimeError(
+                    f"{op_overload} is a TorchScript op, we can't preserve it as is"
+                )
+
+            return True
+
+        if safe:
+            # If we didn't error, it means we can go ahead
+            assert_valid_to_preserve(op_overload)
+
+        saved_tables[op_overload] = op_overload.py_kernels.copy()
+        patched_ops.add(op_overload)
+
+        for override_dispatch_key in _AUTOGRAD_ALIAS_BACKEND_KEYS_TO_OVERRIDE:
+            if override_dispatch_key not in op_overload.py_kernels:
+                # TODO (tmanlaibaatar)https://github.com/pytorch/pytorch/issues/129430
+                op_overload.py_impl(override_dispatch_key)(
+                    autograd_not_implemented(op_overload, deferred_error=True)
+                )
+        if torch._C.DispatchKey.CompositeImplicitAutograd in op_overload.py_kernels:
+            del op_overload.py_kernels[torch._C.DispatchKey.CompositeImplicitAutograd]
+
+        if safe:
+
+            def _(*args, **kwargs):
+                return NotImplemented
+
+            op_overload.py_impl(torch._C.DispatchKey.CompositeImplicitAutograd)(_)
+
+        # For fake tensor prop, we do want to register meta kernel directly
+        if torch._C.DispatchKey.Meta not in op_overload.py_kernels:
+            op_overload.py_impl(torch._C.DispatchKey.Meta)(
+                functools.partial(_register_cia_to_meta, kernel=op_overload)
+            )
+
+        if op_overload in decomp_table:
+            removed_decomps[op_overload] = decomp_table[op_overload]
+            del decomp_table[op_overload]
+
+    try:
+        yield
+    finally:
+        for op in patched_ops:
+            op.py_kernels.clear()
+            op.py_kernels.update(saved_tables[op])
+            op._dispatch_cache.clear()
+
+        for op, decomp in removed_decomps.items():
+            decomp_table[op] = decomp
+
+
+@contextmanager
+def _override_decomp_aten_to_variants():
+    # Preserve variants of aten::to understanding that they are mutating/aliasing
+    # and their CompositeImplicitAutograd kernels will not become NotImplemented.
+    # We will later replace them with aten._to_copy when functionalizing.
+    with _override_composite_implicit_decomp(
+        (torch.ops.aten.to.dtype_layout, torch.ops.aten.to.dtype),
+        {},
+        safe=False,
+    ):
+        yield
+
+
+def _decompose_and_get_gm_with_new_signature_constants(
+    ep,
+    *,
+    decomp_table: Dict[torch._ops.OperatorBase, Callable],
+    _preserve_ops: Tuple[torch._ops.OpOverload],
+    joint_loss_index: Optional[int],
+):
+    from torch._functorch.aot_autograd import aot_export_module
+    from torch._subclasses.fake_tensor import FakeTensorMode
+    from torch.export._trace import (
+        _export_to_aten_ir,
+        _fakify_params_buffers,
+        _ignore_backend_decomps,
+        _verify_nn_module_stack,
+        _verify_placeholder_names,
+        _verify_stack_trace,
+    )
+    from torch.fx.experimental.symbolic_shapes import ShapeEnv
+
+    # TODO Merge this path with inference IR decomp, but it will require some additional work
+    # so I will leave it for now. T200307782
+    if ep.verifier.dialect == "TRAINING":
+        mod = ep.module()
+
+        fake_args = []
+        for node in mod.graph.nodes:
+            if node.op == "placeholder":
+                fake_args.append(node.meta["val"])
+
+        fake_args_unwrapped = pytree.tree_unflatten(fake_args, mod._in_spec)
+        fake_mode = _detect_fake_mode_from_gm(mod)
+        if fake_mode is None:
+            fake_mode = FakeTensorMode(shape_env=ShapeEnv(), export=True)
+
+        # Fix the graph output signature to be tuple if scalar
+        out_spec = mod._out_spec
+
+        orig_arg_names = mod.graph._codegen.pytree_info.orig_args  # type: ignore[attr-defined]
+
+        # aot_export expect the return type to always be a tuple.
+        if out_spec.type not in (list, tuple):
+            out_spec = pytree.TreeSpec(tuple, None, [out_spec])
+
+        mod.graph._codegen = _PyTreeCodeGen(
+            _PyTreeInfo(
+                orig_arg_names,
+                mod._in_spec,
+                out_spec,
+            )
+        )
+
+        mod.recompile()
+
+        # the exported module will store constants & non-persistent buffers such that
+        # retracing treats them as persistent buffers, so we inform the constants lifting pass
+        # and overwrite the new graph signature using the previous program.
+        constant_attrs = _collect_and_set_constant_attrs(
+            ep.graph_signature, ep.constants, mod
+        )
+
+        # get params & buffers after excluding constants
+        fake_params_buffers = _fakify_params_buffers(fake_mode, mod)
+
+        params_buffers_to_node_meta = _collect_param_buffer_metadata(mod)
+
+        with _ignore_backend_decomps(), (
+            fake_mode
+        ), _override_decomp_aten_to_variants(), _override_composite_implicit_decomp(
+            _preserve_ops,
+            decomp_table,
+        ):
+            aten_export_artifact = _export_to_aten_ir(
+                mod,
+                # this requires empty kwargs, but not in pytree.flattened format
+                (
+                    *fake_args_unwrapped[0],
+                    *fake_args_unwrapped[1].values(),
+                ),
+                {},
+                fake_params_buffers,
+                constant_attrs,
+                decomp_table=decomp_table,
+                _check_autograd_state=False,
+            )
+
+        gm = aten_export_artifact.gm
+        new_graph_signature = aten_export_artifact.sig
+
+        _populate_param_buffer_metadata_to_new_gm(
+            params_buffers_to_node_meta, gm, new_graph_signature
+        )
+
+        # overwrite signature for non-persistent buffers
+        new_graph_signature = _overwrite_signature_for_non_persistent_buffers(
+            ep.graph_signature, new_graph_signature
+        )
+
+        _verify_nn_module_stack(gm)
+        _verify_stack_trace(gm)
+        _verify_placeholder_names(gm, new_graph_signature)
+
+        return _remove_unneccessary_copy_op_pass(gm, new_graph_signature)
+
+    old_placeholders = [
+        node for node in ep.graph_module.graph.nodes if node.op == "placeholder"
+    ]
+    fake_args = [node.meta["val"] for node in old_placeholders]
+
+    buffers_to_remove = [name for name, _ in ep.graph_module.named_buffers()]
+    for name in buffers_to_remove:
+        delattr(ep.graph_module, name)
+
+    # TODO(zhxhchen17) Return the new graph_signature directly.
+    fake_mode = detect_fake_mode(fake_args)
+    fake_mode = contextlib.nullcontext() if fake_mode is None else fake_mode
+    with _ignore_backend_decomps(), fake_mode, _override_composite_implicit_decomp(
+        _preserve_ops,
+        decomp_table,
+    ):
+        gm, graph_signature = aot_export_module(
+            ep.graph_module,
+            fake_args,
+            decompositions=decomp_table,
+            trace_joint=True if joint_loss_index is not None else False,
+            output_loss_index=joint_loss_index
+            if joint_loss_index is not None
+            else None,
+        )
+
+    # Update the signatures with the new placeholder names in case they
+    # changed when calling aot_export
+    def update_arg(old_arg, new_ph):
+        if isinstance(old_arg, ConstantArgument):
+            return old_arg
+        elif isinstance(old_arg, TensorArgument):
+            return TensorArgument(name=new_ph.name)
+        elif isinstance(old_arg, SymIntArgument):
+            return SymIntArgument(name=new_ph.name)
+        raise RuntimeError(f"Type of old_arg not supported: {type(old_arg)}")
+
+    new_placeholders = [node for node in gm.graph.nodes if node.op == "placeholder"]
+    new_outputs = list(gm.graph.nodes)[-1].args[0]
+
+    # rename the placeholders
+    assert len(new_placeholders) == len(old_placeholders)
+    for old_ph, new_ph in zip(old_placeholders, new_placeholders):
+        new_ph.name = new_ph.target = old_ph.name
+
+    # handle name collisions with newly decomposed graph nodes
+    name_map = {ph.name: ph.name for ph in new_placeholders}
+    for node in gm.graph.nodes:
+        if node.op == "placeholder":
+            continue
+        node.name = _rename_without_collisions(name_map, node.name, node.name)
+
+    # propagate names to higher order op subgraphs
+    _name_hoo_subgraph_placeholders(gm)
+
+    # Run this pass before creating input/output specs, since size-related CSE/DCE might affect output signature.
+    # Overwrite output specs afterwards.
+    from torch._export.passes._node_metadata_hook import (
+        _node_metadata_hook,
+        _set_node_metadata_hook,
+    )
+    from torch._functorch._aot_autograd.input_output_analysis import _graph_output_names
+
+    if not torch._dynamo.config.do_not_emit_runtime_asserts:
+        stack_trace = (
+            'File "torch/fx/passes/runtime_assert.py", line 24, '
+            "in insert_deferred_runtime_asserts"
+        )
+        shape_env = _get_shape_env(gm)
+        if shape_env is not None:
+            with _set_node_metadata_hook(
+                gm, functools.partial(_node_metadata_hook, stack_trace=stack_trace)
+            ):
+                insert_deferred_runtime_asserts(
+                    gm,
+                    shape_env,
+                    f"exported program: {first_call_function_nn_module_stack(gm.graph)}",
+                    export=True,
+                )
+
+    # update output specs
+    gm.recompile()
+    for i, name in enumerate(_graph_output_names(gm)):
+        if isinstance(new_outputs[i], torch.fx.Node):
+            new_outputs[i].name = name
+
+    # To match the output target with correct input for input mutations
+    # need to find the old to new placeholder map
+    old_new_placeholder_map = {
+        spec.arg.name: new_placeholders[i].name
+        for i, spec in enumerate(ep.graph_signature.input_specs)
+        if not isinstance(spec.arg, ConstantArgument)
+    }
+
+    input_specs = [
+        InputSpec(
+            spec.kind,
+            update_arg(spec.arg, new_placeholders[i]),
+            spec.target,
+            spec.persistent,
+        )
+        for i, spec in enumerate(ep.graph_signature.input_specs)
+    ]
+    output_specs = [
+        OutputSpec(
+            spec.kind,
+            update_arg(spec.arg, new_outputs[i]),
+            old_new_placeholder_map.get(spec.target, spec.target),
+        )
+        for i, spec in enumerate(ep.graph_signature.output_specs)
+    ]
+
+    if joint_loss_index is not None:
+        assert graph_signature.backward_signature is not None
+        gradients = graph_signature.backward_signature.gradients_to_user_inputs
+        assert len(graph_signature.user_inputs) == len(ep.graph_signature.input_specs)
+        specs = {
+            graph_signature.user_inputs[i]: spec
+            for i, spec in enumerate(ep.graph_signature.input_specs)
+            if isinstance(spec.arg, TensorArgument)
+        }
+        for i, node in enumerate(new_outputs[len(output_specs) :]):
+            source = gradients[node.name]
+            spec = specs[source]  # type: ignore[index]
+            if spec.kind == InputKind.PARAMETER:
+                kind = OutputKind.GRADIENT_TO_PARAMETER
+                target = spec.target
+            elif spec.kind == InputKind.USER_INPUT:
+                kind = OutputKind.GRADIENT_TO_USER_INPUT
+                target = source
+            else:
+                raise AssertionError(f"Unknown input kind: {spec.kind}")
+            output_specs.append(
+                OutputSpec(
+                    kind,
+                    TensorArgument(name=node.name),
+                    target,
+                )
+            )
+
+    assert len(new_placeholders) == len(old_placeholders)
+
+    new_graph_signature = ExportGraphSignature(
+        input_specs=input_specs, output_specs=output_specs
+    )
+    # NOTE: aot_export adds symint metadata for placeholders with int
+    # values; since these become specialized, we replace such metadata with
+    # the original values.
+    # Also, set the param/buffer metadata back to the placeholders.
+    for old_node, new_node in zip(old_placeholders, new_placeholders):
+        if not isinstance(old_node.meta["val"], torch.Tensor):
+            new_node.meta["val"] = old_node.meta["val"]
+
+        if (
+            new_node.target in new_graph_signature.inputs_to_parameters
+            or new_node.target in new_graph_signature.inputs_to_buffers
+        ):
+            for k, v in old_node.meta.items():
+                new_node.meta[k] = v
+    return gm, new_graph_signature
+
+
+def _remove_unneccessary_copy_op_pass(
+    gm: torch.fx.GraphModule, new_graph_signature: ExportGraphSignature
+) -> Tuple[torch.fx.GraphModule, ExportGraphSignature]:
+    """
+    Removes redundant copy_ node that was introduced due to mutated buffer.
+    """
+    with gm._set_replace_hook(new_graph_signature.get_replace_hook()):
+        for node in gm.graph.nodes:
+            if node.op == "output":
+                args, _ = pytree.tree_flatten(node.args)
+                for out in args:
+                    if (
+                        isinstance(out, torch.fx.Node)
+                        and out.name in new_graph_signature.buffers_to_mutate
+                    ):
+                        if (
+                            out.op == "call_function"
+                            and out.target == torch.ops.aten.copy.default
+                        ):
+                            out.replace_all_uses_with(out.args[1])  # type: ignore[arg-type]
+                            gm.graph.erase_node(out)
+        gm.recompile()
+    return gm, new_graph_signature
+
+
+def _common_getitem_elimination_pass(
+    gm: torch.fx.GraphModule, graph_signature, module_call_graph
+):
+    with gm._set_replace_hook(graph_signature.get_replace_hook()):
+        for module in gm.modules():
+            if not isinstance(module, torch.fx.GraphModule):
+                continue
+
+            node_id: Dict[torch.fx.Node, str] = {}
+            getitems: Dict[str, torch.fx.Node] = {}
+            for node in list(module.graph.nodes):
+                if node.op == "call_function" and node.target == operator.getitem:
+                    source, idx = node.args
+                    new_id = f"{node_id[source]}.{idx}"
+                    if new_id in getitems:
+                        node.replace_all_uses_with(getitems[new_id])
+                        for entry in module_call_graph:
+                            if entry.signature is not None:
+                                entry.signature.replace_all_uses_with(
+                                    node, getitems[new_id]
+                                )
+                        module.graph.erase_node(node)
+                    else:
+                        getitems[new_id] = node
+                        node_id[node] = new_id
+                else:
+                    node_id[node] = node.name
+
+
+def _decompose_exported_program(
+    ep,
+    *,
+    decomp_table: Dict[torch._ops.OperatorBase, Callable],
+    _preserve_ops: Tuple[torch._ops.OpOverload],
+    joint_loss_index: Optional[int],
+):
+    gm, new_graph_signature = _decompose_and_get_gm_with_new_signature_constants(
+        ep,
+        decomp_table=decomp_table,
+        _preserve_ops=_preserve_ops,
+        joint_loss_index=joint_loss_index,
+    )
+
+    # TODO unfortunately preserving graph-level metadata is not
+    # working well with aot_export. So we manually copy it.
+    # (The node-level meta is addressed above.)
+    gm.meta.update(ep.graph_module.meta)
+
+    new_range_constraints = _get_updated_range_constraints(
+        gm,
+        ep.range_constraints,
+    )
+
+    exported_program = ExportedProgram(
+        root=gm,
+        graph=gm.graph,
+        graph_signature=new_graph_signature,
+        state_dict=ep.state_dict,
+        range_constraints=new_range_constraints,
+        module_call_graph=copy.deepcopy(ep.module_call_graph),
+        example_inputs=ep.example_inputs,
+        constants=ep.constants,
+    )
+    return exported_program
+
+
+class ExportedProgram:
+    """
+    Package of a program from :func:`export`. It contains
+    an :class:`torch.fx.Graph` that represents Tensor computation, a state_dict containing
+    tensor values of all lifted parameters and buffers, and various metadata.
+
+    You can call an ExportedProgram like the original callable traced by
+    :func:`export` with the same calling convention.
+
+    To perform transformations on the graph, use ``.module`` property to access
+    an :class:`torch.fx.GraphModule`. You can then use
+    `FX transformation `_
+    to rewrite the graph. Afterwards, you can simply use :func:`export`
+    again to construct a correct ExportedProgram.
+    """
+
+    def __init__(
+        self,
+        root: Union[torch.nn.Module, Dict[str, Any]],
+        graph: torch.fx.Graph,
+        graph_signature: ExportGraphSignature,
+        state_dict: Dict[str, Union[torch.Tensor, torch.nn.Parameter]],
+        range_constraints: "Dict[sympy.Symbol, Any]",
+        module_call_graph: List[ModuleCallEntry],
+        example_inputs: Optional[Tuple[Tuple[Any, ...], Dict[str, Any]]] = None,
+        constants: Optional[
+            Dict[str, Union[torch.Tensor, FakeScriptObject, torch._C.ScriptObject]]
+        ] = None,
+        *,
+        verifiers: Optional[List[Type[Verifier]]] = None,
+    ):
+        # Remove codegen related things from the graph. It should just be a flat graph.
+        graph._codegen = torch.fx.graph.CodeGen()
+        self._graph_module = _create_graph_module_for_export(root, graph)
+        if isinstance(root, torch.fx.GraphModule):
+            self._graph_module.meta.update(root.meta)
+
+        _common_getitem_elimination_pass(
+            self._graph_module, graph_signature, module_call_graph
+        )
+        self._graph_signature: ExportGraphSignature = graph_signature
+        self._state_dict: Dict[str, Any] = state_dict
+        self._range_constraints: Dict[sympy.Symbol, ValueRanges] = range_constraints
+        assert module_call_graph is not None
+        self._module_call_graph: List[ModuleCallEntry] = module_call_graph
+        self._example_inputs = example_inputs
+
+        self._constants = constants or {}
+
+        verifiers = verifiers or [Verifier]
+        assert all(issubclass(v, Verifier) for v in verifiers)
+        self._verifiers = verifiers
+        # Validate should be always the last step of the constructor.
+        self.validate()
+
+    @property
+    @compatibility(is_backward_compatible=False)
+    def graph_module(self):
+        return self._graph_module
+
+    @property
+    @compatibility(is_backward_compatible=False)
+    def graph(self):
+        return self.graph_module.graph
+
+    @property
+    @compatibility(is_backward_compatible=False)
+    def graph_signature(self):
+        return self._graph_signature
+
+    @property
+    @compatibility(is_backward_compatible=False)
+    def state_dict(self):
+        return self._state_dict
+
+    @compatibility(is_backward_compatible=False)
+    def parameters(self) -> Iterator[torch.nn.Parameter]:
+        """
+        Returns an iterator over original module's parameters.
+        """
+        for _, param in self.named_parameters():
+            yield param
+
+    @compatibility(is_backward_compatible=False)
+    def named_parameters(self) -> Iterator[Tuple[str, torch.nn.Parameter]]:
+        """
+        Returns an iterator over original module parameters, yielding
+        both the name of the parameter as well as the parameter itself.
+        """
+        for param_name in self.graph_signature.parameters:
+            yield param_name, self.state_dict[param_name]
+
+    @compatibility(is_backward_compatible=False)
+    def buffers(self) -> Iterator[torch.Tensor]:
+        """
+        Returns an iterator over original module buffers.
+        """
+        for _, buf in self.named_buffers():
+            yield buf
+
+    @compatibility(is_backward_compatible=False)
+    def named_buffers(self) -> Iterator[Tuple[str, torch.Tensor]]:
+        """
+        Returns an iterator over original module buffers, yielding
+        both the name of the buffer as well as the buffer itself.
+        """
+        non_persistent_buffers = set(self.graph_signature.non_persistent_buffers)
+        for buffer_name in self.graph_signature.buffers:
+            if buffer_name in non_persistent_buffers:
+                yield buffer_name, self.constants[buffer_name]
+            else:
+                yield buffer_name, self.state_dict[buffer_name]
+
+    @property
+    @compatibility(is_backward_compatible=False)
+    def range_constraints(self):
+        return self._range_constraints
+
+    @property
+    @compatibility(is_backward_compatible=False)
+    def module_call_graph(self):
+        return self._module_call_graph
+
+    @property
+    @compatibility(is_backward_compatible=False)
+    def example_inputs(self):
+        return self._example_inputs
+
+    @property
+    @compatibility(is_backward_compatible=False)
+    def call_spec(self):
+        CallSpec = namedtuple("CallSpec", ["in_spec", "out_spec"])
+
+        if len(self.module_call_graph) == 0:
+            return CallSpec(in_spec=None, out_spec=None)
+        assert self.module_call_graph[0].fqn == ""
+        return CallSpec(
+            in_spec=self.module_call_graph[0].signature.in_spec,
+            out_spec=self.module_call_graph[0].signature.out_spec,
+        )
+
+    @property
+    @compatibility(is_backward_compatible=False)
+    def verifier(self) -> Any:
+        return self._verifiers[0]
+
+    @property
+    @compatibility(is_backward_compatible=False)
+    def dialect(self) -> str:
+        assert self._verifiers is not None
+        return self._verifiers[0].dialect
+
+    @property
+    @compatibility(is_backward_compatible=False)
+    def verifiers(self):
+        return self._verifiers
+
+    @property
+    @compatibility(is_backward_compatible=False)
+    def tensor_constants(self):
+        return self._constants
+
+    @property
+    @compatibility(is_backward_compatible=False)
+    def constants(self):
+        return self._constants
+
+    def _get_flat_args_with_check(self, args, kwargs):
+        """Flatten args, kwargs using pytree, then, check specs.
+
+        Args:
+            args: List[Any] original args passed to __call__
+            kwargs: Dict[str, Any] original kwargs passed to __call
+
+        Returns:
+            A tuple of (flat_args, received_spec)
+            flat_args is flattend args / kwargs
+            received_spec is the pytree spec produced while flattening the
+            tuple (args, kwargs)
+        """
+        in_spec = self.call_spec.in_spec
+        if in_spec is not None:
+            kwargs = reorder_kwargs(kwargs, in_spec)
+        flat_args_with_path, received_spec = pytree.tree_flatten_with_path(
+            (args, kwargs)
+        )  # type: ignore[possibly-undefined]
+        self._check_input_constraints(flat_args_with_path)
+        flat_args = tuple(x[1] for x in flat_args_with_path)
+        return flat_args, received_spec
+
+    def _graph_module_flat_inputs(self, args: Any, kwargs: Any) -> Any:
+        """Transform args, kwargs of __call__ to args for graph_module.
+
+        self.graph_module takes stuff from state dict as inputs.
+        The invariant is for ep: ExportedProgram is
+        ep(args, kwargs) ==
+          ep.postprocess(ep.graph_module(ep.graph_module_flat_inputs(args, kwargs)))
+        """
+
+        in_spec = self.call_spec.in_spec
+        flat_args, received_spec = self._get_flat_args_with_check(args, kwargs)
+        if in_spec is not None and not is_equivalent(
+            received_spec, in_spec, _fx_collection_equivalence_fn
+        ):
+            raise ValueError(
+                "Trying to flatten user inputs with exported input tree spec: \n"
+                f"{in_spec}\n"
+                "but actually got inputs with tree spec of: \n"
+                f"{received_spec}"
+            )
+
+        additional_inputs = []
+        for input_ in self.graph_signature.input_specs:
+            if input_.kind == InputKind.USER_INPUT:
+                continue
+            elif input_.kind in (
+                InputKind.PARAMETER,
+                InputKind.BUFFER,
+            ):
+                if input_.persistent is False:
+                    # This is a non-persistent buffer, grab it from our
+                    # constants instead of the state dict.
+                    additional_inputs.append(self.constants[input_.target])
+                else:
+                    additional_inputs.append(self.state_dict[input_.target])
+            elif input_.kind in (
+                InputKind.CONSTANT_TENSOR,
+                InputKind.CUSTOM_OBJ,
+            ):
+                additional_inputs.append(self.constants[input_.target])
+        additional_inputs = tuple(additional_inputs)
+
+        # NOTE: calling convention is first params, then buffers, then args as user supplied them.
+        # See: torch/_functorch/aot_autograd.py#L1034
+        return additional_inputs + flat_args
+
+    def __call__(self, *args: Any, **kwargs: Any) -> Any:
+        raise RuntimeError(
+            "Unable to call ExportedProgram directly. "
+            "You should use `exported_program.module()` instead."
+        )
+
+    def _postprocess_graph_module_outputs(self, res, orig_args, orig_kwargs):
+        """Process potential mutations to the input.
+
+        Because self.graph_module is functional, so mutations has to be written
+        back after execution of graph_module.
+        """
+        import torch._export.error as error
+
+        flat_args, _ = self._get_flat_args_with_check(orig_args, orig_kwargs)
+        if self.call_spec.out_spec is not None:
+            buffer_mutation = self.graph_signature.buffers_to_mutate
+            user_input_mutation = self.graph_signature.user_inputs_to_mutate
+            num_mutated = len(buffer_mutation) + len(user_input_mutation)
+            mutated_values = res[:num_mutated]
+
+            # Exclude dependency token from final result.
+            assertion_dep_token = self.graph_signature.assertion_dep_token
+            if assertion_dep_token is not None:
+                assertion_dep_token_index = next(iter(assertion_dep_token.keys()))
+                res = res[:assertion_dep_token_index]
+
+            res = res[num_mutated:]
+            try:
+                res = pytree.tree_unflatten(res, self.call_spec.out_spec)
+            except Exception:
+                _, received_spec = pytree.tree_flatten(res)
+                raise error.InternalError(  # noqa: B904
+                    "Trying to flatten user outputs with exported output tree spec: \n"
+                    f"{self.call_spec.out_spec}\n"
+                    "but actually got outputs with tree spec of: \n"
+                    f"{received_spec}"
+                )
+            finally:
+                user_inputs = [
+                    spec
+                    for spec in self.graph_signature.input_specs
+                    if spec.kind == InputKind.USER_INPUT
+                ]
+                for i, value in enumerate(mutated_values):
+                    output_spec = self.graph_signature.output_specs[i]
+                    if output_spec.kind == OutputKind.BUFFER_MUTATION:
+                        assert output_spec.target is not None
+                        self.state_dict[output_spec.target] = value
+                    elif output_spec.kind == OutputKind.USER_INPUT_MUTATION:
+                        assert output_spec.target is not None
+                        index = next(
+                            i
+                            for i, spec in enumerate(user_inputs)
+                            if spec.arg.name == output_spec.target
+                        )
+                        flat_args[index].copy_(value)
+                    else:
+                        raise AssertionError(f"Unexpected kind: {output_spec.kind}")
+        return res
+
+    def __str__(self) -> str:
+        graph_module = self.graph_module.print_readable(
+            print_output=False, colored=False
+        ).replace("\n", "\n    ")
+        string = (
+            "ExportedProgram:\n"
+            f"    {graph_module}\n"
+            f"Graph signature: {self.graph_signature}\n"
+            f"Range constraints: {self.range_constraints}\n"
+        )
+        return string
+
+    def module(self) -> torch.nn.Module:
+        """
+        Returns a self contained GraphModule with all the parameters/buffers inlined.
+        """
+        from ._unlift import _unlift_exported_program_lifted_states
+
+        module = _unlift_exported_program_lifted_states(self)
+
+        def _train(self, mode: bool = True):
+            raise NotImplementedError("Calling train() is not supported yet.")
+
+        def _eval(self, mode: bool = True):
+            raise NotImplementedError("Calling eval() is not supported yet.")
+
+        module.train = types.MethodType(_train, module)  # type: ignore[method-assign]
+        module.eval = types.MethodType(_eval, module)  # type: ignore[method-assign]
+        return module
+
+    def _num_lifted_params_buffers(self):
+        return next(
+            (
+                i
+                for i, s in enumerate(self._graph_signature.input_specs)
+                if s.kind == InputKind.USER_INPUT
+            ),
+            len(self._graph_signature.input_specs),
+        )
+
+    @_disable_prexisiting_fake_mode
+    def run_decompositions(
+        self,
+        decomp_table: Optional[Dict[torch._ops.OperatorBase, Callable]] = None,
+        _preserve_ops: Tuple[torch._ops.OpOverload, ...] = (),
+    ) -> "ExportedProgram":
+        """
+        Run a set of decompositions on the exported program and returns a new
+        exported program. By default we will run the Core ATen decompositions to
+        get operators in the
+        `Core ATen Operator Set `_.
+
+        For now, we do not decompose joint graphs.
+        """
+        from torch._decomp import core_aten_decompositions
+
+        if decomp_table is None:
+            decomp_table = core_aten_decompositions()
+
+        return _decompose_exported_program(
+            self,
+            decomp_table=decomp_table,
+            _preserve_ops=_preserve_ops,  # type: ignore[arg-type]
+            joint_loss_index=None,
+        )
+
+    def _transform_do_not_use(self, *passes: PassType) -> "ExportedProgram":
+        pm = PassManager(list(passes))
+        # Since we abstractly run the passes, we need to disable backend decomp here
+        # again.
+        from torch.export._trace import _ignore_backend_decomps
+
+        with _ignore_backend_decomps():
+            res = pm(self.graph_module)
+        transformed_gm = res.graph_module if res is not None else self.graph_module
+        assert transformed_gm is not None
+
+        if transformed_gm is self.graph_module and not res.modified:
+            return self
+
+        # TODO(zhxchen17) Remove this.
+        def _get_updated_graph_signature(
+            old_signature: ExportGraphSignature,
+            new_gm: torch.fx.GraphModule,
+        ) -> ExportGraphSignature:
+            """
+            Update the graph signature's user_input/user_outputs.
+            """
+            new_input_specs = []
+            for i, node in enumerate(new_gm.graph.nodes):
+                if node.op != "placeholder":
+                    break
+
+                assert i < len(
+                    old_signature.input_specs
+                ), "Number of inputs changed after transformation"
+                old_input_spec = old_signature.input_specs[i]
+                arg = (
+                    old_input_spec.arg
+                    if isinstance(
+                        old_input_spec.arg, (ConstantArgument, CustomObjArgument)
+                    )
+                    else type(old_input_spec.arg)(node.name)
+                )
+                new_input_specs.append(
+                    InputSpec(
+                        old_input_spec.kind,
+                        arg,
+                        old_input_spec.target,
+                        old_input_spec.persistent,
+                    )
+                )
+
+            output_node = list(new_gm.graph.nodes)[-1]
+            assert output_node.op == "output"
+
+            new_output_specs = []
+            for i, node in enumerate(output_node.args[0]):
+                assert i < len(
+                    old_signature.output_specs
+                ), "Number of outputs changed after transformation"
+                old_output_spec = old_signature.output_specs[i]
+                arg = (
+                    old_output_spec.arg
+                    if isinstance(
+                        old_output_spec.arg, (ConstantArgument, CustomObjArgument)
+                    )
+                    else type(old_output_spec.arg)(node.name)
+                )
+                new_output_specs.append(
+                    OutputSpec(old_output_spec.kind, arg, old_output_spec.target)
+                )
+
+            new_signature = ExportGraphSignature(
+                input_specs=new_input_specs, output_specs=new_output_specs
+            )
+            return new_signature
+
+        transformed_ep = ExportedProgram(
+            root=transformed_gm,
+            graph=transformed_gm.graph,
+            graph_signature=_get_updated_graph_signature(
+                self.graph_signature, transformed_gm
+            ),
+            state_dict=self.state_dict,
+            range_constraints=_get_updated_range_constraints(
+                transformed_gm,
+                self.range_constraints,
+            ),
+            module_call_graph=copy.deepcopy(self._module_call_graph),
+            example_inputs=self.example_inputs,
+            constants=self.constants,
+            verifiers=self.verifiers,
+        )
+        transformed_ep.graph_module.meta.update(self.graph_module.meta)
+        transformed_ep.graph_module.meta.update(res.graph_module.meta)
+        return transformed_ep
+
+    def _check_input_constraints(self, flat_args_with_path):
+        from torch._export.utils import _check_input_constraints_for_graph
+
+        placeholders = [p for p in self.graph.nodes if p.op == "placeholder"]
+        input_placeholders = [
+            p
+            for p, s in zip(placeholders, self.graph_signature.input_specs)
+            if s.kind == InputKind.USER_INPUT
+        ]
+        _check_input_constraints_for_graph(
+            input_placeholders, flat_args_with_path, self.range_constraints
+        )
+
+    @compatibility(is_backward_compatible=False)
+    def validate(self):
+        self._validate()
+
+    # TODO: remove this
+    @final
+    def _validate(self):
+        assert (
+            len(self.verifiers) > 0
+        ), "ExportedProgram must have at least one verifier."
+        for v in self.verifiers:
+            v().check(self)
+
+    # TODO(zhxchen17) Formalize this.
+    def _update(
+        self, graph_module, graph_signature, *, state_dict=None, verifiers=None
+    ) -> "ExportedProgram":
+        return ExportedProgram(
+            root=graph_module,
+            graph=graph_module.graph,
+            graph_signature=graph_signature,
+            state_dict=state_dict if state_dict is not None else self.state_dict,
+            range_constraints=copy.deepcopy(self.range_constraints),
+            module_call_graph=copy.deepcopy(self._module_call_graph),
+            example_inputs=self.example_inputs,
+            constants=self.constants,
+            verifiers=verifiers if verifiers is not None else self.verifiers,
+        )
+
+
+def _get_shape_env(gm):
+    vals = [
+        node.meta["val"]
+        for node in gm.graph.nodes
+        if node.meta.get("val", None) is not None
+    ]
+    from torch._guards import detect_fake_mode
+
+    fake_mode = detect_fake_mode(vals)
+    if fake_mode is not None:
+        return fake_mode.shape_env
+    for v in vals:
+        if isinstance(v, torch.SymInt):
+            return v.node.shape_env
+
+
+def _get_updated_range_constraints(
+    gm: torch.fx.GraphModule,
+    old_range_constraints: "Optional[Dict[sympy.Symbol, Any]]" = None,
+) -> "Dict[sympy.Symbol, Any]":
+    assert old_range_constraints is not None
+
+    shape_env = _get_shape_env(gm)
+    if shape_env is None:
+        return {}
+
+    range_constraints = copy.copy(old_range_constraints)
+    range_constraints = {
+        k: v for k, v in range_constraints.items() if k not in shape_env.replacements
+    }
+    # Only when we have an unbacked symint, and it's used as constructor inputs,
+    # runtime_var_to_range will make a difference compated to var_to_range.
+    # e.g. [2, oo) -> [0, oo)
+    for k, v in shape_env.var_to_range.items():
+        if k not in shape_env.replacements and k not in range_constraints:
+            range_constraints[k] = v
+    return range_constraints
+
+
+def _create_graph_module_for_export(root, graph):
+    try:
+        gm = torch.fx.GraphModule(root, graph)
+    except SyntaxError:
+        # If custom objects stored in memory are being used in the graph,
+        # the generated python code will result in a syntax error on the custom
+        # object, since it is unable to parse the in-memory object. However
+        # we can still run the graph eagerly through torch.fx.Interpreter,
+        # so we will bypass this error.
+        warnings.warn(
+            "Unable to execute the generated python source code from "
+            "the graph. The graph module will no longer be directly callable, "
+            "but you can still run the ExportedProgram, and if needed, you can "
+            "run the graph module eagerly using torch.fx.Interpreter."
+        )
+        gm = torch.fx.GraphModule(root, torch.fx.Graph())
+        gm._graph = graph
+
+    return gm
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/graph_signature.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/graph_signature.py
new file mode 100644
index 0000000000000000000000000000000000000000..4730cf6febcddfdfbfa0407ec0cd8895507d36a0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/graph_signature.py
@@ -0,0 +1,593 @@
+# mypy: allow-untyped-defs
+import dataclasses
+from enum import auto, Enum
+from typing import Collection, Dict, List, Mapping, Optional, Set, TYPE_CHECKING, Union
+
+from torch._library.fake_class_registry import FakeScriptObject
+
+
+if TYPE_CHECKING:
+    import torch
+    from torch._functorch._aot_autograd.schemas import GraphSignature
+
+__all__ = [
+    "ConstantArgument",
+    "CustomObjArgument",
+    "ExportBackwardSignature",
+    "ExportGraphSignature",
+    "InputKind",
+    "InputSpec",
+    "OutputKind",
+    "OutputSpec",
+    "SymIntArgument",
+    "TensorArgument",
+]
+
+
+@dataclasses.dataclass
+class TensorArgument:
+    name: str
+
+
+@dataclasses.dataclass
+class TokenArgument:
+    name: str
+
+
+@dataclasses.dataclass
+class SymIntArgument:
+    name: str
+
+
+@dataclasses.dataclass
+class CustomObjArgument:
+    name: str
+    class_fqn: str
+    fake_val: Optional[FakeScriptObject] = None
+
+
+@dataclasses.dataclass
+class ConstantArgument:
+    name: str
+    value: Union[int, float, bool, str, None]
+
+
+ArgumentSpec = Union[
+    TensorArgument,
+    SymIntArgument,
+    ConstantArgument,
+    CustomObjArgument,
+    TokenArgument,
+]
+
+
+class InputKind(Enum):
+    USER_INPUT = auto()
+    PARAMETER = auto()
+    BUFFER = auto()
+    CONSTANT_TENSOR = auto()
+    CUSTOM_OBJ = auto()
+    TOKEN = auto()
+
+
+@dataclasses.dataclass
+class InputSpec:
+    kind: InputKind
+    arg: ArgumentSpec
+    target: Optional[str]
+    persistent: Optional[bool] = None
+
+    def __post_init__(self):
+        if self.kind == InputKind.BUFFER:
+            assert (
+                self.persistent is not None
+            ), "Failed to specify persistent flag on BUFFER."
+        assert isinstance(
+            self.arg,
+            (
+                TensorArgument,
+                SymIntArgument,
+                ConstantArgument,
+                CustomObjArgument,
+                TokenArgument,
+            ),
+        ), f"got {type(self.arg)}"
+
+
+class OutputKind(Enum):
+    USER_OUTPUT = auto()
+    LOSS_OUTPUT = auto()
+    BUFFER_MUTATION = auto()
+    GRADIENT_TO_PARAMETER = auto()
+    GRADIENT_TO_USER_INPUT = auto()
+    USER_INPUT_MUTATION = auto()
+    TOKEN = auto()
+
+
+@dataclasses.dataclass
+class OutputSpec:
+    kind: OutputKind
+    arg: ArgumentSpec
+    target: Optional[str]
+
+    def __post_init__(self):
+        assert isinstance(
+            self.arg,
+            (
+                TensorArgument,
+                SymIntArgument,
+                ConstantArgument,
+                TokenArgument,
+                CustomObjArgument,
+            ),
+        ), self.arg
+
+
+@dataclasses.dataclass
+class ExportBackwardSignature:
+    gradients_to_parameters: Dict[str, str]
+    gradients_to_user_inputs: Dict[str, str]
+    loss_output: str
+
+
+@dataclasses.dataclass
+class ExportGraphSignature:
+    """
+    :class:`ExportGraphSignature` models the input/output signature of Export Graph,
+    which is a fx.Graph with stronger invariants gurantees.
+
+    Export Graph is functional and does not access "states" like parameters
+    or buffers within the graph via ``getattr`` nodes. Instead, :func:`export`
+    gurantees that parameters, buffers, and constant tensors are lifted out of
+    the graph as inputs.  Similarly, any mutations to buffers are not included
+    in the graph either, instead the updated values of mutated buffers are
+    modeled as additional outputs of Export Graph.
+
+    The ordering of all inputs and outputs are::
+
+        Inputs = [*parameters_buffers_constant_tensors, *flattened_user_inputs]
+        Outputs = [*mutated_inputs, *flattened_user_outputs]
+
+    e.g. If following module is exported::
+
+        class CustomModule(nn.Module):
+            def __init__(self) -> None:
+                super(CustomModule, self).__init__()
+
+                # Define a parameter
+                self.my_parameter = nn.Parameter(torch.tensor(2.0))
+
+                # Define two buffers
+                self.register_buffer('my_buffer1', torch.tensor(3.0))
+                self.register_buffer('my_buffer2', torch.tensor(4.0))
+
+            def forward(self, x1, x2):
+                # Use the parameter, buffers, and both inputs in the forward method
+                output = (x1 + self.my_parameter) * self.my_buffer1 + x2 * self.my_buffer2
+
+                # Mutate one of the buffers (e.g., increment it by 1)
+                self.my_buffer2.add_(1.0) # In-place addition
+
+                return output
+
+    Resulting Graph would be::
+
+        graph():
+            %arg0_1 := placeholder[target=arg0_1]
+            %arg1_1 := placeholder[target=arg1_1]
+            %arg2_1 := placeholder[target=arg2_1]
+            %arg3_1 := placeholder[target=arg3_1]
+            %arg4_1 := placeholder[target=arg4_1]
+            %add_tensor := call_function[target=torch.ops.aten.add.Tensor](args = (%arg3_1, %arg0_1), kwargs = {})
+            %mul_tensor := call_function[target=torch.ops.aten.mul.Tensor](args = (%add_tensor, %arg1_1), kwargs = {})
+            %mul_tensor_1 := call_function[target=torch.ops.aten.mul.Tensor](args = (%arg4_1, %arg2_1), kwargs = {})
+            %add_tensor_1 := call_function[target=torch.ops.aten.add.Tensor](args = (%mul_tensor, %mul_tensor_1), kwargs = {})
+            %add_tensor_2 := call_function[target=torch.ops.aten.add.Tensor](args = (%arg2_1, 1.0), kwargs = {})
+            return (add_tensor_2, add_tensor_1)
+
+    Resulting ExportGraphSignature would be::
+
+        ExportGraphSignature(
+            input_specs=[
+                InputSpec(kind=, arg=TensorArgument(name='arg0_1'), target='my_parameter'),
+                InputSpec(kind=, arg=TensorArgument(name='arg1_1'), target='my_buffer1'),
+                InputSpec(kind=, arg=TensorArgument(name='arg2_1'), target='my_buffer2'),
+                InputSpec(kind=, arg=TensorArgument(name='arg3_1'), target=None),
+                InputSpec(kind=, arg=TensorArgument(name='arg4_1'), target=None)
+            ],
+            output_specs=[
+                OutputSpec(kind=, arg=TensorArgument(name='add_2'), target='my_buffer2'),
+                OutputSpec(kind=, arg=TensorArgument(name='add_1'), target=None)
+            ]
+        )
+    """
+
+    input_specs: List[InputSpec]
+    output_specs: List[OutputSpec]
+
+    # A list of parameters uniquely identified by mangled fully qualified name
+    @property
+    def parameters(self) -> Collection[str]:
+        return tuple(
+            s.target
+            for s in self.input_specs
+            if s.kind == InputKind.PARAMETER
+            if isinstance(s.target, str)
+        )
+
+    # A list of buffers uniquely identified by mangled fully qualified name
+    @property
+    def buffers(self) -> Collection[str]:
+        return tuple(
+            s.target
+            for s in self.input_specs
+            if s.kind == InputKind.BUFFER
+            if isinstance(s.target, str)
+        )
+
+    @property
+    def non_persistent_buffers(self) -> Collection[str]:
+        return tuple(
+            s.target
+            for s in self.input_specs
+            if s.kind == InputKind.BUFFER
+            if s.persistent is False
+            if isinstance(s.target, str)
+        )
+
+    # A list of lifted constant tensors
+    @property
+    def lifted_tensor_constants(self) -> Collection[str]:
+        return tuple(
+            s.target
+            for s in self.input_specs
+            if s.kind == InputKind.CONSTANT_TENSOR
+            if isinstance(s.target, str)
+        )
+
+    @property
+    def lifted_custom_objs(self) -> Collection[str]:
+        return tuple(
+            s.target
+            for s in self.input_specs
+            if s.kind == InputKind.CUSTOM_OBJ
+            if isinstance(s.target, str)
+        )
+
+    # Graph node names of pytree-flattened inputs of original program
+    @property
+    def user_inputs(self) -> Collection[Union[int, float, bool, None, str]]:
+        user_inputs: List[Union[int, float, bool, None, str]] = []
+        for s in self.input_specs:
+            if s.kind != InputKind.USER_INPUT:
+                continue
+
+            if isinstance(s.arg, (TensorArgument, SymIntArgument, CustomObjArgument)):
+                user_inputs.append(s.arg.name)
+            elif isinstance(s.arg, ConstantArgument):
+                user_inputs.append(s.arg.value)
+            else:
+                raise RuntimeError(f"{s.arg} is not a valid user inputs")
+        return tuple(user_inputs)
+
+    # Graph node names of pytree-flattened outputs of original program
+    @property
+    def user_outputs(self) -> Collection[Union[int, float, bool, None, str]]:
+        user_outputs: List[Union[int, float, bool, None, str]] = []
+        for s in self.output_specs:
+            if s.kind != OutputKind.USER_OUTPUT:
+                continue
+
+            if isinstance(s.arg, (TensorArgument, SymIntArgument)):
+                user_outputs.append(s.arg.name)
+            elif isinstance(s.arg, ConstantArgument):
+                user_outputs.append(s.arg.value)
+            elif isinstance(s.arg, CustomObjArgument):
+                user_outputs.append(s.arg.name)
+            else:
+                raise RuntimeError(f"{s.arg} is not a valid user output")
+        return tuple(user_outputs)
+
+    # A dictionary mapping graph input node names to parameters. If a graph input
+    # name is found in this dictionary, it is guranteed to be a lifted parameter.
+    @property
+    def inputs_to_parameters(self) -> Mapping[str, str]:
+        return _immutable_dict(
+            (s.arg.name, s.target)
+            for s in self.input_specs
+            if s.kind == InputKind.PARAMETER
+            and isinstance(s.arg, TensorArgument)
+            and isinstance(s.target, str)
+        )
+
+    # A dictionary mapping graph input node names to buffers. If a graph input
+    # name is found in this dictionary, it is guranteed to be a lifted buffer.
+    @property
+    def inputs_to_buffers(self) -> Mapping[str, str]:
+        return _immutable_dict(
+            (s.arg.name, s.target)  # type: ignore[union-attr, misc]
+            for s in self.input_specs
+            if s.kind == InputKind.BUFFER
+            and isinstance(s.arg, TensorArgument)
+            and isinstance(s.target, str)
+        )
+
+    # A dictionary mapping graph output node names to buffers that are mutated in the
+    # original program. Buffers that are not mutated will not be found in this dictionary.
+    @property
+    def buffers_to_mutate(self) -> Mapping[str, str]:
+        return _immutable_dict(
+            (s.arg.name, s.target)
+            for s in self.output_specs
+            if s.kind == OutputKind.BUFFER_MUTATION
+            and isinstance(s.arg, TensorArgument)
+            and isinstance(s.target, str)
+        )
+
+    @property
+    def user_inputs_to_mutate(self) -> Mapping[str, str]:
+        return _immutable_dict(
+            (s.arg.name, s.target)
+            for s in self.output_specs
+            if s.kind == OutputKind.USER_INPUT_MUTATION
+            and isinstance(s.arg, TensorArgument)
+            and isinstance(s.target, str)
+        )
+
+    # A dictionary mapping graph input node names to lifted tensor constants.
+    @property
+    def inputs_to_lifted_tensor_constants(self) -> Mapping[str, str]:
+        return _immutable_dict(
+            (s.arg.name, s.target)
+            for s in self.input_specs
+            if s.kind == InputKind.CONSTANT_TENSOR
+            and isinstance(s.arg, TensorArgument)
+            and isinstance(s.target, str)
+        )
+
+    @property
+    def inputs_to_lifted_custom_objs(self) -> Mapping[str, str]:
+        return _immutable_dict(
+            (s.arg.name, s.target)
+            for s in self.input_specs
+            if s.kind == InputKind.CUSTOM_OBJ
+            and isinstance(s.arg, CustomObjArgument)
+            and isinstance(s.target, str)
+        )
+
+    @property
+    def backward_signature(self) -> Optional[ExportBackwardSignature]:
+        loss_output = None
+        gradients_to_parameters: Dict[str, str] = {}
+        gradients_to_user_inputs: Dict[str, str] = {}
+        for spec in self.output_specs:
+            if spec.kind == OutputKind.LOSS_OUTPUT:
+                assert loss_output is None
+                assert isinstance(spec.arg, TensorArgument)
+                loss_output = spec.arg.name
+            elif spec.kind == OutputKind.GRADIENT_TO_PARAMETER:
+                assert isinstance(spec.target, str)
+                assert isinstance(spec.arg, TensorArgument)
+                gradients_to_parameters[spec.arg.name] = spec.target
+            elif spec.kind == OutputKind.GRADIENT_TO_USER_INPUT:
+                assert isinstance(spec.target, str)
+                assert isinstance(spec.arg, TensorArgument)
+                gradients_to_user_inputs[spec.arg.name] = spec.target
+
+        if loss_output is None:
+            return None
+
+        return ExportBackwardSignature(
+            loss_output=loss_output,
+            gradients_to_parameters=gradients_to_parameters,
+            gradients_to_user_inputs=gradients_to_user_inputs,
+        )
+
+    # Map from assertion dependency token index to assertion dep token output
+    # name in output. The shape of output after aot_autograd will be like:
+    # (updated_inputs, user_outputs, dep_token).
+    @property
+    def assertion_dep_token(self) -> Optional[Mapping[int, str]]:
+        return None
+
+    @property
+    def input_tokens(self) -> Collection[str]:
+        input_tokens = []
+        for s in self.input_specs:
+            if s.kind == InputKind.TOKEN:
+                assert isinstance(s.arg, TokenArgument)
+                input_tokens.append(s.arg.name)
+        return tuple(input_tokens)
+
+    @property
+    def output_tokens(self) -> Collection[str]:
+        output_tokens = []
+        for s in self.output_specs:
+            if s.kind == OutputKind.TOKEN:
+                assert isinstance(s.arg, TokenArgument)
+                output_tokens.append(s.arg.name)
+        return tuple(output_tokens)
+
+    def __post_init__(self) -> None:
+        assertion_dep_token = self.assertion_dep_token
+        if assertion_dep_token is None:
+            return
+        assert len(assertion_dep_token) == 1
+        assertion_dep_token_index = next(iter(assertion_dep_token.keys()))
+        assert (
+            len(self.user_outputs) + len(self.buffers_to_mutate)
+            == assertion_dep_token_index
+        )
+
+    def replace_all_uses(self, old: str, new: str):
+        """
+        Replace all uses of the old name with new name in the signature.
+        """
+        assert isinstance(old, str)
+        assert isinstance(new, str)
+        arg_types = (TensorArgument, SymIntArgument, CustomObjArgument, TokenArgument)
+        for o in self.output_specs:
+            if isinstance(o.arg, arg_types):
+                if o.arg.name == old:
+                    o.arg.name = new
+        for i in self.input_specs:
+            if isinstance(i.arg, arg_types):
+                if i.arg.name == old:
+                    i.arg.name = new
+
+    def get_replace_hook(self):
+        def _(old, new, user):
+            if user.op in ("output", "input"):
+                self.replace_all_uses(old.name, new)
+
+        return _
+
+
+def _immutable_dict(items):
+    """
+    Creates a mapping where items cannot be added, deleted, or updated.
+    NOTE: The immutability is shallow (like tuple is an immutable collection).
+    """
+    from types import MappingProxyType
+
+    return MappingProxyType(dict(items))
+
+
+def _make_argument_spec(node, token_names) -> ArgumentSpec:
+    from torch import ScriptObject, SymInt
+    from torch._library.fake_class_registry import FakeScriptObject
+    from torch._subclasses.fake_tensor import FakeTensor
+
+    if isinstance(node, (int, bool, float, type(None), str)):
+        # For const outputs we just directly return this
+        return ConstantArgument(name="", value=node)
+
+    assert (
+        "val" in node.meta
+    ), f"{node} is not a constant or a node with a 'val' metadata field"
+    val = node.meta["val"]
+    if node.name in token_names:
+        return TokenArgument(name=node.name)
+    elif isinstance(val, FakeTensor):
+        return TensorArgument(name=node.name)
+    elif isinstance(val, SymInt):
+        return SymIntArgument(name=node.name)
+    elif isinstance(val, ScriptObject):
+        return CustomObjArgument(name=node.name, class_fqn=val._type().qualified_name())  # type: ignore[attr-defined]
+    elif isinstance(val, FakeScriptObject):
+        return CustomObjArgument(
+            name=node.name, class_fqn=val.script_class_name, fake_val=val
+        )
+    elif isinstance(val, (int, bool, str, float, type(None))):
+        return ConstantArgument(name=node.name, value=val)
+    else:
+        raise AssertionError(
+            f"Encountered an unsupported object of type {type(val)} "
+            f"while writing the metadata for exported program"
+        )
+
+
+def _convert_to_export_graph_signature(
+    graph_signature: "GraphSignature",
+    gm: "torch.fx.GraphModule",
+    non_persistent_buffers: Set[str],
+) -> "ExportGraphSignature":
+    from torch.utils import _pytree as pytree
+
+    is_joint = graph_signature.backward_signature is not None
+
+    # unpack objects
+    user_inputs = set(graph_signature.user_inputs)
+    inputs_to_parameters = graph_signature.inputs_to_parameters
+    inputs_to_buffers = graph_signature.inputs_to_buffers
+    user_outputs = set(graph_signature.user_outputs)
+    buffer_mutations = graph_signature.buffers_to_mutate
+    user_input_mutations = graph_signature.user_inputs_to_mutate
+    grad_params = graph_signature.backward_signature.gradients_to_parameter if is_joint else {}  # type: ignore[union-attr]
+    grad_user_inputs = graph_signature.backward_signature.gradients_to_user_inputs if is_joint else {}  # type: ignore[union-attr]
+    loss_output = graph_signature.backward_signature.loss_output if is_joint else None  # type: ignore[union-attr]
+    input_tokens = graph_signature.input_tokens
+    output_tokens = graph_signature.output_tokens
+
+    inputs = [
+        _make_argument_spec(node, input_tokens)
+        for node in gm.graph.nodes
+        if node.op == "placeholder"
+    ]
+    outputs = [
+        _make_argument_spec(node, output_tokens)
+        for node in pytree.tree_leaves(next(iter(reversed(gm.graph.nodes))).args)
+    ]
+
+    def to_input_spec(inp: ArgumentSpec) -> InputSpec:
+        if isinstance(inp, TokenArgument):
+            return InputSpec(kind=InputKind.TOKEN, arg=inp, target=None)
+
+        if not isinstance(inp, TensorArgument):
+            return InputSpec(kind=InputKind.USER_INPUT, arg=inp, target=None)
+        name = inp.name
+        if name in user_inputs:
+            return InputSpec(kind=InputKind.USER_INPUT, arg=inp, target=None)
+        elif name in inputs_to_parameters:
+            return InputSpec(
+                kind=InputKind.PARAMETER,
+                arg=inp,
+                target=inputs_to_parameters[name],  # type: ignore[index]
+            )
+        elif name in inputs_to_buffers:
+            return InputSpec(
+                kind=InputKind.BUFFER,
+                arg=inp,
+                target=inputs_to_buffers[name],  # type: ignore[index]
+                persistent=(inputs_to_buffers[name] not in non_persistent_buffers),  # type: ignore[index]
+            )
+        else:
+            raise AssertionError(f"Unknown tensor input kind: {name}")
+
+    def to_output_spec(idx: int, o: ArgumentSpec) -> OutputSpec:
+        if isinstance(o, TokenArgument):
+            return OutputSpec(kind=OutputKind.TOKEN, arg=o, target=None)
+
+        if not isinstance(o, TensorArgument):
+            return OutputSpec(kind=OutputKind.USER_OUTPUT, arg=o, target=None)
+        name = o.name
+        if idx < len(buffer_mutations) + len(user_input_mutations) + len(output_tokens):
+            if name in buffer_mutations:
+                return OutputSpec(
+                    kind=OutputKind.BUFFER_MUTATION,
+                    arg=o,
+                    target=buffer_mutations[name],  # type: ignore[index]
+                )
+            elif name in user_input_mutations:
+                return OutputSpec(
+                    kind=OutputKind.USER_INPUT_MUTATION,
+                    arg=o,
+                    target=user_input_mutations[name],  # type: ignore[index]
+                )
+            else:
+                raise AssertionError(f"Unknown tensor mutation kind: {name}")
+        else:
+            if name in user_outputs:
+                return OutputSpec(kind=OutputKind.USER_OUTPUT, arg=o, target=None)
+
+            elif name in grad_params:
+                return OutputSpec(
+                    kind=OutputKind.GRADIENT_TO_PARAMETER,
+                    arg=o,
+                    target=grad_params[name],
+                )
+            elif name in grad_user_inputs:
+                return OutputSpec(
+                    kind=OutputKind.GRADIENT_TO_USER_INPUT,
+                    arg=o,
+                    target=grad_user_inputs[name],
+                )
+            elif name == loss_output:
+                return OutputSpec(kind=OutputKind.LOSS_OUTPUT, arg=o, target=None)
+
+            else:
+                raise AssertionError(f"Unknown tensor output kind: {name}")
+
+    input_specs = [to_input_spec(inp) for inp in inputs]
+    output_specs = [to_output_spec(idx, o) for idx, o in enumerate(outputs)]
+    return ExportGraphSignature(input_specs=input_specs, output_specs=output_specs)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/passes/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/passes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c523b954e88e75165ef56c32a3b8bd5c77e78b9e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/passes/__init__.py
@@ -0,0 +1,69 @@
+from typing import Dict, Union
+
+import torch
+import torch.utils._pytree as pytree
+from torch.export.exported_program import ExportedProgram
+
+
+__all__ = ["move_to_device_pass"]
+
+
+def move_to_device_pass(
+    ep: ExportedProgram, location: Union[torch.device, str, Dict[str, str]]
+) -> ExportedProgram:
+    """
+    Move the exported program to the given device.
+
+    Args:
+        ep (ExportedProgram): The exported program to move.
+        location (Union[torch.device, str, Dict[str, str]]): The device to move the exported program to.
+            If a string, it is interpreted as a device name.
+            If a dict, it is interpreted as a mapping from
+            the existing device to the intended one
+
+    Returns:
+        ExportedProgram: The moved exported program.
+    """
+
+    def _get_new_device(
+        curr_device: torch.device,
+        location: Union[torch.device, str, Dict[str, str]],
+    ) -> str:
+        if isinstance(location, dict):
+            if str(curr_device) in location.keys():
+                return location[str(curr_device)]
+            else:
+                return str(curr_device)
+        else:
+            return str(location)
+
+    # move all the state_dict
+    for k, v in ep.state_dict.items():
+        if isinstance(v, torch.nn.Parameter):
+            ep._state_dict[k] = torch.nn.Parameter(
+                v.to(_get_new_device(v.device, location))
+            )
+        else:
+            ep._state_dict[k] = v.to(_get_new_device(v.device, location))
+
+    # move all the constants
+    for k, v in ep.constants.items():
+        if isinstance(v, torch.Tensor):
+            ep._constants[k] = v.to(_get_new_device(v.device, location))
+
+    for node in ep.graph.nodes:
+        # move all the nodes kwargs with burnt-in device
+        if "device" in node.kwargs:
+            kwargs = node.kwargs.copy()
+            kwargs["device"] = _get_new_device(kwargs["device"], location)
+            node.kwargs = kwargs
+        # move all the tensor metadata
+        node.meta["val"] = pytree.tree_map(
+            lambda v: v.to(_get_new_device(v.device, location))
+            if isinstance(v, torch.Tensor)
+            else v,
+            node.meta.get("val"),
+        )
+
+    ep.validate()
+    return ep
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/passes/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/passes/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..fd054f27dda18643c4351a8d24367bedf8008e3c
Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/passes/__pycache__/__init__.cpython-311.pyc differ
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/unflatten.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/unflatten.py
new file mode 100644
index 0000000000000000000000000000000000000000..992818f1758048ae7f3c0de41971ff5317c0b55d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/export/unflatten.py
@@ -0,0 +1,1258 @@
+# mypy: allow-untyped-defs
+import abc
+import copy
+import operator
+from collections import defaultdict
+from contextlib import contextmanager
+from copy import deepcopy
+from enum import Enum
+from typing import Any, cast, Dict, List, Optional, Set, Tuple, Union
+
+import torch
+import torch.fx._pytree as fx_pytree
+import torch.utils._pytree as pytree
+from torch._library.fake_class_registry import FakeScriptObject
+from torch.export._tree_utils import reorder_kwargs
+from torch.export.exported_program import (
+    ConstantArgument,
+    ExportedProgram,
+    InputKind,
+    ModuleCallSignature,
+    SymIntArgument,
+    TensorArgument,
+)
+from torch.fx._symbolic_trace import is_fx_tracing
+from torch.fx.graph_module import _print_readable
+from torch.utils._pytree import GetAttrKey, SequenceKey
+
+from ._remove_effect_tokens_pass import _remove_effect_tokens
+
+
+__all__ = ["InterpreterModule", "UnflattenedModule", "unflatten", "FlatArgsAdapter"]
+
+
+class _AttrKind(Enum):
+    PARAMETER = "parameter"
+    BUFFER = "buffer"
+    CONSTANT = "constant"
+
+
+RUN_WITH_INTERPRETER = True
+
+
+@contextmanager
+def _disable_interpreter():
+    global RUN_WITH_INTERPRETER
+    old_flag = RUN_WITH_INTERPRETER
+    RUN_WITH_INTERPRETER = False
+    try:
+        yield
+    finally:
+        RUN_WITH_INTERPRETER = old_flag
+
+
+# Assign attribute 'from_obj' to the qualified name 'target' on 'to_module
+# This installs empty Modules where none exist yet if they are subpaths of target
+def _assign_attr(
+    from_obj: Union[torch.Tensor, torch.ScriptObject],
+    to_module: torch.nn.Module,
+    target: str,
+    attr_kind: _AttrKind,
+    persistent: bool = True,
+):
+    *prefix, field = target.split(".")
+    for item in prefix:
+        t = getattr(to_module, item, None)
+
+        if t is None:
+            t = torch.nn.Module()
+            setattr(to_module, item, t)
+        to_module = t
+
+    if attr_kind == _AttrKind.PARAMETER:
+        assert isinstance(from_obj, torch.nn.Parameter)
+        to_module.register_parameter(field, from_obj)
+    elif attr_kind == _AttrKind.BUFFER:
+        assert isinstance(from_obj, torch.Tensor)
+        to_module.register_buffer(field, from_obj, persistent=persistent)
+    elif attr_kind == _AttrKind.CONSTANT:
+        assert not isinstance(
+            from_obj, FakeScriptObject
+        ), "FakeScriptObject should only exist during tracing."
+        assert isinstance(
+            from_obj,
+            (
+                torch.Tensor,
+                torch.ScriptObject,
+            ),
+        )
+        setattr(to_module, field, from_obj)
+
+
+class InterpreterModule(torch.nn.Module):
+    """A module that uses torch.fx.Interpreter to execute instead of the usual
+    codegen that GraphModule uses. This provides better stack trace information
+    and makes it easier to debug execution.
+    """
+
+    def __init__(
+        self,
+        graph: torch.fx.Graph,
+    ):
+        super().__init__()
+        self.graph = graph
+        self.graph.owning_module = self
+        self._run_with_interpeter = RUN_WITH_INTERPRETER
+
+    def forward(self, *args, **kwargs):
+        assert self.graph_module is not None, "Didn't finalize this InterpreterModule"
+        if not is_fx_tracing() and (
+            torch.compiler.is_dynamo_compiling() or not self._run_with_interpeter
+        ):
+            # Dynamo cannot trace through torch.fx.Interpreter, so fall back to
+            # GraphModule codegen in this instance.
+            # Patch the codegened forward to run with this InterpreterModule,
+            # so attribute accesses, etc. are on this module instead.
+            return type(self.graph_module).forward(self, *args, **kwargs)
+        else:
+            if kwargs:
+                # Handle **kwargs. FX only natively supports positional
+                # arguments (through placeholders). So in order to pass in
+                # kwargs, we must correspond the names of the placeholders with
+                # the keys in the kwarg dict.
+                arg_list = list(args)
+                kwarg_names = self.arg_names[len(arg_list) :]
+                for kwarg_name in kwarg_names:
+                    if kwarg_name in kwargs:
+                        arg_list.append(kwargs[kwarg_name])
+
+                # Assert that the kwargs passed in exactly match the positional
+                # arguments specified by the GraphModule. This should be
+                # guaranteed by the unflattening process.
+                assert len(kwarg_names) == len(kwargs)
+                assert len(arg_list) == len(self.arg_names)
+                args = tuple(arg_list)
+
+            return torch.fx.Interpreter(self, graph=self.graph).run(
+                *args, enable_io_processing=False
+            )
+
+    def finalize(self):
+        # We need to "finalize" because GraphModule populates its own state_dict
+        # based on the get_attrs observed in the graph. So we need to fully
+        # construct the graph and call _sink_params before generating this
+        # GraphModule.
+
+        # need to set `graph_module` directly on the dict to avoid it getting
+        # registered as a submodule.
+        self.__dict__["graph_module"] = torch.fx.GraphModule(self, self.graph)
+        self.graph.lint()
+
+        # Cache arg names for kwarg handling (see forward())
+        self.arg_names = []
+        for node in self.graph.nodes:
+            if node.op == "placeholder":
+                self.arg_names.append(node.target)
+
+    def print_readable(
+        self,
+        print_output=True,
+        include_stride=False,
+        include_device=False,
+        colored=False,
+    ):
+        return _print_readable(
+            self,
+            "InterpreterModule",
+            print_output,
+            include_stride,
+            include_device,
+            colored,
+        )
+
+
+class FlatArgsAdapter(abc.ABC):
+    """
+    Adapts input arguments with ``input_spec`` to align ``target_spec``.
+    """
+
+    @abc.abstractmethod
+    def adapt(
+        self,
+        target_spec: pytree.TreeSpec,
+        input_spec: pytree.TreeSpec,
+        input_args: List[Any],
+    ) -> List[Any]:
+        """NOTE: This adapter may mutate given ``input_args_with_path``."""
+        ...
+
+
+class UnflattenedModule(torch.nn.Module):
+    def __init__(
+        self,
+        export_module: ExportedProgram,
+        flat_args_adapter: Optional[FlatArgsAdapter] = None,
+    ):
+        super().__init__()
+        if export_module.graph_signature.backward_signature is not None:
+            raise ValueError("Unflattening on JointExportModule NYI")
+
+        fqn_list = [entry.fqn for entry in export_module.module_call_graph]
+        assert fqn_list[0] == ""
+        export_graph = deepcopy(export_module.graph)
+        self.graph_signature = deepcopy(export_module.graph_signature)
+        self.graph = torch.fx.Graph()
+        self.module_call_graph = deepcopy(export_module.module_call_graph)
+        self.flat_args_adapter = flat_args_adapter
+        # Flag to indicate whether args have been adapted.
+        self.adapted = False
+        self._run_with_interpeter = RUN_WITH_INTERPRETER
+
+        _inplace_buffer_mutations(export_graph, self.graph_signature)
+        _outline_submodules(export_graph, self)
+
+        self.range_constraints = export_module.range_constraints
+        self.equality_constraints: List = []
+
+        # aliasing/unused param or buffer issues:
+        # in strict-mode export, dynamo export will deduplicate aliased tensors,
+        # and ignore unused tensors. For aliasing, this causes issues when some aliases
+        # are unused, and we're unable to match the placeholder node to the correct FQN.
+        # This leads to the graph signature potentially having the wrong target FQN,
+        # and downstream issues where parameters are assigned to the wrong target attribute,
+        # mismatching the relevant placeholder node in the unflattened module.
+        # To resolve this we restore (_assign_attr) all aliased/unused tensors in
+        # the state_dict as module attributes, but only keep the used tensors in the
+        # graph's forward pass (_sink_params).
+        state_dict = export_module.state_dict
+        assigned_params: Set[str] = set()  # tracking unused params
+        id_to_param: Dict[int, torch.nn.Parameter] = {}  # handling weight-sharing
+        for name in self.graph_signature.parameters:  # this loop adds used params
+            param = state_dict[name]
+            if id(param) not in id_to_param:
+                id_to_param[id(param)] = torch.nn.Parameter(
+                    param.clone(), requires_grad=param.requires_grad
+                )
+
+            _assign_attr(
+                id_to_param[id(param)],
+                self,
+                name,
+                attr_kind=_AttrKind.PARAMETER,
+            )
+            assigned_params.add(name)
+
+        non_persistent_buffers = set(self.graph_signature.non_persistent_buffers)
+        assigned_buffers: Set[str] = set()  # tracking unused buffers
+        id_to_buffer: Dict[
+            int, Tuple[torch.nn.Parameter, bool]
+        ] = {}  # handle weight-sharing
+        for name in self.graph_signature.buffers:  # this loop adds used buffers
+            if name in non_persistent_buffers:
+                persistent = False
+                buffer = export_module.constants[name]
+            else:
+                persistent = True
+                buffer = state_dict[name]
+
+            if id(buffer) not in id_to_buffer:
+                id_to_buffer[id(buffer)] = (buffer.clone(), persistent)
+
+            _assign_attr(
+                id_to_buffer[id(buffer)][0],
+                self,
+                name,
+                attr_kind=_AttrKind.BUFFER,
+                persistent=persistent,
+            )
+            assigned_buffers.add(name)
+
+        # restore aliased/unused params and buffers
+        # these appear in state dict but not graph signature
+        for name, tensor in state_dict.items():
+            if name in assigned_params or name in assigned_buffers:  # already assigned
+                continue
+
+            is_buffer = False
+            if id(tensor) in id_to_buffer or not isinstance(
+                tensor, torch.nn.Parameter
+            ):  # aliased buffer
+                is_buffer = True
+
+            if is_buffer:
+                if (
+                    id(tensor) not in id_to_buffer
+                ):  # this is completely unused (not weight-sharing)
+                    id_to_buffer[id(tensor)] = (
+                        tensor,
+                        True,
+                    )  # assign to respect original model
+                _assign_attr(
+                    id_to_buffer[id(tensor)][0],
+                    self,
+                    name,
+                    attr_kind=_AttrKind.BUFFER,
+                    persistent=True,
+                )
+            else:
+                if id(tensor) not in id_to_param:  # this is unused
+                    id_to_param[id(tensor)] = tensor
+                _assign_attr(
+                    id_to_param[id(tensor)],
+                    self,
+                    name,
+                    attr_kind=_AttrKind.PARAMETER,
+                )
+
+        # use id map so we don't double-clone aliased constants
+        id_to_const: Dict[int, Union[torch.Tensor, torch._C.ScriptObject]] = {}
+        for fqn, constant in export_module.constants.items():
+            if id(constant) not in id_to_const:
+                if isinstance(constant, torch.Tensor):
+                    constant = constant.clone()
+                id_to_const[id(constant)] = constant
+            _constant = id_to_const[id(constant)]
+            _assign_attr(
+                _constant,
+                self,
+                fqn,
+                attr_kind=_AttrKind.CONSTANT,
+            )
+
+        # This is to handle parameters/buffers that point to the same tensor
+        # object id -> list of (node_name, target_name)
+        consts_map: Dict[int, List[Tuple[str, str]]] = defaultdict(list)
+        consts_targets: Set[str] = set()
+
+        def add_to_consts_map(obj_id, node_name, target_name):
+            name_list = consts_map[obj_id]
+            name_list.append((node_name, target_name))
+
+        added_params_buffers: Set[str] = set()  # track aliased/unused params, buffers
+        for s in self.graph_signature.input_specs:
+            if s.kind == InputKind.PARAMETER or (
+                s.kind == InputKind.BUFFER and s.persistent
+            ):
+                assert hasattr(s.arg, "name")
+                assert isinstance(s.target, str)
+                add_to_consts_map(
+                    id(export_module.state_dict[s.target]), s.arg.name, s.target
+                )
+                consts_targets.add(s.target)
+                added_params_buffers.add(s.target)
+            elif (
+                (s.kind == InputKind.BUFFER and not s.persistent)
+                or s.kind == InputKind.CONSTANT_TENSOR
+                or s.kind == InputKind.CUSTOM_OBJ
+            ):
+                assert hasattr(s.arg, "name")
+                assert isinstance(s.target, str)
+                add_to_consts_map(
+                    id(export_module.constants[s.target]), s.arg.name, s.target
+                )
+                consts_targets.add(s.target)
+
+        # add constants that are aliased and don't appear in graph signature
+        for const_name, const in export_module.constants.items():
+            if const_name not in consts_targets:
+                assert (
+                    id(const) in consts_map
+                ), "Constants should be either aliased or appear in graph signature"
+                ph_name, _ = consts_map[id(const)][0]
+                add_to_consts_map(id(const), ph_name, const_name)
+                added_params_buffers.add(s.target)
+
+        # add aliased/unused params and buffers that don't appear in graph signature
+        for fqn, tensor in export_module.state_dict.items():
+            if fqn not in added_params_buffers:
+                if id(tensor) not in consts_map:
+                    # completely unused (no weight-sharing), ignore.
+                    # this weight doesn't appear in graph module,
+                    # so won't cause FQN assignment issues
+                    continue
+                ph_name, _ = consts_map[id(tensor)][0]
+                add_to_consts_map(id(tensor), ph_name, fqn)
+
+        # node name -> list of possible targets
+        inputs_to_state: Dict[str, List[str]] = {}
+        for node_target in consts_map.values():
+            targets = [t[1] for t in node_target]
+            for n, _ in node_target:
+                inputs_to_state[n] = targets
+
+        _sink_params(self, inputs_to_state, [])
+
+        # Helper function to check input nodes of `module` has been processed.
+        def check_module_inputs(module, scope):
+            if hasattr(module, "graph"):
+                for node in module.graph.nodes:
+                    # sink_params() should turn placeholders into get_attr nodes
+                    # for attributes that are within scope of the current
+                    # module. We allow attributes to remain as placeholders if
+                    # they are inputs in the original module signature, meaning
+                    # they are a parent module's attribute, and therefore out of
+                    # scope of the current module.
+                    if (
+                        node.op == "placeholder"
+                        and node.name in inputs_to_state
+                        and any(
+                            fqn.split(".")[: len(scope)] == scope
+                            for fqn in inputs_to_state[node.name]
+                        )  # matching scope to avoid wrong assert
+                    ):
+                        raise AssertionError(
+                            f"{node.name} was not sunk into the module {scope} which has the graph: {module.graph}"
+                        )
+            # Recursively check the submodules.
+            for name, submod in module.named_children():
+                scope.append(name)
+                check_module_inputs(submod, scope)
+
+        # Recurively check all input nodes have been processed.
+        check_module_inputs(self, [])
+
+        # Cache so we don't have to compute this every time.
+        # NOTE: this needs to be kept in sync with the placeholders in
+        # self.graph, but currently we have no way to guarantee that.
+        self.input_placeholders = [
+            node for node in self.graph.nodes if node.op == "placeholder"
+        ]
+        self.check_input_constraints = True
+        # TODO(zhxchen17) We can register modules ahead of time instead of reorder later.
+        fqn_order = {fqn: i for i, fqn in enumerate(fqn_list)}
+        # In the case of legacy IR, we might be missing some modules from metadata.
+        for name, _ in self.named_modules(remove_duplicate=False):
+            if name not in fqn_order:
+                fqn_order[name] = len(fqn_order)
+        _reorder_submodules(self, fqn_order)
+        assert [fqn for fqn, _ in self.named_modules(remove_duplicate=False)] == list(
+            fqn_order.keys()
+        )
+        self.graph.lint()
+
+    def _print_graph(self):
+        for fqn, mod in self.named_modules():
+            print(fqn + ":")
+            if hasattr(mod, "graph") and isinstance(mod.graph, torch.fx.Graph):
+                print(mod.graph)
+
+    def forward(self, *args, **kwargs):
+        signature = self.module_call_graph[0].signature
+
+        reordered_kwargs = reorder_kwargs(kwargs, signature.in_spec)
+
+        flat_args_with_path, in_spec = pytree.tree_flatten_with_path(
+            (args, reordered_kwargs)
+        )
+        flat_args = [x[1] for x in flat_args_with_path]
+        if is_fx_tracing():
+            return_val = torch.fx.Interpreter(self, graph=self.graph).run(
+                *flat_args, enable_io_processing=False
+            )
+            # For scalar return value, fx.Graph wraps in a tuple
+            if isinstance(return_val, tuple) and len(return_val) == 1:
+                return return_val[0]
+            return return_val
+
+        if in_spec != signature.in_spec:
+            if not self.adapted:
+                print(
+                    "Input treespec does not match with exported module's: \n"
+                    f"Input treespec: {in_spec}. ",
+                    f"Exported module treespec: {signature.in_spec}",
+                )
+            if self.flat_args_adapter is None:
+                raise TypeError(
+                    "There is no flat args adapter sepcified. "
+                    "Are you sure you are calling this with the right arguments? "
+                )
+            else:
+                if not self.adapted:
+                    print("Adapting flat arg to match exported module's treespec")
+                flat_args = self.flat_args_adapter.adapt(
+                    target_spec=signature.in_spec,
+                    input_spec=in_spec,
+                    input_args=flat_args,
+                )
+                self.adapted = True
+                if len(flat_args) != signature.in_spec.num_leaves:
+                    raise TypeError(
+                        f"Flat args adaption failed, number of args mismatch "
+                        f"Adatped: {len(flat_args)} \n"
+                        f"Exported module: {signature.in_spec.num_leaves}"
+                    )
+
+        if self.check_input_constraints:
+            # Import here to avoid an unfortunate circular dependency.
+            # TODO(suo): untangle this.
+            from torch._export.utils import _check_input_constraints_for_graph
+
+            if self.adapted is True:
+                # TODO(suo): The FlatArgsAdapter returns a list of flat args,
+                # which we don't have keypaths for. For now, just create a dummy
+                # keypath to associate with the arg.
+                new_flat_args_with_path = [  # type: ignore[var-annotated]
+                    ((SequenceKey(idx=0), GetAttrKey(name="")), arg)
+                    for arg in flat_args
+                ]
+            else:
+                new_flat_args_with_path = flat_args_with_path  # type: ignore[assignment]
+
+            _check_input_constraints_for_graph(
+                self.input_placeholders, new_flat_args_with_path, self.range_constraints
+            )
+        if torch.compiler.is_dynamo_compiling() and not self._run_with_interpreter:
+            tree_out = torch.fx.GraphModule(self, self.graph)(*flat_args)
+        else:
+            tree_out = torch.fx.Interpreter(self, graph=self.graph).run(
+                *flat_args, enable_io_processing=False
+            )
+        return pytree.tree_unflatten(tree_out, signature.out_spec)
+
+    def print_readable(
+        self,
+        print_output=True,
+        include_stride=False,
+        include_device=False,
+        colored=False,
+    ):
+        return _print_readable(
+            self,
+            "UnflattenedModule",
+            print_output,
+            include_stride,
+            include_device,
+            colored,
+        )
+
+
+def unflatten(
+    module: ExportedProgram, flat_args_adapter: Optional[FlatArgsAdapter] = None
+) -> UnflattenedModule:
+    """Unflatten an ExportedProgram, producing a module with the same module
+    hierarchy as the original eager module. This can be useful if you are trying
+    to use :mod:`torch.export` with another system that expects a module
+    hierachy instead of the flat graph that :mod:`torch.export` usually produces.
+
+    .. note:: The args/kwargs of unflattened modules will not necessarily match
+        the eager module, so doing a module swap (e.g. :code:`self.submod =
+        new_mod`) will not necessarily work. If you need to swap a module out, you
+        need to set the :code:`preserve_module_call_signature` parameter of
+        :func:`torch.export.export`.
+
+    Args:
+        module (ExportedProgram): The ExportedProgram to unflatten.
+        flat_args_adapter (Optional[FlatArgsAdapter]): Adapt flat args if input TreeSpec does not match with exported module's.
+
+    Returns:
+        An instance of :class:`UnflattenedModule`, which has the same module
+        hierarchy as the original eager module pre-export.
+    """
+    module = _remove_effect_tokens(module)
+    return UnflattenedModule(module, flat_args_adapter)
+
+
+def _inplace_buffer_mutations(graph: torch.fx.Graph, graph_signature) -> None:
+    """Transform buffer mutations from their functionalized form into a copy_
+    node in the graph.
+
+    Functionalization represents buffer mutation by passing the buffer as an input and output. So for example, the eager code:
+        def forward(self, x):
+            self.buffer += x
+            return x * x
+
+    Will become a graph that looks like:
+        def forward(self, buffer, x):
+            mutated_buffer = aten.add(buffer, x)
+            mul = aten.mul(x, x)
+            return (mutated_buffer, mul)
+
+    We want to inplace this into something that looks like the original eager code:
+        def forward(self, buffer, x):
+            mutated_buffer = aten.add(buffer, x)
+            buffer.copy_(mutated_buffer)
+            mul = aten.mul(x, x)
+            return (mul,)
+    """
+    output_node = next(iter(reversed(graph.nodes)))
+    assert output_node.op == "output" and len(output_node.args) == 1
+    return_args = output_node.args[0]
+
+    mutation_node_to_buffer = graph_signature.buffers_to_mutate
+    mutations = return_args[: len(mutation_node_to_buffer)]
+    buffers_to_inputs = {v: k for k, v in graph_signature.inputs_to_buffers.items()}
+    input_name_to_node = {
+        node.name: node for node in graph.nodes if node.op == "placeholder"
+    }
+
+    for mutation in mutations:
+        buffer_name = mutation_node_to_buffer[mutation.name]
+        input_name = buffers_to_inputs[buffer_name]
+        input_node = input_name_to_node[input_name]
+
+        with graph.inserting_after(mutation):
+            new_node = graph.create_node(
+                "call_function", torch.ops.aten.copy_, (input_node, mutation)
+            )
+            for k, v in mutation.meta.items():
+                new_node.meta[k] = v
+        # Replace all uses of the previously functional mutation with our copy_ output.
+        mutation.replace_all_uses_with(new_node, lambda x: x is not new_node)
+
+    # Remove the mutated buffer from the graph outputs, since we don't need to
+    # thread it through anymore. We don't need to handle the inputs, which will
+    # be handled by _sink_params.
+    user_outputs = tuple(
+        return_args[len(mutation_node_to_buffer) :],
+    )
+    output_node.args = ((user_outputs),)
+
+
+def _is_prefix(candidate, target):
+    """Check whether `candidate` is a prefix of `target`."""
+    return len(candidate) < len(target) and target[: len(candidate)] == candidate
+
+
+def _compute_accessor(parent_fqn: str, child_fqn: str) -> str:
+    if parent_fqn == "":
+        # Handle the root module correctly.
+        return child_fqn
+
+    parent_split = parent_fqn.split(".")
+    child_split = child_fqn.split(".")
+
+    # TODO: support skip connection by inlining the child module.
+    if child_split[: len(parent_split)] != parent_split:
+        raise RuntimeError(
+            f"Child module '{child_fqn}' is not a descendant of parent mldule '{parent_fqn}'."
+            "This is currently unsupported."
+            "Please try to make child module attach to parent module direclty."
+        )
+    return ".".join(child_split[len(parent_split) :])
+
+
+def _verify_graph_equivalence(x: torch.nn.Module, y: torch.nn.Module):
+    def graph_dump(graph: torch.fx.Graph) -> str:
+        ret = []
+        nodes_idx: Dict[int, int] = {}
+
+        def arg_dump(arg) -> str:
+            if isinstance(arg, torch.fx.Node):
+                return "%" + str(nodes_idx[id(arg)])
+            return str(arg)
+
+        for i, node in enumerate(graph.nodes):
+            args_dump = [str(arg) for arg in pytree.tree_map(arg_dump, node.args)]
+            args_dump += [
+                f"{key}={value}"
+                for key, value in pytree.tree_map(arg_dump, node.kwargs).items()
+            ]
+            target = node.target if node.op == "call_function" else ""
+            ret.append(f"{i}: {node.op}[{target}]({', '.join(args_dump)})")
+            nodes_idx[id(node)] = i
+        return "\n".join(ret)
+
+    assert graph_dump(x.graph) == graph_dump(y.graph)
+
+
+def _add_spec(gm: torch.nn.Module, spec) -> str:
+    i = 0
+    while hasattr(gm, f"_spec_{i}"):
+        i += 1
+    name = f"_spec_{i}"
+    setattr(gm, name, spec)
+    return name
+
+
+def _generate_flatten(gm: torch.nn.Module, node, spec) -> torch.fx.Node:
+    name = _add_spec(gm, spec)
+    spec_node = gm.graph.get_attr(name)
+    return gm.graph.call_function(fx_pytree.tree_flatten_spec, (node, spec_node))
+
+
+def _generate_unflatten(gm: torch.nn.Module, nodes, spec) -> torch.fx.Node:
+    name = _add_spec(gm, spec)
+    spec_node = gm.graph.get_attr(name)
+    return gm.graph.call_function(pytree.tree_unflatten, (nodes, spec_node))
+
+
+def _get_submodule(mod: torch.nn.Module, target: str):
+    *prefix, field = target.split(".")
+
+    for item in prefix:
+        submod = getattr(mod, item, None)
+
+        if submod is None:
+            return None
+
+        if not isinstance(submod, torch.nn.Module):
+            return None
+
+        mod = submod
+
+    return getattr(mod, field, None)
+
+
+def _add_submodule(mod: torch.nn.Module, target: str, module_to_add: torch.nn.Module):
+    *prefix, field = target.split(".")
+
+    for item in prefix:
+        submod = getattr(mod, item, None)
+
+        if submod is None:
+            submod = torch.nn.Module()
+            setattr(mod, item, submod)
+
+        if not isinstance(submod, torch.nn.Module):
+            return False
+
+        mod = submod
+
+    mod.add_module(field, module_to_add)
+
+
+class _ModuleFrame:
+    def __init__(
+        self,
+        flat_graph: torch.fx.Graph,
+        nodes: Tuple[torch.fx.Node, ...],
+        seen_nodes,
+        seen_modules,
+        parent,
+        module_stack: List[str],
+        module_id,
+        module_call_graph: Dict[str, ModuleCallSignature],
+        module: Optional[torch.nn.Module] = None,
+    ):
+        self.flat_graph = flat_graph
+        self.nodes = nodes
+        self.seen_nodes = seen_nodes
+        self.seen_modules = seen_modules
+        self.parent = parent
+        self.module_stack = module_stack
+        self.module_id = module_id
+
+        self.module_call_graph = module_call_graph
+        self.verbose = False
+
+        self.fqn = self.module_stack[-1]
+        if module is not None:
+            self.module = module
+        else:
+            self.module = InterpreterModule(torch.fx.Graph())
+        if self.module_id in self.seen_modules:
+            self.cached_graph_module = self.seen_modules[self.module_id]
+        else:
+            self.cached_graph_module = None
+            self.seen_modules[self.module_id] = self.module
+
+        self.graph = self.module.graph
+
+        # Mapping of nodes in the flat graph to nodes in this graph.
+        self.node_map: Dict[torch.fx.Node, torch.fx.Node] = {}
+        self.node_to_placeholder = {}
+
+        self.parent_call_module: Optional[torch.fx.Node] = None
+        if parent is not None:
+            accessor = _compute_accessor(parent.fqn, self.fqn)
+            _add_submodule(
+                parent.module,
+                accessor,
+                (
+                    self.module
+                    if self.cached_graph_module is None
+                    else self.cached_graph_module
+                ),
+            )
+            self.parent_call_module = parent.graph.call_module(accessor)
+
+        signature = module_call_graph.get(self.fqn)
+        if signature is not None and self.parent is not None:
+            assert signature.in_spec.num_children == 2
+            args_spec = signature.in_spec.children_specs[0]
+            kwargs_spec = signature.in_spec.children_specs[1]
+            assert args_spec.context is None
+            assert kwargs_spec.context is not None
+
+            with self.graph.inserting_after(None):
+                arg_nodes = []
+                for idx in range(args_spec.num_children):
+                    arg_nodes.append(self.graph.placeholder(f"_positional_arg_{idx}"))
+                kwarg_nodes = {}
+                for name in kwargs_spec.context:
+                    kwarg_nodes[name] = self.graph.placeholder(name)
+                flat_args = _generate_flatten(
+                    self.module,
+                    (tuple(arg_nodes), kwarg_nodes),
+                    signature.in_spec,
+                )
+                for idx, arg in enumerate(signature.inputs):
+                    flat_arg_node = self.graph.create_node(
+                        op="call_function",
+                        target=operator.getitem,
+                        args=(flat_args, idx),
+                        name=(
+                            arg.name
+                            if not isinstance(arg, ConstantArgument)
+                            else f"_constant_{idx}"
+                        ),
+                    )
+                    if isinstance(arg, ConstantArgument):
+                        continue
+
+                    if arg.name in self.seen_nodes:
+                        flat_arg_node.meta = copy.copy(self.seen_nodes[arg.name].meta)
+                        self.node_to_placeholder[
+                            self.seen_nodes[arg.name]
+                        ] = flat_arg_node
+
+            with self.parent.graph.inserting_before(self.parent_call_module):
+                input_nodes: List[Optional[torch.fx.Node]] = []
+                for input in signature.inputs:
+                    if isinstance(input, ConstantArgument) and input.value is None:
+                        input_nodes.append(None)
+                    elif input.name not in self.seen_nodes:
+                        input_nodes.append(None)
+                    else:
+                        assert isinstance(input, (TensorArgument, SymIntArgument))
+                        input_nodes.append(
+                            self.parent.remap_input(self.seen_nodes[input.name])
+                        )
+
+                inputs_node = _generate_unflatten(
+                    self.parent.module,
+                    input_nodes,
+                    signature.in_spec,
+                )
+
+                args_node = self.parent.graph.call_function(
+                    operator.getitem, (inputs_node, 0)
+                )
+                kwargs_node = self.parent.graph.call_function(
+                    operator.getitem, (inputs_node, 1)
+                )
+                arg_nodes = [
+                    self.parent.graph.call_function(operator.getitem, (args_node, i))
+                    for i in range(args_spec.num_children)
+                ]
+                kwarg_nodes = {
+                    k: self.parent.graph.call_function(
+                        operator.getitem, (kwargs_node, k)
+                    )
+                    for k in kwargs_spec.context
+                }
+            assert self.parent_call_module is not None
+            self.parent_call_module.args = tuple(arg_nodes)
+            self.parent_call_module.kwargs = kwarg_nodes
+
+    def add_placeholder(self, x):
+        assert self.fqn != "", f"Cannot add placeholder {x} to root module"
+        assert x.graph is self.flat_graph
+        # x is not in subgraph, create a new placeholder for subgraph
+        with self.graph.inserting_before(None):
+            placeholder_node = self.graph.placeholder(x.name, type_expr=x.type)
+        # copy all meta fields, even if some fields might be irrelvant for
+        # the placeholder node
+        placeholder_node.meta = copy.copy(x.meta)
+        self.node_to_placeholder[x] = placeholder_node
+
+    def copy_sym_call_function(self, x):
+        # This only exists because we deduplicate sym_size nodes in the flat export graph,
+        # and if preserve_module_call_signature is set, we may not be able to pass sym_size
+        # nodes, or their downstream users, as inputs to submodule calls.
+        # To avoid this we copy these call_function nodes with sym_type results.
+        # This should however only be done for sym_type nodes - call_function nodes on tensors
+        # should not be deduplicated in the first place.
+        args = pytree.tree_map_only(torch.fx.Node, self.remap_input, x.args)
+        kwargs = pytree.tree_map_only(torch.fx.Node, self.remap_input, x.kwargs)
+        node = self.graph.call_function(x.target, args, kwargs)
+        node.meta = copy.copy(x.meta)
+        self.node_map[x] = node
+        return node
+
+    def remap_input(self, x):
+        assert x.graph is self.flat_graph
+        if x in self.node_map:
+            return self.node_map[x]
+        self.print(f"remap_input({x})")
+        if x in self.node_to_placeholder:
+            return self.node_to_placeholder[x]
+        elif (
+            x.op == "placeholder"
+            or self.module_call_graph.get(self.fqn) is None
+            # allow placeholder creation if we are not preserving module call signature
+        ):
+            self.add_placeholder(x)
+            if self.parent_call_module is not None:
+                # Important to *prepend* the output to match how we are
+                # inserting placeholder nodes.
+                with self.parent.graph.inserting_before(self.parent_call_module):
+                    self.parent_call_module.insert_arg(0, self.parent.remap_input(x))
+            return self.node_to_placeholder[x]
+        elif x.op == "call_function" and (
+            x.target
+            in (
+                torch.ops.aten.sym_size.int,
+                torch.ops.aten.item.default,
+                torch.ops.aten.unbind.int,
+                torch.ops.aten.sum.dim_IntList,
+                torch.ops.aten.view.default,
+                torch.ops.aten.diff.default,
+            )
+            or (hasattr(x.target, "__module__") and x.target.__module__ == "_operator")
+        ):
+            # export deduplicates sym_size nodes, and may need to re-copy them
+            # if module call signature needs to be preserved
+            self.copy_sym_call_function(x)
+            return self.node_map[x]
+        else:
+            raise RuntimeError(
+                f"Could not run remap_input() on op type: {x.op} for node {x}"
+            )
+
+    def finalize_outputs(self):
+        orig_outputs = []
+
+        signature = self.module_call_graph.get(self.fqn)
+        if signature is not None and self.parent is not None:
+            for output in signature.outputs:
+                if isinstance(output, (TensorArgument, SymIntArgument)):
+                    if output.name in self.seen_nodes:
+                        orig_outputs.append(self.seen_nodes[output.name])
+                    else:
+                        orig_outputs.append(None)
+                else:
+                    raise RuntimeError(
+                        f"Unsupported data type for output node: {output}"
+                    )
+
+            def get_actual_output_node(output):
+                if output is None:
+                    return None
+
+                seen_node = self.seen_nodes[output.name]
+                if seen_node in self.node_map:
+                    return self.node_map[seen_node]
+                elif seen_node in self.node_to_placeholder:
+                    return self.node_to_placeholder[seen_node]
+                else:
+                    raise RuntimeError(
+                        f"Could not find output node {output}. Graph: {self.graph}"
+                    )
+
+            tree_out_node = _generate_unflatten(
+                self.module,
+                tuple(get_actual_output_node(output) for output in orig_outputs),
+                signature.out_spec,
+            )
+            parent_out: Optional[torch.fx.Node] = _generate_flatten(
+                self.parent.module, self.parent_call_module, signature.out_spec
+            )
+            graph_outputs: Union[torch.fx.Node, List[torch.fx.Node]] = tree_out_node
+        else:
+            graph_outputs = []
+            # Iterate through nodes we have copied into self.graph.
+            for orig_node in self.node_map.keys():
+                for user_node in orig_node.users:
+                    if user_node.name not in self.seen_nodes:
+                        # external user node, need to expose as an output
+                        orig_outputs.append(orig_node)
+                        graph_outputs.append(self.node_map[orig_node])
+                        break
+
+            parent_out = self.parent_call_module
+            if len(graph_outputs) == 1:
+                graph_outputs = graph_outputs[0]
+
+        assert isinstance(graph_outputs, (list, torch.fx.Node))
+
+        self.graph.output(graph_outputs)
+
+        # Rewrite outputs in parent module
+        if parent_out is None:
+            return
+
+        parent_out.meta["val"] = (
+            graph_outputs.meta.get("val")
+            if isinstance(graph_outputs, torch.fx.Node)
+            else [o.meta.get("val") for o in graph_outputs]
+        )
+
+        if len(orig_outputs) == 1 and signature is None:
+            self.parent.node_map[orig_outputs[0]] = parent_out
+        else:
+            for i, orig_output in enumerate(orig_outputs):
+                if orig_output is None:
+                    continue
+                # Use Proxy to record getitem access.
+                proxy_out = torch.fx.Proxy(parent_out)[i].node  # type: ignore[index]
+                proxy_out.meta["val"] = orig_output.meta.get("val")
+                self.parent.node_map[orig_output] = proxy_out
+
+        if self.cached_graph_module is not None:
+            _verify_graph_equivalence(self.cached_graph_module, self.module)
+
+    def copy_node(self, node):
+        self.print("copying", node.format_node())
+        self.node_map[node] = self.graph.node_copy(node, self.remap_input)
+        self.seen_nodes[node.name] = node
+
+    def run_outer(self):
+        i = 0
+        for node in self.flat_graph.nodes:
+            self.print(i, node.meta.get("nn_module_stack"), node.format_node())
+            i += 1
+
+        # Copy all graph inputs
+        node_idx: int = 0
+        node = self.nodes[node_idx]
+        while node.op == "placeholder":
+            self.copy_node(node)
+            node_idx += 1
+            node = self.nodes[node_idx]
+
+        self.run_from(node_idx)
+
+        # Copy graph outputs
+        for node in self.flat_graph.nodes:
+            if node.op == "output":
+                self.copy_node(node)
+
+    def print(self, *args, **kwargs):
+        if self.verbose:
+            print(*args, **kwargs)
+
+    def run_from(self, node_idx):
+        module_idx = 0
+        # Walk through the graph, building up a new graph with the right submodules
+        while node_idx < len(self.nodes):
+            node = self.nodes[node_idx]
+            assert node.op != "placeholder"
+
+            self.print()
+            self.print("STEP", node_idx, node.format_node())
+            self.print(self.module_stack)
+            if node.op == "output":
+                if len(self.module_stack) == 1:
+                    # We want the output node of the original graph to be handled
+                    # specially by the outermost stack frame (in run_outer). So
+                    # skip finalization here.
+                    return node_idx
+
+                # We've reached the end of the graph. Wrap up all the existing stack frames.
+                self.finalize_outputs()
+                return node_idx
+
+            if len(node.meta.get("nn_module_stack", {})) == 0:
+                raise RuntimeError(f"Unable to find nn_module_stack for node {node}")
+
+            nn_module_stack = node.meta["nn_module_stack"]
+            from torch._export.passes._node_metadata_hook import (
+                _EMPTY_NN_MODULE_STACK_KEY,
+            )
+
+            if (
+                len(nn_module_stack) == 1
+                and _EMPTY_NN_MODULE_STACK_KEY in nn_module_stack
+            ):
+                # Empty case from the node_metadata_hook
+                node_module_stack = self.module_stack
+            else:
+                node_module_stack = [
+                    path for path, ty in node.meta["nn_module_stack"].values()
+                ]
+
+            if node_module_stack[: len(self.module_stack)] != self.module_stack:
+                # This means that the current module is done executing and the
+                # current node is the beginning of a new module.
+                #
+                # In this case, we should finalize this module and return without
+                # incrementing the node counter.
+                self.finalize_outputs()
+                self.print("outlining", self.fqn)
+                self.print(self.graph)
+                return node_idx
+
+            assert node_module_stack is not None
+
+            if _is_prefix(self.module_stack, node_module_stack):
+                # This means that the current node represents the execution of a new
+                # module.
+                next_module = node_module_stack[len(self.module_stack)]
+                self.print("Creating new stack frame for", next_module)
+                # Run a nested version of module outliner from the current node
+                # counter. Once it is complete, continue from that point.
+                node_idx = _ModuleFrame(
+                    self.flat_graph,
+                    self.nodes,
+                    self.seen_nodes,
+                    self.seen_modules,
+                    self,
+                    self.module_stack + [next_module],
+                    list(node.meta["nn_module_stack"].keys())[len(self.module_stack)],
+                    self.module_call_graph,
+                ).run_from(node_idx)
+                module_idx += 1
+                continue
+
+            # The only remaining possibility is that we are in the right stack
+            # frame. Copy the node into this frame's graph and increment the node counter.
+            assert node_module_stack == self.module_stack
+            self.copy_node(node)
+            node_idx += 1
+
+
+def _outline_submodules(orig_graph: torch.fx.Graph, root_module: UnflattenedModule):
+    seen_nodes: Dict[str, torch.fx.Node] = {}
+    seen_modules: Dict[int, torch.nn.Module] = {}
+    _ModuleFrame(
+        orig_graph,
+        tuple(orig_graph.nodes),
+        seen_nodes,
+        seen_modules,
+        None,
+        [""],
+        "",
+        {
+            entry.fqn: entry.signature
+            for entry in root_module.module_call_graph
+            if entry.signature
+        },
+        module=root_module,
+    ).run_outer()
+
+
+def _reorder_submodules(
+    parent: torch.nn.Module, fqn_order: Dict[str, int], prefix: str = ""
+):
+    # TODO Can be optimized by adding submodules ahead of time.
+    if prefix == "":
+        for fqn in list(fqn_order.keys())[1:]:
+            if _get_submodule(parent, fqn) is None:
+                _add_submodule(parent, fqn, torch.nn.Module())
+
+    children = []
+    for name, child in list(parent._modules.items()):
+        if child is None:
+            continue
+        fqn = prefix + name
+        _reorder_submodules(child, fqn_order, prefix=fqn + ".")
+        delattr(parent, name)
+        children.append((fqn_order[fqn], name, child))
+    children.sort(key=operator.itemgetter(0))
+    for _, name, child in children:
+        parent.register_module(name, child)
+
+
+def _sink_params(
+    module: torch.nn.Module,
+    inputs_to_state: Dict[str, List[str]],
+    scope: List[str],
+):
+    """Sink params, buffers, and constants from graph inputs into get_attr nodes.
+
+    Exported modules are purely functional, so they pass their parameters and
+    buffers in as inputs to the graph.
+
+    To replicate eager's semantics, we need to get them from the module state
+    via get_attr instead.
+
+    module: GraphModule, potentially containining nested submodules.
+    inputs_to_state: mapping graph input names to the corresponding key in the state_dict.
+    scope: tracks where we are in the module hierarchy, so that we can emit the
+        right `getattr(self, "foo.bar")` calls, etc.
+    """
+    # This dict records inputs removed by child modules.
+    # Maps the module object id to the list of placeholder node names
+    # in the child module that were removed.
+    module_id_to_inputs_removed: Dict[int, List[str]] = defaultdict(list)
+
+    # We need to use _modules here instead of named_children(), because we
+    # explicitly want duplicate modules to show up in the traversal.
+    for name, submodule in module._modules.items():
+        submod_id_to_inputs_removed = _sink_params(
+            cast(torch.nn.Module, submodule), inputs_to_state, scope + [name]
+        )
+        for k, v in submod_id_to_inputs_removed.items():
+            module_id_to_inputs_removed[k].extend(v)
+
+    if not hasattr(module, "graph"):
+        # Not all modules have graphs defined, if they are empty modules with no operations (like ParameterList)
+        return module_id_to_inputs_removed
+
+    graph = module.graph
+    inputs = list(filter(lambda n: n.op == "placeholder", graph.nodes))
+    the_last_input = inputs[-1]
+
+    # Also remove from call_module nodes
+    call_module_nodes = filter(lambda n: n.op == "call_module", graph.nodes)
+    for node in call_module_nodes:
+        submodule = _recursive_getattr(module, node.target.split("."))
+        # remove placeholder from call_module node arguments, only if we've
+        # erased the placeholder node in the corresponding _sink_params() call
+        if submodule is not None and id(submodule) in module_id_to_inputs_removed:
+            node.args = tuple(
+                filter(
+                    lambda n: n.name not in module_id_to_inputs_removed[id(submodule)],
+                    node.args,
+                )
+            )
+
+    # Filter out inputs_to_state corresponding to current scope.
+    inputs_to_state_of_scope: Dict[torch.fx.Node, list[str]] = {}
+    for node in inputs:
+        if node.name not in inputs_to_state:
+            continue
+
+        state_name = None
+        for sn in inputs_to_state[node.name]:
+            sn_split = sn.split(".")
+            if sn_split[: len(scope)] == scope:
+                state_name = sn_split
+                break
+
+        # If there's a mismatch beteewn scope name and state name, then
+        # there must be multuple scopes pointing to the same state name,
+        # meaning some modules are shared. In such case, we can simply skip
+        # updating the current node because another later iteration will
+        # take care of this input node when the unique match between scope
+        # and state name occurs.  To make sure this always happen, we should
+        # enforce the invariant that no placeholder node in the unflattened
+        # graph appears in inputs_to_state dict, which means all the extra
+        # input nodes have been handled.
+        if state_name is None:
+            continue
+
+        inputs_to_state_of_scope[node] = state_name
+
+    # Record name of remove inputs for return purpose.
+    inputs_removed: List[str] = []
+
+    for node, state_name in inputs_to_state_of_scope.items():
+        if len(node.users) > 0:
+            attr_path = state_name[len(scope) :]
+            state_attr = _recursive_getattr(module, attr_path)
+            assert isinstance(state_attr, (torch.Tensor, torch.ScriptObject))
+
+            # Make sure the newly created get_attr node is placed after the last placeholder node
+            with graph.inserting_after(the_last_input):
+                new_node = graph.create_node("get_attr", ".".join(attr_path))
+
+            node.replace_all_uses_with(new_node, propagate_meta=True)
+
+        graph.erase_node(node)
+        inputs_removed.append(node.name)
+
+    if isinstance(module, InterpreterModule):
+        module.finalize()
+
+    return {id(module): inputs_removed}
+
+
+def _recursive_getattr(obj, attr_path):
+    for attr in attr_path:
+        if not hasattr(obj, attr):
+            return None
+        obj = getattr(obj, attr)
+
+    return obj
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fft/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fft/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3bc5191c7b57de89817e5401d0db24aac1c6df5e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fft/__init__.py
@@ -0,0 +1,1360 @@
+import sys
+
+import torch
+from torch._C import _add_docstr, _fft  # type: ignore[attr-defined]
+from torch._torch_docs import factory_common_args, common_args
+
+__all__ = ['fft', 'ifft', 'fft2', 'ifft2', 'fftn', 'ifftn',
+           'rfft', 'irfft', 'rfft2', 'irfft2', 'rfftn', 'irfftn',
+           'hfft', 'ihfft', 'fftfreq', 'rfftfreq', 'fftshift', 'ifftshift',
+           'Tensor']
+
+Tensor = torch.Tensor
+
+# Note: This not only adds the doc strings for the spectral ops, but
+# connects the torch.fft Python namespace to the torch._C._fft builtins.
+
+fft = _add_docstr(_fft.fft_fft, r"""
+fft(input, n=None, dim=-1, norm=None, *, out=None) -> Tensor
+
+Computes the one dimensional discrete Fourier transform of :attr:`input`.
+
+Note:
+    The Fourier domain representation of any real signal satisfies the
+    Hermitian property: `X[i] = conj(X[-i])`. This function always returns both
+    the positive and negative frequency terms even though, for real inputs, the
+    negative frequencies are redundant. :func:`~torch.fft.rfft` returns the
+    more compact one-sided representation where only the positive frequencies
+    are returned.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimension.
+
+Args:
+    input (Tensor): the input tensor
+    n (int, optional): Signal length. If given, the input will either be zero-padded
+        or trimmed to this length before computing the FFT.
+    dim (int, optional): The dimension along which to take the one dimensional FFT.
+    norm (str, optional): Normalization mode. For the forward transform
+        (:func:`~torch.fft.fft`), these correspond to:
+
+        * ``"forward"`` - normalize by ``1/n``
+        * ``"backward"`` - no normalization
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the FFT orthonormal)
+
+        Calling the backward transform (:func:`~torch.fft.ifft`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.ifft`
+        the exact inverse.
+
+        Default is ``"backward"`` (no normalization).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> t = torch.arange(4)
+    >>> t
+    tensor([0, 1, 2, 3])
+    >>> torch.fft.fft(t)
+    tensor([ 6.+0.j, -2.+2.j, -2.+0.j, -2.-2.j])
+
+    >>> t = torch.tensor([0.+1.j, 2.+3.j, 4.+5.j, 6.+7.j])
+    >>> torch.fft.fft(t)
+    tensor([12.+16.j, -8.+0.j, -4.-4.j,  0.-8.j])
+""".format(**common_args))
+
+ifft = _add_docstr(_fft.fft_ifft, r"""
+ifft(input, n=None, dim=-1, norm=None, *, out=None) -> Tensor
+
+Computes the one dimensional inverse discrete Fourier transform of :attr:`input`.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimension.
+
+Args:
+    input (Tensor): the input tensor
+    n (int, optional): Signal length. If given, the input will either be zero-padded
+        or trimmed to this length before computing the IFFT.
+    dim (int, optional): The dimension along which to take the one dimensional IFFT.
+    norm (str, optional): Normalization mode. For the backward transform
+        (:func:`~torch.fft.ifft`), these correspond to:
+
+        * ``"forward"`` - no normalization
+        * ``"backward"`` - normalize by ``1/n``
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the IFFT orthonormal)
+
+        Calling the forward transform (:func:`~torch.fft.fft`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.ifft`
+        the exact inverse.
+
+        Default is ``"backward"`` (normalize by ``1/n``).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> t = torch.tensor([ 6.+0.j, -2.+2.j, -2.+0.j, -2.-2.j])
+    >>> torch.fft.ifft(t)
+    tensor([0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j])
+""".format(**common_args))
+
+fft2 = _add_docstr(_fft.fft_fft2, r"""
+fft2(input, s=None, dim=(-2, -1), norm=None, *, out=None) -> Tensor
+
+Computes the 2 dimensional discrete Fourier transform of :attr:`input`.
+Equivalent to :func:`~torch.fft.fftn` but FFTs only the last two dimensions by default.
+
+Note:
+    The Fourier domain representation of any real signal satisfies the
+    Hermitian property: ``X[i, j] = conj(X[-i, -j])``. This
+    function always returns all positive and negative frequency terms even
+    though, for real inputs, half of these values are redundant.
+    :func:`~torch.fft.rfft2` returns the more compact one-sided representation
+    where only the positive frequencies of the last dimension are returned.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the FFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Default: ``s = [input.size(d) for d in dim]``
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        Default: last two dimensions.
+    norm (str, optional): Normalization mode. For the forward transform
+        (:func:`~torch.fft.fft2`), these correspond to:
+
+        * ``"forward"`` - normalize by ``1/n``
+        * ``"backward"`` - no normalization
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the FFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical FFT size.
+        Calling the backward transform (:func:`~torch.fft.ifft2`) with the same
+        normalization mode will apply an overall normalization of ``1/n``
+        between the two transforms. This is required to make
+        :func:`~torch.fft.ifft2` the exact inverse.
+
+        Default is ``"backward"`` (no normalization).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> x = torch.rand(10, 10, dtype=torch.complex64)
+    >>> fft2 = torch.fft.fft2(x)
+
+    The discrete Fourier transform is separable, so :func:`~torch.fft.fft2`
+    here is equivalent to two one-dimensional :func:`~torch.fft.fft` calls:
+
+    >>> two_ffts = torch.fft.fft(torch.fft.fft(x, dim=0), dim=1)
+    >>> torch.testing.assert_close(fft2, two_ffts, check_stride=False)
+
+""".format(**common_args))
+
+ifft2 = _add_docstr(_fft.fft_ifft2, r"""
+ifft2(input, s=None, dim=(-2, -1), norm=None, *, out=None) -> Tensor
+
+Computes the 2 dimensional inverse discrete Fourier transform of :attr:`input`.
+Equivalent to :func:`~torch.fft.ifftn` but IFFTs only the last two dimensions by default.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the IFFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Default: ``s = [input.size(d) for d in dim]``
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        Default: last two dimensions.
+    norm (str, optional): Normalization mode. For the backward transform
+        (:func:`~torch.fft.ifft2`), these correspond to:
+
+        * ``"forward"`` - no normalization
+        * ``"backward"`` - normalize by ``1/n``
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the IFFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical IFFT size.
+        Calling the forward transform (:func:`~torch.fft.fft2`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.ifft2`
+        the exact inverse.
+
+        Default is ``"backward"`` (normalize by ``1/n``).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> x = torch.rand(10, 10, dtype=torch.complex64)
+    >>> ifft2 = torch.fft.ifft2(x)
+
+    The discrete Fourier transform is separable, so :func:`~torch.fft.ifft2`
+    here is equivalent to two one-dimensional :func:`~torch.fft.ifft` calls:
+
+    >>> two_iffts = torch.fft.ifft(torch.fft.ifft(x, dim=0), dim=1)
+    >>> torch.testing.assert_close(ifft2, two_iffts, check_stride=False)
+
+""".format(**common_args))
+
+fftn = _add_docstr(_fft.fft_fftn, r"""
+fftn(input, s=None, dim=None, norm=None, *, out=None) -> Tensor
+
+Computes the N dimensional discrete Fourier transform of :attr:`input`.
+
+Note:
+    The Fourier domain representation of any real signal satisfies the
+    Hermitian property: ``X[i_1, ..., i_n] = conj(X[-i_1, ..., -i_n])``. This
+    function always returns all positive and negative frequency terms even
+    though, for real inputs, half of these values are redundant.
+    :func:`~torch.fft.rfftn` returns the more compact one-sided representation
+    where only the positive frequencies of the last dimension are returned.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the FFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Default: ``s = [input.size(d) for d in dim]``
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        Default: all dimensions, or the last ``len(s)`` dimensions if :attr:`s` is given.
+    norm (str, optional): Normalization mode. For the forward transform
+        (:func:`~torch.fft.fftn`), these correspond to:
+
+        * ``"forward"`` - normalize by ``1/n``
+        * ``"backward"`` - no normalization
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the FFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical FFT size.
+        Calling the backward transform (:func:`~torch.fft.ifftn`) with the same
+        normalization mode will apply an overall normalization of ``1/n``
+        between the two transforms. This is required to make
+        :func:`~torch.fft.ifftn` the exact inverse.
+
+        Default is ``"backward"`` (no normalization).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> x = torch.rand(10, 10, dtype=torch.complex64)
+    >>> fftn = torch.fft.fftn(x)
+
+    The discrete Fourier transform is separable, so :func:`~torch.fft.fftn`
+    here is equivalent to two one-dimensional :func:`~torch.fft.fft` calls:
+
+    >>> two_ffts = torch.fft.fft(torch.fft.fft(x, dim=0), dim=1)
+    >>> torch.testing.assert_close(fftn, two_ffts, check_stride=False)
+
+""".format(**common_args))
+
+ifftn = _add_docstr(_fft.fft_ifftn, r"""
+ifftn(input, s=None, dim=None, norm=None, *, out=None) -> Tensor
+
+Computes the N dimensional inverse discrete Fourier transform of :attr:`input`.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the IFFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Default: ``s = [input.size(d) for d in dim]``
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        Default: all dimensions, or the last ``len(s)`` dimensions if :attr:`s` is given.
+    norm (str, optional): Normalization mode. For the backward transform
+        (:func:`~torch.fft.ifftn`), these correspond to:
+
+        * ``"forward"`` - no normalization
+        * ``"backward"`` - normalize by ``1/n``
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the IFFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical IFFT size.
+        Calling the forward transform (:func:`~torch.fft.fftn`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.ifftn`
+        the exact inverse.
+
+        Default is ``"backward"`` (normalize by ``1/n``).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> x = torch.rand(10, 10, dtype=torch.complex64)
+    >>> ifftn = torch.fft.ifftn(x)
+
+    The discrete Fourier transform is separable, so :func:`~torch.fft.ifftn`
+    here is equivalent to two one-dimensional :func:`~torch.fft.ifft` calls:
+
+    >>> two_iffts = torch.fft.ifft(torch.fft.ifft(x, dim=0), dim=1)
+    >>> torch.testing.assert_close(ifftn, two_iffts, check_stride=False)
+
+""".format(**common_args))
+
+rfft = _add_docstr(_fft.fft_rfft, r"""
+rfft(input, n=None, dim=-1, norm=None, *, out=None) -> Tensor
+
+Computes the one dimensional Fourier transform of real-valued :attr:`input`.
+
+The FFT of a real signal is Hermitian-symmetric, ``X[i] = conj(X[-i])`` so
+the output contains only the positive frequencies below the Nyquist frequency.
+To compute the full output, use :func:`~torch.fft.fft`
+
+Note:
+    Supports torch.half on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimension.
+
+Args:
+    input (Tensor): the real input tensor
+    n (int, optional): Signal length. If given, the input will either be zero-padded
+        or trimmed to this length before computing the real FFT.
+    dim (int, optional): The dimension along which to take the one dimensional real FFT.
+    norm (str, optional): Normalization mode. For the forward transform
+        (:func:`~torch.fft.rfft`), these correspond to:
+
+        * ``"forward"`` - normalize by ``1/n``
+        * ``"backward"`` - no normalization
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the FFT orthonormal)
+
+        Calling the backward transform (:func:`~torch.fft.irfft`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.irfft`
+        the exact inverse.
+
+        Default is ``"backward"`` (no normalization).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> t = torch.arange(4)
+    >>> t
+    tensor([0, 1, 2, 3])
+    >>> torch.fft.rfft(t)
+    tensor([ 6.+0.j, -2.+2.j, -2.+0.j])
+
+    Compare against the full output from :func:`~torch.fft.fft`:
+
+    >>> torch.fft.fft(t)
+    tensor([ 6.+0.j, -2.+2.j, -2.+0.j, -2.-2.j])
+
+    Notice that the symmetric element ``T[-1] == T[1].conj()`` is omitted.
+    At the Nyquist frequency ``T[-2] == T[2]`` is it's own symmetric pair,
+    and therefore must always be real-valued.
+""".format(**common_args))
+
+irfft = _add_docstr(_fft.fft_irfft, r"""
+irfft(input, n=None, dim=-1, norm=None, *, out=None) -> Tensor
+
+Computes the inverse of :func:`~torch.fft.rfft`.
+
+:attr:`input` is interpreted as a one-sided Hermitian signal in the Fourier
+domain, as produced by :func:`~torch.fft.rfft`. By the Hermitian property, the
+output will be real-valued.
+
+Note:
+    Some input frequencies must be real-valued to satisfy the Hermitian
+    property. In these cases the imaginary component will be ignored.
+    For example, any imaginary component in the zero-frequency term cannot
+    be represented in a real output and so will always be ignored.
+
+Note:
+    The correct interpretation of the Hermitian input depends on the length of
+    the original data, as given by :attr:`n`. This is because each input shape
+    could correspond to either an odd or even length signal. By default, the
+    signal is assumed to be even length and odd signals will not round-trip
+    properly. So, it is recommended to always pass the signal length :attr:`n`.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimension.
+    With default arguments, size of the transformed dimension should be (2^n + 1) as argument
+    `n` defaults to even output size = 2 * (transformed_dim_size - 1)
+
+Args:
+    input (Tensor): the input tensor representing a half-Hermitian signal
+    n (int, optional): Output signal length. This determines the length of the
+        output signal. If given, the input will either be zero-padded or trimmed to this
+        length before computing the real IFFT.
+        Defaults to even output: ``n=2*(input.size(dim) - 1)``.
+    dim (int, optional): The dimension along which to take the one dimensional real IFFT.
+    norm (str, optional): Normalization mode. For the backward transform
+        (:func:`~torch.fft.irfft`), these correspond to:
+
+        * ``"forward"`` - no normalization
+        * ``"backward"`` - normalize by ``1/n``
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the real IFFT orthonormal)
+
+        Calling the forward transform (:func:`~torch.fft.rfft`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.irfft`
+        the exact inverse.
+
+        Default is ``"backward"`` (normalize by ``1/n``).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> t = torch.linspace(0, 1, 5)
+    >>> t
+    tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000])
+    >>> T = torch.fft.rfft(t)
+    >>> T
+    tensor([ 2.5000+0.0000j, -0.6250+0.8602j, -0.6250+0.2031j])
+
+    Without specifying the output length to :func:`~torch.fft.irfft`, the output
+    will not round-trip properly because the input is odd-length:
+
+    >>> torch.fft.irfft(T)
+    tensor([0.1562, 0.3511, 0.7812, 1.2114])
+
+    So, it is recommended to always pass the signal length :attr:`n`:
+
+    >>> roundtrip = torch.fft.irfft(T, t.numel())
+    >>> torch.testing.assert_close(roundtrip, t, check_stride=False)
+
+""".format(**common_args))
+
+rfft2 = _add_docstr(_fft.fft_rfft2, r"""
+rfft2(input, s=None, dim=(-2, -1), norm=None, *, out=None) -> Tensor
+
+Computes the 2-dimensional discrete Fourier transform of real :attr:`input`.
+Equivalent to :func:`~torch.fft.rfftn` but FFTs only the last two dimensions by default.
+
+The FFT of a real signal is Hermitian-symmetric, ``X[i, j] = conj(X[-i, -j])``,
+so the full :func:`~torch.fft.fft2` output contains redundant information.
+:func:`~torch.fft.rfft2` instead omits the negative frequencies in the last
+dimension.
+
+Note:
+    Supports torch.half on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the real FFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Default: ``s = [input.size(d) for d in dim]``
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        Default: last two dimensions.
+    norm (str, optional): Normalization mode. For the forward transform
+        (:func:`~torch.fft.rfft2`), these correspond to:
+
+        * ``"forward"`` - normalize by ``1/n``
+        * ``"backward"`` - no normalization
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the real FFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical FFT size.
+        Calling the backward transform (:func:`~torch.fft.irfft2`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.irfft2`
+        the exact inverse.
+
+        Default is ``"backward"`` (no normalization).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> t = torch.rand(10, 10)
+    >>> rfft2 = torch.fft.rfft2(t)
+    >>> rfft2.size()
+    torch.Size([10, 6])
+
+    Compared against the full output from :func:`~torch.fft.fft2`, we have all
+    elements up to the Nyquist frequency.
+
+    >>> fft2 = torch.fft.fft2(t)
+    >>> torch.testing.assert_close(fft2[..., :6], rfft2, check_stride=False)
+
+    The discrete Fourier transform is separable, so :func:`~torch.fft.rfft2`
+    here is equivalent to a combination of :func:`~torch.fft.fft` and
+    :func:`~torch.fft.rfft`:
+
+    >>> two_ffts = torch.fft.fft(torch.fft.rfft(t, dim=1), dim=0)
+    >>> torch.testing.assert_close(rfft2, two_ffts, check_stride=False)
+
+""".format(**common_args))
+
+irfft2 = _add_docstr(_fft.fft_irfft2, r"""
+irfft2(input, s=None, dim=(-2, -1), norm=None, *, out=None) -> Tensor
+
+Computes the inverse of :func:`~torch.fft.rfft2`.
+Equivalent to :func:`~torch.fft.irfftn` but IFFTs only the last two dimensions by default.
+
+:attr:`input` is interpreted as a one-sided Hermitian signal in the Fourier
+domain, as produced by :func:`~torch.fft.rfft2`. By the Hermitian property, the
+output will be real-valued.
+
+Note:
+    Some input frequencies must be real-valued to satisfy the Hermitian
+    property. In these cases the imaginary component will be ignored.
+    For example, any imaginary component in the zero-frequency term cannot
+    be represented in a real output and so will always be ignored.
+
+Note:
+    The correct interpretation of the Hermitian input depends on the length of
+    the original data, as given by :attr:`s`. This is because each input shape
+    could correspond to either an odd or even length signal. By default, the
+    signal is assumed to be even length and odd signals will not round-trip
+    properly. So, it is recommended to always pass the signal shape :attr:`s`.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+    With default arguments, the size of last dimension should be (2^n + 1) as argument
+    `s` defaults to even output size = 2 * (last_dim_size - 1)
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the real FFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Defaults to even output in the last dimension:
+        ``s[-1] = 2*(input.size(dim[-1]) - 1)``.
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        The last dimension must be the half-Hermitian compressed dimension.
+        Default: last two dimensions.
+    norm (str, optional): Normalization mode. For the backward transform
+        (:func:`~torch.fft.irfft2`), these correspond to:
+
+        * ``"forward"`` - no normalization
+        * ``"backward"`` - normalize by ``1/n``
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the real IFFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical IFFT size.
+        Calling the forward transform (:func:`~torch.fft.rfft2`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.irfft2`
+        the exact inverse.
+
+        Default is ``"backward"`` (normalize by ``1/n``).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> t = torch.rand(10, 9)
+    >>> T = torch.fft.rfft2(t)
+
+    Without specifying the output length to :func:`~torch.fft.irfft2`, the output
+    will not round-trip properly because the input is odd-length in the last
+    dimension:
+
+    >>> torch.fft.irfft2(T).size()
+    torch.Size([10, 8])
+
+    So, it is recommended to always pass the signal shape :attr:`s`.
+
+    >>> roundtrip = torch.fft.irfft2(T, t.size())
+    >>> roundtrip.size()
+    torch.Size([10, 9])
+    >>> torch.testing.assert_close(roundtrip, t, check_stride=False)
+
+""".format(**common_args))
+
+rfftn = _add_docstr(_fft.fft_rfftn, r"""
+rfftn(input, s=None, dim=None, norm=None, *, out=None) -> Tensor
+
+Computes the N-dimensional discrete Fourier transform of real :attr:`input`.
+
+The FFT of a real signal is Hermitian-symmetric,
+``X[i_1, ..., i_n] = conj(X[-i_1, ..., -i_n])`` so the full
+:func:`~torch.fft.fftn` output contains redundant information.
+:func:`~torch.fft.rfftn` instead omits the negative frequencies in the
+last dimension.
+
+Note:
+    Supports torch.half on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the real FFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Default: ``s = [input.size(d) for d in dim]``
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        Default: all dimensions, or the last ``len(s)`` dimensions if :attr:`s` is given.
+    norm (str, optional): Normalization mode. For the forward transform
+        (:func:`~torch.fft.rfftn`), these correspond to:
+
+        * ``"forward"`` - normalize by ``1/n``
+        * ``"backward"`` - no normalization
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the real FFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical FFT size.
+        Calling the backward transform (:func:`~torch.fft.irfftn`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.irfftn`
+        the exact inverse.
+
+        Default is ``"backward"`` (no normalization).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> t = torch.rand(10, 10)
+    >>> rfftn = torch.fft.rfftn(t)
+    >>> rfftn.size()
+    torch.Size([10, 6])
+
+    Compared against the full output from :func:`~torch.fft.fftn`, we have all
+    elements up to the Nyquist frequency.
+
+    >>> fftn = torch.fft.fftn(t)
+    >>> torch.testing.assert_close(fftn[..., :6], rfftn, check_stride=False)
+
+    The discrete Fourier transform is separable, so :func:`~torch.fft.rfftn`
+    here is equivalent to a combination of :func:`~torch.fft.fft` and
+    :func:`~torch.fft.rfft`:
+
+    >>> two_ffts = torch.fft.fft(torch.fft.rfft(t, dim=1), dim=0)
+    >>> torch.testing.assert_close(rfftn, two_ffts, check_stride=False)
+
+""".format(**common_args))
+
+irfftn = _add_docstr(_fft.fft_irfftn, r"""
+irfftn(input, s=None, dim=None, norm=None, *, out=None) -> Tensor
+
+Computes the inverse of :func:`~torch.fft.rfftn`.
+
+:attr:`input` is interpreted as a one-sided Hermitian signal in the Fourier
+domain, as produced by :func:`~torch.fft.rfftn`. By the Hermitian property, the
+output will be real-valued.
+
+Note:
+    Some input frequencies must be real-valued to satisfy the Hermitian
+    property. In these cases the imaginary component will be ignored.
+    For example, any imaginary component in the zero-frequency term cannot
+    be represented in a real output and so will always be ignored.
+
+Note:
+    The correct interpretation of the Hermitian input depends on the length of
+    the original data, as given by :attr:`s`. This is because each input shape
+    could correspond to either an odd or even length signal. By default, the
+    signal is assumed to be even length and odd signals will not round-trip
+    properly. So, it is recommended to always pass the signal shape :attr:`s`.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+    With default arguments, the size of last dimension should be (2^n + 1) as argument
+    `s` defaults to even output size = 2 * (last_dim_size - 1)
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the real FFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Defaults to even output in the last dimension:
+        ``s[-1] = 2*(input.size(dim[-1]) - 1)``.
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        The last dimension must be the half-Hermitian compressed dimension.
+        Default: all dimensions, or the last ``len(s)`` dimensions if :attr:`s` is given.
+    norm (str, optional): Normalization mode. For the backward transform
+        (:func:`~torch.fft.irfftn`), these correspond to:
+
+        * ``"forward"`` - no normalization
+        * ``"backward"`` - normalize by ``1/n``
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the real IFFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical IFFT size.
+        Calling the forward transform (:func:`~torch.fft.rfftn`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.irfftn`
+        the exact inverse.
+
+        Default is ``"backward"`` (normalize by ``1/n``).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> t = torch.rand(10, 9)
+    >>> T = torch.fft.rfftn(t)
+
+    Without specifying the output length to :func:`~torch.fft.irfft`, the output
+    will not round-trip properly because the input is odd-length in the last
+    dimension:
+
+    >>> torch.fft.irfftn(T).size()
+    torch.Size([10, 8])
+
+    So, it is recommended to always pass the signal shape :attr:`s`.
+
+    >>> roundtrip = torch.fft.irfftn(T, t.size())
+    >>> roundtrip.size()
+    torch.Size([10, 9])
+    >>> torch.testing.assert_close(roundtrip, t, check_stride=False)
+
+""".format(**common_args))
+
+hfft = _add_docstr(_fft.fft_hfft, r"""
+hfft(input, n=None, dim=-1, norm=None, *, out=None) -> Tensor
+
+Computes the one dimensional discrete Fourier transform of a Hermitian
+symmetric :attr:`input` signal.
+
+Note:
+
+    :func:`~torch.fft.hfft`/:func:`~torch.fft.ihfft` are analogous to
+    :func:`~torch.fft.rfft`/:func:`~torch.fft.irfft`. The real FFT expects
+    a real signal in the time-domain and gives a Hermitian symmetry in the
+    frequency-domain. The Hermitian FFT is the opposite; Hermitian symmetric in
+    the time-domain and real-valued in the frequency-domain. For this reason,
+    special care needs to be taken with the length argument :attr:`n`, in the
+    same way as with :func:`~torch.fft.irfft`.
+
+Note:
+    Because the signal is Hermitian in the time-domain, the result will be
+    real in the frequency domain. Note that some input frequencies must be
+    real-valued to satisfy the Hermitian property. In these cases the imaginary
+    component will be ignored. For example, any imaginary component in
+    ``input[0]`` would result in one or more complex frequency terms which
+    cannot be represented in a real output and so will always be ignored.
+
+Note:
+    The correct interpretation of the Hermitian input depends on the length of
+    the original data, as given by :attr:`n`. This is because each input shape
+    could correspond to either an odd or even length signal. By default, the
+    signal is assumed to be even length and odd signals will not round-trip
+    properly. So, it is recommended to always pass the signal length :attr:`n`.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimension.
+    With default arguments, size of the transformed dimension should be (2^n + 1) as argument
+    `n` defaults to even output size = 2 * (transformed_dim_size - 1)
+
+Args:
+    input (Tensor): the input tensor representing a half-Hermitian signal
+    n (int, optional): Output signal length. This determines the length of the
+        real output. If given, the input will either be zero-padded or trimmed to this
+        length before computing the Hermitian FFT.
+        Defaults to even output: ``n=2*(input.size(dim) - 1)``.
+    dim (int, optional): The dimension along which to take the one dimensional Hermitian FFT.
+    norm (str, optional): Normalization mode. For the forward transform
+        (:func:`~torch.fft.hfft`), these correspond to:
+
+        * ``"forward"`` - normalize by ``1/n``
+        * ``"backward"`` - no normalization
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the Hermitian FFT orthonormal)
+
+        Calling the backward transform (:func:`~torch.fft.ihfft`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.ihfft`
+        the exact inverse.
+
+        Default is ``"backward"`` (no normalization).
+
+Keyword args:
+    {out}
+
+Example:
+
+    Taking a real-valued frequency signal and bringing it into the time domain
+    gives Hermitian symmetric output:
+
+    >>> t = torch.linspace(0, 1, 5)
+    >>> t
+    tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000])
+    >>> T = torch.fft.ifft(t)
+    >>> T
+    tensor([ 0.5000-0.0000j, -0.1250-0.1720j, -0.1250-0.0406j, -0.1250+0.0406j,
+            -0.1250+0.1720j])
+
+    Note that ``T[1] == T[-1].conj()`` and ``T[2] == T[-2].conj()`` is
+    redundant. We can thus compute the forward transform without considering
+    negative frequencies:
+
+    >>> torch.fft.hfft(T[:3], n=5)
+    tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000])
+
+    Like with :func:`~torch.fft.irfft`, the output length must be given in order
+    to recover an even length output:
+
+    >>> torch.fft.hfft(T[:3])
+    tensor([0.1250, 0.2809, 0.6250, 0.9691])
+""".format(**common_args))
+
+ihfft = _add_docstr(_fft.fft_ihfft, r"""
+ihfft(input, n=None, dim=-1, norm=None, *, out=None) -> Tensor
+
+Computes the inverse of :func:`~torch.fft.hfft`.
+
+:attr:`input` must be a real-valued signal, interpreted in the Fourier domain.
+The IFFT of a real signal is Hermitian-symmetric, ``X[i] = conj(X[-i])``.
+:func:`~torch.fft.ihfft` represents this in the one-sided form where only the
+positive frequencies below the Nyquist frequency are included. To compute the
+full output, use :func:`~torch.fft.ifft`.
+
+Note:
+    Supports torch.half on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimension.
+
+Args:
+    input (Tensor): the real input tensor
+    n (int, optional): Signal length. If given, the input will either be zero-padded
+        or trimmed to this length before computing the Hermitian IFFT.
+    dim (int, optional): The dimension along which to take the one dimensional Hermitian IFFT.
+    norm (str, optional): Normalization mode. For the backward transform
+        (:func:`~torch.fft.ihfft`), these correspond to:
+
+        * ``"forward"`` - no normalization
+        * ``"backward"`` - normalize by ``1/n``
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the IFFT orthonormal)
+
+        Calling the forward transform (:func:`~torch.fft.hfft`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.ihfft`
+        the exact inverse.
+
+        Default is ``"backward"`` (normalize by ``1/n``).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> t = torch.arange(5)
+    >>> t
+    tensor([0, 1, 2, 3, 4])
+    >>> torch.fft.ihfft(t)
+    tensor([ 2.0000-0.0000j, -0.5000-0.6882j, -0.5000-0.1625j])
+
+    Compare against the full output from :func:`~torch.fft.ifft`:
+
+    >>> torch.fft.ifft(t)
+    tensor([ 2.0000-0.0000j, -0.5000-0.6882j, -0.5000-0.1625j, -0.5000+0.1625j,
+            -0.5000+0.6882j])
+""".format(**common_args))
+
+hfft2 = _add_docstr(_fft.fft_hfft2, r"""
+hfft2(input, s=None, dim=(-2, -1), norm=None, *, out=None) -> Tensor
+
+Computes the 2-dimensional discrete Fourier transform of a Hermitian symmetric
+:attr:`input` signal. Equivalent to :func:`~torch.fft.hfftn` but only
+transforms the last two dimensions by default.
+
+:attr:`input` is interpreted as a one-sided Hermitian signal in the time
+domain. By the Hermitian property, the Fourier transform will be real-valued.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+    With default arguments, the size of last dimension should be (2^n + 1) as argument
+    `s` defaults to even output size = 2 * (last_dim_size - 1)
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the Hermitian FFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Defaults to even output in the last dimension:
+        ``s[-1] = 2*(input.size(dim[-1]) - 1)``.
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        The last dimension must be the half-Hermitian compressed dimension.
+        Default: last two dimensions.
+    norm (str, optional): Normalization mode. For the forward transform
+        (:func:`~torch.fft.hfft2`), these correspond to:
+
+        * ``"forward"`` - normalize by ``1/n``
+        * ``"backward"`` - no normalization
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the Hermitian FFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical FFT size.
+        Calling the backward transform (:func:`~torch.fft.ihfft2`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.ihfft2`
+        the exact inverse.
+
+        Default is ``"backward"`` (no normalization).
+
+Keyword args:
+    {out}
+
+Example:
+
+    Starting from a real frequency-space signal, we can generate a
+    Hermitian-symmetric time-domain signal:
+    >>> T = torch.rand(10, 9)
+    >>> t = torch.fft.ihfft2(T)
+
+    Without specifying the output length to :func:`~torch.fft.hfftn`, the
+    output will not round-trip properly because the input is odd-length in the
+    last dimension:
+
+    >>> torch.fft.hfft2(t).size()
+    torch.Size([10, 10])
+
+    So, it is recommended to always pass the signal shape :attr:`s`.
+
+    >>> roundtrip = torch.fft.hfft2(t, T.size())
+    >>> roundtrip.size()
+    torch.Size([10, 9])
+    >>> torch.allclose(roundtrip, T)
+    True
+
+""".format(**common_args))
+
+ihfft2 = _add_docstr(_fft.fft_ihfft2, r"""
+ihfft2(input, s=None, dim=(-2, -1), norm=None, *, out=None) -> Tensor
+
+Computes the 2-dimensional inverse discrete Fourier transform of real
+:attr:`input`. Equivalent to :func:`~torch.fft.ihfftn` but transforms only the
+two last dimensions by default.
+
+Note:
+    Supports torch.half on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the Hermitian IFFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Default: ``s = [input.size(d) for d in dim]``
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        Default: last two dimensions.
+    norm (str, optional): Normalization mode. For the backward transform
+        (:func:`~torch.fft.ihfft2`), these correspond to:
+
+        * ``"forward"`` - no normalization
+        * ``"backward"`` - normalize by ``1/n``
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the Hermitian IFFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical IFFT size.
+        Calling the forward transform (:func:`~torch.fft.hfft2`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.ihfft2`
+        the exact inverse.
+
+        Default is ``"backward"`` (normalize by ``1/n``).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> T = torch.rand(10, 10)
+    >>> t = torch.fft.ihfft2(t)
+    >>> t.size()
+    torch.Size([10, 6])
+
+    Compared against the full output from :func:`~torch.fft.ifft2`, the
+    Hermitian time-space signal takes up only half the space.
+
+    >>> fftn = torch.fft.ifft2(t)
+    >>> torch.allclose(fftn[..., :6], rfftn)
+    True
+
+    The discrete Fourier transform is separable, so :func:`~torch.fft.ihfft2`
+    here is equivalent to a combination of :func:`~torch.fft.ifft` and
+    :func:`~torch.fft.ihfft`:
+
+    >>> two_ffts = torch.fft.ifft(torch.fft.ihfft(t, dim=1), dim=0)
+    >>> torch.allclose(t, two_ffts)
+    True
+
+""".format(**common_args))
+
+hfftn = _add_docstr(_fft.fft_hfftn, r"""
+hfftn(input, s=None, dim=None, norm=None, *, out=None) -> Tensor
+
+Computes the n-dimensional discrete Fourier transform of a Hermitian symmetric
+:attr:`input` signal.
+
+:attr:`input` is interpreted as a one-sided Hermitian signal in the time
+domain. By the Hermitian property, the Fourier transform will be real-valued.
+
+Note:
+    :func:`~torch.fft.hfftn`/:func:`~torch.fft.ihfftn` are analogous to
+    :func:`~torch.fft.rfftn`/:func:`~torch.fft.irfftn`. The real FFT expects
+    a real signal in the time-domain and gives Hermitian symmetry in the
+    frequency-domain. The Hermitian FFT is the opposite; Hermitian symmetric in
+    the time-domain and real-valued in the frequency-domain. For this reason,
+    special care needs to be taken with the shape argument :attr:`s`, in the
+    same way as with :func:`~torch.fft.irfftn`.
+
+Note:
+    Some input frequencies must be real-valued to satisfy the Hermitian
+    property. In these cases the imaginary component will be ignored.
+    For example, any imaginary component in the zero-frequency term cannot
+    be represented in a real output and so will always be ignored.
+
+Note:
+    The correct interpretation of the Hermitian input depends on the length of
+    the original data, as given by :attr:`s`. This is because each input shape
+    could correspond to either an odd or even length signal. By default, the
+    signal is assumed to be even length and odd signals will not round-trip
+    properly. It is recommended to always pass the signal shape :attr:`s`.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+    With default arguments, the size of last dimension should be (2^n + 1) as argument
+    `s` defaults to even output size = 2 * (last_dim_size - 1)
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the real FFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Defaults to even output in the last dimension:
+        ``s[-1] = 2*(input.size(dim[-1]) - 1)``.
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        The last dimension must be the half-Hermitian compressed dimension.
+        Default: all dimensions, or the last ``len(s)`` dimensions if :attr:`s` is given.
+    norm (str, optional): Normalization mode. For the forward transform
+        (:func:`~torch.fft.hfftn`), these correspond to:
+
+        * ``"forward"`` - normalize by ``1/n``
+        * ``"backward"`` - no normalization
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the Hermitian FFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical FFT size.
+        Calling the backward transform (:func:`~torch.fft.ihfftn`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.ihfftn`
+        the exact inverse.
+
+        Default is ``"backward"`` (no normalization).
+
+Keyword args:
+    {out}
+
+Example:
+
+    Starting from a real frequency-space signal, we can generate a
+    Hermitian-symmetric time-domain signal:
+    >>> T = torch.rand(10, 9)
+    >>> t = torch.fft.ihfftn(T)
+
+    Without specifying the output length to :func:`~torch.fft.hfftn`, the
+    output will not round-trip properly because the input is odd-length in the
+    last dimension:
+
+    >>> torch.fft.hfftn(t).size()
+    torch.Size([10, 10])
+
+    So, it is recommended to always pass the signal shape :attr:`s`.
+
+    >>> roundtrip = torch.fft.hfftn(t, T.size())
+    >>> roundtrip.size()
+    torch.Size([10, 9])
+    >>> torch.allclose(roundtrip, T)
+    True
+
+""".format(**common_args))
+
+ihfftn = _add_docstr(_fft.fft_ihfftn, r"""
+ihfftn(input, s=None, dim=None, norm=None, *, out=None) -> Tensor
+
+Computes the N-dimensional inverse discrete Fourier transform of real :attr:`input`.
+
+:attr:`input` must be a real-valued signal, interpreted in the Fourier domain.
+The n-dimensional IFFT of a real signal is Hermitian-symmetric,
+``X[i, j, ...] = conj(X[-i, -j, ...])``. :func:`~torch.fft.ihfftn` represents
+this in the one-sided form where only the positive frequencies below the
+Nyquist frequency are included in the last signal dimension. To compute the
+full output, use :func:`~torch.fft.ifftn`.
+
+Note:
+    Supports torch.half on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the Hermitian IFFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Default: ``s = [input.size(d) for d in dim]``
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        Default: all dimensions, or the last ``len(s)`` dimensions if :attr:`s` is given.
+    norm (str, optional): Normalization mode. For the backward transform
+        (:func:`~torch.fft.ihfftn`), these correspond to:
+
+        * ``"forward"`` - no normalization
+        * ``"backward"`` - normalize by ``1/n``
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the Hermitian IFFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical IFFT size.
+        Calling the forward transform (:func:`~torch.fft.hfftn`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.ihfftn`
+        the exact inverse.
+
+        Default is ``"backward"`` (normalize by ``1/n``).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> T = torch.rand(10, 10)
+    >>> ihfftn = torch.fft.ihfftn(T)
+    >>> ihfftn.size()
+    torch.Size([10, 6])
+
+    Compared against the full output from :func:`~torch.fft.ifftn`, we have all
+    elements up to the Nyquist frequency.
+
+    >>> ifftn = torch.fft.ifftn(t)
+    >>> torch.allclose(ifftn[..., :6], ihfftn)
+    True
+
+    The discrete Fourier transform is separable, so :func:`~torch.fft.ihfftn`
+    here is equivalent to a combination of :func:`~torch.fft.ihfft` and
+    :func:`~torch.fft.ifft`:
+
+    >>> two_iffts = torch.fft.ifft(torch.fft.ihfft(t, dim=1), dim=0)
+    >>> torch.allclose(ihfftn, two_iffts)
+    True
+
+""".format(**common_args))
+
+fftfreq = _add_docstr(_fft.fft_fftfreq, r"""
+fftfreq(n, d=1.0, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
+
+Computes the discrete Fourier Transform sample frequencies for a signal of size :attr:`n`.
+
+Note:
+    By convention, :func:`~torch.fft.fft` returns positive frequency terms
+    first, followed by the negative frequencies in reverse order, so that
+    ``f[-i]`` for all :math:`0 < i \leq n/2`` in Python gives the negative
+    frequency terms. For an FFT of length :attr:`n` and with inputs spaced in
+    length unit :attr:`d`, the frequencies are::
+
+        f = [0, 1, ..., (n - 1) // 2, -(n // 2), ..., -1] / (d * n)
+
+Note:
+    For even lengths, the Nyquist frequency at ``f[n/2]`` can be thought of as
+    either negative or positive. :func:`~torch.fft.fftfreq` follows NumPy's
+    convention of taking it to be negative.
+
+Args:
+    n (int): the FFT length
+    d (float, optional): The sampling length scale.
+        The spacing between individual samples of the FFT input.
+        The default assumes unit spacing, dividing that result by the actual
+        spacing gives the result in physical frequency units.
+
+Keyword Args:
+    {out}
+    {dtype}
+    {layout}
+    {device}
+    {requires_grad}
+
+Example:
+
+    >>> torch.fft.fftfreq(5)
+    tensor([ 0.0000,  0.2000,  0.4000, -0.4000, -0.2000])
+
+    For even input, we can see the Nyquist frequency at ``f[2]`` is given as
+    negative:
+
+    >>> torch.fft.fftfreq(4)
+    tensor([ 0.0000,  0.2500, -0.5000, -0.2500])
+
+""".format(**factory_common_args))
+
+rfftfreq = _add_docstr(_fft.fft_rfftfreq, r"""
+rfftfreq(n, d=1.0, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
+
+Computes the sample frequencies for :func:`~torch.fft.rfft` with a signal of size :attr:`n`.
+
+Note:
+    :func:`~torch.fft.rfft` returns Hermitian one-sided output, so only the
+    positive frequency terms are returned. For a real FFT of length :attr:`n`
+    and with inputs spaced in length unit :attr:`d`, the frequencies are::
+
+        f = torch.arange((n + 1) // 2) / (d * n)
+
+Note:
+    For even lengths, the Nyquist frequency at ``f[n/2]`` can be thought of as
+    either negative or positive. Unlike :func:`~torch.fft.fftfreq`,
+    :func:`~torch.fft.rfftfreq` always returns it as positive.
+
+Args:
+    n (int): the real FFT length
+    d (float, optional): The sampling length scale.
+        The spacing between individual samples of the FFT input.
+        The default assumes unit spacing, dividing that result by the actual
+        spacing gives the result in physical frequency units.
+
+Keyword Args:
+    {out}
+    {dtype}
+    {layout}
+    {device}
+    {requires_grad}
+
+Example:
+
+    >>> torch.fft.rfftfreq(5)
+    tensor([0.0000, 0.2000, 0.4000])
+
+    >>> torch.fft.rfftfreq(4)
+    tensor([0.0000, 0.2500, 0.5000])
+
+    Compared to the output from :func:`~torch.fft.fftfreq`, we see that the
+    Nyquist frequency at ``f[2]`` has changed sign:
+    >>> torch.fft.fftfreq(4)
+    tensor([ 0.0000,  0.2500, -0.5000, -0.2500])
+
+""".format(**factory_common_args))
+
+fftshift = _add_docstr(_fft.fft_fftshift, r"""
+fftshift(input, dim=None) -> Tensor
+
+Reorders n-dimensional FFT data, as provided by :func:`~torch.fft.fftn`, to have
+negative frequency terms first.
+
+This performs a periodic shift of n-dimensional data such that the origin
+``(0, ..., 0)`` is moved to the center of the tensor. Specifically, to
+``input.shape[dim] // 2`` in each selected dimension.
+
+Note:
+    By convention, the FFT returns positive frequency terms first, followed by
+    the negative frequencies in reverse order, so that ``f[-i]`` for all
+    :math:`0 < i \leq n/2` in Python gives the negative frequency terms.
+    :func:`~torch.fft.fftshift` rearranges all frequencies into ascending order
+    from negative to positive with the zero-frequency term in the center.
+
+Note:
+    For even lengths, the Nyquist frequency at ``f[n/2]`` can be thought of as
+    either negative or positive. :func:`~torch.fft.fftshift` always puts the
+    Nyquist term at the 0-index. This is the same convention used by
+    :func:`~torch.fft.fftfreq`.
+
+Args:
+    input (Tensor): the tensor in FFT order
+    dim (int, Tuple[int], optional): The dimensions to rearrange.
+        Only dimensions specified here will be rearranged, any other dimensions
+        will be left in their original order.
+        Default: All dimensions of :attr:`input`.
+
+Example:
+
+    >>> f = torch.fft.fftfreq(4)
+    >>> f
+    tensor([ 0.0000,  0.2500, -0.5000, -0.2500])
+
+    >>> torch.fft.fftshift(f)
+    tensor([-0.5000, -0.2500,  0.0000,  0.2500])
+
+    Also notice that the Nyquist frequency term at ``f[2]`` was moved to the
+    beginning of the tensor.
+
+    This also works for multi-dimensional transforms:
+
+    >>> x = torch.fft.fftfreq(5, d=1/5) + 0.1 * torch.fft.fftfreq(5, d=1/5).unsqueeze(1)
+    >>> x
+    tensor([[ 0.0000,  1.0000,  2.0000, -2.0000, -1.0000],
+            [ 0.1000,  1.1000,  2.1000, -1.9000, -0.9000],
+            [ 0.2000,  1.2000,  2.2000, -1.8000, -0.8000],
+            [-0.2000,  0.8000,  1.8000, -2.2000, -1.2000],
+            [-0.1000,  0.9000,  1.9000, -2.1000, -1.1000]])
+
+    >>> torch.fft.fftshift(x)
+    tensor([[-2.2000, -1.2000, -0.2000,  0.8000,  1.8000],
+            [-2.1000, -1.1000, -0.1000,  0.9000,  1.9000],
+            [-2.0000, -1.0000,  0.0000,  1.0000,  2.0000],
+            [-1.9000, -0.9000,  0.1000,  1.1000,  2.1000],
+            [-1.8000, -0.8000,  0.2000,  1.2000,  2.2000]])
+
+    :func:`~torch.fft.fftshift` can also be useful for spatial data. If our
+    data is defined on a centered grid (``[-(N//2), (N-1)//2]``) then we can
+    use the standard FFT defined on an uncentered grid (``[0, N)``) by first
+    applying an :func:`~torch.fft.ifftshift`.
+
+    >>> x_centered = torch.arange(-5, 5)
+    >>> x_uncentered = torch.fft.ifftshift(x_centered)
+    >>> fft_uncentered = torch.fft.fft(x_uncentered)
+
+    Similarly, we can convert the frequency domain components to centered
+    convention by applying :func:`~torch.fft.fftshift`.
+
+    >>> fft_centered = torch.fft.fftshift(fft_uncentered)
+
+    The inverse transform, from centered Fourier space back to centered spatial
+    data, can be performed by applying the inverse shifts in reverse order:
+
+    >>> x_centered_2 = torch.fft.fftshift(torch.fft.ifft(torch.fft.ifftshift(fft_centered)))
+    >>> torch.testing.assert_close(x_centered.to(torch.complex64), x_centered_2, check_stride=False)
+
+
+""")
+
+ifftshift = _add_docstr(_fft.fft_ifftshift, r"""
+ifftshift(input, dim=None) -> Tensor
+
+Inverse of :func:`~torch.fft.fftshift`.
+
+Args:
+    input (Tensor): the tensor in FFT order
+    dim (int, Tuple[int], optional): The dimensions to rearrange.
+        Only dimensions specified here will be rearranged, any other dimensions
+        will be left in their original order.
+        Default: All dimensions of :attr:`input`.
+
+Example:
+
+    >>> f = torch.fft.fftfreq(5)
+    >>> f
+    tensor([ 0.0000,  0.2000,  0.4000, -0.4000, -0.2000])
+
+    A round-trip through :func:`~torch.fft.fftshift` and
+    :func:`~torch.fft.ifftshift` gives the same result:
+
+    >>> shifted = torch.fft.fftshift(f)
+    >>> torch.fft.ifftshift(shifted)
+    tensor([ 0.0000,  0.2000,  0.4000, -0.4000, -0.2000])
+
+""")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/func/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/func/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd0786456dec0f2346e56e76fbfa18b12acd49d5
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/func/__init__.py
@@ -0,0 +1,13 @@
+from torch._functorch.eager_transforms import (
+    vjp,
+    jvp,
+    jacrev,
+    jacfwd,
+    hessian,
+    functionalize,
+    linearize
+)
+from torch._functorch.apis import grad, grad_and_value
+from torch._functorch.functional_call import functional_call, stack_module_state
+from torch._functorch.batch_norm_replacement import replace_all_batch_norm_modules_
+from torch._functorch.apis import vmap
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/futures/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/futures/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1623c44f193d74de2a8d099699cd3439a1f1227
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/futures/__init__.py
@@ -0,0 +1,319 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+from typing import cast, Callable, Generic, List, Optional, Type, TypeVar, Union
+
+import torch
+
+__all__ = ['Future', 'collect_all', 'wait_all']
+
+T = TypeVar("T")
+S = TypeVar("S")
+
+
+class _PyFutureMeta(type(torch._C.Future), type(Generic)):  # type: ignore[misc, no-redef]
+    pass
+
+
+class Future(torch._C.Future, Generic[T], metaclass=_PyFutureMeta):
+    r"""
+    Wrapper around a ``torch._C.Future`` which encapsulates an asynchronous
+    execution of a callable, e.g. :meth:`~torch.distributed.rpc.rpc_async`. It
+    also exposes a set of APIs to add callback functions and set results.
+
+    .. warning:: GPU support is a beta feature, subject to changes.
+    """
+
+    def __init__(self, *, devices: Optional[List[Union[int, str, torch.device]]] = None):
+        r"""
+        Create an empty unset ``Future``. If the future is intended to hold
+        values containing CUDA tensors, (a superset of) their CUDA devices must
+        be specified at construction. (This is only supported if
+        ``torch.cuda.is_available()`` returns ``True``). This is needed to
+        ensure proper CUDA stream synchronization. The child futures, returned
+        by the ``then`` method, will inherit these devices.
+
+        Args:
+            devices(``List[Union[int, str, torch.device]]``, optional): the set
+                of devices on which tensors contained in this future's value are
+                allowed to reside and on which callbacks are allowed to operate.
+        """
+        if devices is None:
+            devices = []
+        super().__init__([torch.device(d) for d in devices])
+
+    def done(self) -> bool:
+        r"""
+        Return ``True`` if this ``Future`` is done. A ``Future`` is done if it
+        has a result or an exception.
+
+        If the value contains tensors that reside on GPUs, ``Future.done()``
+        will return ``True`` even if the asynchronous kernels that are
+        populating those tensors haven't yet completed running on the device,
+        because at such stage the result is already usable, provided one
+        performs the appropriate synchronizations (see :meth:`wait`).
+        """
+        return super().done()
+
+    def wait(self) -> T:
+        r"""
+        Block until the value of this ``Future`` is ready.
+
+        If the value contains tensors that reside on GPUs, then an additional
+        synchronization is performed with the kernels (executing on the device)
+        which may be asynchronously populating those tensors. Such sync is
+        non-blocking, which means that ``wait()`` will insert the necessary
+        instructions in the current streams to ensure that further operations
+        enqueued on those streams will be properly scheduled after the async
+        kernels but, once that is done, ``wait()`` will return, even if those
+        kernels are still running. No further synchronization is required when
+        accessing and using the values, as long as one doesn't change streams.
+
+        Returns:
+            The value held by this ``Future``. If the function (callback or RPC)
+            creating the value has thrown an error, this ``wait`` method will
+            also throw an error.
+        """
+        return super().wait()
+
+    def value(self) -> T:
+        r"""
+        Obtain the value of an already-completed future.
+
+        This method should only be called after a call to :meth:`wait` has
+        completed, or inside a callback function passed to :meth:`then`. In
+        other cases this ``Future`` may not yet hold a value and calling
+        ``value()`` could fail.
+
+        If the value contains tensors that reside on GPUs, then this method will
+        *not* perform any additional synchronization. This should be done
+        beforehand, separately, through a call to :meth:`wait` (except within
+        callbacks, for which it's already being taken care of by :meth:`then`).
+
+        Returns:
+            The value held by this ``Future``. If the function (callback or RPC)
+            creating the value has thrown an error, this ``value()`` method will
+            also throw an error.
+        """
+        return super().value()
+
+    def then(self, callback: Callable[[Future[T]], S]) -> Future[S]:
+        r"""
+        Append the given callback function to this ``Future``, which will be run
+        when the ``Future`` is completed.  Multiple callbacks can be added to
+        the same ``Future``, but the order in which they will be executed cannot
+        be guaranteed (to enforce a certain order consider chaining:
+        ``fut.then(cb1).then(cb2)``). The callback must take one argument, which
+        is the reference to this ``Future``. The callback function can use the
+        :meth:`value` method to get the value. Note that if this ``Future`` is
+        already completed, the given callback will be run immediately inline.
+
+        If the ``Future``'s value contains tensors that reside on GPUs, the
+        callback might be invoked while the async kernels that are populating
+        those tensors haven't yet finished executing on the device. However, the
+        callback will be invoked with some dedicated streams set as current
+        (fetched from a global pool) which will be synchronized with those
+        kernels. Hence any operation performed by the callback on these tensors
+        will be scheduled on the device after the kernels complete. In other
+        words, as long as the callback doesn't switch streams, it can safely
+        manipulate the result without any additional synchronization. This is
+        similar to the non-blocking behavior of :meth:`wait`.
+
+        Similarly, if the callback returns a value that contains tensors that
+        reside on a GPU, it can do so even if the kernels that are producing
+        these tensors are still running on the device, as long as the callback
+        didn't change streams during its execution. If one wants to change
+        streams, one must be careful to re-synchronize them with the original
+        streams, that is, those that were current when the callback was invoked.
+
+        Args:
+            callback(``Callable``): a ``Callable`` that takes this ``Future`` as
+                                    the only argument.
+
+        Returns:
+            A new ``Future`` object that holds the return value of the
+            ``callback`` and will be marked as completed when the given
+            ``callback`` finishes.
+
+        .. note:: Note that if the callback function throws, either
+            through the original future being completed with an exception and
+            calling ``fut.wait()``, or through other code in the callback, the
+            future returned by ``then`` will be marked appropriately with the
+            encountered error. However, if this callback later completes
+            additional futures, those futures are not marked as completed with
+            an error and the user is responsible for handling completion/waiting
+            on those futures independently.
+
+        Example::
+            >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
+            >>> def callback(fut):
+            ...     print(f"RPC return value is {fut.wait()}.")
+            >>> fut = torch.futures.Future()
+            >>> # The inserted callback will print the return value when
+            >>> # receiving the response from "worker1"
+            >>> cb_fut = fut.then(callback)
+            >>> chain_cb_fut = cb_fut.then(
+            ...     lambda x : print(f"Chained cb done. {x.wait()}")
+            ... )
+            >>> fut.set_result(5)
+            RPC return value is 5.
+            Chained cb done. None
+        """
+        return cast(Future[S], super().then(callback))
+
+    def add_done_callback(self, callback: Callable[[Future[T]], None]) -> None:
+        r"""
+        Append the given callback function to this ``Future``, which will be run
+        when the ``Future`` is completed.  Multiple callbacks can be added to
+        the same ``Future``, but the order in which they will be executed cannot
+        be guaranteed. The callback must take one argument, which is the
+        reference to this ``Future``. The callback function can use the
+        :meth:`value` method to get the value. Note that if this ``Future`` is
+        already completed, the given callback will be run inline.
+
+        We recommend that you use the :meth:`then` method as it provides a way
+        to synchronize after your callback has completed. ``add_done_callback``
+        can be cheaper if your callback does not return anything. But both
+        :meth:`then` and ``add_done_callback`` use the same callback
+        registration API under the hood.
+
+        With respect to GPU tensors, this method behaves in the same way as
+        :meth:`then`.
+
+        Args:
+            callback(``Future``): a ``Callable`` that takes in one argument,
+                which is the reference to this ``Future``.
+
+        .. note:: Note that if the callback function throws, either
+            through the original future being completed with an exception and
+            calling ``fut.wait()``, or through other code in the callback,
+            error handling must be carefully taken care of. For example, if
+            this callback later completes additional futures, those futures are
+            not marked as completed with an error and the user is responsible
+            for handling completion/waiting on those futures independently.
+
+        Example::
+            >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
+            >>> def callback(fut):
+            ...     print("This will run after the future has finished.")
+            ...     print(fut.wait())
+            >>> fut = torch.futures.Future()
+            >>> fut.add_done_callback(callback)
+            >>> fut.set_result(5)
+            This will run after the future has finished.
+            5
+        """
+        super().add_done_callback(callback)
+
+    def set_result(self, result: T) -> None:
+        r"""
+        Set the result for this ``Future``, which will mark this ``Future`` as
+        completed and trigger all attached callbacks. Note that a ``Future``
+        cannot be marked completed twice.
+
+        If the result contains tensors that reside on GPUs, this method can be
+        called even if the asynchronous kernels that are populating those
+        tensors haven't yet completed running on the device, provided that the
+        streams on which those kernels were enqueued are set as the current ones
+        when this method is called. Put simply, it's safe to call this method
+        immediately after launching those kernels, without any additional
+        synchronization, as long as one doesn't change streams in between. This
+        method will record events on all the relevant current streams and will
+        use them to ensure proper scheduling for all the consumers of this
+        ``Future``.
+
+        Args:
+            result (object): the result object of this ``Future``.
+
+        Example::
+            >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
+            >>> import threading
+            >>> import time
+            >>> def slow_set_future(fut, value):
+            ...     time.sleep(0.5)
+            ...     fut.set_result(value)
+            >>> fut = torch.futures.Future()
+            >>> t = threading.Thread(
+            ...     target=slow_set_future,
+            ...     args=(fut, torch.ones(2) * 3)
+            ... )
+            >>> t.start()
+            >>> print(fut.wait())
+            tensor([3., 3.])
+            >>> t.join()
+        """
+        super().set_result(result)
+
+    def set_exception(self, result: T) -> None:
+        r"""
+        Set an exception for this ``Future``, which will mark this ``Future`` as
+        completed with an error and trigger all attached callbacks. Note that
+        when calling wait()/value() on this ``Future``, the exception set here
+        will be raised inline.
+
+        Args:
+            result (BaseException): the exception for this ``Future``.
+
+        Example::
+            >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
+            >>> fut = torch.futures.Future()
+            >>> fut.set_exception(ValueError("foo"))
+            >>> fut.wait()
+            Traceback (most recent call last):
+            ...
+            ValueError: foo
+        """
+        assert isinstance(result, Exception), f"{result} is of type {type(result)}, not an Exception."
+
+        def raise_error(fut_result):
+            raise fut_result
+
+        super()._set_unwrap_func(raise_error)
+        self.set_result(result)  # type: ignore[arg-type]
+
+
+def collect_all(futures: List[Future]) -> Future[List[Future]]:
+    r"""
+    Collects the provided :class:`~torch.futures.Future` objects into a single
+    combined :class:`~torch.futures.Future` that is completed when all of the
+    sub-futures are completed.
+
+    Args:
+        futures (list): a list of :class:`~torch.futures.Future` objects.
+
+    Returns:
+        Returns a :class:`~torch.futures.Future` object to a list of the passed
+        in Futures.
+
+    Example::
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
+        >>> fut0 = torch.futures.Future()
+        >>> fut1 = torch.futures.Future()
+        >>> fut = torch.futures.collect_all([fut0, fut1])
+        >>> fut0.set_result(0)
+        >>> fut1.set_result(1)
+        >>> fut_list = fut.wait()
+        >>> print(f"fut0 result = {fut_list[0].wait()}")
+        fut0 result = 0
+        >>> print(f"fut1 result = {fut_list[1].wait()}")
+        fut1 result = 1
+    """
+    return cast(Future[List[Future]], torch._C._collect_all(cast(List[torch._C.Future], futures)))
+
+
+def wait_all(futures: List[Future]) -> List:
+    r"""
+    Waits for all provided futures to be complete, and returns
+    the list of completed values. If any of the futures encounters an error,
+    the method will exit early and report the error not waiting for other
+    futures to complete.
+
+    Args:
+        futures (list): a list of :class:`~torch.futures.Future` object.
+
+    Returns:
+        A list of the completed :class:`~torch.futures.Future` results. This
+        method will throw an error if ``wait`` on any
+        :class:`~torch.futures.Future` throws.
+    """
+    return [fut.wait() for fut in torch._C._collect_all(cast(List[torch._C.Future], futures)).wait()]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/futures/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/futures/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd04cdd09d7fa1a877e64d114db6e037849d7ba1
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/__init__.py
@@ -0,0 +1,89 @@
+r'''
+FX is a toolkit for developers to use to transform ``nn.Module``
+instances. FX consists of three main components: a **symbolic tracer,**
+an **intermediate representation**, and **Python code generation**. A
+demonstration of these components in action:
+
+::
+
+    import torch
+    # Simple module for demonstration
+    class MyModule(torch.nn.Module):
+        def __init__(self) -> None:
+            super().__init__()
+            self.param = torch.nn.Parameter(torch.rand(3, 4))
+            self.linear = torch.nn.Linear(4, 5)
+
+        def forward(self, x):
+            return self.linear(x + self.param).clamp(min=0.0, max=1.0)
+
+    module = MyModule()
+
+    from torch.fx import symbolic_trace
+    # Symbolic tracing frontend - captures the semantics of the module
+    symbolic_traced : torch.fx.GraphModule = symbolic_trace(module)
+
+    # High-level intermediate representation (IR) - Graph representation
+    print(symbolic_traced.graph)
+    """
+    graph():
+        %x : [num_users=1] = placeholder[target=x]
+        %param : [num_users=1] = get_attr[target=param]
+        %add : [num_users=1] = call_function[target=operator.add](args = (%x, %param), kwargs = {})
+        %linear : [num_users=1] = call_module[target=linear](args = (%add,), kwargs = {})
+        %clamp : [num_users=1] = call_method[target=clamp](args = (%linear,), kwargs = {min: 0.0, max: 1.0})
+        return clamp
+    """
+
+    # Code generation - valid Python code
+    print(symbolic_traced.code)
+    """
+    def forward(self, x):
+        param = self.param
+        add = x + param;  x = param = None
+        linear = self.linear(add);  add = None
+        clamp = linear.clamp(min = 0.0, max = 1.0);  linear = None
+        return clamp
+    """
+
+The **symbolic tracer** performs "symbolic execution" of the Python
+code. It feeds fake values, called Proxies, through the code. Operations
+on theses Proxies are recorded. More information about symbolic tracing
+can be found in the :func:`symbolic_trace` and :class:`Tracer`
+documentation.
+
+The **intermediate representation** is the container for the operations
+that were recorded during symbolic tracing. It consists of a list of
+Nodes that represent function inputs, callsites (to functions, methods,
+or :class:`torch.nn.Module` instances), and return values. More information
+about the IR can be found in the documentation for :class:`Graph`. The
+IR is the format on which transformations are applied.
+
+**Python code generation** is what makes FX a Python-to-Python (or
+Module-to-Module) transformation toolkit. For each Graph IR, we can
+create valid Python code matching the Graph's semantics. This
+functionality is wrapped up in :class:`GraphModule`, which is a
+:class:`torch.nn.Module` instance that holds a :class:`Graph` as well as a
+``forward`` method generated from the Graph.
+
+Taken together, this pipeline of components (symbolic tracing ->
+intermediate representation -> transforms -> Python code generation)
+constitutes the Python-to-Python transformation pipeline of FX. In
+addition, these components can be used separately. For example,
+symbolic tracing can be used in isolation to capture a form of
+the code for analysis (and not transformation) purposes. Code
+generation can be used for programmatically generating models, for
+example from a config file. There are many uses for FX!
+
+Several example transformations can be found at the
+`examples `__
+repository.
+'''
+
+from .graph_module import GraphModule
+from ._symbolic_trace import symbolic_trace, Tracer, wrap, PH, ProxyableClassMeta
+from .graph import Graph, CodeGen
+from .node import Node, map_arg, has_side_effect
+from .proxy import Proxy
+from .interpreter import Interpreter as Interpreter, Transformer as Transformer
+from .subgraph_rewriter import replace_pattern
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/__init__.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..0a263dfc5071ddee675ec517f2cbac13b51ce9e8
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/__init__.pyi
@@ -0,0 +1,15 @@
+from torch.fx._symbolic_trace import (
+    symbolic_trace as symbolic_trace,
+    Tracer as Tracer,
+    wrap as wrap,
+)
+from torch.fx.graph import Graph as Graph
+from torch.fx.graph_module import GraphModule as GraphModule
+from torch.fx.interpreter import Interpreter as Interpreter, Transformer as Transformer
+from torch.fx.node import (
+    has_side_effect as has_side_effect,
+    map_arg as map_arg,
+    Node as Node,
+)
+from torch.fx.proxy import Proxy as Proxy
+from torch.fx.subgraph_rewriter import replace_pattern as replace_pattern
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/_compatibility.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/_compatibility.py
new file mode 100644
index 0000000000000000000000000000000000000000..27c1e600036df46ced25e722ebf493c2131beda0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/_compatibility.py
@@ -0,0 +1,36 @@
+from typing import Any, Dict, Callable, TypeVar
+import textwrap
+
+_BACK_COMPAT_OBJECTS : Dict[Any, None] = {}
+_MARKED_WITH_COMPATIBILITY : Dict[Any, None] = {}
+
+_T = TypeVar("_T")
+
+def compatibility(is_backward_compatible: bool) -> Callable[[_T], _T]:
+    if is_backward_compatible:
+
+        def mark_back_compat(fn: _T) -> _T:
+            docstring = textwrap.dedent(getattr(fn, '__doc__', None) or '')
+            docstring += """
+.. note::
+    Backwards-compatibility for this API is guaranteed.
+"""
+            fn.__doc__ = docstring
+            _BACK_COMPAT_OBJECTS.setdefault(fn)
+            _MARKED_WITH_COMPATIBILITY.setdefault(fn)
+            return fn
+
+        return mark_back_compat
+    else:
+
+        def mark_not_back_compat(fn: _T) -> _T:
+            docstring = textwrap.dedent(getattr(fn, '__doc__', None) or '')
+            docstring += """
+.. warning::
+    This API is experimental and is *NOT* backward-compatible.
+"""
+            fn.__doc__ = docstring
+            _MARKED_WITH_COMPATIBILITY.setdefault(fn)
+            return fn
+
+        return mark_not_back_compat
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/_lazy_graph_module.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/_lazy_graph_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a14fce3782e9a33a0d2396f2d57fe24d7730ef2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/_lazy_graph_module.py
@@ -0,0 +1,185 @@
+# mypy: allow-untyped-defs
+from contextlib import contextmanager
+
+from torch.fx import GraphModule
+from torch.fx.graph_module import (
+    _format_import_block,
+    reduce_graph_module,
+    reduce_package_graph_module,
+)
+from torch.package import PackageExporter, sys_importer
+
+from ._compatibility import compatibility
+
+
+_use_lazy_graph_module_flag = False
+_force_skip_lazy_graph_module_flag = False
+
+
+@compatibility(is_backward_compatible=False)
+@contextmanager
+def _force_skip_lazy_graph_module():
+    """
+    Skip using lazy graph module disregarding the setting of _use_lazy_graph_module.
+    Use to skip _LazyGraphModule when testing inductor torchscript related backend.
+
+    torch.jit.script a _LazyGraphModule results in following error:
+        https://gist.github.com/shunting314/5143654c8084aed84ecd19b818258a69
+    """
+    try:
+        global _force_skip_lazy_graph_module_flag
+        prior = _force_skip_lazy_graph_module_flag
+        _force_skip_lazy_graph_module_flag = True
+        yield
+    finally:
+        _force_skip_lazy_graph_module_flag = prior
+
+
+@compatibility(is_backward_compatible=False)
+@contextmanager
+def _use_lazy_graph_module(should_use: bool):
+    try:
+        global _use_lazy_graph_module_flag
+        prior = _use_lazy_graph_module_flag
+        _use_lazy_graph_module_flag = (
+            should_use and not _force_skip_lazy_graph_module_flag
+        )
+        yield
+    finally:
+        _use_lazy_graph_module_flag = prior
+
+
+@compatibility(is_backward_compatible=False)
+def _get_graph_module_cls():
+    return _LazyGraphModule if _use_lazy_graph_module_flag else GraphModule
+
+
+def _make_graph_module(*args, graph_module_cls=None, **kwargs):
+    if graph_module_cls is None:
+        graph_module_cls = _get_graph_module_cls()
+
+    return graph_module_cls(*args, **kwargs)
+
+
+@compatibility(is_backward_compatible=False)
+class _LazyGraphModule(GraphModule):
+    """
+    The main difference between _LazyGraphModule and GraphModule is how recompile happens.
+    GraphModule will do a 'recompile' call to generate python code and the forward method when it's
+    constructed. Later on if the graph get updated, recompile method can be called again to refresh
+    the saved python code and forward method.
+
+    However in some cases especially in inductor, the recompilation can be a waste since we never
+    check the python code for the graph module or call its forward method. A few more concreate
+    examples regarding pattern matching fx passes in inductor:
+    1. some passes will update the graph to be compiled and then call recompile on the GraphModule.
+    2. some passes will trace small pattern function to search it in the graph being compiled and
+       replace the match with the traced graph of a replacement function. The pattern graph and
+       replacement graph are quite small but there are large amount of them. Doing GraphModule.recompile
+       for them in GraphModule.__init__ is also a waste of time.
+
+    However simply skip calling GraphModule.recompile in these scenarios is also dangeruous.
+    People may want to check the python code or call the GraphModule's forward method for debugging purposes.
+
+    The way _LazyGraphModule solves it is, we override the recompile method to just mark the
+    need for recompilation but does not do the actual recompilation. Later on if people really
+    access the compiled python code or call the GraphModule's forward method, we do the real
+    recompilation.
+    """
+
+    @classmethod
+    def from_graphmodule(cls, gm: GraphModule):
+        if isinstance(gm, _LazyGraphModule):
+            return gm
+        else:
+            return _LazyGraphModule(gm, gm.graph)
+
+    @staticmethod
+    def force_recompile(gm):
+        """
+        Sometimes we need force a recompile as a workaround
+        - we want to do the real recompilation before symbolic_trace to avoid error:
+            https://gist.github.com/shunting314/75549c2e82ae07ac1139c94a3583d259
+        """
+        if isinstance(gm, _LazyGraphModule):
+            gm.real_recompile()
+
+    def real_recompile(self):
+        if self._needs_recompile():
+            self._real_recompile()
+
+    @classmethod
+    def _needs_recompile(cls):
+        return cls.forward is cls._lazy_forward
+
+    def _lazy_forward(self, *args, **kwargs):
+        # Call self.real_recompile() rather than self._real_recompile() here.
+        # The _lazy_forward method may be saved and call repeatedly.
+        # Calling self.real_recompile can make sure we skip recompilation if
+        # we have already done so.
+        self.real_recompile()
+        assert not self._needs_recompile()
+
+        # call `__call__` rather than 'forward' since recompilation may
+        # install a wrapper for `__call__` to provide a customized error
+        # message.
+        return self(*args, **kwargs)
+
+    forward = _lazy_forward
+
+    # TODO: we shold handle __reduce_deploy__ the same way as __reduce_package__,
+    # or __reduce__ by calling _real_recompile. But I don't find a good way
+    # to test __reduce_deploy__ out. Also it's very unlikely that LazyGraphModule
+    # will be used in torch::deploy. So it's skipped for now.
+
+    def __reduce_package__(self, exporter: PackageExporter):
+        """
+        Follow GraphModule.__reduce__ but call 'self._real_recompile' rather
+        than 'self.recompile' since for a _LazyGraphModule, self.recompile just
+        mark the need of recompilation and does not return the PythonCode object.
+        """
+        python_code = self._real_recompile()
+        dict_without_graph = self.__dict__.copy()
+        dict_without_graph["_graphmodule_cls_name"] = self.__class__.__name__
+        del dict_without_graph["_graph"]
+
+        generated_module_name = f"fx-generated._{exporter.get_unique_id()}"
+        import_block = _format_import_block(python_code.globals, exporter.importer)
+        module_code = import_block + self.code
+        exporter.save_source_string(generated_module_name, module_code)
+        return (
+            reduce_package_graph_module,
+            (dict_without_graph, generated_module_name),
+        )
+
+    def __reduce__(self):
+        """
+        Follow GraphModule.__reduce__ but call 'self._real_recompile' rather
+        than 'self.recompile' since for a _LazyGraphModule, self.recompile just
+        mark the need of recompilation and does not return the PythonCode object.
+        """
+        python_code = self._real_recompile()
+        dict_without_graph = self.__dict__.copy()
+        import_block = _format_import_block(python_code.globals, sys_importer)
+        del dict_without_graph["_graph"]
+        return (reduce_graph_module, (dict_without_graph, import_block))
+
+    def _real_recompile(self):
+        return super().recompile()
+
+    @classmethod
+    def recompile(cls):
+        cls.forward = cls._lazy_forward
+
+    @property
+    def code(self) -> str:
+        self.real_recompile()
+        return super().code
+
+    def __str__(self) -> str:
+        """
+        str(GraphModule) will access the _code attribute. Make sure recompile
+        happens so _code attribute is available.
+        """
+        self.real_recompile()
+        return super().__str__()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/_pytree.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/_pytree.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ccbfdf048c943e811cd7d9fc092a81d2c30bcea
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/_pytree.py
@@ -0,0 +1,103 @@
+# mypy: allow-untyped-defs
+from collections import namedtuple
+from typing import Any, Callable, Dict, List, NamedTuple, Optional, Tuple, Type
+
+import torch.return_types
+from torch.utils._pytree import PyTree, TreeSpec
+
+
+FlattenFuncSpec = Callable[[PyTree, TreeSpec], List]
+FlattenFuncExactMatchSpec = Callable[[PyTree, TreeSpec], bool]
+
+SUPPORTED_NODES: Dict[Type[Any], FlattenFuncSpec] = {}
+SUPPORTED_NODES_EXACT_MATCH: Dict[Type[Any], Optional[FlattenFuncExactMatchSpec]] = {}
+
+
+def register_pytree_flatten_spec(
+    cls: Type[Any],
+    flatten_fn_spec: FlattenFuncSpec,
+    flatten_fn_exact_match_spec: Optional[FlattenFuncExactMatchSpec] = None,
+) -> None:
+    SUPPORTED_NODES[cls] = flatten_fn_spec
+    SUPPORTED_NODES_EXACT_MATCH[cls] = flatten_fn_exact_match_spec
+
+
+def tree_flatten_spec(
+    pytree: PyTree,
+    spec: TreeSpec,
+    exact_structural_match=False,
+) -> List[Any]:
+    if spec.is_leaf():
+        return [pytree]
+    if spec.type not in SUPPORTED_NODES:
+        raise RuntimeError(
+            f"{type(pytree)} does not have a flatten_fn_spec associated with it. Please register one with "
+            "torch.fx._pytree.register_pytree_flatten_spec.  If you have serialized your model, make "
+            "sure that any custom pytrees have been registered before loading it.",
+        )
+    flatten_fn_spec = SUPPORTED_NODES[spec.type]
+    child_pytrees = flatten_fn_spec(pytree, spec)
+    if exact_structural_match:
+        flatten_fn_exact_match_spec = SUPPORTED_NODES_EXACT_MATCH[spec.type]
+        if flatten_fn_exact_match_spec and not flatten_fn_exact_match_spec(
+            pytree,
+            spec,
+        ):
+            raise RuntimeError(f"Cannot flatten pytree {pytree}, given spec: {spec}")
+    result = []
+    for child, child_spec in zip(child_pytrees, spec.children_specs):
+        flat = tree_flatten_spec(child, child_spec, exact_structural_match)
+        result += flat
+    return result
+
+
+def _dict_flatten_spec(d: Dict[Any, Any], spec: TreeSpec) -> List[Any]:
+    return [d[k] for k in spec.context]
+
+
+def _list_flatten_spec(d: List[Any], spec: TreeSpec) -> List[Any]:
+    return [d[i] for i in range(spec.num_children)]
+
+
+def _tuple_flatten_spec(d: Tuple[Any], spec: TreeSpec) -> List[Any]:
+    return [d[i] for i in range(spec.num_children)]
+
+
+def _namedtuple_flatten_spec(d: NamedTuple, spec: TreeSpec) -> List[Any]:
+    return [d[i] for i in range(spec.num_children)]
+
+
+def _dict_flatten_spec_exact_match(d: Dict[Any, Any], spec: TreeSpec) -> bool:
+    return len(d) == spec.num_children
+
+
+def _list_flatten_spec_exact_match(d: List[Any], spec: TreeSpec) -> bool:
+    return len(d) == spec.num_children
+
+
+def _tuple_flatten_spec_exact_match(d: Tuple[Any], spec: TreeSpec) -> bool:
+    return len(d) == spec.num_children
+
+
+def _namedtuple_flatten_spec_exact_match(d: NamedTuple, spec: TreeSpec) -> bool:
+    return len(d) == spec.num_children
+
+
+register_pytree_flatten_spec(dict, _dict_flatten_spec, _dict_flatten_spec_exact_match)
+register_pytree_flatten_spec(list, _list_flatten_spec, _list_flatten_spec_exact_match)
+register_pytree_flatten_spec(
+    tuple,
+    _tuple_flatten_spec,
+    _tuple_flatten_spec_exact_match,
+)
+for return_type in torch.return_types.all_return_types:
+    register_pytree_flatten_spec(
+        return_type,
+        _tuple_flatten_spec,
+        _tuple_flatten_spec_exact_match,
+    )
+register_pytree_flatten_spec(
+    namedtuple,  # type: ignore[arg-type]
+    _namedtuple_flatten_spec,
+    _namedtuple_flatten_spec_exact_match,
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/_symbolic_trace.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/_symbolic_trace.py
new file mode 100644
index 0000000000000000000000000000000000000000..6693863386513c8fd7bce53b1b3f2cbf8b0bb815
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/_symbolic_trace.py
@@ -0,0 +1,1290 @@
+# mypy: allow-untyped-defs
+import builtins
+import copy
+import contextlib
+import functools
+import inspect
+import math
+import os
+import warnings
+import collections
+from itertools import chain
+from types import CodeType, FunctionType, ModuleType
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    NamedTuple,
+    Optional,
+    Set,
+    Tuple,
+    Type,
+    Union,
+)
+
+import torch
+import torch.utils._pytree as pytree
+from torch._C import ScriptObject  # type: ignore[attr-defined]
+from torch._library.fake_class_registry import FakeScriptObject
+
+from ._compatibility import compatibility
+from .graph import _PyTreeCodeGen, _PyTreeInfo, Graph
+from .graph_module import GraphModule
+from ._lazy_graph_module import _make_graph_module
+from .node import Argument, base_types, map_aggregate
+from .proxy import ParameterProxy, Proxy, TracerBase, Scope, ScopeContextManager
+
+HAS_VARSTUFF = inspect.CO_VARARGS | inspect.CO_VARKEYWORDS
+
+# These need to run in global scope to handle nested calls correctly
+_orig_module_call: Callable = torch.nn.Module.__call__
+_orig_module_getattr: Callable = torch.nn.Module.__getattr__
+
+_proxyable_classes: Dict[Type, None] = {}
+
+_is_fx_tracing_flag = False
+
+
+def is_fx_tracing():
+    return _is_fx_tracing_flag
+
+@compatibility(is_backward_compatible=True)
+class ProxyableClassMeta(type):
+    """
+    ProxyableClassMeta allows you to make construction of a given Python class
+    symbolically traceable. For example::
+
+        import torch
+        import torch.fx
+
+        class TensorPair(metaclass=torch.fx.ProxyableClassMeta):
+            def __init__(self, left, right):
+                self.left, self.right = left, right
+
+            def add(self, other):
+                l = self.left + other.left
+                r = self.right + other.right
+                return TensorPair(l, r)
+
+            def mul(self, other):
+                l = self.left * other.left
+                r = self.right * other.right
+                return TensorPair(l, r)
+
+        def use_tensor_pair_ctor(x : TensorPair, y : torch.Tensor):
+            s = x.add(TensorPair(y, y))
+            return s.mul(x)
+
+        x = TensorPair(torch.randn(5, 3), torch.randn(5, 3))
+        y = torch.randn(5, 3)
+        ref_out = use_tensor_pair_ctor(x, y)
+
+        traced = torch.fx.symbolic_trace(use_tensor_pair_ctor)
+        print(traced.code)
+        '''
+        def forward(self, x : __main___TensorPair, y : torch.Tensor):
+            tensor_pair = __main___TensorPair(y, y);  y = None
+            add = x.add(tensor_pair);  tensor_pair = None
+            mul = add.mul(x);  add = x = None
+            return mul
+        '''
+
+    From this example, we can see that construction of a class (``TensorPair``)
+    defined with ``ProxyableClassMeta`` as metaclass can be recorded in symbolic
+    tracing.
+    """
+
+    def __init__(cls, name, bases, attrs):
+        _proxyable_classes.setdefault(cls)
+        super().__init__(name, bases, attrs)
+
+    def __call__(cls, *args, **kwargs):
+        instance = cls.__new__(cls)  # type: ignore[call-overload]
+
+        if not is_fx_tracing():
+            cls.__init__(instance, *args, **kwargs)  # type: ignore[misc]
+            return instance
+
+        found_proxies = []
+
+        def check_proxy(a):
+            if isinstance(a, Proxy):
+                found_proxies.append(a)
+
+        map_aggregate(args, check_proxy)
+        map_aggregate(kwargs, check_proxy)
+
+        if len(found_proxies) != 0:
+            tracer = found_proxies[0].tracer
+            return tracer.create_proxy("call_function", cls, args, kwargs)
+        else:
+            cls.__init__(instance, *args, **kwargs)  # type: ignore[misc]
+            return instance
+
+
+def _patch_function(fn: FunctionType, nargs: int) -> FunctionType:
+    co = fn.__code__
+    co_flags = co.co_flags & ~HAS_VARSTUFF
+    co_args: tuple
+    if hasattr(co, "co_qualname"):
+        # Python-3.11+ code signature
+        co_args = (
+            nargs,
+            0,
+            0,
+            co.co_nlocals,
+            co.co_stacksize,
+            co_flags,
+            co.co_code,
+            co.co_consts,
+            co.co_names,
+            co.co_varnames,
+            co.co_filename,
+            co.co_name,
+            co.co_qualname,  # type: ignore[attr-defined]
+            co.co_firstlineno,
+            co.co_lnotab,
+            co.co_exceptiontable,  # type: ignore[attr-defined]
+            co.co_freevars,
+            co.co_cellvars,
+        )
+    elif hasattr(co, "co_posonlyargcount"):
+        co_args = (
+            nargs,
+            0,
+            0,
+            co.co_nlocals,
+            co.co_stacksize,
+            co_flags,
+            co.co_code,
+            co.co_consts,
+            co.co_names,
+            co.co_varnames,
+            co.co_filename,
+            co.co_name,
+            co.co_firstlineno,
+            co.co_lnotab,
+            co.co_freevars,
+            co.co_cellvars,
+        )
+    else:
+        co_args = (
+            nargs,
+            0,
+            co.co_nlocals,
+            co.co_stacksize,
+            co_flags,
+            co.co_code,
+            co.co_consts,
+            co.co_names,
+            co.co_varnames,
+            co.co_filename,
+            co.co_name,
+            co.co_firstlineno,
+            co.co_lnotab,
+            co.co_freevars,
+            co.co_cellvars,
+        )
+    new_code = CodeType(*co_args)  # type: ignore[arg-type]
+    return FunctionType(
+        new_code, fn.__globals__, fn.__name__, fn.__defaults__, fn.__closure__
+    )
+
+    # we need to insert placeholder nodes for *args and **kwargs
+    # we can't call this function normally, otherwise it would try to unpack them
+    # instead, let's make python think that args and kwargs are normal variables
+
+
+@compatibility(is_backward_compatible=False)
+class PHBase:
+    """
+    Object representing an input placeholder to `concrete_args`
+    """
+
+    def __repr__(self):
+        return "PH"
+
+
+PH = PHBase()
+
+
+@compatibility(is_backward_compatible=False)
+class PHWithMeta(PHBase):
+    """
+    Object representing an input placeholder to `concrete_args`
+    """
+    def __init__(self, ph_key: Optional[str] = None):
+        super().__init__()
+
+        # Provide a hey for user to identify placeholder node during analysis
+        self.ph_key = ph_key
+
+
+def _transfer_attrs(fr, to):
+    for attr_name in dir(fr):
+        attr_val = getattr(fr, attr_name)
+        if (
+            not callable(attr_val)
+            and not attr_name.startswith("__")
+            and not hasattr(to, attr_name)
+        ):
+            setattr(to, attr_name, attr_val)
+
+
+@compatibility(is_backward_compatible=True)
+class Tracer(TracerBase):
+    # Reference: https://github.com/pytorch/pytorch/issues/54354
+    # The first line of this docstring overrides the one Sphinx generates for the
+    # documentation. We need it so that Sphinx doesn't leak `math`s path from the
+    # build environment (e.g. ` None:
+        # This method's signature is overridden by the first line of this class'
+        # docstring. If this method's signature is modified, the signature that
+        # overrides it also should be modified accordingly.
+
+        """
+        Construct a Tracer object.
+
+        Args:
+
+            autowrap_modules (Tuple[ModuleType]): defaults to `(math, )`,
+                Python modules whose functions should be wrapped automatically
+                without needing to use fx.wrap(). Backward-compatibility for
+                this parameter is guaranteed.
+
+            autowrap_functions (Tuple[Callable, ...]): defaults to `()`,
+                Python functions that should be wrapped automatically without
+                needing to use fx.wrap(). Backward compatibility for this
+                parameter is guaranteed.
+
+            param_shapes_constant (bool): When this flag is set,  calls to shape,
+                size and a few other shape like attributes of a module's parameter
+                will be evaluated directly, rather than returning a new Proxy value
+                for an attribute access. Backward compatibility for this parameter
+                is guaranteed.
+        """
+
+        super().__init__()
+
+        # Functions we will eagerly wrap when we see them while tracing
+        # this captures both `math.sqrt()` and `from math import sqrt` automatically
+        self._autowrap_function_ids: Set[int] = {
+            id(value)
+            for name, value in chain(*[m.__dict__.items() for m in autowrap_modules])
+            if not name.startswith("_") and callable(value)
+        }
+        self._autowrap_function_ids.update({id(f) for f in autowrap_functions})
+
+        # Python modules to apply autowrap to at the start, in addition to
+        # modules we see while tracing
+        self._autowrap_search: List[ModuleType] = list(autowrap_modules)
+        self.param_shapes_constant = param_shapes_constant
+
+        self.submodule_paths: Optional[Dict[torch.nn.Module, str]] = None
+        self.root_module_name: str = ""
+        # Maps the containing module's name to the operator name
+        self.scope = Scope("", None)
+        # Records the module call stack
+        self.module_stack = collections.OrderedDict()
+        # Mapping of node name to module scope
+        self.node_name_to_scope: Dict[str, Tuple[str, type]] = {}
+
+    _qualname_counter: Dict[str, int] = collections.defaultdict(int)
+
+    @compatibility(is_backward_compatible=True)
+    def get_fresh_qualname(self, prefix: str) -> str:
+        """
+        Gets a fresh name for a prefix and returns it. This function ensures
+        that it will not clash with an existing attribute on the graph.
+        """
+        # The idea here is that if the module doesn't have this prefix at all we
+        # should reset the counter to start from the beginning
+        # It's a ... little bit hacky (doesn't cover all cases) but the precise
+        # naming of the prefixes isn't a correctness issue, just a niceness
+        # issue
+        qualname = f"{prefix}0"
+        if not hasattr(self.root, qualname):
+            self._qualname_counter[prefix] = 0
+            return qualname
+
+        i = self._qualname_counter[prefix]
+        while True:
+            qualname = f"{prefix}{i}"
+            i += 1
+            if not hasattr(self.root, qualname):
+                break
+        self._qualname_counter[prefix] = i
+
+        return qualname
+
+    @compatibility(is_backward_compatible=True)
+    def create_arg(self, a: Any) -> "Argument":
+        """
+        A method to specify the behavior of tracing when preparing values to
+        be used as arguments to nodes in the ``Graph``.
+
+        By default, the behavior includes:
+
+        #. Iterate through collection types (e.g. tuple, list, dict) and recursively
+           call ``create_args`` on the elements.
+        #. Given a Proxy object, return a reference to the underlying IR ``Node``
+        #. Given a non-Proxy Tensor object, emit IR for various cases:
+
+            * For a Parameter, emit a ``get_attr`` node referring to that Parameter
+            * For a non-Parameter Tensor, store the Tensor away in a special
+              attribute referring to that attribute.
+
+        This method can be overridden to support more types.
+
+        Args:
+
+            a (Any): The value to be emitted as an ``Argument`` in the ``Graph``.
+
+
+        Returns:
+
+            The value ``a`` converted into the appropriate ``Argument``
+        """
+        # The base tracer is used to construct Graphs when there is no associated
+        # module hierarchy, so it can never create parameter references.
+        # The default tracer adds the ability to refer to parameters when
+        # tracing modules.
+        if isinstance(a, torch.nn.Parameter):
+            for n, p in self.root.named_parameters():
+                if a is p:
+                    return self.create_node("get_attr", n, (), {})
+            raise NameError("parameter is not a member of this module")
+        elif isinstance(a, torch.Tensor):
+            for n_, p_ in self.root.named_buffers():
+                if a is p_:
+                    return self.create_node("get_attr", n_, (), {})
+        elif isinstance(a, torch.nn.Module):
+            for n_, p_ in self.root.named_modules():
+                if a is p_:
+                    return self.create_node("get_attr", n_, (), {})
+        # For NamedTuple instances that appear literally as args, we emit
+        # a node to construct the NamedTuple and use that Node as the argument.
+        if isinstance(a, tuple) and hasattr(a, "_fields"):
+            args = tuple(self.create_arg(elem) for elem in a)
+            return self.create_node("call_function", a.__class__, args, {})
+
+        # Tensors do not have a reliable string repr() from which they can be
+        # constructed (and we probably don't want to rely on that, either), so
+        # for any constant Tensor values we encounter, first search for if they
+        # are an attribute of some module in the module hierarchy. If so, emit
+        # a get_attr to retrieve that tensor. Otherwise, we'll store away the
+        # tensor value into a special attribute on the Module s.t. we can
+        # retrieve it with a get_attr.
+        if isinstance(a, (torch.Tensor, ScriptObject, FakeScriptObject)):
+            qualname: Optional[str] = self.tensor_attrs.get(a)
+
+            # Tensor was not found in the Module hierarchy, stow it away in a
+            # special attribute and set the qualname to refer to that
+            if not qualname:
+                base_name = "_tensor_constant" if isinstance(a, torch.Tensor) else "_torchbind_obj"
+                qualname = self.get_fresh_qualname(base_name)
+                assert isinstance(qualname, str)
+                self.tensor_attrs[a] = qualname
+                setattr(self.root, qualname, a)
+
+            return self.create_node("get_attr", qualname, (), {})
+
+        if type(a) in _proxyable_classes:
+            # This is an instance of a proxyable class for which we did not
+            # witness its construction. Intern this as a constant attribute
+
+            # TODO: binary search
+            qualname = self.get_fresh_qualname(f"_{a.__class__.__name__}_constant_")
+            assert isinstance(qualname, str)
+            setattr(self.root, qualname, a)
+
+            return self.create_node("get_attr", qualname, (), {})
+
+        return super().create_arg(a)
+
+    @compatibility(is_backward_compatible=True)
+    def is_leaf_module(self, m: torch.nn.Module, module_qualified_name: str) -> bool:
+        """
+        A method to specify whether a given ``nn.Module`` is a "leaf" module.
+
+        Leaf modules are the atomic units that appear in
+        the IR, referenced by ``call_module`` calls. By default,
+        Modules in the PyTorch standard library namespace (torch.nn)
+        are leaf modules. All other modules are traced through and
+        their constituent ops are recorded, unless specified otherwise
+        via this parameter.
+
+        Args:
+
+            m (Module): The module being queried about
+            module_qualified_name (str): The path to root of this module. For example,
+                if you have a module hierarchy where submodule ``foo`` contains
+                submodule ``bar``, which contains submodule ``baz``, that module will
+                appear with the qualified name ``foo.bar.baz`` here.
+        """
+        return (
+            (m.__module__.startswith("torch.nn") or m.__module__.startswith("torch.ao.nn"))
+            and not isinstance(m, torch.nn.Sequential)
+        )
+
+    @compatibility(is_backward_compatible=True)
+    def path_of_module(self, mod: torch.nn.Module) -> str:
+        """
+        Helper method to find the qualified name of ``mod`` in the Module hierarchy
+        of ``root``. For example, if ``root`` has a submodule named ``foo``, which has
+        a submodule named ``bar``, passing ``bar`` into this function will return
+        the string "foo.bar".
+
+        Args:
+
+            mod (str): The ``Module`` to retrieve the qualified name for.
+        """
+        # Prefer the O(1) algorithm
+        if self.submodule_paths:
+            path = self.submodule_paths.get(mod)
+            if path is None:
+                raise NameError("module is not installed as a submodule")
+            assert isinstance(path, str)
+            return path
+        # O(N^2) fallback in the case that we didn't store the submodule
+        # paths.
+        else:
+            for n, p in self.root.named_modules():
+                if mod is p:
+                    return n
+            raise NameError("module is not installed as a submodule")
+
+    @compatibility(is_backward_compatible=True)
+    def call_module(
+        self,
+        m: torch.nn.Module,
+        forward: Callable[..., Any],
+        args: Tuple[Any, ...],
+        kwargs: Dict[str, Any],
+    ) -> Any:
+        """
+        Method that specifies the behavior of this ``Tracer`` when it encounters
+        a call to an ``nn.Module`` instance.
+
+        By default, the behavior is to check if the called module is a leaf module
+        via ``is_leaf_module``. If it is, emit a ``call_module`` node referring to
+        ``m`` in the ``Graph``. Otherwise, call the ``Module`` normally, tracing through
+        the operations in its ``forward`` function.
+
+        This method can be overridden to--for example--create nested traced
+        GraphModules, or any other behavior you would want while tracing across
+        ``Module`` boundaries.
+
+        Args:
+
+            m (Module): The module for which a call is being emitted
+            forward (Callable): The forward() method of the ``Module`` to be invoked
+            args (Tuple): args of the module callsite
+            kwargs (Dict): kwargs of the module callsite
+
+        Return:
+
+            The return value from the Module call. In the case that a ``call_module``
+            node was emitted, this is a ``Proxy`` value. Otherwise, it is whatever
+            value was returned from the ``Module`` invocation.
+        """
+        module_qualified_name = self.path_of_module(m)
+        with ScopeContextManager(self.scope, Scope(module_qualified_name, type(m))) as _scope:
+            # module_stack is an ordered dict so writing then deleting the
+            # entry is equivalent to push/pop on a list
+            self.module_stack[_scope.module_path] = (module_qualified_name, _scope.module_type)
+            if not self.is_leaf_module(m, module_qualified_name):
+                ret_val = forward(*args, **kwargs)
+            else:
+                ret_val = self.create_proxy("call_module", module_qualified_name, args, kwargs)
+            key, _ = self.module_stack.popitem(last=True)
+            assert key == _scope.module_path, f" Unexpected key {key}"
+
+        return ret_val
+
+    @compatibility(is_backward_compatible=False)
+    def getattr(self, attr: str, attr_val: Any, parameter_proxy_cache: Dict[str, Any]):
+        """
+        Method that specifies the behavior of this ``Tracer`` when we call getattr
+        on a call to an ``nn.Module`` instance.
+
+        By default, the behavior is to return a proxy value for the attribute. It
+        also stores the proxy value in the ``parameter_proxy_cache``, so that future
+        calls will reuse the proxy rather than creating a new one.
+
+        This method can be overridden to --for example-- not return proxies when
+        querying parameters.
+
+        Args:
+
+            attr (str): The name of the attribute being queried
+            attr_val (Any): The value of the attribute
+            parameter_proxy_cache (Dict[str, Any]): A cache of attr names to proxies
+
+        Return:
+
+            The return value from the getattr call.
+        """
+        def maybe_get_proxy_for_attr(
+            attr_val, collection_to_search, parameter_proxy_cache
+        ):
+            for n, p in collection_to_search:
+                if attr_val is p:
+                    if n not in parameter_proxy_cache:
+                        kwargs = {}
+                        if (
+                            "proxy_factory_fn"
+                            in inspect.signature(self.create_proxy).parameters
+                        ):
+                            kwargs["proxy_factory_fn"] = (
+                                None
+                                if not self.param_shapes_constant
+                                else lambda node: ParameterProxy(
+                                    self, node, n, attr_val
+                                )
+                            )
+                        val_proxy = self.create_proxy("get_attr", n, (), {}, **kwargs)  # type: ignore[arg-type]
+                        parameter_proxy_cache[n] = val_proxy
+                    return parameter_proxy_cache[n]
+            return None
+
+        if isinstance(attr_val, torch.nn.Parameter):
+            maybe_parameter_proxy = maybe_get_proxy_for_attr(
+                attr_val, self.root.named_parameters(), parameter_proxy_cache
+            )
+            if maybe_parameter_proxy is not None:
+                return maybe_parameter_proxy
+
+        if self.proxy_buffer_attributes and isinstance(attr_val, torch.Tensor):
+            maybe_buffer_proxy = maybe_get_proxy_for_attr(
+                attr_val, self.root.named_buffers(), parameter_proxy_cache
+            )
+            if maybe_buffer_proxy is not None:
+                return maybe_buffer_proxy
+
+        return attr_val
+
+    # This method will be refactored
+    @compatibility(is_backward_compatible=False)
+    def create_args_for_root(self, root_fn, is_module, concrete_args=None):
+        """
+        Create ``placeholder`` nodes corresponding to the signature of the ``root``
+        Module. This method introspects root's signature and emits those
+        nodes accordingly, also supporting ``*args`` and ``**kwargs``.
+        """
+        # In some cases, a function or method has been decorated with a wrapper
+        # defined via ``functools.wraps``. In this case, the outer code object
+        # will likely not contain the actual parameters we care about, so unwrap
+        # the function to get to the innermost callable.
+        fn_for_analysis = inspect.unwrap(root_fn)
+        co = fn_for_analysis.__code__
+        total_args = co.co_argcount + co.co_kwonlyargcount
+        orig_args = list(co.co_varnames)
+        names_iter = iter(co.co_varnames)
+        args: List[Any] = []
+        skip_arg_idx = 0
+        if is_module:
+            if total_args == 0:
+                raise RuntimeError(
+                    "``self`` argument cannot be part of *args expansion!"
+                )
+            skip_arg_idx = 1
+            next(names_iter)  # skip self
+            args.append(self.root)
+
+        sig = inspect.signature(fn_for_analysis)
+
+
+        # This covers the very specific case where we are passing in flat
+        # concrete_args as a tuple, but our traced fn takes (*args, **kwargs).
+        # In this case, just take the concrete_args and pass them through.
+        name_idx = 0
+        if isinstance(concrete_args, tuple) and \
+                len(concrete_args) > 0 and \
+                (co.co_flags & HAS_VARSTUFF) and \
+                total_args == 1:
+            for concrete_arg in concrete_args:
+                out = self.create_proxy("placeholder", f"input_{name_idx}", (), {})
+                if isinstance(concrete_arg, PHBase):
+                    if concrete_arg != PH:
+                        # Transfer attrs in the case where you're using a placeholder other
+                        # than the singleton PH (PH has no attributes to transfer).
+                        # Proxies were created out of the placeholders.
+                        # Transfer any metadata (put on the placeholders in the form of
+                        # attributes set by the user) from the placeholder to the
+                        # underlying nodes (the proxy is unwrapped by the user, but
+                        # the metadata should hold).
+                        _transfer_attrs(fr=concrete_arg, to=out.node)
+                args.append(out)
+                name_idx += 1
+            return root_fn, args
+
+        arg_names = [next(names_iter) for idx in range(skip_arg_idx, total_args)]
+        if isinstance(concrete_args, tuple):
+            if len(arg_names) != len(concrete_args):
+                raise RuntimeError(
+                    f"Tracing expected {len(arg_names)} arguments but got {len(concrete_args)} concrete arguments"
+                )
+            concrete_args = dict(zip(arg_names, concrete_args))
+
+        def proxy_placeholder(name):
+            return self._proxy_placeholder(name, concrete_args, sig, fn_for_analysis)
+
+        args.extend(proxy_placeholder(names) for names in arg_names)
+
+        if co.co_kwonlyargcount > 0 or co.co_flags & HAS_VARSTUFF:
+            # TODO: type annotations for *args and **kwargs
+            if co.co_flags & inspect.CO_VARARGS:
+                args.append(proxy_placeholder("*" + next(names_iter)))
+            if co.co_flags & inspect.CO_VARKEYWORDS:
+                args.append(proxy_placeholder("**" + next(names_iter)))
+            root_fn = _patch_function(root_fn, len(args))
+
+        flat_args, in_spec = pytree.tree_flatten(tuple(args))
+        if not all(child.is_leaf() for child in in_spec.children_specs):
+            # In the case that we have pytree-flattened inputs in
+            # `concrete_args`, generate a flattening wrapper around the
+            # original root function and return that.
+            self.graph._codegen = _PyTreeCodeGen(
+                _PyTreeInfo(orig_args[:total_args], in_spec, None)
+            )
+
+            def flatten_fn(*args):
+                tree_args = pytree.tree_unflatten(list(args), in_spec)
+                tree_out = root_fn(*tree_args)
+                out_args, out_spec = pytree.tree_flatten(tree_out)
+                assert isinstance(self.graph._codegen, _PyTreeCodeGen)
+                self.graph._codegen.pytree_info = (
+                    self.graph._codegen.pytree_info._replace(out_spec=out_spec)
+                )
+                return out_args
+
+            return flatten_fn, flat_args
+        return root_fn, args
+
+    @compatibility(is_backward_compatible=True)
+    def trace(
+        self,
+        root: Union[torch.nn.Module, Callable[..., Any]],
+        concrete_args: Optional[Dict[str, Any]] = None,
+    ) -> Graph:
+        """
+        Trace ``root`` and return the corresponding FX ``Graph`` representation. ``root``
+        can either be an ``nn.Module`` instance or a Python callable.
+
+        Note that after this call, ``self.root`` may be different from the ``root`` passed
+        in here. For example, when a free function is passed to ``trace()``, we will
+        create an ``nn.Module`` instance to use as the root and add embedded constants
+        to.
+
+
+        Args:
+
+            root (Union[Module, Callable]): Either a ``Module`` or a function to be
+                traced through. Backwards-compatibility for this parameter is
+                guaranteed.
+            concrete_args (Optional[Dict[str, any]]): Concrete arguments that should
+                not be treated as Proxies. This parameter is experimental and
+                its backwards-compatibility is *NOT* guaranteed.
+
+        Returns:
+
+            A ``Graph`` representing the semantics of the passed-in ``root``.
+        """
+        global _is_fx_tracing_flag
+        old_is_fx_tracing_flag = _is_fx_tracing_flag
+        _is_fx_tracing_flag = True
+        try:
+            if isinstance(root, torch.nn.Module):
+
+                # do real recompilation for _LazyGraphModule before retracing since the trace
+                # method can not trace the _lazy_forward method. Got error:
+                #   https://gist.github.com/shunting314/75549c2e82ae07ac1139c94a3583d259
+                # without this.
+                from torch.fx._lazy_graph_module import _LazyGraphModule
+                _LazyGraphModule.force_recompile(root)
+
+                self.root = root
+
+                assert hasattr(
+                    type(root), self.traced_func_name
+                ), f"traced_func_name={self.traced_func_name} doesn't exist in {type(root).__name__}"
+
+                fn = getattr(type(root), self.traced_func_name)
+                self.root_module_name = root._get_name()
+                self.submodule_paths = {mod: name for name, mod in root.named_modules()}
+            else:
+                self.root = torch.nn.Module()
+                fn = root
+
+            tracer_cls: Optional[Type[Tracer]] = getattr(self, "__class__", None)
+            self.graph = Graph(tracer_cls=tracer_cls)
+            if hasattr(fn, '__code__'):
+                code = fn.__code__
+                self.graph._co_fields = {
+                    'co_name': code.co_name,
+                    'co_filename': code.co_filename,
+                    'co_firstlineno': code.co_firstlineno,
+                }
+
+            # When we encounter a Tensor value that's not a parameter, we look if it
+            # is some other attribute on the model. Construct a dict mapping Tensor
+            # values to the qualified name here for efficiency. This is used downstream
+            # in create_arg
+            self.tensor_attrs: Dict[
+                Union[
+                    torch.Tensor,
+                    ScriptObject,
+                    FakeScriptObject
+                ], str
+            ] = {}
+
+            def collect_tensor_attrs(m: torch.nn.Module, prefix_atoms: List[str]):
+                for k, v in m.__dict__.items():
+                    if isinstance(v, (torch.Tensor, ScriptObject, FakeScriptObject)):
+                        self.tensor_attrs[v] = ".".join(prefix_atoms + [k])
+                for k, v in m.named_children():
+                    collect_tensor_attrs(v, prefix_atoms + [k])
+
+            collect_tensor_attrs(self.root, [])
+
+            assert isinstance(fn, FunctionType)
+
+            fn_globals = fn.__globals__  # run before it gets patched
+            fn, args = self.create_args_for_root(
+                fn, isinstance(root, torch.nn.Module), concrete_args
+            )
+
+            parameter_proxy_cache: Dict[
+                str, Proxy
+            ] = {}  # Reduce number of get_attr calls
+
+            # Method dispatch on parameters is not recorded unless it's directly used.
+            # Thus, we need to insert a proxy when __getattr__ requests a parameter.
+            @functools.wraps(_orig_module_getattr)
+            def module_getattr_wrapper(mod, attr):
+                attr_val = _orig_module_getattr(mod, attr)
+                return self.getattr(attr, attr_val, parameter_proxy_cache)
+
+            @functools.wraps(_orig_module_call)
+            def module_call_wrapper(mod, *args, **kwargs):
+                def forward(*args, **kwargs):
+                    return _orig_module_call(mod, *args, **kwargs)
+
+                _autowrap_check(
+                    patcher,  # type: ignore[has-type]
+                    getattr(getattr(mod, "forward", mod), "__globals__", {}),
+                    self._autowrap_function_ids,
+                )
+                return self.call_module(mod, forward, args, kwargs)
+
+            with _new_patcher() as patcher:
+                # allow duplicate patches to support the case of nested calls
+                patcher.patch_method(
+                    torch.nn.Module,
+                    "__getattr__",
+                    module_getattr_wrapper,
+                    deduplicate=False,
+                )
+                patcher.patch_method(
+                    torch.nn.Module, "__call__", module_call_wrapper, deduplicate=False
+                )
+                _patch_wrapped_functions(patcher)
+                _autowrap_check(patcher, fn_globals, self._autowrap_function_ids)
+                for module in self._autowrap_search:
+                    _autowrap_check(
+                        patcher, module.__dict__, self._autowrap_function_ids
+                    )
+                self.create_node(
+                    "output",
+                    "output",
+                    (self.create_arg(fn(*args)),),
+                    {},
+                    type_expr=fn.__annotations__.get("return", None),
+                )
+
+            self.submodule_paths = None
+        finally:
+            _is_fx_tracing_flag = old_is_fx_tracing_flag
+        return self.graph
+
+    def __deepcopy__(self, memo):
+        # _autowrap_search contains modules, which cannot be deepcopied.
+        new_tracer = Tracer.__new__(Tracer)
+
+        for k, v in self.__dict__.items():
+            if k in {'_autowrap_search'}:
+                new_obj = copy.copy(v)
+            else:
+                new_obj = copy.deepcopy(v, memo)
+
+            new_tracer.__dict__[k] = new_obj
+
+        return new_tracer
+
+    def _proxy_placeholder(self, name, concrete_args, sig, fn_for_analysis):
+        if concrete_args is not None and name in concrete_args:
+            cnt = 0
+
+            def replace_ph(x):
+                nonlocal cnt
+                cnt += 1
+                param = sig.parameters[name]
+                default = (
+                    ()
+                    if param.default is inspect.Parameter.empty
+                    else (param.default,)
+                )
+                out = self.create_proxy(
+                    "placeholder", f"{name}_{str(cnt)}", default, {}
+                )
+                if isinstance(x, PHBase):
+                    if x != PH:
+                        # Transfer attrs in the case where you're using a placeholder other
+                        # than the singleton PH (PH has no attributes to transfer).
+                        # Proxies were created out of the placeholders.
+                        # Transfer any metadata (put on the placeholders in the form of
+                        # attributes set by the user) from the placeholder to the
+                        # underlying nodes (the proxy is unwrapped by the user, but
+                        # the metadata should hold).
+                        _transfer_attrs(fr=x, to=out.node)
+
+                    return out
+                # Union[int, bool] == bool in Python <= 3.6
+                if (
+                    type(x) == bool
+                    or type(x) in base_types
+                    and type(x) != torch.Tensor
+                ):
+                    torch._assert(
+                        out == x,
+                        f"{name} has been specialized to have value {x} but got another value",
+                    )
+                elif x is None:
+                    args = (
+                        out,
+                        f"{name} has been specialized to have value None but got another value",
+                    )
+                    self.create_proxy("call_function", _assert_is_none, args, {})
+                else:
+                    warnings.warn(
+                        f"Was not able to add assertion to guarantee correct input {name} to "
+                        f"specialized function. It is up to the user to make sure that your inputs match the "
+                        f"inputs you specialized the function with."
+                    )
+
+                return x
+
+            return pytree.tree_map(replace_ph, concrete_args[name])
+        if name[0] == "*":
+            default = ()
+        else:
+            param = sig.parameters[name]
+            default = () if param.default is inspect.Parameter.empty else (param.default,)  # type: ignore[assignment]
+        return self.create_proxy(
+            "placeholder",
+            name,
+            default,
+            {},
+            type_expr=fn_for_analysis.__annotations__.get(name, None)
+        )
+
+
+# Dictionary of (id(globals dict), function name) => globals_dict to patch for
+# the purposes of the wrap() API.
+# We key by the globals dict id and function name to ensure we're wrapping a given
+# function only once.
+_wrapped_fns_to_patch: Dict[Tuple[int, str], dict] = {}
+
+# List of methods on classes to wrap (class type, function name)
+# this currently only works for Tensor.* methods that aren't traced properly
+_wrapped_methods_to_patch: List[Tuple[type, str]] = []
+
+if os.environ.get("FX_PATCH_GETITEM") == "1":
+    # This change is needed to trace models like PositionalEmbedding from BERT:
+    # https://github.com/pytorch/benchmark/blob/master/torchbenchmark/models/BERT_pytorch/bert_pytorch/model/embedding/position.py
+    # but causes issues in quantization documented here:
+    # https://github.com/pytorch/pytorch/issues/50710
+    # once that is fixed we can make this the default behavior.
+    _wrapped_methods_to_patch.append((torch.Tensor, "__getitem__"))
+
+
+def _find_proxy(*objects_to_search):
+    """
+    Recursively search a data structure for a Proxy() and return it,
+    return None if not found.
+    """
+    proxy = None
+
+    def find_proxy(x):
+        nonlocal proxy
+        if isinstance(x, Proxy):
+            proxy = x
+
+    map_aggregate(objects_to_search, find_proxy)
+    return proxy
+
+
+def _create_wrapped_func(orig_fn):
+    @functools.wraps(orig_fn)
+    def wrapped(*args, **kwargs):
+        """
+        Given an closed-over ``orig_function`` to invoke, search the args and kwargs for
+        a Proxy object. If there is one, emit a ``call_function`` node to preserve the
+        call to this leaf function directly. Otherwise, just return the results of
+        this function call, as this function is not being traced.
+        """
+        proxy = _find_proxy(args, kwargs)
+        if proxy is not None:
+            return_proxy = proxy.tracer.create_proxy(
+                "call_function", orig_fn, args, kwargs
+            )
+            return_proxy.node.meta["is_wrapped"] = True
+            return return_proxy
+        return orig_fn(*args, **kwargs)
+
+    return wrapped
+
+
+def _create_wrapped_method(cls, name):
+    orig_fn = getattr(cls, name)
+
+    @functools.wraps(orig_fn)
+    def wrapped(*args, **kwargs):
+        """
+        Search the args and kwargs for a Proxy object. If there is one,
+        emit a ``call_method`` node to preserve the call to this method
+        directly. Otherwise, just return the results of this function
+        call, as this function is not being traced.
+        """
+        proxy = _find_proxy(args, kwargs)
+        if proxy is not None:
+            return proxy.tracer.create_proxy("call_method", name, args, kwargs)
+        return orig_fn(*args, **kwargs)
+
+    return wrapped
+
+
+class _PatchedFn(NamedTuple):
+    frame_dict: Any
+    fn_name: str
+    orig_fn: Any
+    new_fn: Any
+
+    def revert(self):
+        raise NotImplementedError
+
+    def patch(self):
+        raise NotImplementedError
+
+
+class _PatchedFnSetItem(_PatchedFn):
+    def revert(self):
+        self.frame_dict[self.fn_name] = self.orig_fn
+
+    def patch(self):
+        self.frame_dict[self.fn_name] = self.new_fn
+
+class _PatchedFnDel(_PatchedFn):
+    def revert(self):
+        del self.frame_dict[self.fn_name]
+
+    def patch(self):
+        self.frame_dict[self.fn_name] = self.new_fn
+
+
+class _PatchedFnSetAttr(_PatchedFn):
+    def revert(self):
+        setattr(self.frame_dict, self.fn_name, self.orig_fn)
+
+    def patch(self):
+        setattr(self.frame_dict, self.fn_name, self.new_fn)
+
+class _Patcher:
+    def __init__(self) -> None:
+        super().__init__()
+        self.patches_made: List[_PatchedFn] = []
+        self.visited: Set[int] = set()
+
+    def patch(
+        self,
+        frame_dict: Dict[str, Any],
+        name: str,
+        new_fn: Callable,
+        deduplicate: bool = True,
+    ):
+        """
+        Replace frame_dict[name] with new_fn until we exit the context manager.
+        """
+        new_fn.__fx_already_patched = deduplicate  # type: ignore[attr-defined]
+        if name not in frame_dict and hasattr(builtins, name):
+            self.patches_made.append(_PatchedFnDel(frame_dict, name, None, new_fn))
+            self.patches_made[-1].patch()
+        elif getattr(frame_dict[name], "__fx_already_patched", False):
+            return  # already patched, no need to do it again
+        else:
+            self.patches_made.append(
+                _PatchedFnSetItem(frame_dict, name, frame_dict[name], new_fn)
+            )
+            self.patches_made[-1].patch()
+
+    def patch_method(
+        self, cls: type, name: str, new_fn: Callable, deduplicate: bool = True
+    ):
+        """
+        Replace object_or_dict.name with new_fn until we exit the context manager.
+        """
+        new_fn.__fx_already_patched = deduplicate  # type: ignore[attr-defined]
+        orig_fn = getattr(cls, name)
+        if getattr(orig_fn, "__fx_already_patched", False):
+            return  # already patched, no need to do it again
+        self.patches_made.append(_PatchedFnSetAttr(cls, name, orig_fn, new_fn))
+        self.patches_made[-1].patch()
+
+    def visit_once(self, thing: Any):
+        """Return True on the first call to with thing, otherwise false"""
+        idx = id(thing)
+        if idx in self.visited:
+            return False
+        self.visited.add(idx)
+        return True
+
+    def revert_all_patches(self):
+        """
+        Remove all the stored patcheds. It doesn't modify patches_made.
+        """
+        for patch in self.patches_made:
+            patch.revert()
+        return self.patches_made
+
+    def reapply_all_patches(self):
+        """
+        Patch all the stored patcheds. It doesn't modify patches_made.
+        """
+        for patch in self.patches_made:
+            patch.patch()
+        return self.patches_made
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """
+        Undo all the changes made via self.patch() and self.patch_method()
+        """
+        while self.patches_made:
+            # unpatch in reverse order to handle duplicates correctly
+            self.patches_made.pop().revert()
+        self.visited.clear()
+
+
+CURRENT_PATCHER: Optional[_Patcher] = None
+
+@contextlib.contextmanager
+def _new_patcher():
+    global CURRENT_PATCHER
+    prior_patcher = CURRENT_PATCHER
+    try:
+        CURRENT_PATCHER = _Patcher()
+        yield CURRENT_PATCHER
+    finally:
+        # Clear all the patches made by when using current patcher.
+        assert CURRENT_PATCHER is not None
+        CURRENT_PATCHER.revert_all_patches()
+        CURRENT_PATCHER = prior_patcher
+
+
+@contextlib.contextmanager
+def _maybe_revert_all_patches():
+    current_patcher = CURRENT_PATCHER
+    patches_made = None
+    patches_removed = None
+    try:
+        if current_patcher is not None:
+            patches_removed = current_patcher.revert_all_patches()
+        yield
+    finally:
+        if current_patcher is not None:
+            patches_made = current_patcher.reapply_all_patches()
+        assert patches_made == patches_removed, "CURRENT_PATCHER was changed during a revert_all_patches"
+
+def _patch_wrapped_functions(patcher: _Patcher):
+    """
+    Go through ``_wrapped_fn_patch_table`` and, for each frame object, wrap
+    the listed global functions in the `_create_wrapped_func` wrapper.
+    """
+    for (_, name), frame_dict in _wrapped_fns_to_patch.copy().items():
+        if name not in frame_dict and hasattr(builtins, name):
+            orig_fn = getattr(builtins, name)
+        else:
+            orig_fn = frame_dict[name]
+        patcher.patch(frame_dict, name, _create_wrapped_func(orig_fn))
+
+    for cls, name in _wrapped_methods_to_patch:
+        patcher.patch_method(cls, name, _create_wrapped_method(cls, name))
+
+
+def _autowrap_check(
+    patcher: _Patcher, frame_dict: Dict[str, Any], function_ids: Set[int]
+):
+    """
+    Some methods, like `math.sqrt` are common enough we want to automatically wrap them as we see them.
+    This method searches a scope for them and patches them if found.
+    """
+    if patcher.visit_once(frame_dict):
+        for name, value in frame_dict.items():
+            if (
+                not name.startswith("_")
+                and callable(value)
+                and id(value) in function_ids
+            ):
+                patcher.patch(frame_dict, name, _create_wrapped_func(value))
+
+
+@compatibility(is_backward_compatible=True)
+def wrap(fn_or_name: Union[str, Callable]):
+    """
+    This function can be called at module-level scope to register fn_or_name as a "leaf function".
+    A "leaf function" will be preserved as a CallFunction node in the FX trace instead of being
+    traced through::
+
+        # foo/bar/baz.py
+        def my_custom_function(x, y):
+            return x * x + y * y
+
+        torch.fx.wrap('my_custom_function')
+
+        def fn_to_be_traced(x, y):
+            # When symbolic tracing, the below call to my_custom_function will be inserted into
+            # the graph rather than tracing it.
+            return my_custom_function(x, y)
+
+    This function can also equivalently be used as a decorator::
+
+        # foo/bar/baz.py
+        @torch.fx.wrap
+        def my_custom_function(x, y):
+            return x * x + y * y
+
+    A wrapped function can be thought of a "leaf function", analogous to the concept of
+    "leaf modules", that is, they are functions that are left as calls in the FX trace
+    rather than traced through.
+
+    Args:
+
+        fn_or_name (Union[str, Callable]): The function or name of the global function to insert into the
+            graph when it's called
+    """
+    if not callable(fn_or_name) and not isinstance(fn_or_name, str):
+        raise RuntimeError(
+            "Unsupported type for global function! Must be either a callable or "
+            "string name"
+        )
+
+    if callable(fn_or_name):
+        assert not isinstance(fn_or_name, str)  # to make mypy happy
+        fn_name = fn_or_name.__name__
+    else:
+        assert isinstance(
+            fn_or_name, str
+        ), "fn_or_name must be a global function or string name"
+        fn_name = fn_or_name
+
+    currentframe = inspect.currentframe()
+    assert currentframe is not None
+    f = currentframe.f_back
+    assert f is not None
+    if f.f_code.co_name != "":
+        raise NotImplementedError("wrap must be called at the top level of a module")
+
+    # consider implementing Callable version of this via _autowrap_function_ids / _autowrap_search
+    # semantics would be slightly different, but would add support `from x import wrapped_function`
+    _wrapped_fns_to_patch[(id(f.f_globals), fn_name)] = f.f_globals
+    return fn_or_name
+
+
+@compatibility(is_backward_compatible=True)
+def symbolic_trace(
+    root: Union[torch.nn.Module, Callable[..., Any]],
+    concrete_args: Optional[Dict[str, Any]] = None,
+) -> GraphModule:
+    """
+    Symbolic tracing API
+
+    Given an ``nn.Module`` or function instance ``root``, this function will return a ``GraphModule``
+    constructed by recording operations seen while tracing through ``root``.
+
+    ``concrete_args`` allows you to partially specialize your function, whether it's to remove control flow or data structures.
+
+    For example::
+
+        def f(a, b):
+            if b == True:
+                return a
+            else:
+                return a*2
+
+    FX can typically not trace through this due to the presence of control
+    flow. However, we can use `concrete_args` to specialize on the value of
+    `b` to trace through this::
+
+        f = fx.symbolic_trace(f, concrete_args={'b': False})
+        assert f(3, False)  == 6
+
+    Note that although you can still pass in different values of `b`, they will be ignored.
+
+    We can also use `concrete_args` to eliminate data-structure handling from
+    our function. This will use pytrees to flatten your input. To avoid
+    overspecializing, pass in `fx.PH` for values that shouldn't be
+    specialized. For example::
+
+        def f(x):
+            out = 0
+            for v in x.values():
+                out += v
+            return out
+        f = fx.symbolic_trace(f, concrete_args={'x': {'a': fx.PH, 'b': fx.PH, 'c': fx.PH}})
+        assert f({'a': 1, 'b': 2, 'c': 4}) == 7
+
+
+    Args:
+        root (Union[torch.nn.Module, Callable]): Module or function to be traced and converted
+            into a Graph representation.
+        concrete_args (Optional[Dict[str, any]]): Inputs to be partially specialized
+
+    Returns:
+        GraphModule: a Module created from the recorded operations from ``root``.
+    """
+    tracer = Tracer()
+    graph = tracer.trace(root, concrete_args)
+    name = (
+        root.__class__.__name__ if isinstance(root, torch.nn.Module) else root.__name__
+    )
+    return _make_graph_module(tracer.root, graph, name)
+
+
+@wrap
+def _assert_is_none(value, msg):
+    assert value is None, msg
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..3dd3780fe0bb36cc47f2669a72a2b9cdc0bdcbf6
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/_utils.py
@@ -0,0 +1,63 @@
+# mypy: allow-untyped-defs
+import sys
+from typing import Dict, Optional
+
+import torch
+from torch._logging import LazyString
+
+
+def lazy_format_graph_code(name, gm, maybe_id=None, **kwargs):
+    """
+    Returns a LazyString that formats the graph code.
+    """
+
+    def format_name():
+        if maybe_id is not None:
+            return f"{name} {maybe_id}"
+        else:
+            return name
+
+    if "print_output" not in kwargs:
+        kwargs["print_output"] = False
+
+    if "colored" in kwargs and not sys.stdout.isatty():
+        kwargs["colored"] = False
+
+    return LazyString(
+        lambda: _format_graph_code(
+            f"===== {format_name()} =====\n",
+            gm.forward.__code__.co_filename,
+            gm.print_readable(**kwargs),
+        )
+    )
+
+
+def _format_graph_code(name, filename, graph_str):
+    """
+    Returns a string that formats the graph code.
+    """
+    return f"TRACED GRAPH\n {name} {filename} {graph_str}\n"
+
+
+def first_call_function_nn_module_stack(graph: torch.fx.Graph) -> Optional[Dict]:
+    """
+    Returns the nn_module_stack of the first call_function node.
+    """
+    for node in graph.nodes:
+        if node.op == "call_function" and "nn_module_stack" in node.meta:
+            return node.meta["nn_module_stack"]
+    return None
+
+
+def get_node_context(node, num_nodes=2) -> str:
+    """
+    Returns a string of the last num_nodes nodes in the graph.
+    """
+    node_contexts = []
+    cur = node
+    for i in range(num_nodes):
+        node_contexts.append(cur.format_node())
+        if cur.op == "root":
+            break
+        cur = cur.prev
+    return "\n".join(node_contexts[::-1])
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/annotate.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/annotate.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1b5b5f2d376115fe25542b3b1260dcd6aaf1aaf
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/annotate.py
@@ -0,0 +1,32 @@
+# mypy: allow-untyped-defs
+from torch.fx.proxy import Proxy
+from ._compatibility import compatibility
+
+@compatibility(is_backward_compatible=False)
+def annotate(val, type):
+    """
+    Annotates a Proxy object with a given type.
+
+    This function annotates a val with a given type if a type of the val is a torch.fx.Proxy object
+    Args:
+        val (object): An object to be annotated if its type is torch.fx.Proxy.
+        type (object): A type to be assigned to a given proxy object as val.
+    Returns:
+        The given val.
+    Raises:
+        RuntimeError: If a val already has a type in its node.
+    """
+    if isinstance(val, Proxy):
+        if val.node.type:
+            raise RuntimeError(f"Tried to annotate a value that already had a type on it!"
+                               f" Existing type is {val.node.type} "
+                               f"and new type is {type}. "
+                               f"This could happen if you tried to annotate a function parameter "
+                               f"value (in which case you should use the type slot "
+                               f"on the function signature) or you called "
+                               f"annotate on the same value twice")
+        else:
+            val.node.type = type
+        return val
+    else:
+        return val
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/config.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..da5120d6edf180f7fbbe88ac342b4d0e4b383e50
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/config.py
@@ -0,0 +1,6 @@
+# Whether to disable showing progress on compilation passes
+# Need to add a new config otherwise wil get a circular import if dynamo config is imported here
+disable_progress = True
+
+# If True this also shows the node names in each pass, for small models this is great but larger models it's quite noisy
+verbose_progress = False
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/__init__.py
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/_backward_state.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/_backward_state.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c742431857c33af22dbc1ad73b5bdfcf6124b9c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/_backward_state.py
@@ -0,0 +1,27 @@
+import torch.fx
+
+
+class BackwardState:
+    """
+    BackwardState is used to pass Python hooks from the forwards pass
+    into the backwards pass in Dynamo+Compiled Autograd.
+
+    It is created by TorchDynamo and has special handling there.
+    Dynamo will pass an empty BackwardState to the forwards, then populate
+    members on it (via setattr) only after the forwards graph is finished.
+    Later on, in CompileAutograd we will inline and add the needed guards
+    on the BackwardState.
+
+    BackwardState is identified and has special handling in AOTAutograd.
+    During AOTAutograd:
+        1) BackwardState is an input to the forwards graph
+        2) It must only be used in the backwards
+        3) It will be empty in the forwards
+        4) In the forwards we add a wrapper to save it
+        5) In the backwards it becomes an input
+        6) There can only be one per graph
+
+    BackwardState requires CompiledAutograd.
+    """
+
+    proxy: torch.fx.Proxy
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/_config.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..f901919fb45c654af2198192a1cf3157aad36bf9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/_config.py
@@ -0,0 +1,88 @@
+import os
+import sys
+from typing import Optional
+
+
+# [@compile_ignored: debug] Uses z3 for validating the guard optimizations transformations.
+translation_validation = (
+    os.environ.get("TORCHDYNAMO_TRANSLATION_VALIDATION", "0") == "1"
+)
+# Timeout (in milliseconds) for z3 finding a solution.
+# [@compile_ignored: debug]
+translation_validation_timeout = int(
+    os.environ.get("TORCHDYNAMO_TRANSLATION_VALIDATION_TIMEOUT", "600000")
+)
+# Disables bisection for translation validation.
+#
+# Translation validation bisection is enabled by default, if translation validation
+# is also enabled. This should help finding guard simplification issues. However,
+# since validation uses Z3 for bisecting, it might take a lot of time.
+#
+# Set this configuration option so as to avoid bisecting.
+# [@compile_ignored: debug]
+translation_validation_no_bisect = (
+    os.environ.get("TORCHDYNAMO_TRANSLATION_NO_BISECT", "0") == "1"
+)
+# Checks whether replaying ShapeEnv events on a freshly constructed one yields
+# the a ShapeEnv with the same state. This should be used only in testing.
+check_shape_env_recorded_events = False
+
+# TODO: Perhaps consider allowing unions for the configs below (so you can hit
+# multiple reps at the same time)
+
+# Give extended debug information if the string representation of a guard
+# matches this.  For example, set this to "Ne(s0, 10)" and whenever we issue
+# this guard, we will generate full Python and C++ backtrace
+# [@compile_ignored: debug]
+extended_debug_guard_added = os.environ.get(
+    "TORCHDYNAMO_EXTENDED_DEBUG_GUARD_ADDED", None
+)
+
+# Give extended debug information when a particular symbol is allocated.  For
+# example, set this to "u2" and whenever we create this symbol, we will
+# generate full Python and C++ backtrace
+# [@compile_ignored: debug]
+extended_debug_create_symbol = os.environ.get(
+    "TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL", None
+)
+
+# Give extended debug information (C++ backtrace) for all extended debug
+# settings as well as errors.  The C++ backtrace is slow and very spammy so we
+# don't include it by default even when you're requesting extended debug.
+# [@compile_ignored: debug]
+extended_debug_cpp = os.environ.get("TORCHDYNAMO_EXTENDED_DEBUG_CPP", "") != ""
+
+# Give extended debug information (line of code) when a torch function
+# is called during export.  This is useful for showing progress and detecting
+# where export might be stuck. Currently only works for strict=False.
+# [@compile_ignored: debug]
+extended_debug_current_loc = (
+    os.environ.get("TORCHEXPORT_EXTENDED_DEBUG_CURRENT_LOC", "0") == "1"
+)
+
+# [@compile_ignored: debug] Show a warning for every specialization
+print_specializations = False
+
+# wraps (un)equalities with 'Not' class after recording the correct expression
+# in the FX graph. This should incorrectly construct the divisible and replacement
+# lists, and incorrectly issue guards.
+inject_EVALUATE_EXPR_flip_equality_TESTING_ONLY = False
+
+# [@compile_ignored: debug] Validate that ShapeEnv's version key is updated correctly
+validate_shape_env_version_key = False
+
+# If we produce more than this many guards on a symbol, force the symbol to
+# get specialized and bail out if this many guards mention this particular
+# symbol.  This may be slightly more aggressive than the true number of guards
+# issued (as we test if we've hit the limit on-the-fly, whereas we may
+# do further simplifications at final guard issuance time that make guards
+# irrelevant.)
+symbol_guard_limit_before_specialize: Optional[int] = None
+
+# This flag changes whether we should use the same symbolic variable to represent input sizes that are the same.
+use_duck_shape = True
+
+from torch.utils._config_module import install_config_module
+
+
+install_config_module(sys.modules[__name__])
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/accelerator_partitioner.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/accelerator_partitioner.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b347762dedba00611fa579b5654cd9718d782b6
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/accelerator_partitioner.py
@@ -0,0 +1,1079 @@
+# mypy: allow-untyped-defs
+import operator
+from collections import deque
+from typing import Dict, List, Set, NamedTuple, Tuple, Deque
+
+import torch
+from torch.fx.passes.graph_manipulation import get_size_of_all_nodes
+from torch.fx.experimental.partitioner_utils import (
+    Partition,
+    Device,
+    PartitionerConfig,
+    get_partition_to_latency_mapping,
+    get_latency_of_partitioned_graph,
+    NodeLatency,
+    get_extra_size_of,
+    PartitionMode,
+)
+from torch.fx.graph_module import GraphModule
+from torch.fx.node import Node, map_arg
+from torch.fx.passes.split_module import split_module
+
+
+class DAGNode:
+    """DAGNode class maintains useful information for a partition (submodule),
+    and its input submodules and output submodules.
+    """
+
+    def __init__(
+        self,
+        submodule_node: Node,
+        input_nodes: List[Node],
+        output_nodes: List[Node],
+        logical_device_ids: List[int],
+        size_bytes: int,
+    ) -> None:
+        self.submodule_node: Node = submodule_node
+        self.input_nodes: List[Node] = input_nodes
+        self.output_nodes: List[Node] = output_nodes
+        self.logical_device_ids: List[int] = logical_device_ids
+        self.size_bytes = size_bytes
+
+    def __str__(self) -> str:
+        return str(self.submodule_node)
+
+
+class DAG:
+    """DAG class contains all the DAG nodes"""
+
+    def __init__(self) -> None:
+        self.nodes: List[DAGNode] = []
+
+    def create_node(
+        self,
+        submodule_node: Node,
+        input_nodes: List[Node],
+        output_nodes: List[Node],
+        logical_devices: List[int],
+        size_bytes: int,
+    ) -> None:
+        node = DAGNode(
+            submodule_node, input_nodes, output_nodes, logical_devices, size_bytes
+        )
+        self.nodes.append(node)
+
+
+class PartitionResult(NamedTuple):
+    """NameTuple used for returning DAG and a new fx module"""
+
+    dag: DAG
+    module_with_submodules: GraphModule
+
+
+"""Followings are some helper functions for partition manipulation"""
+
+
+def reset_partition_device(partitions):
+    for partition in partitions:
+        partition.logical_device_ids = []
+
+
+def combine_two_partitions(
+    partition_0: Partition, partition_1: Partition, partitions: List[Partition]
+) -> None:
+    """Given a list of partitions and its two partitions,
+    combine these two partitions into a new one appending to the partitions
+    and remove the previous two partitions from the list of partitions
+    """
+    partition = Partition(len(partitions))
+    partition.nodes = partition_0.nodes.union(partition_1.nodes)
+    partition.recalculate_mem_size()
+    partitions.append(partition)
+    partitions.remove(partition_0)
+    partitions.remove(partition_1)
+    reorganize_partitions(partitions)
+    return
+
+
+def set_parents_and_children(partitions: List[Partition]) -> None:
+    """Given a list of partitions, mark parents and children for each partition"""
+    # Go through all nodes in a partition.
+    # If a node's user is in other partition,
+    # then the other partition is this partition's children.
+    # This partition is the other partition's parent
+    for partition in partitions:
+        partition.children = set()
+        partition.parents = set()
+    for partition in partitions:
+        for node in partition.nodes:
+            # For each node in the current partition, find its users
+            users = node.users
+            for n in users:
+                # Find which the partition the user node belongs to.
+                # Note that if the node itself is also belongs to that partition,
+                # that partition is not the child of the current partition
+                for p in partitions:
+                    if p != partition and n in p.nodes and node not in p.nodes:
+                        partition.children.add(p)
+                        p.parents.add(partition)
+    return
+
+
+def reorganize_partitions(partitions: List[Partition]) -> None:
+    """Given a list of partitions, reorganize partition id,
+    its parents and its children for each partition
+    """
+    # Rearrange partition ids
+    for i, partition in enumerate(partitions):
+        partition.partition_id = i
+    set_parents_and_children(partitions)
+    return
+
+
+def get_bfs_level_partition(partitions: List[Partition]) -> None:
+    """Given a list of partitions,
+    mark the bfs level for each partition
+    """
+    current_level: Set[Partition] = set()
+    visited: Set[Partition] = set()
+    for partition in partitions:
+        # If a partition has no parent, it should be in root level
+        if len(partition.parents) == 0:
+            current_level.add(partition)
+    next_level: Set[Partition] = set()
+    level = 0
+    # bfs
+    while current_level:
+        partition = current_level.pop()
+        partition.bfs_level = level
+        visited.add(partition)
+        children = partition.children
+        for child in children:
+            if child not in next_level:
+                next_level.add(child)
+        if not current_level:
+            current_level = next_level.copy()
+            next_level = set()
+            level += 1
+    return
+
+
+def get_node_to_partition_mapping(partitions: List[Partition]) -> Dict[Node, int]:
+    """Given a list of partitions,return node to partition mapping"""
+    node_to_partition: Dict[Node, int] = {}
+    for partition in partitions:
+        for node in partition.nodes:
+            node_to_partition[node] = partition.partition_id
+    return node_to_partition
+
+
+def get_logical_id_to_device(devices: List[Device]) -> Dict[int, Device]:
+    """Get a mapping from device logical ID to Device object."""
+    logical_id_to_device: Dict[int, Device] = {}
+    for d in devices:
+        logical_id_to_device[d.logical_id] = d
+    return logical_id_to_device
+
+
+def get_device_partition_stats(
+    partitions: List[Partition], devices: List[Device]
+) -> Tuple[Dict[Device, List[Partition]], Dict[Device, int], List[Partition]]:
+    """Given a list of partitions and a list of devices, returns:
+    1. A mapping from device to partitions on it;
+    2. A mapping from device to its remaining memory size;
+    3. A list of partitions that do not have a device.
+    """
+    # logical id to device
+    logical_id_to_device = get_logical_id_to_device(devices)
+    # Track partitions on device
+    device_to_partitions: Dict[Device, List[Partition]] = {}
+    # Track device's left mem size
+    device_to_left_mem_bytes: Dict[Device, int] = {}
+    for d in devices:
+        device_to_partitions[d] = []
+        device_to_left_mem_bytes[d] = d.available_mem_bytes
+
+    # Deal with the partitions that already have a device
+    # and also collect all partitions without a device (no_device_partitions)
+    no_device_partitions = []
+    for partition in partitions:
+        if partition.logical_device_ids != []:
+            for logical_id in partition.logical_device_ids:
+                device = logical_id_to_device[logical_id]
+                device_to_partitions[device].append(partition)
+                device_to_left_mem_bytes[device] -= partition.used_mem_bytes
+        else:
+            no_device_partitions.append(partition)
+
+    return (
+        device_to_partitions,
+        device_to_left_mem_bytes,
+        no_device_partitions,
+    )
+
+
+def get_device_to_partitions_mapping(
+    partitions: List[Partition], devices: List[Device]
+):
+    """Given a list of partitions and a list of devices,
+    map each partition into a device.
+    """
+
+    def calculate_extra_mem_bytes_needed_for(
+        partition: Partition, partitions: List[Partition]
+    ):
+        all_nodes: Set[Node] = set()
+        for p in partitions:
+            all_nodes = all_nodes.union(p.nodes)
+        if len(all_nodes) == 0:
+            return partition.used_mem_bytes
+        all_nodes = all_nodes.union(partition.nodes)
+        extra_size_needed = 0
+        for node in partition.nodes:
+            extra_size_needed += get_extra_size_of(node, all_nodes)
+        return extra_size_needed
+
+    def find_device_for(partition: Partition):
+        """Given a partition, find a logical device for the partition
+        The algorithm is to put the partition on the device
+        that has just enough mem left for that partition.
+        device_to_left_mem_bytes is a dictionary between device and its left mem size
+        sorted by its left mem size
+        """
+        for d in device_to_left_mem_bytes:
+            extra_size_needed = calculate_extra_mem_bytes_needed_for(
+                partition, device_to_partitions[d]
+            )
+            if extra_size_needed < device_to_left_mem_bytes[d]:
+                device_to_partitions[d].append(partition)
+                partition.logical_device_ids.append(d.logical_id)
+                device_to_left_mem_bytes[d] -= extra_size_needed
+                return True
+        return False
+
+    (
+        device_to_partitions,
+        device_to_left_mem_bytes,
+        no_device_partitions,
+    ) = get_device_partition_stats(partitions, devices)
+
+    # Find devices for all the partitions without a device
+    found_device = True
+    for partition in no_device_partitions:
+        device_to_left_mem_bytes = dict(sorted(device_to_left_mem_bytes.items(), key=operator.itemgetter(1)))
+        found_device = find_device_for(partition)
+        if not found_device:
+            break
+    return found_device
+
+
+def check_dependency(partition):
+    """Given a partition,check if there is a circular dependency on
+    this partition using bfs
+    """
+    visited: Set[Partition] = {partition}
+    queue: Deque[Partition] = deque([partition])
+    while queue:
+        p = queue.popleft()
+        for child in p.children:
+            if child == partition:
+                return True
+            else:
+                if child not in visited:
+                    visited.add(child)
+                    queue.append(child)
+    return False
+
+
+class Partitioner:
+    """A fx module may not fit into one device.
+    Partitioner class helps partition one fx module into submodules (partitions),
+    so that the submodules can be executed crossing different accelerators.
+    The main function of this class is self.partition_graph.
+    It partitions the fx module based on the scheme specified in partition_config
+    A DAG structure is returned
+    along with a new fx module with submodule nodes.
+    """
+
+    def __init__(self) -> None:
+        self.partitions: List[Partition] = []
+        self.node_to_partition: Dict[Node, int] = {}
+        self.devices: List[Device] = []
+
+    def partition_graph(
+        self,
+        fx_module: GraphModule,
+        torch_module: torch.nn.Module,
+        partitioner_config: PartitionerConfig,
+    ) -> PartitionResult:
+        """Given the fx module, torch module and partitioner_config,
+        find the partitions, do the partitions,
+        and then return a DAG and a new fx module with submodule nodes (partitions)
+        """
+        self.graph_module = fx_module
+        self.torch_module = torch_module
+        self.devices = partitioner_config.devices
+        if len(self.devices) == 0:
+            raise RuntimeError("No devices")
+        # Tag the size in bytes to all nodes in the graph_module.
+        get_size_of_all_nodes(self.graph_module)
+        # Check if there are op nodes in the fx module
+        nodes = self.graph_module.graph.nodes
+        if all(node.op in {"placeholder", "get_attr", "output"} for node in nodes):
+            raise RuntimeError("No Partition since no operations in the module")
+        # Calculate total size of the fx module
+        total_size_of_graph = 0
+        for node in nodes:
+            if node.op == "output":
+                break
+            total_size_of_graph += node.size_bytes.total_size
+        # Find the device with the max mem size
+        device_with_max_mem = max(self.devices, key=lambda d: d.available_mem_bytes)
+        # AOT based partition
+        if partitioner_config.mode == PartitionMode.aot_based:
+            self.aot_based_partition(
+                partitioner_config.node_to_partition_mapping,
+                partitioner_config.partition_to_logical_device_mapping,
+            )
+        # Single partition if the whole module can be fit into one device
+        elif total_size_of_graph <= device_with_max_mem.available_mem_bytes:
+            self.find_single_partition(
+                total_size_of_graph, logical_device_id=device_with_max_mem.logical_id
+            )
+        elif total_size_of_graph > sum(d.available_mem_bytes for d in self.devices):
+            raise RuntimeError("Devices have no enough memory for the module")
+        else:
+            # Sparse nn based partition
+            if partitioner_config.mode == PartitionMode.sparse_nn:
+                available_mem_bytes = self.devices[0].available_mem_bytes
+                if not all(
+                    device.available_mem_bytes == available_mem_bytes
+                    for device in self.devices
+                ):
+                    raise RuntimeError("All devices must have same memory size!")
+                # sparse_nn_partition only support same memory size
+                # TODO: add different size support for sparse_nn_partition
+                self.sparse_nn_partition(available_mem_bytes)
+            # Cost aware partition
+            elif partitioner_config.mode == PartitionMode.cost_aware:
+                self.cost_aware_partition(
+                    partitioner_config.transfer_rate_bytes_per_sec,
+                    partitioner_config.node_to_latency_mapping,
+                )
+            # KL based partition
+            elif partitioner_config.mode == PartitionMode.kl_based:
+                self.kl_based_partition(
+                    partitioner_config.transfer_rate_bytes_per_sec,
+                    partitioner_config.node_to_latency_mapping,
+                )
+            else:
+                self.size_based_partition()
+
+        # Saturate host if possible.
+        if partitioner_config.saturate_host:
+            self.saturate_host()
+
+        # Partition the graph module based on the partition assignment.
+        module_with_submodules = self.do_partition()
+
+        # The DAG contains DAGNodes with info of each partition's input nodes, output nodes
+        # and how partitions are connected.
+        dag = self.dump_dag(module_with_submodules)
+        ret = PartitionResult(dag, module_with_submodules)
+        return ret
+
+    def find_single_partition(
+        self, total_size_of_graph, logical_device_id: int = 0
+    ) -> None:
+        """Fit the whole fx module into one device"""
+        partition_0 = self.create_partition()
+        for node in self.graph_module.graph.nodes:
+            if node.op == "output":
+                # Skip the output node, but there can
+                # be nodes after the output in certain cases.
+                continue
+            partition_0.nodes.add(node)
+        partition_0.used_mem_bytes = total_size_of_graph
+        partition_0.logical_device_ids = [logical_device_id]
+        # Get the node to partition mapping
+        self.node_to_partition = get_node_to_partition_mapping(self.partitions)
+        return
+
+    def size_based_partition(self) -> None:
+        """This method is to partition the fx module based on memory size.
+        It uses greedy approach. The result may not be the best.
+        The basic idea is:
+        Step 1:
+        Find a device which has enough memory to fit the current node, create a empty partition
+        with the size of that device.
+        Then keep adding the following nodes into the partition until the partition is full.
+        Step 2:
+        Repeat Step 1 until no device left
+        Step 3:
+        If some nodes are left, create a partition for each left node (single node partition).
+        and then try to map those partitions into logical devices with enough mem left.
+        """
+
+        def find_device_based_on_size(node) -> Device:
+            """Given a node, this function is to find a logical device
+            that could fit the node.
+            """
+            mem_size_needed = get_extra_size_of(node, set())
+            device = Device("", -1, -1)
+            for d in self.devices:
+                if (
+                    d not in occupied_devices
+                    and d.available_mem_bytes >= mem_size_needed
+                ):
+                    device = d
+                    break
+            if device.available_mem_bytes < 0:
+                raise RuntimeError(str(node) + "is too large to fit any device")
+            occupied_devices.append(device)
+            return device
+
+        # Track partition and its left mem size
+        partition_to_left_mem_bytes: Dict[Partition, int] = {}
+        # Track all the devices that have been used
+        occupied_devices: List[Device] = []
+        partition = self.create_partition()
+        for node in self.graph_module.graph.nodes:
+            if node.op in {"call_module", "call_method", "call_function"}:
+                # Check if there are devices left
+                if len(self.partitions) <= len(self.devices):
+                    total_size_of_input_nodes = get_extra_size_of(node, partition.nodes)
+                    # Check if the current partition is the very first partition
+                    if partition.used_mem_bytes == 0:
+                        # Find a device to fit the first node, return available mem size
+                        device = find_device_based_on_size(node)
+                        occupied_devices.append(device)
+                        # Update partition and its left mem size
+                        partition_to_left_mem_bytes[
+                            partition
+                        ] = device.available_mem_bytes
+                        # Update available mem for the current partition
+                        partition.logical_device_ids.append(device.logical_id)
+                    else:
+                        # The current partition is not the first partition
+                        # Check if the current node can fit into current partition
+                        if (
+                            partition_to_left_mem_bytes[partition]
+                            < total_size_of_input_nodes
+                        ):
+                            # Check if no device is left
+                            if len(self.partitions) == len(self.devices):
+                                # No device is left
+                                # Put the previous partitions into a list (non_single_node_partitions)
+                                non_single_node_partitions = self.partitions[:]
+                                # Create the first single node partition for the current node
+                                self.create_single_node_partition(node)
+                                continue
+                            # Some devices are still left
+                            # Create a new partition with a mem size that is enough for the current node
+                            device = find_device_based_on_size(node)
+                            partition = self.create_partition()
+                            total_size_of_input_nodes = get_extra_size_of(
+                                node, partition.nodes
+                            )
+                            partition_to_left_mem_bytes[
+                                partition
+                            ] = device.available_mem_bytes
+                            partition.logical_device_ids.append(device.logical_id)
+                    partition.add_node(node)
+                    partition_to_left_mem_bytes[partition] -= total_size_of_input_nodes
+                # Create single node partitions if no device is left
+                else:
+                    self.create_single_node_partition(node)
+        reorganize_partitions(self.partitions)
+        # Get the node to partition mapping
+        self.node_to_partition = get_node_to_partition_mapping(self.partitions)
+        # Mapping all partitions into device
+        found_partition_to_device_mapping = get_device_to_partitions_mapping(
+            self.partitions, self.devices
+        )
+        if not found_partition_to_device_mapping:
+            raise RuntimeError("Cannot Get a Valid Partition to Logical Device Mapping")
+        return
+
+    def saturate_host(self) -> None:
+        """Saturate host by assigning replicates to unused devices with enough memory.
+        It uses a greedy approach to find a next available set of devices to place all split
+        partitions: For each used device, it searches for an idle device with minimal memory
+        size that can hold all the partition located on that device; If the search is successful
+        for all used devices, it then assigns the new devices' logical ID to the corresponding
+        partition.
+        """
+        (
+            device_to_partitions,
+            device_to_left_mem_bytes,
+            no_device_partitions,
+        ) = get_device_partition_stats(self.partitions, self.devices)
+
+        assert (
+            len(no_device_partitions) == 0
+        ), f"Expect no_device_partitions has 0 device, but get {len(no_device_partitions)}"
+
+        # Devices that hold partitions
+        used_devices = [d for d in self.devices if len(device_to_partitions[d]) > 0]
+        # Track replicates of the assigned devices
+        replicated_device_to_used_device: Dict[Device, Device] = {}
+
+        while len(used_devices) * 2 + len(replicated_device_to_used_device) <= len(
+            self.devices
+        ):
+            # Success flag for this round
+            success = True
+            # Devices that have not been assigned
+            idle_devices = [
+                d
+                for d in self.devices
+                if d not in used_devices and d not in replicated_device_to_used_device
+            ]
+            # Temporary mapping from replicated device to original device
+            temp_replicate_mapping = {}
+
+            # Find a new device to replicate all partitions on an used device
+            for used_device in used_devices:
+                # Idle devices that have enough memory
+                available_devices = [
+                    d
+                    for d in idle_devices
+                    if d.available_mem_bytes
+                    >= used_device.available_mem_bytes
+                    - device_to_left_mem_bytes[used_device]
+                ]
+                if len(available_devices) == 0:
+                    success = False
+                    break
+                new_device = min(available_devices, key=lambda d: d.available_mem_bytes)
+                idle_devices.remove(new_device)
+                temp_replicate_mapping[new_device] = used_device
+
+            if not success:
+                break
+            replicated_device_to_used_device.update(temp_replicate_mapping)
+
+        # Update logical device IDs assigned to the partitions
+        for (
+            replicate_device,
+            original_device,
+        ) in replicated_device_to_used_device.items():
+            logical_id = replicate_device.logical_id
+            for partition in device_to_partitions[original_device]:
+                partition.logical_device_ids.append(logical_id)
+        for p in self.partitions:
+            print(p.logical_device_ids)
+
+    def do_partition(self) -> GraphModule:
+        """Return a new fx module with submodule nodes (partitions)."""
+        module_with_submodules = split_module(
+            self.graph_module,
+            self.torch_module,
+            lambda node: self.node_to_partition[node],
+        )
+        return module_with_submodules
+
+    def dump_dag(self, module_with_submodules: GraphModule) -> DAG:
+        """Return the dag structure and the new fx module with submodules."""
+        dag = DAG()
+        for node in module_with_submodules.graph.nodes:
+            if node.op == "output":
+                break
+            if node.op in {"placeholder", "get_attr"}:
+                continue
+            if node.target == operator.__getitem__:
+                continue
+            input_nodes: Dict[Node, None] = {}
+            map_arg(node.args, input_nodes.setdefault)
+            map_arg(node.kwargs, input_nodes.setdefault)
+            # When a node has two or more output nodes,
+            # it outputs its result to 'getitem' nodes.
+            # Those 'getitem' nodes are the output node for this node.
+            # Otherwise, the output node is this node itself.
+            if len(node.users) > 1:
+                output_nodes = list(node.users)
+            else:
+                output_nodes = [node]
+            partition_id = int(node.name.rsplit("_", 1)[-1])
+            device_ids = self.partitions[partition_id].logical_device_ids
+            size_bytes = self.partitions[partition_id].used_mem_bytes
+            dag.create_node(
+                node, list(input_nodes), output_nodes, device_ids, size_bytes
+            )
+        return dag
+
+    def create_partition(self) -> Partition:
+        """Create a partition and append it to self.partitions."""
+        partition_id = len(self.partitions)
+        partition = Partition(partition_id)
+        self.partitions.append(partition)
+        return partition
+
+    def create_single_node_partition(self, node):
+        """Create a partition for a single node"""
+        partition = self.create_partition()
+        partition.add_node(node)
+        return
+
+    def sparse_nn_partition(self, available_mem_bytes: int) -> None:
+        """This method partition a sparse nn module.
+        It is size based partition but different from size_based_partition,
+        it only works when all the devices have same memory size (available_mem_bytes).
+        In the future, devices with different mem sizes will be supported like size_based_partition.
+        It first traverse all the nodes and do the partitions based on the same memory size.
+        If the current partition has no enough memory left for a new op node
+        (call_module, call_method, call_function), a new partition is created.
+        When crossing the boundary between non-embedding nodes and embedding nodes,
+        a new partition is created regardlessly.
+        For example, if the current node is a non-embedding node but the next node is an
+        embedding node, a new partition is created for the next node.
+        After the partition, the partitions are combined as much as possible.
+        The rule is that a non-embedding partition only
+        combines with another non-embedding one.
+        So as the embedding partitions.
+        """
+
+        def combine_partitions_based_on_size(
+            partitions: List[Partition], available_mem_bytes: int
+        ) -> None:
+            """Combining small partitions together to keep as less partitions as possible.
+            Here is an example of the algorithm to do this:
+            Assume some partitions, we first sort them based on partition used memory size.
+            [(partition_4, 1), (partition_3, 1), (partition_2, 2), (partition_1, 7), (partition_0, 9)]
+            The available memory is 10.
+            step 1: self.find_partition_to_combine_based_on_size()
+            First, mark bfs level for each partition
+            Second, look the smallest partition, partition_4: 10 - 1 = 9
+            It means any partition has a used memory equal or less than 9 could combine this partition
+            We go from the largest and selection partition_0.
+            Check the bfs level for two partitions, if the level difference is less than 2,
+            it can be combined.
+            step 2: repeat step 1 until no partitions can be combined
+            """
+            find_combination = True
+            while find_combination:
+                # Sort partitions based on memory size
+                sorted_partitions = sorted(partitions, key=lambda p: p.used_mem_bytes)
+                # Mark bfs level
+                get_bfs_level_partition(self.partitions)
+                find_combination, partitions = find_partition_to_combine_based_on_size(
+                    sorted_partitions, available_mem_bytes, partitions
+                )
+            return
+
+        def calculate_mem_bytes_needed(p1, p2):
+            """Given two partitions, calculate how many mem bytes
+            are needed if two partitions are combined
+            """
+            nodes = p1.nodes.union(p2.nodes)
+            mem_bytes_needed = 0
+            for node in nodes:
+                mem_bytes_needed += get_extra_size_of(node, nodes)
+            return mem_bytes_needed
+
+        def find_partition_to_combine_based_on_size(
+            sorted_partitions: List[Partition],
+            available_mem_bytes: int,
+            partitions: List[Partition],
+        ) -> Tuple[bool, List[Partition]]:
+            """step 1 in combine_partition_based_on_size()"""
+            find_combination = False
+            smallest_partition = sorted_partitions.pop(0)
+            for p in sorted_partitions[::-1]:
+                if abs(smallest_partition.bfs_level - p.bfs_level) <= 1:
+                    # Calculate how many bytes needed if combined
+                    mem_bytes_needed = calculate_mem_bytes_needed(p, smallest_partition)
+                    if mem_bytes_needed <= available_mem_bytes:
+                        combine_two_partitions(p, smallest_partition, self.partitions)
+                        partitions.remove(smallest_partition)
+                        partitions.remove(p)
+                        partitions.append(self.partitions[-1])
+                        find_combination = True
+                        break
+            return find_combination, partitions
+
+        def reset_partition_in_sparse_nn(partition, new_partition=True):
+            """If crossing the boundary between non-embedding nodes and
+            embedding nodes, create a new partition
+            """
+            if in_embedding_region:
+                embedding_partitions.append(partition)
+            else:
+                non_embedding_partitions.append(partition)
+            if new_partition:
+                partition = self.create_partition()
+                partition.left_mem_bytes = available_mem_bytes
+                return partition
+            return None
+
+        def is_embedding_node(node: Node) -> bool:
+            """Check if a node is an embedding node"""
+            if node.op == "call_module":
+                submodule = self.graph_module
+                for atom in str(node.target).split("."):
+                    if not hasattr(submodule, atom):
+                        raise RuntimeError(
+                            f"Module {submodule} has no attribute {atom}"
+                        )
+                    submodule = getattr(submodule, atom)
+                    if "Embedding" in str(submodule):
+                        return True
+            return False
+
+        # Track embedding partitions and non-embedding partitions separately
+        embedding_partitions: List[Partition] = []
+        non_embedding_partitions: List[Partition] = []
+        # A Flag to check the boundary
+        in_embedding_region: bool = False
+        partition = self.create_partition()
+        for node in self.graph_module.graph.nodes:
+            if node.op in {"call_module", "call_method", "call_function"}:
+                # Check if crossing the boundary between embedding nodes and non embedding nodes
+                if is_embedding_node(node) != in_embedding_region:
+                    # Crossing the boundary
+                    # Check if the current partition is an empty partition
+                    if partition.used_mem_bytes != 0:
+                        # The current partition isn't an empty partition. Create a new one.
+                        partition = reset_partition_in_sparse_nn(partition)
+                    in_embedding_region = not in_embedding_region
+                total_size_of_input_nodes = get_extra_size_of(node, partition.nodes)
+                if (
+                    total_size_of_input_nodes + partition.used_mem_bytes
+                    > available_mem_bytes
+                ):
+                    partition = reset_partition_in_sparse_nn(partition)
+                    total_size_of_input_nodes = get_extra_size_of(node, partition.nodes)
+                    if total_size_of_input_nodes > available_mem_bytes:
+                        raise RuntimeError(
+                            node.target + "is too large to fit into a device"
+                        )
+                partition.add_node(node)
+        reset_partition_in_sparse_nn(partition, new_partition=False)
+        # Set parents and children for partitions
+        set_parents_and_children(self.partitions)
+        # Combining non-embedding partitions
+        combine_partitions_based_on_size(non_embedding_partitions, available_mem_bytes)
+        # Combining embedding partitions
+        combine_partitions_based_on_size(embedding_partitions, available_mem_bytes)
+        total_size_of_non_embedding_partitions = 0
+        for partition in non_embedding_partitions:
+            total_size_of_non_embedding_partitions += partition.used_mem_bytes
+        # Check if devices are enough for all partitions
+        if len(embedding_partitions) > len(self.devices):
+            msg = (
+                "Need "
+                + str(len(embedding_partitions))
+                + " devices, but only "
+                + str(len(self.devices))
+                + " provided"
+            )
+            raise RuntimeError(msg)
+        occupied_devices = []
+        for i, partition in enumerate(embedding_partitions):
+            # Check if all non-embedding partitions can fit into embedding partition devices
+            if (
+                total_size_of_non_embedding_partitions + partition.used_mem_bytes
+                > available_mem_bytes
+            ):
+                raise RuntimeError(
+                    "partition_"
+                    + str(partition.partition_id)
+                    + "(embedding partition) and non embedding partitions can not fit into one device"
+                )
+            else:
+                # Add logical device to the partition
+                partition.logical_device_ids = [self.devices[i].logical_id]
+                occupied_devices.append(self.devices[i].logical_id)
+        # Add logical devices to the non_embedding_partitions
+        for partition in non_embedding_partitions:
+            partition.logical_device_ids = occupied_devices
+        # Get the node to partition mapping
+        self.node_to_partition = get_node_to_partition_mapping(self.partitions)
+        return
+
+    def cost_aware_partition(
+        self,
+        transfer_rate_bytes_per_sec: float,
+        node_to_latency_mapping: Dict[Node, NodeLatency],
+    ) -> None:
+        """This method is to partition the fx module based on the cost.
+        The cost is the total latency of running the whole fx module.
+        In partitioner_utils.py, the cost model is built.
+        The cost aware partition algorithm is:
+        #1. At every beginning, each node is a partition.
+            Then we map all the partitions to the devices
+            and calculate the cost
+        #2. Then try to pre-combine any two of the partitions if the two
+            partitions can be combined.
+            (the bfs level is less than 2 or two partitions are connected and
+            can find partition to device mapping)
+            See if any partition pair could reduce the current cost.
+            Choose the pair that shows the minimum cost and then combine them
+        #3. Repeat #2 until the cost cannot be reduced.
+        """
+
+        def try_combining_partitions(p0_index, p1_index, partitions) -> float:
+            """Given two partitions and a list of partitions, combine these two partitions
+            and see what is the cost of the modified partition list
+            """
+            p0 = partitions[p0_index]
+            p1 = partitions[p1_index]
+            """If two partitions' bfs level are less than 2 or two partitions are connected to each other,
+               then they can be combined
+            """
+            if (
+                (abs(p0.bfs_level - p1.bfs_level) <= 1)
+                or (p0 in p1.parents)
+                or p0 in (p1.children)
+            ):
+                combine_two_partitions(p0, p1, partitions)
+                # Check if a circular dependency exists after combining
+                if check_dependency(partitions[-1]):
+                    return float("inf")
+                # Check if the modified partition list can be mapped to devices after combination
+                reset_partition_device(partitions)
+                found_deivce = get_device_to_partitions_mapping(
+                    partitions, self.devices
+                )
+                if not found_deivce:
+                    return float("inf")
+                # Calculate the new cost
+                partition_to_latency_mapping = get_partition_to_latency_mapping(
+                    partitions, node_to_latency_mapping
+                )
+                cost = get_latency_of_partitioned_graph(
+                    partitions,
+                    partition_to_latency_mapping,
+                    transfer_rate_bytes_per_sec,
+                )
+                return cost
+            # If two partition can not be combined, the cost is inf
+            return float("inf")
+
+        def search_combination(
+            transfer_rate_bytes_per_sec, node_to_latency_mapping
+        ) -> bool:
+            """Given transfer rate between partitions and each node's latency,
+            find two partitions to combine so the cost of the partitions can
+            be reduced.
+            The algorithm is :
+            1. Go through all the partition pairs and see
+            if any pair of partitions can be combined.
+            2. Calculate the cost after the combination.
+            3. Select the minimum cost and combine its corresponding partition pair.
+            """
+            partition_to_latency_mapping = get_partition_to_latency_mapping(
+                self.partitions, node_to_latency_mapping
+            )
+            cost = get_latency_of_partitioned_graph(
+                self.partitions,
+                partition_to_latency_mapping,
+                transfer_rate_bytes_per_sec,
+            )
+            if len(self.partitions) == 1:
+                return False
+            partition_pair: List[int] = []
+            for i in range(len(self.partitions) - 1):
+                for j in range(i + 1, len(self.partitions)):
+                    # Try to combine the partition pair
+                    # and see the new cost after combination
+                    new_cost = try_combining_partitions(i, j, self.partitions[:])
+                    if new_cost <= cost:
+                        partition_pair = [i, j]
+                        cost = new_cost
+                    reorganize_partitions(self.partitions)
+            # If a partition pair is found, combine them
+            if len(partition_pair) != 0:
+                p0 = self.partitions[partition_pair[0]]
+                p1 = self.partitions[partition_pair[1]]
+                combine_two_partitions(p0, p1, self.partitions)
+            get_bfs_level_partition(self.partitions)
+            reset_partition_device(self.partitions)
+            get_device_to_partitions_mapping(self.partitions, self.devices)
+            return len(partition_pair) != 0
+
+        for node in self.graph_module.graph.nodes:
+            if node.op not in {"placeholder", "get_attr", "output"}:
+                self.create_single_node_partition(node)
+        # Set up parent partitions and children partitions for each partition
+        set_parents_and_children(self.partitions)
+        # Get bfs level for each partition
+        get_bfs_level_partition(self.partitions)
+        find_combination = True
+        while find_combination:
+            # Search for a pair partition to generate the minimum new cost,
+            # then combine them
+            find_combination = search_combination(
+                transfer_rate_bytes_per_sec, node_to_latency_mapping
+            )
+        # Make sure all partitions are set up correctly
+        reorganize_partitions(self.partitions)
+        # Set up node to partition mapping
+        self.node_to_partition = get_node_to_partition_mapping(self.partitions)
+        return
+
+    def kl_based_partition(
+        self,
+        transfer_rate_bytes_per_sec: float,
+        node_to_latency_mapping: Dict[Node, NodeLatency],
+    ) -> None:
+        """This function is a cost aware partition based
+        on Kernighan-Lin algorithm.
+        First, the graph is partitioned using size_based_partition.
+        Then, each node is swapped with any other node in a different
+        partition, and at the same time, the cost is estimated after
+        the swapping.
+        For example, we have nodes n0, n1, n2, n3 and n4.
+        Using size_based_partition, n0 and n1 are in Partition p0.
+        n2, n3 and n4 in Partition p1. The current cost is estimated.
+        We first tried using n0 to swap with n2 from the other partition.
+        Then we see that swapping n0 and n2 shows a lower cost
+        than the current cost and it is the minimum among other pairs like
+        (n0, None)(This means moving n0 to Partition without swapping other nodes),
+        (n0, n3) and (n0, n4). We swap n0 and n2 and set the new cost
+        as the current cost.
+        Then We repeat this process for all the other nodes until all swapping pairs
+        are tried.
+        """
+
+        def swap_nodes(n0, n1, p0, p1):
+            # Either n0 or n1 could be None
+            # That means we simply move the node
+            # to another partition
+            if n0 is not None:
+                p0.remove_node(n0)
+                p1.add_node(n0)
+            if n1 is not None:
+                p0.add_node(n1)
+                p1.remove_node(n1)
+
+        def try_swap_nodes(
+            n0, n1, p0, p1, node_to_latency_mapping, transfer_rate_per_sec
+        ):
+            cost = float("inf")
+            swap_nodes(n0, n1, p0, p1)
+            # Reorganize partitions after swapping
+            reorganize_partitions(self.partitions)
+            # Check if there is a circular dependency after swapping
+            if (not check_dependency(p0)) and (not check_dependency(p1)):
+                reset_partition_device(self.partitions)
+                partition_to_latency_mapping = get_partition_to_latency_mapping(
+                    self.partitions, node_to_latency_mapping
+                )
+                # Check if all partitions can be mapped to logical devices after swapping
+                found_device = get_device_to_partitions_mapping(
+                    self.partitions, self.devices
+                )
+                if not found_device:
+                    cost = float("inf")
+                else:
+                    cost = get_latency_of_partitioned_graph(
+                        self.partitions,
+                        partition_to_latency_mapping,
+                        transfer_rate_bytes_per_sec,
+                    )
+            # Swap back and reset all partitions back to original
+            swap_nodes(n1, n0, p0, p1)
+            reorganize_partitions(self.partitions)
+            reset_partition_device(self.partitions)
+            get_device_to_partitions_mapping(self.partitions, self.devices)
+            return cost
+
+        def swap_node_to_partition(
+            node, p0, p1, node_to_latency_mapping, transfer_rate_per_sec
+        ):
+            """This function helps to swap one node from partition p0
+            with all the nodes in another partition p1
+            """
+            p1_nodes = list(p1.nodes) + [None]
+            min_cost = float("inf")
+            node_pair: List[Node] = []
+            for n1 in p1_nodes:
+                # Ignore the node if it is not a op node
+                if n1 is not None and n1.op in {"placeholder", "get_attr"}:
+                    continue
+                # Try swapping node in p0 with n1 in p1
+                cost = try_swap_nodes(
+                    node, n1, p0, p1, node_to_latency_mapping, transfer_rate_per_sec
+                )
+                if cost < min_cost:
+                    node_pair = [node, n1]
+                    min_cost = cost
+            return cost, node_pair  # type: ignore[possibly-undefined]
+
+        # First use size_base_partition
+        self.size_based_partition()
+        partition_to_latency_mapping = get_partition_to_latency_mapping(
+            self.partitions, node_to_latency_mapping
+        )
+        # Calculate the cost of the partitions
+        cost = get_latency_of_partitioned_graph(
+            self.partitions, partition_to_latency_mapping, transfer_rate_bytes_per_sec
+        )
+        # Keep tracking the node pair that shows the better cost
+        node_pair: List[Node] = []
+        # Keep tracking the partition pair of node pair
+        partition_pair: List[Partition] = []
+        # Collect all the op nodes from the graph
+        op_nodes = []
+        for n in self.graph_module.graph.nodes:
+            if n.op not in {"placeholder", "get_attr", "output"}:
+                op_nodes.append(n)
+        for node in op_nodes:
+            # Find which partition the current node belongs
+            p0_index = self.node_to_partition[node]
+            p0 = self.partitions[p0_index]
+            # Go through all the other partitions to swap
+            # with other nodes from those partitions
+            for p1_index, _ in enumerate(self.partitions):
+                if p0_index != p1_index:
+                    p1 = self.partitions[p1_index]
+                    new_cost, new_node_pair = swap_node_to_partition(
+                        node,
+                        p0,
+                        p1,
+                        node_to_latency_mapping,
+                        transfer_rate_bytes_per_sec,
+                    )
+                    # Update the cost
+                    # Track the swapped node pair and their partitions
+                    if new_cost < cost:
+                        cost = new_cost
+                        node_pair = new_node_pair
+                        partition_pair = [p0, p1]
+            # Do the swapping after trying all the nodes from a partition
+            if len(node_pair) != 0:
+                swap_nodes(
+                    node_pair[0], node_pair[1], partition_pair[0], partition_pair[1]
+                )
+                reorganize_partitions(self.partitions)
+                get_device_to_partitions_mapping(self.partitions, self.devices)
+        reorganize_partitions(self.partitions)
+        # Mapping the device to the partition
+        get_device_to_partitions_mapping(self.partitions, self.devices)
+        return
+
+    def aot_based_partition(
+        self, node_to_partition_mapping, partition_to_logical_device_mapping
+    ):
+        """This function helps to rebuild the partitions given the nodes and its
+        corresponding partition id
+        """
+        partition_id_to_partition_mapping: Dict[int, Partition] = {}
+        self.node_to_partition = node_to_partition_mapping
+        for node in self.node_to_partition:
+            partition_id = self.node_to_partition[node]
+            # If the requested partition has not been created, create the partition
+            if partition_id not in partition_id_to_partition_mapping:
+                partition = Partition(partition_id)
+                self.partitions.append(partition)
+                partition_id_to_partition_mapping[partition_id] = partition
+                partition.logical_device_ids = partition_to_logical_device_mapping[
+                    partition_id
+                ]
+            else:
+                partition = partition_id_to_partition_mapping[
+                    self.node_to_partition[node]
+                ]
+            # Add the current node into the partition
+            partition.add_node(node)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/const_fold.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/const_fold.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f395c0ad7bdc2d86f1a24f95facf7dcbbc27a34
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/const_fold.py
@@ -0,0 +1,292 @@
+# mypy: allow-untyped-defs
+import re
+from typing import Callable, Dict, Optional, Set, Union
+
+import torch.fx
+from torch.fx.node import map_arg
+from torch.fx.passes.split_module import split_module
+
+
+__all__ = ['FoldedGraphModule', 'get_unique_attr_name_in_module', 'split_const_subgraphs']
+
+class FoldedGraphModule(torch.fx.GraphModule):
+    """
+    FoldedGraphModule is a GraphModule which also contains another
+    `const_subgraph_module` representing a subgraph which has all const attr
+    inputs and which can be run once before running the main standard
+    `graph`. The `const_output_names` are the ordered list names of attrs which
+    represent what each respective output from the const_subgraph should be set
+    on which attrs.
+    """
+
+    def __init__(
+        self,
+        root: torch.nn.Module,
+        graph: torch.fx.Graph,
+        const_subgraph: Optional[torch.fx.Graph] = None,
+        fx_const_folded_attrs_name: Optional[str] = None,
+        device_for_folded_attrs: str = "cuda",
+    ):
+        super().__init__(root, graph)
+        self.const_subgraph_module = (
+            None
+            if const_subgraph is None
+            else torch.fx.GraphModule(root, const_subgraph)
+        )
+        self.has_folding_been_run = False
+        self.fx_const_folded_attrs_name = fx_const_folded_attrs_name
+        self.device_for_folded_attrs = device_for_folded_attrs
+
+    def __call__(self, *args, **kwargs):
+        if not self.has_folding_been_run:
+            self.run_folding()
+        return super().__call__(*args)
+
+    def run_folding(self):
+        # If there's no const subgraph module or attr output names to use, return
+        # early as there is no const folding to perform.
+        if (
+            self.const_subgraph_module is None
+            or self.fx_const_folded_attrs_name is None
+        ):
+            return
+
+        assert not self.has_folding_been_run
+        self.has_folding_been_run = True
+
+        # Actually run const folding subgraph. Note that single attr const fold
+        # subgraphs output a single Tensor while multiple outputs are returned as
+        # Tuple[Tensor,].
+        folded_attrs = self.const_subgraph_module()
+
+        def _create_param(i):
+            return torch.nn.Parameter(
+                i.detach().clone()
+                if not isinstance(i, int)
+                else torch.Tensor([i]).to(device=self.device_for_folded_attrs),
+                requires_grad=i.requires_grad if isinstance(i, torch.Tensor) else False,
+            )
+
+        params = (
+            torch.nn.ParameterList([_create_param(i) for i in folded_attrs])
+            if isinstance(folded_attrs, tuple)
+            else _create_param(folded_attrs)
+        )
+        setattr(self, self.fx_const_folded_attrs_name, params)
+
+
+def _inline_module(gm: torch.fx.GraphModule, inline_mod_name: str):
+    """
+    Given `gm` and some graph module which is called with target name `inline_mod_name`,
+    this helper will inline all of the nodes from that called graph module into `gm`.
+    """
+    # Fetch the inner graph module that we want to inline inside `gm`.
+    inline_mod = dict(gm.named_modules())[inline_mod_name]
+    assert isinstance(inline_mod, torch.fx.GraphModule)
+    call_mod_node_to_replace = None
+    for node in gm.graph.nodes:
+        if node.op == "call_module" and node.target == inline_mod_name:
+            call_mod_node_to_replace = node
+            break
+    assert call_mod_node_to_replace is not None
+
+    # Now actually do the swap. Note that we have to keep track of new nodes that are
+    # copied into `gm` -- we do this via replacement_mapping.
+    call_mod_args = call_mod_node_to_replace.args
+    replacement_mapping: Dict[torch.fx.Node, torch.fx.Node] = {}
+    ph_count = 0
+
+    def replacement_fn(node):
+        new_node = replacement_mapping[node]
+        new_node.meta = node.meta.copy()
+        return new_node
+
+    for inline_node in inline_mod.graph.nodes:
+        if inline_node.op == "placeholder":
+            replacement_mapping[inline_node] = call_mod_args[ph_count]
+            ph_count += 1
+            continue
+
+        if inline_node.op == "output":
+            outputs = inline_node.args[0]
+            output_replacements = map_arg(outputs, replacement_fn)
+            call_mod_node_to_replace.replace_all_uses_with(output_replacements)
+            continue
+
+        with gm.graph.inserting_before(call_mod_node_to_replace):
+            new_node = gm.graph.node_copy(inline_node, replacement_fn)
+        replacement_mapping[inline_node] = new_node
+
+    gm.graph.eliminate_dead_code()
+
+
+def get_unique_attr_name_in_module(mod_traced: torch.fx.GraphModule, name: str) -> str:
+    """
+    Make sure the name is unique (in a module) and can represents an attr.
+    """
+    # Delete all characters that are illegal in a Python identifier.
+    name = re.sub("[^0-9a-zA-Z_]+", "_", name)
+    if name[0].isdigit():
+        name = f"_{name}"
+    # Now make sure it is in fact unique to the module by incrementing suffix value.
+    while hasattr(mod_traced, name):
+        match = re.match(r"(.*)_(\d+)$", name)
+        if match is None:
+            name = name + "_1"
+        else:
+            base, num = match.group(1, 2)
+            name = f"{base}_{int(num) + 1}"
+
+    return name
+
+
+def split_const_subgraphs(
+    module: Union[torch.nn.Module, torch.fx.GraphModule],
+    skip_folding_node_fn: Optional[Callable[[torch.fx.Node], bool]] = None,
+    device_for_folded_attrs: str = "cpu",
+) -> FoldedGraphModule:
+    """
+    Looks through `module` for any nodes that have all constant attribute inputs
+    and separates them out into their own constant subgraph, and returns a
+    FoldedGraphModule which runs that constant subgraph on the first run to set
+    attributes on the module prior to running the non-constant portion of the
+    graph.
+    """
+    if not isinstance(module, torch.fx.GraphModule):
+        mod_traced = torch.fx.symbolic_trace(module)
+    else:
+        mod_traced = module
+
+    # Build up a list of const_nodes, defined as nodes that are themselves
+    # get_attrs, or have all get_attr or other constant node inputs.
+    const_nodes: Set[torch.fx.Node] = set()
+    found_const_folding = False
+    for node in mod_traced.graph.nodes:
+        # Skip over placeholders/outputs because they can't be const folded and
+        # we don't want to add tags to them.
+        if node.op in {"placeholder", "output"}:
+            continue
+
+        # If the node itself is constant, or all of its inputs are constant,
+        # then tag it as constant.
+        if node.op != "get_attr" and not set(node.all_input_nodes).issubset(
+            const_nodes
+        ):
+            continue
+
+        # If provided skip folding function says to skip, then skip.
+        if skip_folding_node_fn and skip_folding_node_fn(node):
+            continue
+
+        # Skip folding side-effectful functions
+        if node.is_impure():
+            continue
+
+        # Must be a constant foldable node at this point.
+        const_nodes.add(node)
+        if node.op != "get_attr":
+            found_const_folding = True
+
+    # If we did not find any const folding then return early without a const fold subgraph.
+    if not found_const_folding:
+        return FoldedGraphModule(mod_traced, mod_traced.graph)
+
+    # Partition the module into two: submod_0 for constant folding subgraph, and
+    # submod_1 for the rest.
+    def mod_partition(node: torch.fx.Node):
+        return 0 if node in const_nodes else 1
+
+    split = split_module(mod_traced, module, mod_partition)
+
+    const_mod_name, non_const_mod_name = "submod_0", "submod_1"
+    # Safely get submod_1 in case there are no non-const nodes
+    const_gm, non_const_gm = split.submod_0, getattr(split, non_const_mod_name, None)
+
+    # The module that a call_module node refers to gets copied to submodules during split.
+    # The path to the module also gets inlined, i.e. mod.a.b -> mod_a_b. Here we need to
+    # attach inlined modules to `split` as it's the owning module now.
+    for node in non_const_gm.graph.nodes if non_const_gm else []:
+        if node.op == "call_module":
+            setattr(split, node.target, getattr(non_const_gm, node.target))
+    for node in const_gm.graph.nodes:
+        if node.op == "call_module":
+            setattr(split, node.target, getattr(const_gm, node.target))
+
+    # split_module currently does not use get_attrs for attrs. Instead it passes
+    # them in as args from the parent module, which used get_attrs. Here we set
+    # them as get_attrs inside const_gm, allowing for running folding without
+    # somehow a priori knowing the attrs that should be passed as args. We can
+    # unconditionally do this for all placeholders because we know all
+    # placeholders to const_gm must be constants accessible via get_attr.
+    call_const_gm_args = None
+    for node in split.graph.nodes:
+        if node.op == "call_module":
+            if node.target == const_mod_name:
+                call_const_gm_args = node.args
+                break
+    assert call_const_gm_args is not None
+
+    # Here we do the actual replacement of placeholders to get_attrs. Note that here we
+    # set the const_gm.graph into a new root_const_gm with split as the root module,
+    # because we are fetching attributes directly from the root module, instead of
+    # fetching them from const_gm. Example: The const_gm must have some format like:
+    # graph():
+    #    %inp : [num_users=1] = placeholder[target=const_inp]
+    #    %add : [num_users=1] = call_function[target=operator.add](args = (%inp, %inp), kwargs = {})
+    #    return add
+    # We replace that with the following, which does not have any placeholders:
+    # graph():
+    #    %inp_1 : [num_users=1] = get_attr[target=const_inp]
+    #    %add : [num_users=1] = call_function[target=operator.add](args = (%inp_1, %inp_1), kwargs = {})
+    #    return add
+    root_const_gm = torch.fx.GraphModule(split, const_gm.graph)
+    for node in root_const_gm.graph.nodes:
+        if node.op == "output":
+            multiple_outputs = isinstance(node.args[0], tuple)
+            continue
+        if node.op != "placeholder":
+            continue
+        in_node = next(n for n in call_const_gm_args if n.name == node.target)
+        assert in_node.op == "get_attr"
+        with root_const_gm.graph.inserting_before(node):
+            new_node = root_const_gm.graph.get_attr(in_node.target)
+        new_node.meta = node.meta.copy()
+        node.replace_all_uses_with(new_node)
+        root_const_gm.graph.erase_node(node)
+    assert "multiple_outputs" in locals()
+
+    # Now find the call to const_gm inside split, and replace it with a getattr to the
+    # folded tensor(s) that result from constant folding. Note that we don't need to
+    # worry about whether this is one or more tensors because the original graph
+    # correctly uses getitem to extract individual tensors if there are multiple folded.
+    fx_const_folded_attrs_name = get_unique_attr_name_in_module(
+        mod_traced, "_FX_CONST_FOLDED_ATTRS"
+    )
+    setattr(
+        split,
+        fx_const_folded_attrs_name,
+        torch.nn.ParameterList() if multiple_outputs else torch.nn.Parameter(),  # type: ignore[possibly-undefined]
+    )
+    for node in split.graph.nodes:
+        if node.op == "call_module" and node.target == const_mod_name:
+            with node.graph.inserting_before(node):
+                folded_attrs = node.graph.get_attr(fx_const_folded_attrs_name)
+            folded_attrs.meta = node.meta.copy()
+            node.replace_all_uses_with(folded_attrs)
+            break
+
+    # Finally, inline the non-constant submod (if it exists) into the split submod.
+    # This is so that the original caller who may have passed in a graph module will
+    # get back out a graph module whose graph is traced to the same granularity.
+    if hasattr(split, non_const_mod_name):
+        _inline_module(split, non_const_mod_name)
+
+    split.graph.eliminate_dead_code()
+
+    return FoldedGraphModule(
+        split,
+        split.graph,
+        root_const_gm.graph,
+        fx_const_folded_attrs_name,
+        device_for_folded_attrs,
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/debug.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/debug.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3c482319f2ef290ef3e1e64d86d521e9d9324ea
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/debug.py
@@ -0,0 +1,32 @@
+# mypy: allow-untyped-defs
+import torch.fx as fx
+
+def set_trace(gm: fx.GraphModule) -> fx.GraphModule:
+    """
+    Sets a breakpoint in `gm`'s generated python code. It drops into pdb when
+    `gm` gets run.
+
+    Args:
+        gm: graph module to insert breakpoint. It is then recompiled for it to
+            take effect.
+
+    Returns:
+        the `gm` with breakpoint inserted.
+    """
+    def insert_pdb(body):
+        return ["import pdb; pdb.set_trace()\n", *body]
+
+    with gm.graph.on_generate_code(
+        make_transformer=lambda cur_transform: (
+            # new code transformer to register
+            lambda body: (
+                insert_pdb(
+                    cur_transform(body) if cur_transform
+                    else body
+                )
+            )
+        )
+    ):
+        gm.recompile()
+
+    return gm
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/graph_gradual_typechecker.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/graph_gradual_typechecker.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb49795a06fac79d50b58a90a47d36bbff724b9e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/graph_gradual_typechecker.py
@@ -0,0 +1,916 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+from functools import reduce
+import torch
+import operator
+from torch.fx.tensor_type import Dyn, is_consistent, TensorType, is_more_precise
+from typing import Callable, Dict
+from torch.fx.node import Target, Node
+from torch.nn.modules.batchnorm import BatchNorm2d
+from torch.nn.modules.conv import Conv2d
+from torch.fx.experimental.refinement_types import Equality
+import itertools
+
+from torch.fx.experimental.unification import Var  # type: ignore[attr-defined]
+
+import sympy
+
+_INFERENCE_RULES: Dict[Target, Callable] = {}
+_REFINEMENT_RULES: Dict[Target, Callable] = {}
+_RULES: Dict[Target, Callable] = {}
+
+
+def expand_to_tensor_dim(t, n):
+    """
+    Expand a type to the desired tensor dimension if possible
+    Raise an error otherwise.
+    - t is the given type
+    - n is a number of dimensions to expand to
+    """
+    if t == Dyn:
+        dims = [Dyn] * n
+        return TensorType(tuple(dims))
+    elif isinstance(t, TensorType):
+        if len(t.__args__) != n:
+            raise TypeError(f'Cannot extend tensor. Tensor {t} has rank {len(t.__args__)}. It should have rank {n}')
+        return t
+    else:
+        raise TypeError(f'Cannot match the type {t}')
+
+
+def broadcast_types(t1, t2):
+    """
+    Applies broadcasting to both given types such that they
+    become consistent with eachother and returns two new
+    resulting types
+    """
+
+    # if either type is Dyn, do nothing since the types are already consistent
+    if t1 == Dyn or t2 == Dyn or isinstance(t1, Var) or isinstance(t2, Var):
+        return t1, t2
+
+    if isinstance(t1, TensorType) and isinstance(t2, TensorType):
+        s1 = len(t1.__args__)
+        s2 = len(t2.__args__)
+
+        new_t1 = list(t1.__args__)
+        new_t2 = list(t2.__args__)
+
+        # We make the types the same length which is the first requirement
+        # for consistency
+        if s1 > s2:
+            for i in range(s1 - s2):
+                new_t2.insert(0, 1)
+
+        elif s2 > s1:
+            for i in range(s2 - s1):
+                new_t1.insert(0, 1)
+
+        # we replace occurrences of "1" with each tensor with
+        # the corresponding type from the other tensor
+        for i, (x, y) in enumerate(zip(new_t1, new_t2)):
+            if x == 1:
+                new_t1[i] = y
+            elif y == 1:
+                new_t2[i] = x
+
+        # at this point our tensors should be consistent
+        # and we can apply the element-wise operation and find the right dimension
+        # for the output of the operation
+        (t1, t2) = TensorType(tuple(new_t1)), TensorType(tuple(new_t2))
+        return (t1, t2)
+    else:
+        raise TypeError(f'Cannot broadcast types {t1} and {t2}')
+
+def register_inference_rule(call_target):
+    def register(fn):
+        if call_target in _INFERENCE_RULES:
+            raise RuntimeError(f'Inference rule already registered for {call_target}!')
+        _INFERENCE_RULES[call_target] = fn
+        return fn
+    return register
+
+def register_refinement_rule(call_target):
+    def register(fn):
+        if call_target in _REFINEMENT_RULES:
+            raise RuntimeError(f'Refinement rule already registered for {call_target}!')
+        _REFINEMENT_RULES[call_target] = fn
+        return fn
+    return register
+
+def register_algebraic_expressions_inference_rule(call_target):
+    def register(fn):
+        if call_target in _RULES:
+            raise RuntimeError(f'Rule already registered for {call_target}!')
+        _RULES[call_target] = fn
+        return fn
+    return register
+
+@register_inference_rule(torch.add)
+@register_inference_rule(operator.add)
+def add_inference_rule(n: Node):
+    """
+    Apply the addition inference rule. This includes:
+    - scalar addition
+    - broadcasting semantics
+
+    Note that we always return the least precise type between
+    the operands (after applying broadcasting) to be the final type of the operation
+
+    Note that we do not modify the operand types themselves after applying broadcasting
+    to them. We only use them to calculate the final type
+    """
+    assert isinstance(n.args[0], Node)
+    assert isinstance(n.args[1], Node)
+    t1 = n.args[0].type
+    t2 = n.args[1].type
+
+    # handle scalar addition
+    if t1 == int and isinstance(t2, TensorType):
+        n.type = t2
+        return n.type
+
+    # handle scalar addition
+    elif t2 == int and isinstance(t1, TensorType):
+        n.type = t1
+        return n.type
+
+    # we bring the new types to the point where
+    # we can check for consistency
+    # any inconsistency would not have been caused
+    # by broadcasting at this point
+    (new_t1, new_t2) = broadcast_types(t1, t2)
+
+    if new_t1 != t1 or new_t2 != t2:
+        n.meta['broadcast'] = True
+        n.meta[str(n.args[0])] = new_t1
+        n.meta[str(n.args[1])] = new_t2
+
+    else:
+        n.meta['broadcast'] = False
+
+    new_t1 = t1 if not n.meta['broadcast'] else new_t1
+    new_t2 = t2 if not n.meta['broadcast'] else new_t2
+
+    # we check for consistency between the new types
+    if is_consistent(new_t1, new_t2):
+        # we return the less precise type because
+        # broadcasting may have happened
+        # for operands with shape [1,2,Dyn] and [1,2,1]
+        # we have to assign the node [1,2,Dyn]
+        if is_more_precise(new_t1, new_t2):
+            n.type = new_t2
+        else:
+            n.type = new_t1
+        return n.type
+    else:
+        raise TypeError(f'Cannot add arguments {n.args[0]} ({ n.args[0].type}) and {n.args[1]} ({ n.args[1].type}) in node {n}.'
+                        f' Types should match ')
+
+@register_inference_rule(getattr)
+def get_attr_inference_rule(n: Node, traced):
+    """
+    The current getattr rule only handles the shape attribute
+    Can be extended to other attributes
+    The most representitive type we have is "Dyn" but the system
+    can be extended with more types, such as a type to represent shapes
+    """
+    attr_node = n.args[0]
+    attr_name = n.args[1]
+
+    if attr_name == "shape":
+        n.type = Dyn
+    else:
+        raise TypeError("Not yet implemented")
+
+    # TODO. We leave it like this till we add a type to represent tensor sizes
+    return n.type
+
+@register_inference_rule(torch.transpose)
+def transpose_inference_rule(n: Node):
+    """
+    We check that dimensions for the transpose operations
+    are within range of the tensor type of the node
+    """
+    if n.target == torch.transpose:
+        assert isinstance(n.args[0], Node)
+        t = n.args[0].type
+
+        assert isinstance(n.args[1], int)
+        assert isinstance(n.args[2], int)
+        dim1, dim2 = n.args[1], n.args[2]
+
+        if t == Dyn:
+            n.type = Dyn
+            return n.type
+
+        elif isinstance(t, TensorType):
+            if 0 <= dim1 < len(t.__args__) and 0 <= dim2 < len(t.__args__):
+                new_type = list(t.__args__)
+                new_type[dim1], new_type[dim2] = new_type[dim2], new_type[dim1]
+                final = TensorType(new_type)
+                n.type = get_greatest_upper_bound(n.type, final)
+                return n.type
+            else:
+                raise TypeError(f'Cannot transpose {dim1} and {dim2} in type {t} for node {n}')
+        else:
+            raise TypeError(f'Cannot transpose {dim1} and {dim2} in type {t} for node {n}')
+
+
+@register_inference_rule(torch.reshape)
+def reshape_inference_rule(n: Node):
+    """
+    Without dynamism, the rule checks that the
+    product of the elements of the argument tensor
+    type is equal to the product of the elements
+    of the required shape. We gradualize this rule
+    by adding a case to handle fully dynamic input
+    as well as input where some of the tensor dimensions
+    are unknown. In this case we check for divisibility
+    """
+    assert isinstance(n.args[0], Node)
+    t1 = n.args[0].type
+
+    assert isinstance(n.args[1], list)
+    t2 = n.args[1]
+    t2_type = TensorType([Dyn if elem == -1 else elem for elem in t2])
+
+    # if we do not know the original tensor dimension,
+    # we return the required dimension
+    if t1 == Dyn:
+        n.type = t2_type
+        return t2_type
+
+    # if any of the dimensions are unknown,
+    # we check for divisibility
+    elif isinstance(t1, TensorType):
+        assert isinstance(t1, TensorType)
+        a = [e if e != Dyn else 1 for e in t1.__args__]
+        p1 = reduce(operator.mul, a)
+        p2 = reduce(operator.mul, t2)
+        if p1 % p2 == 0 or p2 % p1 == 0:
+            n.type = t2_type
+            return t2_type
+        else:
+            raise TypeError(f'Cannot reshape in node {n} from {t1} to {t2_type}')
+    else:
+        raise TypeError(f'Cannot reshape in node {n} from {t1} to {t2_type}')
+
+@register_inference_rule(BatchNorm2d)
+def bn2d_inference_rule(n: Node, module_instance):
+    """
+    Given a BatchNorm2D instance and a node check the following conditions:
+    - the input type can be expanded to a size 4 tensor: t =  (x_1, x_2, x_3, x_4)
+    - the current node type can be expanded to a size 4 tensor: t' =  (x_1', x_2', x_3', x_4')
+    - t is consistent with t'
+    - x_2 is consistent with the module's num_features
+    - x_2' is consistent with the module's num_features
+    output type: the more precise type of t and t'
+    """
+    assert isinstance(n.args[0], Node)
+    n.args[0].type = expand_to_tensor_dim(n.args[0].type, 4)
+    arg_type = n.args[0].type
+    n.type = expand_to_tensor_dim(n.type, 4)
+
+    # we check the conditions on the incoming argument
+    # and any existing annotation
+    # we also check for consistency between both annotations
+    if is_consistent(arg_type.__args__[1], module_instance.num_features) and \
+            is_consistent(n.type.__args__[1], module_instance.num_features) and \
+            is_consistent(arg_type, n.type):
+
+        # we choose the more precise type
+        # to be the node type
+        # so if an incoming argument has more type information
+        # we set this node's type to be the argument type
+        n.type = get_greatest_upper_bound(arg_type, n.type)
+        return n.type
+    else:
+        raise TypeError(f'Cannot apply {module_instance} with input type {arg_type} and existing type {n.type} on {n}')
+
+
+def calculate_out_dimension(d_in, module_instance, index):
+    """
+    For calculating h_in and w_out according to the conv2D documentation
+    """
+    padding = (module_instance.padding, module_instance.padding) \
+        if isinstance(module_instance.padding, int) else module_instance.padding
+    kernel_size = (module_instance.kernel_size, module_instance.kernel_size) \
+        if isinstance(module_instance.kernel_size, int) else module_instance.kernel_size
+    stride = (module_instance.stride, module_instance.stride) \
+        if isinstance(module_instance.stride, int) else module_instance.stride
+    dilation = (module_instance.dilation, module_instance.dilation) \
+        if isinstance(module_instance.dilation, int) else module_instance.dilation
+
+    DIMENSION_TYPES = (int, sympy.Symbol)
+
+    if d_in == Dyn:
+        return Dyn
+
+    elif isinstance(d_in, DIMENSION_TYPES):
+        n = d_in + 2 * padding[index] - \
+            dilation[index] * \
+            (kernel_size[index] - 1) - 1
+
+        return (n // stride[0]) + 1
+
+    else:
+        raise TypeError(f'{d_in} in {module_instance} must be a number or Dyn. Received {type(d_in)}')
+
+
+def get_greatest_upper_bound(type1, type2):
+    """
+    Get the most precise type that's consistent with the given types
+    """
+    if type1 == Dyn:
+        return type2
+    elif type2 == Dyn:
+        return type1
+    elif isinstance(type1, TensorType) and isinstance(type2, TensorType):
+        if not is_consistent(type1, type2):
+            raise TypeError(f'Inconsistent types {type1}, {type2}')
+        gub = [t1 if is_more_precise(t1, t2) else t2 for (t1, t2) in zip(type1.__args__, type2.__args__)]
+        return TensorType(tuple(gub))
+
+
+@register_inference_rule(Conv2d)
+def conv2d_inference_rule(n: Node, module_instance):
+    """
+    Given a Conv2D instance and a node check the following conditions:
+    - the input type can be expanded to a size 4 tensor: t =  (x_1, x_2, H, W)
+    - the current node type can be expanded to a size 4 tensor: t' =  (x_1', x_2', x_3', x_4')
+    - x_2 is consistent with the module's in_channels
+    - let o = (x_1, out_channels, H_out, W_out)
+    then the output is the greatest upper bound of o and the existing node type t'.
+    """
+    assert isinstance(n.args[0], Node)
+    n.args[0].type = expand_to_tensor_dim(n.args[0].type, 4)
+    arg_type = n.args[0].type
+    curr_node_type = expand_to_tensor_dim(n.type, 4)
+
+    if is_consistent(arg_type.__args__[1], module_instance.in_channels):
+        w_in = arg_type.__args__[3]
+        h_in = arg_type.__args__[2]
+        h_out = calculate_out_dimension(h_in, module_instance, 0)
+        w_out = calculate_out_dimension(w_in, module_instance, 1)
+        new_type = TensorType((arg_type.__args__[0], module_instance.out_channels, h_out, w_out))
+        gub = get_greatest_upper_bound(new_type, curr_node_type)
+        n.type = gub
+        return n.type
+    else:
+        raise TypeError(f'Cannot apply {module_instance} with input type { arg_type} and existing type {n.type} on {n}')
+
+
+@register_inference_rule(torch.nn.ReLU)
+def relu_inference_rule(n: Node, module_instance):
+    """
+    Input and output shapes should be equal.
+    """
+    assert isinstance(n.args[0], Node)
+
+    if n.args[0].type == Dyn and isinstance(n.type, TensorType):
+        n.args[0].type = expand_to_tensor_dim(n.args[0].type, len(n.type.__args__))
+
+    if isinstance(n.args[0].type, TensorType):
+        n.type = get_greatest_upper_bound(n.args[0].type, n.type)
+    return n.type
+
+
+def maxpool2d_check(typ, module_instance):
+    """
+    Applies the maxpool2d shape information to the input
+    this affects the last two dimensions
+    """
+    new_type_list = list(typ.__args__)
+    if len(new_type_list) == 4 or len(new_type_list) == 3:
+        w_in = new_type_list[-1]
+        h_in = new_type_list[-2]
+
+        h_out = calculate_out_dimension(h_in, module_instance, 0)
+        w_out = calculate_out_dimension(w_in, module_instance, 1)
+
+        new_type_list[-1] = w_out
+        new_type_list[-2] = h_out
+        return TensorType(tuple(new_type_list))
+
+    else:
+        raise TypeError(f'Wrong size {typ} for {module_instance}')
+
+
+@register_inference_rule(torch.nn.MaxPool2d)
+def maxpool2d_inference_rule(n: Node, module_instance):
+    """
+    Given a MaxPool2D instance and a node check the following conditions:
+    - Input size matches size 3 or 4
+    - Current node type is consistent with the output type we will calculate
+    - Input size matches output size and the last two dimensions of the output
+      are w_out and h_out. The remaining dimensions are the same as the input
+    - Our final result is the greatest upper bound of the output we calculate
+      and the current node type.
+    """
+    assert isinstance(n.args[0], Node)
+
+    if n.args[0].type == Dyn and isinstance(n.type, TensorType):
+        n.args[0].type = expand_to_tensor_dim(n.args[0].type, len(n.type.__args__))
+    if isinstance(n.args[0].type, TensorType):
+        output = maxpool2d_check(n.args[0].type, module_instance)
+        n.type = get_greatest_upper_bound(output, n.type)
+    return n.type
+
+
+
+def linear_check(tensor_type, module_instance):
+    """
+    Checks that an input tensor type satisfies the conditions for linear operation
+    and returns the output type based on in and out features given by module_instance
+    """
+    if len(tensor_type.__args__) >= 2:
+        if is_consistent(module_instance.in_features, tensor_type.__args__[-1]):
+            new_type_args = list(tensor_type.__args__)
+            new_type_args[-1] = module_instance.out_features
+            return TensorType(tuple(new_type_args))
+        else:
+            raise TypeError(f'Inconsistent {module_instance.in_features} and {tensor_type.__args__[-1]} in {module_instance}')
+    else:
+        raise TypeError(f'Type {tensor_type} must have rank 2 or more.')
+
+
+@register_inference_rule(torch.nn.Linear)
+def linear_inference_rule(n: Node, module_instance):
+    """
+    Applies the shape information to the input then gets the greatest upper bound
+    of the resulting type and the existing type
+    """
+    assert isinstance(n.args[0], Node)
+    if n.args[0].type == Dyn and isinstance(n.type, TensorType):
+        n.args[0].type = expand_to_tensor_dim(n.args[0].type, len(n.type.__args__))
+    if isinstance(n.args[0].type, TensorType):
+        output_type = linear_check(n.args[0].type, module_instance)
+        n.type = get_greatest_upper_bound(output_type, n.type)
+    return n.type
+
+
+def adaptiveavgpool2d_check(tensor_type, module_instance):
+    output_size = module_instance.output_size
+    if isinstance(output_size, int):
+        output_size = [output_size, output_size]
+    elif isinstance(output_size, tuple):
+        output_size = list(output_size)
+        if output_size[0] is None:
+            output_size[0] = output_size[1]
+        if output_size[1] is None:
+            output_size[1] = output_size[0]
+
+    new_type_list = list(tensor_type.__args__)
+
+    if len(tensor_type.__args__) == 4 or len(tensor_type.__args__) == 3:
+        new_type_list[-1] = output_size[1]
+        new_type_list[-2] = output_size[0]
+
+        return TensorType(tuple(new_type_list))
+
+    else:
+        raise TypeError(f'Tensor ranks must be 3 or 4. Got {tensor_type}')
+
+@register_inference_rule(torch.nn.AdaptiveAvgPool2d)
+def adaptiveavgpool2d_inference_rule(n: Node, module_instance):
+    """
+    The input and output sizes should be the same except for the last
+    two dimensions taken from the input, which represent width and height
+    """
+    assert isinstance(n.args[0], Node)
+    if n.args[0].type == Dyn and isinstance(n.type, TensorType):
+        n.args[0].type = expand_to_tensor_dim(n.args[0].type, len(n.type.__args__))
+    if isinstance(n.args[0].type, TensorType):
+        output_type = adaptiveavgpool2d_check(n.args[0].type, module_instance)
+        n.type = get_greatest_upper_bound(n.type, output_type)
+    return n.type
+
+def flatten_check(tensor_type, start_dim, end_dim):
+    l = len(tensor_type.__args__)
+
+    start_dim = l if start_dim == -1 else abs(start_dim)
+    end_dim = l + end_dim + 1 if end_dim < 0 else end_dim + 1
+
+    if 0 <= start_dim <= (l - 1) and 0 <= end_dim <= l and start_dim < end_dim:
+        my_args = list(tensor_type.__args__)
+        lhs = my_args[0:start_dim]
+        rhs = my_args[end_dim:]
+        mid = my_args[start_dim:end_dim]
+        if Dyn in mid:
+            mid = [Dyn]
+        else:
+            mid = [reduce(operator.mul, my_args[start_dim:end_dim])]
+        new_type_list = lhs + mid + rhs
+        return TensorType(tuple(new_type_list))
+    else:
+        raise TypeError(f'Incompatible dimensions {start_dim}, {end_dim - 1} in type {tensor_type}')
+
+@register_inference_rule(torch.flatten)
+def flatten_inference_rule(n: Node):
+    """
+    Applies the flatten shape information to the input then gets the
+    greatest upper bound of the resulting type and the existing type
+    """
+    assert isinstance(n.args[0], Node)
+
+    # set the default start and end dims
+    start_dim = 1
+    end_dim = -1
+
+    if len(n.args) > 1:
+        assert isinstance(n.args[1], int)
+        start_dim = n.args[1]
+
+    if len(n.args) > 2:
+        assert isinstance(n.args[2], int)
+        end_dim = n.args[2]
+
+    if n.args[0].type == Dyn and isinstance(n.type, TensorType):
+        n.args[0].type = expand_to_tensor_dim(n.args[0].type, len(n.type.__args__))
+
+    if isinstance(n.args[0].type, TensorType):
+        output_type = flatten_check(n.args[0].type, start_dim, end_dim)
+        n.type = get_greatest_upper_bound(output_type , n.type)
+
+    return n.type
+
+class GraphTypeChecker:
+    def __init__(self, env, traced):
+        self.env = env
+        self.traced = traced
+
+    def type_check(self):
+        """
+        A gradual type checker for graphs
+        Effect: every node's field type will be
+        populated with a type after type-checking is done
+        """
+        graph = self.traced.graph
+
+        # type check every node with gradual type rules
+        # if any node does not type check return false
+        for n in graph.nodes:
+            self.type_check_node(n)
+        return True
+
+    def type_check_node(self, n: Node):
+        """
+        Type check a given fx node.
+        Current operations:
+        - Reshape
+        - Transpose
+        - Add
+        - Relu
+        - conv2d
+        - batchnorm2d
+        - flatten
+        - maxpool2d
+        - adaptiveavgpool2d
+        - linear
+        """
+        if n.type is None:
+            n.type = Dyn
+
+        if n.op == 'placeholder':
+            return n.type
+
+        elif n.op == 'get_attr':
+            t = get_parameter(self.traced, n.target)  # type: ignore[arg-type]
+            if isinstance(t.data, torch.Tensor):
+                n.type = TensorType(t.data.shape)
+            return n.type
+
+        elif n.op == 'call_function':
+            if n.target == getattr:
+                assert getattr in _INFERENCE_RULES
+                return _INFERENCE_RULES[n.target](n, self.traced)
+
+            elif n.target in _INFERENCE_RULES:
+                return _INFERENCE_RULES[n.target](n)
+            else:
+                raise RuntimeError(f'No inference rule registered for target {n.target}!')
+
+        elif n.op == 'call_module':
+            module_instance = self.traced.get_submodule(n.target)
+            if type(module_instance) in _INFERENCE_RULES:
+                return _INFERENCE_RULES[type(module_instance)](n, module_instance)
+            else:
+                raise RuntimeError(f'No inference rule registered for class {type(module_instance)}!')
+
+        elif n.op == 'output':
+            def get_node_type(a):
+                return a.type
+            n.type = torch.fx.node.map_arg(n.args[0], get_node_type)
+            return n.type
+
+        else:
+            raise NotImplementedError(f"Method {n.op} not yet implemented")
+
+
+@register_refinement_rule(Conv2d)
+def conv_refinement_rule(n: Node):
+    """
+    The equality constraints are between the first dimension of
+    the input and output
+    """
+    res = []
+    assert isinstance(n.args[0], Node)
+    arg_type = n.args[0].type
+    if isinstance(arg_type, TensorType) and isinstance(n.type, TensorType):
+        res = [Equality(arg_type.__args__[0], n.type.__args__[0])]
+        return res
+
+
+@register_refinement_rule(torch.nn.Linear)
+def linear_refinement_rule(n: Node):
+    """
+    The equality constraints are between the first dimension of
+    the input and output
+    """
+    res = []
+    assert isinstance(n.args[0], Node)
+    arg_type = n.args[0].type
+    if isinstance(arg_type, TensorType) and isinstance(n.type, TensorType):
+        res = [Equality(arg_type.__args__[0], n.type.__args__[0])]
+    return res
+
+@register_refinement_rule(BatchNorm2d)
+@register_refinement_rule(torch.nn.ReLU)
+def all_eq(n: Node):
+    """
+    For operations where the input shape is equal to the output shape
+    """
+    res = []
+    assert isinstance(n.args[0], Node)
+    arg_type = n.args[0].type
+    if isinstance(arg_type, TensorType) and isinstance(n.type, TensorType):
+        args1 = arg_type.__args__
+        args2 = n.type.__args__
+        res = [Equality(args1[i], args2[i]) for i in range(len(args1))]
+    return res
+
+
+@register_refinement_rule(torch.nn.AdaptiveAvgPool2d)
+@register_refinement_rule(torch.nn.MaxPool2d)
+def first_two_eq(n: Node):
+    """
+    For operations where the first two dimensions of the input and output shape
+    are equal
+    """
+    res = []
+    assert isinstance(n.args[0], Node)
+    arg_type = n.args[0].type
+    if isinstance(arg_type, TensorType) and isinstance(n.type, TensorType):
+        args1 = arg_type.__args__
+        args2 = n.type.__args__
+        res = [Equality(args1[0], args2[0]), Equality(args1[1], args2[1])]
+    return res
+
+
+@register_refinement_rule(torch.add)
+@register_refinement_rule(operator.add)
+def element_wise_eq(n: Node):
+    """
+    For element-wise operations and handles broadcasting.
+    Note that after applying broadcasting to the arguments
+    we are able to determine if certain dimensions have not been broadcast
+    if they are symbolicallu equal.
+
+    in this case, we can establish equality between those dimensions and the
+    corresponding output dimensions.
+
+    Note that it takes two iterations for this result. One iteration to establish
+    equality between certain dimensions of the operands (requiring the whole solver
+    including unification) and another iteration to establish equality between the operands
+    and the resulting type, requiring another round of constraint generation and unificaiton.
+    """
+    res = []
+    if isinstance(n.args[0], Node) and isinstance(n.args[1], Node):
+        arg_type1 = n.args[0].type
+        arg_type2 = n.args[1].type
+        if isinstance(arg_type1, TensorType) and isinstance(arg_type2, TensorType) and isinstance(n.type, TensorType):
+            args1, args2 = broadcast_types(arg_type1, arg_type2)
+            # by this point, we know that args1 and args2 are the same size.
+            a1 = args1.__args__
+            a2 = args2.__args__
+            a3 = n.type.__args__
+
+            # we would be here in the second iteration where we establish equality
+            # between operand type dimensions and the resulting type dimensions
+            r = []
+            for x, y, z in zip(a1, a2, a3):
+                if x == y:
+                    r.append(Equality(x, z))
+            res = r
+    return res
+
+
+@register_refinement_rule(torch.flatten)
+def flatten_refinement_rule(n: Node):
+    """
+    Generates equality constraints between the dimensions of the input and output
+    that will not be involved in the flatten operation
+    """
+    assert isinstance(n.args[0], Node)
+
+    eq_const = []
+
+    start_dim = 1
+    end_dim = -1
+
+    if len(n.args) > 1:
+        assert isinstance(n.args[1], int)
+        start_dim = n.args[1]
+
+    if len(n.args) > 2:
+        assert isinstance(n.args[2], int)
+        end_dim = n.args[2]
+
+    if isinstance(n.type, TensorType) and isinstance(n.args[0].type, TensorType):
+        l = len(n.type.__args__)
+        arg_type = n.args[0].type
+        start_dim = l if start_dim == -1 else start_dim
+        end_dim = l + end_dim + 1 if end_dim < 0 else end_dim + 1
+
+        for t1, t2 in zip(n.type.__args__[0:start_dim], arg_type.__args__[0:start_dim]):
+            eq_const.append(Equality(t1, t2))
+
+        for t1, t2 in zip(n.type.__args__[end_dim:], arg_type.__args__[end_dim:]):
+            eq_const.append(Equality(t1, t2))
+    return eq_const
+
+
+@register_algebraic_expressions_inference_rule(Conv2d)
+def conv_rule(n: Node, module_instance):
+    """
+    Represents the outout in terms of an algrbraic expression w.r.t
+    the input when possible
+    """
+    assert isinstance(n.args[0], Node)
+    arg_type = n.args[0].type
+    if isinstance(arg_type, TensorType) and isinstance(n.type, TensorType):
+        w_in = arg_type.__args__[3]
+        h_in = arg_type.__args__[2]
+        h_out = calculate_out_dimension(h_in, module_instance, 0)
+        w_out = calculate_out_dimension(w_in, module_instance, 1)
+        new_type = TensorType((n.type.__args__[0], n.type.__args__[1], h_out, w_out))
+        n.type = new_type
+        return new_type
+
+class Refine:
+    """
+    Symbolic shape inference.
+    Generates constraints over type variables.
+    Currently all constraints are equality constraints.
+    """
+    def __init__(self, traced):
+        self.constraints = []
+        self.traced = traced
+        self.symbol_iter = itertools.count(start=0, step=1)
+
+    def refine(self):
+        """
+        Generates constraints for
+        every node in the graph based on
+        the operation.
+        """
+        graph = self.traced.graph
+        for n in graph.nodes:
+            self.refine_node(n)
+        return True
+
+    def symbolic_relations(self):
+        """
+        Infers algebraic relations
+        """
+        graph = self.traced.graph
+        for n in graph.nodes:
+            self.infer_symbolic_relations(n)
+        return True
+
+    def replace_dyn_with_fresh_var(self, typ):
+        """
+        Replace all unknown types with fresh type variables.
+        """
+        if typ == Dyn:
+            new_symbol = Var(next(self.symbol_iter))
+            return new_symbol
+        elif isinstance(typ, TensorType):
+            new_args = [self.replace_dyn_with_fresh_var(a) for a in typ.__args__]
+            return TensorType(tuple(new_args))
+        elif isinstance(typ, list):
+            return [self.replace_dyn_with_fresh_var(t) for t in typ]
+        elif isinstance(typ, tuple):
+            return (self.replace_dyn_with_fresh_var(t) for t in typ)
+        else:
+            return typ
+
+
+    def convert_to_sympy_symbols(self, typ):
+        """
+        Replace all unknown types with fresh type variables.
+        """
+        if isinstance(typ, Var):
+            return sympy.symbols(str(typ))
+        elif isinstance(typ, TensorType):
+            new_args = [self.convert_to_sympy_symbols(a) for a in typ.__args__]
+            return TensorType(tuple(new_args))
+        elif isinstance(typ, list):
+            return [self.convert_to_sympy_symbols(t) for t in typ]
+        elif isinstance(typ, tuple):
+            return (self.convert_to_sympy_symbols(t) for t in typ)
+        else:
+            return typ
+
+    def refine_node(self, n: Node):
+        """
+        Returns a list of equality constraints for
+        call_module and call_function nodes.
+        Models the relation between input and output dimensions
+        using constraints in case they are both tensors.
+        All operations used in resnet50 are defined.
+        """
+        if n.type is None:
+            n.type = Dyn
+
+        n.type = self.replace_dyn_with_fresh_var(n.type)
+
+        if n.op == 'call_function':
+            if n.target in _REFINEMENT_RULES:
+                self.constraints += _REFINEMENT_RULES[n.target](n)
+            else:
+                pass
+
+        if n.op == 'call_module':
+            module_instance = self.traced.get_submodule(n.target)
+            if type(module_instance) in _REFINEMENT_RULES:
+                self.constraints += _REFINEMENT_RULES[type(module_instance)](n)
+            else:
+                pass
+
+        if n.op == 'output':
+            def get_node_type(a):
+                return a.type
+            n.type = torch.fx.node.map_arg(n.args[0], get_node_type)
+            return n.type
+
+        else:
+            pass
+
+    def infer_symbolic_relations(self, n: Node):
+        n.type = self.convert_to_sympy_symbols(n.type)
+        if n.op == 'call_function':
+            if n.target in _RULES:
+                return _RULES[n.target](n)
+            else:
+                pass
+
+        if n.op == 'call_module':
+            module_instance = self.traced.get_submodule(n.target)
+            if type(module_instance) in _RULES:
+                return _RULES[type(module_instance)](n, module_instance)
+            else:
+                pass
+
+        if n.op == 'output':
+            def get_node_type(a):
+                return a.type
+            n.type = torch.fx.node.map_arg(n.args[0], get_node_type)
+            return n.type
+
+        else:
+            pass
+
+def get_parameter(traced, target: str):
+    """
+    Returns the parameter given by ``target`` if it exists,
+    otherwise throws an error.
+
+    See the docstring for ``get_submodule`` for a more detailed
+    explanation of this method's functionality as well as how to
+    correctly specify ``target``.
+
+    Args:
+        target: The fully-qualified string name of the Parameter
+            to look for. (See ``get_submodule`` for how to specify a
+            fully-qualified string.)
+
+    Returns:
+        torch.nn.Parameter: The Parameter referenced by ``target``
+
+    Raises:
+        AttributeError: If the target string references an invalid
+            path or resolves to something that is not an
+            ``nn.Parameter``
+    """
+    module_path, _, param_name = target.rpartition(".")
+
+    mod: torch.nn.Module = traced.get_submodule(module_path)
+
+    if not hasattr(mod, param_name):
+        raise AttributeError(mod._get_name() + " has no attribute `" + param_name + "`")
+
+    param: torch.nn.Parameter = getattr(mod, param_name)
+
+    return param
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/merge_matmul.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/merge_matmul.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1a634b2602a06bcadbd039815e32c9ae226cf65
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/merge_matmul.py
@@ -0,0 +1,172 @@
+# mypy: allow-untyped-defs
+import torch
+
+from torch.fx.node import Node
+from torch.fx._symbolic_trace import symbolic_trace
+from torch.fx.passes.tools_common import legalize_graph
+import itertools
+import operator
+
+from typing import Dict, List, Tuple
+
+
+def split_result_tensors(
+    result: torch.Tensor, inputs: List[torch.Tensor]
+) -> Tuple[torch.Tensor, ...]:
+    """
+    A free function for use in the merge_matmul graph transformation below that
+    splits the output from a merged matmul into the individual results for each
+    input tensor.
+
+    Arguments:
+        result: The merged matmul result tensor.
+        inputs: The list of inputs that were merged into one for the matmul.
+
+    Returns:
+        List of matmul results for each input tensor.
+    """
+    # When fx tracer is running, x.shape[0] will be torch.fx.Attribute but we
+    # need an int even when tracing
+    if isinstance(result, torch.fx.Proxy):
+        splits = [0] * len(inputs)
+    else:
+        splits = [x.shape[0] for x in inputs]
+
+    return torch.split(result, splits)
+
+
+def may_depend_on(a: Node, b: Node, search_depth: int = 6):
+    """
+    Determine if one node depends on another in a torch.fx.Graph.
+
+    Arguments:
+        a: The node that may have a dependency on b.
+        b: The node that a may have a dependency on.
+        search_depth: In the case of an indirect dependency, this function
+                        searches upto this many nodes away in search of a
+                        data dependency. If none is found, the function
+                        makes the conservative assumption that there is a
+                        dependency.
+
+    Returns:
+        True if a may depend on b, False if it definitely does not.
+    """
+    # Equivalence is defined as dependence.
+    if a == b:
+        return True
+
+    # If a has no inputs, it cannot depend on b.
+    if len(a.all_input_nodes) == 0:
+        return False
+
+    # If the search depth has been exhausted and no conclusion has been
+    # reached, assume that there is a data dependency.
+    if search_depth == 0:
+        return True
+
+    # Recursively check all inputs of a.
+    for inp in a.all_input_nodes:
+        if may_depend_on(inp, b, search_depth - 1):
+            return True
+
+    return False
+
+
+def are_nodes_independent(nodes: List[Node]):
+    """
+    Check if all of the given nodes are pairwise-data independent.
+
+    Arguments:
+        nodes: The nodes to check for data dependencies.
+
+    Returns:
+        True if any pair in nodes has a data dependency.
+    """
+    # For each pair in nodes:
+    for i, j in itertools.combinations(nodes, 2):
+        if may_depend_on(i, j) or may_depend_on(j, i):
+            return False
+
+    return True
+
+
+def merge_matmul(in_mod: torch.nn.Module):
+    """
+    A graph transformation that merges matrix multiplication operations that share the same right-hand
+    side operand into one large matrix multiplication.
+               ____      _________        _________
+      ----    |    |    |         |     M|  A * C  |
+    M| A  |  T| B  | * K|    C    | =    |---------|
+      ---- ,  |    |    |         |     T|  B * C  |
+       K       ----      ---------        ---------
+                K            R                R
+    """
+    gm = symbolic_trace(in_mod)
+
+    rhs_users: Dict[Node, List[Node]] = {}
+    lhs_users: Dict[Node, List[Node]] = {}
+
+    # Populate rhs_users and lhs_users - maps from LHS/RHS matrix multiply operands to
+    # the matmul of which they are the LHS/RHS.
+    for node in gm.graph.nodes:
+        if node.op != "call_function" or node.target is not torch.matmul:
+            continue
+
+        lhs, rhs = node.args
+
+        # TODO: Properly handle aliasing caused by get_attr. For now,
+        # use the attribute name as the operand if the node is a
+        # get_attr.
+        lhs = lhs.target if lhs.op == "get_attr" else lhs
+        rhs = rhs.target if rhs.op == "get_attr" else rhs
+
+        lhs_users.setdefault(lhs, []).append(node)
+        rhs_users.setdefault(rhs, []).append(node)
+
+    for rhs, mms in rhs_users.items():
+        # There must be at least matmuls for a merge to make sense.
+        if len(mms) < 2:
+            continue
+
+        # All matmuls must not depend on each other directly or indirectly
+        # in order for the merge to be possible.
+        if not are_nodes_independent(mms):
+            continue
+
+        lhs_vals = [mm.args[0] for mm in mms]
+
+        # Merge the matmul.
+        # Collect a list of LHS operands and the single RHS operand.
+        lhs = [gm.graph.get_attr(l) if isinstance(l, str) else l for l in lhs_vals]
+        rhs = gm.graph.get_attr(rhs) if isinstance(rhs, str) else rhs
+
+        # Concatenate all the LHS operands.
+        merge_mm_cat = gm.graph.call_function(torch.cat, (lhs,), {})
+
+        # Multiply the concatenated LHS operands with the one RHS. This will produce
+        # the same results as all the individual matmuls involving rhs in the original graph,
+        # but they will all be concatenated together.
+        merge_mm = gm.graph.call_function(torch.matmul, (merge_mm_cat, rhs,), {})
+
+        # Split the result of the merged matmul using the shapes of the LHS operands
+        # to ascertain how large each chunk should be.
+        merge_mm_split = gm.graph.call_function(
+            split_result_tensors, (merge_mm, lhs), {}
+        )
+        merge_mm_res = [
+            gm.graph.call_function(operator.getitem, (merge_mm_split, out), {})
+            for out in range(len(lhs))
+        ]
+
+        # Replace all uses of the original, unmerged matmuls with the equivalent split chunk from the merged matmul.
+        for old, new in zip(mms, merge_mm_res):
+            old.replace_all_uses_with(new)
+            gm.graph.erase_node(old)
+
+        # All of the new nodes created above were inserted at the end, so we need to sort
+        # the nodes topologically to make sure all definitions precede uses.
+        legalize_graph(gm)
+
+    gm.recompile()
+    gm.graph.lint()
+    return gm
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/meta_tracer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/meta_tracer.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd2c9b11ab76b15ae310a6b4ea9e89cf26cf3670
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/meta_tracer.py
@@ -0,0 +1,269 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.fx
+import warnings
+import functools
+import builtins
+
+from typing import Any, Callable, Dict, Optional, Union
+
+def embedding_override(self, input):
+    return torch.empty(*input.shape, self.weight.shape[-1], device='meta')
+
+
+def nn_layernorm_override(self, input):
+    return input
+
+
+def torch_relu_override(x):
+    return x
+
+
+def torch_nn_relu_override(self, x):
+    return x
+
+
+def functional_relu_override(x, inplace=False):
+    assert not inplace, 'dont support inplace functional.relu for metatensor analysis'
+    return x
+
+
+def torch_where_override(condition, x, y):
+    # torch.where returns the broadcasted tensor of condition, x, and y,
+    # so hack it by using addition
+    return condition.to(device='meta') + x.to(device='meta') + y.to(device='meta')
+
+
+def torch_abs_override(input, *, out=None):
+    assert out is None, 'Dont support in-place abs for MetaTensor analysis'
+    return input
+
+manual_meta_overrides : Dict[Callable, Callable] = {
+    torch.nn.Embedding: embedding_override,
+    torch.nn.LayerNorm: nn_layernorm_override,
+    torch.relu: torch_relu_override,
+    torch.nn.functional.relu: functional_relu_override,
+    torch.nn.ReLU: torch_nn_relu_override,
+    torch.where: torch_where_override,
+    torch.abs: torch_abs_override,
+}
+
+def gen_constructor_wrapper(target):
+    @functools.wraps(target)
+    def wrapper(*args, **kwargs):
+        proxy = None
+
+        def check_has_proxy(v):
+            if isinstance(v, torch.fx.Proxy):
+                nonlocal proxy
+                proxy = v
+        torch.fx.node.map_aggregate(args, check_has_proxy)
+        torch.fx.node.map_aggregate(kwargs, check_has_proxy)
+
+        if proxy is not None:
+            return proxy.tracer.create_proxy('call_function', target, args, kwargs)
+        else:
+            return target(*args, **kwargs)
+    return wrapper, target
+
+class MetaProxy(torch.fx.Proxy):
+    def install_tensor_meta(self, tensor_meta):
+        self._tensor_meta = tensor_meta
+
+    def size(self, dim=None):
+        if hasattr(self, '_tensor_meta') and self._tensor_meta is not None:
+            return self._tensor_meta.size(*[dim] if dim else [])
+        return self.tracer.create_proxy('call_method', 'size', (self, dim) if dim else (self,), {})
+
+    def dim(self):
+        if hasattr(self, '_tensor_meta') and self._tensor_meta is not None:
+            return self._tensor_meta.dim()
+        return self.tracer.create_proxy('call_method', 'dim', (self,), {})
+
+    @property
+    def shape(self):
+        if hasattr(self, '_tensor_meta') and self._tensor_meta is not None:
+            return self._tensor_meta.shape
+        return self.tracer.create_proxy('call_function', builtins.getattr, (self, 'shape'), {})
+
+    @property
+    def dtype(self):
+        if hasattr(self, '_tensor_meta') and self._tensor_meta is not None:
+            return self._tensor_meta.dtype
+        return self.tracer.create_proxy('call_function', builtins.getattr, (self, 'dtype'), {})
+
+    @property
+    def device(self):
+        # Hack so we can track when devices are used. During meta-tensor propagation,
+        # replace these values with a constant 'meta'
+        return MetaDeviceAttribute(self, 'device')
+
+    def __getattr__(self, k):
+        if k == '_tensor_meta':
+            return self.__getattribute__(k)
+        # note: not added to the graph yet, if this is a method call
+        # we peephole optimize to the method invocation
+        return MetaAttribute(self, k)
+
+class MetaAttribute(MetaProxy):
+    def __init__(self, root, attr: str):
+
+        self.root = root
+        self.attr = attr
+        self.tracer = root.tracer
+        self._node = None
+
+    @property
+    def node(self):
+        # the node for attributes is added lazily, since most will just be method calls
+        # which do not rely on the getitem call
+        if self._node is None:
+            self._node = self.tracer.create_proxy('call_function', getattr, (self.root, self.attr), {}).node
+        return self._node
+
+    def __call__(self, *args, **kwargs):
+        return self.tracer.create_proxy('call_method', self.attr, (self.root,) + args, kwargs)
+
+class MetaDeviceAttribute(MetaAttribute):
+    pass
+
+def proxys_to_metas(v):
+    if isinstance(v, MetaDeviceAttribute):
+        return 'meta'
+    if isinstance(v, torch.fx.Proxy):
+        assert isinstance(v, MetaProxy), f'Expected MetaProxy but got {type(v)}'
+        assert hasattr(v, '_tensor_meta'), 'MetaProxy does not have an associated meta'
+        return v._tensor_meta
+    return v
+
+class MetaTracer(torch.fx.Tracer):
+    allow_insert_stateless_mods : bool = True
+
+    _TORCH_METHODS_TO_PATCH = ['arange', 'zeros', 'ones', 'full_like', 'eye']
+
+    def create_proxy(self, kind, target, args, kwargs, name=None, type_expr=None, proxy_factory_fn=None):
+        rv = super().create_proxy(kind, target, args, kwargs, name, type_expr, proxy_factory_fn)
+
+        if kind == 'placeholder' and target in self.meta_args:
+            rv.install_tensor_meta(self.meta_args[target])
+            return rv
+
+        if target in self.orig_fns:
+            # NOTE: tensor constructors in PyTorch define the `device` argument as
+            # *kwargs-only*. That is why this works. If you add methods to
+            # _TORCH_METHODS_TO_PATCH that do not define `device` as kwarg-only,
+            # this will break and you will likely see issues where we cannot infer
+            # the size of the output.
+            if 'device' in kwargs:
+                kwargs['device'] = 'meta'
+
+        try:
+            args_metas = torch.fx.node.map_aggregate(args, proxys_to_metas)
+            kwargs_metas = torch.fx.node.map_aggregate(kwargs, proxys_to_metas)
+
+            if kind == 'call_function':
+                meta_target = manual_meta_overrides.get(target, target)
+                meta_out = meta_target(*args_metas, **kwargs_metas)
+            elif kind == 'call_method':
+                meta_out = getattr(args_metas[0], target)(*args_metas[1:], **kwargs_metas)  # type: ignore[index]
+            elif kind == 'call_module':
+                assert hasattr(self, 'orig_forward')
+                self._disable_module_getattr = True
+                try:
+                    mod = self.root.get_submodule(target)
+                    mod_type = type(mod)
+                    if mod_type in manual_meta_overrides:
+                        meta_out = manual_meta_overrides[mod_type](mod, *args_metas, **kwargs_metas)  # type: ignore[misc, arg-type]
+                    else:
+                        meta_out = self.orig_forward(*args_metas, **kwargs_metas)
+                finally:
+                    self._disable_module_getattr = False
+            elif kind == 'get_attr':
+                self._disable_module_getattr = True
+                try:
+                    attr_itr = self.root
+                    atoms = target.split('.')
+                    for atom in atoms:
+                        attr_itr = getattr(attr_itr, atom)
+                    assert isinstance(attr_itr, torch.Tensor)
+                    meta_out = attr_itr.to(device='meta')
+                finally:
+                    self._disable_module_getattr = False
+            else:
+                return rv
+
+            # TODO
+            assert isinstance(rv, torch.fx.Proxy), 'Dont support composite output yet'
+            rv.install_tensor_meta(meta_out)
+        except Exception as e:
+            warnings.warn(f'Could not compute metadata for {kind} target {target}: {e}')
+
+        return rv
+
+    def getattr(self, attr, attr_val, parameter_proxy_cache):
+        if getattr(self, '_disable_module_getattr', False):
+            return attr_val
+        else:
+            return super().getattr(attr, attr_val, parameter_proxy_cache)
+
+    def call_module(self, m, forward, args, kwargs):
+        self.orig_forward = forward
+        return super().call_module(m, forward, args, kwargs)
+
+    def _insert_module_as_submodule(self, mod: torch.nn.Module) -> str:
+        """
+        Helper method which tries to insert a module that was not declared as submodule.
+        """
+        idx = 0
+        mod_name = mod.__class__.__name__.lower()
+        path = f"{mod_name}_{idx}"
+        while hasattr(self.root, path):
+            path = f"{mod_name}_{idx}"
+            idx += 1
+
+        self.root.add_module(path, mod)
+        return path
+
+    def path_of_module(self, mod: torch.nn.Module) -> str:
+        try:
+            return super().path_of_module(mod)
+        except NameError as e:
+            if self.allow_insert_stateless_mods and len(list(mod.parameters())) == 0 and len(list(mod.buffers())) == 0:
+                path = self._insert_module_as_submodule(mod)
+                self.prev_module = path
+                return path
+            raise
+
+    def proxy(self, node):
+        return MetaProxy(node, self)
+
+    def trace(self, root, meta_args : Dict[str, torch.Tensor], concrete_args=None):  # type: ignore[override]
+        assert isinstance(meta_args, dict)
+        self.meta_args = meta_args
+
+        self.patched_torch_methods = {
+            target: gen_constructor_wrapper(getattr(torch, target)) for target in self._TORCH_METHODS_TO_PATCH
+        }
+        self.orig_fns = set()
+
+        for name, (wrapper, orig) in self.patched_torch_methods.items():
+            setattr(torch, name, wrapper)
+            self.orig_fns.add(orig)
+
+        try:
+            graph = super().trace(root, concrete_args)
+            graph._tracer_extras = {'meta_args': meta_args}
+            return graph
+        finally:
+            for name, (_, orig) in self.patched_torch_methods.items():
+                setattr(torch, name, orig)
+
+
+def symbolic_trace(root : Union[torch.nn.Module, Callable[..., Any]],
+                   meta_args : Optional[Dict[str, torch.Tensor]] = None,
+                   concrete_args: Optional[Dict[str, Any]] = None) -> torch.fx.GraphModule:
+    tracer = MetaTracer()
+    graph = tracer.trace(root, meta_args, concrete_args)  # type: ignore[arg-type]
+    name = root.__class__.__name__ if isinstance(root, torch.nn.Module) else root.__name__
+    gm = torch.fx.GraphModule(tracer.root, graph, name)
+    return gm
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/__init__.py
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/constraint.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/constraint.py
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index 0000000000000000000000000000000000000000..4693a62de24025c7bb1029297255141bf48cb07f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/constraint.py
@@ -0,0 +1,558 @@
+# mypy: allow-untyped-defs
+from torch.fx.experimental.migrate_gradual_types.operation import op_add, op_sub, op_mul, op_div, \
+    op_mod, op_gt, op_lt, op_neq, op_eq
+from torch.fx.tensor_type import TensorType, Dyn
+
+
+class Constraint:
+    pass
+
+
+class Conj(Constraint):
+    def __init__(self, conjuncts):
+        """
+        :param conjuncts: Conjunction of constraints
+        """
+        self.conjucts = conjuncts
+
+    def __eq__(self, other):
+        if isinstance(other, Conj):
+            return self.conjucts == other.conjucts and self.conjucts == other.conjucts
+        else:
+            return False
+
+    def __repr__(self):
+        return f'And({self.conjucts})'
+
+
+class Disj(Constraint):
+    def __init__(self, disjuncts):
+        """
+        :param disjuncts: Disjunction of constraints
+        """
+        self.disjuncts = disjuncts
+
+    def __eq__(self, other):
+        if isinstance(other, Disj):
+            return self.disjuncts == other.disjuncts and self.disjuncts == other.disjuncts
+        else:
+            return False
+
+    def __repr__(self):
+        return f'Or({self.disjuncts})'
+
+
+class Prod(Constraint):
+    def __init__(self, products):
+        """
+        :param products: lists of dimensions to multiply
+        """
+        self.products = products
+
+    def __eq__(self, other):
+        if isinstance(other, Prod):
+            return self.products == other.products and self.products == other.products
+        else:
+            return False
+
+    def __repr__(self):
+        return f'Product({self.products})'
+
+
+class T(Constraint):
+    """
+    True
+    """
+    def __init__(self) -> None:
+        pass
+
+    def __eq__(self, other):
+        return isinstance(other, T)
+
+    def __repr__(self):
+        return 'True'
+
+class F(Constraint):
+    """
+    False
+    """
+    def __init__(self) -> None:
+        pass
+
+    def __eq__(self, other):
+        return isinstance(other, F)
+
+    def __repr__(self):
+        return 'False'
+
+
+class BinaryConstraint(Constraint):
+    """
+    Represents all binary operations
+    """
+    def __init__(self, lhs, rhs, op):
+        """
+        :param lhs: lhs of the constraint
+        :param rhs: rhs of the constraint
+        :param op: string representing the operation
+        """
+        self.lhs = lhs
+        self.rhs = rhs
+        self.op = op
+
+    def __eq__(self, other):
+        if isinstance(other, BinaryConstraint):
+            return self.lhs == other.lhs and self.rhs == other.rhs and self.op == other.op
+        else:
+            return False
+
+    def __repr__(self):
+        return f'({self.lhs} {self.op} {self.rhs})'
+
+
+class BinConstraintT(BinaryConstraint):
+    """
+    Binary constraints about tensors
+    """
+    def __init__(self, lhs, rhs, op):
+        assert (isinstance(lhs, (TVar, TensorType, int)) or lhs == Dyn) and \
+               (isinstance(rhs, (TVar, TensorType, int)) or rhs == Dyn)
+        super().__init__(lhs, rhs, op)
+
+    def __eq__(self, other):
+        return super().__eq__(other)
+
+
+class BinConstraintD(BinaryConstraint):
+    """
+    Binary constraints about dimensions
+    """
+    def __init__(self, lhs, rhs, op):
+        assert is_algebraic_expression(lhs) or is_dim(lhs) or is_bool_expr(lhs)
+        assert is_algebraic_expression(rhs) or is_dim(rhs) or is_bool_expr(rhs)
+
+        super().__init__(lhs, rhs, op)
+
+    def __eq__(self, other):
+        return super().__eq__(other)
+
+
+
+class TGreatestUpperBound(Constraint):
+    """
+    Greatest Upper bound for tensors with dynamic type
+    """
+    def __init__(self, res, rhs1, rhs2):
+        """
+        :param res: tensor variable that stores the result of the outout
+        :param rhs1: tensor or tensor variable
+        :param rhs2: tensor or tensor variabke
+        """
+        self.res = res
+        self.rhs1 = rhs1
+        self.rhs2 = rhs2
+
+    def __repr__(self):
+        return f'{self.res} = {self.rhs1}\u2294*{self.rhs2}'
+
+    def __eq__(self, other):
+        if isinstance(other, TGreatestUpperBound):
+            return self.res == other.res and self.rhs1 == other.rhs1 and self.rhs2 == other.rhs2
+        else:
+            return False
+
+
+class DGreatestUpperBound(Constraint):
+    """
+    Greatest Upper bound for dimensions
+    """
+    def __init__(self, res, rhs1, rhs2):
+        """
+        :param res: Dimension variable to store the result
+        :param rhs1: dimension variable 1
+        :param rhs2: dimension variable 2
+        """
+        assert is_dim(res)
+        assert is_dim(rhs1)
+        assert is_dim(rhs2)
+
+        self.res = res
+        self.rhs1 = rhs1
+        self.rhs2 = rhs2
+
+    def __repr__(self):
+        return f'{self.res} = {self.rhs1}\u2294{self.rhs2}'
+
+    def __eq__(self, other):
+        if isinstance(other, DGreatestUpperBound):
+            return self.res == other.res and self.rhs1 == other.rhs1 and self.rhs2 == other.rhs2
+        else:
+            return False
+
+
+class CanReshape(Constraint):
+    """
+    can_reshape constraint
+    """
+    def __init__(self, src, target):
+        """
+        :param src: tensor variable
+        :param target: tensor
+        """
+        self.src = src
+        self.target = target
+
+    def __repr__(self):
+        return f'can-reshape({self.src}, {self.target})'
+
+    def __eq__(self, other):
+        if isinstance(other, CanReshape):
+            return self.src == other.src and self.target == other.target
+        else:
+            return False
+
+
+class IndexSelect(Constraint):
+
+    def __init__(self, tensor_size, input_var, dim_replace, index, output):
+        """
+        Args:
+            input_var: input to index_select
+            tensor_size: tensor size we are considering
+            dim_replace: the dimension of the output at "index"
+            index: location of the dimensions to replace in the input
+            output: variable to store the result
+        """
+        assert isinstance(input_var, TVar)
+        assert isinstance(output, TVar)
+        assert isinstance(dim_replace, DVar) or dim_replace == Dyn
+        assert isinstance(index, int)
+
+        self.input_var = input_var
+        self.tensor_size = tensor_size
+        self.dim_replace = dim_replace
+        self.index = index
+        self.output = output
+
+    def __repr__(self):
+
+        return f' {self.output} = ' \
+               f'IndexSelect({self.input_var}, ' \
+               f'tensor_size: {self.tensor_size}, ' \
+               f'{self.dim_replace}, ' \
+               f'{self.index})'
+
+    def __eq__(self, other):
+        if isinstance(other, IndexSelect):
+            return self.tensor_size == other.tensor_size and \
+                self.dim_replace == other.dim_replace and \
+                self.index == other.index and \
+                self.output == other.output and \
+                self.input_var == other.input_var
+        else:
+            return False
+
+
+class Transpose(Constraint):
+
+    def __init__(self, tensor_size, input_var, index1, index2, output):
+        """
+        Args:
+            tensor_size: current tensor size
+            input_var: variable to hold input
+            index1: dimension 1
+            index2: dimension 2
+            output: output that stores result
+        """
+        assert isinstance(input_var, TVar)
+        assert isinstance(output, TVar)
+        assert isinstance(index1, int)
+        assert isinstance(index2, int)
+
+        self.input_var = input_var
+        self.tensor_size = tensor_size
+        self.index1 = index1
+        self.index2 = index2
+        self.output = output
+
+    def __repr__(self):
+
+        return f' {self.output} = ' \
+               f'Transpose({self.input_var}, ' \
+               f'tensor_size: {self.tensor_size}, ' \
+               f'{self.index1}, ' \
+               f'{self.index2})'
+
+    def __eq__(self, other):
+        if isinstance(other, Transpose):
+            return self.tensor_size == other.tensor_size and \
+                self.index1 == other.index1 and \
+                self.index2 == other.index2 and \
+                self.output == other.output and \
+                self.input_var == other.input_var
+        else:
+            return False
+
+
+class GetItem(Constraint):
+
+    def __init__(self, tensor_size, index, res, input_var):
+        """
+        Constraint for getting item given a tensor size
+        :param tensor_size: actual number
+        :param index: actual number representing the index
+        :param res: dimension variable to carry the item we get
+        :param input_var: a tensor variable from which we will get item
+        """
+        assert isinstance(res, DVar)
+
+        self.res = res
+        self.tensor_size = tensor_size
+        self.index = index
+        self.input_var = input_var
+
+    def __repr__(self):
+        return f' {self.res} = GetItem({self.input_var}, tensor_size: {self.tensor_size}, {self.index})'
+
+    def __eq__(self, other):
+        if isinstance(other, GetItem):
+            return self.res == other.res and \
+                self.tensor_size == other.tensor_size and \
+                self.index == other.index and \
+                self.input_var == other.input_var
+        else:
+            return False
+
+class GetItemTensor(Constraint):
+
+    def __init__(self, tensor_size, index_tuple, res, input_var):
+        """
+        Constraint for getting item given a tensor size
+        However, when the argument is a tuple, we will
+        expect a tensor
+        :param tensor_size: actual number representing the rank
+        :param index_tuple: tuple for indexing
+        :param res: tensor variable to carry the item we get
+        :param input_var: a tensor variable from which we will get item
+        """
+        assert isinstance(res, TVar)
+
+        self.res = res
+        self.tensor_size = tensor_size
+        self.index_tuple = index_tuple
+        self.input_var = input_var
+
+    def __repr__(self):
+        return f' {self.res} = GetItemT({self.input_var}, tensor_size: {self.tensor_size}, {self.index_tuple})'
+
+    def __eq__(self, other):
+        if isinstance(other, GetItemTensor):
+            return self.res == other.res and \
+                self.tensor_size == other.tensor_size and \
+                self.index_tuple == other.index_tuple and \
+                self.input_var == other.input_var
+        else:
+            return False
+
+class CalcConv(Constraint):
+
+    def __init__(self, conv_result, input_var, c_out, kernel, padding, stride, dilation, matching_constraint_vars):
+        """
+        :param conv_result: the convolution result
+        :param input_var: input to convolution
+        :param c_out: output chanel type
+        :param kernel: kernel tuple
+        """
+        self.conv_result = conv_result
+        self.input_var = input_var
+        self.c_out = c_out
+        self.kernel = kernel
+        self.padding = padding
+        self.stride = stride
+        self.dilation = dilation
+        self.matching_constraint = matching_constraint_vars
+
+    def __repr__(self):
+        return f'{self.conv_result} =' \
+               f' calc-conv({self.input_var},' \
+               f' {self.c_out}, {self.kernel}, ' \
+               f'{self.padding}, {self.stride},' \
+               f' {self.dilation})'
+
+    def __eq__(self, other):
+        if isinstance(other, CalcConv):
+            return self.conv_result == other.conv_result and self.input_var == other.input_var and \
+                self.c_out == other.c_out and self.kernel == other.kernel and self.padding == other.padding \
+                and self.stride == other.stride and self.dilation == other.dilation \
+                and self.matching_constraint == other.matching_constraint
+        else:
+            return False
+
+
+class CalcMaxPool(Constraint):
+
+    def __init__(self, maxpool_result, input_var, kernel, padding, stride, dilation, matching_constraint_vars):
+        """
+        :param maxpool_result: the result of maxpool
+        :param input_var: input to convolution
+        :param kernel: kernel tuple
+        """
+        self.maxpool_result = maxpool_result
+        self.input_var = input_var
+        self.kernel = kernel
+        self.padding = padding
+        self.stride = stride
+        self.dilation = dilation
+        self.matching_constraint = matching_constraint_vars
+
+    def __repr__(self):
+        return f'{self.maxpool_result} =' \
+               f' calc-maxpool({self.input_var},' \
+               f'  {self.kernel}, ' \
+               f'{self.padding}, {self.stride},' \
+               f' {self.dilation})'
+
+    def __eq__(self, other):
+        if isinstance(other, CalcMaxPool):
+            return self.maxpool_result == other.maxpool_result and self.input_var == other.input_var \
+                and self.kernel == other.kernel and self.padding == other.padding \
+                and self.stride == other.stride and self.dilation == other.dilation \
+                and self.matching_constraint == other.matching_constraint
+        else:
+            return False
+
+
+class ApplyBroadcasting(Constraint):
+    def __init__(self, res1, res2, input1, input2):
+        """
+        :param res1: resulting tensor 1
+        :param res2: resulting tensor 2
+        :param input1: tensor variable 1
+        :param input2: tensor variable 2
+        """
+        self.res1 = res1
+        self.res2 = res2
+        self.input1 = input1
+        self.input2 = input2
+
+    def __eq__(self, other):
+        if isinstance(other, ApplyBroadcasting):
+            return self.res1 == other.res1 \
+                and self.res2 == other.res2 \
+                and self.input1 == other.input1 \
+                and self.input2 == other.input2
+        else:
+            return False
+
+    def __repr__(self):
+        return f'{self.res1}, {self.res2} ='f' apply-broadcasting({self.input1},' f' {self.input2})'
+
+
+class CalcProduct(Constraint):
+    """
+    Given correct dimensions, calculate the product for flatten accounting for Dyn
+    """
+    def __init__(self, start, end, flattened, dims_to_flatten):
+        """
+        :param start: start index
+        :param end: end index
+        :param flattened: variable to store the product
+        :param dims_to_flatten: the type which we will flatten
+        """
+        assert isinstance(dims_to_flatten, list)
+        assert isinstance(flattened, TVar)
+        assert isinstance(start, int)
+        assert isinstance(end, int)
+
+        self.start = start
+        self.end = end
+        self.dims_to_flatten = dims_to_flatten
+        self.flattened = flattened
+
+    def __eq__(self, other):
+        if isinstance(other, CalcProduct):
+            return self.start == other.start and self.end == other.end and \
+                self.dims_to_flatten == other.dims_to_flatten and self.flattened == other.flattened
+
+        else:
+            return False
+
+    def __repr__(self):
+        return f'{self.flattened} = CalcProduct({self.start}, {self.end}, {self.dims_to_flatten})'
+
+
+class TVar:
+    """
+    Tensor variable with no tensor constructor
+    """
+    def __init__(self, tvar):
+        """
+        :param tvar: tensor variable
+        """
+        self.tvar = tvar
+
+    def __repr__(self):
+        return f'TV({self.tvar})'
+
+    def __eq__(self, other):
+        if isinstance(other, TVar):
+            return self.tvar == other.tvar
+        else:
+            return False
+
+
+class DVar:
+    """
+    Dimension variable
+    """
+    def __init__(self, c):
+        """
+        :param c: character or number
+        """
+        self.c = c
+
+    def __repr__(self):
+        return f'DV({self.c})'
+
+    def __eq__(self, other):
+        if isinstance(other, DVar):
+            return self.c == other.c
+        else:
+            return False
+
+
+class BVar:
+    """
+    Boolean variable
+    """
+    def __init__(self, c):
+        """
+        :param c: character or number
+        """
+        self.c = c
+
+    def __repr__(self):
+        return f'BV({self.c})'
+
+    def __eq__(self, other):
+        if isinstance(other, BVar):
+            return self.c == other.c
+        else:
+            return False
+
+
+def is_algebraic_expression(constraint):
+    if isinstance(constraint, BinConstraintD):
+        return constraint.op in [op_add, op_sub, op_div, op_mul, op_mod]
+    else:
+        return isinstance(constraint, Prod)
+
+
+def is_bool_expr(constraint):
+    if isinstance(constraint, BinConstraintD):
+        return constraint.op in [op_gt, op_lt, op_neq, op_eq]
+    else:
+        return isinstance(constraint, (BVar, Conj, Disj))
+
+def is_dim(d):
+    return isinstance(d, (DVar, int)) or d == Dyn
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/constraint_generator.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/constraint_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..952dde662f2ab8cb2a0613b7163266dc2a421758
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/constraint_generator.py
@@ -0,0 +1,1281 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import torch
+import operator
+import warnings
+from typing import Callable, Dict, Iterable
+
+from torch.fx._symbolic_trace import _assert_is_none
+from torch.fx.experimental.migrate_gradual_types.constraint import ApplyBroadcasting, CalcProduct, \
+    Disj, TGreatestUpperBound, CalcMaxPool, CalcConv, Conj, BinConstraintT, CanReshape, BinConstraintD, GetItem, T, F, \
+    TVar, DVar, GetItemTensor, IndexSelect, Transpose, DGreatestUpperBound
+from torch.fx.experimental.migrate_gradual_types.operation import \
+    op_eq, op_matching, op_consistency, op_leq, op_precision, op_gt, op_div, op_sub, op_neq, op_lt, op_add, op_mul
+from torch.fx.node import Target, Node
+from torch.fx.experimental.migrate_gradual_types.util import gen_tensor_dims, gen_nat_constraints, gen_dvar, gen_tvar, \
+    gen_bvar
+
+from torch.fx.tensor_type import Dyn, TensorType
+from torch.nn.modules.conv import Conv2d
+from torch.nn.modules.batchnorm import BatchNorm2d
+
+_INFERENCE_RULES: Dict[Target, Callable] = {}
+
+MAX_TENSOR_RANK = 4
+
+def register_inference_rule(call_target):
+    def register(fn):
+        if call_target in _INFERENCE_RULES:
+            raise RuntimeError(f'Inference rule already registered for {call_target}!')
+        _INFERENCE_RULES[call_target] = fn
+        return fn
+    return register
+
+
+def generate_flatten_constraints(start_dim, end_dim, input, flattened, n, counter):
+    d, counter = gen_tensor_dims(n, counter)
+    c1 = BinConstraintT(input, TensorType(d), op_eq)
+    start_dim = n if start_dim == -1 else abs(start_dim)
+    end_dim = n + end_dim + 1 if end_dim < 0 else end_dim + 1
+    c2 = CalcProduct(start_dim, end_dim, flattened, d)
+    nat_constraints = gen_nat_constraints(d)
+    return Conj([c1, c2, *nat_constraints]), counter
+
+
+@register_inference_rule(getattr)
+def get_attr_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    If the attribute is "device" then the tensor shape is preserved
+    """
+    assert isinstance(n.args[0], Node)
+    assert isinstance(n.args[1], str)
+    output, counter = gen_tvar(counter)
+    symbols[n] = output
+
+    input = symbols[n.args[0]]
+    attr = n.args[1]
+
+    if attr == 'device':
+        return [BinConstraintT(input, output, op_eq)], counter
+    else:
+        raise NotImplementedError('Not yet implemented')
+
+@register_inference_rule(torch.bmm)
+def bmm_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    Constraints that match the input to a size 3 tensor
+    and switch the dimensions according to the rules
+    of batch multiplication
+    """
+    assert isinstance(n.args[0], Node)
+    assert isinstance(n.args[1], Node)
+
+    bmm_output, counter = gen_tvar(counter)
+    symbols[n] = bmm_output
+
+    bmm_input1 = symbols[n.args[0]]
+    bmm_input2 = symbols[n.args[1]]
+
+    dims_input1, counter = gen_tensor_dims(3, counter)
+    dims_input2, counter = gen_tensor_dims(3, counter)
+
+    inputs_dyn = Conj([BinConstraintT(bmm_input1, Dyn, op_eq),
+                       BinConstraintT(bmm_input2, Dyn, op_eq),
+                       BinConstraintT(bmm_output, Dyn, op_eq)])
+
+    input1_dyn = Conj([BinConstraintT(bmm_input1, Dyn, op_eq),
+                       BinConstraintT(bmm_input2, TensorType(dims_input2), op_eq),
+                       BinConstraintT(bmm_output, TensorType([dims_input2[0], Dyn, dims_input2[2]]), op_eq)])
+
+    input2_dyn = Conj([BinConstraintT(bmm_input2, Dyn, op_eq),
+                       BinConstraintT(bmm_input1, TensorType(dims_input1), op_eq),
+                       BinConstraintT(bmm_output, TensorType([dims_input1[0], dims_input1[1], Dyn]), op_eq)])
+
+    consistency_constraints = [BinConstraintD(dims_input1[0], dims_input2[0], op_consistency)]
+
+    batch_size, counter = gen_dvar(counter)
+
+    inputs_are_tensors = Conj([BinConstraintT(bmm_input1, TensorType(dims_input1), op_eq),
+                               BinConstraintT(bmm_input2, TensorType(dims_input2), op_eq),
+                               BinConstraintT(bmm_output, TensorType([batch_size, dims_input1[1], dims_input2[2]]), op_eq),
+                               *consistency_constraints, DGreatestUpperBound(batch_size, dims_input1[0], dims_input2[0])])
+
+    return [Disj([inputs_dyn, input1_dyn, input2_dyn, inputs_are_tensors])], counter
+
+
+@register_inference_rule("index_select")
+def index_select_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    We constrain the second argument to a vector or Dyn.
+    The output replaces the input with the shape of the vector
+    at the position given by the index (first argument)
+    """
+    # print(n.args)
+    assert isinstance(n.args[0], Node)
+    assert isinstance(n.args[1], int)
+    assert isinstance(n.args[2], Node)
+
+
+
+    index_select, counter = gen_tvar(counter)
+    symbols[n] = index_select
+
+    dims, counter = gen_tensor_dims(1, counter)
+
+    # equality constraint
+    is_size_1 = BinConstraintT(symbols[n.args[2]], TensorType(dims), op_eq)
+    is_dyn = BinConstraintT(symbols[n.args[2]], Dyn, op_eq)
+
+    c2 = Conj([is_size_1, Disj([IndexSelect(i + 1, symbols[n.args[0]], dims[0], n.args[1], index_select)
+                                for i in range(MAX_TENSOR_RANK)])])
+    c3 = Conj([is_dyn, Disj([IndexSelect(i + 1, symbols[n.args[0]], Dyn, n.args[1], index_select)
+                             for i in range(MAX_TENSOR_RANK)])])
+
+    return [Disj([c2, c3])], counter
+
+
+@register_inference_rule("expand")
+def expand_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    We generate the exact constraints as we do for tensor additions but we constraint
+    the rank of this expression to be equal to len(n.args[1:]) so that only
+    those cases get considered for the output
+    """
+    assert isinstance(n.args[0], Node)
+
+    # define the output for expand
+    expand, counter = gen_tvar(counter)
+    symbols[n] = expand
+
+    # since we do not have two nodes here, we will construct an argument variable
+    e1 = symbols[n.args[0]]
+    e2, counter = gen_tvar(counter)
+
+    e2_nat_constraints = []
+    for arg in n.args[1:]:
+        assert isinstance(arg, (Node, int))
+        if isinstance(arg, Node):
+            assert isinstance(symbols[arg], DVar)
+            e2_nat_constraints.append(BinConstraintD(0, symbols[arg], op_leq))
+
+    e2_constraint = BinConstraintT(e2, TensorType([arg if isinstance(arg, int) else symbols[arg] for arg in n.args[1:]]), op_eq)
+
+    constraints, counter = gen_broadcasting_constraints(e1, e2, symbols, counter, expand)
+
+    # constraint the output size
+    dims, counter = gen_tensor_dims(len(n.args[1:]), counter)
+    nat_constraints = gen_nat_constraints(dims)
+    c = [BinConstraintT(expand, TensorType(dims), op_eq), *nat_constraints, e2_constraint, *e2_nat_constraints]
+    constraints += c
+
+    return constraints, counter
+
+
+@register_inference_rule(torch.nn.functional.gelu)
+@register_inference_rule(torch.nn.functional.dropout)
+@register_inference_rule(torch.nn.functional.softmax)
+@register_inference_rule("detach")
+@register_inference_rule("to")
+@register_inference_rule("int")
+@register_inference_rule("long")
+@register_inference_rule("contiguous")
+@register_inference_rule(torch.ones)
+@register_inference_rule(torch.zeros)
+def equality_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    We generate the constraint: input = output
+    """
+    output, counter = gen_tvar(counter)
+    symbols[n] = output
+
+    if isinstance(n.args[0], Node):
+        input = symbols[n.args[0]]
+        if isinstance(input, TVar):
+            return [BinConstraintT(input, output, op_eq)], counter
+
+        # then we have dimension variables
+        else:
+            for arg in n.args:
+                assert isinstance(symbols[arg], DVar)
+        my_size = [symbols[arg] for arg in n.args]
+        return [BinConstraintT(output, TensorType(my_size), op_eq)], counter
+
+    elif isinstance(n.args[0], tuple):
+        # then the tuple is the size
+        assert len(n.args[0]) <= 4
+        my_size = [symbols[arg] for arg in n.args[0]]
+        return [BinConstraintT(output, TensorType(my_size), op_eq)], counter
+    else:
+        raise NotImplementedError('Method not yet implemented')
+
+
+@register_inference_rule("transpose")
+def transpose_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    Can be considered as a sequence of two index selects, so we generate constraints accordingly
+    """
+    assert isinstance(n.args[0], Node)
+    assert isinstance(n.args[1], int)
+    assert isinstance(n.args[2], int)
+
+    output, counter = gen_tvar(counter)
+    symbols[n] = output
+
+    from_arg = symbols[n.args[0]]
+    assert isinstance(from_arg, TVar)
+
+    # input and output are dyn
+    is_dyn = Conj([BinConstraintT(from_arg, Dyn, op_eq), BinConstraintT(output, Dyn, op_eq)])
+
+    # or input is a tensor and we actually do the replacement
+    c3 = Disj([Transpose(i + 1, from_arg, n.args[1], n.args[2], output) for i in range(MAX_TENSOR_RANK)])
+
+    return [Disj([is_dyn, c3])], counter
+
+
+@register_inference_rule("type_as")
+def type_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    We generate the constraint: input = output
+    """
+    assert isinstance(n.args[0], Node)
+    assert isinstance(n.args[1], Node)
+
+    output, counter = gen_tvar(counter)
+    symbols[n] = output
+
+    from_arg = symbols[n.args[0]]
+    to_arg = symbols[n.args[1]]
+
+    assert isinstance(from_arg, TVar)
+    assert isinstance(to_arg, TVar)
+
+    return [BinConstraintT(from_arg, to_arg, op_consistency),
+            BinConstraintT(output, to_arg, op_eq)], counter
+
+@register_inference_rule("masked_fill_")
+def masked_fill_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    Similar to addition. For now we implement the constraints when
+    the argument is a boolean tensor. There is also a case for when
+    it is a condition. We will leave this out for now.
+    """
+
+    assert isinstance(n.args[0], Node)
+    assert isinstance(n.args[1], Node)
+
+    # We will retrieve the type variables from the symbol table
+    # and confirm they are tensor variables
+
+    e1 = symbols[n.args[0]]
+    e2 = symbols[n.args[1]]
+
+    if isinstance(e1, TVar) and isinstance(e2, TVar):
+        masked_fill_tensor, counter = gen_tvar(counter)
+        symbols[n] = masked_fill_tensor
+        return gen_broadcasting_constraints(e1, e2, symbols, counter, masked_fill_tensor)
+    else:
+        raise NotImplementedError('Not yet implemented')
+
+
+@register_inference_rule(torch.nn.functional.embedding)
+def embedding_inference_rule_functional(n: Node, symbols, constraints, counter):
+    assert isinstance(n.args[0], Node)
+
+    embedding_dim_weights = symbols[n.args[1]]
+
+    # will treat this as a static shape. So we will not use matching.
+    weight_dims, counter = gen_tensor_dims(2, counter)
+    equality_constraint = BinConstraintT(embedding_dim_weights, TensorType(weight_dims), op_eq)
+    embedding_dim = weight_dims[1]
+    constraints, counter = gen_embedding_rules(n, symbols, embedding_dim, counter)
+    return [equality_constraint] + constraints, counter
+
+
+@register_inference_rule(torch.nn.modules.sparse.Embedding)
+def embedding_inference_rule(n: Node, module_instance, symbols, constraints, counter):
+    """
+    The output shape differs from the input shape in the last dimension
+    """
+    assert isinstance(n.args[0], Node)
+    return gen_embedding_rules(n, symbols, module_instance.embedding_dim, counter)
+
+
+def gen_embedding_rules(n: Node, symbols, embedding_dim, counter):
+
+    embedding_output, counter = gen_tvar(counter)
+    symbols[n] = embedding_output
+    embedding_input = symbols[n.args[0]]
+
+    input_dyn = BinConstraintT(embedding_input, Dyn, op_eq)
+    output_dyn = BinConstraintT(embedding_output, Dyn, op_eq)
+
+    c1 = Conj([input_dyn, output_dyn])
+    c2 = []
+
+    for i in range(1, MAX_TENSOR_RANK):
+        new_dims, counter = gen_tensor_dims(i, counter)
+        nat_constraints = gen_nat_constraints(new_dims)
+
+        # we consider all tensor sizes and append embedding_dim to the end of the output dimension in all cases
+        c_tensor_i = Conj([BinConstraintT(embedding_input, TensorType(new_dims), op_eq),
+                           BinConstraintT(embedding_output, TensorType(new_dims + [embedding_dim]), op_eq)] +
+                          nat_constraints)
+        c2.append(c_tensor_i)
+
+    return [Disj([c1, Disj(c2)])], counter
+
+
+@register_inference_rule(torch.tensor)
+def tensor_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    If the tensor is a scalar, we will skip it since we
+    do not support scalars yet. We will add support in the future
+    if it's needed. For our examples so far, scalars are not needed.
+    """
+    return [], counter
+
+
+@register_inference_rule("reshape")
+@register_inference_rule("view")
+def view_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    Similar to reshape but with an extra condition on the strides
+    """
+    assert isinstance(n.args[0], Node)
+
+    # generate the new variable
+    my_view, counter = gen_tvar(counter)
+    symbols[n] = my_view
+
+
+    src_var = symbols[n.args[0]]
+    t2 = [symbols[elem] if isinstance(elem, Node) else elem for elem in n.args[1:]]  # target shape
+    t2_type = []
+    num_constraints = []
+
+    for t in t2:
+        if t == -1:
+            var, counter = gen_dvar(counter)
+            t2_type.append(var)
+            num_constraints.append(BinConstraintD(var, Dyn, op_neq))
+
+        else:
+            num_constraints.append(BinConstraintD(t, Dyn, op_neq))
+            t2_type.append(t)
+
+    t2_type = TensorType(t2_type)  # type: ignore[assignment]
+
+    c1 = BinConstraintT(my_view, t2_type, op_eq)
+    c2 = CanReshape(src_var, t2_type)
+
+    # TODO: add the extra check mentioned here:
+    # https://pytorch.org/docs/stable/generated/torch.Tensor.view.html#torch.Tensor.view
+
+    return [c1, c2] + num_constraints, counter  # type: ignore[operator]
+
+
+@register_inference_rule("size")
+def size_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    The constraint is just lhs = rhs.
+    Ex: size = input_ids.size()
+    """
+
+
+    if len(n.args) == 1:
+        # generate the new variable
+        size, counter = gen_tvar(counter)
+        symbols[n] = size
+        input = symbols[n.args[0]]
+        c = BinConstraintT(input, size, op_eq)
+        return [c], counter
+
+    elif len(n.args) == 2:
+        # TODO: review this rule; should input = dyn; output = dyn be included here?
+        if isinstance(n.args[1], int):
+            # generate the new variable
+            size_index, counter = gen_dvar(counter)
+            symbols[n] = size_index
+            input = symbols[n.args[0]]
+            c2 = [GetItem(i + 1, n.args[1], size_index, input) for i in range(MAX_TENSOR_RANK)]
+            c3 = BinConstraintD(0, size_index, op_leq)
+
+            input_dyn = BinConstraintT(input, Dyn, op_eq)
+            output_dyn = BinConstraintD(size_index, Dyn, op_eq)
+            c1 = Conj([input_dyn, output_dyn])
+
+            return [Disj([c1, Conj([Disj(c2), c3])])], counter
+
+        else:
+            raise NotImplementedError
+
+    else:
+        raise NotImplementedError
+
+
+def range_check(i, n):
+    """
+    Checks if an index i is within range of a size n list
+    Args:
+        i: index
+        n: list size
+
+    Returns: Boolean
+    """
+    if i >= 0:
+        return T() if i < n else F()
+    else:
+        return T() if i >= n else F()
+
+
+@register_inference_rule(torch.cumsum)
+def cumsum_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    Input and output shapes should be equal
+    We should verify that the index is valid
+    """
+    assert isinstance(n.args[0], Node)
+    arg_1 = n.args[1] if len(n.args) > 1 else n.kwargs["dim"]
+    assert isinstance(arg_1, int)
+
+    output, counter = gen_tvar(counter)
+    symbols[n] = output
+    input = symbols[n.args[0]]
+
+    input_dyn = BinConstraintT(input, Dyn, op_eq)
+    output_dyn = BinConstraintT(output, Dyn, op_eq)
+    c1 = Conj([input_dyn, output_dyn])
+    c2 = []
+    for i in range(1, MAX_TENSOR_RANK + 1):
+        new_dims, counter = gen_tensor_dims(i, counter)
+
+        nat_constraints = gen_nat_constraints(new_dims)
+
+        c_tensor_i = Conj([BinConstraintT(input, TensorType(new_dims), op_eq),
+                           BinConstraintT(output, TensorType(new_dims), op_eq)] +
+                          [range_check(arg_1, i)] + nat_constraints)
+
+        c2.append(c_tensor_i)
+    dyn_or_tensor = Disj([c1, Disj(c2)])
+    return [dyn_or_tensor], counter
+
+
+@register_inference_rule(_assert_is_none)
+def assert_inference_rule(n: Node, symbols, constraints, counter):
+    assert len(n.users) == 0
+    return [], counter
+
+
+@register_inference_rule(operator.getitem)
+def getitem_inference_rule(n: Node, symbols, constraints, counter):
+    assert isinstance(n.args[0], Node)
+
+    # dimension output case
+    if isinstance(n.args[1], int):
+        # create and store the new dimension variable
+        get_item_output, counter = gen_dvar(counter)
+        symbols[n] = get_item_output
+
+        # retrieve arg variables
+        get_item_arg = symbols[n.args[0]]
+        assert isinstance(get_item_arg, TVar)
+
+
+        # if the input is dynamic, we accept any index and return
+        # a dynamic dimension as output
+        input_dyn = BinConstraintT(get_item_arg, Dyn, op_eq)
+        output_dyn = BinConstraintD(get_item_output, Dyn, op_eq)
+        c1 = Conj([input_dyn, output_dyn])
+
+        # if the input is a tensor,
+        # generate a getItem constraint which will be expanded based on the
+        # tensor dimension.
+
+        c2 = [GetItem(i + 1, n.args[1], get_item_output, get_item_arg) for i in range(MAX_TENSOR_RANK)]
+
+
+        # since the output is a dimension, we make sure it's a natural number
+        # added as a conjunction to the disjunction of c2
+        c3 = BinConstraintD(0, get_item_output, op_leq)
+        return [Disj([c1, Conj([Disj(c2), c3])])], counter
+
+    # tensor output case
+    elif isinstance(n.args[1], tuple):
+        # create and store the new tensor variable
+        get_item_output, counter = gen_tvar(counter)
+        symbols[n] = get_item_output
+
+        # retrieve arg variables
+        if n.args[0] in symbols:
+            get_item_arg = symbols[n.args[0]]
+            assert isinstance(get_item_arg, TVar)
+
+            input_dyn = BinConstraintT(get_item_arg, Dyn, op_eq)
+            output_dyn = BinConstraintT(get_item_output, Dyn, op_eq)  # type: ignore[assignment]
+            c1 = Conj([input_dyn, output_dyn])
+
+            c2 = [GetItemTensor(i + 1, n.args[1], get_item_output, get_item_arg)  # type: ignore[misc]
+                  for i in range(MAX_TENSOR_RANK)]
+        else:
+            # TODO: we should figure out why there is a key-error here.
+            return [], counter
+
+        return [Disj([c1, *c2])], counter
+
+    else:
+        raise RuntimeError('Method not yet implemented')
+
+
+@register_inference_rule(operator.gt)
+def gt_inference_rule(n: Node, symbols, constraints, counter):
+    assert isinstance(n.args[0], (Node, int))
+    assert isinstance(n.args[1], (Node, int))
+
+    # We make sure this node will not be used again. We do not
+    # generate a constraint about that node. Only about the operands.
+
+    e1 = symbols[n.args[0]] if isinstance(n.args[0], Node) else n.args[0]
+    e2 = symbols[n.args[1]] if isinstance(n.args[1], Node) else n.args[1]
+
+    if isinstance(n.args[0], Node) and isinstance(n.args[1], Node):
+        if isinstance(e1, TVar) and isinstance(e2, TVar):
+            gt_tensor, counter = gen_tvar(counter)
+            symbols[n] = gt_tensor
+            return gen_broadcasting_constraints(e1, e2, symbols, counter, gt_tensor)
+
+        elif isinstance(e1, DVar) and isinstance(e2, DVar):
+            # This is meant to be used for flow analysis only
+            gt_constraint = BinConstraintD(e1, e2, op_gt)
+
+            my_gt, counter = gen_bvar(counter)
+            equality_constraint = BinConstraintD(my_gt, gt_constraint, op_eq)
+            return [equality_constraint], counter
+
+        else:
+            raise RuntimeError('Sort Mismatch')
+
+    elif isinstance(n.args[0], Node) and not isinstance(n.args[1], Node):
+        if isinstance(e1, DVar):
+            # This is meant to be used for flow analysis only
+            gt_constraint = BinConstraintD(e1, e2, op_gt)
+
+            my_gt, counter = gen_bvar(counter)
+            equality_constraint = BinConstraintD(my_gt, gt_constraint, op_eq)
+            return [equality_constraint], counter
+
+        elif isinstance(e1, TVar) and isinstance(e2, int):
+            # then we made the wrong assumption about the argument being a tensor
+            # so we should fix the assumption
+            warnings.warn(f'Made the wrong assumption for node {n}. Correctness not guaranteed.')
+
+            new_e1, counter = gen_dvar(counter)
+            symbols[n.args[0]] = new_e1
+            symbols[n.args[0]]
+
+            gt_constraint = BinConstraintD(new_e1, e2, op_gt)
+
+            my_gt, counter = gen_bvar(counter)
+            equality_constraint = BinConstraintD(my_gt, gt_constraint, op_eq)
+            return [equality_constraint], counter
+
+        else:
+            raise NotImplementedError('Method not yet implemented')
+
+    else:
+        raise NotImplementedError('Method not yet implemented')
+
+
+@register_inference_rule(operator.eq)
+def eq_inference_rule(n: Node, symbols, constraints, counter):
+    assert isinstance(n.args[0], (Node, int))
+    assert isinstance(n.args[1], (Node, int))
+
+    e1 = symbols[n.args[0]] if isinstance(n.args[0], Node) else n.args[0]
+    e2 = symbols[n.args[1]] if isinstance(n.args[1], Node) else n.args[1]
+
+    if isinstance(n.args[0], Node) and isinstance(n.args[1], Node):
+        if isinstance(e1, TVar) and isinstance(e2, TVar):
+            eq_tensor, counter = gen_tvar(counter)
+            symbols[n] = eq_tensor
+            return gen_broadcasting_constraints(e1, e2, symbols, counter, eq_tensor)
+
+        elif isinstance(e1, DVar) and isinstance(e2, DVar):
+            # This is meant to be used for flow analysis only
+            eq_constraint = BinConstraintD(e1, e2, op_eq)
+
+            my_eq, counter = gen_bvar(counter)
+            equality_constraint = BinConstraintD(my_eq, eq_constraint, op_eq)
+            return [equality_constraint], counter
+
+        else:
+            raise RuntimeError('Sort Mismatch')
+
+    elif isinstance(n.args[0], Node) and not isinstance(n.args[1], Node):
+        if isinstance(e1, DVar):
+            # This is meant to be used for flow analysis only
+            eq_constraint = BinConstraintD(e1, e2, op_eq)
+
+            my_eq, counter = gen_bvar(counter)
+            equality_constraint = BinConstraintD(my_eq, eq_constraint, op_eq)
+            return [equality_constraint], counter
+        else:
+            raise NotImplementedError('Method not yet implemented')
+    else:
+        raise NotImplementedError('Method not yet implemented')
+
+@register_inference_rule(operator.ne)
+def neq_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    Translates to inconsistent in gradual types.
+    To prove inequality, we should prove that
+    tensors are either different sizes or
+    disagree on at least one dimension
+
+    This is a WIP (works when the condition
+    is false. We are working on making this operation work
+    when the condition is true as well)
+    """
+    assert isinstance(n.args[0], Node)
+    assert isinstance(n.args[1], tuple)
+
+    # implementing for size 3 and 4
+    if len(n.args[1]) == 3:
+
+        assert isinstance(n.args[1][0], (Node, int))
+        assert isinstance(n.args[1][1], (Node, int))
+        assert isinstance(n.args[1][2], (Node, int))
+
+        lhs = symbols[n.args[0]]
+
+        b, counter = gen_tensor_dims(4, counter)
+        input_is_size3 = BinConstraintT(lhs, TensorType([b[0], b[1], b[2]]), op_eq)
+
+        d1 = n.args[1][0] if isinstance(n.args[1][0], int) else symbols[n.args[1][0]]
+        d2 = n.args[1][1] if isinstance(n.args[1][1], int) else symbols[n.args[1][1]]
+        d3 = n.args[1][2] if isinstance(n.args[1][2], int) else symbols[n.args[1][2]]
+
+        # dimensions not equal
+        my_ne, counter = gen_bvar(counter)
+        neq_1 = BinConstraintD(d1, b[0], op_neq)
+        neq_2 = BinConstraintD(d2, b[1], op_neq)
+        neq_3 = BinConstraintD(d3, b[2], op_neq)
+
+        # dimensions inconsistent
+        dims_inconsistent1 = Conj([BinConstraintD(d1, Dyn, op_neq), BinConstraintD(b[0], Dyn, op_neq), neq_1])
+        dims_inconsistent2 = Conj([BinConstraintD(d2, Dyn, op_neq), BinConstraintD(b[1], Dyn, op_neq), neq_2])
+        dims_inconsistent3 = Conj([BinConstraintD(d3, Dyn, op_neq), BinConstraintD(b[2], Dyn, op_neq), neq_3])
+
+        dims_inconsistent = Disj([dims_inconsistent1, dims_inconsistent2, dims_inconsistent3])
+
+        # we are covering size 3 and 4 only for now
+        ne_constraint = Conj([input_is_size3, dims_inconsistent])
+
+        my_ne, counter = gen_bvar(counter)
+        equality_constraint = BinConstraintD(my_ne, ne_constraint, op_eq)
+
+    elif len(n.args[1]) == 4:
+
+        assert isinstance(n.args[1][0], (Node, int))
+        assert isinstance(n.args[1][1], (Node, int))
+        assert isinstance(n.args[1][2], (Node, int))
+        assert isinstance(n.args[1][3], (Node, int))
+
+        lhs = symbols[n.args[0]]
+
+        b1, counter = gen_dvar(counter)
+        b2, counter = gen_dvar(counter)
+        b3, counter = gen_dvar(counter)
+        b4, counter = gen_dvar(counter)
+
+        input_is_size4 = BinConstraintT(lhs, TensorType([b1, b2, b3, b4]), op_eq)
+
+        d1 = n.args[1][0] if isinstance(n.args[1][0], int) else symbols[n.args[1][0]]
+        d2 = n.args[1][1] if isinstance(n.args[1][1], int) else symbols[n.args[1][1]]
+        d3 = n.args[1][2] if isinstance(n.args[1][2], int) else symbols[n.args[1][2]]
+        d4 = n.args[1][3] if isinstance(n.args[1][3], int) else symbols[n.args[1][3]]
+
+        # dimensions not equal
+        my_ne, counter = gen_bvar(counter)
+        neq_1 = BinConstraintD(d1, b1, op_neq)
+        neq_2 = BinConstraintD(d2, b2, op_neq)
+        neq_3 = BinConstraintD(d3, b3, op_neq)
+        neq_4 = BinConstraintD(d4, b4, op_neq)
+
+        # dimensions to inconsistent
+        dims_inconsistent1 = Conj([BinConstraintD(d1, Dyn, op_neq), BinConstraintD(b1, Dyn, op_neq), neq_1])
+        dims_inconsistent2 = Conj([BinConstraintD(d2, Dyn, op_neq), BinConstraintD(b2, Dyn, op_neq), neq_2])
+        dims_inconsistent3 = Conj([BinConstraintD(d3, Dyn, op_neq), BinConstraintD(b3, Dyn, op_neq), neq_3])
+        dims_inconsistent4 = Conj([BinConstraintD(d4, Dyn, op_neq), BinConstraintD(b3, Dyn, op_neq), neq_4])
+
+        dims_inconsistent = Disj([dims_inconsistent1, dims_inconsistent2, dims_inconsistent3, dims_inconsistent4])
+
+        ne_constraint = Conj([input_is_size4, dims_inconsistent])
+
+        my_ne, counter = gen_bvar(counter)
+
+        equality_constraint = BinConstraintD(my_ne, ne_constraint, op_eq)
+
+    else:
+        raise NotImplementedError('Method not yet implemented')
+
+    return [equality_constraint], counter
+
+
+@register_inference_rule(operator.lt)
+def lt_inference_rule(n: Node, symbols, constraints, counter):
+    assert isinstance(n.args[0], (Node, int))
+    assert isinstance(n.args[1], (Node, int))
+
+    # We make sure this node will not be used again. We do not
+    # generate a constraint about that node. Only about the operands.
+
+    e1 = symbols[n.args[0]] if isinstance(n.args[0], Node) else n.args[0]
+    e2 = symbols[n.args[1]] if isinstance(n.args[1], Node) else n.args[1]
+
+    if isinstance(n.args[0], Node) and isinstance(n.args[1], Node):
+        if isinstance(e1, TVar) and isinstance(e2, TVar):
+            lt_tensor, counter = gen_tvar(counter)
+            symbols[n] = lt_tensor
+            return gen_broadcasting_constraints(e1, e2, symbols, counter, lt_tensor)
+
+        elif isinstance(e1, DVar) and isinstance(e2, DVar):
+            # This is meant to be used for flow analysis only
+            lt_constraint = BinConstraintD(e1, e2, op_lt)
+
+            my_lt, counter = gen_bvar(counter)
+            equality_constraint = BinConstraintD(my_lt, lt_constraint, op_eq)
+            return [equality_constraint], counter
+
+        else:
+            raise RuntimeError('Sort Mismatch')
+
+    elif isinstance(n.args[0], Node) and not isinstance(n.args[1], Node):
+        if isinstance(e1, DVar):
+            # This is meant to be used for flow analysis only
+            lt_constraint = BinConstraintD(e1, e2, op_lt)
+
+            my_lt, counter = gen_bvar(counter)
+            equality_constraint = BinConstraintD(my_lt, lt_constraint, op_eq)
+            return [equality_constraint], counter
+        else:
+            raise NotImplementedError('Method not yet implemented')
+
+    else:
+        raise NotImplementedError('Method not yet implemented')
+
+
+@register_inference_rule(torch.full)
+def full_inference_rule(n: Node, symbols, constraints, counter):
+    full, counter = gen_tvar(counter)
+    symbols[n] = full
+    res = []
+
+    assert isinstance(n.args[0], Iterable)
+    for arg in n.args[0]:
+        dim = arg if isinstance(arg, int) else symbols[arg]
+        res.append(dim)
+    c = BinConstraintT(full, TensorType(list(res)), op_eq)  # type: ignore[arg-type]
+    return [c], counter
+
+
+# TODO normalize index
+@register_inference_rule(torch.arange)
+def arange_inference_rule(n: Node, symbols, constraints, counter):
+    start = 0
+    step = 1
+
+    if len(n.args) == 1:
+        end = symbols[n.args[0]]
+    else:
+        raise NotImplementedError('Not yet implemented')
+
+    # int((end - start) / step)
+    d1, counter = gen_dvar(counter)
+    size_constraint = BinConstraintD(d1, BinConstraintD(BinConstraintD(end, start, op_sub), step, op_div), op_eq)
+    arange, counter = gen_tvar(counter)
+    symbols[n] = arange
+
+    # either the a parameter is a number or it is Dyn
+    c1 = Disj([BinConstraintD(end, Dyn, op_eq),
+               BinConstraintD(start, Dyn, op_eq),
+               BinConstraintD(step, Dyn, op_eq)])
+    c2 = BinConstraintD(d1, Dyn, op_eq)
+    both_dyn = Conj([c1, c2])
+
+    c11 = Conj([BinConstraintD(end, Dyn, op_neq),
+                BinConstraintD(start, Dyn, op_neq),
+                BinConstraintD(step, Dyn, op_neq)])
+    c22 = BinConstraintD(d1, Dyn, op_neq)
+    both_numbers = Conj([c11, c22, size_constraint])
+
+    return [BinConstraintT(arange, TensorType([d1]), op_eq), Disj([both_dyn, both_numbers])], counter
+
+def gen_broadcasting_constraints(e1, e2, symbols, counter, output_var):
+    # additional vars that don't correspond to expressions
+    e11, counter = gen_tvar(counter)
+    e22, counter = gen_tvar(counter)
+
+    # generate constraints
+    c1 = TGreatestUpperBound(output_var, e11, e22)
+    c2 = ApplyBroadcasting(e11, e22, e1, e2)
+    c3 = BinConstraintT(e11, e22, op_consistency)
+    return [c1, c2, c3], counter
+
+
+@register_inference_rule(operator.mul)
+@register_inference_rule(torch.ne)
+@register_inference_rule("ne")
+@register_inference_rule(torch.add)
+@register_inference_rule(operator.add)
+def broadcasting_inference_rule(n: Node, symbols, constraints, counter):
+
+    op_code = None
+    if n.target == operator.add or n.target == torch.add:
+        op_code = op_add
+    elif n.target == operator.mul:
+        op_code = op_mul
+
+    if isinstance(n.args[0], Node) and isinstance(n.args[1], Node):
+        if isinstance(symbols[n.args[0]], TVar) and isinstance(symbols[n.args[1]], TVar):
+            my_output, counter = gen_tvar(counter)
+            symbols[n] = my_output
+            e1 = symbols[n.args[0]]
+            e2 = symbols[n.args[1]]
+
+            return gen_broadcasting_constraints(e1, e2, symbols, counter, my_output)
+        else:
+            raise NotImplementedError('Method not yet implemented')
+
+    elif isinstance(n.args[0], Node) and isinstance(n.args[1], (int, float)):
+        if isinstance(symbols[n.args[0]], TVar):
+            my_output, counter = gen_tvar(counter)
+            symbols[n] = my_output
+            e1 = symbols[n.args[0]]
+            return [BinConstraintT(my_output, e1, op_eq)], counter
+        elif isinstance(symbols[n.args[0]], DVar):
+            my_output, counter = gen_dvar(counter)
+            symbols[n] = my_output
+            e1 = symbols[n.args[0]]
+
+            # we will propagate the runtime value here since this is regular addition
+            c = Conj([BinConstraintD(my_output, BinConstraintD(e1, n.args[1], op_code), op_eq),
+                      BinConstraintD(0, my_output, op_leq)])
+            return [c], counter
+
+    elif isinstance(n.args[1], Node) and isinstance(n.args[0], (int, float)):
+        if isinstance(symbols[n.args[1]], TVar):
+            my_output, counter = gen_tvar(counter)
+            symbols[n] = my_output
+            e2 = symbols[n.args[1]]
+            return [BinConstraintT(my_output, e2, op_eq)], counter
+        elif isinstance(symbols[n.args[1]], DVar):
+            my_output, counter = gen_dvar(counter)
+            symbols[n] = my_output
+            e2 = symbols[n.args[1]]
+
+            # we will propagate the runtime value here since this is regular addition
+            c = Conj([BinConstraintD(my_output, BinConstraintD(e2, n.args[0], op_code), op_eq),
+                      BinConstraintD(0, my_output, op_leq)])
+            return [c], counter
+
+        else:
+            raise NotImplementedError('Method not yet implemented')
+
+    else:
+        # TODO generate add constraints for scalar addition
+        raise NotImplementedError('Addition not yet implemented')
+
+
+@register_inference_rule(torch.flatten)
+def flatten_inference_rule(n: Node, symbols, constraints, counter):
+    assert isinstance(n.args[0], Node)
+
+    # generate the new variable
+    flattened, counter = gen_tvar(counter)
+    symbols[n] = flattened
+
+    input = symbols[n.args[0]]
+
+    # set the default start and end dims
+    start_dim = 1
+    end_dim = -1
+
+    if len(n.args) > 1:
+        assert isinstance(n.args[1], int)
+        start_dim = n.args[1]
+
+    if len(n.args) > 2:
+        assert isinstance(n.args[2], int)
+        end_dim = n.args[2]
+
+    c1 = BinConstraintT(input, Dyn, op_eq)
+    c2 = BinConstraintT(flattened, Dyn, op_eq)
+    both_dyn = Conj([c1, c2])
+
+    const = []
+    for i in range(1, MAX_TENSOR_RANK + 1):
+        c, counter = generate_flatten_constraints(start_dim, end_dim, input, flattened, i, counter)
+        const.append(c)
+
+    return [Disj([both_dyn, *const])], counter
+
+
+@register_inference_rule(torch.nn.functional.layer_norm)
+def layer_norm_functional(n: Node, symbols, constraints, counter):
+    """
+    We generate the constraint: input = output
+    """
+    assert isinstance(n.args[0], Node)
+    return gen_layer_norm_constraints(n, n.args[1], symbols, counter)
+
+
+@register_inference_rule(torch.nn.LayerNorm)
+def layer_norm_inference_rule(n: Node, module_instance, symbols, constraints, counter):
+    """
+    Input and output shapes should be equal.
+    Input should be consistent with the normalized_shape
+    """
+    assert isinstance(n.args[0], Node)
+    return gen_layer_norm_constraints(n, module_instance.normalized_shape, symbols, counter)
+
+
+def gen_layer_norm_constraints(n: Node, normalized_shape, symbols, counter):
+    output, counter = gen_tvar(counter)
+    symbols[n] = output
+    input = symbols[n.args[0]]
+
+    input_dyn = BinConstraintT(input, Dyn, op_eq)
+    output_dyn = BinConstraintT(output, Dyn, op_eq)
+
+    c1 = Conj([input_dyn, output_dyn])
+
+    c2 = []
+    for i in range(1, MAX_TENSOR_RANK + 1):
+        new_dims_rhs, counter = gen_tensor_dims(i, counter)
+        nat_constraints = gen_nat_constraints(new_dims_rhs)
+
+        c_tensor_i = Conj([BinConstraintT(input, TensorType(new_dims_rhs), op_eq),
+                           BinConstraintT(output, TensorType(new_dims_rhs), op_eq)] +
+                          add_layer_norm_constraints(new_dims_rhs, list(normalized_shape)) +
+                          nat_constraints)
+        c2.append(c_tensor_i)
+    return [Disj([c1, Disj(c2)])], counter
+
+@register_inference_rule(torch.nn.Dropout)
+@register_inference_rule(torch.nn.ReLU)
+def relu_inference_rule(n: Node, module_instance, symbols, constraints, counter):
+    """
+    Input and output shapes should be equal.
+    """
+    assert isinstance(n.args[0], Node)
+    output, counter = gen_tvar(counter)
+    symbols[n] = output
+    input = symbols[n.args[0]]
+    assert isinstance(input, TVar)
+    return [BinConstraintT(input, output, op_eq)], counter
+
+
+@register_inference_rule(torch.nn.Linear)
+def linear_inference_rule(n: Node, module_instance, symbols, constraints, counter):
+    """
+    Input and output sizes should be the same except for the last dimension
+    If the input is Dyn, then so should the output
+    """
+    assert isinstance(n.args[0], Node)
+    return linear_constraints(n, module_instance.in_features, module_instance.out_features, symbols, counter)
+
+
+@register_inference_rule("dim")  # type: ignore[attr-defined]
+def torch_dim_inference_rule(n: Node, symbols, constraints, counter):
+    assert isinstance(n.args[0], Node)
+    my_dim, counter = gen_dvar(counter)
+    symbols[n] = my_dim
+    input = symbols[n.args[0]]
+
+    input_dyn = BinConstraintT(input, Dyn, op_eq)
+    output_dyn = BinConstraintD(my_dim, Dyn, op_eq)
+
+    c1 = []
+
+    for i in range(1, MAX_TENSOR_RANK + 1):
+        new_dims_rhs_1, counter = gen_tensor_dims(i, counter)
+
+        c_tensor_i = Conj([BinConstraintT(input, TensorType(new_dims_rhs_1), op_eq),
+                           BinConstraintD(my_dim, i, op_eq)])
+        c1.append(c_tensor_i)
+
+    return [Disj([Conj([input_dyn, output_dyn]), Disj(c1)])], counter
+
+
+@register_inference_rule(torch._C._nn.linear)  # type: ignore[attr-defined]
+def torch_linear_inference_rule(n: Node, symbols, constraints, counter):
+    assert isinstance(n.args[0], Node)
+    weight_dims, counter = gen_tensor_dims(2, counter)
+    equality_constraint = BinConstraintT(symbols[n.args[1]], TensorType(weight_dims), op_eq)
+    constraints, counter = linear_constraints(n, weight_dims[1], weight_dims[0], symbols, counter)
+    return [equality_constraint] + constraints, counter
+
+
+def linear_constraints(n: Node, in_features, out_features, symbols, counter):
+    linear_output, counter = gen_tvar(counter)
+    symbols[n] = linear_output
+    linear_input = symbols[n.args[0]]
+
+    input_dyn = BinConstraintT(linear_input, Dyn, op_eq)
+    output_dyn = BinConstraintT(linear_output, Dyn, op_eq)
+
+    c1 = Conj([input_dyn, output_dyn])
+
+    c2 = []
+    for i in range(1, MAX_TENSOR_RANK + 1):
+        new_dims_rhs_1, counter = gen_tensor_dims(i, counter)
+        new_dims_rhs_2, counter = gen_tensor_dims(i, counter)
+
+        nat_constraints = gen_nat_constraints(new_dims_rhs_1 + new_dims_rhs_2)
+
+        c_tensor_i = Conj([BinConstraintT(linear_input, TensorType(new_dims_rhs_1), op_eq),
+                           BinConstraintT(linear_output, TensorType(new_dims_rhs_2), op_eq)] +
+                          add_linear_constraints(new_dims_rhs_1, new_dims_rhs_2, in_features, out_features) +
+                          nat_constraints)
+        c2.append(c_tensor_i)
+    return [Disj([c1, Disj(c2)])], counter
+
+def add_layer_norm_constraints(input_dim, normalized_dim):
+    """
+    The constraints say that the type has te form: [*, 1024, 1024]
+     while the normalized_dim have the form [1024, 1024]
+    Args:
+        input_dim: Input shape of layer norm
+        normalized_dim: normalized_dim parameter of the module instance
+
+    """
+
+    # in this case we return false since there's a pattern mismatch
+    if len(normalized_dim) > len(input_dim):
+        return [F()]
+
+    else:
+        constraints = []
+        for i, n in zip(reversed(input_dim), reversed(normalized_dim)):
+            constraints.append(BinConstraintD(i, n, op_consistency))
+        return constraints
+
+
+def add_linear_constraints(dims1, dims2, in_features, out_features):
+    assert len(dims1) == len(dims2)
+    constraints = []
+    for i in range(len(dims1)):
+        if i == len(dims1) - 1:
+            constraints.append(BinConstraintD(dims1[i], in_features, op_consistency))
+            constraints.append(BinConstraintD(dims2[i], out_features, op_eq))
+        else:
+            constraints.append(BinConstraintD(dims1[i], dims2[i], op_eq))
+
+    return constraints
+
+
+@register_inference_rule(torch.reshape)
+def reshape_inference_rule(n: Node, symbols, constraints, counter):
+    assert isinstance(n.args[0], Node)
+
+    # generate the new variable
+    my_reshape, counter = gen_tvar(counter)
+    symbols[n] = my_reshape
+
+    src_var = symbols[n.args[0]]
+    t2 = n.args[1]
+    t2_type = TensorType([Dyn if elem == -1 else elem for elem in t2])  # type: ignore[union-attr]
+    c1 = BinConstraintT(my_reshape, t2_type, op_eq)  # type: ignore[union-attr]
+    c2 = CanReshape(src_var, t2_type)
+
+    return [c1, c2], counter
+
+
+@register_inference_rule(BatchNorm2d)
+def batchnorm_inference_rule(n: Node, module_instance, symbols, constraints, counter):
+    assert isinstance(n.args[0], Node)
+
+    # generate the new variable
+    batchnorm_output, counter = gen_tvar(counter)
+    symbols[n] = batchnorm_output
+    batchnorm_input = symbols[n.args[0]]
+
+    # dim vars
+    d1, counter = gen_dvar(counter)
+    d2, counter = gen_dvar(counter)
+    d3, counter = gen_dvar(counter)
+    d4, counter = gen_dvar(counter)
+
+    nat_constraints = gen_nat_constraints([d1, d2, d3, d4])
+
+    c1 = BinConstraintT(batchnorm_input, TensorType([d1, d2, d3, d4]), op_matching)
+    c2 = BinConstraintT(batchnorm_input, batchnorm_output, op_eq)
+    return [c1, c2, *nat_constraints], counter
+
+
+@register_inference_rule(torch.nn.AdaptiveAvgPool2d)
+def adaptive_inference_rule(n: Node, module_instance, symbols, constraints, counter):
+    assert isinstance(n.args[0], Node)
+
+    avg_pool, counter = gen_tvar(counter)
+
+    symbols[n] = avg_pool
+    input_var = symbols[n.args[0]]
+
+    # dim vars
+    d1, counter = gen_dvar(counter)
+    d2, counter = gen_dvar(counter)
+    d3, counter = gen_dvar(counter)
+    d4, counter = gen_dvar(counter)
+    nat_constraints = gen_nat_constraints([d1, d2, d3, d4])
+    c1 = BinConstraintT(input_var, TensorType([d1, d2, d3, d4]), op_matching)
+    c2 = BinConstraintT(avg_pool, TensorType([d1, d2, module_instance.output_size[0], module_instance.output_size[1]]), op_eq)
+
+    return [c1, c2, *nat_constraints], counter
+
+
+@register_inference_rule(Conv2d)
+def conv2d_inference_rule(n: Node, module_instance, symbols, constraints, counter):
+    assert isinstance(n.args[0], Node)
+
+    my_conv, counter = gen_tvar(counter)
+    symbols[n] = my_conv
+    input_var = symbols[n.args[0]]
+
+    # dim vars
+    [d1, d2, d3, d4], counter = gen_tensor_dims(MAX_TENSOR_RANK, counter)
+
+    # c1 = Matching(input_var, TensorType([d1, d2, d3, d4]))
+    c1 = BinConstraintT(input_var, TensorType([d1, d2, d3, d4]), op_matching)
+
+    # c2 = DConsistency(module_instance.in_channels, d2)
+    c2 = BinConstraintD(module_instance.in_channels, d2, op_consistency)
+
+    c3 = CalcConv(my_conv, input_var,
+                  module_instance.out_channels,
+                  module_instance.kernel_size,
+                  module_instance.padding,
+                  module_instance.stride,
+                  module_instance.dilation, [d1, d2, d3, d4])
+
+    nat_constraints = gen_nat_constraints([d1, d2, d3, d4])
+
+    return [c1, c2, c3, *nat_constraints], counter
+
+
+@register_inference_rule(torch.nn.MaxPool2d)
+def maxpool_inference_rule(n: Node, module_instance, symbols, constraints, counter):
+    assert isinstance(n.args[0], Node)
+    maxpool, counter = gen_tvar(counter)
+    symbols[n] = maxpool
+    input_var = symbols[n.args[0]]
+
+    # dim vars
+    [d1, d2, d3, d4], counter = gen_tensor_dims(MAX_TENSOR_RANK, counter)
+
+    c1 = BinConstraintT(input_var, TensorType([d1, d2, d3, d4]), op_matching)
+
+    c2 = CalcMaxPool(maxpool, input_var, module_instance.kernel_size, module_instance.padding,
+                     module_instance.stride, module_instance.dilation, [d1, d2, d3, d4])
+
+    nat_constraints = gen_nat_constraints([d1, d2, d3, d4])
+
+    return [c1, c2, *nat_constraints], counter
+
+
+class ConstraintGenerator:
+    def __init__(self, traced, graph=None):
+        self.traced = traced  # traced or tracer.root
+        self.traced_params = dict(self.traced.named_parameters())
+        self.constraints = []
+        self.symbol_dict = {}
+        self.graph = traced.graph if hasattr(traced, 'graph') else graph
+
+
+    def generate_constraints(self, counter=0):
+        """
+        Iterate through every node and generate constraints
+        Effect: self.constraints will be populated with the final constraints
+        """
+        graph = self.graph
+
+        all_constraints = []
+
+        for n in graph.nodes:
+            (constraints, counter) = self.generate_constraints_node(n, counter)
+            all_constraints += constraints
+
+        return Conj(all_constraints), counter
+
+    def generate_constraints_node(self, n: Node, counter):
+        """
+        Generate constraints the given node:
+        Currently supported operations:
+        - Reshape
+        - Add
+        - conv2d
+        """
+
+        if n.op == 'placeholder':
+            x, counter = gen_tvar(counter)
+            self.symbol_dict[n] = x
+
+            my_type = n.type
+
+            if n.type != Dyn and (not isinstance(n.type, TensorType)):
+                if n.type == torch.nn.parameter.Parameter:
+                    # since we have a parameter, the shape must be static
+                    assert 'example_value' in n.meta
+                    my_type = TensorType(n.meta['example_value'].size())
+                else:
+                    my_type = Dyn
+
+            c1 = BinConstraintT(my_type, x, op_precision)
+            c2 = BinConstraintT(x, MAX_TENSOR_RANK, op_leq)
+            return [c1, c2], counter
+
+        elif n.op == 'call_function':
+            if n.target in _INFERENCE_RULES:
+                return _INFERENCE_RULES[n.target](n, self.symbol_dict, self.constraints, counter)
+            else:
+                raise RuntimeError(f'No inference rule registered for target {n.target}!')
+
+        elif n.op == 'call_module':
+
+            module_instance = self.traced.get_submodule(n.target)
+            if type(module_instance) in _INFERENCE_RULES:
+                return _INFERENCE_RULES[type(module_instance)](n,
+                                                               module_instance,
+                                                               self.symbol_dict,
+                                                               self.constraints, counter)
+            else:
+                raise RuntimeError(f'No inference rule registered for class {type(module_instance)}!')
+
+        elif n.op == 'call_method':
+            if n.target in _INFERENCE_RULES:
+                return _INFERENCE_RULES[n.target](n, self.symbol_dict, self.constraints, counter)
+            else:
+                raise RuntimeError(f'No inference rule registered for target {n.target}!')
+
+        elif n.op == 'get_attr':
+            t = self.traced_params.get(n.target, None)
+
+            if isinstance(t, torch.Tensor):
+                if len(t.shape) > 0:
+                    res = list(t.shape)
+                    attr_type = TensorType(res)
+                    output, counter = gen_tvar(counter)
+                    self.symbol_dict[n] = output
+                    return [BinConstraintT(output, attr_type, op_eq)], counter
+                else:
+                    # scalar?
+                    return [], counter
+            else:
+                return [], counter
+
+        elif n.op == 'output':
+            return [], counter
+
+        else:
+            raise NotImplementedError(f"Method {n.op} not yet implemented")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/constraint_transformation.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/constraint_transformation.py
new file mode 100644
index 0000000000000000000000000000000000000000..439e3d6195e654147f5f583b6b13fa9611757372
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/constraint_transformation.py
@@ -0,0 +1,1040 @@
+# mypy: ignore-errors
+import copy
+import itertools
+from torch.fx.experimental.migrate_gradual_types.constraint_generator import BinConstraintT, MAX_TENSOR_RANK
+from torch.fx.experimental.migrate_gradual_types.constraint import T, BinConstraintD, Conj, Constraint, DVar, TVar, \
+    Transpose
+from torch.fx.experimental.migrate_gradual_types.constraint import Disj, TGreatestUpperBound
+from torch.fx.experimental.migrate_gradual_types.constraint import DGreatestUpperBound
+from torch.fx.experimental.migrate_gradual_types.constraint import CalcConv, CalcMaxPool
+from torch.fx.experimental.migrate_gradual_types.constraint import CalcProduct, CanReshape
+from torch.fx.experimental.migrate_gradual_types.constraint import ApplyBroadcasting, Prod, F, GetItem, GetItemTensor, IndexSelect
+from torch.fx.experimental.migrate_gradual_types.operation import op_eq, op_precision, op_leq, op_matching
+from torch.fx.experimental.migrate_gradual_types.operation import op_consistency, op_neq
+from torch.fx.experimental.migrate_gradual_types.operation import op_mul, op_add, op_sub, op_div, op_mod
+from torch.fx.experimental.migrate_gradual_types.util import gen_tensor_dims, gen_nat_constraints, gen_dvar
+from torch.fx.tensor_type import TensorType, Dyn
+from typing import Callable, Dict, List
+
+_TRANSFORMATION_RULES: Dict[Constraint, Callable] = {}
+
+
+def register_transformation_rule(call_target):
+    def register(fn):
+        if call_target in _TRANSFORMATION_RULES:
+            raise RuntimeError(f'Transformation rule already registered for {call_target}!')
+        _TRANSFORMATION_RULES[call_target] = fn
+        return fn
+    return register
+
+
+def valid_index(index, dims):
+    """
+    Given a list of dimensions, checks if an index is valid in the list
+    """
+    try:
+        dims[index]
+        return T()
+    except IndexError:
+        return F()
+
+
+@register_transformation_rule(Transpose)
+def transform_transpose(constraint, counter):
+    """
+    Similar to a sequence of two index-selects
+    """
+    dims, counter = gen_tensor_dims(constraint.tensor_size, counter)
+    is_valid_index1 = valid_index(constraint.index1, dims)
+    is_valid_index2 = valid_index(constraint.index2, dims)
+    new_dims = copy.deepcopy(dims)
+    nat_constraints = gen_nat_constraints(dims)
+
+    if is_valid_index1 == T() and is_valid_index2 == T():
+        new_dims[constraint.index1] = dims[constraint.index2]
+        new_dims[constraint.index2] = dims[constraint.index1]
+
+    transformed_constraint = Conj([BinConstraintT(constraint.input_var, TensorType(dims), op_eq),
+                                   *nat_constraints,
+                                   is_valid_index1, is_valid_index2,
+                                   BinConstraintT(constraint.output, TensorType(new_dims), op_eq)])
+    return transformed_constraint, counter
+
+
+@register_transformation_rule(IndexSelect)
+def transform_index_select(constraint, counter):
+    """
+    The constraints consider the given tensor size, checks if the index is valid
+    and if so, generates a constraint for replacing the input dimension
+    with the required dimension
+    """
+    dims, counter = gen_tensor_dims(constraint.tensor_size, counter)
+    is_valid_index = valid_index(constraint.index, dims)
+    nat_constraints = gen_nat_constraints(dims)
+
+    # if the index is valid then replace the input dimension with the new dimension
+    # otherwise the dimension will not be replaced and the clause will contain False
+    if is_valid_index == T():
+        new_dims = copy.deepcopy(dims)
+        new_dims[constraint.index] = constraint.dim_replace
+
+    transformed_constraint = Conj([BinConstraintT(constraint.input_var, TensorType(dims), op_eq),
+                                   *nat_constraints,
+                                   is_valid_index,
+                                   BinConstraintT(constraint.output, TensorType(new_dims), op_eq)])
+
+    # print(constraints)
+    return transformed_constraint, counter
+
+
+@register_transformation_rule(GetItem)
+def transform_get_item(constraint, counter):
+    """
+    generate an equality of the form:
+    t = [a1, ..., an]
+    then generate constraints that check if the given index is valid
+    given this particular tensor size.
+    If the index is valid, generate a constraint to get the item
+    Note that we already handled the Dyn input case in the previous
+    step.
+    Args:
+        constraint: GetItem which assumes we are getting an item from a tensor (not Dyn)
+        counter: variable tracking
+    Returns: simplified constraints for GetItem
+
+    """
+    dims, counter = gen_tensor_dims(constraint.tensor_size, counter)
+    nat_constraints = gen_nat_constraints(dims)
+
+
+    is_valid_index = valid_index(constraint.index, dims)
+
+    all_constraints = [BinConstraintT(constraint.input_var, TensorType(dims), op_eq),
+                       *nat_constraints,
+                       is_valid_index]
+
+    # if the index is valid, we generate a constraint for getting an item
+    # otherwise this clause will have been UNSAT due to the wrong index
+    if is_valid_index == T():
+        all_constraints.append(BinConstraintD(constraint.res, dims[constraint.index], op_eq))
+
+    return Conj(all_constraints), counter
+
+def valid_index_tensor(index, dims):
+    """
+    if the slice instances exceed the length of the dimensions
+    then this is a type error so we return False
+    """
+    slice_count = 0
+    for s in index:
+        if isinstance(s, slice):
+            slice_count += 1
+    if slice_count > len(dims):
+        return F()
+    else:
+        return T()
+
+@register_transformation_rule(GetItemTensor)
+def transform_get_item_tensor(constraint, counter):
+    """
+    When the index is a tuple, then the output will be a tensor
+    TODO: we have to check if this is the case for all HF models
+
+    The cases we are covering here are a tuple with one of:
+     - slice with default argument
+     - None
+
+     None appends 1 to the input tensor dimensions
+     so each occurrence of 'None' increases the rank by 1
+
+     slice with default arguments does not change the rank
+    """
+    assert isinstance(constraint.index_tuple, tuple)
+
+
+    # generate a result tensor of the expected size
+    dims, counter = gen_tensor_dims(constraint.tensor_size, counter)
+    nat_constraints = gen_nat_constraints(dims)
+
+    # generate a place-holder list of the right rank
+    # where "slice" does not contribute to the rank and "None" does
+    none_c = constraint.index_tuple.count(None)
+    resulting_tensor_dims = (none_c + len(dims)) * [None]
+
+    dim_index = 0
+    for i in range(len(constraint.index_tuple)):
+
+        # append 1 to the right location of the resulting tensor
+        if constraint.index_tuple[i] is None:
+            resulting_tensor_dims[i] = 1
+
+        elif constraint.index_tuple[i] == slice(None, None, None):
+            pass
+
+        else:
+            raise NotImplementedError('Method not yet implemented')
+
+    # append the remaining dimensions to the right location
+    dim_index = 0
+    for i in range(len(resulting_tensor_dims)):
+        if resulting_tensor_dims[i] is None:
+            resulting_tensor_dims[i] = dims[dim_index]
+            dim_index += 1
+
+    # check if the index is valid
+    is_valid_index = valid_index_tensor(constraint.index_tuple, dims)
+
+    # check if the resulting tensor is within bounds
+    if len(resulting_tensor_dims) > 4:
+        return F(), counter
+
+    else:
+        constraints = [BinConstraintT(constraint.input_var, TensorType(dims), op_eq),
+                       BinConstraintT(constraint.res, TensorType(resulting_tensor_dims), op_eq),
+                       *nat_constraints,
+                       is_valid_index]
+        return Conj(constraints), counter
+
+
+@register_transformation_rule(BinConstraintT)
+def generate_binconstraint_t(constraint, counter):
+    """
+    Transform binary constraints for tensors
+    """
+
+    # precision constraints
+    if constraint.op == op_precision:
+        if constraint.lhs == Dyn:
+            return T(), counter
+        elif isinstance(constraint.lhs, TensorType):
+            is_fully_static = all(d != Dyn for d in constraint.lhs.__args__)
+            if is_fully_static:
+                return BinConstraintT(constraint.lhs, constraint.rhs, op_eq), counter
+            else:
+                new_dims = []
+
+                for _ in range(len(constraint.lhs.__args__)):
+                    dim, counter = gen_dvar(counter)
+                    new_dims.append(dim)
+
+                new_dim_constraints = [BinConstraintD(old_dim, new_dim, op_precision) for
+                                       new_dim, old_dim in zip(new_dims, constraint.lhs.__args__)] + \
+                                      [BinConstraintT(constraint.rhs, TensorType(new_dims), op_eq)] + \
+                                      [BinConstraintD(1, new_dim, op_leq) for
+                                       new_dim in new_dims]
+                return Conj(new_dim_constraints), counter
+
+    # matching
+    elif constraint.op == op_matching:
+        assert isinstance(constraint.rhs, TensorType)
+        d1 = constraint.rhs.__args__[0]
+        d2 = constraint.rhs.__args__[1]
+        d3 = constraint.rhs.__args__[2]
+        d4 = constraint.rhs.__args__[3]
+
+        conj = [BinConstraintT(constraint.lhs, Dyn, op_eq),
+                BinConstraintD(d1, Dyn, op_eq),
+                BinConstraintD(d2, Dyn, op_eq),
+                BinConstraintD(d3, Dyn, op_eq),
+                BinConstraintD(d4, Dyn, op_eq)]
+        return Disj([Conj(conj),
+                     BinConstraintT(constraint.lhs, TensorType([d1, d2, d3, d4]), op_eq)]), counter
+
+    elif constraint.op == op_consistency:
+        c_dyn = Disj([BinConstraintT(constraint.lhs, Dyn, op_eq), BinConstraintT(constraint.rhs, Dyn, op_eq)])
+        [c_tensor_1, c_tensor_2, c_tensor_3, c_tensor_4], counter = gen_consistency_constraints(constraint, counter)
+
+        return Disj([c_dyn, c_tensor_1, c_tensor_2, c_tensor_3, c_tensor_4]), counter
+
+    elif constraint.op == op_leq:
+        assert isinstance(constraint.rhs, int)
+        disj = [BinConstraintT(constraint.lhs, Dyn, op_eq)]
+        for i in range(1, constraint.rhs + 1):
+            dims = []
+            for j in range(1, i + 1):
+                dim_var, counter = gen_dvar(counter)
+                dims.append(dim_var)
+            disj.append(BinConstraintT(constraint.lhs, TensorType(dims), op_eq))
+        return Disj(disj), counter
+    else:
+        return constraint, counter
+
+
+@register_transformation_rule(BinConstraintD)
+def generate_binconstraint_d(constraint, counter):
+    """
+    Transform binary constraints for dimensions
+    """
+    if constraint.op == op_precision:
+        if isinstance(constraint.lhs, int):
+            return BinConstraintD(constraint.lhs, constraint.rhs, op_eq), counter
+        elif constraint.lhs == Dyn:
+            return T(), counter
+
+    elif constraint.op == op_consistency:
+        return Disj([BinConstraintD(constraint.lhs, constraint.rhs, op_eq),
+                     BinConstraintD(constraint.rhs, Dyn, op_eq), BinConstraintD(constraint.lhs, Dyn, op_eq)]), counter
+
+    else:
+        return constraint, counter
+
+
+@register_transformation_rule(Conj)
+def generate_conj(constraint, counter):
+    """
+    Transform conjunctions
+    """
+    new = []
+    for c in constraint.conjucts:
+        new_c, counter = transform_constraint(c, counter)
+        new.append(new_c)
+    return Conj(new), counter
+
+
+@register_transformation_rule(Disj)
+def generate_disj(constraint, counter):
+    """
+    Transform disjunctions
+    """
+    new = []
+    for c in constraint.disjuncts:
+        new_c, counter = transform_constraint(c, counter)
+        new.append(new_c)
+    return Disj(new), counter
+
+
+@register_transformation_rule(TGreatestUpperBound)
+def generate_gub(constraint, counter):
+    """
+    Transform greatest upper bound for tensors. Results in equality and Greatest Upper Bound
+    on dimensions
+    """
+    c1 = Conj([Disj([BinConstraintT(constraint.rhs1, Dyn, op_eq),
+                     BinConstraintT(constraint.rhs2, Dyn, op_eq)]), BinConstraintT(constraint.res, Dyn, op_eq)])
+
+    [c2, c3, c4, c5], counter = gen_greatest_upper_bound(constraint, counter)
+
+    return Disj([c1, c2, c3, c4, c5]), counter
+
+
+@register_transformation_rule(DGreatestUpperBound)
+def generate_d_gub(constraint, counter):
+    """
+    Transform greatest upper bound for dimensions into equality constraints
+    """
+    c1 = Conj([BinConstraintD(constraint.rhs1, Dyn, op_eq), BinConstraintD(constraint.res, constraint.rhs2, op_eq)])
+    c2 = Conj([BinConstraintD(constraint.rhs2, Dyn, op_eq), BinConstraintD(constraint.res, constraint.rhs1, op_eq)])
+    c3 = Conj([BinConstraintD(constraint.rhs2, constraint.rhs1, op_eq), BinConstraintD(constraint.res, constraint.rhs1, op_eq)])
+    return Disj([c1, c2, c3]), counter
+
+
+@register_transformation_rule(CalcConv)
+def generate_calc_conv(constraint, counter):
+    d, counter = gen_tensor_dims(4, counter)
+    conv_result = TensorType([d[0], d[1], d[2], d[3]])
+
+    # the convolution result is a tensor of size 4
+    c1 = BinConstraintT(constraint.conv_result, conv_result, op_eq)
+
+    # the second dimension of the output is equal to the output channels
+    c2 = Conj([BinConstraintD(d[1], constraint.c_out, op_eq), BinConstraintD(d[1], Dyn, op_neq)])
+
+    # the input corresponds to the output in the first dimension of the convolution
+    c3 = BinConstraintD(constraint.matching_constraint[0], d[0], op_eq)
+
+    c4, c5 = calc_last_two_dims(constraint, d)
+
+    leq_constraints = Conj([BinConstraintD(0, d[0], op_leq),
+                            BinConstraintD(0, d[1], op_leq),
+                            BinConstraintD(0, d[2], op_leq),
+                            BinConstraintD(0, d[3], op_leq)])
+
+    return Conj([c1, c2, c3, c4, c5, leq_constraints]), counter
+
+
+@register_transformation_rule(CalcMaxPool)
+def generate_calc_maxpool(constraint, counter):
+    """
+    Transform maxpool constraints
+    """
+    d, counter = gen_tensor_dims(4, counter)
+    maxpool_result = TensorType([d[0], d[1], d[2], d[3]])
+
+    # the maxpool result is a tensor of size 4
+    c1 = BinConstraintT(constraint.maxpool_result, maxpool_result, op_eq)
+
+    # the input corresponds to the output in the first and second dimension of maxpool
+    c2 = BinConstraintD(constraint.matching_constraint[1], d[1], op_eq)
+    c3 = BinConstraintD(constraint.matching_constraint[0], d[0], op_eq)
+    c4, c5 = calc_last_two_dims(constraint, d)
+
+    leq_constraints = Conj([BinConstraintD(0, d[0], op_leq),
+                            BinConstraintD(0, d[1], op_leq),
+                            BinConstraintD(0, d[2], op_leq),
+                            BinConstraintD(0, d[3], op_leq)])
+
+    return Conj([c1, c2, c3, c4, c5, leq_constraints]), counter
+
+
+@register_transformation_rule(CalcProduct)
+def generate_calc_product(constraint, counter):
+    """
+    Transform flatten constraints
+    """
+    start = constraint.start
+    end = constraint.end
+    dims = constraint.dims_to_flatten
+    flattened = constraint.flattened
+    n = len(constraint.dims_to_flatten)
+
+    # this will be evaluated right here
+    boundary_check = (0 <= start and start < end and end <= n)
+
+    c_boundary = T() if boundary_check else F()
+
+    lhs = dims[0:start]
+    rhs = dims[end:]
+    mid = dims[start:end]
+
+    all_possibilities = generate_all_int_dyn_dim_possibilities(mid)
+
+    all_constraints = []
+
+    for p in all_possibilities:
+        p = list(p)
+        # this tells us there is a dynamic variable
+        contains_dyn = not all(constraint.op == op_neq for constraint in p)
+        if contains_dyn:
+            mid_var = [Dyn]
+            total_constraints = lhs + mid_var + rhs
+            if len(total_constraints) > 4:
+                all_constraints.append(F())
+            else:
+                all_constraints.append(Conj([BinConstraintT(flattened, TensorType(lhs + mid_var + rhs), op_eq)] + p))
+        else:
+            new_var, counter = gen_dvar(counter)
+            mid_eq_prod = Conj([BinConstraintD(new_var, Prod(mid), op_eq), BinConstraintD(new_var, Dyn, op_neq)])
+            mid_var = [new_var]
+            total_constraints = lhs + mid_var + rhs
+            if len(total_constraints) > 4:
+                all_constraints.append(F())
+            else:
+                all_constraints.append(Conj([BinConstraintT(flattened, TensorType(lhs + mid_var + rhs), op_eq), mid_eq_prod] + p))
+
+    return Conj([Disj(all_constraints), c_boundary]), counter
+
+
+@register_transformation_rule(CanReshape)
+def generate_reshape(constraint, counter):
+    """
+    Transform reshape constraints
+    """
+    d, counter = gen_tensor_dims(4, counter)
+
+    d1 = d[0]
+    d2 = d[1]
+    d3 = d[2]
+    d4 = d[3]
+
+    target = constraint.target.__args__
+
+    is_fully_static = all(d != Dyn for d in target)
+
+    # dynamic tensor
+    c1_dyn = BinConstraintT(constraint.src, Dyn, op_eq)
+    c2_tensor1 = BinConstraintT(constraint.src, TensorType([d1]), op_eq)
+    c2_tensor2 = BinConstraintT(constraint.src, TensorType([d1, d2]), op_eq)
+    c2_tensor3 = BinConstraintT(constraint.src, TensorType([d1, d2, d3]), op_eq)
+    c2_tensor4 = BinConstraintT(constraint.src, TensorType([d1, d2, d3, d4]), op_eq)
+
+    d1_eq_dyn = BinConstraintD(d1, Dyn, op_eq)
+    d1_neq_dyn = BinConstraintD(d1, Dyn, op_neq)
+
+    d2_eq_dyn = BinConstraintD(d2, Dyn, op_eq)
+    d2_neq_dyn = BinConstraintD(d2, Dyn, op_neq)
+
+    d3_eq_dyn = BinConstraintD(d3, Dyn, op_eq)
+    d3_neq_dyn = BinConstraintD(d3, Dyn, op_neq)
+
+    d4_eq_dyn = BinConstraintD(d3, Dyn, op_eq)
+    d4_neq_dyn = BinConstraintD(d3, Dyn, op_neq)
+
+    nat_d1 = BinConstraintD(0, d1, op_leq)
+    nat_d2 = BinConstraintD(0, d2, op_leq)
+    nat_d3 = BinConstraintD(0, d3, op_leq)
+    nat_d4 = BinConstraintD(0, d4, op_leq)
+
+    if is_fully_static:
+        # size 1 tensor
+        c3_tensor1 = Disj([d1_eq_dyn,
+                           (Conj([d1_neq_dyn,
+                                  BinConstraintD(d1, Prod(target), op_eq)]))])
+        all_tensor_1 = Conj([c2_tensor1, c3_tensor1])
+
+        # size 2 tensor
+        all_tensor_2 = Conj([c2_tensor2, gen_all_reshape_possibilities([d1, d2], target)])
+
+        # size 3 tensor
+        all_tensor_3 = Conj([c2_tensor3, gen_all_reshape_possibilities([d1, d2, d3], target)])
+
+        # size 4 tensor
+        all_tensor_4 = Conj([c2_tensor4, gen_all_reshape_possibilities([d1, d2, d3, d4], target)])
+
+        return Conj([Disj([c1_dyn, all_tensor_1, all_tensor_2, all_tensor_3, all_tensor_4]),
+                     nat_d1, nat_d2, nat_d3, nat_d4]), counter
+
+    # then there must be exactly one occurrence of dyn
+    else:
+        new_target = []
+
+        for n in target:
+            if n != Dyn:
+                new_target.append(n)
+
+        # tensor 1
+        c3_tensor1 = Disj([d1_eq_dyn,
+                           (Conj([d1_neq_dyn,
+                                  is_dim_div_by_target(new_target, d1)]))])
+        all_tensor_1 = Conj([c2_tensor1, c3_tensor1])
+
+        # tensor 2
+        c21 = Disj([d1_eq_dyn, d2_eq_dyn])
+        c22 = Conj([d1_neq_dyn, d2_neq_dyn, is_dim_div_by_target(new_target, Prod([d1, d2]))])
+        all_tensor_2 = Conj([c2_tensor2, Disj([c21, c22])])
+
+        # tensor 3
+        c31 = Disj([d1_eq_dyn, d2_eq_dyn, d3_eq_dyn])
+        c32 = Conj([d1_neq_dyn, d2_neq_dyn, d3_neq_dyn, is_dim_div_by_target(new_target, Prod([d1, d2, d3]))])
+        all_tensor_3 = Conj([c2_tensor3, Disj([c31, c32])])
+
+        # tensor 4
+        c41 = Disj([d1_eq_dyn, d2_eq_dyn, d3_eq_dyn, d4_eq_dyn])
+        c42 = Conj([d1_neq_dyn, d2_neq_dyn, d3_neq_dyn, d4_neq_dyn, is_dim_div_by_target(new_target, Prod([d1, d2, d3, d4]))])
+        all_tensor_4 = Conj([c2_tensor4, Disj([c41, c42])])
+
+        return Conj([Disj([c1_dyn, all_tensor_1, all_tensor_2, all_tensor_3, all_tensor_4]),
+                     nat_d1, nat_d2, nat_d3, nat_d4]), counter
+
+
+@register_transformation_rule(ApplyBroadcasting)
+def generate_broadcasting(constraint, counter):
+    """
+    Transform broadcasting constraints
+    """
+    e11, e12 = constraint.res1, constraint.res2
+    e1, e2 = constraint.input1, constraint.input2
+
+    e1_dyn = BinConstraintT(e1, Dyn, op_eq)
+    e2_dyn = BinConstraintT(e2, Dyn, op_eq)
+
+    # Introduce dimensions
+    e1_equal_e11 = BinConstraintT(e1, e11, op_eq)
+    e2_equal_e12 = BinConstraintT(e2, e12, op_eq)
+
+    # dyn possibility
+    e1_dyn_constraint = Conj([e1_dyn, e1_equal_e11, e2_equal_e12])
+    e2_dyn_constraint = Conj([e2_dyn, e1_equal_e11, e2_equal_e12])
+
+    # tensor possibility
+    # generate dimensions to create tensors of size 1
+    final_tensor_1_constraint, _, _, nat_dims_1, counter = \
+        gen_broadcasting_constraints(e1, e2, e11, e12, 1, counter)
+
+    # generate dimensions to create tensors of size 2
+    final_tensor_2_constraint_no_padding, final_tensor_2_constraint_padding_arg1, \
+        final_tensor_2_constraint_padding_arg2, nat_dims_2, counter = \
+        gen_broadcasting_constraints(e1, e2, e11, e12, 2, counter)
+
+    # generate dimensions to create tensors of size 3
+    final_tensor_3_constraint_no_padding, final_tensor_3_constraint_padding_arg1, \
+        final_tensor_3_constraint_padding_arg2, nat_dims_3, counter = \
+        gen_broadcasting_constraints(e1, e2, e11, e12, 3, counter)
+
+    # generate dimensions to create tensors of size 4
+    final_tensor_4_constraint_no_padding, final_tensor_4_constraint_padding_arg1, \
+        final_tensor_4_constraint_padding_arg2, nat_dims_4, counter = \
+        gen_broadcasting_constraints(e1, e2, e11, e12, 4, counter)
+
+    final_result = Disj([
+        e1_dyn_constraint,
+        e2_dyn_constraint,
+        final_tensor_1_constraint,
+        final_tensor_2_constraint_no_padding,
+        final_tensor_2_constraint_padding_arg1,
+        final_tensor_2_constraint_padding_arg2,
+        final_tensor_3_constraint_no_padding,
+        final_tensor_3_constraint_padding_arg1,
+        final_tensor_3_constraint_padding_arg2,
+        final_tensor_4_constraint_no_padding,
+        final_tensor_4_constraint_padding_arg1,
+        final_tensor_4_constraint_padding_arg2
+    ])
+
+    return Conj([final_result, *nat_dims_1, *nat_dims_2, *nat_dims_3, *nat_dims_4]), counter
+
+
+def transform_constraint(constraint: Constraint, counter: int):
+    """
+    Transforms a constraint into a simpler constraint.
+    Ex: precision and consistency are transformed to equality
+    Args:
+        constraint: constraint to be transformed
+        counter: for variable tracking
+
+    Returns: Constraint
+
+    """
+    if type(constraint) in _TRANSFORMATION_RULES:
+        return _TRANSFORMATION_RULES[type(constraint)](constraint, counter)
+
+    else:
+        return constraint, counter
+
+
+
+
+def calc_last_two_dims(constraint, d: List[DVar]):
+    """
+    Generates constraints for the last two dimensions of a convolution or a maxpool output
+    Args:
+        constraint: CalcConv or CalcMaxPool
+        d: The list of output dimensions
+
+    Returns: Constraints for calculating the last two dimensions of the output
+
+    """
+
+    assert isinstance(constraint, (CalcConv, CalcMaxPool))
+
+    b3 = constraint.matching_constraint[2]
+    b4 = constraint.matching_constraint[3]
+
+    b3_dyn = Conj([BinConstraintD(d[2], Dyn, op_eq), BinConstraintD(b3, Dyn, op_eq)])
+    b4_dyn = Conj([BinConstraintD(d[3], Dyn, op_eq), BinConstraintD(b4, Dyn, op_eq)])
+
+    d3_not_dyn = Conj([BinConstraintD(d[2], Dyn, op_neq), BinConstraintD(b3, Dyn, op_neq)])
+    d4_not_dyn = Conj([BinConstraintD(d[3], Dyn, op_neq), BinConstraintD(b4, Dyn, op_neq)])
+
+    # transform parameters into tuples incase they are not already
+    padding = (constraint.padding, constraint.padding) \
+        if isinstance(constraint.padding, int) else constraint.padding
+    kernel = (constraint.kernel, constraint.kernel) \
+        if isinstance(constraint.kernel, int) else constraint.kernel
+    stride = (constraint.stride, constraint.stride) \
+        if isinstance(constraint.stride, int) else constraint.stride
+    dilation = (constraint.dilation, constraint.dilation) \
+        if isinstance(constraint.dilation, int) else constraint.dilation
+
+    f1 = BinConstraintD(b3, BinConstraintD(2, padding[0], op_mul), op_add)
+    f2 = BinConstraintD(dilation[0], BinConstraintD(kernel[0], 1, op_sub), op_mul)
+    f3 = BinConstraintD(BinConstraintD(BinConstraintD(f1, f2, op_sub), 1, op_sub), stride[0], op_div)
+    f4 = BinConstraintD(f3, 1, op_add)
+
+    c4 = Disj([b3_dyn, Conj([d3_not_dyn, BinConstraintD(d[2], f4, op_eq)])])
+
+    f11 = BinConstraintD(b4, BinConstraintD(2, padding[1], op_mul), op_add)
+    f22 = BinConstraintD(dilation[1], BinConstraintD(kernel[1], 1, op_sub), op_mul)
+    f33 = BinConstraintD(BinConstraintD(BinConstraintD(f11, f22, op_sub), 1, op_sub), stride[1], op_div)
+    f44 = BinConstraintD(f33, 1, op_add)
+
+    c5 = Disj([b4_dyn, Conj([d4_not_dyn, BinConstraintD(d[3], f44, op_eq)])])
+
+    return c4, c5
+
+
+def generate_all_int_dyn_dim_possibilities(my_list: List[DVar]):
+    """
+    Generate all possibilities of being equal or not equal to dyn for my_list
+    Args:
+        my_list: List of tensor dimensions
+
+    Returns: A list of a list of constraints. Each list of constraints corresponds to
+    one possibility about the values of the dimension variables
+    """
+    # generate all possibilities of being equal or not equal to dyn for my_list
+    eq_possibilities = [BinConstraintD(my_list[i], Dyn, op_eq) for i in range(len(my_list))]
+    neq_possibilities = [BinConstraintD(my_list[i], Dyn, op_neq) for i in range(len(my_list))]
+    d_possibilities = []
+
+    for i in zip(eq_possibilities, neq_possibilities):
+        d_possibilities.append(list(i))
+    all_possibilities = list(itertools.product(*d_possibilities))
+    return all_possibilities
+
+
+def is_target_div_by_dim(target: List[int], dim: List[DVar]):
+    """
+    Generate constraints to check if the target dimensions are divisible by the input dimensions
+    Args:
+        target: Target dimensions
+        dim: Input dimensions
+
+    Returns: Constraints to check divisibility
+
+    """
+    return BinConstraintD(BinConstraintD(Prod(target), dim, op_mod), 0, op_eq)
+
+
+def is_dim_div_by_target(target: List[int], dim: List[DVar]):
+    """
+    Generate constraints to check if the input dimensions is divisible by the target dimensions
+    Args:
+        target: Target dimensions
+        dim:  Input dimensions
+
+    Returns: Constraints to check divisibility
+
+    """
+    return BinConstraintD(BinConstraintD(dim, Prod(target), op_mod), 0, op_eq)
+
+
+def gen_all_reshape_possibilities(list_of_dims, target):
+    """
+    Consider all possibilities what the input dimensions could be (number or dynamic)
+    Then generate the appropriate constraints using multiplication or mod depending on the possibility
+    The possibilities we consider here are the cross product of being equal to dyn or not equal to dyn
+    for the input. Target is fixed because at most one dimension could be dyn.
+    We have different cases for this.
+
+    Args:
+        list_of_dims: The input list of dimensions
+        target: The tensor we want to reshape to
+
+    Returns: A disjunction of transformed reshape constraints
+
+    """
+    all_possibilities = generate_all_int_dyn_dim_possibilities(list_of_dims)
+
+    all_constraints = []
+
+    for p in all_possibilities:
+        to_multiply = []
+
+        p = list(p)
+
+        for constraint in p:
+            assert isinstance(constraint, BinConstraintD)
+            if constraint.op == op_neq:
+                to_multiply.append(constraint.lhs)
+
+        if not to_multiply:
+            all_constraints.append(Conj(p))
+
+        elif len(to_multiply) < len(list_of_dims):
+            all_constraints.append(Conj(p + [is_target_div_by_dim(target, Prod(to_multiply))]))
+        else:
+            all_constraints.append(Conj(p + [BinConstraintD(Prod(list_of_dims),
+                                                            Prod(target), op_eq)]))
+
+    return Disj(all_constraints)
+
+
+def broadcast_dim(tensor_input1, tensor_input2, res1, res2, index, padding=False):
+    """
+    Apply broadcasting to the 'index' dimension of tensor_input1.
+    Args:
+        tensor_input1: should represent [d1, ..., d_index, ...] where d_index = 1
+        tensor_input2: represents the second input
+        res1: broadcasted result 1
+        res2: broadcasted result 2
+        index: the index to broadcast
+        padding: If padding was used, then tensor_input1[index] does not exist
+
+    Returns:
+
+    """
+    if tensor_input1[index] is None:
+        assert padding
+
+
+    if not padding:
+        # then the inputs are the same length so they all have dimensions at "index"
+        return Conj([BinConstraintD(tensor_input1[index], 1, op_eq),
+                     BinConstraintD(res1[index], res2[index], op_eq),
+                     BinConstraintD(res2[index], tensor_input2[index], op_eq)])
+
+    else:
+        # we don't set the input dimension to 1, since it doesn't exist.
+        return Conj([BinConstraintD(res1[index], res2[index], op_eq),
+                     BinConstraintD(res2[index], tensor_input2[index], op_eq)])
+
+
+def apply_padding(e1_var: TVar,
+                  e11: BinConstraintT,
+                  e2: BinConstraintT,
+                  e12: BinConstraintT,
+                  d2: List[DVar],
+                  d11: List[DVar],
+                  d12: List[DVar],
+                  counter: int):
+    """
+    We are considering the possibility where one input has less dimensions than
+    another input, so we apply padding to the broadcasted results
+
+    Args:
+        e1_var: Variable representing the first input where padding will be
+        e11: constraint of the form e11 = Tensortype[d1, ..., dn]
+        e2:  constraint of the form e2 = Tensortype[d1, ..., dn]
+        e12: constraint of the form e11 = Tensortype[d1, ..., dn]
+        d2: Tensor variables for the second input
+        d11: Tensor variables for the broadcasted first input
+        d12: Tensor variables for the broadcasted second input
+        counter: variable tracking
+
+    Returns: A new constraint whose goal is to apply padding to the broadcasted result
+
+    """
+
+    res = []
+
+    # pad the shorter input with None so we can pass it to the broadcasting helper function
+    for i in range(1, len(d2)):
+
+        d1, counter = gen_tensor_dims(i, counter)
+
+        nat_constraints = gen_nat_constraints(d1 + d2 + d11 + d12)
+
+        e1 = BinConstraintT(e1_var, TensorType(d1), op_eq)
+
+        simulate_padding = [None] * (len(d2) - i)
+
+        assert len(simulate_padding + d1) == len(d2)
+
+        broadcast_padding = []
+
+        # for every padding size, we also consider broadcasting
+        for j in range(len(d2) - i):
+            broadcast_padding.append(broadcast_dim(simulate_padding, d2, d11, d12, j, True))
+
+        # we consider the possibilities for broadcasting for every dimension. Since we already
+        # padded d1, we do not consider it while broadcasting
+        all_broadcasting_possibilities = generate_all_broadcasting_possibilities_no_padding(d1,
+                                                                                            d2[(len(d2) - i):],
+                                                                                            d11[(len(d2) - i):],
+                                                                                            d12[(len(d2) - i):])
+        # combine all constraints into a conjunction
+        c = Conj([e1, e11, e2, e12,
+                  *broadcast_padding,
+                  all_broadcasting_possibilities,
+                  *nat_constraints
+                  ])
+        res.append(c)
+
+    return Disj(res), counter
+
+
+def no_broadcast_dim_with_index(d1: List[DVar],
+                                d2: List[DVar],
+                                d3: List[DVar],
+                                d4: List[DVar],
+                                i: int):
+    """
+    Args:
+        d1: input 1
+        d2: input 2
+        d3: simulated broadcasting for input 1
+        d4: simulated broadcasting for input 2
+        i: the rank of the resulting tensor addition
+
+    Returns: Constraints for when no broadcasting occurs
+    """
+    return Conj([
+        Disj([
+            Conj([BinConstraintD(d1[i], 1, op_eq),
+                  BinConstraintD(d2[i], 1, op_eq)]),
+
+            Conj([BinConstraintD(d1[i], 1, op_neq),
+                  BinConstraintD(d2[i], 1, op_neq)])]),
+
+        BinConstraintD(d1[i], d3[i], op_eq),
+        BinConstraintD(d2[i], d4[i], op_eq)])
+
+
+
+def gen_lists_of_dims(num_tensors: int, dim_size: int, counter: int):
+    """
+    Generate lists of DVar to represent tensor dimensions
+    Args:
+        num_tensors: the required number of tensors
+        dim_size: the number of dimensions for each tensor
+        counter: variable tracking
+
+    Returns: A list of a list of tensor dimensions
+
+    """
+    res = []
+
+    for _ in range(num_tensors):
+        dims, counter = gen_tensor_dims(dim_size, counter)
+        res.append(dims)
+
+    return res, counter
+
+
+def create_equality_constraints_for_broadcasting(e1: TVar,
+                                                 e2: TVar,
+                                                 e11: TVar,
+                                                 e12: TVar,
+                                                 d1: List[DVar],
+                                                 d2: List[DVar],
+                                                 d11: List[DVar],
+                                                 d12: List[DVar]):
+    """
+    Create equality constraints for when no broadcasting occurs
+    Args:
+        e1: Input 1
+        e2: Input 2
+        e11: Broadcasted input 1
+        e12: Broadcasted input 2
+        d1: Variables that store dimensions for e1
+        d2: Variables that store dimensions for e2
+        d11: Variables that store dimensions for e11
+        d12: Variables that store dimensions for e22
+
+    Returns: Four equality constraints
+
+    """
+
+    e1_tensor = BinConstraintT(e1, TensorType(d1), op_eq)
+    e11_tensor = BinConstraintT(e11, TensorType(d11), op_eq)
+    e2_tensor = BinConstraintT(e2, TensorType(d2), op_eq)
+    e12_tensor = BinConstraintT(e12, TensorType(d12), op_eq)
+    return [e1_tensor, e11_tensor, e2_tensor, e12_tensor]
+
+
+def gen_consistency_constraints(constraint: Constraint, counter: int):
+    """
+    Args:
+        constraint: Consistency constraint on tensors
+        counter: for variable tracking
+
+    Returns: Equality and consistency constraints on dimensions
+
+    """
+
+    all_constraints = []
+
+    for i in range(1, MAX_TENSOR_RANK + 1):
+        new_dims_rhs_1, counter = gen_tensor_dims(i, counter)
+        new_dims_rhs_2, counter = gen_tensor_dims(i, counter)
+
+        nat_constraints = gen_nat_constraints(new_dims_rhs_1 + new_dims_rhs_2)
+
+        c_tensor_i = Conj([BinConstraintT(constraint.lhs, TensorType(new_dims_rhs_1), op_eq),
+                           BinConstraintT(constraint.rhs, TensorType(new_dims_rhs_2), op_eq)] +
+                          [BinConstraintD(d1, d2, op_consistency) for
+                           d1, d2 in zip(new_dims_rhs_1, new_dims_rhs_2)] + nat_constraints)
+
+        all_constraints.append(c_tensor_i)
+
+    return all_constraints, counter
+
+
+def gen_greatest_upper_bound(constraint: TGreatestUpperBound, counter: int):
+    """
+    Args:
+        constraint: Greatest upper bound on tensors
+        counter: variable tracking
+
+    Returns: A set of equality constraints and DGreatestUpperBound constraints
+
+    """
+
+    all_constraints = []
+
+    for i in range(1, MAX_TENSOR_RANK + 1):
+        c = []
+        dims1, counter = gen_tensor_dims(i, counter)
+        c1tensor = TensorType(dims1)
+
+        dims2, counter = gen_tensor_dims(i, counter)
+        c2tensor = TensorType(dims2)
+
+        dims3, counter = gen_tensor_dims(i, counter)
+        c3tensor = TensorType(dims3)
+
+        c += [BinConstraintT(constraint.rhs1, c1tensor, op_eq),
+              BinConstraintT(constraint.rhs2, c2tensor, op_eq),
+              BinConstraintT(constraint.res, c3tensor, op_eq)] + \
+            gen_nat_constraints(dims1 + dims2 + dims3)
+
+        assert len(c3tensor.__args__) == len(c1tensor.__args__) == len(c2tensor.__args__)
+        for i in range(len(c3tensor.__args__)):
+            c.append(DGreatestUpperBound(c3tensor.__args__[i],
+                                         c1tensor.__args__[i],
+                                         c2tensor.__args__[i]))
+
+        all_constraints.append(Conj(c))
+    return all_constraints, counter
+
+
+def generate_all_broadcasting_possibilities_no_padding(d1: List[DVar], d2: List[DVar], d11: List[DVar], d12: List[DVar]):
+    """
+    Generate broadcasting constraints assuming no padding. Broadcasting can happen at any dimension.
+    We look at all combinations for all dimensions in d1 and d2
+    Args:
+        d1: input1 dimensions
+        d2: input2 dimensions
+        d11: broadcasted input1 dimensions
+        d12: broadcasted input2 dimensions
+
+    Returns: broadcasting constraints relating the input dimensions to the broadcasted dimensions
+
+    """
+
+    size = len(d1)
+
+    res2 = []
+
+    for i in range(size):
+        t1 = broadcast_dim(d1, d2, d11, d12, i)
+        t2 = broadcast_dim(d2, d1, d12, d11, i)
+        t3 = no_broadcast_dim_with_index(d1, d2, d11, d12, i)
+
+        res2.append(Disj([t1, t2, t3]))
+
+    return Conj(res2)
+
+
+def gen_broadcasting_constraints(e1: TVar, e2: TVar, e11: TVar, e12: TVar, i: int, counter: int):
+    """
+    Simulates broadcasting on e1 and e2 and returns the results
+    respectively in e11 and e12. Because of gradual types,
+    e1 and e2 may not be equal. Similarly, e11 and e12 may not
+    be equal. e11 and e12 should be guaranteed to be consistent
+    as they represent the shapes of the tensors to be added after
+    broadcasting.
+    Args:
+        e1: TVar representing the type of input 1
+        e2: TVar representing the type of input 2
+        e11: TVar representing the representing broadcasted input 1
+        e12: TVar representing the representing broadcasted input 2
+        i: The rank of the resulting type of addition
+        counter: for variable tracking
+
+    Returns: Simplified broadcasting constraints
+
+    """
+    dims, counter = gen_lists_of_dims(4, i, counter)
+    [d1, d2, d3, d4] = dims
+    nat_dims_i = gen_nat_constraints(list(itertools.chain.from_iterable(dims)))
+
+    initialize_tensors_constraints = create_equality_constraints_for_broadcasting(e1, e2, e11, e12,
+                                                                                  d1, d2, d3, d4)
+
+    [e1_tensor, e11_tensor, e2_tensor, e12_tensor] = initialize_tensors_constraints
+
+    # without padding, broadcast all possibilities for tensors of size i
+    final_tensor_constraint_no_padding = Conj([*initialize_tensors_constraints,
+                                               generate_all_broadcasting_possibilities_no_padding(d1, d2, d3, d4)])
+
+    # with padding, broadcast all possibilities for tensors of size i
+    final_tensor_constraint_padding_arg1, counter = \
+        apply_padding(e1, e11_tensor, e2_tensor, e12_tensor, d2, d3, d4, counter)
+
+    final_tensor_constraint_padding_arg2, counter = \
+        apply_padding(e2, e12_tensor, e1_tensor, e11_tensor, d1, d4, d3, counter)
+
+    return final_tensor_constraint_no_padding, \
+        final_tensor_constraint_padding_arg1, \
+        final_tensor_constraint_padding_arg2, nat_dims_i, counter
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/operation.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/operation.py
new file mode 100644
index 0000000000000000000000000000000000000000..432cd570bebbfc37e2c5b5a167cb124a8907f39d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/operation.py
@@ -0,0 +1,14 @@
+op_add = '+'
+op_sub = '-'
+op_mul = '*'
+op_div = '/'
+op_eq = '='
+op_neq = '!='
+op_imp = '=>'
+op_matching = '\u22b3'  # (contains)
+op_consistency = '~'
+op_precision = '\u2291'  # (square image of or equal to)
+op_leq = '\u2264'  # less-than or equal to
+op_lt = '<'
+op_gt = '>'
+op_mod = '%'
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/transform_to_z3.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/transform_to_z3.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8cf70006cd84c662f2f2ffd36e208b54bc1bbea
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/transform_to_z3.py
@@ -0,0 +1,349 @@
+# mypy: allow-untyped-defs
+from torch.fx.experimental.migrate_gradual_types.constraint import Conj, Disj, T, F, BinConstraintT, BVar, is_bool_expr
+from torch.fx.experimental.migrate_gradual_types.constraint import BinConstraintD, TVar, DVar
+from torch.fx.experimental.migrate_gradual_types.constraint import Prod, is_algebraic_expression, is_dim
+from torch.fx.experimental.migrate_gradual_types.constraint_generator import ConstraintGenerator
+from torch.fx.experimental.migrate_gradual_types.constraint_transformation import transform_constraint
+from torch.fx.experimental.migrate_gradual_types.operation import op_add, op_eq, op_neq, op_gt, op_lt
+from torch.fx.experimental.migrate_gradual_types.operation import op_leq, op_sub, op_div, op_mul, op_mod
+from torch.fx.tensor_type import TensorType, Dyn
+
+try:
+    import z3  # type: ignore[import]
+    from torch.fx.experimental.migrate_gradual_types.z3_types import tensor_type, z3_dyn, D
+    HAS_Z3 = True
+
+    def transform_to_z3(constraint, counter, dimension_dict):
+        if isinstance(constraint, Conj):
+            conjuncts = []
+            for c in constraint.conjucts:
+                new_c, counter = transform_to_z3(c, counter, dimension_dict)
+                conjuncts.append(new_c)
+            return z3.And(conjuncts), counter
+
+        elif isinstance(constraint, Disj):
+            disjuncts = []
+            for c in constraint.disjuncts:
+                new_c, counter = transform_to_z3(c, counter, dimension_dict)
+                disjuncts.append(new_c)
+            return z3.Or(disjuncts), counter
+
+        elif isinstance(constraint, T):
+            return True, counter
+
+        elif isinstance(constraint, F):
+            return False, counter
+
+        elif isinstance(constraint, BinConstraintT):
+            if constraint.op == op_eq:
+                lhs, counter = transform_var(constraint.lhs, counter, dimension_dict)
+                rhs, counter = transform_var(constraint.rhs, counter, dimension_dict)
+                return (lhs == rhs), counter
+
+            else:
+                raise NotImplementedError('Method not yet implemented')
+
+        elif isinstance(constraint, BinConstraintD):
+            if constraint.op == op_eq:
+
+                if isinstance(constraint.lhs, BVar) and is_bool_expr(constraint.rhs):
+                    transformed_rhs, counter = transform_to_z3(constraint.rhs, counter, dimension_dict)
+                    transformed_lhs = z3.Bool(constraint.lhs.c)
+                    return transformed_lhs == transformed_rhs, counter
+
+                elif is_dim(constraint.lhs) and is_dim(constraint.rhs):
+                    # with dimension transformations we consider the encoding
+                    lhs, counter = transform_dimension(constraint.lhs, counter, dimension_dict)
+                    rhs, counter = transform_dimension(constraint.rhs, counter, dimension_dict)
+                    return lhs == rhs, counter
+
+                else:
+                    # then we have an algebraic expression which means that we disregard the
+                    # first element of the encoding
+                    lhs, counter = transform_algebraic_expression(constraint.lhs, counter, dimension_dict)
+                    rhs, counter = transform_algebraic_expression(constraint.rhs, counter, dimension_dict)
+                    return lhs == rhs, counter
+
+            # The assumption here is that the LHS and RHS must be dimensions
+            elif constraint.op == op_neq:
+                assert is_dim(constraint.lhs)
+                assert is_dim(constraint.rhs)
+                lhs, counter = transform_dimension(constraint.lhs, counter, dimension_dict)
+                rhs, counter = transform_dimension(constraint.rhs, counter, dimension_dict)
+                if constraint.rhs == Dyn or constraint.lhs == Dyn:
+                    if constraint.rhs == Dyn:
+                        return lhs.arg(0) == 1, counter
+                    elif constraint.lhs == Dyn:
+                        return rhs.arg(0) == 1, counter
+
+                # if one of the instances is a number
+                elif isinstance(constraint.lhs, int) or isinstance(constraint.rhs, int):
+                    if isinstance(constraint.lhs, int):
+                        return z3.Or([rhs.arg(0) == 0, z3.And([rhs.arg(0) == 1, lhs.arg(1) != rhs.arg(1)])]), counter
+
+                    elif isinstance(constraint.rhs, int):
+                        return z3.Or([lhs.arg(0) == 0, z3.And([lhs.arg(0) == 1, lhs.arg(1) != rhs.arg(1)])]), counter
+
+                else:
+                    return z3.Or([z3.And([lhs.arg(0) == 0, rhs.arg(0) != 0]),
+                                  z3.And([lhs.arg(0) != 0, rhs.arg(0) == 0]),
+                                  z3.And([lhs.arg(0) != 0, rhs.arg(0) != 0, lhs.arg(1) != rhs.arg(1)])]), counter
+
+
+            elif constraint.op == op_leq:
+                # if the dimensions are not dyn, this will come into effect
+                # there would have been another constraint specifying if a given dimension
+                # is dyn or not
+                assert is_dim(constraint.lhs) and is_dim(constraint.rhs)
+                lhs, counter = transform_algebraic_expression(constraint.lhs, counter, dimension_dict)
+                rhs, counter = transform_algebraic_expression(constraint.rhs, counter, dimension_dict)
+                return lhs <= rhs, counter
+
+            elif constraint.op == op_gt:
+                assert is_dim(constraint.lhs) and is_dim(constraint.rhs)
+                lhs, counter = transform_algebraic_expression(constraint.lhs, counter, dimension_dict)
+                rhs, counter = transform_algebraic_expression(constraint.rhs, counter, dimension_dict)
+                return lhs > rhs, counter
+
+            elif constraint.op == op_lt:
+                assert is_dim(constraint.lhs) and is_dim(constraint.rhs)
+                lhs, counter = transform_algebraic_expression(constraint.lhs, counter, dimension_dict)
+                rhs, counter = transform_algebraic_expression(constraint.rhs, counter, dimension_dict)
+                return lhs < rhs, counter
+
+            else:
+                raise NotImplementedError('operation not yet implemented')
+
+        else:
+            raise NotImplementedError('Operation not yet implemented')
+
+
+    def transform_var(tensor, counter, dimension_dict):
+        """
+        Transforms tensor variables to a format understood by z3
+        Args:
+            tensor: Tensor variable or a tensor type potentially with variable dimensions
+        Returns: Transformed variable to a z3 format
+
+        """
+        if isinstance(tensor, TensorType):
+            res = []
+            for t in tensor.__args__:
+                transformed, counter = transform_dimension(t, counter, dimension_dict)
+                res.append(transformed)
+
+            assert len(res) <= 4
+            if len(tensor.__args__) == 1:
+                return tensor_type.tensor1(res[0]), counter
+            elif len(tensor.__args__) == 2:
+                return tensor_type.tensor2(res[0], res[1]), counter
+            elif len(tensor.__args__) == 3:
+                return tensor_type.tensor3(res[0], res[1], res[2]), counter
+            elif len(tensor.__args__) == 4:
+                return tensor_type.tensor4(res[0], res[1], res[2], res[3]), counter
+
+        elif tensor == Dyn:
+            return z3_dyn, counter
+
+        elif isinstance(tensor, TVar):
+            return z3.Const(tensor.tvar, tensor_type), counter
+
+    def transform_dimension(dimension, counter, dimension_dict):
+        """
+        Takes a dimension variable or a number and transforms it to a tuple
+        according to our scheme
+        Args:
+            dimension: The dimension to be transformed
+            counter: variable tracking
+
+        Returns:  tuple and the current counter
+
+        """
+        if dimension == Dyn:
+            counter += 1
+            return D(0, z3.Int(counter)), counter
+        elif isinstance(dimension, int):
+            return D(1, dimension), counter
+        elif isinstance(dimension, DVar):
+            if dimension.c in dimension_dict:
+                return D(z3.Int(dimension_dict[dimension.c]), z3.Int(dimension.c)), counter
+            else:
+                counter += 1
+                dimension_dict[dimension.c] = counter
+                return D(z3.Int(counter), z3.Int(dimension.c)), counter
+
+
+    def transform_algebraic_expression(expr, counter, dimension_dict):
+        """
+        Transforms an algebraic expression to z3 format
+        Args:
+            expr: An expression is either a dimension variable or an algebraic-expression
+
+
+        Returns: the transformed expression
+
+        """
+        assert is_algebraic_expression(expr) or is_dim(expr)
+
+        if is_dim(expr):
+            transformed, counter = transform_dimension(expr, counter, dimension_dict)
+            return transformed.arg(1), counter
+
+        elif isinstance(expr, Prod):
+
+            dims = []
+            for dim in expr.products:
+                assert is_dim(dim)
+                d, counter = transform_dimension(dim, counter, dimension_dict)
+                dims.append(d.arg(1))
+            return z3.Product(dims), counter
+
+        elif is_algebraic_expression(expr):
+
+            lhs, counter = transform_algebraic_expression(expr.lhs, counter, dimension_dict)
+            rhs, counter = transform_algebraic_expression(expr.rhs, counter, dimension_dict)
+
+            if expr.op == op_sub:
+                c = lhs - rhs
+
+            elif expr.op == op_add:
+                c = lhs + rhs
+
+            elif expr.op == op_div:
+                c = lhs / rhs
+
+            elif expr.op == op_mul:
+                c = lhs * rhs
+
+            elif expr.op == op_mod:
+                c = lhs % rhs
+
+            else:
+                raise NotImplementedError('operation not yet implemented')
+
+            return c, counter
+
+        else:
+            raise RuntimeError
+
+
+    def transform_all_constraints(traced, counter=0):
+        """
+        Given a trace, generates constraints and transforms them to z3 format
+
+        """
+        dimension_dict = {}  # type: ignore[var-annotated]
+
+        generator = ConstraintGenerator(traced)
+        new_constraints, counter = generator.generate_constraints(counter)
+
+        # print(new_constraints.conjucts[0])
+        # print(*new_constraints.conjucts, sep='\n')
+
+        # transform precision, matching, consistency till obtaining a fixed point
+        new_constraints, counter = iterate_till_fixed_point(new_constraints, counter)
+        # print(new_constraints)
+        # print(new_constraints.conjucts)
+        # new_constraints.conjucts = new_constraints.conjucts[:-1]
+        # print(*new_constraints.conjucts, sep='\n')
+
+        transformed, counter = transform_to_z3(new_constraints, counter, dimension_dict)
+        # print(transformed)
+        return transformed
+
+    def iterate_till_fixed_point(constraints, counter):
+        """
+        Transform constraints till reaching a fixed point
+        """
+        old_c = None
+        while old_c != constraints:
+            old_c = constraints
+            constraints, counter = transform_constraint(constraints, counter)
+        return constraints, counter
+
+    def transform_all_constraints_trace_time(tracer_root, graph, node, counter=0):
+        """
+        Takes a node and a graph and generates two sets of constraints.
+        One set constraints the node's constraints and another set
+        constraints the negation of the node's constraints
+        Args:
+            tracer_root: the root for getting the module instances
+            graph: the graph so far in the tracing process
+            node: node that represents a conditional
+            counter: variable tracking
+
+        Returns: Two sets of constraints. One with a conjunction with the
+        the conditional constraint and the other with a conjunction with
+        its negation.
+
+        """
+        dimension_dict = {}  # type: ignore[var-annotated]
+
+        generator = ConstraintGenerator(tracer_root, graph)
+        new_constraints, counter = generator.generate_constraints(counter)
+
+        condition_constraint = new_constraints.conjucts[-1]
+
+        # we know the constraint is a conjunction where the last constraint is about the conditional
+        # so remove the last constraint
+        new_constraints.conjucts = new_constraints.conjucts[:-1]
+
+        # transform precision, matching, consistency till obtaining a fixed point
+        new_constraints, counter = iterate_till_fixed_point(new_constraints, counter)
+
+
+        # since the function returns a list of one element, we get the first element
+        # we are only interested in the RHS in this case because the LHS just stores
+        # the result
+
+        # we make sure the constraint is of the form:
+        # c = b where b is a boolean expression
+        # and we consider b (constraint.rhs) for transformation
+        assert isinstance(condition_constraint.lhs, BVar)
+        assert is_bool_expr(condition_constraint.rhs)
+        condition_constraint_rhs = condition_constraint.rhs
+
+        # transform the condition constraint
+        condition_constraint_rhs, counter = iterate_till_fixed_point(condition_constraint_rhs, counter)
+
+        transformed, counter = transform_to_z3(new_constraints, counter, dimension_dict)
+
+        transformed_condition_constraint, counter = transform_to_z3(condition_constraint_rhs, counter, dimension_dict)
+
+        negation_transformed_condition_constraint = z3.Not(transformed_condition_constraint)
+
+        return z3.And([transformed, transformed_condition_constraint]), \
+            z3.And([transformed, negation_transformed_condition_constraint])
+
+
+    def evaluate_conditional_with_constraints(tracer_root, graph, node, counter=0, user_constraints=None):
+        """
+        Given an IR and a node representing a conditional, evaluate the conditional
+        and its negation
+        Args:
+            tracer_root: Tracer root for module instances
+            node: The node to be evaluated
+
+        Returns: the results of evaluating the condition and the negation with
+        the rest of the constraints
+
+        """
+
+        transformed_positive, transformed_negative = \
+            transform_all_constraints_trace_time(tracer_root, graph, node, counter)
+
+        s = z3.Solver()
+        s.add(transformed_positive)
+        if user_constraints is not None:
+            s.add(user_constraints)
+        condition = s.check()
+
+        s = z3.Solver()
+        s.add(transformed_negative)
+        if user_constraints is not None:
+            s.add(user_constraints)
+        negation = s.check()
+        return condition, negation
+
+except ImportError:
+    HAS_Z3 = False
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/util.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..99f94609f2650b6642bdce586907f757b032409b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/util.py
@@ -0,0 +1,53 @@
+# mypy: allow-untyped-defs
+from torch.fx.experimental.migrate_gradual_types.constraint import TVar, DVar, BinConstraintD, \
+    BVar
+from torch.fx.experimental.migrate_gradual_types.operation import op_leq
+
+
+def gen_tvar(curr):
+    """
+    Generate a tensor variable
+    :param curr: The current counter
+    :return: a tensor variable and the updated counter
+    """
+    curr += 1
+    return TVar(curr), curr
+
+
+def gen_dvar(curr):
+    """
+    Generate a dimension variable
+    :param curr: the current counter
+    :return: a dimension variable and an updated counter
+    """
+    curr += 1
+    return DVar(curr), curr
+
+def gen_bvar(curr):
+    """
+    Generate a boolean variable
+    :param curr: the current counter
+    :return: a boolean variable and an updated counter
+    """
+    curr += 1
+    return BVar(curr), curr
+
+def gen_tensor_dims(n, curr):
+    """
+    Generate a list of tensor dimensions
+    :param n:  the number of dimensions
+    :param curr: the current counter
+    :return: a list of dimension variables and an updated counter
+    """
+    dims = []
+    for _ in range(n):
+        dvar, curr = gen_dvar(curr)
+        dims.append(dvar)
+    return dims, curr
+
+
+def gen_nat_constraints(list_of_dims):
+    """
+    Generate natural number constraints for dimensions
+    """
+    return [BinConstraintD(0, d, op_leq) for d in list_of_dims]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/z3_types.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/z3_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..897a79d5697573a51f5886d5e9965a98e2c4cf6a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/migrate_gradual_types/z3_types.py
@@ -0,0 +1,29 @@
+try:
+    import z3  # type: ignore[import]
+    HAS_Z3 = True
+    # dynamic type
+    dyn = z3.DeclareSort('Dyn')
+    dyn_type = z3.Const('dyn', dyn)
+
+    # dimension
+    dim = z3.Datatype('dim')
+    dim.declare('dim', ('0', z3.IntSort()), ('1', z3.IntSort()))
+    dim = dim.create()
+
+    # tensors
+    tensor_type = z3.Datatype('TensorType')
+    tensor_type.declare('Dyn', ('dyn', dyn))
+    tensor_type.declare('tensor1', ('0', dim))
+    tensor_type.declare('tensor2', ('0', dim), ('1', dim))
+    tensor_type.declare('tensor3', ('0', dim), ('1', dim), ('2', dim))
+    tensor_type.declare('tensor4', ('0', dim), ('1', dim), ('2', dim), ('3', dim))
+    tensor_type = tensor_type.create()
+
+    # create dimension
+    D = dim.dim
+
+    z3_dyn = tensor_type.Dyn(dyn_type)
+
+
+except ImportError:
+    HAS_Z3 = False
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/normalize.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/normalize.py
new file mode 100644
index 0000000000000000000000000000000000000000..30b076a72bee2e4bcb01bd25ed4f7d73b757edb5
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/normalize.py
@@ -0,0 +1,163 @@
+# mypy: allow-untyped-defs
+import operator
+from typing import Any, Callable, Dict, Tuple, Optional
+
+import torch
+import torch.fx
+import torch.fx as fx
+from torch.fx import Transformer, Proxy
+from torch.fx.node import Argument, Target, Node, map_aggregate
+from torch.fx.operator_schemas import (
+    normalize_module,
+    normalize_function,
+    create_type_hint,
+)
+
+from .schema_type_annotation import AnnotateTypesWithSchema
+
+
+class NormalizeArgs(Transformer):
+    """
+    Normalize arguments to Python targets. This means that
+    `args/kwargs` will be matched up to the module/functional's
+    signature and rewritten to exclusively kwargs in positional order
+    if `normalize_to_only_use_kwargs` is true. Also populates default
+    values. Does not support positional-only parameters or varargs
+    parameters (*args, **kwargs).
+
+    If the nodes have 'type' metadata, it will use it to disambiguate
+    overloads. Otherwise, it will throw an error.
+
+    Example usage:
+        m = torchvision.models.resnet18()
+        traced = torch.fx.symbolic_trace(m)
+        traced = NormalizeArgs(traced).transform()
+    """
+
+    def __init__(
+        self, module: torch.fx.GraphModule, normalize_to_only_use_kwargs: bool = True
+    ):
+        super().__init__(module)
+        self.node_map: Dict[Proxy, Node] = {}
+        self.normalize_to_only_use_kwargs = normalize_to_only_use_kwargs
+
+    def run_node(self, n: Node) -> Any:
+        args, kwargs = self.fetch_args_kwargs_from_env(n)
+
+        def get_type(arg):
+            if isinstance(arg, fx.Node):
+                return n.meta["type"] if "type" in n.meta else None
+            return type(arg)
+
+        arg_types = map_aggregate(n.args, get_type)
+        assert isinstance(arg_types, tuple)
+        arg_types = tuple([create_type_hint(i) for i in arg_types])
+        kwarg_types = {k: get_type(v) for k, v in kwargs.items()}
+        if n.op == "call_function":
+            out = self.call_function(n.target, args, kwargs, arg_types, kwarg_types)
+        else:
+            out = super().run_node(n)
+        if n.op != "output":
+            self.node_map[out] = n
+            out.node.meta = n.meta
+            out.node.type = n.type
+        return out
+
+    def call_function(
+        self,
+        target: Target,
+        args: Tuple[Argument, ...],
+        kwargs: Dict[str, Any],
+        arg_types: Optional[Tuple[Any, ...]] = None,
+        kwarg_types: Optional[Dict[str, Any]] = None,
+    ):
+        assert callable(target)
+        new_args_and_kwargs = normalize_function(
+            target,
+            args,  # type: ignore[arg-type]
+            kwargs,
+            arg_types,  # type: ignore[arg-type]
+            kwarg_types,
+            self.normalize_to_only_use_kwargs,
+        )
+        if new_args_and_kwargs:
+            new_args, new_kwargs = new_args_and_kwargs
+            return self.tracer.create_proxy(
+                "call_function", target, new_args, new_kwargs
+            )
+        else:
+            return super().call_function(target, args, kwargs)
+
+    def call_module(
+        self, target: Target, args: Tuple[Argument, ...], kwargs: Dict[str, Any]
+    ):
+        assert isinstance(target, str)
+        new_args_and_kwargs = normalize_module(
+            self.module,
+            target,
+            args,  # type: ignore[arg-type]
+            kwargs,
+            self.normalize_to_only_use_kwargs,
+        )
+        if new_args_and_kwargs:
+            new_args, new_kwargs = new_args_and_kwargs
+            return super().call_module(target, new_args, new_kwargs)
+        else:
+            return super().call_module(target, args, kwargs)
+
+
+class NormalizeOperators(AnnotateTypesWithSchema):
+    """
+    Normalize callsites that are different ways of "spelling" the same
+    invocation into a single, canonical call. Currently supports:
+
+    1. Normalize operators (e.g. operator.add) to the `torch` ops they
+       ultimately invoke (e.g. torch.add) when it is possible to statically
+       reason that
+
+    Example usage:
+
+        m = torchvision.models.resnet18()
+
+        traced = torch.fx.symbolic_trace(m)
+
+        traced = NormalizeOperators(traced).transform()
+    """
+
+    binary_magic_method_remap: Dict[
+        Callable[[Any, Any], Any], Callable[[Any, Any], Any]
+    ] = {
+        torch.add: operator.add,
+        torch.mul: operator.mul,
+        torch.sub: operator.sub,
+        torch.div: operator.truediv,
+        torch.floor_divide: operator.floordiv,
+        torch.remainder: operator.mod,
+        torch.eq: operator.eq,
+        torch.ne: operator.ne,
+        torch.lt: operator.lt,
+        torch.le: operator.le,
+        torch.gt: operator.gt,
+        torch.ge: operator.ge,
+    }
+
+    def call_function(
+        self, target: Target, args: Tuple[Argument, ...], kwargs: Dict[str, Any]
+    ):
+        # Normalize operators according to the magic methods implemented on tensors here:
+        # https://github.com/pytorch/pytorch/blob/28c5d90b679c6b38bf4183ec99f16d933c2f1bcd/tools/autograd/templates/python_variable_methods.cpp#L1137 # noqa: B950
+
+        assert callable(target)
+
+        if target in self.binary_magic_method_remap:
+            if len(args) != 2:
+                return super().call_function(target, args, kwargs)
+            lhs, rhs = args
+
+            return super().call_function(
+                target=self.binary_magic_method_remap[target],
+                args=(lhs, rhs),
+                kwargs={},
+            )
+
+        return super().call_function(target, args, kwargs)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/optimization.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/optimization.py
new file mode 100644
index 0000000000000000000000000000000000000000..8362c0cb88ac167c4c7eb5409128489b9c3d75e9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/optimization.py
@@ -0,0 +1,409 @@
+# mypy: allow-untyped-defs
+import torch.fx as fx
+from torch.fx.node import Argument, Target
+from torch.nn.utils.fusion import fuse_conv_bn_eval
+from typing import Type, Dict, Any, Tuple, Iterable, Optional, List, cast
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.fx.passes.shape_prop import ShapeProp
+import copy
+from collections import defaultdict
+import torch.utils.mkldnn as th_mkldnn
+import operator
+import time
+import logging
+from enum import Enum
+
+def _parent_name(target : str) -> Tuple[str, str]:
+    """
+    Splits a qualname into parent path and last atom.
+    For example, `foo.bar.baz` -> (`foo.bar`, `baz`)
+    """
+    *parent, name = target.rsplit('.', 1)
+    return parent[0] if parent else '', name
+
+# Works for length 2 patterns with 2 modules
+def matches_module_pattern(pattern: Iterable[Type], node: fx.Node, modules: Dict[str, Any]):
+    if len(node.args) == 0:
+        return False
+    nodes: Tuple[Any, fx.Node] = (node.args[0], node)
+    for expected_type, current_node in zip(pattern, nodes):
+        if not isinstance(current_node, fx.Node):
+            return False
+        if current_node.op != 'call_module':
+            return False
+        if not isinstance(current_node.target, str):
+            return False
+        if current_node.target not in modules:
+            return False
+        if type(modules[current_node.target]) is not expected_type:
+            return False
+    return True
+
+
+def replace_node_module(node: fx.Node, modules: Dict[str, Any], new_module: torch.nn.Module):
+    assert isinstance(node.target, str)
+    parent_name, name = _parent_name(node.target)
+    modules[node.target] = new_module
+    setattr(modules[parent_name], name, new_module)
+
+def fuse(model: torch.nn.Module, inplace=False, no_trace=False) -> torch.nn.Module:
+    """
+    Fuses convolution/BN layers for inference purposes. Will deepcopy your
+    model by default, but can modify the model inplace as well.
+    """
+    patterns = [(nn.Conv1d, nn.BatchNorm1d),
+                (nn.Conv2d, nn.BatchNorm2d),
+                (nn.Conv3d, nn.BatchNorm3d)]
+    if not inplace:
+        model = copy.deepcopy(model)
+    if not no_trace or not isinstance(model, torch.fx.GraphModule):
+        fx_model = fx.symbolic_trace(model)
+    else:
+        fx_model = model
+    modules = dict(fx_model.named_modules())
+    new_graph = copy.deepcopy(fx_model.graph)
+
+    for pattern in patterns:
+        for node in new_graph.nodes:
+            if matches_module_pattern(pattern, node, modules):
+                if len(node.args[0].users) > 1:  # Output of conv is used by other nodes
+                    continue
+                conv = modules[node.args[0].target]
+                bn = modules[node.target]
+                if not bn.track_running_stats:
+                    continue
+                fused_conv = fuse_conv_bn_eval(conv, bn)
+                replace_node_module(node.args[0], modules, fused_conv)
+                node.replace_all_uses_with(node.args[0])
+                new_graph.erase_node(node)
+    return fx.GraphModule(fx_model, new_graph)
+
+def remove_dropout(model: nn.Module) -> nn.Module:
+    """
+    Removes all dropout layers from the module.
+    """
+    fx_model = fx.symbolic_trace(model)
+
+    class DropoutRemover(torch.fx.Transformer):
+        def call_module(self, target : Target, args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+            if isinstance(self.submodules[target], nn.Dropout):
+                assert len(args) == 1
+                return args[0]
+            else:
+                return super().call_module(target, args, kwargs)
+    return DropoutRemover(fx_model).transform()
+
+def extract_subgraph(orig_module: nn.Module, nodes: List[fx.Node], inputs: List[fx.Node], outputs: List[fx.Node]):
+    """
+    Given lists of nodes from an existing graph that represent a subgraph, returns a submodule that executes that subgraph.
+    """
+    new_graph = fx.Graph()
+    env: Dict[fx.Node, fx.Node] = {}
+    for input in inputs:
+        new_node = new_graph.placeholder(input.name)
+        env[input] = new_node
+    for node in nodes:
+        new_node = new_graph.node_copy(node, lambda x: env[x])
+        env[node] = new_node
+    new_graph.output([env[output] for output in outputs])
+    new_graph.lint()
+    return fx.GraphModule(orig_module, new_graph)
+
+mkldnn_supported = [
+    nn.Conv2d, nn.Linear, nn.BatchNorm2d, nn.ReLU, nn.MaxPool2d, nn.AvgPool2d, nn.AdaptiveAvgPool2d,
+    torch.relu, torch.transpose, torch.sigmoid,
+    F.relu, F.avg_pool2d, F.adaptive_avg_pool2d
+]
+# These are operators that may not be convertible into MKLDNN ops (e.g. the
+# args are scalar values). Thus, we only include them in the subgraph if their
+# arguments are already in MKLDNN.
+# TODO: Determine whether this can be removed after type inference.
+mkldnn_supported_unknown = [operator.add, operator.mul]
+mkldnn_map = {
+    nn.Conv2d: th_mkldnn.MkldnnConv2d,
+    nn.Linear: th_mkldnn.MkldnnLinear,
+    nn.BatchNorm2d: lambda a, _: th_mkldnn.MkldnnBatchNorm(a)
+}
+
+
+def modules_to_mkldnn(nodes: List[fx.Node], modules: Dict[str, nn.Module]):
+    """
+    For each node, if it's a module that can be preconverted into MKLDNN,
+    then we do so and create a mapping to allow us to convert from the MKLDNN
+    version of the module to the original.
+    """
+    old_modules: Dict[nn.Module, nn.Module] = {}
+    for node in nodes:
+        if node.op == 'call_module':
+            assert isinstance(node.target, str)
+            cur_module = modules[node.target]
+            if type(cur_module) in mkldnn_map:
+                new_module = mkldnn_map[type(cur_module)](cur_module, torch.float)
+                assert isinstance(new_module, nn.Module)
+                old_modules[new_module] = copy.deepcopy(cur_module)
+                replace_node_module(node, modules, new_module)
+    return old_modules
+
+def reset_modules(nodes: List[fx.Node], modules: Dict[str, nn.Module], old_modules: Dict[nn.Module, nn.Module]):
+    """
+    Maps each module that's been changed with `modules_to_mkldnn` back to its
+    original.
+    """
+    for node in nodes:
+        if node.op == 'call_module':
+            assert (isinstance(node.target, str))
+            cur_module = modules[node.target]
+            if cur_module in old_modules:
+                replace_node_module(node, modules, old_modules[cur_module])
+
+class MklSubgraph:
+    def __init__(self, fx_graph: fx.Graph):
+        self.fx_graph = fx_graph
+        self.nodes: List[fx.Node] = []
+        self.start_nodes: List[fx.Node] = []
+        self.end_nodes: List[fx.Node] = []
+
+def gen_mkl_autotuner(example_inputs, iters=10, warmup=1):
+    """
+    This generates a heuristic that can be passed into `optimize_for_inference` that
+    determines whether a subgraph should be run in MKL by running it with the example_inputs.
+
+    Example usage:
+        heuristic = gen_mkl_autotuner(example_inputs, iters=10)
+        fast_model = optimization.optimize_for_inference(model, heuristic)
+    """
+    fx_model = None
+    old_modules = None
+
+    def use_mkl_heuristic(graph: MklSubgraph) -> bool:
+        nonlocal fx_model, old_modules
+        input_nodes = graph.start_nodes
+        if fx_model is None:
+            fx_model = graph.fx_graph.owning_module
+            old_modules = graph.fx_graph.old_modules  # type: ignore[attr-defined]
+            ShapeProp(fx_model).propagate(example_inputs)
+        sample_inputs = [torch.randn(node.shape) for node in input_nodes]  # type: ignore[attr-defined]
+        output_args = cast(List[fx.Node], [node.args[0] for node in graph.end_nodes])
+        submodule = extract_subgraph(fx_model, graph.nodes, input_nodes, output_args)
+
+        def benchmark(f):
+            for _ in range(warmup):
+                f()
+            begin = time.time()
+            for _ in range(iters):
+                out = f()
+            return time.time() - begin
+
+        mkl_time = benchmark(lambda: [i.to_dense() for i in submodule(*[i.to_mkldnn() for i in sample_inputs])])
+
+        reset_modules(submodule.graph.nodes, dict(submodule.named_modules()), old_modules)
+        no_mkl_time = benchmark(lambda: submodule(*sample_inputs))
+        return mkl_time < no_mkl_time
+    return use_mkl_heuristic
+
+def use_mkl_length(graph: MklSubgraph) -> bool:
+    """
+    This is a heuristic that can be passed into `optimize_for_inference` that
+    determines whether a subgraph should be run in MKL by checking if there
+    are more than 2 nodes in it
+    """
+    return len(graph.nodes) > 2
+
+class UnionFind:
+    def __init__(self, n):
+        self.parent: List[Optional[int]] = [None] * n
+        self.size: List[int] = [0] * n
+
+    def make_set(self, v: int):
+        self.parent[v] = v
+        self.size[v] = 1
+
+    def find(self, v: int) -> int:
+        par = self.parent[v]
+        if v == par:
+            return v
+        assert par is not None
+        self.parent[v] = self.find(par)
+        return cast(int, self.parent[v])
+
+    def join(self, a: int, b: int):
+        a, b = self.find(a), self.find(b)
+        if a == b:
+            return a
+        if self.size[a] < self.size[b]:
+            a, b = b, a
+        self.parent[b] = a
+        self.size[a] += self.size[b]
+
+def optimize_for_inference(
+    model: torch.nn.Module,
+    pass_config: Optional[Dict[str, Any]] = None,
+    tracer: Type[fx.Tracer] = fx.Tracer
+) -> torch.nn.Module:
+    """
+    Performs a set of optimization passes to optimize a model for the
+    purposes of inference. Specifically, the passes that are run are:
+    1. Conv/BN fusion
+    2. Dropout removal
+    3. MKL layout optimizations
+
+    The third optimization takes a function `use_mkl_heuristic` that's used
+    to determine whether a subgraph should be explicitly run in MKL layout.
+
+    Note: As FX does not currently handle aliasing, this pass currently
+    assumes nothing aliases. If that isn't true, use at your own risk.
+    """
+    default_pass_config = {
+        "conv_bn_fuse": True,
+        "remove_dropout": True,
+        "mkldnn_layout_optimize": {'heuristic': use_mkl_length},
+    }
+    if pass_config is None:
+        pass_config = {}
+    default_pass_config.update(pass_config)
+
+    if default_pass_config["conv_bn_fuse"]:
+        model = fuse(model)
+    if default_pass_config["remove_dropout"]:
+        model = remove_dropout(model)
+    if default_pass_config["mkldnn_layout_optimize"] is False:
+        return model
+    if not isinstance(default_pass_config["mkldnn_layout_optimize"], dict):
+        raise RuntimeError("mkldnn_layout_optimize config is not a dict")
+    if "heuristic" not in default_pass_config["mkldnn_layout_optimize"]:
+        raise RuntimeError("Heuristic not found in mkldnn_layout_optimize config")
+    use_mkl_heuristic = default_pass_config["mkldnn_layout_optimize"]["heuristic"]
+
+    cur_tracer = tracer()
+    fx_graph = cur_tracer.trace(copy.deepcopy(model))
+    fx_model = fx.GraphModule(cur_tracer.root, fx_graph)
+    modules: Dict[str, nn.Module] = dict(model.named_modules())
+
+    class MklSupport(Enum):
+        NO = 1
+        YES = 2
+        UNKNOWN = 3
+
+    # Inserts to_mkldnn and to_dense around every node we want to be a MKLDNN node.
+    # If the op is in `mkldnn_supported` then we always treat it as a MKLDNN node.
+    # However, if it's in `mkldnn_supported_unknown`, then we only treat it as
+    # a MKLDNN node if its inputs are MKLDNN nodes.
+    for node in list(fx_graph.nodes):
+        supports_mkldnn = MklSupport.NO
+        if node.op == 'call_module':
+            cur_module = modules[node.target]
+            if type(cur_module) in mkldnn_supported:
+                supports_mkldnn = MklSupport.YES
+                sample_parameter = next(cur_module.parameters(), None)
+                if sample_parameter is not None:
+                    assert sample_parameter.dtype == torch.float, "this pass is only for torch.float modules"
+                    assert sample_parameter.device == torch.device('cpu'), "this pass is only for CPU modules"
+        elif node.op == 'call_function':
+            if node.target in mkldnn_supported:
+                supports_mkldnn = MklSupport.YES
+            elif node.target in mkldnn_supported_unknown:
+                supports_mkldnn = MklSupport.UNKNOWN
+
+        if supports_mkldnn != MklSupport.NO:
+            if supports_mkldnn == MklSupport.UNKNOWN:
+                if not any(arg.target == 'to_dense' for arg in node.args):
+                    continue
+            with fx_graph.inserting_before(node):
+                mkldnn_args = fx.map_arg(node.args, lambda n: fx_graph.call_method('to_mkldnn', (n, )))
+
+            node.args = cast(Tuple[fx.node.Argument], mkldnn_args)
+
+            with fx_graph.inserting_after(node):
+                dense_x = fx_graph.create_node('call_method', 'to_dense', (node,))
+                node.replace_all_uses_with(dense_x)
+                dense_x.args = (node,)
+
+    # Does pre-conversion of all modules into MKLDNN (when possible)
+    old_modules = modules_to_mkldnn(list(fx_graph.nodes), modules)
+    fx_graph.old_modules = old_modules  # type: ignore[attr-defined]
+
+    # optimizes all a -> to_dense -> to_mkldnn -> b patterns into a -> b
+    for node in fx_graph.nodes:
+        if node.op == 'call_method' and node.target == 'to_dense':
+            prv_node = node.args[0]
+            users = list(node.users)
+            for user in users:
+                if user.op == 'call_method' and user.target == 'to_mkldnn':
+                    user.replace_all_uses_with(prv_node)
+                    fx_graph.erase_node(user)
+            if len(node.users) == 0:
+                fx_graph.erase_node(node)
+
+
+    num_nodes = len(fx_graph.nodes)
+    uf = UnionFind(num_nodes)
+
+    def get_color(n):
+        if hasattr(n, 'color'):  # Current node is part of a MKL subgraph
+            return uf.find(n.color)
+        if hasattr(n, 'start_color'):  # Current node is input to MKL subgraph
+            return uf.find(n.start_color)
+        return None
+
+
+    # This code is to find each MKLDNN subgraph. Each MKLDNN subgraph consists
+    # of input nodes (which are only `to_mkldnn` calls), output nodes
+    # (`to_dense` calls), and intermediate nodes, which are run entirely on
+    # MKLDNN layout tensors.
+    #
+    # Specifically, this code does a flood fill on a directed acyclic graph
+    # (DAG), starting from each possible "start node" (i.e: `to_mkldnn` nodes).
+    # If every node only had one input, this would be sufficient. However, in
+    # the case that a node has multiple inputs coming from different start
+    # nodes (i.e. colors), we need to join these 2 colors into 1. That's done
+    # using a Disjoint Set Union.
+    for cur_idx, node in enumerate(fx_graph.nodes):
+        if node.op == 'call_method' and node.target == 'to_mkldnn':
+            node.start_color = cur_idx
+            uf.make_set(cur_idx)
+        elif node.op == 'call_method' and node.target == 'to_dense':
+            assert get_color(node.args[0]) is not None
+            node.end_color = get_color(node.args[0])
+        else:
+            cur_colors = [get_color(i) for i in node.all_input_nodes if isinstance(i, fx.Node) if get_color(i) is not None]
+
+            if len(cur_colors) == 0:
+                continue
+            assert not any(i is None for i in cur_colors)
+            cur_colors = sorted(cur_colors)
+            node.color = cur_colors[0]
+            for other_color in cur_colors[1:]:
+                uf.join(cur_colors[0], other_color)
+
+
+    mkldnn_graphs: Dict[int, MklSubgraph] = defaultdict(lambda: MklSubgraph(fx_graph))
+    for node in fx_graph.nodes:
+        if hasattr(node, 'color'):
+            mkldnn_graphs[uf.find(node.color)].nodes.append(node)
+        if hasattr(node, 'start_color'):
+            mkldnn_graphs[uf.find(node.start_color)].start_nodes.append(node)
+        if hasattr(node, 'end_color'):
+            mkldnn_graphs[uf.find(node.end_color)].end_nodes.append(node)
+
+
+    # Now that we have all the subgraphs, we need to decide which MKLDNN
+    # subgraphs we actually want to keep in MKLDNN.
+    for graph in mkldnn_graphs.values():
+        if not use_mkl_heuristic(graph):
+            for node in graph.start_nodes + graph.end_nodes:
+                prv = node.args[0]
+                node.replace_all_uses_with(prv)
+                fx_graph.erase_node(node)
+            reset_modules(graph.nodes, modules, old_modules)
+
+    mkldnn_conversions = 0
+    for node in fx_graph.nodes:
+        if node.target == 'to_mkldnn' or node.target == 'to_dense':
+            mkldnn_conversions += 1
+
+    logging.getLogger(__name__).info("mkldnn conversions: %s", mkldnn_conversions)
+    fx_graph.lint()
+    result = fx.GraphModule(model, fx_graph)
+    return result
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/partitioner_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/partitioner_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..796c65a4302284b94ffd3bf9bd0bf9dfcab496f3
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/partitioner_utils.py
@@ -0,0 +1,318 @@
+# mypy: allow-untyped-defs
+from enum import Enum
+from typing import NamedTuple, Dict, List, Set
+
+from torch.fx.node import Node, map_arg
+
+
+class Partition:
+    """Partition class contains all the information about an individual partition.
+    It also provides necessary methods for manipulation the partition.
+    """
+
+    def __init__(self, partition_id: int) -> None:
+        self.nodes: Set[Node] = set()
+        self.partition_id = partition_id
+        self.parents: Set[Partition] = set()
+        self.children: Set[Partition] = set()
+        self.bfs_level: int = -1
+        self.used_mem_bytes: int = 0
+        self.logical_device_ids: List[int] = []
+
+    def __str__(self):
+        return str(self.partition_id)
+
+    def recalculate_mem_size(self):
+        self.used_mem_bytes = 0
+        for node in self.nodes:
+            self.used_mem_bytes += get_extra_size_of(node, self.nodes)
+
+    def add_node(self, node):
+        input_nodes: Dict[Node, None] = {}
+        map_arg(node.args, input_nodes.setdefault)
+        map_arg(node.kwargs, input_nodes.setdefault)
+        # Add current node's input nodes if they are placeholder or constants
+        for n in input_nodes:
+            if n.op in {"placeholder", "get_attr"}:
+                self.nodes.add(n)
+        self.nodes.add(node)
+        self.recalculate_mem_size()
+
+    def remove_node(self, node):
+        # Remove a node only if the node is in the partition
+        if node in self.nodes:
+            self.nodes.remove(node)
+            # Collect the node's input nodes
+            input_nodes: Dict[Node, None] = {}
+            map_arg(node.args, input_nodes.setdefault)
+            map_arg(node.kwargs, input_nodes.setdefault)
+            # Check if an input node is a placeholder or get_attr,
+            # and this input node is not used by some other nodes in this partition,
+            # the remove this input node
+            for input_node in input_nodes:
+                if all(
+                    n not in self.nodes for n in input_node.users
+                ) and input_node.op in {"placeholder", "get_attr"}:
+                    self.nodes.remove(input_node)
+            self.recalculate_mem_size()
+
+
+class Device(NamedTuple):
+    name: str
+    available_mem_bytes: int
+    logical_id: int
+
+
+class NodeLatency(NamedTuple):
+    # Latency due to the memory bandwidth
+    mem_latency_sec: float
+    # Latency due to the computation
+    computer_latency_sec: float
+
+
+class PartitionLatency(NamedTuple):
+    # Sum of all nodes' memory latency on the critical path
+    mem_latency_sec: float
+    # Sum of all nodes' compute latency on the critical path
+    computer_latency_sec: float
+    # Latency of the critical path
+    overall_latency_sec: float
+
+
+class PartitionMode(Enum):
+    size_based = 0
+    sparse_nn = 1
+    cost_aware = 2
+    kl_based = 3
+    aot_based = 4
+
+
+class PartitionerConfig(NamedTuple):
+    devices: List[Device]
+    mode: PartitionMode = PartitionMode.size_based
+    transfer_rate_bytes_per_sec: float = 0.0
+    node_to_latency_mapping: Dict[Node, NodeLatency] = {}
+    node_to_partition_mapping: Dict[Node, int] = {}
+    partition_to_logical_device_mapping: Dict[int, List[int]] = {}
+    # Saturate host by replicating partitions to the remaining idle devices.
+    saturate_host: bool = False
+
+
+def get_extra_size_of(node: Node, nodes: Set[Node]) -> int:
+    """Given a node and a set of nodes,
+    this function return the extra size that needed
+    if this node is included in this set.
+    """
+    # Find all its input nodes
+    input_nodes: Dict[Node, None] = {}
+    map_arg(node.args, input_nodes.setdefault)
+    map_arg(node.kwargs, input_nodes.setdefault)
+    # Calculate total size of related nodes
+    total_size_of_input_nodes = 0
+    for n in input_nodes:
+        # Make sure this node hasn't been in this set yet
+        if n not in nodes:
+            size_bytes = getattr(n, "size_bytes", None)
+            if size_bytes:
+                total_size_of_input_nodes += size_bytes.output_size
+            else:
+                raise RuntimeError("node has no size_bytes attr")
+    # Don't forget the op node itself
+    size_bytes = getattr(node, "size_bytes", None)
+    if size_bytes:
+        total_size_of_input_nodes += size_bytes.total_size
+    else:
+        raise RuntimeError("node has no size_bytes attr")
+    return total_size_of_input_nodes
+
+
+def get_latency_of_one_partition(
+    partition: Partition, node_to_latency_mapping: Dict[Node, NodeLatency]
+) -> PartitionLatency:
+    """Given a partition and its nodes' latency, return a PartitionLatency for this partition"""
+
+    def get_top_nodes(partition: Partition) -> List[Node]:
+        """Given a partition, return a list of nodes on the top bfs level"""
+        top_nodes: List[Node] = []
+        for node in partition.nodes:
+            # Skip placeholder and get_attr nodes
+            if node.op in {"placeholder", "get_attr"}:
+                continue
+            input_nodes: Dict[Node, None] = {}
+            map_arg(node.args, input_nodes.setdefault)
+            map_arg(node.kwargs, input_nodes.setdefault)
+            # If a node has no input nodes in this partition,
+            # or its input nodes in this partition are placeholders and get_attrs
+            # this node is on the top bfs level in this partition
+            if not any(
+                n in partition.nodes and n.op not in {"placeholder", "get_attr"}
+                    for n in input_nodes
+            ):
+                top_nodes.append(node)
+        return top_nodes
+
+    def dfs_helper(node: Node, partition_latency) -> PartitionLatency:
+        """Given a top node of a partition, this function returns
+        the latency of the critical path in the partition
+        """
+        node_latency = node_to_latency_mapping[node]
+        # Calculate the current overall latency of the partition
+        overall_latency_sec = partition_latency.overall_latency_sec + max(
+            node_latency.computer_latency_sec, node_latency.mem_latency_sec
+        )
+        # Update the mem latency of this path
+        mem_latency_sec = (
+            partition_latency.mem_latency_sec + node_latency.mem_latency_sec
+        )
+        # Update the compute latency of this path
+        computer_latency_sec = (
+            partition_latency.computer_latency_sec + node_latency.computer_latency_sec
+        )
+        # Get all users of this node that are in this partition
+        users = set(node.users).intersection(partition.nodes)
+        if users:
+            max_latency = PartitionLatency(
+                mem_latency_sec=0.0, computer_latency_sec=0.0, overall_latency_sec=0.0
+            )
+            for n in users:
+                # Get new partition latency recursively
+                new_partition_latency = dfs_helper(
+                    n,
+                    PartitionLatency(
+                        mem_latency_sec, computer_latency_sec, overall_latency_sec
+                    ),
+                )
+                if (
+                    new_partition_latency.overall_latency_sec
+                    > max_latency.overall_latency_sec
+                ):
+                    max_latency = new_partition_latency
+            return max_latency
+        # If there is no user, the node is at bottom of the partition
+        return PartitionLatency(
+            mem_latency_sec, computer_latency_sec, overall_latency_sec
+        )
+
+    # Main part starts
+    # Get all top level nodes of this partition
+    top_nodes = get_top_nodes(partition)
+    critical_path_latency = PartitionLatency(
+        mem_latency_sec=0.0, computer_latency_sec=0.0, overall_latency_sec=0.0
+    )
+    # Go through all top nodes and find the largest latency (critical pass latency)
+    for node in top_nodes:
+        partition_latency = dfs_helper(
+            node,
+            PartitionLatency(
+                mem_latency_sec=0.0, computer_latency_sec=0.0, overall_latency_sec=0.0
+            ),
+        )
+        if (
+            partition_latency.overall_latency_sec
+            > critical_path_latency.overall_latency_sec
+        ):
+            critical_path_latency = partition_latency
+    return critical_path_latency
+
+
+def get_partition_to_latency_mapping(
+    partitions: List[Partition], node_to_latency_mapping: Dict[Node, NodeLatency]
+) -> Dict[Partition, PartitionLatency]:
+    """Given all the partitions and node_to_latency_mapping dictionary,
+    return a mapping dictionary of each partition to its overall latency
+    """
+    partition_to_latency_mapping: Dict[Partition, PartitionLatency] = {}
+    # Go through each partition and get its latency
+    for partition in partitions:
+        partition_latency = get_latency_of_one_partition(
+            partition, node_to_latency_mapping
+        )
+        partition_to_latency_mapping[partition] = partition_latency
+    return partition_to_latency_mapping
+
+
+def get_comm_latency_between(
+    parent_partition: Partition,
+    child_partition: Partition,
+    transfer_rate_bytes_per_sec: float,
+):
+    """Given two partitions (parent and child),
+    calculate the communication latency between the two.
+    """
+    # If two partitions are on the same device, the comm latency is 0.
+    if (
+        parent_partition.logical_device_ids != []
+        and child_partition.logical_device_ids != []
+        and parent_partition.logical_device_ids == child_partition.logical_device_ids
+    ):
+        return 0.0
+    # Keep tracking the communication size between parent and child
+    comm_size = 0
+    # Keep tracking all the counted node
+    visited_nodes = set()
+    # Go through all nodes in the child partition
+    # If a node has input nodes from the parent partition,
+    # the output size of those input nodes will be counted
+    # and added to comm_size
+    for node in child_partition.nodes:
+        input_nodes: Dict[Node, None] = {}
+        map_arg(node.args, input_nodes.setdefault)
+        map_arg(node.kwargs, input_nodes.setdefault)
+        for n in input_nodes:
+            if n in parent_partition.nodes and n not in visited_nodes:
+                size_bytes = getattr(n, "size_bytes", None)
+                if size_bytes is not None:
+                    comm_size += size_bytes.output_size
+                visited_nodes.add(n)
+    return comm_size / transfer_rate_bytes_per_sec
+
+
+def get_latency_of_partitioned_graph(
+    partitions: List[Partition],
+    partition_to_latency_mapping: Dict[Partition, PartitionLatency],
+    transfer_rate_bytes_per_sec: float,
+):
+    """Given all partitions in a graph, find the critical path among all partitions
+    and return its latency as the latency of the whole graph
+    """
+
+    def dfs_helper(partition: Partition, latency_so_far_sec: float) -> float:
+        """This function helps to recursively get the latency of a path of partitions"""
+        # Update latency by adding current partition's latency
+        latency_so_far_sec += partition_to_latency_mapping[
+            partition
+        ].overall_latency_sec
+        children = partition.children
+        if partition.children:
+            max_latency_sec = 0.0
+            for child in partition.children:
+                # Calculate latency between
+                comm_latency_sec = get_comm_latency_between(
+                    partition, child, transfer_rate_bytes_per_sec
+                )
+                new_latency_sec = dfs_helper(
+                    child, latency_so_far_sec + comm_latency_sec
+                )
+                if new_latency_sec > max_latency_sec:
+                    max_latency_sec = new_latency_sec
+            return max_latency_sec
+        return latency_so_far_sec
+
+    def get_top_partitions(partitions: List[Partition]) -> List[Partition]:
+        """This function is to return all the partitions without parents
+        as the starting points of all the paths
+        """
+        top_partitions = []
+        for partition in partitions:
+            # If a partition has no parents, then it is a top partition
+            if len(partition.parents) == 0:
+                top_partitions.append(partition)
+        return top_partitions
+
+    top_partitions = get_top_partitions(partitions)
+    critical_path_latency_sec = 0.0
+    for partition in top_partitions:
+        latency_sec = dfs_helper(partition, 0.0)
+        if latency_sec > critical_path_latency_sec:
+            critical_path_latency_sec = latency_sec
+    return critical_path_latency_sec
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/proxy_tensor.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/proxy_tensor.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a5771d2ae8f7a2446eb58fa28973a8fbb629145
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/proxy_tensor.py
@@ -0,0 +1,2215 @@
+# mypy: allow-untyped-decorators
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from __future__ import annotations
+
+import functools
+import inspect
+import logging
+import operator
+import traceback
+import typing
+import typing_extensions
+import warnings
+import weakref
+from collections import defaultdict
+from contextlib import contextmanager, ExitStack, nullcontext
+from dataclasses import dataclass
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Generator,
+    List,
+    Mapping,
+    Optional,
+    overload,
+    Protocol,
+    Sequence,
+    Tuple,
+    Type,
+    TYPE_CHECKING,
+    TypeVar,
+    Union,
+)
+from typing_extensions import Concatenate, ParamSpec, Self
+from weakref import WeakKeyDictionary
+
+import torch
+import torch._ops
+import torch.fx as fx
+import torch.fx.traceback as fx_traceback
+import torch.utils._pytree as pytree
+from torch import SymBool, SymInt, Tensor
+from torch._dispatch.python import enable_python_dispatcher
+from torch._library.fake_class_registry import FakeScriptObject
+from torch._subclasses.fake_impls import fast_detach
+from torch._subclasses.fake_tensor import (
+    FakeTensor,
+    FakeTensorMode,
+    is_fake,
+    unset_fake_temporarily,
+)
+from torch._subclasses.meta_utils import is_sparse_any
+from torch.fx import GraphModule, Proxy, Tracer
+from torch.fx.graph_module import _assign_attr
+from torch.fx.node import _side_effectful_need_to_be_preserved_pre_dispatch
+from torch.fx.passes.shape_prop import _extract_tensor_metadata
+from torch.nn import Module
+from torch.overrides import TorchFunctionMode
+from torch.utils._python_dispatch import (
+    _disable_infra_mode,
+    _push_mode,
+    _unset_infra_mode,
+    TorchDispatchMode,
+)
+from torch.utils._stats import count
+from torch.utils._thunk import Thunk
+from torch.utils._traceback import CapturedTraceback
+from torch.utils.weak import _WeakHashRef, WeakIdKeyDictionary, WeakTensorKeyDictionary
+
+from ._backward_state import BackwardState
+from .sym_node import SymNode
+
+
+if TYPE_CHECKING:
+    import types
+    from collections.abc import MutableMapping
+
+    import sympy
+
+    from torch._ops import OpOverload
+    from torch.fx._symbolic_trace import PHBase
+    from torch.types import IntLikeType
+
+__all__ = [
+    "PythonKeyTracer",
+    "dispatch_trace",
+    "make_fx",
+    "DecompositionInterpreter",
+    "py_sym_types",
+    "get_innermost_proxy_mode",
+    "get_proxy_mode",
+    "handle_sym_dispatch",
+    "maybe_enable_thunkify",
+    "maybe_disable_thunkify",
+]
+
+_ProxyTracer = Union["PythonKeyTracer", "_GraphAppendingTracerEx"]
+
+_AnyScriptObject = (torch.ScriptObject, FakeScriptObject)
+_AnyScriptObjectType = Union[torch.ScriptObject, FakeScriptObject]
+
+aten = torch.ops.aten
+prim = torch.ops.prim
+
+log = logging.getLogger(__name__)
+not_implemented_log = torch._logging.getArtifactLogger(__name__, "not_implemented")
+
+CURRENT_DECOMPOSITION_TABLE: Mapping[OpOverload, Callable] = {}
+
+CONSTANT_NUMEL_LIMIT = 1
+
+T = TypeVar("T")
+U = TypeVar("U")
+_P = ParamSpec("_P")
+R = TypeVar("R")
+
+null_ctx_type = type(nullcontext)
+# We currently convert all SymInt to proxies before we use them.
+# This could plausibly be handled at the Dynamo level.
+pytree.register_pytree_node(
+    torch.Size,
+    lambda xs: (list(xs), None),
+    lambda xs, _: tuple(xs),
+    flatten_with_keys_fn=lambda xs: (
+        [(pytree.SequenceKey(i), x) for i, x in enumerate(xs)],
+        None,
+    ),
+)
+
+
+def fake_signature(fn: Callable[_P, R], nargs: int) -> Callable[_P, R]:
+    """FX gets confused by varargs, de-confuse it"""
+    argnames = ",".join(f"arg{i}" for i in range(nargs))
+    return eval(f"lambda {argnames}: fn({argnames})", {"fn": fn})
+
+
+@contextmanager
+def decompose(
+    decomposition_table: Optional[Mapping[OpOverload, Callable]]
+) -> Generator[Mapping[OpOverload, Callable], None, None]:
+    global CURRENT_DECOMPOSITION_TABLE
+    old_decomposition_table = CURRENT_DECOMPOSITION_TABLE
+    CURRENT_DECOMPOSITION_TABLE = decomposition_table or {}
+    try:
+        yield CURRENT_DECOMPOSITION_TABLE
+    finally:
+        CURRENT_DECOMPOSITION_TABLE = old_decomposition_table
+
+
+# ensure we cannot collide with other properties
+proxy_slot = object()
+
+
+class _NoDefault:
+    pass
+
+
+no_default = _NoDefault()
+
+from torch.types import py_sym_types, PySymType
+
+
+class _HasMeta(Protocol):
+    meta: Dict[str, PySymType]
+
+
+def is_sym_node(node: _HasMeta) -> bool:
+    assert hasattr(node, "meta"), "All nodes traced with proxy_tensor should have meta"
+    return "val" in node.meta and isinstance(node.meta["val"], py_sym_types)
+
+
+@overload
+def set_proxy_slot(obj: Tensor, tracer: _ProxyTracer, proxy: _ProxyTensor) -> None:
+    ...
+
+
+@overload
+def set_proxy_slot(
+    obj: _AnyScriptObjectType, tracer: _ProxyTracer, proxy: Proxy
+) -> None:
+    ...
+
+
+@overload
+def set_proxy_slot(
+    obj: PySymType, tracer: _ProxyTracer, proxy: _PySymProxyType
+) -> None:
+    ...
+
+
+def set_proxy_slot(
+    obj: Union[PySymType, _AnyScriptObjectType, Tensor],
+    tracer: _ProxyTracer,
+    proxy: object,
+) -> None:
+    log.debug("set_proxy_slot %s (%s) %s", obj, id(obj), proxy)
+    if isinstance(obj, Tensor):
+        # We DO want to clobber proxies whenever we run an inplace operation
+        # on a tensor, and it affects the metadata on the proxy.
+        assert isinstance(proxy, _ProxyTensor)
+        tracer.tensor_tracker[obj] = proxy
+    elif isinstance(obj, (_AnyScriptObject)):
+        # We DO want to clobber proxies, with a similar rationale as for tensors.
+        assert isinstance(proxy, Proxy)
+        tracer.script_object_tracker[obj] = proxy
+    else:
+        # NB: Never clobber pre-existing proxy.  Although the proxies
+        # are in principle equivalent, when we do graph partitioning
+        # we need there not to be spurious dependencies on tangent inputs.
+        # This works because primals get their SymInts set first, and
+        # THEN later we allocate tangent inputs.  Make sure if a SymInt
+        # is derivable from a primal that we use that.
+        assert isinstance(obj, py_sym_types), type(obj)
+        if obj not in tracer.symnode_tracker:
+            tracer.symnode_tracker[obj] = typing.cast(_PySymProxyType, proxy)
+
+            # WAR: python test/dynamo/test_subclasses.py
+            # TestNestedTensor.test_basic_autograd
+            #
+            # AOTAutograd doesn't pass the "outer sizes" as an actual argument
+            # to make_fx, but it is made use of internally in AOTAutograd's
+            # call to tensor unflatten.  Because the outer sizes isn't passed
+            # as an argument, it is therefore untracked.  However, it turns
+            # out you luck out, because *Dynamo* will manually add the outer
+            # sizes as an argument so you can fix up the proxy'ness.
+            #
+            # This is probably fixed in
+            # https://github.com/pytorch/pytorch/pull/125941/
+            import sympy
+
+            if isinstance(obj.node.expr, sympy.Symbol):
+                tracer.sympy_expr_tracker[obj.node.expr] = proxy
+
+
+def has_proxy_slot(obj: Tensor, tracer: _ProxyTracer) -> bool:
+    assert isinstance(obj, (Tensor, SymNode)), type(obj)
+    return bool(get_proxy_slot(obj, tracer, False, lambda _: True))
+
+
+_PySymProxyType = Thunk[Proxy]
+
+
+@overload
+def get_proxy_slot(
+    obj: Tensor,
+    tracer: _ProxyTracer,
+) -> _ProxyTensor:
+    ...
+
+
+@overload
+def get_proxy_slot(
+    obj: Tensor,
+    tracer: _ProxyTracer,
+    default: U,
+) -> Union[_ProxyTensor, U]:
+    ...
+
+
+@overload
+def get_proxy_slot(
+    obj: Tensor,
+    tracer: _ProxyTracer,
+    default: U,
+    transform: Callable[[_ProxyTensor], R],
+) -> Union[R, U]:
+    ...
+
+
+@overload
+def get_proxy_slot(
+    obj: _AnyScriptObjectType,
+    tracer: _ProxyTracer,
+) -> Proxy:
+    ...
+
+
+@overload
+def get_proxy_slot(
+    obj: _AnyScriptObjectType,
+    tracer: _ProxyTracer,
+    default: U,
+) -> Union[Proxy, U]:
+    ...
+
+
+@overload
+def get_proxy_slot(
+    obj: _AnyScriptObjectType,
+    tracer: _ProxyTracer,
+    default: U,
+    transform: Callable[[Proxy], R],
+) -> Union[R, U]:
+    ...
+
+
+@overload
+def get_proxy_slot(
+    obj: PySymType,
+    tracer: _ProxyTracer,
+) -> _PySymProxyType:
+    ...
+
+
+@overload
+def get_proxy_slot(
+    obj: PySymType,
+    tracer: _ProxyTracer,
+    default: T,
+) -> Union[T, _PySymProxyType]:
+    ...
+
+
+@overload
+def get_proxy_slot(
+    obj: PySymType,
+    tracer: _ProxyTracer,
+    default: U,
+    transform: Callable[[_PySymProxyType], R],
+) -> Union[R, U]:
+    ...
+
+
+# the default argument is what to return if the slot is not set.
+# the transform argument is handy if you need to extract a subfield from
+# the successfully looked up result (but NOT the default.)
+def get_proxy_slot(
+    obj: Union[Tensor, _AnyScriptObjectType, PySymType],
+    tracer: _ProxyTracer,
+    default: object = no_default,
+    transform: Callable = lambda x: x,
+) -> object:
+    tracker: Any
+    if isinstance(obj, Tensor):
+        tracker = tracer.tensor_tracker
+    elif isinstance(obj, _AnyScriptObject):
+        tracker = tracer.script_object_tracker
+    else:
+        assert isinstance(obj, py_sym_types), type(obj)
+        tracker = tracer.symnode_tracker
+
+    if obj not in tracker:
+        # Last ditch
+        if isinstance(obj, py_sym_types) and obj.node.expr in tracer.sympy_expr_tracker:
+            value = tracer.sympy_expr_tracker[obj.node.expr]
+        else:
+            if isinstance(default, _NoDefault):
+                raise RuntimeError(
+                    f"{obj} ({id(obj)})is not tracked with proxy for {tracer}"
+                )
+            return default
+    else:
+        value = tracker[obj]
+    res = transform(value)
+    return res
+
+
+def snapshot_fake(val: Tensor) -> Optional[Tensor]:
+    # val.detach() will also eventually call fast_detach(),
+    # but this saves us a full trip into __torch_dispatch__
+    # (snapshot_fake is called a lot)
+    if isinstance(val, FakeTensor):
+        return fast_detach(val.fake_mode, val)
+    else:
+        return val.detach()
+
+
+_ExtractValType = Optional[
+    Union[
+        PySymType,
+        _AnyScriptObjectType,
+        BackwardState,
+        List["_ExtractValType"],
+        Tuple["_ExtractValType", ...],
+        Dict[str, "_ExtractValType"],
+        Tensor,
+        int,
+        float,
+        bool,
+    ]
+]
+
+
+def extract_val(val: _ExtractValType) -> _ExtractValType:
+    if is_fake(val):
+        return snapshot_fake(val)
+    elif isinstance(val, py_sym_types):
+        return val
+    elif isinstance(val, _AnyScriptObject):
+        return val
+    elif isinstance(val, BackwardState):
+        return val
+    elif isinstance(val, (list, tuple)):
+        return val.__class__([extract_val(x) for x in val])
+    elif isinstance(val, dict):
+        return {k: extract_val(v) for k, v in val.items()}
+    elif isinstance(val, Tensor):
+        if not val.is_sparse:
+            # NB: Kinda hacky, but we should try to get val as the metadata
+            # everywhere
+            # TODO: This doesn't properly track storages.  A more robust
+            # approach would be to maintain a per-trace FakeTensorMode and
+            # from_real_tensor to create fake values (don't forget to
+            # snapshot_fake)
+            fake_tensor_mode = FakeTensorMode(allow_fallback_kernels=True)
+            with fake_tensor_mode:
+                return torch.empty_strided(
+                    val.shape, val.stride(), device=val.device, dtype=val.dtype
+                )
+        else:
+            return None
+    elif isinstance(val, (int, float, bool)):
+        return val
+    elif val is None:
+        return None
+
+    typing_extensions.assert_never(val)
+
+
+@contextmanager
+def _enable_thunkify(
+    tracer: _ProxyTracer, *, enable: bool = True
+) -> Generator[None, None, None]:
+    """
+    Enable thunkification inside the context manager.  Thunkification prevents
+    SymNode computation from directly being traced into an FX graph; instead,
+    the compute is only added to the graph if it is actually used.  This helps
+    us track SymNode compute when it is computed (since we need /something/
+    to put in the tracker) even if it is unlikely to be used.
+    """
+    old = tracer.enable_thunkify
+    tracer.enable_thunkify = enable
+    try:
+        yield
+    finally:
+        tracer.enable_thunkify = old
+
+
+@contextmanager
+def maybe_disable_thunkify() -> Generator[None, None, None]:
+    """Within a context, disable thunkification.  See :func:`maybe_enable_thunkify`
+    for more details.  This is helpful if you have a wrapper function which
+    you want to enable thunkification on, but in some segment on the inside (say,
+    the original user function), you want to disable thunkification as you know
+    it is not needed there.
+    """
+    proxy_mode = get_proxy_mode()
+    if proxy_mode is not None:
+        with _enable_thunkify(proxy_mode.tracer, enable=False):
+            yield
+    else:
+        yield
+
+
+@contextmanager
+def maybe_enable_thunkify() -> Generator[None, None, None]:
+    """Within this context manager, if you are doing make_fx tracing, we will thunkify
+    all SymNode compute and avoid tracing it into the graph unless it is actually needed.
+    You should prefer to avoid using this as much as possible, as lazy evaluation of
+    SymNode tracing can lead to long chains of thunks which will stack overflow
+    if you evaluate them.  However, this is currently sometimes necessary as there
+    are buggy parts of PT2 which will fail with "s0 is not tracked with proxy" error
+    due to insufficient tracing of SymNode computation.
+    """
+    proxy_mode = get_proxy_mode()
+    if proxy_mode is not None:
+        with _enable_thunkify(proxy_mode.tracer):
+            yield
+    else:
+        yield
+
+
+# Note [invariants for node meta 'val']
+# What invariants do we have for the 'val' set on the FX node?  It has accurate
+# metadata... but only for metadata that exists "below" all other subsystems
+# (most notably autograd, but also vmap, functorch transforms, etc).  This means
+# you can get the dtype, shape, stride, storage, but you CANNOT get requires_grad,
+# grad_fn, _base (_base actually may be set due to recursive call to
+# ADInplaceOrView, but you shouldn't rely on it.)
+def set_meta(proxy: Proxy, val: _ExtractValType) -> Proxy:
+    proxy.node.meta["val"] = extract_val(val)
+
+    with _enable_thunkify(proxy.tracer):  # type: ignore[arg-type]
+        # Best effort tensor_meta setting; prefer using val!
+        if is_fake(val):
+            proxy.node.meta["tensor_meta"] = _extract_tensor_metadata(val)
+        elif isinstance(val, Tensor) and not val.is_sparse:
+            proxy.node.meta["tensor_meta"] = _extract_tensor_metadata(val)
+    return proxy
+
+
+def thunkify(
+    tracer: _ProxyTracer, f: Callable[_P, R], *args: _P.args, **kwargs: _P.kwargs
+) -> Thunk[R]:
+    """
+    Delays computation of f until it's called again
+    Also caches the result
+    """
+    if tracer.enable_thunkify:
+        return Thunk(functools.partial(f, *args, **kwargs))
+    else:
+        r = f(*args, **kwargs)
+        return Thunk(lambda: r)
+
+
+def track_tensor(
+    tensor: Tensor, proxy: Proxy, *, constant: Optional[Tensor], tracer: _ProxyTracer
+) -> None:
+    def try_set_proxy_slot(
+        outer_s: IntLikeType,
+        proxy_callable: Callable[Concatenate[PySymType, _P], Proxy],
+        *args: _P.args,
+        **kwargs: _P.kwargs,
+    ) -> None:
+        assert callable(proxy_callable)
+        if isinstance(outer_s, SymInt):
+            with _enable_thunkify(tracer):
+                set_proxy_slot(
+                    outer_s,
+                    tracer,
+                    thunkify(tracer, proxy_callable, outer_s, *args, **kwargs),
+                )
+
+    # The basic idea is that we need to associate each tensor/SymInt
+    # with a Proxy.  How do we setup this association?  We just store
+    # the proxy on the proxy slot of the object, keyed on the tracer
+    # (so that if we have multiple tracers at the same time, they
+    # don't clobber each other.)
+    for i, s in enumerate(tensor.shape):
+        try_set_proxy_slot(
+            s,
+            lambda x, i: set_meta(
+                tracer.create_proxy(
+                    "call_function", torch.ops.aten.sym_size.int, (proxy, i), {}
+                ),
+                x,
+            ),
+            i,
+        )
+
+    if not is_sparse_any(tensor):
+        for i, s in enumerate(tensor.stride()):
+            try_set_proxy_slot(
+                s,
+                lambda x, i: set_meta(
+                    tracer.create_proxy(
+                        "call_function", torch.ops.aten.sym_stride.int, (proxy, i), {}
+                    ),
+                    x,
+                ),
+                i,
+            )
+
+    try_set_proxy_slot(
+        tensor.numel(),
+        lambda x: set_meta(
+            tracer.create_proxy(
+                "call_function", torch.ops.aten.sym_numel.default, (proxy,), {}
+            ),
+            x,
+        ),
+    )
+    if not is_sparse_any(tensor):
+        try_set_proxy_slot(
+            tensor.storage_offset(),
+            lambda x: set_meta(
+                tracer.create_proxy(
+                    "call_function",
+                    torch.ops.aten.sym_storage_offset.default,
+                    (proxy,),
+                    {},
+                ),
+                x,
+            ),
+        )
+    set_proxy_slot(tensor, tracer, _ProxyTensor(proxy, constant))
+
+
+_NestedProxys = Union[
+    Proxy, Sequence["_NestedProxys"], Mapping[object, "_NestedProxys"]
+]
+_NestedTensors = Union[
+    Tensor, Sequence["_NestedTensors"], Mapping[object, "_NestedTensors"]
+]
+
+
+def track_tensor_tree(
+    inner_res: T,
+    proxy_res: _NestedProxys,
+    *,
+    constant: Optional[_NestedTensors],
+    tracer: _ProxyTracer,
+) -> T:
+    # NB: We call set_unbacked_bindings only on the *topmost* call to
+    # track_tensor_tree, not recursive calls.  This is because there must
+    # be only ONE unbacked_binding proxy call, and it should be the one
+    # where all of the unbacked SymInts actually first come into existence.
+    # If you call this again on the inner proxies for the tuple projections,
+    # you will have multiple unbacked_bindings for the same symbol, but
+    # they're not going to show up anywhere.
+    #
+    # I was briefly deceived into setting unbacked bindings recursively when
+    # working on https://github.com/pytorch/pytorch/pull/133585 because I
+    # observed that some extra unbacked bindings were needed to handle some
+    # higher order operator code.  But actually it looks like this was
+    # just an unrelated bug that needed to be fixed separately.
+    _set_unbacked_bindings(inner_res, proxy_res)
+
+    def wrap_with_proxy(
+        e: object, proxy: _NestedProxys, constant: Optional[_NestedTensors]
+    ) -> None:
+        if isinstance(e, Tensor):
+            assert isinstance(proxy, Proxy)
+            assert constant is None or isinstance(constant, Tensor)
+            track_tensor(e, proxy, tracer=tracer, constant=constant)
+            set_meta(proxy, e)
+        elif isinstance(e, py_sym_types):
+            assert isinstance(proxy, Proxy)
+            # NB: eagerly set meta here, so that the numbering is in order
+            set_meta(proxy, e)
+            set_proxy_slot(e, tracer, thunkify(tracer, lambda: proxy))
+        elif isinstance(e, _AnyScriptObject):
+            assert isinstance(proxy, Proxy)
+            set_proxy_slot(e, tracer, proxy)
+            set_meta(proxy, e)
+        elif isinstance(e, (tuple, list)):
+            # example use case: allreduce_ returns ([tensor], work)
+            if isinstance(proxy, fx.Proxy):
+                set_meta(proxy, e)
+
+            def get_constant(
+                c: Optional[_NestedTensors], idx: int
+            ) -> Optional[_NestedTensors]:
+                if c is None:
+                    return None
+                else:
+                    assert isinstance(c, (list, tuple))
+                    return c[idx]
+
+            for idx, ee in enumerate(e):
+                # Use an indexer here - if proxy is a List then it will unwrap
+                # it. If it's a Proxy then it will proxy the getelem.
+                wrap_with_proxy(ee, proxy[idx], get_constant(constant, idx))  # type: ignore[index]
+
+        elif isinstance(e, dict):
+            # example use case: triton_kernel_wrapper takes arguments as kwargs
+
+            # In theory we could support const-prop when proxy-tensor-tracing
+            # operators that returns dicts of tensors, but we have no use case
+            # for it today (since the only op we currently trace that can
+            # return a dict is triton_kernel_wrapper_functional/mutation,
+            # which does not participate in const-prop)
+            assert constant is None
+
+            if isinstance(proxy, fx.Proxy):
+                set_meta(proxy, e)
+
+            for key, val in e.items():
+                wrap_with_proxy(val, proxy[key], None)  # type: ignore[index]
+
+        elif isinstance(e, BackwardState):
+            assert isinstance(proxy, Proxy)
+            set_meta(proxy, e)
+            e.proxy = proxy
+        else:
+            # intentionally pass on primitives
+            pass
+
+    wrap_with_proxy(inner_res, proxy_res, constant)
+
+    return inner_res
+
+
+@dataclass
+class _ProxyTensor:
+    proxy: Proxy
+    constant: Optional[Tensor]
+
+
+def fetch_sym_proxy(
+    tracer: _ProxyTracer,
+) -> Callable[[PySymType], Union[bool, int, float, Proxy]]:
+    def inner(e: PySymType) -> Union[int, bool, float, Proxy]:
+        n = e.node
+        if n.constant is not None:
+            return n.constant
+        if e.node.expr.is_number:
+            if isinstance(e, SymBool):
+                return bool(e.node.expr)
+            elif isinstance(e, SymInt):
+                return int(e.node.expr)
+            return float(e.node.expr)
+        else:
+            assert isinstance(e, py_sym_types)
+            # NB: we REQUIRE all symints to be tracked
+            return get_proxy_slot(e, tracer).force()
+
+    return inner
+
+
+@overload
+def fetch_object_proxy(tracer: _ProxyTracer, t: Tensor) -> Union[_ProxyTensor, Tensor]:
+    ...
+
+
+@overload
+def fetch_object_proxy(
+    tracer: _ProxyTracer, t: _AnyScriptObjectType
+) -> Union[Proxy, _AnyScriptObjectType]:
+    ...
+
+
+@overload
+def fetch_object_proxy(
+    tracer: _ProxyTracer, t: PySymType
+) -> Union[_PySymProxyType, PySymType]:
+    ...
+
+
+def fetch_object_proxy(
+    tracer: _ProxyTracer, t: Union[Tensor, _AnyScriptObjectType, PySymType]
+) -> object:
+    return get_proxy_slot(t, tracer, t)
+
+
+HANDLED_TYPES = (Tensor, torch.nn.Parameter, FakeTensor)
+
+
+def _maybe_record_pointwise_barrier(
+    func: object, proxy_mode: ProxyTorchDispatchMode
+) -> None:
+    """
+    Records pointwise operators in user program (non decomposed) that were output in fp16/bf16
+    """
+    if proxy_mode.decomp_layers or not proxy_mode.emulate_precision_casts:
+        return
+
+    if (
+        not isinstance(func, torch._ops.OpOverload)
+        or torch.Tag.pointwise not in func.tags
+    ):
+        return
+
+    last_node = next(iter(reversed(proxy_mode.tracer.graph.nodes)))
+    t = last_node.meta.get("val")
+    if not isinstance(t, torch.Tensor) or t.dtype not in (
+        torch.bfloat16,
+        torch.float16,
+    ):
+        return
+
+    last_node.meta["low_precision_pointwise_barrier"] = True
+
+
+def proxy_call(
+    proxy_mode: ProxyTorchDispatchMode,
+    func: OpOverload,
+    pre_dispatch: bool,
+    args: Tuple[object, ...],
+    kwargs: Dict[str, object],
+) -> object:
+    unrecognized_types: List[Type] = []
+    flat_args_kwargs, spec = pytree.tree_flatten((args, kwargs))
+
+    def can_handle_tensor(x: Tensor) -> bool:
+        r = type(x) in HANDLED_TYPES or has_proxy_slot(x, proxy_mode.tracer)
+        if proxy_mode._allow_fake_constant:
+            r = r or type(x) in (torch._subclasses.FakeTensor,)
+        if not r:
+            unrecognized_types.append(type(x))
+        return r
+
+    # If there are any tensor subclasses, we need to handle those tensor subclasses first
+    # TODO: we could use types to test this
+    if not all(can_handle_tensor(x) for x in flat_args_kwargs if isinstance(x, Tensor)):
+        not_implemented_log.debug(
+            "ProxyTensorMode tensors without proxy had unrecognized subclasses: %s",
+            unrecognized_types,
+        )
+        return NotImplemented
+
+    r = maybe_handle_decomp(proxy_mode, func, args, kwargs)
+    if r is not NotImplemented:
+        _maybe_record_pointwise_barrier(func, proxy_mode)
+        return r
+
+    # For pre-autograd tracing, we do not want to run CompositeImplicit decomps.
+    if not pre_dispatch and func not in [
+        torch.ops.aten.size.default,
+        torch.ops.aten.stride.default,
+        torch.ops.aten.storage_offset.default,
+    ]:
+        with proxy_mode:
+            r = func.decompose(*args, **kwargs)
+            if r is not NotImplemented:
+                return r
+
+    tracer = proxy_mode.tracer
+    f_flat_args_kwargs = [
+        (
+            fetch_object_proxy(tracer, x)
+            if isinstance(x, (Tensor, _AnyScriptObject))
+            else x
+        )
+        for x in flat_args_kwargs
+    ]
+
+    # If there are SymInts, we also should not consider this constant.
+    # However, fake tensor handling of SymInts is sufficiently broken that
+    # I couldn't write a test for this case
+    all_constant = (
+        not any(
+            t.constant is None
+            for t in f_flat_args_kwargs
+            if isinstance(t, _ProxyTensor)
+        )
+        # TODO: maybe constant SymInts should also be allowed?  Not sure if
+        # this can happen
+        and not any(isinstance(x, py_sym_types) for x in flat_args_kwargs)
+    )
+
+    if torch.Tag.data_dependent_output in func.tags:
+        # Check if all of the Tensor inputs are constants
+        if all_constant:
+            const_flat_args_kwargs = [
+                t.constant if isinstance(t, _ProxyTensor) else t
+                for t in f_flat_args_kwargs
+            ]
+            const_args, const_kwargs = pytree.tree_unflatten(
+                const_flat_args_kwargs, spec
+            )
+            with unset_fake_temporarily():
+                return func(*const_args, **const_kwargs)
+        # If any of the Tensor inputs are "real" (not FakeTensor), we may
+        # incorrectly burn in constants by allowing this access.  Raise
+        # an error in this case
+        if proxy_mode._error_on_data_dependent_ops and pytree.tree_all_only(
+            Tensor, lambda t: not is_fake(t), (args, kwargs)
+        ):
+            raise RuntimeError(
+                f"It appears that you're trying to get value out of a tracing tensor with {func} - erroring out! "
+                "It's likely that this is caused by data-dependent control flow or similar.  "
+                "It may be possible to trace this with dynamic shapes; try setting tracing_mode='symbolic' "
+                "in your make_fx call."
+            )
+
+    proxy_flat_args_kwargs = [
+        e.proxy if isinstance(e, _ProxyTensor) else e for e in f_flat_args_kwargs
+    ]
+    proxy_flat_args_kwargs = [
+        (fetch_sym_proxy(proxy_mode.tracer)(e) if isinstance(e, py_sym_types) else e)
+        for e in proxy_flat_args_kwargs
+    ]
+    proxy_args, proxy_kwargs = pytree.tree_unflatten(proxy_flat_args_kwargs, spec)
+
+    # When we trace through a torch.tensor invocation, you never actually
+    # see a torch.ops.aten.tensor call. Instead, the way this function is
+    # implemented internally is that we allocate a plain tensor (this is
+    # *guaranteed* to be a plain tensor, we disable all modes when doing
+    # so), and then call at::lift_fresh on it (to give modes a chance to do
+    # their stuff).  Furthermore, the tensor argument to lift_fresh is guaranteed
+    # to be freshly allocated, so we want lift_fresh to be a no-op (directly
+    # returning the input argument).
+    #
+    # Here is the basic problem: when we trace this sequence of executions
+    # into an FX graph, what happens to this call sequence?  Traditionally,
+    # tensor constants get interned as buffers on the FX GraphModule.  But
+    # this is dangerous.  Consider:
+    #
+    #       x = torch.tensor(1)
+    #       x.add_(2)
+    #
+    # Naively, this traces into:
+    #
+    #       t = self._tensor_constant0  # initialized to torch.tensor(1)
+    #       x = torch.ops.aten.lift_fresh(t)
+    #       x.add_(2)
+    #
+    # If lift_fresh returns t directly, the subsequent add_ call will
+    # modify the tensor constant. Really, the problem is we've violated
+    # the invariant the argument to lift is fresh.  So what we should
+    # preserve the invariant by replacing lift_fresh with lift_fresh_copy:
+    #
+    #       t = self._tensor_constant0  # initialized to torch.tensor(1)
+    #       x = torch.ops.aten.lift_fresh_copy(t)
+    #       x.add_(2)
+    #
+    # This is what the overload modification does.
+    if func is torch.ops.aten.lift_fresh.default:
+        func = torch.ops.aten.lift_fresh_copy.default
+
+    proxy_out = proxy_mode.tracer.create_proxy(
+        "call_function",
+        func,
+        proxy_args,
+        proxy_kwargs,
+        name=proxy_mode.tracer.graph._target_to_str(func.overloadpacket.__name__),
+    )
+
+    with _enable_thunkify(proxy_mode.tracer):
+        out = func(*args, **kwargs)
+
+    # In some circumstances, we will be tracing in a situation where a tensor
+    # is *statically* known to be a constant (currently, this only happens if
+    # you run torch.tensor; deterministic factory functions like torch.arange
+    # don't get this treatment).  When the tensor in question is small, it's
+    # helpful to due constant propagation in case we call item() (in which
+    # case we can return the constant value that is known, rather than give
+    # an error.)  The logic here tests if constant propagation is possible
+    # (because all of the inputs are constant).  If so, we disable fake tensor
+    # mode (if it is on) and do true compute on the constant.
+    #
+    # It's worth highlighting that we're making a policy decision here.
+    # There is a potential that the tensor is actually quite large, and we
+    # don't actually want to run the compute.  The tensor being quite large
+    # is one of the reasons why factory functions don't get this treatment
+    # (since they can be quite large; if a parameter is initialized to a
+    # constant value it will be!)  Similarly, there is also a potential
+    # to run an operator that blows up the size of a small tensor; we don't
+    # protect against this case, but we could force, e.g., only single
+    # element constant computation by testing the numel of the result before
+    # propagating const-ness.  Similarly, we don't require the constant to
+    # live on CPU, but we could.
+    any_constant = any(
+        t.constant is not None
+        for t in f_flat_args_kwargs
+        if isinstance(t, _ProxyTensor)
+    )
+
+    constant = None
+
+    def tensor_numel_in_limit(t: Tensor) -> bool:
+        return t.numel() <= CONSTANT_NUMEL_LIMIT
+
+    # If this is a lift, the input tensor is guaranteed to be a
+    # constant, so we keep a copy of the original argument along so
+    # we can query it if we're asked to item() it at some later point
+    if (
+        func is torch.ops.aten.lift_fresh_copy.default
+        and out.numel() <= CONSTANT_NUMEL_LIMIT
+    ):
+        with unset_fake_temporarily():
+            assert isinstance(args[0], (Proxy, Tensor)), type(args[0])
+            constant = args[0].clone()
+    elif (
+        torch.Tag.nondeterministic_seeded not in func.tags
+        and all_constant
+        and any_constant
+        and pytree.tree_all_only(Tensor, tensor_numel_in_limit, out)
+    ):
+        # NB: do NOT include factories as constants
+        with unset_fake_temporarily():
+            const_flat_args_kwargs = [
+                t.constant if isinstance(t, _ProxyTensor) else t
+                for t in f_flat_args_kwargs
+            ]
+            const_args, const_kwargs = pytree.tree_unflatten(
+                const_flat_args_kwargs, spec
+            )
+            constant = func(*const_args, **const_kwargs)
+    else:
+        constant = None
+
+    track_tensor_tree(out, proxy_out, constant=constant, tracer=tracer)
+    _maybe_record_pointwise_barrier(func, proxy_mode)
+    return out
+
+
+class _SymNodeDict:
+    """
+    Wrapper around a dictionary that will hash SymInts with their nodes
+    """
+
+    def __init__(self) -> None:
+        self.sym_node_dict: Dict[PySymType, _PySymProxyType] = {}
+
+    def __setitem__(self, key: PySymType, value: _PySymProxyType) -> None:
+        self.sym_node_dict[key.node] = value
+
+    def __getitem__(self, key: PySymType) -> _PySymProxyType:
+        return self.sym_node_dict[key.node]
+
+    def __contains__(self, key: PySymType) -> bool:
+        return key.node in self.sym_node_dict
+
+    def get(
+        self, key: PySymType, default: Optional[_PySymProxyType] = None
+    ) -> _PySymProxyType:
+        # dict.get()'s annotation doesn't accept `None` when the value type
+        # isn't Optional.
+        return self.sym_node_dict.get(key.node, default)  # type: ignore[arg-type]
+
+    def __iter__(self) -> Any:
+        raise NotImplementedError
+
+    def __len__(self) -> int:
+        return len(self.sym_node_dict)
+
+
+class PythonKeyTracer(Tracer):
+    script_object_tracker: MutableMapping[_AnyScriptObjectType, Proxy]
+    symnode_tracker: _SymNodeDict
+    sympy_expr_tracker: Dict[sympy.Symbol, object]
+    tensor_tracker: MutableMapping[Tensor, _ProxyTensor]
+    torch_fn_counts: Dict[OpOverload, int]
+    enable_thunkify: bool = False
+
+    def __init__(self) -> None:
+        super().__init__(autowrap_modules=())  # type: ignore[arg-type]
+        self.tensor_tracker = WeakTensorKeyDictionary()
+        self.symnode_tracker = _SymNodeDict()
+        self.script_object_tracker = WeakIdKeyDictionary(
+            dict=None, ref_type=_WeakHashRef
+        )
+        self.sympy_expr_tracker = dict()
+
+        # Stores the torch function that was called during tracing
+        self.torch_fn_metadata = None
+        # Stores the counts for every torch function called. This is to help
+        # distinguish between different calls to the same torch function.
+        self.torch_fn_counts = {}
+        self.enable_thunkify = False
+
+    # In general, we don't want to make modules leaves. In principle, users of
+    # this tracer might want to override this in order to turn a couple specific
+    # modules into leaves in the traced graph.
+    def call_module(
+        self,
+        m: Module,
+        forward: Callable[..., Any],
+        args: Tuple[Any, ...],
+        kwargs: Dict[str, Any],
+    ) -> Any:
+        return forward(*args, **kwargs)
+
+    # We don't want to turn getattr calls into proxies. So we just return the actual value.
+    def getattr(
+        self, attr: str, attr_val: object, parameter_proxy_cache: Dict[str, Proxy]
+    ) -> object:
+        return attr_val
+
+    def create_arg(self, a: object) -> fx.node.Node:
+        if isinstance(a, torch.nn.Parameter):
+            for n, p in self.root.named_parameters():
+                if a is p:
+                    return self.create_node("get_attr", n, (), {})
+
+            qualname = self.get_fresh_qualname("_param_constant")
+            setattr(self.root, qualname, a)
+
+            return self.create_node("get_attr", qualname, (), {})
+        elif isinstance(a, py_sym_types):
+            assert a.node.constant is not None
+            return a.node.constant
+        return super().create_arg(a)  # type: ignore[return-value]
+
+    @overload
+    def unwrap_proxy(self, e: Tensor) -> Union[Proxy, Tensor]:
+        ...
+
+    @overload
+    def unwrap_proxy(self, e: PySymType) -> Union[Proxy, PySymType]:
+        ...
+
+    @overload
+    def unwrap_proxy(
+        self, e: _AnyScriptObjectType
+    ) -> Union[Proxy, _AnyScriptObjectType]:
+        ...
+
+    def unwrap_proxy(self, e: T) -> object:
+        if isinstance(e, Tensor):
+            return get_proxy_slot(e, self, e, lambda x: x.proxy)
+        elif isinstance(e, py_sym_types):
+            return get_proxy_slot(e, self, e, lambda e: e.force())
+        elif isinstance(e, _AnyScriptObject):
+            return get_proxy_slot(e, self, e)
+        else:
+            return e
+
+
+@contextmanager
+def _temp_remove_pre_dispatch_torch_function_mode() -> Generator[None, None, None]:
+    from torch.overrides import _len_torch_function_stack, _pop_mode, _push_mode
+
+    temp_elements = []
+    pre_dispatch_mode = None
+
+    while _len_torch_function_stack() > 0:
+        mode = _pop_mode()
+        if isinstance(mode, PreDispatchTorchFunctionMode):
+            pre_dispatch_mode = mode
+            break
+        else:
+            temp_elements.append(mode)
+
+    for mode in reversed(temp_elements):
+        _push_mode(mode)
+
+    try:
+        yield
+
+    finally:
+        if pre_dispatch_mode is not None:
+            count = len(temp_elements)
+            while count > 0:
+                mode = _pop_mode()
+                count -= 1
+
+            temp_elements.append(pre_dispatch_mode)
+
+            for mode in reversed(temp_elements):
+                _push_mode(mode)
+
+
+@torch._disable_dynamo
+def dispatch_trace(
+    root: Union[Module, Callable],
+    tracer: Tracer,
+    concrete_args: Optional[Tuple[Any, ...]] = None,
+) -> GraphModule:
+    graph = tracer.trace(root, concrete_args)  # type: ignore[arg-type]
+
+    # NB: be careful not to DCE .item() calls
+    def impure_pred(n: fx.Node) -> bool:
+        from .symbolic_shapes import is_accessor_node
+
+        # Always defer to the built-in notion of impure
+        if n.is_impure():
+            return True
+
+        # Accessors always OK to DCE
+        if is_accessor_node(n):
+            return False
+
+        # If the operator in question takes SymInt args to SymInt output,
+        # we assume it's pure and OK to DCE
+        if (
+            isinstance(n.meta.get("val"), py_sym_types)
+            and
+            # NB: constant args ok
+            all(
+                isinstance(a.meta.get("val"), py_sym_types)
+                for a in n.args
+                if isinstance(a, fx.Node)
+            )
+        ):
+            return False
+
+        # No idea, just assume it's not OK
+        return True
+
+    graph.eliminate_dead_code(impure_pred)
+    from torch._inductor.fx_passes.dedupe_symint_uses import dedupe_symints
+
+    dedupe_symints(graph)
+    name = root.__class__.__name__ if isinstance(root, Module) else root.__name__
+    return fx._lazy_graph_module._make_graph_module(tracer.root, graph, name)
+
+
+def wrap_key(
+    f: Callable[_P, R], tensors: _P.args, tracer: _ProxyTracer, pre_dispatch: bool
+) -> Callable[_P, R]:
+    flat_tensors, tensors_spec = pytree.tree_flatten(tensors)
+
+    @functools.wraps(f)
+    def wrapped(*proxies: _P.args, **_unused: _P.kwargs) -> R:
+        flat_proxies, proxies_spec = pytree.tree_flatten(proxies)
+        assert len(flat_proxies) == len(flat_tensors)
+        with disable_proxy_modes_tracing() as m:
+            assert isinstance(m, ProxyTorchDispatchMode)
+            track_tensor_tree(flat_tensors, flat_proxies, constant=None, tracer=tracer)
+
+        def get_tensor_proxy_slot(t: Tensor) -> Union[Tensor, Proxy]:
+            return get_proxy_slot(t, tracer, t, lambda x: x.proxy)
+
+        out = f(*tensors)
+        out = pytree.tree_map_only(Tensor, get_tensor_proxy_slot, out)
+        out = pytree.tree_map_only(
+            _AnyScriptObject, lambda t: get_proxy_slot(t, tracer, t, lambda x: x), out
+        )
+
+        def get_sym_proxy_slot(t: PySymType) -> Proxy:
+            return get_proxy_slot(t, tracer).force()
+
+        out = pytree.tree_map_only(py_sym_types, get_sym_proxy_slot, out)
+        return out
+
+    return wrapped
+
+
+# TODO: Make downstream users of this work with OperatorBase
+ORIGINAL_ATEN: Optional[object] = None
+
+
+@contextmanager
+def set_original_aten_op(func: OpOverload) -> Generator[None, None, None]:
+    global ORIGINAL_ATEN
+    if ORIGINAL_ATEN is None and fx_traceback.has_preserved_node_meta():
+        ORIGINAL_ATEN = func
+        fx_traceback.current_meta["original_aten"] = func
+        try:
+            yield
+        finally:
+            ORIGINAL_ATEN = None
+            fx_traceback.current_meta["original_aten"] = None
+    else:
+        yield
+
+
+class TorchFunctionMetadataMode(TorchFunctionMode):
+    def __init__(self, tracer: _ProxyTracer) -> None:
+        self.tracer = tracer
+
+    def __torch_function__(
+        self,
+        func: OpOverload,
+        types: Tuple[torch._C._TensorMeta, ...],
+        args: Tuple[object, ...] = (),
+        kwargs: Optional[Dict[str, object]] = None,
+    ) -> object:
+        kwargs = kwargs or {}
+        self.tracer.torch_fn_metadata = func
+        self.tracer.torch_fn_counts[func] = self.tracer.torch_fn_counts.get(func, 0) + 1
+        return func(*args, **kwargs)
+
+
+# This mode is **only** used for pre_dispatch tracing.
+# In particular, we need to make sure that autograd/autocast API's
+# that do not desugar into dispatcher operators stay in the graph.
+class PreDispatchTorchFunctionMode(TorchFunctionMode):
+    def __init__(self, tracer: _ProxyTracer) -> None:
+        self.tracer = tracer
+
+    def __torch_function__(
+        self,
+        func: OpOverload,
+        types: Tuple[torch._C._TensorMeta, ...],
+        args: Tuple[object, ...] = (),
+        kwargs: Optional[Dict[str, object]] = None,
+    ) -> object:
+        kwargs = kwargs or {}
+        if func in _side_effectful_need_to_be_preserved_pre_dispatch:
+            # It's for passing the export verifier which needs to verify the meta['val']
+            # TODO(tmanlaibaatar): we should systematically couple it with expoert verifier,
+            # instead of hardcoding it here.
+            node = self.tracer.create_node("call_function", func, args, {})  # type: ignore[arg-type]
+            if func is torch._C._set_grad_enabled:
+                node.meta["val"] = None
+            return node
+            # Don't actually run the function! We just want to trace the calls
+            # into a graph. We don't actualy want to change global autograd state.
+        return func(*args, **kwargs)
+
+
+class ProxyTorchDispatchMode(TorchDispatchMode):
+    # Ensure this is read-only; this exists only for legacy reasons
+    @property
+    def enable_tracing(self) -> bool:
+        return True
+
+    def __init__(
+        self,
+        tracer: _ProxyTracer,
+        tracing_mode: str,
+        pre_dispatch: bool = False,
+        _allow_fake_constant: bool = False,
+        _error_on_data_dependent_ops: bool = True,
+    ) -> None:
+        dk = torch._C.DispatchKey.PreDispatch if pre_dispatch else None
+        super().__init__(dk)
+        self.tracer = tracer
+        self.tracing_mode = tracing_mode
+        self.pre_dispatch = pre_dispatch
+        self._allow_fake_constant = _allow_fake_constant
+        self._error_on_data_dependent_ops = _error_on_data_dependent_ops
+        # Indicates to our torch_dispatch dispatching infra that
+        # this is an "infra" mode with lower dispatching precedence.
+        self._mode_key = torch._C._TorchDispatchModeKey.PROXY
+        # Every time we enter a mode, we maintain a stack telling us what the previous
+        # ProxyTorchDispatchMode state was (if there was any).
+        # This lets us properly reset the state on exit.
+        self.enter_stack: List[Optional[ProxyTorchDispatchMode]] = []
+        self.decomp_layers = 0
+        from torch._inductor import config
+
+        self.emulate_precision_casts = config.emulate_precision_casts
+
+    @count
+    def __torch_dispatch__(
+        self,
+        func: OpOverload,
+        types: Tuple[torch._C._TensorMeta, ...],
+        args: Tuple[object, ...] = (),
+        kwargs: Optional[Dict[str, object]] = None,
+    ) -> object:
+        with set_original_aten_op(func):
+            kwargs = kwargs or {}
+
+            if func in (prim.device.default,):
+                return func(*args, **kwargs)
+
+            return proxy_call(self, func, self.pre_dispatch, args, kwargs)
+
+    def __enter__(self) -> Self:
+        # Stash and store the previous proxy mode (there may or may not be one)
+        maybe_prev_proxy_mode = _unset_infra_mode(torch._C._TorchDispatchModeKey.PROXY)
+        self.enter_stack.append(maybe_prev_proxy_mode)
+        return super().__enter__()
+
+    def __exit__(
+        self,
+        exc_type: Optional[Type[BaseException]],
+        exc_value: Optional[BaseException],
+        traceback: Optional[types.TracebackType],
+    ) -> Optional[bool]:
+        b = super().__exit__(exc_type, exc_value, traceback)
+
+        # Re-enable the previous proxy mode, if there was one.
+        mb_previous_proxy_mode = self.enter_stack.pop()
+        if mb_previous_proxy_mode is not None:
+            _push_mode(mb_previous_proxy_mode)
+
+        return b
+
+    @classmethod
+    def is_infra_mode(cls) -> bool:
+        return True
+
+    def _compute_proxy(
+        self, func: OpOverload, args: Tuple[object, ...], out: PySymType
+    ) -> Proxy:
+        n_args = tuple(
+            get_proxy_slot(a, self.tracer).force().node
+            if isinstance(a, py_sym_types)
+            else a
+            for a in args
+        )
+
+        # func doesn't have a __torch_function__ that Proxy can interpose, so
+        # we gotta do it manually
+        n_out = self.tracer.create_node("call_function", func, n_args, {})  # type: ignore[arg-type]
+        p_out = fx.Proxy(n_out, self.tracer)
+        set_meta(p_out, out)
+        return p_out
+
+    def __sym_dispatch__(
+        self,
+        func: OpOverload,
+        types: Tuple[torch._C._TensorMeta, ...],
+        args: Tuple[object, ...],
+        kwargs: Dict[str, object],
+    ) -> object:
+        # Peephole optimize multiply by one
+        # NB: be careful not to trigger guards here!
+        if func == operator.mul:
+            if isinstance(args[1], int) and args[1] == 1:
+                return args[0]
+            elif isinstance(args[0], int) and args[0] == 1:
+                return args[1]
+
+        # For speed, we assume there are no nested data structures
+        # (otherwise we could use tree_map)
+        # We also assume there are no keyword arguments.
+        assert not kwargs
+        out = func(*args, **kwargs)
+
+        # If func returned a constant, we don't need to trace; we have
+        # determined that the result is constant (no matter if the inputs
+        # were symbolic) and it is no longer necessary to trace the
+        # computation.  This could occur if func triggered some guards.
+        if isinstance(out, py_sym_types):
+            p_out_thunk = thunkify(
+                self.tracer, self._compute_proxy, func=func, args=args, out=out
+            )
+            set_proxy_slot(out, self.tracer, p_out_thunk)
+
+        return out
+
+
+class _GraphAppendingTracerEx(fx.proxy.GraphAppendingTracer):
+    script_object_tracker: MutableMapping[_AnyScriptObjectType, Proxy]
+    symnode_tracker: MutableMapping[PySymType, _PySymProxyType]
+    tensor_tracker: MutableMapping[Tensor, _ProxyTensor]
+    sympy_expr_tracker: Dict[sympy.Symbol, object]
+    torch_fn_metadata: Optional[OpOverload]
+    torch_fn_counts: Dict[OpOverload, int]
+    enable_thunkify: bool = False
+
+    def __init__(self, graph: fx.graph.Graph) -> None:
+        super().__init__(graph)
+        self.symnode_tracker = weakref.WeakKeyDictionary()
+        self.tensor_tracker = WeakTensorKeyDictionary()
+        self.sympy_expr_tracker = {}
+        self.script_object_tracker = WeakIdKeyDictionary(
+            dict=None, ref_type=_WeakHashRef
+        )
+        # Stores the torch function that was called during tracing
+        self.torch_fn_metadata = None
+        # Stores the counts for every torch function called. This is to help
+        # distinguish between different calls to the same torch function.
+        self.torch_fn_counts = {}
+
+
+# TODO: I'm not sure what the point of this class is; you can just
+# make_fx through a regular Interpreter
+class DecompositionInterpreter(fx.Interpreter):
+    def __init__(
+        self,
+        module: fx.GraphModule,
+        new_graph: fx.Graph,
+        decomposition_table: Optional[Mapping[OpOverload, Callable]] = None,
+        **kwargs: object,
+    ) -> None:
+        super().__init__(module, **kwargs)  # type: ignore[arg-type]
+        self.new_graph = new_graph
+        self.tracer = _GraphAppendingTracerEx(self.new_graph)
+        # Blegh
+        self.decomposition_table = decomposition_table or {}
+        self.mode = ProxyTorchDispatchMode(self.tracer, tracing_mode="real")
+
+    def placeholder(
+        self, target: str, args: Tuple[object, ...], kwargs: Dict[str, object]  # type: ignore[override]
+    ) -> object:
+        out = super().placeholder(target, args, kwargs)  # type: ignore[arg-type]
+        proxy = fx.Proxy(self.new_graph.placeholder(target), self.tracer)
+        track_tensor_tree(out, proxy, constant=None, tracer=self.tracer)
+        # TODO handle case where the first character of target is '*'
+        return out
+
+    def get_attr(
+        self, target: str, args: Tuple[object, ...], kwargs: Dict[str, object]  # type: ignore[override]
+    ) -> object:
+        out = super().get_attr(target, args, kwargs)  # type: ignore[arg-type]
+        proxy = fx.Proxy(self.new_graph.get_attr(target), self.tracer)
+        track_tensor_tree(out, proxy, constant=None, tracer=self.tracer)
+        return out
+
+    # call_function, call_method, call_module get traced automatically by the outer mode.
+
+    def output(
+        self, target: str, args: Tuple[object, ...], kwargs: Dict[str, object]  # type: ignore[override]
+    ) -> object:
+        out = super().output(target, args, kwargs)  # type: ignore[arg-type]
+
+        def get_proxy_node(x: _ProxyTensor) -> fx.node.Node:
+            return x.proxy.node
+
+        def unwrap(e: Tensor) -> Union[Tensor, fx.Node]:
+            return get_proxy_slot(e, self.tracer, e, get_proxy_node)
+
+        self.new_graph.output(pytree.tree_map(unwrap, out))
+        return out
+
+    def run(self, *args: object, **kwargs: object) -> object:
+        # Should enter the mode at least once for being able to restore it later
+        # See: https://github.com/pytorch/pytorch/pull/82549#discussion_r934782025
+        with decompose(self.decomposition_table), self.mode:
+            return super().run(*args, **kwargs)  # type: ignore[arg-type]
+
+
+def wrapper_and_args_for_make_fx(
+    func: Callable[..., R], args: Tuple[object, ...], kwargs: Dict[str, object]
+) -> Tuple[Callable[[List[object]], R], List[object]]:
+    # make_fx doesn't support kwargs, so we need to do this flattening
+    # and then unflatten the args before calling func
+    flat_args, spec = pytree.tree_flatten((args, kwargs))
+
+    def wrapped(flat_args: List[object]) -> R:
+        fn_args, fn_kwargs = pytree.tree_unflatten(flat_args, spec)
+        return func(*fn_args, **fn_kwargs)
+
+    return wrapped, flat_args
+
+
+@contextmanager
+def disable_autocast_cache() -> Generator[None, None, None]:
+    old_value = torch.is_autocast_cache_enabled()
+    torch.set_autocast_cache_enabled(False)
+    try:
+        yield
+    finally:
+        torch.set_autocast_cache_enabled(old_value)
+
+
+class _ModuleNotInstalledAsSubmoduleError(NameError):
+    pass
+
+
+# Base class for inline _ModuleStackTracer.__init__.AttrProxy
+class _AttrProxy:
+    def reset_proxy_mapping(self, base: Module, path: str) -> None:
+        pass
+
+
+class _ModuleStackTracer(PythonKeyTracer):
+    r"""Customized version of PythonKeyTracer that retains module stack
+    information in node.meta["nn_module_stack"].
+
+    FX symbolic trace actually does this already, but it relies on `self.root`
+    being the actual module being traced. Since make_fx traces a lambda of our
+    creation, things don't work properly.
+
+    So for this version we hold onto a reference to the original module
+    (scope_root) and use that to match the path. Also when we see,
+            A
+           / \
+          B   C
+           \ /
+            D
+    we want to record the path as A.B.D by recording only one path.
+    See Note [Preserving the nn module stack metadata during export non-strict mode]  # noqa: W605
+    """
+
+    def __init__(self, scope_root: GraphModule) -> None:
+        super().__init__()
+        self.scope_root = scope_root
+        self.proxy_paths: WeakKeyDictionary[_AttrProxy, str] = WeakKeyDictionary()
+        self.attr_proxy_map: WeakKeyDictionary[Module, _AttrProxy] = WeakKeyDictionary()
+        self.proxy_modules: WeakKeyDictionary[_AttrProxy, Module] = WeakKeyDictionary()
+        self.counter = 0
+
+        self.module_id_cache = defaultdict(list)
+        for name, mod in self.scope_root.named_modules(remove_duplicate=False):
+            self.module_id_cache[id(mod)].append(name)
+
+        # Build a wrapper around _AttrProxy to provide the tracer. We can't
+        # store it on _AttrProxy itself beceause we mimic the underlying class
+        # (including its attributes).
+        tracer = self
+
+        class AttrProxy(_AttrProxy):
+            def __init__(self, base: Module, path: str) -> None:
+                # Class is modified to be a subclass of torch.nn.Module
+                # Warning: We blow away our own attributes here to mimic the base class
+                # - so don't expect `self.x` to do anything useful.
+                self.__class__ = type(
+                    base.__class__.__name__,
+                    (self.__class__, base.__class__),
+                    {},
+                )
+                self.__dict__ = base.__dict__
+                self.__class__.__module__ = base.__class__.__module__
+                self.__class__.__qualname__ = base.__class__.__qualname__
+                self.reset_proxy_mapping(base, path)
+
+            def reset_proxy_mapping(self, base: Module, path: str) -> None:
+                tracer.proxy_paths[self] = path
+                tracer.proxy_modules[self] = base
+
+            def __getattr__(self, name: str) -> AttrProxy:
+                assert isinstance(self, Module)
+                # Calling into torch.nn.Module.__getattr__ with super(),
+                # That __getattr__ is patched to be module_getattr_wrapper in _symbolic_trace.py.
+                # which then calls into _ModuleStackTracer.getattr
+                attr_val = super().__getattr__(name)  # type: ignore[misc]
+                if isinstance(attr_val, AttrProxy):
+                    attr_val = tracer.proxy_modules[attr_val]
+                elif not isinstance(attr_val, Module):
+                    return attr_val
+                if attr_val not in tracer.attr_proxy_map:
+                    tracer.attr_proxy_map[attr_val] = AttrProxy(
+                        attr_val, tracer.proxy_paths[self] + "." + name
+                    )
+                else:
+                    # NOTE [caching AttrProxy]. Caching ensures a 1-1 mapping between AttrProxy and the actual attr_val.
+                    # 1. We reset the proxy_mapping to solve the diamond shape reference problem: we want to record the
+                    # path as A.B.D instead of A.C.D (the purpose of _ModuleStackTracer).
+                    # 2. Instead of creating a new AttrProxy, we just reset the proxy_mapping of existing one. This is to avoid
+                    # dynamo creating multiple guards for the same attr_val but different AttrProxy when exporting
+                    # a model that calls torch.compile (e.g when a model uses torch.cond.)
+                    tracer.attr_proxy_map[attr_val].reset_proxy_mapping(
+                        attr_val, tracer.proxy_paths[self] + "." + name
+                    )
+                return tracer.attr_proxy_map[attr_val]
+
+            def get_base(self) -> Module:
+                return tracer.proxy_modules[self]
+
+            @property
+            def _modules(self) -> Dict[str, AttrProxy]:
+                assert "_modules" in self.__dict__
+                submodules = self.__dict__["_modules"]
+                assert isinstance(submodules, dict)
+                return {
+                    key: AttrProxy(value, tracer.proxy_paths[self] + "." + str(key))
+                    for key, value in submodules.items()
+                }
+
+        self.proxy_type = AttrProxy
+
+    def path_of_module(self, mod: Module) -> str:
+        """
+        Use tracked access path during tracing instead of the default BFS behavior.
+        Still use all the possible module paths to verify the result.
+        """
+        if mod is self.scope_root:
+            return ""
+
+        if isinstance(mod, _AttrProxy):
+            return self.proxy_paths[mod]
+
+        try:
+            return Tracer.path_of_module(self, mod)
+        except NameError as e:
+            raise _ModuleNotInstalledAsSubmoduleError from e
+
+    def getattr(
+        self, attr: str, attr_val: object, parameter_proxy_cache: Dict[str, Proxy]
+    ) -> object:
+        if not isinstance(attr_val, Module) or isinstance(attr_val, fx.GraphModule):
+            return super().getattr(attr, attr_val, parameter_proxy_cache)
+        if isinstance(attr_val, _AttrProxy):
+            return attr_val
+
+        # See NOTE [caching AttrProxy].
+        if attr_val not in self.attr_proxy_map:
+            self.attr_proxy_map[attr_val] = self.proxy_type(attr_val, attr)
+        else:
+            self.attr_proxy_map[attr_val].reset_proxy_mapping(attr_val, attr)
+        return self.attr_proxy_map[attr_val]
+
+    def trace(  # type: ignore[override]
+        self, root: Union[Module, Callable], concrete_args: Optional[Dict[str, object]]
+    ) -> fx.Graph:
+        res = super().trace(root, concrete_args)
+
+        # Since we are making _AttrProxy mimic the original
+        # submodule, when someone registers a module directly
+        # to the tracer while tracing, the proxy object gets registered
+        # first. So we need to replace the proxy modules with the real ones
+        # This can happen during HOO tracing
+        proxy_module_names_to_be_replaced: List[Tuple[str, _AttrProxy]] = []
+        for name, module in self.root.named_modules():
+            if module in self.proxy_modules:
+                proxy_module_names_to_be_replaced.append((name, module))
+
+        def _delete_proxy_attr(obj: Module, target: str) -> bool:
+            # Copied from fx/graph_module.py
+            # Customized it for proxy type
+            atoms = target.split(".")
+            path, target_submod = atoms[:-1], atoms[-1]
+            assert isinstance(obj, Module)
+            mod = obj
+
+            # Get the parent module
+            for item in path:
+                if not hasattr(mod, item):
+                    return False
+
+                mod = getattr(mod, item)
+
+                if not isinstance(mod, (_AttrProxy, Module)):
+                    return False
+
+            if not hasattr(mod, target_submod):
+                return False
+
+            # At least the leaf module should be proxy type.
+            if not isinstance(getattr(mod, target_submod), _AttrProxy):
+                return False
+
+            delattr(mod, target_submod)
+            return True
+
+        for proxy_module_name, proxy_module in proxy_module_names_to_be_replaced:
+            _delete_proxy_attr(self.root, proxy_module_name)
+            actual_module = self.proxy_modules[proxy_module]
+            _assign_attr(actual_module, self.root, proxy_module_name)
+
+        return res
+
+    def call_module(
+        self,
+        m: Module,
+        forward: Callable,
+        args: Tuple[object, ...],
+        kwargs: Dict[str, object],
+    ) -> None:
+        """PythonKeyTracer overrides call_module to avoid the scope handling,
+        but we actually want it.
+        """
+        from torch._dynamo import OptimizedModule
+
+        # FIXME (tmanlaibaatar)
+        # When we call torch.compile inside HOO, we will end up
+        # invoking a module that is not registered on the root. For
+        # now, we just inline them. But once we start supporting
+        # mark_strict in export, we do need to properly handle this.
+        # Right now, it doesn't matter because current non-strict
+        # use cases don't need to work with HOO.
+        if isinstance(m, (OptimizedModule, GraphModule)):
+            return forward(*args, **kwargs)
+
+        try:
+            return Tracer.call_module(self, m, forward, args, kwargs)
+        except _ModuleNotInstalledAsSubmoduleError as e:
+            warnings.warn(
+                f"Unable to find the path of the module {m}. "
+                "This might be because the module was not properly registered "
+                "as a submodule, which is not good practice. We will trace "
+                "through the module without recording stack information."
+            )
+            return forward(*args, **kwargs)
+
+    def is_leaf_module(self, m: Module, module_qualified_name: str) -> bool:
+        return False
+
+    def create_node(self, *args: object, **kwargs: object) -> fx.node.Node:
+        """
+        Create node and add on metadata.
+        Add nn_module_stack here instead of TracerBase,
+        since calls to make_fx() might not want to record module stack metadata.
+        Add torch_fn by looking at torch_fn_metadata and torch_fn_counts.
+        Add stack_trace by filtering out forward() stack frames.
+        """
+        node = super().create_node(*args, **kwargs)  # type: ignore[arg-type]
+
+        # nn_module_stack
+        if node.op not in ["placeholder", "output"]:
+            if "nn_module_stack" not in node.meta:
+                node.meta["nn_module_stack"] = self.module_stack
+            # convert nn_module_stack from Dict[key, (FQN, class)] -> Dict[str, Tuple[str, str]]
+            for key, (fqn, mod_cls) in node.meta["nn_module_stack"].items():
+                if isinstance(mod_cls, type):
+                    node.meta["nn_module_stack"][key] = (
+                        fqn,
+                        mod_cls.__module__ + "." + mod_cls.__qualname__,
+                    )
+
+        # torch_fn
+        if (
+            node.op == "call_function"
+            and self.torch_fn_metadata is not None
+            and "torch_fn" not in node.meta
+        ):
+            node.meta["torch_fn"] = (
+                f"{self.torch_fn_metadata.__name__}_{self.torch_fn_counts[self.torch_fn_metadata]}",
+                f"{self.torch_fn_metadata.__class__.__name__}.{self.torch_fn_metadata.__name__}",
+            )
+
+        # stack_trace
+        if "stack_trace" not in node.meta and node.op not in ["placeholder", "output"]:
+            user_frame_summary = CapturedTraceback.extract().summary()
+            if user_frame_summary:
+                # we retain frames from forward() calls, or ops
+                # located in torch/__init__.py (e.g. sym_int, sym_constrain_range, vmap)
+                stack_trace = [
+                    frame
+                    for frame in user_frame_summary
+                    if (
+                        frame.name == "forward"
+                        or frame.filename.endswith("torch/__init__.py")
+                    )
+                ]
+                # filter out forward() frames from fx/_symbolic_trace.py, export/_trace.py
+                # this is hardcoded, but leads to a much cleaner stack trace
+                stack_trace = [
+                    frame
+                    for frame in stack_trace
+                    if not (
+                        frame.filename.endswith("fx/_symbolic_trace.py")
+                        or frame.filename.endswith("export/_trace.py")
+                    )
+                ]
+                if (
+                    stack_trace
+                ):  # empty list for strict mode, dynamo should handle stack_trace
+                    stack_trace = traceback.StackSummary.from_list(stack_trace)
+                    node.meta["stack_trace"] = "".join(stack_trace.format()).strip()
+
+        return node
+
+
+class _MakefxTracer:
+    def __init__(
+        self,
+        decomposition_table: Optional[Mapping[OpOverload, Callable]],
+        tracing_mode: str,
+        _allow_non_fake_inputs: bool,
+        pre_dispatch: bool,
+        record_module_stack: bool,
+        _allow_fake_constant: bool,
+        _error_on_data_dependent_ops: bool,
+    ) -> None:
+        # Configurations that are used to initialize the context managers and their states.
+        # Should not modify them during tracing.
+        self.decomposition_table: Dict[OpOverload, Callable] = dict(
+            decomposition_table or {}
+        )
+        self.decomposition_table.setdefault(
+            torch.ops.aten.sym_numel.default, torch._decomp.decompositions.sym_numel
+        )
+        self.tracing_mode: str = tracing_mode
+        self._allow_non_fake_inputs: bool = _allow_non_fake_inputs
+        self.pre_dispatch: bool = pre_dispatch
+        self.record_module_stack: bool = record_module_stack
+        self._allow_fake_constant: bool = _allow_fake_constant
+        self._error_on_data_dependent_ops: bool = _error_on_data_dependent_ops
+
+        # All context managers and their states should be initialized before tracing based on the inputs
+        # and configurations. After tracing, their states should be cleaned except for shape_env.
+        # Remember to specify how to intialize it from user inputs and from parent tracer whenever
+        # adding new modes in _MakefxTracer.
+        self.fake_tensor_mode: Optional[FakeTensorMode] = None
+        self.proxy_mode: Union[nullcontext, ProxyTorchDispatchMode] = nullcontext()
+        self.proxy_function_mode: Union[
+            nullcontext, PreDispatchTorchFunctionMode
+        ] = nullcontext()
+        self.fx_tracer: Optional[PythonKeyTracer] = None
+        self.python_dispatcher_mode: Union[nullcontext, Any] = nullcontext()
+        self.torch_fn_metadata_mode: Union[
+            nullcontext, TorchFunctionMetadataMode
+        ] = nullcontext()
+
+    def _checkpoint_modes(self) -> List[Any]:
+        return [
+            self.fake_tensor_mode,
+            self.proxy_mode,
+            self.proxy_function_mode,
+            self.fx_tracer,
+            self.python_dispatcher_mode,
+            self.torch_fn_metadata_mode,
+        ]
+
+    def _restore_modes(
+        self,
+        prev_fake_tensor_mode: Optional[FakeTensorMode],
+        prev_proxy_mode: Union[nullcontext, ProxyTorchDispatchMode],
+        prev_proxy_function_mode: Union[nullcontext, PreDispatchTorchFunctionMode],
+        prev_fx_tracer: Optional[PythonKeyTracer],
+        prev_python_dispatcher_mode: Union[nullcontext, Any],
+        prev_torch_fn_metadata_mode: Union[nullcontext, TorchFunctionMetadataMode],
+    ) -> None:
+        self.fake_tensor_mode = prev_fake_tensor_mode
+        self.proxy_mode = prev_proxy_mode
+        self.proxy_function_mode = prev_proxy_function_mode
+        self.fx_tracer = prev_fx_tracer
+        self.python_dispatcher_mode = prev_python_dispatcher_mode
+        self.torch_fn_metadata_mode = prev_torch_fn_metadata_mode
+
+    @contextmanager
+    def _init_modes_from_inputs(
+        self, f: Callable, args: Tuple[object, ...]
+    ) -> Generator[None, None, None]:
+        prev_modes = self._checkpoint_modes()
+        try:
+            # Avoid importing sympy at a module level
+            from .symbolic_shapes import ShapeEnv
+
+            if hasattr(f, "_orig_mod") and self.record_module_stack:
+                scope_root = f._orig_mod
+                self.fx_tracer = _ModuleStackTracer(scope_root)
+            else:
+                self.fx_tracer = PythonKeyTracer()
+
+            if self.tracing_mode == "fake":
+                import torch._dynamo
+
+                fake_tensor_mode = torch._dynamo.utils.detect_fake_mode(args)
+                if fake_tensor_mode is None:
+                    import torch._functorch.config as _config
+
+                    with _config.patch(fake_tensor_allow_unsafe_data_ptr_access=False):
+                        fake_tensor_mode = FakeTensorMode(
+                            allow_fallback_kernels=True,
+                            allow_non_fake_inputs=self._allow_non_fake_inputs,
+                            shape_env=ShapeEnv(),
+                            static_shapes=True,
+                        )
+                self.fake_tensor_mode = fake_tensor_mode
+            elif self.tracing_mode == "symbolic":
+                import torch._dynamo
+
+                fake_tensor_mode = torch._dynamo.utils.detect_fake_mode(args)
+                if fake_tensor_mode is None:
+                    shape_env = ShapeEnv()
+                    import torch._functorch.config as _config
+
+                    with _config.patch(fake_tensor_allow_unsafe_data_ptr_access=False):
+                        fake_tensor_mode = FakeTensorMode(
+                            allow_fallback_kernels=False,
+                            allow_non_fake_inputs=self._allow_non_fake_inputs,
+                            shape_env=shape_env,
+                        )
+                assert (
+                    fake_tensor_mode.shape_env is not None
+                ), "shape_env should be set if tracing with 'symbolic'"
+                self.fake_tensor_mode = fake_tensor_mode
+            else:
+                if not self.tracing_mode == "real":
+                    raise AssertionError(
+                        f"Unexpected tracing type: {self.tracing_mode}"
+                    )
+
+            self._construct_modes_with_fx_tracer(self.fx_tracer)
+            yield
+        finally:
+            self._restore_modes(*prev_modes)
+
+    def _construct_modes_with_fx_tracer(self, fx_tracer: _ProxyTracer) -> None:
+        self.proxy_mode = ProxyTorchDispatchMode(
+            fx_tracer,
+            self.tracing_mode,
+            pre_dispatch=self.pre_dispatch,
+            _allow_fake_constant=self._allow_fake_constant,
+            _error_on_data_dependent_ops=self._error_on_data_dependent_ops,
+        )
+
+        if self.pre_dispatch:
+            self.proxy_function_mode = PreDispatchTorchFunctionMode(fx_tracer)
+
+        # pre-autograd tracing uses per-dispatch-key modes,
+        # which requires the python dispatcher
+        if self.tracing_mode == "symbolic" or self.pre_dispatch:
+            self.python_dispatcher_mode = enable_python_dispatcher()
+
+        self.torch_fn_metadata_mode = TorchFunctionMetadataMode(fx_tracer)
+
+    @contextmanager
+    def _init_modes_from_parent(
+        self, parent_tracer: _MakefxTracer
+    ) -> Generator[None, None, None]:
+        # By default, subtracer creates new modes based on parent tracer's config.
+        # However, there are cases where we want to share the same modes with parent tracer
+        # For example, fake_tensor_mode, we want the example value's fake_mode of parent graph and subgraphs to be the same.
+        prev_modes = self._checkpoint_modes()
+        try:
+            self.fake_tensor_mode = parent_tracer.fake_tensor_mode
+
+            def _create_sub_fx_tracer(parent_tracer: _ProxyTracer) -> PythonKeyTracer:
+                if type(parent_tracer) == PythonKeyTracer:
+                    return PythonKeyTracer()
+                elif type(parent_tracer) == _ModuleStackTracer:
+                    return _ModuleStackTracer(parent_tracer.scope_root)
+                else:
+                    raise RuntimeError(
+                        f"Unexpected tracer type: {type(parent_tracer)}."
+                    )
+
+            assert parent_tracer.fx_tracer is not None
+            self.fx_tracer = _create_sub_fx_tracer(parent_tracer.fx_tracer)
+            self._construct_modes_with_fx_tracer(self.fx_tracer)
+            yield
+        finally:
+            self._restore_modes(*prev_modes)
+
+    def _trace_inner(self, f: Callable, *args: object) -> GraphModule:
+        phs = pytree.tree_map(lambda _: torch.fx._symbolic_trace.PH, args)
+
+        def _wrap_fake(args: T) -> T:
+            arg_count = 0
+
+            def inner_wrap_fake(x: object) -> object:
+                nonlocal arg_count
+                # TODO: it would be nice to line these up with the names
+                # FX will choose for the placeholders, but we don't
+                # actually know what the names will be at this point yet
+                # NB: the Source here is actually meaningless
+                from torch._dynamo.source import ConstantSource
+
+                assert self.fake_tensor_mode is not None
+                source = ConstantSource(f"input{arg_count}")
+                if isinstance(x, Tensor):
+                    arg_count += 1
+                    return self.fake_tensor_mode.from_tensor(x, source=source)
+                # NB: don't match on bools
+                elif type(x) is int and self.tracing_mode == "symbolic":
+                    assert (
+                        self.fake_tensor_mode.shape_env is not None
+                    ), "shape_env should be set if tracing with 'symbolic'"
+                    return self.fake_tensor_mode.shape_env.create_symintnode(
+                        self.fake_tensor_mode.shape_env.create_symbol(
+                            x, source, positive=None
+                        ),
+                        hint=x,
+                        source=source,
+                    )
+                elif isinstance(x, torch.ScriptObject):
+                    return torch._library.fake_class_registry.maybe_to_fake_obj(
+                        self.fake_tensor_mode, x
+                    )
+
+                assert not isinstance(
+                    x, FakeScriptObject
+                ), f"ScriptObject {x} has been fakified. Cannot wrap_fake it again."
+                return x
+
+            wrap_fn_map = {
+                "real": lambda x: x,
+                "fake": inner_wrap_fake,
+                "symbolic": inner_wrap_fake,
+            }
+            return pytree.tree_map(wrap_fn_map[self.tracing_mode], args)
+
+        def _wrap_func(f: Callable[_P, R], phs: Sequence[PHBase]) -> Callable[_P, R]:
+            if (
+                not hasattr(inspect.unwrap(f), "__code__")
+                or inspect.unwrap(f).__code__.co_flags & inspect.CO_VARARGS
+            ):
+                # FX doesn't support varargs, so we gotta fake up a wrapper
+                # TODO: Would be nice to fix this at the source...
+                return fake_signature(f, len(phs))
+            return f
+
+        args = _wrap_fake(args)
+        func = _wrap_func(f, phs)
+        # We disable the autocast cache as the autocast cache causes type conversions on parameters to
+        # check a cache, which introduces untracked tensors into the graph
+        #
+        # We also disable tracing by any other tensor proxy-based tracers except the current. The
+        # purpose of `make_fx` is to produce graphmodules as a side effect; its internal execution is
+        # thus irrelevant to any external functional trace.
+        proxy_mode: ProxyTorchDispatchMode = typing.cast(
+            ProxyTorchDispatchMode, self.proxy_mode
+        )
+        with ExitStack() as stack:
+            stack.enter_context(decompose(self.decomposition_table))
+            if self.fake_tensor_mode:
+                stack.enter_context(self.fake_tensor_mode)
+            stack.enter_context(self.python_dispatcher_mode)
+            stack.enter_context(self.proxy_function_mode)
+            stack.enter_context(self.torch_fn_metadata_mode)
+            stack.enter_context(proxy_mode)
+            stack.enter_context(disable_autocast_cache())
+            stack.enter_context(_set_make_fx_tracer(self))
+
+            assert self.fx_tracer is not None
+            t = dispatch_trace(
+                wrap_key(func, args, self.fx_tracer, self.pre_dispatch),
+                tracer=self.fx_tracer,
+                concrete_args=tuple(phs),
+            )
+
+        # TODO: kind of a bad way to do it, should maybe figure out a better way
+        if self.tracing_mode == "symbolic":
+            assert self.fake_tensor_mode is not None
+            t.shape_env = self.fake_tensor_mode.shape_env
+        return t
+
+    def trace(self, f: Callable, *args: object) -> fx.GraphModule:
+        with self._init_modes_from_inputs(f, args):
+            return self._trace_inner(f, *args)
+
+    def trace_subgraph(self, f: Callable, *args: object) -> GraphModule:
+        # Create a new tracer based on parent's config
+        sub_tracer = _MakefxTracer(
+            self.decomposition_table,
+            "real",
+            self._allow_non_fake_inputs,
+            self.pre_dispatch,
+            self.record_module_stack,
+            self._allow_fake_constant,
+            self._error_on_data_dependent_ops,
+        )
+        with sub_tracer._init_modes_from_parent(self):
+            return sub_tracer._trace_inner(f, *args)
+
+
+_CURRENT_MAKE_FX_TRACER: Optional[_MakefxTracer] = None
+
+
+@contextmanager
+def _set_make_fx_tracer(tracer: _MakefxTracer) -> Generator[None, None, None]:
+    global _CURRENT_MAKE_FX_TRACER
+    prev_tracer = _CURRENT_MAKE_FX_TRACER
+    try:
+        _CURRENT_MAKE_FX_TRACER = tracer
+        yield
+    finally:
+        _CURRENT_MAKE_FX_TRACER = prev_tracer
+
+
+def make_fx(
+    f: Callable,
+    decomposition_table: Optional[Mapping[OpOverload, Callable]] = None,
+    tracing_mode: str = "real",
+    _allow_non_fake_inputs: bool = False,
+    *,
+    pre_dispatch: bool = False,
+    record_module_stack: bool = False,
+    _allow_fake_constant: bool = False,
+    _error_on_data_dependent_ops: bool = True,
+) -> Callable[..., GraphModule]:
+    """
+    Given a function f, return a new function which when executed with valid
+    arguments to f, returns an FX GraphModule representing the set of operations that
+    were executed during the course of execution.
+    """
+
+    assert tracing_mode in ["real", "fake", "symbolic"]
+
+    make_fx_tracer = _MakefxTracer(
+        decomposition_table,
+        tracing_mode,
+        _allow_non_fake_inputs,
+        pre_dispatch,
+        record_module_stack,
+        _allow_fake_constant,
+        _error_on_data_dependent_ops,
+    )
+
+    @functools.wraps(f)
+    def wrapped(*args: object) -> GraphModule:
+        return make_fx_tracer.trace(f, *args)
+
+    return wrapped
+
+
+def get_torch_dispatch_modes() -> List[TorchDispatchMode]:
+    return torch.utils._python_dispatch._get_current_dispatch_mode_stack()
+
+
+# TODO: this is a legacy name, there is only ever one proxy mode as it's an
+# infra mode
+def get_innermost_proxy_mode() -> Optional[ProxyTorchDispatchMode]:
+    return get_proxy_mode()
+
+
+def get_proxy_mode() -> Optional[ProxyTorchDispatchMode]:
+    """
+    Current the currently active proxy tracing mode, or None if
+    we are not currently tracing.  This includes pre-dispatch proxy
+    tracing.
+    """
+    pre_dispatch_mode = torch._ops._get_dispatch_mode_pre_dispatch(
+        torch._C._TorchDispatchModeKey.PROXY
+    )
+    mode = torch._C._get_dispatch_mode(torch._C._TorchDispatchModeKey.PROXY)
+    assert (
+        pre_dispatch_mode is None or mode is None
+    ), f"pre_dispatch_mode={pre_dispatch_mode}, mode={mode}"
+    return pre_dispatch_mode or mode
+
+
+def handle_sym_dispatch(func: Callable[_P, R], args: _P.args, kwargs: _P.kwargs) -> R:
+    """
+    Call into the currently active proxy tracing mode to do a
+    SymInt/SymFloat/SymBool dispatch trace on a function that operates on
+    these arguments.
+    """
+    mode = get_proxy_mode()
+    assert mode
+    # Have to do it manually, because we're not doing the normal torch
+    # dispatch machinery which disables it for us
+    with disable_proxy_modes_tracing():
+        # TODO: properly compute types
+        types: List[Type] = []
+        return mode.__sym_dispatch__(func, types, args, kwargs)  # type: ignore[arg-type, return-value]
+
+
+@contextmanager
+def disable_proxy_modes_tracing() -> Generator[ProxyTorchDispatchMode, None, None]:
+    return _disable_infra_mode(torch._C._TorchDispatchModeKey.PROXY)
+
+
+def maybe_handle_decomp(
+    proxy_mode: ProxyTorchDispatchMode,
+    op: OpOverload,
+    args: Tuple[object, ...],
+    kwargs: Dict[str, object],
+) -> object:
+    if op in CURRENT_DECOMPOSITION_TABLE:
+        with proxy_mode:
+            proxy_mode.decomp_layers += 1
+            out = CURRENT_DECOMPOSITION_TABLE[op](*args, **kwargs)
+            proxy_mode.decomp_layers -= 1
+            return out
+
+    return NotImplemented
+
+
+def get_isolated_graphmodule(
+    func: Callable,
+    args: Tuple[object, ...],
+    kwargs: Dict[str, object],
+    tracing_mode: str = "real",
+    decomposition_table: Optional[Mapping[OpOverload, Callable]] = None,
+) -> GraphModule:
+    """A helper function used to get the GraphModule for the given func.
+
+    It's expected to be used in the ProxyTensor tracing context.
+    It detaches the args and kwargs from the current tracer so that the trace of
+    the current graph module can be created without any side-effects.
+    """
+    wrapped, all_args = wrapper_and_args_for_make_fx(func, args, kwargs)
+
+    with disable_proxy_modes_tracing():
+        gm = make_fx(
+            wrapped, decomposition_table=decomposition_table, tracing_mode=tracing_mode
+        )(all_args)
+    return gm
+
+
+def _set_unbacked_bindings(out: object, out_proxy: _NestedProxys) -> None:
+    """A helper function for setting up unbacked_bindings on the destination FX graph."""
+    from .symbolic_shapes import compute_unbacked_bindings
+
+    # Can't use detect_fake_mode here,
+    #
+    # python test/distributed/_tensor/test_dtensor_compile.py -k
+    # test_tp_compile_fullgraph_is_seq_parallel_False
+    #
+    # will fail.  Very strange, it probably isn't right for them to be using
+    # two fake modes there...
+    fake_mode = torch._C._get_dispatch_mode(torch._C._TorchDispatchModeKey.FAKE)
+    if fake_mode and fake_mode.shape_env:
+        if symbol_to_path := compute_unbacked_bindings(fake_mode.shape_env, out):
+            assert isinstance(out_proxy, Proxy), out_proxy
+            out_proxy.node.meta["unbacked_bindings"] = symbol_to_path
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/recording.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/recording.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b6410be41c40308e4947bb97311eb64d3c41aa3
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/recording.py
@@ -0,0 +1,512 @@
+# mypy: allow-untyped-defs
+import functools
+import inspect
+import itertools
+import logging
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.utils._pytree as pytree
+
+
+log = logging.getLogger(__name__)
+trace_shape_events_log = torch._logging.getArtifactLogger(
+    __name__, "trace_shape_events"
+)
+
+
+__all__ = [
+    "ShapeEnvEvent",
+    "record_shapeenv_event",
+    "replay_shape_env_events",
+    "FakeTensorMeta",
+    "shape_env_check_state_equal",
+    "NotEqualError",
+]
+
+# [Note: Recording ShapeEnv Events]
+# =================================
+#
+# What is a ShapeEnv event?
+# -------------------------
+# We consider a ShapeEnv event every function call (ShapeEnv method or
+# independent function) that modifies the state of the ShapeEnv instance.
+# Such calls are recorded alongside their positional and keyword arguments,
+# so that it may be replayed over a different ShapeEnv instance.
+#
+# See [Note: ShapeEnv State Equality] for what is considered the state
+# of a ShapeEnv instance.
+#
+# What is it for?
+# ---------------
+# ShapeEnv events recording is used for reconstructing the ShapeEnv in an
+# arbitrary state in time.
+#
+# Being able to arbitrarily replay events like so is useful, mainly for
+# translation validation bisection. i.e. if a ValidationException has been
+# raised, find the earliest point in time where the translation validation
+# fails.
+#
+# Besides that, it also allows us to inspect the given instance and,
+# for example, check the guards that would actually be issued at that point.
+#
+# What kind of arguments can be stored in an event?
+# -------------------------------------------------
+# There's no specific rule for what cannot be used as an argument.
+# That said, pay special attention to the following cases:
+#
+#   1. Tensor inputs: there are some tests that check whether the inputs
+#      were garbage collected after execution. These will fail if there's
+#      an event that is holding a reference to those inputs.
+#
+#   2. ShapeEnv arguments: if there is an argument of ShapeEnv type, that
+#      will be automatically replaced by the new given ShapeEnv instance.
+#
+#   3. SymTypes arguments: they also hold references to ShapeEnv. So,
+#      whenever we see them, we create a new instance, replacing the
+#      ShapeEnv reference.
+#
+#   4. FX nodes: specifically, FX nodes from the FX graph for symbolic
+#      shapes. That argument must be replaced when replaying the event at
+#      ShapeEnvEvent.run, since it has to reference a node from the given
+#      instance, and not from the recorded instance.
+
+
+# Event class for reconstructing ShapeEnv at arbitrary time.
+#
+# Represents a method call that mutates ShapeEnv in a way that affects the
+# issued guards, when ShapeEnv.produce_guards is called.
+@dataclass
+class ShapeEnvEvent:
+    # ShapeEnv method.
+    f: Callable
+
+    # Arguments and keyword arguments called with.
+    args: Optional[List[Any]] = None
+    kwargs: Optional[Dict[str, Any]] = None
+
+    # List of tracked_fakes at the time the method was called.
+    tracked_fakes: Optional[List[Any]] = None
+
+    # Name of the captured event.
+    # Used for special handling of particular methods.
+    name: Optional[str] = None
+
+    # Replay itself, but using shape_env as self.
+    def run(self, shape_env=None) -> Any:
+        from torch.fx.experimental.symbolic_shapes import (
+            is_symbolic,
+            ShapeEnv,
+            SymTypes,
+        )
+
+        # Special handling for the constructor event.
+        if self.f is ShapeEnv:
+            assert shape_env is None and self.args is None and self.kwargs is not None
+            return ShapeEnv(**self.kwargs)
+
+        assert shape_env is not None
+        args = list(self.args or [])
+        kwargs = dict(self.kwargs or {})
+
+        # Replace any argument of type ShapeEnv by the given one.
+        args, kwargs = pytree.tree_map_only(
+            ShapeEnv, lambda _: shape_env, (args, kwargs)
+        )
+
+        # Replace any argument of type SymTypes by a new instance,
+        # replacing its ShapeEnv reference.
+        args, kwargs = pytree.tree_map_only(
+            lambda x: isinstance(x, SymTypes) and is_symbolic(x),
+            lambda a: type(a)(a.node.with_shape_env(shape_env)),
+            (args, kwargs),
+        )
+
+        # Converts FX nodes using the mapping argument.
+        def maybe_convert_node(x: Any) -> Any:
+            if not isinstance(x, torch.fx.Node):
+                # Don't do anything to x if it's not an FX node.
+                return x
+
+            # If, at some point, we created an FX node, it means that translation validation is on.
+            # It also means we are building an FX graph for symbolic shapes at shape_env.graph, and
+            # we are tracking node names at shape_env.name_to_node.
+            assert hasattr(shape_env, "name_to_node")
+            name_to_node = shape_env.name_to_node  # type: ignore[attr-defined]
+            assert x.name in name_to_node
+            return name_to_node[x.name]
+
+        # Replaces the value of an specific argument by the result of fn.
+        def replacearg(index: int, key: str, fn: Callable):
+            if index < len(args):
+                args[index] = fn(args[index])
+            if key in kwargs:
+                kwargs[key] = fn(kwargs[key])
+
+        if self.is_create_fx_call_function():
+            # ShapeEnv.create_fx_call_function:
+            # "args" parameter is a tuple of FX nodes from the FX graph of the old ShapeEnv.
+            # They must be replaced, since a "call_function" FX node with this tuple as argument
+            # will be added to the FX graph of the new shape_env.
+            replacearg(
+                index=2,
+                key="args",
+                fn=lambda args: tuple(maybe_convert_node(a) for a in args),
+            )
+        if self.is_evaluate_expr() or self.is_defer_runtime_assert():
+            # ShapeEnv.evaluate_expr and ShapeEnv.defer_runtime_assert:
+            # "fx_node" parameter is an (optional) FX node that represents the evaluate expression.
+            # They must be replaced, since it will be part of a "call_function" FX node for
+            # torch._assert, which will be added to the FX graph of the new shape_env.
+            replacearg(index=3, key="fx_node", fn=maybe_convert_node)
+
+        # Actually call the method with the converted arguments.
+        return self.f(*args, **kwargs)
+
+    def __str__(self) -> str:
+        name = self.name if self.name is not None else self.f.__name__
+        return f"event: {name} ({self.args}, {self.kwargs})"
+
+    def is_create_fx_call_function(self) -> bool:
+        return self.name == "_create_fx_call_function"
+
+    def is_evaluate_expr(self) -> bool:
+        return self.name == "evaluate_expr"
+
+    def is_defer_runtime_assert(self) -> bool:
+        return self.name == "defer_runtime_assert"
+
+
+NEST = 0
+
+
+# Extracts a ShapeEnv instance inside args and kwargs.
+# Specifically, it looks for:
+#   1. ShapeEnv arguments
+#   2. SymInt, SymFloat, or SymBool arguments
+# If we find more than one object of any of the above types, we
+# also check that the ShapeEnv instance is the same for all of them.
+def _extract_shape_env_and_assert_equal(args, kwargs):
+    from torch.fx.experimental.symbolic_shapes import is_symbolic, ShapeEnv, SymTypes
+
+    def assert_equal(old: Optional[ShapeEnv], new: ShapeEnv) -> ShapeEnv:
+        if old is not None:
+            assert old is new, "call with different ShapeEnv"
+        return new
+
+    shape_env = None
+    for val in itertools.chain(args, kwargs.values()):
+        if isinstance(val, ShapeEnv):
+            shape_env = assert_equal(shape_env, val)
+        if isinstance(val, SymTypes) and is_symbolic(val):
+            shape_env = assert_equal(shape_env, val.node.shape_env)
+
+    return shape_env
+
+
+# Decorator for recording the given function as a replayable event.
+#
+# This decorator should be used at every function that mutates the state of
+# ShapeEnv in some way that affects the resulting issued guards (i.e. when
+# ShapeEnv.produce_guards is called).
+#
+# save_tracked_fakes: saves a snapshot of the TrackedFake list.
+# This is used when calling ShapeEnv.produce_guards at arbitrary points in time.
+#
+# When to save the list of TrackedFake?
+# =====================================
+# We should save the list of TrackedFake whenever the translation validation
+# bisection may actually stop and call the produce_guards method at the moment
+# right after the recorded function was played. In other words, since the
+# bisection bisects through torch._assert calls, we should save in all methods
+# that adds a torch._assert call to the symbolic shapes FX graph.
+#
+# At the moment, there are 2 methods that save the list:
+#   - ShapeEnv.evaluate_expr
+#   - ShapeEnv.defer_runtime_assert
+def record_shapeenv_event(*, save_tracked_fakes: bool = False) -> Callable:
+    def decorator(fn: Callable) -> Callable:
+        assert callable(fn)
+        args = inspect.getfullargspec(fn).args
+        assert args and args[0] == "self", (
+            "record_shapeenv_event should only wrap methods on ShapeEnv; refactor your "
+            "code so that it calls into a method on ShapeEnv"
+        )
+        name = fn.__name__
+
+        @functools.wraps(fn)
+        def wrapper(*args, **kwargs):
+            from torch.fx.experimental.symbolic_shapes import ShapeEnv
+
+            assert isinstance(args[0], ShapeEnv)
+
+            global NEST
+
+            trace_shape_events_log.debug(
+                "%scall %s(*%r, **%r)", " " * NEST, name, args[1:], kwargs
+            )
+            NEST += 1
+
+            def retlog(r):
+                trace_shape_events_log.debug("%s-> %s", " " * (NEST - 1), r)
+                return r
+
+            try:
+                if args[0].is_recording:  # type: ignore[has-type]
+                    # If ShapeEnv is already recording an event, call the wrapped
+                    # function directly.
+                    #
+                    # NB: here, we skip the check of whether all ShapeEnv instances
+                    # are equal, in favor of a faster dispatch.
+                    return retlog(fn(*args, **kwargs))
+
+                # Retrieve an instance of ShapeEnv.
+                # Assumption: the collection of args and kwargs may not reference
+                # different ShapeEnv instances.
+                self = _extract_shape_env_and_assert_equal(args, kwargs)
+
+                # If we are calling this function without any ShapeEnv instance
+                # alive in its arguments, we don't record and call the original.
+                if self is None:
+                    return retlog(fn(*args, **kwargs))
+
+                # Otherwise, start recording and call the function.
+                with self._recording():
+                    # Take a snapshot of the current tracked_fakes.
+                    tracked_fakes = (
+                        self._snapshot_tracked_fakes() if save_tracked_fakes else None
+                    )
+                    # Record the event for 'fn'.
+                    event = ShapeEnvEvent(
+                        fn, list(args), kwargs, tracked_fakes, name=fn.__name__
+                    )
+                    # Play the event on this ShapeEnv.
+                    # NB: It's important to put the event first, because running
+                    # the event can trigger internal events that must be ordered
+                    # after this event.  However, if an exception happens, we do
+                    # NOT want to have the event in the list, so pop it off from
+                    # the record if an error happened
+                    self.events.append(event)
+                    try:
+                        return retlog(event.run(self))
+                    except Exception:
+                        self.events.pop()
+                        raise
+
+            except Exception:
+                log.error(  # noqa: G201
+                    "failed while running %s(*%s, **%s)",
+                    name,
+                    args[1:],
+                    kwargs,
+                    exc_info=log.isEnabledFor(logging.INFO),
+                )
+                raise
+
+            finally:
+                NEST -= 1
+
+        return wrapper
+
+    return decorator
+
+
+# Replays the ShapeEnvEvents list.
+# It assumes the first event is the constructor call.
+#
+# fn: transforms an old FX node into one corresponding to the newly created ShapeEnv.
+def replay_shape_env_events(events):
+    from torch.fx.experimental.symbolic_shapes import ShapeEnv
+
+    constructor_event = events[0]
+    assert constructor_event.f == ShapeEnv
+
+    # Constructs the new ShapeEnv.
+    shape_env = constructor_event.run()
+
+    for event in events[1:]:
+        try:
+            # Actually replays each event.
+            # We need to call create_mapping_fn every time, since the node list might
+            # change after each event is replayed.
+            event.run(shape_env)
+        except Exception as e:
+            log.error("failed when running event: %s", event)
+            raise
+
+    return shape_env
+
+
+# FakeTensor metadata.
+# This is to be used in place of FakeTensor placeholders when calling
+# ShapeEnv.produce_guards.
+@dataclass
+class FakeTensorMeta:
+    tensor_size: Tuple[Union[int, torch.SymInt], ...]
+    tensor_stride: Tuple[Union[int, torch.SymInt], ...]
+    tensor_storage_offset: Union[int, torch.SymInt]
+    is_nested: bool
+
+    def size(self) -> Tuple[Union[int, torch.SymInt], ...]:
+        return self.tensor_size
+
+    def stride(self) -> Tuple[Union[int, torch.SymInt], ...]:
+        return self.tensor_stride
+
+    def storage_offset(self) -> Union[int, torch.SymInt]:
+        return self.tensor_storage_offset
+
+    def dim(self) -> int:
+        return len(self.tensor_size)
+
+    @staticmethod
+    def from_fake(fake) -> "FakeTensorMeta":
+        return FakeTensorMeta(
+            fake.size(), fake.stride(), fake.storage_offset(), fake.is_nested
+        )
+
+
+# [Note: ShapeEnv State Equality]
+# ===============================
+#
+# What is considered ShapeEnv state?
+# ----------------------------------
+# We consider to be the state of a ShapeEnv instance everything that
+# is not in the inline tuple inside remove_nonstate_variables function.
+# That is: the fields within ShapeEnv that modify the flow of execution
+# of the program.
+#
+# So, for example: the replacements field might influence on how an
+# expression is simplified. That, in turn, may result in a guard being
+# statically known (i.e. not added).
+#
+# On the other hand, var_to_stack serves only changes what is printed
+# in the screen, i.e. used only for debugging purposes. Therefore, we
+# should not consider it when comparing states.
+#
+# What to do on NotEqualError?
+# ----------------------------
+# Here are a few possible causes for getting a NotEqualError raised:
+#
+#   1. New field that does not belong in the ShapeEnv state.
+#      For example: log field of type ShapeEnvLoggerAdapter. Different
+#      ShapeEnv instances will always have different ShapeEnvLoggerAdapter
+#      instances, i.e. equality comparison would fail.
+#      Solution: add it to the inlined tuple inside remove_nonstate_variables
+#      function inside check_equal method.
+#
+#   2. New field that is not directly comparable across instances.
+#      For example: guards field of type List[ShapeGuard]. More specifically,
+#      the ShapeGuard type holds an expression and a stack information
+#      for debugging purposes. When replaying the even on a new ShapeEnv
+#      instance, the stack would be different, which would trigger this error.
+#      Solution: add a special case to the map_value function inside
+#      check_equal function.
+#
+#   3. Mutation of ShapeEnv on some not recorded function.
+#      If a mutation of the state of ShapeEnv happens inside a function
+#      that is not recorded (or that no caller in the stack is recorded),
+#      then, the replayed ShapeEnv won't catch that.
+#      Solution: decorate the function with record_shape_env_event.
+
+
+# Checks whether the state of two ShapeEnv are equal w.r.t. the guards
+# returned by ShapeEnv.produce_guards.
+def shape_env_check_state_equal(env1, env2, non_state_variable_names, map_value):
+    # Collect and remove variables that don't necessarily represent the state
+    # of a ShapeEnv. Note: we copy the dictionary so that we don't modify the
+    # instance itself.
+    env1_vars = vars(env1).copy()
+    env2_vars = vars(env2).copy()
+
+    for v in non_state_variable_names:
+        if v in env1_vars:
+            env1_vars.pop(v)
+        if v in env2_vars:
+            env2_vars.pop(v)
+
+    # Function for transforming the mismatched values into string.
+    # Needed, since dict and set entries order might not be the same every time.
+    def value_to_str(value: Any) -> str:
+        if isinstance(value, dict):
+            return (
+                "{"
+                + ", ".join(f"{k}: {value[k]}" for k in sorted(value.keys(), key=str))
+                + "}"
+            )
+        if isinstance(value, set):
+            return "{" + ", ".join(f"{v}" for v in sorted(value)) + "}"
+        return str(value)
+
+    # Compares env1_vars with env2_vars.
+    # Here, we allow the value of each field to be mapped, so that we appropriately
+    # compare the two values.
+    def compare_vars(
+        map_value: Callable[[str, Any], Any]
+    ) -> List[Tuple[str, str, str]]:
+        env1_set, env2_set = set(env1_vars), set(env2_vars)
+
+        # First, compare the set of keys in each vars dictionary.
+        if env1_set != env2_set:
+            raise NotEqualError(
+                "field set mismatch:",
+                [
+                    (
+                        "found unique fields:",
+                        str(sorted(env1_set - env2_set)),
+                        str(sorted(env2_set - env1_set)),
+                    ),
+                ],
+            )
+
+        # Then, sort the keys, and compare the mapped values of each key.
+        sorted_keys = list(env1_set)
+        sorted_keys.sort()
+
+        mapped_dict = [
+            (k, map_value(k, env1_vars[k]), map_value(k, env2_vars[k]))
+            for k in sorted_keys
+        ]
+
+        # Return a list of tuples representing the fields that did not match
+        # alongside their respective mapped values.
+        return [
+            (f"{k}: values don't match.", value_to_str(val1), value_to_str(val2))
+            for k, val1, val2 in mapped_dict
+            if val1 != val2
+        ]
+
+    # Accumulate the mismatching fields.
+    errors = compare_vars(map_value)
+
+    if len(errors) > 0:
+        raise NotEqualError("field values don't match:", errors)
+
+
+class NotEqualError(Exception):
+    def __init__(
+        self,
+        msg: str,
+        mismatched: List[Tuple[str, str, str]],
+    ) -> None:
+        details = "\n".join(
+            [
+                "\n".join(
+                    [
+                        f"==> {inner_msg}",
+                        f"  >  Left: {str1}",
+                        f"  > Right: {str2}",
+                    ]
+                )
+                for inner_msg, str1, str2 in mismatched
+            ]
+        )
+
+        super().__init__(
+            f"""\
+ShapeEnv not equal: {msg}
+
+{details}
+"""
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/refinement_types.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/refinement_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..a33ddf3710a4a77c3fa5e13fd69f0570b856a8a0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/refinement_types.py
@@ -0,0 +1,17 @@
+# mypy: allow-untyped-defs
+class Equality:
+    def __init__(self, lhs, rhs):
+        self.lhs = lhs
+        self.rhs = rhs
+
+    def __str__(self):
+        return f'{self.lhs} = {self.rhs}'
+
+    def __repr__(self):
+        return f'{self.lhs} = {self.rhs}'
+
+    def __eq__(self, other):
+        if isinstance(other, Equality):
+            return self.lhs == other.lhs and self.rhs == other.rhs
+        else:
+            return False
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/rewriter.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/rewriter.py
new file mode 100644
index 0000000000000000000000000000000000000000..3647ca59153b4fa695f76d1718dfac51ee553609
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/rewriter.py
@@ -0,0 +1,128 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import ast
+import inspect
+import textwrap
+import copy
+import functools
+from types import FunctionType
+from typing import cast, Union, Callable, Dict, Optional, Any
+from torch.fx._symbolic_trace import Tracer
+from torch.fx.graph import Graph
+from torch._sources import normalize_source_lines
+import torch
+
+class AST_Rewriter(ast.NodeTransformer):
+    """
+    Take a FunctionType object representing a `forward` method, then
+    perform an AST rewrite to swap out nodes that are not symbolically
+    traceable with a callsite to the FX alternative.
+
+    To support swapping out an AST node, define a new `visit` method on
+    that node. For more details, see:
+    https://docs.python.org/3/library/ast.html#ast.NodeTransformer
+    """
+
+    # This function checks for new keys added in the globals dict. TorchDynamo
+    # can insert new keys in the global dict and upset the check. Therefore, put
+    # a disable here. This function is an optimization pass and not really
+    # suitable for dynamo tracing anyways.
+    @torch._dynamo.disable
+    def rewrite(self, fn: FunctionType):
+
+        # Normalize the source lines
+        sourcelines, _ = inspect.getsourcelines(fn)
+        sourcelines = normalize_source_lines(sourcelines)
+        source = ''.join(sourcelines)
+        normalized_str = textwrap.dedent(source)
+
+        # Rewrite the original AST
+        source_ast = ast.parse(normalized_str)
+        dest_ast = ast.fix_missing_locations(self.visit(source_ast))
+
+        # Pull out the compiled function from the newly-created Module
+        code = compile(dest_ast, "", "exec")
+        globals_dict = copy.copy(fn.__globals__)
+        keys_before = set(globals_dict.keys())
+        exec(code, globals_dict)
+        new_keys = list(set(globals_dict.keys()) - keys_before)
+        assert len(new_keys) == 1
+        fn_compiled = globals_dict[new_keys[0]]
+
+        # return the compiled function with the original globals
+        def change_func_globals(f, globals):
+            """Based on https://stackoverflow.com/a/13503277/2988730 (@unutbu)"""
+            # __globals__ is a private member of the function class
+            # so we have to copy the function, f, all of its member, except f.__globals__
+            g = FunctionType(
+                f.__code__,
+                globals,
+                name=f.__name__,
+                argdefs=f.__defaults__,
+                closure=f.__closure__,
+            )
+            g = functools.update_wrapper(g, f)
+            g.__kwdefaults__ = copy.copy(f.__kwdefaults__)  # type:ignore[attr-defined]
+            return g
+        # Return the correct FunctionType object
+        return change_func_globals(fn_compiled, globals=fn.__globals__)
+
+    def visit_Assert(self, node):
+        """
+        Swap out the Assert node (Python's `assert`) with a callsite to the
+        symbolically-traceable torch._assert function
+        """
+        # Create the Call node
+        n = ast.parse('torch._assert()', mode='eval')
+        assert isinstance(n, ast.Expression)
+        call_node = n.body
+        assert isinstance(call_node, ast.Call)
+        msg = node.msg if node.msg else ast.Constant(value="", kind=None)
+        call_node.args = [node.test, msg]
+
+        # Ensure that the new node conforms to the Python AST grammar
+        expr_wrapper = ast.Expr(value=call_node)
+
+        # Return the new Call node to signify that we want to use it as
+        # a replacement for the original _assert node
+        return ast.copy_location(expr_wrapper, node)
+
+    def visit_AnnAssign(self, node):
+        """
+        Swap out Python's AnnAssign with an Assign node where the annotation function is called.
+        Example:
+             Original:
+             y: Tensor_Type(1,2,3, Dyn) = f2(x)
+            Output:
+             y = annotate(f2(x),Tensor_Type((1,2,3,Dyn)))
+        """
+        return ast.Assign(targets=[node.target], value=ast.Call(
+            func=ast.Name(id='annotate', ctx=ast.Load()),
+            args=[node.value, node.annotation], keywords=[]))
+
+
+class RewritingTracer(Tracer):
+    def trace(self, root: Union[torch.nn.Module, Callable], concrete_args: Optional[Dict[str, Any]] = None) -> Graph:
+        return super().trace(_rewrite(root), concrete_args)
+
+
+def _rewrite(fn: Union[torch.nn.Module, Callable]) -> Union[torch.nn.Module, Callable]:
+    if isinstance(fn, torch.nn.Module):
+        # Rewrite this module's `forward` as well as the `forward`s of
+        # all of this module's recursive descendents. Return the new,
+        # rewritten module hierarchy.
+        def rewrite_module(m : torch.nn.Module):
+            class RewrittenModule(torch.nn.Module):
+                def __init__(self, orig):
+                    super().__init__()
+                    for k, v in orig.__dict__.items():
+                        if isinstance(v, torch.nn.Module):
+                            self.__dict__[k] = copy.copy(rewrite_module(v))
+                        else:
+                            self.__dict__[k] = copy.copy(v)
+            RewrittenModule.forward = AST_Rewriter().rewrite(cast(FunctionType, m.forward))
+            return RewrittenModule(m)
+        return rewrite_module(fn)
+    else:
+        # Rewrite this single free function
+        return AST_Rewriter().rewrite(cast(FunctionType, fn))
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/schema_type_annotation.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/schema_type_annotation.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c7ab78706cb913d054fe36be7b9a17c2c8a46c9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/schema_type_annotation.py
@@ -0,0 +1,112 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.fx
+import inspect
+from typing import Any, Dict, Optional, Tuple
+from torch.fx.node import Argument, Target
+from torch._jit_internal import boolean_dispatched
+from torch.fx.operator_schemas import _torchscript_type_to_python_type
+
+from torch.fx import Transformer
+
+class AnnotateTypesWithSchema(Transformer):
+    """
+    Use Python function signatures to annotate types for `Nodes` within an FX graph.
+    This pulls out Python function signatures for:
+
+        1. Standard `torch.nn` Module calls
+        2. `torch.nn.functional` calls
+        3. Attribute fetches via `get_attr`
+
+    Example usage:
+
+        m = torchvision.models.resnet18()
+
+        traced = torch.fx.symbolic_trace(m)
+
+        traced = AnnotateTypesWithSchema(traced).transform()
+
+    """
+    def __init__(self, module : torch.nn.Module, annotate_functionals : bool = True,
+                 annotate_modules : bool = True, annotate_get_attrs : bool = True):
+        super().__init__(module)
+        self.annotate_functionals = annotate_functionals
+        self.annotate_modules = annotate_modules
+        self.annotate_get_attrs = annotate_get_attrs
+
+    def call_function(self, target : Target, args : Tuple[Argument, ...], kwargs : Dict[str, Any]):
+        python_ret_type = None
+        if self.annotate_functionals and target.__module__ == 'torch.nn.functional':
+            target_for_analysis = target
+            if target in boolean_dispatched:
+                # HACK: `boolean_dispatch` as used in `torch.nn.functional` makes it so that we have
+                # a 2-way dispatch based on a boolean value. Here we check that the `true` and `false`
+                # branches of the dispatch have exactly the same signature. If they do, use the `true`
+                # branch signature for analysis. Otherwise, leave this un-normalized
+                assert not isinstance(target, str)
+                dispatched = boolean_dispatched[target]
+                if_true, if_false = dispatched['if_true'], dispatched['if_false']
+                # TODO: can we emit the union of these? What are the implications on TorchScript
+                # compilation?
+                if inspect.signature(if_true).return_annotation != inspect.signature(if_false).return_annotation:
+                    return super().call_function(target, args, kwargs)
+                target_for_analysis = if_true
+
+            python_ret_type = self._extract_python_return_type(target_for_analysis)
+
+        return_proxy = super().call_function(target, args, kwargs)
+        return_proxy.node.type = return_proxy.node.type if return_proxy.node.type else python_ret_type
+        return return_proxy
+
+    def call_module(self, target : Target, args : Tuple[Argument, ...], kwargs : Dict[str, Any]):
+        python_ret_type = None
+        assert isinstance(target, str)
+        submod = self.fetch_attr(target)
+        if self.annotate_modules and hasattr(submod.__class__, '__name__'):
+            classname = submod.__class__.__name__
+            if getattr(torch.nn, classname, None) == submod.__class__:
+                python_ret_type = self._extract_python_return_type(submod.forward)
+        return_proxy = super().call_module(target, args, kwargs)
+        return_proxy.node.type = return_proxy.node.type if return_proxy.node.type else python_ret_type
+        return return_proxy
+
+    def get_attr(self, target : torch.fx.node.Target, args : Tuple[Argument, ...], kwargs : Dict[str, Any]):
+        attr_proxy = super().get_attr(target, args, kwargs)
+
+        if self.annotate_get_attrs:
+            module_itr = self.module
+            assert isinstance(target, str)
+            atoms = target.split('.')
+            for i, atom in enumerate(atoms):
+                if not hasattr(module_itr, atom):
+                    raise RuntimeError(f'Node referenced nonextent target {".".join(atoms[:i])}!')
+                module_itr = getattr(module_itr, atom)
+
+            maybe_inferred_ts_type = torch._C._jit_try_infer_type(module_itr)
+            if maybe_inferred_ts_type.success():
+                python_type = _torchscript_type_to_python_type(maybe_inferred_ts_type.type())
+                attr_proxy.node.type = python_type if not attr_proxy.node.type else attr_proxy.node.type
+
+        return attr_proxy
+
+    def _extract_python_return_type(self, target : Target) -> Optional[Any]:
+        """
+        Given a Python call target, try to extract the Python return annotation
+        if it is available, otherwise return None
+
+        Args:
+
+            target (Callable): Python callable to get return annotation for
+
+        Returns:
+
+            Optional[Any]: Return annotation from the `target`, or None if it was
+                not available.
+        """
+        assert callable(target)
+        try:
+            sig = inspect.signature(target)
+        except (ValueError, TypeError):
+            return None
+
+        return sig.return_annotation if sig.return_annotation is not inspect.Signature.empty else None
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/sym_node.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/sym_node.py
new file mode 100644
index 0000000000000000000000000000000000000000..268f56e3214fb99e6344af127006a8b7e34c9c00
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/sym_node.py
@@ -0,0 +1,1554 @@
+# mypy: allow-untyped-defs
+"""
+This file does three things:
+- Contains the definition of SymNode
+- Installs all the magic methods into SymBool, SymFloat, SymFloat at import time
+- Does not depend on sympy at import time
+
+As this file is imported from within torch/__init__.py we do not want it to depend on SymPy
+to avoid having to load SymPy at import time, as doing so is *very* slow.
+"""
+
+import builtins
+import itertools
+import logging
+import math
+import operator
+import sys
+from functools import lru_cache, update_wrapper
+from typing import Optional, Type, TYPE_CHECKING, Union
+
+import torch
+
+# NB: The sym_* functions are used via getattr() and must be imported here.
+from torch import (  # noqa: F401
+    sym_float,
+    sym_ite,
+    sym_max,
+    sym_min,
+    sym_not,
+    SymBool,
+    SymFloat,
+    SymInt,
+)
+
+
+if TYPE_CHECKING:
+    from torch.fx.experimental.symbolic_shapes import ShapeEnv
+
+log = logging.getLogger(__name__)
+sym_node_log = torch._logging.getArtifactLogger(__name__, "sym_node")
+
+
+__all__ = ["SymNode", "method_to_operator", "magic_methods"]
+
+
+from torch.types import py_sym_types as SymTypes
+
+
+def _to_symtype(t):
+    if t is bool:
+        return SymBool
+    if t is int:
+        return SymInt
+    if t is float:
+        return SymFloat
+    return t
+
+
+# TODO: An incomplete list
+# 1. Set variables to be equal when we do equality
+# 2. Specialize on 0/1 when we do subtraction
+class SymNode:
+    """
+    This is a type erased SymInt/SymFloat which we use to do actual operations.
+    End users don't touch this.  Magic methods are NOT defined on this object.
+    """
+
+    def __init__(
+        self,
+        expr,
+        shape_env,
+        pytype,
+        hint: Optional[Union[int, float, bool]],
+        constant=None,
+        fx_node=None,
+    ):
+        self._expr = expr
+        self.shape_env = shape_env
+        self.pytype = pytype
+
+        # What's the difference between hint and constant?
+        #
+        # - A constant is known to be invariant across invocations of the model;
+        #   it will always be this value.  We only really know this when we
+        #   encounter an honest-to-goodness literal (when wrapping it into
+        #   a SymNode, we set constant.)  Most of the time, constant is None
+        #
+        # - A hint is a *particular* value from the particular run we are
+        #   tracing, but it may vary the next time around.  It's useful to
+        #   keep this around, as if we need a concrete value from a SymNode,
+        #   we will return the hint and guard on the expression that produced
+        #   it giving the same hint next time around.  The hint is not
+        #   guaranteed to be set either: if you have an unbacked SymNode,
+        #   there won't be any hint; it was the result of some tensor-dependent
+        #   computation, but we don't know what it actually is because we
+        #   haven't actually run the tensor computation.
+        #
+        # If _hint is None, we will query maybe_evaluate_static(compute_hint=True)
+        # in hopes that we've learned enough about the unbacked symints to
+        # discharge the hint; otherwise, you're likely to just error out.
+        #
+        # (A previous version of this system had some optimizations to only
+        # recompute when it was possible we had learned enough about the
+        # unbacked symint that a hint was now possible, but as we added more
+        # potential refinements to unbacked symints this got harder to keep
+        # in sync, so we've deleted it for now.)
+
+        def compute_hint():
+            from torch.fx.experimental.symbolic_shapes import free_unbacked_symbols
+
+            # This occasionally gets exercised by, e.g.,
+            # convert_shape_to_symint.  It's just a nicety so you don't HAVE
+            # to have a correct hint on hand when making a SymNode.
+            # Don't attempt to compute for unbacked, this can be quite
+            # expensive.
+            if free_unbacked_symbols(self.expr):
+                return None
+            hint = self.shape_env._maybe_evaluate_static(self.expr, compute_hint=True)
+            if hint is not None:
+                hint = self.pytype(hint) if not isinstance(hint, SymTypes) else hint
+            return hint
+
+        if hint is not None:
+            assert type(hint) is pytype or type(hint) is _to_symtype(pytype), (
+                "Cannot create SymNode of type "
+                f"{pytype} with incompatible hint of type {type(hint)}"
+            )
+            if self.shape_env and self.shape_env._translation_validation_enabled:
+                # This is technically not TV, but this assert is expensive so
+                # let's only do it when we're already doing expensive things
+                computed_hint = compute_hint()
+                assert (
+                    hint == computed_hint
+                ), f"{hint} != {computed_hint} (for {self.expr})"
+        else:
+            hint = compute_hint()
+        self._hint = hint
+        self.constant: Optional[Union[int, float, bool]] = constant
+
+        # Record the FX node of the current node if we are doing translation
+        # validation. They will be used for building the input assertions for
+        # the translation validation problem.
+        tx_validation_en = (
+            self.shape_env and self.shape_env._translation_validation_enabled
+        )
+        self.fx_node = tx_validation_en and fx_node
+
+    def with_shape_env(self, shape_env: "ShapeEnv") -> "SymNode":
+        return SymNode(
+            self._expr, shape_env, self.pytype, self._hint, self.constant, self.fx_node
+        )
+
+    def _value_eq(self, other: "SymNode") -> bool:
+        # Purposely don't include the shape_env in the eq.
+        return (
+            self._expr == other._expr
+            and self.pytype == other.pytype
+            and self._hint == other._hint
+            and self.constant == other.constant
+            and self.fx_node == other.fx_node
+        )
+
+    def _value_hash(self) -> int:
+        # Purposely don't include the shape_env in the hash.
+        return hash((self._expr, self.pytype, self._hint, self.constant, self.fx_node))
+
+    @property
+    def expr(self):
+        return self.shape_env.replace(self._expr)
+
+    @property
+    def hint(self):
+        return self._hint
+
+    def has_hint(self):
+        return self._hint is not None
+
+    def require_hint(self, fallback=None):
+        if self._hint is None:
+            if fallback is not None:
+                return fallback
+            # NB: we expect this to raise
+            return self.shape_env.size_hint(self.expr)
+        return self._hint
+
+    def maybe_as_int(self):
+        if self.expr.is_number:
+            return int(self.expr)
+        else:
+            return None
+
+    # NB: This does conversions, not sure if this is good or not
+    def maybe_as_float(self):
+        import sympy
+
+        if isinstance(self.expr, sympy.Float):
+            return float(self.expr)
+        else:
+            return None
+
+    def maybe_as_bool(self):
+        import sympy
+
+        if self.expr is sympy.true:
+            return True
+        elif self.expr is sympy.false:
+            return False
+        else:
+            return None
+
+    def is_int(self):
+        return self.pytype is int
+
+    def is_float(self):
+        return self.pytype is float
+
+    def is_bool(self):
+        return self.pytype is bool
+
+    def is_nested_int(self):
+        # Unbacked SymInts cannot be nested int today
+        return (
+            self._hint is not None
+            and isinstance(self._hint, SymInt)
+            and self._hint.node.is_nested_int()
+        )
+
+    def wrap_int(self, num):
+        assert type(num) is int
+        import sympy
+
+        return SymNode(
+            sympy.Integer(num), self.shape_env, int, num, constant=num, fx_node=num
+        )
+
+    def wrap_float(self, num):
+        assert type(num) is float
+        import sympy
+
+        return SymNode(
+            sympy.Float(num), self.shape_env, float, num, constant=num, fx_node=num
+        )
+
+    def wrap_bool(self, num):
+        assert type(num) is bool
+        import sympy
+
+        return SymNode(
+            sympy.true if num else sympy.false,
+            self.shape_env,
+            bool,
+            num,
+            constant=num,
+            fx_node=num,
+        )
+
+    def clone(self):
+        return self
+
+    def str(self):
+        return f"{self.expr}"
+
+    def __str__(self):
+        return self.str()
+
+    def __repr__(self):
+        rep = [
+            f"SymNode({self._expr}, shape_env={self.shape_env}, pytype={self.pytype}",
+        ]
+        if self._hint is not None:
+            rep.append(f"hint={self._hint}")
+        if self.constant is not None:
+            rep.append(f"constant={self.constant}")
+        if self.fx_node is not None:
+            rep.append(f"fx_node={self.fx_node}")
+        return ", ".join(rep) + ")"
+
+    def _graph_repr(self) -> builtins.str:
+        # Representation used by GraphModule to create a pythonic version of a graph
+        return self.str()
+
+    # These methods call the metaprogrammed methods, they're hand written
+    # here so we get good stack traces
+    def abs(self) -> "SymNode":
+        return self._abs()  # type: ignore[attr-defined]
+
+    def pos(self) -> "SymNode":
+        return self._pos()  # type: ignore[attr-defined]
+
+    def round(self, ndigits=None) -> "SymNode":
+        return self._round(ndigits)  # type: ignore[attr-defined]
+
+    def trunc(self) -> "SymNode":
+        return self._trunc()  # type: ignore[attr-defined]
+
+    def add(self, other) -> "SymNode":
+        return self._add(other)  # type: ignore[attr-defined]
+
+    def sub(self, other) -> "SymNode":
+        return self._sub(other)  # type: ignore[attr-defined]
+
+    def mul(self, other) -> "SymNode":
+        return self._mul(other)  # type: ignore[attr-defined]
+
+    def mod(self, other) -> "SymNode":
+        return self._mod(other)  # type: ignore[attr-defined]
+
+    def float_pow(self, other) -> "SymNode":
+        return self._float_pow(other)  # type: ignore[attr-defined]
+
+    def pow_by_natural(self, other) -> "SymNode":
+        return self._pow_by_natural(other)  # type: ignore[attr-defined]
+
+    def and_(self, other) -> "SymNode":
+        return self._and_(other)  # type: ignore[attr-defined]
+
+    def or_(self, other) -> "SymNode":
+        return self._or_(other)  # type: ignore[attr-defined]
+
+    def float_truediv(self, other) -> "SymNode":
+        return self._float_truediv(other)  # type: ignore[attr-defined]
+
+    def int_truediv(self, other) -> "SymNode":
+        return self._int_truediv(other)  # type: ignore[attr-defined]
+
+    def int_floordiv(self, other) -> "SymNode":
+        return self._int_floordiv(other)  # type: ignore[attr-defined]
+
+    def lshift(self, other) -> "SymNode":
+        return self._lshift(other)  # type: ignore[attr-defined]
+
+    def rshift(self, other) -> "SymNode":
+        return self._rshift(other)  # type: ignore[attr-defined]
+
+    def sym_not(self) -> "SymNode":  # noqa: F811
+        return self._sym_not()  # type: ignore[attr-defined]
+
+    def eq(self, other) -> "SymNode":
+        return self._eq(other)  # type: ignore[attr-defined]
+
+    def ne(self, other) -> "SymNode":
+        return self._ne(other)  # type: ignore[attr-defined]
+
+    def gt(self, other) -> "SymNode":
+        return self._gt(other)  # type: ignore[attr-defined]
+
+    def lt(self, other) -> "SymNode":
+        return self._lt(other)  # type: ignore[attr-defined]
+
+    def le(self, other) -> "SymNode":
+        return self._le(other)  # type: ignore[attr-defined]
+
+    def ge(self, other) -> "SymNode":
+        return self._ge(other)  # type: ignore[attr-defined]
+
+    def floor(self) -> "SymNode":
+        return self._floor()  # type: ignore[attr-defined]
+
+    def is_integer(self) -> "SymNode":
+        return self._is_integer()  # type: ignore[attr-defined]
+
+    def sym_float(self) -> "SymNode":  # noqa: F811
+        return self._sym_float()  # type: ignore[attr-defined]
+
+    def sym_int(self) -> "SymNode":
+        return self._sym_int()  # type: ignore[attr-defined]
+
+    def ceil(self) -> "SymNode":
+        return self._ceil()  # type: ignore[attr-defined]
+
+    def neg(self) -> "SymNode":
+        return self._neg()  # type: ignore[attr-defined]
+
+    def sym_min(self, other) -> "SymNode":  # noqa: F811
+        return self._sym_min(other)  # type: ignore[attr-defined]
+
+    def sym_max(self, other) -> "SymNode":  # noqa: F811
+        return self._sym_max(other)  # type: ignore[attr-defined]
+
+    def sym_ite(self, then_val, else_val) -> "SymNode":
+        return self._sym_ite(then_val, else_val)  # type: ignore[attr-defined]
+
+    def is_contiguous(self, sizes, strides) -> "SymNode":
+        return self._is_contiguous(sizes, strides)  # type: ignore[attr-defined]
+
+    def is_channels_last_contiguous_2d(self, sizes, strides) -> "SymNode":
+        return self._is_channels_last_contiguous_2d(sizes, strides)  # type: ignore[attr-defined]
+
+    def is_channels_last_contiguous_3d(self, sizes, strides) -> "SymNode":
+        return self._is_channels_last_contiguous_3d(sizes, strides)  # type: ignore[attr-defined]
+
+    def is_channels_last_strides_2d(self, sizes, strides) -> "SymNode":
+        return self._is_channels_last_strides_2d(sizes, strides)  # type: ignore[attr-defined]
+
+    def is_channels_last_strides_3d(self, sizes, strides) -> "SymNode":
+        return self._is_channels_last_strides_3d(sizes, strides)  # type: ignore[attr-defined]
+
+    def is_non_overlapping_and_dense_indicator(self, sizes, strides) -> "SymNode":
+        return self._is_non_overlapping_and_dense_indicator(sizes, strides)  # type: ignore[attr-defined]
+
+    # Make C++ happy
+    def sym_or(self, other):
+        return self.or_(other)
+
+    def sym_and(self, other):
+        return self.and_(other)
+
+    # There is no int_truediv available from C++
+    def truediv(self, other):
+        return self.float_truediv(other)
+
+    def floordiv(self, other) -> "SymNode":
+        return self.int_floordiv(other)
+
+    # We didn't bind integer pow in C++
+    def pow(self, other):
+        return self.float_pow(other)
+
+    def is_non_overlapping_and_dense(self, sizes, strides):
+        return self.is_non_overlapping_and_dense_indicator(sizes, strides).eq(to_node(self, 1))  # type: ignore[attr-defined]
+
+    def int_(self):
+        return self.guard_int("", 0)  # NB: uses Python backtrace
+
+    # You can manually trigger a guard with this function
+    def guard_int(self, file, line):
+        # TODO: use the file/line for some useful diagnostic on why a
+        # guard occurred
+        r = self.shape_env.evaluate_expr(self.expr, self.hint, fx_node=self.fx_node)
+        try:
+            return int(r)
+        except Exception:
+            log.warning("Failed to convert to int: %s", r)
+            raise
+
+    def guard_float(self, file, line):
+        # TODO: use the file/line for some useful diagnostic on why a
+        # guard occurred
+        r = self.shape_env.evaluate_expr(self.expr, self.hint, fx_node=self.fx_node)
+        try:
+            return float(r)
+        except Exception:
+            log.warning("Failed to convert to float: %s", r)
+            raise
+
+    def guard_bool(self, file, line):
+        # TODO: use the file/line for some useful diagnostic on why a
+        # guard occurred
+        r = self.shape_env.evaluate_expr(self.expr, self.hint, fx_node=self.fx_node)
+        try:
+            return bool(r)
+        except Exception:
+            log.warning("Failed to convert to bool: %s", r)
+            raise
+
+    def expect_true(self, file, line):
+        from torch.fx.experimental.symbolic_shapes import free_unbacked_symbols
+
+        if (
+            self.has_hint()
+            and not free_unbacked_symbols(self.expr)
+            and not self.shape_env.prefer_deferred_runtime_asserts_over_guards
+        ):
+            # OK to generate guards
+            return self.guard_bool(file, line)
+        # Generate a deferred runtime assert (this might actually end up doing
+        # a regular guard if we can!)
+        # TODO: file/line here is very important, because the assert has been
+        # deferred so you can't backtrace easily
+        return self.shape_env.defer_runtime_assert(
+            self.expr, f"{file}:{line}", fx_node=self.fx_node
+        )
+
+    def expect_size(self, file, line):
+        from torch.fx.experimental.symbolic_shapes import _advise_is_size
+
+        b = self.ge(self.wrap_int(0))
+        # Generate a deferred runtime assert
+        r = b.expect_true(file, line)
+        # Refine compile time range, but only if it's unbacked.
+        # If you refine range for hinted variables, you can end up making
+        # improper deductions since compile time reasoning may be
+        # incompatible with runtime reasoning.
+        if r and not self.has_hint():
+            _advise_is_size(SymInt(self))
+        return r
+
+    def guard_size_oblivious(self, file, line):
+        """
+        Like guard_bool, but if we encounter unbacked symbols, if those symbols
+        are size-like, we will treat them as >= 2 for the purposes of the analysis.
+
+        This CHANGES the runtime semantics, but all size-oblivious sites have been
+        audited to ensure that the runtime semantics don't change in a material way.
+        Acceptable runtime semantic changes are, e.g., squeeze() no longer dropping
+        an unbacked one size, or a tensor reporting as non-contiguous even if it's
+        contiguous if it would have been reported contiguous due to being empty.
+        """
+        # TODO: use the file/line for some useful diagnostic on why a
+        # guard occurred
+        r = self.shape_env.evaluate_expr(
+            self.expr, self.hint, fx_node=self.fx_node, size_oblivious=True
+        )
+        try:
+            return bool(r)
+        except Exception:
+            log.warning("Failed to convert to bool: %s", r)
+            raise
+
+    def bool_(self):
+        return self.guard_bool("", 0)
+
+    def is_symbolic(self):
+        return True
+
+    def nested_int(self):
+        return None
+
+    def is_constant(self):
+        return False
+
+
+# TODO: this probably needs the sizes-strides eval functions
+METHOD_TO_OPERATOR = {
+    "pos": operator.pos,
+    "abs": operator.abs,
+    "add": operator.add,
+    "and": operator.and_,
+    "ceil": math.ceil,
+    "eq": operator.eq,
+    "floor": math.floor,
+    "trunc": math.trunc,
+    "int_floordiv": operator.floordiv,
+    "ge": operator.ge,
+    "gt": operator.gt,
+    "is_integer": lambda x: x.is_integer(),
+    "le": operator.le,
+    "lshift": operator.lshift,
+    "lt": operator.lt,
+    "mod": operator.mod,
+    "mul": operator.mul,
+    "ne": operator.ne,
+    "neg": operator.neg,
+    "or": operator.or_,
+    "float_pow": operator.pow,
+    "pow_by_natural": operator.pow,
+    "round": builtins.round,
+    "rshift": operator.rshift,
+    "sub": operator.sub,
+    "sym_float": sym_float,
+    "sym_ite": sym_ite,
+    "sym_max": sym_max,
+    "sym_min": sym_min,
+    "sym_not": sym_not,
+    "float_truediv": operator.truediv,
+    "int_truediv": operator.truediv,
+}
+
+unary_magic_methods = {
+    "abs",
+    "sym_float",
+    "sym_int",
+    "ceil",
+    "floor",
+    "neg",
+    "sym_not",
+    "pos",
+    "trunc",
+}
+
+
+# Adding math ops: sqrt, cos, sin, ...
+def _get_sym_node_fn(name):
+    def fn(self):
+        return getattr(self, f"_sym_{name}")()
+
+    return fn
+
+
+math_op_names = (
+    "sqrt",
+    "cos",
+    "cosh",
+    "sin",
+    "sinh",
+    "tan",
+    "tanh",
+    "asin",
+    "acos",
+    "atan",
+)
+for name in math_op_names:
+    sym_name = f"sym_{name}"
+    priv_sym_name = f"_{sym_name}"
+    setattr(SymNode, sym_name, _get_sym_node_fn(name))
+    METHOD_TO_OPERATOR[sym_name] = getattr(torch, priv_sym_name)
+    unary_magic_methods.add(sym_name)
+    __all__.append(sym_name)
+
+
+# Unary methods that are not magic methods
+unary_nonmagic_methods = {
+    "is_integer",
+}
+
+unary_methods = unary_magic_methods | unary_nonmagic_methods
+
+# Most methods are only registered on SymInt and SymFloat
+# Some methods are only be registered on SymBool
+only_bool_magic_methods = {"and", "or", "sym_not", "sym_ite"}
+# Methods that implicitly convert SymBool into SymInt
+bool_becomes_int_magic_methods = {"add", "sub", "mul"}
+# Methods that are also on SymBool, in addition to on SymInt and SymFloat
+also_bool_magic_methods = {"eq"}
+bool_magic_methods = only_bool_magic_methods | also_bool_magic_methods
+
+# Methods that are only for float
+only_float_magic_methods = {"is_integer", "round", "sym_int"}
+
+
+magic_methods_on_operator_with_trailing_underscore = {"and", "or"}
+
+
+always_float_magic_methods = {"int_truediv", "float_truediv", "sym_float", "float_pow"}
+
+for name in math_op_names:
+    sym_name = f"sym_{name}"
+    always_float_magic_methods.add(sym_name)
+
+
+always_int_magic_methods = {"ceil", "floor", "trunc", "pow_by_natural"}
+always_bool_magic_methods = {
+    "eq",
+    "ne",
+    "gt",
+    "lt",
+    "le",
+    "ge",
+    "and",
+    "or",
+    "sym_not",
+    "is_non_overlapping_and_dense",
+    "is_integer",
+}
+
+# Methods that have a `__foo__` as well as `__rfoo__`
+
+
+def _sympy_float_truediv(a, b):
+    from torch.utils._sympy.functions import FloatTrueDiv
+
+    return FloatTrueDiv(a, b)
+
+
+def _sympy_int_truediv(a, b):
+    from torch.utils._sympy.functions import IntTrueDiv
+
+    return IntTrueDiv(a, b)
+
+
+def _sympy_floordiv(a, b):
+    from torch.utils._sympy.functions import FloorDiv
+
+    return FloorDiv(a, b)
+
+
+def _sympy_mod(a, b):
+    from torch.utils._sympy.functions import Mod, PythonMod
+
+    if a.is_nonnegative and b.is_nonnegative:
+        return Mod(a, b)
+    else:
+        return PythonMod(a, b)
+
+
+def _sympy_pow_by_natural(a, b):
+    from torch.utils._sympy.functions import PowByNatural
+
+    return PowByNatural(a, b)
+
+
+def _sympy_float_pow(a, b):
+    from torch.utils._sympy.functions import FloatPow
+
+    return FloatPow(a, b)
+
+
+def _sympy_and(a, b):
+    import sympy
+
+    return sympy.And(a, b)
+
+
+def _sympy_or(a, b):
+    import sympy
+
+    return sympy.Or(a, b)
+
+
+def _sympy_lshift(a, b):
+    from torch.utils._sympy.functions import LShift
+
+    return LShift(a, b)
+
+
+def _sympy_rshift(a, b):
+    from torch.utils._sympy.functions import RShift
+
+    return RShift(a, b)
+
+
+reflectable_magic_methods = {
+    "add": operator.add,
+    "sub": operator.sub,
+    "mul": operator.mul,
+    "mod": _sympy_mod,
+    "pow_by_natural": _sympy_pow_by_natural,
+    "float_pow": _sympy_float_pow,
+    "and": _sympy_and,
+    "or": _sympy_or,
+    "float_truediv": _sympy_float_truediv,
+    "int_truediv": _sympy_int_truediv,
+    "int_floordiv": _sympy_floordiv,
+    "lshift": _sympy_lshift,
+    "rshift": _sympy_rshift,
+}
+
+
+def _floor_ceil_helper(a, fn):
+    import sympy
+
+    if isinstance(a, sympy.Mul):
+        aa = a.args
+        if len(aa) == 2 and isinstance(aa[0], sympy.Float) and aa[1].is_integer:
+            coef = sympy.Integer(aa[0])
+            if aa[0] == coef:  # structural equality test
+                return coef * aa[1]
+    if (
+        isinstance(a, sympy.Float)
+        and a == sympy.Integer(a)
+        or isinstance(a, sympy.Integer)
+    ):
+        return sympy.Integer(a)
+    return fn(a)
+
+
+def _sympy_floor(a):
+    from torch.utils._sympy.functions import FloorToInt
+
+    return FloorToInt(a)
+
+
+# NB: this is Python trunc semantics which returns an int.  Do NOT use this to
+# represent torch.trunc (which is float to float)
+def _sympy_trunc(a):
+    from torch.utils._sympy.functions import TruncToInt
+
+    return TruncToInt(a)
+
+
+def _sympy_ceil(a):
+    from torch.utils._sympy.functions import CeilToInt
+
+    return CeilToInt(a)
+
+
+def _sympy_eq(a, b):
+    import sympy
+
+    return sympy.Eq(a, b)
+
+
+def _sympy_ne(a, b):
+    import sympy
+
+    return sympy.Ne(a, b)
+
+
+def _sympy_gt(a, b):
+    import sympy
+
+    return sympy.Gt(a, b)
+
+
+def _sympy_lt(a, b):
+    import sympy
+
+    return sympy.Lt(a, b)
+
+
+def _sympy_le(a, b):
+    import sympy
+
+    return sympy.Le(a, b)
+
+
+def _sympy_ge(a, b):
+    import sympy
+
+    return sympy.Ge(a, b)
+
+
+def _sympy_min(a, b):
+    from torch.utils._sympy.functions import Min
+
+    return Min(a, b)
+
+
+def _sympy_max(a, b):
+    from torch.utils._sympy.functions import Max
+
+    return Max(a, b)
+
+
+def _sympy_ite(a, t, f):
+    import sympy
+
+    return sympy.Piecewise((t, a), (f, True))
+
+
+current_module = sys.modules[__name__]
+
+
+def _get_sym_math_fn(name):
+    def fn(a):
+        import torch.utils._sympy.functions
+
+        return getattr(torch.utils._sympy.functions, f"OpaqueUnaryFn_{name}")(a)
+
+    return fn
+
+
+for name in math_op_names:
+    priv_sympy_name = f"_sympy_{name}"
+    fn = _get_sym_math_fn(name)
+    fn.__qualname__ = fn.__name__ = priv_sympy_name
+    setattr(current_module, priv_sympy_name, fn)
+
+del fn, name, priv_sympy_name  # type: ignore[possibly-undefined]
+
+
+def _sympy_abs(a):
+    import sympy
+
+    return sympy.Abs(a)
+
+
+def _sympy_round(number, ndigits=None):
+    from torch.utils._sympy.functions import RoundDecimal, RoundToInt
+
+    if ndigits is None:
+        return RoundToInt(number)
+    else:
+        return RoundDecimal(number, ndigits)
+
+
+def _sympy_sym_float(a):
+    from torch.utils._sympy.functions import ToFloat
+
+    # NB: Cannot use a * 1.0 here, because 0 * 1.0 is 0 which incorrectly
+    # reports that it is an integer
+    return ToFloat(a)
+
+
+def _sympy_is_integer(a):
+    import sympy
+
+    from torch.utils._sympy.functions import ToFloat
+
+    return sympy.Eq(ToFloat(sympy.floor(a)), a)
+
+
+magic_methods = {
+    **reflectable_magic_methods,
+    "sym_not": operator.invert,
+    "pos": operator.pos,
+    "eq": _sympy_eq,
+    "ne": _sympy_ne,
+    "gt": _sympy_gt,
+    "lt": _sympy_lt,
+    "le": _sympy_le,
+    "ge": _sympy_ge,
+    "floor": _sympy_floor,
+    "trunc": _sympy_trunc,
+    "sym_float": _sympy_sym_float,
+    "ceil": _sympy_ceil,
+    "neg": operator.neg,
+    "sym_min": _sympy_min,
+    "sym_max": _sympy_max,
+    "sym_ite": _sympy_ite,
+    "abs": _sympy_abs,
+    "round": _sympy_round,
+    "is_integer": _sympy_is_integer,
+}
+
+
+for name in math_op_names:
+    sym_name = f"sym_{name}"
+    magic_methods[sym_name] = getattr(current_module, f"_sympy_{name}")
+
+del name, sym_name, math_op_names, current_module  # type: ignore[possibly-undefined]
+
+
+def sympy_is_contiguous(sizes, strides):
+    dim = len(sizes)
+    return sympy_is_contiguous_generic(sizes, strides, list(range(dim - 1, -1, -1)))
+
+
+def sympy_is_contiguous_generic(sizes, strides, dim_order):
+    import sympy
+
+    dim = len(sizes)
+
+    if len(dim_order) != dim:
+        return sympy.false
+
+    is_contiguous = sympy.true
+    z = sympy.Integer(1)
+    # Contiguous if the strides make sense (or the dim is size 1)
+    for d in dim_order:
+        is_contiguous &= sympy.Eq(sizes[d], sympy.Integer(1)) | sympy.Eq(strides[d], z)
+        z *= sizes[d]
+    # OR if any size is zero
+    for d in range(dim):
+        is_contiguous |= sympy.Eq(sizes[d], sympy.Integer(0))
+    return is_contiguous
+
+
+# NB: There is a TODO in C++ to allow omitting the batch dim.  If that
+# happens you will need to refactor this
+
+
+def sympy_is_channels_last_contiguous_2d(sizes, strides):
+    return sympy_is_contiguous_generic(sizes, strides, [1, 3, 2, 0])
+
+
+def sympy_is_channels_last_contiguous_3d(sizes, strides):
+    return sympy_is_contiguous_generic(sizes, strides, [1, 4, 3, 2, 0])
+
+
+def sympy_is_channels_last_strides_generic(sizes, strides, dim_order):
+    import sympy
+
+    from torch.utils._sympy.functions import Max
+
+    dim = len(sizes)
+
+    if dim != len(dim_order):
+        return sympy.false
+
+    m = sympy.Integer(0)
+    r = sympy.true
+
+    # special case for trivial C dimension. default to NCHW
+    r &= sympy.Ne(strides[1], 0)
+
+    for d in dim_order:
+        r &= sympy.Ne(sizes[d], 0) & (strides[d] >= m)
+        # Fallback to NCHW as default layout for ambiguous cases
+        # This is the flaw of implicit memory_format from strides.
+        # N111 tensor with identical strides for size 1 dimension;
+        # Two cases could lead us here:
+        # a. N111 contiguous Tensor ([N,1,1,1]@[1,1,1,1])
+        # b. N11W contiguous Tensor sliced on the W-dimension.
+        # ([N,1,1,1]@[W,W,W,W])
+        if d == 0:
+            r &= sympy.Ne(m, strides[1])
+        # This is necessary to:
+        # 1. distinguish the memory_format of N1H1;
+        #     [H, 1, 1, 1] channels_last stride
+        #     [H, H, 1, 1] contiguous stride
+        # 2. permutation of 1C1W:
+        #     [1, C, 1, H]@[HC, H, H, 1] transpose(1, 3)
+        #     [1, H, 1, C]@[HC, 1, H, H] shouldn't be identified as
+        #     channels_last
+        m = strides[d] * Max(sizes[d], 1)
+
+    return r
+
+
+def sympy_is_channels_last_strides_2d(sizes, strides):
+    return sympy_is_channels_last_strides_generic(sizes, strides, [1, 3, 2, 0])
+
+
+def sympy_is_channels_last_strides_3d(sizes, strides):
+    return sympy_is_channels_last_strides_generic(sizes, strides, [1, 4, 3, 2, 0])
+
+
+def _sympy_is_non_overlapping_and_dense_indicator(sizes, strides):
+    from torch.utils._sympy.functions import IsNonOverlappingAndDenseIndicator
+
+    return IsNonOverlappingAndDenseIndicator(*sizes, *strides)
+
+
+sizes_strides_methods = {
+    # TODO: These could also be done with indicators, maybe it is better
+    # for reasoning to do it that way
+    "is_contiguous": sympy_is_contiguous,
+    "is_channels_last_contiguous_2d": sympy_is_channels_last_contiguous_2d,
+    "is_channels_last_contiguous_3d": sympy_is_channels_last_contiguous_3d,
+    "is_channels_last_strides_2d": sympy_is_channels_last_strides_2d,
+    "is_channels_last_strides_3d": sympy_is_channels_last_strides_3d,
+    "is_non_overlapping_and_dense_indicator": _sympy_is_non_overlapping_and_dense_indicator,
+}
+
+alternate_impl_if_hinted_methods = {
+    "sym_min": builtins.min,
+    "sym_max": builtins.max,
+}
+
+
+def to_node(self, num):
+    if isinstance(num, SymTypes):
+        return num.node
+    elif type(num) is bool:
+        return self.wrap_bool(num)
+    elif type(num) is int:
+        return self.wrap_int(num)
+    elif type(num) is float:
+        return self.wrap_float(num)
+    else:
+        # NotImplemented is important so that Python tries the
+        # other magic method
+        return NotImplemented
+
+
+def wrap_node(x):
+    # TODO: let C++ also take advantage of this
+    if isinstance(x, SymNode) and x.constant is not None:
+        return x.constant
+    if x.is_int():
+        return SymInt(x)
+    elif x.is_float():
+        return SymFloat(x)
+    elif x.is_bool():
+        return SymBool(x)
+    else:
+        raise AssertionError(f"unrecognized return type {x}")
+
+
+def method_to_operator(method):
+    return METHOD_TO_OPERATOR[method]
+
+
+def _make_node_magic(method, func):
+    func = lru_cache(256)(func)
+
+    if method in magic_methods_on_operator_with_trailing_underscore:
+        method_attr = f"{method}_"
+    else:
+        method_attr = method
+
+    def binary_magic_impl(self, other):
+        from torch.fx.experimental.proxy_tensor import (
+            get_proxy_mode,
+            handle_sym_dispatch,
+        )
+        from torch.fx.experimental.symbolic_shapes import safe_expand
+
+        op = method_to_operator(method)
+
+        out_hint = None
+        if self.hint is not None and other.hint is not None:
+            out_hint = op(self.hint, other.hint)
+
+        alternate_impl = alternate_impl_if_hinted_methods.get(method)
+        if alternate_impl and out_hint is not None:
+            return to_node(self, alternate_impl(wrap_node(self), wrap_node(other)))
+
+        if get_proxy_mode():
+            return to_node(
+                self, handle_sym_dispatch(op, (wrap_node(self), wrap_node(other)), {})
+            )
+        assert isinstance(other, SymNode)
+        try:
+            if method == "mod":
+                from torch.utils._sympy.functions import Mod, PythonMod
+
+                # Special handling for mod that requires access to the value
+                # ranges
+                shape_env = self.shape_env
+                if (
+                    self.expr.is_nonnegative
+                    or shape_env.bound_sympy(self.expr).lower >= 0
+                ) and (
+                    other.expr.is_nonnegative
+                    or shape_env.bound_sympy(other.expr).lower >= 0
+                ):
+                    out = Mod(self.expr, other.expr)
+                else:
+                    out = PythonMod(self.expr, other.expr)
+            else:
+                # TODO: consider constant prop here
+                out = func(self.expr, other.expr)
+        except Exception:
+            log.warning("failed to eval %s(%s, %s)", method, self.expr, other.expr)
+            raise
+        out = safe_expand(out)
+        sym_node_log.debug("%s %s %s -> %s", method, self.expr, other.expr, out)
+        pytype: Type
+        # This is not strictly correct. In Python, a**b may return complex when
+        # a < 0 and b is a float: (-1)**2.1. Same for sympy.sqrt(-3.14). This
+        # returns a float while both arguments are ints: 2**(-1). Also, max and
+        # min do not type promote. To avoid having data-dependent control flow
+        # here, we just set the type to float if one of the args is a float. In
+        # case of a type mismatch, we assume that it will be detected during
+        # evaluation.
+        if method in always_float_magic_methods:
+            pytype = float
+        elif method in always_bool_magic_methods:
+            pytype = bool
+        elif self.pytype is float or other.pytype is float:
+            pytype = float
+        else:
+            pytype = self.pytype
+
+        if (
+            pytype is not None
+            and out_hint is not None
+            and not isinstance(out_hint, SymTypes)
+        ):
+            out_hint = pytype(out_hint)
+
+        # Create a FX node that corresponds to the operation being applied to
+        # this node.
+        fx_node, _ = self.shape_env._create_fx_call_function(
+            op, (self.fx_node, other.fx_node)
+        )
+        return SymNode(out, self.shape_env, pytype, out_hint, fx_node=fx_node)
+
+    def unary_magic_impl(self):
+        from torch.fx.experimental.proxy_tensor import (
+            get_proxy_mode,
+            handle_sym_dispatch,
+        )
+        from torch.fx.experimental.symbolic_shapes import safe_expand
+
+        op = method_to_operator(method)
+        if get_proxy_mode():
+            return to_node(self, handle_sym_dispatch(op, (wrap_node(self),), {}))
+        # TODO: consider constant prop here
+        expr = self.expr
+        if method == "floor" or method == "ceiling":
+            expr = self.shape_env._simplify_floor_div(expr)
+
+        try:
+            out = func(expr)
+        except Exception:
+            log.warning("failed to eval %s(%s)", method, expr)
+            raise
+        sym_node_log.debug("%s %s -> %s", func, expr, out)
+        out_hint = None
+        if self.hint is not None:
+            out_hint = op(self.hint)
+        out = safe_expand(out)
+        pytype: Type
+        if method in always_int_magic_methods:
+            pytype = int
+        elif method in always_bool_magic_methods:
+            pytype = bool
+        elif method in always_float_magic_methods:
+            pytype = float
+        else:
+            pytype = self.pytype
+
+        fx_node, _ = self.shape_env._create_fx_call_function(op, (self.fx_node,))
+        return SymNode(out, self.shape_env, pytype, out_hint, fx_node=fx_node)
+
+    if method in unary_methods:
+        setattr(SymNode, f"_{method_attr}", unary_magic_impl)
+    elif method == "sym_ite":
+
+        def sym_ite_impl(pred_node, then_node, else_node):
+            from torch.fx.experimental.proxy_tensor import (
+                get_proxy_mode,
+                handle_sym_dispatch,
+            )
+            from torch.fx.experimental.symbolic_shapes import safe_expand
+
+            out_hint = then_node.hint if pred_node.hint else else_node.hint
+            if get_proxy_mode():
+                return to_node(
+                    pred_node,
+                    handle_sym_dispatch(
+                        sym_ite,
+                        (
+                            wrap_node(pred_node),
+                            wrap_node(then_node),
+                            wrap_node(else_node),
+                        ),
+                        {},
+                    ),
+                )
+
+            try:
+                out = func(pred_node.expr, then_node.expr, else_node.expr)
+            except Exception:
+                log.warning(
+                    "failed to eval %s(%s, %s, %s)",
+                    method,
+                    pred_node.expr,
+                    then_node.expr,
+                    else_node.expr,
+                )
+                raise
+
+            out = safe_expand(out)
+            fx_node, _ = pred_node.shape_env._create_fx_call_function(
+                sym_ite, (pred_node.fx_node, then_node.fx_node, else_node.fx_node)
+            )
+            return SymNode(
+                out, pred_node.shape_env, then_node.pytype, out_hint, fx_node=fx_node
+            )
+
+        setattr(SymNode, f"_{method_attr}", sym_ite_impl)
+    elif method == "round":
+
+        def round_impl(self, ndigits=None):
+            from torch.fx.experimental.proxy_tensor import (
+                get_proxy_mode,
+                handle_sym_dispatch,
+            )
+            from torch.fx.experimental.symbolic_shapes import safe_expand
+
+            op = builtins.round
+            if get_proxy_mode():
+                return to_node(
+                    self, handle_sym_dispatch(op, (wrap_node(self), ndigits), {})
+                )
+
+            expr = self.expr
+            try:
+                out = func(expr, ndigits)
+            except Exception:
+                log.warning("failed to eval %s(%s, ndigits=%s)", method, expr, ndigits)
+                raise
+
+            out = safe_expand(out)
+
+            if ndigits is None:
+                pytype = int
+            else:
+                pytype = self.pytype
+
+            out_hint = None
+            if self.hint is not None:
+                out_hint = op(self.hint, ndigits)
+
+            # Internally, None is used as sentinel to indicate that a something is not a node on an FX graph. At the
+            # same time, there is no way to wrap a plain None into an FX node. Thus, there is no way to pass None here
+            # without triggering some asserts that check whether we are mixing FX nodes with untracked arguments. The
+            # hack down below works, because all round function down the line all take ndigits=None as default in their
+            # signature.
+            # TODO: Remove the args construction below if a different sentinel is used by FX.
+            # ezyang(May 2024): LOL
+            args = [self.fx_node]
+            if ndigits is not None:
+                args.append(ndigits)
+            fx_node, _ = self.shape_env._create_fx_call_function(op, tuple(args))
+            return SymNode(out, self.shape_env, pytype, out_hint, fx_node=fx_node)
+
+        setattr(SymNode, f"_{method_attr}", round_impl)
+    else:
+        setattr(SymNode, f"_{method_attr}", binary_magic_impl)
+
+
+def _make_node_sizes_strides(method, func):
+    # NB: don't LRU cache, lots of arguments
+
+    def sizes_strides_impl(self, sizes, strides):
+        from torch.fx.experimental.proxy_tensor import (
+            get_proxy_mode,
+            handle_sym_dispatch,
+        )
+
+        op = getattr(sys.modules[__name__], method)
+        if get_proxy_mode():
+            return to_node(
+                self,
+                handle_sym_dispatch(
+                    op,
+                    ([wrap_node(s) for s in sizes], [wrap_node(s) for s in strides]),
+                    {},
+                ),
+            )
+        size_exprs = [s.expr for s in sizes]
+        stride_exprs = [s.expr for s in strides]
+        try:
+            out = func(size_exprs, stride_exprs)
+        except Exception:
+            log.warning("failed to eval %s(%s, %s)", method, size_exprs, stride_exprs)
+            raise
+        # bool is never expandable
+
+        size_hints = []
+        out_hint = None
+        for s in sizes:
+            if s.hint is None:
+                break
+            size_hints.append(s.hint)
+        else:
+            stride_hints = []
+            for s in strides:
+                if s.hint is None:
+                    break
+                stride_hints.append(s.hint)
+            else:
+                out_hint = op(size_hints, stride_hints)
+
+        # NB: This is the indicator function, not the actual bool!
+        pytype: Type
+        if method.endswith("_indicator"):
+            pytype = int
+        else:
+            pytype = bool
+        return SymNode(out, self.shape_env, pytype, out_hint)
+
+    setattr(SymNode, f"_{method}", sizes_strides_impl)
+
+    # TODO: This is technically hotpath, but in the ideal end state
+    # guards on this will resolve at a higher level so you never
+    # spend time in this code
+    def sizes_strides_user(sizes, strides):
+        import sympy
+
+        from torch.fx.experimental.symbolic_shapes import (
+            eval_is_non_overlapping_and_dense,
+        )
+
+        for a in itertools.chain(sizes, strides):
+            if isinstance(a, SymInt):
+                return wrap_node(
+                    getattr(a.node, method)(
+                        [to_node(a.node, b) for b in sizes],
+                        [to_node(a.node, b) for b in strides],
+                    )
+                )
+        if method == "is_non_overlapping_and_dense_indicator":
+            return eval_is_non_overlapping_and_dense(sizes, strides)
+        else:
+            # TODO: this is an awful implementation
+            return bool(
+                func(
+                    [sympy.sympify(a) for a in sizes],
+                    [sympy.sympify(a) for a in strides],
+                )
+            )
+
+    # Skip for is_non_overlapping_and_dense_indicator
+    if not hasattr(sys.modules[__name__], method):
+        setattr(sys.modules[__name__], method, sizes_strides_user)
+
+
+for method, func in magic_methods.items():
+    _make_node_magic(method, func)
+
+for method, func in sizes_strides_methods.items():
+    _make_node_sizes_strides(method, func)
+
+
+def _make_user_magic(method, user_type):
+    # User magic takes care of wrapping the other operand into a node,
+    # so that our internal logic can assume everything is nodes
+
+    if method in magic_methods_on_operator_with_trailing_underscore:
+        method_attr = f"sym_{method}"
+    else:
+        method_attr = method
+
+    def get_constant(x: Union[SymInt, int, SymFloat, float, SymBool, bool]):
+        if isinstance(x, (int, float, bool)):
+            return x
+        if isinstance(x, SymBool):
+            return x.node.guard_bool("", 0)
+        raise AssertionError("expect to be called with constant SymBools")
+
+    def is_constant(x):
+        if isinstance(x, (int, float, bool)):
+            return True
+        if isinstance(x, (SymInt, SymFloat, SymBool)):
+            return x.node.is_constant()
+        return False
+
+    # Promotion rules for binary operations.  NB: we preserve PYTHON semantics
+    #   - if args are same type, do nothing
+    #   - if one arg is float, promote other arg to float
+    #       - nb: this applies to floordiv, even though output is integral
+    #       (it's still float)
+    #   - pow is funny business
+    #       - if both ints
+    #       - trigger a guard on exponent >= 0
+    #           - if non-negative, output is int
+    #           - otherwise, output is float
+    #   - otherwise, promote other arg to float
+    #       - nb: complex is impossible to handle correctly lol, with
+    #       negative base and integral float need to diverge semantics and
+    #       just always return complex.  Neener neener pretend this problem
+    #       doesn't exist
+    #   - equality is pain: Python does the fancy thing where it unpacks the
+    #     mantissa from the float and then compares that against the int.
+    #     Which means it is able to tell that
+    #     9007199254740993 != 9007199254740992. (rather than if the LHS was
+    #     promoted to float, in which case it would have truncated to the RHS
+    #     and subsequently been equal).  We'll model this exactly by having
+    #     special mixed type equality operations.  Unfortunately, we need to
+    #     do this for all comparison operations (maybe I'll only implement
+    #     compare)
+    #   - sym_ite mumble mumble really shouldn't allow mixed but whatever
+
+    if method in bool_becomes_int_magic_methods:
+
+        def promote(x):
+            """Implements True+True=2, which works in python but not sympy"""
+            if isinstance(x, SymBool):
+                return SymInt(x.node.wrap_int(int(x)))
+            return x
+
+    else:
+
+        def promote(x):
+            return x
+
+    def promote2(self, other):
+        # TODO: Remove eq and other relations from this list.
+        # CPython has fancy implementations for these to get as much precision
+        # as possible instead of just promoting to float64 and praying, so we
+        # need to handle them specially too.
+        # Also, note that int_truediv doesn't go through this path: both
+        # arguments are "int" so there isn't any promotion
+        if method not in [
+            "add",
+            "sub",
+            "mul",
+            "mod",
+            "float_pow",
+            "float_truediv",
+            "int_floordiv",
+            "sym_min",
+            "sym_max",
+            # TODO: remove these
+            "eq",
+            "ne",
+            "gt",
+            "lt",
+            "le",
+            "ge",
+        ]:
+            return self, other
+        f_self = isinstance(self, (float, torch.SymFloat))
+        f_other = isinstance(other, (float, torch.SymFloat))
+        if f_self or f_other:
+            if not f_self:
+                self = torch.sym_float(self)
+            if not f_other:
+                other = torch.sym_float(other)
+        return self, other
+
+    # Before and after performing the operation, check if any operands are constant.
+    # If so, extract out the constant values first. If `self` itself is a
+    # constant, then "redispatch" by calling back into the operator. Sometimes
+    # this means that operations involving SymBool return plain bools.
+    # Alternatively, we could also rewrap into constant Symbool (i.e. by
+    # implementing wrap_bool in ConstantSymNodeImpl), but we're not doing that
+    # today for no particular reason.
+    def unary_magic_impl(self):
+        self = promote(self)
+        if is_constant(self):
+            return (method_to_operator(method))(get_constant(self))
+        return wrap_node(getattr(self.node, method_attr)())
+
+    def binary_magic_impl(self, other):
+        if not isinstance(other, (int, float, bool, SymInt, SymFloat, SymBool)):
+            return NotImplemented
+        sym_node_log.debug("MAGIC %s %s %s", method, self, other)
+        self = promote(self)
+        other = promote(other)
+        self, other = promote2(self, other)
+        if is_constant(self):
+            return (method_to_operator(method))(get_constant(self), other)
+        if is_constant(other):
+            other = get_constant(other)
+        other_node = to_node(self.node, other)
+        if other_node is NotImplemented:
+            return NotImplemented
+        ret = wrap_node(getattr(self.node, method_attr)(other_node))
+        return get_constant(ret) if is_constant(ret) else ret
+
+    def rbinary_magic_impl(self, other):
+        if not isinstance(other, (int, float, bool, SymInt, SymFloat, SymBool)):
+            return NotImplemented
+        self = promote(self)
+        other = promote(other)
+        self, other = promote2(self, other)
+        if is_constant(self):
+            return (method_to_operator(method))(get_constant(self), other)
+        if is_constant(other):
+            other = get_constant(other)
+        other_node = to_node(self.node, other)
+        if other_node is NotImplemented:
+            return NotImplemented
+        ret = wrap_node(getattr(other_node, method_attr)(self.node))
+        return get_constant(ret) if is_constant(ret) else ret
+
+    if method in unary_magic_methods:
+        setattr(user_type, f"__{method}__", unary_magic_impl)
+    elif method in unary_nonmagic_methods:
+        orig = getattr(user_type, method)
+        setattr(user_type, method, update_wrapper(unary_magic_impl, orig))
+    elif method == "sym_ite":
+
+        def sym_ite_magic_impl(pred, then_val, else_val):
+            pred_node = pred.node
+            then_node = to_node(pred_node, then_val)
+            else_node = to_node(pred_node, else_val)
+            if then_node is NotImplemented or else_node is NotImplemented:
+                return NotImplemented
+            assert (
+                isinstance(then_node, SymNode)
+                and isinstance(else_node, SymNode)
+                and then_node.pytype == else_node.pytype
+            )
+            ret = wrap_node(getattr(pred.node, method_attr)(then_node, else_node))
+            return get_constant(ret) if ret.node.is_constant() else ret
+
+        setattr(user_type, f"__{method}__", sym_ite_magic_impl)
+    elif method == "round":
+
+        def round_magic_impl(self, ndigits=None):
+            if is_constant(self):
+                return builtins.round(get_constant(self), ndigits)
+
+            return wrap_node(getattr(self.node, method)(ndigits))
+
+        setattr(user_type, f"__{method}__", round_magic_impl)
+    else:
+        setattr(user_type, f"__{method}__", binary_magic_impl)
+        if method in reflectable_magic_methods:
+            setattr(user_type, f"__r{method}__", rbinary_magic_impl)
+
+
+for method, func in magic_methods.items():  # type: ignore[assignment]
+    if method in only_bool_magic_methods:
+        _make_user_magic(method, SymBool)
+        continue
+    if method in only_float_magic_methods:
+        _make_user_magic(method, SymFloat)
+        continue
+    if method in also_bool_magic_methods or method in bool_becomes_int_magic_methods:
+        _make_user_magic(method, SymBool)
+    _make_user_magic(method, SymInt)
+    _make_user_magic(method, SymFloat)
+
+del method
+del func
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/symbolic_shapes.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/symbolic_shapes.py
new file mode 100644
index 0000000000000000000000000000000000000000..369708d2c2dbc0c886773ad3d7fbf42c63cfd8f9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/symbolic_shapes.py
@@ -0,0 +1,5603 @@
+# mypy: ignore-errors
+
+"""
+``torch.fx.experimental.symbolic_shapes`` provides interfaces for interacting with
+our symbolic shapes reasoning system that is used heavily in torch.compile.  Although
+this is not generally considered public API, when writing framework code in PyTorch
+as well as extensions to PyTorch (e.g., in custom operator implementations), you may
+need to make use of these APIs to setup dynamic shapes support appropriately.
+"""
+
+import builtins
+import collections
+import functools
+import inspect
+import itertools
+import logging
+import math
+import operator
+import os
+import re
+import sys
+import threading
+import traceback
+from collections import defaultdict
+from contextlib import contextmanager
+from dataclasses import dataclass, field
+from enum import Enum
+import atexit
+from typing import (
+    Any,
+    cast,
+    Callable,
+    Dict,
+    Iterable,
+    List,
+    Optional,
+    Sequence,
+    Set,
+    Tuple,
+    Type,
+    Union,
+    TYPE_CHECKING
+)
+from typing_extensions import TypeAlias
+
+import torch
+import torch.fx
+import torch.fx.traceback as fx_traceback
+from torch.fx.experimental import _config as config
+
+from torch.fx.experimental.recording import (
+    FakeTensorMeta,
+    ShapeEnvEvent,
+    record_shapeenv_event,
+    replay_shape_env_events,
+    shape_env_check_state_equal
+)
+from torch.fx.experimental.sym_node import SymNode, SymTypes
+from torch._logging import trace_structured, structured
+
+# NB: The sym_* functions are used via getattr() and must be imported here.
+from torch import SymBool, SymFloat, SymInt
+from torch._guards import ShapeGuard, Source, TracingContext
+from torch.utils._python_dispatch import is_traceable_wrapper_subclass
+from torch.utils._sympy.functions import (
+    Application, FloorDiv, Mod, PythonMod, IsNonOverlappingAndDenseIndicator, CleanDiv, FloorToInt, CeilToInt
+)
+from torch.utils._sympy.solve import try_solve
+from torch.utils._sympy.numbers import int_oo
+from torch.utils._sympy.value_ranges import bound_sympy, SymPyValueRangeAnalysis, ValueRanges, ValueRangeError
+from torch.utils._sympy.singleton_int import SingletonInt
+from torch.utils._traceback import format_frame, CapturedTraceback
+from torch._utils_internal import signpost_event
+from torch._subclasses.meta_utils import is_sparse_any
+import torch.utils._pytree as pytree
+from torch.utils._sympy.symbol import SymT, make_symbol, symbol_is_type
+
+from torch._logging import LazyString
+
+if TYPE_CHECKING:
+    from torch._dynamo.source import TensorPropertySource
+
+InputList = List
+DimList = List
+
+log = logging.getLogger(__name__)
+
+import sympy
+from sympy.printing.str import StrPrinter
+from sympy.printing.precedence import precedence, PRECEDENCE
+
+class GuardOnDataDependentSymNode(RuntimeError):
+    cond: sympy.Expr
+
+    def __init__(self, cond, *args):
+        super().__init__(*args)
+        self.cond = cond
+
+class PendingUnbackedSymbolNotFound(RuntimeError):
+    pass
+
+aten = torch._ops.ops.aten  # type: ignore[has-type]
+
+__all__ = [
+    "has_symbolic_sizes_strides", "create_contiguous", "ShapeEnv", "is_concrete_int",
+    "guard_int", "guard_float", "guard_scalar", "canonicalize_bool_expr",
+    "hint_int", "SYMPY_INTERP", "free_symbols", "is_symbol_binding_fx_node",
+    "is_concrete_bool", "is_nested_int", "SHAPEENV_EVENT_KEY", "CURRENT_NODE_KEY",
+    "has_free_symbols", "sym_eq", "SymbolicContext", "StatelessSymbolicContext",
+    "StatefulSymbolicContext", "SubclassSymbolicContext", "statically_known_true",
+    "guard_size_oblivious", "check_consistent",
+    "compute_unbacked_bindings", "ConvertIntKey",
+    "rebind_unbacked", "resolve_unbacked_bindings", "is_accessor_node",
+]
+
+# FX node metadata keys for symbolic shape FX graph.
+SHAPEENV_EVENT_KEY = "shapeenv_event"
+CURRENT_NODE_KEY = "current_node"
+
+
+def log_lru_cache_stats(wrapped_f):
+    log.debug("lru_cache_stats %s: %s", wrapped_f.__name__, wrapped_f.cumulative_cache_info())
+
+
+# Wrapper on lru_cache that reports statistics at process end
+def lru_cache(maxsize):
+    def inner(f):
+        wrapped_f = functools.lru_cache(maxsize)(f)
+        old_cache_clear = wrapped_f.cache_clear
+        prev_hits = 0
+        prev_misses = 0
+
+        # TODO: There's a ref-cycle here (wrapped_f -> cumulative_cache_info
+        # -> wrapped_f) but cannot be solved with weakref as wrapped_f is not
+        # weakref'able on some versions of Python
+
+        def cumulative_cache_info():
+            cur = wrapped_f.cache_info()
+            return functools._CacheInfo(
+                prev_hits + cur.hits,
+                prev_misses + cur.misses,
+                cur.maxsize,
+                cur.currsize,
+            )
+
+        def new_cache_clear():
+            nonlocal prev_hits, prev_misses
+            cur = wrapped_f.cache_info()
+            prev_hits += cur.hits
+            prev_misses += cur.misses
+            old_cache_clear()
+
+        wrapped_f.cache_clear = new_cache_clear
+        wrapped_f.cumulative_cache_info = cumulative_cache_info
+        if log.isEnabledFor(logging.DEBUG):
+            atexit.register(log_lru_cache_stats, wrapped_f)
+        return wrapped_f
+
+    return inner
+
+# These are modules that contain generic code for interacting with ShapeEnv
+# which are unlikely to identify a particular interesting guard statement
+@lru_cache(None)
+def uninteresting_files() -> Set[str]:
+    import torch._inductor.sizevars
+    import torch._library.fake_impl
+    import torch._subclasses.meta_utils
+    import torch._subclasses.fake_tensor
+    mods = [
+        sys.modules[__name__],
+        torch.fx.experimental.recording,
+        torch.fx.experimental.sym_node,
+        torch.fx.interpreter,
+        torch,
+        torch._inductor.sizevars,
+        torch._library.fake_impl,
+        torch._subclasses.meta_utils,
+        torch._subclasses.fake_tensor,
+    ]
+    return {inspect.getfile(m) for m in mods}
+
+# We don't bother with the metaclass as all of the dispatching logic happens
+# entirely from Python
+#
+# Didn't bother with ancestors for now, unlikely to have multiple modes for
+# symints right now
+
+class ConstraintViolationError(RuntimeError):
+    pass
+
+def has_symbolic_sizes_strides(elem) -> bool:
+    return elem._has_symbolic_sizes_strides
+
+Int = Union[torch.SymInt, int]
+
+def create_contiguous(shape: Sequence[Int]) -> List[Int]:
+    strides: List[Int] = [1]
+    for dim in reversed(shape[:-1]):
+        strides.append(dim * strides[-1])
+    return list(reversed(strides))
+
+def hint_int(a: Union[torch.SymInt, int], fallback: Optional[int] = None) -> int:
+    """
+    Retrieve the hint for an int (based on the underlying real values as observed
+    at runtime).  If no hint is available (e.g., because data dependent shapes),
+    if fallback is not None, use that instead (otherwise raise an error).
+    """
+    if isinstance(a, torch.SymInt):
+        return a.node.require_hint(fallback)
+    assert type(a) is int, a
+    return a
+
+Scalar = Union[torch.SymInt, torch.SymFloat, torch.SymBool, int, float, bool]
+
+def has_hint(a: Scalar) -> bool:
+    if isinstance(a, SymTypes):
+        return a.node.has_hint()
+    return True
+
+def is_concrete_int(a: Union[int, SymInt]) -> bool:
+    r""" Utility to check if underlying object
+    in SymInt is concrete value. Also returns
+    true if integer is passed in.
+
+    Args:
+        a (SymInt or int): Object to test if it int
+    """
+    assert isinstance(a, (SymInt, int))
+
+    if isinstance(a, int):
+        return True
+
+    if isinstance(a.node.expr, sympy.core.numbers.Integer):
+        return True
+
+    return False
+
+# In obscure Meta only situations, sympy.logic.boolalg doesn't exist at runtime.
+# So make sure only type checker evaluates this alias.
+# Xref: https://www.internalfb.com/diff/D53324783
+SympyBoolean: TypeAlias = "sympy.logic.boolalg.Boolean"
+
+def guard_size_oblivious(expr: Union[torch.SymBool, bool]) -> bool:
+    """
+    Perform a guard on a symbolic boolean expression in a size oblivious way.
+    This is typically used when a non-oblivious test would result in a guard
+    on a data dependent value of which we don't know the value of at compile time.
+    When a guard is tested this way, we may diverge in behavior from how regular
+    PyTorch semantics would treat it.  For more information, see
+    https://github.com/pytorch/pytorch/pull/118579
+    """
+    if isinstance(expr, torch.SymBool):
+        return expr.node.guard_size_oblivious("", 0)
+    else:
+        assert isinstance(expr, bool), expr
+        return expr
+
+def check_consistent(new, old) -> None:
+    """
+    Test that two "meta" values (typically either Tensor or SymInt) have
+    the same values, e.g., after retracing.  If we don't understand the
+    quantities in question, we'll just skip the consistency check.
+    """
+    # TODO: do boolean equality test too, see
+    # https://github.com/pytorch/pytorch/issues/124110
+    scalar_types = (torch.SymInt, torch.SymFloat, int, float)
+
+    if isinstance(new, torch.Tensor):
+        assert isinstance(old, torch.Tensor)
+        torch._check(old.dim() == new.dim(), lambda: f"{old.shape} != {new.shape} (old != new)")
+        # Do this manually so that each individual test is irrefutable
+        # (TODO: should be a helper for this, maybe sym_eq?  That
+        # gives us a compound expression and I'm not sure it
+        # simplifies right now)
+        for i, j in zip(old.shape, new.shape):
+            torch._check(i == j, lambda: f"{old.shape} != {new.shape} (old != new)")
+    # NB: bool is subclass of int
+    elif isinstance(new, scalar_types) and not isinstance(new, bool):
+        assert isinstance(old, scalar_types) and not isinstance(old, bool), f"{old} != {new}"
+        torch._check(old == new, lambda: f"{old} != {new} (old != new)")
+
+def resolve_unbacked_bindings(shape_env, bindings):
+    if bindings is None:
+        return None
+    return {
+        shape_env.unbacked_renamings.get(k, k): v
+        for k, v in bindings.items()
+    }
+
+def rebind_unbacked(shape_env, n: torch.fx.Node, result):
+    """
+    Suppose we are retracing a pre-existing FX graph that previously had
+    fake tensor propagation (and therefore unbacked SymInts).  When we retrace,
+    we re-propagate fake tensors, which results in new unbacked SymInts.
+    When this happens, we need to tell the shape environment about the equivalence
+    of the old and new unbacked SymInts.  Pass us the old torch.fx.Node (which
+    has the old binding information) and the new result (which we can extract the
+    new unbacked SymInts out from).
+    """
+    from torch._dynamo.tensor_version_op import _tensor_version
+
+    # Inputs never need rebinding
+    if n.op == "placeholder":
+        return
+
+    if bindings := resolve_unbacked_bindings(shape_env, n.meta.get("unbacked_bindings")):
+        for raw_u0, path in bindings.items():
+            u1 = pytree.key_get(result, path)
+            # tensor_version ops get specialized after AOTAutograd, it's OK,
+            # we don't actually want to do asserts on them.  This is all a bit
+            # questionable though
+            if isinstance(u1, int) and n.target is _tensor_version:
+                log.info("rebind_unbacked: discard _tensor_version %s %s -> %s", raw_u0, path, u1)
+                continue
+            raw_u1 = u1.node.expr
+            # Simplify SymBool binding
+            if (
+                isinstance(raw_u1, sympy.Piecewise) and
+                len(raw_u1.args) == 2 and
+                raw_u1.args[0][0] == 1 and
+                isinstance(eq := raw_u1.args[0][1], sympy.Eq) and
+                isinstance(new_raw_u1 := eq.lhs, sympy.Symbol) and
+                shape_env.var_to_range[new_raw_u1].issubset(ValueRanges(0, 1)) and
+                eq.rhs == 1 and
+                raw_u1.args[1] == (0, True)
+            ):
+                # This is what the pattern match above is testing
+                repacked = _sympy_cast_symbool_to_symint_guardless(sympy.Eq(new_raw_u1, 1))
+                assert repacked == raw_u1, f"{repacked} != {raw_u1}"
+                # Cancel the to_int(to_bool(x)). This is sound because x in
+                # [0, 1]
+                raw_u1 = new_raw_u1
+            assert isinstance(raw_u1, sympy.Symbol)
+            # The old and new could be the same if you improperly hit the memo
+            # while retracing.  Make sure you updated FakeTensorMode.epoch
+            assert raw_u0 != raw_u1, f"{raw_u0} possible memo disaster"
+            # Reuse the OLD symbol name
+            shape_env._rename_unbacked_to(raw_u1, raw_u0)
+
+# NB: You could try to expand this to cover more cases by simply
+# detecting whenever you have an int output, but this is a bit
+# dangerous in case someone adds a function that returns an int but is
+# mutating.  So manually whitelist for now.
+def is_accessor_node(node: torch.fx.Node) -> bool:
+    # Dynamo only exercised condition
+    if (
+        node.op == "call_method"
+        and isinstance(node.args[0].meta.get("example_value"), torch.Tensor)
+        and node.target in ["size", "stride", "storage_offset", "item"]
+    ):
+        return True
+    if node.op == "call_function" and node.target in [
+        torch.ops.aten.sym_size,
+        torch.ops.aten.sym_size.default,
+        torch.ops.aten.sym_size.int,
+        torch.ops.aten.sym_stride,
+        torch.ops.aten.sym_stride.default,
+        torch.ops.aten.sym_stride.int,
+        torch.ops.aten.sym_storage_offset,
+        torch.ops.aten.sym_storage_offset.default,
+        torch.ops.aten.sym_numel.default,
+    ]:
+        return True
+    return False
+
+def canonicalize_bool_expr(expr: SympyBoolean) -> SympyBoolean:
+    r""" Canonicalize a boolean expression by transforming it into a lt / le
+    inequality and moving all the non-constant terms to the rhs.
+    We canonicalize And / Ors / Not via cnf and then canonicalize their subexpr
+    recursively
+    nb. sympy.Rel.canonical is not good enough https://github.com/sympy/sympy/issues/25924
+
+    Args:
+        expr (sympy.Expr): Expression to canonicalize
+    """
+    # Canonicalise an inequality by transforming it into a lt / le
+    # inequality and moving all the non-constant terms to the rhs
+    # We canonicalise And / Ors / Not via cnf
+    # nb. Relational.canonical in sympy is broken
+    # https://github.com/sympy/sympy/issues/25924
+
+    if not isinstance(expr, (sympy.Rel, sympy.And, sympy.Or, sympy.Not, sympy.Eq, sympy.Ne)):
+        return expr
+
+    if isinstance(expr, (sympy.And, sympy.Or, sympy.Not)):
+        expr = sympy.logic.boolalg.to_cnf(expr)
+    return _canonicalize_bool_expr_impl(expr)
+
+def _canonicalize_bool_expr_impl(expr: SympyBoolean) -> SympyBoolean:
+    """
+    After canonicalization, we are guaranteed to have eliminated Ge/Gt relations
+    (rewriting them to Le/Lt, respectively).
+    """
+    if isinstance(expr, (sympy.And, sympy.Or)):
+        return type(expr)(*map(canonicalize_bool_expr, expr.args))
+
+    opposite = {sympy.Gt: sympy.Lt, sympy.Ge: sympy.Le}
+    if isinstance(expr, tuple(opposite.keys())):
+        rhs = expr.lhs - expr.rhs
+        t = opposite[type(expr)]
+    else:
+        assert isinstance(expr, (sympy.Lt, sympy.Le, sympy.Eq, sympy.Ne))
+        rhs = expr.rhs - expr.lhs
+        t = type(expr)
+
+    def is_neg(t):
+        return t.is_negative or (isinstance(t, sympy.Mul) and t.args[0].is_negative)
+
+    lhs = 0
+    rhs = _reduce_to_lowest_terms(rhs)
+    if isinstance(rhs, sympy.Add):
+        pos = []
+        neg = []
+        for term in rhs.args:
+            if is_neg(term):
+                neg.append(-term)
+            else:
+                pos.append(term)
+        lhs = sympy.Add(*neg)
+        rhs = sympy.Add(*pos)
+    elif is_neg(rhs):
+        # lhs == 0
+        lhs, rhs = -rhs, 0
+    return t(lhs, rhs)
+
+
+def _reduce_to_lowest_terms(expr: sympy.Expr) -> sympy.Expr:
+    """
+    Eliminates any integer factor from a given expression.
+    E.g., 6x + 4y reduces to 3x + 2y.
+
+    Useful when an expression is == or != to 0.
+    """
+    def integer_coefficient(x):
+        if isinstance(x, sympy.Integer):
+            return abs(int(x))
+        elif isinstance(x, sympy.Mul):
+            return math.prod([abs(int(arg)) for arg in x.args if isinstance(arg, sympy.Integer)])
+        else:
+            return 1
+
+    if isinstance(expr, sympy.Add):
+        atoms = expr.args
+        factor = functools.reduce(math.gcd, map(integer_coefficient, atoms))
+        atoms = [x / factor for x in atoms]
+        return sympy.Add(*atoms)
+    else:
+        return expr / integer_coefficient(expr)
+
+
+def is_concrete_bool(a: Union[bool, SymBool]) -> bool:
+    r""" Utility to check if underlying object
+    in SymBool is concrete value. Also returns
+    true if integer is passed in.
+    Args:
+        a (SymBool or bool): Object to test if it bool
+    """
+    assert isinstance(a, (SymBool, bool))
+
+    if isinstance(a, bool):
+        return True
+
+    if isinstance(a.node.expr, (sympy.logic.boolalg.BooleanTrue, sympy.logic.boolalg.BooleanFalse)):
+        return True
+
+    return False
+
+def is_nested_int(s):
+    return isinstance(s, torch.SymInt) and s.node.is_nested_int()
+
+def _iterate_exprs(val: Union[SymInt, torch.Tensor]) -> Iterable[sympy.Basic]:
+    if isinstance(val, SymTypes):
+        # This allow applies to the jagged layout NestedTensor case as
+        # nested ints are not symbolic
+        if is_symbolic(val):
+            yield val.node.expr
+    elif isinstance(val, sympy.Basic):
+        yield val
+    elif isinstance(val, (int, float, bool)):
+        pass
+    elif isinstance(val, (tuple, list)):
+        for s in val:
+            yield from _iterate_exprs(s)
+    elif is_sparse_any(val):
+        yield from _iterate_exprs(val.size())
+    elif isinstance(val, torch.Tensor):
+        yield from _iterate_exprs(val.size())
+        yield from _iterate_exprs(val.stride())
+        yield from _iterate_exprs(val.storage_offset())
+    elif val is None:
+        pass
+    else:
+        raise AssertionError(f"cannot extract sympy expressions from {val} {type(val)}")
+
+def free_symbols(val: Union[SymInt, sympy.Expr, torch.Tensor]) -> Set[sympy.Symbol]:
+    if val is None:
+        return set()
+    itr = _iterate_exprs(val)
+    # we need at least 1 to call union, so we hand code the identity
+    try:
+        first_expr = next(itr)
+    except StopIteration:
+        return set()
+
+    return first_expr.free_symbols.union(*(e.free_symbols for e in itr))
+
+def has_free_symbols(val: Union[SymInt, torch.Tensor]) -> bool:
+    """Faster version of bool(free_symbols(val))"""
+    return not all(e.is_number for e in _iterate_exprs(val))
+
+# Like free_symbols, but filtered to only report unbacked symbols
+def free_unbacked_symbols(x):
+    # NB: keep synced with is_unbacked_symint
+    return {s for s in free_symbols(x) if symbol_is_type(s, (SymT.UNBACKED_INT, SymT.UNBACKED_FLOAT))}
+
+# WARNING: Don't use this on Dynamo produced graphs, they don't have meta
+# setup!
+def is_symbol_binding_fx_node(node) -> Optional[sympy.Symbol]:
+    if (
+        "val" in node.meta and
+        isinstance(node.meta["val"], torch.SymInt) and
+        isinstance(node.meta["val"].node.expr, sympy.Symbol) and
+        (node.op == "placeholder" or free_unbacked_symbols(node.meta["val"].node.expr))
+    ):
+        return node.meta["val"].node.expr
+    return None
+
+def find_symbol_binding_fx_nodes(graph):
+    r = {}
+    # NB: Prefer first occurrence of symbol
+    for node in graph.nodes:
+        if is_symbol_binding_fx_node(node) and node.meta["val"].node.expr not in r:
+            r[node.meta["val"].node.expr] = node
+    return r
+
+
+# Analogous to ConvertIntSource
+@dataclass(frozen=True)
+class ConvertIntKey:
+    def __str__(self) -> str:
+        return ".cast_symbool_to_symint_guardless()"
+
+    def get(self, b: bool) -> int:
+        """Get the int value from bool"""
+        return cast_symbool_to_symint_guardless(b)
+
+
+@dataclass(frozen=True)
+class CallMethodKey:
+    name: str
+
+    def __str__(self) -> str:
+        return f".{self.name}()"
+
+    def get(self, o: Any) -> Any:
+        """Call the method on object"""
+        return getattr(o, self.name)()
+
+
+@dataclass(frozen=True)
+class InnerTensorKey:
+    inner_name: str
+
+    def __str__(self) -> str:
+        return f".{self.inner_name}"
+
+    def get(self, o: Any) -> Any:
+        """Get the inner tensor attribute"""
+        return getattr(o, self.inner_name)
+
+
+@dataclass(frozen=True)
+class DivideByKey:
+    divisor: int
+
+    def __str__(self) -> str:
+        return f".__floordiv__({self.divisor})"
+
+    def get(self, o: int) -> int:
+        """Divide object by divisor"""
+        return o // self.divisor
+
+
+def compute_unbacked_bindings(shape_env, example_value, old_example_value=None, peek=False):
+    """
+    After having run fake tensor propagation and producing example_value
+    result, traverse example_value looking for freshly bound unbacked
+    symbols and record their paths for later.  It is an error if
+    we have allocated an unbacked SymInt but it cannot be found in
+    example_value.  (NB: this means if you have a multi-output
+    function, you must call this on the tuple of tensor output, you
+    cannot wait!)
+
+    The peek parameter lets you check out what the bindings are without
+    changing the affected list.  This is primarily useful for ensuring
+    unbacked_var_to_val is promptly populated when propagate_real_tensors is on.
+    """
+    if shape_env is None:
+        return
+    fs = shape_env.pending_fresh_unbacked_symbols
+    pending = set(fs)
+    if pending:
+        if not peek:
+            log.info("compute_unbacked_bindings %s", fs)
+            fs.clear()
+
+        def free_unbacked_symbols_with_path(
+            a, path, real=None
+        ) -> Dict[sympy.Symbol, pytree.KeyPath]:
+            r = {}
+            if isinstance(a, (tuple, list)):
+                for i in range(len(a)):
+                    r.update(
+                        free_unbacked_symbols_with_path(
+                            a[i], path + (pytree.SequenceKey(i),),
+                            real=real[i] if real is not None else None
+                        )
+                    )
+            elif is_traceable_wrapper_subclass(a):
+                # TODO: Determine if this is correct
+                attrs, _ = a.__tensor_flatten__()
+                for attr in attrs:
+                    sub = getattr(a, attr)
+                    r.update(
+                        free_unbacked_symbols_with_path(sub, path + (InnerTensorKey(attr),))
+                    )
+            elif isinstance(a, torch.Tensor):
+                r.update(
+                    free_unbacked_symbols_with_path(
+                        a.size(), path + (CallMethodKey("size"),),
+                        real=a.real_tensor.size() if a.real_tensor is not None else None
+                    )
+                )
+                r.update(
+                    free_unbacked_symbols_with_path(
+                        a.stride(), path + (CallMethodKey("stride"),),
+                        real=a.real_tensor.stride() if a.real_tensor is not None else None
+                    )
+                )
+                r.update(
+                    free_unbacked_symbols_with_path(
+                        a.storage_offset(), path + (CallMethodKey("storage_offset"),),
+                        real=a.real_tensor.storage_offset() if a.real_tensor is not None else None
+                    )
+                )
+
+            # NB: Intentionally access _expr, not expr, do not want
+            # simplification!
+            elif (
+                isinstance(a, (torch.SymInt, torch.SymFloat))
+                and isinstance(s := a.node._expr, sympy.Symbol)
+                and s in pending
+            ):
+                r[s] = path
+                if real is not None:
+                    shape_env.set_unbacked_var_to_val(s, real)
+                pending.remove(s)
+            # When an unbacked SymInt is perfectly divisible by an integer
+            # constant, we replace it with the integer constant to improve
+            # reasoning capabilities.  However, in synthetic examples, it is
+            # then possible that the factor never is explicitly allocated.
+            # Fortunately, we can compute it by division.
+            elif (
+                isinstance(a, torch.SymInt)
+                and isinstance(s := a.node._expr, sympy.Mul)
+                and len(s.args) == 2
+                and isinstance(lhs := s.args[0], sympy.Integer)
+                and isinstance(rhs := s.args[1], sympy.Symbol)
+                and rhs in pending
+            ):
+                # TODO: DivideByKey needs to test divisibility at runtime!
+                r[s] = path + (DivideByKey(int(lhs)),)
+                if real is not None:
+                    shape_env.set_unbacked_var_to_val(s, real // int(lhs))
+                pending.remove(rhs)
+            # The annoyance here arises from the fact that SymBool is
+            # allocated by allocating a SymInt and then testing if it's equal
+            # to one.  So you have a complicated binding site logic for this.
+            elif (
+                isinstance(a, torch.SymBool)
+                and isinstance(s := a.node._expr, sympy.Eq)
+                # This must match create_unbacked_symbool EXACTLY
+                and isinstance(s.lhs, sympy.Symbol)
+                and s.rhs == 1
+                and s.lhs in pending
+            ):
+                r[s.lhs] = path + (ConvertIntKey(),)
+                if real is not None:
+                    shape_env.set_unbacked_var_to_val(s, int(real))
+                pending.remove(s.lhs)
+
+            return r
+
+        symbol_to_path = free_unbacked_symbols_with_path(example_value, ())
+        if not peek and pending:
+            extra = (
+                repr((example_value.stride(), example_value.storage_offset()))
+                if isinstance(example_value, torch.Tensor)
+                else ""
+            )
+            raise PendingUnbackedSymbolNotFound(
+                f"Pending unbacked symbols {pending} not in returned outputs {example_value} {extra}.\n"
+                "Did you accidentally call new_dynamic_size() or item() more times "
+                "than you needed to in your fake implementation?\n"
+                "For more help, see https://docs.google.com/document/d/1RWrH-3wLEpzR9kCS6gGBNen_-Fs-8PVbWWFE5AcgeWE/edit"
+            )
+
+        # Why do we have to do some rebinding here?  If the original FX node
+        # wasn't a binding site because you had a memo hit, but post
+        # translation you aren't a memo hit anymore, there's now a new binding
+        # site... but we know (because it's the same FX node) that the value
+        # is actually the same, they're just not obviously equal anymore.
+        #
+        # The logic here is written carefully, because unlike the
+        # bind_unbacked case, we are not guaranteed to have a symbol for
+        # old_sym.  If we have a symbol, do regular rename unbacked to; but if
+        # we don't, we need to specially eliminate the fresh unbacked symbol
+        # (NB: we are /trusting/ that the memoization is correct, and that we
+        # don't need to generate a new runtime assert.  This is load bearing,
+        # as repropagation can happen after we've frozen runtime asserts.)
+        if old_example_value is not None:
+            for keypath in symbol_to_path.values():
+                old_sym = pytree.key_get(old_example_value, keypath)
+                new_sym = pytree.key_get(example_value, keypath)
+                if (
+                    isinstance(new_sym, SymTypes) and
+                    isinstance(new_s := new_sym.node.expr, sympy.Symbol)
+                ):
+                    if isinstance(old_sym, SymTypes) and (old_s := old_sym.node.expr) != new_s:
+                        if isinstance(old_s, sympy.Symbol):
+                            shape_env._rename_unbacked_to(new_s, old_s)
+                        else:
+                            shape_env._eliminate_unbacked(new_s, old_s)
+                    elif not isinstance(old_sym, SymTypes):
+                        shape_env._eliminate_unbacked(new_s, sympy.sympify(old_sym))
+
+        return symbol_to_path
+
+def definitely_true(a):
+    """
+    Returns True only if we can tell that a is True, possibly introducing
+    a guard in the process.  If a depends on some unbacked SymInt, we may
+    return False even though there may exist a possible value of the SymInt
+    that would cause the expression to return True.
+
+    When is it appropriate to use definitely_true?  First, if you can use
+    a higher level combinator like parallel_or/parallel_and, prefer using
+    those instead, they are definitely safe (modulo short-circuiting).
+    Second, it can be used if the program would behave equivalently if
+    definitely_true always returned False (parallel_or/parallel_and are
+    examples of this pattern, modulo short-circuiting).  Finally, it even
+    be OK if the program wouldn't behave equivalently, so long as the
+    change is semantics preserving.  It can be semantics preserving if
+    the program errors in more cases than it did previously (but otherwise
+    behaves identically), or if it changes some quantity in a way that
+    doesn't matter (e.g., strides often fall in this bucket.)
+    """
+    if isinstance(a, SymBool):
+        if a.node.has_hint():
+            return guard_bool(a)
+        else:
+            return False
+    return bool(a)
+
+def definitely_false(a):
+    """
+    Returns True only if we can tell that a is False, possibly introducing
+    a guard in the process.  If a depends on some unbacked SymInt, we may
+    return False even though there may exist a possible value of the SymInt
+    that would cause the expression a to be False.  See definitely_true
+    for more usage guidance.
+    """
+    if isinstance(a, SymBool):
+        if a.node.has_hint():
+            return not guard_bool(a)
+        else:
+            return False
+    return not bool(a)
+
+def statically_known_true(x: Union[bool, SymBool]) -> bool:
+    """Returns True if x can be simplified to a constant and is true.
+
+    .. note::
+        This function doesn't introduce new guards, so the expression may end
+        up evaluating to true at runtime even if this function returns False.
+
+    Args:
+        x (bool, SymBool): The expression to try statically evaluating
+
+    """
+    if isinstance(x, SymBool):
+        expr = x.node.expr
+        shape_env = x.node.shape_env
+        try:
+            simplified = shape_env._maybe_evaluate_static(expr)
+            if simplified is not None:
+                return bool(simplified)
+        except Exception:
+            log.debug("Could not simplify %s", expr)
+        return False
+    assert isinstance(x, bool)
+    return x
+
+
+def parallel_or(*args):
+    """
+    Evaluate the logical OR of several arguments, avoiding guarding on
+    unbacked SymInts if another argument is definitely True.
+    """
+    if any(statically_known_true(a) for a in args):
+        return True
+    if any(definitely_true(a) for a in args):
+        return True
+    return any(args)
+
+def parallel_and(*args):
+    """
+    Evaluate the logical FALSE of several arguments, avoiding guarding on
+    unbacked SymInts if another argument is definitely False.
+    """
+    if any(statically_known_true(torch.sym_not(a)) for a in args):
+        return False
+    if any(definitely_false(a) for a in args):
+        return False
+    return all(args)
+
+def sym_eq(x, y):
+    """
+    Like ==, but when run on list/tuple, it will recursively test equality
+    and use sym_and to join the results together, without guarding.
+    """
+    if (isinstance(x, tuple) and isinstance(y, tuple)) or (isinstance(x, list) and isinstance(y, list)):
+        if len(x) != len(y):
+            return False
+        return functools.reduce(operator.and_, map(sym_eq, x, y), True)
+    elif isinstance(x, (int, torch.SymInt)) and isinstance(y, (int, torch.SymInt)):
+        return x == y
+    else:
+        raise AssertionError(f"unexpected sym_eq between {type(x)} {type(y)}")
+
+def guard_scalar(a):
+    if isinstance(a, (SymBool, bool)):
+        return guard_bool(a)
+    elif isinstance(a, (SymInt, int)):
+        return guard_int(a)
+    elif isinstance(a, (SymFloat, float)):
+        return guard_float(a)
+    else:
+        raise AssertionError(f"unrecognized scalar {a}")
+
+
+def _constrain_symbol_range(shape_env, s: sympy.Symbol, compiler_min: int, compiler_max: int):
+    shape_env.constrain_symbol_range(s, compiler_min, compiler_max)
+
+
+def _advise_is_size(a):
+    """
+    Don't use this directly; use torch._check_is_size instead.
+
+    This is a softer version of _constrain_range_for_size (with min=0,
+    max=Inf).  Instead of forcibly constraining a variable (and erroring if we
+    failed to constrain it), it will simply advise us that a size is
+    constrained in some way.  We will always defer a runtime assert for this
+    constraint if we cannot prove it at compile-time, but we we only
+    *sometimes* learn useful extra information at compile-time with this
+    information.  This is in contrast to constrain_range_for_size, where if
+    you don't call that on a fresh unbacked symint, chances are we will choke.
+
+    TODO: Make Dynamo handle this appropriately if this is seen in Dynamo-ed
+    code.  Right now this is only really used in code with AOTAutograd trace
+    through, so it is not a big problem that this isn't supported, but in
+    principle all of this code should be Dynamo'able too.
+
+    TODO: I didn't support min/max because I didn't have a use case where this
+    actually helped.  In principle we can support it, it just makes the
+    implementation below more complicated.
+    """
+
+    # This must always succeed, because the sole allowed caller _check_is_size
+    # was responsible for expect_true'ing this
+    # This assert triggers expensive sym compute, do not do it until its cheap.
+    # assert a >= 0
+
+    # NB: it's important not to constrain range for size for *hinted* SymInts,
+    # because it is not only unsound, it will immediately trip our asserts
+    # that hints have to be consistent with static analysis!  If you somehow
+    # have an unbounded SymInt that later constrains to 1, this will be
+    # inconsistent with the range
+    if (
+        isinstance(a, SymInt)
+        and isinstance(a.node, SymNode)
+        and isinstance(a.node.expr, sympy.Symbol)
+        and a.node.shape_env.is_unbacked_symint(a.node.expr)
+    ):
+        _constrain_range_for_size(a)
+
+def _constrain_range_for_size(a, min: Optional[int] = None, max: Optional[int] = None):
+    """
+    This function is NOT INTENDED to be used by itself.
+    """
+
+    if isinstance(a, (SymFloat, SymBool)):
+        raise ValueError("Constraining SymFloat/SymBool is nyi")
+
+    assert isinstance(a, SymInt), "can only constrain range for SymInt"
+    assert isinstance(a.node.expr, sympy.Symbol), "constraining non-Symbols NYI"
+
+    a.node.shape_env._constrain_range_for_size(a.node.expr, min, max)
+
+
+# inclusive both ways
+def constrain_range(a, *, min: Optional[int], max: Optional[int] = None):
+    """
+    Applies a constraint that the passed in SymInt must lie between min-max
+    inclusive-inclusive, WITHOUT introducing a guard on the SymInt (meaning
+    that it can be used on unbacked SymInts).  If min/max are None, we assume
+    that the dimension is unbounded in that direction.  Repeated application
+    of constrain_range intersects the ranges.  This is a fairly low level API
+    that doesn't have a lot of safety guarantees (TODO: provide higher level
+    APIs).
+
+    Currently, we use this API in the following circumstance: when we allocate
+    an unbacked SymInt, denoting an integer quantity which is data dependent,
+    we ordinarily do not know anything about what values it may take.  This
+    means that any sort of guard on it will immediately fail.  However, in
+    many cases, we know something about the unbacked SymInt: for example, we
+    know that nonzero(x).size(0) must be >= 0.  We use constrain_range to
+    narrow the possible range, declaring that negative symbols are impossible.
+    This permits to definitely answer True to queries like 'nnz >= 0', even if
+    we don't know what the actual (hinted) value of 'nnz' is.  In fact, we
+    actually use constrain_range to unsoundly discharge common guards: for an
+    unbacked SymInt produced by nonzero, we will also assume that it is not
+    equal to 0/1 (even though these are perfectly possible values at runtime),
+    because we generally expect graphs that are valid for N=2 to also be valid
+    for N=1.
+    """
+    if min is None:
+        min = -int_oo
+    if max is None:
+        max = int_oo
+
+    if max < min:
+        raise ValueError(
+            "Maximum value to constrain_as_size can't be less than the specified min value, "
+            "received min={min} and max={max}"
+        )
+
+    if isinstance(a, int):
+        if not (min <= a <= max):
+            raise ValueError(f"Invalid value {a} for range [{min}:{max}]")
+        return
+
+    a.node.shape_env._constrain_range(a.node.expr, min, max)
+
+def constrain_unify(a: torch.SymInt, b: torch.SymInt) -> None:
+    """
+    Given two SymInts, constrain them so that they must be equal.  NB:
+    this will not work with SymInts that represent nontrivial expressions
+    (yet!)
+    """
+    if not isinstance(a, SymInt):
+        if not isinstance(b, SymInt):
+            assert a == b
+            return
+        else:
+            shape_env = b.node.shape_env
+    else:
+        shape_env = a.node.shape_env
+
+    shape_env._constrain_unify(a, b)
+
+# Assume that a boolean is true for the purposes of subsequent symbolic
+# reasoning.  This will keep track of corresponding runtime checks to verify
+# that the result is upheld: either as a regular guard, or as a special set
+# of asserts which are triggered when an unbacked SymInt is allocated.
+#
+# DO NOT use this function for these cases:
+#
+#  - This is inappropriate for "branching" conditions (where both
+#    true and false result in valid programs).  We will always assume
+#    the condition evaluates true, and so it will never be possible
+#    to trace the false condition when you use it.  For true branching
+#    on unbacked SymInts, you must use torch.cond; if you incorrectly
+#    use expect_true in this case, you will make the false branch
+#    unreachable (as we will simply assume that only the true branch
+#    is ever exercised).
+#
+#  - This is inappropriate for situations where you know some other system
+#    invariant guarantees that this property holds, since you don't
+#    really need to insert a runtime check in that case.  Use something
+#    like constrain_range in that case.
+#
+# This API has a hitch.  To avoid having to reimplement error reporting
+# capabilities, this function CAN return False.  The invariant is that
+# the surrounding code must raise an error when this function returns
+# False.  This is quite low level, so we recommend using other functions
+# like check() which enforce this in a more intuitive way.
+#
+# By the way, this name is a nod to the __builtin_expect macro,
+# which is used similarly (but unlike __builtin_expect, you MUST fail
+# in the unlikely branch.)  (I think expect is a good name; in recent
+# versions of C++, this is replaced with [[likely]], which is weaker
+# and not accurate for this function!)
+def expect_true(a, skip: int = 0):
+    if isinstance(a, SymBool):
+        # TODO: check perf implications of this
+        frame = inspect.currentframe()
+        for _ in range(skip + 1):  # always run this loop at least once
+            frame = frame.f_back
+        return a.node.expect_true(frame.f_code.co_filename, frame.f_lineno)
+    assert type(a) is bool, a
+    return a
+
+def guard_bool(a):
+    if isinstance(a, SymBool):
+        return a.node.guard_bool("", 0)  # NB: uses Python backtrace
+    assert type(a) is bool, a
+    return a
+
+def guard_int(a):
+    if isinstance(a, SymInt):
+        return a.node.guard_int("", 0)  # NB: uses Python backtrace
+    assert type(a) is int, a
+    return a
+
+def guard_float(a):
+    if isinstance(a, SymFloat):
+        return a.node.guard_float("", 0)  # NB: uses Python backtrace
+    assert isinstance(a, float), a
+    return a
+
+# Given a GraphModule, return all the FakeTensors for all the placeholders
+def fx_placeholder_vals(gm):
+    return [n.meta['val'] for n in gm.graph.nodes if n.op == "placeholder"]
+
+def fx_placeholder_targets(gm):
+    return [n.target for n in gm.graph.nodes if n.op == "placeholder"]
+
+# Given a GraphModule and arguments to run it with, evaluate that the guards
+# for its associated ShapeEnv are satisfied by the passed arguments.  This
+# WILL check for duck sizing.
+def eval_guards(gm, *args, ignore_static=True):
+    return gm.shape_env.evaluate_guards_for_args(fx_placeholder_vals(gm), args, ignore_static=ignore_static)
+
+def bind_symbols(gm, *args):
+    return gm.shape_env.bind_symbols(fx_placeholder_vals(gm), args)
+
+class DimDynamic(Enum):
+    """
+    Controls how to perform symbol allocation for a dimension.  It is always
+    sound to default this to DYNAMIC, but the policies DUCK and STATIC can
+    result in better trace-time and compile-time performance, as they reduce
+    the number of allocated symbols and generally make your graph more static.
+
+    NB: If we notice you've applied a constraint to the dimension, we will
+    force it to DYNAMIC for simplicity.
+
+    DimDynamic is controlled by a variety of higher level UX features.
+    Currently:
+
+    - In eager mode, the default policy is DUCK.
+        - The default is changed to STATIC with assume_static_by_default.
+        - An individual dim is marked DYNAMIC if you mark_dynamic_dim.
+    - In export mode, the default policy is STATIC.
+        - An individual dim is marked DYNAMIC if you specify it in
+          dynamic_shapes passed to export.
+    """
+    # Treat the dimension symbolically
+    DYNAMIC = 0
+    # Treat the dimension symbolically, but if its hint matches another
+    # dynamic dimension, unify the two symbols ("duck sizing")
+    DUCK = 1
+    # Treat the dimension statically based on its hint
+    STATIC = 2
+    # Treat the dimension as a size-like unbacked
+    SIZE_LIKE_UNBACKED = 3
+    # Infer the strides from stride. If size is static, strides will be static as well.
+    INFER_STRIDE = 4
+
+
+# NB: These constraints affect both clients and backends: given some
+# constraint C, the client must pass inputs that satisfy the constraint,
+# while a backend must not introduce guards BEYOND this constraint.
+# For clarity, we document the implications on both sides for both the client
+# and the backend.
+#
+# NB: These constraints are on a *single* dimension.  In principle, we could
+# also have multi-dimension constraints, but our guess is that this is not
+# actually useful and so we are not supporting it right now.
+#
+# NB: Strict constraints are typically only suitable for export, as in eager
+# a backend like inductor may validly introduce extra, discretionary guards
+# to improve performance of code.  A StrictMinMaxConstraint would be brittle
+# under future optimizations performed by inductor; we don't guarantee
+# eager code with StrictMinMaxConstraint will keep working in the future!
+
+@dataclass(frozen=True)
+class Constraint:
+    warn_only: bool
+
+@dataclass(frozen=True)
+class StrictMinMaxConstraint(Constraint):
+    """
+    For clients: the size at this dimension must be within 'vr' (which
+    specifies a lower and upper bound, inclusive-inclusive) AND it
+    must be non-negative and should not be 0 or 1 (but see NB below).
+
+    For backends: there must not be any guards on this dimension which
+    are not implied by the given lower and upper bound.  Regardless of
+    the lower bound, the backend can assume the size is non-negative
+    and that it is not 0 or 1.
+
+    An unbounded StrictMinMaxConstraint can be thought of as a strict version
+    of "RelaxedUnspecConstraint".
+
+    NB: Export will often unsoundly assume that a graph works for 0/1, even
+    though at trace time we assumed size is not 0 or 1.  The idea is that
+    if we produce a graph that works for a range of values, it will be OK
+    for N=0/1 too.
+    """
+    vr: ValueRanges
+
+    def render(self, source: Source):
+        """Format the constrain equation"""
+        # TODO: better printing for -oo and oo
+        return f"{self.vr.lower} <= {source.name()} <= {self.vr.upper}"
+
+@dataclass(frozen=True)
+class RelaxedUnspecConstraint(Constraint):
+    """
+    For clients: no explicit constraint; constraint is whatever is implicitly
+    inferred by guards from tracing.
+
+    For backends: there must exist at least TWO possible values for the
+    size at this dimension which satisfy the guards for this dimension.
+
+    In other words, this constraint helps us distinguish between "we don't
+    care if this dimension specializes or not" versus "this dimension must be
+    unspecialized."  However, this constraint doesn't say very much about what
+    specialization is permitted; for example, if we guard on a size being
+    even, this would still be acceptable under an unspec constraint.  This
+    makes RelaxedUnspecConstraint useful for eager mode, where your backend compiler
+    may add constraints to otherwise dynamic dimensions; we can't assert that
+    there are NO guards as this is brittle because compilers should be able to
+    add extra constraints.  If you want to assert that there are no guards,
+    use StrictMinMaxConstraint with an unbounded ValueRanges.
+    """
+    def render(self, source: Source):
+        return f"RelaxedUnspecConstraint({source.name()})"
+
+# NB: None here indicates the client constraint is whatever is implicitly
+# inferred by guards from tracing, and that a backend can add whatever guards
+# it wants (including fully specializing the value).
+DimConstraint = Union[StrictMinMaxConstraint, RelaxedUnspecConstraint, None]
+
+@dataclass(frozen=True)
+class EqualityConstraint(Constraint):
+    """
+    Represent and decide various kinds of equality constraints between input sources.
+
+    A "source pair" is a pair of input sources for dynamic dimensions that
+    are specified equal. We represent `source_pairs` in a union-find forest
+    so that we can efficiently check whether two such sources are transitively equal.
+
+    A "derived equality" relates an input source to an expression over a root.
+    The root can be another input source, corresponding to some dynamic dimension,
+    or a phantom symbol that does not directly represent any dynamic dimension. We
+    represent `derived_equalities` involving input sources in a transitively-closed map
+    so that we can efficiently check whether an input source is transitively equal to
+    a given expression over another input source.
+    (NOTE: In contrast, it is easy to decide whether an input source is transitively equal
+    to a given expression over a phantom symbol; such expressions are already in canonical
+    form and so the problem reduces to symbolic expression equality.)
+    """
+    source_pairs: List[Tuple[Source, Source]]
+    derived_equalities: List[Tuple[Source, Union[Source, sympy.Symbol], Callable[[sympy.Expr], sympy.Expr]]]
+    phantom_symbols: List[sympy.Symbol]
+
+    def __post_init__(self):
+        """Pre-processing to answer queries `is_equal` and `is_derived` below.
+
+        Example: Suppose we are given:
+          source_pairs [a = b, b = c]
+          derived_equalities [d = c + 1, e = d - 1]
+        We first construct a union find with source_pairs:
+          _parents = {a: a, b: a, c: a}
+        Then we compute canonical symbolic expressions, recursively applying derived_equalities
+        until we bottom out:
+          _defs = {d: c + 1, e: (c + 1) - 1 aka c}
+        """
+
+        # self._parents is a map from input sources to input sources where, conceptually,
+        # these are directed edges in a union-find forest
+        _parents: Dict[Source, Source] = {}
+        object.__setattr__(self, "_parents", _parents)
+        # self._defs is a map from input sources to "canonical" symbolic expressions,
+        # i.e., unary expressions with symbols that corresponds to regular Dims (i.e.,
+        # not derived Dims)
+        _defs: Dict[Source, sympy.Expr] = {}
+        object.__setattr__(self, "_defs", _defs)
+
+        for source1, source2 in self.source_pairs:
+            # preprocess into a union-find forest
+            self._union(self._find(source1), self._find(source2))
+        for source, root, fn in self.derived_equalities:
+            # preprocess into a transitively-closed map
+            # NOTE(avik): we reuse the union-find forest for canonicalizing input sources
+            if isinstance(root, sympy.Symbol):
+                self._defs[self._find(source)] = fn(root)
+            else:
+                self._defs[self._find(source)] = fn(self._rewrite(root))
+
+    def _find(self, source):
+        # chase edges to find the root of this equivalence class
+        if source in self._parents:
+            return self._find(self._parents[source])
+        else:
+            return source
+
+    def _union(self, root1, root2):
+        # merge two equivalence classes by adding an edge from one root to the other
+        if root1 != root2:
+            self._parents[root1] = root2
+
+    def _rewrite(self, src):
+        # always represent the given source by the root of its equivalence class
+        src = self._find(src)
+        if src in self._defs:
+            # simply look up the definition if it exists
+            # NOTE(avik): This works because definitions are always transitively-closed;
+            # otherwise we would have to do recursive rewriting.
+            return self._defs[src]
+        else:
+            # otherwise, create a symbol representing the source
+            return sympy.Symbol(src.name())
+
+    def is_equal(self, source1, source2):
+        return (
+            # check whether source1 and source2 have the same root
+            self._find(source1) == self._find(source2) or
+            # check whether source1 is derived equal to source2
+            self.is_derived(source1, source2, lambda x: x)
+        )
+
+    def is_derived(self, src, symbol_src, fn):
+        # check whether both src and symbol_src have the same definition
+        return self._rewrite(src) == fn(self._rewrite(symbol_src))
+
+
+def _assert_symbol_context(symbolic_context):
+    assert isinstance(symbolic_context, SymbolicContext), "Invalid symbolic_context object"
+    assert type(symbolic_context) is not SymbolicContext, "Illegal usage of symbolic_context ABC"
+
+def _is_supported_equivalence(expr):
+    # Currently supported Dim ops are linear expressions with integer coefficients.
+    # So check that expr only contains +, *, ints, and a single occurrence of a symbol.
+    # (See also documentation of dynamic_shapes._DerivedDim.)
+    if isinstance(expr, (sympy.Add, sympy.Mul)):
+        if len(expr.args) > 2:
+            return False
+        lhs, rhs = expr.args
+        return (
+            (_is_supported_equivalence(lhs) and isinstance(rhs, sympy.Integer)) or
+            (isinstance(lhs, sympy.Integer) and _is_supported_equivalence(rhs))
+        )
+    return isinstance(expr, sympy.Symbol)
+
+def _has_uninterpretable_sympy_function(expr) -> bool:
+    """
+    Add functions that our sympy interpreter can't reify into FX nodes
+    """
+    return expr.has(
+        torch.utils._sympy.functions.ToFloat,
+        torch.utils._sympy.functions.TruncToInt,
+        torch.utils._sympy.functions.CeilToInt,
+    )
+
+@dataclass(frozen=True)
+class SymbolicContext:
+    """
+    Data structure specifying how we should create symbols in
+    ``create_symbolic_sizes_strides_storage_offset``; e.g., should
+    they be static or dynamic.
+
+    This is an abstract base class because we are probably going to add
+    another version of this that says "use exactly these SymInts, don't
+    allocate fresh symbols."
+    """
+
+
+@dataclass(frozen=True)
+class StatelessSymbolicContext(SymbolicContext):
+    """
+    Create symbols in ``create_symbolic_sizes_strides_storage_offset`` via
+    a symbolic_context determination as given by ``DimDynamic`` and ``DimConstraint``.
+    This will cause fresh symbols to be allocated
+    """
+    dynamic_sizes: DimList[DimDynamic]
+    dynamic_strides: DimList[DimDynamic] = None
+    constraint_sizes: DimList[DimConstraint] = None
+    constraint_strides: DimList[DimConstraint] = None
+    # If the tensor is a view, this should be populated for the base. It contains
+    # information on how to allocate symbols when recursively fakeifying the base
+    # during view fake-ification.
+    view_base_context: Optional[SymbolicContext] = None
+    # TODO: add storage offset and stride symbolic_context
+
+    def __post_init__(self):
+        if self.dynamic_strides is None:
+            object.__setattr__(self, 'dynamic_strides', [DimDynamic.INFER_STRIDE] * len(self.dynamic_sizes))
+        if self.constraint_sizes is None:
+            object.__setattr__(self, 'constraint_sizes', [None] * len(self.dynamic_sizes))
+        if self.constraint_strides is None:
+            object.__setattr__(self, 'constraint_strides', [None] * len(self.dynamic_sizes))
+        assert all(stride in (DimDynamic.INFER_STRIDE, DimDynamic.DYNAMIC, DimDynamic.DUCK) for stride in self.dynamic_strides)
+
+
+# note [Tensor Fakification and Symbol Caching]
+#
+# As of the time of this note, dynamo creates a fresh fake tensor mode for backends.
+# The reason we do this is because there are certain classes of operations, namely,
+# metadata mutations, that change tensor size, stride, etc. This means that the fake tensor
+# state at the end of a dynamo trace is different than the fake tensor state at the beginning
+# of a trace. Backends like aot_autograd need a fresh fake tensor to correctly track metadata mutation,
+# view relationships, etc.
+#
+# As we create a new fake mode, we also lose the memoization that comes with it. Rather than
+# transfer the memoization cache, we instead transfer the shape env. However, with this
+# comes nuance - as dynamo is selective in how it makes symbolic shapes. Due to strategies in
+# automatic dynamic and constraints, the policy for which dims are dynamic is nuanced and varies across
+# recompilations.
+#
+# In order to preserve the symbolic decisions made during dynamo tensor fakification, we pass
+# a StatefulSymbolicContext at creation time. This object is tracked, per tensor, on the TracingContext.
+# The lifecycle of this object should match the lifecycle of the original dynamo tracked tensor, and it is
+# safe to reuse this object as many times as necessary to create a fake tensor. Fake tensors
+# created with new fake modes should produce the same exact symbols as the original, providing the same shape_env
+# is used.
+# TODO(voz): Shape env validation
+@dataclass(frozen=True)
+class StatefulSymbolicContext(StatelessSymbolicContext):
+    """
+    Create symbols in ``create_symbolic_sizes_strides_storage_offset`` via
+    a symbolic_context determination as given by a cache of Source:Symbol. A cache hit
+    will reuse a stored symbol, and a cache miss will write to this cache.
+
+    This behaves like StatelessSymbolicContext, except the cache supersedes the
+    other values - dynamic_sizes and constraint_sizes will not be read if we cache
+    hit.
+
+    It is the cache owners responsibility to maintain the lifecycle of the cache
+    w/r/t different shape_envs, clearing, etc.
+    """
+    tensor_source: Source = None
+    # Why is this keyd on int first?
+    # That integer is actually the id of the shape_env. This cache short-circuits symbol
+    # creation, and we must store it per shape env. Now, while tracing invariants are a single
+    # shape env per tracing context, and every new frame gets a new shape_env. So where would we have
+    # multiple shape envs? The answer lies in recording. When we are replaying, replay_shape_env_events
+    # is invoked, and creates a new shape_env. Replaying events against this new shape_env will
+    # cause it to fail with unknown symbols, as the symbols cached here will skip creation, and never
+    # get recorded in var_to_val, etc.
+    # TODO(voz): consider a weakref to the shape_env here
+    shape_env_to_source_to_symbol_cache : Dict[int, Dict["TensorPropertySource", "sympy.Expr"]] = None
+
+    def __post_init__(self):
+        super().__post_init__()
+        # The None default is annoying, but required because of dataclass limitations
+        assert self.tensor_source is not None
+        if not self.shape_env_to_source_to_symbol_cache:
+            object.__setattr__(self, 'shape_env_to_source_to_symbol_cache', {})
+
+
+@dataclass(frozen=True)
+class SubclassSymbolicContext(StatefulSymbolicContext):
+    """
+    The correct symbolic context for a given inner tensor of a traceable tensor subclass
+    may differ from that of the outer symbolic context. This structure allows for this
+    flexibility, with inner symbolic contexts mapped via attr -> symbolic context.
+    """
+    inner_contexts: Dict[str, SymbolicContext] = None
+
+    def __post_init__(self):
+        super().__post_init__()
+        if self.inner_contexts is None:
+            self.inner_contexts = {}
+
+
+def is_symbolic(val: Union[int, SymInt, float, SymFloat, bool, SymBool]) -> bool:
+    if isinstance(val, (int, float, bool)):
+        return False
+    return val.node.is_symbolic()
+
+IndicatorTypes = (IsNonOverlappingAndDenseIndicator,)
+
+@lru_cache(256)
+def safe_expand(r):
+    if hasattr(r, 'expand'):
+        try:
+            return sympy.expand(r)
+        except RecursionError:
+            log.warning("RecursionError in sympy.expand(%s)", r)
+            return r
+    else:
+        return r
+
+def error():
+    raise AssertionError("shouldn't be hit")
+
+
+# TODO: Deduplicate this with torch/_prims_common/__init__.py
+def eval_is_non_overlapping_and_dense(sizes, strides):
+    return int(guard_bool(_eval_is_non_overlapping_and_dense(sizes, strides)))
+
+def _eval_is_non_overlapping_and_dense(sizes, strides):
+    dim = len(sizes)
+
+    # Short-circuits for tensors of rank one, which are
+    # non-overlapping and "dense" if their stride is one
+    # or it is a 0/1 element tensor
+    if dim == 1:
+        return strides[0] == 1 or sizes[0] < 2
+
+    # Checks that there exists a permutation of the strides s.t. the tensor would be contiguous
+    # Sorts (length, stride) pairs by stride
+    lengths_and_strides = sorted(
+        zip(sizes, strides), key=operator.itemgetter(1)
+    )
+
+    # Unlike the C++ code, we don't move the 0/1 size dimensions to the
+    # end.  So we have to keep going for this code.
+    expected_stride = 1
+    for length, stride in lengths_and_strides:
+
+        if length == 1:
+            continue
+
+        if stride != expected_stride:
+            return False
+
+        expected_stride *= length
+
+    return True
+
+
+def _sympy_cast_symbool_to_symint_guardless(x: sympy.Expr) -> sympy.Expr:
+    return sympy.Piecewise((1, x), (0, True))
+
+
+def cast_symbool_to_symint_guardless(symbool: torch.SymBool) -> torch.SymInt:
+    if isinstance(symbool, bool):
+        return 1 if symbool else 0
+    int_sym = _sympy_cast_symbool_to_symint_guardless(symbool.node.expr)
+    return symbool.node.shape_env.create_symintnode(int_sym, hint=int(symbool.node.require_hint()) if has_hint(symbool) else None)
+
+SYMPY_INTERP = {
+    'Abs': operator.abs,
+    'Eq': operator.eq,
+    'Ne': operator.ne,
+    'Gt': operator.gt,
+    'Lt': operator.lt,
+    'Le': operator.le,
+    'Ge': operator.ge,
+    'Min': min,
+    'Max': max,
+    'Mod': operator.mod,
+    'PythonMod': operator.mod,
+    'FloorDiv': operator.floordiv,
+    'TrueDiv': operator.truediv,
+    'PowByNatural': operator.pow,
+    'IsNonOverlappingAndDenseIndicator': eval_is_non_overlapping_and_dense,
+    'floor': math.floor,
+    'ceiling': math.ceil,
+    'FloorToInt': math.floor,
+    'FloatPow': math.pow,
+    'CeilToInt': math.ceil,
+    'cast_symbool_to_symint_guardless': cast_symbool_to_symint_guardless,
+    'RoundToInt': builtins.round,
+    'RoundDecimal': builtins.round,
+    'TruncToInt': math.trunc,
+    'IntTrueDiv': operator.truediv,
+    'FloatTrueDiv': operator.truediv,
+    'ToFloat': builtins.float,
+}
+
+
+def _lru_cache(fn, maxsize=None):
+    """
+    Wrapper around lru_cache that clears when new info about shapes has been
+    updated.
+
+    Use lru_cache if the output is always the same, regardless of the
+    constraints we know now (i.e. evaluate_expr)
+
+    Use _lru_cache otherwise.
+
+    Also note that this depends on _update_version_counter being called on the
+    shape environment whenever the constraints are updated, otherwise the cache
+    will not be cleared.
+    """
+    fn_cache = lru_cache(maxsize)(fn)
+    prior_version = 0
+
+    if config.validate_shape_env_version_key:
+        prior_key = None
+
+        @functools.wraps(fn)
+        def wrapper(self, *args, **kwargs):
+            nonlocal prior_version, prior_key
+            if prior_key is None:
+                prior_key = self._get_key()
+
+            if prior_version != self._version_counter:
+                fn_cache.cache_clear()
+                prior_version = self._version_counter
+                prior_key = self._get_key()
+            else:
+                assert prior_key == self._get_key(), \
+                    "ShapeEnv cache key changed without version being updated!"
+
+            return fn_cache(self, *args, **kwargs)
+
+    else:
+
+        @functools.wraps(fn)
+        def wrapper(self, *args, **kwargs):
+            nonlocal prior_version
+            if prior_version != self._version_counter:
+                fn_cache.cache_clear()
+                prior_version = self._version_counter
+
+            return fn_cache(self, *args, **kwargs)
+
+    wrapper.cache_clear = fn_cache.cache_clear
+    wrapper.cache_info = fn_cache.cache_info  # type: ignore[attr-defined]
+    return wrapper
+
+
+# This is pretty similar to ShapeGuard but it also comes with a message,
+# and is exclusively used for things that MUST be true (unlike guards,
+# which can evaluate False, in which case you just choose not to use
+# a particular specialization)
+@dataclass(frozen=True)
+class RuntimeAssert:
+    expr: sympy.Expr
+    msg: str = field(repr=False)
+    stack: str = field(repr=False)
+
+
+# Used for printing SymExprs in compile_fx
+class SymExprPrinter(StrPrinter):
+    def _print_Float(self, expr):
+        return str(float(expr))
+
+
+class ShapeGuardPrinter(SymExprPrinter):
+    def __init__(
+        self,
+        symbol_to_source,
+        source_ref,
+        var_to_sources,
+    ):
+        super().__init__()
+        self.symbol_to_source = symbol_to_source
+        self.source_ref = source_ref
+        self.var_to_sources = var_to_sources
+
+    def _print_Not(self, expr):
+        return 'not {}'.format(self.parenthesize(expr.args[0], PRECEDENCE["Not"]))
+
+    def _print_And(self, expr):
+        return self.stringify(expr.args, " and ", PRECEDENCE["And"])
+
+    def _print_Or(self, expr):
+        return self.stringify(expr.args, " or ", PRECEDENCE["Or"])
+
+    def _print_Symbol(self, expr) -> str:
+        assert isinstance(expr, sympy.Symbol), str(type(expr))
+
+        def repr_symbol_to_source():
+            return repr({
+                symbol: [s.name() for s in sources]
+                for symbol, sources in self.symbol_to_source.items()
+            })
+
+        assert self.symbol_to_source.get(expr), (
+            f"{expr} (could be from {[s.name() for s in self.var_to_sources[expr]]}) "
+            f"not in {repr_symbol_to_source()}.  If this assert is failing, it could be "
+            "due to the issue described in https://github.com/pytorch/pytorch/pull/90665"
+        )
+        return self.source_ref(self.symbol_to_source[expr][0])
+
+
+class LoggingShapeGuardPrinter(ShapeGuardPrinter):
+    def __init__(self, var_to_sources):
+        super().__init__(var_to_sources, lambda n: n.name(), var_to_sources)
+
+
+class DynamicDimConstraintPrinter(StrPrinter):
+    """
+    Printer for dynamic dim constraints.
+    - Instead of symbol s_k it prints its source t.size()[i]
+    - Instead of Eq(_, _), Mod(_, _), etc. it prints _ == _, _ % _, etc.
+
+    We use this to suggest code for specifying dynamic dim constraints.
+    """
+    def __init__(self, symbol_to_source, source_name_to_debug_name):
+        super().__init__()
+        self.symbol_to_source = symbol_to_source
+        self.source_name_to_debug_name = source_name_to_debug_name
+
+    def _print_Symbol(self, expr) -> str:
+        assert isinstance(expr, sympy.Symbol), str(type(expr))
+        assert self.symbol_to_source.get(expr), (
+            f"Unknown symbol {expr} created by constraints solver"
+        )
+        return self.symbol_to_source[expr][0].name()
+
+    def _print_Relational(self, expr):
+        return f'{self.parenthesize(expr.lhs, precedence(expr))} {expr.rel_op} {self.parenthesize(expr.rhs, precedence(expr))}'
+
+
+class DimConstraints:
+    """
+    Custom solver for a system of constraints on symbolic dimensions.
+    Solutions are "static" values or simplified "dynamic" constraints.
+    """
+
+    def __init__(
+        self,
+        symbol_to_source,
+        var_to_val,
+        marked_dynamic,
+        source_name_to_debug_name,
+    ):
+        # We try to solve systems of inequalities with 1 free variable.
+        self._univariate_inequalities: Dict[sympy.Symbol, Set[sympy.Expr]] = defaultdict(set)
+        # Among them, we prioritize solving for a free variable that has equalities.
+        # NOTE: _symbols_with_equalities is always a subset of _univariate_inequalities.keys()
+        # and removing a symbol from the former => removing it from the latter.
+        self._symbols_with_equalities: Set[sympy.Symbol] = set()
+        # A solution of a free variable with equalities becomes a substitution.
+        # We use these substitutions to simplify other constraints.
+        # NOTE: removing a symbol from _symbols_with_equalities => adding it to _substitutions.
+        self._substitutions: Dict[sympy.Symbol, sympy.Integer] = {}
+
+        # In general, constraints may have // and % operations.
+        # Of course, // can be expressed in terms of / and %.
+        # Our inequality solver can handle / but not %. So we need to transform them away.
+        # We do so by using the values of variables as hints to evaluate %.
+        # For soundness we record additional congruence guards and solve them separately.
+        self._var_to_val: Dict[sympy.Symbol, sympy.Integer] = var_to_val
+        self._congruences: Set[sympy.Expr] = defaultdict(set)
+
+        # We do not try to (directly) solve inequalities with > 1 free variables.
+        # NOTE: free variables in these inequalities cannot also be in _substitutions.
+        self._multivariate_inequalities: Set[sympy.Expr] = set()
+
+        # We park external equalities between free variables here.
+        self._symbolic_equivalences: List[Tuple[Source, sympy.Expr]] = []
+
+        # Solutions come in two forms:
+        # - (static) specializations
+        # - (dynamic) inequalities / congruences
+        self._static_results: Set[str] = set()
+        self._dynamic_results: Set[str] = set()
+
+        # printer for solutions
+        self._dcp = DynamicDimConstraintPrinter(symbol_to_source, source_name_to_debug_name)
+
+        # inconsistencies found on substituting with concrete values / static solutions
+        self._inconsistencies: List[str] = []
+
+        # symbols that are marked dynamic
+        self._marked_dynamic = marked_dynamic
+
+        # track supported sympy functions and subtract from list of all sympy functions
+        self._supported_sympy_functions: Set[sympy.Function] = {
+            Application,
+            Mod,
+            PythonMod,
+            FloorDiv,
+        }
+        self._enumerate_sympy_functions()
+
+    def rewrite_with_congruences(self, s, expr):
+        """
+        Eliminate expressions of the form b // d and b % d while adding congruences of the form b % d == k.
+        This leaves rational operators (in particular of the form b / d) that our inequality solver can handle.
+        We solve the added congruences separately (using our congruence solver, see below).
+        """
+        def mod_handler(*args):
+            # Suppose that we have an expression of the form b % d with free variable s.
+            # Using the value of s as a "hint," we can evaluate b % d to a value k.
+            # Then we can rewrite b % d to k while adding the guard b % d == k.
+
+            # NOTE(avik): This abstraction is provably sound but, in general, incomplete. It is complete IFF
+            # the original expression always evaluates to a constant value (i.e., it does not vary with s).
+            # In other words,
+            # - solutions of s with the rewritten expression are guaranteed to also be solutions of s with
+            #   the original expression;
+            # - while it may be possible to find solutions of s with the original expression that are not
+            #   solutions with the rewritten expression, in that case the original expression cannot evaluate
+            #   to the same value for all solutions of s.
+            #
+            # Should we be worried about this incompleteness? No, because of the following reasons:
+            # 1. It unblocks dramatic simplification that would not be otherwise possible with current tech
+            #    (i.e., "don't let perfect be the enemy of the good").
+            # 2. We already have a tradition of using hints to add guards in the compiler for making progress.
+            # 3. We have not yet seen a counterexample arise in practice! In particular, any congruence guards
+            #    we generate (or simplify to) seem to be of the form b % d == k where k is a constant.
+            #
+            # Here's a theoretical counterexample: 3*s % (s + 1) == s - 2, that is satisfied by all s >= 2.
+            # With any hint (say) s = k, we'd rewrite this to: 3*s % (s + 1) == k - 2. But, substituting, we
+            # would then get k - 2 == s - 2, and thus s = k as the (only, constant) solution!
+            base, divisor = args
+            base, divisor = self.rewrite_with_congruences(s, base), self.rewrite_with_congruences(s, divisor)
+            mod_reduced = base.xreplace(self._var_to_val) % divisor.xreplace(self._var_to_val)
+            congruence = (base - mod_reduced) % divisor
+            if congruence != 0:
+                self._congruences[s].add(congruence)
+            return mod_reduced
+
+        def floor_div_handler(*args):
+            # Suppose that we have an expression of the form b // d with free variable s.
+            # Using the value of s, we can evaluate b % d to a value k.
+            # Then we can rewrite b // d to (b - k) / d, while adding the guard b % d == k.
+
+            # NOTE(avik): This is exactly equivalent to rewriting b // d as (b - (b % d)) / d
+            # and eliminating b % d as above.
+            base, divisor = args
+            base, divisor = self.rewrite_with_congruences(s, base), self.rewrite_with_congruences(s, divisor)
+            mod_reduced = base.xreplace(self._var_to_val) % divisor.xreplace(self._var_to_val)
+            congruence = (base - mod_reduced) % divisor
+            if congruence != 0:
+                self._congruences[s].add(congruence)
+            # NB: Must not be CleanDiv, it needs to be regular sympy division
+            # so inequality solver works.  This is sort of problematic for
+            # is_integer tests though haha
+            return (base - mod_reduced) / divisor
+
+        if expr.has(Mod):
+            expr = expr.replace(Mod, mod_handler)
+        # 7 // -3 is -3, 7 % -3 is -2, and 7 - (-2) / -3 is -3.0 so negative
+        # arguments should be OK.
+        if expr.has(PythonMod):
+            expr = expr.replace(PythonMod, mod_handler)
+        if expr.has(FloorDiv):
+            expr = expr.replace(FloorDiv, floor_div_handler)
+        return expr
+
+    def _enumerate_sympy_functions(self):
+        module = torch.utils._sympy.functions
+        all_functions = set()
+        for attr in dir(module):
+            if isinstance(func := getattr(module, attr), sympy.FunctionClass):
+                all_functions.add(func)
+        self._unsupported_sympy_functions = all_functions.difference(self._supported_sympy_functions)
+
+    def _has_unsupported_sympy_function(self, expr) -> bool:
+        """
+        Tracks list of sympy.Functions the export solver doesn't know how to handle.
+        """
+        return expr.has(*self._unsupported_sympy_functions)
+
+    def add(self, expr) -> bool:
+        """Add an expression to the set of constraints.
+
+        Return whether the expression is a trivial constraint (i.e., an obvious tautology).
+        """
+        if expr == sympy.true:
+            return True
+        orig_expr = expr
+        orig_reduced = orig_expr.xreplace(self._var_to_val)
+        # TODO(avik): https://github.com/pytorch/pytorch/issues/101093
+        # It is possible that `expr` will fail the consistency check because of
+        # precision errors. Specifically, on substituting its free symbols with
+        # their concrete values, we might end up comparing floats. Until we have
+        # a fix for this issue, we delay raising such failures. See solve().
+        if orig_reduced == sympy.false:
+            self._inconsistencies.append(f"{orig_expr} is inconsistent!")
+        if isinstance(expr, sympy.Ne) or self._has_unsupported_sympy_function(expr):
+            # we're not going to do anything useful with these, so drop them
+            return False
+        free_symbols = expr.free_symbols
+        assert free_symbols, f"Did not expect constraint with no free variables: {expr}"
+        if len(free_symbols) > 1:
+            # multivariate: record and move on
+            self._multivariate_inequalities.add(expr)
+        else:
+            # univariate: can solve these immediately
+            s = next(iter(free_symbols))
+            # eliminate // and % (see documentation of `rewrite_with_congruences` above)
+            old_n_congruences = len(self._congruences[s])
+            expr = self.rewrite_with_congruences(s, expr)
+            new_n_congruences = len(self._congruences[s])
+            if expr == sympy.true:
+                return old_n_congruences == new_n_congruences
+            reduced = expr.xreplace(self._var_to_val)
+            if reduced == sympy.false:
+                self._inconsistencies.append(
+                    f"{expr}, obtained by rewriting {orig_expr} with congruences, "
+                    "is inconsistent!"
+                )
+            if isinstance(expr, sympy.Eq):
+                # special status for symbols that have equalities (see `solve` below)
+                self._symbols_with_equalities.add(s)
+            self._univariate_inequalities[s].add(expr)
+        return False
+
+    def add_equality(self, source, expr):
+        """Add an equality constraint"""
+        if expr.is_number:
+            # specialization, right here
+            self._static_results.add(f"{source.name()} == {expr}")
+        else:
+            # these will resolve to either specializations or dynamic equality constraints
+            self._symbolic_equivalences.append((source, expr))
+
+    def _reduce_congruences(self):
+        reduced_congruences = {}
+        for s, congruences in self._congruences.items():
+            remainder_modulus_pairs = []
+            congruences_to_check = set()
+            for congruence in congruences:
+                base, divisor = congruence.args
+                # We are given a congruence of the form base % divisor == 0 with a free variable s. So:
+                # - we transform this into an equation of the form base = divisor * tmp;
+                # - we solve this equation for s to get a linear solution with free variable tmp.
+                tmp = sympy.Symbol("reduce_congruences_tmp", integer=True)
+                symbol, solution = sympy.solve_linear(base - divisor * tmp, symbols=[s])
+                # See https://docs.sympy.org/latest/modules/solvers/solvers.html#sympy.solvers.solvers.solve_linear
+                # for how to interpret the results.
+                if s == symbol:
+                    # This means the solution is of the form s = modulus*tmp + remainder.
+                    modulus, remainder = sympy.polys.polytools.div(solution, tmp)
+                    if isinstance(modulus, sympy.Integer) and isinstance(remainder, sympy.Integer):
+                        # Make sure 0 <= remainder <= modulus.
+                        remainder = remainder % modulus
+                        remainder_modulus_pairs.append((remainder, modulus))
+                        continue
+                # This means that we did not get a unique solution to the equation.
+                # No problem, we will check it.
+                congruences_to_check.add(congruence)
+            # Finally we solve for a congruence s such that s = r_i mod m_i for each (r_i, m_i).
+            # The solution will be a congruence of the form s = r mod m.
+            # NOTE(avik): Since the given m_i may not be pairwise coprime, we can't just use CRT.
+            if remainder_modulus_pairs:
+                remainder, modulus = sympy.ntheory.modular.solve_congruence(*remainder_modulus_pairs)
+                reduced_congruences[s] = {(s - remainder) % modulus}
+                substitution = {s: modulus * sympy.Symbol("tmp", integer=True) + remainder}
+                reduced_congruences[s].update(
+                    congruence for congruence in congruences_to_check
+                    if not sympy.checksol(congruence, substitution)
+                )
+            else:
+                reduced_congruences[s] = congruences_to_check
+
+        return reduced_congruences
+
+    def _raise_inconsistencies(self):
+        if self._inconsistencies:
+            msg = "\n".join(self._inconsistencies)
+            self._inconsistencies.clear()
+            raise ValueError(f"The following inconsistencies were found:\n{msg}")
+
+    def solve(self):
+        """Solve the system of constraint equations to find simplified constraints
+        """
+        self._raise_inconsistencies()
+        # as long as there are symbols with equalities, solve for them
+        # NOTE(avik): this is guaranteed to terminate (#iterations <= #symbols)
+        while self._symbols_with_equalities:
+            s = self._symbols_with_equalities.pop()
+            exprs = self._univariate_inequalities.pop(s)
+            solution = sympy.solvers.inequalities.reduce_inequalities(exprs, s)
+            if isinstance(solution, sympy.And):
+                solution = next((arg for arg in solution.args if isinstance(arg, sympy.Eq)), solution)
+            assert isinstance(solution, sympy.Eq), f"Expected an equality constraint for {s}, got {solution}"
+            symbol, val = solution.args
+            assert symbol == s, f"Expected a constraint on {s} instead of on {symbol}"
+            # because this is univariate, the solution is a specialization
+            self._static_results.add(f"{self._dcp.symbol_to_source[s][0].name()} == {val}")
+            # add this as a substitution to simplify other constraints
+            self._substitutions[s] = val
+
+            # simplify multivariate inequalities: some of them will now become univariate!
+            multivariate_inequalities = self._multivariate_inequalities
+            self._multivariate_inequalities = set()
+            for expr in multivariate_inequalities:
+                self.add(expr.xreplace({s: self._substitutions[s]}))
+            self._raise_inconsistencies()
+
+        # solve linear congruences
+        # NOTE(avik): We do not need to solve them for symbols that have already been specialized.
+        reduced_congruences = self._reduce_congruences()
+        for s, congruences in reduced_congruences.items():
+            for congruence in congruences:
+                # any congruence that cannot be checked becomes a dynamic constraint as well
+                if s not in self._substitutions or not sympy.checksol(congruence, {s: self._substitutions[s]}):
+                    if self._is_supported_congruence(congruence):
+                        base, divisor = congruence.args
+                        tmp_name = f"_{self._dcp.source_name_to_debug_name[self._dcp.symbol_to_source[s][0].name()]}"
+                        tmp = sympy.Symbol(tmp_name, integer=True)
+                        from torch._dynamo.source import ConstantSource
+                        self._dcp.symbol_to_source[tmp] = [ConstantSource(tmp_name)]
+                        r = try_solve(sympy.Eq(base, divisor * tmp), s)
+                        self._dynamic_results.add(self._dcp.doprint(sympy.Eq(s, r[1])))
+
+        # remaining symbols have only pure inequalities (no equalities)
+        for s, exprs in self._univariate_inequalities.items():
+            try:
+                solution = sympy.solvers.inequalities.reduce_inequalities(exprs, s)
+                # because this is univariate, the solution is a dynamic (range) constraint
+                if isinstance(solution, sympy.Or):
+                    solution = next(iter(arg for arg in solution.args if arg.xreplace(self._var_to_val)))
+                if isinstance(solution, sympy.And):
+                    for arg in solution.args:
+                        self._dynamic_results.add(self._dcp.doprint(arg))
+                else:
+                    self._dynamic_results.add(self._dcp.doprint(solution))
+            except (NotImplementedError, AssertionError) as e:
+                log.warning("Failed to reduce inequalities: %s", e)
+                for expr in exprs:
+                    self._dynamic_results.add(self._dcp.doprint(expr))
+
+        # simplify symbolic equivalences: some of them will now become specializations!
+        symbolic_equivalences = self._symbolic_equivalences
+        self._symbolic_equivalences = []
+        for source, expr in symbolic_equivalences:
+            self.add_equality(source, expr.xreplace(self._substitutions))
+
+        # remaining symbolic equivalences become dynamic equality constraints
+        for source, expr in self._symbolic_equivalences:
+            self._dynamic_results.add(f"{source.name()} == {self._dcp.doprint(expr)}")
+
+    @classmethod
+    def _is_supported_congruence(cls, congruence):
+        base, divisor = congruence.args
+        # Congruences that can be currently expressed with supported Dim ops are
+        # of the form (x + a) % b == 0, where x is a Dim and a and b are constants.
+        # This allows us to derive x as b*y - a for some Dim y.
+        # (See also documentation of dynamic_shapes._DerivedDim.)
+        if isinstance(base, sympy.Add):
+            lhs, rhs = base.args
+            cond = (
+                (isinstance(lhs, sympy.Symbol) and isinstance(rhs, sympy.Integer)) or
+                (isinstance(lhs, sympy.Integer) and isinstance(rhs, sympy.Symbol))
+            )
+        else:
+            cond = isinstance(base, sympy.Symbol)
+        cond = cond and isinstance(divisor, sympy.Integer)
+        return cond
+
+    def forced_specializations(self):
+        """Returns a dictionary of the names of symbols to their specialized value
+        """
+        def debug_name(src):
+            name = src.name()
+            if self._dcp.source_name_to_debug_name:
+                return f"{self._dcp.source_name_to_debug_name[name]} = {name}"
+            else:
+                return name
+
+        return {
+            debug_name(self._dcp.symbol_to_source[s][0]): val
+            for s, val in self._substitutions.items()
+            if s in self._marked_dynamic
+        }
+
+    def _is_derived_dim(self, dim):
+        return isinstance(dim, torch.export.dynamic_shapes._DerivedDim)
+
+    def _is_dim(self, dim):
+        return (
+            isinstance(dim, torch.export.dynamic_shapes._Dim)
+            and not isinstance(dim, torch.export.dynamic_shapes._DerivedDim)
+        )
+
+    def _process_derived_dim_roots(
+        self,
+        results: Dict[str, Dict[str, Any]],
+        name_to_dim: Dict[str, Any],
+    ) -> None:
+        '''
+        Here we resolve 2 concerns with derived dims suggested fixes: 1) newly introduced roots,
+        and 2) root swapping.
+
+        1) Newly introduced roots appear with modulo guards, e.g. Mod(dx, 2) = 0 suggests
+        dx is a derived dim equal to 2 * _dx, introducing a new root _dx. Currently the final
+        suggested fixes handle this correctly, but we can get intermediate results that look like
+        {"dy": {"eq": "dx + 1"}, "dx": {"eq": "2 * _dx + 1, "min": 3, "max": 15}}
+        and this routine prettifies this by unifying to a single root, and making each suggestion
+        either a derived dim or min/max range, not both.
+
+        2) With suggested fixes for derived dims, roots can be swapped,
+        e.g. dx, dx - 1 -> dy + 1, dy. Here we don't want to print out the attached name,
+        since this leads to messages like "dx - 1 = Dim("dx - 1", ...)".
+        Instead we evaluate the new root value, and remove results for its derivations.
+
+        First we find all the original roots (specified in dynamic_shapes), that are found in the
+        values of results (i.e. used for computing suggesting fix values). These original roots
+        (suppose `dx`) are either specialized, unchanged, refined, or swapped
+        (expressed as a derived dim). If any of the first 3 cases happen, we suggest `dx`'s value
+        in results, and remove suggestions for derivations of `dx`, assuming the derived relation
+        is valid. If swapped, we find the new root, and use the fix to evaluate `dx`'s new value,
+        and then do the same with `dx`'s derivations.
+
+        Assuming the originally specified derived relations are correct is valid, because:
+            1) if the relations are plain wrong (e.g. input shape = (6, 4) with spec (dx, dx - 1))
+               produce_guards() will catch this and crash before hand.
+            2) if the relations are numerically correct but do not match the emitted guard,
+               for example:
+
+                    def forward(self, x, y):
+                        return x.reshape([-1]) + y  # guard: s0 * 2 = s1
+                    inputs = (torch.randn(6, 2), torch.randn(12))
+                    dx = Dim("dx", min=2, max=32)
+                    dynamic_shapes={"x": (dx, 2), "y": (dx + 6, )}  # this matches values but not op
+
+               then this leads to 2 linear equations, and a) produce_guards() is able to solve for
+               the unique solution of dx = 6 and specialize, and b) the export constraint solver will
+               raise an issue due to range constraints (a unique solution means not all values in a
+               range satisfy a guard) and also force specializations.
+        '''
+        from torch.export.dynamic_shapes import Dim
+
+        def _check_same_range(c, dim):
+            # returns True if c & dim are both min/max ranges with same values
+            return (
+                self._is_dim(dim)
+                and ("min" in c or "max" in c)
+                and (
+                    (dim.min < 2 and c.get("min", 2) == 2)
+                    or dim.min == c.get("min", 2)
+                )  # let pass if analysis min = 2 and specified min = 0/1
+                and dim.max == c.get("max", int_oo)
+            )
+
+        # 1) newly introduced roots
+        # this part we handle adding newly introduced roots
+        # these arise from guards like "x.shape[0] % 3 == 0"
+        # leading to suggested fixes like "dx = 3*_dx"
+        # extract _dx, and find appropriate min/max values
+        #
+        # before, we have something like:
+        # {"dx": {"eq": 3*_dx+1, "min": 4, "max": 10}, "dy": dx+1, "dz": dx+2}
+        # we want instead:
+        # {"_dx": {"min": 1, "max": 4}, "dx": 3*_dx+1, "dy": 3*_dx+2, "dz": 3*_dx+3}
+        introduced_roots: Dict[str, str] = {}  # map new root -> old root
+        for k, c in list(results.items()):
+            if "eq" in c and isinstance(c["eq"], sympy.Expr):  # derived dim
+                root = next(iter(c["eq"].free_symbols))
+                if str(root) not in name_to_dim:
+                    introduced_roots[str(root)] = k
+                    # calculate necessary min & max
+                    modulus, remainder = sympy.polys.polytools.div(c["eq"], root)
+                    c_min = c.get("min", 2)
+                    min_ = math.ceil((c_min - remainder) / modulus)
+                    c_max = c.get("max", int_oo)
+                    max_ = math.floor((c_max - remainder) / modulus)
+                    # create result & dim
+                    results[str(root)] = {"min": min_, "max": max_}
+                    name_to_dim[str(root)] = Dim(str(root), min=min_, max=max_)
+                    # remove old root min/max bounds
+                    c.pop("min", None)
+                    c.pop("max", None)
+
+        # alter derivations that depend on old root, to unify to new root
+        # e.g. dx=3*_dx+1, dy=dx+1 -> dy=3*_dx+2
+        for old_root in introduced_roots.values():
+            for k, c in list(results.items()):
+                if (
+                    "eq" in c
+                    and isinstance(c["eq"], sympy.Expr)
+                    and str(symbol := next(iter(c["eq"].free_symbols))) == old_root
+                ):  # derived dim with root = old_root
+                    new_root_expr = results[str(old_root)]["eq"]  # dx=3*_dx+1
+                    new_expr = c["eq"].subs({symbol: new_root_expr})  # dy=(3*_dx+1)+1
+                    c["eq"] = new_expr
+
+        # 2) root swapping
+        # collect all the original roots that are used for calculating values of suggested fixes
+        # this consists of:
+        # 1) {"dx": {"min": ..., "max": ...}} -> dx: refined root dim
+        # 2) {"dy": "dx + 1"} -> dx: root for suggested fix
+        modified_roots: Set[str] = set()
+        for k, c in results.items():
+            if k not in name_to_dim:  # _dynamo.export() may handle source directly
+                continue
+            if self._is_dim(name_to_dim[k]) and ("min" in c or "max" in c):  # case 1)
+                modified_roots.add(k)
+            elif "eq" in c and isinstance(c["eq"], sympy.Expr):  # case 2)
+                root = next(iter(c["eq"].free_symbols))
+                assert root is not None
+                modified_roots.add(str(root))
+
+        # exclude newly introduced roots, we've already processed these
+        modified_roots = modified_roots.difference(introduced_roots)
+
+        # evaluate the new value for each root
+        # this is now either 1) unchanged, 2) refined with a new range,
+        # or 3) specialized to a concrete value
+        modified_root_values: Dict[str, Dict[str, Any]] = {}
+        for root in modified_roots:
+            swapped_root = True
+            if root in results:
+                c = results[root]
+                if (
+                    ("min" in c or "max" in c)  # range
+                    or isinstance(c["eq"], int)  # specialized
+                ):
+                    # here, the original root is a root Dim or concrete value in results.
+                    # if it is a derived dim, it is swapped, and we handle that below.
+                    if not _check_same_range(c, name_to_dim[root]):  # ignore if unchanged
+                        modified_root_values[root] = c
+                    swapped_root = False
+
+            if swapped_root:
+                # if the original root has been swapped in results, that means the new root
+                # is a range (if it had specialized, the original root would have too).
+                # find this new root, and solve for the original root's range.
+                for k, c in results.items():
+                    if k not in name_to_dim:
+                        continue
+                    dim = name_to_dim[k]
+                    if dim.__class__.__name__ == "_DerivedDim" and dim.root.__name__ == root:
+                        # only look for min/max root, otherwise root would have specialized
+                        if "min" in c or "max" in c:
+                            expr = sympy.sympify(k)
+                            s = next(iter(expr.free_symbols))
+                            result = {
+                                "min": try_solve(sympy.Eq(expr, c["min"]), s)[1],  # type: ignore[arg-type]
+                                "max": try_solve(sympy.Eq(expr, c["max"]), s)[1],  # type: ignore[arg-type]
+                            }
+                            if not _check_same_range(result, name_to_dim[root]):  # ignore if unchanged
+                                modified_root_values[root] = result
+                                break
+
+        # filter out results where the key is a derived dim (e.g. {"dx - 1" : 4})
+        # we only want to suggest fixes for the root, to avoid derived names.
+        # also, remove anything in modified_roots, since we either add new modified values after this,
+        # or have decided they are unchanged.
+        for k in list(results.keys()):
+            if k not in name_to_dim:
+                continue
+            if self._is_derived_dim(name_to_dim[k]) or k in modified_roots:
+                del results[k]
+
+        # update results with modified root values
+        # now results has the following properties:
+        # - only contains original roots as keys
+        # - each root is now either specialized, refined, or derived from another original root
+        results.update(modified_root_values)
+
+    def prettify_results(
+        self,
+        original_signature: inspect.Signature,
+        dynamic_shapes: Optional[Union[Dict[str, Any], Tuple[Any], List[Any]]] = None,
+        constraint_violation_error=None,
+        forced_specializations=None,
+    ):
+        """Format a message for constraint violation erros"""
+        from torch.export.dynamic_shapes import _get_dim_name_mapping
+        if not self._dcp.source_name_to_debug_name:
+            # nothing to do
+            return ""
+
+        def transform(s, inverse=False):
+            for k, v in self._dcp.source_name_to_debug_name.items():
+                s = s.replace(k, v) if not inverse else s.replace(v, k)
+            return s
+
+        results = defaultdict(dict)
+        if dynamic_shapes is None:
+            dynamic_shapes = {}
+
+        def flip(op):
+            if op == "<=":
+                return ">="
+            if op == ">=":
+                return "<="
+            if op == "<":
+                return ">"
+            if op == ">":
+                return "<"
+            assert op == "=="
+            return op
+
+        def relation_with_digit(expr, op, digit):
+            if op == "<=":
+                results[expr]["max"] = digit
+            elif op == "<":
+                results[expr]["max"] = digit - 1
+            elif op == ">=":
+                results[expr]["min"] = digit
+            elif op == ">":
+                results[expr]["min"] = digit + 1
+            else:
+                assert op == "=="
+                results[expr]["eq"] = digit
+
+        # retrieve dynamic shapes
+        name_to_dim = _get_dim_name_mapping(dynamic_shapes)
+
+        for s in self._static_results.union(self._dynamic_results):
+            t = transform(s)
+            if t == s:
+                continue
+            left, op, right = re.split(r"( == | <= | >= | < | > )", t)
+            op = op.strip()
+            if op == "==" and left == right:
+                continue
+            if right.isdigit():
+                relation_with_digit(left, op, int(right))
+            elif left.isdigit():
+                relation_with_digit(right, flip(op), int(left))
+            else:
+                assert op == "==", t
+                results[left]["eq"] = sympy.sympify(right)
+
+        # order forced specializations based on name
+        forced_specializations = {
+            k: forced_specializations[k]
+            for k in sorted(
+                forced_specializations.keys(),
+                key=lambda x: x.split(" = ")[1],
+            )
+        }
+
+        buf = ""
+        if forced_specializations:
+            debug_names = set()
+            for k in forced_specializations:
+                dim = name_to_dim[k.split(" = ")[0]]
+                if self._is_derived_dim(dim):
+                    debug_names.add(dim.root.__name__)
+                else:
+                    debug_names.add(dim.__name__)
+
+            buf += (
+                f"Specializations unexpectedly required ({', '.join(sorted(debug_names))})! "
+                'For more information, run with TORCH_LOGS="+dynamic".\n'
+            )
+            for s, val in forced_specializations.items():
+                buf += f"  - solving the guards generated for {s} resulted in a specialized value of {val}.\n"
+
+        self._process_derived_dim_roots(results, name_to_dim)
+
+        dims = []
+        others = []
+
+        # order results by source name
+        results = {
+            k: results[k] for k in sorted(
+                results.keys(),
+                key=lambda x: transform(x, inverse=True),
+            )
+        }
+        for k, c in results.items():
+            if "eq" in c:
+                other = c["eq"]
+                if isinstance(other, int):
+                    others.append(f"{k} = {other}")
+                elif _is_supported_equivalence(other):
+                    others.append(f"{k} = {other}")
+            else:
+                min_ = c.get("min", None)
+                if min_ == 2:
+                    min_ = None
+                max_ = c.get("max", None)
+                if min_ is not None and max_ is not None:
+                    dims.append(f"{k} = Dim('{k}', min={min_}, max={max_})")
+                elif min_ is not None:
+                    dims.append(f"{k} = Dim('{k}', min={min_})")
+                elif max_ is not None:
+                    dims.append(f"{k} = Dim('{k}', max={max_})")
+                else:
+                    dims.append(f"{k} = Dim('{k}')")
+
+        # results will get filtered out if no new suggestions,
+        # this can happen if guards are too complex.
+        # in that case don't suggest fix
+        if dims or others:
+            buf += "\nSuggested fixes:\n  "
+            buf += "\n  ".join(dims + others)
+
+        return buf
+
+
+TLS = threading.local()
+
+
+@dataclass(frozen=True)
+class ShapeEnvSettings:
+    """
+    Encapsulates all shape env settings that could potentially affect
+    FakeTensor dispatch. Used when creating dispatch cache keys.
+    """
+
+    allow_scalar_outputs: bool
+    allow_dynamic_output_shape_ops: bool
+    assume_static_by_default: bool
+    specialize_zero_one: bool
+    duck_shape: bool
+    prefer_deferred_runtime_asserts_over_guards: bool
+    allow_complex_guards_as_runtime_asserts: bool
+
+
+class ShapeEnv:
+    # This is a wrapper over the actual __init__ function.
+    #
+    # Where to add a new constructor parameter to ShapeEnv?
+    # =====================================================
+    # This __init__ function should be used only for parameters related to event recording.
+    # These are parameters that we don't wish to pass down the road to new ShapeEnv instances
+    # created from replaying events.
+    #
+    # If you wish to add a parameter to the constructor of ShapeEnv, unrelated to event
+    # recording, do so in the _init function.
+    def __init__(
+        self, *,
+        should_record_events: Optional[bool] = None,
+        tracked_fakes: Optional[List[Any]] = None,
+        **kwargs
+    ) -> None:
+        self._init(**kwargs)
+
+        # Disable event recording when replaying.
+        kwargs["should_record_events"] = False
+
+        from torch.fx.experimental.validator import translation_validation_enabled
+        self._translation_validation_enabled = translation_validation_enabled()
+
+        # If not specified, enable event recording if both:
+        #   - Translation validation is on
+        #   - Translation validation bisection is not disabled
+        self.should_record_events = (
+            should_record_events
+            if should_record_events is not None
+            else (
+                self._translation_validation_enabled
+                and not config.translation_validation_no_bisect
+            )
+        )
+
+        # Enable event recording check if both:
+        #   - It should record events
+        #   - The recording check is enabled
+        self.check_recorded_events = (
+            self.should_record_events and config.check_shape_env_recorded_events
+        )
+
+        # This will make sure we only record the top-level function call.
+        self.is_recording = not self.should_record_events
+        # Keep track of the list of tracked fakes.
+        self.tracked_fakes = tracked_fakes
+        # List of events for reconstructing ShapeEnv at arbitrary points in time.
+        self.events: List[ShapeEnvEvent] = (
+            [ShapeEnvEvent(ShapeEnv, kwargs=kwargs)] if self.should_record_events else []
+        )
+
+        # FakeTensor per-ShapeEnv operation cache. This is used for caching
+        # operations that contain symbolic shapes which have guards on the
+        # ShapeEnv (so are ShapeEnv-dependent).
+        #
+        # NOTE: It's important that SymNodes in this cache have their ShapeEnv
+        # stripped otherwise you end up with cycles which can only be cleaned
+        # with the GC.
+        self.fake_tensor_cache: Dict[torch._subclasses.fake_tensor._DispatchCacheKey,
+                                     torch._subclasses.fake_tensor._DispatchCacheEntry] = {}
+
+    # Pro-tip: if you add new field to ShapeEnv, this affects some accept
+    # tests.  Accept their output with:
+    #
+    #   EXPECTTEST_ACCEPT=1 python test/dynamo/test_dynamic_shapes.py -k test_shape_env_equal
+    #
+    def _init(
+        self, *,
+        allow_scalar_outputs=True,
+        allow_dynamic_output_shape_ops=True,
+        # NB: These are legacy configuration that help us make good choices
+        # when the constraint/dynamic dims are not explicitly passed to us.
+        # Ideally we will fix all call sites to be explicit and not have
+        # implicit choices, but this apparently was pretty involved.
+        assume_static_by_default=False,
+        # Note - On 0/1 specialization
+        #
+        # The following options affect decisions we make about eager
+        # specialization.  Disabling them will increase trace time (as we do
+        # more symbolic reasoning) and can also harm the quality of generated
+        # code (because inductor may not be able to specialize for bounds
+        # being equal--although if we later respecialize because of a guard,
+        # your code may be just as good as it was before.)
+        #
+        # When True, eagerly specialize input sizes which have 0/1.
+        specialize_zero_one=True,
+        # When True, assume input sizes which have the same size are
+        # symbolically equal.
+        duck_shape: Optional[bool] = None,
+        # For debugging
+        co_fields=None,
+        # When True, whenever safe, we will generate a deferred runtime assert
+        # instead of a guard whenever we know that an expression must be True,
+        # otherwise it would be an error, even for backed SymInts (where we
+        # could ostensibly unconditionally generate guards).  This is useful
+        # for export, where preventing "error checking" sizes from showing up
+        # in guards is helpful, since these guards in some sense are overly
+        # pedantic.  See also https://github.com/pytorch/pytorch/issues/121749
+        prefer_deferred_runtime_asserts_over_guards=False,
+        # When True, does not emit or raise constraint violation errors on
+        # implicit guards generated by ops, and defers to runtime assertions
+        # in the graph instead. For export.
+        allow_complex_guards_as_runtime_asserts=False,
+        # XXX Add any new settings that could affect FakeTensor evaluation
+        # to: torch._subclasses.fake_tensor._ShapeEnvSettings
+    ):
+        if duck_shape is None:
+            duck_shape = config.use_duck_shape
+
+        self.settings = ShapeEnvSettings(
+            # Not directly used by ShapeEnv; indirectly used by FakeTensor
+            allow_scalar_outputs=allow_scalar_outputs,
+            allow_dynamic_output_shape_ops=allow_dynamic_output_shape_ops,
+            # End
+            assume_static_by_default=assume_static_by_default,
+            specialize_zero_one=specialize_zero_one,
+            duck_shape=duck_shape,
+            prefer_deferred_runtime_asserts_over_guards=prefer_deferred_runtime_asserts_over_guards,
+            allow_complex_guards_as_runtime_asserts=allow_complex_guards_as_runtime_asserts,
+        )
+
+        self.guards: List[ShapeGuard] = []
+        # Maps symbolic ints to their original concrete values
+        # Currently populated from tensors
+        self.var_to_val: Dict[sympy.Symbol, sympy.Integer] = {}
+        # Like var_to_val, but only set when propagate_real_tensors is on.
+        # Used as last resort to avoid GuardOnDataDependent error
+        self.unbacked_var_to_val: Dict[sympy.Symbol, sympy.Integer] = {}
+        # Maps symbolic ints to their min/max range.  These ranges
+        # are conservative: the int MUST fall in the range, but the
+        # range may contain ints which may not actually appear in
+        # practice
+        self.var_to_range: Dict[sympy.Symbol, ValueRanges] = {}
+        self.source_name_to_debug_name: Dict[str, str] = {}
+        self.var_to_sources: Dict[sympy.Symbol, List[Source]] = {}
+        self.var_to_stack: Dict[sympy.Symbol, CapturedTraceback] = {}
+        # Maps from sympy ints to expressions representing them
+        # Populated from equality guards (i.e. a.shape[0] == b.shape[0])
+        self.replacements: Dict[sympy.Symbol, sympy.Expr] = {}
+        self.unbacked_renamings: Dict[sympy.Symbol, sympy.Symbol] = {}
+        # Set holds a % b expressions that evaluate to 0.
+        self.divisible: Set[sympy.Expr] = set()
+        # Set that holds "size-like" symbols.  When we perform
+        # "size-oblivious" tests, these can be assumed to be >= 2.
+        self.size_like: Set[sympy.Symbol] = set()
+        # Duck-shaping says that if two input tensors have the same size,
+        # they get assigned the same symbolic variable
+        self.val_to_var: Dict[int, sympy.Expr] = {}
+        if specialize_zero_one:
+            self.val_to_var = {0: sympy.Integer(0), 1: sympy.Integer(1)}
+        self.unbacked_symfloat_counter = itertools.count()
+        self.unbacked_symint_counter = itertools.count()
+        # Similar to guards, but these MUST evaluate to true and can
+        # only be evaluated at runtime midway through (i.e., they always
+        # involve unbacked symints)
+        #
+        # For efficiency reasons, we index in the following way.  Suppose you have
+        # a runtime assert i0 + i1 <= s1.  We pick the most recently allocated
+        # symbol in the source expression and add the assert to the list for
+        # that symbol e.g., {i1: [i0 + i1 <= s1]}.
+        #
+        # We access the runtime asserts in two situations:
+        #
+        #   - When we are guarding on an expression, we will attempt to
+        #     statically evaluate it, in case the unbacked SymInts can
+        #     simplify away.  If we have a runtime assert, we may be able
+        #     to discharge the guard entirely.  We only need to attempt
+        #     runtime asserts that mention freevars of the expression in
+        #     question.
+        #
+        #   - When we are performing codegen (in Inductor for eager, or
+        #     when finalizing the export FX graph), we need to know what
+        #     extra runtime asserts to insert.  Whenever an unbacked
+        #     SymInt comes into scope, all runtime asserts involving it
+        #     become eligible for insertion (so long as all of their other
+        #     free unbacked symbols are also in scope).  We technically
+        #     can handle any choice of key by kicking inexpressible asserts
+        #     to the next unbacked symbol to wait on, but if we choose the
+        #     latest key, an assert will only show up at the moment when
+        #     we can actually codegen it.
+        self.deferred_runtime_asserts: Dict[sympy.Symbol, List[RuntimeAssert]] = {}
+        # This exists so we can efficiently invalidate the cache (it's used as
+        # part of the cache key); otherwise we'd have to iterate through
+        # deferred_runtime_asserts to compute its length
+        self.num_deferred_runtime_asserts = 0
+        self.log = log
+        self.log.debug("create_env")
+        self.frozen = False
+        self.runtime_asserts_frozen = False
+        self.dim_constraints: Optional[DimConstraints] = None
+        self.counter = collections.Counter()
+        # Mapping from sympy.Symbol to the number of guards which mention this
+        # symbol
+        self.symbol_guard_counter = collections.Counter()
+        # A selection of important fields on co_field; solely used for
+        # signpost_event
+        self.co_fields = co_fields if co_fields else {}
+
+        # Whenever we allocate a fresh unbacked Symbol, we add it to this
+        # pending list.  Unbacked symbol allocation can occur at unpredictable
+        # points during meta tensor propagation, but at some point, the we
+        # have to know what the binding site for an unbacked symbol is, and
+        # this is computed when we actually place the node in the graph.  The
+        # important thing is that we always actually handle every unaccounted
+        # for unbacked symbol, so this list helps us keep track of them and
+        # then make sure they are all accounted for.
+        #
+        # We could potentially give rise to errors earlier by lexically
+        # scoping when we do propagation, and only allowing unbacked symbols
+        # to be allocated at this point in time.  However this is inconvenient
+        # to do in Dynamo, because fake tensor propagation is far from when we
+        # analyze binding sites (set_example_value), so we do it in a more
+        # mutatey way.
+        #
+        # NB: fresh unbacked symbols NEVER get substitutions applied to them,
+        # they are binding sites!
+        self.pending_fresh_unbacked_symbols: List[sympy.Symbol] = []
+
+        # Version counter used to invalidate cached values
+        self._prev_cache_key = self._get_key()
+        self._version_counter = 0
+
+        # Cache for FX nodes.
+        # Maps an already built node a tuple of:
+        #   1. node's target
+        #   2. list of arguments
+        # This drastically reduces the size of the FX graph, avoiding
+        # duplicated nodes.
+        self.fx_node_cache: Dict[Tuple[Callable, Tuple[Any, ...]], torch.fx.Node] = {}
+        self.source_to_symbol: Dict[str, sympy.Symbol] = {}
+
+        # Suppose you want to replace an unbacked symbol with another
+        # unbacked symbol.  This is error prone because you can cause
+        # references to unbacked symbols to time travel backwards.  E.g.,
+        #
+        # u1 = x.item()
+        # ... use of u1 ...
+        # u2 = y.item()
+        # u3 = z.item()
+        # torch._check(u1 == u2 + u3)
+        #
+        # If you replace u1 with u2 + u3, then the use of u1 now
+        # references u2 and u3 prior to them actually being bound at
+        # runtime.
+        #
+        # To control for this, we track the order unbacked symbols
+        # were allocated, and only allow substitutions if they respect
+        # the dependency from this order; an unbacked symbol can only
+        # be substituted with unbacked symbols that come before it in the
+        # order.
+        #
+        # This also imposes an ordering on the unbacked symbol binding
+        # sites themselves: you are not allowed to reorder unbacked symbol
+        # bindings.  At the moment, this is not tracked, but we potentially
+        # could track this at the IR level using a higher order operator
+        # with something like effect token tracking.
+        self.unbacked_alloc_order: Dict[sympy.Symbol, int] = {}
+
+        from torch.fx.experimental.validator import translation_validation_enabled
+        self._translation_validation_enabled = translation_validation_enabled()
+
+        if self._translation_validation_enabled:
+            from torch.fx.experimental.validator import TranslationValidator
+
+            self.validator = TranslationValidator()
+            self.graph = torch.fx.Graph()
+            # Create an output graph and start inserting before that.
+            # This is needed when 'deepcopy'-ing this object.
+            self.graph.inserting_before(self.graph.output(None))
+
+            # Mapping of each node name to the node itself.
+            #
+            # This is useful for matching an FX node from a recorded ShapeEnv.graph
+            # to the FX node of the ShapeEnv we are running the event on.
+            #
+            # Whenever you add a node to self.graph, you must add a mapping to this
+            # variable. Otherwise, the built FX graph on the replayed ShapeEnv will
+            # not be valid.
+            self.name_to_node: Dict[str, torch.fx.Node] = {}
+
+    @property
+    def allow_scalar_outputs(self):
+        return self.settings.allow_scalar_outputs
+
+    @property
+    def allow_dynamic_output_shape_ops(self):
+        return self.settings.allow_dynamic_output_shape_ops
+
+    @property
+    def assume_static_by_default(self):
+        return self.settings.assume_static_by_default
+
+    @property
+    def specialize_zero_one(self):
+        return self.settings.specialize_zero_one
+
+    @property
+    def duck_shape(self):
+        return self.settings.duck_shape
+
+    @property
+    def prefer_deferred_runtime_asserts_over_guards(self):
+        return self.settings.prefer_deferred_runtime_asserts_over_guards
+
+    @property
+    def allow_complex_guards_as_runtime_asserts(self):
+        return self.settings.allow_complex_guards_as_runtime_asserts
+
+    def check_equal(self, other: "ShapeEnv") -> None:
+        """Compare another ShapeEnv for equivalence
+        """
+        # ShapeEnv fields that are not relevant for the outcome of
+        # ShapeEnv.produce_guards call:
+        #   - Debugging variables
+        #   - Translation validation related variables
+        #   - Events recording related variables
+        non_state_variable_names = (
+            "counter",
+            "log",
+            "var_to_stack",
+            "fx_node_cache",
+            "graph",
+            "validator",
+            "check_recorded_events",
+            "should_record_events",
+            "is_recording",
+            "tracked_fakes",
+            "events",
+            "source_name_to_debug_name",
+            "_prev_cache_key",
+            "_version_counter",
+            "dim_constraints",
+        )
+
+        # Mapping of the value of each to-be-compared field into the values that
+        # should actually be compared.
+        #
+        # You should modify this if, for example, the field that holds state and
+        # debugging information. e.g. ShapeGuard holds the actual guard (sympy.Expr)
+        # and the stack when it was added to the set of guards. In order to compare
+        # it, we throw away the stack information.
+        def map_value(key: str, value: Any) -> Any:
+            if key in ("unbacked_symfloat_counter", "unbacked_symint_counter"):
+                from copy import copy
+
+                # For itertools.count(), we compare the next integer returned
+                # by the count iterators. Not that we need to copy the iterator
+                # first. Otherwise we are mutating the object.
+                return next(copy(value))
+            elif key == "guards":
+                # Transform the list of ShapeGuard into a list of expressions.
+                return [g.expr for g in value]
+            elif key == "deferred_runtime_asserts":
+                # Transform the list of RuntimeAsserts into a list of expressions.
+                return {s: [ra.expr for ra in ras] for s, ras in value.items()}
+            elif key == "name_to_node":
+                # Compare just the set of keys is the same.
+                return set(value.keys())
+            elif key in ("symbol_guard_counter", "pending_fresh_unbacked_symbols", "fake_tensor_cache"):
+                # Skip this for comparisons
+                return None
+            return value
+
+        shape_env_check_state_equal(self, other, non_state_variable_names, map_value)
+
+    def _snapshot_tracked_fakes(self) -> Optional[List[Any]]:
+        if self.tracked_fakes is None:
+            return None
+
+        from torch._dynamo.variables.builder import TrackedFake
+
+        def maybe_transform_fake(fake: TrackedFake):
+            inner_fake = fake.fake \
+                if isinstance(fake.fake, (torch.SymInt, torch.SymFloat)) \
+                else FakeTensorMeta.from_fake(fake.fake)
+            # Even though TrackedFake accepts either a Union[SymInt, FakeTensor], here we give it a
+            # FakeTensorMeta for two reasons:
+            #   1. this is all the information we need when recording ShapeEnvEvents.
+            #   2. it works even if each TrackedFake changes its metadata.
+            return TrackedFake(inner_fake, fake.source, fake.symbolic_context)  # type: ignore[arg-type]
+
+        return [maybe_transform_fake(fake) for fake in self.tracked_fakes]
+
+    def _last_event_index(self) -> int:
+        return len(self.events) - 1
+
+    @contextmanager
+    def _recording(self):
+        self.is_recording = True
+        try:
+            yield
+        finally:
+            self.is_recording = False
+
+    @record_shapeenv_event()
+    def _eliminate_unbacked(self, orig_s: sympy.Symbol, new_s: sympy.Expr):
+        self._set_replacement(orig_s, new_s, "eliminate_unbacked")
+
+    @record_shapeenv_event()
+    def set_unbacked_var_to_val(self, k: sympy.Symbol, v: int) -> None:
+        """Used only when propagate_real_tensors; registers a value for an
+        unbacked symbol, which can be used last resort to resolve hints."""
+        self.unbacked_var_to_val[k] = sympy.sympify(v)
+
+    # Unlike set_replacement, this records a shapeenv event
+    @record_shapeenv_event()
+    def _rename_unbacked_to(self, orig_s: sympy.Symbol, new_s: sympy.Symbol):
+        assert isinstance(orig_s, sympy.Symbol), orig_s
+        assert isinstance(new_s, sympy.Symbol), new_s
+        assert free_unbacked_symbols(new_s), new_s
+        assert free_unbacked_symbols(orig_s), orig_s
+        dest = self.replacements.get(orig_s)
+        assert not free_unbacked_symbols(dest), f"{orig_s} -> {dest}"
+        self._set_replacement(orig_s, new_s, "rename_unbacked_to")
+        self.unbacked_renamings[orig_s] = new_s
+        if dest is not None:
+            self._set_replacement(new_s, dest, "rename_unbacked_to_dest")
+
+    @record_shapeenv_event()
+    def _constrain_range_for_size(self, a: sympy.Symbol, min: Optional[int] = None, max: Optional[int] = None):
+        if min is None:
+            min = 0
+        if max is None:
+            max = int_oo
+
+        if max < min:
+            raise ValueError(
+                "Maximum value to constrain_as_size can't be less than the specified min value, "
+                "received min={min} and max={max}"
+            )
+
+        self.constrain_symbol_range(
+            a,
+            compiler_min=min,
+            compiler_max=max,
+        )
+        self.size_like.add(a)
+
+    @record_shapeenv_event()
+    def _constrain_range(self, a: sympy.Expr, min: int, max: int):
+        if isinstance(a, sympy.Integer):
+            if not (min <= int(a) <= max):
+                raise ValueRangeError(f"Invalid value {int(a)} for range [{min}:{max}]")
+            return
+
+        # TODO: Shouldn't we install a guard if the symbol is backed?  Or is the
+        # semantics that this is an "unchecked" assert (but it this actually
+        # something useful?  Might be better to restrict only for unbacked
+        # SymInt).
+        if isinstance(a, sympy.Symbol):
+            self.constrain_symbol_range(
+                a,
+                compiler_min=min,
+                compiler_max=max,
+            )
+
+    @record_shapeenv_event()
+    def _constrain_unify(self, a, b):
+        """
+        Given two SymInts, constrain them so that they must be equal.  NB:
+        this will not work with SymInts that represent nontrivial expressions
+        (yet!)
+        """
+        # TODO: this does not install a deferred runtime assert yet
+
+        # TODO: Maybe dedupe this with _maybe_guard_rel?
+        # Update Feb 2024: this is extra important to do, this doesn't handle
+        # unbacked replacements properly nor does it generate deferred runtime
+        # asserts
+        if not isinstance(a, SymInt):
+            if not isinstance(b, SymInt):
+                assert a == b
+            else:
+                assert isinstance(b.node.expr, sympy.Symbol), "constraining non-Symbols NYI"
+                assert b.node.shape_env is self
+                self.replacements[b.node.expr] = sympy.Integer(a)
+        else:
+            # TODO: Actually, we can support this as long as one of them is a symbol.
+            # NB: We can't actually do "unification" as our operators are not
+            # injective
+            assert isinstance(a.node.expr, sympy.Symbol), "constraining non-Symbols NYI"
+            assert a.node.shape_env is self
+            if not isinstance(b, SymInt):
+                self.replacements[a.node.expr] = sympy.Integer(b)
+            else:
+                assert a.node.shape_env is b.node.shape_env
+                assert isinstance(b.node.expr, sympy.Symbol), "constraining non-Symbols NYI"
+                new_var = self._find(a.node.expr)
+                self.replacements[b.node.expr] = new_var
+
+    def _ignore_fresh_unbacked_symbols_tls(self):
+        return getattr(TLS, "ignore_fresh_unbacked_symbols", False)
+
+    @record_shapeenv_event()
+    def _ignore_fresh_unbacked_symbols_enter(self):
+        TLS.ignore_fresh_unbacked_symbols = True
+
+    @record_shapeenv_event()
+    def _ignore_fresh_unbacked_symbols_exit(self):
+        TLS.ignore_fresh_unbacked_symbols = False
+
+    @contextmanager
+    def ignore_fresh_unbacked_symbols(self):
+        """
+        Indicates that the newly allocated unbacked SymInts are being
+        discarded
+        """
+        self._ignore_fresh_unbacked_symbols_enter()
+        try:
+            yield
+        finally:
+            self._ignore_fresh_unbacked_symbols_exit()
+
+    @record_shapeenv_event()
+    def freeze(self):
+        """Freeze this ShapeEnv to stop accumulating guards
+
+        A frozen ShapeEnv will ignore any further guards generated on it and
+        only emit a warning which may lead to accuracy problems.
+        """
+        self.frozen = True
+
+    @record_shapeenv_event()
+    def freeze_runtime_asserts(self):
+        """Freeze this ShapeEnv to stop adding deferred runtime asserts.
+
+        We will error if you try to install a new runtime assert when it is
+        frozen.  This would indicate a lowering violation, or perhaps something
+        we know statically is already True but we are checking it again in a way
+        that is not clearly dischargeable.
+        """
+        # self.prefer_deferred_runtime_asserts_over_guards = False
+        self.runtime_asserts_frozen = True
+
+    def _create_symbol_for_source(self, source: Source) -> Optional[sympy.Symbol]:
+        if not self._translation_validation_enabled:
+            return None
+        srcname = source.name()
+        if source not in self.source_to_symbol:
+            self.source_to_symbol[srcname] = sympy.Symbol(srcname, integer=True)
+        return self.source_to_symbol[srcname]
+
+    def _add_z3var(self, symbol: sympy.Symbol, type: Type) -> None:
+        if self._translation_validation_enabled:
+            self.validator.add_var(symbol, type)
+
+    def _add_target_expr(self, expr) -> None:
+        if self._translation_validation_enabled:
+            self.validator.add_target_expr(expr)
+
+    def _add_assertion(self, expr) -> None:
+        if self._translation_validation_enabled:
+            self.validator.add_assertion(expr)
+
+    def _check_translation_validate(self) -> None:
+        if self._translation_validation_enabled:
+            self.validator.validate()
+
+    @record_shapeenv_event()
+    def _create_fx_call_function(
+            self,
+            op: Callable,
+            args: Tuple,
+    ) -> Tuple[Optional[torch.fx.Node], bool]:
+        # Cache this tuple in order to avoid duplicated nodes.
+        node_key = (op, args)
+        # Flags whether the returned node was cached or not.
+        fresh = False
+
+        if self._translation_validation_enabled and node_key not in self.fx_node_cache:
+
+            # Presence of None in the arguments implies that we should ignore this operation.
+            if any(a is None for a in args):
+                # We check if we are not mixing SymNode that should not be ignored
+                # (fx_node is not None) with those that should (fx_node is None).
+                assert all(not isinstance(a, torch.fx.Node) for a in args)
+                return None, fresh
+
+            fresh = True
+
+            # If translation validation is enabled, all arguments must have its
+            # own FX node.
+            assert all(a is not None for a in args), f"missing arg in FX graph ({op.__name__}): {args}"
+            node = self.fx_node_cache[node_key] = self.graph.call_function(op, args)
+            self.name_to_node[node.name] = node
+
+        return self.fx_node_cache.get(node_key, None), fresh
+
+    def _create_fx_placeholder_and_z3var(
+            self,
+            symbol: sympy.Symbol,
+            type: Type,
+    ) -> Optional[torch.fx.Node]:
+        if not self._translation_validation_enabled:
+            return None
+
+        node_key = (self.graph.placeholder, (symbol,))
+
+        # Check if we haven't added this symbol already.
+        # If so, skip the placeholder creation, as it
+        # generates invalid Python code.
+        if node_key not in self.fx_node_cache:
+            # Add a Z3 variable according to 'type'.
+            self._add_z3var(symbol, type)
+            # Create the FX placeholder out of a mangled name.
+            mangled_name = re.sub(r'[^a-zA-Z0-9]', '_', re.sub(r'[()]', '', symbol.name))
+            node = self.fx_node_cache[node_key] = self.graph.placeholder(mangled_name)
+            self.name_to_node[node.name] = node
+            # Attach the 'symbol' to the placeholder so that we can retrieve
+            # the Z3 variable later.
+            node.meta["symbol"] = symbol
+
+        return self.fx_node_cache[node_key]
+
+    def _remove_fx_node(self, node: Optional[torch.fx.Node]) -> None:
+        if self._translation_validation_enabled and node is not None:
+            self.name_to_node.pop(node.name)
+            self.graph.erase_node(node)
+
+    def _add_fx_node_metadata(self, node: torch.fx.Node) -> None:
+        from torch._dynamo.utils import get_current_node
+
+        if self.should_record_events:
+            node.meta[SHAPEENV_EVENT_KEY] = self._last_event_index()
+            node.meta[CURRENT_NODE_KEY] = get_current_node()
+
+    def _suppress_guards_tls(self):
+        return getattr(TLS, "suppress_guards", False)
+
+    @record_shapeenv_event()
+    def _suppress_guards_enter(self):
+        TLS.suppress_guards = True
+
+    @record_shapeenv_event()
+    def _suppress_guards_exit(self):
+        TLS.suppress_guards = False
+
+    @contextmanager
+    def suppress_guards(self):
+        """Context manager to ignore all guards generated inside"""
+        self._suppress_guards_enter()
+        try:
+            yield
+        finally:
+            self._suppress_guards_exit()
+
+    def _get_key(self):
+        """
+        Defines the current "state" of the guards we've accumulated in this ShapeEnv.
+        Determines when we need to invalidate our cache
+        """
+        return (len(self.replacements), len(self.divisible), self.num_deferred_runtime_asserts, len(self.unbacked_var_to_val))
+
+    def _update_version_counter(self):
+        # The shape environment is queried orders of magnitude more often than
+        # it is changed, so we summarise the cache key into a linearly
+        # increasing version counter which is cheaper to check in _lru_cache
+
+        # Only update version counter if the state actually changed
+        cur_key = self._get_key()
+        if self._prev_cache_key != cur_key:
+            self._prev_cache_key = cur_key
+            self._version_counter += 1
+
+    def _produce_dyn_sizes(self,
+                           ex_size: Sequence[int],
+                           source: Source,
+                           symbolic_context: SymbolicContext
+                           ) -> List[sympy.Expr]:
+        return self._produce_dyn_sizes_from_int_tuple(tuple(ex_size), source, symbolic_context)
+
+    def _produce_dyn_sizes_from_int_tuple(self,
+                                          tensor_size: Tuple[int],
+                                          source: Source,
+                                          symbolic_context: SymbolicContext,
+                                          ) -> List[sympy.Expr]:
+        assert all(not is_symbolic(val) for val in tensor_size), f"Expect size to be a plain tuple of ints but got {tensor_size}"
+        from torch._dynamo.source import TensorPropertySource, TensorProperty
+        _assert_symbol_context(symbolic_context)
+        dynamic_dims = symbolic_context.dynamic_sizes
+        constraint_dims = symbolic_context.constraint_sizes
+        size = []
+        for i, val in enumerate(tensor_size):
+            size.append(self.create_symbol(
+                val,
+                TensorPropertySource(source, TensorProperty.SIZE, i),
+                dynamic_dims[i],
+                constraint_dims[i],
+                symbolic_context=symbolic_context
+            ))
+        return size
+
+    def create_symbolic_sizes_strides_storage_offset(
+        self,
+        ex: torch.Tensor,
+        source: Source,
+        *,
+        symbolic_context: Optional[SymbolicContext] = None,
+    ):
+        """
+        Returns a list of symbolic sizes and strides for the given tensor.
+        We try our best to express stride in terms of the sizes, so as to not
+        introduce new symbolic variables.
+        """
+
+        ex_size = tuple(self._maybe_specialize_sym_int_with_hint(sz) for sz in ex.size())
+        ex_stride = tuple(self._maybe_specialize_sym_int_with_hint(sd) for sd in ex.stride())
+        ex_storage_offset = self._maybe_specialize_sym_int_with_hint(ex.storage_offset())
+
+        return self._create_symbolic_sizes_strides_storage_offset(
+            ex_size,
+            ex_stride,
+            ex_storage_offset,
+            [_is_dim_dynamic(ex, i) for i in range(ex.dim())],
+            source,
+            symbolic_context=symbolic_context,
+        )
+
+    # Dynamo may want to wrap FakeTensors with SymInt sizes up e.g. make_fx(opt_f(), tracing_mode="symbolic").
+    # We create symbols in shape_env using the backed hints behind SymInt.
+
+    # Case 1: when SymInt is backed, dynamo can proceed with FakeTensors that have concrete shape.
+    # produce_guards will trigger specializations on the outer stuff
+
+    # Case 2: when the SymInt is unbacked, we will throw an data dependent error in require_hint().
+    #
+    # It's probably good for now but it's important to note that this approach has implications for
+    # the original shape_env when checking guards in different order.
+
+    # Example:
+    # ---------
+    # Consider a function "opt_f" as shown below:
+
+    # @torch.compile()
+    # def opt_f(x: bool, y: Tensor):
+    #   if x == True:
+    #     return y + torch.randn([4])
+    #   else:
+    #     return y
+    # Depending on the sequence of calls, we might install two different sets of guards:
+
+    # 1. opt_f(False, y):
+    #    - "x == False" (always works for any size y)
+
+    # 2. opt_f(True, y):
+    #    - Triggers recompilation and results in guards like:
+    #      - "x == True and y.size(0) == 4"
+    #      - (or "y.size(0) == 4 and x == True")
+
+    # The order of checking the guards matters. In this specific example:
+    # If True branch guard check precedes False branch and for True branch, y.size(0) check precedes x == True,
+    # we may have an unnessary shape speciliazation for y.
+    def _maybe_specialize_sym_int_with_hint(self, maybe_sym) -> int:
+        assert isinstance(maybe_sym, (int, torch.SymInt))
+        if is_symbolic(maybe_sym):
+            assert maybe_sym.node.shape_env is not self, \
+                "expect the symbol is created from an shape env other than current one."
+            return maybe_sym.node.require_hint()
+        return maybe_sym
+
+    @record_shapeenv_event()
+    def _create_symbolic_sizes_strides_storage_offset(
+        self,
+        ex_size: Sequence[int],
+        ex_stride: Sequence[int],
+        ex_storage_offset: int,
+        is_dim_dynamic: Sequence[bool],
+        source: Source,
+        *,
+        symbolic_context: Optional[SymbolicContext] = None,
+    ):
+        dim = len(ex_size)
+
+        # Reimplement the legacy behavior
+        if symbolic_context is None:
+            constraint_sizes = [None] * dim
+            constraint_strides = [None] * dim
+            dynamic_dims = []
+            dynamic_strides = []
+            for i in range(dim):
+                # NB: This is encapsulation breaking!  Legacy behavior was
+                # bad.
+                if is_dim_dynamic[i]:
+                    r = DimDynamic.DYNAMIC
+                elif self.assume_static_by_default:
+                    r = DimDynamic.STATIC
+                else:
+                    r = DimDynamic.DUCK
+                dynamic_dims.append(r)
+                dynamic_strides.append(r)
+            dynamic_dims = [DimDynamic.DUCK] * dim
+            dynamic_strides = [DimDynamic.INFER_STRIDE] * dim
+            # symbolic_context is None - set one
+            symbolic_context = StatelessSymbolicContext(
+                dynamic_sizes=dynamic_dims,
+                dynamic_strides=dynamic_strides,
+                constraint_sizes=constraint_sizes,
+                constraint_strides=constraint_strides,
+            )
+        # We got a StatelessSymbolicContext
+        _assert_symbol_context(symbolic_context)
+        constraint_sizes = symbolic_context.constraint_sizes
+        constraint_strides = symbolic_context.constraint_strides
+        dynamic_sizes = symbolic_context.dynamic_sizes
+        dynamic_strides = symbolic_context.dynamic_strides
+
+        # TODO: make this configurable from outside symbolic_context; we made a symbolic_context
+        # decision here where if all sizes are static, we are going to
+        # specialize all of the inner strides/offset too. We don't have to
+        # do this, and arguably we should ALWAYS allow for dynamic offset,
+        # this is cheap.
+        # TODO: This should be DYNAMIC, using DUCK for BC
+        dynamic_offset = DimDynamic.STATIC if all(r == DimDynamic.STATIC for r in dynamic_sizes) else DimDynamic.DUCK
+        are_sizes_static = all(r == DimDynamic.STATIC for r in dynamic_sizes)
+
+        assert len(dynamic_sizes) == dim, f"{len(dynamic_sizes)} != {dim}"
+        assert len(dynamic_strides) == dim, f"{len(dynamic_sizes)} != {dim}"
+        assert len(constraint_sizes) == dim
+        assert len(constraint_strides) == dim
+
+        from torch._dynamo.source import TensorPropertySource, TensorProperty
+        size: List[sympy.Expr] = self._produce_dyn_sizes_from_int_tuple(ex_size, source, symbolic_context)
+        stride: List[Optional[sympy.Expr]] = [None] * len(size)
+        for i, val in enumerate(ex_stride):
+            if val in (0, 1):
+                stride[i] = sympy.Integer(val)
+        while any(x is None for x in stride):
+            candidates = {
+                ex_size[i] * ex_stride[i]: size[i] * stride[i]
+                for i in range(len(size))
+                if stride[i] is not None and ex_stride[i] >= 0
+            }
+
+            # iterate over unbound strides in sorted order
+            def _nested_int_aware_sort(tup):
+                return (
+                    # Order nested ints by their coefficients.
+                    # 1 here to order nested ints after non-nested-ints.
+                    (1, tup[0].node.nested_int_coeff(), tup[1]) if is_nested_int(tup[0])
+                    else (0, *tup)
+                )
+            val_list = sorted(
+                [(ex_stride[i], i) for i in range(len(stride)) if stride[i] is None],
+                key=_nested_int_aware_sort,
+            )
+            for _, i in val_list:
+                # Set stride to a candidate only for DimDynamic.INFER_STRIDE
+                if stride[i] is None and dynamic_strides[i] == DimDynamic.INFER_STRIDE and ex_stride[i] in candidates:
+                    stride[i] = candidates[ex_stride[i]]
+                    candidates[ex_size[i] * ex_stride[i]] = size[i] * stride[i]
+
+            if any(x is None for x in stride):
+                # bind the smallest unbound stride to a new variable
+                val, i = min(
+                    [
+                        (ex_stride[i], i)
+                        for i in range(len(stride))
+                        if stride[i] is None
+                    ], key=_nested_int_aware_sort
+                )
+                # Set INFER_STRIDE to STATIC or DUCK depending on sizes
+                dyn_stride = dynamic_strides[i]
+                if dynamic_strides[i] == DimDynamic.INFER_STRIDE:
+                    dyn_stride = DimDynamic.STATIC if are_sizes_static else DimDynamic.DUCK
+                stride[i] = self.create_symbol(
+                    val,
+                    TensorPropertySource(source, TensorProperty.STRIDE, i),
+                    dynamic_dim=dyn_stride,
+                    constraint_dim=constraint_strides[i],
+                    symbolic_context=symbolic_context,
+                )
+        assert all(x is not None for x in stride)
+
+        sym_sizes = [
+            self.create_symintnode(
+                sym,
+                hint=hint,
+                source=TensorPropertySource(source, TensorProperty.SIZE, i),
+            )
+            for i, (sym, hint) in enumerate(zip(size, ex_size))
+        ]
+        sym_stride = []
+        for i, stride_expr in enumerate(stride):
+            # NB: Don't duck size the stride; instead use the expression
+            # we computed
+            assert stride_expr is not None
+            sym_stride.append(self.create_symintnode(
+                stride_expr, hint=ex_stride[i], source=TensorPropertySource(source, TensorProperty.STRIDE, i)))
+        sym_storage_offset = self.create_symintnode(
+            self.create_symbol(
+                ex_storage_offset,
+                TensorPropertySource(source, TensorProperty.STORAGE_OFFSET),
+                dynamic_dim=dynamic_offset,
+                constraint_dim=None,
+                symbolic_context=symbolic_context
+            ),
+            hint=ex_storage_offset,
+            source=TensorPropertySource(source, TensorProperty.STORAGE_OFFSET))
+        return tuple(sym_sizes), tuple(sym_stride), sym_storage_offset
+
+    @record_shapeenv_event()
+    def create_symintnode(
+            self,
+            sym: "sympy.Expr",
+            *,
+            hint: Optional[int],
+            source: Optional[Source] = None,
+    ):
+        """Create a SymInt value from a symbolic expression
+
+        If you know what the current hint value of the SymInt to be created
+        is, pass it into hint.  Otherwise, pass None and we will make our best
+        guess
+
+        """
+        source_name = source.name() if source else None
+
+        if self._translation_validation_enabled and source is not None:
+            # Create a new symbol for this source.
+            symbol = self._create_symbol_for_source(source)
+            assert symbol is not None
+
+            # Create a new FX placeholder and Z3 variable for 'symbol'.
+            fx_node = self._create_fx_placeholder_and_z3var(symbol, int)
+
+            # Add an equality assertion for the newly created symbol and 'sym'.
+            self._add_assertion(sympy.Eq(symbol, sym))
+        else:
+            fx_node = None
+
+        if isinstance(sym, sympy.Integer):
+            if hint is not None:
+                assert int(sym) == hint
+            out = int(sym)
+        else:
+            # How can this occur? When we mark_unbacked, we end up with a real
+            # tensor that has hints for all sizes, but we MUST NOT create a
+            # SymNode with a hint, because we're hiding the hint from our eyes
+            # with the unbacked Symbol.  And in fact, the hint compute may be
+            # inconsistent with size oblivious tests.
+            if free_unbacked_symbols(sym):
+                hint = None
+            out = SymInt(SymNode(sym, self, int, hint, fx_node=fx_node))
+        return out
+
+    @record_shapeenv_event()
+    def create_symfloatnode(
+            self,
+            sym: "sympy.Expr",
+            *,
+            hint: Optional[int],
+            source: Optional[Source] = None,
+    ):
+        """Create a SymFloat value from a symbolic expression"""
+        source_name = source.name() if source else None
+
+        if self._translation_validation_enabled and source is not None:
+            # Create a new symbol for this source.
+            symbol = self._create_symbol_for_source(source)
+            assert symbol is not None
+
+            # Create a new FX placeholder and Z3 variable for 'symbol'.
+            fx_node = self._create_fx_placeholder_and_z3var(symbol, float)
+
+            # Add an equality assertion for the newly created symbol and 'sym'.
+            self._add_assertion(sympy.Eq(symbol, sym))
+        else:
+            fx_node = None
+
+        if isinstance(sym, sympy.Float):
+            if hint is not None:
+                assert float(sym) == hint
+            out = float(sym)
+        else:
+            # You could give this the same treatment as SymInt above if
+            # you supported mark_unbacked on a float, but it's a kind of
+            # strange thing to do though because floats don't get 0/1
+            # specialization anyway
+            if free_unbacked_symbols(sym):
+                assert hint is None, sym
+            out = SymFloat(SymNode(sym, self, float, hint, fx_node=fx_node))
+        return out
+
+    @record_shapeenv_event()
+    def create_unspecified_symint_and_symbol(self, value, source, dynamic_dim):
+        """Create a SymInt wrapping a new unspecified symbol"""
+        return self.create_symintnode(
+            self.create_unspecified_symbol(
+                value,
+                source=source,
+                dynamic_dim=dynamic_dim,
+            ),
+            hint=value,
+            source=source,
+        )
+
+    def create_symboolnode(self, sym: "sympy.Expr"):
+        """Create a SymBool object from a sympy boolean expression"""
+        # This function is only being used in serialization, so we do not track it
+        # for validation.
+        return SymBool(SymNode(sym, self, bool, None))
+
+    def _log_create_unbacked_symbol(self, prefix: str, symbol, vr: ValueRanges):
+        is_debug = config.extended_debug_create_symbol is not None and str(symbol) in config.extended_debug_create_symbol.split(',')
+        fsummary, maybe_user_loc, maybe_extra_debug = self._get_stack_summary(is_debug)
+        log.info(
+            "%s %s [%s, %s]%s (%s)%s",
+            prefix, symbol, vr.lower, vr.upper, maybe_user_loc, format_frame(fsummary), maybe_extra_debug, stack_info=is_debug
+        )
+
+    @record_shapeenv_event()
+    def create_unbacked_symfloat(self):
+        """Create a symbolic float without a hint value
+        """
+        symbol: sympy.Symbol = make_symbol(SymT.UNBACKED_FLOAT, next(self.unbacked_symfloat_counter))
+        self.counter["create_unbacked_symbol"] += 1
+        if not self._ignore_fresh_unbacked_symbols_tls():
+            self.pending_fresh_unbacked_symbols.append(symbol)
+        self.var_to_stack[symbol] = CapturedTraceback.extract(skip=1)
+        vr = self.var_to_range[symbol] = ValueRanges.unknown()
+        assert vr.is_float
+
+        # Create a new FX placeholder and Z3 variable for 'symbol'.
+        fx_node = self._create_fx_placeholder_and_z3var(symbol, float)
+
+        self._log_create_unbacked_symbol("create_unbacked_symfloat", symbol, vr)
+
+        return SymFloat(SymNode(symbol, self, float, None, fx_node=fx_node))
+
+    @record_shapeenv_event()
+    def create_unbacked_symint(self):
+        """Create a symbolic integer without a hint value
+        """
+        symbol: sympy.Symbol = make_symbol(SymT.UNBACKED_INT, next(self.unbacked_symint_counter), integer=True)
+        if not self._ignore_fresh_unbacked_symbols_tls():
+            self.pending_fresh_unbacked_symbols.append(symbol)
+        self.counter["create_unbacked_symbol"] += 1
+        self.var_to_stack[symbol] = CapturedTraceback.extract(skip=1)
+        vr = self.var_to_range[symbol] = self._default_unspecified_value_range()
+        assert vr.is_int
+
+        # Create a new FX placeholder and Z3 variable for 'symbol'.
+        fx_node = self._create_fx_placeholder_and_z3var(symbol, int)
+
+        self._log_create_unbacked_symbol("create_unbacked_symint", symbol, vr)
+
+        return SymInt(SymNode(symbol, self, int, None, fx_node=fx_node))
+
+    def is_unbacked_symint(self, symbol: sympy.Symbol) -> bool:
+        """Check if a sympy symbol matches the naming convention for unbacked symbols
+        """
+        return symbol_is_type(symbol, SymT.UNBACKED_INT)
+
+    @record_shapeenv_event()
+    def create_unbacked_symbool(self):
+        """Create a symbolic boolean without a hint value
+        """
+        symbol: sympy.Symbol = make_symbol(SymT.UNBACKED_INT, next(self.unbacked_symint_counter), integer=True)
+        if not self._ignore_fresh_unbacked_symbols_tls():
+            self.pending_fresh_unbacked_symbols.append(symbol)
+        self.counter["create_unbacked_symbol"] += 1
+        self.var_to_stack[symbol] = CapturedTraceback.extract(skip=1)
+        vr = self.var_to_range[symbol] = ValueRanges(0, 1)
+        assert vr.is_int
+
+        # Create a new FX placeholder and Z3 variable for 'symbol'.
+        fx_node = self._create_fx_placeholder_and_z3var(symbol, bool)
+
+        self._log_create_unbacked_symbol("create_unbacked_symbool", symbol, vr)
+
+        return SymBool(SymNode(sympy.Eq(symbol, 1), self, bool, None, fx_node=fx_node))
+
+    @record_shapeenv_event()
+    def create_unspecified_symbol(
+        self,
+        val: Union[int, SymInt, float, SymFloat],
+        source: Source,
+        dynamic_dim: DimDynamic = DimDynamic.DUCK,
+        constraint_dim: DimConstraint = None,  # NB: includes None
+    ) -> "sympy.Expr":
+        """Create a symbol with an unspecified value
+
+        Compared to standard symbols we do not assume the value is positive,
+        nor do we specialze on zero or one values.
+        """
+        # 'positive' is None for unspecified symbols, since we can't
+        # assume that it will be neither positive nor negative.
+
+        # We don't want to specialize zero one val for unspecified symbol
+        # so that we can always get a new symbol despite val.
+        return self.create_symbol(
+            val,
+            source,
+            dynamic_dim,
+            constraint_dim,
+            positive=None,
+            do_not_specialize_zero_one=True,
+            symbolic_context=None)
+
+    @record_shapeenv_event()
+    def create_symbol(
+        self,
+        val: int,
+        source: Source,
+        dynamic_dim: DimDynamic = DimDynamic.DUCK,
+        constraint_dim: DimConstraint = None,  # NB: includes None
+        positive: Optional[bool] = True,
+        do_not_specialize_zero_one: bool = False,
+        symbolic_context=None,
+    ) -> "sympy.Expr":
+        """Create a new symbol which is tracked by this ShapeEnv
+        """
+        # check if constraint_dim is actually static integer
+        if isinstance(constraint_dim, StrictMinMaxConstraint) and constraint_dim.vr.lower == constraint_dim.vr.upper:
+            dynamic_dim = DimDynamic.STATIC
+            if constraint_dim.vr.lower != val:
+                raise ConstraintViolationError(
+                    f"Static shape constraint of {constraint_dim.vr.lower} does not match input size of {val}, "
+                    f"for {source.name()}"
+                )
+            if symbolic_context:
+                symbolic_context.dynamic_sizes[source.idx] = dynamic_dim
+                symbolic_context.constraint_sizes[source.idx] = None
+            constraint_dim = None
+
+        # see note [Tensor Fakification and Symbol Caching]
+        source_name = source.name()
+        if (isinstance(symbolic_context, StatefulSymbolicContext)
+                and id(self) not in symbolic_context.shape_env_to_source_to_symbol_cache):
+            symbolic_context.shape_env_to_source_to_symbol_cache[id(self)] = {}
+
+        if (isinstance(symbolic_context, StatefulSymbolicContext)
+                and source_name
+                and (source_name in symbolic_context.shape_env_to_source_to_symbol_cache[id(self)])):
+            return symbolic_context.shape_env_to_source_to_symbol_cache[id(self)][source_name]
+
+        if dynamic_dim is DimDynamic.SIZE_LIKE_UNBACKED:
+            out = self.create_unbacked_symint().node.expr
+            self._constrain_range_for_size(out)
+            # TODO: maybe put the hint somewhere
+            if isinstance(symbolic_context, StatefulSymbolicContext) and source_name:
+                symbolic_context.shape_env_to_source_to_symbol_cache[id(self)][source_name] = out
+            return out
+
+        if do_not_specialize_zero_one:
+            specialize_zero_one = False
+        else:
+            specialize_zero_one = self.specialize_zero_one
+
+        assert isinstance(source, Source), f"{type(source)} {source}"
+        assert not (positive and val < 0), f"positive set for negative value: {val}"
+        # It's always sound to allocate a symbol as DYNAMIC.  If the user
+        # constrained the symbol, force the symbolic_context to DYNAMIC, because our
+        # constraint code will do weird stuff if, e.g., it's duck shaped
+        if constraint_dim is not None:
+            dynamic_dim = DimDynamic.DYNAMIC
+
+        if dynamic_dim is DimDynamic.STATIC:
+            out = sympy.Integer(val)
+            if isinstance(symbolic_context, StatefulSymbolicContext) and source_name:
+                symbolic_context.shape_env_to_source_to_symbol_cache[id(self)][source_name] = out
+            return out
+
+        elif dynamic_dim is DimDynamic.DUCK:
+            # duck_shape can be used to globally turn off duck shaping, even
+            # if it was requested
+            duck = self.duck_shape
+        elif dynamic_dim is DimDynamic.DYNAMIC:
+            duck = False
+        else:
+            raise AssertionError(f"unhandled dynamic_dim {dynamic_dim}")
+
+        if val in (0, 1) and specialize_zero_one:
+            r = self.val_to_var[val]
+        elif not duck or val not in self.val_to_var:
+            # If we're not duck shaping, we always create a new symbol
+            # Even if we're duck shaping, if we haven't seen this particular
+            # value before, we also create a new symbol
+            if type(val) is int or is_nested_int(val):
+                sympy_expr = make_symbol(SymT.SIZE, len(self.var_to_val), positive=positive, integer=True)
+            else:
+                sympy_expr = make_symbol(SymT.FLOAT, len(self.var_to_val), positive=positive, real=True)
+            # We always associate vars to vals
+            if isinstance(val, int):
+                self.var_to_val[sympy_expr] = sympy.Integer(val)
+            elif isinstance(val, float):
+                self.var_to_val[sympy_expr] = sympy.Float(val)
+            else:
+                # Only used for jagged layout nested tensors
+                self.var_to_val[sympy_expr] = SingletonInt(val.node.nested_int(), coeff=val.node.nested_int_coeff())
+
+            # Do the appending later, because we always want to populate this
+            self.var_to_sources[sympy_expr] = []
+            # Create a Z3 variable for the new symbol.
+            self._add_z3var(sympy_expr, int)
+
+            if duck:
+                # Make sure to reuse this symbol for subsequent duck shaping
+                self.val_to_var[val] = sympy_expr
+
+            if isinstance(val, int):
+                if positive:
+                    # Add assertions for the newly created symbols
+                    self._add_assertion(sympy_expr > 1)
+
+                    # Apply default range, which assumes not zero-one
+                    self.var_to_range[sympy_expr] = self._default_value_range()
+                else:
+                    self.var_to_range[sympy_expr] = self._default_unspecified_value_range()
+
+                # Small performance optimization: if we have a min-max constraint,
+                # we can proactively narrow to that range
+                if isinstance(constraint_dim, StrictMinMaxConstraint):
+                    assert not duck
+                    self.var_to_range[sympy_expr] &= constraint_dim.vr
+
+                vr = self.var_to_range[sympy_expr]
+                assert vr.is_int
+
+                if val not in vr:
+                    raise ConstraintViolationError(f"{val} not in range [{vr.lower}, {vr.upper}]")
+
+                range_str = f"[{vr.lower}, {vr.upper}]"
+            elif isinstance(val, float):
+                self.var_to_range[sympy_expr] = vr = ValueRanges(-sympy.oo, sympy.oo)
+                range_str = f"[{vr.lower}, {vr.upper}]"
+                assert vr.is_float
+            else:
+                # Skip var_range logic for SingletonInt
+                # Only used for jagged layout nested tensors
+                range_str = ""
+
+            r = sympy_expr
+
+            is_debug = (
+                config.extended_debug_create_symbol is not None and
+                str(sympy_expr) in config.extended_debug_create_symbol.split(',')
+            )
+            maybe_more_info = ""
+            if not is_debug:
+                maybe_more_info = (
+                    ", for more info run with "
+                    f'TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="{sympy_expr}"'
+                )
+            fsummary, maybe_user_loc, maybe_extra_debug = self._get_stack_summary(is_debug)
+            self.log.info(
+                "create_symbol %s = %s for %s %s%s (%s)%s%s",
+                sympy_expr, val, source.name(), range_str,
+                maybe_user_loc, format_frame(fsummary), maybe_more_info, maybe_extra_debug, stack_info=is_debug
+            )
+
+            self.counter["create_symbol"] += 1
+        else:
+            # This implements duck-shaping: input sizes that match are assigned
+            # the same symint
+            r = self.val_to_var[val]
+            self.log.debug("create_symbol %s duck sized %s", r, source.name())
+
+        if isinstance(r, sympy.Symbol):
+            r_sources = self.var_to_sources[r]
+            r_sources.append(source)
+            if not source.is_ephemeral() and r_sources[0].is_ephemeral():
+                # prefer non-ephemeral source first since it may be guarded on later
+                r_sources[0], r_sources[-1] = r_sources[-1], r_sources[0]
+
+            # This ensures we get zeros in symbol_guard_counts, which makes
+            # some queries simpler (since we will accumulate mass on 0 this
+            # way)
+            self.symbol_guard_counter[r] = 0
+
+        if isinstance(symbolic_context, StatefulSymbolicContext) and source_name:
+            symbolic_context.shape_env_to_source_to_symbol_cache[id(self)][source_name] = r
+        return r
+
+    def add_var_to_val(self, expr: sympy.Symbol, val: int):
+        """ Adds a new symbol to the symbolic environment. """
+        log.debug("add_var_to_val %s %s", expr, val, stack_info=True)
+        assert expr not in self.var_to_val, f"{expr} already exists"
+        self.var_to_val[expr] = sympy.Integer(val)
+
+    def _debug_name(self, source):
+        src_name = source.name()
+        return self.source_name_to_debug_name.get(src_name, src_name)
+
+    def _render_range_for_constraint_violation(self, source, c):
+        if isinstance(c, StrictMinMaxConstraint):
+            lower, upper = c.vr.lower, c.vr.upper
+            default = self._default_value_range()
+            if lower <= default.lower:
+                lower = None
+            if upper >= default.upper:
+                upper = None
+            c_render = f"{self._debug_name(source)} = {source.name()} in the specified range"
+            if lower is not None and upper is not None:
+                c_render += f" {lower} <= {self._debug_name(source)} <= {upper}"
+            elif lower is None and upper is not None:
+                c_render += f" {self._debug_name(source)} <= {upper}"
+            elif lower is not None and upper is None:
+                c_render += f" {lower} <= {self._debug_name(source)}"
+            return c_render
+        return c.render(source)
+
+    def produce_guards(
+        self,
+        placeholders,
+        sources,
+        source_ref=lambda n: n.name(),
+        *,
+        guards: List[ShapeGuard] = None,
+        input_contexts: Optional[DimList[SymbolicContext]] = None,
+        # Encodes user-specified input shape equations of the form s = s' and s = fn(s').
+        # (See docs on EqualityConstraint for details of the encoding.)
+        equalities_inputs: Optional[EqualityConstraint] = None,
+        _simplified=False,
+        # Indicates if we should produce guards for known static values.
+        ignore_static=True,
+    ) -> List[str]:
+        """
+        Generates a list of guards strings which, when evaluated in a context that
+        defines tensors for all the sources, returns True or False depending
+        on if the guards in the list evaluated to True or not.  Primarily used by Dynamo,
+        but this is also helpful for manual testing of guards (see
+        evaluate_guards_for_args)
+
+        For convenience in testing, a source is allowed to be a str,
+        in which case we will assume it is a LocalSource
+
+        simplified lets you omit duck sizing, equality and 0/1 guards.
+        This is useful for testing when you don't care about the boilerplate
+        guards, and it may be helpful for user output too (be careful though;
+        some equality guards are nontrivial!  It would be nice to get simplified
+        output to print them too).  It's private because it's not
+        intended for normal use
+        """
+        self.log.info("produce_guards")
+
+        # Check if we get to the same ShapeEnv state by replaying the recorded events.
+        # This will create a new ShapeEnv instance, and call all recorded function
+        # calls on this new instance. Finally, it will check whether this new instance
+        # has equal state.
+        #
+        # It's important that we do it in the begining of this function, since it modifies
+        # self.dim_constraints through its execution. Changes that happen in this method
+        # aren't interesting, since this is the function call we wish to reproduce at the
+        # end. If we wish to simply reproduce ShapeEnv instances even after this call,
+        # this method should also be recorded.
+        if self.check_recorded_events:
+            shape_env = replay_shape_env_events(self.events)
+            self.check_equal(shape_env)
+
+        assert len(placeholders) == len(sources), f"len({placeholders}) != len({sources})"
+        Tensorlike = (torch.Tensor, FakeTensorMeta)
+
+        def _create_no_constraints_context(t):
+            return StatelessSymbolicContext(
+                # Ignored; only the constraints part is relevant below.
+                dynamic_sizes=[DimDynamic.DYNAMIC] * t.dim(),
+                dynamic_strides=[DimDynamic.INFER_STRIDE] * t.dim(),
+                constraint_sizes=[None] * t.dim(),
+                constraint_strides=[None] * t.dim()
+            )
+
+        # Expand optional inputs, or verify invariants are upheld
+        if input_contexts is None:
+            input_contexts = [
+                _create_no_constraints_context(t) if isinstance(t, Tensorlike)
+                else None for t in placeholders
+            ]
+        else:
+            assert len(input_contexts) == len(placeholders)
+            for i, (t, context) in enumerate(zip(placeholders, input_contexts)):
+                if isinstance(t, Tensorlike):
+                    if context is None:
+                        input_contexts[i] = _create_no_constraints_context(t)
+                else:
+                    assert isinstance(t, (SymInt, int, SymFloat, float))
+                    assert not isinstance(context, list)
+
+        # It took a lot of sweat to figure out the algorithm here.  Let's
+        # explain how it works.
+        #
+        # The ShapeEnv lifecycle looks something like this:
+        #
+        # - For each input, you either generate a fresh Sympy symbol (s0) to
+        #   represent its value (a binding site), or you reuse some
+        #   preexisting symbol or expression, skipping the symbol allocation
+        #   (e.g., duck sizing to a preexisting symbol, or expressing a
+        #   stride as a multiplication of a separate stride and size.)
+        #   Naively, you might expect to bind a fresh Sympy symbol for
+        #   every input, but this is fairly wasteful as most of these
+        #   symbols immediately simplify away, and if you don't eagerly
+        #   specialize, e.g., 0/1 symbols, you end up with very complicated
+        #   expressions that are not optimizable in practice.
+        #
+        # - You perform some compute on these symbols, occasionally
+        #   introducing guards on boolean expressions on these symbols.
+        #   In particular, whenever we guard on equality (_maybe_guard_rel),
+        #   we can simplify shapes; e.g., when s0 == s1 * 2, we can now
+        #   replace all occurrences of s0 with s1 * 2.  Sometimes, a
+        #   boolean expression evaluation doesn't introduce a guard, as
+        #   the guard is already entailed by the simplifications we have
+        #   applied.
+        #
+        # - In the end, you have a bunch of replacements (saying how to
+        #   simplify shapes) and a bunch of guards (all the equality guards
+        #   are trivial, because they're covered by the replacements).
+        #
+        # From the ShapeEnv, we must generate a Python expression that, when
+        # evaluated on a set of inputs, tells us whether or not these boolean
+        # expressions would have evaluated in the same way.  However,
+        # we cannot easily compute this, as we elide recording boolean
+        # expressions when we think they are vacuously true.  Thus, we seek
+        # an approximation: we must generate an expression, if true, would have
+        # produced an "equivalent" ShapeEnv, which would answer guard
+        # expressions in the same way.
+        #
+        # Our notion of equivalence is a bit subtle.  For example, consider
+        # the ShapeEnv created from an input of size (5, 4) versus (4, 4)
+        # (no other guards.)  Duck sizing would generate (s0, s1) in the first
+        # case but (s0, s0) in the second.  We do NOT assume that size
+        # variables are disjoint; so in fact a graph that assumes the input
+        # could be (s0, s1) subsumes (s0, s0) (setting s0 == s1), but not
+        # vice versa.  However, consider an analogous case (1,) versus (2,).
+        # Duck sizing generates (1,) and (s0,); the (s0,) graph does NOT
+        # subsume the (1,) graph because we assume that any size variables
+        # is NOT 0/1 (and make simplifications according to this; e.g., if
+        # we queried s0 == 0, we would immediately return False without
+        # returning a guard.)
+        #
+        # So, it is perhaps easier to flip things on their head: the guard
+        # expressions we generate here say what simplifications are valid,
+        # and what are not.  Below, we explain each of the guard expressions
+        # we generate
+
+        # TODO: Make this more efficient by binding all the size/stride/offsets
+        # to locals before performing tests on them.
+
+        from torch._dynamo.source import TensorPropertySource, TensorProperty
+
+        # Actual codegen must be delayed as we don't necessarily know what
+        # the symbol mapping is
+        input_guards = []
+
+        symbol_to_source = collections.defaultdict(list)
+        symbol_to_constraints = collections.defaultdict(set)
+        constraint_violations : List[Tuple[bool, str, Callable[[], str]]] = []
+
+        def record_constraint_violation(warn_only, debug_name, msg, hint=None):
+            constraint_violations.append(
+                (warn_only, debug_name, lambda: f"{msg}{hint()}" if hint else msg)
+            )
+
+        def is_dim(src):
+            return isinstance(src, TensorPropertySource) and src.prop is TensorProperty.SIZE
+
+        if equalities_inputs:
+            source_index = {}
+            for i, src in enumerate(sources):
+                source_index[src.name()] = i
+
+            def get_expression(tensor_dim_src):
+                fake = placeholders[source_index[tensor_dim_src.base.name()]]
+                symint = fake.shape[tensor_dim_src.idx]
+                if isinstance(symint, torch.SymInt):
+                    return symint.node.expr
+                else:
+                    assert type(symint) is int, f"Expected int, got {type(symint)}"
+                    return symint
+
+            for src1, src2 in equalities_inputs.source_pairs:
+                expr1, expr2 = get_expression(src1), get_expression(src2)
+                # Check whether given input shape values satisfy a specified equation s = s'.
+                # - Raise when the equation was violated by the given input shape values.
+                # - Otherwise issue a guard to constrain them.
+                concrete_val = self.evaluate_expr(sympy.Eq(expr1, expr2))
+                if not concrete_val:
+                    raise ConstraintViolationError(
+                        f"{src1.name()} = {expr1 if isinstance(expr1, int) else expr1.xreplace(self.var_to_val)}"
+                        " is not equal to "
+                        f"{src2.name()} = {expr2 if isinstance(expr2, int) else expr2.xreplace(self.var_to_val)}"
+                    )
+
+            for src, root, fn in equalities_inputs.derived_equalities:
+                expr1 = get_expression(src)
+                # recall that root is either a phantom symbol or an input source
+                expr2, debug_name = (
+                    (root, self.var_to_sources[root][0].name()) if isinstance(root, sympy.Symbol)
+                    else (get_expression(root), self._debug_name(root))
+                )
+                expr2_ = fn(expr2)
+                # Check whether given input shape values satisfy a specified equation s = fn(s').
+                # - Raise when the equation was violated by the given input shape values.
+                # - Otherwise issue a guard to constrain them.
+                concrete_val = self.evaluate_expr(sympy.Eq(expr1, expr2_))
+                if not concrete_val:
+                    raise ConstraintViolationError(
+                        f"Expected input {src.name()} to be equal to "
+                        f"{fn(sympy.Symbol(debug_name))}, "
+                        f"where {debug_name} = {expr2.xreplace(self.var_to_val)}, "
+                        f"but got {expr1.xreplace(self.var_to_val)}"
+                    )
+
+            for phantom_symbol in equalities_inputs.phantom_symbols:
+                # we created additional phantom symbols that are not input shape dimensions
+                symbol_to_source[phantom_symbol].extend(self.var_to_sources[phantom_symbol])
+
+        # How do we know what the value of s0 is?  Fresh variables can only be
+        # bound by inputs, so there MUST be some other input which binds the
+        # variable.  If there is no such input, this is an error in our
+        # system.  We record where all symbols come from, to help you diagnose
+        # why those symbols didn't occur.
+        #
+        # In fact, generally speaking it is only possible for the "outermost"
+        # user of a ShapeEnv to evaluate the guards, because some inputs may
+        # not be available to inner levels.  For example, Dynamo can guard on
+        # tensors that never actually become graph arguments (they are
+        # pruned).  In this case, only Dynamo knows about these arguments.
+        def track_symint(source, val, constraint=None):
+            log.debug("track_symint %s %s %s", LazyString(source.name), val, constraint)
+            assert not isinstance(val, SymInt) or is_symbolic(val)
+
+            if isinstance(val, SymInt) and val.node.maybe_as_int() is not None:
+                val = val.node.maybe_as_int()
+
+            if isinstance(val, SymInt):
+                s = val.node.expr
+                if isinstance(s, sympy.Symbol):
+                    symbol_to_source[s].append(source)
+                    if (
+                        constraint is not None
+                        and not isinstance(constraint, RelaxedUnspecConstraint)
+                    ):
+                        symbol_to_constraints[s].add(constraint)
+                else:
+                    constraint_violated = False
+                    if isinstance(constraint, StrictMinMaxConstraint):
+                        # try inferring the ranges of the expr s
+                        sym_vrs = {x: self.var_to_range.get(x, None) for x in s.free_symbols}
+                        if any(vr is None for vr in sym_vrs.values()):
+                            # some of the free symbols in s don't have ranges
+                            constraint_violated = True
+                    elif isinstance(constraint, RelaxedUnspecConstraint):
+                        if s.is_number:
+                            i = int(s)
+                            # Don't complain about 0/1 specialization, we
+                            # expect to have to compile in this case anyway
+                            if i not in (0, 1):
+                                constraint_violated = True
+                    if constraint_violated:
+                        def hint(s):
+                            sexpr = ShapeGuardPrinter(symbol_to_source, source_ref, self.var_to_sources).doprint(s)
+                            return f"{sexpr}."
+
+                        var_with_range = self._render_range_for_constraint_violation(source, constraint)
+                        msg = (
+                            f"Not all values of {var_with_range} are valid because "
+                            f"{self._debug_name(source)} was inferred to be equal to "
+                        )
+                        record_constraint_violation(
+                            constraint.warn_only,
+                            self._debug_name(source),
+                            msg,
+                            hint=functools.partial(hint, s),
+                        )
+
+                input_guards.append((source, s))
+            else:
+                s = sympy.Integer(val)
+                input_guards.append((source, s))
+                constraint_violated = False
+                if isinstance(constraint, StrictMinMaxConstraint):
+                    if not (s == constraint.vr.lower == constraint.vr.upper):  # allow static constraints
+                        constraint_violated = True
+                elif isinstance(constraint, RelaxedUnspecConstraint):
+                    # Don't complain about 0/1 specialization, we
+                    # expect to have to compile in this case anyway
+                    if val not in (0, 1):
+                        constraint_violated = True
+                if constraint_violated:
+                    var_with_range = self._render_range_for_constraint_violation(source, constraint)
+                    msg = (
+                        f"Not all values of {var_with_range} are valid because "
+                        f"{self._debug_name(source)} was inferred to be a constant ({val})."
+                    )
+                    record_constraint_violation(constraint.warn_only, self._debug_name(source), msg)
+
+        def track_symfloat(source, val):
+            log.debug("track_symfloat %s %s", LazyString(source.name), val)
+            assert not isinstance(val, SymFloat) or is_symbolic(val)
+
+            if isinstance(val, SymFloat) and val.node.maybe_as_float() is not None:
+                val = val.node.maybe_as_float()
+
+            if isinstance(val, SymFloat):
+                s = val.node.expr
+                if isinstance(s, sympy.Symbol):
+                    symbol_to_source[s].append(source)
+                input_guards.append((source, s))
+            else:
+                s = sympy.Float(val)
+                input_guards.append((source, s))
+
+        for t, source, context in zip(placeholders, sources, input_contexts):
+            if isinstance(source, str):
+                from torch._dynamo.source import LocalSource
+                source = LocalSource(source)
+            assert isinstance(source, Source)
+            if t is None:
+                continue
+            if isinstance(t, (SymInt, int)):
+                track_symint(source, t)
+                continue
+            elif isinstance(t, (SymFloat, float)):
+                track_symfloat(source, t)
+                continue
+            assert isinstance(t, Tensorlike)
+            if is_traceable_wrapper_subclass(t):
+                from torch._dynamo.source import AttrSource
+
+                assert isinstance(context, SubclassSymbolicContext)
+
+                # For subclasses, we need to track symints on BOTH the outer
+                # and inner tensors.
+                sources_tensors_constraints = [
+                    (source, t, context.constraint_sizes, context.constraint_strides)
+                ]
+                attrs, _ = t.__tensor_flatten__()
+                for attr in attrs:
+                    inner_t = getattr(t, attr)
+                    inner_context = context.inner_contexts[attr]
+                    sources_tensors_constraints.append((
+                        AttrSource(source, attr),
+                        inner_t,
+                        inner_context.constraint_sizes,
+                        inner_context.constraint_strides
+                    ))
+            else:
+                sources_tensors_constraints = [(source, t, context.constraint_sizes, context.constraint_strides)]
+
+            for src, curr_t, constraint_size, constraint_stride in sources_tensors_constraints:
+                if is_sparse_any(curr_t):
+                    for i, ss in enumerate(curr_t.size()):
+                        property_source = TensorPropertySource(src, TensorProperty.SIZE, i)
+                        track_symint(property_source, ss, constraint_size[i])
+                else:
+                    for i, ss in enumerate(curr_t.size()):
+                        property_source = TensorPropertySource(src, TensorProperty.SIZE, i)
+                        track_symint(property_source, ss, constraint_size[i])
+                    for i, ss in enumerate(curr_t.stride()):
+                        property_source = TensorPropertySource(src, TensorProperty.STRIDE, i)
+                        track_symint(property_source, ss, constraint_stride[i])
+                    track_symint(TensorPropertySource(src, TensorProperty.STORAGE_OFFSET), curr_t.storage_offset())
+
+        # 1. Every input must equal the final simplified symbolic expression
+        #    stored on the placeholder.  Given a placeholder (s0*2, s1),
+        #    if we have an input (2, 3), we must show s0*2 == 2 and s1 == 3.
+        #    This does a lot of work: it covers duck sizing and equality guards.
+        exprs = []
+        self.dim_constraints = DimConstraints(
+            symbol_to_source,
+            self.var_to_val,
+            set(symbol_to_constraints.keys()),
+            self.source_name_to_debug_name,
+        )
+
+        if not _simplified:
+            for source, expr in input_guards:
+                if self._translation_validation_enabled:
+                    # Ignore sources that were not turned into SymInts.
+                    srcname = source.name()
+                    if srcname in self.source_to_symbol:
+                        self._add_target_expr(sympy.Eq(self.source_to_symbol[srcname], expr))
+
+                # Small optimization
+                if (
+                    isinstance(expr, sympy.Symbol) and
+                    symbol_to_source.get(expr) and
+                    source == symbol_to_source[expr][0]
+                ):
+                    continue
+
+                # This logic excludes static values found on tensors from guarding, because
+                # dynamo's check_tensor_fn does that (see guards.cpp).
+                # However, for non tensor sources, we still need to guard here.
+                if ignore_static and isinstance(source, TensorPropertySource):
+                    if expr.is_number:
+                        self.log.debug("Skipping guard %s", f"{source_ref(source)} == {expr}")
+                        continue
+
+                if is_dim(source):
+                    self.dim_constraints.add_equality(source, expr)
+
+                sexpr = ShapeGuardPrinter(symbol_to_source, source_ref, self.var_to_sources).doprint(expr)
+                exprs.append(f"{source_ref(source)} == {sexpr}")
+                if (
+                    isinstance(source, TensorPropertySource)
+                    and source.prop is TensorProperty.SIZE
+                    and equalities_inputs
+                    and len(expr.free_symbols) == 1
+                ):
+                    symbol = next(iter(expr.free_symbols))
+                    if (
+                        isinstance(expr, sympy.Symbol) and
+                        expr in symbol_to_constraints and
+                        not equalities_inputs.is_equal(source, symbol_to_source[expr][0])
+                    ):
+                        msg = (
+                            f"The values of {self._debug_name(source)} = {source.name()} and "
+                            f"{self._debug_name(symbol_to_source[expr][0])} = {symbol_to_source[expr][0].name()} "
+                            "must always be equal."
+                        )
+                        record_constraint_violation(equalities_inputs.warn_only, self._debug_name(source), msg)
+
+                    if (
+                        not isinstance(expr, sympy.Symbol) and
+                        symbol in symbol_to_constraints and
+                        not equalities_inputs.is_derived(source, symbol_to_source[symbol][0], lambda x: expr.xreplace({symbol: x}))
+                    ):
+                        src = symbol_to_source[symbol][0]
+                        msg = (
+                            f"The values of {self._debug_name(source)} = {source.name()} must always be related to "
+                            f"the values of {self._debug_name(src)} = {src.name()} by "
+                            f"{self._debug_name(source)} = {expr.xreplace({symbol: sympy.sympify(self._debug_name(src))})}."
+                        )
+                        record_constraint_violation(equalities_inputs.warn_only, self._debug_name(source), msg)
+
+                # NB: Not necessary to report constraint violations here:
+                # constraints are guaranteed to be on symbols (we've already
+                # caught constants and non-atomic expressions), so we only
+                # have relational constraints, but we don't support those
+                # at the moment
+
+        # 2. Every guard must evaluate to True (but remember many guards
+        #    like s0 == s1*2 because trivial due to simplification)
+        issued = set()
+
+        def issue_guard(guard: ShapeGuard) -> None:
+            expr = self.simplify(guard.expr)
+
+            # Avoid re-issueing the same guard.
+            if expr in issued:
+                return
+
+            issued.add(expr)
+
+            try:
+                is_trivial = False
+                if any(is_dim(source) for s in expr.free_symbols for source in symbol_to_source[s]):
+                    is_trivial = self.dim_constraints.add(expr)
+                guard_expr = ShapeGuardPrinter(symbol_to_source, source_ref, self.var_to_sources).doprint(expr)
+                exprs.append(guard_expr)
+                self._add_target_expr(expr)
+                # A non-relational constraint on a single sizevar can violate
+                # a constraint
+                if not is_trivial and len(expr.free_symbols) == 1:
+                    symbol = next(iter(expr.free_symbols))
+                    source = symbol_to_source[symbol][0]
+                    constraints = symbol_to_constraints[symbol]
+                    for c in constraints:
+                        if isinstance(c, StrictMinMaxConstraint):
+                            var_with_range = self._render_range_for_constraint_violation(source, c)
+                            msg = (
+                                f"Not all values of {var_with_range} "
+                                f"satisfy the generated guard {guard_expr}."
+                            )
+                            record_constraint_violation(c.warn_only, self._debug_name(source), msg)
+                        elif isinstance(c, RelaxedUnspecConstraint):
+                            # This is fine, we allow guards here as long as it
+                            # didn't constrain it to one value  (we don't
+                            # actually know this; this depends on our
+                            # ValueRanges reasoning capability)
+                            pass
+                        else:
+                            raise AssertionError(f"unrecognized constraint {c}")
+            except Exception:
+                self.log.warning("Failing guard allocated at: \n%s", ''.join(guard.stack.format()))
+                raise
+
+        # First, issue all guards.
+        # This removes all the checks that follow from bounds
+        # We could simply emit those and also the bounds 2 <= size when necessary
+        for guard in (guards if guards is not None else self.guards):
+            if self._maybe_evaluate_static(guard.expr, axioms=()) is not None:
+                continue
+            issue_guard(guard)
+
+        # Because there are guards that export's constraint solver can suggest good fixes for, that we may have
+        # deferred as runtime asserts, and that produce_guards() alone won't do anything with (e.g. divisiblity guards),
+        # we want to send runtime asserts to export's constraint solver too. These will still stay in the graph as asserts,
+        # but export's constraint solver can decide whether to do anything with them (i.e. raise an error and provide
+        # suggested fixes, or decide it's out of scope and leave as a runtime assert in the graph).
+        for ra in self.deferred_runtime_asserts.get(None, []):
+            if self._maybe_evaluate_static(ra.expr, axioms=()) is not None:
+                continue
+            expr = self.simplify(ra.expr)
+            self.dim_constraints.add(expr)
+
+        # 3. Every symbol must be within its value range (this handles 0/1
+        # specialization too).
+        for symbol, sources in symbol_to_source.items():
+            r = self.var_to_range.get(symbol)
+            if r is None:
+                if symbol not in self.var_to_range:
+                    continue
+                r = self.var_to_range[symbol]
+
+            assert sources
+            bounds = []
+            if r.lower not in (-sympy.oo, -int_oo):
+                if any(is_dim(source) for source in sources):
+                    self.dim_constraints.add(sympy.Ge(symbol, r.lower))
+                # Only print lower bound in simplified mode if it is not the
+                # default
+                if not _simplified or r.lower != self._default_value_range().lower:
+                    bounds.append(str(r.lower))
+            bounds.append(source_ref(sources[0]))
+            if r.upper not in (sympy.oo, int_oo):
+                if any(is_dim(source) for source in sources):
+                    self.dim_constraints.add(sympy.Le(symbol, r.upper))
+                # nontrivial upper bound is always interesting
+                bounds.append(str(r.upper))
+            if len(bounds) > 1:
+                exprs.append(" <= ".join(bounds))
+
+                # Check constraints
+                constraints = symbol_to_constraints[symbol]
+                for c in constraints:
+                    if isinstance(c, StrictMinMaxConstraint):
+                        # TODO: With int_oo, I think this condition is a noop
+                        # now
+                        if not (c.vr & self._default_value_range()).issubset(r):
+                            source = sources[0]
+
+                            expr = sympy.And(sympy.Le(r.lower, symbol), sympy.Le(symbol, r.upper))
+                            guard_expr = ShapeGuardPrinter(symbol_to_source, source_ref, self.var_to_sources).doprint(expr)
+                            var_with_range = self._render_range_for_constraint_violation(source, c)
+                            msg = (
+                                f"Not all values of {var_with_range} satisfy the generated guard {guard_expr}"
+                            )
+                            record_constraint_violation(
+                                c.warn_only,
+                                self._debug_name(source),
+                                msg,
+                            )
+            # We NaN specialize, which means similar to 0/1 specialization we
+            # should assume that the float is NOT nan.  This is load bearing
+            # if you have something like an equality guard, nan will play
+            # merry hell with the reasoning.
+            if symbol_is_type(symbol, SymT.FLOAT):
+                exprs.append(f"not __math_isnan({source_ref(sources[0])})")
+
+        if constraint_violations:
+            warn_msgs = []
+            error_msgs = []
+            debug_names = set()
+            for warn_only, debug_name, msg in constraint_violations:
+                if warn_only:
+                    msg = f"  {len(warn_msgs) + 1}. {msg()}"
+                    warn_msgs.append(msg)
+                else:
+                    msg = f"  - {msg()}"
+                    error_msgs.append(msg)
+                    debug_names.add(debug_name)
+            if len(error_msgs) > 0:
+                debug_names = ', '.join(sorted(debug_names))
+                err = '\n'.join(error_msgs)
+                raise ConstraintViolationError(
+                    f"Constraints violated ({debug_names})! "
+                    'For more information, run with TORCH_LOGS="+dynamic".\n'
+                    f"{err}"
+                )
+            elif len(warn_msgs) > 0:
+                log.debug("%s Warning only constraints violated", len(warn_msgs))
+
+        signpost_event(
+            "dynamic",
+            "produce_guards",
+            {
+                **self.co_fields,
+                **self.counter,
+                "num_guards": len(exprs),
+                "free_symbols": sum(1 for v in symbol_to_source.values() if v),
+                # The keys are meaningless from an aggregate perspective, so
+                # don't include them.  Biggest first.
+                "symbol_guard_counts": sorted(self.symbol_guard_counter.values(), reverse=True),
+            },
+        )
+
+        if self._translation_validation_enabled:
+            from torch.fx.experimental.validator import PopulateValidator
+
+            # Add all deferred runtime assertions; these are not technically
+            # handled by produce_guards but we need to put them in the target
+            # set
+            for ras in self.deferred_runtime_asserts.values():
+                for ra in ras:
+                    self._add_target_expr(ra.expr)
+
+            # Add value range bound guards for all symbols with no trivial bounds.
+            # Reason: '_maybe_evaluate_static' may eliminate guards based on the
+            # refined value ranges.
+            for sym, vr in self.var_to_range.items():
+                if vr.lower not in (-sympy.oo, -int_oo):
+                    self._add_target_expr(sympy.Le(vr.lower, sym))
+                if vr.upper not in (sympy.oo, int_oo):
+                    self._add_target_expr(sympy.Le(sym, vr.upper))
+
+            # Before validating, populate the input of the validator with the
+            # built FX graph.
+            with fx_traceback.preserve_node_meta():
+                PopulateValidator(self.graph, self.validator).run()
+
+        # Only run translation validation when we are not passing custom guards
+        if guards is None:
+            self._check_translation_validate()
+        return exprs
+
+    def produce_guards_expression(
+        self,
+        placeholders,
+        *,
+        guards: Optional[List[ShapeGuard]] = None,
+        ignore_static=True
+    ):
+        """
+        Expected to be used with evaluate_guards_expression(). Produces the guards
+        for the given placeholders and returns a string expression to be evaluated
+        by evaluate_guards_expression given concrete values for the placeholders.
+        """
+        from torch._dynamo.source import LocalSource
+        arg_names = [f"t{i}" for i in range(len(placeholders))]
+        produced_guards = self.produce_guards(
+            placeholders,
+            [LocalSource(a) for a in arg_names],
+            guards=guards,
+            ignore_static=ignore_static,
+        )
+        if produced_guards:
+            return " and ".join(produced_guards)
+        return None
+
+    def evaluate_symexpr(self, code):
+        """
+        To be used by compile_fx to evaluate symexprs
+        """
+        args = {str(e): val for e, val in self.var_to_val.items()}
+        return eval(code, SYMPY_INTERP, args)
+
+    def evaluate_guards_expression(self, code, args):
+        """
+        Expected to be used with produce_guards_expression(). Evaluates an expression
+        generated by produce_guards_expression for the given concrete args.
+        """
+        arg_names = [f"t{i}" for i in range(len(args))]
+        return eval(code, SYMPY_INTERP, {"L": dict(zip(arg_names, args))})
+
+    def evaluate_guards_for_args(self, placeholders, args, *, ignore_static=True):
+        """Generate guards for a graph's placeholder values and evaluate the guards with args
+        """
+        code = self.produce_guards_expression(placeholders, ignore_static=ignore_static)
+        if code:
+            return self.evaluate_guards_expression(code, args)
+        return True
+
+    def get_pruned_guards(self, symints):
+        """
+        Get a list of guards, but pruned so it only provides guards that
+        reference symints from the passed in input
+        """
+        symints = {s.node.expr for s in symints if isinstance(s.node.expr, sympy.Symbol)}
+        guards = []
+        for g in self.guards:
+            if all(s in symints for s in g.expr.free_symbols):
+                guards.append(g)
+        return guards
+
+    def bind_symbols(self, placeholders, args):
+        """
+        Given a paired list of placeholders (fake tensors with
+        symbolic sizes) and concrete arguments (regular tensors
+        with real sizes), returns a dictionary mapping each
+        symbol to its real value.  So for example, if you
+        have a placeholder with size (s0, s1), binding
+        (2, 4) to it will give you {s0: 2, s1: 4}.  This is
+        not guaranteed to bind ALL symbols in the ShapeEnv;
+        we can't bind a symbol if it doesn't occur in any placeholder,
+        and symbols that already have replacements won't get bindings.
+
+        This is a little duplicative with evaluate_guards but
+        it's different enough that it seemed cleanest to make
+        another copy.  This assumes the guards are already checked,
+        though if it's cheap we'll check for shenanigans
+        """
+        bindings: Dict[sympy.Symbol, int] = {}
+
+        def bind_symint(arg, val):
+            if isinstance(val, SymInt):
+                s = val.node.expr
+
+                if isinstance(s, sympy.Symbol):
+                    if s in bindings:
+                        assert bindings[s] == arg, f"{bindings[s]} != {arg}"
+                    else:
+                        bindings[s] = arg
+                elif isinstance(-s, sympy.Symbol):
+                    if -s in bindings:
+                        assert bindings[-s] == -arg, f"{bindings[-s]} != {-arg}"
+                    else:
+                        bindings[-s] = -arg
+
+        for t, arg in zip(placeholders, args):
+            if t is None:
+                continue
+            if isinstance(t, SymInt):
+                bind_symint(arg, t)
+                continue
+            assert isinstance(t, torch.Tensor)
+            for i, s in enumerate(t.size()):
+                bind_symint(arg.size(i), s)
+            for i, s in enumerate(t.stride()):
+                bind_symint(arg.stride(i), s)
+            bind_symint(arg.storage_offset(), t.storage_offset())
+
+        return bindings
+
+    def get_nontrivial_guards(self):
+        """Returns a list of guard expressions that aren't statically known (i.e. not trivial)"""
+        return [self.simplify(guard.expr) for guard in self.guards if self._maybe_evaluate_static(guard.expr, axioms=()) is None]
+
+    def format_guards(self, verbose=False):
+        """Format this shape env's guard expressions with optional traceback info if verbose"""
+        def format_tb(tb):
+            if not verbose:
+                return ""
+            return f"\n   Guarded at:\n{''.join('   ' + l for l in tb.format())}"
+
+        return '\n'.join(f" - {guard.expr}{format_tb(guard.stack)}" for guard in self.guards)
+
+    def bound_sympy(self, expr: sympy.Expr, size_oblivious: bool = False) -> ValueRanges:
+        """Given a sympy expression, computes a ValueRanges bound for what values it can be"""
+        var_to_range = {x: self.var_to_range.get(x, None) for x in expr.free_symbols}
+        if size_oblivious:
+            # Clamp values of size-like variables
+            # NB: discarding the old upper bound in intentional, per
+            # https://github.com/pytorch/pytorch/pull/123675
+            for x in self.size_like & var_to_range.keys():
+                if var_to_range[x] is not None:
+                    # NB: do NOT set upper to 2 ** 48, we're using this solely
+                    # to determine if we can do size-like replacement, the
+                    # upper bound is irrelevant here
+                    var_to_range[x] = ValueRanges(2, int_oo)
+                    assert var_to_range[x].is_int
+        return bound_sympy(expr, var_to_range)
+
+    @_lru_cache
+    def get_axioms(self, symbols: Optional[Tuple["sympy.Symbol"]] = None, compute_hint: bool = False) -> Tuple["sympy.Expr"]:
+        """
+        Given the symbols in an expression, it returns all the runtime asserts that have those symbols
+        concatenated with all the guards.
+        If symbols is None, it returns all the runtime asserts (and all the guards)
+        """
+        if symbols is None:
+            runtime_asserts = (r.expr
+                               for rs in self.deferred_runtime_asserts.values()
+                               for r in rs)
+        else:
+            runtime_asserts = (r.expr
+                               for s in symbols if s not in self.var_to_val
+                               for r in self.deferred_runtime_asserts.get(s, ()))
+        guards = (g.expr for g in self.guards)
+        axioms = itertools.chain(guards, runtime_asserts)
+        if compute_hint:
+            axioms = (canonicalize_bool_expr(a.xreplace(self.var_to_val)) for a in axioms)
+        return tuple(dict.fromkeys(axioms).keys())
+
+    @lru_cache(None)
+    def get_implications(self,
+                         e: "sympy.Expr") -> Tuple[Tuple["sympy.Expr", 'sympy.logic.boolalg.BooleanAtom']]:
+        """ Given a expression, it returns a list of predicates that follow from it """
+        equiv = {}
+
+        def add_expr(expr):
+            expr = canonicalize_bool_expr(expr)
+            if isinstance(expr, (sympy.Eq, sympy.Ne)):
+                # No need to canonicalize
+                # TODO We could further canonicalize Eq ordering the lhs and rhs somehow
+                # With this, we could remove the need for the commutativity part
+                opposite = sympy.Eq if isinstance(expr, sympy.Ne) else sympy.Ne
+                # Commutativity of == and !=
+                equiv[type(expr)(expr.lhs, expr.rhs)] = sympy.true
+                equiv[type(expr)(expr.rhs, expr.lhs)] = sympy.true
+                equiv[opposite(expr.lhs, expr.rhs)] = sympy.false
+                equiv[opposite(expr.rhs, expr.lhs)] = sympy.false
+            else:
+                # Expr and negation
+                equiv[expr] = sympy.true
+                equiv[canonicalize_bool_expr(sympy.Not(expr))] = sympy.false
+
+        add_expr(e)
+        # Other relational expressions this expression implies
+        if isinstance(e, sympy.Eq):
+            add_expr(sympy.Le(e.lhs, e.rhs))
+            add_expr(sympy.Ge(e.lhs, e.rhs))
+        elif isinstance(e, sympy.Lt):
+            add_expr(sympy.Le(e.lhs, e.rhs))
+            add_expr(sympy.Ne(e.lhs, e.rhs))
+            if e.lhs.is_integer and e.rhs.is_integer:
+                add_expr(sympy.Le(e.lhs, e.rhs - 1))
+        elif isinstance(e, sympy.Le):
+            add_expr(sympy.Lt(e.lhs, e.rhs + 1))
+        return tuple(equiv.items())
+
+    @_lru_cache
+    def _maybe_evaluate_static(
+        self, expr: "sympy.Expr", *, unbacked_only: bool = False, compute_hint: bool = False,
+        size_oblivious: bool = False, axioms: Optional[Tuple[sympy.Expr]] = None,
+        var_to_range: Optional[Tuple[Tuple[sympy.Symbol, ValueRanges]]] = None
+    ) -> "Optional[sympy.Expr]":
+        """
+        Tries to evaluate expr without introducing guards
+
+        If unbacked_only == True, then we only do substitutions on
+        unbacked SymInts (leaving regular hinted integers alone).  This could
+        result in an expression that still contains backed SymInts, which you
+        could then potentially guard on.
+
+        Use compute_hint == True if you are trying to compute a non-binding
+        hint for the particular hint values of backed SymInts, e.g., if
+        s0 happens to be 3 this run, compute_hint will subsitute s0 with 3.
+        """
+
+        # axioms with compute hint NYE
+        assert not compute_hint or not axioms
+
+        if var_to_range is None:
+            var_ranges = self.var_to_range
+        else:
+            var_ranges = dict(var_to_range)
+
+        expr = self.simplify(expr)
+
+        if compute_hint:
+            expr = expr.xreplace(self.var_to_val)
+
+        expr = canonicalize_bool_expr(expr)
+
+        # Pattern matching
+        symbols = tuple(expr.free_symbols)
+        if axioms is None:
+            axioms = self.get_axioms(symbols, compute_hint=compute_hint)
+        subst = {}
+        for e in axioms:
+            if e.free_symbols.issubset(expr.free_symbols):
+                subst.update(dict(self.get_implications(e)))
+
+        expr = expr.xreplace(subst)
+
+        symbols = tuple(expr.free_symbols)
+
+        # Simplify making use of value range lower bound
+        new_shape_env = {}
+        new_range_env = {}
+        for idx, k in enumerate(symbols):
+            if isinstance(self.var_to_val.get(k, None), SingletonInt):
+                # Skip var_ranges logic for SingletonInt which is only used
+                # for jagged layout NestedTensors today
+                continue
+            vr = var_ranges[k]
+            if size_oblivious and k in self.size_like:
+                lower = max(2, vr.lower)
+                # Clamping size-oblivious to some quantity below sys.maxsize
+                # helps us determine that f(u0) != sys.maxsize, which is a
+                # test that is looking for sys.maxsize as a sentinel, but you
+                # don't really want to worry about it for unbacked SymInts.
+                # This is similar to the flavor where size oblivious omits
+                # 0/1, it changes semantics but in a benign way.
+                upper = min(2 ** 48, vr.upper)
+                # This is a bit dodgy: what this means is that there was a
+                # size-like unbacked symbol whose upper bound < 2.  This
+                # causes... problems.
+                if lower <= upper:
+                    vr = ValueRanges(lower, upper)
+            else:
+                lower = vr.lower
+            # Don't do anything if we don't have a nontrivial lower bound
+            # Also don't do anything if we asked only to simplify unbacked
+            # SymInt
+            if (
+                lower is -int_oo or
+                (unbacked_only and k in self.var_to_val) or
+                not vr.is_int
+            ):
+                new_range_env[k] = vr
+                continue
+            # The goal is to take our symbols which have various lower bounds
+            # and reallocate them into new symbols which are exactly positive;
+            # e.g., if we have s0 in [2, inf], we want to turn it into ess0 in
+            # [1, inf], where s0 = ess0 + 1.  This gives the most information
+            # to sympy for subsequent simplifications.
+            #
+            # Positive means >= 1
+            # Positive - 1 means >= 0
+            # Positive + lower - 1 means >= lower
+            # The new symbol 's' is "too low", so when we substitute it in
+            # we have to increase it by offset (and conversely, the new
+            # variables have to have their value range bounds adjusted as
+            # well)
+            s = sympy.Symbol(f"evaluate_static_shape_{idx}", positive=True, integer=True)
+
+            # Note:
+            #   Offset might be a fraction(e.g. aten.split.Tensor), but shapes are always integers.
+            #   Sympy might give unexepected results when comparing an integer with a non-integer
+            #   Therefore, we cast offset to int here.
+            #   For example:
+            #       shape_0 = sympy.Symbol("shape_0", positive=True, integer=True)
+            #       expr = sympy.Eq(shape_0 - 1/3, 4)
+            #       expr.xreplace({}) # False
+            offset = int(lower - 1)
+            new_shape_env[k] = s + offset
+            new_range_env[s] = SymPyValueRangeAnalysis.add(vr, -offset)
+
+        try:
+            new_expr = expr.xreplace(new_shape_env)
+        except RecursionError:
+            log.warning("RecursionError in sympy.xreplace(%s, %s)", expr, new_shape_env)
+            self.counter["sympy_recursion_error"] += 1
+            return None
+
+        # We need to canonicalize, as after expand we may have something like `a + b = a` and
+        # sympy will not simplify the a. The two appeareances of the a will then make value ranges
+        # analysis give lose bounds
+        new_expr = canonicalize_bool_expr(safe_expand(new_expr))
+        if new_expr.is_number:
+            return new_expr
+
+        # This is bad to do, the replacement with division leaves us with
+        # rationals when atom.args[0] is addition, e.g., sympy will happily
+        # turn (s0 + s1) // 2 into s0 / 2 + s1 / 2.  Needless complication!
+        """
+        floor_div_replace = {}
+        for atom in new_expr.atoms(FloorDiv):
+            floor_div_replace[atom] = sympy.floor(atom.args[0] / atom.args[1])
+        new_expr = safe_expand(new_expr.xreplace(floor_div_replace))
+        # TODO: when unbacked_only, can sometimes early return even when there
+        # are still free symbols
+        if new_expr.is_number:
+            return new_expr
+        """
+
+        # Check if the range can solve it statically
+        out = bound_sympy(new_expr, new_range_env)
+        if out.is_singleton():
+            return out.lower
+
+        return new_expr if unbacked_only else None
+
+    @_lru_cache
+    def replace(self, expr: "sympy.Expr") -> "sympy.Expr":
+        """Apply symbol replacements to any symbols in the given expression
+        """
+        replacements = {s: self._find(cast(sympy.Symbol, s)) for s in expr.free_symbols}
+        return safe_expand(expr.xreplace(replacements))
+
+    @_lru_cache
+    def _update_divisible(self):
+        new_divisible = set()
+        for k in self.divisible:
+            res = self.replace(k)
+            if not res.is_number:
+                new_divisible.add(k)
+
+        self.divisible = new_divisible
+        self._update_version_counter()
+
+    @_lru_cache
+    def simplify(self, expr: "sympy.Expr") -> "sympy.Expr":
+        """Use known constraints and replacements to simplify the given expr
+        """
+        expr = self.replace(expr)
+        # TODO it would seem that this pass is not necessary given the
+        # below replacement of // with /, but for nested FloorDivs
+        # the non-recursive replacement doesn't work, and
+        # recursive makes it hard to look up divisibility,
+        # because existing divisibility info has FloorDiv in it, not /
+        # for now just do a separate pass to catch common nested case
+        if expr.has(FloorDiv):
+            self._update_divisible()
+            div_replacements = {}
+            for atom in expr.atoms(FloorDiv):
+                base, divisor = atom.args
+                if isinstance(divisor, FloorDiv):
+                    base1, divisor1 = divisor.args
+                    if self.replace(Mod(base, divisor)) in self.divisible and \
+                            base == base1 and self.replace(Mod(base1, divisor1)) in self.divisible:
+                        div_replacements[atom] = divisor1
+            expr = expr.xreplace(div_replacements)
+            expr = safe_expand(expr)
+        if expr.has(FloorDiv):
+            div_replacements = {}
+            pows = expr.atoms(sympy.Pow)
+            rationals = expr.atoms(sympy.Rational).difference(expr.atoms(sympy.Integer))
+            for fd in expr.atoms(FloorDiv):
+                base, divisor = fd.args
+                if self.replace(Mod(base, divisor)) in self.divisible:
+                    div_replacements[fd] = CleanDiv(base, divisor)
+            new_expr = expr.xreplace(div_replacements)
+            new_expr = safe_expand(new_expr)
+            new_pows = new_expr.atoms(sympy.Pow)
+            new_rationals = new_expr.atoms(sympy.Rational).difference(new_expr.atoms(sympy.Integer))
+            # divisions simplified away
+            if new_pows.issubset(pows) and new_rationals.issubset(rationals):
+                expr = new_expr
+        return expr
+
+    @lru_cache(256)
+    def size_hint(self, expr: "sympy.Expr", *, allow_none=False):
+        """
+        Gets a size hint for a given expression from the underlying shapes we had.
+        Does not introduce a guard, so only use this when you can guarantee that
+        your code is still valid for arbitrary shapes (such as optimization decisions)
+        """
+        result_expr = safe_expand(expr).xreplace(self.var_to_val)
+        if not result_expr.is_number:
+
+            from torch.utils._sympy.singleton_int import SingletonInt
+
+            if isinstance(result_expr, SingletonInt):
+                return None
+            r = self._maybe_evaluate_static(result_expr, compute_hint=True)
+            if r is not None:
+                return r
+            if allow_none:
+                return None
+
+            if self.unbacked_var_to_val:
+                unsound_expr = result_expr.xreplace(self.unbacked_var_to_val)
+                if not unsound_expr.free_symbols:
+                    log.warning("propagate_real_tensors size_hint(%s) -> %s", expr, unsound_expr)
+                    trace_structured(
+                        "propagate_real_tensors",
+                        metadata_fn=lambda: {
+                            "expr": repr(expr),
+                            "result": repr(unsound_expr),
+                            "stack": structured.from_traceback(CapturedTraceback.extract(skip=1).summary()),
+                        },
+                    )
+                    self.defer_runtime_assert(
+                        sympy.Eq(result_expr, unsound_expr),
+                        f"propagate_real_tensors: {result_expr} == {unsound_expr}"
+                    )
+                    return unsound_expr
+
+            raise self._make_data_dependent_error(result_expr, expr)
+        return result_expr
+
+    # NB: keep in sync with size_hint
+    @lru_cache(256)
+    def has_hint(self, expr: "sympy.Expr"):
+        result_expr = safe_expand(expr).xreplace(self.var_to_val)
+        return result_expr.is_number or self._maybe_evaluate_static(result_expr) is not None
+
+    def _make_data_dependent_error(self, expr, unhinted_expr, *, size_oblivious_result: Optional[bool] = None):
+        # TODO: in a Dynamo context, having user code, and having the
+        # name of the local, will be much better
+        size_like_symbols = []
+        for s in expr.free_symbols:
+            stacktrace = ''.join(self.var_to_stack[s].format())
+            self.log.debug("Data dependent variable '%s' allocated at:\n%s", s, stacktrace)
+            if s in self.size_like:
+                size_like_symbols.append(s)
+        size_oblivious_result_msg = ""
+        if size_oblivious_result is not None:
+            size_oblivious_result_msg = (
+                f"ATTENTION: guard_size_oblivious would fix the error, evaluating expression to {size_oblivious_result}.\n"
+                "Maybe you need to add guard_size_oblivious to framework code, see doc below for more guidance.\n\n"
+            )
+        fsummary, maybe_user_loc, maybe_extra_debug = self._get_stack_summary(True)
+        if expr.is_integer:
+            desc = "Could not extract specialized integer from data-dependent expression"
+        else:
+            desc = "Could not guard on data-dependent expression"
+        msg = (
+            f"{desc} {expr} (unhinted: {unhinted_expr}).  "
+            f"(Size-like symbols: {', '.join(map(str, size_like_symbols)) or 'none'})\n\n"
+            f"{size_oblivious_result_msg}"
+            "Potential framework code culprit (scroll up for full backtrace):\n"
+            f"{''.join(traceback.StackSummary.from_list([fsummary]).format())}\n"
+            'For more information, run with TORCH_LOGS="dynamic"\n'
+            "For extended logs when we create symbols, also add "
+            f"TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL=\"{','.join(map(str, expr.free_symbols))}\"\n"
+            "If you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\n"
+            "For more debugging help, see "
+            "https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing\n" +
+            maybe_extra_debug
+            # TODO: Help text about how to use our runtime tests to fix this
+            # problem
+        )
+        return GuardOnDataDependentSymNode(expr, msg)
+
+    def _update_var_to_range(self, symbol, vr):
+        lower, upper = vr.lower, vr.upper
+
+        # If we have a size-like unbacked SymInt, refuse to refine the range to be
+        # less than two.  This is because when we intersect this range
+        # with [2, inf] for size oblivious tests, the range would be
+        # unsatisfiable.  In other words, once you have a size-like
+        # unbacked SymInt, we can never learn that it is exactly zero or one,
+        # because we would now give inconsistent results for all size
+        # oblivous tests!
+        if upper < 2 and symbol in self.size_like:
+            upper = 2
+
+        # Updates the range and the guards corresponding to each bound of the symbol.
+        if symbol not in self.var_to_range:
+            r = ValueRanges(lower, upper)
+            self.log.debug("_update_var_to_range %s = %s (new)", symbol, r)
+            self.var_to_range[symbol] = r
+        else:
+            old = self.var_to_range[symbol]
+            new = old & ValueRanges(lower, upper)
+            if new != old:
+                self.var_to_range[symbol] = new
+                self.log.debug("_update_var_to_range %s = %s (update)", symbol, new)
+
+        if (v := self.var_to_val.get(symbol)) is not None:
+            r = self.var_to_range[symbol]
+            assert v in r, f"{v} not in {r}"
+
+    def _set_replacement(self, a: "sympy.Symbol", tgt: "sympy.Expr", msg: str) -> None:
+        """
+        Adds or updates a replacement for a symbol.
+        Use this instead of `self.replacements[a] = tgt`.
+        """
+
+        if tgt == self.replacements.get(a, None):
+            return
+
+        # Precondition: a == tgt
+        assert isinstance(a, sympy.Symbol)
+
+        if self.allow_complex_guards_as_runtime_asserts and not _is_supported_equivalence(tgt):
+            return  # continuing leads to placeholder shapes having complex expressions that we can't resolve
+
+        # Handles nested tensor symbolic variables which don't have
+        # var_to_range bounds
+        tgt_bound = None
+        if a in self.var_to_range:
+            src_bound = self.var_to_range[a]
+
+            # First, refine the value range of a based on the computed value range
+            # of tgt.  This is always OK to do, even if we decide not to do the
+            # substitution in the end.  This might be a no-op, if a already has
+            # a tighter bound
+            tgt_bound = self.bound_sympy(tgt)
+            self._update_var_to_range(a, tgt_bound)
+
+            # Next, check if we can update the range of free symbols in tgt
+            # based on the range in a. But only do it if:
+            #  - the source bound non-trivially improves over what we get out of
+            #    the existing bounds.
+            #  - the replacement is univariate and we can invert the tgt expression
+            if not tgt_bound.issubset(src_bound) and len(tgt.free_symbols) == 1:
+                b = next(iter(tgt.free_symbols))
+                # Try to invert the equality
+                r = try_solve(sympy.Eq(a, tgt), b, floordiv_inequality=False)
+                if r is not None:
+                    self.log.debug("set_replacement: solve for %s in %s == %s gives %s", b, a, tgt, r)
+                    # The solution here can be non-integral, for example, if
+                    # we have s0 = 2*s1, then s1 = s0/2.  What we would like
+                    # to do is calculated the bounds in arbitrary precision,
+                    # and then requantize the bound to integers when we are
+                    # done.
+                    rat_b_bound = self.bound_sympy(r[1])
+                    b_bound = ValueRanges(CeilToInt(rat_b_bound.lower), FloorToInt(rat_b_bound.upper))
+                    self._update_var_to_range(b, b_bound)
+                    tgt_bound = self.bound_sympy(tgt)
+                    assert tgt_bound.issubset(src_bound)
+
+            # TODO: Should we propagate size-like-ness?
+            #
+            # Pros: if u0 is size-like, intuitively u0 == u1 should cause u1
+            # to become size-like.
+            #
+            # Cons: if u0 is size-like, what about u0 - 1 == u1?  You CAN'T
+            # propagate in this case, because what if u0 == 0, then u1 is negative
+            # and clearly isn't a size.  So, at minimum, any f(x) whose value
+            # range isn't [0, inf] given x in [0, inf] cannot propagate
+            # size-like-ness.  But there are many situations where you could
+            # imagine u1 is going to be size-like and actually you just didn't
+            # have a refined enough value range on u0.  Since even innocuous
+            # looking arithmetic operations can destroy size-like-ness, it's
+            # best to not propagate it at all and force the user to annotate it
+            # as necessary.
+            #
+            # Compromise: we preserve size-like-ness only for exact equality
+            # and nothing else.
+            if a in self.size_like and isinstance(tgt, sympy.Symbol):
+                self.size_like.add(tgt)
+            elif isinstance(tgt, sympy.Symbol) and tgt in self.size_like:
+                self.size_like.add(a)
+
+            # Now, decide if we will do the substitution.
+            #
+            #  - If the source has a non-trivial range, only substitute if
+            #    we preserve this range.  Note that we may have propagated
+            #    the src_range to free variables in tgt when tgt is univariate
+            #    and we could find an inverse, which helps us achieve this.
+            #    This ensures we never "forget" about user defined ranges,
+            #    even if they end up being defined on composite formulas
+            #    like s0 + s1.
+            #
+            #  - If the variable is unbacked, only substitute if the substitution
+            #    would preserve the bounds also under size-like-ness conditions.
+
+            if not tgt_bound.issubset(src_bound):
+                self.log.debug("skipped set_replacement %s = %s (%s) [%s not subset of %s]", a, tgt, msg, tgt_bound, src_bound)
+                return
+            elif a in self.size_like:
+                tgt_bound_so = self.bound_sympy(tgt, size_oblivious=True)
+                src_bound_so = self.bound_sympy(a, size_oblivious=True)
+                if not tgt_bound_so.issubset(src_bound_so):
+                    self.log.debug("skipped set_replacement %s = %s (%s) "
+                                   "[%s not subset of %s (size-oblivious conditions)]", a, tgt, msg, tgt_bound_so, src_bound_so)
+                    return
+
+        if isinstance(tgt, (sympy.Integer, sympy.Float)):
+            # specializing to a constant, which is likely unexpected (unless
+            # you specified dynamic=True)
+
+            user_tb = TracingContext.extract_stack()
+            trace_structured(
+                "symbolic_shape_specialization",
+                metadata_fn=lambda: {
+                    "symbol": repr(a),
+                    "sources": [s.name() for s in self.var_to_sources.get(a, [])],
+                    "value": repr(tgt),
+                    "reason": msg,
+                    "stack": structured.from_traceback(CapturedTraceback.extract(skip=1).summary()),
+                    "user_stack": structured.from_traceback(user_tb) if user_tb else None,
+                }
+            )
+
+            if config.print_specializations:
+                self.log.warning("Specializing %s to %s", self.var_to_sources[a][0].name(), tgt)
+                self.log.debug("SPECIALIZATION", stack_info=True)
+        log.info("set_replacement %s = %s (%s) %s", a, tgt, msg, tgt_bound)
+        self.replacements[a] = tgt
+        self._update_version_counter()
+
+        # When specializing 'a == tgt', the equality should be also conveyed to
+        # Z3, in case an expression uses 'a'.
+        self._add_target_expr(sympy.Eq(a, tgt))
+
+    def _add_divisible(self, expr: "sympy.Expr"):
+        self.divisible.add(expr)
+        self._update_version_counter()
+
+    @_lru_cache
+    @record_shapeenv_event()
+    def _find(self, a: "sympy.Symbol") -> "sympy.Expr":
+        """
+        Implements a DSU-like algorithm to find the variable that represents a
+        Also handles transitive non-identity replacements.
+
+        a: b + c
+        c: d
+        """
+        if a not in self.replacements:
+            return a
+        res = self.replacements[a]
+        cur_replace = {s: self._find(s) for s in res.free_symbols}
+        replaced, changed = self.replacements[a]._xreplace(cur_replace)
+        if changed:
+            self._set_replacement(a, replaced, "find")
+        return self.replacements[a]
+
+    @lru_cache(256)
+    def _maybe_guard_rel(self, expr: "sympy.Rel") -> None:
+        """
+        The relational guard is guarded to be true.  Use this information to
+        simplify shapes (i.e. a == b or a % 5 == 0)
+        """
+        assert isinstance(expr, sympy.Rel)
+
+        # A good example of what goes wrong if you don't do this is
+        # python test/functorch/test_aotdispatch.py -k
+        # test_aot_autograd_symbolic_module_exhaustive_nn_LazyConv3d_cpu_float32
+        if isinstance(expr, sympy.Ne):
+            return
+
+        free = list(expr.free_symbols)
+
+        assert len(free) > 0, f"The expression should not be static by this point: {expr}"
+        # In case of really gnarly expression, we don't blow up
+        if len(free) > 5:
+            return
+
+        # Prioritize unbacked symints for solving by ordering them last.
+        # Prefer to simplify out lexicographically higher symbols (i.e. simplify out s4 over s3).
+        #   (NB: this unfortunately isn't strictly equivalent to simplifying out newer symbols)
+        # Prefer to simplify out symbols with ephemeral sources.
+        def _smart_symbol_sort(x):
+            has_only_ephemeral_sources = (
+                x in self.var_to_sources and all(s.is_ephemeral() for s in self.var_to_sources[x])
+            )
+            # NB: size_hint is int, not sympy.Expr, do not use int_oo here
+            size = self.size_hint(x, allow_none=True) or sys.maxsize
+            name = x.name
+            # 1 puts ephemeral sourced symbols first when sorting in reverse
+            return (1 if has_only_ephemeral_sources else 0, size, name)
+
+        free = sorted(free, key=_smart_symbol_sort, reverse=True)  # type: ignore[attr-defined]
+        lhs = expr.lhs
+        rhs = expr.rhs
+
+        self._refine_ranges(expr)
+
+        # The rest of this stuff is for equality only
+        if not isinstance(expr, sympy.Eq):
+            return
+
+        if not expr.has(Mod):
+            try:
+                floor_div_atoms = lhs.atoms(FloorDiv).union(rhs.atoms(FloorDiv))
+                if len(floor_div_atoms) > 0 and any(a.divisor != 1 for a in floor_div_atoms):
+                    raise NotImplementedError
+
+                # Never replace unbacked symbols with other unbacked symbols.
+                # This is error prone because you can cause references to
+                # unbacked symbols to time travel backwards.  E.g.,
+                #
+                # u1 = x.item()
+                # ... use of u1 ...
+                # u2 = y.item()
+                # u3 = z.item()
+                # torch._check(u1 == u2 + u3)
+                #
+                # If you replace u1 with u2 + u3, then the use of u1 now
+                # references u2 and u3 prior to them actually being bound at
+                # runtime.  It's pretty inconvenient to setup control
+                # dependencies for substitutions, so ban it entirely.
+                def trivial_solve(lhs, rhs):
+                    if isinstance(lhs, sympy.Symbol):
+                        if free_unbacked_symbols(lhs) and not free_unbacked_symbols(rhs):
+                            return True
+                        if symbol_is_type(lhs, SymT.FLOAT):
+                            return True
+                        # TODO: Maybe trivial solutions for int should also be
+                        # done?
+                    return False
+
+                # short-circuit when no solving is needed
+                if trivial_solve(lhs, rhs):
+                    self._set_replacement(lhs, self._find(rhs), "trivial_lhs")
+                elif trivial_solve(rhs, lhs):
+                    self._set_replacement(rhs, self._find(lhs), "trivial_rhs")
+                else:
+                    r = try_solve(expr, free[0], floordiv_inequality=False)
+                    if r is not None and all(t.is_integer for t in sympy.preorder_traversal(r[1])):
+                        new_var = self._find(r[1])
+                        ok = len(free_unbacked_symbols(new_var)) == 0
+                        if ok:
+                            self._set_replacement(cast(sympy.Symbol, free[0]), new_var, "solve")
+            except NotImplementedError:
+                pass
+        if expr.has(Mod):
+            mod_expr = next(iter(expr.atoms(Mod)))
+            try:
+                r = try_solve(expr, mod_expr, floordiv_inequality=False)
+                if r is not None and r[1] == 0:
+                    self._add_divisible(mod_expr)
+                    # This is a little bit of extra logic to make things like
+                    # torch.empty(i0, q).view(c, -1, q) work out
+                    p, q = mod_expr.args
+                    if isinstance(q, sympy.Number) and isinstance(p, sympy.Mul) and len(p.args) == 2:
+                        c, i0 = p.args
+                        # Given Mod(c * i0, q) == 0
+                        if (
+                            isinstance(c, sympy.Number) and
+                            isinstance(i0, sympy.Symbol) and
+                            self.is_unbacked_symint(i0)
+                        ):
+                            # We have Mod(i0, q / c) == 0, which means we can
+                            # rewrite i0 as (q / gcd(q, c)) * i1
+                            d = q / sympy.gcd(q, c)  # TODO: CleanDiv?
+                            i1 = self.create_unbacked_symint().node.expr
+                            # Propagate the value ranges.  It doesn't really
+                            # matter if we use truediv or floordiv, because we
+                            # have established divisibility.
+                            self._update_var_to_range(i1, SymPyValueRangeAnalysis.floordiv(
+                                self.var_to_range[i0], ValueRanges.wrap(d)
+                            ))
+                            # Propagate size-like-ness
+                            if i0 in self.size_like:
+                                self.size_like.add(i1)
+                            self._set_replacement(i0, d * i1, "divisibility")
+
+            except NotImplementedError:
+                pass
+        return
+
+    # See: Note - On 0/1 specialization
+    def _default_value_range(self) -> ValueRanges:
+        lower = 2 if self.specialize_zero_one else 0
+        return ValueRanges(lower, int_oo)
+
+    def _default_unspecified_value_range(self) -> ValueRanges:
+        return ValueRanges(-int_oo, int_oo)
+
+    @_lru_cache
+    def _simplify_floor_div(self, expr):
+        floor_divs = tuple(expr.atoms(FloorDiv))
+        # we expect floor_divs to be exact,
+        # and thus add the guards for the exact floordivs,
+        # even if tracing doesn't require them otherwise
+        for fd in reversed(floor_divs):
+            base, divisor = fd.args
+            mod_expr = Mod(base, divisor)
+            eq_expr = sympy.Eq(mod_expr, 0)
+            # add necessary mod guards
+            self.evaluate_expr(eq_expr)
+        return self.simplify(expr)
+
+    # We're about to add a guard/runtime assert, check if the ShapeEnv is frozen
+    # and if so issue a warning
+    def _check_frozen(self, expr, concrete_val):
+        if self.frozen:
+            self.counter["ignored_backward_guard"] += 1
+            signpost_event(
+                "dynamic",
+                "evaluate_expr_frozen",
+                {
+                    **self.co_fields,
+                    "ignored_guard": f"{expr} == {concrete_val}",
+                    # no version = original state (this signpost is expected)
+                    # version 2 = dynamic backwards is eagerly compiled
+                    "version": 2,
+                },
+            )
+            log.warning("Ignored guard %s == %s, this could result in accuracy problems", expr, concrete_val, stack_info=True)
+
+
+    def _get_stack_summary(self, is_debug: bool = False):
+        fsummary = None
+        frame = inspect.currentframe()
+        try:
+            while frame is not None:
+                if frame.f_code.co_filename not in uninteresting_files():
+                    fsummary = traceback.FrameSummary(
+                        frame.f_code.co_filename,
+                        frame.f_lineno,
+                        frame.f_code.co_name,
+                    )
+                    break
+                frame = frame.f_back
+        finally:
+            del frame
+
+        # NB: this stack is truncated, but it's fine because the main
+        # stack_info will give you the rest of the info you need
+        maybe_user_loc = ""
+        user_tb = TracingContext.extract_stack()
+        if user_tb:
+            maybe_user_loc = " at " + format_frame(user_tb[-1])
+
+        maybe_extra_debug = ""
+        if is_debug and user_tb:
+            maybe_extra_debug = (
+                '\nUser Stack (most recent call last):\n' +
+                '  (snipped, see stack below for prefix)\n' +
+                ''.join(traceback.format_list(user_tb))
+            )
+        if is_debug and config.extended_debug_cpp:
+            cpp_stack = CapturedTraceback.extract(cpp=True)
+            maybe_extra_debug += "\nC++ stack trace:\n" + ''.join(cpp_stack.format())
+        elif is_debug:
+            maybe_extra_debug += (
+                "\nFor C++ stack trace, run with "
+                "TORCHDYNAMO_EXTENDED_DEBUG_CPP=1"
+            )
+
+        return fsummary, maybe_user_loc, maybe_extra_debug
+
+    def _log_guard(self, prefix: str, g, forcing_spec: bool):
+        if self.log.isEnabledFor(logging.INFO):
+            str_g = str(g)
+            is_debug = config.extended_debug_guard_added is not None and str_g == config.extended_debug_guard_added
+            fsummary, maybe_user_loc, maybe_extra_debug = self._get_stack_summary(is_debug)
+            maybe_more_info = ""
+            if not is_debug:
+                maybe_more_info = (
+                    ", for more info run with "
+                    f'TORCHDYNAMO_EXTENDED_DEBUG_GUARD_ADDED="{str_g}"'
+                )
+            self.log.info(
+                "%s %s [guard added]%s (%s)%s%s",
+                prefix if not forcing_spec else f"{prefix} (forcing_spec)",
+                str_g,
+                maybe_user_loc,
+                format_frame(fsummary),
+                maybe_more_info,
+                maybe_extra_debug,
+                stack_info=is_debug,
+            )
+
+    @lru_cache(256)
+    @record_shapeenv_event(save_tracked_fakes=True)
+    def evaluate_expr(self, orig_expr: "sympy.Expr", hint=None, fx_node=None,
+                      size_oblivious: bool = False, *, forcing_spec: bool = False):
+        try:
+            return self._evaluate_expr(orig_expr, hint, fx_node, size_oblivious, forcing_spec=forcing_spec)
+        except Exception:
+            self.log.warning(
+                "failed during evaluate_expr(%s, hint=%s, size_oblivious=%s, forcing_spec=%s",
+                orig_expr, hint, size_oblivious, forcing_spec
+            )
+            raise
+
+    def _evaluate_expr(self, orig_expr: "sympy.Expr", hint=None, fx_node=None,
+                       size_oblivious: bool = False, *, forcing_spec: bool = False):
+        """
+        Given an expression, evaluates it, adding guards if necessary
+        """
+
+        # TODO: split conjunctions and evaluate them separately
+
+        # Don't track this one
+        @functools.lru_cache(None)
+        def compute_concrete_val():
+            if hint is None:
+                return self.size_hint(orig_expr)
+            else:
+                return sympy.sympify(hint)
+
+        # Check if:
+        #   1. 'translation_validation' is set
+        #   2. the corresponding 'fx_node' is not 'None'
+        #   3. the guard should not be suppressed
+        #
+        # If all of the above check, we create an FX node representing the
+        # actual expression to be guarded.
+        node = None
+        fresh = False
+        if (
+                self._translation_validation_enabled
+                and fx_node is not None
+                and not self._suppress_guards_tls()
+                and not size_oblivious
+        ):
+            concrete_val = compute_concrete_val()
+            if concrete_val is sympy.true:
+                node, fresh = self._create_fx_call_function(torch._assert, (fx_node,))
+            elif concrete_val is sympy.false:
+                neg, _ = self._create_fx_call_function(operator.not_, (fx_node,))
+                node, fresh = self._create_fx_call_function(torch._assert, (neg,))
+            else:
+                eql, _ = self._create_fx_call_function(operator.eq, (fx_node, concrete_val))
+                node, fresh = self._create_fx_call_function(torch._assert, (eql,))
+
+            assert node is not None
+            # If this is a fresh node, we have to remember the event index that
+            # corresponds to this assertion node.
+            # Reason: so that, given an assertion node, we can replay the ShapeEnv
+            # events until the point where this assertion node was freshly created.
+            if fresh:
+                self._add_fx_node_metadata(node)
+
+        # After creating the FX node corresponding to orig_expr, we must make sure that
+        # no error will be raised until the end of this function.
+        #
+        # Reason: the translation validation may become invalid otherwise.
+        #
+        # If an error is raised before the end of this function, we remove the FX node
+        # inserted, and re-raise the error.
+        guard = None
+        tb = None
+
+        try:
+            if orig_expr.is_number:
+                self.log.debug("eval %s [trivial]", orig_expr)
+                if hint is not None:
+                    assert orig_expr == hint, f"{orig_expr} != {hint}"
+                return orig_expr
+
+            expr = orig_expr
+
+            static_expr = self._maybe_evaluate_static(expr,
+                                                      size_oblivious=size_oblivious)
+            if static_expr is not None:
+                self.log.debug("eval %s == %s [statically known]", orig_expr, static_expr)
+                if hint is not None:
+                    assert static_expr == hint, f"{static_expr} != {hint}"
+                return static_expr
+
+            transmute_into_runtime_assert = False
+
+            concrete_val = None
+            if not (expr.free_symbols <= self.var_to_val.keys()):
+                # TODO: dedupe this with _maybe_evaluate_static
+                # Attempt to eliminate the unbacked SymInt
+                new_expr = self._maybe_evaluate_static(expr, unbacked_only=True)
+                if not (new_expr.free_symbols <= self.var_to_val.keys()):
+                    size_oblivious_result = None
+                    if not size_oblivious:
+                        size_oblivious_result = self._maybe_evaluate_static(
+                            expr,
+                            size_oblivious=True
+                        )
+
+                    # Last ditch
+                    if (
+                        self.unbacked_var_to_val and
+                        not (unsound_result := orig_expr.xreplace(self.unbacked_var_to_val)).free_symbols
+                    ):
+                        log.warning("propagate_real_tensors evaluate_expr(%s) -> %s", orig_expr, unsound_result)
+                        trace_structured(
+                            "propagate_real_tensors",
+                            metadata_fn=lambda: {
+                                "expr": repr(orig_expr),
+                                "result": repr(unsound_result),
+                                "stack": structured.from_traceback(CapturedTraceback.extract(skip=1).summary()),
+                            },
+                        )
+                        transmute_into_runtime_assert = True
+                        concrete_val = unsound_result
+                    else:
+                        raise self._make_data_dependent_error(
+                            expr.xreplace(self.var_to_val),
+                            expr,
+                            size_oblivious_result=size_oblivious_result
+                        )
+                else:
+                    expr = new_expr
+
+            if concrete_val is None:
+                concrete_val = compute_concrete_val()
+            self._check_frozen(expr, concrete_val)
+
+            if (
+                    config.inject_EVALUATE_EXPR_flip_equality_TESTING_ONLY
+                    and isinstance(hint, bool)
+                    and isinstance(expr, (sympy.Eq, sympy.Ne))
+            ):
+                expr = sympy.Not(expr)
+
+            # Turn this into a boolean expression, no longer need to consult
+            # concrete_val
+            if concrete_val is sympy.true:
+                g = expr
+            elif concrete_val is sympy.false:
+                g = sympy.Not(expr)
+            else:
+                g = sympy.Eq(expr, concrete_val)  # type: ignore[arg-type]
+
+            if transmute_into_runtime_assert:
+                self.defer_runtime_assert(
+                    g,
+                    f"propagate_real_tensors: {orig_expr} == {unsound_result}"
+                )
+                return concrete_val
+
+            if not self._suppress_guards_tls():
+                if isinstance(g, sympy.Rel):
+                    # TODO: If we successfully eliminate a symbol via equality, it
+                    # is not actually necessary to save a guard for the equality,
+                    # as we will implicitly generate a guard when we match that
+                    # input against the symbol.  Probably the easiest way to
+                    # implement this is to have maybe_guard_rel return a bool
+                    # saying if it "subsumed" the guard (and therefore the guard
+                    # is no longer necessary)
+                    self._maybe_guard_rel(g)
+
+                if not self.allow_complex_guards_as_runtime_asserts:
+                    # at this point, we've evaluated the concrete expr value, and have
+                    # flipped/negated the guard if necessary. Now we know what to guard
+                    # or defer to runtime assert on.
+                    stack = CapturedTraceback.extract(skip=1)
+                    guard = ShapeGuard(g, stack)
+                    self.guards.append(guard)
+                else:
+                    # it's fine to defer simple guards here without checking,
+                    # the _maybe_guard_rel() call above will set replacements if possible,
+                    # and so the result here will be statically known
+                    self.defer_runtime_assert(g, f"evaluate_expr: {orig_expr}")
+
+        except Exception:
+            if fresh:
+                self._remove_fx_node(node)
+            raise
+        else:
+            if not self._suppress_guards_tls():
+                if guard is not None:  # we might have deferred this to runtime assert
+                    self._log_guard("eval", g, forcing_spec=forcing_spec)
+
+                    for s in g.free_symbols:
+                        self.symbol_guard_counter[s] += 1
+                        # Forcing_spec to avoid infinite recursion
+                        if (
+                            not forcing_spec and
+                            config.symbol_guard_limit_before_specialize is not None and
+                            self.symbol_guard_counter[s] > config.symbol_guard_limit_before_specialize
+                        ):
+                            # Force specialization
+                            self.log.info(
+                                "symbol_guard_limit_before_specialize=%s exceeded on %s",
+                                config.symbol_guard_limit_before_specialize,
+                                s
+                            )
+                            self.evaluate_expr(s, forcing_spec=True)
+            else:
+                self._log_guard("eval [guard suppressed]", g, forcing_spec=forcing_spec)
+
+        return concrete_val
+
+    def cleanup(self):
+        """
+        Break reference cycles.
+
+        This destroys the stacks. If you really want to keep them, we
+        just need some way to break references on code objects.
+        """
+        for g in self.guards:
+            g.stack.cleanup()
+        for s in self.var_to_stack.values():
+            s.cleanup()
+        for ras in self.deferred_runtime_asserts.values():
+            for ra in ras:
+                ra.stack.cleanup()
+
+    @record_shapeenv_event(save_tracked_fakes=True)
+    def defer_runtime_assert(self, orig_expr: "sympy.Expr", msg, fx_node=None):
+        """Create an assert that is checked at runtime
+
+        Args:
+            orig_expr (sympy.Expr): Boolean expression to assert is true
+            msg (str): Message to display on assertion failure
+            fx_node (Optional, torch.fx.Node): node in ``self.graph`` corresponding
+                to the expression, if applicable
+
+        """
+        expr = orig_expr
+
+        # TODO: split conjunctions and evaluate them separately
+
+        static_expr = self._maybe_evaluate_static(expr)
+        if static_expr is not None:
+            self.log.debug("runtime_assert %s == %s [statically known]", orig_expr, static_expr)
+            return static_expr
+
+        # Attempt to eliminate the unbacked SymInt
+        new_expr = self._maybe_evaluate_static(expr, unbacked_only=True)
+        if not self.prefer_deferred_runtime_asserts_over_guards and new_expr.free_symbols <= self.var_to_val.keys():
+            # Do a normal guard
+            return self.evaluate_expr(new_expr, fx_node=fx_node)
+        # NB: Don't use new_expr as expr; it could contain gunk like shape0
+        # which we don't want to guard on
+
+        # OK, we're definitely doing a runtime assert now
+        if (
+            self._translation_validation_enabled
+            and fx_node is not None
+            and not self._suppress_guards_tls()
+        ):
+            node, fresh = self._create_fx_call_function(torch._assert, (fx_node,))
+            assert node is not None
+            if fresh:
+                self._add_fx_node_metadata(node)
+
+        if not self._suppress_guards_tls():
+            # If you're here because of this assert, read Note [Backwards runtime asserts]
+            # in torch/_inductor/graph.py
+            assert not self.runtime_asserts_frozen, expr
+
+            self._check_frozen(expr, sympy.true)
+
+            # eliminate symbols on equality tests / refine ranges
+            if isinstance(expr, sympy.Rel):
+                self._maybe_guard_rel(expr)
+
+            # canonicalise to remove equations that are trivially equal
+            orig_expr = expr
+            expr = canonicalize_bool_expr(expr)
+            stack = CapturedTraceback.extract(skip=1)
+            ra = RuntimeAssert(expr, msg, stack)
+            # TODO: Do this in a way that is less janky than int(s.name[1:])
+            cands = sorted((s for s in expr.free_symbols if symbol_is_type(s, SymT.UNBACKED_INT)), key=lambda s: int(s.name[1:]))
+            # Is None when prefer_deferred_runtime_asserts_over_guards=True
+            # and the guard in question has no unbacked SymInts in front
+            ix = cands[-1] if cands else None
+            self.deferred_runtime_asserts.setdefault(ix, []).append(ra)
+            self.num_deferred_runtime_asserts += 1
+            self._update_version_counter()
+            self._log_guard("runtime_assert", orig_expr, forcing_spec=False)
+        else:
+            self._log_guard("runtime_assert [guard suppressed]", orig_expr, forcing_spec=False)
+
+        return True
+
+    # Refines the ranges of the variables present in 'guard'.
+    #
+    # This function tries to refine the range of the variables inside
+    # 'guard' by reasoning about it. Specifically, when 'guard' is a
+    # 'sympy.Relational' operation.
+    #
+    # It does mainly 3 things:
+    #   1. Tries to isolate a variable in the left-hand side
+    #   2. Compute the value range of the right-hand side
+    #   3. Update the value range of the variable, if better
+    def _refine_ranges(self, expr: sympy.Expr) -> None:
+        expr = self.simplify(expr)
+
+        for symbol in expr.free_symbols:
+            assert isinstance(symbol, sympy.Symbol)
+
+            if isinstance(self.var_to_val.get(symbol, None), SingletonInt):
+                # Skip var_to_range logic for SingletonInt which is only used
+                # for jagged layout NestedTensors today
+                continue
+
+            r = try_solve(expr, symbol)
+
+            if r is None or not (symbol.is_integer and r[1].is_integer):
+                # Range refinement only supports integer symbols for now.
+                # There are lots of SymPy bugs when it comes to comparing
+                # reals and integers, so we skip that for now.
+                continue
+
+            r_expr, rhs = r
+            vr = self.var_to_range[symbol]
+            lower, upper = vr.lower, vr.upper
+
+            rhs_vr = bound_sympy(rhs, self.var_to_range)
+
+            # Let's suppose that we have a preexisting range for x [0, 100].
+            # Now, we issue a guard x > y, where the range for y is [50, 150].
+            # Then, lower = 0, rhs_vr.lower = 50 and therefore refinement can happen,
+            # refining x to [51, 100], since x must be greater than y, but the lowest
+            # y could be is 50.
+            #
+            # sympy.Eq may update both lower and upper bounds.
+            # sympy.G{t,e} may update the lower bound, only.
+            # sympy.L{t,e} may update the upper bound, only.
+            if lower < rhs_vr.lower and isinstance(r_expr, (sympy.Eq, sympy.Ge, sympy.Gt)):
+                # Strictly greater relations allow us to refine a bit more, since
+                # x < y implies that the lower bound for x is: y + 1.
+                lower = rhs_vr.lower + int(isinstance(r_expr, sympy.Gt))
+            if upper > rhs_vr.upper and isinstance(r_expr, (sympy.Eq, sympy.Le, sympy.Lt)):
+                upper = rhs_vr.upper - int(isinstance(r_expr, sympy.Lt))
+
+            # Do nothing if the new value range is no better than what we already have.
+            if vr == ValueRanges(lower, upper):
+                continue
+
+            # Updates the range and the guards corresponding to each bound of the symbol.
+            self._update_var_to_range(symbol, ValueRanges(lower, upper))
+            # If the range is refined to singleton, set replacement
+            if self.var_to_range[symbol].is_singleton():
+                self._set_replacement(symbol, self.var_to_range[symbol].lower, "range_refined_to_singleton")
+
+            # Clears the cache, since this update can change the result.
+            self._maybe_evaluate_static.cache_clear()
+
+    @lru_cache(maxsize=None)
+    @record_shapeenv_event()
+    def constrain_symbol_range(self, s: sympy.Symbol, compiler_min: int, compiler_max: int):
+        upd_vr = ValueRanges(compiler_min, compiler_max)
+        old_vr = self.var_to_range.get(s, ValueRanges.unknown())
+        self._update_var_to_range(s, upd_vr)
+        if (new_vr := self.var_to_range[s]) != old_vr:
+            log.info("constrain_symbol_range %s [%s, %s]", s, new_vr.lower, new_vr.upper)
+
+
+def _is_int(expr):
+    return isinstance(expr, SymInt) and expr.node.expr.is_number
+
+# WARNING: This is legacy, DO NOT USE
+def _is_dim_dynamic(t, d):
+    return hasattr(t, "_dynamo_dynamic_indices") and d in t._dynamo_dynamic_indices
+
+class PropagateUnbackedSymInts(torch.fx.Interpreter):
+    def run_node(self, n: torch.fx.Node):
+        """
+        Run an FX node, propagating unbacked Symbol bindings to the new fake tensor
+        """
+        from torch._guards import detect_fake_mode
+
+        result = super().run_node(n)
+        rebind_unbacked(detect_fake_mode().shape_env, n, result)
+        return result
+
+
+def _find_user_code_frame():
+    frame = inspect.currentframe()
+    while frame is not None:
+        if not frame.f_code.co_filename.startswith(
+            os.path.dirname(inspect.getfile(torch)) + os.path.sep
+        ):
+            break
+        frame = frame.f_back
+    return frame
+
+
+def _blame_user_code(e, frame):
+    frame_summary = traceback.FrameSummary(
+        frame.f_code.co_filename,
+        frame.f_lineno,
+        frame.f_code.co_name,
+    )
+    msg = e.args[0]
+    msg += (
+        '\n\nThe following call raised this error:\n' +
+        ''.join(traceback.StackSummary.from_list([frame_summary]).format())
+    )
+    e.args = (msg,)
+
+
+class _PythonPrinter(sympy.printing.str.StrPrinter):
+    """
+    Util printer that replaces sympy symbols with their source-level names
+    and renders sympy relational operators (e.g., Eq, Ne, Ge, Le) inline
+    (i.e., as ==, !=, >, <).
+    """
+
+    def __init__(self, src_map):
+        super().__init__()
+        self.src_map = src_map
+
+    def _print_Symbol(self, sym):
+        return self.src_map[sym.name][0]
+
+    def _print_Relational(self, expr):
+        lhs = self.parenthesize(expr.lhs, sympy.printing.precedence.precedence(expr))
+        rel_op = expr.rel_op
+        rhs = self.parenthesize(expr.rhs, sympy.printing.precedence.precedence(expr))
+        return f"{lhs} {rel_op} {rhs}"
+
+
+def _suggest_torch_checks(e, src_map):
+    # extract the unresolved condition on unbacked symints in the error
+    cond = e.cond
+    diff = ", ".join(s.name for s in cond.free_symbols if s.name not in src_map)
+    if diff:
+        log.warning("Unable to find user code corresponding to {%s}", diff)
+        return
+    printer = _PythonPrinter(src_map)
+    msg = e.args[0]
+    msg += "\nTo fix the error, insert one of the following checks before this call:"
+    # suggested fixes to resolve `cond`` are to tell the compiler to assume
+    # either `cond` or its negation (the user will need to select which)
+    suggested_fixes = [
+        f"torch._check({printer.doprint(cond)})",
+        f"torch._check({printer.doprint(sympy.Not(cond))})",
+    ]
+    for i, fix in enumerate(suggested_fixes):
+        msg += f"\n  {i+1}. {fix}"
+    src_mapped = ', '.join(
+        f"`{s}` with {' or '.join(src_map[s])}"
+        for s in sorted(s.name for s in cond.free_symbols)
+    )
+    msg += f"\n\n(These suggested fixes were derived by replacing {src_mapped} in {cond} and its negation.)"
+    e.args = (msg,)
+
+
+def _suggest_fixes_for_data_dependent_error_non_strict(e):
+    """
+    Given a raised data-dependent error, add the following to the error message:
+    1. the closest user code location that raised the error;
+    2. suggested fixes for the error in terms of live variables at that location.
+    """
+
+    # walk the stack up from the data-dependent error until a non-torch frame is found
+    frame = _find_user_code_frame()
+    if frame is not None:
+        # add frame info to error message
+        _blame_user_code(e, frame)
+
+        # map symbol names reachable via frame locals to their source-level names
+        src_map = defaultdict(list)
+        for var, val in frame.f_locals.items():
+            # figure out how to access any symbol inside `val` through `var`
+            for path, leaf in pytree.tree_leaves_with_path(val):
+                name = var + pytree.keystr(path)
+                if isinstance(leaf, torch.SymInt):
+                    src_map[str(leaf.node.expr)].append(name)
+                elif isinstance(leaf, torch.Tensor):
+                    for i, dim in enumerate(leaf.shape):
+                        if isinstance(dim, torch.SymInt):
+                            src_map[str(dim.node.expr)].append(f"{name}.shape[{i}]")
+
+        # add suggested torch.check()s based on `src_map` to the error message
+        # replacing unbacked symints in the unresolved condition in the error
+        _suggest_torch_checks(e, src_map)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..31446d0e61253d7f722a3235e6e4c5788b4b01ba
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/__init__.py
@@ -0,0 +1,4 @@
+# mypy: disable-error-code=attr-defined
+from .core import unify, reify  # noqa: F403
+from .more import unifiable  # noqa: F403
+from .variable import var, isvar, vars, variables, Var  # noqa: F403
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/core.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/core.py
new file mode 100644
index 0000000000000000000000000000000000000000..0893c385bbc9ae5506069913d387689853f79d55
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/core.py
@@ -0,0 +1,119 @@
+# mypy: allow-untyped-defs
+from collections.abc import Iterator  # type: ignore[import]
+from functools import partial
+
+from .unification_tools import assoc  # type: ignore[import]
+from .utils import transitive_get as walk
+from .variable import isvar
+from .dispatch import dispatch
+
+__all__ = ["reify", "unify"]
+
+###############
+# Reification #
+###############
+
+@dispatch(Iterator, dict)
+def _reify(t, s):
+    return map(partial(reify, s=s), t)
+    # return (reify(arg, s) for arg in t)
+_reify
+
+@dispatch(tuple, dict)  # type: ignore[no-redef]
+def _reify(t, s):
+    return tuple(reify(iter(t), s))
+_reify
+
+@dispatch(list, dict)  # type: ignore[no-redef]
+def _reify(t, s):
+    return list(reify(iter(t), s))
+_reify
+
+@dispatch(dict, dict)  # type: ignore[no-redef]
+def _reify(d, s):
+    return {k: reify(v, s) for k, v in d.items()}
+_reify
+
+@dispatch(object, dict)  # type: ignore[no-redef]
+def _reify(o, s):
+    return o  # catch all, just return the object
+
+def reify(e, s):
+    """ Replace variables of expression with substitution
+    >>> # xdoctest: +SKIP
+    >>> x, y = var(), var()
+    >>> e = (1, x, (3, y))
+    >>> s = {x: 2, y: 4}
+    >>> reify(e, s)
+    (1, 2, (3, 4))
+    >>> e = {1: x, 3: (y, 5)}
+    >>> reify(e, s)
+    {1: 2, 3: (4, 5)}
+    """
+    if isvar(e):
+        return reify(s[e], s) if e in s else e
+    return _reify(e, s)
+
+###############
+# Unification #
+###############
+
+seq = tuple, list, Iterator
+
+@dispatch(seq, seq, dict)
+def _unify(u, v, s):
+    if len(u) != len(v):
+        return False
+    for uu, vv in zip(u, v):  # avoiding recursion
+        s = unify(uu, vv, s)
+        if s is False:
+            return False
+    return s
+#
+# @dispatch((set, frozenset), (set, frozenset), dict)
+# def _unify(u, v, s):
+#     i = u & v
+#     u = u - i
+#     v = v - i
+#     return _unify(sorted(u), sorted(v), s)
+#
+#
+# @dispatch(dict, dict, dict)
+# def _unify(u, v, s):
+#     if len(u) != len(v):
+#         return False
+#     for key, uval in iteritems(u):
+#         if key not in v:
+#             return False
+#         s = unify(uval, v[key], s)
+#         if s is False:
+#             return False
+#     return s
+#
+#
+# @dispatch(object, object, dict)
+# def _unify(u, v, s):
+#     return False  # catch all
+
+
+@dispatch(object, object, dict)
+def unify(u, v, s):  # no check at the moment
+    """ Find substitution so that u == v while satisfying s
+    >>> x = var('x')
+    >>> unify((1, x), (1, 2), {})
+    {~x: 2}
+    """
+    u = walk(u, s)
+    v = walk(v, s)
+    if u == v:
+        return s
+    if isvar(u):
+        return assoc(s, u, v)
+    if isvar(v):
+        return assoc(s, v, u)
+    return _unify(u, v, s)
+unify
+
+@dispatch(object, object)  # type: ignore[no-redef]
+def unify(u, v):
+    return unify(u, v, {})
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/dispatch.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/dispatch.py
new file mode 100644
index 0000000000000000000000000000000000000000..93039ce75070fec8da52d03067d5c0b851a79b50
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/dispatch.py
@@ -0,0 +1,6 @@
+from functools import partial
+from .multipledispatch import dispatch  # type: ignore[import]
+
+namespace = {}  # type: ignore[var-annotated]
+
+dispatch = partial(dispatch, namespace=namespace)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/match.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/match.py
new file mode 100644
index 0000000000000000000000000000000000000000..96583ef324ded18eb93e6cea52fcc12d4fc16f3f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/match.py
@@ -0,0 +1,122 @@
+# mypy: allow-untyped-defs
+from .core import unify, reify  # type: ignore[attr-defined]
+from .variable import isvar
+from .utils import _toposort, freeze
+from .unification_tools import groupby, first  # type: ignore[import]
+
+
+class Dispatcher:
+    def __init__(self, name):
+        self.name = name
+        self.funcs = {}
+        self.ordering = []
+
+    def add(self, signature, func):
+        self.funcs[freeze(signature)] = func
+        self.ordering = ordering(self.funcs)
+
+    def __call__(self, *args, **kwargs):
+        func, s = self.resolve(args)
+        return func(*args, **kwargs)
+
+    def resolve(self, args):
+        n = len(args)
+        for signature in self.ordering:
+            if len(signature) != n:
+                continue
+            s = unify(freeze(args), signature)
+            if s is not False:
+                result = self.funcs[signature]
+                return result, s
+        raise NotImplementedError("No match found. \nKnown matches: "
+                                  + str(self.ordering) + "\nInput: " + str(args))
+
+    def register(self, *signature):
+        def _(func):
+            self.add(signature, func)
+            return self
+        return _
+
+
+class VarDispatcher(Dispatcher):
+    """ A dispatcher that calls functions with variable names
+    >>> # xdoctest: +SKIP
+    >>> d = VarDispatcher('d')
+    >>> x = var('x')
+    >>> @d.register('inc', x)
+    ... def f(x):
+    ...     return x + 1
+    >>> @d.register('double', x)
+    ... def f(x):
+    ...     return x * 2
+    >>> d('inc', 10)
+    11
+    >>> d('double', 10)
+    20
+    """
+    def __call__(self, *args, **kwargs):
+        func, s = self.resolve(args)
+        d = {k.token: v for k, v in s.items()}
+        return func(**d)
+
+
+global_namespace = {}  # type: ignore[var-annotated]
+
+
+def match(*signature, **kwargs):
+    namespace = kwargs.get('namespace', global_namespace)
+    dispatcher = kwargs.get('Dispatcher', Dispatcher)
+
+    def _(func):
+        name = func.__name__
+
+        if name not in namespace:
+            namespace[name] = dispatcher(name)
+        d = namespace[name]
+
+        d.add(signature, func)
+
+        return d
+    return _
+
+
+def supercedes(a, b):
+    """ ``a`` is a more specific match than ``b`` """
+    if isvar(b) and not isvar(a):
+        return True
+    s = unify(a, b)
+    if s is False:
+        return False
+    s = {k: v for k, v in s.items() if not isvar(k) or not isvar(v)}
+    if reify(a, s) == a:
+        return True
+    if reify(b, s) == b:
+        return False
+
+
+# Taken from multipledispatch
+def edge(a, b, tie_breaker=hash):
+    """ A should be checked before B
+    Tie broken by tie_breaker, defaults to ``hash``
+    """
+    if supercedes(a, b):
+        if supercedes(b, a):
+            return tie_breaker(a) > tie_breaker(b)
+        else:
+            return True
+    return False
+
+
+# Taken from multipledispatch
+def ordering(signatures):
+    """ A sane ordering of signatures to check, first to last
+    Topological sort of edges as given by ``edge`` and ``supercedes``
+    """
+    signatures = list(map(tuple, signatures))
+    edges = [(a, b) for a in signatures for b in signatures if edge(a, b)]
+    edges = groupby(first, edges)
+    for s in signatures:
+        if s not in edges:
+            edges[s] = []
+    edges = {k: [b for a, b in v] for k, v in edges.items()}  # type: ignore[attr-defined, assignment]
+    return _toposort(edges)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/more.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/more.py
new file mode 100644
index 0000000000000000000000000000000000000000..2228448a71a1fd19647a972be0a4f44d8c1c9f54
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/more.py
@@ -0,0 +1,118 @@
+# mypy: allow-untyped-defs
+from .core import unify, reify  # type: ignore[attr-defined]
+from .dispatch import dispatch
+
+
+def unifiable(cls):
+    """ Register standard unify and reify operations on class
+    This uses the type and __dict__ or __slots__ attributes to define the
+    nature of the term
+    See Also:
+    >>> # xdoctest: +SKIP
+    >>> class A(object):
+    ...     def __init__(self, a, b):
+    ...         self.a = a
+    ...         self.b = b
+    >>> unifiable(A)
+    
+    >>> x = var('x')
+    >>> a = A(1, 2)
+    >>> b = A(1, x)
+    >>> unify(a, b, {})
+    {~x: 2}
+    """
+    _unify.add((cls, cls, dict), unify_object)
+    _reify.add((cls, dict), reify_object)
+
+    return cls
+
+
+#########
+# Reify #
+#########
+
+
+def reify_object(o, s):
+    """ Reify a Python object with a substitution
+    >>> # xdoctest: +SKIP
+    >>> class Foo(object):
+    ...     def __init__(self, a, b):
+    ...         self.a = a
+    ...         self.b = b
+    ...     def __str__(self):
+    ...         return "Foo(%s, %s)"%(str(self.a), str(self.b))
+    >>> x = var('x')
+    >>> f = Foo(1, x)
+    >>> print(f)
+    Foo(1, ~x)
+    >>> print(reify_object(f, {x: 2}))
+    Foo(1, 2)
+    """
+    if hasattr(o, '__slots__'):
+        return _reify_object_slots(o, s)
+    else:
+        return _reify_object_dict(o, s)
+
+
+def _reify_object_dict(o, s):
+    obj = object.__new__(type(o))
+    d = reify(o.__dict__, s)
+    if d == o.__dict__:
+        return o
+    obj.__dict__.update(d)
+    return obj
+
+
+def _reify_object_slots(o, s):
+    attrs = [getattr(o, attr) for attr in o.__slots__]
+    new_attrs = reify(attrs, s)
+    if attrs == new_attrs:
+        return o
+    else:
+        newobj = object.__new__(type(o))
+        for slot, attr in zip(o.__slots__, new_attrs):
+            setattr(newobj, slot, attr)
+        return newobj
+
+
+@dispatch(slice, dict)
+def _reify(o, s):
+    """ Reify a Python ``slice`` object """
+    return slice(*reify((o.start, o.stop, o.step), s))
+
+
+#########
+# Unify #
+#########
+
+
+def unify_object(u, v, s):
+    """ Unify two Python objects
+    Unifies their type and ``__dict__`` attributes
+    >>> # xdoctest: +SKIP
+    >>> class Foo(object):
+    ...     def __init__(self, a, b):
+    ...         self.a = a
+    ...         self.b = b
+    ...     def __str__(self):
+    ...         return "Foo(%s, %s)"%(str(self.a), str(self.b))
+    >>> x = var('x')
+    >>> f = Foo(1, x)
+    >>> g = Foo(1, 2)
+    >>> unify_object(f, g, {})
+    {~x: 2}
+    """
+    if type(u) != type(v):
+        return False
+    if hasattr(u, '__slots__'):
+        return unify([getattr(u, slot) for slot in u.__slots__],
+                     [getattr(v, slot) for slot in v.__slots__],
+                     s)
+    else:
+        return unify(u.__dict__, v.__dict__, s)
+
+
+@dispatch(slice, slice, dict)
+def _unify(u, v, s):
+    """ Unify a Python ``slice`` object """
+    return unify((u.start, u.stop, u.step), (v.start, v.stop, v.step), s)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/multipledispatch/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/multipledispatch/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0295af0ea6b6b92836e034c1d28cfdf69b1d3ba
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/multipledispatch/__init__.py
@@ -0,0 +1,3 @@
+from .core import dispatch
+from .dispatcher import (Dispatcher, halt_ordering, restart_ordering,
+                         MDNotImplementedError)
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/multipledispatch/conflict.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/multipledispatch/conflict.py
new file mode 100644
index 0000000000000000000000000000000000000000..7187330ead257a30d3d18504ec4787d005a41cca
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/multipledispatch/conflict.py
@@ -0,0 +1,121 @@
+# mypy: allow-untyped-defs
+from .utils import _toposort, groupby
+from .variadic import isvariadic
+import operator
+
+__all__ = ["AmbiguityWarning", "supercedes", "consistent", "ambiguous", "ambiguities", "super_signature",
+           "edge", "ordering"]
+
+class AmbiguityWarning(Warning):
+    pass
+
+
+def supercedes(a, b):
+    """ A is consistent and strictly more specific than B """
+    if len(a) < len(b):
+        # only case is if a is empty and b is variadic
+        return not a and len(b) == 1 and isvariadic(b[-1])
+    elif len(a) == len(b):
+        return all(map(issubclass, a, b))
+    else:
+        # len(a) > len(b)
+        p1 = 0
+        p2 = 0
+        while p1 < len(a) and p2 < len(b):
+            cur_a = a[p1]
+            cur_b = b[p2]
+            if not (isvariadic(cur_a) or isvariadic(cur_b)):
+                if not issubclass(cur_a, cur_b):
+                    return False
+                p1 += 1
+                p2 += 1
+            elif isvariadic(cur_a):
+                assert p1 == len(a) - 1
+                return p2 == len(b) - 1 and issubclass(cur_a, cur_b)
+            elif isvariadic(cur_b):
+                assert p2 == len(b) - 1
+                if not issubclass(cur_a, cur_b):
+                    return False
+                p1 += 1
+        return p2 == len(b) - 1 and p1 == len(a)
+
+
+def consistent(a, b):
+    """ It is possible for an argument list to satisfy both A and B """
+
+    # Need to check for empty args
+    if not a:
+        return not b or isvariadic(b[0])
+    if not b:
+        return not a or isvariadic(a[0])
+
+    # Non-empty args check for mutual subclasses
+    if len(a) == len(b):
+        return all(issubclass(aa, bb) or issubclass(bb, aa)
+                   for aa, bb in zip(a, b))
+    else:
+        p1 = 0
+        p2 = 0
+        while p1 < len(a) and p2 < len(b):
+            cur_a = a[p1]
+            cur_b = b[p2]
+            if not issubclass(cur_b, cur_a) and not issubclass(cur_a, cur_b):
+                return False
+            if not (isvariadic(cur_a) or isvariadic(cur_b)):
+                p1 += 1
+                p2 += 1
+            elif isvariadic(cur_a):
+                p2 += 1
+            elif isvariadic(cur_b):
+                p1 += 1
+        # We only need to check for variadic ends
+        # Variadic types are guaranteed to be the last element
+        return (isvariadic(cur_a) and p2 == len(b) or  # type: ignore[possibly-undefined]
+                isvariadic(cur_b) and p1 == len(a))  # type: ignore[possibly-undefined]
+
+
+def ambiguous(a, b):
+    """ A is consistent with B but neither is strictly more specific """
+    return consistent(a, b) and not (supercedes(a, b) or supercedes(b, a))
+
+
+def ambiguities(signatures):
+    """ All signature pairs such that A is ambiguous with B """
+    signatures = list(map(tuple, signatures))
+    return {(a, b) for a in signatures for b in signatures
+            if hash(a) < hash(b)
+            and ambiguous(a, b)
+            and not any(supercedes(c, a) and supercedes(c, b)
+            for c in signatures)}
+
+
+def super_signature(signatures):
+    """ A signature that would break ambiguities """
+    n = len(signatures[0])
+    assert all(len(s) == n for s in signatures)
+
+    return [max((type.mro(sig[i]) for sig in signatures), key=len)[0]
+            for i in range(n)]
+
+
+def edge(a, b, tie_breaker=hash):
+    """ A should be checked before B
+    Tie broken by tie_breaker, defaults to ``hash``
+    """
+    # A either supercedes B and B does not supercede A or if B does then call
+    # tie_breaker
+    return supercedes(a, b) and (not supercedes(b, a) or tie_breaker(a) > tie_breaker(b))
+
+
+def ordering(signatures):
+    """ A sane ordering of signatures to check, first to last
+    Topological sort of edges as given by ``edge`` and ``supercedes``
+    """
+    signatures = list(map(tuple, signatures))
+    edges = [(a, b) for a in signatures for b in signatures if edge(a, b)]
+    edges = groupby(operator.itemgetter(0), edges)
+    for s in signatures:
+        if s not in edges:
+            edges[s] = []
+    edges = {k: [b for a, b in v] for k, v in edges.items()}  # type: ignore[assignment, attr-defined]
+    return _toposort(edges)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/multipledispatch/core.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/multipledispatch/core.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b5bdbc963014e8f76b1f1ac3624eab56bf3b274
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/multipledispatch/core.py
@@ -0,0 +1,84 @@
+# mypy: allow-untyped-defs
+import inspect
+import sys
+
+from .dispatcher import Dispatcher, MethodDispatcher
+
+global_namespace = {}  # type: ignore[var-annotated]
+
+__all__ = ["dispatch", "ismethod"]
+
+def dispatch(*types, **kwargs):
+    """ Dispatch function on the types of the inputs
+    Supports dispatch on all non-keyword arguments.
+    Collects implementations based on the function name.  Ignores namespaces.
+    If ambiguous type signatures occur a warning is raised when the function is
+    defined suggesting the additional method to break the ambiguity.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> @dispatch(int)
+        ... def f(x):
+        ...     return x + 1
+        >>> @dispatch(float)
+        ... def f(x):
+        ...     return x - 1
+        >>> # xdoctest: +SKIP
+        >>> f(3)
+        4
+        >>> f(3.0)
+        2.0
+        >>> # Specify an isolated namespace with the namespace keyword argument
+        >>> my_namespace = {}
+        >>> @dispatch(int, namespace=my_namespace)
+        ... def foo(x):
+        ...     return x + 1
+        >>> # Dispatch on instance methods within classes
+        >>> class MyClass(object):
+        ...     @dispatch(list)
+        ...     def __init__(self, data):
+        ...         self.data = data
+        ...     @dispatch(int)
+        ...     def __init__(self, datum):
+        ...         self.data = [datum]
+        >>> MyClass([1, 2, 3]).data
+        [1, 2, 3]
+        >>> MyClass(3).data
+        [3]
+    """
+    namespace = kwargs.get('namespace', global_namespace)
+
+    types = tuple(types)
+
+    def _df(func):
+        name = func.__name__
+
+        if ismethod(func):
+            dispatcher = inspect.currentframe().f_back.f_locals.get(  # type: ignore[union-attr]
+                name,  # type: ignore[union-attr]
+                MethodDispatcher(name),
+            )
+        else:
+            if name not in namespace:
+                namespace[name] = Dispatcher(name)
+            dispatcher = namespace[name]
+
+        dispatcher.add(types, func)
+        return dispatcher
+    return _df
+
+
+def ismethod(func):
+    """ Is func a method?
+    Note that this has to work as the method is defined but before the class is
+    defined.  At this stage methods look like functions.
+    """
+    if hasattr(inspect, "signature"):
+        signature = inspect.signature(func)
+        return signature.parameters.get('self', None) is not None
+    else:
+        if sys.version_info.major < 3:
+            spec = inspect.getargspec(func)  # type: ignore[attr-defined]
+        else:
+            spec = inspect.getfullargspec(func)  # type: ignore[union-attr, assignment]
+        return spec and spec.args and spec.args[0] == 'self'
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1d28201d04191b8ed269595e73ee05a41044a24
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py
@@ -0,0 +1,427 @@
+# mypy: allow-untyped-defs
+from warnings import warn
+import inspect
+from typing_extensions import deprecated
+from .conflict import ordering, ambiguities, super_signature, AmbiguityWarning
+from .utils import expand_tuples
+from .variadic import Variadic, isvariadic
+import itertools as itl
+
+__all__ = ["MDNotImplementedError", "ambiguity_warn", "halt_ordering", "restart_ordering", "variadic_signature_matches_iter",
+           "variadic_signature_matches", "Dispatcher", "source", "MethodDispatcher", "str_signature", "warning_text"]
+
+class MDNotImplementedError(NotImplementedError):
+    """ A NotImplementedError for multiple dispatch """
+
+
+def ambiguity_warn(dispatcher, ambiguities):
+    """ Raise warning when ambiguity is detected
+    Parameters
+    ----------
+    dispatcher : Dispatcher
+        The dispatcher on which the ambiguity was detected
+    ambiguities : set
+        Set of type signature pairs that are ambiguous within this dispatcher
+    See Also:
+        Dispatcher.add
+        warning_text
+    """
+    warn(warning_text(dispatcher.name, ambiguities), AmbiguityWarning)
+
+
+@deprecated(
+    "`halt_ordering` is deprecated, you can safely remove this call.",
+    category=FutureWarning,
+)
+def halt_ordering():
+    """Deprecated interface to temporarily disable ordering."""
+
+
+@deprecated(
+    "`restart_ordering` is deprecated, if you would like to eagerly order the dispatchers, "
+    "you should call the `reorder()` method on each dispatcher.",
+    category=FutureWarning,
+)
+def restart_ordering(on_ambiguity=ambiguity_warn):
+    """Deprecated interface to temporarily resume ordering."""
+
+
+def variadic_signature_matches_iter(types, full_signature):
+    """Check if a set of input types matches a variadic signature.
+    Notes
+    -----
+    The algorithm is as follows:
+    Initialize the current signature to the first in the sequence
+    For each type in `types`:
+        If the current signature is variadic
+            If the type matches the signature
+                yield True
+            Else
+                Try to get the next signature
+                If no signatures are left we can't possibly have a match
+                    so yield False
+        Else
+            yield True if the type matches the current signature
+            Get the next signature
+    """
+    sigiter = iter(full_signature)
+    sig = next(sigiter)
+    for typ in types:
+        matches = issubclass(typ, sig)
+        yield matches
+        if not isvariadic(sig):
+            # we're not matching a variadic argument, so move to the next
+            # element in the signature
+            sig = next(sigiter)
+    else:
+        try:
+            sig = next(sigiter)
+        except StopIteration:
+            assert isvariadic(sig)
+            yield True
+        else:
+            # We have signature items left over, so all of our arguments
+            # haven't matched
+            yield False
+
+
+def variadic_signature_matches(types, full_signature):
+    # No arguments always matches a variadic signature
+    assert full_signature
+    return all(variadic_signature_matches_iter(types, full_signature))
+
+
+class Dispatcher:
+    """ Dispatch methods based on type signature
+    Use ``dispatch`` to add implementations
+    Examples
+    --------
+    >>> # xdoctest: +SKIP("bad import name")
+    >>> from multipledispatch import dispatch
+    >>> @dispatch(int)
+    ... def f(x):
+    ...     return x + 1
+    >>> @dispatch(float)
+    ... def f(x):
+    ...     return x - 1
+    >>> f(3)
+    4
+    >>> f(3.0)
+    2.0
+    """
+    __slots__ = '__name__', 'name', 'funcs', '_ordering', '_cache', 'doc'
+
+    def __init__(self, name, doc=None):
+        self.name = self.__name__ = name
+        self.funcs = {}
+        self.doc = doc
+
+        self._cache = {}
+
+    def register(self, *types, **kwargs):
+        """ register dispatcher with new implementation
+        >>> # xdoctest: +SKIP
+        >>> f = Dispatcher('f')
+        >>> @f.register(int)
+        ... def inc(x):
+        ...     return x + 1
+        >>> @f.register(float)
+        ... def dec(x):
+        ...     return x - 1
+        >>> @f.register(list)
+        ... @f.register(tuple)
+        ... def reverse(x):
+        ...     return x[::-1]
+        >>> f(1)
+        2
+        >>> f(1.0)
+        0.0
+        >>> f([1, 2, 3])
+        [3, 2, 1]
+        """
+        def _df(func):
+            self.add(types, func, **kwargs)   # type: ignore[call-arg]
+            return func
+        return _df
+
+    @classmethod
+    def get_func_params(cls, func):
+        if hasattr(inspect, "signature"):
+            sig = inspect.signature(func)
+            return sig.parameters.values()
+
+    @classmethod
+    def get_func_annotations(cls, func):
+        """ get annotations of function positional parameters
+        """
+        params = cls.get_func_params(func)
+        if params:
+            Parameter = inspect.Parameter
+
+            params = (param for param in params
+                      if param.kind in
+                      (Parameter.POSITIONAL_ONLY,
+                       Parameter.POSITIONAL_OR_KEYWORD))
+
+            annotations = tuple(
+                param.annotation
+                for param in params)
+
+            if all(ann is not Parameter.empty for ann in annotations):
+                return annotations
+
+    def add(self, signature, func):
+        """ Add new types/method pair to dispatcher
+        >>> # xdoctest: +SKIP
+        >>> D = Dispatcher('add')
+        >>> D.add((int, int), lambda x, y: x + y)
+        >>> D.add((float, float), lambda x, y: x + y)
+        >>> D(1, 2)
+        3
+        >>> D(1, 2.0)
+        Traceback (most recent call last):
+        ...
+        NotImplementedError: Could not find signature for add: 
+        >>> # When ``add`` detects a warning it calls the ``on_ambiguity`` callback
+        >>> # with a dispatcher/itself, and a set of ambiguous type signature pairs
+        >>> # as inputs.  See ``ambiguity_warn`` for an example.
+        """
+        # Handle annotations
+        if not signature:
+            annotations = self.get_func_annotations(func)
+            if annotations:
+                signature = annotations
+
+        # Handle union types
+        if any(isinstance(typ, tuple) for typ in signature):
+            for typs in expand_tuples(signature):
+                self.add(typs, func)
+            return
+
+        new_signature = []
+
+        for index, typ in enumerate(signature, start=1):
+            if not isinstance(typ, (type, list)):
+                str_sig = ', '.join(c.__name__ if isinstance(c, type)
+                                    else str(c) for c in signature)
+                raise TypeError(f"Tried to dispatch on non-type: {typ}\n"
+                                f"In signature: <{str_sig}>\n"
+                                f"In function: {self.name}")
+
+            # handle variadic signatures
+            if isinstance(typ, list):
+                if index != len(signature):
+                    raise TypeError(
+                        'Variadic signature must be the last element'
+                    )
+
+                if len(typ) != 1:
+                    raise TypeError(
+                        'Variadic signature must contain exactly one element. '
+                        'To use a variadic union type place the desired types '
+                        'inside of a tuple, e.g., [(int, str)]'
+                    )
+                new_signature.append(Variadic[typ[0]])
+            else:
+                new_signature.append(typ)
+
+        self.funcs[tuple(new_signature)] = func
+        self._cache.clear()
+
+        try:
+            del self._ordering
+        except AttributeError:
+            pass
+
+    @property
+    def ordering(self):
+        try:
+            return self._ordering
+        except AttributeError:
+            return self.reorder()
+
+    def reorder(self, on_ambiguity=ambiguity_warn):
+        self._ordering = od = ordering(self.funcs)
+        amb = ambiguities(self.funcs)
+        if amb:
+            on_ambiguity(self, amb)
+        return od
+
+    def __call__(self, *args, **kwargs):
+        types = tuple([type(arg) for arg in args])
+        try:
+            func = self._cache[types]
+        except KeyError as e:
+            func = self.dispatch(*types)
+            if not func:
+                raise NotImplementedError(
+                    f'Could not find signature for {self.name}: <{str_signature(types)}>') from e
+            self._cache[types] = func
+        try:
+            return func(*args, **kwargs)
+
+        except MDNotImplementedError as e:
+            funcs = self.dispatch_iter(*types)
+            next(funcs)  # burn first
+            for func in funcs:
+                try:
+                    return func(*args, **kwargs)
+                except MDNotImplementedError:
+                    pass
+
+            raise NotImplementedError(
+                "Matching functions for "
+                f"{self.name}: <{str_signature(types)}> found, but none completed successfully",) from e
+
+    def __str__(self):
+        return f""
+    __repr__ = __str__
+
+    def dispatch(self, *types):
+        """Determine appropriate implementation for this type signature
+        This method is internal.  Users should call this object as a function.
+        Implementation resolution occurs within the ``__call__`` method.
+        >>> # xdoctest: +SKIP
+        >>> from multipledispatch import dispatch
+        >>> @dispatch(int)
+        ... def inc(x):
+        ...     return x + 1
+        >>> implementation = inc.dispatch(int)
+        >>> implementation(3)
+        4
+        >>> print(inc.dispatch(float))
+        None
+        See Also:
+          ``multipledispatch.conflict`` - module to determine resolution order
+        """
+
+        if types in self.funcs:
+            return self.funcs[types]
+
+        try:
+            return next(self.dispatch_iter(*types))
+        except StopIteration:
+            return None
+
+    def dispatch_iter(self, *types):
+
+        n = len(types)
+        for signature in self.ordering:
+            if len(signature) == n and all(map(issubclass, types, signature)):
+                result = self.funcs[signature]
+                yield result
+            elif len(signature) and isvariadic(signature[-1]):
+                if variadic_signature_matches(types, signature):
+                    result = self.funcs[signature]
+                    yield result
+
+    @deprecated("`resolve()` is deprecated, use `dispatch(*types)`", category=FutureWarning)
+    def resolve(self, types):
+        """ Determine appropriate implementation for this type signature
+        .. deprecated:: 0.4.4
+            Use ``dispatch(*types)`` instead
+        """
+        return self.dispatch(*types)
+
+    def __getstate__(self):
+        return {'name': self.name,
+                'funcs': self.funcs}
+
+    def __setstate__(self, d):
+        self.name = d['name']
+        self.funcs = d['funcs']
+        self._ordering = ordering(self.funcs)
+        self._cache = {}
+
+    @property
+    def __doc__(self):
+        docs = [f"Multiply dispatched method: {self.name}"]
+
+        if self.doc:
+            docs.append(self.doc)
+
+        other = []
+        for sig in self.ordering[::-1]:
+            func = self.funcs[sig]
+            if func.__doc__:
+                s = f'Inputs: <{str_signature(sig)}>\n'
+                s += '-' * len(s) + '\n'
+                s += func.__doc__.strip()
+                docs.append(s)
+            else:
+                other.append(str_signature(sig))
+
+        if other:
+            docs.append('Other signatures:\n    ' + '\n    '.join(other))
+
+        return '\n\n'.join(docs)
+
+    def _help(self, *args):
+        return self.dispatch(*map(type, args)).__doc__
+
+    def help(self, *args, **kwargs):
+        """ Print docstring for the function corresponding to inputs """
+        print(self._help(*args))
+
+    def _source(self, *args):
+        func = self.dispatch(*map(type, args))
+        if not func:
+            raise TypeError("No function found")
+        return source(func)
+
+    def source(self, *args, **kwargs):
+        """ Print source code for the function corresponding to inputs """
+        print(self._source(*args))
+
+
+def source(func):
+    s = f'File: {inspect.getsourcefile(func)}\n\n'
+    s = s + inspect.getsource(func)
+    return s
+
+
+class MethodDispatcher(Dispatcher):
+    """ Dispatch methods based on type signature
+    See Also:
+        Dispatcher
+    """
+    __slots__ = ('obj', 'cls')
+
+    @classmethod
+    def get_func_params(cls, func):
+        if hasattr(inspect, "signature"):
+            sig = inspect.signature(func)
+            return itl.islice(sig.parameters.values(), 1, None)
+
+    def __get__(self, instance, owner):
+        self.obj = instance
+        self.cls = owner
+        return self
+
+    def __call__(self, *args, **kwargs):
+        types = tuple([type(arg) for arg in args])
+        func = self.dispatch(*types)
+        if not func:
+            raise NotImplementedError(f'Could not find signature for {self.name}: <{str_signature(types)}>')
+        return func(self.obj, *args, **kwargs)
+
+
+def str_signature(sig):
+    """ String representation of type signature
+    >>> str_signature((int, float))
+    'int, float'
+    """
+    return ', '.join(cls.__name__ for cls in sig)
+
+
+def warning_text(name, amb):
+    """ The text for ambiguity warnings """
+    text = f"\nAmbiguities exist in dispatched function {name}\n\n"
+    text += "The following signatures may result in ambiguous behavior:\n"
+    for pair in amb:
+        text += "\t" + \
+                ', '.join('[' + str_signature(s) + ']' for s in pair) + "\n"
+    text += "\n\nConsider making the following additions:\n\n"
+    text += '\n\n'.join(['@dispatch(' + str_signature(super_signature(s))
+                         + f')\ndef {name}(...)' for s in amb])
+    return text
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..77702e8ccb7f4ff8e36d6d03bc29a2d16b23d80a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py
@@ -0,0 +1,126 @@
+# mypy: allow-untyped-defs
+from collections import OrderedDict
+
+__all__ = ["raises", "expand_tuples", "reverse_dict", "groupby", "typename"]
+
+def raises(err, lamda):
+    try:
+        lamda()
+        return False
+    except err:
+        return True
+
+
+def expand_tuples(L):
+    """
+    >>> expand_tuples([1, (2, 3)])
+    [(1, 2), (1, 3)]
+    >>> expand_tuples([1, 2])
+    [(1, 2)]
+    """
+    if not L:
+        return [()]
+    elif not isinstance(L[0], tuple):
+        rest = expand_tuples(L[1:])
+        return [(L[0],) + t for t in rest]
+    else:
+        rest = expand_tuples(L[1:])
+        return [(item,) + t for t in rest for item in L[0]]
+
+
+# Taken from theano/theano/gof/sched.py
+# Avoids licensing issues because this was written by Matthew Rocklin
+def _toposort(edges):
+    """ Topological sort algorithm by Kahn [1] - O(nodes + vertices)
+    inputs:
+        edges - a dict of the form {a: {b, c}} where b and c depend on a
+    outputs:
+        L - an ordered list of nodes that satisfy the dependencies of edges
+    >>> _toposort({1: (2, 3), 2: (3, )})
+    [1, 2, 3]
+    >>> # Closely follows the wikipedia page [2]
+    >>> # [1] Kahn, Arthur B. (1962), "Topological sorting of large networks",
+    >>> # Communications of the ACM
+    >>> # [2] http://en.wikipedia.org/wiki/Toposort#Algorithms
+    """
+    incoming_edges = reverse_dict(edges)
+    incoming_edges = OrderedDict((k, set(val))
+                                 for k, val in incoming_edges.items())
+    S = OrderedDict.fromkeys(v for v in edges if v not in incoming_edges)
+    L = []
+
+    while S:
+        n, _ = S.popitem()
+        L.append(n)
+        for m in edges.get(n, ()):
+            assert n in incoming_edges[m]
+            incoming_edges[m].remove(n)
+            if not incoming_edges[m]:
+                S[m] = None
+    if any(incoming_edges.get(v, None) for v in edges):
+        raise ValueError("Input has cycles")
+    return L
+
+
+def reverse_dict(d):
+    """Reverses direction of dependence dict
+    >>> d = {'a': (1, 2), 'b': (2, 3), 'c':()}
+    >>> reverse_dict(d)  # doctest: +SKIP
+    {1: ('a',), 2: ('a', 'b'), 3: ('b',)}
+    :note: dict order are not deterministic. As we iterate on the
+        input dict, it make the output of this function depend on the
+        dict order. So this function output order should be considered
+        as undeterministic.
+    """
+    result = OrderedDict()  # type: ignore[var-annotated]
+    for key in d:
+        for val in d[key]:
+            result[val] = result.get(val, ()) + (key,)
+    return result
+
+
+# Taken from toolz
+# Avoids licensing issues because this version was authored by Matthew Rocklin
+def groupby(func, seq):
+    """ Group a collection by a key function
+    >>> names = ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith', 'Frank']
+    >>> groupby(len, names)  # doctest: +SKIP
+    {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']}
+    >>> iseven = lambda x: x % 2 == 0
+    >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8])  # doctest: +SKIP
+    {False: [1, 3, 5, 7], True: [2, 4, 6, 8]}
+    See Also:
+        ``countby``
+    """
+
+    d = OrderedDict()  # type: ignore[var-annotated]
+    for item in seq:
+        key = func(item)
+        if key not in d:
+            d[key] = []
+        d[key].append(item)
+    return d
+
+
+def typename(type):
+    """Get the name of `type`.
+    Parameters
+    ----------
+    type : Union[Type, Tuple[Type]]
+    Returns
+    -------
+    str
+        The name of `type` or a tuple of the names of the types in `type`.
+    Examples
+    --------
+    >>> typename(int)
+    'int'
+    >>> typename((int, float))
+    '(int, float)'
+    """
+    try:
+        return type.__name__
+    except AttributeError:
+        if len(type) == 1:
+            return typename(*type)
+        return f"({', '.join(map(typename, type))})"
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py
new file mode 100644
index 0000000000000000000000000000000000000000..49e546e1ea2672c9c7225e4c3fbc8301a53f299f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py
@@ -0,0 +1,92 @@
+# mypy: allow-untyped-defs
+from .utils import typename
+
+__all__ = ["VariadicSignatureType", "isvariadic", "VariadicSignatureMeta", "Variadic"]
+
+class VariadicSignatureType(type):
+    # checking if subclass is a subclass of self
+    def __subclasscheck__(cls, subclass):
+        other_type = (subclass.variadic_type if isvariadic(subclass)
+                      else (subclass,))
+        return subclass is cls or all(
+            issubclass(other, cls.variadic_type) for other in other_type  # type: ignore[attr-defined]
+        )
+
+    def __eq__(cls, other):
+        """
+        Return True if other has the same variadic type
+        Parameters
+        ----------
+        other : object (type)
+            The object (type) to check
+        Returns
+        -------
+        bool
+            Whether or not `other` is equal to `self`
+        """
+        return (isvariadic(other) and
+                set(cls.variadic_type) == set(other.variadic_type))  # type: ignore[attr-defined]
+
+    def __hash__(cls):
+        return hash((type(cls), frozenset(cls.variadic_type)))  # type: ignore[attr-defined]
+
+
+def isvariadic(obj):
+    """Check whether the type `obj` is variadic.
+    Parameters
+    ----------
+    obj : type
+        The type to check
+    Returns
+    -------
+    bool
+        Whether or not `obj` is variadic
+    Examples
+    --------
+    >>> # xdoctest: +SKIP
+    >>> isvariadic(int)
+    False
+    >>> isvariadic(Variadic[int])
+    True
+    """
+    return isinstance(obj, VariadicSignatureType)
+
+
+class VariadicSignatureMeta(type):
+    """A metaclass that overrides ``__getitem__`` on the class. This is used to
+    generate a new type for Variadic signatures. See the Variadic class for
+    examples of how this behaves.
+    """
+    def __getitem__(cls, variadic_type):
+        if not (isinstance(variadic_type, (type, tuple)) or type(variadic_type)):
+            raise ValueError("Variadic types must be type or tuple of types"
+                             " (Variadic[int] or Variadic[(int, float)]")
+
+        if not isinstance(variadic_type, tuple):
+            variadic_type = variadic_type,
+        return VariadicSignatureType(
+            f'Variadic[{typename(variadic_type)}]',
+            (),
+            dict(variadic_type=variadic_type, __slots__=())
+        )
+
+
+class Variadic(metaclass=VariadicSignatureMeta):
+    """A class whose getitem method can be used to generate a new type
+    representing a specific variadic signature.
+    Examples
+    --------
+    >>> # xdoctest: +SKIP
+    >>> Variadic[int]  # any number of int arguments
+    
+    >>> Variadic[(int, str)]  # any number of one of int or str arguments
+    
+    >>> issubclass(int, Variadic[int])
+    True
+    >>> issubclass(int, Variadic[(int, str)])
+    True
+    >>> issubclass(str, Variadic[(int, str)])
+    True
+    >>> issubclass(float, Variadic[(int, str)])
+    False
+    """
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/unification_tools.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/unification_tools.py
new file mode 100644
index 0000000000000000000000000000000000000000..d06d9bef771c4c3871edc869522fa3e2d26f5be8
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/unification_tools.py
@@ -0,0 +1,396 @@
+# mypy: allow-untyped-defs
+import collections
+import operator
+from functools import reduce
+from collections.abc import Mapping
+
+__all__ = ['merge', 'merge_with', 'valmap', 'keymap', 'itemmap',
+           'valfilter', 'keyfilter', 'itemfilter',
+           'assoc', 'dissoc', 'assoc_in', 'update_in', 'get_in']
+
+
+def _get_factory(f, kwargs):
+    factory = kwargs.pop('factory', dict)
+    if kwargs:
+        raise TypeError(f"{f.__name__}() got an unexpected keyword argument '{kwargs.popitem()[0]}'")
+    return factory
+
+
+def merge(*dicts, **kwargs):
+    """ Merge a collection of dictionaries
+
+    >>> merge({1: 'one'}, {2: 'two'})
+    {1: 'one', 2: 'two'}
+
+    Later dictionaries have precedence
+
+    >>> merge({1: 2, 3: 4}, {3: 3, 4: 4})
+    {1: 2, 3: 3, 4: 4}
+
+    See Also:
+        merge_with
+    """
+    if len(dicts) == 1 and not isinstance(dicts[0], Mapping):
+        dicts = dicts[0]
+    factory = _get_factory(merge, kwargs)
+
+    rv = factory()
+    for d in dicts:
+        rv.update(d)
+    return rv
+
+
+def merge_with(func, *dicts, **kwargs):
+    """ Merge dictionaries and apply function to combined values
+
+    A key may occur in more than one dict, and all values mapped from the key
+    will be passed to the function as a list, such as func([val1, val2, ...]).
+
+    >>> merge_with(sum, {1: 1, 2: 2}, {1: 10, 2: 20})
+    {1: 11, 2: 22}
+
+    >>> merge_with(first, {1: 1, 2: 2}, {2: 20, 3: 30})  # doctest: +SKIP
+    {1: 1, 2: 2, 3: 30}
+
+    See Also:
+        merge
+    """
+    if len(dicts) == 1 and not isinstance(dicts[0], Mapping):
+        dicts = dicts[0]
+    factory = _get_factory(merge_with, kwargs)
+
+    result = factory()
+    for d in dicts:
+        for k, v in d.items():
+            if k not in result:
+                result[k] = [v]
+            else:
+                result[k].append(v)
+    return valmap(func, result, factory)
+
+
+def valmap(func, d, factory=dict):
+    """ Apply function to values of dictionary
+
+    >>> bills = {"Alice": [20, 15, 30], "Bob": [10, 35]}
+    >>> valmap(sum, bills)  # doctest: +SKIP
+    {'Alice': 65, 'Bob': 45}
+
+    See Also:
+        keymap
+        itemmap
+    """
+    rv = factory()
+    rv.update(zip(d.keys(), map(func, d.values())))
+    return rv
+
+
+def keymap(func, d, factory=dict):
+    """ Apply function to keys of dictionary
+
+    >>> bills = {"Alice": [20, 15, 30], "Bob": [10, 35]}
+    >>> keymap(str.lower, bills)  # doctest: +SKIP
+    {'alice': [20, 15, 30], 'bob': [10, 35]}
+
+    See Also:
+        valmap
+        itemmap
+    """
+    rv = factory()
+    rv.update(zip(map(func, d.keys()), d.values()))
+    return rv
+
+
+def itemmap(func, d, factory=dict):
+    """ Apply function to items of dictionary
+
+    >>> accountids = {"Alice": 10, "Bob": 20}
+    >>> itemmap(reversed, accountids)  # doctest: +SKIP
+    {10: "Alice", 20: "Bob"}
+
+    See Also:
+        keymap
+        valmap
+    """
+    rv = factory()
+    rv.update(map(func, d.items()))
+    return rv
+
+
+def valfilter(predicate, d, factory=dict):
+    """ Filter items in dictionary by value
+
+    >>> iseven = lambda x: x % 2 == 0
+    >>> d = {1: 2, 2: 3, 3: 4, 4: 5}
+    >>> valfilter(iseven, d)
+    {1: 2, 3: 4}
+
+    See Also:
+        keyfilter
+        itemfilter
+        valmap
+    """
+    rv = factory()
+    for k, v in d.items():
+        if predicate(v):
+            rv[k] = v
+    return rv
+
+
+def keyfilter(predicate, d, factory=dict):
+    """ Filter items in dictionary by key
+
+    >>> iseven = lambda x: x % 2 == 0
+    >>> d = {1: 2, 2: 3, 3: 4, 4: 5}
+    >>> keyfilter(iseven, d)
+    {2: 3, 4: 5}
+
+    See Also:
+        valfilter
+        itemfilter
+        keymap
+    """
+    rv = factory()
+    for k, v in d.items():
+        if predicate(k):
+            rv[k] = v
+    return rv
+
+
+def itemfilter(predicate, d, factory=dict):
+    """ Filter items in dictionary by item
+
+    >>> def isvalid(item):
+    ...     k, v = item
+    ...     return k % 2 == 0 and v < 4
+
+    >>> d = {1: 2, 2: 3, 3: 4, 4: 5}
+    >>> itemfilter(isvalid, d)
+    {2: 3}
+
+    See Also:
+        keyfilter
+        valfilter
+        itemmap
+    """
+    rv = factory()
+    for item in d.items():
+        if predicate(item):
+            k, v = item
+            rv[k] = v
+    return rv
+
+
+def assoc(d, key, value, factory=dict):
+    """ Return a new dict with new key value pair
+
+    New dict has d[key] set to value. Does not modify the initial dictionary.
+
+    >>> assoc({'x': 1}, 'x', 2)
+    {'x': 2}
+    >>> assoc({'x': 1}, 'y', 3)   # doctest: +SKIP
+    {'x': 1, 'y': 3}
+    """
+    d2 = factory()
+    d2.update(d)
+    d2[key] = value
+    return d2
+
+
+def dissoc(d, *keys, **kwargs):
+    """ Return a new dict with the given key(s) removed.
+
+    New dict has d[key] deleted for each supplied key.
+    Does not modify the initial dictionary.
+
+    >>> dissoc({'x': 1, 'y': 2}, 'y')
+    {'x': 1}
+    >>> dissoc({'x': 1, 'y': 2}, 'y', 'x')
+    {}
+    >>> dissoc({'x': 1}, 'y') # Ignores missing keys
+    {'x': 1}
+    """
+    factory = _get_factory(dissoc, kwargs)
+    d2 = factory()
+
+    if len(keys) < len(d) * .6:
+        d2.update(d)
+        for key in keys:
+            if key in d2:
+                del d2[key]
+    else:
+        remaining = set(d)
+        remaining.difference_update(keys)
+        for k in remaining:
+            d2[k] = d[k]
+    return d2
+
+
+def assoc_in(d, keys, value, factory=dict):
+    """ Return a new dict with new, potentially nested, key value pair
+
+    >>> purchase = {'name': 'Alice',
+    ...             'order': {'items': ['Apple', 'Orange'],
+    ...                       'costs': [0.50, 1.25]},
+    ...             'credit card': '5555-1234-1234-1234'}
+    >>> assoc_in(purchase, ['order', 'costs'], [0.25, 1.00]) # doctest: +SKIP
+    {'credit card': '5555-1234-1234-1234',
+     'name': 'Alice',
+     'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}}
+    """
+    return update_in(d, keys, lambda x: value, value, factory)
+
+
+def update_in(d, keys, func, default=None, factory=dict):
+    """ Update value in a (potentially) nested dictionary
+
+    inputs:
+    d - dictionary on which to operate
+    keys - list or tuple giving the location of the value to be changed in d
+    func - function to operate on that value
+
+    If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the
+    original dictionary with v replaced by func(v), but does not mutate the
+    original dictionary.
+
+    If k0 is not a key in d, update_in creates nested dictionaries to the depth
+    specified by the keys, with the innermost value set to func(default).
+
+    >>> inc = lambda x: x + 1
+    >>> update_in({'a': 0}, ['a'], inc)
+    {'a': 1}
+
+    >>> transaction = {'name': 'Alice',
+    ...                'purchase': {'items': ['Apple', 'Orange'],
+    ...                             'costs': [0.50, 1.25]},
+    ...                'credit card': '5555-1234-1234-1234'}
+    >>> update_in(transaction, ['purchase', 'costs'], sum) # doctest: +SKIP
+    {'credit card': '5555-1234-1234-1234',
+     'name': 'Alice',
+     'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}}
+
+    >>> # updating a value when k0 is not in d
+    >>> update_in({}, [1, 2, 3], str, default="bar")
+    {1: {2: {3: 'bar'}}}
+    >>> update_in({1: 'foo'}, [2, 3, 4], inc, 0)
+    {1: 'foo', 2: {3: {4: 1}}}
+    """
+    ks = iter(keys)
+    k = next(ks)
+
+    rv = inner = factory()
+    rv.update(d)
+
+    for key in ks:
+        if k in d:
+            d = d[k]
+            dtemp = factory()
+            dtemp.update(d)
+        else:
+            d = dtemp = factory()
+
+        inner[k] = inner = dtemp
+        k = key
+
+    if k in d:
+        inner[k] = func(d[k])
+    else:
+        inner[k] = func(default)
+    return rv
+
+
+def get_in(keys, coll, default=None, no_default=False):
+    """ Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys.
+
+    If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless
+    ``no_default`` is specified, then it raises KeyError or IndexError.
+
+    ``get_in`` is a generalization of ``operator.getitem`` for nested data
+    structures such as dictionaries and lists.
+
+    >>> transaction = {'name': 'Alice',
+    ...                'purchase': {'items': ['Apple', 'Orange'],
+    ...                             'costs': [0.50, 1.25]},
+    ...                'credit card': '5555-1234-1234-1234'}
+    >>> get_in(['purchase', 'items', 0], transaction)
+    'Apple'
+    >>> get_in(['name'], transaction)
+    'Alice'
+    >>> get_in(['purchase', 'total'], transaction)
+    >>> get_in(['purchase', 'items', 'apple'], transaction)
+    >>> get_in(['purchase', 'items', 10], transaction)
+    >>> get_in(['purchase', 'total'], transaction, 0)
+    0
+    >>> get_in(['y'], {}, no_default=True)
+    Traceback (most recent call last):
+        ...
+    KeyError: 'y'
+
+    See Also:
+        itertoolz.get
+        operator.getitem
+    """
+    try:
+        return reduce(operator.getitem, keys, coll)
+    except (KeyError, IndexError, TypeError):
+        if no_default:
+            raise
+        return default
+
+
+def getter(index):
+    if isinstance(index, list):
+        if len(index) == 1:
+            index = index[0]
+            return lambda x: (x[index],)
+        elif index:
+            return operator.itemgetter(*index)
+        else:
+            return lambda x: ()
+    else:
+        return operator.itemgetter(index)
+
+
+def groupby(key, seq):
+    """ Group a collection by a key function
+
+    >>> names = ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith', 'Frank']
+    >>> groupby(len, names)  # doctest: +SKIP
+    {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']}
+
+    >>> iseven = lambda x: x % 2 == 0
+    >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8])  # doctest: +SKIP
+    {False: [1, 3, 5, 7], True: [2, 4, 6, 8]}
+
+    Non-callable keys imply grouping on a member.
+
+    >>> groupby('gender', [{'name': 'Alice', 'gender': 'F'},
+    ...                    {'name': 'Bob', 'gender': 'M'},
+    ...                    {'name': 'Charlie', 'gender': 'M'}]) # doctest:+SKIP
+    {'F': [{'gender': 'F', 'name': 'Alice'}],
+     'M': [{'gender': 'M', 'name': 'Bob'},
+           {'gender': 'M', 'name': 'Charlie'}]}
+
+    Not to be confused with ``itertools.groupby``
+
+    See Also:
+        countby
+    """
+    if not callable(key):
+        key = getter(key)
+    d = collections.defaultdict(lambda: [].append)  # type: ignore[var-annotated]
+    for item in seq:
+        d[key(item)](item)
+    rv = {}
+    for k, v in d.items():
+        rv[k] = v.__self__  # type: ignore[var-annotated, attr-defined]
+    return rv
+
+
+def first(seq):
+    """ The first element in a sequence
+
+    >>> first('ABC')
+    'A'
+    """
+    return next(iter(seq))
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..609fe59d43f45863e65d42ba9732a8bb095ebb11
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/utils.py
@@ -0,0 +1,106 @@
+# mypy: allow-untyped-defs
+__all__ = ["hashable", "transitive_get", "raises", "reverse_dict", "xfail", "freeze"]
+def hashable(x):
+    try:
+        hash(x)
+        return True
+    except TypeError:
+        return False
+
+
+def transitive_get(key, d):
+    """ Transitive dict.get
+    >>> d = {1: 2, 2: 3, 3: 4}
+    >>> d.get(1)
+    2
+    >>> transitive_get(1, d)
+    4
+    """
+    while hashable(key) and key in d:
+        key = d[key]
+    return key
+
+
+def raises(err, lamda):
+    try:
+        lamda()
+        return False
+    except err:
+        return True
+
+
+# Taken from theano/theano/gof/sched.py
+# Avoids licensing issues because this was written by Matthew Rocklin
+def _toposort(edges):
+    """ Topological sort algorithm by Kahn [1] - O(nodes + vertices)
+    inputs:
+        edges - a dict of the form {a: {b, c}} where b and c depend on a
+    outputs:
+        L - an ordered list of nodes that satisfy the dependencies of edges
+    >>> # xdoctest: +SKIP
+    >>> _toposort({1: (2, 3), 2: (3, )})
+    [1, 2, 3]
+    Closely follows the wikipedia page [2]
+    [1] Kahn, Arthur B. (1962), "Topological sorting of large networks",
+    Communications of the ACM
+    [2] http://en.wikipedia.org/wiki/Toposort#Algorithms
+    """
+    incoming_edges = reverse_dict(edges)
+    incoming_edges = {k: set(val) for k, val in incoming_edges.items()}
+    S = ({v for v in edges if v not in incoming_edges})
+    L = []
+
+    while S:
+        n = S.pop()
+        L.append(n)
+        for m in edges.get(n, ()):
+            assert n in incoming_edges[m]
+            incoming_edges[m].remove(n)
+            if not incoming_edges[m]:
+                S.add(m)
+    if any(incoming_edges.get(v, None) for v in edges):
+        raise ValueError("Input has cycles")
+    return L
+
+
+def reverse_dict(d):
+    """Reverses direction of dependence dict
+    >>> d = {'a': (1, 2), 'b': (2, 3), 'c':()}
+    >>> reverse_dict(d)  # doctest: +SKIP
+    {1: ('a',), 2: ('a', 'b'), 3: ('b',)}
+    :note: dict order are not deterministic. As we iterate on the
+        input dict, it make the output of this function depend on the
+        dict order. So this function output order should be considered
+        as undeterministic.
+    """
+    result = {}  # type: ignore[var-annotated]
+    for key in d:
+        for val in d[key]:
+            result[val] = result.get(val, ()) + (key,)
+    return result
+
+
+def xfail(func):
+    try:
+        func()
+        raise Exception("XFailed test passed")  # pragma:nocover  # noqa: TRY002
+    except Exception:
+        pass
+
+
+def freeze(d):
+    """ Freeze container to hashable form
+    >>> freeze(1)
+    1
+    >>> freeze([1, 2])
+    (1, 2)
+    >>> freeze({1: 2}) # doctest: +SKIP
+    frozenset([(1, 2)])
+    """
+    if isinstance(d, dict):
+        return frozenset(map(freeze, d.items()))
+    if isinstance(d, set):
+        return frozenset(map(freeze, d))
+    if isinstance(d, (tuple, list)):
+        return tuple(map(freeze, d))
+    return d
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/variable.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/variable.py
new file mode 100644
index 0000000000000000000000000000000000000000..66e97a3a766361cd10e586857104cd71118dcc96
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unification/variable.py
@@ -0,0 +1,86 @@
+# mypy: allow-untyped-defs
+from contextlib import contextmanager
+from .utils import hashable
+from .dispatch import dispatch
+
+_global_logic_variables = set()  # type: ignore[var-annotated]
+_glv = _global_logic_variables
+
+
+class Var:
+    """ Logic Variable """
+
+    _id = 1
+
+    def __new__(cls, *token):
+        if len(token) == 0:
+            token = f"_{Var._id}"  # type: ignore[assignment]
+            Var._id += 1
+        elif len(token) == 1:
+            token = token[0]
+
+        obj = object.__new__(cls)
+        obj.token = token  # type: ignore[attr-defined]
+        return obj
+
+    def __str__(self):
+        return "~" + str(self.token)  # type: ignore[attr-defined]
+    __repr__ = __str__
+
+    def __eq__(self, other):
+        return type(self) == type(other) and self.token == other.token  # type: ignore[attr-defined]
+
+    def __hash__(self):
+        return hash((type(self), self.token))  # type: ignore[attr-defined]
+
+
+def var():
+    return lambda *args: Var(*args)
+
+
+def vars():
+    return lambda n: [var() for i in range(n)]
+
+
+@dispatch(Var)
+def isvar(v):
+    return True
+
+isvar
+
+
+@dispatch(object)  # type: ignore[no-redef]
+def isvar(o):
+    return not not _glv and hashable(o) and o in _glv
+
+
+@contextmanager
+def variables(*variables):
+    """
+    Context manager for logic variables
+
+    Example:
+        >>> # xdoctest: +SKIP("undefined vars")
+        >>> from __future__ import with_statement
+        >>> with variables(1):
+        ...     print(isvar(1))
+        True
+        >>> print(isvar(1))
+        False
+        >>> # Normal approach
+        >>> from unification import unify
+        >>> x = var('x')
+        >>> unify(x, 1)
+        {~x: 1}
+        >>> # Context Manager approach
+        >>> with variables('x'):
+        ...     print(unify('x', 1))
+        {'x': 1}
+    """
+    old_global_logic_variables = _global_logic_variables.copy()
+    _global_logic_variables.update(set(variables))
+    try:
+        yield
+    finally:
+        _global_logic_variables.clear()
+        _global_logic_variables.update(old_global_logic_variables)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unify_refinements.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unify_refinements.py
new file mode 100644
index 0000000000000000000000000000000000000000..cad0a33425bf894540fb3a84f0127a58b43ff237
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/unify_refinements.py
@@ -0,0 +1,121 @@
+# mypy: allow-untyped-defs
+from torch.fx.experimental.graph_gradual_typechecker import Refine
+from torch.fx.tensor_type import TensorType
+from torch.fx.experimental.unification import Var, unify  # type: ignore[attr-defined]
+
+
+def infer_symbolic_types_single_pass(traced):
+    """
+    Calls our symbolic inferencer once.
+    """
+    r = Refine(traced)
+    r.refine()
+    mgu = unify_eq(r.constraints)
+    substitute_all_types(traced.graph, mgu)
+
+def infer_symbolic_types(traced):
+    """
+    Calls our symbolic inferencer twice.
+    This is useful when one pass is not enough
+    to infer all the information such as the case
+    for braodcasting.
+    """
+    r = Refine(traced)
+    r.refine()
+    mgu = unify_eq(r.constraints)
+    substitute_all_types(traced.graph, mgu)
+
+    r = Refine(traced)
+    r.refine()
+    mgu = unify_eq(r.constraints)
+    substitute_all_types(traced.graph, mgu)
+
+    r.symbolic_relations()
+
+def convert_eq(list_of_eq):
+    """
+    Convert equality constraints in the right format
+    to be used by unification library.
+    """
+    lhs = []
+    rhs = []
+    for eq in list_of_eq:
+        lhs.append(eq.lhs)
+        rhs.append(eq.rhs)
+    return tuple(lhs), tuple(rhs)
+
+
+def unify_eq(list_of_eq):
+    """
+    Apply unification to a set of
+    equality constraints
+    """
+    lhs, rhs = convert_eq(list_of_eq)
+    return unify(lhs, rhs)
+
+
+def substitute_solution_one_type(mapping, t):
+    """
+    Apply the most general unifier to a type
+    """
+    if isinstance(t, Var):
+        if t in mapping.keys():
+            return mapping[t]
+        else:
+            return t
+
+    elif isinstance(t, TensorType):
+        new_type = []
+        for typ in t.__args__:
+            if typ in mapping.keys():
+                new_type.append(mapping[typ])
+            else:
+                new_type.append(typ)
+        return TensorType(tuple(new_type))
+
+    elif isinstance(t, list):
+        new_type = []
+        for typ in t:
+            new_type.append(substitute_solution_one_type(mapping, typ))
+        return new_type
+
+    elif isinstance(t, tuple):
+        new_type = []
+        for typ in t:
+            new_type.append(substitute_solution_one_type(mapping, typ))
+        return tuple(new_type)
+
+    else:
+        return t
+
+
+def substitute_all_types(graph, mapping):
+    """
+    Apply the most general unifier to all types in a graph
+    till reaching a fixed point. If the input and output graph
+    are the same, we converge.
+    """
+    flag = True
+    while flag:
+        flag = False
+        for k in mapping:
+            old_mapping_val = mapping[k]
+            if mapping[k] in mapping.keys():
+                new_key = mapping[k]
+                mapping[k] = mapping[new_key]
+            if old_mapping_val != mapping[k]:
+                flag = True
+
+    for n in graph.nodes:
+        n.type = substitute_solution_one_type(mapping, n.type)
+
+def check_for_type_equality(g1, g2):
+    """
+    A check equality to be used in fixed points.
+    We do not use graph equality but instead type
+    equality.
+    """
+    for n, m in zip(g1.nodes, g2.nodes):
+        if n.type != m.type:
+            return False
+    return True
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/validator.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/validator.py
new file mode 100644
index 0000000000000000000000000000000000000000..163479d3cd4c6fba17d469d6f9c04c21b4818953
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/experimental/validator.py
@@ -0,0 +1,787 @@
+# mypy: allow-untyped-defs
+import functools
+import logging
+import math
+import operator
+import sympy
+import builtins
+
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
+
+import torch
+import torch.fx
+import torch.fx.traceback as fx_traceback
+
+from torch._dynamo.exc import TorchDynamoException
+from torch.fx.node import Argument, Target
+from torch.utils._sympy.interp import sympy_interp
+from torch._dynamo.utils import dynamo_timed
+
+log = logging.getLogger(__name__)
+
+try:
+    import z3  # type: ignore[import]
+
+    # Translation Validation for Dynamo guards
+    # ========================================
+    #
+    # Checks whether optimizations applied to the collected guards are
+    # valid. In other words, whether the guard function we actually run
+    # does not have false positives (unsound).
+    #
+    # In order to do so, we build the guards using 2 different information
+    # attached to each 'SymNode':
+    #   1. SymPy expressions
+    #   2. FX nodes
+    #
+    # SymPy expressions have implicit optimizations baked within itself,
+    # which may have a few bugs. On the other hand, we build the FX graph
+    # manually, with no optimizations enabled. This gives us access to
+    # the "ground truth".
+    #
+    # We then convert into Z3 expressions both the SymPy expressions
+    # (see [Note: SympyToZ3]) that reach 'ShapeEnv.produce_guards' function
+    # and the FX nodes (see [Note: PopulateValidator]) that go through
+    # 'ShapeEnv.evaluate_expr' function. Finally, we run the validation.
+    # (see [Note: TranslationValidator])
+
+    # Better Z3 to string implementation (for a small fraction of Z3).
+    #
+    # Here are the things we clean before showing the Z3 expression:
+    #   - Rename a few ops (e.g. "Distinct" ==> "!=")
+    #
+    #   - Ignore ToInt and ToReal operations:
+    #     usually they don't really matter
+    #
+    #   - Transform (ToInt (/ ...)) into (idiv ...):
+    #     this is the pattern for floor division
+    #
+    #   - Collect a chain of the same operations into one
+    def z3str(e: z3.ExprRef) -> str:
+        assert z3.is_expr(e), f"unsupported expression type: {e}"
+
+        def get_args_str(e: z3.ExprRef) -> List[str]:
+            return [z3str(e.arg(i)) for i in range(e.num_args())]
+
+        # First, we simplify the given expression.
+        # This is done using rewriting rules, so shouldn't take long.
+        e = z3.simplify(e)
+
+
+        # Only support function applications.
+        # Even Z3 "variables" are, in fact, function applications.
+        if not z3.is_app(e):
+            raise ValueError(f"can't print Z3 expression: {e}")
+
+        if z3.is_int_value(e) or z3.is_rational_value(e):
+            return e.as_string()  # type: ignore[attr-defined]
+
+        decl = e.decl()
+        kind = decl.kind()
+        op = str(decl)
+        args = get_args_str(e)
+
+        if kind == z3.Z3_OP_POWER:
+            op = "pow"
+
+        elif kind in (z3.Z3_OP_ADD, z3.Z3_OP_MUL):
+            # Collect the arguments of chains of ADD and MUL.
+            # This is safe, since they are associative.
+
+            def collect_str_args(e):
+                if not (z3.is_app(e) and e.decl().kind() == kind):
+                    return [z3str(e)]
+                else:
+                    return [
+                        x
+                        for i in range(e.num_args())
+                        for x in collect_str_args(e.arg(i))
+                    ]
+
+            args = collect_str_args(e)
+
+        elif kind == z3.Z3_OP_NOT:
+            # Revert some conversions that z3.simplify applies:
+            #   - a != b ==> (Not (== a b)) ==> (!= a b)
+            #   - a < b ==> (Not (<= b a)) ==> (> b a)
+            #   - a > b ==> (Not (<= a b)) ==> (> a b)
+
+            assert e.num_args() == 1
+            arg = e.arg(0)
+
+            assert z3.is_app(arg)
+            argkind = arg.decl().kind()
+
+            logic_inverse = {
+                z3.Z3_OP_EQ: "!=",
+                z3.Z3_OP_LE: ">",
+                z3.Z3_OP_GE: "<",
+            }
+
+            if argkind in logic_inverse:
+                op = logic_inverse[argkind]
+                args = get_args_str(arg)
+
+        elif kind in (z3.Z3_OP_TO_INT, z3.Z3_OP_TO_REAL):
+            assert e.num_args() == 1
+            argstr = z3str(e.arg(0))
+
+            # Check if it's the floor division pattern.
+            if argstr.startswith("(/"):
+                return "(idiv" + argstr[2:]
+
+            # Otherwise, just ignore it.
+            return argstr
+
+        elif kind == z3.Z3_OP_UNINTERPRETED:
+            assert e.num_args() == 0
+            return str(decl)
+
+        string = op + " " + " ".join(args)
+        return f"({string.rstrip()})"
+
+    # Implementation of Python semantics as Z3 expressions.
+    #
+    # Z3 Real-Int theory has operators with semantics that differ that of
+    # Python. Therefore, in order to get it right, we need to implement
+    # the (Python) semantics we are relying on in Z3.
+    @dataclass
+    class _Z3Ops:
+        # Validator used for adding assertions as needed.
+        # e.g. div(a, b) requires b != 0.
+        validator: "TranslationValidator"
+
+        # The 2 functions below are used for conditionally casting between
+        # integer and reals.
+        #
+        # Returns a real expression from 'x'.
+        @staticmethod
+        def to_real(x: z3.ArithRef) -> z3.ArithRef:
+            return x if x.is_real() else z3.ToReal(x)
+
+        # Returns an integer expression from 'x'.
+        @staticmethod
+        def to_int(x: z3.ArithRef) -> z3.ArithRef:
+            return x if x.is_int() else z3.ToInt(x)
+
+        # Implements Python division semantics.
+        def div(self, numerator: z3.ArithRef, denominator: z3.ArithRef) -> z3.ArithRef:
+            self.validator.add_assertion(denominator != 0)  # type: ignore[arg-type]
+            return _Z3Ops.to_real(numerator) / _Z3Ops.to_real(denominator)
+
+        def floor(self, number: z3.ArithRef) -> z3.ArithRef:
+            # Z3 ToInt function rounds a real number towards negative infinity.
+            return _Z3Ops.to_int(number)
+
+        # Python semantics for 'FloorDiv' states that before applying the floor
+        # function, the operands are converted to their common type.
+        def floordiv(self, numerator: z3.ArithRef, denominator: z3.ArithRef) -> z3.ArithRef:
+            cast_result_to_real = numerator.is_real() or denominator.is_real()
+            result = _Z3Ops.to_int(self.div(numerator, denominator))
+            # Since the 'result' is already an integer, we just have to check
+            # whether we should cast it to real.
+            return _Z3Ops.to_real(result) if cast_result_to_real else result
+
+        def ceil(self, number: z3.ArithRef) -> z3.ArithRef:
+            return z3.If(
+                self.floor(number) < number,
+                self.floor(number + 1),
+                number
+            )  # type: ignore[return-value]
+
+        def max(self, a: z3.ArithRef, b: z3.ArithRef) -> z3.ArithRef:
+            return z3.If(a > b, a, b)  # type: ignore[return-value]
+
+        def min(self, a: z3.ArithRef, b: z3.ArithRef) -> z3.ArithRef:
+            return z3.If(a < b, a, b)  # type: ignore[return-value]
+
+        # Python semantics for 'Mod' is defined as: p % q = p - floordiv(p, q) * q
+        # It should work with both integer and reals.
+        def mod(self, p: z3.ArithRef, q: z3.ArithRef) -> z3.ArithRef:
+            return p - self.floordiv(p, q) * q
+
+        def pow(self, base: z3.ArithRef, exp: z3.ArithRef) -> z3.ArithRef:
+            # Z3 can't handle complex numbers very well.
+            self.validator.add_assertion(z3.Or(base != 0, exp > 0))  # type: ignore[arg-type]
+            return base ** exp
+
+        def sqrt(self, number: z3.ArithRef) -> z3.ArithRef:
+            # Square-root:
+            # 1. Only work with reals
+            number = _Z3Ops.to_real(number)
+            # 2. The number should be positive or zero.
+            #    Otherwise, Z3 returns 'unknown'.
+            self.validator.add_assertion(number >= 0)
+            return number ** 0.5
+
+        def abs(self, number: z3.ArithRef) -> z3.ArithRef:
+            return z3.Abs(number)
+
+        def round_to_int(self, number: z3.ArithRef) -> z3.ArithRef:
+            # Pythons builtin 'round' implements the 'round half to even' strategy
+            # See https://en.wikipedia.org/wiki/Rounding#Rounding_half_to_even
+            # z3 has an equivalent z3.fpRoundToIntegral(z3.RoundNearestTiesToEven(), ...), but this only applies to
+            # floating point numbers, which is different from real numbers that we are dealing with here.
+            # Instead, we implement 'round half to even' in terms of 'round half up' (floor(x + 0.5)) and
+            # 'round half down' (ceil(x - 0.5)).
+            # Assuming 'round half up' is the default case, we need to correct ..., -3.5, -1.5, 0.5, 2.5, 4.5, ...
+            # to round down, i.e. use the 'round half down' strategy
+            return z3.If(
+                self.mod(number, z3.IntVal(2)) == 0.5,
+                self.ceil(number - 0.5),
+                self.floor(number + 0.5),
+            )
+
+    # Lifts a callable to be used in Z3.
+    #
+    # This function replaces the given 'op' by a function that:
+    #
+    #   1. Lifts the arguments into Z3 (i.e. make them inhabitants of Z3)
+    #
+    #   2. Calls an operation that corresponds to 'op', but works with Z3
+    #      inhabitants (left as is if it works as is)
+    def z3op(op: Callable, validator: "TranslationValidator") -> Callable:
+        # Operations that have booleans as their argument.
+        # This is needed because the argument of some FX nodes were
+        # literal integers, instead of booleans. So, whenever this flag
+        # is set, we also convert ints to booleans.
+        boolean_ops = {operator.not_, operator.and_, operator.or_}
+        as_bool = op in boolean_ops
+
+        # Lifts the function into 'z3.ExprRef' domain.
+        def lift(func):
+            def wrap(a) -> z3.ExprRef:
+                if isinstance(a, (z3.ArithRef, z3.BoolRef)):
+                    return a
+                # Convert it into a Z3 value, if it is some of the supported
+                # types below.
+                if isinstance(a, bool) or (as_bool and isinstance(a, int)):
+                    return z3.BoolVal(bool(a))
+                if isinstance(a, (int, sympy.Integer)):
+                    return z3.IntVal(int(a))
+                if isinstance(a, (float, sympy.Float)):
+                    return z3.RealVal(float(a))
+                raise ValueError(f"can't lift type: {type(a)}")
+
+            @functools.wraps(func)
+            def wrapper(*args):
+                # Lifts the arguments into a list of Z3 inhabitants.
+                wrapped_args = (wrap(a) for a in args)
+                # Run the function on the Z3 expressions.
+                return func(*wrapped_args)
+
+            return wrapper
+
+        ops = _Z3Ops(validator)
+        replacement_map = {
+            # Operator module.
+            operator.not_: lift(z3.Not),
+            operator.and_: lift(z3.And),
+            operator.or_: lift(z3.Or),
+            operator.floordiv: lift(ops.floordiv),
+            operator.truediv: lift(ops.div),
+            operator.mod: lift(ops.mod),
+            operator.abs: lift(ops.abs),
+            builtins.round: lift(ops.round_to_int),
+
+            # Math module.
+            math.ceil: lift(ops.ceil),
+            math.floor: lift(ops.floor),
+
+            # Torch module.
+            torch.sym_float: lift(ops.to_real),
+            torch.sym_max: lift(ops.max),
+            torch.sym_min: lift(ops.min),
+            torch.sym_ite: lift(lambda b, t, f: t if b else f),
+            torch._sym_sqrt: lift(ops.sqrt),  # type: ignore[attr-defined]
+            # Not lifted because we only use this function as a
+            # marker for adding the expression as validator input.
+            torch._assert: torch._assert,
+        }
+        return replacement_map[op] if op in replacement_map else lift(op)
+
+    # Processes an FX graph, populating the given validator.
+    #
+    # [Note: PopulateValidator]
+    # This class walks through each node in the FX graph, translating
+    # them into the Z3 world.
+    #
+    # Then, whenever it finds an 'torch._assert' call_function operation,
+    # it adds the Z3 expression corresponding to the argument as validator
+    # input.
+    class PopulateValidator(torch.fx.Interpreter):
+        def __init__(self, graph: torch.fx.Graph, validator: "TranslationValidator"):
+            # Reference to the translation validator.
+            self.validator = validator
+
+            # Build the graph module and call `Interpreter` constructor.
+            module = torch.fx.GraphModule(root={}, graph=graph)
+            super().__init__(module, garbage_collect_values=True)
+
+        def placeholder(self, target: Target, args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
+            symbol = fx_traceback.get_current_meta()["symbol"]
+            return self.validator.z3var(symbol)
+
+        def call_function(self, target: Target, args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
+            if target != torch._assert:
+                # Lift and runs the node target function
+                return super().call_function(z3op(target, self.validator), args, kwargs)  # type: ignore[arg-type]
+            # Adds the Z3 expression corresponding to the first argument
+            # as a validator input.
+            assert len(args) == 1, f"expected 1 argument on assertion. Got: {len(args)} "
+            self.validator.add_source_expr(args[0])  # type: ignore[arg-type]
+
+    # Translates SymPy expressions into Z3 expressions.
+    #
+    # [Note: SympyToZ3]
+    # At the time of the translation, all free variables present in the
+    # SymPy expression being translated must be already mapped to a Z3
+    # integer variable.
+    class SympyToZ3:
+        OPERATOR_HANDLES = {"add", "mul", "eq", "ne", "lt", "gt", "le", "ge"}
+
+        def __init__(
+                self,
+                validator: "TranslationValidator",
+        ) -> None:
+            self._validator = validator
+            self._ops = _Z3Ops(self._validator)
+
+        def constant(self, value: Any, dtype: torch.dtype) -> z3.ExprRef:
+            # TODO: Probably OK to relax this and allow lower precision
+            if dtype is torch.int64:
+                return z3.IntVal(int(value))
+            if dtype is torch.double:
+                return z3.RealVal(float(value))
+            if dtype is torch.bool:
+                return z3.BoolVal(bool(value))
+            raise ValueError(f"unsupported dtype (SympyToZ3): {dtype}")
+
+        def to_dtype(self, x: z3.ArithRef, dtype: torch.dtype) -> z3.ArithRef:
+            if dtype == torch.float64:
+                return z3.ToReal(x)
+            raise NotImplementedError(f"to_dtype {dtype} NYI")
+
+        def trunc_to_int(self, x: z3.ArithRef, dtype: torch.dtype) -> z3.ArithRef:
+            return z3.ToInt(x)
+
+        def round_to_int(self, x: z3.ArithRef, dtype: torch.dtype) -> z3.ArithRef:
+            return self._ops.round_to_int(x)
+
+        def int_truediv(self, numerator: z3.ArithRef, denominator: z3.ArithRef) -> z3.ArithRef:
+            return self._ops.div(numerator, denominator)
+
+        def truediv(self, numerator: z3.ArithRef, denominator: z3.ArithRef) -> z3.ArithRef:
+            return self._ops.div(numerator, denominator)
+
+        def floordiv(self, numerator: z3.ArithRef, denominator: z3.ArithRef) -> z3.ArithRef:
+            return self._ops.floordiv(numerator, denominator)
+
+        def div(self, numerator: z3.ArithRef, denominator: z3.ArithRef) -> z3.ArithRef:
+            return self._ops.floordiv(numerator, denominator)
+
+        def pow(self, base: z3.ArithRef, exp: z3.ArithRef) -> z3.ArithRef:
+            return self._ops.pow(base, exp)
+
+        def pow_by_natural(self, base: z3.ArithRef, exp: z3.ArithRef) -> z3.ArithRef:
+            return self._ops.pow(base, exp)
+
+        def mod(self, p: z3.ArithRef, q: z3.ArithRef) -> z3.ArithRef:
+            return self._ops.mod(p, q)
+
+        def ceil_to_int(self, x: z3.ArithRef, dtype: torch.dtype) -> z3.ArithRef:
+            return self._ops.ceil(x)
+
+        def floor_to_int(self, x: z3.ArithRef, dtype: torch.dtype) -> z3.ArithRef:
+            return self._ops.floor(x)
+
+        def __getattr__(self, name: str) -> Any:
+            REPLACEMENT = {
+                "and_": z3.And,
+                "or_": z3.Or,
+                "not_": z3.Not,
+                "floor": self._ops.floor,
+                "ceil": self._ops.ceil,
+                "minimum": self._ops.min,
+                "maximum": self._ops.max,
+            }
+
+            if name in REPLACEMENT:
+                return REPLACEMENT[name]
+            if name in self.OPERATOR_HANDLES:
+                return getattr(operator, name)
+            raise AttributeError(f"unhandled operator: {name}")
+
+        def run(self, expr: sympy.Basic) -> z3.ExprRef:
+            return sympy_interp(self, self._validator.symbols, expr)  # type: ignore[arg-type]
+
+    # Dynamo guards translation validator.
+    #
+    # [Note: TranslationValidator]
+    # Verifies whether the guards issued by 'ShapeEnv.produce_guards' are sound.
+    # That is: whether those (target) guards only yield TRUE whenever the original,
+    # unoptimized, (source) guards yield TRUE.
+    #
+    # More concretely, given 'source' and 'target' guard expressions, we wish to
+    # check whether the following expression holds:
+    #
+    # Not(And(source)) AND And(target)
+    #
+    # i.e. whether there is an assignment of the free variables where the opposite
+    # happens: target is TRUE, but source is FALSE.
+    class TranslationValidator:
+        def __init__(self) -> None:
+            log.debug("new instance")
+
+            # Mapping of SymPy symbols to Z3 variables.
+            self.symbols: Dict[sympy.Symbol, z3.ExprRef] = {}
+
+            # Set of source Z3 expressions.
+            # They represent the generated guards without any kind of
+            # simplification or transformation.
+            self._source_exprs: Set[z3.BoolRef] = set()
+
+            # Set of target Z3 expressions.
+            # They represent the actual checked guards at runtime. They might
+            # be simplified or transformed versions of the source guards.
+            self._target_exprs: Set[z3.BoolRef] = set()
+
+            # Set of Z3 expressions representing assertions over both the
+            # source and target expressions.
+            self._assertions: Set[z3.BoolRef] = set()
+
+        # Retrieves the corresponding Z3 variable.
+        def z3var(self, symbol: sympy.Symbol) -> z3.ExprRef:
+            assert symbol in self.symbols, f"Z3 variable not found for: {symbol}"
+            return self.symbols[symbol]
+
+        # Create a variable in Z3 of 'type' for 'symbol', if it doesn't already exists.
+        def add_var(self, symbol: sympy.Symbol, type: Type) -> z3.ExprRef:
+            if symbol in self.symbols:
+                return self.symbols[symbol]
+
+            log.debug("new variable: %s (%s)", symbol.name, type.__name__)
+
+            if type is int:
+                var = z3.Int(symbol.name)
+
+                # If 'symbol' is positive (SymPy assumption), we have to
+                # convey it to Z3 as well.
+                if symbol.is_positive:  # type: ignore[attr-defined]
+                    self._target_exprs.add(var > 0)
+            elif type is float:
+                var = z3.Real(symbol.name)
+            elif type is bool:
+                var = z3.Bool(symbol.name)
+            else:
+                raise RuntimeError(f"unsupported type for Z3 variable: {type}")
+
+            self.symbols[symbol] = var
+            return var
+
+        # Checks whether all symbols were already added.
+        def _check_freesymbols(self, e: sympy.Basic) -> None:
+            for s in e.free_symbols:
+                assert isinstance(s, sympy.Symbol)
+                # Call 'z3var' just to check whether there's already a
+                # Z3 variable corresponding to 's'.
+                self.z3var(s)
+
+
+        def to_z3_boolean_expr(self, e: sympy.Basic) -> z3.BoolRef:
+            z3expr = SympyToZ3(self).run(e)
+            assert isinstance(z3expr, z3.BoolRef), f"expected boolean expression. Got: {z3expr}"
+            return z3expr
+
+        def add_source_expr(self, e: z3.BoolRef) -> None:
+            if e not in self._source_exprs:
+                log.debug("add source guard: %s", z3str(e))
+            self._source_exprs.add(e)
+
+        def add_target_expr(self, e: sympy.Expr) -> None:
+            self._check_freesymbols(e)
+            z3expr = self.to_z3_boolean_expr(e)
+            if e not in self._target_exprs:
+                log.debug("add target guard: %s", z3str(z3expr))
+            self._target_exprs.add(z3expr)
+
+        def add_assertion(self, e: Union[z3.BoolRef, sympy.Basic]) -> None:
+            if isinstance(e, sympy.Basic):
+                self._check_freesymbols(e)
+                ref = self.to_z3_boolean_expr(e)
+            else:
+                ref = e
+            assert isinstance(ref, z3.BoolRef)
+            if ref not in self._assertions:
+                log.debug("add assertion: %s", z3str(ref))
+            self._assertions.add(ref)
+
+        def validate(self) -> None:
+            with dynamo_timed("TranslationValidator.validate"):
+                return self._validate()
+
+        def _validate(self) -> None:
+            if len(self._source_exprs) == 0 or len(self._target_exprs) == 0:
+                # If there are no source/target expressions, there's nothing we really
+                # wish to prove. So, we just return.
+                return None
+
+            # Here, we use "QF_NRA" logic for the solver:
+            #   "Quantifier-free Non-linear Real Arithmetic".
+            #
+            # Most of the guards expressions have:
+            #   1. arithmetic between integer and reals
+            #   2. no quantifiers
+            #   3. potentially non-linear.
+            #
+            # Although there's also "QF_NIRA" (mixed integer-real arithmetic),
+            # "QF_NRA" seems to work better on 'dynamo/test_dynamic_shapes.py'.
+            solver = z3.SolverFor("QF_NRA")
+            # Set a timeout for finding a solution.
+            solver.set(timeout=translation_validation_timeout())
+
+            # Add all the assertions to the solver.
+            for assertion in self._assertions:
+                solver.add(assertion)
+
+            # "Is there any case where it's TRUE for the target expressions,
+            #  but FALSE for the source expressions?"
+            solver.add(z3.Not(z3.And(*self._source_exprs)))
+            solver.add(*self._target_exprs)
+
+            log.debug("translation validation: start")
+            r = solver.check()
+            if r == z3.sat:
+                # Target expressions are unsound.
+                # Log the found model and the source expressions that failed.
+                model = solver.model()
+                raise ValidationException(
+                    model, self._assertions, self._target_exprs,
+                    failed_source_exprs=[
+                        inp for inp in self._source_exprs if not model.evaluate(inp)
+                    ]
+                )
+            else:
+                if r == z3.unknown:
+                    # Could not find a solution. It didn't fail, but it also
+                    # didn't succeed. Canceling the validation execution (keyboard
+                    # interrupt) also gets to this branch.
+                    log.warning("translation validation: could not validate: got z3.unknown")
+                else:
+                    # Target expressions are sound.
+                    assert r == z3.unsat
+                    log.debug("translation validation: success")
+
+except ImportError:
+    _HAS_Z3 = False
+
+    __all__ = [
+        "translation_validation_enabled", "translation_validation_timeout",
+        "ValidationException", "BisectValidationException",
+    ]
+
+else:
+    _HAS_Z3 = True
+
+    __all__ = [
+        "z3str", "z3op", "PopulateValidator", "SympyToZ3", "TranslationValidator",
+        "translation_validation_enabled", "translation_validation_timeout",
+        "ValidationException", "BisectValidationException",
+    ]
+
+from torch.fx.experimental import _config as config
+
+def translation_validation_enabled() -> bool:
+    # Checks everytime this function is called, in case the Dynamo
+    # option is set, but Z3 is not installed.
+    _assert_z3_installed_if_tv_set()
+    return _HAS_Z3 and config.translation_validation
+
+
+def translation_validation_timeout() -> int:
+    return config.translation_validation_timeout
+
+
+def _assert_z3_installed_if_tv_set():
+    assert _HAS_Z3 or not config.translation_validation, (
+        "translation validation requires Z3 package. Please, either install "
+        "z3-solver or disable translation validation."
+    )
+
+
+class ValidationException(TorchDynamoException):
+    def __init__(self, model, assertions, target_exprs, failed_source_exprs):
+        assert _HAS_Z3
+
+        def symbolstr(sym) -> str:
+            return f"{sym}: {model[sym]}"
+
+        def joinlines(xs) -> str:
+            return "\n".join(f"  ==> {x}" for x in xs)
+
+        model_str = joinlines(sorted(map(symbolstr, model)))
+        assertions_str = joinlines(sorted(map(z3str, assertions)))
+        target_exprs_str = joinlines(sorted(map(z3str, target_exprs)))
+        failed_source_exprs_str = joinlines(sorted(map(z3str, failed_source_exprs)))
+
+        self.msg = "translation validation failed."
+        self.details = f"""\
+Model:
+{model_str}
+
+Assertions:
+{assertions_str}
+
+Target Expressions:
+{target_exprs_str}
+
+Failed Source Expressions:
+{failed_source_exprs_str}"""
+
+    def __str__(self):
+        return f"{self.msg}\n\n{self.details}"
+
+
+class BisectValidationException(TorchDynamoException):
+    def __init__(self, validation_exc, expr, failed_action, traced_node):
+        self.msg = f"translation validation failed when {failed_action}: {expr}"
+        self.details = f"""\
+Failure occurred while running node:
+    {traced_node.format_node()}
+
+{validation_exc.details}"""
+
+    def __str__(self):
+        return f"{self.msg}\n\n{self.details}"
+
+# Checks when this module is loaded.
+_assert_z3_installed_if_tv_set()
+
+# Translation validation bisection.
+#
+# Bisect into the torch._assert nodes recorded in the shape_env FX graph, and raise
+# the earliest ValidationException.
+#
+# As guards are added by ShapeEnv.evaluate_expr calls, some simplification errors
+# might be silently happening. This function tries to nail down exactly at which
+# point things went wrong from a validation perspective.
+def bisect(shape_env):
+    from torch.fx.experimental.symbolic_shapes import ShapeEnv, SHAPEENV_EVENT_KEY, CURRENT_NODE_KEY
+    from torch.fx.experimental.recording import FakeTensorMeta, ShapeEnvEvent, replay_shape_env_events
+
+    events = shape_env.events
+
+    # Retrieves the ShapeEnvEvent associated with node.
+    def get_node_event(node: torch.fx.Node) -> ShapeEnvEvent:
+        assert SHAPEENV_EVENT_KEY in node.meta
+        return events[node.meta[SHAPEENV_EVENT_KEY]]
+
+    # Creates a new instance of fake, but updating every symbolic value's ShapeEnv
+    # reference to the one given as argument.
+    #
+    # This is needed so as not to simplify a symbolic expression using a ShapeEnv
+    # "from the future", where it may have a different set of replacements.
+    def new_with_shape_env(shape_env: ShapeEnv, fake) -> Any:
+        if isinstance(fake, int):
+            return fake
+        if isinstance(fake, torch.SymInt):
+            return torch.SymInt(fake.node.with_shape_env(shape_env))
+        assert isinstance(fake, FakeTensorMeta)
+        return FakeTensorMeta(
+            tuple(new_with_shape_env(shape_env, s) for s in fake.size()),
+            tuple(new_with_shape_env(shape_env, s) for s in fake.stride()),
+            new_with_shape_env(shape_env, fake.storage_offset()),
+            fake.is_nested,
+        )
+
+    # Checks whether the given shape_env fails when produce_guards is called.
+    def check_shapeenv_fails(shape_env: ShapeEnv, tracked_fakes: Optional[List[Any]]) -> Optional[ValidationException]:
+        assert tracked_fakes is not None
+        try:
+            # This produce_guards call is a best-effort replication, since we
+            # don't populate EqualityConstraint list. Reason: we would also have
+            # to save OutputGraph.tracked_fakes_id_to_source.
+            shape_env.produce_guards(
+                [new_with_shape_env(shape_env, a.fake) for a in tracked_fakes],
+                [a.source for a in tracked_fakes],
+                input_contexts=[a.symbolic_context for a in tracked_fakes],
+            )
+            return None
+        except ValidationException as e:
+            return e
+
+    # Checks whether the ShapeEnv reconstructed by replaying the events until
+    # node is created fails when produce_guards is called.
+    def check_node_fails(node: torch.fx.Node) -> Optional[ValidationException]:
+        number = node.meta[SHAPEENV_EVENT_KEY]
+        # Reconstruct shape_env until the event at event_number.
+        shape_env = replay_shape_env_events(events[:number + 1])
+        shape_env.graph.lint()
+        return check_shapeenv_fails(shape_env, events[number].tracked_fakes)
+
+    last_exception = check_shapeenv_fails(shape_env, shape_env._snapshot_tracked_fakes())
+
+    if not last_exception:
+        # We don't actually fail due to a produce_guards call.
+        # Stop and don't bisect.
+        log.info("translation validation succeeded: no errors found.")
+        return
+
+    if not shape_env.should_record_events or config.translation_validation_no_bisect:
+        # Bisection is off.
+        # Return the last ValidationException we got.
+        raise last_exception
+
+    # Cache the raised exception (if any) at each bisection point.
+    exception = {}
+
+    # Bisection happens on the assertion nodes of the recorded FX graph for
+    # dynamic shapes.
+    assert_nodes = [node for node in shape_env.graph.nodes if node.target == torch._assert]
+
+    # Preparing the indices for binary search.
+    left, mid, right = 0, 0, len(assert_nodes) - 1
+
+    while left < right:
+        mid = (left + right) // 2
+
+        node = assert_nodes[mid]
+        log.debug("bisecting at %s: %s", mid, get_node_event(node))
+
+        # Check whether the new shape_env raises a ValidationException or not.
+        exception[mid] = check_node_fails(node)
+
+        if exception[mid]:
+            right = mid
+        else:
+            left = mid + 1
+
+    assert left in exception and isinstance(exception[left], ValidationException)
+
+    node = assert_nodes[left]
+    event = get_node_event(node)
+
+    if event.is_evaluate_expr():
+        failed_action = "evaluating"
+    else:
+        assert event.is_defer_runtime_assert(), f"unexpected event type: {event}"
+        failed_action = "adding runtime assert"
+
+    args = event.args
+    assert args is not None
+    assert len(args) >= 2, (
+        f"bisecting expects {event.name} to have at least 2 positional arguments. "
+        f"Got: {len(args)}"
+    )
+    assert isinstance(args[1], sympy.Basic), (
+        f"bisecting expects {event.name} to have a SymPy expression as its second argument. "
+        f"Got: {type(args[1])}"
+    )
+
+    raise BisectValidationException(
+        exception[left],
+        expr=args[1],
+        failed_action=failed_action,
+        traced_node=node.meta[CURRENT_NODE_KEY],
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/graph.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4fdd79fcbb280236c0dd139fa2baffa5c53a061
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/graph.py
@@ -0,0 +1,1796 @@
+# mypy: allow-untyped-defs
+from collections import defaultdict
+from .node import Node, Argument, Target, map_arg, _type_repr, _get_qualified_name
+import torch.utils._pytree as pytree
+from . import _pytree as fx_pytree
+from ._compatibility import compatibility
+from torch._C import _NodeIter
+
+import os
+import contextlib
+from typing import TYPE_CHECKING, Callable, Any, List, Dict, NamedTuple, Optional, Tuple, Set, FrozenSet, Type, Iterable
+from dataclasses import dataclass
+from contextlib import contextmanager
+import copy
+import enum
+import torch
+import keyword
+import re
+import builtins
+import math
+import warnings
+import inspect
+
+__all__ = ["PythonCode", "CodeGen", "Graph"]
+
+if TYPE_CHECKING:
+    from .graph_module import GraphModule  # noqa: F401
+    from ._symbolic_trace import Tracer   # noqa: F401
+
+
+# Mapping of builtins to their `typing` equivalent.
+_origin_type_map = {
+    list: List,
+    dict: Dict,
+    set: Set,
+    frozenset: FrozenSet,
+    tuple: Tuple,
+}
+
+
+# Signature for functions thattransforms the body (`list[str]`) of the
+# generated code
+TransformCodeFunc = Callable[[List[str]], List[str]]
+
+
+class _CustomBuiltin(NamedTuple):
+    """Additional objs that we add to every graph's globals.
+
+    The repr() for some standard library objects is not valid Python code without
+    an import. For common objects of this sort, we bundle them in the globals of
+    every FX graph.
+    """
+    # How to import this object from the standard library.
+    import_str: str
+    # The actual object, produced from that import string.
+    obj: Any
+
+_custom_builtins: Dict[str, _CustomBuiltin] = {}
+
+
+def _register_custom_builtin(name: str, import_str: str, obj: Any):
+    _custom_builtins[name] = _CustomBuiltin(import_str, obj)
+
+
+_register_custom_builtin('inf', 'from math import inf', math.inf)
+_register_custom_builtin('nan', 'from math import nan', math.nan)
+_register_custom_builtin('NoneType', 'NoneType = type(None)', type(None))
+_register_custom_builtin('torch', 'import torch', torch)
+_register_custom_builtin('device', 'from torch import device', torch.device)
+_register_custom_builtin('fx_pytree', 'import torch.fx._pytree as fx_pytree', fx_pytree)
+_register_custom_builtin('pytree', 'import torch.utils._pytree as pytree', pytree)
+
+
+def _is_magic(x: str) -> bool:
+    return x.startswith('__') and x.endswith('__')
+
+
+def _snake_case(s: str) -> str:
+    """
+    Transforms the given string ``s`` to a Python-style variable name
+
+    Examples:
+        ``mod.snake_case`` -> ``mod.snake_case``
+        ``mod.pascalCase``-> ``mod.pascal_case``
+        ``mod.ALL_CAPS`` -> ``mod.all_caps``
+    """
+    chars = []
+    prev_lower = False
+    for c in s:
+        if prev_lower and c.isupper():
+            chars.append('_')
+        chars.append(c.lower())
+        prev_lower = c.islower()
+    return ''.join(chars)
+
+
+def _is_from_torch(obj: Any) -> bool:
+    module_name = getattr(obj, '__module__', None)
+    if module_name is not None:
+        base_module = module_name.partition('.')[0]
+        return (
+            base_module == 'torch' and
+            not module_name.startswith("torch._dynamo.") and
+            not module_name.startswith("torch._inductor.")
+        )
+
+    name = getattr(obj, '__name__', None)
+    # exclude torch because torch.torch.torch.torch works. idk mang
+    if name is not None and name != 'torch':
+        for guess in [torch, torch.nn.functional]:
+            if getattr(guess, name, None) is obj:
+                return True
+
+    return False
+
+
+class _Namespace:
+    """A context for associating names uniquely with objects.
+
+    The following invariants are enforced:
+    - Each object gets a single name.
+    - Each name is unique within a given namespace.
+    - Names generated do not shadow builtins, unless the object is indeed that builtin.
+    """
+    def __init__(self):
+        self._obj_to_name: Dict[Any, str] = {}
+        self._unassociated_names = set()
+        self._used_names: Set[str] = set()
+        self._base_count: Dict[str, int] = defaultdict(int)
+
+        self._illegal_char_regex = re.compile('[^0-9a-zA-Z_]+')
+        self._name_suffix_regex = re.compile(r"(.*)_(\d+)$")
+
+    def create_name(self, candidate: str, obj: Optional[Any]) -> str:
+        """Create a unique name.
+
+        Arguments:
+            candidate: used as the basis for the unique name, relevant to the user.
+            obj: If not None, an object that will be associated with the unique name.
+        """
+        if obj is not None and obj in self._obj_to_name:
+            return self._obj_to_name[obj]
+
+        # delete all characters that are illegal in a Python identifier
+        candidate = self._illegal_char_regex.sub('_', candidate)
+
+        if not candidate:
+            candidate = '_unnamed'
+
+        if candidate[0].isdigit():
+            candidate = f'_{candidate}'
+
+        match = self._name_suffix_regex.match(candidate)
+        if match is None:
+            base = candidate
+            num = None
+        else:
+            base, num_str = match.group(1, 2)
+            num = int(num_str)
+
+        candidate = base if num is None else f'{base}_{num}'
+        if not num:
+            num = self._base_count[base]
+
+        while candidate in self._used_names or self._is_illegal_name(candidate, obj):
+            num += 1
+            candidate = f'{base}_{num}'
+
+        self._used_names.add(candidate)
+        self._base_count[base] = num
+        if obj is None:
+            self._unassociated_names.add(candidate)
+        else:
+            self._obj_to_name[obj] = candidate
+        return candidate
+
+    def associate_name_with_obj(self, name: str, obj: Any):
+        """Associate a unique name with an object.
+
+        Neither `name` nor `obj` should be associated already.
+        """
+        assert obj not in self._obj_to_name
+        assert name in self._unassociated_names
+        self._obj_to_name[obj] = name
+        self._unassociated_names.remove(name)
+
+    def _is_illegal_name(self, name: str, obj: Any) -> bool:
+        # 1. keywords are never allowed as names.
+        if name in keyword.kwlist:
+            return True
+
+        # 2. Can't shadow a builtin name, unless you *are* that builtin.
+        if name in builtins.__dict__:
+            return obj is not builtins.__dict__[name]
+
+        # 3. Can't shadow our custom builtins either
+        if name in _custom_builtins:
+            return obj is not _custom_builtins[name].obj
+
+        return False
+
+    def _rename_object(self, obj: Any, name: str):
+        assert obj in self._obj_to_name
+        self._obj_to_name[obj] = name
+        self._used_names.add(name)
+
+dtype_abbrs = {
+    torch.bfloat16: 'bf16',
+    torch.float64: 'f64',
+    torch.float32: 'f32',
+    torch.float16: 'f16',
+    torch.float8_e4m3fn: 'f8e4m3fn',
+    torch.float8_e5m2: 'f8e5m2',
+    torch.float8_e4m3fnuz: 'f8e4m3fnuz',
+    torch.float8_e5m2fnuz: 'f8e5m2fnuz',
+    torch.complex32: 'c32',
+    torch.complex64: 'c64',
+    torch.complex128: 'c128',
+    torch.int8: 'i8',
+    torch.int16: 'i16',
+    torch.int32: 'i32',
+    torch.int64: 'i64',
+    torch.bool: 'b8',
+    torch.uint8: 'u8',
+    torch.uint16: 'u16',
+    torch.uint32: 'u32',
+    torch.uint64: 'u64',
+    torch.bits16: 'b16',
+}
+
+@compatibility(is_backward_compatible=True)
+@dataclass
+class PythonCode:
+    """
+    Represents all the information necessary to exec or save a graph as Python code.
+    """
+    # Python source code for the forward function definition.
+    src: str
+    # Values in global scope during execution of `src_def`.
+    globals: Dict[str, Any]
+    # Optional mapping from the forward function's line number to
+    # node index.
+    _lineno_map: Optional[Dict[int, Optional[int]]]
+
+
+def _format_target(base: str, target: str) -> str:
+    elems = target.split('.')
+    r = base
+    for e in elems:
+        if not e.isidentifier():
+            r = f'getattr({r}, "{e}")'
+        else:
+            r = f'{r}.{e}'
+    return r
+
+class _InsertPoint:
+    def __init__(self, graph, new_insert):
+        self.graph = graph
+        self.orig_insert, graph._insert = graph._insert, new_insert
+
+    def __enter__(self):
+        pass
+
+    def __exit__(self, type, value, tb):
+        self.graph._insert = self.orig_insert
+
+class _node_list:
+    def __init__(self, graph: 'Graph', direction: str = '_next'):
+        assert direction in ['_next', '_prev']
+        self.graph = graph
+        self.direction = direction
+
+    def __len__(self):
+        return self.graph._len
+
+    def __iter__(self):
+        assert self.direction == "_prev" or self.direction == "_next"
+        yield from _NodeIter(self.graph._root, self.direction == "_prev")
+
+    def __reversed__(self):
+        return _node_list(self.graph, '_next' if self.direction == '_prev' else '_prev')
+
+class _PyTreeInfo(NamedTuple):
+    """
+    Contains extra info stored when we're using Pytrees
+    """
+    orig_args: List[str]
+    in_spec: pytree.TreeSpec
+    out_spec: Optional[pytree.TreeSpec]
+
+@dataclass(frozen=True)
+class _ParsedStackTrace:
+    """
+    Represents the top-most frame of a parsed stack trace
+    """
+    file: str
+    lineno: str
+    name: str
+    code: str
+
+    def get_summary_str(self):
+        return f'File: {self.file}:{self.lineno} in {self.name}, code: {self.code}'
+
+# get File:lineno code from stack_trace
+def _parse_stack_trace(stack_trace: str):
+    if stack_trace is None:
+        return None
+    pattern = re.compile(r"^File \"(.+)\", line (\d+), in (.+)$")
+    lines = stack_trace.strip().split('\n')
+    # stacktrace should have innermost frame last, so we
+    # iterate backwards to find the first line that starts
+    # with 'File '
+    summary_str = ""
+    for idx in range(len(lines) - 2, -1, -1):
+        line = lines[idx].strip()
+        matches = pattern.match(line)
+        if matches:
+            file = matches.group(1)
+            lineno = matches.group(2)
+            name = matches.group(3)
+            # next line should be the code
+            code = lines[idx + 1].strip()
+            return _ParsedStackTrace(file, lineno, name, code)
+    return None
+
+@compatibility(is_backward_compatible=False)
+class CodeGen:
+    def __init__(self):
+        self._body_transformer: Optional[TransformCodeFunc] = None
+        self._func_name: str = "forward"
+
+    def gen_fn_def(self, free_vars: List[str], maybe_return_annotation: str) -> str:
+        """
+        Given the free variables and a return annotation, generates the beginning of the FX function.
+        By default, `gen_fn_def(['a', 'b'], '') == 'def {self._func_name}(a, b):'`
+        """
+        # If the original function didn't have self as its first argument, we
+        # would have added it.
+        if len(free_vars) == 0 or free_vars[0] != 'self':
+            free_vars.insert(0, 'self')
+        return f"def {self._func_name}({', '.join(free_vars)}){maybe_return_annotation}:"
+
+    def generate_output(self, output_args: Argument) -> str:
+        """
+        Given the output arguments, generates the return statement of the FX function.
+        Note: The returned statement should not be indented.
+        """
+        return f'return {repr(output_args)}'
+
+    def process_inputs(self, *args: Any) -> Any:
+        """
+        Transforms the inputs so that the graph can take them as arguments, as
+        non-default codegen may result in the inputs to the function being
+        different from the inputs to the graph.
+
+        If the graph was directly runnable, this invariant should hold true
+        `f.graph.process_outputs(f.graph(*f.graph.process_inputs(*inputs))) == f(*inputs)`
+        """
+        return args
+
+    def process_outputs(self, outputs: Any) -> Any:
+        """
+        Transforms the outputs of the graph to be identical to the codegen.
+
+        See ``process_inputs`` for more details.
+        """
+        return outputs
+
+    def additional_globals(self) -> List[Tuple[str, Any]]:
+        """
+        If your codegen uses extra global values, add tuples of (identifier,reference to the value) here.
+        For example, return ['List', typing.List] if you need ``List`` in the global context.
+        """
+        return []
+
+    def _gen_python_code(
+        self, nodes, root_module: str, namespace: _Namespace, *,
+        verbose: bool = False, include_stride: bool = False, include_device: bool = False, colored: bool = False
+    ) -> PythonCode:
+        free_vars: List[str] = []
+        body: List[str] = []
+        globals_: Dict[str, Any] = {}
+        wrapped_fns: Dict[str, None] = {}
+
+        # Wrap string in list to pass by reference
+        maybe_return_annotation : List[str] = ['']
+        include_stride = include_stride or (os.environ.get("FX_GRAPH_SHOW_STRIDE", "0") == "1")
+        include_device = include_device or (os.environ.get("FX_GRAPH_SHOW_DEVICE", "0") == "1")
+
+        def add_global(name_hint: str, obj: Any):
+            """Add an obj to be tracked as a global.
+
+            We call this for names that reference objects external to the
+            Graph, like functions or types.
+
+            Returns: the global name that should be used to reference 'obj' in generated source.
+            """
+            if _is_from_torch(obj) and obj != torch.device:  # to support registering torch.device
+                # HACK: workaround for how torch custom ops are registered. We
+                # can't import them like normal modules so they must retain their
+                # fully qualified name.
+                return _get_qualified_name(obj)
+
+            # normalize the name hint to get a proper identifier
+            global_name = namespace.create_name(name_hint, obj)
+
+            if global_name in globals_:
+                assert globals_[global_name] is obj
+                return global_name
+            globals_[global_name] = obj
+            return global_name
+
+        # Pre-fill the globals table with registered builtins.
+        for name, (_, obj) in _custom_builtins.items():
+            add_global(name, obj)
+
+        def type_repr(o : Any):
+            if o == ():
+                # Empty tuple is used for empty tuple type annotation Tuple[()]
+                return '()'
+
+            typename = _type_repr(o)
+
+            if hasattr(o, '__origin__'):
+                # This is a generic type, e.g. typing.List[torch.Tensor]
+                origin_type = _origin_type_map.get(o.__origin__, o.__origin__)
+                origin_typename = add_global(_type_repr(origin_type), origin_type)
+
+                if hasattr(o, '__args__'):
+                    # Assign global names for each of the inner type variables.
+                    args = [type_repr(arg) for arg in o.__args__]
+
+                    if len(args) == 0:
+                        # Bare type, such as `typing.Tuple` with no subscript
+                        # This code-path used in Python < 3.9
+                        return origin_typename
+
+                    return f'{origin_typename}[{",".join(args)}]'
+                else:
+                    # Bare type, such as `typing.Tuple` with no subscript
+                    # This code-path used in Python 3.9+
+                    return origin_typename
+
+            # Common case: this is a regular module name like 'foo.bar.baz'
+            return add_global(typename, o)
+
+        codes = {
+            "yellow": "\033[33m",
+            "cyan": "\033[36m",
+            "green": "\033[32m",
+            "blue": "\033[34m",
+            "red": "\033[31m",
+            "dim": "\033[2m",
+            "dim_blue": "\033[2m\033[34m",
+            "dim_green": "\033[2m\033[32m",
+            "reset": "\033[0m",
+        }
+
+        def make_wrapper_func(name):
+            def f(s):
+                if colored:
+                    return f"{codes[name]}{s}{codes['reset']}"
+                return s
+            return f
+
+        yellow = make_wrapper_func("yellow")
+        cyan = make_wrapper_func("cyan")
+        red = make_wrapper_func("red")
+        green = make_wrapper_func("green")
+        dim_green = make_wrapper_func("dim_green")
+        dim = make_wrapper_func("dim")
+        dim_blue = make_wrapper_func("dim_blue")
+        blue = make_wrapper_func("blue")
+
+        def _get_repr(arg: Any) -> str:
+            # Handle NamedTuples (if it has `_fields`) via add_global.
+            if isinstance(arg, tuple) and hasattr(arg, '_fields'):
+                qualified_name = _get_qualified_name(type(arg))
+                global_name = add_global(qualified_name, type(arg))
+                return f"{global_name}{repr(tuple(arg))}"
+            elif isinstance(arg, (torch._ops.OpOverload, torch._ops.HigherOrderOperator)):
+                qualified_name = _get_qualified_name(arg)
+                global_name = add_global(qualified_name, arg)
+                return f"{global_name}"
+            elif isinstance(arg, enum.Enum):
+                cls = arg.__class__
+                clsname = add_global(cls.__name__, cls)
+                return f"{clsname}.{arg.name}"
+            elif isinstance(arg, Node):
+                return repr(arg)
+            elif isinstance(arg, torch.Tensor):
+                size = list(arg.size())
+                dtype = str(arg.dtype).split(".")[-1]
+                return f"torch.Tensor(size={size}, dtype={dtype})"
+            else:
+                return blue(repr(arg))
+
+
+        def _format_args(args: Tuple[Argument, ...], kwargs: Dict[str, Argument]) -> str:
+            args_s = ', '.join(_get_repr(a) for a in args)
+            kwargs_s = ', '.join(f'{k} = {_get_repr(v)}' for k, v in kwargs.items())
+            if args_s and kwargs_s:
+                return f'{args_s}, {kwargs_s}'
+            return args_s or kwargs_s
+
+        # Run through reverse nodes and record the first instance of a use
+        # of a given node. This represents the *last* use of the node in the
+        # execution order of the program, which we will use to free unused
+        # values
+        node_to_last_use : Dict[Node, Node] = {}
+        user_to_last_uses : Dict[Node, List[Node]] = {}
+
+        def register_last_uses(n : Node, user : Node):
+            if n not in node_to_last_use:
+                node_to_last_use[n] = user
+                user_to_last_uses.setdefault(user, []).append(n)
+
+        for node in reversed(nodes):
+            map_arg(node.args, lambda n: register_last_uses(n, node))
+            map_arg(node.kwargs, lambda n: register_last_uses(n, node))
+
+        def delete_unused_values(user : Node):
+            """
+            Delete values after their last use. This ensures that values that are
+            not used in the remainder of the code are freed and the memory usage
+            of the code is optimal.
+            """
+            if user.op == 'placeholder':
+                return
+            if user.op == 'output':
+                body.append('\n')
+                return
+            nodes_to_delete = user_to_last_uses.get(user, [])
+
+            if len(user.users.keys()) == 0:
+                # This node is not used by any others. however it's also not
+                # removed by DCE since side-effect. We want to free it's outputs
+                # right after its execution done to save memory.
+                nodes_to_delete.append(user)
+
+            if len(nodes_to_delete):
+                to_delete_str = ' = '.join([repr(n) for n in nodes_to_delete] + ['None'])
+                body.append(f';  {dim(to_delete_str)}\n')
+            else:
+                body.append('\n')
+
+        prev_stacktrace = None
+
+        def append_stacktrace_summary(node : Node):
+            """
+            Append a summary of the stacktrace to the generated code. This is
+            useful for debugging.
+            """
+            nonlocal prev_stacktrace
+
+            if node.op not in {'placeholder', 'output'}:
+                if node.stack_trace:
+                    if node.stack_trace != prev_stacktrace:
+                        prev_stacktrace = node.stack_trace
+                        summary_str = ""
+
+                        if parsed_stack_trace := _parse_stack_trace(node.stack_trace):
+                            summary_str = parsed_stack_trace.get_summary_str()
+
+                        body.append(f'\n {dim("# " + summary_str)}\n')
+                elif prev_stacktrace != "":
+                    prev_stacktrace = ""
+                    no_stacktrace_msg = "# No stacktrace found for following nodes"
+                    body.append(f'\n{dim(no_stacktrace_msg)}\n')
+
+        def stringify_shape(shape : Iterable) -> str:
+            return f"[{', '.join(str(x) for x in shape)}]"
+
+        def emit_node(node : Node):
+            maybe_type_annotation = '' if node.type is None else f' : {type_repr(node.type)}'
+
+            if verbose:
+                # override annotation with more detailed information
+                from torch.fx.experimental.proxy_tensor import py_sym_types
+                from torch.fx.passes.shape_prop import TensorMetadata
+
+                meta_val = node.meta.get('val', node.meta.get('tensor_meta', node.meta.get('example_value', None)))
+                # use string as annotation, to make it valid python code
+
+                if isinstance(meta_val, torch.Tensor):
+                    stride_annotation = f"{stringify_shape(meta_val.stride())}" if include_stride else ""
+                    device_annotation = f"{meta_val.device}" if include_device else ""
+                    maybe_type_annotation = \
+                        f': "{red(dtype_abbrs[meta_val.dtype])}{blue(stringify_shape(meta_val.shape))}' \
+                        f'{dim_blue(stride_annotation)}{dim_green(device_annotation)}"'
+                elif isinstance(meta_val, py_sym_types):
+                    maybe_type_annotation = f': "Sym({meta_val})"'
+                elif isinstance(meta_val, TensorMetadata):
+                    maybe_type_annotation = f': "{dtype_abbrs[meta_val.dtype]}{stringify_shape(meta_val.shape)}"'
+
+            if node.op == 'placeholder':
+                assert isinstance(node.target, str)
+                maybe_default_arg = '' if not node.args else f' = {_get_repr(node.args[0])}'
+                free_vars.append(f'{node.target}{maybe_type_annotation}{maybe_default_arg}')
+                raw_name = node.target.replace('*', '')
+                if raw_name != repr(node):
+                    body.append(f'{repr(node)} = {raw_name}\n')
+                return
+            elif node.op == 'call_method':
+                assert isinstance(node.target, str)
+                body.append(
+                    f'{repr(node)}{maybe_type_annotation} = {_format_target(_get_repr(node.args[0]), node.target)}'
+                    f'({_format_args(node.args[1:], node.kwargs)})')
+                return
+            elif node.op == 'call_function':
+                assert callable(node.target)
+                # pretty print operators
+                if getattr(node.target, "__module__", "") == '_operator' and node.target.__name__ in magic_methods:
+                    assert isinstance(node.args, tuple)
+                    body.append(f'{repr(node)}{maybe_type_annotation} = '
+                                f'{magic_methods[node.target.__name__].format(*(_get_repr(a) for a in node.args))}')
+                    return
+
+                # pretty print inplace operators; required for jit.script to work properly
+                # not currently supported in normal FX graphs, but generated by torchdynamo
+                if getattr(node.target, "__module__", "") == '_operator' and node.target.__name__ in inplace_methods:
+                    body.append(f'{inplace_methods[node.target.__name__].format(*(_get_repr(a) for a in node.args))};  '
+                                f'{repr(node)}{maybe_type_annotation} = {_get_repr(node.args[0])}')
+                    return
+
+                qualified_name = _get_qualified_name(node.target)
+                global_name = add_global(qualified_name, node.target)
+                # special case for getattr: node.args could be 2-argument or 3-argument
+                # 2-argument: attribute access; 3-argument: fall through to attrib function call with default value
+                if global_name == 'getattr' and \
+                   isinstance(node.args, tuple) and \
+                   isinstance(node.args[1], str) and \
+                   node.args[1].isidentifier() and \
+                   len(node.args) == 2:
+                    body.append(f'{repr(node)}{maybe_type_annotation} = {_format_target(_get_repr(node.args[0]), node.args[1])}')
+                    return
+                body.append(f'{repr(node)}{maybe_type_annotation} = {global_name}({_format_args(node.args, node.kwargs)})')
+                if node.meta.get('is_wrapped', False):
+                    wrapped_fns.setdefault(global_name)
+                return
+            elif node.op == 'call_module':
+                assert isinstance(node.target, str)
+                body.append(f'{repr(node)}{maybe_type_annotation} = '
+                            f'{_format_target(root_module, node.target)}({_format_args(node.args, node.kwargs)})')
+                return
+            elif node.op == 'get_attr':
+                assert isinstance(node.target, str)
+                body.append(f'{repr(node)}{maybe_type_annotation} = {_format_target(root_module, node.target)}')
+                return
+            elif node.op == 'output':
+                if node.type is not None:
+                    maybe_return_annotation[0] = f" -> {type_repr(node.type)}"
+                body.append(self.generate_output(node.args[0]))
+                return
+            raise NotImplementedError(f'node: {node.op} {node.target}')
+
+        for i, node in enumerate(nodes):
+            # NOTE: emit_node does not emit a string with newline. It depends
+            # on delete_unused_values to append one
+            if verbose:
+                append_stacktrace_summary(node)
+            # emit a counter comment to keep track of
+            # node index, which will be deleted later
+            # after going through _body_transformer
+            body.append(f"# COUNTER: {i}\n")
+            emit_node(node)
+            delete_unused_values(node)
+
+        if len(body) == 0:
+            # If the Graph has no non-placeholder nodes, no lines for the body
+            # have been emitted. To continue to have valid Python code, emit a
+            # single pass statement
+            body.append('pass\n')
+
+
+
+        if len(wrapped_fns) > 0:
+            wrap_name = add_global('wrap', torch.fx.wrap)
+            wrap_stmts = '\n'.join([f'{wrap_name}("{name}")' for name in wrapped_fns])
+        else:
+            wrap_stmts = ''
+
+        if self._body_transformer:
+            body = self._body_transformer(body)
+
+        for name, value in self.additional_globals():
+            add_global(name, value)
+
+        prologue = self.gen_fn_def(free_vars, maybe_return_annotation[0])
+
+        # remove counter and generate lineno to node index mapping
+        lineno_map: Dict[int, Optional[int]] = {}
+        prologue_len = prologue.count('\n') + 1
+        new_lines: List[str] = []
+        cur_idx = None
+        for line in ''.join(body).split('\n'):
+            counter = re.search(r"# COUNTER: (\d+)", line)
+            if counter and counter.group(1) is not None:
+                cur_idx = int(counter.group(1))
+            else:
+                lineno_map[len(new_lines) + prologue_len] = cur_idx
+                new_lines.append(line)
+
+        code = "\n".join(new_lines).lstrip('\n')
+        code = '\n'.join('    ' + line for line in code.split('\n'))
+
+        fn_code = f"""
+{wrap_stmts}
+
+{prologue}
+{code}"""
+        return PythonCode(fn_code, globals_, _lineno_map=lineno_map)
+
+
+# Ideally, we'd like to refactor all of the pytree logic into this codegen
+# class. Unfortunately, there are 3 areas we currently need extra logic in FX.
+# 1. In the initial symbolic trace, the pytree logic is tied up with `concrete_args`.
+# 2. In the FX graph, we need to access 2 attributes - in_spec and out_spec.
+#    Since we can't access .graph within the FX forward, we need to copy the attribute to the module.
+# 3. We currently can't register the pytree imports with `add_global` - not sure why.
+class _PyTreeCodeGen(CodeGen):
+    def __init__(self, pytree_info: _PyTreeInfo):
+        super().__init__()
+        self.pytree_info: _PyTreeInfo = pytree_info
+
+    def process_inputs(self, *inputs: Any) -> Any:
+        flat_args = pytree.arg_tree_leaves(*inputs)
+        return flat_args
+
+    def process_outputs(self, out: Any) -> Any:
+        if self.pytree_info is None or self.pytree_info.out_spec is None:
+            return out
+        if not isinstance(out, (list, tuple)):
+            out = [out]
+        assert self.pytree_info.out_spec is not None
+        return pytree.tree_unflatten(out, self.pytree_info.out_spec)
+
+    def gen_fn_def(self, free_vars, maybe_return_annotation):
+        # Given a user function/model:
+        #   myargs = [myargs0, myargs1]
+        #   mykwargs = {'mykwargs0': ..., 'mykwargs1': ...}
+        #   def forward(self, mypos, *myargs, mykey=None, **mykwargs):
+        #
+        # The generated code flattens all keywords into positional arguments for `forward()`
+        #   e.g forward(self, mypos, myargs0, myargs1, mykey, mykwargs0, mykwargs1):
+        #
+        # Within `forward`, `tree_flatten_spec``still parses args and kwargs separately
+        #   e.g. tree_flatten_spec(([mypos, myargs0, myargs1],
+        #                           {'mykey':mykey, 'mykwargs0':mykwargs0, 'mykwargs1':mykwargs1}),
+        #                          self._in_spec)
+        #
+        # If the user function/model does not have keywords, the dict is suppressed from tree_flatten_spec
+        #   e.g. tree_flatten_spec([mypos, myargs0, myargs1]), self._in_spec)
+        if self.pytree_info is None:
+            return super().gen_fn_def(free_vars, maybe_return_annotation)
+
+        fn_args = self.pytree_info.orig_args
+        has_orig_self = (fn_args[0] == 'self') if len(fn_args) > 0 else False
+        if has_orig_self:
+            free_vars.insert(0, 'self')
+        fn_definition = super().gen_fn_def(fn_args[:], maybe_return_annotation)
+
+        if len(free_vars) > 0:  # pytree has placeholders in it
+            # when kwargs is present, in_spec is tuple(args, kwargs)
+            has_args_kwargs_tuple = self.pytree_info.in_spec.type == tuple and \
+                self.pytree_info.in_spec.num_children == 2 and \
+                self.pytree_info.in_spec.children_specs[0].type == tuple and \
+                self.pytree_info.in_spec.children_specs[1].type == dict
+            fn_kwargs = '{}'
+            fn_signature = f"[{', '.join(fn_args)}], self._in_spec"
+            if has_args_kwargs_tuple:
+                count_args = self.pytree_info.in_spec.children_specs[0].num_children
+                fn_args = self.pytree_info.orig_args[:count_args]
+                fn_kwargs = '{' + ', '.join(f"'{k}':{v}" for k, v in zip(
+                                  self.pytree_info.in_spec.children_specs[1].context,
+                                  self.pytree_info.orig_args[count_args:])) + '}'
+                fn_signature = f"([{', '.join(fn_args)}], {fn_kwargs}), self._in_spec"
+
+            # in Python, `var1: annotation1, var2: annotation2 = function_call()` is invalid.
+            # we need to split it to two lines:
+            # one for annotation: `var1: annotation1; var2: annotation2;` (note the semicolon)
+            # one for code: `var1, var2, = function_call()`
+            without_annotation = [x.split(":")[0] for x in free_vars]
+            has_annotation = [x + "; " for x in free_vars if ":" in x]
+            if len(has_annotation) > 0:
+                fn_definition += "\n    " + "".join(has_annotation) + "\n"
+            fn_definition += f"""
+    {', '.join(without_annotation)}, = fx_pytree.tree_flatten_spec({fn_signature})"""
+        return fn_definition
+
+    def generate_output(self, output_args):
+        if self.pytree_info and self.pytree_info.out_spec:
+            return f'return pytree.tree_unflatten({repr(output_args)}, self._out_spec)'
+        else:
+            return super().generate_output(output_args)
+
+class _FindNodesLookupTable:
+    """
+    Side table for the graph for the purpose of doing fast queries
+    """
+    def __init__(self):
+        self.table: Dict[Tuple[str, Optional[Target]], Dict[Node, None]] = defaultdict(dict)
+
+    def _key(self, node) -> Tuple[str, Optional[Target]]:
+        return (node.op, node.target if node.op == "call_function" else None)
+
+    def __contains__(self, node) -> bool:
+        return node in self.table[self._key(node)]
+
+    def insert(self, node: Node) -> None:
+        self.table[self._key(node)][node] = None
+
+    def remove(self, node: Node) -> None:
+        self.table[self._key(node)].pop(node)
+
+    def find_nodes(self, *, op: str, target: Optional['Target'] = None):
+        if op == "call_function":
+            assert target is not None
+            return dict(self.table[(op, target)]).keys()
+
+        if target is None:
+            return dict(self.table[(op, None)]).keys()
+
+        # op is call_method, get_attr, call_module
+        return [node for node in self.table[(op, None)].keys() if node.target == target]
+
+@compatibility(is_backward_compatible=True)
+class Graph:
+    """
+    ``Graph`` is the main data structure used in the FX Intermediate Representation.
+    It consists of a series of ``Node`` s, each representing callsites (or other
+    syntactic constructs). The list of ``Node`` s, taken together, constitute a
+    valid Python function.
+
+    For example, the following code
+
+    .. code-block:: python
+
+        import torch
+        import torch.fx
+
+        class MyModule(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.param = torch.nn.Parameter(torch.rand(3, 4))
+                self.linear = torch.nn.Linear(4, 5)
+
+            def forward(self, x):
+                return torch.topk(torch.sum(self.linear(x + self.linear.weight).relu(), dim=-1), 3)
+
+        m = MyModule()
+        gm = torch.fx.symbolic_trace(m)
+
+    Will produce the following Graph::
+
+        print(gm.graph)
+
+    .. code-block:: text
+
+        graph(x):
+            %linear_weight : [num_users=1] = self.linear.weight
+            %add_1 : [num_users=1] = call_function[target=operator.add](args = (%x, %linear_weight), kwargs = {})
+            %linear_1 : [num_users=1] = call_module[target=linear](args = (%add_1,), kwargs = {})
+            %relu_1 : [num_users=1] = call_method[target=relu](args = (%linear_1,), kwargs = {})
+            %sum_1 : [num_users=1] = call_function[target=torch.sum](args = (%relu_1,), kwargs = {dim: -1})
+            %topk_1 : [num_users=1] = call_function[target=torch.topk](args = (%sum_1, 3), kwargs = {})
+            return topk_1
+
+    For the semantics of operations represented in the ``Graph``, please see :class:`Node`.
+    """
+
+    @compatibility(is_backward_compatible=True)
+    def __init__(self, owning_module: Optional["GraphModule"] = None, tracer_cls: Optional[Type["Tracer"]] = None,
+                 tracer_extras: Optional[Dict[str, Any]] = None):
+        """
+        Construct an empty Graph.
+        """
+        self._root : Node = Node(self, '', 'root', '', (), {})
+        self._used_names : Dict[str, int] = {}  # base name -> number
+        self._insert = self._root.prepend
+        self._len = 0
+        self._graph_namespace = _Namespace()
+        self._owning_module = owning_module
+        self._tracer_cls = tracer_cls
+        self._tracer_extras = tracer_extras
+        self._codegen = CodeGen()
+        self._co_fields : Dict[str, Any] = {}
+        self._find_nodes_lookup_table = _FindNodesLookupTable()
+
+    @property
+    def owning_module(self):
+        return self._owning_module
+
+    @owning_module.setter
+    def owning_module(self, mod: Optional["GraphModule"]):
+        self._owning_module = mod
+
+    @property
+    def nodes(self) -> _node_list:
+        """
+        Get the list of Nodes that constitute this Graph.
+
+        Note that this ``Node`` list representation is a doubly-linked list. Mutations
+        during iteration (e.g. delete a Node, add a Node) are safe.
+
+        Returns:
+
+            A doubly-linked list of Nodes. Note that ``reversed`` can be called on
+            this list to switch iteration order.
+        """
+        return _node_list(self)
+
+    @compatibility(is_backward_compatible=False)
+    def find_nodes(self, *, op: str, target: Optional['Target'] = None, sort: bool = True):
+        """
+        Allows for fast query of nodes
+
+        Args:
+
+            op (str): the name of the operation
+
+            target (Optional[Target]): the target of the node. For call_function,
+                the target is required. For other ops, the target is optional.
+
+            sort (bool): whether to return nodes in the order they appear on
+                         on the graph.
+
+        Returns:
+
+            Iteratable of nodes with the requested op and target.
+        """
+        node_list = self._find_nodes_lookup_table.find_nodes(op=op, target=target)
+        if sort:
+            return sorted(node_list)
+        return node_list
+
+    @compatibility(is_backward_compatible=True)
+    def graph_copy(self, g : 'Graph', val_map : Dict[Node, Node], return_output_node=False) -> 'Optional[Argument]':
+        """
+        Copy all nodes from a given graph into ``self``.
+
+        Args:
+
+            g (Graph): The source graph from which to copy Nodes.
+
+            val_map (Dict[Node, Node]): a dictionary that will be populated with a mapping
+                from nodes in ``g`` to nodes in ``self``. Note that ``val_map`` can be passed
+                in with values in it already to override copying of certain values.
+
+        Returns:
+
+            The value in ``self`` that is now equivalent to the output value in ``g``,
+            if ``g`` had an ``output`` node. ``None`` otherwise.
+        """
+        for node in g.nodes:
+            if node in val_map:
+                continue
+            if node.op == 'output':
+                rv = map_arg(node.args[0], lambda n: val_map[n])
+                return rv if not return_output_node else (rv, node)
+            val_map[node] = self.node_copy(node, lambda n : val_map[n])
+        return None
+
+    def __deepcopy__(self, memo=None) -> 'Graph':
+        """
+        Explicitly implement __deepcopy__ to prevent excessive recursion depth
+        from the default implementation. This uses graph_copy to copy the nodes
+        in an iterative way, rather than recursive. It also populates the
+        memoization table to prevent unnecessary copies (e.g. references to
+        nodes or other parts of the Graph from a custom GraphModule implementation.
+        """
+        memo = memo if memo else {}
+        g = Graph(tracer_cls=self._tracer_cls)
+        output_vals = g.graph_copy(self, val_map=memo, return_output_node=True)
+        g._codegen = copy.deepcopy(self._codegen)
+        assert isinstance(output_vals, tuple)
+        output_val, old_output_node = output_vals
+        new_output_node = g.output(output_val, type_expr=getattr(old_output_node, 'type', None))
+        new_output_node.meta = copy.copy(old_output_node.meta)
+        return g
+
+    @compatibility(is_backward_compatible=True)
+    def create_node(self, op: str, target: 'Target',
+                    args: Optional[Tuple['Argument', ...]] = None,
+                    kwargs: Optional[Dict[str, 'Argument']] = None,
+                    name: Optional[str] = None,
+                    type_expr: Optional[Any] = None) -> Node:
+        """
+        Create a ``Node`` and add it to the ``Graph`` at the current insert-point.
+        Note that the current insert-point can be set via :meth:`Graph.inserting_before`
+        and :meth:`Graph.inserting_after`.
+
+        Args:
+            op (str): the opcode for this Node. One of 'call_function', 'call_method', 'get_attr',
+                'call_module', 'placeholder', or 'output'. The semantics of these opcodes are
+                described in the ``Graph`` docstring.
+
+            args (Optional[Tuple[Argument, ...]]): is a tuple of arguments to this node.
+
+            kwargs (Optional[Dict[str, Argument]]): the kwargs of this Node
+
+            name (Optional[str]): an optional string name for the ``Node``.
+                This will influence the name of the value assigned to in the
+                Python generated code.
+
+            type_expr (Optional[Any]): an optional type annotation representing the
+                Python type the output of this node will have.
+
+        Returns:
+
+            The newly-created and inserted node.
+        """
+        assert op in ('call_function', 'call_method', 'get_attr', 'call_module', 'placeholder', 'output')
+        args = () if args is None else args
+        kwargs = {} if kwargs is None else kwargs
+        assert isinstance(args, tuple), "args must be a tuple"
+        assert isinstance(kwargs, dict), "kwargs must be a dict"
+
+        candidate = name if name is not None else self._target_to_str(target)
+        name = self._graph_namespace.create_name(candidate, None)
+        n = Node(self, name, op, target, args, kwargs, type_expr)
+
+        if self.owning_module is not None and getattr(self.owning_module, "_create_node_hooks", None) is not None:
+            for f in self.owning_module._create_node_hooks:
+                f(n)
+
+        self._graph_namespace.associate_name_with_obj(name, n)
+
+        self._insert(n)
+        self._find_nodes_lookup_table.insert(n)
+        self._len += 1
+        return n
+
+    @compatibility(is_backward_compatible=False)
+    def process_inputs(self, *args):
+        """
+        Processes args so that they can be passed to the FX graph.
+        """
+        return self._codegen.process_inputs(*args)
+
+    @compatibility(is_backward_compatible=False)
+    def process_outputs(self, out):
+        return self._codegen.process_outputs(out)
+
+
+    @compatibility(is_backward_compatible=True)
+    def erase_node(self, to_erase : Node) -> None:
+        """
+        Erases a ``Node`` from the ``Graph``. Throws an exception if
+        there are still users of that node in the ``Graph``.
+
+        Args:
+
+            to_erase (Node): The ``Node`` to erase from the ``Graph``.
+        """
+        if len(to_erase.users) > 0:
+            raise RuntimeError(f'Tried to erase Node {to_erase} but it still had {len(to_erase.users)} '
+                               f'users in the graph: {to_erase.users}!')
+        if to_erase.graph != self:
+            raise RuntimeError(f"Attempting to remove {to_erase} from wrong graph!")
+        if to_erase._erased:
+            warnings.warn(f"erase_node({to_erase}) on an already erased node")
+            return
+
+        if self.owning_module is not None and getattr(self.owning_module, "_erase_node_hooks", None) is not None:
+            for f in self.owning_module._erase_node_hooks:
+                f(to_erase)
+
+        self._find_nodes_lookup_table.remove(to_erase)
+        to_erase._remove_from_list()
+        to_erase._erased = True  # iterators may retain handles to erased nodes
+        self._len -= 1
+
+        # Null out this Node's argument nodes so that the Nodes referred to
+        # can update their ``users`` accordingly
+        new_args = map_arg(to_erase.args, lambda n: None)
+        assert isinstance(new_args, tuple)
+        to_erase.args = new_args
+        new_kwargs = map_arg(to_erase.kwargs, lambda n: None)
+        assert isinstance(new_kwargs, dict)
+        to_erase.kwargs = new_kwargs
+
+    @compatibility(is_backward_compatible=True)
+    def inserting_before(self, n: Optional[Node] = None):
+        """Set the point at which create_node and companion methods will insert into the graph.
+        When used within a 'with' statement, this will temporary set the insert point and
+        then restore it when the with statement exits::
+
+            with g.inserting_before(n):
+                ... # inserting before node n
+            ... # insert point restored to what it was previously
+            g.inserting_before(n) #  set the insert point permanently
+
+        Args:
+
+            n (Optional[Node]): The node before which to insert. If None this will insert before
+                the beginning of the entire graph.
+
+        Returns:
+            A resource manager that will restore the insert point on ``__exit__``.
+        """
+        if n is None:
+            return self.inserting_after(self._root)
+        assert n.graph == self, "Node to insert before is not in graph."
+        return _InsertPoint(self, n.prepend)
+
+    @compatibility(is_backward_compatible=True)
+    def inserting_after(self, n: Optional[Node] = None):
+        """Set the point at which create_node and companion methods will insert into the graph.
+        When used within a 'with' statement, this will temporary set the insert point and
+        then restore it when the with statement exits::
+
+            with g.inserting_after(n):
+                ... # inserting after node n
+            ... # insert point restored to what it was previously
+            g.inserting_after(n) #  set the insert point permanently
+
+        Args:
+
+            n (Optional[Node]): The node before which to insert. If None this will insert after
+                the beginning of the entire graph.
+
+        Returns:
+            A resource manager that will restore the insert point on ``__exit__``.
+        """
+        if n is None:
+            return self.inserting_before(self._root)
+        assert n.graph == self, "Node to insert after is not in graph."
+        return _InsertPoint(self, n.append)
+
+    @compatibility(is_backward_compatible=True)
+    def placeholder(self, name: str, type_expr: Optional[Any] = None,
+                    default_value : Any = inspect.Signature.empty) -> Node:
+        """
+        Insert a ``placeholder`` node into the Graph. A ``placeholder`` represents
+        a function input.
+
+        Args:
+
+            name (str): A name for the input value. This corresponds to the name
+                of the positional argument to the function this ``Graph`` represents.
+
+            type_expr (Optional[Any]): an optional type annotation representing the
+                Python type the output of this node will have. This is needed in some
+                cases for proper code generation (e.g. when the function is used
+                subsequently in TorchScript compilation).
+
+            default_value (Any): The default value this function argument should take
+                on. NOTE: to allow for `None` as a default value, `inspect.Signature.empty`
+                should be passed as this argument to specify that the parameter does _not_
+                have a default value.
+
+        .. note::
+            The same insertion point and type expression rules apply for this method
+            as ``Graph.create_node``.
+        """
+        args = () if default_value is inspect.Signature.empty else (default_value,)
+        return self.create_node('placeholder', name, args=args, type_expr=type_expr)
+
+    @compatibility(is_backward_compatible=True)
+    def get_attr(self, qualified_name: str, type_expr: Optional[Any] = None) -> Node:
+        """
+        Insert a ``get_attr`` node into the Graph. A ``get_attr`` ``Node`` represents the
+        fetch of an attribute from the ``Module`` hierarchy.
+
+        Args:
+
+            qualified_name (str): the fully-qualified name of the attribute to be retrieved.
+                For example, if the traced Module has a submodule named ``foo``, which has a
+                submodule named ``bar``, which has an attribute named ``baz``, the qualified
+                name ``foo.bar.baz`` should be passed as ``qualified_name``.
+
+            type_expr (Optional[Any]): an optional type annotation representing the
+                Python type the output of this node will have.
+
+
+        Returns:
+
+            The newly-created and inserted ``get_attr`` node.
+
+        .. note::
+            The same insertion point and type expression rules apply for this method
+            as ``Graph.create_node``.
+        """
+        def _get_attr_reference_exists(mod: torch.nn.Module, qualified_name: str) -> bool:
+            module_path, _, name = qualified_name.rpartition(".")
+
+            try:
+                submod: torch.nn.Module = mod.get_submodule(module_path)
+            except AttributeError:
+                warnings.warn(f"Failed to fetch module {module_path}!")
+                return False
+
+            if not hasattr(submod, name):
+                return False
+
+            res = getattr(submod, name)
+
+            if (not isinstance(res, torch.nn.Module)
+                    and not isinstance(res, torch.nn.Parameter)
+                    and name not in submod._buffers):
+                return False
+
+            return True
+
+        if (self.owning_module and
+                not _get_attr_reference_exists(self.owning_module, qualified_name)):
+            warnings.warn("Attempted to insert a get_attr Node with no "
+                          "underlying reference in the owning "
+                          "GraphModule! Call "
+                          "GraphModule.add_submodule to add the "
+                          "necessary submodule, "
+                          "GraphModule.add_parameter to add the "
+                          "necessary Parameter, or "
+                          "nn.Module.register_buffer to add the "
+                          "necessary buffer", stacklevel=2)
+        return self.create_node('get_attr', qualified_name, type_expr=type_expr)
+
+    @compatibility(is_backward_compatible=True)
+    def call_module(self,
+                    module_name: str,
+                    args: Optional[Tuple['Argument', ...]] = None,
+                    kwargs: Optional[Dict[str, 'Argument']] = None,
+                    type_expr: Optional[Any] = None) -> Node:
+        """
+        Insert a ``call_module`` ``Node`` into the ``Graph``. A ``call_module`` node
+        represents a call to the forward() function of a ``Module`` in the ``Module``
+        hierarchy.
+
+        Args:
+
+            module_name (str): The qualified name of the ``Module`` in the ``Module``
+                hierarchy to be called. For example, if the traced ``Module`` has a
+                submodule named ``foo``, which has a submodule named ``bar``, the
+                qualified name ``foo.bar`` should be passed as ``module_name`` to
+                call that module.
+
+            args (Optional[Tuple[Argument, ...]]): The positional arguments to be passed
+                to the called method. Note that this should *not* include a ``self`` argument.
+
+            kwargs (Optional[Dict[str, Argument]]): The keyword arguments to be passed
+                to the called method
+
+            type_expr (Optional[Any]): an optional type annotation representing the
+                Python type the output of this node will have.
+
+        Returns:
+
+            The newly-created and inserted ``call_module`` node.
+
+        .. note::
+            The same insertion point and type expression rules apply for this method
+            as :meth:`Graph.create_node`.
+        """
+        if (self.owning_module and
+                self.owning_module.get_submodule(module_name) is None):
+            warnings.warn("Attempted to insert a call_module Node with "
+                          "no underlying reference in the owning "
+                          "GraphModule! Call "
+                          "GraphModule.add_submodule to add the "
+                          "necessary submodule")
+        return self.create_node('call_module', module_name, args, kwargs, type_expr=type_expr)
+
+    @compatibility(is_backward_compatible=True)
+    def call_method(self,
+                    method_name: str,
+                    args: Optional[Tuple['Argument', ...]] = None,
+                    kwargs: Optional[Dict[str, 'Argument']] = None,
+                    type_expr: Optional[Any] = None) -> Node:
+        """
+        Insert a ``call_method`` ``Node`` into the ``Graph``. A ``call_method`` node
+        represents a call to a given method on the 0th element of ``args``.
+
+        Args:
+
+            method_name (str): The name of the method to apply to the self argument.
+                For example, if args[0] is a ``Node`` representing a ``Tensor``,
+                then to call ``relu()`` on that ``Tensor``, pass ``relu`` to ``method_name``.
+
+            args (Optional[Tuple[Argument, ...]]): The positional arguments to be passed
+                to the called method. Note that this *should* include a ``self`` argument.
+
+            kwargs (Optional[Dict[str, Argument]]): The keyword arguments to be passed
+                to the called method
+
+            type_expr (Optional[Any]): an optional type annotation representing the
+                Python type the output of this node will have.
+
+        Returns:
+
+            The newly created and inserted ``call_method`` node.
+
+        .. note::
+            The same insertion point and type expression rules apply for this method
+            as :meth:`Graph.create_node`.
+        """
+        return self.create_node('call_method', method_name, args, kwargs, type_expr=type_expr)
+
+    @compatibility(is_backward_compatible=True)
+    def call_function(self,
+                      the_function: Callable[..., Any],
+                      args: Optional[Tuple['Argument', ...]] = None,
+                      kwargs: Optional[Dict[str, 'Argument']] = None,
+                      type_expr: Optional[Any] = None) -> Node:
+        """
+        Insert a ``call_function`` ``Node`` into the ``Graph``. A ``call_function`` node
+        represents a call to a Python callable, specified by ``the_function``.
+
+        Args:
+
+            the_function (Callable[..., Any]): The function to be called. Can be any PyTorch
+                operator, Python function, or member of the ``builtins`` or ``operator``
+                namespaces.
+
+            args (Optional[Tuple[Argument, ...]]): The positional arguments to be passed
+                to the called function.
+
+            kwargs (Optional[Dict[str, Argument]]): The keyword arguments to be passed
+                to the called function
+
+            type_expr (Optional[Any]): an optional type annotation representing the
+                Python type the output of this node will have.
+
+        Returns:
+
+            The newly created and inserted ``call_function`` node.
+
+        .. note::
+            The same insertion point and type expression rules apply for this method
+            as :meth:`Graph.create_node`.
+        """
+        return self.create_node('call_function', the_function, args, kwargs, type_expr=type_expr)
+
+    @compatibility(is_backward_compatible=True)
+    def node_copy(self, node: Node, arg_transform: Callable[[Node], 'Argument'] = lambda x: x) -> Node:
+        """
+        Copy a node from one graph into another. ``arg_transform`` needs to transform arguments from
+        the graph of node to the graph of self. Example::
+
+            # Copying all the nodes in `g` into `new_graph`
+            g : torch.fx.Graph = ...
+            new_graph = torch.fx.graph()
+            value_remap = {}
+            for node in g.nodes:
+                value_remap[node] = new_graph.node_copy(node, lambda n : value_remap[n])
+
+        Args:
+
+            node (Node): The node to copy into ``self``.
+
+            arg_transform (Callable[[Node], Argument]): A function that transforms
+                ``Node`` arguments in node's ``args`` and ``kwargs`` into the
+                equivalent argument in ``self``. In the simplest case, this should
+                retrieve a value out of a table mapping Nodes in the original
+                graph to ``self``.
+        """
+        args = map_arg(node.args, arg_transform)
+        kwargs = map_arg(node.kwargs, arg_transform)
+        assert isinstance(args, tuple)
+        assert isinstance(kwargs, dict)
+        result_node = self.create_node(node.op, node.target, args, kwargs, node.name, node.type)
+        result_node.meta = copy.copy(node.meta)
+        return result_node
+
+    @compatibility(is_backward_compatible=True)
+    def output(self, result: 'Argument', type_expr: Optional[Any] = None):
+        """
+        Insert an ``output`` ``Node`` into the ``Graph``. An ``output`` node represents
+        a ``return`` statement in Python code. ``result`` is the value that should
+        be returned.
+
+        Args:
+
+            result (Argument): The value to be returned.
+
+            type_expr (Optional[Any]): an optional type annotation representing the
+                Python type the output of this node will have.
+
+        .. note::
+
+            The same insertion point and type expression rules apply for this method
+            as ``Graph.create_node``.
+        """
+        return self.create_node(op='output', target='output', args=(result,), type_expr=type_expr)
+
+    def _target_to_str(self, target : Target) -> str:
+        if callable(target):
+            op = target.__name__
+        else:
+            assert isinstance(target, str)
+            op = target
+            if _is_magic(op):
+                op = op[2:-2]
+        op = _snake_case(op)
+        return op
+
+    @compatibility(is_backward_compatible=True)
+    def python_code(
+        self, root_module: str, *,
+        verbose: bool = False, include_stride: bool = False, include_device: bool = False, colored: bool = False
+    ) -> PythonCode:
+        """
+        Turn this ``Graph`` into valid Python code.
+
+        Args:
+
+            root_module (str): The name of the root module on which to look-up
+                qualified name targets. This is usually 'self'.
+
+        Returns:
+
+            A PythonCode object, consisting of two fields:
+                src: the Python source code representing the object
+                globals: a dictionary of global names in `src` -> the objects that they reference.
+        """
+        # NOTE: [Graph Namespaces]
+        #
+        # There are two types of symbols in generated Python source code:
+        # locals and globals.
+        #   Locals are locally defined by the output of a node in the Graph.
+        #   Globals are references to external objects, like functions or types.
+        #
+        # When generating Python code, we need to make sure to name things
+        # appropriately. In particular:
+        # - All names should be unique, to avoid weird shadowing bugs.
+        # - These names need to be consistent, e.g. a object should always be
+        #   referenced by the same name.
+        #
+        # To do this, we create a new namespace just for this source. All names
+        # that get printed must come from this namespace.
+        #
+        # Why can't we re-use node.name? Because it was generated within the
+        # namespace `self._graph_namespace`. In order to provide uniqueness
+        # over both locals (node.name) *and* globals, we create a completely
+        # new namespace to put all identifiers in.
+        namespace = _Namespace()
+
+        # Override Node's repr to generate a valid name within our namespace.
+        # Since repr() is designed to produce a valid Python expression, it
+        # makes sense to re-use it. This way, it's easy to print something like
+        # Tuple[Node, Node] by simply calling repr() on it. Node's __repr__ is
+        # implemented cooperatively to allow this.
+        def node_repr(n: Node):
+            return namespace.create_name(n.name, n)
+
+        @contextmanager
+        def override_node_repr(graph: Graph):
+            orig_repr_fns = {}
+            for node in graph.nodes:
+                orig_repr_fns[node] = node._repr_fn
+                node._repr_fn = node_repr
+            try:
+                yield None
+            finally:
+                # restore the original repr functions
+                for node in graph.nodes:
+                    node._repr_fn = orig_repr_fns[node]
+
+        with override_node_repr(self):
+            return self._python_code(
+                root_module, namespace,
+                verbose=verbose, include_stride=include_stride, include_device=include_device, colored=colored
+            )
+
+    def _python_code(
+        self, root_module: str, namespace: _Namespace, *,
+        verbose: bool = False, include_stride: bool = False, include_device: bool = False, colored: bool = False,
+    ) -> PythonCode:
+        return self._codegen._gen_python_code(
+            self.nodes, root_module, namespace,
+            verbose=verbose, include_stride=include_stride, include_device=include_device, colored=colored
+        )
+
+
+    def __str__(self) -> str:
+        """
+        Return a human-readable (not machine-readable) string representation
+        of this Graph
+        """
+        placeholder_names : List[str] = []
+        # This is a one-element array just so ``format_node`` can modify the closed
+        # over value
+        maybe_return_typename : List[str] = ['']
+
+        node_strs = [node.format_node(placeholder_names) for node in self.nodes]
+        param_str = ', '.join(placeholder_names)
+        s = f'graph({param_str}){maybe_return_typename[0]}:'
+        for node_str in node_strs:
+            if node_str:
+                s += '\n    ' + node_str
+        return s
+
+    @compatibility(is_backward_compatible=True)
+    def print_tabular(self):
+        """
+        Prints the intermediate representation of the graph in tabular
+        format. Note that this API requires the ``tabulate`` module to be
+        installed.
+        """
+        try:
+            from tabulate import tabulate
+        except ImportError:
+            print("`print_tabular` relies on the library `tabulate`, "
+                  "which could not be found on this machine. Run `pip "
+                  "install tabulate` to install the library.")
+            raise
+
+        node_specs = [[n.op, n.name, n.target, n.args, n.kwargs]
+                      for n in self.nodes]
+        print(tabulate(node_specs,
+              headers=['opcode', 'name', 'target', 'args', 'kwargs']))
+
+    @compatibility(is_backward_compatible=True)
+    def lint(self):
+        """
+        Runs various checks on this Graph to make sure it is well-formed. In
+        particular:
+        - Checks Nodes have correct ownership (owned by this graph)
+        - Checks Nodes appear in topological order
+        - If this Graph has an owning GraphModule, checks that targets
+        exist in that GraphModule
+        """
+
+        # Check topo order
+        def check_arg(arg : Node, n : Optional[Node] = None) -> None:
+            context_str = f' of Node \'{n}\' ' if n else ' '
+            if arg.graph is not self:
+                raise RuntimeError(f'Argument \'{arg}\'{context_str}does not belong to this Graph, '
+                                   f'but was used as an argument! If you are copying nodes from another graph, make '
+                                   f'sure to use ``arg_transform`` on node_copy() to remap values\n{self}')
+            if arg not in seen_values:
+                raise RuntimeError(f'Argument \'{arg}\'{context_str}was used before it has been '
+                                   f'defined! Please check that Nodes in the graph are topologically ordered\n{self}')
+
+        seen_names : Set[str] = set()
+        seen_values : Set[Node] = set()
+        for node in self.nodes:
+            if node.op not in ['placeholder', 'call_method', 'call_module', 'call_function', 'get_attr', 'output']:
+                raise RuntimeError(f'Node {node} had unknown opcode {node.op}!')
+            if node.graph is not self:
+                raise RuntimeError(f'Node \'{node}\' does not belong to this Graph!')
+            if node not in self._find_nodes_lookup_table:
+                raise RuntimeError(f"Node '{node}' is not added to the side table")
+            map_arg(node.args, lambda arg: check_arg(arg, node))
+            map_arg(node.kwargs, lambda arg: check_arg(arg, node))
+            seen_values.add(node)
+
+            if node.name in seen_names:
+                raise RuntimeError(f'Node redefined name {node.name}!')
+            seen_names.add(node.name)
+
+        # Check targets are legit
+        if self.owning_module:
+            num_warnings = 0
+            MAX_WARNINGS = 5
+            for node in self.nodes:
+                if node.op == 'call_function':
+                    if not callable(node.target):
+                        raise ValueError(f'Node {node} target {node.target} has type {torch.typename(node.target)} but '
+                                         'a Callable is expected')
+                else:
+                    if not isinstance(node.target, str):
+                        raise ValueError(f'Node {node} target {node.target} has type {torch.typename(node.target)} but '
+                                         'a str is expected')
+                if node.op in ['get_attr', 'call_module']:
+                    target_atoms = node.target.split('.')
+                    m_itr = self.owning_module
+                    for i, atom in enumerate(target_atoms):
+                        new_m_itr = getattr(m_itr, atom, None)
+                        seen_qualname = '.'.join(target_atoms[:i])
+                        if new_m_itr is None:
+                            raise RuntimeError(f'Node {node} target {node.target} references nonexistent attribute '
+                                               f'{atom} of {seen_qualname}')
+                        if (node.op == "call_module"
+                                and not isinstance(new_m_itr, torch.nn.Module)):
+                            raise RuntimeError(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
+                                               'not reference an nn.Module')
+                        elif (node.op == "get_attr"
+                              and not isinstance(new_m_itr, torch.nn.Module)
+                              and not isinstance(new_m_itr, torch.nn.Parameter)
+                              and atom not in m_itr._buffers):
+                            if num_warnings < MAX_WARNINGS:
+                                # Don't emit this warning too frequently,
+                                # for very large graphs this can become very expensive
+                                # from a performance perspective.
+                                warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
+                                              'not reference an nn.Module, nn.Parameter, or buffer, which is '
+                                              'what \'get_attr\' Nodes typically target')
+                            num_warnings += 1
+                        else:
+                            m_itr = new_m_itr
+            if num_warnings > MAX_WARNINGS:
+                warnings.warn(
+                    f'Additional {num_warnings - MAX_WARNINGS} warnings '
+                    'suppressed about get_attr references'
+                )
+
+    @compatibility(is_backward_compatible=True)
+    def eliminate_dead_code(self, is_impure_node: Optional[Callable[[Node], bool]] = None):
+        """
+        Remove all dead code from the graph, based on each node's number of
+        users, and whether the nodes have any side effects. The graph must be
+        topologically sorted before calling.
+
+        Args:
+            is_impure_node (Optional[Callable[[Node], bool]]): A function that returns
+            whether a node is impure. If this is None, then the default behavior is to
+            use Node.is_impure.
+
+        Returns:
+          bool: Whether the graph was changed as a result of the pass.
+
+        Example:
+
+        Before dead code is eliminated, `a` from `a = x + 1` below has no users
+        and thus can be eliminated from the graph without having an effect.
+
+        .. code-block:: python
+
+            def forward(self, x):
+                a = x + 1
+                return x + self.attr_1
+
+        After dead code is eliminated, `a = x + 1` has been removed, and the rest
+        of `forward` remains.
+
+        .. code-block:: python
+
+            def forward(self, x):
+                return x + self.attr_1
+
+        .. warning::
+
+            Dead code elimination has some heuristics to avoid removing
+            side-effectful nodes (see Node.is_impure) but in general coverage
+            is very bad, so you should assume that this method is not sound
+            to call unless you know that your FX graph consists entirely
+            of functional operations or you supply your own custom
+            function for detecting side-effectful nodes.
+        """
+        # Lint the graph first to make sure its topologically sorted, otherwise
+        # DCE below will not behave as expected.
+        self.lint()
+
+        def has_side_effect(node):
+            if is_impure_node is not None:
+                return is_impure_node(node)
+            return node.is_impure()
+
+        # Reverse iterate so that when we remove a node, any nodes used as an
+        # input to that node have an updated user count that no longer reflects
+        # the removed node.
+        changed = False
+        for node in reversed(self.nodes):
+            if not has_side_effect(node) and len(node.users) == 0:
+                self.erase_node(node)
+                changed = True
+
+        return changed
+
+    @compatibility(is_backward_compatible=False)
+    def set_codegen(self, codegen: CodeGen):
+        self._codegen = codegen
+
+    @compatibility(is_backward_compatible=False)
+    def on_generate_code(
+        self,
+        make_transformer: Callable[[Optional[TransformCodeFunc]], TransformCodeFunc]
+    ):
+        """Register a transformer function when python code is generated
+
+        Args:
+            make_transformer (Callable[[Optional[TransformCodeFunc]], TransformCodeFunc]):
+                a function that returns a code transformer to be registered.
+                This function is called by `on_generate_code` to obtain the
+                code transformer.
+
+                This function is also given as its input the currently
+                registered code transformer (or None if nothing is registered),
+                in case it is not desirable to overwrite it. This is useful to
+                chain code transformers together.
+
+        Returns:
+            a context manager that when used in a `with` statement, to automatically
+            restore the previously registered code transformer.
+
+        Example:
+
+        .. code-block:: python
+
+
+            gm: fx.GraphModule = ...
+
+            # This is a code transformer we want to register. This code
+            # transformer prepends a pdb import and trace statement at the very
+            # beginning of the generated torch.fx code to allow for manual
+            # debugging with the PDB library.
+            def insert_pdb(body):
+                return ["import pdb; pdb.set_trace()\\n", *body]
+
+            # Registers `insert_pdb`, and overwrites the current registered
+            # code transformer (given by `_` to the lambda):
+            gm.graph.on_generate_code(
+                lambda _: insert_pdb
+            )
+
+            # Or alternatively, registers a code transformer which first
+            # runs `body` through existing registered transformer, then
+            # through `insert_pdb`:
+            gm.graph.on_generate_code(
+                lambda current_trans: (
+                    lambda body: insert_pdb(
+                        current_trans(body) if current_trans
+                        else body
+                    )
+                )
+            )
+
+            gm.recompile()
+            gm(*inputs)  # drops into pdb
+
+
+        This function can also be used as a context manager, with the benefit to
+        automatically restores the previously registered code transformer:
+
+        .. code-block:: python
+
+            # ... continue from previous example
+
+            with gm.graph.on_generate_code(lambda _: insert_pdb):
+                # do more stuff with `gm`...
+                gm.recompile()
+                gm(*inputs)  # drops into pdb
+
+            # now previous code transformer is restored (but `gm`'s code with pdb
+            # remains - that means you can run `gm` with pdb here too, until you
+            # run next `recompile()`).
+        """
+        on_gen_code_old = self._codegen._body_transformer
+        self._codegen._body_transformer = make_transformer(on_gen_code_old)
+
+        @contextlib.contextmanager
+        def on_generate_code_context_manager():
+            try:
+                yield
+            finally:
+                self._codegen._body_transformer = on_gen_code_old
+
+        return on_generate_code_context_manager()
+
+
+reflectable_magic_methods = {
+    'add': '{} + {}',
+    'sub': '{} - {}',
+    'mul': '{} * {}',
+    'floordiv': '{} // {}',
+    'truediv': '{} / {}',
+    'div': '{} / {}',
+    'mod': '{} % {}',
+    'pow': '{} ** {}',
+    'lshift': '{} << {}',
+    'rshift': '{} >> {}',
+    'and_': '{} & {}',
+    'or_': '{} | {}',
+    'xor': '{} ^ {}',
+    'getitem': '{}[{}]',
+    'matmul': '{} @ {}',
+}
+
+magic_methods = dict({
+    'eq': '{} == {}',
+    'ne': '{} != {}',
+    'lt': '{} < {}',
+    'gt': '{} > {}',
+    'le': '{} <= {}',
+    'ge': '{} >= {}',
+    'pos': '+{}',
+    'neg': '-{}',
+    'invert': '~{}'}, **reflectable_magic_methods)
+
+inplace_methods = {
+    'iadd': '{} += {}',
+    'iand': '{} &= {}',
+    'ifloordiv': '{} //= {}',
+    'ilshift': '{} <<= {}',
+    'imod': '{} %= {}',
+    'imul': '{} *= {}',
+    'imatmul': '{} @= {}',
+    'ior': '{} |= {}',
+    'ipow': '{} **= {}',
+    'irshift': '{} >>= {}',
+    'isub': '{} -= {}',
+    'itruediv': '{} /= {}',
+    'ixor': '{} ^= {}',
+    'setitem': '{}[{}] = {}',
+}
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/graph_module.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/graph_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..76dac29512bdccd7cb93a5afe8107e124f8961b7
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/graph_module.py
@@ -0,0 +1,955 @@
+# mypy: allow-untyped-defs
+import contextlib
+import copy
+import itertools
+import linecache
+import os
+import sys
+import traceback
+import warnings
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Optional, Set, Type, Union
+
+import torch
+import torch.nn as nn
+import torch.overrides
+from torch.nn.modules.module import _addindent
+from torch.package import Importer, PackageExporter, PackageImporter, sys_importer
+
+from ._compatibility import compatibility
+from .graph import _custom_builtins, _is_from_torch, _PyTreeCodeGen, Graph, PythonCode
+
+__all__ = [
+    "reduce_graph_module",
+    "reduce_package_graph_module",
+    "reduce_deploy_graph_module",
+    "GraphModule",
+]
+
+_USER_PRESERVED_ATTRIBUTES_KEY = "_user_preserved_attributes"
+
+# Normal exec loses the source code, however we can work with
+# the linecache module to recover it.
+# Using _exec_with_source will add it to our local cache
+# and then tools like TorchScript will be able to get source info.
+class _EvalCacheLoader:
+    def __init__(self):
+        self.eval_cache = {}
+        self.next_id = 0
+
+    def cache(self, src: str, globals: Dict[str, Any], co_fields=None):
+        """Store the source in a private cache, and add a lazy entry in linecache
+        that allows the source to be retrieved by 'filename'.
+
+        Args:
+            src (str): The module source to cache
+            globals (dict): The module globals
+
+        Returns:
+            str: The cache key (and dummy filename) generated for src.
+        """
+
+        key = self._get_key()
+        if co_fields:
+            key += f" from {co_fields['co_filename']}:{co_fields['co_firstlineno']} in {co_fields['co_name']}"
+        self.eval_cache[key] = src
+
+        # Don't mutate globals so that this loader is only used
+        # to populate linecache, and doesn't interact with other modules
+        # that might check `__loader__`
+        globals_copy = globals.copy()
+        globals_copy["__file__"] = key
+        globals_copy["__name__"] = key
+        globals_copy["__loader__"] = self
+        linecache.lazycache(key, globals_copy)
+
+        return key
+
+    # Part of the loader protocol (PEP 302)
+    # linecache will use this method when trying to find source code
+    def get_source(self, module_name) -> Optional[str]:
+        if module_name in self.eval_cache:
+            return self.eval_cache[module_name]
+        return None
+
+    def _get_key(self):
+        key = f".{self.next_id}"
+        self.next_id += 1
+        return key
+
+
+_loader = _EvalCacheLoader()
+
+
+def _exec_with_source(src: str, globals: Dict[str, Any], co_fields=None):
+    key = _loader.cache(src, globals, co_fields)
+    exec(compile(src, key, "exec"), globals)
+
+
+def _forward_from_src(src: str, globals: Dict[str, Any], co_fields=None):
+    return _method_from_src(
+        method_name="forward", src=src, globals=globals, co_fields=co_fields
+    )
+
+
+def _method_from_src(
+    method_name: str, src: str, globals: Dict[str, Any], co_fields=None
+) -> Callable:
+    # avoid mutating the passed in dict
+    globals_copy = globals.copy()
+    _exec_with_source(src, globals_copy, co_fields)
+    fn = globals_copy[method_name]
+    del globals_copy[method_name]
+    return fn
+
+
+def _format_import_statement(name: str, obj: Any, importer: Importer) -> str:
+    if name in _custom_builtins:
+        return _custom_builtins[name].import_str
+    if _is_from_torch(name):
+        return "import torch"
+    module_name, attr_name = importer.get_name(obj)
+    return f"from {module_name} import {attr_name} as {name}"
+
+
+def _format_import_block(globals: Dict[str, Any], importer: Importer):
+    import_strs: Set[str] = {_format_import_statement(name, obj, importer) for name, obj in globals.items()}
+    # Sort the imports so we have a stable import block that allows us to
+    # hash the graph module and get a consistent key for use in a cache.
+    return "\n".join(sorted(import_strs))
+
+
+@compatibility(is_backward_compatible=True)
+def reduce_graph_module(body: Dict[Any, Any], import_block: str) -> torch.nn.Module:
+    # BC: attribute name was changed from `code` to `_code` to facilitate
+    # making `code` into a property and adding a docstring to it
+    fn_src = body.get("_code") or body["code"]
+    forward = _forward_from_src(import_block + fn_src, {})
+    return _deserialize_graph_module(forward, body)
+
+
+@compatibility(is_backward_compatible=True)
+def reduce_package_graph_module(
+    importer: PackageImporter, body: Dict[Any, Any], generated_module_name: str
+) -> torch.nn.Module:
+    forward = importer.import_module(generated_module_name).forward
+    return _deserialize_graph_module(forward, body)
+
+
+@compatibility(is_backward_compatible=True)
+def reduce_deploy_graph_module(
+    importer: PackageImporter, body: Dict[Any, Any], import_block: str
+) -> torch.nn.Module:
+    ns = {}
+    ns["__builtins__"] = importer.patched_builtins
+    fn_src = body.get("_code")
+    assert fn_src is not None
+    forward = _forward_from_src(import_block + fn_src, ns)
+    return _deserialize_graph_module(forward, body)
+
+
+# We create a dummy class here because symbolic_trace pulls the forward()
+# function off of the class, rather than the instance. This class is used
+# in _deserialize_graph_module() below.
+class _CodeOnlyModule(torch.nn.Module):
+    def __init__(self, body):
+        super().__init__()
+        self.__dict__ = body
+
+
+def _deserialize_graph_module(forward, body: Dict[Any, Any], graph_module_cls=None) -> torch.nn.Module:
+    """
+    Deserialize a GraphModule given the dictionary of the original module,
+    using the code to reconstruct the graph. We delete the actual graph before
+    saving the dictionary so that changes to the in-memory graph format do not
+    get serialized.
+    """
+
+    # Try to retrieve the forward source in a backward-compatible way
+    _CodeOnlyModule.forward = forward
+
+    tracer_cls = body.get("_tracer_cls")
+    if tracer_cls is None:
+        from ._symbolic_trace import Tracer
+
+        tracer_cls = Tracer
+
+    graphmodule_cls_name = body.get("_graphmodule_cls_name", "GraphModule")
+
+    # This is a workaround for a mypy linter issue related to
+    # passing base class as an argument - https://github.com/python/mypy/issues/5865.
+    cls_tracer: Any = tracer_cls
+
+    class KeepModules(cls_tracer):
+        # we shouldn't trace into any of the submodules,
+        # because they were not traced in the original GraphModule
+        def is_leaf_module(self, _: torch.nn.Module, __: str) -> bool:
+            return True
+
+    com = _CodeOnlyModule(body)
+
+    tracer_extras = body.get("_tracer_extras", {})
+    graph = KeepModules().trace(com, **tracer_extras)
+
+    # Manually set Tracer class on the reconstructed Graph, to avoid
+    # referencing the private local subclass KeepModules.
+    graph._tracer_cls = tracer_cls
+    from ._lazy_graph_module import _make_graph_module
+    gm = _make_graph_module(com, graph, class_name=graphmodule_cls_name, graph_module_cls=graph_module_cls)
+
+    # The GraphModule constructor only retains attributes referenced by the graph.
+    # In this case, our goal is return a GraphModule as close to identical as the one
+    # put into the package. If any additional attributes were present in body,
+    # we should keep them.
+    for k, v in body.items():
+        if not hasattr(gm, k):
+            setattr(gm, k, v)
+    return gm
+
+
+# copy an attribute value with qualified name 'target' from 'from_module' to 'to_module'
+# This installs empty Modules where none exist yet if they are subpaths of target
+def _copy_attr(from_module: torch.nn.Module, to_module: torch.nn.Module, target: str):
+    *prefix, field = target.split(".")
+    for item in prefix:
+        f = getattr(from_module, item)
+        t = getattr(to_module, item, None)
+        if f is t:
+            # we have already installed one of its parents
+            # (e.g. target = root.linear.weight, but we have already installed root.linear)
+            # once we install a parent, we no longer need to copy the children
+            # since all the needed properties will already be present
+            return
+
+        if t is None:
+            t = torch.nn.Module()
+            setattr(to_module, item, t)
+        from_module, to_module = f, t
+
+    orig = getattr(from_module, field)
+    # If it is a tensor and not a parameter attribute of a module, it should be a named buffer.
+    # So, we register it as a named buffer in the target module.
+    if isinstance(orig, torch.Tensor) and not isinstance(orig, torch.nn.Parameter):
+        to_module.register_buffer(field, orig)
+    else:
+        setattr(to_module, field, orig)
+
+
+# Assign attribute 'from_obj' to the qualified name 'target' on 'to_module
+# This installs empty Modules where none exist yet if they are subpaths of target
+def _assign_attr(from_obj: Any, to_module: torch.nn.Module, target: str):
+    *prefix, field = target.split(".")
+    for item in prefix:
+        t = getattr(to_module, item, None)
+
+        if t is None:
+            t = torch.nn.Module()
+            setattr(to_module, item, t)
+        to_module = t
+
+    # If it is a tensor and not a parameter attribute of a module, it should be a named buffer.
+    # So, we register it as a named buffer in the target module.
+    if isinstance(from_obj, torch.Tensor) and not isinstance(
+        from_obj, torch.nn.Parameter
+    ):
+        to_module.register_buffer(field, from_obj)
+    else:
+        setattr(to_module, field, from_obj)
+
+
+def _print_readable(
+    module,
+    module_name,
+    print_output=True,
+    include_stride=False,
+    include_device=False,
+    colored=False,
+):
+    graph = module.graph
+    assert graph is not None and isinstance(graph, torch.fx.Graph), "print_readable must be used on a module with a graph"
+
+    verbose_python_code = graph.python_code(
+        root_module="self",
+        verbose=True,
+        include_stride=include_stride,
+        include_device=include_device,
+        colored=colored,
+    )
+    module_code = verbose_python_code.src
+    module_code = module_code.lstrip("\n")
+    module_code = f"class {module_name}(torch.nn.Module):\n" + module_code
+    module_code = _addindent(module_code, 4)
+
+    submodule_code_list = [""]
+    for submodule_name, submodule in module.named_children():
+        if hasattr(submodule, "graph"):
+            submodule_code_list.append(
+                _print_readable(
+                    submodule,
+                    submodule_name,
+                    print_output=False,
+                    include_stride=include_stride,
+                    include_device=include_device,
+                    colored=colored,
+                )
+            )
+    submodule_code = "\n".join(submodule_code_list)
+    submodule_code = _addindent(submodule_code, 4)
+
+    output = module_code + submodule_code
+    if print_output:
+        print(module_code + submodule_code)
+    return output
+
+
+class _WrappedCall:
+    def __init__(self, cls, cls_call):
+        self.cls = cls
+        self.cls_call = cls_call
+
+    # Previously, if an error occurred when valid
+    # symbolically-traced code was run with an invalid input, the
+    # user would see the source of the error as coming from
+    # `File "`, where N is some number. We use
+    # this function to generate a more informative error message. We
+    # return the traceback itself, a message explaining that the
+    # error occurred in a traced Module's generated forward
+    # function, and five lines of context surrounding the faulty
+    # line
+    @staticmethod
+    def _generate_error_message(frame_summary: traceback.FrameSummary) -> str:
+        # auxiliary variables (for readability)
+        err_lineno = frame_summary.lineno
+        assert err_lineno is not None
+        line = frame_summary.line
+        assert line is not None
+        err_line_len = len(line)
+        all_src_lines = linecache.getlines(frame_summary.filename)
+
+        # constituent substrings of the error message
+        tb_repr = torch._dynamo.disable(traceback.format_exc)()
+        custom_msg = (
+            "Call using an FX-traced Module, "
+            f"line {err_lineno} of the traced Module's "
+            "generated forward function:"
+        )
+        before_err = "".join(all_src_lines[err_lineno - 2 : err_lineno])
+        marker = "~" * err_line_len + "~~~ <--- HERE"
+        err_and_after_err = "\n".join(all_src_lines[err_lineno : err_lineno + 2])
+
+        # joined message
+        return "\n".join([tb_repr, custom_msg, before_err, marker, err_and_after_err])
+
+    def __call__(self, obj, *args, **kwargs):
+        try:
+            if self.cls_call is not None:
+                return self.cls_call(obj, *args, **kwargs)
+            else:
+                return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
+        except Exception as e:
+            assert e.__traceback__
+            topmost_framesummary: traceback.FrameSummary = (
+                traceback.StackSummary.extract(traceback.walk_tb(e.__traceback__))[-1]
+            )  # type: ignore[arg-type]
+            if "eval_with_key" in topmost_framesummary.filename:
+                print(
+                    _WrappedCall._generate_error_message(topmost_framesummary),
+                    file=sys.stderr,
+                )
+                raise e.with_traceback(None)  # noqa: B904
+            else:
+                raise e
+
+@compatibility(is_backward_compatible=True)
+class GraphModule(torch.nn.Module):
+    """
+    GraphModule is an nn.Module generated from an fx.Graph. Graphmodule has a
+    ``graph`` attribute, as well as ``code`` and ``forward`` attributes generated
+    from that ``graph``.
+
+    .. warning::
+
+        When ``graph`` is reassigned, ``code`` and ``forward`` will be automatically
+        regenerated. However, if you edit the contents of the ``graph`` without reassigning
+        the ``graph`` attribute itself, you must call ``recompile()`` to update the generated
+        code.
+    """
+
+    def __new__(cls: "Type[GraphModule]", *args, **kwargs):
+        # each instance of a graph module needs its own forward method
+        # so create a new singleton class for each instance.
+        # it is a subclass of the user-defined class, the only difference
+        # is an extra layer to install the forward method
+
+        # address issue described at https://github.com/pytorch/pytorch/issues/63883
+        # in other words, traverse class hierarchy to fix the redundant class definition problem
+        for t in cls.__mro__:
+            c = t.__qualname__.split(".")[-1]
+            if c != "GraphModuleImpl":
+                cls = t
+                break
+
+        class GraphModuleImpl(cls):  # type: ignore[misc, valid-type]
+            pass
+
+        return super().__new__(GraphModuleImpl)
+
+    @compatibility(is_backward_compatible=True)
+    def __init__(
+        self,
+        root: Union[torch.nn.Module, Dict[str, Any]],
+        graph: Graph,
+        class_name: str = "GraphModule",
+    ):
+        """
+        Construct a GraphModule.
+
+        Args:
+
+            root (Union[torch.nn.Module, Dict[str, Any]):
+                ``root`` can either be an nn.Module instance or a Dict mapping strings to any attribute type.
+                In the case that ``root`` is a Module, any references to Module-based objects (via qualified
+                name) in the Graph's Nodes' ``target`` field will be copied over from the respective place
+                within ``root``'s Module hierarchy into the GraphModule's module hierarchy.
+                In the case that ``root`` is a dict, the qualified name found in a Node's ``target`` will be
+                looked up directly in the dict's keys. The object mapped to by the Dict will be copied
+                over into the appropriate place within the GraphModule's module hierarchy.
+
+            graph (Graph): ``graph`` contains the nodes this GraphModule should use for code generation
+
+            class_name (str): ``name`` denotes the name of this GraphModule for debugging purposes. If it's unset, all
+                error messages will report as originating from ``GraphModule``. It may be helpful to set this
+                to ``root``'s original name or a name that makes sense within the context of your transform.
+        """
+        super().__init__()
+        self.__class__.__name__ = class_name
+        if isinstance(root, torch.nn.Module):
+            if hasattr(root, "training"):
+                self.training = root.training
+
+            # When we pickle/unpickle graph module, we don't want to drop any module or attributes.
+            if isinstance(root, _CodeOnlyModule):
+                for k, _ in root.named_children():
+                    _copy_attr(root, self, k)
+
+                for k, _ in root.named_buffers():
+                    _copy_attr(root, self, k)
+
+                for k, _ in root.named_parameters():
+                    _copy_attr(root, self, k)
+
+            for node in graph.nodes:
+                if node.op in ["get_attr", "call_module"]:
+                    assert isinstance(node.target, str)
+                    _copy_attr(root, self, node.target)
+        elif isinstance(root, dict):
+            targets_to_copy = []
+            for node in graph.nodes:
+                if node.op in ["get_attr", "call_module"]:
+                    assert isinstance(node.target, str)
+                    if node.target not in root:
+                        raise RuntimeError(
+                            "Node "
+                            + str(node)
+                            + " referenced target "
+                            + node.target
+                            + " but that target was not provided in ``root``!"
+                        )
+                    targets_to_copy.append(node.target)
+            # Sort targets in ascending order of the # of atoms.
+            # This will ensure that less deeply nested attributes are assigned
+            # before more deeply nested attributes. For example, foo.bar
+            # will be assigned before foo.bar.baz. Otherwise, we might assign
+            # the user-provided ``foo.bar`` and wipe out the previously-assigned
+            # ``foo.bar.baz``
+            targets_to_copy.sort(key=lambda t: t.count("."))
+            for target_to_copy in targets_to_copy:
+                _assign_attr(root[target_to_copy], self, target_to_copy)
+        else:
+            raise RuntimeError("Unsupported type " + str(root) + " passed for root!")
+
+        self.graph = graph
+
+        # Store the Tracer class responsible for creating a Graph separately as part of the
+        # GraphModule state, except when the Tracer is defined in a local namespace.
+        # Locally defined Tracers are not pickleable. This is needed because torch.package will
+        # serialize a GraphModule without retaining the Graph, and needs to use the correct Tracer
+        # to re-create the Graph during deserialization.
+        self._tracer_cls = None
+        if (
+            self.graph._tracer_cls
+            and "" not in self.graph._tracer_cls.__qualname__
+        ):
+            self._tracer_cls = self.graph._tracer_cls
+
+        self._tracer_extras = {}
+        if self.graph._tracer_extras:
+            self._tracer_extras = self.graph._tracer_extras
+
+        # Dictionary to store metadata
+        self.meta: Dict[str, Any] = {}
+        self._replace_hook = None
+        self._create_node_hooks: List[Callable] = []
+        self._erase_node_hooks: List[Callable] = []
+
+    # TorchScript breaks trying to compile the graph setter because of the
+    # continued string literal. Issue here: https://github.com/pytorch/pytorch/issues/44842
+    #
+    # Shouldn't be an issue since these methods shouldn't be used in TorchScript anyway
+    __jit_unused_properties__ = ["graph"]
+
+    @property
+    def graph(self) -> Graph:
+        """
+        Return the ``Graph`` underlying this ``GraphModule``
+        """
+        return self._graph
+
+    @graph.setter
+    def graph(self, g: Graph) -> None:
+        """
+        Set the underlying ``Graph`` for this ``GraphModule``. This will internally
+        recompile the ``GraphModule`` so that the generated ``forward()`` function
+        corresponds to ``g``
+        """
+        assert isinstance(g, Graph), f"Expected a Graph instance, but got {type(g)}"
+        self._graph = g
+        g.owning_module = self
+        self.recompile()
+
+    @compatibility(is_backward_compatible=False)
+    def to_folder(self, folder: Union[str, os.PathLike], module_name: str = "FxModule"):
+        """Dumps out module to ``folder`` with ``module_name`` so that it can be
+        imported with ``from  import ``
+
+        Args:
+
+            folder (Union[str, os.PathLike]): The folder to write the code out to
+
+            module_name (str): Top-level name to use for the ``Module`` while
+                writing out the code
+        """
+        folder = Path(folder)
+        Path(folder).mkdir(exist_ok=True)
+        torch.save(self.state_dict(), folder / "state_dict.pt")
+        tab = " " * 4
+        custom_builtins = "\n".join([v.import_str for v in _custom_builtins.values()])
+        model_str = f"""
+import torch
+{custom_builtins}
+
+from torch.nn import *
+class {module_name}(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+"""
+
+        def _gen_model_repr(module_name: str, module: torch.nn.Module) -> Optional[str]:
+            safe_reprs = [
+                nn.Linear,
+                nn.Conv1d,
+                nn.Conv2d,
+                nn.Conv3d,
+                nn.BatchNorm1d,
+                nn.BatchNorm2d,
+                nn.BatchNorm3d,
+            ]
+            if type(module) in safe_reprs:
+                return f"{module.__repr__()}"
+            else:
+                return None
+
+        blobified_modules = []
+        for module_name, module in self.named_children():
+            module_str = _gen_model_repr(module_name, module)
+            if module_str is None:
+                module_file = folder / f"{module_name}.pt"
+                torch.save(module, module_file)
+                blobified_modules.append(module_name)
+                module_repr = module.__repr__().replace("\r", " ").replace("\n", " ")
+                # weights_only=False as this is legacy code that saves the model
+                module_str = f"torch.load(r'{module_file}', weights_only=False) # {module_repr}"
+            model_str += f"{tab*2}self.{module_name} = {module_str}\n"
+
+        for buffer_name, buffer in self._buffers.items():
+            if buffer is None:
+                continue
+            model_str += f"{tab*2}self.register_buffer('{buffer_name}', torch.empty({list(buffer.shape)}, dtype={buffer.dtype}))\n"
+
+        for param_name, param in self._parameters.items():
+            if param is None:
+                continue
+            model_str += f"{tab*2}self.{param_name} = torch.nn.Parameter(torch.empty({list(param.shape)}, dtype={param.dtype}))\n"
+
+        model_str += (
+            f"{tab*2}self.load_state_dict(torch.load(r'{folder}/state_dict.pt'))\n"
+        )
+        model_str += f"{_addindent(self.code, 4)}\n"
+
+        module_file = folder / "module.py"
+        module_file.write_text(model_str)
+
+        init_file = folder / "__init__.py"
+        init_file.write_text("from .module import *")
+
+        if len(blobified_modules) > 0:
+            warnings.warn(
+                "Was not able to save the following children modules as reprs -"
+                f"saved as pickled files instead: {blobified_modules}"
+            )
+
+    @compatibility(is_backward_compatible=True)
+    def add_submodule(self, target: str, m: torch.nn.Module) -> bool:
+        """
+        Adds the given submodule to ``self``.
+
+        This installs empty Modules where none exist yet if they are
+        subpaths of ``target``.
+
+        Args:
+            target: The fully-qualified string name of the new submodule
+                (See example in ``nn.Module.get_submodule`` for how to
+                specify a fully-qualified string.)
+            m: The submodule itself; the actual object we want to
+                install in the current Module
+
+        Return:
+            bool: Whether or not the submodule could be inserted. For
+                this method to return True, each object in the chain
+                denoted by ``target`` must either a) not exist yet,
+                or b) reference an ``nn.Module`` (not a parameter or
+                other attribute)
+        """
+        *prefix, field = target.split(".")
+        mod: torch.nn.Module = self
+
+        for item in prefix:
+
+            submod = getattr(mod, item, None)
+
+            if submod is None:
+                submod = torch.nn.Module()
+                setattr(mod, item, submod)
+
+            if not isinstance(submod, torch.nn.Module):
+                return False
+
+            mod = submod
+
+        mod.add_module(field, m)
+        return True
+
+    @compatibility(is_backward_compatible=True)
+    def delete_submodule(self, target: str) -> bool:
+        """
+        Deletes the given submodule from ``self``.
+
+        The module will not be deleted if ``target`` is not a valid
+        target.
+
+        Args:
+            target: The fully-qualified string name of the new submodule
+                (See example in ``nn.Module.get_submodule`` for how to
+                specify a fully-qualified string.)
+
+        Returns:
+            bool: Whether or not the target string referenced a
+                submodule we want to delete. A return value of ``False``
+                means that the ``target`` was not a valid reference to
+                a submodule.
+        """
+        atoms = target.split(".")
+        path, target_submod = atoms[:-1], atoms[-1]
+        mod: torch.nn.Module = self
+
+        # Get the parent module
+        for item in path:
+
+            if not hasattr(mod, item):
+                return False
+
+            mod = getattr(mod, item)
+
+            if not isinstance(mod, torch.nn.Module):
+                return False
+
+        if not hasattr(mod, target_submod):
+            return False
+
+        if not isinstance(getattr(mod, target_submod), torch.nn.Module):
+            return False
+
+        delattr(mod, target_submod)
+        return True
+
+    @compatibility(is_backward_compatible=True)
+    def delete_all_unused_submodules(self) -> None:
+        """
+        Deletes all unused submodules from ``self``.
+
+        A Module is considered "used" if any one of the following is
+        true:
+        1. It has children that are used
+        2. Its forward is called directly via a ``call_module`` node
+        3. It has a non-Module attribute that is used from a
+        ``get_attr`` node
+
+        This method can be called to clean up an ``nn.Module`` without
+        manually calling ``delete_submodule`` on each unused submodule.
+        """
+        used: List[str] = []
+
+        for node in self.graph.nodes:
+
+            if node.op == "call_module" or node.op == "get_attr":
+
+                # A list of strings representing the different parts
+                # of the path. For example, `foo.bar.baz` gives us
+                # ["foo", "bar", "baz"]
+                fullpath = node.target.split(".")
+
+                # If we're looking at multiple parts of a path, join
+                # join them with a dot. Otherwise, return that single
+                # element without doing anything to it.
+                def join_fn(x: str, y: str) -> str:
+                    return ".".join([x, y] if y else [x])
+
+                # Progressively collect all the names of intermediate
+                # modules. For example, if we have the target
+                # `foo.bar.baz`, we'll add `foo`, `foo.bar`, and
+                # `foo.bar.baz` to the list.
+                used.extend(itertools.accumulate(fullpath, join_fn))
+
+                # For a `call_module` node, also register all recursive submodules
+                # as used
+                if node.op == "call_module":
+                    try:
+                        submod = self.get_submodule(node.target)
+
+                        for submod_name, _ in submod.named_modules():
+                            if submod_name != "":
+                                used.append(".".join([node.target, submod_name]))
+                    except AttributeError:
+                        # Node referenced nonexistent submodule, don't need to
+                        # worry about GCing anything
+                        pass
+
+        to_delete = [name for name, _ in self.named_modules() if name not in used]
+
+        for name in to_delete:
+            self.delete_submodule(name)
+
+    @property
+    def code(self) -> str:
+        """
+        Return the Python code generated from the ``Graph`` underlying this
+        ``GraphModule``.
+        """
+        if not hasattr(self, "_code"):
+            raise RuntimeError(
+                "Code has not been generated! Please report a bug to PyTorch"
+            )
+        return self._code
+
+    @compatibility(is_backward_compatible=True)
+    def recompile(self) -> PythonCode:
+        """
+        Recompile this GraphModule from its ``graph`` attribute. This should be
+        called after editing the contained ``graph``, otherwise the generated
+        code of this ``GraphModule`` will be out of date.
+        """
+        if isinstance(self._graph._codegen, _PyTreeCodeGen):
+            self._in_spec = self._graph._codegen.pytree_info.in_spec
+            self._out_spec = self._graph._codegen.pytree_info.out_spec
+        python_code = self._graph.python_code(root_module="self")
+        self._code = python_code.src
+        self._lineno_map = python_code._lineno_map
+
+        cls = type(self)
+        co_fields = self._graph._co_fields if hasattr(self._graph, "_co_fields") else {}
+        cls.forward = _forward_from_src(self._code, python_code.globals, co_fields)
+
+        # Determine whether this class explicitly defines a __call__ implementation
+        # to wrap. If it does, save it in order to have wrapped_call invoke it.
+        # If it does not, wrapped_call can use a dynamic call to super() instead.
+        # In most cases, super().__call__ should be torch.nn.Module.__call__.
+        # We do not want to hold a reference to Module.__call__ here; doing so will
+        # bypass patching of torch.nn.Module.__call__ done while symbolic tracing.
+        cls_call = cls.__call__ if "__call__" in vars(cls) else None
+
+        if "_wrapped_call" not in vars(cls):
+            cls._wrapped_call = _WrappedCall(cls, cls_call)  # type: ignore[attr-defined]
+
+        def call_wrapped(self, *args, **kwargs):
+            return self._wrapped_call(self, *args, **kwargs)
+
+        cls.__call__ = call_wrapped  # type: ignore[method-assign]
+
+        return python_code
+
+    # Passing Tracer as argument allows subclasses extending fx.GraphModule
+    # define their own Tracer (extending fx.Tracer).
+    def __reduce_deploy__(self, importer: Importer):
+        dict_without_graph = self.__dict__.copy()
+        dict_without_graph["_graphmodule_cls_name"] = self.__class__.__name__
+        del dict_without_graph["_graph"]
+
+        python_code = self.recompile()
+        import_block = _format_import_block(python_code.globals, importer)
+        return (reduce_deploy_graph_module, (dict_without_graph, import_block))
+
+    def __reduce_package__(self, exporter: PackageExporter):
+        dict_without_graph = self.__dict__.copy()
+        dict_without_graph["_graphmodule_cls_name"] = self.__class__.__name__
+        del dict_without_graph["_graph"]
+
+        generated_module_name = f"fx-generated._{exporter.get_unique_id()}"
+        python_code = self.recompile()
+        import_block = _format_import_block(python_code.globals, exporter.importer)
+        module_code = import_block + self.code
+        exporter.save_source_string(generated_module_name, module_code)
+        return (
+            reduce_package_graph_module,
+            (dict_without_graph, generated_module_name),
+        )
+
+    def __reduce__(self):
+        """
+        Serialization of GraphModule. We serialize only the generated code, not
+        the underlying ``Graph``. This is because ``Graph`` does not have on-disk
+        backward-compatibility guarantees, whereas Python source code does.
+        On the deserialization side, we symbolically trace through the generated
+        code to regenerate the underlying ``Graph``
+        """
+        dict_without_graph = self.__dict__.copy()
+
+        python_code = self.recompile()
+        import_block = _format_import_block(python_code.globals, sys_importer)
+        del dict_without_graph["_graph"]
+        return (reduce_graph_module, (dict_without_graph, import_block))
+
+    def _deepcopy_init(self):
+        return GraphModule.__init__
+
+    # because __reduce__ is defined for serialization,
+    # we need to define deepcopy otherwise it will call __reduce__
+    # and cause symbolic tracing to occur every time we try to copy the object
+    def __deepcopy__(self, memo):
+        res = type(self).__new__(type(self))
+        memo[id(self)] = res
+        fake_mod = _CodeOnlyModule(copy.deepcopy(self.__dict__, memo))
+        self._deepcopy_init()(res, fake_mod, fake_mod.__dict__["_graph"])
+        # hooks are lost during `GraphModule.__init__`, so we need to copy over
+        # them explicitly, note right now we are only copying state_dict related
+        # hooks, to reduce bc-related issues, we can copy forward/backward related
+        # hooks in the future as well if needed
+        extra_preserved_attrs = [
+            "_state_dict_hooks",
+            "_load_state_dict_pre_hooks",
+            "_load_state_dict_post_hooks",
+            "_replace_hook",
+            "_create_node_hooks",
+            "_erase_node_hooks"
+        ]
+        for attr in extra_preserved_attrs:
+            if attr in self.__dict__:
+                setattr(res, attr, copy.deepcopy(self.__dict__[attr], memo))
+        res.meta = copy.deepcopy(getattr(self, "meta", {}), memo)
+        if _USER_PRESERVED_ATTRIBUTES_KEY in res.meta:
+            for attr_name, attr in res.meta[_USER_PRESERVED_ATTRIBUTES_KEY].items():
+                setattr(res, attr_name, attr)
+        return res
+
+    def __copy__(self):
+        from ._lazy_graph_module import _make_graph_module
+        res = _make_graph_module(self, self.graph)
+        res.meta = getattr(self, "meta", {})
+        return res
+
+    @compatibility(is_backward_compatible=False)
+    def print_readable(self, print_output=True, include_stride=False, include_device=False, colored=False):
+        """
+        Return the Python code generated for current GraphModule and its children GraphModules
+        """
+        return _print_readable(
+            self,
+            self._get_name(),
+            print_output,
+            include_stride,
+            include_device,
+            colored,
+        )
+
+    def __str__(self) -> str:
+        orig_str = super().__str__()
+        print_readable_reminder = (
+            "# To see more debug info, please use `graph_module.print_readable()`"
+        )
+        return "\n".join([orig_str, self._code, print_readable_reminder])
+
+    def _replicate_for_data_parallel(self):
+        new_gm = self.__copy__()
+        new_gm._is_replica = True
+        return new_gm
+
+    @contextlib.contextmanager
+    def _set_replace_hook(self, f):
+        """
+        Takes a callable which will be called everytime when we replace a node
+        to a new node, or change the node's name. Callable takes three arguments:
+        the old node we're changing, and NAME of the new node, followed by the
+        user node which consumes the old node to be replaced.
+        """
+        assert callable(f), "Replace hook must be a callable."
+        prev, self._replace_hook = self._replace_hook, f
+        try:
+            yield
+        finally:
+            self._replace_hook = prev
+
+    def _register_create_node_hook(self, f):
+        """
+        Takes a callable which will be called after we create a new node. The
+        callable takes the newly created node as input and returns None.
+        """
+        assert callable(f), "create_node hook must be a callable."
+        self._create_node_hooks.append(f)
+
+    def _unregister_create_node_hook(self, f):
+        """
+        Takes a callable which was previously registered to be called after we create a node.
+        This function will unregister that callable so it is no longer invoked on node creation.
+        """
+        assert callable(f), "create_node hook must be a callable."
+        self._create_node_hooks.remove(f)
+
+    def _register_erase_node_hook(self, f):
+        """
+        Takes a callable which will be called after we erase a node. The
+        callable takes the node that is being erased as input and returns None.
+        """
+        assert callable(f), "erase_node hook must be a callable."
+        self._erase_node_hooks.append(f)
+
+    def _unregister_erase_node_hook(self, f):
+        """
+        Takes a callable which was previously registered to be called after we erase a node.
+        This function will unregister that callable so it is no longer invoked on node erasure.
+        """
+        assert callable(f), "erase_node hook must be a callable."
+        self._erase_node_hooks.remove(f)
+
+# workarounds for issues in __torch_function__
+
+# WAR for __torch_function__ not handling tensor lists,
+# fix is in https://github.com/pytorch/pytorch/pull/34725
+# orig_cat = torch.cat
+# def patched_cat(*args, **kwargs):
+#     tensors = args[0]
+#     for t in tensors:
+#         if isinstance(t, Proxy):
+#             return t.__torch_function__(patched_cat, (), args, kwargs)
+#     return orig_cat(*args, **kwargs)
+# patched_cat.__module__ = 'torch'
+# patched_cat.__name__ = 'cat'
+# torch.cat = patched_cat
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/immutable_collections.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/immutable_collections.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ff29cba474dcb613cd529b881193e20e1325856
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/immutable_collections.py
@@ -0,0 +1,117 @@
+# mypy: allow-untyped-defs
+from typing import Any, Dict, Iterable, List, Tuple
+
+from torch.utils._pytree import (
+    _dict_flatten,
+    _dict_flatten_with_keys,
+    _dict_unflatten,
+    _list_flatten,
+    _list_flatten_with_keys,
+    _list_unflatten,
+    Context,
+    register_pytree_node,
+)
+
+from ._compatibility import compatibility
+
+
+__all__ = ["immutable_list", "immutable_dict"]
+
+_help_mutation = """\
+If you are attempting to modify the kwargs or args of a torch.fx.Node object,
+instead create a new copy of it and assign the copy to the node:
+    new_args = ... # copy and mutate args
+    node.args = new_args
+"""
+
+
+def _no_mutation(self, *args, **kwargs):
+    raise NotImplementedError(
+        f"'{type(self).__name__}' object does not support mutation. {_help_mutation}",
+    )
+
+
+def _create_immutable_container(base, mutable_functions):
+    container = type("immutable_" + base.__name__, (base,), {})
+    for attr in mutable_functions:
+        setattr(container, attr, _no_mutation)
+    return container
+
+
+immutable_list = _create_immutable_container(
+    list,
+    (
+        "__delitem__",
+        "__iadd__",
+        "__imul__",
+        "__setitem__",
+        "append",
+        "clear",
+        "extend",
+        "insert",
+        "pop",
+        "remove",
+        "reverse",
+        "sort",
+    ),
+)
+immutable_list.__reduce__ = lambda self: (immutable_list, (tuple(iter(self)),))
+immutable_list.__hash__ = lambda self: hash(tuple(self))
+
+compatibility(is_backward_compatible=True)(immutable_list)
+
+immutable_dict = _create_immutable_container(
+    dict,
+    (
+        "__delitem__",
+        "__ior__",
+        "__setitem__",
+        "clear",
+        "pop",
+        "popitem",
+        "setdefault",
+        "update",
+    ),
+)
+immutable_dict.__reduce__ = lambda self: (immutable_dict, (iter(self.items()),))
+immutable_dict.__hash__ = lambda self: hash(tuple(self.items()))
+compatibility(is_backward_compatible=True)(immutable_dict)
+
+
+# Register immutable collections for PyTree operations
+def _immutable_dict_flatten(d: Dict[Any, Any]) -> Tuple[List[Any], Context]:
+    return _dict_flatten(d)
+
+
+def _immutable_dict_unflatten(
+    values: Iterable[Any],
+    context: Context,
+) -> Dict[Any, Any]:
+    return immutable_dict(_dict_unflatten(values, context))
+
+
+def _immutable_list_flatten(d: List[Any]) -> Tuple[List[Any], Context]:
+    return _list_flatten(d)
+
+
+def _immutable_list_unflatten(
+    values: Iterable[Any],
+    context: Context,
+) -> List[Any]:
+    return immutable_list(_list_unflatten(values, context))
+
+
+register_pytree_node(
+    immutable_dict,
+    _immutable_dict_flatten,
+    _immutable_dict_unflatten,
+    serialized_type_name="torch.fx.immutable_collections.immutable_dict",
+    flatten_with_keys_fn=_dict_flatten_with_keys,
+)
+register_pytree_node(
+    immutable_list,
+    _immutable_list_flatten,
+    _immutable_list_unflatten,
+    serialized_type_name="torch.fx.immutable_collections.immutable_list",
+    flatten_with_keys_fn=_list_flatten_with_keys,
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/interpreter.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/interpreter.py
new file mode 100644
index 0000000000000000000000000000000000000000..c75407583137d30e377a6b4e8c734858b7b5063b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/interpreter.py
@@ -0,0 +1,520 @@
+# mypy: allow-untyped-defs
+from .graph_module import GraphModule
+from ._lazy_graph_module import _make_graph_module
+from .graph import Graph
+from .node import Argument, Node, Target, map_arg, map_aggregate
+from .proxy import Proxy
+from ._symbolic_trace import Tracer
+from ._compatibility import compatibility
+from . import config
+import torch.fx.traceback as fx_traceback
+import torch
+from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
+import inspect
+from contextlib import contextmanager
+from torch.hub import tqdm
+
+__all__ = ['Interpreter', 'Transformer']
+
+@compatibility(is_backward_compatible=True)
+class Interpreter:
+    """
+    An Interpreter executes an FX graph Node-by-Node. This pattern
+    can be useful for many things, including writing code
+    transformations as well as analysis passes.
+
+    Methods in the Interpreter class can be overridden to customize
+    the behavior of execution. The map of overrideable methods
+    in terms of call hierarchy::
+
+        run()
+            +-- run_node
+                +-- placeholder()
+                +-- get_attr()
+                +-- call_function()
+                +-- call_method()
+                +-- call_module()
+                +-- output()
+
+    Example:
+
+        Suppose we want to swap all instances of ``torch.neg`` with
+        ``torch.sigmoid`` and vice versa (including their ``Tensor``
+        method equivalents). We could subclass Interpreter like so::
+
+            class NegSigmSwapInterpreter(Interpreter):
+                def call_function(self, target : Target,
+                                  args : Tuple, kwargs : Dict) -> Any:
+                    if target == torch.sigmoid:
+                        return torch.neg(*args, **kwargs)
+                    return super().call_function(n)
+
+                def call_method(self, target : Target,
+                                args : Tuple, kwargs : Dict) -> Any:
+                    if target == 'neg':
+                        call_self, *args_tail = args
+                        return call_self.sigmoid(*args_tail, **kwargs)
+                    return super().call_method(n)
+
+            def fn(x):
+                return torch.sigmoid(x).neg()
+
+            gm = torch.fx.symbolic_trace(fn)
+            input = torch.randn(3, 4)
+            result = NegSigmSwapInterpreter(gm).run(input)
+            torch.testing.assert_close(result, torch.neg(input).sigmoid())
+
+    Args:
+        module (torch.nn.Module): The module to be executed
+        garbage_collect_values (bool): Whether to delete values after their last
+            use within the Module's execution. This ensures optimal memory usage during
+            execution. This can be disabled to, for example, examine all of the intermediate
+            values in the execution by looking at the ``Interpreter.env`` attribute.
+        graph (Optional[Graph]): If passed, the interpreter will execute this
+            graph instead of `module.graph`, using the provided `module`
+            argument to satisfy any requests for state.
+    """
+    @compatibility(is_backward_compatible=True)
+    def __init__(self, module: torch.nn.Module, garbage_collect_values: bool = True, graph: Optional[Graph] = None):
+        self.module = module
+        self.submodules = dict(self.module.named_modules())
+        if graph is not None:
+            self.graph = graph
+        else:
+            self.graph = self.module.graph
+        self.env : Dict[Node, Any] = {}
+        self.name = "Interpreter"
+        self.garbage_collect_values = garbage_collect_values
+        self.extra_traceback = True
+
+        if self.garbage_collect_values:
+            # Run through reverse nodes and record the first instance of a use
+            # of a given node. This represents the *last* use of the node in the
+            # execution order of the program, which we will use to free unused
+            # values
+            node_to_last_use : Dict[Node, Node] = {}
+            self.user_to_last_uses : Dict[Node, List[Node]] = {}
+
+            def register_last_uses(n : Node, user : Node):
+                if n not in node_to_last_use:
+                    node_to_last_use[n] = user
+                    self.user_to_last_uses.setdefault(user, []).append(n)
+
+            for node in reversed(self.graph.nodes):
+                map_arg(node.args, lambda n: register_last_uses(n, node))
+                map_arg(node.kwargs, lambda n: register_last_uses(n, node))
+
+    @compatibility(is_backward_compatible=True)
+    def run(self, *args, initial_env : Optional[Dict[Node, Any]] = None, enable_io_processing : bool = True) -> Any:
+        """
+        Run `module` via interpretation and return the result.
+
+        Args:
+            *args: The arguments to the Module to run, in positional order
+            initial_env (Optional[Dict[Node, Any]]): An optional starting environment for execution.
+                This is a dict mapping `Node` to any value. This can be used, for example, to
+                pre-populate results for certain `Nodes` so as to do only partial evaluation within
+                the interpreter.
+            enable_io_processing (bool): If true, we process the inputs and outputs with graph's process_inputs and
+                process_outputs function first before using them.
+
+        Returns:
+            Any: The value returned from executing the Module
+        """
+        self.env = initial_env if initial_env is not None else {}
+
+        # Positional function args are consumed left-to-right by
+        # `placeholder` nodes. Use an iterator to keep track of
+        # position and extract those values.
+        if enable_io_processing:
+            args = self.graph.process_inputs(*args)
+        self.args_iter : Iterator[Any] = iter(args)
+        pbar = tqdm(total=len(self.graph.nodes),
+                    desc=f"{self.name}: {str(list(self.graph.nodes)) if config.verbose_progress else ''}",
+                    initial=0, position=0, leave=True, disable=config.disable_progress, delay=0)
+
+        for node in self.graph.nodes:
+            pbar.update(1)
+            if node in self.env:
+                # Short circuit if we have this value. This could
+                # be used, for example, for partial evaluation
+                # where the caller has pre-populated `env` with
+                # values for a subset of the program.
+                continue
+
+            try:
+                self.env[node] = self.run_node(node)
+            except Exception as e:
+                if self.extra_traceback:
+                    msg = f"While executing {node.format_node()}"
+                    msg = f'{e.args[0]}\n\n{msg}' if e.args else str(msg)
+                    msg += f"\nOriginal traceback:\n{node.stack_trace}"
+                    e.args = (msg,) + e.args[1:]
+                    if isinstance(e, KeyError):
+                        raise RuntimeError(*e.args) from e
+                raise
+
+            if self.garbage_collect_values:
+                for to_delete in self.user_to_last_uses.get(node, []):
+                    del self.env[to_delete]
+
+            if node.op == 'output':
+                output_val = self.env[node]
+                return self.graph.process_outputs(output_val) if enable_io_processing else output_val
+
+    @compatibility(is_backward_compatible=True)
+    def boxed_run(self, args_list):
+        """
+        Run `module` via interpretation and return the result.  This uses the "boxed"
+        calling convention, where you pass a list of arguments, which will be cleared
+        by the interpreter.  This ensures that input tensors are promptly deallocated.
+        """
+        args_iter = iter(args_list)
+        env = {}
+        for n in self.graph.nodes:
+            if n.op == "placeholder":
+                env[n] = next(args_iter)
+        args_list.clear()
+        return self.run(initial_env=env)
+
+    @contextmanager
+    def _set_current_node(self, node):
+        with fx_traceback.set_current_meta(node):
+            yield
+
+    @compatibility(is_backward_compatible=True)
+    def run_node(self, n : Node) -> Any:
+        """
+        Run a specific node ``n`` and return the result.
+        Calls into placeholder, get_attr, call_function,
+        call_method, call_module, or output depending
+        on ``node.op``
+
+        Args:
+            n (Node): The Node to execute
+
+        Returns:
+            Any: The result of executing ``n``
+        """
+        with self._set_current_node(n):
+            args, kwargs = self.fetch_args_kwargs_from_env(n)
+            assert isinstance(args, tuple)
+            assert isinstance(kwargs, dict)
+            return getattr(self, n.op)(n.target, args, kwargs)
+
+    # Main Node running APIs
+    @compatibility(is_backward_compatible=True)
+    def placeholder(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+        """
+        Execute a ``placeholder`` node. Note that this is stateful:
+        ``Interpreter`` maintains an internal iterator over
+        arguments passed to ``run`` and this method returns
+        next() on that iterator.
+
+        Args:
+            target (Target): The call target for this node. See
+                `Node `__ for
+                details on semantics
+            args (Tuple): Tuple of positional args for this invocation
+            kwargs (Dict): Dict of keyword arguments for this invocation
+
+        Returns:
+            Any: The argument value that was retrieved.
+        """
+        assert isinstance(target, str)
+        if target.startswith('*'):
+            # For a starred parameter e.g. `*args`, retrieve all
+            # remaining values from the args list.
+            return list(self.args_iter)
+        else:
+            try:
+                return next(self.args_iter)
+            except StopIteration as si:
+                if len(args) > 0:
+                    return args[0]
+                else:
+                    raise RuntimeError(f'Expected positional argument for parameter {target}, but one was not passed in!') from si
+
+    @compatibility(is_backward_compatible=True)
+    def get_attr(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+        """
+        Execute a ``get_attr`` node. Will retrieve an attribute
+        value from the ``Module`` hierarchy of ``self.module``.
+
+        Args:
+            target (Target): The call target for this node. See
+                `Node `__ for
+                details on semantics
+            args (Tuple): Tuple of positional args for this invocation
+            kwargs (Dict): Dict of keyword arguments for this invocation
+
+        Return:
+            Any: The value of the attribute that was retrieved
+        """
+        assert isinstance(target, str)
+        return self.fetch_attr(target)
+
+    @compatibility(is_backward_compatible=True)
+    def call_function(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+        """
+        Execute a ``call_function`` node and return the result.
+
+        Args:
+            target (Target): The call target for this node. See
+                `Node `__ for
+                details on semantics
+            args (Tuple): Tuple of positional args for this invocation
+            kwargs (Dict): Dict of keyword arguments for this invocation
+
+        Return
+            Any: The value returned by the function invocation
+        """
+        assert not isinstance(target, str)
+
+        # Execute the function and return the result
+        return target(*args, **kwargs)
+
+    @compatibility(is_backward_compatible=True)
+    def call_method(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+        """
+        Execute a ``call_method`` node and return the result.
+
+        Args:
+            target (Target): The call target for this node. See
+                `Node `__ for
+                details on semantics
+            args (Tuple): Tuple of positional args for this invocation
+            kwargs (Dict): Dict of keyword arguments for this invocation
+
+        Return
+            Any: The value returned by the method invocation
+        """
+        # args[0] is the `self` object for this method call
+        self_obj, *args_tail = args
+
+        # Execute the method and return the result
+        assert isinstance(target, str)
+        return getattr(self_obj, target)(*args_tail, **kwargs)
+
+    @compatibility(is_backward_compatible=True)
+    def call_module(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+        """
+        Execute a ``call_module`` node and return the result.
+
+        Args:
+            target (Target): The call target for this node. See
+                `Node `__ for
+                details on semantics
+            args (Tuple): Tuple of positional args for this invocation
+            kwargs (Dict): Dict of keyword arguments for this invocation
+
+        Return
+            Any: The value returned by the module invocation
+        """
+        # Retrieve executed args and kwargs values from the environment
+
+        # Execute the method and return the result
+        assert isinstance(target, str)
+        submod = self.fetch_attr(target)
+
+        return submod(*args, **kwargs)
+
+    @compatibility(is_backward_compatible=True)
+    def output(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+        """
+        Execute an ``output`` node. This really just retrieves
+        the value referenced by the ``output`` node and returns it.
+
+        Args:
+            target (Target): The call target for this node. See
+                `Node `__ for
+                details on semantics
+            args (Tuple): Tuple of positional args for this invocation
+            kwargs (Dict): Dict of keyword arguments for this invocation
+
+        Return:
+            Any: The return value referenced by the output node
+        """
+        return args[0]
+
+    # Helper methods
+    @compatibility(is_backward_compatible=True)
+    def fetch_attr(self, target : str):
+        """
+        Fetch an attribute from the ``Module`` hierarchy of ``self.module``.
+
+        Args:
+            target (str): The fully-qualified name of the attribute to fetch
+
+        Return:
+            Any: The value of the attribute.
+        """
+        target_atoms = target.split('.')
+        attr_itr = self.module
+        for i, atom in enumerate(target_atoms):
+            if not hasattr(attr_itr, atom):
+                raise RuntimeError(f"Node referenced nonexistent target {'.'.join(target_atoms[:i+1])}")
+            attr_itr = getattr(attr_itr, atom)
+        return attr_itr
+
+    @compatibility(is_backward_compatible=True)
+    def fetch_args_kwargs_from_env(self, n : Node) -> Tuple[Tuple, Dict]:
+        """
+        Fetch the concrete values of ``args`` and ``kwargs`` of node ``n``
+        from the current execution environment.
+
+        Args:
+            n (Node): The node for which ``args`` and ``kwargs`` should be fetched.
+
+        Return:
+            Tuple[Tuple, Dict]: ``args`` and ``kwargs`` with concrete values for ``n``.
+        """
+        args = self.map_nodes_to_values(n.args, n)
+        assert isinstance(args, tuple)
+        kwargs = self.map_nodes_to_values(n.kwargs, n)
+        assert isinstance(kwargs, dict)
+        return args, kwargs
+
+    @compatibility(is_backward_compatible=True)
+    def map_nodes_to_values(self, args : Argument, n : Node) -> Argument:
+        """
+        Recursively descend through ``args`` and look up the concrete value
+        for each ``Node`` in the current execution environment.
+
+        Args:
+            args (Argument): Data structure within which to look up concrete values
+
+            n (Node): Node to which ``args`` belongs. This is only used for error reporting.
+        """
+        def load_arg(n_arg : Node) -> Any:
+            if n_arg not in self.env:
+                raise RuntimeError(f'Node {n} referenced nonexistent value {n_arg}! Run Graph.lint() '
+                                   f'to diagnose such issues')
+            return self.env[n_arg]
+        return map_arg(args, load_arg)
+
+@compatibility(is_backward_compatible=True)
+class Transformer(Interpreter):
+    """
+    ``Transformer`` is a special type of interpreter that produces a
+    new ``Module``. It exposes a ``transform()`` method that returns
+    the transformed ``Module``. ``Transformer`` does not require
+    arguments to run, as ``Interpreter`` does. ``Transformer`` works
+    entirely symbolically.
+
+    Example:
+
+        Suppose we want to swap all instances of ``torch.neg`` with
+        ``torch.sigmoid`` and vice versa (including their ``Tensor``
+        method equivalents). We could subclass ``Transformer`` like so::
+
+            class NegSigmSwapXformer(Transformer):
+                def call_function(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+                    if target == torch.sigmoid:
+                        return torch.neg(*args, **kwargs)
+                    return super().call_function(n)
+
+                def call_method(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+                    if target == 'neg':
+                        call_self, *args_tail = args
+                        return call_self.sigmoid(*args_tail, **kwargs)
+                    return super().call_method(n)
+
+            def fn(x):
+                return torch.sigmoid(x).neg()
+
+            gm = torch.fx.symbolic_trace(fn)
+
+            transformed : torch.nn.Module = NegSigmSwapXformer(gm).transform()
+            input = torch.randn(3, 4)
+            torch.testing.assert_close(transformed(input), torch.neg(input).sigmoid())
+
+    Args:
+        module (GraphModule): The ``Module`` to be transformed.
+    """
+
+    @compatibility(is_backward_compatible=True)
+    def __init__(self, module):
+        super().__init__(module)
+        self.new_graph = Graph()
+        self.new_graph.set_codegen(module.graph._codegen)
+
+        class TransformerTracer(Tracer):
+            def __init__(self, graph: Graph):
+                super().__init__()
+                self.graph = graph
+                self.tensor_attrs: Dict[torch.Tensor, str] = {}  # type: ignore[assignment]
+
+            def is_leaf_module(self, _, __) -> bool:
+                return True
+
+        self.tracer = TransformerTracer(self.new_graph)
+        self.tracer.root = module
+
+    @compatibility(is_backward_compatible=True)
+    def placeholder(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Proxy:
+        """
+        Execute a ``placeholder`` node. In ``Transformer``, this is
+        overridden to insert a new ``placeholder`` into the output
+        graph.
+
+        Args:
+            target (Target): The call target for this node. See
+                `Node `__ for
+                details on semantics
+            args (Tuple): Tuple of positional args for this invocation
+            kwargs (Dict): Dict of keyword arguments for this invocation
+        """
+        assert isinstance(target, str)
+        default_value = next(iter(args)) if args else inspect.Signature.empty
+        return Proxy(self.new_graph.placeholder(target, default_value=default_value), self.tracer)
+
+    @compatibility(is_backward_compatible=True)
+    def get_attr(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Proxy:
+        """
+        Execute a ``get_attr`` node. In ``Transformer``, this is
+        overridden to insert a new ``get_attr`` node into the output
+        graph.
+
+        Args:
+            target (Target): The call target for this node. See
+                `Node `__ for
+                details on semantics
+            args (Tuple): Tuple of positional args for this invocation
+            kwargs (Dict): Dict of keyword arguments for this invocation
+        """
+        assert isinstance(target, str)
+        return self.tracer.create_proxy("get_attr", target, args, kwargs)
+
+    @compatibility(is_backward_compatible=True)
+    def call_module(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+        # Override so that the leaf module policy from `self.tracer` is respected.
+        assert isinstance(target, str)
+        submod = self.fetch_attr(target)
+        return self.tracer.call_module(submod, submod.forward, args, kwargs)
+
+    @compatibility(is_backward_compatible=True)
+    def call_function(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
+        # Override so that functions that were wrapped are still wrapped.
+        return self.tracer.create_proxy('call_function', target, args, kwargs)
+
+    @compatibility(is_backward_compatible=True)
+    def transform(self) -> GraphModule:
+        """
+        Transform ``self.module`` and return the transformed
+        ``GraphModule``.
+        """
+        with fx_traceback.preserve_node_meta():
+            result = super().run(enable_io_processing=False)
+        if result is not None:
+            def strip_proxy(a : Union[Argument, Proxy]) -> Any:
+                return a.node if isinstance(a, Proxy) else a
+            new_output_node = self.new_graph.output(map_aggregate(result, strip_proxy))
+            # also preserve the metadata from the old output node, if it exists
+            old_output_node = list(self.graph.nodes)[-1]
+            assert old_output_node.op == "output"
+            for k, v in old_output_node.meta.items():
+                new_output_node.meta[k] = v
+
+
+        return _make_graph_module(self.module, self.new_graph)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/node.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/node.py
new file mode 100644
index 0000000000000000000000000000000000000000..f84b23e29ddf855f799b3de6ce1bd4ccf3da3dd4
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/node.py
@@ -0,0 +1,788 @@
+# Nodes represent a definition of a value in our graph of operators.
+from typing import TYPE_CHECKING, Union, Callable, Any, Tuple, List, Optional, Dict, Set
+from ._compatibility import compatibility
+from .immutable_collections import immutable_dict, immutable_list
+import torch
+import builtins
+import types
+import inspect
+import warnings
+from torch.fx.operator_schemas import normalize_function, normalize_module, ArgsKwargsPair
+from .._ops import ops as _ops
+from torch._C import _NodeBase
+
+if TYPE_CHECKING:
+    from .graph import Graph
+
+__all__ = ['Node', 'map_arg', 'map_aggregate', "has_side_effect"]
+
+BaseArgumentTypes = Union[str, int, float, bool, complex, torch.dtype,
+                          torch.Tensor, torch.device, torch.memory_format, torch.layout, torch._ops.OpOverload,
+                          torch.SymInt, torch.SymBool, torch.SymFloat]
+base_types = BaseArgumentTypes.__args__  # type: ignore[attr-defined]
+
+Target = Union[Callable[..., Any], str]
+
+Argument = Optional[Union[
+    Tuple[Any, ...],  # actually Argument, but mypy can't represent recursive types
+    List[Any],  # actually Argument
+    Dict[str, Any],  # actually Argument
+    slice,  # Slice[Argument, Argument, Argument], but slice is not a templated type in typing
+    range,
+    'Node',
+    BaseArgumentTypes
+]]
+
+_legal_ops = dict.fromkeys(['placeholder', 'call_method', 'call_module', 'call_function', 'get_attr', 'output', 'root'])
+
+_side_effectful_need_to_be_preserved_pre_dispatch: Set[Callable] = {
+    torch._C._set_grad_enabled,
+    torch.amp._enter_autocast,
+    torch.amp._exit_autocast,
+}
+
+# TODO: Either refactor this into 2 functions 1 dce for functional graphs and 1 dce for all graphs,
+# or add logic to correctly mark all inplace ops as side effectful.
+_side_effectful_functions: Set[Callable] = {
+    torch._assert,
+    torch._assert_async,
+    _ops.aten._assert_async.msg,
+    _ops.aten._assert_scalar.default,
+    _ops.aten.sym_constrain_range.default,
+    _ops.aten.sym_constrain_range_for_size.default,
+    _ops.profiler._record_function_enter,
+    _ops.profiler._record_function_enter_new,
+    _ops.profiler._record_function_exit,
+    _ops.inductor.accumulate_grad_.default,
+} | _side_effectful_need_to_be_preserved_pre_dispatch
+if hasattr(_ops.inductor, "resize_storage_bytes_"):
+    _side_effectful_functions.add(_ops.inductor.resize_storage_bytes_.default)
+
+
+@compatibility(is_backward_compatible=False)
+def has_side_effect(fn: Callable) -> Callable:
+    _side_effectful_functions.add(fn)
+    return fn
+
+
+# this is fixed on master, WAR for 1.5
+def _find_module_of_method(orig_method: Callable[..., Any]) -> str:
+    name = orig_method.__name__
+    module = orig_method.__module__
+    if module is not None:
+        return module
+    for guess in [torch, torch.nn.functional]:
+        if getattr(guess, name, None) is orig_method:
+            return guess.__name__
+    raise RuntimeError(f'cannot find module for {orig_method}')
+
+# Borrowed from CPython typing module
+# https://github.com/python/cpython/blob/f90dc36c15d7fee0efaf6d39e97be0bdf2683e93/Lib/typing.py#L156
+def _type_repr(obj: object) -> str:
+    """Return the repr() of an object, special-casing types (internal helper).
+    If obj is a type, we return a shorter version than the default
+    type.__repr__, based on the module and qualified name, which is
+    typically enough to uniquely identify a type.  For everything
+    else, we fall back on repr(obj).
+    """
+    if isinstance(obj, type):
+        if obj.__module__ == 'builtins':
+            return obj.__qualname__
+        return f'{obj.__module__}.{obj.__qualname__}'
+    if obj is ...:
+        return '...'
+    if isinstance(obj, types.FunctionType):
+        return obj.__name__
+    return repr(obj)
+
+def _get_qualified_name(func: Callable[..., Any]) -> str:
+    # things like getattr just appear in builtins
+    if getattr(builtins, func.__name__, None) is func:
+        return func.__name__
+    # torch.Tensor.{fn}
+    if (isinstance(func, (types.MethodDescriptorType, types.WrapperDescriptorType))
+       and func is getattr(torch.Tensor, func.__name__, None)):
+        return f"torch.Tensor.{func.__name__}"
+    name = func.__name__
+    if name == "":
+        # For lambdas, try to get their defining name in the module
+        try:
+            name = inspect.getsource(func).split("=")[0].strip()
+        except Exception as e:
+            raise RuntimeError("Unable to represent lambda") from e
+    module = _find_module_of_method(func)
+    module = module.replace('torch._ops', 'torch.ops')  # WAR for bug in how torch.ops assigns module
+    # Fixup segment_reduce mismatch
+    if module == "torch" and name == "segment_reduce":
+        name = "_" + name
+    return f'{module}.{name}'
+
+def _format_arg(arg: object, max_list_len: float = float('inf')) -> str:
+    if hasattr(arg, '_custom_fx_repr_fn'):
+        return arg._custom_fx_repr_fn()
+    elif isinstance(arg, list):
+        items = ', '.join(_format_arg(a) for idx, a in enumerate(arg) if idx < max_list_len)
+        maybe_len = '' if len(arg) < max_list_len + 1 else f', ...[total_len={len(arg)}]'
+        return f'[{items}{maybe_len}]'
+    elif isinstance(arg, tuple):
+        items = ', '.join(_format_arg(a) for idx, a in enumerate(arg) if idx < max_list_len)
+        maybe_len = '' if len(arg) < max_list_len + 1 else f', ...[total_len={len(arg)}]'
+        maybe_comma = ',' if len(arg) == 1 else ''
+        return f'({items}{maybe_comma}{maybe_len})'
+    elif isinstance(arg, dict):
+        items_str = ', '.join(f'{k}: {_format_arg(v)}' for k, v in arg.items())
+        return f'{{{items_str}}}'
+
+    if isinstance(arg, Node):
+        return '%' + str(arg)
+    else:
+        return str(arg)
+
+@compatibility(is_backward_compatible=True)
+class Node(_NodeBase):
+    """
+    ``Node`` is the data structure that represents individual operations within
+    a ``Graph``. For the most part, Nodes represent callsites to various entities,
+    such as operators, methods, and Modules (some exceptions include nodes that
+    specify function inputs and outputs). Each ``Node`` has a function specified
+    by its ``op`` property. The ``Node`` semantics for each value of ``op`` are as follows:
+
+    - ``placeholder`` represents a function input. The ``name`` attribute specifies the name this value will take on.
+      ``target`` is similarly the name of the argument. ``args`` holds either: 1) nothing, or 2) a single argument
+      denoting the default parameter of the function input. ``kwargs`` is don't-care. Placeholders correspond to
+      the function parameters (e.g. ``x``) in the graph printout.
+    - ``get_attr`` retrieves a parameter from the module hierarchy. ``name`` is similarly the name the result of the
+      fetch is assigned to. ``target`` is the fully-qualified name of the parameter's position in the module hierarchy.
+      ``args`` and ``kwargs`` are don't-care
+    - ``call_function`` applies a free function to some values. ``name`` is similarly the name of the value to assign
+      to. ``target`` is the function to be applied. ``args`` and ``kwargs`` represent the arguments to the function,
+      following the Python calling convention
+    - ``call_module`` applies a module in the module hierarchy's ``forward()`` method to given arguments. ``name`` is
+      as previous. ``target`` is the fully-qualified name of the module in the module hierarchy to call.
+      ``args`` and ``kwargs`` represent the arguments to invoke the module on, *excluding the self argument*.
+    - ``call_method`` calls a method on a value. ``name`` is as similar. ``target`` is the string name of the method
+      to apply to the ``self`` argument. ``args`` and ``kwargs`` represent the arguments to invoke the module on,
+      *including the self argument*
+    - ``output`` contains the output of the traced function in its ``args[0]`` attribute. This corresponds to the "return" statement
+      in the Graph printout.
+    """
+    _args: Tuple['Argument', ...]
+    _kwargs: Dict[str, 'Argument']
+
+    @compatibility(is_backward_compatible=True)
+    def __init__(self, graph: 'Graph', name: str, op: str, target: 'Target',
+                 args: Tuple['Argument', ...], kwargs: Dict[str, 'Argument'],
+                 return_type : Optional[Any] = None) -> None:
+        """
+        Instantiate an instance of ``Node``. Note: most often, you want to use the
+        Graph APIs, i.e. ``Graph.call_module``, ``Graph.call_method``, etc. rather
+        than instantiating a ``Node`` directly.
+
+        Args:
+            graph (Graph): The ``Graph`` to which this ``Node`` should belong.
+
+            name (str): The name to which the output of this ``Node`` should be assigned
+
+            op (str): The opcode for this ``Node``. Can be one of 'placeholder',
+                'call_method', 'call_module', 'call_function', 'get_attr',
+                'output'
+
+            target ('Target'): The target this op should call. See the broader
+                ``Node`` docstring for more details.
+
+            args (Tuple['Argument']): The args to be passed to ``target``
+
+            kwargs (Dict[str, 'Argument']): The kwargs to be passed to ``target``
+
+            return_type (Optional[Any]): The python type expression representing the
+                type of the output of this node. This field can be used for
+                annotation of values in the generated code or for other types
+                of analyses.
+        """
+        super().__init__()
+        self.graph = graph
+        self.name = name  # unique name of value being created
+        assert op in _legal_ops
+        self.op = op  # the kind of operation = placeholder|call_method|call_module|call_function|get_attr
+        if op == 'call_function':
+            if not callable(target):
+                raise ValueError(f'Node [graph = {graph}, name = \'{name}\'] target {target} has type {torch.typename(target)} '
+                                 'but a Callable is expected')
+        else:
+            if not isinstance(target, str):
+                raise ValueError(f'Node [graph = {graph}, name = \'{name}\'] target {target} has type {torch.typename(target)} '
+                                 'but a str is expected')
+        self.target = target  # for method/module/function, the name of the method/module/function/attr
+        # being invoked, e.g add, layer1, or torch.add
+
+        # All `Node`-valued inputs. Key is the Node, value is don't-care.
+        # The public API for this is `all_input_nodes`, this private attribute
+        # should not be accessed directly.
+        self._input_nodes : Dict[Node, None] = {}
+        self.__update_args_kwargs(args, kwargs)
+
+        # All of the nodes that use the value produced by this Node
+        # Note one user may correspond to several uses, e.g. the node fo ``x + x``
+        # would appear once here, but represents two uses.
+        #
+        # Is a dict to act as an "ordered set". Keys are significant, value dont-care
+        self.users : Dict[Node, None] = {}
+        # Type expression representing the output value of this node.
+        # This should contain the same class of Type objects that would appear
+        # as type annotations for function inputs/outputs.
+        #
+        # For placeholder nodes, this value will be used to type-annotate the
+        # generated function parameters.
+        # For the return node, this value will be used to type-annotate the
+        # generated function return type. (Note this is a special case. ``return``
+        # does not produce a value, it's more of a notation. Thus, this value
+        # describes the type of args[0] in the ``return`` node.
+        self.type : Optional[Any] = return_type
+        self._sort_key: Any = ()
+
+        # If set, use this fn to print this node
+        self._repr_fn : Optional[Callable[[Node], str]] = None
+
+        # Dictionary to store metadata passes need to do their
+        # transformations. This metadata is preserved across node copies
+        self.meta : Dict[str, Any] = {}
+
+    def __getstate__(self) -> Dict[str, Any]:
+        state = self.__dict__.copy()
+        state["_erased"] = self._erased
+        state["_prev"] = self._prev
+        state["_next"] = self._next
+        return state
+
+    def __setstate__(self, state: Dict[str, Any]) -> None:
+        _erased = state.pop("_erased")
+        _prev = state.pop("_prev")
+        _next = state.pop("_next")
+        self.__dict__.update(state)
+        self._erased = _erased
+        self._prev = _prev
+        self._next = _next
+
+    @property
+    def next(self) -> 'Node':
+        """
+        Returns the next ``Node`` in the linked list of Nodes.
+
+        Returns:
+
+            The next ``Node`` in the linked list of Nodes.
+        """
+        return self._next
+
+    @property
+    def prev(self) -> 'Node':
+        """
+        Returns the previous ``Node`` in the linked list of Nodes.
+
+        Returns:
+
+            The previous ``Node`` in the linked list of Nodes.
+        """
+        return self._prev
+
+    @compatibility(is_backward_compatible=True)
+    def prepend(self, x: 'Node') -> None:
+        """
+        Insert x before this node in the list of nodes in the graph. Example::
+
+            Before: p -> self
+                    bx -> x -> ax
+            After:  p -> x -> self
+                    bx -> ax
+
+        Args:
+            x (Node): The node to put before this node. Must be a member of the same graph.
+        """
+        assert self.graph == x.graph, "Attempting to move a Node into a different Graph"
+        if self == x:
+            warnings.warn("Trying to prepend a node to itself. This behavior has no effect on the graph.")
+            return
+        x._remove_from_list()
+        p = self._prev
+        p._next, x._prev = x, p
+        x._next, self._prev = self, x
+
+        # compute x._sort_key
+        psk = x._prev._sort_key
+        nsk = x._next._sort_key
+        if len(psk) > len(nsk):
+            idx: int
+            *prefix, idx = psk[:len(nsk) + 1]
+            x._sort_key = (*prefix, idx + 1)
+        elif len(psk) < len(nsk):
+            *prefix, idx = nsk[:len(psk) + 1]
+            x._sort_key = (*prefix, idx - 1)
+        else:  # same length, increase length by 1
+            x._sort_key = (*psk, 0)
+
+    def __gt__(self, other: 'Node') -> bool:
+        return self._sort_key > other._sort_key
+
+    def __lt__(self, other: 'Node') -> bool:
+        return self._sort_key < other._sort_key
+
+    def __ge__(self, other: 'Node') -> bool:
+        return self > other or self == other
+
+    def __le__(self, other: 'Node') -> bool:
+        return self < other or self == other
+
+    @compatibility(is_backward_compatible=True)
+    def append(self, x: 'Node') -> None:
+        """
+        Insert ``x`` after this node in the list of nodes in the graph.
+        Equivalent to ``self.next.prepend(x)``
+
+        Args:
+            x (Node): The node to put after this node. Must be a member of the same graph.
+        """
+        self._next.prepend(x)
+
+    def _remove_from_list(self) -> None:
+        p, n = self._prev, self._next
+        p._next, n._prev = n, p
+
+    @property
+    def args(self) -> Tuple[Argument, ...]:
+        """
+        The tuple of arguments to this ``Node``. The interpretation of arguments
+        depends on the node's opcode. See the :class:`Node` docstring for more
+        information.
+
+        Assignment to this property is allowed. All accounting of uses and users
+        is updated automatically on assignment.
+        """
+        return self._args
+
+    @args.setter
+    def args(self, a : Tuple[Argument, ...]) -> None:
+        """
+        Set the tuple of arguments to this Node. The interpretation of arguments
+        depends on the node's opcode. See the ``fx.Graph`` docstring for more
+        information.
+        """
+        # DO NOT CALL `__update_args_kwargs` directly. The correct way to
+        # set `args` is via direct assignment, i.e. `node.args = new_args`
+        self.__update_args_kwargs(a, self._kwargs)
+
+    @property
+    def kwargs(self) -> Dict[str, Argument]:
+        """
+        The dict of keyword arguments to this ``Node``. The interpretation of arguments
+        depends on the node's opcode. See the :class:`Node` docstring for more
+        information.
+
+        Assignment to this property is allowed. All accounting of uses and users
+        is updated automatically on assignment.
+        """
+        return self._kwargs
+
+    @kwargs.setter
+    def kwargs(self, k : Dict[str, Argument]) -> None:
+        """
+        Set the dict of kwargs to this Node. The interpretation of arguments
+        depends on the node's opcode. See the ``fx.Graph`` docstring for more
+        information.
+        """
+        # DO NOT CALL `__update_args_kwargs` directly. The correct way to
+        # set `args` is via direct assignment, i.e. `node.kwargs = new_kwargs`
+        self.__update_args_kwargs(self._args, k)
+
+    @property
+    def all_input_nodes(self) -> List['Node']:
+        """
+        Return all Nodes that are inputs to this Node. This is equivalent to
+        iterating over ``args`` and ``kwargs`` and only collecting the values that
+        are Nodes.
+
+        Returns:
+
+            List of ``Nodes`` that appear in the ``args`` and ``kwargs`` of this
+            ``Node``, in that order.
+        """
+        return list(self._input_nodes.keys())
+
+    @compatibility(is_backward_compatible=True)
+    def update_arg(self, idx : int, arg : Argument) -> None:
+        """
+        Update an existing positional argument to contain the new value
+        ``arg``. After calling, ``self.args[idx] == arg``.
+
+        Args:
+
+            idx (int): The index into ``self.args`` of the element to update
+            arg (Argument): The new argument value to write into ``args``
+        """
+        args = list(self.args)
+        args[idx] = arg
+        self.args = tuple(args)
+
+    @compatibility(is_backward_compatible=True)
+    def insert_arg(self, idx : int, arg : Argument) -> None:
+        """
+        Insert an positional argument to the argument list with given index.
+
+        Args:
+
+            idx (int): The index of the element in ``self.args`` to be inserted before.
+            arg (Argument): The new argument value to insert into ``args``
+        """
+        assert 0 <= idx <= len(self.args), "insert_args index must be between 0 and len(self.args)"
+        args_left = self.args[:idx]
+        args_right = self.args[idx:]
+
+        self._args = args_left + (arg,) + args_right
+
+        _new_input_nodes: Dict[Node, None] = {}
+        map_arg(arg, _new_input_nodes.setdefault)
+
+        for new_use in _new_input_nodes.keys():
+            if new_use not in self._input_nodes:
+                self._input_nodes.setdefault(new_use)
+                new_use.users.setdefault(self)
+
+    @compatibility(is_backward_compatible=True)
+    def update_kwarg(self, key : str, arg : Argument) -> None:
+        """
+        Update an existing keyword argument to contain the new value
+        ``arg``. After calling, ``self.kwargs[key] == arg``.
+
+        Args:
+
+            key (str): The key in ``self.kwargs`` of the element to update
+            arg (Argument): The new argument value to write into ``kwargs``
+        """
+        self.kwargs = {**self.kwargs, key: arg}
+
+    @property
+    def stack_trace(self) -> Optional[str]:
+        """
+        Return the Python stack trace that was recorded during tracing, if any.
+        When traced with fx.Tracer, this property is usually populated by
+        `Tracer.create_proxy`. To record stack traces during tracing for debug purposes,
+        set `record_stack_traces = True` on the `Tracer` instance.
+        When traced with dynamo, this property will be populated by default by
+        `OutputGraph.create_proxy`.
+
+        stack_trace would have the innermost frame at the end of the string.
+        """
+        return self.meta.get("stack_trace", None)
+
+    @stack_trace.setter
+    def stack_trace(self, trace : Optional[str]) -> None:
+        self.meta["stack_trace"] = trace
+
+    def __update_args_kwargs(self, new_args : Tuple['Argument', ...], new_kwargs : Dict[str, 'Argument']) -> None:
+        """
+        This API is internal. Do *not* call it directly.
+        """
+        def update_users_and_input_nodes(n: Any) -> Any:
+            if isinstance(n, Node):
+                self._input_nodes.setdefault(n)
+                n.users.setdefault(self)
+            return n
+
+        # Clear prior users and input_nodes
+        for old_use in self._input_nodes.keys():
+            old_use.users.pop(self)
+        self._input_nodes = {}
+
+        # We do three things in a single pass of the args
+        # - Normalize list->immutable_list, dict->immutable_dict, etc
+        # - Populate self._input_nodes
+        # - Populate arg.users[self] for each arg
+        self._args = map_aggregate(new_args, update_users_and_input_nodes)  # type: ignore[assignment]
+        self._kwargs = map_aggregate(new_kwargs, update_users_and_input_nodes)  # type: ignore[assignment]
+
+    def __repr__(self) -> str:
+        if self._repr_fn:
+            return self._repr_fn(self)
+        return self.name
+
+    def _pretty_print_target(self, target: object) -> str:
+        """
+        Make target printouts more user-friendly.
+        1) builtins will be printed as `builtins.xyz`
+        2) operators will be printed as `operator.xyz`
+        3) other callables will be printed with qualified name, e.g. torch.add
+        """
+        if isinstance(target, str):
+            return target
+        if hasattr(target, '__module__'):
+            name = getattr(target, '__name__', None)
+            if name is None:
+                # Just to be defensive, if we don't have `__name__`, get the
+                # qualname. Not sure if this happens for any members of `operator`
+                # or `builtins`. This fallback path is not as good, since e.g.
+                # things in `operator` have `_operator` as their __module__.
+                # TODO: THIS IS BROKEN: _get_qualified_name calls `__name__`
+                return _get_qualified_name(target)  # type: ignore[arg-type]
+            if target.__module__ == 'builtins':
+                return f'builtins.{name}'
+            elif target.__module__ == '_operator':
+                return f'operator.{name}'
+        return _get_qualified_name(target)  # type: ignore[arg-type]
+
+    @compatibility(is_backward_compatible=True)
+    def format_node(self,
+                    placeholder_names: Optional[List[str]] = None,
+                    maybe_return_typename: Optional[List[str]] = None) -> Optional[str]:
+        """
+        Return a descriptive string representation of ``self``.
+
+        This method can be used with no arguments as a debugging
+        utility.
+
+        This function is also used internally in the ``__str__`` method
+        of ``Graph``. Together, the strings in ``placeholder_names``
+        and ``maybe_return_typename`` make up the signature of the
+        autogenerated ``forward`` function in this Graph's surrounding
+        GraphModule. ``placeholder_names`` and ``maybe_return_typename``
+        should not be used otherwise.
+
+        Args:
+            placeholder_names: A list that will store formatted strings
+                representing the placeholders in the generated
+                ``forward`` function. Internal use only.
+            maybe_return_typename: A single-element list that will store
+                a formatted string representing the output of the
+                generated ``forward`` function. Internal use only.
+
+        Returns:
+            str: If 1) we're using ``format_node`` as an internal helper
+                in the ``__str__`` method of ``Graph``, and 2) ``self``
+                is a placeholder Node, return ``None``. Otherwise,
+                return a  descriptive string representation of the
+                current Node.
+        """
+        if self.op == 'placeholder':
+            assert isinstance(self.target, str)
+            arg_str = self.target
+            arg_str += arg_str + f': {_type_repr(self.type)}' if self.type else ''
+            if placeholder_names:
+                placeholder_names.append(arg_str)
+                return None
+            maybe_typename = f'{_type_repr(self.type)} ' if self.type else ''
+            default_val = '(default=' + str(self.args[0]) + ')' if self.args else ''
+            return f'%{self.name} : {maybe_typename}[num_users={len(self.users)}] = {self.op}[target={self.target}]{default_val}'
+        elif self.op == 'get_attr':
+            maybe_typename = f'{_type_repr(self.type)} ' if self.type is not None else ''
+            return f'%{self.name} : {maybe_typename}[num_users={len(self.users)}] = ' \
+                   f'{self.op}[target={self._pretty_print_target(self.target)}]'
+        elif self.op == 'output':
+            if self.type and maybe_return_typename:
+                maybe_return_typename[0] = f' -> {_type_repr(self.type)}'
+            return f'return {self.args[0]}'
+        else:
+            maybe_typename = f'{_type_repr(self.type)} ' if self.type is not None else ''
+            return f'%{self.name} : {maybe_typename}[num_users={len(self.users)}] = ' \
+                   f'{self.op}[target={self._pretty_print_target(self.target)}](' \
+                   f'args = {_format_arg(self.args)}, kwargs = {_format_arg(self.kwargs)})'
+
+    @compatibility(is_backward_compatible=True)
+    def replace_all_uses_with(self,
+                              replace_with: 'Node',
+                              delete_user_cb: Callable[['Node'], bool] = lambda user: True,
+                              *,
+                              propagate_meta: bool = False
+                              ) -> List['Node']:
+        """
+        Replace all uses of ``self`` in the Graph with the Node ``replace_with``.
+
+        Args:
+
+            replace_with (Node): The node to replace all uses of ``self`` with.
+            delete_user_cb (Callable): Callback that is called to determine
+              whether a given user of the self node should be removed.
+            propagate_meta (bool): Whether or not to copy all properties
+              on the .meta field of the original node onto the replacement node.
+              For safety, this is only valid to do if the replacement node
+              doesn't already have an existing .meta field.
+
+        Returns:
+
+            The list of Nodes on which this change was made.
+        """
+        if propagate_meta:
+            assert len(replace_with.meta) == 0, \
+                'Called node.replace_all_uses_with(replace_with, propagate_meta=True), ' \
+                'but replace_with already has .meta keys'
+            for k, v in self.meta.items():
+                replace_with.meta[k] = v
+        to_process = list(self.users)
+        skipped = []
+        m = self.graph.owning_module
+        for use_node in to_process:
+            if not delete_user_cb(use_node):
+                skipped.append(use_node)
+                continue
+
+            def maybe_replace_node(n : Node) -> Node:
+                if n == self:
+                    return replace_with
+                else:
+                    return n
+
+            if getattr(m, "_replace_hook", None):
+                m._replace_hook(old=self, new=replace_with.name, user=use_node)
+
+            new_args = map_arg(use_node.args, maybe_replace_node)
+            new_kwargs = map_arg(use_node.kwargs, maybe_replace_node)
+            assert isinstance(new_args, tuple)
+            assert isinstance(new_kwargs, dict)
+            use_node.__update_args_kwargs(new_args, new_kwargs)
+
+        assert len(self.users) - len(skipped) == 0
+        return [n for n in to_process if n not in skipped]
+
+    @compatibility(is_backward_compatible=False)
+    def is_impure(self) -> bool:
+        """
+        Returns whether this op is impure, i.e. if its op is a placeholder or
+        output, or if a call_function or call_module which is impure.
+
+        Returns:
+
+            bool: If the op is impure or not.
+        """
+        if self.op in {"placeholder", "output"}:
+            return True
+
+        # Check if an impure function based on schema.
+        if self.op == "call_function":
+            schema = getattr(self.target, "_schema", None)
+            schema_mutable = schema is not None and schema.is_mutable
+            return schema_mutable or self.target in _side_effectful_functions
+
+        # Check if an impure module.
+        if self.op == "call_module":
+            assert (
+                self.graph.owning_module is not None
+            ), "self.graph.owning_module not set for purity check"
+            target_mod = self.graph.owning_module.get_submodule(self.target)
+            assert (
+                target_mod is not None
+            ), f"Did not find expected submodule target {self.target}"
+            return getattr(target_mod, "_is_impure", False)
+
+        return False
+
+    @compatibility(is_backward_compatible=False)
+    def normalized_arguments(
+            self, root : torch.nn.Module, arg_types : Optional[Tuple[Any]] = None,
+            kwarg_types : Optional[Dict[str, Any]] = None,
+            normalize_to_only_use_kwargs : bool = False) -> Optional[ArgsKwargsPair]:
+        """
+        Returns normalized arguments to Python targets. This means that
+        `args/kwargs` will be matched up to the module/functional's
+        signature and return exclusively kwargs in positional order
+        if `normalize_to_only_use_kwargs` is true.
+        Also populates default values. Does not support positional-only
+        parameters or varargs parameters.
+
+        Supports module calls.
+
+        May require `arg_types` and `kwarg_types` in order to disambiguate overloads.
+
+        Args:
+            root (torch.nn.Module): Module upon which to resolve module targets.
+            arg_types (Optional[Tuple[Any]]): Tuple of arg types for the args
+            kwarg_types (Optional[Dict[str, Any]]): Dict of arg types for the kwargs
+            normalize_to_only_use_kwargs (bool): Whether to normalize to only use kwargs.
+
+        Returns:
+
+            Returns NamedTuple ArgsKwargsPair, or `None` if not successful.
+        """
+        if self.op == 'call_function':
+            assert callable(self.target)
+            return normalize_function(self.target, self.args, self.kwargs, arg_types, kwarg_types)  # type: ignore[arg-type]
+        elif self.op == 'call_module':
+            assert isinstance(self.target, str)
+            return normalize_module(root, self.target, self.args, self.kwargs)  # type: ignore[arg-type]
+
+        return None
+
+    @compatibility(is_backward_compatible=True)
+    def replace_input_with(self, old_input: 'Node', new_input: 'Node') -> None:
+        """
+        Loop through input nodes of ``self``, and replace all instances of
+        ``old_input`` with ``new_input``.
+
+        Args:
+
+            old_input (Node): The old input node to be replaced.
+            new_input (Node): The new input node to replace ``old_input``.
+        """
+        def maybe_replace_node(n : Node) -> Node:
+            return new_input if n == old_input else n
+
+        m = self.graph.owning_module
+        if getattr(m, "_replace_hook", None):
+            m._replace_hook(old=old_input, new=new_input.name, user=self)
+
+        new_args = map_arg(self.args, maybe_replace_node)
+        new_kwargs = map_arg(self.kwargs, maybe_replace_node)
+        assert isinstance(new_args, tuple)
+        assert isinstance(new_kwargs, dict)
+        self.__update_args_kwargs(new_args, new_kwargs)
+
+    def _rename(self, candidate: str) -> None:
+        if candidate == self.name:
+            return
+        name = self.graph._graph_namespace.create_name(candidate, None)
+        self.name = name
+        self.graph._graph_namespace._rename_object(self, name)
+
+    def __setattr__(self, name: str, value: Any) -> None:
+        if name == 'name' and hasattr(self, "name"):
+            m = self.graph.owning_module
+            if getattr(m, "_replace_hook", None):
+                assert isinstance(value, str)
+                for user in self.users:
+                    m._replace_hook(old=self, new=value, user=user)
+        update = False
+        if (
+                hasattr(self, name) and
+                hasattr(self.graph, "_find_nodes_lookup_table") and
+                self in self.graph._find_nodes_lookup_table
+        ):
+            update = True
+            self.graph._find_nodes_lookup_table.remove(self)
+        object.__setattr__(self, name, value)
+        if update:
+            self.graph._find_nodes_lookup_table.insert(self)
+
+@compatibility(is_backward_compatible=True)
+def map_arg(a: Argument, fn: Callable[[Node], Argument]) -> Argument:
+    """
+    Apply fn to each Node appearing arg. arg may be a list, tuple, slice, or dict with string keys.
+    """
+    assert callable(fn), "torch.fx.map_arg(a, fn): fn must be a callable"
+    return map_aggregate(a, lambda x: fn(x) if isinstance(x, Node) else x)
+
+@compatibility(is_backward_compatible=True)
+def map_aggregate(a: Argument, fn: Callable[[Argument], Argument]) -> Argument:
+    """
+    Apply fn to each Node appearing arg. arg may be a list, tuple, slice, or dict with string keys.
+    """
+    if isinstance(a, tuple):
+        t = tuple([map_aggregate(elem, fn) for elem in a])
+        # Support NamedTuple (if it has `_fields`) by repacking into original type.
+        return t if not hasattr(a, '_fields') else type(a)(*t)  # type: ignore[arg-type]
+    elif isinstance(a, list):
+        return immutable_list([map_aggregate(elem, fn) for elem in a])
+    elif isinstance(a, dict):
+        rv = immutable_dict()
+        for k, v in a.items():
+            dict.__setitem__(rv, k, map_aggregate(v, fn))
+        return rv
+    elif isinstance(a, slice):
+        return slice(map_aggregate(a.start, fn), map_aggregate(a.stop, fn), map_aggregate(a.step, fn))
+    else:
+        return fn(a)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/operator_schemas.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/operator_schemas.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a5beed5285d9a208d7a28ebdeeb79e1bdf2c19e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/operator_schemas.py
@@ -0,0 +1,451 @@
+# mypy: allow-untyped-defs
+import torch
+import inspect
+import numbers
+import types
+import typing
+import enum
+import warnings
+from typing import Any, Callable, Dict, List, Optional, Tuple, NamedTuple, cast, TYPE_CHECKING
+from torch._jit_internal import boolean_dispatched
+from ._compatibility import compatibility
+from torch._ops import OpOverloadPacket, OpOverload
+
+if TYPE_CHECKING:
+    from .node import Argument
+
+__all__ = ["ArgsKwargsPair", "check_for_mutable_operation", "get_signature_for_torch_op", "create_type_hint",
+           "type_matches", "normalize_function", "normalize_module"]
+
+@compatibility(is_backward_compatible=False)
+class ArgsKwargsPair(NamedTuple):
+    """
+    Simple named tuple for wrapping args/kwargs pairs.
+    """
+    args: Tuple[Any, ...]
+    kwargs: Dict[str, Any]
+
+_manual_overrides : Dict[Callable, List[inspect.Signature]] = {}
+
+def _nonzero_schemas():
+    signatures = []
+
+    def nonzero(self):
+        pass
+    signatures.append(inspect.signature(nonzero))
+
+    def nonzero(self, *, as_tuple : bool):  # type: ignore[no-redef]
+        pass
+    signatures.append(inspect.signature(nonzero))
+
+    return signatures
+
+_manual_overrides[torch.nonzero] = _nonzero_schemas()
+
+class _FakeGlobalNamespace:
+    def __getattr__(self, name):
+        if name == 'torch':
+            return torch
+        raise RuntimeError('Expected a torch namespace lookup')
+
+_type_eval_globals = {'Tensor' : torch.Tensor, 'Device' : torch.device, 'Layout' : torch.layout,
+                      'number' : numbers.Number, 'Future' : torch.jit.Future,
+                      'AnyEnumType' : enum.Enum, 'QScheme' : torch.qscheme,
+                      '__torch__': _FakeGlobalNamespace(), 'NoneType': type(None),
+                      'Storage': torch.UntypedStorage,
+                      't': typing.TypeVar('t')}
+for k in dir(typing):
+    _type_eval_globals[k] = getattr(typing, k)
+
+def _torchscript_type_to_python_type(ts_type : 'torch._C.JitType') -> Any:
+    """
+    Convert a TorchScript type to a Python type (including subtypes) via
+    eval'ing the annotation_str. _type_eval_globals sets up expressions
+    like "List" and "Future" to map to actual types (typing.List and jit.Future)
+    """
+    return eval(ts_type.annotation_str, _type_eval_globals)
+
+def _torchscript_schema_to_signature_impl(ts_schema : torch._C.FunctionSchema) -> inspect.Signature:
+    from inspect import Parameter
+    parameters : List[Parameter] = []
+    for arg in ts_schema.arguments:
+        arg_type = _torchscript_type_to_python_type(arg.type)
+        default = arg.default_value if arg.has_default_value() else Parameter.empty
+        # TODO: Figure out if this is safe. It seems like when generating the type signatures for
+        # PythonArgParser, we emit signatures with `input` instead of `self` as the first tensor
+        # argument name. Downstream, if someone converts that positional argument to a keyword
+        # argument, the name mismatch will break things, so here we're going to normalize the
+        # name to "input"
+        name = arg.name if arg.name != 'self' else 'input'
+        kind = Parameter.KEYWORD_ONLY if arg.kwarg_only else Parameter.POSITIONAL_OR_KEYWORD
+        # "from" is a keyword therefore it must be a POSITIONAL_ONLY argument
+        if name == "from":
+            assert kind == Parameter.POSITIONAL_OR_KEYWORD
+            # ParameterKind type is internal implementation detail to inspec package
+            # which makes it hard to do type annotation
+            kind = Parameter.POSITIONAL_ONLY  # type: ignore[assignment]
+            # This renders all previous arguments to positional only
+            for idx, p in enumerate(parameters):
+                assert p.kind == Parameter.POSITIONAL_OR_KEYWORD
+                parameters[idx] = Parameter(name=p.name, kind=Parameter.POSITIONAL_ONLY, default=p.default, annotation=p.annotation)
+        parameters.append(Parameter(name=name, kind=kind, default=default, annotation=arg_type))
+    return_types = [_torchscript_type_to_python_type(ret.type) for ret in ts_schema.returns]
+    if len(return_types) == 0:
+        return_type = None
+    elif len(return_types) == 1:
+        return_type = return_types[0]
+    else:
+        return_type = tuple(return_types)
+
+    return inspect.Signature(parameters, return_annotation=return_type)
+
+_SCHEMA_TO_SIGNATURE_CACHE : Dict[Tuple[str, str], inspect.Signature] = {}
+
+def _torchscript_schema_to_signature(ts_schema : torch._C.FunctionSchema) -> inspect.Signature:
+    # Cached as it's called in the hot path of FakeTensor dispatch
+    cache_key = ts_schema.name, ts_schema.overload_name
+    cache_val = _SCHEMA_TO_SIGNATURE_CACHE.get(cache_key)
+    if cache_val is not None:
+        return cache_val
+
+    res = _torchscript_schema_to_signature_impl(ts_schema)
+    _SCHEMA_TO_SIGNATURE_CACHE[cache_key] = res
+    return res
+
+@compatibility(is_backward_compatible=False)
+def check_for_mutable_operation(target : Callable, args : Tuple['Argument', ...], kwargs : Dict[str, 'Argument']):
+    signatures, schemas = get_signature_for_torch_op(target, return_schemas=True)
+
+    if signatures and schemas:
+        matched_schemas = []
+
+        # Iterate through all of the schema until we find one that matches
+        # If one matches, populate `new_args_and_kwargs` with the new args/kwargs
+        # values. If none matches, `new_args_and_kwargs` will be None
+        for candidate_signature, schema in zip(signatures, schemas):
+            try:
+                candidate_signature.bind(*args, **kwargs)
+                matched_schemas.append((candidate_signature, schema))
+            except TypeError as e:
+                continue
+
+        def throw_if_mutable(schema):
+            if schema.is_mutable:
+                raise RuntimeError(f'Tried to trace mutable operation {schema}. FX only supports functional '
+                                   f'code, so operations that mutate operands in-place (e.g. via `out` arguments) '
+                                   f'are not supported')
+
+        if len(matched_schemas) == 0:
+            # Did not match any schema. Cannot check for mutation
+            pass
+        elif len(matched_schemas) == 1:
+            # Matched exactly one schema, unambiguous
+            _, schema_to_check = matched_schemas[0]
+            throw_if_mutable(schema_to_check)
+        else:
+            # Ambiguous schema match. Since mutability checking is best effort,
+            # do nothing.
+            pass
+
+@compatibility(is_backward_compatible=False)
+def get_signature_for_torch_op(op : Callable, return_schemas : bool = False):
+    """
+    Given an operator on the `torch` namespace, return a list of `inspect.Signature`
+    objects corresponding to the overloads of that op.. May return `None` if a signature
+    could not be retrieved.
+
+    Args:
+        op (Callable): An operator on the `torch` namespace to look up a signature for
+
+    Returns:
+        Optional[List[inspect.Signature]]: A list of signatures for the overloads of this
+            operator, or None if the operator signatures could not be retrieved. If
+            return_schemas=True, returns a tuple containing the optional Python signatures
+            and the optional TorchScript Function signature
+    """
+    if isinstance(op, OpOverload):
+        schemas = [op._schema]
+    elif isinstance(op, OpOverloadPacket):
+        schemas = [getattr(op, overload)._schema for overload in op.overloads()]
+    else:
+        override = _manual_overrides.get(op)
+        if override:
+            return (override, None) if return_schemas else None
+
+        aten_fn = torch.jit._builtins._find_builtin(op)
+
+        if aten_fn is None:
+            return (None, None) if return_schemas else None
+        schemas = torch._C._jit_get_schemas_for_operator(aten_fn)
+
+    signatures = [_torchscript_schema_to_signature(schema) for schema in schemas]
+    return (signatures, schemas) if return_schemas else signatures
+
+@compatibility(is_backward_compatible=False)
+def create_type_hint(x):
+    """
+    Produces a type hint for the given argument.
+
+    The :func:`create_type_hint` looks for a type hint compatible with the input argument `x`.
+
+    If `x` is a `list` or `tuple`, it looks for an object in the list whose type is a superclass
+    of the rest, and uses that as `base_type` for the `List` or `Tuple` to be returned.
+    If no such object is found, it defaults to `List[Any]`.
+
+    If `x` is neither a `list` nor a `tuple`, it returns `x`.
+    """
+    try:
+        if isinstance(x, (list, tuple)):
+            # todo(chilli): Figure out the right way for mypy to handle this
+            if isinstance(x, list):
+                def ret_type(x):
+                    return List[x]  # type: ignore[valid-type]
+            else:
+                def ret_type(x):
+                    return Tuple[x, ...]
+            if len(x) == 0:
+                return ret_type(Any)
+            base_type = x[0]
+            for t in x:
+                if issubclass(t, base_type):
+                    continue
+                elif issubclass(base_type, t):
+                    base_type = t
+                else:
+                    return ret_type(Any)
+            return ret_type(base_type)
+    except Exception as e:
+        # We tried to create a type hint for list but failed.
+        warnings.warn(f"We were not able to successfully create type hint from the type {x}")
+    return x
+
+@compatibility(is_backward_compatible=False)
+def type_matches(signature_type : Any, argument_type : Any):
+    sig_origin_type = getattr(signature_type, '__origin__', signature_type)
+
+    if signature_type is argument_type:
+        return True
+
+    # Union types in signature. Given type needs to match one of the
+    # contained types in the Union
+    if sig_origin_type is typing.Union and signature_type != argument_type:
+        sig_contained = signature_type.__args__
+        return any(type_matches(c, argument_type) for c in sig_contained)
+
+    if signature_type is List[int] and argument_type is int:
+        # int can be promoted to List[int]
+        return True
+
+    if getattr(signature_type, '__origin__', None) in {list, List}:
+        sig_el_type = signature_type.__args__[0]
+        if not inspect.isclass(sig_el_type):
+            warnings.warn(
+                f"Does not support nested parametric types, got {signature_type}. Please file a bug.")
+            return False
+        if getattr(argument_type, '__origin__', None) in {list, List}:
+            return issubclass(argument_type.__args__[0], sig_el_type)
+
+        def is_homogeneous_tuple(t):
+            if getattr(t, "__origin__", None) not in {tuple, Tuple}:
+                return False
+            contained = t.__args__
+            if t.__args__ == ((),):  # Tuple[()].__args__ == ((),) for some reason
+                return True
+            return all((c is Ellipsis) or issubclass(c, sig_el_type) for c in contained)
+
+        # Tuple[T] is accepted for List[T] parameters
+        return is_homogeneous_tuple(argument_type)
+
+    # Dtype is an int in schemas
+    if signature_type is int and argument_type is torch.dtype:
+        return True
+
+    if signature_type is numbers.Number and argument_type in {int, float}:
+        return True
+    if inspect.isclass(argument_type) and inspect.isclass(signature_type):
+        return issubclass(argument_type, signature_type)
+
+    return False
+
+@compatibility(is_backward_compatible=False)
+def normalize_function(
+        target: Callable, args: Tuple[Any], kwargs : Optional[Dict[str, Any]] = None, arg_types : Optional[Tuple[Any]] = None,
+        kwarg_types : Optional[Dict[str, Any]] = None,
+        normalize_to_only_use_kwargs : bool = False) -> Optional[ArgsKwargsPair]:
+    """
+    Returns normalized arguments to PyTorch functions. This means that
+    `args/kwargs` will be matched up to the functional's
+    signature and return exclusively kwargs in positional order if
+    `normalize_to_only_use_kwargs` is True.
+    Also populates default values. Does not support positional-only
+    parameters or varargs parameters (*args, **kwargs). Does not support modules.
+
+    May require `arg_types` and `kwarg_types` in order to disambiguate overloads.
+
+    Args:
+        target (Callable): Function that we are normalizing
+        args (Tuple[Any]): Tuple of args to the function
+        kwargs (Optional[Dict[str, Any]]): Dict of kwargs to the function
+        arg_types (Optional[Tuple[Any]]): Tuple of arg types for the args
+        kwarg_types (Optional[Dict[str, Any]]): Dict of arg types for the kwargs
+        normalize_to_only_use_kwargs (bool): Whether to normalize to only use kwargs.
+
+    Returns:
+
+        Returns normalized_args_and_kwargs, or `None` if not successful.
+    """
+    if kwargs is None:
+        kwargs = {}
+    new_args_and_kwargs = None
+    if not isinstance(target, types.BuiltinFunctionType) and not (
+        isinstance(target, (OpOverloadPacket, OpOverload))
+    ):
+        target_for_analysis = target
+        if target in boolean_dispatched:
+            # HACK: `boolean_dispatch` as used in `torch.nn.functional` makes it so that we have
+            # a 2-way dispatch based on a boolean value. Here we check that the `true` and `false`
+            # branches of the dispatch have exactly the same signature. If they do, use the `true`
+            # branch signature for analysis. Otherwise, leave this un-normalized
+            assert not isinstance(target, str)
+            dispatched = boolean_dispatched[target]
+            if_true, if_false = dispatched['if_true'], dispatched['if_false']
+            if inspect.signature(if_true).parameters != inspect.signature(if_false).parameters:
+                return None
+            target_for_analysis = if_true
+
+        assert callable(target_for_analysis)
+        sig = inspect.signature(inspect.unwrap(target_for_analysis))
+        new_args_and_kwargs = _args_kwargs_to_normalized_args_kwargs(sig, args, kwargs, normalize_to_only_use_kwargs)
+    else:
+        assert callable(target)
+        torch_op_schemas = get_signature_for_torch_op(target)
+        matched_schemas = []
+        if torch_op_schemas:
+            # Iterate through all of the schema until we find one that matches
+            # If one matches, populate `new_args_and_kwargs` with the new args/kwargs
+            # values. If none matches, `new_args_and_kwargs` will be None
+            for candidate_signature in torch_op_schemas:
+                try:
+                    candidate_signature.bind(*args, **kwargs)
+                    matched_schemas.append(candidate_signature)
+                except TypeError as e:
+                    continue
+
+            if len(matched_schemas) == 0:
+                # Did not match any schema. Cannot normalize
+                pass
+            elif len(matched_schemas) == 1:
+                # Matched exactly one schema, unambiguous
+                new_args_and_kwargs = _args_kwargs_to_normalized_args_kwargs(matched_schemas[0], args, kwargs,
+                                                                             normalize_to_only_use_kwargs)
+            else:
+                if arg_types is not None or kwarg_types is not None:
+                    arg_types = arg_types if arg_types else cast(Tuple[Any], ())
+                    kwarg_types = kwarg_types if kwarg_types else {}
+                    for candidate_signature in torch_op_schemas:
+                        sig_matches = True
+                        try:
+                            bound_types = candidate_signature.bind(*arg_types, **kwarg_types)
+                            for arg_name, arg_type in bound_types.arguments.items():
+                                param = candidate_signature.parameters[arg_name]
+                                sig_matches = sig_matches and type_matches(param.annotation, arg_type)
+                        except TypeError as e:
+                            sig_matches = False
+                        if sig_matches:
+                            new_args_and_kwargs = _args_kwargs_to_normalized_args_kwargs(candidate_signature, args, kwargs,
+                                                                                         normalize_to_only_use_kwargs)
+                            break
+                else:
+                    # Matched more than one schema. In this situation, the caller must provide the types of
+                    # the arguments of the overload they expect.
+                    schema_printouts = '\n'.join(str(schema) for schema in matched_schemas)
+                    raise RuntimeError(f'Tried to normalize arguments to {torch.typename(target)} but '
+                                       f'the schema match was ambiguous! Please provide argument types to '
+                                       f'the normalize_arguments() call. Available schemas:\n{schema_printouts}')
+
+    return new_args_and_kwargs
+
+@compatibility(is_backward_compatible=False)
+def normalize_module(
+        root: torch.nn.Module, target: str, args: Tuple[Any], kwargs : Optional[Dict[str, Any]] = None,
+        normalize_to_only_use_kwargs : bool = False) -> Optional[ArgsKwargsPair]:
+    """
+    Returns normalized arguments to PyTorch modules. This means that
+    `args/kwargs` will be matched up to the functional's
+    signature and return exclusively kwargs in positional order if
+    `normalize_to_only_use_kwargs` is True.
+    Also populates default values. Does not support positional-only
+    parameters or varargs parameters (*args, **kwargs).
+
+    Args:
+        root (nn.Module): root module upon which we query modules
+        target (Callable): Function that we are normalizing
+        args (Tuple[Any]): Tuple of args to the function
+        kwargs (Optional[Dict[str, Any]]): Dict of kwargs to the function
+        normalize_to_only_use_kwargs (bool): Whether to normalize to only use kwargs.
+
+    Returns:
+
+        Returns normalized_args_and_kwargs, or `None` if not successful.
+    """
+    try:
+        submod = root.get_submodule(target)
+    except AttributeError as e:
+        raise RuntimeError(f"Tried to normalize node with target {target} but root did not "
+                           f"have that target!") from e
+    if hasattr(submod.__class__, '__name__'):
+        classname = submod.__class__.__name__
+        if getattr(torch.nn, classname, None) == submod.__class__:
+            sig = inspect.signature(inspect.unwrap(submod.forward))
+            if kwargs is None:
+                kwargs = {}
+            new_args_and_kwargs = _args_kwargs_to_normalized_args_kwargs(sig, args, kwargs,
+                                                                         normalize_to_only_use_kwargs)
+            return new_args_and_kwargs
+    return None
+
+def _args_kwargs_to_normalized_args_kwargs(sig : inspect.Signature, args : Tuple[Any, ...],
+                                           kwargs : Dict[str, Any],
+                                           normalize_to_only_use_kwargs : bool) -> Optional[ArgsKwargsPair]:
+    """
+    Given a call target, args, and kwargs, return the arguments normalized into
+    an ArgsKwargsPair, or None if the type signature is not supported by
+    this normalization.
+
+    Args:
+
+        sig (inspect.Signature): Signature object for the target
+        args (Tuple): Arguments that appear at the callsite for `target`
+        kwargs (Dict): Keyword arguments that appear at the callsite for `target`
+        normalize_to_only_use_kwargs (bool): Whether to normalize to only use kwargs.
+
+    Returns:
+
+        Optional[ArgsKwargsPair]: Normalized args and kwargs for `target`, or `None` if
+            this target is not supported.
+    """
+
+    # Don't currently support positional-only
+    # or varargs (*args, **kwargs) signatures
+    supported_parameter_types = {
+        inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.KEYWORD_ONLY}
+    if any(p.kind not in supported_parameter_types for p in sig.parameters.values()):
+        # Add an exception for one signature, which is common for random/uniform, i.e.:
+        # Tensor(a!) self, float from=0, float to=1, *, Generator? generator=None
+        # `from` is Python keyword and as such functions with that signature should have
+        # positional-only args, but at the same time they could be dispatched as kwargs
+        if list(sig.parameters.keys()) != ['input', 'from', 'to', 'generator']:
+            return None
+
+    bound_args = sig.bind(*args, **kwargs)
+    bound_args.apply_defaults()
+
+    new_kwargs : Dict[str, Any] = {}
+    new_args : List[Any] = []
+    for i, param in enumerate(sig.parameters):
+        if not normalize_to_only_use_kwargs and i < len(args):
+            new_args.append(bound_args.arguments[param])
+        else:
+            new_kwargs[param] = bound_args.arguments[param]
+
+    return ArgsKwargsPair(tuple(new_args), new_kwargs)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f83a2f248fcde4fd3947ee0ea82505a8bb7af976
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/__init__.py
@@ -0,0 +1,12 @@
+from . import graph_drawer
+from . import graph_manipulation
+from . import net_min_base
+from . import operator_support
+from . import param_fetch
+from . import reinplace
+from . import runtime_assert
+from . import shape_prop
+from . import split_module
+from . import split_utils
+from . import splitter_base
+from . import tools_common
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/annotate_getitem_nodes.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/annotate_getitem_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..0399cef526205f8f82a0c53555bc16fdab67a550
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/annotate_getitem_nodes.py
@@ -0,0 +1,44 @@
+import operator
+
+import torch
+
+
+def annotate_getitem_nodes(graph: torch.fx.Graph) -> None:
+    """
+    Annotate the type of getitem nodes, inferred from the type of sequence node.
+    If sequence node is not annotated with a type, do nothing.
+    Currently support getitem nodes from Tuple, List, and NamedTuple sequence node.
+
+    This is helpful since annotations on local names within function are lost during FX transforms.
+    Adding back known type annotation for getitem nodes to improve jit scriptability.
+
+    Args:
+        graph (Graph): The graph to be annotated
+    """
+    for node in graph.nodes:
+        if node.target == operator.getitem:
+            sequence_node, index_node = node.args
+            if not sequence_node.type:
+                continue
+            # container types
+            if hasattr(sequence_node.type, "_name"):
+                parameterized_types = sequence_node.type.__args__
+                if sequence_node.type._name == "Tuple":
+                    if len(parameterized_types) == 2 and isinstance(
+                        parameterized_types[1], type(...)
+                    ):
+                        node.type = parameterized_types[0]
+                    else:
+                        assert len(parameterized_types) > index_node
+                        node_type = parameterized_types[index_node]
+                        node.type = node_type
+                elif sequence_node.type._name == "List":
+                    assert len(parameterized_types) == 1
+                    node.type = parameterized_types[0]
+            # NamedTuple type
+            elif hasattr(sequence_node.type, "__annotations__"):
+                if sequence_node.type == torch.Tensor:
+                    continue
+                sequence_node_field_types = sequence_node.type.__annotations__
+                field_name = sequence_node.type._fields[index_node]
+                node.type = sequence_node_field_types[field_name]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/backends/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/backends/__init__.py
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/backends/cudagraphs.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/backends/cudagraphs.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f48165b7dab41d9c8dd30ef49a2752868740989
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/backends/cudagraphs.py
@@ -0,0 +1,57 @@
+# mypy: allow-untyped-defs
+import torch
+from torch.fx.passes.infra.partitioner import CapabilityBasedPartitioner
+from torch.fx.passes.operator_support import OperatorSupport
+from torch.fx.passes.tools_common import CALLABLE_NODE_OPS
+from torch.fx.passes.fake_tensor_prop import FakeTensorProp
+from torch.utils import _pytree as pytree
+
+import operator
+
+class CudaGraphsSupport(OperatorSupport):
+    # TODO: why is submodules passed here
+    def is_node_supported(self, submodules, node: torch.fx.Node) -> bool:
+        if node.op not in CALLABLE_NODE_OPS:
+            return False
+
+        if node.target in [torch.ops.aten.embedding_dense_backward.default]:
+            return False
+
+        if node.target in [operator.getitem]:
+            return True
+
+        found_not_cuda = False
+
+        def meta_fk(meta):
+            return meta["val"] if "val" in meta else meta["fake_result"]
+
+        def find_not_cuda(t):
+            nonlocal found_not_cuda
+            if isinstance(t, torch.Tensor) and t.device.type != 'cuda':
+                found_not_cuda = True
+
+        for n in node.all_input_nodes:
+            pytree.tree_map_(find_not_cuda, meta_fk(n.meta))
+
+        pytree.tree_map_(find_not_cuda, meta_fk(node.meta))
+
+        # NB: factory function is accounted for because the result would be
+        # cpu or cuda
+
+        return not found_not_cuda
+
+def partition_cudagraphs(gm, inputs):
+    """
+    Partition an FX graph into sub-GraphModules that can be validly run under
+    CUDA graphs.  For a subgraph to be runnable under CUDA, all of the operations
+    must involve CUDA tensors only/
+    """
+
+    FakeTensorProp(gm).propagate(*inputs)
+    supported_ops = CudaGraphsSupport()
+    # TODO: single node partition may be wrong due to the pessimization
+    # from copying in and out the data.  Check in benchmarks, perhaps
+    partitioner = CapabilityBasedPartitioner(gm, supported_ops, allows_single_node_partition=True)
+    partitions = partitioner.propose_partitions()
+    fused_graph = partitioner.fuse_partitions(partitions)
+    return fused_graph
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/dialect/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/dialect/__init__.py
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/dialect/common/cse_pass.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/dialect/common/cse_pass.py
new file mode 100644
index 0000000000000000000000000000000000000000..577f445e7b316c61657625705f11f38daa1511a4
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/dialect/common/cse_pass.py
@@ -0,0 +1,113 @@
+# mypy: allow-untyped-defs
+from typing import Dict, Tuple, Any
+
+import torch
+from torch.fx.passes.infra.pass_base import PassBase, PassResult
+from torch.utils._pytree import tree_flatten
+
+from torch.fx import GraphModule, Graph
+from torch.fx import Node
+
+aten = torch.ops.aten
+
+
+# stateful ops are banned from CSE
+rand_ops = {aten.dropout, aten._fused_dropout, aten._standard_gamma, aten.bernoulli, aten.multinomial, aten.native_dropout, aten.normal, aten.poisson, aten.binomial, aten.rrelu, aten.rand_like, aten.rand, aten.randint, aten.randn, aten.randperm}  # noqa: E501,B950
+
+inplace_ops = {aten.add_, aten.sub_, aten.mul_, aten.div_, aten.pow_, aten.lerp_, aten.relu_, aten.sigmoid_, aten.tanh_}  # noqa: E501
+
+
+@torch.fx._compatibility.compatibility(is_backward_compatible=False)
+def get_CSE_banned_ops():
+    return rand_ops.union(inplace_ops)
+
+
+@torch.fx._compatibility.compatibility(is_backward_compatible=False)
+class CSEPass(PassBase):
+
+    def __init__(self, banned_ops=None):
+        """
+        This version of CSE Pass aims to be dialect agnostic, and it's implemented purely based on the connectivity between fx.Node.
+
+        For functional dialects, user would only need to specify the random ops in ban list.
+
+        Warning: CSE Pass cannot be safely applied on a FX graph in non-functional dialects.
+        If your dialect contains stateful operators, please customized the banned_ops.
+
+        """
+        if banned_ops is None:
+            banned_ops = set()
+        self.banned_ops = banned_ops
+        super().__init__()
+
+    def call(self, graph_module: GraphModule) -> PassResult:
+        """
+        Return a new copy of torch.fx.GraphModule with CSE applied to the input graph
+
+        Example usage:
+
+        from torch.fx.experimental.proxy_tensor import make_fx
+        def f(a):
+            b = a * a
+            c = a * a
+            return b+c
+
+        p = CSEPass()
+        traced_graph = make_fx(f)(torch.tensor(1))
+        print(traced_graph)
+        result = p(traced_graph)
+        print(result.graph_module)
+        """
+        def get_aten_target(node):
+            if hasattr(node.target, 'overloadpacket'):
+                return node.target.overloadpacket
+            return node.target
+
+        modified = False
+        new_graph = Graph()
+        env: Dict[Node, Node] = {}  # map from node in the old graph to node in the new graph
+        hash_env: Dict[Tuple[torch._ops.OpOverload, int], Node] = {}  # map from hash to a node in the new graph
+        token_map: Dict[Tuple[torch._ops.OpOverload, int], Dict[str, Any]] = {}  # map from hash to token
+        for n in graph_module.graph.nodes:
+            # The placeholder, output, and get_attr nodes are copied to the new graph without change
+            # do not CSE away random operations
+            if n.op == 'placeholder' or n.op == 'output' or n.op == 'get_attr' or get_aten_target(n) in self.banned_ops:
+                new_node = new_graph.node_copy(n, lambda x: env[x])
+                env[n] = new_node
+            else:  # n.op == 'call_function', should never see n.op == 'call_module' or 'call_method'
+                # substitute args and kwargs members to their mapping in env if exists
+                # specs can be used to reconstruct nested list/dictionaries
+                def substitute(arg_list):
+                    arg_list, spec = tree_flatten(arg_list)
+                    for i in range(len(arg_list)):
+                        v = arg_list[i]
+                        if isinstance(v, Node) and v in env:
+                            arg_list[i] = env[v]
+                    return tuple(arg_list), spec
+                args, args_spec = substitute(n.args)
+                kwargs, kwargs_spec = substitute(n.kwargs)
+
+                # each token corresponds to a unique node
+                # nodes with the same token can be substituted
+                token = {"target": n.target, "args": args, "args_spec": args_spec,
+                         "kwargs": kwargs, "kwargs_spec": kwargs_spec}
+
+                # hash substituted args to a number, do not hash specs because specs are not hashable
+                hash_arg = hash((args, kwargs))
+                hash_val = (n.target, hash_arg)
+
+                # check if a node has a substitute and can be eliminated
+                hash_val_in_hash_env = hash_val in hash_env
+                if hash_val_in_hash_env and token_map[hash_val] == token:
+                    modified = True  # substitution happens and the graph is modified
+                    env[n] = hash_env[hash_val]
+                    continue
+
+                new_node = new_graph.node_copy(n, lambda x: env[x])
+                env[n] = new_node
+                if not hash_val_in_hash_env:
+                    hash_env[hash_val] = new_node
+                    token_map[hash_val] = token
+
+        csed_gm = GraphModule(graph_module, new_graph)
+        return PassResult(csed_gm, modified)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/fake_tensor_prop.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/fake_tensor_prop.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b40207e0f8048cb98cc99c147b979d9791e8d01
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/fake_tensor_prop.py
@@ -0,0 +1,70 @@
+# mypy: allow-untyped-defs
+from typing import Optional
+
+import torch.fx
+from torch.fx import Node
+from torch.fx.node import map_aggregate
+from torch.fx._compatibility import compatibility
+from torch._subclasses.fake_tensor import FakeTensorMode, FakeTensor
+from torch.fx.experimental.proxy_tensor import snapshot_fake, py_sym_types
+
+__all__ = ['FakeTensorProp']
+
+@compatibility(is_backward_compatible=False)
+class FakeTensorProp(torch.fx.Interpreter):
+    """
+    Execute an FX graph Node-by-Node and record a fake tensor representing
+    the metadata for the node.  Unlike ShapeProp, (1) this propagation
+    is cheap--it does the propagation with meta tensors which do not actually
+    store data, and (2) the fake tensors have much more fine grained information,
+    e.g., they have accurate alias information that can be consulted by looking
+    at the storages.
+
+    Args:
+         module (GraphModule): The module to be executed
+         mode (Optional[FakeTensorMode]): The dispatch mode used to execute computation indicated by each FX Node.
+    """
+    def __init__(self, module: torch.fx.GraphModule, mode: Optional[FakeTensorMode] = None):
+        super().__init__(module)
+        if mode is None:
+            mode = FakeTensorMode()
+        self._mode = mode
+        mode.epoch += 1
+        mode.reset_nt_tensor_id_counter()
+
+    def run_node(self, n: Node):
+        from torch.fx.experimental.symbolic_shapes import rebind_unbacked, compute_unbacked_bindings
+
+        result = super().run_node(n)
+        rebind_unbacked(self._mode.shape_env, n, result)
+
+        def extract_val(obj):
+            if isinstance(obj, FakeTensor):
+                return snapshot_fake(obj)
+            elif isinstance(obj, torch.Tensor):
+                # TODO: How is it possible that we get a non fake tensor?  We
+                # should be running under the mode...
+                return snapshot_fake(self._mode.from_tensor(obj, static_shapes=True))
+            elif isinstance(obj, py_sym_types):
+                return obj
+            else:
+                return None
+
+        meta = map_aggregate(result, extract_val)
+        if meta is not None:
+            n.meta['val'] = meta
+            if (shape_env := self._mode.shape_env) and (symbol_to_path := compute_unbacked_bindings(shape_env, result)):
+                n.meta["unbacked_bindings"] = symbol_to_path
+
+        return result
+
+    def propagate(self, *args):
+        fake_args = [
+            self._mode.from_tensor(a) if isinstance(a, torch.Tensor) else a
+            for a in args
+        ]
+        return self.propagate_dont_convert_inputs(*fake_args)
+
+    def propagate_dont_convert_inputs(self, *args):
+        with self._mode:
+            return super().run(*args)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/graph_drawer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/graph_drawer.py
new file mode 100644
index 0000000000000000000000000000000000000000..975b2b6171780e9705603196ef7596dc6ea24f55
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/graph_drawer.py
@@ -0,0 +1,443 @@
+# mypy: allow-untyped-defs
+
+import hashlib
+from itertools import chain
+from typing import Any, Dict, Optional, TYPE_CHECKING
+
+import torch
+import torch.fx
+from torch.fx._compatibility import compatibility
+from torch.fx.graph import _parse_stack_trace
+from torch.fx.node import _format_arg, _get_qualified_name
+from torch.fx.operator_schemas import normalize_function
+from torch.fx.passes.shape_prop import TensorMetadata
+
+
+try:
+    import pydot
+
+    HAS_PYDOT = True
+except ModuleNotFoundError:
+    HAS_PYDOT = False
+    pydot = None
+
+
+__all__ = ["FxGraphDrawer"]
+
+_COLOR_MAP = {
+    "placeholder": '"AliceBlue"',
+    "call_module": "LemonChiffon1",
+    "get_param": "Yellow2",
+    "get_attr": "LightGrey",
+    "output": "PowderBlue",
+}
+
+_HASH_COLOR_MAP = [
+    "CadetBlue1",
+    "Coral",
+    "DarkOliveGreen1",
+    "DarkSeaGreen1",
+    "GhostWhite",
+    "Khaki1",
+    "LavenderBlush1",
+    "LightSkyBlue",
+    "MistyRose1",
+    "MistyRose2",
+    "PaleTurquoise2",
+    "PeachPuff1",
+    "Salmon",
+    "Thistle1",
+    "Thistle3",
+    "Wheat1",
+]
+
+_WEIGHT_TEMPLATE = {
+    "fillcolor": "Salmon",
+    "style": '"filled,rounded"',
+    "fontcolor": "#000000",
+}
+
+if HAS_PYDOT:
+    @compatibility(is_backward_compatible=False)
+    class FxGraphDrawer:
+        """
+        Visualize a torch.fx.Graph with graphviz
+        Basic usage:
+            g = FxGraphDrawer(symbolic_traced, "resnet18")
+            g.get_dot_graph().write_svg("a.svg")
+        """
+
+        def __init__(
+            self,
+            graph_module: torch.fx.GraphModule,
+            name: str,
+            ignore_getattr: bool = False,
+            ignore_parameters_and_buffers: bool = False,
+            skip_node_names_in_args: bool = True,
+            parse_stack_trace: bool = False,
+            dot_graph_shape: Optional[str] = None,
+            normalize_args: bool = False,
+        ):
+            self._name = name
+            self.dot_graph_shape = (
+                dot_graph_shape if dot_graph_shape is not None else "record"
+            )
+            self.normalize_args = normalize_args
+            _WEIGHT_TEMPLATE["shape"] = self.dot_graph_shape
+
+            self._dot_graphs = {
+                name: self._to_dot(
+                    graph_module, name, ignore_getattr, ignore_parameters_and_buffers, skip_node_names_in_args, parse_stack_trace
+                )
+            }
+
+            for node in graph_module.graph.nodes:
+                if node.op != "call_module":
+                    continue
+
+                leaf_node = self._get_leaf_node(graph_module, node)
+
+                if not isinstance(leaf_node, torch.fx.GraphModule):
+                    continue
+
+                self._dot_graphs[f"{name}_{node.target}"] = self._to_dot(
+                    leaf_node,
+                    f"{name}_{node.target}",
+                    ignore_getattr,
+                    ignore_parameters_and_buffers,
+                    skip_node_names_in_args,
+                    parse_stack_trace,
+                )
+
+        def get_dot_graph(self, submod_name=None) -> pydot.Dot:
+            """
+            Visualize a torch.fx.Graph with graphviz
+            Example:
+                >>> # xdoctest: +REQUIRES(module:pydot)
+                >>> # xdoctest: +REQUIRES(module:ubelt)
+                >>> # define module
+                >>> class MyModule(torch.nn.Module):
+                >>>     def __init__(self) -> None:
+                >>>         super().__init__()
+                >>>         self.linear = torch.nn.Linear(4, 5)
+                >>>     def forward(self, x):
+                >>>         return self.linear(x).clamp(min=0.0, max=1.0)
+                >>> module = MyModule()
+                >>> # trace the module
+                >>> symbolic_traced = torch.fx.symbolic_trace(module)
+                >>> # setup output file
+                >>> import ubelt as ub
+                >>> dpath = ub.Path.appdir('torch/tests/FxGraphDrawer').ensuredir()
+                >>> fpath = dpath / 'linear.svg'
+                >>> # draw the graph
+                >>> g = FxGraphDrawer(symbolic_traced, "linear")
+                >>> g.get_dot_graph().write_svg(fpath)
+            """
+            if submod_name is None:
+                return self.get_main_dot_graph()
+            else:
+                return self.get_submod_dot_graph(submod_name)
+
+        def get_main_dot_graph(self) -> pydot.Dot:
+            return self._dot_graphs[self._name]
+
+        def get_submod_dot_graph(self, submod_name) -> pydot.Dot:
+            return self._dot_graphs[f"{self._name}_{submod_name}"]
+
+        def get_all_dot_graphs(self) -> Dict[str, pydot.Dot]:
+            return self._dot_graphs
+
+        def _get_node_style(self, node: torch.fx.Node) -> Dict[str, str]:
+
+            template = {
+                "shape": self.dot_graph_shape,
+                "fillcolor": "#CAFFE3",
+                "style": '"filled,rounded"',
+                "fontcolor": "#000000",
+            }
+            if node.op in _COLOR_MAP:
+                template["fillcolor"] = _COLOR_MAP[node.op]
+            else:
+                # Use a random color for each node; based on its name so it's stable.
+                target_name = node._pretty_print_target(node.target)
+                target_hash = int(hashlib.md5(target_name.encode()).hexdigest()[:8], 16)
+                template["fillcolor"] = _HASH_COLOR_MAP[target_hash % len(_HASH_COLOR_MAP)]
+            return template
+
+        def _get_leaf_node(
+            self, module: torch.nn.Module, node: torch.fx.Node
+        ) -> torch.nn.Module:
+            py_obj = module
+            assert isinstance(node.target, str)
+            atoms = node.target.split(".")
+            for atom in atoms:
+                if not hasattr(py_obj, atom):
+                    raise RuntimeError(
+                        str(py_obj) + " does not have attribute " + atom + "!"
+                    )
+                py_obj = getattr(py_obj, atom)
+            return py_obj
+
+        def _typename(self, target: Any) -> str:
+            if isinstance(target, torch.nn.Module):
+                ret = torch.typename(target)
+            elif isinstance(target, str):
+                ret = target
+            else:
+                ret = _get_qualified_name(target)
+
+            # Escape "{" and "}" to prevent dot files like:
+            # https://gist.github.com/SungMinCho/1a017aab662c75d805c5954d62c5aabc
+            # which triggers `Error: bad label format (...)` from dot
+            return ret.replace("{", r"\{").replace("}", r"\}")
+
+        # shorten path to avoid drawing long boxes
+        # for full path = '/home/weif/pytorch/test.py'
+        # return short path = 'pytorch/test.py'
+        def _shorten_file_name(
+            self,
+            full_file_name: str,
+            truncate_to_last_n: int = 2,
+        ):
+            splits = full_file_name.split('/')
+            if len(splits) >= truncate_to_last_n:
+                return '/'.join(splits[-truncate_to_last_n:])
+            return full_file_name
+
+
+        def _get_node_label(
+            self,
+            module: torch.fx.GraphModule,
+            node: torch.fx.Node,
+            skip_node_names_in_args: bool,
+            parse_stack_trace: bool,
+        ) -> str:
+            def _get_str_for_args_kwargs(arg):
+                if isinstance(arg, tuple):
+                    prefix, suffix = r"|args=(\l", r",\n)\l"
+                    arg_strs_list = [_format_arg(a, max_list_len=8) for a in arg]
+                elif isinstance(arg, dict):
+                    prefix, suffix = r"|kwargs={\l", r",\n}\l"
+                    arg_strs_list = [
+                        f"{k}: {_format_arg(v, max_list_len=8)}"
+                        for k, v in arg.items()
+                    ]
+                else:  # Fall back to nothing in unexpected case.
+                    return ""
+
+                # Strip out node names if requested.
+                if skip_node_names_in_args:
+                    arg_strs_list = [a for a in arg_strs_list if "%" not in a]
+                if len(arg_strs_list) == 0:
+                    return ""
+                arg_strs = prefix + r",\n".join(arg_strs_list) + suffix
+                if len(arg_strs_list) == 1:
+                    arg_strs = arg_strs.replace(r"\l", "").replace(r"\n", "")
+                return arg_strs.replace("{", r"\{").replace("}", r"\}")
+
+
+            label = "{" + f"name=%{node.name}|op_code={node.op}\n"
+
+            if node.op == "call_module":
+                leaf_module = self._get_leaf_node(module, node)
+                label += r"\n" + self._typename(leaf_module) + r"\n|"
+                extra = ""
+                if hasattr(leaf_module, "__constants__"):
+                    extra = r"\n".join(
+                        [f"{c}: {getattr(leaf_module, c)}" for c in leaf_module.__constants__]  # type: ignore[union-attr]
+                    )
+                label += extra + r"\n"
+            else:
+                label += f"|target={self._typename(node.target)}" + r"\n"
+                if self.normalize_args:
+                    try:
+                        args, kwargs = normalize_function(  # type: ignore[misc]
+                            node.target, node.args, node.kwargs, normalize_to_only_use_kwargs=True  # type: ignore[arg-type]
+                        )
+                    except Exception:
+                        # Fallback to not normalizing if there's an exception.
+                        # Some functions need overloads specified to normalize.
+                        args, kwargs = node.args, node.kwargs
+                else:
+                    args, kwargs = node.args, node.kwargs
+                if len(args) > 0:
+                    label += _get_str_for_args_kwargs(args)
+                if len(kwargs) > 0:
+                    label += _get_str_for_args_kwargs(kwargs)
+                label += f"|num_users={len(node.users)}" + r"\n"
+
+            tensor_meta = node.meta.get('tensor_meta')
+            label += self._tensor_meta_to_label(tensor_meta)
+
+            # for original fx graph
+            # print buf=buf0, n_origin=6
+            buf_meta = node.meta.get('buf_meta', None)
+            if buf_meta is not None:
+                label += f"|buf={buf_meta.name}" + r"\n"
+                label += f"|n_origin={buf_meta.n_origin}" + r"\n"
+
+            # for original fx graph
+            # print file:lineno code
+            if parse_stack_trace and node.stack_trace is not None:
+                parsed_stack_trace = _parse_stack_trace(node.stack_trace)
+                fname = self._shorten_file_name(parsed_stack_trace.file)
+                label += f"|file={fname}:{parsed_stack_trace.lineno} {parsed_stack_trace.code}" + r"\n"
+
+
+            return label + "}"
+
+        def _tensor_meta_to_label(self, tm) -> str:
+            if tm is None:
+                return ""
+            elif isinstance(tm, TensorMetadata):
+                return self._stringify_tensor_meta(tm)
+            elif isinstance(tm, list):
+                result = ""
+                for item in tm:
+                    result += self._tensor_meta_to_label(item)
+                return result
+            elif isinstance(tm, dict):
+                result = ""
+                for v in tm.values():
+                    result += self._tensor_meta_to_label(v)
+                return result
+            elif isinstance(tm, tuple):
+                result = ""
+                for item in tm:
+                    result += self._tensor_meta_to_label(item)
+                return result
+            else:
+                raise RuntimeError(f"Unsupported tensor meta type {type(tm)}")
+
+        def _stringify_tensor_meta(self, tm: TensorMetadata) -> str:
+            result = ""
+            if not hasattr(tm, "dtype"):
+                print("tm", tm)
+            result += "|" + "dtype" + "=" + str(tm.dtype) + r"\n"
+            result += "|" + "shape" + "=" + str(tuple(tm.shape)) + r"\n"
+            result += "|" + "requires_grad" + "=" + str(tm.requires_grad) + r"\n"
+            result += "|" + "stride" + "=" + str(tm.stride) + r"\n"
+            if tm.is_quantized:
+                assert tm.qparams is not None
+                assert "qscheme" in tm.qparams
+                qscheme = tm.qparams["qscheme"]
+                if qscheme in {
+                        torch.per_tensor_affine,
+                        torch.per_tensor_symmetric,
+                }:
+                    result += "|" + "q_scale" + "=" + str(tm.qparams["scale"]) + r"\n"
+                    result += "|" + "q_zero_point" + "=" + str(tm.qparams["zero_point"]) + r"\n"
+                elif qscheme in {
+                        torch.per_channel_affine,
+                        torch.per_channel_symmetric,
+                        torch.per_channel_affine_float_qparams,
+                }:
+                    result += "|" + "q_per_channel_scale" + "=" + str(tm.qparams["scale"]) + r"\n"
+                    result += "|" + "q_per_channel_zero_point" + "=" + str(tm.qparams["zero_point"]) + r"\n"
+                    result += "|" + "q_per_channel_axis" + "=" + str(tm.qparams["axis"]) + r"\n"
+                else:
+                    raise RuntimeError(f"Unsupported qscheme: {qscheme}")
+                result += "|" + "qscheme" + "=" + str(tm.qparams["qscheme"]) + r"\n"
+            return result
+
+        def _get_tensor_label(self, t: torch.Tensor) -> str:
+            return str(t.dtype) + str(list(t.shape)) + r"\n"
+
+        # when parse_stack_trace=True
+        # print file:lineno code
+        def _to_dot(
+            self,
+            graph_module: torch.fx.GraphModule,
+            name: str,
+            ignore_getattr: bool,
+            ignore_parameters_and_buffers: bool,
+            skip_node_names_in_args: bool,
+            parse_stack_trace: bool,
+        ) -> pydot.Dot:
+            """
+            Actual interface to visualize a fx.Graph. Note that it takes in the GraphModule instead of the Graph.
+            If ignore_parameters_and_buffers is True, the parameters and buffers
+            created with the module will not be added as nodes and edges.
+            """
+
+            # "TB" means top-to-bottom rank direction in layout
+            dot_graph = pydot.Dot(name, rankdir="TB")
+
+
+            buf_name_to_subgraph = {}
+
+            for node in graph_module.graph.nodes:
+                if ignore_getattr and node.op == "get_attr":
+                    continue
+
+                style = self._get_node_style(node)
+                dot_node = pydot.Node(
+                    node.name, label=self._get_node_label(graph_module, node, skip_node_names_in_args, parse_stack_trace), **style
+                )
+
+                current_graph = dot_graph
+
+                buf_meta = node.meta.get('buf_meta', None)
+                if buf_meta is not None and buf_meta.n_origin > 1:
+                    buf_name = buf_meta.name
+                    if buf_name not in buf_name_to_subgraph:
+                        buf_name_to_subgraph[buf_name] = pydot.Cluster(buf_name, label=buf_name)
+                    current_graph = buf_name_to_subgraph.get(buf_name)
+
+                current_graph.add_node(dot_node)
+
+                def get_module_params_or_buffers():
+                    for pname, ptensor in chain(
+                        leaf_module.named_parameters(), leaf_module.named_buffers()
+                    ):
+                        pname1 = node.name + "." + pname
+                        label1 = (
+                            pname1 + "|op_code=get_" + "parameter"
+                            if isinstance(ptensor, torch.nn.Parameter)
+                            else "buffer" + r"\l"
+                        )
+                        dot_w_node = pydot.Node(
+                            pname1,
+                            label="{" + label1 + self._get_tensor_label(ptensor) + "}",
+                            **_WEIGHT_TEMPLATE,
+                        )
+                        dot_graph.add_node(dot_w_node)
+                        dot_graph.add_edge(pydot.Edge(pname1, node.name))
+
+                if node.op == "call_module":
+                    leaf_module = self._get_leaf_node(graph_module, node)
+
+                    if not ignore_parameters_and_buffers and not isinstance(leaf_module, torch.fx.GraphModule):
+                        get_module_params_or_buffers()
+
+            for subgraph in buf_name_to_subgraph.values():
+                subgraph.set('color', 'royalblue')
+                subgraph.set('penwidth', '2')
+                dot_graph.add_subgraph(subgraph)
+
+            for node in graph_module.graph.nodes:
+                if ignore_getattr and node.op == "get_attr":
+                    continue
+
+                for user in node.users:
+                    dot_graph.add_edge(pydot.Edge(node.name, user.name))
+
+            return dot_graph
+
+else:
+    if not TYPE_CHECKING:
+        @compatibility(is_backward_compatible=False)
+        class FxGraphDrawer:
+            def __init__(
+                self,
+                graph_module: torch.fx.GraphModule,
+                name: str,
+                ignore_getattr: bool = False,
+                ignore_parameters_and_buffers: bool = False,
+                skip_node_names_in_args: bool = True,
+                parse_stack_trace: bool = False,
+                dot_graph_shape: Optional[str] = None,
+                normalize_args: bool = False,
+            ):
+                raise RuntimeError('FXGraphDrawer requires the pydot package to be installed. Please install '
+                                   'pydot through your favorite Python package manager.')
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/graph_manipulation.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/graph_manipulation.py
new file mode 100644
index 0000000000000000000000000000000000000000..36c59cb31af05c689826ff338d710605a68d9587
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/graph_manipulation.py
@@ -0,0 +1,111 @@
+# mypy: allow-untyped-defs
+from typing import Any, Dict, List, NamedTuple, Optional
+
+import torch
+from torch.fx._compatibility import compatibility
+from torch.fx.graph import Graph
+from torch.fx.graph_module import GraphModule
+from torch.fx.node import (
+    map_arg,
+    Node,
+    Target,
+)
+from torch.fx.passes.shape_prop import ShapeProp
+
+__all__ = ['replace_target_nodes_with', 'size_bytes', 'get_size_of_all_nodes', 'get_tensor_meta',
+           'get_size_of_node']
+
+@compatibility(is_backward_compatible=False)
+def replace_target_nodes_with(
+    fx_module: GraphModule,
+    old_op: str,
+    old_target: Target,
+    new_op: str,
+    new_target: Target,
+):
+    """Modifies all nodes in fx_module.graph.nodes which match the specified op code and target,
+    and updates them to match the new op code and target"""
+    new_graph = Graph()
+    val_map: Dict[Node, Node] = {}
+    for node in fx_module.graph.nodes:
+        if node.op == old_op and node.target == old_target:
+            args = map_arg(node.args, lambda n: val_map[n])
+            kwargs = map_arg(node.kwargs, lambda n: val_map[n])
+            assert isinstance(args, tuple)
+            assert isinstance(kwargs, dict)
+            val_map[node] = new_graph.create_node(
+                new_op, new_target, args, kwargs, node.name
+            )
+        else:
+            val_map[node] = new_graph.node_copy(node, lambda n: val_map[n])
+    fx_module.graph = new_graph
+
+
+@compatibility(is_backward_compatible=False)
+class size_bytes(NamedTuple):
+    output_size: int
+    total_size: int
+
+
+@compatibility(is_backward_compatible=False)
+def get_size_of_all_nodes(
+    fx_module: GraphModule, args: Optional[List[torch.Tensor]] = None
+) -> None:
+    """Given a fx graph module, update each node with its total size (weights + bias + output)
+    and its output_size(output). For a non-module node, the total size is the output size.
+    return total size"""
+    if args is not None:
+        # Mark shape and dtype for each node (node.shape and node.dtype)
+        ShapeProp(fx_module).propagate(*args)
+    # Calculate the total size of the whole fx graph
+    total_size_of_graph = 0.0
+    for node in fx_module.graph.nodes:
+        if node.op == "output":
+            break
+        node.size_bytes = get_size_of_node(fx_module, node)
+    return
+
+
+@compatibility(is_backward_compatible=False)
+def get_tensor_meta(node: Node) -> Any:
+    tensor_meta = node.meta.get("tensor_meta")
+
+    if not tensor_meta:
+        raise RuntimeError(
+            f"Node {node} has no tensor metadata associated with it! "
+            f"Check that shape propagation has run."
+        )
+
+    return tensor_meta
+
+
+@compatibility(is_backward_compatible=False)
+def get_size_of_node(fx_module: GraphModule, node: Node) -> size_bytes:
+    """Given a node with node.dtype and node.shape, return its total size and its output size.
+    total_size = weights + bias + output_size
+    """
+    # Total num of elements
+    total_num_of_elems = 0
+    # For a module, conside all parameters
+    if node.op == "call_module":
+        submodule_dict = dict(fx_module.named_modules())
+        submodule = submodule_dict[node.target]
+        parameters = submodule.named_parameters()
+        # Parameters are named tuples
+        for name, p in parameters:
+            total_num_of_elems += p.numel()
+    # Don't forget the output size
+    # node.shape is the shape of this node's output
+    tensor_meta = get_tensor_meta(node)
+    output_elem = tensor_meta.shape.numel()
+    total_num_of_elems += output_elem
+    # Assume for now if it's quantized then it's qint8 or quint8
+    if tensor_meta.is_quantized:
+        size_per_elem_bytes = torch._empty_affine_quantized(
+            [], dtype=tensor_meta.dtype
+        ).element_size()
+    else:
+        size_per_elem_bytes = torch.tensor([], dtype=tensor_meta.dtype).element_size()
+    total_size = size_per_elem_bytes * total_num_of_elems
+    output_size = size_per_elem_bytes * output_elem
+    return size_bytes(output_size, total_size)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/graph_transform_observer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/graph_transform_observer.py
new file mode 100644
index 0000000000000000000000000000000000000000..6390b7cee4954c1b1c8bf9fbc0040df77e146d12
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/graph_transform_observer.py
@@ -0,0 +1,91 @@
+# mypy: allow-untyped-defs
+import os
+from typing import Optional
+
+from torch.fx._compatibility import compatibility
+from torch.fx.graph_module import GraphModule
+
+from .graph_drawer import FxGraphDrawer
+
+
+__all__ = ["GraphTransformObserver"]
+
+
+@compatibility(is_backward_compatible=False)
+class GraphTransformObserver:
+    __pass_count = 0
+
+    def __init__(self, gm: GraphModule, passname: str, log_url: Optional[str] = None):
+        # If log_url is None, we don't log anything
+        self.log_url = log_url
+        if self.log_url is None:
+            return
+        GraphTransformObserver.__pass_count += 1
+        self.gm = gm
+        self.passname = passname
+
+        self.input_dot_graph = FxGraphDrawer(
+            self.gm,
+            self.passname,
+            ignore_getattr=True,
+            ignore_parameters_and_buffers=True,
+        ).get_dot_graph()
+
+    @classmethod
+    def get_current_pass_count(cls):
+        return cls.__pass_count
+
+    def __enter__(self):
+        if self.log_url is None or self.gm is None:
+            return self
+
+        self.erased_nodes = set()
+        self.created_nodes = set()
+        self.gm._register_create_node_hook(self.on_node_creation)
+        self.gm._register_erase_node_hook(self.on_node_erase)
+
+        return self
+
+    def __exit__(self, type, value, tb):
+        if self.log_url is None or self.gm is None:
+            return
+
+        self.gm._unregister_create_node_hook(self.on_node_creation)
+        self.gm._unregister_erase_node_hook(self.on_node_erase)
+
+        if len(self.created_nodes) > 0 or len(self.erased_nodes) > 0:
+            for e in self.input_dot_graph.get_node_list():
+                if e.get_name() in self.erased_nodes:
+                    e.obj_dict["attributes"]["fillcolor"] = "yellow"
+                else:
+                    e.obj_dict["attributes"]["fillcolor"] = "grey"
+            self.input_dot_graph.write(
+                os.path.join(
+                    self.log_url,
+                    f"pass_{GraphTransformObserver.__pass_count}_{self.passname}_input_graph.dot",
+                )
+            )
+
+            output_dot_graph = FxGraphDrawer(
+                self.gm,
+                self.passname,
+                ignore_getattr=True,
+                ignore_parameters_and_buffers=True,
+            ).get_dot_graph()
+            for e in output_dot_graph.get_node_list():
+                if e.get_name() in self.created_nodes:
+                    e.obj_dict["attributes"]["fillcolor"] = "yellow"
+                else:
+                    e.obj_dict["attributes"]["fillcolor"] = "grey"
+            output_dot_graph.write(
+                os.path.join(
+                    self.log_url,
+                    f"pass_{GraphTransformObserver.__pass_count}_{self.passname}_output_graph.dot",
+                )
+            )
+
+    def on_node_creation(self, node):
+        self.created_nodes.add(node.name)
+
+    def on_node_erase(self, node):
+        self.erased_nodes.add(node.name)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/infra/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/infra/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..657b6a93014f428eece18ec896136c81bc3949f3
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/infra/__init__.py
@@ -0,0 +1,2 @@
+
+from . import pass_manager
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/infra/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/infra/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/infra/partitioner.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/infra/partitioner.py
new file mode 100644
index 0000000000000000000000000000000000000000..271f90a7b75e8b63b9541d344a00476bf2d1e4ab
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/infra/partitioner.py
@@ -0,0 +1,335 @@
+# mypy: allow-untyped-defs
+from torch.fx.passes.utils.fuser_utils import fuse_by_partitions
+import collections
+import itertools
+import logging
+
+from copy import copy
+from typing import Dict, Iterable, List, Optional, Sequence, Set
+
+from torch.fx.graph_module import GraphModule
+from torch.fx.node import Node, _get_qualified_name
+from torch.fx.passes.operator_support import OperatorSupportBase
+
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.WARNING)
+
+class Partition:
+    def __init__(self, id: Optional[int] = None, nodes: Optional[Iterable[Node]] = None):
+        self.id = id
+        self.nodes = dict.fromkeys(nodes) if nodes is not None else {}
+
+    def __repr__(self) -> str:
+        return str(self.nodes)
+
+    def add_node(self, node: Node):
+        self.nodes.update({node: None})
+
+    def remove_node(self, node: Node):
+        del self.nodes[node]
+
+    def size(self):
+        return len(self.nodes)
+
+class _DependencyViewer:
+    def __init__(self, graph_module: GraphModule):
+        self.upstreams = collections.defaultdict(set)
+        self.downstreams = collections.defaultdict(set)
+
+        for node in graph_module.graph.nodes:
+            for input_node in node.all_input_nodes:
+                # add input_node and input_node's upstream dependency
+                self.upstreams[node].add(input_node)
+                self.upstreams[node].update(self.upstreams[input_node])
+
+        for node in reversed(graph_module.graph.nodes):
+            for output_node in node.users:
+                # add output_node and output_node's downstream dependency
+                self.downstreams[node].add(output_node)
+                self.downstreams[node].update(self.downstreams[output_node])
+
+    def downstreams_of(self, node: Node) -> Set[Node]:
+        return self.downstreams[node]
+
+    def upstreams_of(self, node: Node) -> Set[Node]:
+        return self.upstreams[node]
+
+class CapabilityBasedPartitioner:
+
+    def __init__(self,
+                 graph_module: GraphModule,
+                 operator_support: OperatorSupportBase,
+                 allows_single_node_partition: bool = False,
+                 non_compute_ops: Optional[Sequence[str]] = None,
+                 allowed_single_node_partition_ops: Optional[Sequence[str]] = None,
+                 ) -> None:
+        self.graph_module = graph_module
+        self.operator_support = operator_support
+        self.allows_single_node_partition = allows_single_node_partition
+        self.non_compute_ops = non_compute_ops if non_compute_ops is not None else []
+        self.allowed_single_node_partition_ops = (
+            allowed_single_node_partition_ops
+            if allowed_single_node_partition_ops is not None
+            else []
+        )
+        self.dependency_viewer = _DependencyViewer(graph_module)
+
+    def __is_node_supported(self, node: Node) -> bool:
+        return (
+            self.operator_support.is_node_supported(dict(self.graph_module.named_modules()), node)
+        )
+
+    def propose_partitions(self) -> List[Partition]:
+        # partition_map is a mapping from partition id to a set of partition id's.
+        # The value set contains all the partition ids that can be reached by doing a
+        # DFS starting from the partition id in the key.
+        partition_map : Dict[int, Set] = collections.defaultdict(set)
+
+        # assumptions: nodes in candidate list is sorted in topological order
+        assignment: Dict[Node, int] = {}   # mapping from node to partition_id
+        partitions_by_id: Dict[int, Partition] = {}  # mapping from partition_id to partition
+        new_partition_id = itertools.count()
+
+        # try to merge partition other_id into partition self_id
+        # merge only happens if the end graph doesn't contain cyclic dependency
+        # returns `True` when merge happens, `False` otherwise.
+        def maybe_merge_partition(self_id: int, other_id: int):
+            # merged_nodes is the union of nodes in two partition to-be-merged
+            merged_nodes = copy(partitions_by_id[self_id].nodes)
+            merged_nodes.update(partitions_by_id[other_id].nodes)
+
+            def dfs_iter_find_cycle(all_user_nodes: Set[Node]):
+                for user_node in all_user_nodes:
+                    visited_partition_ids = set()
+
+                    for path_node in self.dependency_viewer.downstreams_of(user_node):
+                        # If any of the nodes in the dfs path of this node are in the merged_nodes
+                        # list then there is a cycle in the graph.
+                        if path_node in merged_nodes:
+                            return True
+
+                        # If any of the nodes in the dfs path of this node are in the assignment
+                        # map then we have to make sure that the partitions that these nodes belong
+                        # to do not form a cycle with the current partitions being merged. This means
+                        # iterating through all the nodes in all the parititons that are traversed in
+                        # the dfs path and checking if they are in the merged_nodes list.
+                        if path_node in assignment:
+                            partition_id = assignment[path_node]
+                            # If the partition id has already been visited then we know that it doesn't
+                            # form a cycle with the current partitions being merged.
+                            if partition_id in visited_partition_ids:
+                                continue
+                            p_map = partition_map[partition_id]
+                            if self_id in p_map or other_id in p_map:
+                                return True
+
+                            visited_partition_ids.add(partition_id)
+
+                return False
+
+            # check if merge would create cyclic dependency.
+            all_user_nodes = set()
+            for node in merged_nodes:
+                for user_node in node.users:
+                    if user_node not in merged_nodes:
+                        all_user_nodes.add(user_node)
+
+            if dfs_iter_find_cycle(all_user_nodes):
+                # return false indicating cyclic dependency found and
+                # merge is aborted
+                return False
+
+            # no cyclic dependency found, move forward with the merge
+            # updating partition nodes
+            partitions_by_id[self_id].nodes = merged_nodes
+            # updating assignment map
+            for node in partitions_by_id[other_id].nodes:
+                assignment[node] = self_id
+            # delete other partition
+            del partitions_by_id[other_id]
+
+            partition_map[self_id] = partition_map[self_id].union(partition_map[other_id])
+            del partition_map[other_id]
+
+            return True
+
+        def merge_single_node(node: Node, id: Optional[int]):
+            def _update_partition_map(node: Node, id: int):
+                # Iterate through all the downstream nodes of this node and update the partition map
+                # to indicate that there is a path from the partition id of this node to the target
+                # partition id.
+                downstream_nodes = self.dependency_viewer.downstreams_of(node)
+                for curr_node in downstream_nodes:
+                    target_id = assignment.get(curr_node, None)
+                    if target_id is not None:
+                        partition_map[id].add(target_id)
+
+                # Iterate through all the upstream nodes of this node and update the partition map
+                # to indicate that there is a path from the partition id of the upstream node to the
+                # current node's partition id.
+                upstream_nodes = self.dependency_viewer.upstreams_of(node)
+                for curr_node in upstream_nodes:
+                    source_id = assignment.get(curr_node, None)
+                    if source_id is not None:
+                        partition_map[source_id].add(id)
+
+            if node in assignment:
+                partitions_by_id[assignment[node]].remove_node(node)
+
+            if id is None:
+                assignment.pop(node)
+            elif id not in partitions_by_id:
+                assignment[node] = id
+                partitions_by_id[id] = Partition(id=id, nodes=[node])
+                _update_partition_map(node, id)
+            else:
+                assignment[node] = id
+                partitions_by_id[id].add_node(node)
+                _update_partition_map(node, id)
+
+        logger.debug("Proposing partitions...")
+
+        for node in reversed(self.graph_module.graph.nodes):
+            # use Dict as an ordered set to ensure deterministic partitioning result, don't care value
+            merge_candidates: Dict[int, None] = {}
+
+            # Note a limited horizontal fusion is enabled:
+            #   when `node` is not supported, the code below attempts to fuse consumer of `node`.
+            #
+            # I don't see a need to add a knob to disable horizontal fusion yet, we can short-cut
+            # the fusion by adding an `else` block here to skip horizontal fusion.
+            if self.__is_node_supported(node) and node not in assignment:
+                partition_id = next(new_partition_id)
+                merge_single_node(node, partition_id)
+                merge_candidates[partition_id] = None
+
+            # merge all possible partitions
+            for node in assignment:
+                merge_candidates[assignment[node]] = None
+
+            merge_candidates_list = list(merge_candidates.keys())
+            if len(merge_candidates_list) > 1:
+                self_id = merge_candidates_list[0]
+                for other_id in merge_candidates_list[1:]:
+                    # note: merge partition `other_id` into partition `self_id` if
+                    # it doesn't create cyclic dependency in the graph, otherwise,
+                    # this is a no-op
+                    maybe_merge_partition(self_id, other_id)
+
+        # post processing to re-assign "getitem" nodes into upstream partition
+        logger.debug("Reassigning getitem nodes to its producer node's partition...")
+        nodes_reassignment: Dict[Node, int] = {}
+        for node in self.graph_module.graph.nodes:
+            is_tuple_output = True
+            for user in node.users:
+                if user.op != "call_function" or \
+                   _get_qualified_name(user.target) != "_operator.getitem":     # type: ignore[arg-type]
+                    is_tuple_output = False
+                    break
+
+            # node has tuple outputs, re-assign all following getitem node into node's partition
+            if is_tuple_output:
+                id = assignment.get(node, None)     # type: ignore[arg-type]
+                for user in node.users:
+                    if assignment.get(user, None) != id:    # type: ignore[arg-type]
+                        nodes_reassignment[user] = id  # type: ignore[assignment]
+        for node, id in nodes_reassignment.items():
+            merge_single_node(node, id)
+
+        # filter out single node partitions
+        if not self.allows_single_node_partition:
+            logger.debug("Filtering out single node partitions...")
+            default_non_compute_ops = {"torch.ops.aten.view", "_operator.getitem"}
+            non_compute_ops = default_non_compute_ops.union(set(self.non_compute_ops))
+            partitions_to_remove: List[int] = []
+            for id, partition in partitions_by_id.items():
+                compute_node_count = 0
+                for node in partition.nodes:
+                    if node.op == "call_function":
+                        assert callable(node.target)
+                        if _get_qualified_name(node.target) not in non_compute_ops:
+                            compute_node_count += 1
+                        if _get_qualified_name(node.target) in self.allowed_single_node_partition_ops:
+                            compute_node_count += 1
+                if compute_node_count <= 1:
+                    partitions_to_remove.append(id)
+            for id in partitions_to_remove:
+                del partitions_by_id[id]
+
+        logger.debug("Partitions proposed:")
+        for id, partition in partitions_by_id.items():
+            logger.debug("partition #%s: %s", id, [node.name for node in partition.nodes])
+
+        return [partition for partition in partitions_by_id.values() if partition.size() > 0]
+
+    def fuse_partitions(self, partitions: List[Partition], prefix: str = "fused_") -> GraphModule:
+        logger.debug("Fusing partitions...")
+        # fuse_by_partitions expects partitions in List[List[Node]]: [ [node0, node1], [node2, node3] ]
+        return fuse_by_partitions(
+            self.graph_module,
+            [list(partition.nodes) for partition in partitions],
+            prefix=prefix,
+        )
+
+    # remove non-compute-ops that sits at the boundary of a partition.
+    def remove_bookend_non_compute_ops(self, partitions: List[Partition]):
+        non_compute_ops = set(self.non_compute_ops)
+
+        def is_non_compute_node(node: Node):
+            return node.op == "call_function" and \
+                _get_qualified_name(node.target) in non_compute_ops  # type: ignore[arg-type]
+
+        # cache transparent nodes
+        transparent_input_nodes: Dict[Node, bool] = {}
+        transparent_output_nodes: Dict[Node, bool] = {}
+
+        def is_transparent_input_node(node: Node, partition: Set[Node], removed_nodes: Set[Node]):
+            if node.op == "placeholder" or (node not in partition) or (node in removed_nodes):
+                return True
+            if node in transparent_input_nodes:
+                return transparent_input_nodes[node]
+            if is_non_compute_node(node):
+                for input_n in node.all_input_nodes:
+                    if not is_transparent_input_node(input_n, partition, removed_nodes):
+                        transparent_input_nodes[node] = False
+                        return False
+                transparent_input_nodes[node] = True
+                return True
+            transparent_input_nodes[node] = False
+            return False
+
+        def is_transparent_output_node(node: Node, partition: Set[Node], removed_nodes: Set[Node]):
+            if node.op == "placeholder" or (node not in partition) or (node in removed_nodes):
+                return True
+            if node in transparent_output_nodes:
+                return transparent_output_nodes[node]
+            if is_non_compute_node(node):
+                for output_n in node.users:
+                    if not is_transparent_output_node(output_n, partition, removed_nodes):
+                        transparent_output_nodes[node] = False
+                        return False
+                transparent_output_nodes[node] = True
+                return True
+            transparent_output_nodes[node] = False
+            return False
+
+        for partition in partitions:
+            # Note it's ok to use `set` here, since we are only query if a node
+            # has been removed. We are NEVER going to iterate on nodes inside
+            # the set.
+            remove_node: Set[Node] = set()
+            for node in partition.nodes:
+                if is_non_compute_node(node) and \
+                    (is_transparent_input_node(node, set(partition.nodes), remove_node) or
+                     is_transparent_output_node(node, set(partition.nodes), remove_node)):
+                    remove_node.add(node)
+
+            if len(remove_node) != 0:
+                for node in remove_node:
+                    partition.nodes.pop(node, None)
+
+    def partition_and_fuse(self, prefix: str = "fused_") -> GraphModule:
+        partitions = self.propose_partitions()
+        fused_gm = self.fuse_partitions(partitions, prefix=prefix)
+        return fused_gm
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/infra/pass_base.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/infra/pass_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f5b64eafbb60ffca5e6539c7811f2f003ce9c1f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/infra/pass_base.py
@@ -0,0 +1,73 @@
+# mypy: allow-untyped-defs
+import abc
+from collections import namedtuple
+from typing import Optional
+
+from torch.fx.graph_module import GraphModule
+from torch.fx._compatibility import compatibility
+
+
+__all__ = ['PassResult', 'PassBase']
+
+@compatibility(is_backward_compatible=False)
+class PassResult(namedtuple("PassResult", ["graph_module", "modified"])):
+    """
+    Result of a pass:
+        graph_module: The modified graph module
+        modified: A flag for if the pass has modified the graph module
+    """
+    def __new__(cls, graph_module, modified):
+        return super().__new__(cls, graph_module, modified)
+
+@compatibility(is_backward_compatible=False)
+class PassBase(abc.ABC):
+    """
+    Base interface for implementing passes.
+
+    It is required to implement the `call` function so that we can directly
+    pass instances of the Pass directly to the PassManager and call them as a
+    function.
+
+    We can directly pass an instance of a class implementing this interface into
+    the PassManager's `passes` attribute.
+    """
+
+    def __call__(self, graph_module: GraphModule) -> Optional[PassResult]:
+        """
+        Runs the precondition check, the pass itself, and the postcondition check.
+        """
+
+        self.requires(graph_module)
+        res = self.call(graph_module)
+        self.ensures(graph_module)
+        return res
+
+    @abc.abstractmethod
+    def call(self, graph_module: GraphModule) -> Optional[PassResult]:
+        """
+        The pass that is run through the given graph module. To implement a
+        pass, it is required to implement this function.
+
+        Args:
+            graph_module: The graph module we will run a pass on
+        """
+
+    def requires(self, graph_module: GraphModule) -> None:  # noqa: B027
+        """
+        This function will be called before the pass is run and will check that
+        the given graph module contains the preconditions needed to run the
+        pass. It is not required to implement this function.
+
+        Args:
+            graph_module: The graph module we will run checks on
+        """
+
+    def ensures(self, graph_module: GraphModule) -> None:  # noqa: B027
+        """
+        This function will be called after the pass is run and will check that
+        the given graph module contains the postconditions needed to run the
+        pass. It is not required to implement this function.
+
+        Args:
+            graph_module: The graph module we will run checks on
+        """
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/infra/pass_manager.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/infra/pass_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..29540fa447eb193e4b52335178e30172b8e72728
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/infra/pass_manager.py
@@ -0,0 +1,302 @@
+# mypy: allow-untyped-defs
+import inspect
+import logging
+from queue import Queue
+from functools import wraps
+from typing import Callable, Dict, List
+
+import torch.nn as nn
+from torch.fx.graph_module import GraphModule
+from torch.fx._compatibility import compatibility
+from torch.fx.passes.infra.pass_base import PassResult
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.WARNING)
+
+__all__ = ['pass_result_wrapper', 'this_before_that_pass_constraint', 'PassManager']
+
+@compatibility(is_backward_compatible=False)
+def pass_result_wrapper(fn: Callable) -> Callable:
+    """
+    Wrapper for passes which currently do not return a PassResult.
+    This wrapper makes them return a PassResult containing the modified object
+    and True for the "modified" flag.
+
+    Args:
+        fn (Callable[Module, Any])
+
+    Returns:
+        wrapped_fn (Callable[Module, PassResult])
+    """
+    if fn is None:
+        return None
+
+    @wraps(fn)
+    def wrapped_fn(gm):
+        res = fn(gm)
+        if res is None:
+            return PassResult(gm, True)
+        if isinstance(res, PassResult):
+            return res
+        elif isinstance(res, nn.Module):
+            return PassResult(res, True)
+
+    if not inspect.isfunction(fn):
+        wrapped_fn.__name__ = type(fn).__name__
+
+    return wrapped_fn
+
+def _validate_pass_schedule_constraint(
+    constraint: Callable[[Callable, Callable], bool], passes: List[Callable]
+) -> None:
+    for i, a in enumerate(passes):
+        for j, b in enumerate(passes[i + 1 :]):
+            if constraint(a, b):
+                continue
+            raise RuntimeError(
+                f"pass schedule constraint violated. Expected {a} before {b}"
+                f" but found {a} at index {i} and {b} at index{j} in pass"
+                f" list."
+            )
+
+def _topological_sort_passes(
+    passes: List[Callable], constraints: List[Callable]
+) -> List[Callable]:
+    """
+    Args
+        passes: Passes that we are ordering
+        constraints: Constraints applied on these passes
+
+    Returns
+        A sorted list of callables and a boolean of if a circular dependency
+        existed
+    """
+    if len(constraints) == 0:
+        return passes
+
+    # Contruct a graph mapping nodes to a list of their users
+    graph: Dict[Callable, List[Callable]] = {p : [] for p in passes}
+    indegree_map: Dict[Callable, int] = dict.fromkeys(passes, 0)
+    candidates: Queue = Queue()
+    for a in passes:
+        for b in passes:
+            if a == b:
+                continue
+
+            for constraint in constraints:
+                if not constraint(a, b):
+                    graph[b].append(a)
+                    indegree_map[a] += 1
+
+        if indegree_map[a] == 0:
+            candidates.put(a)
+
+    visited: Dict[Callable, bool] = dict.fromkeys(passes, False)
+    sorted_passes: List[Callable] = []
+
+    while not candidates.empty():
+        p = candidates.get()
+        sorted_passes.append(p)
+        visited[p] = True
+
+        for n in graph[p]:
+            if not visited[n]:
+                indegree_map[n] -= 1
+                if indegree_map[n] == 0:
+                    candidates.put(n)
+
+    # Check if there are unvisited nodes (aka cycles in the graph)
+    cycle_passes = list(filter(lambda p: indegree_map[p] != 0, indegree_map.keys()))
+    if len(cycle_passes) != 0:
+        error = f"Circular dependency detected within the following passes: {cycle_passes}"
+        raise RuntimeError(error)
+
+    return sorted_passes
+
+@compatibility(is_backward_compatible=False)
+def this_before_that_pass_constraint(this: Callable, that: Callable) -> Callable:
+    """
+    Defines a partial order ('depends on' function) where `this` must occur
+    before `that`.
+
+    For example, the following pass list and constraint list would be invalid.
+    ```
+    passes = [pass_b, pass_a]
+
+    constraints = [
+        this_before_that_pass_constraint(pass_a, pass_b)
+    ]
+    ```
+
+    Args:
+        this (Callable): pass which should occur first
+        that (Callable): pass which should occur later
+
+    Returns:
+        depends_on (Callable[[Object, Object], bool]
+    """
+
+    def depends_on(a: Callable, b: Callable):
+        return a != that or b != this
+
+    return depends_on
+
+
+@compatibility(is_backward_compatible=False)
+class PassManager:
+    """
+    Construct a PassManager.
+
+    Collects passes and constraints. This defines the pass schedule, manages
+    pass constraints and pass execution.
+
+    Args:
+        passes (Optional[List[Callable]]): List of passes. A pass is a
+            callable which modifies an object and returns a PassResult
+        constraint (Optional[List[Callable]]): List of constraints. A
+            constraint is a callable which takes two passes (A, B) and returns
+            True if A depends on B and False otherwise. See implementation of
+            `this_before_that_pass_constraint` for example.
+        steps (int): Max number of times we run the passes (default = 1).
+        run_checks_after_each_pass (bool): Whether to run checks and linting
+            after each pass
+        suppress_check_failures (bool): Whether to raise errors when running
+            checks
+    """
+
+    passes: List[Callable[[nn.Module], PassResult]]
+    constraints: List[Callable[[Callable, Callable], bool]]
+    _validated: bool = False
+    steps: int = 1
+
+    def __init__(
+        self,
+        passes=None,
+        constraints=None,
+        steps=None,
+        run_checks_after_each_pass: bool = False,
+        suppress_check_failures: bool = False,
+    ):
+        self.passes = passes or []
+        self.constraints = constraints or []
+        if steps:
+            self.steps = steps
+
+        self.run_checks_after_each_pass = run_checks_after_each_pass
+        self.suppress_check_failures = suppress_check_failures
+
+    def add_pass(self, _pass: Callable):
+        """
+        Adds a pass into the current list of passes.
+        """
+        self.passes.append(_pass)
+        self._validated = False
+
+    def add_constraint(self, constraint: Callable):
+        """
+        Adds a constraint into the current list of constraints.
+        """
+        self.constraints.append(constraint)
+        self._validated = False
+
+    def validate_constraints(self):
+        """
+        Validates that current pass schedule defined by `self.passes` is valid
+        according to all constraints in `self.constraints`
+        """
+        if self._validated:
+            return
+        for constraint in self.constraints:
+            _validate_pass_schedule_constraint(constraint, self.passes)
+        self._validated = True
+
+    def solve_constraints(self):
+        """
+        Finds a valid traversal order based on the given constraints and orders
+        the passes based on this order.
+
+        If a circular dependency exists between the constraints and steps = 1,
+        then we will raise an error because if steps != 1 this means that we
+        will re-run the passes, allowing for circular dependencies.
+        """
+        self.passes = _topological_sort_passes(self.passes, self.constraints)
+        self._validated = True
+
+    def add_checks(self, check: Callable) -> None:
+        """
+        Adds a function which takes runs various checks on a given graph module.
+        This function is run before and after each pass if the
+        `run_checks_after_each_pass` flag is enabled.
+        """
+        sig = inspect.signature(check)
+
+        if len(list(sig.parameters.values())) != 1:
+            raise TypeError("PassManager check function should only take in one variable, a module")
+
+        setattr(self, "check", check)  # noqa: B010
+
+    def check(self, module: nn.Module) -> None:
+        pass
+
+    def __call__(self, module: nn.Module) -> PassResult:
+        """
+        Runs a list of passes in the order based on `self.passes` on the given
+        graph module. Each time a pass is run, checks and linting will be run on
+        the graph module if `run_checks_after_each_pass` is set.
+
+        If the module is a graph module, we will run the list of passes until
+        the graph stops changing, or until `steps` number of times.
+        """
+        # Order the passes based on the constraints
+        if not self._validated:
+            self.solve_constraints()
+
+        # Check graph invariants
+        self.check(module)
+
+        # Run the set of passes `steps` number of times or until the graph stops
+        # changing
+        overall_modified = False
+        for _ in range(self.steps):
+            modified = False
+
+            # Run the set of passes on the graph module
+            for i, fn in enumerate(self.passes):
+                fn_name = fn.__name__ if inspect.isfunction(fn) else type(fn).__name__
+                logger.debug("Running pass '%s'", fn_name)
+
+                try:
+                    res = fn(module)
+
+                    if not isinstance(res, PassResult) and not hasattr(
+                        res, "graph_module"
+                    ):
+                        raise TypeError(
+                            f"The result of the pass {fn_name} should be type PassResult."
+                            + "Please wrap it with pass_result_wrapper()"
+                        )
+                    module = res.graph_module
+                    modified = modified or res.modified
+
+                    if isinstance(module, GraphModule):
+                        logger.debug("Graph after pass '%s': %s", fn_name, module.graph)
+                        module.recompile()
+
+                    # Check graph invariants
+                    if self.run_checks_after_each_pass:
+                        self.check(module)
+
+                except Exception as e:
+                    prev_pass_names = [
+                        p.__name__ if inspect.isfunction(p) else type(p).__name__
+                        for p in self.passes[:i]
+                    ]
+                    msg = f"An error occurred when running the '{fn_name}' pass after the following passes: {prev_pass_names}"
+                    raise Exception(msg) from e  # noqa: TRY002
+
+            # If the graph no longer changes, then we can stop running these passes
+            overall_modified = overall_modified or modified
+            if not modified:
+                break
+
+        return PassResult(module, overall_modified)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/net_min_base.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/net_min_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..6182972e670eaf078c5b82c32626bab1ba98f11e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/net_min_base.py
@@ -0,0 +1,924 @@
+# mypy: allow-untyped-defs
+import logging
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, List, Optional, Tuple
+
+import torch
+import torch.fx
+
+from torch.fx._compatibility import compatibility
+from torch.fx.node import map_arg
+
+from .shape_prop import ShapeProp
+from .split_utils import split_by_tags
+from .tools_common import (
+    CALLABLE_NODE_OPS,
+    FxNetAccFusionsFinder,
+    Names,
+    NodeList,
+    NodeSet,
+    TensorOrTensors,
+    Tensors,
+)
+
+__all__ = [
+    "FxNetMinimizerBadModuleError",
+    "FxNetMinimizerRunFuncError",
+    "FxNetMinimizerResultMismatchError",
+]
+
+_LOGGER = logging.getLogger(__name__)
+
+
+@compatibility(is_backward_compatible=False)
+class FxNetMinimizerBadModuleError(Exception):
+    """
+    Raised if failed to split out a minimize module
+    """
+
+
+
+@compatibility(is_backward_compatible=False)
+class FxNetMinimizerRunFuncError(Exception):
+    """
+    Raised if error occurs during run_a or run_b functions
+    """
+
+
+
+@compatibility(is_backward_compatible=False)
+class FxNetMinimizerResultMismatchError(Exception):
+    """
+    Raised if comparing function thinks the results are mismatching.
+    """
+
+
+
+@dataclass
+class _MinimizerSettingBase:
+    """
+    Args:
+    `accumulate_error`: Instead of using a's input for both converted module to verify
+    , use the previous outputs of each converted module as input to accumulate the
+    errors.
+
+    `traverse_method`: "sequential" or "binary" or "accumulate"
+    Determine the way of traverse the nodes in FX module.
+
+    `find_all`: Minimizer will go through the entire model and return all problematic nodes.
+
+    `return_intermediate`: If true, when using `run_nodes()` function to run the
+    model, intermediate results of all the ops will be returned as output.
+    """
+
+    accumulate_error: bool = False
+    traverse_method: str = "sequential"
+    find_all: bool = False
+    return_intermediate: bool = False
+
+    def __str__(self):
+        settings_str = "FX Minimizer Settings:\n"
+
+        for k, v in vars(self).items():
+            settings_str += f"\t{k}: {v}\n"
+
+        return settings_str
+
+
+class _MinimizerBase:
+    """
+    This class is used to automatically find problematic nodes in a model. It takes a FX
+    graphmodule and generate some submodules while traverse the graph. Then two functions
+    `run_a` and `run_b` will be used to run the same submodule and a function `compare_fn`
+    will be used to compare the results.
+
+    Currently we provides two ways to traverse the graph and generate submodules.
+        1. Sequential traversal: this will traverse the graph node by node and generate
+           one submodule with one sigle node.
+        2. Binary searching: this will do a binary search style traversal on the graph.
+
+    For internal Users, a guide can be found here https://fb.quip.com/HDtuAgiKGfkP.
+    """
+
+    def __init__(
+        self,
+        module: torch.fx.GraphModule,
+        sample_input: Tensors,
+        compare_fn: Callable[
+            [TensorOrTensors, TensorOrTensors, Names], Tuple[float, bool]
+        ],
+        settings: _MinimizerSettingBase,
+        module_exporter: Optional[
+            Callable[
+                [Tensors, torch.fx.GraphModule, str],
+                None
+            ]
+        ] = None,
+        exclusion_fn: Optional[
+            Callable[[NodeList, int, int], None]
+        ] = None,
+    ):
+        assert isinstance(module, torch.fx.GraphModule)
+
+        self.module = module
+        self.sample_input = sample_input
+        self.compare_fn = compare_fn
+        self.module_exporter = module_exporter
+        self.settings = settings
+        self.exclusion_fn = exclusion_fn
+
+        # Stores outputs of run_a function
+        self.a_outputs: Dict[str, Any] = {}
+
+        # Stores outputs of run_b function
+        self.b_outputs: Dict[str, Any] = {}
+
+        # Stores the results of compare_fn
+        self.results: Dict[Any, Any] = {}
+
+        # Stores the report for the runs
+        self.reports: List[List[str]] = []
+
+        # Current iteration
+        self.iteration: int = 0
+
+        callable_nodes = {
+            node for node in self.module.graph.nodes if node.op in CALLABLE_NODE_OPS
+        }
+        ShapeProp(self.module).propagate(*self.sample_input)
+        self.fusions = FxNetAccFusionsFinder(self.module, callable_nodes)()
+
+        # Check if number of input in sample_input matches the number of placeholders
+        placeholders = [
+            node.name for node in self.module.graph.nodes if node.op == "placeholder"
+        ]
+        assert len(placeholders) == len(self.sample_input)
+
+        # Store sample_input
+        for i, name in enumerate(placeholders):
+            self.a_outputs[name] = sample_input[i]
+            self.b_outputs[name] = sample_input[i]
+
+    def run_a(self, mod: torch.fx.GraphModule, inputs: Tensors, report_idx: int = -1) -> TensorOrTensors:
+        """
+        Run `mod` with `inputs` and generate output. The output will be compared with
+        output of run_b().
+        """
+        raise RuntimeError("run_a() is not implemented.")
+
+    def run_b(self, mod: torch.fx.GraphModule, inputs: Tensors, report_idx: int = -1) -> TensorOrTensors:
+        """
+        Run `mod` with `inputs` and generate output. The output will be compared with
+        output of run_a().
+        """
+        raise RuntimeError("run_b() is not implemented.")
+
+    def _store_outputs(
+        self,
+        a_result: TensorOrTensors,
+        b_result: TensorOrTensors,
+        submodule: torch.fx.GraphModule,
+    ):
+        """
+        Store the outputs of self.run_a() and self.run_b() into self.a_outputs and
+        self.b_outputs, so that we can use them when execute preceding nodes that
+        use those outputs as inputs.
+
+        Args:
+            a_result: Output of self.run_a(). Could be a tensor or tensors.
+            b_result: Output of self.run_b(). Could be a tensor or tensors.
+            submodule: The module that generates a_result and b_result.
+        """
+        output_node = next(
+            node for node in submodule.graph.nodes if node.op == "output"
+        )
+
+        # Only one output
+        if isinstance(output_node.args[0], torch.fx.Node):
+            self.a_outputs[output_node.args[0].name] = a_result
+            self.b_outputs[output_node.args[0].name] = b_result
+        # Multiple outputs
+        else:
+            for i, arg in enumerate(output_node.args[0]):
+                self.a_outputs[arg.name] = a_result[i]
+                self.b_outputs[arg.name] = b_result[i]
+
+    def _get_submod_inputs(
+        self, main_module: torch.fx.GraphModule, submod_path: str
+    ) -> Tuple[Tensors, Tensors]:
+        """
+        Try get submodule inputs from stored outputs. If not found then use
+        torch_glow.get_submod_inputs to get the inputs.
+
+        If accumulate_error is False, use a_input for run_a() and run_b()
+        otherwise use a_input for run_a and b_input for run_b.
+
+        Args:
+            main_module: Top-levlel fx module.
+            submod_path: Path to the submodule we want to run and compare results.
+
+        Returns:
+            a_input: List of tensor(s) that will be used by run_a() as submodule inputs.
+            b_input: List of tensor(s) that will be used by run_b() as submodule inputs.
+        """
+        a_input = []
+        b_input = []
+        submodule = getattr(main_module, submod_path)
+        placeholders = [
+            node.name for node in submodule.graph.nodes if node.op == "placeholder"
+        ]
+
+        # If all placeholder can be found in stored outputs, use stored
+        # outputs as inputs. Otherwise, use `torch_glow.get_submod_inputs`
+        # to get the inputs.
+        if set(placeholders) <= self.a_outputs.keys():
+            for name in placeholders:
+                a_input.append(self.a_outputs[name])
+                b_input.append(self.b_outputs[name])
+        else:
+            if self.settings.accumulate_error:
+                print(f"Can't find previous stored outputs named {placeholders}!")
+
+            def get_inputs(self: torch.nn.Module, inputs: Any):
+                nonlocal a_input
+                a_input = inputs
+
+            # Use forward hook to get the inputs to the submodule
+            handle = submodule.register_forward_pre_hook(get_inputs)
+            main_module(*self.sample_input)
+            handle.remove()
+
+            b_input = a_input
+
+        if not self.settings.accumulate_error:
+            return a_input, a_input
+
+        return a_input, b_input
+
+    def _tag_nodes(self, selected_nodes: NodeSet):
+        """
+        Tag selected nodes with tag "minimize". Nodes with the same tags will
+        be split to the same submodule afterwards.
+
+        Args:
+            selected_nodes: Nodes that we want to minimize. We will tag those nodes
+                with "minimize", all preceding nodes with "main_0" and all following
+                nodes with "main_1".
+        """
+        for node in self.module.graph.nodes:
+            if node.op not in CALLABLE_NODE_OPS:
+                continue
+
+            if node in selected_nodes:
+                node.tag = "minimize"
+            elif any(
+                n.tag in {"minimize", "main_1"}
+                for n in node.all_input_nodes
+                if n.op in CALLABLE_NODE_OPS
+            ):
+                node.tag = "main_1"
+            else:
+                node.tag = "main_0"
+
+    def _build_submodule(self, nodes: NodeSet) -> Tuple[torch.fx.GraphModule, str]:
+        """
+        Split self.module so that one submodule consists of `nodes` and only `nodes`.
+
+        Args:
+            nodes: Nodes that we want to include in the minimize submodule.
+
+        Returns:
+            split_module (torch.fx.GraphModule): the module after split.
+            submodule_name (str): the name of the submodule that consists of `nodes`.
+        """
+        # Color provided nodes
+        self._tag_nodes(nodes)
+
+        # Split module based on coloring
+        split_module = split_by_tags(self.module, ["main_0", "minimize", "main_1"])
+
+        # Find submodule containing colored nodes
+        submodule_name: str = ""
+        for child_name, _ in split_module.named_children():  # type: ignore[union-attr]
+            # Skip submodules we're not interested in at the moment
+            if "minimize" not in child_name:
+                continue
+
+            if submodule_name == "":
+                submodule_name = child_name
+            else:
+                raise FxNetMinimizerBadModuleError(
+                    f"Expected only one minimize submodule with nodes {nodes}"
+                )
+
+        if submodule_name == "":
+            raise FxNetMinimizerBadModuleError(
+                f"Minimize submodule was not found with nodes {nodes}"
+            )
+
+        return split_module, submodule_name  # type: ignore[return-value]
+
+    def _run_and_compare(
+        self,
+        split_module: torch.fx.GraphModule,
+        submod_name: str,
+        output_names: Names,
+        report_idx: int = -1
+    ):
+        """
+        Run the submodule in `split_module` that has name `submod_name`
+        using `self.run_a` and `self.run_b` and compare their results.
+
+        Args:
+            split_module: Main module that contains the minimize submodule.
+            submod_name: Name of the minimize submodule.
+            output_names: Names of the node we want to output. If None, we
+                will use the original output.
+        """
+        submodule = getattr(split_module, submod_name)
+        a_input, b_input = self._get_submod_inputs(split_module, submod_name)
+
+        if len(self.reports) == 0:
+            self.reports.append([])
+            self.iteration = 1
+
+        report = self.reports[report_idx if report_idx >= 0 else self.iteration - 1]
+        report.append("Run and compare ...")
+
+        if output_names:
+            output_nodes: NodeList = []
+            for node in submodule.graph.nodes:
+                if node.op == "output":
+                    submodule.graph.erase_node(node)
+
+                if node.name in output_names:
+                    output_nodes.append(node)
+
+            submodule.graph.output(
+                output_nodes[0] if len(output_nodes) == 1 else tuple(output_nodes)
+            )
+            submodule.graph.lint()
+            submodule.recompile()
+
+        # Use name of args in output node as key to store comparison result
+        for node in submodule.graph.nodes:
+            if node.op == "output":
+                result_key = map_arg(node.args, lambda x: x.name)
+
+        try:
+            a_result = self.run_a(submodule, a_input, report_idx)
+            b_result = self.run_b(submodule, b_input, report_idx)
+            self._store_outputs(a_result, b_result, submodule)
+        except Exception as e:
+            report.append(f"Exception raised when running {submod_name}: {e}")
+            raise FxNetMinimizerRunFuncError(  # noqa: B904
+                f"Exception raised when running {submod_name}: {e}"
+            )
+
+        # Compare results
+        names: Names = output_names
+        if output_names is None:
+            names = [str(v) for v in result_key]  # type: ignore[possibly-undefined]
+
+        numeric_result, bool_result = self.compare_fn(a_result, b_result, names)
+
+        self.results[result_key] = numeric_result  # type: ignore[possibly-undefined]
+        report.append(f"Numerical accuracy = {numeric_result}")
+        if not bool_result:
+            report.append(f"Result mismatch for {result_key}")
+            if self.module_exporter:
+                self.module_exporter(
+                    a_input, submodule, str(result_key[0]) + "_cpu",  # type: ignore[index]
+                )
+                self.module_exporter(
+                    b_input, submodule, str(result_key[0]) + "_acc",  # type: ignore[index]
+                )
+            raise FxNetMinimizerResultMismatchError(f"Result mismatch for {result_key}")
+
+    def _binary_search_impl(
+        self, all_nodes: NodeList, start_idx: int, end_idx: int
+    ) -> NodeSet:
+        """
+        Recursive binary search implementation.
+        """
+        culprits: NodeSet = set()
+        nodes: NodeList = all_nodes[start_idx:end_idx]
+
+        report: List[str] = []
+        if self.exclusion_fn is not None:
+            self.exclusion_fn(nodes, start_idx, end_idx)
+            if len(nodes) == 0:
+                report = ["All nodes are excluded by user"]
+                self.reports.append(report)
+                return culprits
+
+        first_node_name = nodes[0].name
+        output_node_name = nodes[-1].name
+        self.iteration += 1
+        self.reports.append(report)
+        report.append(f"Binary search iteration {self.iteration}")
+        report.append(
+            f"From node index {start_idx}:{first_node_name} to {end_idx-1}:{output_node_name}. "
+            f"Size of the interested node list is {len(nodes)}"
+        )
+        cur_nodes: NodeSet = set(nodes)
+
+        try:
+            split_module, submod_name = self._build_submodule(cur_nodes)
+            self._run_and_compare(split_module, submod_name, [output_node_name])
+
+        except (FxNetMinimizerRunFuncError, FxNetMinimizerResultMismatchError):
+
+            if len(nodes) == 1:
+                report.append(
+                    f"This is the last node in the sub-module. "
+                    f"Search in the current branch is successful with culprit = {cur_nodes}."
+                )
+                self.print_report(report)
+                return cur_nodes
+
+            report.append(
+                "Proceed to split and lower the halves of the current "
+                "sub-module individually."
+            )
+            self.print_report(report)
+
+            mid = len(nodes) // 2
+            culprits = self._binary_search_impl(all_nodes, start_idx, start_idx + mid)
+
+            if len(culprits) != 0 and not self.settings.find_all:
+                return culprits
+
+            culprits = self._binary_search_impl(all_nodes, start_idx + mid, end_idx)
+
+            if len(culprits) == 0:
+                report.append(
+                    f"Further split and lowering found no errors. "
+                    f"Unable to minimize the submodule with list of nodes: {nodes}"
+                )
+                self.print_report(report)
+
+            return culprits
+        else:
+            report.append("No discrepancy found.")
+            self.print_report(report)
+            return set()
+
+    def _binary_traverse(self, nodes: NodeList) -> NodeSet:
+        """
+        Binary search on `nodes` for culprit.
+        """
+        return self._binary_search_impl(nodes, 0, len(nodes))
+
+    def _sequential_traverse(self, nodes: NodeList) -> NodeSet:
+        """
+        Traverse `nodes` one by one and determine if any of them is a culprit.
+        """
+        culprits: NodeSet = set()
+
+        for node in nodes:
+            report: List[str] = []
+            self.reports.append(report)
+            self.iteration += 1
+            report.append(f"Sequential traverse iteration {self.iteration}.")
+            report.append(f"Visit node: {node.name}")
+
+            _LOGGER.info("Visit node: %s", node.name)
+            node_list: NodeList = [node]
+            if self.exclusion_fn is not None:
+                self.exclusion_fn(node_list, -1, -1)
+                if len(node_list) == 0:
+                    report.append(f"User exclusion : {node.name}")
+                    self.print_report(report)
+                    if not self.settings.find_all:
+                        return culprits
+                    else:
+                        continue
+
+            cur_nodes: NodeSet = {node}
+
+            if node in self.fusions:
+                cur_nodes = self.fusions[node]
+
+            try:
+                split_module, submod_name = self._build_submodule(cur_nodes)
+                self._run_and_compare(split_module, submod_name, [node.name])
+                self.print_report(report)
+            except (FxNetMinimizerResultMismatchError):
+                culprits.add(node)
+                report.append(f"Found culprit from numeric error: {node}")
+                self.print_report(report)
+                if not self.settings.find_all:
+                    return culprits
+            except (FxNetMinimizerRunFuncError):
+                culprits.update(cur_nodes)
+                report.append(f"Found culprit from run error: {node}")
+                self.print_report(report)
+                if not self.settings.find_all:
+                    return culprits
+
+        return culprits
+
+
+    def _block_traverse_impl(self, nodes: NodeList, start_idx: int, end_idx: int, find_last_node: bool) -> int:
+        """
+        Recursive block search implementation.
+        find_last_node: If True, search for the last node which result in numerics difference
+        if False: find first node in sorted node list
+        """
+        report: List[str] = []
+
+        mid = (start_idx + end_idx) // 2
+        cur_nodes_list: NodeList = nodes[:mid + 1] if find_last_node else nodes[mid:]
+
+        if self.exclusion_fn:
+            self.exclusion_fn(cur_nodes_list, -1, -1)
+
+        cur_nodes = set(cur_nodes_list)
+
+        first_node_name = cur_nodes_list[0].name
+        last_node_name = cur_nodes_list[-1].name
+        target_node_name = last_node_name if find_last_node else first_node_name
+
+        self.iteration += 1
+        self.reports.append(report)
+        report.extend(
+            [
+                "=" * 30,
+                f"Block search iteration {self.iteration}",
+            ]
+        )
+        report.extend(
+            [
+                f"Search for {'last' if find_last_node else 'first'} node in culprits",
+                f"From node index {start_idx}:{nodes[start_idx].name} to {end_idx}:{nodes[end_idx].name}. ",
+                f"Subgraph constructed by {first_node_name} to {last_node_name}",
+                f"Targeting node: {target_node_name}",
+                f"Size of the interested node list is {end_idx - start_idx + 1}",
+            ]
+        )
+        report_idx = len(self.reports) - 1
+
+        try:
+            split_module, submod_name = self._build_submodule(cur_nodes)
+            self._run_and_compare(split_module, submod_name, [last_node_name], report_idx)
+        except (FxNetMinimizerResultMismatchError, FxNetMinimizerRunFuncError):
+            report.append(f"Culprits found from node {first_node_name} to {last_node_name}.")
+
+            if start_idx == mid:
+                report.extend(
+                    [
+                        "This is the last node in the sub-module. ",
+                        "Search in the current branch is successful with node :",
+                        f"{start_idx}, node name: {nodes[start_idx].name}."
+                    ]
+                )
+                self.print_report(report)
+                return start_idx
+
+            report.append(
+                "Proceed to split and lower the halves of the current "
+                "sub-module individually."
+            )
+            self.print_report(report)
+
+            if find_last_node:
+                return self._block_traverse_impl(nodes, start_idx, mid, find_last_node)
+            else:
+                return self._block_traverse_impl(nodes, mid + 1, end_idx, find_last_node)
+        else:
+            report.append(f"Culprits not found from node start to {mid}:{nodes[mid].name}.")
+
+            if start_idx == mid:
+                report.extend(
+                    [
+                        "This is the last node in the sub-module. ",
+                        "Search in the current branch is successful with node",
+                        f"{start_idx}, node name: {nodes[start_idx].name}.",
+                    ]
+                )
+                self.print_report(report)
+                return start_idx + 1 if find_last_node else start_idx - 1
+
+            report.append(
+                "Proceed to split and lower the halves of the current "
+                "sub-module individually."
+            )
+            self.print_report(report)
+
+            if find_last_node:
+                return self._block_traverse_impl(nodes, mid + 1, end_idx, find_last_node)
+            else:
+                return self._block_traverse_impl(nodes, start_idx, mid, find_last_node)
+
+
+    def _block_traverse(self, nodes: NodeList, find_last_node: Optional[bool]) -> NodeSet:
+        """
+        Traverse topologically sorted node list
+        Find minimium block (start_idx, end_idx) which contains the culprit
+        1st pass: search for end_idx by finding the last node in culprit block
+        where Numerical accuracy (0, end_idx) > threshold
+        2nd pass: search for start_idx by finding the first node in culprit block
+        where Numerical accuracy (start_idx, end_idx) < threshold
+        Form minimum block by (start_idx - 1, end_idx)
+        """
+        culprits: NodeSet = set()
+        first_node_name = nodes[0].name
+        last_node_name = nodes[-1].name
+        last_node_report = [f"Block search from {first_node_name} to {last_node_name}"]
+        last_node_report.append("*" * 50)
+        self.reports.append(last_node_report)
+
+        start_idx = 0
+        end_idx = len(nodes) - 1
+        run_both = True if find_last_node is None else False
+
+        # step 1: find (0, end_idx) of culprit block
+        if run_both or find_last_node:
+            last_node_report.append("Start searching for last node in culprit")
+            self.print_report(last_node_report)
+            end_idx = self._block_traverse_impl(nodes, start_idx, end_idx, True)
+            last_node_report.extend(
+                [
+                    "Finish Pass 1",
+                    f"Find end_idx = {end_idx}:{nodes[end_idx].name}"
+                ]
+            )
+            self.print_report(last_node_report)
+
+        # step 2: reduce culprit block to (start_idx, end_idx)
+        if run_both or not find_last_node:
+            first_node_report = ["Start searching for first node in culprit"]
+            self.print_report(first_node_report)
+            start_idx = self._block_traverse_impl(nodes[0:end_idx + 1], start_idx, end_idx, False)
+            first_node_report.append("*" * 50)
+            self.reports.append(first_node_report)
+            first_node_report.extend(
+                [
+                    "Finish Pass 2",
+                    f"Find start_idx = {start_idx}:{nodes[start_idx].name}"
+                ]
+            )
+            self.print_report(first_node_report)
+
+        # step 3: form module with minimum culprits
+        culprits.update(nodes[start_idx:end_idx + 1])
+        result_report = [f"Finish searching, found minimum block ({nodes[start_idx]},{nodes[end_idx]})"]
+        self.reports.append(result_report)
+        self.print_report(result_report)
+        return culprits
+
+
+    def _defined_traverse(self, nodes: NodeList) -> NodeSet:
+        """
+        run user defined `nodes` and determine if it is a culprit.
+        """
+        culprits: NodeSet = set()
+        if self.exclusion_fn is not None:
+            self.exclusion_fn(nodes, -1, -1)
+        if len(nodes) == 0:
+            report = ["All nodes are excluded by user"]
+            self.reports.append(report)
+            return culprits
+
+        first_node_name = nodes[0].name
+        output_node_name = nodes[-1].name
+        report = [f"Defined graph from {first_node_name} to {output_node_name}"]
+        cur_nodes: NodeSet = set(nodes)
+        try:
+            split_module, submod_name = self._build_submodule(cur_nodes)
+            self._run_and_compare(split_module, submod_name, [output_node_name])
+            self.print_report(report)
+        except (FxNetMinimizerResultMismatchError, FxNetMinimizerRunFuncError):
+            report.append(f"Found culprit {cur_nodes}")
+            self.print_report(report)
+            return culprits
+
+        return culprits
+
+    def _accumulate_traverse(self, nodes: NodeList) -> NodeSet:
+        culprits: NodeSet = set()
+        nodes_to_run: NodeSet = set()
+
+        # find_all is not supported for accumulate traversal because all the
+        # ops run on NNPI. So we return after the first op that raises error.
+        if self.settings.find_all:
+            print("'Find All' mode is not supported in accumulate traversal.")
+            return culprits
+
+        for node in nodes:
+            report: List[str] = []
+            self.reports.append(report)
+            self.iteration += 1
+            report.append(f"Accumulate traverse iteration {self.iteration}.")
+
+            nodes_to_run.add(node)
+
+            node_name = node.name
+            if node_name is not None and isinstance(node_name, tuple):
+                node_name = node_name[0]
+            assert node_name is not None and isinstance(
+                node_name, str
+            ), f"minimize: node_name: {node_name}"
+
+            report.append(f"Add node: {node_name}")
+
+            try:
+                split_module, submod_name = self._build_submodule(nodes_to_run)
+                self._run_and_compare(split_module, submod_name, [node_name])
+                self.print_report(report)
+            except (FxNetMinimizerResultMismatchError, FxNetMinimizerRunFuncError):
+                culprits.add(node)
+                report.append(f"Found culprit {node}")
+                self.print_report(report)
+                return culprits
+
+        return culprits
+
+    def _skip_traverse_impl(self, all_nodes: NodeList, start_idx: int, end_idx: int) -> NodeSet:
+        """
+        Skip certain nodes in graph based on settings
+        """
+        culprits: NodeSet = set()
+        nodes: NodeList = all_nodes[start_idx:end_idx]
+        cur_nodes: NodeSet = set(nodes)
+        if self.exclusion_fn is not None:
+            self.exclusion_fn(nodes, start_idx, end_idx)
+            cur_nodes = set(nodes)
+        else:
+            for node in nodes:
+                if node in self.fusions:
+                    cur_nodes.update(self.fusions[node])
+        report: List[str] = []
+        self.reports.append(report)
+        self.iteration += 1
+        report.append(f" Nodes block {self.iteration}.")
+        report.append(
+            f"From node index {start_idx} to {end_idx-1}. "
+            f"Size of the interested node list is {len(nodes)}"
+        )
+
+        try:
+            split_module, submod_name = self._build_submodule(cur_nodes)
+            self._run_and_compare(split_module, submod_name, [])
+        except (FxNetMinimizerResultMismatchError):
+            culprits.update(cur_nodes)
+            report.append(f"Found culprit from numeric error: {cur_nodes}")
+            self.print_report(report)
+            return culprits
+        except (FxNetMinimizerRunFuncError):
+            culprits.update(cur_nodes)
+            report.append(f"Found culprit from run error: {cur_nodes}")
+            self.print_report(report)
+            return culprits
+        else:
+            report.append("No discrepancy found.")
+            self.print_report(report)
+            return set()
+
+
+    def _skip_traverse(self, all_nodes: NodeList, skip_nodes: List) -> NodeSet:
+        """
+        Skip certain nodes in graph based on settings
+        """
+        start_idx = 0
+        num_nodes = len(all_nodes)
+        idx = 0
+        culprits = set()
+        while idx < num_nodes:
+            node = all_nodes[idx]
+            if (node.name in skip_nodes):  # skip the node
+                if idx > start_idx:
+                    culprits = self._skip_traverse_impl(all_nodes, start_idx, idx)
+                start_idx = idx + 1
+            elif idx == num_nodes - 1 and start_idx <= idx:  # last node
+                culprits = self._skip_traverse_impl(all_nodes, start_idx, idx + 1)
+            idx += 1
+
+        return culprits
+
+
+
+    def _collect_nodes(self, start: Optional[str], end: Optional[str]) -> NodeList:
+        """
+        Collect nodes in the model that between nodes with name of `start` and `end`.
+        These two nodes are also included.
+        """
+        nodes: NodeList = []
+        add_node = start is None
+
+        for node in self.module.graph.nodes:
+            if node.op not in CALLABLE_NODE_OPS:
+                continue
+
+            if node.name == start:
+                add_node = True
+
+            if add_node:
+                nodes.append(node)
+
+            if node.name == end:
+                break
+
+        return nodes
+
+    def run_nodes(self, start: Optional[str] = None, end: Optional[str] = None):
+        """
+        Run part of the model from `start` node to `end` node. If `start` is None
+        then we start from the beginning of the model. If `end` is None then we
+        stop at the end of the model.
+
+        Args:
+            start: The name of the node which is the first node of the submodule
+                we want to run. If set to None, then we'll start with the first
+                node of the model.
+            end: The name of the node which is the last node of the submodule we
+                want to run. If set to None, we'll end with the last node of the
+                model.
+        """
+        nodes = self._collect_nodes(start, end)
+        cur_nodes = set(nodes)
+
+        for node in nodes:
+            if node in self.fusions:
+                cur_nodes.update(self.fusions[node])
+
+        output_names = []
+        if self.settings.return_intermediate:
+            output_names = [node.name for node in nodes]
+
+        try:
+            split_module, submod_name = self._build_submodule(cur_nodes)
+            self._run_and_compare(split_module, submod_name, output_names)
+        except (
+            FxNetMinimizerRunFuncError,
+            FxNetMinimizerResultMismatchError,
+        ) as e:
+            print(e)
+
+    def print_report(self, report: List[str]):
+        for i in range(len(report)):
+            if i > 0:
+                print(" . " + report[i])
+            else:
+                print(report[i])
+
+    def print_reports(self):
+        for report in self.reports:
+            self.print_report(report)
+
+    def minimize(
+        self,
+        start: Optional[str] = None,
+        end: Optional[str] = None,
+        skip_nodes: Optional[List] = None,
+        find_last_node: Optional[bool] = None,
+    ) -> NodeSet:
+        """
+        Minimizing the model from node with name `start` to node with name `end` base
+        on self.settings. Find culprits that causes FxNetMinimizerRunFuncError or
+        FxNetMinimizerResultMismatchError errors.
+
+        Args:
+            start: The name of the node where we want to start minimizing. If set
+                to None, then we'll start with the first node of the model.
+            end: The name of the node where we want to terminate minimizing. If
+                set to None, we'll end with the last node of the model.
+            skip_nodes: The names of nodes where we want to skip during minimizing.
+                It'll create subgraphs without these skip nodes under the hood.
+                Only applicable in mode "skip".
+            find_last_node: True if only last_node of a culprits is needed in mode "block".
+                False if only the first_node of a culprits is needed.
+                Only applicable in mode "block".
+
+        Returns:
+            nodes: A list of nodes that causes FxNetMinimizerRunFuncError or
+                FxNetMinimizerResultMismatchError errors during minimizing.
+        """
+
+        print(self.settings)
+        print(self.module.graph)
+
+        nodes = self._collect_nodes(start, end)
+
+        if self.settings.traverse_method == "sequential":
+            return self._sequential_traverse(nodes)
+
+        if self.settings.traverse_method == "binary":
+            return self._binary_traverse(nodes)
+
+        if self.settings.traverse_method == "accumulate":
+            return self._accumulate_traverse(nodes)
+
+        if self.settings.traverse_method == "skip":
+            if (skip_nodes is None):
+                raise RuntimeError("'skip_nodes' can't be None when 'traverse_method' is 'skip'.")
+            return self._skip_traverse(nodes, skip_nodes)
+
+        if self.settings.traverse_method == "defined":
+            return self._defined_traverse(nodes)
+
+        if self.settings.traverse_method == "block":
+            return self._block_traverse(nodes, find_last_node)
+
+        raise RuntimeError(f"Unknown traverse method {self.settings.traverse_method}!")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/operator_support.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/operator_support.py
new file mode 100644
index 0000000000000000000000000000000000000000..57edabc0a55ae6ddb0738ea83edb49b2d5823930
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/operator_support.py
@@ -0,0 +1,215 @@
+# mypy: allow-untyped-defs
+import abc
+import typing as t
+
+import torch
+import torch.fx
+from torch.fx._compatibility import compatibility
+from .shape_prop import TensorMetadata
+from .tools_common import get_node_target, CALLABLE_NODE_OPS
+
+
+__all__ = ['OperatorSupportBase', 'OperatorSupport', 'create_op_support', 'chain', 'OpSupports', 'any_chain']
+
+# fx.Node.target typename, as returned by `get_node_target()`
+TargetTypeName = str
+
+# Arguments' dtypes for a given node, see `OperatorSupport`
+SupportedArgumentDTypes = t.Optional[
+    t.Tuple[
+        t.Sequence[t.Sequence[torch.dtype]],
+        t.Dict[str, t.Sequence[torch.dtype]],
+    ]
+]
+
+SupportDict = t.Mapping[TargetTypeName, SupportedArgumentDTypes]
+
+
+@compatibility(is_backward_compatible=False)
+class OperatorSupportBase(abc.ABC):
+    """Interface for determining if a fx.Node is supported by a backend"""
+    @abc.abstractmethod
+    def is_node_supported(
+        self, submodules: t.Mapping[str, torch.nn.Module], node: torch.fx.Node
+    ) -> bool:
+        raise NotImplementedError
+
+
+@compatibility(is_backward_compatible=False)
+class OperatorSupport(OperatorSupportBase):
+    """
+    `_support_dict` maps node.target typename to supported inputs dtypes.
+
+    node.target typename is retrieved using helper function `get_node_target()`
+
+    If supported inputs dtypes is None, it means any dtype is supported, else
+    we should see a tuple like (([dtypes], ...), {"name":[dtypes], ...}).
+
+    The first tuple ([dtypes], ...) indicates what dtypes are supported for
+    inputs in node.args and the second dict {"name": [dtypes], ...} indicates
+    what dtypes are supported for inputs in node.kwargs.
+
+    For inputs in args, if we don't want to check it, we can put None there,
+    e.g. (None, [torch.float]) indicates that we don't care about the type of
+    the first input in args. And for inputs in kwargs, if not listed, will not
+    be checked.
+    """
+
+    _support_dict: SupportDict
+
+    def __init__(
+        self,
+        support_dict: t.Optional[SupportDict] = None
+    ):
+        self._support_dict = support_dict or {}
+
+    def is_node_supported(
+        self, submodules: t.Mapping[str, torch.nn.Module], node: torch.fx.Node
+    ) -> bool:
+        """
+        Args:
+            `submodules`: mapping from module name to the module. This can be
+                          retrieved by calling model.named_modules().
+
+            `node`: a Fx node that we want to determine whether it's supported.
+
+        Returns:
+            `is_supported`: whether the arg `node` is supported.
+        """
+        if node.op not in CALLABLE_NODE_OPS:
+            return True
+
+        target = get_node_target(submodules, node)
+
+        # Target not found in _support_dict meaning that we don't support this op at all
+        if target not in self._support_dict:
+            return False
+
+        # The rule for target is None meaning that we accept any dtype
+        if self._support_dict[target] is None:
+            return True
+
+        args_dtypes, kwargs_dtypes = self._support_dict[target]  # type: ignore[misc]
+
+        # Check args dtypes
+        for i, dtypes in enumerate(args_dtypes):
+            if len(node.args) <= i:
+                break
+
+            # None indicates we don't care about the dtype of args[i]
+            if dtypes is None:
+                continue
+
+            # If arg is not a node then we don't check it
+            if not isinstance(node.args[i], torch.fx.Node):
+                continue
+
+            arg_dtype = _get_arg_dtype(node.args[i])  # type: ignore[arg-type]
+            if arg_dtype not in dtypes:
+                return False
+
+        # Check kwargs dtypes
+        for k, dtypes in kwargs_dtypes.items():
+            if k not in node.kwargs:
+                continue
+
+            # If arg is not a node then we don't check it
+            if not isinstance(node.kwargs[k], torch.fx.Node):
+                continue
+
+            kwarg_dtype = _get_arg_dtype(node.kwargs[k])  # type: ignore[arg-type]
+            if kwarg_dtype not in dtypes:
+                return False
+
+        return True
+
+
+# ======================================================================
+# Functional interfaces and utils for defining basic operator support logic
+# and composing them into more complex ones
+# ======================================================================
+
+IsNodeSupported = t.Callable[[t.Mapping[str, torch.nn.Module], torch.fx.Node], bool]
+
+
+@compatibility(is_backward_compatible=False)
+def create_op_support(is_node_supported: IsNodeSupported) -> OperatorSupportBase:
+    """Wraps a `IsNodeSupported` function into an `OperatorSupportBase` instance
+
+    `IsNodeSupported` has the same call signature as
+    `OperatorSupportBase.is_node_supported`
+    """
+    class FunctionalOperatorSupport(OperatorSupportBase):
+        def is_node_supported(
+                self, submodules: t.Mapping[str, torch.nn.Module], node: torch.fx.Node
+        ) -> bool:
+            return is_node_supported(submodules, node)
+    return FunctionalOperatorSupport()
+
+
+@compatibility(is_backward_compatible=False)
+def chain(*op_support: OperatorSupportBase) -> OperatorSupportBase:
+    """Combines a sequence of `OperatorSupportBase` instances to form a single `OperatorSupportBase`
+    instance by evaluating each input `OperatorSupportBase` instance, and returns False if
+    any of it reports False.
+    """
+    def _chain(submods, node) -> bool:
+        return all(
+            x.is_node_supported(submods, node)
+            for x in op_support
+        )
+    return create_op_support(_chain)
+
+
+@compatibility(is_backward_compatible=False)
+def any_chain(*op_support: OperatorSupportBase) -> OperatorSupportBase:
+    """Combines a sequence of `OperatorSupportBase` instances to form a single `OperatorSupportBase`
+    instance by evaluating each input `OperatorSupportBase` instance, and returns True if
+    any of it reports True.
+    """
+    def _any_chain(submods, node) -> bool:
+        return any(
+            x.is_node_supported(submods, node)
+            for x in op_support
+        )
+    return create_op_support(_any_chain)
+
+
+@compatibility(is_backward_compatible=False)
+class OpSupports:
+    """A set of atomic `OperatorSupportBase` instances that can be combined together
+    to form more complex operator support logic.
+    """
+    @classmethod
+    def decline_if_input_dtype(cls, dtype: torch.dtype) -> OperatorSupportBase:
+        """Report a node as non-supported, if any of its arguments is of dtype"""
+
+        def _decline_if_input_dtype(
+            submodules: t.Mapping[str, torch.nn.Module],
+            node: torch.fx.Node,
+        ) -> bool:
+            for arg in node.all_input_nodes:
+                arg_dtype = _get_arg_dtype(arg)
+                if arg_dtype == dtype:
+                    return False
+            return True
+        return create_op_support(_decline_if_input_dtype)
+
+    @classmethod
+    def decline_if_node_in_names(cls, disallow_set: t.Set[str]) -> OperatorSupportBase:
+        """
+        If a node has a name that is in the disallow set, reported it as non-supported.
+        """
+        def _decline_if_node_in_names(
+            submodules: t.Mapping[str, torch.nn.Module],
+            node: torch.fx.Node,
+        ) -> bool:
+            return node.name not in disallow_set
+        return create_op_support(_decline_if_node_in_names)
+
+
+def _get_arg_dtype(arg: torch.fx.Node) -> t.Any:
+    assert isinstance(arg, torch.fx.Node)
+    tensor_meta = arg.meta.get("tensor_meta")  # type: ignore[union-attr]
+    dtype = tensor_meta.dtype if isinstance(tensor_meta, TensorMetadata) else arg.meta["type"]
+    return dtype
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/param_fetch.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/param_fetch.py
new file mode 100644
index 0000000000000000000000000000000000000000..5979e29fcc6b2650a1f73be4845e2ad3dcda0920
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/param_fetch.py
@@ -0,0 +1,66 @@
+from torch.fx.graph_module import GraphModule
+from typing import Any, Callable, Dict, List, Tuple, Type
+import torch
+import torch.nn as nn
+
+from torch.fx._compatibility import compatibility
+
+__all__ = ['default_matching', 'extract_attrs_for_lowering', 'lift_lowering_attrs_to_nodes']
+
+# Matching method matches the attribute name of current version to the attribute name of `target_version`
+@compatibility(is_backward_compatible=False)
+def default_matching(name: str, target_version: int) -> str:
+    """Default matching method
+    """
+    return name
+
+# This dict maps the nn.Module class name to the attribute name list that we want to fetch for lowering.
+# The first integer in the tuple is the version number of the nn.Module class when we create the parameter list.
+# If there's a version mismatch then it means the parameter names in the book might be mismatched with nn.Module.
+module_fetch_book: Dict[Type, Tuple[int, List[str], Callable[[str, int], str]]] = {
+    torch.nn.modules.linear.Linear: (1, ["weight", "bias"], default_matching),
+    torch.nn.modules.conv.Conv2d: (
+        1, ["weight", "bias", "kernel_size", "stride", "padding", "dilation", "groups", "padding_mode"], default_matching
+    ),
+    torch.nn.modules.batchnorm.BatchNorm2d: (2, ["weight", "bias", "running_mean", "running_var", "eps"], default_matching),
+    torch.nn.modules.pooling.AdaptiveAvgPool2d: (1, [], default_matching),
+    torch.nn.modules.pooling.MaxPool2d: (
+        1, ["kernel_size", "stride", "padding", "dilation", "return_indices", "ceil_mode"], default_matching
+    ),
+    torch.nn.modules.activation.ReLU: (1, ["inplace"], default_matching),
+}
+
+@compatibility(is_backward_compatible=False)
+def extract_attrs_for_lowering(mod: nn.Module) -> Dict[str, Any]:
+    """If `mod` is in `module_fetch_book`, fetch the mod's attributes that in the `module_fetch_book`
+    after checking module's version is compatible with the `module_fetch_book`.
+    """
+    attrs_for_lowering: Dict[str, Any] = {}
+    attrs_for_lowering["name"] = torch.typename(mod)
+
+    if type(mod) in module_fetch_book:
+        version, param_to_fetch, matching_method = module_fetch_book[type(mod)]
+        if version < mod._version:
+            raise RuntimeError(f"Fetcher version {version} try to fetch {torch.typename(mod)} version {mod._version}, "
+                               "please upgrade the module_fetch_book, open an issue and @842974287 "
+                               "or report a bug to AIACC team directly.")
+        for attr in param_to_fetch:
+            attrs_for_lowering[attr] = getattr(mod, matching_method(attr, mod._version))
+    else:
+        raise RuntimeError(f"{torch.typename(mod)} is not in the module_fetch_book yet, "
+                           "please add it to the module_fetch_book, open an issue and @842974287 "
+                           "or report a bug to AIACC team directly.")
+    return attrs_for_lowering
+
+@compatibility(is_backward_compatible=False)
+def lift_lowering_attrs_to_nodes(fx_module: GraphModule) -> None:
+    """Recursively traverse all `fx_module` nodes and fetch the module's attributes if the node is a leaf module.
+    """
+    submodules = dict(fx_module.named_modules())
+
+    for node in fx_module.graph.nodes:
+        if node.op == "call_module":
+            if isinstance(submodules[node.target], GraphModule):
+                lift_lowering_attrs_to_nodes(submodules[node.target])
+            else:
+                node.attrs_for_lowering = extract_attrs_for_lowering(submodules[node.target])
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/pass_manager.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/pass_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..3cc4ff5e07090588ec92edd49111012f4a69ba6b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/pass_manager.py
@@ -0,0 +1,254 @@
+# mypy: allow-untyped-defs
+from functools import wraps
+from inspect import unwrap
+from typing import Callable, List, Optional
+import logging
+
+logger = logging.getLogger(__name__)
+
+__all__ = [
+    "PassManager",
+    "inplace_wrapper",
+    "log_hook",
+    "loop_pass",
+    "this_before_that_pass_constraint",
+    "these_before_those_pass_constraint",
+]
+
+# for callables which modify object inplace and return something other than
+# the object on which they act
+def inplace_wrapper(fn: Callable) -> Callable:
+    """
+    Convenience wrapper for passes which modify an object inplace. This
+    wrapper makes them return the modified object instead.
+
+    Args:
+        fn (Callable[Object, Any])
+
+    Returns:
+        wrapped_fn (Callable[Object, Object])
+    """
+
+    @wraps(fn)
+    def wrapped_fn(gm):
+        val = fn(gm)
+        return gm
+
+    return wrapped_fn
+
+def log_hook(fn: Callable, level=logging.INFO) -> Callable:
+    """
+    Logs callable output.
+
+    This is useful for logging output of passes. Note inplace_wrapper replaces
+    the pass output with the modified object. If we want to log the original
+    output, apply this wrapper before inplace_wrapper.
+
+
+    ```
+    def my_pass(d: Dict) -> bool:
+        changed = False
+        if 'foo' in d:
+            d['foo'] = 'bar'
+            changed = True
+        return changed
+
+    pm = PassManager(
+        passes=[
+            inplace_wrapper(log_hook(my_pass))
+        ]
+    )
+    ```
+
+    Args:
+        fn (Callable[Type1, Type2])
+        level: logging level (e.g. logging.INFO)
+
+    Returns:
+        wrapped_fn (Callable[Type1, Type2])
+    """
+    @wraps(fn)
+    def wrapped_fn(gm):
+        val = fn(gm)
+        logger.log(level, "Ran pass %s\t Return value: %s", fn, val)
+        return val
+
+    return wrapped_fn
+
+
+
+def loop_pass(base_pass: Callable, n_iter: Optional[int] = None, predicate: Optional[Callable] = None):
+    """
+    Convenience wrapper for passes which need to be applied multiple times.
+
+    Exactly one of `n_iter`or `predicate` must be specified.
+
+    Args:
+        base_pass (Callable[Object, Object]): pass to be applied in loop
+        n_iter (int, optional): number of times to loop pass
+        predicate (Callable[Object, bool], optional):
+
+    """
+    assert (n_iter is not None) ^ (
+        predicate is not None
+    ), "Exactly one of `n_iter`or `predicate` must be specified."
+
+    @wraps(base_pass)
+    def new_pass(source):
+        output = source
+        if n_iter is not None and n_iter > 0:
+            for _ in range(n_iter):
+                output = base_pass(output)
+        elif predicate is not None:
+            while predicate(output):
+                output = base_pass(output)
+        else:
+            raise RuntimeError(
+                f"loop_pass must be given positive int n_iter (given "
+                f"{n_iter}) xor predicate (given {predicate})"
+            )
+        return output
+
+    return new_pass
+
+
+# Pass Schedule Constraints:
+#
+# Implemented as 'depends on' operators. A constraint is satisfied iff a list
+# has a valid partial ordering according to this comparison operator.
+def _validate_pass_schedule_constraint(
+    constraint: Callable[[Callable, Callable], bool], passes: List[Callable]
+):
+    for i, a in enumerate(passes):
+        for j, b in enumerate(passes[i + 1 :]):
+            if constraint(a, b):
+                continue
+            raise RuntimeError(
+                f"pass schedule constraint violated. Expected {a} before {b}"
+                f" but found {a} at index {i} and {b} at index{j} in pass"
+                f" list."
+            )
+
+
+def this_before_that_pass_constraint(this: Callable, that: Callable):
+    """
+    Defines a partial order ('depends on' function) where `this` must occur
+    before `that`.
+    """
+
+    def depends_on(a: Callable, b: Callable):
+        return a != that or b != this
+
+    return depends_on
+
+
+def these_before_those_pass_constraint(these: Callable, those: Callable):
+    """
+    Defines a partial order ('depends on' function) where `these` must occur
+    before `those`. Where the inputs are 'unwrapped' before comparison.
+
+    For example, the following pass list and constraint list would be invalid.
+    ```
+    passes = [
+        loop_pass(pass_b, 3),
+        loop_pass(pass_a, 5),
+    ]
+
+    constraints = [
+        these_before_those_pass_constraint(pass_a, pass_b)
+    ]
+    ```
+
+    Args:
+        these (Callable): pass which should occur first
+        those (Callable): pass which should occur later
+
+    Returns:
+        depends_on (Callable[[Object, Object], bool]
+    """
+
+    def depends_on(a: Callable, b: Callable):
+        return unwrap(a) != those or unwrap(b) != these
+
+    return depends_on
+
+
+class PassManager:
+    """
+    Construct a PassManager.
+
+    Collects passes and constraints. This defines the pass schedule, manages
+    pass constraints and pass execution.
+
+    Args:
+        passes (Optional[List[Callable]]): list of passes. A pass is a
+            callable which modifies an object and returns modified object
+        constraint (Optional[List[Callable]]): list of constraints. A
+            constraint is a callable which takes two passes (A, B) and returns
+            True if A depends on B and False otherwise. See implementation of
+            `this_before_that_pass_constraint` for example.
+    """
+
+    passes: List[Callable]
+    constraints: List[Callable]
+    _validated: bool = False
+
+    def __init__(
+        self,
+        passes=None,
+        constraints=None,
+    ):
+        self.passes = passes or []
+        self.constraints = constraints or []
+
+    @classmethod
+    def build_from_passlist(cls, passes):
+        pm = PassManager(passes)
+        # TODO(alexbeloi): add constraint management/validation
+        return pm
+
+    def add_pass(self, _pass: Callable):
+        self.passes.append(_pass)
+        self._validated = False
+
+    def add_constraint(self, constraint):
+        self.constraints.append(constraint)
+        self._validated = False
+
+    def remove_pass(self, _passes: List[str]):
+        if _passes is None:
+            return
+        passes_left = []
+        for ps in self.passes:
+            if ps.__name__ not in _passes:
+                passes_left.append(ps)
+        self.passes = passes_left
+        self._validated = False
+
+    def replace_pass(self, _target, _replacement):
+        passes_left = []
+        for ps in self.passes:
+            if ps.__name__ == _target.__name__:
+                passes_left.append(_replacement)
+            else:
+                passes_left.append(ps)
+        self.passes = passes_left
+        self._validated = False
+
+    def validate(self):
+        """
+        Validates that current pass schedule defined by `self.passes` is valid
+        according to all constraints in `self.constraints`
+        """
+        if self._validated:
+            return
+        for constraint in self.constraints:
+            _validate_pass_schedule_constraint(constraint, self.passes)
+        self._validated = True
+
+    def __call__(self, source):
+        self.validate()
+        out = source
+        for _pass in self.passes:
+            out = _pass(out)
+        return out
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/reinplace.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/reinplace.py
new file mode 100644
index 0000000000000000000000000000000000000000..76435b9d318af1b5d62d6dce3b9e8c83fd615516
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/reinplace.py
@@ -0,0 +1,675 @@
+# mypy: allow-untyped-defs
+import torch
+from torch.fx import Node
+from torch.fx._compatibility import compatibility
+from torch._subclasses.fake_tensor import FakeTensorMode, FakeTensor
+from torch.utils._pytree import tree_map_only
+from torch.utils import _pytree as pytree
+from torch.multiprocessing.reductions import StorageWeakRef
+
+import _operator
+from enum import Enum
+import itertools
+from typing import Set, Dict
+from collections import defaultdict
+
+__all__ = ['reinplace']
+
+class _ViewType(Enum):
+    NonView = 0
+    SingleOutputView = 1
+    MultiOutputView = 2
+
+def _is_view_op(tgt):
+    if tgt is not None and isinstance(tgt, torch._ops.OpOverload):
+        schema = tgt._schema
+        if len(schema.arguments) > 0:
+            first_arg = schema.arguments[0]
+            # check if op is a view
+            return first_arg.alias_info is not None and not first_arg.alias_info.is_write
+
+def _get_view_type(tgt) -> _ViewType:
+    if tgt is not None and isinstance(tgt, torch._ops.OpOverload):
+        schema = tgt._schema
+        if len(schema.arguments) > 0:
+            first_arg = schema.arguments[0]
+            # check if op is a view
+            if first_arg.alias_info is not None and not first_arg.alias_info.is_write:
+                # check if op is a multi-output view
+                if '*' in first_arg.alias_info.after_set:
+                    return _ViewType.MultiOutputView
+                else:
+                    return _ViewType.SingleOutputView
+    return _ViewType.NonView
+
+
+# Stores a bunch of metadata related to functionalization each node.
+# Relevant metadata:
+# n.meta['fake_result']: FakeTensor (same type as the output of the node, but with FakeTenors instead of Tensors)
+#   The fake tensor output from running the current node
+# n.meta['view_of']: Node
+#   If the current node n is a view of some base tensor, the 'view_of' field tells us which
+#   view node was used to generate the current node (a view tensor).
+#   This information actually makes `fake_result` redundant, but we can use `fake_result`
+#   to sanity check that our aliasing information is correct.
+@compatibility(is_backward_compatible=False)
+class _FunctionalizationMetadataProp(torch.fx.Interpreter):
+
+    def run_node(self, node: Node):
+        self.node_counter += 1
+        result = super().run_node(node)
+        node.meta['fake_result'] = result
+        node.meta['node_idx'] = self.node_counter
+
+        # (1) Update metadata with the list of nodes that are used by this node
+        # copy_() doesn't read from its first argument; it writes to it, overwriting previous data.
+        # We don't want to treat it as "being used as an input".
+        node_args = node.args
+        if node.target is torch.ops.aten.copy_.default:
+            node_args = node_args[1:]
+
+        # (2) Update metadata to track aliasing information about view tensor nodes.
+        if node.op == 'call_function':
+            view_type = _get_view_type(node.target)
+            if view_type == _ViewType.SingleOutputView:
+                assert isinstance(node.args[0], Node)
+                node.meta['view_of'] = node.args[0]
+            elif view_type == _ViewType.MultiOutputView:
+                self.multi_output_view_nodes[node] = node.args[0]
+
+            # Check if we returned a multi-output view,
+            # and we're now grabbing the individual views from the output.
+            #
+            # For multi-output views, we want to map each output view to the base,
+            # but this mapping involves two separate nodes in FX IR.
+            # e.g. "a, b = x_1.split(...)" becomes:
+            #    %split_tensor : [num_users=2] = call_function[target=torch.ops.aten.split.Tensor](args = (%x_1, 2), kwargs = {})
+            #    %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%split_tensor, 0), kwargs = {})
+            #    %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%split_tensor, 1), kwargs = {})
+            # And we'd like to set:
+            #    getitem1.meta['view_of'] = x_1
+            elif node.target is _operator.getitem:
+                list_arg = node.args[0]
+                maybe_base_of_view = self.multi_output_view_nodes.get(list_arg, None)
+                if maybe_base_of_view is not None:
+                    # Note: we could also track indexing info here for multi-output views.
+                    # I don't think this metadata is strictly needed for de-functionalization.
+                    assert isinstance(maybe_base_of_view, Node)
+                    node.meta['view_of'] = maybe_base_of_view
+
+        if 'view_of' in node.meta:
+            # We're linking the current node with its first argument as views.
+            # Assert here that this is actually the case, and their storages are the same.
+            assert isinstance(node.meta['fake_result'], FakeTensor)
+            assert isinstance(node.meta['view_of'].meta['fake_result'], FakeTensor)
+            view_storage = StorageWeakRef(node.meta['fake_result']._typed_storage())
+            base_storage = StorageWeakRef(node.meta['view_of'].meta['fake_result']._typed_storage())
+            assert view_storage == base_storage
+        return result
+
+
+
+    def propagate(self, *args):
+        self.multi_output_view_nodes = {}
+        self.node_counter = -1
+
+        with FakeTensorMode() as mode:
+            fake_args = [mode.from_tensor(a) if isinstance(a, torch.Tensor) else a for a in args]
+            return super().run(*fake_args)
+
+def _schemas_match(functional_schema, inplace_schema):
+    names_match = inplace_schema.name.endswith("_") and inplace_schema.name[:-1] == functional_schema.name
+    arg_types_match = len(functional_schema.arguments) == len(inplace_schema.arguments) and all(
+        a1.type == a2.type for a1, a2 in zip(functional_schema.arguments, inplace_schema.arguments))
+    # for the inplace op, its first argument should be mutable
+    assert inplace_schema.arguments[0].alias_info is not None and inplace_schema.arguments[0].alias_info.is_write
+    # and its remaining arguments shouldn't be.
+    assert all(a.alias_info is None for a in inplace_schema.arguments[1:])
+    return names_match and arg_types_match
+
+# TODO: this should be beefed up to be able to properly re-inplace with:
+# - mutating ops (e.g. _fused_moving_avg_obs_fq_helper)
+# - out= ops (e.g. angle -> angle.out)
+# TODO: we should also figure this info out using torchgen.
+def _maybe_get_inplace_op(op):
+    # __module__ seems broken; it returns torch._ops.aten which doesn't exist
+    if not isinstance(op, torch._ops.OpOverload):
+        return None
+    # Some view ops have inplace variants (as_strided_, etc),
+    # but we do NOT want the reinplacing pass to directly add these into the program.
+    # (they'll require extra special handling, aren't aren't really useful for perf anyway)
+    if _is_view_op(op):
+        return None
+    op_namespace = op.__module__.split(".")[-1]
+    op_base_name = op.overloadpacket.__name__
+    maybe_namespace_module = getattr(torch.ops, op_namespace)
+    maybe_inplace_op = None if maybe_namespace_module is None else getattr(maybe_namespace_module, f'{op_base_name}_', None)
+    if maybe_inplace_op is None:
+        return None
+
+    inplace_overloads = [
+        getattr(maybe_inplace_op, overload_name) for overload_name in maybe_inplace_op.overloads()
+    ]
+    inplace_overloads_with_matching_schemas = [
+        f
+        for f in inplace_overloads
+        if _schemas_match(op._schema, f._schema)
+    ]
+    # Just because foo() and foo_() are both existing operators,
+    # They aren't guaranteed to have compatible schemas.
+    # For example, pow.Scalar(Scalar self, Tensor exponent) has no valid inplace variant,
+    # Even though several overloads of pow_ exist.
+    if len(inplace_overloads_with_matching_schemas) == 0:
+        return None
+    assert len(inplace_overloads_with_matching_schemas) == 1
+    inplace_op = inplace_overloads_with_matching_schemas[0]
+    return inplace_op
+
+_VIEW_INVERSE_MAP = {
+    torch.ops.aten.diagonal_scatter.default: torch.ops.aten.diagonal.default,
+    torch.ops.aten.select_scatter.default: torch.ops.aten.select.int,
+    torch.ops.aten.slice_scatter.default: torch.ops.aten.slice.Tensor,
+    torch.ops.aten.as_strided_scatter.default: torch.ops.aten.as_strided.default,
+}
+
+# This function, given a set of set of (aliased) tensor nodes,
+# Returns any nodes in the graph that *use* any of the aliases, that occur *after* op_index
+# in the node ordering.
+def _get_all_later_node_usages(tensor_aliases: Set[Node], op_index: int):
+    def _add_if_tensor(x, set_):
+        if isinstance(x, FakeTensor):
+            set_.add(StorageWeakRef(x._typed_storage()))
+
+    nodes_used_after = set()
+    for t in tensor_aliases:
+        # get all nodes that use the current alias
+        usage_nodes = t.users
+        for n in usage_nodes:
+            # We only care about usages after the current node
+            if 'node_idx' not in n.meta or n.meta['node_idx'] <= op_index:
+                continue
+            # We also don't care about intermediate view ops.
+            # They only matter if their output is then used elsewhere
+            # (either in an out-of-place op, or as an output to the function).
+            if n in tensor_aliases:
+                if isinstance(n.target, torch._ops.OpOverload) or n.target == _operator.getitem:
+                    continue
+            nodes_used_after.add(n)
+    return nodes_used_after
+
+# Given an op that we're trying to re-inplace, "b = foo(a)",
+# And given a {view}_scatter op that shows up later in the graph, "y = {view}_scatter(base, x, args...)"
+# Then re-inplacing `foo()` would allow us to remove the `{view}_scatter` op entirely, IF:
+# If there are any aliases in the alias_set(a) that satisfy:
+# (1) The base of "alias", "alias_base", has the same size/stride/offset metadata as "base"
+# (2) The output of running {view}(alias, args...) gives you the same size/stride/offset metadata
+#     as "alias"
+def _get_view_inverse_node_usages(later_node_usages: Set[Node], self_aliases: Set[Node]) -> Set[Node]:
+    def matching_view_metadata(a, b):
+        return a.size() == b.size() and \
+            a.stride() == b.stride() and \
+            a.storage_offset() == b.storage_offset()
+
+    view_inverse_nodes = set()
+    # Go through them in node order, so we can see chains of view_scatter ops.
+    for n in sorted(later_node_usages, key=lambda x: x.meta['node_idx']):
+        if n.target not in _VIEW_INVERSE_MAP:
+            continue
+        base = n.args[0]
+        mutated_view = n.args[1]
+        assert isinstance(base, Node)
+        assert isinstance(base.meta['fake_result'], FakeTensor)
+        assert isinstance(mutated_view, Node)
+        assert isinstance(mutated_view.meta['fake_result'], FakeTensor)
+        # Check that this view_inverse op actually corresponds to taking doing the inverse
+        # of one of our existing self_alias nodes.
+        original_view = _VIEW_INVERSE_MAP[n.target]
+        for self_alias in self_aliases:
+            # We're looking for some alias of the self arg, "alias",
+            # that was created from some op `alias = foo(base, args...)`
+            # such that the current _scatter op "inverts" that foo call.
+            # We can check that by running the original op again, and checking that the strides match.
+            if 'view_of' not in self_alias.meta:
+                continue
+            self_alias_base = self_alias.meta['view_of']
+            try:
+                # The we're trying to re-use the args from the view_scatter call inside of the corresponding
+                # view op, which might throw. This just indicates that view_scatter op isn't a valid inverse
+                # of the current alias we're looking at.
+                view_replay_metadata = original_view(self_alias_base.meta['fake_result'], *n.args[2:], **n.kwargs)
+                expected_metadata = self_alias.meta['fake_result']
+                # If the alias and its base both have matching metadata, then this view_scatter op is valid to re-inplace.
+                if matching_view_metadata(self_alias_base.meta['fake_result'], base.meta['fake_result']) and \
+                        matching_view_metadata(view_replay_metadata, expected_metadata):
+                    view_inverse_nodes.add(n)
+            except Exception:
+                continue
+
+    return view_inverse_nodes
+
+
+@compatibility(is_backward_compatible=True)
+def reinplace(gm, *sample_args):
+    """
+    Given an fx.GraphModule, modifies it to perform "reinplacing",
+    mutating the nodes of the graph.
+    We look for out-of-place op call sites like `b = a.add(...)`,
+    and convert them to be inplace (`b = a.add_(...)`),
+    as long as the input to the current operator ("a") isn't re-used
+    anywhere later in the graph.
+
+    This pass currently expects to operate on a **functional, ATen** graph.
+    This can be obtained by running `make_fx(functionalize(f))`.
+
+    Sample inputs are needed to determine aliasing relationships of the inputs.
+    In general, we can't reinplace node `b = a.add(...)` if "a" aliases any of the
+    inputs to the program.
+
+    Given a node "b = foo(a, args...) the algorithm for re-inplacing is as follows:
+
+    (1) Perform some initial checks on the metadata of "a" and "args..."
+        that can disqualify them from being reinplaced.
+
+      (1a) Check that the self argument we're attempting to reinplace
+           has acceptable dtype/size metadata to reinplace with.
+
+           For example, if we have:
+             a = torch.ones(1)
+             b = torch.ones(10)
+             out = torch.add(a, b)
+           We can't turn that into
+             a.add_(b)
+           Because that would require resizing "a".
+
+           Similarly, we can't convert torch.ge(a, b) into a.ge_(b),
+           because that would require changing a's dtype (from e.g. float32 to bool).
+           Note that in this specific example, we could technically do better..
+
+           If we see the pattern:
+             a_1 = a.ge(b)
+             a_2 = aten._to_copy(a_1, a.dtype)
+           Then we this should be valid to completely re-inplace
+           (this is exactly what functionalization will emit when it sees a.ge_(b)).
+
+           This optimization is only really important for user programs
+           that directly use inplace comparison ops though.
+
+           We also cannot re-inplace on tensors that have overlapping memory,
+           e.g. torch.ones(1).expand(4, 4).add_(1)
+
+      (1b) Check if "a" is an alias of any of the program inputs.
+
+          If it is, skip and move to the next node.
+          Inplace'ing an op that would cause it to mutate a program is not sound,
+          because that would be a side effect visible to the user.
+
+          NOTE: there's a future optimization that we should make:
+          if "a" is a (alias of a)  program input, but later in the program
+          there is a node that looks like "a.copy_(...)",
+          Then re-inplacing is ok to do - we are temporarily re-using a's buffer,
+          which will later be overwritten by the copy_() call.
+
+          This will be an important optimization to have for programs that mutate
+          their inputs. It currently isn't implemented though.
+
+      (1c) Check if "a" and "args..." alias
+
+          For example, re-inplacing to create code like the below
+          isn't guaranteed to be sound:
+
+            aten.mul_(a, a)
+
+    (2) Check that "a" and all of its outstanding aliases are not used anywhere
+        later in the graph. If this is the case, then it's safe to re-inplace
+        to "b = foo_(a)".
+
+        There are a few caveats to this, explained in more detail below:
+        (a) If "a" is used later as an argument to a view op, that is okay.
+            It's only a problem if "a" (or that view) is later passed
+            into a normal operator, or if it is returned as the program output.
+        (b) If "a" is a repeat argument in `foo()`, then don't reinplace.
+            Most ATen kernels don't make any guarantees that this is sound,
+            e.g. if you do aten.mul_(a, a).
+            So we'll just ban re-inplacing in this case.
+            It's only a problem if "a" (or that view) is later passed
+        (c) If "a" is used as an input into a view "inverse" / "scatter"
+            operator, it is potentially fine to re-inplace
+            (and remove that scatter operator from the graph).
+            See below for a more detailed example.
+
+        NOTE: there is an optimization in this step that is crucial
+        to fully recovering performance from functionalization.
+
+        Given this program:
+        def f(x):
+            a = torch.ops.aten.add(x, x)
+            b = torch.ops.aten.diagonal(a)
+            torch.ops.aten.fill_(b, 0)
+            return d
+
+        Functionalization will emit the following:
+        def f(x):
+            a = torch.ops.aten.add(x, x)
+            b = torch.ops.aten.diagonal(a, 0, 1)
+            b_updated = torch.ops.aten.fill(b, 0)
+            a_updated = torch.ops.aten.diagonal_scatter(a, b_updated, 0, 1)
+            return a_updated
+
+        Ordinarily, we would not be able to reinplace the fill,
+        because "b" aliases with "a" which is used by the diagonal_scatter call.
+
+        "re-inplacing" is on the hook for figuring out that it is ok to
+        completely, the expensive diagonal_scatter call, if we re-inplace the add().
+
+        So, for every `alias in alias_set(a)`, instead of checking
+        that "alias" is not used anywhere later in the graph,
+        we check that
+            EITHER:
+          (a) alias is not used anywhere later in the graph
+            OR:
+          (b) alias is used exactly once later on in the graph,
+              in the following op:
+
+                out = foo_scatter(alias, x, args...)
+
+              where the following must hold:
+                (i) "foo_scatter" is the "inverse" operator for foo.
+                    This only applies to "foo" ops that are view operators,
+                    which view into a subset of the original tensor's memory.
+                    In practice, there are ~4 operators where this applies:
+                      diagonal -> diagonal_scatter
+                      slice -> slice_scatter
+                      select -> select_scatter
+                      as_strided -> as_strided_scatter
+                (ii) "args..." are the same between the foo() and foo_scatter() calls.
+
+    (3) Perform the actual re-inplacing on foo!
+
+      (3b) is the common case, but special care is needed for {view}_scatter (3a)
+
+      (3a) {view}_scatter ops.
+
+        Consider this program:
+          a = torch.zeros(2, 2)
+          b = torch.ones(2)
+          a[0] = b
+
+        Post functionalization, that will look like:
+          a = torch.zeros(2)
+          b = torch.ones(1)
+          a_updated = torch.select_scatter(a, b, 0, 0)
+
+        In this case though, there is no "functional" op to re-inplace!
+        Instead, we'd like to directly remove toe select_scatter call.
+        We already know from (3) that this is valid,
+        because "a" has no later usages in the graph.
+
+        We perform the re-inplacing on the {view}_scatter op like so
+        Before:
+          a_updated = torch.select_scatter(a, b, args...)
+        After:
+          a_slice = a.select(a, args...)
+          a_slice.copy_(b)
+
+      (3b) Otherwise, replace the functional op with its inplace variant.
+        Before:
+          b = foo(a, args...)
+        After:
+          a.foo_(args...)
+
+    (4) Finally, after converting either:
+          Before:
+            b = foo(a)
+          After:
+            foo_(a)
+        or
+          Before:
+            b = {slice}_scatter(a, mutated_slice, args...)
+          After:
+            slice = {slice}(a, args...)
+            slice.copy_(mutated_slice)
+
+        We now need to find all later nodes that use "b" as an argument
+        and update them to take in "a" instead.
+
+        Note that for the majority of inplace ops, this isn't actually necessary
+        (because most inplace ops return "self" as their output).
+        This isn't generally true for all mutable ops though, which is why
+        we need to actually replace all of the arguments.
+
+        We also need to update our metadata of Dict[StorageWeakRef, Set[Node]],
+        That maps a given tensor storage to the set of all nodes that take in that storage
+        as an input.
+        Specifically, re-inplacing `b = foo(a)` causes "a" and "b"'s sets to get fused
+        together.
+
+    (5) Any "view_inverse/scatter" nodes that were identified as "it's ok to ignore them"
+        during step (3) get manually deleted from the graph.
+        Their outputs are no longer used, so technically standard DCE would be able
+        to do this, but we can no longer run FX's DCE pass now that we have mutable
+        ops in the graph.
+    """
+    _FunctionalizationMetadataProp(gm).propagate(*sample_args)
+
+    # Useful debug printing
+    # def _print(x):
+    # if isinstance(x, FakeTensor):
+    # print(f'fake_result: {StorageWeakRef(x._typed_storage()).cdata}')
+
+    # for n in gm.graph.nodes:
+    # print(n.format_node())
+    # if hasattr(n, 'meta'):
+    # print(f'node_idx: {n.meta["node_idx"]}')
+    # if 'fake_result' in n.meta:
+    # tree_map(_print, n.meta['fake_result'])
+    # if 'view_of' in n.meta:
+    # print(f'view_of: {str(n.meta["view_of"])}')
+    # print()
+
+    # We need to know which nodes correspond to inputs (or their aliases)
+    # so we know not to re-inplace them.
+    # NOTE: later, we'll need to add an optimization for fully recovering performance
+    # on programs that mutate inputs.
+    input_storages = {
+        StorageWeakRef(
+            node.meta['fake_result']._typed_storage()
+        ) for node in gm.graph.nodes if (node.op == 'placeholder' and isinstance(node.meta['fake_result'], torch.Tensor))}
+
+    # We also need to know for a given node, what are all of its aliasing nodes.
+    storage_to_nodes: Dict[StorageWeakRef, Set[Node]] = defaultdict(set)
+    for n in gm.graph.nodes:
+        if 'fake_result' in n.meta:
+            # Tree-mapping because some ops can return lists of tensors.
+            def _add_to_map(x):
+                if isinstance(x, FakeTensor):
+                    storage_to_nodes[StorageWeakRef(x._typed_storage())].add(n)
+            pytree.tree_map_(_add_to_map, n.meta['fake_result'])
+
+    # inplace-ify functional ops, subject to the constraints written below.
+    all_later_view_inverse_nodes_to_delete = set()
+    for idx, node in enumerate(gm.graph.nodes):
+        if node.op == 'call_function':
+
+            # Today, the re-inplace pass on directly acts on:
+            # - functional ops with an inplace variant
+            # - {view}_scatter ops that can be potentially removed from the graph.
+            # Both of these ops take in tensor first args, so filtering on this condition
+            # makes the later code simpler.
+            # We should revisit this at some point though, particularly when we also want
+            # the reinplacer to be able to handle out= and mutable operators
+            # and tensorlist first args (like `_foreach_` ops).
+            if not isinstance(node.target, torch._ops.OpOverload):
+                continue
+            if len(node.target._schema.arguments) < 1:
+                continue
+            if type(node.target._schema.arguments[0].type) != torch.TensorType:
+                continue
+
+            # Step 1a: Check that the self argument we're attempting to reinplace
+            # has the same size/stride as the output.
+            # For example, we shouldn't try to reinplace torch.add(scalar_tensor, larger_tensor)
+            # As it would require resizing scalar_tensor.
+            # (We could potentially swizzle this into larger_tensor.add_(scalar_tensor),
+            # this is probably an optimization to revisit later).
+            self_arg = node.args[0]
+            self_flattened = pytree.tree_leaves(self_arg.meta['fake_result'])
+            node_flattened = pytree.tree_leaves(node.meta['fake_result'])
+            self_has_wrong_metadata = False
+            if len(self_flattened) == len(node_flattened):
+                for self_meta, node_meta in zip(self_flattened, node_flattened):
+                    if self_meta.numel() != node_meta.numel():
+                        self_has_wrong_metadata = True
+                    if self_meta.dtype != node_meta.dtype:
+                        self_has_wrong_metadata = True
+                    # We also cannot re-inplace on tensors that have internal memory overlap.
+                    # e.g. torch.ones(1).expand(4, 4).add_(1)
+                    if torch._debug_has_internal_overlap(self_meta) == 1:
+                        self_has_wrong_metadata = True
+            # Here, we (optimistically) assume that a.resize(b) is valid to re-inplace,
+            # Since users should never really be calling the functional "torch.ops.aten.resize"
+            # op directly in their programs.
+            if self_has_wrong_metadata and node.target != torch.ops.aten.resize.default:
+                continue
+
+            # Step 1b: ensure that the op we're trying to re-inplace isn't a program input
+            self_arg_name = self_arg.name
+            self_arg_storage = StorageWeakRef(self_arg.meta['fake_result']._typed_storage())
+            if self_arg_storage in input_storages:
+                # TODO: later, add the optimization for handling `copy_()` calls in the graph.
+                continue
+            if len([x for x in node.args if x is self_arg]) > 1:
+                # Step 1c:
+                # Calling stuff like aten.mul_(a, a) isn't guaranteed to be sound,
+                # so we prevent re-inplacing in this case.
+                continue
+
+            self_arg_storage = StorageWeakRef(self_arg.meta['fake_result']._typed_storage())
+            self_aliases = storage_to_nodes[self_arg_storage]
+
+            # First, we find all later usages of any of the aliases of self_arg.
+            later_node_usages = _get_all_later_node_usages(self_aliases, node.meta['node_idx'])
+            # Then, we check if any of those later usages are actually view_scatter ops
+            # that are safe to fully remove.
+            later_view_inverse_node_usages = _get_view_inverse_node_usages(later_node_usages, self_aliases)
+
+            # Step 2: Check to see if the input to the op is re-used later in the graph.
+            # If not (same goes for its aliases), then this op is safe to re-in place.
+            # This is a slightly roundabout way to check that there are no later usages of the current self argument.
+            # (later_view_inverse_node_usages corresponds to "view_scatter" nodes that we are allowed to delete)
+            can_reinplace = len(later_node_usages - later_view_inverse_node_usages) == 0
+            if not can_reinplace:
+                continue
+
+            # Step 3a: Special handling for when we see *_scatter operators.
+            # When we see an operator like `b = torch.slice_scatter(a, ...)`,
+            # instead of trying to "inplace" it into a.slice_scatter_(..._),
+            # we would prefer to remove it from the graph entirely,
+            # and instead copy_() the slice directly into the larger tensor.
+            # See the description of the algorithm for a full example.
+            if node.target in _VIEW_INVERSE_MAP and node not in all_later_view_inverse_nodes_to_delete:
+                view_op = _VIEW_INVERSE_MAP[node.target]
+                # Before:
+                #   base_updated = torch.ops.aten.slice_scatter.default(base, mutated_slice, args...)
+                # After:
+                #   slice = torch.ops.aten.slice.default(base, args...)
+                #   slice.copy_(mutated_slice)
+                with gm.graph.inserting_before(node):
+                    mutated_slice_node = node.args[1]
+                    remaining_slice_args = node.args[2:]
+                    slice_node = gm.graph.create_node(
+                        'call_function', view_op, (self_arg,) + tuple(remaining_slice_args), node.kwargs)
+                    copy_node = gm.graph.create_node(
+                        'call_function', torch.ops.aten.copy_.default, (slice_node, mutated_slice_node,), {})
+                # Add the slice_scatter node to our "nodes to delete" list.
+                all_later_view_inverse_nodes_to_delete.add(node)
+
+
+            else:
+                # Step 3b: Check to see if this operator has an inplace variant.
+                maybe_inplace_op = _maybe_get_inplace_op(node.target)
+                if maybe_inplace_op is None:
+                    continue
+                # And if so, replace it with its inplace variant.
+                node.target = maybe_inplace_op
+
+            # At this point, 'storage_to_nodes' will be stale.
+            # Now that we're inplacing `b = foo(a)`, we need to effectively
+            # union together the dict values for b and a's storage.
+            # Hmm... morally I think we also want to keep the `fake_result` metadata
+            # up to date here, but I'm not sure how easy it is to do.
+            # Maybe it's fine to wait until the end of the pass to update it.
+            curr_node_storage = StorageWeakRef(node.meta['fake_result']._typed_storage())
+            storage_to_nodes[self_arg_storage].update(storage_to_nodes[curr_node_storage])
+            storage_to_nodes[curr_node_storage].update(storage_to_nodes[self_arg_storage])
+
+            # Need to remember the view_scatter view nodes we found so we can remove them alter.
+            all_later_view_inverse_nodes_to_delete.update(later_view_inverse_node_usages)
+
+            # Step 4:
+            # Now that we've replaced b = a.foo() with a.foo_(),
+            # We need to replace any later usages of "b" with "a"
+            for old in itertools.chain([node], later_view_inverse_node_usages):
+                new = old.args[0]
+                nodes_to_update = [n for n in old.users if n.meta['node_idx'] > node.meta['node_idx']]
+                for node_to_update in nodes_to_update:
+                    new_args = []
+                    args = node_to_update.args
+
+                    def replace_arg(a):
+                        if a == old:
+                            return new
+                        return a
+
+                    # First, replace usages of "b" with "a"
+                    node_to_update.args = tree_map_only(Node, replace_arg, node_to_update.args)
+                    node_to_update.kwargs = tree_map_only(Node, replace_arg, node_to_update.kwargs)
+
+                    # Second, update our storage_to_nodes data structure.
+                    old_flattened_res = pytree.tree_leaves(old.meta['fake_result'])
+                    node_flattened_res = pytree.tree_leaves(node_to_update.meta['fake_result'])
+
+                    old_res_storage = {
+                        StorageWeakRef(
+                            x._typed_storage()
+                        ) for x in old_flattened_res if isinstance(x, FakeTensor)}
+                    node_res_storage = {
+                        StorageWeakRef(
+                            x._typed_storage()
+                        ) for x in node_flattened_res if isinstance(x, FakeTensor)}
+
+                    # This will happen if we're updating a view op, e.g.
+                    # e.g. replacing
+                    #     x = view(old)
+                    #     x = view(new)
+                    # When that happens, we need to make sure to keep our
+                    # storage mapping up to date.
+                    #
+                    # We're checking for len(...) == 1 here because all view ops are guaranteed to return either a single tensor,
+                    # or multiple tensors that all share the same storage.
+                    # We can't just check equality because we might encounter FX nodes that return zero tensor outputs.
+                    if len(old_res_storage) == 1 and len(node_res_storage) == 1 and old_res_storage == node_res_storage:
+                        new_flattened_res = pytree.tree_leaves(new.meta['fake_result'])
+                        new_res_storage = {
+                            StorageWeakRef(
+                                x._typed_storage()
+                            ) for x in new_flattened_res if isinstance(x, FakeTensor)}
+                        assert len(new_res_storage) == 1
+                        (old_ref,) = old_res_storage
+                        (new_ref,) = new_res_storage
+                        (node_ref,) = node_res_storage
+                        # Technically, "old_ref" and all its aliases will remain
+                        # in our mapping.
+                        # That should be fine though, since we deleted "old"
+                        # from the graph at this point.
+                        storage_to_nodes[node_ref].update(storage_to_nodes[new_ref])
+                        storage_to_nodes[new_ref].update(storage_to_nodes[node_ref])
+
+    # Step 4: delete any _scatter nodes that we de-functionalized
+    # Need to take care not to delete any of these nodes until after *all* modifications
+    # to the graph are finished.
+    for to_delete in all_later_view_inverse_nodes_to_delete:
+        gm.graph.erase_node(to_delete)
+
+
+    gm.recompile()
+    return gm
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/runtime_assert.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/runtime_assert.py
new file mode 100644
index 0000000000000000000000000000000000000000..01803600d021ab59b42222f6163f23d82f5d29ed
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/runtime_assert.py
@@ -0,0 +1,605 @@
+# mypy: allow-untyped-defs
+import functools
+import logging
+import operator
+import sys
+from typing import Any, Dict, Optional, Set, TYPE_CHECKING
+
+
+# Import sympy and ShapeEnv during TYPE_CHECKING since importing sympy is slow
+if TYPE_CHECKING:
+    import sympy
+
+    from torch.fx.experimental.symbolic_shapes import ShapeEnv
+else:
+    ShapeEnv = Any
+
+import torch
+import torch.utils._pytree as pytree
+from torch import fx
+from torch._subclasses.meta_utils import is_sparse_any
+from torch.fx._compatibility import compatibility
+from torch.fx._utils import lazy_format_graph_code
+from torch.fx.experimental.proxy_tensor import py_sym_types
+from torch.fx.experimental.sym_node import SymNode
+from torch.fx.graph_module import GraphModule
+
+
+__all__ = ["insert_deferred_runtime_asserts"]
+
+log = logging.getLogger(__name__)
+graph_code_log = torch._logging.getArtifactLogger(__name__, "graph_code")
+
+
+def _get_example_value(node: fx.Node) -> Optional[str]:
+    """
+    Get the example value key for a node, since dynamo uses "example_value"
+    while non-strict export uses "val.
+    """
+    if "example_value" in node.meta:
+        return node.meta["example_value"]
+    elif "val" in node.meta:
+        return node.meta["val"]
+    else:
+        return None
+
+
+def _get_sym_val(node: fx.Node) -> Optional["sympy.Expr"]:
+    val = _get_example_value(node)
+    if isinstance(val, py_sym_types):
+        return val.node.expr
+    return None
+
+
+@compatibility(is_backward_compatible=True)
+def insert_deferred_runtime_asserts(
+    gm: GraphModule,
+    shape_env: ShapeEnv,
+    name: str,
+    export: bool = False,
+) -> None:
+    """
+    During tracing, we may have discovered that some data-dependent values
+    had runtime assert on them; e.g., torch.empty(x.item()) induces a runtime
+    that x.item() >= 0.  This asserts can happen unpredictably during fake
+    tensor propagation, so we cannot conveniently insert them into the FX graph
+    when they occur.  Instead, we accumulate them in the ShapeEnv, and in this
+    pass insert them into the graph as proper tests.
+
+    This pass also deduplicates size-related computation, CSE-ing ops that produce
+    symbolic values and/or are involved in runtime asserts. Additionally, shape calls
+    (size/stride/storage_offset) are turned into compute on input sizes if possible,
+    allowing intermediate tensors to be freed earlier. For example, here dynamo will
+    DCE the cat and repeat calls:
+
+        z = torch.cat([x, x], dim=0)  # 2*s0
+        w = z.repeat(y.shape[0])  # 2*s0*s1
+        _w = w.shape[0]
+        # something with _w, but not w ...
+
+        # turns into ->
+        _w0 = 2 * s0
+        _w = _w0 * s1
+
+        # where s0, s1 are either SymInt graph inputs, or the result of added size calls
+
+    Redundant torch._check or torch.ops.aten._assert_scalar.default calls that assert
+    the same expression, and redundant constrain_range calls are also deduplicated.
+    Additionally, because single-symbol bound checks (e.g. u0 >= 0, u0 <= 5) accumulate
+    information in the ShapeEnv, the ShapeEnv contains min/max bounds for each symbol,
+    and we delete all previous calls, adding bound checks at the end of this pass.
+    """
+
+    # Import sympy locally
+    import sympy
+
+    from torch._export.passes._node_metadata_hook import _set_node_metadata_hook
+    from torch.fx.experimental.symbolic_shapes import (
+        _has_uninterpretable_sympy_function,
+        CallMethodKey,
+        cast_symbool_to_symint_guardless,
+        ConvertIntKey,
+        DivideByKey,
+        free_symbols,
+        InnerTensorKey,
+        resolve_unbacked_bindings,
+    )
+    from torch.utils._sympy.numbers import int_oo
+    from torch.utils._sympy.reference import PythonReferenceAnalysis
+    from torch.utils._sympy.value_ranges import ValueRanges
+
+    # TODO: Request simplification on runtime asserts before emitting them
+    ras_by_symbol = shape_env.deferred_runtime_asserts.copy()
+    graph = gm.graph
+    graph_code_log.debug(
+        "%s",
+        lazy_format_graph_code(
+            f"pre insert_deferred_runtime_asserts {name}", gm, colored=True
+        ),
+    )
+
+    # We are going to mutate the dict
+    expr_to_proxy: Dict[sympy.Expr, fx.Proxy] = {}
+    placeholders = set()
+    first_non_placeholder = None
+    for node in graph.nodes:
+        if node.op != "placeholder":
+            first_non_placeholder = node
+            break
+        else:
+            placeholders.add(node)
+
+    def _is_intermediate_tensor_sym_call(node: fx.Node) -> bool:
+        """
+        If a size/stride/storage offset call on an intermediate tensor,
+        we can try to compute the value from input shapes instead.
+        """
+        return (
+            (val := _get_sym_val(node)) is not None
+            and not isinstance(val, sympy.Number)
+            # this holds back from reifying anything in torch.utils._sympy.functions.py that's unsupported
+            and not _has_uninterpretable_sympy_function(val)
+            and any(
+                isinstance(arg, fx.Node)
+                and isinstance(_get_example_value(arg), (torch.Tensor, torch.Size))
+                and arg.op != "placeholder"
+                for arg in node.args
+            )
+        )
+
+    # Figure out what key to use, val or example_value
+    val_key = "val"
+    for node in graph.nodes:
+        if "example_value" in node.meta:
+            val_key = "example_value"
+            break
+        elif "val" in node.meta:
+            break
+
+    def _node_metadata_hook(
+        node: torch.fx.Node,
+        stack_trace: Optional[str] = None,
+        nn_module_stack: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        fake_args = [
+            _get_example_value(arg) if isinstance(arg, torch.fx.Node) else arg
+            for arg in node.args
+        ]
+        try:
+            node.meta[val_key] = node.target(*fake_args)  # type: ignore[operator]
+        except NotImplementedError:
+            # This can happen when attempting to reify a symbol with an unsupported call_function node,
+            # e.g. with NestedTensors + sym_size.int via match_symbol().
+            # This seems to be fine, as the node gets CSE'd and deleted later in favor of a SymInt graph input.
+            pass
+        if stack_trace is not None:
+            node.meta["stack_trace"] = stack_trace
+        if nn_module_stack is not None:
+            node.meta["nn_module_stack"] = nn_module_stack
+
+    # Track asserts/checks we've added
+    added_asserts: Set[sympy.Expr] = set()
+    constrained_unbacked_symbols: Set[sympy.Symbol] = set()
+
+    def _sympy_interp(expr_to_proxy, expr):
+        # sympy_interp() with hash consing
+        from sympy import Integer, Number, Symbol
+        from sympy.logic.boolalg import BooleanAtom
+
+        from torch.utils._sympy.interp import _run_sympy_handler, sympy_interp
+
+        # hash cons
+        if expr in expr_to_proxy:
+            return expr_to_proxy[expr]
+        # base cases, don't cache
+        if isinstance(expr, (Integer, Number, Symbol, BooleanAtom)):
+            return sympy_interp(PythonReferenceAnalysis, expr_to_proxy, expr)
+
+        # hash cons on arguments, run expr handler
+        expr_to_proxy[expr] = _run_sympy_handler(
+            PythonReferenceAnalysis,
+            [_sympy_interp(expr_to_proxy, arg) for arg in expr.args],
+            expr,
+        )
+        return expr_to_proxy[expr]
+
+    def _is_bound_expr_for_symbol(expr: "sympy.Expr") -> bool:
+        # This is probably unnecessary, but since torch._check() calls for single-symbol bounds
+        # like u0 >= 0, 10 >= u0 accumulate range info in the ShapeEnv, we designate these calls as redundant
+        # and instead add 2 runtime asserts at the end of this pass, if the min/max bounds are non-trivial.
+        if len(expr.args) != 2 or expr.func not in (sympy.LessThan, sympy.GreaterThan):
+            return False
+        lhs, rhs = expr.args
+        return (isinstance(lhs, sympy.Symbol) and isinstance(rhs, sympy.Number)) or (
+            isinstance(rhs, sympy.Symbol) and isinstance(lhs, sympy.Number)
+        )
+
+    def add_runtime_asserts(ras):
+        for ra in ras:
+            if (
+                # redundant
+                ra.expr in added_asserts
+                # if we've already added a constrain_range call for this symbol,
+                # then single-symbol bound asserts like u0 >= 0, u0 <= 5 are redundant.
+                or (
+                    len(ra.expr.free_symbols) == 1
+                    and next(iter(ra.expr.free_symbols)) in constrained_unbacked_symbols
+                    and _is_bound_expr_for_symbol(ra.expr)
+                )
+                # don't try to reify sympy functions we can't turn into FX nodes
+                or _has_uninterpretable_sympy_function(ra.expr)
+            ):
+                continue
+
+            log.debug("inserting runtime assert %s", ra.expr)
+            # Need to process ALL free symbols, not just unbacked ones
+            fvs = free_symbols(ra.expr)
+            missing = fvs - expr_to_proxy.keys()
+            if missing:
+                i1 = min(missing, key=str)
+                # TODO: Remove relaxing assert on unbacked_symint https://github.com/pytorch/pytorch/issues/119689
+                # assert shape_env.is_unbacked_symint(i1), i1
+                ras_by_symbol.setdefault(i1, []).append(ra)
+            else:
+                # Convert the sympy expression into a sequence of FX
+                # nodes
+                with _set_node_metadata_hook(gm, _node_metadata_hook):
+                    res = _sympy_interp(expr_to_proxy, ra.expr).node
+                    graph.call_function(
+                        torch.ops.aten._assert_scalar.default,
+                        # TODO: use ra.msg here, but it's pretty
+                        # useless right now
+                        (
+                            res,
+                            f"Runtime assertion failed for expression {ra.expr} on node '{res}'",
+                        ),
+                    )
+                added_asserts.add(ra.expr)
+
+    nodes = list(graph.nodes)
+    for i, node in enumerate(nodes[:-1]):
+        # Placeholders can match symbols, but when we destructure them
+        # with size we have to make sure we insert the nodes after all
+        # the placeholders
+        with graph.inserting_before(
+            nodes[i + 1] if node not in placeholders else first_non_placeholder
+        ):
+            # Unfortunately, this logic still must remain because manual
+            # make_fx calls may not explicitly bind all symbolic ints as
+            # arguments to the function, so we must infer it from the other
+            # arguments
+            if (
+                node in placeholders
+                and (example_value := _get_example_value(node)) is not None
+            ):
+
+                def match_symbol(symint, cb):
+                    if (
+                        isinstance(symint, torch.SymInt)
+                        and isinstance(symint.node, SymNode)
+                        and isinstance(s := symint.node.expr, sympy.Symbol)
+                        and s not in expr_to_proxy
+                    ):
+                        with _set_node_metadata_hook(gm, _node_metadata_hook):
+                            expr_to_proxy[s] = fx.Proxy(cb())
+                        log.debug("expr_to_proxy[%s] = %s", s, expr_to_proxy[s])
+
+                match_symbol(example_value, lambda: node)
+                if isinstance(t := example_value, torch.Tensor):
+                    for i, s in enumerate(t.size()):
+                        match_symbol(
+                            s,
+                            lambda: graph.call_function(
+                                torch.ops.aten.sym_size.int, (node, i)
+                            ),
+                        )
+                    if not is_sparse_any(t):
+                        for i, s in enumerate(t.stride()):
+                            match_symbol(
+                                s,
+                                lambda: graph.call_function(
+                                    torch.ops.aten.sym_stride.int, (node, i)
+                                ),
+                            )
+                        match_symbol(
+                            t.storage_offset(),
+                            lambda: graph.call_function(
+                                torch.ops.aten.sym_storage_offset.default, (node,)
+                            ),
+                        )
+
+            # Handle asserts that aren't associated with any symbol.  This
+            # doesn't really have to be in the loop as it will only run once,
+            # it just needs to happen right after the placeholders.
+            # insert this after placeholders & added sym nodes, and before non-placeholders.
+            if node == first_non_placeholder:
+                add_runtime_asserts(ras_by_symbol.pop(None, []))  # type: ignore[call-overload]
+
+            # deduplicate asserts already present in graph
+            if node.target in (
+                torch._check,
+                torch.ops.aten._assert_scalar.default,
+            ):
+                if (
+                    node.args[0] == True  # noqa: E712
+                    or (assert_expr := _get_sym_val(node.args[0])) in expr_to_proxy
+                    or (
+                        assert_expr is not None
+                        and _is_bound_expr_for_symbol(assert_expr)
+                    )
+                ):
+                    arg = node.args[0]
+                    gm.graph.erase_node(node)
+                    if isinstance(arg, fx.Node) and not arg.users:
+                        gm.graph.erase_node(arg)
+                else:
+                    added_asserts.add(assert_expr)  # type: ignore[arg-type]
+
+            # hash cons, replace function calls that return torch.SymInts with direct references to
+            # FX nodes built up to reify the sympy expression.
+            if (
+                node.op != "placeholder"
+                and (sym_expr := _get_sym_val(node)) is not None
+            ):
+                # this guards against deleting calls like item() that produce new untracked symbols
+                new_untracked_symbols = sym_expr.free_symbols - expr_to_proxy.keys()
+                # this guards against deleting calls that produce unbacked bindings we haven't yet seen.
+                # in this case looking at sym_expr.free_symbols might not be enough, if the example value has a hint
+                # (is backed), but produces an unbacked symbol. In this case keep the node alive.
+                new_unbacked_bindings = (
+                    resolve_unbacked_bindings(
+                        shape_env, node.meta.get("unbacked_bindings", {})
+                    ).keys()
+                    - expr_to_proxy.keys()
+                )
+
+                # maybe re-reify expression, replace current node
+                if (
+                    sym_expr in expr_to_proxy
+                    or (  # example value is redundant
+                        _is_intermediate_tensor_sym_call(node)
+                        # shape call on intermediate tensor, turn into computation on input shapes
+                        and not new_untracked_symbols
+                    )
+                ) and not new_unbacked_bindings:
+                    if _is_intermediate_tensor_sym_call(
+                        node
+                    ):  # reify from input shapes
+                        with _set_node_metadata_hook(
+                            gm,
+                            functools.partial(
+                                _node_metadata_hook,
+                                stack_trace=node.meta.get("stack_trace"),
+                                nn_module_stack=node.meta.get("nn_module_stack"),
+                            ),
+                        ):
+                            expr_to_proxy[sym_expr] = _sympy_interp(expr_to_proxy, sym_expr)  # type: ignore[arg-type]
+                        # won't try DCE-ing tensor compute here
+                    hash_node = expr_to_proxy[sym_expr].node  # type: ignore[arg-type]
+                    node.replace_all_uses_with(hash_node)
+                    gm.graph.erase_node(node)
+                    log.debug(
+                        "CSE node %s -> %s for expr %s", node, hash_node, sym_expr
+                    )
+
+                # store node in hash cons, don't delete/replace
+                elif sym_expr not in expr_to_proxy and not isinstance(
+                    sym_expr, (sympy.Number, sympy.logic.boolalg.BooleanAtom)
+                ):  # don't hash cons primitives
+                    expr_to_proxy[sym_expr] = fx.Proxy(node)  # type: ignore[arg-type]
+
+            # We add sym_constrain_range calls for symbols later in any case if they're size-like or range-constrained,
+            # so calls before that are redundant.
+            if node.target in (
+                torch.ops.aten.sym_constrain_range.default,
+                torch.ops.aten.sym_constrain_range_for_size.default,
+            ):
+                gm.graph.erase_node(node)
+
+            defs = []
+
+            # AOTAutograd will create new symbols as the unbacked_bindings keys, which PropagateSymInts will set as
+            # equivalent, but the refinement calls we perform in this pass may struggle with associating the two.
+            # More concretely, when re-exporting/tracing, constraining only the new symbol may not communicate enough
+            # information about the old symbol when we re-export, raising errors on data-dependent guards.
+            # Call resolve_unbacked_bindings() to get the original symbol if present, otherwise we take it as is.
+            if unbacked_bindings := resolve_unbacked_bindings(
+                shape_env, node.meta.get("unbacked_bindings")
+            ):
+                for s, keypath in unbacked_bindings.items():
+                    defs.append(s)
+
+                    # TODO: some CSE when generating these nodes can probably
+                    # help reduce graph size and improve compile time
+                    def go(node, keypath):
+                        if keypath == ():
+                            return node
+                        if (
+                            len(keypath) >= 2
+                            and isinstance(keypath[0], CallMethodKey)
+                            and isinstance(keypath[1], pytree.SequenceKey)
+                        ):
+                            if keypath[0].name == "size":
+                                return go(
+                                    graph.call_function(
+                                        torch.ops.aten.sym_size.int,
+                                        (node, keypath[1].idx),
+                                    ),
+                                    keypath[2:],
+                                )
+                            if keypath[0].name == "stride":
+                                return go(
+                                    graph.call_function(
+                                        torch.ops.aten.sym_stride.int,
+                                        (node, keypath[1].idx),
+                                    ),
+                                    keypath[2:],
+                                )
+                            return go(
+                                graph.call_method(
+                                    keypath[0].name, (node, keypath[1].idx)
+                                ),
+                                keypath[2:],
+                            )
+                        elif isinstance(keypath[0], CallMethodKey):
+                            return go(
+                                graph.call_method(keypath[0].name, (node,)), keypath[1:]
+                            )
+                        elif isinstance(keypath[0], pytree.SequenceKey):
+                            return go(
+                                graph.call_function(
+                                    operator.getitem, (node, keypath[0].idx)
+                                ),
+                                keypath[1:],
+                            )
+                        elif isinstance(keypath[0], ConvertIntKey):
+                            return go(
+                                graph.call_function(
+                                    cast_symbool_to_symint_guardless, (node,)
+                                ),
+                                keypath[1:],
+                            )
+                        elif isinstance(keypath[0], DivideByKey):
+                            # TODO: need to assert divisibility
+                            return go(
+                                graph.call_function(
+                                    operator.floordiv, (node, keypath[0].divisor)
+                                ),
+                                keypath[1:],
+                            )
+                        elif isinstance(keypath[0], InnerTensorKey):
+                            return go(
+                                graph.call_function(
+                                    getattr, (node, keypath[0].inner_name)
+                                ),
+                                keypath[1:],
+                            )
+                        else:
+                            raise AssertionError(f"unrecognized keypath {keypath}")
+
+                    if s not in expr_to_proxy:
+                        with _set_node_metadata_hook(gm, _node_metadata_hook):
+                            expr_to_proxy[s] = fx.Proxy(go(node, keypath))
+                        log.debug("expr_to_proxy[%s] = %s", s, expr_to_proxy[s])
+
+            for i0 in defs:
+                ras = ras_by_symbol.pop(i0, [])
+                # Before we perform any asserts, first apply range
+                # refinement.  This is important, because if we are going
+                # to retrace the graph (and we typically are if we send
+                # the graph to AOTAutograd), we need to make sure we apply
+                # range refinement (ala _check_is_size) first, BEFORE we
+                # run any of the asserts.  Otherwise, we may decide to
+                # perform substitutions based on the asserts which we then
+                # can't back out, because value ranges can only be applied
+                # to asserts.)
+                #
+                # A perhaps better long term plan is to avoid this order
+                # dependence by making it possible to refine ranges on
+                # arbitrary expressions, not just symbols.  But it is not
+                # so easy to make use of this information, see
+                # https://twitter.com/ezyang/status/1745801370299482492
+                # We actually made an attempt at this in
+                # https://github.com/pytorch/pytorch/pull/119043
+                # which didn't work.
+                #
+                # Another ideas for how to do this:
+                # - Have bound_sympy be the source of truth of the ranges of any expression
+                # - Cache intermediate results for every subexpression of bound_sympy
+                # - This cache should be possible to edit to refine ranges
+                #
+                # One issue with this proposal is that if
+                # we have a bound on 2x, we are not going to be able to
+                # apply it for 4x.  Similarly, we may have bounds for an
+                # equivalent expression that we are not applying because
+                # it's not a perfect match (e.g. x < y vs y > x)".
+                #
+                # The first issue we already have it and it's impossible
+                # to solve in general, so any implementation on a best
+                # effort basis should do.
+                #
+                # The second issue is a preexisting one. It can be mitigated
+                # with a normalisation algorithm. In general, it may also
+                # be on a best effort basis, but since our grammar is not
+                # terribly difficult, chances are we could even fully
+                # normalise SymPy expressions... who knows.
+                if i0 in constrained_unbacked_symbols:
+                    continue  # constrain symbol just once
+
+                if i0 in shape_env.size_like:
+                    if export:
+                        graph.call_function(
+                            torch.ops.aten.sym_constrain_range_for_size.default,
+                            (expr_to_proxy[i0].node,),
+                        )
+                    else:
+                        graph.call_function(
+                            torch._check_is_size, (expr_to_proxy[i0].node,)
+                        )
+
+                vr = shape_env.var_to_range[i0]
+                if vr.is_int and vr.upper == sys.maxsize - 1:
+                    # treat upper bound == sys.maxsize - 1 for int symbols as +oo
+                    # to avoid redundant runtime assert
+                    vr = ValueRanges(vr.lower, int_oo)
+                if not shape_env._default_unspecified_value_range().issubset(vr):
+                    # The runtime range is constrained, so add a runtime
+                    # assert and also explicitly refine the range
+                    # (refinement should not be necessary once runtime
+                    # asserts cause refinement, but that's NYI)
+                    def convert(s):
+                        if s in (int_oo, -int_oo):
+                            return None
+                        try:
+                            return int(s)
+                        except TypeError:
+                            return None
+
+                    if (
+                        expr_to_proxy[i0].node.target
+                        != cast_symbool_to_symint_guardless
+                    ):
+                        # TODO(pianpwk): calling sym_constrain_range_for_size or adding bound asserts
+                        # raises AOTAutograd errors on cast_symbool_to_symint_guardless
+
+                        with _set_node_metadata_hook(
+                            gm,
+                            functools.partial(
+                                _node_metadata_hook,
+                                stack_trace=node.meta.get("stack_trace"),
+                                nn_module_stack=node.meta.get("nn_module_stack"),
+                            ),
+                        ):
+                            if (min_val := convert(vr.lower)) is not None:
+                                ge = _sympy_interp(expr_to_proxy, i0 >= min_val).node
+                                graph.call_function(
+                                    torch.ops.aten._assert_scalar.default,
+                                    (
+                                        ge,
+                                        f"Runtime assertion failed for expression {i0 >= min_val} on node '{ge}'",
+                                    ),
+                                )
+                                added_asserts.add(i0 >= min_val)
+                            if (max_val := convert(vr.upper)) is not None:
+                                le = _sympy_interp(expr_to_proxy, i0 <= max_val).node
+                                graph.call_function(
+                                    torch.ops.aten._assert_scalar.default,
+                                    (
+                                        le,
+                                        f"Runtime assertion failed for expression {i0 <= max_val} on node '{le}'",
+                                    ),
+                                )
+                                added_asserts.add(i0 <= max_val)
+
+                constrained_unbacked_symbols.add(i0)
+                add_runtime_asserts(ras)
+
+    # delete unused reified symbols
+    for expr, proxy in expr_to_proxy.items():
+        if (
+            isinstance(expr, sympy.Symbol)
+            and proxy.node.op != "placeholder"  # keep placeholders intact
+            and not proxy.node.users
+        ):
+            log.debug("deleting unused reified symbol for %s", expr)
+            gm.graph.erase_node(proxy.node)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/shape_prop.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/shape_prop.py
new file mode 100644
index 0000000000000000000000000000000000000000..dcaee3f82113907132ea852e9f74dc73a5bb10d9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/shape_prop.py
@@ -0,0 +1,196 @@
+# mypy: ignore-errors
+
+import torch
+import torch.fx
+import traceback
+
+from torch._dispatch.python import enable_python_dispatcher
+from torch.fx.node import Node, map_aggregate
+from typing import Any, Tuple, NamedTuple, Optional, Dict
+from torch.fx._compatibility import compatibility
+from torch._guards import detect_fake_mode
+from torch._subclasses.meta_utils import is_sparse_any
+
+__all__ = ['TensorMetadata', 'ShapeProp']
+
+@compatibility(is_backward_compatible=True)
+class TensorMetadata(NamedTuple):
+    # TensorMetadata is a structure containing pertinent information
+    # about a tensor within a PyTorch program.
+
+    # General Tensor metadata
+    shape : torch.Size
+    dtype : torch.dtype
+    requires_grad : bool
+    stride : Tuple[int, ...]
+    memory_format : Optional[torch.memory_format]
+
+    # Quantization metadata
+    is_quantized : bool
+    qparams: Dict[str, Any]
+
+def _extract_tensor_metadata(result : torch.Tensor, include_contiguity=True) -> TensorMetadata:
+    """
+    Extract a TensorMetadata NamedTuple describing `result`.
+    """
+    shape = result.shape
+    dtype = result.dtype
+    requires_grad = result.requires_grad
+    stride = result.stride() if not is_sparse_any(result) else None
+
+    memory_format = None
+
+    if include_contiguity and not is_sparse_any(result):
+        memory_formats = {
+            torch.contiguous_format,
+            torch.channels_last,
+            torch.channels_last_3d,
+        }
+        for query_format in memory_formats:
+            if result.is_contiguous(memory_format=query_format):
+                memory_format = query_format
+                break
+
+    is_quantized = result.is_quantized
+    qparams: Dict[str, Any] = {}
+    if is_quantized:
+        qscheme = result.qscheme()
+        qparams["qscheme"] = qscheme
+        if qscheme in {torch.per_tensor_affine, torch.per_tensor_symmetric}:
+            qparams["scale"] = result.q_scale()  # type: ignore[assignment]
+            qparams["zero_point"] = result.q_zero_point()  # type: ignore[assignment]
+        elif qscheme in {torch.per_channel_affine, torch.per_channel_affine_float_qparams, torch.per_channel_symmetric}:
+            # In this branch, scale and zero_point are expected to be tensors,
+            # we store the values as immutable_list in TensorMetadata for
+            # easier serialization downstream
+            qparams["scale"] = result.q_per_channel_scales().tolist()  # type: ignore[assignment]
+            qparams["zero_point"] = result.q_per_channel_zero_points().tolist()  # type: ignore[assignment]
+            qparams["axis"] = result.q_per_channel_axis()  # type: ignore[assignment]
+
+    return TensorMetadata(
+        shape, dtype, requires_grad, stride, memory_format, is_quantized, qparams)
+
+@compatibility(is_backward_compatible=True)
+class ShapeProp(torch.fx.Interpreter):
+    """
+    Execute an FX graph Node-by-Node and
+    record the shape and type of the result
+    into the corresponding node.
+
+    Example:
+         In this example, we record the shape
+         and data type of a module given
+         an example input ``torch.randn(50, D_in)``.
+         We print the name, shape and dtype of each node.
+
+        class TwoLayerNet(torch.nn.Module):
+            def __init__(self, D_in, H, D_out):
+                super().__init__()
+                self.linear1 = torch.nn.Linear(D_in, H)
+                self.linear2 = torch.nn.Linear(H, D_out)
+            def forward(self, x):
+                h_relu = self.linear1(x).clamp(min=0)
+                y_pred = self.linear2(h_relu)
+                return y_pred
+        N, D_in, H, D_out = 64, 1000, 100, 10
+        x = torch.randn(N, D_in)
+        y = torch.randn(N, D_out)
+        model = TwoLayerNet(D_in, H, D_out)
+        gm = torch.fx.symbolic_trace(model)
+        sample_input = torch.randn(50, D_in)
+        ShapeProp(gm).propagate(sample_input)
+
+        for node in gm.graph.nodes:
+            print(node.name, node.meta['tensor_meta'].dtype,
+                node.meta['tensor_meta'].shape)
+
+        The output of this code is:
+
+        x torch.float32 torch.Size([50, 1000])
+        linear1 torch.float32 torch.Size([50, 100])
+        clamp_1 torch.float32 torch.Size([50, 100])
+        linear2 torch.float32 torch.Size([50, 10])
+        output torch.float32 torch.Size([50, 10])
+
+    Args:
+         module (GraphModule): The module to be executed
+         fake_mode (FakeTensorMode): A fake mode for copying the gm
+
+    """
+    def __init__(self, gm, fake_mode=None):
+        super().__init__(gm)
+        if fake_mode is None:
+            fake_mode = detect_fake_mode()
+        if fake_mode is not None:
+            from torch._dynamo.utils import deepcopy_to_fake_tensor
+            # Note:
+            # We need fake execution cause the inputs are fake, however, we cannot fakify the module
+            # - because we need to write to the tensor_meta of the real module. So we fakify to
+            # produce a result (L131 below), to extract tensor meta, and then keep going.
+            #
+            # If we were to fakify, we would write to the wrong node, and then downstream fusion
+            # would be missing the tensor_meta.
+            #
+            # See torch/_inductor/overrides.py for where this is called upstream of fusion.
+            self.fake_module = deepcopy_to_fake_tensor(self.module, fake_mode)
+            self.fake_mode = fake_mode
+        else:
+            self.fake_module = None
+            self.fake_mode = None
+
+        self.real_module = self.module
+
+    def run_node(self, n : Node) -> Any:
+        try:
+            if self.fake_module is not None:
+                # Hacky swap. Alternatively, we could do this with overriding
+                # call_module and get_attr.
+                self.module = self.fake_module
+            try:
+                if self.fake_mode is not None:
+                    with self.fake_mode, enable_python_dispatcher():
+                        result = super().run_node(n)
+                else:
+                    result = super().run_node(n)
+            finally:
+                self.module = self.real_module
+        except Exception as e:
+            traceback.print_exc()
+            raise RuntimeError(
+                f"ShapeProp error for: node={n.format_node()} with "
+                f"meta={n.meta}"
+            ) from e
+
+        found_tensor = False
+
+        def extract_tensor_meta(obj):
+            if isinstance(obj, torch.Tensor):
+                nonlocal found_tensor
+                found_tensor = True
+                return _extract_tensor_metadata(obj)
+            else:
+                return obj
+
+        meta = map_aggregate(result, extract_tensor_meta)
+        if found_tensor:
+            n.meta['tensor_meta'] = meta
+
+        n.meta['type'] = type(result)
+        return result
+
+    def propagate(self, *args):
+        """
+        Run `module` via interpretation and return the result and
+        record the shape and type of each node.
+
+        Args:
+            *args (Tensor): the sample input.
+
+        Returns:
+            Any: The value returned from executing the Module
+        """
+        if self.fake_mode is not None:
+            fake_args = [self.fake_mode.from_tensor(t) if isinstance(t, torch.Tensor) else t for t in args]
+        else:
+            fake_args = args
+        return super().run(*fake_args)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/split_module.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/split_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..1881beaf2ece1362b8d89b83c5e8cf891bcecf75
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/split_module.py
@@ -0,0 +1,575 @@
+# mypy: allow-untyped-defs
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Set
+from collections import OrderedDict
+import logging
+
+import torch
+from torch.fx._compatibility import compatibility
+from torch.fx.graph_module import GraphModule
+from torch.fx.node import Node
+from torch.fx._utils import lazy_format_graph_code
+
+
+__all__ = ["Partition", "split_module"]
+log = _LOGGER = logging.getLogger(__name__)
+
+@compatibility(is_backward_compatible=True)
+class Partition:
+    def __init__(self, name: str):
+        self.name: str = name
+        self.submod_name = f"submod_{name}"
+        self.node_names: List[str] = []
+        self.inputs: Dict[str, None] = {}
+        self.outputs: Dict[str, None] = {}
+        self.dependencies: Dict[str, None] = {}
+        self.dependents: Dict[str, None] = {}
+        self.graph: torch.fx.graph.Graph = torch.fx.graph.Graph()
+        self.environment: Dict[Node, Node] = {}
+        self.targets: Dict[str, Any] = {}
+
+    def __repr__(self) -> str:
+        return (
+            f"name: {self.name},\n"
+            f" nodes: {self.node_names},\n"
+            f" inputs: {self.inputs},\n"
+            f" outputs: {self.outputs},\n"
+            f" partitions depended on: {self.dependencies},\n"
+            f" partition dependents: {self.dependents}"
+        )
+
+
+# Creates subgraphs out of main graph
+@compatibility(is_backward_compatible=True)
+def split_module(
+    m: GraphModule,
+    root_m: torch.nn.Module,
+    split_callback: Callable[[Node], int],
+    qualname_map: Optional[Dict[str, str]] = None,
+    keep_original_order: Optional[bool] = False,
+    keep_original_node_name: Optional[bool] = False,
+):
+    """
+    Creates subgraphs out of main graph
+
+    Args:
+        m (GraphModule): Graph module to split
+        root_m (torch.nn.Module): root nn module. Not currently used. Included
+            because the root nn module is usually transformed via
+            torch.fx._symbolic_trace.symbolic_trace (see example below)
+        split_callback (Callable[[Node], int]): Callable function
+            that maps a given Node instance to a numeric partition identifier.
+            split_module will use this function as the policy for which operations
+            appear in which partitions in the output Module.
+        qualname_map: Optional[Dict[str, str]]: optional output parameter that returns a
+            mapping from new target names in the module after split to old target
+            names in the original module.
+        keep_original_order: Optional[bool]: keep the original order of the GraphModule
+            or use the Topological order of the new constructed GraphModule
+
+
+    Returns:
+        GraphModule: the module after split.
+
+    Example:
+
+        This is a sample setup:
+
+            import torch
+            from torch.fx.symbolic_trace import symbolic_trace
+            from torch.fx.graph_module import GraphModule
+            from torch.fx.node import Node
+            from torch.fx.passes.split_module import split_module
+
+            class MyModule(torch.nn.Module):
+                def __init__(self) -> None:
+                    super().__init__()
+                    self.param = torch.nn.Parameter(torch.rand(3, 4))
+                    self.linear = torch.nn.Linear(4, 5)
+
+                def forward(self, x, y):
+                    z = self.linear(x + self.param).clamp(min=0.0, max=1.0)
+                    w = self.linear(y).clamp(min=0.0, max=1.0)
+                    return z + w
+
+            # symbolically trace model
+            my_module = MyModule()
+            my_module_traced = symbolic_trace(my_module)
+
+            # random mod partitioning
+            partition_counter = 0
+            NPARTITIONS = 3
+
+            def mod_partition(node: Node):
+                global partition_counter
+                partition = partition_counter % NPARTITIONS
+                partition_counter = (partition_counter + 1) % NPARTITIONS
+                return partition
+
+            # split module in module with submodules
+            module_with_submodules = split_module(
+                my_module_traced, my_module, mod_partition
+            )
+
+        Output looks like this. Original graph is broken into partitions
+
+            > print(module_with_submodules)
+            GraphModule(
+                (submod_0): GraphModule(
+                    (linear): Linear(in_features=4, out_features=5, bias=True)
+                )
+                (submod_1): GraphModule(
+                    (linear): Linear(in_features=4, out_features=5, bias=True)
+                )
+                (submod_2): GraphModule()
+            )
+
+            def forward(self, x, y):
+                param = self.param
+                submod_0 = self.submod_0(x, param, y);  x = param = y = None
+                getitem = submod_0[0]
+                getitem_1 = submod_0[1];  submod_0 = None
+                submod_1 = self.submod_1(getitem, getitem_1);  getitem = getitem_1 = None
+                getitem_2 = submod_1[0]
+                getitem_3 = submod_1[1];  submod_1 = None
+                submod_2 = self.submod_2(getitem_2, getitem_3);  getitem_2 = getitem_3 = None
+                return submod_2
+
+        Output of split module is the same as output of input traced module.
+        This is an example within a test setting:
+
+            > orig_out = my_module_traced(x, y)
+            > submodules_out = module_with_submodules(x, y)
+            > self.assertEqual(orig_out, submodules_out)
+            True
+    """
+
+    log.debug(
+        "%s",
+        lazy_format_graph_code(
+            "pre split_module", m, colored=True
+        ),
+    )
+
+    def construct_graph(
+        node: Node,
+        base_mod_env: Dict[str, Node],
+        base_mod_attrs: Dict[str, torch.fx.graph_module.GraphModule],
+    ):
+        if node.op == "placeholder":
+            default_value = (
+                node.args[0] if len(node.args) > 0 else inspect.Signature.empty
+            )
+            if keep_original_node_name:
+                args = () if default_value is inspect.Signature.empty else (default_value,)
+                base_mod_env[node.name] = base_mod_graph.create_node('placeholder', node.name, args=args, type_expr=node.type)  # type: ignore[arg-type]
+            else:
+                base_mod_env[node.name] = base_mod_graph.placeholder(
+                    node.target, type_expr=node.type, default_value=default_value  # type: ignore[arg-type]
+                )
+            base_mod_env[node.name].meta = node.meta.copy()
+        elif node.op == "get_attr":
+            base_mod_env[node.name] = base_mod_graph.get_attr(node.target)  # type: ignore[arg-type]
+            base_mod_env[node.name].meta = node.meta.copy()
+            attr_val = m
+            for atom in node.target.split("."):  # type: ignore[union-attr]
+                if not hasattr(attr_val, atom):
+                    raise AttributeError(f"Node target {node.target} not found!")
+                attr_val = getattr(attr_val, atom)
+            base_mod_attrs[node.target] = attr_val  # type: ignore[index]
+        return base_mod_env, base_mod_attrs
+
+    import sympy
+
+    partitions: Dict[str, Partition] = {}
+    orig_nodes: Dict[str, Node] = {}
+    symbol_to_node: Dict[sympy.Symbol, Node] = {}
+
+    def record_cross_partition_use(
+        def_node: Node, use_node: Optional[Node]
+    ):  # noqa: B950
+        from torch.fx.experimental.symbolic_shapes import free_symbols
+
+        defined = getattr(def_node, "_fx_partition", None)
+        used = getattr(use_node, "_fx_partition", None)
+
+        log.debug(
+            "record_cross_partition_use %s (%s) %s (%s)",
+            def_node.name, defined, use_node.name if use_node is not None else "-", used
+        )
+
+        if defined != used:
+            if defined is not None:
+                def_partition = partitions[defined]
+                def_partition.outputs.setdefault(def_node.name)
+                if used is not None:
+                    def_partition.dependents.setdefault(used)
+
+            if used is not None:
+                use_partition = partitions[used]
+                use_partition.inputs.setdefault(def_node.name)
+                # We have made def_node an input to the use_partition.  If
+                # this input has symbolic symbols in its size, those also must
+                # be made as inputs to the partition
+                if (def_val := def_node.meta.get("example_value")) is not None:
+                    for s in sorted(free_symbols(def_val), key=str):
+                        s_node = symbol_to_node[s]
+                        use_partition.inputs.setdefault(s_node.name)
+                        if symbol_to_node[s].op != "placeholder":
+                            # If the node that defines the symbol is not a
+                            # placeholder, we must make it an output of the
+                            # partition.  Note that this may be in a different
+                            # partition than defined!  Although, this doesn't
+                            # really make a difference for correctness, since
+                            # defined is guaranteed to have the symbol in
+                            # scope and can return it; you just get less
+                            # optimal codegen in this case.
+                            s_defined = getattr(s_node, "_fx_partition", None)
+                            if s_defined is not None:
+                                s_def_partition = partitions[s_defined]
+                                s_def_partition.outputs.setdefault(s_node.name)
+                                s_def_partition.dependents.setdefault(used)
+                if defined is not None:
+                    use_partition.dependencies.setdefault(defined)
+
+    def instantiate_node_partition_mapping(node):
+        partition_name = str(split_callback(node))
+        log.debug("instantiate_node_partition_mapping %s (%s)", node.name, partition_name)
+
+        # add node to partitions
+        partition = partitions.get(partition_name)
+        if partition is None:
+            partitions[partition_name] = partition = Partition(partition_name)
+
+        partition.node_names.append(node.name)
+        node._fx_partition = partition_name
+
+    # Global State Nodes are nodes which by their global state effects,
+    # "taint" all downstream nodes while they are active.
+    GLOBAL_STATE_NODES = [
+        torch.amp._enter_autocast,
+        torch.amp._exit_autocast,
+        torch._C._set_grad_enabled
+    ]
+
+    # For grad regions:
+    # ------------------------
+    # 1. first region: we do nothing
+    # 2. subsequent regions: we insert the set_grad at the beginning
+    grad_regions: OrderedDict[Node, Set[int]] = OrderedDict()
+
+    # For autocast regions:
+    # ------------------------
+    # 1. first region: we will only insert the _exit at the end
+    # 2. intermediate regions: we will insert both the
+    #    _enter at the beginning and _exit at the end
+    # 3. last region: we will only insert _enter at the beginning
+    # We will do so in the order in which the autocasts were instantiated.
+    autocast_regions: OrderedDict[Node, Set[int]] = OrderedDict()
+    autocast_exits: Dict[Node, Optional[Node]] = {}
+
+    active_grad = None
+    active_autocasts = set()
+
+    for node in m.graph.nodes:
+        # This will prefer placeholder bindings, because those come first.
+        # This is a little dangerous though: it is possible that an unbacked
+        # symbol is used without any binding site for it, in which case we
+        # will get a KeyError not able to find it.  I'd like to fix this by
+        # having passes.runtime_assert establish some invariants that I can
+        # rely on later, but this needs some extra work.  Quick fix first.
+        # See https://github.com/pytorch/pytorch/issues/130534
+        if (
+            (val := node.meta.get("example_value")) is not None and
+            isinstance(val, torch.SymInt) and
+            isinstance(s0 := val.node.expr, sympy.Symbol) and
+            s0 not in symbol_to_node
+        ):
+            symbol_to_node[val.node.expr] = node
+
+        if node.op in ["placeholder", "get_attr", "output"]:
+            continue
+
+        instantiate_node_partition_mapping(node)
+
+        if node.op == "call_function" and node.target in GLOBAL_STATE_NODES:
+            if node.target == torch._C._set_grad_enabled:
+                assert len(node.args) == 1
+                assert isinstance(node.args[0], bool)
+                active_grad = node
+                grad_regions[active_grad] = set({split_callback(node)})
+            elif node.target == torch.amp._enter_autocast:
+                # Should all be python constants
+                assert all(not isinstance(arg, Node) for arg in node.args)
+                active_autocasts.add(node)
+                autocast_regions[node] = set({split_callback(node)})
+                autocast_exits[node] = None
+            elif node.target == torch.amp._exit_autocast:
+                assert len(node.args) == 1
+                autocast_regions[node.args[0]].add(split_callback(node))
+                active_autocasts.remove(node.args[0])
+                autocast_exits[node.args[0]] = node
+
+        if active_grad is not None:
+            grad_regions[active_grad].add(split_callback(node))
+
+        for a in active_autocasts:
+            autocast_regions[a].add(split_callback(node))
+
+    assert all(v is not None for v in autocast_exits.values()), "autocast must exit"
+
+    autocast_regions = {k: sorted(v) for k, v in autocast_regions.items()}
+    grad_regions = {k: sorted(v) for k, v in grad_regions.items()}
+
+    if _LOGGER.isEnabledFor(logging.DEBUG):
+        _LOGGER.debug("autocast_regions: %s", autocast_regions)
+        _LOGGER.debug("grad_regions: %s", grad_regions)
+
+    assert_monotonically_increasing = bool(autocast_regions) or bool(grad_regions)
+
+    # split nodes into partitions
+    highest_partition = -1
+    for node in m.graph.nodes:
+        orig_nodes[node.name] = node
+
+        # TODO currently placeholders/parameters aren't put into random partitions,
+        # rather they're added to the graphs where they are used down below
+        if node.op in ["placeholder", "get_attr"]:
+            continue
+        if node.op == "output":
+            torch.fx.graph.map_arg(
+                node.args[0], lambda n: record_cross_partition_use(n, None)
+            )
+            continue
+
+        if assert_monotonically_increasing:
+            pid = split_callback(node)
+            assert highest_partition <= pid, \
+                ("autocast or set_grad_enabled require monotonically increasing partitions:"
+                 f"highest: {highest_partition}, this node's: {pid}")
+            highest_partition = pid
+
+        # do not capture cross-partition dependencies for global state nodes as they will be
+        # self-contained - their setup and unwind will be isolated to each partition submodule.
+        if node.target not in GLOBAL_STATE_NODES:
+            torch.fx.graph.map_arg(
+                node.args, lambda def_node: record_cross_partition_use(def_node, node)
+            )
+            torch.fx.graph.map_arg(
+                node.kwargs, lambda def_node: record_cross_partition_use(def_node, node)
+            )  # noqa: B950
+
+    original_partition_order = list(partitions.keys())
+    # find partitions with no dependencies
+    root_partitions: List[str] = []
+    for partition_name, partition in partitions.items():
+        if not len(partition.dependencies):
+            root_partitions.append(partition_name)
+
+    # check partitions for circular dependencies and create topological partition ordering
+    sorted_partitions: List[str] = []
+    while root_partitions:
+        root_partition = root_partitions.pop()
+        sorted_partitions.append(root_partition)
+        for dependent in partitions[root_partition].dependents:
+            partitions[dependent].dependencies.pop(root_partition)
+            if not partitions[dependent].dependencies:
+                root_partitions.append(dependent)
+    if len(sorted_partitions) != len(partitions):
+        raise RuntimeError("cycle exists between partitions!")
+
+    # Enter prelude
+    for regions_mapping in [autocast_regions, grad_regions]:
+        for node, regions in regions_mapping.items():
+            assert len(regions) > 0
+            partitions[str(regions[0])].environment[node] = node
+            for r in regions[1:]:
+                partition = partitions[str(r)]
+                new_node = partition.graph.create_node(
+                    op=node.op,
+                    target=node.target,
+                    args=tuple(arg for arg in node.args),
+                    kwargs={},
+                    type_expr=node.type,
+                )
+                new_node.meta = node.meta.copy()  # is it really a good idea to copy this?
+                partition.environment[node] = new_node
+
+    # add placeholders to partition inputs
+    for partition_name in sorted_partitions:
+        partition = partitions[partition_name]
+        for inp in partition.inputs:
+            placeholder = partition.graph.placeholder(
+                inp,
+                type_expr=orig_nodes[inp].type,
+            )
+            placeholder.meta = orig_nodes[inp].meta.copy()
+            partition.environment[orig_nodes[inp]] = placeholder
+
+    # Transform nodes and collect targets for partition's submodule
+    for node in m.graph.nodes:
+        if hasattr(node, "_fx_partition"):
+            partition = partitions[node._fx_partition]
+
+            # swap out old graph nodes in kw/args with references to new nodes in this submodule
+            environment = partition.environment
+            gathered_args = torch.fx.graph.map_arg(node.args, lambda n: environment[n])
+            gathered_kwargs = torch.fx.graph.map_arg(
+                node.kwargs, lambda n: environment[n]
+            )
+
+            if node.op not in ["call_module", "get_attr"]:
+                target = node.target
+            else:
+                target_atoms = node.target.split(".")
+                target_attr = m
+                for atom in target_atoms:
+                    if not hasattr(target_attr, atom):
+                        raise AttributeError(f"Operator target {node.target} not found!")
+                    target_attr = getattr(target_attr, atom)
+                # target = target_atoms[-1]
+                target = "_".join(target_atoms)
+                partition.targets[target] = target_attr
+                # Fill in the passed-in mapping from new qualname to old qualname
+                if qualname_map is not None:
+                    # When creating the split module later, the submodules will have
+                    # path prefix matching the corresponding partition's submod_name
+                    qualname = f"{partition.submod_name}.{target}"
+                    qualname_map[qualname] = node.target
+
+            assert isinstance(gathered_args, tuple)
+            assert isinstance(gathered_kwargs, dict)
+            name = node.name if keep_original_node_name else None
+            new_node = partition.graph.create_node(
+                op=node.op,
+                target=target,
+                args=gathered_args,
+                kwargs=gathered_kwargs,
+                type_expr=node.type,
+                name=name,
+            )
+            new_node.meta = node.meta.copy()
+            partition.environment[node] = new_node
+
+    # Exit epilogue
+    for regions_mapping in [autocast_regions]:
+        for node in reversed(regions_mapping):
+            regions = regions_mapping[node]
+            assert len(regions) > 0
+            for r in regions[:-1]:
+                partition = partitions[str(r)]
+                exit_node = autocast_exits[node]
+                assert exit_node is not None, "Missing exit node"
+                new_node = partition.graph.create_node(
+                    op=exit_node.op,
+                    target=exit_node.target,
+                    args=(partition.environment[node],),
+                    kwargs={},
+                    type_expr=exit_node.type,
+                )
+                new_node.meta = exit_node.meta.copy()  # is it really a good idea to copy this?
+
+    # original module environment dict mapping node names to nodes
+    orig_mod_env: Dict[str, Node] = {}
+    # Set up values to construct base module
+    base_mod_env: Dict[str, Node] = {}
+    base_mod_graph: torch.fx.graph.Graph = torch.fx.graph.Graph()
+    base_mod_attrs: Dict[str, torch.fx.graph_module.GraphModule] = {}
+    if not keep_original_order:
+        for node in m.graph.nodes:
+            base_mod_env, base_mod_attrs = construct_graph(
+                node, base_mod_env, base_mod_attrs
+            )
+
+    else:
+        # Go through the graph to construct the mapping dict
+        for node in m.graph.nodes:
+            orig_mod_env[node.name] = node
+
+    # Do some things iterating over the partitions in topological order again:
+    # 1) Finish off submodule Graphs by setting corresponding outputs
+    # 2) Construct GraphModules for each submodule
+    # 3) Construct the base graph by emitting calls to those submodules in
+    #    topological order or original order specified by keep_original_order
+
+    construct_order_partitions = (
+        sorted_partitions if not keep_original_order else original_partition_order
+    )
+
+    already_constructed_attr_nodes = set()
+
+    # We actually need to insert the placeholder nodes in the original order
+    # otherwise graph signature will be wrong.
+    original_order = [node for node in m.graph.nodes if node.op == "placeholder"]
+
+    for partition_name in construct_order_partitions:
+        partition = partitions[partition_name]
+
+        # Set correct output values
+        output_vals = tuple(
+            partition.environment[orig_nodes[name]] for name in partition.outputs
+        )
+
+        # skip output node generation if there are no output values
+        num_output_vals = len(output_vals)
+        if num_output_vals == 1:
+            partition.graph.output(output_vals[0])
+        elif num_output_vals > 1:
+            partition.graph.output(output_vals)
+
+        if keep_original_order:
+            # first get the attr nodes required by this partition
+            orig_mod_attr_nodes: List[Node] = [
+                orig_mod_env[key] for key in partition.inputs if key not in original_order
+            ]
+
+            for node in original_order:
+                if node in already_constructed_attr_nodes:
+                    continue  # already added this attr to the base graph
+                base_mod_env, based_mod_attrs = construct_graph(
+                    node, base_mod_env, base_mod_attrs
+                )
+                already_constructed_attr_nodes.add(node)
+
+            # Construct GraphModule for this partition
+            for node in orig_mod_attr_nodes:  # type: ignore[attr-defined]
+                if node in already_constructed_attr_nodes:
+                    continue
+                base_mod_env, base_mod_attrs = construct_graph(
+                    node, base_mod_env, base_mod_attrs
+                )
+                already_constructed_attr_nodes.add(node)
+
+        base_mod_attrs[partition.submod_name] = torch.fx.graph_module.GraphModule(
+            partition.targets, partition.graph
+        )  # noqa: B950
+
+        # Emit call in base graph to this submodule
+        output_val = base_mod_graph.call_module(
+            partition.submod_name,
+            tuple(base_mod_env[name] for name in partition.inputs),
+        )
+
+        num_outputs = len(partition.outputs)
+        if num_outputs > 1:
+            # Unpack multiple return values from submodule
+            output_val_proxy = torch.fx.proxy.Proxy(output_val)
+            for i, output_name in enumerate(partition.outputs):
+                base_mod_env[output_name] = output_val_proxy[i].node  # type: ignore[index]
+        elif num_outputs == 1:
+            base_mod_env[next(iter(partition.outputs))] = output_val
+
+    for node in m.graph.nodes:
+        if node.op == "output":
+            base_mod_graph.output(
+                torch.fx.graph.map_arg(node.args[0], lambda n: base_mod_env[n.name])
+            )  # noqa: B950
+
+    ret = torch.fx.graph_module.GraphModule(base_mod_attrs, base_mod_graph)
+    log.debug(
+        "%s",
+        lazy_format_graph_code(
+            "post split_module", ret, colored=True
+        ),
+    )
+    return ret
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/split_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/split_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c003966983f3f90c1620a2acfe9742cfb4f6fa9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/split_utils.py
@@ -0,0 +1,303 @@
+# mypy: allow-untyped-defs
+import copy
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Tuple, Type, Union
+
+import torch.fx
+from torch.fx._compatibility import compatibility
+from torch.fx.graph import map_arg
+from torch.fx.passes.utils import HolderModule, lift_subgraph_as_module
+
+from .tools_common import NodeList
+
+__all__ = ["getattr_recursive", "setattr_recursive", "Component", "split_by_tags"]
+
+
+@compatibility(is_backward_compatible=False)
+def getattr_recursive(obj, name):
+    for layer in name.split("."):
+        if hasattr(obj, layer):
+            obj = getattr(obj, layer)
+        else:
+            return None
+    return obj
+
+
+@compatibility(is_backward_compatible=False)
+def setattr_recursive(obj, attr, value):
+    if "." not in attr:
+        setattr(obj, attr, value)
+    else:
+        layer = attr.split(".")
+        setattr_recursive(getattr(obj, layer[0]), ".".join(layer[1:]), value)
+
+
+@compatibility(is_backward_compatible=False)
+@dataclass
+class Component:
+    """
+    A component serves as a container for a subgraph we want to create afterwards.
+    """
+
+    graph: torch.fx.Graph
+    order: int
+    name: str
+
+    # Stores the placeholder nodes in `graph`.
+    input_placeholders: List = field(default_factory=list)
+
+    # Store the nodes in original graph that are placeholder in `graph`.
+    orig_inputs: List = field(default_factory=list)
+
+    # Store the nodes in original graph that are outputs in `graph`.
+    orig_outputs: List = field(default_factory=list)
+
+    # Mapping from get_attr node in original graph to get_attr node in `graph`.
+    getattr_maps: Dict[torch.fx.Node, torch.fx.Node] = field(default_factory=dict)
+    constructor_args: List[str] = field(default_factory=list)
+    gm: Optional[torch.fx.GraphModule] = None
+
+
+@compatibility(is_backward_compatible=False)
+def split_by_tags(
+    gm: torch.fx.GraphModule,
+    tags: List[str],
+    return_fqn_mapping: bool = False,
+    return_tuple: bool = False,
+    GraphModuleCls: Type[torch.fx.GraphModule] = torch.fx.GraphModule,
+) -> Union[torch.fx.GraphModule, Tuple[torch.fx.GraphModule, Dict[str, str]]]:
+    """
+    Splits a GraphModule using tags on its graph nodes. We honor the order of
+    tags. For example, we have tags = ["a", "b", "c"], the function will create
+    the initial submodules in the order of "a", "b", "c".
+
+    To set a tag:
+    gm.graph.nodes[idx].tag = "mytag"
+
+    This will result in all nodes with the same tag being extracted and placed in their
+    own submodule. For placeholder, output and get_attr node, the tag is ignored. placeholder
+    and output nodes are created when needed while get_attr nodes get copied to submodules
+    where they are used.
+
+    Given the following module def:
+
+    class SimpleModule(torch.nn.Module):
+        def __init__(self) -> None:
+            super().__init__()
+            self.linear1 = torch.nn.Linear(...)
+            self.linear2 = torch.nn.Linear(...)
+            self.linear3 = torch.nn.Linear(...)
+
+        def forward(self, in1, in2):
+            r1 = self.linear1(in1)
+            r2 = self.linear2(in2)
+            r3 = torch.cat([r1, r2])
+            return self.linear3(r3)
+
+    Marking the node corresponding to in1 with the tag sc.REQUEST_ONLY.lower() results in the following split:
+
+    ro:
+    def forward(self, in1):
+        self = self.root
+        linear1 = self.linear1(in1)
+        return linear1
+
+    main:
+    def forward(self, in2, linear1):
+        self = self.root
+        linear2 = self.linear2(in2)
+        cat_1 = torch.cat([linear1, linear2])
+        linear3 = self.linear3(cat_1)
+        return linear3
+
+    main:
+    def forward(self, in1, in2):
+        self = self.root
+        ro_0 = self.ro_0(in1)
+        main_1 = self.main_1(in2, ro_0)
+        return main_1
+
+    Returns:
+        split_gm: torch fx graph after split
+        orig_to_split_fqn_mapping: a map between the original fqn and the fqn
+            after split for call_module and get_attr.
+    """
+
+    def flatten(x: torch.fx.node.Argument) -> NodeList:
+        """
+        Stores nodes in x to a list and returns the list.
+        """
+        r: NodeList = []
+        map_arg(x, r.append)
+        return r
+
+    # Mapping from node in original module to node in created submodule.
+    node_remapping: Dict[torch.fx.Node, torch.fx.Node] = {}
+
+    # Mapping from node in original module or created submodules to
+    # corresponding component.
+    node_to_component: Dict[torch.fx.Node, Component] = {}
+
+    # Mapping from tag to the corresponding component.
+    tag_to_component: Dict[str, Component] = {}
+
+    # Stores all components.
+    all_components: List[Component] = []
+
+    # Stores nodes that will be used in main graph.
+    used_in_main: Dict[torch.fx.Node, None] = {}
+
+    # Main graph after split.
+    main_g = torch.fx.Graph()
+
+    # Mapping from node in original module to node in main graph after split.
+    main_remapping: Dict[torch.fx.Node, torch.fx.Node] = {}
+
+    # Output node of original module.
+    output_node: Optional[torch.fx.Node] = None
+
+    # Create a component for each tag, we don't expect to create other components afterwards.
+    for tag in tags:
+        comp = Component(torch.fx.Graph(), len(all_components), f"{tag}")
+        all_components.append(comp)
+        tag_to_component[tag] = comp
+
+    # Traverse the nodes in original graph and take care of them.
+    for node in gm.graph.nodes:
+        if node.op == "output":
+            if output_node is not None:
+                raise RuntimeError("Multiple output nodes in graph!")
+            output_node = node
+            continue
+
+        # Placeholders in the original graph get copied to main graph.
+        if node.op == "placeholder":
+            main_remapping[node] = main_g.placeholder(node.name, type_expr=node.type)
+            main_remapping[node].meta = copy.copy(node.meta)
+            continue
+
+        # Get_attr nodes are ignored because we are not tagging them.
+        # Instead, we copy them directly to the submodules use them afterwards.
+        if node.op == "get_attr":
+            continue
+
+        # Now we process callable nodes which are nodes with op of call_module,
+        # call_function or call_method. Every callable nodes should be tagged.
+        assert hasattr(node, "tag")
+
+        upstream_components = [
+            node_to_component[x]
+            for x in flatten(node.args) + flatten(node.kwargs)
+            if x.op not in {"placeholder", "get_attr"}
+        ]
+
+        comp = tag_to_component[node.tag]
+        node_to_component[node] = comp
+
+        # Max order of upperstream components.
+        mx = max((c.order for c in upstream_components), default=0)
+
+        # Expect the component for `node` has higher order then its upstream components.
+        assert comp.order >= mx
+
+        # Map a input of `node` to nodes in the component's graph.
+        def remap_func(x):
+            # If input is a get_attr node, copy it to current component's graph.
+            # Returns the get_attr node in current component's graph.
+            if x.op == "get_attr":
+                if x not in comp.getattr_maps:
+                    comp.getattr_maps[x] = comp.graph.get_attr(
+                        x.target, type_expr=x.type
+                    )
+                return comp.getattr_maps[x]
+
+            # If input is not a placeholder, it should have been put into a component
+            # already. If it's the current component then we return the corresponding
+            # node in the component.
+            if x.op != "placeholder" and node_to_component[x] == comp:
+                return node_remapping[x]
+
+            # If input is a placeholder or it's in other components, we want to make it
+            # as a placeholder in current component's graph.
+            if x not in comp.orig_inputs:
+                comp.orig_inputs.append(x)
+                placeholder = comp.graph.placeholder(x.name, type_expr=x.type)
+                placeholder.meta = copy.copy(x.meta)
+                comp.input_placeholders.append(placeholder)
+                used_in_main[x] = None
+
+            return comp.input_placeholders[comp.orig_inputs.index(x)]
+
+        n = comp.graph.node_copy(node, remap_func)
+        n.tag = node.tag  # type: ignore[attr-defined]
+        node_remapping[node] = n
+        node_to_component[n] = comp
+
+    if output_node is None:
+        raise RuntimeError("Graph had no output node!")
+
+    for x in flatten(output_node.args[0]):
+        if x.op == "get_attr":
+            # We don't need components mapping for nodes of type "get_attr"
+            # that are consumed by the output. Only need to make sure we create
+            # corresponding counterparts in the resulting graph.
+            main_remapping[x] = main_g.get_attr(x.name, type_expr=x.type)
+        else:
+            # All component results consumed by the output node should be
+            # marked as "used in main".
+            used_in_main[x] = None
+
+    # If a node is used in main graph then we mark it as an output in the component
+    # it belongs to.
+    for n in used_in_main:
+        if n.op != "placeholder":
+            node_to_component[n].orig_outputs.append(n)
+
+    # Now we create a graphmodule for each component.
+    orig_to_split_fqn_mapping: Dict[str, str] = {}
+    for comp in all_components:
+        outs = tuple(map(node_remapping.__getitem__, comp.orig_outputs))
+
+        if return_tuple:
+            comp.graph.output(outs)
+        else:
+            # Take care of the args of FX output node. If there's a single
+            # output then the output node args is like (output_single), else
+            # if there're multiple outputs then the output node args is like
+            # ((output_0, output_1, ...)).
+            comp.graph.output(outs[0] if len(outs) == 1 else outs)
+
+        comp.gm, comp_orig_to_split_fqn_mapping = lift_subgraph_as_module(
+            gm, subgraph=comp.graph, comp_name=comp.name
+        )
+        orig_to_split_fqn_mapping.update(comp_orig_to_split_fqn_mapping)
+
+        # Create a call_module node in main graph.
+        main_node = main_g.call_module(
+            comp.name,
+            args=tuple(map(main_remapping.__getitem__, comp.orig_inputs)),
+            kwargs=None,
+        )
+
+        if len(outs) == 1 and not return_tuple:
+            main_remapping[comp.orig_outputs[0]] = main_node
+        else:
+            for i, o in enumerate(comp.orig_outputs):
+                # Use Proxy to record getitem access.
+                main_remapping[o] = torch.fx.Proxy(main_node)[i].node  # type: ignore[index]
+
+    main_g.output(map_arg(output_node.args[0], main_remapping.__getitem__))
+    main_root = HolderModule({comp.name: comp.gm for comp in all_components})
+    main_g._codegen = gm.graph._codegen
+
+    # If the output nodes consumes get_attr directly in the original graph,
+    # then we need to make sure get_attr is copied to the new graph.
+    for x in flatten(output_node.args[0]):
+        if x.op == "get_attr":
+            setattr(main_root, x.name, getattr_recursive(gm, x.target))  # type: ignore[arg-type]
+
+    result_gm = GraphModuleCls(main_root, main_g)
+    if return_fqn_mapping:
+        return result_gm, orig_to_split_fqn_mapping
+
+    return result_gm
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/splitter_base.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/splitter_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..70b117c8ca374f33585901831b5435e5c3d9fbcc
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/splitter_base.py
@@ -0,0 +1,898 @@
+# mypy: allow-untyped-defs
+import argparse
+import copy
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import NamedTuple, Sequence, Iterable, Any, List, Dict, Optional, Tuple
+import logging
+
+import torch
+from torch.fx.passes.graph_manipulation import get_size_of_node
+from torch.fx.node import map_arg
+from torch.fx._compatibility import compatibility
+
+from .operator_support import (
+    get_node_target,
+    OperatorSupportBase,
+)
+from .graph_drawer import FxGraphDrawer
+from .shape_prop import ShapeProp
+from .split_utils import split_by_tags
+from .tools_common import (
+    FxNetAccFusionsFinder,
+    CALLABLE_NODE_OPS,
+    Tensors,
+    NodeList,
+    NodeSet,
+    is_node_output_tensor,
+)
+
+
+__all__ = ['FxNetAccNodesFinder', 'FxNetSplitterInternalError', 'Subgraph', 'SplitResult', 'generate_inputs_for_submodules']
+_LOGGER = logging.getLogger(__name__)
+
+DEFAULT_MIN_ACC_MODULE_SIZE = 1
+DEFAULT_SKIP_FUSION = False
+DEFAULT_ALLOW_NON_TENSOR = False
+
+class _SplitterSettingBase:
+    def __init__(
+        self,
+        min_acc_module_size=DEFAULT_MIN_ACC_MODULE_SIZE,
+        skip_fusion=DEFAULT_SKIP_FUSION,
+        allow_non_tensor=DEFAULT_ALLOW_NON_TENSOR,
+        max_acc_splits: int = -1,
+    ):
+        parser = argparse.ArgumentParser()
+        parser.add_argument(
+            "--min-acc-module-size",
+            "--min_acc_module_size",
+            required=False,
+            type=int,
+            help="Minimum size limit of an accelerator subgraph.",
+        )
+        parser.add_argument(
+            "--max-acc-splits",
+            "--max_acc_splits",
+            required=False,
+            type=int,
+            help="Enforce a maximum number of split subgraphs.",
+        )
+        parser.add_argument(
+            "--skip-fusion",
+            "--skip_fusion",
+            default=False,
+            action="store_true",
+            help="If true then no fusion groups. Fusion group is used to "
+            "enforce no non-tensor data flow between submodules. If we don't "
+            "have this constrain, setting this to false is recommended as it "
+            "can reduce overhead.",
+        )
+        parser.add_argument(
+            "--allow-non-tensor",
+            "--allow_non_tensor",
+            default=False,
+            action="store_true",
+            help="For some backends non-tensor data flow between cpu and them "
+            "are not allowed. Therefore, if a node supported by accelerator but "
+            "it has non-tensor inputs or outputs to a cpu node we would want to "
+            "consider it as a cpu node during splitting. However, for some backends "
+            "we might not care about non-tensor data flow and we can set this option "
+            "to true to disable the functionality that prevent non-tensor data flow.",
+        )
+        args, unknown = parser.parse_known_args()
+
+        self.min_acc_module_size: int = args.min_acc_module_size if args.min_acc_module_size else min_acc_module_size
+        self.skip_fusion: bool = args.skip_fusion if args.skip_fusion else skip_fusion
+        self.allow_non_tensor: bool = args.allow_non_tensor if args.allow_non_tensor else allow_non_tensor
+        self.max_acc_splits: int = max_acc_splits
+
+
+@compatibility(is_backward_compatible=False)
+class FxNetAccNodesFinder:
+    """
+    Finds a set of nodes that can be supported on ACC, excluding nodes that have non-tensor
+    input/output to cpu nodes to prevent non-tensor data flow between backends and cpu.
+
+    I.e. if we have a chain:
+
+    ACC_NODE_1 -> ACC_NODE_2 -> ACC_NODE_3 -> CPU_NODE_1
+
+    where every ACC node produces non-tensor output, then they all should be treated as CPU nodes.
+
+    This behavior can be turned off by passing allow_non_tensor=True.
+    """
+
+    def __init__(
+        self,
+        module: torch.fx.GraphModule,
+        operator_support: OperatorSupportBase,
+        allow_non_tensor: bool,
+    ):
+        self.module = module
+        self.operator_support = operator_support
+        self.allow_non_tensor = allow_non_tensor
+        self.acc_nodes: NodeSet = set()
+
+    def reduce_acc_nodes_non_tensor_input_helper(
+        self, cpu_worklist: NodeList
+    ):
+        """
+        Transitively excludes nodes from ACC supported set.
+        For every node in the worklist:
+        - removes its downstream ACC nodes from ACC supported set,
+        - if any downstream ACC node produces non-tensor output,
+          then it gets added into the worklist.
+        """
+        while cpu_worklist:
+            node = cpu_worklist.pop(0)
+
+            for user in node.users:
+                if user in self.acc_nodes:
+                    self.acc_nodes.remove(user)
+                    if not is_node_output_tensor(user):
+                        cpu_worklist.append(user)
+
+    def reduce_acc_nodes_non_tensor_input(self):
+        """
+        Excludes nodes from ACC supported set that have direct
+        upstream CPU nodes that produce non-tensor outputs.
+        """
+        non_tensor_cpu_nodes: NodeList = []
+
+        for node in self.module.graph.nodes:
+            if node.op not in CALLABLE_NODE_OPS:
+                continue
+            if node in self.acc_nodes:
+                continue
+            if is_node_output_tensor(node):
+                continue
+            non_tensor_cpu_nodes.append(node)
+
+        self.reduce_acc_nodes_non_tensor_input_helper(non_tensor_cpu_nodes)
+
+    def reduce_acc_nodes_non_tensor_output(self):
+        """
+        Excludes nodes from ACC supported set that produce non-tensor
+        outputs and have downstream CPU nodes.
+        """
+        while True:
+            new_cpu_nodes: NodeList = []
+
+            for acc_node in self.acc_nodes:
+                if is_node_output_tensor(acc_node):
+                    continue
+                for user in acc_node.users:
+                    if user not in self.acc_nodes:
+                        new_cpu_nodes.append(acc_node)
+                        break
+
+            if not new_cpu_nodes:
+                break
+
+            for new_cpu_node in new_cpu_nodes:
+                self.acc_nodes.remove(new_cpu_node)
+
+            self.reduce_acc_nodes_non_tensor_input_helper(new_cpu_nodes)
+
+    def __call__(self) -> NodeSet:
+        submodules = dict(self.module.named_modules())
+        self.acc_nodes = {
+            n
+            for n in self.module.graph.nodes
+            if n.op in CALLABLE_NODE_OPS
+            and self.operator_support.is_node_supported(submodules, n)
+        }
+
+        if not self.allow_non_tensor:
+            self.reduce_acc_nodes_non_tensor_input()
+            self.reduce_acc_nodes_non_tensor_output()
+
+        return self.acc_nodes
+
+@compatibility(is_backward_compatible=False)
+class FxNetSplitterInternalError(Exception):
+    pass
+
+@compatibility(is_backward_compatible=False)
+@dataclass
+class Subgraph:
+    is_acc: bool
+    nodes: NodeList
+    device_ordinal: Optional[int] = None
+
+@compatibility(is_backward_compatible=False)
+class SplitResult(NamedTuple):
+    """
+    Stores the results of the splitter.
+
+    Attributes:
+        split_module: root module after splitting.
+        submodule_inputs: a dict that maps submodule name to its inputs.
+        non_acc_submodule_prefix: the prefix for non acc submodules. For
+            acc submodule the prefix is alwasy "_run_on_acc_".
+    """
+
+    split_module: torch.fx.GraphModule
+    submodule_inputs: Dict[str, Any]
+    non_acc_submodule_prefix: str
+
+
+@compatibility(is_backward_compatible=False)
+def generate_inputs_for_submodules(
+    model: torch.nn.Module,
+    inputs: Sequence[Any],
+    target_submodules: Iterable[str],
+    deepcopy: bool = False,
+) -> Dict[str, Any]:
+    """
+    Generate inputs for targeting submdoules in the given model. Note that if two submodules refer to the same obj, this
+    function doesn't work.
+
+    Args:
+        model: root model.
+        inputs: inputs to the root model.
+        target_submodules: submodules that we want to generate inputs for.
+
+    Returns:
+        A dict that maps from submodule name to its inputs.
+    """
+
+    handles = []
+    results = {}
+    submodule_to_names = {mod: name for name, mod in model.named_modules()}
+
+    def pre_forward(module, module_inputs):
+        results[submodule_to_names[module]] = copy.deepcopy(module_inputs) if deepcopy else module_inputs
+
+    for name, mod in model.named_modules():
+        if name in target_submodules:
+            handles.append(mod.register_forward_pre_hook(pre_forward))
+
+    def clean_up_handles():
+        for h in handles:
+            h.remove()
+
+    try:
+        with torch.no_grad():
+            model(*inputs)
+    except Exception as e:
+        clean_up_handles()
+        raise e
+
+    clean_up_handles()
+    return results
+
+
+class _SplitterBase:
+    """
+    Splits a GraphModule into sub-GraphModules for execution on CPU or the accelerator.
+    Output is a GraphModule with supported and unsupported operators grouped into as few sub-GraphModules as possible.
+    Assumes that only "call_module", "call_function" and "call_method" from FX IR can potentially be executed on the accelerator.
+
+    Given the following graph:
+          ==> b ==>
+        //         \\
+       a             d
+        \\         //
+          ==> c ==>
+
+    class SimpleModule(torch.nn.Module):
+        def forward(self, a):
+            b = torch.sin(a)
+            c = torch.cos(a)
+            d = b + c
+            return d
+
+    and providing "operator_support" that indicates that 'b' and 'c' can be executed on the accelerator,
+    we will get the following split result:
+
+    main:
+    def forward(self, a):
+        run_on_acc_0_0 = self._run_on_acc_0_0(a)
+        getitem = run_on_acc_0_0[0]
+        getitem_1 = run_on_acc_0_0[1]
+        run_on_cpu_1_1 = self._run_on_cpu_1_1(getitem, getitem_1)
+        return run_on_cpu_1_1
+
+    _run_on_acc_0_0:
+    def forward(self, a):
+        sin_1 = torch.sin(a)
+        cos_1 = torch.cos(a)
+        return (sin_1, cos_1)
+
+    _run_on_cpu_1_1:
+    def forward(self, sin_1, cos_1):
+        add_1 = sin_1 + cos_1
+        return add_1
+    """
+
+    # PCIe bandwidth for the backend, default to 100 GB/s
+    PCIe_BW = 100 * 2 ** 30
+
+    def __init__(
+        self,
+        module: torch.fx.GraphModule,
+        sample_input: Sequence[Any],
+        operator_support: OperatorSupportBase,
+        settings: _SplitterSettingBase,
+        non_acc_submodule_name: str = "_run_on_cpu_",
+        return_tuple: bool = False,
+    ):
+        """
+        Preprocesses graph before splitting:
+        - finds nodes supported by ACC,
+        - finds fusion groups for ACC nodes having non-tensor IO,
+        - builds a graph of direct dependencies,
+        - builds a map of fused nodes to their fusions.
+        As a result we get self.acc_nodes, self.deps and self.fusions.
+        """
+        assert isinstance(module, torch.fx.GraphModule)
+
+        self.module = module
+        ShapeProp(self.module).propagate(*sample_input)
+
+        self.settings = settings
+        self.operator_support = operator_support
+        self.sample_input = sample_input
+        self.acc_nodes = FxNetAccNodesFinder(self.module, self.operator_support, self.settings.allow_non_tensor)()
+
+        if self.settings.skip_fusion:
+            self.fusions = {}
+        else:
+            self.fusions = FxNetAccFusionsFinder(module, self.acc_nodes)()
+
+        # Modify deps to add more deps for fused nodes
+        self.deps = self.find_deps()
+        self.update_deps_for_fusions()
+
+        self.non_acc_submodule_name = non_acc_submodule_name
+        self._node_submodule_map: Dict[str, str] = {}
+        self._return_tuple = return_tuple
+
+        self.tags: List[str] = []
+
+    # ===============================================================
+    # Helpers for ctor and initial state
+    # ===============================================================
+
+    def get_node_submodule_map(self) -> Dict[str, str]:
+        """ Returns a map from node name to submodule name, e.g.
+            node: main_module_impl_impl_over_arch_unary_multiple_embedding
+              _pooling_embedding_pooling_sparse_entity_equivalence_key
+              _proxy_embedding_bag
+            maps to submodule name of: _run_on_acc_1
+        """
+        return self._node_submodule_map
+
+    def find_deps(self) -> Dict[torch.fx.Node, NodeSet]:
+        """
+        Builds a graph of node dependencies. Leaf nodes don't have any
+        dependencies and the "output" node doesn't have nodes depending on it.
+
+        Resulting graph has only direct dependencies, i.e. there are no
+        transitive dependencies.
+        """
+        deps: Dict[torch.fx.Node, NodeSet] = defaultdict(set)
+        for node in self.module.graph.nodes:
+            if node.op not in CALLABLE_NODE_OPS:
+                continue
+
+            for user in node.users:
+                if user.op != "output":
+                    deps[user].add(node)
+        return deps
+
+    def update_deps_for_fusions(self):
+        """
+        Updates graph of dependencies so that:
+        - nodes from the same fusion depend on the same set of outer nodes,
+        - outer nodes depending on a fusion depend on all nodes in that fusion.
+        """
+        for node in self.fusions:
+            fusion = self.fusions[node]
+            for fused_neighbor in fusion:
+                self.deps[node].update(self.deps[fused_neighbor] - fusion)
+
+                for user in fused_neighbor.users:
+                    if user not in fusion:
+                        self.deps[user].add(node)
+
+    # ===============================================================
+    # Helpers for preview
+    # ===============================================================
+
+    def _lower_model_to_backend(
+        self, mod: torch.fx.GraphModule, inputs: Tensors
+    ) -> torch.nn.Module:
+        """
+        Lower the model to a backend.
+        """
+
+        return mod
+
+    def _find_culprit(
+        self, mod: torch.fx.GraphModule, inputs: Tensors
+    ) -> str:
+        """
+        When an error occurs during lowering or running the lowered mod, we use this
+        function to find culprits in the `mod` that causes the error.
+        """
+
+        return "Unable to find a culprit because _find_culprit() function is not implemented."
+
+    def _draw_graph_based_on_node_support(
+        self, mod: torch.fx.GraphModule, supported_nodes: NodeList
+    ):
+        color_map = {
+            "default": "AliceBlue",
+            "supported": "chartreuse1",
+            "unsupported": "crimson",
+        }
+
+        class CustomDrawer(FxGraphDrawer):
+            def _get_node_style(self, node):
+                template = super()._get_node_style(node)
+                if node in supported_nodes:
+                    template["fillcolor"] = color_map["supported"]
+                elif node.op in CALLABLE_NODE_OPS:
+                    template["fillcolor"] = color_map["unsupported"]
+                else:
+                    template["fillcolor"] = color_map["default"]
+
+                return template
+
+        drawer = CustomDrawer(mod, "node_support", ignore_getattr=True)
+        dot_graph = drawer.get_main_dot_graph()
+        # pyre-fixme[16]: `pydot.Dot` has no attribute `write_raw`.
+        dot_graph.write_raw("node_support.dot")
+
+    def node_support_preview(self, dump_graph: bool = False):
+        submodules = dict(self.module.named_modules())
+
+        supported_nodes: NodeList = []
+        supported_node_types = defaultdict(set)
+        unsupported_node_types = defaultdict(set)
+
+        def get_dtype(arg):
+            tensor_meta = arg.meta.get("tensor_meta")
+            return getattr(tensor_meta, "dtype", None)
+
+        for node in self.module.graph.nodes:
+            if node.op not in CALLABLE_NODE_OPS:
+                continue
+
+            target = get_node_target(submodules, node)
+
+            # Store dtype of arg in node.args. If arg doesn't have dtype, i.e. not a tensor, we'll store None.
+            arg_dtypes = [
+                get_dtype(arg) if isinstance(arg, torch.fx.Node) else None
+                for arg in node.args
+            ]
+
+            # Find last non-None element. If all elements are None, return max_len.
+            last_index = len(arg_dtypes) - next(
+                (
+                    i
+                    for i, dtype in enumerate(reversed(arg_dtypes))
+                    if dtype is not None
+                ),
+                len(arg_dtypes),
+            )
+
+            # Strip None elements at the end.
+            arg_dtypes_tuple = tuple(arg_dtypes[:last_index])
+            kwarg_dtypes_tuple = tuple(
+                (k, get_dtype(arg))
+                for k, arg in node.kwargs.items()
+                if isinstance(arg, torch.fx.Node)
+            )
+
+            if self.operator_support.is_node_supported(submodules, node):
+                supported_nodes.append(node)
+                supported_node_types[target].add((arg_dtypes_tuple, kwarg_dtypes_tuple))
+            else:
+                unsupported_node_types[target].add((arg_dtypes_tuple, kwarg_dtypes_tuple))
+
+        if dump_graph:
+            self._draw_graph_based_on_node_support(self.module, supported_nodes)
+
+        reports = "\nSupported node types in the model:\n"
+        for t, dtypes in supported_node_types.items():
+            for arg_dtypes_tuple, kwarg_dtypes_tuple in dtypes:
+                reports += f"{t}: ({arg_dtypes_tuple}, {dict(kwarg_dtypes_tuple)})\n"
+
+        reports += "\nUnsupported node types in the model:\n"
+        for t, dtypes in unsupported_node_types.items():
+            for arg_dtypes_tuple, kwarg_dtypes_tuple in dtypes:
+                reports += f"{t}: ({arg_dtypes_tuple}, {dict(kwarg_dtypes_tuple)})\n"
+
+        print(reports)
+
+        # Return reports for testing purpose
+        return reports
+
+    def split_preview(self, dump_graph: bool = False):
+        reports = ""
+        subgraphs = self.put_nodes_into_subgraphs()
+        acc_subgraphs_num = len([g for g in subgraphs if g.is_acc])
+        cpu_subgraphs_num = len(subgraphs) - acc_subgraphs_num
+        reports += f"Before removing small acc subgraphs, total {len(subgraphs)} subgraphs are created:"
+        reports += f" {acc_subgraphs_num} acc subgraphs and {cpu_subgraphs_num} cpu subgraphs.\n"
+
+        subgraphs = self.remove_small_acc_subgraphs(subgraphs)
+        acc_subgraphs_num = len([g for g in subgraphs if g.is_acc])
+        cpu_subgraphs_num = len(subgraphs) - acc_subgraphs_num
+        reports += f"After removing small acc subgraphs, total {len(subgraphs)} subgraphs are created:"
+        reports += f" {acc_subgraphs_num} acc subgraphs and {cpu_subgraphs_num} cpu subgraphs.\n"
+
+        for i, subgraph in enumerate(subgraphs):
+            reports += f"_run_on_acc_{i}: " if subgraph.is_acc else f"{self.non_acc_submodule_name}{i}: "
+            reports += f"{len(subgraph.nodes)} node(s)\n"
+
+        self.tag(subgraphs)
+        split_mod = self.split(remove_tag=True)
+        split_mod.eval()
+
+        if dump_graph:
+            drawer = FxGraphDrawer(
+                split_mod, "preview", ignore_getattr=True
+            )
+            dot_graphs = drawer.get_all_dot_graphs()
+            for name, dot_graph in dot_graphs.items():
+                # pyre-fixme[16]: `pydot.Dot` has no attribute `write_raw`.
+                dot_graph.write_raw(f"{name}.dot")
+
+        max_qps: float = self.PCIe_BW
+        bottleneck_module = ""
+
+        for node in split_mod.graph.nodes:
+            if node.op == "call_module" and "acc" in node.target:
+                reports += f"\nProcessing acc submodule {node.target}\n"
+
+                submod = getattr(split_mod, node.target)
+
+                def get_submod_inputs(main_mod, submod, example_inputs):
+                    sub_inputs = None
+
+                    def get_inputs(self, inputs):
+                        nonlocal sub_inputs
+                        sub_inputs = inputs
+
+                    handle = submod.register_forward_pre_hook(get_inputs)
+                    main_mod(*example_inputs)
+                    handle.remove()
+                    return sub_inputs
+
+                submod_inputs = get_submod_inputs(
+                    split_mod, submod, self.sample_input
+                )
+                ShapeProp(submod).propagate(*submod_inputs)
+
+                total_input_bytes = 0
+                total_output_bytes = 0
+
+                reports += "Checking inputs...\n"
+                for n in submod.graph.nodes:
+                    if n.op == "placeholder":
+                        if not is_node_output_tensor(n):
+                            reports += f"Input {n.name} is not a tensor, this might cause problems during lowering!\n"
+                        else:
+                            total_input_bytes += get_size_of_node(submod, n)[0]
+                    if n.op == "output":
+                        output_node = n
+
+                reports += "Checking outputs...\n"
+
+                def get_bytes(node: torch.fx.Node):
+                    nonlocal total_output_bytes
+                    nonlocal reports
+                    if not is_node_output_tensor(node):
+                        reports += f"Output {node.name} is not a tensor, this might cause problems during lowering!\n"
+                    else:
+                        total_output_bytes += get_size_of_node(submod, node)[0]
+
+                map_arg(output_node.args, get_bytes)  # type: ignore[possibly-undefined]
+                qps = self.PCIe_BW / max(total_input_bytes, total_output_bytes)
+                reports += f"Total input size in bytes is {total_input_bytes}, total output size in bytes is {total_output_bytes},"
+                reports += f" theoretical max qps (bounds by PCIe bandwidth) for this submodule is {qps}.\n"
+
+                if qps < max_qps:
+                    max_qps = qps
+                    bottleneck_module = node.target
+
+                try:
+                    lowered_submod = self._lower_model_to_backend(submod, submod_inputs)
+                except RuntimeError:
+                    reports += "Run into an error during lowering!\n"
+                    reports += self._find_culprit(submod, submod_inputs)
+                    continue
+
+                try:
+                    lowered_submod(*submod_inputs)
+                except RuntimeError:
+                    reports += "Run into an error during inference!\n"
+                    reports += self._find_culprit(submod, submod_inputs)
+                else:
+                    reports += "Lowering and running succeed!\n"
+
+        reports += f"\nTheoretical max qps (bounds by PCIe bandwidth) for this model is {max_qps},"
+        reports += f" bottleneck is submodule {bottleneck_module}."
+        print(reports)
+
+        # return the reports for testing purposes
+        return reports
+
+    # ===============================================================
+    # Helpers for extend_acc_subgraph() method
+    # ===============================================================
+
+    def find_reverse_deps(
+        self, tag_id: Optional[int] = None
+    ) -> Dict[torch.fx.Node, NodeSet]:
+        """
+        Builds reversed topological node dependencies, if tag_id is specified,
+        we ignore nodes that are in later subgraph i.e. nodes have greater tag_id.
+        """
+        result: Dict[torch.fx.Node, NodeSet] = defaultdict(set)
+
+        for node in self.module.graph.nodes:
+            if node.op not in CALLABLE_NODE_OPS:
+                continue
+
+            for user in node.users:
+                if user.op not in CALLABLE_NODE_OPS:
+                    continue
+
+                if tag_id is None or (int(user.tag.split("_")[-1]) < tag_id):
+                    result[node].add(user)
+
+        return result
+
+    def update_reverse_deps_for_fusions(
+        self, deps: Dict[torch.fx.Node, NodeSet]
+    ):
+        processed_node = set()
+
+        for node, fusion in self.fusions.items():
+            if node in processed_node:
+                continue
+
+            new_dep = set()
+
+            # Create a new dependency set which include all the
+            # dependencies of the nodes in the fusion group
+            for n in fusion:
+                new_dep.update(deps[n])
+
+            # Exclude nodes in the fusion
+            new_dep.difference_update(fusion)
+
+            # Update dependency
+            for n in fusion:
+                deps[n] = new_dep
+
+                for arg in n.all_input_nodes:
+                    if arg not in fusion:
+                        deps[arg].update(fusion)
+
+                processed_node.add(n)
+
+    def find_parent_nodes_of_subgraph(self, tag: str) -> NodeSet:
+        """
+        Finds parent nodes of the `tag` subgraph.
+
+        Traverse the inputs of nodes in the subgraph, if input doesn't belong to the subgraph
+        and is not a placeholder, we consider it as the parent node of the subgraph.
+        """
+        parent_nodes = set()
+
+        for node in self.module.graph.nodes:
+            if node.op in CALLABLE_NODE_OPS and node.tag == tag:
+                for arg in node.all_input_nodes:
+                    if arg.op in CALLABLE_NODE_OPS and arg.tag != tag:
+                        parent_nodes.add(arg)
+
+        return parent_nodes
+
+    def extend_acc_subgraph(self, tag: str):
+        """
+        Extend the acc subgraph with `tag` going the reversed topological direction.
+        """
+        # Dict that maps node to its users and ignore users that
+        # are in the subgraph that has greater tag
+        deps = self.find_reverse_deps(tag_id=int(tag.split("_")[-1]))
+        self.update_reverse_deps_for_fusions(deps)
+
+        # Parent nodes of the subgraph
+        parent_nodes = self.find_parent_nodes_of_subgraph(tag)
+
+        visited_nodes: NodeSet = set()
+
+        while parent_nodes:
+            node = None
+
+            # Find a acc node that depends on visited nodes only
+            for n in parent_nodes:
+                if deps[n] <= visited_nodes and n in self.acc_nodes:
+                    node = n
+                    break
+
+            if node is None:
+                break
+
+            # Put the node into `tag` subgraph
+            node.tag = tag  # type: ignore[attr-defined]
+            parent_nodes.remove(node)
+            visited_nodes.add(node)
+
+            # If node is in a fusion group, add all fusion buddies to parent nodes
+            if node in self.fusions:
+                for fusion_node in self.fusions[node]:
+                    if fusion_node not in visited_nodes:
+                        parent_nodes.add(fusion_node)
+
+            # Add inputs of the node to parent nodes
+            for arg in node.all_input_nodes:
+                if arg.op in CALLABLE_NODE_OPS and arg not in visited_nodes:
+                    parent_nodes.add(arg)
+
+    # ===============================================================
+    # Helpers for split() method
+    # ===============================================================
+
+    def starter_nodes(self) -> Tuple[NodeSet, NodeSet]:
+        """
+        Finds nodes that consume module inputs or get_attr nodes.
+        """
+        starter_cpu_nodes: NodeSet = set()
+        starter_acc_nodes: NodeSet = set()
+        for node in self.module.graph.nodes:
+            if node.op not in {"placeholder", "get_attr"}:
+                continue
+            for user in node.users:
+                if user in self.acc_nodes:
+                    starter_acc_nodes.add(user)
+                else:
+                    starter_cpu_nodes.add(user)
+        return starter_cpu_nodes, starter_acc_nodes
+
+    def put_nodes_into_subgraphs(self) -> List[Subgraph]:
+        # We start graph traversal from leaf nodes
+        current_cpu_nodes, current_acc_nodes = self.starter_nodes()
+        visited_nodes: NodeSet = set()
+
+        # Determine which subgraph to start from based on which subgraph has
+        # 0-dep node
+        acc_subgraph: bool = not any(len(self.deps[n]) == 0 for n in current_cpu_nodes)
+
+        current_subgraph_nodes: NodeList = []
+
+        # Result accumulator
+        subgraphs: List[Subgraph] = []
+        while current_cpu_nodes or current_acc_nodes:
+            # Find the first node that should belong to the current subgraph and has all dependencies resolved
+            current_nodes = current_acc_nodes if acc_subgraph else current_cpu_nodes
+            node = next(
+                (n for n in current_nodes if self.deps[n] <= visited_nodes),
+                None,
+            )
+
+            # If nothing was found, then it's time to flip the mode and start a new subgraph
+            if node is None:
+                if not current_subgraph_nodes:
+                    raise FxNetSplitterInternalError("Subgraph can't be empty")
+
+                subgraphs.append(
+                    Subgraph(is_acc=acc_subgraph, nodes=current_subgraph_nodes)
+                )
+                acc_subgraph = not acc_subgraph
+                current_subgraph_nodes = []
+                continue
+
+            current_nodes.remove(node)
+            visited_nodes.add(node)
+            current_subgraph_nodes.append(node)
+
+            # Add fusion buddies
+            if node in self.fusions:
+                if node in self.acc_nodes:
+                    current_acc_nodes.update(self.fusions[node] - visited_nodes)
+                else:
+                    current_cpu_nodes.update(self.fusions[node] - visited_nodes)
+
+            # Put depending nodes into the queue
+            for user in node.users:
+                if user.op not in CALLABLE_NODE_OPS:
+                    continue
+
+                # Add downstream nodes
+                if user in self.acc_nodes:
+                    current_acc_nodes.add(user)
+                else:
+                    current_cpu_nodes.add(user)
+
+        # Check if the last subgraph was not created
+        if current_subgraph_nodes:
+            subgraphs.append(
+                Subgraph(is_acc=acc_subgraph, nodes=current_subgraph_nodes)
+            )
+
+        if not subgraphs:
+            raise FxNetSplitterInternalError("Couldn't create subgraphs")
+
+        return subgraphs
+
+    def remove_small_acc_subgraphs(self, subgraphs: List[Subgraph]) -> List[Subgraph]:
+        """
+        This pass finds ACC submodules with less than specified size and merges
+        them with adjacent CPU submodules.
+        """
+        result: List[Subgraph] = []
+        for subgraph in subgraphs:
+            if subgraph.is_acc:
+                if len(subgraph.nodes) >= self.settings.min_acc_module_size:
+                    result.append(subgraph)
+                else:
+                    print(
+                        "Eliminating acc subgraph because it's smaller than the threshold: "
+                        f"{len(subgraph.nodes)} < {self.settings.min_acc_module_size}"
+                    )
+                    if result:
+                        result[-1].nodes.extend(subgraph.nodes)
+                    else:
+                        subgraph.is_acc = False
+                        result.append(subgraph)
+            else:
+                if result and not result[-1].is_acc:
+                    result[-1].nodes.extend(subgraph.nodes)
+                else:
+                    result.append(subgraph)
+        return result
+
+    def tag(self, subgraphs: List[Subgraph]):
+        self.tags = []
+        for subgraph in subgraphs:
+            tag = f"_run_on_acc_{len(self.tags)}" if subgraph.is_acc else f"{self.non_acc_submodule_name}{len(self.tags)}"
+            self.tags.append(tag)
+            for node in subgraph.nodes:
+                if hasattr(node, "tag"):
+                    raise FxNetSplitterInternalError(f"Node {node} was already tagged")
+
+                node.tag = tag  # type: ignore[attr-defined]
+                self._node_submodule_map[node.name] = tag
+
+    def split(self, remove_tag: bool = False) -> torch.fx.GraphModule:
+        split_module = split_by_tags(self.module, self.tags, return_tuple=self._return_tuple)
+        if remove_tag:
+            for node in self.module.graph.nodes:
+                if hasattr(node, "tag"):
+                    del node.tag
+        return split_module  # type: ignore[return-value]
+
+    def __call__(self) -> torch.fx.GraphModule:
+        subgraphs = self.put_nodes_into_subgraphs()
+        subgraphs = self.remove_small_acc_subgraphs(subgraphs)
+        acc_subgraphs_count = len([s for s in subgraphs if s.is_acc])
+        non_acc_subgraphs_count = len(subgraphs) - acc_subgraphs_count
+        print(f"Got {acc_subgraphs_count} acc subgraphs and {non_acc_subgraphs_count} non-acc subgraphs")
+        self.tag(subgraphs)
+        return self.split()
+
+    def generate_split_results(self) -> SplitResult:
+        split_module = self()
+        submodule_names = []
+        for name, mod in split_module.named_children():
+            submodule_names.append(name)
+        if (
+            self.settings.max_acc_splits > 0
+            and len(submodule_names) > self.settings.max_acc_splits
+        ):
+            raise ValueError(
+                "Cannot fulfill max_acc_splits limit. "
+                "This may cause split fragmentation and "
+                "result in performance issues."
+            )
+
+        submodule_inputs = generate_inputs_for_submodules(split_module, self.sample_input, submodule_names)
+        return SplitResult(split_module, submodule_inputs, self.non_acc_submodule_name)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/tests/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/tests/__init__.py
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--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/tests/test_pass_manager.py
@@ -0,0 +1,58 @@
+import unittest
+
+from ..pass_manager import (
+    inplace_wrapper,
+    PassManager,
+    these_before_those_pass_constraint,
+    this_before_that_pass_constraint,
+)
+
+
+class TestPassManager(unittest.TestCase):
+    def test_pass_manager_builder(self) -> None:
+        passes = [lambda x: 2 * x for _ in range(10)]
+        pm = PassManager(passes)
+        pm.validate()
+
+    def test_this_before_that_pass_constraint(self) -> None:
+        passes = [lambda x: 2 * x for _ in range(10)]
+        pm = PassManager(passes)
+
+        # add unfulfillable constraint
+        pm.add_constraint(this_before_that_pass_constraint(passes[-1], passes[0]))
+
+        self.assertRaises(RuntimeError, pm.validate)
+
+    def test_these_before_those_pass_constraint(self) -> None:
+        passes = [lambda x: 2 * x for _ in range(10)]
+        constraint = these_before_those_pass_constraint(passes[-1], passes[0])
+        pm = PassManager(
+            [inplace_wrapper(p) for p in passes]
+        )
+
+        # add unfulfillable constraint
+        pm.add_constraint(constraint)
+
+        self.assertRaises(RuntimeError, pm.validate)
+
+    def test_two_pass_managers(self) -> None:
+        """Make sure we can construct the PassManager twice and not share any
+        state between them"""
+
+        passes = [lambda x: 2 * x for _ in range(3)]
+        constraint = these_before_those_pass_constraint(passes[0], passes[1])
+        pm1 = PassManager()
+        for p in passes:
+            pm1.add_pass(p)
+        pm1.add_constraint(constraint)
+        output1 = pm1(1)
+        self.assertEqual(output1, 2 ** 3)
+
+        passes = [lambda x: 3 * x for _ in range(3)]
+        constraint = these_before_those_pass_constraint(passes[0], passes[1])
+        pm2 = PassManager()
+        for p in passes:
+            pm2.add_pass(p)
+        pm2.add_constraint(constraint)
+        output2 = pm2(1)
+        self.assertEqual(output2, 3 ** 3)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/tools_common.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/tools_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..aac071ace8c2daff5c727e462f34bb47ff0f820a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/tools_common.py
@@ -0,0 +1,303 @@
+# mypy: allow-untyped-defs
+from typing import List, Tuple, Union, Dict, Any, Set, Mapping, Optional
+import collections
+from dataclasses import dataclass
+import operator
+
+import torch
+import torch.fx
+from torch.fx.node import _get_qualified_name
+from torch.fx._compatibility import compatibility
+
+__all__ = ['get_acc_ops_name', 'get_node_target', 'is_node_output_tensor', 'FxNetAccFusionsFinder', 'legalize_graph']
+
+Tensors = Union[Tuple[torch.Tensor], List[torch.Tensor]]
+TensorOrTensors = Union[torch.Tensor, Tensors]
+NodeList = List[torch.fx.Node]
+NodeSet = Set[torch.fx.Node]
+Names = List[str]
+CALLABLE_NODE_OPS = {"call_module", "call_function", "call_method"}
+
+
+@compatibility(is_backward_compatible=False)
+def get_acc_ops_name(k):
+    if isinstance(k, str):
+        return k
+    elif k.__module__ and "acc_ops" in k.__module__:
+        return f"acc_ops.{k.__name__}"
+    else:
+        module = k.__module__.replace('torch._ops', 'torch.ops')  # WAR for bug in how torch.ops assigns module
+        return f"{module if module else ''}.{k.__name__}"
+
+
+@compatibility(is_backward_compatible=False)
+def get_node_target(submodules: Mapping[str, torch.nn.Module], node: torch.fx.Node) -> str:
+    """
+    Given a `node` returns its target typename.
+
+    For "call_method" node, return node.target which is the name of that method being called.
+    This could potential lead to conflict but should be okay because normally it's on a tensor.
+
+    For "call_function" node, return typename of node.target.
+
+    For "call_module" node, return typename of the module that node.target point to.
+
+    If seeing "_VariableFunctionsClass" in the target name string, it will be replaced by
+    "torch". e.g. _VariableFunctionsClass.relu would become torch.relu.
+    """
+
+    assert node.op in CALLABLE_NODE_OPS, (
+        "Expect op types of " + ", ".join(CALLABLE_NODE_OPS) + f", but found {node.op}"
+    )
+
+    if node.op == "call_module":
+        assert isinstance(node.target, str)
+        submod = submodules[node.target]
+        submod_type = getattr(submod, "_base_class_origin", type(submod))
+        return get_acc_ops_name(submod_type)
+    elif node.op == "call_function":
+        target: Any = node.target
+        return (
+            f"acc_ops.{target.__name__}"
+            if target.__module__ is not None and "acc_ops" in target.__module__
+            else _get_qualified_name(target)
+        )
+    else:
+        assert isinstance(node.target, str)
+        return node.target
+
+@compatibility(is_backward_compatible=False)
+def is_node_output_tensor(node: torch.fx.Node) -> bool:
+    """Checks if the node output produces a Tensor or not.
+
+    NOTE: This requires to run `ShapeProp` on the containing fx graph before
+    calling this function. This is because it works by checking the `type`
+    metadata on the node. This metadata is produced by the `ShapeProp`.
+    """
+    type_ = node.meta.get("type", None)
+    return type_ is not None and issubclass(type_, torch.Tensor)
+
+@compatibility(is_backward_compatible=False)
+class FxNetAccFusionsFinder:
+    """
+    Finds groups of connected ACC nodes that pass non-tensor data between each other.
+    Such groups are called fusion groups.
+    """
+
+    def __init__(self, module: torch.fx.GraphModule, acc_nodes: NodeSet):
+        self.module = module
+        self.nodes = list(module.graph.nodes)
+        self.acc_nodes = acc_nodes
+
+    @dataclass
+    class FusionGroup:
+        # The smallest idx of nodes in the fusion group after topological sorting all the nodes in the model.
+        top_node_idx: int
+
+        # Nodes in this fusion group.
+        nodes: NodeSet
+
+        # Inputs to this fusion group.
+        inputs: NodeSet
+
+        # Nodes that in the fusion group that haven't been processed yet.
+        nodes_need_process: NodeSet
+
+        def add_node(self, node):
+            """
+            Add a node to fusion group.
+            """
+            if node in self.nodes:
+                return
+
+            self.nodes_need_process.add(node)
+            self.nodes.add(node)
+            self.inputs.discard(node)
+            self.inputs.update(
+                {
+                    n
+                    for n in node.all_input_nodes
+                    if n.op in CALLABLE_NODE_OPS and n not in self.nodes
+                }
+            )
+
+    def recursive_add_node(
+        self,
+        fusion_group: "FxNetAccFusionsFinder.FusionGroup",
+        inputs: Union[NodeSet, NodeList],
+        visited: Optional[NodeSet] = None,
+    ):
+        """
+        Start from inputs and going reverse topological order. If any upstream node
+        is in the fusion group, add all the nodes in this path to fusion group.
+        """
+        for arg in inputs:
+            # skip the node if already seen
+            if visited is not None:
+                if arg in visited:
+                    continue
+                visited.add(arg)
+
+            # Skip placeholder and get_attr because they won't be in the fusion group.
+            if arg.op not in CALLABLE_NODE_OPS:
+                continue
+
+            # If the node has smaller idx, it's already an upstream node of the fusion
+            # group. We don't need to check it anymore.
+            if self.nodes.index(arg) < fusion_group.top_node_idx:
+                continue
+
+            # If the node is in the fusion group, return True.
+            if arg in fusion_group.nodes:
+                return True
+
+            # Check the upstream nodes of the node, if any of them is in the fusion group
+            # we'll add this node to fusion group and return True.
+            if self.recursive_add_node(fusion_group, arg.all_input_nodes, visited):
+                fusion_group.add_node(arg)
+                return True
+
+        return False
+
+    def __call__(self) -> Dict[torch.fx.Node, NodeSet]:
+        result: Dict[torch.fx.Node, NodeSet] = {}
+        acc_nodes = list(self.acc_nodes)
+
+        for node in acc_nodes:
+            if node in result:
+                continue
+            if node.op not in CALLABLE_NODE_OPS:
+                continue
+            if "tensor_meta" in node.meta:
+                continue
+            if node not in self.acc_nodes:
+                continue
+
+            fusion_group: FxNetAccFusionsFinder.FusionGroup = self.FusionGroup(
+                top_node_idx=self.nodes.index(node),
+                nodes={node},
+                inputs=set(node.all_input_nodes),
+                nodes_need_process={node},
+            )
+            while fusion_group.nodes_need_process:
+                node = fusion_group.nodes_need_process.pop()
+                self.recursive_add_node(
+                    fusion_group,
+                    fusion_group.inputs,
+                    visited=set(),
+                )
+
+                # Optionally add downstream nodes
+                if "tensor_meta" not in node.meta:
+                    for user in node.users:
+                        if user.op not in CALLABLE_NODE_OPS:
+                            continue
+                        if user in fusion_group.nodes:
+                            continue
+
+                        fusion_group.add_node(user)
+                        self.recursive_add_node(
+                            fusion_group,
+                            fusion_group.inputs,
+                            visited=set(),
+                        )
+
+                # Add some upstream nodes
+                for arg in node.all_input_nodes:
+                    if arg.op not in CALLABLE_NODE_OPS:
+                        continue
+                    if "tensor_meta" in arg.meta:
+                        continue
+                    if arg in fusion_group.nodes:
+                        continue
+
+                    fusion_group.add_node(arg)
+                    fusion_group.top_node_idx = min(
+                        fusion_group.top_node_idx, self.nodes.index(arg)
+                    )
+                    self.recursive_add_node(
+                        fusion_group,
+                        fusion_group.inputs,
+                        visited=set(),
+                    )
+
+            if not (set(fusion_group.nodes) <= self.acc_nodes):
+                self.acc_nodes -= fusion_group.nodes
+            else:
+                for n in fusion_group.nodes:
+                    result[n] = fusion_group.nodes
+
+        return result
+
+
+@compatibility(is_backward_compatible=False)
+def legalize_graph(gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
+    """
+    Replace the graph of the given GraphModule with one that contains the same nodes as the
+    original, but in topologically sorted order.
+
+    This is used by the merge_matmul transformation below, which disturbs the topologically sorted
+    order of its input GraphModule, so that this order is restored before further transformation.
+
+    Arguments:
+        gm: The graph module to topologically sort. It is modified in-place.
+
+    Returns:
+        The graph module in-place sorted
+    """
+
+    # These operators are used for making runtime assertions before any
+    # data-dependent operators occur. We want to prioritize sorting these to
+    # ensure that these assertions appear before any data-dependent operations
+    # in the graph.
+    PRIORITIZED_OPS = [
+        operator.add,
+        operator.mul,
+        operator.sub,
+        operator.floordiv,
+        operator.truediv,
+        operator.mod,
+        operator.le,
+        operator.lt,
+        operator.ge,
+        operator.gt,
+        operator.eq,
+        operator.ne,
+        torch.ops.aten.sym_constrain_range.default,
+        torch.ops.aten.sym_constrain_range_for_size.default,
+        torch.ops.aten._assert_async.msg,
+        torch.ops.aten.scalar_tensor.default,
+        torch.ops.aten._assert_scalar.default,
+    ]
+
+    indeg = dict.fromkeys(gm.graph.nodes, 0)
+    new_graph = torch.fx.Graph()
+    # Track how many unfulfilled dependencies each node has
+    for node in gm.graph.nodes:
+        for user in node.users:
+            indeg[user] += 1
+    queue: collections.deque = collections.deque()
+    # Add all nodes with no dependencies to the queue
+    for node in gm.graph.nodes:
+        if indeg[node] == 0:
+            queue.append(node)
+    env: Dict[torch.fx.Node, torch.fx.Node] = {}
+    # Pop nodes from the queue, and add nodes that have had all their
+    # dependencies fulfilled
+    while len(queue) > 0:
+        cur = queue.popleft()
+        env[cur] = new_graph.node_copy(cur, lambda x: env[x])
+        for user in cur.users:
+            indeg[user] -= 1
+            if indeg[user] == 0:
+                if user.op == "call_function" and user.target in PRIORITIZED_OPS:
+                    queue.appendleft(user)
+                else:
+                    queue.append(user)
+    # If the new graph's size is not as large as the old one, then there must be
+    # a cycle (i.e. some node's dependencies were not satisfied.)
+    if len(new_graph.nodes) < len(gm.graph.nodes):
+        raise RuntimeError(f"Input graph has cycles, unable to add {[node for node in indeg if indeg[node] != 0]}")
+    new_graph._codegen = gm.graph._codegen
+    gm.graph = new_graph
+    return gm
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/utils/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a7970ba4c283e851430ed0025e1ed5c772eb7b1
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/utils/__init__.py
@@ -0,0 +1 @@
+from .common import lift_subgraph_as_module, HolderModule, compare_graphs
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/utils/common.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/utils/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba2ae45aabf5dcd26a0baee6b5acc11229cf1b88
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/utils/common.py
@@ -0,0 +1,96 @@
+# mypy: allow-untyped-defs
+from typing import Dict, Tuple
+
+from torch.fx._compatibility import compatibility
+from torch.fx.graph import Graph
+
+from torch.fx.graph_module import GraphModule
+from torch.fx.passes.utils.matcher_utils import SubgraphMatcher
+from torch.nn import Module
+
+
+__all__ = ["HolderModule", "lift_subgraph_as_module", "compare_graphs"]
+
+
+@compatibility(is_backward_compatible=False)
+class HolderModule(Module):
+    """
+    HolderModule is used to copy all the attributes from original module to submodules
+    that uses the attributes
+    """
+
+    def __init__(self, d):
+        super().__init__()
+        for k, v in d.items():
+            self.add_module(k, v)
+
+
+@compatibility(is_backward_compatible=False)
+def lift_subgraph_as_module(
+    gm: GraphModule,
+    subgraph: Graph,
+    comp_name: str = "",
+    class_name: str = "GraphModule",
+) -> Tuple[GraphModule, Dict[str, str]]:
+    """
+    Create a GraphModule for subgraph, which copies the necessary attributes from the original parent graph_module.
+
+    Args:
+        gm (GraphModule): parent graph module
+
+        subgraph (Graph): a valid subgraph that contains copied nodes from the parent graph
+
+        comp_name (str): name for the new component
+
+        class_name (str): name for the submodule
+
+    """
+
+    # Loop through all module calls (call_module) and param fetches (get_attr)
+    # in this component, creating HolderModules as necessary to match the path.
+    # e.g. if in the original module there's a get_attr node fetches "conv.weight".
+    # We create a HolderModule as root -> add a HolderModule named "conv" ->
+    # make "weight" a attribute of "conv" HolderModule and point to conv.weight in
+    # the original module.
+    submodule = HolderModule({})
+    orig_to_split_fqn_mapping: Dict[str, str] = {}
+    for n in subgraph.nodes:
+        if n.op not in ("call_module", "get_attr"):
+            continue
+
+        target = n.target
+        assert isinstance(target, str)
+        target_name_parts = target.split(".")
+        curr = submodule
+        orig_gm = gm
+
+        for name in target_name_parts[:-1]:
+            if not hasattr(curr, name):
+                curr.add_module(name, HolderModule({}))
+
+            curr = getattr(curr, name)
+            orig_gm = getattr(orig_gm, name)
+
+        leaf_node_name = target_name_parts[-1]
+        leaf_node = getattr(orig_gm, leaf_node_name)
+
+        orig_to_split_fqn_mapping[target] = f"{comp_name}.{target}"
+        # Relies on custom __setattr__ magic.
+        setattr(curr, leaf_node_name, leaf_node)
+
+    return GraphModule(submodule, subgraph, class_name), orig_to_split_fqn_mapping
+
+
+@compatibility(is_backward_compatible=False)
+def compare_graphs(left: Graph, right: Graph) -> bool:
+    """
+    Return True if two graphs are identical, i.e they
+        - have the same number of outputs in the same order
+        - have the same number of inputs in the same order
+        - have the same set of nodes, and identical connectivity
+    """
+
+    matcher = SubgraphMatcher(left, match_output=True, match_placeholder=True)
+    matches = matcher.match(right)
+
+    return len(matches) > 0
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/utils/fuser_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/utils/fuser_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..324e8a67801564cf69fac7fef318951fc34223b7
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/utils/fuser_utils.py
@@ -0,0 +1,236 @@
+# mypy: allow-untyped-defs
+import copy
+from queue import SimpleQueue
+from typing import List, Dict, Tuple
+
+import torch.fx
+from torch.fx.graph_module import GraphModule
+from torch.fx.graph import Graph
+from torch.fx.node import Node
+from torch.fx.passes.tools_common import NodeList, NodeSet, legalize_graph
+from torch.fx.passes.utils import lift_subgraph_as_module
+from torch.fx._compatibility import compatibility
+
+@compatibility(is_backward_compatible=False)
+def topo_sort(nodes: NodeList) -> NodeList:
+    # sort nodes according to the topological order
+    indegree_map = dict.fromkeys(nodes, 0)
+    candidates: SimpleQueue = SimpleQueue()
+
+    for node in nodes:
+        for n in node.all_input_nodes:
+            if n in indegree_map:
+                indegree_map[node] += 1
+        if indegree_map[node] == 0:
+            candidates.put(node)
+
+    sorted_nodes: NodeList = []
+    while not candidates.empty():
+        node = candidates.get()
+        sorted_nodes.append(node)
+
+        for n in node.users:
+            if n in indegree_map:
+                indegree_map[n] -= 1
+                if indegree_map[n] == 0:
+                    candidates.put(n)
+
+    assert len(nodes) == len(sorted_nodes), "topological sorted nodes doesn't have same length as input nodes"
+
+    return sorted_nodes
+
+
+@compatibility(is_backward_compatible=False)
+def validate_partition(partition: NodeList) -> bool:
+    # verify the partition does't form a dependency cycle in the original graph
+    # returns True for valid partition, False for invalid
+
+    partition_set = set(partition)
+
+    outputs: NodeList = []
+    for node in partition_set:
+        for user_node in node.users:
+            if user_node not in partition_set:
+                # external user node, need to expose as an output
+                outputs.append(user_node)
+
+    # Perform BFS on the partition outputs.
+    # If it reaches a node within the partition, then it found a cycle.
+    # This function takes the ownership of `root_nodes` and may modify it.
+    def bfs_find_cycle(root_nodes: NodeList) -> bool:
+        # Set used to exclude nodes that have already been visited.
+        # If a node has been visited, that node and all its children have
+        # been checked for cycles.
+        visited: NodeSet = set()
+
+        # Start with `root_nodes` and traverse through (toward child nodes)
+        # their connected sub-graph. Nodes in `visited` won't be added
+        # to `queue` again.
+        queue: NodeList = root_nodes
+        while queue:
+            current = queue.pop()
+            visited.add(current)
+            if current in partition_set:
+                # Started from partition's `output` nodes, and reached
+                # another node in partition. Cycle!
+                return True
+            for user_node in current.users:
+                if user_node in visited:
+                    continue
+                queue.append(user_node)
+        # `root_nodes` don't cause cycle.
+        return False
+
+    # Use all output nodes as roots to traverse
+    # the graph to check cycles.
+    if bfs_find_cycle(outputs):
+        return False
+
+    return True
+
+
+@compatibility(is_backward_compatible=False)
+def fuse_as_graphmodule(gm: GraphModule,
+                        nodes: NodeList,
+                        module_name: str) -> Tuple[GraphModule, Tuple[Node, ...], Tuple[Node, ...]]:
+
+    """
+    Fuse nodes in graph_module into a GraphModule.
+
+    Args:
+        gm (GraphModule): target graph_module
+
+        nodes (List[Node]): list of nodes in `gm` to fuse, where the node must be topologically sorted
+
+        module_name: class name for the fused GraphModule
+
+    Returns:
+        fused_gm (GraphModule): fused graph module, where its node is a copy of `nodes` in `gm`
+
+        original_inputs (Tuple[Node, ...]): input nodes to `nodes` in original `gm`
+
+        original_outputs (Tuple[Node, ...]): consumer nodes of `nodes` in original `gm`
+
+    """
+
+    # assumption: nodes are already sorted in topo order
+
+    for node in nodes:
+        assert node.graph.owning_module is gm, f"{node} doesn't belong to passed in graph module {gm._get_name()}"
+        assert not node._erased, f"{node} has been removed from owning graph"
+        assert node in gm.graph.nodes, f"{node} is not found in graph module {gm._get_name()}"
+
+    # validates partition doesn't introduce dependency circles in the graph
+    assert validate_partition(nodes), "Invalid partition, found dependency cycles"
+
+    subgraph = Graph()
+
+    node_to_placeholder: Dict[Node, Node] = {}  # mapping of nodes from old graph to placeholder in new graph
+    node_map: Dict[Node, Node] = {}       # mapping of nodes from old graph to new graph
+
+    # handles inputs through graph.node_copy's arg_transform functions
+    def remap_inputs(x):
+        if x.op == "get_attr":
+            # TODO: do we really need copy the get_attr node into the graph?
+            # do something here
+            pass
+
+        if x in nodes:
+            # x is inside subgraph, return the copied node
+            # the node should have been copied aleady, as we are copying graph in the topological order
+            return node_map[x]
+
+        if x not in node_to_placeholder:
+            # x is not in subgraph, create a new placeholder for subgraph
+            placeholder_node = subgraph.placeholder(x.name, type_expr=x.type)
+            # copy all meta fields, even if some fields might be irrelvant for the placeholder node
+            placeholder_node.meta = copy.copy(x.meta)
+            node_to_placeholder[x] = placeholder_node
+
+        return node_to_placeholder[x]
+
+    # copy nodes in topological order
+    for node in nodes:
+        new_node = subgraph.node_copy(node, remap_inputs)
+        node_map[node] = new_node
+
+    # handles outputs
+    output_mapping: Dict[Node, Node] = {}  # mapping from old output to new outputs
+
+    for node in nodes:
+        for user_node in node.users:
+            if user_node not in nodes:
+                # external user node, need to expose as an output
+                output_mapping[node] = node_map[node]
+
+    # outs contain nodes in the new subgraph
+    outs = tuple(output_mapping.values())
+
+    # Take care of the args of FX output node. If there's a single
+    # output then the output node args is like (output_single), else
+    # if there're multiple outputs then the output node args is like
+    # ((output_0, output_1, ...)).
+    subgraph.output(outs[0] if len(outs) == 1 else outs)
+
+    # lint to ensure correctness
+    subgraph.lint()
+    fused_gm: GraphModule
+    fused_gm, _ = lift_subgraph_as_module(gm, subgraph, comp_name="", class_name=module_name)
+
+    # sub_gm's input nodes in the original module
+    original_inputs: Tuple[Node, ...] = tuple(node_to_placeholder.keys())
+
+    # sub_gm's outputs node in the original module
+    original_outputs: Tuple[Node, ...] = tuple(output_mapping.keys())
+
+    return fused_gm, original_inputs, original_outputs
+
+
+@compatibility(is_backward_compatible=False)
+def insert_subgm(gm: GraphModule, sub_gm: GraphModule, orig_inputs: Tuple[Node, ...], orig_outputs: Tuple[Node, ...]):
+    # add sub_gm into gm
+    submodule_name = sub_gm.__class__.__name__
+    gm.add_submodule(submodule_name, sub_gm)
+
+    # Create a call_module node in main graph.
+    module_node = gm.graph.call_module(
+        submodule_name,
+        args=orig_inputs,
+        kwargs=None)
+
+    if len(orig_outputs) == 1:
+        # main_remapping[comp.orig_outputs[0]] = module_node
+        orig_outputs[0].replace_all_uses_with(module_node, propagate_meta=True)
+    else:
+        for i, orig_output in enumerate(orig_outputs):
+            # Use Proxy to record getitem access.
+            proxy_out = torch.fx.Proxy(module_node)[i].node  # type: ignore[index]
+            orig_output.replace_all_uses_with(proxy_out, propagate_meta=True)
+
+        module_node.meta["val"] = tuple(orig_output.meta.get("val", None) for orig_output in orig_outputs)
+    return gm
+
+@compatibility(is_backward_compatible=False)
+def erase_nodes(gm: GraphModule, nodes: NodeList):
+
+    # erase original nodes in inversed topological order
+    for node in reversed(nodes):
+        gm.graph.erase_node(node)
+
+
+@compatibility(is_backward_compatible=False)
+def fuse_by_partitions(gm: GraphModule, partitions: List[NodeList], prefix: str = "fused_") -> GraphModule:
+    for partition_id, nodes in enumerate(partitions):
+        sorted_nodes = topo_sort(nodes)
+
+        submodule_name = prefix + str(partition_id)
+        sub_gm, orig_inputs, orig_outputs = fuse_as_graphmodule(gm, sorted_nodes, submodule_name)
+
+        insert_subgm(gm, sub_gm, orig_inputs, orig_outputs)
+
+        erase_nodes(gm, sorted_nodes)
+
+    # topological sort original gm with newly created sub_gm
+    legalize_graph(gm)
+
+    return gm
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/utils/matcher_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/utils/matcher_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..56b9d96348e8de5619f2a78682090d5e4448c5ce
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/utils/matcher_utils.py
@@ -0,0 +1,401 @@
+# mypy: allow-untyped-defs
+from dataclasses import dataclass, field
+from collections import defaultdict
+import copy
+import torch
+from torch.fx import (
+    Node,
+    Graph,
+)
+from torch.fx._compatibility import compatibility
+from typing import Dict, List, Set, Any, Union, Tuple
+import logging
+import os
+
+__all__ = ['SubgraphMatcher', 'InternalMatch']
+
+# Set`PYTORCH_MATCHER_LOGLEVEL=INFO` to see debug logs
+def _init_logger():
+    logger = logging.getLogger(__name__)
+
+    level = os.environ.get('PYTORCH_MATCHER_LOGLEVEL', 'WARNING').upper()
+    logger.setLevel(level)
+    console = logging.StreamHandler()
+    formatter = logging.Formatter("%(filename)s > %(message)s")
+    console.setFormatter(formatter)
+    console.setLevel(level)
+    # add the handlers to the logger
+    logger.addHandler(console)
+    logger.propagate = False
+    return logger
+
+logger = _init_logger()
+
+@compatibility(is_backward_compatible=False)
+@dataclass
+class InternalMatch:
+    # Nodes from which the match was found
+    anchors: List[Node]
+    # Maps nodes in the pattern subgraph to nodes in the larger graph
+    nodes_map: Dict[Node, Node] = field(default_factory=dict)
+
+    # nodes in target graph that are matched placeholder in pattern
+    placeholder_nodes: List[Node] = field(default_factory=list)
+
+    # nodes in matched subgraph returned by output
+    returning_nodes: List[Node] = field(default_factory=list)
+
+    # map from a string name to a node in the target graph
+    # only available if the matcher is `SubgraphMatcherWithNameNodesMap`
+    name_node_map: Dict[str, Node] = field(default_factory=dict)
+
+    def __copy__(self):
+        return InternalMatch(anchors=self.anchors, nodes_map=self.nodes_map.copy(),
+                             placeholder_nodes=self.placeholder_nodes.copy(),
+                             returning_nodes=self.returning_nodes.copy())
+
+@compatibility(is_backward_compatible=False)
+class SubgraphMatcher:
+    def __init__(self, pattern: Graph,
+                 match_output: bool = False,
+                 match_placeholder: bool = False,
+                 remove_overlapping_matches: bool = True,
+                 ignore_literals: bool = False) -> None:
+        """
+        Args:
+            pattern: the targeted matching pattern, represented in fx.Graph.
+            match_output: If True, output node in the pattern graph will be treated as a part of the targeted pattern.
+                If False, output node is ignored during match.
+            match_placeholder: If True, placeholder node in the pattern graph will be treated as a part of
+                the targeted pattern. If False, placeholder nodes will be used a wildcard.
+            remove_overlapping_matches: If True, in the case of overlapping matches, only the first match
+                will be returned.
+            ignore_literals: If True, will not check if literals are equal and
+                will instead treat them as wildcards.
+        """
+
+        self.pattern = pattern
+        self.match_output = match_output
+        self.match_placeholder = match_placeholder
+        self.remove_overlapping_matches = remove_overlapping_matches
+        self.ignore_literals = ignore_literals
+
+        if len(pattern.nodes) == 0:
+            raise ValueError("SubgraphMatcher cannot be initialized with an empty pattern")
+
+        for node in pattern.nodes:
+            if node.op != "output":
+                assert len(node.users) > 0, \
+                       "SubgraphMatcher cannot be initialized with an pattern with dead code"
+
+        # TODO: assert pattern is a connected graph
+
+        self.pattern_placeholder_nodes = [n for n in pattern.nodes if n.op == "placeholder"]
+        output_node = next(iter(reversed(pattern.nodes)))
+        # nodes returned by outputs
+        self.pattern_returning_nodes: List[Node] = output_node.all_input_nodes
+
+        self.pattern_anchors: List[Node] = []
+        if match_output:
+            self.pattern_anchors = [output_node]
+        else:
+            # If a node has output_node as the ONLY user, then this node is a graph sink,
+            # and should be matched against as an anchor
+            self.pattern_anchors = [n for n in output_node.all_input_nodes if len(n.users) == 1]
+
+    def _match_attributes(self, pn: Node, gn: Node) -> bool:
+        # Attributes matching is complicated. Right now we only support matching constant tensor
+        assert isinstance(pn.target, str), f"pn.target {pn.target} must be a string."
+        assert isinstance(gn.target, str), f"gn.target {gn.target} must be a string."
+
+        # TODO(tmanlaibaatar) should probably make this actual API
+        def _getattr(model: torch.fx.GraphModule, attr_name: str):
+            *prefix, field = attr_name.split(".")
+            t = model
+            for item in prefix:
+                t = getattr(t, item, None)  # type: ignore[assignment]
+                assert t is not None
+
+            return getattr(t, field)
+
+        pn_value = _getattr(pn.graph.owning_module, pn.target)
+        gn_value = _getattr(gn.graph.owning_module, gn.target)
+
+        if type(pn_value) != type(gn_value):
+            return False
+
+        # Don't require exact match on tensor values.
+        if isinstance(pn_value, torch.Tensor):
+            return isinstance(gn_value, torch.Tensor)
+        else:
+            raise RuntimeError(f"Unsupported type {pn_value} when matching attributes")
+        return False
+
+    def _nodes_are_equal(self, pn: Node, gn: Node) -> bool:
+        # if exact match for placeholder is not required, then use placeholder as a wildcard
+        if not self.match_placeholder and pn.op == "placeholder":
+            return True
+
+        if pn.op == gn.op:
+            if pn.op == "placeholder" or pn.op == "output":
+                return True
+            elif pn.op == "get_attr":
+                return self._match_attributes(pn, gn)
+            return pn.target == gn.target
+        return False
+
+    def _is_contained(self, nodes_map: Dict[Node, Node]) -> bool:
+        # `lookup` represents all the nodes in `original_graph`
+        # that are part of `pattern`
+
+        # Placeholders can be used by other nodes in the graphs
+        lookup: Dict[Node, Node] = {gn : pn for pn, gn in nodes_map.items() if pn.op != "placeholder"}
+
+        for gn, pn in lookup.items():
+            # nodes returned by output are allowed to be used in other areas of the graph
+            if pn in self.pattern_returning_nodes:
+                continue
+
+            for user in gn.users:
+                # If this node has users that were not in `lookup`, then it must leak out of the
+                # pattern subgraph
+                if user not in lookup:
+                    return False
+        return True
+
+    def _remove_overlapping_matches(self, matches: List[InternalMatch]) -> List[InternalMatch]:
+        non_overlapping_matches: List[InternalMatch] = []
+        nodes_matched: Set[Node] = set()
+
+        for match in matches:
+            found_overlap = False
+            for pn, gn in match.nodes_map.items():
+                if pn.op not in {"placeholder", "output"} and gn in nodes_matched:
+                    found_overlap = True
+                    break
+
+            if not found_overlap:
+                non_overlapping_matches.append(match)
+                for pn, gn in match.nodes_map.items():
+                    if pn.op not in {"placeholder", "output"}:
+                        nodes_matched.add(gn)
+        return non_overlapping_matches
+
+    def _match_literals(self, pn: Any, gn: Any, match: InternalMatch) -> bool:
+        assert not (isinstance(pn, Node) and isinstance(gn, Node)), "pn and gn cannot both be Node"
+
+        if isinstance(pn, Node) and not isinstance(gn, Node):
+            if pn.op == "placeholder":
+                # Check if we've already matched these nodes in the current
+                # traversal
+                if pn in match.nodes_map:
+                    return match.nodes_map[pn] == gn
+
+                match.nodes_map[pn] = gn
+                return True
+            else:
+                return False
+        elif not isinstance(pn, Node) and isinstance(gn, Node):
+            return False
+        else:
+            return type(gn) == type(pn) and gn == pn
+
+    def _match_nodes(self, pn: Node, gn: Node, match: InternalMatch) -> bool:
+        logger.info("  matching %s to %s", pn, gn)
+
+        assert isinstance(pn, Node) and isinstance(gn, Node), str(f"pn and gn must be Node, pn: {pn}, gn: {gn}")
+
+        # Check if we've already matched these nodes in the current
+        # traversal
+        if pn in match.nodes_map:
+            return match.nodes_map[pn] == gn
+
+        # TODO: use a more efficient way to check if gn is matched before: two-way dict
+        if gn in match.nodes_map.values():
+            return False
+
+        if not self._nodes_are_equal(pn, gn):
+            return False
+
+        # Optimistically mark `pn` as a match for `gn`, and save a local copy of match
+        saved_match = copy.copy(match)
+        match.nodes_map[pn] = gn
+
+        # Placeholder is a wildcard and can be matched with any python object
+        # (including list/tuple)
+        if pn.op == "placeholder":
+            return True
+
+        # Recursively traverse upwards to check if `pn` is a true
+        # match for `gn`
+        match_found = True
+
+        def _match_args(args1: Union[List, Tuple], args2: Union[List, Tuple]) -> bool:
+            if len(args1) != len(args2):
+                return False
+
+            for a1, a2 in zip(args1, args2):
+                if isinstance(a1, Node) and isinstance(a2, Node):
+                    matched = self._match_nodes(a1, a2, match)
+                elif isinstance(a1, (list, tuple)) and isinstance(a2, (list, tuple)):
+                    matched = _match_args(a1, a2)
+                else:
+                    matched = self._match_literals(a1, a2, match) or self.ignore_literals
+
+                if not matched:
+                    return False
+
+            return True
+
+        # Flatten all args/kwargs into 1 list of args
+        pn_args, gn_args = None, None
+        if (
+            (len(pn.args) != len(gn.args) or list(pn.kwargs.keys()) != list(gn.kwargs.keys())) and
+            pn.op == "call_function" and
+            isinstance(pn.target, torch._ops.OpOverload)
+        ):
+            args_schema = pn.target._schema.arguments
+
+            def get_all_arguments(orig_args, orig_kwargs):
+                all_args = []
+                for i, schema in enumerate(args_schema):
+                    if schema.name in orig_kwargs:
+                        all_args.append(orig_kwargs[schema.name])
+                    elif not schema.kwarg_only and i < len(orig_args):
+                        all_args.append(orig_args[i])
+                    else:
+                        all_args.append(schema.default_value)
+                return all_args
+
+            pn_args = get_all_arguments(pn.args, pn.kwargs)
+            gn_args = get_all_arguments(gn.args, gn.kwargs)
+
+        elif len(pn.args) == len(gn.args) and list(pn.kwargs.keys()) == list(gn.kwargs.keys()):
+            pn_args = list(pn.args)
+            gn_args = list(gn.args)
+            pn_args.extend(list(pn.kwargs.values()))
+            gn_args.extend(list(gn.kwargs.values()))
+        else:
+            match_found = False
+
+        match_found = (
+            match_found and
+            pn_args is not None and
+            gn_args is not None and
+            _match_args(pn_args, gn_args)
+        )
+
+        if not match_found:
+            # revert to saved_match before matching with current node
+            match = copy.copy(saved_match)
+            return False
+
+        return True
+
+    def match(self, graph: Graph) -> List[InternalMatch]:
+        """
+        Returns:
+            The matched subgraphs.
+            Thre returned subgraph would be fully self-contained, meaning the nodes (except placeholder
+            and nodes returned by output) can only be consumed by nodes within the matched subgraph.
+
+        Subgraph pattern matcher is implemented with the backtracking style in the following steps:
+
+        1. We first identify all the anchor nodes in the pattern graph. The anchor nodes
+        are the "sinks" (nodes with no user other than the output node) of the pattern graph.
+        One pattern graph could have multiple anchors if it has multiple return values.
+
+        2. In the target graph, we identify the potential candidate nodes that can be matched
+        with each anchor. These anchor-candidate pairs are the starting points for
+        pairwise per-node matching.
+
+        3. For each anchor-candidate pair, we simultaneously traverse backwards (DFS) in both
+        pattern and target graphs. For every pattern nodes along traversal path, we compare it
+        against the target nodes. In case any comparison failed, the match for this anchor-candidate
+        pair fails. A match is found when DFS completes traversing the graph. See `self._match_nodes`
+        for more details.
+
+        4. In the case of multiple anchors, every anchor will need to find a match using step 3.
+        In addition, the matches found between anchors need to have a common intersection node
+        in order for the match to be valid. This is implemented with backtracking. See `backtracking`
+        for more details.
+
+        Notice: graph traversal must be done in the reverser order because a tensor can have multiple
+        consumers, but can only have a single producer. Only with reverser order, we can we jointly
+        traverse the pattern and target graph in a deterministic path.
+
+        Warning: In theory, this backtracking algorithm have an **exponential** time complexity. However,
+        in practice, it's unlikely to blow up.
+
+        """
+        from torch.fx.passes.utils.fuser_utils import validate_partition
+
+        # find candidate nodes to match with pattern anchors
+        match_candidates: Dict[Node, List[Node]] = defaultdict(list)
+        for pattern_anchor in self.pattern_anchors:
+            for node in graph.nodes:
+                if self._nodes_are_equal(pattern_anchor, node):
+                    match_candidates[pattern_anchor].append(node)
+        match_candidates_list = list(match_candidates.items())
+
+        logger.info("Initial match_candidates_list: %s\n", match_candidates_list)
+
+        matches: List[InternalMatch] = []
+
+        def backtracking(anchor_index, match):
+            if anchor_index == len(match_candidates_list):
+                match.placeholder_nodes = [match.nodes_map[pn] for pn in self.pattern_placeholder_nodes]
+                match.returning_nodes = [match.nodes_map[pn] for pn in self.pattern_returning_nodes]
+                matches.append(match)
+
+                logger.info("Found a match: %s\n", match)
+                return
+
+            pattern_anchor, candidate_nodes = match_candidates_list[anchor_index]
+            saved_match = copy.copy(match)
+
+            for node in candidate_nodes:
+                logger.info("Trying to match anchor %s to %s", pattern_anchor, node)
+
+                match_found = self._match_nodes(pattern_anchor, node, match)
+                if match_found:
+                    # match next anchor
+                    backtracking(anchor_index + 1, match)
+                else:
+                    logger.info("Failed to match anchor %s to %s\n", pattern_anchor, node)
+
+                # revert to saved_match before matching with current anchor
+                match = copy.copy(saved_match)
+
+        match = InternalMatch(anchors=self.pattern_anchors)
+        if match_candidates_list:
+            backtracking(0, match)
+
+        # filter out the matches where the subgraph is not fully_contained
+        before = len(matches)
+        matches = [match for match in matches if self._is_contained(match.nodes_map)]
+        after = len(matches)
+        if before != after:
+            logger.info("Filtered out %s matches because they are not fully contained", before - after)
+
+        # filter out the matches that form a cycle if the subgraph is fused
+        valid_matches = []
+        for match in matches:
+            matched_compute_nodes = \
+                [gn for pn, gn in match.nodes_map.items() if pn.op not in {"placeholder", "output"}]
+            if validate_partition(matched_compute_nodes):
+                valid_matches.append(match)
+        if len(valid_matches) != len(matches):
+            logger.info("Filtered out %s matches because \
+                          matched subgraph would form a cycle if fused", len(matches) - len(valid_matches))
+
+        if self.remove_overlapping_matches:
+            before = len(valid_matches)
+            matches = self._remove_overlapping_matches(valid_matches)
+            after = len(matches)
+            if before != after:
+                logger.info("Filtered out %s matches because matched subgraphs are overlapping", before - after)
+
+        logger.info("Matches returned: %s", matches)
+
+        return matches
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8482dca74b1002ad5f4e4a66b8dd30b800fc2765
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py
@@ -0,0 +1,114 @@
+from typing import Dict, List, Tuple
+
+from torch.fx import Graph, GraphModule, Node
+from torch.fx._compatibility import compatibility
+
+from .matcher_utils import InternalMatch, SubgraphMatcher
+
+
+__all__ = ["SubgraphMatcherWithNameNodeMap"]
+
+
+def _split_to_graph_and_name_node_map(
+    gm: GraphModule,
+) -> Tuple[GraphModule, Dict[str, Node]]:
+    from torch.fx.graph import _PyTreeInfo
+    from torch.utils._pytree import tree_flatten, tree_unflatten
+
+    name_node_map = {}
+    for n in gm.graph.nodes:
+        if n.op == "output":
+            assert gm._out_spec is not None
+            output = tree_unflatten(n.args[0], gm._out_spec)
+            assert isinstance(
+                output, tuple
+            ), "Expecting the pattern graph to return a tuple"
+            assert (
+                len(output) >= 2
+            ), "Expecting the pattern graph to have at least two outputs"
+            *out, name_node_map = output
+            flattened, out_spec = tree_flatten(out)
+            assert isinstance(
+                name_node_map, Dict
+            ), "Expecting the input graph to have a dict output as the last element"
+            n.args = (flattened,)
+            orig_pytree_info = gm._graph._codegen.pytree_info  # type: ignore[attr-defined]
+            gm._graph._codegen.pytree_info = _PyTreeInfo(  # type: ignore[attr-defined]
+                orig_pytree_info.orig_args, orig_pytree_info.in_spec, out_spec
+            )
+    gm.recompile()
+    return gm, name_node_map
+
+
+@compatibility(is_backward_compatible=False)
+class SubgraphMatcherWithNameNodeMap(SubgraphMatcher):
+    """Extends SubgraphMatcher to support querying the matched subgraph nodes through node name,
+    this requires pattern to have specific format (returning and additional dictionary at the output,
+    that has node name as key, and the node in the pattern graph as value, see Example for more details)
+
+    Difference with SubgraphMatcher is that it takes a `pattern_gm` GraphModule as input during
+    initialization since we need to modify the graph (which requires `recompile` the GraphModule)
+
+    Example::
+        def pattern(x, weight):
+            conv = F.conv2d(x, weight)
+            relu = F.relu(conv)
+            return relu, {"conv": conv, "relu": relu}
+
+        def target_graph(x, weight):
+            conv = F.conv2d(x, weight)
+            relu = F.relu(conv)
+            relu *= 2
+            return relu
+
+        pattern_gm = capture_pre_autograd_graph(pattern, example_inputs)
+        target_gm = capture_pre_autograd_graph(target_graph, example_inputs)
+        matcher = SubgraphMatcherWithNameNodeMap(pattern_gm)
+        matches = matcher.match(target_gm)
+        for match in matches:
+            match.name_node_map["conv"].meta["annotation"] = ...
+
+    """
+
+    def __init__(
+        self,
+        pattern_gm: GraphModule,
+        match_output: bool = False,
+        match_placeholder: bool = False,
+        remove_overlapping_matches: bool = True,
+        ignore_literals: bool = False,
+    ) -> None:
+        pattern_gm, name_node_map = _split_to_graph_and_name_node_map(pattern_gm)
+        self.name_node_map = name_node_map
+        super().__init__(
+            pattern_gm.graph,
+            match_output,
+            match_placeholder,
+            remove_overlapping_matches,
+            ignore_literals,
+        )
+
+    def match(self, graph: Graph) -> List[InternalMatch]:
+        """The returned InternalMatch will have name_node_map populated with a map
+        from node name (str) to the target node, e.g.
+        {"conv": target_conv_ndoe, "relu": target_relu_node}
+
+        this requires the pattern graph returns an additional
+        output of node name to node, e.g. instead of:
+        ```
+        def pattern(...):
+            ...
+            return relu
+        ```
+        we should do:
+        ```
+        def pattern(...):
+            ...
+            return relu, {"conv": conv, "relu": relu}
+        ``` instead
+        """
+        internal_matches = super().match(graph)
+        for internal_match in internal_matches:
+            for k, n in self.name_node_map.items():
+                internal_match.name_node_map[k] = internal_match.nodes_map[n]
+        return internal_matches
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/utils/source_matcher_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/utils/source_matcher_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a4f072644cdbb01edab042ad42a8a617cce8314
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/passes/utils/source_matcher_utils.py
@@ -0,0 +1,154 @@
+from dataclasses import dataclass, field
+from torch.fx.graph import Graph
+from torch.fx.node import Node
+from torch.fx._compatibility import compatibility
+from typing import Dict, List, Any, Type, Optional, Callable
+import logging
+import os
+
+
+__all__ = ['get_source_partitions', 'check_subgraphs_connected', 'SourcePartition']
+
+# Set`PYTORCH_MATCHER_LOGLEVEL=INFO` to see debug logs
+def _init_logger() -> logging.Logger:
+    logger = logging.getLogger(__name__)
+
+    level = os.environ.get('PYTORCH_MATCHER_LOGLEVEL', 'WARNING').upper()
+    logger.setLevel(level)
+    console = logging.StreamHandler()
+    formatter = logging.Formatter("%(filename)s > %(message)s")
+    console.setFormatter(formatter)
+    console.setLevel(level)
+    # add the handlers to the logger
+    logger.addHandler(console)
+    logger.propagate = False
+    return logger
+
+logger = _init_logger()
+
+
+@compatibility(is_backward_compatible=False)
+@dataclass
+class SourcePartition:
+    # Nodes in a particular partition
+    nodes: List[Node]
+
+    # The source these nodes decomposed from
+    source: Any
+
+    # Nodes in the graph that are needed as inputs to the partition
+    input_nodes: List[Node] = field(default_factory=list)
+
+    # Nodes in the partition that are being used by nodes outside of the
+    # partition
+    output_nodes: List[Node] = field(default_factory=list)
+
+    # Parameters that are being used
+    params: List[Node] = field(default_factory=list)
+
+
+@compatibility(is_backward_compatible=False)  # type: ignore[misc]
+def get_source_partitions(
+    graph: Graph,
+    wanted_sources: List[Any],
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+) -> Dict[Any, List[SourcePartition]]:
+    """
+    Args:
+        graph: The graph we want to partition
+        wanted_sources: List of sources of nodes that were decomposed from this
+            source. This can be a function (ex. torch.nn.functional.linear) or a
+            leaf module type (ex. torch.nn.Linear).
+
+    Returns:
+        Dictionary mapping sources that were given to a list of SourcePartitions
+        that correspond to the list of nodes that were decomposed from the given
+        source.
+    """
+    modules: Dict[Type, Dict[str, List[Node]]] = {}
+
+    for node in graph.nodes:
+        # The metadata source_fn should contain a tuple of a unique name for the
+        # source, and the source function if the node is decomposed from a
+        # function, or the type of module if the node is decomposed from a leaf
+        # module
+
+        # TODO: Bypass "torch_fn" when "source_fn_stack" because now "torch_fn" can
+        # be different from "source_fn_stack", for example for the add_ node
+        # decomposed from batch norm. We should remove the check on "source_fn_stack"
+        # after we fix "torch_fn". T199561090
+        if ((source_fn_st := node.meta.get("source_fn_stack", None)) is None and
+           (torch_fn := node.meta.get("torch_fn", None)) is not None):
+            node_fqn, source_fn = torch_fn
+            source_fn_name = source_fn.split(".")[1]
+            if source_fn_name in wanted_sources:
+                diff_modules = modules.setdefault(source_fn_name, {})
+                partition = diff_modules.setdefault(node_fqn, [])
+                partition.append(node)
+
+
+        if (source_fn_st := node.meta.get("source_fn_stack", None)) is not None:
+            source_fn = source_fn_st[-1]
+            if source_fn[1] in wanted_sources:
+                diff_modules = modules.setdefault(source_fn[1], {})
+                partition = diff_modules.setdefault(source_fn[0], [])
+                partition.append(node)
+
+    def make_partition(nodes: List[Node], module_type: Type) -> SourcePartition:
+        input_nodes = set()
+        output_nodes = set()
+        params = set()
+        for node in nodes:
+            for arg in node.args:
+                if isinstance(arg, Node) and arg not in nodes:
+                    input_nodes.add(arg)
+
+            if node.op == "get_attr":
+                params.add(node)
+
+            for user in node.users.keys():
+                if user not in nodes:
+                    output_nodes.add(node)
+
+        return SourcePartition(
+            nodes,
+            module_type,
+            list(input_nodes),
+            list(output_nodes),
+            list(params),  # type: ignore[arg-type]
+        )
+
+    ret: Dict[Type[Any], List[SourcePartition]] = {}
+
+    if filter_fn:
+        # for each partition, we apply filter_fn to filter out all partitions that doesn't satisfy the
+        # filter condition
+        filtered_modules = {}
+        for tp, name_to_partition in modules.items():
+            filtered_name_to_partition = {
+                name: partition
+                for name, partition in name_to_partition.items()
+                if all(map(filter_fn, partition))
+            }
+            filtered_modules[tp] = filtered_name_to_partition
+        modules = filtered_modules
+
+    for k, v in modules.items():
+        ret[k] = [make_partition(partition, k) for partition in v.values()]
+
+    return ret
+
+
+@compatibility(is_backward_compatible=False)  # type: ignore[misc]
+def check_subgraphs_connected(subgraph1: SourcePartition, subgraph2: SourcePartition) -> bool:
+    """
+    Given two subgraphs A and B (in the form of a list of nodes), checks if
+    A has nodes connecting to at least one node in B -- aka there exists a node
+    in B that uses a node in A (not the other way around).
+    """
+
+    for node in reversed(subgraph1.nodes):
+        for user in node.users.keys():
+            if user in subgraph2.nodes:
+                return True
+    return False
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/proxy.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/proxy.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b86a1c609f918d63dbcddf791cddf3d72b9f924
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/proxy.py
@@ -0,0 +1,609 @@
+# mypy: ignore-errors
+
+import enum
+import dis
+import copy
+import sys
+import torch
+import inspect
+import operator
+import collections
+import logging
+
+from dataclasses import is_dataclass, fields
+
+
+from .graph import magic_methods, reflectable_magic_methods, Graph
+from torch.utils._traceback import CapturedTraceback
+from typing import Tuple, Dict, OrderedDict, Optional, Any, Iterator, Callable
+from .node import Target, Node, Argument, base_types, map_aggregate
+from ._compatibility import compatibility
+from .operator_schemas import check_for_mutable_operation
+import torch.fx.traceback as fx_traceback
+
+__all__ = ['TracerBase', 'GraphAppendingTracer', 'TraceError',
+           'Proxy', 'Attribute', 'ParameterProxy', 'Scope',
+           'ScopeContextManager']
+
+
+log = logging.getLogger(__name__)
+
+
+@compatibility(is_backward_compatible=False)
+class Scope:
+    """ Scope object that records the module path and the module type
+    of a module. Scope is used to track the information of the module
+    that contains a Node in a Graph of GraphModule. For example::
+
+        class Sub(torch.nn.Module):
+            def forward(self, x):
+                # This will be a call_method Node in GraphModule,
+                # scope for this would be (module_path="sub", module_type=Sub)
+                return x.transpose(1, 2)
+
+        class M(torch.nn.Module):
+            def __init__(self) -> None:
+                self.sub = Sub()
+
+            def forward(self, x):
+                # This will be a call_method Node as well,
+                # scope for this would be (module_path="", None)
+                x = x.transpose(1, 2)
+                x = self.sub(x)
+                return x
+
+    """
+
+    def __init__(self, module_path: str, module_type: Any):
+        super().__init__()
+        self.module_path = module_path
+        self.module_type = module_type
+
+
+@compatibility(is_backward_compatible=False)
+class ScopeContextManager:
+    """ A context manager to track the Scope of Node during symbolic tracing.
+    When entering a forward function of a Module, we'll update the scope information of
+    the current module, and when we exit, we'll restore the previous scope information.
+    """
+
+    def __init__(
+        self,
+        scope: Scope,
+        current_scope: Scope,
+    ):
+        super().__init__()
+        # Keep a copy of prev scope to restore on exit
+        self._prev_scope = copy.copy(scope)
+        # Update scope to current scope
+        scope.module_path = current_scope.module_path
+        scope.module_type = current_scope.module_type
+        # Save a reference so we can restore it
+        self._scope = scope
+
+    def __enter__(self):
+        return self._scope
+
+    def __exit__(self, *args):
+        self._scope.module_path = self._prev_scope.module_path
+        self._scope.module_type = self._prev_scope.module_type
+        return
+
+
+_COPY_META_FIELDS = [
+    "nn_module_stack",
+    "torch_fn",
+    "source_fn_stack",
+    "original_aten",
+    "recompute",
+    "ac_graph_id",
+    "from_node",
+    "quantization_tag",  # TODO deprecated
+    "_numeric_debug_handle",  # TODO deprecated
+    "custom",
+    "partitioner_tag"
+]
+
+
+@compatibility(is_backward_compatible=True)
+class TracerBase:
+    graph: Graph
+    record_stack_traces : bool = False
+    # Feature flag for mutable schema checking
+    # Enableby default in 1.12
+    check_mutable_operations : bool = False
+    # Feature flag for assert tracing
+    trace_asserts : bool = False
+    # Feature flag for proxying accesses to buffer values
+    proxy_buffer_attributes : bool = False
+
+    # Name of the function to be traced. It will only be used when
+    # ``root`` is an instance of ``nn.Module``
+    traced_func_name: str = "forward"
+
+    # Maps the containing module's name to the operator name
+    scope : Scope
+
+    # Records the module call stack
+    module_stack: OrderedDict[str, Tuple[str, Any]]
+
+    # Mapping of node name to module scope
+    node_name_to_scope: Dict[str, Tuple[str, type]]
+
+    @compatibility(is_backward_compatible=True)
+    def create_node(self, kind : str, target : Target,
+                    args : Tuple[Argument, ...], kwargs : Dict[str, Argument], name : Optional[str] = None,
+                    type_expr : Optional[Any] = None) -> Node:
+        """
+        Inserts a graph node given target, args, kwargs, and name.
+
+        This method can be overridden to do extra checking, validation, or
+        modification of values used in node creation. For example, one might
+        want to disallow in-place operations from being recorded.
+        """
+
+        if kind == 'call_function' and self.check_mutable_operations:
+            check_for_mutable_operation(target, args, kwargs)
+
+        node = self.graph.create_node(kind, target, args, kwargs, name, type_expr)
+        # TODO node_name_to_scope will be depreciated in favor of
+        # node.meta['nn_module_stack']
+        self.node_name_to_scope[node.name] = (
+            self.scope.module_path,
+            self.scope.module_type,
+        )
+        # Optionally set stack trace on the created Node for debugging purposes
+        if fx_traceback.has_preserved_node_meta():
+            current_meta: Dict[str, Any] = fx_traceback.get_current_meta()
+
+            stack_trace = current_meta.get("stack_trace")
+            if stack_trace:
+                node.stack_trace = stack_trace
+            # Explicitly set the stack_trace, nn_module_stack and source_fn on the node.meta
+            # If other meta fields are needed, they can be added here
+            for field in _COPY_META_FIELDS:
+                if field in current_meta:
+                    node.meta[field] = copy.copy(current_meta[field])
+
+            # Here we decrement to account for the sequence_nr having
+            # just been incremented while tracing this lowered aten op.
+            new_seq_nr = torch.autograd._get_sequence_nr() - 1
+            # The sequence_nr increments every time a new autograd Node
+            # is created. During the FWD pass we store the sequence_nr
+            # corresponding to the last autograd Node created on this fx
+            # node's meta.  A single aten op can create multiple autograd
+            # nodes as is the case with in-place foreach ops. During the
+            # BWD pass we retrieve the sequence_nr stored on the current
+            # executing autograd Node. See NOTE [ Sequence Number ].
+            if current_meta.get("in_grad_fn", 0) > 0:
+                new_seq_nr = current_meta["grad_fn_seq_nr"][-1]
+            node.meta["seq_nr"] = new_seq_nr
+
+        elif self.module_stack:
+            node.meta['nn_module_stack'] = copy.copy(self.module_stack)
+
+        log.debug("create_node %s", node)
+        return node
+
+    @compatibility(is_backward_compatible=True)
+    def proxy(self, node: Node) -> 'Proxy':
+        return Proxy(node, self)
+
+    @compatibility(is_backward_compatible=True)
+    def create_proxy(self, kind: str, target: Target, args: Tuple[Any, ...], kwargs: Dict[str, Any],
+                     name: Optional[str] = None, type_expr : Optional[Any] = None,
+                     proxy_factory_fn: Callable[[Node], 'Proxy'] = None):
+        '''
+        Create a Node from the given arguments, then return the Node
+        wrapped in a Proxy object.
+
+        If kind = 'placeholder', then we're creating a Node that
+        represents the parameter of a function. If we need to encode
+        a default parameter, we use the ``args`` tuple. ``args`` is
+        otherwise empty for ``placeholder`` Nodes.
+        '''
+
+        args_ = self.create_arg(args)
+        kwargs_ = self.create_arg(kwargs)
+        assert isinstance(args_, tuple)
+        assert isinstance(kwargs_, dict)
+
+        node = self.create_node(kind, target, args_, kwargs_, name, type_expr)
+
+        if not proxy_factory_fn:
+            proxy = self.proxy(node)
+        else:
+            proxy = proxy_factory_fn(node)
+
+        if self.record_stack_traces and not proxy.node.stack_trace:
+            proxy.node.stack_trace = ''.join(CapturedTraceback.extract().format())
+
+
+        return proxy
+
+    def _find_user_frame(self):
+        """
+        Find the Python stack frame executing the user code during
+        symbolic tracing.
+        """
+        # We have to do a little dance here. Basically, walk up the callstack and
+        # record the first frame not in the pytorch source. This is the frame executing
+        # the user code during tracing.
+        frame = inspect.currentframe()
+
+        pt_files = ['torch/fx/proxy.py',
+                    'torch/fx/_symbolic_trace.py',
+                    'torch/fx/experimental/proxy_tensor.py',
+                    'torch/_ops.py',
+                    'torch/_tensor.py',
+                    'torch/utils/_python_dispatch.py',
+                    'torch/_prims_common/wrappers.py',
+                    'torch/_refs/__init__.py',
+                    'torch/_refs/nn/functional/__init__.py',
+                    'torch/utils/_stats.py',
+                    ]
+        while frame:
+            frame = frame.f_back
+            if frame and all(not frame.f_code.co_filename.endswith(file) for file in pt_files):
+                break
+
+        if not frame:
+            return None
+
+        return frame
+
+    @compatibility(is_backward_compatible=True)
+    def create_arg(self, a: Any) -> Argument:
+        """
+        A method that lowers the objects seen as arguments during symbolic evaluation
+        into Argument types that can be stored in IR.
+
+        Can be override to support more trace-specific types.
+        """
+        if not isinstance(a, Proxy) and hasattr(a, '__fx_create_arg__'):
+            return a.__fx_create_arg__(self)
+        # aggregates
+        elif isinstance(a, tuple) and hasattr(a, '_fields'):
+            # NamedTuple constructors don't seem to like getting a generator
+            # expression as an argument to their constructor, so build this
+            # intermediate tuple and unpack it into the NamedTuple constructor
+            args = tuple(self.create_arg(elem) for elem in a)
+            return type(a)(*args)  # type: ignore[arg-type]
+        elif isinstance(a, (tuple, list)):
+            return type(a)(self.create_arg(elem) for elem in a)
+        elif isinstance(a, dict):
+            r = {}
+            for k, v in a.items():
+                # Check for invalid dict keys. We do not want a Proxy to appear
+                # anywhere within the key. Since keys can be collection types,
+                # we iterate through the key with map_aggregate
+                k = self.create_arg(k)
+
+                def no_node(arg):
+                    if isinstance(arg, Node):
+                        raise RuntimeError("Keys for dictionaries used as an argument cannot contain a "
+                                           f"Node. Got key: {k}")
+                map_aggregate(k, no_node)
+
+                r[k] = self.create_arg(v)
+            return r
+        elif isinstance(a, slice):
+            return slice(self.create_arg(a.start), self.create_arg(a.stop), self.create_arg(a.step))
+
+        elif isinstance(a, range):
+            return range(self.create_arg(a.start), self.create_arg(a.stop), self.create_arg(a.step))
+
+        elif isinstance(a, (torch._ops.OpOverload, torch._ops.HigherOrderOperator)):
+            return a
+
+        if isinstance(a, Proxy):
+            # base case: we unwrap the Proxy object
+            return a.node
+
+        if is_dataclass(a):
+            kwargs = {field.name: self.create_arg(getattr(a, field.name)) for field in fields(a)}
+            return self.create_node("call_function", a.__class__, (), kwargs)
+
+        elif isinstance(a, (*base_types, enum.Enum)) or a is None or a is ...:
+            return a
+        raise NotImplementedError(f"argument of type: {type(a)}")
+
+    @compatibility(is_backward_compatible=True)
+    def to_bool(self, obj: 'Proxy') -> bool:
+        """Called when a proxy object is being converted to a boolean, such as
+        when used in control flow.  Normally we don't know what to do because
+        we don't know the value of the proxy, but a custom tracer can attach more
+        information to the graph node using create_node and can choose to return a value.
+        """
+        raise TraceError('symbolically traced variables cannot be used as inputs to control flow')
+
+    @compatibility(is_backward_compatible=True)
+    def iter(self, obj: 'Proxy') -> Iterator:
+        """Called when a proxy object is being iterated over, such as
+        when used in control flow.  Normally we don't know what to do because
+        we don't know the value of the proxy, but a custom tracer can attach more
+        information to the graph node using create_node and can choose to return an iterator.
+        """
+        raise TraceError('Proxy object cannot be iterated. This can be '
+                         'attempted when the Proxy is used in a loop or'
+                         ' as a *args or **kwargs function argument. '
+                         'See the torch.fx docs on pytorch.org for a '
+                         'more detailed explanation of what types of '
+                         'control flow can be traced, and check out the'
+                         ' Proxy docstring for help troubleshooting '
+                         'Proxy iteration errors')
+
+    @compatibility(is_backward_compatible=True)
+    def keys(self, obj: 'Proxy') -> Any:
+        """Called when a proxy object is has the keys() method called.
+        This is what happens when ** is called on a proxy. This should return an
+        iterator it ** is suppose to work in your custom tracer.
+        """
+        return Attribute(obj, 'keys')()
+
+
+# used in Proxy object when just appending to the graph while not tracing.
+@compatibility(is_backward_compatible=True)
+class GraphAppendingTracer(TracerBase):
+    def __init__(self, graph: Graph):
+        super().__init__()
+        self.graph = graph
+        self.scope = Scope("", None)
+        self.module_stack = collections.OrderedDict()
+        self.node_name_to_scope = {}
+
+@compatibility(is_backward_compatible=False)
+def assert_fn(x):
+    assert x
+
+@compatibility(is_backward_compatible=True)
+class TraceError(ValueError):
+    pass
+
+@compatibility(is_backward_compatible=True)
+class Proxy:
+    """
+    ``Proxy`` objects are ``Node`` wrappers that flow through the
+    program during symbolic tracing and record all the operations
+    (``torch`` function calls, method calls, operators) that they touch
+    into the growing FX Graph.
+
+    If you're doing graph transforms, you can wrap your own ``Proxy``
+    method around a raw ``Node`` so that you can use the overloaded
+    operators to add additional things to a ``Graph``.
+
+    ``Proxy`` objects cannot be iterated. In other words, the symbolic
+    tracer will throw an error if a ``Proxy`` is used in a loop or as
+    an ``*args``/``**kwargs`` function argument.
+
+    There are two main ways around this:
+    1. Factor out the untraceable logic into a top-level function and
+    use ``fx.wrap`` on it.
+    2. If the control flow is static (i.e. the loop trip count is
+    based on some hyperparameter), the code can be kept in its original
+    position and refactored into something like::
+
+        for i in range(self.some_hyperparameter):
+            indexed_item = proxied_value[i]
+
+    For a more detailed description into the Proxy internals, check out
+    the "Proxy" section in `torch/fx/README.md`
+    """
+
+    @compatibility(is_backward_compatible=True)
+    def __init__(self, node: Node, tracer: 'Optional[TracerBase]' = None):
+        if tracer is None:
+            # This allows you to create a Proxy object around a raw Node
+            tracer = GraphAppendingTracer(node.graph)
+        self.tracer = tracer
+        self.node = node
+
+    def __repr__(self) -> str:
+        return f'Proxy({self.node.name})'
+
+    def __getattr__(self, k) -> 'Attribute':
+        # note: not added to the graph yet, if this is a method call
+        # we peephole optimize to the method invocation
+        return Attribute(self, k)
+
+    def __getstate__(self) -> Dict:
+        return self.__dict__
+
+    def __deepcopy__(self, memo) -> Dict:
+        # We have to explicitly override this method, because otherwise deepcopy
+        # will go to __getattr__(self, "__deepcopy__") and return a
+        # Attribute(__deepcopy__), and may go into an infinite loop in some cases.
+        import copy
+        new_dict = {}
+        for k, v in self.__dict__.items():
+            try:
+                new_obj = copy.deepcopy(v, memo)
+            except Exception:
+                log.warning(
+                    "Shallow copy %s of Proxy because it cannot be deepcopied. "
+                    "Proxy is created for node %s", k, self.node.name)
+                new_obj = copy.copy(v)
+            new_dict[k] = new_obj
+        assert "node" in new_dict
+        assert "tracer" in new_dict
+        new_proxy = Proxy(new_dict["node"], new_dict["tracer"])
+        for k, v in new_dict.items():
+            new_proxy.__dict__[k] = v
+        return new_proxy
+
+    def __setstate__(self, d):
+        # This is called when being unpickled/loaded.
+        self.__dict__ = d
+
+    def __call__(self, *args, **kwargs) -> 'Proxy':
+        return self.tracer.create_proxy('call_method', '__call__', (self,) + args, kwargs)
+
+    def __iter__(self) -> Iterator['Proxy']:
+        frame = inspect.currentframe()
+        assert frame is not None
+        calling_frame = frame.f_back
+        assert calling_frame is not None
+        inst_list = list(dis.get_instructions(calling_frame.f_code))
+        if sys.version_info >= (3, 11):
+            from bisect import bisect_left
+            inst_idx = bisect_left(inst_list, calling_frame.f_lasti, key=lambda x: x.offset)
+        else:
+            inst_idx = calling_frame.f_lasti // 2
+        inst = inst_list[inst_idx]
+        if inst.opname == 'UNPACK_SEQUENCE':
+            return (self[i] for i in range(inst.argval))  # type: ignore[index]
+
+        return self.tracer.iter(self)
+
+    def __abs__(self):
+        return self.tracer.create_proxy('call_function', operator.abs, (self,), {})
+
+    def __bool__(self) -> bool:
+        if self.tracer.trace_asserts:
+            # check if this boolean is used in an assertion, bytecode pattern for assertions
+            # is pretty stable for Python 3.7--3.9
+            frame = inspect.currentframe()
+            assert frame is not None
+            calling_frame = frame.f_back
+            assert calling_frame is not None
+            insts = list(dis.get_instructions(calling_frame.f_code))
+            if sys.version_info >= (3, 11):
+                from bisect import bisect_left
+                cur = bisect_left(insts, calling_frame.f_lasti, key=lambda x: x.offset)
+            else:
+                cur = calling_frame.f_lasti // 2
+            inst = insts[cur]
+
+            if inst.opname == 'POP_JUMP_IF_TRUE':
+                first = insts[cur + 1]
+                assert inst.arg is not None
+                last = insts[inst.arg // 2 - 1]
+                starts_with_assert = (first.opname == 'LOAD_GLOBAL' and first.argval == 'AssertionError'
+                                      or first.opname == 'LOAD_ASSERTION_ERROR')
+                if starts_with_assert and last.opname == 'RAISE_VARARGS':
+                    self.tracer.create_proxy('call_function', assert_fn, (self,), {})
+                    return True
+
+        return self.tracer.to_bool(self)
+
+    @compatibility(is_backward_compatible=True)
+    def keys(self):
+        return self.tracer.keys(self)
+
+    def __len__(self):
+        raise RuntimeError("'len' is not supported in symbolic tracing by default. If you want "
+                           "this call to be recorded, please call torch.fx.wrap('len') at "
+                           "module scope")
+
+    @classmethod
+    def __torch_function__(cls, orig_method, types, args=None, kwargs=None):
+        args = args if args else ()
+        kwargs = kwargs if kwargs else {}
+
+        tracers : Dict[Any, None] = {}
+
+        def find_tracer(a):
+            if isinstance(a, cls):
+                tracers[a.tracer] = None
+        torch.fx.node.map_aggregate(args, find_tracer)
+        torch.fx.node.map_aggregate(kwargs, find_tracer)
+
+        if len(tracers) > 1:
+            raise RuntimeError(f'Found multiple different tracers {list(tracers.keys())} while '
+                               f'trying to trace operations {orig_method}')
+        tracer = next(iter(tracers.keys()))
+
+        if isinstance(orig_method, torch._C.ScriptMethod):
+            args = (orig_method.owner,) + args
+            return tracer.create_proxy('call_method', orig_method.name, args, kwargs)
+        if torch.overrides.is_tensor_method_or_property(orig_method):
+            return tracer.create_proxy('call_method', orig_method.__name__, args, kwargs)
+        else:
+            if isinstance(orig_method, torch._ops.HigherOrderOperator):
+                # TODO: Define how to symbolically trace HigherOrderOperators
+                raise RuntimeError("Unable to symbolically trace HigherOrderOperators")
+            return tracer.create_proxy('call_function', orig_method, args, kwargs,
+                                       name=tracer.graph._target_to_str(orig_method.__name__))
+
+
+@compatibility(is_backward_compatible=True)
+class Attribute(Proxy):
+    @compatibility(is_backward_compatible=True)
+    def __init__(self, root: Proxy, attr: str):
+        self.root = root
+        self.attr = attr
+        self.tracer = root.tracer
+        self._node: Optional[Node] = None
+
+    @property
+    def node(self):
+        # the node for attributes is added lazily, since most will just be method calls
+        # which do not rely on the getitem call
+        if self._node is None:
+            self._node = self.tracer.create_proxy('call_function', getattr, (self.root, self.attr), {}).node
+        return self._node
+
+    def __call__(self, *args, **kwargs):
+        return self.tracer.create_proxy('call_method', self.attr, (self.root,) + args, kwargs)
+
+
+@compatibility(is_backward_compatible=False)
+class ParameterProxy(Proxy):
+    """
+    A special proxy which lets "shape", "size", "dim", and a few other
+    attribute accesses pass through to the underlying  module parameter object,
+    so that conditional tests on these attributes will not throw exception during tracing
+    """
+    def __init__(self, tracer: TracerBase, node: Node, name, param):
+        super().__init__(node, tracer)
+        assert isinstance(param, torch.nn.Parameter)
+        self.param = param
+        self.name = name
+
+    def __repr__(self) -> str:
+        return f'ParameterProxy({self.name})'
+
+    @property
+    def shape(self):
+        return self.param.shape
+
+    def size(self):
+        return self.param.size()
+
+    def dim(self):
+        return self.param.dim()
+
+    @property
+    def ndim(self):
+        return self.param.ndim
+
+    def numel(self):
+        return self.param.numel()
+
+    def nelement(self):
+        return self.param.nelement()
+
+
+for method in magic_methods:
+    def _scope(method):
+        def impl(*args, **kwargs):
+            tracer = args[0].tracer
+            target = getattr(operator, method)
+            return tracer.create_proxy('call_function', target, args, kwargs)
+        impl.__name__ = method
+        as_magic = f'__{method.strip("_")}__'
+        setattr(Proxy, as_magic, impl)
+    _scope(method)
+
+def _define_reflectable(orig_method_name):
+    method_name = f'__r{orig_method_name.strip("_")}__'
+
+    def impl(self, rhs):
+        target = getattr(operator, orig_method_name)
+        return self.tracer.create_proxy('call_function', target, (rhs, self), {})
+    impl.__name__ = method_name
+    impl.__qualname__ = method_name
+    setattr(Proxy, method_name, impl)
+
+for orig_method_name in reflectable_magic_methods:
+    _define_reflectable(orig_method_name)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/subgraph_rewriter.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/subgraph_rewriter.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f2cb743d2cdd9ba09c605432a144ccba97327da
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/subgraph_rewriter.py
@@ -0,0 +1,348 @@
+from .graph_module import GraphModule
+from .graph import Graph
+from .node import Node
+from ._symbolic_trace import symbolic_trace
+from ._compatibility import compatibility
+
+import copy
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, List, NamedTuple, Optional, Set, Union, TYPE_CHECKING
+import torch
+
+if TYPE_CHECKING:
+    from .passes.utils.matcher_with_name_node_map_utils import InternalMatch
+
+__all__ = ['Match', 'replace_pattern', 'replace_pattern_with_filters', "ReplacedPatterns"]
+
+@compatibility(is_backward_compatible=True)
+class Match(NamedTuple):
+    # Node from which the match was found
+    anchor: Node
+    # Maps nodes in the pattern subgraph to nodes in the larger graph
+    nodes_map: Dict[Node, Node]
+
+@compatibility(is_backward_compatible=False)
+@dataclass
+class ReplacedPatterns:
+    # Node from which the match was found
+    anchor: Node
+    # Maps nodes in the pattern subgraph to nodes in the larger graph
+    nodes_map: Dict[Node, Node]
+    # List of nodes that were added into the graph
+    replacements: List[Node]
+
+def _replace_attributes(gm: GraphModule, replacement: torch.nn.Module) -> None:
+    gm.delete_all_unused_submodules()
+
+    if isinstance(replacement, GraphModule):
+        replacement.graph.lint()
+
+    def try_get_attr(gm: torch.nn.Module, target: str) -> Optional[Any]:
+        module_path, _, attr_name = target.rpartition(".")
+        try:
+            mod: torch.nn.Module = gm.get_submodule(module_path)
+        except AttributeError:
+            return None
+        attr = getattr(mod, attr_name, None)
+        return attr
+
+    for node in gm.graph.nodes:
+        if node.op == "call_module" or node.op == "get_attr":
+
+            gm_attr = try_get_attr(gm, node.target)
+            replacement_attr = try_get_attr(replacement, node.target)
+
+            # CASE 1: This target already exists as an attribute in our
+            # result GraphModule. Whether or not it exists in
+            # `replacement`, the existing submodule takes precedence.
+            if gm_attr is not None:
+                continue
+
+            # CASE 2: The target exists as an attribute in `replacement`
+            # only, so we need to copy it over.
+            elif replacement_attr is not None:
+                new_attr = copy.deepcopy(replacement_attr)
+                if isinstance(replacement_attr, torch.nn.Module):
+                    gm.add_submodule(node.target, new_attr)
+                else:
+                    setattr(gm, node.target, new_attr)
+
+            # CASE 3: The target doesn't exist as an attribute in `gm`
+            # or `replacement`
+            else:
+                raise RuntimeError('Attempted to create a "', node.op,
+                                   '" node during subgraph rewriting '
+                                   f"with target {node.target}, but "
+                                   "the referenced attribute does not "
+                                   "exist in the replacement GraphModule")
+
+    gm.graph.lint()
+
+
+@compatibility(is_backward_compatible=True)
+def replace_pattern(
+    gm: GraphModule,
+    pattern: Union[Callable, GraphModule],
+    replacement: Union[Callable, GraphModule]
+) -> List[Match]:
+    """
+    Matches all possible non-overlapping sets of operators and their
+    data dependencies (``pattern``) in the Graph of a GraphModule
+    (``gm``), then replaces each of these matched subgraphs with another
+    subgraph (``replacement``).
+
+    Args:
+        ``gm``: The GraphModule that wraps the Graph to operate on
+        ``pattern``: The subgraph to match in ``gm`` for replacement
+        ``replacement``: The subgraph to replace ``pattern`` with
+
+    Returns:
+        List[Match]: A list of ``Match`` objects representing the places
+        in the original graph that ``pattern`` was matched to. The list
+        is empty if there are no matches. ``Match`` is defined as:
+
+        .. code-block:: python
+
+            class Match(NamedTuple):
+                # Node from which the match was found
+                anchor: Node
+                # Maps nodes in the pattern subgraph to nodes in the larger graph
+                nodes_map: Dict[Node, Node]
+
+    Examples:
+
+    .. code-block:: python
+
+        import torch
+        from torch.fx import symbolic_trace, subgraph_rewriter
+
+        class M(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+
+            def forward(self, x, w1, w2):
+                m1 = torch.cat([w1, w2]).sum()
+                m2 = torch.cat([w1, w2]).sum()
+                return x + torch.max(m1) + torch.max(m2)
+
+        def pattern(w1, w2):
+            return torch.cat([w1, w2]).sum()
+
+        def replacement(w1, w2):
+            return torch.stack([w1, w2])
+
+        traced_module = symbolic_trace(M())
+
+        subgraph_rewriter.replace_pattern(traced_module, pattern, replacement)
+
+    The above code will first match ``pattern`` in the ``forward``
+    method of ``traced_module``. Pattern-matching is done based on
+    use-def relationships, not node names. For example, if you had
+    ``p = torch.cat([a, b])`` in ``pattern``, you could match
+    ``m = torch.cat([a, b])`` in the original ``forward`` function,
+    despite the variable names being different (``p`` vs ``m``).
+
+    The ``return`` statement in ``pattern`` is matched based on its
+    value only; it may or may not match to the ``return`` statement in
+    the larger graph. In other words, the pattern doesn't have to extend
+    to the end of the larger graph.
+
+    When the pattern is matched, it will be removed from the larger
+    function and replaced by ``replacement``. If there are multiple
+    matches for ``pattern`` in the larger function, each non-overlapping
+    match will be replaced. In the case of a match overlap, the first
+    found match in the set of overlapping matches will be replaced.
+    ("First" here being defined as the first in a topological ordering
+    of the Nodes' use-def relationships. In most cases, the first Node
+    is the parameter that appears directly after ``self``, while the
+    last Node is whatever the function returns.)
+
+    One important thing to note is that the parameters of the
+    ``pattern`` Callable must be used in the Callable itself,
+    and the parameters of the ``replacement`` Callable must match
+    the pattern. The first rule is why, in the above code block, the
+    ``forward`` function has parameters ``x, w1, w2``, but the
+    ``pattern`` function only has parameters ``w1, w2``. ``pattern``
+    doesn't use ``x``, so it shouldn't specify ``x`` as a parameter.
+    As an example of the second rule, consider replacing
+
+    .. code-block:: python
+
+        def pattern(x, y):
+            return torch.neg(x) + torch.relu(y)
+
+    with
+
+    .. code-block:: python
+
+        def replacement(x, y):
+            return torch.relu(x)
+
+    In this case, ``replacement`` needs the same number of parameters
+    as ``pattern`` (both ``x`` and ``y``), even though the parameter
+    ``y`` isn't used in ``replacement``.
+
+    After calling ``subgraph_rewriter.replace_pattern``, the generated
+    Python code looks like this:
+
+    .. code-block:: python
+
+        def forward(self, x, w1, w2):
+            stack_1 = torch.stack([w1, w2])
+            sum_1 = stack_1.sum()
+            stack_2 = torch.stack([w1, w2])
+            sum_2 = stack_2.sum()
+            max_1 = torch.max(sum_1)
+            add_1 = x + max_1
+            max_2 = torch.max(sum_2)
+            add_2 = add_1 + max_2
+            return add_2
+    """
+    match_and_replacements = _replace_pattern(gm, pattern, replacement)
+    return [Match(anchor=m.anchor, nodes_map=m.nodes_map) for m in match_and_replacements]
+
+
+# Experimental API, not backward compatible
+@compatibility(is_backward_compatible=False)
+def replace_pattern_with_filters(
+    gm: GraphModule,
+    pattern: Union[Callable, Graph, GraphModule],
+    replacement: Union[Callable, Graph, GraphModule],
+    match_filters: Optional[List[Callable[["InternalMatch", Graph, Graph], bool]]] = None,
+    ignore_literals: bool = False,
+) -> List[ReplacedPatterns]:
+    """
+    See replace_pattern for documentation. This function is an overload with an additional match_filter argument.
+
+    Args:
+        ``match_filters``: A list of functions that take in
+            (match: InternalMatch, original_graph: Graph, pattern_graph: Graph) and return a boolean indicating
+            whether the match satisfies the condition.
+            See matcher_utils.py for definition of InternalMatch.
+    """
+
+    return _replace_pattern(gm, pattern, replacement, match_filters, ignore_literals)
+
+
+def _replace_pattern(
+    gm: GraphModule,
+    pattern: Union[Callable, Graph, GraphModule],
+    replacement: Union[Callable, Graph, GraphModule],
+    match_filters: Optional[List[Callable[["InternalMatch", Graph, Graph], bool]]] = None,
+    ignore_literals: bool = False,
+) -> List[ReplacedPatterns]:
+
+    from torch.fx.passes.utils.matcher_utils import SubgraphMatcher, InternalMatch
+
+    if match_filters is None:
+        match_filters = []
+
+    # Get the graphs for `gm`, `pattern`, `replacement`
+    original_graph: Graph = gm.graph
+
+    if isinstance(pattern, GraphModule):
+        pattern_graph = pattern.graph
+    elif isinstance(pattern, Graph):
+        pattern_graph = pattern
+    else:
+        pattern_graph = symbolic_trace(pattern).graph
+
+    if isinstance(replacement, GraphModule):
+        replacement_graph = replacement.graph
+    elif isinstance(replacement, Graph):
+        replacement_graph = replacement
+    else:
+        replacement_graph = symbolic_trace(replacement).graph
+
+    matcher = SubgraphMatcher(pattern_graph, match_output=False, match_placeholder=False,
+                              remove_overlapping_matches=True, ignore_literals=ignore_literals)
+    _matches: List[InternalMatch] = matcher.match(original_graph)
+
+    # Filter out matches that don't match the filter
+    _matches = [
+        m for m in _matches
+        if all(match_filter(m, original_graph, pattern_graph)
+               for match_filter in match_filters)
+    ]
+
+    replacement_placeholders = [n for n in replacement_graph.nodes if n.op == "placeholder"]
+
+    # As we progressively replace nodes, we'll need to keep track of how the match results should change
+    match_changed_node: Dict[Node, Node] = {}
+
+    match_and_replacements = []
+    for match in _matches:
+
+        # Build connecting between replacement graph's input and original graph input producer node
+
+        # Initialize `val_map` with mappings from placeholder nodes in
+        # `replacement` to their corresponding node in `original_graph`
+        assert len(match.placeholder_nodes) == len(replacement_placeholders)
+        val_map: Dict[Node, Node] = {}
+        for rn, gn in zip(replacement_placeholders, match.placeholder_nodes):
+            if isinstance(gn, Node):
+                val_map[rn] = match_changed_node.get(gn, gn)
+                if gn != val_map[rn]:
+                    # Update match.placeholder_nodes and match.nodes_map with the node that replaced gn
+                    gn_ind = match.placeholder_nodes.index(gn)
+                    match.placeholder_nodes[gn_ind] = match_changed_node[gn]
+                    map_key = list(match.nodes_map.keys())[list(match.nodes_map.values()).index(gn)]
+                    match.nodes_map[map_key] = match_changed_node[gn]
+            else:
+                val_map[rn] = gn
+
+        # Copy the replacement graph over
+        user_nodes: Set[Node] = set()
+        for n in match.returning_nodes:
+            user_nodes.update(n.users)
+        assert user_nodes, "The returning_nodes should have at least one user node"
+
+        if len(user_nodes) == 1:
+            first_user_node = next(iter(user_nodes))
+        else:
+            # If there are multiple user nodes, we need to find the first user node
+            # in the current execution order of the `original_graph`
+            for n in original_graph.nodes:
+                if n in user_nodes:
+                    first_user_node = n
+                    break
+
+        with original_graph.inserting_before(first_user_node):  # type: ignore[possibly-undefined]
+            copied_returning_nodes = original_graph.graph_copy(replacement_graph, val_map)
+
+        if isinstance(copied_returning_nodes, Node):
+            copied_returning_nodes = (copied_returning_nodes, )
+
+        # Get a list of nodes that have been replaced into the graph
+        replacement_nodes: List[Node] = [v for v in val_map.values() if v not in match.placeholder_nodes]
+
+        # Hook the output Node of the replacement subgraph in to the
+        # original Graph at the correct location
+        assert len(match.returning_nodes) == len(copied_returning_nodes)  # type: ignore[arg-type]
+        for gn, copied_node in zip(match.returning_nodes, copied_returning_nodes):  # type: ignore[arg-type]
+            gn.replace_all_uses_with(copied_node)
+            match_changed_node[gn] = copied_node
+        # Remove the original nodes
+        for node in reversed(pattern_graph.nodes):
+            if node.op != "placeholder" and node.op != "output":
+                gn = match.nodes_map[node]
+                gm.graph.erase_node(gn)
+
+        match_and_replacements.append(
+            ReplacedPatterns(
+                anchor=match.anchors[0],
+                nodes_map=match.nodes_map,
+                replacements=replacement_nodes
+            )
+        )
+
+    # Update the passed-in GraphModule to reflect the new state of
+    # `original_graph`
+    gm.recompile()
+
+    # If `replacement` was an nn.Module, we'll need to make sure that
+    # all the submodules have been copied over correctly
+    if isinstance(replacement, torch.nn.Module):
+        _replace_attributes(gm, replacement)
+
+    return match_and_replacements
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/tensor_type.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/tensor_type.py
new file mode 100644
index 0000000000000000000000000000000000000000..83b5a9f8faf65e86813d55926ef275fa19ef2013
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/tensor_type.py
@@ -0,0 +1,105 @@
+# mypy: allow-untyped-defs
+from torch.fx.experimental.unification import Var  # type: ignore[attr-defined]
+
+from ._compatibility import compatibility
+
+
+@compatibility(is_backward_compatible=False)
+class TensorType:
+    """
+    TensorType defines a type for tensors, which consists of a list of dimensions.
+    Example:
+        class M(torch.nn.Module):
+            def forward(self, x:TensorType((1,2,3, Dyn)), y:TensorType((1,2,3, Dyn))):
+                return torch.add(x, y)
+    """
+
+    def __init__(self, dim):
+        self.__origin__ = TensorType
+        self.__args__ = dim
+
+    def __repr__(self):
+        return f'TensorType[{self.__args__}]'
+
+    def __eq__(self, other):
+        if isinstance(other, self.__class__):
+            return list(self.__args__) == list(other.__args__)
+        else:
+            return False
+
+    @staticmethod
+    def __class_getitem__(*args):
+        if len(args) == 1 and isinstance(args[0], tuple):
+            args = args[0]
+        return TensorType(tuple(args))
+
+
+class _DynType:
+    """
+    _DynType defines a type which stands for the absence of type information.
+    """
+    def __init__(self) -> None:
+        self.__name__ = '_DynType'
+
+    def __eq__(self, other):
+        return isinstance(other, self.__class__)
+
+    def __str__(self):
+        return "Dyn"
+
+    def __repr__(self):
+        return "Dyn"
+
+
+Dyn = _DynType()
+
+@compatibility(is_backward_compatible=False)
+def is_consistent(t1, t2):
+    """
+    A binary relation denoted by ~ that determines if t1 is consistent with t2.
+    The relation is reflexive, symmetric but not transitive.
+    returns True if t1 and t2 are consistent and False otherwise.
+    Example:
+        Dyn ~ TensorType((1,2,3))
+        int ~ Dyn
+        int ~ int
+        TensorType((1,Dyn,3)) ~ TensorType((1,2,3))
+    """
+
+    if t1 == t2:
+        return True
+
+    if t1 == Dyn or t2 == Dyn or isinstance(t1, Var) or isinstance(t2, Var):
+        return True
+
+    if isinstance(t1, TensorType) and isinstance(t2, TensorType):
+        return len(t1.__args__) == len(t2.__args__) and \
+            all(is_consistent(elem1, elem2) for elem1, elem2 in zip(t1.__args__, t2.__args__))
+    else:
+        return False
+
+
+@compatibility(is_backward_compatible=False)
+def is_more_precise(t1, t2):
+    """
+    A binary relation denoted by <= that determines if t1 is more precise than t2.
+    The relation is reflexive and transitive.
+    returns True if t1 is more precise than t2 and False otherwise.
+    Example:
+        Dyn >= TensorType((1,2,3))
+        int >= Dyn
+        int >= int
+        TensorType((1,Dyn,3)) <= TensorType((1,2,3))
+    """
+    if t1 == t2:
+        return True
+
+    if isinstance(t2, _DynType):
+        return True
+
+    if isinstance(t1, TensorType) and isinstance(t2, TensorType):
+        return len(t1.__args__) == len(t2.__args__) and \
+            all(is_more_precise(elem1, elem2) for elem1, elem2 in zip(t1.__args__, t2.__args__))
+
+    else:
+        return False
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/traceback.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/traceback.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e72a8011f63ab44a64b8ece6ccbe4b2875fdda6
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/fx/traceback.py
@@ -0,0 +1,104 @@
+# mypy: allow-untyped-defs
+import traceback
+from contextlib import contextmanager
+from typing import List, Any, Dict
+from ._compatibility import compatibility
+
+__all__ = ['preserve_node_meta', 'has_preserved_node_meta',
+           'set_stack_trace', 'set_grad_fn_seq_nr', 'reset_grad_fn_seq_nr',
+           'format_stack', 'set_current_meta', 'get_current_meta']
+
+current_meta: Dict[str, Any] = {}
+should_preserve_node_meta = False
+
+
+@compatibility(is_backward_compatible=False)
+@contextmanager
+def preserve_node_meta():
+    global should_preserve_node_meta
+    global current_meta
+
+    saved_should_preserve_node_meta = should_preserve_node_meta
+    # Shallow copy is OK since fields of current_meta are not mutated
+    saved_current_meta = current_meta.copy()
+    try:
+        should_preserve_node_meta = True
+        yield
+    finally:
+        should_preserve_node_meta = saved_should_preserve_node_meta
+        current_meta = saved_current_meta
+
+
+@compatibility(is_backward_compatible=False)
+def set_stack_trace(stack : List[str]):
+    global current_meta
+
+    if should_preserve_node_meta and stack:
+        current_meta["stack_trace"] = "".join(stack)
+
+
+@compatibility(is_backward_compatible=False)
+def set_grad_fn_seq_nr(seq_nr):
+    global current_meta
+
+    if should_preserve_node_meta:
+        # The seq_nr is captured by eager mode in the grad_fn during forward
+        current_meta["grad_fn_seq_nr"] = current_meta.get("grad_fn_seq_nr", []) + [seq_nr]
+        current_meta["in_grad_fn"] = current_meta.get("in_grad_fn", 0) + 1
+
+
+@compatibility(is_backward_compatible=False)
+def reset_grad_fn_seq_nr():
+    # NB: reset state properly, this would be helpful towards supporting
+    #     reentrant autograd if we actually wanted to do that.
+    global current_meta
+    if should_preserve_node_meta:
+        current_level = current_meta.get("in_grad_fn", 0)
+        assert current_level > 0
+        if current_level == 1:
+            del current_meta["in_grad_fn"]
+            del current_meta["grad_fn_seq_nr"]
+        else:
+            current_meta["in_grad_fn"] = current_level - 1
+            current_meta["grad_fn_seq_nr"] = current_meta["grad_fn_seq_nr"][:-1]
+
+
+@compatibility(is_backward_compatible=False)
+def format_stack() -> List[str]:
+    if should_preserve_node_meta:
+        return [current_meta.get("stack_trace", "")]
+    else:
+        # fallback to traceback.format_stack()
+        return traceback.format_list(traceback.extract_stack()[:-1])
+
+
+@compatibility(is_backward_compatible=False)
+def has_preserved_node_meta() -> bool:
+    return should_preserve_node_meta
+
+
+@compatibility(is_backward_compatible=False)
+@contextmanager
+def set_current_meta(node):
+    global current_meta
+    if should_preserve_node_meta and node.meta:
+        saved_meta = current_meta
+        try:
+            current_meta = node.meta.copy()
+
+            # Append (node.name, node.target) onto "from_node" for provenance tracking
+            if "from_node" not in current_meta:
+                current_meta["from_node"] = [(node.name, node.target)]
+            elif current_meta["from_node"][-1][0] != node.name:
+                current_meta["from_node"] = current_meta["from_node"] + [(node.name, node.target)]
+
+            yield
+        finally:
+            current_meta = saved_meta
+    else:
+        yield
+
+
+@compatibility(is_backward_compatible=False)
+def get_current_meta() -> Dict[str, Any]:
+    return current_meta
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/clog.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/clog.h
new file mode 100644
index 0000000000000000000000000000000000000000..bf09cd0cb6de4ff632807ad2e58df9e402906878
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/clog.h
@@ -0,0 +1,123 @@
+/*
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include 
+#include 
+#include 
+
+#define CLOG_NONE 0
+#define CLOG_FATAL 1
+#define CLOG_ERROR 2
+#define CLOG_WARNING 3
+#define CLOG_INFO 4
+#define CLOG_DEBUG 5
+
+#ifndef CLOG_VISIBILITY
+#if defined(__ELF__)
+#define CLOG_VISIBILITY __attribute__((__visibility__("internal")))
+#elif defined(__MACH__)
+#define CLOG_VISIBILITY __attribute__((__visibility__("hidden")))
+#else
+#define CLOG_VISIBILITY
+#endif
+#endif
+
+#ifndef CLOG_ARGUMENTS_FORMAT
+#if defined(__GNUC__)
+#define CLOG_ARGUMENTS_FORMAT __attribute__((__format__(__printf__, 1, 2)))
+#else
+#define CLOG_ARGUMENTS_FORMAT
+#endif
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+CLOG_VISIBILITY void clog_vlog_debug(
+    const char* module,
+    const char* format,
+    va_list args);
+CLOG_VISIBILITY void clog_vlog_info(
+    const char* module,
+    const char* format,
+    va_list args);
+CLOG_VISIBILITY void clog_vlog_warning(
+    const char* module,
+    const char* format,
+    va_list args);
+CLOG_VISIBILITY void clog_vlog_error(
+    const char* module,
+    const char* format,
+    va_list args);
+CLOG_VISIBILITY void clog_vlog_fatal(
+    const char* module,
+    const char* format,
+    va_list args);
+
+#define CLOG_DEFINE_LOG_DEBUG(log_debug_function_name, module, level)   \
+  CLOG_ARGUMENTS_FORMAT                                                 \
+  inline static void log_debug_function_name(const char* format, ...) { \
+    if (level >= CLOG_DEBUG) {                                          \
+      va_list args;                                                     \
+      va_start(args, format);                                           \
+      clog_vlog_debug(module, format, args);                            \
+      va_end(args);                                                     \
+    }                                                                   \
+  }
+
+#define CLOG_DEFINE_LOG_INFO(log_info_function_name, module, level)    \
+  CLOG_ARGUMENTS_FORMAT                                                \
+  inline static void log_info_function_name(const char* format, ...) { \
+    if (level >= CLOG_INFO) {                                          \
+      va_list args;                                                    \
+      va_start(args, format);                                          \
+      clog_vlog_info(module, format, args);                            \
+      va_end(args);                                                    \
+    }                                                                  \
+  }
+
+#define CLOG_DEFINE_LOG_WARNING(log_warning_function_name, module, level) \
+  CLOG_ARGUMENTS_FORMAT                                                   \
+  inline static void log_warning_function_name(const char* format, ...) { \
+    if (level >= CLOG_WARNING) {                                          \
+      va_list args;                                                       \
+      va_start(args, format);                                             \
+      clog_vlog_warning(module, format, args);                            \
+      va_end(args);                                                       \
+    }                                                                     \
+  }
+
+#define CLOG_DEFINE_LOG_ERROR(log_error_function_name, module, level)   \
+  CLOG_ARGUMENTS_FORMAT                                                 \
+  inline static void log_error_function_name(const char* format, ...) { \
+    if (level >= CLOG_ERROR) {                                          \
+      va_list args;                                                     \
+      va_start(args, format);                                           \
+      clog_vlog_error(module, format, args);                            \
+      va_end(args);                                                     \
+    }                                                                   \
+  }
+
+#define CLOG_DEFINE_LOG_FATAL(log_fatal_function_name, module, level)   \
+  CLOG_ARGUMENTS_FORMAT                                                 \
+  inline static void log_fatal_function_name(const char* format, ...) { \
+    if (level >= CLOG_FATAL) {                                          \
+      va_list args;                                                     \
+      va_start(args, format);                                           \
+      clog_vlog_fatal(module, format, args);                            \
+      va_end(args);                                                     \
+    }                                                                   \
+    abort();                                                            \
+  }
+
+#ifdef __cplusplus
+} /* extern "C" */
+#endif
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/cpuinfo.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/cpuinfo.h
new file mode 100644
index 0000000000000000000000000000000000000000..8bb1db4e96470883bcc04a0d803770a3fc093015
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/cpuinfo.h
@@ -0,0 +1,2245 @@
+#pragma once
+#ifndef CPUINFO_H
+#define CPUINFO_H
+
+#ifndef __cplusplus
+#include 
+#endif
+
+#ifdef __APPLE__
+#include 
+#endif
+
+#include 
+
+/* Identify architecture and define corresponding macro */
+
+#if defined(__i386__) || defined(__i486__) || defined(__i586__) || defined(__i686__) || defined(_M_IX86)
+#define CPUINFO_ARCH_X86 1
+#endif
+
+#if defined(__x86_64__) || defined(__x86_64) || defined(_M_X64) || defined(_M_AMD64)
+#define CPUINFO_ARCH_X86_64 1
+#endif
+
+#if defined(__arm__) || defined(_M_ARM)
+#define CPUINFO_ARCH_ARM 1
+#endif
+
+#if defined(__aarch64__) || defined(_M_ARM64)
+#define CPUINFO_ARCH_ARM64 1
+#endif
+
+#if defined(__PPC64__) || defined(__powerpc64__) || defined(_ARCH_PPC64)
+#define CPUINFO_ARCH_PPC64 1
+#endif
+
+#if defined(__asmjs__)
+#define CPUINFO_ARCH_ASMJS 1
+#endif
+
+#if defined(__wasm__)
+#if defined(__wasm_simd128__)
+#define CPUINFO_ARCH_WASMSIMD 1
+#else
+#define CPUINFO_ARCH_WASM 1
+#endif
+#endif
+
+#if defined(__riscv)
+#if (__riscv_xlen == 32)
+#define CPUINFO_ARCH_RISCV32 1
+#elif (__riscv_xlen == 64)
+#define CPUINFO_ARCH_RISCV64 1
+#endif
+#endif
+
+/* Define other architecture-specific macros as 0 */
+
+#ifndef CPUINFO_ARCH_X86
+#define CPUINFO_ARCH_X86 0
+#endif
+
+#ifndef CPUINFO_ARCH_X86_64
+#define CPUINFO_ARCH_X86_64 0
+#endif
+
+#ifndef CPUINFO_ARCH_ARM
+#define CPUINFO_ARCH_ARM 0
+#endif
+
+#ifndef CPUINFO_ARCH_ARM64
+#define CPUINFO_ARCH_ARM64 0
+#endif
+
+#ifndef CPUINFO_ARCH_PPC64
+#define CPUINFO_ARCH_PPC64 0
+#endif
+
+#ifndef CPUINFO_ARCH_ASMJS
+#define CPUINFO_ARCH_ASMJS 0
+#endif
+
+#ifndef CPUINFO_ARCH_WASM
+#define CPUINFO_ARCH_WASM 0
+#endif
+
+#ifndef CPUINFO_ARCH_WASMSIMD
+#define CPUINFO_ARCH_WASMSIMD 0
+#endif
+
+#ifndef CPUINFO_ARCH_RISCV32
+#define CPUINFO_ARCH_RISCV32 0
+#endif
+
+#ifndef CPUINFO_ARCH_RISCV64
+#define CPUINFO_ARCH_RISCV64 0
+#endif
+
+#if CPUINFO_ARCH_X86 && defined(_MSC_VER)
+#define CPUINFO_ABI __cdecl
+#elif CPUINFO_ARCH_X86 && defined(__GNUC__)
+#define CPUINFO_ABI __attribute__((__cdecl__))
+#else
+#define CPUINFO_ABI
+#endif
+
+#define CPUINFO_CACHE_UNIFIED 0x00000001
+#define CPUINFO_CACHE_INCLUSIVE 0x00000002
+#define CPUINFO_CACHE_COMPLEX_INDEXING 0x00000004
+
+struct cpuinfo_cache {
+	/** Cache size in bytes */
+	uint32_t size;
+	/** Number of ways of associativity */
+	uint32_t associativity;
+	/** Number of sets */
+	uint32_t sets;
+	/** Number of partitions */
+	uint32_t partitions;
+	/** Line size in bytes */
+	uint32_t line_size;
+	/**
+	 * Binary characteristics of the cache (unified cache, inclusive cache,
+	 * cache with complex indexing).
+	 *
+	 * @see CPUINFO_CACHE_UNIFIED, CPUINFO_CACHE_INCLUSIVE,
+	 * CPUINFO_CACHE_COMPLEX_INDEXING
+	 */
+	uint32_t flags;
+	/** Index of the first logical processor that shares this cache */
+	uint32_t processor_start;
+	/** Number of logical processors that share this cache */
+	uint32_t processor_count;
+};
+
+struct cpuinfo_trace_cache {
+	uint32_t uops;
+	uint32_t associativity;
+};
+
+#define CPUINFO_PAGE_SIZE_4KB 0x1000
+#define CPUINFO_PAGE_SIZE_1MB 0x100000
+#define CPUINFO_PAGE_SIZE_2MB 0x200000
+#define CPUINFO_PAGE_SIZE_4MB 0x400000
+#define CPUINFO_PAGE_SIZE_16MB 0x1000000
+#define CPUINFO_PAGE_SIZE_1GB 0x40000000
+
+struct cpuinfo_tlb {
+	uint32_t entries;
+	uint32_t associativity;
+	uint64_t pages;
+};
+
+/** Vendor of processor core design */
+enum cpuinfo_vendor {
+	/** Processor vendor is not known to the library, or the library failed
+	   to get vendor information from the OS. */
+	cpuinfo_vendor_unknown = 0,
+
+	/* Active vendors of modern CPUs */
+
+	/**
+	 * Intel Corporation. Vendor of x86, x86-64, IA64, and ARM processor
+	 * microarchitectures.
+	 *
+	 * Sold its ARM design subsidiary in 2006. The last ARM processor design
+	 * was released in 2004.
+	 */
+	cpuinfo_vendor_intel = 1,
+	/** Advanced Micro Devices, Inc. Vendor of x86 and x86-64 processor
+	   microarchitectures. */
+	cpuinfo_vendor_amd = 2,
+	/** ARM Holdings plc. Vendor of ARM and ARM64 processor
+	   microarchitectures. */
+	cpuinfo_vendor_arm = 3,
+	/** Qualcomm Incorporated. Vendor of ARM and ARM64 processor
+	   microarchitectures. */
+	cpuinfo_vendor_qualcomm = 4,
+	/** Apple Inc. Vendor of ARM and ARM64 processor microarchitectures. */
+	cpuinfo_vendor_apple = 5,
+	/** Samsung Electronics Co., Ltd. Vendir if ARM64 processor
+	   microarchitectures. */
+	cpuinfo_vendor_samsung = 6,
+	/** Nvidia Corporation. Vendor of ARM64-compatible processor
+	   microarchitectures. */
+	cpuinfo_vendor_nvidia = 7,
+	/** MIPS Technologies, Inc. Vendor of MIPS processor microarchitectures.
+	 */
+	cpuinfo_vendor_mips = 8,
+	/** International Business Machines Corporation. Vendor of PowerPC
+	   processor microarchitectures. */
+	cpuinfo_vendor_ibm = 9,
+	/** Ingenic Semiconductor. Vendor of MIPS processor microarchitectures.
+	 */
+	cpuinfo_vendor_ingenic = 10,
+	/**
+	 * VIA Technologies, Inc. Vendor of x86 and x86-64 processor
+	 * microarchitectures.
+	 *
+	 * Processors are designed by Centaur Technology, a subsidiary of VIA
+	 * Technologies.
+	 */
+	cpuinfo_vendor_via = 11,
+	/** Cavium, Inc. Vendor of ARM64 processor microarchitectures. */
+	cpuinfo_vendor_cavium = 12,
+	/** Broadcom, Inc. Vendor of ARM processor microarchitectures. */
+	cpuinfo_vendor_broadcom = 13,
+	/** Applied Micro Circuits Corporation (APM). Vendor of ARM64 processor
+	   microarchitectures. */
+	cpuinfo_vendor_apm = 14,
+	/**
+	 * Huawei Technologies Co., Ltd. Vendor of ARM64 processor
+	 * microarchitectures.
+	 *
+	 * Processors are designed by HiSilicon, a subsidiary of Huawei.
+	 */
+	cpuinfo_vendor_huawei = 15,
+	/**
+	 * Hygon (Chengdu Haiguang Integrated Circuit Design Co., Ltd), Vendor
+	 * of x86-64 processor microarchitectures.
+	 *
+	 * Processors are variants of AMD cores.
+	 */
+	cpuinfo_vendor_hygon = 16,
+	/** SiFive, Inc. Vendor of RISC-V processor microarchitectures. */
+	cpuinfo_vendor_sifive = 17,
+
+	/* Active vendors of embedded CPUs */
+
+	/** Texas Instruments Inc. Vendor of ARM processor microarchitectures.
+	 */
+	cpuinfo_vendor_texas_instruments = 30,
+	/** Marvell Technology Group Ltd. Vendor of ARM processor
+	 * microarchitectures.
+	 */
+	cpuinfo_vendor_marvell = 31,
+	/** RDC Semiconductor Co., Ltd. Vendor of x86 processor
+	   microarchitectures. */
+	cpuinfo_vendor_rdc = 32,
+	/** DM&P Electronics Inc. Vendor of x86 processor microarchitectures. */
+	cpuinfo_vendor_dmp = 33,
+	/** Motorola, Inc. Vendor of PowerPC and ARM processor
+	   microarchitectures. */
+	cpuinfo_vendor_motorola = 34,
+
+	/* Defunct CPU vendors */
+
+	/**
+	 * Transmeta Corporation. Vendor of x86 processor microarchitectures.
+	 *
+	 * Now defunct. The last processor design was released in 2004.
+	 * Transmeta processors implemented VLIW ISA and used binary translation
+	 * to execute x86 code.
+	 */
+	cpuinfo_vendor_transmeta = 50,
+	/**
+	 * Cyrix Corporation. Vendor of x86 processor microarchitectures.
+	 *
+	 * Now defunct. The last processor design was released in 1996.
+	 */
+	cpuinfo_vendor_cyrix = 51,
+	/**
+	 * Rise Technology. Vendor of x86 processor microarchitectures.
+	 *
+	 * Now defunct. The last processor design was released in 1999.
+	 */
+	cpuinfo_vendor_rise = 52,
+	/**
+	 * National Semiconductor. Vendor of x86 processor microarchitectures.
+	 *
+	 * Sold its x86 design subsidiary in 1999. The last processor design was
+	 * released in 1998.
+	 */
+	cpuinfo_vendor_nsc = 53,
+	/**
+	 * Silicon Integrated Systems. Vendor of x86 processor
+	 * microarchitectures.
+	 *
+	 * Sold its x86 design subsidiary in 2001. The last processor design was
+	 * released in 2001.
+	 */
+	cpuinfo_vendor_sis = 54,
+	/**
+	 * NexGen. Vendor of x86 processor microarchitectures.
+	 *
+	 * Now defunct. The last processor design was released in 1994.
+	 * NexGen designed the first x86 microarchitecture which decomposed x86
+	 * instructions into simple microoperations.
+	 */
+	cpuinfo_vendor_nexgen = 55,
+	/**
+	 * United Microelectronics Corporation. Vendor of x86 processor
+	 * microarchitectures.
+	 *
+	 * Ceased x86 in the early 1990s. The last processor design was released
+	 * in 1991. Designed U5C and U5D processors. Both are 486 level.
+	 */
+	cpuinfo_vendor_umc = 56,
+	/**
+	 * Digital Equipment Corporation. Vendor of ARM processor
+	 * microarchitecture.
+	 *
+	 * Sold its ARM designs in 1997. The last processor design was released
+	 * in 1997.
+	 */
+	cpuinfo_vendor_dec = 57,
+};
+
+/**
+ * Processor microarchitecture
+ *
+ * Processors with different microarchitectures often have different instruction
+ * performance characteristics, and may have dramatically different pipeline
+ * organization.
+ */
+enum cpuinfo_uarch {
+	/** Microarchitecture is unknown, or the library failed to get
+	   information about the microarchitecture from OS */
+	cpuinfo_uarch_unknown = 0,
+
+	/** Pentium and Pentium MMX microarchitecture. */
+	cpuinfo_uarch_p5 = 0x00100100,
+	/** Intel Quark microarchitecture. */
+	cpuinfo_uarch_quark = 0x00100101,
+
+	/** Pentium Pro, Pentium II, and Pentium III. */
+	cpuinfo_uarch_p6 = 0x00100200,
+	/** Pentium M. */
+	cpuinfo_uarch_dothan = 0x00100201,
+	/** Intel Core microarchitecture. */
+	cpuinfo_uarch_yonah = 0x00100202,
+	/** Intel Core 2 microarchitecture on 65 nm process. */
+	cpuinfo_uarch_conroe = 0x00100203,
+	/** Intel Core 2 microarchitecture on 45 nm process. */
+	cpuinfo_uarch_penryn = 0x00100204,
+	/** Intel Nehalem and Westmere microarchitectures (Core i3/i5/i7 1st
+	   gen). */
+	cpuinfo_uarch_nehalem = 0x00100205,
+	/** Intel Sandy Bridge microarchitecture (Core i3/i5/i7 2nd gen). */
+	cpuinfo_uarch_sandy_bridge = 0x00100206,
+	/** Intel Ivy Bridge microarchitecture (Core i3/i5/i7 3rd gen). */
+	cpuinfo_uarch_ivy_bridge = 0x00100207,
+	/** Intel Haswell microarchitecture (Core i3/i5/i7 4th gen). */
+	cpuinfo_uarch_haswell = 0x00100208,
+	/** Intel Broadwell microarchitecture. */
+	cpuinfo_uarch_broadwell = 0x00100209,
+	/** Intel Sky Lake microarchitecture (14 nm, including
+	   Kaby/Coffee/Whiskey/Amber/Comet/Cascade/Cooper Lake). */
+	cpuinfo_uarch_sky_lake = 0x0010020A,
+	/** DEPRECATED (Intel Kaby Lake microarchitecture). */
+	cpuinfo_uarch_kaby_lake = 0x0010020A,
+	/** Intel Palm Cove microarchitecture (10 nm, Cannon Lake). */
+	cpuinfo_uarch_palm_cove = 0x0010020B,
+	/** Intel Sunny Cove microarchitecture (10 nm, Ice Lake). */
+	cpuinfo_uarch_sunny_cove = 0x0010020C,
+
+	/** Pentium 4 with Willamette, Northwood, or Foster cores. */
+	cpuinfo_uarch_willamette = 0x00100300,
+	/** Pentium 4 with Prescott and later cores. */
+	cpuinfo_uarch_prescott = 0x00100301,
+
+	/** Intel Atom on 45 nm process. */
+	cpuinfo_uarch_bonnell = 0x00100400,
+	/** Intel Atom on 32 nm process. */
+	cpuinfo_uarch_saltwell = 0x00100401,
+	/** Intel Silvermont microarchitecture (22 nm out-of-order Atom). */
+	cpuinfo_uarch_silvermont = 0x00100402,
+	/** Intel Airmont microarchitecture (14 nm out-of-order Atom). */
+	cpuinfo_uarch_airmont = 0x00100403,
+	/** Intel Goldmont microarchitecture (Denverton, Apollo Lake). */
+	cpuinfo_uarch_goldmont = 0x00100404,
+	/** Intel Goldmont Plus microarchitecture (Gemini Lake). */
+	cpuinfo_uarch_goldmont_plus = 0x00100405,
+
+	/** Intel Knights Ferry HPC boards. */
+	cpuinfo_uarch_knights_ferry = 0x00100500,
+	/** Intel Knights Corner HPC boards (aka Xeon Phi). */
+	cpuinfo_uarch_knights_corner = 0x00100501,
+	/** Intel Knights Landing microarchitecture (second-gen MIC). */
+	cpuinfo_uarch_knights_landing = 0x00100502,
+	/** Intel Knights Hill microarchitecture (third-gen MIC). */
+	cpuinfo_uarch_knights_hill = 0x00100503,
+	/** Intel Knights Mill Xeon Phi. */
+	cpuinfo_uarch_knights_mill = 0x00100504,
+
+	/** Intel/Marvell XScale series. */
+	cpuinfo_uarch_xscale = 0x00100600,
+
+	/** AMD K5. */
+	cpuinfo_uarch_k5 = 0x00200100,
+	/** AMD K6 and alike. */
+	cpuinfo_uarch_k6 = 0x00200101,
+	/** AMD Athlon and Duron. */
+	cpuinfo_uarch_k7 = 0x00200102,
+	/** AMD Athlon 64, Opteron 64. */
+	cpuinfo_uarch_k8 = 0x00200103,
+	/** AMD Family 10h (Barcelona, Istambul, Magny-Cours). */
+	cpuinfo_uarch_k10 = 0x00200104,
+	/**
+	 * AMD Bulldozer microarchitecture
+	 * Zambezi FX-series CPUs, Zurich, Valencia and Interlagos Opteron CPUs.
+	 */
+	cpuinfo_uarch_bulldozer = 0x00200105,
+	/**
+	 * AMD Piledriver microarchitecture
+	 * Vishera FX-series CPUs, Trinity and Richland APUs, Delhi, Seoul, Abu
+	 * Dhabi Opteron CPUs.
+	 */
+	cpuinfo_uarch_piledriver = 0x00200106,
+	/** AMD Steamroller microarchitecture (Kaveri APUs). */
+	cpuinfo_uarch_steamroller = 0x00200107,
+	/** AMD Excavator microarchitecture (Carizzo APUs). */
+	cpuinfo_uarch_excavator = 0x00200108,
+	/** AMD Zen microarchitecture (12/14 nm Ryzen and EPYC CPUs). */
+	cpuinfo_uarch_zen = 0x00200109,
+	/** AMD Zen 2 microarchitecture (7 nm Ryzen and EPYC CPUs). */
+	cpuinfo_uarch_zen2 = 0x0020010A,
+	/** AMD Zen 3 microarchitecture. */
+	cpuinfo_uarch_zen3 = 0x0020010B,
+	/** AMD Zen 4 microarchitecture. */
+	cpuinfo_uarch_zen4 = 0x0020010C,
+
+	/** NSC Geode and AMD Geode GX and LX. */
+	cpuinfo_uarch_geode = 0x00200200,
+	/** AMD Bobcat mobile microarchitecture. */
+	cpuinfo_uarch_bobcat = 0x00200201,
+	/** AMD Jaguar mobile microarchitecture. */
+	cpuinfo_uarch_jaguar = 0x00200202,
+	/** AMD Puma mobile microarchitecture. */
+	cpuinfo_uarch_puma = 0x00200203,
+
+	/** ARM7 series. */
+	cpuinfo_uarch_arm7 = 0x00300100,
+	/** ARM9 series. */
+	cpuinfo_uarch_arm9 = 0x00300101,
+	/** ARM 1136, ARM 1156, ARM 1176, or ARM 11MPCore. */
+	cpuinfo_uarch_arm11 = 0x00300102,
+
+	/** ARM Cortex-A5. */
+	cpuinfo_uarch_cortex_a5 = 0x00300205,
+	/** ARM Cortex-A7. */
+	cpuinfo_uarch_cortex_a7 = 0x00300207,
+	/** ARM Cortex-A8. */
+	cpuinfo_uarch_cortex_a8 = 0x00300208,
+	/** ARM Cortex-A9. */
+	cpuinfo_uarch_cortex_a9 = 0x00300209,
+	/** ARM Cortex-A12. */
+	cpuinfo_uarch_cortex_a12 = 0x00300212,
+	/** ARM Cortex-A15. */
+	cpuinfo_uarch_cortex_a15 = 0x00300215,
+	/** ARM Cortex-A17. */
+	cpuinfo_uarch_cortex_a17 = 0x00300217,
+
+	/** ARM Cortex-A32. */
+	cpuinfo_uarch_cortex_a32 = 0x00300332,
+	/** ARM Cortex-A35. */
+	cpuinfo_uarch_cortex_a35 = 0x00300335,
+	/** ARM Cortex-A53. */
+	cpuinfo_uarch_cortex_a53 = 0x00300353,
+	/** ARM Cortex-A55 revision 0 (restricted dual-issue capabilities
+	   compared to revision 1+). */
+	cpuinfo_uarch_cortex_a55r0 = 0x00300354,
+	/** ARM Cortex-A55. */
+	cpuinfo_uarch_cortex_a55 = 0x00300355,
+	/** ARM Cortex-A57. */
+	cpuinfo_uarch_cortex_a57 = 0x00300357,
+	/** ARM Cortex-A65. */
+	cpuinfo_uarch_cortex_a65 = 0x00300365,
+	/** ARM Cortex-A72. */
+	cpuinfo_uarch_cortex_a72 = 0x00300372,
+	/** ARM Cortex-A73. */
+	cpuinfo_uarch_cortex_a73 = 0x00300373,
+	/** ARM Cortex-A75. */
+	cpuinfo_uarch_cortex_a75 = 0x00300375,
+	/** ARM Cortex-A76. */
+	cpuinfo_uarch_cortex_a76 = 0x00300376,
+	/** ARM Cortex-A77. */
+	cpuinfo_uarch_cortex_a77 = 0x00300377,
+	/** ARM Cortex-A78. */
+	cpuinfo_uarch_cortex_a78 = 0x00300378,
+
+	/** ARM Neoverse N1. */
+	cpuinfo_uarch_neoverse_n1 = 0x00300400,
+	/** ARM Neoverse E1. */
+	cpuinfo_uarch_neoverse_e1 = 0x00300401,
+	/** ARM Neoverse V1. */
+	cpuinfo_uarch_neoverse_v1 = 0x00300402,
+	/** ARM Neoverse N2. */
+	cpuinfo_uarch_neoverse_n2 = 0x00300403,
+	/** ARM Neoverse V2. */
+	cpuinfo_uarch_neoverse_v2 = 0x00300404,
+
+	/** ARM Cortex-X1. */
+	cpuinfo_uarch_cortex_x1 = 0x00300501,
+	/** ARM Cortex-X2. */
+	cpuinfo_uarch_cortex_x2 = 0x00300502,
+	/** ARM Cortex-X3. */
+	cpuinfo_uarch_cortex_x3 = 0x00300503,
+	/** ARM Cortex-X4. */
+	cpuinfo_uarch_cortex_x4 = 0x00300504,
+
+	/** ARM Cortex-A510. */
+	cpuinfo_uarch_cortex_a510 = 0x00300551,
+	/** ARM Cortex-A520. */
+	cpuinfo_uarch_cortex_a520 = 0x00300552,
+	/** ARM Cortex-A710. */
+	cpuinfo_uarch_cortex_a710 = 0x00300571,
+	/** ARM Cortex-A715. */
+	cpuinfo_uarch_cortex_a715 = 0x00300572,
+	/** ARM Cortex-A720. */
+	cpuinfo_uarch_cortex_a720 = 0x00300573,
+
+	/** Qualcomm Scorpion. */
+	cpuinfo_uarch_scorpion = 0x00400100,
+	/** Qualcomm Krait. */
+	cpuinfo_uarch_krait = 0x00400101,
+	/** Qualcomm Kryo. */
+	cpuinfo_uarch_kryo = 0x00400102,
+	/** Qualcomm Falkor. */
+	cpuinfo_uarch_falkor = 0x00400103,
+	/** Qualcomm Saphira. */
+	cpuinfo_uarch_saphira = 0x00400104,
+
+	/** Nvidia Denver. */
+	cpuinfo_uarch_denver = 0x00500100,
+	/** Nvidia Denver 2. */
+	cpuinfo_uarch_denver2 = 0x00500101,
+	/** Nvidia Carmel. */
+	cpuinfo_uarch_carmel = 0x00500102,
+
+	/** Samsung Exynos M1 (Exynos 8890 big cores). */
+	cpuinfo_uarch_exynos_m1 = 0x00600100,
+	/** Samsung Exynos M2 (Exynos 8895 big cores). */
+	cpuinfo_uarch_exynos_m2 = 0x00600101,
+	/** Samsung Exynos M3 (Exynos 9810 big cores). */
+	cpuinfo_uarch_exynos_m3 = 0x00600102,
+	/** Samsung Exynos M4 (Exynos 9820 big cores). */
+	cpuinfo_uarch_exynos_m4 = 0x00600103,
+	/** Samsung Exynos M5 (Exynos 9830 big cores). */
+	cpuinfo_uarch_exynos_m5 = 0x00600104,
+
+	/* Deprecated synonym for Cortex-A76 */
+	cpuinfo_uarch_cortex_a76ae = 0x00300376,
+	/* Deprecated names for Exynos. */
+	cpuinfo_uarch_mongoose_m1 = 0x00600100,
+	cpuinfo_uarch_mongoose_m2 = 0x00600101,
+	cpuinfo_uarch_meerkat_m3 = 0x00600102,
+	cpuinfo_uarch_meerkat_m4 = 0x00600103,
+
+	/** Apple A6 and A6X processors. */
+	cpuinfo_uarch_swift = 0x00700100,
+	/** Apple A7 processor. */
+	cpuinfo_uarch_cyclone = 0x00700101,
+	/** Apple A8 and A8X processor. */
+	cpuinfo_uarch_typhoon = 0x00700102,
+	/** Apple A9 and A9X processor. */
+	cpuinfo_uarch_twister = 0x00700103,
+	/** Apple A10 and A10X processor. */
+	cpuinfo_uarch_hurricane = 0x00700104,
+	/** Apple A11 processor (big cores). */
+	cpuinfo_uarch_monsoon = 0x00700105,
+	/** Apple A11 processor (little cores). */
+	cpuinfo_uarch_mistral = 0x00700106,
+	/** Apple A12 processor (big cores). */
+	cpuinfo_uarch_vortex = 0x00700107,
+	/** Apple A12 processor (little cores). */
+	cpuinfo_uarch_tempest = 0x00700108,
+	/** Apple A13 processor (big cores). */
+	cpuinfo_uarch_lightning = 0x00700109,
+	/** Apple A13 processor (little cores). */
+	cpuinfo_uarch_thunder = 0x0070010A,
+	/** Apple A14 / M1 processor (big cores). */
+	cpuinfo_uarch_firestorm = 0x0070010B,
+	/** Apple A14 / M1 processor (little cores). */
+	cpuinfo_uarch_icestorm = 0x0070010C,
+	/** Apple A15 / M2 processor (big cores). */
+	cpuinfo_uarch_avalanche = 0x0070010D,
+	/** Apple A15 / M2 processor (little cores). */
+	cpuinfo_uarch_blizzard = 0x0070010E,
+
+	/** Cavium ThunderX. */
+	cpuinfo_uarch_thunderx = 0x00800100,
+	/** Cavium ThunderX2 (originally Broadcom Vulkan). */
+	cpuinfo_uarch_thunderx2 = 0x00800200,
+
+	/** Marvell PJ4. */
+	cpuinfo_uarch_pj4 = 0x00900100,
+
+	/** Broadcom Brahma B15. */
+	cpuinfo_uarch_brahma_b15 = 0x00A00100,
+	/** Broadcom Brahma B53. */
+	cpuinfo_uarch_brahma_b53 = 0x00A00101,
+
+	/** Applied Micro X-Gene. */
+	cpuinfo_uarch_xgene = 0x00B00100,
+
+	/* Hygon Dhyana (a modification of AMD Zen for Chinese market). */
+	cpuinfo_uarch_dhyana = 0x01000100,
+
+	/** HiSilicon TaiShan v110 (Huawei Kunpeng 920 series processors). */
+	cpuinfo_uarch_taishan_v110 = 0x00C00100,
+};
+
+struct cpuinfo_processor {
+	/** SMT (hyperthread) ID within a core */
+	uint32_t smt_id;
+	/** Core containing this logical processor */
+	const struct cpuinfo_core* core;
+	/** Cluster of cores containing this logical processor */
+	const struct cpuinfo_cluster* cluster;
+	/** Physical package containing this logical processor */
+	const struct cpuinfo_package* package;
+#if defined(__linux__)
+	/**
+	 * Linux-specific ID for the logical processor:
+	 * - Linux kernel exposes information about this logical processor in
+	 * /sys/devices/system/cpu/cpu/
+	 * - Bit  in the cpu_set_t identifies this logical processor
+	 */
+	int linux_id;
+#endif
+#if defined(_WIN32) || defined(__CYGWIN__)
+	/** Windows-specific ID for the group containing the logical processor.
+	 */
+	uint16_t windows_group_id;
+	/**
+	 * Windows-specific ID of the logical processor within its group:
+	 * - Bit  in the KAFFINITY mask identifies this
+	 * logical processor within its group.
+	 */
+	uint16_t windows_processor_id;
+#endif
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	/** APIC ID (unique x86-specific ID of the logical processor) */
+	uint32_t apic_id;
+#endif
+	struct {
+		/** Level 1 instruction cache */
+		const struct cpuinfo_cache* l1i;
+		/** Level 1 data cache */
+		const struct cpuinfo_cache* l1d;
+		/** Level 2 unified or data cache */
+		const struct cpuinfo_cache* l2;
+		/** Level 3 unified or data cache */
+		const struct cpuinfo_cache* l3;
+		/** Level 4 unified or data cache */
+		const struct cpuinfo_cache* l4;
+	} cache;
+};
+
+struct cpuinfo_core {
+	/** Index of the first logical processor on this core. */
+	uint32_t processor_start;
+	/** Number of logical processors on this core */
+	uint32_t processor_count;
+	/** Core ID within a package */
+	uint32_t core_id;
+	/** Cluster containing this core */
+	const struct cpuinfo_cluster* cluster;
+	/** Physical package containing this core. */
+	const struct cpuinfo_package* package;
+	/** Vendor of the CPU microarchitecture for this core */
+	enum cpuinfo_vendor vendor;
+	/** CPU microarchitecture for this core */
+	enum cpuinfo_uarch uarch;
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	/** Value of CPUID leaf 1 EAX register for this core */
+	uint32_t cpuid;
+#elif CPUINFO_ARCH_ARM || CPUINFO_ARCH_ARM64
+	/** Value of Main ID Register (MIDR) for this core */
+	uint32_t midr;
+#endif
+	/** Clock rate (non-Turbo) of the core, in Hz */
+	uint64_t frequency;
+};
+
+struct cpuinfo_cluster {
+	/** Index of the first logical processor in the cluster */
+	uint32_t processor_start;
+	/** Number of logical processors in the cluster */
+	uint32_t processor_count;
+	/** Index of the first core in the cluster */
+	uint32_t core_start;
+	/** Number of cores on the cluster */
+	uint32_t core_count;
+	/** Cluster ID within a package */
+	uint32_t cluster_id;
+	/** Physical package containing the cluster */
+	const struct cpuinfo_package* package;
+	/** CPU microarchitecture vendor of the cores in the cluster */
+	enum cpuinfo_vendor vendor;
+	/** CPU microarchitecture of the cores in the cluster */
+	enum cpuinfo_uarch uarch;
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	/** Value of CPUID leaf 1 EAX register of the cores in the cluster */
+	uint32_t cpuid;
+#elif CPUINFO_ARCH_ARM || CPUINFO_ARCH_ARM64
+	/** Value of Main ID Register (MIDR) of the cores in the cluster */
+	uint32_t midr;
+#endif
+	/** Clock rate (non-Turbo) of the cores in the cluster, in Hz */
+	uint64_t frequency;
+};
+
+#define CPUINFO_PACKAGE_NAME_MAX 48
+
+struct cpuinfo_package {
+	/** SoC or processor chip model name */
+	char name[CPUINFO_PACKAGE_NAME_MAX];
+	/** Index of the first logical processor on this physical package */
+	uint32_t processor_start;
+	/** Number of logical processors on this physical package */
+	uint32_t processor_count;
+	/** Index of the first core on this physical package */
+	uint32_t core_start;
+	/** Number of cores on this physical package */
+	uint32_t core_count;
+	/** Index of the first cluster of cores on this physical package */
+	uint32_t cluster_start;
+	/** Number of clusters of cores on this physical package */
+	uint32_t cluster_count;
+};
+
+struct cpuinfo_uarch_info {
+	/** Type of CPU microarchitecture */
+	enum cpuinfo_uarch uarch;
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	/** Value of CPUID leaf 1 EAX register for the microarchitecture */
+	uint32_t cpuid;
+#elif CPUINFO_ARCH_ARM || CPUINFO_ARCH_ARM64
+	/** Value of Main ID Register (MIDR) for the microarchitecture */
+	uint32_t midr;
+#endif
+	/** Number of logical processors with the microarchitecture */
+	uint32_t processor_count;
+	/** Number of cores with the microarchitecture */
+	uint32_t core_count;
+};
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+bool CPUINFO_ABI cpuinfo_initialize(void);
+
+void CPUINFO_ABI cpuinfo_deinitialize(void);
+
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+/* This structure is not a part of stable API. Use cpuinfo_has_x86_* functions
+ * instead. */
+struct cpuinfo_x86_isa {
+#if CPUINFO_ARCH_X86
+	bool rdtsc;
+#endif
+	bool rdtscp;
+	bool rdpid;
+	bool sysenter;
+#if CPUINFO_ARCH_X86
+	bool syscall;
+#endif
+	bool msr;
+	bool clzero;
+	bool clflush;
+	bool clflushopt;
+	bool mwait;
+	bool mwaitx;
+#if CPUINFO_ARCH_X86
+	bool emmx;
+#endif
+	bool fxsave;
+	bool xsave;
+#if CPUINFO_ARCH_X86
+	bool fpu;
+	bool mmx;
+	bool mmx_plus;
+#endif
+	bool three_d_now;
+	bool three_d_now_plus;
+#if CPUINFO_ARCH_X86
+	bool three_d_now_geode;
+#endif
+	bool prefetch;
+	bool prefetchw;
+	bool prefetchwt1;
+#if CPUINFO_ARCH_X86
+	bool daz;
+	bool sse;
+	bool sse2;
+#endif
+	bool sse3;
+	bool ssse3;
+	bool sse4_1;
+	bool sse4_2;
+	bool sse4a;
+	bool misaligned_sse;
+	bool avx;
+	bool avxvnni;
+	bool fma3;
+	bool fma4;
+	bool xop;
+	bool f16c;
+	bool avx2;
+	bool avx512f;
+	bool avx512pf;
+	bool avx512er;
+	bool avx512cd;
+	bool avx512dq;
+	bool avx512bw;
+	bool avx512vl;
+	bool avx512ifma;
+	bool avx512vbmi;
+	bool avx512vbmi2;
+	bool avx512bitalg;
+	bool avx512vpopcntdq;
+	bool avx512vnni;
+	bool avx512bf16;
+	bool avx512fp16;
+	bool avx512vp2intersect;
+	bool avx512_4vnniw;
+	bool avx512_4fmaps;
+	bool amx_bf16;
+	bool amx_tile;
+	bool amx_int8;
+	bool amx_fp16;
+	bool avx_vnni_int8;
+	bool avx_vnni_int16;
+	bool avx_ne_convert;
+	bool hle;
+	bool rtm;
+	bool xtest;
+	bool mpx;
+#if CPUINFO_ARCH_X86
+	bool cmov;
+	bool cmpxchg8b;
+#endif
+	bool cmpxchg16b;
+	bool clwb;
+	bool movbe;
+#if CPUINFO_ARCH_X86_64
+	bool lahf_sahf;
+#endif
+	bool fs_gs_base;
+	bool lzcnt;
+	bool popcnt;
+	bool tbm;
+	bool bmi;
+	bool bmi2;
+	bool adx;
+	bool aes;
+	bool vaes;
+	bool pclmulqdq;
+	bool vpclmulqdq;
+	bool gfni;
+	bool rdrand;
+	bool rdseed;
+	bool sha;
+	bool rng;
+	bool ace;
+	bool ace2;
+	bool phe;
+	bool pmm;
+	bool lwp;
+};
+
+extern struct cpuinfo_x86_isa cpuinfo_isa;
+#endif
+
+static inline bool cpuinfo_has_x86_rdtsc(void) {
+#if CPUINFO_ARCH_X86_64
+	return true;
+#elif CPUINFO_ARCH_X86
+#if defined(__ANDROID__)
+	return true;
+#else
+	return cpuinfo_isa.rdtsc;
+#endif
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_rdtscp(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.rdtscp;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_rdpid(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.rdpid;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_clzero(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.clzero;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_mwait(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.mwait;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_mwaitx(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.mwaitx;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_fxsave(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.fxsave;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_xsave(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.xsave;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_fpu(void) {
+#if CPUINFO_ARCH_X86_64
+	return true;
+#elif CPUINFO_ARCH_X86
+#if defined(__ANDROID__)
+	return true;
+#else
+	return cpuinfo_isa.fpu;
+#endif
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_mmx(void) {
+#if CPUINFO_ARCH_X86_64
+	return true;
+#elif CPUINFO_ARCH_X86
+#if defined(__ANDROID__)
+	return true;
+#else
+	return cpuinfo_isa.mmx;
+#endif
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_mmx_plus(void) {
+#if CPUINFO_ARCH_X86_64
+	return true;
+#elif CPUINFO_ARCH_X86
+#if defined(__ANDROID__)
+	return true;
+#else
+	return cpuinfo_isa.mmx_plus;
+#endif
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_3dnow(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.three_d_now;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_3dnow_plus(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.three_d_now_plus;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_3dnow_geode(void) {
+#if CPUINFO_ARCH_X86_64
+	return false;
+#elif CPUINFO_ARCH_X86
+#if defined(__ANDROID__)
+	return false;
+#else
+	return cpuinfo_isa.three_d_now_geode;
+#endif
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_prefetch(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.prefetch;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_prefetchw(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.prefetchw;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_prefetchwt1(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.prefetchwt1;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_daz(void) {
+#if CPUINFO_ARCH_X86_64
+	return true;
+#elif CPUINFO_ARCH_X86
+#if defined(__ANDROID__)
+	return true;
+#else
+	return cpuinfo_isa.daz;
+#endif
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_sse(void) {
+#if CPUINFO_ARCH_X86_64
+	return true;
+#elif CPUINFO_ARCH_X86
+#if defined(__ANDROID__)
+	return true;
+#else
+	return cpuinfo_isa.sse;
+#endif
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_sse2(void) {
+#if CPUINFO_ARCH_X86_64
+	return true;
+#elif CPUINFO_ARCH_X86
+#if defined(__ANDROID__)
+	return true;
+#else
+	return cpuinfo_isa.sse2;
+#endif
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_sse3(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+#if defined(__ANDROID__)
+	return true;
+#else
+	return cpuinfo_isa.sse3;
+#endif
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_ssse3(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+#if defined(__ANDROID__)
+	return true;
+#else
+	return cpuinfo_isa.ssse3;
+#endif
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_sse4_1(void) {
+#if CPUINFO_ARCH_X86_64
+#if defined(__ANDROID__)
+	return true;
+#else
+	return cpuinfo_isa.sse4_1;
+#endif
+#elif CPUINFO_ARCH_X86
+	return cpuinfo_isa.sse4_1;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_sse4_2(void) {
+#if CPUINFO_ARCH_X86_64
+#if defined(__ANDROID__)
+	return true;
+#else
+	return cpuinfo_isa.sse4_2;
+#endif
+#elif CPUINFO_ARCH_X86
+	return cpuinfo_isa.sse4_2;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_sse4a(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.sse4a;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_misaligned_sse(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.misaligned_sse;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avx(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avxvnni(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avxvnni;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_fma3(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.fma3;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_fma4(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.fma4;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_xop(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.xop;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_f16c(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.f16c;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avx2(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx2;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avx512f(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx512f;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avx512pf(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx512pf;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avx512er(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx512er;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avx512cd(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx512cd;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avx512dq(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx512dq;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avx512bw(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx512bw;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avx512vl(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx512vl;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avx512ifma(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx512ifma;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avx512vbmi(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx512vbmi;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avx512vbmi2(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx512vbmi2;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avx512bitalg(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx512bitalg;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avx512vpopcntdq(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx512vpopcntdq;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avx512vnni(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx512vnni;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avx512bf16(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx512bf16;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avx512fp16(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx512fp16;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avx512vp2intersect(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx512vp2intersect;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avx512_4vnniw(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx512_4vnniw;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_avx512_4fmaps(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx512_4fmaps;
+#else
+	return false;
+#endif
+}
+
+/* [NOTE] Intel Advanced Matrix Extensions (AMX) detection
+ *
+ * I.  AMX is a new extensions to the x86 ISA to work on matrices, consists of
+ *   1) 2-dimentional registers (tiles), hold sub-matrices from larger matrices in memory
+ *   2) Accelerator called Tile Matrix Multiply (TMUL), contains instructions operating on tiles
+ *
+ * II. Platforms that supports AMX:
+ * +-----------------+-----+----------+----------+----------+----------+
+ * |    Platforms    | Gen | amx-bf16 | amx-tile | amx-int8 | amx-fp16 |
+ * +-----------------+-----+----------+----------+----------+----------+
+ * | Sapphire Rapids | 4th |   YES    |   YES    |   YES    |    NO    |
+ * +-----------------+-----+----------+----------+----------+----------+
+ * | Emerald Rapids  | 5th |   YES    |   YES    |   YES    |    NO    |
+ * +-----------------+-----+----------+----------+----------+----------+
+ * | Granite Rapids  | 6th |   YES    |   YES    |   YES    |   YES    |
+ * +-----------------+-----+----------+----------+----------+----------+
+ *
+ * Reference: https://www.intel.com/content/www/us/en/products/docs
+ *    /accelerator-engines/advanced-matrix-extensions/overview.html
+ */
+static inline bool cpuinfo_has_x86_amx_bf16(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.amx_bf16;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_amx_tile(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.amx_tile;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_amx_int8(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.amx_int8;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_amx_fp16(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.amx_fp16;
+#else
+	return false;
+#endif
+}
+
+/*
+ * Intel AVX Vector Neural Network Instructions (VNNI) INT8
+ * Supported Platfroms: Sierra Forest, Arrow Lake, Lunar Lake
+ */
+static inline bool cpuinfo_has_x86_avx_vnni_int8(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx_vnni_int8;
+#else
+	return false;
+#endif
+}
+
+/*
+ * Intel AVX Vector Neural Network Instructions (VNNI) INT16
+ * Supported Platfroms: Arrow Lake, Lunar Lake
+ */
+static inline bool cpuinfo_has_x86_avx_vnni_int16(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx_vnni_int16;
+#else
+	return false;
+#endif
+}
+
+/*
+ * A new set of instructions, which can convert low precision floating point
+ * like BF16/FP16 to high precision floating point FP32, as well as convert FP32
+ * elements to BF16. This instruction allows the platform to have improved AI
+ * capabilities and better compatibility.
+ *
+ * Supported Platforms: Sierra Forest, Arrow Lake, Lunar Lake
+ */
+static inline bool cpuinfo_has_x86_avx_ne_convert(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.avx_ne_convert;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_hle(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.hle;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_rtm(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.rtm;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_xtest(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.xtest;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_mpx(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.mpx;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_cmov(void) {
+#if CPUINFO_ARCH_X86_64
+	return true;
+#elif CPUINFO_ARCH_X86
+	return cpuinfo_isa.cmov;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_cmpxchg8b(void) {
+#if CPUINFO_ARCH_X86_64
+	return true;
+#elif CPUINFO_ARCH_X86
+	return cpuinfo_isa.cmpxchg8b;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_cmpxchg16b(void) {
+#if CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.cmpxchg16b;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_clwb(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.clwb;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_movbe(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.movbe;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_lahf_sahf(void) {
+#if CPUINFO_ARCH_X86
+	return true;
+#elif CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.lahf_sahf;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_lzcnt(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.lzcnt;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_popcnt(void) {
+#if CPUINFO_ARCH_X86_64
+#if defined(__ANDROID__)
+	return true;
+#else
+	return cpuinfo_isa.popcnt;
+#endif
+#elif CPUINFO_ARCH_X86
+	return cpuinfo_isa.popcnt;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_tbm(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.tbm;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_bmi(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.bmi;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_bmi2(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.bmi2;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_adx(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.adx;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_aes(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.aes;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_vaes(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.vaes;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_pclmulqdq(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.pclmulqdq;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_vpclmulqdq(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.vpclmulqdq;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_gfni(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.gfni;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_rdrand(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.rdrand;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_rdseed(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.rdseed;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_x86_sha(void) {
+#if CPUINFO_ARCH_X86 || CPUINFO_ARCH_X86_64
+	return cpuinfo_isa.sha;
+#else
+	return false;
+#endif
+}
+
+#if CPUINFO_ARCH_ARM || CPUINFO_ARCH_ARM64
+/* This structure is not a part of stable API. Use cpuinfo_has_arm_* functions
+ * instead. */
+struct cpuinfo_arm_isa {
+#if CPUINFO_ARCH_ARM
+	bool thumb;
+	bool thumb2;
+	bool thumbee;
+	bool jazelle;
+	bool armv5e;
+	bool armv6;
+	bool armv6k;
+	bool armv7;
+	bool armv7mp;
+	bool armv8;
+	bool idiv;
+
+	bool vfpv2;
+	bool vfpv3;
+	bool d32;
+	bool fp16;
+	bool fma;
+
+	bool wmmx;
+	bool wmmx2;
+	bool neon;
+#endif
+#if CPUINFO_ARCH_ARM64
+	bool atomics;
+	bool bf16;
+	bool sve;
+	bool sve2;
+	bool i8mm;
+	bool sme;
+	uint32_t svelen;
+#endif
+	bool rdm;
+	bool fp16arith;
+	bool dot;
+	bool jscvt;
+	bool fcma;
+	bool fhm;
+
+	bool aes;
+	bool sha1;
+	bool sha2;
+	bool pmull;
+	bool crc32;
+};
+
+extern struct cpuinfo_arm_isa cpuinfo_isa;
+#endif
+
+static inline bool cpuinfo_has_arm_thumb(void) {
+#if CPUINFO_ARCH_ARM
+	return cpuinfo_isa.thumb;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_thumb2(void) {
+#if CPUINFO_ARCH_ARM
+	return cpuinfo_isa.thumb2;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_v5e(void) {
+#if CPUINFO_ARCH_ARM
+	return cpuinfo_isa.armv5e;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_v6(void) {
+#if CPUINFO_ARCH_ARM
+	return cpuinfo_isa.armv6;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_v6k(void) {
+#if CPUINFO_ARCH_ARM
+	return cpuinfo_isa.armv6k;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_v7(void) {
+#if CPUINFO_ARCH_ARM
+	return cpuinfo_isa.armv7;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_v7mp(void) {
+#if CPUINFO_ARCH_ARM
+	return cpuinfo_isa.armv7mp;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_v8(void) {
+#if CPUINFO_ARCH_ARM64
+	return true;
+#elif CPUINFO_ARCH_ARM
+	return cpuinfo_isa.armv8;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_idiv(void) {
+#if CPUINFO_ARCH_ARM64
+	return true;
+#elif CPUINFO_ARCH_ARM
+	return cpuinfo_isa.idiv;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_vfpv2(void) {
+#if CPUINFO_ARCH_ARM
+	return cpuinfo_isa.vfpv2;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_vfpv3(void) {
+#if CPUINFO_ARCH_ARM64
+	return true;
+#elif CPUINFO_ARCH_ARM
+	return cpuinfo_isa.vfpv3;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_vfpv3_d32(void) {
+#if CPUINFO_ARCH_ARM64
+	return true;
+#elif CPUINFO_ARCH_ARM
+	return cpuinfo_isa.vfpv3 && cpuinfo_isa.d32;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_vfpv3_fp16(void) {
+#if CPUINFO_ARCH_ARM64
+	return true;
+#elif CPUINFO_ARCH_ARM
+	return cpuinfo_isa.vfpv3 && cpuinfo_isa.fp16;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_vfpv3_fp16_d32(void) {
+#if CPUINFO_ARCH_ARM64
+	return true;
+#elif CPUINFO_ARCH_ARM
+	return cpuinfo_isa.vfpv3 && cpuinfo_isa.fp16 && cpuinfo_isa.d32;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_vfpv4(void) {
+#if CPUINFO_ARCH_ARM64
+	return true;
+#elif CPUINFO_ARCH_ARM
+	return cpuinfo_isa.vfpv3 && cpuinfo_isa.fma;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_vfpv4_d32(void) {
+#if CPUINFO_ARCH_ARM64
+	return true;
+#elif CPUINFO_ARCH_ARM
+	return cpuinfo_isa.vfpv3 && cpuinfo_isa.fma && cpuinfo_isa.d32;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_fp16_arith(void) {
+#if CPUINFO_ARCH_ARM || CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.fp16arith;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_bf16(void) {
+#if CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.bf16;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_wmmx(void) {
+#if CPUINFO_ARCH_ARM
+	return cpuinfo_isa.wmmx;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_wmmx2(void) {
+#if CPUINFO_ARCH_ARM
+	return cpuinfo_isa.wmmx2;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_neon(void) {
+#if CPUINFO_ARCH_ARM64
+	return true;
+#elif CPUINFO_ARCH_ARM
+	return cpuinfo_isa.neon;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_neon_fp16(void) {
+#if CPUINFO_ARCH_ARM64
+	return true;
+#elif CPUINFO_ARCH_ARM
+	return cpuinfo_isa.neon && cpuinfo_isa.fp16;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_neon_fma(void) {
+#if CPUINFO_ARCH_ARM64
+	return true;
+#elif CPUINFO_ARCH_ARM
+	return cpuinfo_isa.neon && cpuinfo_isa.fma;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_neon_v8(void) {
+#if CPUINFO_ARCH_ARM64
+	return true;
+#elif CPUINFO_ARCH_ARM
+	return cpuinfo_isa.neon && cpuinfo_isa.armv8;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_atomics(void) {
+#if CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.atomics;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_neon_rdm(void) {
+#if CPUINFO_ARCH_ARM || CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.rdm;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_neon_fp16_arith(void) {
+#if CPUINFO_ARCH_ARM
+	return cpuinfo_isa.neon && cpuinfo_isa.fp16arith;
+#elif CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.fp16arith;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_fhm(void) {
+#if CPUINFO_ARCH_ARM || CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.fhm;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_neon_dot(void) {
+#if CPUINFO_ARCH_ARM || CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.dot;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_neon_bf16(void) {
+#if CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.bf16;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_jscvt(void) {
+#if CPUINFO_ARCH_ARM || CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.jscvt;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_fcma(void) {
+#if CPUINFO_ARCH_ARM || CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.fcma;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_i8mm(void) {
+#if CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.i8mm;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_aes(void) {
+#if CPUINFO_ARCH_ARM || CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.aes;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_sha1(void) {
+#if CPUINFO_ARCH_ARM || CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.sha1;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_sha2(void) {
+#if CPUINFO_ARCH_ARM || CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.sha2;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_pmull(void) {
+#if CPUINFO_ARCH_ARM || CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.pmull;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_crc32(void) {
+#if CPUINFO_ARCH_ARM || CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.crc32;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_sve(void) {
+#if CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.sve;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_sve_bf16(void) {
+#if CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.sve && cpuinfo_isa.bf16;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_sve2(void) {
+#if CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.sve2;
+#else
+	return false;
+#endif
+}
+
+// Function to get the max SVE vector length on ARM CPU's which support SVE.
+static inline uint32_t cpuinfo_get_max_arm_sve_length(void) {
+#if CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.svelen * 8; // bytes * 8 = bit length(vector length)
+#else
+	return 0;
+#endif
+}
+
+static inline bool cpuinfo_has_arm_sme(void) {
+#if CPUINFO_ARCH_ARM64
+	return cpuinfo_isa.sme;
+#else
+	return false;
+#endif
+}
+
+#if CPUINFO_ARCH_RISCV32 || CPUINFO_ARCH_RISCV64
+/* This structure is not a part of stable API. Use cpuinfo_has_riscv_* functions
+ * instead. */
+struct cpuinfo_riscv_isa {
+	/**
+	 * Keep fields in line with the canonical order as defined by
+	 * Section 27.11 Subset Naming Convention.
+	 */
+	/* RV32I/64I/128I Base ISA. */
+	bool i;
+#if CPUINFO_ARCH_RISCV32
+	/* RV32E Base ISA. */
+	bool e;
+#endif
+	/* Integer Multiply/Divide Extension. */
+	bool m;
+	/* Atomic Extension. */
+	bool a;
+	/* Single-Precision Floating-Point Extension. */
+	bool f;
+	/* Double-Precision Floating-Point Extension. */
+	bool d;
+	/* Compressed Extension. */
+	bool c;
+	/* Vector Extension. */
+	bool v;
+};
+
+extern struct cpuinfo_riscv_isa cpuinfo_isa;
+#endif
+
+static inline bool cpuinfo_has_riscv_i(void) {
+#if CPUINFO_ARCH_RISCV32 || CPUINFO_ARCH_RISCV64
+	return cpuinfo_isa.i;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_riscv_e(void) {
+#if CPUINFO_ARCH_RISCV32
+	return cpuinfo_isa.e;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_riscv_m(void) {
+#if CPUINFO_ARCH_RISCV32 || CPUINFO_ARCH_RISCV64
+	return cpuinfo_isa.m;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_riscv_a(void) {
+#if CPUINFO_ARCH_RISCV32 || CPUINFO_ARCH_RISCV64
+	return cpuinfo_isa.a;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_riscv_f(void) {
+#if CPUINFO_ARCH_RISCV32 || CPUINFO_ARCH_RISCV64
+	return cpuinfo_isa.f;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_riscv_d(void) {
+#if CPUINFO_ARCH_RISCV32 || CPUINFO_ARCH_RISCV64
+	return cpuinfo_isa.d;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_riscv_g(void) {
+	// The 'G' extension is simply shorthand for 'IMAFD'.
+	return cpuinfo_has_riscv_i() && cpuinfo_has_riscv_m() && cpuinfo_has_riscv_a() && cpuinfo_has_riscv_f() &&
+		cpuinfo_has_riscv_d();
+}
+
+static inline bool cpuinfo_has_riscv_c(void) {
+#if CPUINFO_ARCH_RISCV32 || CPUINFO_ARCH_RISCV64
+	return cpuinfo_isa.c;
+#else
+	return false;
+#endif
+}
+
+static inline bool cpuinfo_has_riscv_v(void) {
+#if CPUINFO_ARCH_RISCV32 || CPUINFO_ARCH_RISCV64
+	return cpuinfo_isa.v;
+#else
+	return false;
+#endif
+}
+
+const struct cpuinfo_processor* CPUINFO_ABI cpuinfo_get_processors(void);
+const struct cpuinfo_core* CPUINFO_ABI cpuinfo_get_cores(void);
+const struct cpuinfo_cluster* CPUINFO_ABI cpuinfo_get_clusters(void);
+const struct cpuinfo_package* CPUINFO_ABI cpuinfo_get_packages(void);
+const struct cpuinfo_uarch_info* CPUINFO_ABI cpuinfo_get_uarchs(void);
+const struct cpuinfo_cache* CPUINFO_ABI cpuinfo_get_l1i_caches(void);
+const struct cpuinfo_cache* CPUINFO_ABI cpuinfo_get_l1d_caches(void);
+const struct cpuinfo_cache* CPUINFO_ABI cpuinfo_get_l2_caches(void);
+const struct cpuinfo_cache* CPUINFO_ABI cpuinfo_get_l3_caches(void);
+const struct cpuinfo_cache* CPUINFO_ABI cpuinfo_get_l4_caches(void);
+
+const struct cpuinfo_processor* CPUINFO_ABI cpuinfo_get_processor(uint32_t index);
+const struct cpuinfo_core* CPUINFO_ABI cpuinfo_get_core(uint32_t index);
+const struct cpuinfo_cluster* CPUINFO_ABI cpuinfo_get_cluster(uint32_t index);
+const struct cpuinfo_package* CPUINFO_ABI cpuinfo_get_package(uint32_t index);
+const struct cpuinfo_uarch_info* CPUINFO_ABI cpuinfo_get_uarch(uint32_t index);
+const struct cpuinfo_cache* CPUINFO_ABI cpuinfo_get_l1i_cache(uint32_t index);
+const struct cpuinfo_cache* CPUINFO_ABI cpuinfo_get_l1d_cache(uint32_t index);
+const struct cpuinfo_cache* CPUINFO_ABI cpuinfo_get_l2_cache(uint32_t index);
+const struct cpuinfo_cache* CPUINFO_ABI cpuinfo_get_l3_cache(uint32_t index);
+const struct cpuinfo_cache* CPUINFO_ABI cpuinfo_get_l4_cache(uint32_t index);
+
+uint32_t CPUINFO_ABI cpuinfo_get_processors_count(void);
+uint32_t CPUINFO_ABI cpuinfo_get_cores_count(void);
+uint32_t CPUINFO_ABI cpuinfo_get_clusters_count(void);
+uint32_t CPUINFO_ABI cpuinfo_get_packages_count(void);
+uint32_t CPUINFO_ABI cpuinfo_get_uarchs_count(void);
+uint32_t CPUINFO_ABI cpuinfo_get_l1i_caches_count(void);
+uint32_t CPUINFO_ABI cpuinfo_get_l1d_caches_count(void);
+uint32_t CPUINFO_ABI cpuinfo_get_l2_caches_count(void);
+uint32_t CPUINFO_ABI cpuinfo_get_l3_caches_count(void);
+uint32_t CPUINFO_ABI cpuinfo_get_l4_caches_count(void);
+
+/**
+ * Returns upper bound on cache size.
+ */
+uint32_t CPUINFO_ABI cpuinfo_get_max_cache_size(void);
+
+/**
+ * Identify the logical processor that executes the current thread.
+ *
+ * There is no guarantee that the thread will stay on the same logical processor
+ * for any time. Callers should treat the result as only a hint, and be prepared
+ * to handle NULL return value.
+ */
+const struct cpuinfo_processor* CPUINFO_ABI cpuinfo_get_current_processor(void);
+
+/**
+ * Identify the core that executes the current thread.
+ *
+ * There is no guarantee that the thread will stay on the same core for any
+ * time. Callers should treat the result as only a hint, and be prepared to
+ * handle NULL return value.
+ */
+const struct cpuinfo_core* CPUINFO_ABI cpuinfo_get_current_core(void);
+
+/**
+ * Identify the microarchitecture index of the core that executes the current
+ * thread. If the system does not support such identification, the function
+ * returns 0.
+ *
+ * There is no guarantee that the thread will stay on the same type of core for
+ * any time. Callers should treat the result as only a hint.
+ */
+uint32_t CPUINFO_ABI cpuinfo_get_current_uarch_index(void);
+
+/**
+ * Identify the microarchitecture index of the core that executes the current
+ * thread. If the system does not support such identification, the function
+ * returns the user-specified default value.
+ *
+ * There is no guarantee that the thread will stay on the same type of core for
+ * any time. Callers should treat the result as only a hint.
+ */
+uint32_t CPUINFO_ABI cpuinfo_get_current_uarch_index_with_default(uint32_t default_uarch_index);
+
+#ifdef __cplusplus
+} /* extern "C" */
+#endif
+
+#endif /* CPUINFO_H */
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl.h
new file mode 100644
index 0000000000000000000000000000000000000000..bc74bf644f4b628018d7a9103ba63320abc466d5
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl.h
@@ -0,0 +1,22 @@
+/*******************************************************************************
+* Copyright 2020 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef DNNL_H
+#define DNNL_H
+
+#include "oneapi/dnnl/dnnl.h"
+
+#endif /* DNNL_H */
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_config.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_config.h
new file mode 100644
index 0000000000000000000000000000000000000000..48925e1e3ab49ae135c6e9c4c501aa2f5e030913
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_config.h
@@ -0,0 +1,22 @@
+/*******************************************************************************
+* Copyright 2020 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef DNNL_CONFIG_H
+#define DNNL_CONFIG_H
+
+#include "oneapi/dnnl/dnnl_config.h"
+
+#endif /* DNNL_CONFIG_H */
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_debug.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_debug.h
new file mode 100644
index 0000000000000000000000000000000000000000..5044971832bbbe56127920a527508b207a803eea
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_debug.h
@@ -0,0 +1,22 @@
+/*******************************************************************************
+* Copyright 2020 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef DNNL_DEBUG_H
+#define DNNL_DEBUG_H
+
+#include "oneapi/dnnl/dnnl_debug.h"
+
+#endif /* DNNL_DEBUG_H */
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_ocl.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_ocl.h
new file mode 100644
index 0000000000000000000000000000000000000000..ad731150b28babe7bd5a911acd8de70c57e85254
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_ocl.h
@@ -0,0 +1,22 @@
+/*******************************************************************************
+* Copyright 2020 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef DNNL_OCL_H
+#define DNNL_OCL_H
+
+#include "oneapi/dnnl/dnnl_ocl.h"
+
+#endif /* DNNL_OCL_H */
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_sycl.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_sycl.h
new file mode 100644
index 0000000000000000000000000000000000000000..4501598c2f461021f0fa818e95fd1972ce2d3ace
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_sycl.h
@@ -0,0 +1,22 @@
+/*******************************************************************************
+* Copyright 2020 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef DNNL_SYCL_H
+#define DNNL_SYCL_H
+
+#include "oneapi/dnnl/dnnl_sycl.h"
+
+#endif /* DNNL_SYCL_H */
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_sycl_types.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_sycl_types.h
new file mode 100644
index 0000000000000000000000000000000000000000..a4a854a4cf138103f4c53030083e119cc0732cf1
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_sycl_types.h
@@ -0,0 +1,22 @@
+/*******************************************************************************
+* Copyright 2020 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef DNNL_SYCL_TYPES_H
+#define DNNL_SYCL_TYPES_H
+
+#include "oneapi/dnnl/dnnl_sycl_types.h"
+
+#endif /* DNNL_SYCL_TYPES_H */
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_threadpool.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_threadpool.h
new file mode 100644
index 0000000000000000000000000000000000000000..e27e584a65ed16740d4fde93da3a1a049dd111aa
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_threadpool.h
@@ -0,0 +1,22 @@
+/*******************************************************************************
+* Copyright 2020 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef DNNL_THREADPOOL_H
+#define DNNL_THREADPOOL_H
+
+#include "oneapi/dnnl/dnnl_threadpool.h"
+
+#endif /* DNNL_THREADPOOL_H */
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_types.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_types.h
new file mode 100644
index 0000000000000000000000000000000000000000..6f4261b712dc37ec2416ba60c0c68bb30f6995e0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_types.h
@@ -0,0 +1,22 @@
+/*******************************************************************************
+* Copyright 2020 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef DNNL_TYPES_H
+#define DNNL_TYPES_H
+
+#include "oneapi/dnnl/dnnl_types.h"
+
+#endif /* DNNL_TYPES_H */
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_version.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_version.h
new file mode 100644
index 0000000000000000000000000000000000000000..32a3d5cf839b1d593f069520febfd60b323730e9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/dnnl_version.h
@@ -0,0 +1,22 @@
+/*******************************************************************************
+* Copyright 2020 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef DNNL_VERSION_H
+#define DNNL_VERSION_H
+
+#include "oneapi/dnnl/dnnl_version.h"
+
+#endif /* DNNL_VERSION_H */
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/experiments-config.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/experiments-config.h
new file mode 100644
index 0000000000000000000000000000000000000000..7c0cba4acdaef0784e7b96bfd6e755254d3eecb4
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/experiments-config.h
@@ -0,0 +1,25 @@
+// Copyright 2023 Google LLC
+//
+// This source code is licensed under the BSD-style license found in the
+// LICENSE file in the root directory of this source tree.
+
+#pragma once
+
+#include 
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+struct xnn_experiment_config {
+  bool adaptive_avx_optimization;
+};
+
+struct xnn_experiment_config* xnn_get_experiment_config();
+
+void xnn_experiment_enable_adaptive_avx_optimization();
+
+
+#ifdef __cplusplus
+}  // extern "C"
+#endif
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/fp16.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/fp16.h
new file mode 100644
index 0000000000000000000000000000000000000000..9d7366e997dadef17922225bcbb489288f6f9cdc
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/fp16.h
@@ -0,0 +1,11 @@
+#pragma once
+#ifndef FP16_H
+#define FP16_H
+
+#include 
+
+#if defined(PSIMD_H)
+#include 
+#endif
+
+#endif /* FP16_H */
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/fxdiv.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/fxdiv.h
new file mode 100644
index 0000000000000000000000000000000000000000..2c35038d97c55c524bb97caba2e3560cab9da504
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/fxdiv.h
@@ -0,0 +1,425 @@
+#pragma once
+#ifndef FXDIV_H
+#define FXDIV_H
+
+#if defined(__cplusplus) && (__cplusplus >= 201103L)
+	#include 
+	#include 
+	#include 
+#elif !defined(__OPENCL_VERSION__)
+	#include 
+	#include 
+	#include 
+#endif
+
+#if defined(_MSC_VER)
+	#include 
+	#if defined(_M_IX86) || defined(_M_X64)
+		#include 
+	#endif
+#endif
+
+#ifndef FXDIV_USE_INLINE_ASSEMBLY
+	#define FXDIV_USE_INLINE_ASSEMBLY 0
+#endif
+
+static inline uint64_t fxdiv_mulext_uint32_t(uint32_t a, uint32_t b) {
+#if defined(_MSC_VER) && defined(_M_IX86)
+	return (uint64_t) __emulu((unsigned int) a, (unsigned int) b);
+#else
+	return (uint64_t) a * (uint64_t) b;
+#endif
+}
+
+static inline uint32_t fxdiv_mulhi_uint32_t(uint32_t a, uint32_t b) {
+#if defined(__OPENCL_VERSION__)
+	return mul_hi(a, b);
+#elif defined(__CUDA_ARCH__)
+	return (uint32_t) __umulhi((unsigned int) a, (unsigned int) b);
+#elif defined(_MSC_VER) && defined(_M_IX86)
+	return (uint32_t) (__emulu((unsigned int) a, (unsigned int) b) >> 32);
+#elif defined(_MSC_VER) && defined(_M_ARM)
+	return (uint32_t) _MulUnsignedHigh((unsigned long) a, (unsigned long) b);
+#else
+	return (uint32_t) (((uint64_t) a * (uint64_t) b) >> 32);
+#endif
+}
+
+static inline uint64_t fxdiv_mulhi_uint64_t(uint64_t a, uint64_t b) {
+#if defined(__OPENCL_VERSION__)
+	return mul_hi(a, b);
+#elif defined(__CUDA_ARCH__)
+	return (uint64_t) __umul64hi((unsigned long long) a, (unsigned long long) b);
+#elif defined(_MSC_VER) && defined(_M_X64)
+	return (uint64_t) __umulh((unsigned __int64) a, (unsigned __int64) b);
+#elif defined(__GNUC__) && defined(__SIZEOF_INT128__)
+	return (uint64_t) (((((unsigned __int128) a) * ((unsigned __int128) b))) >> 64);
+#else
+	const uint32_t a_lo = (uint32_t) a;
+	const uint32_t a_hi = (uint32_t) (a >> 32);
+	const uint32_t b_lo = (uint32_t) b;
+	const uint32_t b_hi = (uint32_t) (b >> 32);
+
+	const uint64_t t = fxdiv_mulext_uint32_t(a_hi, b_lo) +
+		(uint64_t) fxdiv_mulhi_uint32_t(a_lo, b_lo);
+	return fxdiv_mulext_uint32_t(a_hi, b_hi) + (t >> 32) +
+		((fxdiv_mulext_uint32_t(a_lo, b_hi) + (uint64_t) (uint32_t) t) >> 32);
+#endif
+}
+
+static inline size_t fxdiv_mulhi_size_t(size_t a, size_t b) {
+#if SIZE_MAX == UINT32_MAX
+	return (size_t) fxdiv_mulhi_uint32_t((uint32_t) a, (uint32_t) b);
+#elif SIZE_MAX == UINT64_MAX
+	return (size_t) fxdiv_mulhi_uint64_t((uint64_t) a, (uint64_t) b);
+#else
+	#error Unsupported platform
+#endif
+}
+
+struct fxdiv_divisor_uint32_t {
+	uint32_t value;
+	uint32_t m;
+	uint8_t s1;
+	uint8_t s2;
+};
+
+struct fxdiv_result_uint32_t {
+	uint32_t quotient;
+	uint32_t remainder;
+};
+
+struct fxdiv_divisor_uint64_t {
+	uint64_t value;
+	uint64_t m;
+	uint8_t s1;
+	uint8_t s2;
+};
+
+struct fxdiv_result_uint64_t {
+	uint64_t quotient;
+	uint64_t remainder;
+};
+
+struct fxdiv_divisor_size_t {
+	size_t value;
+	size_t m;
+	uint8_t s1;
+	uint8_t s2;
+};
+
+struct fxdiv_result_size_t {
+	size_t quotient;
+	size_t remainder;
+};
+
+static inline struct fxdiv_divisor_uint32_t fxdiv_init_uint32_t(uint32_t d) {
+	struct fxdiv_divisor_uint32_t result = { d };
+	if (d == 1) {
+		result.m = UINT32_C(1);
+		result.s1 = 0;
+		result.s2 = 0;
+	} else {
+		#if defined(__OPENCL_VERSION__)
+			const uint32_t l_minus_1 = 31 - clz(d - 1);
+		#elif defined(__CUDA_ARCH__)
+			const uint32_t l_minus_1 = 31 - __clz((int) (d - 1));
+		#elif defined(_MSC_VER) && (defined(_M_IX86) || defined(_M_X64) || defined(_M_ARM) || defined(_M_ARM64))
+			unsigned long l_minus_1;
+			_BitScanReverse(&l_minus_1, (unsigned long) (d - 1));
+		#elif defined(__GNUC__) && (defined(__i386__) || defined(__x86_64__)) && FXDIV_USE_INLINE_ASSEMBLY
+			uint32_t l_minus_1;
+			__asm__("BSRL %[d_minus_1], %[l_minus_1]"
+				: [l_minus_1] "=r" (l_minus_1)
+				: [d_minus_1] "r" (d - 1)
+				: "cc");
+		#elif defined(__GNUC__)
+			const uint32_t l_minus_1 = 31 - __builtin_clz(d - 1);
+		#else
+			/* Based on Algorithm 2 from Hacker's delight */
+
+			uint32_t l_minus_1 = 0;
+			uint32_t x = d - 1;
+			uint32_t y = x >> 16;
+			if (y != 0) {
+				l_minus_1 += 16;
+				x = y;
+			}
+			y = x >> 8;
+			if (y != 0) {
+				l_minus_1 += 8;
+				x = y;
+			}
+			y = x >> 4;
+			if (y != 0) {
+				l_minus_1 += 4;
+				x = y;
+			}
+			y = x >> 2;
+			if (y != 0) {
+				l_minus_1 += 2;
+				x = y;
+			}
+			if ((x & 2) != 0) {
+				l_minus_1 += 1;
+			}
+		#endif
+		uint32_t u_hi = (UINT32_C(2) << (uint32_t) l_minus_1) - d;
+
+		/* Division of 64-bit number u_hi:UINT32_C(0) by 32-bit number d, 32-bit quotient output q */
+		#if defined(__GNUC__) && defined(__i386__) && FXDIV_USE_INLINE_ASSEMBLY
+			uint32_t q;
+			__asm__("DIVL %[d]"
+				: "=a" (q), "+d" (u_hi)
+				: [d] "r" (d), "a" (0)
+				: "cc");
+		#elif (defined(_MSC_VER) && _MSC_VER >= 1920) && !defined(__clang__) && !defined(__INTEL_COMPILER) && (defined(_M_IX86) || defined(_M_X64))
+			unsigned int remainder;
+			const uint32_t q = (uint32_t) _udiv64((unsigned __int64) ((uint64_t) u_hi << 32), (unsigned int) d, &remainder);
+		#else
+			const uint32_t q = ((uint64_t) u_hi << 32) / d;
+		#endif
+
+		result.m = q + UINT32_C(1);
+		result.s1 = 1;
+		result.s2 = (uint8_t) l_minus_1;
+	}
+	return result;
+}
+
+static inline struct fxdiv_divisor_uint64_t fxdiv_init_uint64_t(uint64_t d) {
+	struct fxdiv_divisor_uint64_t result = { d };
+	if (d == 1) {
+		result.m = UINT64_C(1);
+		result.s1 = 0;
+		result.s2 = 0;
+	} else {
+		#if defined(__OPENCL_VERSION__)
+			const uint32_t nlz_d = clz(d);
+			const uint32_t l_minus_1 = 63 - clz(d - 1);
+		#elif defined(__CUDA_ARCH__)
+			const uint32_t nlz_d = __clzll((long long) d);
+			const uint32_t l_minus_1 = 63 - __clzll((long long) (d - 1));
+		#elif defined(_MSC_VER) && (defined(_M_X64) || defined(_M_ARM64))
+			unsigned long l_minus_1;
+			_BitScanReverse64(&l_minus_1, (unsigned __int64) (d - 1));
+			unsigned long bsr_d;
+			_BitScanReverse64(&bsr_d, (unsigned __int64) d);
+			const uint32_t nlz_d = bsr_d ^ 0x3F;
+		#elif defined(_MSC_VER) && (defined(_M_IX86) || defined(_M_ARM))
+			const uint64_t d_minus_1 = d - 1;
+			const uint8_t d_is_power_of_2 = (d & d_minus_1) == 0;
+			unsigned long l_minus_1;
+			if ((uint32_t) (d_minus_1 >> 32) == 0) {
+				_BitScanReverse(&l_minus_1, (unsigned long) d_minus_1);
+			} else {
+				_BitScanReverse(&l_minus_1, (unsigned long) (uint32_t) (d_minus_1 >> 32));
+				l_minus_1 += 32;
+			}
+			const uint32_t nlz_d = ((uint8_t) l_minus_1 ^ UINT8_C(0x3F)) - d_is_power_of_2;
+		#elif defined(__GNUC__) && defined(__x86_64__) && FXDIV_USE_INLINE_ASSEMBLY
+			uint64_t l_minus_1;
+			__asm__("BSRQ %[d_minus_1], %[l_minus_1]"
+				: [l_minus_1] "=r" (l_minus_1)
+				: [d_minus_1] "r" (d - 1)
+				: "cc");
+		#elif defined(__GNUC__)
+			const uint32_t l_minus_1 = 63 - __builtin_clzll(d - 1);
+			const uint32_t nlz_d = __builtin_clzll(d);
+		#else
+			/* Based on Algorithm 2 from Hacker's delight */
+			const uint64_t d_minus_1 = d - 1;
+			const uint32_t d_is_power_of_2 = (d & d_minus_1) == 0;
+			uint32_t l_minus_1 = 0;
+			uint32_t x = (uint32_t) d_minus_1;
+			uint32_t y = d_minus_1 >> 32;
+			if (y != 0) {
+				l_minus_1 += 32;
+				x = y;
+			}
+			y = x >> 16;
+			if (y != 0) {
+				l_minus_1 += 16;
+				x = y;
+			}
+			y = x >> 8;
+			if (y != 0) {
+				l_minus_1 += 8;
+				x = y;
+			}
+			y = x >> 4;
+			if (y != 0) {
+				l_minus_1 += 4;
+				x = y;
+			}
+			y = x >> 2;
+			if (y != 0) {
+				l_minus_1 += 2;
+				x = y;
+			}
+			if ((x & 2) != 0) {
+				l_minus_1 += 1;
+			}
+			const uint32_t nlz_d = (l_minus_1 ^ UINT32_C(0x3F)) - d_is_power_of_2;
+		#endif
+		uint64_t u_hi = (UINT64_C(2) << (uint32_t) l_minus_1) - d;
+
+		/* Division of 128-bit number u_hi:UINT64_C(0) by 64-bit number d, 64-bit quotient output q */
+		#if defined(__GNUC__) && defined(__x86_64__) && FXDIV_USE_INLINE_ASSEMBLY
+			uint64_t q;
+			__asm__("DIVQ %[d]"
+				: "=a" (q), "+d" (u_hi)
+				: [d] "r" (d), "a" (UINT64_C(0))
+				: "cc");
+		#elif 0 && defined(__GNUC__) && defined(__SIZEOF_INT128__)
+			/* GCC, Clang, and Intel Compiler fail to inline optimized implementation and call into support library for 128-bit division */
+			const uint64_t q = (uint64_t) (((unsigned __int128) u_hi << 64) / ((unsigned __int128) d));
+		#elif (defined(_MSC_VER) && _MSC_VER >= 1920) && !defined(__clang__) && !defined(__INTEL_COMPILER) && defined(_M_X64)
+			unsigned __int64 remainder;
+			const uint64_t q = (uint64_t) _udiv128((unsigned __int64) u_hi, 0, (unsigned __int64) d, &remainder);
+		#else
+			/* Implementation based on code from Hacker's delight */
+
+			/* Normalize divisor and shift divident left */
+			d <<= nlz_d;
+			u_hi <<= nlz_d;
+			/* Break divisor up into two 32-bit digits */
+			const uint64_t d_hi = (uint32_t) (d >> 32);
+			const uint32_t d_lo = (uint32_t) d;
+
+			/* Compute the first quotient digit, q1 */
+			uint64_t q1 = u_hi / d_hi;
+			uint64_t r1 = u_hi - q1 * d_hi;
+
+			while ((q1 >> 32) != 0 || fxdiv_mulext_uint32_t((uint32_t) q1, d_lo) > (r1 << 32)) {
+				q1 -= 1;
+				r1 += d_hi;
+				if ((r1 >> 32) != 0) {
+					break;
+				}
+			}
+
+			/* Multiply and subtract. */
+			u_hi = (u_hi << 32) - q1 * d;
+
+			/* Compute the second quotient digit, q0 */
+			uint64_t q0 = u_hi / d_hi;
+			uint64_t r0 = u_hi - q0 * d_hi;
+
+			while ((q0 >> 32) != 0 || fxdiv_mulext_uint32_t((uint32_t) q0, d_lo) > (r0 << 32)) {
+				q0 -= 1;
+				r0 += d_hi;
+				if ((r0 >> 32) != 0) {
+					break;
+				}
+			}
+			const uint64_t q = (q1 << 32) | (uint32_t) q0;
+		#endif
+		result.m = q + UINT64_C(1);
+		result.s1 = 1;
+		result.s2 = (uint8_t) l_minus_1;
+	}
+	return result;
+}
+
+static inline struct fxdiv_divisor_size_t fxdiv_init_size_t(size_t d) {
+#if SIZE_MAX == UINT32_MAX
+	const struct fxdiv_divisor_uint32_t uint_result = fxdiv_init_uint32_t((uint32_t) d);
+#elif SIZE_MAX == UINT64_MAX
+	const struct fxdiv_divisor_uint64_t uint_result = fxdiv_init_uint64_t((uint64_t) d);
+#else
+	#error Unsupported platform
+#endif
+	struct fxdiv_divisor_size_t size_result = {
+		(size_t) uint_result.value,
+		(size_t) uint_result.m,
+		uint_result.s1,
+		uint_result.s2
+	};
+	return size_result;
+}
+
+static inline uint32_t fxdiv_quotient_uint32_t(uint32_t n, const struct fxdiv_divisor_uint32_t divisor) {
+	const uint32_t t = fxdiv_mulhi_uint32_t(n, divisor.m);
+	return (t + ((n - t) >> divisor.s1)) >> divisor.s2;
+}
+
+static inline uint64_t fxdiv_quotient_uint64_t(uint64_t n, const struct fxdiv_divisor_uint64_t divisor) {
+	const uint64_t t = fxdiv_mulhi_uint64_t(n, divisor.m);
+	return (t + ((n - t) >> divisor.s1)) >> divisor.s2;
+}
+
+static inline size_t fxdiv_quotient_size_t(size_t n, const struct fxdiv_divisor_size_t divisor) {
+#if SIZE_MAX == UINT32_MAX
+	const struct fxdiv_divisor_uint32_t uint32_divisor = {
+		(uint32_t) divisor.value,
+		(uint32_t) divisor.m,
+		divisor.s1,
+		divisor.s2
+	};
+	return fxdiv_quotient_uint32_t((uint32_t) n, uint32_divisor);
+#elif SIZE_MAX == UINT64_MAX
+	const struct fxdiv_divisor_uint64_t uint64_divisor = {
+		(uint64_t) divisor.value,
+		(uint64_t) divisor.m,
+		divisor.s1,
+		divisor.s2
+	};
+	return fxdiv_quotient_uint64_t((uint64_t) n, uint64_divisor);
+#else
+	#error Unsupported platform
+#endif
+}
+
+static inline uint32_t fxdiv_remainder_uint32_t(uint32_t n, const struct fxdiv_divisor_uint32_t divisor) {
+	const uint32_t quotient = fxdiv_quotient_uint32_t(n, divisor);
+	return n - quotient * divisor.value;
+}
+
+static inline uint64_t fxdiv_remainder_uint64_t(uint64_t n, const struct fxdiv_divisor_uint64_t divisor) {
+	const uint64_t quotient = fxdiv_quotient_uint64_t(n, divisor);
+	return n - quotient * divisor.value;
+}
+
+static inline size_t fxdiv_remainder_size_t(size_t n, const struct fxdiv_divisor_size_t divisor) {
+	const size_t quotient = fxdiv_quotient_size_t(n, divisor);
+	return n - quotient * divisor.value;
+}
+
+static inline uint32_t fxdiv_round_down_uint32_t(uint32_t n, const struct fxdiv_divisor_uint32_t granularity) {
+	const uint32_t quotient = fxdiv_quotient_uint32_t(n, granularity);
+	return quotient * granularity.value;
+}
+
+static inline uint64_t fxdiv_round_down_uint64_t(uint64_t n, const struct fxdiv_divisor_uint64_t granularity) {
+	const uint64_t quotient = fxdiv_quotient_uint64_t(n, granularity);
+	return quotient * granularity.value;
+}
+
+static inline size_t fxdiv_round_down_size_t(size_t n, const struct fxdiv_divisor_size_t granularity) {
+	const size_t quotient = fxdiv_quotient_size_t(n, granularity);
+	return quotient * granularity.value;
+}
+
+static inline struct fxdiv_result_uint32_t fxdiv_divide_uint32_t(uint32_t n, const struct fxdiv_divisor_uint32_t divisor) {
+	const uint32_t quotient = fxdiv_quotient_uint32_t(n, divisor);
+	const uint32_t remainder = n - quotient * divisor.value;
+	struct fxdiv_result_uint32_t result = { quotient, remainder };
+	return result;
+}
+
+static inline struct fxdiv_result_uint64_t fxdiv_divide_uint64_t(uint64_t n, const struct fxdiv_divisor_uint64_t divisor) {
+	const uint64_t quotient = fxdiv_quotient_uint64_t(n, divisor);
+	const uint64_t remainder = n - quotient * divisor.value;
+	struct fxdiv_result_uint64_t result = { quotient, remainder };
+	return result;
+}
+
+static inline struct fxdiv_result_size_t fxdiv_divide_size_t(size_t n, const struct fxdiv_divisor_size_t divisor) {
+	const size_t quotient = fxdiv_quotient_size_t(n, divisor);
+	const size_t remainder = n - quotient * divisor.value;
+	struct fxdiv_result_size_t result = { quotient, remainder };
+	return result;
+}
+
+#endif /* FXDIV_H */
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/libshm.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/libshm.h
new file mode 100644
index 0000000000000000000000000000000000000000..28024aa2338d1f46ce280abeb92a633f89be1385
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/libshm.h
@@ -0,0 +1,46 @@
+#pragma once
+
+#include 
+
+#ifdef __cplusplus
+
+void libshm_init(const char* manager_exec_path);
+
+// Superclass to run a constructor before at::RefcountedMapAllocator
+class THManagedMapAllocatorInit {
+ protected:
+  THManagedMapAllocatorInit(const char* manager_handle, const char* filename);
+  std::string manager_handle_;
+};
+
+// Like a at::RefcountedMapAllocator, but it also makes use of an external
+// shared memory manager process to ensure that shared memory regions actually
+// get freed in the end (even if processes lose the memory).
+class THManagedMapAllocator : private THManagedMapAllocatorInit,
+                              public at::RefcountedMapAllocator {
+ public:
+  THManagedMapAllocator(
+      const char* manager_handle,
+      const char* filename,
+      int flags,
+      size_t size);
+
+  void close() override;
+
+  ~THManagedMapAllocator() override {
+    close();
+  }
+
+  static at::DataPtr makeDataPtr(
+      const char* manager_handle,
+      const char* filename,
+      int flags,
+      size_t size);
+  static THManagedMapAllocator* fromDataPtr(const at::DataPtr&);
+
+  const char* manager_handle() const {
+    return manager_handle_.c_str();
+  }
+};
+
+#endif
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/nnpack.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/nnpack.h
new file mode 100644
index 0000000000000000000000000000000000000000..97b5ff390076e9ab7ae91e67bfc0d78736aaeffd
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/nnpack.h
@@ -0,0 +1,659 @@
+#pragma once
+
+#include 
+#include 
+#include 
+
+#include 
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @brief Status code for any NNPACK function call.
+ */
+enum nnp_status {
+	/** The call succeeded, and all output arguments now contain valid data. */
+	nnp_status_success = 0,
+	/** NNPACK function was called with batch_size == 0. */
+	nnp_status_invalid_batch_size = 2,
+	/** NNPACK function was called with channels == 0. */
+	nnp_status_invalid_channels = 3,
+	/** NNPACK function was called with input_channels == 0. */
+	nnp_status_invalid_input_channels = 4,
+	/** NNPACK function was called with output_channels == 0. */
+	nnp_status_invalid_output_channels = 5,
+	/** NNPACK function was called with input_size.height == 0 or input_size.width == 0 */
+	nnp_status_invalid_input_size = 10,
+	/** NNPACK function was called with input_stride.height == 0 or input_stride.width == 0 */
+	nnp_status_invalid_input_stride = 11,
+	/** NNPACK function was called with input_padding not less than respective kernel (or pooling) size, i.e.:
+	 *
+	 *  - input_padding.left   >= kernel_size.width  (>= pooling_size.width)
+	 *  - input_padding.right  >= kernel_size.width  (>= pooling_size.width)
+	 *  - input_padding.top    >= kernel_size.height (>= pooling_size.height)
+	 *  - input_padding.bottom >= kernel_size.height (>= pooling_size.height)
+	 */
+	nnp_status_invalid_input_padding = 12,
+	/** NNPACK function was called with kernel_size.height == 0 or kernel_size.width == 0 */
+	nnp_status_invalid_kernel_size = 13,
+	/** NNPACK function was called with pooling_size.height == 0 or pooling_size.width == 0 */
+	nnp_status_invalid_pooling_size = 14,
+	/** NNPACK function was called with pooling_stride.height == 0 or pooling_stride.width == 0 */
+	nnp_status_invalid_pooling_stride = 15,
+	/** NNPACK function was called with convolution algorithm not in nnp_convolution_algorithm enumeration */
+	nnp_status_invalid_algorithm = 16,
+	/** NNPACK function was called with convolution transform strategy not in nnp_convolution_transform_strategy enum */
+	nnp_status_invalid_transform_strategy = 17,
+	/** NNPACK function was called with output_subsampling.height == 0 or output_subsampling.width == 0 */
+	nnp_status_invalid_output_subsampling = 13,
+	/** NNPACK function was called with activation not in nnp_activation enum */
+	nnp_status_invalid_activation = 14,
+	/** NNPACK function was called with invalid activation parameters */
+	nnp_status_invalid_activation_parameters = 15,
+
+	/** NNPACK does not support the particular input size for the function */
+	nnp_status_unsupported_input_size = 20,
+	/** NNPACK does not support the particular input stride for the function */
+	nnp_status_unsupported_input_stride = 21,
+	/** NNPACK does not support the particular input padding for the function */
+	nnp_status_unsupported_input_padding = 22,
+	/** NNPACK does not support the particular kernel size for the function */
+	nnp_status_unsupported_kernel_size = 23,
+	/** NNPACK does not support the particular pooling size for the function */
+	nnp_status_unsupported_pooling_size = 24,
+	/** NNPACK does not support the particular pooling stride for the function */
+	nnp_status_unsupported_pooling_stride = 25,
+	/** NNPACK does not support the particular convolution algorithm for the function */
+	nnp_status_unsupported_algorithm = 26,
+	/** NNPACK does not support the particular convolution transform strategy for the algorithm */
+	nnp_status_unsupported_transform_strategy = 27,
+	/** NNPACK does not support the particular activation function for the function */
+	nnp_status_unsupported_activation = 28,
+	/** NNPACK does not support the particular activation function parameters for the function */
+	nnp_status_unsupported_activation_parameters = 29, 
+
+	/** NNPACK function was called before the library was initialized */
+	nnp_status_uninitialized = 50,
+	/** NNPACK does not implement this function for the host CPU */
+	nnp_status_unsupported_hardware = 51,
+	/** NNPACK failed to allocate memory for temporary buffers */
+	nnp_status_out_of_memory = 52,
+	/** Scratch space buffer is too small */
+	nnp_status_insufficient_buffer = 53,
+	/** Scratch space buffer is not properly aligned */
+	nnp_status_misaligned_buffer = 54
+};
+
+/**
+ * @brief Activation applied applied after a convolutional or fully-connected layer.
+ */
+enum nnp_activation {
+	/** Identity activation f(x) := x, i.e. no transformation */
+	nnp_activation_identity = 0,
+	/** ReLU activation f(x) := max(0, x) */
+	nnp_activation_relu = 1,
+};
+
+/**
+ * @brief Algorithm for computing convolutional layers.
+ */
+enum nnp_convolution_algorithm {
+	/** Let NNPACK choose the algorithm depending on layer parameters */
+	nnp_convolution_algorithm_auto = 0,
+	/** Tiled convolution based on 2D Fourier transform with 8x8 blocks. Supports kernels up to 8x8. */
+	nnp_convolution_algorithm_ft8x8 = 1,
+	/** Tiled convolution based on 2D Fourier transform with 16x16 blocks. Supports kernels up to 16x16. */
+	nnp_convolution_algorithm_ft16x16 = 2,
+	/** Tiled convolution based on 2D Winograd transform F(3x3, 6x6) with 8x8 blocks. Supports only 3x3 kernels. */
+	nnp_convolution_algorithm_wt8x8 = 3,
+	/** Direct convolution via implicit GEMM. */
+	nnp_convolution_algorithm_implicit_gemm = 4,
+	/** Direct convolution implementation. */
+	nnp_convolution_algorithm_direct = 5,
+	/**
+	 * Tiled convolution based on 2D Winograd transform F(3x3, 6x6) with 8x8 blocks in FP16.
+	 * Supports only 3x3 kernels. Implemented only for new ARM processors (with NEON-HP),
+	 * on non-supported processors falls back to nnp_convolution_algorithm_wt8x8.
+	 */
+	nnp_convolution_algorithm_wt8x8_fp16 = 6,
+};
+
+enum nnp_convolution_transform_strategy {
+	nnp_convolution_transform_strategy_compute = 1,
+	nnp_convolution_transform_strategy_precompute = 2,
+	nnp_convolution_transform_strategy_reuse = 3
+};
+
+/* For backward compatibility */
+#define nnp_convolution_transform_strategy_block_based nnp_convolution_transform_strategy_compute
+#define nnp_convolution_transform_strategy_tuple_based nnp_convolution_transform_strategy_compute
+
+/**
+ * @brief Size of images, kernels, and pooling filters in NNPACK.
+ */
+struct nnp_size {
+	/** Width (horizontal size) of an image, kernel, or pooling filter. */
+	size_t width;
+	/** Height (vertical size) of an image, kernel, or pooling filter. */
+	size_t height;
+};
+
+/**
+ * @brief Padding of images in NNPACK.
+ */
+struct nnp_padding {
+	/** Padding above the image data */
+	size_t top;
+	/** Padding on the right of image data */
+	size_t right;
+	/** Padding below the image data */
+	size_t bottom;
+	/** Padding on the left of image data */
+	size_t left;
+};
+
+/**
+ * @brief Profiling information about time spent in different phases of a function call.
+ */
+struct nnp_profile {
+	/** Time spent inside the function call, in seconds. */
+	double total;
+	/** Time spend on transformation of the input or input gradient tensor, in seconds. */
+	double input_transform;
+	/** Time spend on transformation of the kernel or kernel gradient tensor, in seconds. */
+	double kernel_transform;
+	/** Time spend on transformation of the output or output gradient tensor, in seconds. */
+	double output_transform;
+	/** Time spend on multiplication-accumulation of transformed coefficients, in seconds. */
+	double block_multiplication;
+};
+
+enum nnp_status nnp_initialize(void);
+
+enum nnp_status nnp_deinitialize(void);
+
+/**
+ * @brief Computes output of a 2D convolutional layer from input and kernel tensors.
+ * @details This function targets training of convolutional neural networks and performs forward propagation.
+ *          It is optimized for moderate minibatch sizes (64-128) and can be inefficient on a small minibatch.
+ *          For minibatch size 1, use nnp_convolution_inference for optimal performance.
+ * @param algorithm The type of algorithm to use for convolution. Possible values are:
+ *
+ *    - nnp_convolution_algorithm_auto    -- let the function choose the algorithm.
+ *    - nnp_convolution_algorithm_ft8x8   -- tiled convolution based on 2D Fourier transform with 8x8 blocks.
+ *                                           Supports kernels up to 8x8.
+ *    - nnp_convolution_algorithm_ft16x16 -- tiled convolution based on 2D Fourier transform with 16x16 blocks.
+ *                                           Supports kernels up to 16x16.
+ *    - nnp_convolution_algorithm_wt8x8   -- tiled convolution based on 2D Winograd transform F(3x3, 6x6).
+ *                                           Supports only 3x3 kernels.
+ *
+ * @param batch_size The number of images on the input and output of the convolutional layer.
+ * @param input_channels The number of channels (AKA features, dimensions) in the input images.
+ * @param output_channels The number of channels (AKA features, dimensions) in the output images.
+ * @param input_size Size of input images, excluding implicit zero-padding.
+ * @param input_padding Implicit zero-padding of input images.
+ * @param kernel_size Kernel size.
+ * @param[in]  input  A 4D tensor input[batch_size][input_channels][input_size.height][input_size.width].
+ * @param[in]  kernel A 4D tensor kernel[output_channels][input_channels][kernel_size.height][kernel_size.width].
+ * @param[in]  bias   A 1D array bias[output_channels].
+ * @param[out] output A 4D tensor output[batch_size][output_channels][output_size.height][output_size.width] where
+ *                        output_size.height = (input_padding.top + input_size.height + input_padding.bottom) -
+ *                                             (kernel_size.height - 1)
+ *                        output_size.width  = (input_padding.left + input_size.width + input_padding.right) -
+ *                                             (kernel_size.width - 1)
+ * @param threadpool A thread pool for parallelization of the computation.
+ *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
+ * @param[out] profile An optional pointer to profiling structure.
+ *                     If provided, the structure would record time spent in different phases of the computation.
+ */
+
+enum nnp_status nnp_convolution_output(
+	enum nnp_convolution_algorithm algorithm,
+	size_t batch_size,
+	size_t input_channels,
+	size_t output_channels,
+	struct nnp_size input_size,
+	struct nnp_padding input_padding,
+	struct nnp_size kernel_size,
+	const float* input,
+	const float* kernel,
+	const float* bias,
+	float* output,
+	void* workspace_buffer,
+	size_t* workspace_size,
+	enum nnp_activation activation,
+	const void* activation_parameters,
+	pthreadpool_t threadpool,
+	struct nnp_profile* profile);
+
+/**
+ * @brief Computes gradient of input of a 2D convolutional layer from gradient of output and kernel tensors.
+ * @details This function targets training of convolutional neural networks and performs backward propagation.
+ *          It is optimized for moderate minibatch sizes (64-128) and can be inefficient on a small minibatch.
+ * @param algorithm The type of algorithm to use for convolution. Possible values are:
+ *
+ *    - nnp_convolution_algorithm_auto    -- let the function choose the algorithm.
+ *    - nnp_convolution_algorithm_ft8x8   -- tiled convolution based on 2D Fourier transform with 8x8 blocks.
+ *                                           Supports kernels up to 8x8.
+ *    - nnp_convolution_algorithm_ft16x16 -- tiled convolution based on 2D Fourier transform with 16x16 blocks.
+ *                                           Supports kernels up to 16x16.
+ *    - nnp_convolution_algorithm_wt8x8   -- tiled convolution based on 2D Winograd transform F(3x3, 6x6).
+ *                                           Supports only 3x3 kernels.
+ *
+ * @param batch_size The number of images (and their gradients) on the input and output of the convolutional layer.
+ * @param input_channels The number of channels (AKA features, dimensions) in the input images (and gradients).
+ * @param output_channels The number of channels (AKA features, dimensions) in the output images (and gradients).
+ * @param input_size Size of input images and their gradients, excluding implicit zero-padding.
+ * @param input_padding Implicit zero-padding of input images.
+ * @param kernel_size Kernel size.
+ * @param[in]  grad_output A 4D tensor grad_output[batch_size][output_channels][output_size.height][output_size.width]
+ *                         where
+ *                           output_size.height = (input_padding.top + input_size.height + input_padding.bottom) -
+ *                                                (kernel_size.height - 1)
+ *                           output_size.width  = (input_padding.left + input_size.width + input_padding.right) -
+ *                                                (kernel_size.width - 1)
+ * @param[in]  kernel      A 4D tensor kernel[output_channels][input_channels][kernel_size.height][kernel_size.width].
+ * @param[out] grad_input  A 4D tensor grad_input[batch_size][input_channels][input_size.height][input_size.width].
+ * @param threadpool A thread pool for parallelization of the computation.
+ *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
+ * @param[out] profile An optional pointer to profiling structure.
+ *                     If provided, the structure would record time spent in different phases of the computation.
+ */
+enum nnp_status nnp_convolution_input_gradient(
+	enum nnp_convolution_algorithm algorithm,
+	size_t batch_size,
+	size_t input_channels,
+	size_t output_channels,
+	struct nnp_size input_size,
+	struct nnp_padding input_padding,
+	struct nnp_size kernel_size,
+	const float* grad_output,
+	const float* kernel,
+	float* grad_input,
+	void* workspace_buffer,
+	size_t* workspace_size,
+	enum nnp_activation activation,
+	const void* activation_parameters,
+	pthreadpool_t threadpool,
+	struct nnp_profile* profile);
+
+/**
+ * @brief Computes gradient of kernel of a 2D convolutional layer from gradient of output and input tensors.
+ * @details This function targets training of convolutional neural networks and performs backward propagation.
+ *          It is optimized for moderate minibatch sizes (64-128) and can be inefficient on a small minibatch.
+ * @param algorithm The type of algorithm to use for convolution. Possible values are:
+ *
+ *    - nnp_convolution_algorithm_auto    -- let the function choose the algorithm.
+ *    - nnp_convolution_algorithm_ft8x8   -- tiled convolution based on 2D Fourier transform with 8x8 blocks.
+ *                                           Supports kernels up to 8x8.
+ *    - nnp_convolution_algorithm_ft16x16 -- tiled convolution based on 2D Fourier transform with 16x16 blocks.
+ *                                           Supports kernels up to 16x16.
+ *
+ * @param batch_size The number of images (and their gradients) on the input and output of the convolutional layer.
+ * @param input_channels The number of channels (AKA features, dimensions) in the input images.
+ * @param output_channels The number of channels (AKA features, dimensions) in the output images (and gradients).
+ * @param input_size Size of input images and their gradients, excluding implicit zero-padding.
+ * @param input_padding Implicit zero-padding of input images.
+ * @param kernel_size Kernel size.
+ * @param[in]  input       A 4D tensor input[batch_size][input_channels][input_size.height][input_size.width].
+ * @param[in]  grad_output A 4D tensor grad_output[batch_size][output_channels][output_size.height][output_size.width]
+ *                         where
+ *                           output_size.height = (input_padding.top + input_size.height + input_padding.bottom) -
+ *                                                (kernel_size.height - 1)
+ *                           output_size.width  = (input_padding.left + input_size.width + input_padding.right) -
+ *                                                (kernel_size.width - 1)
+ * @param[out] grad_kernel A 4D tensor
+ *                         grad_kernel[output_channels][input_channels][kernel_size.height][kernel_size.width].
+ * @param threadpool A thread pool for parallelization of the computation.
+ *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
+ * @param[out] profile An optional pointer to profiling structure.
+ *                     If provided, the structure would record time spent in different phases of the computation.
+ */
+enum nnp_status nnp_convolution_kernel_gradient(
+	enum nnp_convolution_algorithm algorithm,
+	size_t batch_size,
+	size_t input_channels,
+	size_t output_channels,
+	struct nnp_size input_size,
+	struct nnp_padding input_padding,
+	struct nnp_size kernel_size,
+	const float* input,
+	const float* grad_output,
+	float* grad_kernel,
+	void* workspace_buffer,
+	size_t* workspace_size,
+	enum nnp_activation activation,
+	const void* activation_parameters,
+	pthreadpool_t threadpool,
+	struct nnp_profile* profile);
+
+/**
+ * @brief Computes output of a 2D convolutional layer for a single input image and a kernel tensor.
+ * @details This function targets prediction with convolutional neural networks and performs forward propagation.
+ * @param algorithm The type of algorithm to use for convolution. Possible values are:
+ *
+ *    - nnp_convolution_algorithm_auto    -- let the function choose the algorithm.
+ *    - nnp_convolution_algorithm_ft8x8   -- tiled convolution based on 2D Fourier transform with 8x8 blocks.
+ *                                           Supports kernels up to 8x8.
+ *    - nnp_convolution_algorithm_ft16x16 -- tiled convolution based on 2D Fourier transform with 16x16 blocks.
+ *                                           Supports kernels up to 16x16.
+ *    - nnp_convolution_algorithm_wt8x8   -- tiled convolution based on 2D Winograd transform F(3x3, 6x6).
+ *                                           Supports only 3x3 kernels.
+ *
+ * @param transform_strategy A strategy that guides computation of kernel transforms coefficients.
+ *                           Possible values are:
+ *
+ *    - nnp_convolution_transform_strategy_block_based -- do multiplication-accumulations on blocks of transformed
+ *                                                        coefficients.
+ *    - nnp_convolution_transform_strategy_tuple_based -- do multiplication-accumulations on tuples of transformed
+ *                                                        coefficients.
+ *
+ * @param input_channels The number of channels (AKA features, dimensions) in the input image.
+ * @param output_channels The number of channels (AKA features, dimensions) in the output image.
+ * @param input_size Size of input image, excluding implicit zero-padding.
+ * @param input_padding Implicit zero-padding of input image.
+ * @param kernel_size Kernel size.
+ * @param output_subsampling Subsample region for output, also known as convolution stride.
+ * @param[in]  input  A 3D tensor input[input_channels][input_size.height][input_size.width].
+ * @param[in]  kernel A 4D tensor kernel[output_channels][input_channels][kernel_size.height][kernel_size.width].
+ * @param[in]  bias   A 1D array bias[output_channels].
+ * @param[out] output A 3D tensor output[output_channels][output_size.height][output_size.width] where
+ *                        output_size.height = (input_padding.top + input_size.height + input_padding.bottom) -
+ *                                             (kernel_size.height - 1)
+ *                        output_size.width  = (input_padding.left + input_size.width + input_padding.right) -
+ *                                             (kernel_size.width - 1)
+ * @param[in] workspace_buffer Buffer for scratch memory used during computation. Buffer must be aligned on 64 bytes.
+ *                             If workspace_buffer is NULL and workspace_size is non-NULL, NNPACK would store the size
+ *                             of required workspace memory at the workspace_size location, and exit without
+ *                             computations.
+ *                             If workspace_buffer is NULL and workspace_size is NULL, NNPACK would allocate memory
+ *                             before and deallocate after this computation, potentially at significant runtime cost.
+ * @param[in,out] workspace_size Pointer to the size of workspace buffer.
+ *                               If workspace_buffer is NULL, NNPACK will write the size of required scratch memory to
+ *                               the location specified by this pointer.
+ *                               If workspace_buffer is non-NULL, NNPACK expects workspace_size to specify the size of
+ *                               the buffer, in bytes.
+ *                               If workspace_size is NULL, workspace_buffer must be NULL as well. In this case NNPACK
+ *                               would allocate memory before and deallocate after this computation, potentially at
+ *                               significant runtime cost.
+ * @param threadpool A thread pool for parallelization of the computation.
+ *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
+ * @param[out] profile An optional pointer to profiling structure.
+ *                     If provided, the structure would record time spent in different phases of the computation.
+ */
+enum nnp_status nnp_convolution_inference(
+	enum nnp_convolution_algorithm algorithm,
+	enum nnp_convolution_transform_strategy transform_strategy,
+	size_t input_channels,
+	size_t output_channels,
+	struct nnp_size input_size,
+	struct nnp_padding input_padding,
+	struct nnp_size kernel_size,
+	struct nnp_size output_subsampling,
+	const float* input,
+	const float* kernel,
+	const float* bias,
+	float* output,
+	void* workspace_buffer,
+	size_t* workspace_size,
+	enum nnp_activation activation,
+	const void* activation_parameters,
+	pthreadpool_t threadpool,
+	struct nnp_profile* profile);
+
+/**
+ * @brief Computes output of a fully connected layer from input and kernel matrices.
+ * @details This function targets training of convolutional neural networks and performs forward propagation.
+ *          It is optimized for moderate minibatch sizes (64-128) and can be inefficient on a small minibatch.
+ *          For minibatch size 1, use nnp_fully_connected_inference for optimal performance.
+ * @param batch_size The number of vectors on the input and output of the fully connected layer.
+ * @param input_channels The number of channels (AKA features, dimensions) in the input matrix.
+ * @param output_channels The number of channels (AKA features, dimensions) in the output matrix.
+ * @param[in]  input  A 2D matrix input[batch_size][input_channels].
+ * @param[in]  kernel A 2D matrix kernel[output_channels][input_channels].
+ * @param[out] output A 2D matrix output[batch_size][output_channels].
+ * @param threadpool A thread pool for parallelization of the computation.
+ *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
+ */
+enum nnp_status nnp_fully_connected_output(
+	size_t batch_size,
+	size_t input_channels,
+	size_t output_channels,
+	const float input[],
+	const float kernel[],
+	float output[],
+	pthreadpool_t threadpool,
+	struct nnp_profile* profile);
+
+/**
+ * @brief Computes output of a fully connected layer for a single input vector and a kernel matrix.
+ * @details This function targets prediction with convolutional neural networks and performs forward propagation.
+ * @param input_channels The number of channels (AKA features, dimensions) in the input vector.
+ * @param output_channels The number of channels (AKA features, dimensions) in the output vector.
+ * @param[in]  input  A 1D array input[input_channels] of FP32 elements.
+ * @param[in]  kernel A 2D matrix kernel[output_channels][input_channels] of FP32 elements.
+ * @param[out] output A 1D array output[output_channels] of FP32 elements.
+ * @param threadpool A thread pool for parallelization of the computation.
+ *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
+ */
+enum nnp_status nnp_fully_connected_inference(
+	size_t input_channels,
+	size_t output_channels,
+	const float* input,
+	const float* kernel,
+	float* output,
+	pthreadpool_t threadpool);
+
+/**
+ * @brief Computes output of a fully connected layer for a single input vector and a kernel matrix.
+ * @details This function targets prediction with convolutional neural networks and performs forward propagation.
+ * @param input_channels The number of channels (AKA features, dimensions) in the input vector.
+ * @param output_channels The number of channels (AKA features, dimensions) in the output vector.
+ * @param[in]  input  A 1D array input[input_channels] of FP32 elements.
+ * @param[in]  kernel A 2D matrix kernel[output_channels][input_channels] of FP16 (ARM alternative format) elements.
+ * @param[out] output A 1D array output[output_channels] of FP32 elements.
+ * @param threadpool A thread pool for parallelization of the computation.
+ *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
+ */
+enum nnp_status nnp_fully_connected_inference_f16f32(
+	size_t input_channels,
+	size_t output_channels,
+	const float* input,
+	const void* kernel,
+	float* output,
+	pthreadpool_t threadpool);
+
+/**
+ * @brief Computes output of a max-pooling layer for an input tensor.
+ * @details This function targets both prediction and training of convolutional neural networks and performs forward
+ *          propagation. Is is optimized for both large and small minibatch sizes.
+ * @param batch_size The number of images on the input and output of the max-pooling layer.
+ * @param channels   The number of channels (AKA features, dimensions) in both input and output images.
+ * @param input_size Size of input images, excluding implicit zero-padding.
+ * @param input_padding Implicit padding of input images. The padding pixels are ignored by the pooling filter, but
+ *                      affect the output size.
+ * @param pooling_size   Size of the pooling filter. Only 2x2 filter are currently supported.
+ * @param pooling_stride Stride of the pooling filter. Only 2x2 strides are currently supported.
+ * @param[in]  input  A 4D tensor input[batch_size][channels][input_size.height][input_size.width].
+ * @param[out] output A 4D tensor output[batch_size][channels][output_size.height][output_size.width] where
+ *                    output_size.height = ceil(
+ *                      (input_padding.top + input_size.height + input_padding.bottom - pooling_size.height) /
+ *                        pooling_stride.height) + 1
+ *                    output_size.width = ceil(
+ *                      (input_padding.left + input_size.width + input_padding.right - pooling_size.width) /
+ *                        pooling_stride.width) + 1
+ * @param threadpool A thread pool for parallelization of the computation.
+ *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
+ */
+enum nnp_status nnp_max_pooling_output(
+	size_t batch_size,
+	size_t channels,
+	struct nnp_size input_size,
+	struct nnp_padding input_padding,
+	struct nnp_size pooling_size,
+	struct nnp_size pooling_stride,
+	const float input[],
+	float output[],
+	pthreadpool_t threadpool);
+
+/**
+ * @brief Computes output of a softmax layer for an input matrix.
+ * @details This function targets both prediction and training of convolutional neural networks and performs forward
+ *          propagation. Is is optimized for both large and small minibatch sizes.
+ * @param batch_size The number of vectors on the input and output of the softmax layer.
+ * @param channels   The number of channels (AKA features, dimensions) in both input and output vectors.
+ * @param[in]  input  A 2D matrix input[batch_size][channels].
+ * @param[out] output A 2D matrix output[batch_size][channels].
+ * @param threadpool A thread pool for parallelization of the computation.
+ *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
+ */
+enum nnp_status nnp_softmax_output(
+    size_t batch_size,
+    size_t channels,
+    const float input[],
+    float output[],
+    pthreadpool_t threadpool);
+
+/**
+ * @brief Computes output of a rectified linear unit (ReLU) layer for an input matrix.
+ * @details This function targets both prediction and training of convolutional neural networks and performs forward
+ *          propagation. Is is optimized for both large and small minibatch sizes.
+ * @param batch_size The number of vectors on the input and output of the ReLU layer.
+ * @param channels   The number of channels (AKA features, dimensions) in both input and output matrices.
+ * @param[in]  input  A 2D matrix input[batch_size][channels].
+ * @param[out] output A 2D matrix output[batch_size][channels].
+ * @param threadpool A thread pool for parallelization of the computation.
+ *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
+ */
+enum nnp_status nnp_relu_output(
+	size_t batch_size,
+	size_t channels,
+	const float input[],
+	float output[],
+	float negative_slope,
+	pthreadpool_t threadpool);
+
+/**
+ * @brief Computes gradient of input of a rectified linear unit (ReLU) layer from gradient of output and input matrices.
+ * @details This function targets training of convolutional neural networks and performs backward propagation.
+ *          Is is optimized for both large and small minibatch sizes.
+ * @param batch_size The number of vectors on the input and output of the ReLU layer.
+ * @param channels   The number of channels (AKA features, dimensions) in both input and output matrices.
+ * @param[in]  input  A 2D matrix input[batch_size][channels].
+ * @param[out] output A 2D matrix output[batch_size][channels].
+ * @param threadpool A thread pool for parallelization of the computation.
+ *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
+ */
+enum nnp_status nnp_relu_input_gradient(
+	size_t batch_size,
+	size_t channels,
+	const float grad_output[],
+	const float input[],
+	float grad_input[],
+	float negative_slope,
+	pthreadpool_t threadpool);
+
+#ifdef __cplusplus
+} /* extern "C" */
+#endif
+
+#ifdef __cplusplus
+// Backward compatible implementations for nnp_convolution_*, if we are in C++
+// mode.
+inline enum nnp_status nnp_convolution_output(
+	enum nnp_convolution_algorithm algorithm,
+	size_t batch_size,
+	size_t input_channels,
+	size_t output_channels,
+	struct nnp_size input_size,
+	struct nnp_padding input_padding,
+	struct nnp_size kernel_size,
+	const float input[],
+	const float kernel[],
+	const float bias[],
+	float output[],
+	pthreadpool_t threadpool,
+	struct nnp_profile* profile)
+{
+	return nnp_convolution_output(
+		algorithm,
+		batch_size, input_channels, output_channels,
+		input_size, input_padding, kernel_size,
+		input, kernel, bias, output,
+		NULL, NULL,
+		nnp_activation_identity, NULL, threadpool, profile);
+}
+
+inline enum nnp_status nnp_convolution_input_gradient(
+	enum nnp_convolution_algorithm algorithm,
+	size_t batch_size,
+	size_t input_channels,
+	size_t output_channels,
+	struct nnp_size input_size,
+	struct nnp_padding input_padding,
+	struct nnp_size kernel_size,
+	const float grad_output[],
+	const float kernel[],
+	float grad_input[],
+	pthreadpool_t threadpool,
+	struct nnp_profile* profile)
+{
+	return nnp_convolution_input_gradient(
+		algorithm,
+		batch_size, input_channels, output_channels,
+		input_size, input_padding, kernel_size,
+		grad_output, kernel, grad_input,
+		NULL, NULL,
+		nnp_activation_identity, NULL, threadpool, profile);
+}
+
+inline enum nnp_status nnp_convolution_kernel_gradient(
+	enum nnp_convolution_algorithm algorithm,
+	size_t batch_size,
+	size_t input_channels,
+	size_t output_channels,
+	struct nnp_size input_size,
+	struct nnp_padding input_padding,
+	struct nnp_size kernel_size,
+	const float input[],
+	const float grad_output[],
+	float grad_kernel[],
+	pthreadpool_t threadpool,
+	struct nnp_profile* profile)
+{
+	return nnp_convolution_kernel_gradient(
+		algorithm,
+		batch_size, input_channels, output_channels,
+		input_size, input_padding, kernel_size,
+		input, grad_output, grad_kernel,
+		NULL, NULL,
+		nnp_activation_identity, NULL, threadpool, profile);
+}
+
+inline enum nnp_status nnp_convolution_inference(
+	enum nnp_convolution_algorithm algorithm,
+	enum nnp_convolution_transform_strategy transform_strategy,
+	size_t input_channels,
+	size_t output_channels,
+	struct nnp_size input_size,
+	struct nnp_padding input_padding,
+	struct nnp_size kernel_size,
+	struct nnp_size output_subsampling,
+	const float input[],
+	const float kernel[],
+	const float bias[],
+	float output[],
+	pthreadpool_t threadpool,
+	struct nnp_profile* profile) {
+	return nnp_convolution_inference(
+		algorithm, transform_strategy,
+		input_channels, output_channels,
+		input_size, input_padding, kernel_size, output_subsampling,
+		input, kernel, bias, output, NULL, NULL,
+		nnp_activation_identity, NULL,
+		threadpool, profile);
+}
+
+#endif // __cplusplus
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/psimd.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/psimd.h
new file mode 100644
index 0000000000000000000000000000000000000000..b7cb65d799c98931a73b3184511b1bd8c2b30ec0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/psimd.h
@@ -0,0 +1,1384 @@
+#pragma once
+#ifndef PSIMD_H
+#define PSIMD_H
+
+#if defined(__CUDA_ARCH__)
+	/* CUDA compiler */
+	#define PSIMD_INTRINSIC __forceinline__ __device__
+#elif defined(__OPENCL_VERSION__)
+	/* OpenCL compiler */
+	#define PSIMD_INTRINSIC inline static
+#elif defined(__INTEL_COMPILER)
+	/* Intel compiler, even on Windows */
+	#define PSIMD_INTRINSIC inline static __attribute__((__always_inline__))
+#elif defined(__GNUC__)
+	/* GCC-compatible compiler (gcc/clang/icc) */
+	#define PSIMD_INTRINSIC inline static __attribute__((__always_inline__))
+#elif defined(_MSC_VER)
+	/* MSVC-compatible compiler (cl/icl/clang-cl) */
+	#define PSIMD_INTRINSIC __forceinline static
+#elif defined(__cplusplus)
+	/* Generic C++ compiler */
+	#define PSIMD_INTRINSIC inline static
+#elif defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L)
+	/* Generic C99 compiler */
+	#define PSIMD_INTRINSIC inline static
+#else
+	/* Generic C compiler */
+	#define PSIMD_INTRINSIC static
+#endif
+
+#if defined(__GNUC__) || defined(__clang__)
+	#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+		#include 
+	#endif
+
+	#if defined(__SSE2__)
+		#include 
+	#endif
+
+	#if defined(__SSE3__)
+		#include 
+	#endif
+
+	#if defined(__SSSE3__)
+		#include 
+	#endif
+
+	#if defined(__SSE4_1__)
+		#include 
+	#endif
+
+	#if defined(__SSE4_2__)
+		#include 
+	#endif
+
+	#if defined(__AVX__)
+		#include 
+	#endif
+#elif defined(_MSC_VER)
+	#include 
+#endif
+
+#if defined(__cplusplus)
+	#define PSIMD_CXX_SYNTAX
+#elif defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L)
+	#define PSIMD_C11_SYNTAX
+#elif defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L)
+	#define PSIMD_C99_SYNTAX
+#else
+	#define PSIMD_C89_SYNTAX
+#endif
+
+#if defined(__cplusplus) && (__cplusplus >= 201103L)
+	#include 
+	#include 
+#elif !defined(__OPENCL_VERSION__)
+	#include 
+	#include 
+#endif
+
+#if defined(__GNUC__) || defined(__clang__)
+	#define PSIMD_HAVE_F64 0
+	#define PSIMD_HAVE_F32 1
+	#define PSIMD_HAVE_U8 1
+	#define PSIMD_HAVE_S8 1
+	#define PSIMD_HAVE_U16 1
+	#define PSIMD_HAVE_S16 1
+	#define PSIMD_HAVE_U32 1
+	#define PSIMD_HAVE_S32 1
+	#define PSIMD_HAVE_U64 0
+	#define PSIMD_HAVE_S64 0
+
+	typedef int8_t   psimd_s8  __attribute__((vector_size(16), aligned(1)));
+	typedef uint8_t  psimd_u8  __attribute__((vector_size(16), aligned(1)));
+	typedef int16_t  psimd_s16 __attribute__((vector_size(16), aligned(2)));
+	typedef uint16_t psimd_u16 __attribute__((vector_size(16), aligned(2)));
+	typedef int32_t  psimd_s32 __attribute__((vector_size(16), aligned(4)));
+	typedef uint32_t psimd_u32 __attribute__((vector_size(16), aligned(4)));
+	typedef float    psimd_f32 __attribute__((vector_size(16), aligned(4)));
+
+	typedef struct {
+		psimd_s8 lo;
+		psimd_s8 hi;
+	} psimd_s8x2;
+
+	typedef struct {
+		psimd_u8 lo;
+		psimd_u8 hi;
+	} psimd_u8x2;
+
+	typedef struct {
+		psimd_s16 lo;
+		psimd_s16 hi;
+	} psimd_s16x2;
+
+	typedef struct {
+		psimd_u16 lo;
+		psimd_u16 hi;
+	} psimd_u16x2;
+
+	typedef struct {
+		psimd_s32 lo;
+		psimd_s32 hi;
+	} psimd_s32x2;
+
+	typedef struct {
+		psimd_u32 lo;
+		psimd_u32 hi;
+	} psimd_u32x2;
+
+	typedef struct {
+		psimd_f32 lo;
+		psimd_f32 hi;
+	} psimd_f32x2;
+
+	/* Bit casts */
+	PSIMD_INTRINSIC psimd_u32x2 psimd_cast_s32x2_u32x2(psimd_s32x2 v) {
+		return (psimd_u32x2) { .lo = (psimd_u32) v.lo, .hi = (psimd_u32) v.hi };
+	}
+
+	PSIMD_INTRINSIC psimd_f32x2 psimd_cast_s32x2_f32x2(psimd_s32x2 v) {
+		return (psimd_f32x2) { .lo = (psimd_f32) v.lo, .hi = (psimd_f32) v.hi };
+	}
+
+	PSIMD_INTRINSIC psimd_s32x2 psimd_cast_u32x2_s32x2(psimd_u32x2 v) {
+		return (psimd_s32x2) { .lo = (psimd_s32) v.lo, .hi = (psimd_s32) v.hi };
+	}
+
+	PSIMD_INTRINSIC psimd_f32x2 psimd_cast_u32x2_f32x2(psimd_u32x2 v) {
+		return (psimd_f32x2) { .lo = (psimd_f32) v.lo, .hi = (psimd_f32) v.hi };
+	}
+
+	PSIMD_INTRINSIC psimd_s32x2 psimd_cast_f32x2_s32x2(psimd_f32x2 v) {
+		return (psimd_s32x2) { .lo = (psimd_s32) v.lo, .hi = (psimd_s32) v.hi };
+	}
+
+	PSIMD_INTRINSIC psimd_u32x2 psimd_cast_f32x2_u32x2(psimd_f32x2 v) {
+		return (psimd_u32x2) { .lo = (psimd_u32) v.lo, .hi = (psimd_u32) v.hi };
+	}
+
+	/* Swap */
+	PSIMD_INTRINSIC void psimd_swap_s8(psimd_s8 a[1], psimd_s8 b[1]) {
+		const psimd_s8 new_a = *b;
+		const psimd_s8 new_b = *a;
+		*a = new_a;
+		*b = new_b;
+	}
+
+	PSIMD_INTRINSIC void psimd_swap_u8(psimd_u8 a[1], psimd_u8 b[1]) {
+		const psimd_u8 new_a = *b;
+		const psimd_u8 new_b = *a;
+		*a = new_a;
+		*b = new_b;
+	}
+
+	PSIMD_INTRINSIC void psimd_swap_s16(psimd_s16 a[1], psimd_s16 b[1]) {
+		const psimd_s16 new_a = *b;
+		const psimd_s16 new_b = *a;
+		*a = new_a;
+		*b = new_b;
+	}
+
+	PSIMD_INTRINSIC void psimd_swap_u16(psimd_u16 a[1], psimd_u16 b[1]) {
+		const psimd_u16 new_a = *b;
+		const psimd_u16 new_b = *a;
+		*a = new_a;
+		*b = new_b;
+	}
+
+	PSIMD_INTRINSIC void psimd_swap_s32(psimd_s32 a[1], psimd_s32 b[1]) {
+		const psimd_s32 new_a = *b;
+		const psimd_s32 new_b = *a;
+		*a = new_a;
+		*b = new_b;
+	}
+
+	PSIMD_INTRINSIC void psimd_swap_u32(psimd_u32 a[1], psimd_u32 b[1]) {
+		const psimd_u32 new_a = *b;
+		const psimd_u32 new_b = *a;
+		*a = new_a;
+		*b = new_b;
+	}
+
+	PSIMD_INTRINSIC void psimd_swap_f32(psimd_f32 a[1], psimd_f32 b[1]) {
+		const psimd_f32 new_a = *b;
+		const psimd_f32 new_b = *a;
+		*a = new_a;
+		*b = new_b;
+	}
+
+	/* Zero-initialization */
+	PSIMD_INTRINSIC psimd_s8 psimd_zero_s8(void) {
+		return (psimd_s8) { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 };
+	}
+
+	PSIMD_INTRINSIC psimd_u8 psimd_zero_u8(void) {
+		return (psimd_u8) { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 };
+	}
+
+	PSIMD_INTRINSIC psimd_s16 psimd_zero_s16(void) {
+		return (psimd_s16) { 0, 0, 0, 0, 0, 0, 0, 0 };
+	}
+
+	PSIMD_INTRINSIC psimd_u16 psimd_zero_u16(void) {
+		return (psimd_u16) { 0, 0, 0, 0, 0, 0, 0, 0 };
+	}
+
+	PSIMD_INTRINSIC psimd_s32 psimd_zero_s32(void) {
+		return (psimd_s32) { 0, 0, 0, 0 };
+	}
+
+	PSIMD_INTRINSIC psimd_u32 psimd_zero_u32(void) {
+		return (psimd_u32) { 0, 0, 0, 0 };
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_zero_f32(void) {
+		return (psimd_f32) { 0.0f, 0.0f, 0.0f, 0.0f };
+	}
+
+	/* Initialization to the same constant */
+	PSIMD_INTRINSIC psimd_s8 psimd_splat_s8(int8_t c) {
+		return (psimd_s8) { c, c, c, c, c, c, c, c, c, c, c, c, c, c, c, c };
+	}
+
+	PSIMD_INTRINSIC psimd_u8 psimd_splat_u8(uint8_t c) {
+		return (psimd_u8) { c, c, c, c, c, c, c, c, c, c, c, c, c, c, c, c };
+	}
+
+	PSIMD_INTRINSIC psimd_s16 psimd_splat_s16(int16_t c) {
+		return (psimd_s16) { c, c, c, c, c, c, c, c };
+	}
+
+	PSIMD_INTRINSIC psimd_u16 psimd_splat_u16(uint16_t c) {
+		return (psimd_u16) { c, c, c, c, c, c, c, c };
+	}
+
+	PSIMD_INTRINSIC psimd_s32 psimd_splat_s32(int32_t c) {
+		return (psimd_s32) { c, c, c, c };
+	}
+
+	PSIMD_INTRINSIC psimd_u32 psimd_splat_u32(uint32_t c) {
+		return (psimd_u32) { c, c, c, c };
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_splat_f32(float c) {
+		return (psimd_f32) { c, c, c, c };
+	}
+
+	/* Load vector */
+	PSIMD_INTRINSIC psimd_s8 psimd_load_s8(const void* address) {
+		return *((const psimd_s8*) address);
+	}
+
+	PSIMD_INTRINSIC psimd_u8 psimd_load_u8(const void* address) {
+		return *((const psimd_u8*) address);
+	}
+
+	PSIMD_INTRINSIC psimd_s16 psimd_load_s16(const void* address) {
+		return *((const psimd_s16*) address);
+	}
+
+	PSIMD_INTRINSIC psimd_u16 psimd_load_u16(const void* address) {
+		return *((const psimd_u16*) address);
+	}
+
+	PSIMD_INTRINSIC psimd_s32 psimd_load_s32(const void* address) {
+		return *((const psimd_s32*) address);
+	}
+
+	PSIMD_INTRINSIC psimd_u32 psimd_load_u32(const void* address) {
+		return *((const psimd_u32*) address);
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_load_f32(const void* address) {
+		return *((const psimd_f32*) address);
+	}
+
+	PSIMD_INTRINSIC psimd_s8 psimd_load_splat_s8(const void* address) {
+		return psimd_splat_s8(*((const int8_t*) address));
+	}
+
+	PSIMD_INTRINSIC psimd_u8 psimd_load_splat_u8(const void* address) {
+		return psimd_splat_u8(*((const uint8_t*) address));
+	}
+
+	PSIMD_INTRINSIC psimd_s16 psimd_load_splat_s16(const void* address) {
+		return psimd_splat_s16(*((const int16_t*) address));
+	}
+
+	PSIMD_INTRINSIC psimd_u16 psimd_load_splat_u16(const void* address) {
+		return psimd_splat_u16(*((const uint16_t*) address));
+	}
+
+	PSIMD_INTRINSIC psimd_s32 psimd_load_splat_s32(const void* address) {
+		return psimd_splat_s32(*((const int32_t*) address));
+	}
+
+	PSIMD_INTRINSIC psimd_u32 psimd_load_splat_u32(const void* address) {
+		return psimd_splat_u32(*((const uint32_t*) address));
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_load_splat_f32(const void* address) {
+		return psimd_splat_f32(*((const float*) address));
+	}
+
+	PSIMD_INTRINSIC psimd_s32 psimd_load1_s32(const void* address) {
+		return (psimd_s32) { *((const int32_t*) address), 0, 0, 0 };
+	}
+
+	PSIMD_INTRINSIC psimd_u32 psimd_load1_u32(const void* address) {
+		return (psimd_u32) { *((const uint32_t*) address), 0, 0, 0 };
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_load1_f32(const void* address) {
+		return (psimd_f32) { *((const float*) address), 0.0f, 0.0f, 0.0f };
+	}
+
+	PSIMD_INTRINSIC psimd_s32 psimd_load2_s32(const void* address) {
+		const int32_t* address_s32 = (const int32_t*) address;
+		return (psimd_s32) { address_s32[0], address_s32[1], 0, 0 };
+	}
+
+	PSIMD_INTRINSIC psimd_u32 psimd_load2_u32(const void* address) {
+		const uint32_t* address_u32 = (const uint32_t*) address;
+		return (psimd_u32) { address_u32[0], address_u32[1], 0, 0 };
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_load2_f32(const void* address) {
+		const float* address_f32 = (const float*) address;
+		return (psimd_f32) { address_f32[0], address_f32[1], 0.0f, 0.0f };
+	}
+
+	PSIMD_INTRINSIC psimd_s32 psimd_load3_s32(const void* address) {
+		const int32_t* address_s32 = (const int32_t*) address;
+		return (psimd_s32) { address_s32[0], address_s32[1], address_s32[2], 0 };
+	}
+
+	PSIMD_INTRINSIC psimd_u32 psimd_load3_u32(const void* address) {
+		const uint32_t* address_u32 = (const uint32_t*) address;
+		return (psimd_u32) { address_u32[0], address_u32[1], address_u32[2], 0 };
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_load3_f32(const void* address) {
+		const float* address_f32 = (const float*) address;
+		return (psimd_f32) { address_f32[0], address_f32[1], address_f32[2], 0.0f };
+	}
+
+	PSIMD_INTRINSIC psimd_s32 psimd_load4_s32(const void* address) {
+		return psimd_load_s32(address);
+	}
+
+	PSIMD_INTRINSIC psimd_u32 psimd_load4_u32(const void* address) {
+		return psimd_load_u32(address);
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_load4_f32(const void* address) {
+		return psimd_load_f32(address);
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_load_stride2_f32(const void* address) {
+		const psimd_f32 v0x1x = psimd_load_f32(address);
+		const psimd_f32 vx2x3 = psimd_load_f32((const float*) address + 3);
+		#if defined(__clang__)
+			return __builtin_shufflevector(v0x1x, vx2x3, 0, 2, 5, 7);
+		#else
+			return __builtin_shuffle(v0x1x, vx2x3, (psimd_s32) { 0, 2, 5, 7 });
+		#endif
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_load1_stride2_f32(const void* address) {
+		return psimd_load_f32(address);
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_load2_stride2_f32(const void* address) {
+		const float* address_f32 = (const float*) address;
+		return (psimd_f32) { address_f32[0], address_f32[2], 0.0f, 0.0f };
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_load3_stride2_f32(const void* address) {
+		const psimd_f32 v0x1x = psimd_load_f32(address);
+		const psimd_f32 v2zzz = psimd_load1_f32((const float*) address + 2);
+		#if defined(__clang__)
+			return __builtin_shufflevector(v0x1x, v2zzz, 0, 2, 4, 6);
+		#else
+			return __builtin_shuffle(v0x1x, v2zzz, (psimd_s32) { 0, 2, 4, 6 });
+		#endif
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_load4_stride2_f32(const void* address) {
+		return psimd_load_stride2_f32(address);
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_load_stride_f32(const void* address, size_t stride) {
+		const float* address0_f32 = (const float*) address;
+		const float* address1_f32 = address0_f32 + stride;
+		const float* address2_f32 = address1_f32 + stride;
+		const float* address3_f32 = address2_f32 + stride;
+		return (psimd_f32) { *address0_f32, *address1_f32, *address2_f32, *address3_f32 };
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_load1_stride_f32(const void* address, size_t stride) {
+		return psimd_load1_f32(address);
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_load2_stride_f32(const void* address, size_t stride) {
+		const float* address_f32 = (const float*) address;
+		return (psimd_f32) { address_f32[0], address_f32[stride], 0.0f, 0.0f };
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_load3_stride_f32(const void* address, size_t stride) {
+		const float* address0_f32 = (const float*) address;
+		const float* address1_f32 = address0_f32 + stride;
+		const float* address2_f32 = address1_f32 + stride;
+		return (psimd_f32) { *address0_f32, *address1_f32, *address2_f32, 0.0f };
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_load4_stride_f32(const void* address, size_t stride) {
+		return psimd_load_stride_f32(address, stride);
+	}
+
+	/* Store vector */
+	PSIMD_INTRINSIC void psimd_store_s8(void* address, psimd_s8 value) {
+		*((psimd_s8*) address) = value;
+	}
+
+	PSIMD_INTRINSIC void psimd_store_u8(void* address, psimd_u8 value) {
+		*((psimd_u8*) address) = value;
+	}
+
+	PSIMD_INTRINSIC void psimd_store_s16(void* address, psimd_s16 value) {
+		*((psimd_s16*) address) = value;
+	}
+
+	PSIMD_INTRINSIC void psimd_store_u16(void* address, psimd_u16 value) {
+		*((psimd_u16*) address) = value;
+	}
+
+	PSIMD_INTRINSIC void psimd_store_s32(void* address, psimd_s32 value) {
+		*((psimd_s32*) address) = value;
+	}
+
+	PSIMD_INTRINSIC void psimd_store_u32(void* address, psimd_u32 value) {
+		*((psimd_u32*) address) = value;
+	}
+
+	PSIMD_INTRINSIC void psimd_store_f32(void* address, psimd_f32 value) {
+		*((psimd_f32*) address) = value;
+	}
+
+	PSIMD_INTRINSIC void psimd_store1_s32(void* address, psimd_s32 value) {
+		*((int32_t*) address) = value[0];
+	}
+
+	PSIMD_INTRINSIC void psimd_store1_u32(void* address, psimd_u32 value) {
+		*((uint32_t*) address) = value[0];
+	}
+
+	PSIMD_INTRINSIC void psimd_store1_f32(void* address, psimd_f32 value) {
+		*((float*) address) = value[0];
+	}
+
+	PSIMD_INTRINSIC void psimd_store2_s32(void* address, psimd_s32 value) {
+		int32_t* address_s32 = (int32_t*) address;
+		address_s32[0] = value[0];
+		address_s32[1] = value[1];
+	}
+
+	PSIMD_INTRINSIC void psimd_store2_u32(void* address, psimd_u32 value) {
+		uint32_t* address_u32 = (uint32_t*) address;
+		address_u32[0] = value[0];
+		address_u32[1] = value[1];
+	}
+
+	PSIMD_INTRINSIC void psimd_store2_f32(void* address, psimd_f32 value) {
+		float* address_f32 = (float*) address;
+		address_f32[0] = value[0];
+		address_f32[1] = value[1];
+	}
+
+	PSIMD_INTRINSIC void psimd_store3_s32(void* address, psimd_s32 value) {
+		int32_t* address_s32 = (int32_t*) address;
+		address_s32[0] = value[0];
+		address_s32[1] = value[1];
+		address_s32[2] = value[2];
+	}
+
+	PSIMD_INTRINSIC void psimd_store3_u32(void* address, psimd_u32 value) {
+		uint32_t* address_u32 = (uint32_t*) address;
+		address_u32[0] = value[0];
+		address_u32[1] = value[1];
+		address_u32[2] = value[2];
+	}
+
+	PSIMD_INTRINSIC void psimd_store3_f32(void* address, psimd_f32 value) {
+		float* address_f32 = (float*) address;
+		address_f32[0] = value[0];
+		address_f32[1] = value[1];
+		address_f32[2] = value[2];
+	}
+
+	PSIMD_INTRINSIC void psimd_store4_s32(void* address, psimd_s32 value) {
+		psimd_store_s32(address, value);
+	}
+
+	PSIMD_INTRINSIC void psimd_store4_u32(void* address, psimd_u32 value) {
+		psimd_store_u32(address, value);
+	}
+
+	PSIMD_INTRINSIC void psimd_store4_f32(void* address, psimd_f32 value) {
+		psimd_store_f32(address, value);
+	}
+
+	PSIMD_INTRINSIC void psimd_store_stride_f32(void* address, size_t stride, psimd_f32 value) {
+		float* address0_f32 = (float*) address;
+		float* address1_f32 = address0_f32 + stride;
+		float* address2_f32 = address1_f32 + stride;
+		float* address3_f32 = address2_f32 + stride;
+		*address0_f32 = value[0];
+		*address1_f32 = value[1];
+		*address2_f32 = value[2];
+		*address3_f32 = value[3];
+	}
+
+	PSIMD_INTRINSIC void psimd_store1_stride_f32(void* address, size_t stride, psimd_f32 value) {
+		psimd_store1_f32(address, value);
+	}
+
+	PSIMD_INTRINSIC void psimd_store2_stride_f32(void* address, size_t stride, psimd_f32 value) {
+		float* address_f32 = (float*) address;
+		address_f32[0]      = value[0];
+		address_f32[stride] = value[1];
+	}
+
+	PSIMD_INTRINSIC void psimd_store3_stride_f32(void* address, size_t stride, psimd_f32 value) {
+		float* address0_f32 = (float*) address;
+		float* address1_f32 = address0_f32 + stride;
+		float* address2_f32 = address1_f32 + stride;
+		*address0_f32 = value[0];
+		*address1_f32 = value[1];
+		*address2_f32 = value[2];
+	}
+
+	/* Vector addition */
+	PSIMD_INTRINSIC psimd_s8 psimd_add_s8(psimd_s8 a, psimd_s8 b) {
+		return a + b;
+	}
+
+	PSIMD_INTRINSIC psimd_u8 psimd_add_u8(psimd_u8 a, psimd_u8 b) {
+		return a + b;
+	}
+
+	PSIMD_INTRINSIC psimd_s16 psimd_add_s16(psimd_s16 a, psimd_s16 b) {
+		return a + b;
+	}
+
+	PSIMD_INTRINSIC psimd_u16 psimd_add_u16(psimd_u16 a, psimd_u16 b) {
+		return a + b;
+	}
+
+	PSIMD_INTRINSIC psimd_s32 psimd_add_s32(psimd_s32 a, psimd_s32 b) {
+		return a + b;
+	}
+
+	PSIMD_INTRINSIC psimd_u32 psimd_add_u32(psimd_u32 a, psimd_u32 b) {
+		return a + b;
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_add_f32(psimd_f32 a, psimd_f32 b) {
+		#if defined(__ARM_ARCH_7A__) && defined(__ARM_NEON__) && !defined(__FAST_MATH__)
+			return (psimd_f32) vaddq_f32((float32x4_t) a, (float32x4_t) b);
+		#else
+			return a + b;
+		#endif
+	}
+
+	/* Vector subtraction */
+	PSIMD_INTRINSIC psimd_s8 psimd_sub_s8(psimd_s8 a, psimd_s8 b) {
+		return a - b;
+	}
+
+	PSIMD_INTRINSIC psimd_u8 psimd_sub_u8(psimd_u8 a, psimd_u8 b) {
+		return a - b;
+	}
+
+	PSIMD_INTRINSIC psimd_s16 psimd_sub_s16(psimd_s16 a, psimd_s16 b) {
+		return a - b;
+	}
+
+	PSIMD_INTRINSIC psimd_u16 psimd_sub_u16(psimd_u16 a, psimd_u16 b) {
+		return a - b;
+	}
+
+	PSIMD_INTRINSIC psimd_s32 psimd_sub_s32(psimd_s32 a, psimd_s32 b) {
+		return a - b;
+	}
+
+	PSIMD_INTRINSIC psimd_u32 psimd_sub_u32(psimd_u32 a, psimd_u32 b) {
+		return a - b;
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_sub_f32(psimd_f32 a, psimd_f32 b) {
+		#if defined(__ARM_ARCH_7A__) && defined(__ARM_NEON__) && !defined(__FAST_MATH__)
+			return (psimd_f32) vsubq_f32((float32x4_t) a, (float32x4_t) b);
+		#else
+			return a - b;
+		#endif
+	}
+
+	/* Vector multiplication */
+	PSIMD_INTRINSIC psimd_s8 psimd_mul_s8(psimd_s8 a, psimd_s8 b) {
+		return a * b;
+	}
+
+	PSIMD_INTRINSIC psimd_u8 psimd_mul_u8(psimd_u8 a, psimd_u8 b) {
+		return a * b;
+	}
+
+	PSIMD_INTRINSIC psimd_s16 psimd_mul_s16(psimd_s16 a, psimd_s16 b) {
+		return a * b;
+	}
+
+	PSIMD_INTRINSIC psimd_u16 psimd_mul_u16(psimd_u16 a, psimd_u16 b) {
+		return a * b;
+	}
+
+	PSIMD_INTRINSIC psimd_s32 psimd_mul_s32(psimd_s32 a, psimd_s32 b) {
+		return a * b;
+	}
+
+	PSIMD_INTRINSIC psimd_u32 psimd_mul_u32(psimd_u32 a, psimd_u32 b) {
+		return a * b;
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_mul_f32(psimd_f32 a, psimd_f32 b) {
+		#if defined(__ARM_ARCH_7A__) && defined(__ARM_NEON__) && !defined(__FAST_MATH__)
+			return (psimd_f32) vmulq_f32((float32x4_t) a, (float32x4_t) b);
+		#else
+			return a * b;
+		#endif
+	}
+
+	/* Quasi-Fused Multiply-Add */
+	PSIMD_INTRINSIC psimd_f32 psimd_qfma_f32(psimd_f32 a, psimd_f32 b, psimd_f32 c) {
+		#if defined(__aarch64__) || defined(__ARM_NEON__) && defined(__ARM_FEATURE_FMA)
+			return (psimd_f32) vfmaq_f32((float32x4_t) a, (float32x4_t) b, (float32x4_t) c);
+		#elif (defined(__x86_64__) || defined(__i386__) || defined(__i686__)) && defined(__FMA__)
+			return (psimd_f32) _mm_fmadd_ps((__m128) b, (__m128) c, (__m128) a);
+		#elif (defined(__x86_64__) || defined(__i386__) || defined(__i686__)) && defined(__FMA4__)
+			return (psimd_f32) _mm_macc_ps((__m128) b, (__m128) c, (__m128) a);
+		#elif defined(__wasm__) && defined(__wasm_simd128__) && defined(__clang__) && PSIMD_ENABLE_WASM_QFMA
+			return (psimd_f32) __builtin_wasm_qfma_f32x4(a, b, c);
+		#else
+			return a + b * c;
+		#endif
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_div_f32(psimd_f32 a, psimd_f32 b) {
+		return a / b;
+	}
+
+	/* Vector and */
+	PSIMD_INTRINSIC psimd_f32 psimd_andmask_f32(psimd_s32 mask, psimd_f32 v) {
+		return (psimd_f32) (mask & (psimd_s32) v);
+	}
+
+	/* Vector and-not */
+	PSIMD_INTRINSIC psimd_f32 psimd_andnotmask_f32(psimd_s32 mask, psimd_f32 v) {
+		return (psimd_f32) (~mask & (psimd_s32) v);
+	}
+
+	/* Vector blend */
+	PSIMD_INTRINSIC psimd_s8 psimd_blend_s8(psimd_s8 mask, psimd_s8 a, psimd_s8 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_s8) vbslq_s8((uint8x16_t) mask, (int8x16_t) a, (int8x16_t) b);
+		#elif defined(__wasm__) && defined(__wasm_simd128__) && defined(__clang__)
+			return (psimd_s8) __builtin_wasm_bitselect(a, b, mask);
+		#else
+			return (mask & a) | (~mask & b);
+		#endif
+	}
+
+	PSIMD_INTRINSIC psimd_u8 psimd_blend_u8(psimd_s8 mask, psimd_u8 a, psimd_u8 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_u8) vbslq_u8((uint8x16_t) mask, (uint8x16_t) a, (uint8x16_t) b);
+		#elif defined(__wasm__) && defined(__wasm_simd128__) && defined(__clang__)
+			return (psimd_u8) __builtin_wasm_bitselect(a, b, mask);
+		#else
+			return (psimd_u8) ((mask & (psimd_s8) a) | (~mask & (psimd_s8) b));
+		#endif
+	}
+	
+	PSIMD_INTRINSIC psimd_s16 psimd_blend_s16(psimd_s16 mask, psimd_s16 a, psimd_s16 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_s16) vbslq_s16((uint16x8_t) mask, (int16x8_t) a, (int16x8_t) b);
+		#elif defined(__wasm__) && defined(__wasm_simd128__) && defined(__clang__)
+			return (psimd_s16) __builtin_wasm_bitselect(a, b, mask);
+		#else
+			return (mask & a) | (~mask & b);
+		#endif
+	}
+
+	PSIMD_INTRINSIC psimd_u16 psimd_blend_u16(psimd_s16 mask, psimd_u16 a, psimd_u16 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_u16) vbslq_u16((uint16x8_t) mask, (uint16x8_t) a, (uint16x8_t) b);
+		#elif defined(__wasm__) && defined(__wasm_simd128__) && defined(__clang__)
+			return (psimd_u16) __builtin_wasm_bitselect(a, b, mask);
+		#else
+			return (psimd_u16) ((mask & (psimd_s16) a) | (~mask & (psimd_s16) b));
+		#endif
+	}
+	
+	PSIMD_INTRINSIC psimd_s32 psimd_blend_s32(psimd_s32 mask, psimd_s32 a, psimd_s32 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_s32) vbslq_s32((uint32x4_t) mask, (int32x4_t) a, (int32x4_t) b);
+		#elif defined(__wasm__) && defined(__wasm_simd128__) && defined(__clang__)
+			return (psimd_s32) __builtin_wasm_bitselect(a, b, mask);
+		#else
+			return (mask & a) | (~mask & b);
+		#endif
+	}
+
+	PSIMD_INTRINSIC psimd_u32 psimd_blend_u32(psimd_s32 mask, psimd_u32 a, psimd_u32 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_u32) vbslq_u32((uint32x4_t) mask, (uint32x4_t) a, (uint32x4_t) b);
+		#elif defined(__wasm__) && defined(__wasm_simd128__) && defined(__clang__)
+			return (psimd_u32) __builtin_wasm_bitselect(a, b, mask);
+		#else
+			return (psimd_u32) ((mask & (psimd_s32) a) | (~mask & (psimd_s32) b));
+		#endif
+	}
+	
+	PSIMD_INTRINSIC psimd_f32 psimd_blend_f32(psimd_s32 mask, psimd_f32 a, psimd_f32 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_f32) vbslq_f32((uint32x4_t) mask, (float32x4_t) a, (float32x4_t) b);
+		#elif defined(__wasm__) && defined(__wasm_simd128__) && defined(__clang__)
+			return (psimd_f32) __builtin_wasm_bitselect(a, b, mask);
+		#else
+			return (psimd_f32) ((mask & (psimd_s32) a) | (~mask & (psimd_s32) b));
+		#endif
+	}
+
+	/* Vector blend on sign */
+	PSIMD_INTRINSIC psimd_s8 psimd_signblend_s8(psimd_s8 x, psimd_s8 a, psimd_s8 b) {
+		return psimd_blend_s8(x >> psimd_splat_s8(7), a, b);
+	}
+
+	PSIMD_INTRINSIC psimd_u8 psimd_signblend_u8(psimd_s8 x, psimd_u8 a, psimd_u8 b) {
+		return psimd_blend_u8((x >> psimd_splat_s8(7)), a, b);
+	}
+
+	PSIMD_INTRINSIC psimd_s16 psimd_signblend_s16(psimd_s16 x, psimd_s16 a, psimd_s16 b) {
+		return psimd_blend_s16(x >> psimd_splat_s16(15), a, b);
+	}
+
+	PSIMD_INTRINSIC psimd_u16 psimd_signblend_u16(psimd_s16 x, psimd_u16 a, psimd_u16 b) {
+		return psimd_blend_u16((x >> psimd_splat_s16(15)), a, b);
+	}
+
+	PSIMD_INTRINSIC psimd_s32 psimd_signblend_s32(psimd_s32 x, psimd_s32 a, psimd_s32 b) {
+		return psimd_blend_s32(x >> psimd_splat_s32(31), a, b);
+	}
+
+	PSIMD_INTRINSIC psimd_u32 psimd_signblend_u32(psimd_s32 x, psimd_u32 a, psimd_u32 b) {
+		return psimd_blend_u32((x >> psimd_splat_s32(31)), a, b);
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_signblend_f32(psimd_f32 x, psimd_f32 a, psimd_f32 b) {
+		const psimd_s32 mask = (psimd_s32) x >> psimd_splat_s32(31);
+		return psimd_blend_f32(mask, a, b);
+	}
+
+	/* Vector absolute value */
+	PSIMD_INTRINSIC psimd_f32 psimd_abs_f32(psimd_f32 v) {
+		const psimd_s32 mask = (psimd_s32) psimd_splat_f32(-0.0f);
+		return (psimd_f32) ((psimd_s32) v & ~mask);
+	}
+
+	/* Vector negation */
+	PSIMD_INTRINSIC psimd_f32 psimd_neg_f32(psimd_f32 v) {
+		const psimd_s32 mask = (psimd_s32) psimd_splat_f32(-0.0f);
+		return (psimd_f32) ((psimd_s32) v ^ mask);
+	}
+
+	/* Vector maximum */
+	PSIMD_INTRINSIC psimd_s8 psimd_max_s8(psimd_s8 a, psimd_s8 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_s8) vmaxq_s8((int8x16_t) a, (int8x16_t) b);
+		#else
+			return psimd_blend_s8(a > b, a, b);
+		#endif
+	}
+
+	PSIMD_INTRINSIC psimd_u8 psimd_max_u8(psimd_u8 a, psimd_u8 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_u8) vmaxq_u8((uint8x16_t) a, (uint8x16_t) b);
+		#else
+			return psimd_blend_u8(a > b, a, b);
+		#endif
+	}
+
+	PSIMD_INTRINSIC psimd_s16 psimd_max_s16(psimd_s16 a, psimd_s16 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_s16) vmaxq_s16((int16x8_t) a, (int16x8_t) b);
+		#else
+			return psimd_blend_s16(a > b, a, b);
+		#endif
+	}
+
+	PSIMD_INTRINSIC psimd_u16 psimd_max_u16(psimd_u16 a, psimd_u16 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_u16) vmaxq_u16((uint16x8_t) a, (uint16x8_t) b);
+		#else
+			return psimd_blend_u16(a > b, a, b);
+		#endif
+	}
+
+	PSIMD_INTRINSIC psimd_s32 psimd_max_s32(psimd_s32 a, psimd_s32 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_s32) vmaxq_s32((int32x4_t) a, (int32x4_t) b);
+		#else
+			return psimd_blend_s32(a > b, a, b);
+		#endif
+	}
+
+	PSIMD_INTRINSIC psimd_u32 psimd_max_u32(psimd_u32 a, psimd_u32 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_u32) vmaxq_u32((uint32x4_t) a, (uint32x4_t) b);
+		#else
+			return psimd_blend_u32(a > b, a, b);
+		#endif
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_max_f32(psimd_f32 a, psimd_f32 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_f32) vmaxq_f32((float32x4_t) a, (float32x4_t) b);
+		#elif defined(__wasm__) && defined(__wasm_simd128__) && defined(__clang__)
+			return __builtin_wasm_max_f32x4(a, b);
+		#else
+			return psimd_blend_f32(a > b, a, b);
+		#endif
+	}
+
+	/* Vector minimum */
+	PSIMD_INTRINSIC psimd_s8 psimd_min_s8(psimd_s8 a, psimd_s8 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_s8) vminq_s8((int8x16_t) a, (int8x16_t) b);
+		#else
+			return psimd_blend_s8(a < b, a, b);
+		#endif
+	}
+
+	PSIMD_INTRINSIC psimd_u8 psimd_min_u8(psimd_u8 a, psimd_u8 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_u8) vminq_u8((uint8x16_t) a, (uint8x16_t) b);
+		#else
+			return psimd_blend_u8(a < b, a, b);
+		#endif
+	}
+
+	PSIMD_INTRINSIC psimd_s16 psimd_min_s16(psimd_s16 a, psimd_s16 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_s16) vminq_s16((int16x8_t) a, (int16x8_t) b);
+		#else
+			return psimd_blend_s16(a < b, a, b);
+		#endif
+	}
+
+	PSIMD_INTRINSIC psimd_u16 psimd_min_u16(psimd_u16 a, psimd_u16 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_u16) vminq_u16((uint16x8_t) a, (uint16x8_t) b);
+		#else
+			return psimd_blend_u16(a < b, a, b);
+		#endif
+	}
+
+	PSIMD_INTRINSIC psimd_s32 psimd_min_s32(psimd_s32 a, psimd_s32 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_s32) vminq_s32((int32x4_t) a, (int32x4_t) b);
+		#else
+			return psimd_blend_s32(a < b, a, b);
+		#endif
+	}
+
+	PSIMD_INTRINSIC psimd_u32 psimd_min_u32(psimd_u32 a, psimd_u32 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_u32) vminq_u32((uint32x4_t) a, (uint32x4_t) b);
+		#else
+			return psimd_blend_u32(a < b, a, b);
+		#endif
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_min_f32(psimd_f32 a, psimd_f32 b) {
+		#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_f32) vminq_f32((float32x4_t) a, (float32x4_t) b);
+		#elif defined(__wasm__) && defined(__wasm_simd128__) && defined(__clang__)
+			return __builtin_wasm_min_f32x4(a, b);
+		#else
+			return psimd_blend_f32(a < b, a, b);
+		#endif
+	}
+
+	PSIMD_INTRINSIC psimd_f32 psimd_cvt_s32_f32(psimd_s32 v) {
+		#if defined(__clang__)
+			return __builtin_convertvector(v, psimd_f32);
+		#elif defined(__ARM_NEON__) || defined(__ARM_NEON)
+			return (psimd_f32) vcvtq_f32_s32((int32x4_t) v);
+		#elif defined(__SSE2__)
+			return (psimd_f32) _mm_cvtepi32_ps((__m128i) v);
+		#else
+			return (psimd_f32) { (float) v[0], (float) v[1], (float) v[2], (float) v[3] };
+		#endif
+	}
+
+	/* Broadcast vector element */
+	#if defined(__clang__)
+		PSIMD_INTRINSIC psimd_f32 psimd_splat0_f32(psimd_f32 v) {
+			return __builtin_shufflevector(v, v, 0, 0, 0, 0);
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_splat1_f32(psimd_f32 v) {
+			return __builtin_shufflevector(v, v, 1, 1, 1, 1);
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_splat2_f32(psimd_f32 v) {
+			return __builtin_shufflevector(v, v, 2, 2, 2, 2);
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_splat3_f32(psimd_f32 v) {
+			return __builtin_shufflevector(v, v, 3, 3, 3, 3);
+		}
+	#else
+		PSIMD_INTRINSIC psimd_f32 psimd_splat0_f32(psimd_f32 v) {
+			return __builtin_shuffle(v, (psimd_s32) { 0, 0, 0, 0 });
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_splat1_f32(psimd_f32 v) {
+			return __builtin_shuffle(v, (psimd_s32) { 1, 1, 1, 1 });
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_splat2_f32(psimd_f32 v) {
+			return __builtin_shuffle(v, (psimd_s32) { 2, 2, 2, 2 });
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_splat3_f32(psimd_f32 v) {
+			return __builtin_shuffle(v, (psimd_s32) { 3, 3, 3, 3 });
+		}
+	#endif
+
+	/* Reversal of vector elements */
+	#if defined(__clang__)
+		PSIMD_INTRINSIC psimd_s8 psimd_reverse_s8(psimd_s8 v) {
+			return __builtin_shufflevector(v, v, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
+		}
+
+		PSIMD_INTRINSIC psimd_u8 psimd_reverse_u8(psimd_u8 v) {
+			return __builtin_shufflevector(v, v, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
+		}
+
+		PSIMD_INTRINSIC psimd_s16 psimd_reverse_s16(psimd_s16 v) {
+			return __builtin_shufflevector(v, v, 7, 6, 5, 4, 3, 2, 1, 0);
+		}
+
+		PSIMD_INTRINSIC psimd_u16 psimd_reverse_u16(psimd_u16 v) {
+			return __builtin_shufflevector(v, v, 7, 6, 5, 4, 3, 2, 1, 0);
+		}
+
+		PSIMD_INTRINSIC psimd_s32 psimd_reverse_s32(psimd_s32 v) {
+			return __builtin_shufflevector(v, v, 3, 2, 1, 0);
+		}
+
+		PSIMD_INTRINSIC psimd_u32 psimd_reverse_u32(psimd_u32 v) {
+			return __builtin_shufflevector(v, v, 3, 2, 1, 0);
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_reverse_f32(psimd_f32 v) {
+			return __builtin_shufflevector(v, v, 3, 2, 1, 0);
+		}
+	#else
+		PSIMD_INTRINSIC psimd_s8 psimd_reverse_s8(psimd_s8 v) {
+			return __builtin_shuffle(v, (psimd_s8) { 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 });
+		}
+
+		PSIMD_INTRINSIC psimd_u8 psimd_reverse_u8(psimd_u8 v) {
+			return __builtin_shuffle(v, (psimd_s8) { 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 });
+		}
+
+		PSIMD_INTRINSIC psimd_s16 psimd_reverse_s16(psimd_s16 v) {
+			return __builtin_shuffle(v, (psimd_s16) { 7, 6, 5, 4, 3, 2, 1, 0 });
+		}
+
+		PSIMD_INTRINSIC psimd_u16 psimd_reverse_u16(psimd_u16 v) {
+			return __builtin_shuffle(v, (psimd_s16) { 7, 6, 5, 4, 3, 2, 1, 0 });
+		}
+
+		PSIMD_INTRINSIC psimd_s32 psimd_reverse_s32(psimd_s32 v) {
+			return __builtin_shuffle(v, (psimd_s32) { 3, 2, 1, 0 });
+		}
+
+		PSIMD_INTRINSIC psimd_u32 psimd_reverse_u32(psimd_u32 v) {
+			return __builtin_shuffle(v, (psimd_s32) { 3, 2, 1, 0 });
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_reverse_f32(psimd_f32 v) {
+			return __builtin_shuffle(v, (psimd_s32) { 3, 2, 1, 0 });
+		}
+	#endif
+
+	/* Interleaving of vector elements */
+	#if defined(__clang__)
+		PSIMD_INTRINSIC psimd_s16 psimd_interleave_lo_s16(psimd_s16 a, psimd_s16 b) {
+			return __builtin_shufflevector(a, b, 0, 8+0, 1, 8+1, 2, 8+2, 3, 8+3);
+		}
+
+		PSIMD_INTRINSIC psimd_s16 psimd_interleave_hi_s16(psimd_s16 a, psimd_s16 b) {
+			return __builtin_shufflevector(a, b, 4, 8+4, 5, 8+5, 6, 8+6, 7, 8+7);
+		}
+
+		PSIMD_INTRINSIC psimd_u16 psimd_interleave_lo_u16(psimd_u16 a, psimd_u16 b) {
+			return __builtin_shufflevector(a, b, 0, 8+0, 1, 8+1, 2, 8+2, 3, 8+3);
+		}
+
+		PSIMD_INTRINSIC psimd_u16 psimd_interleave_hi_u16(psimd_u16 a, psimd_u16 b) {
+			return __builtin_shufflevector(a, b, 4, 8+4, 5, 8+5, 6, 8+6, 7, 8+7);
+		}
+
+		PSIMD_INTRINSIC psimd_s32 psimd_interleave_lo_s32(psimd_s32 a, psimd_s32 b) {
+			return __builtin_shufflevector(a, b, 0, 4+0, 1, 4+1);
+		}
+
+		PSIMD_INTRINSIC psimd_s32 psimd_interleave_hi_s32(psimd_s32 a, psimd_s32 b) {
+			return __builtin_shufflevector(a, b, 2, 4+2, 3, 4+3);
+		}
+
+		PSIMD_INTRINSIC psimd_u32 psimd_interleave_lo_u32(psimd_u32 a, psimd_u32 b) {
+			return __builtin_shufflevector(a, b, 0, 4+0, 1, 4+1);
+		}
+
+		PSIMD_INTRINSIC psimd_u32 psimd_interleave_hi_u32(psimd_u32 a, psimd_u32 b) {
+			return __builtin_shufflevector(a, b, 2, 4+2, 3, 4+3);
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_interleave_lo_f32(psimd_f32 a, psimd_f32 b) {
+			return __builtin_shufflevector(a, b, 0, 4+0, 1, 4+1);
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_interleave_hi_f32(psimd_f32 a, psimd_f32 b) {
+			return __builtin_shufflevector(a, b, 2, 4+2, 3, 4+3);
+		}
+	#else
+		PSIMD_INTRINSIC psimd_s16 psimd_interleave_lo_s16(psimd_s16 a, psimd_s16 b) {
+			return __builtin_shuffle(a, b, (psimd_s16) { 0, 8+0, 1, 8+1, 2, 8+2, 3, 8+3 });
+		}
+
+		PSIMD_INTRINSIC psimd_s16 psimd_interleave_hi_s16(psimd_s16 a, psimd_s16 b) {
+			return __builtin_shuffle(a, b, (psimd_s16) { 4, 8+4, 5, 8+5, 6, 8+6, 7, 8+7 });
+		}
+
+		PSIMD_INTRINSIC psimd_u16 psimd_interleave_lo_u16(psimd_u16 a, psimd_u16 b) {
+			return __builtin_shuffle(a, b, (psimd_s16) { 0, 8+0, 1, 8+1, 2, 8+2, 3, 8+3 });
+		}
+
+		PSIMD_INTRINSIC psimd_u16 psimd_interleave_hi_u16(psimd_u16 a, psimd_u16 b) {
+			return __builtin_shuffle(a, b, (psimd_s16) { 4, 8+4, 5, 8+5, 6, 8+6, 7, 8+7 });
+		}
+
+		PSIMD_INTRINSIC psimd_s32 psimd_interleave_lo_s32(psimd_s32 a, psimd_s32 b) {
+			return __builtin_shuffle(a, b, (psimd_s32) { 0, 4+0, 1, 4+1 });
+		}
+
+		PSIMD_INTRINSIC psimd_s32 psimd_interleave_hi_s32(psimd_s32 a, psimd_s32 b) {
+			return __builtin_shuffle(a, b, (psimd_s32) { 2, 4+2, 3, 4+3 });
+		}
+
+		PSIMD_INTRINSIC psimd_u32 psimd_interleave_lo_u32(psimd_u32 a, psimd_u32 b) {
+			return __builtin_shuffle(a, b, (psimd_s32) { 0, 4+0, 1, 4+1 });
+		}
+
+		PSIMD_INTRINSIC psimd_u32 psimd_interleave_hi_u32(psimd_u32 a, psimd_u32 b) {
+			return __builtin_shuffle(a, b, (psimd_s32) { 2, 4+2, 3, 4+3 });
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_interleave_lo_f32(psimd_f32 a, psimd_f32 b) {
+			return __builtin_shuffle(a, b, (psimd_s32) { 0, 4+0, 1, 4+1 });
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_interleave_hi_f32(psimd_f32 a, psimd_f32 b) {
+			return __builtin_shuffle(a, b, (psimd_s32) { 2, 4+2, 3, 4+3 });
+		}
+	#endif
+
+	/* Concatenation of low/high vector elements */
+	#if defined(__clang__)
+		PSIMD_INTRINSIC psimd_s16 psimd_concat_lo_s16(psimd_s16 a, psimd_s16 b) {
+			return __builtin_shufflevector(a, b, 0, 1, 2, 3, 8+0, 8+1, 8+2, 8+3);
+		}
+
+		PSIMD_INTRINSIC psimd_s16 psimd_concat_hi_s16(psimd_s16 a, psimd_s16 b) {
+			return __builtin_shufflevector(a, b, 4, 5, 6, 7, 8+4, 8+5, 8+6, 8+7);
+		}
+
+		PSIMD_INTRINSIC psimd_u16 psimd_concat_lo_u16(psimd_u16 a, psimd_u16 b) {
+			return __builtin_shufflevector(a, b, 0, 1, 2, 3, 8+0, 8+1, 8+2, 8+3);
+		}
+
+		PSIMD_INTRINSIC psimd_u16 psimd_concat_hi_u16(psimd_u16 a, psimd_u16 b) {
+			return __builtin_shufflevector(a, b, 4, 5, 6, 7, 8+4, 8+5, 8+6, 8+7);
+		}
+
+		PSIMD_INTRINSIC psimd_s32 psimd_concat_lo_s32(psimd_s32 a, psimd_s32 b) {
+			return __builtin_shufflevector(a, b, 0, 1, 4+0, 4+1);
+		}
+
+		PSIMD_INTRINSIC psimd_s32 psimd_concat_hi_s32(psimd_s32 a, psimd_s32 b) {
+			return __builtin_shufflevector(a, b, 2, 3, 4+2, 4+3);
+		}
+
+		PSIMD_INTRINSIC psimd_u32 psimd_concat_lo_u32(psimd_u32 a, psimd_u32 b) {
+			return __builtin_shufflevector(a, b, 0, 1, 4+0, 4+1);
+		}
+
+		PSIMD_INTRINSIC psimd_u32 psimd_concat_hi_u32(psimd_u32 a, psimd_u32 b) {
+			return __builtin_shufflevector(a, b, 2, 3, 4+2, 4+3);
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_concat_lo_f32(psimd_f32 a, psimd_f32 b) {
+			return __builtin_shufflevector(a, b, 0, 1, 4+0, 4+1);
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_concat_hi_f32(psimd_f32 a, psimd_f32 b) {
+			return __builtin_shufflevector(a, b, 2, 3, 4+2, 4+3);
+		}
+	#else
+		PSIMD_INTRINSIC psimd_s16 psimd_concat_lo_s16(psimd_s16 a, psimd_s16 b) {
+			return __builtin_shuffle(a, b, (psimd_s16) { 0, 1, 2, 3, 8+0, 8+1, 8+2, 8+3 });
+		}
+
+		PSIMD_INTRINSIC psimd_s16 psimd_concat_hi_s16(psimd_s16 a, psimd_s16 b) {
+			return __builtin_shuffle(a, b, (psimd_s16) { 4, 5, 6, 7, 8+4, 8+5, 8+6, 8+7 });
+		}
+
+		PSIMD_INTRINSIC psimd_u16 psimd_concat_lo_u16(psimd_u16 a, psimd_u16 b) {
+			return __builtin_shuffle(a, b, (psimd_s16) { 0, 1, 2, 3, 8+0, 8+1, 8+2, 8+3 });
+		}
+
+		PSIMD_INTRINSIC psimd_u16 psimd_concat_hi_u16(psimd_u16 a, psimd_u16 b) {
+			return __builtin_shuffle(a, b, (psimd_s16) { 4, 5, 6, 7, 8+4, 8+5, 8+6, 8+7 });
+		}
+
+		PSIMD_INTRINSIC psimd_s32 psimd_concat_lo_s32(psimd_s32 a, psimd_s32 b) {
+			return __builtin_shuffle(a, b, (psimd_s32) { 0, 1, 4+0, 4+1 });
+		}
+
+		PSIMD_INTRINSIC psimd_s32 psimd_concat_hi_s32(psimd_s32 a, psimd_s32 b) {
+			return __builtin_shuffle(a, b, (psimd_s32) { 2, 3, 4+2, 4+3 });
+		}
+
+		PSIMD_INTRINSIC psimd_u32 psimd_concat_lo_u32(psimd_u32 a, psimd_u32 b) {
+			return __builtin_shuffle(a, b, (psimd_s32) { 0, 1, 4+0, 4+1 });
+		}
+
+		PSIMD_INTRINSIC psimd_u32 psimd_concat_hi_u32(psimd_u32 a, psimd_u32 b) {
+			return __builtin_shuffle(a, b, (psimd_s32) { 2, 3, 4+2, 4+3 });
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_concat_lo_f32(psimd_f32 a, psimd_f32 b) {
+			return __builtin_shuffle(a, b, (psimd_s32) { 0, 1, 4+0, 4+1 });
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_concat_hi_f32(psimd_f32 a, psimd_f32 b) {
+			return __builtin_shuffle(a, b, (psimd_s32) { 2, 3, 4+2, 4+3 });
+		}
+	#endif
+
+	/* Concatenation of even/odd vector elements */
+	#if defined(__clang__)
+		PSIMD_INTRINSIC psimd_s8 psimd_concat_even_s8(psimd_s8 a, psimd_s8 b) {
+			return __builtin_shufflevector(a, b,
+				0, 2, 4, 6, 8, 10, 12, 14, 16+0, 16+2, 16+4, 16+6, 16+8, 16+10, 16+12, 16+14);
+		}
+
+		PSIMD_INTRINSIC psimd_s8 psimd_concat_odd_s8(psimd_s8 a, psimd_s8 b) {
+			return __builtin_shufflevector(a, b,
+				1, 3, 5, 7, 9, 11, 13, 15, 16+1, 16+3, 16+5, 16+7, 16+9, 16+11, 16+13, 16+15);
+		}
+
+		PSIMD_INTRINSIC psimd_u8 psimd_concat_even_u8(psimd_u8 a, psimd_u8 b) {
+			return __builtin_shufflevector(a, b,
+				0, 2, 4, 6, 8, 10, 12, 14, 16+0, 16+2, 16+4, 16+6, 16+8, 16+10, 16+12, 16+14);
+		}
+
+		PSIMD_INTRINSIC psimd_u8 psimd_concat_odd_u8(psimd_u8 a, psimd_u8 b) {
+			return __builtin_shufflevector(a, b,
+				1, 3, 5, 7, 9, 11, 13, 15, 16+1, 16+3, 16+5, 16+7, 16+9, 16+11, 16+13, 16+15);
+		}
+
+		PSIMD_INTRINSIC psimd_s16 psimd_concat_even_s16(psimd_s16 a, psimd_s16 b) {
+			return __builtin_shufflevector(a, b, 0, 2, 4, 6, 8+0, 8+2, 8+4, 8+6);
+		}
+
+		PSIMD_INTRINSIC psimd_s16 psimd_concat_odd_s16(psimd_s16 a, psimd_s16 b) {
+			return __builtin_shufflevector(a, b, 1, 3, 5, 7, 8+1, 8+3, 8+5, 8+7);
+		}
+
+		PSIMD_INTRINSIC psimd_u16 psimd_concat_even_u16(psimd_u16 a, psimd_u16 b) {
+			return __builtin_shufflevector(a, b, 0, 2, 4, 6, 8+0, 8+2, 8+4, 8+6);
+		}
+
+		PSIMD_INTRINSIC psimd_u16 psimd_concat_odd_u16(psimd_u16 a, psimd_u16 b) {
+			return __builtin_shufflevector(a, b, 1, 3, 5, 7, 8+1, 8+3, 8+5, 8+7);
+		}
+
+		PSIMD_INTRINSIC psimd_s32 psimd_concat_even_s32(psimd_s32 a, psimd_s32 b) {
+			return __builtin_shufflevector(a, b, 0, 2, 4+0, 4+2);
+		}
+
+		PSIMD_INTRINSIC psimd_s32 psimd_concat_odd_s32(psimd_s32 a, psimd_s32 b) {
+			return __builtin_shufflevector(a, b, 1, 3, 4+1, 4+3);
+		}
+
+		PSIMD_INTRINSIC psimd_u32 psimd_concat_even_u32(psimd_u32 a, psimd_u32 b) {
+			return __builtin_shufflevector(a, b, 0, 2, 4+0, 4+2);
+		}
+
+		PSIMD_INTRINSIC psimd_u32 psimd_concat_odd_u32(psimd_u32 a, psimd_u32 b) {
+			return __builtin_shufflevector(a, b, 1, 3, 4+1, 4+3);
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_concat_even_f32(psimd_f32 a, psimd_f32 b) {
+			return __builtin_shufflevector(a, b, 0, 2, 4+0, 4+2);
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_concat_odd_f32(psimd_f32 a, psimd_f32 b) {
+			return __builtin_shufflevector(a, b, 1, 3, 4+1, 4+3);
+		}
+	#else
+		PSIMD_INTRINSIC psimd_s8 psimd_concat_even_s8(psimd_s8 a, psimd_s8 b) {
+			return __builtin_shuffle(a, b,
+				(psimd_s8) { 0, 2, 4, 6, 8, 10, 12, 14, 16+0, 16+2, 16+4, 16+6, 16+8, 16+10, 16+12, 16+14 });
+		}
+
+		PSIMD_INTRINSIC psimd_s8 psimd_concat_odd_s8(psimd_s8 a, psimd_s8 b) {
+			return __builtin_shuffle(a, b,
+				(psimd_s8) { 1, 3, 5, 7, 9, 11, 13, 15, 16+1, 16+3, 16+5, 16+7, 16+9, 16+11, 16+13, 16+15 });
+		}
+
+		PSIMD_INTRINSIC psimd_u8 psimd_concat_even_u8(psimd_u8 a, psimd_u8 b) {
+			return __builtin_shuffle(a, b,
+				(psimd_s8) { 0, 2, 4, 6, 8, 10, 12, 14, 16+0, 16+2, 16+4, 16+6, 16+8, 16+10, 16+12, 16+14 });
+		}
+
+		PSIMD_INTRINSIC psimd_u8 psimd_concat_odd_u8(psimd_u8 a, psimd_u8 b) {
+			return __builtin_shuffle(a, b,
+				(psimd_s8) { 1, 3, 5, 7, 9, 11, 13, 15, 16+1, 16+3, 16+5, 16+7, 16+9, 16+11, 16+13, 16+15 });
+		}
+
+		PSIMD_INTRINSIC psimd_s16 psimd_concat_even_s16(psimd_s16 a, psimd_s16 b) {
+			return __builtin_shuffle(a, b, (psimd_s16) { 0, 2, 4, 6, 8+0, 8+2, 8+4, 8+6 });
+		}
+
+		PSIMD_INTRINSIC psimd_s16 psimd_concat_odd_s16(psimd_s16 a, psimd_s16 b) {
+			return __builtin_shuffle(a, b, (psimd_s16) { 1, 3, 5, 7, 8+1, 8+3, 8+5, 8+7 });
+		}
+
+		PSIMD_INTRINSIC psimd_u16 psimd_concat_even_u16(psimd_u16 a, psimd_u16 b) {
+			return __builtin_shuffle(a, b, (psimd_s16) { 0, 2, 4, 6, 8+0, 8+2, 8+4, 8+6 });
+		}
+
+		PSIMD_INTRINSIC psimd_u16 psimd_concat_odd_u16(psimd_u16 a, psimd_u16 b) {
+			return __builtin_shuffle(a, b, (psimd_s16) { 1, 3, 5, 7, 8+1, 8+3, 8+5, 8+7 });
+		}
+
+		PSIMD_INTRINSIC psimd_s32 psimd_concat_even_s32(psimd_s32 a, psimd_s32 b) {
+			return __builtin_shuffle(a, b, (psimd_s32) { 0, 2, 4+0, 4+2 });
+		}
+
+		PSIMD_INTRINSIC psimd_s32 psimd_concat_odd_s32(psimd_s32 a, psimd_s32 b) {
+			return __builtin_shuffle(a, b, (psimd_s32) { 1, 3, 4+1, 4+3 });
+		}
+
+		PSIMD_INTRINSIC psimd_u32 psimd_concat_even_u32(psimd_u32 a, psimd_u32 b) {
+			return __builtin_shuffle(a, b, (psimd_s32) { 0, 2, 4+0, 4+2 });
+		}
+
+		PSIMD_INTRINSIC psimd_u32 psimd_concat_odd_u32(psimd_u32 a, psimd_u32 b) {
+			return __builtin_shuffle(a, b, (psimd_s32) { 1, 3, 4+1, 4+3 });
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_concat_even_f32(psimd_f32 a, psimd_f32 b) {
+			return __builtin_shuffle(a, b, (psimd_s32) { 0, 2, 4+0, 4+2 });
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_concat_odd_f32(psimd_f32 a, psimd_f32 b) {
+			return __builtin_shuffle(a, b, (psimd_s32) { 1, 3, 4+1, 4+3 });
+		}
+	#endif
+
+	/* Vector reduce */
+	#if defined(__clang__)
+		PSIMD_INTRINSIC psimd_f32 psimd_allreduce_sum_f32(psimd_f32 v) {
+			const psimd_f32 temp = v + __builtin_shufflevector(v, v, 2, 3, 0, 1);
+			return temp + __builtin_shufflevector(temp, temp, 1, 0, 3, 2);
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_allreduce_max_f32(psimd_f32 v) {
+			const psimd_f32 temp = psimd_max_f32(v, __builtin_shufflevector(v, v, 2, 3, 0, 1));
+			return psimd_max_f32(temp, __builtin_shufflevector(temp, temp, 1, 0, 3, 2));
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_allreduce_min_f32(psimd_f32 v) {
+			const psimd_f32 temp = psimd_min_f32(v, __builtin_shufflevector(v, v, 2, 3, 0, 1));
+			return psimd_min_f32(temp, __builtin_shufflevector(temp, temp, 1, 0, 3, 2));
+		}
+
+		PSIMD_INTRINSIC float psimd_reduce_sum_f32(psimd_f32 v) {
+			const psimd_f32 temp = v + __builtin_shufflevector(v, v, 2, 3, -1, -1);
+			const psimd_f32 result = temp + __builtin_shufflevector(temp, temp, 1, -1, -1, -1);
+			return result[0];
+		}
+
+		PSIMD_INTRINSIC float psimd_reduce_max_f32(psimd_f32 v) {
+			const psimd_f32 temp = psimd_max_f32(v, __builtin_shufflevector(v, v, 2, 3, -1, -1));
+			const psimd_f32 result = psimd_max_f32(temp, __builtin_shufflevector(temp, temp, 1, -1, -1, -1));
+			return result[0];
+		}
+
+		PSIMD_INTRINSIC float psimd_reduce_min_f32(psimd_f32 v) {
+			const psimd_f32 temp = psimd_min_f32(v, __builtin_shufflevector(v, v, 2, 3, -1, -1));
+			const psimd_f32 result = psimd_min_f32(temp, __builtin_shufflevector(temp, temp, 1, -1, -1, -1));
+			return result[0];
+		}
+	#else
+		PSIMD_INTRINSIC psimd_f32 psimd_allreduce_sum_f32(psimd_f32 v) {
+			const psimd_f32 temp = v + __builtin_shuffle(v, (psimd_s32) { 2, 3, 0, 1 });
+			return temp + __builtin_shuffle(temp, (psimd_s32) { 1, 0, 3, 2 });
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_allreduce_max_f32(psimd_f32 v) {
+			const psimd_f32 temp = psimd_max_f32(v, __builtin_shuffle(v, (psimd_s32) { 2, 3, 0, 1 }));
+			return psimd_max_f32(temp, __builtin_shuffle(temp, (psimd_s32) { 1, 0, 3, 2 }));
+		}
+
+		PSIMD_INTRINSIC psimd_f32 psimd_allreduce_min_f32(psimd_f32 v) {
+			const psimd_f32 temp = psimd_min_f32(v, __builtin_shuffle(v, (psimd_s32) { 2, 3, 0, 1 }));
+			return psimd_min_f32(temp, __builtin_shuffle(temp, (psimd_s32) { 1, 0, 3, 2 }));
+		}
+
+		PSIMD_INTRINSIC float psimd_reduce_sum_f32(psimd_f32 v) {
+			const psimd_f32 result = psimd_allreduce_sum_f32(v);
+			return result[0];
+		}
+
+		PSIMD_INTRINSIC float psimd_reduce_max_f32(psimd_f32 v) {
+			const psimd_f32 result = psimd_allreduce_max_f32(v);
+			return result[0];
+		}
+
+		PSIMD_INTRINSIC float psimd_reduce_min_f32(psimd_f32 v) {
+			const psimd_f32 result = psimd_allreduce_min_f32(v);
+			return result[0];
+		}
+	#endif
+#endif
+
+#endif /* PSIMD_H */
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/pthreadpool.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/pthreadpool.h
new file mode 100644
index 0000000000000000000000000000000000000000..953ccc4cc24070aa4897fabc081cba466e34170a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/pthreadpool.h
@@ -0,0 +1,2555 @@
+#ifndef PTHREADPOOL_H_
+#define PTHREADPOOL_H_
+
+#include 
+#include 
+
+typedef struct pthreadpool* pthreadpool_t;
+
+typedef void (*pthreadpool_task_1d_t)(void*, size_t);
+typedef void (*pthreadpool_task_1d_with_thread_t)(void*, size_t, size_t);
+typedef void (*pthreadpool_task_1d_tile_1d_t)(void*, size_t, size_t);
+typedef void (*pthreadpool_task_2d_t)(void*, size_t, size_t);
+typedef void (*pthreadpool_task_2d_with_thread_t)(void*, size_t, size_t, size_t);
+typedef void (*pthreadpool_task_2d_tile_1d_t)(void*, size_t, size_t, size_t);
+typedef void (*pthreadpool_task_2d_tile_2d_t)(void*, size_t, size_t, size_t, size_t);
+typedef void (*pthreadpool_task_3d_t)(void*, size_t, size_t, size_t);
+typedef void (*pthreadpool_task_3d_tile_1d_t)(void*, size_t, size_t, size_t, size_t);
+typedef void (*pthreadpool_task_3d_tile_1d_with_thread_t)(void*, size_t, size_t, size_t, size_t, size_t);
+typedef void (*pthreadpool_task_3d_tile_2d_t)(void*, size_t, size_t, size_t, size_t, size_t);
+typedef void (*pthreadpool_task_4d_t)(void*, size_t, size_t, size_t, size_t);
+typedef void (*pthreadpool_task_4d_tile_1d_t)(void*, size_t, size_t, size_t, size_t, size_t);
+typedef void (*pthreadpool_task_4d_tile_2d_t)(void*, size_t, size_t, size_t, size_t, size_t, size_t);
+typedef void (*pthreadpool_task_5d_t)(void*, size_t, size_t, size_t, size_t, size_t);
+typedef void (*pthreadpool_task_5d_tile_1d_t)(void*, size_t, size_t, size_t, size_t, size_t, size_t);
+typedef void (*pthreadpool_task_5d_tile_2d_t)(void*, size_t, size_t, size_t, size_t, size_t, size_t, size_t);
+typedef void (*pthreadpool_task_6d_t)(void*, size_t, size_t, size_t, size_t, size_t, size_t);
+typedef void (*pthreadpool_task_6d_tile_1d_t)(void*, size_t, size_t, size_t, size_t, size_t, size_t, size_t);
+typedef void (*pthreadpool_task_6d_tile_2d_t)(void*, size_t, size_t, size_t, size_t, size_t, size_t, size_t, size_t);
+
+typedef void (*pthreadpool_task_1d_with_id_t)(void*, uint32_t, size_t);
+typedef void (*pthreadpool_task_2d_tile_1d_with_id_t)(void*, uint32_t, size_t, size_t, size_t);
+typedef void (*pthreadpool_task_2d_tile_2d_with_id_t)(void*, uint32_t, size_t, size_t, size_t, size_t);
+typedef void (*pthreadpool_task_3d_tile_1d_with_id_t)(void*, uint32_t, size_t, size_t, size_t, size_t);
+typedef void (*pthreadpool_task_3d_tile_2d_with_id_t)(void*, uint32_t, size_t, size_t, size_t, size_t, size_t);
+typedef void (*pthreadpool_task_4d_tile_2d_with_id_t)(void*, uint32_t, size_t, size_t, size_t, size_t, size_t, size_t);
+
+typedef void (*pthreadpool_task_2d_tile_1d_with_id_with_thread_t)(void*, uint32_t, size_t, size_t, size_t, size_t);
+typedef void (*pthreadpool_task_3d_tile_1d_with_id_with_thread_t)(void*, uint32_t, size_t, size_t, size_t, size_t, size_t);
+
+
+/**
+ * Disable support for denormalized numbers to the maximum extent possible for
+ * the duration of the computation.
+ *
+ * Handling denormalized floating-point numbers is often implemented in
+ * microcode, and incurs significant performance degradation. This hint
+ * instructs the thread pool to disable support for denormalized numbers before
+ * running the computation by manipulating architecture-specific control
+ * registers, and restore the initial value of control registers after the
+ * computation is complete. The thread pool temporary disables denormalized
+ * numbers on all threads involved in the computation (i.e. the caller threads,
+ * and potentially worker threads).
+ *
+ * Disabling denormalized numbers may have a small negative effect on results'
+ * accuracy. As various architectures differ in capabilities to control
+ * processing of denormalized numbers, using this flag may also hurt results'
+ * reproducibility across different instruction set architectures.
+ */
+#define PTHREADPOOL_FLAG_DISABLE_DENORMALS 0x00000001
+
+/**
+ * Yield worker threads to the system scheduler after the operation is finished.
+ *
+ * Force workers to use kernel wait (instead of active spin-wait by default) for
+ * new commands after this command is processed. This flag affects only the
+ * immediate next operation on this thread pool. To make the thread pool always
+ * use kernel wait, pass this flag to all parallelization functions.
+ */
+#define PTHREADPOOL_FLAG_YIELD_WORKERS 0x00000002
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * Create a thread pool with the specified number of threads.
+ *
+ * @param  threads_count  the number of threads in the thread pool.
+ *    A value of 0 has special interpretation: it creates a thread pool with as
+ *    many threads as there are logical processors in the system.
+ *
+ * @returns  A pointer to an opaque thread pool object if the call is
+ *    successful, or NULL pointer if the call failed.
+ */
+pthreadpool_t pthreadpool_create(size_t threads_count);
+
+/**
+ * Query the number of threads in a thread pool.
+ *
+ * @param  threadpool  the thread pool to query.
+ *
+ * @returns  The number of threads in the thread pool.
+ */
+size_t pthreadpool_get_threads_count(pthreadpool_t threadpool);
+
+/**
+ * Process items on a 1D grid.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range; i++)
+ *     function(context, i);
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each item.
+ * @param context     the first argument passed to the specified function.
+ * @param range       the number of items on the 1D grid to process. The
+ *    specified function will be called once for each item.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_1d(
+	pthreadpool_t threadpool,
+	pthreadpool_task_1d_t function,
+	void* context,
+	size_t range,
+	uint32_t flags);
+
+/**
+ * Process items on a 1D grid passing along the current thread id.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range; i++)
+ *     function(context, thread_index, i);
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each item.
+ * @param context     the first argument passed to the specified function.
+ * @param range       the number of items on the 1D grid to process. The
+ *    specified function will be called once for each item.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_1d_with_thread(
+	pthreadpool_t threadpool,
+	pthreadpool_task_1d_with_thread_t function,
+	void* context,
+	size_t range,
+	uint32_t flags);
+
+/**
+ * Process items on a 1D grid using a microarchitecture-aware task function.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   uint32_t uarch_index = cpuinfo_initialize() ?
+ *       cpuinfo_get_current_uarch_index() : default_uarch_index;
+ *   if (uarch_index > max_uarch_index) uarch_index = default_uarch_index;
+ *   for (size_t i = 0; i < range; i++)
+ *     function(context, uarch_index, i);
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool           the thread pool to use for parallelisation. If
+ *    threadpool is NULL, all items are processed serially on the calling
+ *    thread.
+ * @param function             the function to call for each item.
+ * @param context              the first argument passed to the specified
+ *    function.
+ * @param default_uarch_index  the microarchitecture index to use when
+ *    pthreadpool is configured without cpuinfo, cpuinfo initialization failed,
+ *    or index returned by cpuinfo_get_current_uarch_index() exceeds the
+ *    max_uarch_index value.
+ * @param max_uarch_index      the maximum microarchitecture index expected by
+ *    the specified function. If the index returned by
+ *    cpuinfo_get_current_uarch_index() exceeds this value, default_uarch_index
+ *    will be used instead. default_uarch_index can exceed max_uarch_index.
+ * @param range                the number of items on the 1D grid to process.
+ *    The specified function will be called once for each item.
+ * @param flags                a bitwise combination of zero or more optional
+ *    flags (PTHREADPOOL_FLAG_DISABLE_DENORMALS or
+ *    PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_1d_with_uarch(
+	pthreadpool_t threadpool,
+	pthreadpool_task_1d_with_id_t function,
+	void* context,
+	uint32_t default_uarch_index,
+	uint32_t max_uarch_index,
+	size_t range,
+	uint32_t flags);
+
+/**
+ * Process items on a 1D grid with specified maximum tile size.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range; i += tile)
+ *     function(context, i, min(range - i, tile));
+ *
+ * When the call returns, all items have been processed and the thread pool is
+ * ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool,
+ *    the calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each tile.
+ * @param context     the first argument passed to the specified function.
+ * @param range       the number of items on the 1D grid to process.
+ * @param tile        the maximum number of items on the 1D grid to process in
+ *    one function call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_1d_tile_1d(
+	pthreadpool_t threadpool,
+	pthreadpool_task_1d_tile_1d_t function,
+	void* context,
+	size_t range,
+	size_t tile,
+	uint32_t flags);
+
+/**
+ * Process items on a 2D grid.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       function(context, i, j);
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each item.
+ * @param context     the first argument passed to the specified function.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 2D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 2D grid.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_2d(
+	pthreadpool_t threadpool,
+	pthreadpool_task_2d_t function,
+	void* context,
+	size_t range_i,
+	size_t range_j,
+	uint32_t flags);
+
+/**
+ * Process items on a 2D grid passing along the current thread id.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       function(context, thread_index, i, j);
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each item.
+ * @param context     the first argument passed to the specified function.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 2D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 2D grid.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_2d_with_thread(
+	pthreadpool_t threadpool,
+	pthreadpool_task_2d_with_thread_t function,
+	void* context,
+	size_t range_i,
+	size_t range_j,
+	uint32_t flags);
+
+/**
+ * Process items on a 2D grid with the specified maximum tile size along the
+ * last grid dimension.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j += tile_j)
+ *       function(context, i, j, min(range_j - j, tile_j));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each tile.
+ * @param context     the first argument passed to the specified function.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 2D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 2D grid.
+ * @param tile_j      the maximum number of items along the second dimension of
+ *    the 2D grid to process in one function call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_2d_tile_1d(
+	pthreadpool_t threadpool,
+	pthreadpool_task_2d_tile_1d_t function,
+	void* context,
+	size_t range_i,
+	size_t range_j,
+	size_t tile_j,
+	uint32_t flags);
+
+/**
+ * Process items on a 2D grid with the specified maximum tile size along the
+ * last grid dimension using a microarchitecture-aware task function.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   uint32_t uarch_index = cpuinfo_initialize() ?
+ *       cpuinfo_get_current_uarch_index() : default_uarch_index;
+ *   if (uarch_index > max_uarch_index) uarch_index = default_uarch_index;
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j += tile_j)
+ *       function(context, uarch_index, i, j, min(range_j - j, tile_j));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each tile.
+ * @param context     the first argument passed to the specified function.
+ * @param default_uarch_index  the microarchitecture index to use when
+ *    pthreadpool is configured without cpuinfo, cpuinfo initialization failed,
+ *    or index returned by cpuinfo_get_current_uarch_index() exceeds the
+ *    max_uarch_index value.
+ * @param max_uarch_index      the maximum microarchitecture index expected by
+ *    the specified function. If the index returned by
+ *    cpuinfo_get_current_uarch_index() exceeds this value, default_uarch_index
+ *    will be used instead. default_uarch_index can exceed max_uarch_index.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 2D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 2D grid.
+ * @param tile_j      the maximum number of items along the second dimension of
+ *    the 2D grid to process in one function call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_2d_tile_1d_with_uarch(
+	pthreadpool_t threadpool,
+	pthreadpool_task_2d_tile_1d_with_id_t function,
+	void* context,
+	uint32_t default_uarch_index,
+	uint32_t max_uarch_index,
+	size_t range_i,
+	size_t range_j,
+	size_t tile_j,
+	uint32_t flags);
+
+/**
+ * Process items on a 2D grid with the specified maximum tile size along the
+ * last grid dimension using a microarchitecture-aware task function and passing
+ * along the current thread id.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   uint32_t uarch_index = cpuinfo_initialize() ?
+ *       cpuinfo_get_current_uarch_index() : default_uarch_index;
+ *   if (uarch_index > max_uarch_index) uarch_index = default_uarch_index;
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j += tile_j)
+ *       function(context, uarch_index, thread_index, i, j, min(range_j - j, tile_j));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each tile.
+ * @param context     the first argument passed to the specified function.
+ * @param default_uarch_index  the microarchitecture index to use when
+ *    pthreadpool is configured without cpuinfo, cpuinfo initialization failed,
+ *    or index returned by cpuinfo_get_current_uarch_index() exceeds the
+ *    max_uarch_index value.
+ * @param max_uarch_index      the maximum microarchitecture index expected by
+ *    the specified function. If the index returned by
+ *    cpuinfo_get_current_uarch_index() exceeds this value, default_uarch_index
+ *    will be used instead. default_uarch_index can exceed max_uarch_index.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 2D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 2D grid.
+ * @param tile_j      the maximum number of items along the second dimension of
+ *    the 2D grid to process in one function call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_2d_tile_1d_with_uarch_with_thread(
+	pthreadpool_t threadpool,
+	pthreadpool_task_2d_tile_1d_with_id_with_thread_t function,
+	void* context,
+	uint32_t default_uarch_index,
+	uint32_t max_uarch_index,
+	size_t range_i,
+	size_t range_j,
+	size_t tile_j,
+	uint32_t flags);
+
+/**
+ * Process items on a 2D grid with the specified maximum tile size along each
+ * grid dimension.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i += tile_i)
+ *     for (size_t j = 0; j < range_j; j += tile_j)
+ *       function(context, i, j,
+ *         min(range_i - i, tile_i), min(range_j - j, tile_j));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each tile.
+ * @param context     the first argument passed to the specified function.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 2D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 2D grid.
+ * @param tile_j      the maximum number of items along the first dimension of
+ *    the 2D grid to process in one function call.
+ * @param tile_j      the maximum number of items along the second dimension of
+ *    the 2D grid to process in one function call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_2d_tile_2d(
+	pthreadpool_t threadpool,
+	pthreadpool_task_2d_tile_2d_t function,
+	void* context,
+	size_t range_i,
+	size_t range_j,
+	size_t tile_i,
+	size_t tile_j,
+	uint32_t flags);
+
+/**
+ * Process items on a 2D grid with the specified maximum tile size along each
+ * grid dimension using a microarchitecture-aware task function.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   uint32_t uarch_index = cpuinfo_initialize() ?
+ *       cpuinfo_get_current_uarch_index() : default_uarch_index;
+ *   if (uarch_index > max_uarch_index) uarch_index = default_uarch_index;
+ *   for (size_t i = 0; i < range_i; i += tile_i)
+ *     for (size_t j = 0; j < range_j; j += tile_j)
+ *       function(context, uarch_index, i, j,
+ *         min(range_i - i, tile_i), min(range_j - j, tile_j));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool           the thread pool to use for parallelisation. If
+ *    threadpool is NULL, all items are processed serially on the calling
+ *    thread.
+ * @param function             the function to call for each tile.
+ * @param context              the first argument passed to the specified
+ *    function.
+ * @param default_uarch_index  the microarchitecture index to use when
+ *                             pthreadpool is configured without cpuinfo,
+ *                             cpuinfo initialization failed, or index returned
+ *                             by cpuinfo_get_current_uarch_index() exceeds
+ *                             the max_uarch_index value.
+ * @param max_uarch_index      the maximum microarchitecture index expected
+ *                             by the specified function. If the index returned
+ *                             by cpuinfo_get_current_uarch_index() exceeds this
+ *                             value, default_uarch_index will be used instead.
+ *                             default_uarch_index can exceed max_uarch_index.
+ * @param range_i              the number of items to process along the first
+ *    dimension of the 2D grid.
+ * @param range_j              the number of items to process along the second
+ *    dimension of the 2D grid.
+ * @param tile_j               the maximum number of items along the first
+ *    dimension of the 2D grid to process in one function call.
+ * @param tile_j               the maximum number of items along the second
+ *    dimension of the 2D grid to process in one function call.
+ * @param flags                a bitwise combination of zero or more optional
+ *    flags (PTHREADPOOL_FLAG_DISABLE_DENORMALS or
+ *    PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_2d_tile_2d_with_uarch(
+	pthreadpool_t threadpool,
+	pthreadpool_task_2d_tile_2d_with_id_t function,
+	void* context,
+	uint32_t default_uarch_index,
+	uint32_t max_uarch_index,
+	size_t range_i,
+	size_t range_j,
+	size_t tile_i,
+	size_t tile_j,
+	uint32_t flags);
+
+/**
+ * Process items on a 3D grid.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k++)
+ *         function(context, i, j, k);
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each tile.
+ * @param context     the first argument passed to the specified function.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 3D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 3D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 3D grid.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_3d(
+	pthreadpool_t threadpool,
+	pthreadpool_task_3d_t function,
+	void* context,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	uint32_t flags);
+
+/**
+ * Process items on a 3D grid with the specified maximum tile size along the
+ * last grid dimension.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k += tile_k)
+ *         function(context, i, j, k, min(range_k - k, tile_k));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each tile.
+ * @param context     the first argument passed to the specified function.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 3D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 3D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 3D grid.
+ * @param tile_k      the maximum number of items along the third dimension of
+ *    the 3D grid to process in one function call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_3d_tile_1d(
+	pthreadpool_t threadpool,
+	pthreadpool_task_3d_tile_1d_t function,
+	void* context,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t tile_k,
+	uint32_t flags);
+
+/**
+ * Process items on a 3D grid with the specified maximum tile size along the
+ * last grid dimension and passing along the current thread id.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k += tile_k)
+ *         function(context, thread_index, i, j, k, min(range_k - k, tile_k));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each tile.
+ * @param context     the first argument passed to the specified function.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 3D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 3D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 3D grid.
+ * @param tile_k      the maximum number of items along the third dimension of
+ *    the 3D grid to process in one function call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_3d_tile_1d_with_thread(
+  pthreadpool_t threadpool,
+  pthreadpool_task_3d_tile_1d_with_thread_t function,
+  void* context,
+  size_t range_i,
+  size_t range_j,
+  size_t range_k,
+  size_t tile_k,
+  uint32_t flags);
+
+/**
+ * Process items on a 3D grid with the specified maximum tile size along the
+ * last grid dimension using a microarchitecture-aware task function.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   uint32_t uarch_index = cpuinfo_initialize() ?
+ *       cpuinfo_get_current_uarch_index() : default_uarch_index;
+ *   if (uarch_index > max_uarch_index) uarch_index = default_uarch_index;
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k += tile_k)
+ *         function(context, uarch_index, i, j, k, min(range_k - k, tile_k));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool           the thread pool to use for parallelisation. If
+ *    threadpool is NULL, all items are processed serially on the calling
+ *    thread.
+ * @param function             the function to call for each tile.
+ * @param context              the first argument passed to the specified
+ *    function.
+ * @param default_uarch_index  the microarchitecture index to use when
+ *    pthreadpool is configured without cpuinfo, cpuinfo initialization failed,
+ *    or index returned by cpuinfo_get_current_uarch_index() exceeds the
+ *    max_uarch_index value.
+ * @param max_uarch_index      the maximum microarchitecture index expected by
+ *    the specified function. If the index returned by
+ *    cpuinfo_get_current_uarch_index() exceeds this value, default_uarch_index
+ *    will be used instead. default_uarch_index can exceed max_uarch_index.
+ * @param range_i              the number of items to process along the first
+ *    dimension of the 3D grid.
+ * @param range_j              the number of items to process along the second
+ *    dimension of the 3D grid.
+ * @param range_k              the number of items to process along the third
+ *    dimension of the 3D grid.
+ * @param tile_k               the maximum number of items along the third
+ *    dimension of the 3D grid to process in one function call.
+ * @param flags                a bitwise combination of zero or more optional
+ *    flags (PTHREADPOOL_FLAG_DISABLE_DENORMALS or
+ *    PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_3d_tile_1d_with_uarch(
+	pthreadpool_t threadpool,
+	pthreadpool_task_3d_tile_1d_with_id_t function,
+	void* context,
+	uint32_t default_uarch_index,
+	uint32_t max_uarch_index,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t tile_k,
+	uint32_t flags);
+
+/**
+ * Process items on a 3D grid with the specified maximum tile size along the
+ * last grid dimension using a microarchitecture-aware task function and passing
+ * along the current thread id.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   uint32_t uarch_index = cpuinfo_initialize() ?
+ *       cpuinfo_get_current_uarch_index() : default_uarch_index;
+ *   if (uarch_index > max_uarch_index) uarch_index = default_uarch_index;
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k += tile_k)
+ *         function(context, uarch_index, thread_index, i, j, k, min(range_k - k, tile_k));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool           the thread pool to use for parallelisation. If
+ *    threadpool is NULL, all items are processed serially on the calling
+ *    thread.
+ * @param function             the function to call for each tile.
+ * @param context              the first argument passed to the specified
+ *    function.
+ * @param default_uarch_index  the microarchitecture index to use when
+ *    pthreadpool is configured without cpuinfo, cpuinfo initialization failed,
+ *    or index returned by cpuinfo_get_current_uarch_index() exceeds the
+ *    max_uarch_index value.
+ * @param max_uarch_index      the maximum microarchitecture index expected by
+ *    the specified function. If the index returned by
+ *    cpuinfo_get_current_uarch_index() exceeds this value, default_uarch_index
+ *    will be used instead. default_uarch_index can exceed max_uarch_index.
+ * @param range_i              the number of items to process along the first
+ *    dimension of the 3D grid.
+ * @param range_j              the number of items to process along the second
+ *    dimension of the 3D grid.
+ * @param range_k              the number of items to process along the third
+ *    dimension of the 3D grid.
+ * @param tile_k               the maximum number of items along the third
+ *    dimension of the 3D grid to process in one function call.
+ * @param flags                a bitwise combination of zero or more optional
+ *    flags (PTHREADPOOL_FLAG_DISABLE_DENORMALS or
+ *    PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_3d_tile_1d_with_uarch_with_thread(
+	pthreadpool_t threadpool,
+	pthreadpool_task_3d_tile_1d_with_id_with_thread_t function,
+	void* context,
+	uint32_t default_uarch_index,
+	uint32_t max_uarch_index,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t tile_k,
+	uint32_t flags);
+
+/**
+ * Process items on a 3D grid with the specified maximum tile size along the
+ * last two grid dimensions.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j += tile_j)
+ *       for (size_t k = 0; k < range_k; k += tile_k)
+ *         function(context, i, j, k,
+ *           min(range_j - j, tile_j), min(range_k - k, tile_k));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each tile.
+ * @param context     the first argument passed to the specified function.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 3D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 3D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 3D grid.
+ * @param tile_j      the maximum number of items along the second dimension of
+ *    the 3D grid to process in one function call.
+ * @param tile_k      the maximum number of items along the third dimension of
+ *    the 3D grid to process in one function call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_3d_tile_2d(
+	pthreadpool_t threadpool,
+	pthreadpool_task_3d_tile_2d_t function,
+	void* context,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t tile_j,
+	size_t tile_k,
+	uint32_t flags);
+
+/**
+ * Process items on a 3D grid with the specified maximum tile size along the
+ * last two grid dimensions using a microarchitecture-aware task function.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   uint32_t uarch_index = cpuinfo_initialize() ?
+ *       cpuinfo_get_current_uarch_index() : default_uarch_index;
+ *   if (uarch_index > max_uarch_index) uarch_index = default_uarch_index;
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j += tile_j)
+ *       for (size_t k = 0; k < range_k; k += tile_k)
+ *         function(context, uarch_index, i, j, k,
+ *           min(range_j - j, tile_j), min(range_k - k, tile_k));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool           the thread pool to use for parallelisation. If
+ *    threadpool is NULL, all items are processed serially on the calling
+ *    thread.
+ * @param function             the function to call for each tile.
+ * @param context              the first argument passed to the specified
+ *    function.
+ * @param default_uarch_index  the microarchitecture index to use when
+ *    pthreadpool is configured without cpuinfo, cpuinfo initialization failed,
+ *    or index returned by cpuinfo_get_current_uarch_index() exceeds the
+ *    max_uarch_index value.
+ * @param max_uarch_index      the maximum microarchitecture index expected by
+ *    the specified function. If the index returned by
+ *    cpuinfo_get_current_uarch_index() exceeds this value, default_uarch_index
+ *    will be used instead. default_uarch_index can exceed max_uarch_index.
+ * @param range_i              the number of items to process along the first
+ *    dimension of the 3D grid.
+ * @param range_j              the number of items to process along the second
+ *    dimension of the 3D grid.
+ * @param range_k              the number of items to process along the third
+ *    dimension of the 3D grid.
+ * @param tile_j               the maximum number of items along the second
+ *    dimension of the 3D grid to process in one function call.
+ * @param tile_k               the maximum number of items along the third
+ *    dimension of the 3D grid to process in one function call.
+ * @param flags                a bitwise combination of zero or more optional
+ *    flags (PTHREADPOOL_FLAG_DISABLE_DENORMALS or
+ *    PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_3d_tile_2d_with_uarch(
+	pthreadpool_t threadpool,
+	pthreadpool_task_3d_tile_2d_with_id_t function,
+	void* context,
+	uint32_t default_uarch_index,
+	uint32_t max_uarch_index,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t tile_j,
+	size_t tile_k,
+	uint32_t flags);
+
+/**
+ * Process items on a 4D grid.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k++)
+ *         for (size_t l = 0; l < range_l; l++)
+ *           function(context, i, j, k, l);
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each tile.
+ * @param context     the first argument passed to the specified function.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 4D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 4D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 4D grid.
+ * @param range_l     the number of items to process along the fourth dimension
+ *    of the 4D grid.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_4d(
+	pthreadpool_t threadpool,
+	pthreadpool_task_4d_t function,
+	void* context,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t range_l,
+	uint32_t flags);
+
+/**
+ * Process items on a 4D grid with the specified maximum tile size along the
+ * last grid dimension.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k++)
+ *         for (size_t l = 0; l < range_l; l += tile_l)
+ *           function(context, i, j, k, l, min(range_l - l, tile_l));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each tile.
+ * @param context     the first argument passed to the specified function.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 4D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 4D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 4D grid.
+ * @param range_l     the number of items to process along the fourth dimension
+ *    of the 4D grid.
+ * @param tile_l      the maximum number of items along the fourth dimension of
+ *    the 4D grid to process in one function call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_4d_tile_1d(
+	pthreadpool_t threadpool,
+	pthreadpool_task_4d_tile_1d_t function,
+	void* context,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t range_l,
+	size_t tile_l,
+	uint32_t flags);
+
+/**
+ * Process items on a 4D grid with the specified maximum tile size along the
+ * last two grid dimensions.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k += tile_k)
+ *         for (size_t l = 0; l < range_l; l += tile_l)
+ *           function(context, i, j, k, l,
+ *             min(range_k - k, tile_k), min(range_l - l, tile_l));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each tile.
+ * @param context     the first argument passed to the specified function.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 4D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 4D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 4D grid.
+ * @param range_l     the number of items to process along the fourth dimension
+ *    of the 4D grid.
+ * @param tile_k      the maximum number of items along the third dimension of
+ *    the 4D grid to process in one function call.
+ * @param tile_l      the maximum number of items along the fourth dimension of
+ *    the 4D grid to process in one function call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_4d_tile_2d(
+	pthreadpool_t threadpool,
+	pthreadpool_task_4d_tile_2d_t function,
+	void* context,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t range_l,
+	size_t tile_k,
+	size_t tile_l,
+	uint32_t flags);
+
+/**
+ * Process items on a 4D grid with the specified maximum tile size along the
+ * last two grid dimensions using a microarchitecture-aware task function.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   uint32_t uarch_index = cpuinfo_initialize() ?
+ *       cpuinfo_get_current_uarch_index() : default_uarch_index;
+ *   if (uarch_index > max_uarch_index) uarch_index = default_uarch_index;
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k += tile_k)
+ *         for (size_t l = 0; l < range_l; l += tile_l)
+ *           function(context, uarch_index, i, j, k, l,
+ *             min(range_k - k, tile_k), min(range_l - l, tile_l));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool           the thread pool to use for parallelisation. If
+ *    threadpool is NULL, all items are processed serially on the calling
+ *    thread.
+ * @param function             the function to call for each tile.
+ * @param context              the first argument passed to the specified
+ *    function.
+ * @param default_uarch_index  the microarchitecture index to use when
+ *    pthreadpool is configured without cpuinfo, cpuinfo initialization failed,
+ *    or index returned by cpuinfo_get_current_uarch_index() exceeds the
+ *    max_uarch_index value.
+ * @param max_uarch_index      the maximum microarchitecture index expected by
+ *    the specified function. If the index returned by
+ *    cpuinfo_get_current_uarch_index() exceeds this value, default_uarch_index
+ *    will be used instead. default_uarch_index can exceed max_uarch_index.
+ * @param range_i              the number of items to process along the first
+ *    dimension of the 4D grid.
+ * @param range_j              the number of items to process along the second
+ *    dimension of the 4D grid.
+ * @param range_k              the number of items to process along the third
+ *    dimension of the 4D grid.
+ * @param range_l              the number of items to process along the fourth
+ *    dimension of the 4D grid.
+ * @param tile_k               the maximum number of items along the third
+ *    dimension of the 4D grid to process in one function call.
+ * @param tile_l               the maximum number of items along the fourth
+ *    dimension of the 4D grid to process in one function call.
+ * @param flags                a bitwise combination of zero or more optional
+ *    flags (PTHREADPOOL_FLAG_DISABLE_DENORMALS or
+ *    PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_4d_tile_2d_with_uarch(
+	pthreadpool_t threadpool,
+	pthreadpool_task_4d_tile_2d_with_id_t function,
+	void* context,
+	uint32_t default_uarch_index,
+	uint32_t max_uarch_index,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t range_l,
+	size_t tile_k,
+	size_t tile_l,
+	uint32_t flags);
+
+/**
+ * Process items on a 5D grid.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k++)
+ *         for (size_t l = 0; l < range_l; l++)
+ *           for (size_t m = 0; m < range_m; m++)
+ *             function(context, i, j, k, l, m);
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each tile.
+ * @param context     the first argument passed to the specified function.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 5D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 5D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 5D grid.
+ * @param range_l     the number of items to process along the fourth dimension
+ *    of the 5D grid.
+ * @param range_m     the number of items to process along the fifth dimension
+ *    of the 5D grid.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_5d(
+	pthreadpool_t threadpool,
+	pthreadpool_task_5d_t function,
+	void* context,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t range_l,
+	size_t range_m,
+	uint32_t flags);
+
+/**
+ * Process items on a 5D grid with the specified maximum tile size along the
+ * last grid dimension.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k++)
+ *         for (size_t l = 0; l < range_l; l++)
+ *           for (size_t m = 0; m < range_m; m += tile_m)
+ *             function(context, i, j, k, l, m, min(range_m - m, tile_m));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each tile.
+ * @param context     the first argument passed to the specified function.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 5D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 5D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 5D grid.
+ * @param range_l     the number of items to process along the fourth dimension
+ *    of the 5D grid.
+ * @param range_m     the number of items to process along the fifth dimension
+ *    of the 5D grid.
+ * @param tile_m      the maximum number of items along the fifth dimension of
+ *    the 5D grid to process in one function call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_5d_tile_1d(
+	pthreadpool_t threadpool,
+	pthreadpool_task_5d_tile_1d_t function,
+	void* context,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t range_l,
+	size_t range_m,
+	size_t tile_m,
+	uint32_t flags);
+
+/**
+ * Process items on a 5D grid with the specified maximum tile size along the
+ * last two grid dimensions.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k++)
+ *         for (size_t l = 0; l < range_l; l += tile_l)
+ *           for (size_t m = 0; m < range_m; m += tile_m)
+ *             function(context, i, j, k, l, m,
+ *               min(range_l - l, tile_l), min(range_m - m, tile_m));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each tile.
+ * @param context     the first argument passed to the specified function.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 5D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 5D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 5D grid.
+ * @param range_l     the number of items to process along the fourth dimension
+ *    of the 5D grid.
+ * @param range_m     the number of items to process along the fifth dimension
+ *    of the 5D grid.
+ * @param tile_l      the maximum number of items along the fourth dimension of
+ *    the 5D grid to process in one function call.
+ * @param tile_m      the maximum number of items along the fifth dimension of
+ *    the 5D grid to process in one function call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_5d_tile_2d(
+	pthreadpool_t threadpool,
+	pthreadpool_task_5d_tile_2d_t function,
+	void* context,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t range_l,
+	size_t range_m,
+	size_t tile_l,
+	size_t tile_m,
+	uint32_t flags);
+
+/**
+ * Process items on a 6D grid.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k++)
+ *         for (size_t l = 0; l < range_l; l++)
+ *           for (size_t m = 0; m < range_m; m++)
+ *             for (size_t n = 0; n < range_n; n++)
+ *               function(context, i, j, k, l, m, n);
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each tile.
+ * @param context     the first argument passed to the specified function.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 6D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 6D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 6D grid.
+ * @param range_l     the number of items to process along the fourth dimension
+ *    of the 6D grid.
+ * @param range_m     the number of items to process along the fifth dimension
+ *    of the 6D grid.
+ * @param range_n     the number of items to process along the sixth dimension
+ *    of the 6D grid.
+ * @param tile_n      the maximum number of items along the sixth dimension of
+ *    the 6D grid to process in one function call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_6d(
+	pthreadpool_t threadpool,
+	pthreadpool_task_6d_t function,
+	void* context,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t range_l,
+	size_t range_m,
+	size_t range_n,
+	uint32_t flags);
+
+/**
+ * Process items on a 6D grid with the specified maximum tile size along the
+ * last grid dimension.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k++)
+ *         for (size_t l = 0; l < range_l; l++)
+ *           for (size_t m = 0; m < range_m; m++)
+ *             for (size_t n = 0; n < range_n; n += tile_n)
+ *               function(context, i, j, k, l, m, n, min(range_n - n, tile_n));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each tile.
+ * @param context     the first argument passed to the specified function.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 6D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 6D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 6D grid.
+ * @param range_l     the number of items to process along the fourth dimension
+ *    of the 6D grid.
+ * @param range_m     the number of items to process along the fifth dimension
+ *    of the 6D grid.
+ * @param range_n     the number of items to process along the sixth dimension
+ *    of the 6D grid.
+ * @param tile_n      the maximum number of items along the sixth dimension of
+ *    the 6D grid to process in one function call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_6d_tile_1d(
+	pthreadpool_t threadpool,
+	pthreadpool_task_6d_tile_1d_t function,
+	void* context,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t range_l,
+	size_t range_m,
+	size_t range_n,
+	size_t tile_n,
+	uint32_t flags);
+
+/**
+ * Process items on a 6D grid with the specified maximum tile size along the
+ * last two grid dimensions.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k++)
+ *         for (size_t l = 0; l < range_l; l++)
+ *           for (size_t m = 0; m < range_m; m += tile_m)
+ *             for (size_t n = 0; n < range_n; n += tile_n)
+ *               function(context, i, j, k, l, m, n,
+ *                 min(range_m - m, tile_m), min(range_n - n, tile_n));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param function    the function to call for each tile.
+ * @param context     the first argument passed to the specified function.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 6D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 6D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 6D grid.
+ * @param range_l     the number of items to process along the fourth dimension
+ *    of the 6D grid.
+ * @param range_m     the number of items to process along the fifth dimension
+ *    of the 6D grid.
+ * @param range_n     the number of items to process along the sixth dimension
+ *    of the 6D grid.
+ * @param tile_m      the maximum number of items along the fifth dimension of
+ *    the 6D grid to process in one function call.
+ * @param tile_n      the maximum number of items along the sixth dimension of
+ *    the 6D grid to process in one function call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+void pthreadpool_parallelize_6d_tile_2d(
+	pthreadpool_t threadpool,
+	pthreadpool_task_6d_tile_2d_t function,
+	void* context,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t range_l,
+	size_t range_m,
+	size_t range_n,
+	size_t tile_m,
+	size_t tile_n,
+	uint32_t flags);
+
+/**
+ * Terminates threads in the thread pool and releases associated resources.
+ *
+ * @warning  Accessing the thread pool after a call to this function constitutes
+ *    undefined behaviour and may cause data corruption.
+ *
+ * @param[in,out]  threadpool  The thread pool to destroy.
+ */
+void pthreadpool_destroy(pthreadpool_t threadpool);
+
+#ifndef PTHREADPOOL_NO_DEPRECATED_API
+
+/* Legacy API for compatibility with pre-existing users (e.g. NNPACK) */
+#if defined(__GNUC__)
+	#define PTHREADPOOL_DEPRECATED __attribute__((__deprecated__))
+#else
+	#define PTHREADPOOL_DEPRECATED
+#endif
+
+typedef void (*pthreadpool_function_1d_t)(void*, size_t);
+typedef void (*pthreadpool_function_1d_tiled_t)(void*, size_t, size_t);
+typedef void (*pthreadpool_function_2d_t)(void*, size_t, size_t);
+typedef void (*pthreadpool_function_2d_tiled_t)(void*, size_t, size_t, size_t, size_t);
+typedef void (*pthreadpool_function_3d_tiled_t)(void*, size_t, size_t, size_t, size_t, size_t, size_t);
+typedef void (*pthreadpool_function_4d_tiled_t)(void*, size_t, size_t, size_t, size_t, size_t, size_t, size_t, size_t);
+
+void pthreadpool_compute_1d(
+	pthreadpool_t threadpool,
+	pthreadpool_function_1d_t function,
+	void* argument,
+	size_t range) PTHREADPOOL_DEPRECATED;
+
+void pthreadpool_compute_1d_tiled(
+	pthreadpool_t threadpool,
+	pthreadpool_function_1d_tiled_t function,
+	void* argument,
+	size_t range,
+	size_t tile) PTHREADPOOL_DEPRECATED;
+
+void pthreadpool_compute_2d(
+	pthreadpool_t threadpool,
+	pthreadpool_function_2d_t function,
+	void* argument,
+	size_t range_i,
+	size_t range_j) PTHREADPOOL_DEPRECATED;
+
+void pthreadpool_compute_2d_tiled(
+	pthreadpool_t threadpool,
+	pthreadpool_function_2d_tiled_t function,
+	void* argument,
+	size_t range_i,
+	size_t range_j,
+	size_t tile_i,
+	size_t tile_j) PTHREADPOOL_DEPRECATED;
+
+void pthreadpool_compute_3d_tiled(
+	pthreadpool_t threadpool,
+	pthreadpool_function_3d_tiled_t function,
+	void* argument,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t tile_i,
+	size_t tile_j,
+	size_t tile_k) PTHREADPOOL_DEPRECATED;
+
+void pthreadpool_compute_4d_tiled(
+	pthreadpool_t threadpool,
+	pthreadpool_function_4d_tiled_t function,
+	void* argument,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t range_l,
+	size_t tile_i,
+	size_t tile_j,
+	size_t tile_k,
+	size_t tile_l) PTHREADPOOL_DEPRECATED;
+
+#endif /* PTHREADPOOL_NO_DEPRECATED_API */
+
+#ifdef __cplusplus
+} /* extern "C" */
+#endif
+
+#ifdef __cplusplus
+
+namespace libpthreadpool {
+namespace detail {
+namespace {
+
+template
+void call_wrapper_1d(void* arg, size_t i) {
+	(*static_cast(arg))(i);
+}
+
+template
+void call_wrapper_1d_tile_1d(void* arg, size_t range_i, size_t tile_i) {
+	(*static_cast(arg))(range_i, tile_i);
+}
+
+template
+void call_wrapper_2d(void* functor, size_t i, size_t j) {
+	(*static_cast(functor))(i, j);
+}
+
+template
+void call_wrapper_2d_tile_1d(void* functor,
+		                         size_t i, size_t range_j, size_t tile_j)
+{
+	(*static_cast(functor))(i, range_j, tile_j);
+}
+
+template
+void call_wrapper_2d_tile_2d(void* functor,
+		                         size_t range_i, size_t range_j,
+		                         size_t tile_i, size_t tile_j)
+{
+	(*static_cast(functor))(range_i, range_j, tile_i, tile_j);
+}
+
+template
+void call_wrapper_3d(void* functor, size_t i, size_t j, size_t k) {
+	(*static_cast(functor))(i, j, k);
+}
+
+template
+void call_wrapper_3d_tile_1d(void* functor,
+		                         size_t i, size_t j, size_t range_k,
+		                         size_t tile_k)
+{
+	(*static_cast(functor))(i, j, range_k, tile_k);
+}
+
+template
+void call_wrapper_3d_tile_2d(void* functor,
+		                         size_t i, size_t range_j, size_t range_k,
+		                         size_t tile_j, size_t tile_k)
+{
+	(*static_cast(functor))(i, range_j, range_k, tile_j, tile_k);
+}
+
+template
+void call_wrapper_4d(void* functor, size_t i, size_t j, size_t k, size_t l) {
+	(*static_cast(functor))(i, j, k, l);
+}
+
+template
+void call_wrapper_4d_tile_1d(void* functor,
+		                         size_t i, size_t j, size_t k, size_t range_l,
+		                         size_t tile_l)
+{
+	(*static_cast(functor))(i, j, k, range_l, tile_l);
+}
+
+template
+void call_wrapper_4d_tile_2d(void* functor,
+		                         size_t i, size_t j, size_t range_k, size_t range_l,
+		                         size_t tile_k, size_t tile_l)
+{
+	(*static_cast(functor))(i, j, range_k, range_l, tile_k, tile_l);
+}
+
+template
+void call_wrapper_5d(void* functor, size_t i, size_t j, size_t k, size_t l, size_t m) {
+	(*static_cast(functor))(i, j, k, l, m);
+}
+
+template
+void call_wrapper_5d_tile_1d(void* functor,
+		                         size_t i, size_t j, size_t k, size_t l, size_t range_m,
+		                         size_t tile_m)
+{
+	(*static_cast(functor))(i, j, k, l, range_m, tile_m);
+}
+
+template
+void call_wrapper_5d_tile_2d(void* functor,
+		                         size_t i, size_t j, size_t k, size_t range_l, size_t range_m,
+		                         size_t tile_l, size_t tile_m)
+{
+	(*static_cast(functor))(i, j, k, range_l, range_m, tile_l, tile_m);
+}
+
+template
+void call_wrapper_6d(void* functor, size_t i, size_t j, size_t k, size_t l, size_t m, size_t n) {
+	(*static_cast(functor))(i, j, k, l, m, n);
+}
+
+template
+void call_wrapper_6d_tile_1d(void* functor,
+		                         size_t i, size_t j, size_t k, size_t l, size_t m, size_t range_n,
+		                         size_t tile_n)
+{
+	(*static_cast(functor))(i, j, k, l, m, range_n, tile_n);
+}
+
+template
+void call_wrapper_6d_tile_2d(void* functor,
+		                         size_t i, size_t j, size_t k, size_t l, size_t range_m, size_t range_n,
+		                         size_t tile_m, size_t tile_n)
+{
+	(*static_cast(functor))(i, j, k, l, range_m, range_n, tile_m, tile_n);
+}
+
+}  /* namespace */
+}  /* namespace detail */
+}  /* namespace libpthreadpool */
+
+/**
+ * Process items on a 1D grid.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range; i++)
+ *     functor(i);
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param functor     the functor to call for each item.
+ * @param range       the number of items on the 1D grid to process. The
+ *    specified functor will be called once for each item.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+template
+inline void pthreadpool_parallelize_1d(
+	pthreadpool_t threadpool,
+	const T& functor,
+	size_t range,
+	uint32_t flags = 0)
+{
+	pthreadpool_parallelize_1d(
+		threadpool,
+		&libpthreadpool::detail::call_wrapper_1d,
+		const_cast(static_cast(&functor)),
+		range,
+		flags);
+}
+
+/**
+ * Process items on a 1D grid with specified maximum tile size.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range; i += tile)
+ *     functor(i, min(range - i, tile));
+ *
+ * When the call returns, all items have been processed and the thread pool is
+ * ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool,
+ *    the calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param functor     the functor to call for each tile.
+ * @param range       the number of items on the 1D grid to process.
+ * @param tile        the maximum number of items on the 1D grid to process in
+ *    one functor call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+template
+inline void pthreadpool_parallelize_1d_tile_1d(
+	pthreadpool_t threadpool,
+	const T& functor,
+	size_t range,
+	size_t tile,
+	uint32_t flags = 0)
+{
+	pthreadpool_parallelize_1d_tile_1d(
+		threadpool,
+		&libpthreadpool::detail::call_wrapper_1d_tile_1d,
+		const_cast(static_cast(&functor)),
+		range,
+		tile,
+		flags);
+}
+
+/**
+ * Process items on a 2D grid.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       functor(i, j);
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param functor     the functor to call for each item.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 2D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 2D grid.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+template
+inline void pthreadpool_parallelize_2d(
+	pthreadpool_t threadpool,
+	const T& functor,
+	size_t range_i,
+	size_t range_j,
+	uint32_t flags = 0)
+{
+	pthreadpool_parallelize_2d(
+		threadpool,
+		&libpthreadpool::detail::call_wrapper_2d,
+		const_cast(static_cast(&functor)),
+		range_i,
+		range_j,
+		flags);
+}
+
+/**
+ * Process items on a 2D grid with the specified maximum tile size along the
+ * last grid dimension.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j += tile_j)
+ *       functor(i, j, min(range_j - j, tile_j));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param functor     the functor to call for each tile.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 2D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 2D grid.
+ * @param tile_j      the maximum number of items along the second dimension of
+ *    the 2D grid to process in one functor call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+template
+inline void pthreadpool_parallelize_2d_tile_1d(
+	pthreadpool_t threadpool,
+	const T& functor,
+	size_t range_i,
+	size_t range_j,
+	size_t tile_j,
+	uint32_t flags = 0)
+{
+	pthreadpool_parallelize_2d_tile_1d(
+		threadpool,
+		&libpthreadpool::detail::call_wrapper_2d_tile_1d,
+		const_cast(static_cast(&functor)),
+		range_i,
+		range_j,
+		tile_j,
+		flags);
+}
+
+/**
+ * Process items on a 2D grid with the specified maximum tile size along each
+ * grid dimension.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i += tile_i)
+ *     for (size_t j = 0; j < range_j; j += tile_j)
+ *       functor(i, j,
+ *         min(range_i - i, tile_i), min(range_j - j, tile_j));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param functor     the functor to call for each tile.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 2D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 2D grid.
+ * @param tile_j      the maximum number of items along the first dimension of
+ *    the 2D grid to process in one functor call.
+ * @param tile_j      the maximum number of items along the second dimension of
+ *    the 2D grid to process in one functor call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+template
+inline void pthreadpool_parallelize_2d_tile_2d(
+	pthreadpool_t threadpool,
+	const T& functor,
+	size_t range_i,
+	size_t range_j,
+	size_t tile_i,
+	size_t tile_j,
+	uint32_t flags = 0)
+{
+	pthreadpool_parallelize_2d_tile_2d(
+		threadpool,
+		&libpthreadpool::detail::call_wrapper_2d_tile_2d,
+		const_cast(static_cast(&functor)),
+		range_i,
+		range_j,
+		tile_i,
+		tile_j,
+		flags);
+}
+
+/**
+ * Process items on a 3D grid.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k++)
+ *         functor(i, j, k);
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param functor     the functor to call for each tile.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 3D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 3D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 3D grid.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+template
+inline void pthreadpool_parallelize_3d(
+	pthreadpool_t threadpool,
+	const T& functor,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	uint32_t flags = 0)
+{
+	pthreadpool_parallelize_3d(
+		threadpool,
+		&libpthreadpool::detail::call_wrapper_3d,
+		const_cast(static_cast(&functor)),
+		range_i,
+		range_j,
+		range_k,
+		flags);
+}
+
+/**
+ * Process items on a 3D grid with the specified maximum tile size along the
+ * last grid dimension.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k += tile_k)
+ *         functor(i, j, k, min(range_k - k, tile_k));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param functor     the functor to call for each tile.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 3D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 3D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 3D grid.
+ * @param tile_k      the maximum number of items along the third dimension of
+ *    the 3D grid to process in one functor call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+template
+inline void pthreadpool_parallelize_3d_tile_1d(
+	pthreadpool_t threadpool,
+	const T& functor,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t tile_k,
+	uint32_t flags = 0)
+{
+	pthreadpool_parallelize_3d_tile_1d(
+		threadpool,
+		&libpthreadpool::detail::call_wrapper_3d_tile_1d,
+		const_cast(static_cast(&functor)),
+		range_i,
+		range_j,
+		range_k,
+		tile_k,
+		flags);
+}
+
+/**
+ * Process items on a 3D grid with the specified maximum tile size along the
+ * last two grid dimensions.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j += tile_j)
+ *       for (size_t k = 0; k < range_k; k += tile_k)
+ *         functor(i, j, k,
+ *           min(range_j - j, tile_j), min(range_k - k, tile_k));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param functor     the functor to call for each tile.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 3D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 3D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 3D grid.
+ * @param tile_j      the maximum number of items along the second dimension of
+ *    the 3D grid to process in one functor call.
+ * @param tile_k      the maximum number of items along the third dimension of
+ *    the 3D grid to process in one functor call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+template
+inline void pthreadpool_parallelize_3d_tile_2d(
+	pthreadpool_t threadpool,
+	const T& functor,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t tile_j,
+	size_t tile_k,
+	uint32_t flags = 0)
+{
+	pthreadpool_parallelize_3d_tile_2d(
+		threadpool,
+		&libpthreadpool::detail::call_wrapper_3d_tile_2d,
+		const_cast(static_cast(&functor)),
+		range_i,
+		range_j,
+		range_k,
+		tile_j,
+		tile_k,
+		flags);
+}
+
+/**
+ * Process items on a 4D grid.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k++)
+ *         for (size_t l = 0; l < range_l; l++)
+ *           functor(i, j, k, l);
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param functor     the functor to call for each tile.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 4D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 4D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 4D grid.
+ * @param range_l     the number of items to process along the fourth dimension
+ *    of the 4D grid.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+template
+inline void pthreadpool_parallelize_4d(
+	pthreadpool_t threadpool,
+	const T& functor,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t range_l,
+	uint32_t flags = 0)
+{
+	pthreadpool_parallelize_4d(
+		threadpool,
+		&libpthreadpool::detail::call_wrapper_4d,
+		const_cast(static_cast(&functor)),
+		range_i,
+		range_j,
+		range_k,
+		range_l,
+		flags);
+}
+
+/**
+ * Process items on a 4D grid with the specified maximum tile size along the
+ * last grid dimension.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k++)
+ *         for (size_t l = 0; l < range_l; l += tile_l)
+ *           functor(i, j, k, l, min(range_l - l, tile_l));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param functor     the functor to call for each tile.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 4D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 4D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 4D grid.
+ * @param range_l     the number of items to process along the fourth dimension
+ *    of the 4D grid.
+ * @param tile_l      the maximum number of items along the fourth dimension of
+ *    the 4D grid to process in one functor call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+template
+inline void pthreadpool_parallelize_4d_tile_1d(
+	pthreadpool_t threadpool,
+	const T& functor,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t range_l,
+	size_t tile_l,
+	uint32_t flags = 0)
+{
+	pthreadpool_parallelize_4d_tile_1d(
+		threadpool,
+		&libpthreadpool::detail::call_wrapper_4d_tile_1d,
+		const_cast(static_cast(&functor)),
+		range_i,
+		range_j,
+		range_k,
+		range_l,
+		tile_l,
+		flags);
+}
+
+/**
+ * Process items on a 4D grid with the specified maximum tile size along the
+ * last two grid dimensions.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k += tile_k)
+ *         for (size_t l = 0; l < range_l; l += tile_l)
+ *           functor(i, j, k, l,
+ *             min(range_k - k, tile_k), min(range_l - l, tile_l));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param functor     the functor to call for each tile.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 4D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 4D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 4D grid.
+ * @param range_l     the number of items to process along the fourth dimension
+ *    of the 4D grid.
+ * @param tile_k      the maximum number of items along the third dimension of
+ *    the 4D grid to process in one functor call.
+ * @param tile_l      the maximum number of items along the fourth dimension of
+ *    the 4D grid to process in one functor call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+template
+inline void pthreadpool_parallelize_4d_tile_2d(
+	pthreadpool_t threadpool,
+	const T& functor,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t range_l,
+	size_t tile_k,
+	size_t tile_l,
+	uint32_t flags = 0)
+{
+	pthreadpool_parallelize_4d_tile_2d(
+		threadpool,
+		&libpthreadpool::detail::call_wrapper_4d_tile_2d,
+		const_cast(static_cast(&functor)),
+		range_i,
+		range_j,
+		range_k,
+		range_l,
+		tile_k,
+		tile_l,
+		flags);
+}
+
+/**
+ * Process items on a 5D grid.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k++)
+ *         for (size_t l = 0; l < range_l; l++)
+ *           for (size_t m = 0; m < range_m; m++)
+ *             functor(i, j, k, l, m);
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param functor     the functor to call for each tile.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 5D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 5D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 5D grid.
+ * @param range_l     the number of items to process along the fourth dimension
+ *    of the 5D grid.
+ * @param range_m     the number of items to process along the fifth dimension
+ *    of the 5D grid.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+template
+inline void pthreadpool_parallelize_5d(
+	pthreadpool_t threadpool,
+	const T& functor,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t range_l,
+	size_t range_m,
+	uint32_t flags = 0)
+{
+	pthreadpool_parallelize_5d(
+		threadpool,
+		&libpthreadpool::detail::call_wrapper_5d,
+		const_cast(static_cast(&functor)),
+		range_i,
+		range_j,
+		range_k,
+		range_l,
+		range_m,
+		flags);
+}
+
+/**
+ * Process items on a 5D grid with the specified maximum tile size along the
+ * last grid dimension.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k++)
+ *         for (size_t l = 0; l < range_l; l++)
+ *           for (size_t m = 0; m < range_m; m += tile_m)
+ *             functor(i, j, k, l, m, min(range_m - m, tile_m));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param functor     the functor to call for each tile.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 5D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 5D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 5D grid.
+ * @param range_l     the number of items to process along the fourth dimension
+ *    of the 5D grid.
+ * @param range_m     the number of items to process along the fifth dimension
+ *    of the 5D grid.
+ * @param tile_m      the maximum number of items along the fifth dimension of
+ *    the 5D grid to process in one functor call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+template
+inline void pthreadpool_parallelize_5d_tile_1d(
+	pthreadpool_t threadpool,
+	const T& functor,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t range_l,
+	size_t range_m,
+	size_t tile_m,
+	uint32_t flags = 0)
+{
+	pthreadpool_parallelize_5d_tile_1d(
+		threadpool,
+		&libpthreadpool::detail::call_wrapper_5d_tile_1d,
+		const_cast(static_cast(&functor)),
+		range_i,
+		range_j,
+		range_k,
+		range_l,
+		range_m,
+		tile_m,
+		flags);
+}
+
+/**
+ * Process items on a 5D grid with the specified maximum tile size along the
+ * last two grid dimensions.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k++)
+ *         for (size_t l = 0; l < range_l; l += tile_l)
+ *           for (size_t m = 0; m < range_m; m += tile_m)
+ *             functor(i, j, k, l, m,
+ *               min(range_l - l, tile_l), min(range_m - m, tile_m));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param functor     the functor to call for each tile.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 5D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 5D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 5D grid.
+ * @param range_l     the number of items to process along the fourth dimension
+ *    of the 5D grid.
+ * @param range_m     the number of items to process along the fifth dimension
+ *    of the 5D grid.
+ * @param tile_l      the maximum number of items along the fourth dimension of
+ *    the 5D grid to process in one functor call.
+ * @param tile_m      the maximum number of items along the fifth dimension of
+ *    the 5D grid to process in one functor call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+template
+inline void pthreadpool_parallelize_5d_tile_2d(
+	pthreadpool_t threadpool,
+	const T& functor,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t range_l,
+	size_t range_m,
+	size_t tile_l,
+	size_t tile_m,
+	uint32_t flags = 0)
+{
+	pthreadpool_parallelize_5d_tile_2d(
+		threadpool,
+		&libpthreadpool::detail::call_wrapper_5d_tile_2d,
+		const_cast(static_cast(&functor)),
+		range_i,
+		range_j,
+		range_k,
+		range_l,
+		range_m,
+		tile_l,
+		tile_m,
+		flags);
+}
+
+/**
+ * Process items on a 6D grid.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k++)
+ *         for (size_t l = 0; l < range_l; l++)
+ *           for (size_t m = 0; m < range_m; m++)
+ *             for (size_t n = 0; n < range_n; n++)
+ *               functor(i, j, k, l, m, n);
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param functor     the functor to call for each tile.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 6D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 6D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 6D grid.
+ * @param range_l     the number of items to process along the fourth dimension
+ *    of the 6D grid.
+ * @param range_m     the number of items to process along the fifth dimension
+ *    of the 6D grid.
+ * @param range_n     the number of items to process along the sixth dimension
+ *    of the 6D grid.
+ * @param tile_n      the maximum number of items along the sixth dimension of
+ *    the 6D grid to process in one functor call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+template
+inline void pthreadpool_parallelize_6d(
+	pthreadpool_t threadpool,
+	const T& functor,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t range_l,
+	size_t range_m,
+	size_t range_n,
+	uint32_t flags = 0)
+{
+	pthreadpool_parallelize_6d(
+		threadpool,
+		&libpthreadpool::detail::call_wrapper_6d,
+		const_cast(static_cast(&functor)),
+		range_i,
+		range_j,
+		range_k,
+		range_l,
+		range_m,
+		range_n,
+		flags);
+}
+
+/**
+ * Process items on a 6D grid with the specified maximum tile size along the
+ * last grid dimension.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k++)
+ *         for (size_t l = 0; l < range_l; l++)
+ *           for (size_t m = 0; m < range_m; m++)
+ *             for (size_t n = 0; n < range_n; n += tile_n)
+ *               functor(i, j, k, l, m, n, min(range_n - n, tile_n));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param functor     the functor to call for each tile.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 6D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 6D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 6D grid.
+ * @param range_l     the number of items to process along the fourth dimension
+ *    of the 6D grid.
+ * @param range_m     the number of items to process along the fifth dimension
+ *    of the 6D grid.
+ * @param range_n     the number of items to process along the sixth dimension
+ *    of the 6D grid.
+ * @param tile_n      the maximum number of items along the sixth dimension of
+ *    the 6D grid to process in one functor call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+template
+inline void pthreadpool_parallelize_6d_tile_1d(
+	pthreadpool_t threadpool,
+	const T& functor,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t range_l,
+	size_t range_m,
+	size_t range_n,
+	size_t tile_n,
+	uint32_t flags = 0)
+{
+	pthreadpool_parallelize_6d_tile_1d(
+		threadpool,
+		&libpthreadpool::detail::call_wrapper_6d_tile_1d,
+		const_cast(static_cast(&functor)),
+		range_i,
+		range_j,
+		range_k,
+		range_l,
+		range_m,
+		range_n,
+		tile_n,
+		flags);
+}
+
+/**
+ * Process items on a 6D grid with the specified maximum tile size along the
+ * last two grid dimensions.
+ *
+ * The function implements a parallel version of the following snippet:
+ *
+ *   for (size_t i = 0; i < range_i; i++)
+ *     for (size_t j = 0; j < range_j; j++)
+ *       for (size_t k = 0; k < range_k; k++)
+ *         for (size_t l = 0; l < range_l; l++)
+ *           for (size_t m = 0; m < range_m; m += tile_m)
+ *             for (size_t n = 0; n < range_n; n += tile_n)
+ *               functor(i, j, k, l, m, n,
+ *                 min(range_m - m, tile_m), min(range_n - n, tile_n));
+ *
+ * When the function returns, all items have been processed and the thread pool
+ * is ready for a new task.
+ *
+ * @note If multiple threads call this function with the same thread pool, the
+ *    calls are serialized.
+ *
+ * @param threadpool  the thread pool to use for parallelisation. If threadpool
+ *    is NULL, all items are processed serially on the calling thread.
+ * @param functor     the functor to call for each tile.
+ * @param range_i     the number of items to process along the first dimension
+ *    of the 6D grid.
+ * @param range_j     the number of items to process along the second dimension
+ *    of the 6D grid.
+ * @param range_k     the number of items to process along the third dimension
+ *    of the 6D grid.
+ * @param range_l     the number of items to process along the fourth dimension
+ *    of the 6D grid.
+ * @param range_m     the number of items to process along the fifth dimension
+ *    of the 6D grid.
+ * @param range_n     the number of items to process along the sixth dimension
+ *    of the 6D grid.
+ * @param tile_m      the maximum number of items along the fifth dimension of
+ *    the 6D grid to process in one functor call.
+ * @param tile_n      the maximum number of items along the sixth dimension of
+ *    the 6D grid to process in one functor call.
+ * @param flags       a bitwise combination of zero or more optional flags
+ *    (PTHREADPOOL_FLAG_DISABLE_DENORMALS or PTHREADPOOL_FLAG_YIELD_WORKERS)
+ */
+template
+inline void pthreadpool_parallelize_6d_tile_2d(
+	pthreadpool_t threadpool,
+	const T& functor,
+	size_t range_i,
+	size_t range_j,
+	size_t range_k,
+	size_t range_l,
+	size_t range_m,
+	size_t range_n,
+	size_t tile_m,
+	size_t tile_n,
+	uint32_t flags = 0)
+{
+	pthreadpool_parallelize_6d_tile_2d(
+		threadpool,
+		&libpthreadpool::detail::call_wrapper_6d_tile_2d,
+		const_cast(static_cast(&functor)),
+		range_i,
+		range_j,
+		range_k,
+		range_l,
+		range_m,
+		range_n,
+		tile_m,
+		tile_n,
+		flags);
+}
+
+#endif  /* __cplusplus */
+
+#endif /* PTHREADPOOL_H_ */
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/qnnpack_func.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/qnnpack_func.h
new file mode 100644
index 0000000000000000000000000000000000000000..10bbc000192d7e03745e2cf3fb263a9655cde00c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/qnnpack_func.h
@@ -0,0 +1,166 @@
+#pragma once
+
+#include 
+#include 
+
+namespace qnnpack {
+class PrePackConvWeights final {
+ public:
+  PrePackConvWeights(
+      const pytorch_qnnp_operator_t convolution,
+      const uint8_t* kernel_zero_points,
+      const uint8_t* kernel,
+      const int32_t* bias);
+
+  void* getPackedWeights() const
+  {
+    return packed_weights_;
+  }
+
+  int64_t getOutputChannels() const
+  {
+    return output_channels_;
+  }
+
+  ~PrePackConvWeights()
+  {
+    if (packed_weights_ != nullptr) {
+      free(packed_weights_);
+    }
+  }
+
+  PrePackConvWeights() = delete;
+  PrePackConvWeights(const PrePackConvWeights&) = delete;
+  PrePackConvWeights& operator=(const PrePackConvWeights&) = delete;
+
+ private:
+  void* packed_weights_ = nullptr;
+  int64_t output_channels_;
+};
+
+class PackBMatrix final {
+ public:
+  PackBMatrix(
+      size_t input_channels,
+      size_t output_channels,
+      const uint8_t* kernel_zero_points,
+      const float* requantization_scale,
+      const uint8_t* kernel,
+      const int32_t* bias);
+
+  // This constructor is to be used for dynamic mode
+  // quantization. In dynamic mode, we dont yet support
+  // per channel quantization, and paying the cost of
+  // memory allocation for per channel zero point and
+  // requant scale will hurt performance.
+  PackBMatrix(
+      size_t input_channels,
+      size_t output_channels,
+      const uint8_t kernel_zero_point,
+      const float requantization_scale,
+      const uint8_t* kernel,
+      const int32_t* bias);
+
+  void* getPackedWeights() const
+  {
+    return packed_weights_;
+  }
+
+  void unpackWeights(
+      const uint8_t* kernel_zero_points,
+      int8_t* kernel
+    ) const;
+
+  size_t getInputChannels() const
+  {
+    return input_channels_;
+  }
+
+  size_t getOutputChannels() const
+  {
+    return output_channels_;
+  }
+
+  ~PackBMatrix()
+  {
+    if (packed_weights_ != nullptr) {
+      free(packed_weights_);
+    }
+  }
+
+  PackBMatrix() = delete;
+  PackBMatrix(const PackBMatrix&) = delete;
+  PackBMatrix& operator=(const PackBMatrix&) = delete;
+
+ private:
+  void* packed_weights_ = nullptr;
+  size_t input_channels_;
+  size_t output_channels_;
+};
+
+enum pytorch_qnnp_status qnnpackLinear(
+    const size_t batch_size,
+    const size_t input_channels,
+    const size_t output_channels,
+    const uint8_t input_zero_point,
+    const uint8_t* kernel_zero_points,
+    const float* requantization_scales,
+    const uint8_t output_zero_point,
+    const uint8_t output_min,
+    const uint8_t output_max,
+    const uint8_t* input,
+    const size_t input_stride,
+    void* packed_weights,
+    uint8_t* output,
+    const size_t output_stride,
+    pthreadpool_t threadpool);
+
+enum pytorch_qnnp_status qnnpackConv(
+    const pytorch_qnnp_operator_t convolution,
+    void* packed_weights,
+    const size_t batch_size,
+    const size_t input_depth,
+    const size_t input_height,
+    const size_t input_width,
+    const uint8_t input_zero_point,
+    const uint8_t* input,
+    const uint8_t* kernel_zero_points,
+    const float* requantization_scales,
+    const uint8_t output_zero_point,
+    const uint8_t output_min,
+    const uint8_t output_max,
+    uint8_t* output,
+    pthreadpool_t threadpool);
+
+enum pytorch_qnnp_status qnnpackDeConv(
+    const pytorch_qnnp_operator_t deconvolution,
+    void* packed_weights,
+    const size_t batch_size,
+    const size_t input_height,
+    const size_t input_width,
+    const uint8_t input_zero_point,
+    const uint8_t* input,
+    const uint8_t* kernel_zero_points,
+    const float* requantization_scales,
+    const uint8_t output_zero_point,
+    const uint8_t output_min,
+    const uint8_t output_max,
+    uint8_t* output,
+    pthreadpool_t threadpool);
+
+enum pytorch_qnnp_status qnnpackLinearDynamic(
+    const size_t batch_size,
+    const size_t input_channels,
+    const size_t output_channels,
+    const uint8_t input_zero_point,
+    const uint8_t* kernel_zero_points,
+    const float* dequantization_scales,
+    const uint8_t* input,
+    const size_t input_stride,
+    void* packed_weights,
+    const float* bias,
+    float* output,
+    const size_t output_stride,
+    pthreadpool_t threadpool);
+
+} // namespace qnnpack
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/sleef.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/sleef.h
new file mode 100644
index 0000000000000000000000000000000000000000..292ac5b8be30c5766679ce2dd562014fdf50d4f2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/sleef.h
@@ -0,0 +1,4170 @@
+//   Copyright Naoki Shibata and contributors 2010 - 2023.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+
+#ifndef __SLEEF_H__
+#define __SLEEF_H__
+
+#define SLEEF_VERSION_MAJOR 3
+#define SLEEF_VERSION_MINOR 6
+#define SLEEF_VERSION_PATCHLEVEL 0
+
+#include 
+#include 
+
+#if defined (__GNUC__) || defined (__clang__) || defined(__INTEL_COMPILER)
+#define SLEEF_CONST __attribute__((const))
+#define SLEEF_INLINE __attribute__((always_inline))
+#elif defined(_MSC_VER)
+#define SLEEF_CONST
+#define SLEEF_INLINE __forceinline
+#endif
+
+#if defined(__AVX2__) || defined(__aarch64__) || defined(__arm__) || defined(__powerpc64__) || defined(__zarch__)
+#ifndef FP_FAST_FMA
+#define FP_FAST_FMA
+#endif
+#ifndef FP_FAST_FMAF
+#define FP_FAST_FMAF
+#endif
+#endif
+
+#if defined(_MSC_VER) && !defined(__STDC__)
+#define __STDC__ 1
+#endif
+
+#if (defined(__MINGW32__) || defined(__MINGW64__) || defined(__CYGWIN__) || defined(_MSC_VER)) && !defined(SLEEF_STATIC_LIBS)
+#ifdef SLEEF_IMPORT_IS_EXPORT
+#define SLEEF_IMPORT __declspec(dllexport)
+#else // #ifdef SLEEF_IMPORT_IS_EXPORT
+#define SLEEF_IMPORT __declspec(dllimport)
+#if (defined(_MSC_VER))
+#pragma comment(lib,"sleef.lib")
+#endif // #if (defined(_MSC_VER))
+#endif // #ifdef SLEEF_IMPORT_IS_EXPORT
+#else // #if (defined(__MINGW32__) || defined(__MINGW64__) || defined(__CYGWIN__) || defined(_MSC_VER)) && !defined(SLEEF_STATIC_LIBS)
+#define SLEEF_IMPORT
+#endif // #if (defined(__MINGW32__) || defined(__MINGW64__) || defined(__CYGWIN__) || defined(_MSC_VER)) && !defined(SLEEF_STATIC_LIBS)
+
+#if (defined(__GNUC__) || defined(__CLANG__)) && (defined(__i386__) || defined(__x86_64__))
+#include 
+#endif
+
+#if (defined(_MSC_VER))
+#include 
+#endif
+
+#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+#include 
+#endif
+
+#if defined(__ARM_FEATURE_SVE)
+#include 
+#endif
+
+#if defined(__VSX__) && defined(__PPC64__) && defined(__LITTLE_ENDIAN__)
+#include 
+typedef __vector double       SLEEF_VECTOR_DOUBLE;
+typedef __vector float        SLEEF_VECTOR_FLOAT;
+typedef __vector int          SLEEF_VECTOR_INT;
+typedef __vector unsigned int SLEEF_VECTOR_UINT;
+typedef __vector long long SLEEF_VECTOR_LONGLONG;
+typedef __vector unsigned long long SLEEF_VECTOR_ULONGLONG;
+#endif
+
+#if defined(__VX__) && defined(__VEC__)
+#ifndef SLEEF_VECINTRIN_H_INCLUDED
+#include 
+#define SLEEF_VECINTRIN_H_INCLUDED
+#endif
+typedef __vector double       SLEEF_VECTOR_DOUBLE;
+typedef __vector float        SLEEF_VECTOR_FLOAT;
+typedef __vector int          SLEEF_VECTOR_INT;
+typedef __vector unsigned int SLEEF_VECTOR_UINT;
+typedef __vector long long SLEEF_VECTOR_LONGLONG;
+typedef __vector unsigned long long SLEEF_VECTOR_ULONGLONG;
+#endif
+
+//
+
+#if defined(SLEEF_ENABLE_OMP_SIMD) && (defined(__GNUC__) || defined(__CLANG__)) && !defined(__INTEL_COMPILER)
+#if defined(__aarch64__)
+//#define SLEEF_PRAGMA_OMP_SIMD_DP _Pragma ("omp declare simd simdlen(2) notinbranch")
+//#define SLEEF_PRAGMA_OMP_SIMD_SP _Pragma ("omp declare simd simdlen(4) notinbranch")
+//#elif defined(__x86_64__) && defined(__AVX512F__)
+//#define SLEEF_PRAGMA_OMP_SIMD_DP _Pragma ("omp declare simd simdlen(8) notinbranch")
+//#define SLEEF_PRAGMA_OMP_SIMD_SP _Pragma ("omp declare simd simdlen(16) notinbranch")
+#elif defined(__x86_64__) && defined(__AVX__)
+#define SLEEF_PRAGMA_OMP_SIMD_DP _Pragma ("omp declare simd simdlen(4) notinbranch")
+#define SLEEF_PRAGMA_OMP_SIMD_SP _Pragma ("omp declare simd simdlen(8) notinbranch")
+#elif defined(__x86_64__) && defined(__SSE2__)
+#define SLEEF_PRAGMA_OMP_SIMD_DP _Pragma ("omp declare simd simdlen(2) notinbranch")
+#define SLEEF_PRAGMA_OMP_SIMD_SP _Pragma ("omp declare simd simdlen(4) notinbranch")
+#endif
+#endif
+
+#ifndef SLEEF_PRAGMA_OMP_SIMD_DP
+#define SLEEF_PRAGMA_OMP_SIMD_DP
+#define SLEEF_PRAGMA_OMP_SIMD_SP
+#endif
+
+//
+
+#ifndef SLEEF_FP_ILOGB0
+#define SLEEF_FP_ILOGB0 ((int)0x80000000)
+#endif
+
+#ifndef SLEEF_FP_ILOGBNAN
+#define SLEEF_FP_ILOGBNAN ((int)2147483647)
+#endif
+
+//
+
+SLEEF_IMPORT void *Sleef_malloc(size_t z);
+SLEEF_IMPORT void Sleef_free(void *ptr);
+SLEEF_IMPORT uint64_t Sleef_currentTimeMicros();
+
+#if defined(__i386__) || defined(__x86_64__) || defined(_MSC_VER)
+SLEEF_IMPORT void Sleef_x86CpuID(int32_t out[4], uint32_t eax, uint32_t ecx);
+#endif
+
+//
+
+#if defined(__riscv_v)
+#include 
+typedef vfloat64m2_t Sleef_vfloat64m1_t_2;
+typedef vfloat32m2_t Sleef_vfloat32m1_t_2;
+typedef vfloat64m4_t Sleef_vfloat64m2_t_2;
+typedef vfloat32m4_t Sleef_vfloat32m2_t_2;
+#define Sleef_vfloat64m1_t_2_DEFINED
+#define Sleef_vfloat32m1_t_2_DEFINED
+#define Sleef_vfloat64m2_t_2_DEFINED
+#define Sleef_vfloat32m2_t_2_DEFINED
+#endif
+
+#ifndef Sleef_double2_DEFINED
+#define Sleef_double2_DEFINED
+typedef struct {
+  double x, y;
+} Sleef_double2;
+#endif
+
+#ifndef Sleef_float2_DEFINED
+#define Sleef_float2_DEFINED
+typedef struct {
+  float x, y;
+} Sleef_float2;
+#endif
+
+#ifdef __cplusplus
+extern "C"
+{
+#endif
+
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_sin_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_cos_u35(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double2 Sleef_sincos_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_tan_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_asin_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_acos_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_atan_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_atan2_u35(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_log_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_cbrt_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_sin_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_cos_u10(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double2 Sleef_sincos_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_tan_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_asin_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_acos_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_atan_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_atan2_u10(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_log_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_cbrt_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_exp_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_pow_u10(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_sinh_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_cosh_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_tanh_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_sinh_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_cosh_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_tanh_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_asinh_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_acosh_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_atanh_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_exp2_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_exp10_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_exp2_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_exp10_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_expm1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_log10_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_log2_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_log2_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_log1p_u10(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double2 Sleef_sincospi_u05(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double2 Sleef_sincospi_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_sinpi_u05(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_cospi_u05(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_ldexp(double, int);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST int Sleef_ilogb(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_fma(double, double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_sqrt(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_sqrt_u05(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_sqrt_u35(double);
+
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_hypot_u05(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_hypot_u35(double, double);
+
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_fabs(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_copysign(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_fmax(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_fmin(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_fdim(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_trunc(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_floor(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_ceil(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_round(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_rint(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_nextafter(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_frfrexp(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST int Sleef_expfrexp(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_fmod(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_remainder(double, double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double2 Sleef_modf(double);
+
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_lgamma_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_tgamma_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_erf_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_erfc_u15(double);
+
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_sinf_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_cosf_u35(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float2 Sleef_sincosf_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_tanf_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_asinf_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_acosf_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_atanf_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_atan2f_u35(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_logf_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_cbrtf_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_sinf_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_cosf_u10(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float2 Sleef_sincosf_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_fastsinf_u3500(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_fastcosf_u3500(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_tanf_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_asinf_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_acosf_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_atanf_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_atan2f_u10(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_logf_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_cbrtf_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_expf_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_powf_u10(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_fastpowf_u3500(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_sinhf_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_coshf_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_tanhf_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_sinhf_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_coshf_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_tanhf_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_asinhf_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_acoshf_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_atanhf_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_exp2f_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_exp10f_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_exp2f_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_exp10f_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_expm1f_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_log10f_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_log2f_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_log2f_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_log1pf_u10(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float2 Sleef_sincospif_u05(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float2 Sleef_sincospif_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_sinpif_u05(float d);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_cospif_u05(float d);
+SLEEF_IMPORT SLEEF_CONST float Sleef_ldexpf(float, int);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST int Sleef_ilogbf(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_fmaf(float, float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_sqrtf(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_sqrtf_u05(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_sqrtf_u35(float);
+
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_hypotf_u05(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_hypotf_u35(float, float);
+
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_fabsf(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_copysignf(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_fmaxf(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_fminf(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_fdimf(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_truncf(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_floorf(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_ceilf(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_roundf(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_rintf(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_nextafterf(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_frfrexpf(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST int Sleef_expfrexpf(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_fmodf(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_remainderf(float, float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float2 Sleef_modff(float);
+
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_lgammaf_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_tgammaf_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_erff_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_erfcf_u15(float);
+#ifdef __SSE2__
+
+#ifndef Sleef___m128d_2_DEFINED
+typedef struct {
+  __m128d x, y;
+} Sleef___m128d_2;
+#define Sleef___m128d_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sind2_u35(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cosd2_u35(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_sincosd2_u35(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_tand2_u35(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_asind2_u35(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_acosd2_u35(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_atand2_u35(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_atan2d2_u35(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_logd2_u35(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cbrtd2_u35(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sind2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cosd2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_sincosd2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_tand2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_asind2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_acosd2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_atand2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_atan2d2_u10(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_logd2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cbrtd2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_expd2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_powd2_u10(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sinhd2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_coshd2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_tanhd2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sinhd2_u35(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_coshd2_u35(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_tanhd2_u35(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fastsind2_u3500(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fastcosd2_u3500(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fastpowd2_u3500(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_asinhd2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_acoshd2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_atanhd2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_exp2d2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_exp2d2_u35(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_exp10d2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_exp10d2_u35(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_expm1d2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_log10d2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_log2d2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_log2d2_u35(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_log1pd2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_sincospid2_u05(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_sincospid2_u35(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sinpid2_u05(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cospid2_u05(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_ldexpd2(__m128d, __m128i);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_ilogbd2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fmad2(__m128d, __m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sqrtd2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sqrtd2_u05(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sqrtd2_u35(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_hypotd2_u05(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_hypotd2_u35(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fabsd2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_copysignd2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fmaxd2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fmind2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fdimd2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_truncd2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_floord2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_ceild2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_roundd2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_rintd2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_nextafterd2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_frfrexpd2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_expfrexpd2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fmodd2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_remainderd2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_modfd2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_lgammad2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_tgammad2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_erfd2_u10(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_erfcd2_u15(__m128d);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntd2(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrd2(int);
+
+#ifndef Sleef___m128_2_DEFINED
+typedef struct {
+  __m128 x, y;
+} Sleef___m128_2;
+#define Sleef___m128_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sinf4_u35(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cosf4_u35(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_sincosf4_u35(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_tanf4_u35(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_asinf4_u35(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_acosf4_u35(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_atanf4_u35(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_atan2f4_u35(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_logf4_u35(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cbrtf4_u35(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sinf4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cosf4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_sincosf4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_tanf4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_asinf4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_acosf4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_atanf4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_atan2f4_u10(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_logf4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cbrtf4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_expf4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_powf4_u10(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sinhf4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_coshf4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_tanhf4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sinhf4_u35(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_coshf4_u35(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_tanhf4_u35(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fastsinf4_u3500(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fastcosf4_u3500(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fastpowf4_u3500(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_asinhf4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_acoshf4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_atanhf4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_exp2f4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_exp2f4_u35(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_exp10f4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_exp10f4_u35(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_expm1f4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_log10f4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_log2f4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_log2f4_u35(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_log1pf4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_sincospif4_u05(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_sincospif4_u35(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sinpif4_u05(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cospif4_u05(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fmaf4(__m128, __m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sqrtf4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sqrtf4_u05(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sqrtf4_u35(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_hypotf4_u05(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_hypotf4_u35(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fabsf4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_copysignf4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fmaxf4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fminf4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fdimf4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_truncf4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_floorf4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_ceilf4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_roundf4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_rintf4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_nextafterf4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_frfrexpf4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fmodf4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_remainderf4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_modff4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_lgammaf4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_tgammaf4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_erff4_u10(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_erfcf4_u15(__m128);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntf4(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrf4(int);
+#endif
+#ifdef __SSE2__
+
+#ifndef Sleef___m128d_2_DEFINED
+typedef struct {
+  __m128d x, y;
+} Sleef___m128d_2;
+#define Sleef___m128d_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sind2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_sind2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cosd2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_cosd2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_sincosd2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_cinz_sincosd2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_tand2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_tand2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_asind2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_asind2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_acosd2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_acosd2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_atand2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_atand2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_atan2d2_u35sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_atan2d2_u35sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_logd2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_logd2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cbrtd2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_cbrtd2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sind2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_sind2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cosd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_cosd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_sincosd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_cinz_sincosd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_tand2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_tand2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_asind2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_asind2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_acosd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_acosd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_atand2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_atand2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_atan2d2_u10sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_atan2d2_u10sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_logd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_logd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cbrtd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_cbrtd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_expd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_expd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_powd2_u10sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_powd2_u10sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sinhd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_sinhd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_coshd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_coshd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_tanhd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_tanhd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sinhd2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_sinhd2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_coshd2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_coshd2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_tanhd2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_tanhd2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fastsind2_u3500sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_fastsind2_u3500sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fastcosd2_u3500sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_fastcosd2_u3500sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fastpowd2_u3500sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_fastpowd2_u3500sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_asinhd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_asinhd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_acoshd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_acoshd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_atanhd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_atanhd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_exp2d2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_exp2d2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_exp2d2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_exp2d2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_exp10d2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_exp10d2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_exp10d2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_exp10d2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_expm1d2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_expm1d2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_log10d2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_log10d2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_log2d2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_log2d2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_log2d2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_log2d2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_log1pd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_log1pd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_sincospid2_u05sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_cinz_sincospid2_u05sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_sincospid2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_cinz_sincospid2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sinpid2_u05sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_sinpid2_u05sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cospid2_u05sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_cospid2_u05sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_ldexpd2_sse2(__m128d, __m128i);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_ldexpd2_sse2(__m128d, __m128i);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_ilogbd2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_cinz_ilogbd2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fmad2_sse2(__m128d, __m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_fmad2_sse2(__m128d, __m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sqrtd2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_sqrtd2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sqrtd2_u05sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_sqrtd2_u05sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sqrtd2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_sqrtd2_u35sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_hypotd2_u05sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_hypotd2_u05sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_hypotd2_u35sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_hypotd2_u35sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fabsd2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_fabsd2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_copysignd2_sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_copysignd2_sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fmaxd2_sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_fmaxd2_sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fmind2_sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_fmind2_sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fdimd2_sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_fdimd2_sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_truncd2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_truncd2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_floord2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_floord2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_ceild2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_ceild2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_roundd2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_roundd2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_rintd2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_rintd2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_nextafterd2_sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_nextafterd2_sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_frfrexpd2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_frfrexpd2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_expfrexpd2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_cinz_expfrexpd2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fmodd2_sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_fmodd2_sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_remainderd2_sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_remainderd2_sse2(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_modfd2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_cinz_modfd2_sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_lgammad2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_lgammad2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_tgammad2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_tgammad2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_erfd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_erfd2_u10sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_erfcd2_u15sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_erfcd2_u15sse2(__m128d);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntd2_sse2(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrd2_sse2(int);
+
+#ifndef Sleef___m128_2_DEFINED
+typedef struct {
+  __m128 x, y;
+} Sleef___m128_2;
+#define Sleef___m128_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sinf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_sinf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cosf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_cosf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_sincosf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_cinz_sincosf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_tanf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_tanf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_asinf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_asinf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_acosf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_acosf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_atanf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_atanf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_atan2f4_u35sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_atan2f4_u35sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_logf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_logf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cbrtf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_cbrtf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sinf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_sinf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cosf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_cosf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_sincosf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_cinz_sincosf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_tanf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_tanf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_asinf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_asinf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_acosf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_acosf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_atanf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_atanf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_atan2f4_u10sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_atan2f4_u10sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_logf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_logf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cbrtf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_cbrtf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_expf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_expf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_powf4_u10sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_powf4_u10sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sinhf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_sinhf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_coshf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_coshf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_tanhf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_tanhf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sinhf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_sinhf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_coshf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_coshf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_tanhf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_tanhf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fastsinf4_u3500sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_fastsinf4_u3500sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fastcosf4_u3500sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_fastcosf4_u3500sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fastpowf4_u3500sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_fastpowf4_u3500sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_asinhf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_asinhf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_acoshf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_acoshf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_atanhf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_atanhf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_exp2f4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_exp2f4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_exp2f4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_exp2f4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_exp10f4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_exp10f4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_exp10f4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_exp10f4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_expm1f4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_expm1f4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_log10f4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_log10f4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_log2f4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_log2f4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_log2f4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_log2f4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_log1pf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_log1pf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_sincospif4_u05sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_cinz_sincospif4_u05sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_sincospif4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_cinz_sincospif4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sinpif4_u05sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_sinpif4_u05sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cospif4_u05sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_cospif4_u05sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fmaf4_sse2(__m128, __m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_fmaf4_sse2(__m128, __m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sqrtf4_sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_sqrtf4_sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sqrtf4_u05sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_sqrtf4_u05sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sqrtf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_sqrtf4_u35sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_hypotf4_u05sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_hypotf4_u05sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_hypotf4_u35sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_hypotf4_u35sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fabsf4_sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_fabsf4_sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_copysignf4_sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_copysignf4_sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fmaxf4_sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_fmaxf4_sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fminf4_sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_fminf4_sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fdimf4_sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_fdimf4_sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_truncf4_sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_truncf4_sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_floorf4_sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_floorf4_sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_ceilf4_sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_ceilf4_sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_roundf4_sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_roundf4_sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_rintf4_sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_rintf4_sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_nextafterf4_sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_nextafterf4_sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_frfrexpf4_sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_frfrexpf4_sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fmodf4_sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_fmodf4_sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_remainderf4_sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_remainderf4_sse2(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_modff4_sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_cinz_modff4_sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_lgammaf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_lgammaf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_tgammaf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_tgammaf4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_erff4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_erff4_u10sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_erfcf4_u15sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_erfcf4_u15sse2(__m128);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntf4_sse2(int);
+SLEEF_IMPORT SLEEF_CONST int Sleef_cinz_getIntf4_sse2(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrf4_sse2(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_cinz_getPtrf4_sse2(int);
+#endif
+#ifdef __SSE2__
+
+#ifndef Sleef___m128d_2_DEFINED
+typedef struct {
+  __m128d x, y;
+} Sleef___m128d_2;
+#define Sleef___m128d_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sind2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_sind2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cosd2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_cosd2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_sincosd2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_cinz_sincosd2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_tand2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_tand2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_asind2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_asind2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_acosd2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_acosd2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_atand2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_atand2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_atan2d2_u35sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_atan2d2_u35sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_logd2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_logd2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cbrtd2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_cbrtd2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sind2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_sind2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cosd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_cosd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_sincosd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_cinz_sincosd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_tand2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_tand2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_asind2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_asind2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_acosd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_acosd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_atand2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_atand2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_atan2d2_u10sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_atan2d2_u10sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_logd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_logd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cbrtd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_cbrtd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_expd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_expd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_powd2_u10sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_powd2_u10sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sinhd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_sinhd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_coshd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_coshd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_tanhd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_tanhd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sinhd2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_sinhd2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_coshd2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_coshd2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_tanhd2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_tanhd2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fastsind2_u3500sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_fastsind2_u3500sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fastcosd2_u3500sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_fastcosd2_u3500sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fastpowd2_u3500sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_fastpowd2_u3500sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_asinhd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_asinhd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_acoshd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_acoshd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_atanhd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_atanhd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_exp2d2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_exp2d2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_exp2d2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_exp2d2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_exp10d2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_exp10d2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_exp10d2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_exp10d2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_expm1d2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_expm1d2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_log10d2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_log10d2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_log2d2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_log2d2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_log2d2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_log2d2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_log1pd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_log1pd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_sincospid2_u05sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_cinz_sincospid2_u05sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_sincospid2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_cinz_sincospid2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sinpid2_u05sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_sinpid2_u05sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cospid2_u05sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_cospid2_u05sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_ldexpd2_sse4(__m128d, __m128i);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_ldexpd2_sse4(__m128d, __m128i);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_ilogbd2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_cinz_ilogbd2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fmad2_sse4(__m128d, __m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_fmad2_sse4(__m128d, __m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sqrtd2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_sqrtd2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sqrtd2_u05sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_sqrtd2_u05sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sqrtd2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_sqrtd2_u35sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_hypotd2_u05sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_hypotd2_u05sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_hypotd2_u35sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_hypotd2_u35sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fabsd2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_fabsd2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_copysignd2_sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_copysignd2_sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fmaxd2_sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_fmaxd2_sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fmind2_sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_fmind2_sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fdimd2_sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_fdimd2_sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_truncd2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_truncd2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_floord2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_floord2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_ceild2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_ceild2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_roundd2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_roundd2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_rintd2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_rintd2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_nextafterd2_sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_nextafterd2_sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_frfrexpd2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_frfrexpd2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_expfrexpd2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_cinz_expfrexpd2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fmodd2_sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_fmodd2_sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_remainderd2_sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_remainderd2_sse4(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_modfd2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_cinz_modfd2_sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_lgammad2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_lgammad2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_tgammad2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_tgammad2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_erfd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_erfd2_u10sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_erfcd2_u15sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cinz_erfcd2_u15sse4(__m128d);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntd2_sse4(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrd2_sse4(int);
+
+#ifndef Sleef___m128_2_DEFINED
+typedef struct {
+  __m128 x, y;
+} Sleef___m128_2;
+#define Sleef___m128_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sinf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_sinf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cosf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_cosf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_sincosf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_cinz_sincosf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_tanf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_tanf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_asinf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_asinf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_acosf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_acosf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_atanf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_atanf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_atan2f4_u35sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_atan2f4_u35sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_logf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_logf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cbrtf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_cbrtf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sinf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_sinf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cosf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_cosf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_sincosf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_cinz_sincosf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_tanf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_tanf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_asinf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_asinf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_acosf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_acosf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_atanf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_atanf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_atan2f4_u10sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_atan2f4_u10sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_logf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_logf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cbrtf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_cbrtf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_expf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_expf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_powf4_u10sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_powf4_u10sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sinhf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_sinhf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_coshf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_coshf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_tanhf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_tanhf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sinhf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_sinhf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_coshf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_coshf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_tanhf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_tanhf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fastsinf4_u3500sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_fastsinf4_u3500sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fastcosf4_u3500sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_fastcosf4_u3500sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fastpowf4_u3500sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_fastpowf4_u3500sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_asinhf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_asinhf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_acoshf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_acoshf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_atanhf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_atanhf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_exp2f4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_exp2f4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_exp2f4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_exp2f4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_exp10f4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_exp10f4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_exp10f4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_exp10f4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_expm1f4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_expm1f4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_log10f4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_log10f4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_log2f4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_log2f4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_log2f4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_log2f4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_log1pf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_log1pf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_sincospif4_u05sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_cinz_sincospif4_u05sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_sincospif4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_cinz_sincospif4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sinpif4_u05sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_sinpif4_u05sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cospif4_u05sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_cospif4_u05sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fmaf4_sse4(__m128, __m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_fmaf4_sse4(__m128, __m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sqrtf4_sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_sqrtf4_sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sqrtf4_u05sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_sqrtf4_u05sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sqrtf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_sqrtf4_u35sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_hypotf4_u05sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_hypotf4_u05sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_hypotf4_u35sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_hypotf4_u35sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fabsf4_sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_fabsf4_sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_copysignf4_sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_copysignf4_sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fmaxf4_sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_fmaxf4_sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fminf4_sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_fminf4_sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fdimf4_sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_fdimf4_sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_truncf4_sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_truncf4_sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_floorf4_sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_floorf4_sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_ceilf4_sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_ceilf4_sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_roundf4_sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_roundf4_sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_rintf4_sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_rintf4_sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_nextafterf4_sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_nextafterf4_sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_frfrexpf4_sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_frfrexpf4_sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fmodf4_sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_fmodf4_sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_remainderf4_sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_remainderf4_sse4(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_modff4_sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_cinz_modff4_sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_lgammaf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_lgammaf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_tgammaf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_tgammaf4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_erff4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_erff4_u10sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_erfcf4_u15sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cinz_erfcf4_u15sse4(__m128);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntf4_sse4(int);
+SLEEF_IMPORT SLEEF_CONST int Sleef_cinz_getIntf4_sse4(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrf4_sse4(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_cinz_getPtrf4_sse4(int);
+#endif
+#ifdef __AVX__
+
+#ifndef Sleef___m256d_2_DEFINED
+typedef struct {
+  __m256d x, y;
+} Sleef___m256d_2;
+#define Sleef___m256d_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sind4_u35(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cosd4_u35(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_sincosd4_u35(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_tand4_u35(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_asind4_u35(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_acosd4_u35(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_atand4_u35(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_atan2d4_u35(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_logd4_u35(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cbrtd4_u35(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sind4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cosd4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_sincosd4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_tand4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_asind4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_acosd4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_atand4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_atan2d4_u10(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_logd4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cbrtd4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_expd4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_powd4_u10(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sinhd4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_coshd4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_tanhd4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sinhd4_u35(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_coshd4_u35(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_tanhd4_u35(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fastsind4_u3500(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fastcosd4_u3500(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fastpowd4_u3500(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_asinhd4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_acoshd4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_atanhd4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_exp2d4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_exp2d4_u35(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_exp10d4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_exp10d4_u35(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_expm1d4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_log10d4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_log2d4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_log2d4_u35(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_log1pd4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_sincospid4_u05(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_sincospid4_u35(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sinpid4_u05(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cospid4_u05(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_ldexpd4(__m256d, __m128i);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_ilogbd4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fmad4(__m256d, __m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sqrtd4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sqrtd4_u05(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sqrtd4_u35(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_hypotd4_u05(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_hypotd4_u35(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fabsd4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_copysignd4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fmaxd4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fmind4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fdimd4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_truncd4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_floord4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_ceild4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_roundd4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_rintd4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_nextafterd4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_frfrexpd4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_expfrexpd4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fmodd4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_remainderd4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_modfd4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_lgammad4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_tgammad4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_erfd4_u10(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_erfcd4_u15(__m256d);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntd4(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrd4(int);
+
+#ifndef Sleef___m256_2_DEFINED
+typedef struct {
+  __m256 x, y;
+} Sleef___m256_2;
+#define Sleef___m256_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sinf8_u35(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cosf8_u35(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_sincosf8_u35(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_tanf8_u35(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_asinf8_u35(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_acosf8_u35(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_atanf8_u35(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_atan2f8_u35(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_logf8_u35(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cbrtf8_u35(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sinf8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cosf8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_sincosf8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_tanf8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_asinf8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_acosf8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_atanf8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_atan2f8_u10(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_logf8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cbrtf8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_expf8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_powf8_u10(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sinhf8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_coshf8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_tanhf8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sinhf8_u35(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_coshf8_u35(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_tanhf8_u35(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fastsinf8_u3500(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fastcosf8_u3500(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fastpowf8_u3500(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_asinhf8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_acoshf8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_atanhf8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_exp2f8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_exp2f8_u35(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_exp10f8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_exp10f8_u35(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_expm1f8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_log10f8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_log2f8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_log2f8_u35(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_log1pf8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_sincospif8_u05(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_sincospif8_u35(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sinpif8_u05(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cospif8_u05(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fmaf8(__m256, __m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sqrtf8(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sqrtf8_u05(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sqrtf8_u35(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_hypotf8_u05(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_hypotf8_u35(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fabsf8(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_copysignf8(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fmaxf8(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fminf8(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fdimf8(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_truncf8(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_floorf8(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_ceilf8(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_roundf8(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_rintf8(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_nextafterf8(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_frfrexpf8(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fmodf8(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_remainderf8(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_modff8(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_lgammaf8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_tgammaf8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_erff8_u10(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_erfcf8_u15(__m256);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntf8(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrf8(int);
+#endif
+#ifdef __AVX__
+
+#ifndef Sleef___m256d_2_DEFINED
+typedef struct {
+  __m256d x, y;
+} Sleef___m256d_2;
+#define Sleef___m256d_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sind4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_sind4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cosd4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_cosd4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_sincosd4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_cinz_sincosd4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_tand4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_tand4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_asind4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_asind4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_acosd4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_acosd4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_atand4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_atand4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_atan2d4_u35avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_atan2d4_u35avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_logd4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_logd4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cbrtd4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_cbrtd4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sind4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_sind4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cosd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_cosd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_sincosd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_cinz_sincosd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_tand4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_tand4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_asind4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_asind4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_acosd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_acosd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_atand4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_atand4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_atan2d4_u10avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_atan2d4_u10avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_logd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_logd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cbrtd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_cbrtd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_expd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_expd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_powd4_u10avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_powd4_u10avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sinhd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_sinhd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_coshd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_coshd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_tanhd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_tanhd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sinhd4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_sinhd4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_coshd4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_coshd4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_tanhd4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_tanhd4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fastsind4_u3500avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_fastsind4_u3500avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fastcosd4_u3500avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_fastcosd4_u3500avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fastpowd4_u3500avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_fastpowd4_u3500avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_asinhd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_asinhd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_acoshd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_acoshd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_atanhd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_atanhd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_exp2d4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_exp2d4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_exp2d4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_exp2d4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_exp10d4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_exp10d4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_exp10d4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_exp10d4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_expm1d4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_expm1d4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_log10d4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_log10d4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_log2d4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_log2d4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_log2d4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_log2d4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_log1pd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_log1pd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_sincospid4_u05avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_cinz_sincospid4_u05avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_sincospid4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_cinz_sincospid4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sinpid4_u05avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_sinpid4_u05avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cospid4_u05avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_cospid4_u05avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_ldexpd4_avx(__m256d, __m128i);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_ldexpd4_avx(__m256d, __m128i);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_ilogbd4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_cinz_ilogbd4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fmad4_avx(__m256d, __m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_fmad4_avx(__m256d, __m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sqrtd4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_sqrtd4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sqrtd4_u05avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_sqrtd4_u05avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sqrtd4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_sqrtd4_u35avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_hypotd4_u05avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_hypotd4_u05avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_hypotd4_u35avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_hypotd4_u35avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fabsd4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_fabsd4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_copysignd4_avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_copysignd4_avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fmaxd4_avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_fmaxd4_avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fmind4_avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_fmind4_avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fdimd4_avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_fdimd4_avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_truncd4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_truncd4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_floord4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_floord4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_ceild4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_ceild4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_roundd4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_roundd4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_rintd4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_rintd4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_nextafterd4_avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_nextafterd4_avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_frfrexpd4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_frfrexpd4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_expfrexpd4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_cinz_expfrexpd4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fmodd4_avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_fmodd4_avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_remainderd4_avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_remainderd4_avx(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_modfd4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_cinz_modfd4_avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_lgammad4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_lgammad4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_tgammad4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_tgammad4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_erfd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_erfd4_u10avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_erfcd4_u15avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cinz_erfcd4_u15avx(__m256d);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntd4_avx(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrd4_avx(int);
+
+#ifndef Sleef___m256_2_DEFINED
+typedef struct {
+  __m256 x, y;
+} Sleef___m256_2;
+#define Sleef___m256_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sinf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_sinf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cosf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_cosf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_sincosf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_cinz_sincosf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_tanf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_tanf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_asinf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_asinf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_acosf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_acosf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_atanf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_atanf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_atan2f8_u35avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_atan2f8_u35avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_logf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_logf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cbrtf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_cbrtf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sinf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_sinf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cosf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_cosf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_sincosf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_cinz_sincosf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_tanf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_tanf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_asinf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_asinf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_acosf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_acosf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_atanf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_atanf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_atan2f8_u10avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_atan2f8_u10avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_logf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_logf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cbrtf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_cbrtf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_expf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_expf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_powf8_u10avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_powf8_u10avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sinhf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_sinhf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_coshf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_coshf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_tanhf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_tanhf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sinhf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_sinhf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_coshf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_coshf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_tanhf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_tanhf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fastsinf8_u3500avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_fastsinf8_u3500avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fastcosf8_u3500avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_fastcosf8_u3500avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fastpowf8_u3500avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_fastpowf8_u3500avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_asinhf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_asinhf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_acoshf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_acoshf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_atanhf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_atanhf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_exp2f8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_exp2f8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_exp2f8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_exp2f8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_exp10f8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_exp10f8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_exp10f8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_exp10f8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_expm1f8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_expm1f8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_log10f8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_log10f8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_log2f8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_log2f8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_log2f8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_log2f8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_log1pf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_log1pf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_sincospif8_u05avx(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_cinz_sincospif8_u05avx(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_sincospif8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_cinz_sincospif8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sinpif8_u05avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_sinpif8_u05avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cospif8_u05avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_cospif8_u05avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fmaf8_avx(__m256, __m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_fmaf8_avx(__m256, __m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sqrtf8_avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_sqrtf8_avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sqrtf8_u05avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_sqrtf8_u05avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sqrtf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_sqrtf8_u35avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_hypotf8_u05avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_hypotf8_u05avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_hypotf8_u35avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_hypotf8_u35avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fabsf8_avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_fabsf8_avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_copysignf8_avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_copysignf8_avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fmaxf8_avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_fmaxf8_avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fminf8_avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_fminf8_avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fdimf8_avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_fdimf8_avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_truncf8_avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_truncf8_avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_floorf8_avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_floorf8_avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_ceilf8_avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_ceilf8_avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_roundf8_avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_roundf8_avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_rintf8_avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_rintf8_avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_nextafterf8_avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_nextafterf8_avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_frfrexpf8_avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_frfrexpf8_avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fmodf8_avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_fmodf8_avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_remainderf8_avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_remainderf8_avx(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_modff8_avx(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_cinz_modff8_avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_lgammaf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_lgammaf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_tgammaf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_tgammaf8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_erff8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_erff8_u10avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_erfcf8_u15avx(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cinz_erfcf8_u15avx(__m256);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntf8_avx(int);
+SLEEF_IMPORT SLEEF_CONST int Sleef_cinz_getIntf8_avx(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrf8_avx(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_cinz_getPtrf8_avx(int);
+#endif
+#ifdef __AVX__
+
+#ifndef Sleef___m256d_2_DEFINED
+typedef struct {
+  __m256d x, y;
+} Sleef___m256d_2;
+#define Sleef___m256d_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sind4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_sind4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cosd4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_cosd4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_sincosd4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_finz_sincosd4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_tand4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_tand4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_asind4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_asind4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_acosd4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_acosd4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_atand4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_atand4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_atan2d4_u35fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_atan2d4_u35fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_logd4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_logd4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cbrtd4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_cbrtd4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sind4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_sind4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cosd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_cosd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_sincosd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_finz_sincosd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_tand4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_tand4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_asind4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_asind4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_acosd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_acosd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_atand4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_atand4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_atan2d4_u10fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_atan2d4_u10fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_logd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_logd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cbrtd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_cbrtd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_expd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_expd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_powd4_u10fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_powd4_u10fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sinhd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_sinhd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_coshd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_coshd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_tanhd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_tanhd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sinhd4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_sinhd4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_coshd4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_coshd4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_tanhd4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_tanhd4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fastsind4_u3500fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_fastsind4_u3500fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fastcosd4_u3500fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_fastcosd4_u3500fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fastpowd4_u3500fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_fastpowd4_u3500fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_asinhd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_asinhd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_acoshd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_acoshd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_atanhd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_atanhd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_exp2d4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_exp2d4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_exp2d4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_exp2d4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_exp10d4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_exp10d4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_exp10d4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_exp10d4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_expm1d4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_expm1d4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_log10d4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_log10d4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_log2d4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_log2d4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_log2d4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_log2d4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_log1pd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_log1pd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_sincospid4_u05fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_finz_sincospid4_u05fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_sincospid4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_finz_sincospid4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sinpid4_u05fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_sinpid4_u05fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cospid4_u05fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_cospid4_u05fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_ldexpd4_fma4(__m256d, __m128i);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_ldexpd4_fma4(__m256d, __m128i);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_ilogbd4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_finz_ilogbd4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fmad4_fma4(__m256d, __m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_fmad4_fma4(__m256d, __m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sqrtd4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_sqrtd4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sqrtd4_u05fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_sqrtd4_u05fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sqrtd4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_sqrtd4_u35fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_hypotd4_u05fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_hypotd4_u05fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_hypotd4_u35fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_hypotd4_u35fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fabsd4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_fabsd4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_copysignd4_fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_copysignd4_fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fmaxd4_fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_fmaxd4_fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fmind4_fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_fmind4_fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fdimd4_fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_fdimd4_fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_truncd4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_truncd4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_floord4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_floord4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_ceild4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_ceild4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_roundd4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_roundd4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_rintd4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_rintd4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_nextafterd4_fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_nextafterd4_fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_frfrexpd4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_frfrexpd4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_expfrexpd4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_finz_expfrexpd4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fmodd4_fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_fmodd4_fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_remainderd4_fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_remainderd4_fma4(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_modfd4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_finz_modfd4_fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_lgammad4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_lgammad4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_tgammad4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_tgammad4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_erfd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_erfd4_u10fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_erfcd4_u15fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_erfcd4_u15fma4(__m256d);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntd4_fma4(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrd4_fma4(int);
+
+#ifndef Sleef___m256_2_DEFINED
+typedef struct {
+  __m256 x, y;
+} Sleef___m256_2;
+#define Sleef___m256_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sinf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_sinf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cosf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_cosf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_sincosf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_finz_sincosf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_tanf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_tanf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_asinf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_asinf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_acosf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_acosf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_atanf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_atanf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_atan2f8_u35fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_atan2f8_u35fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_logf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_logf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cbrtf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_cbrtf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sinf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_sinf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cosf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_cosf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_sincosf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_finz_sincosf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_tanf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_tanf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_asinf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_asinf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_acosf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_acosf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_atanf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_atanf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_atan2f8_u10fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_atan2f8_u10fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_logf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_logf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cbrtf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_cbrtf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_expf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_expf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_powf8_u10fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_powf8_u10fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sinhf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_sinhf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_coshf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_coshf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_tanhf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_tanhf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sinhf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_sinhf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_coshf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_coshf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_tanhf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_tanhf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fastsinf8_u3500fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_fastsinf8_u3500fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fastcosf8_u3500fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_fastcosf8_u3500fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fastpowf8_u3500fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_fastpowf8_u3500fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_asinhf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_asinhf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_acoshf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_acoshf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_atanhf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_atanhf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_exp2f8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_exp2f8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_exp2f8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_exp2f8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_exp10f8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_exp10f8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_exp10f8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_exp10f8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_expm1f8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_expm1f8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_log10f8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_log10f8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_log2f8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_log2f8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_log2f8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_log2f8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_log1pf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_log1pf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_sincospif8_u05fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_finz_sincospif8_u05fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_sincospif8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_finz_sincospif8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sinpif8_u05fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_sinpif8_u05fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cospif8_u05fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_cospif8_u05fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fmaf8_fma4(__m256, __m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_fmaf8_fma4(__m256, __m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sqrtf8_fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_sqrtf8_fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sqrtf8_u05fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_sqrtf8_u05fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sqrtf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_sqrtf8_u35fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_hypotf8_u05fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_hypotf8_u05fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_hypotf8_u35fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_hypotf8_u35fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fabsf8_fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_fabsf8_fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_copysignf8_fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_copysignf8_fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fmaxf8_fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_fmaxf8_fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fminf8_fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_fminf8_fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fdimf8_fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_fdimf8_fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_truncf8_fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_truncf8_fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_floorf8_fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_floorf8_fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_ceilf8_fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_ceilf8_fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_roundf8_fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_roundf8_fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_rintf8_fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_rintf8_fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_nextafterf8_fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_nextafterf8_fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_frfrexpf8_fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_frfrexpf8_fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fmodf8_fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_fmodf8_fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_remainderf8_fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_remainderf8_fma4(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_modff8_fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_finz_modff8_fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_lgammaf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_lgammaf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_tgammaf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_tgammaf8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_erff8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_erff8_u10fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_erfcf8_u15fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_erfcf8_u15fma4(__m256);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntf8_fma4(int);
+SLEEF_IMPORT SLEEF_CONST int Sleef_finz_getIntf8_fma4(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrf8_fma4(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_finz_getPtrf8_fma4(int);
+#endif
+#ifdef __AVX__
+
+#ifndef Sleef___m256d_2_DEFINED
+typedef struct {
+  __m256d x, y;
+} Sleef___m256d_2;
+#define Sleef___m256d_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sind4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_sind4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cosd4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_cosd4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_sincosd4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_finz_sincosd4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_tand4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_tand4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_asind4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_asind4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_acosd4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_acosd4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_atand4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_atand4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_atan2d4_u35avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_atan2d4_u35avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_logd4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_logd4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cbrtd4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_cbrtd4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sind4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_sind4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cosd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_cosd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_sincosd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_finz_sincosd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_tand4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_tand4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_asind4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_asind4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_acosd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_acosd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_atand4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_atand4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_atan2d4_u10avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_atan2d4_u10avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_logd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_logd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cbrtd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_cbrtd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_expd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_expd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_powd4_u10avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_powd4_u10avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sinhd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_sinhd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_coshd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_coshd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_tanhd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_tanhd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sinhd4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_sinhd4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_coshd4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_coshd4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_tanhd4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_tanhd4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fastsind4_u3500avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_fastsind4_u3500avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fastcosd4_u3500avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_fastcosd4_u3500avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fastpowd4_u3500avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_fastpowd4_u3500avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_asinhd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_asinhd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_acoshd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_acoshd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_atanhd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_atanhd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_exp2d4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_exp2d4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_exp2d4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_exp2d4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_exp10d4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_exp10d4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_exp10d4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_exp10d4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_expm1d4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_expm1d4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_log10d4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_log10d4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_log2d4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_log2d4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_log2d4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_log2d4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_log1pd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_log1pd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_sincospid4_u05avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_finz_sincospid4_u05avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_sincospid4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_finz_sincospid4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sinpid4_u05avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_sinpid4_u05avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_cospid4_u05avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_cospid4_u05avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_ldexpd4_avx2(__m256d, __m128i);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_ldexpd4_avx2(__m256d, __m128i);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_ilogbd4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_finz_ilogbd4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fmad4_avx2(__m256d, __m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_fmad4_avx2(__m256d, __m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sqrtd4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_sqrtd4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sqrtd4_u05avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_sqrtd4_u05avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_sqrtd4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_sqrtd4_u35avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_hypotd4_u05avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_hypotd4_u05avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_hypotd4_u35avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_hypotd4_u35avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fabsd4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_fabsd4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_copysignd4_avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_copysignd4_avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fmaxd4_avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_fmaxd4_avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fmind4_avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_fmind4_avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fdimd4_avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_fdimd4_avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_truncd4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_truncd4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_floord4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_floord4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_ceild4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_ceild4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_roundd4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_roundd4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_rintd4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_rintd4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_nextafterd4_avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_nextafterd4_avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_frfrexpd4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_frfrexpd4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_expfrexpd4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_finz_expfrexpd4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_fmodd4_avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_fmodd4_avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_remainderd4_avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_remainderd4_avx2(__m256d, __m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_modfd4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256d_2 Sleef_finz_modfd4_avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_lgammad4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_lgammad4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_tgammad4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_tgammad4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_erfd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_erfd4_u10avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_erfcd4_u15avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST __m256d Sleef_finz_erfcd4_u15avx2(__m256d);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntd4_avx2(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrd4_avx2(int);
+
+#ifndef Sleef___m256_2_DEFINED
+typedef struct {
+  __m256 x, y;
+} Sleef___m256_2;
+#define Sleef___m256_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sinf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_sinf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cosf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_cosf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_sincosf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_finz_sincosf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_tanf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_tanf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_asinf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_asinf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_acosf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_acosf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_atanf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_atanf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_atan2f8_u35avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_atan2f8_u35avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_logf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_logf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cbrtf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_cbrtf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sinf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_sinf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cosf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_cosf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_sincosf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_finz_sincosf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_tanf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_tanf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_asinf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_asinf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_acosf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_acosf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_atanf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_atanf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_atan2f8_u10avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_atan2f8_u10avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_logf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_logf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cbrtf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_cbrtf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_expf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_expf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_powf8_u10avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_powf8_u10avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sinhf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_sinhf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_coshf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_coshf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_tanhf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_tanhf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sinhf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_sinhf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_coshf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_coshf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_tanhf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_tanhf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fastsinf8_u3500avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_fastsinf8_u3500avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fastcosf8_u3500avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_fastcosf8_u3500avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fastpowf8_u3500avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_fastpowf8_u3500avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_asinhf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_asinhf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_acoshf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_acoshf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_atanhf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_atanhf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_exp2f8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_exp2f8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_exp2f8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_exp2f8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_exp10f8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_exp10f8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_exp10f8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_exp10f8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_expm1f8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_expm1f8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_log10f8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_log10f8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_log2f8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_log2f8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_log2f8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_log2f8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_log1pf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_log1pf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_sincospif8_u05avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_finz_sincospif8_u05avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_sincospif8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_finz_sincospif8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sinpif8_u05avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_sinpif8_u05avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_cospif8_u05avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_cospif8_u05avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fmaf8_avx2(__m256, __m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_fmaf8_avx2(__m256, __m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sqrtf8_avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_sqrtf8_avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sqrtf8_u05avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_sqrtf8_u05avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_sqrtf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_sqrtf8_u35avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_hypotf8_u05avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_hypotf8_u05avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_hypotf8_u35avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_hypotf8_u35avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fabsf8_avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_fabsf8_avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_copysignf8_avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_copysignf8_avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fmaxf8_avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_fmaxf8_avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fminf8_avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_fminf8_avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fdimf8_avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_fdimf8_avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_truncf8_avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_truncf8_avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_floorf8_avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_floorf8_avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_ceilf8_avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_ceilf8_avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_roundf8_avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_roundf8_avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_rintf8_avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_rintf8_avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_nextafterf8_avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_nextafterf8_avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_frfrexpf8_avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_frfrexpf8_avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_fmodf8_avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_fmodf8_avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_remainderf8_avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_remainderf8_avx2(__m256, __m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_modff8_avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST Sleef___m256_2 Sleef_finz_modff8_avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_lgammaf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_lgammaf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_tgammaf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_tgammaf8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_erff8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_erff8_u10avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_erfcf8_u15avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST __m256 Sleef_finz_erfcf8_u15avx2(__m256);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntf8_avx2(int);
+SLEEF_IMPORT SLEEF_CONST int Sleef_finz_getIntf8_avx2(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrf8_avx2(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_finz_getPtrf8_avx2(int);
+#endif
+#ifdef __SSE2__
+
+#ifndef Sleef___m128d_2_DEFINED
+typedef struct {
+  __m128d x, y;
+} Sleef___m128d_2;
+#define Sleef___m128d_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sind2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_sind2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cosd2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_cosd2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_sincosd2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_finz_sincosd2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_tand2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_tand2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_asind2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_asind2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_acosd2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_acosd2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_atand2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_atand2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_atan2d2_u35avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_atan2d2_u35avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_logd2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_logd2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cbrtd2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_cbrtd2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sind2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_sind2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cosd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_cosd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_sincosd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_finz_sincosd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_tand2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_tand2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_asind2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_asind2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_acosd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_acosd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_atand2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_atand2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_atan2d2_u10avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_atan2d2_u10avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_logd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_logd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cbrtd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_cbrtd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_expd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_expd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_powd2_u10avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_powd2_u10avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sinhd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_sinhd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_coshd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_coshd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_tanhd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_tanhd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sinhd2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_sinhd2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_coshd2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_coshd2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_tanhd2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_tanhd2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fastsind2_u3500avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_fastsind2_u3500avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fastcosd2_u3500avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_fastcosd2_u3500avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fastpowd2_u3500avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_fastpowd2_u3500avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_asinhd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_asinhd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_acoshd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_acoshd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_atanhd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_atanhd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_exp2d2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_exp2d2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_exp2d2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_exp2d2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_exp10d2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_exp10d2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_exp10d2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_exp10d2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_expm1d2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_expm1d2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_log10d2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_log10d2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_log2d2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_log2d2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_log2d2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_log2d2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_log1pd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_log1pd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_sincospid2_u05avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_finz_sincospid2_u05avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_sincospid2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_finz_sincospid2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sinpid2_u05avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_sinpid2_u05avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_cospid2_u05avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_cospid2_u05avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_ldexpd2_avx2128(__m128d, __m128i);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_ldexpd2_avx2128(__m128d, __m128i);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_ilogbd2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_finz_ilogbd2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fmad2_avx2128(__m128d, __m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_fmad2_avx2128(__m128d, __m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sqrtd2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_sqrtd2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sqrtd2_u05avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_sqrtd2_u05avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_sqrtd2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_sqrtd2_u35avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_hypotd2_u05avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_hypotd2_u05avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_hypotd2_u35avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_hypotd2_u35avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fabsd2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_fabsd2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_copysignd2_avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_copysignd2_avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fmaxd2_avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_fmaxd2_avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fmind2_avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_fmind2_avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fdimd2_avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_fdimd2_avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_truncd2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_truncd2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_floord2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_floord2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_ceild2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_ceild2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_roundd2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_roundd2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_rintd2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_rintd2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_nextafterd2_avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_nextafterd2_avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_frfrexpd2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_frfrexpd2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_expfrexpd2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128i Sleef_finz_expfrexpd2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_fmodd2_avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_fmodd2_avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_remainderd2_avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_remainderd2_avx2128(__m128d, __m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_modfd2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128d_2 Sleef_finz_modfd2_avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_lgammad2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_lgammad2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_tgammad2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_tgammad2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_erfd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_erfd2_u10avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_erfcd2_u15avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST __m128d Sleef_finz_erfcd2_u15avx2128(__m128d);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntd2_avx2128(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrd2_avx2128(int);
+
+#ifndef Sleef___m128_2_DEFINED
+typedef struct {
+  __m128 x, y;
+} Sleef___m128_2;
+#define Sleef___m128_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sinf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_sinf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cosf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_cosf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_sincosf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_finz_sincosf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_tanf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_tanf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_asinf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_asinf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_acosf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_acosf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_atanf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_atanf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_atan2f4_u35avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_atan2f4_u35avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_logf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_logf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cbrtf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_cbrtf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sinf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_sinf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cosf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_cosf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_sincosf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_finz_sincosf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_tanf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_tanf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_asinf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_asinf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_acosf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_acosf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_atanf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_atanf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_atan2f4_u10avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_atan2f4_u10avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_logf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_logf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cbrtf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_cbrtf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_expf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_expf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_powf4_u10avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_powf4_u10avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sinhf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_sinhf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_coshf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_coshf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_tanhf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_tanhf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sinhf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_sinhf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_coshf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_coshf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_tanhf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_tanhf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fastsinf4_u3500avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_fastsinf4_u3500avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fastcosf4_u3500avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_fastcosf4_u3500avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fastpowf4_u3500avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_fastpowf4_u3500avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_asinhf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_asinhf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_acoshf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_acoshf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_atanhf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_atanhf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_exp2f4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_exp2f4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_exp2f4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_exp2f4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_exp10f4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_exp10f4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_exp10f4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_exp10f4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_expm1f4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_expm1f4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_log10f4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_log10f4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_log2f4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_log2f4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_log2f4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_log2f4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_log1pf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_log1pf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_sincospif4_u05avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_finz_sincospif4_u05avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_sincospif4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_finz_sincospif4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sinpif4_u05avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_sinpif4_u05avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_cospif4_u05avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_cospif4_u05avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fmaf4_avx2128(__m128, __m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_fmaf4_avx2128(__m128, __m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sqrtf4_avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_sqrtf4_avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sqrtf4_u05avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_sqrtf4_u05avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_sqrtf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_sqrtf4_u35avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_hypotf4_u05avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_hypotf4_u05avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_hypotf4_u35avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_hypotf4_u35avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fabsf4_avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_fabsf4_avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_copysignf4_avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_copysignf4_avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fmaxf4_avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_fmaxf4_avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fminf4_avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_fminf4_avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fdimf4_avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_fdimf4_avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_truncf4_avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_truncf4_avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_floorf4_avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_floorf4_avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_ceilf4_avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_ceilf4_avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_roundf4_avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_roundf4_avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_rintf4_avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_rintf4_avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_nextafterf4_avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_nextafterf4_avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_frfrexpf4_avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_frfrexpf4_avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_fmodf4_avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_fmodf4_avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_remainderf4_avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_remainderf4_avx2128(__m128, __m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_modff4_avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST Sleef___m128_2 Sleef_finz_modff4_avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_lgammaf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_lgammaf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_tgammaf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_tgammaf4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_erff4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_erff4_u10avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_erfcf4_u15avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST __m128 Sleef_finz_erfcf4_u15avx2128(__m128);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntf4_avx2128(int);
+SLEEF_IMPORT SLEEF_CONST int Sleef_finz_getIntf4_avx2128(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrf4_avx2128(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_finz_getPtrf4_avx2128(int);
+#endif
+#ifdef __AVX512F__
+
+#ifndef Sleef___m512d_2_DEFINED
+typedef struct {
+  __m512d x, y;
+} Sleef___m512d_2;
+#define Sleef___m512d_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sind8_u35(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cosd8_u35(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_sincosd8_u35(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_tand8_u35(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_asind8_u35(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_acosd8_u35(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_atand8_u35(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_atan2d8_u35(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_logd8_u35(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cbrtd8_u35(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sind8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cosd8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_sincosd8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_tand8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_asind8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_acosd8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_atand8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_atan2d8_u10(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_logd8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cbrtd8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_expd8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_powd8_u10(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sinhd8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_coshd8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_tanhd8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sinhd8_u35(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_coshd8_u35(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_tanhd8_u35(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fastsind8_u3500(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fastcosd8_u3500(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fastpowd8_u3500(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_asinhd8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_acoshd8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_atanhd8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_exp2d8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_exp2d8_u35(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_exp10d8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_exp10d8_u35(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_expm1d8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_log10d8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_log2d8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_log2d8_u35(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_log1pd8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_sincospid8_u05(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_sincospid8_u35(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sinpid8_u05(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cospid8_u05(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_ldexpd8(__m512d, __m256i);
+SLEEF_IMPORT SLEEF_CONST __m256i Sleef_ilogbd8(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fmad8(__m512d, __m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sqrtd8(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sqrtd8_u05(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sqrtd8_u35(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_hypotd8_u05(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_hypotd8_u35(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fabsd8(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_copysignd8(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fmaxd8(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fmind8(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fdimd8(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_truncd8(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_floord8(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_ceild8(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_roundd8(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_rintd8(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_nextafterd8(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_frfrexpd8(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m256i Sleef_expfrexpd8(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fmodd8(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_remainderd8(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_modfd8(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_lgammad8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_tgammad8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_erfd8_u10(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_erfcd8_u15(__m512d);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntd8(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrd8(int);
+
+#ifndef Sleef___m512_2_DEFINED
+typedef struct {
+  __m512 x, y;
+} Sleef___m512_2;
+#define Sleef___m512_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sinf16_u35(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cosf16_u35(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_sincosf16_u35(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_tanf16_u35(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_asinf16_u35(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_acosf16_u35(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_atanf16_u35(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_atan2f16_u35(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_logf16_u35(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cbrtf16_u35(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sinf16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cosf16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_sincosf16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_tanf16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_asinf16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_acosf16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_atanf16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_atan2f16_u10(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_logf16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cbrtf16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_expf16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_powf16_u10(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sinhf16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_coshf16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_tanhf16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sinhf16_u35(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_coshf16_u35(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_tanhf16_u35(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fastsinf16_u3500(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fastcosf16_u3500(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fastpowf16_u3500(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_asinhf16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_acoshf16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_atanhf16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_exp2f16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_exp2f16_u35(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_exp10f16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_exp10f16_u35(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_expm1f16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_log10f16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_log2f16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_log2f16_u35(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_log1pf16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_sincospif16_u05(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_sincospif16_u35(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sinpif16_u05(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cospif16_u05(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fmaf16(__m512, __m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sqrtf16(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sqrtf16_u05(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sqrtf16_u35(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_hypotf16_u05(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_hypotf16_u35(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fabsf16(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_copysignf16(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fmaxf16(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fminf16(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fdimf16(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_truncf16(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_floorf16(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_ceilf16(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_roundf16(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_rintf16(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_nextafterf16(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_frfrexpf16(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fmodf16(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_remainderf16(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_modff16(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_lgammaf16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_tgammaf16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_erff16_u10(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_erfcf16_u15(__m512);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntf16(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrf16(int);
+#endif
+#ifdef __AVX512F__
+
+#ifndef Sleef___m512d_2_DEFINED
+typedef struct {
+  __m512d x, y;
+} Sleef___m512d_2;
+#define Sleef___m512d_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sind8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_sind8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cosd8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_cosd8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_sincosd8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_finz_sincosd8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_tand8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_tand8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_asind8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_asind8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_acosd8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_acosd8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_atand8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_atand8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_atan2d8_u35avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_atan2d8_u35avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_logd8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_logd8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cbrtd8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_cbrtd8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sind8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_sind8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cosd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_cosd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_sincosd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_finz_sincosd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_tand8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_tand8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_asind8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_asind8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_acosd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_acosd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_atand8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_atand8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_atan2d8_u10avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_atan2d8_u10avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_logd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_logd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cbrtd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_cbrtd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_expd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_expd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_powd8_u10avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_powd8_u10avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sinhd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_sinhd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_coshd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_coshd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_tanhd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_tanhd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sinhd8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_sinhd8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_coshd8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_coshd8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_tanhd8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_tanhd8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fastsind8_u3500avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_fastsind8_u3500avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fastcosd8_u3500avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_fastcosd8_u3500avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fastpowd8_u3500avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_fastpowd8_u3500avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_asinhd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_asinhd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_acoshd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_acoshd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_atanhd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_atanhd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_exp2d8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_exp2d8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_exp2d8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_exp2d8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_exp10d8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_exp10d8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_exp10d8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_exp10d8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_expm1d8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_expm1d8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_log10d8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_log10d8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_log2d8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_log2d8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_log2d8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_log2d8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_log1pd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_log1pd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_sincospid8_u05avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_finz_sincospid8_u05avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_sincospid8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_finz_sincospid8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sinpid8_u05avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_sinpid8_u05avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cospid8_u05avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_cospid8_u05avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_ldexpd8_avx512f(__m512d, __m256i);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_ldexpd8_avx512f(__m512d, __m256i);
+SLEEF_IMPORT SLEEF_CONST __m256i Sleef_ilogbd8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m256i Sleef_finz_ilogbd8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fmad8_avx512f(__m512d, __m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_fmad8_avx512f(__m512d, __m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sqrtd8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_sqrtd8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sqrtd8_u05avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_sqrtd8_u05avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sqrtd8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_sqrtd8_u35avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_hypotd8_u05avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_hypotd8_u05avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_hypotd8_u35avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_hypotd8_u35avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fabsd8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_fabsd8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_copysignd8_avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_copysignd8_avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fmaxd8_avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_fmaxd8_avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fmind8_avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_fmind8_avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fdimd8_avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_fdimd8_avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_truncd8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_truncd8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_floord8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_floord8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_ceild8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_ceild8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_roundd8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_roundd8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_rintd8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_rintd8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_nextafterd8_avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_nextafterd8_avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_frfrexpd8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_frfrexpd8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m256i Sleef_expfrexpd8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m256i Sleef_finz_expfrexpd8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fmodd8_avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_fmodd8_avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_remainderd8_avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_remainderd8_avx512f(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_modfd8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_finz_modfd8_avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_lgammad8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_lgammad8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_tgammad8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_tgammad8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_erfd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_erfd8_u10avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_erfcd8_u15avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_finz_erfcd8_u15avx512f(__m512d);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntd8_avx512f(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrd8_avx512f(int);
+
+#ifndef Sleef___m512_2_DEFINED
+typedef struct {
+  __m512 x, y;
+} Sleef___m512_2;
+#define Sleef___m512_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sinf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_sinf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cosf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_cosf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_sincosf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_finz_sincosf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_tanf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_tanf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_asinf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_asinf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_acosf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_acosf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_atanf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_atanf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_atan2f16_u35avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_atan2f16_u35avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_logf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_logf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cbrtf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_cbrtf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sinf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_sinf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cosf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_cosf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_sincosf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_finz_sincosf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_tanf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_tanf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_asinf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_asinf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_acosf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_acosf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_atanf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_atanf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_atan2f16_u10avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_atan2f16_u10avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_logf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_logf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cbrtf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_cbrtf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_expf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_expf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_powf16_u10avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_powf16_u10avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sinhf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_sinhf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_coshf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_coshf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_tanhf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_tanhf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sinhf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_sinhf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_coshf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_coshf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_tanhf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_tanhf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fastsinf16_u3500avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_fastsinf16_u3500avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fastcosf16_u3500avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_fastcosf16_u3500avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fastpowf16_u3500avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_fastpowf16_u3500avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_asinhf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_asinhf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_acoshf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_acoshf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_atanhf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_atanhf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_exp2f16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_exp2f16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_exp2f16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_exp2f16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_exp10f16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_exp10f16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_exp10f16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_exp10f16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_expm1f16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_expm1f16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_log10f16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_log10f16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_log2f16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_log2f16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_log2f16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_log2f16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_log1pf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_log1pf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_sincospif16_u05avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_finz_sincospif16_u05avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_sincospif16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_finz_sincospif16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sinpif16_u05avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_sinpif16_u05avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cospif16_u05avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_cospif16_u05avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fmaf16_avx512f(__m512, __m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_fmaf16_avx512f(__m512, __m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sqrtf16_avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_sqrtf16_avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sqrtf16_u05avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_sqrtf16_u05avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sqrtf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_sqrtf16_u35avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_hypotf16_u05avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_hypotf16_u05avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_hypotf16_u35avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_hypotf16_u35avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fabsf16_avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_fabsf16_avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_copysignf16_avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_copysignf16_avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fmaxf16_avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_fmaxf16_avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fminf16_avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_fminf16_avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fdimf16_avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_fdimf16_avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_truncf16_avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_truncf16_avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_floorf16_avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_floorf16_avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_ceilf16_avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_ceilf16_avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_roundf16_avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_roundf16_avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_rintf16_avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_rintf16_avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_nextafterf16_avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_nextafterf16_avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_frfrexpf16_avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_frfrexpf16_avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fmodf16_avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_fmodf16_avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_remainderf16_avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_remainderf16_avx512f(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_modff16_avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_finz_modff16_avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_lgammaf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_lgammaf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_tgammaf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_tgammaf16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_erff16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_erff16_u10avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_erfcf16_u15avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_finz_erfcf16_u15avx512f(__m512);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntf16_avx512f(int);
+SLEEF_IMPORT SLEEF_CONST int Sleef_finz_getIntf16_avx512f(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrf16_avx512f(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_finz_getPtrf16_avx512f(int);
+#endif
+#ifdef __AVX512F__
+
+#ifndef Sleef___m512d_2_DEFINED
+typedef struct {
+  __m512d x, y;
+} Sleef___m512d_2;
+#define Sleef___m512d_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sind8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_sind8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cosd8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_cosd8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_sincosd8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_cinz_sincosd8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_tand8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_tand8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_asind8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_asind8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_acosd8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_acosd8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_atand8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_atand8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_atan2d8_u35avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_atan2d8_u35avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_logd8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_logd8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cbrtd8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_cbrtd8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sind8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_sind8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cosd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_cosd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_sincosd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_cinz_sincosd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_tand8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_tand8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_asind8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_asind8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_acosd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_acosd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_atand8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_atand8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_atan2d8_u10avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_atan2d8_u10avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_logd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_logd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cbrtd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_cbrtd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_expd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_expd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_powd8_u10avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_powd8_u10avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sinhd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_sinhd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_coshd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_coshd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_tanhd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_tanhd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sinhd8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_sinhd8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_coshd8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_coshd8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_tanhd8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_tanhd8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fastsind8_u3500avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_fastsind8_u3500avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fastcosd8_u3500avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_fastcosd8_u3500avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fastpowd8_u3500avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_fastpowd8_u3500avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_asinhd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_asinhd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_acoshd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_acoshd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_atanhd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_atanhd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_exp2d8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_exp2d8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_exp2d8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_exp2d8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_exp10d8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_exp10d8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_exp10d8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_exp10d8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_expm1d8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_expm1d8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_log10d8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_log10d8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_log2d8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_log2d8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_log2d8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_log2d8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_log1pd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_log1pd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_sincospid8_u05avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_cinz_sincospid8_u05avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_sincospid8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_cinz_sincospid8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sinpid8_u05avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_sinpid8_u05avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cospid8_u05avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_cospid8_u05avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_ldexpd8_avx512fnofma(__m512d, __m256i);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_ldexpd8_avx512fnofma(__m512d, __m256i);
+SLEEF_IMPORT SLEEF_CONST __m256i Sleef_ilogbd8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m256i Sleef_cinz_ilogbd8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fmad8_avx512fnofma(__m512d, __m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_fmad8_avx512fnofma(__m512d, __m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sqrtd8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_sqrtd8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sqrtd8_u05avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_sqrtd8_u05avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_sqrtd8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_sqrtd8_u35avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_hypotd8_u05avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_hypotd8_u05avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_hypotd8_u35avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_hypotd8_u35avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fabsd8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_fabsd8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_copysignd8_avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_copysignd8_avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fmaxd8_avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_fmaxd8_avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fmind8_avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_fmind8_avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fdimd8_avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_fdimd8_avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_truncd8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_truncd8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_floord8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_floord8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_ceild8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_ceild8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_roundd8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_roundd8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_rintd8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_rintd8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_nextafterd8_avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_nextafterd8_avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_frfrexpd8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_frfrexpd8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m256i Sleef_expfrexpd8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m256i Sleef_cinz_expfrexpd8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_fmodd8_avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_fmodd8_avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_remainderd8_avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_remainderd8_avx512fnofma(__m512d, __m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_modfd8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512d_2 Sleef_cinz_modfd8_avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_lgammad8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_lgammad8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_tgammad8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_tgammad8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_erfd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_erfd8_u10avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_erfcd8_u15avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST __m512d Sleef_cinz_erfcd8_u15avx512fnofma(__m512d);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntd8_avx512fnofma(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrd8_avx512fnofma(int);
+
+#ifndef Sleef___m512_2_DEFINED
+typedef struct {
+  __m512 x, y;
+} Sleef___m512_2;
+#define Sleef___m512_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sinf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_sinf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cosf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_cosf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_sincosf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_cinz_sincosf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_tanf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_tanf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_asinf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_asinf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_acosf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_acosf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_atanf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_atanf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_atan2f16_u35avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_atan2f16_u35avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_logf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_logf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cbrtf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_cbrtf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sinf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_sinf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cosf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_cosf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_sincosf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_cinz_sincosf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_tanf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_tanf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_asinf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_asinf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_acosf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_acosf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_atanf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_atanf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_atan2f16_u10avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_atan2f16_u10avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_logf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_logf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cbrtf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_cbrtf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_expf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_expf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_powf16_u10avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_powf16_u10avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sinhf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_sinhf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_coshf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_coshf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_tanhf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_tanhf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sinhf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_sinhf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_coshf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_coshf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_tanhf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_tanhf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fastsinf16_u3500avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_fastsinf16_u3500avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fastcosf16_u3500avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_fastcosf16_u3500avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fastpowf16_u3500avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_fastpowf16_u3500avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_asinhf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_asinhf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_acoshf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_acoshf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_atanhf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_atanhf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_exp2f16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_exp2f16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_exp2f16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_exp2f16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_exp10f16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_exp10f16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_exp10f16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_exp10f16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_expm1f16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_expm1f16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_log10f16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_log10f16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_log2f16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_log2f16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_log2f16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_log2f16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_log1pf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_log1pf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_sincospif16_u05avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_cinz_sincospif16_u05avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_sincospif16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_cinz_sincospif16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sinpif16_u05avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_sinpif16_u05avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cospif16_u05avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_cospif16_u05avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fmaf16_avx512fnofma(__m512, __m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_fmaf16_avx512fnofma(__m512, __m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sqrtf16_avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_sqrtf16_avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sqrtf16_u05avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_sqrtf16_u05avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_sqrtf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_sqrtf16_u35avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_hypotf16_u05avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_hypotf16_u05avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_hypotf16_u35avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_hypotf16_u35avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fabsf16_avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_fabsf16_avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_copysignf16_avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_copysignf16_avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fmaxf16_avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_fmaxf16_avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fminf16_avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_fminf16_avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fdimf16_avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_fdimf16_avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_truncf16_avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_truncf16_avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_floorf16_avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_floorf16_avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_ceilf16_avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_ceilf16_avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_roundf16_avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_roundf16_avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_rintf16_avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_rintf16_avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_nextafterf16_avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_nextafterf16_avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_frfrexpf16_avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_frfrexpf16_avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_fmodf16_avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_fmodf16_avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_remainderf16_avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_remainderf16_avx512fnofma(__m512, __m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_modff16_avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST Sleef___m512_2 Sleef_cinz_modff16_avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_lgammaf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_lgammaf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_tgammaf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_tgammaf16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_erff16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_erff16_u10avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_erfcf16_u15avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST __m512 Sleef_cinz_erfcf16_u15avx512fnofma(__m512);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntf16_avx512fnofma(int);
+SLEEF_IMPORT SLEEF_CONST int Sleef_cinz_getIntf16_avx512fnofma(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrf16_avx512fnofma(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_cinz_getPtrf16_avx512fnofma(int);
+#endif
+#ifdef __STDC__
+
+#ifndef Sleef_double_2_DEFINED
+typedef Sleef_double2 Sleef_double_2;
+#define Sleef_double_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST double Sleef_sind1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_sind1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cosd1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_cosd1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_sincosd1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_cinz_sincosd1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_tand1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_tand1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_asind1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_asind1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_acosd1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_acosd1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_atand1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_atand1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_atan2d1_u35purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_atan2d1_u35purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_logd1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_logd1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cbrtd1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_cbrtd1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_sind1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_sind1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cosd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_cosd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_sincosd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_cinz_sincosd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_tand1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_tand1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_asind1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_asind1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_acosd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_acosd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_atand1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_atand1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_atan2d1_u10purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_atan2d1_u10purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_logd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_logd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cbrtd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_cbrtd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_expd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_expd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_powd1_u10purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_powd1_u10purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_sinhd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_sinhd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_coshd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_coshd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_tanhd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_tanhd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_sinhd1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_sinhd1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_coshd1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_coshd1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_tanhd1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_tanhd1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_fastsind1_u3500purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_fastsind1_u3500purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_fastcosd1_u3500purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_fastcosd1_u3500purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_fastpowd1_u3500purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_fastpowd1_u3500purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_asinhd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_asinhd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_acoshd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_acoshd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_atanhd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_atanhd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_exp2d1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_exp2d1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_exp2d1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_exp2d1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_exp10d1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_exp10d1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_exp10d1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_exp10d1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_expm1d1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_expm1d1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_log10d1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_log10d1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_log2d1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_log2d1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_log2d1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_log2d1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_log1pd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_log1pd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_sincospid1_u05purec(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_cinz_sincospid1_u05purec(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_sincospid1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_cinz_sincospid1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_sinpid1_u05purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_sinpid1_u05purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cospid1_u05purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_cospid1_u05purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_ldexpd1_purec(double, int32_t);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_ldexpd1_purec(double, int32_t);
+SLEEF_IMPORT SLEEF_CONST int32_t Sleef_ilogbd1_purec(double);
+SLEEF_IMPORT SLEEF_CONST int32_t Sleef_cinz_ilogbd1_purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_fmad1_purec(double, double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_fmad1_purec(double, double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_sqrtd1_purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_sqrtd1_purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_sqrtd1_u05purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_sqrtd1_u05purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_sqrtd1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_sqrtd1_u35purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_hypotd1_u05purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_hypotd1_u05purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_hypotd1_u35purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_hypotd1_u35purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_fabsd1_purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_fabsd1_purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_copysignd1_purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_copysignd1_purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_fmaxd1_purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_fmaxd1_purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_fmind1_purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_fmind1_purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_fdimd1_purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_fdimd1_purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_truncd1_purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_truncd1_purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_floord1_purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_floord1_purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_ceild1_purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_ceild1_purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_roundd1_purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_roundd1_purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_rintd1_purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_rintd1_purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_nextafterd1_purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_nextafterd1_purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_frfrexpd1_purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_frfrexpd1_purec(double);
+SLEEF_IMPORT SLEEF_CONST int32_t Sleef_expfrexpd1_purec(double);
+SLEEF_IMPORT SLEEF_CONST int32_t Sleef_cinz_expfrexpd1_purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_fmodd1_purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_fmodd1_purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_remainderd1_purec(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_remainderd1_purec(double, double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_modfd1_purec(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_cinz_modfd1_purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_lgammad1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_lgammad1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_tgammad1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_tgammad1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_erfd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_erfd1_u10purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_erfcd1_u15purec(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cinz_erfcd1_u15purec(double);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntd1_purec(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrd1_purec(int);
+
+#ifndef Sleef_float_2_DEFINED
+typedef Sleef_float2 Sleef_float_2;
+#define Sleef_float_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST float Sleef_sinf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_sinf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cosf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_cosf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_sincosf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_cinz_sincosf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_tanf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_tanf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_asinf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_asinf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_acosf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_acosf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_atanf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_atanf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_atan2f1_u35purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_atan2f1_u35purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_logf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_logf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cbrtf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_cbrtf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_sinf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_sinf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cosf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_cosf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_sincosf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_cinz_sincosf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_tanf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_tanf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_asinf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_asinf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_acosf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_acosf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_atanf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_atanf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_atan2f1_u10purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_atan2f1_u10purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_logf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_logf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cbrtf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_cbrtf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_expf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_expf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_powf1_u10purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_powf1_u10purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_sinhf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_sinhf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_coshf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_coshf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_tanhf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_tanhf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_sinhf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_sinhf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_coshf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_coshf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_tanhf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_tanhf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_fastsinf1_u3500purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_fastsinf1_u3500purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_fastcosf1_u3500purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_fastcosf1_u3500purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_fastpowf1_u3500purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_fastpowf1_u3500purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_asinhf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_asinhf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_acoshf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_acoshf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_atanhf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_atanhf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_exp2f1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_exp2f1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_exp2f1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_exp2f1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_exp10f1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_exp10f1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_exp10f1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_exp10f1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_expm1f1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_expm1f1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_log10f1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_log10f1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_log2f1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_log2f1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_log2f1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_log2f1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_log1pf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_log1pf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_sincospif1_u05purec(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_cinz_sincospif1_u05purec(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_sincospif1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_cinz_sincospif1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_sinpif1_u05purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_sinpif1_u05purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cospif1_u05purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_cospif1_u05purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_fmaf1_purec(float, float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_fmaf1_purec(float, float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_sqrtf1_purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_sqrtf1_purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_sqrtf1_u05purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_sqrtf1_u05purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_sqrtf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_sqrtf1_u35purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_hypotf1_u05purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_hypotf1_u05purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_hypotf1_u35purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_hypotf1_u35purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_fabsf1_purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_fabsf1_purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_copysignf1_purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_copysignf1_purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_fmaxf1_purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_fmaxf1_purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_fminf1_purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_fminf1_purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_fdimf1_purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_fdimf1_purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_truncf1_purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_truncf1_purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_floorf1_purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_floorf1_purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_ceilf1_purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_ceilf1_purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_roundf1_purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_roundf1_purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_rintf1_purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_rintf1_purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_nextafterf1_purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_nextafterf1_purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_frfrexpf1_purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_frfrexpf1_purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_fmodf1_purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_fmodf1_purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_remainderf1_purec(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_remainderf1_purec(float, float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_modff1_purec(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_cinz_modff1_purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_lgammaf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_lgammaf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_tgammaf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_tgammaf1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_erff1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_erff1_u10purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_erfcf1_u15purec(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cinz_erfcf1_u15purec(float);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntf1_purec(int);
+SLEEF_IMPORT SLEEF_CONST int Sleef_cinz_getIntf1_purec(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrf1_purec(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_cinz_getPtrf1_purec(int);
+#endif
+#ifdef __STDC__
+
+#ifndef Sleef_double_2_DEFINED
+typedef Sleef_double2 Sleef_double_2;
+#define Sleef_double_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST double Sleef_sind1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_sind1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cosd1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_cosd1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_sincosd1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_finz_sincosd1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_tand1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_tand1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_asind1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_asind1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_acosd1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_acosd1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_atand1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_atand1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_atan2d1_u35purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_atan2d1_u35purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_logd1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_logd1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cbrtd1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_cbrtd1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_sind1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_sind1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cosd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_cosd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_sincosd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_finz_sincosd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_tand1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_tand1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_asind1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_asind1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_acosd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_acosd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_atand1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_atand1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_atan2d1_u10purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_atan2d1_u10purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_logd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_logd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cbrtd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_cbrtd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_expd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_expd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_powd1_u10purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_powd1_u10purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_sinhd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_sinhd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_coshd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_coshd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_tanhd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_tanhd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_sinhd1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_sinhd1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_coshd1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_coshd1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_tanhd1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_tanhd1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_fastsind1_u3500purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_fastsind1_u3500purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_fastcosd1_u3500purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_fastcosd1_u3500purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_fastpowd1_u3500purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_fastpowd1_u3500purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_asinhd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_asinhd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_acoshd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_acoshd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_atanhd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_atanhd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_exp2d1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_exp2d1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_exp2d1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_exp2d1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_exp10d1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_exp10d1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_exp10d1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_exp10d1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_expm1d1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_expm1d1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_log10d1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_log10d1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_log2d1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_log2d1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_log2d1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_log2d1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_log1pd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_log1pd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_sincospid1_u05purecfma(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_finz_sincospid1_u05purecfma(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_sincospid1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_finz_sincospid1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_sinpid1_u05purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_sinpid1_u05purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_cospid1_u05purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_cospid1_u05purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_ldexpd1_purecfma(double, int32_t);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_ldexpd1_purecfma(double, int32_t);
+SLEEF_IMPORT SLEEF_CONST int32_t Sleef_ilogbd1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST int32_t Sleef_finz_ilogbd1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_fmad1_purecfma(double, double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_fmad1_purecfma(double, double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_sqrtd1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_sqrtd1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_sqrtd1_u05purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_sqrtd1_u05purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_sqrtd1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_sqrtd1_u35purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_hypotd1_u05purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_hypotd1_u05purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_hypotd1_u35purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_hypotd1_u35purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_fabsd1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_fabsd1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_copysignd1_purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_copysignd1_purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_fmaxd1_purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_fmaxd1_purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_fmind1_purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_fmind1_purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_fdimd1_purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_fdimd1_purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_truncd1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_truncd1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_floord1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_floord1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_ceild1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_ceild1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_roundd1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_roundd1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_rintd1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_rintd1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_nextafterd1_purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_nextafterd1_purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_frfrexpd1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_frfrexpd1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST int32_t Sleef_expfrexpd1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST int32_t Sleef_finz_expfrexpd1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_fmodd1_purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_fmodd1_purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_remainderd1_purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_remainderd1_purecfma(double, double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_modfd1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_finz_modfd1_purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_lgammad1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_lgammad1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_tgammad1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_tgammad1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_erfd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_erfd1_u10purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_erfcd1_u15purecfma(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_finz_erfcd1_u15purecfma(double);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntd1_purecfma(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrd1_purecfma(int);
+
+#ifndef Sleef_float_2_DEFINED
+typedef Sleef_float2 Sleef_float_2;
+#define Sleef_float_2_DEFINED
+#endif
+
+SLEEF_IMPORT SLEEF_CONST float Sleef_sinf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_sinf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cosf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_cosf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_sincosf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_finz_sincosf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_tanf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_tanf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_asinf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_asinf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_acosf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_acosf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_atanf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_atanf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_atan2f1_u35purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_atan2f1_u35purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_logf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_logf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cbrtf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_cbrtf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_sinf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_sinf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cosf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_cosf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_sincosf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_finz_sincosf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_tanf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_tanf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_asinf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_asinf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_acosf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_acosf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_atanf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_atanf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_atan2f1_u10purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_atan2f1_u10purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_logf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_logf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cbrtf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_cbrtf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_expf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_expf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_powf1_u10purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_powf1_u10purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_sinhf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_sinhf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_coshf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_coshf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_tanhf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_tanhf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_sinhf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_sinhf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_coshf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_coshf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_tanhf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_tanhf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_fastsinf1_u3500purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_fastsinf1_u3500purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_fastcosf1_u3500purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_fastcosf1_u3500purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_fastpowf1_u3500purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_fastpowf1_u3500purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_asinhf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_asinhf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_acoshf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_acoshf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_atanhf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_atanhf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_exp2f1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_exp2f1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_exp2f1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_exp2f1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_exp10f1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_exp10f1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_exp10f1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_exp10f1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_expm1f1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_expm1f1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_log10f1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_log10f1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_log2f1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_log2f1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_log2f1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_log2f1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_log1pf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_log1pf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_sincospif1_u05purecfma(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_finz_sincospif1_u05purecfma(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_sincospif1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_finz_sincospif1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_sinpif1_u05purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_sinpif1_u05purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_cospif1_u05purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_cospif1_u05purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_fmaf1_purecfma(float, float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_fmaf1_purecfma(float, float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_sqrtf1_purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_sqrtf1_purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_sqrtf1_u05purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_sqrtf1_u05purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_sqrtf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_sqrtf1_u35purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_hypotf1_u05purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_hypotf1_u05purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_hypotf1_u35purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_hypotf1_u35purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_fabsf1_purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_fabsf1_purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_copysignf1_purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_copysignf1_purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_fmaxf1_purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_fmaxf1_purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_fminf1_purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_fminf1_purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_fdimf1_purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_fdimf1_purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_truncf1_purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_truncf1_purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_floorf1_purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_floorf1_purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_ceilf1_purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_ceilf1_purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_roundf1_purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_roundf1_purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_rintf1_purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_rintf1_purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_nextafterf1_purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_nextafterf1_purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_frfrexpf1_purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_frfrexpf1_purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_fmodf1_purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_fmodf1_purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_remainderf1_purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_remainderf1_purecfma(float, float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_modff1_purecfma(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_finz_modff1_purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_lgammaf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_lgammaf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_tgammaf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_tgammaf1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_erff1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_erff1_u10purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_erfcf1_u15purecfma(float);
+SLEEF_IMPORT SLEEF_CONST float Sleef_finz_erfcf1_u15purecfma(float);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntf1_purecfma(int);
+SLEEF_IMPORT SLEEF_CONST int Sleef_finz_getIntf1_purecfma(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrf1_purecfma(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_finz_getPtrf1_purecfma(int);
+#endif
+#ifdef __STDC__
+
+#ifndef Sleef_double_2_DEFINED
+typedef Sleef_double2 Sleef_double_2;
+#define Sleef_double_2_DEFINED
+#endif
+
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_sind1_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_cosd1_u35(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_sincosd1_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_tand1_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_asind1_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_acosd1_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_atand1_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_atan2d1_u35(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_logd1_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_cbrtd1_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_sind1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_cosd1_u10(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_sincosd1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_tand1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_asind1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_acosd1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_atand1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_atan2d1_u10(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_logd1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_cbrtd1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_expd1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_powd1_u10(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_sinhd1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_coshd1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_tanhd1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_sinhd1_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_coshd1_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_tanhd1_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_fastsind1_u3500(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_fastcosd1_u3500(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_fastpowd1_u3500(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_asinhd1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_acoshd1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_atanhd1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_exp2d1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_exp2d1_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_exp10d1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_exp10d1_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_expm1d1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_log10d1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_log2d1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_log2d1_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_log1pd1_u10(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_sincospid1_u05(double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_sincospid1_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_sinpid1_u05(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_cospid1_u05(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_ldexpd1(double, int32_t);
+SLEEF_IMPORT SLEEF_CONST int32_t Sleef_ilogbd1(double);
+SLEEF_IMPORT SLEEF_CONST double Sleef_fmad1(double, double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_sqrtd1(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_sqrtd1_u05(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_sqrtd1_u35(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_hypotd1_u05(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_hypotd1_u35(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_fabsd1(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_copysignd1(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_fmaxd1(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_fmind1(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_fdimd1(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_truncd1(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_floord1(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_ceild1(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_roundd1(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_rintd1(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_nextafterd1(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_frfrexpd1(double);
+SLEEF_IMPORT SLEEF_CONST int32_t Sleef_expfrexpd1(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_fmodd1(double, double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_remainderd1(double, double);
+SLEEF_IMPORT SLEEF_CONST Sleef_double_2 Sleef_modfd1(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_lgammad1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_tgammad1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_erfd1_u10(double);
+SLEEF_PRAGMA_OMP_SIMD_DP SLEEF_IMPORT SLEEF_CONST double Sleef_erfcd1_u15(double);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntd1(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrd1(int);
+
+#ifndef Sleef_float_2_DEFINED
+typedef Sleef_float2 Sleef_float_2;
+#define Sleef_float_2_DEFINED
+#endif
+
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_sinf1_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_cosf1_u35(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_sincosf1_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_tanf1_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_asinf1_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_acosf1_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_atanf1_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_atan2f1_u35(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_logf1_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_cbrtf1_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_sinf1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_cosf1_u10(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_sincosf1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_tanf1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_asinf1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_acosf1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_atanf1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_atan2f1_u10(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_logf1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_cbrtf1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_expf1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_powf1_u10(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_sinhf1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_coshf1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_tanhf1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_sinhf1_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_coshf1_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_tanhf1_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_fastsinf1_u3500(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_fastcosf1_u3500(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_fastpowf1_u3500(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_asinhf1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_acoshf1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_atanhf1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_exp2f1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_exp2f1_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_exp10f1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_exp10f1_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_expm1f1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_log10f1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_log2f1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_log2f1_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_log1pf1_u10(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_sincospif1_u05(float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_sincospif1_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_sinpif1_u05(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_cospif1_u05(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_fmaf1(float, float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_sqrtf1(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_sqrtf1_u05(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_sqrtf1_u35(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_hypotf1_u05(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_hypotf1_u35(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_fabsf1(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_copysignf1(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_fmaxf1(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_fminf1(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_fdimf1(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_truncf1(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_floorf1(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_ceilf1(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_roundf1(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_rintf1(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_nextafterf1(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_frfrexpf1(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_fmodf1(float, float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_remainderf1(float, float);
+SLEEF_IMPORT SLEEF_CONST Sleef_float_2 Sleef_modff1(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_lgammaf1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_tgammaf1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_erff1_u10(float);
+SLEEF_PRAGMA_OMP_SIMD_SP SLEEF_IMPORT SLEEF_CONST float Sleef_erfcf1_u15(float);
+SLEEF_IMPORT SLEEF_CONST int Sleef_getIntf1(int);
+SLEEF_IMPORT SLEEF_CONST void *Sleef_getPtrf1(int);
+#endif
+
+//
+
+#ifdef __cplusplus
+} // extern "C"
+#endif
+
+#endif // #ifndef __SLEEF_H__
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/xnnpack.h b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/xnnpack.h
new file mode 100644
index 0000000000000000000000000000000000000000..e71be0fd57ffc1ef2cc67b2fc8fb20fc4288a1d2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/include/xnnpack.h
@@ -0,0 +1,6172 @@
+// Copyright (c) Facebook, Inc. and its affiliates.
+// All rights reserved.
+//
+// Copyright 2019 Google LLC
+//
+// This source code is licensed under the BSD-style license found in the
+// LICENSE file in the root directory of this source tree.
+
+#pragma once
+
+#include 
+#include 
+#include 
+
+#include 
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/// The number of bytes XNNPACK may read beyond array bounds.
+/// The caller must allocate at least this many extra bytes after the tensor data passed to XNNPACK.
+///
+/// Note: XNNPACK reads, but never writes beyond array bounds.
+#define XNN_EXTRA_BYTES 16
+
+/// Maximum number of dimensions in tensor shape.
+#define XNN_MAX_TENSOR_DIMS 6
+
+/// Allow sparse inference in a Runtime.
+///
+/// Note: this flag hints XNNPACK to consider sparse inference, but does not guarantee it.
+#define XNN_FLAG_HINT_SPARSE_INFERENCE 0x00000001
+
+/// Allow IEEE FP16 inference in a Runtime.
+///
+/// Note: this flag hints XNNPACK to consider IEEE FP16 inference, but does not guarantee it.
+#define XNN_FLAG_HINT_FP16_INFERENCE 0x00000002
+
+/// Force IEEE FP16 inference in a Runtime, and fail if FP16 inference is not possible.
+///
+/// Note: this flag guarantees that XNNPACK will use IEEE FP16 inference, or fail to create the Runtime object.
+/// Warning: on x86 systems FP16 computations will be emulated at a substantial performance cost.
+#define XNN_FLAG_FORCE_FP16_INFERENCE 0x00000004
+
+/// Enable timing of each operator's runtime.
+#define XNN_FLAG_BASIC_PROFILING 0x00000008
+
+/// Enable the just-in-time compiler.
+#define XNN_FLAG_JIT 0x00000010
+
+/// The convolution operator represents a depthwise convolution, and use HWGo layout for filters.
+#define XNN_FLAG_DEPTHWISE_CONVOLUTION 0x00000001
+
+/// Assume transposed weights in a fully connected operator.
+#define XNN_FLAG_TRANSPOSE_WEIGHTS 0x00000001
+
+/// The operator assumes NHWC layout for the input, regardless of the output layout.
+#define XNN_FLAG_INPUT_NHWC 0x00000002
+
+/// Match "SAME" padding in TensorFlow. Exact padding values are computed dynamically depending on input size.
+#define XNN_FLAG_TENSORFLOW_SAME_PADDING 0x00000004
+
+/// Assume transposed weights in a batch matrix multiply operator.
+#define XNN_FLAG_TRANSPOSE_B XNN_FLAG_TRANSPOSE_WEIGHTS
+
+/// Assume transposed input in a batch matrix multiply operator.
+#define XNN_FLAG_TRANSPOSE_A 0x00000002
+
+/// Implicitly flatten and reshape input of a Fully Connected operator into a 2D tensor.
+#define XNN_FLAG_TENSORFLOW_RESHAPE_2D 0x00000004
+
+/// Match behaviour of TensorFlow 1.x.
+#define XNN_FLAG_TENSORFLOW_LEGACY_MODE 0x00000004
+
+/// Static weights of the FP16 operator are in FP32 format.
+#define XNN_FLAG_FP32_STATIC_WEIGHTS 0x00000008
+
+/// Align corners of input and output images in resize operations.
+#define XNN_FLAG_ALIGN_CORNERS 0x00000008
+
+/// Yield worker threads of the thread pool to the system scheduler after the inference.
+#define XNN_FLAG_YIELD_WORKERS 0x00000010
+
+/// Use transient indirection buffer to reduce memory footprint
+#define XNN_FLAG_TRANSIENT_INDIRECTION_BUFFER 0x00000020
+
+/// Reduce the dimensions.
+#define XNN_FLAG_REDUCE_DIMS 0x00000040
+
+/// The number of entries in an array of xnn_dynamic_quantization_params that XNNPACK may read beyond array bounds.
+/// The caller must allocate at least this many extra xnn_dynamic_quantization_params before passing the array to XNNPACK.
+///
+/// Note: XNNPACK reads, but never writes beyond array bounds.
+#define XNN_EXTRA_QUANTIZATION_PARAMS 8
+
+struct xnn_dynamic_quantization_params {
+  int32_t zero_point;
+  float scale;
+};
+
+/// Status code for any XNNPACK function call.
+enum xnn_status {
+  /// The call succeeded, and all output arguments now contain valid data.
+  xnn_status_success = 0,
+  xnn_status_uninitialized = 1,
+  xnn_status_invalid_parameter = 2,
+  xnn_status_invalid_state = 3,
+  xnn_status_unsupported_parameter = 4,
+  xnn_status_unsupported_hardware = 5,
+  xnn_status_out_of_memory = 6,
+  xnn_status_reallocation_required = 7,
+};
+
+struct xnn_allocator {
+  /// User-specified pointer that will be passed as-is to all functions in this structure.
+  void* context;
+  /// Pointer to a function to be called for general memory allocation.
+  ///
+  /// @param context - The user-specified pointer from xnn_allocator structure.
+  /// @param size - The size of the memory block to allocate, in bytes.
+  ///
+  /// @returns Pointer to the allocated memory block of at least @ref size bytes.
+  ///          If allocation fails, the function must return NULL.
+  void* (*allocate)(void* context, size_t size);
+  /// Pointer to a function to be called for general memory re-allocation, i.e. to increase or shrink a previously
+  /// allocated memory block. The content of the old memory block is copied to the new memory block.
+  ///
+  /// @param context - The user-specified pointer from xnn_allocator structure.
+  /// @param pointer - Pointer to a memory block allocated by @ref allocate or @ref reallocate functions. Can be NULL.
+  ///                  If the pointer is NULL, the @ref reallocate call is equivalent to an @ref allocate call.
+  /// @param size - The new size of the memory block to allocate, in bytes.
+  ///
+  /// @returns Pointer to the newly allocated memory block of at least @ref size bytes with the content of the previous
+  ///          memory block.
+  ///          If allocation fails, the function must return NULL, but must not release the previous memory block.
+  void* (*reallocate)(void* context, void* pointer, size_t size);
+  /// Pointer to a function to be called for general memory de-allocation.
+  ///
+  /// @param context - The user-specified pointer from xnn_allocator structure.
+  /// @param pointer - Pointer to a memory block allocated by @ref allocate or @ref reallocate functions. Can be NULL.
+  ///                  If the pointer is NULL, the @ref deallocate call is a no-op.
+  void (*deallocate)(void* context, void* pointer);
+  /// Pointer to a function to be called for aligned memory allocation.
+  ///
+  /// @param context - The user-specified pointer from xnn_allocator structure.
+  /// @param alignment - The alignment of the memory block to allocate, in bytes. Alignment is always a power-of-2.
+  /// @param size - The size of the memory block to allocate, in bytes.
+  ///
+  /// @returns Pointer to the allocated memory block of at least @ref size bytes.
+  ///          If allocation fails, the function must return NULL.
+  void* (*aligned_allocate)(void* context, size_t alignment, size_t size);
+  /// Pointer to a function to be called for aligned memory de-allocation.
+  ///
+  /// @param context - The user-specified pointer from xnn_allocator structure.
+  /// @param pointer - Pointer to a memory block allocated by @ref aligned_allocate function. Can be NULL.
+  ///                  If the pointer is NULL, the @ref aligned_deallocate call is a no-op.
+  void (*aligned_deallocate)(void* context, void* pointer);
+};
+
+/// Initialize XNNPACK library.
+///
+/// XNNPACK must be successfully initialized before use. During initialization, XNNPACK populates internal structures
+/// depending on the host processor. Initialization can be time-consuming.
+///
+/// @param[in] allocator - structure with function pointers to be use for memory allocation and de-allocation.
+///                        If this argument is NULL, system-provided memory management functions (e.g. malloc/free)
+///                        will be used.
+///
+/// @retval xnn_status_success - XNNPACK is successfully initialized and ready to use.
+/// @retval xnn_status_out_of_memory - initialization failed due to out-of-memory condition.
+/// @retval xnn_status_unsupported_hardware - initialization failed because the host processor does not satisfy the
+///                                           minimum hardware requirements for XNNPACK. E.g. this may happen on x86
+///                                           processors without SSE2 extension, or on 32-bit ARM processors without
+///                                           the NEON SIMD extension.
+enum xnn_status xnn_initialize(const struct xnn_allocator* allocator);
+
+/// Deinitialize XNNPACK library.
+///
+/// To avoid memory and resource leaks, users must call xnn_deinitialize once for each successful xnn_initialize call.
+///
+/// @retval xnn_status_success - deinitialization call succeeded.
+enum xnn_status xnn_deinitialize(void);
+
+/// Subgraph is an abstract representation of a neural network model.
+/// Subgraph objects are used to define Values (tensors) and Nodes (operators) comprising the model.
+typedef struct xnn_subgraph* xnn_subgraph_t;
+
+/// Create a empty Subgraph object.
+///
+/// @param external_value_ids - number of Value IDs to reserve for communication with external graph representation.
+///                             The Subgraph object would avoid creating internal Value IDs in the
+///                             [0, reserved_value_ids-1] range.
+/// @param flags - binary features of the subgraph. No supported flags are currently defined.
+/// @param subgraph_out - pointer to the variable that will be initialized with a handle to the Subgraph object upon
+///                       successful return.
+enum xnn_status xnn_create_subgraph(
+  uint32_t external_value_ids,
+  uint32_t flags,
+  xnn_subgraph_t* subgraph_out);
+
+/// Destroy a Subgraph object, as well as Values, and Nodes associated with the subgraph.
+///
+/// @param subgraph - the Subgraph object to destroy.
+enum xnn_status xnn_delete_subgraph(
+  xnn_subgraph_t subgraph);
+
+#define XNN_VALUE_FLAG_EXTERNAL_INPUT  0x00000001
+#define XNN_VALUE_FLAG_EXTERNAL_OUTPUT 0x00000002
+#define XNN_VALUE_FLAG_PERSISTENT      0x00000004
+
+#define XNN_INVALID_VALUE_ID UINT32_MAX
+
+/// Type of elements in a Value object.
+enum xnn_datatype {
+  /// Invalid data type. Valid Values never have this datatype.
+  xnn_datatype_invalid = 0,
+  /// IEEE754 single-precision floating-point.
+  xnn_datatype_fp32 = 1,
+  /// IEEE754 half-precision floating-point.
+  xnn_datatype_fp16 = 2,
+  /// Quantized 8-bit signed integer with shared per-Value quantization parameters.
+  xnn_datatype_qint8 = 3,
+  /// Quantized 8-bit unsigned integer with shared per-Value quantization parameters.
+  xnn_datatype_quint8 = 4,
+  /// Quantized 32-bit signed integer with shared per-Value quantization parameters.
+  xnn_datatype_qint32 = 5,
+  /// Quantized 8-bit signed integer with shared per-channel quantization parameters.
+  xnn_datatype_qcint8 = 6,
+  /// Quantized 32-bit signed integer with shared per-channel quantization parameters.
+  xnn_datatype_qcint32 = 7,
+  /// Quantized 4-bit signed integer with shared per-channel quantization parameters.
+  xnn_datatype_qcint4 = 8,
+  /// Dynamically quantized 8-bit signed integer with per-batch quantization parameters.
+  xnn_datatype_qdint8 = 9,
+};
+
+/// Define a tensor-type Value and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Value.
+/// @param datatype - type of the tensor elements.
+/// @param num_dims - number of dimensions in the shape.
+/// @param dims - pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL.
+///               XNNPACK does not keep any pointers to this array after the function returns.
+/// @param data - pointer to static data used for tensor initialization. If the tensor is not statically initialized,
+///               this pointer must be is NULL. If non-NULL, the life-time of the static data must exceed the life-time
+///               of the Subgraph object, and of any Runtime objects created from the Subgraph.
+/// @param external_id - external ID for the Value. The ID must be within the range of reversed Value IDs specified on
+///                      the Subgraph creation. If the external ID is XNN_INVALID_VALUE_ID, an internal ID will be
+///                      created for the Value.
+/// @param flags - binary features of the Value. Supported values are any combination of XNN_VALUE_FLAG_EXTERNAL_INPUT
+///                and XNN_VALUE_FLAG_EXTERNAL_OUTPUT.
+/// @param id_out - pointer to the variable that will be initialized with the Value ID upon successful return. If a
+///                 valid @a external_id was provided, the variable will be initialized with the @a external_id value.
+enum xnn_status xnn_define_tensor_value(
+  xnn_subgraph_t subgraph,
+  enum xnn_datatype datatype,
+  size_t num_dims,
+  const size_t* dims,
+  const void* data,
+  uint32_t external_id,
+  uint32_t flags,
+  uint32_t* id_out);
+
+/// Define a quantized tensor-type Value and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Value.
+/// @param datatype - type of the tensor elements.
+/// @param zero_point - offset from zero to subtract from the quantized elements in the Value.
+/// @param scale - multiplication factor to convert quantized elements to real representation.
+/// @param num_dims - number of dimensions in the shape.
+/// @param dims - pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL.
+///               XNNPACK does not keep any pointers to this array after the function returns.
+/// @param data - pointer to static data used for tensor initialization. If the tensor is not statically initialized,
+///               this pointer must be is NULL. If non-NULL, the life-time of the static data must exceed the life-time
+///               of the Subgraph object, and of any Runtime objects created from the Subgraph.
+/// @param external_id - external ID for the Value. The ID must be within the range of reversed Value IDs specified on
+///                      the Subgraph creation. If the external ID is XNN_INVALID_VALUE_ID, an internal ID will be
+///                      created for the Value.
+/// @param flags - binary features of the Value. Supported values are any combination of XNN_VALUE_FLAG_EXTERNAL_INPUT
+///                and XNN_VALUE_FLAG_EXTERNAL_OUTPUT.
+/// @param id_out - pointer to the variable that will be initialized with the Value ID upon successful return. If a
+///                 valid @a external_id was provided, the variable will be initialized with the @a external_id value.
+enum xnn_status xnn_define_quantized_tensor_value(
+  xnn_subgraph_t subgraph,
+  enum xnn_datatype datatype,
+  int32_t zero_point,
+  float scale,
+  size_t num_dims,
+  const size_t* dims,
+  const void* data,
+  uint32_t external_id,
+  uint32_t flags,
+  uint32_t* id_out);
+
+enum xnn_status xnn_define_channelwise_quantized_tensor_value(
+  xnn_subgraph_t subgraph,
+  enum xnn_datatype datatype,
+  const float* scale,
+  size_t num_dims,
+  size_t channel_dim,
+  const size_t* dims,
+  const void* data,
+  uint32_t external_id,
+  uint32_t flags,
+  uint32_t* id_out);
+
+/// Validate the dimensions, channel_dim, zero point, datatype, and scale of a quantized tensor-type.
+///
+/// @param datatype - type of the tensor elements.
+/// @param zero_point - offset from zero to subtract from the quantized elements in the Value.
+/// @param scale - multiplication factor to convert quantized elements to real representation.
+/// @param num_dims - number of dimensions in the shape.
+/// @param dims - pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL.
+///               XNNPACK does not keep any pointers to this array after the function returns.
+enum xnn_status xnn_validate_quantized_tensor(
+  enum xnn_datatype datatype,
+  int32_t zero_point,
+  float scale,
+  size_t num_dims,
+  const size_t* dims);
+
+/// Validate the dimensions, channel_dim, zero point, datatype, and scales of a channelwise quantized tensor-type.
+///
+/// @param datatype - type of the tensor elements.
+/// @param zero_point - offset from zero to subtract from the quantized elements in the Value.
+/// @param scale - per-channel multiplication factors to convert quantized elements to real representation.
+/// @param num_dims - number of dimensions in the shape.
+/// @param channel_dim - index of the channel dimension in the tensor with per-channel quantization parameters.
+///                      Typically this is the first dimension (dimension #0) of the filter tensors in the Convolution,
+///                      Deconvolution, and Fully Connected operators and the last dimension of the filter tensors in
+///                      the Depthwise Convolution operators.
+/// @param dims - pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL.
+///               XNNPACK does not keep any pointers to this array after the function returns.
+enum xnn_status xnn_validate_channelwise_quantized_tensor(
+  enum xnn_datatype datatype,
+  int32_t zero_point,
+  const float* scale,
+  size_t num_dims,
+  size_t channel_dim,
+  const size_t* dims);
+
+/// Define a channelwise quantized tensor-type Value and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Value.
+/// @param datatype - type of the tensor elements.
+/// @param zero_point - offset from zero to subtract from the quantized elements in the Value.
+/// @param scale - per-channel multiplication factors to convert quantized elements to real representation.
+/// @param num_dims - number of dimensions in the shape.
+/// @param channel_dim - index of the channel dimension in the tensor with per-channel quantization parameters.
+///                      Typically this is the first dimension (dimension #0) of the filter tensors in the Convolution,
+///                      Deconvolution, and Fully Connected operators and the last dimension of the filter tensors in
+///                      the Depthwise Convolution operators.
+/// @param dims - pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL.
+///               XNNPACK does not keep any pointers to this array after the function returns.
+/// @param data - pointer to static data used for tensor initialization. If the tensor is not statically initialized,
+///               this pointer must be is NULL. If non-NULL, the life-time of the static data must exceed the life-time
+///               of the Subgraph object, and of any Runtime objects created from the Subgraph.
+/// @param external_id - external ID for the Value. The ID must be within the range of reversed Value IDs specified on
+///                      the Subgraph creation. If the external ID is XNN_INVALID_VALUE_ID, an internal ID will be
+///                      created for the Value.
+/// @param flags - binary features of the Value. Supported values are any combination of XNN_VALUE_FLAG_EXTERNAL_INPUT
+///                and XNN_VALUE_FLAG_EXTERNAL_OUTPUT.
+/// @param id_out - pointer to the variable that will be initialized with the Value ID upon successful return. If a
+///                 valid @a external_id was provided, the variable will be initialized with the @a external_id value.
+enum xnn_status xnn_define_channelwise_quantized_tensor_value_v2(
+  xnn_subgraph_t subgraph,
+  enum xnn_datatype datatype,
+  int32_t zero_point,
+  const float* scale,
+  size_t num_dims,
+  size_t channel_dim,
+  const size_t* dims,
+  const void* data,
+  uint32_t external_id,
+  uint32_t flags,
+  uint32_t* id_out);
+
+/// Define a dynamically quantized tensor-type Value and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Value.
+/// @param datatype - type of the tensor elements.
+/// @param num_dims - number of dimensions in the shape.
+/// @param num_non_batch_dims - number of non-batch dimensions in the shape. The leading (num_dims - num_non_batch_dims)
+///                             dimensions will be flattened and treated as batch size. A set of quantization parameters
+///                             will be calculated for each batch element.
+/// @param dims - pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL.
+///               XNNPACK does not keep any pointers to this array after the function returns.
+/// @param external_id - external ID for the Value. The ID must be within the range of reversed Value IDs specified on
+///                      the Subgraph creation. If the external ID is XNN_INVALID_VALUE_ID, an internal ID will be
+///                      created for the Value.
+/// @param flags - binary features of the Value. No supported flags are currently defined.
+/// @param id_out - pointer to the variable that will be initialized with the Value ID upon successful return. If a
+///                 valid @a external_id was provided, the variable will be initialized with the @a external_id value.
+enum xnn_status xnn_define_dynamically_quantized_tensor_value(
+  xnn_subgraph_t subgraph,
+  enum xnn_datatype datatype,
+  size_t num_dims,
+  size_t num_nonbatch_dims,
+  const size_t* dims,
+  uint32_t external_id,
+  uint32_t flags,
+  uint32_t* id_out);
+
+/// Define a Convert Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
+///                    shape must match the shape of the input tensor.
+/// @param flags - binary features of the Convert Node. No supported flags are currently defined.
+enum xnn_status xnn_define_convert(
+  xnn_subgraph_t subgraph,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a 2D Convolution Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_padding_top - implicit zero-padding above 2D input data. Must be 0 if XNN_FLAG_TENSORFLOW_SAME_PADDING
+///                            flag is specified.
+/// @param input_padding_right - implicit zero-padding to the right of 2D input data. Must be 0 if
+///                              XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
+/// @param input_padding_bottom - implicit zero-padding below 2D input data. Must be 0 if
+///                               XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
+/// @param input_padding_left - implicit zero-padding to the left of 2D input data. Must be 0 if
+///                             XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
+/// @param kernel_height - kernel (filter) height.
+/// @param kernel_width - kernel (filter) width.
+/// @param subsampling_height - height of subsampling region for convolution output (convolution height stride).
+/// @param subsampling_width - width of subsampling region for convolution output (convolution width stride).
+/// @param dilation_height - dilation of kernel elements along the height dimension.
+/// @param dilation_width - dilation of kernel elements along the width dimension.
+/// @param groups - number of convolution groups.
+/// @param group_input_channels - number of input channels per group.
+/// @param group_output_channels - number of output channels per group.
+/// @param output_min - lower bound for clipping output values.
+/// @param output_max - upper bound for clipping output values.
+/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
+///                   with [N, IH, IW, groups * group_input_channels] dimensions
+/// @param filter_id - Value ID for the filter tensor. The filter tensor must ge a 4D tensor defined in the @a subgraph
+///                    with [groups * group_output_channels, kernel_height, kernel_width, group_input_channels]
+///                    dimensions.
+/// @param bias_id - Value ID for the bias tensor, or XNN_INVALID_VALUE_ID for a 2D Convolution Node without a bias. If
+///                  present, the bias tensor must be a 1D tensor defined in the @a subgraph with [groups *
+///                  group_output_channels] dimensions.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
+///                    with [N, OH, OW, groups * group_output_channels] dimensions.
+/// @param flags - binary features of the 2D Convolution Node. The only currently supported values is
+///                XNN_FLAG_TENSORFLOW_SAME_PADDING.
+enum xnn_status xnn_define_convolution_2d(
+  xnn_subgraph_t subgraph,
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t kernel_height,
+  uint32_t kernel_width,
+  uint32_t subsampling_height,
+  uint32_t subsampling_width,
+  uint32_t dilation_height,
+  uint32_t dilation_width,
+  uint32_t groups,
+  size_t group_input_channels,
+  size_t group_output_channels,
+  float output_min,
+  float output_max,
+  uint32_t input_id,
+  uint32_t filter_id,
+  uint32_t bias_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a 2D Deconvolution (Transposed Convolution) Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param padding_top - implicit padding above 2D output data.
+/// @param padding_right - implicit padding to the right of 2D output data.
+/// @param padding_bottom - implicit padding below 2D output data.
+/// @param padding_left - implicit padding to the left of 2D output data.
+/// @param adjustment_height - additional elements in the bottom of the 2D output data.
+/// @param adjustment_width - additional elements to the right of the 2D output data.
+/// @param kernel_height - kernel (filter) height.
+/// @param kernel_width - kernel (filter) width.
+/// @param upsampling_height - height of upsampling region for deconvolution input (deconvolution height stride).
+/// @param upsampling_width - width of upsampling region for deconvolution input (deconvolution width stride).
+/// @param dilation_height - dilation of kernel elements along the height dimension.
+/// @param dilation_width - dilation of kernel elements along the width dimension.
+/// @param groups - number of convolution groups.
+/// @param group_input_channels - number of input channels per group.
+/// @param group_output_channels - number of output channels per group.
+/// @param output_min - lower bound for clipping output values.
+/// @param output_max - upper bound for clipping output values.
+/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
+///                   with [N, IH, IW, groups * group_input_channels] dimensions
+/// @param filter_id - Value ID for the filter tensor. The filter tensor must ge a 4D tensor defined in the @a subgraph
+///                    with [groups * group_output_channels, kernel_height, kernel_width, group_input_channels]
+///                    dimensions.
+/// @param bias_id - Value ID for the bias tensor, or XNN_INVALID_VALUE_ID for a 2D Convolution Node without a bias. If
+///                  present, the bias tensor must be a 1D tensor defined in the @a subgraph with
+///                  [groups * group_output_channels] dimensions.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
+///                    with [N, OH, OW, groups * group_output_channels] dimensions.
+/// @param flags - binary features of the 2D Deconvolution Node. No supported flags are currently defined.
+enum xnn_status xnn_define_deconvolution_2d(
+  xnn_subgraph_t subgraph,
+  uint32_t padding_top,
+  uint32_t padding_right,
+  uint32_t padding_bottom,
+  uint32_t padding_left,
+  uint32_t adjustment_height,
+  uint32_t adjustment_width,
+  uint32_t kernel_height,
+  uint32_t kernel_width,
+  uint32_t upsampling_height,
+  uint32_t upsampling_width,
+  uint32_t dilation_height,
+  uint32_t dilation_width,
+  uint32_t groups,
+  size_t group_input_channels,
+  size_t group_output_channels,
+  float output_min,
+  float output_max,
+  uint32_t input_id,
+  uint32_t filter_id,
+  uint32_t bias_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a 2D Depthwise Convolution Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_padding_top - implicit zero-padding above 2D input data. Must be 0 if XNN_FLAG_TENSORFLOW_SAME_PADDING
+///                            flag is specified.
+/// @param input_padding_right - implicit zero-padding to the right of 2D input data. Must be 0 if
+///                              XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
+/// @param input_padding_bottom - implicit zero-padding below 2D input data. Must be 0 if
+///                               XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
+/// @param input_padding_left - implicit zero-padding to the left of 2D input data. Must be 0 if
+///                             XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
+/// @param kernel_height - kernel (filter) height.
+/// @param kernel_width - kernel (filter) width.
+/// @param subsampling_height - height of subsampling region for convolution output (convolution height stride).
+/// @param subsampling_width - width of subsampling region for convolution output (convolution width stride).
+/// @param dilation_height - dilation of kernel elements along the height dimension.
+/// @param dilation_width - dilation of kernel elements along the width dimension.
+/// @param depth_multiplier - ratio of output channels to input channels.
+/// @param input_channels - number of input channels.
+/// @param output_min - lower bound for clipping output values.
+/// @param output_max - upper bound for clipping output values.
+/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
+///                   with [N, IH, IW, input_channels] dimensions
+/// @param filter_id - Value ID for the filter tensor. The filter tensor must ge a 4D tensor defined in the @a subgraph
+///                    with [1, kernel_height, kernel_width, input_channels * depth_multiplier] dimensions.
+/// @param bias_id - Value ID for the bias tensor, or XNN_INVALID_VALUE_ID for a 2D Depthwise Convolution Node without
+///                  a bias. If present, the bias tensor must be a 1D tensor defined in the @a subgraph with
+///                  [input_channels * depth_multiplier] dimensions.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
+///                    with [N, OH, OW, input_channels * depth_multiplier] dimensions.
+/// @param flags - binary features of the 2D Depthwise Convolution Node. The only currently supported values is
+///                XNN_FLAG_TENSORFLOW_SAME_PADDING.
+enum xnn_status xnn_define_depthwise_convolution_2d(
+  xnn_subgraph_t subgraph,
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t kernel_height,
+  uint32_t kernel_width,
+  uint32_t subsampling_height,
+  uint32_t subsampling_width,
+  uint32_t dilation_height,
+  uint32_t dilation_width,
+  uint32_t depth_multiplier,
+  size_t input_channels,
+  float output_min,
+  float output_max,
+  uint32_t input_id,
+  uint32_t filter_id,
+  uint32_t bias_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Depth To Space Node 2D and add it to a Subgraph.
+///
+/// The Depth To Space 2D Node rearranges data from depth into blocks of spatial data (a reverse transform to
+/// Space To Depth). For a given input pixel, an output square of pixels with side @a block_size is formed from values
+/// in the corresponding number of its channels. The output depth is therefore @a block_size x @a block_size times
+/// smaller than that of the input.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param block_size - the size of the spatial block.
+/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
+///                   with [N, IH, IW, OC * block_size * block_size] dimensions.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
+///                    with [N, IH * block_size, IW * block_size, OC] dimensions.
+/// @param flags - binary features of the input_channels Node. No supported flags are currently defined.
+enum xnn_status xnn_define_depth_to_space_2d(
+  xnn_subgraph_t subgraph,
+  uint32_t block_size,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+enum xnn_status xnn_define_depth_to_space(
+  xnn_subgraph_t subgraph,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t block_size,
+  uint32_t flags);
+
+/// Define a 1D Global Average Pooling Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param output_min - lower bound for clipping output values.
+/// @param output_max - upper bound for clipping output values.
+/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 2 or more dimensions
+///                   defined in the @a subgraph. Averaging is performed across the second-innermost dimension.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 2 or more
+///                    dimensions defined in the @a subgraph.
+/// @param flags - binary features of the 1D Global Average Pooling Node. The only currently supported value is
+///                XNN_FLAG_REDUCE_DIMS.
+enum xnn_status xnn_define_global_average_pooling_1d(
+  xnn_subgraph_t subgraph,
+  float output_min,
+  float output_max,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a 2D Global Average Pooling Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param output_min - lower bound for clipping output values.
+/// @param output_max - upper bound for clipping output values.
+/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 3 or more dimensions
+///                   defined in the @a subgraph. Averaging is performed across the second- and third-innermost
+///                   dimensions.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 3 or more
+///                    dimensions defined in the @a subgraph.
+/// @param flags - binary features of the 2D Global Average Pooling Node. The only currently supported value is
+///                XNN_FLAG_REDUCE_DIMS.
+enum xnn_status xnn_define_global_average_pooling_2d(
+  xnn_subgraph_t subgraph,
+  float output_min,
+  float output_max,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a 1D Global Sum Pooling Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param output_min - lower bound for clipping output values.
+/// @param output_max - upper bound for clipping output values.
+/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 2 or more dimensions
+///                   defined in the @a subgraph. Averaging is performed across the second-innermost dimension.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 2 or more
+///                    dimensions defined in the @a subgraph.
+/// @param flags - binary features of the 1D Global Sum Pooling Node. The only currently supported value is
+///                XNN_FLAG_REDUCE_DIMS.
+enum xnn_status xnn_define_global_sum_pooling_1d(
+  xnn_subgraph_t subgraph,
+  float output_min,
+  float output_max,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a 2D Global Sum Pooling Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param output_min - lower bound for clipping output values.
+/// @param output_max - upper bound for clipping output values.
+/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 3 or more dimensions
+///                   defined in the @a subgraph. Averaging is performed across the second- and third-innermost
+///                   dimensions.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 3 or more
+///                    dimensions defined in the @a subgraph.
+/// @param flags - binary features of the 2D Global Sum Pooling Node. The only currently supported value is
+///                XNN_FLAG_REDUCE_DIMS.
+enum xnn_status xnn_define_global_sum_pooling_2d(
+  xnn_subgraph_t subgraph,
+  float output_min,
+  float output_max,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a 2D Average Pooling Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_padding_top - implicit zero-padding above 2D input data. Must be 0 if XNN_FLAG_TENSORFLOW_SAME_PADDING
+///                            flag is specified.
+/// @param input_padding_right - implicit zero-padding to the right of 2D input data. Must be 0 if
+///                              XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
+/// @param input_padding_bottom - implicit zero-padding below 2D input data. Must be 0 if
+///                               XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
+/// @param input_padding_left - implicit zero-padding to the left of 2D input data. Must be 0 if
+///                             XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
+/// @param pooling_height - pooling (kernel) height.
+/// @param pooling_width - pooling (kernel) width.
+/// @param stride_height - displacing of the pooling window in the vertical dimension of the input pixels corresponding
+///                        to vertically adjacent output pixels.
+/// @param stride_width - displacing of the pooling window in the horizontal dimension of the input pixels corresponding
+///                        to horizontally adjacent output pixels.
+/// @param output_min - lower bound for clipping output values.
+/// @param output_max - upper bound for clipping output values.
+/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
+///                   with [N, IH, IW, channels] dimensions
+/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
+///                    with [N, OH, OW, channels] dimensions.
+/// @param flags - binary features of the 2D Average Pooling Node. The only currently supported values is
+///                XNN_FLAG_TENSORFLOW_SAME_PADDING.
+enum xnn_status xnn_define_average_pooling_2d(
+  xnn_subgraph_t subgraph,
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t pooling_height,
+  uint32_t pooling_width,
+  uint32_t stride_height,
+  uint32_t stride_width,
+  float output_min,
+  float output_max,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Fully Connected Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param output_min - lower bound for clipping output values.
+/// @param output_max - upper bound for clipping output values.
+/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the
+///                   @a subgraph. If XNN_FLAG_TENSORFLOW_RESHAPE_2D is not specified, the input tensor must be at least
+///                   1D and its last dimension must match the last dimension of the filter tensor. In particular, if
+///                   input is a 2D tensor, it must have [batch_size, input_channels] dimensions.
+///                   If XNN_FLAG_TENSORFLOW_RESHAPE_2D is specified, the number of elements in the input tensor must be
+///                   divisible by the input_channels. The tensor will be first flattened into a 1D tensor of
+///                   [num_input_elements] dimensions, then reshaped into a 2D tensor of
+///                   [num_input_elements / input_channels, input_channels] dimensions where num_input_elements is the
+///                   total number of elements in the input tensor.
+/// @param filter_id - Value ID for the filter tensor. The filter tensor must a 2D tensor defined in the @a subgraph.
+///                    If the XNN_FLAG_TRANSPOSE_WEIGHTS flag is not specified, the filter tensor must have
+///                    [output_channels, input_channels] dimensions. If the XNN_FLAG_TRANSPOSE_WEIGHTS flag is
+///                    specified, the filter tensor must have [input_channels, output_channels] dimensions.
+/// @param bias_id - Value ID for the bias tensor, or XNN_INVALID_VALUE_ID for a Fully Connected Node without a bias.
+///                  If present, the bias tensor must be a 1D tensor defined in the @a subgraph with [output_channels]
+///                  dimensions.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph.
+///                    If XNN_FLAG_TENSORFLOW_RESHAPE_2D is not specified, the output tensor must have the same
+///                    dimensionality as the input tensor, all its dimensions but the last one must match the
+///                    corresponding dimensions of the input tensor, and the last dimensions of the output tensor must
+///                    match the first dimension of the filter tensor. In particular, if input is a 2D tensor, output
+///                    must be a 2D tensor of [batch_size, output_channels] dimensions.
+///                    If XNN_FLAG_TENSORFLOW_RESHAPE_2D is specified, output must be a 2D tensor of
+///                    [num_input_elements / input_channels, output_channels] dimensions where num_input_elements is the
+///                    total number of elements in the input tensor.
+/// @param flags - binary features of the Fully Connected Node. The only currently supported values are
+///                XNN_FLAG_TENSORFLOW_RESHAPE_2D and XNN_FLAG_TRANSPOSE_WEIGHTS.
+enum xnn_status xnn_define_fully_connected(
+  xnn_subgraph_t subgraph,
+  float output_min,
+  float output_max,
+  uint32_t input_id,
+  uint32_t filter_id,
+  uint32_t bias_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Sparse Fully Connected Node and add it to a Subgraph.
+///
+/// This operator is experimental, and will be removed in the future.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param output_min - lower bound for clipping output values.
+/// @param output_max - upper bound for clipping output values.
+/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the
+///                   @a subgraph. If XNN_FLAG_TENSORFLOW_RESHAPE_2D is not specified, the input tensor must be at least
+///                   1D and its last dimension must match the last dimension of the filter tensor. In particular, if
+///                   input is a 2D tensor, it must have [batch_size, input_channels] dimensions.
+///                   If XNN_FLAG_TENSORFLOW_RESHAPE_2D is specified, the number of elements in the input tensor must be
+///                   divisible by the input_channels. The tensor will be first flattened into a 1D tensor of
+///                   [num_input_elements] dimensions, then reshaped into a 2D tensor of
+///                   [num_input_elements / input_channels, input_channels] dimensions where num_input_elements is the
+///                   total number of elements in the input tensor.
+/// @param filter_id - Value ID for the filter tensor. The filter tensor must a 2D tensor defined in the @a subgraph.
+///                    If the XNN_FLAG_TRANSPOSE_WEIGHTS flag is not specified, the filter tensor must have
+///                    [output_channels, input_channels] dimensions. If the XNN_FLAG_TRANSPOSE_WEIGHTS flag is
+///                    specified, the filter tensor must have [input_channels, output_channels] dimensions.
+/// @param bias_id - Value ID for the bias tensor, or XNN_INVALID_VALUE_ID for a Fully Connected Node without a bias.
+///                  If present, the bias tensor must be a 1D tensor defined in the @a subgraph with [output_channels]
+///                  dimensions.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph.
+///                    If XNN_FLAG_TENSORFLOW_RESHAPE_2D is not specified, the output tensor must have the same
+///                    dimensionality as the input tensor, all its dimensions but the last one must match the
+///                    corresponding dimensions of the input tensor, and the last dimensions of the output tensor must
+///                    match the first dimension of the filter tensor. In particular, if input is a 2D tensor, output
+///                    must be a 2D tensor of [batch_size, output_channels] dimensions.
+///                    If XNN_FLAG_TENSORFLOW_RESHAPE_2D is specified, output must be a 2D tensor of
+///                    [num_input_elements / input_channels, output_channels] dimensions where num_input_elements is the
+///                    total number of elements in the input tensor.
+/// @param flags - binary features of the Fully Connected Node. The only currently supported values are
+///                XNN_FLAG_TENSORFLOW_RESHAPE_2D and XNN_FLAG_TRANSPOSE_WEIGHTS.
+enum xnn_status xnn_define_fully_connected_sparse(
+  xnn_subgraph_t subgraph,
+  float output_min,
+  float output_max,
+  uint32_t input_id,
+  uint32_t filter_id,
+  uint32_t bias_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a 2D Max Pooling Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_padding_top - implicit zero-padding above 2D input data. Must be 0 if XNN_FLAG_TENSORFLOW_SAME_PADDING
+///                            flag is specified.
+/// @param input_padding_right - implicit zero-padding to the right of 2D input data. Must be 0 if
+///                              XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
+/// @param input_padding_bottom - implicit zero-padding below 2D input data. Must be 0 if
+///                               XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
+/// @param input_padding_left - implicit zero-padding to the left of 2D input data. Must be 0 if
+///                             XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
+/// @param pooling_height - pooling (kernel) height.
+/// @param pooling_width - pooling (kernel) width.
+/// @param stride_height - displacing of the pooling window in the vertical dimension of the input pixels corresponding
+///                        to vertically adjacent output pixels.
+/// @param stride_width - displacing of the pooling window in the horizontal dimension of the input pixels corresponding
+///                        to horizontally adjacent output pixels.
+/// @param dilation_height - dilation of pooling elements along the height dimension.
+/// @param dilation_width - dilation of pooling elements along the width dimension.
+/// @param output_min - lower bound for clipping output values.
+/// @param output_max - upper bound for clipping output values.
+/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
+///                   with [N, IH, IW, channels] dimensions
+/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
+///                    with [N, OH, OW, channels] dimensions.
+/// @param flags - binary features of the 2D Max Pooling Node. The only currently supported values is
+///                XNN_FLAG_TENSORFLOW_SAME_PADDING.
+enum xnn_status xnn_define_max_pooling_2d(
+  xnn_subgraph_t subgraph,
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t pooling_height,
+  uint32_t pooling_width,
+  uint32_t stride_height,
+  uint32_t stride_width,
+  uint32_t dilation_height,
+  uint32_t dilation_width,
+  float output_min,
+  float output_max,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a 2D ArgMax Pooling Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_padding_top - implicit zero-padding above 2D input data.
+/// @param input_padding_right - implicit zero-padding to the right of 2D input data.
+/// @param input_padding_bottom - implicit zero-padding below 2D input data.
+/// @param input_padding_left - implicit zero-padding to the left of 2D input data.
+/// @param pooling_height - pooling (kernel) height. Vertical stride between pooling regions match this value.
+/// @param pooling_width - pooling (kernel) width. Horizontal stride between pooling regions match this value.
+/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
+///                   with [N, IH, IW, channels] dimensions
+/// @param output_value_id - Value ID for the output tensor with the maximum values in the pools. The output tensor must
+///                          be a 4D tensor defined in the @a subgraph with [N, OH, OW, channels] dimensions.
+/// @param output_index_id - Value ID for the output tensor with the indexes of the maximum values in the pools. The
+///                          output tensor must be a 4D tensor defined in the @a subgraph with [N, OH, OW, channels]
+///                          dimensions.
+/// @param flags - binary features of the 2D ArgMax Pooling Node. No supported flags are currently defined.
+enum xnn_status xnn_define_argmax_pooling_2d(
+  xnn_subgraph_t subgraph,
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t pooling_height,
+  uint32_t pooling_width,
+  uint32_t input_id,
+  uint32_t output_value_id,
+  uint32_t output_index_id,
+  uint32_t flags);
+
+/// Define a 2D UnPooling Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param padding_top - implicit padding above 2D output data.
+/// @param padding_right - implicit padding to the right of 2D output data.
+/// @param padding_bottom - implicit padding below 2D output data.
+/// @param padding_left - implicit padding to the left of 2D output data.
+/// @param pooling_height - height of the pooling window.
+/// @param pooling_width - width of the pooling window.
+/// @param input_value_id - Value ID for the input tensor with the max-pooling values to invert. The input value tensor
+///                         must be a 4D tensor defined in the @a subgraph with [N, IH, IW, channels] dimensions.
+/// @param input_index_id - Value ID for the input tensor with the indices of the per-pool maximum values produced by
+///                         a 2D UnPooling Node. The input tensor must be a 4D tensor defined in the @a subgraph with
+///                         [N, IH, IW, channels] dimensions.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
+///                    with [N, OH, OW, channels] dimensions.
+/// @param flags - binary features of the 2D UnPooling Node. No supported flags are currently defined.
+enum xnn_status xnn_define_unpooling_2d(
+  xnn_subgraph_t subgraph,
+  uint32_t padding_top,
+  uint32_t padding_right,
+  uint32_t padding_bottom,
+  uint32_t padding_left,
+  uint32_t pooling_height,
+  uint32_t pooling_width,
+  uint32_t input_value_id,
+  uint32_t input_index_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a 2-Input Add Node and add it to a Subgraph.
+///
+/// The 2-Input Add Node computes elementwise addition of two tensor inputs with numpy broadcasting rules.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param output_min - lower bound for clipping output values.
+/// @param output_max - upper bound for clipping output values.
+/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
+///                    the @a subgraph with each dimension either equal to the corresponding dimension of the second
+///                    input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
+///                    that dimension.
+/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
+///                    the @a subgraph with each dimension either equal to the corresponding dimension of the first
+///                    input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
+///                    that dimension.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
+///                    in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
+///                    of the two inputs.
+/// @param flags - binary features of the Add Node. No supported flags are currently defined.
+enum xnn_status xnn_define_add2(
+  xnn_subgraph_t subgraph,
+  float output_min,
+  float output_max,
+  uint32_t input1_id,
+  uint32_t input2_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a 2-Input Multiply Node and add it to a Subgraph.
+///
+/// The 2-Input Multiply Node computes elementwise multiplication of two tensor inputs with numpy broadcasting rules.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param output_min - lower bound for clipping output values.
+/// @param output_max - upper bound for clipping output values.
+/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
+///                    the @a subgraph with each dimension either equal to the corresponding dimension of the second
+///                    input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
+///                    that dimension.
+/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
+///                    the @a subgraph with each dimension either equal to the corresponding dimension of the first
+///                    input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
+///                    that dimension.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
+///                    in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
+///                    of the two inputs.
+/// @param flags - binary features of the Multiply Node. No supported flags are currently defined.
+enum xnn_status xnn_define_multiply2(
+  xnn_subgraph_t subgraph,
+  float output_min,
+  float output_max,
+  uint32_t input1_id,
+  uint32_t input2_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+// Cap operations applied to logits (Q * K) of attention operator.
+enum xnn_attention_logits_cap_type {
+  // No capping.
+  xnn_attention_logits_cap_type_none = 0,
+  // Cap the absolute values of logits by tanh: tanh(logits / cap) * cap
+  xnn_attention_logits_cap_type_tanh
+};
+
+// Params when the cap type is xnn_attention_logits_cap_type_tanh.
+struct xnn_attention_logits_cap_tanh_params {
+  float cap;
+};
+
+/// Define a Scaled Dot-Product Attention Node and add it to a Subgraph.
+///
+/// This operator is experimental.
+///
+/// The Scaled Dot-Product Attention Node computes a multi-head or multi-query scaled dot attention on the query, key,
+/// and value tensors.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param cap_type - type of cap to be applied to the logits.
+/// @param cap_params - parameters for the cap. Must be a pointer to xnn_attention_logits_cap_tanh_params if cap_type
+///                     is xnn_attention_logits_cap_type_tanh.
+/// @param query_id - Value ID for the query tensor. The query tensor must be a 3+-dimensional tensor defined in the
+///                   @a subgraph with the dimensions as [*, H, T, C], where H/T/C are the heads/tokens/channels, and *
+///                   is the 0 or more dimensions treated as batch size.
+/// @param key_id - Value ID for the key tensor. The key tensor must be a 2+--dimensional tensor defined in the
+///                 @a subgraph. It can have the same number of dimensions as the query, with the dimensions as
+///                 [*, H, U, C] (multi-head), or have 1 less dimension than the query, with the dimensions as
+///                 as [*, U, C] (multi-query, number of heads omitted implies single head), where H/U/C are the
+///                 heads/key_value_tokens/channels, and * is the 0 or more dimensions treated as batch size. These
+///                 batch size dimensions must be the same as query.
+/// @param value_id - Value ID for the value tensor. The value tensor must be a 2+--dimensional tensor defined in the
+///                   @a subgraph. It can have the same number of dimensions as the query, with the dimensions as
+///                   [*, H, U, D] (multi-head), or have 1 less dimension than the query, with the dimensions as
+///                   as [*, U, D] (multi-query, number of heads omitted implies single head), where H/U/D are the
+///                   heads/key_value_tokens/value_channels, and * is the 0 or more dimensions treated as batch size.
+///                   These batch size dimensions must be the same as query and key.
+/// @param scale_id - Value ID for the scale tensor. The scale tensor must be a 1D tensor defined in the @a subgraph
+///                   with [C] dimensions. The query tensor is multiplied with this scale tensor before the dot product
+///                   with the key tensor.
+/// @param mask_id - Value ID for the mask tensor. The mask tensor must be a 2D tensor defined in the @a subgraph with
+///                  [T, U] dimensions. The mask tensor is added to the logits (query dot value).
+/// @param output_id - Value ID for the output tensor. The output tensor must be a 3+-dimensional tensor defined in the
+///                    @a subgraph with the dimensions as [*, H, T, D], where H/T/D are the heads/tokens/value_channels,
+///                    and * is the 0 or more dimensions treated as batch size. These batch size dimensions must be the
+///                    same as query, key, and value.
+/// @param flags - binary features of the Scaled Dot Product Attention Node. No supported flags are currently defined.
+enum xnn_status xnn_define_scaled_dot_product_attention(
+  xnn_subgraph_t subgraph,
+  enum xnn_attention_logits_cap_type cap_type,
+  const void* cap_params,
+  uint32_t query_id,
+  uint32_t key_id,
+  uint32_t value_id,
+  uint32_t scale_id,
+  uint32_t mask_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Subtract Node and add it to a Subgraph.
+///
+/// The Subtract Node computes elementwise subtraction of two tensor inputs with numpy broadcasting rules.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param output_min - lower bound for clipping output values.
+/// @param output_max - upper bound for clipping output values.
+/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
+///                    the @a subgraph with each dimension either equal to the corresponding dimension of the second
+///                    input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
+///                    that dimension.
+/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
+///                    the @a subgraph with each dimension either equal to the corresponding dimension of the first
+///                    input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
+///                    that dimension.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
+///                    in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
+///                    of the two inputs.
+/// @param flags - binary features of the Subtract Node. No supported flags are currently defined.
+enum xnn_status xnn_define_subtract(
+  xnn_subgraph_t subgraph,
+  float output_min,
+  float output_max,
+  uint32_t input1_id,
+  uint32_t input2_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Divide Node and add it to a Subgraph.
+///
+/// The Divide Node computes elementwise division of two tensor inputs with numpy broadcasting rules.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param output_min - lower bound for clipping output values.
+/// @param output_max - upper bound for clipping output values.
+/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
+///                    the @a subgraph with each dimension either equal to the corresponding dimension of the second
+///                    input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
+///                    that dimension.
+/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
+///                    the @a subgraph with each dimension either equal to the corresponding dimension of the first
+///                    input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
+///                    that dimension.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
+///                    in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
+///                    of the two inputs.
+/// @param flags - binary features of the Divide Node. No supported flags are currently defined.
+enum xnn_status xnn_define_divide(
+  xnn_subgraph_t subgraph,
+  float output_min,
+  float output_max,
+  uint32_t input1_id,
+  uint32_t input2_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a 2-Input Maximum Node and add it to a Subgraph.
+///
+/// The 2-Input Maximum Node computes elementwise maximum of two tensor inputs with numpy broadcasting rules.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
+///                    the @a subgraph with each dimension either equal to the corresponding dimension of the second
+///                    input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
+///                    that dimension.
+/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
+///                    the @a subgraph with each dimension either equal to the corresponding dimension of the first
+///                    input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
+///                    that dimension.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
+///                    in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
+///                    of the two inputs.
+/// @param flags - binary features of the Maximum Node. No supported flags are currently defined.
+enum xnn_status xnn_define_maximum2(
+  xnn_subgraph_t subgraph,
+  uint32_t input1_id,
+  uint32_t input2_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a 2-Input Minimum Node and add it to a Subgraph.
+///
+/// The 2-Input Minimum Node computes elementwise minimum of two tensor inputs with numpy broadcasting rules.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
+///                    the @a subgraph with each dimension either equal to the corresponding dimension of the second
+///                    input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
+///                    that dimension.
+/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
+///                    the @a subgraph with each dimension either equal to the corresponding dimension of the first
+///                    input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
+///                    that dimension.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
+///                    in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
+///                    of the two inputs.
+/// @param flags - binary features of the Minimum Node. No supported flags are currently defined.
+enum xnn_status xnn_define_minimum2(
+  xnn_subgraph_t subgraph,
+  uint32_t input1_id,
+  uint32_t input2_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Squared Difference Node and add it to a Subgraph.
+///
+/// The Squared Difference Node computes elementwise squared difference of two tensor inputs with numpy broadcasting
+/// rules.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
+///                    the @a subgraph with each dimension either equal to the corresponding dimension of the second
+///                    input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
+///                    that dimension.
+/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
+///                    the @a subgraph with each dimension either equal to the corresponding dimension of the first
+///                    input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
+///                    that dimension.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
+///                    in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
+///                    of the two inputs.
+/// @param flags - binary features of the Squared Difference Node. No supported flags are currently defined.
+enum xnn_status xnn_define_squared_difference(
+  xnn_subgraph_t subgraph,
+  uint32_t input1_id,
+  uint32_t input2_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Constant Pad Node with static padding specification and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param pre_paddings - number of padding elements to insert before input elements for every dimension. This array
+///                       must have as many elements as the number of dimensions in the input tensor.
+/// @param post_paddings - number of padding elements to insert after input elements for every dimension. This array
+///                        must have as many elements as the number of dimensions in the input tensor.
+/// @param padding_value - constant value used to initialize padding elements.
+/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
+///                    shape must match the shape of the input tensor with padding.
+/// @param flags - binary features of the Constant Pad Node. No supported flags are currently defined.
+enum xnn_status xnn_define_static_constant_pad(
+  xnn_subgraph_t subgraph,
+  const size_t* pre_paddings,
+  const size_t* post_paddings,
+  float padding_value,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Mean Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param num_reduction_axes - number of axes along which mean is computed.
+/// @param reduction_axes - axes along which mean is computed.
+/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with at least
+///                   @a num_reduction_axes dimensions defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor defined in the
+///                    @a subgraph with @a num_reduction_axes fewer dimensions than the input tensor (if
+///                    XNN_FLAG_REDUCE_DIMS is specified), or has same dimension rank but the dimension at
+///                    @a reduction_axes reduced to 1 (if XNN_FLAG_REDUCE_DIMS is not specified).
+/// @param flags - binary features of the Mean Node. The only currently supported value is XNN_FLAG_REDUCE_DIMS
+enum xnn_status xnn_define_static_mean(
+  xnn_subgraph_t subgraph,
+  size_t num_reduction_axes,
+  const size_t* reduction_axes,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a 2-Input Concatenate Node and add it to a Subgraph.
+///
+/// The 2-Input Concatenate Node concatenates two tensors along a specified axis.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param axis - the axis to concatenate the two input tensors along
+/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
+///                    the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
+///                    second input.
+/// @param input2_id - Value ID for the second input tensor. The input tensor must be an N-dimensional tensor defined in
+///                    the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
+///                    first input.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a N-dimensional tensor defined
+///                    in the @a subgraph with each dimension equal to the dimension of both inputs, except the axis
+///                    dimension, where it is the sum of the corresponding dimensions of both inputs.
+/// @param flags - binary features of the Concatenate Node. No supported flags are currently defined.
+enum xnn_status xnn_define_concatenate2(
+  xnn_subgraph_t subgraph,
+  size_t axis,
+  uint32_t input1_id,
+  uint32_t input2_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a 3-Input Concatenate Node and add it to a Subgraph.
+///
+/// The 3-Input Concatenate Node concatenates three tensors along a specified axis.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param axis - the axis to concatenate the three input tensors along
+/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
+///                    the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
+///                    other inputs.
+/// @param input2_id - Value ID for the second input tensor. The input tensor must be an N-dimensional tensor defined in
+///                    the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
+///                    other inputs.
+/// @param input3_id - Value ID for the third input tensor. The input tensor must be an N-dimensional tensor defined in
+///                    the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
+///                    other inputs.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a N-dimensional tensor defined
+///                    in the @a subgraph with each dimension equal to the dimension of all inputs, except the axis
+///                    dimension, where it is the sum of the corresponding dimensions of all inputs.
+/// @param flags - binary features of the Concatenate Node. No supported flags are currently defined.
+enum xnn_status xnn_define_concatenate3(
+  xnn_subgraph_t subgraph,
+  size_t axis,
+  uint32_t input1_id,
+  uint32_t input2_id,
+  uint32_t input3_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a 4-Input Concatenate Node and add it to a Subgraph.
+///
+/// The 4-Input Concatenate Node concatenates four tensors along a specified axis.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param axis - the axis to concatenate the four input tensors along
+/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
+///                    the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
+///                    other inputs.
+/// @param input2_id - Value ID for the second input tensor. The input tensor must be an N-dimensional tensor defined in
+///                    the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
+///                    other inputs.
+/// @param input3_id - Value ID for the third input tensor. The input tensor must be an N-dimensional tensor defined in
+///                    the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
+///                    other inputs.
+/// @param input4_id - Value ID for the fourth input tensor. The input tensor must be an N-dimensional tensor defined in
+///                    the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
+///                    other inputs.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a N-dimensional tensor defined
+///                    in the @a subgraph with each dimension equal to the dimension of all inputs, except the axis
+///                    dimension, where it is the sum of the corresponding dimensions of all inputs.
+/// @param flags - binary features of the Concatenate Node. No supported flags are currently defined.
+enum xnn_status xnn_define_concatenate4(
+  xnn_subgraph_t subgraph,
+  size_t axis,
+  uint32_t input1_id,
+  uint32_t input2_id,
+  uint32_t input3_id,
+  uint32_t input4_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+enum xnn_status xnn_define_concatenate5(
+  xnn_subgraph_t subgraph,
+  size_t axis,
+  uint32_t input1_id,
+  uint32_t input2_id,
+  uint32_t input3_id,
+  uint32_t input4_id,
+  uint32_t input5_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Copy Node and add it to a Subgraph.
+///
+/// The Copy Node copies an input tensor to an output tensor.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_id - Value ID for the first input tensor. The input tensor must be defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
+///                    shape must match the shape of the input tensor.
+/// @param flags - binary features of the Copy Node. No supported flags are currently defined.
+enum xnn_status xnn_define_copy(
+  xnn_subgraph_t subgraph,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a 2-Output Split Node and add it to a Subgraph.
+///
+/// The 2-Output Split Node splits an input tensor into two output tensors along a specified axis evenly.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param split_dim - the dimension to split the input tensor along
+/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the @a
+///                   subgraph.
+/// @param output1_id - Value ID for the first output tensor. The output tensor must be an N-dimensional tensor defined
+///                     in the @a subgraph with each dimension, except the axis, equal to the corresponding dimension
+///                     of the second output. The split_dim dimension is half of the input's split_dim.
+/// @param output2_id - Value ID for the second output tensor. The output tensor must be an N-dimensional tensor
+///                     defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
+///                     dimension of the first output. The split_dim dimension is half of the input's split_dim.
+/// @param flags - binary features of the Split Node. No supported flags are currently defined.
+enum xnn_status xnn_define_even_split2(
+  xnn_subgraph_t subgraph,
+  size_t split_dim,
+  uint32_t input_id,
+  uint32_t output1_id,
+  uint32_t output2_id,
+  uint32_t flags);
+
+/// Define a 3-Output Split Node and add it to a Subgraph.
+///
+/// The 3-Output Split Node splits an input tensor into three output tensors along a specified axis evenly.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param split_dim - the dimension to split the input tensor along
+/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the @a
+///                   subgraph.
+/// @param output1_id - Value ID for the first output tensor. The output tensor must be an N-dimensional tensor defined
+///                     in the @a subgraph with each dimension, except the axis, equal to the corresponding dimension
+///                     of the second and third output. The split_dim dimension is one third of the input's split_dim.
+/// @param output2_id - Value ID for the second output tensor. The output tensor must be an N-dimensional tensor
+///                     defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
+///                     dimension of the first and third output. The split_dim dimension is one third of the input's
+///                     split_dim.
+/// @param output3_id - Value ID for the third output tensor. The output tensor must be an N-dimensional tensor
+///                     defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
+///                     dimension of the second and third output. The split_dim dimension is one third of the input's
+///                     split_dim.
+/// @param flags - binary features of the Split Node. No supported flags are currently defined.
+enum xnn_status xnn_define_even_split3(
+  xnn_subgraph_t subgraph,
+  size_t split_dim,
+  uint32_t input_id,
+  uint32_t output1_id,
+  uint32_t output2_id,
+  uint32_t output3_id,
+  uint32_t flags);
+
+/// Define a 4-Output Split Node and add it to a Subgraph.
+///
+/// The 4-Output Split Node splits an input tensor into four output tensors along a specified axis evenly.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param split_dim - the dimension to split the input tensor along
+/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the @a
+///                   subgraph.
+/// @param output1_id - Value ID for the first output tensor. The output tensor must be an N-dimensional tensor defined
+///                     in the @a subgraph with each dimension, except the axis, equal to the corresponding dimension
+///                     of the other output tensors. The split_dim dimension is one fourth of the input's split_dim.
+/// @param output2_id - Value ID for the second output tensor. The output tensor must be an N-dimensional tensor
+///                     defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
+///                     dimension of the other output tensors. The split_dim dimension is one fourth of the input's
+///                     split_dim.
+/// @param output3_id - Value ID for the third output tensor. The output tensor must be an N-dimensional tensor
+///                     defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
+///                     dimension of the other output tensors. The split_dim dimension is one fourth of the input's
+///                     split_dim.
+/// @param output4_id - Value ID for the fourth output tensor. The output tensor must be an N-dimensional tensor
+///                     defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
+///                     dimension of the other output tensors. The split_dim dimension is one fourth of the input's
+///                     split_dim.
+/// @param flags - binary features of the Split Node. No supported flags are currently defined.
+enum xnn_status xnn_define_even_split4(
+  xnn_subgraph_t subgraph,
+  size_t split_dim,
+  uint32_t input_id,
+  uint32_t output1_id,
+  uint32_t output2_id,
+  uint32_t output3_id,
+  uint32_t output4_id,
+  uint32_t flags);
+
+/// Define a Reshape Node with static shape specification and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param num_dims - number of shape dimensions in the output tensor.
+/// @param new_shape - shape dimensions of the output tensor.
+/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
+///                    shape must match the shape of the input tensor with padding.
+/// @param flags - binary features of the Reshape Node. No supported flags are currently defined.
+enum xnn_status xnn_define_static_reshape(
+  xnn_subgraph_t subgraph,
+  size_t num_dims,
+  const size_t* new_shape,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Node that reshapes a tensor to two dimensions, retaining the
+/// trailing dimension, and add it to a Subgraph.
+///
+/// This operator is experimental.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_id - Value ID for the input tensor. The input tensor must be
+///                   defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be
+///                    defined in the @a subgraph, and its
+///                    size must match the shape of the input tensor with
+///                    padding.
+/// @param flags - binary features of the Reshape Node. No supported flags are
+///                currently defined.
+enum xnn_status xnn_define_reshape_2d(xnn_subgraph_t subgraph,
+                                      uint32_t input_id, uint32_t output_id,
+                                      uint32_t flags);
+
+/// Define a 2D Resize Bilinear Node with static output height & width specification and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param new_height - height dimension of the output tensor.
+/// @param new_width - width dimension of the output tensor.
+/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
+///                   with [N, H, W, C] dimensions.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
+///                    with [N, new_height, new_width, C] dimensions.
+/// @param flags - binary features of the 2D Resize Bilinear Node. The only currently supported values are
+///                XNN_FLAG_TENSORFLOW_LEGACY_MODE and XNN_FLAG_ALIGN_CORNERS, which are mutually exclusive.
+enum xnn_status xnn_define_static_resize_bilinear_2d(
+  xnn_subgraph_t subgraph,
+  size_t new_height,
+  size_t new_width,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a PReLU (Parametric ReLU) Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
+///                   with [N, H, W, channels] dimensions.
+/// @param slope_id - Value ID for the slope tensor. The slope tensor must be a 1D tensor defined in the @a subgraph with
+///                   [channels] dimensions.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
+///                    with [N, H, W, channels] dimensions.
+/// @param flags - binary features of the PReLU Node. No supported flags are currently defined.
+enum xnn_status xnn_define_prelu(
+  xnn_subgraph_t subgraph,
+  uint32_t input_id,
+  uint32_t slope_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a RoPE (Rotary Positional Embeddings) Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param max_tokens - maximum possible number of tokens (maximum sequence length) of the input/output tensors.
+/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
+///                   with [batch, tokens, heads, channels] dimensions.
+/// @param weights_id - Value ID for the weights tensor. The weights tensor must be a 2D tensor defined in the
+///                     @a subgraph with [max_tokens, channels] dimensions.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
+///                    with [batch, tokens, heads, channels] dimensions.
+/// @param flags - binary features of the RoPE Node. No supported flags are currently defined.
+enum xnn_status xnn_define_rope(
+  xnn_subgraph_t subgraph,
+  size_t max_sequence_size,
+  uint32_t input_id,
+  uint32_t weights_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Abs Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
+///                    shape must match the shape of the input tensor.
+/// @param flags - binary features of the Abs Node. No supported flags are currently defined.
+enum xnn_status xnn_define_abs(
+  xnn_subgraph_t subgraph,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Bankers' Rounding Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
+///                    shape must match the shape of the input tensor.
+/// @param flags - binary features of the Bankers' Rounding Node. No supported flags are currently defined.
+enum xnn_status xnn_define_bankers_rounding(
+  xnn_subgraph_t subgraph,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Batch Matrix Multiply Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
+///                    the @a subgraph. It must be at least 3D. The first N-2 dimensions must match the second input
+///                    tensor. The last 2 dimensions are [M, K]. If XNN_FLAG_TRANSPOSE_B is not specified, the last
+///                    dimension must match the second last dimension of the second input tensor. If
+///                    XNN_FLAG_TRANSPOSE_B is specified, the last dimension must match the last dimension of the
+///                    second input tensor.
+/// @param input2_id - Value ID for the second input tensor. The input tensor must be an N-dimensional tensor defined
+///                    in the @a subgraph. It must be at least 3D. The first N-2 dimensions must match the first input
+///                    tensor. If XNN_FLAG_TRANSPOSE_B is not specified, the last 2 dimensions are [K, N], and the
+///                    second last dimension must match the last dimension of the first input tensor. If
+///                    XNN_FLAG_TRANSPOSE_B is specified, the last 2 dimensions are [N, K], and the last dimension must
+///                    match the last dimension of the first input tensor.
+/// @param output_id - Value ID for the output tensor. The output tensor must be an N-dimensional tensor defined in the
+///                    @a subgraph. It must be at least 3D. The first N-2 dimensions must match the first and second
+///                    input tensors . The last 2 dimensions must be [M, N].
+/// @param flags - binary features of the Batch Matrix Multiply Node. The only currently supported value is
+///                XNN_FLAG_TRANSPOSE_B.
+enum xnn_status xnn_define_batch_matrix_multiply(
+  xnn_subgraph_t subgraph,
+  uint32_t input1_id,
+  uint32_t input2_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Ceiling Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
+///                    shape must match the shape of the input tensor.
+/// @param flags - binary features of the Ceiling Node. No supported flags are currently defined.
+enum xnn_status xnn_define_ceiling(
+  xnn_subgraph_t subgraph,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Clamp Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param output_min - lower bound for clipping output values.
+/// @param output_max - upper bound for clipping output values.
+/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
+///                    shape must match the shape of the input tensor.
+/// @param flags - binary features of the Clamp Node. No supported flags are currently defined.
+enum xnn_status xnn_define_clamp(
+  xnn_subgraph_t subgraph,
+  float output_min,
+  float output_max,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define an ELU (Exponential Linear Unit) Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param alpha - scale factor for negative output elements.
+/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
+///                    shape must match the shape of the input tensor.
+/// @param flags - binary features of the ELU Node. No supported flags are currently defined.
+enum xnn_status xnn_define_elu(
+  xnn_subgraph_t subgraph,
+  float alpha,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Floor Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
+///                    shape must match the shape of the input tensor.
+/// @param flags - binary features of the Floor Node. No supported flags are currently defined.
+enum xnn_status xnn_define_floor(
+  xnn_subgraph_t subgraph,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a HardSwish Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
+///                    shape must match the shape of the input tensor.
+/// @param flags - binary features of the HardSwish Node. No supported flags are currently defined.
+enum xnn_status xnn_define_hardswish(
+  xnn_subgraph_t subgraph,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Leaky ReLU Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param negative_slope - scale factor for negative input elements.
+/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
+///                    shape must match the shape of the input tensor.
+/// @param flags - binary features of the Leaky ReLU Node. No supported flags are currently defined.
+enum xnn_status xnn_define_leaky_relu(
+  xnn_subgraph_t subgraph,
+  float negative_slope,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Negate Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
+///                    shape must match the shape of the input tensor.
+/// @param flags - binary features of the Negate Node. No supported flags are currently defined.
+enum xnn_status xnn_define_negate(
+  xnn_subgraph_t subgraph,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Sigmoid Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
+///                    shape must match the shape of the input tensor.
+/// @param flags - binary features of the Sigmoid Node. No supported flags are currently defined.
+enum xnn_status xnn_define_sigmoid(
+  xnn_subgraph_t subgraph,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a SoftMax Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph, and have at
+///                   least one dimension.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
+///                    shape must match the shape of the input tensor.
+/// @param flags - binary features of the SoftMax Node. No supported flags are currently defined.
+enum xnn_status xnn_define_softmax(
+  xnn_subgraph_t subgraph,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Space To Depth 2D Node and add it to a Subgraph.
+///
+/// The Space To Depth 2D Node rearranges blocks of spatial data into blocks (a reverse transform to Depth To Space 2D).
+/// For a given input pixel, an output square of pixels with side @a block_size is formed from values in the
+/// corresponding number of its channels. The output depth is therefore @a block_size x @a block_size times greater
+/// than that of the input.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param block_size - the size of the spatial block.
+/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
+///                   with [N, IH * block_size, IW * block_size, OC] dimensions.
+/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
+///                    with [N, IH, IW, OC * block_size * block_size] dimensions.
+/// @param flags - binary features of the input_channels Node. No supported flags are currently defined.
+enum xnn_status xnn_define_space_to_depth_2d(
+  xnn_subgraph_t subgraph,
+  uint32_t block_size,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Square Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
+///                    shape must match the shape of the input tensor.
+/// @param flags - binary features of the Square Node. No supported flags are currently defined.
+enum xnn_status xnn_define_square(
+  xnn_subgraph_t subgraph,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Square Root Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
+///                    shape must match the shape of the input tensor.
+/// @param flags - binary features of the Square Root Node. No supported flags are currently defined.
+enum xnn_status xnn_define_square_root(
+  xnn_subgraph_t subgraph,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Reciprocal Square Root Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_id - Value ID for the input tensor. The input tensor must be
+/// defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be
+/// defined in the @a subgraph, and its
+///                    shape must match the shape of the input tensor.
+/// @param flags - binary features of the Square Root Node. No supported flags
+/// are currently defined.
+enum xnn_status xnn_define_reciprocal_square_root(xnn_subgraph_t subgraph,
+                                                  uint32_t input_id,
+                                                  uint32_t output_id,
+                                                  uint32_t flags);
+
+/// Define a Static Slice Node add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param num_dims - number of shape dimensions in the input and output tensor.
+/// @param offsets - offsets in each dimension of the input tensor. This array must have @a num_dims elements.
+/// @param sizes - size of each dimension in output tensor. This array must have @a num_dims elements.
+/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
+///                    dimensions must match @a sizes.
+/// @param flags - binary features of the Static Slice Node. No supported flags are currently defined.
+enum xnn_status xnn_define_static_slice(
+  xnn_subgraph_t subgraph,
+  size_t num_dims,
+  const size_t* offsets,
+  const size_t* sizes,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Static Transpose Node and add it to a Subgraph.
+///
+/// The Static Transpose Node applies a generalized transpose to the input tensor using the permuation in perm.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in
+///                   the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be an N-dimensional tensor defined
+///                    in the @a subgraph with each dimension equal to its corresponding permuted input dimension.
+/// @param num_dims - the number of permutation dimensions. This must be equal to the number of input dimensions.
+/// @param perm - The permutation of the axis of the input tensor. The perm array must must contain 0 to N-1 in the
+///               permuted order.
+/// @param flags - binary features of the Static Transpose Node. No supported flags are currently defined.
+enum xnn_status xnn_define_static_transpose(
+  xnn_subgraph_t subgraph,
+  size_t num_dims,
+  const size_t* perm,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Define a Tanh Node and add it to a Subgraph.
+///
+/// @param subgraph - a Subgraph object that will own the created Node.
+/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
+/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
+///                    shape must match the shape of the input tensor.
+/// @param flags - binary features of the Tanh Node. No supported flags are currently defined.
+enum xnn_status xnn_define_tanh(
+  xnn_subgraph_t subgraph,
+  uint32_t input_id,
+  uint32_t output_id,
+  uint32_t flags);
+
+/// Code cache is a cache for JIT generated code.
+typedef struct xnn_code_cache* xnn_code_cache_t;
+
+/// Weights cache can be finalized in these ways:
+enum xnn_weights_cache_finalization_kind {
+  /// Weights cache is finalized, no insert operations into the weights cache is allowed, even if the "inserted"
+  /// weights already exist in thee cache. Weights cache memory will also be trimmed to page boundary and set to
+  /// read-only (to prevent writes).
+  xnn_weights_cache_finalization_kind_hard,
+  /// Weights cache will be finalized with some extra space at the end, this allows for "inserting" into the cache only
+  /// if the weights are already in the cache, and errors on inserting uncached weights. There is memory overhead.
+  xnn_weights_cache_finalization_kind_soft,
+};
+
+/// A combination of multiple factors to uniquely locate the weights cache.
+struct xnn_weights_cache_look_up_key {
+  /// The unique seed for each ukernel. It is guaranteed that each ukernel provides
+  /// a consistent and identical seed.
+  uint32_t seed;
+  /// Pointer to the original kernel.
+  const void* kernel;
+  /// Pointer to the original bias, could be NULL.
+  const void* bias;
+};
+
+/// A group of function pointers to manage weights cache. All functions may be
+/// called on multi threads.
+struct xnn_weights_cache_provider {
+  /// User-specified pointer that will be passed as-is to all functions in this
+  /// structure.
+  void* context;
+
+  /// Looks up the tuple of {cache_key, kernel, bias} in the cache. If it is found,
+  /// returns the offset to the found entry for reuse. Otherwise, returns SIZE_MAX.
+  /// @param context - The user-specified pointer from xnn_weights_cache_provider structure.
+  /// @param cache_key - The key used to locate the weights cache entry.
+  size_t (*look_up)(void* context, const struct xnn_weights_cache_look_up_key* cache_key);
+
+  /// Ensures that cache has enough space for `n` bytes. Returns the address to
+  /// store weight cache. Returns NULL if fails to reserve space.
+  /// @param context - The user-specified pointer from xnn_weights_cache_provider structure.
+  /// @param n - size to be reserved.
+  void* (*reserve_space)(void* context, size_t n);
+
+  /// Looks up packed weights at `ptr` in the cache. If it is found, reuse it.
+  /// Otherwise, it is added to the cache. Returns the offset to the cache.
+  /// @param context - The user-specified pointer from xnn_weights_cache_provider structure.
+  /// @param cache_key - The key used to locate the weights cache entry.
+  /// @param ptr - pointer pointing to the packed weight.
+  /// @param size - size of the packed weight.
+  size_t (*look_up_or_insert)(void* context, const struct xnn_weights_cache_look_up_key* cache_key, void* ptr, size_t size);
+
+  /// Returns whether the cache is finalized.
+  /// @param context - The user-specified pointer from xnn_weights_cache_provider structure.
+  bool (*is_finalized)(void* context);
+
+  /// Returns the absolute pointer corresponding to `offset`, where the offset is returned from
+  /// `look_up` or `get_or_insert`. This function must be called after finalize.
+  /// @param context - The user-specified pointer from xnn_weights_cache_provider structure.
+  /// @param offset - offset to the start of internal buffer
+  void* (*offset_to_addr)(void* context, size_t offset);
+
+  /// Destroy a weights cache object, as well as memory used for the cache.
+  /// @param context - The user-specified pointer from xnn_weights_cache_provider structure.
+  enum xnn_status (*delete_cache)(void* context);
+};
+
+/// Weights cache is a cache for packed weights. It can be reused between runtimes.
+typedef struct xnn_weights_cache_provider* xnn_weights_cache_t;
+
+/// Create a weights cache object specifying the initial size of weights cache (in bytes).
+///
+/// @param[in] size - initial capacity of the weights cache (in bytes), i.e. it can hold size bytes without growing.
+/// @param weights_cache_out - pointer to the variable that will be initialized to a handle to the weights cache provider
+///                            upon successful return. Once created, the weights cache provider can be shared between
+///                            different Runtime objects.
+enum xnn_status xnn_create_weights_cache_with_size(size_t size, xnn_weights_cache_t* weights_cache_out);
+
+enum xnn_status xnn_create_weights_cache(xnn_weights_cache_t* weights_cache_out);
+
+/// Finalizes the weights cache. The kind of finalization is specified by `finalization_kind`.
+/// @param weights_cache - the weights cache object to finalize.
+/// @param finalization_kind - the kind of finalization.
+enum xnn_status xnn_finalize_weights_cache(
+  xnn_weights_cache_t weights_cache,
+  enum xnn_weights_cache_finalization_kind finalization_kind);
+
+/// Destroy a weights cache object, as well as memory used for the cache.
+/// @param weights_cache - the weights cache object to destroy.
+enum xnn_status xnn_delete_weights_cache(xnn_weights_cache_t weights_cache);
+
+typedef struct xnn_workspace* xnn_workspace_t;
+
+/// Create a workspace object.
+/// @param workspace_out - pointer to the variable that will be initialized to a handle to the workspace object upon
+///                        successful return. Once created, the workspace can be shared between different Runtime
+///                        objects.
+enum xnn_status xnn_create_workspace(xnn_workspace_t* workspace_out);
+/// Destroy a workspace object, as well as memory used by the workspace. Object destruction can be deferred until all
+/// Runtime objects created with this workspace are destroyed.
+/// @param workspace - the workspace object to destroy.
+enum xnn_status xnn_release_workspace(xnn_workspace_t workspace);
+
+/// Runtime is a combination of an execution plan for subgraph Nodes and a memory manager for subgraph Values.
+typedef struct xnn_runtime* xnn_runtime_t;
+
+enum xnn_profile_info {
+  /// Returns a size_t containing the number of operators.
+  xnn_profile_info_num_operators,
+  /// Returns a char[] containing the null character separated names of all operators.
+  xnn_profile_info_operator_name,
+  /// Returns a uint64_t[] with the runtimes of all operators in the same order as xnn_profile_info_operator_name.
+  xnn_profile_info_operator_timing,
+};
+
+/// Return profile information for all operators.
+///
+/// @param runtime - a Runtime object created with @ref xnn_create_runtime, @ref xnn_create_runtime_v2 or
+///                  @ref xnn_create_runtime_v3.
+/// @param param_name - type of profile information required.
+/// @param param_value_size - the size in bytes of memory pointed to by param_value. If this is not sufficient then
+///                           param_value_size_ret will be set to the required size and xnn_status_out_of_memory will be
+///                           returned.
+/// @param param_value - a pointer to memory location where appropriate values for a given param_value will be written.
+/// @param param_value_size_ret - returns number of bytes required to write the result if param_value_size is not
+///                               sufficient.
+enum xnn_status xnn_get_runtime_profiling_info(xnn_runtime_t runtime,
+                                               enum xnn_profile_info param_name,
+                                               size_t param_value_size,
+                                               void* param_value,
+                                               size_t* param_value_size_ret);
+
+/// Create a Runtime object from a subgraph.
+///
+/// @param subgraph - a Subgraph object with all Values and Nodes that would be handled by the runtime. No Values or
+///                   Nodes can be added to the runtime once it is constructed.
+/// @param weights_cache - a cache for packed weights. The runtime will look up and reuse packed weights in this cache,
+///                        this will reduce memory allocated for packed weights.
+/// @param workspace - a workspace to hold internal tensors. The runtime will allocate space used for internal tensors
+///                    and track them using workspace. Workspace can be shared and reused across different runtimes. If
+///                    workspace is NULL, there will be no sharing: each runtime has its own workspace.
+/// @param threadpool - the thread pool to be used for parallelisation of computations in the runtime. If the thread
+///                     pool is NULL, the computation would run on the caller thread without parallelization.
+/// @param flags - binary features of the runtime. The only currently supported values are
+///                XNN_FLAG_HINT_SPARSE_INFERENCE, XNN_FLAG_HINT_FP16_INFERENCE, XNN_FLAG_FORCE_FP16_INFERENCE,
+///                XNN_FLAG_YIELD_WORKERS, and XNN_FLAG_TRANSIENT_INDIRECTION_BUFFER. If XNN_FLAG_YIELD_WORKERS is
+///                specified, worker threads would be yielded to the system scheduler after processing the last operator
+///                in the Runtime. If XNN_FLAG_TRANSIENT_INDIRECTION_BUFFER is specified, convolution operators will
+///                initialize indirection buffers on each inference run using temporary memory in the workspace, instead
+///                of initializing persistent indirection buffers once.
+/// @param runtime_out - pointer to the variable that will be initialized with a handle to the Runtime object upon
+///                      successful return. Once constructed, the Runtime object is independent of the Subgraph object
+///                      used to create it.
+enum xnn_status xnn_create_runtime_v4(
+  xnn_subgraph_t subgraph,
+  xnn_weights_cache_t weights_cache,
+  xnn_workspace_t workspace,
+  pthreadpool_t threadpool,
+  uint32_t flags,
+  xnn_runtime_t* runtime_out);
+
+enum xnn_status xnn_create_runtime_v3(
+  xnn_subgraph_t subgraph,
+  xnn_weights_cache_t weights_cache,
+  pthreadpool_t threadpool,
+  uint32_t flags,
+  xnn_runtime_t* runtime_out);
+
+enum xnn_status xnn_create_runtime_v2(
+  xnn_subgraph_t subgraph,
+  pthreadpool_t threadpool,
+  uint32_t flags,
+  xnn_runtime_t* runtime_out);
+
+enum xnn_status xnn_create_runtime(
+  xnn_subgraph_t subgraph,
+  xnn_runtime_t* runtime_out);
+
+struct xnn_external_value {
+  uint32_t id;
+  void* data;
+};
+
+/// Reshape an external value.
+///
+/// @param external_id - external ID for the Value. The ID must be within the range of reversed Value IDs specified on
+///                      the Subgraph creation. If the external ID is XNN_INVALID_VALUE_ID, an internal ID will be
+///                      created for the Value.
+/// @param num_dims - number of dimensions in the shape.
+/// @param dims - pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL.
+///               XNNPACK does not keep any pointers to this array after the function returns.
+enum xnn_status xnn_reshape_external_value(
+  xnn_runtime_t runtime,
+  uint32_t external_id,
+  size_t num_dims,
+  const size_t* dims);
+
+/// Get the external value shape.
+///
+/// @param external_id - external ID for the Value. The ID must be within the range of reversed Value IDs specified on
+///                      the Subgraph creation. The external ID can not be XNN_INVALID_VALUE_ID.
+/// @param num_dims -  A valid pointer into which the number of dimensions in the shape will be written. It can not be larger than XNN_MAX_TENSOR_DIMS.
+/// @param dims - pointer to an array of @a num_dims shape dimensions. This pointer can't be NULL. It must be large enough to hold
+///               at least @a num_dims elements. XNNPACK does not keep any pointers to this array after the function returns.
+enum xnn_status xnn_get_external_value_shape(
+  xnn_runtime_t runtime,
+  uint32_t external_id,
+  size_t* num_dims,
+  size_t* dims);
+
+/// Reshape the XNNPACK runtime.
+///
+/// Propgates the shapes of input tensors through the graph to determine the shapes of intermediate and output tensors.
+/// Memory is allocated if required. Output tensor shapes are returned by xnn_get_external_value_shape.
+///
+/// @param runtime - a Runtime object created with @ref xnn_create_runtime or @ref xnn_create_runtime_v2.
+enum xnn_status xnn_reshape_runtime(
+  xnn_runtime_t runtime);
+
+/// Deprecated. Use xnn_reshape_runtime and xnn_setup_runtime_v2.
+///
+/// Setup data pointers for external inputs and outputs in a Runtime object and
+/// allocate memory.
+///
+/// @param runtime - a Runtime object created with @ref xnn_create_runtime or @ref xnn_create_runtime_v2.
+/// @param num_external_values - the number of external inputs and outputs specified in this call. This number must
+///                              match the number of external inputs and outputs in the runtime, i.e. all external
+///                              inputs and outputs in the runtime must be specified in one call.
+/// @param external_values - array with location information for all external inputs and outputs in the runtime.
+enum xnn_status xnn_setup_runtime(
+  xnn_runtime_t runtime,
+  size_t num_external_values,
+  const struct xnn_external_value* external_values);
+
+/// Setup data pointers for external inputs and outputs in a Runtime object.
+/// Should be called after xnn_reshape_runtime.
+///
+/// @param runtime - a Runtime object created with @ref xnn_create_runtime or @ref xnn_create_runtime_v2.
+/// @param num_external_values - the number of external inputs and outputs specified in this call. This number must
+///                              match the number of external inputs and outputs in the runtime, i.e. all external
+///                              inputs and outputs in the runtime must be specified in one call.
+/// @param external_values - array with location information for all external inputs and outputs in the runtime.
+enum xnn_status xnn_setup_runtime_v2(
+  xnn_runtime_t runtime,
+  size_t num_external_values,
+  const struct xnn_external_value* external_values);
+
+/// Execute forward pass for all operators in the runtime.
+///
+/// @param runtime - the Runtime object with the execution plan to invoke.
+enum xnn_status xnn_invoke_runtime(
+  xnn_runtime_t runtime);
+
+/// Destroy a Runtime object, as well as operators and memory associated with it.
+///
+/// @param runtime - the Runtime object to destroy.
+enum xnn_status xnn_delete_runtime(
+  xnn_runtime_t runtime);
+
+typedef struct xnn_operator* xnn_operator_t;
+
+enum xnn_status xnn_run_operator(
+  xnn_operator_t op,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_delete_operator(
+  xnn_operator_t op);
+
+
+/// Operator API:
+/// - create operator will create and populate a xnn_operator_t
+/// - reshape operator will update fields in xnn_operator_t with shape/dimensions and parallelization information
+/// - setup operator will update pointers to input and outputs
+/// Each supported operator must have a create, reshape, and setup function. (Optionally a run function.)
+/// Operators listed below are in alphabetical order by operator name; within each operator, we sort alphabetically by
+/// data layout and type. We also group create, reshape, setup (and optionally run) functions of each operator together.
+
+enum xnn_status xnn_create_abs_nc_f16(
+  uint32_t flags,
+  xnn_operator_t* abs_op_out);
+
+enum xnn_status xnn_reshape_abs_nc_f16(
+  xnn_operator_t abs_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_abs_nc_f16(
+  xnn_operator_t abs_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_abs_nc_f32(
+  uint32_t flags,
+  xnn_operator_t* abs_op_out);
+
+enum xnn_status xnn_reshape_abs_nc_f32(
+  xnn_operator_t abs_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_abs_nc_f32(
+  xnn_operator_t abs_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_run_abs_nc_f32(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const float* input,
+  float* output,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_add_nd_f16(
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* add_op_out);
+
+enum xnn_status xnn_reshape_add_nd_f16(
+  xnn_operator_t add_op,
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_add_nd_f16(
+  xnn_operator_t add_op,
+  const void* input1,
+  const void* input2,
+  void* output);
+
+enum xnn_status xnn_create_add_nd_f32(
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* add_op_out);
+
+enum xnn_status xnn_reshape_add_nd_f32(
+  xnn_operator_t add_op,
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_add_nd_f32(
+  xnn_operator_t add_op,
+  const float* input1,
+  const float* input2,
+  float* output);
+
+enum xnn_status xnn_run_add_nd_f32(
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  const float* input1,
+  const float* input2,
+  float* output,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_add_nd_qs8(
+  int8_t input1_zero_point,
+  float input1_scale,
+  int8_t input2_zero_point,
+  float input2_scale,
+  int8_t output_zero_point,
+  float output_scale,
+  int8_t output_min,
+  int8_t output_max,
+  uint32_t flags,
+  xnn_operator_t* add_op_out);
+
+enum xnn_status xnn_reshape_add_nd_qs8(
+  xnn_operator_t add_op,
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_add_nd_qs8(
+  xnn_operator_t add_op,
+  const int8_t* input1,
+  const int8_t* input2,
+  int8_t* output);
+
+enum xnn_status xnn_run_add_nd_qs8(
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  int8_t input1_zero_point,
+  float input1_scale,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  int8_t input2_zero_point,
+  float input2_scale,
+  const int8_t* input1,
+  const int8_t* input2,
+  int8_t* output,
+  int8_t output_zero_point,
+  float output_scale,
+  int8_t output_min,
+  int8_t output_max,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_add_nd_qu8(
+  uint8_t input1_zero_point,
+  float input1_scale,
+  uint8_t input2_zero_point,
+  float input2_scale,
+  uint8_t output_zero_point,
+  float output_scale,
+  uint8_t output_min,
+  uint8_t output_max,
+  uint32_t flags,
+  xnn_operator_t* add_op_out);
+
+enum xnn_status xnn_reshape_add_nd_qu8(
+  xnn_operator_t add_op,
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_add_nd_qu8(
+  xnn_operator_t add_op,
+  const uint8_t* input1,
+  const uint8_t* input2,
+  uint8_t* output);
+
+enum xnn_status xnn_run_add_nd_qu8(
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  uint8_t input1_zero_point,
+  float input1_scale,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  uint8_t input2_zero_point,
+  float input2_scale,
+  const uint8_t* input1,
+  const uint8_t* input2,
+  uint8_t* output,
+  uint8_t output_zero_point,
+  float output_scale,
+  uint8_t output_min,
+  uint8_t output_max,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_argmax_pooling2d_nhwc_f32(
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t pooling_height,
+  uint32_t pooling_width,
+  uint32_t flags,
+  xnn_operator_t* argmax_pooling_op_out);
+
+enum xnn_status xnn_reshape_argmax_pooling2d_nhwc_f32(
+  xnn_operator_t argmax_pooling_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t channels,
+  size_t input_pixel_stride,
+  size_t output_pixel_stride,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_argmax_pooling2d_nhwc_f32(
+  xnn_operator_t argmax_pooling_op,
+  void* workspace,
+  const float* input,
+  float* output,
+  uint32_t* index);
+
+enum xnn_status xnn_create_average_pooling2d_nhwc_f16(
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t pooling_height,
+  uint32_t pooling_width,
+  uint32_t stride_height,
+  uint32_t stride_width,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* average_pooling_op_out);
+
+enum xnn_status xnn_reshape_average_pooling2d_nhwc_f16(
+  xnn_operator_t average_pooling_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t channels,
+  size_t input_pixel_stride,
+  size_t output_pixel_stride,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_average_pooling2d_nhwc_f16(
+  xnn_operator_t average_pooling_op,
+  void* workspace,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_average_pooling2d_nhwc_f32(
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t pooling_height,
+  uint32_t pooling_width,
+  uint32_t stride_height,
+  uint32_t stride_width,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* average_pooling_op_out);
+
+enum xnn_status xnn_reshape_average_pooling2d_nhwc_f32(
+  xnn_operator_t average_pooling_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t channels,
+  size_t input_pixel_stride,
+  size_t output_pixel_stride,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_average_pooling2d_nhwc_f32(
+  xnn_operator_t average_pooling_op,
+  void* workspace,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_create_average_pooling2d_nhwc_qu8(
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t pooling_height,
+  uint32_t pooling_width,
+  uint32_t stride_height,
+  uint32_t stride_width,
+  uint8_t input_zero_point,
+  float input_scale,
+  uint8_t output_zero_point,
+  float output_scale,
+  uint8_t output_min,
+  uint8_t output_max,
+  uint32_t flags,
+  xnn_operator_t* average_pooling_op_out);
+
+enum xnn_status xnn_reshape_average_pooling2d_nhwc_qu8(
+  xnn_operator_t average_pooling_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t channels,
+  size_t input_pixel_stride,
+  size_t output_pixel_stride,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_average_pooling2d_nhwc_qu8(
+  xnn_operator_t average_pooling_op,
+  void* workspace,
+  const uint8_t* input,
+  uint8_t* output);
+
+enum xnn_status xnn_create_bankers_rounding_nc_f16(
+  uint32_t flags,
+  xnn_operator_t* rounding_op_out);
+
+enum xnn_status xnn_reshape_bankers_rounding_nc_f16(
+  xnn_operator_t rounding_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_bankers_rounding_nc_f16(
+  xnn_operator_t rounding_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_bankers_rounding_nc_f32(
+  uint32_t flags,
+  xnn_operator_t* rounding_op_out);
+
+enum xnn_status xnn_reshape_bankers_rounding_nc_f32(
+  xnn_operator_t rounding_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_bankers_rounding_nc_f32(
+  xnn_operator_t rounding_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_run_bankers_rounding_nc_f32(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const float* input,
+  float* output,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_batch_matrix_multiply_nc_f16(
+  uint32_t flags,
+  xnn_operator_t* batch_matrix_multiply_op);
+
+enum xnn_status xnn_reshape_batch_matrix_multiply_nc_f16(
+  xnn_operator_t batch_matrix_multiply_op,
+  size_t batch_size,
+  size_t m,
+  size_t k,
+  size_t n,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_batch_matrix_multiply_nc_f16(
+  xnn_operator_t batch_matrix_multiply_op,
+  void* workspace,
+  const void* lhs_input,
+  const void* rhs_input,
+  void* output);
+
+enum xnn_status xnn_create_batch_matrix_multiply_nc_f32(
+  uint32_t flags,
+  xnn_operator_t* batch_matrix_multiply_op);
+
+enum xnn_status xnn_reshape_batch_matrix_multiply_nc_f32(
+  xnn_operator_t batch_matrix_multiply_op,
+  size_t batch_size,
+  size_t m,
+  size_t k,
+  size_t n,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_batch_matrix_multiply_nc_f32(
+  xnn_operator_t batch_matrix_multiply_op,
+  void* workspace,
+  const float* lhs_input,
+  const float* rhs_input,
+  float* output);
+
+enum xnn_status xnn_create_ceiling_nc_f16(
+  uint32_t flags,
+  xnn_operator_t* ceiling_op_out);
+
+enum xnn_status xnn_reshape_ceiling_nc_f16(
+  xnn_operator_t ceiling_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_ceiling_nc_f16(
+  xnn_operator_t ceiling_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_ceiling_nc_f32(
+  uint32_t flags,
+  xnn_operator_t* ceiling_op_out);
+
+enum xnn_status xnn_run_ceiling_nc_f32(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const float* input,
+  float* output,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_reshape_ceiling_nc_f32(
+  xnn_operator_t ceiling_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_ceiling_nc_f32(
+  xnn_operator_t ceiling_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_create_channel_shuffle_nc_x8(
+  size_t groups,
+  size_t group_channels,
+  size_t input_stride,
+  size_t output_stride,
+  uint32_t flags,
+  xnn_operator_t* channel_shuffle_op_out);
+
+enum xnn_status xnn_reshape_channel_shuffle_nc_x8(
+  xnn_operator_t channel_shuffle_op,
+  size_t batch_size,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_channel_shuffle_nc_x8(
+  xnn_operator_t channel_shuffle_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_channel_shuffle_nc_x32(
+  size_t groups,
+  size_t group_channels,
+  size_t input_stride,
+  size_t output_stride,
+  uint32_t flags,
+  xnn_operator_t* channel_shuffle_op_out);
+
+enum xnn_status xnn_reshape_channel_shuffle_nc_x32(
+  xnn_operator_t channel_shuffle_op,
+  size_t batch_size,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_channel_shuffle_nc_x32(
+  xnn_operator_t channel_shuffle_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_clamp_nc_f16(
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* clamp_op_out);
+
+enum xnn_status xnn_reshape_clamp_nc_f16(
+  xnn_operator_t clamp_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_clamp_nc_f16(
+  xnn_operator_t clamp_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_clamp_nc_f32(
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* clamp_op_out);
+
+enum xnn_status xnn_reshape_clamp_nc_f32(
+  xnn_operator_t clamp_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_clamp_nc_f32(
+  xnn_operator_t clamp_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_run_clamp_nc_f32(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const float* input,
+  float* output,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_clamp_nc_s8(
+  int8_t output_min,
+  int8_t output_max,
+  uint32_t flags,
+  xnn_operator_t* clamp_op_out);
+
+enum xnn_status xnn_reshape_clamp_nc_s8(
+  xnn_operator_t clamp_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_clamp_nc_s8(
+  xnn_operator_t clamp_op,
+  const int8_t* input,
+  int8_t* output);
+
+enum xnn_status xnn_create_clamp_nc_u8(
+  uint8_t output_min,
+  uint8_t output_max,
+  uint32_t flags,
+  xnn_operator_t* clamp_op_out);
+
+enum xnn_status xnn_reshape_clamp_nc_u8(
+  xnn_operator_t clamp_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_clamp_nc_u8(
+  xnn_operator_t clamp_op,
+  const uint8_t* input,
+  uint8_t* output);
+
+enum xnn_status xnn_create_constant_pad_nd_x8(
+  const void* padding_value,
+  uint32_t flags,
+  xnn_operator_t* constant_pad_op_out);
+
+enum xnn_status xnn_reshape_constant_pad_nd_x8(
+  xnn_operator_t constant_pad_op,
+  size_t num_dims,
+  const size_t* input_shape,
+  const size_t* pre_padding,
+  const size_t* post_padding,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_constant_pad_nd_x8(
+  xnn_operator_t constant_pad_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_run_constant_pad_nd_x8(
+  uint32_t flags,
+  size_t num_dims,
+  const size_t* input_shape,
+  const size_t* pre_paddings,
+  const size_t* post_paddings,
+  const void* input,
+  void* output,
+  const void* padding_value,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_constant_pad_nd_x16(
+  const void* padding_value,
+  uint32_t flags,
+  xnn_operator_t* constant_pad_op_out);
+
+enum xnn_status xnn_reshape_constant_pad_nd_x16(
+  xnn_operator_t constant_pad_op,
+  size_t num_dims,
+  const size_t* input_shape,
+  const size_t* pre_padding,
+  const size_t* post_padding,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_constant_pad_nd_x16(
+  xnn_operator_t constant_pad_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_run_constant_pad_nd_x16(
+  uint32_t flags,
+  size_t num_dims,
+  const size_t* input_shape,
+  const size_t* pre_paddings,
+  const size_t* post_paddings,
+  const void* input,
+  void* output,
+  const void* padding_value,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_constant_pad_nd_x32(
+  const void* padding_value,
+  uint32_t flags,
+  xnn_operator_t* constant_pad_op_out);
+
+enum xnn_status xnn_reshape_constant_pad_nd_x32(
+  xnn_operator_t constant_pad_op,
+  size_t num_dims,
+  const size_t* input_shape,
+  const size_t* pre_padding,
+  const size_t* post_padding,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_constant_pad_nd_x32(
+  xnn_operator_t constant_pad_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_run_constant_pad_nd_x32(
+  uint32_t flags,
+  size_t num_dims,
+  const size_t* input_shape,
+  const size_t* pre_paddings,
+  const size_t* post_paddings,
+  const void* input,
+  void* output,
+  const void* padding_value,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_convert_nc_f16_f32(
+  uint32_t flags,
+  xnn_operator_t* convert_op_out);
+
+enum xnn_status xnn_reshape_convert_nc_f16_f32(
+  xnn_operator_t convert_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_convert_nc_f16_f32(
+  xnn_operator_t convert_op,
+  const void* input,
+  float* output);
+
+enum xnn_status xnn_run_convert_nc_f16_f32(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const void* input,
+  float* output,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_convert_nc_f16_qd8(
+  uint32_t flags,
+  xnn_operator_t* convert_op_out);
+
+enum xnn_status xnn_reshape_convert_nc_f16_qd8(
+  xnn_operator_t convert_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+// quantization_params must be padded with at least XNN_EXTRA_QUANTIZATION_PARAMS entries.
+enum xnn_status xnn_setup_convert_nc_f16_qd8(
+  xnn_operator_t convert_op,
+  const void* input,
+  int8_t* output,
+  struct xnn_dynamic_quantization_params* quantization_params);
+
+enum xnn_status xnn_create_convert_nc_f32_qd8(
+  uint32_t flags,
+  xnn_operator_t* convert_op_out);
+
+enum xnn_status xnn_reshape_convert_nc_f32_qd8(
+  xnn_operator_t convert_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+// quantization_params must be padded with at least XNN_EXTRA_QUANTIZATION_PARAMS entries.
+enum xnn_status xnn_setup_convert_nc_f32_qd8(
+  xnn_operator_t convert_op,
+  const float* input,
+  int8_t* output,
+  struct xnn_dynamic_quantization_params* quantization_params);
+
+enum xnn_status xnn_create_convert_nc_f32_f16(
+  uint32_t flags,
+  xnn_operator_t* convert_op_out);
+
+enum xnn_status xnn_reshape_convert_nc_f32_f16(
+  xnn_operator_t convert_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_convert_nc_f32_f16(
+  xnn_operator_t convert_op,
+  const float* input,
+  void* output);
+
+enum xnn_status xnn_run_convert_nc_f32_f16(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const float* input,
+  void* output,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_convert_nc_f32_qs8(
+  float output_scale,
+  int8_t output_zero_point,
+  int8_t output_min,
+  int8_t output_max,
+  uint32_t flags,
+  xnn_operator_t* convert_op_out);
+
+enum xnn_status xnn_reshape_convert_nc_f32_qs8(
+  xnn_operator_t convert_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_convert_nc_f32_qs8(
+  xnn_operator_t convert_op,
+  const float* input,
+  int8_t* output);
+
+enum xnn_status xnn_run_convert_nc_f32_qs8(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const float* input,
+  int8_t* output,
+  float output_scale,
+  int8_t output_zero_point,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_convert_nc_f32_qu8(
+  float output_scale,
+  uint8_t output_zero_point,
+  uint8_t output_min,
+  uint8_t output_max,
+  uint32_t flags,
+  xnn_operator_t* convert_op_out);
+
+enum xnn_status xnn_reshape_convert_nc_f32_qu8(
+  xnn_operator_t convert_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_convert_nc_f32_qu8(
+  xnn_operator_t convert_op,
+  const float* input,
+  uint8_t* output);
+
+enum xnn_status xnn_run_convert_nc_f32_qu8(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const float* input,
+  uint8_t* output,
+  float output_scale,
+  uint8_t output_zero_point,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_convert_nc_qs8(
+  float input_scale,
+  int8_t input_zero_point,
+  float output_scale,
+  int8_t output_zero_point,
+  uint32_t flags,
+  xnn_operator_t* convert_op_out);
+
+enum xnn_status xnn_reshape_convert_nc_qs8(
+  xnn_operator_t convert_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_convert_nc_qs8(
+  xnn_operator_t convert_op,
+  const int8_t* input,
+  int8_t* output);
+
+enum xnn_status xnn_create_convert_nc_qs8_f16(
+  float input_scale,
+  int8_t input_zero_point,
+  uint32_t flags,
+  xnn_operator_t* convert_op_out);
+
+enum xnn_status xnn_reshape_convert_nc_qs8_f16(
+  xnn_operator_t convert_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_convert_nc_qs8_f16(
+  xnn_operator_t convert_op,
+  const int8_t* input,
+  void* output);
+
+enum xnn_status xnn_create_convert_nc_qs8_f32(
+  float input_scale,
+  int8_t input_zero_point,
+  uint32_t flags,
+  xnn_operator_t* convert_op_out);
+
+enum xnn_status xnn_reshape_convert_nc_qs8_f32(
+  xnn_operator_t convert_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_convert_nc_qs8_f32(
+  xnn_operator_t convert_op,
+  const int8_t* input,
+  float* output);
+
+enum xnn_status xnn_run_convert_nc_qs8_f32(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const int8_t* input,
+  float* output,
+  float input_scale,
+  int8_t input_zero_point,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_convert_nc_qs16_qs8(
+  float input_scale,
+  float output_scale,
+  int8_t output_zero_point,
+  uint32_t flags,
+  xnn_operator_t* convert_op_out);
+
+enum xnn_status xnn_reshape_convert_nc_qs16_qs8(
+  xnn_operator_t convert_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_convert_nc_qs16_qs8(
+  xnn_operator_t convert_op,
+  const int16_t* input,
+  int8_t* output);
+
+enum xnn_status xnn_run_convert_nc_qs16_qs8(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const int16_t* input,
+  int8_t* output,
+  float input_scale,
+  float output_scale,
+  int8_t output_zero_point,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_convert_nc_qu8(
+  float input_scale,
+  uint8_t input_zero_point,
+  float output_scale,
+  uint8_t output_zero_point,
+  uint32_t flags,
+  xnn_operator_t* convert_op_out);
+
+enum xnn_status xnn_reshape_convert_nc_qu8(
+  xnn_operator_t convert_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_convert_nc_qu8(
+  xnn_operator_t convert_op,
+  const uint8_t* input,
+  uint8_t* output);
+
+enum xnn_status xnn_create_convert_nc_qu8_f32(
+  float input_scale,
+  uint8_t input_zero_point,
+  uint32_t flags,
+  xnn_operator_t* convert_op_out);
+
+enum xnn_status xnn_reshape_convert_nc_qu8_f32(
+  xnn_operator_t convert_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_convert_nc_qu8_f32(
+  xnn_operator_t convert_op,
+  const uint8_t* input,
+  float* output);
+
+enum xnn_status xnn_run_convert_nc_qu8_f32(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const uint8_t* input,
+  float* output,
+  float input_scale,
+  uint8_t input_zero_point,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_convolution2d_nchw_f16(
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t kernel_height,
+  uint32_t kernel_width,
+  uint32_t subsampling_height,
+  uint32_t subsampling_width,
+  uint32_t dilation_height,
+  uint32_t dilation_width,
+  uint32_t groups,
+  size_t group_input_channels,
+  size_t group_output_channels,
+  size_t input_channel_stride,
+  size_t output_channel_stride,
+  const void* kernel,
+  const void* bias,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* convolution_op_out);
+
+enum xnn_status xnn_reshape_convolution2d_nchw_f16(
+  xnn_operator_t convolution_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_convolution2d_nchw_f16(
+  xnn_operator_t convolution_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_convolution2d_nchw_f32(
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t kernel_height,
+  uint32_t kernel_width,
+  uint32_t subsampling_height,
+  uint32_t subsampling_width,
+  uint32_t dilation_height,
+  uint32_t dilation_width,
+  uint32_t groups,
+  size_t group_input_channels,
+  size_t group_output_channels,
+  size_t input_channel_stride,
+  size_t output_channel_stride,
+  const float* kernel,
+  const float* bias,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* convolution_op_out);
+
+enum xnn_status xnn_reshape_convolution2d_nchw_f32(
+  xnn_operator_t convolution_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_convolution2d_nchw_f32(
+  xnn_operator_t convolution_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_create_convolution2d_nhwc_f16(
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t kernel_height,
+  uint32_t kernel_width,
+  uint32_t subsampling_height,
+  uint32_t subsampling_width,
+  uint32_t dilation_height,
+  uint32_t dilation_width,
+  uint32_t groups,
+  size_t group_input_channels,
+  size_t group_output_channels,
+  size_t input_channel_stride,
+  size_t output_channel_stride,
+  const void* kernel,
+  const void* bias,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* convolution_op_out);
+
+enum xnn_status xnn_reshape_convolution2d_nhwc_f16(
+  xnn_operator_t convolution_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_convolution2d_nhwc_f16(
+  xnn_operator_t convolution_op,
+  void* workspace,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_convolution2d_nhwc_f32(
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t kernel_height,
+  uint32_t kernel_width,
+  uint32_t subsampling_height,
+  uint32_t subsampling_width,
+  uint32_t dilation_height,
+  uint32_t dilation_width,
+  uint32_t groups,
+  size_t group_input_channels,
+  size_t group_output_channels,
+  size_t input_channel_stride,
+  size_t output_channel_stride,
+  const float* kernel,
+  const float* bias,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* convolution_op_out);
+
+// Forward declare.
+struct xnn_post_operation;
+
+/// Create a convolution operator with a number of post operations. The
+/// convolution operator created using this function does not have output_min
+/// and output_max. The list of operators in post_operations will be applied in
+/// order. Convolution with post operations is only supported on JIT platforms
+/// and when JIT is enabled.
+enum xnn_status xnn_create_fused_convolution2d_nhwc_f32(
+    uint32_t input_padding_top,
+    uint32_t input_padding_right,
+    uint32_t input_padding_bottom,
+    uint32_t input_padding_left,
+    uint32_t kernel_height,
+    uint32_t kernel_width,
+    uint32_t subsampling_height,
+    uint32_t subsampling_width,
+    uint32_t dilation_height,
+    uint32_t dilation_width,
+    uint32_t groups,
+    size_t group_input_channels,
+    size_t group_output_channels,
+    size_t input_channel_stride,
+    size_t output_channel_stride,
+    const float* kernel,
+    const float* bias,
+    size_t num_post_operations,
+    struct xnn_post_operation* post_operations,
+    uint32_t flags,
+    xnn_code_cache_t code_cache,
+    xnn_weights_cache_t weights_cache,
+    xnn_operator_t* convolution_op_out);
+
+enum xnn_status xnn_reshape_convolution2d_nhwc_f32(
+  xnn_operator_t convolution_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_convolution2d_nhwc_f32(
+  xnn_operator_t convolution_op,
+  void* workspace,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_create_convolution2d_nhwc_qd8_f16_qc8w(
+    uint32_t input_padding_top, uint32_t input_padding_right,
+    uint32_t input_padding_bottom, uint32_t input_padding_left,
+    uint32_t kernel_height, uint32_t kernel_width, uint32_t subsampling_height,
+    uint32_t subsampling_width, uint32_t dilation_height,
+    uint32_t dilation_width, uint32_t groups, size_t group_input_channels,
+    size_t group_output_channels, size_t input_channel_stride,
+    size_t output_channel_stride, const float* kernel_scale,
+    const int8_t* kernel, const float* bias, float output_min, float output_max,
+    uint32_t flags, xnn_code_cache_t code_cache,
+    xnn_weights_cache_t weights_cache, xnn_operator_t* convolution_op_out);
+
+enum xnn_status xnn_create_convolution2d_nhwc_qd8_f32_qc8w(
+    uint32_t input_padding_top, uint32_t input_padding_right,
+    uint32_t input_padding_bottom, uint32_t input_padding_left,
+    uint32_t kernel_height, uint32_t kernel_width, uint32_t subsampling_height,
+    uint32_t subsampling_width, uint32_t dilation_height,
+    uint32_t dilation_width, uint32_t groups, size_t group_input_channels,
+    size_t group_output_channels, size_t input_channel_stride,
+    size_t output_channel_stride, const float* kernel_scale,
+    const int8_t* kernel, const float* bias, float output_min, float output_max,
+    uint32_t flags, xnn_code_cache_t code_cache,
+    xnn_weights_cache_t weights_cache, xnn_operator_t* convolution_op_out);
+
+enum xnn_status xnn_create_convolution2d_nhwc_qs8(
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t kernel_height,
+  uint32_t kernel_width,
+  uint32_t subsampling_height,
+  uint32_t subsampling_width,
+  uint32_t dilation_height,
+  uint32_t dilation_width,
+  uint32_t groups,
+  size_t group_input_channels,
+  size_t group_output_channels,
+  size_t input_channel_stride,
+  size_t output_channel_stride,
+  int8_t input_zero_point,
+  float input_scale,
+  float kernel_scale,
+  const int8_t* kernel,
+  const int32_t* bias,
+  int8_t output_zero_point,
+  float output_scale,
+  int8_t output_min,
+  int8_t output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* convolution_op_out);
+
+enum xnn_status xnn_reshape_convolution2d_nhwc_qd8_f16_qc8w(
+    xnn_operator_t convolution_op, size_t batch_size, size_t input_height,
+    size_t input_width, size_t* workspace_size, size_t* workspace_alignment,
+    size_t* output_height_out, size_t* output_width_out,
+    pthreadpool_t threadpool);
+
+enum xnn_status xnn_reshape_convolution2d_nhwc_qd8_f32_qc8w(
+    xnn_operator_t convolution_op, size_t batch_size, size_t input_height,
+    size_t input_width, size_t* workspace_size, size_t* workspace_alignment,
+    size_t* output_height_out, size_t* output_width_out,
+    pthreadpool_t threadpool);
+
+enum xnn_status xnn_reshape_convolution2d_nhwc_qs8(
+  xnn_operator_t convolution_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_convolution2d_nhwc_qd8_f16_qc8w(
+    xnn_operator_t convolution_op, void* workspace, const int8_t* input,
+    void* output,
+    const struct xnn_dynamic_quantization_params* quantization_params);
+
+enum xnn_status xnn_setup_convolution2d_nhwc_qd8_f32_qc8w(
+    xnn_operator_t convolution_op, void* workspace, const int8_t* input,
+    float* output,
+    const struct xnn_dynamic_quantization_params* quantization_params);
+
+enum xnn_status xnn_setup_convolution2d_nhwc_qs8(
+  xnn_operator_t convolution_op,
+  void* workspace,
+  const int8_t* input,
+  int8_t* output);
+
+enum xnn_status xnn_create_convolution2d_nhwc_qs8_qc8w(
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t kernel_height,
+  uint32_t kernel_width,
+  uint32_t subsampling_height,
+  uint32_t subsampling_width,
+  uint32_t dilation_height,
+  uint32_t dilation_width,
+  uint32_t groups,
+  size_t group_input_channels,
+  size_t group_output_channels,
+  size_t input_channel_stride,
+  size_t output_channel_stride,
+  int8_t input_zero_point,
+  float input_scale,
+  const float* kernel_scale,
+  const int8_t* kernel,
+  const int32_t* bias,
+  int8_t output_zero_point,
+  float output_scale,
+  int8_t output_min,
+  int8_t output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* convolution_op_out);
+
+enum xnn_status xnn_reshape_convolution2d_nhwc_qs8_qc8w(
+  xnn_operator_t convolution_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_convolution2d_nhwc_qs8_qc8w(
+  xnn_operator_t convolution_op,
+  void* workspace,
+  const int8_t* input,
+  int8_t* output);
+
+enum xnn_status xnn_create_convolution2d_nhwc_qu8(
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t kernel_height,
+  uint32_t kernel_width,
+  uint32_t subsampling_height,
+  uint32_t subsampling_width,
+  uint32_t dilation_height,
+  uint32_t dilation_width,
+  uint32_t groups,
+  size_t group_input_channels,
+  size_t group_output_channels,
+  size_t input_channel_stride,
+  size_t output_channel_stride,
+  uint8_t input_zero_point,
+  float input_scale,
+  uint8_t kernel_zero_point,
+  float kernel_scale,
+  const uint8_t* kernel,
+  const int32_t* bias,
+  uint8_t output_zero_point,
+  float output_scale,
+  uint8_t output_min,
+  uint8_t output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* convolution_op_out);
+
+enum xnn_status xnn_reshape_convolution2d_nhwc_qu8(
+  xnn_operator_t convolution_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_convolution2d_nhwc_qu8(
+  xnn_operator_t convolution_op,
+  void* workspace,
+  const uint8_t* input,
+  uint8_t* output);
+
+enum xnn_status xnn_create_copy_nc_x8(
+  uint32_t flags,
+  xnn_operator_t* copy_op_out);
+
+enum xnn_status xnn_reshape_copy_nc_x8(
+  xnn_operator_t copy_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_copy_nc_x8(
+  xnn_operator_t copy_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_copy_nc_x16(
+  uint32_t flags,
+  xnn_operator_t* copy_op_out);
+
+enum xnn_status xnn_reshape_copy_nc_x16(
+  xnn_operator_t copy_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_copy_nc_x16(
+  xnn_operator_t copy_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_copy_nc_x32(
+  uint32_t flags,
+  xnn_operator_t* copy_op_out);
+
+enum xnn_status xnn_reshape_copy_nc_x32(
+  xnn_operator_t copy_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_copy_nc_x32(
+  xnn_operator_t copy_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_run_copy_nc_x32(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const uint32_t* input,
+  uint32_t* output,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_deconvolution2d_nhwc_f16(
+  uint32_t output_padding_top,
+  uint32_t output_padding_right,
+  uint32_t output_padding_bottom,
+  uint32_t output_padding_left,
+  uint32_t kernel_height,
+  uint32_t kernel_width,
+  uint32_t stride_height,
+  uint32_t stride_width,
+  uint32_t dilation_height,
+  uint32_t dilation_width,
+  uint32_t groups,
+  size_t group_input_channels,
+  size_t group_output_channels,
+  size_t input_pixel_stride,
+  size_t output_pixel_stride,
+  const void* kernel,
+  const void* bias,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* deconvolution_op_out);
+
+enum xnn_status xnn_reshape_deconvolution2d_nhwc_f16(
+  xnn_operator_t deconvolution_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  uint32_t adjustment_height,
+  uint32_t adjustment_width,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_deconvolution2d_nhwc_f16(
+  xnn_operator_t deconvolution_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_deconvolution2d_nhwc_f32(
+  uint32_t output_padding_top,
+  uint32_t output_padding_right,
+  uint32_t output_padding_bottom,
+  uint32_t output_padding_left,
+  uint32_t kernel_height,
+  uint32_t kernel_width,
+  uint32_t stride_height,
+  uint32_t stride_width,
+  uint32_t dilation_height,
+  uint32_t dilation_width,
+  uint32_t groups,
+  size_t group_input_channels,
+  size_t group_output_channels,
+  size_t input_pixel_stride,
+  size_t output_pixel_stride,
+  const float* kernel,
+  const float* bias,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* deconvolution_op_out);
+
+enum xnn_status xnn_reshape_deconvolution2d_nhwc_f32(
+  xnn_operator_t deconvolution_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  uint32_t adjustment_height,
+  uint32_t adjustment_width,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_deconvolution2d_nhwc_f32(
+  xnn_operator_t deconvolution_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_create_deconvolution2d_nhwc_qs8(
+  uint32_t output_padding_top,
+  uint32_t output_padding_right,
+  uint32_t output_padding_bottom,
+  uint32_t output_padding_left,
+  uint32_t kernel_height,
+  uint32_t kernel_width,
+  uint32_t stride_height,
+  uint32_t stride_width,
+  uint32_t dilation_height,
+  uint32_t dilation_width,
+  uint32_t groups,
+  size_t group_input_channels,
+  size_t group_output_channels,
+  size_t input_pixel_stride,
+  size_t output_pixel_stride,
+  int8_t input_zero_point,
+  float input_scale,
+  float kernel_scale,
+  const int8_t* kernel,
+  const int32_t* bias,
+  int8_t output_zero_point,
+  float output_scale,
+  int8_t output_min,
+  int8_t output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* deconvolution_op_out);
+
+enum xnn_status xnn_reshape_deconvolution2d_nhwc_qs8(
+  xnn_operator_t deconvolution_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  uint32_t adjustment_height,
+  uint32_t adjustment_width,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_deconvolution2d_nhwc_qs8(
+  xnn_operator_t deconvolution_op,
+  const int8_t* input,
+  int8_t* output);
+
+enum xnn_status xnn_create_deconvolution2d_nhwc_qu8(
+  uint32_t output_padding_top,
+  uint32_t output_padding_right,
+  uint32_t output_padding_bottom,
+  uint32_t output_padding_left,
+  uint32_t kernel_height,
+  uint32_t kernel_width,
+  uint32_t stride_height,
+  uint32_t stride_width,
+  uint32_t dilation_height,
+  uint32_t dilation_width,
+  uint32_t groups,
+  size_t group_input_channels,
+  size_t group_output_channels,
+  size_t input_pixel_stride,
+  size_t output_pixel_stride,
+  uint8_t input_zero_point,
+  float input_scale,
+  uint8_t kernel_zero_point,
+  float kernel_scale,
+  const uint8_t* kernel,
+  const int32_t* bias,
+  uint8_t output_zero_point,
+  float output_scale,
+  uint8_t output_min,
+  uint8_t output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* deconvolution_op_out);
+
+enum xnn_status xnn_reshape_deconvolution2d_nhwc_qu8(
+  xnn_operator_t deconvolution_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  uint32_t adjustment_height,
+  uint32_t adjustment_width,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_deconvolution2d_nhwc_qu8(
+  xnn_operator_t deconvolution_op,
+  const uint8_t* input,
+  uint8_t* output);
+
+enum xnn_status xnn_create_depth_to_space_nchw2nhwc_x16(
+  uint32_t block_size,
+  uint32_t flags,
+  xnn_operator_t* depth_to_space_op_out);
+
+enum xnn_status xnn_reshape_depth_to_space_nchw2nhwc_x16(
+  xnn_operator_t depth_to_space_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t input_channels,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  size_t* output_channels_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_depth_to_space_nchw2nhwc_x16(
+  xnn_operator_t depth_to_space_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_depth_to_space_nchw2nhwc_x32(
+  uint32_t block_size,
+  uint32_t flags,
+  xnn_operator_t* depth_to_space_op_out);
+
+enum xnn_status xnn_reshape_depth_to_space_nchw2nhwc_x32(
+  xnn_operator_t depth_to_space_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t input_channels,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  size_t* output_channels_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_depth_to_space_nchw2nhwc_x32(
+  xnn_operator_t depth_to_space_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_depth_to_space_nhwc_x8(
+  uint32_t block_size,
+  uint32_t flags,
+  xnn_operator_t* depth_to_space_op_out);
+
+enum xnn_status xnn_reshape_depth_to_space_nhwc_x8(
+  xnn_operator_t depth_to_space_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t input_channels,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  size_t* output_channels_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_depth_to_space_nhwc_x8(
+  xnn_operator_t depth_to_space_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_depth_to_space_nhwc_x16(
+  uint32_t block_size,
+  uint32_t flags,
+  xnn_operator_t* depth_to_space_op_out);
+
+enum xnn_status xnn_reshape_depth_to_space_nhwc_x16(
+  xnn_operator_t depth_to_space_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t input_channels,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  size_t* output_channels_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_depth_to_space_nhwc_x16(
+  xnn_operator_t depth_to_space_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_depth_to_space_nhwc_x32(
+  uint32_t block_size,
+  uint32_t flags,
+  xnn_operator_t* depth_to_space_op_out);
+
+enum xnn_status xnn_reshape_depth_to_space_nhwc_x32(
+  xnn_operator_t depth_to_space_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t input_channels,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  size_t* output_channels_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_depth_to_space_nhwc_x32(
+  xnn_operator_t depth_to_space_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_divide_nd_f16(
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* divide_op_out);
+
+enum xnn_status xnn_reshape_divide_nd_f16(
+  xnn_operator_t divide_op,
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_divide_nd_f16(
+  xnn_operator_t divide_op,
+  const void* input1,
+  const void* input2,
+  void* output);
+
+enum xnn_status xnn_create_divide_nd_f32(
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* divide_op_out);
+
+enum xnn_status xnn_reshape_divide_nd_f32(
+  xnn_operator_t divide_op,
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_divide_nd_f32(
+  xnn_operator_t divide_op,
+  const float* input1,
+  const float* input2,
+  float* output);
+
+enum xnn_status xnn_run_divide_nd_f32(
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  const float* input1,
+  const float* input2,
+  float* output,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_dynamic_fully_connected_nc_f16(
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* dynamic_fully_connected_op_out);
+
+enum xnn_status xnn_reshape_dynamic_fully_connected_nc_f16(
+  xnn_operator_t dynamic_fully_connected_op,
+  size_t batch_size,
+  size_t input_channels,
+  size_t output_channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_dynamic_fully_connected_nc_f16(
+  xnn_operator_t dynamic_fully_connected_op,
+  void* workspace,
+  const void* input,
+  const void* kernel,
+  const void* bias,
+  void* output);
+
+enum xnn_status xnn_create_dynamic_fully_connected_nc_f32(
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* dynamic_fully_connected_op_out);
+
+enum xnn_status xnn_reshape_dynamic_fully_connected_nc_f32(
+  xnn_operator_t dynamic_fully_connected_op,
+  size_t batch_size,
+  size_t input_channels,
+  size_t output_channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_dynamic_fully_connected_nc_f32(
+  xnn_operator_t dynamic_fully_connected_op,
+  void* workspace,
+  const float* input,
+  const float* kernel,
+  const float* bias,
+  float* output);
+
+enum xnn_status xnn_create_elu_nc_f16(
+  float alpha,
+  uint32_t flags,
+  xnn_operator_t* elu_op_out);
+
+enum xnn_status xnn_reshape_elu_nc_f16(
+  xnn_operator_t elu_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_elu_nc_f16(
+  xnn_operator_t elu_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_elu_nc_f32(
+  float alpha,
+  uint32_t flags,
+  xnn_operator_t* elu_op_out);
+
+enum xnn_status xnn_reshape_elu_nc_f32(
+  xnn_operator_t elu_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_elu_nc_f32(
+  xnn_operator_t elu_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_run_elu_nc_f32(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const float* input,
+  float* output,
+  float alpha,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_elu_nc_qs8(
+  float alpha,
+  int8_t input_zero_point,
+  float input_scale,
+  int8_t output_zero_point,
+  float output_scale,
+  int8_t output_min,
+  int8_t output_max,
+  uint32_t flags,
+  xnn_operator_t* elu_op_out);
+
+enum xnn_status xnn_reshape_elu_nc_qs8(
+  xnn_operator_t elu_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_elu_nc_qs8(
+  xnn_operator_t elu_op,
+  const int8_t* input,
+  int8_t* output);
+
+enum xnn_status xnn_create_floor_nc_f16(
+  uint32_t flags,
+  xnn_operator_t* floor_op_out);
+
+enum xnn_status xnn_reshape_floor_nc_f16(
+  xnn_operator_t floor_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_floor_nc_f16(
+  xnn_operator_t floor_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_floor_nc_f32(
+  uint32_t flags,
+  xnn_operator_t* floor_op_out);
+
+enum xnn_status xnn_reshape_floor_nc_f32(
+  xnn_operator_t floor_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_floor_nc_f32(
+  xnn_operator_t floor_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_run_floor_nc_f32(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const float* input,
+  float* output,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_fully_connected_nc_f16(
+  size_t input_channels,
+  size_t output_channels,
+  size_t input_stride,
+  size_t output_stride,
+  const void* kernel,
+  const void* bias,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* fully_connected_op_out);
+
+enum xnn_status xnn_reshape_fully_connected_nc_f16(
+  xnn_operator_t fully_connected_op,
+  size_t batch_size,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_fully_connected_nc_f16(
+  xnn_operator_t fully_connected_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_fully_connected_nc_f32(
+  size_t input_channels,
+  size_t output_channels,
+  size_t input_stride,
+  size_t output_stride,
+  const float* kernel,
+  const float* bias,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* fully_connected_op_out);
+
+enum xnn_status xnn_reshape_fully_connected_nc_f32(
+  xnn_operator_t fully_connected_op,
+  size_t batch_size,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_fully_connected_nc_f32(
+  xnn_operator_t fully_connected_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_create_fully_connected_nc_f32_qc4w(
+  size_t input_channels,
+  size_t output_channels,
+  size_t input_stride,
+  size_t output_stride,
+  uint8_t kernel_zero_point,
+  const float* kernel_scale,
+  const uint8_t* kernel,
+  const float* bias,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* fully_connected_op_out);
+
+enum xnn_status xnn_reshape_fully_connected_nc_f32_qc4w(
+  xnn_operator_t fully_connected_op,
+  size_t batch_size,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_fully_connected_nc_f32_qc4w(
+  xnn_operator_t fully_connected_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_create_fully_connected_nc_f32_qc8w(
+  size_t input_channels,
+  size_t output_channels,
+  size_t input_stride,
+  size_t output_stride,
+  const float* kernel_scale,
+  const int8_t* kernel,
+  const float* bias,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* fully_connected_op_out);
+
+enum xnn_status xnn_reshape_fully_connected_nc_f32_qc8w(
+  xnn_operator_t fully_connected_op,
+  size_t batch_size,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_fully_connected_nc_f32_qc8w(
+  xnn_operator_t fully_connected_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_create_fully_connected_nc_qd8_f16_qc4w(
+  size_t input_channels,
+  size_t output_channels,
+  size_t input_stride,
+  size_t output_stride,
+  uint8_t kernel_zero_point,
+  const float* kernel_scale,
+  const void* kernel,
+  const float* bias,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* fully_connected_op_out);
+
+enum xnn_status xnn_setup_fully_connected_nc_qd8_f16_qc4w(
+  xnn_operator_t fully_connected_op,
+  const int8_t* input,
+  void* output,
+  const struct xnn_dynamic_quantization_params* quantization_params);
+
+enum xnn_status xnn_reshape_fully_connected_nc_qd8_f16_qc4w(
+  xnn_operator_t fully_connected_op,
+  size_t batch_size,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_fully_connected_nc_qd8_f32_qc4w(
+  size_t input_channels,
+  size_t output_channels,
+  size_t input_stride,
+  size_t output_stride,
+  uint8_t kernel_zero_point,
+  const float* kernel_scale,
+  const void* kernel,
+  const float* bias,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* fully_connected_op_out);
+
+enum xnn_status xnn_setup_fully_connected_nc_qd8_f32_qc4w(
+  xnn_operator_t fully_connected_op,
+  const int8_t* input,
+  float* output,
+  const struct xnn_dynamic_quantization_params* quantization_params);
+
+enum xnn_status xnn_reshape_fully_connected_nc_qd8_f32_qc4w(
+  xnn_operator_t fully_connected_op,
+  size_t batch_size,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_fully_connected_nc_qd8_f16_qc8w(
+  size_t input_channels,
+  size_t output_channels,
+  size_t input_stride,
+  size_t output_stride,
+  const float* kernel_scale,
+  const int8_t* kernel,
+  const float* bias,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* fully_connected_op_out);
+
+enum xnn_status xnn_setup_fully_connected_nc_qd8_f16_qc8w(
+  xnn_operator_t fully_connected_op,
+  const int8_t* input,
+  void* output,
+  const struct xnn_dynamic_quantization_params* quantization_params);
+
+enum xnn_status xnn_reshape_fully_connected_nc_qd8_f16_qc8w(
+  xnn_operator_t fully_connected_op,
+  size_t batch_size,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_fully_connected_nc_qd8_f32_qc8w(
+  size_t input_channels,
+  size_t output_channels,
+  size_t input_stride,
+  size_t output_stride,
+  const float* kernel_scale,
+  const int8_t* kernel,
+  const float* bias,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* fully_connected_op_out);
+
+enum xnn_status xnn_setup_fully_connected_nc_qd8_f32_qc8w(
+  xnn_operator_t fully_connected_op,
+  const int8_t* input,
+  float* output,
+  const struct xnn_dynamic_quantization_params* quantization_params);
+
+enum xnn_status xnn_reshape_fully_connected_nc_qd8_f32_qc8w(
+  xnn_operator_t fully_connected_op,
+  size_t batch_size,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_fully_connected_nc_qs8(
+  size_t input_channels,
+  size_t output_channels,
+  size_t input_stride,
+  size_t output_stride,
+  int8_t input_zero_point,
+  float input_scale,
+  float kernel_scale,
+  const int8_t* kernel,
+  const int32_t* bias,
+  int8_t output_zero_point,
+  float output_scale,
+  int8_t output_min,
+  int8_t output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* fully_connected_op_out);
+
+enum xnn_status xnn_reshape_fully_connected_nc_qs8(
+  xnn_operator_t fully_connected_op,
+  size_t batch_size,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_fully_connected_nc_qs8(
+  xnn_operator_t fully_connected_op,
+  const int8_t* input,
+  int8_t* output);
+
+enum xnn_status xnn_create_fully_connected_nc_qs8_qc8w(
+  size_t input_channels,
+  size_t output_channels,
+  size_t input_stride,
+  size_t output_stride,
+  int8_t input_zero_point,
+  float input_scale,
+  const float* kernel_scale,
+  const int8_t* kernel,
+  const int32_t* bias,
+  int8_t output_zero_point,
+  float output_scale,
+  int8_t output_min,
+  int8_t output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* fully_connected_op_out);
+
+enum xnn_status xnn_reshape_fully_connected_nc_qs8_qc8w(
+  xnn_operator_t fully_connected_op,
+  size_t batch_size,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_fully_connected_nc_qs8_qc8w(
+  xnn_operator_t fully_connected_op,
+  const int8_t* input,
+  int8_t* output);
+
+enum xnn_status xnn_create_fully_connected_nc_qu8(
+  size_t input_channels,
+  size_t output_channels,
+  size_t input_stride,
+  size_t output_stride,
+  uint8_t input_zero_point,
+  float input_scale,
+  uint8_t kernel_zero_point,
+  float kernel_scale,
+  const uint8_t* kernel,
+  const int32_t* bias,
+  uint8_t output_zero_point,
+  float output_scale,
+  uint8_t output_min,
+  uint8_t output_max,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* fully_connected_op_out);
+
+enum xnn_status xnn_reshape_fully_connected_nc_qu8(
+  xnn_operator_t fully_connected_op,
+  size_t batch_size,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_fully_connected_nc_qu8(
+  xnn_operator_t fully_connected_op,
+  const uint8_t* input,
+  uint8_t* output);
+
+enum xnn_status xnn_create_global_average_pooling_ncw_f16(
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* global_average_pooling_op_out);
+
+enum xnn_status xnn_reshape_global_average_pooling_ncw_f16(
+  xnn_operator_t global_average_pooling_op,
+  size_t batch_size,
+  size_t width,
+  size_t channels,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_global_average_pooling_ncw_f16(
+  xnn_operator_t global_average_pooling_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_global_average_pooling_ncw_f32(
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* global_average_pooling_op_out);
+
+enum xnn_status xnn_reshape_global_average_pooling_ncw_f32(
+  xnn_operator_t global_average_pooling_op,
+  size_t batch_size,
+  size_t width,
+  size_t channels,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_global_average_pooling_ncw_f32(
+  xnn_operator_t global_average_pooling_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_create_global_average_pooling_nwc_f16(
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* global_average_pooling_op_out);
+
+enum xnn_status xnn_reshape_global_average_pooling_nwc_f16(
+  xnn_operator_t global_average_pooling_op,
+  size_t batch_size,
+  size_t width,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_global_average_pooling_nwc_f16(
+  xnn_operator_t global_average_pooling_op,
+  void* workspace,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_global_average_pooling_nwc_f32(
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* global_average_pooling_op_out);
+
+enum xnn_status xnn_reshape_global_average_pooling_nwc_f32(
+  xnn_operator_t global_average_pooling_op,
+  size_t batch_size,
+  size_t width,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_global_average_pooling_nwc_f32(
+  xnn_operator_t global_average_pooling_op,
+  void* workspace,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_create_global_average_pooling_nwc_qs8(
+  int8_t input_zero_point,
+  float input_scale,
+  int8_t output_zero_point,
+  float output_scale,
+  int8_t output_min,
+  int8_t output_max,
+  uint32_t flags,
+  xnn_operator_t* global_average_pooling_op_out);
+
+enum xnn_status xnn_reshape_global_average_pooling_nwc_qs8(
+  xnn_operator_t global_average_pooling_op,
+  size_t batch_size,
+  size_t width,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_global_average_pooling_nwc_qs8(
+  xnn_operator_t global_average_pooling_op,
+  void* workspace,
+  const int8_t* input,
+  int8_t* output);
+
+enum xnn_status xnn_create_global_average_pooling_nwc_qu8(
+  uint8_t input_zero_point,
+  float input_scale,
+  uint8_t output_zero_point,
+  float output_scale,
+  uint8_t output_min,
+  uint8_t output_max,
+  uint32_t flags,
+  xnn_operator_t* global_average_pooling_op_out);
+
+enum xnn_status xnn_reshape_global_average_pooling_nwc_qu8(
+  xnn_operator_t global_average_pooling_op,
+  size_t batch_size,
+  size_t width,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_global_average_pooling_nwc_qu8(
+  xnn_operator_t global_average_pooling_op,
+  void* workspace,
+  const uint8_t* input,
+  uint8_t* output);
+
+enum xnn_status xnn_create_global_sum_pooling_nwc_f16(
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* global_sum_pooling_op_out);
+
+enum xnn_status xnn_reshape_global_sum_pooling_nwc_f16(
+  xnn_operator_t global_sum_pooling_op,
+  size_t batch_size,
+  size_t width,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_global_sum_pooling_nwc_f16(
+  xnn_operator_t global_sum_pooling_op,
+  void* workspace,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_global_sum_pooling_nwc_f32(
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* global_sum_pooling_op_out);
+
+enum xnn_status xnn_reshape_global_sum_pooling_nwc_f32(
+  xnn_operator_t global_sum_pooling_op,
+  size_t batch_size,
+  size_t width,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_global_sum_pooling_nwc_f32(
+  xnn_operator_t global_sum_pooling_op,
+  void* workspace,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_create_hardswish_nc_f16(
+  uint32_t flags,
+  xnn_operator_t* hardswish_op_out);
+
+enum xnn_status xnn_reshape_hardswish_nc_f16(
+  xnn_operator_t hardswish_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_hardswish_nc_f16(
+  xnn_operator_t hardswish_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_hardswish_nc_f32(
+  uint32_t flags,
+  xnn_operator_t* hardswish_op_out);
+
+enum xnn_status xnn_reshape_hardswish_nc_f32(
+  xnn_operator_t hardswish_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_hardswish_nc_f32(
+  xnn_operator_t hardswish_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_run_hardswish_nc_f32(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const float* input,
+  float* output,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_leaky_relu_nc_f16(
+  float negative_slope,
+  uint32_t flags,
+  xnn_operator_t* leaky_relu_op_out);
+
+enum xnn_status xnn_reshape_leaky_relu_nc_f16(
+  xnn_operator_t leaky_relu_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_leaky_relu_nc_f16(
+  xnn_operator_t leaky_relu_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_leaky_relu_nc_f32(
+  float negative_slope,
+  uint32_t flags,
+  xnn_operator_t* leaky_relu_op_out);
+
+enum xnn_status xnn_reshape_leaky_relu_nc_f32(
+  xnn_operator_t leaky_relu_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_leaky_relu_nc_f32(
+  xnn_operator_t leaky_relu_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_run_leaky_relu_nc_f32(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const float* input,
+  float* output,
+  float negative_slope,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_leaky_relu_nc_qs8(
+  float negative_slope,
+  int8_t input_zero_point,
+  float input_scale,
+  int8_t output_zero_point,
+  float output_scale,
+  uint32_t flags,
+  xnn_operator_t* leaky_relu_op_out);
+
+enum xnn_status xnn_reshape_leaky_relu_nc_qs8(
+  xnn_operator_t leaky_relu_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_leaky_relu_nc_qs8(
+  xnn_operator_t leaky_relu_op,
+  const int8_t* input,
+  int8_t* output);
+
+enum xnn_status xnn_create_leaky_relu_nc_qu8(
+  float negative_slope,
+  uint8_t input_zero_point,
+  float input_scale,
+  uint8_t output_zero_point,
+  float output_scale,
+  uint32_t flags,
+  xnn_operator_t* leaky_relu_op_out);
+
+enum xnn_status xnn_reshape_leaky_relu_nc_qu8(
+  xnn_operator_t leaky_relu_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_leaky_relu_nc_qu8(
+  xnn_operator_t leaky_relu_op,
+  const uint8_t* input,
+  uint8_t* output);
+
+enum xnn_status xnn_create_max_pooling2d_nhwc_f16(
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t pooling_height,
+  uint32_t pooling_width,
+  uint32_t stride_height,
+  uint32_t stride_width,
+  uint32_t dilation_height,
+  uint32_t dilation_width,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* max_pooling_op_out);
+
+enum xnn_status xnn_reshape_max_pooling2d_nhwc_f16(
+  xnn_operator_t max_pooling_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t channels,
+  size_t input_pixel_stride,
+  size_t output_pixel_stride,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_max_pooling2d_nhwc_f16(
+  xnn_operator_t max_pooling_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_max_pooling2d_nhwc_f32(
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t pooling_height,
+  uint32_t pooling_width,
+  uint32_t stride_height,
+  uint32_t stride_width,
+  uint32_t dilation_height,
+  uint32_t dilation_width,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* max_pooling_op_out);
+
+enum xnn_status xnn_reshape_max_pooling2d_nhwc_f32(
+  xnn_operator_t max_pooling_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t channels,
+  size_t input_pixel_stride,
+  size_t output_pixel_stride,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_max_pooling2d_nhwc_f32(
+  xnn_operator_t max_pooling_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_create_max_pooling2d_nhwc_s8(
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t pooling_height,
+  uint32_t pooling_width,
+  uint32_t stride_height,
+  uint32_t stride_width,
+  uint32_t dilation_height,
+  uint32_t dilation_width,
+  int8_t output_min,
+  int8_t output_max,
+  uint32_t flags,
+  xnn_operator_t* max_pooling_op_out);
+
+enum xnn_status xnn_reshape_max_pooling2d_nhwc_s8(
+  xnn_operator_t max_pooling_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t channels,
+  size_t input_pixel_stride,
+  size_t output_pixel_stride,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_max_pooling2d_nhwc_s8(
+  xnn_operator_t max_pooling_op,
+  const int8_t* input,
+  int8_t* output);
+
+enum xnn_status xnn_create_max_pooling2d_nhwc_u8(
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t pooling_height,
+  uint32_t pooling_width,
+  uint32_t stride_height,
+  uint32_t stride_width,
+  uint32_t dilation_height,
+  uint32_t dilation_width,
+  uint8_t output_min,
+  uint8_t output_max,
+  uint32_t flags,
+  xnn_operator_t* max_pooling_op_out);
+
+enum xnn_status xnn_reshape_max_pooling2d_nhwc_u8(
+  xnn_operator_t max_pooling_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t channels,
+  size_t input_pixel_stride,
+  size_t output_pixel_stride,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_max_pooling2d_nhwc_u8(
+  xnn_operator_t max_pooling_op,
+  const uint8_t* input,
+  uint8_t* output);
+
+enum xnn_status xnn_create_maximum_nd_f16(
+  uint32_t flags,
+  xnn_operator_t* maximum_op_out);
+
+enum xnn_status xnn_reshape_maximum_nd_f16(
+  xnn_operator_t maximum_op,
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_maximum_nd_f16(
+  xnn_operator_t maximum_op,
+  const void* input1,
+  const void* input2,
+  void* output);
+
+enum xnn_status xnn_create_maximum_nd_f32(
+  uint32_t flags,
+  xnn_operator_t* maximum_op_out);
+
+enum xnn_status xnn_reshape_maximum_nd_f32(
+  xnn_operator_t maximum_op,
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_maximum_nd_f32(
+  xnn_operator_t maximum_op,
+  const float* input1,
+  const float* input2,
+  float* output);
+
+enum xnn_status xnn_run_maximum_nd_f32(
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  const float* input1,
+  const float* input2,
+  float* output,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_mean_nd_f16(
+  uint32_t flags,
+  xnn_operator_t* mean_op_out);
+
+enum xnn_status xnn_reshape_mean_nd_f16(
+  xnn_operator_t mean_op,
+  size_t num_reduction_axes,
+  const size_t* reduction_axes,
+  size_t num_input_dims,
+  const size_t* input_shape,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_mean_nd_f16(
+  xnn_operator_t mean_op,
+  void* workspace,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_mean_nd_f32(
+  uint32_t flags,
+  xnn_operator_t* mean_op_out);
+
+enum xnn_status xnn_reshape_mean_nd_f32(
+  xnn_operator_t mean_op,
+  size_t num_reduction_axes,
+  const size_t* reduction_axes,
+  size_t num_input_dims,
+  const size_t* input_shape,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_mean_nd_f32(
+  xnn_operator_t mean_op,
+  void* workspace,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_create_minimum_nd_f16(
+  uint32_t flags,
+  xnn_operator_t* minimum_op_out);
+
+enum xnn_status xnn_reshape_minimum_nd_f16(
+  xnn_operator_t minimum_op,
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_minimum_nd_f16(
+  xnn_operator_t minimum_op,
+  const void* input1,
+  const void* input2,
+  void* output);
+
+enum xnn_status xnn_create_minimum_nd_f32(
+  uint32_t flags,
+  xnn_operator_t* minimum_op_out);
+
+enum xnn_status xnn_reshape_minimum_nd_f32(
+  xnn_operator_t minimum_op,
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_minimum_nd_f32(
+  xnn_operator_t minimum_op,
+  const float* input1,
+  const float* input2,
+  float* output);
+
+enum xnn_status xnn_run_minimum_nd_f32(
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  const float* input1,
+  const float* input2,
+  float* output,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_multiply_nd_f16(
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* multiply_op_out);
+
+enum xnn_status xnn_reshape_multiply_nd_f16(
+  xnn_operator_t multiply_op,
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_multiply_nd_f16(
+  xnn_operator_t multiply_op,
+  const void* input1,
+  const void* input2,
+  void* output);
+
+enum xnn_status xnn_create_multiply_nd_f32(
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* multiply_op_out);
+
+enum xnn_status xnn_reshape_multiply_nd_f32(
+  xnn_operator_t multiply_op,
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_multiply_nd_f32(
+  xnn_operator_t multiply_op,
+  const float* input1,
+  const float* input2,
+  float* output);
+
+enum xnn_status xnn_run_multiply_nd_f32(
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  const float* input1,
+  const float* input2,
+  float* output,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_multiply_nd_qs8(
+  int8_t input1_zero_point,
+  float input1_scale,
+  int8_t input2_zero_point,
+  float input2_scale,
+  int8_t output_zero_point,
+  float output_scale,
+  int8_t output_min,
+  int8_t output_max,
+  uint32_t flags,
+  xnn_operator_t* multiply_op_out);
+
+enum xnn_status xnn_reshape_multiply_nd_qs8(
+  xnn_operator_t multiply_op,
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_multiply_nd_qs8(
+  xnn_operator_t multiply_op,
+  const int8_t* input1,
+  const int8_t* input2,
+  int8_t* output);
+
+enum xnn_status xnn_run_multiply_nd_qs8(
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  int8_t input1_zero_point,
+  float input1_scale,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  int8_t input2_zero_point,
+  float input2_scale,
+  const int8_t* input1,
+  const int8_t* input2,
+  int8_t* output,
+  int8_t output_zero_point,
+  float output_scale,
+  int8_t output_min,
+  int8_t output_max,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_multiply_nd_qu8(
+  uint8_t input1_zero_point,
+  float input1_scale,
+  uint8_t input2_zero_point,
+  float input2_scale,
+  uint8_t output_zero_point,
+  float output_scale,
+  uint8_t output_min,
+  uint8_t output_max,
+  uint32_t flags,
+  xnn_operator_t* multiply_op_out);
+
+enum xnn_status xnn_reshape_multiply_nd_qu8(
+  xnn_operator_t multiply_op,
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_multiply_nd_qu8(
+  xnn_operator_t multiply_op,
+  const uint8_t* input1,
+  const uint8_t* input2,
+  uint8_t* output);
+
+enum xnn_status xnn_run_multiply_nd_qu8(
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  uint8_t input1_zero_point,
+  float input1_scale,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  uint8_t input2_zero_point,
+  float input2_scale,
+  const uint8_t* input1,
+  const uint8_t* input2,
+  uint8_t* output,
+  uint8_t output_zero_point,
+  float output_scale,
+  uint8_t output_min,
+  uint8_t output_max,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_negate_nc_f16(
+  uint32_t flags,
+  xnn_operator_t* negate_op_out);
+
+enum xnn_status xnn_reshape_negate_nc_f16(
+  xnn_operator_t negate_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_negate_nc_f16(
+  xnn_operator_t negate_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_negate_nc_f32(
+  uint32_t flags,
+  xnn_operator_t* negate_op_out);
+
+enum xnn_status xnn_reshape_negate_nc_f32(
+  xnn_operator_t negate_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_negate_nc_f32(
+  xnn_operator_t negate_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_run_negate_nc_f32(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const float* input,
+  float* output,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_prelu_nc_f16(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  const void* negative_slope,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* prelu_op_out);
+
+enum xnn_status xnn_reshape_prelu_nc_f16(
+  xnn_operator_t prelu_op,
+  size_t batch_size,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_prelu_nc_f16(
+  xnn_operator_t prelu_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_prelu_nc_f32(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  const float* negative_slope,
+  uint32_t flags,
+  xnn_code_cache_t code_cache,
+  xnn_weights_cache_t weights_cache,
+  xnn_operator_t* prelu_op_out);
+
+enum xnn_status xnn_reshape_prelu_nc_f32(
+  xnn_operator_t prelu_op,
+  size_t batch_size,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_prelu_nc_f32(
+  xnn_operator_t prelu_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_create_resize_bilinear2d_nchw_f32(
+  size_t output_height,
+  size_t output_width,
+  uint32_t flags,
+  xnn_operator_t* resize_op_out);
+
+enum xnn_status xnn_reshape_resize_bilinear2d_nchw_f32(
+  xnn_operator_t resize_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t channels,
+  size_t input_pixel_stride,
+  size_t output_pixel_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_resize_bilinear2d_nchw_f32(
+  xnn_operator_t resize_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_create_resize_bilinear2d_nchw_f16(
+  size_t output_height,
+  size_t output_width,
+  uint32_t flags,
+  xnn_operator_t* resize_op_out);
+
+enum xnn_status xnn_reshape_resize_bilinear2d_nchw_f16(
+  xnn_operator_t resize_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t channels,
+  size_t input_pixel_stride,
+  size_t output_pixel_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_resize_bilinear2d_nchw_f16(
+  xnn_operator_t resize_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_resize_bilinear2d_nhwc_f16(
+  size_t output_height,
+  size_t output_width,
+  uint32_t flags,
+  xnn_operator_t* resize_op_out);
+
+enum xnn_status xnn_reshape_resize_bilinear2d_nhwc_f16(
+  xnn_operator_t resize_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t channels,
+  size_t input_pixel_stride,
+  size_t output_pixel_stride,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_resize_bilinear2d_nhwc_f16(
+  xnn_operator_t resize_op,
+  void* workspace,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_resize_bilinear2d_nhwc_f32(
+  size_t output_height,
+  size_t output_width,
+  uint32_t flags,
+  xnn_operator_t* resize_op_out);
+
+enum xnn_status xnn_reshape_resize_bilinear2d_nhwc_f32(
+  xnn_operator_t resize_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t channels,
+  size_t input_pixel_stride,
+  size_t output_pixel_stride,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_resize_bilinear2d_nhwc_f32(
+  xnn_operator_t resize_op,
+  void* workspace,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_create_resize_bilinear2d_nhwc_s8(
+  size_t output_height,
+  size_t output_width,
+  uint32_t flags,
+  xnn_operator_t* resize_op_out);
+
+enum xnn_status xnn_reshape_resize_bilinear2d_nhwc_s8(
+  xnn_operator_t resize_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t channels,
+  size_t input_pixel_stride,
+  size_t output_pixel_stride,
+  size_t* workspace_size,
+  size_t* workspace,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_resize_bilinear2d_nhwc_s8(
+  xnn_operator_t resize_op,
+  void* workspace,
+  const int8_t* input,
+  int8_t* output);
+
+enum xnn_status xnn_create_resize_bilinear2d_nhwc_u8(
+  size_t output_height,
+  size_t output_width,
+  uint32_t flags,
+  xnn_operator_t* resize_op_out);
+
+enum xnn_status xnn_reshape_resize_bilinear2d_nhwc_u8(
+  xnn_operator_t resize_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t channels,
+  size_t input_pixel_stride,
+  size_t output_pixel_stride,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_resize_bilinear2d_nhwc_u8(
+  xnn_operator_t resize_op,
+  void* workspace,
+  const uint8_t* input,
+  uint8_t* output);
+
+enum xnn_status xnn_create_rope_nthc_f16(
+  size_t max_tokens,
+  uint32_t flags,
+  xnn_operator_t* rope_op_out);
+
+enum xnn_status xnn_reshape_rope_nthc_f16(
+  xnn_operator_t rope_op,
+  size_t batch_size,
+  size_t tokens,
+  size_t heads,
+  size_t channels,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_rope_nthc_f16(
+  xnn_operator_t rope_op,
+  const void* input,
+  const void* weights,
+  void* output);
+
+enum xnn_status xnn_create_rope_nthc_f32(
+  size_t max_tokens,
+  uint32_t flags,
+  xnn_operator_t* rope_op_out);
+
+enum xnn_status xnn_reshape_rope_nthc_f32(
+  xnn_operator_t rope_op,
+  size_t batch_size,
+  size_t tokens,
+  size_t heads,
+  size_t channels,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_rope_nthc_f32(
+  xnn_operator_t rope_op,
+  const float* input,
+  const float* weights,
+  float* output);
+
+// N: batch size
+// H: number of heads
+// T: tokens (sequence length)
+// C: channels (head dimension)
+enum xnn_status xnn_create_scaled_dot_product_attention_nhtc_f16(
+  enum xnn_attention_logits_cap_type cap_type,
+  const void* cap_params,
+  uint32_t flags,
+  xnn_operator_t* attention_op_out);
+
+enum xnn_status xnn_reshape_scaled_dot_product_attention_nhtc_f16(
+  xnn_operator_t attention_op,
+  size_t batch_size,
+  size_t query_heads,
+  // Number of tokens in query.
+  size_t query_tokens,
+  size_t key_value_heads,
+  // Number of tokens in key/value. For self-attention, this is same as tokens.
+  size_t key_value_tokens,
+  size_t query_key_channels,
+  size_t value_channels,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  pthreadpool_t threadpool);
+
+// Query is of dimension [batch_size, query_heads, query_tokens, channels].
+// Key and value are of dimension [batch_size, key_value_heads, key_value_tokens, channels].
+// Scale is of dimension [channels].
+// Mask is of dimension [query_tokens, key_value_tokens].
+enum xnn_status xnn_setup_scaled_dot_product_attention_nhtc_f16(
+  xnn_operator_t attention_op,
+  void* workspace,
+  const void* query,
+  const void* key,
+  const void* value,
+  const void* scale,
+  const void* mask,
+  void* output);
+
+// N: batch size
+// H: number of heads
+// T: tokens (sequence length)
+// C: channels (head dimension)
+enum xnn_status xnn_create_scaled_dot_product_attention_nhtc_f32(
+  enum xnn_attention_logits_cap_type cap_type,
+  const void* cap_params,
+  uint32_t flags,
+  xnn_operator_t* attention_op_out);
+
+enum xnn_status xnn_reshape_scaled_dot_product_attention_nhtc_f32(
+  xnn_operator_t attention_op,
+  size_t batch_size,
+  size_t query_heads,
+  // Number of tokens in query.
+  size_t query_tokens,
+  size_t key_value_heads,
+  // Number of tokens in key/value. For self-attention, this is same as tokens.
+  size_t key_value_tokens,
+  size_t query_key_channels,
+  size_t value_channels,
+  size_t* workspace_size,
+  size_t* workspace_alignment,
+  pthreadpool_t threadpool);
+
+// Query is of dimension [batch_size, query_heads, query_tokens, query_key_channels].
+// Key and value are of dimension [batch_size, key_value_heads, key_value_tokens, query_key_channels].
+// Scale is of dimension [query_key_channels].
+// Mask is of dimension [query_tokens, key_value_tokens].
+// Output is of dimension [batch_size, query_heads, query_tokens, value_channels].
+enum xnn_status xnn_setup_scaled_dot_product_attention_nhtc_f32(
+  xnn_operator_t attention_op,
+  void* workspace,
+  const float* query,
+  const float* key,
+  const float* value,
+  const float* scale,
+  const float* mask,
+  float* output);
+
+enum xnn_status xnn_create_sigmoid_nc_f16(
+  uint32_t flags,
+  xnn_operator_t* sigmoid_op_out);
+
+enum xnn_status xnn_reshape_sigmoid_nc_f16(
+  xnn_operator_t sigmoid_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_sigmoid_nc_f16(
+  xnn_operator_t sigmoid_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_sigmoid_nc_f32(
+  uint32_t flags,
+  xnn_operator_t* sigmoid_op_out);
+
+enum xnn_status xnn_reshape_sigmoid_nc_f32(
+  xnn_operator_t sigmoid_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_sigmoid_nc_f32(
+  xnn_operator_t sigmoid_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_run_sigmoid_nc_f32(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const float* input,
+  float* output,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_sigmoid_nc_qs8(
+  int8_t input_zero_point,
+  float input_scale,
+  int8_t output_zero_point,
+  float output_scale,
+  int8_t output_min,
+  int8_t output_max,
+  uint32_t flags,
+  xnn_operator_t* sigmoid_op_out);
+
+enum xnn_status xnn_reshape_sigmoid_nc_qs8(
+  xnn_operator_t sigmoid_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_sigmoid_nc_qs8(
+  xnn_operator_t sigmoid_op,
+  const int8_t* input,
+  int8_t* output);
+
+enum xnn_status xnn_create_sigmoid_nc_qu8(
+  uint8_t input_zero_point,
+  float input_scale,
+  uint8_t output_zero_point,
+  float output_scale,
+  uint8_t output_min,
+  uint8_t output_max,
+  uint32_t flags,
+  xnn_operator_t* sigmoid_op_out);
+
+enum xnn_status xnn_reshape_sigmoid_nc_qu8(
+  xnn_operator_t sigmoid_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_sigmoid_nc_qu8(
+  xnn_operator_t sigmoid_op,
+  const uint8_t* input,
+  uint8_t* output);
+
+enum xnn_status xnn_create_slice_nd_x16(
+  uint32_t flags,
+  xnn_operator_t* slice_op_out);
+
+enum xnn_status xnn_reshape_slice_nd_x16(
+  xnn_operator_t slice_op,
+  size_t num_dims,
+  const size_t* input_shape,
+  const size_t* offsets,
+  const size_t* sizes,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_slice_nd_x16(
+  xnn_operator_t slice_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_slice_nd_x32(
+  uint32_t flags,
+  xnn_operator_t* slice_op_out);
+
+enum xnn_status xnn_reshape_slice_nd_x32(
+  xnn_operator_t slice_op,
+  size_t num_dims,
+  const size_t* input_shape,
+  const size_t* offsets,
+  const size_t* sizes,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_slice_nd_x32(
+  xnn_operator_t slice_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_run_slice_nd_x32(
+  size_t num_dims,
+  const size_t* input_shape,
+  const size_t* offsets,
+  const size_t* sizes,
+  const void* input,
+  void* output,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_softmax_nc_f16(
+  uint32_t flags,
+  xnn_operator_t* softmax_op_out);
+
+enum xnn_status xnn_reshape_softmax_nc_f16(
+  xnn_operator_t softmax_op,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_softmax_nc_f16(
+  xnn_operator_t softmax_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_softmax_nc_f32(
+  uint32_t flags,
+  xnn_operator_t* softmax_op_out);
+
+enum xnn_status xnn_reshape_softmax_nc_f32(
+  xnn_operator_t softmax_op,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_softmax_nc_f32(
+  xnn_operator_t softmax_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_create_softmax_nc_qu8(
+  float input_scale,
+  uint8_t output_zero_point,
+  float output_scale,
+  uint32_t flags,
+  xnn_operator_t* softmax_op_out);
+
+enum xnn_status xnn_reshape_softmax_nc_qu8(
+  xnn_operator_t softmax_op,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_softmax_nc_qu8(
+  xnn_operator_t softmax_op,
+  const uint8_t* input,
+  uint8_t* output);
+
+enum xnn_status xnn_create_space_to_depth_nhwc_x16(
+  uint32_t block_size,
+  uint32_t flags,
+  xnn_operator_t* space_to_depth_op_out);
+
+enum xnn_status xnn_reshape_space_to_depth_nhwc_x16(
+  xnn_operator_t space_to_depth_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t input_channels,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  size_t* output_channels_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_space_to_depth_nhwc_x16(
+  xnn_operator_t space_to_depth_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_space_to_depth_nhwc_x32(
+  uint32_t block_size,
+  uint32_t flags,
+  xnn_operator_t* space_to_depth_op_out);
+
+enum xnn_status xnn_reshape_space_to_depth_nhwc_x32(
+  xnn_operator_t space_to_depth_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t input_channels,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  size_t* output_channels_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_space_to_depth_nhwc_x32(
+  xnn_operator_t space_to_depth_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_square_nc_f16(
+  uint32_t flags,
+  xnn_operator_t* square_op_out);
+
+enum xnn_status xnn_reshape_square_nc_f16(
+  xnn_operator_t square_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_square_nc_f16(
+  xnn_operator_t square_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_square_nc_f32(
+  uint32_t flags,
+  xnn_operator_t* square_op_out);
+
+enum xnn_status xnn_reshape_square_nc_f32(
+  xnn_operator_t square_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_square_nc_f32(
+  xnn_operator_t square_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_run_square_nc_f32(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const float* input,
+  float* output,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_square_root_nc_f16(
+  uint32_t flags,
+  xnn_operator_t* sqrt_op_out);
+
+enum xnn_status xnn_reshape_square_root_nc_f16(
+  xnn_operator_t sqrt_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_square_root_nc_f16(
+  xnn_operator_t sqrt_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_square_root_nc_f32(
+  uint32_t flags,
+  xnn_operator_t* sqrt_op_out);
+
+enum xnn_status xnn_reshape_square_root_nc_f32(
+  xnn_operator_t sqrt_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_square_root_nc_f32(
+  xnn_operator_t sqrt_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_run_square_root_nc_f32(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const float* input,
+  float* output,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_reciprocal_square_root_nc_f32(
+    uint32_t flags, xnn_operator_t* sqrt_op_out);
+
+enum xnn_status xnn_reshape_reciprocal_square_root_nc_f32(
+    xnn_operator_t sqrt_op, size_t batch_size, size_t channels,
+    size_t input_stride, size_t output_stride, pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_reciprocal_square_root_nc_f32(xnn_operator_t sqrt_op,
+                                                        const float* input,
+                                                        float* output);
+
+enum xnn_status xnn_run_reciprocal_square_root_nc_f32(
+    size_t channels, size_t input_stride, size_t output_stride,
+    size_t batch_size, const float* input, float* output, uint32_t flags,
+    pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_squared_difference_nd_f16(
+  uint32_t flags,
+  xnn_operator_t* squared_difference_op_out);
+
+enum xnn_status xnn_reshape_squared_difference_nd_f16(
+  xnn_operator_t squared_difference_op,
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_squared_difference_nd_f16(
+  xnn_operator_t squared_difference_op,
+  const void* input1,
+  const void* input2,
+  void* output);
+
+enum xnn_status xnn_create_squared_difference_nd_f32(
+  uint32_t flags,
+  xnn_operator_t* squared_difference_op_out);
+
+enum xnn_status xnn_reshape_squared_difference_nd_f32(
+  xnn_operator_t squared_difference_op,
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_squared_difference_nd_f32(
+  xnn_operator_t squared_difference_op,
+  const float* input1,
+  const float* input2,
+  float* output);
+
+enum xnn_status xnn_run_squared_difference_nd_f32(
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  const float* input1,
+  const float* input2,
+  float* output,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_subtract_nd_f16(
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* subtract_op_out);
+
+enum xnn_status xnn_reshape_subtract_nd_f16(
+  xnn_operator_t subtract_op,
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_subtract_nd_f16(
+  xnn_operator_t subtract_op,
+  const void* input1,
+  const void* input2,
+  void* output);
+
+enum xnn_status xnn_create_subtract_nd_f32(
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  xnn_operator_t* subtract_op_out);
+
+enum xnn_status xnn_reshape_subtract_nd_f32(
+  xnn_operator_t subtract_op,
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_subtract_nd_f32(
+  xnn_operator_t subtract_op,
+  const float* input1,
+  const float* input2,
+  float* output);
+
+enum xnn_status xnn_run_subtract_nd_f32(
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  const float* input1,
+  const float* input2,
+  float* output,
+  float output_min,
+  float output_max,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_subtract_nd_qs8(
+  int8_t input1_zero_point,
+  float input1_scale,
+  int8_t input2_zero_point,
+  float input2_scale,
+  int8_t output_zero_point,
+  float output_scale,
+  int8_t output_min,
+  int8_t output_max,
+  uint32_t flags,
+  xnn_operator_t* subtract_op_out);
+
+enum xnn_status xnn_reshape_subtract_nd_qs8(
+  xnn_operator_t subtract_op,
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_subtract_nd_qs8(
+  xnn_operator_t subtract_op,
+  const int8_t* input1,
+  const int8_t* input2,
+  int8_t* output);
+
+enum xnn_status xnn_run_subtract_nd_qs8(
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  int8_t input1_zero_point,
+  float input1_scale,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  int8_t input2_zero_point,
+  float input2_scale,
+  const int8_t* input1,
+  const int8_t* input2,
+  int8_t* output,
+  int8_t output_zero_point,
+  float output_scale,
+  int8_t output_min,
+  int8_t output_max,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_subtract_nd_qu8(
+  uint8_t input1_zero_point,
+  float input1_scale,
+  uint8_t input2_zero_point,
+  float input2_scale,
+  uint8_t output_zero_point,
+  float output_scale,
+  uint8_t output_min,
+  uint8_t output_max,
+  uint32_t flags,
+  xnn_operator_t* subtract_op_out);
+
+enum xnn_status xnn_reshape_subtract_nd_qu8(
+  xnn_operator_t subtract_op,
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_subtract_nd_qu8(
+  xnn_operator_t subtract_op,
+  const uint8_t* input1,
+  const uint8_t* input2,
+  uint8_t* output);
+
+enum xnn_status xnn_run_subtract_nd_qu8(
+  size_t num_input1_dims,
+  const size_t* input1_shape,
+  uint8_t input1_zero_point,
+  float input1_scale,
+  size_t num_input2_dims,
+  const size_t* input2_shape,
+  uint8_t input2_zero_point,
+  float input2_scale,
+  const uint8_t* input1,
+  const uint8_t* input2,
+  uint8_t* output,
+  uint8_t output_zero_point,
+  float output_scale,
+  uint8_t output_min,
+  uint8_t output_max,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_tanh_nc_f16(
+  uint32_t flags,
+  xnn_operator_t* tanh_op_out);
+
+enum xnn_status xnn_reshape_tanh_nc_f16(
+  xnn_operator_t tanh_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_tanh_nc_f16(
+  xnn_operator_t tanh_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_tanh_nc_f32(
+  uint32_t flags,
+  xnn_operator_t* tanh_op_out);
+
+enum xnn_status xnn_reshape_tanh_nc_f32(
+  xnn_operator_t tanh_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_tanh_nc_f32(
+  xnn_operator_t tanh_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_run_tanh_nc_f32(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const float* input,
+  float* output,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_tanh_nc_qs8(
+  int8_t input_zero_point,
+  float input_scale,
+  int8_t output_zero_point,
+  float output_scale,
+  int8_t output_min,
+  int8_t output_max,
+  uint32_t flags,
+  xnn_operator_t* tanh_op_out);
+
+enum xnn_status xnn_reshape_tanh_nc_qs8(
+  xnn_operator_t tanh_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_tanh_nc_qs8(
+  xnn_operator_t tanh_op,
+  const int8_t* input,
+  int8_t* output);
+
+enum xnn_status xnn_create_tanh_nc_qu8(
+  uint8_t input_zero_point,
+  float input_scale,
+  uint8_t output_zero_point,
+  float output_scale,
+  uint8_t output_min,
+  uint8_t output_max,
+  uint32_t flags,
+  xnn_operator_t* tanh_op_out);
+
+enum xnn_status xnn_reshape_tanh_nc_qu8(
+  xnn_operator_t tanh_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_tanh_nc_qu8(
+  xnn_operator_t tanh_op,
+  const uint8_t* input,
+  uint8_t* output);
+
+enum xnn_status xnn_create_transpose_nd_x8(
+  uint32_t flags,
+  xnn_operator_t* transpose_op_out);
+
+enum xnn_status xnn_reshape_transpose_nd_x8(
+  xnn_operator_t transpose_op,
+  size_t num_dims,
+  const size_t* input_shape,
+  const size_t* output_perm,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_transpose_nd_x8(
+  xnn_operator_t transpose_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_run_transpose_nd_x8(
+  const void* input,
+  void* output,
+  size_t num_dims,
+  const size_t* input_shape,
+  const size_t* output_perm,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_transpose_nd_x16(
+  uint32_t flags,
+  xnn_operator_t* transpose_op_out);
+
+enum xnn_status xnn_reshape_transpose_nd_x16(
+  xnn_operator_t transpose_op,
+  size_t num_dims,
+  const size_t* input_shape,
+  const size_t* output_perm,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_transpose_nd_x16(
+  xnn_operator_t transpose_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_run_transpose_nd_x16(
+  const void* input,
+  void* output,
+  size_t num_dims,
+  const size_t* input_shape,
+  const size_t* output_perm,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_transpose_nd_x32(
+  uint32_t flags,
+  xnn_operator_t* transpose_op_out);
+
+enum xnn_status xnn_reshape_transpose_nd_x32(
+  xnn_operator_t transpose_op,
+  size_t num_dims,
+  const size_t* input_shape,
+  const size_t* output_perm,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_transpose_nd_x32(
+  xnn_operator_t transpose_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_run_transpose_nd_x32(
+  const void* input,
+  void* output,
+  size_t num_dims,
+  const size_t* input_shape,
+  const size_t* output_perm,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_transpose_nd_x64(
+  uint32_t flags,
+  xnn_operator_t* transpose_op_out);
+
+enum xnn_status xnn_reshape_transpose_nd_x64(
+  xnn_operator_t transpose_op,
+  size_t num_dims,
+  const size_t* input_shape,
+  const size_t* output_perm,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_transpose_nd_x64(
+  xnn_operator_t transpose_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_run_transpose_nd_x64(
+  const void* input,
+  void* output,
+  size_t num_dims,
+  const size_t* input_shape,
+  const size_t* output_perm,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_truncation_nc_f16(
+  uint32_t flags,
+  xnn_operator_t* truncation_op_out);
+
+enum xnn_status xnn_reshape_truncation_nc_f16(
+  xnn_operator_t truncation_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_truncation_nc_f16(
+  xnn_operator_t truncation_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_truncation_nc_f32(
+  uint32_t flags,
+  xnn_operator_t* truncation_op_out);
+
+enum xnn_status xnn_reshape_truncation_nc_f32(
+  xnn_operator_t truncation_op,
+  size_t batch_size,
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_truncation_nc_f32(
+  xnn_operator_t truncation_op,
+  const float* input,
+  float* output);
+
+enum xnn_status xnn_run_truncation_nc_f32(
+  size_t channels,
+  size_t input_stride,
+  size_t output_stride,
+  size_t batch_size,
+  const float* input,
+  float* output,
+  uint32_t flags,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_create_unpooling2d_nhwc_x32(
+  uint32_t input_padding_top,
+  uint32_t input_padding_right,
+  uint32_t input_padding_bottom,
+  uint32_t input_padding_left,
+  uint32_t pooling_height,
+  uint32_t pooling_width,
+  size_t channels,
+  size_t input_pixel_stride,
+  size_t output_pixel_stride,
+  uint32_t flags,
+  xnn_operator_t* unpooling_op_out);
+
+enum xnn_status xnn_reshape_unpooling2d_nhwc_x32(
+  xnn_operator_t unpooling_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_unpooling2d_nhwc_x32(
+  xnn_operator_t unpooling_op,
+  const void* input,
+  const uint32_t* index,
+  void* output);
+
+enum xnn_status xnn_create_slice_nd_x8(
+  uint32_t flags,
+  xnn_operator_t* slice_op_out);
+
+enum xnn_status xnn_reshape_slice_nd_x8(
+  xnn_operator_t slice_op,
+  size_t num_dims,
+  const size_t* input_shape,
+  const size_t* offsets,
+  const size_t* sizes,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_slice_nd_x8(
+  xnn_operator_t slice_op,
+  const void* input,
+  void* output);
+
+enum xnn_status xnn_create_space_to_depth_nhwc_x8(
+  uint32_t block_size,
+  uint32_t flags,
+  xnn_operator_t* space_to_depth_op_out);
+
+enum xnn_status xnn_reshape_space_to_depth_nhwc_x8(
+  xnn_operator_t space_to_depth_op,
+  size_t batch_size,
+  size_t input_height,
+  size_t input_width,
+  size_t input_channels,
+  size_t* output_height_out,
+  size_t* output_width_out,
+  size_t* output_channels_out,
+  pthreadpool_t threadpool);
+
+enum xnn_status xnn_setup_space_to_depth_nhwc_x8(
+  xnn_operator_t space_to_depth_op,
+  const void* input,
+  void* output);
+
+#ifdef __cplusplus
+}  // extern "C"
+#endif
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec776b9047500742154e8ab4e8e32b47e6cdfaa7
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/__init__.py
@@ -0,0 +1,294 @@
+# mypy: allow-untyped-defs
+import warnings
+from contextlib import contextmanager
+from typing import Any, Iterator
+
+import torch._C
+
+# These are imported so users can access them from the `torch.jit` module
+from torch._jit_internal import (
+    _Await,
+    _drop,
+    _IgnoreContextManager,
+    _isinstance,
+    _overload,
+    _overload_method,
+    export,
+    Final,
+    Future,
+    ignore,
+    is_scripting,
+    unused,
+)
+from torch.jit._async import fork, wait
+from torch.jit._await import _awaitable, _awaitable_nowait, _awaitable_wait
+from torch.jit._decomposition_utils import _register_decomposition
+from torch.jit._freeze import freeze, optimize_for_inference, run_frozen_optimizations
+from torch.jit._fuser import (
+    fuser,
+    last_executed_optimized_graph,
+    optimized_execution,
+    set_fusion_strategy,
+)
+from torch.jit._ir_utils import _InsertPoint
+from torch.jit._script import (
+    _ScriptProfile,
+    _unwrap_optional,
+    Attribute,
+    CompilationUnit,
+    interface,
+    RecursiveScriptClass,
+    RecursiveScriptModule,
+    script,
+    script_method,
+    ScriptFunction,
+    ScriptModule,
+    ScriptWarning,
+)
+from torch.jit._serialization import (
+    jit_module_from_flatbuffer,
+    load,
+    save,
+    save_jit_module_to_flatbuffer,
+)
+from torch.jit._trace import (
+    _flatten,
+    _get_trace_graph,
+    _script_if_tracing,
+    _unique_state_dict,
+    is_tracing,
+    ONNXTracedModule,
+    TopLevelTracedModule,
+    trace,
+    trace_module,
+    TracedModule,
+    TracerWarning,
+    TracingCheckError,
+)
+from torch.utils import set_module
+
+
+__all__ = [
+    "Attribute",
+    "CompilationUnit",
+    "Error",
+    "Future",
+    "ScriptFunction",
+    "ScriptModule",
+    "annotate",
+    "enable_onednn_fusion",
+    "export",
+    "export_opnames",
+    "fork",
+    "freeze",
+    "interface",
+    "ignore",
+    "isinstance",
+    "load",
+    "onednn_fusion_enabled",
+    "optimize_for_inference",
+    "save",
+    "script",
+    "script_if_tracing",
+    "set_fusion_strategy",
+    "strict_fusion",
+    "trace",
+    "trace_module",
+    "unused",
+    "wait",
+]
+
+# For backwards compatibility
+_fork = fork
+_wait = wait
+_set_fusion_strategy = set_fusion_strategy
+
+
+def export_opnames(m):
+    r"""
+    Generate new bytecode for a Script module.
+
+    Returns what the op list would be for a Script Module based off the current code base.
+
+    If you have a LiteScriptModule and want to get the currently present
+    list of ops call _export_operator_list instead.
+    """
+    return torch._C._export_opnames(m._c)
+
+
+# torch.jit.Error
+Error = torch._C.JITException
+set_module(Error, "torch.jit")
+# This is not perfect but works in common cases
+Error.__name__ = "Error"
+Error.__qualname__ = "Error"
+
+
+# for use in python if using annotate
+def annotate(the_type, the_value):
+    """Use to give type of `the_value` in TorchScript compiler.
+
+    This method is a pass-through function that returns `the_value`, used to hint TorchScript
+    compiler the type of `the_value`. It is a no-op when running outside of TorchScript.
+
+    Though TorchScript can infer correct type for most Python expressions, there are some cases where
+    type inference can be wrong, including:
+
+    - Empty containers like `[]` and `{}`, which TorchScript assumes to be container of `Tensor`
+    - Optional types like `Optional[T]` but assigned a valid value of type `T`, TorchScript would assume
+      it is type `T` rather than `Optional[T]`
+
+    Note that `annotate()` does not help in `__init__` method of `torch.nn.Module` subclasses because it
+    is executed in eager mode. To annotate types of `torch.nn.Module` attributes,
+    use :meth:`~torch.jit.Attribute` instead.
+
+    Example:
+
+    .. testcode::
+
+        import torch
+        from typing import Dict
+
+        @torch.jit.script
+        def fn():
+            # Telling TorchScript that this empty dictionary is a (str -> int) dictionary
+            # instead of default dictionary type of (str -> Tensor).
+            d = torch.jit.annotate(Dict[str, int], {})
+
+            # Without `torch.jit.annotate` above, following statement would fail because of
+            # type mismatch.
+            d["name"] = 20
+
+    .. testcleanup::
+
+        del fn
+
+    Args:
+        the_type: Python type that should be passed to TorchScript compiler as type hint for `the_value`
+        the_value: Value or expression to hint type for.
+
+    Returns:
+        `the_value` is passed back as return value.
+    """
+    return the_value
+
+
+def script_if_tracing(fn):
+    """
+    Compiles ``fn`` when it is first called during tracing.
+
+    ``torch.jit.script`` has a non-negligible start up time when it is first called due to
+    lazy-initializations of many compiler builtins. Therefore you should not use
+    it in library code. However, you may want to have parts of your library work
+    in tracing even if they use control flow. In these cases, you should use
+    ``@torch.jit.script_if_tracing`` to substitute for
+    ``torch.jit.script``.
+
+    Args:
+        fn: A function to compile.
+
+    Returns:
+        If called during tracing, a :class:`ScriptFunction` created by `torch.jit.script` is returned.
+        Otherwise, the original function `fn` is returned.
+    """
+    return _script_if_tracing(fn)
+
+
+# for torch.jit.isinstance
+def isinstance(obj, target_type):
+    """
+    Provide container type refinement in TorchScript.
+
+    It can refine parameterized containers of the List, Dict, Tuple, and Optional types. E.g. ``List[str]``,
+    ``Dict[str, List[torch.Tensor]]``, ``Optional[Tuple[int,str,int]]``. It can also
+    refine basic types such as bools and ints that are available in TorchScript.
+
+    Args:
+        obj: object to refine the type of
+        target_type: type to try to refine obj to
+    Returns:
+        ``bool``: True if obj was successfully refined to the type of target_type,
+            False otherwise with no new type refinement
+
+
+    Example (using ``torch.jit.isinstance`` for type refinement):
+    .. testcode::
+
+        import torch
+        from typing import Any, Dict, List
+
+        class MyModule(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+
+            def forward(self, input: Any): # note the Any type
+                if torch.jit.isinstance(input, List[torch.Tensor]):
+                    for t in input:
+                        y = t.clamp(0, 0.5)
+                elif torch.jit.isinstance(input, Dict[str, str]):
+                    for val in input.values():
+                        print(val)
+
+        m = torch.jit.script(MyModule())
+        x = [torch.rand(3,3), torch.rand(4,3)]
+        m(x)
+        y = {"key1":"val1","key2":"val2"}
+        m(y)
+    """
+    return _isinstance(obj, target_type)
+
+
+class strict_fusion:
+    """
+    Give errors if not all nodes have been fused in inference, or symbolically differentiated in training.
+
+    Example:
+    Forcing fusion of additions.
+
+    .. code-block:: python
+
+        @torch.jit.script
+        def foo(x):
+            with torch.jit.strict_fusion():
+                return x + x + x
+
+    """
+
+    def __init__(self) -> None:
+        if not torch._jit_internal.is_scripting():
+            warnings.warn("Only works in script mode")
+
+    def __enter__(self):
+        pass
+
+    def __exit__(self, type: Any, value: Any, tb: Any) -> None:
+        pass
+
+
+# Context manager for globally hiding source ranges when printing graphs.
+# Note that these functions are exposed to Python as static members of the
+# Graph class, so mypy checks need to be skipped.
+@contextmanager
+def _hide_source_ranges() -> Iterator[None]:
+    old_enable_source_ranges = torch._C.Graph.global_print_source_ranges  # type: ignore[attr-defined]
+    try:
+        torch._C.Graph.set_global_print_source_ranges(False)  # type: ignore[attr-defined]
+        yield
+    finally:
+        torch._C.Graph.set_global_print_source_ranges(old_enable_source_ranges)  # type: ignore[attr-defined]
+
+
+def enable_onednn_fusion(enabled: bool):
+    """Enable or disables onednn JIT fusion based on the parameter `enabled`."""
+    torch._C._jit_set_llga_enabled(enabled)
+
+
+def onednn_fusion_enabled():
+    """Return whether onednn JIT fusion is enabled."""
+    return torch._C._jit_llga_enabled()
+
+
+del Any
+
+if not torch._C._jit_init():
+    raise RuntimeError("JIT initialization failed")
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_async.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_async.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ccde01d4a425632fa942df18300110620c0248a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_async.py
@@ -0,0 +1,102 @@
+# mypy: allow-untyped-defs
+"""Async API.
+
+This module contains the API for parallelism in TorchScript, notably:
+    * torch.jit.fork
+    * torch.jit.wait
+
+This is not intended to be imported directly; please use the exposed
+functionalities in `torch.jit`.
+"""
+
+import torch
+from torch._jit_internal import Future
+from torch.jit._builtins import _register_builtin
+from torch.utils import set_module
+
+
+set_module(Future, "torch.jit")
+
+
+def fork(func, *args, **kwargs):
+    r"""
+    Create an asynchronous task executing `func` and a reference to the value of the result of this execution.
+
+    `fork` will return immediately, so the return value of `func` may not have been computed yet. To force completion
+    of the task and access the return value invoke `torch.jit.wait` on the Future. `fork` invoked
+    with a `func` which returns `T` is typed as `torch.jit.Future[T]`. `fork` calls can be arbitrarily
+    nested, and may be invoked with positional and keyword arguments.
+    Asynchronous execution will only occur when run in TorchScript. If run in pure python,
+    `fork` will not execute in parallel. `fork` will also not execute in parallel when invoked
+    while tracing, however the `fork` and `wait` calls will be captured in the exported IR Graph.
+
+    .. warning::
+        `fork` tasks will execute non-deterministically. We recommend only spawning
+        parallel fork tasks for pure functions that do not modify their inputs,
+        module attributes, or global state.
+
+    Args:
+        func (callable or torch.nn.Module):  A Python function or `torch.nn.Module`
+            that will be invoked. If executed in TorchScript, it will execute asynchronously,
+            otherwise it will not. Traced invocations of fork will be captured in the IR.
+        ``*args``, ``**kwargs``: arguments to invoke `func` with.
+    Returns:
+        `torch.jit.Future[T]`: a reference to the execution of `func`. The value `T`
+        can only be accessed by forcing completion of `func` through `torch.jit.wait`.
+
+    Example (fork a free function):
+
+    .. code-block:: python
+
+        import torch
+        from torch import Tensor
+        def foo(a : Tensor, b : int) -> Tensor:
+            return a + b
+        def bar(a):
+            fut : torch.jit.Future[Tensor] = torch.jit.fork(foo, a, b=2)
+            return torch.jit.wait(fut)
+        script_bar = torch.jit.script(bar)
+        input = torch.tensor(2)
+        # only the scripted version executes asynchronously
+        assert script_bar(input) == bar(input)
+        # trace is not run asynchronously, but fork is captured in IR
+        graph = torch.jit.trace(bar, (input,)).graph
+        assert "fork" in str(graph)
+
+    Example (fork a module method):
+
+    .. code-block:: python
+
+        import torch
+        from torch import Tensor
+        class AddMod(torch.nn.Module):
+            def forward(self, a: Tensor, b : int):
+                return a + b
+        class Mod(torch.nn.Module):
+            def __init__(self) -> None:
+                super(self).__init__()
+                self.mod = AddMod()
+            def forward(self, input):
+                fut = torch.jit.fork(self.mod, a, b=2)
+                return torch.jit.wait(fut)
+        input = torch.tensor(2)
+        mod = Mod()
+        assert mod(input) == torch.jit.script(mod).forward(input)
+    """
+    return torch._C.fork(func, *args, **kwargs)
+
+
+def wait(future):
+    r"""
+    Force completion of a `torch.jit.Future[T]` asynchronous task, returning the result of the task.
+
+    See :func:`~fork` for docs and examples.
+    Args:
+        future (torch.jit.Future[T]): an asynchronous task reference, created through `torch.jit.fork`
+    Returns:
+        `T`: the return value of the completed task
+    """
+    return torch._C.wait(future)
+
+
+_register_builtin(wait, "aten::wait")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_await.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_await.py
new file mode 100644
index 0000000000000000000000000000000000000000..b00da7b3384b7a263aeab613b3e613c4bd3c3667
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_await.py
@@ -0,0 +1,27 @@
+# mypy: allow-untyped-defs
+import torch
+from torch._jit_internal import _Await
+from torch.jit._builtins import _register_builtin
+from torch.utils import set_module
+
+
+set_module(_Await, "torch.jit")
+
+
+def _awaitable(func, *args, **kwargs):
+    r"""Create Await object that will call specified functioni with specified args, when it is requested for the result."""
+    return torch._C._awaitable(func, *args, **kwargs)
+
+
+def _awaitable_wait(aw):
+    r"""Request await the result of execution, if Await is not completed yet, the func will be called immediately."""
+    return torch._C._awaitable_wait(aw)
+
+
+def _awaitable_nowait(o):
+    r"""Create completed Await with specified result."""
+    return torch._C._awaitable_nowait(o)
+
+
+_register_builtin(_awaitable_wait, "prim::awaitable_wait")
+_register_builtin(_awaitable_nowait, "prim::awaitable_nowait")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_builtins.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_builtins.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3edc688d9e14b33eccc146c91f171f90af8ab26
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_builtins.py
@@ -0,0 +1,193 @@
+# mypy: allow-untyped-defs
+import cmath
+import math
+import warnings
+from collections import OrderedDict
+from typing import Dict, Optional
+
+import torch
+import torch.backends.cudnn as cudnn
+from torch.nn.modules.utils import (
+    _list_with_default,
+    _pair,
+    _quadruple,
+    _single,
+    _triple,
+)
+
+
+_builtin_table: Optional[Dict[int, str]] = None
+
+_modules_containing_builtins = (torch, torch._C._nn, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._sparse, torch._C._special)  # type: ignore[attr-defined] # noqa: B950
+
+_builtin_ops = [
+    # Pairs of (function, op_name)
+    (_pair, "aten::_pair"),
+    (_quadruple, "aten::_quadruple"),
+    (_single, "aten::_single"),
+    (_triple, "aten::_triple"),
+    (_list_with_default, "aten::list_with_default"),
+    (OrderedDict, "aten::dict"),
+    (dict, "aten::dict"),
+    (cudnn.is_acceptable, "aten::cudnn_is_acceptable"),
+    (math.ceil, "aten::ceil"),
+    (math.copysign, "aten::copysign"),
+    (math.erf, "aten::erf"),
+    (math.erfc, "aten::erfc"),
+    (math.exp, "aten::exp"),
+    (math.expm1, "aten::expm1"),
+    (math.fabs, "aten::fabs"),
+    (math.floor, "aten::floor"),
+    (math.gamma, "aten::gamma"),
+    (math.lgamma, "aten::lgamma"),
+    (math.log, "aten::log"),
+    (math.log10, "aten::log10"),
+    (math.log1p, "aten::log1p"),
+    (math.pow, "aten::pow"),
+    (math.sqrt, "aten::sqrt"),
+    (math.isnan, "aten::isnan"),
+    (math.asinh, "aten::asinh"),
+    (math.atanh, "aten::atanh"),
+    (math.cosh, "aten::cosh"),
+    (math.sinh, "aten::sinh"),
+    (math.tanh, "aten::tanh"),
+    (math.acos, "aten::acos"),
+    (math.asin, "aten::asin"),
+    (math.atan, "aten::atan"),
+    (math.atan2, "aten::atan2"),
+    (math.cos, "aten::cos"),
+    (math.sin, "aten::sin"),
+    (math.tan, "aten::tan"),
+    (math.asinh, "aten::asinh"),
+    (math.atanh, "aten::atanh"),
+    (math.acosh, "aten::acosh"),
+    (math.fmod, "aten::fmod"),
+    (math.modf, "aten::modf"),
+    (math.factorial, "aten::factorial"),
+    (math.frexp, "aten::frexp"),
+    (math.isinf, "aten::isinf"),
+    (math.degrees, "aten::degrees"),
+    (math.radians, "aten::radians"),
+    (cmath.isnan, "aten::isnan"),
+    (cmath.isfinite, "aten::isfinite"),
+    (cmath.isinf, "aten::isinf"),
+    (cmath.phase, "aten::angle"),
+    (cmath.rect, "aten::polar"),
+    (cmath.log, "aten::log"),
+    (cmath.log10, "aten::log10"),
+    (cmath.sqrt, "aten::sqrt"),
+    (cmath.exp, "aten::exp"),
+    (cmath.sin, "aten::sin"),
+    (cmath.tan, "aten::tan"),
+    (cmath.cos, "aten::cos"),
+    (cmath.asin, "aten::asin"),
+    (cmath.acos, "aten::acos"),
+    (cmath.atan, "aten::atan"),
+    (cmath.sinh, "aten::sinh"),
+    (cmath.cosh, "aten::cosh"),
+    (cmath.tanh, "aten::tanh"),
+    (cmath.asinh, "aten::asinh"),
+    (cmath.acosh, "aten::acosh"),
+    (cmath.atanh, "aten::atanh"),
+    (math.ldexp, "aten::ldexp"),
+    (torch._assert, "aten::_assert"),
+    (torch.autograd.grad, "aten::grad"),
+    (torch.autograd.backward, "aten::backward"),
+    (torch._C._infer_size, "aten::_infer_size"),
+    (torch.nn.functional._no_grad_embedding_renorm_, "aten::_no_grad_embedding_renorm_"),  # type: ignore[attr-defined]
+    (torch.nn.functional.assert_int_or_pair, "aten::_assert_int_or_pair"),
+    (torch.nn.init._no_grad_fill_, "aten::_no_grad_fill_"),
+    (torch.nn.init._no_grad_normal_, "aten::_no_grad_normal_"),
+    (torch.nn.init._no_grad_uniform_, "aten::_no_grad_uniform_"),
+    (torch.nn.init._no_grad_zero_, "aten::_no_grad_zero_"),
+    (torch._C._get_tracing_state, "aten::_get_tracing_state"),
+    (torch._C._get_cpu_capability, "aten::_get_cpu_capability"),
+    (warnings.warn, "aten::warn"),
+    (torch._VF.stft, "aten::stft"),  # type: ignore[attr-defined]
+    (torch._VF.istft, "aten::istft"),  # type: ignore[attr-defined]
+    (torch._VF.cdist, "aten::cdist"),  # type: ignore[attr-defined]
+    (torch._VF.norm, "aten::norm"),  # type: ignore[attr-defined]
+    (torch._VF.unique_dim, "aten::unique_dim"),
+    (torch._VF.unique_consecutive, "aten::unique_consecutive"),  # type: ignore[attr-defined]
+    (torch._VF.nuclear_norm, "aten::nuclear_norm"),
+    (torch._VF.frobenius_norm, "aten::frobenius_norm"),
+    (torch._VF.tensordot, "aten::tensordot"),  # type: ignore[attr-defined]
+]
+
+# ops in torch.functional are bound to torch
+# in these cases, we want to resolve the function to their python implementation
+# instead looking up a builtin "aten::" schema
+
+
+def _gen_torch_functional_registered_ops():
+    # eventually ops should encompass all of torch/functional.py, (torch.functional.__all__)
+    # but we are currently only able to compile some of the functions. additionally,
+    # some functions directly map to their aten:: implementations.
+    # TODO: add support for more ops
+    ops = [
+        "stft",
+        "istft",
+        "lu",
+        "cdist",
+        "norm",
+        "unique",
+        "unique_consecutive",
+        "tensordot",
+    ]
+    return {getattr(torch.functional, name) for name in ops}
+
+
+_functional_registered_ops = _gen_torch_functional_registered_ops()
+
+
+def _is_special_functional_bound_op(fn):
+    return fn in _functional_registered_ops
+
+
+# lazily built to ensure the correct initialization order
+def _get_builtin_table():
+    global _builtin_table
+    if _builtin_table is not None:
+        return _builtin_table
+    _builtin_table = {}
+
+    def register_all(mod):
+        for name in dir(mod):
+            v = getattr(mod, name)
+            if (
+                callable(v)
+                and not _is_special_functional_bound_op(v)
+                and v is not torch.no_grad
+                and v is not torch.autocast
+            ):
+                # Fixup inconsistency in segment_reduce
+                if name == "_segment_reduce":
+                    name = name[1:]
+                _builtin_ops.append((v, "aten::" + name))
+
+    for mod in _modules_containing_builtins:
+        register_all(mod)
+
+    _builtin_ops.append((math.gcd, "aten::gcd"))
+    _builtin_ops.append((math.isfinite, "aten::isfinite"))
+    _builtin_ops.append((math.remainder, "aten::mathremainder"))  # type: ignore[attr-defined]
+
+    import torch.distributed.autograd as dist_autograd
+
+    if dist_autograd.is_available():
+        _builtin_ops.append((dist_autograd.get_gradients, "aten::get_gradients"))
+        _builtin_ops.append((dist_autograd.backward, "aten::dist_backward"))
+
+    # populate the _builtin_table from _builtin_ops
+    for builtin, aten_op in _builtin_ops:
+        _builtin_table[id(builtin)] = aten_op
+
+    return _builtin_table
+
+
+def _register_builtin(fn, op):
+    _get_builtin_table()[id(fn)] = op
+
+
+def _find_builtin(fn):
+    return _get_builtin_table().get(id(fn))
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_check.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_check.py
new file mode 100644
index 0000000000000000000000000000000000000000..f708ee87f3089a199232a353572cdd84258e21c2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_check.py
@@ -0,0 +1,249 @@
+# mypy: allow-untyped-defs
+import ast
+import inspect
+import textwrap
+import warnings
+
+import torch
+
+
+class AttributeTypeIsSupportedChecker(ast.NodeVisitor):
+    """Check the ``__init__`` method of a given ``nn.Module``.
+
+    It ensures that all instance-level attributes can be properly initialized.
+
+    Specifically, we do type inference based on attribute values...even
+    if the attribute in question has already been typed using
+    Python3-style annotations or ``torch.jit.annotate``. This means that
+    setting an instance-level attribute to ``[]`` (for ``List``),
+    ``{}`` for ``Dict``), or ``None`` (for ``Optional``) isn't enough
+    information for us to properly initialize that attribute.
+
+    An object of this class can walk a given ``nn.Module``'s AST and
+    determine if it meets our requirements or not.
+
+    Known limitations
+    1. We can only check the AST nodes for certain constructs; we can't
+    ``eval`` arbitrary expressions. This means that function calls,
+    class instantiations, and complex expressions that resolve to one of
+    the "empty" values specified above will NOT be flagged as
+    problematic.
+    2. We match on string literals, so if the user decides to use a
+    non-standard import (e.g. `from typing import List as foo`), we
+    won't catch it.
+
+    Example:
+        .. code-block:: python
+
+            class M(torch.nn.Module):
+                def fn(self):
+                    return []
+
+                def __init__(self) -> None:
+                    super().__init__()
+                    self.x: List[int] = []
+
+                def forward(self, x: List[int]):
+                    self.x = x
+                    return 1
+
+        The above code will pass the ``AttributeTypeIsSupportedChecker``
+        check since we have a function call in ``__init__``. However,
+        it will still fail later with the ``RuntimeError`` "Tried to set
+        nonexistent attribute: x. Did you forget to initialize it in
+        __init__()?".
+
+    Args:
+        nn_module - The instance of ``torch.nn.Module`` whose
+            ``__init__`` method we wish to check
+    """
+
+    def check(self, nn_module: torch.nn.Module) -> None:
+        source_lines = inspect.getsource(nn_module.__class__.__init__)
+
+        # Ignore comments no matter the indentation
+        def is_useless_comment(line):
+            line = line.strip()
+            return line.startswith("#") and not line.startswith("# type:")
+
+        source_lines = "\n".join(
+            [l for l in source_lines.split("\n") if not is_useless_comment(l)]
+        )
+
+        # This AST only contains the `__init__` method of the nn.Module
+        init_ast = ast.parse(textwrap.dedent(source_lines))
+
+        # Get items annotated in the class body
+        self.class_level_annotations = list(nn_module.__annotations__.keys())
+
+        # Flag for later
+        self.visiting_class_level_ann = False
+
+        self.visit(init_ast)
+
+    def _is_empty_container(self, node: ast.AST, ann_type: str) -> bool:
+        if ann_type == "List":
+            # Assigning `[]` to a `List` type gives you a Node where
+            # value=List(elts=[], ctx=Load())
+            if not isinstance(node, ast.List):
+                return False
+            if node.elts:
+                return False
+        elif ann_type == "Dict":
+            # Assigning `{}` to a `Dict` type gives you a Node where
+            # value=Dict(keys=[], values=[])
+            if not isinstance(node, ast.Dict):
+                return False
+            if node.keys:
+                return False
+        elif ann_type == "Optional":
+            # Assigning `None` to an `Optional` type gives you a
+            # Node where value=Constant(value=None, kind=None)
+            if not isinstance(node, ast.Constant):
+                return False
+            if node.value:  # type: ignore[attr-defined]
+                return False
+
+        return True
+
+    def visit_Assign(self, node):
+        """Store assignment state when assigning to a Call Node.
+
+        If we're visiting a Call Node (the right-hand side of an
+        assignment statement), we won't be able to check the variable
+        that we're assigning to (the left-hand side of an assignment).
+        Because of this, we need to store this state in visitAssign.
+        (Luckily, we only have to do this if we're assigning to a Call
+        Node, i.e. ``torch.jit.annotate``. If we're using normal Python
+        annotations, we'll be visiting an AnnAssign Node, which has its
+        target built in.)
+        """
+        try:
+            if (
+                isinstance(node.value, ast.Call)
+                and node.targets[0].attr in self.class_level_annotations
+            ):
+                self.visiting_class_level_ann = True
+        except AttributeError:
+            return
+        self.generic_visit(node)
+        self.visiting_class_level_ann = False
+
+    def visit_AnnAssign(self, node):
+        """Visit an AnnAssign node in an ``nn.Module``'s ``__init__`` method.
+
+        It checks if it conforms to our attribute annotation rules."""
+        # If we have a local variable
+        try:
+            if node.target.value.id != "self":
+                return
+        except AttributeError:
+            return
+
+        # If we have an attribute that's already been annotated at the
+        # class level
+        if node.target.attr in self.class_level_annotations:
+            return
+
+        # TODO @ansley: add `Union` once landed
+
+        # NB: Even though `Tuple` is a "container", we don't want to
+        # check for it here. `Tuple` functions as an type with an
+        # "infinite" number of subtypes, in the sense that you can have
+        # `Tuple[())]`, `Tuple[T1]`, `Tuple[T2]`, `Tuple[T1, T2]`,
+        # `Tuple[T2, T1]` and so on, and none of these subtypes can be
+        # used in place of the other. Therefore, assigning an empty
+        # tuple in `__init__` CORRECTLY means that that variable
+        # cannot be reassigned later to a non-empty tuple. Same
+        # deal with `NamedTuple`
+
+        containers = {"List", "list", "Dict", "dict", "Optional"}
+
+        # If we're not evaluating one of the specified problem types
+        try:
+            if node.annotation.value.id not in containers:
+                return
+        except AttributeError:
+            # To evaluate a base type (`str`, `int`, etc.), we would
+            # have needed to get the name through `node.annotation.id`
+            # instead of `node.annotation.value.id`. Seems that we're
+            # not evaluating one of our "containers"
+            return
+
+        # Check if the assigned variable is empty
+        ann_type = node.annotation.value.id
+        if not self._is_empty_container(node.value, ann_type):
+            return
+
+        warnings.warn(
+            "The TorchScript type system doesn't support "
+            "instance-level annotations on empty non-base "
+            "types in `__init__`. Instead, either 1) use a "
+            "type annotation in the class body, or 2) wrap "
+            "the type in `torch.jit.Attribute`."
+        )
+
+    def visit_Call(self, node):
+        """Determine if a Call node is 'torch.jit.annotate' in __init__.
+
+        Visit a Call node in an ``nn.Module``'s ``__init__``
+        method and determine if it's ``torch.jit.annotate``. If so,
+        see if it conforms to our attribute annotation rules.
+        """
+        # If we have an attribute that's already been annotated at the
+        # class level
+        if self.visiting_class_level_ann:
+            return
+
+        # If this isn't a call to `torch.jit.annotate`
+        try:
+            if (
+                node.func.value.value.id != "torch"
+                or node.func.value.attr != "jit"
+                or node.func.attr != "annotate"
+            ):
+                self.generic_visit(node)
+            elif (
+                node.func.value.value.id != "jit" or node.func.value.attr != "annotate"
+            ):
+                self.generic_visit(node)
+        except AttributeError:
+            # Looks like we didn't even have the right node structure
+            # to check for `torch.jit.annotate` in the first place
+            self.generic_visit(node)
+
+        # Invariant: we have a `torch.jit.annotate` or a
+        # `torch.annotate` call
+
+        # A Call Node for `torch.jit.annotate` should have an `args`
+        # list of length 2 where args[0] represents the annotation and
+        # args[1] represents the actual value
+        if len(node.args) != 2:
+            return
+
+        if not isinstance(node.args[0], ast.Subscript):
+            return
+
+        # See notes in `visit_AnnAssign` r.e. containers
+
+        containers = {"List", "Dict", "Optional"}
+
+        try:
+            ann_type = node.args[0].value.id  # type: ignore[attr-defined]
+        except AttributeError:
+            return
+
+        if ann_type not in containers:
+            return
+
+        # Check if the assigned variable is empty
+        if not self._is_empty_container(node.args[1], ann_type):
+            return
+
+        warnings.warn(
+            "The TorchScript type system doesn't support "
+            "instance-level annotations on empty non-base "
+            "types in `__init__`. Instead, either 1) use a "
+            "type annotation in the class body, or 2) wrap "
+            "the type in `torch.jit.Attribute`."
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_dataclass_impls.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_dataclass_impls.py
new file mode 100644
index 0000000000000000000000000000000000000000..2dc1dfba076f254c9fb4e4ca20fb7acff3246a7d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_dataclass_impls.py
@@ -0,0 +1,190 @@
+# mypy: allow-untyped-defs
+# Functions for synthesizing magic methods for JIT-compiled dataclasses
+import ast
+import dataclasses
+import inspect
+import os
+from functools import partial
+from typing import Callable, Dict, List
+
+from torch._jit_internal import FAKE_FILENAME_PREFIX, is_optional
+from torch._sources import ParsedDef, SourceContext
+
+
+def _get_fake_filename(cls, method_name):
+    return os.path.join(FAKE_FILENAME_PREFIX, cls.__name__, method_name)
+
+
+def compose_fn(cls, name: str, body_lines: List[str], signature: str) -> ParsedDef:
+    body = "\n".join(f"  {b}" for b in body_lines)
+    decl = f"def {name}{signature}:\n{body}"
+
+    # Parse the function declaration
+    try:
+        py_ast = ast.parse(decl)
+    except SyntaxError as e:
+        # This should only happen if there's some unforeseeable change
+        # in the dataclasses module that makes our synthesized code fail
+        raise RuntimeError(
+            f"TorchScript failed to synthesize dataclass method '{name}' for class '{cls.__name__}'. "
+            "Please file a bug report at "
+        ) from e
+    fake_filename = _get_fake_filename(cls, name)
+    # Parse the function
+    return ParsedDef(
+        py_ast,
+        ctx=SourceContext(
+            source=decl, filename=fake_filename, file_lineno=0, leading_whitespace_len=0
+        ),
+        source=decl,
+        filename=fake_filename,
+        file_lineno=0,
+    )
+
+
+def synthesize__init__(cls) -> ParsedDef:
+    # Supporting default factories in the way that people expect would sort of require us to
+    # allow compiling lambda functions, which is not currently supported.
+    if any(
+        field.default_factory is not dataclasses.MISSING
+        for field in dataclasses.fields(cls)
+    ):
+        raise NotImplementedError(
+            "Default factory initializers are not supported in TorchScript dataclasses"
+        )
+
+    # Simply read off the generated __init__ signature from CPython's implementation. It'll be
+    # almost correct except for InitVar annotations, which we need to handle specially.
+    signature = inspect.signature(cls.__init__)
+
+    # Handle InitVars if needed (only works on Python 3.8+, when a `type` attribute was added to InitVar);
+    # see CPython commit here https://github.com/python/cpython/commit/01ee12ba35a333e8a6a25c4153c4a21838e9585c
+    init_vars: List[str] = []
+    params = []
+    for name, param in signature.parameters.items():
+        ann = param.annotation
+
+        if isinstance(ann, dataclasses.InitVar):
+            # The TorchScript interpreter can't handle InitVar annotations, so we unwrap the underlying type here
+            init_vars.append(name)
+            params.append(param.replace(annotation=ann.type))  # type: ignore[attr-defined]
+        else:
+            params.append(param)
+
+    signature = signature.replace(parameters=params)
+
+    body = [
+        # Assign all attributes to self
+        f"self.{field.name} = {field.name}"
+        for field in dataclasses.fields(cls)
+        if field.init and field.name not in init_vars
+    ]
+    # Call user's impl of __post_init__ if it exists
+    if hasattr(cls, "__post_init__"):
+        body.append("self.__post_init__(" + ", ".join(init_vars) + ")")
+
+    return compose_fn(cls, "__init__", body or ["pass"], signature=str(signature))
+
+
+# This is a placeholder at the moment since the TorchScript interpreter doesn't call __repr__
+def synthesize__repr__(cls) -> ParsedDef:
+    return compose_fn(
+        cls,
+        "__repr__",
+        [
+            f"return '{cls.__name__}("
+            + ", ".join(
+                [
+                    f"{field.name}=self.{field.name}"
+                    for field in dataclasses.fields(cls)
+                    if field.repr
+                ]
+            )
+            + ")'"
+        ],
+        signature="(self) -> str",
+    )
+
+
+def synthesize__hash__(cls) -> ParsedDef:
+    return compose_fn(
+        cls,
+        "__hash__",
+        [
+            # This is just a placeholder to prevent compilation from failing; this won't even get called at
+            # all right now because the TorchScript interpreter doesn't call custom __hash__ implementations
+            "raise NotImplementedError('__hash__ is not supported for dataclasses in TorchScript')"
+        ],
+        signature="(self) -> int",
+    )
+
+
+# Implementation for __eq__ and __ne__
+def synthesize_equality(cls, name: str, converse: str) -> ParsedDef:
+    return synthesize_comparison(
+        cls,
+        name,
+        allow_eq=True,
+        raise_on_none=False,
+        inner=[f"if val1 {converse} val2: return False"],
+    )
+
+
+def synthesize_inequality(cls, name: str, op: str, allow_eq: bool) -> ParsedDef:
+    return synthesize_comparison(
+        cls,
+        name,
+        allow_eq,
+        raise_on_none=True,
+        inner=[
+            f"if val1 {op} val2: return True",
+            f"elif val2 {op} val1: return False",
+        ],
+    )
+
+
+def synthesize_comparison(
+    cls, name: str, allow_eq: bool, raise_on_none: bool, inner: List[str]
+) -> ParsedDef:
+    body = []
+    for field in dataclasses.fields(cls):
+        if not field.compare:
+            continue
+
+        body.extend(
+            [
+                f"val1 = self.{field.name}",
+                f"val2 = other.{field.name}",
+            ]
+        )
+        body.extend(
+            inner
+            if not is_optional(field.type)
+            else [
+                # Type refinement for optional fields; we need this to avoid type errors from the interpreter
+                "if val1 is not None and val2 is not None:",
+                *["  " + line for line in inner],
+                "elif (val1 is None) != (val2 is None):",
+                f"  raise TypeError('Cannot compare {cls.__name__} with None')"
+                if raise_on_none
+                else "  return False",
+            ]
+        )
+
+    body.append(f"return {allow_eq}")
+    return compose_fn(
+        cls, name, body, signature=f"(self, other: {cls.__name__}) -> bool"
+    )
+
+
+DATACLASS_MAGIC_METHODS: Dict[str, Callable] = {
+    "__init__": synthesize__init__,
+    "__repr__": synthesize__repr__,
+    "__hash__": synthesize__hash__,
+    "__eq__": partial(synthesize_equality, name="__eq__", converse="!="),
+    "__ne__": partial(synthesize_equality, name="__ne__", converse="=="),
+    "__lt__": partial(synthesize_inequality, name="__lt__", op="<", allow_eq=False),
+    "__le__": partial(synthesize_inequality, name="__le__", op="<", allow_eq=True),
+    "__gt__": partial(synthesize_inequality, name="__gt__", op=">", allow_eq=False),
+    "__ge__": partial(synthesize_inequality, name="__ge__", op=">", allow_eq=True),
+}
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_decomposition_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_decomposition_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..795f9da8e073a1197f81ddf379738a7f93605d7c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_decomposition_utils.py
@@ -0,0 +1,12 @@
+# mypy: allow-untyped-defs
+import torch
+from torch._ops import OpOverload, OpOverloadPacket
+
+
+def _register_decomposition(op: OpOverload, graph: torch._C.Graph):
+    assert not isinstance(
+        op, OpOverloadPacket
+    ), f"Must pass specific op overload, not overload packet, found {op}"
+    assert isinstance(op, OpOverload)
+
+    torch._C._jit_register_decomposition_for_schema(op._schema, graph)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_decompositions.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_decompositions.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc63ee7394a7165dded62996656c396a30579b23
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_decompositions.py
@@ -0,0 +1,137 @@
+# mypy: allow-untyped-defs
+import torch
+from torch import Tensor
+
+
+aten = torch.ops.aten
+import inspect
+import warnings
+from typing import Callable, Dict, List, Optional, Set, TypeVar
+from typing_extensions import ParamSpec
+
+from torch.types import Number
+
+
+decomposition_table: Dict[str, torch.jit.ScriptFunction] = {}
+function_name_set: Set[str] = set()
+
+_T = TypeVar("_T")
+_P = ParamSpec("_P")
+
+
+def check_decomposition_has_type_annotations(f):
+    inspect_empty = inspect._empty  # type: ignore[attr-defined]
+    sig = inspect.signature(f)
+    for param in sig.parameters.values():
+        assert (
+            param.annotation != inspect_empty
+        ), f"No signature on param {param.name} for function {f.name}"
+
+    assert (
+        sig.return_annotation != inspect_empty
+    ), f"No return annotation for function {f.name}"
+
+
+def signatures_match(decomposition_sig, torch_op_sig):
+    decomp_params = decomposition_sig.parameters
+    op_params = torch_op_sig.parameters
+
+    if len(decomp_params) != len(op_params):
+        return False
+
+    for decomp_param, op_param in zip(decomp_params.values(), op_params.values()):
+        # can't check full equality yet because not all fields are correcly deduced
+        # in the torch_op_sig - like default value
+        # can't check 'kind' bc
+        # kwarg-only values with defaults not yet supported in TS
+        inspect_empty = inspect._empty  # type: ignore[attr-defined]
+        for field in ["name", "annotation"]:
+            if field == "name" and decomp_param.name == "self":
+                warnings.warn("PyTorch uses 'input' instead of 'self' on public api")
+
+            if getattr(decomp_param, field) != getattr(op_param, field):
+                return False
+
+        decomp_default = decomp_param.default
+        op_default = op_param.default
+        # default value not always correctly inferred as being present on torch schema,
+        # but if specified on both they should be equal
+        if decomp_default != inspect_empty and op_default != inspect_empty:
+            if decomp_default != op_default:
+                return False
+
+    return decomposition_sig.return_annotation == torch_op_sig.return_annotation
+
+
+def register_decomposition(
+    aten_op: torch._ops.OpOverload,
+    registry: Optional[Dict[str, torch.jit.ScriptFunction]] = None,
+) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]:
+    def decomposition_decorator(f: Callable[_P, _T]) -> Callable[_P, _T]:
+        nonlocal registry
+        if registry is None:
+            registry = decomposition_table
+
+        assert isinstance(aten_op, torch._ops.OpOverload)
+
+        # Need unique name for jit function serialization
+        assert (
+            f.__name__ not in function_name_set
+        ), f"Duplicated function name {f.__name__}"
+        function_name_set.add(f.__name__)
+
+        scripted_func = torch.jit.script(f)
+        torch._C._jit_pass_inline(scripted_func.graph)
+
+        for _ in range(2):
+            torch._C._jit_pass_peephole(scripted_func.graph)
+            torch._C._jit_pass_constant_propagation(scripted_func.graph)
+
+        registry[str(aten_op._schema)] = scripted_func
+        return f
+
+    return decomposition_decorator
+
+
+# TODO: replace torch.sigmoid -> aten.sigmoid
+
+
+@register_decomposition(aten.var.correction)
+def var_decomposition(
+    input: Tensor,
+    dim: Optional[List[int]] = None,
+    correction: Optional[Number] = None,
+    keepdim: bool = False,
+) -> Tensor:
+    if dim is None:
+        dim_i: List[int] = []
+        dim = dim_i
+
+    if isinstance(dim, (tuple, list)) and len(dim) == 0:
+        n = input.numel()
+    else:
+        n = 1
+        for dim_i in dim:  # type: ignore[assignment]
+            n *= input.shape[dim_i]  # type: ignore[call-overload]
+
+    mean = aten.mean(input, dim, True)
+    sub = input - mean
+    sq = sub * sub
+    sum = aten.sum(sq, dim, keepdim)
+
+    if correction is None:
+        denom = float(n - 1)
+    else:
+        if isinstance(correction, int):
+            denom = float(n - correction)
+        elif isinstance(correction, float):
+            denom = float(n) - correction
+        else:
+            raise RuntimeError("correction must be int or float")
+
+    return sum / max(0, denom)
+
+
+@register_decomposition(aten.var.default)
+def var(input: Tensor, unbiased: bool = True) -> Tensor:
+    return var_decomposition(input, correction=(1 if unbiased else 0))
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_freeze.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_freeze.py
new file mode 100644
index 0000000000000000000000000000000000000000..e496bd74762554e4909362e8eebcb2711be04789
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_freeze.py
@@ -0,0 +1,228 @@
+# mypy: allow-untyped-defs
+"""Freezing.
+
+This is not intended to be imported directly; please use the exposed
+functionalities in `torch.jit`.
+"""
+
+from typing import List, Optional
+
+import torch
+from torch.jit._script import RecursiveScriptModule, ScriptModule
+
+
+def freeze(
+    mod, preserved_attrs: Optional[List[str]] = None, optimize_numerics: bool = True
+):
+    r"""Freeze ScriptModule, inline submodules, and attributes as constants.
+
+    Freezing a :class:`ScriptModule` will clone it and attempt to inline the cloned
+    module's submodules, parameters, and attributes as constants in the TorchScript IR Graph.
+    By default, `forward` will be preserved, as well as attributes & methods specified in
+    `preserved_attrs`. Additionally, any attribute that is modified within a preserved
+    method will be preserved.
+
+    Freezing currently only accepts ScriptModules that are in eval mode.
+
+    Freezing applies generic optimization that will speed up your model regardless of machine.
+    To further optimize using server-specific settings, run `optimize_for_inference` after
+    freezing.
+
+    Args:
+        mod (:class:`ScriptModule`): a module to be frozen
+        preserved_attrs (Optional[List[str]]): a list of attributes to preserve in addition to the forward method.
+            Attributes modified in preserved methods will also be preserved.
+        optimize_numerics (bool): If ``True``, a set of optimization passes will be run that does not strictly
+            preserve numerics. Full details of optimization can be found at `torch.jit.run_frozen_optimizations`.
+
+    Returns:
+        Frozen :class:`ScriptModule`.
+
+    Example (Freezing a simple module with a Parameter):
+
+    .. testcode::
+        import torch
+        class MyModule(torch.nn.Module):
+            def __init__(self, N, M):
+                super().__init__()
+                self.weight = torch.nn.Parameter(torch.rand(N, M))
+                self.linear = torch.nn.Linear(N, M)
+
+            def forward(self, input):
+                output = self.weight.mm(input)
+                output = self.linear(output)
+                return output
+
+        scripted_module = torch.jit.script(MyModule(2, 3).eval())
+        frozen_module = torch.jit.freeze(scripted_module)
+        # parameters have been removed and inlined into the Graph as constants
+        assert len(list(frozen_module.named_parameters())) == 0
+        # See the compiled graph as Python code
+        print(frozen_module.code)
+
+    Example (Freezing a module with preserved attributes)
+
+    .. testcode::
+        import torch
+        class MyModule2(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.modified_tensor = torch.tensor(10.)
+                self.version = 1
+
+            def forward(self, input):
+                self.modified_tensor += 1
+                return input + self.modified_tensor
+
+        scripted_module = torch.jit.script(MyModule2().eval())
+        frozen_module = torch.jit.freeze(scripted_module, preserved_attrs=["version"])
+        # we've manually preserved `version`, so it still exists on the frozen module and can be modified
+        assert frozen_module.version == 1
+        frozen_module.version = 2
+        # `modified_tensor` is detected as being mutated in the forward, so freezing preserves
+        # it to retain model semantics
+        assert frozen_module(torch.tensor(1)) == torch.tensor(12)
+        # now that we've run it once, the next result will be incremented by one
+        assert frozen_module(torch.tensor(1)) == torch.tensor(13)
+
+    Note:
+        Freezing submodule attributes is also supported:
+        frozen_module = torch.jit.freeze(scripted_module, preserved_attrs=["submodule.version"])
+
+    Note:
+        If you're not sure why an attribute is not being inlined as a constant, you can run
+        `dump_alias_db` on frozen_module.forward.graph to see if freezing has detected the
+        attribute is being modified.
+
+    Note:
+        Because freezing makes weights constants and removes module hierarchy, `to` and other
+        nn.Module methods to manipulate device or dtype no longer work. As a workaround,
+        You can remap devices by specifying `map_location` in `torch.jit.load`, however
+        device-specific logic may have been baked into the model.
+    """
+    if not isinstance(mod, ScriptModule):
+        raise RuntimeError(
+            "Freezing expects a ScriptModule as input. "
+            "Please use torch.jit.script or torch.jit.trace to script your 'nn.Module'."
+        )
+
+    if mod.training:
+        raise RuntimeError(
+            "Freezing is currently only implemented for modules in eval mode. "
+            "Please call .eval() on your module before freezing."
+        )
+
+    preserved_attrs = preserved_attrs if preserved_attrs is not None else []
+
+    out = RecursiveScriptModule(torch._C._freeze_module(mod._c, preserved_attrs))
+    RecursiveScriptModule._finalize_scriptmodule(out)
+
+    preserved_methods = [x for x in preserved_attrs if mod._c._has_method(x)]
+    run_frozen_optimizations(out, optimize_numerics, preserved_methods)
+
+    return out
+
+
+def run_frozen_optimizations(
+    mod, optimize_numerics: bool = True, preserved_methods: Optional[List[str]] = None
+):
+    r"""
+    Run a series of optimizations looking for patterns that occur in frozen graphs.
+
+    The current set of optimizations includes:
+        - Dropout Removal
+        - Pretranspose Linear Layers
+        - Concat Linear Layers with same input Tensor
+        - Conv -> Batchnorm folding
+        - Conv -> Add/Sub folding
+        - Conv -> Mul/Div folding
+
+    Args:
+        mod (:class:`ScriptModule`): a frozen module to be optimized
+
+        optimize_numerics (bool): If ``True``, a set of optimization passes will be run that does not strictly
+        preserve numerics. These optimizations preserve default rtol and atol of `torch.testing.assert_close`
+        when applied on a single transformation, however in a module where many transformations are applied
+        the rtol or atol may no longer fall within the default `assert_close` tolerance. Conv -> Batchnorm folding,
+        Conv-Add/Sub, and Conv -> Mul/Div folding all may alter numerics.
+
+    Returns:
+        None
+
+    Note:
+        In rare occassions, this can result in slower execution.
+
+    Example (Freezing a module with Conv->Batchnorm)
+    .. code-block:: python
+        import torch
+        in_channels, out_channels = 3, 32
+        conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=True)
+        bn = torch.nn.BatchNorm2d(out_channels, eps=.001)
+        mod = torch.nn.Sequential(conv, bn)
+        # set optimize to False here, by default freezing runs run_frozen_optimizations
+        frozen_mod = torch.jit.freeze(torch.jit.script(mod.eval()), optimize=False)
+        # inspect frozen mod
+        assert "batch_norm" in str(frozen_mod.graph)
+        torch.jit.run_frozen_optimizations(frozen_mod)
+        assert "batch_norm" not in str(frozen_mod.graph)
+
+    """
+    if mod._c._has_method("forward"):
+        torch._C._jit_pass_optimize_frozen_graph(mod.graph, optimize_numerics)
+
+    if preserved_methods is None:
+        preserved_methods = []
+
+    for method in preserved_methods:
+        torch._C._jit_pass_optimize_frozen_graph(
+            mod.__getattr__(method).graph, optimize_numerics
+        )
+
+
+def optimize_for_inference(
+    mod: ScriptModule, other_methods: Optional[List[str]] = None
+) -> ScriptModule:
+    """
+    Perform a set of optimization passes to optimize a model for the purposes of inference.
+
+    If the model is not already frozen, optimize_for_inference
+    will invoke `torch.jit.freeze` automatically.
+
+    In addition to generic optimizations that should speed up your model regardless
+    of environment, prepare for inference will also bake in build specific settings
+    such as the presence of CUDNN or MKLDNN, and may in the future make transformations
+    which speed things up on one machine but slow things down on another. Accordingly,
+    serialization is not implemented following invoking `optimize_for_inference` and
+    is not guaranteed.
+
+    This is still in prototype, and may have the potential to slow down your model.
+    Primary use cases that have been targeted so far have been vision models on cpu
+    and gpu to a lesser extent.
+
+    Example (optimizing a module with Conv->Batchnorm)::
+
+        import torch
+        in_channels, out_channels = 3, 32
+        conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=True)
+        bn = torch.nn.BatchNorm2d(out_channels, eps=.001)
+        mod = torch.nn.Sequential(conv, bn)
+        frozen_mod = torch.jit.optimize_for_inference(torch.jit.script(mod.eval()))
+        assert "batch_norm" not in str(frozen_mod.graph)
+        # if built with MKLDNN, convolution will be run with MKLDNN weights
+        assert "MKLDNN" in frozen_mod.graph
+    """
+    if not isinstance(mod, ScriptModule):
+        raise RuntimeError(
+            "optimize_for_inference expects a ScriptModule as input. "
+            "Please use torch.jit.script or torch.jit.trace to script your 'nn.Module'."
+        )
+
+    if other_methods is None:
+        other_methods = []
+
+    if hasattr(mod, "training"):
+        mod = freeze(mod.eval(), preserved_attrs=other_methods)
+
+    torch._C._jit_pass_optimize_for_inference(mod._c, other_methods)
+
+    return mod
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_fuser.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_fuser.py
new file mode 100644
index 0000000000000000000000000000000000000000..7466800402d27a954be8f5631df0c9517d10189a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_fuser.py
@@ -0,0 +1,161 @@
+# mypy: allow-untyped-defs
+import contextlib
+from typing import List, Tuple
+
+import torch
+
+
+@contextlib.contextmanager
+def optimized_execution(should_optimize):
+    """Context manager that controls whether the JIT's executor will run optimizations before executing a function."""
+    stored_flag = torch._C._get_graph_executor_optimize()
+    torch._C._set_graph_executor_optimize(should_optimize)
+    try:
+        yield
+    finally:
+        torch._C._set_graph_executor_optimize(stored_flag)
+
+
+@contextlib.contextmanager
+def fuser(name):
+    """Context manager that facilitates switching between backend fusers.
+
+    Valid names:
+    * ``fuser0`` - enables only legacy fuser
+    * ``fuser1`` - enables only NNC
+    * ``fuser2`` - enables only nvFuser
+    * ``fuser3`` - enables oneDNN Graph
+    """
+    old_cpu_fuse = torch._C._jit_can_fuse_on_cpu()
+    old_gpu_fuse = torch._C._jit_can_fuse_on_gpu()
+    old_texpr_fuser_state = torch._C._jit_texpr_fuser_enabled()
+    old_nvfuser_state = torch._C._jit_nvfuser_enabled()
+    old_llga_state = torch._C._jit_llga_enabled()
+    if name == "fuser0":  # legacy fuser
+        torch._C._jit_override_can_fuse_on_cpu(True)
+        torch._C._jit_override_can_fuse_on_gpu(True)
+        torch._C._jit_set_texpr_fuser_enabled(False)
+        torch._C._jit_set_nvfuser_enabled(False)
+        torch._C._jit_set_llga_enabled(False)
+    elif name == "fuser1":  # NNC
+        old_profiling_executor = torch._C._jit_set_profiling_executor(True)
+        old_profiling_mode = torch._C._get_graph_executor_optimize(True)
+        torch._C._jit_override_can_fuse_on_cpu(True)
+        torch._C._jit_override_can_fuse_on_gpu(True)
+        torch._C._jit_set_texpr_fuser_enabled(True)
+        torch._C._jit_set_nvfuser_enabled(False)
+        torch._C._jit_set_llga_enabled(False)
+    elif name == "fuser2":  # nvFuser
+        torch._C._jit_override_can_fuse_on_cpu(False)
+        torch._C._jit_override_can_fuse_on_gpu(False)
+        torch._C._jit_set_texpr_fuser_enabled(False)
+        torch._C._jit_set_nvfuser_enabled(True)
+        torch._C._jit_set_llga_enabled(False)
+    elif name == "fuser3":  # oneDNN Graph
+        old_profiling_executor = torch._C._jit_set_profiling_executor(True)
+        old_profiling_mode = torch._C._get_graph_executor_optimize(True)
+        torch._C._jit_override_can_fuse_on_cpu(True)
+        torch._C._jit_override_can_fuse_on_gpu(False)
+        torch._C._jit_set_texpr_fuser_enabled(True)
+        torch._C._jit_set_nvfuser_enabled(False)
+        torch._C._jit_set_llga_enabled(True)
+    elif name == "none":  # Turn Pytorch fuser off
+        torch._C._jit_override_can_fuse_on_cpu(False)
+        torch._C._jit_override_can_fuse_on_gpu(False)
+        torch._C._jit_set_texpr_fuser_enabled(False)
+        torch._C._jit_set_nvfuser_enabled(False)
+        torch._C._jit_set_llga_enabled(False)
+    else:
+        raise Exception(f"unrecognized fuser option (name: {name})")  # noqa: TRY002
+    try:
+        yield
+    finally:
+        if name in ["fuser1", "fuser3"]:  # NNC or oneDNN Graph
+            torch._C._jit_set_profiling_executor(old_profiling_executor)  # type: ignore[possibly-undefined]
+            torch._C._get_graph_executor_optimize(old_profiling_mode)  # type: ignore[possibly-undefined]
+        # recover the previous values
+        torch._C._jit_override_can_fuse_on_cpu(old_cpu_fuse)
+        torch._C._jit_override_can_fuse_on_gpu(old_gpu_fuse)
+        torch._C._jit_set_texpr_fuser_enabled(old_texpr_fuser_state)
+        torch._C._jit_set_nvfuser_enabled(old_nvfuser_state)
+        torch._C._jit_set_llga_enabled(old_llga_state)
+
+
+last_executed_optimized_graph = torch._C._last_executed_optimized_graph
+
+
+def _get_differentiable_graph_node(node, diff_node):
+    if node.kind() == "prim::DifferentiableGraph":
+        diff_node.append(node)
+    else:
+        for block in node.blocks():
+            for n in block.nodes():
+                _get_differentiable_graph_node(n, diff_node)
+
+
+def _graph_for(self, *args, **kwargs):
+    return _script_method_graph_for(self, self, *args, **kwargs)
+
+
+def _script_method_graph_for(self, parent, *args, **kwargs):
+    try:
+        dbs = parent.get_debug_state()
+        eps = list(dbs.execution_plans.values())
+        assert len(eps) == 1
+        graph = eps[0].graph.copy()
+
+        # graph_executor_states for differentiable node
+        fw_states = eps[0].code.differentiable_op_executor_states()
+        diff_nodes: List[torch._C.Node] = []
+        for n in graph.nodes():
+            _get_differentiable_graph_node(n, diff_nodes)
+
+        assert len(fw_states) == len(diff_nodes)
+        # swap each differentiable graph with optimized graph in their execution plan
+        for n, state in zip(diff_nodes, fw_states):
+            fw_execution_plans = list(state.execution_plans.values())
+            # we can only update the subgraph when there's a unique execution
+            # plan. Avoid assert here so we would skip the ones that can't be
+            # updated while try the best effort to update other nodes.
+            if len(fw_execution_plans) == 1:
+                n.g_("Subgraph", fw_execution_plans[0].graph)
+
+        return graph
+    except Exception:
+        # fallback approach, we just ran the graph and return the recorded optimized
+        # graph
+        self(*args, **kwargs)
+        return last_executed_optimized_graph()
+
+
+def set_fusion_strategy(strategy: List[Tuple[str, int]]):
+    """Set the type and number of specializations that can occur during fusion.
+
+    Usage: provide a list of pairs (type, depth) where type is one of "STATIC" or "DYNAMIC"
+    and depth is an integer.
+
+    Behavior - static vs dynamic:
+        In STATIC fusion, fused ops are compiled to have fixed input shapes. The shape is determined
+        based on some initial profiling runs.
+        In DYNAMIC fusion, fused ops are compiled to have variable input shapes, so that multiple
+        shapes are possible.
+
+    In both cases, we also recompile on new striding behavior, device, or dtype.
+
+    Behavior - fallback functions & depth:
+        When an input doesn't match the format required by the specialized compiled op, it will run
+        a fallback function. Fallback functions are recursively be compiled and specialized based
+        on the observed tensor shapes. Since compilation can be slow, the "depth" parameter is provided to
+        limit the number of specializations that can be compiled, before giving up on recompiling and
+        falling back to a completely un-fused, un-specialized implementation.
+
+    The list of (type, depth) pairs controls the type of specializations and the number of
+    specializations. For example: [("STATIC", 2), ("DYNAMIC", 2)] indicates that the first
+    two specializations will use static fusions, the following two specializations will use
+    dynamic fusion, and any inputs that satisfy none of the 4 options will run an
+    unfused implementation.
+
+    NB: in the future, if more as more fusion backends are added there may be more granular
+    apis for specific fusers.
+    """
+    return torch._C._jit_set_fusion_strategy(strategy)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_ir_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_ir_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..52b953624a3a18d2fb3f06f79044ab6ac583af76
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_ir_utils.py
@@ -0,0 +1,26 @@
+# mypy: allow-untyped-defs
+from typing import Union
+
+import torch
+
+
+class _InsertPoint:
+    def __init__(
+        self,
+        insert_point_graph: torch._C.Graph,
+        insert_point: Union[torch._C.Node, torch._C.Block],
+    ):
+        self.insert_point = insert_point
+        self.g = insert_point_graph
+        self.guard = None
+
+    def __enter__(self):
+        self.prev_insert_point = self.g.insertPoint()
+        self.g.setInsertPoint(self.insert_point)
+
+    def __exit__(self, *args):
+        self.g.setInsertPoint(self.prev_insert_point)
+
+
+def insert_point_guard(self, insert_point: Union[torch._C.Node, torch._C.Block]):
+    return _InsertPoint(self, insert_point)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_logging.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_logging.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0687023386ee7745af4e2cb0731f76528125477
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_logging.py
@@ -0,0 +1,11 @@
+import torch
+
+
+add_stat_value = torch.ops.prim.AddStatValue
+
+set_logger = torch._C._logging_set_logger
+LockingLogger = torch._C.LockingLogger
+AggregationType = torch._C.AggregationType
+NoopLogger = torch._C.NoopLogger
+
+time_point = torch.ops.prim.TimePoint
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_monkeytype_config.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_monkeytype_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..366a58ac6afd30907d7ca73aab531d1dc02ea1f9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_monkeytype_config.py
@@ -0,0 +1,194 @@
+# mypy: allow-untyped-defs
+import inspect
+import sys
+import typing
+from collections import defaultdict
+from pathlib import Path
+from types import CodeType
+from typing import Dict, Iterable, List, Optional
+
+import torch
+
+
+_IS_MONKEYTYPE_INSTALLED = True
+try:
+    import monkeytype  # type: ignore[import]
+    from monkeytype import trace as monkeytype_trace
+    from monkeytype.config import _startswith, LIB_PATHS  # type: ignore[import]
+    from monkeytype.db.base import (  # type: ignore[import]
+        CallTraceStore,
+        CallTraceStoreLogger,
+        CallTraceThunk,
+    )
+    from monkeytype.tracing import CallTrace, CodeFilter  # type: ignore[import]
+except ImportError:
+    _IS_MONKEYTYPE_INSTALLED = False
+
+
+# Checks whether a class is defind in `torch.*` modules
+def is_torch_native_class(cls):
+    if not hasattr(cls, "__module__"):
+        return False
+
+    parent_modules = cls.__module__.split(".")
+    if not parent_modules:
+        return False
+
+    root_module = sys.modules.get(parent_modules[0])
+    return root_module is torch
+
+
+def get_type(type):
+    """Convert the given type to a torchScript acceptable format."""
+    if isinstance(type, str):
+        return type
+    elif inspect.getmodule(type) == typing:
+        # If the type is a type imported from typing
+        # like Tuple, List, Dict then replace `typing.`
+        # with a null string. This needs to be done since
+        # typing.List is not accepted by TorchScript.
+        type_to_string = str(type)
+        return type_to_string.replace(type.__module__ + ".", "")
+    elif is_torch_native_class(type):
+        # If the type is a subtype of torch module, then TorchScript expects a fully qualified name
+        # for the type which is obtained by combining the module name and type name.
+        return type.__module__ + "." + type.__name__
+    else:
+        # For all other types use the name for the type.
+        return type.__name__
+
+
+def get_optional_of_element_type(types):
+    """Extract element type, return as `Optional[element type]` from consolidated types.
+
+    Helper function to extracts the type of the element to be annotated to Optional
+    from the list of consolidated types and returns `Optional[element type]`.
+    TODO: To remove this check once Union support lands.
+    """
+    elem_type = types[1] if type(None) == types[0] else types[0]
+    elem_type = get_type(elem_type)
+
+    # Optional type is internally converted to Union[type, NoneType], which
+    # is not supported yet in TorchScript. Hence, representing the optional type as string.
+    return "Optional[" + elem_type + "]"
+
+
+def get_qualified_name(func):
+    return func.__qualname__
+
+
+if _IS_MONKEYTYPE_INSTALLED:
+
+    class JitTypeTraceStoreLogger(CallTraceStoreLogger):
+        """A JitTypeCallTraceLogger that stores logged traces in a CallTraceStore."""
+
+        def __init__(self, store: CallTraceStore):
+            super().__init__(store)
+
+        def log(self, trace: CallTrace) -> None:
+            self.traces.append(trace)
+
+    class JitTypeTraceStore(CallTraceStore):
+        def __init__(self) -> None:
+            super().__init__()
+            # A dictionary keeping all collected CallTrace
+            # key is fully qualified name of called function
+            # value is list of all CallTrace
+            self.trace_records: Dict[str, list] = defaultdict(list)
+
+        def add(self, traces: Iterable[CallTrace]):
+            for t in traces:
+                qualified_name = get_qualified_name(t.func)
+                self.trace_records[qualified_name].append(t)
+
+        def filter(
+            self,
+            qualified_name: str,
+            qualname_prefix: Optional[str] = None,
+            limit: int = 2000,
+        ) -> List[CallTraceThunk]:
+            return self.trace_records[qualified_name]
+
+        def analyze(self, qualified_name: str) -> Dict:
+            # Analyze the types for the given module
+            # and create a dictionary of all the types
+            # for arguments.
+            records = self.trace_records[qualified_name]
+            all_args = defaultdict(set)
+            for record in records:
+                for arg, arg_type in record.arg_types.items():
+                    all_args[arg].add(arg_type)
+            return all_args
+
+        def consolidate_types(self, qualified_name: str) -> Dict:
+            all_args = self.analyze(qualified_name)
+            # If there are more types for an argument,
+            # then consolidate the type to `Any` and replace the entry
+            # by type `Any`.
+            for arg, types in all_args.items():
+                types = list(types)
+                type_length = len(types)
+                if type_length == 2 and type(None) in types:
+                    # TODO: To remove this check once Union suppport in TorchScript lands.
+                    all_args[arg] = get_optional_of_element_type(types)
+                elif type_length > 1:
+                    all_args[arg] = "Any"
+                elif type_length == 1:
+                    all_args[arg] = get_type(types[0])
+            return all_args
+
+        def get_args_types(self, qualified_name: str) -> Dict:
+            return self.consolidate_types(qualified_name)
+
+    class JitTypeTraceConfig(monkeytype.config.Config):
+        def __init__(self, s: JitTypeTraceStore):
+            super().__init__()
+            self.s = s
+
+        def trace_logger(self) -> JitTypeTraceStoreLogger:
+            """Return a JitCallTraceStoreLogger that logs to the configured trace store."""
+            return JitTypeTraceStoreLogger(self.trace_store())
+
+        def trace_store(self) -> CallTraceStore:
+            return self.s
+
+        def code_filter(self) -> Optional[CodeFilter]:
+            return jit_code_filter
+
+else:
+    # When MonkeyType is not installed, we provide dummy class definitions
+    # for the below classes.
+    class JitTypeTraceStoreLogger:  # type:  ignore[no-redef]
+        def __init__(self) -> None:
+            pass
+
+    class JitTypeTraceStore:  # type:  ignore[no-redef]
+        def __init__(self) -> None:
+            self.trace_records = None
+
+    class JitTypeTraceConfig:  # type:  ignore[no-redef]
+        def __init__(self) -> None:
+            pass
+
+    monkeytype_trace = None  # type: ignore[assignment]  # noqa: F811
+
+
+def jit_code_filter(code: CodeType) -> bool:
+    """Codefilter for Torchscript to trace forward calls.
+
+    The custom CodeFilter is required while scripting a FX Traced forward calls.
+    FX Traced forward calls have `code.co_filename` start with '<' which is used
+    to exclude tracing of stdlib and site-packages in the default code filter.
+    Since we need all forward calls to be traced, this custom code filter
+    checks for code.co_name to be 'forward' and enables tracing for all such calls.
+    The code filter is similar to default code filter for monkeytype and
+    excludes tracing of stdlib and site-packages.
+    """
+    # Filter code without a source file and exclude this check for 'forward' calls.
+    if code.co_name != "forward" and (
+        not code.co_filename or code.co_filename[0] == "<"
+    ):
+        return False
+
+    filename = Path(code.co_filename).resolve()
+    return not any(_startswith(filename, lib_path) for lib_path in LIB_PATHS)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_passes/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_passes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_passes/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_passes/__pycache__/__init__.cpython-311.pyc
new file mode 100644
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_passes/__pycache__/_property_propagation.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_passes/__pycache__/_property_propagation.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..70f44e609a4895ef5fc7c246ef9dee840c1b6ed4
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_passes/_property_propagation.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_passes/_property_propagation.py
new file mode 100644
index 0000000000000000000000000000000000000000..1537f7bc4147359af3f1e0ea4229352d37303921
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_passes/_property_propagation.py
@@ -0,0 +1,47 @@
+# mypy: allow-untyped-defs
+"""
+Tools to help with tensor property propagation.
+
+This is not intended to be imported directly; please use the exposed
+functionalities in `torch.jit`.
+"""
+
+from typing import Any, List
+
+import torch
+from torch import TensorType
+from torch._C import Graph
+
+
+def apply_input_props_using_example(graph: Graph, example_input: List[Any]):
+    """
+    Applies properties for each tensor in the graph inputs
+    using the example supplied.
+    """
+    graph_inputs = list(graph.inputs())
+    if len(graph_inputs) == 0:
+        return
+
+    # Strip self args off for methods
+    in_0 = graph_inputs[0]
+    if isinstance(in_0.type(), torch._C.ClassType) and in_0.debugName() == "self":
+        graph_inputs = graph_inputs[1:]
+
+    if not len(graph_inputs) == len(example_input):
+        raise RuntimeError(
+            "Number of inputs in graph does not match number of inputs in the example"
+        )
+
+    for i, (graph_i, example_i) in enumerate(zip(graph_inputs, example_input)):
+        if example_i is None:
+            continue  # Skip the type check
+
+        if isinstance(example_i, torch.Tensor) != isinstance(
+            graph_i.type(), TensorType
+        ):
+            raise RuntimeError(
+                f"Input {i} does not match type of example", graph_i, example_i
+            )
+
+        if isinstance(example_i, torch.Tensor):
+            graph_i.setType(TensorType.create_from_tensor(example_i))  # type: ignore[arg-type]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_pickle.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_pickle.py
new file mode 100644
index 0000000000000000000000000000000000000000..5517499e926001603cdb8f0696a7dfdc7df1a964
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_pickle.py
@@ -0,0 +1,38 @@
+# mypy: allow-untyped-defs
+# These functions are referenced from the pickle archives produced by
+# ScriptModule.save()
+
+
+# These (`build_*`) functions used to be used by `pickler.cpp` to specify
+# the type of the list for certain special types, but now all lists get
+# a type attached and restored via `restore_type_tag` below. The legacy
+# functions should stick around for backwards-compatibility.
+
+
+def build_intlist(data):
+    return data
+
+
+def build_tensorlist(data):
+    return data
+
+
+def build_doublelist(data):
+    return data
+
+
+def build_boollist(data):
+    return data
+
+
+def build_tensor_from_id(data):
+    if isinstance(data, int):
+        # just the id, can't really do anything
+        return data
+
+
+def restore_type_tag(value, type_str):
+    # The type_ptr is used by the jit unpickler to restore the full static type
+    # to container types like list when they are re-loaded, but this doesn't
+    # matter for Python, so just return the plain value
+    return value
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_recursive.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_recursive.py
new file mode 100644
index 0000000000000000000000000000000000000000..d489b51d3cd5d01610811ec39e85b18bc9790fd3
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_recursive.py
@@ -0,0 +1,1071 @@
+# mypy: allow-untyped-defs
+import collections
+import functools
+import inspect
+import sys
+import textwrap
+import types
+import warnings
+from typing import Dict, List, Set, Type
+
+import torch
+import torch._jit_internal as _jit_internal
+from torch._sources import fake_range
+from torch.jit._builtins import _find_builtin
+from torch.jit._check import AttributeTypeIsSupportedChecker
+from torch.jit._state import _add_script_class, _get_script_class, _python_cu
+from torch.jit.frontend import (
+    get_class_properties,
+    get_default_args,
+    get_jit_class_def,
+    get_jit_def,
+)
+from torch.nn import Module
+
+
+ScriptMethodStub = collections.namedtuple(
+    "ScriptMethodStub", ("resolution_callback", "def_", "original_method")
+)
+PropertyStub = collections.namedtuple("PropertyStub", ("resolution_callback", "def_"))
+
+
+# TODO: there should be a more principled way of doing this.
+ignored_attributes = [
+    "_version",
+    "_parameters",
+    "_buffers",
+    "_non_persistent_buffers_set",
+    "_backward_hooks",
+    "_backward_pre_hooks",
+    "_forward_hooks",
+    "_forward_hooks_with_kwargs",
+    "_forward_pre_hooks",
+    "_forward_pre_hooks_with_kwargs",
+    "_forward_hooks_always_called",
+    "_state_dict_hooks",
+    "_state_dict_pre_hooks",
+    "_load_state_dict_pre_hooks",
+    "_load_state_dict_post_hooks",
+    "_modules",
+    "_initializing",
+    "dump_patches",
+]
+
+
+def _compile_and_register_class(obj, rcb, qualified_name):
+    script_class = _get_script_class(obj)
+
+    if not script_class:
+        ast = get_jit_class_def(obj, obj.__name__)
+        defaults = torch.jit.frontend.get_default_args_for_class(obj)
+        script_class = torch._C._jit_script_class_compile(
+            qualified_name, ast, defaults, rcb
+        )
+        _add_script_class(obj, script_class)
+
+    return script_class
+
+
+def make_stub(func, name):
+    rcb = _jit_internal.createResolutionCallbackFromClosure(func)
+    ast = get_jit_def(func, name, self_name="RecursiveScriptModule")
+    return ScriptMethodStub(rcb, ast, func)
+
+
+def make_stub_from_method(nn_module, method_name):
+    func = getattr(nn_module, method_name)
+    if isinstance(func, ScriptMethodStub):
+        return func
+    # Make sure the name present in the resulting AST will match the name
+    # requested here. The only time they don't match is if you do something
+    # like:
+    #   def _forward(self):
+    #       pass
+    #   forward = _forward
+    # In this case, the actual function object will have the name `_forward`,
+    # even though we requested a stub for `forward`.
+    return make_stub(func, method_name)
+
+
+def make_stubs_from_exported_methods(mod):
+    stubs = []
+    for name in dir(mod):
+        item = getattr(mod, name, None)
+        if (
+            _jit_internal.get_torchscript_modifier(item)
+            is _jit_internal.FunctionModifiers.EXPORT
+        ):
+            stubs.append(make_stub_from_method(mod, name))
+
+    return stubs
+
+
+def jit_ignored_properties(module):
+    user_annotated_ignored_attributes = getattr(
+        module, "__jit_ignored_attributes__", []
+    )
+
+    def get_properties_names(module):
+        return {k for k, v in vars(module).items() if isinstance(v, property)}
+
+    properties = get_properties_names(type(module))
+    user_annoted_ignored_properties = set()
+
+    for ignored_attr in user_annotated_ignored_attributes:
+        if ignored_attr in properties:
+            user_annoted_ignored_properties.add(ignored_attr)
+    return user_annoted_ignored_properties
+
+
+# base types that can be constants
+# in addition, tuples and lists of these base types are also considered constants
+# If you edit this list, then you also need to edit the handlers in
+# ConstantValue in jit/script/init.cpp
+_constant_types = (
+    bool,
+    float,
+    int,
+    str,
+    type(None),
+    torch.device,
+    torch.layout,
+    torch.dtype,
+)
+
+
+def _get_valid_constant(attr, v, owner_type):
+    if isinstance(v, _constant_types):
+        return v
+    elif isinstance(v, (tuple, list)):
+        return tuple(_get_valid_constant(attr, x, owner_type) for x in v)
+    constants = ", ".join(torch.typename(typ) for typ in _constant_types)
+    raise TypeError(
+        textwrap.dedent(
+            f"""
+        '{torch.typename(type(v))}' object in attribute '{owner_type}.{attr}' is not a valid constant.
+        Valid constants are:
+        1. a nn.ModuleList
+        2. a value of type {{{constants}}}
+        3. a list or tuple of (2)
+        """
+        )
+    )
+
+
+class SourceContext(torch._C._jit_tree_views.SourceRangeFactory):
+    def __init__(self, source, filename, file_lineno, leading_whitespace_len):
+        super().__init__(source, filename, file_lineno, leading_whitespace_len)
+
+
+def get_annotations(obj):
+    if sys.version_info < (3, 10):
+        return getattr(obj, "__annotations__", {})
+    # In Python-3.10+ it is recommended to use inspect.get_annotations
+    # See https://docs.python.org/3.10/howto/annotations.html
+    # But also, in 3.10 annotations from base class are not inherited
+    # by unannotated derived one, so they must be manually extracted
+    annotations = inspect.get_annotations(obj)
+    if annotations:
+        return annotations
+
+    def get_cls_annotations(cls):
+        cls_annotations = inspect.get_annotations(cls)
+        if cls_annotations:
+            return cls_annotations
+        for base in cls.__bases__:
+            cls_annotations = get_cls_annotations(base)
+            if cls_annotations:
+                return cls_annotations
+        return {}
+
+    cls = obj if isinstance(obj, type) else type(obj)
+    return get_cls_annotations(cls)
+
+
+def infer_concrete_type_builder(nn_module, share_types=True):
+    """
+    Build a ConcreteModuleTypeBuilder from an nn.Module.
+
+    This ConcreteModuleType doesn't have a JIT type associated with it yet, it
+    must be filled in by the caller.
+    """
+    concrete_type_builder = torch._C.ConcreteModuleTypeBuilder(type(nn_module))
+    if isinstance(nn_module, (torch.nn.ModuleDict)):
+        concrete_type_builder.set_module_dict()
+    if isinstance(nn_module, (torch.nn.ModuleList, torch.nn.Sequential)):
+        concrete_type_builder.set_module_list()
+    if isinstance(nn_module, (torch.nn.ParameterList)):
+        concrete_type_builder.set_parameter_list()
+    if isinstance(nn_module, (torch.nn.ParameterDict)):
+        concrete_type_builder.set_parameter_dict()
+
+    class_annotations = get_annotations(nn_module)
+    if isinstance(nn_module, (torch.ao.quantization.QuantWrapper)):
+        class_annotations = {}
+
+    # Get user-annotated ignored attributes.
+    user_annotated_ignored_attributes = getattr(
+        nn_module, "__jit_ignored_attributes__", []
+    )
+    concrete_type_builder.add_ignored_attributes(user_annotated_ignored_attributes)
+    ignored_properties = jit_ignored_properties(nn_module)
+
+    # try to infer the type from type annotation or from the object itself
+    def infer_type(name, item):
+        # The forward function from Module is special; never use this annotations; we
+        # need to infer type directly using JIT.  I originally wanted to write
+        # this test as isinstance(class_annotations[name], Callable) but
+        # isinstance on typing things doesn't seem to work: isinstance(list, Callable)
+        # is also true!
+        inferred = False
+        try:
+            if (
+                name in class_annotations
+                and class_annotations[name]
+                != torch.nn.Module.__annotations__["forward"]
+            ):
+                ann_to_type = torch.jit.annotations.ann_to_type(
+                    class_annotations[name], fake_range()
+                )
+                attr_type = torch._C.InferredType(ann_to_type)
+            elif isinstance(item, torch.jit.Attribute):
+                ann_to_type = torch.jit.annotations.ann_to_type(item.type, fake_range())
+                attr_type = torch._C.InferredType(ann_to_type)
+            else:
+                attr_type = torch._C._jit_try_infer_type(item)
+                inferred = True
+        except RuntimeError as re:
+            raise RuntimeError(f"Error inferring type for {name}: {item}: {re}") from re
+
+        return attr_type, inferred
+
+    added_names = set()
+
+    for name, item in nn_module._parameters.items():
+        if name in user_annotated_ignored_attributes:
+            continue
+
+        assert item is None or isinstance(item, torch.Tensor)
+        attr_type, _ = infer_type(name, item)
+        # We currently have the invariant in various places in our code
+        # that parameters must be Tensors. However, the nn.Module API also
+        # allows NoneType parameters. These parameters are not returned as
+        # part of `parameters()` and its variants, but are available
+        # through direct attribute access.
+        concrete_type_builder.add_attribute(name, attr_type.type(), True, False)
+        added_names.add(name)
+
+    for name, item in nn_module._buffers.items():
+        if name in user_annotated_ignored_attributes:
+            continue
+
+        assert item is None or isinstance(item, torch.Tensor)
+        attr_type, _ = infer_type(name, item)
+        concrete_type_builder.add_attribute(name, attr_type.type(), False, True)
+        added_names.add(name)
+
+    for name, item in nn_module._modules.items():
+        if name in user_annotated_ignored_attributes:
+            continue
+
+        attr_type, _ = infer_type(name, item)
+        if item is None:
+            # Modules can be None. We don't have direct support for optional
+            # Modules, so the register it as an NoneType attribute instead.
+            concrete_type_builder.add_attribute(name, attr_type.type(), False, False)
+            continue
+        if attr_type.success():
+            assert attr_type.type().is_interface_type()
+            # if the type can be inferred, it should be a module interface type
+            sub_concrete_type = torch._C.ConcreteModuleType.from_jit_type(
+                attr_type.type()
+            )
+        else:
+            # otherwise we get the concrete module type for item and add it to concrete_type
+            sub_concrete_type = get_module_concrete_type(item, share_types)
+        concrete_type_builder.add_module(name, sub_concrete_type)
+
+        added_names.add(name)
+
+    # populate constants_set
+    constants_set = set(getattr(nn_module, "__constants__", ()))
+
+    # Constants annotated via `Final[T]` rather than being added to `__constants__`
+    for name, ann in class_annotations.items():
+        if torch._jit_internal.is_final(ann):
+            constants_set.add(name)
+
+    for name in constants_set:
+        if name in added_names:
+            # TODO: We should really error in this case, but its bc-breaking so
+            # we need to warn for at least one release
+            if name in nn_module._modules:
+                hint = "submodule"
+            elif name in nn_module._buffers:
+                hint = "buffer"
+            elif name in nn_module._parameters:
+                hint = "parameter"
+            else:
+                raise AssertionError(
+                    "added_names must be submodule, parameter, or buffer"
+                )
+
+            warnings.warn(
+                f"'{name}' was found in ScriptModule constants, "
+                f" but it is a non-constant {hint}. Consider removing it."
+            )
+            continue
+        if not hasattr(nn_module, name):
+            # TODO: We should really error in this case, but its bc-breaking so
+            # we need to warn for at least one release
+            warnings.warn(
+                f"'{name}' was found in ScriptModule constants, "
+                "but was not actually set in __init__. "
+                "Consider removing it."
+            )
+            continue
+        value = getattr(nn_module, name)
+        concrete_type_builder.add_constant(
+            name, _get_valid_constant(name, value, type(nn_module).__name__)
+        )
+        added_names.add(name)
+
+    # populate overloads
+    overloads = getattr(nn_module, "__overloads__", {})
+    # update with any annotated overloads
+    overloads.update(
+        get_overload_name_mapping(
+            get_overload_annotations(nn_module, ignored_properties)
+        )
+    )
+    for name, overloaded_names in overloads.items():
+        concrete_type_builder.add_overload(name, overloaded_names)
+
+    for name, value in nn_module.__dict__.items():
+        if name in ignored_attributes or name.startswith("__"):
+            # Python objects have lots of random attributes attached to them;
+            # PyTorch adds a few more. Prevent these from getting compiled.
+            continue
+
+        if name in user_annotated_ignored_attributes:
+            continue
+
+        if name in added_names:
+            # Don't re-add anything we already added
+            continue
+
+        isoverloadpacket = isinstance(value, torch._ops.OpOverloadPacket)
+        if isoverloadpacket:
+            value = value.op
+        # Handle Python function attributes
+        if inspect.isfunction(value):
+            try:
+                scripted_fn = torch.jit.script(value)
+                concrete_type_builder.add_function_attribute(
+                    name, torch._C._jit_try_infer_type(scripted_fn).type(), value
+                )
+            except Exception as e:
+                # If we fail to script the function, it isn't a hard error.
+                # Instead, we will add it to the list of attributes we failed
+                # to convert, with the compilation error.
+                hint = (
+                    "(This function exists as an attribute on the Python module, "
+                    "but we failed to compile it to a TorchScript function. "
+                    f"\nThe error stack is reproduced here:\n{e}"
+                )
+                concrete_type_builder.add_failed_attribute(name, hint)
+
+            continue
+
+        # Handle calls to builtin functions (either bespoke builtins from torch.jit._builtins or
+        # a call to an aten function like torch.add)
+        builtin_symbol_name = _find_builtin(value)
+        if builtin_symbol_name:
+            concrete_type_builder.add_builtin_function(name, builtin_symbol_name)
+            continue
+
+        # Handle Script function attributes
+        if isinstance(value, torch.jit.ScriptFunction):
+            concrete_type_builder.add_function_attribute(
+                name, torch._C._jit_try_infer_type(value).type(), value
+            )
+            continue
+
+        # If we got here, this is a regular "data" attribute, add it to the concrete type
+        attr_type, inferred = infer_type(name, value)
+        if attr_type.success():
+            concrete_type_builder.add_attribute(name, attr_type.type(), False, False)
+        else:
+            # TODO: could add more detail here. For example, what the user should do
+            # when the pytype is `list` or `NoneType`
+            inferred_msg = (
+                "Its type was inferred; try adding a type annotation for the attribute."
+                if inferred
+                else ""
+            )
+            additional_info = f"{attr_type.reason()}. {inferred_msg}"
+            hint = (
+                "(This attribute exists on the Python module, "
+                f"but we failed to convert Python type: '{torch.typename(type(value))}' "
+                f"to a TorchScript type. {additional_info})"
+            )
+            concrete_type_builder.add_failed_attribute(name, hint)
+
+    # add hooks to concrete type
+    for hook in nn_module._forward_hooks.values():
+        concrete_type_builder.add_forward_hook(hook)
+    for pre_hook in nn_module._forward_pre_hooks.values():
+        concrete_type_builder.add_forward_pre_hook(pre_hook)
+
+    return concrete_type_builder
+
+
+class ConcreteTypeStore:
+    type_store: Dict[Type[Module], List[torch._C.ConcreteModuleType]]
+    methods_compiled: Set[torch._C.ConcreteModuleType]
+
+    def __init__(self) -> None:
+        # Python module type => List[ConcreteModuleType)]
+        self.type_store = {}
+        # ConcreteTypes that have had their methods already compiled
+        self.methods_compiled = set()
+
+    def get_or_create_concrete_type(self, nn_module):
+        """Infer a ConcreteType from this `nn.Module` instance. Underlying JIT types are re-used if possible."""
+        concrete_type_builder = infer_concrete_type_builder(nn_module)
+
+        nn_module_type = type(nn_module)
+        if nn_module_type not in self.type_store:
+            self.type_store[nn_module_type] = []
+
+        # Search the type store for an already-available JIT type
+        known_types = self.type_store[nn_module_type]
+        for known_type in known_types:
+            if known_type.equals(concrete_type_builder):
+                return known_type
+
+        # We didn't find anything; generate a new JIT type from this concrete type
+        concrete_type = concrete_type_builder.build()
+        self.type_store[nn_module_type].append(concrete_type)
+        return concrete_type
+
+
+concrete_type_store = ConcreteTypeStore()
+
+
+def create_methods_and_properties_from_stubs(
+    concrete_type, method_stubs, property_stubs
+):
+    method_defs = [m.def_ for m in method_stubs]
+    method_rcbs = [m.resolution_callback for m in method_stubs]
+    method_defaults = [get_default_args(m.original_method) for m in method_stubs]
+
+    property_defs = [p.def_ for p in property_stubs]
+    property_rcbs = [p.resolution_callback for p in property_stubs]
+
+    concrete_type._create_methods_and_properties(
+        property_defs, property_rcbs, method_defs, method_rcbs, method_defaults
+    )
+
+
+def create_hooks_from_stubs(concrete_type, hook_stubs, pre_hook_stubs):
+    hook_defs = [h.def_ for h in hook_stubs]
+    hook_rcbs = [h.resolution_callback for h in hook_stubs]
+
+    pre_hook_defs = [h.def_ for h in pre_hook_stubs]
+    pre_hook_rcbs = [h.resolution_callback for h in pre_hook_stubs]
+
+    concrete_type._create_hooks(hook_defs, hook_rcbs, pre_hook_defs, pre_hook_rcbs)
+
+
+def get_module_concrete_type(nn_module, share_types=True):
+    """
+    Get a concrete type for nn_modules.
+
+    If share_types is True, the concrete type is fetched from concrete_type_store.
+    If it is False, a new concrete type is created without first searching concrete_type_store.
+
+    Args:
+        nn_module:  The original Python nn.Module that we are creating a ScriptModule for.
+        share_types = Whether to share underlying JIT types between modules (if possible).
+
+    Returns:
+        A concrete type for nn_module.
+    """
+    assert isinstance(nn_module, Module)
+    if isinstance(nn_module, torch.jit.ScriptModule) and hasattr(
+        nn_module, "_concrete_type"
+    ):
+        return nn_module._concrete_type
+
+    if share_types:
+        # Look into the store of cached JIT types
+        concrete_type = concrete_type_store.get_or_create_concrete_type(nn_module)
+    else:
+        # Get a concrete type directly, without trying to re-use an existing JIT
+        # type from the type store.
+        concrete_type_builder = infer_concrete_type_builder(nn_module, share_types)
+        concrete_type_builder.set_poisoned()
+        concrete_type = concrete_type_builder.build()
+
+    return concrete_type
+
+
+def create_script_class(obj):
+    """
+    Create and return a RecursiveScriptClass instance from a Python object.
+
+    Arguments:
+        obj: A Python object.
+    """
+    qualified_class_name = _jit_internal._qualified_name(type(obj))
+    rcb = _jit_internal.createResolutionCallbackForClassMethods(type(obj))
+    # Script the type of obj if it hasn't already been scripted.
+    _compile_and_register_class(type(obj), rcb, qualified_class_name)
+    class_ty = _python_cu.get_class(qualified_class_name)
+    # Create an empty torch._C.ScriptObject with the scripted type.
+    cpp_object = torch._C._create_object_with_type(class_ty)
+    # Copy all of the attributes over to the torch._C.ScriptObject.
+    for name, value in obj.__dict__.items():
+        cpp_object.setattr(name, value)
+
+    # Wrap the torch._C.ScriptObject in a RecursiveScriptClass instance.
+    return wrap_cpp_class(cpp_object)
+
+
+def create_script_module(nn_module, stubs_fn, share_types=True, is_tracing=False):
+    """
+    Create a new ScriptModule from an nn.Module.
+
+    Args:
+        nn_module:  The original Python nn.Module that we are creating a ScriptModule for.
+        stubs_fn:  Lambda that takes an nn.Module and generates a list of ScriptMethodStubs to compile.
+        share_types:  Whether to share underlying JIT types between modules (if possible).
+            NOTE: Only set to False this when we cannot guarantee type sharing will work
+                correctly. This only happens today for traced modules, where the same
+                module can produce different traced methods depending on the inputs.
+        is_tracing: Whether this function is called during tracing or scripting. If tracing,
+                we don't need to do AttributeTypeIsSupportedChecker because all the unsupported
+                attributes will be baked as constant in the tracing graph. In addition,
+                this check significantly slows down the traced modules when the module size is big.
+    """
+    assert not isinstance(nn_module, torch.jit.RecursiveScriptModule)
+    check_module_initialized(nn_module)
+    concrete_type = get_module_concrete_type(nn_module, share_types)
+    if not is_tracing:
+        AttributeTypeIsSupportedChecker().check(nn_module)
+    return create_script_module_impl(nn_module, concrete_type, stubs_fn)
+
+
+def create_script_module_impl(nn_module, concrete_type, stubs_fn):
+    """
+    Convert an nn.Module to a RecursiveScriptModule.
+
+    Args:
+        nn_module:  The original Python nn.Module that we are creating a ScriptModule for.
+        concrete_type:  The fully initialized ConcreteType of the module.
+        stubs_fn:  Lambda that takes an nn.Module and generates a list of ScriptMethodStubs to compile.
+    """
+    cpp_module = torch._C._create_module_with_type(concrete_type.jit_type)
+    method_stubs = stubs_fn(nn_module)
+    property_stubs = get_property_stubs(nn_module)
+    hook_stubs, pre_hook_stubs = get_hook_stubs(nn_module)
+
+    user_annotated_ignored_attributes = getattr(
+        nn_module, "__jit_ignored_attributes__", []
+    )
+    ignored_properties = jit_ignored_properties(nn_module)
+
+    def init_fn(script_module):
+        # Initialize the ScriptModule:
+        # 1. Copy the attributes/parameters/buffers from the original `nn_module` to the new ScriptModule.
+        for name in concrete_type.get_attributes().keys():
+            orig_value = getattr(nn_module, name)
+            orig_value = (
+                orig_value.value
+                if isinstance(orig_value, torch.jit.Attribute)
+                else orig_value
+            )
+            cpp_module.setattr(name, orig_value)
+
+        # 2. Copy the submodules from the original `nn_module` to the new ScriptModule,
+        #    recursively scripting them.
+        for name, sub_concrete_type in concrete_type.get_modules():
+            orig_value = getattr(nn_module, name)
+            assert isinstance(
+                orig_value, Module
+            ), f"Expected Module but got {type(orig_value)}"
+            module_type = sub_concrete_type.jit_type
+            if isinstance(module_type, torch._C.InterfaceType):
+                # use the interface inference rule to compile the module
+                scripted = interface_script(module_type, orig_value)
+            elif isinstance(orig_value, torch.jit.ScriptModule):
+                scripted = orig_value
+            else:
+                # always reuse the provided stubs_fn to infer the methods to compile
+                scripted = create_script_module_impl(
+                    orig_value, sub_concrete_type, stubs_fn
+                )
+
+            cpp_module.setattr(name, scripted)
+            script_module._modules[name] = scripted
+
+        # 3. Copy @ignored/@unused methods and attrs from the original `nn_module` to the new ScriptModule.
+        #    This ensures we can access these Python methods on the ScriptModule.
+        for name in dir(nn_module):
+            if name in ignored_properties:
+                continue
+            item = getattr(nn_module, name, None)
+            if inspect.ismethod(item) and _jit_internal.is_ignored_fn(item):
+                unbound_function = getattr(nn_module, name).__func__
+                bound_method = unbound_function.__get__(script_module)
+                setattr(script_module, name, bound_method)
+            elif concrete_type.is_ignored_attribute(name):
+                setattr(script_module, name, item)
+
+        # For convenience, attach the concrete type to the new ScriptModule
+        script_module._concrete_type = concrete_type
+
+    # Actually create the ScriptModule, initializing it with the function we just defined
+    script_module = torch.jit.RecursiveScriptModule._construct(cpp_module, init_fn)
+
+    # Compile methods if necessary
+    if concrete_type not in concrete_type_store.methods_compiled:
+        create_methods_and_properties_from_stubs(
+            concrete_type, method_stubs, property_stubs
+        )
+        # Create hooks after methods to ensure no name collisions between hooks and methods.
+        # If done before, hooks can overshadow methods that aren't exported.
+        create_hooks_from_stubs(concrete_type, hook_stubs, pre_hook_stubs)
+        torch._C._run_emit_module_hook(cpp_module)
+        concrete_type_store.methods_compiled.add(concrete_type)
+
+    # Copy the forward hooks and pre-hooks to the new ScriptModule
+    # to allow the hooks to be run from eager as ScriptFunctions
+    for idx, fn in enumerate(script_module._c._get_forward_pre_hooks()):
+        script_module._forward_pre_hooks[idx] = fn
+    for idx, fn in enumerate(script_module._c._get_forward_hooks()):
+        script_module._forward_hooks[idx] = fn
+
+    # Special handling so methods like __len__ work in script methods on classes derived from containers
+    if (
+        isinstance(
+            nn_module, (torch.nn.ModuleList, torch.nn.Sequential, torch.nn.ModuleDict)
+        )
+        and "__len__" not in cpp_module._method_names()
+    ):
+        script_module.define(f"def __len__(self):\n   return {len(nn_module)}\n")
+    if (
+        isinstance(nn_module, torch.nn.ModuleDict)
+        and "__contains__" not in cpp_module._method_names()
+    ):
+        if len(nn_module.keys()):
+            keys = repr(list(nn_module.keys()))
+            script_module.define(
+                f"def __contains__(self, key: str):\n   return key in {keys}\n"
+            )
+        else:
+            script_module.define("def __contains__(self, key: str):\n   return False\n")
+
+    # Make the compiled methods available to the Python ScriptModule class.
+    for method_stub in method_stubs:
+        if method_stub.original_method is None:
+            # define()'d methods don't have an Python original_method, so we
+            # don't need to do any Python re-wrapping stuff
+            continue
+
+        name = method_stub.original_method.__name__
+        if name != method_stub.def_.name().name:
+            # TODO: Why skip this? Because @torch.jit._overload_method will
+            # mangle the name of the function.
+            continue
+        script_method = cpp_module._get_method(name)
+
+        # Wrap the original to propagate docstrings and such.
+        # TODO: we don't currently do this functions that are recursively
+        # compiled, we should.
+        wrapped_script_method = functools.wraps(method_stub.original_method)(
+            script_method
+        )
+
+        # Add the methods to the script_module directly. This ensures they will
+        # be found first when `name` is looked up (as opposed to the stubs or
+        # nn.Module.forward)
+        script_module.__dict__[name] = wrapped_script_method
+
+    # Make module properties available on the Python ScriptModule class.
+    for property_stub in property_stubs:
+        property_name = property_stub.def_.name().name
+        fget = cpp_module._get_method(property_stub.def_.getter_name().name)
+        # Setter is optional, so it may not exist.
+        setter_name = property_stub.def_.setter_name()
+        fset = cpp_module._get_method(setter_name.name) if setter_name else None
+        script_module.__dict__[property_name] = property(property_name, fget, fset)  # type: ignore[arg-type]
+
+    # copy over python methods to script module if they aren't defined on the script module
+    # this is currently an internal api used only on module containers
+    for name in dir(nn_module):
+        if name in ignored_properties:
+            continue
+        item = getattr(nn_module, name, None)
+        if (
+            _jit_internal.get_torchscript_modifier(item)
+            is _jit_internal.FunctionModifiers.COPY_TO_SCRIPT_WRAPPER
+        ):
+            add_python_attr_to_scripted_model(script_module, nn_module, name)
+
+    return script_module
+
+
+# We define shims of certain attributes on the RecursiveScriptModule to support
+# magic methods. To check if a script model defines an attribute we need
+# to also check that the attribute is not the shim
+def script_model_defines_attr(script_model, attr):
+    script_attr = getattr(script_model, attr, None)
+    if script_attr is None:
+        return False
+    default_attr = getattr(torch.jit.RecursiveScriptModule, attr, None)
+    if default_attr is None:
+        return False
+    return script_attr != default_attr
+
+
+def add_python_attr_to_scripted_model(script_model, orig, attr):
+    if hasattr(orig, attr) and script_model_defines_attr(script_model, attr):
+        setattr(script_model, attr, getattr(orig, attr))
+
+
+def get_overload_annotations(mod, jit_ignored_properties):
+    # original function => [(mangled overload name, overload function)]
+    overloads = {}
+
+    for name in dir(type(mod)):
+        if name in jit_ignored_properties:
+            continue
+        item = getattr(mod, name, None)
+        if not callable(item):
+            continue
+
+        # builtin functions like repr() in python 2 do not have __module__ defined
+        if hasattr(item, "__module__") and item.__module__ is not None:
+            method_overloads = _jit_internal._get_overloaded_methods(
+                item, mod.__class__
+            )
+            if method_overloads is None:
+                continue
+
+            if item.__func__ in method_overloads:
+                raise RuntimeError(
+                    _jit_internal.get_overload_no_implementation_error_message(
+                        "method", item.__func__
+                    )
+                )
+
+            names = [name + "__" + str(i) for i in range(len(method_overloads))]
+            overloads[item] = list(zip(names, method_overloads))
+
+    return overloads
+
+
+def get_overload_name_mapping(overload_info):
+    # Same format as __overloads__
+    # original function => [overload names]
+    overload_name_mappings: Dict[str, List[str]] = {}
+    for orig_fn, overloads in overload_info.items():
+        original_name = orig_fn.__name__
+        if original_name not in overload_name_mappings:
+            overload_name_mappings[original_name] = []
+
+        for overload_name, _ in overloads:
+            overload_name_mappings[original_name].append(overload_name)
+    return overload_name_mappings
+
+
+def _check_no_signature(func):
+    signature = torch.jit.annotations.get_signature(
+        func, None, fake_range(), inspect.ismethod(func)
+    )
+    if signature is None:
+        qual_name = _jit_internal._qualified_name(func)
+        raise RuntimeError(
+            f"Must explicitly add type annotations to overloaded functions: {qual_name}"
+        )
+
+
+def make_stubs_for_overloads(overload_info):
+    overload_stubs = []
+    for orig_fn, overloads in overload_info.items():
+        orig_ast = get_jit_def(
+            orig_fn, orig_fn.__name__, self_name="RecursiveScriptModule"
+        )
+        for overload_name, overload_fn in overloads:
+            _check_no_signature(overload_fn)
+            over_ast = get_jit_def(
+                overload_fn, overload_fn.__name__, self_name="RecursiveScriptModule"
+            )
+            new_ast = torch._C._replace_overloaded_method_decl(
+                over_ast.decl(), orig_ast, overload_name
+            )
+            _rcb = _jit_internal.createResolutionCallbackFromClosure(orig_fn)
+            overload_stubs.append(ScriptMethodStub(_rcb, new_ast, overload_fn))
+    return overload_stubs
+
+
+def check_module_initialized(mod):
+    assert isinstance(mod, torch.nn.Module)
+    if not hasattr(mod, "_parameters"):
+        raise RuntimeError(
+            f"'{torch.typename(type(mod))}' has not been initialized, did you forget to call 'super()'?"
+        )
+
+    # This is to avoid importing torch.distributed.nn
+    if not hasattr(mod, "remote_parameters"):
+        for name, param in mod._parameters.items():
+            if param is not None and torch.nn.parameter.is_lazy(param):
+                raise RuntimeError(
+                    f"'{torch.typename(type(mod))}' has uninitialized parameters {name}. Did you forget to run a forward pass?"
+                )
+        for name, buf in mod._buffers.items():
+            if buf is not None and torch.nn.parameter.is_lazy(buf):
+                raise RuntimeError(
+                    f"'{torch.typename(type(mod))}' has uninitialized buffers {name}. Did you forget to run a forward pass?"
+                )
+
+
+def infer_methods_to_compile(nn_module):
+    """Implement the default rules for which methods should act as starting points for compilation.
+
+    (TODO add a link when the rules are published).
+    """
+    check_module_initialized(nn_module)
+    user_annotated_ignored_attributes = getattr(
+        nn_module, "__jit_ignored_attributes__", []
+    )
+    ignored_properties = jit_ignored_properties(nn_module)
+
+    methods: List[str] = []
+    if hasattr(nn_module, "forward") and not _jit_internal.is_ignored_fn(
+        nn_module.forward
+    ):
+        forward_func = getattr(nn_module.forward, "__func__", None)
+        module_forward = getattr(torch.nn.Module, "forward", None)
+        if forward_func != module_forward:
+            methods = ["forward"]
+
+    exported = []
+    for name in dir(nn_module):
+        if name in ignored_properties:
+            continue
+        item = getattr(nn_module, name, None)
+        if (
+            _jit_internal.get_torchscript_modifier(item)
+            is _jit_internal.FunctionModifiers.EXPORT
+        ):
+            exported.append(name)
+
+    methods = methods + exported
+
+    overload_name_mappings = dict(getattr(nn_module, "__overloads__", {}))
+    overload_info = get_overload_annotations(nn_module, ignored_properties)
+    overload_name_mappings.update(get_overload_name_mapping(overload_info))
+    overload_stubs = make_stubs_for_overloads(overload_info)
+
+    nn_module.__overloads__ = overload_name_mappings
+
+    # we shouldn't directly compile overloaded methods, just its overloads
+    def ignore_overloaded(method_name):
+        return method_name not in overload_name_mappings
+
+    filtered_methods = filter(ignore_overloaded, methods)
+
+    # Unique the methods. We don't want to use a set to store the methods because it
+    # introduces non-determinism to compile order.
+    uniquer: Set[str] = set()
+    uniqued_methods = []
+    for name in filtered_methods:
+        if name in uniquer:
+            continue
+        uniqued_methods.append(name)
+        uniquer.add(name)
+
+    stubs = []
+    for method in uniqued_methods:
+        stubs.append(make_stub_from_method(nn_module, method))
+    return overload_stubs + stubs
+
+
+def get_hook_stubs(nn_module):
+    """Return forward hook and pre_hook ScriptModuleStubs."""
+    check_module_initialized(nn_module)
+    hook_map: Dict = {}
+
+    hook_stubs = []
+    for hook in nn_module._forward_hooks.values():
+        if hook.__name__ in hook_map:
+            if id(hook) != id(hook_map[hook.__name__]):
+                raise RuntimeError(
+                    f"Hook '{hook.__name__}' on {type(nn_module).__name__} "
+                    "has at least two different python definitions."
+                    " Please use unique names for all hooks."
+                )
+        else:
+            hook_map[hook.__name__] = hook
+        hook_stubs.append(make_stub(hook, hook.__name__))
+
+    pre_hook_stubs = []
+    for pre_hook in nn_module._forward_pre_hooks.values():
+        if pre_hook.__name__ in hook_map:
+            if id(pre_hook) != id(hook_map[pre_hook.__name__]):
+                raise RuntimeError(
+                    f"Pre-hook '{pre_hook.__name__}' on {type(nn_module).__name__} "
+                    "has at least two different python definitions."
+                    " Please use unique names for all hooks."
+                )
+        else:
+            hook_map[pre_hook.__name__] = pre_hook
+        pre_hook_stubs.append(make_stub(pre_hook, pre_hook.__name__))
+
+    return hook_stubs, pre_hook_stubs
+
+
+def get_property_stubs(nn_module):
+    """Create property stubs for the properties of the module by creating method stubs for the getter and setter."""
+    module_ty = type(nn_module)
+    properties_asts = get_class_properties(module_ty, self_name="RecursiveScriptModule")
+    rcbs = {}
+
+    for name in dir(module_ty):
+        item = getattr(module_ty, name, None)
+        if isinstance(item, property):
+            if not item.fget:
+                raise RuntimeError(
+                    f"Property {name} of {nn_module.__name__} must have a getter"
+                )
+
+            rcbs[name] = _jit_internal.createResolutionCallbackFromClosure(item.fget)
+
+    stubs = [PropertyStub(rcbs[ast.name().name], ast) for ast in properties_asts]
+    return stubs
+
+
+def interface_script(mod_interface, nn_module):
+    """
+    Make a ScriptModule from an nn.Module, using the interface methods rule for determining which methods to compile.
+
+    Args:
+        mod_interface: the interface type that the module have
+        nn_module:  The original Python nn.Module that we are creating a ScriptModule for.
+    """
+    if isinstance(nn_module, torch.jit.ScriptModule):
+        return nn_module
+
+    check_module_initialized(nn_module)
+
+    def infer_interface_methods_to_compile(nn_module):
+        """Rule to infer the methods from the interface type.
+
+        It is used to know which methods need to act as starting points for compilation.
+        """
+        stubs = []
+        for method in mod_interface.getMethodNames():
+            stubs.append(make_stub_from_method(nn_module, method))
+        return stubs
+
+    return create_script_module(nn_module, infer_interface_methods_to_compile)
+
+
+def try_compile_fn(fn, loc):
+    if _jit_internal.is_ignored_fn(fn):
+        # Don't do anything for @ignore'd functions
+        return None
+
+    if isinstance(fn, torch.nn.Module):
+        # Since modules are callable pybind recognizes them as functions, but
+        # don't do anything for them
+        return None
+
+    if not inspect.isfunction(fn) and not inspect.ismethod(fn):
+        raise RuntimeError(
+            f"`{fn}` is not a function. Recursive scripting only supports "
+            "Python functions or methods currently.\n"
+            f"Consider manually annotating `{fn}` with @torch.jit.script."
+        )
+
+    # The object returned by __prepare_scriptable__ might have a different closure.
+    # Resolve it here to get the right resolution callback.
+    fn = fn.__prepare_scriptable__() if hasattr(fn, "__prepare_scriptable__") else fn  # type: ignore[operator]
+
+    # We don't have the actual scope where the function was defined, but we can
+    # extract the necessary info from the closed over variables on the function
+    # object
+    rcb = _jit_internal.createResolutionCallbackFromClosure(fn)
+    return torch.jit.script(fn, _rcb=rcb)
+
+
+def wrap_cpp_class(cpp_class):
+    """Wrap this torch._C.Object in a Python RecursiveScriptClass."""
+    return torch.jit.RecursiveScriptClass(cpp_class)
+
+
+def wrap_cpp_module(cpp_module):
+    """Wrap this torch._C.ScriptModule in a Python ScriptModule, recursively for all submodules."""
+
+    def init_fn(script_module):
+        for name, cpp_module in torch._C.ModuleDict(script_module._c).items():
+            setattr(script_module, name, wrap_cpp_module(cpp_module))
+        script_module._concrete_type = torch._C.ConcreteModuleType.from_jit_type(
+            script_module._c._type()
+        )
+
+        for idx, fn in enumerate(script_module._c._get_forward_pre_hooks()):
+            script_module._forward_pre_hooks[idx] = fn
+        for idx, fn in enumerate(script_module._c._get_forward_hooks()):
+            script_module._forward_hooks[idx] = fn
+
+    return torch.jit.RecursiveScriptModule._construct(cpp_module, init_fn)
+
+
+def compile_unbound_method(concrete_type, fn):
+    if _jit_internal.is_ignored_fn(fn):
+        return None
+    stub = make_stub(fn, fn.__name__)
+    with torch._jit_internal._disable_emit_hooks():
+        # We don't want to call the hooks here since the graph that is calling
+        # this function is not yet complete
+        create_methods_and_properties_from_stubs(concrete_type, (stub,), ())
+    return stub
+
+
+def lazy_bind(concrete_type, unbound_method):
+    """
+    Return a function that lazily binds `unbound_method` to a provided Module IValue, then invokes the method.
+
+    We do this so that any Python shenanigans that
+    will poison type sharing are impossible at compile time.
+    """
+
+    def lazy_binding_method(cpp_module, *args):
+        def init_fn(script_module):
+            orig_class = concrete_type.py_class
+
+            # Copy @ignored/@unused methods from the original module to the new one.
+            # This ensures they are available during execution.
+            for name in dir(orig_class):
+                item = getattr(orig_class, name, None)
+                if _jit_internal.is_ignored_fn(item):
+                    setattr(script_module, name, item)
+
+            # Copy constants over so they are available during execution.
+            for name, value in concrete_type.get_constants().items():
+                setattr(script_module, name, value)
+
+        script_module = torch.jit.RecursiveScriptModule._construct(cpp_module, init_fn)
+        method = types.MethodType(unbound_method, script_module)
+        return method(*args)
+
+    # make the lazy binding method "look like" the original method
+    lazy_binding_method.original_fn = unbound_method  # type: ignore[attr-defined]
+    lazy_binding_method.__name__ = unbound_method.__name__
+    torch._jit_internal.copy_torchscript_modifier(unbound_method, lazy_binding_method)
+
+    return lazy_binding_method
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_script.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_script.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f90e5a6d84d2e0762e5111017c0286cefa60793
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_script.py
@@ -0,0 +1,1727 @@
+"""TorchScript.
+
+This module contains functionality to support the JIT's scripting frontend, notably:
+    - torch.jit.script
+
+This is not intended to be imported directly; please use the exposed
+functionalities in `torch.jit`.
+"""
+import collections
+import copy
+import enum
+import functools
+import inspect
+import pickle
+import warnings
+from typing import Any, Callable, Dict, List, Set, Tuple, Union
+
+import torch
+import torch._jit_internal as _jit_internal
+from torch._classes import classes
+from torch._jit_internal import _get_model_id, _qualified_name
+from torch._utils_internal import log_torchscript_usage
+from torch.jit._builtins import _register_builtin
+from torch.jit._fuser import _graph_for, _script_method_graph_for
+from torch.jit._monkeytype_config import (
+    JitTypeTraceConfig,
+    JitTypeTraceStore,
+    monkeytype_trace,
+)
+from torch.jit._recursive import (
+    _compile_and_register_class,
+    infer_methods_to_compile,
+    ScriptMethodStub,
+    wrap_cpp_module,
+)
+from torch.jit._state import (
+    _enabled,
+    _set_jit_function_cache,
+    _set_jit_overload_cache,
+    _try_get_jit_cached_function,
+    _try_get_jit_cached_overloads,
+)
+from torch.jit.frontend import get_default_args, get_jit_class_def, get_jit_def
+from torch.nn import Module
+from torch.overrides import (
+    has_torch_function,
+    has_torch_function_unary,
+    has_torch_function_variadic,
+)
+from torch.package import PackageExporter, PackageImporter
+from torch.utils import set_module
+
+from ._serialization import validate_map_location
+
+
+type_trace_db = JitTypeTraceStore()  # DB to hold all call traces from MonkeyType
+
+torch._C.ScriptMethod.graph_for = _script_method_graph_for  # type: ignore[attr-defined]
+torch._C.ScriptFunction.graph_for = _graph_for  # type: ignore[attr-defined]
+ScriptFunction = torch._C.ScriptFunction
+ScriptFunction.__doc__ = """
+Functionally equivalent to a :class:`ScriptModule`, but represents a single
+function and does not have any attributes or Parameters.
+"""
+set_module(ScriptFunction, "torch.jit")
+
+
+# Throws an error if a jit function is pickled.
+# Helps to avoid Python crashes for Python versions 3.9.5 + when protocol 0 or 1 is given as an argument.
+def _reduce(cls):
+    raise pickle.PickleError("ScriptFunction cannot be pickled")
+
+
+ScriptFunction.__reduce__ = _reduce  # type: ignore[assignment]
+
+
+if _enabled:
+    Attribute = collections.namedtuple("Attribute", ["value", "type"])
+else:
+
+    def Attribute(value, type):  # type: ignore[no-redef]
+        return value
+
+
+Attribute.__doc__ = """
+    This method is a pass-through function that returns `value`, mostly
+    used to indicate to the TorchScript compiler that the left-hand side
+    expression is a class instance attribute with type of `type`. Note that
+    `torch.jit.Attribute` should only be used in `__init__` method of `jit.ScriptModule`
+    subclasses.
+
+    Though TorchScript can infer correct type for most Python expressions, there are some cases where
+    type inference can be wrong, including:
+
+    - Empty containers like `[]` and `{}`, which TorchScript assumes to be container of `Tensor`
+    - Optional types like `Optional[T]` but assigned a valid value of type `T`, TorchScript would assume
+      it is type `T` rather than `Optional[T]`
+
+    In eager mode, it is simply a pass-through function that returns `value`
+    without other implications.
+
+    Example:
+
+    .. testcode::
+
+        import torch
+        from typing import Dict
+
+        class AttributeModule(torch.jit.ScriptModule):
+            def __init__(self) -> None:
+                super().__init__()
+                self.foo = torch.jit.Attribute(0.1, float)
+
+                # we should be able to use self.foo as a float here
+                assert 0.0 < self.foo
+
+                self.names_ages = torch.jit.Attribute({}, Dict[str, int])
+                self.names_ages["someone"] = 20
+                assert isinstance(self.names_ages["someone"], int)
+
+        m = AttributeModule()
+        # m will contain two attributes
+        # 1. foo of type float
+        # 2. names_ages of type Dict[str, int]
+
+    .. testcleanup::
+
+        del AttributeModule
+        del m
+
+    Note: it's now preferred to instead use type annotations instead of `torch.jit.Attribute`:
+
+    .. testcode::
+
+        import torch
+        from typing import Dict
+
+        class AttributeModule(torch.nn.Module):
+            names: Dict[str, int]
+
+            def __init__(self) -> None:
+                super().__init__()
+                self.names = {}
+
+        m = AttributeModule()
+
+    .. testcleanup::
+
+        del AttributeModule
+        del m
+
+    Args:
+        value: An initial value to be assigned to attribute.
+        type: A Python type
+
+    Returns:
+        Returns `value`
+"""
+
+
+def _get_type_trace_db():
+    # This is a private API. Use of this for external purposes is discouraged.
+    return type_trace_db
+
+
+# Gets a function from the name of a method on a type
+def _get_function_from_type(cls, name):
+    return getattr(cls, name, None)
+
+
+# ScriptClasses must be new-style classes because we construct them using their
+# __new__ method.
+def _is_new_style_class(cls):
+    if hasattr(cls, "__class__"):
+        return "__dict__" in dir(cls) or hasattr(cls, "__slots__")
+
+
+# These OrderedDictWrapper classes replace the actual OrderedDicts in
+# module with versions that get/set properties inside of Module.
+# This allows us to reuse most of nn.Module while still storing the
+# data in C++.
+# Each OrderedDict needs to support:
+#  x not in view
+#  x in view
+#  view[name] = ...
+#  view.values()
+#  del view[name]
+#  view.items()
+#  view.keys()
+#  len(view)
+
+
+class OrderedDictWrapper:
+    def __init__(self, _c):
+        self._c = _c
+
+    def keys(self):
+        return [k for k, v in self.items()]
+
+    def values(self):
+        return [v for k, v in self.items()]
+
+    def __len__(self):
+        return len(self.values())
+
+    def __delitem__(self, k):
+        raise RuntimeError("cannot delete methods or parameters of a script module")
+
+    def items(self):
+        return self._c.items()
+
+    def __setitem__(self, k, v):
+        if k not in self:
+            raise RuntimeError(
+                f"Can't add a new parameter after ScriptModule construction. Tried to add '{k}"
+            )
+        self._c.setattr(k, v)
+
+    def __contains__(self, k):
+        return self._c.contains(k)
+
+    def __getitem__(self, k):
+        if k not in self:
+            raise KeyError(k)
+        return self._c.getattr(k)
+
+
+class OrderedModuleDict(OrderedDictWrapper):
+    def __init__(self, module, python_dict):
+        super().__init__(torch._C.ModuleDict(module))
+        # contains _both_ script modules and non-script python-only modules
+
+        # because script modules are subclassed in python and the
+        # C++ Module class will not hold references to them,
+        # to ensure that you always get the same python value here
+        # we store it in the python dict as well
+        self._python_modules = python_dict
+
+    def items(self):
+        r = self._python_modules.items()
+        return r
+
+    def __contains__(self, k):
+        return k in self._python_modules
+
+    def __setitem__(self, k, v):
+        # Cases where sub-module can be re-assigned after ScriptModule construction
+        # 1. If the attr is an module interface type, it's guaranteed that the module is
+        #    not inlined in the graph, so it's safe to swap a new ScriptModule in.
+        # 2. if the new value if a ScriptModule with the same JIT type, IR won't change
+        #    and it's legit to swap a new module in.
+        # In these two cases we allow swapping a new scripted module and update the
+        # corresponding python module dict to keep sync.
+        # Note: the value to be swapped in has to be ScriptModule instead of nn.Module,
+        # otherwise it's illegal and we throw error.
+        if isinstance(v, ScriptModule):
+            self._c.setattr(k, v)
+            self._python_modules[k] = v
+        else:
+            raise RuntimeError(
+                "Cannot re-assign modules in a ScriptModule with non-scripted "
+                f"module, tried to replace existing module '{k}': {v}"
+            )
+
+    def __getitem__(self, k):
+        return self._python_modules[k]
+
+
+# For each user-defined class that subclasses ScriptModule, this meta-class:
+# (1) finds all the methods annotated with @script_method in a ScriptModule and
+#     removes them from the class attributes
+# (2) puts a wrapper around the class's __init__ method to recursively compile
+#     all of the script_methods with the module after the original __init__ has
+#     run. This has to occur after the user-defined __init__ so that submodules and
+#     parameters are initialized _before_ the script compiler resolve references to
+#     `self.param` or `self.module`.
+class ScriptMeta(type):
+    def __init__(cls, name, bases, attrs):  # noqa: B902
+        # Aggregate all the ScriptMethods and constants from superclasses
+        cls._methods: Dict[str, Any] = {}
+        cls._constants_set = set(getattr(cls, "__constants__", ()))
+        for base in reversed(bases):
+            for k, v in getattr(base, "_methods", {}).items():
+                cls._methods[k] = v
+            base_constants: Set = getattr(base, "_constants_set", set())
+            cls._constants_set = cls._constants_set.union(base_constants)
+
+        # find all the script methods of the current class
+        for k, v in sorted(attrs.items()):
+            if isinstance(v, ScriptMethodStub):
+                delattr(cls, k)
+                cls._methods[v.original_method.__name__] = v
+
+        if getattr(cls, "_disable_script_meta", False):
+            # We leave built-in ScriptModule types alone, since this metaclass
+            # is only for compiling user classes that inherit from
+            # ScriptModule.
+            super().__init__(name, bases, attrs)
+            return
+
+        original_init = getattr(cls, "__init__", lambda self: None)
+
+        @functools.wraps(original_init)
+        def init_then_script(self, *args, **kwargs):
+            num_methods = len(cls._methods)
+            original_init(self, *args, **kwargs)
+            added_methods_in_init = len(cls._methods) > num_methods
+
+            if type(self) == cls:
+
+                def make_stubs(module):
+                    cls = type(module)
+                    if hasattr(cls, "_methods"):
+                        return [v for k, v in sorted(cls._methods.items())]
+                    else:
+                        return infer_methods_to_compile(module)
+
+                self.__dict__[
+                    "_actual_script_module"
+                ] = torch.jit._recursive.create_script_module(
+                    self, make_stubs, share_types=not added_methods_in_init
+                )
+
+                # Delete the Python attributes that now shadow the ScriptModule
+                # ones, so that __getattr__ and __setattr__ will properly find
+                # the scripted versions.
+                concrete_type = self._actual_script_module._concrete_type
+                for name in concrete_type.get_attributes():
+                    delattr(self, name)
+                for name, _ in concrete_type.get_modules():
+                    delattr(self, name)
+                for name in ("_parameters", "_buffers", "_modules"):
+                    delattr(self, name)
+
+        cls.__init__ = init_then_script  # type: ignore[misc]
+        super().__init__(name, bases, attrs)
+
+
+class _CachedForward:
+    def __get__(self, obj, cls):
+        return self.__getattr__("forward")  # type: ignore[attr-defined]
+
+
+class ScriptWarning(Warning):
+    pass
+
+
+def script_method(fn):
+    if not _enabled:
+        return fn
+    # NOTE: we need to traverse two frames here because the meta-class frame
+    # for ScriptModule will be present, as opposed to invoking @script on a
+    # a function or invoking define() on a CompilationUnit.
+    # The stack will look like:
+    #
+    # 0. createResolutionCallback()
+    # 1. script_method()
+    # 2. ScriptModule metaclass frame
+    # 3. Surrounding scope
+    #
+    # createResolutionCallback internally adds 1 to get us to the scope of this
+    # function (the calling function). Adding 2 gets us to the proper surrounding scope.
+    _rcb = _jit_internal.createResolutionCallbackFromFrame(frames_up=2)
+    ast = get_jit_def(fn, fn.__name__, self_name="ScriptModule")
+    return ScriptMethodStub(_rcb, ast, fn)
+
+
+class ConstMap:
+    def __init__(self, const_mapping):
+        self.const_mapping = const_mapping
+
+    def __getattr__(self, attr):
+        return self.const_mapping[attr]
+
+
+def unpackage_script_module(
+    importer: PackageImporter, script_module_id: str
+) -> torch.nn.Module:
+    """
+    Call by ``torch.package.PackageImporter``'s Pickler's ``persistent_load`` function.
+
+    Performs work of loading and returning a ScriptModule from a ``torch.package`` archive.
+    """
+    if not isinstance(importer.zip_reader, torch._C.PyTorchFileReader):
+        raise RuntimeError(
+            "Loading ScriptObjects from a PackageImporter created from a "
+            "directory is not supported. Use a package archive file instead."
+        )
+    cu = torch._C.CompilationUnit()
+    cpp_module = torch._C._import_ir_module_from_package(
+        cu,
+        importer.zip_reader,
+        importer.storage_context,
+        validate_map_location(importer.last_map_location),
+        script_module_id,
+    )
+    return wrap_cpp_module(cpp_module)
+
+
+if _enabled:
+    _magic_methods = [
+        "__iter__",
+        "__len__",
+        "__neg__",
+        "__mul__",
+        "__contains__",
+        "__add__",
+        "__sub__",
+        "__pow__",
+        "__truediv__",
+        "__mod__",
+        "__ne__",
+        "__eq__",
+        "__lt__",
+        "__gt__",
+        "__le__",
+        "__ge__",
+        "__and__",
+        "__or__",
+        "__xor__",
+        "__getitem__",
+        "__setitem__",
+        "__call__",
+        "__int__",
+        "__float__",
+        "__bool__",
+        "__str__",
+        "__enter__",
+        "__exit__",
+    ]
+
+    class RecursiveScriptClass:
+        """Wrapper for a TorchScript class instance for use in Python.
+
+        An analogue of RecursiveScriptModule for regular objects that are not modules.
+        This class is a wrapper around a torch._C.ScriptObject that represents an instance
+        of a TorchScript class and allows it to be used in Python.
+
+        Attributes:
+            _c [torch._C.ScriptObject]: The C++ object to which attribute lookups and method
+                calls are forwarded.
+            _props [Dict[str, property]]: A dictionary of properties fetched from self._c and
+                exposed on this wrppaer.
+        """
+
+        def __init__(self, cpp_class):
+            super().__init__()
+            self.__dict__["_initializing"] = True
+            self._c = cpp_class
+
+            # Add wrapped object's properties to this class instance.
+            self._props = {
+                prop.name: property(prop.getter, prop.setter)
+                for prop in self._c._properties()
+            }
+
+            self.__dict__["_initializing"] = False
+
+        def __getattr__(self, attr):
+            if self.__dict__.get("_initializing"):
+                return super().__getattr__(attr)  # type: ignore[misc]
+
+            if attr in self._props:
+                return self._props[attr].fget()  # type: ignore[call-arg, misc]
+
+            return getattr(self._c, attr)
+
+        def __setattr__(self, attr, value):
+            if self.__dict__.get("_initializing"):
+                return super().__setattr__(attr, value)
+
+            if attr in self._props:
+                return self._props[attr].fset(value)  # type: ignore[call-arg, misc]
+
+            setattr(self._c, attr, value)
+
+        # Delegate calls to magic methods like __len__ to the C++ module backing the
+        # RecursiveScriptClass.
+        def forward_magic_method(self, method_name, *args, **kwargs):
+            if not self._c._has_method(method_name):
+                raise TypeError
+
+            self_method = self.__getattr__(method_name)
+            return self_method(*args, **kwargs)
+
+        def __getstate__(self):
+            raise pickle.PickleError("ScriptClasses cannot be pickled")
+
+        def __iadd__(self, other):
+            if self._c._has_method("__iadd__"):
+                return self.forward_magic_method("__iadd__", other)
+            else:
+                return self.forward_magic_method("__add__", other)
+
+    for method_name in _magic_methods:
+
+        def method_template(self, *args, **kwargs):
+            return self.forward_magic_method(method_name, *args, **kwargs)
+
+        setattr(RecursiveScriptClass, method_name, method_template)
+
+    # this is a Python 'non-data descriptor' that causes the first access
+    # to ScriptModule's forward to look up the forward method and stash
+    # it in the objects dict. Due to the standard rules for attribute lookup,
+    # subsequent lookups will just directly return the previously looked up method.
+    # This is necessary because nn.Module defines forward as a method. If we
+    # did nothing, __getattr__ would not be called. Instead we'd get nn.Module.forward
+    # which always throws an exception.
+
+    class ScriptModule(Module, metaclass=ScriptMeta):
+        r"""Wrapper for C++ torch::jit::Module with methods, attributes, and parameters.
+
+        A wrapper around C++ ``torch::jit::Module``. ``ScriptModule``\s
+        contain methods, attributes, parameters, and
+        constants. These can be accessed the same way as on a normal ``nn.Module``.
+        """
+
+        __jit_unused_properties__ = [
+            "code",
+            "code_with_constants",
+            "graph",
+            "inlined_graph",
+            "original_name",
+        ]
+
+        def __init__(self) -> None:
+            super().__init__()
+
+        forward: Callable[..., Any] = _CachedForward()  # type: ignore[assignment]
+
+        def __getattr__(self, attr):
+            if "_actual_script_module" not in self.__dict__:
+                return super().__getattr__(attr)
+            return getattr(self._actual_script_module, attr)
+
+        def __setattr__(self, attr, value):
+            if "_actual_script_module" not in self.__dict__:
+                # Unwrap torch.jit.Attribute into a regular setattr + record
+                # the provided type in __annotations__.
+                #
+                # This ensures that if we use the attr again in `__init__`, it
+                # will look like the actual value, not an instance of Attribute.
+                if isinstance(value, Attribute):
+                    # NB: Ensure that we set __annotations__ on the specific
+                    # class in question, and not on a superclass (which would
+                    # be wrong wrong wrong!).
+                    # See also https://github.com/pytorch/pytorch/issues/39463
+                    if "__annotations__" not in self.__class__.__dict__:
+                        self.__class__.__annotations__ = {}
+                    self.__annotations__[attr] = value.type
+                    value = value.value
+                return super().__setattr__(attr, value)
+
+            setattr(self._actual_script_module, attr, value)
+
+        def define(self, src):
+            if "_actual_script_module" in self.__dict__:
+                # If we have completed initialization, just defer to the
+                # backing RecursiveScriptModule to eagerly compile the provided
+                # source.
+                return self._actual_script_module.define(src)
+
+            # Otherwise, we are still in the object's __init__.
+            # In that case, add `src` as a stub to be compiled.
+            #
+            # We use frames_up=1 to get to the proper surrounding scope. The stack
+            # will look like:
+            # 0. createResolutionCallback
+            # 1. define()
+            # 2. surrounding scope.
+            #
+            # createResolutionCallback internally adds 1 to get us to our frame, then
+            # we add 1 to get to the proper surrounding scope.
+            rcb = _jit_internal.createResolutionCallbackFromFrame(frames_up=1)
+            ast = torch._C._parse_source_def(src)
+            self._methods[ast.name().name] = ScriptMethodStub(rcb, ast, None)
+
+        def _replicate_for_data_parallel(self):
+            return self._actual_script_module._replicate_for_data_parallel()
+
+        def __reduce_package__(self, exporter: PackageExporter):
+            """Save a ScriptModule inside of a ``torch.package`` archive.
+
+            Called by ``torch.package.PackageExporter``'s Pickler's ``persistent_id`` when
+            saving TorchScript objects. Performs act of saving a ScriptModule inside of
+            a ``torch.package`` archive.
+
+            Returns method to load the ScriptModule from a ``torch.package.PackageImporter``'s
+            Pickler's ``persistent_load`` function.
+            """
+            script_module_id = exporter.get_unique_id()
+            exporter.script_module_serializer.serialize(self._c, int(script_module_id))
+            return (unpackage_script_module, (script_module_id,))
+
+    class RecursiveScriptModule(ScriptModule):
+        # XXX: RecursiveScriptModule inherits from ScriptModule for the sole
+        # reason that it retains the existing isinstance(ScriptModule)
+        # behavior.
+        r"""Retain the existing isinstance(ScriptModule) behavior.
+
+        The core data structure in TorchScript is the ``ScriptModule``. It is an
+        analogue of torch's ``nn.Module`` and represents an entire model as a tree of
+        submodules. Like normal modules, each individual module in a ``ScriptModule`` can
+        have submodules, parameters, and methods. In ``nn.Module``\s methods are implemented
+        as Python functions, but in ``ScriptModule``\s methods are implemented as
+        TorchScript functions, a statically-typed subset of Python that contains all
+        of PyTorch's built-in Tensor operations. This difference allows your
+        ``ScriptModule``\s code to run without the need for a Python interpreter.
+
+        ``ScriptModule``\s should not be created manually, instead use
+        either :func:`tracing ` or :func:`scripting `.
+        Tracing and scripting can be applied incrementally and :ref:`composed as necessary `.
+
+        * Tracing records the tensor operations as executed with a set of example inputs and uses these
+          operations to construct a computation graph. You can use the full dynamic behavior of Python with tracing,
+          but values other than Tensors and control flow aren't captured in the graph.
+
+        * Scripting inspects the Python code of the model
+          and compiles it to TorchScript. Scripting allows the use of many `types`_ of values and supports dynamic control flow.
+          Many, but not all features of Python are supported by the compiler, so changes to the source code may be necessary.
+        """
+
+        _disable_script_meta = True
+
+        def __init__(self, cpp_module):
+            self.__dict__["_initializing"] = True
+            self._c = cpp_module
+            super().__init__()
+            # Delete the 'training' attribute set up by `Module.__init__`. It
+            # will get set on the underlying cpp module, so we delete it here
+            # to avoid this version shadowing the cpp module version.
+            delattr(self, "training")
+
+        @staticmethod
+        def _construct(cpp_module, init_fn):
+            """
+            Construct a RecursiveScriptModule that's ready for use.
+
+            PyTorch code should use this to construct a RecursiveScriptModule instead
+            of instead of calling `__init__` directly, as it makes sure the
+            object is properly finalized (and in the future, we may take
+            control of how the RecursiveScriptModule instance is created).
+
+            Args:
+                cpp_module:  The C++ Module that will hold the actual state of
+                             this RecursiveScriptModule instance.
+                init_fn:  Lambda that initializes the RecursiveScriptModule passed to it.
+            """
+            script_module = RecursiveScriptModule(cpp_module)
+            init_fn(script_module)
+
+            # Finalize the ScriptModule: replace the nn.Module state with our
+            # custom implementations and flip the _initializing bit.
+            RecursiveScriptModule._finalize_scriptmodule(script_module)
+            return script_module
+
+        @staticmethod
+        def _finalize_scriptmodule(script_module):
+            script_module._parameters = OrderedDictWrapper(
+                torch._C.ParameterDict(script_module._c)
+            )
+            script_module._buffers = OrderedDictWrapper(
+                torch._C.BufferDict(script_module._c)
+            )
+            script_module._modules = OrderedModuleDict(
+                script_module._c, script_module._modules
+            )
+            script_module._initializing = False
+
+        def _reconstruct(self, cpp_module):
+            """
+            Re-construct an instance of RecursiveScriptModule using an instance of a C++ module.
+
+            Args:
+                cpp_module: The C++ module that this RecursiveScriptModule will be rebuilt around.
+            """
+            self.__init__(cpp_module)  # type: ignore[misc]
+
+            # Copy the concrete type from the C++ module to this ScriptModule.
+            self._concrete_type = torch._C.ConcreteModuleType.from_jit_type(
+                self._c._type()
+            )
+
+            # Copy submodules from the C++ module to this ScriptModule.
+            modules = {}
+            for name, cpp_module in torch._C.ModuleDict(self._c).items():
+                modules[name] = wrap_cpp_module(cpp_module)
+            self._modules = OrderedModuleDict(self._c, modules)  # type: ignore[assignment]
+
+            # Copy parameters and buffers.
+            self._parameters = OrderedDictWrapper(torch._C.ParameterDict(self._c))  # type: ignore[assignment]
+            self._buffers = OrderedDictWrapper(torch._C.BufferDict(self._c))  # type: ignore[assignment]
+
+            # Get rid of the functions from the old C++ module.
+            self.__dict__ = {
+                k: v
+                for k, v in self.__dict__.items()
+                if not isinstance(v, torch._C.ScriptMethod)
+            }
+            self.__dict__["_initializing"] = False
+
+        @property
+        def graph(self):
+            r"""Return a string representation of the internal graph for the ``forward`` method.
+
+            See :ref:`interpreting-graphs` for details.
+            """
+            return self._c._get_method("forward").graph
+
+        @property
+        def inlined_graph(self):
+            r"""
+            Return a string representation of the internal graph for the ``forward`` method.
+
+            This graph will be preprocessed to inline all function and method calls.
+            See :ref:`interpreting-graphs` for details.
+            """
+            return self.forward.inlined_graph  # type: ignore[attr-defined]
+
+        @property
+        def code(self):
+            r"""
+            Return a pretty-printed representation (as valid Python syntax) of the internal graph for the ``forward`` method.
+
+            See :ref:`inspecting-code` for details.
+            """
+            return self.forward.code  # type: ignore[attr-defined]
+
+        @property
+        def code_with_constants(self):
+            r"""Return a tuple.
+
+            Returns a tuple of:
+
+            [0] a pretty-printed representation (as valid Python syntax) of
+            the internal graph for the ``forward`` method. See `code`.
+            [1] a ConstMap following the CONSTANT.cN format of the output in [0].
+            The indices in the [0] output are keys to the underlying constant's values.
+
+            See :ref:`inspecting-code` for details.
+            """
+            r = self.forward.code_with_constants  # type: ignore[attr-defined]
+            return (r[0], ConstMap(r[1]))
+
+        def save(self, f, **kwargs):
+            r"""Save with a file-like object.
+
+            save(f, _extra_files={})
+
+            See :func:`torch.jit.save ` which accepts a file-like object.
+            This function, torch.save(), converts the object to a string, treating it as a path.
+            DO NOT confuse these two functions when it comes to the 'f' parameter functionality.
+            """
+            return self._c.save(str(f), **kwargs)
+
+        def _save_for_lite_interpreter(self, *args, **kwargs):
+            r"""Add (or update) the bytecode session to the script model.
+
+            _save_for_lite_interpreter(f)
+
+            The updated model is used
+            in lite interpreter for mobile applications.
+
+            Args:
+                f: a string containing a file name.
+                _extra_files: Map from filename to contents which will be stored as part of 'f'.
+
+            """
+            return self._c._save_for_mobile(*args, **kwargs)
+
+        def _save_to_buffer_for_lite_interpreter(self, *args, **kwargs):
+            return self._c._save_to_buffer_for_mobile(*args, **kwargs)
+
+        def save_to_buffer(self, *args, **kwargs):
+            return self._c.save_to_buffer(*args, **kwargs)
+
+        def get_debug_state(self, *args, **kwargs):
+            return self._c.get_debug_state()
+
+        def extra_repr(self):
+            return f"original_name={self.original_name}"
+
+        def graph_for(self, *args, **kwargs):
+            return self.forward.graph_for(self, *args, **kwargs)  # type: ignore[attr-defined]
+
+        @property
+        def original_name(self):
+            if type(self) == str(self._c._type().name()):
+                return ""
+            return str(self._c._type().name())
+
+        def define(self, src):
+            # We use frames_up=1 to get to the proper surrounding scope. The stack
+            # will look like:
+            # 0. createResolutionCallback
+            # 1. define()
+            # 2. surrounding scope.
+            #
+            # createResolutionCallback internally adds 1 to get us to our frame, then
+            # we add 1 to get to the proper surrounding scope.
+            rcb = _jit_internal.createResolutionCallbackFromFrame(frames_up=1)
+            self._c._define(self._concrete_type, src, rcb)
+
+        def __getattr__(self, attr):
+            if "_initializing" not in self.__dict__:
+                raise RuntimeError(
+                    "ScriptModule has not been initialized, did you forget to call super's init?"
+                )
+
+            if self._initializing:
+                return super().__getattr__(attr)
+
+            # _modules check is before hasattr since modules are included as attributes in _c,
+            # but we want to get the python wrapper from _modules instead of the raw _c object.
+            if attr in self._modules:
+                return self._modules[attr]
+            elif self._c.hasattr(attr):
+                return self._c.getattr(attr)
+            elif self._c._has_method(attr):
+                script_method = self._c._get_method(attr)
+                # cache method so future calls do not go through __getattr__
+                # to improve invocation performance
+                self.__dict__[attr] = script_method
+                return script_method
+
+            return super().__getattr__(attr)
+
+        def __setattr__(self, attr, value):
+            if self._initializing:
+                return super().__setattr__(attr, value)
+
+            if attr in self._modules:
+                self._modules[attr] = value
+            elif self._c.hasattr(attr):
+                self._c.setattr(attr, value)
+            elif (
+                hasattr(self, "_concrete_type")
+                and attr in self._concrete_type.get_constants().keys()
+            ):
+                # TODO: we don't have _concrete_type set after load(), and in general we lose constant information.
+                # We should encode constants as class type attributes (or something) so it persists across save/load.
+                raise AttributeError(
+                    f"Cannot mutate TorchScript constant value: '{attr}'. Value: '{value}'"
+                )
+            else:
+                # We allow setting Python attributes on the ScriptModule, for
+                # when people want to stash some convenience info on it.
+                # TODO: it's possible that the following is confusing:
+                #   s = torch.jit.script(...)
+                #   s.python_attr = ...
+                #   s.save()   <--- this doesn't have `python_attr`
+                # It's fairly trivial to save enough info to warn in this case.
+                return super().__setattr__(attr, value)
+
+        def __copy__(self):
+            return torch.jit._recursive.wrap_cpp_module(copy.copy(self._c))
+
+        def __deepcopy__(self, memo):
+            return torch.jit._recursive.wrap_cpp_module(copy.deepcopy(self._c, memo))
+
+        # Python magic methods do method lookups on an object's class type, instead of looking up
+        # the method defines on the class instance. In order to continue to expose the magic methods
+        # of builtin-containers (ModuleList, Sequential, ModuleDict) to Python, we
+        # define magic methods here as a shim to the correct attribute.
+        def forward_magic_method(self, method_name, *args, **kwargs):
+            self_method = getattr(self, method_name)
+            if getattr(self_method, "__func__", None) == getattr(
+                RecursiveScriptModule, method_name
+            ):
+                raise NotImplementedError
+            return self_method(*args, **kwargs)
+
+        def __iter__(self):
+            return self.forward_magic_method("__iter__")
+
+        def __getitem__(self, idx):
+            return self.forward_magic_method("__getitem__", idx)
+
+        def __len__(self):
+            return self.forward_magic_method("__len__")
+
+        def __contains__(self, key):
+            return self.forward_magic_method("__contains__", key)
+
+        # dir is defined by the base nn.Module, so instead of throwing if
+        # it is not overridden, we call into the nn.Module __dir__ method
+        def __dir__(self):
+            self_method = self.__dir__
+            if (
+                self_method.__func__  # type: ignore[attr-defined]
+                == _get_function_from_type(RecursiveScriptModule, "__dir__")
+            ):
+                return super().__dir__()
+            return self_method()
+
+        # to resolve bool(value), Python looks if __bool__ is defined then __iter__
+        # is defined then returns true for classes. Since __iter__() on this
+        # class throws if it isn't overridden, we define __bool__ to preserve default behavior
+        def __bool__(self):
+            self_method = self.__bool__
+            if (
+                self_method.__func__  # type: ignore[attr-defined]
+                == _get_function_from_type(RecursiveScriptModule, "__bool__")
+            ):
+                return True
+            return self_method()
+
+        def _replicate_for_data_parallel(self):
+            # we have to initialize ScriptModule properly so that
+            # it works with pybind11
+            def init_fn(script_module):
+                # Don't do anything here, we'll initialize the ScriptModule below
+                return
+
+            return RecursiveScriptModule._construct(
+                self._c._replicate_for_data_parallel(), init_fn
+            )
+
+    # Need to copy all RecursiveScriptModule methods to ScriptModule.
+    #
+    # This is because `super().foo()` does not use
+    # `__getattr__` to look up `foo`. So we need to make each method available on
+    # the ScriptModule manually.
+    for name, item in RecursiveScriptModule.__dict__.items():
+        if not callable(item) and not isinstance(item, property):
+            continue
+        if name.startswith("__") or hasattr(ScriptModule, name):
+            continue
+        # We can copy over the implementation wholesale because besides the
+        # `super()` thing above, ScriptModule behaves exactly like
+        # RecursiveScriptModule
+        setattr(ScriptModule, name, item)
+
+    def _get_methods(cls):
+        import inspect
+
+        # In Python 3 unbound methods are functions, but in Python 2 they are methods
+        return inspect.getmembers(
+            cls, predicate=lambda x: inspect.isfunction(x) or inspect.ismethod(x)
+        )
+
+    _compiled_methods_allowlist = {
+        "forward",
+        "register_buffer",
+        "register_parameter",
+        "register_module",
+        "add_module",
+        "_apply",
+        "apply",
+        "cuda",
+        "cpu",
+        "to",
+        "type",
+        "float",
+        "double",
+        "half",
+        "state_dict",
+        "_save_to_state_dict",
+        "load_state_dict",
+        "_load_from_state_dict",
+        "_named_members",
+        "parameters",
+        "named_parameters",
+        "buffers",
+        "named_buffers",
+        "children",
+        "named_children",
+        "modules",
+        "named_modules",
+        "zero_grad",
+        "share_memory",
+        "_get_name",
+        "extra_repr",
+        "_slow_forward",
+        "_tracing_name",
+        "eval",
+        "train",
+        "get_extra_state",
+        "set_extra_state",
+    }
+
+    def _make_fail(name):
+        def fail(self, *args, **kwargs):
+            raise RuntimeError(name + " is not supported on ScriptModules")
+
+        return fail
+
+    for name, method in _get_methods(torch.nn.Module):
+        if name.startswith("__") or name.endswith("_call_impl"):
+            continue
+        if (
+            name not in RecursiveScriptModule.__dict__
+            and name not in _compiled_methods_allowlist
+        ):
+            setattr(RecursiveScriptModule, method.__name__, _make_fail(name))
+
+
+else:
+    # TODO MAKE SURE THAT DISABLING WORKS
+    class RecursiveScriptClass:  # type: ignore[no-redef]
+        pass
+
+    class ScriptModule(torch.nn.Module):  # type: ignore[no-redef]
+        def __init__(self, arg=None):
+            super().__init__()
+
+    class RecursiveScriptModule(ScriptModule):  # type: ignore[no-redef]
+        def __init__(self, arg=None):
+            super().__init__()
+
+
+def call_prepare_scriptable_func_impl(obj, memo):
+    if not isinstance(obj, torch.nn.Module):
+        return obj
+
+    obj_id = id(obj)
+
+    # If obj_id is in memo, obj has already been prepared or is being
+    # prepared in another call up the stack.
+    if obj_id in memo:
+        return memo[id(obj)]
+
+    obj = obj.__prepare_scriptable__() if hasattr(obj, "__prepare_scriptable__") else obj  # type: ignore[operator]
+    # Record obj in memo to avoid infinite recursion in the case of cycles in the module
+    # hierarchy when recursing below.
+    memo[obj_id] = obj
+
+    new_obj_dict = {}
+
+    for name, sub_module in obj.__dict__.items():
+        if name == "_modules":
+            for k, v in sub_module.items():
+                sub_module[k] = call_prepare_scriptable_func_impl(v, memo)
+            new_obj_dict[name] = sub_module
+        elif isinstance(sub_module, torch.nn.Module) and not isinstance(
+            sub_module, ScriptModule
+        ):
+            new_obj_dict[name] = call_prepare_scriptable_func_impl(sub_module, memo)
+        else:
+            new_obj_dict[name] = sub_module
+
+    for k, v in new_obj_dict.items():
+        obj.__dict__[name] = v
+
+    return obj
+
+
+def call_prepare_scriptable_func(obj):
+    memo: Dict[int, torch.nn.Module] = {}
+    return call_prepare_scriptable_func_impl(obj, memo)
+
+
+def create_script_dict(obj):
+    """
+    Create a ``torch._C.ScriptDict`` instance with the data from ``obj``.
+
+    Args:
+        obj (dict): The Python dictionary that is used to initialize the ``ScriptDict``
+                    returned by this function.
+
+    Returns:
+        An instance of ``torch._C.ScriptDict`` that has the same data as ``obj``
+        and can be passed between Python and TorchScript with reference semantics and
+        zero copy overhead.
+    """
+    return torch._C.ScriptDict(obj)  # type: ignore[attr-defined]
+
+
+def create_script_list(obj, type_hint=None):
+    """
+    Create a ``torch._C.ScriptList`` instance with the data from ``obj``.
+
+    Args:
+        obj (dict): The Python list that is used to initialize the ``ScriptList``
+                    returned by this function.
+    Returns:
+        An instance of ``torch._C.ScriptList`` that has the same data as ``obj``
+        and can be passed between Python and TorchScript with reference semantics and
+        zero copy overhead.
+    """
+    return torch._C.ScriptList(obj)  # type: ignore[attr-defined]
+
+
+_TOPLEVEL: bool = True
+
+
+def _script_impl(
+    obj,
+    optimize=None,
+    _frames_up=0,
+    _rcb=None,
+    example_inputs: Union[List[Tuple], Dict[Callable, List[Tuple]], None] = None,
+):
+    global type_trace_db
+
+    if optimize is not None:
+        warnings.warn(
+            "`optimize` is deprecated and has no effect. "
+            "Use `with torch.jit.optimized_execution()` instead",
+            FutureWarning,
+            stacklevel=3,
+        )
+
+    # No-op for modules, functions, class instances that are already scripted
+    if isinstance(obj, RecursiveScriptClass):
+        return obj
+    if isinstance(obj, ScriptModule):
+        return obj
+    if isinstance(obj, ScriptFunction):
+        return obj
+
+    if example_inputs:
+        # If MonkeyType is installed, enable profile directed type annotation
+        # Check if example_inputs are defined and generate call traces
+        # for the method by running eager mode version of the method with
+        # the provide example inputs. This logs all the traces in type_trace_db
+        type_trace_db = JitTypeTraceStore()
+        if monkeytype_trace:
+            monkeytype_config = JitTypeTraceConfig(type_trace_db)
+            with monkeytype_trace(monkeytype_config):
+                if isinstance(example_inputs, Dict):
+                    # If the obj is an nn.Module or a class, then each method is
+                    # executed with the arguments provided in the example inputs.
+                    # example inputs here will be of type Dict(class.method, (arguments))
+                    # This is used to infer type annotations for those methods
+                    # which are not called directly under the hood of monkeytype.
+                    for module, example_input in example_inputs.items():
+                        for example in example_input:
+                            module(*example)
+                elif isinstance(example_inputs, List):
+                    for examples in example_inputs:
+                        obj(*examples)
+                else:
+                    raise ValueError(
+                        "Error: Unable to infer types. Please format the inputs to type `List[Tuple]`"
+                        " or `Dict[Callable, List[Tuple]]` to be run with MonkeyType."
+                    )
+        else:
+            warnings.warn(
+                "Warning: monkeytype is not installed. Please install https://github.com/Instagram/MonkeyType "
+                "to enable Profile-Directed Typing in TorchScript. Refer to "
+                "https://github.com/Instagram/MonkeyType/blob/master/README.rst to install MonkeyType. "
+            )
+
+    if isinstance(obj, torch.nn.Module):
+        obj = call_prepare_scriptable_func(obj)
+        return torch.jit._recursive.create_script_module(
+            obj, torch.jit._recursive.infer_methods_to_compile
+        )
+    else:
+        obj = obj.__prepare_scriptable__() if hasattr(obj, "__prepare_scriptable__") else obj  # type: ignore[operator]
+
+    if isinstance(obj, dict):
+        return create_script_dict(obj)
+    if isinstance(obj, list):
+        return create_script_list(obj)
+
+    if inspect.isclass(obj):
+        qualified_name = _qualified_name(obj)
+        # If this type is a `nn.Module` subclass, they probably meant to pass
+        # an instance instead of a Module
+        if issubclass(obj, torch.nn.Module):
+            raise RuntimeError(
+                f"Type '{obj}' cannot be compiled since it inherits from nn.Module, pass an instance instead"
+            )
+
+        # Enums are automatically usable in TorchScript, explicitly scripting
+        # is not necessary, but not harmful either.
+        if issubclass(obj, enum.Enum):
+            return obj
+
+        if not _is_new_style_class(obj):
+            raise RuntimeError(
+                "TorchScript classes must be new-style classes. "
+                "Please inherit from 'object'."
+            )
+        if len(obj.mro()) > 2:
+            raise RuntimeError(
+                "TorchScript classes does not support inheritance yet. "
+                "Please directly inherit from 'object'."
+            )
+        if _rcb is None:
+            _rcb = _jit_internal.createResolutionCallbackFromFrame(_frames_up + 1)
+        _compile_and_register_class(obj, _rcb, qualified_name)
+        return obj
+    elif inspect.isfunction(obj) or inspect.ismethod(obj):
+        qualified_name = _qualified_name(obj)
+        # this is a decorated fn, and we need to the underlying fn and its rcb
+        if hasattr(obj, "__script_if_tracing_wrapper"):
+            obj = obj.__original_fn  # type: ignore[union-attr]
+            _rcb = _jit_internal.createResolutionCallbackFromClosure(obj)
+
+        # some functions are explicitly marked as not supported in script mode
+        if hasattr(obj, "__script_unsupported"):
+            raise RuntimeError("TorchScript error: " + obj.__script_unsupported)
+
+        _check_directly_compile_overloaded(obj)
+        maybe_already_compiled_fn = _try_get_jit_cached_function(obj)
+        if maybe_already_compiled_fn:
+            maybe_already_compiled_fn._torchdynamo_inline = obj  # type: ignore[attr-defined]
+            return maybe_already_compiled_fn
+        ast = get_jit_def(obj, obj.__name__)
+        if _rcb is None:
+            _rcb = _jit_internal.createResolutionCallbackFromClosure(obj)
+        fn = torch._C._jit_script_compile(
+            qualified_name, ast, _rcb, get_default_args(obj)
+        )
+        # Forward docstrings
+        fn.__doc__ = obj.__doc__
+        # Allow torch.compile() to inline
+        fn._torchdynamo_inline = obj  # type: ignore[attr-defined]
+        _set_jit_function_cache(obj, fn)
+        return fn
+    else:
+        return torch.jit._recursive.create_script_class(obj)
+
+
+def script(
+    obj,
+    optimize=None,
+    _frames_up=0,
+    _rcb=None,
+    example_inputs: Union[List[Tuple], Dict[Callable, List[Tuple]], None] = None,
+):
+    r"""Script the function.
+
+    Scripting a function or ``nn.Module`` will inspect the source code, compile
+    it as TorchScript code using the TorchScript compiler, and return a :class:`ScriptModule` or
+    :class:`ScriptFunction`. TorchScript itself is a subset of the Python language, so not all
+    features in Python work, but we provide enough functionality to compute on
+    tensors and do control-dependent operations. For a complete guide, see the
+    :ref:`language-reference`.
+
+    Scripting a dictionary or list copies the data inside it into a TorchScript instance than can be
+    subsequently passed by reference between Python and TorchScript with zero copy overhead.
+
+    ``torch.jit.script`` can be used as a function for modules, functions, dictionaries and lists
+     and as a decorator ``@torch.jit.script`` for :ref:`torchscript-classes` and functions.
+
+    Args:
+        obj (Callable, class, or nn.Module):  The ``nn.Module``, function, class type,
+                                                  dictionary, or list to compile.
+        example_inputs (Union[List[Tuple], Dict[Callable, List[Tuple]], None]): Provide example inputs
+            to annotate the arguments for a function or ``nn.Module``.
+
+    Returns:
+        If ``obj`` is ``nn.Module``, ``script`` returns
+        a :class:`ScriptModule` object. The returned :class:`ScriptModule` will
+        have the same set of sub-modules and parameters as the
+        original ``nn.Module``. If ``obj`` is a standalone function,
+        a :class:`ScriptFunction` will be returned. If ``obj`` is a ``dict``, then
+        ``script`` returns an instance of `torch._C.ScriptDict`. If ``obj`` is a ``list``,
+        then ``script`` returns an instance of `torch._C.ScriptList`.
+
+    **Scripting a function**
+        The ``@torch.jit.script`` decorator will construct a :class:`ScriptFunction`
+        by compiling the body of the function.
+
+        Example (scripting a function):
+
+        .. testcode::
+
+            import torch
+
+            @torch.jit.script
+            def foo(x, y):
+                if x.max() > y.max():
+                    r = x
+                else:
+                    r = y
+                return r
+
+            print(type(foo))  # torch.jit.ScriptFunction
+
+            # See the compiled graph as Python code
+            print(foo.code)
+
+            # Call the function using the TorchScript interpreter
+            foo(torch.ones(2, 2), torch.ones(2, 2))
+
+        .. testoutput::
+            :hide:
+
+            ...
+
+    ****Scripting a function using example_inputs**
+        Example inputs can be used to annotate a function arguments.
+
+        Example (annotating a function before scripting):
+
+        .. testcode::
+
+            import torch
+
+            def test_sum(a, b):
+                return a + b
+
+            # Annotate the arguments to be int
+            scripted_fn = torch.jit.script(test_sum, example_inputs=[(3, 4)])
+
+            print(type(scripted_fn))  # torch.jit.ScriptFunction
+
+            # See the compiled graph as Python code
+            print(scripted_fn.code)
+
+            # Call the function using the TorchScript interpreter
+            scripted_fn(20, 100)
+
+        .. testoutput::
+            :hide:
+
+            ...
+
+    **Scripting an nn.Module**
+        Scripting an ``nn.Module`` by default will compile the ``forward`` method and recursively
+        compile any methods, submodules, and functions called by ``forward``. If a ``nn.Module`` only uses
+        features supported in TorchScript, no changes to the original module code should be necessary. ``script``
+        will construct :class:`ScriptModule` that has copies of the attributes, parameters, and methods of
+        the original module.
+
+        Example (scripting a simple module with a Parameter):
+
+        .. testcode::
+
+            import torch
+
+            class MyModule(torch.nn.Module):
+                def __init__(self, N, M):
+                    super().__init__()
+                    # This parameter will be copied to the new ScriptModule
+                    self.weight = torch.nn.Parameter(torch.rand(N, M))
+
+                    # When this submodule is used, it will be compiled
+                    self.linear = torch.nn.Linear(N, M)
+
+                def forward(self, input):
+                    output = self.weight.mv(input)
+
+                    # This calls the `forward` method of the `nn.Linear` module, which will
+                    # cause the `self.linear` submodule to be compiled to a `ScriptModule` here
+                    output = self.linear(output)
+                    return output
+
+            scripted_module = torch.jit.script(MyModule(2, 3))
+
+        Example (scripting a module with traced submodules):
+
+        .. testcode::
+
+            import torch
+            import torch.nn as nn
+            import torch.nn.functional as F
+
+            class MyModule(nn.Module):
+                def __init__(self) -> None:
+                    super().__init__()
+                    # torch.jit.trace produces a ScriptModule's conv1 and conv2
+                    self.conv1 = torch.jit.trace(nn.Conv2d(1, 20, 5), torch.rand(1, 1, 16, 16))
+                    self.conv2 = torch.jit.trace(nn.Conv2d(20, 20, 5), torch.rand(1, 20, 16, 16))
+
+                def forward(self, input):
+                    input = F.relu(self.conv1(input))
+                    input = F.relu(self.conv2(input))
+                    return input
+
+            scripted_module = torch.jit.script(MyModule())
+
+        To compile a method other than ``forward`` (and recursively compile anything it calls), add
+        the :func:`@torch.jit.export ` decorator to the method. To opt out of compilation
+        use :func:`@torch.jit.ignore ` or :func:`@torch.jit.unused `.
+
+        Example (an exported and ignored method in a module)::
+
+            import torch
+            import torch.nn as nn
+
+            class MyModule(nn.Module):
+                def __init__(self) -> None:
+                    super().__init__()
+
+                @torch.jit.export
+                def some_entry_point(self, input):
+                    return input + 10
+
+                @torch.jit.ignore
+                def python_only_fn(self, input):
+                    # This function won't be compiled, so any
+                    # Python APIs can be used
+                    import pdb
+                    pdb.set_trace()
+
+                def forward(self, input):
+                    if self.training:
+                        self.python_only_fn(input)
+                    return input * 99
+
+            scripted_module = torch.jit.script(MyModule())
+            print(scripted_module.some_entry_point(torch.randn(2, 2)))
+            print(scripted_module(torch.randn(2, 2)))
+
+        Example ( Annotating forward of nn.Module using example_inputs)::
+
+            import torch
+            import torch.nn as nn
+            from typing import NamedTuple
+
+            class MyModule(NamedTuple):
+            result: List[int]
+
+            class TestNNModule(torch.nn.Module):
+                def forward(self, a) -> MyModule:
+                    result = MyModule(result=a)
+                    return result
+
+            pdt_model = TestNNModule()
+
+            # Runs the pdt_model in eager model with the inputs provided and annotates the arguments of forward
+            scripted_model = torch.jit.script(pdt_model, example_inputs={pdt_model: [([10, 20, ], ), ], })
+
+            # Run the scripted_model with actual inputs
+            print(scripted_model([20]))
+    """
+    if not _enabled:
+        return obj
+    try:
+        global _TOPLEVEL
+        prev = _TOPLEVEL
+        _TOPLEVEL = False
+        ret = _script_impl(
+            obj=obj,
+            optimize=optimize,
+            _frames_up=_frames_up + 1,
+            _rcb=_rcb,
+            example_inputs=example_inputs,
+        )
+
+        if prev:
+            log_torchscript_usage("script", model_id=_get_model_id(ret))
+
+        return ret
+    finally:
+        _TOPLEVEL = prev
+
+
+# overloads are registered in _jit_internal and compiled here so that _overload
+# can be used in nn/functional.py without an import cycle
+
+
+def _check_overload_defaults(impl_defaults, overload_defaults, loc):
+    for name, overload_value in overload_defaults.items():
+        if name not in impl_defaults or impl_defaults[name] != overload_value:
+            raise torch.jit.frontend.FrontendError(
+                loc,
+                "Default parameters on overloads do not affect the runtime so they "
+                "must equal to the default parameter on the implementation function. Found on "
+                f"parameter {name}",
+            )
+
+
+def _compile_function_with_overload(overload_fn, qual_name, impl_fn):
+    overload_decl = get_jit_def(overload_fn, overload_fn.__name__).decl()
+    overload_signature = torch.jit.annotations.get_signature(
+        overload_fn, None, None, inspect.ismethod(overload_fn)
+    )
+    impl_ast = get_jit_def(impl_fn, impl_fn.__name__)
+    overload_defaults = get_default_args(overload_fn)
+    implementation_defaults = get_default_args(impl_fn)
+    _rcb = _jit_internal.createResolutionCallbackFromClosure(impl_fn)
+    _check_overload_defaults(
+        implementation_defaults, overload_defaults, overload_decl.range()
+    )
+    fn = torch._C._jit_script_compile_overload(
+        qual_name,
+        overload_decl,
+        impl_ast,
+        _rcb,
+        implementation_defaults,
+        overload_signature,
+    )
+    return fn
+
+
+def _get_overloads(obj):
+    # check for cached compiled fns
+    existing_compiled_fns = _try_get_jit_cached_overloads(obj)
+    qual_name = _qualified_name(obj)
+    uncompiled_overloads = _jit_internal._get_fn_overloads(qual_name)
+    if uncompiled_overloads is None:
+        return existing_compiled_fns
+
+    if obj in uncompiled_overloads:
+        raise RuntimeError(
+            _jit_internal.get_overload_no_implementation_error_message("function", obj)
+        )
+
+    compiled_fns = []
+    for overload_fn in uncompiled_overloads:
+        compiled_fns.append(
+            _compile_function_with_overload(overload_fn, qual_name, obj)
+        )
+
+    if existing_compiled_fns:
+        compiled_fns = existing_compiled_fns + compiled_fns
+
+    # cache compilation, remove information stored to do compilation
+    _set_jit_overload_cache(obj, compiled_fns)
+    _jit_internal._clear_fn_overloads(qual_name)
+    return compiled_fns
+
+
+def _check_directly_compile_overloaded(obj):
+    qual_name = _qualified_name(obj)
+    if _jit_internal._get_fn_overloads(qual_name) or _try_get_jit_cached_overloads(obj):
+        raise RuntimeError(
+            f"Function {qual_name} cannot be directly compiled because it"
+            " is overloaded. It must be used in a context of a function"
+            " where its inputs can determine which overload to call."
+        )
+
+
+def interface(obj):
+    r"""Decorate to annotate classes or modules of different types.
+
+    This decorator can be used to define an interface that can be used to annotate
+    classes or modules of different types. This can be used for to annotate a submodule
+    or attribute class that could have different types that implement the same
+    interface, or which could be swapped at runtime; or to store a list of modules or
+    classes of varying types.
+
+    It is sometimes used to implement "Callables" - functions or modules that implement
+    an interface but whose implementations differ and which can be swapped out.
+
+    Example:
+    .. testcode::
+
+        import torch
+        from typing import List
+
+        @torch.jit.interface
+        class InterfaceType:
+            def run(self, x: torch.Tensor) -> torch.Tensor:
+                pass
+
+        # implements InterfaceType
+        @torch.jit.script
+        class Impl1:
+            def run(self, x: torch.Tensor) -> torch.Tensor:
+                return x.relu()
+
+        class Impl2(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.val = torch.rand(())
+
+            @torch.jit.export
+            def run(self, x: torch.Tensor) -> torch.Tensor:
+                return x + self.val
+
+        def user_fn(impls: List[InterfaceType], idx: int, val: torch.Tensor) -> torch.Tensor:
+            return impls[idx].run(val)
+
+        user_fn_jit = torch.jit.script(user_fn)
+
+        impls = [Impl1(), torch.jit.script(Impl2())]
+        val = torch.rand(4, 4)
+        user_fn_jit(impls, 0, val)
+        user_fn_jit(impls, 1, val)
+    """
+    if not inspect.isclass(obj):
+        raise RuntimeError("interface must be applied to a class")
+    if not _is_new_style_class(obj):
+        raise RuntimeError("TorchScript interfaces must inherit from 'object'")
+
+    # Expected MRO is:
+    #   User module
+    #   torch.nn.modules.module.Module
+    #   object
+    is_module_interface = issubclass(obj, torch.nn.Module) and len(obj.mro()) == 3
+
+    if not is_module_interface and len(obj.mro()) > 2:
+        raise RuntimeError(
+            "TorchScript interface does not support inheritance yet. "
+            "Please directly inherit from 'object' or 'nn.Module'."
+        )
+
+    qualified_name = _qualified_name(obj)
+    rcb = _jit_internal.createResolutionCallbackFromFrame(1)
+    # if this type is a `nn.Module` subclass, generate a module interface type
+    # instead of a class interface type; a module interface type only compiles
+    # the user provided methods as part of the interface
+    ast = get_jit_class_def(obj, obj.__name__)
+    mangled_classname = torch._C._jit_script_interface_compile(
+        qualified_name, ast, rcb, is_module_interface
+    )
+    obj.__torch_script_interface__ = mangled_classname
+    return obj
+
+
+def _recursive_compile_class(obj, loc):
+    _qual_name = _qualified_name(obj)
+    # We're starting a new compilation, so update the error call stack in
+    # case it fails
+    error_stack = torch._C.CallStack(_qual_name, loc)
+    rcb = _jit_internal.createResolutionCallbackForClassMethods(obj)
+    return _compile_and_register_class(obj, rcb, _qual_name)
+
+
+CompilationUnit = torch._C.CompilationUnit
+set_module(CompilationUnit, "torch.jit")
+
+
+def pad(s: str, padding: int, offset: int = 0, char: str = " "):
+    if padding >= len(s):
+        padding -= len(s)
+    return "".join([char for _ in range(padding + offset)]) + s
+
+
+class _ScriptProfileColumn:
+    def __init__(self, header: str, alignment: int = 4, offset: int = 0):
+        self.header = header
+        self.alignment = alignment
+        self.offset = offset
+        self.rows: Dict[int, Any] = {}
+
+    def add_row(self, lineno: int, value: Any):
+        self.rows[lineno] = value
+
+    def materialize(self):
+        max_length = len(self.header)
+        rows: List[Tuple[int, str]] = []
+        for key, value in self.rows.items():
+            cell = str(value)
+            rows.append((key, cell))
+            max_length = max(len(cell), max_length)
+
+        if self.alignment > 0:
+            padding = max_length + self.alignment
+            padding -= padding % self.alignment
+        else:
+            padding = 0
+
+        rows = [(key, pad(cell, padding, self.offset)) for key, cell in rows]
+        return pad(self.header, padding, self.offset), rows
+
+
+class _ScriptProfileTable:
+    def __init__(self, cols: List[_ScriptProfileColumn], source_range: List[int]):
+        self.cols = cols
+        self.source_range = source_range
+
+    def dump_string(self):
+        outputs: List[str] = []
+        cells: List[Tuple[str, Dict[int, str]]] = []
+        header_buffer = ""
+        for col in self.cols:
+            header, rows = col.materialize()
+            header_buffer += header
+            cells.append((header, dict(rows)))
+
+        outputs.append(header_buffer)
+        outputs.append(pad("", len(header_buffer), 0, "="))
+        for line in self.source_range:
+            row_buffer = ""
+            for header, rows in cells:
+                cell = rows.get(line)
+                if cell is None:
+                    row_buffer += pad("", len(header))
+                else:
+                    row_buffer += cell
+            outputs.append(row_buffer)
+        return "\n".join(outputs)
+
+
+class _ScriptProfile:
+    def __init__(self) -> None:
+        self.profile = classes.profiling._ScriptProfile()
+
+    def enable(self):
+        self.profile.enable()
+
+    def disable(self):
+        self.profile.disable()
+
+    def dump_string(self) -> str:
+        outputs: List[str] = []
+        for source_stats in self.profile._dump_stats():
+            source_ref = source_stats.source()
+            source_lines = source_ref.text().splitlines()
+            dedent = min(len(line) - len(line.lstrip(" ")) for line in source_lines)
+            source_lines = [line[dedent:] for line in source_lines]
+
+            start_line = source_ref.starting_lineno()
+            end_line = start_line + len(source_lines)
+            source_range = range(start_line, end_line)
+            lineno = _ScriptProfileColumn("Line #")
+            hits = _ScriptProfileColumn("Hits")
+            time_ns = _ScriptProfileColumn("Time (ns)")
+            line_contents = _ScriptProfileColumn("Line Contents", 0, 1)
+            stats = source_stats.line_map()
+            for line in source_range:
+                lineno.add_row(line, line)
+                line_contents.add_row(line, source_lines[line - start_line])
+                stat = stats.get(line)
+                if stat is not None:
+                    hits.add_row(line, stat.count())
+                    time_ns.add_row(line, stat.duration_ns())
+
+            table = _ScriptProfileTable(
+                [lineno, hits, time_ns, line_contents], list(source_range)
+            )
+            outputs.append(table.dump_string())
+        return "\n\n".join(outputs)
+
+    def dump(self):
+        print(self.dump_string())
+
+
+def _unwrap_optional(x):
+    assert x is not None, "Unwrapping null optional"
+    return x
+
+
+_register_builtin(_unwrap_optional, "aten::_unwrap_optional")
+_register_builtin(_jit_internal.is_scripting, "aten::is_scripting")
+_register_builtin(has_torch_function, "aten::has_torch_function")
+_register_builtin(has_torch_function_unary, "aten::has_torch_function")
+_register_builtin(has_torch_function_variadic, "aten::has_torch_function")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_script.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_script.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..bafbb63340ec7d2e9c2625b288a709f7b239eb49
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_script.pyi
@@ -0,0 +1,296 @@
+# mypy: allow-untyped-defs
+# mypy: disable-error-code="type-arg"
+from typing import Any, Callable, NamedTuple, overload, TypeVar
+from typing_extensions import Never, TypeAlias
+
+from _typeshed import Incomplete
+
+import torch
+from torch._classes import classes as classes
+from torch._jit_internal import _qualified_name as _qualified_name
+from torch.jit._builtins import _register_builtin as _register_builtin
+from torch.jit._fuser import (
+    _graph_for as _graph_for,
+    _script_method_graph_for as _script_method_graph_for,
+)
+from torch.jit._monkeytype_config import (
+    JitTypeTraceConfig as JitTypeTraceConfig,
+    JitTypeTraceStore as JitTypeTraceStore,
+    monkeytype_trace as monkeytype_trace,
+)
+from torch.jit._recursive import (
+    _compile_and_register_class as _compile_and_register_class,
+    infer_methods_to_compile as infer_methods_to_compile,
+    ScriptMethodStub as ScriptMethodStub,
+    wrap_cpp_module as wrap_cpp_module,
+)
+from torch.jit._serialization import validate_map_location as validate_map_location
+from torch.jit._state import (
+    _enabled as _enabled,
+    _set_jit_function_cache as _set_jit_function_cache,
+    _set_jit_overload_cache as _set_jit_overload_cache,
+    _try_get_jit_cached_function as _try_get_jit_cached_function,
+    _try_get_jit_cached_overloads as _try_get_jit_cached_overloads,
+)
+from torch.jit.frontend import (
+    get_default_args as get_default_args,
+    get_jit_class_def as get_jit_class_def,
+    get_jit_def as get_jit_def,
+)
+from torch.nn import Module as Module
+from torch.overrides import (
+    has_torch_function as has_torch_function,
+    has_torch_function_unary as has_torch_function_unary,
+    has_torch_function_variadic as has_torch_function_variadic,
+)
+from torch.package import (
+    PackageExporter as PackageExporter,
+    PackageImporter as PackageImporter,
+)
+from torch.utils import set_module as set_module
+
+ScriptFunction = torch._C.ScriptFunction
+
+type_trace_db: JitTypeTraceStore
+
+# Defined in torch/csrc/jit/python/script_init.cpp
+ResolutionCallback: TypeAlias = Callable[[str], Callable[..., Any]]
+_ClassVar = TypeVar("_ClassVar", bound=type)
+
+def _reduce(cls) -> None: ...
+
+class Attribute(NamedTuple):
+    value: Incomplete
+    type: Incomplete
+
+def _get_type_trace_db(): ...
+def _get_function_from_type(cls, name): ...
+def _is_new_style_class(cls): ...
+
+class OrderedDictWrapper:
+    _c: Incomplete
+    def __init__(self, _c) -> None: ...
+    def keys(self): ...
+    def values(self): ...
+    def __len__(self) -> int: ...
+    def __delitem__(self, k) -> None: ...
+    def items(self): ...
+    def __setitem__(self, k, v) -> None: ...
+    def __contains__(self, k) -> bool: ...
+    def __getitem__(self, k): ...
+
+class OrderedModuleDict(OrderedDictWrapper):
+    _python_modules: Incomplete
+    def __init__(self, module, python_dict) -> None: ...
+    def items(self): ...
+    def __contains__(self, k) -> bool: ...
+    def __setitem__(self, k, v) -> None: ...
+    def __getitem__(self, k): ...
+
+class ScriptMeta(type):
+    def __init__(cls, name, bases, attrs) -> None: ...
+
+class _CachedForward:
+    def __get__(self, obj, cls): ...
+
+class ScriptWarning(Warning): ...
+
+def script_method(fn): ...
+
+class ConstMap:
+    const_mapping: Incomplete
+    def __init__(self, const_mapping) -> None: ...
+    def __getattr__(self, attr): ...
+
+def unpackage_script_module(
+    importer: PackageImporter,
+    script_module_id: str,
+) -> torch.nn.Module: ...
+
+_magic_methods: Incomplete
+
+class RecursiveScriptClass:
+    _c: Incomplete
+    _props: Incomplete
+    def __init__(self, cpp_class) -> None: ...
+    def __getattr__(self, attr): ...
+    def __setattr__(self, attr, value) -> None: ...
+    def forward_magic_method(self, method_name, *args, **kwargs): ...
+    def __getstate__(self) -> None: ...
+    def __iadd__(self, other): ...
+
+def method_template(self, *args, **kwargs): ...
+
+class ScriptModule(Module, metaclass=ScriptMeta):
+    __jit_unused_properties__: Incomplete
+    def __init__(self) -> None: ...
+    forward: Callable[..., Any]
+    def __getattr__(self, attr): ...
+    def __setattr__(self, attr, value) -> None: ...
+    def define(self, src): ...
+    def _replicate_for_data_parallel(self): ...
+    def __reduce_package__(self, exporter: PackageExporter): ...
+    # add __jit_unused_properties__
+    @property
+    def code(self) -> str: ...
+    @property
+    def code_with_constants(self) -> tuple[str, ConstMap]: ...
+    @property
+    def graph(self) -> torch.Graph: ...
+    @property
+    def inlined_graph(self) -> torch.Graph: ...
+    @property
+    def original_name(self) -> str: ...
+
+class RecursiveScriptModule(ScriptModule):
+    _disable_script_meta: bool
+    _c: Incomplete
+    def __init__(self, cpp_module) -> None: ...
+    @staticmethod
+    def _construct(cpp_module, init_fn): ...
+    @staticmethod
+    def _finalize_scriptmodule(script_module) -> None: ...
+    _concrete_type: Incomplete
+    _modules: Incomplete
+    _parameters: Incomplete
+    _buffers: Incomplete
+    __dict__: Incomplete
+    def _reconstruct(self, cpp_module) -> None: ...
+    def save(self, f, **kwargs): ...
+    def _save_for_lite_interpreter(self, *args, **kwargs): ...
+    def _save_to_buffer_for_lite_interpreter(self, *args, **kwargs): ...
+    def save_to_buffer(self, *args, **kwargs): ...
+    def get_debug_state(self, *args, **kwargs): ...
+    def extra_repr(self): ...
+    def graph_for(self, *args, **kwargs): ...
+    def define(self, src) -> None: ...
+    def __getattr__(self, attr): ...
+    def __setattr__(self, attr, value) -> None: ...
+    def __copy__(self): ...
+    def __deepcopy__(self, memo): ...
+    def forward_magic_method(self, method_name, *args, **kwargs): ...
+    def __iter__(self): ...
+    def __getitem__(self, idx): ...
+    def __len__(self) -> int: ...
+    def __contains__(self, key) -> bool: ...
+    def __dir__(self): ...
+    def __bool__(self) -> bool: ...
+    def _replicate_for_data_parallel(self): ...
+
+def _get_methods(cls): ...
+
+_compiled_methods_allowlist: Incomplete
+
+def _make_fail(name): ...
+def call_prepare_scriptable_func_impl(obj, memo): ...
+def call_prepare_scriptable_func(obj): ...
+def create_script_dict(obj): ...
+def create_script_list(obj, type_hint: Incomplete | None = ...): ...
+@overload
+def script(
+    obj: type[Module],
+    optimize: bool | None = None,
+    _frames_up: int = 0,
+    _rcb: ResolutionCallback | None = None,
+    example_inputs: list[tuple] | dict[Callable, list[tuple]] | None = None,
+) -> Never: ...
+@overload
+def script(  # type: ignore[misc]
+    obj: dict,
+    optimize: bool | None = None,
+    _frames_up: int = 0,
+    _rcb: ResolutionCallback | None = None,
+    example_inputs: list[tuple] | dict[Callable, list[tuple]] | None = None,
+) -> torch.ScriptDict: ...
+@overload
+def script(  # type: ignore[misc]
+    obj: list,
+    optimize: bool | None = None,
+    _frames_up: int = 0,
+    _rcb: ResolutionCallback | None = None,
+    example_inputs: list[tuple] | dict[Callable, list[tuple]] | None = None,
+) -> torch.ScriptList: ...
+@overload
+def script(  # type: ignore[misc]
+    obj: Module,
+    optimize: bool | None = None,
+    _frames_up: int = 0,
+    _rcb: ResolutionCallback | None = None,
+    example_inputs: list[tuple] | dict[Callable, list[tuple]] | None = None,
+) -> RecursiveScriptModule: ...
+@overload
+def script(  # type: ignore[misc]
+    obj: _ClassVar,
+    optimize: bool | None = None,
+    _frames_up: int = 0,
+    _rcb: ResolutionCallback | None = None,
+    example_inputs: list[tuple] | dict[Callable, list[tuple]] | None = None,
+) -> _ClassVar: ...
+@overload
+def script(  # type: ignore[misc]
+    obj: Callable,
+    optimize: bool | None = None,
+    _frames_up: int = 0,
+    _rcb: ResolutionCallback | None = None,
+    example_inputs: list[tuple] | dict[Callable, list[tuple]] | None = None,
+) -> ScriptFunction: ...
+@overload
+def script(
+    obj: Any,
+    optimize: bool | None = None,
+    _frames_up: int = 0,
+    _rcb: ResolutionCallback | None = None,
+    example_inputs: list[tuple] | dict[Callable, list[tuple]] | None = None,
+) -> RecursiveScriptClass: ...
+@overload
+def script(
+    obj,
+    optimize: Incomplete | None = ...,
+    _frames_up: int = ...,
+    _rcb: Incomplete | None = ...,
+    example_inputs: list[tuple] | dict[Callable, list[tuple]] | None = ...,
+): ...
+def _check_overload_defaults(impl_defaults, overload_defaults, loc) -> None: ...
+def _compile_function_with_overload(overload_fn, qual_name, impl_fn): ...
+def _get_overloads(obj): ...
+def _check_directly_compile_overloaded(obj) -> None: ...
+def interface(obj): ...
+def _recursive_compile_class(obj, loc): ...
+
+CompilationUnit: Incomplete
+
+def pad(s: str, padding: int, offset: int = ..., char: str = ...): ...
+
+class _ScriptProfileColumn:
+    header: Incomplete
+    alignment: Incomplete
+    offset: Incomplete
+    rows: Incomplete
+    def __init__(
+        self,
+        header: str,
+        alignment: int = ...,
+        offset: int = ...,
+    ) -> None: ...
+    def add_row(self, lineno: int, value: Any): ...
+    def materialize(self): ...
+
+class _ScriptProfileTable:
+    cols: Incomplete
+    source_range: Incomplete
+    def __init__(
+        self,
+        cols: list[_ScriptProfileColumn],
+        source_range: list[int],
+    ) -> None: ...
+    def dump_string(self): ...
+
+class _ScriptProfile:
+    profile: Incomplete
+    def __init__(self) -> None: ...
+    def enable(self) -> None: ...
+    def disable(self) -> None: ...
+    def dump_string(self) -> str: ...
+    def dump(self) -> None: ...
+
+def _unwrap_optional(x): ...
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_serialization.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_serialization.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f1be34bbb3dd50be4ddf39140d47021873dbd79
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_serialization.py
@@ -0,0 +1,273 @@
+# mypy: allow-untyped-defs
+"""Serialization.
+
+This module contains functionality for serializing TorchScript modules, notably:
+    * torch.jit.save
+    * torch.jit.load
+
+This is not intended to be imported directly; please use the exposed
+functionalities in `torch.jit`.
+"""
+
+import os
+
+import torch
+from torch._jit_internal import _get_model_id
+from torch._utils_internal import log_torchscript_usage
+from torch.jit._recursive import wrap_cpp_module
+from torch.serialization import validate_cuda_device
+
+
+def save(m, f, _extra_files=None):
+    r"""
+    Save an offline version of this module for use in a separate process.
+
+    The saved module serializes all of the methods, submodules, parameters, and
+    attributes of this module. It can be loaded into the C++ API using
+    ``torch::jit::load(filename)`` or into the Python API with
+    :func:`torch.jit.load `.
+
+    To be able to save a module, it must not make any calls to native Python
+    functions.  This means that all submodules must be subclasses of
+    :class:`ScriptModule` as well.
+
+    .. DANGER::
+        All modules, no matter their device, are always loaded onto the CPU
+        during loading.  This is different from :func:`torch.load`'s semantics
+        and may change in the future.
+
+    Args:
+        m: A :class:`ScriptModule` to save.
+        f: A file-like object (has to implement write and flush) or a string
+           containing a file name.
+        _extra_files: Map from filename to contents which will be stored as part of `f`.
+
+    .. note::
+        torch.jit.save attempts to preserve the behavior of some operators
+        across versions. For example, dividing two integer tensors in
+        PyTorch 1.5 performed floor division, and if the module
+        containing that code is saved in PyTorch 1.5 and loaded in PyTorch 1.6
+        its division behavior will be preserved. The same module saved in
+        PyTorch 1.6 will fail to load in PyTorch 1.5, however, since the
+        behavior of division changed in 1.6, and 1.5 does not know how to
+        replicate the 1.6 behavior.
+
+    Example:
+    .. testcode::
+
+        import torch
+        import io
+
+        class MyModule(torch.nn.Module):
+            def forward(self, x):
+                return x + 10
+
+        m = torch.jit.script(MyModule())
+
+        # Save to file
+        torch.jit.save(m, 'scriptmodule.pt')
+        # This line is equivalent to the previous
+        m.save("scriptmodule.pt")
+
+        # Save to io.BytesIO buffer
+        buffer = io.BytesIO()
+        torch.jit.save(m, buffer)
+
+        # Save with extra files
+        extra_files = {'foo.txt': b'bar'}
+        torch.jit.save(m, 'scriptmodule.pt', _extra_files=extra_files)
+    """
+    log_torchscript_usage("save", model_id=_get_model_id(m))
+    if _extra_files is None:
+        _extra_files = {}
+    if isinstance(f, (str, os.PathLike)):
+        m.save(f, _extra_files=_extra_files)
+    else:
+        ret = m.save_to_buffer(_extra_files=_extra_files)
+        f.write(ret)
+
+
+def load(f, map_location=None, _extra_files=None, _restore_shapes=False):
+    r"""
+    Load a :class:`ScriptModule` or :class:`ScriptFunction` previously saved with :func:`torch.jit.save `.
+
+    All previously saved modules, no matter their device, are first loaded onto CPU,
+    and then are moved to the devices they were saved from. If this fails (e.g.
+    because the run time system doesn't have certain devices), an exception is
+    raised.
+
+    Args:
+        f: a file-like object (has to implement read, readline, tell, and seek),
+            or a string containing a file name
+        map_location (string or torch.device): A simplified version of
+            ``map_location`` in `torch.jit.save` used to dynamically remap
+            storages to an alternative set of devices.
+        _extra_files (dictionary of filename to content): The extra
+            filenames given in the map would be loaded and their content
+            would be stored in the provided map.
+        _restore_shapes (bool): Whether or not to retrace the module on load using stored inputs
+
+    Returns:
+        A :class:`ScriptModule` object.
+
+    Example:
+    .. testcode::
+
+        import torch
+        import io
+
+        torch.jit.load('scriptmodule.pt')
+
+        # Load ScriptModule from io.BytesIO object
+        with open('scriptmodule.pt', 'rb') as f:
+            buffer = io.BytesIO(f.read())
+
+        # Load all tensors to the original device
+        torch.jit.load(buffer)
+
+        # Load all tensors onto CPU, using a device
+        buffer.seek(0)
+        torch.jit.load(buffer, map_location=torch.device('cpu'))
+
+        # Load all tensors onto CPU, using a string
+        buffer.seek(0)
+        torch.jit.load(buffer, map_location='cpu')
+
+        # Load with extra files.
+        extra_files = {'foo.txt': ''}  # values will be replaced with data
+        torch.jit.load('scriptmodule.pt', _extra_files=extra_files)
+        print(extra_files['foo.txt'])
+
+    .. testoutput::
+        :hide:
+
+        ...
+
+    .. testcleanup::
+
+        import os
+        os.remove("scriptmodule.pt")
+    """
+    if isinstance(f, (str, os.PathLike)):
+        if not os.path.exists(f):  # type: ignore[type-var]
+            raise ValueError(f"The provided filename {f} does not exist")  # type: ignore[str-bytes-safe]
+        if os.path.isdir(f):
+            raise ValueError(f"The provided filename {f} is a directory")  # type: ignore[str-bytes-safe]
+
+    map_location = validate_map_location(map_location)
+    if _extra_files is None:
+        _extra_files = {}
+
+    cu = torch._C.CompilationUnit()
+    if isinstance(f, (str, os.PathLike)):
+        cpp_module = torch._C.import_ir_module(cu, os.fspath(f), map_location, _extra_files, _restore_shapes)  # type: ignore[call-arg]
+    else:
+        cpp_module = torch._C.import_ir_module_from_buffer(
+            cu, f.read(), map_location, _extra_files, _restore_shapes
+        )  # type: ignore[call-arg]
+
+    # TODO: Pretty sure this approach loses ConstSequential status and such
+    ret = wrap_cpp_module(cpp_module)
+    log_torchscript_usage("load", model_id=_get_model_id(ret))
+    return ret
+
+
+def validate_map_location(map_location=None):
+    if isinstance(map_location, str):
+        map_location = torch.device(map_location)
+    elif not (map_location is None or isinstance(map_location, torch.device)):
+        raise ValueError(
+            "map_location should be either None, string or torch.device, "
+            "but got type: " + str(type(map_location))
+        )
+
+    if str(map_location).startswith("cuda"):
+        validate_cuda_device(map_location)
+
+    return map_location
+
+
+def jit_module_from_flatbuffer(f):
+    if isinstance(f, (str, os.PathLike)):
+        f = os.fspath(f)
+        return wrap_cpp_module(torch._C._load_jit_module_from_file(f))
+    else:
+        return wrap_cpp_module(torch._C._load_jit_module_from_bytes(f.read()))
+
+
+def save_jit_module_to_flatbuffer(m, f, _extra_files=None):
+    r"""
+    Save an offline version of this module for use in a separate process.
+
+    The saved module serializes all of the methods, submodules, parameters, and
+    attributes of this module. It can be loaded into the C++ API using
+    ``torch::jit::load_jit_module_from_file(filename)`` or into the Python API with
+    :func:`torch.jit.jit_module_from_flatbuffer`.
+
+    To be able to save a module, it must not make any calls to native Python
+    functions.  This means that all submodules must be subclasses of
+    :class:`ScriptModule` as well.
+
+    .. DANGER::
+        All modules, no matter their device, are always loaded onto the CPU
+        during loading.  This is different from :func:`torch.load`'s semantics
+        and may change in the future.
+
+    Args:
+        m: A :class:`ScriptModule` to save.
+        f: A string for file path
+
+
+    Example:
+    .. testcode::
+
+        import torch
+        import io
+
+        class MyModule(torch.nn.Module):
+            def forward(self, x):
+                return x + 10
+
+        m = torch.jit.script(MyModule())
+
+        # Save to file
+        torch.jit.save_jit_module_to_flatbuffer(m, 'scriptmodule.ff')
+    """
+    extra_files = _extra_files
+    if extra_files is None:
+        extra_files = {}
+
+    if isinstance(f, (str, os.PathLike)):
+        f = os.fspath(f)
+        torch._C._save_jit_module(m._c, f, extra_files)
+    else:
+        s = torch._C._save_jit_module_to_bytes(m._c, extra_files)
+        f.write(s)
+
+
+def get_flatbuffer_module_info(path_or_file):
+    r"""Get some information regarding a model file in flatbuffer format.
+
+    Args:
+        path_or_file: Either str, Path or file like object (BytesIO OK).
+            If it's str or Path, we will read the file referenced by that
+            path as Bytes.
+
+    Returns:
+        A dict with metadata on what that file contains, currently looks like
+        this:
+        {
+            'bytecode_version': 4,  # int
+            'operator_version': 4,  # int
+            'function_names': {
+                '__torch__.___torch_mangle_0.Foo.forward'}, # set
+            'type_names': set(),  # set
+            'opname_to_num_args': {'aten::linear': 3} # Dict[str, int]
+        }
+    """
+    if isinstance(path_or_file, (str, os.PathLike)):
+        with open(path_or_file, "rb") as f:
+            all_bytes = f.read()
+    else:
+        all_bytes = path_or_file.read()
+    return torch._C._get_module_info_from_flatbuffer(all_bytes)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_shape_functions.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_shape_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..56c4a8cb36e3a922589563cc160845f1bc94eb22
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_shape_functions.py
@@ -0,0 +1,1477 @@
+# mypy: allow-untyped-defs
+import math
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+
+number = Union[int, float]
+# flake8: noqa
+
+###
+# There are generated files that depend on this file
+# To re-generate, please run from the root of the repo:
+# python torchgen/shape_functions/gen_jit_shape_functions.py
+
+# How to test:
+# After regenerating files, compile PyTorch.
+# Then run: ./build/bin/test_jit --gtest_filter=TestShapeGraphLinting.Basic
+# If you have enabled opinfo testing for the op, also run:
+# python test/test_ops_jit.py TestJitCPU.test_variant_consistency_jit_[FAILING_OP]_cpu_float32
+# to reproduce errors from opinfo tests.
+
+# Example PR: https://github.com/pytorch/pytorch/pull/80860/files
+####
+
+import torch
+
+
+def broadcast(a: List[int], b: List[int]):
+    dimsA = len(a)
+    dimsB = len(b)
+    ndim = max(dimsA, dimsB)
+    expandedSizes: List[int] = []
+
+    for i in range(ndim):
+        offset = ndim - 1 - i
+        dimA = dimsA - 1 - offset
+        dimB = dimsB - 1 - offset
+        sizeA = a[dimA] if (dimA >= 0) else 1
+        sizeB = b[dimB] if (dimB >= 0) else 1
+
+        if sizeA != sizeB and sizeA != 1 and sizeB != 1:
+            # TODO: only assertion error is bound in C++ compilation right now
+            raise AssertionError(
+                f"The size of tensor a {sizeA} must match the size of tensor b ({sizeB}) at non-singleton dimension {i}"
+            )
+
+        expandedSizes.append(sizeB if sizeA == 1 else sizeA)
+
+    return expandedSizes
+
+
+def broadcast_three(a: List[int], b: List[int], c: List[int]):
+    return broadcast(broadcast(a, b), c)
+
+
+def broadcast_one_three(a: List[int], b: Any, c: List[int]):
+    return broadcast(a, c)
+
+
+def adaptive_avg_pool2d(self: List[int], out: List[int]):
+    assert len(out) == 2
+    assert len(self) == 3 or len(self) == 4
+    for i in range(1, len(self)):
+        assert self[i] != 0
+
+    shape: List[int] = []
+    for i in range(0, len(self) - 2):
+        shape.append(self[i])
+    for elem in out:
+        shape.append(elem)
+    return shape
+
+
+def _copy(self: List[int]):
+    out: List[int] = []
+    for elem in self:
+        out.append(elem)
+    return out
+
+
+def unary(self: List[int]):
+    return _copy(self)
+
+
+def broadcast_inplace(a: List[int], b: List[int]):
+    dimsA = len(a)
+    dimsB = len(b)
+    if dimsB > dimsA:
+        raise AssertionError(
+            f"The dims of tensor b ({dimsB}) must be less than or equal tothe dims of tensor a ({dimsA}) "
+        )
+    for dimA in range(dimsA):
+        dimB = dimsB - dimsA + dimA
+        sizeA = a[dimA]
+        sizeB = b[dimB] if (dimB >= 0) else 1
+        if sizeA != sizeB and sizeB != 1:
+            # TODO: only assertion error is bound in C++ compilation right now
+            raise AssertionError(
+                "The size of tensor a {} must match the size of tensor b ("
+                "{}) at non-singleton dimension {}".format(sizeA, sizeB, dimA)
+            )
+    return _copy(a)
+
+
+def expand(self: List[int], sizes: List[int]):
+    assert len(sizes) >= len(self)
+    ndim = len(sizes)
+    tensor_dim = len(self)
+    if ndim == 0:
+        return _copy(sizes)
+    out: List[int] = []
+    for i in range(ndim):
+        offset = ndim - 1 - i
+        dim = tensor_dim - 1 - offset
+        size = self[dim] if dim >= 0 else 1
+        targetSize = sizes[i]
+        if targetSize == -1:
+            assert dim >= 0
+            targetSize = size
+        if size != targetSize:
+            assert size == 1
+            size = targetSize
+        out.append(size)
+    return out
+
+
+def expand_one_unused(self: List[int], sizes: List[int], inp0: Any):
+    return expand(self, sizes)
+
+
+def infer_size_impl(shape: List[int], numel: int) -> List[int]:
+    newsize = 1
+    infer_dim: Optional[int] = None
+    for dim in range(len(shape)):
+        if shape[dim] == -1:
+            if infer_dim is not None:
+                raise AssertionError("only one dimension can be inferred")
+            infer_dim = dim
+        elif shape[dim] >= 0:
+            newsize *= shape[dim]
+        else:
+            raise AssertionError("invalid shape dimensions")
+    if not (
+        numel == newsize
+        or (infer_dim is not None and newsize > 0 and numel % newsize == 0)
+    ):
+        raise AssertionError("invalid shape")
+    out = _copy(shape)
+    if infer_dim is not None:
+        out[infer_dim] = numel // newsize
+    return out
+
+
+def numel(sizes: List[int]):
+    numel = 1
+    for elem in sizes:
+        numel *= elem
+    return numel
+
+
+def view(self: List[int], sizes: List[int]):
+    return infer_size_impl(sizes, numel(self))
+
+
+def view_one_unused(self: List[int], sizes: List[int], *, implicit: bool = False):
+    return view(self, sizes)
+
+
+def sum_mean_dim(
+    self: List[int], opt_dims: Optional[List[int]], keep_dim: bool, dt: Any
+):
+    out: List[int] = []
+    if opt_dims is None or len(opt_dims) == 0:
+        dims: List[int] = list(range(len(self)))
+    else:
+        dims = opt_dims
+
+    for idx in range(len(self)):
+        is_mean_dim: bool = False
+        for reduce_dim in dims:
+            if idx == maybe_wrap_dim(reduce_dim, len(self)):
+                is_mean_dim = True
+        if is_mean_dim:
+            if keep_dim:
+                out.append(1)
+        else:
+            out.append(self[idx])
+    return out
+
+
+def max_dim(self: List[int], dim: int, keep_dim: bool):
+    out = sum_mean_dim(self, [dim], keep_dim, None)
+    return out, out
+
+
+# note: python already rounds down towards negative infinity on integer division, special arithmetic not needed
+def div_rtn(x: int, y: int):
+    return x // y
+
+
+def pooling_output_shape_pad_lr(
+    inputSize: int,
+    kernelSize: int,
+    pad_l: int,
+    pad_r: int,
+    stride: int,
+    dilation: int,
+    ceil_mode: bool,
+):
+    outputSize = (
+        div_rtn(
+            inputSize
+            + pad_l
+            + pad_r
+            - dilation * (kernelSize - 1)
+            - 1
+            + (stride - 1 if ceil_mode else 0),
+            stride,
+        )
+        + 1
+    )
+    if ceil_mode:
+        if (outputSize - 1) * stride >= inputSize + pad_l:
+            outputSize = outputSize - 1
+    return outputSize
+
+
+def pooling_output_shape(
+    inputSize: int,
+    kernelSize: int,
+    pad_l: int,
+    stride: int,
+    dilation: int,
+    ceil_mode: bool,
+):
+    assert stride != 0, "stride should not be zeero"
+    return pooling_output_shape_pad_lr(
+        inputSize, kernelSize, pad_l, pad_l, stride, dilation, ceil_mode
+    )
+
+
+def pool2d_shape_check(
+    input: List[int],
+    kH: int,
+    kW: int,
+    dH: int,
+    dW: int,
+    padH: int,
+    padW: int,
+    dilationH: int,
+    dilationW: int,
+    nInputPlane: int,
+    inputHeight: int,
+    inputWidth: int,
+    outputHeight: int,
+    outputWidth: int,
+):
+    ndim = len(input)
+    nOutputPlane = nInputPlane
+
+    assert kW > 0 and kH > 0
+    assert dW > 0 and dH > 0
+    assert dilationH > 0 and dilationW > 0
+
+    valid_dims = input[1] != 0 and input[2] != 0
+    assert (
+        ndim == 3
+        and input[0] != 0
+        and valid_dims
+        or (ndim == 4 and valid_dims and input[3] != 0)
+    )
+
+    assert kW // 2 >= padW and kH // 2 >= padH
+    assert outputWidth >= 1 and outputHeight >= 1
+
+
+def max_pool2d(
+    input: List[int],
+    kernel_size: List[int],
+    stride: List[int],
+    padding: List[int],
+    dilation: List[int],
+    ceil_mode: bool,
+):
+    assert (
+        len(kernel_size) == 1 or len(kernel_size) == 2
+    ), "max_pool2d: kernel_size must either be a single int, or a tuple of two ints"
+    kH = kernel_size[0]
+    kW = kH if len(kernel_size) == 1 else kernel_size[1]
+
+    assert (
+        len(stride) == 0 or len(stride) == 1 or len(stride) == 2
+    ), "max_pool2d: stride must either be omitted, a single int, or a tuple of two ints"
+    dH = kH if len(stride) == 0 else stride[0]
+    if len(stride) == 0:
+        dW = kW
+    elif len(stride) == 1:
+        dW = dH
+    else:
+        dW = stride[1]
+
+    assert (
+        len(padding) == 1 or len(padding) == 2
+    ), "max_pool2d: padding must either be a single int, or a tuple of two ints"
+    padH = padding[0]
+    padW = padH if len(padding) == 1 else padding[1]
+
+    assert (
+        len(dilation) == 1 or len(dilation) == 2
+    ), "max_pool2d: dilation must be either a single int, or a tuple of two ints"
+    dilationH = dilation[0]
+    dilationW = dilationH if len(dilation) == 1 else dilation[1]
+
+    assert len(input) == 3 or len(input) == 4
+
+    nbatch = input[-4] if len(input) == 4 else 1
+    nInputPlane = input[-3]
+    inputHeight = input[-2]
+    inputWidth = input[-1]
+
+    outputHeight = pooling_output_shape(inputHeight, kH, padH, dH, dilationH, ceil_mode)
+    outputWidth = pooling_output_shape(inputWidth, kW, padW, dW, dilationW, ceil_mode)
+
+    pool2d_shape_check(
+        input,
+        kH,
+        kW,
+        dH,
+        dW,
+        padH,
+        padW,
+        dilationH,
+        dilationW,
+        nInputPlane,
+        inputHeight,
+        inputWidth,
+        outputHeight,
+        outputWidth,
+    )
+
+    if len(input) == 3:
+        return [nInputPlane, outputHeight, outputWidth]
+    else:
+        return [nbatch, nInputPlane, outputHeight, outputWidth]
+
+
+def max_pool2d_with_indices(
+    input: List[int],
+    kernel_size: List[int],
+    stride: List[int],
+    padding: List[int],
+    dilation: List[int],
+    ceil_mode: bool,
+):
+    out = max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
+    return (out, out)
+
+
+def upsample_nearest2d(
+    input: List[int],
+    output_size: Optional[List[int]],
+    scale_factors: Optional[List[float]],
+):
+    out: List[int] = []
+    out.append(input[0])
+    out.append(input[1])
+
+    if scale_factors is None and output_size is None:
+        assert 0, "Either output_size or scale_factors must be presented"
+
+    if output_size is not None:
+        assert (
+            scale_factors is None
+        ), "Must specify exactly one of output_size and scale_factors"
+        assert len(output_size) == 2
+        out.append(output_size[0])
+        out.append(output_size[1])
+
+    if scale_factors is not None:
+        assert (
+            output_size is None
+        ), "Must specify exactly one of output_size and scale_factors"
+        assert len(scale_factors) == 2
+        out.append(int(input[2] * scale_factors[0]))
+        out.append(int(input[3] * scale_factors[1]))
+
+    return out
+
+
+def mm(self: List[int], mat2: List[int]):
+    assert len(self) == 2, "self must be a matrix"
+    assert len(mat2) == 2, "mat2 must be a matrix"
+
+    assert self[1] == mat2[0]
+    return [self[0], mat2[1]]
+
+
+def dot(self: List[int], tensor: List[int]):
+    assert len(self) == 1 and len(tensor) == 1
+    assert self[0] == tensor[0]
+    out: List[int] = []
+    return out
+
+
+def mv(self: List[int], vec: List[int]):
+    assert len(self) == 2 and len(vec) == 1
+    assert self[1] == vec[0]
+    # TODO: return self
+    return [self[0]]
+
+
+def unsqueeze(li: List[int], dim: int):
+    dim = maybe_wrap_dim(dim, len(li) + 1)
+    out = _copy(li)
+    out.insert(dim, 1)
+    return out
+
+
+def squeeze_nodim(li: List[int]):
+    out: List[int] = []
+    for i in range(len(li)):
+        if li[i] != 1:
+            out.append(li[i])
+    return out
+
+
+def squeeze(li: List[int], dim: int):
+    out: List[int] = []
+    wrapped_dim = maybe_wrap_dim(dim, len(li))
+    for i in range(len(li)):
+        if i == wrapped_dim:
+            if li[i] != 1:
+                out.append(li[i])
+        else:
+            out.append(li[i])
+    return out
+
+
+def squeeze_dims(li: List[int], dims: List[int]):
+    if len(dims) == 0:
+        return li
+    wrapped_dims = _copy(dims)
+    for i in range(len(dims)):
+        wrapped_dims[i] = maybe_wrap_dim(wrapped_dims[i], len(li))
+    result: List[int] = []
+    for i in range(len(li)):
+        if li[i] == 1:
+            if i not in wrapped_dims:
+                result.append(li[i])
+        else:
+            result.append(li[i])
+    return result
+
+
+def index_select(self: List[int], dim: int, index: List[int]):
+    dim = maybe_wrap_dim(dim, len(self))
+    numel = multiply_integers(index)
+    assert len(index) <= 1
+    assert dim == 0 or dim < len(self)
+    result_size: List[int] = []
+    for i in range(len(self)):
+        if dim == i:
+            result_size.append(numel)
+        else:
+            result_size.append(self[i])
+    return result_size
+
+
+def embedding(
+    weight: List[int],
+    indices: List[int],
+    padding_idx: int = -1,
+    scale_grad_by_freq: bool = False,
+    sparse: bool = False,
+):
+    assert len(weight) == 2
+    if len(indices) == 1:
+        return index_select(weight, 0, indices)
+    size = _copy(indices)
+    size.append(weight[1])
+    return size
+
+
+def max_int():
+    return 9223372036854775807
+
+
+def slice(
+    self: List[int], dim: int, start: Optional[int], end: Optional[int], step: int
+):
+    ndim = len(self)
+    assert ndim != 0
+    dim = maybe_wrap_dim(dim, ndim)
+    start_val = start if start is not None else 0
+    end_val = end if end is not None else max_int()
+    assert step > 0
+    if start_val == max_int():
+        start_val = 0
+    if start_val < 0:
+        start_val += self[dim]
+    if end_val < 0:
+        end_val += self[dim]
+    if start_val < 0:
+        start_val = 0
+    elif start_val > self[dim]:
+        start_val = self[dim]
+    if end_val < start_val:
+        end_val = start_val
+    elif end_val >= self[dim]:
+        end_val = self[dim]
+    slice_len = end_val - start_val
+    out = _copy(self)
+    out[dim] = (slice_len + step - 1) // step
+    return out
+
+
+def check_cat_no_zero_dim(tensors: List[List[int]]):
+    for tensor in tensors:
+        assert len(tensor) > 0
+
+
+def legacy_cat_wrap_dim(dim: int, tensor_sizes: List[List[int]]):
+    out_dim: Optional[int] = None
+    for size in tensor_sizes:
+        if not (len(size) == 1 and size[0] == 0):
+            if out_dim is None:
+                out_dim = maybe_wrap_dim(dim, len(size))
+    if out_dim is None:
+        out_dim = dim
+    return out_dim
+
+
+def should_skip(tensor: List[int]):
+    return numel(tensor) == 0 and len(tensor) == 1
+
+
+def check_cat_shape_except_dim(
+    first: List[int], second: List[int], dimension: int, index: int
+):
+    first_dims = len(first)
+    second_dims = len(second)
+    assert first_dims == second_dims, "Tensors must have same number of dimensions"
+    for dim in range(0, first_dims):
+        if dim != dimension:
+            assert (
+                first[dim] == second[dim]
+            ), "Sizes of tensors must match except in dimension"
+
+
+def cat(tensors: List[List[int]], dim: int):
+    check_cat_no_zero_dim(tensors)
+    dim = legacy_cat_wrap_dim(dim, tensors)
+    assert len(tensors) > 0
+    not_skipped_tensor: Optional[List[int]] = None
+    for tensor in tensors:
+        if not should_skip(tensor):
+            not_skipped_tensor = tensor
+    if not_skipped_tensor is None:
+        return [0]
+
+    cat_dim_size = 0
+
+    for i in range(len(tensors)):
+        tensor = tensors[i]
+        if not should_skip(tensor):
+            check_cat_shape_except_dim(not_skipped_tensor, tensor, dim, i)
+            cat_dim_size = cat_dim_size + tensor[dim]
+
+    result_size = _copy(not_skipped_tensor)
+    result_size[dim] = cat_dim_size
+    return result_size
+
+
+def stack(tensors: List[List[int]], dim: int):
+    unsqueezed_tensors: List[List[int]] = []
+    for tensor in tensors:
+        unsqueezed = unsqueeze(tensor, dim)
+        unsqueezed_tensors.append(unsqueezed)
+    return cat(unsqueezed_tensors, dim)
+
+
+def select(self: List[int], dim: int, index: int):
+    ndim = len(self)
+    assert ndim != 0
+    dim = maybe_wrap_dim(dim, ndim)
+    size = self[dim]
+    assert not (index < -size or index >= size)
+    if index < 0:
+        index += size
+    out: List[int] = []
+    for i in range(ndim):
+        if i != dim:
+            out.append(self[i])
+    return out
+
+
+def matmul(tensor1: List[int], tensor2: List[int]):
+    dim_tensor1 = len(tensor1)
+    dim_tensor2 = len(tensor2)
+    if dim_tensor1 == 1 and dim_tensor2 == 1:
+        return dot(tensor1, tensor2)
+    elif dim_tensor1 == 2 and dim_tensor2 == 1:
+        return mv(tensor1, tensor2)
+    elif dim_tensor1 == 1 and dim_tensor2 == 2:
+        return squeeze(mm(unsqueeze(tensor1, 0), tensor2), 0)
+    elif dim_tensor1 == 2 and dim_tensor2 == 2:
+        return mm(tensor1, tensor2)
+    elif dim_tensor1 >= 1 and dim_tensor2 >= 1:
+        # We are multiplying b1 x n x m1 by x2 x m2 x p (where b1 can be a list);
+        # we track m1 vs m2 separately even though they must match for nicer error messages
+        n = tensor1[-2] if dim_tensor1 > 1 else 1
+        m1 = tensor1[-1]
+        batch_tensor1: List[int] = []
+        # TODO: handling of slice
+        for i in range(dim_tensor1 - 2):
+            batch_tensor1.append(tensor1[i])
+        m2 = tensor2[-1] if dim_tensor2 > 1 else 1
+        p = tensor2[-1]
+        batch_tensor2: List[int] = []
+        # TODO: handling of slice
+        for i in range(dim_tensor2 - 2):
+            batch_tensor2.append(tensor2[i])
+
+        # expand the batch portion (i.e. cut off matrix dimensions and expand rest)
+        expand_batch_portion = broadcast(batch_tensor1, batch_tensor2)
+
+        # todo: copy ?
+        output_shape = expand_batch_portion
+        if dim_tensor1 > 1:
+            output_shape.append(n)
+
+        if dim_tensor2 > 1:
+            output_shape.append(p)
+
+        return output_shape
+    else:
+        assert False, "both  arguments to matmul need to be at least 1D"
+
+
+def t(self: List[int]):
+    assert len(self) <= 2
+    self_len = len(self)
+    if self_len == 0:
+        out: List[int] = []
+        return out
+    elif self_len == 1:
+        return [self[0]]
+    else:
+        return [self[1], self[0]]
+
+
+def transpose(self: List[int], dim0: int, dim1: int):
+    ndims = len(self)
+    dim0 = maybe_wrap_dim(dim0, ndims)
+    dim1 = maybe_wrap_dim(dim1, ndims)
+    if dim0 == dim1:
+        return _copy(self)
+    out: List[int] = []
+    for i in range(ndims):
+        if i == dim0:
+            out.append(self[dim1])
+        elif i == dim1:
+            out.append(self[dim0])
+        else:
+            out.append(self[i])
+    return out
+
+
+def linear(input: List[int], weight: List[int], bias: Optional[List[int]]):
+    out = matmul(input, t(weight))
+    if bias is not None:
+        assert broadcast(bias, out) == out
+    return out
+
+
+def addmm(self: List[int], mat1: List[int], mat2: List[int], beta: Any, alpha: Any):
+    return broadcast(self, mm(mat1, mat2))
+
+
+def check_non_negative(array: List[int]) -> bool:
+    # TODO: look into rewriting with early return and getting loop unrolling to fire
+    non_negative = False
+    for val in array:
+        if val < 0:
+            non_negative = True
+    return non_negative
+
+
+def check_shape_forward(
+    input: List[int],
+    weight_sizes: List[int],
+    bias: Optional[List[int]],
+    stride: List[int],
+    padding: List[int],
+    dilation: List[int],
+    groups: int,
+):
+    k = len(input)
+    weight_dim = len(weight_sizes)
+
+    # TODO: assertions could be expanded with the error messages
+    assert not check_non_negative(padding)
+    assert not check_non_negative(stride)
+
+    assert weight_dim == k
+    assert weight_sizes[0] >= groups
+    assert (weight_sizes[0] % groups) == 0
+    # only handling not transposed
+    assert input[1] == weight_sizes[1] * groups
+    assert bias is None or (len(bias) == 1 and bias[0] == weight_sizes[0])
+
+    for i in range(2, k):
+        assert (input[i] + 2 * padding[i - 2]) >= (
+            dilation[i - 2] * (weight_sizes[i] - 1) + 1
+        )
+
+    # this is not handling transposed convolution yet
+
+
+def conv_output_size(
+    input_size: List[int],
+    weight_size: List[int],
+    bias: Optional[List[int]],
+    stride: List[int],
+    padding: List[int],
+    dilation: List[int],
+    groups: int,
+):
+    check_shape_forward(
+        input_size, weight_size, bias, stride, padding, dilation, groups
+    )
+
+    has_dilation = len(dilation) > 0
+    dim = len(input_size)
+    output_size: List[int] = []
+    input_batch_size_dim = 0
+    weight_output_channels_dim = 0
+    output_size.append(input_size[input_batch_size_dim])
+    output_size.append(weight_size[weight_output_channels_dim])
+
+    for d in range(2, dim):
+        dilation_ = dilation[d - 2] if has_dilation else 1
+        kernel = dilation_ * (weight_size[d] - 1) + 1
+        output_size.append(
+            (input_size[d] + (2 * padding[d - 2]) - kernel) // stride[d - 2] + 1
+        )
+    return output_size
+
+
+def conv1d(
+    input: List[int],
+    weight: List[int],
+    bias: Optional[List[int]],
+    stride: List[int],
+    padding: List[int],
+    dilation: List[int],
+    groups: int,
+):
+    assert len(weight) == 3
+    assert len(input) == 3
+    return conv_output_size(input, weight, bias, stride, padding, dilation, groups)
+
+
+def conv2d(
+    input: List[int],
+    weight: List[int],
+    bias: Optional[List[int]],
+    stride: List[int],
+    padding: List[int],
+    dilation: List[int],
+    groups: int,
+):
+    assert len(weight) == 4
+    assert len(input) == 4
+    return conv_output_size(input, weight, bias, stride, padding, dilation, groups)
+
+
+def conv_backwards(
+    grad_output: List[int],
+    input: List[int],
+    weight: List[int],
+    biases: Optional[List[int]],
+):
+    # Bias gradient is always generated regardess of if biases is supplied
+    return _copy(input), _copy(weight), [grad_output[1]]
+
+
+def conv_transpose2d_input(
+    input: List[int],
+    weight: List[int],
+    bias: Optional[List[int]] = None,
+    stride: Optional[List[int]] = None,
+    padding: Optional[List[int]] = None,
+    output_padding: Optional[List[int]] = None,
+    groups: int = 1,
+    dilation: Optional[List[int]] = None,
+) -> List[int]:
+    if stride is None:
+        stride = [1, 1]
+    if padding is None:
+        padding = [0, 0]
+    if output_padding is None:
+        output_padding = [0, 0]
+    if dilation is None:
+        dilation = [1, 1]
+    has_dilation = len(dilation) > 0
+    dim = len(input)
+    output_size: List[int] = []
+    input_batch_size_dim = 0
+    weight_output_channels_dim = 1
+    output_size.append(input[input_batch_size_dim])
+    output_size.append(weight[weight_output_channels_dim] * groups)
+
+    for d in range(2, dim):
+        dilation_ = dilation[d - 2] if has_dilation else 1
+        kernel = dilation_ * (weight[d] - 1)
+        output_size.append(
+            (input[d] - 1) * stride[d - 2]
+            - 2 * padding[d - 2]
+            + kernel
+            + output_padding[d - 2]
+            + 1
+        )
+    return output_size
+
+
+def conv_forwards(
+    input: List[int],
+    weight: List[int],
+    bias: Optional[List[int]],
+    stride: List[int],
+    padding: List[int],
+    dilation: List[int],
+    transposed: bool,
+    output_padding: List[int],
+    groups: int,
+) -> List[int]:
+    has_dilation = len(dilation) > 0
+    has_output_padding = len(output_padding) > 0
+    dim = len(input)
+    output_size: List[int] = []
+    input_batch_size_dim = 0
+    weight_output_channels_dim = 1 if transposed else 0
+    output_size.append(input[input_batch_size_dim])
+    if transposed:
+        output_size.append(weight[weight_output_channels_dim] * groups)
+    else:
+        output_size.append(weight[weight_output_channels_dim])
+
+    for d in range(2, dim):
+        dilation_ = dilation[d - 2] if has_dilation else 1
+        output_padding_ = output_padding[d - 2] if has_output_padding else 0
+        if transposed:
+            kernel = dilation_ * (weight[d] - 1)
+            output_size.append(
+                (input[d] - 1) * stride[d - 2]
+                - 2 * padding[d - 2]
+                + kernel
+                + output_padding_
+                + 1
+            )
+        else:
+            kernel = dilation_ * (weight[d] - 1) + 1
+            output_size.append(
+                (input[d] + (2 * padding[d - 2]) - kernel) // stride[d - 2] + 1
+            )
+    return output_size
+
+
+def _conv_forwards(
+    input: List[int],
+    weight: List[int],
+    bias: Optional[List[int]],
+    stride: List[int],
+    padding: List[int],
+    dilation: List[int],
+    transposed: bool,
+    output_padding: List[int],
+    groups: int,
+    benchmark: bool,
+    deterministic: bool,
+    cudnn_enabled: bool,
+    allow_tf32: bool,
+) -> List[int]:
+    return conv_forwards(
+        input,
+        weight,
+        bias,
+        stride,
+        padding,
+        dilation,
+        transposed,
+        output_padding,
+        groups,
+    )
+
+
+def batch_norm(
+    input: List[int],
+    weight: Optional[List[int]],
+    bias: Optional[List[int]],
+    running_mean: Optional[List[int]],
+    running_var: Optional[List[int]],
+    training: bool,
+    momentum: float,
+    eps: float,
+    cudnn_enabled: bool,
+):
+    out: List[int] = []
+    for elem in input:
+        out.append(elem)
+    return out
+
+
+def conv3d(
+    input: List[int],
+    weight: List[int],
+    bias: Optional[List[int]],
+    stride: List[int],
+    padding: List[int],
+    dilation: List[int],
+    groups: int,
+):
+    assert len(weight) == 5
+    assert len(input) == 5
+    return conv_output_size(input, weight, bias, stride, padding, dilation, groups)
+
+
+def maybe_wrap_dim(dim: int, dim_post_expr: int, wrap_scalar: bool = True):
+    if dim_post_expr <= 0:
+        assert wrap_scalar
+        dim_post_expr = 1
+    min = -dim_post_expr
+    max = dim_post_expr - 1
+    assert not (dim < min or dim > max)
+    if dim < 0:
+        dim += dim_post_expr
+    return dim
+
+
+def zero_dim_tensor(input: Any):
+    out: List[int] = []
+    return out
+
+
+def multiply_integers(li: List[int]):
+    out = 1
+    for elem in li:
+        out = out * elem
+    return out
+
+
+def arange_end(end: number, inp0: Any, inp1: Any, inp2: Any, inp3: Any):
+    assert end >= 0
+    return [int(math.ceil(end))]
+
+
+def arange_start(
+    start: number, end: number, inp0: Any, inp1: Any, inp2: Any, inp3: Any
+):
+    assert end >= 0
+    assert end >= start
+    return [int(math.ceil(end - start))]
+
+
+def arange_start_step(
+    start: number, end: number, step: number, inp0: Any, inp1: Any, inp2: Any, inp3: Any
+):
+    assert step != 0
+    if step < 0:
+        assert start >= end
+    else:
+        assert end >= start
+    return [int(math.ceil((end - start) / step))]
+
+
+def permute(input: List[int], dims: List[int]):
+    assert len(input) == len(dims)
+    ndim = len(dims)
+    seen_dims: List[int] = []
+    newSizes: List[int] = []
+    for i in range(ndim):
+        dim = maybe_wrap_dim(dims[i], ndim)
+        seen_dims.append(dim)
+        newSizes.append(input[dim])
+    for i in range(1, ndim):
+        for j in range(i):
+            assert seen_dims[i] != seen_dims[j]
+    return newSizes
+
+
+def movedim(self: List[int], source: List[int], destination: List[int]) -> List[int]:
+    self_dim = len(self)
+    if self_dim <= 1:
+        return self
+    normalized_src: List[int] = []
+    normalized_dst: List[int] = []
+    for i in range(len(source)):
+        normalized_src.append(maybe_wrap_dim(source[i], self_dim))
+        normalized_dst.append(maybe_wrap_dim(destination[i], self_dim))
+    order = [-1 for i in range(self_dim)]
+    src_dims = [i for i in range(self_dim)]
+    dst_dims = [i for i in range(self_dim)]
+
+    for i in range(len(source)):
+        order[normalized_dst[i]] = normalized_src[i]
+        src_dims[normalized_src[i]] = -1
+        dst_dims[normalized_dst[i]] = -1
+
+    source_dims: List[int] = []
+    destination_dims: List[int] = []
+    for ele in src_dims:
+        if ele != -1:
+            source_dims.append(ele)
+    for ele in dst_dims:
+        if ele != -1:
+            destination_dims.append(ele)
+
+    rest_dim = self_dim - len(source)
+    for i in range(rest_dim):
+        order[destination_dims[i]] = source_dims[i]
+    return permute(self, order)
+
+
+def flatten(input: List[int], start_dim: int, end_dim: int):
+    start_dim = maybe_wrap_dim(start_dim, len(input))
+    end_dim = maybe_wrap_dim(end_dim, len(input))
+    assert start_dim <= end_dim
+    if len(input) == 0:
+        return [1]
+    if start_dim == end_dim:
+        # TODO: return self
+        out: List[int] = []
+        for elem in input:
+            out.append(elem)
+        return out
+    slice_numel = 1
+    for i in range(start_dim, end_dim + 1):
+        slice_numel *= input[i]
+    # TODO: use slicing when slice optimization has landed
+    # slice_numel = multiply_integers(input[start_dim:end_dim - start_dim + 1])
+    shape: List[int] = []
+    for i in range(start_dim):
+        shape.append(input[i])
+    shape.append(slice_numel)
+    for i in range(end_dim + 1, len(input)):
+        shape.append(input[i])
+    return shape
+
+
+def nonzero_lower_bound(input: List[int]):
+    return [0, len(input)]
+
+
+def nonzero_upper_bound(input: List[int]):
+    return [numel(input), len(input)]
+
+
+def _reduce_along_dim(self: List[int], dim: int, keepdim: bool):
+    dim = maybe_wrap_dim(dim, len(self))
+    out: List[int] = []
+    for i, self_dim in enumerate(self):
+        if i == dim:
+            if keepdim:
+                out.append(1)
+        else:
+            out.append(self_dim)
+    return out
+
+
+def argmax(
+    self: List[int], dim: Optional[int] = None, keepdim: bool = False
+) -> List[int]:
+    if dim is None:
+        return []
+    return _reduce_along_dim(self, dim, keepdim)
+
+
+def bmm(self: List[int], mat2: List[int]) -> List[int]:
+    assert len(self) == 3, "bmm only supports 3D tensors"
+    assert len(mat2) == 3, "bmm only supports 3D tensors"
+    assert self[0] == mat2[0], "mismatching batch dimension"
+    assert self[2] == mat2[1], "mismatching contracting dimension"
+    return [self[0], self[1], mat2[2]]
+
+
+def _shape_as_tensor(self: List[int]) -> List[int]:
+    return [len(self)]
+
+
+def topk(self: List[int], k: int, dim: int = -1) -> Tuple[List[int], List[int]]:
+    if len(self) == 0:
+        result: List[int] = []
+    else:
+        assert (
+            k <= self[dim]
+        ), f"k ({k}) is too big for dimension {dim} of size {self[dim]}"
+        result = _copy(self)
+        result[dim] = k
+    return result, result
+
+
+def nll_loss_forward(
+    self: List[int], target: List[int], weight: Optional[List[int]], reduction: int
+) -> Tuple[List[int], List[int]]:
+    # This is taken shamelessly from the meta function in LossNLL.cpp
+    self_dim = len(self)
+    target_dim = len(target)
+    assert 0 < self_dim <= 2
+    assert target_dim <= 1
+    no_batch_dim = self_dim == 1 and target_dim == 0
+    assert no_batch_dim or (self[0] == target[0])
+    n_classes = self[-1]
+    scalar_shape: List[int] = []
+    assert weight is None or (len(weight) == 1 and weight[0] == n_classes)
+    if reduction == 0 and self_dim == 2:
+        reduction_shape = [self[0]]
+    else:
+        reduction_shape = scalar_shape
+    return reduction_shape, scalar_shape
+
+
+def native_layer_norm(
+    input: List[int], normalized_shape: List[int]
+) -> Tuple[List[int], List[int], List[int]]:
+    reduction_shape: List[int] = []
+    num_unreduced_dimensions = len(input) - len(normalized_shape)
+    assert num_unreduced_dimensions >= 0
+    for i in range(num_unreduced_dimensions):
+        reduction_shape.append(input[i])
+    for i in range(num_unreduced_dimensions, len(input)):
+        reduction_shape.append(1)
+    return _copy(input), reduction_shape, reduction_shape
+
+
+def native_batch_norm(
+    input: List[int],
+    weight: Optional[List[int]],
+    bias: Optional[List[int]],
+    running_mean: Optional[List[int]],
+    running_var: Optional[List[int]],
+    training: bool,
+) -> Tuple[List[int], List[int], List[int]]:
+    if training:
+        _size = [input[1]]
+    else:
+        _size = [0]
+    return _copy(input), _size, _size
+
+
+def _batch_norm_with_update(
+    input: List[int],
+    weight: Optional[List[int]],
+    bias: Optional[List[int]],
+    running_mean: Optional[List[int]],
+    running_var: Optional[List[int]],
+) -> Tuple[List[int], List[int], List[int], List[int]]:
+    _size = [input[1]]
+    return _copy(input), _size, _size, [0]
+
+
+def cross_entropy_loss(
+    self: List[int],
+    target: List[int],
+    weight: Optional[List[int]] = None,
+    reduction: int = 1,
+    ignore_index: int = -100,
+    label_smoothing: float = 0.0,
+) -> List[int]:
+    result_shape = nll_loss_forward(self, target, weight, reduction)[0]
+    return result_shape
+
+
+"""
+Currently deferring the enabling of this, as part of the propoasal to suspend
+adding ops.
+There are currently cases in the test case where this is being called
+in the SSA opinfo tests with with unexpected values (eg list of two ints, see the first
+opinfo test). The behavoir of index is significantly dependent on the inputs.
+
+This could be an error with how we are matching up shape functions, or that this
+function needs to just implement everything.
+
+def index_Tensor(self: List[int], indices: List[Optional[List[int]]]) -> List[int]:
+    assert len(indices) <= len(self), "More indices than dimensions to index"
+    broadcasted_shape: List[int] = []
+    for index_tensor_shape in indices:
+        if index_tensor_shape is not None:
+            broadcasted_shape = broadcast(broadcasted_shape, index_tensor_shape)
+    return broadcasted_shape
+"""
+
+ScriptFn = torch._C.ScriptFunction
+shape_compute_graph_mapping: Dict[str, ScriptFn] = {}
+bounded_compute_graph_mapping: Dict[str, Tuple[ScriptFn, ScriptFn]] = {}
+script_func_map: Dict[Callable, ScriptFn] = {}
+
+
+def process_func(func: Callable):
+    if func not in script_func_map:
+        scripted_func = torch.jit.script(func)
+
+        torch._C._jit_pass_inline(scripted_func.graph)
+
+        for _ in range(2):
+            torch._C._jit_pass_peephole(scripted_func.graph)
+            torch._C._jit_pass_constant_propagation(scripted_func.graph)
+
+        script_func_map[func] = scripted_func
+    return script_func_map[func]
+
+
+def add_shape_compute_mapping(operator_schema: str, func: Callable):
+    global shape_compute_graph_mapping
+
+    shape_compute_graph_mapping[operator_schema] = process_func(func)
+
+
+def add_bounded_compute_mapping(
+    operator_schema: str, lower_bound_func: Callable, upper_bound_func: Callable
+):
+    # Adds a shape compute function for both upper and lower bounds
+    fns = (process_func(lower_bound_func), process_func(upper_bound_func))
+    bounded_compute_graph_mapping[operator_schema] = fns
+
+
+add_shape_compute_mapping(
+    "aten::contiguous(Tensor(a) self, *, MemoryFormat memory_format=contiguous_format) -> Tensor(a)",
+    unary,
+)
+add_shape_compute_mapping(
+    "aten::rsub.Tensor(Tensor self, Scalar other, Scalar alpha=1) -> Tensor", unary
+)
+add_shape_compute_mapping(
+    "aten::dropout(Tensor input, float p, bool train) -> Tensor", unary
+)
+add_shape_compute_mapping(
+    "aten::adaptive_avg_pool2d(Tensor self, int[2] output_size) -> Tensor",
+    adaptive_avg_pool2d,
+)
+add_shape_compute_mapping(
+    "prim::NumToTensor.Scalar(Scalar a) -> Tensor", zero_dim_tensor
+)
+add_shape_compute_mapping("prim::NumToTensor.bool(bool a) -> Tensor", zero_dim_tensor)
+add_shape_compute_mapping(
+    "aten::zeros(int[] size, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None) -> (Tensor)",
+    unary,
+)
+add_shape_compute_mapping(
+    "aten::to.dtype(Tensor(a) self, int dtype, bool non_blocking=False, bool copy=False, int? memory_format=None) -> (Tensor(a))",
+    unary,
+)
+add_shape_compute_mapping(
+    "aten::arange(Scalar end, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None) -> (Tensor)",
+    arange_end,
+)
+add_shape_compute_mapping(
+    "aten::arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor",
+    arange_start,
+)
+add_shape_compute_mapping(
+    "aten::arange.start_step(Scalar start, Scalar end, Scalar step, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor",
+    arange_start_step,
+)
+add_shape_compute_mapping("aten::squeeze(Tensor(a) self) -> Tensor(a)", squeeze_nodim)
+add_shape_compute_mapping(
+    "aten::squeeze.dim(Tensor(a) self, int dim) -> Tensor(a)", squeeze
+)
+add_shape_compute_mapping(
+    "aten::squeeze.dims(Tensor(a) self, int[] dim) -> Tensor(a)", squeeze_dims
+)
+add_shape_compute_mapping(
+    "aten::unsqueeze(Tensor(a) self, int dim) -> Tensor(a)", unsqueeze
+)
+add_shape_compute_mapping(
+    "aten::slice.Tensor(Tensor(a) self, int dim=0, int? start=None, int? end=None, int step=1) -> Tensor(a)",
+    slice,
+)
+add_shape_compute_mapping(
+    "aten::select.int(Tensor(a) self, int dim, int index) -> Tensor(a)", select
+)
+add_shape_compute_mapping(
+    "aten::index_select(Tensor self, int dim, Tensor index) -> Tensor", index_select
+)
+add_shape_compute_mapping(
+    "aten::layer_norm(Tensor input, int[] normalized_shape, Tensor? weight=None, Tensor? bias=None, "
+    "float eps=1e-05, bool cudnn_enable=True) -> Tensor",
+    unary,
+)
+add_shape_compute_mapping(
+    "aten::softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor", unary
+)
+add_shape_compute_mapping(
+    "aten::_no_grad_embedding_renorm_(Tensor weight, Tensor input, float max_norm, float norm_type) -> Tensor",
+    unary,
+)
+add_shape_compute_mapping(
+    "aten::embedding_renorm_(Tensor(a!) self, Tensor indices, float max_norm, float norm_type) -> Tensor(a!)",
+    unary,
+)
+add_shape_compute_mapping(
+    "aten::embedding(Tensor weight, Tensor indices, int padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor",
+    embedding,
+)
+add_shape_compute_mapping("aten::mm(Tensor self, Tensor mat2) -> Tensor", mm)
+add_shape_compute_mapping("aten::dot(Tensor self, Tensor tensor) -> Tensor", dot)
+add_shape_compute_mapping("aten::mv(Tensor self, Tensor vec) -> Tensor", mv)
+add_shape_compute_mapping("aten::matmul(Tensor self, Tensor other) -> Tensor", matmul)
+add_shape_compute_mapping(
+    "aten::linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensor", linear
+)
+add_shape_compute_mapping(
+    "aten::max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor",
+    max_pool2d,
+)
+add_shape_compute_mapping(
+    "aten::max_pool2d_with_indices(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor)",
+    max_pool2d_with_indices,
+)
+add_shape_compute_mapping("aten::t(Tensor(a) self) -> Tensor(a)", t)
+add_shape_compute_mapping(
+    "aten::transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a)", transpose
+)
+add_shape_compute_mapping(
+    "aten::conv1d(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, int[1] padding=0, int[1] dilation=1, int groups=1) -> Tensor",
+    conv1d,
+)
+add_shape_compute_mapping(
+    "aten::conv2d(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1, int groups=1) -> Tensor",
+    conv2d,
+)
+add_shape_compute_mapping(
+    "aten::batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, bool cudnn_enabled) -> Tensor",
+    batch_norm,
+)
+add_shape_compute_mapping(
+    "aten::conv3d(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1, int groups=1) -> Tensor",
+    conv3d,
+)
+add_shape_compute_mapping(
+    "aten::convolution_backward(Tensor grad_output, Tensor input, Tensor weight, int[]? bias_sizes, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)",
+    conv_backwards,
+)
+add_shape_compute_mapping(
+    "aten::convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups) -> Tensor",
+    conv_forwards,
+)
+add_shape_compute_mapping(
+    "aten::_convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -> Tensor",
+    _conv_forwards,
+)
+add_shape_compute_mapping(
+    "aten::conv_transpose2d.input(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] output_padding=0, int groups=1, int[2] dilation=1) -> Tensor",
+    conv_transpose2d_input,
+)
+add_shape_compute_mapping(
+    "aten::flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a)",
+    flatten,
+)
+add_shape_compute_mapping("aten::cat(Tensor[] tensors, int dim=0) -> Tensor", cat)
+add_shape_compute_mapping("aten::stack(Tensor[] tensors, int dim=0) -> Tensor", stack)
+add_shape_compute_mapping(
+    "aten::permute(Tensor(a) self, int[] dims) -> Tensor(a)", permute
+)
+add_shape_compute_mapping(
+    "aten::movedim.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a)",
+    movedim,
+)
+add_shape_compute_mapping("aten::view(Tensor(a) self, int[] size) -> Tensor(a)", view)
+add_shape_compute_mapping(
+    "aten::expand_as(Tensor(a) self, Tensor other) -> Tensor(a)", expand
+)
+add_shape_compute_mapping(
+    "aten::expand(Tensor(a) self, int[] size, *, bool implicit=False) -> Tensor(a)",
+    expand_one_unused,
+)
+add_shape_compute_mapping(
+    "aten::mean.dim(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor",
+    sum_mean_dim,
+)
+add_shape_compute_mapping(
+    "aten::sum.dim_IntList(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor",
+    sum_mean_dim,
+)
+add_shape_compute_mapping(
+    "aten::max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)",
+    max_dim,
+)
+add_shape_compute_mapping(
+    "aten::mean(Tensor self, *, ScalarType? dtype=None) -> Tensor", zero_dim_tensor
+)
+add_shape_compute_mapping(
+    "aten::sum(Tensor self, *, ScalarType? dtype=None) -> Tensor", zero_dim_tensor
+)
+add_shape_compute_mapping(
+    "aten::addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor",
+    addmm,
+)
+add_shape_compute_mapping(
+    "aten::upsample_nearest2d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -> (Tensor)",
+    upsample_nearest2d,
+)
+add_shape_compute_mapping(
+    "aten::quantize_per_tensor(Tensor self, float scale, int zero_point, ScalarType dtype) -> Tensor",
+    unary,
+)
+add_shape_compute_mapping(
+    "aten::quantize_per_tensor.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, ScalarType dtype) -> Tensor",
+    unary,
+)
+add_shape_compute_mapping("aten::dequantize(Tensor self) -> Tensor", unary)
+add_shape_compute_mapping(
+    "quantized::add(Tensor qa, Tensor qb, float scale, int zero_point) -> Tensor qc",
+    broadcast,
+)
+add_shape_compute_mapping(
+    "aten::argmax(Tensor self, int? dim=None, bool keepdim=False) -> Tensor", argmax
+)
+add_shape_compute_mapping("aten::bmm(Tensor self, Tensor mat2) -> Tensor", bmm)
+add_shape_compute_mapping(
+    "aten::_shape_as_tensor(Tensor self) -> Tensor", _shape_as_tensor
+)
+add_shape_compute_mapping(
+    "aten::topk(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices)",
+    topk,
+)
+add_shape_compute_mapping(
+    "aten::nll_loss_forward(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index) -> (Tensor output, Tensor total_weight)",
+    nll_loss_forward,
+)
+add_shape_compute_mapping(
+    "aten::native_layer_norm(Tensor input, int[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor)",
+    native_layer_norm,
+)
+add_shape_compute_mapping(
+    "aten::native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)",
+    native_batch_norm,
+)
+add_shape_compute_mapping(
+    "aten::_native_batch_norm_legit(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)",
+    native_batch_norm,
+)
+add_shape_compute_mapping(
+    "aten::_native_batch_norm_legit.no_stats(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)",
+    native_batch_norm,
+)
+add_shape_compute_mapping(
+    "_batch_norm_with_update(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor)",
+    _batch_norm_with_update,
+)
+
+add_shape_compute_mapping(
+    "aten::cross_entropy_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, float label_smoothing=0.0) -> Tensor",
+    cross_entropy_loss,
+)
+# add_shape_compute_mapping("aten::index.Tensor(Tensor self, Tensor?[] indices) -> Tensor", index_Tensor)
+
+# TODO: migrate over all of symbolic_shape_registry_util.cpp
+# These are duplicated here so that the functions will be serialiazed
+add_shape_compute_mapping(
+    "aten::lerp.Tensor(Tensor self, Tensor end, Tensor weight) -> Tensor",
+    broadcast_three,
+)
+add_shape_compute_mapping(
+    "aten::where.ScalarSelf(Tensor condition, Scalar self, Tensor other) -> Tensor",
+    broadcast_one_three,
+)
+add_shape_compute_mapping(
+    "aten::add_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)",
+    broadcast_inplace,
+)
+
+# quantized_conv_prepack TODO
+
+# Shape Compute Fn with upper and lower bounds
+add_bounded_compute_mapping(
+    "aten::nonzero(Tensor self) -> (Tensor)", nonzero_lower_bound, nonzero_upper_bound
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_state.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_state.py
new file mode 100644
index 0000000000000000000000000000000000000000..18456ebd38687c32ef68f947ac512a74acfef59d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_state.py
@@ -0,0 +1,127 @@
+# mypy: allow-untyped-defs
+"""JIT-related state.
+
+This module stores various pieces of Python-global state relating to the JIT.
+
+This is not intended to be imported directly; please the exposed
+functionalities in `torch.jit`.
+"""
+import os
+import weakref
+from typing import Any, Dict, Type
+
+import torch
+
+
+class EnabledProxy:
+    """Stores whether the JIT is enabled or not.
+
+    This is just a wrapper for a bool, so that we get reference semantics
+    """
+
+    def __init__(self) -> None:
+        self.enabled = self.parse_env(
+            "PYTORCH_JIT", True, "> Using PyTorch JIT", "> PyTorch JIT DISABLED"
+        )
+
+    def parse_env(self, name, default, true_message, false_message):
+        value = os.environ.get(name)
+        if value is None:
+            return default
+        if value.lower() in {"1", "true", "yes"}:
+            return True
+        elif value.lower() in {"0", "false", "no"}:
+            return False
+        if value == "1v":
+            print(true_message)
+            return True
+        elif value == "0v":
+            print(false_message)
+            return False
+        raise ValueError(f"Unknown setting of {name}. Try using 0 or 1.")
+
+    def __bool__(self):
+        return self.enabled
+
+
+_enabled = EnabledProxy()
+
+
+def disable():
+    _enabled.enabled = False
+
+
+def enable():
+    _enabled.enabled = True
+
+
+# The Python CompilationUnit. All functions and modules defined in Python will
+# live in here. It's defined in Python because doing in cpp creates static
+# destruction order issues.
+_python_cu = torch._C.CompilationUnit()
+
+
+# python class => ScriptClass mapping
+_script_classes: Dict[Type[Any], Type[Any]] = {}
+_name_to_pyclass: Dict[str, Type[Any]] = {}
+
+
+def _add_script_class(python_class, script_class):
+    _script_classes[python_class] = script_class
+    _name_to_pyclass[script_class.qualified_name()] = python_class
+
+
+def _get_script_class(python_class):
+    override = getattr(python_class, "_jit_override_qualname", None)
+    if override is not None:
+        python_class = _get_python_class(override)
+    return _script_classes.get(python_class, None)
+
+
+def _get_python_class(qualified_name):
+    return _name_to_pyclass.get(qualified_name, None)
+
+
+def _clear_class_state():
+    _script_classes.clear()
+    _name_to_pyclass.clear()
+
+
+# Caching: we currently cache compilation of free functions and overloaded functions.
+# To cache free functions we hold a weak ref to the function object and
+# map to the compiled fn's qualified name.
+# To cache overloaded functions we hold a weak ref to the function obj and
+# map to all of its overloaded compiled fns.
+# In the future we could consider caching more types of objects so that
+# aliasing is preserved across separate compilations of the same object.
+
+_jit_caching_layer: weakref.WeakKeyDictionary = weakref.WeakKeyDictionary()
+_jit_function_overload_caching: weakref.WeakKeyDictionary = weakref.WeakKeyDictionary()
+
+
+def _try_get_jit_cached_overloads(key):
+    qual_names = _jit_function_overload_caching.get(key, None)
+    if qual_names:
+        return [_python_cu.find_function(qual_name) for qual_name in qual_names]
+    else:
+        return None
+
+
+def _set_jit_overload_cache(key, compiled_fns):
+    _jit_function_overload_caching[key] = [fn.qualified_name for fn in compiled_fns]
+
+
+def _try_get_jit_cached_function(key):
+    if getattr(key, "__disable_jit_function_caching__", False) is True:
+        return None
+    qual_name = _jit_caching_layer.get(key, None)
+    if qual_name:
+        return _python_cu.find_function(qual_name)
+    else:
+        return None
+
+
+def _set_jit_function_cache(key, value):
+    # only free functions currently supported
+    assert isinstance(value, torch.jit.ScriptFunction)
+    _jit_caching_layer[key] = value.qualified_name
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_trace.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_trace.py
new file mode 100644
index 0000000000000000000000000000000000000000..1dbcdb6a3ca2a86644696f05857b00156427f274
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/_trace.py
@@ -0,0 +1,1503 @@
+# mypy: allow-untyped-defs
+"""Tracing.
+
+This module contains functionality to support the JIT's tracing frontend, notably:
+    * torch.jit.trace
+    * torch.jit.trace_module
+
+This is not intended to be imported directly; please use the exposed
+functionalities in `torch.jit`.
+"""
+
+import contextlib
+import copy
+import functools
+import inspect
+import os
+import re
+import warnings
+from enum import Enum
+from typing import Any, Callable, Dict, List, Optional, Set, TypeVar
+from typing_extensions import ParamSpec
+
+import torch
+from torch._jit_internal import (
+    _get_model_id,
+    _qualified_name,
+    get_callable_argument_names,
+    is_scripting,
+)
+from torch.autograd import function
+from torch.jit._script import _CachedForward, script, ScriptModule
+from torch.jit._state import _enabled, _python_cu
+from torch.nn import Module
+from torch.testing._comparison import default_tolerances
+
+
+_flatten = torch._C._jit_flatten
+_unflatten = torch._C._jit_unflatten
+
+R = TypeVar("R", covariant=True)  # return type (always covariant)
+P = ParamSpec("P")
+
+
+def _create_interpreter_name_lookup_fn(frames_up=1):
+    def _get_interpreter_name_for_var(var):
+        frame = inspect.currentframe()
+        if not frame:
+            raise RuntimeError("failed to inspect frame")
+
+        i = 0
+        while i < frames_up + 1:
+            frame = frame.f_back
+            if not frame:
+                raise RuntimeError("failed to get frame")
+            i += 1
+
+        f_locals = frame.f_locals
+        f_globals = frame.f_globals
+
+        for k, v in f_locals.items():
+            if isinstance(v, torch.Tensor) and var is v:
+                return k if k != "self" else ""
+        return ""
+
+    return _get_interpreter_name_for_var
+
+
+def _unique_state_dict(module, keep_vars=False):
+    # since Parameter.detach() always creates a new torch.Tensor instance,
+    # id(v) doesn't work with it. So we always get the Parameter or Buffer
+    # as values, and deduplicate the params using Parameters and Buffers
+    state_dict = module.state_dict(keep_vars=True)
+    filtered_dict = type(state_dict)()
+    seen_ids: Set[int] = set()
+    for k, v in state_dict.items():
+        if id(v) in seen_ids:
+            continue
+        seen_ids.add(id(v))
+        if keep_vars:
+            filtered_dict[k] = v
+        else:
+            filtered_dict[k] = v.detach()
+    return filtered_dict
+
+
+class ONNXTracedModule(torch.nn.Module):
+    def __init__(
+        self,
+        inner,
+        strict=True,
+        force_outplace=False,
+        return_inputs=False,
+        return_inputs_states=False,
+    ):
+        super().__init__()
+        # inner may be a Module, or it may be an arbitrary callable
+        # If it's a Module, we get its parameters automatically, which lets
+        # us avoid a special casing functions versus modules.
+        self.inner = inner
+        self.strict = strict
+        self._force_outplace = force_outplace
+        self._return_inputs = return_inputs
+        self._return_inputs_states = return_inputs_states
+
+    def forward(self, *args: torch.Tensor):
+        in_vars, in_desc = _flatten(args)
+        # NOTE: use full state, because we need it for BatchNorm export
+        # This differs from the compiler path, which doesn't support it at the moment.
+        module_state = list(_unique_state_dict(self, keep_vars=True).values())
+
+        ret_inputs = []
+        inputs_states = []
+        outs = []
+
+        def wrapper(*args):
+            in_args: List[torch.Tensor] = []
+            for i in range(len(in_vars)):
+                if not isinstance(args[i], torch.Tensor):
+                    raise RuntimeError("Expected Tensor argument")
+                in_args.append(args[i])
+
+            trace_inputs = _unflatten(in_args, in_desc)
+
+            if self._return_inputs:
+                ret_inputs.append(
+                    tuple(x.clone(memory_format=torch.preserve_format) for x in args)
+                )
+            if self._return_inputs_states:
+                inputs_states.append(_unflatten(in_args, in_desc))
+            outs.append(self.inner(*trace_inputs))
+            if self._return_inputs_states:
+                inputs_states[0] = (inputs_states[0], trace_inputs)
+            out_vars, _ = _flatten(outs)
+            if len(out_vars) == 1:
+                return out_vars[0]
+            else:
+                return tuple(out_vars)
+
+        graph, out = torch._C._create_graph_by_tracing(
+            wrapper,
+            in_vars + module_state,
+            _create_interpreter_name_lookup_fn(),
+            self.strict,
+            self._force_outplace,
+        )
+
+        if self._return_inputs:
+            return graph, outs[0], ret_inputs[0]
+        if self._return_inputs_states:
+            return graph, outs[0], inputs_states[0]
+        else:
+            return graph, outs[0]
+
+
+def _clone_inputs(args):
+    def clone_input(a):
+        if a is None:
+            return None
+        elif isinstance(a, torch.Tensor):
+            # TODO: figure out one liner to .clone() and set requires_grad
+            v = (
+                a.detach()
+                .clone(memory_format=None if a.is_mkldnn else torch.preserve_format)
+                .requires_grad_(a.requires_grad)
+            )
+            if a.grad is not None:
+                v.grad = clone_input(v.grad)
+            return v
+        else:
+            return a.clone(memory_format=torch.preserve_format)
+
+    return function._nested_map(
+        lambda x: isinstance(x, torch.Tensor), clone_input, condition_msg="tensors"
+    )(args)
+
+
+# This is purely for developer debugging.  We are not going to advertise it.
+_JIT_TIME = os.environ.get("PYTORCH_JIT_TIME", False)  # CUDA-only timing
+_JIT_DISABLE = os.environ.get("PYTORCH_JIT_DISABLE", False)
+_JIT_STATS = os.environ.get("PYTORCH_JIT_STATS", False)
+
+
+@contextlib.contextmanager
+def _time(trace_name, name, time=True):
+    if (not _JIT_TIME and not time) or not torch.cuda.is_available():
+        yield
+        return
+    stream = torch.cuda.current_stream()
+    start = torch.cuda.Event(enable_timing=True)
+    end = torch.cuda.Event(enable_timing=True)
+    stream.record_event(start)
+    try:
+        yield
+    finally:
+        stream.record_event(end)
+        end.synchronize()
+        print(f"{trace_name} {name} time: {start.elapsed_time(end)} ms")
+
+
+def verify(model, args, loss_fn=torch.sum, devices=None):
+    """
+    Verify that a JIT compiled model has the same behavior as its uncompiled version along with its backwards pass.
+
+    If your model returns multiple outputs,
+    you must also specify a `loss_fn` to produce a loss for which
+    the backwards will be computed.
+
+    This function has side-effects (e.g., it executes your model / saves and loads
+    parameters), so don't expect the model to come out exactly the same as what
+    you passed in.
+
+    Args:
+        model (compiled torch.nn.Module or function): the module/function to be
+            verified.  The module/function definition MUST have been decorated with
+            `@torch.jit.compile`.
+        args (tuple or Tensor): the positional arguments to pass to the
+            compiled function/module to be verified.  A non-tuple is assumed to
+            be a single positional argument to be passed to the model.
+        loss_fn (function, optional): the loss function to be applied to
+            the output of the model, before backwards is invoked.  By default,
+            we assume that a model returns a single result, and we :func:`torch.sum`
+            before calling backwards; if this is inappropriate, you can pass your
+            own loss function.  Note that if a model returns a tuple of results,
+            these are passed as separate positional arguments to `loss_fn`.
+        devices (iterable of device IDs, optional): the GPU devices which the
+            compiled module will be run on.  This determines the RNG state we
+            must save when running both compiled and uncompiled versions of the model.
+    """
+    # TODO: In principle, we track device information in our trace, so it
+    # should be possible to check if our execution actually obeyed the 'devices'
+    # the user provided.
+
+    # TODO: Consider adding a utility function to torch.jit to test
+    # for this case
+    if not isinstance(model, torch._C.CompiledFunction):  # type: ignore[attr-defined]
+        raise TypeError(
+            "Cannot verify an uncompiled module.  Add @torch.jit.compile to compile it"
+        )
+    is_module = isinstance(model, Module)
+
+    if not isinstance(args, tuple):
+        args = (args,)
+
+    saved_args = _clone_inputs(args)
+    if is_module:
+        saved_state = copy.deepcopy(model.state_dict())
+
+    def run_fwd_bwd(args, force_trace=False, assert_compiled=False):
+        params = list(model.parameters()) if is_module else []
+        in_vars, _ = _flatten((args, params))
+        # We use a special API to reset the trace and compile it from scratch.
+        compiled_fn = model
+        if force_trace:
+            compiled_fn.clear_cache()
+        if assert_compiled:
+            hits = compiled_fn.hits
+        out = model(*args)
+        if assert_compiled and compiled_fn.hits == hits:  # type: ignore[possibly-undefined]
+            raise RuntimeError("failed to use the compiled function")
+        if not isinstance(out, tuple):
+            out = (out,)
+        if loss_fn == torch.sum and len(out) != 1:
+            raise ValueError(
+                f"Model returns {len(out)} outputs, but default loss function "
+                "(torch.sum) can only handle a single output"
+            )
+        out_vars, _ = _flatten(out)
+        saved_outs = [
+            v.detach().clone(memory_format=torch.preserve_format) for v in out_vars
+        ]
+        loss = loss_fn(*out)
+        grads = torch.autograd.grad([loss], in_vars)
+        # TODO: I'm not sure if the clone here is necessary but it is safer
+        saved_grads = [
+            v.detach().clone(memory_format=torch.preserve_format) for v in grads
+        ]
+        return (saved_outs, saved_grads)
+
+    with torch.random.fork_rng(devices, _caller="torch.jit.verify"):
+        uncompiled_outs, uncompiled_grads = run_fwd_bwd(args, force_trace=True)
+        assert model.has_trace_for(*args)
+
+    if is_module:
+        model.load_state_dict(saved_state)  # type: ignore[possibly-undefined]
+    compiled_outs, compiled_grads = run_fwd_bwd(args, assert_compiled=True)
+
+    _verify_equal(uncompiled_outs, compiled_outs)
+    _verify_equal(uncompiled_grads, compiled_grads)
+
+
+def _verify_equal(xs, ys):
+    for x, y in zip(xs, ys):
+        if x.sub(y).abs().max() > 1e-6:
+            raise RuntimeError("JIT and real computation mismatch")
+
+
+def indent(s):
+    return "\n".join(["\t" + line for line in s.splitlines()])
+
+
+class TracingCheckError(Exception):
+    def __init__(self, graph_diff_error, tensor_compare_error, extra_msg=None):
+        self.message = "Tracing failed sanity checks!\n"
+        if extra_msg is not None:
+            self.message += extra_msg + "\n"
+        if graph_diff_error is not None:
+            self.message += "ERROR: Graphs differed across invocations!\n"
+            self.message += indent(graph_diff_error) + "\n"
+        if tensor_compare_error is not None:
+            self.message += (
+                "ERROR: Tensor-valued Constant nodes differed in value "
+                "across invocations. This often indicates that the tracer has"
+                " encountered untraceable code.\n"
+            )
+            self.message += indent(tensor_compare_error) + "\n"
+        super().__init__(self.message)
+
+
+# Check the traced module against a set of user-provided validation inputs
+@torch.no_grad()
+def _check_trace(
+    check_inputs,
+    func,
+    traced_func,
+    check_tolerance,
+    strict,
+    force_outplace,
+    is_trace_module,
+    _module_class,
+    example_inputs_is_kwarg=False,
+):
+    # Note: tracing is independent of optimizations, which consume the trace
+    for inputs in check_inputs:
+        if isinstance(inputs, torch.Tensor):
+            inputs = (inputs,)
+
+        if is_trace_module:
+            copied_dict = {}
+            for name, data in inputs.items():
+                copied_dict[name] = _clone_inputs(data)
+            check_mod = torch.jit.trace_module(
+                getattr(func, "__self__", func),
+                copied_dict,
+                check_trace=False,
+                strict=strict,
+                _force_outplace=force_outplace,
+                _module_class=_module_class,
+                _compilation_unit=torch._C.CompilationUnit(),
+                example_inputs_is_kwarg=example_inputs_is_kwarg,
+                _store_inputs=False,
+            )
+            check_mod_func = check_mod._c._get_method(traced_func.name)
+            inputs = inputs[traced_func.name]
+            if (
+                isinstance(inputs, (torch.Tensor))
+                or isinstance(inputs, dict)
+                and not example_inputs_is_kwarg
+            ):
+                inputs = (inputs,)
+        else:
+            if example_inputs_is_kwarg:
+                check_mod = torch.jit.trace(
+                    func,
+                    check_trace=False,
+                    strict=strict,
+                    _force_outplace=force_outplace,
+                    _module_class=_module_class,
+                    example_kwarg_inputs=_clone_inputs(inputs),
+                    _store_inputs=False,
+                )
+            else:
+                check_mod = torch.jit.trace(
+                    func,
+                    _clone_inputs(inputs),
+                    check_trace=False,
+                    strict=strict,
+                    _force_outplace=force_outplace,
+                    _module_class=_module_class,
+                    _store_inputs=False,
+                )
+            check_mod_func = check_mod
+
+        def graph_diagnostic_info():
+            mod_canonicalized = torch._C._jit_pass_canonicalize(traced_func.graph)
+            torch._C._jit_pass_inline(mod_canonicalized)
+            torch._C._jit_pass_erase_shape_information(mod_canonicalized)
+            mod_str = str(mod_canonicalized)
+            mod_str = re.sub(r"___torch_mangle_[0-9]+\.", "", mod_str)
+            check_canonicalized = torch._C._jit_pass_canonicalize(check_mod_func.graph)
+            torch._C._jit_pass_inline(check_canonicalized)
+            torch._C._jit_pass_erase_shape_information(check_canonicalized)
+            check_str = str(check_canonicalized)
+            check_str = re.sub(r"___torch_mangle_[0-9]+\.", "", check_str)
+
+            graph_diff_errors = None
+            if mod_str != check_str:
+                import difflib
+
+                graph_diff = difflib.ndiff(
+                    mod_str.splitlines(True), check_str.splitlines(True)
+                )
+                graph_diff_errors = "Graph diff:\n" + indent("".join(graph_diff)) + "\n"
+
+                for n_mod, n_check in zip(
+                    mod_canonicalized.nodes(), check_canonicalized.nodes()
+                ):
+                    if str(n_mod) != str(n_check):
+                        graph_diff_errors += "First diverging operator:\n"
+                        node_diff = difflib.ndiff(
+                            str(n_mod).splitlines(True), str(n_check).splitlines(True)
+                        )
+                        source_printout = (
+                            "Node diff:\n" + indent("".join(node_diff)) + "\n"
+                        )
+                        mod_stack = n_mod.sourceRange()
+                        if mod_stack:
+                            source_printout += (
+                                "Trace source location:\n" + indent(mod_stack) + "\n"
+                            )
+                        check_stack = n_check.sourceRange()
+                        if check_stack:
+                            source_printout += (
+                                "Check source location:\n" + indent(check_stack) + "\n"
+                            )
+                        graph_diff_errors += source_printout
+
+                        break  # For now, only print out the first pair of nodes that diverges
+
+            tensor_compare_errors = None
+            # Check Tensor-valued constant nodes
+            for n_mod, n_check in zip(
+                mod_canonicalized.nodes(), check_canonicalized.nodes()
+            ):
+                if n_mod.kind() != n_check.kind():
+                    break  # Graphs have already diverged
+
+                if n_mod.kind() == "prim::Constant" and not (
+                    n_mod.mustBeNone() or n_check.mustBeNone()
+                ):
+                    if not n_mod.hasAttribute("value"):
+                        continue
+                    if n_mod.kindOf("value") != "t" or n_check.kindOf("value") != "t":
+                        continue
+
+                    mod_tensor_val = n_mod.t("value")
+                    check_tensor_val = n_check.t("value")
+
+                    try:
+                        torch.testing.assert_close(
+                            mod_tensor_val, check_tensor_val, equal_nan=True
+                        )
+                    except (RuntimeError, AssertionError) as e:
+                        if tensor_compare_errors is None:
+                            tensor_compare_errors = ""
+                        tensor_compare_errors += "Node:\n" + indent(str(n_mod)) + "\n"
+                        compare_stack = n_mod.sourceRange()
+                        if compare_stack:
+                            tensor_compare_errors += (
+                                "Source Location:\n" + indent(compare_stack) + "\n"
+                            )
+                        tensor_compare_errors += "Comparison exception: " + indent(
+                            str(e)
+                        )
+
+                        break  # For now, only print the first diverging pair
+
+            return graph_diff_errors, tensor_compare_errors
+
+        def wrap_retval(x):
+            return x if isinstance(x, tuple) else (x,)
+
+        def run_mod_and_filter_tensor_outputs(mod, inputs, running_what):
+            try:
+                if isinstance(inputs, dict) and example_inputs_is_kwarg:
+                    outs = wrap_retval(mod(**inputs))
+                else:
+                    outs = wrap_retval(mod(*_clone_inputs(inputs)))
+                outs = [out for out in outs if isinstance(out, torch.Tensor)]
+                return outs
+            except Exception as e:
+                graph_diff_errors, tensor_compare_errors = graph_diagnostic_info()
+                msg = f"encountered an exception while running the {running_what} with test inputs.\nException:\n{indent(str(e))}"
+                raise TracingCheckError(
+                    graph_diff_errors,
+                    tensor_compare_errors,
+                    extra_msg=msg,
+                ) from e
+
+        has_warned = [False]
+
+        def maybe_warn_nondeterministic():
+            if has_warned[0]:
+                return
+            has_warned[0] = True
+            nondeterm_ops = [
+                op for op in traced_func.graph.nodes() if op.isNondeterministic()
+            ]
+            if len(nondeterm_ops) > 0:
+                nondeterministic_ops_warning = "Trace had nondeterministic nodes. "
+                nondeterministic_ops_warning += (
+                    "Did you forget call .eval() on your model? Nodes:\n"
+                )
+                nondeterministic_ops_warning += "\n".join(
+                    [indent(str(op)) for op in nondeterm_ops][:20]
+                )
+                nondeterministic_ops_warning += (
+                    "\nThis may cause errors in trace checking. To disable trace checking,"
+                    " pass check_trace=False to torch.jit.trace()"
+                )
+                warnings.warn(
+                    nondeterministic_ops_warning, category=TracerWarning, stacklevel=5
+                )
+
+        def compare_outputs(original, reference, match_what):
+            all_ok = True
+            for i, (orig, ref) in enumerate(zip(original, reference)):
+                try:
+                    if orig.is_quantized:
+                        orig = orig.dequantize()
+                    if ref.is_quantized:
+                        ref = ref.dequantize()
+                    if orig.is_mkldnn:
+                        orig = orig.to_dense()
+                    if ref.is_mkldnn:
+                        ref = ref.to_dense()
+                    if ref.is_complex() or orig.is_complex():
+                        torch.testing.assert_close(
+                            orig.to(torch.cdouble),
+                            ref.to(torch.cdouble),
+                            rtol=check_tolerance,
+                            atol=default_tolerances(orig, ref)[1],
+                            equal_nan=True,
+                        )
+                    else:
+                        if orig.is_mps or ref.is_mps:
+                            torch.testing.assert_close(
+                                orig.float(),
+                                ref.float(),
+                                rtol=check_tolerance,
+                                atol=default_tolerances(orig, ref)[1],
+                                equal_nan=True,
+                            )
+                        elif getattr(orig, "is_nested", None) or getattr(
+                            ref, "is_nested", None
+                        ):
+                            assert getattr(orig, "is_nested", None) == getattr(
+                                ref, "is_nested", None
+                            )
+                            for t_orig, t_ref in zip(orig.unbind(), ref.unbind()):
+                                torch.testing.assert_close(
+                                    t_orig.double(),
+                                    t_ref.double(),
+                                    rtol=check_tolerance,
+                                    atol=default_tolerances(t_orig, t_ref)[1],
+                                    equal_nan=True,
+                                )
+                        else:
+                            torch.testing.assert_close(
+                                orig.double(),
+                                ref.double(),
+                                rtol=check_tolerance,
+                                atol=default_tolerances(orig, ref)[1],
+                                equal_nan=True,
+                            )
+                except AssertionError as e:
+                    maybe_warn_nondeterministic()
+                    warnings.warn(
+                        "Output nr "
+                        + str(i + 1)
+                        + ". of the traced function does not match "
+                        "the corresponding output of the "
+                        + match_what
+                        + ". Detailed error:\n"
+                        + str(e),
+                        category=TracerWarning,
+                        stacklevel=4,
+                    )
+                    all_ok = False
+
+            return all_ok
+
+        traced_outs = run_mod_and_filter_tensor_outputs(traced_func, inputs, "trace")
+        fn_outs = run_mod_and_filter_tensor_outputs(func, inputs, "Python function")
+        if compare_outputs(traced_outs, fn_outs, "Python function"):
+            check_outs = run_mod_and_filter_tensor_outputs(
+                check_mod_func, inputs, "repeated trace"
+            )
+            compare_outputs(traced_outs, check_outs, "repeated trace")
+
+        diag_info = graph_diagnostic_info()
+        if any(info is not None for info in diag_info):
+            raise TracingCheckError(*diag_info)
+
+
+class TracerWarning(Warning):
+    @staticmethod
+    def ignore_lib_warnings():
+        # We ignore warnings from all submodules excluding the JIT, because we need them e.g. for _check_trace
+        warnings.filterwarnings(
+            "ignore", category=TracerWarning, module="torch.(?!jit)"
+        )
+        warnings.filterwarnings("ignore", "torch::jit::fuser::cuda")
+
+
+# We ignore the tracer warnings coming form inside the library, because all our shape
+# checks in nn will trigger them.
+TracerWarning.ignore_lib_warnings()
+torch._C._tracer_warn_use_python()
+
+
+def make_tuple(example_inputs):
+    if isinstance(example_inputs, (torch.Tensor, dict)):
+        return (example_inputs,)
+    # done primarily so that weird iterables fail here and not pybind11 code
+    if not isinstance(example_inputs, tuple):
+        return tuple(example_inputs)
+    return example_inputs
+
+
+def make_module(mod, _module_class, _compilation_unit):
+    if isinstance(mod, ScriptModule):
+        return mod
+    elif torch._jit_internal.module_has_exports(mod):
+        infer_methods_stubs_fn = torch.jit._recursive.make_stubs_from_exported_methods
+        return torch.jit._recursive.create_script_module(
+            mod, infer_methods_stubs_fn, share_types=False, is_tracing=True
+        )
+    else:
+        if _module_class is None:
+            _module_class = TopLevelTracedModule
+        return _module_class(mod, _compilation_unit=_compilation_unit)
+
+
+def wrap_check_inputs(check_inputs):
+    if check_inputs is None:
+        return None
+
+    return [{"forward": c} for c in check_inputs]
+
+
+def analyze_ts_result_with_export_result(export, trace):
+    import torch.utils._pytree as pytree
+
+    flat_export = pytree.tree_leaves(export)
+    flat_trace = pytree.tree_leaves(trace)
+
+    for orig, loaded in zip(flat_export, flat_trace):
+        if orig.layout != loaded.layout:
+            return False
+        # mkldnn is not supported for torch.allclose
+        if orig.layout == torch._mkldnn:  # type: ignore[attr-defined]
+            return True
+        if type(orig) != type(loaded):
+            return False
+
+        if isinstance(orig, torch._subclasses.FakeTensor):
+            # Skip for FakeTensor.
+            return True
+        elif isinstance(orig, torch.Tensor):
+            if orig.dtype != loaded.dtype:
+                return False
+            if not torch.allclose(orig, loaded):
+                return False
+        else:
+            if orig != loaded:
+                return False
+    return True
+
+
+def _trace_impl(
+    func,
+    example_inputs=None,
+    optimize=None,
+    check_trace=True,
+    check_inputs=None,
+    check_tolerance=1e-5,
+    strict=True,
+    _force_outplace=False,
+    _module_class=None,
+    _compilation_unit=_python_cu,
+    example_kwarg_inputs=None,
+    _store_inputs=True,
+):
+    if isinstance(func, torch.jit.ScriptModule):
+        # it is hard to trace it because the forward method on ScriptModule is already defined, so it
+        # would result in an error.
+        warnings.warn(
+            "The input to trace is already a ScriptModule, tracing it is a no-op. Returning the object as is."
+        )
+        return func
+
+    if isinstance(func, torch.nn.Module):
+        if example_inputs is None:
+            if isinstance(example_kwarg_inputs, dict):
+                example_inputs = example_kwarg_inputs
+            else:
+                raise RuntimeError("example_kwarg_inputs should be a dict")
+        return trace_module(
+            func,
+            {"forward": example_inputs},
+            None,
+            check_trace,
+            wrap_check_inputs(check_inputs),
+            check_tolerance,
+            strict,
+            _force_outplace,
+            _module_class,
+            example_inputs_is_kwarg=isinstance(example_kwarg_inputs, dict),
+            _store_inputs=_store_inputs,
+        )
+    if (
+        hasattr(func, "__self__")
+        and isinstance(func.__self__, torch.nn.Module)
+        and func.__name__ == "forward"
+    ):
+        if example_inputs is None:
+            if isinstance(example_kwarg_inputs, dict):
+                example_inputs = example_kwarg_inputs
+            else:
+                raise RuntimeError("example_kwarg_inputs should be a dict")
+        return trace_module(
+            func.__self__,
+            {"forward": example_inputs},
+            None,
+            check_trace,
+            wrap_check_inputs(check_inputs),
+            check_tolerance,
+            strict,
+            _force_outplace,
+            _module_class,
+            example_inputs_is_kwarg=isinstance(example_kwarg_inputs, dict),
+            _store_inputs=_store_inputs,
+        )
+
+    # Special case for common case of passing a single Tensor
+    if (
+        isinstance(example_inputs, (torch.Tensor, dict))
+        and example_kwarg_inputs is None
+    ):
+        example_inputs = (example_inputs,)
+    # done primarily so that weird iterables fail here and not pybind11 code
+    elif example_kwarg_inputs is None and not isinstance(example_inputs, tuple):
+        example_inputs = tuple(example_inputs)
+
+    var_lookup_fn = _create_interpreter_name_lookup_fn(0)
+
+    if hasattr(func, "__self__") and isinstance(func.__self__, torch.nn.Module):
+        raise AttributeError(
+            "trace doesn't support compiling individual module's functions.\n"
+            "Please use trace_module"
+        )
+
+    name = _qualified_name(func)
+    if isinstance(example_kwarg_inputs, dict):
+        example_inputs = example_kwarg_inputs
+        traced = torch._C._create_function_from_trace_with_dict(
+            name,
+            func,
+            example_kwarg_inputs,
+            var_lookup_fn,
+            strict,
+            _force_outplace,
+            get_callable_argument_names(func),
+        )
+    else:
+        traced = torch._C._create_function_from_trace(
+            name,
+            func,
+            example_inputs,
+            var_lookup_fn,
+            strict,
+            _force_outplace,
+            get_callable_argument_names(func),
+        )
+
+    # Check the trace against new traces created from user-specified inputs
+    if check_trace:
+        if check_inputs is not None:
+            _check_trace(
+                check_inputs,
+                func,
+                traced,
+                check_tolerance,
+                strict,
+                _force_outplace,
+                False,
+                _module_class,
+                example_inputs_is_kwarg=isinstance(example_kwarg_inputs, dict),
+            )
+        else:
+            _check_trace(
+                [example_inputs],
+                func,
+                traced,
+                check_tolerance,
+                strict,
+                _force_outplace,
+                False,
+                _module_class,
+                example_inputs_is_kwarg=isinstance(example_kwarg_inputs, dict),
+            )
+
+    # Allow torch.compile() to inline
+    traced._torchdynamo_inline = func  # type: ignore[attr-defined]
+    return traced
+
+
+class _ExportType(str, Enum):
+    DIRECT_EXPORT = "DIRECT_EXPORT"
+    TRACE_AND_EXPORT = "TRACE_AND_EXPORT"
+    SOURCE_TO_SOURCE = "SOURCE_TO_SOURCE"
+
+    def __str__(self) -> str:
+        return self.value
+
+
+class _ExportOutcome(str, Enum):
+    SUCCESS = "SUCCESS"
+    FAILED_TO_EXPORT = "FAILED_TO_EXPORT"
+    FAILED_TO_RUN = "FAILED_TO_RUN"
+    ACCURACY_ERROR = "ACCURACY_ERROR"
+
+    def __str__(self) -> str:
+        return self.value
+
+
+def trace(
+    func,
+    example_inputs=None,
+    optimize=None,
+    check_trace=True,
+    check_inputs=None,
+    check_tolerance=1e-5,
+    strict=True,
+    _force_outplace=False,
+    _module_class=None,
+    _compilation_unit=_python_cu,
+    example_kwarg_inputs=None,
+    _store_inputs=True,
+):
+    r"""
+    Trace a function and return an executable  or :class:`ScriptFunction` that will be optimized using just-in-time compilation.
+
+    Tracing is ideal for code that operates only on
+    ``Tensor``\\s and lists, dictionaries, and
+    tuples of ``Tensor``\\s.
+
+    Using `torch.jit.trace` and `torch.jit.trace_module`, you can turn an
+    existing module or Python function into a TorchScript
+    :class:`ScriptFunction` or :class:`ScriptModule`. You must provide example
+    inputs, and we run the function, recording the operations performed on all
+    the tensors.
+
+    * The resulting recording of a standalone function produces `ScriptFunction`.
+    * The resulting recording of `nn.Module.forward` or `nn.Module` produces
+      `ScriptModule`.
+
+    This module also contains any parameters that the original
+    module had as well.
+
+    Warning:
+        Tracing only correctly records functions and modules which are not data
+        dependent (e.g., do not have conditionals on data in tensors) and do not have
+        any untracked external dependencies (e.g., perform input/output or
+        access global variables). Tracing only records operations done when the given
+        function is run on the given tensors. Therefore, the returned
+        `ScriptModule` will always run the same traced graph on any input. This
+        has some important implications when your module is expected to run
+        different sets of operations, depending on the input and/or the module
+        state. For example,
+
+        * Tracing will not record any control-flow like if-statements or loops.
+          When this control-flow is constant across your module, this is fine
+          and it often inlines the control-flow decisions. But sometimes the
+          control-flow is actually part of the model itself. For instance, a
+          recurrent network is a loop over the (possibly dynamic) length of an
+          input sequence.
+        * In the returned :class:`ScriptModule`, operations that have different
+          behaviors in ``training`` and ``eval`` modes will always behave as if
+          it is in the mode it was in during tracing, no matter which mode the
+          `ScriptModule` is in.
+
+        In cases like these, tracing would not be appropriate and
+        :func:`scripting ` is a better choice. If you trace
+        such models, you may silently get incorrect results on subsequent
+        invocations of the model. The tracer will try to emit warnings when
+        doing something that may cause an incorrect trace to be produced.
+
+    Args:
+        func (callable or torch.nn.Module):  A Python function or `torch.nn.Module`
+            that will be run with `example_inputs`. `func` arguments and return
+            values  must be tensors or (possibly nested) tuples that contain
+            tensors. When a module is passed `torch.jit.trace`, only the
+            ``forward`` method is run and traced (see :func:`torch.jit.trace
+            ` for details).
+
+    Keyword arguments:
+        example_inputs (tuple or torch.Tensor or None, optional): A tuple of example
+            inputs that will be passed to the function while tracing.
+            Default: ``None``. Either this argument or ``example_kwarg_inputs``
+            should be specified. The resulting trace can be run with inputs of
+            different types and shapes assuming the traced operations support those
+            types and shapes. `example_inputs` may also be a single Tensor in which
+            case it is automatically wrapped in a tuple. When the value is None,
+            ``example_kwarg_inputs`` should be specified.
+
+        check_trace (``bool``, optional): Check if the same inputs run through
+            traced code produce the same outputs. Default: ``True``. You might want
+            to disable this if, for example, your network contains non-
+            deterministic ops or if you are sure that the network is correct despite
+            a checker failure.
+
+        check_inputs (list of tuples, optional): A list of tuples of input
+            arguments that should be used to check the trace against what is
+            expected. Each tuple is equivalent to a set of input arguments that
+            would be specified in ``example_inputs``. For best results, pass in
+            a set of checking inputs representative of the space of shapes and
+            types of inputs you expect the network to see.  If not specified,
+            the original ``example_inputs`` are used for checking
+        check_tolerance (float, optional): Floating-point comparison tolerance
+            to use in the checker procedure.  This can be used to relax the
+            checker strictness in the event that results diverge numerically
+            for a known reason, such as operator fusion.
+        strict (``bool``, optional): run the tracer in a strict mode or not
+            (default: ``True``). Only turn this off when you want the tracer to
+            record your mutable container types (currently ``list``/``dict``)
+            and you are sure that the container you are using in your
+            problem is a ``constant`` structure and does not get used as
+            control flow (if, for) conditions.
+        example_kwarg_inputs (dict, optional): This parameter is a pack of keyword
+            arguments of example inputs that will be passed to the function while
+            tracing. Default: ``None``. Either this argument or ``example_inputs``
+            should be specified. The dict will be unpacking by the arguments name
+            of the traced function. If the keys of the dict don't not match with
+            the traced function's arguments name, a runtime exception will be raised.
+
+    Returns:
+        If `func` is `nn.Module` or ``forward`` of `nn.Module`, `trace` returns
+        a :class:`ScriptModule` object with a single ``forward`` method
+        containing the traced code.  The returned `ScriptModule` will
+        have the same set of sub-modules and parameters as the original
+        ``nn.Module``.  If ``func`` is a standalone function, ``trace``
+        returns `ScriptFunction`.
+
+    Example (tracing a function):
+
+    .. testcode::
+
+        import torch
+
+        def foo(x, y):
+            return 2 * x + y
+
+        # Run `foo` with the provided inputs and record the tensor operations
+        traced_foo = torch.jit.trace(foo, (torch.rand(3), torch.rand(3)))
+
+        # `traced_foo` can now be run with the TorchScript interpreter or saved
+        # and loaded in a Python-free environment
+
+    Example (tracing an existing module)::
+
+        import torch
+        import torch.nn as nn
+
+        class Net(nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.conv = nn.Conv2d(1, 1, 3)
+
+            def forward(self, x):
+                return self.conv(x)
+
+        n = Net()
+        example_weight = torch.rand(1, 1, 3, 3)
+        example_forward_input = torch.rand(1, 1, 3, 3)
+
+        # Trace a specific method and construct `ScriptModule` with
+        # a single `forward` method
+        module = torch.jit.trace(n.forward, example_forward_input)
+
+        # Trace a module (implicitly traces `forward`) and construct a
+        # `ScriptModule` with a single `forward` method
+        module = torch.jit.trace(n, example_forward_input)
+
+    """
+    if not _enabled:
+        return func
+    if optimize is not None:
+        warnings.warn(
+            "`optimize` is deprecated and has no effect. "
+            "Use `with torch.jit.optimized_execution()` instead",
+            FutureWarning,
+            stacklevel=2,
+        )
+
+    from torch._utils_internal import (
+        check_if_torch_exportable,
+        log_torch_jit_trace_exportability,
+        log_torchscript_usage,
+    )
+
+    traced_func = _trace_impl(
+        func,
+        example_inputs,
+        optimize,
+        check_trace,
+        check_inputs,
+        check_tolerance,
+        strict,
+        _force_outplace,
+        _module_class,
+        _compilation_unit,
+        example_kwarg_inputs,
+        _store_inputs,
+    )
+    log_torchscript_usage("trace", model_id=_get_model_id(traced_func))
+
+    if check_if_torch_exportable():
+        from torch._export.converter import TS2EPConverter
+        from torch.export._trace import (
+            _convert_ts_to_export_experimental,
+            _process_jit_trace_inputs_for_export,
+        )
+
+        traced_func_for_export = _trace_impl(
+            func,
+            example_inputs=example_inputs,
+            optimize=optimize,
+            check_trace=False,
+            check_inputs=check_inputs,
+            check_tolerance=check_tolerance,
+            strict=strict,
+            _force_outplace=_force_outplace,
+            _module_class=_module_class,
+            _compilation_unit=_compilation_unit,
+            example_kwarg_inputs=example_kwarg_inputs,
+            _store_inputs=_store_inputs,
+        )
+
+        export_args, _ = _process_jit_trace_inputs_for_export(
+            example_inputs, example_kwarg_inputs
+        )
+
+        def _log_exportability(func_to_export, export_func, export_args, export_type):
+            try:
+                traced_result = func_to_export(*export_args)
+            except Exception as e:
+                _ = e
+                log_torch_jit_trace_exportability(
+                    "trace", str(export_type), str(_ExportOutcome.SUCCESS), "succeeded"
+                )
+                return
+
+            try:
+                ep_module = export_func(func_to_export, export_args)
+            except Exception as e:
+                log_torch_jit_trace_exportability(
+                    "trace",
+                    str(export_type),
+                    str(_ExportOutcome.FAILED_TO_EXPORT),
+                    str(e),
+                )
+                return
+
+            try:
+                export = ep_module(*export_args)
+            except Exception as e:
+                log_torch_jit_trace_exportability(
+                    "trace", str(export_type), str(_ExportOutcome.FAILED_TO_RUN), str(e)
+                )
+                return
+
+            if not analyze_ts_result_with_export_result(export, traced_result):
+                log_torch_jit_trace_exportability(
+                    "trace",
+                    str(export_type),
+                    str(_ExportOutcome.ACCURACY_ERROR),
+                    "accuracy error",
+                )
+                return
+
+            log_torch_jit_trace_exportability(
+                "trace", str(export_type), str(_ExportOutcome.SUCCESS), "succeeded"
+            )
+
+        def _direct_export_and_lower(func, export_args):
+            return torch.export.export(func, export_args, strict=False).module()
+
+        def _convert_ts_to_export_source_to_source(func, export_args):
+            return TS2EPConverter(func, export_args).convert().module()
+
+        # torch.jit.trace is noop when the original module is torch.jit.ScriptModule
+        if not isinstance(traced_func_for_export, torch.jit.ScriptModule):
+            _log_exportability(
+                traced_func_for_export,
+                _direct_export_and_lower,
+                export_args,
+                _ExportType.DIRECT_EXPORT,
+            )
+
+        _log_exportability(
+            traced_func_for_export,
+            _convert_ts_to_export_experimental,
+            export_args,
+            _ExportType.TRACE_AND_EXPORT,
+        )
+        _log_exportability(
+            traced_func_for_export,
+            _convert_ts_to_export_source_to_source,
+            export_args,
+            _ExportType.SOURCE_TO_SOURCE,
+        )
+
+    return traced_func
+
+
+_trace_module_map: Optional[Dict[Any, Any]] = None
+
+
+def trace_module(
+    mod,
+    inputs,
+    optimize=None,
+    check_trace=True,
+    check_inputs=None,
+    check_tolerance=1e-5,
+    strict=True,
+    _force_outplace=False,
+    _module_class=None,
+    _compilation_unit=_python_cu,
+    example_inputs_is_kwarg=False,
+    _store_inputs=True,
+):
+    """
+    Trace a module and return an executable :class:`ScriptModule` that will be optimized using just-in-time compilation.
+
+    When a module is passed to :func:`torch.jit.trace `, only
+    the ``forward`` method is run and traced. With ``trace_module``, you can specify a dictionary of
+    method names to example inputs to trace (see the ``inputs``) argument below.
+
+    See :func:`torch.jit.trace ` for more information on tracing.
+
+    Args:
+        mod (torch.nn.Module):  A ``torch.nn.Module`` containing methods whose names are
+                                specified in ``inputs``. The given methods will be compiled
+                                as a part of a single `ScriptModule`.
+        inputs (dict):  A dict containing sample inputs indexed by method names in ``mod``.
+                                The inputs will be passed to methods whose names correspond to inputs'
+                                keys while tracing.
+                                ``{ 'forward' : example_forward_input, 'method2': example_method2_input}``
+    Keyword arguments:
+        check_trace (``bool``, optional): Check if the same inputs run through
+                                      traced code produce the same outputs. Default: ``True``. You might want
+                                      to disable this if, for example, your network contains non-
+                                      deterministic ops or if you are sure that the network is correct despite
+                                      a checker failure.
+
+        check_inputs (list of dicts, optional): A list of dicts of input arguments that should be used
+                                                 to check the trace against what is expected. Each tuple
+                                                 is equivalent to a set of input arguments that would
+                                                 be specified in ``inputs``. For best results, pass in a
+                                                 set of checking inputs representative of the space of
+                                                 shapes and types of inputs you expect the network to see.
+                                                 If not specified, the original ``inputs`` are used for checking
+        check_tolerance (float, optional): Floating-point comparison tolerance to use in the checker procedure.
+                                           This can be used to relax the checker strictness in the event that
+                                           results diverge numerically for a known reason, such as operator fusion.
+        example_inputs_is_kwarg (``bool``, optional): This parameter indicate whether the example inputs is a pack
+                                           pack of keyword arguments. Default: ``False``.
+
+    Returns:
+        A :class:`ScriptModule` object with a single ``forward`` method containing the traced code.
+        When ``func`` is a ``torch.nn.Module``, the returned :class:`ScriptModule` will have the same set of
+        sub-modules and parameters as ``func``.
+
+    Example (tracing a module with multiple methods)::
+
+        import torch
+        import torch.nn as nn
+
+        class Net(nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.conv = nn.Conv2d(1, 1, 3)
+
+            def forward(self, x):
+                return self.conv(x)
+
+            def weighted_kernel_sum(self, weight):
+                return weight * self.conv.weight
+
+
+        n = Net()
+        example_weight = torch.rand(1, 1, 3, 3)
+        example_forward_input = torch.rand(1, 1, 3, 3)
+
+        # Trace a specific method and construct `ScriptModule` with
+        # a single `forward` method
+        module = torch.jit.trace(n.forward, example_forward_input)
+
+        # Trace a module (implicitly traces `forward`) and construct a
+        # `ScriptModule` with a single `forward` method
+        module = torch.jit.trace(n, example_forward_input)
+
+        # Trace specific methods on a module (specified in `inputs`), constructs
+        # a `ScriptModule` with `forward` and `weighted_kernel_sum` methods
+        inputs = {'forward' : example_forward_input, 'weighted_kernel_sum' : example_weight}
+        module = torch.jit.trace_module(n, inputs)
+
+    """
+    if not _enabled:
+        return mod
+    if optimize is not None:
+        warnings.warn(
+            "`optimize` is deprecated and has no effect. "
+            "Use `with torch.jit.optimized_execution()` instead",
+            FutureWarning,
+            stacklevel=2,
+        )
+
+    var_lookup_fn = _create_interpreter_name_lookup_fn(0)
+
+    if not isinstance(mod, torch.nn.Module):
+        raise AttributeError("expected torch.nn.Module as the first argument")
+
+    if not isinstance(inputs, dict):
+        raise AttributeError("expected a dictionary of (method_name, input) pairs")
+
+    old_module_map = torch.jit._trace._trace_module_map
+    try:
+        trace_module_map: Dict[Any, Any] = {}
+
+        def register_submods(mod, prefix):
+            for name, child in mod.named_children():
+                submod_qualname = prefix + "." + name
+                trace_module_map[child] = submod_qualname
+                register_submods(child, submod_qualname)
+
+        trace_module_map["__module"] = mod
+        torch.jit._trace._trace_module_map = trace_module_map
+        register_submods(mod, "__module")
+
+        module = make_module(mod, _module_class, _compilation_unit)
+
+        for method_name, example_inputs in inputs.items():
+            if method_name == "forward":
+                # "forward" is a special case because we need to trace
+                # `Module.__call__`, which sets up some extra tracing, but uses
+                # argument names of the real `Module.forward` method.
+                func = mod
+                forward_method = getattr(mod, method_name)
+                argument_names = get_callable_argument_names(forward_method)
+            else:
+                func = getattr(mod, method_name)
+                argument_names = get_callable_argument_names(func)
+
+            if isinstance(example_inputs, dict) and example_inputs_is_kwarg:
+                # Raise exception when the user provided key names are not aligned with forward() method's arguments' name/
+                for key in example_inputs:
+                    if key not in argument_names:
+                        valid_arguments = "[" + ",".join(argument_names) + "]"
+                        raise NameError(
+                            f"""'{key}' is not in forward() method's arguments,
+                         valid arguments name are {valid_arguments}"""
+                        )
+                module._c._create_method_from_trace_with_dict(
+                    method_name,
+                    func,
+                    example_inputs,
+                    var_lookup_fn,
+                    strict,
+                    _force_outplace,
+                    argument_names,
+                    _store_inputs,
+                )
+            else:
+                example_inputs = make_tuple(example_inputs)
+                module._c._create_method_from_trace(
+                    method_name,
+                    func,
+                    example_inputs,
+                    var_lookup_fn,
+                    strict,
+                    _force_outplace,
+                    argument_names,
+                    _store_inputs,
+                )
+
+            check_trace_method = module._c._get_method(method_name)
+
+            # Check the trace against new traces created from user-specified inputs
+            if check_trace:
+                if check_inputs is not None:
+                    _check_trace(
+                        check_inputs,
+                        func,
+                        check_trace_method,
+                        check_tolerance,
+                        strict,
+                        _force_outplace,
+                        True,
+                        _module_class,
+                        example_inputs_is_kwarg=example_inputs_is_kwarg,
+                    )
+                else:
+                    _check_trace(
+                        [inputs],
+                        func,
+                        check_trace_method,
+                        check_tolerance,
+                        strict,
+                        _force_outplace,
+                        True,
+                        _module_class,
+                        example_inputs_is_kwarg=example_inputs_is_kwarg,
+                    )
+    finally:
+        torch.jit._trace._trace_module_map = old_module_map
+
+    return module
+
+
+def is_tracing():
+    """Return a boolean value.
+
+    Returns ``True`` in tracing (if a function is called during the
+    tracing of code with ``torch.jit.trace``) and ``False`` otherwise.
+    """
+    if is_scripting():
+        return False
+    return torch._C._is_tracing()
+
+
+class TracedModule(ScriptModule):
+    _disable_script_meta = True
+
+    def __init__(self, orig, id_set=None, _compilation_unit=None):
+        # XXX: orig can be a nn.Module or a function!
+        super().__init__()
+        assert isinstance(orig, torch.nn.Module)
+
+        # Copy a subset of `orig` to a temporary nn.Module.
+        # This is a way to customize what will actually get compiled by create_script_module
+        id_set = set()
+
+        # This allows us to preserve the original module's qualified name by defining a new
+        # type with the attribute _jit_override_qualname. In torch._jit_internal._qualified_name
+        # we have a special case that will look up this attribute to override whatever qualname
+        # we would get from the python type system
+        class QualnameWrapper(torch.nn.Module):
+            pass
+
+        QualnameWrapper._jit_override_qualname = torch._jit_internal._qualified_name(  # type: ignore[attr-defined]
+            type(orig)
+        )
+
+        tmp_module = QualnameWrapper()
+
+        def check_unique(param):
+            if param in id_set:
+                raise ValueError(
+                    "TracedModules don't support parameter sharing between modules"
+                )
+            id_set.add(param)
+
+        tmp_module.training = orig.training
+
+        for name, param in orig._parameters.items():
+            if param is not None:
+                tmp_module._parameters[name] = param
+                check_unique(param)
+        for name, buf in orig._buffers.items():
+            if buf is not None:
+                tmp_module._buffers[name] = buf
+                check_unique(buf)
+        for name, val in orig.__dict__.items():
+            if (
+                torch._C._jit_is_script_object(val)
+                and name not in orig._parameters
+                and name not in orig._buffers
+            ):
+                setattr(tmp_module, name, val)
+
+        if orig._backward_hooks:
+            raise ValueError(
+                "Modules that have backward hooks assigned can't be compiled: "
+                + str(orig)
+            )
+
+        for name, submodule in orig._modules.items():
+            if submodule is None:
+                continue
+            tmp_module._modules[name] = make_module(
+                submodule, TracedModule, _compilation_unit=None
+            )
+
+        script_module = torch.jit._recursive.create_script_module(
+            tmp_module, lambda module: (), share_types=False, is_tracing=True
+        )
+
+        self.__dict__["_name"] = type(orig).__name__
+        self.__dict__["_actual_script_module"] = script_module
+        for name in ("_parameters", "_buffers", "_modules", "training"):
+            delattr(self, name)
+
+    def forward(self, *args, **kwargs):
+        raise RuntimeError("Trace submodules cannot be called.")
+
+    def __getattr__(self, attr):
+        if "_actual_script_module" not in self.__dict__:
+            return super().__getattr__(attr)
+        return getattr(self._actual_script_module, attr)
+
+    def __setattr__(self, attr, value):
+        if "_actual_script_module" not in self.__dict__:
+            return super().__setattr__(attr, value)
+        setattr(self._actual_script_module, attr, value)
+
+    def _get_name(self):
+        return self._name
+
+    def extra_repr(self):
+        return f"original_name={self._name}"
+
+
+class TopLevelTracedModule(TracedModule):
+    forward: Callable[..., Any] = _CachedForward()  # type: ignore[assignment]
+
+    def _reconstruct(self, cpp_module):
+        """
+        Re-construct an instance of TopLevelTracedModule using an instance of a C++ module.
+
+        Args:
+            cpp_module: The C++ module that this TopLevelTracedModule will be rebuilt around.
+        """
+        self.__dict__["_actual_script_module"]._reconstruct(cpp_module)
+
+
+def _script_if_tracing(fn: Callable[P, R]) -> Callable[P, R]:
+    @functools.wraps(fn)
+    def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+        if not is_tracing():
+            # Not tracing, don't do anything
+            return fn(*args, **kwargs)
+
+        compiled_fn: Callable[P, R] = script(wrapper.__original_fn)  # type: ignore[attr-defined]
+        return compiled_fn(*args, **kwargs)
+
+    wrapper.__original_fn = fn  # type: ignore[attr-defined]
+    wrapper.__script_if_tracing_wrapper = True  # type: ignore[attr-defined]
+
+    return wrapper
+
+
+def _get_trace_graph(
+    f,
+    args=(),
+    kwargs=None,
+    strict=True,
+    _force_outplace=False,
+    return_inputs=False,
+    _return_inputs_states=False,
+):
+    """Return a tuple on tracing a function or model.
+
+    .. warning::
+        This function is internal-only and should only be used by the ONNX
+        exporter. If you are trying to get a graph through tracing, please go
+        through the public API instead::
+
+            trace = torch.jit.trace(nn.LSTMCell(), (input, hidden))
+            trace_graph = trace.graph
+
+    Trace a function or model, returning a tuple consisting of the both the
+    *trace* of an execution, as well as the original return value. If return_inputs,
+    also returns the trace inputs as part of the tuple
+
+    Tracing is guaranteed not to change the semantics of the function/module
+    that is traced.
+
+    Args:
+        f (torch.nn.Module or function): the function or module
+            to be traced.
+        args (tuple or Tensor): the positional arguments to pass to the
+            function/module to be traced.  A non-tuple is assumed to
+            be a single positional argument to be passed to the model.
+        kwargs (dict): the keyword arguments to pass to the function/module
+            to be traced.
+
+    Example (trace a cell):
+
+    .. testcode::
+
+        trace = torch.jit.trace(nn.LSTMCell(), (input, hidden))
+    """
+    if kwargs is None:
+        kwargs = {}
+    if not isinstance(args, tuple):
+        args = (args,)
+    outs = ONNXTracedModule(
+        f, strict, _force_outplace, return_inputs, _return_inputs_states
+    )(*args, **kwargs)
+    return outs
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/annotations.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/annotations.py
new file mode 100644
index 0000000000000000000000000000000000000000..0337342edaf97be421b037dafdde199a3d29af42
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/annotations.py
@@ -0,0 +1,551 @@
+# mypy: allow-untyped-defs
+import ast
+import builtins
+import dis
+import enum
+import inspect
+import re
+import typing
+import warnings
+from textwrap import dedent
+from typing import Type
+
+import torch
+from torch._C import (
+    _GeneratorType,
+    AnyType,
+    AwaitType,
+    BoolType,
+    ComplexType,
+    DeviceObjType,
+    DictType,
+    EnumType,
+    FloatType,
+    FutureType,
+    InterfaceType,
+    IntType,
+    ListType,
+    NoneType,
+    NumberType,
+    OptionalType,
+    StreamObjType,
+    StringType,
+    TensorType,
+    TupleType,
+    UnionType,
+)
+from torch._jit_internal import (  # type: ignore[attr-defined]
+    _Await,
+    _qualified_name,
+    Any,
+    BroadcastingList1,
+    BroadcastingList2,
+    BroadcastingList3,
+    Dict,
+    Future,
+    is_await,
+    is_dict,
+    is_future,
+    is_ignored_fn,
+    is_list,
+    is_optional,
+    is_tuple,
+    is_union,
+    List,
+    Optional,
+    Tuple,
+    Union,
+)
+from torch._sources import get_source_lines_and_file
+
+from ._state import _get_script_class
+
+
+if torch.distributed.rpc.is_available():
+    from torch._C import RRefType
+    from torch._jit_internal import is_rref, RRef
+
+from torch._ops import OpOverloadPacket
+
+
+class Module:
+    def __init__(self, name, members):
+        self.name = name
+        self.members = members
+
+    def __getattr__(self, name):
+        try:
+            return self.members[name]
+        except KeyError:
+            raise RuntimeError(
+                f"Module {self.name} has no member called {name}"
+            ) from None
+
+
+class EvalEnv:
+    env = {
+        "torch": Module("torch", {"Tensor": torch.Tensor}),
+        "Tensor": torch.Tensor,
+        "typing": Module("typing", {"Tuple": Tuple}),
+        "Tuple": Tuple,
+        "List": List,
+        "Dict": Dict,
+        "Optional": Optional,
+        "Union": Union,
+        "Future": Future,
+        "Await": _Await,
+    }
+
+    def __init__(self, rcb):
+        self.rcb = rcb
+        if torch.distributed.rpc.is_available():
+            self.env["RRef"] = RRef
+
+    def __getitem__(self, name):
+        if name in self.env:
+            return self.env[name]
+        if self.rcb is not None:
+            return self.rcb(name)
+        return getattr(builtins, name, None)
+
+
+def get_signature(fn, rcb, loc, is_method):
+    if isinstance(fn, OpOverloadPacket):
+        signature = try_real_annotations(fn.op, loc)
+    else:
+        signature = try_real_annotations(fn, loc)
+    if signature is not None and is_method:
+        # If this is a method, then the signature will include a type for
+        # `self`, but type comments do not contain a `self`. So strip it
+        # away here so everything is consistent (`inspect.ismethod` does
+        # not work here since `fn` is unbound at this point)
+        param_types, return_type = signature
+        param_types = param_types[1:]
+        signature = (param_types, return_type)
+
+    if signature is None:
+        type_line, source = None, None
+        try:
+            source = dedent("".join(get_source_lines_and_file(fn)[0]))
+            type_line = get_type_line(source)
+        except TypeError:
+            pass
+        # This might happen both because we failed to get the source of fn, or
+        # because it didn't have any annotations.
+        if type_line is not None:
+            signature = parse_type_line(type_line, rcb, loc)
+
+    return signature
+
+
+def is_function_or_method(the_callable):
+    # A stricter version of `inspect.isroutine` that does not pass for built-in
+    # functions
+    return inspect.isfunction(the_callable) or inspect.ismethod(the_callable)
+
+
+def is_vararg(the_callable):
+    if not is_function_or_method(the_callable) and callable(the_callable):  # noqa: B004
+        # If `the_callable` is a class, de-sugar the call so we can still get
+        # the signature
+        the_callable = the_callable.__call__
+
+    if is_function_or_method(the_callable):
+        return inspect.getfullargspec(the_callable).varargs is not None
+    else:
+        return False
+
+
+def get_param_names(fn, n_args):
+    if isinstance(fn, OpOverloadPacket):
+        fn = fn.op
+
+    if (
+        not is_function_or_method(fn)
+        and callable(fn)
+        and is_function_or_method(fn.__call__)
+    ):  # noqa: B004
+        # De-sugar calls to classes
+        fn = fn.__call__
+
+    if is_function_or_method(fn):
+        if is_ignored_fn(fn):
+            fn = inspect.unwrap(fn)
+        return inspect.getfullargspec(fn).args
+    else:
+        # The `fn` was not a method or function (maybe a class with a __call__
+        # method, so use a default param name list)
+        return [str(i) for i in range(n_args)]
+
+
+def check_fn(fn, loc):
+    # Make sure the function definition is not a class instantiation
+    try:
+        source = dedent("".join(get_source_lines_and_file(fn)[0]))
+    except (OSError, TypeError):
+        return
+    if source is None:
+        return
+
+    py_ast = ast.parse(source)
+    if len(py_ast.body) == 1 and isinstance(py_ast.body[0], ast.ClassDef):
+        raise torch.jit.frontend.FrontendError(
+            loc,
+            f"Cannot instantiate class '{py_ast.body[0].name}' in a script function",
+        )
+    if len(py_ast.body) != 1 or not isinstance(py_ast.body[0], ast.FunctionDef):
+        raise torch.jit.frontend.FrontendError(
+            loc, "Expected a single top-level function"
+        )
+
+
+def _eval_no_call(stmt, glob, loc):
+    """Evaluate statement as long as it does not contain any method/function calls."""
+    bytecode = compile(stmt, "", mode="eval")
+    for insn in dis.get_instructions(bytecode):
+        if "CALL" in insn.opname:
+            raise RuntimeError(
+                f"Type annotation should not contain calls, but '{stmt}' does"
+            )
+    return eval(bytecode, glob, loc)  # type: ignore[arg-type] # noqa: P204
+
+
+def parse_type_line(type_line, rcb, loc):
+    """Parse a type annotation specified as a comment.
+
+    Example inputs:
+        # type: (Tensor, torch.Tensor) -> Tuple[Tensor]
+        # type: (Tensor, Tuple[Tensor, Tensor]) -> Tensor
+    """
+    arg_ann_str, ret_ann_str = split_type_line(type_line)
+
+    try:
+        arg_ann = _eval_no_call(arg_ann_str, {}, EvalEnv(rcb))
+    except (NameError, SyntaxError) as e:
+        raise RuntimeError(
+            "Failed to parse the argument list of a type annotation"
+        ) from e
+
+    if not isinstance(arg_ann, tuple):
+        arg_ann = (arg_ann,)
+
+    try:
+        ret_ann = _eval_no_call(ret_ann_str, {}, EvalEnv(rcb))
+    except (NameError, SyntaxError) as e:
+        raise RuntimeError(
+            "Failed to parse the return type of a type annotation"
+        ) from e
+
+    arg_types = [ann_to_type(ann, loc) for ann in arg_ann]
+    return arg_types, ann_to_type(ret_ann, loc)
+
+
+def get_type_line(source):
+    """Try to find the line containing a comment with the type annotation."""
+    type_comment = "# type:"
+
+    lines = source.split("\n")
+    lines = list(enumerate(lines))
+    type_lines = list(filter(lambda line: type_comment in line[1], lines))
+    # `type: ignore` comments may be needed in JIT'ed functions for mypy, due
+    # to the hack in torch/_VF.py.
+
+    # An ignore type comment can be of following format:
+    #   1) type: ignore
+    #   2) type: ignore[rule-code]
+    # This ignore statement must be at the end of the line
+
+    # adding an extra backslash before the space, to avoid triggering
+    # one of the checks in .github/workflows/lint.yml
+    type_pattern = re.compile("# type:\\ ignore(\\[[a-zA-Z-]+\\])?$")
+    type_lines = list(filter(lambda line: not type_pattern.search(line[1]), type_lines))
+
+    if len(type_lines) == 0:
+        # Catch common typo patterns like extra spaces, typo in 'ignore', etc.
+        wrong_type_pattern = re.compile("#[\t ]*type[\t ]*(?!: ignore(\\[.*\\])?$):")
+        wrong_type_lines = list(
+            filter(lambda line: wrong_type_pattern.search(line[1]), lines)
+        )
+        if len(wrong_type_lines) > 0:
+            raise RuntimeError(
+                "The annotation prefix in line "
+                + str(wrong_type_lines[0][0])
+                + " is probably invalid.\nIt must be '# type:'"
+                + "\nSee PEP 484 (https://www.python.org/dev/peps/pep-0484/#suggested-syntax-for-python-2-7-and-straddling-code)"  # noqa: B950
+                + "\nfor examples"
+            )
+        return None
+    elif len(type_lines) == 1:
+        # Only 1 type line, quit now
+        return type_lines[0][1].strip()
+
+    # Parse split up argument types according to PEP 484
+    # https://www.python.org/dev/peps/pep-0484/#suggested-syntax-for-python-2-7-and-straddling-code
+    return_line = None
+    parameter_type_lines = []
+    for line_num, line in type_lines:
+        if "# type: (...) -> " in line:
+            return_line = (line_num, line)
+            break
+        elif type_comment in line:
+            parameter_type_lines.append(line)
+    if return_line is None:
+        raise RuntimeError(
+            "Return type line '# type: (...) -> ...' not found on multiline "
+            "type annotation\nfor type lines:\n"
+            + "\n".join([line[1] for line in type_lines])
+            + "\n(See PEP 484 https://www.python.org/dev/peps/pep-0484/#suggested-syntax-for-python-2-7-and-straddling-code)"
+        )
+
+    def get_parameter_type(line):
+        item_type = line[line.find(type_comment) + len(type_comment) :]
+        return item_type.strip()
+
+    types = map(get_parameter_type, parameter_type_lines)
+    parameter_types = ", ".join(types)
+
+    return return_line[1].replace("...", parameter_types)
+
+
+def split_type_line(type_line):
+    """Split the comment with the type annotation into parts for argument and return types.
+
+    For example, for an input of:
+        # type: (Tensor, torch.Tensor) -> Tuple[Tensor, Tensor]
+
+    This function will return:
+        ("(Tensor, torch.Tensor)", "Tuple[Tensor, Tensor]")
+
+    """
+    start_offset = len("# type:")
+    try:
+        arrow_pos = type_line.index("->")
+    except ValueError:
+        raise RuntimeError(
+            "Syntax error in type annotation (couldn't find `->`)"
+        ) from None
+    return type_line[start_offset:arrow_pos].strip(), type_line[arrow_pos + 2 :].strip()
+
+
+def try_real_annotations(fn, loc):
+    """Try to use the Py3.5+ annotation syntax to get the type."""
+    try:
+        # Note: anything annotated as `Optional[T]` will automatically
+        # be returned as `Union[T, None]` per
+        # https://github.com/python/typing/blob/master/src/typing.py#L850
+        sig = inspect.signature(fn)
+    except ValueError:
+        return None
+
+    all_annots = [sig.return_annotation] + [
+        p.annotation for p in sig.parameters.values()
+    ]
+    if all(ann is sig.empty for ann in all_annots):
+        return None
+
+    arg_types = [ann_to_type(p.annotation, loc) for p in sig.parameters.values()]
+    return_type = ann_to_type(sig.return_annotation, loc)
+    return arg_types, return_type
+
+
+# Finds common type for enum values belonging to an Enum class. If not all
+# values have the same type, AnyType is returned.
+def get_enum_value_type(e: Type[enum.Enum], loc):
+    enum_values: List[enum.Enum] = list(e)
+    if not enum_values:
+        raise ValueError(f"No enum values defined for: '{e.__class__}'")
+
+    types = {type(v.value) for v in enum_values}
+    ir_types = [try_ann_to_type(t, loc) for t in types]
+
+    # If Enum values are of different types, an exception will be raised here.
+    # Even though Python supports this case, we chose to not implement it to
+    # avoid overcomplicate logic here for a rare use case. Please report a
+    # feature request if you find it necessary.
+    res = torch._C.unify_type_list(ir_types)
+    if not res:
+        return AnyType.get()
+    return res
+
+
+def is_tensor(ann):
+    if issubclass(ann, torch.Tensor):
+        return True
+
+    if issubclass(
+        ann,
+        (
+            torch.LongTensor,
+            torch.DoubleTensor,
+            torch.FloatTensor,
+            torch.IntTensor,
+            torch.ShortTensor,
+            torch.HalfTensor,
+            torch.CharTensor,
+            torch.ByteTensor,
+            torch.BoolTensor,
+        ),
+    ):
+        warnings.warn(
+            "TorchScript will treat type annotations of Tensor "
+            "dtype-specific subtypes as if they are normal Tensors. "
+            "dtype constraints are not enforced in compilation either."
+        )
+        return True
+
+    return False
+
+
+def _fake_rcb(inp):
+    return None
+
+
+def try_ann_to_type(ann, loc, rcb=None):
+    ann_args = typing.get_args(ann)  # always returns a tuple!
+
+    if ann is inspect.Signature.empty:
+        return TensorType.getInferred()
+    if ann is None:
+        return NoneType.get()
+    if inspect.isclass(ann) and is_tensor(ann):
+        return TensorType.get()
+    if is_tuple(ann):
+        # Special case for the empty Tuple type annotation `Tuple[()]`
+        if len(ann_args) == 1 and ann_args[0] == ():
+            return TupleType([])
+        return TupleType([try_ann_to_type(a, loc) for a in ann_args])
+    if is_list(ann):
+        elem_type = try_ann_to_type(ann_args[0], loc)
+        if elem_type:
+            return ListType(elem_type)
+    if is_dict(ann):
+        key = try_ann_to_type(ann_args[0], loc)
+        value = try_ann_to_type(ann_args[1], loc)
+        # Raise error if key or value is None
+        if key is None:
+            raise ValueError(
+                f"Unknown type annotation: '{ann_args[0]}' at {loc.highlight()}"
+            )
+        if value is None:
+            raise ValueError(
+                f"Unknown type annotation: '{ann_args[1]}' at {loc.highlight()}"
+            )
+        return DictType(key, value)
+    if is_optional(ann):
+        if issubclass(ann_args[1], type(None)):
+            contained = ann_args[0]
+        else:
+            contained = ann_args[1]
+        valid_type = try_ann_to_type(contained, loc)
+        msg = "Unsupported annotation {} could not be resolved because {} could not be resolved. At\n{}"
+        assert valid_type, msg.format(repr(ann), repr(contained), repr(loc))
+        return OptionalType(valid_type)
+    if is_union(ann):
+        # TODO: this is hack to recognize NumberType
+        if set(ann_args) == {int, float, complex}:
+            return NumberType.get()
+        inner: List = []
+        # We need these extra checks because both `None` and invalid
+        # values will return `None`
+        # TODO: Determine if the other cases need to be fixed as well
+        for a in typing.get_args(ann):
+            if a is None:
+                inner.append(NoneType.get())
+            maybe_type = try_ann_to_type(a, loc)
+            msg = "Unsupported annotation {} could not be resolved because {} could not be resolved. At\n{}"
+            assert maybe_type, msg.format(repr(ann), repr(maybe_type), repr(loc))
+            inner.append(maybe_type)
+        return UnionType(inner)  # type: ignore[arg-type]
+    if torch.distributed.rpc.is_available() and is_rref(ann):
+        return RRefType(try_ann_to_type(ann_args[0], loc))
+    if is_future(ann):
+        return FutureType(try_ann_to_type(ann_args[0], loc))
+    if is_await(ann):
+        elementType = try_ann_to_type(ann_args[0], loc) if ann_args else AnyType.get()
+        return AwaitType(elementType)
+    if ann is float:
+        return FloatType.get()
+    if ann is complex:
+        return ComplexType.get()
+    if ann is int or ann is torch.SymInt:
+        return IntType.get()
+    if ann is str:
+        return StringType.get()
+    if ann is bool:
+        return BoolType.get()
+    if ann is Any:
+        return AnyType.get()
+    if ann is type(None):
+        return NoneType.get()
+    if inspect.isclass(ann) and hasattr(ann, "__torch_script_interface__"):
+        return InterfaceType(ann.__torch_script_interface__)
+    if ann is torch.device:
+        return DeviceObjType.get()
+    if ann is torch.Generator:
+        return _GeneratorType.get()
+    if ann is torch.Stream:
+        return StreamObjType.get()
+    if ann is torch.dtype:
+        return IntType.get()  # dtype not yet bound in as its own type
+    if inspect.isclass(ann) and issubclass(ann, enum.Enum):
+        if _get_script_class(ann) is None:
+            scripted_class = torch.jit._script._recursive_compile_class(ann, loc)
+            name = scripted_class.qualified_name()
+        else:
+            name = _qualified_name(ann)
+        return EnumType(name, get_enum_value_type(ann, loc), list(ann))
+    if inspect.isclass(ann):
+        maybe_script_class = _get_script_class(ann)
+        if maybe_script_class is not None:
+            return maybe_script_class
+        if torch._jit_internal.can_compile_class(ann):
+            return torch.jit._script._recursive_compile_class(ann, loc)
+
+    # Maybe resolve a NamedTuple to a Tuple Type
+    if rcb is None:
+        rcb = _fake_rcb
+    return torch._C._resolve_type_from_object(ann, loc, rcb)
+
+
+def ann_to_type(ann, loc, rcb=None):
+    the_type = try_ann_to_type(ann, loc, rcb)
+    if the_type is not None:
+        return the_type
+    raise ValueError(f"Unknown type annotation: '{ann}' at {loc.highlight()}")
+
+
+__all__ = [
+    "Any",
+    "List",
+    "BroadcastingList1",
+    "BroadcastingList2",
+    "BroadcastingList3",
+    "Tuple",
+    "is_tuple",
+    "is_list",
+    "Dict",
+    "is_dict",
+    "is_optional",
+    "is_union",
+    "TensorType",
+    "TupleType",
+    "FloatType",
+    "ComplexType",
+    "IntType",
+    "ListType",
+    "StringType",
+    "DictType",
+    "AnyType",
+    "Module",
+    # TODO: Consider not exporting these during wildcard import (reserve
+    # that for the types; for idiomatic typing code.)
+    "get_signature",
+    "check_fn",
+    "get_param_names",
+    "parse_type_line",
+    "get_type_line",
+    "split_type_line",
+    "try_real_annotations",
+    "try_ann_to_type",
+    "ann_to_type",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/frontend.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/frontend.py
new file mode 100644
index 0000000000000000000000000000000000000000..7eb2d768c5259b5328b1fe121d0e49a3b61b67d3
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/frontend.py
@@ -0,0 +1,1286 @@
+# mypy: allow-untyped-defs
+import ast
+import dataclasses
+import inspect
+import re
+import string
+import sys
+from collections import namedtuple
+from textwrap import dedent
+from typing import List, Tuple  # noqa: F401
+
+import torch
+import torch.jit.annotations
+from torch import _jit_internal
+from torch._C._jit_tree_views import (
+    Apply,
+    Assert,
+    Assign,
+    Attribute,
+    AugAssign,
+    BinOp,
+    Break,
+    ClassDef,
+    Const,
+    Continue,
+    Decl,
+    Def,
+    Delete,
+    DictComp,
+    DictLiteral,
+    Dots,
+    EmptyTypeAnnotation,
+    ExprStmt,
+    FalseLiteral,
+    For,
+    Ident,
+    If,
+    ListComp,
+    ListLiteral,
+    NoneLiteral,
+    Param,
+    Pass,
+    Property,
+    Raise,
+    Return,
+    Select,
+    SliceExpr,
+    Starred,
+    Stmt,
+    StringLiteral,
+    Subscript,
+    TernaryIf,
+    TrueLiteral,
+    TupleLiteral,
+    UnaryOp,
+    Var,
+    While,
+    With,
+    WithItem,
+)
+from torch._jit_internal import (  # noqa: F401
+    _is_drop_fn,
+    FunctionModifiers,
+    is_static_fn,
+    should_drop,
+)
+from torch._sources import (
+    get_source_lines_and_file,
+    make_source_context,
+    parse_def,
+    ParsedDef as _ParsedDef,
+)
+from torch.jit._dataclass_impls import DATACLASS_MAGIC_METHODS
+from torch.jit._monkeytype_config import get_qualified_name, monkeytype_trace
+
+
+_IS_ASTUNPARSE_INSTALLED = False
+try:
+    import astunparse  # type: ignore[import]
+
+    _IS_ASTUNPARSE_INSTALLED = True
+except ImportError:
+    pass
+
+# Borrowed from cPython implementation
+# https://github.com/python/cpython/blob/561612d8456cfab5672c9b445521113b847bd6b3/Lib/textwrap.py#L411#
+
+_reserved_prefix = "__jit"
+_reserved_names = {"print"}
+_identifier_chars = set(string.ascii_lowercase + string.ascii_uppercase + string.digits)
+
+
+def is_reserved_name(name):
+    return name.startswith(_reserved_prefix) or name in _reserved_names
+
+
+pretty_node_names = {
+    ast.FunctionDef: "function definitions",
+    ast.For: "for loops",
+    ast.Delete: "del statements",
+    ast.ClassDef: "class definitions",
+    ast.With: "with statements",
+    ast.Raise: "raise statements",
+    ast.Assert: "assertions",
+    ast.Import: "import statements",
+    ast.ImportFrom: "import statements",
+    ast.Global: "global variables",
+    ast.Break: "break statements",
+    ast.Continue: "continue statements",
+}
+
+node_start_tokens = {
+    ast.FunctionDef: "def",
+    ast.For: "for",
+    ast.Delete: "del",
+    ast.ClassDef: "class",
+    ast.With: "with",
+    ast.Raise: "raise",
+    ast.Assert: "assert",
+    ast.Import: "import",
+    ast.ImportFrom: "from",
+    ast.Global: "global",
+    ast.Break: "break",
+    ast.Continue: "continue",
+}
+
+pretty_node_names.update(
+    {
+        ast.AsyncFunctionDef: "async function definitions",
+        ast.AsyncFor: "async for loops",
+        ast.AsyncWith: "async with statements",
+        ast.Try: "try blocks",
+        ast.Nonlocal: "nonlocal variables",
+    }
+)
+
+node_start_tokens.update(
+    {
+        ast.AsyncFunctionDef: "async def",
+        ast.AsyncFor: "async for",
+        ast.AsyncWith: "async with",
+        ast.Try: "try",
+        ast.Nonlocal: "nonlocal",
+    }
+)
+
+pretty_node_names.update(
+    {
+        ast.AnnAssign: "annotated assignments",
+    }
+)
+# NB: no specific token for AnnAssign
+
+
+class FrontendError(Exception):
+    def __init__(self, source_range, msg):
+        self.source_range = source_range
+        self.msg = msg
+
+        # This has to be instantiated here so the ErrorReport is accurate to the
+        # call stack when the FrontendError was raised
+        self.error_report = torch._C.ErrorReport(self.source_range)
+
+    def __str__(self):
+        return self.msg + self.error_report.what().lstrip()
+
+
+class NotSupportedError(FrontendError):
+    pass
+
+
+class UnsupportedNodeError(NotSupportedError):
+    def __init__(self, ctx, offending_node, reason=""):
+        # If we don't have a specific token, we default to length of 1
+        node_type = type(offending_node)
+        range_len = len(node_start_tokens.get(node_type, " "))
+        source_range = ctx.make_range(
+            offending_node.lineno,
+            offending_node.col_offset,
+            offending_node.col_offset + range_len,
+        )
+        feature_name = pretty_node_names.get(node_type, node_type.__name__)
+        msg = f"{feature_name} {reason + ' ' if reason else ''}aren't supported"
+        super().__init__(source_range, msg)
+
+
+class FrontendTypeError(FrontendError):
+    pass
+
+
+def build_withitems(ctx, items):
+    items = [build_withitem(ctx, i) for i in items]
+    return list(items)
+
+
+def build_stmts(ctx, stmts):
+    stmts = [build_stmt(ctx, s) for s in stmts]
+    return list(filter(None, stmts))
+
+
+def get_class_properties(cls, self_name):
+    """
+    Get a list of Property objects representing the properties of a class.
+
+    Args:
+        cls:  The class to get properties of.
+        self_name: The name of the class that the properties should belong to.
+    Returns:
+        A list of Property objects corresponding to the properties of cls. Property
+        here refers to the subclass of TreeView.
+    """
+    props = inspect.getmembers(cls, predicate=lambda m: isinstance(m, property))
+    # Any property that should not compiled must be in this list on the Module.
+    unused_properties = getattr(cls, "__jit_unused_properties__", [])
+
+    # Create Property TreeView objects from inspected property objects.
+    properties = []
+    for prop in props:
+        if prop[0] not in unused_properties and not should_drop(prop[1].fget):
+            getter = get_jit_def(
+                prop[1].fget, f"__{prop[0]}_getter", self_name=self_name
+            )
+            setter = (
+                get_jit_def(prop[1].fset, f"__{prop[0]}_setter", self_name=self_name)
+                if prop[1].fset
+                else None
+            )
+            properties.append(
+                Property(getter.range(), Ident(getter.range(), prop[0]), getter, setter)
+            )
+
+    return properties
+
+
+def get_class_assigns(ctx, cls_ast):
+    assigns = []
+
+    def maybe_build_assign(builder, entry):
+        nonlocal assigns
+        try:
+            assigns.append(builder(ctx, entry))
+        except NotSupportedError:
+            pass
+
+    for entry in cls_ast.body:
+        if isinstance(entry, ast.Assign):
+            maybe_build_assign(StmtBuilder.build_Assign, entry)
+        elif isinstance(entry, ast.AnnAssign):
+            maybe_build_assign(StmtBuilder.build_AnnAssign, entry)
+    return assigns
+
+
+def get_jit_class_def(cls, self_name):
+    """Get definitions for each method within the current class independently.
+
+    Args:
+        cls: The class to get definition of.
+        self_name: The name of the class that the properties should belong to.
+
+    Returns:
+        torch._C._jit_tree_views.ClassDef: A representation of the class,
+            the methods in the class and their definition as a tree.
+    """
+    # TODO: proper overriding analysis when implementing class inheritance
+    methods = inspect.getmembers(
+        cls,
+        predicate=lambda m: (inspect.ismethod(m) or inspect.isfunction(m))
+        and not is_static_fn(cls, m.__name__)
+        and m.__name__ in cls.__dict__
+        and not _is_drop_fn(m),
+    )
+
+    def is_classmethod(fn):
+        return inspect.ismethod(fn) and getattr(fn, "__self__", None) == cls
+
+    # Get and parse the source code for this class
+    sourcelines, file_lineno, filename = get_source_lines_and_file(
+        cls, torch._C.ErrorReport.call_stack()
+    )
+    source = "".join(sourcelines)
+
+    dedent_src = dedent(source)
+    py_ast = ast.parse(dedent_src)
+
+    class_ast = py_ast.body[0]
+    assert isinstance(class_ast, ast.ClassDef)
+
+    # Special case for dataclasses. In general we need access to the source code for
+    # an object in order to JIT compile it. But the dataclasses module dynamically synthesizes
+    # magic methods for classes, and we can't get the source code for these methods. As a
+    # workaround, we synthesize TorchScript-friendly implementations ourselves.
+    if dataclasses.is_dataclass(cls):
+        # Detect whether the user manually implemented any of the magic methods. If they did,
+        # we don't want to synthesize/override them.
+        overrides = {
+            method.name
+            for method in class_ast.body
+            if isinstance(method, ast.FunctionDef)
+            and method.name in DATACLASS_MAGIC_METHODS
+        }
+        for i, (name, _) in enumerate(methods):
+            # Is this a magic method we can synthesize?
+            synthesizer_fn = DATACLASS_MAGIC_METHODS.get(name)
+            if synthesizer_fn and name not in overrides:
+                parsed_def = synthesizer_fn(cls)
+                methods[i] = name, parsed_def
+                func = getattr(cls, name)
+                _jit_internal.loader.cache(func, parsed_def.source)
+
+    method_defs = [
+        get_jit_def(obj, name, self_name=self_name, is_classmethod=is_classmethod(obj))
+        for (name, obj) in methods
+    ]
+    properties = get_class_properties(cls, self_name)
+
+    leading_whitespace_len = len(source.split("\n", 1)[0]) - len(
+        dedent_src.split("\n", 1)[0]
+    )
+    ctx = make_source_context(
+        source, filename, file_lineno, leading_whitespace_len, False
+    )
+    assigns = get_class_assigns(ctx, class_ast)
+
+    return build_class_def(ctx, class_ast, method_defs, properties, self_name, assigns)
+
+
+def get_jit_def(fn, def_name, self_name=None, is_classmethod=False):
+    """
+    Build a JIT AST (TreeView) from the given function.
+
+    Args:
+        fn: A function object to compile or a pre-parsed ParsedDef object
+        def_name: The name to give to the resulting AST object. This is not
+            always the same as `fn.__name__`, for example:
+                def _forward(self):
+                    ...
+                forward = _forward
+            In this case, the `__name__` attribute of the function object is "_forward",
+            but we want the result AST to have the name "forward".
+        self_name: If this function is a method, what the type name of `self` is.
+    """
+    parsed_def = parse_def(fn) if not isinstance(fn, _ParsedDef) else fn
+    type_line = torch.jit.annotations.get_type_line(parsed_def.source)
+    fn_def = parsed_def.ast.body[0]
+
+    if is_classmethod:
+        arg_name = fn_def.args.args[0].arg
+        # Insert a statement that assigns the first argument to the class
+        assign_stmt = ast.parse(f"{arg_name} = {self_name}").body[0]
+        fn_def.body.insert(0, assign_stmt)
+
+    # Swap out the function signature and body if it is unused
+    if should_drop(fn):
+        unused_fn_def = ast.parse(
+            'def unused_fn(self: Any):\n\traise RuntimeError("Cannot call @unused methods")'
+        )
+        if len(unused_fn_def.body) != 1 or not isinstance(
+            unused_fn_def.body[0], ast.FunctionDef
+        ):
+            raise RuntimeError(
+                f"Expected a single top-level function: {parsed_def.filename}:{parsed_def.file_lineno}"
+            )
+        unused_def = unused_fn_def.body[0]
+        fn_def.body = unused_def.body
+        # kwarg/vararg not supported by `build_def`
+        fn_def.args.kwarg = fn_def.args.vararg = None
+        for arg in fn_def.args.args + fn_def.args.kwonlyargs:
+            # Replace potentially unsupported type annotations by "Any"
+            arg.annotation = unused_def.args.args[0].annotation
+        if _is_drop_fn(fn):
+            # Dropping potentially unsupported return type annotation for jit._drop
+            fn_def.returns = None
+            fn_def.type_comment = None
+
+    # If MonkeyType is installed, get all the consolidated type traces
+    # for the arguments from type_trace_db
+    type_trace_db = torch.jit._script._get_type_trace_db()
+    pdt_arg_types = None
+    if monkeytype_trace and not isinstance(fn, _ParsedDef):  # type: ignore[truthy-function]
+        qualname = get_qualified_name(fn)
+        pdt_arg_types = type_trace_db.get_args_types(qualname)
+
+    return build_def(
+        parsed_def.ctx,
+        fn_def,
+        type_line,
+        def_name,
+        self_name=self_name,
+        pdt_arg_types=pdt_arg_types,
+    )
+
+
+# TODO: more robust handling of recognizing ignore context manager
+def is_torch_jit_ignore_context_manager(stmt):
+    # checks if the statement is torch.jit.ignore context manager
+    if isinstance(stmt.items[0].context_expr, ast.Call):
+        # extract torch part
+        function = stmt.items[0].context_expr.func
+        if isinstance(function, ast.Attribute):
+            attr_name = function.attr
+            attr_value = function.value
+            if attr_name == "_IgnoreContextManager" and isinstance(
+                attr_value, ast.Attribute
+            ):
+                # there should be at most two nested attributes (e.g torch.jit._IgnoreContextManager)
+                if attr_value.attr == "jit" and isinstance(attr_value.value, ast.Name):
+                    if attr_value.value.id == "torch":
+                        return True
+    return False
+
+
+class Builder:
+    def __call__(self, ctx, node):
+        method = getattr(self, "build_" + node.__class__.__name__, None)
+        if method is None:
+            raise UnsupportedNodeError(ctx, node)
+        return method(ctx, node)
+
+
+def build_class_def(ctx, py_def, methods, properties, self_name, assigns):
+    r = ctx.make_range(
+        py_def.lineno, py_def.col_offset, py_def.col_offset + len("class")
+    )
+    return ClassDef(
+        Ident(r, self_name), [Stmt(method) for method in methods], properties, assigns
+    )
+
+
+def build_def(ctx, py_def, type_line, def_name, self_name=None, pdt_arg_types=None):
+    body = py_def.body
+    r = ctx.make_range(py_def.lineno, py_def.col_offset, py_def.col_offset + len("def"))
+
+    param_list = build_param_list(ctx, py_def.args, self_name, pdt_arg_types)
+    return_type = None
+    if getattr(py_def, "returns", None) is not None:
+        return_type = build_expr(ctx, py_def.returns)
+
+    decl = Decl(r, param_list, return_type)
+    is_method = self_name is not None
+    if type_line is not None:
+        type_comment_decl = torch._C.parse_type_comment(type_line)
+        decl = torch._C.merge_type_from_type_comment(decl, type_comment_decl, is_method)
+
+    return Def(Ident(r, def_name), decl, build_stmts(ctx, body))
+
+
+_vararg_kwarg_err = (
+    "Compiled functions can't take variable number of arguments "
+    "or use keyword-only arguments with defaults"
+)
+
+
+def build_param_list(ctx, py_args, self_name, pdt_arg_types=None):
+    if py_args.kwarg is not None:
+        expr = py_args.kwarg
+        ctx_range = ctx.make_range(
+            expr.lineno, expr.col_offset - 1, expr.col_offset + len(expr.arg)
+        )
+        raise NotSupportedError(ctx_range, _vararg_kwarg_err)
+    if py_args.vararg is not None:
+        expr = py_args.vararg
+        ctx_range = ctx.make_range(
+            expr.lineno, expr.col_offset - 1, expr.col_offset + len(expr.arg)
+        )
+        raise NotSupportedError(ctx_range, _vararg_kwarg_err)
+    if len(py_args.kw_defaults) > 0:
+        # kw_defaults is a list of the values for the kwargs (which default to None),
+        # so they don't actually have line numbers.
+        for arg in py_args.kw_defaults:
+            if arg is not None:
+                ctx_range = build_expr(ctx, arg).range()
+                raise NotSupportedError(ctx_range, _vararg_kwarg_err)
+
+    # List of Tuple of args and type as inferred by profile directed typing
+    arg_and_types = [
+        (
+            arg,
+            pdt_arg_types[arg.arg]
+            if pdt_arg_types and bool(pdt_arg_types[arg.arg])
+            else None,
+        )
+        for arg in py_args.args
+    ]
+    arg_and_types_kwonlyargs = [
+        (
+            arg,
+            pdt_arg_types[arg.arg]
+            if pdt_arg_types and bool(pdt_arg_types[arg.arg])
+            else None,
+        )
+        for arg in py_args.kwonlyargs
+    ]
+
+    result = [
+        build_param(ctx, arg, self_name, kwarg_only=False, pdt_arg_type=arg_type)
+        for arg, arg_type in arg_and_types
+    ]
+    result += [
+        build_param(ctx, arg, self_name, kwarg_only=True, pdt_arg_type=arg_type)
+        for arg, arg_type in arg_and_types_kwonlyargs
+    ]
+    return result
+
+
+def build_param(ctx, py_arg, self_name, kwarg_only, pdt_arg_type=None):
+    # NB: In Python3 py_arg is a pair of (str arg, expr? annotation)
+    name = py_arg.arg
+    r = ctx.make_range(py_arg.lineno, py_arg.col_offset, py_arg.col_offset + len(name))
+    if getattr(py_arg, "annotation", None) is not None:
+        annotation_expr = build_expr(ctx, py_arg.annotation)
+    elif pdt_arg_type:
+        annotation_expr = Var(Ident(r, pdt_arg_type))
+    elif self_name is not None and name == "self":
+        annotation_expr = Var(Ident(r, self_name))
+    else:
+        annotation_expr = EmptyTypeAnnotation(r)
+    return Param(annotation_expr, Ident(r, name), kwarg_only)
+
+
+def build_ignore_context_manager(ctx, stmt):
+    InputType = namedtuple("InputType", ["name", "ann"])
+    OutputType = namedtuple("OutputType", ["name", "ann"])
+
+    def process_ins_outs(args):
+        # parse the context manager to figure out inputs and outputs
+        # with their annotated types
+        # TODO: add input, output validator
+        inputs = []
+        outputs = []
+        for arg in args:
+            var_name = arg.arg
+            var_ann = arg.value.value
+            var_decl_type, var_ann = var_ann.split(":")
+            if var_decl_type == "inp":
+                inputs.append(InputType(var_name, var_ann))
+            if var_decl_type == "out":
+                outputs.append(OutputType(var_name, var_ann))
+        return inputs, outputs
+
+    def create_unique_name_ext(ctx, stmt):
+        # extension will be based on the full path filename plus
+        # the line number of original context manager
+        fn = re.sub(r"[^a-zA-Z0-9_]", "_", ctx.filename)
+        return f"{fn}_{stmt.lineno}"
+
+    def build_return_ann_stmt(outputs):
+        return_type_ann = ""
+        return_statement_str = "return "
+        if len(outputs) == 0:
+            return_type_ann += " -> None"
+        if len(outputs) == 1:
+            return_type_ann = " -> " + outputs[0].ann
+            return_statement_str += outputs[0].name
+        if len(outputs) > 1:
+            return_type_ann = " -> Tuple"
+            return_type_ann += "[" + ", ".join([var.ann for var in outputs]) + "]"
+            return_statement_str += ", ".join([var.name for var in outputs])
+        return return_type_ann, return_statement_str
+
+    def build_args(args):
+        return ", ".join([arg.name for arg in args])
+
+    inputs, outputs = process_ins_outs(stmt.items[0].context_expr.keywords)
+
+    # build the replacement function str with given inputs and outputs
+    ignore_function_name = "func_ignore_" + create_unique_name_ext(ctx, stmt)
+    ignore_function_str = "\ndef " + ignore_function_name
+    ignore_function_str += (
+        "(" + ", ".join([var.name + " :" + var.ann for var in inputs]) + ")"
+    )
+
+    return_ann, return_stmt = build_return_ann_stmt(outputs)
+    ignore_function_str += return_ann + ": pass"
+
+    # first create the functionDef object from just declaration
+    ignore_function = ast.parse(ignore_function_str).body[0]
+
+    # dump the body of context manager to dummy function
+    ignore_function.body = stmt.body  # type: ignore[attr-defined]
+
+    # insert return statement to the function
+    return_stmt = ast.parse(return_stmt).body[0]
+    ignore_function.body.append(return_stmt)  # type: ignore[attr-defined]
+
+    # registers the custom function in the global context
+    ignore_func_str = "@torch.jit.ignore\n" + astunparse.unparse(ignore_function)
+    ignore_func_str += f'\nglobals()["{ignore_function_name}"] = {ignore_function_name}'
+    exec(ignore_func_str)  # noqa: P204
+
+    # build the statements as:
+    # , , ... = torch.jit.frontend.(, )
+    assign_str_lhs = build_args(outputs)
+    # this function will be registered in torch.jit.frontend module by default
+    assign_str_rhs = (
+        f"torch.jit.frontend.{ignore_function_name}(" + build_args(inputs) + ")"
+    )
+
+    if len(outputs) > 0:
+        assign_str = assign_str_lhs + " = " + assign_str_rhs
+    else:
+        assign_str = assign_str_rhs
+    assign_ast = ast.parse(assign_str).body[0]
+    return assign_ast
+
+
+def get_default_args(fn):
+    """
+    Get a dictionary of default arguments for a function.
+
+    Args:
+        fn: Callable - The function to inspect for default arguments.
+    Returns:
+        (Dict[str, Any]): mapping argument names to their default values if
+        :attr:`fn` is not None, else empty dictionary.
+    """
+    if fn is None:
+        return {}
+
+    signature = inspect.signature(fn)
+
+    return {
+        k: v.default
+        for k, v in signature.parameters.items()
+        if v.default is not inspect.Parameter.empty
+    }
+
+
+def get_default_args_for_class(cls):
+    """
+    Get default arguments for all methods in a class (except for static methods).
+
+    Args:
+        cls: type - The class type to inspect for default arguments.
+    Returns:
+        A Dict[str, Dict[str, Any]] which maps each method name to a Dict[str, Any]
+        that maps each argument name to its default value.
+    """
+    # Get methods (except static methods because those are compiled separately as
+    # if they were independent script functions).
+    methods = inspect.getmembers(
+        cls,
+        predicate=lambda m: (inspect.ismethod(m) or inspect.isfunction(m))
+        and not is_static_fn(cls, m.__name__)
+        and m.__name__ in cls.__dict__,
+    )
+
+    # Get method defaults. Property defaults do not need to be considered
+    # because setters cannot be invoked without a value.
+    defaults = {
+        method_name: get_default_args(method_impl)
+        for method_name, method_impl in methods
+    }
+
+    return defaults
+
+
+class WithItemBuilder(Builder):
+    @staticmethod
+    def build_withitem(ctx, item):
+        lineno = item.context_expr.lineno
+        start = item.context_expr.col_offset
+        end = start + len(pretty_node_names[ast.With])
+        op_vars = item.optional_vars
+        r = ctx.make_range(lineno, start, end)
+
+        return WithItem(
+            r,
+            build_expr(ctx, item.context_expr),
+            build_expr(ctx, op_vars) if op_vars else None,
+        )
+
+
+class StmtBuilder(Builder):
+    augassign_map = {
+        ast.Add: "+",
+        ast.Sub: "-",
+        ast.Mult: "*",
+        ast.Div: "/",
+        ast.Mod: "%",
+        ast.BitOr: "|",
+        ast.BitAnd: "&",
+        ast.BitXor: "^",
+        ast.LShift: "<<",
+        ast.RShift: ">>",
+        ast.Pow: "**",
+    }
+
+    @staticmethod
+    def build_Expr(ctx, stmt):
+        value = stmt.value
+        if value.__class__.__name__ == "Str":
+            # If a statement is a string literal expression,
+            # then it is a docstring. Just ignore it.
+            return None
+        else:
+            return ExprStmt(build_expr(ctx, value))
+
+    @staticmethod
+    def build_Assign(ctx, stmt):
+        rhs = build_expr(ctx, stmt.value)
+        lhs = [build_expr(ctx, x) for x in stmt.targets]
+        return Assign(lhs, rhs)
+
+    @staticmethod
+    def build_AnnAssign(ctx, stmt):
+        if stmt.value is None:
+            raise UnsupportedNodeError(ctx, stmt, reason="without assigned value")
+
+        # Disallow type annotations on instance attributes outside of __init__
+        if (
+            type(stmt.target) == ast.Attribute
+            and stmt.target.value.id == "self"  # type: ignore[attr-defined]
+            and ctx.funcname != "__init__"
+        ):
+            start = stmt.col_offset
+            end = start + len(f"self.{stmt.target.attr}")
+            if hasattr(stmt.annotation, "id"):
+                end += len(f": {stmt.annotation.id}")
+            sr = ctx.make_range(stmt.lineno, start, end)
+            raise ValueError(
+                "Type annotations on instance attributes must be declared in "
+                f"__init__, not '{ctx.funcname}': {sr}"
+            )
+
+        rhs = build_expr(ctx, stmt.value)
+        lhs = build_expr(ctx, stmt.target)
+        the_type = build_expr(ctx, stmt.annotation)
+        return Assign([lhs], rhs, the_type)
+
+    @staticmethod
+    def build_Delete(ctx, stmt):
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len("del"))
+
+        return Delete(r, [build_expr(ctx, target) for target in stmt.targets])
+
+    @staticmethod
+    def build_Return(ctx, stmt):
+        r = ctx.make_range(
+            stmt.lineno, stmt.col_offset, stmt.col_offset + len("return")
+        )
+        return Return(r, None if stmt.value is None else build_expr(ctx, stmt.value))
+
+    @staticmethod
+    def build_Raise(ctx, stmt):
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len("raise"))
+        expr = build_expr(ctx, stmt.exc)
+        return Raise(r, expr)
+
+    @staticmethod
+    def build_Assert(ctx, stmt):
+        r = ctx.make_range(
+            stmt.lineno, stmt.col_offset, stmt.col_offset + len("assert")
+        )
+        test = build_expr(ctx, stmt.test)
+        msg = build_expr(ctx, stmt.msg) if stmt.msg is not None else None
+        return Assert(r, test, msg)
+
+    @staticmethod
+    def build_AugAssign(ctx, stmt):
+        lhs = build_expr(ctx, stmt.target)
+        rhs = build_expr(ctx, stmt.value)
+        op = type(stmt.op)
+        if op in StmtBuilder.augassign_map:
+            op_token = StmtBuilder.augassign_map[op]
+        else:
+            raise NotSupportedError(
+                find_before(ctx, rhs.range().start, "=", offsets=(-1, 0)),
+                "unsupported kind of augmented assignment: " + op.__name__,
+            )
+        return AugAssign(lhs, op_token, rhs)
+
+    @staticmethod
+    def build_While(ctx, stmt):
+        if stmt.orelse:
+            # TODO: try to recover the location of else:? Python doesn't give us useful
+            # annotations in this case
+            raise NotSupportedError(
+                None, "else branches of while loops aren't supported"
+            )
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len("while"))
+        return While(r, build_expr(ctx, stmt.test), build_stmts(ctx, stmt.body))
+
+    @staticmethod
+    def build_For(ctx, stmt):
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len("for"))
+        if stmt.orelse:
+            raise NotSupportedError(r, "else branches of for loops aren't supported")
+
+        return For(
+            r,
+            [build_expr(ctx, stmt.target)],
+            [build_expr(ctx, stmt.iter)],
+            build_stmts(ctx, stmt.body),
+        )
+
+    @staticmethod
+    def build_If(ctx, stmt):
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len("if"))
+        return If(
+            r,
+            build_expr(ctx, stmt.test),
+            build_stmts(ctx, stmt.body),
+            build_stmts(ctx, stmt.orelse),
+        )
+
+    @staticmethod
+    def build_Print(ctx, stmt):
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len("print"))
+        if stmt.dest:
+            raise NotSupportedError(
+                r, "print statements with non-default destinations aren't supported"
+            )
+        args = [build_expr(ctx, val) for val in stmt.values]
+        return ExprStmt(Apply(Var(Ident(r, "print")), args, []))
+
+    @staticmethod
+    def build_Pass(ctx, stmt):
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len("pass"))
+        return Pass(r)
+
+    @staticmethod
+    def build_Break(ctx, stmt):
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len("break"))
+        return Break(r)
+
+    @staticmethod
+    def build_Continue(ctx, stmt):
+        r = ctx.make_range(
+            stmt.lineno, stmt.col_offset, stmt.col_offset + len("continue")
+        )
+        return Continue(r)
+
+    @staticmethod
+    def build_With(ctx, stmt):
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len("with"))
+        # Handle ignore context manager
+        if is_torch_jit_ignore_context_manager(stmt):
+            if not _IS_ASTUNPARSE_INSTALLED:
+                raise RuntimeError(
+                    "torch.jit._IgnoreContextManager requires installing Python library `astunparse`, \
+                                   please install it in your Python environment"
+                )
+            assign_ast = build_ignore_context_manager(ctx, stmt)
+            return build_stmt(ctx, assign_ast)
+        return With(r, build_withitems(ctx, stmt.items), build_stmts(ctx, stmt.body))
+
+
+class ExprBuilder(Builder):
+    binop_map = {
+        ast.Add: "+",
+        ast.Sub: "-",
+        ast.Mult: "*",
+        ast.Div: "/",
+        ast.Pow: "**",
+        ast.Mod: "%",
+        ast.FloorDiv: "//",
+        ast.BitAnd: "&",
+        ast.BitXor: "^",
+        ast.BitOr: "|",
+        ast.LShift: "<<",
+        ast.RShift: ">>",
+    }
+
+    binop_map[ast.MatMult] = "@"
+
+    unop_map = {
+        ast.Not: "not",
+        ast.USub: "-",
+        ast.Invert: "~",
+    }
+
+    boolop_map = {
+        ast.And: "and",
+        ast.Or: "or",
+    }
+
+    cmpop_map = {
+        ast.Eq: "==",
+        ast.NotEq: "!=",
+        ast.LtE: "<=",
+        ast.Lt: "<",
+        ast.GtE: ">=",
+        ast.Gt: ">",
+        ast.Is: "is",
+        ast.IsNot: "is not",
+        ast.In: "in",
+        ast.NotIn: "not in",
+    }
+
+    @staticmethod
+    def build_Attribute(ctx, expr):
+        base = build_expr(ctx, expr.value)
+        # expr.attr is just a string, so it's not annotated in any way, so we have
+        # to build the range manually
+        source = ctx.source.encode("utf-8")
+
+        def get_char(index):
+            return chr(source[index])
+
+        start_pos = base.range().end + 1
+        while get_char(start_pos) in string.whitespace:  # Skip whitespace
+            start_pos += 1
+        end_pos = start_pos + len(expr.attr)
+        name_range = ctx.make_raw_range(start_pos, end_pos)
+        return Select(base, Ident(name_range, expr.attr))
+
+    @staticmethod
+    def build_Call(ctx, expr):
+        func = build_expr(ctx, expr.func)
+        args = [build_expr(ctx, py_arg) for py_arg in expr.args]
+        if hasattr(expr, "starargs") and expr.starargs:
+            stararg_expr = build_expr(ctx, expr.starargs)
+            args += [Starred(stararg_expr.range(), stararg_expr)]
+        kwargs = []
+        for kw in expr.keywords:
+            kw_expr = build_expr(ctx, kw.value)
+            # XXX: we could do a better job at figuring out the range for the name here
+            if not kw.arg:
+                raise NotSupportedError(
+                    kw_expr.range(), "keyword-arg expansion is not supported"
+                )
+            kwargs.append(Attribute(Ident(kw_expr.range(), kw.arg), kw_expr))
+        return Apply(func, args, kwargs)
+
+    @staticmethod
+    def build_Ellipsis(ctx, expr):
+        r = ctx.make_range(
+            expr.lineno, expr.col_offset, expr.col_offset + 3
+        )  # len("...") == 3
+        return Dots(r)
+
+    @staticmethod
+    def build_Name(ctx, expr):
+        r = ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + len(expr.id))
+        if expr.id.startswith(_reserved_prefix):
+            raise NotSupportedError(
+                r,
+                "names of variables used in JIT-ed functions "
+                "can't start with " + _reserved_prefix,
+            )
+        if expr.id == "True":
+            return TrueLiteral(r)
+        elif expr.id == "False":
+            return FalseLiteral(r)
+        elif expr.id == "None":
+            return NoneLiteral(r)
+        elif expr.id == "Ellipsis":
+            return Dots(r)
+        return Var(Ident(r, expr.id))
+
+    @staticmethod
+    def build_NameConstant(ctx, expr):
+        r = ctx.make_range(
+            expr.lineno, expr.col_offset, expr.col_offset + len(str(expr.value))
+        )
+        if expr.value is True:
+            return TrueLiteral(r)
+        elif expr.value is False:
+            return FalseLiteral(r)
+        elif expr.value is None:
+            return NoneLiteral(r)
+        elif expr.value == Ellipsis:
+            return Dots(r)
+        else:
+            raise ValueError("Name constant value unsupported: " + str(expr.value))
+
+    @staticmethod
+    def build_BinOp(ctx, expr):
+        lhs = build_expr(ctx, expr.left)
+        rhs = build_expr(ctx, expr.right)
+        op = type(expr.op)
+
+        if op == ast.Div and not ctx.uses_true_division:
+            err_range = ctx.make_raw_range(lhs.range().end, rhs.range().start)
+            raise FrontendError(
+                err_range,
+                "Division of ints in TorchScript uses Python 3 true "
+                "division semantics. Please put `from __future__ "
+                "import division` at the top of your file",
+            )
+        op_token = ExprBuilder.binop_map.get(op)
+        if op_token is None:
+            err_range = ctx.make_raw_range(lhs.range().end, rhs.range().start)
+            raise NotSupportedError(
+                err_range, "unsupported binary operator: " + op.__name__
+            )
+        return BinOp(op_token, lhs, rhs)
+
+    @staticmethod
+    def build_UnaryOp(ctx, expr):
+        sub_expr = build_expr(ctx, expr.operand)
+        op = type(expr.op)
+        op_token = ExprBuilder.unop_map.get(op)
+        if op_token is None:
+            raise NotSupportedError(
+                expr.range(), "unsupported unary operator: " + op.__name__
+            )
+        r = ctx.make_range(
+            expr.lineno, expr.col_offset, expr.col_offset + len(op_token)
+        )
+        return UnaryOp(r, op_token, sub_expr)
+
+    @staticmethod
+    def build_BoolOp(ctx, expr):
+        if len(expr.values) < 2:
+            raise AssertionError(
+                "expected at least 2 values in BoolOp, but got " + str(len(expr.values))
+            )
+        sub_exprs = [build_expr(ctx, sub_expr) for sub_expr in expr.values]
+        op = type(expr.op)
+        op_token = ExprBuilder.boolop_map.get(op)
+        if op_token is None:
+            err_range = ctx.make_raw_range(
+                sub_exprs[0].range().end, sub_exprs[1].range().start
+            )
+            raise NotSupportedError(
+                err_range, "unsupported boolean operator: " + op.__name__
+            )
+        lhs = sub_exprs[0]
+        for rhs in sub_exprs[1:]:
+            lhs = BinOp(op_token, lhs, rhs)
+        return lhs
+
+    @staticmethod
+    def build_IfExp(ctx, expr):
+        return TernaryIf(
+            build_expr(ctx, expr.test),
+            build_expr(ctx, expr.body),
+            build_expr(ctx, expr.orelse),
+        )
+
+    @staticmethod
+    def build_Compare(ctx, expr):
+        operands = [build_expr(ctx, e) for e in [expr.left] + list(expr.comparators)]
+        result = None
+        for lhs, op_, rhs in zip(operands, expr.ops, operands[1:]):
+            op = type(op_)
+            op_token = ExprBuilder.cmpop_map.get(op)
+            r = ctx.make_raw_range(lhs.range().end, rhs.range().start)
+            if op_token is None:
+                raise NotSupportedError(
+                    r, "unsupported comparison operator: " + op.__name__
+                )
+
+            if op == ast.NotIn:
+                # NB: `not in` is just `not( in )`, so we don't introduce new tree view
+                # but just make it a nested call in our tree view structure
+                in_expr = BinOp("in", lhs, rhs)
+                cmp_expr = UnaryOp(r, "not", in_expr)
+            else:
+                cmp_expr = BinOp(op_token, lhs, rhs)
+
+            if result is None:
+                result = cmp_expr
+            else:
+                result = BinOp("and", result, cmp_expr)
+        return result
+
+    @staticmethod
+    def build_Subscript(ctx, expr):
+        def build_SliceExpr(ctx, base, slice_expr):
+            lower = (
+                build_expr(ctx, slice_expr.lower)
+                if slice_expr.lower is not None
+                else None
+            )
+            upper = (
+                build_expr(ctx, slice_expr.upper)
+                if slice_expr.upper is not None
+                else None
+            )
+            step = (
+                build_expr(ctx, slice_expr.step)
+                if slice_expr.step is not None
+                else None
+            )
+            return SliceExpr(base.range(), lower, upper, step)
+
+        def build_Index(ctx, base, index_expr):
+            if isinstance(index_expr.value, ast.Tuple):
+                raise NotSupportedError(
+                    base.range(),
+                    "slicing multiple dimensions with tuples not supported yet",
+                )
+            return build_expr(ctx, index_expr.value)
+
+        def build_ExtSlice(ctx, base, extslice):
+            sub_exprs = []
+            for expr in extslice.dims:
+                sub_type = type(expr)
+                if sub_type is ast.Index:
+                    sub_exprs.append(build_Index(ctx, base, expr))
+                elif sub_type is ast.Slice:
+                    sub_exprs.append(build_SliceExpr(ctx, base, expr))
+                elif sub_type is ast.Constant and expr.value is Ellipsis:
+                    sub_exprs.append(Dots(base.range()))
+                else:
+                    raise NotSupportedError(
+                        base.range(),
+                        f"slicing multiple dimensions with {sub_type} not supported",
+                    )
+            return sub_exprs
+
+        base = build_expr(ctx, expr.value)
+        sub_type = type(expr.slice)
+        if sub_type is ast.Index:
+            if isinstance(expr.slice.value, ast.Tuple):
+                # N-dimensional indexing using Tuple: x[(i, j, k)] is equivalent to x[i, j, k]
+                # XXX: Indexing using a list is **different**! It triggers advanced indexing.
+                indices = [
+                    build_expr(ctx, index_expr) for index_expr in expr.slice.value.elts
+                ]
+                if not indices:
+                    # `col_offset` is an int, but `end_col_offset` is
+                    # `Optional[int]`. The magic number is here to make
+                    # sure we can parse `()` on any machine
+                    r = ctx.make_range(
+                        expr.lineno,
+                        expr.slice.value.col_offset,
+                        expr.slice.value.col_offset + 2,
+                    )
+                    tup = TupleLiteral(r, [])
+                    indices.append(tup)
+                return Subscript(base, indices)
+            else:
+                return Subscript(base, [build_expr(ctx, expr.slice.value)])
+        elif sub_type is ast.Slice:
+            return Subscript(base, [build_SliceExpr(ctx, base, expr.slice)])
+        elif sub_type is ast.ExtSlice:
+            return Subscript(base, build_ExtSlice(ctx, base, expr.slice))
+        elif sys.version_info >= (
+            3,
+            9,
+        ):  # In Python3.9 array indicies are not wrapped in ast.Index
+            if sub_type is ast.Tuple:
+                # N-dimensional indexing using Tuple: x[(i, j, k)] is equivalent to x[i, j, k]
+                indices = []
+                for index_expr in expr.slice.elts:
+                    if isinstance(index_expr, ast.Slice):
+                        indices.append(build_SliceExpr(ctx, base, index_expr))
+                    else:
+                        indices.append(build_expr(ctx, index_expr))
+                # Special-case logic for `typing.Tuple[()]`
+                if not indices:
+                    # See note above r.e. magic number
+                    r = ctx.make_range(
+                        expr.lineno, expr.slice.col_offset, expr.slice.col_offset + 2
+                    )
+                    tup = TupleLiteral(r, [])
+                    indices.append(tup)
+                return Subscript(base, indices)
+            return Subscript(base, [build_expr(ctx, expr.slice)])
+        else:  # Ellipsis (can only happen in Python 2)
+            raise NotSupportedError(base.range(), "ellipsis is not supported")
+
+    @staticmethod
+    def build_List(ctx, expr):
+        return ListLiteral(
+            ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + 1),
+            [build_expr(ctx, e) for e in expr.elts],
+        )
+
+    @staticmethod
+    def build_Tuple(ctx, expr):
+        return TupleLiteral(
+            ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + 1),
+            [build_expr(ctx, e) for e in expr.elts],
+        )
+
+    @staticmethod
+    def build_Dict(ctx, expr):
+        range = ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + 1)
+        if expr.keys and not expr.keys[0]:
+            raise NotSupportedError(
+                range, "Dict expansion (e.g. `{**dict}`) is not supported"
+            )
+        return DictLiteral(
+            range,
+            [build_expr(ctx, e) for e in expr.keys],
+            [build_expr(ctx, e) for e in expr.values],
+        )
+
+    @staticmethod
+    def build_Num(ctx, expr):
+        value = str(expr.value)
+        r = ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + len(value))
+        return Const(r, value)
+
+    @staticmethod
+    def build_Constant(ctx, expr):
+        value = expr.value
+        if value is None or isinstance(value, bool):
+            # NB: this check has to happen before the int check because bool is
+            # a subclass of int
+            return ExprBuilder.build_NameConstant(ctx, expr)
+        if isinstance(value, (int, float, complex)):
+            return ExprBuilder.build_Num(ctx, expr)
+        elif isinstance(value, str):
+            return ExprBuilder.build_Str(ctx, expr)
+        elif isinstance(value, type(Ellipsis)):
+            return ExprBuilder.build_Ellipsis(ctx, expr)
+        else:
+            error_range = ctx.make_range(
+                expr.lineno, expr.col_offset, expr.col_offset + len(str(value))
+            )
+            raise FrontendError(error_range, "Unknown Constant expression type")
+
+    @staticmethod
+    def build_Str(ctx, expr):
+        value = str(expr.value)
+        r = ctx.make_range(
+            expr.lineno, expr.col_offset, expr.col_offset + len(value) + 1
+        )
+        return StringLiteral(r, value)
+
+    @staticmethod
+    def build_JoinedStr(ctx, expr):
+        s = ""
+        args = []
+        for value in expr.values:
+            r = ctx.make_range(value.lineno, value.col_offset, value.col_offset + 1)
+            if isinstance(value, ast.FormattedValue):
+                if value.conversion != -1:
+                    raise NotSupportedError(r, "Don't support conversion in JoinedStr")
+                if value.format_spec is not None:
+                    raise NotSupportedError(r, "Don't support formatting in JoinedStr")
+                s += "{}"
+                args.append(build_expr(ctx, value.value))
+            elif isinstance(value, ast.Constant):
+                s += value.value
+            else:
+                raise NotSupportedError(r, "Unsupported value in JoinedStr")
+
+        r = ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + 1)
+        return Apply(Select(StringLiteral(r, s), Ident(r, "format")), args, [])
+
+    @staticmethod
+    def build_ListComp(ctx, stmt):
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset)
+        if len(stmt.generators) != 1:
+            raise NotSupportedError(r, "Only a single generator is currently supported")
+
+        if len(stmt.generators[0].ifs) != 0:
+            raise NotSupportedError(r, "Comprehension ifs are not supported yet")
+
+        elt_expr = build_expr(ctx, stmt.elt)
+        target_expr = build_expr(ctx, stmt.generators[0].target)
+        iter_expr = build_expr(ctx, stmt.generators[0].iter)
+
+        return ListComp(r, elt_expr, target_expr, iter_expr)
+
+    @staticmethod
+    def build_GeneratorExp(ctx, stmt):
+        # Convert Generator expression to ListComp
+        return ExprBuilder.build_ListComp(ctx, stmt)
+
+    @staticmethod
+    def build_DictComp(ctx, stmt):
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset)
+        if len(stmt.generators) != 1:
+            raise NotSupportedError(r, "Only a single generator is currently supported")
+
+        if len(stmt.generators[0].ifs) != 0:
+            raise NotSupportedError(r, "Comprehension ifs are not supported yet")
+
+        key_expr = build_expr(ctx, stmt.key)
+        value_expr = build_expr(ctx, stmt.value)
+        target_expr = build_expr(ctx, stmt.generators[0].target)
+        iter_expr = build_expr(ctx, stmt.generators[0].iter)
+
+        return DictComp(r, key_expr, value_expr, target_expr, iter_expr)
+
+    @staticmethod
+    def build_Starred(ctx, expr):
+        r = ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + 1)
+        return Starred(r, build_expr(ctx, expr.value))
+
+
+build_expr = ExprBuilder()
+build_stmt = StmtBuilder()
+build_withitem = WithItemBuilder()
+
+
+def find_before(ctx, pos, substr, offsets=(0, 0)):
+    new_pos = ctx.source[:pos].rindex(substr)
+    return ctx.make_raw_range(new_pos + offsets[0], new_pos + len(substr) + offsets[1])
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/generate_bytecode.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/generate_bytecode.py
new file mode 100644
index 0000000000000000000000000000000000000000..f66bf7bfc4c1b25a35b342ab6792af025266a3a8
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/generate_bytecode.py
@@ -0,0 +1,34 @@
+# mypy: allow-untyped-defs
+from typing import List
+
+from torch._C import _compile_graph_to_code_table, _generate_upgraders_graph
+
+
+def format_bytecode(table):
+    # given a nested tuple, convert it to nested list
+    def listify(content):
+        if not isinstance(content, tuple):
+            return content
+        return [listify(i) for i in content]
+
+    formatted_table = {}
+    for entry in table:
+        identifier = entry[0]
+        content = entry[1]
+        content = listify(content)
+        formatted_table[identifier] = content
+    return formatted_table
+
+
+def generate_upgraders_bytecode() -> List:
+    yaml_content = []
+    upgraders_graph_map = _generate_upgraders_graph()
+    for upgrader_name, upgrader_graph in upgraders_graph_map.items():
+        bytecode_table = _compile_graph_to_code_table(upgrader_name, upgrader_graph)
+        entry = {upgrader_name: format_bytecode(bytecode_table)}
+        yaml_content.append(entry)
+    return yaml_content
+
+
+if __name__ == "__main__":
+    raise RuntimeError("This file is not meant to be run directly")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/mobile/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/mobile/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..593dabf2fc43ca075d604977a0d254ea02f3ba55
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/mobile/__init__.py
@@ -0,0 +1,232 @@
+# mypy: allow-untyped-defs
+import os
+
+import torch
+from torch.jit._serialization import validate_map_location
+
+
+def _load_for_lite_interpreter(f, map_location=None):
+    r"""
+    Load a :class:`LiteScriptModule` saved with :func:`torch.jit._save_for_lite_interpreter`.
+
+    Args:
+        f: a file-like object (has to implement read, readline, tell, and seek),
+            or a string containing a file name
+        map_location: a string or torch.device used to dynamically remap
+            storages to an alternative set of devices.
+
+    Returns:
+        A :class:`LiteScriptModule` object.
+
+    Example:
+
+    .. testcode::
+
+        import torch
+        import io
+
+        # Load LiteScriptModule from saved file path
+        torch.jit._load_for_lite_interpreter('lite_script_module.pt')
+
+        # Load LiteScriptModule from io.BytesIO object
+        with open('lite_script_module.pt', 'rb') as f:
+            buffer = io.BytesIO(f.read())
+
+        # Load all tensors to the original device
+        torch.jit.mobile._load_for_lite_interpreter(buffer)
+    """
+    if isinstance(f, (str, os.PathLike)):
+        if not os.path.exists(f):
+            raise ValueError(f"The provided filename {f} does not exist")
+        if os.path.isdir(f):
+            raise ValueError(f"The provided filename {f} is a directory")
+
+    map_location = validate_map_location(map_location)
+
+    if isinstance(f, (str, os.PathLike)):
+        cpp_module = torch._C._load_for_lite_interpreter(os.fspath(f), map_location)
+    else:
+        cpp_module = torch._C._load_for_lite_interpreter_from_buffer(
+            f.read(), map_location
+        )
+
+    return LiteScriptModule(cpp_module)
+
+
+class LiteScriptModule:
+    def __init__(self, cpp_module):
+        self._c = cpp_module
+        super().__init__()
+
+    def __call__(self, *input):
+        return self._c.forward(input)
+
+    def find_method(self, method_name):
+        return self._c.find_method(method_name)
+
+    def forward(self, *input):
+        return self._c.forward(input)
+
+    def run_method(self, method_name, *input):
+        return self._c.run_method(method_name, input)
+
+
+def _export_operator_list(module: LiteScriptModule):
+    r"""Return a set of root operator names (with overload name) that are used by any method in this mobile module."""
+    return torch._C._export_operator_list(module._c)
+
+
+def _get_model_bytecode_version(f_input) -> int:
+    r"""Take a file-like object to return an integer.
+
+    Args:
+        f_input: a file-like object (has to implement read, readline, tell, and seek),
+            or a string containing a file name
+
+    Returns:
+        version: An integer. If the integer is -1, the version is invalid. A warning
+            will show in the log.
+
+    Example:
+    .. testcode::
+
+        from torch.jit.mobile import _get_model_bytecode_version
+
+        # Get bytecode version from a saved file path
+        version = _get_model_bytecode_version("path/to/model.ptl")
+
+    """
+    if isinstance(f_input, (str, os.PathLike)):
+        if not os.path.exists(f_input):
+            raise ValueError(f"The provided filename {f_input} does not exist")
+        if os.path.isdir(f_input):
+            raise ValueError(f"The provided filename {f_input} is a directory")
+
+    if isinstance(f_input, (str, os.PathLike)):
+        return torch._C._get_model_bytecode_version(os.fspath(f_input))
+    else:
+        return torch._C._get_model_bytecode_version_from_buffer(f_input.read())
+
+
+def _get_mobile_model_contained_types(f_input) -> int:
+    r"""Take a file-like object and return a set of string, like ("int", "Optional").
+
+    Args:
+        f_input: a file-like object (has to implement read, readline, tell, and seek),
+            or a string containing a file name
+
+    Returns:
+        type_list: A set of string, like ("int", "Optional"). These are types used in bytecode.
+
+    Example:
+
+    .. testcode::
+
+        from torch.jit.mobile import _get_mobile_model_contained_types
+
+        # Get type list from a saved file path
+        type_list = _get_mobile_model_contained_types("path/to/model.ptl")
+
+    """
+    if isinstance(f_input, (str, os.PathLike)):
+        if not os.path.exists(f_input):
+            raise ValueError(f"The provided filename {f_input} does not exist")
+        if os.path.isdir(f_input):
+            raise ValueError(f"The provided filename {f_input} is a directory")
+
+    if isinstance(f_input, (str, os.PathLike)):
+        return torch._C._get_mobile_model_contained_types(os.fspath(f_input))
+    else:
+        return torch._C._get_mobile_model_contained_types_from_buffer(f_input.read())
+
+
+def _backport_for_mobile(f_input, f_output, to_version):
+    r"""Take a input string containing a file name (file-like object) and a new destination to return a boolean.
+
+    Args:
+        f_input: a file-like object (has to implement read, readline, tell, and seek),
+            or a string containing a file name
+        f_output: path to new model destination
+        to_version: the expected output model bytecode version
+    Returns:
+        success: A boolean. If backport success, return true, otherwise false
+    """
+    if isinstance(f_input, (str, os.PathLike)):
+        if not os.path.exists(f_input):
+            raise ValueError(f"The provided filename {f_input} does not exist")
+        if os.path.isdir(f_input):
+            raise ValueError(f"The provided filename {f_input} is a directory")
+
+    if (isinstance(f_input, (str, os.PathLike))) and (
+        isinstance(f_output, (str, os.PathLike))
+    ):
+        return torch._C._backport_for_mobile(
+            os.fspath(f_input), os.fspath(f_output), to_version
+        )
+    else:
+        return torch._C._backport_for_mobile_from_buffer(
+            f_input.read(), str(f_output), to_version
+        )
+
+
+def _backport_for_mobile_to_buffer(f_input, to_version):
+    r"""Take a string containing a file name (file-like object).
+
+    Args:
+        f_input: a file-like object (has to implement read, readline, tell, and seek),
+            or a string containing a file name
+
+    """
+    if isinstance(f_input, (str, os.PathLike)):
+        if not os.path.exists(f_input):
+            raise ValueError(f"The provided filename {f_input} does not exist")
+        if os.path.isdir(f_input):
+            raise ValueError(f"The provided filename {f_input} is a directory")
+
+    if isinstance(f_input, (str, os.PathLike)):
+        return torch._C._backport_for_mobile_to_buffer(os.fspath(f_input), to_version)
+    else:
+        return torch._C._backport_for_mobile_from_buffer_to_buffer(
+            f_input.read(), to_version
+        )
+
+
+def _get_model_ops_and_info(f_input):
+    r"""Retrieve the root (top level) operators of a model and their corresponding compatibility info.
+
+    These root operators can call other operators within them (traced ops), and
+    a root op can call many different traced ops depending on internal code paths in the root op.
+    These traced ops are not returned by this function. Those operators are abstracted into the
+    runtime as an implementation detail (and the traced ops themselves can also call other operators)
+    making retrieving them difficult and their value from this api negligible since they will differ
+    between which runtime version the model is run on. Because of this, there is a false positive this
+    api can't prevent in a compatibility usecase. All the root ops of a model are present in a
+    target runtime, but not all the traced ops are which prevents a model from being able to run.
+    Args:
+        f_input: a file-like object (has to implement read, readline, tell, and seek),
+            or a string containing a file name
+
+    Returns:
+        Operators and info: A Dictionary mapping strings (the qualified names of the root operators)
+        of the model to their OperatorInfo structs.
+
+    Example:
+
+    .. testcode::
+
+        from torch.jit.mobile import _get_model_ops_and_info
+
+        # Get bytecode version from a saved file path
+        ops_and_info = _get_model_ops_and_info("path/to/model.ptl")
+
+    """
+    if isinstance(f_input, (str, os.PathLike)):
+        if not os.path.exists(f_input):
+            raise ValueError(f"The provided filename {f_input} does not exist")
+        if os.path.isdir(f_input):
+            raise ValueError(f"The provided filename {f_input} is a directory")
+
+    if isinstance(f_input, (str, os.PathLike)):
+        return torch._C._get_model_ops_and_info(os.fspath(f_input))
+    else:
+        return torch._C._get_model_ops_and_info(f_input.read())
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/mobile/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/mobile/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d84df8d725a35ece39786ea56fec28c0e6827fa9
Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/mobile/__pycache__/__init__.cpython-311.pyc differ
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/quantized.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/quantized.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2500c1f1b9fed481ab713953746294dc52668af
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/quantized.py
@@ -0,0 +1,100 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+class QuantizedLinear(torch.jit.ScriptModule):
+    def __init__(self, other):
+        raise RuntimeError(
+            "torch.jit.QuantizedLinear is no longer supported. Please use "
+            "torch.ao.nn.quantized.dynamic.Linear instead."
+        )
+
+
+# FP16 weights
+class QuantizedLinearFP16(torch.jit.ScriptModule):
+    def __init__(self, other):
+        super().__init__()
+        raise RuntimeError(
+            "torch.jit.QuantizedLinearFP16 is no longer supported. "
+            "Please use the torch.ao.nn.quantized.dynamic.Linear instead."
+        )
+
+
+# Quantized RNN cell implementations
+class QuantizedRNNCellBase(torch.jit.ScriptModule):
+    def __init__(self, other):
+        raise RuntimeError(
+            "torch.jit.QuantizedRNNCellBase is no longer supported. "
+            "Please use the torch.ao.nn.quantized.dynamic.RNNCell instead."
+        )
+
+
+class QuantizedRNNCell(QuantizedRNNCellBase):
+    def __init__(self, other):
+        raise RuntimeError(
+            "torch.jit.QuantizedRNNCell is no longer supported. "
+            "Please use the torch.ao.nn.quantized.dynamic.RNNCell instead."
+        )
+
+
+class QuantizedLSTMCell(QuantizedRNNCellBase):
+    def __init__(self, other):
+        super().__init__(other)
+        raise RuntimeError(
+            "torch.jit.QuantizedLSTMCell is no longer supported. "
+            "Please use the torch.ao.nn.quantized.dynamic.LSTMCell instead."
+        )
+
+
+class QuantizedGRUCell(QuantizedRNNCellBase):
+    def __init__(self, other):
+        super().__init__(other)
+        raise RuntimeError(
+            "torch.jit.QuantizedGRUCell is no longer supported. "
+            "Please use the torch.ao.nn.quantized.dynamic.GRUCell instead."
+        )
+
+
+class QuantizedRNNBase(torch.jit.ScriptModule):
+    def __init__(self, other, dtype=torch.int8):
+        raise RuntimeError(
+            "torch.jit.QuantizedRNNBase is no longer supported. "
+            "Please use the torch.ao.nn.quantized.dynamic instead."
+        )
+
+
+class QuantizedLSTM(QuantizedRNNBase):
+    def __init__(self, other, dtype):
+        raise RuntimeError(
+            "torch.jit.QuantizedLSTM is no longer supported. "
+            "Please use the torch.ao.nn.quantized.dynamic.LSTM instead."
+        )
+
+
+class QuantizedGRU(QuantizedRNNBase):
+    def __init__(self, *args, **kwargs):
+        raise RuntimeError(
+            "torch.jit.QuantizedGRU is no longer supported. "
+            "Please use the torch.ao.nn.quantized.dynamic.GRU instead."
+        )
+
+
+def quantize_rnn_cell_modules(module):
+    raise RuntimeError(
+        "quantize_rnn_cell_modules function is no longer supported. "
+        "Please use torch.ao.quantization.quantize_dynamic API instead."
+    )
+
+
+def quantize_linear_modules(module, dtype=torch.int8):
+    raise RuntimeError(
+        "quantize_linear_modules function is no longer supported. "
+        "Please use torch.ao.quantization.quantize_dynamic API instead."
+    )
+
+
+def quantize_rnn_modules(module, dtype=torch.int8):
+    raise RuntimeError(
+        "quantize_rnn_modules function is no longer supported. "
+        "Please use torch.ao.quantization.quantize_dynamic API instead."
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/supported_ops.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/supported_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..791a11a9b3aa7dcac62cc3a2c129985abaf85bc1
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/supported_ops.py
@@ -0,0 +1,344 @@
+# mypy: allow-untyped-defs
+import inspect
+import textwrap
+
+import torch.jit
+from torch.jit._builtins import _find_builtin
+
+
+# this file is for generating documentation using sphinx autodoc
+# > help(torch.jit.supported_ops) will also give a nice listed of the
+# supported ops programmatically
+
+
+def _hidden(name):
+    return name.startswith("_") and not name.startswith("__")
+
+
+def _emit_type(type):
+    return str(type)
+
+
+def _emit_arg(indent, i, arg):
+    v = f"{arg.name} : {_emit_type(arg.type)}"
+    default = arg.default_value
+    if default is not None:
+        v = f"{v}={str(default)}"
+    if i > 0:
+        v = f"\n{' ' * indent}{v}"
+    return v
+
+
+def _emit_args(indent, arguments):
+    return ",".join(_emit_arg(indent, i, arg) for i, arg in enumerate(arguments))
+
+
+def _emit_ret(ret):
+    return _emit_type(ret.type)
+
+
+def _emit_rets(returns):
+    if len(returns) == 1:
+        return _emit_ret(returns[0])
+    return f"Tuple[{', '.join(_emit_ret(r) for r in returns)}]"
+
+
+def _emit_schema(mod, name, schema, arg_start=0, padding=4):
+    if mod is None:
+        qualified_name = name
+    else:
+        qualified_name = f"{mod}.{name}"
+    schema_str = (
+        f"{qualified_name}"
+        f"({_emit_args(len(qualified_name) + 1 + padding, schema.arguments[arg_start:])}) "
+        f"-> {_emit_rets(schema.returns)}"
+    )
+    return schema_str
+
+
+def _get_tensor_ops():
+    def is_tensor_method(schema):
+        if len(schema.arguments) == 0:
+            return False
+        self = schema.arguments[0]
+        if self.name != "self":
+            return False
+        if not self.type.isSubtypeOf(torch._C.TensorType.get()):
+            return False
+        return True
+
+    methods = []
+    # discover methods
+    for elem in dir(torch.Tensor):
+        if not _hidden(elem):
+            schemas = torch._C._jit_get_schemas_for_operator("aten::" + elem)
+            for schema in schemas:
+                if is_tensor_method(schema):
+                    methods.append(_emit_schema("Tensor", elem, schema, arg_start=1))
+
+    return "Supported Tensor Methods", methods
+
+
+def _get_nn_functional_ops():
+    functions = []
+
+    # Iterate over torch.nn.functional
+    mod = torch.nn.functional
+    name = mod.__name__
+    for elem in dir(torch.nn.functional):
+        attr = getattr(mod, elem)
+        if not inspect.isfunction(attr) or _hidden(elem[0]):
+            # Ignore non-functions and internal methods
+            continue
+
+        attr_module = inspect.getmodule(attr)
+        if not attr_module:
+            raise RuntimeError(f"Module for {attr} not found")
+
+        if "torch.nn.functional" not in attr_module.__name__:
+            # Ignore functions from outside torch.nn.functional
+            continue
+
+        try:
+            # compile fn, get schema
+            scripted = torch.jit.script(attr)
+            scripted_schema = scripted.schema
+            functions.append(_emit_schema(name, elem, scripted_schema))
+        except:  # noqa: B001,E722
+            # Skip interpolate / boolean dispatched things
+            pass
+
+    # Iterate over modules that we know contain a lot of builtins
+    for mod in torch.jit._builtins._modules_containing_builtins:
+        name = mod.__name__
+        for elem in dir(mod):
+            builtin = _find_builtin(getattr(mod, elem))
+            if builtin is not None:
+                schemas = torch._C._jit_get_schemas_for_operator(builtin)
+                for schema in schemas:
+                    # remove _tan but not __and__
+                    if not _hidden(elem):
+                        functions.append(_emit_schema(name, elem, schema))
+    return "Supported PyTorch Functions", functions
+
+
+def _get_builtins_helper():
+    builtins = []
+    for fn, _builtin_name in torch.jit._builtins._builtin_ops:
+        mod = inspect.getmodule(fn)
+
+        if not hasattr(fn, "__name__"):
+            # typing classes
+            continue
+        if not mod:
+            continue
+        if _hidden(fn.__name__) or _hidden(fn.__qualname__) or _hidden(mod.__name__):
+            # skip internal-only methods
+            continue
+
+        if "torch._C" in mod.__name__:
+            continue
+
+        builtins.append((fn, _builtin_name))
+
+    return builtins
+
+
+def _is_math_fn(fn):
+    mod = inspect.getmodule(fn)
+    if not mod:
+        raise RuntimeError(f"Module for {fn} not found")
+
+    return mod.__name__ == "math"
+
+
+def _get_torchscript_builtins():
+    functions = []
+    builtins = filter(lambda fn: not _is_math_fn(fn[0]), _get_builtins_helper())
+    builtins_list = list(builtins)
+    # Iterate over the specially added builtins
+    for fn, _builtin_name in builtins_list:
+        mod = inspect.getmodule(fn)
+        if not mod:
+            raise RuntimeError(f"Module for {fn} not found")
+        builtin = _find_builtin(fn)
+        if builtin is not None:
+            schemas = torch._C._jit_get_schemas_for_operator(builtin)
+            for schema in schemas:
+                functions.append(_emit_schema(mod.__name__, fn.__name__, schema))
+
+    return "TorchScript Builtin Functions", functions
+
+
+def _get_math_builtins():
+    functions = []
+    builtins = filter(lambda fn: _is_math_fn(fn[0]), _get_builtins_helper())
+    builtins_list = list(builtins)
+    # Iterate over the specially added builtins
+    for fn, _builtin_name in builtins_list:
+        mod = inspect.getmodule(fn)
+        if not mod:
+            raise RuntimeError(f"Module for {fn} not found")
+        builtin = _find_builtin(fn)
+        if builtin is not None:
+            schemas = torch._C._jit_get_schemas_for_operator(builtin)
+            for schema in schemas:
+                schema_str = _emit_schema(mod.__name__, fn.__name__, schema)
+                if "Tensor" in schema_str:
+                    # Skip Tensor ops that have the same name as math functions
+                    # (they will show up in the tensor methods section)
+                    continue
+                functions.append(schema)
+
+    return "``math`` Module", functions
+
+
+def _get_global_builtins():
+    # Taken from the 'globals' map in torch/csrc/jit/frontend/ir_emitter.cpp
+    supported_builtins = [
+        "print",
+        "tuple",
+        "float",
+        "complex",
+        "int",
+        "bool",
+        "str",
+        "getattr",
+        "hasattr",
+        "isinstance",
+        "len",
+        "hex",
+        "oct",
+        "round",
+        "hash",
+        "min",
+        "max",
+        "abs",
+        "all",
+        "divmod",
+        "list",
+        "ord",
+        "chr",
+        "bin",
+        "range",
+        "zip",
+        "enumerate",
+        "sorted",
+    ]
+
+    op_renames = {
+        "bool": "aten::Bool",
+        "int": "aten::Int",
+        "float": "aten::Float",
+        "complex": "aten::Complex",
+        "abs": "prim::abs",
+        "max": "prim::max",
+        "min": "prim::min",
+        "range": "fake::does_not_exist",
+    }
+
+    schemaless_op_explanations = {
+        "print": "Print any value",
+        "tuple": "Lists cannot be converted to tuples with this method since their size is not statically known",
+        "getattr": "Attribute name must be a literal string",
+        "hasattr": "Attribute name must be a literal string",
+        "isinstance": "Result is static",
+        "zip": "Arguments must be iterable. See :ref:`Iterables ` for details.",
+        "enumerate": "Arguments must be iterable. See :ref:`Iterables ` for details.",
+        "range": "Can only be used as an iterator in a for loop",
+    }
+
+    magic_methods = [
+        ("complex", "__complex__"),
+        ("float", "__float__"),
+        ("int", "__int__"),
+        ("bool", "__bool__"),
+        ("str", "__str__"),
+        ("len", "__len__"),
+        ("hex", "__hex__"),
+        ("oct", "__oct__"),
+    ]
+
+    magic_methods_rows = []
+    for fn, magic_method in magic_methods:
+        magic_methods_rows.append(f'"{fn}", "``{magic_method}``"')
+
+    schematized_ops = []
+    schemaless_ops = []
+
+    for fn in supported_builtins:
+        op_name = f"aten::{fn}"
+        if fn in op_renames:
+            op_name = op_renames[fn]
+        schemas = torch._C._jit_get_schemas_for_operator(op_name)
+        for s in schemas:
+            schematized_ops.append(_emit_schema(None, fn, s, padding=0))
+        if len(schemas) > 0:
+            schematized_ops.append("")
+        else:
+            table_row = (
+                f'":external+python:py:obj:`{fn}`", "{schemaless_op_explanations[fn]}"'
+            )
+            schemaless_ops.append(table_row)
+
+    schematized_ops_str = "\n".join(schematized_ops)
+    schemaless_ops_str = "\n".join(schemaless_ops)
+    magic_methods_rows_str = "\n".join(magic_methods_rows)
+    schematized_ops_str = textwrap.indent(schematized_ops_str, "\t")
+    schemaless_ops_str = textwrap.indent(schemaless_ops_str, "\t")
+    magic_methods_rows_str = textwrap.indent(magic_methods_rows_str, "\t")
+    section = f"""
+The functions in the following table are supported but do not have a static schema
+
+.. csv-table::
+    :header: "Function", "Note"
+
+{schemaless_ops_str}
+
+The following functions will use the corresponding magic method on :any:`TorchScript classes`
+
+.. csv-table::
+    :header: "Function", "Magic Method"
+
+{magic_methods_rows_str}
+
+These built-in functions use the schema
+
+.. rst-class:: codeblock-height-limiter
+
+::
+
+{schematized_ops_str}
+    """
+
+    return "Python Built-in Functions", section
+
+
+def _list_supported_ops():
+    def emit_block(decls):
+        return "\n.. rst-class:: codeblock-height-limiter\n\n::\n\n{}\n".format(
+            "".join(f"    {d}\n\n" for d in decls)
+        )
+
+    body = ""
+    op_gathering_fns = (
+        _get_tensor_ops,
+        _get_nn_functional_ops,
+        _get_torchscript_builtins,
+        _get_global_builtins,
+        _get_math_builtins,
+    )
+    for fn in op_gathering_fns:
+        header, items = fn()
+        link_target = header.replace("`", "").replace("-", "").lower().replace(" ", "-")
+        if isinstance(items, str):
+            section = f"{header}\n{'~' * len(header)}\n{items}\n"
+        else:
+            section = f"{header}\n{'~' * len(header)}\n{emit_block(items)}"
+        section = f".. _{link_target}:" + "\n\n" + section
+        body += section
+
+    return body
+
+
+__doc__ = _list_supported_ops()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/unsupported_tensor_ops.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/unsupported_tensor_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..46b0a000bd618c8bb9ba0b9a345d8a061a69a1fc
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/jit/unsupported_tensor_ops.py
@@ -0,0 +1,78 @@
+# mypy: allow-untyped-defs
+from textwrap import dedent
+from typing import Any, Dict
+
+import torch.jit
+
+
+def execWrapper(code, glob, loc):
+    exec(code, glob, loc)
+
+
+def _gen_unsupported_methods_properties():
+    tensor_attrs = set(filter(lambda x: x[0] != "_", dir(torch.Tensor)))
+    tensor = torch.tensor([2])
+    funcs_template = dedent(
+        """
+    def func(x):
+        return x.{op}()
+    """
+    )
+
+    deprecated_apis = {
+        "volatile",
+        "resize",
+        "reinforce",
+        "new",
+        "name",
+        "map2_",
+        "has_names",
+        "grad_fn",
+        "resize_as",
+    }
+    tensor_attrs = tensor_attrs - deprecated_apis
+
+    properties = []
+    methods = []
+    sorted_tensor_attrs = sorted(tensor_attrs, key=lambda x: x.lower())
+    for attr in sorted_tensor_attrs:
+        funcs_str = funcs_template.format(op=attr)
+        scope: Dict[str, Any] = {}
+        execWrapper(funcs_str, globals(), scope)
+        try:
+            cu = torch.jit.CompilationUnit(funcs_str)
+        except Exception as e:
+            if "nonexistent attribute" not in repr(e):
+                continue
+            attr_repr = repr(getattr(tensor, attr))
+            if "bound method" in attr_repr or "built-in method" in attr_repr:
+                methods.append(attr)
+            else:
+                properties.append(attr)
+
+    mapped_methods = ("\t*  :meth:`~torch.Tensor." + x + r"`" for x in methods)
+    mapped_properties = ("\t*  :attr:`~torch.Tensor." + x + r"`" for x in properties)
+    return "\n".join(mapped_methods), "\n".join(mapped_properties)
+
+
+def _list_unsupported_tensor_ops():
+    header = """\n\n
+Unsupported Tensor Methods
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    """
+    methods, properties = _gen_unsupported_methods_properties()
+    return (
+        header
+        + "\n"
+        + methods
+        + """
+
+Unsupported Tensor Properties
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    """
+        + "\n"
+        + properties
+    )
+
+
+__doc__ = _list_unsupported_tensor_ops()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/lib/libshm.so b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/lib/libshm.so
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/lib/libtorch.so b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/lib/libtorch.so
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/lib/libtorch_global_deps.so b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/lib/libtorch_global_deps.so
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/linalg/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/linalg/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..cef76fec1107d5a47d8fd84857de9f846aa3070d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/linalg/__init__.py
@@ -0,0 +1,2853 @@
+import torch
+from torch._C import _add_docstr, _linalg  # type: ignore[attr-defined]
+
+LinAlgError = torch._C._LinAlgError  # type: ignore[attr-defined]
+
+Tensor = torch.Tensor
+
+common_notes = {
+    "experimental_warning": """This function is "experimental" and it may change in a future PyTorch release.""",
+    "sync_note": "When inputs are on a CUDA device, this function synchronizes that device with the CPU.",
+    "sync_note_ex": r"When the inputs are on a CUDA device, this function synchronizes only when :attr:`check_errors`\ `= True`.",
+    "sync_note_has_ex": ("When inputs are on a CUDA device, this function synchronizes that device with the CPU. "
+                         "For a version of this function that does not synchronize, see :func:`{}`.")
+}
+
+
+# Note: This not only adds doc strings for functions in the linalg namespace, but
+# also connects the torch.linalg Python namespace to the torch._C._linalg builtins.
+
+cross = _add_docstr(_linalg.linalg_cross, r"""
+linalg.cross(input, other, *, dim=-1, out=None) -> Tensor
+
+
+Computes the cross product of two 3-dimensional vectors.
+
+Supports input of float, double, cfloat and cdouble dtypes. Also supports batches
+of vectors, for which it computes the product along the dimension :attr:`dim`.
+It broadcasts over the batch dimensions.
+
+Args:
+    input (Tensor): the first input tensor.
+    other (Tensor): the second input tensor.
+    dim  (int, optional): the dimension along which to take the cross-product. Default: `-1`.
+
+Keyword args:
+    out (Tensor, optional): the output tensor. Ignored if `None`. Default: `None`.
+
+Example:
+    >>> a = torch.randn(4, 3)
+    >>> a
+    tensor([[-0.3956,  1.1455,  1.6895],
+            [-0.5849,  1.3672,  0.3599],
+            [-1.1626,  0.7180, -0.0521],
+            [-0.1339,  0.9902, -2.0225]])
+    >>> b = torch.randn(4, 3)
+    >>> b
+    tensor([[-0.0257, -1.4725, -1.2251],
+            [-1.1479, -0.7005, -1.9757],
+            [-1.3904,  0.3726, -1.1836],
+            [-0.9688, -0.7153,  0.2159]])
+    >>> torch.linalg.cross(a, b)
+    tensor([[ 1.0844, -0.5281,  0.6120],
+            [-2.4490, -1.5687,  1.9792],
+            [-0.8304, -1.3037,  0.5650],
+            [-1.2329,  1.9883,  1.0551]])
+    >>> a = torch.randn(1, 3)  # a is broadcast to match shape of b
+    >>> a
+    tensor([[-0.9941, -0.5132,  0.5681]])
+    >>> torch.linalg.cross(a, b)
+    tensor([[ 1.4653, -1.2325,  1.4507],
+            [ 1.4119, -2.6163,  0.1073],
+            [ 0.3957, -1.9666, -1.0840],
+            [ 0.2956, -0.3357,  0.2139]])
+""")
+
+cholesky = _add_docstr(_linalg.linalg_cholesky, r"""
+linalg.cholesky(A, *, upper=False, out=None) -> Tensor
+
+Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+the **Cholesky decomposition** of a complex Hermitian or real symmetric positive-definite matrix
+:math:`A \in \mathbb{K}^{n \times n}` is defined as
+
+.. math::
+
+    A = LL^{\text{H}}\mathrlap{\qquad L \in \mathbb{K}^{n \times n}}
+
+where :math:`L` is a lower triangular matrix with real positive diagonal (even in the complex case) and
+:math:`L^{\text{H}}` is the conjugate transpose when :math:`L` is complex, and the transpose when :math:`L` is real-valued.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+""" + fr"""
+.. note:: {common_notes["sync_note_has_ex"].format("torch.linalg.cholesky_ex")}
+""" + r"""
+
+.. seealso::
+
+        :func:`torch.linalg.cholesky_ex` for a version of this operation that
+        skips the (slow) error checking by default and instead returns the debug
+        information. This makes it a faster way to check if a matrix is
+        positive-definite.
+
+        :func:`torch.linalg.eigh` for a different decomposition of a Hermitian matrix.
+        The eigenvalue decomposition gives more information about the matrix but it
+        slower to compute than the Cholesky decomposition.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions
+                consisting of symmetric or Hermitian positive-definite matrices.
+
+Keyword args:
+    upper (bool, optional): whether to return an upper triangular matrix.
+        The tensor returned with upper=True is the conjugate transpose of the tensor
+        returned with upper=False.
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Raises:
+    RuntimeError: if the :attr:`A` matrix or any matrix in a batched :attr:`A` is not Hermitian
+                  (resp. symmetric) positive-definite. If :attr:`A` is a batch of matrices,
+                  the error message will include the batch index of the first matrix that fails
+                  to meet this condition.
+
+Examples::
+
+    >>> A = torch.randn(2, 2, dtype=torch.complex128)
+    >>> A = A @ A.T.conj() + torch.eye(2) # creates a Hermitian positive-definite matrix
+    >>> A
+    tensor([[2.5266+0.0000j, 1.9586-2.0626j],
+            [1.9586+2.0626j, 9.4160+0.0000j]], dtype=torch.complex128)
+    >>> L = torch.linalg.cholesky(A)
+    >>> L
+    tensor([[1.5895+0.0000j, 0.0000+0.0000j],
+            [1.2322+1.2976j, 2.4928+0.0000j]], dtype=torch.complex128)
+    >>> torch.dist(L @ L.T.conj(), A)
+    tensor(4.4692e-16, dtype=torch.float64)
+
+    >>> A = torch.randn(3, 2, 2, dtype=torch.float64)
+    >>> A = A @ A.mT + torch.eye(2)  # batch of symmetric positive-definite matrices
+    >>> L = torch.linalg.cholesky(A)
+    >>> torch.dist(L @ L.mT, A)
+    tensor(5.8747e-16, dtype=torch.float64)
+""")
+
+cholesky_ex = _add_docstr(_linalg.linalg_cholesky_ex, r"""
+linalg.cholesky_ex(A, *, upper=False, check_errors=False, out=None) -> (Tensor, Tensor)
+
+Computes the Cholesky decomposition of a complex Hermitian or real
+symmetric positive-definite matrix.
+
+This function skips the (slow) error checking and error message construction
+of :func:`torch.linalg.cholesky`, instead directly returning the LAPACK
+error codes as part of a named tuple ``(L, info)``. This makes this function
+a faster way to check if a matrix is positive-definite, and it provides an
+opportunity to handle decomposition errors more gracefully or performantly
+than :func:`torch.linalg.cholesky` does.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+If :attr:`A` is not a Hermitian positive-definite matrix, or if it's a batch of matrices
+and one or more of them is not a Hermitian positive-definite matrix,
+then ``info`` stores a positive integer for the corresponding matrix.
+The positive integer indicates the order of the leading minor that is not positive-definite,
+and the decomposition could not be completed.
+``info`` filled with zeros indicates that the decomposition was successful.
+If ``check_errors=True`` and ``info`` contains positive integers, then a RuntimeError is thrown.
+
+""" + fr"""
+.. note:: {common_notes["sync_note_ex"]}
+
+.. warning:: {common_notes["experimental_warning"]}
+""" + r"""
+
+.. seealso::
+        :func:`torch.linalg.cholesky` is a NumPy compatible variant that always checks for errors.
+
+Args:
+    A (Tensor): the Hermitian `n \times n` matrix or the batch of such matrices of size
+                    `(*, n, n)` where `*` is one or more batch dimensions.
+
+Keyword args:
+    upper (bool, optional): whether to return an upper triangular matrix.
+        The tensor returned with upper=True is the conjugate transpose of the tensor
+        returned with upper=False.
+    check_errors (bool, optional): controls whether to check the content of ``infos``. Default: `False`.
+    out (tuple, optional): tuple of two tensors to write the output to. Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> A = torch.randn(2, 2, dtype=torch.complex128)
+    >>> A = A @ A.t().conj()  # creates a Hermitian positive-definite matrix
+    >>> L, info = torch.linalg.cholesky_ex(A)
+    >>> A
+    tensor([[ 2.3792+0.0000j, -0.9023+0.9831j],
+            [-0.9023-0.9831j,  0.8757+0.0000j]], dtype=torch.complex128)
+    >>> L
+    tensor([[ 1.5425+0.0000j,  0.0000+0.0000j],
+            [-0.5850-0.6374j,  0.3567+0.0000j]], dtype=torch.complex128)
+    >>> info
+    tensor(0, dtype=torch.int32)
+
+""")
+
+inv = _add_docstr(_linalg.linalg_inv, r"""
+linalg.inv(A, *, out=None) -> Tensor
+
+Computes the inverse of a square matrix if it exists.
+Throws a `RuntimeError` if the matrix is not invertible.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+for a matrix :math:`A \in \mathbb{K}^{n \times n}`,
+its **inverse matrix** :math:`A^{-1} \in \mathbb{K}^{n \times n}` (if it exists) is defined as
+
+.. math::
+
+    A^{-1}A = AA^{-1} = \mathrm{I}_n
+
+where :math:`\mathrm{I}_n` is the `n`-dimensional identity matrix.
+
+The inverse matrix exists if and only if :math:`A` is `invertible`_. In this case,
+the inverse is unique.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices
+then the output has the same batch dimensions.
+
+""" + fr"""
+.. note:: {common_notes["sync_note_has_ex"].format("torch.linalg.inv_ex")}
+""" + r"""
+
+.. note::
+    Consider using :func:`torch.linalg.solve` if possible for multiplying a matrix on the left by
+    the inverse, as::
+
+        linalg.solve(A, B) == linalg.inv(A) @ B  # When B is a matrix
+
+    It is always preferred to use :func:`~solve` when possible, as it is faster and more
+    numerically stable than computing the inverse explicitly.
+
+.. seealso::
+
+        :func:`torch.linalg.pinv` computes the pseudoinverse (Moore-Penrose inverse) of matrices
+        of any shape.
+
+        :func:`torch.linalg.solve` computes :attr:`A`\ `.inv() @ \ `:attr:`B` with a
+        numerically stable algorithm.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions
+                consisting of invertible matrices.
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Raises:
+    RuntimeError: if the matrix :attr:`A` or any matrix in the batch of matrices :attr:`A` is not invertible.
+
+Examples::
+
+    >>> A = torch.randn(4, 4)
+    >>> Ainv = torch.linalg.inv(A)
+    >>> torch.dist(A @ Ainv, torch.eye(4))
+    tensor(1.1921e-07)
+
+    >>> A = torch.randn(2, 3, 4, 4)  # Batch of matrices
+    >>> Ainv = torch.linalg.inv(A)
+    >>> torch.dist(A @ Ainv, torch.eye(4))
+    tensor(1.9073e-06)
+
+    >>> A = torch.randn(4, 4, dtype=torch.complex128)  # Complex matrix
+    >>> Ainv = torch.linalg.inv(A)
+    >>> torch.dist(A @ Ainv, torch.eye(4))
+    tensor(7.5107e-16, dtype=torch.float64)
+
+.. _invertible:
+    https://en.wikipedia.org/wiki/Invertible_matrix#The_invertible_matrix_theorem
+""")
+
+solve_ex = _add_docstr(_linalg.linalg_solve_ex, r"""
+linalg.solve_ex(A, B, *, left=True, check_errors=False, out=None) -> (Tensor, Tensor)
+
+A version of :func:`~solve` that does not perform error checks unless :attr:`check_errors`\ `= True`.
+It also returns the :attr:`info` tensor returned by `LAPACK's getrf`_.
+
+""" + fr"""
+.. note:: {common_notes["sync_note_ex"]}
+
+.. warning:: {common_notes["experimental_warning"]}
+""" + r"""
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions.
+
+Keyword args:
+    left (bool, optional): whether to solve the system :math:`AX=B` or :math:`XA = B`. Default: `True`.
+    check_errors (bool, optional): controls whether to check the content of ``infos`` and raise
+                                   an error if it is non-zero. Default: `False`.
+    out (tuple, optional): tuple of two tensors to write the output to. Ignored if `None`. Default: `None`.
+
+Returns:
+    A named tuple `(result, info)`.
+
+Examples::
+
+    >>> A = torch.randn(3, 3)
+    >>> Ainv, info = torch.linalg.solve_ex(A)
+    >>> torch.dist(torch.linalg.inv(A), Ainv)
+    tensor(0.)
+    >>> info
+    tensor(0, dtype=torch.int32)
+
+.. _LAPACK's getrf:
+    https://www.netlib.org/lapack/explore-html/dd/d9a/group__double_g_ecomputational_ga0019443faea08275ca60a734d0593e60.html
+""")
+
+inv_ex = _add_docstr(_linalg.linalg_inv_ex, r"""
+linalg.inv_ex(A, *, check_errors=False, out=None) -> (Tensor, Tensor)
+
+Computes the inverse of a square matrix if it is invertible.
+
+Returns a namedtuple ``(inverse, info)``. ``inverse`` contains the result of
+inverting :attr:`A` and ``info`` stores the LAPACK error codes.
+
+If :attr:`A` is not an invertible matrix, or if it's a batch of matrices
+and one or more of them is not an invertible matrix,
+then ``info`` stores a positive integer for the corresponding matrix.
+The positive integer indicates the diagonal element of the LU decomposition of
+the input matrix that is exactly zero.
+``info`` filled with zeros indicates that the inversion was successful.
+If ``check_errors=True`` and ``info`` contains positive integers, then a RuntimeError is thrown.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+""" + fr"""
+.. note:: {common_notes["sync_note_ex"]}
+
+.. warning:: {common_notes["experimental_warning"]}
+""" + r"""
+
+.. seealso::
+
+        :func:`torch.linalg.inv` is a NumPy compatible variant that always checks for errors.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions
+                    consisting of square matrices.
+    check_errors (bool, optional): controls whether to check the content of ``info``. Default: `False`.
+
+Keyword args:
+    out (tuple, optional): tuple of two tensors to write the output to. Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> A = torch.randn(3, 3)
+    >>> Ainv, info = torch.linalg.inv_ex(A)
+    >>> torch.dist(torch.linalg.inv(A), Ainv)
+    tensor(0.)
+    >>> info
+    tensor(0, dtype=torch.int32)
+
+""")
+
+det = _add_docstr(_linalg.linalg_det, r"""
+linalg.det(A, *, out=None) -> Tensor
+
+Computes the determinant of a square matrix.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+.. seealso::
+
+        :func:`torch.linalg.slogdet` computes the sign and natural logarithm of the absolute
+        value of the determinant of square matrices.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions.
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> A = torch.randn(3, 3)
+    >>> torch.linalg.det(A)
+    tensor(0.0934)
+
+    >>> A = torch.randn(3, 2, 2)
+    >>> torch.linalg.det(A)
+    tensor([1.1990, 0.4099, 0.7386])
+""")
+
+slogdet = _add_docstr(_linalg.linalg_slogdet, r"""
+linalg.slogdet(A, *, out=None) -> (Tensor, Tensor)
+
+Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix.
+
+For complex :attr:`A`, it returns the sign and the natural logarithm of the modulus of the
+determinant, that is, a logarithmic polar decomposition of the determinant.
+
+The determinant can be recovered as `sign * exp(logabsdet)`.
+When a matrix has a determinant of zero, it returns `(0, -inf)`.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+.. seealso::
+
+        :func:`torch.linalg.det` computes the determinant of square matrices.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions.
+
+Keyword args:
+    out (tuple, optional): output tuple of two tensors. Ignored if `None`. Default: `None`.
+
+Returns:
+    A named tuple `(sign, logabsdet)`.
+
+    `sign` will have the same dtype as :attr:`A`.
+
+    `logabsdet` will always be real-valued, even when :attr:`A` is complex.
+
+Examples::
+
+    >>> A = torch.randn(3, 3)
+    >>> A
+    tensor([[ 0.0032, -0.2239, -1.1219],
+            [-0.6690,  0.1161,  0.4053],
+            [-1.6218, -0.9273, -0.0082]])
+    >>> torch.linalg.det(A)
+    tensor(-0.7576)
+    >>> torch.logdet(A)
+    tensor(nan)
+    >>> torch.linalg.slogdet(A)
+    torch.return_types.linalg_slogdet(sign=tensor(-1.), logabsdet=tensor(-0.2776))
+""")
+
+eig = _add_docstr(_linalg.linalg_eig, r"""
+linalg.eig(A, *, out=None) -> (Tensor, Tensor)
+
+Computes the eigenvalue decomposition of a square matrix if it exists.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+the **eigenvalue decomposition** of a square matrix
+:math:`A \in \mathbb{K}^{n \times n}` (if it exists) is defined as
+
+.. math::
+
+    A = V \operatorname{diag}(\Lambda) V^{-1}\mathrlap{\qquad V \in \mathbb{C}^{n \times n}, \Lambda \in \mathbb{C}^n}
+
+This decomposition exists if and only if :math:`A` is `diagonalizable`_.
+This is the case when all its eigenvalues are different.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+The returned eigenvalues are not guaranteed to be in any specific order.
+
+.. note:: The eigenvalues and eigenvectors of a real matrix may be complex.
+
+""" + fr"""
+.. note:: {common_notes["sync_note"]}
+""" + r"""
+
+.. warning:: This function assumes that :attr:`A` is `diagonalizable`_ (for example, when all the
+             eigenvalues are different). If it is not diagonalizable, the returned
+             eigenvalues will be correct but :math:`A \neq V \operatorname{diag}(\Lambda)V^{-1}`.
+
+.. warning:: The returned eigenvectors are normalized to have norm `1`.
+             Even then, the eigenvectors of a matrix are not unique, nor are they continuous with respect to
+             :attr:`A`. Due to this lack of uniqueness, different hardware and software may compute
+             different eigenvectors.
+
+             This non-uniqueness is caused by the fact that multiplying an eigenvector by
+             by :math:`e^{i \phi}, \phi \in \mathbb{R}` produces another set of valid eigenvectors
+             of the matrix.  For this reason, the loss function shall not depend on the phase of the
+             eigenvectors, as this quantity is not well-defined.
+             This is checked when computing the gradients of this function. As such,
+             when inputs are on a CUDA device, the computation of the gradients
+             of this function synchronizes that device with the CPU.
+
+
+.. warning:: Gradients computed using the `eigenvectors` tensor will only be finite when
+             :attr:`A` has distinct eigenvalues.
+             Furthermore, if the distance between any two eigenvalues is close to zero,
+             the gradient will be numerically unstable, as it depends on the eigenvalues
+             :math:`\lambda_i` through the computation of
+             :math:`\frac{1}{\min_{i \neq j} \lambda_i - \lambda_j}`.
+
+.. seealso::
+
+        :func:`torch.linalg.eigvals` computes only the eigenvalues.
+        Unlike :func:`torch.linalg.eig`, the gradients of :func:`~eigvals` are always
+        numerically stable.
+
+        :func:`torch.linalg.eigh` for a (faster) function that computes the eigenvalue decomposition
+        for Hermitian and symmetric matrices.
+
+        :func:`torch.linalg.svd` for a function that computes another type of spectral
+        decomposition that works on matrices of any shape.
+
+        :func:`torch.linalg.qr` for another (much faster) decomposition that works on matrices of
+        any shape.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions
+                consisting of diagonalizable matrices.
+
+Keyword args:
+    out (tuple, optional): output tuple of two tensors. Ignored if `None`. Default: `None`.
+
+Returns:
+    A named tuple `(eigenvalues, eigenvectors)` which corresponds to :math:`\Lambda` and :math:`V` above.
+
+    `eigenvalues` and `eigenvectors` will always be complex-valued, even when :attr:`A` is real. The eigenvectors
+    will be given by the columns of `eigenvectors`.
+
+Examples::
+
+    >>> A = torch.randn(2, 2, dtype=torch.complex128)
+    >>> A
+    tensor([[ 0.9828+0.3889j, -0.4617+0.3010j],
+            [ 0.1662-0.7435j, -0.6139+0.0562j]], dtype=torch.complex128)
+    >>> L, V = torch.linalg.eig(A)
+    >>> L
+    tensor([ 1.1226+0.5738j, -0.7537-0.1286j], dtype=torch.complex128)
+    >>> V
+    tensor([[ 0.9218+0.0000j,  0.1882-0.2220j],
+            [-0.0270-0.3867j,  0.9567+0.0000j]], dtype=torch.complex128)
+    >>> torch.dist(V @ torch.diag(L) @ torch.linalg.inv(V), A)
+    tensor(7.7119e-16, dtype=torch.float64)
+
+    >>> A = torch.randn(3, 2, 2, dtype=torch.float64)
+    >>> L, V = torch.linalg.eig(A)
+    >>> torch.dist(V @ torch.diag_embed(L) @ torch.linalg.inv(V), A)
+    tensor(3.2841e-16, dtype=torch.float64)
+
+.. _diagonalizable:
+    https://en.wikipedia.org/wiki/Diagonalizable_matrix#Definition
+""")
+
+eigvals = _add_docstr(_linalg.linalg_eigvals, r"""
+linalg.eigvals(A, *, out=None) -> Tensor
+
+Computes the eigenvalues of a square matrix.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+the **eigenvalues** of a square matrix :math:`A \in \mathbb{K}^{n \times n}` are defined
+as the roots (counted with multiplicity) of the polynomial `p` of degree `n` given by
+
+.. math::
+
+    p(\lambda) = \operatorname{det}(A - \lambda \mathrm{I}_n)\mathrlap{\qquad \lambda \in \mathbb{C}}
+
+where :math:`\mathrm{I}_n` is the `n`-dimensional identity matrix.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+The returned eigenvalues are not guaranteed to be in any specific order.
+
+.. note:: The eigenvalues of a real matrix may be complex, as the roots of a real polynomial may be complex.
+
+          The eigenvalues of a matrix are always well-defined, even when the matrix is not diagonalizable.
+
+""" + fr"""
+.. note:: {common_notes["sync_note"]}
+""" + r"""
+
+.. seealso::
+
+        :func:`torch.linalg.eig` computes the full eigenvalue decomposition.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions.
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Returns:
+    A complex-valued tensor containing the eigenvalues even when :attr:`A` is real.
+
+Examples::
+
+    >>> A = torch.randn(2, 2, dtype=torch.complex128)
+    >>> L = torch.linalg.eigvals(A)
+    >>> L
+    tensor([ 1.1226+0.5738j, -0.7537-0.1286j], dtype=torch.complex128)
+
+    >>> torch.dist(L, torch.linalg.eig(A).eigenvalues)
+    tensor(2.4576e-07)
+""")
+
+eigh = _add_docstr(_linalg.linalg_eigh, r"""
+linalg.eigh(A, UPLO='L', *, out=None) -> (Tensor, Tensor)
+
+Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+the **eigenvalue decomposition** of a complex Hermitian or real symmetric matrix
+:math:`A \in \mathbb{K}^{n \times n}` is defined as
+
+.. math::
+
+    A = Q \operatorname{diag}(\Lambda) Q^{\text{H}}\mathrlap{\qquad Q \in \mathbb{K}^{n \times n}, \Lambda \in \mathbb{R}^n}
+
+where :math:`Q^{\text{H}}` is the conjugate transpose when :math:`Q` is complex, and the transpose when :math:`Q` is real-valued.
+:math:`Q` is orthogonal in the real case and unitary in the complex case.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+:attr:`A` is assumed to be Hermitian (resp. symmetric), but this is not checked internally, instead:
+
+- If :attr:`UPLO`\ `= 'L'` (default), only the lower triangular part of the matrix is used in the computation.
+- If :attr:`UPLO`\ `= 'U'`, only the upper triangular part of the matrix is used.
+
+The eigenvalues are returned in ascending order.
+
+""" + fr"""
+.. note:: {common_notes["sync_note"]}
+""" + r"""
+
+.. note:: The eigenvalues of real symmetric or complex Hermitian matrices are always real.
+
+.. warning:: The eigenvectors of a symmetric matrix are not unique, nor are they continuous with
+             respect to :attr:`A`. Due to this lack of uniqueness, different hardware and
+             software may compute different eigenvectors.
+
+             This non-uniqueness is caused by the fact that multiplying an eigenvector by
+             `-1` in the real case or by :math:`e^{i \phi}, \phi \in \mathbb{R}` in the complex
+             case produces another set of valid eigenvectors of the matrix.
+             For this reason, the loss function shall not depend on the phase of the eigenvectors, as
+             this quantity is not well-defined.
+             This is checked for complex inputs when computing the gradients of this function. As such,
+             when inputs are complex and are on a CUDA device, the computation of the gradients
+             of this function synchronizes that device with the CPU.
+
+.. warning:: Gradients computed using the `eigenvectors` tensor will only be finite when
+             :attr:`A` has distinct eigenvalues.
+             Furthermore, if the distance between any two eigenvalues is close to zero,
+             the gradient will be numerically unstable, as it depends on the eigenvalues
+             :math:`\lambda_i` through the computation of
+             :math:`\frac{1}{\min_{i \neq j} \lambda_i - \lambda_j}`.
+
+.. warning:: User may see pytorch crashes if running `eigh` on CUDA devices with CUDA versions before 12.1 update 1
+             with large ill-conditioned matrices as inputs.
+             Refer to :ref:`Linear Algebra Numerical Stability` for more details.
+             If this is the case, user may (1) tune their matrix inputs to be less ill-conditioned,
+             or (2) use :func:`torch.backends.cuda.preferred_linalg_library` to
+             try other supported backends.
+
+.. seealso::
+
+        :func:`torch.linalg.eigvalsh` computes only the eigenvalues of a Hermitian matrix.
+        Unlike :func:`torch.linalg.eigh`, the gradients of :func:`~eigvalsh` are always
+        numerically stable.
+
+        :func:`torch.linalg.cholesky` for a different decomposition of a Hermitian matrix.
+        The Cholesky decomposition gives less information about the matrix but is much faster
+        to compute than the eigenvalue decomposition.
+
+        :func:`torch.linalg.eig` for a (slower) function that computes the eigenvalue decomposition
+        of a not necessarily Hermitian square matrix.
+
+        :func:`torch.linalg.svd` for a (slower) function that computes the more general SVD
+        decomposition of matrices of any shape.
+
+        :func:`torch.linalg.qr` for another (much faster) decomposition that works on general
+        matrices.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions
+                consisting of symmetric or Hermitian matrices.
+    UPLO ('L', 'U', optional): controls whether to use the upper or lower triangular part
+                               of :attr:`A` in the computations. Default: `'L'`.
+
+Keyword args:
+    out (tuple, optional): output tuple of two tensors. Ignored if `None`. Default: `None`.
+
+Returns:
+    A named tuple `(eigenvalues, eigenvectors)` which corresponds to :math:`\Lambda` and :math:`Q` above.
+
+    `eigenvalues` will always be real-valued, even when :attr:`A` is complex.
+    It will also be ordered in ascending order.
+
+    `eigenvectors` will have the same dtype as :attr:`A` and will contain the eigenvectors as its columns.
+
+Examples::
+    >>> A = torch.randn(2, 2, dtype=torch.complex128)
+    >>> A = A + A.T.conj()  # creates a Hermitian matrix
+    >>> A
+    tensor([[2.9228+0.0000j, 0.2029-0.0862j],
+            [0.2029+0.0862j, 0.3464+0.0000j]], dtype=torch.complex128)
+    >>> L, Q = torch.linalg.eigh(A)
+    >>> L
+    tensor([0.3277, 2.9415], dtype=torch.float64)
+    >>> Q
+    tensor([[-0.0846+-0.0000j, -0.9964+0.0000j],
+            [ 0.9170+0.3898j, -0.0779-0.0331j]], dtype=torch.complex128)
+    >>> torch.dist(Q @ torch.diag(L.cdouble()) @ Q.T.conj(), A)
+    tensor(6.1062e-16, dtype=torch.float64)
+
+    >>> A = torch.randn(3, 2, 2, dtype=torch.float64)
+    >>> A = A + A.mT  # creates a batch of symmetric matrices
+    >>> L, Q = torch.linalg.eigh(A)
+    >>> torch.dist(Q @ torch.diag_embed(L) @ Q.mH, A)
+    tensor(1.5423e-15, dtype=torch.float64)
+""")
+
+eigvalsh = _add_docstr(_linalg.linalg_eigvalsh, r"""
+linalg.eigvalsh(A, UPLO='L', *, out=None) -> Tensor
+
+Computes the eigenvalues of a complex Hermitian or real symmetric matrix.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+the **eigenvalues** of a complex Hermitian or real symmetric  matrix :math:`A \in \mathbb{K}^{n \times n}`
+are defined as the roots (counted with multiplicity) of the polynomial `p` of degree `n` given by
+
+.. math::
+
+    p(\lambda) = \operatorname{det}(A - \lambda \mathrm{I}_n)\mathrlap{\qquad \lambda \in \mathbb{R}}
+
+where :math:`\mathrm{I}_n` is the `n`-dimensional identity matrix.
+The eigenvalues of a real symmetric or complex Hermitian matrix are always real.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+The eigenvalues are returned in ascending order.
+
+:attr:`A` is assumed to be Hermitian (resp. symmetric), but this is not checked internally, instead:
+
+- If :attr:`UPLO`\ `= 'L'` (default), only the lower triangular part of the matrix is used in the computation.
+- If :attr:`UPLO`\ `= 'U'`, only the upper triangular part of the matrix is used.
+
+""" + fr"""
+.. note:: {common_notes["sync_note"]}
+""" + r"""
+
+.. seealso::
+
+        :func:`torch.linalg.eigh` computes the full eigenvalue decomposition.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions
+                consisting of symmetric or Hermitian matrices.
+    UPLO ('L', 'U', optional): controls whether to use the upper or lower triangular part
+                               of :attr:`A` in the computations. Default: `'L'`.
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Returns:
+    A real-valued tensor containing the eigenvalues even when :attr:`A` is complex.
+    The eigenvalues are returned in ascending order.
+
+Examples::
+
+    >>> A = torch.randn(2, 2, dtype=torch.complex128)
+    >>> A = A + A.T.conj()  # creates a Hermitian matrix
+    >>> A
+    tensor([[2.9228+0.0000j, 0.2029-0.0862j],
+            [0.2029+0.0862j, 0.3464+0.0000j]], dtype=torch.complex128)
+    >>> torch.linalg.eigvalsh(A)
+    tensor([0.3277, 2.9415], dtype=torch.float64)
+
+    >>> A = torch.randn(3, 2, 2, dtype=torch.float64)
+    >>> A = A + A.mT  # creates a batch of symmetric matrices
+    >>> torch.linalg.eigvalsh(A)
+    tensor([[ 2.5797,  3.4629],
+            [-4.1605,  1.3780],
+            [-3.1113,  2.7381]], dtype=torch.float64)
+""")
+
+householder_product = _add_docstr(_linalg.linalg_householder_product, r"""
+householder_product(A, tau, *, out=None) -> Tensor
+
+Computes the first `n` columns of a product of Householder matrices.
+
+Let :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`, and
+let :math:`A \in \mathbb{K}^{m \times n}` be a matrix with columns :math:`a_i \in \mathbb{K}^m`
+for :math:`i=1,\ldots,m` with :math:`m \geq n`. Denote by :math:`b_i` the vector resulting from
+zeroing out the first :math:`i-1` components of :math:`a_i` and setting to `1` the :math:`i`-th.
+For a vector :math:`\tau \in \mathbb{K}^k` with :math:`k \leq n`, this function computes the
+first :math:`n` columns of the matrix
+
+.. math::
+
+    H_1H_2 ... H_k \qquad\text{with}\qquad H_i = \mathrm{I}_m - \tau_i b_i b_i^{\text{H}}
+
+where :math:`\mathrm{I}_m` is the `m`-dimensional identity matrix and :math:`b^{\text{H}}` is the
+conjugate transpose when :math:`b` is complex, and the transpose when :math:`b` is real-valued.
+The output matrix is the same size as the input matrix :attr:`A`.
+
+See `Representation of Orthogonal or Unitary Matrices`_ for further details.
+
+Supports inputs of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if the inputs are batches of matrices then
+the output has the same batch dimensions.
+
+.. seealso::
+
+        :func:`torch.geqrf` can be used together with this function to form the `Q` from the
+        :func:`~qr` decomposition.
+
+        :func:`torch.ormqr` is a related function that computes the matrix multiplication
+        of a product of Householder matrices with another matrix.
+        However, that function is not supported by autograd.
+
+.. warning::
+    Gradient computations are only well-defined if :math:`\tau_i \neq \frac{1}{||a_i||^2}`.
+    If this condition is not met, no error will be thrown, but the gradient produced may contain `NaN`.
+
+Args:
+    A (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
+    tau (Tensor): tensor of shape `(*, k)` where `*` is zero or more batch dimensions.
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Raises:
+    RuntimeError: if :attr:`A` doesn't satisfy the requirement `m >= n`,
+                  or :attr:`tau` doesn't satisfy the requirement `n >= k`.
+
+Examples::
+
+    >>> A = torch.randn(2, 2)
+    >>> h, tau = torch.geqrf(A)
+    >>> Q = torch.linalg.householder_product(h, tau)
+    >>> torch.dist(Q, torch.linalg.qr(A).Q)
+    tensor(0.)
+
+    >>> h = torch.randn(3, 2, 2, dtype=torch.complex128)
+    >>> tau = torch.randn(3, 1, dtype=torch.complex128)
+    >>> Q = torch.linalg.householder_product(h, tau)
+    >>> Q
+    tensor([[[ 1.8034+0.4184j,  0.2588-1.0174j],
+            [-0.6853+0.7953j,  2.0790+0.5620j]],
+
+            [[ 1.4581+1.6989j, -1.5360+0.1193j],
+            [ 1.3877-0.6691j,  1.3512+1.3024j]],
+
+            [[ 1.4766+0.5783j,  0.0361+0.6587j],
+            [ 0.6396+0.1612j,  1.3693+0.4481j]]], dtype=torch.complex128)
+
+.. _Representation of Orthogonal or Unitary Matrices:
+    https://www.netlib.org/lapack/lug/node128.html
+""")
+
+ldl_factor = _add_docstr(_linalg.linalg_ldl_factor, r"""
+linalg.ldl_factor(A, *, hermitian=False, out=None) -> (Tensor, Tensor)
+
+Computes a compact representation of the LDL factorization of a Hermitian or symmetric (possibly indefinite) matrix.
+
+When :attr:`A` is complex valued it can be Hermitian (:attr:`hermitian`\ `= True`)
+or symmetric (:attr:`hermitian`\ `= False`).
+
+The factorization is of the form the form :math:`A = L D L^T`.
+If :attr:`hermitian` is `True` then transpose operation is the conjugate transpose.
+
+:math:`L` (or :math:`U`) and :math:`D` are stored in compact form in ``LD``.
+They follow the format specified by `LAPACK's sytrf`_ function.
+These tensors may be used in :func:`torch.linalg.ldl_solve` to solve linear systems.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+""" + fr"""
+.. note:: {common_notes["sync_note_has_ex"].format("torch.linalg.ldl_factor_ex")}
+""" + r"""
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions
+                consisting of symmetric or Hermitian matrices.
+
+Keyword args:
+    hermitian (bool, optional): whether to consider the input to be Hermitian or symmetric.
+                                For real-valued matrices, this switch has no effect. Default: `False`.
+    out (tuple, optional): tuple of two tensors to write the output to. Ignored if `None`. Default: `None`.
+
+Returns:
+    A named tuple `(LD, pivots)`.
+
+Examples::
+
+    >>> A = torch.randn(3, 3)
+    >>> A = A @ A.mT # make symmetric
+    >>> A
+    tensor([[7.2079, 4.2414, 1.9428],
+            [4.2414, 3.4554, 0.3264],
+            [1.9428, 0.3264, 1.3823]])
+    >>> LD, pivots = torch.linalg.ldl_factor(A)
+    >>> LD
+    tensor([[ 7.2079,  0.0000,  0.0000],
+            [ 0.5884,  0.9595,  0.0000],
+            [ 0.2695, -0.8513,  0.1633]])
+    >>> pivots
+    tensor([1, 2, 3], dtype=torch.int32)
+
+.. _LAPACK's sytrf:
+    https://www.netlib.org/lapack/explore-html/d3/db6/group__double_s_ycomputational_gad91bde1212277b3e909eb6af7f64858a.html
+""")
+
+ldl_factor_ex = _add_docstr(_linalg.linalg_ldl_factor_ex, r"""
+linalg.ldl_factor_ex(A, *, hermitian=False, check_errors=False, out=None) -> (Tensor, Tensor, Tensor)
+
+This is a version of :func:`~ldl_factor` that does not perform error checks unless :attr:`check_errors`\ `= True`.
+It also returns the :attr:`info` tensor returned by `LAPACK's sytrf`_.
+``info`` stores integer error codes from the backend library.
+A positive integer indicates the diagonal element of :math:`D` that is zero.
+Division by 0 will occur if the result is used for solving a system of linear equations.
+``info`` filled with zeros indicates that the factorization was successful.
+If ``check_errors=True`` and ``info`` contains positive integers, then a `RuntimeError` is thrown.
+
+""" + fr"""
+.. note:: {common_notes["sync_note_ex"]}
+
+.. warning:: {common_notes["experimental_warning"]}
+""" + r"""
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions
+                consisting of symmetric or Hermitian matrices.
+
+Keyword args:
+    hermitian (bool, optional): whether to consider the input to be Hermitian or symmetric.
+                                For real-valued matrices, this switch has no effect. Default: `False`.
+    check_errors (bool, optional): controls whether to check the content of ``info`` and raise
+                                   an error if it is non-zero. Default: `False`.
+    out (tuple, optional): tuple of three tensors to write the output to. Ignored if `None`. Default: `None`.
+
+Returns:
+    A named tuple `(LD, pivots, info)`.
+
+Examples::
+
+    >>> A = torch.randn(3, 3)
+    >>> A = A @ A.mT # make symmetric
+    >>> A
+    tensor([[7.2079, 4.2414, 1.9428],
+            [4.2414, 3.4554, 0.3264],
+            [1.9428, 0.3264, 1.3823]])
+    >>> LD, pivots, info = torch.linalg.ldl_factor_ex(A)
+    >>> LD
+    tensor([[ 7.2079,  0.0000,  0.0000],
+            [ 0.5884,  0.9595,  0.0000],
+            [ 0.2695, -0.8513,  0.1633]])
+    >>> pivots
+    tensor([1, 2, 3], dtype=torch.int32)
+    >>> info
+    tensor(0, dtype=torch.int32)
+
+.. _LAPACK's sytrf:
+    https://www.netlib.org/lapack/explore-html/d3/db6/group__double_s_ycomputational_gad91bde1212277b3e909eb6af7f64858a.html
+""")
+
+ldl_solve = _add_docstr(_linalg.linalg_ldl_solve, r"""
+linalg.ldl_solve(LD, pivots, B, *, hermitian=False, out=None) -> Tensor
+
+Computes the solution of a system of linear equations using the LDL factorization.
+
+:attr:`LD` and :attr:`pivots` are the compact representation of the LDL factorization and
+are expected to be computed by :func:`torch.linalg.ldl_factor_ex`.
+:attr:`hermitian` argument to this function should be the same
+as the corresponding arguments in :func:`torch.linalg.ldl_factor_ex`.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+""" + fr"""
+.. warning:: {common_notes["experimental_warning"]}
+""" + r"""
+
+Args:
+    LD (Tensor): the `n \times n` matrix or the batch of such matrices of size
+                      `(*, n, n)` where `*` is one or more batch dimensions.
+    pivots (Tensor): the pivots corresponding to the LDL factorization of :attr:`LD`.
+    B (Tensor): right-hand side tensor of shape `(*, n, k)`.
+
+Keyword args:
+    hermitian (bool, optional): whether to consider the decomposed matrix to be Hermitian or symmetric.
+                                For real-valued matrices, this switch has no effect. Default: `False`.
+    out (tuple, optional): output tensor. `B` may be passed as `out` and the result is computed in-place on `B`.
+                           Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> A = torch.randn(2, 3, 3)
+    >>> A = A @ A.mT # make symmetric
+    >>> LD, pivots, info = torch.linalg.ldl_factor_ex(A)
+    >>> B = torch.randn(2, 3, 4)
+    >>> X = torch.linalg.ldl_solve(LD, pivots, B)
+    >>> torch.linalg.norm(A @ X - B)
+    >>> tensor(0.0001)
+""")
+
+lstsq = _add_docstr(_linalg.linalg_lstsq, r"""
+torch.linalg.lstsq(A, B, rcond=None, *, driver=None) -> (Tensor, Tensor, Tensor, Tensor)
+
+Computes a solution to the least squares problem of a system of linear equations.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+the **least squares problem** for a linear system :math:`AX = B` with
+:math:`A \in \mathbb{K}^{m \times n}, B \in \mathbb{K}^{m \times k}` is defined as
+
+.. math::
+
+    \min_{X \in \mathbb{K}^{n \times k}} \|AX - B\|_F
+
+where :math:`\|-\|_F` denotes the Frobenius norm.
+
+Supports inputs of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if the inputs are batches of matrices then
+the output has the same batch dimensions.
+
+:attr:`driver` chooses the backend function that will be used.
+For CPU inputs the valid values are `'gels'`, `'gelsy'`, `'gelsd`, `'gelss'`.
+To choose the best driver on CPU consider:
+
+- If :attr:`A` is well-conditioned (its `condition number`_ is not too large), or you do not mind some precision loss.
+
+  - For a general matrix: `'gelsy'` (QR with pivoting) (default)
+  - If :attr:`A` is full-rank: `'gels'` (QR)
+
+- If :attr:`A` is not well-conditioned.
+
+  - `'gelsd'` (tridiagonal reduction and SVD)
+  - But if you run into memory issues: `'gelss'` (full SVD).
+
+For CUDA input, the only valid driver is `'gels'`, which assumes that :attr:`A` is full-rank.
+
+See also the `full description of these drivers`_
+
+:attr:`rcond` is used to determine the effective rank of the matrices in :attr:`A`
+when :attr:`driver` is one of (`'gelsy'`, `'gelsd'`, `'gelss'`).
+In this case, if :math:`\sigma_i` are the singular values of `A` in decreasing order,
+:math:`\sigma_i` will be rounded down to zero if :math:`\sigma_i \leq \text{rcond} \cdot \sigma_1`.
+If :attr:`rcond`\ `= None` (default), :attr:`rcond` is set to the machine precision of the dtype of :attr:`A` times `max(m, n)`.
+
+This function returns the solution to the problem and some extra information in a named tuple of
+four tensors `(solution, residuals, rank, singular_values)`. For inputs :attr:`A`, :attr:`B`
+of shape `(*, m, n)`, `(*, m, k)` respectively, it contains
+
+- `solution`: the least squares solution. It has shape `(*, n, k)`.
+- `residuals`: the squared residuals of the solutions, that is, :math:`\|AX - B\|_F^2`.
+  It has shape equal to the batch dimensions of :attr:`A`.
+  It is computed when `m > n` and every matrix in :attr:`A` is full-rank,
+  otherwise, it is an empty tensor.
+  If :attr:`A` is a batch of matrices and any matrix in the batch is not full rank,
+  then an empty tensor is returned. This behavior may change in a future PyTorch release.
+- `rank`: tensor of ranks of the matrices in :attr:`A`.
+  It has shape equal to the batch dimensions of :attr:`A`.
+  It is computed when :attr:`driver` is one of (`'gelsy'`, `'gelsd'`, `'gelss'`),
+  otherwise it is an empty tensor.
+- `singular_values`: tensor of singular values of the matrices in :attr:`A`.
+  It has shape `(*, min(m, n))`.
+  It is computed when :attr:`driver` is one of (`'gelsd'`, `'gelss'`),
+  otherwise it is an empty tensor.
+
+.. note::
+    This function computes `X = \ `:attr:`A`\ `.pinverse() @ \ `:attr:`B` in a faster and
+    more numerically stable way than performing the computations separately.
+
+.. warning::
+    The default value of :attr:`rcond` may change in a future PyTorch release.
+    It is therefore recommended to use a fixed value to avoid potential
+    breaking changes.
+
+Args:
+    A (Tensor): lhs tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
+    B (Tensor): rhs tensor of shape `(*, m, k)` where `*` is zero or more batch dimensions.
+    rcond (float, optional): used to determine the effective rank of :attr:`A`.
+                             If :attr:`rcond`\ `= None`, :attr:`rcond` is set to the machine
+                             precision of the dtype of :attr:`A` times `max(m, n)`. Default: `None`.
+
+Keyword args:
+    driver (str, optional): name of the LAPACK/MAGMA method to be used.
+        If `None`, `'gelsy'` is used for CPU inputs and `'gels'` for CUDA inputs.
+        Default: `None`.
+
+Returns:
+    A named tuple `(solution, residuals, rank, singular_values)`.
+
+Examples::
+
+    >>> A = torch.randn(1,3,3)
+    >>> A
+    tensor([[[-1.0838,  0.0225,  0.2275],
+         [ 0.2438,  0.3844,  0.5499],
+         [ 0.1175, -0.9102,  2.0870]]])
+    >>> B = torch.randn(2,3,3)
+    >>> B
+    tensor([[[-0.6772,  0.7758,  0.5109],
+         [-1.4382,  1.3769,  1.1818],
+         [-0.3450,  0.0806,  0.3967]],
+        [[-1.3994, -0.1521, -0.1473],
+         [ 1.9194,  1.0458,  0.6705],
+         [-1.1802, -0.9796,  1.4086]]])
+    >>> X = torch.linalg.lstsq(A, B).solution # A is broadcasted to shape (2, 3, 3)
+    >>> torch.dist(X, torch.linalg.pinv(A) @ B)
+    tensor(1.5152e-06)
+
+    >>> S = torch.linalg.lstsq(A, B, driver='gelsd').singular_values
+    >>> torch.dist(S, torch.linalg.svdvals(A))
+    tensor(2.3842e-07)
+
+    >>> A[:, 0].zero_()  # Decrease the rank of A
+    >>> rank = torch.linalg.lstsq(A, B).rank
+    >>> rank
+    tensor([2])
+
+.. _condition number:
+    https://pytorch.org/docs/main/linalg.html#torch.linalg.cond
+.. _full description of these drivers:
+    https://www.netlib.org/lapack/lug/node27.html
+""")
+
+matrix_power = _add_docstr(_linalg.linalg_matrix_power, r"""
+matrix_power(A, n, *, out=None) -> Tensor
+
+Computes the `n`-th power of a square matrix for an integer `n`.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+If :attr:`n`\ `= 0`, it returns the identity matrix (or batch) of the same shape
+as :attr:`A`. If :attr:`n` is negative, it returns the inverse of each matrix
+(if invertible) raised to the power of `abs(n)`.
+
+.. note::
+    Consider using :func:`torch.linalg.solve` if possible for multiplying a matrix on the left by
+    a negative power as, if :attr:`n`\ `> 0`::
+
+        torch.linalg.solve(matrix_power(A, n), B) == matrix_power(A, -n)  @ B
+
+    It is always preferred to use :func:`~solve` when possible, as it is faster and more
+    numerically stable than computing :math:`A^{-n}` explicitly.
+
+.. seealso::
+
+        :func:`torch.linalg.solve` computes :attr:`A`\ `.inverse() @ \ `:attr:`B` with a
+        numerically stable algorithm.
+
+Args:
+    A (Tensor): tensor of shape `(*, m, m)` where `*` is zero or more batch dimensions.
+    n (int): the exponent.
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Raises:
+    RuntimeError: if :attr:`n`\ `< 0` and the matrix :attr:`A` or any matrix in the
+                  batch of matrices :attr:`A` is not invertible.
+
+Examples::
+
+    >>> A = torch.randn(3, 3)
+    >>> torch.linalg.matrix_power(A, 0)
+    tensor([[1., 0., 0.],
+            [0., 1., 0.],
+            [0., 0., 1.]])
+    >>> torch.linalg.matrix_power(A, 3)
+    tensor([[ 1.0756,  0.4980,  0.0100],
+            [-1.6617,  1.4994, -1.9980],
+            [-0.4509,  0.2731,  0.8001]])
+    >>> torch.linalg.matrix_power(A.expand(2, -1, -1), -2)
+    tensor([[[ 0.2640,  0.4571, -0.5511],
+            [-1.0163,  0.3491, -1.5292],
+            [-0.4899,  0.0822,  0.2773]],
+            [[ 0.2640,  0.4571, -0.5511],
+            [-1.0163,  0.3491, -1.5292],
+            [-0.4899,  0.0822,  0.2773]]])
+""")
+
+matrix_rank = _add_docstr(_linalg.linalg_matrix_rank, r"""
+linalg.matrix_rank(A, *, atol=None, rtol=None, hermitian=False, out=None) -> Tensor
+
+Computes the numerical rank of a matrix.
+
+The matrix rank is computed as the number of singular values
+(or eigenvalues in absolute value when :attr:`hermitian`\ `= True`)
+that are greater than :math:`\max(\text{atol}, \sigma_1 * \text{rtol})` threshold,
+where :math:`\sigma_1` is the largest singular value (or eigenvalue).
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+If :attr:`hermitian`\ `= True`, :attr:`A` is assumed to be Hermitian if complex or
+symmetric if real, but this is not checked internally. Instead, just the lower
+triangular part of the matrix is used in the computations.
+
+If :attr:`rtol` is not specified and :attr:`A` is a matrix of dimensions `(m, n)`,
+the relative tolerance is set to be :math:`\text{rtol} = \max(m, n) \varepsilon`
+and :math:`\varepsilon` is the epsilon value for the dtype of :attr:`A` (see :class:`.finfo`).
+If :attr:`rtol` is not specified and :attr:`atol` is specified to be larger than zero then
+:attr:`rtol` is set to zero.
+
+If :attr:`atol` or :attr:`rtol` is a :class:`torch.Tensor`, its shape must be broadcastable to that
+of the singular values of :attr:`A` as returned by :func:`torch.linalg.svdvals`.
+
+.. note::
+    This function has NumPy compatible variant `linalg.matrix_rank(A, tol, hermitian=False)`.
+    However, use of the positional argument :attr:`tol` is deprecated in favor of :attr:`atol` and :attr:`rtol`.
+
+""" + fr"""
+.. note:: The matrix rank is computed using a singular value decomposition
+          :func:`torch.linalg.svdvals` if :attr:`hermitian`\ `= False` (default) and the eigenvalue
+          decomposition :func:`torch.linalg.eigvalsh` when :attr:`hermitian`\ `= True`.
+          {common_notes["sync_note"]}
+""" + r"""
+
+Args:
+    A (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
+    tol (float, Tensor, optional): [NumPy Compat] Alias for :attr:`atol`. Default: `None`.
+
+Keyword args:
+    atol (float, Tensor, optional): the absolute tolerance value. When `None` it's considered to be zero.
+                                    Default: `None`.
+    rtol (float, Tensor, optional): the relative tolerance value. See above for the value it takes when `None`.
+                                    Default: `None`.
+    hermitian(bool): indicates whether :attr:`A` is Hermitian if complex
+                     or symmetric if real. Default: `False`.
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> A = torch.eye(10)
+    >>> torch.linalg.matrix_rank(A)
+    tensor(10)
+    >>> B = torch.eye(10)
+    >>> B[0, 0] = 0
+    >>> torch.linalg.matrix_rank(B)
+    tensor(9)
+
+    >>> A = torch.randn(4, 3, 2)
+    >>> torch.linalg.matrix_rank(A)
+    tensor([2, 2, 2, 2])
+
+    >>> A = torch.randn(2, 4, 2, 3)
+    >>> torch.linalg.matrix_rank(A)
+    tensor([[2, 2, 2, 2],
+            [2, 2, 2, 2]])
+
+    >>> A = torch.randn(2, 4, 3, 3, dtype=torch.complex64)
+    >>> torch.linalg.matrix_rank(A)
+    tensor([[3, 3, 3, 3],
+            [3, 3, 3, 3]])
+    >>> torch.linalg.matrix_rank(A, hermitian=True)
+    tensor([[3, 3, 3, 3],
+            [3, 3, 3, 3]])
+    >>> torch.linalg.matrix_rank(A, atol=1.0, rtol=0.0)
+    tensor([[3, 2, 2, 2],
+            [1, 2, 1, 2]])
+    >>> torch.linalg.matrix_rank(A, atol=1.0, rtol=0.0, hermitian=True)
+    tensor([[2, 2, 2, 1],
+            [1, 2, 2, 2]])
+""")
+
+norm = _add_docstr(_linalg.linalg_norm, r"""
+linalg.norm(A, ord=None, dim=None, keepdim=False, *, out=None, dtype=None) -> Tensor
+
+Computes a vector or matrix norm.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+
+Whether this function computes a vector or matrix norm is determined as follows:
+
+- If :attr:`dim` is an `int`, the vector norm will be computed.
+- If :attr:`dim` is a `2`-`tuple`, the matrix norm will be computed.
+- If :attr:`dim`\ `= None` and :attr:`ord`\ `= None`,
+  :attr:`A` will be flattened to 1D and the `2`-norm of the resulting vector will be computed.
+- If :attr:`dim`\ `= None` and :attr:`ord` `!= None`, :attr:`A` must be 1D or 2D.
+
+:attr:`ord` defines the norm that is computed. The following norms are supported:
+
+======================     =========================  ========================================================
+:attr:`ord`                norm for matrices          norm for vectors
+======================     =========================  ========================================================
+`None` (default)           Frobenius norm             `2`-norm (see below)
+`'fro'`                    Frobenius norm             -- not supported --
+`'nuc'`                    nuclear norm               -- not supported --
+`inf`                      `max(sum(abs(x), dim=1))`  `max(abs(x))`
+`-inf`                     `min(sum(abs(x), dim=1))`  `min(abs(x))`
+`0`                        -- not supported --        `sum(x != 0)`
+`1`                        `max(sum(abs(x), dim=0))`  as below
+`-1`                       `min(sum(abs(x), dim=0))`  as below
+`2`                        largest singular value     as below
+`-2`                       smallest singular value    as below
+other `int` or `float`     -- not supported --        `sum(abs(x)^{ord})^{(1 / ord)}`
+======================     =========================  ========================================================
+
+where `inf` refers to `float('inf')`, NumPy's `inf` object, or any equivalent object.
+
+.. seealso::
+
+        :func:`torch.linalg.vector_norm` computes a vector norm.
+
+        :func:`torch.linalg.matrix_norm` computes a matrix norm.
+
+        The above functions are often clearer and more flexible than using :func:`torch.linalg.norm`.
+        For example, `torch.linalg.norm(A, ord=1, dim=(0, 1))` always
+        computes a matrix norm, but with `torch.linalg.vector_norm(A, ord=1, dim=(0, 1))` it is possible
+        to compute a vector norm over the two dimensions.
+
+Args:
+    A (Tensor): tensor of shape `(*, n)` or `(*, m, n)` where `*` is zero or more batch dimensions
+    ord (int, float, inf, -inf, 'fro', 'nuc', optional): order of norm. Default: `None`
+    dim (int, Tuple[int], optional): dimensions over which to compute
+        the vector or matrix norm. See above for the behavior when :attr:`dim`\ `= None`.
+        Default: `None`
+    keepdim (bool, optional): If set to `True`, the reduced dimensions are retained
+        in the result as dimensions with size one. Default: `False`
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+    dtype (:class:`torch.dtype`, optional): If specified, the input tensor is cast to
+        :attr:`dtype` before performing the operation, and the returned tensor's type
+        will be :attr:`dtype`. Default: `None`
+
+Returns:
+    A real-valued tensor, even when :attr:`A` is complex.
+
+Examples::
+
+    >>> from torch import linalg as LA
+    >>> a = torch.arange(9, dtype=torch.float) - 4
+    >>> a
+    tensor([-4., -3., -2., -1.,  0.,  1.,  2.,  3.,  4.])
+    >>> B = a.reshape((3, 3))
+    >>> B
+    tensor([[-4., -3., -2.],
+            [-1.,  0.,  1.],
+            [ 2.,  3.,  4.]])
+
+    >>> LA.norm(a)
+    tensor(7.7460)
+    >>> LA.norm(B)
+    tensor(7.7460)
+    >>> LA.norm(B, 'fro')
+    tensor(7.7460)
+    >>> LA.norm(a, float('inf'))
+    tensor(4.)
+    >>> LA.norm(B, float('inf'))
+    tensor(9.)
+    >>> LA.norm(a, -float('inf'))
+    tensor(0.)
+    >>> LA.norm(B, -float('inf'))
+    tensor(2.)
+
+    >>> LA.norm(a, 1)
+    tensor(20.)
+    >>> LA.norm(B, 1)
+    tensor(7.)
+    >>> LA.norm(a, -1)
+    tensor(0.)
+    >>> LA.norm(B, -1)
+    tensor(6.)
+    >>> LA.norm(a, 2)
+    tensor(7.7460)
+    >>> LA.norm(B, 2)
+    tensor(7.3485)
+
+    >>> LA.norm(a, -2)
+    tensor(0.)
+    >>> LA.norm(B.double(), -2)
+    tensor(1.8570e-16, dtype=torch.float64)
+    >>> LA.norm(a, 3)
+    tensor(5.8480)
+    >>> LA.norm(a, -3)
+    tensor(0.)
+
+Using the :attr:`dim` argument to compute vector norms::
+
+    >>> c = torch.tensor([[1., 2., 3.],
+    ...                   [-1, 1, 4]])
+    >>> LA.norm(c, dim=0)
+    tensor([1.4142, 2.2361, 5.0000])
+    >>> LA.norm(c, dim=1)
+    tensor([3.7417, 4.2426])
+    >>> LA.norm(c, ord=1, dim=1)
+    tensor([6., 6.])
+
+Using the :attr:`dim` argument to compute matrix norms::
+
+    >>> A = torch.arange(8, dtype=torch.float).reshape(2, 2, 2)
+    >>> LA.norm(A, dim=(1,2))
+    tensor([ 3.7417, 11.2250])
+    >>> LA.norm(A[0, :, :]), LA.norm(A[1, :, :])
+    (tensor(3.7417), tensor(11.2250))
+""")
+
+vector_norm = _add_docstr(_linalg.linalg_vector_norm, r"""
+linalg.vector_norm(x, ord=2, dim=None, keepdim=False, *, dtype=None, out=None) -> Tensor
+
+Computes a vector norm.
+
+If :attr:`x` is complex valued, it computes the norm of :attr:`x`\ `.abs()`
+
+Supports input of float, double, cfloat and cdouble dtypes.
+
+This function does not necessarily treat multidimensional :attr:`x` as a batch of
+vectors, instead:
+
+- If :attr:`dim`\ `= None`, :attr:`x` will be flattened before the norm is computed.
+- If :attr:`dim` is an `int` or a `tuple`, the norm will be computed over these dimensions
+  and the other dimensions will be treated as batch dimensions.
+
+This behavior is for consistency with :func:`torch.linalg.norm`.
+
+:attr:`ord` defines the vector norm that is computed. The following norms are supported:
+
+======================   ===============================
+:attr:`ord`              vector norm
+======================   ===============================
+`2` (default)            `2`-norm (see below)
+`inf`                    `max(abs(x))`
+`-inf`                   `min(abs(x))`
+`0`                      `sum(x != 0)`
+other `int` or `float`   `sum(abs(x)^{ord})^{(1 / ord)}`
+======================   ===============================
+
+where `inf` refers to `float('inf')`, NumPy's `inf` object, or any equivalent object.
+
+:attr:`dtype` may be used to perform the computation in a more precise dtype.
+It is semantically equivalent to calling ``linalg.vector_norm(x.to(dtype))``
+but it is faster in some cases.
+
+.. seealso::
+
+        :func:`torch.linalg.matrix_norm` computes a matrix norm.
+
+Args:
+    x (Tensor): tensor, flattened by default, but this behavior can be
+        controlled using :attr:`dim`.
+    ord (int, float, inf, -inf, 'fro', 'nuc', optional): order of norm. Default: `2`
+    dim (int, Tuple[int], optional): dimensions over which to compute
+        the norm. See above for the behavior when :attr:`dim`\ `= None`.
+        Default: `None`
+    keepdim (bool, optional): If set to `True`, the reduced dimensions are retained
+        in the result as dimensions with size one. Default: `False`
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+    dtype (:class:`torch.dtype`, optional): type used to perform the accumulation and the return.
+        If specified, :attr:`x` is cast to :attr:`dtype` before performing the operation,
+        and the returned tensor's type will be :attr:`dtype` if real and of its real counterpart if complex.
+        :attr:`dtype` may be complex if :attr:`x` is complex, otherwise it must be real.
+        :attr:`x` should be convertible without narrowing to :attr:`dtype`. Default: None
+
+Returns:
+    A real-valued tensor, even when :attr:`x` is complex.
+
+Examples::
+
+    >>> from torch import linalg as LA
+    >>> a = torch.arange(9, dtype=torch.float) - 4
+    >>> a
+    tensor([-4., -3., -2., -1.,  0.,  1.,  2.,  3.,  4.])
+    >>> B = a.reshape((3, 3))
+    >>> B
+    tensor([[-4., -3., -2.],
+            [-1.,  0.,  1.],
+            [ 2.,  3.,  4.]])
+    >>> LA.vector_norm(a, ord=3.5)
+    tensor(5.4345)
+    >>> LA.vector_norm(B, ord=3.5)
+    tensor(5.4345)
+""")
+
+matrix_norm = _add_docstr(_linalg.linalg_matrix_norm, r"""
+linalg.matrix_norm(A, ord='fro', dim=(-2, -1), keepdim=False, *, dtype=None, out=None) -> Tensor
+
+Computes a matrix norm.
+
+If :attr:`A` is complex valued, it computes the norm of :attr:`A`\ `.abs()`
+
+Support input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices: the norm will be computed over the
+dimensions specified by the 2-tuple :attr:`dim` and the other dimensions will
+be treated as batch dimensions. The output will have the same batch dimensions.
+
+:attr:`ord` defines the matrix norm that is computed. The following norms are supported:
+
+======================   ========================================================
+:attr:`ord`              matrix norm
+======================   ========================================================
+`'fro'` (default)        Frobenius norm
+`'nuc'`                  nuclear norm
+`inf`                    `max(sum(abs(x), dim=1))`
+`-inf`                   `min(sum(abs(x), dim=1))`
+`1`                      `max(sum(abs(x), dim=0))`
+`-1`                     `min(sum(abs(x), dim=0))`
+`2`                      largest singular value
+`-2`                     smallest singular value
+======================   ========================================================
+
+where `inf` refers to `float('inf')`, NumPy's `inf` object, or any equivalent object.
+
+Args:
+    A (Tensor): tensor with two or more dimensions. By default its
+        shape is interpreted as `(*, m, n)` where `*` is zero or more
+        batch dimensions, but this behavior can be controlled using :attr:`dim`.
+    ord (int, inf, -inf, 'fro', 'nuc', optional): order of norm. Default: `'fro'`
+    dim (Tuple[int, int], optional): dimensions over which to compute the norm. Default: `(-2, -1)`
+    keepdim (bool, optional): If set to `True`, the reduced dimensions are retained
+        in the result as dimensions with size one. Default: `False`
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+    dtype (:class:`torch.dtype`, optional): If specified, the input tensor is cast to
+        :attr:`dtype` before performing the operation, and the returned tensor's type
+        will be :attr:`dtype`. Default: `None`
+
+Returns:
+    A real-valued tensor, even when :attr:`A` is complex.
+
+Examples::
+
+    >>> from torch import linalg as LA
+    >>> A = torch.arange(9, dtype=torch.float).reshape(3, 3)
+    >>> A
+    tensor([[0., 1., 2.],
+            [3., 4., 5.],
+            [6., 7., 8.]])
+    >>> LA.matrix_norm(A)
+    tensor(14.2829)
+    >>> LA.matrix_norm(A, ord=-1)
+    tensor(9.)
+    >>> B = A.expand(2, -1, -1)
+    >>> B
+    tensor([[[0., 1., 2.],
+            [3., 4., 5.],
+            [6., 7., 8.]],
+
+            [[0., 1., 2.],
+            [3., 4., 5.],
+            [6., 7., 8.]]])
+    >>> LA.matrix_norm(B)
+    tensor([14.2829, 14.2829])
+    >>> LA.matrix_norm(B, dim=(0, 2))
+    tensor([ 3.1623, 10.0000, 17.2627])
+""")
+
+matmul = _add_docstr(_linalg.linalg_matmul, r"""
+linalg.matmul(input, other, *, out=None) -> Tensor
+
+Alias for :func:`torch.matmul`
+""")
+
+diagonal = _add_docstr(_linalg.linalg_diagonal, r"""
+linalg.diagonal(A, *, offset=0, dim1=-2, dim2=-1) -> Tensor
+
+Alias for :func:`torch.diagonal` with defaults :attr:`dim1`\ `= -2`, :attr:`dim2`\ `= -1`.
+""")
+
+multi_dot = _add_docstr(_linalg.linalg_multi_dot, r"""
+linalg.multi_dot(tensors, *, out=None)
+
+Efficiently multiplies two or more matrices by reordering the multiplications so that
+the fewest arithmetic operations are performed.
+
+Supports inputs of float, double, cfloat and cdouble dtypes.
+This function does not support batched inputs.
+
+Every tensor in :attr:`tensors` must be 2D, except for the first and last which
+may be 1D. If the first tensor is a 1D vector of shape `(n,)` it is treated as a row vector
+of shape `(1, n)`, similarly if the last tensor is a 1D vector of shape `(n,)` it is treated
+as a column vector of shape `(n, 1)`.
+
+If the first and last tensors are matrices, the output will be a matrix.
+However, if either is a 1D vector, then the output will be a 1D vector.
+
+Differences with `numpy.linalg.multi_dot`:
+
+- Unlike `numpy.linalg.multi_dot`, the first and last tensors must either be 1D or 2D
+  whereas NumPy allows them to be nD
+
+.. warning:: This function does not broadcast.
+
+.. note:: This function is implemented by chaining :func:`torch.mm` calls after
+          computing the optimal matrix multiplication order.
+
+.. note:: The cost of multiplying two matrices with shapes `(a, b)` and `(b, c)` is
+          `a * b * c`. Given matrices `A`, `B`, `C` with shapes `(10, 100)`,
+          `(100, 5)`, `(5, 50)` respectively, we can calculate the cost of different
+          multiplication orders as follows:
+
+          .. math::
+
+             \begin{align*}
+             \operatorname{cost}((AB)C) &= 10 \times 100 \times 5 + 10 \times 5 \times 50 = 7500 \\
+             \operatorname{cost}(A(BC)) &= 10 \times 100 \times 50 + 100 \times 5 \times 50 = 75000
+             \end{align*}
+
+          In this case, multiplying `A` and `B` first followed by `C` is 10 times faster.
+
+Args:
+    tensors (Sequence[Tensor]): two or more tensors to multiply. The first and last
+        tensors may be 1D or 2D. Every other tensor must be 2D.
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> from torch.linalg import multi_dot
+
+    >>> multi_dot([torch.tensor([1, 2]), torch.tensor([2, 3])])
+    tensor(8)
+    >>> multi_dot([torch.tensor([[1, 2]]), torch.tensor([2, 3])])
+    tensor([8])
+    >>> multi_dot([torch.tensor([[1, 2]]), torch.tensor([[2], [3]])])
+    tensor([[8]])
+
+    >>> A = torch.arange(2 * 3).view(2, 3)
+    >>> B = torch.arange(3 * 2).view(3, 2)
+    >>> C = torch.arange(2 * 2).view(2, 2)
+    >>> multi_dot((A, B, C))
+    tensor([[ 26,  49],
+            [ 80, 148]])
+""")
+
+svd = _add_docstr(_linalg.linalg_svd, r"""
+linalg.svd(A, full_matrices=True, *, driver=None, out=None) -> (Tensor, Tensor, Tensor)
+
+Computes the singular value decomposition (SVD) of a matrix.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+the **full SVD** of a matrix
+:math:`A \in \mathbb{K}^{m \times n}`, if `k = min(m,n)`, is defined as
+
+.. math::
+
+    A = U \operatorname{diag}(S) V^{\text{H}}
+    \mathrlap{\qquad U \in \mathbb{K}^{m \times m}, S \in \mathbb{R}^k, V \in \mathbb{K}^{n \times n}}
+
+where :math:`\operatorname{diag}(S) \in \mathbb{K}^{m \times n}`,
+:math:`V^{\text{H}}` is the conjugate transpose when :math:`V` is complex, and the transpose when :math:`V` is real-valued.
+The matrices  :math:`U`, :math:`V` (and thus :math:`V^{\text{H}}`) are orthogonal in the real case, and unitary in the complex case.
+
+When `m > n` (resp. `m < n`) we can drop the last `m - n` (resp. `n - m`) columns of `U` (resp. `V`) to form the **reduced SVD**:
+
+.. math::
+
+    A = U \operatorname{diag}(S) V^{\text{H}}
+    \mathrlap{\qquad U \in \mathbb{K}^{m \times k}, S \in \mathbb{R}^k, V \in \mathbb{K}^{k \times n}}
+
+where :math:`\operatorname{diag}(S) \in \mathbb{K}^{k \times k}`.
+In this case, :math:`U` and :math:`V` also have orthonormal columns.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+The returned decomposition is a named tuple `(U, S, Vh)`
+which corresponds to :math:`U`, :math:`S`, :math:`V^{\text{H}}` above.
+
+The singular values are returned in descending order.
+
+The parameter :attr:`full_matrices` chooses between the full (default) and reduced SVD.
+
+The :attr:`driver` kwarg may be used in CUDA with a cuSOLVER backend to choose the algorithm used to compute the SVD.
+The choice of a driver is a trade-off between accuracy and speed.
+
+- If :attr:`A` is well-conditioned (its `condition number`_ is not too large), or you do not mind some precision loss.
+
+  - For a general matrix: `'gesvdj'` (Jacobi method)
+  - If :attr:`A` is tall or wide (`m >> n` or `m << n`): `'gesvda'` (Approximate method)
+
+- If :attr:`A` is not well-conditioned or precision is relevant: `'gesvd'` (QR based)
+
+By default (:attr:`driver`\ `= None`), we call `'gesvdj'` and, if it fails, we fallback to `'gesvd'`.
+
+Differences with `numpy.linalg.svd`:
+
+- Unlike `numpy.linalg.svd`, this function always returns a tuple of three tensors
+  and it doesn't support `compute_uv` argument.
+  Please use :func:`torch.linalg.svdvals`, which computes only the singular values,
+  instead of `compute_uv=False`.
+
+.. note:: When :attr:`full_matrices`\ `= True`, the gradients with respect to `U[..., :, min(m, n):]`
+          and `Vh[..., min(m, n):, :]` will be ignored, as those vectors can be arbitrary bases
+          of the corresponding subspaces.
+
+.. warning:: The returned tensors `U` and `V` are not unique, nor are they continuous with
+             respect to :attr:`A`.
+             Due to this lack of uniqueness, different hardware and software may compute
+             different singular vectors.
+
+             This non-uniqueness is caused by the fact that multiplying any pair of singular
+             vectors :math:`u_k, v_k` by `-1` in the real case or by
+             :math:`e^{i \phi}, \phi \in \mathbb{R}` in the complex case produces another two
+             valid singular vectors of the matrix.
+             For this reason, the loss function shall not depend on this :math:`e^{i \phi}` quantity,
+             as it is not well-defined.
+             This is checked for complex inputs when computing the gradients of this function. As such,
+             when inputs are complex and are on a CUDA device, the computation of the gradients
+             of this function synchronizes that device with the CPU.
+
+.. warning:: Gradients computed using `U` or `Vh` will only be finite when
+             :attr:`A` does not have repeated singular values. If :attr:`A` is rectangular,
+             additionally, zero must also not be one of its singular values.
+             Furthermore, if the distance between any two singular values is close to zero,
+             the gradient will be numerically unstable, as it depends on the singular values
+             :math:`\sigma_i` through the computation of
+             :math:`\frac{1}{\min_{i \neq j} \sigma_i^2 - \sigma_j^2}`.
+             In the rectangular case, the gradient will also be numerically unstable when
+             :attr:`A` has small singular values, as it also depends on the computation of
+             :math:`\frac{1}{\sigma_i}`.
+
+.. seealso::
+
+        :func:`torch.linalg.svdvals` computes only the singular values.
+        Unlike :func:`torch.linalg.svd`, the gradients of :func:`~svdvals` are always
+        numerically stable.
+
+        :func:`torch.linalg.eig` for a function that computes another type of spectral
+        decomposition of a matrix. The eigendecomposition works just on square matrices.
+
+        :func:`torch.linalg.eigh` for a (faster) function that computes the eigenvalue decomposition
+        for Hermitian and symmetric matrices.
+
+        :func:`torch.linalg.qr` for another (much faster) decomposition that works on general
+        matrices.
+
+Args:
+    A (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
+    full_matrices (bool, optional): controls whether to compute the full or reduced
+                                    SVD, and consequently,
+                                    the shape of the returned tensors
+                                    `U` and `Vh`. Default: `True`.
+
+Keyword args:
+    driver (str, optional): name of the cuSOLVER method to be used. This keyword argument only works on CUDA inputs.
+        Available options are: `None`, `gesvd`, `gesvdj`, and `gesvda`.
+        Default: `None`.
+    out (tuple, optional): output tuple of three tensors. Ignored if `None`.
+
+Returns:
+    A named tuple `(U, S, Vh)` which corresponds to :math:`U`, :math:`S`, :math:`V^{\text{H}}` above.
+
+    `S` will always be real-valued, even when :attr:`A` is complex.
+    It will also be ordered in descending order.
+
+    `U` and `Vh` will have the same dtype as :attr:`A`. The left / right singular vectors will be given by
+    the columns of `U` and the rows of `Vh` respectively.
+
+Examples::
+
+    >>> A = torch.randn(5, 3)
+    >>> U, S, Vh = torch.linalg.svd(A, full_matrices=False)
+    >>> U.shape, S.shape, Vh.shape
+    (torch.Size([5, 3]), torch.Size([3]), torch.Size([3, 3]))
+    >>> torch.dist(A, U @ torch.diag(S) @ Vh)
+    tensor(1.0486e-06)
+
+    >>> U, S, Vh = torch.linalg.svd(A)
+    >>> U.shape, S.shape, Vh.shape
+    (torch.Size([5, 5]), torch.Size([3]), torch.Size([3, 3]))
+    >>> torch.dist(A, U[:, :3] @ torch.diag(S) @ Vh)
+    tensor(1.0486e-06)
+
+    >>> A = torch.randn(7, 5, 3)
+    >>> U, S, Vh = torch.linalg.svd(A, full_matrices=False)
+    >>> torch.dist(A, U @ torch.diag_embed(S) @ Vh)
+    tensor(3.0957e-06)
+
+.. _condition number:
+    https://pytorch.org/docs/main/linalg.html#torch.linalg.cond
+.. _the resulting vectors will span the same subspace:
+    https://en.wikipedia.org/wiki/Singular_value_decomposition#Singular_values,_singular_vectors,_and_their_relation_to_the_SVD
+""")
+
+svdvals = _add_docstr(_linalg.linalg_svdvals, r"""
+linalg.svdvals(A, *, driver=None, out=None) -> Tensor
+
+Computes the singular values of a matrix.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+The singular values are returned in descending order.
+
+.. note:: This function is equivalent to NumPy's `linalg.svd(A, compute_uv=False)`.
+
+""" + fr"""
+.. note:: {common_notes["sync_note"]}
+""" + r"""
+
+.. seealso::
+
+        :func:`torch.linalg.svd` computes the full singular value decomposition.
+
+Args:
+    A (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
+
+Keyword args:
+    driver (str, optional): name of the cuSOLVER method to be used. This keyword argument only works on CUDA inputs.
+        Available options are: `None`, `gesvd`, `gesvdj`, and `gesvda`.
+        Check :func:`torch.linalg.svd` for details.
+        Default: `None`.
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Returns:
+    A real-valued tensor, even when :attr:`A` is complex.
+
+Examples::
+
+    >>> A = torch.randn(5, 3)
+    >>> S = torch.linalg.svdvals(A)
+    >>> S
+    tensor([2.5139, 2.1087, 1.1066])
+
+    >>> torch.dist(S, torch.linalg.svd(A, full_matrices=False).S)
+    tensor(2.4576e-07)
+""")
+
+cond = _add_docstr(_linalg.linalg_cond, r"""
+linalg.cond(A, p=None, *, out=None) -> Tensor
+
+Computes the condition number of a matrix with respect to a matrix norm.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+the **condition number** :math:`\kappa` of a matrix
+:math:`A \in \mathbb{K}^{n \times n}` is defined as
+
+.. math::
+
+    \kappa(A) = \|A\|_p\|A^{-1}\|_p
+
+The condition number of :attr:`A` measures the numerical stability of the linear system `AX = B`
+with respect to a matrix norm.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+:attr:`p` defines the matrix norm that is computed. The following norms are supported:
+
+=========    =================================
+:attr:`p`    matrix norm
+=========    =================================
+`None`       `2`-norm (largest singular value)
+`'fro'`      Frobenius norm
+`'nuc'`      nuclear norm
+`inf`        `max(sum(abs(x), dim=1))`
+`-inf`       `min(sum(abs(x), dim=1))`
+`1`          `max(sum(abs(x), dim=0))`
+`-1`         `min(sum(abs(x), dim=0))`
+`2`          largest singular value
+`-2`         smallest singular value
+=========    =================================
+
+where `inf` refers to `float('inf')`, NumPy's `inf` object, or any equivalent object.
+
+For :attr:`p` is one of `('fro', 'nuc', inf, -inf, 1, -1)`, this function uses
+:func:`torch.linalg.norm` and :func:`torch.linalg.inv`.
+As such, in this case, the matrix (or every matrix in the batch) :attr:`A` has to be square
+and invertible.
+
+For :attr:`p` in `(2, -2)`, this function can be computed in terms of the singular values
+:math:`\sigma_1 \geq \ldots \geq \sigma_n`
+
+.. math::
+
+    \kappa_2(A) = \frac{\sigma_1}{\sigma_n}\qquad \kappa_{-2}(A) = \frac{\sigma_n}{\sigma_1}
+
+In these cases, it is computed using :func:`torch.linalg.svdvals`. For these norms, the matrix
+(or every matrix in the batch) :attr:`A` may have any shape.
+
+.. note :: When inputs are on a CUDA device, this function synchronizes that device with the CPU
+           if :attr:`p` is one of `('fro', 'nuc', inf, -inf, 1, -1)`.
+
+.. seealso::
+
+        :func:`torch.linalg.solve` for a function that solves linear systems of square matrices.
+
+        :func:`torch.linalg.lstsq` for a function that solves linear systems of general matrices.
+
+Args:
+    A (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions
+                    for :attr:`p` in `(2, -2)`, and of shape `(*, n, n)` where every matrix
+                    is invertible for :attr:`p` in `('fro', 'nuc', inf, -inf, 1, -1)`.
+    p (int, inf, -inf, 'fro', 'nuc', optional):
+        the type of the matrix norm to use in the computations (see above). Default: `None`
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Returns:
+    A real-valued tensor, even when :attr:`A` is complex.
+
+Raises:
+    RuntimeError:
+        if :attr:`p` is one of `('fro', 'nuc', inf, -inf, 1, -1)`
+        and the :attr:`A` matrix or any matrix in the batch :attr:`A` is not square
+        or invertible.
+
+Examples::
+
+    >>> A = torch.randn(3, 4, 4, dtype=torch.complex64)
+    >>> torch.linalg.cond(A)
+    >>> A = torch.tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]])
+    >>> torch.linalg.cond(A)
+    tensor([1.4142])
+    >>> torch.linalg.cond(A, 'fro')
+    tensor(3.1623)
+    >>> torch.linalg.cond(A, 'nuc')
+    tensor(9.2426)
+    >>> torch.linalg.cond(A, float('inf'))
+    tensor(2.)
+    >>> torch.linalg.cond(A, float('-inf'))
+    tensor(1.)
+    >>> torch.linalg.cond(A, 1)
+    tensor(2.)
+    >>> torch.linalg.cond(A, -1)
+    tensor(1.)
+    >>> torch.linalg.cond(A, 2)
+    tensor([1.4142])
+    >>> torch.linalg.cond(A, -2)
+    tensor([0.7071])
+
+    >>> A = torch.randn(2, 3, 3)
+    >>> torch.linalg.cond(A)
+    tensor([[9.5917],
+            [3.2538]])
+    >>> A = torch.randn(2, 3, 3, dtype=torch.complex64)
+    >>> torch.linalg.cond(A)
+    tensor([[4.6245],
+            [4.5671]])
+""")
+
+pinv = _add_docstr(_linalg.linalg_pinv, r"""
+linalg.pinv(A, *, atol=None, rtol=None, hermitian=False, out=None) -> Tensor
+
+Computes the pseudoinverse (Moore-Penrose inverse) of a matrix.
+
+The pseudoinverse may be `defined algebraically`_
+but it is more computationally convenient to understand it `through the SVD`_
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+If :attr:`hermitian`\ `= True`, :attr:`A` is assumed to be Hermitian if complex or
+symmetric if real, but this is not checked internally. Instead, just the lower
+triangular part of the matrix is used in the computations.
+
+The singular values (or the norm of the eigenvalues when :attr:`hermitian`\ `= True`)
+that are below :math:`\max(\text{atol}, \sigma_1 \cdot \text{rtol})` threshold are
+treated as zero and discarded in the computation,
+where :math:`\sigma_1` is the largest singular value (or eigenvalue).
+
+If :attr:`rtol` is not specified and :attr:`A` is a matrix of dimensions `(m, n)`,
+the relative tolerance is set to be :math:`\text{rtol} = \max(m, n) \varepsilon`
+and :math:`\varepsilon` is the epsilon value for the dtype of :attr:`A` (see :class:`.finfo`).
+If :attr:`rtol` is not specified and :attr:`atol` is specified to be larger than zero then
+:attr:`rtol` is set to zero.
+
+If :attr:`atol` or :attr:`rtol` is a :class:`torch.Tensor`, its shape must be broadcastable to that
+of the singular values of :attr:`A` as returned by :func:`torch.linalg.svd`.
+
+.. note:: This function uses :func:`torch.linalg.svd` if :attr:`hermitian`\ `= False` and
+          :func:`torch.linalg.eigh` if :attr:`hermitian`\ `= True`.
+          For CUDA inputs, this function synchronizes that device with the CPU.
+
+.. note::
+    Consider using :func:`torch.linalg.lstsq` if possible for multiplying a matrix on the left by
+    the pseudoinverse, as::
+
+        torch.linalg.lstsq(A, B).solution == A.pinv() @ B
+
+    It is always preferred to use :func:`~lstsq` when possible, as it is faster and more
+    numerically stable than computing the pseudoinverse explicitly.
+
+.. note::
+    This function has NumPy compatible variant `linalg.pinv(A, rcond, hermitian=False)`.
+    However, use of the positional argument :attr:`rcond` is deprecated in favor of :attr:`rtol`.
+
+.. warning::
+    This function uses internally :func:`torch.linalg.svd` (or :func:`torch.linalg.eigh`
+    when :attr:`hermitian`\ `= True`), so its derivative has the same problems as those of these
+    functions. See the warnings in :func:`torch.linalg.svd` and :func:`torch.linalg.eigh` for
+    more details.
+
+.. seealso::
+
+        :func:`torch.linalg.inv` computes the inverse of a square matrix.
+
+        :func:`torch.linalg.lstsq` computes :attr:`A`\ `.pinv() @ \ `:attr:`B` with a
+        numerically stable algorithm.
+
+Args:
+    A (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
+    rcond (float, Tensor, optional): [NumPy Compat]. Alias for :attr:`rtol`. Default: `None`.
+
+Keyword args:
+    atol (float, Tensor, optional): the absolute tolerance value. When `None` it's considered to be zero.
+                                    Default: `None`.
+    rtol (float, Tensor, optional): the relative tolerance value. See above for the value it takes when `None`.
+                                    Default: `None`.
+    hermitian(bool, optional): indicates whether :attr:`A` is Hermitian if complex
+                               or symmetric if real. Default: `False`.
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> A = torch.randn(3, 5)
+    >>> A
+    tensor([[ 0.5495,  0.0979, -1.4092, -0.1128,  0.4132],
+            [-1.1143, -0.3662,  0.3042,  1.6374, -0.9294],
+            [-0.3269, -0.5745, -0.0382, -0.5922, -0.6759]])
+    >>> torch.linalg.pinv(A)
+    tensor([[ 0.0600, -0.1933, -0.2090],
+            [-0.0903, -0.0817, -0.4752],
+            [-0.7124, -0.1631, -0.2272],
+            [ 0.1356,  0.3933, -0.5023],
+            [-0.0308, -0.1725, -0.5216]])
+
+    >>> A = torch.randn(2, 6, 3)
+    >>> Apinv = torch.linalg.pinv(A)
+    >>> torch.dist(Apinv @ A, torch.eye(3))
+    tensor(8.5633e-07)
+
+    >>> A = torch.randn(3, 3, dtype=torch.complex64)
+    >>> A = A + A.T.conj()  # creates a Hermitian matrix
+    >>> Apinv = torch.linalg.pinv(A, hermitian=True)
+    >>> torch.dist(Apinv @ A, torch.eye(3))
+    tensor(1.0830e-06)
+
+.. _defined algebraically:
+    https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse#Existence_and_uniqueness
+.. _through the SVD:
+    https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse#Singular_value_decomposition_(SVD)
+""")
+
+matrix_exp = _add_docstr(_linalg.linalg_matrix_exp, r"""
+linalg.matrix_exp(A) -> Tensor
+
+Computes the matrix exponential of a square matrix.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+this function computes the **matrix exponential** of :math:`A \in \mathbb{K}^{n \times n}`, which is defined as
+
+.. math::
+    \mathrm{matrix\_exp}(A) = \sum_{k=0}^\infty \frac{1}{k!}A^k \in \mathbb{K}^{n \times n}.
+
+If the matrix :math:`A` has eigenvalues :math:`\lambda_i \in \mathbb{C}`,
+the matrix :math:`\mathrm{matrix\_exp}(A)` has eigenvalues :math:`e^{\lambda_i} \in \mathbb{C}`.
+
+Supports input of bfloat16, float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions.
+
+Example::
+
+    >>> A = torch.empty(2, 2, 2)
+    >>> A[0, :, :] = torch.eye(2, 2)
+    >>> A[1, :, :] = 2 * torch.eye(2, 2)
+    >>> A
+    tensor([[[1., 0.],
+             [0., 1.]],
+
+            [[2., 0.],
+             [0., 2.]]])
+    >>> torch.linalg.matrix_exp(A)
+    tensor([[[2.7183, 0.0000],
+             [0.0000, 2.7183]],
+
+             [[7.3891, 0.0000],
+              [0.0000, 7.3891]]])
+
+    >>> import math
+    >>> A = torch.tensor([[0, math.pi/3], [-math.pi/3, 0]]) # A is skew-symmetric
+    >>> torch.linalg.matrix_exp(A) # matrix_exp(A) = [[cos(pi/3), sin(pi/3)], [-sin(pi/3), cos(pi/3)]]
+    tensor([[ 0.5000,  0.8660],
+            [-0.8660,  0.5000]])
+""")
+
+
+solve = _add_docstr(_linalg.linalg_solve, r"""
+linalg.solve(A, B, *, left=True, out=None) -> Tensor
+
+Computes the solution of a square system of linear equations with a unique solution.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+this function computes the solution :math:`X \in \mathbb{K}^{n \times k}` of the **linear system** associated to
+:math:`A \in \mathbb{K}^{n \times n}, B \in \mathbb{K}^{n \times k}`, which is defined as
+
+.. math:: AX = B
+
+If :attr:`left`\ `= False`, this function returns the matrix :math:`X \in \mathbb{K}^{n \times k}` that solves the system
+
+.. math::
+
+    XA = B\mathrlap{\qquad A \in \mathbb{K}^{k \times k}, B \in \mathbb{K}^{n \times k}.}
+
+This system of linear equations has one solution if and only if :math:`A` is `invertible`_.
+This function assumes that :math:`A` is invertible.
+
+Supports inputs of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if the inputs are batches of matrices then
+the output has the same batch dimensions.
+
+Letting `*` be zero or more batch dimensions,
+
+- If :attr:`A` has shape `(*, n, n)` and :attr:`B` has shape `(*, n)` (a batch of vectors) or shape
+  `(*, n, k)` (a batch of matrices or "multiple right-hand sides"), this function returns `X` of shape
+  `(*, n)` or `(*, n, k)` respectively.
+- Otherwise, if :attr:`A` has shape `(*, n, n)` and  :attr:`B` has shape `(n,)`  or `(n, k)`, :attr:`B`
+  is broadcasted to have shape `(*, n)` or `(*, n, k)` respectively.
+  This function then returns the solution of the resulting batch of systems of linear equations.
+
+.. note::
+    This function computes `X = \ `:attr:`A`\ `.inverse() @ \ `:attr:`B` in a faster and
+    more numerically stable way than performing the computations separately.
+
+.. note::
+    It is possible to compute the solution of the system :math:`XA = B` by passing the inputs
+    :attr:`A` and :attr:`B` transposed and transposing the output returned by this function.
+
+.. note::
+    :attr:`A` is allowed to be a non-batched `torch.sparse_csr_tensor`, but only with `left=True`.
+
+""" + fr"""
+.. note:: {common_notes["sync_note_has_ex"].format("torch.linalg.solve_ex")}
+""" + r"""
+
+.. seealso::
+
+        :func:`torch.linalg.solve_triangular` computes the solution of a triangular system of linear
+        equations with a unique solution.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions.
+    B (Tensor): right-hand side tensor of shape `(*, n)` or  `(*, n, k)` or `(n,)` or `(n, k)`
+                according to the rules described above
+
+Keyword args:
+    left (bool, optional): whether to solve the system :math:`AX=B` or :math:`XA = B`. Default: `True`.
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Raises:
+    RuntimeError: if the :attr:`A` matrix is not invertible or any matrix in a batched :attr:`A`
+                  is not invertible.
+
+Examples::
+
+    >>> A = torch.randn(3, 3)
+    >>> b = torch.randn(3)
+    >>> x = torch.linalg.solve(A, b)
+    >>> torch.allclose(A @ x, b)
+    True
+    >>> A = torch.randn(2, 3, 3)
+    >>> B = torch.randn(2, 3, 4)
+    >>> X = torch.linalg.solve(A, B)
+    >>> X.shape
+    torch.Size([2, 3, 4])
+    >>> torch.allclose(A @ X, B)
+    True
+
+    >>> A = torch.randn(2, 3, 3)
+    >>> b = torch.randn(3, 1)
+    >>> x = torch.linalg.solve(A, b) # b is broadcasted to size (2, 3, 1)
+    >>> x.shape
+    torch.Size([2, 3, 1])
+    >>> torch.allclose(A @ x, b)
+    True
+    >>> b = torch.randn(3)
+    >>> x = torch.linalg.solve(A, b) # b is broadcasted to size (2, 3)
+    >>> x.shape
+    torch.Size([2, 3])
+    >>> Ax = A @ x.unsqueeze(-1)
+    >>> torch.allclose(Ax, b.unsqueeze(-1).expand_as(Ax))
+    True
+
+.. _invertible:
+    https://en.wikipedia.org/wiki/Invertible_matrix#The_invertible_matrix_theorem
+""")
+
+solve_triangular = _add_docstr(_linalg.linalg_solve_triangular, r"""
+linalg.solve_triangular(A, B, *, upper, left=True, unitriangular=False, out=None) -> Tensor
+
+Computes the solution of a triangular system of linear equations with a unique solution.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+this function computes the solution :math:`X \in \mathbb{K}^{n \times k}` of the **linear system**
+associated to the triangular matrix :math:`A \in \mathbb{K}^{n \times n}` without zeros on the diagonal
+(that is, it is `invertible`_) and the rectangular matrix , :math:`B \in \mathbb{K}^{n \times k}`,
+which is defined as
+
+.. math:: AX = B
+
+The argument :attr:`upper` signals whether :math:`A` is upper or lower triangular.
+
+If :attr:`left`\ `= False`, this function returns the matrix :math:`X \in \mathbb{K}^{n \times k}` that
+solves the system
+
+.. math::
+
+    XA = B\mathrlap{\qquad A \in \mathbb{K}^{k \times k}, B \in \mathbb{K}^{n \times k}.}
+
+If :attr:`upper`\ `= True` (resp. `False`) just the upper (resp. lower) triangular half of :attr:`A`
+will be accessed. The elements below the main diagonal will be considered to be zero and will not be accessed.
+
+If :attr:`unitriangular`\ `= True`, the diagonal of :attr:`A` is assumed to be ones and will not be accessed.
+
+The result may contain `NaN` s if the diagonal of :attr:`A` contains zeros or elements that
+are very close to zero and :attr:`unitriangular`\ `= False` (default) or if the input matrix
+has very small eigenvalues.
+
+Supports inputs of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if the inputs are batches of matrices then
+the output has the same batch dimensions.
+
+.. seealso::
+
+        :func:`torch.linalg.solve` computes the solution of a general square system of linear
+        equations with a unique solution.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` (or `(*, k, k)` if :attr:`left`\ `= False`)
+                where `*` is zero or more batch dimensions.
+    B (Tensor): right-hand side tensor of shape `(*, n, k)`.
+
+Keyword args:
+    upper (bool): whether :attr:`A` is an upper or lower triangular matrix.
+    left (bool, optional): whether to solve the system :math:`AX=B` or :math:`XA = B`. Default: `True`.
+    unitriangular (bool, optional): if `True`, the diagonal elements of :attr:`A` are assumed to be
+                                    all equal to `1`. Default: `False`.
+    out (Tensor, optional): output tensor. `B` may be passed as `out` and the result is computed in-place on `B`.
+                            Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> A = torch.randn(3, 3).triu_()
+    >>> B = torch.randn(3, 4)
+    >>> X = torch.linalg.solve_triangular(A, B, upper=True)
+    >>> torch.allclose(A @ X, B)
+    True
+
+    >>> A = torch.randn(2, 3, 3).tril_()
+    >>> B = torch.randn(2, 3, 4)
+    >>> X = torch.linalg.solve_triangular(A, B, upper=False)
+    >>> torch.allclose(A @ X, B)
+    True
+
+    >>> A = torch.randn(2, 4, 4).tril_()
+    >>> B = torch.randn(2, 3, 4)
+    >>> X = torch.linalg.solve_triangular(A, B, upper=False, left=False)
+    >>> torch.allclose(X @ A, B)
+    True
+
+.. _invertible:
+    https://en.wikipedia.org/wiki/Invertible_matrix#The_invertible_matrix_theorem
+""")
+
+lu_factor = _add_docstr(_linalg.linalg_lu_factor, r"""
+linalg.lu_factor(A, *, bool pivot=True, out=None) -> (Tensor, Tensor)
+
+Computes a compact representation of the LU factorization with partial pivoting of a matrix.
+
+This function computes a compact representation of the decomposition given by :func:`torch.linalg.lu`.
+If the matrix is square, this representation may be used in :func:`torch.linalg.lu_solve`
+to solve system of linear equations that share the matrix :attr:`A`.
+
+The returned decomposition is represented as a named tuple `(LU, pivots)`.
+The ``LU`` matrix has the same shape as the input matrix ``A``. Its upper and lower triangular
+parts encode the non-constant elements of ``L`` and ``U`` of the LU decomposition of ``A``.
+
+The returned permutation matrix is represented by a 1-indexed vector. `pivots[i] == j` represents
+that in the `i`-th step of the algorithm, the `i`-th row was permuted with the `j-1`-th row.
+
+On CUDA, one may use :attr:`pivot`\ `= False`. In this case, this function returns the LU
+decomposition without pivoting if it exists.
+
+Supports inputs of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if the inputs are batches of matrices then
+the output has the same batch dimensions.
+
+""" + fr"""
+.. note:: {common_notes["sync_note_has_ex"].format("torch.linalg.lu_factor_ex")}
+""" + r"""
+.. warning:: The LU decomposition is almost never unique, as often there are different permutation
+             matrices that can yield different LU decompositions.
+             As such, different platforms, like SciPy, or inputs on different devices,
+             may produce different valid decompositions.
+
+             Gradient computations are only supported if the input matrix is full-rank.
+             If this condition is not met, no error will be thrown, but the gradient may not be finite.
+             This is because the LU decomposition with pivoting is not differentiable at these points.
+
+.. seealso::
+
+        :func:`torch.linalg.lu_solve` solves a system of linear equations given the output of this
+        function provided the input matrix was square and invertible.
+
+        :func:`torch.lu_unpack` unpacks the tensors returned by :func:`~lu_factor` into the three
+        matrices `P, L, U` that form the decomposition.
+
+        :func:`torch.linalg.lu` computes the LU decomposition with partial pivoting of a possibly
+        non-square matrix. It is a composition of :func:`~lu_factor` and :func:`torch.lu_unpack`.
+
+        :func:`torch.linalg.solve` solves a system of linear equations. It is a composition
+        of :func:`~lu_factor` and :func:`~lu_solve`.
+
+Args:
+    A (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
+
+Keyword args:
+    pivot (bool, optional): Whether to compute the LU decomposition with partial pivoting, or the regular LU
+                            decomposition. :attr:`pivot`\ `= False` not supported on CPU. Default: `True`.
+    out (tuple, optional): tuple of two tensors to write the output to. Ignored if `None`. Default: `None`.
+
+Returns:
+    A named tuple `(LU, pivots)`.
+
+Raises:
+    RuntimeError: if the :attr:`A` matrix is not invertible or any matrix in a batched :attr:`A`
+                  is not invertible.
+
+Examples::
+
+    >>> A = torch.randn(2, 3, 3)
+    >>> B1 = torch.randn(2, 3, 4)
+    >>> B2 = torch.randn(2, 3, 7)
+    >>> LU, pivots = torch.linalg.lu_factor(A)
+    >>> X1 = torch.linalg.lu_solve(LU, pivots, B1)
+    >>> X2 = torch.linalg.lu_solve(LU, pivots, B2)
+    >>> torch.allclose(A @ X1, B1)
+    True
+    >>> torch.allclose(A @ X2, B2)
+    True
+
+.. _invertible:
+    https://en.wikipedia.org/wiki/Invertible_matrix#The_invertible_matrix_theorem
+""")
+
+lu_factor_ex = _add_docstr(_linalg.linalg_lu_factor_ex, r"""
+linalg.lu_factor_ex(A, *, pivot=True, check_errors=False, out=None) -> (Tensor, Tensor, Tensor)
+
+This is a version of :func:`~lu_factor` that does not perform error checks unless :attr:`check_errors`\ `= True`.
+It also returns the :attr:`info` tensor returned by `LAPACK's getrf`_.
+
+""" + fr"""
+.. note:: {common_notes["sync_note_ex"]}
+
+.. warning:: {common_notes["experimental_warning"]}
+""" + r"""
+
+Args:
+    A (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
+
+Keyword args:
+    pivot (bool, optional): Whether to compute the LU decomposition with partial pivoting, or the regular LU
+                            decomposition. :attr:`pivot`\ `= False` not supported on CPU. Default: `True`.
+    check_errors (bool, optional): controls whether to check the content of ``infos`` and raise
+                                   an error if it is non-zero. Default: `False`.
+    out (tuple, optional): tuple of three tensors to write the output to. Ignored if `None`. Default: `None`.
+
+Returns:
+    A named tuple `(LU, pivots, info)`.
+
+.. _LAPACK's getrf:
+    https://www.netlib.org/lapack/explore-html/dd/d9a/group__double_g_ecomputational_ga0019443faea08275ca60a734d0593e60.html
+""")
+
+lu_solve = _add_docstr(_linalg.linalg_lu_solve, r"""
+linalg.lu_solve(LU, pivots, B, *, left=True, adjoint=False, out=None) -> Tensor
+
+Computes the solution of a square system of linear equations with a unique solution given an LU decomposition.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+this function computes the solution :math:`X \in \mathbb{K}^{n \times k}` of the **linear system** associated to
+:math:`A \in \mathbb{K}^{n \times n}, B \in \mathbb{K}^{n \times k}`, which is defined as
+
+.. math:: AX = B
+
+where :math:`A` is given factorized as returned by :func:`~lu_factor`.
+
+If :attr:`left`\ `= False`, this function returns the matrix :math:`X \in \mathbb{K}^{n \times k}` that solves the system
+
+.. math::
+
+    XA = B\mathrlap{\qquad A \in \mathbb{K}^{k \times k}, B \in \mathbb{K}^{n \times k}.}
+
+If  :attr:`adjoint`\ `= True` (and :attr:`left`\ `= True`), given an LU factorization of :math:`A`
+this function function returns the :math:`X \in \mathbb{K}^{n \times k}` that solves the system
+
+.. math::
+
+    A^{\text{H}}X = B\mathrlap{\qquad A \in \mathbb{K}^{k \times k}, B \in \mathbb{K}^{n \times k}.}
+
+where :math:`A^{\text{H}}` is the conjugate transpose when :math:`A` is complex, and the
+transpose when :math:`A` is real-valued. The :attr:`left`\ `= False` case is analogous.
+
+Supports inputs of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if the inputs are batches of matrices then
+the output has the same batch dimensions.
+
+Args:
+    LU (Tensor): tensor of shape `(*, n, n)` (or `(*, k, k)` if :attr:`left`\ `= True`)
+                 where `*` is zero or more batch dimensions as returned by :func:`~lu_factor`.
+    pivots (Tensor): tensor of shape `(*, n)` (or `(*, k)` if :attr:`left`\ `= True`)
+                     where `*` is zero or more batch dimensions as returned by :func:`~lu_factor`.
+    B (Tensor): right-hand side tensor of shape `(*, n, k)`.
+
+Keyword args:
+    left (bool, optional): whether to solve the system :math:`AX=B` or :math:`XA = B`. Default: `True`.
+    adjoint (bool, optional): whether to solve the system :math:`AX=B` or :math:`A^{\text{H}}X = B`. Default: `False`.
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> A = torch.randn(3, 3)
+    >>> LU, pivots = torch.linalg.lu_factor(A)
+    >>> B = torch.randn(3, 2)
+    >>> X = torch.linalg.lu_solve(LU, pivots, B)
+    >>> torch.allclose(A @ X, B)
+    True
+
+    >>> B = torch.randn(3, 3, 2)   # Broadcasting rules apply: A is broadcasted
+    >>> X = torch.linalg.lu_solve(LU, pivots, B)
+    >>> torch.allclose(A @ X, B)
+    True
+
+    >>> B = torch.randn(3, 5, 3)
+    >>> X = torch.linalg.lu_solve(LU, pivots, B, left=False)
+    >>> torch.allclose(X @ A, B)
+    True
+
+    >>> B = torch.randn(3, 3, 4)   # Now solve for A^T
+    >>> X = torch.linalg.lu_solve(LU, pivots, B, adjoint=True)
+    >>> torch.allclose(A.mT @ X, B)
+    True
+
+.. _invertible:
+    https://en.wikipedia.org/wiki/Invertible_matrix#The_invertible_matrix_theorem
+""")
+
+lu = _add_docstr(_linalg.linalg_lu, r"""
+lu(A, *, pivot=True, out=None) -> (Tensor, Tensor, Tensor)
+
+Computes the LU decomposition with partial pivoting of a matrix.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+the **LU decomposition with partial pivoting** of a matrix
+:math:`A \in \mathbb{K}^{m \times n}` is defined as
+
+.. math::
+
+    A = PLU\mathrlap{\qquad P \in \mathbb{K}^{m \times m}, L \in \mathbb{K}^{m \times k}, U \in \mathbb{K}^{k \times n}}
+
+where `k = min(m,n)`, :math:`P` is a `permutation matrix`_, :math:`L` is lower triangular with ones on the diagonal
+and :math:`U` is upper triangular.
+
+If :attr:`pivot`\ `= False` and :attr:`A` is on GPU, then the **LU decomposition without pivoting** is computed
+
+.. math::
+
+    A = LU\mathrlap{\qquad L \in \mathbb{K}^{m \times k}, U \in \mathbb{K}^{k \times n}}
+
+When :attr:`pivot`\ `= False`, the returned matrix :attr:`P` will be empty.
+The LU decomposition without pivoting `may not exist`_ if any of the principal minors of :attr:`A` is singular.
+In this case, the output matrix may contain `inf` or `NaN`.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+.. seealso::
+
+        :func:`torch.linalg.solve` solves a system of linear equations using the LU decomposition
+        with partial pivoting.
+
+.. warning:: The LU decomposition is almost never unique, as often there are different permutation
+             matrices that can yield different LU decompositions.
+             As such, different platforms, like SciPy, or inputs on different devices,
+             may produce different valid decompositions.
+
+.. warning:: Gradient computations are only supported if the input matrix is full-rank.
+             If this condition is not met, no error will be thrown, but the gradient
+             may not be finite.
+             This is because the LU decomposition with pivoting is not differentiable at these points.
+
+Args:
+    A (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
+    pivot (bool, optional): Controls whether to compute the LU decomposition with partial pivoting or
+        no pivoting. Default: `True`.
+
+Keyword args:
+    out (tuple, optional): output tuple of three tensors. Ignored if `None`. Default: `None`.
+
+Returns:
+    A named tuple `(P, L, U)`.
+
+Examples::
+
+    >>> A = torch.randn(3, 2)
+    >>> P, L, U = torch.linalg.lu(A)
+    >>> P
+    tensor([[0., 1., 0.],
+            [0., 0., 1.],
+            [1., 0., 0.]])
+    >>> L
+    tensor([[1.0000, 0.0000],
+            [0.5007, 1.0000],
+            [0.0633, 0.9755]])
+    >>> U
+    tensor([[0.3771, 0.0489],
+            [0.0000, 0.9644]])
+    >>> torch.dist(A, P @ L @ U)
+    tensor(5.9605e-08)
+
+    >>> A = torch.randn(2, 5, 7, device="cuda")
+    >>> P, L, U = torch.linalg.lu(A, pivot=False)
+    >>> P
+    tensor([], device='cuda:0')
+    >>> torch.dist(A, L @ U)
+    tensor(1.0376e-06, device='cuda:0')
+
+.. _permutation matrix:
+    https://en.wikipedia.org/wiki/Permutation_matrix
+.. _may not exist:
+    https://en.wikipedia.org/wiki/LU_decomposition#Definitions
+""")
+
+tensorinv = _add_docstr(_linalg.linalg_tensorinv, r"""
+linalg.tensorinv(A, ind=2, *, out=None) -> Tensor
+
+Computes the multiplicative inverse of :func:`torch.tensordot`.
+
+If `m` is the product of the first :attr:`ind` dimensions of :attr:`A` and `n` is the product of
+the rest of the dimensions, this function expects `m` and `n` to be equal.
+If this is the case, it computes a tensor `X` such that
+`tensordot(\ `:attr:`A`\ `, X, \ `:attr:`ind`\ `)` is the identity matrix in dimension `m`.
+`X` will have the shape of :attr:`A` but with the first :attr:`ind` dimensions pushed back to the end
+
+.. code:: text
+
+    X.shape == A.shape[ind:] + A.shape[:ind]
+
+Supports input of float, double, cfloat and cdouble dtypes.
+
+.. note:: When :attr:`A` is a `2`-dimensional tensor and :attr:`ind`\ `= 1`,
+          this function computes the (multiplicative) inverse of :attr:`A`
+          (see :func:`torch.linalg.inv`).
+
+.. note::
+    Consider using :func:`torch.linalg.tensorsolve` if possible for multiplying a tensor on the left
+    by the tensor inverse, as::
+
+        linalg.tensorsolve(A, B) == torch.tensordot(linalg.tensorinv(A), B)  # When B is a tensor with shape A.shape[:B.ndim]
+
+    It is always preferred to use :func:`~tensorsolve` when possible, as it is faster and more
+    numerically stable than computing the pseudoinverse explicitly.
+
+.. seealso::
+
+        :func:`torch.linalg.tensorsolve` computes
+        `torch.tensordot(tensorinv(\ `:attr:`A`\ `), \ `:attr:`B`\ `)`.
+
+Args:
+    A (Tensor): tensor to invert. Its shape must satisfy
+                    `prod(\ `:attr:`A`\ `.shape[:\ `:attr:`ind`\ `]) ==
+                    prod(\ `:attr:`A`\ `.shape[\ `:attr:`ind`\ `:])`.
+    ind (int): index at which to compute the inverse of :func:`torch.tensordot`. Default: `2`.
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Raises:
+    RuntimeError: if the reshaped :attr:`A` is not invertible or the product of the first
+                  :attr:`ind` dimensions is not equal to the product of the rest.
+
+Examples::
+
+    >>> A = torch.eye(4 * 6).reshape((4, 6, 8, 3))
+    >>> Ainv = torch.linalg.tensorinv(A, ind=2)
+    >>> Ainv.shape
+    torch.Size([8, 3, 4, 6])
+    >>> B = torch.randn(4, 6)
+    >>> torch.allclose(torch.tensordot(Ainv, B), torch.linalg.tensorsolve(A, B))
+    True
+
+    >>> A = torch.randn(4, 4)
+    >>> Atensorinv = torch.linalg.tensorinv(A, ind=1)
+    >>> Ainv = torch.linalg.inv(A)
+    >>> torch.allclose(Atensorinv, Ainv)
+    True
+""")
+
+tensorsolve = _add_docstr(_linalg.linalg_tensorsolve, r"""
+linalg.tensorsolve(A, B, dims=None, *, out=None) -> Tensor
+
+Computes the solution `X` to the system `torch.tensordot(A, X) = B`.
+
+If `m` is the product of the first :attr:`B`\ `.ndim`  dimensions of :attr:`A` and
+`n` is the product of the rest of the dimensions, this function expects `m` and `n` to be equal.
+
+The returned tensor `x` satisfies
+`tensordot(\ `:attr:`A`\ `, x, dims=x.ndim) == \ `:attr:`B`.
+`x` has shape :attr:`A`\ `[B.ndim:]`.
+
+If :attr:`dims` is specified, :attr:`A` will be reshaped as
+
+.. code:: text
+
+    A = movedim(A, dims, range(len(dims) - A.ndim + 1, 0))
+
+Supports inputs of float, double, cfloat and cdouble dtypes.
+
+.. seealso::
+
+        :func:`torch.linalg.tensorinv` computes the multiplicative inverse of
+        :func:`torch.tensordot`.
+
+Args:
+    A (Tensor): tensor to solve for. Its shape must satisfy
+                    `prod(\ `:attr:`A`\ `.shape[:\ `:attr:`B`\ `.ndim]) ==
+                    prod(\ `:attr:`A`\ `.shape[\ `:attr:`B`\ `.ndim:])`.
+    B (Tensor): tensor of shape :attr:`A`\ `.shape[:\ `:attr:`B`\ `.ndim]`.
+    dims (Tuple[int], optional): dimensions of :attr:`A` to be moved.
+        If `None`, no dimensions are moved. Default: `None`.
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Raises:
+    RuntimeError: if the reshaped :attr:`A`\ `.view(m, m)` with `m` as above  is not
+                  invertible or the product of the first :attr:`ind` dimensions is not equal
+                  to the product of the rest of the dimensions.
+
+Examples::
+
+    >>> A = torch.eye(2 * 3 * 4).reshape((2 * 3, 4, 2, 3, 4))
+    >>> B = torch.randn(2 * 3, 4)
+    >>> X = torch.linalg.tensorsolve(A, B)
+    >>> X.shape
+    torch.Size([2, 3, 4])
+    >>> torch.allclose(torch.tensordot(A, X, dims=X.ndim), B)
+    True
+
+    >>> A = torch.randn(6, 4, 4, 3, 2)
+    >>> B = torch.randn(4, 3, 2)
+    >>> X = torch.linalg.tensorsolve(A, B, dims=(0, 2))
+    >>> X.shape
+    torch.Size([6, 4])
+    >>> A = A.permute(1, 3, 4, 0, 2)
+    >>> A.shape[B.ndim:]
+    torch.Size([6, 4])
+    >>> torch.allclose(torch.tensordot(A, X, dims=X.ndim), B, atol=1e-6)
+    True
+""")
+
+qr = _add_docstr(_linalg.linalg_qr, r"""
+qr(A, mode='reduced', *, out=None) -> (Tensor, Tensor)
+
+Computes the QR decomposition of a matrix.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+the **full QR decomposition** of a matrix
+:math:`A \in \mathbb{K}^{m \times n}` is defined as
+
+.. math::
+
+    A = QR\mathrlap{\qquad Q \in \mathbb{K}^{m \times m}, R \in \mathbb{K}^{m \times n}}
+
+where :math:`Q` is orthogonal in the real case and unitary in the complex case,
+and :math:`R` is upper triangular with real diagonal (even in the complex case).
+
+When `m > n` (tall matrix), as `R` is upper triangular, its last `m - n` rows are zero.
+In this case, we can drop the last `m - n` columns of `Q` to form the
+**reduced QR decomposition**:
+
+.. math::
+
+    A = QR\mathrlap{\qquad Q \in \mathbb{K}^{m \times n}, R \in \mathbb{K}^{n \times n}}
+
+The reduced QR decomposition agrees with the full QR decomposition when `n >= m` (wide matrix).
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+The parameter :attr:`mode` chooses between the full and reduced QR decomposition.
+If :attr:`A` has shape `(*, m, n)`, denoting `k = min(m, n)`
+
+- :attr:`mode`\ `= 'reduced'` (default): Returns `(Q, R)` of shapes `(*, m, k)`, `(*, k, n)` respectively.
+  It is always differentiable.
+- :attr:`mode`\ `= 'complete'`: Returns `(Q, R)` of shapes `(*, m, m)`, `(*, m, n)` respectively.
+  It is differentiable for `m <= n`.
+- :attr:`mode`\ `= 'r'`: Computes only the reduced `R`. Returns `(Q, R)` with `Q` empty and `R` of shape `(*, k, n)`.
+  It is never differentiable.
+
+Differences with `numpy.linalg.qr`:
+
+- :attr:`mode`\ `= 'raw'` is not implemented.
+- Unlike `numpy.linalg.qr`, this function always returns a tuple of two tensors.
+  When :attr:`mode`\ `= 'r'`, the `Q` tensor is an empty tensor.
+
+.. warning:: The elements in the diagonal of `R` are not necessarily positive.
+             As such, the returned QR decomposition is only unique up to the sign of the diagonal of `R`.
+             Therefore, different platforms, like NumPy, or inputs on different devices,
+             may produce different valid decompositions.
+
+.. warning:: The QR decomposition is only well-defined if the first `k = min(m, n)` columns
+             of every matrix in :attr:`A` are linearly independent.
+             If this condition is not met, no error will be thrown, but the QR produced
+             may be incorrect and its autodiff may fail or produce incorrect results.
+
+Args:
+    A (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
+    mode (str, optional): one of `'reduced'`, `'complete'`, `'r'`.
+                          Controls the shape of the returned tensors. Default: `'reduced'`.
+
+Keyword args:
+    out (tuple, optional): output tuple of two tensors. Ignored if `None`. Default: `None`.
+
+Returns:
+    A named tuple `(Q, R)`.
+
+Examples::
+
+    >>> A = torch.tensor([[12., -51, 4], [6, 167, -68], [-4, 24, -41]])
+    >>> Q, R = torch.linalg.qr(A)
+    >>> Q
+    tensor([[-0.8571,  0.3943,  0.3314],
+            [-0.4286, -0.9029, -0.0343],
+            [ 0.2857, -0.1714,  0.9429]])
+    >>> R
+    tensor([[ -14.0000,  -21.0000,   14.0000],
+            [   0.0000, -175.0000,   70.0000],
+            [   0.0000,    0.0000,  -35.0000]])
+    >>> (Q @ R).round()
+    tensor([[  12.,  -51.,    4.],
+            [   6.,  167.,  -68.],
+            [  -4.,   24.,  -41.]])
+    >>> (Q.T @ Q).round()
+    tensor([[ 1.,  0.,  0.],
+            [ 0.,  1., -0.],
+            [ 0., -0.,  1.]])
+    >>> Q2, R2 = torch.linalg.qr(A, mode='r')
+    >>> Q2
+    tensor([])
+    >>> torch.equal(R, R2)
+    True
+    >>> A = torch.randn(3, 4, 5)
+    >>> Q, R = torch.linalg.qr(A, mode='complete')
+    >>> torch.dist(Q @ R, A)
+    tensor(1.6099e-06)
+    >>> torch.dist(Q.mT @ Q, torch.eye(4))
+    tensor(6.2158e-07)
+""")
+
+vander = _add_docstr(_linalg.linalg_vander, r"""
+vander(x, N=None) -> Tensor
+
+Generates a Vandermonde matrix.
+
+Returns the Vandermonde matrix :math:`V`
+
+.. math::
+
+    V = \begin{pmatrix}
+            1 & x_1 & x_1^2 & \dots & x_1^{N-1}\\
+            1 & x_2 & x_2^2 & \dots & x_2^{N-1}\\
+            1 & x_3 & x_3^2 & \dots & x_3^{N-1}\\
+            \vdots & \vdots & \vdots & \ddots &\vdots \\
+            1 & x_n & x_n^2 & \dots & x_n^{N-1}
+        \end{pmatrix}.
+
+for `N > 1`.
+If :attr:`N`\ `= None`, then `N = x.size(-1)` so that the output is a square matrix.
+
+Supports inputs of float, double, cfloat, cdouble, and integral dtypes.
+Also supports batches of vectors, and if :attr:`x` is a batch of vectors then
+the output has the same batch dimensions.
+
+Differences with `numpy.vander`:
+
+- Unlike `numpy.vander`, this function returns the powers of :attr:`x` in ascending order.
+  To get them in the reverse order call ``linalg.vander(x, N).flip(-1)``.
+
+Args:
+    x (Tensor): tensor of shape `(*, n)` where `*` is zero or more batch dimensions
+                consisting of vectors.
+
+Keyword args:
+    N (int, optional): Number of columns in the output. Default: `x.size(-1)`
+
+Example::
+
+    >>> x = torch.tensor([1, 2, 3, 5])
+    >>> linalg.vander(x)
+    tensor([[  1,   1,   1,   1],
+            [  1,   2,   4,   8],
+            [  1,   3,   9,  27],
+            [  1,   5,  25, 125]])
+    >>> linalg.vander(x, N=3)
+    tensor([[ 1,  1,  1],
+            [ 1,  2,  4],
+            [ 1,  3,  9],
+            [ 1,  5, 25]])
+""")
+
+vecdot = _add_docstr(_linalg.linalg_vecdot, r"""
+linalg.vecdot(x, y, *, dim=-1, out=None) -> Tensor
+
+Computes the dot product of two batches of vectors along a dimension.
+
+In symbols, this function computes
+
+.. math::
+
+    \sum_{i=1}^n \overline{x_i}y_i.
+
+over the dimension :attr:`dim` where :math:`\overline{x_i}` denotes the conjugate for complex
+vectors, and it is the identity for real vectors.
+
+Supports input of half, bfloat16, float, double, cfloat, cdouble and integral dtypes.
+It also supports broadcasting.
+
+Args:
+    x (Tensor): first batch of vectors of shape `(*, n)`.
+    y (Tensor): second batch of vectors of shape `(*, n)`.
+
+Keyword args:
+    dim (int): Dimension along which to compute the dot product. Default: `-1`.
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> v1 = torch.randn(3, 2)
+    >>> v2 = torch.randn(3, 2)
+    >>> linalg.vecdot(v1, v2)
+    tensor([ 0.3223,  0.2815, -0.1944])
+    >>> torch.vdot(v1[0], v2[0])
+    tensor(0.3223)
+""")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d00ba1e8d5aff6a18490ac7b16a629ac36e3dcb5
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/__init__.py
@@ -0,0 +1,57 @@
+from torch.masked._ops import (
+    _canonical_dim,
+    _combine_input_and_mask,
+    _generate_docstring,
+    _input_mask,
+    _output_mask,
+    _reduction_identity,
+    _where,
+    amax,
+    amin,
+    argmax,
+    argmin,
+    cumprod,
+    cumsum,
+    log_softmax,
+    logaddexp,
+    logsumexp,
+    mean,
+    median,
+    norm,
+    normalize,
+    prod,
+    softmax,
+    softmin,
+    std,
+    sum,
+    var,
+)
+from torch.masked.maskedtensor.core import is_masked_tensor, MaskedTensor
+from torch.masked.maskedtensor.creation import as_masked_tensor, masked_tensor
+
+
+__all__ = [
+    "amax",
+    "amin",
+    "argmax",
+    "argmin",
+    "as_masked_tensor",
+    "cumprod",
+    "cumsum",
+    "is_masked_tensor",
+    "log_softmax",
+    "logaddexp",
+    "logsumexp",
+    "masked_tensor",
+    "MaskedTensor",
+    "mean",
+    "median",
+    "norm",
+    "normalize",
+    "prod",
+    "softmax",
+    "softmin",
+    "std",
+    "sum",
+    "var",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/__pycache__/_ops.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/__pycache__/_ops.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/_docs.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/_docs.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa130bbefbc5caa7373459ef2fc3dc5292239948
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/_docs.py
@@ -0,0 +1,1177 @@
+# This file is generated, do not modify it!
+#
+# To update this file, run the update masked docs script as follows:
+#
+#   python tools/update_masked_docs.py
+#
+# The script must be called from an environment where the development
+# version of torch package can be imported and is functional.
+#
+
+amax_docstring = """amax(input, dim, *, keepdim=False, dtype=None, mask=None) -> Tensor
+
+Returns maximum of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+
+The identity value of maximum operation, which is used to start the
+reduction, depends on input dtype. For instance, for float32, uint8,
+and int32 dtypes, the identity values are ``-inf``, ``0``, and ``-2147483648``, respectively.
+
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in maximum computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of maximum operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int or tuple of ints, optional): the dimension or dimensions to reduce.
+      Default: None that is equivalent to ``tuple(range(input.ndim))``.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3, -2, -1], [ 0, 1, 2]])
+    >>> input
+    tensor([[-3, -2, -1],
+            [ 0,  1,  2]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.amax(input, 1, mask=mask)
+    tensor([                  -1, -9223372036854775808])
+"""
+
+amin_docstring = """amin(input, dim, *, keepdim=False, dtype=None, mask=None) -> Tensor
+
+Returns minimum of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+
+The identity value of minimum operation, which is used to start the
+reduction, depends on input dtype. For instance, for float32, uint8,
+and int32 dtypes, the identity values are ``inf``, ``255``, and ``2147483647``, respectively.
+
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in minimum computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of minimum operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int or tuple of ints, optional): the dimension or dimensions to reduce.
+      Default: None that is equivalent to ``tuple(range(input.ndim))``.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3, -2, -1], [ 0, 1, 2]])
+    >>> input
+    tensor([[-3, -2, -1],
+            [ 0,  1,  2]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.amin(input, 1, mask=mask)
+    tensor([                 -3, 9223372036854775807])
+"""
+
+argmax_docstring = """argmax(input, dim, *, keepdim=False, dtype=None, mask=None) -> Tensor
+Returns argmax of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+The identity value of argmax operation, which is used to start the
+reduction, depends on input dtype. For instance, for float32, uint8,
+and int32 dtypes, the identity values are ``-inf``, ``0``, and ``-2147483648``, respectively.
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in argmax computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of argmax operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int): the dimension along which argmax is computed.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+Example::
+
+    >>> input = tensor([[-3, -2, -1], [ 0, 1, 2]])
+    >>> input
+    tensor([[-3, -2, -1],
+            [ 0,  1,  2]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.argmax(input, 1, mask=mask)
+    tensor([2, 0])
+"""
+
+argmin_docstring = """argmin(input, dim, *, keepdim=False, dtype=None, mask=None) -> Tensor
+Returns argmin of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+The identity value of argmin operation, which is used to start the
+reduction, depends on input dtype. For instance, for float32, uint8,
+and int32 dtypes, the identity values are ``inf``, ``255``, and ``2147483647``, respectively.
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in argmin computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of argmin operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int): the dimension along which argmin is computed.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+Example::
+
+    >>> input = tensor([[-3, -2, -1], [ 0, 1, 2]])
+    >>> input
+    tensor([[-3, -2, -1],
+            [ 0,  1,  2]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.argmin(input, 1, mask=mask)
+    tensor([0, 0])
+"""
+
+cumprod_docstring = """cumprod(input, dim, *, dtype=None, mask=None) -> Tensor
+
+Returns cumulative_prod of all the slices in the :attr:`input` tensor
+along :attr:`dim` while the :attr:`input` elements are masked out
+according to the boolean tensor :attr:`mask`.
+
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. Cumsum of i-th element in ``x`` is
+defined as ``prod(x[:i])``.
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True then
+the corresponding element in :attr:`input` tensor will be included in
+cumulative_prod computation, otherwise the element is ignored.
+
+The values of masked-out elements of the output tensor have undefined
+value: it may or may not be set to zero or nan; the choice may correspond to
+the value that leads to the most efficient storage of :attr:`output`
+tensor.
+
+The mask of the cumulative_prod output tensor can be computed as
+``torch.broadcast_to(mask, input.shape)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int): the dimension along which cumulative_prod is computed.
+
+Keyword args:
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3., -2., -1.], [ 0., 1., 2.]])
+    >>> input
+    tensor([[-3., -2., -1.],
+            [ 0.,  1.,  2.]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.cumprod(input, 1, mask=mask)
+    tensor([[-3., -3.,  3.],
+            [ 1.,  1.,  1.]])
+"""
+
+cumsum_docstring = """cumsum(input, dim, *, dtype=None, mask=None) -> Tensor
+
+Returns cumulative_sum of all the slices in the :attr:`input` tensor
+along :attr:`dim` while the :attr:`input` elements are masked out
+according to the boolean tensor :attr:`mask`.
+
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. Cumsum of i-th element in ``x`` is
+defined as ``sum(x[:i])``.
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True then
+the corresponding element in :attr:`input` tensor will be included in
+cumulative_sum computation, otherwise the element is ignored.
+
+The values of masked-out elements of the output tensor have undefined
+value: it may or may not be set to zero or nan; the choice may correspond to
+the value that leads to the most efficient storage of :attr:`output`
+tensor.
+
+The mask of the cumulative_sum output tensor can be computed as
+``torch.broadcast_to(mask, input.shape)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int): the dimension along which cumulative_sum is computed.
+
+Keyword args:
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3., -2., -1.], [ 0., 1., 2.]])
+    >>> input
+    tensor([[-3., -2., -1.],
+            [ 0.,  1.,  2.]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.cumsum(input, 1, mask=mask)
+    tensor([[-3., -3., -4.],
+            [ 0.,  0.,  0.]])
+"""
+
+log_softmax_docstring = """log_softmax(input, dim, *, dtype=None, mask=None) -> Tensor
+
+Returns log_softmax of all the slices in the :attr:`input` tensor
+along :attr:`dim` while the :attr:`input` elements are masked out
+according to the boolean tensor :attr:`mask`.
+
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. LogSoftmax of i-th element in ``x`` is
+defined as ``log(exp(x[i])/sum(exp(x)))``.
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True then
+the corresponding element in :attr:`input` tensor will be included in
+log_softmax computation, otherwise the element is ignored.
+
+The values of masked-out elements of the output tensor have undefined
+value: it may or may not be set to zero or nan; the choice may correspond to
+the value that leads to the most efficient storage of :attr:`output`
+tensor.
+
+The mask of the log_softmax output tensor can be computed as
+``torch.broadcast_to(mask, input.shape)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int): the dimension along which log_softmax is computed.
+
+Keyword args:
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3., -2., -1.], [ 0., 1., 2.]])
+    >>> input
+    tensor([[-3., -2., -1.],
+            [ 0.,  1.,  2.]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.log_softmax(input, 1, mask=mask)
+    tensor([[-2.1269,    -inf, -0.1269],
+            [    nan,     nan,     nan]])
+"""
+
+logsumexp_docstring = """logsumexp(input, dim, *, keepdim=False, dtype=None, mask=None) -> Tensor
+
+Returns logsumexp of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+
+The identity value of logsumexp operation, which is used to start the reduction, is ``-2147483648``.
+
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in logsumexp computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of logsumexp operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int or tuple of ints, optional): the dimension or dimensions to reduce.
+      Default: None that is equivalent to ``tuple(range(input.ndim))``.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3, -2, -1], [ 0, 1, 2]])
+    >>> input
+    tensor([[-3, -2, -1],
+            [ 0,  1,  2]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.logsumexp(input, 1, mask=mask)
+    tensor([                   0, -9223372036854775808])
+"""
+
+mean_docstring = """mean(input, dim, *, keepdim=False, dtype=None, mask=None) -> Tensor
+
+Returns mean of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+
+By definition, the identity value of a mean operation is the mean
+value of the tensor. If all elements of the input tensor along given
+dimension(s) :attr:`dim` are masked-out, the identity value of the
+mean is undefined.  Due to this ambiguity, the elements of output
+tensor with strided layout, that correspond to fully masked-out
+elements, have ``nan`` values.
+
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in mean computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of mean operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int or tuple of ints, optional): the dimension or dimensions to reduce.
+      Default: None that is equivalent to ``tuple(range(input.ndim))``.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3, -2, -1], [ 0, 1, 2]])
+    >>> input
+    tensor([[-3, -2, -1],
+            [ 0,  1,  2]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.mean(input, 1, mask=mask)
+    tensor([-2., nan])
+"""
+
+median_docstring = """median(input, dim, *, keepdim=False, dtype=None, mask=None) -> Tensor
+Returns median of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+By definition, the identity value of a median operation is the median
+value of the tensor. If all elements of the input tensor along given
+dimension(s) :attr:`dim` are masked-out, the identity value of the
+median is undefined.  Due to this ambiguity, the elements of output
+tensor with strided layout, that correspond to fully masked-out
+elements, have ``nan`` values.
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in median computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of median operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int): the dimension along which median is computed.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+Example::
+
+    >>> input = tensor([[-3., -2., -1.], [ 0., 1., 2.]])
+    >>> input
+    tensor([[-3., -2., -1.],
+            [ 0.,  1.,  2.]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.median(input, 1, mask=mask)
+    tensor([-3., nan])
+"""
+
+norm_docstring = """norm(input, ord, dim, *, keepdim=False, dtype=None, mask=None) -> Tensor
+
+Returns norm of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+
+The identity value of norm operation, which is used to start the
+reduction, is ``0.0``, except for ``ord=-inf`` it is
+``inf``.
+
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in norm computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of norm operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    ord (int, float, optional): the order of vector norm. Default: 2.
+      See :func:`torch.linalg.vector_norm` for a list of supported norms.
+    dim (int or tuple of ints, optional): the dimension or dimensions to reduce.
+      Default: None that is equivalent to ``tuple(range(input.ndim))``.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3., -2., -1.], [ 0., 1., 2.]])
+    >>> input
+    tensor([[-3., -2., -1.],
+            [ 0.,  1.,  2.]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.norm(input, 2.0, 1, mask=mask)
+    tensor([3.1623, 0.0000])
+"""
+
+normalize_docstring = """normalize(input, ord, dim, *, eps=1e-12, dtype=None, mask=None) -> Tensor
+
+Returns normalize of all the slices in the :attr:`input` tensor
+along :attr:`dim` while the :attr:`input` elements are masked out
+according to the boolean tensor :attr:`mask`.
+
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. Normalize of i-th element in ``x`` is
+defined as ``x[i]/max(norm(x, p), eps)``.
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True then
+the corresponding element in :attr:`input` tensor will be included in
+normalize computation, otherwise the element is ignored.
+
+The values of masked-out elements of the output tensor have undefined
+value: it may or may not be set to zero or nan; the choice may correspond to
+the value that leads to the most efficient storage of :attr:`output`
+tensor.
+
+The mask of the normalize output tensor can be computed as
+``torch.broadcast_to(mask, input.shape)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    ord (int, float): the order of vector norm. Default: 2.
+      See :func:`torch.linalg.vector_norm` for a list of supported norms.
+    dim (int): the dimension along which normalize is computed.
+
+Keyword args:
+    eps (float, optional): small value to avoid division by zero. Default: 1e-12.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3., -2., -1.], [ 0., 1., 2.]])
+    >>> input
+    tensor([[-3., -2., -1.],
+            [ 0.,  1.,  2.]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.normalize(input, 2.0, 1, mask=mask)
+    tensor([[-0.9487,  0.0000, -0.3162],
+            [ 0.0000,  0.0000,  0.0000]])
+"""
+
+prod_docstring = """prod(input, dim, *, keepdim=False, dtype=None, mask=None) -> Tensor
+
+Returns product of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+
+The identity value of product operation, which is used to start the reduction, is ``1``.
+
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in product computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of product operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int or tuple of ints, optional): the dimension or dimensions to reduce.
+      Default: None that is equivalent to ``tuple(range(input.ndim))``.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3, -2, -1], [ 0, 1, 2]])
+    >>> input
+    tensor([[-3, -2, -1],
+            [ 0,  1,  2]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.prod(input, 1, mask=mask)
+    tensor([3, 1])
+"""
+
+softmax_docstring = """softmax(input, dim, *, dtype=None, mask=None) -> Tensor
+
+Returns softmax of all the slices in the :attr:`input` tensor
+along :attr:`dim` while the :attr:`input` elements are masked out
+according to the boolean tensor :attr:`mask`.
+
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. Softmax of i-th element in ``x`` is
+defined as ``exp(x[i])/sum(exp(x))``.
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True then
+the corresponding element in :attr:`input` tensor will be included in
+softmax computation, otherwise the element is ignored.
+
+The values of masked-out elements of the output tensor have undefined
+value: it may or may not be set to zero or nan; the choice may correspond to
+the value that leads to the most efficient storage of :attr:`output`
+tensor.
+
+The mask of the softmax output tensor can be computed as
+``torch.broadcast_to(mask, input.shape)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int): the dimension along which softmax is computed.
+
+Keyword args:
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3., -2., -1.], [ 0., 1., 2.]])
+    >>> input
+    tensor([[-3., -2., -1.],
+            [ 0.,  1.,  2.]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.softmax(input, 1, mask=mask)
+    tensor([[0.1192, 0.0000, 0.8808],
+            [   nan,    nan,    nan]])
+"""
+
+softmin_docstring = """softmin(input, dim, *, dtype=None, mask=None) -> Tensor
+
+Returns softmin of all the slices in the :attr:`input` tensor
+along :attr:`dim` while the :attr:`input` elements are masked out
+according to the boolean tensor :attr:`mask`.
+
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. Softmin of i-th element in ``x`` is
+defined as ``exp(-x[i])/sum(exp(-x))``.
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True then
+the corresponding element in :attr:`input` tensor will be included in
+softmin computation, otherwise the element is ignored.
+
+The values of masked-out elements of the output tensor have undefined
+value: it may or may not be set to zero or nan; the choice may correspond to
+the value that leads to the most efficient storage of :attr:`output`
+tensor.
+
+The mask of the softmin output tensor can be computed as
+``torch.broadcast_to(mask, input.shape)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int): the dimension along which softmin is computed.
+
+Keyword args:
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3., -2., -1.], [ 0., 1., 2.]])
+    >>> input
+    tensor([[-3., -2., -1.],
+            [ 0.,  1.,  2.]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.softmin(input, 1, mask=mask)
+    tensor([[0.8808, 0.0000, 0.1192],
+            [   nan,    nan,    nan]])
+"""
+
+std_docstring = """std(input, dim, unbiased, *, keepdim=False, dtype=None, mask=None) -> Tensor
+Returns standard_deviation of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+The identity value of sample standard deviation operation is undefined. The
+elements of output tensor with strided layout, that correspond to
+fully masked-out elements, have ``nan`` values.
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in standard_deviation computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of standard_deviation operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int or tuple of ints, optional): the dimension or dimensions to reduce.
+      Default: None that is equivalent to ``tuple(range(input.ndim))``.
+    unbiased (bool): when True, use Bessel's correction, otherwise, compute
+      the uncorrected sample variance.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+Example::
+
+    >>> input = tensor([[-3, -2, -1], [ 0, 1, 2]])
+    >>> input
+    tensor([[-3, -2, -1],
+            [ 0,  1,  2]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.std(input, 1, False, mask=mask)
+    tensor([1., nan])
+"""
+
+sum_docstring = """sum(input, dim, *, keepdim=False, dtype=None, mask=None) -> Tensor
+
+Returns sum of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+
+The identity value of sum operation, which is used to start the reduction, is ``0``.
+
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in sum computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of sum operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int or tuple of ints, optional): the dimension or dimensions to reduce.
+      Default: None that is equivalent to ``tuple(range(input.ndim))``.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3, -2, -1], [ 0, 1, 2]])
+    >>> input
+    tensor([[-3, -2, -1],
+            [ 0,  1,  2]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.sum(input, 1, mask=mask)
+    tensor([-4,  0])
+"""
+
+var_docstring = """var(input, dim, unbiased, *, keepdim=False, dtype=None, mask=None) -> Tensor
+Returns variance of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+The identity value of sample variance operation is undefined. The
+elements of output tensor with strided layout, that correspond to
+fully masked-out elements, have ``nan`` values.
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in variance computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of variance operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int or tuple of ints, optional): the dimension or dimensions to reduce.
+      Default: None that is equivalent to ``tuple(range(input.ndim))``.
+    unbiased (bool): when True, use Bessel's correction, otherwise, compute
+      the uncorrected sample variance.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+Example::
+
+    >>> input = tensor([[-3, -2, -1], [ 0, 1, 2]])
+    >>> input
+    tensor([[-3, -2, -1],
+            [ 0,  1,  2]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.var(input, 1, False, mask=mask)
+    tensor([1., nan])
+"""
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/_ops.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d8151197a207e550647a4bc19a1ed615eff5cdd
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/_ops.py
@@ -0,0 +1,1796 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import warnings
+from typing import Any, List, Optional, Tuple, TYPE_CHECKING, Union
+
+import torch
+from torch import sym_float, Tensor
+from torch._prims_common import corresponding_real_dtype
+from torch.masked import _docs
+from torch.masked.maskedtensor.core import is_masked_tensor, MaskedTensor
+from torch.masked.maskedtensor.creation import as_masked_tensor
+
+
+if TYPE_CHECKING:
+    from torch.types import _dtype as DType
+
+    DimOrDims = Optional[Union[int, Tuple[int], List[int]]]
+else:
+    # The JIT doesn't understand Union, nor torch.dtype here
+    DType = int
+    DimOrDims = Optional[Tuple[int]]
+
+
+__all__: List[str] = []
+
+# All masked reduction/normalization operations have the same
+# signatures. Here we introduce docstring templates that are applied
+# to docstrings of reduction/normalization functions via
+# _apply_docstring_templates decorator.
+
+
+def _apply_docstring_templates(func):
+    """Decorator that applies docstring templates to function docstring
+    and returns the function instance.
+    """
+
+    doc_string = getattr(_docs, f"{func.__name__}_docstring", None)
+    if doc_string is None:
+        warnings.warn(
+            f"No documentation string available for {func.__name__}."
+            " PyTorch team should run `python tools/update_masked_docs.py`"
+            " to generate the missing docstrings."
+        )
+    else:
+        func.__doc__ = doc_string
+
+    # Expose function as public symbol
+    __all__.append(func.__name__)
+
+    return func
+
+
+def _generate_docstring(func):
+    """A utility function called from tools/update_masked_docs.py
+    script to update the module torch.masked._docs.py
+    """
+    docstring_templates = dict(
+        reduction_signature="""\
+{function_name}(input, {operation_args}, *, {operation_kwargs}) -> Tensor""",
+        reduction_descr="""\
+Returns {operation name} of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.""",
+        reduction_args="""\
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in {operation name} computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of {operation name} operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    {args_declarations}
+
+Keyword args:
+    {kwargs_declarations}""",
+        reduction_example="""\
+Example::
+
+    >>> input = {example_input}
+    >>> input
+    {indent_example_input}
+    >>> mask = {example_mask}
+    >>> mask
+    {indent_example_mask}
+    >>> {full_function_name}(input, {example_args}, mask=mask)
+    {indent_example_output}
+""",
+        reduction_identity="""\
+The identity value of {operation name} operation, which is used to start the reduction, is ``{identity_int32}``.""",
+        reduction_identity_dtype="""\
+The identity value of {operation name} operation, which is used to start the
+reduction, depends on input dtype. For instance, for float32, uint8,
+and int32 dtypes, the identity values are ``{identity_float32}``, ``{identity_uint8}``, and ``{identity_int32}``, respectively.""",
+        normalization_signature="""\
+{function_name}(input, {operation_args}, *, {operation_kwargs}) -> Tensor""",
+        normalization_descr="""\
+Returns {operation name} of all the slices in the :attr:`input` tensor
+along :attr:`dim` while the :attr:`input` elements are masked out
+according to the boolean tensor :attr:`mask`.
+
+{definition}""",
+        normalization_args="""\
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True then
+the corresponding element in :attr:`input` tensor will be included in
+{operation name} computation, otherwise the element is ignored.
+
+The values of masked-out elements of the output tensor have undefined
+value: it may or may not be set to zero or nan; the choice may correspond to
+the value that leads to the most efficient storage of :attr:`output`
+tensor.
+
+The mask of the {operation name} output tensor can be computed as
+``torch.broadcast_to(mask, input.shape)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    {args_declarations}
+
+Keyword args:
+    {kwargs_declarations}""",
+        normalization_example="""\
+Example::
+
+    >>> input = {example_input}
+    >>> input
+    {indent_example_input}
+    >>> mask = {example_mask}
+    >>> mask
+    {indent_example_mask}
+    >>> {full_function_name}(input, {example_args}, mask=mask)
+    {indent_example_output}
+""",
+    )
+
+    args_and_kwargs = dict(
+        # argument name sufficies separated by double underscore will
+        # be removed in the final documentation string.
+        sum=(("dim",), ("keepdim=False", "dtype=None", "mask=None")),
+        prod=(("dim",), ("keepdim=False", "dtype=None", "mask=None")),
+        cumsum=(("dim__as_int",), ("dtype=None", "mask=None")),
+        cumprod=(("dim__as_int",), ("dtype=None", "mask=None")),
+        amin=(("dim",), ("keepdim=False", "dtype=None", "mask=None")),
+        amax=(("dim",), ("keepdim=False", "dtype=None", "mask=None")),
+        argmin=(("dim__as_int",), ("keepdim=False", "dtype=None", "mask=None")),
+        argmax=(("dim__as_int",), ("keepdim=False", "dtype=None", "mask=None")),
+        mean=(("dim",), ("keepdim=False", "dtype=None", "mask=None")),
+        median=(("dim__as_int",), ("keepdim=False", "dtype=None", "mask=None")),
+        norm=(
+            (
+                "ord",
+                "dim",
+            ),
+            ("keepdim=False", "dtype=None", "mask=None"),
+        ),
+        var=(("dim", "unbiased"), ("keepdim=False", "dtype=None", "mask=None")),
+        std=(("dim", "unbiased"), ("keepdim=False", "dtype=None", "mask=None")),
+        logsumexp=(("dim",), ("keepdim=False", "dtype=None", "mask=None")),
+        softmax=(("dim__as_int",), ("dtype=None", "mask=None")),
+        log_softmax=(("dim__as_int",), ("dtype=None", "mask=None")),
+        softmin=(("dim__as_int",), ("dtype=None", "mask=None")),
+        normalize=(
+            (
+                "ord__required",
+                "dim__as_int",
+            ),
+            ("eps=1e-12", "dtype=None", "mask=None"),
+        ),
+    )
+
+    argument_declarations = dict(
+        dim="""\
+dim (int or tuple of ints, optional): the dimension or dimensions to reduce.
+  Default: None that is equivalent to ``tuple(range(input.ndim))``.""",
+        dim__as_int="""\
+dim (int): the dimension along which {operation name} is computed.""",
+        ord="""\
+ord (int, float, optional): the order of vector norm. Default: 2.
+  See :func:`torch.linalg.vector_norm` for a list of supported norms.""",
+        ord__required="""\
+ord (int, float): the order of vector norm. Default: 2.
+  See :func:`torch.linalg.vector_norm` for a list of supported norms.""",
+        unbiased="""\
+unbiased (bool): when True, use Bessel's correction, otherwise, compute
+  the uncorrected sample variance.""",
+        eps="""\
+eps (float, optional): small value to avoid division by zero. Default: {default}.""",
+        keepdim="""\
+keepdim (bool, optional): whether the output tensor has
+  :attr:`dim` retained or not. Default: {default}.""",
+        dtype="""\
+dtype (:class:`torch.dtype`, optional): the desired data type
+  of returned tensor.  If specified, the input tensor is
+  casted to :attr:`dtype` before the operation is
+  performed. Default: {default}.""",
+        mask="""\
+mask (:class:`torch.Tensor`, optional): the boolean tensor
+  containing the binary mask of validity of input tensor
+  elements.
+  Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.""",
+    )
+
+    definitions = dict(
+        softmax="""\
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. Softmax of i-th element in ``x`` is
+defined as ``exp(x[i])/sum(exp(x))``.""",
+        log_softmax="""\
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. LogSoftmax of i-th element in ``x`` is
+defined as ``log(exp(x[i])/sum(exp(x)))``.""",
+        softmin="""\
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. Softmin of i-th element in ``x`` is
+defined as ``exp(-x[i])/sum(exp(-x))``.""",
+        normalize="""\
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. Normalize of i-th element in ``x`` is
+defined as ``x[i]/max(norm(x, p), eps)``.""",
+        cumsum="""\
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. Cumsum of i-th element in ``x`` is
+defined as ``sum(x[:i])``.""",
+        cumprod="""\
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. Cumsum of i-th element in ``x`` is
+defined as ``prod(x[:i])``.""",
+    )
+
+    reduction_names = dict(
+        sum="sum",
+        prod="product",
+        amax="maximum",
+        amin="minimum",
+        argmax="argmax",
+        argmin="argmin",
+        mean="mean",
+        median="median",
+        norm="norm",
+        var="variance",
+        std="standard_deviation",
+        logsumexp="logsumexp",
+    )
+
+    normalization_names = dict(
+        softmax="softmax",
+        log_softmax="log_softmax",
+        softmin="softmin",
+        normalize="normalize",
+        cumsum="cumulative_sum",
+        cumprod="cumulative_prod",
+    )
+
+    operation_names = {}
+    operation_names.update(reduction_names)
+    operation_names.update(normalization_names)
+
+    # Default example data:
+    example_dim = 1
+    example_input = torch.tensor([[-3, -2, -1], [0, 1, 2]])
+    example_mask = torch.tensor([[True, False, True], [False, False, False]])
+    example_args: Tuple[Any, ...]
+    if func.__name__ in {"norm", "normalize"}:
+        example_args = (2.0, example_dim)
+        example_input = example_input.to(dtype=torch.float32)
+    elif func.__name__ in {"var", "std"}:
+        example_args = (example_dim, False)
+    elif func.__name__ == "median":
+        example_args = (example_dim,)
+        example_input = example_input.to(dtype=torch.float32)
+    else:
+        example_args = (example_dim,)
+
+    operation_args: Tuple[str, ...]
+    operation_kwargs: Tuple[str, ...]
+    operation_args, operation_kwargs = args_and_kwargs[func.__name__]
+    arg_declarations = [
+        "\n    ".join(
+            argument_declarations.get(a, f'{a.split("__", 1)[0]}: TBD.').splitlines()
+        )
+        for a in operation_args
+    ]
+    kwarg_declarations = [
+        "\n    ".join(
+            argument_declarations.get(
+                a.split("=", 1)[0], f'{a.split("__", 1)[0]}: TBD.'
+            )
+            .format(default=a.split("=", 1)[1])
+            .splitlines()
+        )
+        for a in operation_kwargs
+    ]
+
+    if func.__name__ in reduction_names:
+        op_kind = "reduction"
+        doc_sections = ["signature", "descr", "identity", "args", "example"]
+    elif func.__name__ in normalization_names:
+        op_kind = "normalization"
+        doc_sections = ["signature", "descr", "args", "example"]
+        example_input = example_input.to(dtype=torch.float32)
+    else:
+        assert 0  # add function name to operation names dictionaries
+    example_output = func(example_input, *example_args, mask=example_mask)
+
+    template_data = {
+        "function_name": func.__name__,
+        "full_function_name": func.__module__ + "." + func.__name__,
+        "operation name": operation_names[func.__name__],
+        "operation_args": ", ".join(a.split("__", 1)[0] for a in operation_args),
+        "operation_kwargs": ", ".join(a.split("__", 1)[0] for a in operation_kwargs),
+        # one-line representation of a tensor:
+        "example_input": " ".join(str(example_input).split()),
+        "example_args": ", ".join(map(str, example_args)),
+        "example_mask": " ".join(str(example_mask).split()),
+        # multi-line representation of a tensor with indent
+        "indent_example_input": ("\n    ").join(str(example_input).splitlines()),
+        "indent_example_mask": ("\n    ").join(str(example_mask).splitlines()),
+        "indent_example_output": ("\n    ").join(str(example_output).splitlines()),
+    }
+
+    if func.__name__ in reduction_names:
+        template_data.update(
+            identity_uint8=_reduction_identity(
+                func.__name__, torch.tensor(0, dtype=torch.uint8)
+            ),
+            identity_int32=_reduction_identity(
+                func.__name__, torch.tensor(0, dtype=torch.int32)
+            ),
+            identity_float32=_reduction_identity(
+                func.__name__, torch.tensor(0, dtype=torch.float32)
+            ),
+        )
+        if func.__name__ == "norm":
+            template_data.update(
+                identity_ord_ninf=_reduction_identity(
+                    func.__name__, torch.tensor(0, dtype=torch.float32), float("-inf")
+                )
+            )
+    elif func.__name__ in normalization_names:
+        template_data.update(definition=definitions[func.__name__])
+    else:
+        assert 0  # add function name to operation names dictionaries
+    template_data.update(
+        args_declarations=("\n    ".join(arg_declarations)).format_map(template_data)
+    )
+    template_data.update(
+        kwargs_declarations=("\n    ".join(kwarg_declarations)).format_map(
+            template_data
+        )
+    )
+
+    # Apply function name info to docstring templates:
+    templates = {
+        k: v.format_map(template_data)
+        for k, v in docstring_templates.items()
+        if k.startswith(op_kind)
+    }
+    templates.update(
+        (k, v.format_map(template_data) if isinstance(v, str) else v)
+        for k, v in template_data.items()
+    )
+
+    # Apply docstring templates to function doctring:
+    if func.__doc__ is None:
+        doc_template = "\n\n".join([f"{{{op_kind}_{sec}}}" for sec in doc_sections])
+    else:
+        doc_template = func.__doc__
+    return doc_template.format_map(templates)
+
+
+def _reduction_identity(op_name: str, input: Tensor, *args):
+    """Return identity value as scalar tensor of a reduction operation on
+    given input, or None, if the identity value cannot be uniquely
+    defined for the given input.
+
+    The identity value of the operation is defined as the initial
+    value to reduction operation that has a property ``op(op_identity,
+    value) == value`` for any value in the domain of the operation.
+    Or put it another way, including or excluding the identity value in
+    a list of operands will not change the reduction result.
+
+    See https://github.com/pytorch/rfcs/pull/27 for more information.
+
+    """
+    dtype: DType = input.dtype
+    device = input.device
+    op_name = op_name.rsplit(".", 1)[-1]  # lstrip module name when present
+    if op_name in {"sum", "cumsum"}:
+        return torch.tensor(0, dtype=dtype, device=device)
+    elif op_name in {"prod", "cumprod"}:
+        return torch.tensor(1, dtype=dtype, device=device)
+    elif op_name in {"amax", "argmax", "logaddexp"}:
+        if torch.is_floating_point(input):
+            return torch.tensor(-torch.inf, dtype=dtype, device=device)
+        elif torch.is_signed(input) or dtype == torch.uint8:
+            return torch.tensor(torch.iinfo(dtype).min, dtype=dtype, device=device)
+    elif op_name in {"logsumexp"}:
+        if torch.is_floating_point(input):
+            return torch.tensor(-torch.inf, dtype=dtype, device=device)
+        elif torch.is_complex(input):
+            return torch.tensor(-torch.inf + 0j, dtype=dtype, device=device)
+        elif torch.is_signed(input) or dtype == torch.uint8:
+            return torch.tensor(torch.iinfo(dtype).min, dtype=dtype, device=device)
+    elif op_name in {"amin", "argmin"}:
+        if torch.is_floating_point(input):
+            return torch.tensor(torch.inf, dtype=dtype, device=device)
+        elif torch.is_signed(input) or dtype == torch.uint8:
+            return torch.tensor(torch.iinfo(dtype).max, dtype=dtype, device=device)
+    elif op_name == "mean":
+        # Strictly speaking, the identity value of the mean operation
+        # is the mean of the input. Since the mean value depends on
+        # the dim argument and it may be a non-scalar tensor, we
+        # consider the identity value of the mean operation ambiguous.
+        # Moreover, the mean value of empty input is undefined.
+        return None
+    elif op_name == "norm":
+        ord = args[0] if args else 2
+        if ord == float("-inf"):
+            assert torch.is_floating_point(input), input.dtype
+            return torch.tensor(torch.inf, dtype=dtype, device=device)
+        return torch.tensor(0, dtype=dtype, device=device)
+    elif op_name == "median":
+        # We use NaN for now because the implementation is currently using torch.nanmedian
+        # and NaN is the identity for that function since it gets ignored
+        dtype = input.dtype if torch.is_floating_point(input) else torch.float
+        return torch.tensor(torch.nan, dtype=dtype, device=device)
+    elif op_name in {"var", "std"}:
+        return None
+    raise NotImplementedError(f"identity of {op_name} on {dtype} input")
+
+
+def _canonical_dim(dim: DimOrDims, ndim: int) -> Tuple[int, ...]:
+    """Return dim argument as a tuple of sorted dim values."""
+    dims: List[int] = []
+    if dim == ():
+        # Currently, `dim=()` in reductions operations means "reduce
+        # over all dimensions" while in future, it will read "no
+        # reduce". See https://github.com/pytorch/pytorch/issues/29137
+        # When gh-29137 is resolved, this if-block must be deleted.
+        dim = None
+    if dim is None:
+        return tuple(range(ndim))
+    ndim = max(ndim, 1)
+    dim_ = (dim,) if isinstance(dim, (int, torch.SymInt)) else dim
+    for d in dim_:
+        if d in dims:
+            raise RuntimeError(f"dim={d} appears multiple times in the list of dims")
+        if d >= ndim or d < -ndim:
+            raise IndexError(
+                f"Dimension out of range (expected to be in range of [{-ndim}, {ndim - 1}], but got {d})"
+            )
+        dims.append(d % ndim)
+    return tuple(sorted(dims))
+
+
+def _sparse_coo_flatten_indices(indices: Tensor, shape: tuple):
+    # Flatted N-D indices to 1-D indices
+    flat_indices = indices.new_zeros(indices.size(1))
+    for d, sz in enumerate(shape):
+        flat_indices.mul_(sz)
+        flat_indices.add_(indices[d])
+    return flat_indices
+
+
+def _any(input: Tensor, dim: tuple, keepdim: bool):
+    # Support torch.any with tuple dim argument.
+    # Workaround of https://github.com/pytorch/pytorch/issues/56586
+    r = input
+    for d in reversed(dim):
+        r = r.any(dim=d, keepdim=keepdim)
+    return r
+
+
+def _sparse_coo_where(mask: Tensor, input: Tensor, fill_value: Tensor) -> Tensor:
+    """Sparse variant of torch.where. Supports sparse COO and hybrid sparse COO tensors.
+
+    _sparse_coo_where implements the following invariant:
+
+      _sparse_coo_where(mask, input, fill_value).to_dense(fill_value) ==
+        torch.where(mask.to_dense(), input.to_dense(), torch.full(input.shape, fill_value))
+
+    where `a == b` means `assertEqual(a, b)`, mask is boolean sparse
+    tensor, and `to_dense(fill_value)` is like `to_dense()` except
+    that the unspecified elements are mapped to `fill_value` rather
+    than to `0`.
+
+    Returns a sparse COO tensor with the following features:
+
+    - all specified elements correspond to masked-in elements that
+      have the values of the input tensor. If there exists a masked-in
+      element (as specified by mask) that is not specified in the
+      input, in the result tensor, the corresponding element has value
+      0. In the dense part of the sparse tensor, the masked-out
+      elements are replaced with fill_value.
+
+    - all unspecified elements correspond to masked-out elements.
+    """
+
+    assert input.layout == torch.sparse_coo
+    assert mask.layout == input.layout
+    assert mask.shape == input.shape
+    assert mask.dense_dim() == input.dense_dim()  # TODO: eliminate this restriction
+
+    input = input.coalesce()
+
+    # For set operations on sparse tensor indices, we'll convert
+    # multi-dimensional indices to 1-D indices for efficiency.
+    input_flat_indices = _sparse_coo_flatten_indices(
+        input.indices(), input.shape[: input.sparse_dim()]
+    )
+    mask_flat_indices = _sparse_coo_flatten_indices(
+        mask.indices(), mask.shape[: mask.sparse_dim()]
+    )
+
+    # the set of mask flat indices that define masked-in elements:
+    if mask.dense_dim() > 0:
+        mask_values = _any(
+            mask.values(), tuple(range(1, input.sparse_dim() + 1)), False
+        )
+    else:
+        mask_values = mask.values()
+    maskin_flat_indices = mask_flat_indices[mask_values.nonzero()[:, 0]]
+
+    def intersection(i1, i2):
+        union, counts = torch.cat([i1, i2]).unique(return_counts=True)
+        return union, torch.where(counts.gt(1))
+
+    def minus(i1, i2):
+        union, counts = torch.cat([i1, i2]).unique(return_counts=True)
+        return intersection(union[torch.where(counts.eq(1))], i1)
+
+    def _apply(a):
+        obj, w = a
+        return obj[w]
+
+    # the set of input flat indices of specified and masked-in elements:
+    maskin_input_flat_indices = _apply(
+        intersection(maskin_flat_indices, input_flat_indices)
+    )
+    _, w = intersection(input_flat_indices, maskin_input_flat_indices)
+
+    # the indices and values of masked-in elements
+    where_input_indices = input.indices()[(slice(None),) + w]
+    where_input_values = input.values()[w]
+
+    if mask.dense_dim() > 0:
+        # apply mask to the dense part of the input values:
+        _, w1 = intersection(mask_flat_indices, maskin_input_flat_indices)
+        where_mask_values = mask.values()[w1]
+        where_input_values = torch.where(
+            where_mask_values, where_input_values, fill_value
+        )
+
+    # the set of flat indices of unspecified input and masked-in elements:
+    maskin_zero_flat_indices = _apply(
+        minus(maskin_flat_indices, maskin_input_flat_indices)
+    )
+
+    # the indices of masked-in zero elements
+    _, w = intersection(mask_flat_indices, maskin_zero_flat_indices)
+    where_zero_indices = mask.indices()[(slice(None),) + w]
+
+    # construct result
+    n = where_zero_indices.size(1)
+    if n == 0:
+        # the input is coalesced, hence input_flat_indices are ordered
+        # and the result is guaranteed to be coalesced:
+        result = torch.sparse_coo_tensor(
+            where_input_indices, where_input_values, input.shape
+        )
+        return result._coalesced_(True)
+
+    where_indices = torch.cat([where_input_indices, where_zero_indices], dim=1)
+    where_values = torch.cat(
+        [
+            where_input_values,
+            where_input_values.new_zeros((n,) + where_input_values.shape[1:]),
+        ]
+    )
+    result = torch.sparse_coo_tensor(where_indices, where_values, input.shape)
+
+    # appending zero elements leads to uncoalesced sparse tensor
+    return result.coalesce()
+
+
+def _sparse_coo_scatter_reduction_helper(
+    op,
+    mask_input: Tensor,
+    dims: Tuple[int, ...],
+    keepdim: bool,
+    dtype: Optional[DType] = None,
+) -> Tensor:
+    reduce = op.__name__
+    valid_reductions = ["sum", "prod", "amax", "amin"]
+    if reduce not in valid_reductions:
+        raise ValueError(
+            f"op must be one of {' '.join(valid_reductions)}, but got {reduce} instead"
+        )
+
+    output_dtype = dtype
+    values, indices = mask_input._values(), mask_input._indices()
+    input_dims = mask_input.dim()
+    num_sparse_dims = mask_input.sparse_dim()
+    reduced_sparse_dims = []
+    retained_sparse_dims = []
+    reduced_dense_dims = []
+
+    # promote dtype if specified
+    if values.dtype != output_dtype:
+        values = values.to(output_dtype)
+
+    if keepdim:
+        output_shape = tuple(
+            1 if i in dims else si for (i, si) in enumerate(mask_input.shape)
+        )
+    else:
+        output_shape = tuple(
+            si for (i, si) in enumerate(mask_input.shape) if i not in dims
+        )
+
+    for d in dims:
+        if d >= input_dims:
+            continue
+
+        if d < num_sparse_dims:
+            reduced_sparse_dims.append(d)
+        else:
+            reduced_dense_dims.append(d + 1 - num_sparse_dims)
+
+    # Reduce dense dimensions
+    if len(reduced_dense_dims) > 0:
+        if reduce == "sum":
+            new_values = values
+            new_values = op(new_values, dim=reduced_dense_dims, keepdim=bool(keepdim))
+        else:
+            # FIXME: Implement reductions for dense dimensions for ops with non-zero reduction identities
+            return NotImplemented
+    else:
+        new_values = values.clone()
+
+    # Reduce sparse dimensions
+    if len(reduced_sparse_dims) == num_sparse_dims:
+        if reduce in {"amax", "amin"} and new_values.size(0) == 0:
+            # IndexError: amax(): Expected reduction dim 0 to have non-zero size.
+            # sum()/prod() return the reduction identity when dim has size 0 but amax()/amin() do not
+            # See https://github.com/pytorch/pytorch/issues/61901
+            new_values = _reduction_identity(reduce, new_values)
+        else:
+            new_values = op(new_values, dim=0)
+        if keepdim:
+            for _ in range(num_sparse_dims):
+                new_values = new_values.unsqueeze(0)
+        return new_values.to(dtype=output_dtype).to_sparse()
+    else:
+        new_indices = indices.clone()
+        if keepdim:
+            # zero out reduced sparse dimensions if keepdim = True
+            # ensures that the call to torch.unique folds duplicated indices together while preserving the dimension
+            new_indices[reduced_sparse_dims, :] = 0
+        else:
+            # remove reduced sparse dimensions if keepdim = False
+            if len(reduced_sparse_dims) > 0:
+                retained_sparse_dims = [
+                    i
+                    for i in range(num_sparse_dims)
+                    if i not in set(reduced_sparse_dims)
+                ]
+                new_indices = new_indices.index_select(
+                    0, torch.tensor(retained_sparse_dims).to(mask_input.device)
+                )
+
+    # Use scatter_reduce to reduce items in the new_values tensor that correspond to the same indices in new_indices
+    if new_indices.numel() > 0:
+        # lexsort indices and get index tensor for scatter reduction
+        new_indices, inverse_indices = torch.unique(
+            new_indices, return_inverse=True, dim=1
+        )
+        out_shape = list(new_values.shape)
+        out_shape[0] = new_indices.shape[1]
+        for _ in range(new_values.ndim - 1):
+            inverse_indices = inverse_indices.unsqueeze(-1)
+        scatter_indices = inverse_indices.expand(new_values.shape)
+        # FIXME: temporary workaround for issue with bfloat16/float16 remove when acctype is implemented for scatter_reduce
+        if output_dtype in {torch.bfloat16, torch.float16}:
+            new_values = new_values.to(torch.float)
+            out = new_values.new_empty(out_shape)
+            new_values = out.scatter_reduce_(
+                0, scatter_indices, new_values, reduce=reduce, include_self=False
+            )
+            new_values = new_values.to(dtype=output_dtype)
+        else:
+            out = new_values.new_empty(out_shape)
+            new_values = out.scatter_reduce_(
+                0, scatter_indices, new_values, reduce=reduce, include_self=False
+            )
+
+    return torch.sparse_coo_tensor(
+        new_indices,
+        new_values,
+        output_shape,
+        dtype=output_dtype,
+        device=mask_input.device,
+    )
+
+
+def _sparse_csr_segment_reduction_helper(
+    op,
+    mask_input: Tensor,
+    dims: Tuple[int, ...],
+    keepdim: bool,
+    dtype: Optional[DType] = None,
+) -> Tensor:
+    # Currently, while sparse CSR is always 2D with no dense dimensions keepdim must be True
+    # FIXME: when dense dimensions are implemented for CSR tensors
+    assert (
+        keepdim
+    ), "reduction operations on CSR tensors with keepdim=False is unsupported"
+    reduce = op.__name__
+    valid_reductions = ["sum", "prod", "mean", "amax", "amin"]
+    if reduce not in valid_reductions:
+        raise ValueError(
+            f"op must be one of {' '.join(valid_reductions)}, but got {reduce} instead"
+        )
+    device = mask_input.device
+    output_dtype = dtype
+    values, crow_indices, col_indices = (
+        mask_input.values(),
+        mask_input.crow_indices(),
+        mask_input.col_indices(),
+    )
+
+    # promote dtype if specified
+    if values.dtype != output_dtype:
+        values = values.to(output_dtype)
+
+    if len(dims) == 0:
+        return mask_input
+    if len(dims) == 1:
+        if dims[0] == 0:
+            new_col_indices, scatter_indices = torch.unique(
+                col_indices, return_inverse=True
+            )
+            new_nnz = new_col_indices.shape[0]
+            new_crow_indices = torch.tensor([0, new_nnz])
+            new_values = values.new_empty(new_col_indices.shape)
+            new_values.scatter_reduce_(
+                0, scatter_indices, values, reduce, include_self=False
+            )
+            new_shape = [1, mask_input.size(1)]
+        else:
+            assert (
+                dims[0] == 1
+            ), "Sparse CSR tensors are 2D and only support reduction along dim 0 or 1."
+            # all intervals new_crow_indices[i] - new_crow_indices[i-1] are 1
+            # except for where crow_indices[i] == crow_indices[i-1] where the interval remains as 0
+            new_crow_indices = torch.cat(
+                (
+                    crow_indices.new_zeros(1),
+                    torch.cumsum(torch.diff(crow_indices) != 0, 0),
+                ),
+                0,
+            )
+            new_nnz = new_crow_indices[-1]
+            new_col_indices = col_indices.new_zeros(new_nnz)
+            new_values = torch._segment_reduce(values, reduce, offsets=crow_indices)  # type: ignore[attr-defined]
+            new_shape = [mask_input.size(0), 1]
+    else:
+        assert len(dims) == 2
+        nnz = min(1, values.numel())
+        if nnz == 1:
+            op_kwargs = {"keepdim": True, "dtype": output_dtype}
+            # amax and amin do not support dtype kwarg
+            if reduce in ["amax", "amin"]:
+                del op_kwargs["dtype"]
+            new_values = op(values, 0, **op_kwargs)
+        else:
+            new_values = torch.empty(0, dtype=output_dtype)
+        new_col_indices = col_indices.new_zeros(nnz)
+        new_crow_indices = torch.tensor([0, nnz])
+        new_shape = [1, nnz]
+
+    return torch.sparse_csr_tensor(
+        new_crow_indices,
+        new_col_indices,
+        new_values,
+        new_shape,
+        dtype=output_dtype,
+        device=device,
+    )
+
+
+def _sparse_csr_where(mask: Tensor, input: Tensor, fill_value: Tensor) -> Tensor:
+    """Sparse variant of torch.where. Supports sparse CSR tensors."""
+    # TODO: implement sparse CSR specific where operator for efficiency
+    return _sparse_coo_where(
+        mask.to_sparse_coo(), input.to_sparse_coo(), fill_value
+    ).to_sparse_csr()
+
+
+def _where(mask: Tensor, input: Tensor, fill_value: Tensor) -> Tensor:
+    """torch.where with sparse inputs support.
+
+    _where implements the following invariant:
+
+      _where(mask, input, fill_value).to_dense(fill_value) ==
+        torch.where(mask.to_dense(), input.to_dense(), torch.full(input.shape, fill_value))
+
+    where `a == b` means `assertEqual(a, b)`, mask is boolean sparse
+    tensor, and `to_dense(fill_value)` is like `to_dense()` except
+    that the unspecified elements are mapped to `fill_value` rather
+    than to `0`.
+
+    Returns a sparse tensor with the following features:
+
+    - all specified elements correspond to masked-in elements that
+      have the values of the input tensor. If there exists a masked-in
+      element (as specified by mask) that is not specified in the
+      input, in the result tensor, the corresponding element has value
+      0. In the dense part of the sparse tensor, the masked-out
+      elements are replaced with fill_value.
+
+    - all unspecified elements correspond to masked-out elements.
+    """
+    if mask.layout == torch.strided:
+        return torch.where(mask, input, fill_value)
+    elif mask.layout == torch.sparse_coo:
+        return _sparse_coo_where(mask, input, fill_value)
+    elif mask.layout == torch.sparse_csr:
+        return _sparse_csr_where(mask, input, fill_value)
+    else:
+        raise ValueError(
+            f"_where expects strided or sparse COO or sparse CSR tensor but got {mask.layout}"
+        )
+
+
+def _input_mask(input: Union[Tensor, MaskedTensor], *args, **kwargs) -> Tensor:
+    """Return canonical input mask.
+
+    A canonical input mask is defined as a boolean mask tensor that
+    shape and layout matches with the shape and the layout of the
+    input.
+
+    The canonical input mask is computed from the :attr:`mask` tensor
+    content to meet the following criteria:
+
+    1. The shape of the canonical input mask is the same as the shape
+       of :attr:`input` tensor. If the mask tensor has a smaller shape
+       than the shape of the :attr:`input`, broadcasting rules will be
+       applied. Downcasting of mask is not supported.
+
+    2. The layout of the canonical input mask is the same as the
+       layout of the :attr:`input` tensor. If the mask has different
+       layout, it will be converted to the expected layout.  In the
+       case of sparse COO layout, the canonical input mask will be
+       coalesced.
+
+    3. The dtype of the canonical input mask is torch.bool. If the
+       mask dtype is not bool then it will be converted to bool dtype
+       using `.to(dtype=bool)` method call.
+
+    4. The elements of the canonical input mask have boolean values
+       copied from the content of the :attr:`mask` tensor (after
+       possible broadcasting and dtype conversion transforms).  In
+       general, the sparsity pattern of the sparse canonical input
+       mask need not to be the same as the sparsity pattern of the
+       sparse :attr:`input` tensor.
+
+    """
+    if input.layout not in {torch.strided, torch.sparse_coo, torch.sparse_csr}:
+        raise ValueError(
+            f"_input_mask expects strided or sparse COO or sparse CSR tensor but got {input.layout}"
+        )
+
+    mask = kwargs.get("mask")
+
+    # default mask
+    if mask is None:
+        raise ValueError("_input_mask requires explicit mask")
+
+    # mask shape must match with input shape
+    if mask.shape != input.shape:
+        if mask.ndim > input.ndim:
+            raise IndexError(
+                "_input_mask expected broadcastable mask (got mask dimensionality higher than of the input)"
+            )
+        if mask.layout == torch.strided:
+            mask = torch.broadcast_to(mask.clone(), input.shape).to(dtype=torch.bool)
+        elif mask.layout == torch.sparse_coo:
+            mask = torch._sparse_broadcast_to(mask, input.shape)
+        else:
+            assert mask.layout == torch.sparse_csr
+            # Broadcasting of CSR tensors is not implemented. Working
+            # around by using COO layout.
+            mask = torch._sparse_broadcast_to(
+                mask.to_sparse(), input.shape
+            ).to_sparse_csr()
+
+    # mask layout must match with input layout
+    if mask.layout != input.layout:
+        if input.layout == torch.strided:
+            mask = mask.to_dense()
+        elif input.layout == torch.sparse_coo:
+            if mask.layout == torch.strided:
+                mask = mask.to_sparse(input.sparse_dim())
+            else:
+                mask = mask.to_sparse()
+        else:
+            assert input.layout == torch.sparse_csr
+            mask = mask.to_sparse_csr()
+
+    # sparse mask must be coalesced
+    if mask.layout == torch.sparse_coo:
+        mask = mask.coalesce()
+
+    # mask is a boolean tensor
+    mask = mask.to(dtype=torch.bool)
+
+    return mask
+
+
+def _output_mask(op, input: Tensor, *args, **kwargs) -> Tensor:
+    """Return output mask of masked operation applied to given arguments."""
+    if callable(op):
+        is_reduction = op.__name__ in {
+            "sum",
+            "prod",
+            "amax",
+            "amin",
+            "argmax",
+            "argmin",
+            "mean",
+            "median",
+            "norm",
+            "var",
+            "std",
+            "logsumexp",
+        }
+        is_normalization = op.__name__ in {
+            "softmax",
+            "log_softmax",
+            "softmin",
+            "normalize",
+            "cumsum",
+            "cumprod",
+        }
+        if is_reduction:
+            if op.__name__ == "norm":
+                if args:
+                    args = args[1:]  # lstrip ord argument
+            dim = args[0] if args else kwargs.get("dim")
+            outmask = _input_mask(input, *args, **kwargs)
+            keepdim = kwargs.get("keepdim", False)
+            dim_ = _canonical_dim(dim, input.ndim)
+            return _any(outmask, dim_, bool(keepdim))
+        elif is_normalization:
+            return _input_mask(input, *args, **kwargs)
+        else:
+            raise ValueError(
+                f"_output_mask expected masked operation (got callable {op.__module__}.{op.__name__})"
+            )
+    else:
+        raise ValueError(
+            f"_output_mask expected masked operation (got {type(op).__name__} object)"
+        )
+
+
+def _combine_input_and_mask(
+    op, input: Union[MaskedTensor, Tensor], mask, *args
+) -> Tensor:
+    def helper(input, mask):
+        if mask is None:
+            return input
+        canonical_mask = _input_mask(input, mask=mask)
+        if callable(op):
+            fill_value = _reduction_identity(op.__name__, input, *args)
+            return _where(canonical_mask, input, fill_value)
+        else:
+            raise ValueError(
+                f"_combine_input_and_mask expected masked operation (got {type(op).__name__} object)"
+            )
+
+    class Combine(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, input, mask):
+            """Return input with masked-out elements eliminated for the given operations."""
+            ctx.save_for_backward(mask)
+
+            if mask is not None:
+                ctx.mark_non_differentiable(mask)
+
+            return helper(input, mask)
+
+        @staticmethod
+        def backward(ctx, grad_output):
+            (mask,) = ctx.saved_tensors
+            grad_data = (
+                grad_output.get_data() if is_masked_tensor(grad_output) else grad_output
+            )
+            result = as_masked_tensor(grad_data, mask)
+            return result, None
+
+    return (
+        Combine.apply(input.get_data(), input.get_mask())  # type: ignore[union-attr]
+        if is_masked_tensor(input)
+        else helper(input, mask)
+    )
+
+
+@_apply_docstring_templates
+def sum(
+    input: Union[Tensor, MaskedTensor],
+    dim: DimOrDims = None,
+    *,
+    keepdim: Optional[bool] = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    # __doc__ is generated by _apply_docstring_templates decorator
+    if dtype is None:
+        # promote integer types to int64 when output dtype is not specified
+        if input.layout == torch.sparse_csr:
+            if input.dtype in {
+                torch.uint8,
+                torch.bool,
+                torch.int8,
+                torch.int16,
+                torch.int32,
+            }:
+                # csr.to(dtype=torch.int64) is not implemented, so
+                # using coo.to on input to ensure the promoted dtype
+                input = input.to_sparse_coo().to(dtype=torch.int64).to_sparse_csr()
+            else:
+                dtype = input.dtype
+        else:
+            dtype = input.dtype
+            if input.dtype in {
+                torch.uint8,
+                torch.bool,
+                torch.int8,
+                torch.int16,
+                torch.int32,
+            }:
+                dtype = torch.int64
+    dim_ = _canonical_dim(dim, input.ndim)
+    mask_input = _combine_input_and_mask(sum, input, mask)
+    if mask_input.layout == torch.strided:
+        return torch.sum(mask_input, dim_, bool(keepdim), dtype=dtype)
+    elif mask_input.layout == torch.sparse_coo:
+        return _sparse_coo_scatter_reduction_helper(
+            torch.sum, mask_input, dim_, bool(keepdim), dtype
+        )
+    elif mask_input.layout == torch.sparse_csr:
+        return torch._sparse_csr_sum(
+            mask_input, dim=list(dim_), keepdim=bool(keepdim), dtype=dtype
+        )
+    else:
+        raise ValueError(
+            f"masked sum expects strided, sparse_coo or sparse_csr tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def prod(
+    input: Union[Tensor, MaskedTensor],
+    dim: DimOrDims = None,
+    *,
+    keepdim: Optional[bool] = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    # __doc__ is generated by _apply_docstring_templates decorator
+    if dtype is None:
+        # promote integer types to int64 when output dtype is not specified
+        if input.layout == torch.sparse_csr:
+            if input.dtype in {
+                torch.uint8,
+                torch.bool,
+                torch.int8,
+                torch.int16,
+                torch.int32,
+            }:
+                # csr.to(dtype=torch.int64) is not implemented, so
+                # using coo.to on input to ensure the promoted dtype
+                input = input.to_sparse_coo().to(dtype=torch.int64).to_sparse_csr()
+            else:
+                dtype = input.dtype
+        else:
+            dtype = input.dtype
+            if input.dtype in {
+                torch.uint8,
+                torch.bool,
+                torch.int8,
+                torch.int16,
+                torch.int32,
+            }:
+                dtype = torch.int64
+    dim_ = _canonical_dim(dim, input.ndim)
+    mask_input = _combine_input_and_mask(prod, input, mask)
+    if mask_input.layout == torch.strided:
+        # Workaround https://github.com/pytorch/pytorch/issues/56586
+        result = mask_input
+        result = result.to(dtype=dtype)
+        for d in reversed(dim_):
+            result = result.prod(dim=d, keepdim=bool(keepdim))
+        return result
+    elif mask_input.layout == torch.sparse_coo:
+        if mask is None:
+            # See comment in the sparse_csr branch, the same issue arises for sparse_coo tensors
+            raise ValueError(
+                "masked prod expects explicit mask for sparse_coo tensor input"
+            )
+        return _sparse_coo_scatter_reduction_helper(
+            torch.prod, mask_input, dim_, bool(keepdim), dtype
+        )
+    elif mask_input.layout == torch.sparse_csr:
+        if mask is None:
+            # mask is None corresponds to all-True mask. The
+            # unspecified elements in the CSR tensor correspond to
+            # zero values. Hence, the prod reduction result is
+            # automatically zero unless all elements are specified.
+            # A semi-optimal way to take this into account is to use:
+            #
+            #   masked_prod(csr, ..., mask=None) == torch._sparse_csr_prod(csr, ...) * all(csr.nonzero(), ...)
+            #
+            # but that requires implementing `all` and `nonzero`
+            # support for sparse csr tensors.
+            raise ValueError(
+                "masked prod expects explicit mask for sparse_csr tensor input"
+            )
+        return torch._sparse_csr_prod(
+            mask_input, dim=list(dim_), keepdim=bool(keepdim), dtype=dtype
+        )
+    else:
+        raise ValueError(
+            f"masked prod expects strided, sparse_coo or sparse_csr tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def cumsum(
+    input: Tensor,
+    dim: int,
+    *,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    if dtype is None:
+        dtype = input.dtype
+    dim_ = _canonical_dim(dim, input.ndim)[0]
+    mask_input = _combine_input_and_mask(sum, input, mask)
+    if mask_input.layout == torch.strided:
+        return torch.cumsum(mask_input, dim_, dtype=dtype).to(dtype=dtype)
+    else:
+        raise ValueError(
+            f"masked cumsum expects strided tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def cumprod(
+    input: Tensor,
+    dim: int,
+    *,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    if dtype is None:
+        dtype = input.dtype
+    dim_ = _canonical_dim(dim, input.ndim)[0]
+    mask_input = _combine_input_and_mask(prod, input, mask)
+    if mask_input.layout == torch.strided:
+        return torch.cumprod(mask_input, dim_, dtype=dtype).to(dtype=dtype)
+    else:
+        raise ValueError(
+            f"masked cumprod expects strided tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def amax(
+    input: Union[Tensor, MaskedTensor],
+    dim: DimOrDims = None,
+    *,
+    keepdim: Optional[bool] = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    """\
+{reduction_signature}
+
+{reduction_descr}
+
+{reduction_identity_dtype}
+
+{reduction_args}
+
+{reduction_example}"""
+    if dtype is None:
+        dtype = input.dtype
+
+    mask_input = _combine_input_and_mask(amax, input, mask)
+    dim_ = _canonical_dim(dim, mask_input.ndim)
+    if mask_input.layout == torch.strided:
+        return torch.amax(mask_input, dim_, bool(keepdim)).to(dtype=dtype)
+    elif mask_input.layout == torch.sparse_coo:
+        if mask is None:
+            # See comment in the sparse_csr branch of prod, a similar issue arises here
+            # where unspecified elements along a dimension may need to be reduced with the result
+            raise ValueError(
+                "masked amax expects explicit mask for sparse_coo tensor input"
+            )
+        return _sparse_coo_scatter_reduction_helper(
+            torch.amax, mask_input, dim_, bool(keepdim), dtype
+        )
+    elif mask_input.layout == torch.sparse_csr:
+        if mask is None:
+            raise ValueError(
+                "masked amax expects explicit mask for sparse_csr tensor input"
+            )
+        return _sparse_csr_segment_reduction_helper(
+            torch.amax, mask_input, dim_, bool(keepdim), dtype
+        )
+    else:
+        raise ValueError(
+            f"masked amax expects strided, sparse_coo or sparse_csr tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def amin(
+    input: Union[Tensor, MaskedTensor],
+    dim: DimOrDims = None,
+    *,
+    keepdim: Optional[bool] = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    """\
+{reduction_signature}
+
+{reduction_descr}
+
+{reduction_identity_dtype}
+
+{reduction_args}
+
+{reduction_example}"""
+    if dtype is None:
+        dtype = input.dtype
+
+    mask_input = _combine_input_and_mask(amin, input, mask)
+    dim_ = _canonical_dim(dim, mask_input.ndim)
+    if mask_input.layout == torch.strided:
+        return torch.amin(mask_input, dim_, bool(keepdim)).to(dtype=dtype)
+    elif mask_input.layout == torch.sparse_coo:
+        if mask is None:
+            # See comment in the sparse_csr branch of prod, a similar issue arises here
+            # where unspecified elements along a dimension may need to be reduced with the result
+            raise ValueError(
+                "masked amax expects explicit mask for sparse_coo tensor input"
+            )
+        return _sparse_coo_scatter_reduction_helper(
+            torch.amin, mask_input, dim_, bool(keepdim), dtype
+        )
+    elif mask_input.layout == torch.sparse_csr:
+        if mask is None:
+            raise ValueError(
+                "masked amin expects explicit mask for sparse_csr tensor input"
+            )
+        return _sparse_csr_segment_reduction_helper(
+            torch.amin, mask_input, dim_, bool(keepdim), dtype
+        )
+    else:
+        raise ValueError(
+            f"masked amin expects strided, sparse_coo or sparse_csr tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def argmax(
+    input: Union[Tensor, MaskedTensor],
+    dim: Optional[int] = None,
+    *,
+    keepdim: Optional[bool] = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    """\
+{reduction_signature}
+{reduction_descr}
+{reduction_identity_dtype}
+{reduction_args}
+{reduction_example}"""
+    if dtype is None:
+        dtype = input.dtype
+    mask_input = _combine_input_and_mask(argmax, input, mask)
+    if mask_input.layout == torch.strided:
+        return torch.argmax(mask_input, dim, bool(keepdim)).to(dtype=dtype)
+    else:
+        raise ValueError(
+            f"masked argmax expects strided tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def argmin(
+    input: Union[Tensor, MaskedTensor],
+    dim: Optional[int] = None,
+    *,
+    keepdim: Optional[bool] = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    """\
+{reduction_signature}
+{reduction_descr}
+{reduction_identity_dtype}
+{reduction_args}
+{reduction_example}"""
+    if dtype is None:
+        dtype = input.dtype
+    mask_input = _combine_input_and_mask(argmin, input, mask)
+    if mask_input.layout == torch.strided:
+        return torch.argmin(mask_input, dim, bool(keepdim)).to(dtype=dtype)
+    else:
+        raise ValueError(
+            f"masked argmin expects strided tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def mean(
+    input: Union[Tensor, MaskedTensor],
+    dim: DimOrDims = None,
+    *,
+    keepdim: Optional[bool] = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    """\
+{reduction_signature}
+
+{reduction_descr}
+
+By definition, the identity value of a mean operation is the mean
+value of the tensor. If all elements of the input tensor along given
+dimension(s) :attr:`dim` are masked-out, the identity value of the
+mean is undefined.  Due to this ambiguity, the elements of output
+tensor with strided layout, that correspond to fully masked-out
+elements, have ``nan`` values.
+
+{reduction_args}
+
+{reduction_example}"""
+    if dtype is None:
+        dtype = input.dtype
+    if input.layout == torch.strided:
+        if mask is None:
+            # TODO: compute count analytically
+            count = sum(
+                torch.ones(input.shape, dtype=torch.int64, device=input.device),
+                dim,
+                keepdim=keepdim,
+            )
+            total = sum(input, dim, keepdim=keepdim, dtype=dtype)
+        else:
+            inmask = _input_mask(input, mask=mask)
+            count = inmask.sum(dim=dim, keepdim=bool(keepdim))
+            total = sum(input, dim, keepdim=keepdim, dtype=dtype, mask=inmask)
+        return total / count
+    elif input.layout == torch.sparse_csr:
+        mask_input = _combine_input_and_mask(mean, input, mask)
+        dim_ = _canonical_dim(dim, mask_input.ndim)
+        if mask is None:
+            raise ValueError(
+                "masked mean expects explicit mask for sparse_csr tensor input"
+            )
+        return _sparse_csr_segment_reduction_helper(
+            torch.mean, mask_input, dim_, bool(keepdim), dtype
+        )
+    else:
+        raise ValueError(
+            f"masked mean expects strided or sparse_csr tensor (got {input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def median(
+    input: Union[Tensor, MaskedTensor],
+    dim: int = -1,
+    *,
+    keepdim: bool = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    """\
+{reduction_signature}
+{reduction_descr}
+By definition, the identity value of a median operation is the median
+value of the tensor. If all elements of the input tensor along given
+dimension(s) :attr:`dim` are masked-out, the identity value of the
+median is undefined.  Due to this ambiguity, the elements of output
+tensor with strided layout, that correspond to fully masked-out
+elements, have ``nan`` values.
+{reduction_args}
+{reduction_example}"""
+    if dtype is None:
+        dtype = input.dtype
+    dim_ = _canonical_dim(dim, input.ndim)[0]
+    is_float = torch.is_floating_point(input)
+    if not is_float:
+        input = input.to(dtype=torch.float)
+    mask_input = _combine_input_and_mask(median, input, mask)
+    if mask_input.layout == torch.strided:
+        output = torch.nanmedian(mask_input, dim_, keepdim).values
+        if is_float:
+            return output
+        elif not is_float and not torch.isnan(output).any():
+            return output.to(dtype=dtype)
+        else:
+            raise ValueError(
+                "masked median expects no fully masked out rows if dtype is not floating point"
+            )
+    else:
+        raise ValueError(
+            f"masked median expects strided tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def logsumexp(
+    input: Tensor,
+    dim: DimOrDims = None,
+    *,
+    keepdim: bool = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    if dtype is None:
+        dtype = input.dtype
+    dim_ = _canonical_dim(dim, input.ndim)
+    mask_input = _combine_input_and_mask(logsumexp, input, mask)
+    if mask_input.layout == torch.strided:
+        return torch.logsumexp(mask_input, dim_, keepdim=keepdim).to(dtype=dtype)
+    else:
+        raise ValueError(
+            f"masked logsumexp expects strided tensor (got {mask_input.layout} tensor)"
+        )
+
+
+# Cannot use _apply_docstring_templates as it is only set up for reductions and normalizations
+def logaddexp(
+    input: Union[Tensor, MaskedTensor],
+    other: Union[Tensor, MaskedTensor],
+    *,
+    dtype: Optional[DType] = None,
+    input_mask: Optional[Tensor] = None,
+    other_mask: Optional[Tensor] = None,
+) -> Tensor:
+    """logaddexp(input, other, *, dtype=None, input_mask=None, other_mask=None) -> Tensor
+
+    Returns logaddexp of all the elements in the :attr:`input` and the :attr:`other`
+    tensor. The :attr:`input` elements are masked out according to the boolean tensor
+    :attr:`input_mask` and the attr:`other` elements are masked out according to the boolean tensor
+    :attr:`other_mask`.
+
+    The shapes of a mask tensor and the tensor to be masked
+    don't need to match, but they must be :ref:`broadcastable
+    ` and the dimensionality of the mask
+    tensor must not be greater than of the tensor to be masked.
+
+    Args:
+        input (Tensor): the input tensor
+        other (Tensor): the second input tensor
+
+    Keyword args:
+        dtype (:class:`torch.dtype`, optional): the desired data type
+          of returned tensor.  If specified, the output tensor is
+          casted to :attr:`dtype` after the operation is
+          performed. Default: None.
+        input_mask (:class:`torch.Tensor`, optional): the boolean tensor
+          containing the binary mask of validity of :attr:`input` tensor elements.
+          Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+        other_mask (:class:`torch.Tensor`, optional): the boolean tensor
+          containing the binary mask of validity of :attr:`other` tensor elements.
+          Default: None that is equivalent to ``torch.ones(other.shape, dtype=torch.bool)``.
+
+    Example::
+
+        >>> input = torch.tensor([-100.0, -200, -300])
+        >>> input
+        tensor([-100., -200., -300.])
+        >>> other = torch.tensor([-1.0, -2, -3])
+        >>> other
+        tensor([-1., -2., -3.])
+        >>> mask = torch.tensor([True, False, True])
+        >>> mask
+        tensor([ True, False,  True])
+        >>> torch.masked._ops.logaddexp(input, other, input_mask=mask, other_mask=mask)
+        tensor([-1., -inf, -3.])"""
+    if dtype is None:
+        dtype = input.dtype
+    if input.layout == torch.strided and other.layout == torch.strided:
+        mask_input = _combine_input_and_mask(logaddexp, input, input_mask)
+        mask_other = _combine_input_and_mask(logaddexp, other, other_mask)
+        return torch.logaddexp(mask_input, mask_other).to(dtype=dtype)
+    else:
+        raise ValueError(
+            f"masked logaddexp expects strided tensors (got {input.layout} tensor for input, {other.layout} for other)"
+        )
+
+
+@_apply_docstring_templates
+def norm(
+    input: Union[Tensor, MaskedTensor],
+    ord: Optional[float] = 2.0,
+    dim: DimOrDims = None,
+    *,
+    keepdim: Optional[bool] = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    """\
+{reduction_signature}
+
+{reduction_descr}
+
+The identity value of norm operation, which is used to start the
+reduction, is ``{identity_float32}``, except for ``ord=-inf`` it is
+``{identity_ord_ninf}``.
+
+{reduction_args}
+
+{reduction_example}"""
+    if dtype is None:
+        dtype = input.dtype
+    mask_input = _combine_input_and_mask(norm, input, mask, ord)
+    if mask_input.layout == torch.strided:
+        dim_ = _canonical_dim(dim, input.ndim)
+        return torch.linalg.vector_norm(
+            mask_input, ord, dim_, bool(keepdim), dtype=dtype
+        )
+    else:
+        raise ValueError(
+            f"masked norm expects strided tensor (got {mask_input.layout} tensor)"
+        )
+
+
+def _std_var(
+    input: Union[Tensor, MaskedTensor],
+    dim: DimOrDims,
+    unbiased: Optional[bool],
+    *,
+    correction_opt: Optional[Union[int, float]],
+    keepdim: Optional[bool],
+    dtype: Optional[DType],
+    mask: Optional[Tensor],
+    take_sqrt: Optional[bool],
+) -> Tensor:
+    assert (
+        unbiased is None or correction_opt is None
+    ), "Only one of unbiased and correction may be given"
+    correction = 1.0
+    if unbiased is not None:
+        correction = 1.0 if unbiased else 0.0
+    if correction_opt is not None:
+        correction = sym_float(correction_opt)
+
+    if dtype is None:
+        dtype = input.dtype
+        if not (dtype.is_floating_point or dtype.is_complex):
+            dtype = torch.float32
+    compute_dtype = dtype
+    if not (compute_dtype.is_floating_point or compute_dtype.is_complex):
+        compute_dtype = torch.float32
+    if input.layout == torch.strided:
+        if mask is None:
+            # TODO: compute count analytically
+            count = sum(
+                torch.ones(input.shape, dtype=torch.int64, device=input.device),
+                dim,
+                keepdim=True,
+            )
+            sample_total = sum(input, dim, keepdim=True, dtype=dtype)
+        else:
+            inmask = _input_mask(input, mask=mask)
+            count = inmask.sum(dim=dim, keepdim=True)
+            sample_total = sum(input, dim, keepdim=True, dtype=dtype, mask=inmask)
+        # TODO: replace torch.subtract/divide/square/maximum with
+        # masked subtract/divide/square/maximum when these will be
+        # available.
+        sample_mean = torch.divide(sample_total, count)
+        x = torch.subtract(input, sample_mean)
+        if mask is None:
+            total = sum(x * x.conj(), dim, keepdim=keepdim, dtype=compute_dtype)
+        else:
+            total = sum(
+                x * x.conj(), dim, keepdim=keepdim, dtype=compute_dtype, mask=inmask  # type: ignore[possibly-undefined]
+            )
+        if not keepdim:
+            count = count.reshape(total.shape)
+        if correction != 0:
+            real_dtype = (
+                corresponding_real_dtype(compute_dtype)
+                if compute_dtype.is_complex
+                else compute_dtype
+            )
+            count = count.to(real_dtype)
+            count = torch.subtract(count, correction)
+            count = torch.maximum(count, count.new_zeros([]))
+        output = torch.divide(total, count).to(dtype=dtype)
+        if take_sqrt:
+            output = torch.sqrt(output)
+        return output
+    else:
+        raise ValueError(
+            f"masked std/var expects strided tensor (got {input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def var(
+    input: Union[Tensor, MaskedTensor],
+    dim: DimOrDims = None,
+    unbiased: Optional[bool] = None,
+    *,
+    correction: Optional[Union[int, float]] = None,
+    keepdim: Optional[bool] = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    """\
+{reduction_signature}
+{reduction_descr}
+The identity value of sample variance operation is undefined. The
+elements of output tensor with strided layout, that correspond to
+fully masked-out elements, have ``nan`` values.
+{reduction_args}
+{reduction_example}"""
+    return _std_var(
+        input=input,
+        dim=dim,
+        unbiased=unbiased,
+        correction_opt=correction,
+        keepdim=keepdim,
+        dtype=dtype,
+        mask=mask,
+        take_sqrt=False,
+    )
+
+
+@_apply_docstring_templates
+def std(
+    input: Union[Tensor, MaskedTensor],
+    dim: DimOrDims = None,
+    unbiased: Optional[bool] = None,
+    *,
+    correction: Optional[int] = None,
+    keepdim: Optional[bool] = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    """\
+{reduction_signature}
+{reduction_descr}
+The identity value of sample standard deviation operation is undefined. The
+elements of output tensor with strided layout, that correspond to
+fully masked-out elements, have ``nan`` values.
+{reduction_args}
+{reduction_example}"""
+    return _std_var(
+        input=input,
+        dim=dim,
+        unbiased=unbiased,
+        correction_opt=correction,
+        keepdim=keepdim,
+        dtype=dtype,
+        mask=mask,
+        take_sqrt=True,
+    )
+
+
+@_apply_docstring_templates
+def softmax(
+    input: Union[Tensor, MaskedTensor],
+    dim: int,
+    *,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    if dtype is None:
+        dtype = input.dtype
+    dim_ = _canonical_dim(dim, input.ndim)[0]
+    mask_input = _combine_input_and_mask(amax, input, mask)
+    if mask_input.layout == torch.strided:
+        return torch.nn.functional.softmax(mask_input, dim_, dtype=dtype)
+    else:
+        raise ValueError(
+            f"masked softmax expects strided tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def log_softmax(
+    input: Union[Tensor, MaskedTensor],
+    dim: int,
+    *,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    if dtype is None:
+        dtype = input.dtype
+    dim_ = _canonical_dim(dim, input.ndim)[0]
+    mask_input = _combine_input_and_mask(amax, input, mask)
+    if mask_input.layout == torch.strided:
+        return torch.nn.functional.log_softmax(mask_input, dim_, dtype=dtype)
+    else:
+        raise ValueError(
+            f"masked log_softmax expects strided tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def softmin(
+    input: Union[Tensor, MaskedTensor],
+    dim: int,
+    *,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    if dtype is None:
+        dtype = input.dtype
+    dim_ = _canonical_dim(dim, input.ndim)[0]
+    mask_input = _combine_input_and_mask(amin, input, mask)
+    if mask_input.layout == torch.strided:
+        return torch.nn.functional.softmin(mask_input, dim_, dtype=dtype)
+    else:
+        raise ValueError(
+            f"masked softmin expects strided tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def normalize(
+    input: Union[Tensor, MaskedTensor],
+    ord: float,
+    dim: int,
+    *,
+    eps: float = 1e-12,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    if dtype is None:
+        dtype = input.dtype
+    dim_ = _canonical_dim(dim, input.ndim)[0]
+    # TODO: eliminate mask_input as unnecessary when using masked divide.
+    mask_input = _combine_input_and_mask(sum, input, mask)
+    if mask_input.layout == torch.strided:
+        nrm_ = norm(input, ord, dim, keepdim=True, dtype=dtype, mask=mask)
+        # TODO: replace torch.maximum with masked maximum when available.
+        denom = torch.maximum(nrm_, nrm_.new_full([], eps))
+        # TODO: replace torch.divide with masked divide when available.
+        return torch.divide(mask_input, denom)
+    else:
+        raise ValueError(
+            f"masked normalize expects strided tensor (got {mask_input.layout} tensor)"
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e38e03c87086cf50d031dd5591f64f65399d6ac1
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/__init__.py
@@ -0,0 +1,8 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+# flake8: noqa
+
+from .binary import _apply_native_binary, _is_native_binary
+from .core import is_masked_tensor, MaskedTensor
+from .passthrough import _apply_pass_through_fn, _is_pass_through_fn
+from .reductions import _apply_reduction, _is_reduction
+from .unary import _apply_native_unary, _is_native_unary
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/_ops_refs.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/_ops_refs.py
new file mode 100644
index 0000000000000000000000000000000000000000..7344a6e801aae261ba53c3a86b66b98b037ca3a0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/_ops_refs.py
@@ -0,0 +1,531 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+from functools import partial
+from typing import Any, Callable, Dict, TYPE_CHECKING
+
+import torch
+
+from .binary import _apply_native_binary, NATIVE_BINARY_FNS, NATIVE_INPLACE_BINARY_FNS
+from .core import (
+    _get_data,
+    _masks_match,
+    _maybe_get_mask,
+    is_masked_tensor,
+    MaskedTensor,
+)
+from .passthrough import _apply_pass_through_fn, PASSTHROUGH_FNS
+from .reductions import (
+    _apply_reduction,
+    NATIVE_REDUCE_FNS,
+    TENSOR_REDUCE_FNS,
+    TORCH_REDUCE_FNS,
+)
+from .unary import _apply_native_unary, NATIVE_INPLACE_UNARY_FNS, NATIVE_UNARY_FNS
+
+
+if TYPE_CHECKING:
+    from torch._ops import OpOverload
+
+
+__all__ = []  # type: ignore[var-annotated]
+
+
+def _check_args_kwargs_length(
+    args, kwargs, error_prefix, len_args=None, len_kwargs=None
+):
+    if len_args is not None and len_args != len(args):
+        raise ValueError(
+            f"{error_prefix}: len(args) must be {len_args} but got {len(args)}"
+        )
+    if len_kwargs is not None and len_kwargs != len(kwargs):
+        raise ValueError(
+            f"{error_prefix}: len(kwargs) must be {len_kwargs} but got {len(kwargs)}"
+        )
+
+
+class _MaskedContiguous(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input):
+        if not is_masked_tensor(input):
+            raise ValueError("MaskedContiguous forward: input must be a MaskedTensor.")
+
+        if input.is_contiguous():
+            return input
+
+        data = input.get_data()
+        mask = input.get_mask()
+
+        return MaskedTensor(data.contiguous(), mask.contiguous())
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return grad_output
+
+
+class _MaskedToDense(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input):
+        if not is_masked_tensor(input):
+            raise ValueError("MaskedToDense forward: input must be a MaskedTensor.")
+
+        if input.layout == torch.strided:
+            return input
+
+        ctx.layout = input.layout
+        data = input.get_data()
+        mask = input.get_mask()
+
+        return MaskedTensor(data.to_dense(), mask.to_dense())
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        layout = ctx.layout
+
+        if layout == torch.sparse_coo:
+            return grad_output.to_sparse_coo()
+        elif layout == torch.sparse_csr:
+            return grad_output.to_sparse_csr()
+        elif layout == torch.strided:
+            return grad_output.to_dense()
+        raise ValueError("to_dense: Unsupported input layout: ", layout)
+
+
+class _MaskedToSparse(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input):
+        if not is_masked_tensor(input):
+            raise ValueError("MaskedToSparse forward: input must be a MaskedTensor.")
+
+        # Following the convention from sparse tensors that to_sparse always means that we convert to sparse_coo
+        if input.layout == torch.sparse_coo:
+            return input
+
+        data = input.get_data()
+        mask = input.get_mask()
+        sparse_mask = mask.to_sparse_coo().coalesce()
+        sparse_data = data.sparse_mask(sparse_mask)
+
+        return MaskedTensor(sparse_data, sparse_mask)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return grad_output.to_dense()
+
+
+class _MaskedToSparseCsr(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input):
+        if not is_masked_tensor(input):
+            raise ValueError("MaskedToSparseCsr forward: input must be a MaskedTensor.")
+
+        if input._masked_data.ndim != 2:
+            raise ValueError(
+                f"Only 2D tensors can be converted to the SparseCsr layout but got shape: {input._masked_data.size()}"
+            )
+
+        if input.layout == torch.sparse_csr:
+            return input
+
+        data = input.get_data()
+        mask = input.get_mask()
+        sparse_mask = mask.to_sparse_csr()
+        sparse_data = data.sparse_mask(sparse_mask)
+
+        return MaskedTensor(sparse_data, sparse_mask)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return grad_output.to_dense()
+
+
+class _MaskedWhere(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, cond, self, other):
+        ctx.mark_non_differentiable(cond)
+        ctx.save_for_backward(cond)
+        return torch.ops.aten.where(cond, self, other)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        (cond,) = ctx.saved_tensors
+
+        def masked_out_like(mt):
+            return MaskedTensor(mt.get_data(), torch.zeros_like(mt.get_mask()).bool())
+
+        return (
+            None,
+            torch.ops.aten.where(cond, grad_output, masked_out_like(grad_output)),
+            torch.ops.aten.where(cond, masked_out_like(grad_output), grad_output),
+        )
+
+
+_MASKEDTENSOR_FUNCTION_TABLE = {}
+
+_function_fn_apply_map = {
+    (
+        tuple(NATIVE_REDUCE_FNS),
+        tuple(TORCH_REDUCE_FNS),
+        tuple(TENSOR_REDUCE_FNS),
+    ): _apply_reduction,
+}
+
+for fn_map_list, apply_fn in _function_fn_apply_map.items():
+    for fn_map in fn_map_list:
+        for fn in fn_map:
+            _MASKEDTENSOR_FUNCTION_TABLE[fn] = partial(apply_fn, fn)
+
+
+def register_function_func(ops):
+    """
+    Used for registering a new __torch_function__ function to MaskedTensor
+    Called via _MASKEDTENSOR_FUNCTION_TABLE[func](*args, **kwargs)
+
+    The code to register a new function looks like:
+
+    @register_function_func(list_of_ops)
+    def foo(func, *args, **kwargs):
+        
+    """
+
+    def wrapper(func):
+        for op in ops:
+            _MASKEDTENSOR_FUNCTION_TABLE[op] = partial(func, op)
+
+    return wrapper
+
+
+@register_function_func(NATIVE_REDUCE_FNS + TORCH_REDUCE_FNS + TENSOR_REDUCE_FNS)
+def _general_function_reductions(func, *args, **kwargs):
+    return _apply_reduction(func, *args, **kwargs)
+
+
+@register_function_func([torch.Tensor.where, torch.where])
+def _function_where(func, *args, **kwargs):
+    _check_args_kwargs_length(
+        args, kwargs, "__torch_function__, torch.where", len_args=3, len_kwargs=0
+    )
+    return _MaskedWhere.apply(*args)
+
+
+@register_function_func([torch.Tensor.contiguous])
+def _function_contiguous(func, *args, **kwargs):
+    return _MaskedContiguous.apply(args[0])
+
+
+@register_function_func([torch.Tensor.to_dense])
+def _function_to_dense(func, *args, **kwargs):
+    return _MaskedToDense.apply(args[0])
+
+
+@register_function_func([torch.Tensor.to_sparse])
+def _function_to_sparse(func, *args, **kwargs):
+    return _MaskedToSparse.apply(args[0])
+
+
+@register_function_func([torch.Tensor.to_sparse_csr])
+def _function_to_sparse_csr(func, *args, **kwargs):
+    return _MaskedToSparseCsr.apply(args[0])
+
+
+_MASKEDTENSOR_DISPATCH_TABLE: Dict["OpOverload", Callable[..., Any]] = {}
+
+
+def register_dispatch_func(aten_ops):
+    """
+    Used for registering a new __torch_dispatch__ function to MaskedTensor
+    Called via _MASKEDTENSOR_DISPATCH_TABLE[func](*args, **kwargs)
+
+    The code to register a new function looks like:
+
+    @register_dispatch_func(list_of_ops)
+    def foo(func, *args, **kwargs):
+        
+    """
+
+    def wrapper(func):
+        for aten_op in aten_ops:
+            _MASKEDTENSOR_DISPATCH_TABLE[aten_op] = partial(func, aten_op)
+
+    return wrapper
+
+
+@register_dispatch_func(NATIVE_REDUCE_FNS + TORCH_REDUCE_FNS + TENSOR_REDUCE_FNS)
+def _general_reduction(func, *args, **kwargs):
+    return _apply_reduction(func, *args, **kwargs)
+
+
+@register_dispatch_func(PASSTHROUGH_FNS)
+def _general_passthrough(func, *args, **kwargs):
+    return _apply_pass_through_fn(func, *args, **kwargs)
+
+
+@register_dispatch_func(NATIVE_UNARY_FNS + NATIVE_INPLACE_UNARY_FNS)
+def _general_unary(func, *args, **kwargs):
+    return _apply_native_unary(func, *args, **kwargs)
+
+
+@register_dispatch_func(NATIVE_BINARY_FNS + NATIVE_INPLACE_BINARY_FNS)
+def _general_binary(func, *args, **kwargs):
+    return _apply_native_binary(func, *args, **kwargs)
+
+
+@register_dispatch_func([torch.ops.aten.stride])
+def stride(func, *args, **kwargs):
+    return None
+
+
+@register_dispatch_func([torch.ops.aten.sym_stride])
+def sym_stride(func, *args, **kwargs):
+    return None
+
+
+@register_dispatch_func([torch.ops.prim.layout])
+def layout(func, *args, **kwargs):
+    return _get_data(args[0]).layout
+
+
+@register_dispatch_func([torch.ops.aten.is_contiguous])
+def is_contiguous(func, *args, **kwargs):
+    data = _get_data(args[0])
+    if data.is_sparse:
+        raise ValueError("MaskedTensors with sparse data do not have is_contiguous")
+    return func(data, *args[1:], **kwargs)
+
+
+@register_dispatch_func([torch.ops.aten.is_strides_like_format])
+def is_strides_like_format(func, *args, **kwargs):
+    data = _get_data(args[0])
+    if data.is_sparse:
+        raise ValueError(
+            "MaskedTensors with sparse data do not have is_strides_like_format"
+        )
+    return func(data, *args[1:], **kwargs)
+
+
+@register_dispatch_func([torch.ops.aten.is_non_overlapping_and_dense])
+def is_non_overlapping_and_dense(func, *args, **kwargs):
+    data = _get_data(args[0])
+    if data.is_sparse:
+        raise ValueError(
+            "MaskedTensors with sparse data do not have is_non_overlapping_and_dense"
+        )
+    return func(data, *args[1:], **kwargs)
+
+
+@register_dispatch_func([torch.ops.aten.contiguous])
+def contiguous(func, *args, **kwargs):
+    if _get_data(args[0]).is_sparse:
+        raise ValueError("MaskedTensors with sparse data do not have contiguous")
+    return _MaskedContiguous.apply(args[0])
+
+
+@register_dispatch_func([torch.ops.aten.new_empty_strided])
+def new_empty_strided(func, *args, **kwargs):
+    _check_args_kwargs_length(args, kwargs, f"__torch_dispatch__, {func}", len_args=3)
+    data = _get_data(args[0])
+    mask = _maybe_get_mask(args[0])
+    if tuple(args[1]) != tuple(data.size()):
+        raise ValueError(
+            f"__torch_dispatch__, {func}: args[1] expected to be the same as data.size()"
+        )
+    if tuple(args[2]) != tuple(data.stride()):
+        raise ValueError(
+            f"__torch_dispatch__, {func}: args[2] expected to be the same as data.stride()"
+        )
+    return MaskedTensor(func(data, args[1], args[2], **kwargs), mask)
+
+
+@register_dispatch_func([torch.ops.aten._local_scalar_dense])
+def _local_scalar_dense(func, *args, **kwargs):
+    if not _maybe_get_mask(args[0]):
+        raise ValueError(f"__torch_dispatch__, {func}: expected a mask tensor")
+    return torch.ops.aten._local_scalar_dense(_get_data(args[0]))
+
+
+@register_dispatch_func([torch.ops.aten.detach, torch.ops.aten.clone])
+def _apply_fn_on_data(func, *args, **kwargs):
+    return MaskedTensor(func(_get_data(args[0])), _maybe_get_mask(args[0]))
+
+
+@register_dispatch_func([torch.ops.aten._to_copy])
+def _to_copy(func, *args, **kwargs):
+    new_data = func(_get_data(args[0]), *args[1:], **kwargs)
+    return MaskedTensor(new_data, _maybe_get_mask(args[0]))
+
+
+@register_dispatch_func([torch.ops.aten._softmax])
+def _softmax(func, *args, **kwargs):
+    _check_args_kwargs_length(
+        args, kwargs, f"__torch_dispatch__, {func}", len_args=3, len_kwargs=0
+    )
+    data = _get_data(args[0])
+    mask = _maybe_get_mask(args[0])
+    result_data = torch.ops.aten._masked_softmax(data, ~mask, args[1], 2)
+    return MaskedTensor(result_data, mask)
+
+
+@register_dispatch_func([torch.ops.aten.ones_like])
+def ones_like(func, *args, **kwargs):
+    _check_args_kwargs_length(args, kwargs, f"__torch_dispatch__, {func}", len_args=1)
+    result_data = func(_get_data(args[0]), **kwargs)
+    return MaskedTensor(result_data, _maybe_get_mask(args[0]))
+
+
+@register_dispatch_func([torch.ops.aten._softmax_backward_data])
+def _softmax_backward_data(func, *args, **kwargs):
+    _check_args_kwargs_length(args, kwargs, f"__torch_dispatch__, {func}", len_args=4)
+    grad, output, dim, input_dtype = args
+    if is_masked_tensor(grad) and is_masked_tensor(output):
+        if not _masks_match(grad, output):
+            raise ValueError(
+                "__torch_dispatch__, {func}: expected the masks of grad and output to match"
+            )
+        grad_data = _get_data(grad)
+        new_grad_data = torch.ops.aten._masked_softmax_backward(
+            grad_data,
+            _get_data(output),
+            ~_maybe_get_mask(grad),
+            dim % grad_data.ndim,
+        )
+        res = MaskedTensor(new_grad_data, _maybe_get_mask(grad))
+        return res
+    else:
+        raise ValueError(
+            f"__torch_dispatch__, {func}: grad and output must both be MaskedTensors"
+        )
+
+
+@register_dispatch_func([torch.ops.aten.copy_])
+def copy_(func, *args, **kwargs):
+    _check_args_kwargs_length(args, kwargs, f"__torch_dispatch__, {func}", len_args=2)
+    if not _masks_match(_maybe_get_mask(args[0]), _maybe_get_mask(args[1])):
+        raise ValueError("args[0] mask and args[1] mask must match but do not")
+    func(_get_data(args[0]), _get_data(args[1]))
+    return args[0]
+
+
+@register_dispatch_func([torch.ops.aten.where])
+def where(func, *args, **kwargs):
+    _check_args_kwargs_length(
+        args, kwargs, f"__torch_dispatch__, {func}", len_args=3, len_kwargs=0
+    )
+    if not torch.is_tensor(args[0]):
+        raise ValueError("__torch_dispatch__, {func}: expected args[0] to be a tensor")
+    mx = args[1]
+    my = args[2]
+    if not is_masked_tensor(mx):
+        mx = MaskedTensor(mx, torch.ones_like(mx, dtype=torch.bool))
+    if not is_masked_tensor(my):
+        my = MaskedTensor(my, torch.ones_like(my, dtype=torch.bool))
+    new_data = func(args[0], mx.get_data(), my.get_data())
+    new_mask = func(args[0], mx.get_mask(), my.get_mask())
+    return MaskedTensor(new_data, new_mask)
+
+
+@register_dispatch_func([torch.ops.aten._to_sparse])
+def _to_sparse(func, *args, **kwargs):
+    _check_args_kwargs_length(
+        args, kwargs, f"__torch_dispatch__, {func}", len_args=1, len_kwargs=0
+    )
+    if not torch.is_tensor(args[0]):
+        raise TypeError("__torch_dispatch__, {func}: expected args[0] to be a tensor")
+    mt = args[0]
+    if not is_masked_tensor(mt):
+        mt = MaskedTensor(mt, torch.ones_like(mt, dtype=torch.bool))
+    if mt.is_sparse_coo():
+        return mt
+    new_mask = func(_maybe_get_mask(args[0])).coalesce()
+    new_data = _get_data(args[0]).sparse_mask(new_mask)
+    return MaskedTensor(new_data, new_mask)
+
+
+@register_dispatch_func([torch.ops.aten._to_sparse_csr])
+def _to_sparse_csr(func, *args, **kwargs):
+    _check_args_kwargs_length(
+        args, kwargs, f"__torch_dispatch__, {func}", len_args=1, len_kwargs=0
+    )
+    if not torch.is_tensor(args[0]):
+        raise ValueError("__torch_dispatch__, {func}: expected args[0] to be a tensor")
+    mt = args[0]
+    if not is_masked_tensor(mt):
+        mt = MaskedTensor(mt, torch.ones_like(mt).bool())
+    if mt.is_sparse_csr():
+        return mt
+    new_mask = func(_maybe_get_mask(args[0]))
+    new_data = _get_data(args[0]).sparse_mask(new_mask)
+    return MaskedTensor(new_data, new_mask)
+
+
+@register_dispatch_func([torch.ops.aten._to_dense])
+def _to_dense(func, *args, **kwargs):
+    _check_args_kwargs_length(
+        args, kwargs, f"__torch_dispatch__, {func}", len_args=1, len_kwargs=0
+    )
+    if not torch.is_tensor(args[0]):
+        raise ValueError("__torch_dispatch__, {func}: expected args[0] to be a tensor")
+    mt = args[0]
+    if not is_masked_tensor(mt):
+        mt = MaskedTensor(mt, torch.ones_like(mt).bool())
+    new_data = func(_get_data(args[0]))
+    new_mask = func(_maybe_get_mask(args[0]))
+    return MaskedTensor(new_data, new_mask)
+
+
+@register_dispatch_func([torch.ops.aten._indices])
+def _indices(func, *args, **kwargs):
+    # Assumes data is sparse
+    _check_args_kwargs_length(
+        args, kwargs, f"__torch_dispatch__, {func}", len_args=1, len_kwargs=0
+    )
+    data = _get_data(args[0]).indices()
+    return MaskedTensor(data, torch.ones_like(data).bool())
+
+
+@register_dispatch_func([torch.ops.aten._values])
+def _values(func, *args, **kwargs):
+    _check_args_kwargs_length(
+        args, kwargs, f"__torch_dispatch__, {func}", len_args=1, len_kwargs=0
+    )
+    data = _get_data(args[0]).values()
+    return MaskedTensor(data, torch.ones_like(data).bool())
+
+
+@register_dispatch_func([torch.ops.aten._sparse_coo_tensor_with_dims_and_tensors])
+def _sparse_coo_tensor_with_dims_and_tensors(func, *args, **kwargs):
+    new_args = list(args)
+    if is_masked_tensor(args[-1]):
+        new_args[-1] = args[-1].get_data()
+    if is_masked_tensor(args[-2]):
+        new_args[-2] = args[-2].get_data()
+
+    new_data = func(*new_args, **kwargs)
+    new_args[-1] = torch.ones_like(new_args[-1])
+    new_mask = func(*new_args, **kwargs).bool()
+
+    return MaskedTensor(new_data, new_mask)
+
+
+@register_dispatch_func([torch.ops.aten.is_same_size])
+def is_same_size(func, *args, **kwargs):
+    _check_args_kwargs_length(args, kwargs, f"__torch_dispatch__, {func}", len_args=2)
+    return _get_data(args[0]).is_same_size(_get_data(args[1]))
+
+
+@register_dispatch_func([torch.ops.aten._is_any_true])
+def _is_any_true(func, *args, **kwargs):
+    _check_args_kwargs_length(
+        args, kwargs, f"__torch_dispatch__, {func}", len_args=1, len_kwargs=0
+    )
+    data = _get_data(args[0])
+    mask = _maybe_get_mask(args[0])
+    if mask is None:
+        raise ValueError(
+            f"__torch_dispatch__, {func}: expected args[0] to be a MaskedTensor"
+        )
+    if data.dtype != torch.bool:
+        raise ValueError(f"__torch_dispatch__, {func}: expected a boolean tensor")
+    if data.is_sparse:
+        raise ValueError(f"MaskedTensors with sparse data do not have {func}")
+
+    return MaskedTensor(func(data & mask), torch.tensor(True))
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/binary.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/binary.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b64cfa0fbd983a3d26f064f4c7f80ef904edada
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/binary.py
@@ -0,0 +1,199 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import torch
+
+from .core import (
+    _map_mt_args_kwargs,
+    _masks_match,
+    _tensors_match,
+    _wrap_result,
+    is_masked_tensor,
+)
+
+
+__all__ = []  # type: ignore[var-annotated]
+
+BINARY_NAMES = [
+    "add",
+    "atan2",
+    "arctan2",
+    "bitwise_and",
+    "bitwise_or",
+    "bitwise_xor",
+    "bitwise_left_shift",
+    "bitwise_right_shift",
+    "div",
+    "divide",
+    "floor_divide",
+    "fmod",
+    "logaddexp",
+    "logaddexp2",
+    "mul",
+    "multiply",
+    "nextafter",
+    "remainder",
+    "sub",
+    "subtract",
+    "true_divide",
+    "eq",
+    "ne",
+    "le",
+    "ge",
+    "greater",
+    "greater_equal",
+    "gt",
+    "less_equal",
+    "lt",
+    "less",
+    "maximum",
+    "minimum",
+    "fmax",
+    "fmin",
+    "not_equal",
+]
+
+INPLACE_BINARY_NAMES = [
+    n + "_"
+    for n in (
+        list(
+            set(BINARY_NAMES)
+            - {
+                "logaddexp",
+                "logaddexp2",
+                "equal",
+                "fmin",
+                "minimum",
+                "maximum",
+                "fmax",
+            }
+        )
+    )
+]
+
+
+def _get_at_least_one_mask(a, b):
+    if not is_masked_tensor(a) and not is_masked_tensor(b):
+        raise TypeError("At least one of `a` and `b` must be a MaskedTensor")
+    if not _masks_match(a, b):
+        raise ValueError("a and b must have matching masks")
+    if is_masked_tensor(a):
+        return a.get_mask()
+    return b.get_mask()
+
+
+def _binary_helper(fn, args, kwargs, inplace):
+    if len(kwargs) != 0:
+        raise ValueError("len(kwargs) must equal 0")
+    for a in args[2:]:
+        if torch.is_tensor(a):
+            raise TypeError(
+                "MaskedTensor binary ops do not support Tensor arguments aside from the lhs and rhs"
+            )
+
+    if not _masks_match(*args[:2]):
+        raise ValueError(
+            "Input masks must match. If you need support for this, please open an issue on Github."
+        )
+
+    data_args, data_kwargs = _map_mt_args_kwargs(args, kwargs, lambda x: x.get_data())
+    mask_args, mask_kwargs = _map_mt_args_kwargs(args, kwargs, lambda x: x.get_mask())
+
+    args0_layout = data_args[0].layout
+    same_layout = (
+        torch.is_tensor(data_args[1]) or is_masked_tensor(data_args[1])
+    ) and (args0_layout == data_args[1].layout)
+
+    if args0_layout == torch.sparse_coo:
+        if same_layout:
+            if not _tensors_match(data_args[0].indices(), data_args[1].indices()):
+                raise ValueError(
+                    "sparse_coo indices must match. If you need support for this, please open an issue on Github."
+                )
+            if data_args[0].size() != data_args[1].size():
+                raise ValueError(
+                    "input1 and input2 must have the same size for binary functions."
+                )
+
+            data_args[1] = data_args[1].values()
+
+        i = data_args[0].indices()
+        size = data_args[0].size()
+        data_args[0] = data_args[0].values()
+        v = fn(*data_args)
+        result_data = torch.sparse_coo_tensor(i, v, size)
+
+    elif args0_layout == torch.sparse_csr:
+        if same_layout:
+            if not (
+                _tensors_match(data_args[0].crow_indices(), data_args[1].crow_indices())
+                and _tensors_match(
+                    data_args[0].col_indices(), data_args[1].col_indices()
+                )
+            ):
+                raise ValueError(
+                    "sparse_csr indices must match. If you need support for this, please open an issue on Github."
+                )
+
+            data_args[1] = data_args[1].values()
+
+        crow = data_args[0].crow_indices()
+        col = data_args[0].col_indices()
+        data_args[0] = data_args[0].values()
+        v = fn(*data_args)
+        result_data = torch.sparse_csr_tensor(crow, col, v)
+
+    else:
+        result_data = fn(*data_args)
+
+    if inplace:
+        args[0]._set_data_mask(result_data, mask_args[0])
+        return args[0]
+    else:
+        result_mask = _get_at_least_one_mask(*args[:2])
+        # sparse tensors don't have strides so we can only expand if the layout is strided
+        if args0_layout == torch.strided:
+            result_mask = result_mask.expand_as(result_data)
+        return _wrap_result(result_data, result_mask)
+
+
+def _torch_binary(fn_name):
+    fn = getattr(torch.ops.aten, fn_name)
+
+    def binary_fn(*args, **kwargs):
+        return _binary_helper(fn, args, kwargs, inplace=False)
+
+    return binary_fn
+
+
+def _torch_inplace_binary(fn_name):
+    fn = getattr(torch.ops.aten, fn_name)
+
+    def binary_fn(*args, **kwargs):
+        return _binary_helper(fn, args, kwargs, inplace=True)
+
+    return binary_fn
+
+
+NATIVE_BINARY_MAP = {
+    getattr(torch.ops.aten, name): _torch_binary(name) for name in BINARY_NAMES
+}
+NATIVE_INPLACE_BINARY_MAP = {
+    getattr(torch.ops.aten, name): _torch_inplace_binary(name)
+    for name in INPLACE_BINARY_NAMES
+}
+
+NATIVE_BINARY_FNS = list(NATIVE_BINARY_MAP.keys())
+NATIVE_INPLACE_BINARY_FNS = list(NATIVE_INPLACE_BINARY_MAP.keys())
+
+
+def _is_native_binary(fn):
+    return fn in NATIVE_BINARY_FNS or fn in NATIVE_INPLACE_BINARY_FNS
+
+
+def _apply_native_binary(fn, *args, **kwargs):
+    if fn in NATIVE_BINARY_FNS:
+        return NATIVE_BINARY_MAP[fn](*args, **kwargs)
+    if fn in NATIVE_INPLACE_BINARY_FNS:
+        return NATIVE_INPLACE_BINARY_MAP[fn](*args, **kwargs)
+    return NotImplemented
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/core.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/core.py
new file mode 100644
index 0000000000000000000000000000000000000000..69850df81c65a1e5a14647bb89ac6bef09a84408
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/core.py
@@ -0,0 +1,359 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import warnings
+from typing import Any
+from typing_extensions import TypeGuard
+
+import torch
+from torch.overrides import get_default_nowrap_functions
+
+
+__all__ = [
+    "MaskedTensor",
+    "is_masked_tensor",
+]
+
+
+def is_masked_tensor(obj: Any, /) -> TypeGuard["MaskedTensor"]:
+    r"""Returns True if the input is a MaskedTensor, else False
+
+    Args:
+        a: any input
+
+    Examples:
+
+        >>> # xdoctest: +SKIP
+        >>> from torch.masked import MaskedTensor
+        >>> data = torch.arange(6).reshape(2,3)
+        >>> mask = torch.tensor([[True, False, False], [True, True, False]])
+        >>> mt = MaskedTensor(data, mask)
+        >>> is_masked_tensor(mt)
+        True
+    """
+    return isinstance(obj, MaskedTensor)
+
+
+def _tensors_match(a, b, exact=True, rtol=1e-05, atol=1e-08):
+    if is_masked_tensor(a) or is_masked_tensor(b):
+        raise ValueError("Neither `a` nor `b` can be a MaskedTensor.")
+    if a.layout != b.layout:
+        raise ValueError(
+            f"`a` and `b` must have the same layout. Got {a.layout} and {b.layout}"
+        )
+
+    if a.dtype != b.dtype:
+        b = b.type(a.dtype)
+    if a.layout == b.layout == torch.sparse_coo:
+        return _tensors_match(a.values(), b.values(), exact) and _tensors_match(
+            a.indices(), b.indices(), exact
+        )
+    elif a.layout == b.layout == torch.sparse_csr:
+        return (
+            _tensors_match(a.crow_indices(), b.crow_indices(), exact)
+            and _tensors_match(a.col_indices(), b.col_indices(), exact)
+            and _tensors_match(a.values(), b.values(), exact)
+        )
+    if exact:
+        return (a.dim() == b.dim()) and torch.eq(a, b).all().item()
+    return (a.dim() == b.dim()) and torch.allclose(a, b, rtol=rtol, atol=atol)
+
+
+def _masks_match(a, b):
+    if is_masked_tensor(a) and is_masked_tensor(b):
+        mask_a = a.get_mask()
+        mask_b = b.get_mask()
+        return _tensors_match(mask_a, mask_b, exact=True)
+    return True
+
+
+def _map_mt_args_kwargs(args, kwargs, map_fn):
+    def _helper(a, map_fn):
+        if is_masked_tensor(a):
+            return map_fn(a)
+        elif torch.is_tensor(a):
+            return a
+        elif isinstance(a, list):
+            a_impl, _ = _map_mt_args_kwargs(a, {}, map_fn)
+            return a_impl
+        elif isinstance(a, tuple):
+            a_impl, _ = _map_mt_args_kwargs(a, {}, map_fn)
+            return tuple(a_impl)
+        else:
+            return a
+
+    if kwargs is None:
+        kwargs = {}
+    impl_args = []
+    for a in args:
+        impl_args.append(_helper(a, map_fn))
+    impl_kwargs = {}
+    for k in kwargs.keys():
+        impl_kwargs[k] = _helper(a, map_fn)
+    return impl_args, impl_kwargs
+
+
+def _wrap_result(result_data, result_mask):
+    if isinstance(result_data, list):
+        return [_wrap_result(r, m) for (r, m) in zip(result_data, result_mask)]
+    if isinstance(result_data, tuple):
+        return tuple(_wrap_result(r, m) for (r, m) in zip(result_data, result_mask))
+    if torch.is_tensor(result_data):
+        return MaskedTensor(result_data, result_mask)
+    # Expect result_data and result_mask to be Tensors only
+    return NotImplemented
+
+
+def _masked_tensor_str(data, mask, formatter):
+    if data.layout in {torch.sparse_coo, torch.sparse_csr}:
+        data = data.to_dense()
+        mask = mask.to_dense()
+    if data.dim() == 1:
+        formatted_elements = [
+            formatter.format(d.item()) if isinstance(d.item(), float) else str(d.item())
+            for d in data
+        ]
+        max_len = max(8 if x[1] else len(x[0]) for x in zip(formatted_elements, ~mask))
+        return (
+            "["
+            + ", ".join(
+                [
+                    "--".rjust(max_len) if m else e
+                    for (e, m) in zip(formatted_elements, ~mask)
+                ]
+            )
+            + "]"
+        )
+    sub_strings = [_masked_tensor_str(d, m, formatter) for (d, m) in zip(data, mask)]
+    sub_strings = ["\n".join(["  " + si for si in s.split("\n")]) for s in sub_strings]
+    return "[\n" + ",\n".join(sub_strings) + "\n]"
+
+
+def _get_data(a):
+    if is_masked_tensor(a):
+        return a._masked_data
+    return a
+
+
+def _maybe_get_mask(a):
+    if is_masked_tensor(a):
+        return a.get_mask()
+    return None
+
+
+class MaskedTensor(torch.Tensor):
+    @staticmethod
+    def __new__(cls, data, mask, requires_grad=False):
+        if is_masked_tensor(data) or not torch.is_tensor(data):
+            raise TypeError("data must be a Tensor")
+        if is_masked_tensor(mask) or not torch.is_tensor(mask):
+            raise TypeError("mask must be a Tensor")
+        # Use a Tensor that of the give size for the wrapper.
+        kwargs = {
+            "device": data.device,
+            "dtype": data.dtype,
+            "layout": data.layout,
+            "requires_grad": requires_grad,
+            "dispatch_sizes_strides_policy": "strides",
+            "dispatch_layout": True,
+        }
+        warnings.warn(
+            (
+                "The PyTorch API of MaskedTensors is in prototype stage "
+                "and will change in the near future. Please open a Github issue "
+                "for features requests and see our documentation on the torch.masked "
+                "module for further information about the project."
+            ),
+            UserWarning,
+            stacklevel=2,
+        )
+        if data.requires_grad:
+            warnings.warn(
+                "It is not recommended to create a MaskedTensor with a tensor that requires_grad. "
+                "To avoid this, you can use data.clone().detach()",
+                UserWarning,
+                stacklevel=2,
+            )
+        return torch.Tensor._make_wrapper_subclass(cls, data.size(), **kwargs)  # type: ignore[attr-defined]
+
+    def _preprocess_data(self, data, mask):
+        from .._ops import _sparse_coo_where, _sparse_csr_where
+
+        if data.layout != mask.layout:
+            raise TypeError("data and mask must have the same layout.")
+        if data.layout == torch.sparse_coo:
+            data = data.coalesce()
+            mask = mask.coalesce()
+            if data._nnz() != mask._nnz():
+                data = _sparse_coo_where(mask, data, torch.tensor(0))
+        elif data.layout == torch.sparse_csr:
+            if data._nnz() != mask._nnz():
+                data = _sparse_csr_where(mask, data, torch.tensor(0))
+
+        # Have to pick awkward names to not conflict with existing fields such as data
+        self._masked_data = data.clone()
+        self._masked_mask = mask.clone()
+
+    def _validate_members(self):
+        data = self._masked_data
+        mask = self.get_mask()
+        if type(data) != type(mask):
+            raise TypeError(
+                f"data and mask must have the same type. Got {type(data)} and {type(mask)}"
+            )
+        if data.layout not in {torch.strided, torch.sparse_coo, torch.sparse_csr}:
+            raise TypeError(f"data layout of {data.layout} is not supported.")
+        if data.layout == torch.sparse_coo:
+            if not _tensors_match(data.indices(), mask.indices(), exact=True):
+                raise ValueError(
+                    "data and mask are both sparse COO tensors but do not have the same indices."
+                )
+        elif data.layout == torch.sparse_csr:
+            if not _tensors_match(
+                data.crow_indices(), mask.crow_indices(), exact=True
+            ) or not _tensors_match(data.col_indices(), mask.col_indices(), exact=True):
+                raise ValueError(
+                    "data and mask are both sparse CSR tensors but do not share either crow or col indices."
+                )
+        if mask.dtype != torch.bool:
+            raise TypeError("mask must have dtype bool.")
+        if not (
+            data.dtype == torch.float16
+            or data.dtype == torch.float32
+            or data.dtype == torch.float64
+            or data.dtype == torch.bool
+            or data.dtype == torch.int8
+            or data.dtype == torch.int16
+            or data.dtype == torch.int32
+            or data.dtype == torch.int64
+        ):
+            raise TypeError(f"{data.dtype} is not supported in MaskedTensor.")
+        if data.dim() != mask.dim():
+            raise ValueError("data.dim() must equal mask.dim()")
+        if data.size() != mask.size():
+            raise ValueError("data.size() must equal mask.size()")
+
+    def __init__(self, data, mask, requires_grad=False):
+        self._preprocess_data(data, mask)
+        self._validate_members()
+
+    @staticmethod
+    def _from_values(data, mask):
+        """Differentiable constructor for MaskedTensor"""
+
+        class Constructor(torch.autograd.Function):
+            @staticmethod
+            def forward(ctx, data, mask):
+                return MaskedTensor(data, mask)
+
+            @staticmethod
+            def backward(ctx, grad_output):
+                return grad_output, None
+
+        result = Constructor.apply(data, mask)
+        return result
+
+    def _set_data_mask(self, data, mask):
+        self._masked_data = data
+        self._masked_mask = mask
+        self._validate_members()
+
+    def __repr__(self):
+        formatter = "{0:8.4f}"
+        if self.dim() == 0:
+            scalar_data = self.get_data().item()
+            data_formatted = (
+                formatter.format(scalar_data)
+                if isinstance(scalar_data, float)
+                else str(scalar_data)
+            )
+            if not self.get_mask().item():
+                data_formatted = "--"
+            return (
+                "MaskedTensor("
+                + data_formatted
+                + ", "
+                + str(self.get_mask().item())
+                + ")"
+            )
+        s = _masked_tensor_str(self.get_data(), self.get_mask(), formatter)
+        s = "\n".join("  " + si for si in s.split("\n"))
+        return "MaskedTensor(\n" + s + "\n)"
+
+    # Seems like this needs to be defined before torch_dispatch to work
+    @classmethod
+    def __torch_function__(cls, func, types, args=(), kwargs=None):
+        kwargs = kwargs or {}
+
+        from ._ops_refs import _MASKEDTENSOR_FUNCTION_TABLE
+
+        if func in _MASKEDTENSOR_FUNCTION_TABLE:
+            return _MASKEDTENSOR_FUNCTION_TABLE[func](*args, **kwargs)
+
+        if not all(issubclass(cls, t) for t in types):
+            return NotImplemented
+        with torch._C.DisableTorchFunctionSubclass():
+            ret = func(*args, **kwargs)
+            if func in get_default_nowrap_functions():
+                return ret
+            else:
+                return torch._tensor._convert(ret, cls)
+
+    @classmethod
+    def unary(cls, fn, data, mask):
+        return MaskedTensor(fn(data), mask)
+
+    @classmethod
+    def __torch_dispatch__(cls, func, types, args, kwargs):
+        func = func.overloadpacket
+
+        from ._ops_refs import _MASKEDTENSOR_DISPATCH_TABLE
+
+        if func in _MASKEDTENSOR_DISPATCH_TABLE:
+            return _MASKEDTENSOR_DISPATCH_TABLE[func](*args, **kwargs)
+
+        msg = (
+            f"{func.__name__} is not implemented in __torch_dispatch__ for MaskedTensor.\n"
+            "If you would like this operator to be supported, please file an issue for a feature request at "
+            "https://github.com/pytorch/maskedtensor/issues with a minimal reproducible code snippet.\n"
+            "In the case that the semantics for the operator are not trivial, it would be appreciated "
+            "to also include a proposal for the semantics."
+        )
+        warnings.warn(msg)
+        return NotImplemented
+
+    def __lt__(self, other):
+        if is_masked_tensor(other):
+            return MaskedTensor(self.get_data() < _get_data(other), self.get_mask())
+        return MaskedTensor(self.get_data() < other, self.get_mask())
+
+    def to_tensor(self, value):
+        return self.get_data().masked_fill(~self.get_mask(), value)
+
+    def get_data(self):
+        class GetData(torch.autograd.Function):
+            @staticmethod
+            def forward(ctx, self):
+                return self._masked_data
+
+            @staticmethod
+            def backward(ctx, grad_output):
+                if is_masked_tensor(grad_output):
+                    return grad_output
+                return MaskedTensor(grad_output, self.get_mask())
+
+        return GetData.apply(self)
+
+    def get_mask(self):
+        return self._masked_mask
+
+    def is_sparse_coo(self):
+        return self.layout == torch.sparse_coo
+
+    def is_sparse_csr(self):
+        return self.layout == torch.sparse_csr
+
+    # Update later to support more sparse layouts
+    @property
+    def is_sparse(self):
+        return self.is_sparse_coo() or self.is_sparse_csr()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/creation.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/creation.py
new file mode 100644
index 0000000000000000000000000000000000000000..a013ef1beb6668c5fc2456c2c5a17f2399fc6be8
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/creation.py
@@ -0,0 +1,23 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+from .core import MaskedTensor
+
+
+__all__ = [
+    "as_masked_tensor",
+    "masked_tensor",
+]
+
+
+# These two factory functions are intended to mirror
+#     torch.tensor - guaranteed to be a leaf node
+#     torch.as_tensor - differentiable constructor that preserves the autograd history
+
+
+def masked_tensor(data, mask, requires_grad=False):
+    return MaskedTensor(data, mask, requires_grad)
+
+
+def as_masked_tensor(data, mask):
+    return MaskedTensor._from_values(data, mask)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/passthrough.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/passthrough.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba13f50c1fee9c9fc10563ffc9f4ff3211c0dca6
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/passthrough.py
@@ -0,0 +1,50 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+"""
+These are functions that should simply be applied to both mask and data.
+Take select or stack as an example. This operation can be applied to
+both the mask and data of a MaskedTensor and the result wrapped into
+a new MaskedTensor as a result.
+"""
+
+import torch
+
+from .core import _map_mt_args_kwargs, _wrap_result
+
+
+__all__ = []  # type: ignore[var-annotated]
+
+
+PASSTHROUGH_FNS = [
+    torch.ops.aten.select,
+    torch.ops.aten.transpose,
+    torch.ops.aten.split,
+    torch.ops.aten.t,
+    torch.ops.aten.slice,
+    torch.ops.aten.slice_backward,
+    torch.ops.aten.select_backward,
+    torch.ops.aten.index,
+    torch.ops.aten.expand,
+    torch.ops.aten.view,
+    torch.ops.aten._unsafe_view,
+    torch.ops.aten._reshape_alias,
+    torch.ops.aten.cat,
+    torch.ops.aten.unsqueeze,
+    torch.ops.aten.unfold,
+    torch.ops.aten.unfold_backward,
+    torch.ops.aten.im2col,
+    torch.ops.aten.col2im,
+    torch.ops.aten.stack,
+]
+
+
+def _is_pass_through_fn(fn):
+    return fn in PASSTHROUGH_FNS
+
+
+def _apply_pass_through_fn(fn, *args, **kwargs):
+    data_args, data_kwargs = _map_mt_args_kwargs(args, kwargs, lambda x: x.get_data())
+    result_data = fn(*data_args, **data_kwargs)
+    mask_args, mask_kwargs = _map_mt_args_kwargs(args, kwargs, lambda x: x.get_mask())
+    result_mask = fn(*mask_args, **mask_kwargs)
+    return _wrap_result(result_data, result_mask)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/reductions.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/reductions.py
new file mode 100644
index 0000000000000000000000000000000000000000..fedab1c12a63786fe950edda37ced9637beed83c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/reductions.py
@@ -0,0 +1,176 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import warnings
+
+import torch
+
+from .core import is_masked_tensor
+from .creation import as_masked_tensor, masked_tensor
+
+
+__all__ = []  # type: ignore[var-annotated]
+
+
+def _masked_all_all(data, mask=None):
+    if mask is None:
+        return data.all()
+    return data.masked_fill(~mask, True).all()
+
+
+def _masked_all_dim(data, dim, keepdim=False, mask=None):
+    if mask is None:
+        return torch.all(data, dim=dim, keepdim=keepdim)
+    return torch.all(data.masked_fill(~mask, True), dim=dim, keepdim=keepdim)
+
+
+def _masked_all(*args, **kwargs):
+    if len(args) == 1 and len(kwargs) == 1:
+        return _masked_all_all(args[0], mask=kwargs["mask"])
+    return _masked_all_dim(*args, **kwargs)
+
+
+def _multidim_any(mask, dim, keepdim):
+    if isinstance(dim, int):
+        return _multidim_any(mask, [dim], keepdim)
+    for d in sorted(dim, reverse=True):
+        mask = torch.any(mask, dim=d, keepdim=keepdim)
+    return mask
+
+
+def _get_masked_fn(fn):
+    if fn == "all":
+        return _masked_all
+    return getattr(torch.masked, fn)
+
+
+def _torch_reduce_all(fn):
+    def reduce_all(self):
+        masked_fn = _get_masked_fn(fn)
+        data = self.get_data()
+        mask = self.get_mask().values() if self.is_sparse else self.get_mask()
+        # When reduction is "all", then torch.argmin/torch.argmax needs to return the index of the
+        # element corresponding to the min/max, but this operation isn't supported correctly for sparse layouts.
+        # Therefore, this implementation calculates it using the strides.
+        if fn == "all":
+            result_data = masked_fn(data, mask=mask)
+
+        elif fn in {"argmin", "argmax"} and self.is_sparse_coo():
+            sparse_idx = masked_fn(data.values(), mask=mask).to(dtype=torch.int)
+            indices = (
+                data.to_sparse_coo().indices()
+                if not self.is_sparse_coo()
+                else data.indices()
+            )
+            idx = indices.unbind(1)[sparse_idx]
+            stride = data.size().numel() / torch.tensor(
+                data.size(), device=data.device
+            ).cumprod(0)
+            result_data = torch.sum(idx * stride)
+
+        # we simply pass in the values for sparse COO/CSR tensors
+        elif self.is_sparse:
+            result_data = masked_fn(masked_tensor(data.values(), mask))
+
+        else:
+            result_data = masked_fn(self, mask=mask)
+
+        return as_masked_tensor(result_data, torch.any(mask))
+
+    return reduce_all
+
+
+def _torch_reduce_dim(fn):
+    def reduce_dim(self, dim, keepdim=False, dtype=None):
+        if self.is_sparse:
+            msg = (
+                f"The sparse version of {fn} is not implemented in reductions.\n"
+                "If you would like this operator to be supported, please file an issue for a feature request at "
+                "https://github.com/pytorch/maskedtensor/issues with a minimal reproducible code snippet.\n"
+                "In the case that the semantics for the operator are not trivial, it would be appreciated "
+                "to also include a proposal for the semantics."
+            )
+            warnings.warn(msg)
+            return NotImplemented
+        if not is_masked_tensor(self):
+            raise TypeError("Input to reduce_dim must be a MaskedTensor")
+
+        masked_fn = _get_masked_fn(fn)
+        data = self.get_data()
+        mask = self.get_mask()
+        if fn == "all":
+            result_data = masked_fn(data, dim=dim, keepdim=keepdim, mask=mask)
+        else:
+            result_data = masked_fn(
+                self, dim=dim, keepdim=keepdim, dtype=dtype, mask=self.get_mask()
+            )
+        return as_masked_tensor(result_data, _multidim_any(mask, dim, keepdim))
+
+    return reduce_dim
+
+
+def _torch_reduce(fn):
+    def reduce_fn(*args, **kwargs):
+        if len(args) == 1 and len(kwargs) == 0:
+            return _torch_reduce_all(fn)(args[0])
+        return _torch_reduce_dim(fn)(*args, **kwargs)
+
+    return reduce_fn
+
+
+def _reduce_dim_args(input, dim, keepdim=False, dtype=None):
+    return input, dim, keepdim, dtype
+
+
+def _torch_grad_reduce(fn):
+    def grad_reduce(*args, **kwargs):
+        if len(args) == 1 and len(kwargs) == 0:
+            return _torch_reduce_all(fn)(args[0])
+        # TODO: autograd.Function doesn't support kwarg
+        input, dim, keepdim, dtype = _reduce_dim_args(*args, **kwargs)
+        return _torch_reduce_dim(fn)(input, dim, keepdim, dtype)
+
+    return grad_reduce
+
+
+REDUCE_NAMES = [
+    "sum",
+    "mean",
+    "amin",
+    "amax",
+    "argmin",
+    "argmax",
+    "prod",
+    "all",
+    "norm",
+    "var",
+    "std",
+]
+
+NATIVE_REDUCE_MAP = {
+    getattr(torch.ops.aten, name): _torch_reduce(name) for name in REDUCE_NAMES
+}
+TORCH_REDUCE_MAP = {
+    getattr(torch, name): _torch_grad_reduce(name) for name in REDUCE_NAMES
+}
+TENSOR_REDUCE_MAP = {
+    getattr(torch.Tensor, name): _torch_grad_reduce(name) for name in REDUCE_NAMES
+}
+
+NATIVE_REDUCE_FNS = list(NATIVE_REDUCE_MAP.keys())
+TORCH_REDUCE_FNS = list(TORCH_REDUCE_MAP.keys())
+TENSOR_REDUCE_FNS = list(TENSOR_REDUCE_MAP.keys())
+
+
+def _is_reduction(fn):
+    return fn in NATIVE_REDUCE_MAP or fn in TORCH_REDUCE_MAP or fn in TENSOR_REDUCE_MAP
+
+
+def _apply_reduction(fn, *args, **kwargs):
+    if fn in NATIVE_REDUCE_MAP:
+        return NATIVE_REDUCE_MAP[fn](*args, **kwargs)
+    if fn in TORCH_REDUCE_MAP:
+        return TORCH_REDUCE_MAP[fn](*args, **kwargs)
+    if fn in TENSOR_REDUCE_MAP:
+        return TENSOR_REDUCE_MAP[fn](*args, **kwargs)
+    return NotImplemented
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/unary.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/unary.py
new file mode 100644
index 0000000000000000000000000000000000000000..790d86ef92e4c8c379d01c20e82835c5d79a358c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/masked/maskedtensor/unary.py
@@ -0,0 +1,190 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import torch
+
+from .core import _map_mt_args_kwargs, _wrap_result
+
+
+__all__ = []  # type: ignore[var-annotated]
+
+
+UNARY_NAMES = [
+    "abs",
+    "absolute",
+    "acos",
+    "arccos",
+    "acosh",
+    "arccosh",
+    "angle",
+    "asin",
+    "arcsin",
+    "asinh",
+    "arcsinh",
+    "atan",
+    "arctan",
+    "atanh",
+    "arctanh",
+    "bitwise_not",
+    "ceil",
+    "clamp",
+    "clip",
+    "conj_physical",
+    "cos",
+    "cosh",
+    "deg2rad",
+    "digamma",
+    "erf",
+    "erfc",
+    "erfinv",
+    "exp",
+    "exp2",
+    "expm1",
+    "fix",
+    "floor",
+    "frac",
+    "lgamma",
+    "log",
+    "log10",
+    "log1p",
+    "log2",
+    "logit",
+    "i0",
+    "isnan",
+    "nan_to_num",
+    "neg",
+    "negative",
+    "positive",
+    "pow",
+    "rad2deg",
+    "reciprocal",
+    "round",
+    "rsqrt",
+    "sigmoid",
+    "sign",
+    "sgn",
+    "signbit",
+    "sin",
+    "sinc",
+    "sinh",
+    "sqrt",
+    "square",
+    "tan",
+    "tanh",
+    "trunc",
+]
+
+INPLACE_UNARY_NAMES = [
+    n + "_"
+    for n in (list(set(UNARY_NAMES) - {"angle", "positive", "signbit", "isnan"}))
+]
+
+# Explicitly tracking functions we know are currently not supported
+# This might be due to missing code gen or because of complex semantics
+UNARY_NAMES_UNSUPPORTED = [
+    "atan2",
+    "arctan2",
+    "bitwise_left_shift",
+    "bitwise_right_shift",
+    "copysign",
+    "float_power",
+    "fmod",
+    "frexp",
+    "gradient",
+    "imag",
+    "ldexp",
+    "lerp",
+    "logical_not",
+    "hypot",
+    "igamma",
+    "igammac",
+    "mvlgamma",
+    "nextafter",
+    "polygamma",
+    "real",
+    "remainder",
+    "true_divide",
+    "xlogy",
+]
+
+
+def _unary_helper(fn, args, kwargs, inplace):
+    if len(kwargs) != 0:
+        raise ValueError(
+            "MaskedTensor unary ops require that len(kwargs) == 0. "
+            "If you need support for this, please open an issue on Github."
+        )
+    for a in args[1:]:
+        if torch.is_tensor(a):
+            raise TypeError(
+                "MaskedTensor unary ops do not support additional Tensor arguments"
+            )
+
+    mask_args, mask_kwargs = _map_mt_args_kwargs(args, kwargs, lambda x: x._masked_mask)
+    data_args, data_kwargs = _map_mt_args_kwargs(args, kwargs, lambda x: x._masked_data)
+
+    if args[0].layout == torch.sparse_coo:
+        data_args[0] = data_args[0].coalesce()
+        s = data_args[0].size()
+        i = data_args[0].indices()
+        data_args[0] = data_args[0].coalesce().values()
+        v = fn(*data_args)
+        result_data = torch.sparse_coo_tensor(i, v, size=s)
+
+    elif args[0].layout == torch.sparse_csr:
+        crow = data_args[0].crow_indices()
+        col = data_args[0].col_indices()
+        data_args[0] = data_args[0].values()
+        v = fn(*data_args)
+        result_data = torch.sparse_csr_tensor(crow, col, v)
+
+    else:
+        result_data = fn(*data_args)
+
+    if inplace:
+        args[0]._set_data_mask(result_data, mask_args[0])
+        return args[0]
+    else:
+        return _wrap_result(result_data, mask_args[0])
+
+
+def _torch_unary(fn_name):
+    fn = getattr(torch.ops.aten, fn_name)
+
+    def unary_fn(*args, **kwargs):
+        return _unary_helper(fn, args, kwargs, inplace=False)
+
+    return unary_fn
+
+
+def _torch_inplace_unary(fn_name):
+    fn = getattr(torch.ops.aten, fn_name)
+
+    def unary_fn(*args, **kwargs):
+        return _unary_helper(fn, args, kwargs, inplace=True)
+
+    return unary_fn
+
+
+NATIVE_UNARY_MAP = {
+    getattr(torch.ops.aten, name): _torch_unary(name) for name in UNARY_NAMES
+}
+NATIVE_INPLACE_UNARY_MAP = {
+    getattr(torch.ops.aten, name): _torch_inplace_unary(name)
+    for name in INPLACE_UNARY_NAMES
+}
+
+NATIVE_UNARY_FNS = list(NATIVE_UNARY_MAP.keys())
+NATIVE_INPLACE_UNARY_FNS = list(NATIVE_INPLACE_UNARY_MAP.keys())
+
+
+def _is_native_unary(fn):
+    return fn in NATIVE_UNARY_FNS or fn in NATIVE_INPLACE_UNARY_FNS
+
+
+def _apply_native_unary(fn, *args, **kwargs):
+    if fn in NATIVE_UNARY_FNS:
+        return NATIVE_UNARY_MAP[fn](*args, **kwargs)
+    if fn in NATIVE_INPLACE_UNARY_FNS:
+        return NATIVE_INPLACE_UNARY_MAP[fn](*args, **kwargs)
+    return NotImplemented
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/monitor/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/monitor/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..36493cd7539c90736a06d08da972eb546645dbda
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/monitor/__init__.py
@@ -0,0 +1,38 @@
+from torch._C._monitor import *  # noqa: F403
+from typing import TYPE_CHECKING
+
+from torch._C._monitor import _WaitCounter  # type: ignore[attr-defined]
+
+if TYPE_CHECKING:
+    from torch.utils.tensorboard import SummaryWriter
+
+
+STAT_EVENT = "torch.monitor.Stat"
+
+
+class TensorboardEventHandler:
+    """
+    TensorboardEventHandler is an event handler that will write known events to
+    the provided SummaryWriter.
+
+    This currently only supports ``torch.monitor.Stat`` events which are logged
+    as scalars.
+
+    Example:
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MONITOR)
+        >>> # xdoctest: +REQUIRES(module:tensorboard)
+        >>> from torch.utils.tensorboard import SummaryWriter
+        >>> from torch.monitor import TensorboardEventHandler, register_event_handler
+        >>> writer = SummaryWriter("log_dir")
+        >>> register_event_handler(TensorboardEventHandler(writer))
+    """
+    def __init__(self, writer: "SummaryWriter") -> None:
+        """
+        Constructs the ``TensorboardEventHandler``.
+        """
+        self._writer = writer
+
+    def __call__(self, event: Event) -> None:
+        if event.name == STAT_EVENT:
+            for k, v in event.data.items():
+                self._writer.add_scalar(k, v, walltime=event.timestamp.timestamp())
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/monitor/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/monitor/__pycache__/__init__.cpython-311.pyc
new file mode 100644
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/mps/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/mps/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc6b00d3c1a719023dd4e10c5a3f62d02b6a2ed4
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/mps/__init__.py
@@ -0,0 +1,166 @@
+# mypy: allow-untyped-defs
+r"""
+This package enables an interface for accessing MPS (Metal Performance Shaders) backend in Python.
+Metal is Apple's API for programming metal GPU (graphics processor unit). Using MPS means that increased
+performance can be achieved, by running work on the metal GPU(s).
+See https://developer.apple.com/documentation/metalperformanceshaders for more details.
+"""
+from typing import Union
+
+import torch
+from torch import Tensor
+
+
+_is_in_bad_fork = getattr(torch._C, "_mps_is_in_bad_fork", lambda: False)
+_default_mps_generator: torch._C.Generator = None  # type: ignore[assignment]
+
+
+# local helper function (not public or exported)
+def _get_default_mps_generator() -> torch._C.Generator:
+    global _default_mps_generator
+    if _default_mps_generator is None:
+        _default_mps_generator = torch._C._mps_get_default_generator()
+    return _default_mps_generator
+
+
+def device_count() -> int:
+    r"""Returns the number of available MPS devices."""
+    return int(torch._C._has_mps and torch._C._mps_is_available())
+
+
+def synchronize() -> None:
+    r"""Waits for all kernels in all streams on a MPS device to complete."""
+    return torch._C._mps_deviceSynchronize()
+
+
+def get_rng_state(device: Union[int, str, torch.device] = "mps") -> Tensor:
+    r"""Returns the random number generator state as a ByteTensor.
+
+    Args:
+        device (torch.device or int, optional): The device to return the RNG state of.
+            Default: ``'mps'`` (i.e., ``torch.device('mps')``, the current MPS device).
+    """
+    return _get_default_mps_generator().get_state()
+
+
+def set_rng_state(
+    new_state: Tensor, device: Union[int, str, torch.device] = "mps"
+) -> None:
+    r"""Sets the random number generator state.
+
+    Args:
+        new_state (torch.ByteTensor): The desired state
+        device (torch.device or int, optional): The device to set the RNG state.
+            Default: ``'mps'`` (i.e., ``torch.device('mps')``, the current MPS device).
+    """
+    new_state_copy = new_state.clone(memory_format=torch.contiguous_format)
+    _get_default_mps_generator().set_state(new_state_copy)
+
+
+def manual_seed(seed: int) -> None:
+    r"""Sets the seed for generating random numbers.
+
+    Args:
+        seed (int): The desired seed.
+    """
+    # the torch.mps.manual_seed() can be called from the global
+    # torch.manual_seed() in torch/random.py. So we need to make
+    # sure mps is available (otherwise we just return without
+    # erroring out)
+    if not torch._C._has_mps:
+        return
+    seed = int(seed)
+    _get_default_mps_generator().manual_seed(seed)
+
+
+def seed() -> None:
+    r"""Sets the seed for generating random numbers to a random number."""
+    _get_default_mps_generator().seed()
+
+
+def empty_cache() -> None:
+    r"""Releases all unoccupied cached memory currently held by the caching
+    allocator so that those can be used in other GPU applications.
+    """
+    torch._C._mps_emptyCache()
+
+
+def set_per_process_memory_fraction(fraction) -> None:
+    r"""Set memory fraction for limiting process's memory allocation on MPS device.
+    The allowed value equals the fraction multiplied by recommended maximum device memory
+    (obtained from Metal API device.recommendedMaxWorkingSetSize).
+    If trying to allocate more than the allowed value in a process, it will raise an out of
+    memory error in allocator.
+
+    Args:
+        fraction(float): Range: 0~2. Allowed memory equals total_memory * fraction.
+
+    .. note::
+       Passing 0 to fraction means unlimited allocations
+       (may cause system failure if out of memory).
+       Passing fraction greater than 1.0 allows limits beyond the value
+       returned from device.recommendedMaxWorkingSetSize.
+    """
+
+    if not isinstance(fraction, float):
+        raise TypeError("Invalid type for fraction argument, must be `float`")
+    if fraction < 0 or fraction > 2:
+        raise ValueError(f"Invalid fraction value: {fraction}. Allowed range: 0~2")
+
+    torch._C._mps_setMemoryFraction(fraction)
+
+
+def current_allocated_memory() -> int:
+    r"""Returns the current GPU memory occupied by tensors in bytes.
+
+    .. note::
+       The returned size does not include cached allocations in
+       memory pools of MPSAllocator.
+    """
+    return torch._C._mps_currentAllocatedMemory()
+
+
+def driver_allocated_memory() -> int:
+    r"""Returns total GPU memory allocated by Metal driver for the process in bytes.
+
+    .. note::
+       The returned size includes cached allocations in MPSAllocator pools
+       as well as allocations from MPS/MPSGraph frameworks.
+    """
+    return torch._C._mps_driverAllocatedMemory()
+
+
+def recommended_max_memory() -> int:
+    r"""Returns recommended max Working set size for GPU memory in bytes.
+
+    .. note::
+       Recommended max working set size for Metal.
+       returned from device.recommendedMaxWorkingSetSize.
+    """
+    return torch._C._mps_recommendedMaxMemory()
+
+
+def is_available() -> bool:
+    return device_count() > 0
+
+
+from . import profiler
+from .event import Event
+
+
+__all__ = [
+    "device_count",
+    "get_rng_state",
+    "manual_seed",
+    "seed",
+    "set_rng_state",
+    "synchronize",
+    "empty_cache",
+    "set_per_process_memory_fraction",
+    "current_allocated_memory",
+    "driver_allocated_memory",
+    "Event",
+    "profiler",
+    "recommended_max_memory",
+    "is_available",
+]
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/mps/event.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/mps/event.py
new file mode 100644
index 0000000000000000000000000000000000000000..d619c027480c3ad6c52744afa76f35ff4cba64c0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/mps/event.py
@@ -0,0 +1,46 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+class Event:
+    r"""Wrapper around an MPS event.
+
+    MPS events are synchronization markers that can be used to monitor the
+    device's progress, to accurately measure timing, and to synchronize MPS streams.
+
+    Args:
+        enable_timing (bool, optional): indicates if the event should measure time
+            (default: ``False``)
+    """
+
+    def __init__(self, enable_timing=False):
+        self.__eventId = torch._C._mps_acquireEvent(enable_timing)
+
+    def __del__(self):
+        # checks if torch._C is already destroyed
+        if hasattr(torch._C, "_mps_releaseEvent") and self.__eventId > 0:
+            torch._C._mps_releaseEvent(self.__eventId)
+
+    def record(self):
+        r"""Records the event in the default stream."""
+        torch._C._mps_recordEvent(self.__eventId)
+
+    def wait(self):
+        r"""Makes all future work submitted to the default stream wait for this event."""
+        torch._C._mps_waitForEvent(self.__eventId)
+
+    def query(self):
+        r"""Returns True if all work currently captured by event has completed."""
+        return torch._C._mps_queryEvent(self.__eventId)
+
+    def synchronize(self):
+        r"""Waits until the completion of all work currently captured in this event.
+        This prevents the CPU thread from proceeding until the event completes.
+        """
+        torch._C._mps_synchronizeEvent(self.__eventId)
+
+    def elapsed_time(self, end_event):
+        r"""Returns the time elapsed in milliseconds after the event was
+        recorded and before the end_event was recorded.
+        """
+        return torch._C._mps_elapsedTimeOfEvents(self.__eventId, end_event.__eventId)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/mps/profiler.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/mps/profiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..4dcd86b30ecbf765cb13c674e602bdf21058f668
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/mps/profiler.py
@@ -0,0 +1,61 @@
+# mypy: allow-untyped-defs
+import contextlib
+
+import torch
+
+
+__all__ = ["start", "stop", "profile"]
+
+
+def start(mode: str = "interval", wait_until_completed: bool = False) -> None:
+    r"""Start OS Signpost tracing from MPS backend.
+
+    The generated OS Signposts could be recorded and viewed in
+    XCode Instruments Logging tool.
+
+    Args:
+        mode(str): OS Signpost tracing mode could be "interval", "event",
+            or both "interval,event".
+            The interval mode traces the duration of execution of the operations,
+            whereas event mode marks the completion of executions.
+            See document `Recording Performance Data`_ for more info.
+        wait_until_completed(bool): Waits until the MPS Stream complete
+            executing each encoded GPU operation. This helps generating single
+            dispatches on the trace's timeline.
+            Note that enabling this option would affect the performance negatively.
+
+    .. _Recording Performance Data:
+       https://developer.apple.com/documentation/os/logging/recording_performance_data
+    """
+    mode_normalized = mode.lower().replace(" ", "")
+    torch._C._mps_profilerStartTrace(mode_normalized, wait_until_completed)
+
+
+def stop():
+    r"""Stops generating OS Signpost tracing from MPS backend."""
+    torch._C._mps_profilerStopTrace()
+
+
+@contextlib.contextmanager
+def profile(mode: str = "interval", wait_until_completed: bool = False):
+    r"""Context Manager to enabling generating OS Signpost tracing from MPS backend.
+
+    Args:
+        mode(str): OS Signpost tracing mode could be "interval", "event",
+            or both "interval,event".
+            The interval mode traces the duration of execution of the operations,
+            whereas event mode marks the completion of executions.
+            See document `Recording Performance Data`_ for more info.
+        wait_until_completed(bool): Waits until the MPS Stream complete
+            executing each encoded GPU operation. This helps generating single
+            dispatches on the trace's timeline.
+            Note that enabling this option would affect the performance negatively.
+
+    .. _Recording Performance Data:
+       https://developer.apple.com/documentation/os/logging/recording_performance_data
+    """
+    try:
+        start(mode, wait_until_completed)
+        yield
+    finally:
+        stop()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/mtia/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/mtia/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..eaa107e973f6d9ce9cbf153d4d544b1dca02e07e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/mtia/__init__.py
@@ -0,0 +1,332 @@
+# mypy: allow-untyped-defs
+r"""
+This package enables an interface for accessing MTIA backend in python
+"""
+
+import threading
+import warnings
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+from torch import device as _device, Tensor
+from torch._utils import _dummy_type, _LazySeedTracker, classproperty
+from torch.types import Device
+
+from ._utils import _get_device_index
+
+
+_device_t = Union[_device, str, int, None]
+
+# torch.mtia.Event/Stream is alias of torch.Event/Stream
+Event = torch.Event
+Stream = torch.Stream
+
+_initialized = False
+_queued_calls: List[
+    Tuple[Callable[[], None], List[str]]
+] = []  # don't invoke these until initialization occurs
+_tls = threading.local()
+_initialization_lock = threading.Lock()
+_lazy_seed_tracker = _LazySeedTracker()
+
+
+def init():
+    _lazy_init()
+
+
+def is_initialized():
+    r"""Return whether PyTorch's MTIA state has been initialized."""
+    return _initialized and not _is_in_bad_fork()
+
+
+def _is_in_bad_fork() -> bool:
+    return torch._C._mtia_isInBadFork()
+
+
+def _lazy_init() -> None:
+    global _initialized, _queued_calls
+    if is_initialized() or hasattr(_tls, "is_initializing"):
+        return
+    with _initialization_lock:
+        # We be double-checking locking, boys! This is OK because
+        # the above test was GIL protected anyway. The inner test
+        # is for when a thread blocked on some other thread which was
+        # doing the initialization; when they get the lock, they will
+        # find there is nothing left to do.
+        if is_initialized():
+            return
+        # It is important to prevent other threads from entering _lazy_init
+        # immediately, while we are still guaranteed to have the GIL, because some
+        # of the C calls we make below will release the GIL
+        if _is_in_bad_fork():
+            raise RuntimeError(
+                "Cannot re-initialize MTIA in forked subprocess. To use MTIA with "
+                "multiprocessing, you must use the 'spawn' start method"
+            )
+        if not _is_compiled():
+            raise AssertionError(
+                "Torch not compiled with MTIA enabled. "
+                "Ensure you have `import mtia.host_runtime.torch_mtia` in your python "
+                "src file and include `//mtia/host_runtime/torch_mtia:torch_mtia` as "
+                "your target dependency!"
+            )
+
+        torch._C._mtia_init()
+        # Some of the queued calls may reentrantly call _lazy_init();
+        # we need to just return without initializing in that case.
+        # However, we must not let any *other* threads in!
+        _tls.is_initializing = True
+
+        for calls in _lazy_seed_tracker.get_calls():
+            if calls:
+                _queued_calls.append(calls)
+
+        try:
+            for queued_call, orig_traceback in _queued_calls:
+                try:
+                    queued_call()
+                except Exception as e:
+                    msg = (
+                        f"MTIA call failed lazily at initialization with error: {str(e)}\n\n"
+                        f"MTIA call was originally invoked at:\n\n{''.join(orig_traceback)}"
+                    )
+                    raise DeferredMtiaCallError(msg) from e
+        finally:
+            delattr(_tls, "is_initializing")
+        _initialized = True
+
+
+class DeferredMtiaCallError(Exception):
+    pass
+
+
+def _is_compiled() -> bool:
+    r"""Return true if compiled with MTIA support."""
+    return torch._C._mtia_isBuilt()
+
+
+def is_available() -> bool:
+    r"""Return true if MTIA device is available"""
+    if not _is_compiled():
+        return False
+    # MTIA has to init devices first to know if there is any devices available.
+    return device_count() > 0
+
+
+def synchronize(device: Optional[_device_t] = None) -> None:
+    r"""Waits for all jobs in all streams on a MTIA device to complete."""
+    with torch.mtia.device(device):
+        return torch._C._mtia_deviceSynchronize()
+
+
+def device_count() -> int:
+    r"""Return the number of MTIA devices available."""
+    return torch._C._accelerator_hooks_device_count()
+
+
+def current_device() -> int:
+    r"""Return the index of a currently selected device."""
+    return torch._C._accelerator_hooks_get_current_device()
+
+
+def current_stream(device: Optional[_device_t] = None) -> Stream:
+    r"""Return the currently selected :class:`Stream` for a given device.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            the currently selected :class:`Stream` for the current device, given
+            by :func:`~torch.mtia.current_device`, if :attr:`device` is ``None``
+            (default).
+    """
+    return torch._C._mtia_getCurrentStream(_get_device_index(device, optional=True))
+
+
+def default_stream(device: Optional[_device_t] = None) -> Stream:
+    r"""Return the default :class:`Stream` for a given device.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            the default :class:`Stream` for the current device, given by
+            :func:`~torch.mtia.current_device`, if :attr:`device` is ``None``
+            (default).
+    """
+    return torch._C._mtia_getDefaultStream(_get_device_index(device, optional=True))
+
+
+def memory_stats(device: Optional[_device_t] = None) -> Dict[str, Any]:
+    r"""Return a dictionary of MTIA memory allocator statistics for a given device.
+
+    Args:
+        device (torch.device or int, optional) selected device. Returns
+            statistics for the current device, given by current_device(),
+            if device is None (default).
+    """
+    if not is_initialized():
+        return {}
+    return torch._C._mtia_memoryStats(_get_device_index(device, optional=True))
+
+
+def set_stream(stream: Stream):
+    r"""Set the current stream.This is a wrapper API to set the stream.
+        Usage of this function is discouraged in favor of the ``stream``
+        context manager.
+
+    Args:
+        stream (Stream): selected stream. This function is a no-op
+            if this argument is ``None``.
+    """
+    if stream is None:
+        return
+    torch._C._mtia_setCurrentStream(stream)
+
+
+def set_device(device: _device_t) -> None:
+    r"""Set the current device.
+
+    Args:
+        device (torch.device or int): selected device. This function is a no-op
+            if this argument is negative.
+    """
+    device = _get_device_index(device)
+    if device >= 0:
+        torch._C._accelerator_hooks_set_current_device(device)
+
+
+class device:
+    r"""Context-manager that changes the selected device.
+
+    Args:
+        device (torch.device or int): device index to select. It's a no-op if
+            this argument is a negative integer or ``None``.
+    """
+
+    def __init__(self, device: Any):
+        self.idx = _get_device_index(device, optional=True)
+        self.prev_idx = -1
+
+    def __enter__(self):
+        self.prev_idx = torch._C._accelerator_hooks_maybe_exchange_device(self.idx)
+
+    def __exit__(self, type: Any, value: Any, traceback: Any):
+        self.idx = torch._C._accelerator_hooks_maybe_exchange_device(self.prev_idx)
+        return False
+
+
+class StreamContext:
+    r"""Context-manager that selects a given stream.
+
+    All MTIA kernels queued within its context will be enqueued on a selected
+    stream.
+
+    Args:
+        Stream (Stream): selected stream. This manager is a no-op if it's
+            ``None``.
+    .. note:: Streams are per-device.
+    """
+
+    cur_stream: Optional["torch.mtia.Stream"]
+
+    def __init__(self, stream: Optional["torch.mtia.Stream"]):
+        self.cur_stream = None
+        self.stream = stream
+        self.idx = _get_device_index(None, True)
+        if not torch.jit.is_scripting():
+            if self.idx is None:
+                self.idx = -1
+
+        self.src_prev_stream = (
+            None if not torch.jit.is_scripting() else torch.mtia.default_stream(None)
+        )
+        self.dst_prev_stream = (
+            None if not torch.jit.is_scripting() else torch.mtia.default_stream(None)
+        )
+
+    def __enter__(self):
+        # Local cur_stream variable for type refinement
+        cur_stream = self.stream
+        # Return if stream is None or MTIA device not available
+        if cur_stream is None or self.idx == -1:
+            return
+        self.src_prev_stream = torch.mtia.current_stream(None)
+
+        # If the stream is not on the current device, then
+        # set the current stream on the device
+        if self.src_prev_stream.device != cur_stream.device:
+            with device(cur_stream.device):
+                self.dst_prev_stream = torch.mtia.current_stream(cur_stream.device)
+        torch.mtia.set_stream(cur_stream)
+
+    def __exit__(self, type: Any, value: Any, traceback: Any):
+        # Local cur_stream variable for type refinement
+        cur_stream = self.stream
+        # If stream is None or no MTIA device available, return
+        if cur_stream is None or self.idx == -1:
+            return
+
+        # Reset the stream on the original device
+        # and destination device
+        if self.src_prev_stream.device != cur_stream.device:  # type: ignore[union-attr]
+            torch.mtia.set_stream(self.dst_prev_stream)  # type: ignore[arg-type]
+        torch.mtia.set_stream(self.src_prev_stream)  # type: ignore[arg-type]
+
+
+def stream(stream: Optional["torch.mtia.Stream"]) -> StreamContext:
+    r"""Wrap around the Context-manager StreamContext that selects a given stream.
+
+    Arguments:
+        stream (Stream): selected stream. This manager is a no-op if it's
+            ``None``.
+    ..Note:: In eager mode stream is of type Stream class while in JIT it doesn't support torch.mtia.stream
+    """
+    return StreamContext(stream)
+
+
+def get_rng_state(device: Union[int, str, torch.device] = "mtia") -> Tensor:
+    r"""Returns the random number generator state as a ByteTensor.
+
+    Args:
+        device (torch.device or int, optional): The device to return the RNG state of.
+            Default: ``'mtia'`` (i.e., ``torch.device('mtia')``, the current mtia device).
+    """
+    warnings.warn(
+        "get_rng_state is not implemented in torch.mtia",
+        UserWarning,
+        stacklevel=2,
+    )
+    return torch.zeros([1], dtype=torch.uint8, device=device)
+
+
+def set_rng_state(
+    new_state: Tensor, device: Union[int, str, torch.device] = "mtia"
+) -> None:
+    r"""Sets the random number generator state.
+
+    Args:
+        new_state (torch.ByteTensor): The desired state
+        device (torch.device or int, optional): The device to set the RNG state.
+            Default: ``'mtia'`` (i.e., ``torch.device('mtia')``, the current mtia device).
+    """
+    warnings.warn(
+        "set_rng_state is not implemented in torch.mtia",
+        UserWarning,
+        stacklevel=2,
+    )
+
+
+__all__ = [
+    "init",
+    "is_available",
+    "is_initialized",
+    "synchronize",
+    "device_count",
+    "current_device",
+    "current_stream",
+    "default_stream",
+    "memory_stats",
+    "set_device",
+    "set_stream",
+    "stream",
+    "device",
+    "set_rng_state",
+    "get_rng_state",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/mtia/_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/mtia/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..090e26f321232f9687c2b348ac602dbb6699b03f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/mtia/_utils.py
@@ -0,0 +1,38 @@
+from typing import Any
+
+import torch
+
+# The _get_device_index has been moved to torch.utils._get_device_index
+from torch._utils import _get_device_index as _torch_get_device_index
+
+
+def _get_device_index(
+    device: Any, optional: bool = False, allow_cpu: bool = False
+) -> int:
+    r"""Get the device index from :attr:`device`, which can be a torch.device object, a Python integer, or ``None``.
+
+    If :attr:`device` is a torch.device object, returns the device index if it
+    is a MTIA device. Note that for a MTIA device without a specified index,
+    i.e., ``torch.device('mtia')``, this will return the current default MTIA
+    device if :attr:`optional` is ``True``. If :attr:`allow_cpu` is ``True``,
+    CPU devices will be accepted and ``-1`` will be returned in this case.
+
+    If :attr:`device` is a Python integer, it is returned as is.
+
+    If :attr:`device` is ``None``, this will return the current default MTIA
+    device if :attr:`optional` is ``True``.
+    """
+    if isinstance(device, int):
+        return device
+    if isinstance(device, str):
+        device = torch.device(device)
+    if isinstance(device, torch.device):
+        if allow_cpu:
+            if device.type not in ["mtia", "cpu"]:
+                raise ValueError(f"Expected a mtia or cpu device, but got: {device}")
+        elif device.type != "mtia":
+            raise ValueError(f"Expected a mtia device, but got: {device}")
+    if not torch.jit.is_scripting():
+        if isinstance(device, torch.mtia.device):
+            return device.idx
+    return _torch_get_device_index(device, optional, allow_cpu)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/multiprocessing/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/multiprocessing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..745c180d8c415c4e52472864d26381e0872f3354
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/multiprocessing/__init__.py
@@ -0,0 +1,100 @@
+# mypy: allow-untyped-defs
+"""torch.multiprocessing is a wrapper around the native :mod:`multiprocessing` module.
+
+It registers custom reducers, that use shared memory to provide shared
+views on the same data in different processes. Once the tensor/storage is moved
+to shared_memory (see :func:`~torch.Tensor.share_memory_`), it will be possible
+to send it to other processes without making any copies.
+
+The API is 100% compatible with the original module - it's enough to change
+``import multiprocessing`` to ``import torch.multiprocessing`` to have all the
+tensors sent through the queues or shared via other mechanisms, moved to shared
+memory.
+
+Because of the similarity of APIs we do not document most of this package
+contents, and we recommend referring to very good docs of the original module.
+"""
+import multiprocessing
+import sys
+
+import torch
+
+from .reductions import init_reductions
+
+
+__all__ = ["set_sharing_strategy", "get_sharing_strategy", "get_all_sharing_strategies"]
+
+
+from multiprocessing import *  # noqa: F403
+
+
+__all__ += multiprocessing.__all__  # noqa: PLE0605 type: ignore[attr-defined]
+
+
+# This call adds a Linux specific prctl(2) wrapper function to this module.
+# See https://github.com/pytorch/pytorch/pull/14391 for more information.
+torch._C._multiprocessing_init()
+
+
+"""Add helper function to spawn N processes and wait for completion of any of
+them. This depends `mp.get_context` which was added in Python 3.4."""
+from .spawn import (
+    ENV_VAR_PARALLEL_START,
+    ProcessContext,
+    ProcessExitedException,
+    ProcessRaisedException,
+    spawn,
+    SpawnContext,
+    start_processes,
+)
+
+
+if sys.platform == "darwin" or sys.platform == "win32":
+    _sharing_strategy = "file_system"
+    _all_sharing_strategies = {"file_system"}
+else:
+    _sharing_strategy = "file_descriptor"
+    _all_sharing_strategies = {"file_descriptor", "file_system"}
+
+
+def set_sharing_strategy(new_strategy):
+    """Set the strategy for sharing CPU tensors.
+
+    Args:
+        new_strategy (str): Name of the selected strategy. Should be one of
+            the values returned by :func:`get_all_sharing_strategies()`.
+    """
+    global _sharing_strategy
+    assert new_strategy in _all_sharing_strategies
+    _sharing_strategy = new_strategy
+
+
+def get_sharing_strategy():
+    """Return the current strategy for sharing CPU tensors."""
+    return _sharing_strategy
+
+
+def get_all_sharing_strategies():
+    """Return a set of sharing strategies supported on a current system."""
+    return _all_sharing_strategies
+
+
+def _set_thread_name(name: str) -> None:
+    """Set the name of the current thread.
+
+    Args:
+        name (str): Name of the current thread.
+    """
+    torch._C._set_thread_name(name)
+
+
+def _get_thread_name() -> str:
+    """Get the name of the current thread.
+
+    Returns:
+        str: Name of the current thread.
+    """
+    return torch._C._get_thread_name()
+
+
+init_reductions()
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/multiprocessing/_atfork.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/multiprocessing/_atfork.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac4a97c10c07ae680765b0f362ef33c4bfb2308b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/multiprocessing/_atfork.py
@@ -0,0 +1,35 @@
+# mypy: allow-untyped-defs
+import sys
+
+
+__all__ = ["register_after_fork"]
+
+if sys.platform == "win32":
+    import multiprocessing.util as _util
+
+    def _register(func):
+        def wrapper(arg):
+            func()
+
+        _util.register_after_fork(_register, wrapper)
+
+else:
+    import os
+
+    def _register(func):
+        os.register_at_fork(after_in_child=func)
+
+
+def register_after_fork(func):
+    """Register a callable to be executed in the child process after a fork.
+
+    Note:
+        In python < 3.7 this will only work with processes created using the
+        ``multiprocessing`` module. In python >= 3.7 it also works with
+        ``os.fork()``.
+
+    Args:
+        func (function): Function taking no arguments to be called in the child after fork
+
+    """
+    _register(func)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/multiprocessing/pool.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/multiprocessing/pool.py
new file mode 100644
index 0000000000000000000000000000000000000000..6915203566469cfaf7170d87894ce03cc8348dd5
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/multiprocessing/pool.py
@@ -0,0 +1,52 @@
+import multiprocessing.pool
+import multiprocessing.util as util
+
+from .queue import SimpleQueue
+
+
+def clean_worker(*args, **kwargs):
+    import gc
+
+    multiprocessing.pool.worker(*args, **kwargs)
+    # Regular multiprocessing workers don't fully clean up after themselves,
+    # so we have to explicitly trigger garbage collection to make sure that all
+    # destructors are called...
+    gc.collect()
+
+
+class Pool(multiprocessing.pool.Pool):
+    """Pool implementation which uses our version of SimpleQueue.
+
+    This lets us pass tensors in shared memory across processes instead of
+    serializing the underlying data.
+    """
+
+    def _setup_queues(self):
+        self._inqueue = SimpleQueue()
+        self._outqueue = SimpleQueue()
+        self._quick_put = self._inqueue._writer.send
+        self._quick_get = self._outqueue._reader.recv
+
+    def _repopulate_pool(self):
+        """Increase the number of pool processes to the specified number.
+
+        Bring the number of pool processes up to the specified number, for use after
+        reaping workers which have exited.
+        """
+        for i in range(self._processes - len(self._pool)):
+            # changed worker -> clean_worker
+            args = (
+                self._inqueue,
+                self._outqueue,
+                self._initializer,
+                self._initargs,
+                self._maxtasksperchild,
+            )
+            if hasattr(self, "_wrap_exception"):
+                args += (self._wrap_exception,)
+            w = self.Process(target=clean_worker, args=args)
+            self._pool.append(w)
+            w.name = w.name.replace("Process", "PoolWorker")
+            w.daemon = True
+            w.start()
+            util.debug("added worker")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/multiprocessing/queue.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/multiprocessing/queue.py
new file mode 100644
index 0000000000000000000000000000000000000000..876bf8d0e7459b60a41b59b0a093608e515ba455
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/multiprocessing/queue.py
@@ -0,0 +1,43 @@
+# mypy: allow-untyped-defs
+import io
+import multiprocessing.queues
+import pickle
+from multiprocessing.reduction import ForkingPickler
+
+
+class ConnectionWrapper:
+    """Proxy class for _multiprocessing.Connection which uses ForkingPickler for object serialization."""
+
+    def __init__(self, conn):
+        self.conn = conn
+
+    def send(self, obj):
+        buf = io.BytesIO()
+        ForkingPickler(buf, pickle.HIGHEST_PROTOCOL).dump(obj)
+        self.send_bytes(buf.getvalue())
+
+    def recv(self):
+        buf = self.recv_bytes()
+        return pickle.loads(buf)
+
+    def __getattr__(self, name):
+        if "conn" in self.__dict__:
+            return getattr(self.conn, name)
+        raise AttributeError(f"'{type(self).__name__}' object has no attribute 'conn'")
+
+
+class Queue(multiprocessing.queues.Queue):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._reader: ConnectionWrapper = ConnectionWrapper(self._reader)
+        self._writer: ConnectionWrapper = ConnectionWrapper(self._writer)
+        self._send = self._writer.send
+        self._recv = self._reader.recv
+
+
+class SimpleQueue(multiprocessing.queues.SimpleQueue):
+    def _make_methods(self):
+        if not isinstance(self._reader, ConnectionWrapper):
+            self._reader: ConnectionWrapper = ConnectionWrapper(self._reader)
+            self._writer: ConnectionWrapper = ConnectionWrapper(self._writer)
+        super()._make_methods()  # type: ignore[misc]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/multiprocessing/reductions.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/multiprocessing/reductions.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa0818571a93c0e9809c4638446e7ebdb15bd87e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/multiprocessing/reductions.py
@@ -0,0 +1,647 @@
+# mypy: allow-untyped-defs
+import multiprocessing
+import os
+import threading
+from multiprocessing.reduction import ForkingPickler
+from multiprocessing.util import register_after_fork
+from typing import Union
+
+import torch
+from torch._namedtensor_internals import check_serializing_named_tensor
+
+
+try:
+    # Early load resource_sharer to prevent a partially initialized instance
+    # from being inherited in a forked child process. The reduce_storage method
+    # requires this module indirectly through DupFd(). The built-in mp.Queue
+    # class pickles arguments in a background thread which may overlap with the
+    # fork.
+    import multiprocessing.resource_sharer
+except ImportError:
+    pass
+
+
+class StorageWeakRef:
+    r"""A weak reference to a Storage.
+
+    The cdata member is a Python number containing the integer representation of
+    the Storage pointer.
+    """
+
+    __slots__ = ["cdata", "_free_weak_ref"]
+
+    def __init__(self, storage):
+        self.cdata = storage._weak_ref()
+        # Save a direct reference to _free_weak_ref because the `torch` module
+        # might be cleared during Python shutdown before this module is cleared.
+        self._free_weak_ref = torch.Storage._free_weak_ref  # type: ignore[attr-defined]
+
+    @classmethod
+    def from_weakref(cls, cdata):
+        instance = cls.__new__(cls)
+        instance.cdata = cdata
+        instance._free_weak_ref = torch.Storage._free_weak_ref  # type: ignore[attr-defined]
+        return instance
+
+    def expired(self):
+        return torch.Storage._expired(self.cdata)  # type: ignore[attr-defined]
+
+    def __del__(self):
+        self._free_weak_ref(self.cdata)
+
+    def __hash__(self):
+        return self.cdata
+
+    def __eq__(self, other):
+        if id(self) == id(other):
+            return True
+        return self.cdata == other.cdata
+
+
+class SharedCache(dict):
+    """Dictionary from multiprocessing handles to StorageWeakRef."""
+
+    def __init__(self) -> None:
+        # free_dead_references() is called if the len exceeds the current
+        # limit. The limit scales with the number of remaining live objects.
+        self.limit = 128
+        # `fork` inherits lock state, so in case we fork when the lock is held,
+        # we register a function to reset the lock to a new object to avoid
+        # possible deadlocks, following python multiprocessing library design.
+        self._after_fork()
+        register_after_fork(self, SharedCache._after_fork)
+
+    def _after_fork(self):
+        self.lock = threading.Lock()
+
+    def get(self, key):
+        with self.lock:
+            return dict.get(self, key)
+
+    def __setitem__(self, key, storage_ref):
+        with self.lock:
+            dict.__setitem__(self, key, storage_ref)
+            if len(self) > self.limit:
+                self.free_dead_references()
+
+    def free_dead_references(self):
+        live = 0
+        for key, storage_ref in list(self.items()):
+            if storage_ref.expired():
+                del self[key]
+            else:
+                live += 1
+        self.limit = max(128, live * 2)
+
+
+# mapping from handles to StorageWeakRef objects
+shared_cache = SharedCache()
+
+
+def rebuild_event(device, handle):
+    return torch.cuda.Event.from_ipc_handle(device, handle)
+
+
+def reduce_event(event):
+    handle = event.ipc_handle()
+    return (rebuild_event, (event.device, handle))
+
+
+def rebuild_tensor(cls, storage, metadata):
+    storage_offset, size, stride, requires_grad = metadata
+    t = torch._utils._rebuild_tensor(storage, storage_offset, size, stride)
+    if cls == torch.nn.parameter.Parameter:
+        # we have to pass requires_grad into constructor, rather than set it as an
+        # attribute later, because it's an important check for Integer Tensors to
+        # have requires_grad=False (or else they raise an error)
+        t = torch.nn.parameter.Parameter(t, requires_grad=requires_grad)
+    else:
+        t.requires_grad = requires_grad
+    return t
+
+
+def rebuild_meta_tensor(
+    tensor_cls,
+    tensor_size,
+    tensor_stride,
+    tensor_offset,
+    dtype,
+    storage_size_bytes,
+    requires_grad,
+):
+    untyped_storage = torch.UntypedStorage(storage_size_bytes, device="meta")
+
+    typed_storage = torch.TypedStorage(
+        wrap_storage=untyped_storage, dtype=dtype, _internal=True
+    )
+
+    t = torch._utils._rebuild_tensor(
+        typed_storage,
+        tensor_offset,
+        tensor_size,
+        tensor_stride,
+    )
+
+    if tensor_cls == torch.nn.parameter.Parameter:
+        # It is crucial for integer tensors to receive
+        # the requires_grad=False as an argument in the constructor
+        t = torch.nn.parameter.Parameter(t, requires_grad=requires_grad)
+    else:
+        t.requires_grad = requires_grad
+
+    return t
+
+
+def rebuild_cuda_tensor(
+    tensor_cls,
+    tensor_size,
+    tensor_stride,
+    tensor_offset,
+    storage_cls,
+    dtype,
+    storage_device,
+    storage_handle,
+    storage_size_bytes,
+    storage_offset_bytes,
+    requires_grad,
+    ref_counter_handle,
+    ref_counter_offset,
+    event_handle,
+    event_sync_required,
+):
+    # If storage_handle is None, storage points to nullptr.
+    if storage_handle is None or storage_size_bytes == 0:
+        storage = storage_cls(0, dtype=dtype, device=storage_device, _internal=True)
+    else:
+        storage = storage_from_cache(
+            storage_cls, (storage_handle, storage_offset_bytes)
+        )
+        if storage is None:
+            torch.cuda._lazy_init()
+            storage = storage_cls._new_shared_cuda(
+                storage_device,
+                storage_handle,
+                storage_size_bytes,
+                storage_offset_bytes,
+                ref_counter_handle,
+                ref_counter_offset,
+                event_handle,
+                event_sync_required,
+            )
+            shared_cache[(storage_handle, storage_offset_bytes)] = StorageWeakRef(
+                storage
+            )
+        else:
+            # We already ref counting this Storage, but producer needs new ref-counters to be released.
+            storage_cls._release_ipc_counter(
+                ref_counter_handle, ref_counter_offset, device=storage_device
+            )
+
+    _storage = (
+        storage
+        if isinstance(storage, torch.UntypedStorage)
+        else storage._untyped_storage
+    )
+
+    t = torch._utils._rebuild_tensor(
+        torch.storage.TypedStorage(wrap_storage=_storage, dtype=dtype, _internal=True),
+        tensor_offset,
+        tensor_size,
+        tensor_stride,
+    )
+
+    if tensor_cls == torch.nn.parameter.Parameter:
+        # It is crucial for integer tensors to receive
+        # the requires_grad=False as an argument in the constructor
+        t = torch.nn.parameter.Parameter(t, requires_grad=requires_grad)
+    else:
+        t.requires_grad = requires_grad
+
+    return t
+
+
+def reduce_tensor(tensor):
+    if tensor.requires_grad and not tensor.is_leaf:
+        raise RuntimeError(
+            "Cowardly refusing to serialize non-leaf tensor which requires_grad, "
+            "since autograd does not support crossing process boundaries.  "
+            "If you just want to transfer the data, call detach() on the tensor "
+            "before serializing (e.g., putting it on the queue)."
+        )
+
+    check_serializing_named_tensor(tensor)
+    torch.utils.hooks.warn_if_has_hooks(tensor)
+
+    # Note [CUDA IPC and the caching allocator]
+    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    # When you send a CUDA tensor over IPC, you might expect that you will
+    # get out the same storage from the other end.  However, the CUDA caching
+    # allocator makes it difficult to preserve this invariant.  Consider
+    # the following situation: a tensor of size 0x100 points to offset 0x20 of
+    # a storage at 0xA100 of size 0x100.  (For simplicity, all of these
+    # sizes are given in bytes).  HOWEVER, with the caching allocator, this storage
+    # might be part of a larger cudaMalloc allocation 0xA000 of size 0x4000.
+    #
+    # When we want to send this CUDA tensor over IPC, we must send the
+    # *entire* cudaMalloc allocation, i.e., the 0xA000 region, not just
+    # the storage 0xA100 (because that is what CUDA supports).  So, on the
+    # other end, there simply isn't any way to say, "Wait, you gave me
+    # a bigger region (0xA000) than the one I wanted (0xA100)".
+    #
+    # OK, so if you sent the cudaMalloc allocation, can you just wrap that up as
+    # one storage itself? No, because this cudaMalloc allocation might contain
+    # storages of mixed types: float, bytes, double... If you make the entire
+    # allocation a single storage of a type A, we'll hit an error when constructing
+    # a tensor of type B on the storage.
+    #
+    # cudaIpcMemHandle is an identifier to access the sender cudaMalloc allocation on the
+    # receiver side. However, cudaIpcMemHandles from each device in a given process may
+    # only be opened by one context per device per other process.
+    # If we open and close a memory handle multiples times in a process, CUDA is allowed
+    # to give it a different address; similarly, once we close the memory, we're not
+    # allowed to access it(and the storage/tensor built on top of it), even if it is
+    # still live in the original process. As we cannot make a cudaMalloc allocation
+    # to a single storage in one go, this requires us to cache the device pointer for
+    # each cudaIpcMemHandle on C++ side to reconstruct types of storages, while keep
+    # the old ones alives.
+    # See [https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__DEVICE.html]
+    #
+    # This is fine, because all we need to do is to save our position in the allocation,
+    # and reconstruct storage and tensor from it.
+    # 0xA000 ->  -------CUDA Allocation------
+    #           |                            |
+    #           |                            |
+    #           |                            |
+    #           |                            |
+    # 0xA100 ->  --------storage1 begin------
+    #           |                            |
+    # 0xA120 ->  --------tensor1 begin ------
+    #           |                            |
+    #           |                            |
+    #           |                            |
+    #           |                            |
+    #           |                            |
+    # 0xA160 ->  --------tensor1 end---------
+    #           |                            |
+    #           |                            |
+    #           |                            |
+    # 0xA200 ->  --------storage1 end--------
+    #           |                            |
+    # 0xE000 ->  --------CUDA allocation-----
+    #
+    # To send tensor1, the following info are required from sender to receiver for
+    # storage recontruction.
+    #   1. cudaIpcMemHandle of 0xA000(which can be mapped to a basePtr in receiver process).
+    #      basePtr may not be exactly 0xA000 since it's a different process.
+    #   2. offset(0xA100) of storage1 in the CUDA allocation.
+    #   3. size of storage1(0x100).
+    #
+    # On receiver side:
+    #   1. Get the devPtr of the MemHandle to access the memory, reconstruct a storage
+    #      of the same type using (basePtr, offset, size).
+    #   2. we can reconstruct the tensor on top of the reconstructed storage
+    #   Tensor(size=0x040, offset=0x020, storage=Storage(data=basePtr+0xA100, size=0x0100))
+    #
+    # This strategy has a few implications:
+    #
+    # 1. When we serialize a CUDA tensor for IPC, we cannot do it all in one
+    #    go (non-compositionally), and this requires to have a global map
+    #    memHandle -> devPtr for each process.
+    #
+    # 2. We MUST NOT let the new IPC tensor be resizable.  Originally, a resize
+    #    of the storage beyond 0x100 would merely have caused us to do a
+    #    reallocation.  You don't really want to do this, but if you did,
+    #    all that would happen is that you would lose IPC sharing.  But if
+    #    you do this in the new world, we will happily let you write out of
+    #    bounds of your "allocation", clobbering unrelated data in the cached
+    #    allocator block.  BAD!
+    #
+    # By the way, in old versions of PyTorch, we supported this situation
+    # natively using a "storage view", which permitted multiple storages to be
+    # views on each other.  But this was the *only* use of storage views, so we
+    # eliminated it so that we could just use tensor views to implement the same
+    # thing.
+    #
+
+    # TODO: Handle distinguishing between subclass and non-subclass versions of NT better
+    # https://github.com/pytorch/pytorch/issues/110543
+    from torch.nested._internal.nested_tensor import NestedTensor
+
+    if tensor.is_nested and not isinstance(tensor, NestedTensor):
+        return reduce_nested_tensor(tensor)
+
+    if tensor.layout in {
+        torch.sparse_coo,
+        torch.sparse_csr,
+        torch.sparse_bsr,
+        torch.sparse_csc,
+        torch.sparse_bsc,
+    }:
+        return reduce_sparse_tensor(tensor)
+
+    storage = tensor._typed_storage()
+
+    if storage._untyped_storage.device.type == "cuda":
+        (
+            device,
+            handle,
+            storage_size_bytes,
+            storage_offset_bytes,
+            ref_counter_handle,
+            ref_counter_offset,
+            event_handle,
+            event_sync_required,
+        ) = storage._share_cuda_()
+        tensor_offset = tensor.storage_offset()
+        shared_cache[handle] = StorageWeakRef(storage)
+        # _backward_hooks purposely omitted here, see
+        # Note [Don't serialize hooks]
+        return (
+            rebuild_cuda_tensor,
+            (
+                type(tensor),
+                tensor.size(),
+                tensor.stride(),
+                tensor_offset,  # tensor offset in its storage
+                type(storage),
+                tensor.dtype,
+                device,
+                handle,  # identifier which CUDA allocation is the storage in.
+                storage_size_bytes,  # size(in bytes) of the storage
+                storage_offset_bytes,  # offset(in bytes) of the storage in the CUDA allocation
+                tensor.requires_grad,
+                ref_counter_handle,
+                ref_counter_offset,
+                event_handle,
+                event_sync_required,
+            ),
+        )
+    elif storage._untyped_storage.device.type == "meta":
+        return (
+            rebuild_meta_tensor,
+            (
+                type(tensor),
+                tensor.size(),
+                tensor.stride(),
+                tensor.storage_offset(),
+                tensor.dtype,
+                tensor.untyped_storage().size(),
+                tensor.requires_grad,
+            ),
+        )
+
+    # _backward_hooks purposely omitted here, see Note [Don't serialize hooks]
+    metadata = (
+        tensor.storage_offset(),
+        tensor.size(),
+        tensor.stride(),
+        tensor.requires_grad,
+    )
+    return (rebuild_tensor, (type(tensor), storage, metadata))
+
+
+def rebuild_nested_tensor(
+    rebuild_buffer_func,
+    rebuild_buffer_args,
+    rebuild_sizes_func,
+    rebuild_sizes_args,
+    rebuild_strides_func,
+    rebuild_strides_args,
+    rebuild_offsets_func,
+    rebuild_offsets_args,
+):
+    buffer = rebuild_buffer_func(*rebuild_buffer_args)
+    sizes = rebuild_sizes_func(*rebuild_sizes_args)
+    strides = rebuild_strides_func(*rebuild_strides_args)
+    offsets = rebuild_offsets_func(*rebuild_offsets_args)
+    return torch._nested_view_from_buffer_copy(buffer, sizes, strides, offsets)
+
+
+def reduce_nested_tensor(nt):
+    rebuild_buffer_func, rebuild_buffer_args = reduce_tensor(nt.values())
+    rebuild_sizes_func, rebuild_sizes_args = reduce_tensor(nt._nested_tensor_size())
+    rebuild_strides_func, rebuild_strides_args = reduce_tensor(
+        nt._nested_tensor_strides()
+    )
+    rebuild_offsets_func, rebuild_offsets_args = reduce_tensor(
+        nt._nested_tensor_storage_offsets()
+    )
+
+    return (
+        rebuild_nested_tensor,
+        (
+            rebuild_buffer_func,
+            rebuild_buffer_args,
+            rebuild_sizes_func,
+            rebuild_sizes_args,
+            rebuild_strides_func,
+            rebuild_strides_args,
+            rebuild_offsets_func,
+            rebuild_offsets_args,
+        ),
+    )
+
+
+def rebuild_sparse_coo_tensor(
+    rebuild_indices_func,
+    rebuild_indices_args,
+    rebuild_values_func,
+    rebuild_values_args,
+    shape,
+    is_coalesced,
+):
+    indices = rebuild_indices_func(*rebuild_indices_args)
+    values = rebuild_values_func(*rebuild_values_args)
+    return torch.sparse_coo_tensor(indices, values, shape, is_coalesced=is_coalesced)
+
+
+def rebuild_sparse_compressed_tensor(
+    rebuild_compressed_indices_func,
+    rebuild_compressed_indices_args,
+    rebuild_plain_indices_func,
+    rebuild_plain_indices_args,
+    rebuild_values_func,
+    rebuild_values_args,
+    shape,
+    layout,
+):
+    compressed_indices = rebuild_compressed_indices_func(
+        *rebuild_compressed_indices_args
+    )
+    plain_indices = rebuild_plain_indices_func(*rebuild_plain_indices_args)
+    values = rebuild_values_func(*rebuild_values_args)
+    return torch.sparse_compressed_tensor(
+        compressed_indices, plain_indices, values, shape, layout=layout
+    )
+
+
+def reduce_sparse_tensor(sparse):
+    if sparse.layout is torch.sparse_coo:
+        rebuild_indices_func, rebuild_indices_args = reduce_tensor(sparse._indices())
+        rebuild_values_func, rebuild_values_args = reduce_tensor(sparse._values())
+        return (
+            rebuild_sparse_coo_tensor,
+            (
+                rebuild_indices_func,
+                rebuild_indices_args,
+                rebuild_values_func,
+                rebuild_values_args,
+                sparse.shape,
+                sparse.is_coalesced(),
+            ),
+        )
+    else:
+        if sparse.layout in {torch.sparse_csr, torch.sparse_bsr}:
+            compressed_indices = sparse.crow_indices()
+            plain_indices = sparse.col_indices()
+        elif sparse.layout in {torch.sparse_csc, torch.sparse_bsc}:
+            compressed_indices = sparse.ccol_indices()
+            plain_indices = sparse.row_indices()
+        else:
+            raise NotImplementedError(sparse.layout)
+        (
+            rebuild_compressed_indices_func,
+            rebuild_compressed_indices_args,
+        ) = reduce_tensor(compressed_indices)
+        rebuild_plain_indices_func, rebuild_plain_indices_args = reduce_tensor(
+            plain_indices
+        )
+        rebuild_values_func, rebuild_values_args = reduce_tensor(sparse.values())
+        return (
+            rebuild_sparse_compressed_tensor,
+            (
+                rebuild_compressed_indices_func,
+                rebuild_compressed_indices_args,
+                rebuild_plain_indices_func,
+                rebuild_plain_indices_args,
+                rebuild_values_func,
+                rebuild_values_args,
+                sparse.shape,
+                sparse.layout,
+            ),
+        )
+
+
+def fd_id(fd):
+    # Returns a tuple which uniquely identifies a file descriptor. In Mac OS,
+    # this doesn't work with shared memory handles, which is why we don't
+    # support the "file_descriptor" sharing method on that platform.
+    stat = os.fstat(fd)
+    return (stat.st_ino, stat.st_dev)
+
+
+def storage_from_cache(cls, key):
+    storage_ref = shared_cache.get(key)
+    if storage_ref is None:
+        return None
+    return torch.UntypedStorage._new_with_weak_ptr(storage_ref.cdata)
+
+
+def rebuild_storage_fd(cls, df, size):
+    fd = df.detach()
+    try:
+        storage = storage_from_cache(cls, fd_id(fd))
+        if storage is not None:
+            return storage
+        storage = cls._new_shared_fd_cpu(fd, size)
+        shared_cache[fd_id(fd)] = StorageWeakRef(storage)
+        return storage
+    finally:
+        os.close(fd)
+
+
+def rebuild_storage_filename(cls, manager, handle, size, dtype=None):
+    storage: Union[torch.TypedStorage, torch.UntypedStorage] = storage_from_cache(
+        cls, handle
+    )
+    if storage is not None:
+        return storage._shared_decref()
+    if dtype is None:
+        storage = torch.UntypedStorage._new_shared_filename_cpu(manager, handle, size)
+    else:
+        byte_size = size * torch._utils._element_size(dtype)
+        untyped_storage: torch.UntypedStorage = (
+            torch.UntypedStorage._new_shared_filename_cpu(manager, handle, byte_size)
+        )
+        storage = torch.TypedStorage(
+            wrap_storage=untyped_storage, dtype=dtype, _internal=True
+        )
+    shared_cache[handle] = StorageWeakRef(storage)
+    return storage._shared_decref()
+
+
+def rebuild_storage_empty(cls):
+    return cls()
+
+
+def rebuild_typed_storage(storage, dtype):
+    return torch.storage.TypedStorage(wrap_storage=storage, dtype=dtype, _internal=True)
+
+
+# Use for torch.storage.TypedStorage
+def reduce_typed_storage(storage):
+    return (rebuild_typed_storage, (storage._untyped_storage, storage.dtype))
+
+
+def rebuild_typed_storage_child(storage, storage_type):
+    return storage_type(wrap_storage=storage, _internal=True)
+
+
+# Use for child classes of torch.storage.TypedStorage, like torch.FloatStorage
+def reduce_typed_storage_child(storage):
+    return (rebuild_typed_storage_child, (storage._untyped_storage, type(storage)))
+
+
+def reduce_storage(storage):
+    from . import get_sharing_strategy
+
+    if storage.is_cuda:
+        raise RuntimeError(
+            "Cannot pickle CUDA storage; try pickling a CUDA tensor instead"
+        )
+    elif storage.device.type == "meta":
+        raise RuntimeError(
+            "Cannot pickle meta storage; try pickling a meta tensor instead"
+        )
+    elif get_sharing_strategy() == "file_system":
+        metadata = storage._share_filename_cpu_()
+        cache_key = metadata[1]
+        rebuild = rebuild_storage_filename
+        if isinstance(storage, torch.TypedStorage):
+            metadata += (storage.dtype,)
+        storage._shared_incref()
+    elif storage.size() == 0:
+        # This is special cased because Empty tensors
+        # (with size 0) cannot be mmapped.
+        return (rebuild_storage_empty, (type(storage),))
+    else:
+        fd, size = storage._share_fd_cpu_()
+        df = multiprocessing.reduction.DupFd(fd)
+        cache_key = fd_id(fd)
+        metadata = (df, size)
+        rebuild = rebuild_storage_fd  # type: ignore[assignment]
+
+    shared_cache[cache_key] = StorageWeakRef(storage)
+    return (rebuild, (type(storage),) + metadata)
+
+
+def init_reductions():
+    ForkingPickler.register(torch.cuda.Event, reduce_event)
+
+    for t in torch._storage_classes:
+        if t.__name__ == "UntypedStorage":
+            ForkingPickler.register(t, reduce_storage)
+        else:
+            ForkingPickler.register(t, reduce_typed_storage_child)
+
+    ForkingPickler.register(torch.storage.TypedStorage, reduce_typed_storage)
+
+    for t in torch._tensor_classes:
+        ForkingPickler.register(t, reduce_tensor)
+
+    # TODO: Maybe this should be in tensor_classes? :)
+    ForkingPickler.register(torch.Tensor, reduce_tensor)
+
+    from torch.nn.parameter import Parameter
+
+    ForkingPickler.register(Parameter, reduce_tensor)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/multiprocessing/spawn.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/multiprocessing/spawn.py
new file mode 100644
index 0000000000000000000000000000000000000000..74bdde0fd97b20355686fc49fdb50a8fe02c5006
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/multiprocessing/spawn.py
@@ -0,0 +1,328 @@
+# mypy: allow-untyped-defs
+import logging
+import multiprocessing
+import multiprocessing.connection
+import os
+import pickle
+import signal
+import sys
+import tempfile
+import time
+import warnings
+from concurrent.futures import as_completed, ThreadPoolExecutor
+from typing import Optional
+
+from . import _prctl_pr_set_pdeathsig  # type: ignore[attr-defined]
+
+
+ENV_VAR_PARALLEL_START = "TORCH_MP_PARALLEL_START"
+
+log = logging.getLogger(__name__)
+
+__all__ = [
+    "ProcessContext",
+    "ProcessException",
+    "ProcessExitedException",
+    "ProcessRaisedException",
+    "spawn",
+    "SpawnContext",
+    "start_processes",
+]
+
+
+class ProcessException(Exception):
+    __slots__ = ["error_index", "error_pid"]
+
+    def __init__(self, msg: str, error_index: int, pid: int):
+        super().__init__(msg)
+        self.msg = msg
+        self.error_index = error_index
+        self.pid = pid
+
+    def __reduce__(self):
+        return type(self), (self.msg, self.error_index, self.pid)
+
+
+class ProcessRaisedException(ProcessException):
+    """Exception raised when a process failed due to an exception raised by the code."""
+
+    def __init__(
+        self,
+        msg: str,
+        error_index: int,
+        error_pid: int,
+    ):
+        super().__init__(msg, error_index, error_pid)
+
+
+class ProcessExitedException(ProcessException):
+    """Exception raised when a process failed due to signal or exited with a specific code."""
+
+    __slots__ = ["exit_code"]
+
+    def __init__(
+        self,
+        msg: str,
+        error_index: int,
+        error_pid: int,
+        exit_code: int,
+        signal_name: Optional[str] = None,
+    ):
+        super().__init__(msg, error_index, error_pid)
+        self.exit_code = exit_code
+        self.signal_name = signal_name
+
+    def __reduce__(self):
+        return (
+            type(self),
+            (self.msg, self.error_index, self.pid, self.exit_code, self.signal_name),
+        )
+
+
+def _wrap(fn, i, args, error_file):
+    # prctl(2) is a Linux specific system call.
+    # On other systems the following function call has no effect.
+    # This is set to ensure that non-daemonic child processes can
+    # terminate if their parent terminates before they do.
+    _prctl_pr_set_pdeathsig(signal.SIGINT)
+
+    try:
+        fn(i, *args)
+    except KeyboardInterrupt:
+        pass  # SIGINT; Killed by parent, do nothing
+    except Exception:
+        # Propagate exception to parent process, keeping original traceback
+        import traceback
+
+        with open(error_file, "wb") as fh:
+            pickle.dump(traceback.format_exc(), fh)
+        sys.exit(1)
+
+
+class ProcessContext:
+    def __init__(self, processes, error_files):
+        self.error_files = error_files
+        self.processes = processes
+        self.sentinels = {
+            process.sentinel: index for index, process in enumerate(processes)
+        }
+
+    def pids(self):
+        return [int(process.pid) for process in self.processes]
+
+    def join(self, timeout=None):
+        r"""Join one or more processes within spawn context.
+
+        Attempt to join one or more processes in this spawn context.
+        If one of them exited with a non-zero exit status, this function
+        kills the remaining processes and raises an exception with the cause
+        of the first process exiting.
+
+        Returns ``True`` if all processes have been joined successfully,
+        ``False`` if there are more processes that need to be joined.
+
+        Args:
+            timeout (float): Wait this long before giving up on waiting.
+        """
+        # Ensure this function can be called even when we're done.
+        if len(self.sentinels) == 0:
+            return True
+
+        # Wait for any process to fail or all of them to succeed.
+        ready = multiprocessing.connection.wait(
+            self.sentinels.keys(),
+            timeout=timeout,
+        )
+
+        error_index = None
+        for sentinel in ready:
+            index = self.sentinels.pop(sentinel)
+            process = self.processes[index]
+            process.join()
+            if process.exitcode != 0:
+                error_index = index
+                break
+
+        # Return if there was no error.
+        if error_index is None:
+            # Return whether or not all processes have been joined.
+            return len(self.sentinels) == 0
+
+        # Assume failure. Terminate processes that are still alive.
+        # Try SIGTERM then SIGKILL if the process isn't going down.
+        # The reason is related to python signal handling is limited
+        # to main thread and if that is in c/c++ land and stuck it won't
+        # to handle it. We have seen processes getting stuck not handling
+        # SIGTERM for the above reason.
+        timeout: int = 30
+        for process in self.processes:
+            if process.is_alive():
+                log.warning("Terminating process %s via signal SIGTERM", process.pid)
+                process.terminate()
+        end = time.monotonic() + timeout
+        for process in self.processes:
+            time_to_wait = max(0, end - time.monotonic())
+            process.join(time_to_wait)
+        for process in self.processes:
+            if process.is_alive():
+                log.warning(
+                    "Unable to shutdown process %s via SIGTERM , forcefully exiting via SIGKILL",
+                    process.pid,
+                )
+                process.kill()
+            process.join()
+
+        # The file will only be created if the process crashed.
+        failed_process = self.processes[error_index]
+        if not os.access(self.error_files[error_index], os.R_OK):
+            exitcode = self.processes[error_index].exitcode
+            if exitcode < 0:
+                try:
+                    name = signal.Signals(-exitcode).name
+                except ValueError:
+                    name = f""
+                raise ProcessExitedException(
+                    "process %d terminated with signal %s" % (error_index, name),
+                    error_index=error_index,
+                    error_pid=failed_process.pid,
+                    exit_code=exitcode,
+                    signal_name=name,
+                )
+            else:
+                raise ProcessExitedException(
+                    "process %d terminated with exit code %d" % (error_index, exitcode),
+                    error_index=error_index,
+                    error_pid=failed_process.pid,
+                    exit_code=exitcode,
+                )
+
+        with open(self.error_files[error_index], "rb") as fh:
+            original_trace = pickle.load(fh)
+        msg = "\n\n-- Process %d terminated with the following error:\n" % error_index
+        msg += original_trace
+        raise ProcessRaisedException(msg, error_index, failed_process.pid)
+
+
+class SpawnContext(ProcessContext):
+    def __init__(self, processes, error_files):
+        warnings.warn("SpawnContext is renamed to ProcessContext since 1.4 release.")
+        super().__init__(processes, error_files)
+
+
+# Note: [start_processes]
+# mp.start_processes handles both start_method='spawn' and 'fork'. It's supposed to be a
+# more generalized API than mp.spawn. Currently we only document mp.spawn as it's the
+# CUDA compatible start_method. However, in environments like Ipython notebooks, 'fork'
+# works better than 'spawn'. Every helper function we created for mp.spawn is indeed
+# general enough, and backends like XLA can reuse them in Colab notebooks as well.
+# Currently we only add this API first, we can consider adding it to documentation as
+# needed in the future.
+def start_processes(
+    fn,
+    args=(),
+    nprocs=1,
+    join=True,
+    daemon=False,
+    start_method="spawn",
+):
+    # To speed up performance in certain cases (see https://github.com/pytorch/pytorch/issues/133010),
+    # this func will start processes in parallel if start_method is 'forkserver'.
+    # Please opt in to this perf optimization by setting env var (TORCH_MP_PARALLEL_START) to 1.
+    # todo: investigate why spawn does not work with threadpool and raises SIGINT
+    if (
+        start_method == "forkserver"
+        and os.environ.get(ENV_VAR_PARALLEL_START, "0") == "1"
+    ):
+        log.info("Starting processes in parallel.")
+        start_parallel = True
+    else:
+        # Set env var TORCH_MP_PARALLEL_START to 0 to disable parallel start
+        start_parallel = False
+
+    mp = multiprocessing.get_context(start_method)
+    error_files = [None] * nprocs
+    processes = [None] * nprocs
+
+    def start_process(i):
+        # Each process is assigned a file to write tracebacks to.  We
+        # use the file being non-empty to indicate an exception
+        # occurred (vs an expected shutdown).  Note: this previously
+        # used a multiprocessing.Queue but that can be prone to
+        # deadlocks, so we went with a simpler solution for a one-shot
+        # message between processes.
+        tf = tempfile.NamedTemporaryFile(
+            prefix="pytorch-errorfile-", suffix=".pickle", delete=False
+        )
+        tf.close()
+        os.unlink(tf.name)
+        process = mp.Process(
+            target=_wrap,
+            args=(fn, i, args, tf.name),
+            daemon=daemon,
+        )
+        process.start()
+        return i, process, tf.name
+
+    if not start_parallel:
+        for i in range(nprocs):
+            idx, process, tf_name = start_process(i)
+            error_files[idx] = tf_name
+            processes[idx] = process
+    else:
+        with ThreadPoolExecutor(max_workers=nprocs) as executor:
+            futures = [executor.submit(start_process, i) for i in range(nprocs)]
+            for fut in as_completed(futures):
+                idx, process, tf_name = fut.result()
+                # idx and process rank needs to be the same.
+                error_files[idx] = tf_name
+                processes[idx] = process
+    context = ProcessContext(processes, error_files)
+    if not join:
+        return context
+
+    # Loop on join until it returns True or raises an exception.
+    while not context.join():
+        pass
+
+
+def spawn(fn, args=(), nprocs=1, join=True, daemon=False, start_method="spawn"):
+    r"""Spawns ``nprocs`` processes that run ``fn`` with ``args``.
+
+    If one of the processes exits with a non-zero exit status, the
+    remaining processes are killed and an exception is raised with the
+    cause of termination. In the case an exception was caught in the
+    child process, it is forwarded and its traceback is included in
+    the exception raised in the parent process.
+
+    Args:
+        fn (function): Function is called as the entrypoint of the
+            spawned process. This function must be defined at the top
+            level of a module so it can be pickled and spawned. This
+            is a requirement imposed by multiprocessing.
+
+            The function is called as ``fn(i, *args)``, where ``i`` is
+            the process index and ``args`` is the passed through tuple
+            of arguments.
+
+        args (tuple): Arguments passed to ``fn``.
+        nprocs (int): Number of processes to spawn.
+        join (bool): Perform a blocking join on all processes.
+        daemon (bool): The spawned processes' daemon flag. If set to True,
+                       daemonic processes will be created.
+        start_method (str): (deprecated) this method will always use ``spawn``
+                               as the start method. To use a different start method
+                               use ``start_processes()``.
+
+    Returns:
+        None if ``join`` is ``True``,
+        :class:`~ProcessContext` if ``join`` is ``False``
+
+    """
+    if start_method != "spawn":
+        msg = (
+            f"This method only supports start_method=spawn (got: {start_method}).\n"
+            "To use a different start_method use:\n\t\t"
+            " torch.multiprocessing.start_processes(...)"
+        )
+        warnings.warn(msg, FutureWarning, stacklevel=2)
+    return start_processes(fn, args, nprocs, join, daemon, start_method="spawn")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nested/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nested/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..38baafa0cf951d7d994c2629a0332b2e1f2496a1
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nested/__init__.py
@@ -0,0 +1,465 @@
+# mypy: allow-untyped-defs
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+from torch import SymInt, Tensor
+from torch._C import _add_docstr, _nested  # type: ignore[attr-defined]
+
+from torch.types import _device as Device, _dtype as DType
+
+__all__ = [
+    "to_padded_tensor",
+    "as_nested_tensor",
+    "nested_tensor",
+    "nested_tensor_from_jagged",
+    "narrow",
+    "masked_select",
+]
+
+# Nested Tensor constructor functions
+
+
+def as_nested_tensor(
+    ts: Union[Tensor, List[Tensor], Tuple[Tensor, ...]],
+    dtype: Optional[DType] = None,
+    device: Optional[Device] = None,
+    layout=None
+) -> Tensor:
+    r"""
+    Constructs a nested tensor preserving autograd history from a tensor or a list / tuple of
+    tensors.
+
+    If a nested tensor is passed, it will be returned directly unless the device / dtype / layout
+    differ. Note that converting device / dtype will result in a copy, while converting layout
+    is not currently supported by this function.
+
+    If a non-nested tensor is passed, it is treated as a batch of constituents of consistent size.
+    A copy will be incurred if the passed device / dtype differ from those of the input OR if
+    the input is non-contiguous. Otherwise, the input's storage will be used directly.
+
+    If a tensor list is provided, tensors in the list are always copied during construction of
+    the nested tensor.
+
+    Args:
+        ts (Tensor or List[Tensor] or Tuple[Tensor]): a tensor to treat as a nested tensor OR a
+            list / tuple of tensors with the same ndim
+
+    Keyword arguments:
+        dtype (:class:`torch.dtype`, optional): the desired type of returned nested tensor.
+            Default: if None, same :class:`torch.dtype` as leftmost tensor in the list.
+        device (:class:`torch.device`, optional): the desired device of returned nested tensor.
+            Default: if None, same :class:`torch.device` as leftmost tensor in the list
+        layout (:class:`torch.layout`, optional): the desired layout of returned nested tensor.
+            Only strided and jagged layouts are supported. Default: if None, the strided layout.
+
+    Example::
+
+        >>> a = torch.arange(3, dtype=torch.float, requires_grad=True)
+        >>> b = torch.arange(5, dtype=torch.float, requires_grad=True)
+        >>> nt = torch.nested.as_nested_tensor([a, b])
+        >>> nt.is_leaf
+        False
+        >>> fake_grad = torch.nested.nested_tensor([torch.ones_like(a), torch.zeros_like(b)])
+        >>> nt.backward(fake_grad)
+        >>> a.grad
+        tensor([1., 1., 1.])
+        >>> b.grad
+        tensor([0., 0., 0., 0., 0.])
+        >>> c = torch.randn(3, 5, requires_grad=True)
+        >>> nt2 = torch.nested.as_nested_tensor(c)
+    """
+    is_tensor_list = isinstance(ts, (list, tuple)) and all(isinstance(t, Tensor) for t in ts)
+    if not isinstance(ts, Tensor) and not is_tensor_list:
+        raise TypeError(
+            "as_nested_tensor(): Expected first argument to be a tensor or a list / tuple of tensors "
+        )
+    # convert tuple -> list if needed
+    if is_tensor_list and not isinstance(ts, list):
+        ts = list(ts)
+
+    if isinstance(ts, Tensor) and ts.dim() < 2:
+        raise RuntimeError("as_nested_tensor(): Expected tensor argument to have dim() > 1")
+
+    if isinstance(ts, Tensor) and ts.is_nested:
+        if layout == ts.layout:
+            # return input directly or input copied to device / dtype
+            return ts.to(device=device, dtype=dtype)
+        else:
+            # TODO: Just use nt.to(layout=layout) when it exists.
+            raise RuntimeError(
+                "as_nested_tensor(): Converting between nested tensor layouts is not supported")
+
+    if layout is None:
+        layout = torch.strided
+    if layout == torch.strided:
+        if isinstance(ts, Tensor):
+            # contiguous() might be necessary to get flattened view.
+            # we could probably be more precise about when to do this as an optimization
+            buffer = ts.contiguous().view(-1).to(device=device, dtype=dtype)
+            nested_sizes = torch.tensor([t.shape for t in ts])
+            return torch._nested_view_from_buffer(
+                buffer,
+                nested_sizes,
+                *torch._nested_compute_contiguous_strides_offsets(nested_sizes))
+        else:
+            assert isinstance(ts, list)
+            return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)
+    elif layout == torch.jagged:
+        if isinstance(ts, Tensor):
+            if device is None:
+                device = ts.device
+
+            # contiguous() might be necessary to get flattened view.
+            # we could probably be more precise about when to do this as an optimization
+            values = ts.contiguous().flatten(0, 1).to(device=device, dtype=dtype)
+            batch_size = ts.shape[0]
+            seq_len = ts.shape[1]
+            offsets = torch.arange(0, batch_size * seq_len + 1, seq_len,
+                                   device=device, dtype=torch.int64)
+
+            from torch.nested._internal.nested_tensor import nested_view_from_values_offsets
+
+            return nested_view_from_values_offsets(
+                values, offsets, min_seqlen=seq_len, max_seqlen=seq_len
+            )
+        else:
+            from torch.nested._internal.nested_tensor import jagged_from_list
+
+            assert isinstance(ts, list)
+            nt, _ = jagged_from_list(ts, offsets=None, device=device, dtype=dtype)
+            return nt
+    else:
+        raise RuntimeError(f"Specified layout is unsupported for nested tensors: {layout}")
+
+
+# Note: This not only adds doc strings for the nested ops, but
+# also connects the torch.nested Python namespace to the torch._C._nested builtins.
+
+to_padded_tensor = _add_docstr(
+    _nested.nested_to_padded_tensor,
+    r"""
+to_padded_tensor(input, padding, output_size=None, out=None) -> Tensor
+
+Returns a new (non-nested) Tensor by padding the :attr:`input` nested tensor.
+The leading entries will be filled with the nested data,
+while the trailing entries will be padded.
+
+.. warning::
+
+    :func:`to_padded_tensor` always copies the underlying data,
+    since the nested and the non-nested tensors differ in memory layout.
+
+Args:
+    padding (float): The padding value for the trailing entries.
+
+Keyword args:
+    output_size (Tuple[int]): The size of the output tensor.
+                              If given, it must be large enough to contain all nested data;
+                              else, will infer by taking the max size of each nested sub-tensor along each dimension.
+    out (Tensor, optional): the output tensor.
+
+Example::
+
+    >>> nt = torch.nested.nested_tensor([torch.randn((2, 5)), torch.randn((3, 4))])
+    nested_tensor([
+      tensor([[ 1.6862, -1.1282,  1.1031,  0.0464, -1.3276],
+              [-1.9967, -1.0054,  1.8972,  0.9174, -1.4995]]),
+      tensor([[-1.8546, -0.7194, -0.2918, -0.1846],
+              [ 0.2773,  0.8793, -0.5183, -0.6447],
+              [ 1.8009,  1.8468, -0.9832, -1.5272]])
+    ])
+    >>> pt_infer = torch.nested.to_padded_tensor(nt, 0.0)
+    tensor([[[ 1.6862, -1.1282,  1.1031,  0.0464, -1.3276],
+             [-1.9967, -1.0054,  1.8972,  0.9174, -1.4995],
+             [ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000]],
+            [[-1.8546, -0.7194, -0.2918, -0.1846,  0.0000],
+             [ 0.2773,  0.8793, -0.5183, -0.6447,  0.0000],
+             [ 1.8009,  1.8468, -0.9832, -1.5272,  0.0000]]])
+    >>> pt_large = torch.nested.to_padded_tensor(nt, 1.0, (2, 4, 6))
+    tensor([[[ 1.6862, -1.1282,  1.1031,  0.0464, -1.3276,  1.0000],
+             [-1.9967, -1.0054,  1.8972,  0.9174, -1.4995,  1.0000],
+             [ 1.0000,  1.0000,  1.0000,  1.0000,  1.0000,  1.0000],
+             [ 1.0000,  1.0000,  1.0000,  1.0000,  1.0000,  1.0000]],
+            [[-1.8546, -0.7194, -0.2918, -0.1846,  1.0000,  1.0000],
+             [ 0.2773,  0.8793, -0.5183, -0.6447,  1.0000,  1.0000],
+             [ 1.8009,  1.8468, -0.9832, -1.5272,  1.0000,  1.0000],
+             [ 1.0000,  1.0000,  1.0000,  1.0000,  1.0000,  1.0000]]])
+    >>> pt_small = torch.nested.to_padded_tensor(nt, 2.0, (2, 2, 2))
+    RuntimeError: Value in output_size is less than NestedTensor padded size. Truncation is not supported.
+
+""",
+)
+
+def nested_tensor(tensor_list, *, dtype=None, layout=None, device=None, requires_grad=False, pin_memory=False) -> Tensor:
+    r"""
+Constructs a nested tensor with no autograd history (also known as a "leaf tensor", see
+:ref:`Autograd mechanics `) from :attr:`tensor_list` a list of tensors.
+
+Args:
+    tensor_list (List[array_like]): a list of tensors, or anything that can be passed to torch.tensor,
+    where each element of the list has the same dimensionality.
+
+Keyword arguments:
+    dtype (:class:`torch.dtype`, optional): the desired type of returned nested tensor.
+        Default: if None, same :class:`torch.dtype` as leftmost tensor in the list.
+    layout (:class:`torch.layout`, optional): the desired layout of returned nested tensor.
+        Only strided and jagged layouts are supported. Default: if None, the strided layout.
+    device (:class:`torch.device`, optional): the desired device of returned nested tensor.
+        Default: if None, same :class:`torch.device` as leftmost tensor in the list
+    requires_grad (bool, optional): If autograd should record operations on the
+        returned nested tensor. Default: ``False``.
+    pin_memory (bool, optional): If set, returned nested tensor would be allocated in
+        the pinned memory. Works only for CPU tensors. Default: ``False``.
+
+Example::
+
+    >>> a = torch.arange(3, dtype=torch.float, requires_grad=True)
+    >>> b = torch.arange(5, dtype=torch.float, requires_grad=True)
+    >>> nt = torch.nested.nested_tensor([a, b], requires_grad=True)
+    >>> nt.is_leaf
+    True
+    """
+    if layout is None:
+        layout = torch.strided
+    if layout == torch.strided:
+        return _nested.nested_tensor(
+            tensor_list,
+            dtype=dtype,
+            device=device,
+            requires_grad=requires_grad,
+            pin_memory=pin_memory)
+    elif layout == torch.jagged:
+        # Need to wrap lists of scalars as tensors
+        list_of_tensors = [t if isinstance(t, Tensor) else torch.as_tensor(t) for t in tensor_list]
+
+        from torch.nested._internal.nested_tensor import jagged_from_list
+
+        with torch.no_grad():
+            nt, _ = jagged_from_list(list_of_tensors, offsets=None, device=device, dtype=dtype)
+
+        nt.requires_grad_(requires_grad)
+        if pin_memory:
+            nt = nt.pin_memory()  # type: ignore[assignment]
+
+        return nt
+    else:
+        raise RuntimeError(f"Specified layout is unsupported for nested tensors: {layout}")
+
+
+def narrow(tensor: Tensor, dim: int, start: Union[int, Tensor], length: Union[int, Tensor], layout=torch.strided) -> Tensor:
+    r"""
+Constructs a nested tensor (which might be a view) from :attr:`tensor`, a strided tensor. This follows
+similar semantics to torch.Tensor.narrow, where in the :attr:`dim`-th dimension the new nested tensor
+shows only the elements in the interval `[start, start+length)`. As nested representations
+allow for a different `start` and `length` at each 'row' of that dimension, :attr:`start` and :attr:`length`
+can also be tensors of shape `tensor.shape[0]`.
+
+There's some differences depending on the layout you use for the nested tensor. If using strided layout,
+torch.narrow will do a copy of the narrowed data into a contiguous NT with strided layout, while
+jagged layout narrow() will create a non-contiguous view of your original strided tensor. This particular
+representation is really useful for representing kv-caches in Transformer models, as specialized
+SDPA kernels can deal with format easily, resulting in performance improvements.
+
+
+Args:
+    tensor (:class:`torch.Tensor`): a strided tensor, which will be used as the underlying data
+        for the nested tensor if using the jagged layout or will be copied for the strided layout.
+    dim (int): the dimension where narrow will be applied. Only `dim=1` is supported for the
+        jagged layout, while strided supports all dim
+    start (Union[int, :class:`torch.Tensor`]): starting element for the narrow operation
+    length (Union[int, :class:`torch.Tensor`]): number of elements taken during the narrow op
+
+Keyword arguments:
+    layout (:class:`torch.layout`, optional): the desired layout of returned nested tensor.
+        Only strided and jagged layouts are supported. Default: if None, the strided layout.
+
+Example::
+
+    >>> starts = torch.tensor([0, 1, 2, 3, 4], dtype=torch.int64)
+    >>> lengths = torch.tensor([3, 2, 2, 1, 5], dtype=torch.int64)
+    >>> narrow_base = torch.randn(5, 10, 20)
+    >>> nt_narrowed = torch.nested.narrow(narrow_base, 1, starts, lengths, layout=torch.jagged)
+    >>> nt_narrowed.is_contiguous()
+    False
+    """
+    if not isinstance(start, (int, SymInt, Tensor)):
+        raise RuntimeError("start must be an integer or a tensor")
+
+    if not isinstance(length, (int, SymInt, Tensor)):
+        raise RuntimeError("length must be an integer or a tensor")
+
+    if layout == torch.strided:
+        if isinstance(start, Tensor) or isinstance(length, Tensor):
+            raise RuntimeError("start and length must be integers for the strided layout NT impl")
+        # TODO: switch to as_nested_tensor(tensor) when it is available
+        nt = as_nested_tensor(torch.unbind(tensor), layout=torch.strided).narrow(dim, start, length)
+    elif layout == torch.jagged:
+        if dim != 1:
+            raise RuntimeError("jagged layout only supports dim=1")
+
+        from torch.nested._internal.nested_tensor import jagged_from_tensor_and_lengths
+
+        if isinstance(start, (int, SymInt)):
+            start = torch.tensor([start], device=tensor.device, dtype=torch.int64)
+
+        if isinstance(length, (int, SymInt)):
+            length = torch.tensor([length], device=tensor.device, dtype=torch.int64)
+
+        nt, _, _ = jagged_from_tensor_and_lengths(tensor, start, length)
+    else:
+        raise RuntimeError(f"Specified layout is unsupported for nested narrow: {layout}")
+
+    return nt
+
+
+def nested_tensor_from_jagged(
+    values: Tensor,
+    offsets: Optional[Tensor] = None,
+    lengths: Optional[Tensor] = None,
+    jagged_dim: Optional[int] = None,
+    min_seqlen: Optional[int] = None,
+    max_seqlen: Optional[int] = None,
+) -> Tensor:
+    r"""
+Constructs a jagged layout nested tensor from the given jagged components. The jagged layout
+consists of a required values buffer with the jagged dimension packed into a single dimension.
+The offsets / lengths metadata determines how this dimension is split into batch elements
+and are expected to be allocated on the same device as the values buffer.
+
+Expected metadata formats:
+    * offsets: Indices within the packed dimension splitting it into heterogeneously-sized
+      batch elements. Example: [0, 2, 3, 6] indicates that a packed jagged dim of size 6
+      should be conceptually split into batch elements of length [2, 1, 3]. Note that both the
+      beginning and ending offsets are required for kernel convenience (i.e. shape batch_size + 1).
+    * lengths: Lengths of the individual batch elements; shape == batch_size. Example: [2, 1, 3]
+      indicates that a packed jagged dim of size 6 should be conceptually split into batch
+      elements of length [2, 1, 3].
+
+Note that it can be useful to provide both offsets and lengths. This describes a nested tensor
+with "holes", where the offsets indicate the start position of each batch item and the length
+specifies the total number of elements (see example below).
+
+The returned jagged layout nested tensor will be a view of the input values tensor.
+
+Args:
+    values (:class:`torch.Tensor`): The underlying buffer in the shape of
+        (sum_B(*), D_1, ..., D_N). The jagged dimension is packed into a single dimension,
+        with the offsets / lengths metadata used to distinguish batch elements.
+    offsets (optional :class:`torch.Tensor`): Offsets into the jagged dimension of shape B + 1.
+    lengths (optional :class:`torch.Tensor`): Lengths of the batch elements of shape B.
+    jagged_dim (optional int): Indicates which dimension in values is the packed jagged
+        dimension. If None, this is set to dim=1 (i.e. the dimension immediately following
+        the batch dimension). Default: None
+    min_seqlen (optional int): If set, uses the specified value as the cached minimum sequence
+        length for the returned nested tensor. This can be a useful alternative to computing
+        this value on-demand, possibly avoiding a GPU -> CPU sync. Default: None
+    max_seqlen (optional int): If set, uses the specified value as the cached maximum sequence
+        length for the returned nested tensor. This can be a useful alternative to computing
+        this value on-demand, possibly avoiding a GPU -> CPU sync. Default: None
+
+Example::
+
+    >>> values = torch.randn(12, 5)
+    >>> offsets = torch.tensor([0, 3, 5, 6, 10, 12])
+    >>> nt = nested_tensor_from_jagged(values, offsets)
+    >>> # 3D shape with the middle dimension jagged
+    >>> nt.shape
+    torch.Size([5, j2, 5])
+    >>> # Length of each item in the batch:
+    >>> offsets.diff()
+    tensor([3, 2, 1, 4, 2])
+
+    >>> values = torch.randn(6, 5)
+    >>> offsets = torch.tensor([0, 2, 3, 6])
+    >>> lengths = torch.tensor([1, 1, 2])
+    >>> # NT with holes
+    >>> nt = nested_tensor_from_jagged(values, offsets, lengths)
+    >>> a, b, c = nt.unbind()
+    >>> # Batch item 1 consists of indices [0, 1)
+    >>> torch.equal(a, values[0:1, :])
+    True
+    >>> # Batch item 2 consists of indices [2, 3)
+    >>> torch.equal(b, values[2:3, :])
+    True
+    >>> # Batch item 3 consists of indices [3, 5)
+    >>> torch.equal(c, values[3:5, :])
+    True
+    """
+    from torch.fx._symbolic_trace import is_fx_tracing
+    if is_fx_tracing():
+        raise RuntimeError(
+            "torch.nested.nested_tensor_from_jagged does not support tracing with fx.symbolic_trace. "
+            "Use fx.wrap to wrap the function that calls nested_tensor_from_jagged."
+        )
+
+    if offsets is None:
+        if lengths is None:
+            raise RuntimeError(
+                "nested_tensor_from_jagged(): At least one of offsets or lengths is required."
+            )
+        else:
+            # TODO: Truly support offsets=None at some point?
+            # For now, just convert lengths -> offsets for kernel convenience
+            offsets = F.pad(lengths.cumsum(0), (1, 0))
+            lengths = None
+
+    if jagged_dim is None:
+        jagged_dim = 1
+
+    from torch.nested._internal.nested_tensor import nested_view_from_values_offsets_lengths
+
+    return nested_view_from_values_offsets_lengths(
+        values, offsets, lengths, ragged_idx=jagged_dim, min_seqlen=min_seqlen, max_seqlen=max_seqlen)
+
+def masked_select(tensor: Tensor, mask: Tensor) -> Tensor:
+    r"""
+    Constructs a nested tensor given a strided tensor input and a strided mask, the resulting jagged layout nested tensor
+    will have values retain values where the mask is equal to True. The dimensionality of the mask is preserved and is
+    represented with the offsets, this is unlike :func:`masked_select` where the output is collapsed to a 1D tensor.
+
+    Args:
+    tensor (:class:`torch.Tensor`): a strided tensor from which the jagged layout nested tensor is constructed from.
+    mask (:class:`torch.Tensor`): a strided mask tensor which is applied to the tensor input
+
+    Example::
+
+    >>> tensor = torch.randn(3, 3)
+    >>> mask = torch.tensor([[False, False, True], [True, False, True], [False, False, True]])
+    >>> nt = torch.nested.masked_select(tensor, mask)
+    >>> nt.shape
+    torch.Size([3, j4])
+    >>> # Length of each item in the batch:
+    >>> nt.offsets().diff()
+    tensor([1, 2, 1])
+
+    >>> tensor = torch.randn(6, 5)
+    >>> mask = torch.tensor([False])
+    >>> nt = torch.nested.masked_select(tensor, mask)
+    >>> nt.shape
+    torch.Size([6, j5])
+    >>> # Length of each item in the batch:
+    >>> nt.offsets().diff()
+    tensor([0, 0, 0, 0, 0, 0])
+    """
+    if tensor.layout != torch.strided:
+        raise RuntimeError(
+            f"torch.nested.masked_select requires a strided tensor, given {tensor.layout}"
+        )
+
+    if mask.layout != torch.strided:
+        raise RuntimeError(
+            f"torch.nested.masked_select requires a strided mask, given: {mask.layout}"
+        )
+    res_values = tensor.masked_select(mask)
+    expanded_mask = mask.expand(tensor.shape)
+    res_lengths = expanded_mask.sum(dim=tensor.ndim - 1).view(-1)
+
+    from torch.nested._internal.nested_tensor import (
+        nested_view_from_values_offsets,
+    )
+
+    return nested_view_from_values_offsets(
+        values=res_values,
+        offsets=F.pad(res_lengths.cumsum(dim=0), (1, 0)),
+    )
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@@ -0,0 +1,564 @@
+# mypy: allow-untyped-defs
+from typing import *  # noqa: F403
+from typing import Tuple
+
+import torch
+from torch._C import DispatchKey, DispatchKeySet
+from torch._prims_common import is_expandable_to
+from torch.utils.weak import WeakTensorKeyDictionary
+
+
+_tensor_id_counter = 0
+_tensor_symint_registry = WeakTensorKeyDictionary()
+
+
+def get_tensor_symint(tensor, *, coeff=1):
+    from torch._subclasses.fake_tensor import FakeTensor
+    from torch._subclasses.functional_tensor import mb_unwrap_functional_tensor
+
+    # NB: Only FakeTensor is associated with a memo
+    tensor = mb_unwrap_functional_tensor(tensor)
+    if isinstance(tensor, FakeTensor):
+        return tensor.get_nested_int(coeff=coeff)
+
+    global _tensor_id_counter
+
+    tensor_symint = _tensor_symint_registry.get(tensor)
+    if tensor_symint is None:
+        tensor_symint = torch._C._get_nested_int(_tensor_id_counter, coeff)
+        _tensor_id_counter += 1
+        _tensor_symint_registry[tensor] = tensor_symint
+    return tensor_symint
+
+
+# SDPA metadata; max / min seqlens are needed for e.g. flash
+def _get_sdpa_extreme_seqlen(func, tensor):
+    return int(func(tensor).item())
+
+
+def _store_val_in_tensor(val) -> torch.Tensor:
+    # hack to get dynamic shapes support: store in a (val, 0) shaped tensor
+    return torch.zeros(val, 0)
+
+
+def _load_val_from_tensor(t: torch.Tensor):
+    return t.shape[0]
+
+
+class NestedTensor(torch.Tensor):
+    _values: torch.Tensor  # type: ignore[assignment]
+    _offsets: torch.Tensor
+    _lengths: Optional[torch.Tensor]
+    # NOTE [ Nested ints for ragged sizes and strides ]
+    #
+    # Jagged layout tensors are tensors that represent a n-dim tensor with a
+    # ragged dimension, but are backed by an (n-1)-dim tensor underneath, e.g.,
+    # a jagged tensor with outer shape [B, x, D] is represented internally by a
+    # tensor with shape [sum(x), D] where we introduce what we call a nested int
+    # denoted as "x" here (but sometimes denoted with "*" to
+    # represent the ragged dimension, and sum(x) represents the dim of the inner
+    # tensor or equivalently the sum of all the sizes of the constituent
+    # tensors' varying lengths.
+    #
+    # We also use nested ints to represent the strides of this tensor.
+    # For example, a jagged tensor with shape [B, x, D] can be strided in two
+    # ways: [xD, D, 1] and [x, 1, sum(x)], where xD represents x multiplied by D
+    _size: Tuple[int, ...]
+    _strides: Tuple[int, ...]
+    # Indicates that the nth dimension is ragged
+    _ragged_idx: int
+    _metadata_cache: Dict[str, Any]
+
+    @staticmethod
+    def __new__(
+        cls,
+        values,
+        offsets,
+        *,
+        lengths=None,
+        **kwargs,
+    ):
+        ks = DispatchKeySet(DispatchKey.NestedTensor)
+        ks = ks.add(DispatchKey.AutogradNestedTensor)
+
+        # Only support jagged for now.
+        assert offsets is not None
+        assert offsets.ndim == 1
+        assert not isinstance(values, NestedTensor)
+        assert values.device == offsets.device
+
+        # Query cache for the symint associated with offsets or lengths
+        # (create a new one if needed).
+        ragged_source = offsets if lengths is None else lengths
+        ragged_size = get_tensor_symint(ragged_source, coeff=1)
+        _ragged_idx = kwargs.get("_ragged_idx", 1)
+        B = offsets.shape[0] - 1
+        if lengths is not None:
+            assert B == lengths.shape[0]
+
+        # subtract 1 to convert to values dim space
+        r = _ragged_idx - 1
+        _size = (B, *values.shape[:r], ragged_size, *values.shape[r + 1 :])
+        stride = values.stride()
+        _strides = (ragged_size * stride[r], *stride)
+
+        r = torch.Tensor._make_wrapper_subclass(  # type: ignore[attr-defined]
+            cls,
+            _size,
+            _strides,
+            0,
+            torch.contiguous_format,
+            values.dtype,
+            torch.jagged,
+            values.device,
+            False,
+            kwargs.get("requires_grad", False),
+            "sizes",
+            False,
+            True,  # dispatch_layout
+            ks,
+            # don't try to calculate storage based on non-zero size
+            storage_size=values.untyped_storage().size(),
+        )
+        r._ragged_idx = _ragged_idx
+        r._size = _size
+        r._strides = _strides
+
+        return r
+
+    def __init__(self, values, offsets, *, lengths=None, **kwargs):
+        super().__init__()
+
+        self._values = values
+        self._offsets = offsets
+        self._lengths = lengths
+
+        # holds properties that are computed lazily
+        self._metadata_cache = kwargs.get("_metadata_cache") or {}
+
+        # collapsed ragged dim must always be dynamic
+        torch._dynamo.maybe_mark_dynamic(self, self._ragged_idx)
+        torch._dynamo.maybe_mark_dynamic(self._values, self._ragged_idx - 1)
+
+        # min / max sequence length should be dynamic if present
+        max_seqlen_tensor = self._metadata_cache.get("max_seqlen", None)
+        if max_seqlen_tensor is not None:
+            torch._dynamo.mark_dynamic(max_seqlen_tensor, 0)
+        min_seqlen_tensor = self._metadata_cache.get("min_seqlen", None)
+        if min_seqlen_tensor is not None:
+            torch._dynamo.mark_dynamic(min_seqlen_tensor, 0)
+
+    def values(self):
+        # dispatch to get proper view relationship
+        return torch._nested_get_values(self)  # type: ignore[attr-defined]
+
+    def offsets(self):
+        return self._offsets
+
+    def lengths(self):
+        return self._lengths
+
+    # Private accessor functions for min / max sequence length. They're
+    # purposefully not @properties because those don't work with PT2 (yet).
+    # These compute / cache if not present.
+    # TODO: Revisit this when @properties are better supported by PT2. I think the ideal
+    # state would be to have public @properties for min / max sequence length that compile
+    # (including setters).
+    def _get_max_seqlen(self):
+        max_seqlen_tensor = self._max_seqlen_tensor
+        if max_seqlen_tensor is None:
+            # compute & cache
+            max_val = _get_sdpa_extreme_seqlen(
+                torch.max,
+                self._offsets.diff() if self._lengths is None else self._lengths,
+            )
+            max_seqlen_tensor = _store_val_in_tensor(max_val)
+            self._metadata_cache["max_seqlen"] = max_seqlen_tensor
+        return _load_val_from_tensor(max_seqlen_tensor)
+
+    def _get_min_seqlen(self):
+        min_seqlen_tensor = self._min_seqlen_tensor
+        if min_seqlen_tensor is None:
+            # compute & cache
+            min_val = _get_sdpa_extreme_seqlen(
+                torch.min,
+                self._offsets.diff() if self._lengths is None else self._lengths,
+            )
+            min_seqlen_tensor = _store_val_in_tensor(min_val)
+            self._metadata_cache["min_seqlen"] = min_seqlen_tensor
+        return _load_val_from_tensor(min_seqlen_tensor)
+
+    # Private accessors used for treating min / max seqlen as inner tensors for
+    # flatten / unflatten. These must be properties to work with the traceable wrapper
+    # subclass logic. These do not compute / cache if not present.
+    @property
+    def _max_seqlen_tensor(self) -> Optional[torch.Tensor]:
+        return self._metadata_cache.get("max_seqlen", None)
+
+    @property
+    def _min_seqlen_tensor(self) -> Optional[torch.Tensor]:
+        return self._metadata_cache.get("min_seqlen", None)
+
+    # These are old private @property accessors that are kept around for internal BC
+    # reasons. TODO: Remove these!
+    @property
+    def _max_seqlen(self):
+        return self._get_max_seqlen()
+
+    @property
+    def _min_seqlen(self):
+        return self._get_min_seqlen()
+
+    def __repr__(self):
+        # We should implement this in torch/_tensor_str.py instead
+        grad_fn_str = (
+            f", requires_grad={self.requires_grad}" if self.requires_grad else ""
+        )
+        if self.grad_fn:
+            grad_fn_str = f", grad_fn={self.grad_fn}"
+        return f"NestedTensor(size={self._size}, offsets={self._offsets}{grad_fn_str}, contiguous={self._lengths is None})"
+
+    def __reduce_ex__(self, proto):
+        state = torch._utils._get_obj_state(self)
+
+        # SymNodes are not serializable
+        assert "_size" in state and "_strides" in state
+        state = dict(state)
+        del state["_size"]
+        del state["_strides"]
+
+        # TODO: Update this to handle the other inner tensors
+        func = NestedTensor
+        args = (self._values, self._offsets)
+        return (torch._tensor._rebuild_from_type_v2, (func, type(self), args, state))
+
+    def __tensor_flatten__(self):
+        ctx = {
+            "requires_grad": self.requires_grad,
+            "ragged_idx": self._ragged_idx,
+        }
+        inner_tensors = ["_values", "_offsets"]
+        if self._lengths is not None:
+            inner_tensors.append("_lengths")
+        if self._min_seqlen_tensor is not None:
+            inner_tensors.append("_min_seqlen_tensor")
+        if self._max_seqlen_tensor is not None:
+            inner_tensors.append("_max_seqlen_tensor")
+        return inner_tensors, ctx
+
+    @staticmethod
+    def __tensor_unflatten__(inner_tensors: Dict, meta, outer_size, outer_stride):
+        from torch._subclasses.fake_tensor import FakeTensor
+
+        # inner tensors: _values, _offsets, [_lengths], [_min_seqlen], [_max_seqlen]
+        assert len(inner_tensors) >= 2 and len(inner_tensors) <= 5
+        values = inner_tensors["_values"]
+        offsets = inner_tensors["_offsets"]
+        lengths = inner_tensors.get("_lengths", None)
+        min_seqlen_tensor = inner_tensors.get("_min_seqlen_tensor", None)
+        max_seqlen_tensor = inner_tensors.get("_max_seqlen_tensor", None)
+
+        metadata_cache = {}
+        if min_seqlen_tensor is not None:
+            metadata_cache["min_seqlen"] = min_seqlen_tensor
+        if max_seqlen_tensor is not None:
+            metadata_cache["max_seqlen"] = max_seqlen_tensor
+        ragged_idx = meta["ragged_idx"]
+
+        # Alternatively, we could make it the caller's responsibility to
+        # cache it. But this heuristic seems simple enough.
+        ragged_source = offsets if lengths is None else lengths
+        if isinstance(ragged_source, FakeTensor):
+            ragged_size = outer_size[ragged_idx]
+            ragged_source.nested_int_memo = ragged_size
+
+        return NestedTensor(
+            values,
+            offsets=offsets,
+            lengths=lengths,
+            requires_grad=meta["requires_grad"],
+            _ragged_idx=ragged_idx,
+            _metadata_cache=metadata_cache,
+        )
+
+    @classmethod
+    def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
+        kwargs = {} if kwargs is None else kwargs
+
+        # Lazy import to avoid circular dependency
+        from .ops import lookup_jagged
+
+        fn = lookup_jagged(func, *args, **kwargs)
+        if fn is not None:
+            return fn(*args, **kwargs)
+
+        raise NotImplementedError(func)
+
+    @classmethod
+    def __torch_function__(cls, func, types, args=(), kwargs=None):
+        if kwargs is None:
+            kwargs = {}
+
+        from torch.fx.experimental.proxy_tensor import maybe_enable_thunkify
+
+        from .ops import jagged_torch_function
+
+        # This should be removed after
+        # https://github.com/pytorch/pytorch/pull/125941/ lands
+        with maybe_enable_thunkify():
+            try:
+                return jagged_torch_function(func, *args, **kwargs)
+            except NotImplementedError:
+                pass
+            with torch._C.DisableTorchFunctionSubclass():
+                return func(*args, **kwargs)
+
+
+# NB: These fake view autograd.Functions are superseded by real view ops. Don't use them!
+# TODO: Remove ViewBufferFromNested, ViewNestedFromBuffer, and buffer_from_jagged once the
+# internal BC period has passed.
+
+
+# Not actually a view!
+class ViewBufferFromNested(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x: NestedTensor):  # type: ignore[override]
+        ctx.save_for_backward(x.offsets())
+        ctx.metadata_cache = x._metadata_cache
+        ctx.ragged_idx = x._ragged_idx
+        return x._values
+
+    @staticmethod
+    def backward(ctx, gO: torch.Tensor):  # type: ignore[override]
+        (offsets,) = ctx.saved_tensors
+        return NestedTensor(
+            gO,
+            offsets=offsets,
+            _metadata_cache=ctx.metadata_cache,
+            _ragged_idx=ctx.ragged_idx,
+        )
+
+
+# Not actually a view!
+class ViewNestedFromBuffer(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        values: torch.Tensor,
+        offsets: torch.Tensor,
+        metadata_cache: Optional[Dict[str, Any]] = None,
+    ):  # type: ignore[override]
+        # maintain BC with this usages of this where the seqlens are stuffed
+        # directly into the metadata cache as non-Tensors / ints
+        if metadata_cache is not None:
+            min_seqlen = metadata_cache.get("min_seqlen", None)
+            max_seqlen = metadata_cache.get("max_seqlen", None)
+            if min_seqlen is not None and not isinstance(min_seqlen, torch.Tensor):
+                metadata_cache["min_seqlen"] = _store_val_in_tensor(min_seqlen)
+            if max_seqlen is not None and not isinstance(max_seqlen, torch.Tensor):
+                metadata_cache["max_seqlen"] = _store_val_in_tensor(max_seqlen)
+        return NestedTensor(
+            values.detach(),
+            offsets=offsets,
+            _metadata_cache=metadata_cache,
+        )
+
+    @staticmethod
+    def backward(ctx, gO: NestedTensor):  # type: ignore[override]
+        return gO._values, None, None
+
+
+def buffer_from_jagged(jagged):
+    return ViewBufferFromNested.apply(jagged)
+
+
+# Need to make it obvious that users should be passing in offsets
+def jagged_from_list(
+    tensors: List[torch.Tensor],
+    offsets: Optional[torch.Tensor],
+    dtype=None,
+    device=None,
+) -> Tuple[NestedTensor, torch.Tensor]:
+    """Constructs a NestedTensor backed by jagged layout from a list of tensors"""
+
+    if not len(set(t.dtype for t in tensors)) == 1:  # noqa: C401
+        raise RuntimeError(
+            "When constructing a nested tensor, all tensors in list must have the same dtype"
+        )
+    if not len(set(t.device for t in tensors)) == 1:  # noqa: C401
+        raise RuntimeError(
+            "When constructing a nested tensor, all tensors in list must be on the same device"
+        )
+
+    # Check that the NT is representable by the jagged layout.
+    # Jagged layout represents (B, *, D_0, D_1, ..., D_N), where the only
+    # raggedness allowed is for the single dim immediately adjacent to the batch dim.
+    sizes = [t.shape for t in tensors]
+    non_first_sizes = [s[1:] for s in sizes]
+    at_most_first_ragged = all(s == non_first_sizes[0] for s in non_first_sizes)
+    if not at_most_first_ragged:
+        raise RuntimeError(
+            "Cannot represent given tensor list as a nested tensor with the jagged layout. "
+            "Note that the jagged layout only represents shapes of the form "
+            "(B, *, D_0, D_1, ..., D_N), with only * allowed to be ragged."
+        )
+
+    # Set properties appropriately.
+    values = torch.cat(tensors, dim=0)
+    to_kwargs = {}
+    if device is not None:
+        to_kwargs["device"] = device
+    if dtype is not None:
+        to_kwargs["dtype"] = dtype
+    values = values.to(**to_kwargs)
+
+    # Calculate jagged offsets if not provided.
+    if offsets is None:
+        # Jagged layout specifies that offsets are stored as int64 on the same device as values.
+        # TODO: An alternative way to construct offsets is to use F.pad. This avoids creating
+        # an extra leaf tensor during the forward, potentially resolving compatibility issues.
+        offsets = torch.cat(
+            [
+                torch.zeros(1, dtype=torch.int64, device=values.device),
+                torch.tensor([s[0] for s in sizes], device=values.device).cumsum(dim=0),
+            ]
+        )
+
+    # compute this now since it's easy
+    min_seqlen = min(t.shape[0] for t in tensors)
+    max_seqlen = max(t.shape[0] for t in tensors)
+    ret_nt = nested_view_from_values_offsets(
+        values, offsets, min_seqlen=min_seqlen, max_seqlen=max_seqlen
+    )
+    return (ret_nt, offsets)  # type: ignore[return-value]
+
+
+def jagged_from_tensor_and_lengths(
+    tensor: torch.Tensor, starts: torch.Tensor, lengths: torch.Tensor
+) -> Tuple[NestedTensor, torch.Tensor, Optional[torch.Tensor]]:
+    """Constructs a NestedTensor backed by jagged layout from a tensor, starts of sequences, and sequence lengths"""
+    batch_size = tensor.shape[0]
+    if is_expandable_to(starts.shape, (batch_size,)) and is_expandable_to(
+        lengths.shape, (batch_size,)
+    ):
+        start_list = starts.expand(batch_size)
+        length_list = lengths.expand(batch_size)
+    else:
+        raise RuntimeError(
+            "When constructing a jagged nested tensor using narrow(), "
+            "your start and length must be Tensors that broadcast to input.shape[0]"
+        )
+
+    # Calculate jagged offsets
+    assert (
+        len(tensor.shape) >= 2
+    ), "tensor must at least be 2D for the nested narrow op to work"
+    max_seq_len = tensor.shape[1]
+    offset_lengths = max_seq_len * torch.arange(
+        0, batch_size, dtype=torch.int64, device=tensor.device
+    )
+    # Jagged layout specifies that offsets are stored as int64 on the same device as values.
+    offsets = torch.cat(
+        [
+            start_list + offset_lengths,
+            (start_list[-1] + offset_lengths[-1] + length_list[-1]).unsqueeze(0),
+        ]
+    )
+
+    # Reshape buffer to flatten the 1st and 2nd dimension (view used to enforce non-copy)
+    if len(tensor.shape) > 2:
+        values = tensor.view(-1, *tensor.shape[2:])
+    else:
+        values = tensor.view(-1)
+
+    # Check if offsets and lengths make it possibly contiguous and return a regular NT
+    is_contiguous = True
+    orig_dim = tensor.shape[1]
+    if torch.any(length_list[1:-1].ne(orig_dim)):
+        is_contiguous = False
+    if torch.any(offsets[1:-2].diff().ne(orig_dim)):
+        is_contiguous = False
+    if offsets[0] + length_list[0] != orig_dim:
+        is_contiguous = False
+
+    actual_max_seqlen = int(torch.max(lengths).item())
+    min_seqlen = int(torch.min(lengths).item())
+
+    if is_contiguous:
+        ret_nt = nested_view_from_values_offsets(
+            values[offsets[0] : offsets[-1]],
+            offsets - offsets[0],
+            min_seqlen=min_seqlen,
+            max_seqlen=actual_max_seqlen,
+        )
+    else:
+        ret_nt = nested_view_from_values_offsets_lengths(
+            values,
+            offsets,
+            length_list,
+            min_seqlen=min_seqlen,
+            max_seqlen=actual_max_seqlen,
+        )
+
+    return (ret_nt, offsets, None if is_contiguous else length_list)
+
+
+# NB: A dummy arg is required so that NestedTensor.__torch_dispatch__() is invoked
+# for _nested_view_from_values_offsets(). Sizes don't matter much, but they shouldn't be
+# 0/1 because the dummy can be fake-ified and we want to avoid specializing.
+# This arg is otherwise unused.
+_dummy_instance: Optional[torch.Tensor] = None
+
+
+def _nt_view_dummy() -> torch.Tensor:
+    global _dummy_instance
+    if _dummy_instance is None:
+        _dummy_instance = NestedTensor(
+            values=torch.zeros(3, 3, device="meta"),
+            offsets=torch.zeros(3, device="meta", dtype=torch.int64),
+        ).detach()
+    return _dummy_instance
+
+
+def nested_view_from_values_offsets(
+    values, offsets, ragged_idx=1, min_seqlen=None, max_seqlen=None
+):
+    min_seqlen_tensor = None
+    if min_seqlen is not None:
+        min_seqlen_tensor = _store_val_in_tensor(min_seqlen)
+
+    max_seqlen_tensor = None
+    if max_seqlen is not None:
+        max_seqlen_tensor = _store_val_in_tensor(max_seqlen)
+
+    return torch._nested_view_from_jagged(  # type: ignore[attr-defined]
+        values,
+        offsets,
+        _nt_view_dummy(),
+        None,
+        ragged_idx,
+        min_seqlen_tensor,
+        max_seqlen_tensor,
+    )  # type: ignore[return-value]
+
+
+def nested_view_from_values_offsets_lengths(
+    values, offsets, lengths, ragged_idx=1, min_seqlen=None, max_seqlen=None
+):
+    min_seqlen_tensor = None
+    if min_seqlen is not None:
+        min_seqlen_tensor = _store_val_in_tensor(min_seqlen)
+
+    max_seqlen_tensor = None
+    if max_seqlen is not None:
+        max_seqlen_tensor = _store_val_in_tensor(max_seqlen)
+
+    return torch._nested_view_from_jagged(  # type: ignore[attr-defined]
+        values,
+        offsets,
+        _nt_view_dummy(),
+        lengths,
+        ragged_idx,
+        min_seqlen_tensor,
+        max_seqlen_tensor,
+    )  # type: ignore[return-value]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nested/_internal/ops.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nested/_internal/ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed9a54f9dca932c99c11a44015a927f090176e12
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nested/_internal/ops.py
@@ -0,0 +1,1675 @@
+# mypy: allow-untyped-defs
+import functools
+import math
+import operator
+from typing import *  # noqa: F403
+
+import torch
+import torch.nn.functional as F
+from torch.fx.operator_schemas import normalize_function
+from torch.nested._internal.sdpa import jagged_scaled_dot_product_attention
+
+from .nested_tensor import NestedTensor
+
+
+__all__: List[Any] = []
+
+JAGGED_OPS_TABLE: Dict[Any, Any] = {}
+
+
+# Simplifying assumption: we assume that the batch dim is always the left-most
+# dim, and the ragged dim is always the second dim.
+def _outer_to_inner_dim(ndim, dim):
+    assert dim >= 0 and dim < ndim
+    return 0 if dim < 2 else dim - 1
+
+
+def _wrap_jagged_dim(
+    ndim, dim, op_name, convert_to_inner_dim=True, allow_batch_dim=False
+):
+    from torch._prims_common import canonicalize_dims
+
+    wrapped = canonicalize_dims(ndim, dim)
+    if wrapped == 1:
+        raise RuntimeError(f"{op_name}(): not supported for NestedTensor on dim=1")
+    elif wrapped == 0 and not allow_batch_dim:
+        raise RuntimeError(f"{op_name}(): not supported for NestedTensor on dim=0")
+    return _outer_to_inner_dim(ndim, wrapped) if convert_to_inner_dim else wrapped
+
+
+def _wrap_jagged_dims(ndim, dims, op_name, ragged_idx=1):
+    """
+    For NestedTensor operators,
+    wraps dimensions to non-negative values,
+    and returns metadata related to reduction dimension(s).
+    """
+    from torch._prims_common import canonicalize_dims
+
+    assert isinstance(
+        dims, (tuple, list)
+    ), f"_wrap_jagged_dims(): cannot iterate over dimensions of type {type(dims)}"
+
+    wrapped_dims = [
+        canonicalize_dims(ndim, d) for d in dims
+    ]  # convert all indices to non-negative values
+
+    operate_on_batch = 0 in wrapped_dims
+    operate_on_ragged = ragged_idx in wrapped_dims
+    operate_on_non_batch = any(d != 0 and d != ragged_idx for d in wrapped_dims)
+
+    outer_to_inner_dim = tuple(
+        _outer_to_inner_dim(ndim, d) for d in wrapped_dims if d != 0
+    )
+
+    return outer_to_inner_dim, operate_on_batch, operate_on_ragged, operate_on_non_batch
+
+
+def check_schema(schema_str: str, func, *args, **kwargs) -> None:
+    named_arg_types = schema_str.split(", ")
+    num_optional_args = [x.endswith("?") for x in named_arg_types].count(True)
+    min_args = len(named_arg_types) - num_optional_args
+
+    # special case: ellipses allows for any number of unchecked args at the end
+    if named_arg_types[-1] == "...":
+        named_arg_types = named_arg_types[:-1]
+    else:
+        if not (len(args) >= min_args and len(args) <= len(named_arg_types)):
+            raise ValueError(
+                f"NestedTensor {func.__name__}({schema_str}): expected at least {min_args} "
+                f"arguments and at most {len(named_arg_types)} arguments, but got: "
+                f"{len(args)} arguments"
+            )
+
+    arg_type_check_fns = {
+        "t": lambda x: isinstance(x, torch.Tensor) and not isinstance(x, NestedTensor),
+        "jt": lambda x: isinstance(x, NestedTensor)
+        and x._lengths is None
+        and x._ragged_idx == 1,  # ops with "jt" require contiguous JT only
+        "jt_all": lambda x: isinstance(
+            x, NestedTensor
+        ),  # ops with "jt_all" can accept all kinds of JT
+        "any": lambda x: True,
+    }
+    for i, named_arg_type in enumerate(named_arg_types):
+        name, arg_type = named_arg_type.split(": ")
+        is_optional = arg_type.endswith("?")
+        normalized_arg_type = arg_type[:-1] if is_optional else arg_type
+        if normalized_arg_type not in arg_type_check_fns.keys():
+            raise AssertionError(f"Unknown arg type: {normalized_arg_type}")
+
+        if i >= len(args):
+            if not is_optional:
+                raise ValueError(
+                    f"NestedTensor {func.__name__}({schema_str}) "
+                    f"missing required argument: {name}"
+                )
+            continue
+
+        _check_fn = arg_type_check_fns[normalized_arg_type]
+
+        def check_fn(x, is_optional=is_optional):
+            if is_optional:
+                return x is None or _check_fn(x)
+            else:
+                return _check_fn(x)
+
+        if not check_fn(args[i]):
+            type_to_desc = {
+                "t": "tensor",
+                "t?": "optional tensor",
+                "jt": "contiguous jagged layout NestedTensor",
+                "jt_all": "jagged layout NestedTensor",
+                "any": "",
+            }
+
+            raise ValueError(
+                f"NestedTensor {func.__name__}({schema_str}): expected {name} to be a "
+                f"{type_to_desc[arg_type]}"
+            )
+
+
+def check_ragged_dim_same(
+    func, a: NestedTensor, a_name: str, b: NestedTensor, b_name: str
+) -> None:
+    # Calling into .shape here
+    if a._size[a._ragged_idx] != b._size[b._ragged_idx]:
+        raise RuntimeError(
+            f"NestedTensor {func.__name__}: expected {a_name} and {b_name} to have the "
+            "same exact offsets tensor."
+        )
+
+
+# returns True if the raggedness-relevant portions of the NT shape
+# match those of the specified size
+def raggedness_matches(nt, size):
+    end = nt._ragged_idx + 1
+    nt_ragged = nt._size[:end]
+    size_ragged = size[:end]
+    return len(nt_ragged) == len(size_ragged) and (
+        all(ns == s or s == -1 for ns, s in zip(nt_ragged, size_ragged))
+    )
+
+
+def squeeze_leading_ones(t):
+    # Note: [ Squeezing leading ones ]
+    #
+    # Squeeze leading ones from t.
+    #
+    # We want:
+    #   (B, j0, ?, ?) + (1, 1, ?, ?) -> (B, j0, ?, ?)
+    #   (B, j0, ?, ?) + (1, 1, 1, ?, ?) -> (1, B, j0, ?, ?)  (not yet supported)
+    #
+    # 1) Squeeze extra ones and grab values from NT
+    #   (1, 1, ?, ?) -> (?, ?)   and   (sum(*), ?, ?) -> (B, j0, ?, ?)
+    # 2) Do dense broadcasting:
+    #   (sum(*), ?, ?) + (?, ?) -> (sum(*), ?, ?)
+    # 3) Construct nested tensor
+    #   (sum(*), ?, ?) -> (B, j0, ?, ?)
+    #
+    # If unsqueezing on the 0th dim becomes supported, we would unsqueeze
+    # at step (4) and we would need to update this function to record how
+    # many ones we unsqueezed.
+    while t.dim() > 0 and t.shape[0] == 1:
+        t = t.squeeze(0)
+    return t
+
+
+def register_func(tables, aten_ops, schema_str):
+    if not isinstance(aten_ops, list):
+        aten_ops = [aten_ops]
+    if not isinstance(tables, list):
+        tables = [tables]
+
+    def wrapper(func):
+        for aten_op in aten_ops:
+
+            def get_inner(aten_op):
+                def inner(*args, **kwargs):
+                    check_schema(schema_str, func, *args, **kwargs)
+                    return func(aten_op, *args, **kwargs)
+
+                return inner
+
+            for table in tables:
+                table[aten_op] = get_inner(aten_op)
+        return func
+
+    return wrapper
+
+
+register_jagged_func = functools.partial(register_func, JAGGED_OPS_TABLE)
+
+
+def lookup_jagged(func, *args, **kwargs) -> Optional[Callable]:
+    dispatch_func = JAGGED_OPS_TABLE.get(func, None)
+    if dispatch_func is not None:
+        return dispatch_func
+
+    # Handle pointwise fallbacks
+    if torch.Tag.pointwise in func.tags:
+        # Assume there aren't additional tensors that aren't the "unary/binary" args
+        num_tensor_args = sum(isinstance(x, torch.Tensor) for x in args)
+        if num_tensor_args == 1:
+            # Build up the check schema string. The first tensor arg is assumed to be
+            # an NJT and other args are sent through as-is.
+            schema_parts = []
+            for arg in func._schema.arguments:
+                if isinstance(arg.type, torch.TensorType):
+                    schema_parts.append(f"{arg.name}: jt_all")
+                    break
+                else:
+                    schema_parts.append(f"{arg.name}: any")
+            schema_parts.append("...")
+            check_schema_str = ", ".join(schema_parts)
+            check_schema(check_schema_str, func, *args, **kwargs)
+            return functools.partial(jagged_unary_pointwise, func)
+        elif num_tensor_args == 2:
+            check_schema("lhs: any, rhs: any, ...", func, *args, **kwargs)
+            return functools.partial(jagged_binary_pointwise, func)
+
+    return None
+
+
+def extract_kwargs(arg):
+    kwargs = {
+        "offsets": arg.offsets(),
+        "_metadata_cache": arg._metadata_cache,
+        "_ragged_idx": arg._ragged_idx,
+    }
+    return kwargs
+
+
+def jagged_unary_pointwise(func, *args, **kwargs):
+    # assume if we get here that there is a single NJT input in the args
+    njt = next(arg for arg in args if isinstance(arg, NestedTensor))
+    return NestedTensor(
+        func(*(arg._values if arg is njt else arg for arg in args), **kwargs),
+        **extract_kwargs(njt),
+    )
+
+
+def jagged_binary_pointwise(func, *args, **kwargs):
+    a, b = args[0], args[1]
+    assert isinstance(a, NestedTensor) or isinstance(b, NestedTensor)
+
+    mismatch_error_msg = (
+        "cannot call binary pointwise function {} with inputs of shapes {} and {}"
+    )
+    # a is NT, b is NT
+    if isinstance(a, NestedTensor) and isinstance(b, NestedTensor):
+        # ex: (B, j0, D) + (B, j0, D)
+        # ex: (B, j0, D) + (B, j0, 1)
+        if raggedness_matches(a, b._size):
+            return NestedTensor(
+                func(a._values, b._values, *args[2:], **kwargs), **extract_kwargs(a)
+            )
+        raise RuntimeError(mismatch_error_msg.format(func.__name__, a._size, b._size))
+    # either a is NT or b is NT at this point
+    a_is_nt = isinstance(a, NestedTensor)
+    extracted_kwargs = extract_kwargs(a) if a_is_nt else extract_kwargs(b)
+
+    # === Handle broadcasting across the batch / ragged dims ===
+
+    # Easy case: take advantage of pre-existing broadcasting logic
+    # ex: (B, j0, ?, ?) + (?) -> (B, j0, ?, ?)
+    # ex: (B, j0, ?, ?) + (?, ?) -> (B, j0, ?, ?)
+    # ex: (B, j0, ?, ?) + (1, 1, ?, ?) -> (B, j0, ?, ?)
+    nt, t = (a, b) if a_is_nt else (b, a)
+    # See Note: [ Squeezing leading ones ]
+    if t.dim() > nt.dim():
+        raise NotImplementedError("NYI: broadcasting NT with T with larger dim")
+    t_squeezed = squeeze_leading_ones(t)
+    if nt.dim() >= t_squeezed.dim() + 2:
+        lhs, rhs = (nt._values, t_squeezed) if a_is_nt else (t_squeezed, nt._values)
+        return NestedTensor(func(lhs, rhs, *args[2:], **kwargs), **extracted_kwargs)
+
+    # Harder case: do manual broadcasting over unbound components
+    # when NT dim == non-NT dim
+    # ex: (B, j0, D_0, D_1) + (B, 1, D_0, D_1) -> (B, j0, D_0, D_1)
+    if a.dim() == b.dim():
+        # ex: (B, j0, D_0, D_1) + (1, 1, D_0, D_1) -> should
+        # be (B, j0, D_0, D_1) but not yet supported
+        if a.shape[0] != b.shape[0]:
+            raise RuntimeError(
+                mismatch_error_msg.format(func.__name__, a.shape, b.shape)
+            )
+
+        # need to use offsets to broadcast across ragged dim properly
+        # NB: inefficient fallback here; Triton codegen can help this
+        # TODO: Make this work with autograd
+        outputs = []
+        for a_comp, b_comp in zip(a.unbind(), b.unbind()):
+            outputs.append(func(a_comp, b_comp, *args[2:], **kwargs))
+        new_values = torch.cat(outputs, dim=0)
+        return NestedTensor(new_values, **extracted_kwargs)
+
+    # ex: (B, j0, D_0, D_1) + (A, B, 1, D_0, D_1) -> error because this breaks the invariant
+    # that ragged dim is wrt left-most batch dim
+    raise RuntimeError(mismatch_error_msg.format(func.__name__, a.shape, b.shape))
+
+
+def jagged_torch_function(func, *args, **kwargs):
+    # SDPA has special kernels that handle nested tensors.
+    # Dispatch to the correct implementation here
+    if func is torch._C._nn.scaled_dot_product_attention:
+        return jagged_scaled_dot_product_attention(*args, **kwargs)
+
+    if func.__name__ == "apply_":
+        func(args[0]._values, *args[1:], **kwargs)
+        return args[0]
+
+    # Handle flatten() here because it's CompositeImplicit.
+    if func.__name__ == "flatten":
+
+        def _flatten_sig(input, start_dim=0, end_dim=-1):
+            pass
+
+        _, new_kwargs = normalize_function(  # type: ignore[misc]
+            _flatten_sig, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+        )
+
+        inp = new_kwargs.pop("input")
+
+        # NB: stay in outer dim space because we're going to redispatch on a NT input
+        start_dim = _wrap_jagged_dim(
+            inp.dim(), new_kwargs["start_dim"], "flatten", convert_to_inner_dim=False
+        )
+        end_dim = _wrap_jagged_dim(
+            inp.dim(), new_kwargs["end_dim"], "flatten", convert_to_inner_dim=False
+        )
+
+        if start_dim == end_dim:
+            return inp
+
+        product = functools.reduce(operator.mul, inp.shape[start_dim : end_dim + 1])
+        new_shape = (*inp.shape[:start_dim], product, *inp.shape[end_dim + 1 :])
+
+        return inp.reshape(*new_shape)
+
+    raise NotImplementedError(func)
+
+
+@register_jagged_func(
+    [
+        torch.ops.aten.is_non_overlapping_and_dense.default,
+        torch.ops.aten.sym_size.default,
+        torch.ops.aten.dim.default,
+        torch.ops.aten.numel.default,
+        torch.ops.aten.sym_numel.default,
+        torch.ops.aten.sym_stride.default,
+        torch.ops.aten.sym_storage_offset.default,
+    ],
+    "self: jt_all",
+)
+def tensor_attr_supported_getter(func, *args, **kwargs):
+    if func == torch.ops.aten.is_non_overlapping_and_dense.default:
+        return False
+
+    if func == torch.ops.aten.sym_size.default:
+        return args[0]._size
+
+    if func == torch.ops.aten.dim.default:
+        return len(args[0]._size)
+
+    if func in (torch.ops.aten.sym_numel.default, torch.ops.aten.numel.default):
+        if args[0]._lengths is not None:
+            return int(sum(args[0]._lengths) * math.prod(args[0]._size[2:]))
+        return args[0]._values.numel()
+
+    if func == torch.ops.aten.sym_stride.default:
+        return args[0]._strides
+
+    if func == torch.ops.aten.sym_storage_offset.default:
+        return args[0]._values.storage_offset()
+
+
+@register_jagged_func(torch.ops.prim.layout.default, "self: jt_all")
+def prim_layout_default(func, *args, **kwargs):
+    return torch.jagged
+
+
+@register_jagged_func(
+    [torch.ops.aten.size.default],
+    "self: jt_all",
+)
+def tensor_attr_unsupported_getter(func, *args, **kwargs):
+    if func == torch.ops.aten.size.default:
+        raise RuntimeError(
+            "NestedTensors does not support directly calling torch.ops.aten.size "
+            "please use `nested_tensor.size()` instead."
+        )
+
+
+@register_jagged_func(torch.ops.aten.is_contiguous.default, "self: jt_all")
+def is_contiguous_general(func, *args, **kwargs):
+    from torch._prims_common import is_contiguous_for_memory_format
+
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+    inp = new_kwargs.pop("input")
+
+    # If created from narrow() check for lengths
+    if inp.lengths() is not None:
+        return False
+
+    new_kwargs["memory_format"] = new_kwargs.get(
+        "memory_format", torch.contiguous_format
+    )
+    if new_kwargs["memory_format"] == torch.preserve_format:
+        return True
+    return is_contiguous_for_memory_format(inp._values, **new_kwargs)
+
+
+register_jagged_func(
+    torch.ops.aten.is_contiguous.memory_format, "self: jt_all, memory_format: any?"
+)(is_contiguous_general)
+
+
+@register_jagged_func(
+    torch.ops.aten.clone.default, "input: jt_all, memory_format: any?"
+)
+def clone_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+
+    new_meta = extract_kwargs(inp)
+
+    if inp._lengths is not None:
+        if new_kwargs["memory_format"] == torch.contiguous_format:
+            # need to copy to remove "holes" non-contiguity / lengths metadata
+            # TODO: write a kernel for this
+            from .nested_tensor import jagged_from_list
+
+            # TODO: We probably want the output to have the same ragged structure / nested int.
+            assert (
+                inp._ragged_idx == 1
+            ), "NJT with ragged_idx != 1 not supported for contiguous clone"
+            contig, _ = jagged_from_list(inp.unbind(), offsets=None)
+            return contig
+        else:
+            # need to preserve any lengths metadata present
+            new_meta["lengths"] = inp._lengths
+
+    return NestedTensor(func(inp._values, **new_kwargs), **new_meta)
+
+
+@register_jagged_func(torch.ops.aten.linear.default, "input: jt, weight: t, bias: t?")
+def linear_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+
+    return NestedTensor(func(inp._values, **new_kwargs), **extract_kwargs(inp))
+
+
+@register_jagged_func(
+    torch.ops.aten.linear_backward.default,
+    "self: jt, grad_output: jt, weight: t, output_mask: any",
+)
+def linear_backward_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+    grad_output = new_kwargs.pop("grad_output")
+    weight = new_kwargs.pop("weight")
+
+    check_ragged_dim_same(func, inp, "self", grad_output, "grad_output")
+    ds = NestedTensor(
+        torch.matmul(grad_output._values, weight), **extract_kwargs(grad_output)
+    )
+    dw = torch.matmul(grad_output._values.transpose(-2, -1), inp._values)
+    db = None  # NYI: gradient for bias, need to reduce over ragged dim
+    return (ds, dw, db)
+
+
+@register_jagged_func(torch.ops.aten.to.dtype, "input: jt_all, dtype: any")
+def to_dtype(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+
+    return NestedTensor(func(inp._values, **new_kwargs), **extract_kwargs(inp))
+
+
+@register_jagged_func(torch.ops.aten._to_copy.default, "self: jt_all")
+def to_copy_default(func, *args, **kwargs):
+    from .nested_tensor import _tensor_symint_registry
+
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+    # don't change layout
+    new_kwargs.pop("layout")
+
+    new_values = func(inp._values, **new_kwargs)
+    new_offsets = inp._offsets.to(device=new_values.device)
+
+    from torch._subclasses.fake_tensor import FakeTensor
+    from torch._subclasses.functional_tensor import (
+        FunctionalTensor,
+        mb_unwrap_functional_tensor,
+    )
+
+    if isinstance(new_offsets, (FakeTensor, FunctionalTensor)):
+        # Temporary hack until we have the union find
+        tgt = mb_unwrap_functional_tensor(new_offsets)
+        src = mb_unwrap_functional_tensor(inp._offsets)
+        tgt.nested_int_memo = src.nested_int_memo
+    else:
+        _tensor_symint_registry[new_offsets] = _tensor_symint_registry[inp._offsets]
+    inp_kwargs = extract_kwargs(inp)
+    inp_kwargs["offsets"] = new_offsets
+
+    return NestedTensor(new_values, **inp_kwargs)
+
+
+@register_jagged_func(
+    torch.ops.aten.copy_.default, "self: jt_all, src: jt_all, non_blocking: any?"
+)
+def copy_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+    inp = new_kwargs.pop("input")
+    src = new_kwargs.pop("src")
+    if inp._size != src._size:
+        raise RuntimeError(
+            "copy_ only supports Nested Tensors that have same size and the exact same offset tensor."
+        )
+    inp.values().copy_(src.values())
+    return inp
+
+
+register_jagged_func(torch.ops.aten.detach.default, "self: jt_all")(
+    jagged_unary_pointwise
+)
+
+
+@register_jagged_func(
+    [
+        torch.ops.aten.empty_like.default,
+        torch.ops.aten.ones_like.default,
+        torch.ops.aten.zeros_like.default,
+        torch.ops.aten.randn_like.default,
+    ],
+    "self: jt_all",
+)
+def like_factory_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+
+    # Default layout is technically torch.strided but only jagged is supported here.
+    # Rather than force users to specify the layout, assume jagged.
+    # This should be set to strided for redispatching on values.
+    new_kwargs["layout"] = torch.strided
+
+    return NestedTensor(func(inp._values, **new_kwargs), **extract_kwargs(inp))
+
+
+@register_jagged_func(torch.ops.aten.zero_.default, "self: jt_all")
+def zero__default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+    func(inp._values)
+    return inp
+
+
+@register_jagged_func(
+    torch.ops.aten._softmax.default, "self: jt_all, dim: any, half_to_float: any"
+)
+def _softmax_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    if isinstance(new_kwargs["dim"], tuple):
+        raise RuntimeError(
+            "softmax(): not supported for dimensions of type 'tuple' for NestedTensor"
+        )
+
+    inp = new_kwargs.pop("input")
+
+    (
+        new_kwargs["dim"],
+        reduce_on_batch,
+        reduce_on_ragged,
+        reduce_on_non_batch,
+    ) = _wrap_jagged_dims(
+        inp.dim(),
+        (new_kwargs["dim"],),
+        "softmax",
+        inp._ragged_idx,
+    )
+
+    if reduce_on_batch:
+        raise RuntimeError(
+            "softmax(): not supported when reducing across the batch dimension for NestedTensor"
+        )
+
+    if reduce_on_ragged and inp._ragged_idx > 1:
+        raise RuntimeError(
+            "softmax(): not supported when reducing along the ragged dimension for ragged_idx > 1 for NestedTensor"
+        )
+
+    if reduce_on_ragged and inp._lengths is not None:
+        raise RuntimeError(
+            "softmax(): not supported where lengths is not None "
+            + "if reducing across the ragged dimension for NestedTensor"
+        )
+
+    new_kwargs["dim"] = new_kwargs["dim"][
+        0
+    ]  # torch.softmax takes in the reduction dimension as an integer
+
+    if reduce_on_ragged:
+        padded_softmax_values = torch.nn.functional.softmax(
+            torch.ops.aten._jagged_to_padded_dense_forward(
+                inp._values.reshape(
+                    inp._values.shape[0], -1
+                ),  # values are required to be 2D tensors for j2pd
+                [inp._offsets],
+                max_lengths=[inp._max_seqlen],  # max length of ragged dimension
+                padding_value=float("-inf"),  # e^-inf = 0
+            ),
+            dim=inp._ragged_idx,
+        )
+
+        softmax_values = torch.ops.aten._padded_dense_to_jagged_forward(
+            padded_softmax_values,
+            [inp._offsets],
+            total_L=inp._values.shape[
+                0
+            ],  # providing this parameter helps avoid a GPU/CPU sync
+        ).reshape(
+            -1, *inp._values.shape[1:]
+        )  # expand softmax_values back to original shape (inp._values.shape)
+
+        return NestedTensor(softmax_values, **extract_kwargs(inp))
+
+    return NestedTensor(func(inp._values, **new_kwargs), **extract_kwargs(inp))
+
+
+@register_jagged_func(
+    torch.ops.aten._softmax_backward_data.default,
+    "grad_output: jt, output: jt, dim: any, input_dtype: any",
+)
+def _softmax_backward(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+    grad_out = new_kwargs.pop("grad_output")
+    output = new_kwargs.pop("output")
+    return NestedTensor(
+        func(grad_out._values, output._values, **new_kwargs), **extract_kwargs(grad_out)
+    )
+
+
+@register_jagged_func(
+    torch.ops.aten.native_dropout.default, "self: jt, float: any, train: any?"
+)
+def native_dropout_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+    out1, out2 = func(inp._values, **new_kwargs)
+    return (
+        NestedTensor(out1, **extract_kwargs(inp)),
+        NestedTensor(out2, **extract_kwargs(inp)),
+    )
+
+
+@register_jagged_func(
+    torch.ops.aten.native_dropout_backward.default,
+    "grad_output: jt, mask: jt, scale: any",
+)
+def native_dropout_backward_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+    grad_output = new_kwargs.pop("grad_output")
+    mask = new_kwargs.pop("mask")
+    return NestedTensor(
+        func(grad_output._values, mask._values, **new_kwargs),
+        **extract_kwargs(grad_output),
+    )
+
+
+@register_jagged_func(torch.ops.aten.prod.dim_int, "self: jt, dim: any, keepdim: any?")
+def prod_dim_int(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+    # TODO: Figure out how to handle this better
+    # keep_dim is required to keep it in jagged format
+    if not new_kwargs["keepdim"]:
+        raise RuntimeError("prod(): keepdim=True must be set for NestedTensor")
+    dim = new_kwargs["dim"]
+    new_kwargs["dim"] = _wrap_jagged_dim(len(inp._size), dim, "prod")
+
+    return NestedTensor(func(inp._values, **new_kwargs), **extract_kwargs(args[0]))
+
+
+@register_jagged_func(
+    torch.ops.aten.split.Tensor, "self: jt, split_size: any, dim: any"
+)
+def split_tensor(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+
+    new_kwargs["dim"] = _wrap_jagged_dim(inp.dim(), new_kwargs["dim"], "split")
+
+    return tuple(
+        NestedTensor(values=x, **extract_kwargs(inp))
+        for x in func(inp._values, **new_kwargs)
+    )
+
+
+@register_jagged_func(
+    torch.ops.aten.split_with_sizes.default, "self: jt, split_sizes: any, dim: any"
+)
+def split_with_sizes_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+
+    new_kwargs["dim"] = _wrap_jagged_dim(
+        inp.dim(), new_kwargs["dim"], "split_with_sizes"
+    )
+
+    return [
+        NestedTensor(values=x, **extract_kwargs(inp))
+        for x in func(inp._values, **new_kwargs)
+    ]
+
+
+@register_jagged_func(
+    torch.ops.aten.narrow.default, "self: jt, dim: any, start: any, length: any"
+)
+def narrow(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+    inp = new_kwargs.pop("input")
+
+    dim = _wrap_jagged_dim(inp.dim(), new_kwargs["dim"], "narrow")
+    values = func(
+        inp._values,
+        dim=dim,
+        start=new_kwargs["start"],
+        length=new_kwargs["length"],
+    )
+    return NestedTensor(values, **extract_kwargs(inp))
+
+
+@register_jagged_func(torch.ops.aten.chunk.default, "self: jt, chunks: any, dim: any?")
+def chunk_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+
+    new_kwargs["dim"] = _wrap_jagged_dim(
+        inp.dim(), new_kwargs["dim"], "chunk", allow_batch_dim=True
+    )
+
+    if new_kwargs["dim"] == 0:
+        chunks = new_kwargs["chunks"]
+        dim0_size = inp._size[0]
+        chunk_size = math.ceil(dim0_size / chunks)
+
+        # get _offsets of the chunks
+        lengths = inp._offsets.diff()
+        chunked_lengths = lengths.chunk(chunks)
+        chunked_offsets = [torch.cumsum(x, dim=0) for x in chunked_lengths]
+        chunked_offsets = [F.pad(x, (1, 0), value=0) for x in chunked_offsets]  # type: ignore[arg-type]
+        nested_kwargs = [
+            {"offsets": per_offsets, "_ragged_idx": inp._ragged_idx}
+            for per_offsets in chunked_offsets
+        ]
+
+        # get _values of the chunks
+        split_sizes = [x.sum().item() for x in chunked_lengths]
+        chunk_values = inp._values.split(split_sizes)
+
+        return [
+            NestedTensor(values=chunk_values[i], **(nested_kwargs[i]))
+            for i in range(0, chunk_size)
+        ]
+    else:
+        return [
+            NestedTensor(values=x, **extract_kwargs(inp))
+            for x in func(inp._values, **new_kwargs)
+        ]
+
+
+@register_jagged_func(torch.ops.aten.unbind.int, "self: jt_all, dim: any?")
+def unbind_int(func, *args, **kwargs):
+    # Note that this specializes on the length of the offsets
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    dim = new_kwargs["dim"]
+    if dim != 0:
+        raise RuntimeError("unbind(): only supported for NestedTensor on dim=0")
+
+    inp = new_kwargs.pop("input")
+    values = inp.values()
+    offsets = inp.offsets()
+    lengths = inp.lengths()
+    ragged_idx = inp._ragged_idx
+
+    if lengths is None:
+        return torch.split(values, offsets.diff().tolist(), dim=(ragged_idx - 1))
+
+    if ragged_idx <= 0:
+        raise RuntimeError(
+            "unbind(): nested tensor ragged_idx out of bounds (should be >= 1)"
+        )
+    for i in range(lengths.shape[0]):
+        if offsets[i] + lengths[i] > values.shape[ragged_idx - 1]:
+            raise RuntimeError(
+                "unbind(): nested tensor offsets and lengths do not match ragged_idx dimension"
+            )
+    return [
+        torch.narrow(values, dim=(ragged_idx - 1), start=offsets[i], length=lengths[i])
+        for i in range(lengths.shape[0])
+    ]
+
+
+@register_jagged_func(torch.ops.aten.squeeze.dim, "self: jt, dim: any")
+def squeeze_dim(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+    values = inp._values
+
+    new_kwargs["dim"] = _wrap_jagged_dim(len(inp._size), new_kwargs["dim"], "squeeze")
+    return NestedTensor(func(values, **new_kwargs), **extract_kwargs(inp))
+
+
+@register_jagged_func(torch.ops.aten.unsqueeze.default, "self: jt, dim: any")
+def unsqueeze_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+    values = inp._values
+
+    # Account for collapsed jagged dim
+    dim = new_kwargs["dim"]
+    new_kwargs["dim"] = _wrap_jagged_dim(len(inp._size) + 1, dim, "unsqueeze")
+    return NestedTensor(func(values, **new_kwargs), **extract_kwargs(inp))
+
+
+@register_jagged_func(torch.ops.aten.cat.default, "tensors: any, dim: any")
+def cat_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    tensors = new_kwargs.pop("tensors")
+
+    # Convert any non-nested to nested
+    nested = [t for t in tensors if t.is_nested]
+    assert len(nested) > 0
+    first = nested[0]
+    tensors = [t if t.is_nested else t.expand_as(first) for t in tensors]
+
+    # Account for collapsed jagged dim
+    dim = new_kwargs["dim"]
+    new_kwargs["dim"] = _wrap_jagged_dim(len(first.shape), dim, "cat")
+
+    return NestedTensor(
+        func([t._values for t in tensors], **new_kwargs), **extract_kwargs(tensors[0])
+    )
+
+
+@register_jagged_func(torch.ops.aten.matmul.default, "self: jt, other: any")
+def matmul_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+    other = new_kwargs.pop("other")
+
+    if inp.is_nested and not other.is_nested:
+        return NestedTensor(
+            func(inp._values, other, **new_kwargs), **extract_kwargs(inp)
+        )
+    elif inp.is_nested and other.is_nested:
+        # BMM with equivalent ragged dims between the two inputs
+        if inp.dim() > 3 and other.dim() > 3 and raggedness_matches(inp, other._size):
+            return NestedTensor(func(inp._values, other._values), **extract_kwargs(inp))
+
+    raise RuntimeError(
+        f"matmul(): not supported between inputs of shapes {inp._size} and {other.shape}"
+    )
+
+
+@register_jagged_func(
+    torch.ops.aten.expand.default, "self: jt, size: any, implicit: any?"
+)
+def expand_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+    size = new_kwargs["size"]
+
+    assert ("implicit" not in new_kwargs) or (not new_kwargs.pop("implicit"))
+    if not raggedness_matches(inp, size):
+        raise RuntimeError(f"expand(): cannot expand shape {inp._size} -> {size}")
+
+    expand_arg = [-1, *size[2:]]
+    return NestedTensor(func(inp._values, expand_arg), **extract_kwargs(inp))
+
+
+@register_jagged_func(torch.ops.aten.expand_as.default, "self: t, other: jt")
+def expand_as_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+    other = new_kwargs.pop("other")
+
+    return NestedTensor(func(inp, other._values), **extract_kwargs(other))
+
+
+@register_jagged_func(torch.ops.aten.where.self, "condition: jt, self: jt, other: jt")
+def where_self(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    condition = new_kwargs.pop("condition")
+    inp = new_kwargs.pop("input")
+    other = new_kwargs.pop("other")
+
+    assert condition._size == other._size == inp._size
+
+    return NestedTensor(
+        func(condition._values, inp._values, other._values, **new_kwargs),
+        **extract_kwargs(condition),
+    )
+
+
+@register_jagged_func(torch.ops.aten._pin_memory.default, "self: jt, device: any?")
+def _pin_memory_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+
+    return NestedTensor(func(inp._values, **new_kwargs), **extract_kwargs(inp))
+
+
+@register_jagged_func(torch.ops.aten.is_pinned.default, "self: jt, device: any?")
+def is_pinned_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+
+    return func(inp._values, **new_kwargs)
+
+
+@register_jagged_func(
+    torch.ops.aten.is_same_size.default, "self: jt_all, other: jt_all"
+)
+def is_same_size_default(func, *args, **kwargs):
+    return args[0]._size == args[1]._size
+
+
+@register_jagged_func(
+    torch.ops.aten.sum.dim_IntList,
+    "self: jt_all, dim: any?, keepdim: any?, dtype: any?",
+)
+def sum_dim_IntList(func, *args, **kwargs):
+    """
+    Performs a sum along the provided tensor dimension.
+    Returns a dense tensor if the ragged dimension is reduced away, else returns a nested tensor.
+    """
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+    inp = new_kwargs.pop("input")
+
+    (
+        new_kwargs["dim"],
+        reduce_on_batch,
+        reduce_on_ragged,
+        reduce_on_non_batch,
+    ) = _wrap_jagged_dims(
+        inp.dim(),
+        new_kwargs["dim"],
+        "sum",
+        inp._ragged_idx,
+    )
+
+    if reduce_on_ragged and inp._lengths is not None:
+        raise RuntimeError(
+            "sum(): not supported where lengths is not None "
+            + "if reducing across the ragged dimension for NestedTensor"
+        )
+
+    if reduce_on_ragged:  # raggedness reduced away --> return dense tensor
+        if (
+            reduce_on_batch
+        ):  # reduction cases: (batch, ragged), (batch, ragged, non-batch), etc.
+            out = func(
+                inp._values, **new_kwargs
+            )  # no need to read offsets --> apply sum directly on values
+        else:
+            if (
+                reduce_on_non_batch
+            ):  # invalid reduction cases: (ragged, non-batch), etc.
+                raise RuntimeError(
+                    "sum(): not supported along a ragged and non-batch dimension for NestedTensor"
+                )
+            # reduction cases: (ragged)
+            values_ragged_dim_outer = inp._values.permute(
+                inp._ragged_idx - 1,  # outer dimension
+                *range(0, inp._ragged_idx - 1),
+                *range(inp._ragged_idx, inp.dim() - 1),
+            )  # shift reduction dimension of values backward to outer dimension
+
+            # _jagged_to_padded_dense_forward requires values to be a 2D tensor
+            # with the ragged dimension as the 0th dimension
+            padded = torch.ops.aten._jagged_to_padded_dense_forward(
+                values_ragged_dim_outer.reshape(values_ragged_dim_outer.shape[0], -1),
+                [inp._offsets],
+                max_lengths=[inp._max_seqlen],
+            )
+
+            padded_ragged_dim_original = padded.view(
+                padded.shape[0],
+                inp._max_seqlen,
+                *values_ragged_dim_outer.shape[
+                    1:
+                ],  # expand non-batch dimensions of padded tensor
+            ).permute(
+                0,
+                *range(2, inp._ragged_idx + 1),
+                1,
+                *range(inp._ragged_idx + 1, inp.dim()),
+            )  # shift reduction dimension of padded tensor forward to original ragged dimension
+
+            out = torch.sum(
+                padded_ragged_dim_original,
+                dim=inp._ragged_idx,
+            )  # need to read offsets --> pad jagged dimension and apply sum
+
+        if new_kwargs["keepdim"]:
+            # TODO: Fix this; it's a bug. should be unsqueezing on ragged_idx
+            out = out.unsqueeze(0)
+        return out
+    else:  # raggedness preserved --> return nested tensor
+        if (
+            reduce_on_batch
+        ):  # invalid reduction cases: (batch), (batch, non-batch), etc.
+            raise RuntimeError(
+                "sum(): not supported along the batch dimension but not the ragged dimension for NestedTensor"
+            )
+        # reduction cases: (non-batch), (non-batch, non-batch), etc.
+        return NestedTensor(
+            func(inp._values, **new_kwargs), **extract_kwargs(inp)
+        )  # apply sum directly on values
+
+
+@register_jagged_func(
+    torch.ops.aten.transpose.int, "self: jt_all, dim0: any, dim1: any"
+)
+def transpose_int(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    from torch._prims_common import canonicalize_dims
+
+    inp = new_kwargs.pop("input")
+    dim0, dim1 = canonicalize_dims(inp.dim(), (new_kwargs["dim0"], new_kwargs["dim1"]))
+
+    if inp._lengths is not None:
+        raise ValueError(
+            "transpose(): not supported on jagged layout nested tensor with holes"
+        )
+
+    # To support the SDPA API, inputs need to have the ragged idx transposed to dim 2
+    # instead of 1, although the internal Flash and mem-effn implementations will
+    # use the inputs with raggedness in dim 1.
+    if dim0 == inp._ragged_idx or dim1 == inp._ragged_idx:
+        if dim0 == 0 or dim1 == 0:
+            raise ValueError(
+                "Transpose is not supported on the batch dimension for jagged NT"
+            )
+        if dim0 == inp._ragged_idx:
+            to_dim = dim1
+        else:
+            to_dim = dim0
+        inp_kwargs = extract_kwargs(inp)
+        inp_kwargs["_ragged_idx"] = to_dim
+        return NestedTensor(
+            inp.values().transpose(
+                _outer_to_inner_dim(len(inp._size), dim0),
+                _outer_to_inner_dim(len(inp._size), dim1),
+            ),
+            **inp_kwargs,
+        )
+
+    new_kwargs["dim0"] = _wrap_jagged_dim(inp.dim(), new_kwargs["dim0"], "transpose")
+    new_kwargs["dim1"] = _wrap_jagged_dim(inp.dim(), new_kwargs["dim1"], "transpose")
+
+    return NestedTensor(func(inp._values, **new_kwargs), **extract_kwargs(inp))
+
+
+@register_jagged_func(torch.ops.aten.permute.default, "self: jt_all, dims: any")
+def permute_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+    inp = new_kwargs.pop("input")
+    dims = new_kwargs.pop("dims")
+    inp_kwargs = extract_kwargs(inp)
+    inp_dim = len(inp._size)
+
+    # The first two checks are the same as the checks in the normal permute implementation
+    if inp_dim != len(dims):
+        raise ValueError(
+            f"permute(): number of dimensions in the tensor input ({inp_dim}) "
+            + f"does not match the length of the desired ordering of dimensions ({len(dims)}).",
+        )
+
+    from torch._prims_common import canonicalize_dims
+
+    canonicalized_dims = canonicalize_dims(inp_dim, dims)
+
+    if len(canonicalized_dims) != len(set(canonicalized_dims)):
+        raise ValueError("permute(): duplicate dims are not allowed.")
+
+    if inp._lengths is not None:
+        raise ValueError(
+            "permute(): not supported on jagged layout nested tensor with holes"
+        )
+    if canonicalized_dims[0] != 0:
+        raise ValueError(
+            "Permute is not supported on the batch dimension for jagged NT"
+        )
+    inp_kwargs["_ragged_idx"] = canonicalized_dims.index(inp._ragged_idx)
+    inner_dims = [_outer_to_inner_dim(inp_dim, dim) for dim in canonicalized_dims[1:]]
+    new_kwargs["dims"] = inner_dims
+    return NestedTensor(func(inp._values, **new_kwargs), **inp_kwargs)
+
+
+@register_jagged_func(
+    [torch.ops.aten.view.default, torch.ops.aten._unsafe_view.default],
+    "self: jt_all, size: any",
+)
+def view_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+    size = new_kwargs.pop("size")
+
+    if inp._ragged_idx != 1 and tuple(inp._size) != tuple(size):
+        raise RuntimeError(
+            f"view(): does not support ragged_idx != 1 except when inp._size == size. "
+            f"inp._size is ({inp._size}) and size is ({size})."
+        )
+
+    # Ensure specified size still includes batch and ragged dims
+    if len(size) < 3 or not raggedness_matches(inp, size):
+        raise RuntimeError(f"view(): cannot view shape {inp._size} as {size}")
+
+    # outer size: the size of the NT, e.g. [3, j0, 10]
+    # inner size: the size of the values, e.g. [8, 10] (e.g. for offsets = [0, 3, 5, 8])
+    # this function gets inner_size[inner_idx] for a given inner_idx.
+    #
+    # example: for outer size [a, b, c, j0, d, e, f]
+    #                         assume that j0 is ragged, other are concrete integers
+    #                         and ragged_idx=3
+    # inner size will be      [b, c, inp._values.size(ragged_idx), d, e, f]
+    # therefore:
+    #    inner_size[0] = outer_size[1]
+    #    inner_size[1] = outer_size[2]
+    #    inner_size[0] = inp._values.size(ragged_idx - 1)
+    #    inner_size[3] = outer_size[4]
+    #    inner_size[4] = outer_size[5]
+    def get_inner_size(inner_idx):
+        nonlocal inp, size
+        if inner_idx == inp._ragged_idx - 1:
+            return inp._values.size(inner_idx)
+        else:
+            return size[inner_idx + 1]
+
+    inner_size = [get_inner_size(i) for i in range(len(size) - 1)]
+
+    return NestedTensor(func(inp._values, inner_size), **extract_kwargs(inp))
+
+
+@register_jagged_func(
+    torch.ops.aten.native_layer_norm.default,
+    "input: jt_all, normalized_shape: any, weight: any?, bias: any?, eps: any",
+)
+def native_layer_norm_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+
+    if inp.dim() <= 2:
+        raise RuntimeError(
+            "layer_norm(): not supported for NestedTensor objects with 2 or fewer dimensions"
+        )
+
+    normalized_shape = new_kwargs["normalized_shape"]
+    ragged_size = inp.shape[inp._ragged_idx]
+
+    num_dims_not_normalized = inp.dim() - len(normalized_shape)
+
+    if (
+        num_dims_not_normalized == 0
+    ):  # error if trying to normalize over the batch dimension
+        raise RuntimeError(
+            "layer_norm(): not supported when normalizing over the batch dimension for NestedTensor"
+        )
+
+    if ragged_size in normalized_shape and inp._lengths is not None:
+        raise RuntimeError(
+            "layer_norm(): not supported where lengths is not None if operating on the ragged dimension for NestedTensor"
+        )
+
+    if (
+        ragged_size in normalized_shape
+    ):  # special handling for normalizing over the ragged dimension
+        padded_input = torch.ops.aten._jagged_to_padded_dense_forward(
+            inp._values.flatten(
+                start_dim=inp._ragged_idx
+            ),  # _jagged_to_padded_dense_forward requires values to be a 2D tensor
+            [inp._offsets],
+            max_lengths=[inp._max_seqlen],  # max length of ragged dimension
+        )
+
+        padded_mask = torch.ops.aten._jagged_to_padded_dense_forward(
+            torch.ones((inp._values.shape[0], 1), device=inp.device, dtype=inp.dtype),
+            [inp._offsets],
+            max_lengths=[inp._max_seqlen],  # max length of ragged dimension
+        ).expand(
+            padded_input.shape
+        )  # mask elements outside of the ragged dimension and expand to the same shape as padded input (3D dense tensor)
+
+        ragged_lengths = (
+            inp._offsets.diff().unsqueeze(1).unsqueeze(1) * padded_input.shape[2]
+        )  # ragged dim * inner dim, since we sum over dims (1, 2) (the layer on which we normalize)
+
+        mean = (
+            torch.sum(
+                padded_input,
+                dim=(1, 2),
+                keepdim=True,
+            )
+            / ragged_lengths
+        )  # a sum over (1, 2) ensures layer norm, whereas a sum over (1) would be an instance norm
+
+        padded_normalized = (
+            padded_input - mean
+        ) * padded_mask  # mask elements outside of the ragged dimension size for correct variance calculation
+
+        variance = (
+            torch.sum(
+                torch.square(padded_normalized),
+                dim=(1, 2),
+                keepdim=True,
+            )
+            / ragged_lengths
+        )  # a sum over (1, 2) ensures layer norm, whereas a sum over (1) would be an instance norm
+
+        std = torch.sqrt(variance + new_kwargs["eps"])
+        padded_layer_norm = padded_normalized / std
+
+        jagged_layer_norm_values = torch.ops.aten._padded_dense_to_jagged_forward(
+            padded_layer_norm,
+            [inp._offsets],
+            total_L=inp._values.shape[
+                0
+            ],  # providing this parameter helps avoid a GPU/CPU sync
+        ).unflatten(
+            -1, inp.shape[inp._ragged_idx + 1 :]
+        )  # unflatten last dimension back into original nested tensor shape, e.g. (B, *, WH) --> (B, *, W, H)
+
+        return (
+            NestedTensor(jagged_layer_norm_values, **extract_kwargs(inp)),
+            mean,
+            std,
+        )
+
+    output, mean, std = func(inp._values, **new_kwargs)
+    return (NestedTensor(output, **extract_kwargs(inp)), mean, std)
+
+
+@register_jagged_func(
+    torch.ops.aten.native_layer_norm_backward.default,
+    "grad_out: jt, input: jt, normalized_shape: any, mean: any, rstd: any, weight: any?, bias: any?, output_mask: any",
+)
+def native_layer_norm_backward_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+    grad_out = new_kwargs.pop("grad_out")
+    inp = new_kwargs.pop("input")
+    d_input, d_gamma, d_beta = func(grad_out._values, inp._values, **new_kwargs)
+    if d_input is None:
+        return (None, d_gamma, d_beta)
+
+    return (NestedTensor(d_input, **extract_kwargs(inp)), d_gamma, d_beta)
+
+
+@register_jagged_func(torch.ops.aten.select.int, "self: jt, dim: any, index: any")
+def select_int(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+    new_kwargs["dim"] = _wrap_jagged_dim(
+        inp.dim(), new_kwargs["dim"], "select", allow_batch_dim=True
+    )
+
+    # handle batch dim slicing via unbind() for now
+    # TODO: make this more efficient
+    if new_kwargs["dim"] == 0:
+        return inp.unbind()[new_kwargs["index"]]
+
+    return NestedTensor(func(inp._values, **new_kwargs), **extract_kwargs(inp))
+
+
+@register_jagged_func(
+    torch.ops.aten.slice.Tensor,
+    "self: jt, dim: any?, start: any?, end: any?, step: any?",
+)
+def slice_tensor(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+    new_kwargs["dim"] = _wrap_jagged_dim(inp.dim(), new_kwargs["dim"], "slice")
+
+    return NestedTensor(func(inp._values, **new_kwargs), **extract_kwargs(inp))
+
+
+@register_jagged_func(
+    torch.ops.aten.convolution.default,
+    "input: jt, weight: t, bias: t?, stride: any, padding: any, "
+    "dilation: any, transposed: any, output_padding: any, groups: any",
+)
+def convolution_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+
+    return NestedTensor(func(inp._values, **new_kwargs), **extract_kwargs(inp))
+
+
+@register_jagged_func(
+    torch.ops.aten.mean.dim, "self: jt_all, dim: any?, keepdim: any?, dtype: any?"
+)
+def mean_dim(func, *args, **kwargs):
+    """
+    Performs a mean along the provided tensor dimension.
+    Returns a dense tensor if the ragged dimension is reduced away, else returns a nested tensor.
+    """
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    if len(new_kwargs["dim"]) > 1:
+        raise RuntimeError(
+            "mean(): not supported across multiple dimensions for NestedTensor"
+        )
+
+    inp = new_kwargs.pop("input")
+
+    (
+        new_kwargs["dim"],
+        reduce_on_batch,
+        reduce_on_ragged,
+        reduce_on_non_batch,
+    ) = _wrap_jagged_dims(
+        inp.dim(),
+        new_kwargs["dim"],
+        "mean",
+        inp._ragged_idx,
+    )
+
+    if reduce_on_batch:
+        raise RuntimeError(
+            "mean(): not supported along the batch dimension but not the ragged dimension for NestedTensor"
+        )
+
+    if reduce_on_ragged and inp._lengths is not None:
+        raise RuntimeError(
+            "mean(): not supported where lengths is not None "
+            + "if reducing across the ragged dimension for NestedTensor"
+        )
+
+    if not new_kwargs["keepdim"]:
+        raise RuntimeError("mean(): not supported when keepdim=False for NestedTensor")
+
+    if reduce_on_ragged:  # raggedness reduced away
+        torch_sum = torch.sum(inp, dim=inp._ragged_idx, keepdim=new_kwargs["keepdim"])
+
+        # for every non-batch dimension,
+        #   unsqueeze lengths into the same shape as the PyTorch sum,
+        #   as the extra dimensions must all be divided by the same length
+        lengths = inp._offsets.diff()
+        for _ in range(inp.dim() - 2):
+            lengths = lengths.unsqueeze(-1)
+
+        return torch_sum / lengths.broadcast_to(torch_sum.shape)
+
+    return NestedTensor(
+        func(inp._values, **new_kwargs), **extract_kwargs(inp)
+    )  # raggedness preserved
+
+
+@register_jagged_func(torch.ops.aten.stack.default, "tensors: any, dim: any")
+def stack_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    # guaranteed this is non-empty if we got here
+    tensors = new_kwargs.pop("tensors")
+    for t in tensors:
+        if not isinstance(t, NestedTensor):
+            raise RuntimeError("stack(): expected all nested tensors inputs")
+
+        if t.dim() != tensors[0].dim():
+            raise RuntimeError(
+                "stack(): expected all nested tensors to have the same dim"
+            )
+
+        if not raggedness_matches(t, tensors[0].shape):
+            raise RuntimeError(
+                "stack(): expected all nested tensors to have the same nested structure"
+            )
+
+    new_kwargs["dim"] = _wrap_jagged_dim(
+        tensors[0].dim() + 1, new_kwargs["dim"], "stack"
+    )
+
+    return NestedTensor(
+        func([t._values for t in tensors], **new_kwargs), **extract_kwargs(tensors[0])
+    )
+
+
+@register_jagged_func(
+    torch.ops.aten.embedding.default,
+    "weight: t, indices: jt, padding_idx: any?, scale_grad_by_freq: any?, sparse: any?",
+)
+def embedding_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    # guaranteed this is non-empty if we got here
+    indices = new_kwargs.pop("indices")
+    weight = new_kwargs.pop("weight")
+
+    return NestedTensor(
+        func(weight, indices._values, **new_kwargs), **extract_kwargs(indices)
+    )
+
+
+@register_jagged_func(
+    [
+        torch.ops.aten.values.default,
+        torch.ops.aten._nested_get_values.default,
+    ],
+    "self: jt_all",
+)
+def values_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+
+    # TODO: Handle inference mode properly.
+    # See https://github.com/pytorch/pytorch/issues/112024#issuecomment-1779554292
+    return inp._values.detach()
+
+
+@register_jagged_func(torch.ops.aten.all.default, "self: jt_all")
+def all_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+
+    return func(inp._values)
+
+
+@register_jagged_func(
+    torch.ops.aten._nested_view_from_jagged.default,
+    "values: t, offsets: t, dummy: jt_all, lengths: t?, ragged_idx: any?, min_seqlen: t?, max_seqlen: t?",
+)
+def _nested_view_from_jagged_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    values, offsets, lengths = (
+        new_kwargs["input"],
+        new_kwargs["offsets"],
+        new_kwargs["lengths"],
+    )
+    ragged_idx = new_kwargs["ragged_idx"]
+    min_seqlen = new_kwargs["min_seqlen"]
+    max_seqlen = new_kwargs["max_seqlen"]
+    metadata_cache = {}
+    if min_seqlen is not None:
+        metadata_cache["min_seqlen"] = min_seqlen
+    if max_seqlen is not None:
+        metadata_cache["max_seqlen"] = max_seqlen
+
+    return NestedTensor(
+        values,
+        offsets,
+        lengths=lengths,
+        _ragged_idx=ragged_idx,
+        _metadata_cache=metadata_cache,
+    )
+
+
+@register_jagged_func(torch.ops.aten._nested_get_offsets.default, "self: jt_all")
+def _nested_get_offsets(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+    return inp._offsets
+
+
+@register_jagged_func(torch.ops.aten._nested_get_lengths.default, "self: jt_all")
+def _nested_get_lengths(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+    return inp._lengths
+
+
+@register_jagged_func(torch.ops.aten._nested_get_ragged_idx.default, "self: jt_all")
+def _nested_get_ragged_idx(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+    return inp._ragged_idx
+
+
+@register_jagged_func(torch.ops.aten._nested_get_min_seqlen.default, "self: jt_all")
+def _nested_get_min_seqlen(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+    return inp._metadata_cache.get("min_seqlen", None)
+
+
+@register_jagged_func(torch.ops.aten._nested_get_max_seqlen.default, "self: jt_all")
+def _nested_get_max_seqlen(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+
+    inp = new_kwargs.pop("input")
+    return inp._metadata_cache.get("max_seqlen", None)
+
+
+# If a section of the Nested Tensor is fully masked out we still retain the section with a length of 0
+@register_jagged_func(torch.ops.aten.masked_select.default, "self: jt, mask: any")
+def masked_select_default(func, *args, **kwargs):
+    _, new_kwargs = normalize_function(  # type: ignore[misc]
+        func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
+    )
+    inp = new_kwargs.pop("input")
+    mask = new_kwargs.pop("mask")
+
+    if inp.ndim > 2:
+        raise RuntimeError("masked_select only support 2-D selections currently")
+    elif inp.shape != mask.shape:
+        raise RuntimeError(
+            f"Mask with shape {mask.shape} is not compatible with input's shape {inp.shape}"
+        )
+    res_values = inp._values.masked_select(mask.values())
+    mask_cumsum = F.pad(mask.values().cumsum(dim=0), (1, 0))  # type: ignore[arg-type]
+
+    args = extract_kwargs(inp)
+    args["offsets"] = mask_cumsum[inp._offsets]
+    return NestedTensor(
+        values=res_values,
+        **args,
+    )
+
+
+# Make the dummy available on the C++ side.
+@register_jagged_func(torch.ops.aten._nested_get_jagged_dummy.default, "self: any")
+def _nested_get_jagged_dummy(func, *args, **kwargs):
+    from torch.nested._internal.nested_tensor import _nt_view_dummy
+
+    return _nt_view_dummy()
+
+
+with torch.library._scoped_library("aten", "IMPL") as aten:
+    aten.impl("_nested_get_jagged_dummy", _nested_get_jagged_dummy, "CPU")
+    aten.impl("_nested_get_jagged_dummy", _nested_get_jagged_dummy, "CUDA")
+    aten.impl("_nested_get_jagged_dummy", _nested_get_jagged_dummy, "Meta")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nested/_internal/sdpa.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nested/_internal/sdpa.py
new file mode 100644
index 0000000000000000000000000000000000000000..578904af9469717e68de86be821aa84110e9ea80
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nested/_internal/sdpa.py
@@ -0,0 +1,871 @@
+# mypy: allow-untyped-defs
+import logging
+from typing import Optional, Tuple
+
+import torch
+import torch.nn
+import torch.nn.functional as F
+from torch.backends.cuda import (
+    can_use_efficient_attention,
+    can_use_flash_attention,
+    flash_sdp_enabled,
+    math_sdp_enabled,
+    mem_efficient_sdp_enabled,
+    SDPAParams,
+)
+from torch.nn.attention import SDPBackend
+
+from .nested_tensor import NestedTensor
+
+
+log = logging.getLogger(__name__)
+
+
+def _validate_sdpa_input(
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attn_mask: Optional[torch.Tensor] = None,
+    dropout_p=0.0,
+    is_causal=False,
+    scale=None,
+):
+    if (
+        not isinstance(query, NestedTensor)
+        or not isinstance(key, NestedTensor)
+        or not isinstance(value, NestedTensor)
+    ):
+        raise ValueError(
+            f"Expected query, key, and value to be nested tensors, "
+            f"but got query.is_nested: {query.is_nested}, key.is_nested: {key.is_nested}, "
+            f"and value.is_nested: {value.is_nested} instead."
+        )
+    if query.dtype != key.dtype or query.dtype != value.dtype:
+        raise ValueError(
+            f"Expected query, key, and value to have the same dtype, "
+            f"but got query.dtype: {query.dtype}, key.dtype: {key.dtype}, "
+            f"and value.dtype: {value.dtype} instead."
+        )
+    if query.device != key.device or query.device != value.device:
+        raise ValueError(
+            f"Expected query, key, and value to have the same device type, "
+            f"but got query.device: {query.device}, key.device: {key.device}, "
+            f"and value.device: {value.device} instead."
+        )
+    if query.dim() < 3 or key.dim() < 3 or value.dim() < 3:
+        raise ValueError(
+            f"Expected query, key, and value to all be  at least 3 dimensional, but got query.dim: "
+            f"{query.dim()}, key.dim: {key.dim()} and value.dim: {value.dim()} instead."
+        )
+    if query._ragged_idx != key._ragged_idx or query._ragged_idx != value._ragged_idx:
+        raise ValueError(
+            f"Expected query, key, and value to all be ragged on the same dimension, but got ragged "
+            f"dims {query._ragged_idx}, {key._ragged_idx}, and {value._ragged_idx}, respectively."
+        )
+    if attn_mask is not None:
+        # TODO: Figure out whether masks are actually supported for this layout or not
+        raise ValueError("Masks are not yet supported!")
+        if attn_mask.dtype != torch.bool and attn_mask.dtype != query.dtype:
+            raise ValueError(
+                f"Expected attn_mask dtype to be bool or to match query dtype, but got attn_mask.dtype: "
+                f"{attn_mask.dtype}, and query.dtype: {query.dtype} instead."
+            )
+
+
+def _check_batch_size_nested(params: SDPAParams, debug=False) -> bool:
+    # This is expected to be called after check_tensor_shapes ensuring that the
+    # size() calls won't error since the inputs are all 4 dimensional
+    q_batch_size = params.query.size(0)
+    k_batch_size = params.key.size(0)
+    v_batch_size = params.value.size(0)
+
+    # num_heads logic for nested input is checked in
+    # check_for_seq_len_0_nested_tensor as there is handling there to make sure
+    # num_heads is not ragged
+    return q_batch_size == k_batch_size and q_batch_size == v_batch_size
+
+
+def _check_head_dim_size_flash_nested(params: SDPAParams, debug=False) -> bool:
+    max_size = 256
+    query_size_last = params.query.size(-1)
+    key_size_last = params.key.size(-1)
+    value_size_last = params.value.size(-1)
+    same_head_dim_size = (
+        query_size_last == key_size_last and query_size_last == value_size_last
+    )
+    if not (
+        same_head_dim_size
+        and (query_size_last % 8 == 0)
+        and (query_size_last <= max_size)
+    ):
+        if debug:
+            log.warning(
+                "For NestedTensor inputs, Flash attention requires q,k,v to have the same "
+                "last dimension and to be a multiple of 8 and less than or equal to 256. "
+                "Got Query.size(-1): %d, Key.size(-1): %d, Value.size(-1): %d instead.",
+                query_size_last,
+                key_size_last,
+                value_size_last,
+            )
+        return False
+    return True
+
+
+def _check_for_seq_len_0_and_consistent_head_dim_nested_helper(
+    param: torch.Tensor, param_name: str, debug=False
+) -> bool:
+    assert isinstance(param, NestedTensor), "param should be a jagged NT"
+
+    if param._ragged_idx == 1:
+        # num_head_dims is ragged
+        if debug:
+            log.warning(
+                "Fused kernels do not support ragged num_head_dims, %s has a ragged num_heads.",
+                param_name,
+            )
+        return False
+
+    # This is being called inside sdp with shape [batch, heads, {seq_len}, dim]
+    if param._get_min_seqlen() == 0:
+        if debug:
+            log.warning(
+                "Fused kernels do not support seq_len == 0, %s has a seq len of 0.",
+                param_name,
+            )
+        return False
+
+    return True
+
+
+def _try_broadcast_param_size(q_size, k_size, v_size, param_name, debug=False) -> bool:
+    max_size = max(q_size, k_size, v_size)
+    if (
+        (q_size != max_size and q_size != 1)
+        or (k_size != max_size and k_size != 1)
+        or (v_size != max_size and v_size != 1)
+    ):
+        if debug:
+            log.warning(
+                "Both fused kernels require query, key and value to have broadcastable %s, "
+                "got Query %s %d, Key %s %d, Value %s %d instead.",
+                param_name,
+                param_name,
+                q_size,
+                param_name,
+                k_size,
+                param_name,
+                v_size,
+            )
+        return False
+    return True
+
+
+def _check_for_seq_len_0_nested(params: SDPAParams, debug=False) -> bool:
+    # When this function is called we are assured that the nt is dim==4
+    q_is_safe = (
+        _check_for_seq_len_0_and_consistent_head_dim_nested_helper(
+            params.query, "query", debug
+        )
+        if params.query.is_nested
+        else True
+    )
+    # short circuit if any is unsafe
+    if not q_is_safe:
+        return False
+
+    k_is_safe = (
+        _check_for_seq_len_0_and_consistent_head_dim_nested_helper(
+            params.key, "key", debug
+        )
+        if params.key.is_nested
+        else True
+    )
+    # short circuit if any is unsafe
+    if not k_is_safe:
+        return False
+
+    v_is_safe = (
+        _check_for_seq_len_0_and_consistent_head_dim_nested_helper(
+            params.value, "value", debug
+        )
+        if params.value.is_nested
+        else True
+    )
+    # short circuit if any is unsafe
+    if not v_is_safe:
+        return False
+
+    # We now know none of the inputs have ragged num_heads, so we can safely
+    # access .size(1)
+    q_num_heads = params.query.size(1)
+    k_num_heads = params.key.size(1)
+    v_num_heads = params.value.size(1)
+    same_num_heads = q_num_heads == k_num_heads and q_num_heads == v_num_heads
+
+    if not same_num_heads:
+        if (
+            params.query.requires_grad
+            or params.key.requires_grad
+            or params.value.requires_grad
+        ):
+            if debug:
+                log.warning(
+                    "Both fused kernels do not support training with broadcasted NT inputs."
+                )
+            return False
+        return _try_broadcast_param_size(
+            q_num_heads, k_num_heads, v_num_heads, "num heads", debug
+        )
+    return True
+
+
+def _can_use_flash_sdpa_jagged(params: SDPAParams, debug=False) -> bool:
+    constraints = (
+        _check_batch_size_nested,
+        _check_head_dim_size_flash_nested,
+        _check_for_seq_len_0_nested,
+    )
+    for constraint in constraints:
+        if not constraint(params, debug):
+            return False
+    return True
+
+
+def _can_use_efficient_sdpa_jagged(params: SDPAParams, debug=False) -> bool:
+    constraints = (
+        _check_batch_size_nested,
+        _check_for_seq_len_0_nested,
+    )
+    for constraint in constraints:
+        if not constraint(params, debug):
+            return False
+    return True
+
+
+def _can_use_math_sdpa_jagged(params: SDPAParams, debug=False) -> bool:
+    if (
+        not params.query.transpose(1, 2).is_contiguous()
+        or not params.key.transpose(1, 2).is_contiguous()
+        or not params.value.transpose(1, 2).is_contiguous()
+    ):
+        if debug:
+            log.warning(
+                "If inputs are nested tensors they must be contiguous after transposing."
+            )
+        return False
+    if params.is_causal:
+        if debug:
+            log.warning(
+                "Nested tensors for query / key are not supported when is_causal=True."
+            )
+        return False
+    return True
+
+
+def _select_sdp_backend(query, key, value, attn_mask, dropout, is_causal, enable_gqa):
+    if (
+        not flash_sdp_enabled()
+        and not mem_efficient_sdp_enabled()
+        and not math_sdp_enabled()
+    ):
+        return SDPBackend.ERROR
+
+    ordering = (
+        SDPBackend.FLASH_ATTENTION,
+        SDPBackend.EFFICIENT_ATTENTION,
+        SDPBackend.MATH,
+    )
+
+    params = SDPAParams(query, key, value, attn_mask, dropout, is_causal, enable_gqa)
+
+    for backend in ordering:
+        if backend == SDPBackend.FLASH_ATTENTION:
+            if can_use_flash_attention(params) and _can_use_flash_sdpa_jagged(params):
+                return SDPBackend.FLASH_ATTENTION
+        if backend == SDPBackend.EFFICIENT_ATTENTION:
+            if can_use_efficient_attention(params) and _can_use_efficient_sdpa_jagged(
+                params
+            ):
+                return SDPBackend.EFFICIENT_ATTENTION
+        if backend == SDPBackend.MATH:
+            if math_sdp_enabled() and _can_use_math_sdpa_jagged(params):
+                return SDPBackend.MATH
+
+    log.warning("Memory efficient kernel not used because:")
+    can_use_efficient_attention(params, debug=True)
+    _can_use_efficient_sdpa_jagged(params, debug=True)
+    log.warning("Flash attention kernel not used because:")
+    can_use_flash_attention(params, debug=True)
+    _can_use_flash_sdpa_jagged(params, debug=True)
+    log.warning("Math attention kernel not used because:")
+    _can_use_math_sdpa_jagged(params, debug=True)
+    return SDPBackend.ERROR
+
+
+def _cumulative_and_max_seq_len_nnz(qkv: torch.Tensor) -> Tuple[torch.Tensor, int, int]:
+    # This function is used to calculate two pieces of metadata that are needed
+    # for use with flash-attention and efficient_attention kernels. They are the
+    # cumulative sequence_length over a batch of sequences and the maximum
+    # sequence length.
+
+    # It returns a tuple of cumulative sequence lengths and the maximum sequence
+    # length, and the last element in the cumulative_sequence_lengths
+    if not isinstance(qkv, NestedTensor):
+        raise ValueError("QKV must be nested for flash cumulative_seq_len calculation.")
+
+    if qkv.lengths() is None:
+        # TODO: Explore performance impact of copying
+        cumulative_seqlen = qkv.offsets().to(dtype=torch.int32, device=qkv.device)
+        max_seqlen = qkv._get_max_seqlen()
+        n_elem = qkv.values().shape[0]
+    else:
+        # TODO: Explore performance impact of copying
+        cumulative_seqlen = (
+            qkv.lengths().cumsum(0).to(dtype=torch.int32, device=qkv.device)
+        )
+        batch_size = qkv.size(0)
+        max_seqlen = qkv._get_max_seqlen()
+        # TODO: Explore performance impact when compiling
+        n_elem = int(cumulative_seqlen[-1].item())
+    return cumulative_seqlen, max_seqlen, n_elem
+
+
+def _is_safe_to_get_storage_as_tensor(tensor: torch.Tensor):
+    # This function checks if a nested tensor is valid for
+    # use with the flash-attention and efficient_attention kernels without
+    # needing to call contiguous on the nested tensor input.
+    # It checks that the storage offsets' adjacent_differences are a constant
+    # mutiple of the previous tensor in the nested tensor and that the strides
+    # are monitonically decreasing. This check is done after calling transpose on
+    # the nested tensor resulting in a Nt of shape [bsz, {seq_len}, num_heads, dim]
+
+    # Returns a boolean indicating if contiguous needs to be called for input
+    assert isinstance(tensor, NestedTensor)
+    offsets = tensor.offsets()
+    strides = tensor._strides
+
+    n_tensors = offsets.size(0) - 1
+    if n_tensors <= 1:
+        return True
+
+    # Check initially that the tensor strides are in strictly descending order
+    prev_stride = strides[1]
+    for stride in strides[2:]:
+        if prev_stride <= stride:
+            # This would mean that the last stride is greater than the seq_len
+            # stride
+            return False
+        prev_stride = stride
+
+    # Congrats you made it!
+    return True
+
+
+def _view_as_dense(
+    tensor: torch.Tensor, Nnz: int, num_heads: int, head_dim: int
+) -> torch.Tensor:
+    if tensor.is_nested:
+        return tensor.values()
+    return tensor.view(Nnz, num_heads, head_dim)
+
+
+# TODO: Next iteration should add test cases and check it works
+# def _sdpa_nested_preprocessing_with_broadcast(query, key, value):
+#     # Query (Batch x Num_heads x {Q_seq_len}  x Dim_per_head)
+#     # Key   (Batch x Num_heads x {KV_seq_len} x Dim_per_head)
+#     # Value (Batch x Num_heads x {KV_seq_len} x Dim_per_head)
+#     q_batch_size = query.size(0)
+#     k_batch_size = key.size(0)
+#     v_batch_size = value.size(0)
+
+#     output_batch_size = max(q_batch_size, k_batch_size, v_batch_size)
+
+#     q_num_heads = query.size(1)
+#     k_num_heads = key.size(1)
+#     v_num_heads = value.size(1)
+
+#     output_num_heads = max(q_num_heads, k_num_heads, v_num_heads)
+
+#     head_dim_qk = query.size(3)
+#     head_dim_v = value.size(3)
+
+#     q_t = query.transpose(1, 2)
+#     k_t = key.transpose(1, 2)
+#     v_t = value.transpose(1, 2)
+
+#     # Checks in sdp_utils ensure that if {*}_batch_size/{*}_num_heads !=
+#     # output_batch_size/num_heads then they are 1
+#     q_batch_size_needs_broadcast = q_batch_size != output_batch_size
+#     k_batch_size_needs_broadcast = k_batch_size != output_batch_size
+#     v_batch_size_needs_broadcast = v_batch_size != output_batch_size
+
+#     # If {*}_batch_size_needs_broadcast, then
+#     # (1) max_seqlen_batch_{*} is given by {*}_t.size(1)
+#     #     this is because needs_broadcast indicates that the batch_size is 1
+#     #     and hence there is only 1 value for seq_len
+#     # (2) The cum_seq_lens are given by [0, {*}_t.size(1), 2 * {*}_t.size(1),
+#     # ..., outut_batch_size * {*}_t.size(1)]
+#     # (3) Nnz_{*} is given by output_batch_size * {*}_t.size(1)
+
+#     if q_batch_size_needs_broadcast or not q_t.is_nested:
+#         max_seqlen_batch_q = q_t.size(1)
+#         cumulative_sequence_length_q = torch.arange(
+#             0,
+#             (output_batch_size + 1) * max_seqlen_batch_q,
+#             max_seqlen_batch_q,
+#             device=q_t.device,
+#             dtype=torch.int32,
+#         )
+#         Nnz_q = output_batch_size * max_seqlen_batch_q
+#     else:
+#         (
+#             cumulative_sequence_length_q,
+#             max_seqlen_batch_q,
+#             Nnz_q,
+#         ) = _cumulative_and_max_seq_len_nnz(q_t)
+
+#     if k_batch_size_needs_broadcast and v_batch_size_needs_broadcast:
+#         assert k_t.size(1) == v_t.size(1)
+#         max_seqlen_batch_kv = k_t.size(1)
+#         cumulative_sequence_length_kv = torch.arange(
+#             0,
+#             (output_batch_size + 1) * max_seqlen_batch_kv,
+#             max_seqlen_batch_kv,
+#             device=k_t.device,
+#             dtype=torch.int32,
+#         )
+#         Nnz_kv = output_batch_size * max_seqlen_batch_kv
+#     else:
+#         cumulative_sequence_length_kv, max_seqlen_batch_kv, Nnz_kv = (
+#             _cumulative_and_max_seq_len_nnz(v_t)
+#             if k_batch_size_needs_broadcast
+#             else _cumulative_and_max_seq_len_nnz(k_t)
+#         )
+
+#     q_num_heads_needs_broadcast = q_num_heads != output_num_heads
+#     k_num_heads_needs_broadcast = k_num_heads != output_num_heads
+#     v_num_heads_needs_broadcast = v_num_heads != output_num_heads
+
+#     if not q_t.is_nested:
+#         query_buffer_reshaped = q_t.expand(
+#             output_batch_size, q_t.size(1), output_num_heads, head_dim_qk
+#         )
+#         query_buffer_reshaped = query_buffer_reshaped.reshape(
+#             Nnz_q, output_num_heads, head_dim_qk
+#         )
+#     else:
+#         if not q_t.is_contiguous() and not _is_safe_to_get_storage_as_tensor(q_t):
+#             q_t = q_t.contiguous()
+#         # If we are broadcasting then Nnz_q will be the output_batch_size since
+#         # seq_len is 1
+#         effective_batch_size_q = (
+#             output_batch_size if q_batch_size_needs_broadcast else Nnz_q
+#         )
+#         query_buffer_reshaped = _view_as_dense(
+#             q_t, effective_batch_size_q, output_num_heads, head_dim_qk
+#         )
+
+#     # If the physical layout of the NestedTensor's storage
+#     # is not: batch, {seq_len}, num_heads, head_dim then we need
+#     # to call contiguous
+#     if not k_t.is_contiguous() and not _is_safe_to_get_storage_as_tensor(k_t):
+#         k_t = k_t.contiguous()
+#     if not v_t.is_contiguous() and not _is_safe_to_get_storage_as_tensor(v_t):
+#         v_t = v_t.contiguous()
+
+#     effective_batch_size_k = (
+#         output_batch_size if k_batch_size_needs_broadcast else Nnz_kv
+#     )
+#     key_buffer_reshaped = _view_as_dense(
+#         k_t, effective_batch_size_k, output_num_heads, head_dim_qk
+#     )
+
+#     effective_batch_size_v = (
+#         output_batch_size if v_batch_size_needs_broadcast else Nnz_kv
+#     )
+#     value_buffer_reshaped = _view_as_dense(
+#         v_t, effective_batch_size_v, output_num_heads, head_dim_v
+#     )
+
+#     if not q_batch_size_needs_broadcast:
+#         output_shape = q_t._size
+#         if head_dim_v != head_dim_qk:
+#             output_shape[-1] = head_dim_v
+#         if q_num_heads_needs_broadcast:
+#             output_shape[1] = output_num_heads
+#     else:
+#         output_shape = torch.empty(3, dtype=torch.int64, device=torch.device("cpu"))
+#         output_shape[0] = q_t.size(1)
+#         output_shape[1] = output_num_heads
+#         output_shape[2] = head_dim_v
+
+#     return (
+#         query_buffer_reshaped,
+#         key_buffer_reshaped,
+#         value_buffer_reshaped,
+#         cumulative_sequence_length_q,
+#         cumulative_sequence_length_kv,
+#         max_seqlen_batch_q,
+#         max_seqlen_batch_kv,
+#         output_shape,
+#     )
+
+
+def _sdpa_nested_preprocessing(query, key, value):
+    # Query (Batch x Num_heads x {Q_seq_len}  x Dim_per_head)
+    # Key   (Batch x Num_heads x {KV_seq_len} x Dim_per_head)
+    # Value (Batch x Num_heads x {KV_seq_len} x Dim_per_head)
+    q_batch_size = query.size(0)
+    k_batch_size = key.size(0)
+    v_batch_size = value.size(0)
+
+    q_num_heads = query.size(1)
+    k_num_heads = key.size(1)
+    v_num_heads = value.size(1)
+
+    if not (q_batch_size == k_batch_size and q_batch_size == v_batch_size) or not (
+        q_num_heads == k_num_heads and k_num_heads == v_num_heads
+    ):
+        raise RuntimeError(
+            "This path is currently not implemented for jagged layout NT."
+        )
+        # return _sdpa_nested_preprocessing_with_broadcast(query, key, value)
+
+    num_heads = query.size(1)
+    head_dim_qk = query.size(3)
+    head_dim_v = value.size(3)
+    q_t = query.transpose(1, 2)
+    k_t = key.transpose(1, 2)
+    v_t = value.transpose(1, 2)
+
+    (
+        cumulative_sequence_length_q,
+        max_seqlen_batch_q,
+        Nnz_q,
+    ) = _cumulative_and_max_seq_len_nnz(q_t)
+    (
+        cumulative_sequence_length_kv,
+        max_seqlen_batch_kv,
+        Nnz_kv,
+    ) = _cumulative_and_max_seq_len_nnz(k_t)
+
+    # [TODO] K and V have to have the same Nnz, should probably torch_check
+    # assume in order to not iterate over v
+
+    # If the physical layout of the NestedTensor's storage
+    # is not: batch, {seq_len}, num_heads, head_dim then we need
+    # to call contiguous
+    if not q_t.is_contiguous() and not _is_safe_to_get_storage_as_tensor(q_t):
+        q_t = q_t.contiguous()
+    if not k_t.is_contiguous() and not _is_safe_to_get_storage_as_tensor(k_t):
+        k_t = k_t.contiguous()
+    if not v_t.is_contiguous() and not _is_safe_to_get_storage_as_tensor(v_t):
+        v_t = v_t.contiguous()
+
+    query_buffer_reshaped = _view_as_dense(q_t, Nnz_q, num_heads, head_dim_qk)
+    key_buffer_reshaped = _view_as_dense(k_t, Nnz_kv, num_heads, head_dim_qk)
+    value_buffer_reshaped = _view_as_dense(v_t, Nnz_kv, num_heads, head_dim_v)
+
+    output_nt_info = {
+        "offsets": q_t.offsets(),
+        "_max_seqlen": q_t._get_max_seqlen(),
+        "_min_seqlen": q_t._get_min_seqlen(),
+    }
+
+    return (
+        query_buffer_reshaped,
+        key_buffer_reshaped,
+        value_buffer_reshaped,
+        cumulative_sequence_length_q,
+        cumulative_sequence_length_kv,
+        max_seqlen_batch_q,
+        max_seqlen_batch_kv,
+        output_nt_info,
+    )
+
+
+def _pad_last_dim(
+    tensor: torch.Tensor, alignment_size: int, slice: bool
+) -> torch.Tensor:
+    # FlashAttentionV2 requires that head dimension be a multiple of 8
+    # This was previously done within the kernel, however
+    # This causes the kernel to maybe alias query, key, value
+    # So instead we pad the head_dimensions to be a multiple of 8
+    # in the composite region
+    last_dim_size = tensor.size(-1)
+    if last_dim_size % alignment_size == 0:
+        return tensor
+    pad_count = alignment_size - (last_dim_size % alignment_size)
+    tensor = torch.nn.functional.pad(tensor, [0, pad_count])
+    if slice:
+        return tensor[..., 0:last_dim_size]
+    return tensor
+
+
+# TODO: coalesce with torch/nn/utils/attention.py
+def _calculate_scale(query, scale):
+    # TODO: Investigate why math.sqrt() isn't properly handled by Dynamo?
+    softmax_scale = scale if scale is not None else torch.sym_sqrt(1.0 / query.size(-1))
+    return softmax_scale
+
+
+def _post_process_flash_output(out: torch.Tensor, og_size):
+    if not out.is_nested and out.size(-1) != og_size:
+        out = out[..., 0:og_size]
+    return out
+
+
+def _is_computing_meta_flops(x):
+    # Note: there's a use case of using meta tensors & the dispatch-based flop counter.
+    # We can use this function to check for this scenario in order to handle it specially.
+    if not torch.jit.is_scripting() and x.device.type == "meta":
+        torch_dispatch_mode_stack = (
+            torch.utils._python_dispatch._get_current_dispatch_mode_stack()
+        )
+        return any(
+            type(x) == torch.utils.flop_counter.FlopCounterMode
+            for x in torch_dispatch_mode_stack
+        )
+    return False
+
+
+def _autocast(
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attn_mask: Optional[torch.Tensor],
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
+    """
+    [Autocasting SDPA for NJT]
+
+    Normal autocasting doesn't work for NJT+SDPA right now:
+    * NJT intercepts the __torch_function__ call for scaled_dot_product_attention, which happens
+      before we get to any aten ops or dispatcher logic; then the torch_function logic calls into
+      efficient attention or flash attention. So, autocasting on the scaled_dot_product_attention
+      op won't work because we never see that aten op.
+    * If we put autocasting on `_flash_attention_forward`, then we'll get autocasting to run, but
+      the kernel selection logic in torch_function handling (ie. jagged_scaled_dot_product_attention)
+      won't work correctly: the kernel selection logic will run before autocasting, and choose
+      a kernel based on the un-autocasted dtypes; but then autocasting will run and the actual
+      attention computation will happen in a different dtype.
+
+    An alternative is to just change the backend selection logic for SDPA+NJT to be autocast-aware
+    and rely on autocasting to do the actual conversions for flash attention / efficient attention.
+    However, by manually doing the actual autocast before the backend selection, we ensure that the
+    autocast handling for backend selection doesn't diverge from the autocast handling for the
+    actual dtype conversions.
+    """
+    device_type = query.device.type
+    # meta device is not supported by autocast, so break early for it
+    if _is_computing_meta_flops(query) or not torch.is_autocast_enabled(device_type):
+        return query, key, value, attn_mask
+
+    def cvt(x):
+        if x is None:
+            return x
+        target_dtype = torch.get_autocast_dtype(device_type)
+        if (
+            (not x.dtype.is_floating_point)
+            or x.dtype == target_dtype
+            or x.dtype == torch.float64
+        ):
+            return x
+        return x.to(target_dtype)
+
+    return cvt(query), cvt(key), cvt(value), cvt(attn_mask)
+
+
+def jagged_scaled_dot_product_attention(
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attn_mask: Optional[torch.Tensor] = None,
+    dropout_p=0.0,
+    is_causal=False,
+    scale=None,
+    enable_gqa=False,
+):
+    query, key, value, attn_mask = _autocast(query, key, value, attn_mask)
+    _validate_sdpa_input(query, key, value, attn_mask, dropout_p, is_causal, scale)
+    # for mypy, ugh
+    assert (
+        isinstance(query, NestedTensor)
+        and isinstance(key, NestedTensor)
+        and isinstance(value, NestedTensor)
+    )
+    from torch.nested._internal.nested_tensor import nested_view_from_values_offsets
+
+    # Special path for non-ragged sequence length (e.g. for SAM where we have a ragged
+    # second batch dim instead). For this case, we can just send the dense buffers through
+    # vanilla SDPA.
+    if query.dim() > 3 and key.dim() > 3 and value.dim() > 3 and query._ragged_idx == 1:
+        output = F.scaled_dot_product_attention(
+            query.values(),
+            key.values(),
+            value.values(),
+            attn_mask=(
+                attn_mask.values() if isinstance(attn_mask, NestedTensor) else attn_mask
+            ),
+            dropout_p=dropout_p,
+            is_causal=is_causal,
+            scale=scale,
+        )
+        return nested_view_from_values_offsets(output, query.offsets())
+
+    compute_logsumexp = query.requires_grad or key.requires_grad or value.requires_grad
+
+    backend_choice = _select_sdp_backend(
+        query, key, value, attn_mask, dropout_p, is_causal, enable_gqa
+    )
+
+    if _is_computing_meta_flops(query):
+        # Backend choice will probably not be correct if we have a meta device,
+        # because backend choice is device-aware. In this case, we mostly just
+        # want to avoid using math backend (which does a .item() call).
+        # Arbitrarily choose flash attention.
+        backend_choice = SDPBackend.FLASH_ATTENTION
+
+    if backend_choice == SDPBackend.FLASH_ATTENTION:
+        og_size = query.size(-1)
+        query_padded = _pad_last_dim(query, 8, False)
+        key_padded = _pad_last_dim(key, 8, False)
+        value_padded = _pad_last_dim(value, 8, False)
+        # We need to calculate the scale based off the OG head dim size
+        og_scale = _calculate_scale(query, scale)
+        (
+            query_buffer_reshaped,
+            key_buffer_reshaped,
+            value_buffer_reshaped,
+            cumulative_sequence_length_q,
+            cumulative_sequence_length_kv,
+            max_seqlen_batch_q,
+            max_seqlen_batch_kv,
+            output_nt_info,
+        ) = _sdpa_nested_preprocessing(query_padded, key_padded, value_padded)
+
+        (
+            attention,
+            logsumexp,
+            philox_seed,
+            philox_offset,
+            debug_attn_mask,
+        ) = torch.ops.aten._flash_attention_forward(
+            query_buffer_reshaped,
+            key_buffer_reshaped,
+            value_buffer_reshaped,
+            cumulative_sequence_length_q,
+            cumulative_sequence_length_kv,
+            max_seqlen_batch_q,
+            max_seqlen_batch_kv,
+            dropout_p,
+            is_causal,
+            False,
+            scale=og_scale,
+        )
+
+        # Reshape output to convert nnz to batch_size and seq_len
+        attention = nested_view_from_values_offsets(
+            attention,  # output from flash_attn is [total_q, num_heads, head_size_og]
+            output_nt_info["offsets"],
+            min_seqlen=output_nt_info["_min_seqlen"],
+            max_seqlen=output_nt_info["_max_seqlen"],
+        ).transpose(1, 2)
+        return _post_process_flash_output(attention, og_size)
+    elif backend_choice == SDPBackend.EFFICIENT_ATTENTION:
+        (
+            query_reshaped,
+            key_reshaped,
+            value_reshaped,
+            cumulative_sequence_length_q,
+            cumulative_sequence_length_kv,
+            max_seqlen_batch_q,
+            max_seqlen_batch_kv,
+            output_nt_info,
+        ) = _sdpa_nested_preprocessing(query, key, value)
+        (
+            attention,
+            log_sumexp,
+            seed,
+            offset,
+            max_seqlen_q,
+            max_seqlen_batch_kv,
+        ) = torch.ops.aten._efficient_attention_forward(
+            query_reshaped.unsqueeze(0),
+            key_reshaped.unsqueeze(0),
+            value_reshaped.unsqueeze(0),
+            None,
+            cumulative_sequence_length_q,
+            cumulative_sequence_length_kv,
+            max_seqlen_batch_q,
+            max_seqlen_batch_kv,
+            dropout_p,
+            int(is_causal),
+            compute_logsumexp,
+            scale=scale,
+        )
+
+        # Reshape output to convert nnz to batch_size and seq_len
+        return nested_view_from_values_offsets(
+            attention.squeeze(0),
+            output_nt_info["offsets"],
+            min_seqlen=output_nt_info["_min_seqlen"],
+            max_seqlen=output_nt_info["_max_seqlen"],
+        ).transpose(1, 2)
+    elif backend_choice == SDPBackend.MATH:
+        # save the offsets and shape of the inputs, so we can reshape the final output
+        # query @ key = attn: [B, D1, j0, D'] @ [B, D1, D' j1] = [B, D1, j0, j1]
+        # attn @ value = out: [B, D1, j0, j1] @ [B, D1, j1, D2] = [B, D1, j0, D2]
+        offsets = query.offsets()
+        d1 = query._size[1]
+        d2 = value._size[-1]
+
+        min_seqlen_tensor = query._metadata_cache.get(
+            "min_seqlen", None
+        )  # type: ignore[attr-defined]
+        max_seqlen_tensor = query._metadata_cache.get(
+            "max_seqlen", None
+        )  # type: ignore[attr-defined]
+
+        # convert jagged layout Nested Tensor to strided layout Nested Tensor
+        # which support the math implementation of SDPA
+        def get_strided_layout_nested_tensor(jagged_layout_nt):
+            lengths = jagged_layout_nt._offsets[1:] - jagged_layout_nt._offsets[:-1]
+            transpose = torch.transpose(jagged_layout_nt, 1, 2)
+            tensor_list = transpose.values().split(list(lengths), dim=0)
+            strided_nt = torch.nested.as_nested_tensor(list(tensor_list))
+            strided_nt = strided_nt.transpose(1, 2).contiguous()
+            return strided_nt
+
+        query = get_strided_layout_nested_tensor(query)
+        key = get_strided_layout_nested_tensor(key)
+        value = get_strided_layout_nested_tensor(value)
+
+        attn_out = torch._scaled_dot_product_attention_math(
+            query, key, value, attn_mask, dropout_p, is_causal, scale=scale
+        )[0]
+
+        from torch.nested._internal.nested_tensor import _load_val_from_tensor
+
+        # convert strided layout Nested Tensor back to jagged layout Nested Tensor
+        attn_out = attn_out.transpose(1, 2).contiguous().values()
+        attn_out = attn_out.view(-1, d1, d2)
+        attn_out = nested_view_from_values_offsets(
+            attn_out,
+            offsets,
+            min_seqlen=(
+                None
+                if min_seqlen_tensor is None
+                else _load_val_from_tensor(min_seqlen_tensor)
+            ),
+            max_seqlen=(
+                None
+                if max_seqlen_tensor is None
+                else _load_val_from_tensor(max_seqlen_tensor)
+            ),
+        ).transpose(1, 2)
+
+        return attn_out
+    else:
+        raise RuntimeError(
+            "No viable backend for scaled_dot_product_attention was found."
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..36c3ae609bb0871214138888d29501c803705598
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/__init__.py
@@ -0,0 +1,62 @@
+# mypy: allow-untyped-defs
+from torch.nn.parameter import (  # usort: skip
+    Buffer as Buffer,
+    Parameter as Parameter,
+    UninitializedBuffer as UninitializedBuffer,
+    UninitializedParameter as UninitializedParameter,
+)
+from torch.nn.modules import *  # usort: skip # noqa: F403
+from torch.nn import (
+    attention as attention,
+    functional as functional,
+    init as init,
+    modules as modules,
+    parallel as parallel,
+    parameter as parameter,
+    utils as utils,
+)
+from torch.nn.parallel import DataParallel as DataParallel
+
+
+def factory_kwargs(kwargs):
+    r"""Return a canonicalized dict of factory kwargs.
+
+    Given kwargs, returns a canonicalized dict of factory kwargs that can be directly passed
+    to factory functions like torch.empty, or errors if unrecognized kwargs are present.
+
+    This function makes it simple to write code like this::
+
+        class MyModule(nn.Module):
+            def __init__(self, **kwargs):
+                factory_kwargs = torch.nn.factory_kwargs(kwargs)
+                self.weight = Parameter(torch.empty(10, **factory_kwargs))
+
+    Why should you use this function instead of just passing `kwargs` along directly?
+
+    1. This function does error validation, so if there are unexpected kwargs we will
+    immediately report an error, instead of deferring it to the factory call
+    2. This function supports a special `factory_kwargs` argument, which can be used to
+    explicitly specify a kwarg to be used for factory functions, in the event one of the
+    factory kwargs conflicts with an already existing argument in the signature (e.g.
+    in the signature ``def f(dtype, **kwargs)``, you can specify ``dtype`` for factory
+    functions, as distinct from the dtype argument, by saying
+    ``f(dtype1, factory_kwargs={"dtype": dtype2})``)
+    """
+    if kwargs is None:
+        return {}
+    simple_keys = {"device", "dtype", "memory_format"}
+    expected_keys = simple_keys | {"factory_kwargs"}
+    if not kwargs.keys() <= expected_keys:
+        raise TypeError(f"unexpected kwargs {kwargs.keys() - expected_keys}")
+
+    # guarantee no input kwargs is untouched
+    r = dict(kwargs.get("factory_kwargs", {}))
+    for k in simple_keys:
+        if k in kwargs:
+            if k in r:
+                raise TypeError(
+                    f"{k} specified twice, in **kwargs and in factory_kwargs"
+                )
+            r[k] = kwargs[k]
+
+    return r
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/_reduction.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/_reduction.py
new file mode 100644
index 0000000000000000000000000000000000000000..93b00dc6feb43df50a95528ab2cb01d1fcac1609
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/_reduction.py
@@ -0,0 +1,60 @@
+import warnings
+from typing import Optional
+
+
+# NB: Keep this file in sync with enums in aten/src/ATen/core/Reduction.h
+
+
+def get_enum(reduction: str) -> int:
+    if reduction == "none":
+        ret = 0
+    elif reduction == "mean":
+        ret = 1
+    elif reduction == "elementwise_mean":
+        warnings.warn(
+            "reduction='elementwise_mean' is deprecated. "
+            "Please use reduction='mean' instead."
+        )
+        ret = 1
+    elif reduction == "sum":
+        ret = 2
+    else:
+        ret = -1  # TODO: remove once JIT exceptions support control flow
+        raise ValueError(f"{reduction} is not a valid value for reduction")
+    return ret
+
+
+# In order to support previous versions, accept boolean size_average and reduce
+# and convert them into the new constants for now
+
+
+# We use these functions in torch/legacy as well, in which case we'll silence the warning
+def legacy_get_string(
+    size_average: Optional[bool],
+    reduce: Optional[bool],
+    emit_warning: bool = True,
+) -> str:
+    warning = "size_average and reduce args will be deprecated, please use reduction='{}' instead."
+
+    if size_average is None:
+        size_average = True
+    if reduce is None:
+        reduce = True
+
+    if size_average and reduce:
+        ret = "mean"
+    elif reduce:
+        ret = "sum"
+    else:
+        ret = "none"
+    if emit_warning:
+        warnings.warn(warning.format(ret))
+    return ret
+
+
+def legacy_get_enum(
+    size_average: Optional[bool],
+    reduce: Optional[bool],
+    emit_warning: bool = True,
+) -> int:
+    return get_enum(legacy_get_string(size_average, reduce, emit_warning))
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/common_types.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/common_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..74661d604c3e60427048b68d0b7cfa733366bd2b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/common_types.py
@@ -0,0 +1,44 @@
+from typing import Optional, Tuple, TypeVar, Union
+
+from torch import Tensor
+
+
+# Create some useful type aliases
+
+# Template for arguments which can be supplied as a tuple, or which can be a scalar which PyTorch will internally
+# broadcast to a tuple.
+# Comes in several variants: A tuple of unknown size, and a fixed-size tuple for 1d, 2d, or 3d operations.
+T = TypeVar("T")
+_scalar_or_tuple_any_t = Union[T, Tuple[T, ...]]
+_scalar_or_tuple_1_t = Union[T, Tuple[T]]
+_scalar_or_tuple_2_t = Union[T, Tuple[T, T]]
+_scalar_or_tuple_3_t = Union[T, Tuple[T, T, T]]
+_scalar_or_tuple_4_t = Union[T, Tuple[T, T, T, T]]
+_scalar_or_tuple_5_t = Union[T, Tuple[T, T, T, T, T]]
+_scalar_or_tuple_6_t = Union[T, Tuple[T, T, T, T, T, T]]
+
+# For arguments which represent size parameters (eg, kernel size, padding)
+_size_any_t = _scalar_or_tuple_any_t[int]
+_size_1_t = _scalar_or_tuple_1_t[int]
+_size_2_t = _scalar_or_tuple_2_t[int]
+_size_3_t = _scalar_or_tuple_3_t[int]
+_size_4_t = _scalar_or_tuple_4_t[int]
+_size_5_t = _scalar_or_tuple_5_t[int]
+_size_6_t = _scalar_or_tuple_6_t[int]
+
+# For arguments which represent optional size parameters (eg, adaptive pool parameters)
+_size_any_opt_t = _scalar_or_tuple_any_t[Optional[int]]
+_size_2_opt_t = _scalar_or_tuple_2_t[Optional[int]]
+_size_3_opt_t = _scalar_or_tuple_3_t[Optional[int]]
+
+# For arguments that represent a ratio to adjust each dimension of an input with (eg, upsampling parameters)
+_ratio_2_t = _scalar_or_tuple_2_t[float]
+_ratio_3_t = _scalar_or_tuple_3_t[float]
+_ratio_any_t = _scalar_or_tuple_any_t[float]
+
+_tensor_list_t = _scalar_or_tuple_any_t[Tensor]
+
+# For the return value of max pooling operations that may or may not return indices.
+# With the proposed 'Literal' feature to Python typing, it might be possible to
+# eventually eliminate this.
+_maybe_indices_t = _scalar_or_tuple_2_t[Tensor]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/cpp.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/cpp.py
new file mode 100644
index 0000000000000000000000000000000000000000..98a61bfb7c428d024753ea3f6c4dc34fdb3bea08
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/cpp.py
@@ -0,0 +1,89 @@
+# mypy: allow-untyped-defs
+"""Functionality for Python <-> C++ frontend inter-op."""
+
+from torch import nn
+
+
+class OrderedDictWrapper:
+    """A wrapper around a C++ OrderedDict.
+
+    It dynamically evaluates the OrderedDict getter on a bound C++ module, such
+    that new changes on the C++ side are picked up. Otherwise accessing e.g.
+    ``cpp_module._parameters`` just once would get a frozen copy of the parameters
+    at the time of access. ``torch.nn.Module`` accesses ``_parameters`` et al. via ``self.__dict__``
+    so using properties does not work.
+    """
+
+    def __init__(self, cpp_module, attr):
+        self.cpp_module = cpp_module
+        self.attr = attr
+
+    @property
+    def cpp_dict(self):
+        return getattr(self.cpp_module, self.attr)
+
+    # Magic methods cannot be assigned dynamically and bypass ``getattr``, so we
+    # must manually override them.
+
+    def items(self):
+        return self.cpp_dict.items()
+
+    def keys(self):
+        return self.cpp_dict.keys()
+
+    def values(self):
+        return self.cpp_dict.values()
+
+    def __iter__(self):
+        return self.cpp_dict.__iter__()
+
+    def __len__(self):
+        return self.cpp_dict.__len__()
+
+    def __contains__(self, key):
+        return self.cpp_dict.__contains__(key)
+
+    def __getitem__(self, key):
+        return self.cpp_dict.__getitem__(key)
+
+
+class ModuleWrapper(nn.Module):
+    """A subclass of ``torch.nn.Module`` that wraps a C++ frontend module and delegates all access."""
+
+    def __init__(self, cpp_module):
+        # Assign before the super class constructor so ``self.training`` can be
+        # assigned to in the super class constructor.
+        self.cpp_module = cpp_module
+        super().__init__()
+        self._parameters = OrderedDictWrapper(cpp_module, "_parameters")  # type: ignore[assignment]
+        self._buffers: OrderedDictWrapper = OrderedDictWrapper(cpp_module, "_buffers")  # type: ignore[assignment]
+        self._modules: OrderedDictWrapper = OrderedDictWrapper(cpp_module, "_modules")  # type: ignore[assignment]
+        for attr in dir(cpp_module):
+            # Skip magic methods and the three attributes above.
+            if not attr.startswith("_"):
+                setattr(self, attr, getattr(self.cpp_module, attr))
+
+    def _apply(self, fn, recurse=True):
+        for param in self.parameters():
+            # Tensors stored in modules are graph leaves, and we don't
+            # want to create copy nodes, so we have to unpack the data.
+            param.data = fn(param.data)
+            if param._grad is not None:
+                param._grad.data = fn(param._grad.data)
+
+        for buf in self.buffers():
+            buf.data = fn(buf.data)
+
+        return self
+
+    # nn.Module defines training as a boolean
+    @property  # type: ignore[override]
+    def training(self):
+        return self.cpp_module.training
+
+    @training.setter
+    def training(self, mode):
+        self.cpp_module.train(mode)
+
+    def __repr__(self):
+        return self.cpp_module.__repr__()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/functional.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/functional.py
new file mode 100644
index 0000000000000000000000000000000000000000..3072ac0fef0f299fbb44a030bcc19a253b53e0e0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/functional.py
@@ -0,0 +1,6290 @@
+"""Functional interface."""
+
+import importlib
+import math
+import warnings
+from typing import Callable, List, Optional, Tuple, TYPE_CHECKING, Union
+
+import torch
+from torch import _VF, sym_int as _sym_int, Tensor
+from torch._C import _add_docstr, _infer_size
+from torch._jit_internal import (
+    _overload,
+    boolean_dispatch,
+    BroadcastingList1,
+    BroadcastingList2,
+    BroadcastingList3,
+)
+from torch._torch_docs import reproducibility_notes, sparse_support_notes, tf32_notes
+from torch.nn import _reduction as _Reduction, grad  # noqa: F401
+from torch.nn.modules.utils import _list_with_default, _pair, _single, _triple
+from torch.overrides import (
+    handle_torch_function,
+    has_torch_function,
+    has_torch_function_unary,
+    has_torch_function_variadic,
+)
+
+
+if TYPE_CHECKING:
+    from torch.types import _dtype as DType
+else:
+    # The JIT doesn't understand Union, nor torch.dtype here
+    DType = int
+
+try:
+    import numpy as np
+except ModuleNotFoundError:
+    np = None
+
+
+conv1d = _add_docstr(
+    torch.conv1d,
+    r"""
+conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor
+
+Applies a 1D convolution over an input signal composed of several input
+planes.
+
+{tf32_note}
+
+See :class:`~torch.nn.Conv1d` for details and output shape.
+
+Note:
+    {cudnn_reproducibility_note}
+
+Note:
+    This operator supports complex data types i.e. ``complex32, complex64, complex128``.
+""".format(
+        **reproducibility_notes, **tf32_notes
+    )
+    + r"""
+
+Args:
+    input: input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iW)`
+    weight: filters of shape :math:`(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kW)`
+    bias: optional bias of shape :math:`(\text{out\_channels})`. Default: ``None``
+    stride: the stride of the convolving kernel. Can be a single number or
+      a one-element tuple `(sW,)`. Default: 1
+    padding: implicit paddings on both sides of the input. Can be a string {'valid', 'same'},
+      single number or a one-element tuple `(padW,)`. Default: 0
+      ``padding='valid'`` is the same as no padding. ``padding='same'`` pads
+      the input so the output has the same shape as the input. However, this mode
+      doesn't support any stride values other than 1.
+
+      .. warning::
+          For ``padding='same'``, if the ``weight`` is even-length and
+          ``dilation`` is odd in any dimension, a full :func:`pad` operation
+          may be needed internally. Lowering performance.
+    dilation: the spacing between kernel elements. Can be a single number or
+      a one-element tuple `(dW,)`. Default: 1
+    groups: split input into groups, :math:`\text{in\_channels}` should be divisible by
+      the number of groups. Default: 1
+
+Examples::
+
+    >>> inputs = torch.randn(33, 16, 30)
+    >>> filters = torch.randn(20, 16, 5)
+    >>> F.conv1d(inputs, filters)
+""",
+)
+
+conv2d = _add_docstr(
+    torch.conv2d,
+    r"""
+conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor
+
+Applies a 2D convolution over an input image composed of several input
+planes.
+
+{tf32_note}
+
+See :class:`~torch.nn.Conv2d` for details and output shape.
+
+Note:
+    {cudnn_reproducibility_note}
+
+Note:
+    This operator supports complex data types i.e. ``complex32, complex64, complex128``.
+""".format(
+        **reproducibility_notes, **tf32_notes
+    )
+    + r"""
+
+Args:
+    input: input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`
+    weight: filters of shape :math:`(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kH , kW)`
+    bias: optional bias tensor of shape :math:`(\text{out\_channels})`. Default: ``None``
+    stride: the stride of the convolving kernel. Can be a single number or a
+      tuple `(sH, sW)`. Default: 1
+    padding: implicit paddings on both sides of the input. Can be a string {'valid', 'same'},
+      single number or a tuple `(padH, padW)`. Default: 0
+      ``padding='valid'`` is the same as no padding. ``padding='same'`` pads
+      the input so the output has the same shape as the input. However, this mode
+      doesn't support any stride values other than 1.
+
+      .. warning::
+          For ``padding='same'``, if the ``weight`` is even-length and
+          ``dilation`` is odd in any dimension, a full :func:`pad` operation
+          may be needed internally. Lowering performance.
+
+    dilation: the spacing between kernel elements. Can be a single number or
+      a tuple `(dH, dW)`. Default: 1
+    groups: split input into groups, both :math:`\text{in\_channels}` and :math:`\text{out\_channels}`
+      should be divisible by the number of groups. Default: 1
+
+Examples::
+
+    >>> # With square kernels and equal stride
+    >>> filters = torch.randn(8, 4, 3, 3)
+    >>> inputs = torch.randn(1, 4, 5, 5)
+    >>> F.conv2d(inputs, filters, padding=1)
+""",
+)  # noqa: E501
+
+conv3d = _add_docstr(
+    torch.conv3d,
+    r"""
+conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor
+
+Applies a 3D convolution over an input image composed of several input
+planes.
+
+{tf32_note}
+
+See :class:`~torch.nn.Conv3d` for details and output shape.
+
+Note:
+    {cudnn_reproducibility_note}
+
+Note:
+    This operator supports complex data types i.e. ``complex32, complex64, complex128``.
+""".format(
+        **reproducibility_notes, **tf32_notes
+    )
+    + r"""
+
+Args:
+    input: input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iT , iH , iW)`
+    weight: filters of shape :math:`(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kT , kH , kW)`
+    bias: optional bias tensor of shape :math:`(\text{out\_channels})`. Default: None
+    stride: the stride of the convolving kernel. Can be a single number or a
+      tuple `(sT, sH, sW)`. Default: 1
+    padding: implicit paddings on both sides of the input. Can be a string {'valid', 'same'},
+      single number or a tuple `(padT, padH, padW)`. Default: 0
+      ``padding='valid'`` is the same as no padding. ``padding='same'`` pads
+      the input so the output has the same shape as the input. However, this mode
+      doesn't support any stride values other than 1.
+
+      .. warning::
+          For ``padding='same'``, if the ``weight`` is even-length and
+          ``dilation`` is odd in any dimension, a full :func:`pad` operation
+          may be needed internally. Lowering performance.
+
+    dilation: the spacing between kernel elements. Can be a single number or
+      a tuple `(dT, dH, dW)`. Default: 1
+    groups: split input into groups, :math:`\text{in\_channels}` should be divisible by
+      the number of groups. Default: 1
+
+Examples::
+
+    >>> filters = torch.randn(33, 16, 3, 3, 3)
+    >>> inputs = torch.randn(20, 16, 50, 10, 20)
+    >>> F.conv3d(inputs, filters)
+""",
+)  # noqa: E501
+
+conv_transpose1d = _add_docstr(
+    torch.conv_transpose1d,
+    r"""
+conv_transpose1d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor
+
+Applies a 1D transposed convolution operator over an input signal
+composed of several input planes, sometimes also called "deconvolution".
+
+{tf32_note}
+
+See :class:`~torch.nn.ConvTranspose1d` for details and output shape.
+
+Note:
+    {cudnn_reproducibility_note}
+""".format(
+        **reproducibility_notes, **tf32_notes
+    )
+    + r"""
+
+Args:
+    input: input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iW)`
+    weight: filters of shape :math:`(\text{in\_channels} , \frac{\text{out\_channels}}{\text{groups}} , kW)`
+    bias: optional bias of shape :math:`(\text{out\_channels})`. Default: None
+    stride: the stride of the convolving kernel. Can be a single number or a
+      tuple ``(sW,)``. Default: 1
+    padding: ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both
+      sides of each dimension in the input. Can be a single number or a tuple
+      ``(padW,)``. Default: 0
+    output_padding: additional size added to one side of each dimension in the
+      output shape. Can be a single number or a tuple ``(out_padW)``. Default: 0
+    groups: split input into groups, :math:`\text{in\_channels}` should be divisible by the
+      number of groups. Default: 1
+    dilation: the spacing between kernel elements. Can be a single number or
+      a tuple ``(dW,)``. Default: 1
+
+Examples::
+
+    >>> inputs = torch.randn(20, 16, 50)
+    >>> weights = torch.randn(16, 33, 5)
+    >>> F.conv_transpose1d(inputs, weights)
+""",
+)
+
+conv_transpose2d = _add_docstr(
+    torch.conv_transpose2d,
+    r"""
+conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor
+
+Applies a 2D transposed convolution operator over an input image
+composed of several input planes, sometimes also called "deconvolution".
+
+{tf32_note}
+
+See :class:`~torch.nn.ConvTranspose2d` for details and output shape.
+
+Note:
+    {cudnn_reproducibility_note}
+""".format(
+        **reproducibility_notes, **tf32_notes
+    )
+    + r"""
+
+Args:
+    input: input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`
+    weight: filters of shape :math:`(\text{in\_channels} , \frac{\text{out\_channels}}{\text{groups}} , kH , kW)`
+    bias: optional bias of shape :math:`(\text{out\_channels})`. Default: None
+    stride: the stride of the convolving kernel. Can be a single number or a
+      tuple ``(sH, sW)``. Default: 1
+    padding: ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both
+      sides of each dimension in the input. Can be a single number or a tuple
+      ``(padH, padW)``. Default: 0
+    output_padding: additional size added to one side of each dimension in the
+      output shape. Can be a single number or a tuple ``(out_padH, out_padW)``.
+      Default: 0
+    groups: split input into groups, :math:`\text{in\_channels}` should be divisible by the
+      number of groups. Default: 1
+    dilation: the spacing between kernel elements. Can be a single number or
+      a tuple ``(dH, dW)``. Default: 1
+
+Examples::
+
+    >>> # With square kernels and equal stride
+    >>> inputs = torch.randn(1, 4, 5, 5)
+    >>> weights = torch.randn(4, 8, 3, 3)
+    >>> F.conv_transpose2d(inputs, weights, padding=1)
+""",
+)  # noqa: E501
+
+conv_transpose3d = _add_docstr(
+    torch.conv_transpose3d,
+    r"""
+conv_transpose3d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor
+
+Applies a 3D transposed convolution operator over an input image
+composed of several input planes, sometimes also called "deconvolution"
+
+{tf32_note}
+
+See :class:`~torch.nn.ConvTranspose3d` for details and output shape.
+
+Note:
+    {cudnn_reproducibility_note}
+""".format(
+        **reproducibility_notes, **tf32_notes
+    )
+    + r"""
+
+Args:
+    input: input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iT , iH , iW)`
+    weight: filters of shape :math:`(\text{in\_channels} , \frac{\text{out\_channels}}{\text{groups}} , kT , kH , kW)`
+    bias: optional bias of shape :math:`(\text{out\_channels})`. Default: None
+    stride: the stride of the convolving kernel. Can be a single number or a
+      tuple ``(sT, sH, sW)``. Default: 1
+    padding: ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both
+      sides of each dimension in the input. Can be a single number or a tuple
+      ``(padT, padH, padW)``. Default: 0
+    output_padding: additional size added to one side of each dimension in the
+      output shape. Can be a single number or a tuple
+      ``(out_padT, out_padH, out_padW)``. Default: 0
+    groups: split input into groups, :math:`\text{in\_channels}` should be divisible by the
+      number of groups. Default: 1
+    dilation: the spacing between kernel elements. Can be a single number or
+      a tuple `(dT, dH, dW)`. Default: 1
+
+Examples::
+
+    >>> inputs = torch.randn(20, 16, 50, 10, 20)
+    >>> weights = torch.randn(16, 33, 3, 3, 3)
+    >>> F.conv_transpose3d(inputs, weights)
+""",
+)  # noqa: E501
+
+conv_tbc = _add_docstr(
+    torch.conv_tbc,
+    r"""
+Applies a 1-dimensional sequence convolution over an input sequence.
+Input and output dimensions are (Time, Batch, Channels) - hence TBC.
+
+Args:
+    input: input tensor of shape :math:`(\text{sequence length} \times batch \times \text{in\_channels})`
+    weight: filter of shape (:math:`\text{kernel width} \times \text{in\_channels} \times \text{out\_channels}`)
+    bias: bias of shape (:math:`\text{out\_channels}`)
+    pad: number of timesteps to pad. Default: 0
+""",
+)
+
+
+# Pooling
+avg_pool1d = _add_docstr(
+    torch.avg_pool1d,
+    r"""
+avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) -> Tensor
+
+Applies a 1D average pooling over an input signal composed of several
+input planes.
+
+See :class:`~torch.nn.AvgPool1d` for details and output shape.
+
+Args:
+    input: input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iW)`
+    kernel_size: the size of the window. Can be a single number or a
+      tuple `(kW,)`
+    stride: the stride of the window. Can be a single number or a tuple
+      `(sW,)`. Default: :attr:`kernel_size`
+    padding: implicit zero paddings on both sides of the input. Can be a
+      single number or a tuple `(padW,)`. Default: 0
+    ceil_mode: when True, will use `ceil` instead of `floor` to compute the
+        output shape. Default: ``False``
+    count_include_pad: when True, will include the zero-padding in the
+        averaging calculation. Default: ``True``
+
+Examples::
+
+    >>> # pool of square window of size=3, stride=2
+    >>> input = torch.tensor([[[1, 2, 3, 4, 5, 6, 7]]], dtype=torch.float32)
+    >>> F.avg_pool1d(input, kernel_size=3, stride=2)
+    tensor([[[ 2.,  4.,  6.]]])
+
+""",
+)
+
+
+avg_pool2d = _add_docstr(
+    torch._C._nn.avg_pool2d,
+    r"""
+avg_pool2d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None) -> Tensor
+
+Applies 2D average-pooling operation in :math:`kH \times kW` regions by step size
+:math:`sH \times sW` steps. The number of output features is equal to the number of
+input planes.
+
+See :class:`~torch.nn.AvgPool2d` for details and output shape.
+
+Args:
+    input: input tensor :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`
+    kernel_size: size of the pooling region. Can be a single number or a
+      tuple `(kH, kW)`
+    stride: stride of the pooling operation. Can be a single number or a
+      tuple `(sH, sW)`. Default: :attr:`kernel_size`
+    padding: implicit zero paddings on both sides of the input. Can be a
+      single number or a tuple `(padH, padW)`. Default: 0
+    ceil_mode: when True, will use `ceil` instead of `floor` in the formula
+        to compute the output shape. Default: ``False``
+    count_include_pad: when True, will include the zero-padding in the
+        averaging calculation. Default: ``True``
+    divisor_override: if specified, it will be used as divisor, otherwise
+         size of the pooling region will be used. Default: None
+""",
+)
+
+avg_pool3d = _add_docstr(
+    torch._C._nn.avg_pool3d,
+    r"""
+avg_pool3d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None) -> Tensor
+
+Applies 3D average-pooling operation in :math:`kT \times kH \times kW` regions by step
+size :math:`sT \times sH \times sW` steps. The number of output features is equal to
+:math:`\lfloor\frac{\text{input planes}}{sT}\rfloor`.
+
+See :class:`~torch.nn.AvgPool3d` for details and output shape.
+
+Args:
+    input: input tensor :math:`(\text{minibatch} , \text{in\_channels} , iT \times iH , iW)`
+    kernel_size: size of the pooling region. Can be a single number or a
+      tuple `(kT, kH, kW)`
+    stride: stride of the pooling operation. Can be a single number or a
+      tuple `(sT, sH, sW)`. Default: :attr:`kernel_size`
+    padding: implicit zero paddings on both sides of the input. Can be a
+      single number or a tuple `(padT, padH, padW)`, Default: 0
+    ceil_mode: when True, will use `ceil` instead of `floor` in the formula
+        to compute the output shape
+    count_include_pad: when True, will include the zero-padding in the
+        averaging calculation
+    divisor_override: if specified, it will be used as divisor, otherwise
+        size of the pooling region will be used. Default: None
+""",
+)
+
+
+def fractional_max_pool2d_with_indices(
+    input: Tensor,
+    kernel_size: BroadcastingList2[int],
+    output_size: Optional[BroadcastingList2[int]] = None,
+    output_ratio: Optional[BroadcastingList2[float]] = None,
+    return_indices: bool = False,
+    _random_samples: Optional[Tensor] = None,
+) -> Tuple[Tensor, Tensor]:  # noqa: D400
+    r"""
+    fractional_max_pool2d(input, kernel_size, output_size=None, output_ratio=None, return_indices=False, _random_samples=None)
+
+    Applies 2D fractional max pooling over an input signal composed of several input planes.
+
+    Fractional MaxPooling is described in detail in the paper `Fractional MaxPooling`_ by Ben Graham
+
+    The max-pooling operation is applied in :math:`kH \times kW` regions by a stochastic
+    step size determined by the target output size.
+    The number of output features is equal to the number of input planes.
+
+    Args:
+        kernel_size: the size of the window to take a max over.
+                     Can be a single number :math:`k` (for a square kernel of :math:`k \times k`)
+                     or a tuple `(kH, kW)`
+        output_size: the target output size of the image of the form :math:`oH \times oW`.
+                     Can be a tuple `(oH, oW)` or a single number :math:`oH` for a square image :math:`oH \times oH`
+        output_ratio: If one wants to have an output size as a ratio of the input size, this option can be given.
+                      This has to be a number or tuple in the range (0, 1)
+        return_indices: if ``True``, will return the indices along with the outputs.
+                        Useful to pass to :func:`~torch.nn.functional.max_unpool2d`.
+
+    Examples::
+        >>> input = torch.randn(20, 16, 50, 32)
+        >>> # pool of square window of size=3, and target output size 13x12
+        >>> F.fractional_max_pool2d(input, 3, output_size=(13, 12))
+        >>> # pool of square window and target output size being half of input image size
+        >>> F.fractional_max_pool2d(input, 3, output_ratio=(0.5, 0.5))
+
+    .. _Fractional MaxPooling:
+        http://arxiv.org/abs/1412.6071
+    """
+    if has_torch_function_variadic(input, _random_samples):
+        return handle_torch_function(
+            fractional_max_pool2d_with_indices,
+            (input, _random_samples),
+            input,
+            kernel_size,
+            output_size=output_size,
+            output_ratio=output_ratio,
+            return_indices=return_indices,
+            _random_samples=_random_samples,
+        )
+    if output_size is None and output_ratio is None:
+        raise ValueError(
+            "fractional_max_pool2d requires specifying either an output_size or an output_ratio"
+        )
+    if output_size is None:
+        assert output_ratio is not None
+        if len(output_ratio) > 2:
+            raise ValueError(
+                "fractional_max_pool2d requires output_ratio to either be a single Int or tuple of Ints."
+            )
+        _output_ratio = _pair(output_ratio)
+        output_size = [
+            int(input.size(-2) * _output_ratio[0]),
+            int(input.size(-1) * _output_ratio[1]),
+        ]
+
+    if _random_samples is None:
+        n_batch = 1 if input.dim() == 3 else input.size(0)
+        _random_samples = torch.rand(
+            n_batch, input.size(-3), 2, dtype=input.dtype, device=input.device
+        )
+    return torch._C._nn.fractional_max_pool2d(
+        input, kernel_size, output_size, _random_samples
+    )
+
+
+def _fractional_max_pool2d(
+    input: Tensor,
+    kernel_size: BroadcastingList2[int],
+    output_size: Optional[BroadcastingList2[int]] = None,
+    output_ratio: Optional[BroadcastingList2[float]] = None,
+    return_indices: bool = False,
+    _random_samples: Optional[Tensor] = None,
+) -> Tensor:
+    if has_torch_function_variadic(input, _random_samples):
+        return handle_torch_function(
+            fractional_max_pool2d,
+            (input, _random_samples),
+            input,
+            kernel_size,
+            output_size=output_size,
+            output_ratio=output_ratio,
+            return_indices=return_indices,
+            _random_samples=_random_samples,
+        )
+    return fractional_max_pool2d_with_indices(
+        input, kernel_size, output_size, output_ratio, return_indices, _random_samples
+    )[0]
+
+
+fractional_max_pool2d = boolean_dispatch(
+    arg_name="return_indices",
+    arg_index=4,
+    default=False,
+    if_true=fractional_max_pool2d_with_indices,
+    if_false=_fractional_max_pool2d,
+    module_name=__name__,
+    func_name="fractional_max_pool2d",
+)
+
+
+def fractional_max_pool3d_with_indices(
+    input: Tensor,
+    kernel_size: BroadcastingList3[int],
+    output_size: Optional[BroadcastingList3[int]] = None,
+    output_ratio: Optional[BroadcastingList3[float]] = None,
+    return_indices: bool = False,
+    _random_samples: Optional[Tensor] = None,
+) -> Tuple[Tensor, Tensor]:  # noqa: D400
+    r"""
+    fractional_max_pool3d(input, kernel_size, output_size=None, output_ratio=None, return_indices=False, _random_samples=None)
+
+    Applies 3D fractional max pooling over an input signal composed of several input planes.
+
+    Fractional MaxPooling is described in detail in the paper `Fractional MaxPooling`_ by Ben Graham
+
+    The max-pooling operation is applied in :math:`kT \times kH \times kW` regions by a stochastic
+    step size determined by the target output size.
+    The number of output features is equal to the number of input planes.
+
+    Args:
+        kernel_size: the size of the window to take a max over.
+                     Can be a single number :math:`k` (for a square kernel of :math:`k \times k \times k`)
+                     or a tuple `(kT, kH, kW)`
+        output_size: the target output size of the form :math:`oT \times oH \times oW`.
+                     Can be a tuple `(oT, oH, oW)` or a single number :math:`oH` for a cubic output
+                     :math:`oH \times oH \times oH`
+        output_ratio: If one wants to have an output size as a ratio of the input size, this option can be given.
+                      This has to be a number or tuple in the range (0, 1)
+        return_indices: if ``True``, will return the indices along with the outputs.
+                        Useful to pass to :func:`~torch.nn.functional.max_unpool3d`.
+
+    Shape:
+        - Input: :math:`(N, C, T_{in}, H_{in}, W_{in})` or :math:`(C, T_{in}, H_{in}, W_{in})`.
+        - Output: :math:`(N, C, T_{out}, H_{out}, W_{out})` or :math:`(C, T_{out}, H_{out}, W_{out})`, where
+          :math:`(T_{out}, H_{out}, W_{out})=\text{output\_size}` or
+          :math:`(T_{out}, H_{out}, W_{out})=\text{output\_ratio} \times (T_{in}, H_{in}, W_{in})`
+
+    Examples::
+        >>> input = torch.randn(20, 16, 50, 32, 16)
+        >>> # pool of cubic window of size=3, and target output size 13x12x11
+        >>> F.fractional_max_pool3d(input, 3, output_size=(13, 12, 11))
+        >>> # pool of cubic window and target output size being half of input size
+        >>> F.fractional_max_pool3d(input, 3, output_ratio=(0.5, 0.5, 0.5))
+
+    .. _Fractional MaxPooling:
+        http://arxiv.org/abs/1412.6071
+    """
+    if has_torch_function_variadic(input, _random_samples):
+        return handle_torch_function(
+            fractional_max_pool3d_with_indices,
+            (input, _random_samples),
+            input,
+            kernel_size,
+            output_size=output_size,
+            output_ratio=output_ratio,
+            return_indices=return_indices,
+            _random_samples=_random_samples,
+        )
+    if output_size is None and output_ratio is None:
+        raise ValueError(
+            "fractional_max_pool3d requires specifying either an output_size or an output_ratio"
+        )
+    if output_size is None:
+        assert output_ratio is not None
+        _output_ratio = _triple(output_ratio)
+        output_size = [
+            int(input.size(-3) * _output_ratio[0]),
+            int(input.size(-2) * _output_ratio[1]),
+            int(input.size(-1) * _output_ratio[2]),
+        ]
+
+    if _random_samples is None:
+        n_batch = 1 if input.dim() == 4 else input.size(0)
+        _random_samples = torch.rand(
+            n_batch, input.size(-4), 3, dtype=input.dtype, device=input.device
+        )
+    return torch._C._nn.fractional_max_pool3d(
+        input, kernel_size, output_size, _random_samples
+    )
+
+
+def _fractional_max_pool3d(
+    input: Tensor,
+    kernel_size: BroadcastingList3[int],
+    output_size: Optional[BroadcastingList3[int]] = None,
+    output_ratio: Optional[BroadcastingList3[float]] = None,
+    return_indices: bool = False,
+    _random_samples: Optional[Tensor] = None,
+) -> Tensor:
+    if has_torch_function_variadic(input, _random_samples):
+        return handle_torch_function(
+            fractional_max_pool3d,
+            (input, _random_samples),
+            input,
+            kernel_size,
+            output_size=output_size,
+            output_ratio=output_ratio,
+            return_indices=return_indices,
+            _random_samples=_random_samples,
+        )
+    return fractional_max_pool3d_with_indices(
+        input, kernel_size, output_size, output_ratio, return_indices, _random_samples
+    )[0]
+
+
+fractional_max_pool3d = boolean_dispatch(
+    arg_name="return_indices",
+    arg_index=4,
+    default=False,
+    if_true=fractional_max_pool3d_with_indices,
+    if_false=_fractional_max_pool3d,
+    module_name=__name__,
+    func_name="fractional_max_pool3d",
+)
+
+
+def max_pool1d_with_indices(
+    input: Tensor,
+    kernel_size: BroadcastingList1[int],
+    stride: Optional[BroadcastingList1[int]] = None,
+    padding: BroadcastingList1[int] = 0,
+    dilation: BroadcastingList1[int] = 1,
+    ceil_mode: bool = False,
+    return_indices: bool = False,
+) -> Tuple[Tensor, Tensor]:  # noqa: D400
+    r"""
+    max_pool1d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False)
+
+    Applies a 1D max pooling over an input signal composed of several input
+    planes.
+
+    .. note::
+        The order of :attr:`ceil_mode` and :attr:`return_indices` is different from
+        what seen in :class:`~torch.nn.MaxPool1d`, and will change in a future release.
+
+    See :class:`~torch.nn.MaxPool1d` for details.
+
+    Args:
+        input: input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iW)`, minibatch dim optional.
+        kernel_size: the size of the window. Can be a single number or a
+            tuple `(kW,)`
+        stride: the stride of the window. Can be a single number or a tuple
+            `(sW,)`. Default: :attr:`kernel_size`
+        padding: Implicit negative infinity padding to be added on both sides, must be >= 0 and <= kernel_size / 2.
+        dilation: The stride between elements within a sliding window, must be > 0.
+        ceil_mode: If ``True``, will use `ceil` instead of `floor` to compute the output shape. This
+                   ensures that every element in the input tensor is covered by a sliding window.
+        return_indices: If ``True``, will return the argmax along with the max values.
+                        Useful for :class:`torch.nn.functional.max_unpool1d` later
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            max_pool1d_with_indices,
+            (input,),
+            input,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            ceil_mode=ceil_mode,
+            return_indices=return_indices,
+        )
+    if stride is None:
+        stride = torch.jit.annotate(List[int], [])
+    return torch.max_pool1d_with_indices(
+        input, kernel_size, stride, padding, dilation, ceil_mode
+    )
+
+
+def _max_pool1d(
+    input: Tensor,
+    kernel_size: BroadcastingList1[int],
+    stride: Optional[BroadcastingList1[int]] = None,
+    padding: BroadcastingList1[int] = 0,
+    dilation: BroadcastingList1[int] = 1,
+    ceil_mode: bool = False,
+    return_indices: bool = False,
+) -> Tensor:
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            max_pool1d,
+            (input,),
+            input,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            ceil_mode=ceil_mode,
+            return_indices=return_indices,
+        )
+    if stride is None:
+        stride = torch.jit.annotate(List[int], [])
+    return torch.max_pool1d(input, kernel_size, stride, padding, dilation, ceil_mode)
+
+
+max_pool1d = boolean_dispatch(
+    arg_name="return_indices",
+    arg_index=6,
+    default=False,
+    if_true=max_pool1d_with_indices,
+    if_false=_max_pool1d,
+    module_name=__name__,
+    func_name="max_pool1d",
+)
+
+
+def max_pool2d_with_indices(
+    input: Tensor,
+    kernel_size: BroadcastingList2[int],
+    stride: Optional[BroadcastingList2[int]] = None,
+    padding: BroadcastingList2[int] = 0,
+    dilation: BroadcastingList2[int] = 1,
+    ceil_mode: bool = False,
+    return_indices: bool = False,
+) -> Tuple[Tensor, Tensor]:  # noqa: D400
+    r"""
+    max_pool2d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False)
+
+    Applies a 2D max pooling over an input signal composed of several input
+    planes.
+
+    .. note::
+        The order of :attr:`ceil_mode` and :attr:`return_indices` is different from
+        what seen in :class:`~torch.nn.MaxPool2d`, and will change in a future release.
+
+    See :class:`~torch.nn.MaxPool2d` for details.
+
+    Args:
+        input: input tensor :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`, minibatch dim optional.
+        kernel_size: size of the pooling region. Can be a single number or a
+            tuple `(kH, kW)`
+        stride: stride of the pooling operation. Can be a single number or a
+            tuple `(sH, sW)`. Default: :attr:`kernel_size`
+        padding: Implicit negative infinity padding to be added on both sides, must be >= 0 and <= kernel_size / 2.
+        dilation: The stride between elements within a sliding window, must be > 0.
+        ceil_mode: If ``True``, will use `ceil` instead of `floor` to compute the output shape. This
+                   ensures that every element in the input tensor is covered by a sliding window.
+        return_indices: If ``True``, will return the argmax along with the max values.
+                        Useful for :class:`torch.nn.functional.max_unpool2d` later
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            max_pool2d_with_indices,
+            (input,),
+            input,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            ceil_mode=ceil_mode,
+            return_indices=return_indices,
+        )
+    if stride is None:
+        stride = torch.jit.annotate(List[int], [])
+    return torch._C._nn.max_pool2d_with_indices(
+        input, kernel_size, stride, padding, dilation, ceil_mode
+    )
+
+
+def _max_pool2d(
+    input: Tensor,
+    kernel_size: BroadcastingList2[int],
+    stride: Optional[BroadcastingList2[int]] = None,
+    padding: BroadcastingList2[int] = 0,
+    dilation: BroadcastingList2[int] = 1,
+    ceil_mode: bool = False,
+    return_indices: bool = False,
+) -> Tensor:
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            max_pool2d,
+            (input,),
+            input,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            ceil_mode=ceil_mode,
+            return_indices=return_indices,
+        )
+    if stride is None:
+        stride = torch.jit.annotate(List[int], [])
+    return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
+
+
+max_pool2d = boolean_dispatch(
+    arg_name="return_indices",
+    arg_index=6,
+    default=False,
+    if_true=max_pool2d_with_indices,
+    if_false=_max_pool2d,
+    module_name=__name__,
+    func_name="max_pool2d",
+)
+
+
+def max_pool3d_with_indices(
+    input: Tensor,
+    kernel_size: BroadcastingList3[int],
+    stride: Optional[BroadcastingList3[int]] = None,
+    padding: BroadcastingList3[int] = 0,
+    dilation: BroadcastingList3[int] = 1,
+    ceil_mode: bool = False,
+    return_indices: bool = False,
+) -> Tuple[Tensor, Tensor]:  # noqa: D400
+    r"""
+    max_pool3d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False)
+
+    Applies a 3D max pooling over an input signal composed of several input
+    planes.
+
+    .. note::
+        The order of :attr:`ceil_mode` and :attr:`return_indices` is different from
+        what seen in :class:`~torch.nn.MaxPool3d`, and will change in a future release.
+
+    See :class:`~torch.nn.MaxPool3d` for details.
+
+    Args:
+        input: input tensor :math:`(\text{minibatch} , \text{in\_channels} , iD, iH , iW)`, minibatch dim optional.
+        kernel_size: size of the pooling region. Can be a single number or a
+                     tuple `(kT, kH, kW)`
+        stride: stride of the pooling operation. Can be a single number or a
+                tuple `(sT, sH, sW)`. Default: :attr:`kernel_size`
+        padding: Implicit negative infinity padding to be added on both sides, must be >= 0 and <= kernel_size / 2.
+        dilation: The stride between elements within a sliding window, must be > 0.
+        ceil_mode: If ``True``, will use `ceil` instead of `floor` to compute the output shape. This
+                   ensures that every element in the input tensor is covered by a sliding window.
+        return_indices: If ``True``, will return the argmax along with the max values.
+                        Useful for :class:`torch.nn.functional.max_unpool3d` later
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            max_pool3d_with_indices,
+            (input,),
+            input,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            ceil_mode=ceil_mode,
+            return_indices=return_indices,
+        )
+    if stride is None:
+        stride = torch.jit.annotate(List[int], [])
+    return torch._C._nn.max_pool3d_with_indices(
+        input, kernel_size, stride, padding, dilation, ceil_mode
+    )
+
+
+def _max_pool3d(
+    input: Tensor,
+    kernel_size: BroadcastingList3[int],
+    stride: Optional[BroadcastingList3[int]] = None,
+    padding: BroadcastingList3[int] = 0,
+    dilation: BroadcastingList3[int] = 1,
+    ceil_mode: bool = False,
+    return_indices: bool = False,
+) -> Tensor:
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            max_pool3d,
+            (input,),
+            input,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            ceil_mode=ceil_mode,
+            return_indices=return_indices,
+        )
+    if stride is None:
+        stride = torch.jit.annotate(List[int], [])
+    return torch.max_pool3d(input, kernel_size, stride, padding, dilation, ceil_mode)
+
+
+max_pool3d = boolean_dispatch(
+    arg_name="return_indices",
+    arg_index=6,
+    default=False,
+    if_true=max_pool3d_with_indices,
+    if_false=_max_pool3d,
+    module_name=__name__,
+    func_name="max_pool3d",
+)
+
+
+def _unpool_output_size(
+    input: Tensor,
+    kernel_size: List[int],
+    stride: List[int],
+    padding: List[int],
+    output_size: Optional[List[int]],
+) -> List[int]:
+    input_size = input.size()
+    default_size = torch.jit.annotate(List[int], [])
+    for d in range(len(kernel_size)):
+        default_size.append(
+            (input_size[-len(kernel_size) + d] - 1) * stride[d]
+            + kernel_size[d]
+            - 2 * padding[d]
+        )
+    if output_size is None:
+        ret = default_size
+    else:
+        if len(output_size) == len(kernel_size) + 2:
+            output_size = output_size[2:]
+        if len(output_size) != len(kernel_size):
+            raise ValueError(
+                "output_size should be a sequence containing "
+                f"{len(kernel_size)} or {len(kernel_size) + 2} elements, but it has a length of '{len(output_size)}'"
+            )
+        for d in range(len(kernel_size)):
+            min_size = default_size[d] - stride[d]
+            max_size = default_size[d] + stride[d]
+            if not (min_size < output_size[d] < max_size):
+                raise ValueError(
+                    f'invalid output_size "{output_size}" (dim {d} must be between {min_size} and {max_size})'
+                )
+
+        ret = output_size
+    return ret
+
+
+def max_unpool1d(
+    input: Tensor,
+    indices: Tensor,
+    kernel_size: BroadcastingList1[int],
+    stride: Optional[BroadcastingList1[int]] = None,
+    padding: BroadcastingList1[int] = 0,
+    output_size: Optional[BroadcastingList1[int]] = None,
+) -> Tensor:
+    r"""Compute a partial inverse of :class:`MaxPool1d`.
+
+    See :class:`~torch.nn.MaxUnpool1d` for details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            max_unpool1d,
+            (input,),
+            input,
+            indices,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            output_size=output_size,
+        )
+    kernel_size = _single(kernel_size)
+    if stride is not None:
+        _stride = _single(stride)
+    else:
+        _stride = kernel_size
+    padding = _single(padding)
+    output_size = _unpool_output_size(input, kernel_size, _stride, padding, output_size)
+    if isinstance(output_size, list):
+        output_size = output_size + [1]
+    else:
+        output_size = output_size + (1,)
+    return torch._C._nn.max_unpool2d(
+        input.unsqueeze(-1), indices.unsqueeze(-1), output_size
+    ).squeeze(-1)
+
+
+def max_unpool2d(
+    input: Tensor,
+    indices: Tensor,
+    kernel_size: BroadcastingList2[int],
+    stride: Optional[BroadcastingList2[int]] = None,
+    padding: BroadcastingList2[int] = 0,
+    output_size: Optional[BroadcastingList2[int]] = None,
+) -> Tensor:
+    r"""Compute a partial inverse of :class:`MaxPool2d`.
+
+    See :class:`~torch.nn.MaxUnpool2d` for details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            max_unpool2d,
+            (input,),
+            input,
+            indices,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            output_size=output_size,
+        )
+    kernel_size = _pair(kernel_size)
+    if stride is not None:
+        _stride = _pair(stride)
+    else:
+        _stride = kernel_size
+    padding = _pair(padding)
+    output_size = _unpool_output_size(input, kernel_size, _stride, padding, output_size)
+    return torch._C._nn.max_unpool2d(input, indices, output_size)
+
+
+def max_unpool3d(
+    input: Tensor,
+    indices: Tensor,
+    kernel_size: BroadcastingList3[int],
+    stride: Optional[BroadcastingList3[int]] = None,
+    padding: BroadcastingList3[int] = 0,
+    output_size: Optional[BroadcastingList3[int]] = None,
+) -> Tensor:
+    r"""Compute a partial inverse of :class:`MaxPool3d`.
+
+    See :class:`~torch.nn.MaxUnpool3d` for details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            max_unpool3d,
+            (input,),
+            input,
+            indices,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            output_size=output_size,
+        )
+    kernel_size = _triple(kernel_size)
+    if stride is not None:
+        _stride = _triple(stride)
+    else:
+        _stride = kernel_size
+    padding = _triple(padding)
+    output_size = _unpool_output_size(input, kernel_size, _stride, padding, output_size)
+    return torch._C._nn.max_unpool3d(input, indices, output_size, _stride, padding)
+
+
+def lp_pool3d(
+    input: Tensor,
+    norm_type: Union[int, float],
+    kernel_size: BroadcastingList3[int],
+    stride: Optional[BroadcastingList3[int]] = None,
+    ceil_mode: bool = False,
+) -> Tensor:
+    r"""
+    Apply a 3D power-average pooling over an input signal composed of several input planes.
+
+    If the sum of all inputs to the power of `p` is
+    zero, the gradient is set to zero as well.
+
+    See :class:`~torch.nn.LPPool3d` for details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            lp_pool3d,
+            (input,),
+            input,
+            norm_type,
+            kernel_size,
+            stride=stride,
+            ceil_mode=ceil_mode,
+        )
+    kd, kw, kh = _triple(kernel_size)
+    if stride is not None:
+        out = avg_pool3d(input.pow(norm_type), kernel_size, stride, 0, ceil_mode)
+    else:
+        out = avg_pool3d(
+            input.pow(norm_type), kernel_size, padding=0, ceil_mode=ceil_mode
+        )
+
+    return (
+        (torch.sign(out) * relu(torch.abs(out))).mul(kd * kw * kh).pow(1.0 / norm_type)
+    )
+
+
+def lp_pool2d(
+    input: Tensor,
+    norm_type: Union[int, float],
+    kernel_size: BroadcastingList2[int],
+    stride: Optional[BroadcastingList2[int]] = None,
+    ceil_mode: bool = False,
+) -> Tensor:
+    r"""
+    Apply a 2D power-average pooling over an input signal composed of several input planes.
+
+    If the sum of all inputs to the power of `p` is
+    zero, the gradient is set to zero as well.
+
+    See :class:`~torch.nn.LPPool2d` for details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            lp_pool2d,
+            (input,),
+            input,
+            norm_type,
+            kernel_size,
+            stride=stride,
+            ceil_mode=ceil_mode,
+        )
+    kw, kh = _pair(kernel_size)
+    if stride is not None:
+        out = avg_pool2d(input.pow(norm_type), kernel_size, stride, 0, ceil_mode)
+    else:
+        out = avg_pool2d(
+            input.pow(norm_type), kernel_size, padding=0, ceil_mode=ceil_mode
+        )
+
+    return (torch.sign(out) * relu(torch.abs(out))).mul(kw * kh).pow(1.0 / norm_type)
+
+
+def lp_pool1d(
+    input: Tensor,
+    norm_type: Union[int, float],
+    kernel_size: int,
+    stride: Optional[BroadcastingList1[int]] = None,
+    ceil_mode: bool = False,
+) -> Tensor:
+    r"""Apply a 1D power-average pooling over an input signal composed of several input planes.
+
+    If the sum of all inputs to the power of `p` is
+    zero, the gradient is set to zero as well.
+
+    See :class:`~torch.nn.LPPool1d` for details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            lp_pool1d,
+            (input,),
+            input,
+            norm_type,
+            kernel_size,
+            stride=stride,
+            ceil_mode=ceil_mode,
+        )
+    if stride is not None:
+        out = avg_pool1d(input.pow(norm_type), kernel_size, stride, 0, ceil_mode)
+    else:
+        out = avg_pool1d(
+            input.pow(norm_type), kernel_size, padding=0, ceil_mode=ceil_mode
+        )
+
+    return (
+        (torch.sign(out) * relu(torch.abs(out))).mul(kernel_size).pow(1.0 / norm_type)
+    )
+
+
+def adaptive_max_pool1d_with_indices(
+    input: Tensor,
+    output_size: BroadcastingList1[int],
+    return_indices: bool = False,
+) -> Tuple[Tensor, Tensor]:  # noqa: D400
+    r"""
+    adaptive_max_pool1d(input, output_size, return_indices=False)
+
+    Applies a 1D adaptive max pooling over an input signal composed of
+    several input planes.
+
+    See :class:`~torch.nn.AdaptiveMaxPool1d` for details and output shape.
+
+    Args:
+        output_size: the target output size (single integer)
+        return_indices: whether to return pooling indices. Default: ``False``
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            adaptive_max_pool1d_with_indices,
+            (input,),
+            input,
+            output_size,
+            return_indices=return_indices,
+        )
+    return torch.adaptive_max_pool1d(input, output_size)
+
+
+def _adaptive_max_pool1d(
+    input: Tensor,
+    output_size: BroadcastingList1[int],
+    return_indices: bool = False,
+) -> Tensor:
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            adaptive_max_pool1d,
+            (input,),
+            input,
+            output_size,
+            return_indices=return_indices,
+        )
+    return adaptive_max_pool1d_with_indices(input, output_size)[0]
+
+
+adaptive_max_pool1d = boolean_dispatch(
+    arg_name="return_indices",
+    arg_index=2,
+    default=False,
+    if_true=adaptive_max_pool1d_with_indices,
+    if_false=_adaptive_max_pool1d,
+    module_name=__name__,
+    func_name="adaptive_max_pool1d",
+)
+
+
+def adaptive_max_pool2d_with_indices(
+    input: Tensor,
+    output_size: BroadcastingList2[int],
+    return_indices: bool = False,
+) -> Tuple[Tensor, Tensor]:  # noqa: D400
+    r"""adaptive_max_pool2d(input, output_size, return_indices=False)
+
+    Applies a 2D adaptive max pooling over an input signal composed of
+    several input planes.
+
+    See :class:`~torch.nn.AdaptiveMaxPool2d` for details and output shape.
+
+    Args:
+        output_size: the target output size (single integer or
+            double-integer tuple)
+        return_indices: whether to return pooling indices. Default: ``False``
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            adaptive_max_pool2d_with_indices,
+            (input,),
+            input,
+            output_size,
+            return_indices=return_indices,
+        )
+    output_size = _list_with_default(output_size, input.size())
+    return torch._C._nn.adaptive_max_pool2d(input, output_size)
+
+
+def _adaptive_max_pool2d(
+    input: Tensor,
+    output_size: BroadcastingList2[int],
+    return_indices: bool = False,
+) -> Tensor:
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            adaptive_max_pool2d,
+            (input,),
+            input,
+            output_size,
+            return_indices=return_indices,
+        )
+    return adaptive_max_pool2d_with_indices(input, output_size)[0]
+
+
+adaptive_max_pool2d = boolean_dispatch(
+    arg_name="return_indices",
+    arg_index=2,
+    default=False,
+    if_true=adaptive_max_pool2d_with_indices,
+    if_false=_adaptive_max_pool2d,
+    module_name=__name__,
+    func_name="adaptive_max_pool2d",
+)
+
+
+def adaptive_max_pool3d_with_indices(
+    input: Tensor,
+    output_size: BroadcastingList3[int],
+    return_indices: bool = False,
+) -> Tuple[Tensor, Tensor]:  # noqa: D400
+    r"""
+    adaptive_max_pool3d(input, output_size, return_indices=False)
+
+    Applies a 3D adaptive max pooling over an input signal composed of
+    several input planes.
+
+    See :class:`~torch.nn.AdaptiveMaxPool3d` for details and output shape.
+
+    Args:
+        output_size: the target output size (single integer or
+            triple-integer tuple)
+        return_indices: whether to return pooling indices. Default: ``False``
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            adaptive_max_pool3d_with_indices,
+            (input,),
+            input,
+            output_size,
+            return_indices=return_indices,
+        )
+    output_size = _list_with_default(output_size, input.size())
+    return torch._C._nn.adaptive_max_pool3d(input, output_size)
+
+
+def _adaptive_max_pool3d(
+    input: Tensor,
+    output_size: BroadcastingList3[int],
+    return_indices: bool = False,
+) -> Tensor:
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            adaptive_max_pool3d,
+            (input,),
+            input,
+            output_size,
+            return_indices=return_indices,
+        )
+    return adaptive_max_pool3d_with_indices(input, output_size)[0]
+
+
+adaptive_max_pool3d = boolean_dispatch(
+    arg_name="return_indices",
+    arg_index=2,
+    default=False,
+    if_true=adaptive_max_pool3d_with_indices,
+    if_false=_adaptive_max_pool3d,
+    module_name=__name__,
+    func_name="adaptive_max_pool3d",
+)
+
+
+adaptive_avg_pool1d = _add_docstr(
+    torch.adaptive_avg_pool1d,
+    r"""
+adaptive_avg_pool1d(input, output_size) -> Tensor
+
+Applies a 1D adaptive average pooling over an input signal composed of
+several input planes.
+
+See :class:`~torch.nn.AdaptiveAvgPool1d` for details and output shape.
+
+Args:
+    output_size: the target output size (single integer)
+""",
+)
+
+
+def adaptive_avg_pool2d(input: Tensor, output_size: BroadcastingList2[int]) -> Tensor:
+    r"""Apply a 2D adaptive average pooling over an input signal composed of several input planes.
+
+    See :class:`~torch.nn.AdaptiveAvgPool2d` for details and output shape.
+
+    Args:
+        output_size: the target output size (single integer or
+            double-integer tuple)
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(adaptive_avg_pool2d, (input,), input, output_size)
+    _output_size = _list_with_default(output_size, input.size())
+    return torch._C._nn.adaptive_avg_pool2d(input, _output_size)
+
+
+def adaptive_avg_pool3d(input: Tensor, output_size: BroadcastingList3[int]) -> Tensor:
+    r"""Apply a 3D adaptive average pooling over an input signal composed of several input planes.
+
+    See :class:`~torch.nn.AdaptiveAvgPool3d` for details and output shape.
+
+    Args:
+        output_size: the target output size (single integer or
+            triple-integer tuple)
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(adaptive_avg_pool3d, (input,), input, output_size)
+    _output_size = _list_with_default(output_size, input.size())
+    return torch._C._nn.adaptive_avg_pool3d(input, _output_size)
+
+
+# Activation functions
+def dropout(
+    input: Tensor,
+    p: float = 0.5,
+    training: bool = True,
+    inplace: bool = False,
+) -> Tensor:
+    r"""During training, randomly zeroes some elements of the input tensor with probability :attr:`p`.
+
+    Uses samples from a Bernoulli distribution.
+
+    See :class:`~torch.nn.Dropout` for details.
+
+    Args:
+        p: probability of an element to be zeroed. Default: 0.5
+        training: apply dropout if is ``True``. Default: ``True``
+        inplace: If set to ``True``, will do this operation in-place. Default: ``False``
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            dropout, (input,), input, p=p, training=training, inplace=inplace
+        )
+    if p < 0.0 or p > 1.0:
+        raise ValueError(f"dropout probability has to be between 0 and 1, but got {p}")
+    return (
+        _VF.dropout_(input, p, training) if inplace else _VF.dropout(input, p, training)
+    )
+
+
+def alpha_dropout(
+    input: Tensor,
+    p: float = 0.5,
+    training: bool = False,
+    inplace: bool = False,
+) -> Tensor:
+    r"""Apply alpha dropout to the input.
+
+    See :class:`~torch.nn.AlphaDropout` for details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            alpha_dropout, (input,), input, p=p, training=training, inplace=inplace
+        )
+    if p < 0.0 or p > 1.0:
+        raise ValueError(f"dropout probability has to be between 0 and 1, but got {p}")
+    return (
+        _VF.alpha_dropout_(input, p, training)
+        if inplace
+        else _VF.alpha_dropout(input, p, training)
+    )
+
+
+def dropout1d(
+    input: Tensor,
+    p: float = 0.5,
+    training: bool = True,
+    inplace: bool = False,
+) -> Tensor:
+    r"""Randomly zero out entire channels (a channel is a 1D feature map).
+
+    For example, the :math:`j`-th channel of the :math:`i`-th sample in the
+    batched input is a 1D tensor :math:`\text{input}[i, j]` of the input tensor.
+    Each channel will be zeroed out independently on every forward call with
+    probability :attr:`p` using samples from a Bernoulli distribution.
+
+    See :class:`~torch.nn.Dropout1d` for details.
+
+    Args:
+        p: probability of a channel to be zeroed. Default: 0.5
+        training: apply dropout if is ``True``. Default: ``True``
+        inplace: If set to ``True``, will do this operation in-place. Default: ``False``
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            dropout1d, (input,), input, p=p, training=training, inplace=inplace
+        )
+    if p < 0.0 or p > 1.0:
+        raise ValueError(f"dropout probability has to be between 0 and 1, but got {p}")
+    inp_dim = input.dim()
+    if inp_dim not in (2, 3):
+        raise RuntimeError(
+            f"dropout1d: Expected 2D or 3D input, but received a {inp_dim}D input. "
+            "Note that dropout1d exists to provide channel-wise dropout on inputs with 1 "
+            "spatial dimension, a channel dimension, and an optional batch dimension "
+            "(i.e. 2D or 3D inputs)."
+        )
+
+    is_batched = inp_dim == 3
+    if not is_batched:
+        input = input.unsqueeze_(0) if inplace else input.unsqueeze(0)
+
+    result = (
+        _VF.feature_dropout_(input, p, training)
+        if inplace
+        else _VF.feature_dropout(input, p, training)
+    )
+
+    if not is_batched:
+        result = result.squeeze_(0) if inplace else result.squeeze(0)
+
+    return result
+
+
+def dropout2d(
+    input: Tensor,
+    p: float = 0.5,
+    training: bool = True,
+    inplace: bool = False,
+) -> Tensor:
+    r"""Randomly zero out entire channels (a channel is a 2D feature map).
+
+    For example, the :math:`j`-th channel of the :math:`i`-th sample in the
+    batched input is a 2D tensor :math:`\text{input}[i, j]` of the input tensor.
+    Each channel will be zeroed out independently on every forward call with
+    probability :attr:`p` using samples from a Bernoulli distribution.
+
+    See :class:`~torch.nn.Dropout2d` for details.
+
+    Args:
+        p: probability of a channel to be zeroed. Default: 0.5
+        training: apply dropout if is ``True``. Default: ``True``
+        inplace: If set to ``True``, will do this operation in-place. Default: ``False``
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            dropout2d, (input,), input, p=p, training=training, inplace=inplace
+        )
+    if p < 0.0 or p > 1.0:
+        raise ValueError(f"dropout probability has to be between 0 and 1, but got {p}")
+    inp_dim = input.dim()
+    if inp_dim not in (3, 4):
+        warn_msg = (
+            f"dropout2d: Received a {inp_dim}-D input to dropout2d, which is deprecated "
+            "and will result in an error in a future release. To retain the behavior "
+            "and silence this warning, please use dropout instead. Note that dropout2d "
+            "exists to provide channel-wise dropout on inputs with 2 spatial dimensions, "
+            "a channel dimension, and an optional batch dimension (i.e. 3D or 4D inputs)."
+        )
+        warnings.warn(warn_msg)
+
+    # TODO: Properly support no-batch-dim inputs. For now, these are NOT supported; passing
+    # a 3D input will perform dropout1d behavior instead. This was done historically and the
+    # behavior is maintained here for now.
+    # See https://github.com/pytorch/pytorch/issues/77081
+    if inp_dim == 3:
+        warnings.warn(
+            "dropout2d: Received a 3D input to dropout2d and assuming that channel-wise "
+            "1D dropout behavior is desired - input is interpreted as shape (N, C, L), where C "
+            "is the channel dim. This behavior will change in a future release to interpret the "
+            "input as one without a batch dimension, i.e. shape (C, H, W). To maintain the 1D "
+            "channel-wise dropout behavior, please switch to using dropout1d instead."
+        )
+
+    result = (
+        _VF.feature_dropout_(input, p, training)
+        if inplace
+        else _VF.feature_dropout(input, p, training)
+    )
+
+    return result
+
+
+def dropout3d(
+    input: Tensor,
+    p: float = 0.5,
+    training: bool = True,
+    inplace: bool = False,
+) -> Tensor:
+    r"""Randomly zero out entire channels (a channel is a 3D feature map).
+
+    For example, the :math:`j`-th channel of the :math:`i`-th sample in the
+    batched input is a 3D tensor :math:`\text{input}[i, j]` of the input tensor.
+    Each channel will be zeroed out independently on every forward call with
+    probability :attr:`p` using samples from a Bernoulli distribution.
+
+    See :class:`~torch.nn.Dropout3d` for details.
+
+    Args:
+        p: probability of a channel to be zeroed. Default: 0.5
+        training: apply dropout if is ``True``. Default: ``True``
+        inplace: If set to ``True``, will do this operation in-place. Default: ``False``
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            dropout3d, (input,), input, p=p, training=training, inplace=inplace
+        )
+    if p < 0.0 or p > 1.0:
+        raise ValueError(f"dropout probability has to be between 0 and 1, but got {p}")
+    inp_dim = input.dim()
+    if inp_dim not in (4, 5):
+        warn_msg = (
+            f"dropout3d: Received a {inp_dim}-D input to dropout3d, which is deprecated "
+            "and will result in an error in a future release. To retain the behavior "
+            "and silence this warning, please use dropout instead. Note that dropout3d "
+            "exists to provide channel-wise dropout on inputs with 3 spatial dimensions, "
+            "a channel dimension, and an optional batch dimension (i.e. 4D or 5D inputs)."
+        )
+        warnings.warn(warn_msg)
+
+    is_batched = inp_dim == 5
+    if not is_batched:
+        input = input.unsqueeze_(0) if inplace else input.unsqueeze(0)
+
+    result = (
+        _VF.feature_dropout_(input, p, training)
+        if inplace
+        else _VF.feature_dropout(input, p, training)
+    )
+
+    if not is_batched:
+        result = result.squeeze_(0) if inplace else result.squeeze(0)
+    return result
+
+
+def feature_alpha_dropout(
+    input: Tensor,
+    p: float = 0.5,
+    training: bool = False,
+    inplace: bool = False,
+) -> Tensor:
+    r"""Randomly masks out entire channels (a channel is a feature map).
+
+    For example, the :math:`j`-th channel of the :math:`i`-th sample in the batch input
+    is a tensor :math:`\text{input}[i, j]` of the input tensor. Instead of
+    setting activations to zero, as in regular Dropout, the activations are set
+    to the negative saturation value of the SELU activation function.
+
+    Each element will be masked independently on every forward call with
+    probability :attr:`p` using samples from a Bernoulli distribution.
+    The elements to be masked are randomized on every forward call, and scaled
+    and shifted to maintain zero mean and unit variance.
+
+    See :class:`~torch.nn.FeatureAlphaDropout` for details.
+
+    Args:
+        p: dropout probability of a channel to be zeroed. Default: 0.5
+        training: apply dropout if is ``True``. Default: ``True``
+        inplace: If set to ``True``, will do this operation in-place. Default: ``False``
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            feature_alpha_dropout,
+            (input,),
+            input,
+            p=p,
+            training=training,
+            inplace=inplace,
+        )
+    if p < 0.0 or p > 1.0:
+        raise ValueError(f"dropout probability has to be between 0 and 1, but got {p}")
+    return (
+        _VF.feature_alpha_dropout_(input, p, training)
+        if inplace
+        else _VF.feature_alpha_dropout(input, p, training)
+    )
+
+
+def _threshold(
+    input: Tensor,
+    threshold: float,
+    value: float,
+    inplace: bool = False,
+) -> Tensor:
+    r"""Apply a threshold to each element of the input Tensor.
+
+    See :class:`~torch.nn.Threshold` for more details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            _threshold, (input,), input, threshold, value, inplace=inplace
+        )
+    if inplace:
+        result = _VF.threshold_(input, threshold, value)
+    else:
+        result = _VF.threshold(input, threshold, value)
+    return result
+
+
+# We define this function as _threshold because it takes an argument
+# named threshold, which clobbers the recursive reference to the
+# function needed for __torch_function__ support
+threshold = _threshold
+
+threshold_ = _add_docstr(
+    _VF.threshold_,
+    r"""
+threshold_(input, threshold, value) -> Tensor
+
+In-place version of :func:`~threshold`.
+""",
+)
+
+
+def relu(input: Tensor, inplace: bool = False) -> Tensor:  # noqa: D400,D402
+    r"""relu(input, inplace=False) -> Tensor
+
+    Applies the rectified linear unit function element-wise. See
+    :class:`~torch.nn.ReLU` for more details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(relu, (input,), input, inplace=inplace)
+    if inplace:
+        result = torch.relu_(input)
+    else:
+        result = torch.relu(input)
+    return result
+
+
+relu_ = _add_docstr(
+    torch.relu_,
+    r"""
+relu_(input) -> Tensor
+
+In-place version of :func:`~relu`.
+""",
+)
+
+
+def glu(input: Tensor, dim: int = -1) -> Tensor:  # noqa: D400,D402
+    r"""
+    glu(input, dim=-1) -> Tensor
+
+    The gated linear unit. Computes:
+
+    .. math ::
+        \text{GLU}(a, b) = a \otimes \sigma(b)
+
+    where `input` is split in half along `dim` to form `a` and `b`, :math:`\sigma`
+    is the sigmoid function and :math:`\otimes` is the element-wise product between matrices.
+
+    See `Language Modeling with Gated Convolutional Networks `_.
+
+    Args:
+        input (Tensor): input tensor
+        dim (int): dimension on which to split the input. Default: -1
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(glu, (input,), input, dim=dim)
+    if input.dim() == 0:
+        raise RuntimeError(
+            "glu does not support scalars because halving size must be even"
+        )
+    return torch._C._nn.glu(input, dim)
+
+
+def hardtanh(
+    input: Tensor,
+    min_val: float = -1.0,
+    max_val: float = 1.0,
+    inplace: bool = False,
+) -> Tensor:  # noqa: D400,D402
+    r"""
+    hardtanh(input, min_val=-1., max_val=1., inplace=False) -> Tensor
+
+    Applies the HardTanh function element-wise. See :class:`~torch.nn.Hardtanh` for more
+    details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            hardtanh, (input,), input, min_val=min_val, max_val=max_val, inplace=inplace
+        )
+    if min_val > max_val:
+        raise ValueError("min_val cannot be greater than max_val")
+    if inplace:
+        result = torch._C._nn.hardtanh_(input, min_val, max_val)
+    else:
+        result = torch._C._nn.hardtanh(input, min_val, max_val)
+    return result
+
+
+hardtanh_ = _add_docstr(
+    torch._C._nn.hardtanh_,
+    r"""
+hardtanh_(input, min_val=-1., max_val=1.) -> Tensor
+
+In-place version of :func:`~hardtanh`.
+""",
+)
+
+
+def relu6(input: Tensor, inplace: bool = False) -> Tensor:  # noqa: D400,D402
+    r"""relu6(input, inplace=False) -> Tensor
+
+    Applies the element-wise function :math:`\text{ReLU6}(x) = \min(\max(0,x), 6)`.
+
+    See :class:`~torch.nn.ReLU6` for more details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(relu6, (input,), input, inplace=inplace)
+    if inplace:
+        result = torch._C._nn.relu6_(input)
+    else:
+        result = torch._C._nn.relu6(input)
+    return result
+
+
+def elu(input: Tensor, alpha: float = 1.0, inplace: bool = False) -> Tensor:
+    r"""Apply the Exponential Linear Unit (ELU) function element-wise.
+
+    See :class:`~torch.nn.ELU` for more details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(elu, (input,), input, alpha=alpha, inplace=inplace)
+    if inplace:
+        result = torch._C._nn.elu_(input, alpha)
+    else:
+        result = torch._C._nn.elu(input, alpha)
+    return result
+
+
+elu_ = _add_docstr(
+    torch._C._nn.elu_,
+    r"""
+elu_(input, alpha=1.) -> Tensor
+
+In-place version of :func:`~elu`.
+""",
+)
+
+
+def selu(input: Tensor, inplace: bool = False) -> Tensor:  # noqa: D400,D402
+    r"""selu(input, inplace=False) -> Tensor
+
+    Applies element-wise,
+    :math:`\text{SELU}(x) = scale * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1)))`,
+    with :math:`\alpha=1.6732632423543772848170429916717` and
+    :math:`scale=1.0507009873554804934193349852946`.
+
+    See :class:`~torch.nn.SELU` for more details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(selu, (input,), input, inplace=inplace)
+    if inplace:
+        result = torch.selu_(input)
+    else:
+        result = torch.selu(input)
+    return result
+
+
+selu_ = _add_docstr(
+    torch.selu_,
+    r"""
+selu_(input) -> Tensor
+
+In-place version of :func:`~selu`.
+""",
+)
+
+
+def celu(
+    input: Tensor,
+    alpha: float = 1.0,
+    inplace: bool = False,
+) -> Tensor:  # noqa: D400,D402
+    r"""celu(input, alpha=1., inplace=False) -> Tensor
+
+    Applies element-wise,
+    :math:`\text{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x/\alpha) - 1))`.
+
+    See :class:`~torch.nn.CELU` for more details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            celu, (input,), input, alpha=alpha, inplace=inplace
+        )
+    if inplace:
+        result = torch.celu_(input, alpha)
+    else:
+        result = torch.celu(input, alpha)
+    return result
+
+
+celu_ = _add_docstr(
+    torch.celu_,
+    r"""
+celu_(input, alpha=1.) -> Tensor
+
+In-place version of :func:`~celu`.
+""",
+)
+
+
+def leaky_relu(
+    input: Tensor,
+    negative_slope: float = 0.01,
+    inplace: bool = False,
+) -> Tensor:  # noqa: D400,D402
+    r"""
+    leaky_relu(input, negative_slope=0.01, inplace=False) -> Tensor
+
+    Applies element-wise,
+    :math:`\text{LeakyReLU}(x) = \max(0, x) + \text{negative\_slope} * \min(0, x)`
+
+    See :class:`~torch.nn.LeakyReLU` for more details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            leaky_relu, (input,), input, negative_slope=negative_slope, inplace=inplace
+        )
+    if inplace:
+        result = torch._C._nn.leaky_relu_(input, negative_slope)
+    else:
+        result = torch._C._nn.leaky_relu(input, negative_slope)
+    return result
+
+
+leaky_relu_ = _add_docstr(
+    torch._C._nn.leaky_relu_,
+    r"""
+leaky_relu_(input, negative_slope=0.01) -> Tensor
+
+In-place version of :func:`~leaky_relu`.
+""",
+)
+
+
+prelu = _add_docstr(
+    torch.prelu,
+    r"""prelu(input, weight) -> Tensor
+
+Applies element-wise the function
+:math:`\text{PReLU}(x) = \max(0,x) + \text{weight} * \min(0,x)` where weight is a
+learnable parameter.
+
+.. note::
+    `weight` is expected to be a scalar or 1-D tensor. If `weight` is 1-D,
+    its size must match the number of input channels, determined by
+    `input.size(1)` when `input.dim() >= 2`, otherwise 1.
+    In the 1-D case, note that when `input` has dim > 2, `weight` can be expanded
+    to the shape of `input` in a way that is not possible using normal
+    :ref:`broadcasting semantics`.
+
+See :class:`~torch.nn.PReLU` for more details.
+""",
+)
+
+
+def rrelu(
+    input: Tensor,
+    lower: float = 1.0 / 8,
+    upper: float = 1.0 / 3,
+    training: bool = False,
+    inplace: bool = False,
+) -> Tensor:  # noqa: D400,D402
+    r"""rrelu(input, lower=1./8, upper=1./3, training=False, inplace=False) -> Tensor
+
+    Randomized leaky ReLU.
+
+    See :class:`~torch.nn.RReLU` for more details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            rrelu,
+            (input,),
+            input,
+            lower=lower,
+            upper=upper,
+            training=training,
+            inplace=inplace,
+        )
+    if inplace:
+        result = torch.rrelu_(input, lower, upper, training)
+    else:
+        result = torch.rrelu(input, lower, upper, training)
+    return result
+
+
+rrelu_ = _add_docstr(
+    torch.rrelu_,
+    r"""
+rrelu_(input, lower=1./8, upper=1./3, training=False) -> Tensor
+
+In-place version of :func:`~rrelu`.
+""",
+)
+
+logsigmoid = _add_docstr(
+    torch._C._nn.log_sigmoid,
+    r"""
+logsigmoid(input) -> Tensor
+
+Applies element-wise :math:`\text{LogSigmoid}(x_i) = \log \left(\frac{1}{1 + \exp(-x_i)}\right)`
+
+See :class:`~torch.nn.LogSigmoid` for more details.
+""",
+)
+
+gelu = _add_docstr(
+    torch._C._nn.gelu,
+    r"""
+gelu(input, approximate = 'none') -> Tensor
+
+When the approximate argument is 'none', it applies element-wise the function
+:math:`\text{GELU}(x) = x * \Phi(x)`
+
+where :math:`\Phi(x)` is the Cumulative Distribution Function for Gaussian Distribution.
+
+When the approximate argument is 'tanh', Gelu is estimated with
+
+.. math::
+    \text{GELU}(x) = 0.5 * x * (1 + \text{Tanh}(\sqrt{2 / \pi} * (x + 0.044715 * x^3)))
+
+See `Gaussian Error Linear Units (GELUs) `_.
+""",
+)
+
+hardshrink = _add_docstr(
+    torch.hardshrink,
+    r"""
+hardshrink(input, lambd=0.5) -> Tensor
+
+Applies the hard shrinkage function element-wise
+
+See :class:`~torch.nn.Hardshrink` for more details.
+""",
+)
+
+
+def tanhshrink(input):  # noqa: D400,D402
+    r"""tanhshrink(input) -> Tensor
+
+    Applies element-wise, :math:`\text{Tanhshrink}(x) = x - \text{Tanh}(x)`
+
+    See :class:`~torch.nn.Tanhshrink` for more details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(tanhshrink, (input,), input)
+    return input - input.tanh()
+
+
+def softsign(input):  # noqa: D400,D402
+    r"""softsign(input) -> Tensor
+
+    Applies element-wise, the function :math:`\text{SoftSign}(x) = \frac{x}{1 + |x|}`
+
+    See :class:`~torch.nn.Softsign` for more details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(softsign, (input,), input)
+    return input / (input.abs() + 1)
+
+
+softplus = _add_docstr(
+    torch._C._nn.softplus,
+    r"""
+softplus(input, beta=1, threshold=20) -> Tensor
+
+Applies element-wise, the function :math:`\text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))`.
+
+For numerical stability the implementation reverts to the linear function
+when :math:`input \times \beta > threshold`.
+
+See :class:`~torch.nn.Softplus` for more details.
+""",
+)
+
+
+def _get_softmax_dim(name: str, ndim: int, stacklevel: int) -> int:
+    warnings.warn(
+        f"Implicit dimension choice for {name} has been deprecated. "
+        "Change the call to include dim=X as an argument.",
+        stacklevel=stacklevel,
+    )
+    if ndim == 0 or ndim == 1 or ndim == 3:
+        ret = 0
+    else:
+        ret = 1
+    return ret
+
+
+def softmin(
+    input: Tensor,
+    dim: Optional[int] = None,
+    _stacklevel: int = 3,
+    dtype: Optional[DType] = None,
+) -> Tensor:
+    r"""Apply a softmin function.
+
+    Note that :math:`\text{Softmin}(x) = \text{Softmax}(-x)`. See softmax definition for mathematical formula.
+
+    See :class:`~torch.nn.Softmin` for more details.
+
+    Args:
+        input (Tensor): input
+        dim (int): A dimension along which softmin will be computed (so every slice
+            along dim will sum to 1).
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
+          If specified, the input tensor is casted to :attr:`dtype` before the operation
+          is performed. This is useful for preventing data type overflows. Default: None.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            softmin, (input,), input, dim=dim, _stacklevel=_stacklevel, dtype=dtype
+        )
+    if dim is None:
+        dim = _get_softmax_dim("softmin", input.dim(), _stacklevel)
+    if dtype is None:
+        ret = (-input).softmax(dim)
+    else:
+        ret = (-input).softmax(dim, dtype=dtype)
+    return ret
+
+
+def softmax(
+    input: Tensor,
+    dim: Optional[int] = None,
+    _stacklevel: int = 3,
+    dtype: Optional[DType] = None,
+) -> Tensor:
+    r"""Apply a softmax function.
+
+    Softmax is defined as:
+
+    :math:`\text{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}`
+
+    It is applied to all slices along dim, and will re-scale them so that the elements
+    lie in the range `[0, 1]` and sum to 1.
+
+    See :class:`~torch.nn.Softmax` for more details.
+
+    Args:
+        input (Tensor): input
+        dim (int): A dimension along which softmax will be computed.
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
+          If specified, the input tensor is casted to :attr:`dtype` before the operation
+          is performed. This is useful for preventing data type overflows. Default: None.
+
+    .. note::
+        This function doesn't work directly with NLLLoss,
+        which expects the Log to be computed between the Softmax and itself.
+        Use log_softmax instead (it's faster and has better numerical properties).
+
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            softmax, (input,), input, dim=dim, _stacklevel=_stacklevel, dtype=dtype
+        )
+    if dim is None:
+        dim = _get_softmax_dim("softmax", input.dim(), _stacklevel)
+    if dtype is None:
+        ret = input.softmax(dim)
+    else:
+        ret = input.softmax(dim, dtype=dtype)
+    return ret
+
+
+def gumbel_softmax(
+    logits: Tensor,
+    tau: float = 1,
+    hard: bool = False,
+    eps: float = 1e-10,
+    dim: int = -1,
+) -> Tensor:
+    r"""
+    Sample from the Gumbel-Softmax distribution (`Link 1`_  `Link 2`_) and optionally discretize.
+
+    Args:
+      logits: `[..., num_features]` unnormalized log probabilities
+      tau: non-negative scalar temperature
+      hard: if ``True``, the returned samples will be discretized as one-hot vectors,
+            but will be differentiated as if it is the soft sample in autograd
+      dim (int): A dimension along which softmax will be computed. Default: -1.
+
+    Returns:
+      Sampled tensor of same shape as `logits` from the Gumbel-Softmax distribution.
+      If ``hard=True``, the returned samples will be one-hot, otherwise they will
+      be probability distributions that sum to 1 across `dim`.
+
+    .. note::
+      This function is here for legacy reasons, may be removed from nn.Functional in the future.
+
+    .. note::
+      The main trick for `hard` is to do  `y_hard - y_soft.detach() + y_soft`
+
+      It achieves two things:
+      - makes the output value exactly one-hot
+      (since we add then subtract y_soft value)
+      - makes the gradient equal to y_soft gradient
+      (since we strip all other gradients)
+
+    Examples::
+        >>> logits = torch.randn(20, 32)
+        >>> # Sample soft categorical using reparametrization trick:
+        >>> F.gumbel_softmax(logits, tau=1, hard=False)
+        >>> # Sample hard categorical using "Straight-through" trick:
+        >>> F.gumbel_softmax(logits, tau=1, hard=True)
+
+    .. _Link 1:
+        https://arxiv.org/abs/1611.00712
+    .. _Link 2:
+        https://arxiv.org/abs/1611.01144
+    """
+    if has_torch_function_unary(logits):
+        return handle_torch_function(
+            gumbel_softmax, (logits,), logits, tau=tau, hard=hard, eps=eps, dim=dim
+        )
+    if eps != 1e-10:
+        warnings.warn("`eps` parameter is deprecated and has no effect.")
+
+    gumbels = (
+        -torch.empty_like(logits, memory_format=torch.legacy_contiguous_format)
+        .exponential_()
+        .log()
+    )  # ~Gumbel(0,1)
+    gumbels = (logits + gumbels) / tau  # ~Gumbel(logits,tau)
+    y_soft = gumbels.softmax(dim)
+
+    if hard:
+        # Straight through.
+        index = y_soft.max(dim, keepdim=True)[1]
+        y_hard = torch.zeros_like(
+            logits, memory_format=torch.legacy_contiguous_format
+        ).scatter_(dim, index, 1.0)
+        ret = y_hard - y_soft.detach() + y_soft
+    else:
+        # Reparametrization trick.
+        ret = y_soft
+    return ret
+
+
+def log_softmax(
+    input: Tensor,
+    dim: Optional[int] = None,
+    _stacklevel: int = 3,
+    dtype: Optional[DType] = None,
+) -> Tensor:
+    r"""Apply a softmax followed by a logarithm.
+
+    While mathematically equivalent to log(softmax(x)), doing these two
+    operations separately is slower and numerically unstable. This function
+    uses an alternative formulation to compute the output and gradient correctly.
+
+    See :class:`~torch.nn.LogSoftmax` for more details.
+
+    Args:
+        input (Tensor): input
+        dim (int): A dimension along which log_softmax will be computed.
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
+          If specified, the input tensor is cast to :attr:`dtype` before the operation
+          is performed. This is useful for preventing data type overflows. Default: None.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            log_softmax, (input,), input, dim=dim, _stacklevel=_stacklevel, dtype=dtype
+        )
+    if dim is None:
+        dim = _get_softmax_dim("log_softmax", input.dim(), _stacklevel)
+    if dtype is None:
+        ret = input.log_softmax(dim)
+    else:
+        ret = input.log_softmax(dim, dtype=dtype)
+    return ret
+
+
+softshrink = _add_docstr(
+    torch._C._nn.softshrink,
+    r"""
+softshrink(input, lambd=0.5) -> Tensor
+
+Applies the soft shrinkage function elementwise
+
+See :class:`~torch.nn.Softshrink` for more details.
+""",
+)
+
+
+def tanh(input):  # noqa: D400,D402
+    r"""tanh(input) -> Tensor
+
+    Applies element-wise,
+    :math:`\text{Tanh}(x) = \tanh(x) = \frac{\exp(x) - \exp(-x)}{\exp(x) + \exp(-x)}`
+
+    See :class:`~torch.nn.Tanh` for more details.
+    """
+    return input.tanh()
+
+
+def sigmoid(input):  # noqa: D400,D402
+    r"""sigmoid(input) -> Tensor
+
+    Applies the element-wise function :math:`\text{Sigmoid}(x) = \frac{1}{1 + \exp(-x)}`
+
+    See :class:`~torch.nn.Sigmoid` for more details.
+    """
+    return input.sigmoid()
+
+
+def hardsigmoid(input: Tensor, inplace: bool = False) -> Tensor:
+    r"""Apply the Hardsigmoid function element-wise.
+
+    .. math::
+        \text{Hardsigmoid}(x) = \begin{cases}
+            0 & \text{if~} x \le -3, \\
+            1 & \text{if~} x \ge +3, \\
+            x / 6 + 1 / 2 & \text{otherwise}
+        \end{cases}
+
+    Args:
+        inplace: If set to ``True``, will do this operation in-place. Default: ``False``
+
+    See :class:`~torch.nn.Hardsigmoid` for more details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(hardsigmoid, (input,), input, inplace=inplace)
+    if inplace:
+        return torch._C._nn.hardsigmoid_(input)
+    return torch._C._nn.hardsigmoid(input)
+
+
+linear = _add_docstr(
+    torch._C._nn.linear,
+    r"""
+linear(input, weight, bias=None) -> Tensor
+
+Applies a linear transformation to the incoming data: :math:`y = xA^T + b`.
+
+This operation supports 2-D :attr:`weight` with :ref:`sparse layout`
+
+{sparse_beta_warning}
+
+This operator supports :ref:`TensorFloat32`.
+
+Shape:
+
+    - Input: :math:`(*, in\_features)` where `*` means any number of
+      additional dimensions, including none
+    - Weight: :math:`(out\_features, in\_features)` or :math:`(in\_features)`
+    - Bias: :math:`(out\_features)` or :math:`()`
+    - Output: :math:`(*, out\_features)` or :math:`(*)`, based on the shape of the weight
+""".format(
+        **sparse_support_notes
+    ),
+)
+
+
+bilinear = _add_docstr(
+    torch.bilinear,
+    r"""
+bilinear(input1, input2, weight, bias=None) -> Tensor
+
+Applies a bilinear transformation to the incoming data:
+:math:`y = x_1^T A x_2 + b`
+
+Shape:
+
+    - input1: :math:`(N, *, H_{in1})` where :math:`H_{in1}=\text{in1\_features}`
+      and :math:`*` means any number of additional dimensions.
+      All but the last dimension of the inputs should be the same.
+    - input2: :math:`(N, *, H_{in2})` where :math:`H_{in2}=\text{in2\_features}`
+    - weight: :math:`(\text{out\_features}, \text{in1\_features},
+      \text{in2\_features})`
+    - bias: :math:`(\text{out\_features})`
+    - output: :math:`(N, *, H_{out})` where :math:`H_{out}=\text{out\_features}`
+      and all but the last dimension are the same shape as the input.
+""",
+)
+
+
+def silu(input: Tensor, inplace: bool = False) -> Tensor:
+    r"""Apply the Sigmoid Linear Unit (SiLU) function, element-wise.
+
+    The SiLU function is also known as the swish function.
+
+    .. math::
+        \text{silu}(x) = x * \sigma(x), \text{where } \sigma(x) \text{ is the logistic sigmoid.}
+
+    .. note::
+        See `Gaussian Error Linear Units (GELUs) `_
+        where the SiLU (Sigmoid Linear Unit) was originally coined, and see
+        `Sigmoid-Weighted Linear Units for Neural Network Function Approximation
+        in Reinforcement Learning `_ and `Swish:
+        a Self-Gated Activation Function `_
+        where the SiLU was experimented with later.
+
+    See :class:`~torch.nn.SiLU` for more details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(silu, (input,), input, inplace=inplace)
+    if inplace:
+        return torch._C._nn.silu_(input)
+    return torch._C._nn.silu(input)
+
+
+def mish(input: Tensor, inplace: bool = False) -> Tensor:
+    r"""Apply the Mish function, element-wise.
+
+    Mish: A Self Regularized Non-Monotonic Neural Activation Function.
+
+    .. math::
+        \text{Mish}(x) = x * \text{Tanh}(\text{Softplus}(x))
+
+    .. note::
+        See `Mish: A Self Regularized Non-Monotonic Neural Activation Function `_
+
+    See :class:`~torch.nn.Mish` for more details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(mish, (input,), input, inplace=inplace)
+    if inplace:
+        return torch._C._nn.mish_(input)
+    return torch._C._nn.mish(input)
+
+
+def hardswish(input: Tensor, inplace: bool = False) -> Tensor:
+    r"""Apply hardswish function, element-wise.
+
+    Follows implementation as described in the paper:
+    `Searching for MobileNetV3`_.
+
+    .. math::
+        \text{Hardswish}(x) = \begin{cases}
+            0 & \text{if~} x \le -3, \\
+            x & \text{if~} x \ge +3, \\
+            x \cdot (x + 3) /6 & \text{otherwise}
+        \end{cases}
+
+    See :class:`~torch.nn.Hardswish` for more details.
+
+    .. _`Searching for MobileNetV3`:
+        https://arxiv.org/abs/1905.02244
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(hardswish, (input,), input, inplace=inplace)
+    if inplace:
+        return torch._C._nn.hardswish_(input)
+    return torch._C._nn.hardswish(input)
+
+
+def _no_grad_embedding_renorm_(
+    weight: Tensor,
+    input: Tensor,
+    max_norm: float,
+    norm_type: float,
+) -> Tuple[Tensor, Tensor]:
+    torch.embedding_renorm_(weight.detach(), input, max_norm, norm_type)
+
+
+def embedding(
+    input: Tensor,
+    weight: Tensor,
+    padding_idx: Optional[int] = None,
+    max_norm: Optional[float] = None,
+    norm_type: float = 2.0,
+    scale_grad_by_freq: bool = False,
+    sparse: bool = False,
+) -> Tensor:
+    r"""Generate a simple lookup table that looks up embeddings in a fixed dictionary and size.
+
+    This module is often used to retrieve word embeddings using indices.
+    The input to the module is a list of indices, and the embedding matrix,
+    and the output is the corresponding word embeddings.
+
+    See :class:`torch.nn.Embedding` for more details.
+
+    .. note::
+        Note that the analytical gradients of this function with respect to
+        entries in :attr:`weight` at the row specified by :attr:`padding_idx`
+        are expected to differ from the numerical ones.
+
+    .. note::
+        Note that `:class:`torch.nn.Embedding` differs from this function in
+        that it initializes the row of :attr:`weight` specified by
+        :attr:`padding_idx` to all zeros on construction.
+
+    Args:
+        input (LongTensor): Tensor containing indices into the embedding matrix
+        weight (Tensor): The embedding matrix with number of rows equal to the maximum possible index + 1,
+            and number of columns equal to the embedding size
+        padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the gradient;
+                                     therefore, the embedding vector at :attr:`padding_idx` is not updated during training,
+                                     i.e. it remains as a fixed "pad".
+        max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
+                                    is renormalized to have norm :attr:`max_norm`.
+                                    Note: this will modify :attr:`weight` in-place.
+        norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.
+        scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse of frequency of
+                                                the words in the mini-batch. Default ``False``.
+        sparse (bool, optional): If ``True``, gradient w.r.t. :attr:`weight` will be a sparse tensor. See Notes under
+                                 :class:`torch.nn.Embedding` for more details regarding sparse gradients.
+
+    Shape:
+        - Input: LongTensor of arbitrary shape containing the indices to extract
+        - Weight: Embedding matrix of floating point type with shape `(V, embedding_dim)`,
+          where V = maximum index + 1 and embedding_dim = the embedding size
+        - Output: `(*, embedding_dim)`, where `*` is the input shape
+
+    Examples::
+
+        >>> # a batch of 2 samples of 4 indices each
+        >>> input = torch.tensor([[1, 2, 4, 5], [4, 3, 2, 9]])
+        >>> # an embedding matrix containing 10 tensors of size 3
+        >>> embedding_matrix = torch.rand(10, 3)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> F.embedding(input, embedding_matrix)
+        tensor([[[ 0.8490,  0.9625,  0.6753],
+                 [ 0.9666,  0.7761,  0.6108],
+                 [ 0.6246,  0.9751,  0.3618],
+                 [ 0.4161,  0.2419,  0.7383]],
+
+                [[ 0.6246,  0.9751,  0.3618],
+                 [ 0.0237,  0.7794,  0.0528],
+                 [ 0.9666,  0.7761,  0.6108],
+                 [ 0.3385,  0.8612,  0.1867]]])
+
+        >>> # example with padding_idx
+        >>> weights = torch.rand(10, 3)
+        >>> weights[0, :].zero_()
+        >>> embedding_matrix = weights
+        >>> input = torch.tensor([[0, 2, 0, 5]])
+        >>> F.embedding(input, embedding_matrix, padding_idx=0)
+        tensor([[[ 0.0000,  0.0000,  0.0000],
+                 [ 0.5609,  0.5384,  0.8720],
+                 [ 0.0000,  0.0000,  0.0000],
+                 [ 0.6262,  0.2438,  0.7471]]])
+    """
+    if has_torch_function_variadic(input, weight):
+        return handle_torch_function(
+            embedding,
+            (input, weight),
+            input,
+            weight,
+            padding_idx=padding_idx,
+            max_norm=max_norm,
+            norm_type=norm_type,
+            scale_grad_by_freq=scale_grad_by_freq,
+            sparse=sparse,
+        )
+    if padding_idx is not None:
+        if padding_idx > 0:
+            assert padding_idx < weight.size(
+                0
+            ), "Padding_idx must be within num_embeddings"
+        elif padding_idx < 0:
+            assert padding_idx >= -weight.size(
+                0
+            ), "Padding_idx must be within num_embeddings"
+            padding_idx = weight.size(0) + padding_idx
+    else:
+        padding_idx = -1
+    if max_norm is not None:
+        # Note [embedding_renorm contiguous]
+        # `embedding_renorm_` will call .contiguous() on input anyways, so we
+        # call it here and take advantage of the improved locality in the
+        # `embedding` call below too.
+        input = input.contiguous()
+        # Note [embedding_renorm set_grad_enabled]
+        # XXX: equivalent to
+        # with torch.no_grad():
+        #   torch.embedding_renorm_
+        # remove once script supports set_grad_enabled
+        _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
+    return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
+
+
+def embedding_bag(
+    input: Tensor,
+    weight: Tensor,
+    offsets: Optional[Tensor] = None,
+    max_norm: Optional[float] = None,
+    norm_type: float = 2,
+    scale_grad_by_freq: bool = False,
+    mode: str = "mean",
+    sparse: bool = False,
+    per_sample_weights: Optional[Tensor] = None,
+    include_last_offset: bool = False,
+    padding_idx: Optional[int] = None,
+) -> Tensor:
+    r"""Compute sums, means or maxes of `bags` of embeddings.
+
+    Calculation is done without instantiating the intermediate embeddings.
+    See :class:`torch.nn.EmbeddingBag` for more details.
+
+    Note:
+        {backward_reproducibility_note}
+
+    Args:
+        input (LongTensor): Tensor containing bags of indices into the embedding matrix
+        weight (Tensor): The embedding matrix with number of rows equal to the maximum possible index + 1,
+            and number of columns equal to the embedding size
+        offsets (LongTensor, optional): Only used when :attr:`input` is 1D. :attr:`offsets` determines
+                             the starting index position of each bag (sequence) in :attr:`input`.
+        max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
+                                    is renormalized to have norm :attr:`max_norm`.
+                                    Note: this will modify :attr:`weight` in-place.
+        norm_type (float, optional): The ``p`` in the ``p``-norm to compute for the :attr:`max_norm` option.
+                                     Default ``2``.
+        scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of
+                                                the words in the mini-batch. Default ``False``.
+                                                Note: this option is not supported when ``mode="max"``.
+        mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag.
+                                 Default: ``"mean"``
+        sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` will be a sparse tensor. See Notes under
+                                 :class:`torch.nn.Embedding` for more details regarding sparse gradients.
+                                 Note: this option is not supported when ``mode="max"``.
+        per_sample_weights (Tensor, optional): a tensor of float / double weights, or None
+            to indicate all weights should be taken to be 1. If specified, :attr:`per_sample_weights`
+            must have exactly the same shape as input and is treated as having the same
+            :attr:`offsets`, if those are not None.
+
+        include_last_offset (bool, optional): if ``True``, the size of offsets is equal to the number of bags + 1.
+            The last element is the size of the input, or the ending index position of the last bag (sequence).
+
+        padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the
+                                     gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated
+                                     during training, i.e. it remains as a fixed "pad". Note that the embedding
+                                     vector at :attr:`padding_idx` is excluded from the reduction.
+
+    Shape:
+        - :attr:`input` (LongTensor) and :attr:`offsets` (LongTensor, optional)
+
+          - If :attr:`input` is 2D of shape `(B, N)`, it will be treated as ``B`` bags (sequences)
+            each of fixed length ``N``, and this will return ``B`` values aggregated in a way
+            depending on the :attr:`mode`. :attr:`offsets` is ignored and required to be ``None`` in this case.
+
+          - If :attr:`input` is 1D of shape `(N)`, it will be treated as a concatenation of
+            multiple bags (sequences). :attr:`offsets` is required to be a 1D tensor containing
+            the starting index positions of each bag in :attr:`input`. Therefore, for :attr:`offsets`
+            of shape `(B)`, :attr:`input` will be viewed as having ``B`` bags.
+            Empty bags (i.e., having 0-length) will have returned vectors filled by zeros.
+
+        - :attr:`weight` (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)`
+
+        - :attr:`per_sample_weights` (Tensor, optional). Has the same shape as :attr:`input`.
+
+        - :attr:`output`: aggregated embedding values of shape `(B, embedding_dim)`
+
+    Examples::
+
+        >>> # an Embedding module containing 10 tensors of size 3
+        >>> embedding_matrix = torch.rand(10, 3)
+        >>> # a batch of 2 samples of 4 indices each
+        >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9])
+        >>> offsets = torch.tensor([0, 4])
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> F.embedding_bag(input, embedding_matrix, offsets)
+        tensor([[ 0.3397,  0.3552,  0.5545],
+                [ 0.5893,  0.4386,  0.5882]])
+
+        >>> # example with padding_idx
+        >>> embedding_matrix = torch.rand(10, 3)
+        >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9])
+        >>> offsets = torch.tensor([0, 4])
+        >>> F.embedding_bag(input, embedding_matrix, offsets, padding_idx=2, mode='sum')
+        tensor([[ 0.0000,  0.0000,  0.0000],
+                [-0.7082,  3.2145, -2.6251]])
+    """
+    if has_torch_function_variadic(input, weight, offsets, per_sample_weights):
+        return handle_torch_function(
+            embedding_bag,
+            (input, weight, offsets, per_sample_weights),
+            input,
+            weight,
+            offsets=offsets,
+            max_norm=max_norm,
+            norm_type=norm_type,
+            scale_grad_by_freq=scale_grad_by_freq,
+            mode=mode,
+            sparse=sparse,
+            per_sample_weights=per_sample_weights,
+            include_last_offset=include_last_offset,
+            padding_idx=padding_idx,
+        )
+    # Check for backward compatibility.
+    # Used to be embedding_bag(weight, input, ...)
+    # Now is     embedding_bag(input, weight, ...)
+    if weight.dtype == torch.long and input.is_floating_point():
+        warnings.warn(
+            "Argument order of nn.functional.embedding_bag was changed. "
+            "Usage `embedding_bag(weight, input, ...)` is deprecated, "
+            "and should now be `embedding_bag(input, weight, ...)`."
+        )
+        weight, input = input, weight
+
+    if per_sample_weights is not None and input.size() != per_sample_weights.size():
+        raise ValueError(
+            f"embedding_bag: If per_sample_weights ({per_sample_weights.shape}) is not None, "
+            f"then it must have the same shape as the input ({input.shape})"
+        )
+
+    if not weight.dim() == 2:
+        raise ValueError(
+            f"weight has to be a 2D Tensor, but got Tensor of dimension {weight.dim()}"
+        )
+
+    if input.dim() == 2:
+        if offsets is not None:
+            type_str = ""
+            # TODO: Remove this once script supports type() calls
+            if not torch.jit.is_scripting():
+                type_str = str(type(offsets))
+            raise ValueError(
+                "if input is 2D, then offsets has to be None"
+                ", as input is treated is a mini-batch of"
+                " fixed length sequences. However, found "
+                f"offsets of type {type_str}"
+            )
+        offsets = torch.arange(
+            0, input.numel(), input.size(1), dtype=input.dtype, device=input.device
+        )
+
+        input = input.reshape(-1)
+        if per_sample_weights is not None:
+            per_sample_weights = per_sample_weights.reshape(-1)
+    elif input.dim() == 1:
+        if offsets is None:
+            raise ValueError("offsets has to be a 1D Tensor but got None")
+        if offsets.dim() != 1:
+            raise ValueError("offsets has to be a 1D Tensor")
+    else:
+        raise ValueError(
+            f"input has to be 1D or 2D Tensor, but got Tensor of dimension {input.dim()}"
+        )
+    if mode == "sum":
+        mode_enum = 0
+    elif mode == "mean":
+        mode_enum = 1
+    elif mode == "max":
+        mode_enum = 2
+
+        if scale_grad_by_freq:
+            raise ValueError(
+                "max mode does not support scaling the gradient by the frequency"
+            )
+
+        if sparse:
+            raise ValueError("max mode does not support sparse weights")
+
+    else:
+        raise ValueError("mode has to be one of sum, mean or max")
+
+    if max_norm is not None:
+        # XXX: equivalent to
+        # with torch.no_grad():
+        #   torch.nembedding_renorm_
+        # remove once script supports set_grad_enabled
+        _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
+
+    if per_sample_weights is not None and mode != "sum":
+        raise NotImplementedError(
+            "embedding_bag: per_sample_weights was not None. "
+            "per_sample_weights is only supported for mode='sum' "
+            f"(got mode='{mode}'). Please open a feature request on GitHub."
+        )
+
+    ret, _, _, _ = torch.embedding_bag(
+        weight,
+        input,
+        offsets,
+        scale_grad_by_freq,
+        mode_enum,
+        sparse,
+        per_sample_weights,
+        include_last_offset,
+        padding_idx,
+    )
+    return ret
+
+
+if embedding_bag.__doc__:
+    embedding_bag.__doc__ = embedding_bag.__doc__.format(**reproducibility_notes)
+
+
+def _verify_batch_size(size: List[int]) -> None:
+    # XXX: JIT script does not support the reduce from functools, and mul op is a
+    # builtin, which cannot be used as a value to a func yet, so rewrite this size
+    # check to a simple equivalent for loop
+    #
+    # TODO: make use of reduce like below when JIT is ready with the missing features:
+    # from operator import mul
+    # from functools import reduce
+    #
+    #   if reduce(mul, size[2:], size[0]) == 1
+    size_prods = size[0]
+    for i in range(len(size) - 2):
+        size_prods *= size[i + 2]
+    if size_prods == 1:
+        raise ValueError(
+            f"Expected more than 1 value per channel when training, got input size {size}"
+        )
+
+
+def batch_norm(
+    input: Tensor,
+    running_mean: Optional[Tensor],
+    running_var: Optional[Tensor],
+    weight: Optional[Tensor] = None,
+    bias: Optional[Tensor] = None,
+    training: bool = False,
+    momentum: float = 0.1,
+    eps: float = 1e-5,
+) -> Tensor:
+    r"""Apply Batch Normalization for each channel across a batch of data.
+
+    See :class:`~torch.nn.BatchNorm1d`, :class:`~torch.nn.BatchNorm2d`,
+    :class:`~torch.nn.BatchNorm3d` for details.
+    """
+    if has_torch_function_variadic(input, running_mean, running_var, weight, bias):
+        return handle_torch_function(
+            batch_norm,
+            (input, running_mean, running_var, weight, bias),
+            input,
+            running_mean,
+            running_var,
+            weight=weight,
+            bias=bias,
+            training=training,
+            momentum=momentum,
+            eps=eps,
+        )
+    if training:
+        _verify_batch_size(input.size())
+
+    return torch.batch_norm(
+        input,
+        weight,
+        bias,
+        running_mean,
+        running_var,
+        training,
+        momentum,
+        eps,
+        torch.backends.cudnn.enabled,
+    )
+
+
+def _verify_spatial_size(size: List[int]) -> None:
+    # Verify that there is > 1 spatial element for instance norm calculation.
+    size_prods = 1
+    for i in range(2, len(size)):
+        size_prods *= size[i]
+    if size_prods == 1:
+        raise ValueError(
+            f"Expected more than 1 spatial element when training, got input size {size}"
+        )
+
+
+def instance_norm(
+    input: Tensor,
+    running_mean: Optional[Tensor] = None,
+    running_var: Optional[Tensor] = None,
+    weight: Optional[Tensor] = None,
+    bias: Optional[Tensor] = None,
+    use_input_stats: bool = True,
+    momentum: float = 0.1,
+    eps: float = 1e-5,
+) -> Tensor:
+    r"""Apply Instance Normalization independently for each channel in every data sample within a batch.
+
+    See :class:`~torch.nn.InstanceNorm1d`, :class:`~torch.nn.InstanceNorm2d`,
+    :class:`~torch.nn.InstanceNorm3d` for details.
+    """
+    if has_torch_function_variadic(input, running_mean, running_var, weight, bias):
+        return handle_torch_function(
+            instance_norm,
+            (input, running_mean, running_var, weight, bias),
+            input,
+            running_mean=running_mean,
+            running_var=running_var,
+            weight=weight,
+            bias=bias,
+            use_input_stats=use_input_stats,
+            momentum=momentum,
+            eps=eps,
+        )
+    if use_input_stats:
+        _verify_spatial_size(input.size())
+    return torch.instance_norm(
+        input,
+        weight,
+        bias,
+        running_mean,
+        running_var,
+        use_input_stats,
+        momentum,
+        eps,
+        torch.backends.cudnn.enabled,
+    )
+
+
+def layer_norm(
+    input: Tensor,
+    normalized_shape: List[int],
+    weight: Optional[Tensor] = None,
+    bias: Optional[Tensor] = None,
+    eps: float = 1e-5,
+) -> Tensor:
+    r"""Apply Layer Normalization for last certain number of dimensions.
+
+    See :class:`~torch.nn.LayerNorm` for details.
+    """
+    if has_torch_function_variadic(input, weight, bias):
+        return handle_torch_function(
+            layer_norm,
+            (input, weight, bias),
+            input,
+            normalized_shape,
+            weight=weight,
+            bias=bias,
+            eps=eps,
+        )
+    return torch.layer_norm(
+        input, normalized_shape, weight, bias, eps, torch.backends.cudnn.enabled
+    )
+
+
+def rms_norm(
+    input: Tensor,
+    normalized_shape: List[int],
+    weight: Optional[Tensor] = None,
+    eps: Optional[float] = None,
+) -> Tensor:
+    r"""Apply Root Mean Square Layer Normalization.
+
+    See :class:`~torch.nn.RMSNorm` for details.
+    """
+    if has_torch_function_variadic(input, weight):
+        return handle_torch_function(
+            rms_norm, (input, weight), input, normalized_shape, weight=weight, eps=eps
+        )
+    return torch.rms_norm(input, normalized_shape, weight, eps)
+
+
+def group_norm(
+    input: Tensor,
+    num_groups: int,
+    weight: Optional[Tensor] = None,
+    bias: Optional[Tensor] = None,
+    eps: float = 1e-5,
+) -> Tensor:
+    r"""Apply Group Normalization for last certain number of dimensions.
+
+    See :class:`~torch.nn.GroupNorm` for details.
+    """
+    if has_torch_function_variadic(input, weight, bias):
+        return handle_torch_function(
+            group_norm,
+            (
+                input,
+                weight,
+                bias,
+            ),
+            input,
+            num_groups,
+            weight=weight,
+            bias=bias,
+            eps=eps,
+        )
+    if input.dim() < 2:
+        raise RuntimeError(
+            f"Expected at least 2 dimensions for input tensor but received {input.dim()}"
+        )
+    _verify_batch_size(
+        [input.size(0) * input.size(1) // num_groups, num_groups]
+        + list(input.size()[2:])
+    )
+    return torch.group_norm(
+        input, num_groups, weight, bias, eps, torch.backends.cudnn.enabled
+    )
+
+
+def local_response_norm(
+    input: Tensor,
+    size: int,
+    alpha: float = 1e-4,
+    beta: float = 0.75,
+    k: float = 1.0,
+) -> Tensor:
+    r"""Apply local response normalization over an input signal.
+
+    The input signal is composed of several input planes, where channels occupy the second dimension.
+    Normalization is applied across channels.
+
+    See :class:`~torch.nn.LocalResponseNorm` for details.
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            local_response_norm, (input,), input, size, alpha=alpha, beta=beta, k=k
+        )
+    dim = input.dim()
+    if dim < 3:
+        raise ValueError(
+            f"Expected 3D or higher dimensionality                          input (got {dim} dimensions)"
+        )
+
+    if input.numel() == 0:
+        return input
+
+    div = input.mul(input)
+    if dim == 3:
+        div = div.unsqueeze(1)
+        div = pad(div, (0, 0, size // 2, (size - 1) // 2))
+        div = avg_pool2d(div, (size, 1), stride=1).squeeze(1)
+    else:
+        sizes = input.size()
+        div = div.view(sizes[0], 1, sizes[1], sizes[2], -1)
+        div = pad(div, (0, 0, 0, 0, size // 2, (size - 1) // 2))
+        div = avg_pool3d(div, (size, 1, 1), stride=1).squeeze(1)
+        div = div.view(sizes)
+    div = div.mul(alpha).add(k).pow(beta)
+    return input / div
+
+
+# loss
+
+
+def ctc_loss(
+    log_probs: Tensor,
+    targets: Tensor,
+    input_lengths: Tensor,
+    target_lengths: Tensor,
+    blank: int = 0,
+    reduction: str = "mean",
+    zero_infinity: bool = False,
+) -> Tensor:
+    r"""Apply the Connectionist Temporal Classification loss.
+
+    See :class:`~torch.nn.CTCLoss` for details.
+
+    Note:
+        {cudnn_reproducibility_note}
+
+    Note:
+        {backward_reproducibility_note}
+
+    Args:
+        log_probs: :math:`(T, N, C)` or :math:`(T, C)` where `C = number of characters in alphabet including blank`,
+            `T = input length`, and `N = batch size`.
+            The logarithmized probabilities of the outputs
+            (e.g. obtained with :func:`torch.nn.functional.log_softmax`).
+        targets: :math:`(N, S)` or `(sum(target_lengths))`.
+            Targets cannot be blank. In the second form, the targets are assumed to be concatenated.
+        input_lengths: :math:`(N)` or :math:`()`.
+            Lengths of the inputs (must each be :math:`\leq T`)
+        target_lengths: :math:`(N)` or :math:`()`.
+            Lengths of the targets
+        blank (int, optional):
+            Blank label. Default :math:`0`.
+        reduction (str, optional): Specifies the reduction to apply to the output:
+            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
+            ``'mean'``: the output losses will be divided by the target lengths and
+            then the mean over the batch is taken, ``'sum'``: the output will be
+            summed. Default: ``'mean'``
+        zero_infinity (bool, optional):
+            Whether to zero infinite losses and the associated gradients.
+            Default: ``False``
+            Infinite losses mainly occur when the inputs are too short
+            to be aligned to the targets.
+
+    Example::
+
+        >>> log_probs = torch.randn(50, 16, 20).log_softmax(2).detach().requires_grad_()
+        >>> targets = torch.randint(1, 20, (16, 30), dtype=torch.long)
+        >>> input_lengths = torch.full((16,), 50, dtype=torch.long)
+        >>> target_lengths = torch.randint(10, 30, (16,), dtype=torch.long)
+        >>> loss = F.ctc_loss(log_probs, targets, input_lengths, target_lengths)
+        >>> loss.backward()
+    """
+    if has_torch_function_variadic(log_probs, targets, input_lengths, target_lengths):
+        return handle_torch_function(
+            ctc_loss,
+            (log_probs, targets, input_lengths, target_lengths),
+            log_probs,
+            targets,
+            input_lengths,
+            target_lengths,
+            blank=blank,
+            reduction=reduction,
+            zero_infinity=zero_infinity,
+        )
+    return torch.ctc_loss(
+        log_probs,
+        targets,
+        input_lengths,
+        target_lengths,
+        blank,
+        _Reduction.get_enum(reduction),
+        zero_infinity,
+    )
+
+
+if ctc_loss.__doc__:
+    ctc_loss.__doc__ = ctc_loss.__doc__.format(**reproducibility_notes)
+
+
+def nll_loss(
+    input: Tensor,
+    target: Tensor,
+    weight: Optional[Tensor] = None,
+    size_average: Optional[bool] = None,
+    ignore_index: int = -100,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+) -> Tensor:
+    r"""Compute the negative log likelihood loss.
+
+    See :class:`~torch.nn.NLLLoss` for details.
+
+    Args:
+        input: :math:`(N, C)` where `C = number of classes` or :math:`(N, C, H, W)`
+            in case of 2D Loss, or :math:`(N, C, d_1, d_2, ..., d_K)` where :math:`K \geq 1`
+            in the case of K-dimensional loss. `input` is expected to be log-probabilities.
+        target: :math:`(N)` where each value is :math:`0 \leq \text{targets}[i] \leq C-1`,
+            or :math:`(N, d_1, d_2, ..., d_K)` where :math:`K \geq 1` for
+            K-dimensional loss.
+        weight (Tensor, optional): a manual rescaling weight given to each
+            class. If given, has to be a Tensor of size `C`
+        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
+            the losses are averaged over each loss element in the batch. Note that for
+            some losses, there multiple elements per sample. If the field :attr:`size_average`
+            is set to ``False``, the losses are instead summed for each minibatch. Ignored
+            when reduce is ``False``. Default: ``True``
+        ignore_index (int, optional): Specifies a target value that is ignored
+            and does not contribute to the input gradient. When :attr:`size_average` is
+            ``True``, the loss is averaged over non-ignored targets. Default: -100
+        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
+            losses are averaged or summed over observations for each minibatch depending
+            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
+            batch element instead and ignores :attr:`size_average`. Default: ``True``
+        reduction (str, optional): Specifies the reduction to apply to the output:
+            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
+            ``'mean'``: the sum of the output will be divided by the number of
+            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
+            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
+            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``
+
+    Example::
+
+        >>> # input is of size N x C = 3 x 5
+        >>> input = torch.randn(3, 5, requires_grad=True)
+        >>> # each element in target has to have 0 <= value < C
+        >>> target = torch.tensor([1, 0, 4])
+        >>> output = F.nll_loss(F.log_softmax(input, dim=1), target)
+        >>> output.backward()
+    """
+    if has_torch_function_variadic(input, target, weight):
+        return handle_torch_function(
+            nll_loss,
+            (input, target, weight),
+            input,
+            target,
+            weight=weight,
+            size_average=size_average,
+            ignore_index=ignore_index,
+            reduce=reduce,
+            reduction=reduction,
+        )
+    if size_average is not None or reduce is not None:
+        reduction = _Reduction.legacy_get_string(size_average, reduce)
+    return torch._C._nn.nll_loss_nd(
+        input, target, weight, _Reduction.get_enum(reduction), ignore_index
+    )
+
+
+def poisson_nll_loss(
+    input: Tensor,
+    target: Tensor,
+    log_input: bool = True,
+    full: bool = False,
+    size_average: Optional[bool] = None,
+    eps: float = 1e-8,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+) -> Tensor:
+    r"""Poisson negative log likelihood loss.
+
+    See :class:`~torch.nn.PoissonNLLLoss` for details.
+
+    Args:
+        input: expectation of underlying Poisson distribution.
+        target: random sample :math:`target \sim \text{Poisson}(input)`.
+        log_input: if ``True`` the loss is computed as
+            :math:`\exp(\text{input}) - \text{target} * \text{input}`, if ``False`` then loss is
+            :math:`\text{input} - \text{target} * \log(\text{input}+\text{eps})`. Default: ``True``
+        full: whether to compute full loss, i. e. to add the Stirling
+            approximation term. Default: ``False``
+            :math:`\text{target} * \log(\text{target}) - \text{target} + 0.5 * \log(2 * \pi * \text{target})`.
+        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
+            the losses are averaged over each loss element in the batch. Note that for
+            some losses, there multiple elements per sample. If the field :attr:`size_average`
+            is set to ``False``, the losses are instead summed for each minibatch. Ignored
+            when reduce is ``False``. Default: ``True``
+        eps (float, optional): Small value to avoid evaluation of :math:`\log(0)` when
+            :attr:`log_input`\ =\ ``False``. Default: 1e-8
+        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
+            losses are averaged or summed over observations for each minibatch depending
+            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
+            batch element instead and ignores :attr:`size_average`. Default: ``True``
+        reduction (str, optional): Specifies the reduction to apply to the output:
+            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
+            ``'mean'``: the sum of the output will be divided by the number of
+            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
+            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
+            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``
+
+    """
+    if has_torch_function_variadic(input, target):
+        return handle_torch_function(
+            poisson_nll_loss,
+            (input, target),
+            input,
+            target,
+            log_input=log_input,
+            full=full,
+            size_average=size_average,
+            eps=eps,
+            reduce=reduce,
+            reduction=reduction,
+        )
+    if size_average is not None or reduce is not None:
+        reduction = _Reduction.legacy_get_string(size_average, reduce)
+    if reduction != "none" and reduction != "mean" and reduction != "sum":
+        ret = input
+        raise ValueError(reduction + " is not a valid value for reduction")
+
+    ret = torch.poisson_nll_loss(
+        input, target, log_input, full, eps, _Reduction.get_enum(reduction)
+    )
+    return ret
+
+
+def gaussian_nll_loss(
+    input: Tensor,
+    target: Tensor,
+    var: Tensor,
+    full: bool = False,
+    eps: float = 1e-6,
+    reduction: str = "mean",
+) -> Tensor:
+    r"""Gaussian negative log likelihood loss.
+
+    See :class:`~torch.nn.GaussianNLLLoss` for details.
+
+    Args:
+        input: expectation of the Gaussian distribution.
+        target: sample from the Gaussian distribution.
+        var: tensor of positive variance(s), one for each of the expectations
+            in the input (heteroscedastic), or a single one (homoscedastic).
+        full (bool, optional): include the constant term in the loss calculation. Default: ``False``.
+        eps (float, optional): value added to var, for stability. Default: 1e-6.
+        reduction (str, optional): specifies the reduction to apply to the output:
+            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
+            ``'mean'``: the output is the average of all batch member losses,
+            ``'sum'``: the output is the sum of all batch member losses.
+            Default: ``'mean'``.
+    """
+    if has_torch_function_variadic(input, target, var):
+        return handle_torch_function(
+            gaussian_nll_loss,
+            (input, target, var),
+            input,
+            target,
+            var,
+            full=full,
+            eps=eps,
+            reduction=reduction,
+        )
+
+    # Check var size
+    # If var.size == input.size, the case is heteroscedastic and no further checks are needed.
+    # Otherwise:
+    if var.size() != input.size():
+        # If var is one dimension short of input, but the sizes match otherwise, then this is a homoscedastic case.
+        # e.g. input.size = (10, 2, 3), var.size = (10, 2)
+        # -> unsqueeze var so that var.shape = (10, 2, 1)
+        # this is done so that broadcasting can happen in the loss calculation
+        if input.size()[:-1] == var.size():
+            var = torch.unsqueeze(var, -1)
+
+        # This checks if the sizes match up to the final dimension, and the final dimension of var is of size 1.
+        # This is also a homoscedastic case.
+        # e.g. input.size = (10, 2, 3), var.size = (10, 2, 1)
+        elif (
+            input.size()[:-1] == var.size()[:-1] and var.size(-1) == 1
+        ):  # Heteroscedastic case
+            pass
+
+        # If none of the above pass, then the size of var is incorrect.
+        else:
+            raise ValueError("var is of incorrect size")
+
+    # Check validity of reduction mode
+    if reduction != "none" and reduction != "mean" and reduction != "sum":
+        raise ValueError(reduction + " is not valid")
+
+    # Entries of var must be non-negative
+    if torch.any(var < 0):
+        raise ValueError("var has negative entry/entries")
+
+    # Clamp for stability
+    var = var.clone()
+    with torch.no_grad():
+        var.clamp_(min=eps)
+
+    # Calculate the loss
+    loss = 0.5 * (torch.log(var) + (input - target) ** 2 / var)
+    if full:
+        loss += 0.5 * math.log(2 * math.pi)
+
+    if reduction == "mean":
+        return loss.mean()
+    elif reduction == "sum":
+        return loss.sum()
+    else:
+        return loss
+
+
+def kl_div(
+    input: Tensor,
+    target: Tensor,
+    size_average: Optional[bool] = None,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+    log_target: bool = False,
+) -> Tensor:
+    r"""Compute the KL Divergence loss.
+
+    Refer - The `Kullback-Leibler divergence Loss
+    `__
+
+    See :class:`~torch.nn.KLDivLoss` for details.
+
+    Args:
+        input: Tensor of arbitrary shape in log-probabilities.
+        target: Tensor of the same shape as input. See :attr:`log_target` for
+            the target's interpretation.
+        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
+            the losses are averaged over each loss element in the batch. Note that for
+            some losses, there multiple elements per sample. If the field :attr:`size_average`
+            is set to ``False``, the losses are instead summed for each minibatch. Ignored
+            when reduce is ``False``. Default: ``True``
+        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
+            losses are averaged or summed over observations for each minibatch depending
+            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
+            batch element instead and ignores :attr:`size_average`. Default: ``True``
+        reduction (str, optional): Specifies the reduction to apply to the output:
+            ``'none'`` | ``'batchmean'`` | ``'sum'`` | ``'mean'``.
+            ``'none'``: no reduction will be applied
+            ``'batchmean'``: the sum of the output will be divided by the batchsize
+            ``'sum'``: the output will be summed
+            ``'mean'``: the output will be divided by the number of elements in the output
+            Default: ``'mean'``
+        log_target (bool): A flag indicating whether ``target`` is passed in the log space.
+            It is recommended to pass certain distributions (like ``softmax``)
+            in the log space to avoid numerical issues caused by explicit ``log``.
+            Default: ``False``
+
+    .. note::
+        :attr:`size_average` and :attr:`reduce` are in the process of being deprecated,
+        and in the meantime, specifying either of those two args will override :attr:`reduction`.
+
+    .. warning::
+        :attr:`reduction` = ``'mean'`` doesn't return the true kl divergence value, please use
+        :attr:`reduction` = ``'batchmean'`` which aligns with KL math definition.
+    """
+    if has_torch_function_variadic(input, target):
+        return handle_torch_function(
+            kl_div,
+            (input, target),
+            input,
+            target,
+            size_average=size_average,
+            reduce=reduce,
+            reduction=reduction,
+            log_target=log_target,
+        )
+    if size_average is not None or reduce is not None:
+        reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
+    else:
+        if reduction == "mean":
+            warnings.warn(
+                "reduction: 'mean' divides the total loss by both the batch size and the support size."
+                "'batchmean' divides only by the batch size, and aligns with the KL div math definition."
+                "'mean' will be changed to behave the same as 'batchmean' in the next major release."
+            )
+
+        # special case for batchmean
+        if reduction == "batchmean":
+            reduction_enum = _Reduction.get_enum("sum")
+        else:
+            reduction_enum = _Reduction.get_enum(reduction)
+
+    reduced = torch.kl_div(input, target, reduction_enum, log_target=log_target)
+
+    if reduction == "batchmean" and input.dim() != 0:
+        reduced = reduced / input.size()[0]
+
+    return reduced
+
+
+def cross_entropy(
+    input: Tensor,
+    target: Tensor,
+    weight: Optional[Tensor] = None,
+    size_average: Optional[bool] = None,
+    ignore_index: int = -100,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+    label_smoothing: float = 0.0,
+) -> Tensor:
+    r"""Compute the cross entropy loss between input logits and target.
+
+    See :class:`~torch.nn.CrossEntropyLoss` for details.
+
+    Args:
+        input (Tensor) : Predicted unnormalized logits;
+            see Shape section below for supported shapes.
+        target (Tensor) : Ground truth class indices or class probabilities;
+            see Shape section below for supported shapes.
+        weight (Tensor, optional): a manual rescaling weight given to each
+            class. If given, has to be a Tensor of size `C`
+        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
+            the losses are averaged over each loss element in the batch. Note that for
+            some losses, there multiple elements per sample. If the field :attr:`size_average`
+            is set to ``False``, the losses are instead summed for each minibatch. Ignored
+            when reduce is ``False``. Default: ``True``
+        ignore_index (int, optional): Specifies a target value that is ignored
+            and does not contribute to the input gradient. When :attr:`size_average` is
+            ``True``, the loss is averaged over non-ignored targets. Note that
+            :attr:`ignore_index` is only applicable when the target contains class indices.
+            Default: -100
+        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
+            losses are averaged or summed over observations for each minibatch depending
+            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
+            batch element instead and ignores :attr:`size_average`. Default: ``True``
+        reduction (str, optional): Specifies the reduction to apply to the output:
+            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
+            ``'mean'``: the sum of the output will be divided by the number of
+            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
+            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
+            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``
+        label_smoothing (float, optional): A float in [0.0, 1.0]. Specifies the amount
+            of smoothing when computing the loss, where 0.0 means no smoothing. The targets
+            become a mixture of the original ground truth and a uniform distribution as described in
+            `Rethinking the Inception Architecture for Computer Vision `__. Default: :math:`0.0`.
+
+    Shape:
+        - Input: Shape :math:`(C)`, :math:`(N, C)` or :math:`(N, C, d_1, d_2, ..., d_K)` with :math:`K \geq 1`
+          in the case of `K`-dimensional loss.
+        - Target: If containing class indices, shape :math:`()`, :math:`(N)` or :math:`(N, d_1, d_2, ..., d_K)` with
+          :math:`K \geq 1` in the case of K-dimensional loss where each value should be between :math:`[0, C)`.
+          If containing class probabilities, same shape as the input and each value should be between :math:`[0, 1]`.
+
+        where:
+
+        .. math::
+            \begin{aligned}
+                C ={} & \text{number of classes} \\
+                N ={} & \text{batch size} \\
+            \end{aligned}
+
+    Examples::
+
+        >>> # Example of target with class indices
+        >>> input = torch.randn(3, 5, requires_grad=True)
+        >>> target = torch.randint(5, (3,), dtype=torch.int64)
+        >>> loss = F.cross_entropy(input, target)
+        >>> loss.backward()
+        >>>
+        >>> # Example of target with class probabilities
+        >>> input = torch.randn(3, 5, requires_grad=True)
+        >>> target = torch.randn(3, 5).softmax(dim=1)
+        >>> loss = F.cross_entropy(input, target)
+        >>> loss.backward()
+    """
+    if has_torch_function_variadic(input, target, weight):
+        return handle_torch_function(
+            cross_entropy,
+            (input, target, weight),
+            input,
+            target,
+            weight=weight,
+            size_average=size_average,
+            ignore_index=ignore_index,
+            reduce=reduce,
+            reduction=reduction,
+            label_smoothing=label_smoothing,
+        )
+    if size_average is not None or reduce is not None:
+        reduction = _Reduction.legacy_get_string(size_average, reduce)
+    return torch._C._nn.cross_entropy_loss(
+        input,
+        target,
+        weight,
+        _Reduction.get_enum(reduction),
+        ignore_index,
+        label_smoothing,
+    )
+
+
+def binary_cross_entropy(
+    input: Tensor,
+    target: Tensor,
+    weight: Optional[Tensor] = None,
+    size_average: Optional[bool] = None,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+) -> Tensor:
+    r"""Measure Binary Cross Entropy between the target and input probabilities.
+
+    See :class:`~torch.nn.BCELoss` for details.
+
+    Args:
+        input: Tensor of arbitrary shape as probabilities.
+        target: Tensor of the same shape as input with values between 0 and 1.
+        weight (Tensor, optional): a manual rescaling weight
+                if provided it's repeated to match input tensor shape
+        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
+            the losses are averaged over each loss element in the batch. Note that for
+            some losses, there multiple elements per sample. If the field :attr:`size_average`
+            is set to ``False``, the losses are instead summed for each minibatch. Ignored
+            when reduce is ``False``. Default: ``True``
+        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
+            losses are averaged or summed over observations for each minibatch depending
+            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
+            batch element instead and ignores :attr:`size_average`. Default: ``True``
+        reduction (str, optional): Specifies the reduction to apply to the output:
+            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
+            ``'mean'``: the sum of the output will be divided by the number of
+            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
+            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
+            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``
+
+    Examples::
+
+        >>> input = torch.randn(3, 2, requires_grad=True)
+        >>> target = torch.rand(3, 2, requires_grad=False)
+        >>> loss = F.binary_cross_entropy(torch.sigmoid(input), target)
+        >>> loss.backward()
+    """
+    if has_torch_function_variadic(input, target, weight):
+        return handle_torch_function(
+            binary_cross_entropy,
+            (input, target, weight),
+            input,
+            target,
+            weight=weight,
+            size_average=size_average,
+            reduce=reduce,
+            reduction=reduction,
+        )
+    if size_average is not None or reduce is not None:
+        reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
+    else:
+        reduction_enum = _Reduction.get_enum(reduction)
+    if target.size() != input.size():
+        raise ValueError(
+            f"Using a target size ({target.size()}) that is different to the input size ({input.size()}) is deprecated. "
+            "Please ensure they have the same size."
+        )
+
+    if weight is not None:
+        new_size = _infer_size(target.size(), weight.size())
+        weight = weight.expand(new_size)
+
+    return torch._C._nn.binary_cross_entropy(input, target, weight, reduction_enum)
+
+
+def binary_cross_entropy_with_logits(
+    input: Tensor,
+    target: Tensor,
+    weight: Optional[Tensor] = None,
+    size_average: Optional[bool] = None,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+    pos_weight: Optional[Tensor] = None,
+) -> Tensor:
+    r"""Calculate Binary Cross Entropy between target and input logits.
+
+    See :class:`~torch.nn.BCEWithLogitsLoss` for details.
+
+    Args:
+        input: Tensor of arbitrary shape as unnormalized scores (often referred to as logits).
+        target: Tensor of the same shape as input with values between 0 and 1
+        weight (Tensor, optional): a manual rescaling weight
+            if provided it's repeated to match input tensor shape
+        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
+            the losses are averaged over each loss element in the batch. Note that for
+            some losses, there multiple elements per sample. If the field :attr:`size_average`
+            is set to ``False``, the losses are instead summed for each minibatch. Ignored
+            when reduce is ``False``. Default: ``True``
+        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
+            losses are averaged or summed over observations for each minibatch depending
+            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
+            batch element instead and ignores :attr:`size_average`. Default: ``True``
+        reduction (str, optional): Specifies the reduction to apply to the output:
+            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
+            ``'mean'``: the sum of the output will be divided by the number of
+            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
+            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
+            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``
+        pos_weight (Tensor, optional): a weight of positive examples to be broadcasted with target.
+            Must be a tensor with equal size along the class dimension to the number of classes.
+            Pay close attention to PyTorch's broadcasting semantics in order to achieve the desired
+            operations. For a target of size [B, C, H, W] (where B is batch size) pos_weight of
+            size [B, C, H, W] will apply different pos_weights to each element of the batch or
+            [C, H, W] the same pos_weights across the batch. To apply the same positive weight
+            along all spatial dimensions for a 2D multi-class target [C, H, W] use: [C, 1, 1].
+            Default: ``None``
+
+    Examples::
+
+         >>> input = torch.randn(3, requires_grad=True)
+         >>> target = torch.empty(3).random_(2)
+         >>> loss = F.binary_cross_entropy_with_logits(input, target)
+         >>> loss.backward()
+    """
+    if has_torch_function_variadic(input, target, weight, pos_weight):
+        return handle_torch_function(
+            binary_cross_entropy_with_logits,
+            (input, target, weight, pos_weight),
+            input,
+            target,
+            weight=weight,
+            size_average=size_average,
+            reduce=reduce,
+            reduction=reduction,
+            pos_weight=pos_weight,
+        )
+    if size_average is not None or reduce is not None:
+        reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
+    else:
+        reduction_enum = _Reduction.get_enum(reduction)
+
+    if not (target.size() == input.size()):
+        raise ValueError(
+            f"Target size ({target.size()}) must be the same as input size ({input.size()})"
+        )
+
+    return torch.binary_cross_entropy_with_logits(
+        input, target, weight, pos_weight, reduction_enum
+    )
+
+
+def smooth_l1_loss(
+    input: Tensor,
+    target: Tensor,
+    size_average: Optional[bool] = None,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+    beta: float = 1.0,
+) -> Tensor:
+    r"""Compute the Smooth L1 loss.
+
+    Function uses a squared term if the absolute
+    element-wise error falls below beta and an L1 term otherwise.
+
+    See :class:`~torch.nn.SmoothL1Loss` for details.
+    """
+    if has_torch_function_variadic(input, target):
+        return handle_torch_function(
+            smooth_l1_loss,
+            (input, target),
+            input,
+            target,
+            size_average=size_average,
+            reduce=reduce,
+            reduction=reduction,
+            beta=beta,
+        )
+    if not (target.size() == input.size()):
+        warnings.warn(
+            f"Using a target size ({target.size()}) that is different to the input size ({input.size()}). "
+            "This will likely lead to incorrect results due to broadcasting. "
+            "Please ensure they have the same size.",
+            stacklevel=2,
+        )
+    if size_average is not None or reduce is not None:
+        reduction = _Reduction.legacy_get_string(size_average, reduce)
+
+    expanded_input, expanded_target = torch.broadcast_tensors(input, target)
+
+    if beta == 0.0:
+        return torch._C._nn.l1_loss(
+            expanded_input, expanded_target, _Reduction.get_enum(reduction)
+        )
+    else:
+        return torch._C._nn.smooth_l1_loss(
+            expanded_input, expanded_target, _Reduction.get_enum(reduction), beta
+        )
+
+
+def huber_loss(
+    input: Tensor,
+    target: Tensor,
+    reduction: str = "mean",
+    delta: float = 1.0,
+) -> Tensor:
+    r"""Compute the Huber loss.
+
+    Function uses a squared term if the absolute
+    element-wise error falls below delta and a delta-scaled L1 term otherwise.
+
+    When delta equals 1, this loss is equivalent to SmoothL1Loss.
+    In general, Huber loss differs from SmoothL1Loss by a factor of delta (AKA beta in Smooth L1).
+
+    See :class:`~torch.nn.HuberLoss` for details.
+    """
+    if has_torch_function_variadic(input, target):
+        return handle_torch_function(
+            huber_loss,
+            (input, target),
+            input,
+            target,
+            reduction=reduction,
+            delta=delta,
+        )
+    if not (target.size() == input.size()):
+        warnings.warn(
+            f"Using a target size ({target.size()}) that is different to the input size ({input.size()}). "
+            "This will likely lead to incorrect results due to broadcasting. "
+            "Please ensure they have the same size.",
+            stacklevel=2,
+        )
+
+    expanded_input, expanded_target = torch.broadcast_tensors(input, target)
+    return torch._C._nn.huber_loss(
+        expanded_input, expanded_target, _Reduction.get_enum(reduction), delta
+    )
+
+
+def l1_loss(
+    input: Tensor,
+    target: Tensor,
+    size_average: Optional[bool] = None,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+) -> Tensor:  # noqa: D400,D402
+    r"""l1_loss(input, target, size_average=None, reduce=None, reduction='mean') -> Tensor
+
+    Function that takes the mean element-wise absolute value difference.
+
+    See :class:`~torch.nn.L1Loss` for details.
+    """
+    if has_torch_function_variadic(input, target):
+        return handle_torch_function(
+            l1_loss,
+            (input, target),
+            input,
+            target,
+            size_average=size_average,
+            reduce=reduce,
+            reduction=reduction,
+        )
+    if not (target.size() == input.size()):
+        warnings.warn(
+            f"Using a target size ({target.size()}) that is different to the input size ({input.size()}). "
+            "This will likely lead to incorrect results due to broadcasting. "
+            "Please ensure they have the same size.",
+            stacklevel=2,
+        )
+    if size_average is not None or reduce is not None:
+        reduction = _Reduction.legacy_get_string(size_average, reduce)
+
+    expanded_input, expanded_target = torch.broadcast_tensors(input, target)
+    return torch._C._nn.l1_loss(
+        expanded_input, expanded_target, _Reduction.get_enum(reduction)
+    )
+
+
+def mse_loss(
+    input: Tensor,
+    target: Tensor,
+    size_average: Optional[bool] = None,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+) -> Tensor:  # noqa: D400,D402
+    r"""mse_loss(input, target, size_average=None, reduce=None, reduction='mean') -> Tensor
+
+    Measures the element-wise mean squared error.
+    See :class:`~torch.nn.MSELoss` for details.
+    """
+    if has_torch_function_variadic(input, target):
+        return handle_torch_function(
+            mse_loss,
+            (input, target),
+            input,
+            target,
+            size_average=size_average,
+            reduce=reduce,
+            reduction=reduction,
+        )
+    if not (target.size() == input.size()):
+        warnings.warn(
+            f"Using a target size ({target.size()}) that is different to the input size ({input.size()}). "
+            "This will likely lead to incorrect results due to broadcasting. "
+            "Please ensure they have the same size.",
+            stacklevel=2,
+        )
+    if size_average is not None or reduce is not None:
+        reduction = _Reduction.legacy_get_string(size_average, reduce)
+
+    expanded_input, expanded_target = torch.broadcast_tensors(input, target)
+    return torch._C._nn.mse_loss(
+        expanded_input, expanded_target, _Reduction.get_enum(reduction)
+    )
+
+
+def margin_ranking_loss(
+    input1: Tensor,
+    input2: Tensor,
+    target: Tensor,
+    margin: float = 0,
+    size_average: Optional[bool] = None,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+) -> Tensor:  # noqa: D400,D402
+    r"""margin_ranking_loss(input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean') -> Tensor
+
+    See :class:`~torch.nn.MarginRankingLoss` for details.
+    """
+    if has_torch_function_variadic(input1, input2, target):
+        return handle_torch_function(
+            margin_ranking_loss,
+            (input1, input2, target),
+            input1,
+            input2,
+            target,
+            margin=margin,
+            size_average=size_average,
+            reduce=reduce,
+            reduction=reduction,
+        )
+    if size_average is not None or reduce is not None:
+        reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
+    else:
+        reduction_enum = _Reduction.get_enum(reduction)
+    if input1.dim() != input2.dim() or input1.dim() != target.dim():
+        raise RuntimeError(
+            f"margin_ranking_loss : All input tensors should have same dimension but got sizes: "
+            f"input1: {input1.size()}, input2: {input2.size()}, target: {target.size()} "
+        )
+    return torch.margin_ranking_loss(input1, input2, target, margin, reduction_enum)
+
+
+def hinge_embedding_loss(
+    input: Tensor,
+    target: Tensor,
+    margin: float = 1.0,
+    size_average: Optional[bool] = None,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+) -> Tensor:  # noqa: D400,D402
+    r"""hinge_embedding_loss(input, target, margin=1.0, size_average=None, reduce=None, reduction='mean') -> Tensor
+
+    See :class:`~torch.nn.HingeEmbeddingLoss` for details.
+    """
+    if has_torch_function_variadic(input, target):
+        return handle_torch_function(
+            hinge_embedding_loss,
+            (input, target),
+            input,
+            target,
+            margin=margin,
+            size_average=size_average,
+            reduce=reduce,
+            reduction=reduction,
+        )
+    if size_average is not None or reduce is not None:
+        reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
+    else:
+        reduction_enum = _Reduction.get_enum(reduction)
+    return torch.hinge_embedding_loss(input, target, margin, reduction_enum)
+
+
+def multilabel_margin_loss(
+    input: Tensor,
+    target: Tensor,
+    size_average: Optional[bool] = None,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+) -> Tensor:  # noqa: D400,D402
+    r"""multilabel_margin_loss(input, target, size_average=None, reduce=None, reduction='mean') -> Tensor
+
+    See :class:`~torch.nn.MultiLabelMarginLoss` for details.
+    """
+    if has_torch_function_variadic(input, target):
+        return handle_torch_function(
+            multilabel_margin_loss,
+            (input, target),
+            input,
+            target,
+            size_average=size_average,
+            reduce=reduce,
+            reduction=reduction,
+        )
+    if size_average is not None or reduce is not None:
+        reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
+    else:
+        reduction_enum = _Reduction.get_enum(reduction)
+    return torch._C._nn.multilabel_margin_loss(input, target, reduction_enum)
+
+
+def soft_margin_loss(
+    input: Tensor,
+    target: Tensor,
+    size_average: Optional[bool] = None,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+) -> Tensor:  # noqa: D400,D402
+    r"""
+    soft_margin_loss(input, target, size_average=None, reduce=None, reduction='mean') -> Tensor
+
+    See :class:`~torch.nn.SoftMarginLoss` for details.
+    """
+    if has_torch_function_variadic(input, target):
+        return handle_torch_function(
+            soft_margin_loss,
+            (input, target),
+            input,
+            target,
+            size_average=size_average,
+            reduce=reduce,
+            reduction=reduction,
+        )
+    if size_average is not None or reduce is not None:
+        reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
+    else:
+        reduction_enum = _Reduction.get_enum(reduction)
+    return torch._C._nn.soft_margin_loss(input, target, reduction_enum)
+
+
+def multilabel_soft_margin_loss(
+    input: Tensor,
+    target: Tensor,
+    weight: Optional[Tensor] = None,
+    size_average: Optional[bool] = None,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+) -> Tensor:  # noqa: D400,D402
+    r"""multilabel_soft_margin_loss(input, target, weight=None, size_average=None, reduce=None, reduction='mean') -> Tensor
+
+    See :class:`~torch.nn.MultiLabelSoftMarginLoss` for details.
+    """
+    if has_torch_function_variadic(input, target, weight):
+        return handle_torch_function(
+            multilabel_soft_margin_loss,
+            (input, target, weight),
+            input,
+            target,
+            weight=weight,
+            size_average=size_average,
+            reduce=reduce,
+            reduction=reduction,
+        )
+    if size_average is not None or reduce is not None:
+        reduction = _Reduction.legacy_get_string(size_average, reduce)
+
+    loss = -(target * logsigmoid(input) + (1 - target) * logsigmoid(-input))
+
+    if weight is not None:
+        loss = loss * weight
+
+    class_dim = input.dim() - 1
+    C = input.size(class_dim)
+    loss = loss.sum(dim=class_dim) / C  # only return N loss values
+
+    if reduction == "none":
+        ret = loss
+    elif reduction == "mean":
+        ret = loss.mean()
+    elif reduction == "sum":
+        ret = loss.sum()
+    else:
+        ret = input
+        raise ValueError(reduction + " is not valid")
+    return ret
+
+
+def cosine_embedding_loss(
+    input1: Tensor,
+    input2: Tensor,
+    target: Tensor,
+    margin: float = 0,
+    size_average: Optional[bool] = None,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+) -> Tensor:  # noqa: D400,D402
+    r"""cosine_embedding_loss(input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean') -> Tensor
+
+    See :class:`~torch.nn.CosineEmbeddingLoss` for details.
+    """
+    if has_torch_function_variadic(input1, input2, target):
+        return handle_torch_function(
+            cosine_embedding_loss,
+            (input1, input2, target),
+            input1,
+            input2,
+            target,
+            margin=margin,
+            size_average=size_average,
+            reduce=reduce,
+            reduction=reduction,
+        )
+    if size_average is not None or reduce is not None:
+        reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
+    else:
+        reduction_enum = _Reduction.get_enum(reduction)
+    return torch.cosine_embedding_loss(input1, input2, target, margin, reduction_enum)
+
+
+def multi_margin_loss(
+    input: Tensor,
+    target: Tensor,
+    p: int = 1,
+    margin: float = 1.0,
+    weight: Optional[Tensor] = None,
+    size_average: Optional[bool] = None,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+) -> Tensor:  # noqa: D400,D402
+    r"""multi_margin_loss(input, target, p=1, margin=1, weight=None, size_average=None, reduce=None, reduction='mean') -> Tensor
+
+    See :class:`~torch.nn.MultiMarginLoss` for details.
+    """
+    if has_torch_function_variadic(input, target, weight):
+        return handle_torch_function(
+            multi_margin_loss,
+            (input, target, weight),
+            input,
+            target,
+            p=p,
+            margin=margin,
+            weight=weight,
+            size_average=size_average,
+            reduce=reduce,
+            reduction=reduction,
+        )
+    if size_average is not None or reduce is not None:
+        reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
+    else:
+        reduction_enum = _Reduction.get_enum(reduction)
+    if p != 1 and p != 2:
+        raise ValueError("only p == 1 and p == 2 supported")
+    if weight is not None:
+        if weight.dim() != 1:
+            raise ValueError("weight must be one-dimensional")
+
+    return torch._C._nn.multi_margin_loss(
+        input, target, p, margin, weight, reduction_enum
+    )
+
+
+pixel_shuffle = _add_docstr(
+    torch.pixel_shuffle,
+    r"""
+pixel_shuffle(input, upscale_factor) -> Tensor
+
+Rearranges elements in a tensor of shape :math:`(*, C \times r^2, H, W)` to a
+tensor of shape :math:`(*, C, H \times r, W \times r)`, where r is the :attr:`upscale_factor`.
+
+See :class:`~torch.nn.PixelShuffle` for details.
+
+Args:
+    input (Tensor): the input tensor
+    upscale_factor (int): factor to increase spatial resolution by
+
+Examples::
+
+    >>> input = torch.randn(1, 9, 4, 4)
+    >>> output = torch.nn.functional.pixel_shuffle(input, 3)
+    >>> print(output.size())
+    torch.Size([1, 1, 12, 12])
+""",
+)
+
+pixel_unshuffle = _add_docstr(
+    torch.pixel_unshuffle,
+    r"""
+pixel_unshuffle(input, downscale_factor) -> Tensor
+
+Reverses the :class:`~torch.nn.PixelShuffle` operation by rearranging elements in a
+tensor of shape :math:`(*, C, H \times r, W \times r)` to a tensor of shape
+:math:`(*, C \times r^2, H, W)`, where r is the :attr:`downscale_factor`.
+
+See :class:`~torch.nn.PixelUnshuffle` for details.
+
+Args:
+    input (Tensor): the input tensor
+    downscale_factor (int): factor to increase spatial resolution by
+
+Examples::
+
+    >>> input = torch.randn(1, 1, 12, 12)
+    >>> output = torch.nn.functional.pixel_unshuffle(input, 3)
+    >>> print(output.size())
+    torch.Size([1, 9, 4, 4])
+""",
+)
+
+channel_shuffle = _add_docstr(
+    torch.channel_shuffle,
+    r"""
+channel_shuffle(input, groups) -> Tensor
+
+Divide the channels in a tensor of shape :math:`(*, C , H, W)`
+into g groups and rearrange them as :math:`(*, C \frac g, g, H, W)`,
+while keeping the original tensor shape.
+
+See :class:`~torch.nn.ChannelShuffle` for details.
+
+Args:
+    input (Tensor): the input tensor
+    groups (int): number of groups to divide channels in and rearrange.
+
+Examples::
+
+    >>> input = torch.randn(1, 4, 2, 2)
+    >>> print(input)
+    [[[[1, 2],
+       [3, 4]],
+      [[5, 6],
+       [7, 8]],
+      [[9, 10],
+       [11, 12]],
+      [[13, 14],
+       [15, 16]],
+     ]]
+    >>> output = torch.nn.functional.channel_shuffle(input, 2)
+    >>> print(output)
+    [[[[1, 2],
+       [3, 4]],
+      [[9, 10],
+       [11, 12]],
+      [[5, 6],
+       [7, 8]],
+      [[13, 14],
+       [15, 16]],
+     ]]
+""",
+)
+
+native_channel_shuffle = _add_docstr(
+    torch.native_channel_shuffle,
+    r"""
+native_channel_shuffle(input, groups) -> Tensor
+
+Native kernel level implementation of the `channel_shuffle`.
+This function might become private in future releases, use with caution.
+
+Divide the channels in a tensor of shape :math:`(*, C , H, W)`
+into g groups and rearrange them as :math:`(*, C \frac g, g, H, W)`,
+while keeping the original tensor shape.
+
+See :class:`~torch.nn.ChannelShuffle` for details.
+
+Args:
+    input (Tensor): the input tensor
+    groups (int): number of groups to divide channels in and rearrange.
+
+Examples::
+
+    >>> input = torch.randn(1, 4, 2, 2)
+    >>> print(input)
+    [[[[1, 2],
+       [3, 4]],
+      [[5, 6],
+       [7, 8]],
+      [[9, 10],
+       [11, 12]],
+      [[13, 14],
+       [15, 16]],
+     ]]
+    >>> output = torch.nn.functional.native_channel_shuffle(input, 2)
+    >>> print(output)
+    [[[[1, 2],
+       [3, 4]],
+      [[9, 10],
+       [11, 12]],
+      [[5, 6],
+       [7, 8]],
+      [[13, 14],
+       [15, 16]],
+     ]]
+""",
+)
+
+
+@_overload
+def upsample(  # noqa: F811
+    input: Tensor,
+    size: Optional[int] = None,
+    scale_factor: Optional[float] = None,
+    mode: str = "nearest",
+    align_corners: Optional[bool] = None,
+) -> Tensor:  # noqa: B950
+    pass
+
+
+@_overload
+def upsample(  # noqa: F811
+    input: Tensor,
+    size: Optional[List[int]] = None,
+    scale_factor: Optional[float] = None,
+    mode: str = "nearest",
+    align_corners: Optional[bool] = None,
+) -> Tensor:  # noqa: B950
+    pass
+
+
+def upsample(  # noqa: F811
+    input,
+    size=None,
+    scale_factor=None,
+    mode="nearest",
+    align_corners=None,
+):
+    r"""Upsample input.
+
+    Provided tensor is upsampled to either the given :attr:`size` or the given
+    :attr:`scale_factor`
+
+    .. warning::
+        This function is deprecated in favor of :func:`torch.nn.functional.interpolate`.
+        This is equivalent with ``nn.functional.interpolate(...)``.
+
+    Note:
+        {backward_reproducibility_note}
+
+    The algorithm used for upsampling is determined by :attr:`mode`.
+
+    Currently temporal, spatial and volumetric upsampling are supported, i.e.
+    expected inputs are 3-D, 4-D or 5-D in shape.
+
+    The input dimensions are interpreted in the form:
+    `mini-batch x channels x [optional depth] x [optional height] x width`.
+
+    The modes available for upsampling are: `nearest`, `linear` (3D-only),
+    `bilinear`, `bicubic` (4D-only), `trilinear` (5D-only)
+
+    Args:
+        input (Tensor): the input tensor
+        size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]):
+            output spatial size.
+        scale_factor (float or Tuple[float]): multiplier for spatial size. Has to match input size if it is a tuple.
+        mode (str): algorithm used for upsampling:
+            ``'nearest'`` | ``'linear'`` | ``'bilinear'`` | ``'bicubic'`` |
+            ``'trilinear'``. Default: ``'nearest'``
+        align_corners (bool, optional): Geometrically, we consider the pixels of the
+            input and output as squares rather than points.
+            If set to ``True``, the input and output tensors are aligned by the
+            center points of their corner pixels, preserving the values at the corner pixels.
+            If set to ``False``, the input and output tensors are aligned by the corner
+            points of their corner pixels, and the interpolation uses edge value padding
+            for out-of-boundary values, making this operation *independent* of input size
+            when :attr:`scale_factor` is kept the same. This only has an effect when :attr:`mode`
+            is ``'linear'``, ``'bilinear'``, ``'bicubic'`` or ``'trilinear'``.
+            Default: ``False``
+
+    .. note::
+        With ``mode='bicubic'``, it's possible to cause overshoot, in other words it can produce
+        negative values or values greater than 255 for images.
+        Explicitly call ``result.clamp(min=0, max=255)`` if you want to reduce the overshoot
+        when displaying the image.
+
+    .. warning::
+        With ``align_corners = True``, the linearly interpolating modes
+        (`linear`, `bilinear`, and `trilinear`) don't proportionally align the
+        output and input pixels, and thus the output values can depend on the
+        input size. This was the default behavior for these modes up to version
+        0.3.1. Since then, the default behavior is ``align_corners = False``.
+        See :class:`~torch.nn.Upsample` for concrete examples on how this
+        affects the outputs.
+
+    """
+    warnings.warn(
+        "`nn.functional.upsample` is deprecated. "
+        "Use `nn.functional.interpolate` instead.",
+        stacklevel=2,
+    )
+    return interpolate(input, size, scale_factor, mode, align_corners)
+
+
+if upsample.__doc__:
+    upsample.__doc__ = upsample.__doc__.format(**reproducibility_notes)
+
+
+def _is_integer(x) -> bool:
+    r"""Type check the input number is an integer.
+
+    Will return True for int, SymInt, Numpy integers and Tensors with integer elements.
+    """
+    if isinstance(x, (int, torch.SymInt)):
+        return True
+    if np is not None and isinstance(x, np.integer):
+        return True
+    return isinstance(x, Tensor) and not x.is_floating_point()
+
+
+@_overload
+def interpolate(  # noqa: F811
+    input: Tensor,
+    size: Optional[int] = None,
+    scale_factor: Optional[List[float]] = None,
+    mode: str = "nearest",
+    align_corners: Optional[bool] = None,
+    recompute_scale_factor: Optional[bool] = None,
+    antialias: bool = False,
+) -> Tensor:  # noqa: B950
+    pass
+
+
+@_overload
+def interpolate(  # noqa: F811
+    input: Tensor,
+    size: Optional[List[int]] = None,
+    scale_factor: Optional[List[float]] = None,
+    mode: str = "nearest",
+    align_corners: Optional[bool] = None,
+    recompute_scale_factor: Optional[bool] = None,
+    antialias: bool = False,
+) -> Tensor:  # noqa: B950
+    pass
+
+
+@_overload
+def interpolate(  # noqa: F811
+    input: Tensor,
+    size: Optional[int] = None,
+    scale_factor: Optional[float] = None,
+    mode: str = "nearest",
+    align_corners: Optional[bool] = None,
+    recompute_scale_factor: Optional[bool] = None,
+    antialias: bool = False,
+) -> Tensor:  # noqa: B950
+    pass
+
+
+@_overload
+def interpolate(  # noqa: F811
+    input: Tensor,
+    size: Optional[List[int]] = None,
+    scale_factor: Optional[float] = None,
+    mode: str = "nearest",
+    align_corners: Optional[bool] = None,
+    recompute_scale_factor: Optional[bool] = None,
+    antialias: bool = False,
+) -> Tensor:
+    pass
+
+
+def interpolate(  # noqa: F811
+    input: Tensor,
+    size: Optional[int] = None,
+    scale_factor: Optional[List[float]] = None,
+    mode: str = "nearest",
+    align_corners: Optional[bool] = None,
+    recompute_scale_factor: Optional[bool] = None,
+    antialias: bool = False,
+) -> Tensor:  # noqa: B950
+    r"""Down/up samples the input.
+
+    Tensor interpolated to either the given :attr:`size` or the given
+    :attr:`scale_factor`
+
+    The algorithm used for interpolation is determined by :attr:`mode`.
+
+    Currently temporal, spatial and volumetric sampling are supported, i.e.
+    expected inputs are 3-D, 4-D or 5-D in shape.
+
+    The input dimensions are interpreted in the form:
+    `mini-batch x channels x [optional depth] x [optional height] x width`.
+
+    The modes available for resizing are: `nearest`, `linear` (3D-only),
+    `bilinear`, `bicubic` (4D-only), `trilinear` (5D-only), `area`, `nearest-exact`
+
+    Args:
+        input (Tensor): the input tensor
+        size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]):
+            output spatial size.
+        scale_factor (float or Tuple[float]): multiplier for spatial size. If `scale_factor` is a tuple,
+            its length has to match the number of spatial dimensions; `input.dim() - 2`.
+        mode (str): algorithm used for upsampling:
+            ``'nearest'`` | ``'linear'`` | ``'bilinear'`` | ``'bicubic'`` |
+            ``'trilinear'`` | ``'area'`` | ``'nearest-exact'``. Default: ``'nearest'``
+        align_corners (bool, optional): Geometrically, we consider the pixels of the
+            input and output as squares rather than points.
+            If set to ``True``, the input and output tensors are aligned by the
+            center points of their corner pixels, preserving the values at the corner pixels.
+            If set to ``False``, the input and output tensors are aligned by the corner
+            points of their corner pixels, and the interpolation uses edge value padding
+            for out-of-boundary values, making this operation *independent* of input size
+            when :attr:`scale_factor` is kept the same. This only has an effect when :attr:`mode`
+            is ``'linear'``, ``'bilinear'``, ``'bicubic'`` or ``'trilinear'``.
+            Default: ``False``
+        recompute_scale_factor (bool, optional): recompute the scale_factor for use in the
+            interpolation calculation. If `recompute_scale_factor` is ``True``, then
+            `scale_factor` must be passed in and `scale_factor` is used to compute the
+            output `size`. The computed output `size` will be used to infer new scales for
+            the interpolation. Note that when `scale_factor` is floating-point, it may differ
+            from the recomputed `scale_factor` due to rounding and precision issues.
+            If `recompute_scale_factor` is ``False``, then `size` or `scale_factor` will
+            be used directly for interpolation. Default: ``None``.
+        antialias (bool, optional): flag to apply anti-aliasing. Default: ``False``. Using anti-alias
+            option together with ``align_corners=False``, interpolation result would match Pillow
+            result for downsampling operation. Supported modes: ``'bilinear'``, ``'bicubic'``.
+
+    .. note::
+        With ``mode='bicubic'``, it's possible to cause overshoot, in other words it can produce
+        negative values or values greater than 255 for images.
+        Explicitly call ``result.clamp(min=0, max=255)`` if you want to reduce the overshoot
+        when displaying the image.
+
+    .. note::
+        Mode ``mode='nearest-exact'`` matches Scikit-Image and PIL nearest neighbours interpolation
+        algorithms and fixes known issues with ``mode='nearest'``. This mode is introduced to keep
+        backward compatibility.
+        Mode ``mode='nearest'`` matches buggy OpenCV's ``INTER_NEAREST`` interpolation algorithm.
+
+    .. note::
+        The gradients for the dtype ``float16`` on CUDA may be inaccurate in the upsample operation
+        when using modes ``['linear', 'bilinear', 'bicubic', 'trilinear', 'area']``.
+        For more details, please refer to the discussion in
+        `issue#104157 `_.
+
+    Note:
+        {backward_reproducibility_note}
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            interpolate,
+            (input,),
+            input,
+            size=size,
+            scale_factor=scale_factor,
+            mode=mode,
+            align_corners=align_corners,
+            recompute_scale_factor=recompute_scale_factor,
+            antialias=antialias,
+        )
+
+    if mode in ("nearest", "area", "nearest-exact"):
+        if align_corners is not None:
+            raise ValueError(
+                "align_corners option can only be set with the "
+                "interpolating modes: linear | bilinear | bicubic | trilinear"
+            )
+    else:
+        if align_corners is None:
+            align_corners = False
+
+    dim = input.dim() - 2  # Number of spatial dimensions.
+
+    # Process size and scale_factor.  Validate that exactly one is set.
+    # Validate its length if it is a list, or expand it if it is a scalar.
+    # After this block, exactly one of output_size and scale_factors will
+    # be non-None, and it will be a list (or tuple).
+    if size is not None and scale_factor is not None:
+        raise ValueError("only one of size or scale_factor should be defined")
+    elif size is not None:
+        assert scale_factor is None
+        scale_factors = None
+        if isinstance(size, (list, tuple)):
+            if len(size) != dim:
+                raise ValueError(
+                    "Input and output must have the same number of spatial dimensions, but got "
+                    f"input with spatial dimensions of {list(input.shape[2:])} and output size of {size}. "
+                    "Please provide input tensor in (N, C, d1, d2, ...,dK) format and "
+                    "output size in (o1, o2, ...,oK) format."
+                )
+            if not torch.jit.is_scripting():
+                if not all(_is_integer(x) for x in size):
+                    raise TypeError(
+                        "expected size to be one of int or Tuple[int] or Tuple[int, int] or "
+                        f"Tuple[int, int, int], but got size with types {[type(x) for x in size]}"
+                    )
+            output_size = size
+        else:
+            output_size = [size for _ in range(dim)]
+    elif scale_factor is not None:
+        assert size is None
+        output_size = None
+        if isinstance(scale_factor, (list, tuple)):
+            if len(scale_factor) != dim:
+                raise ValueError(
+                    "Input and scale_factor must have the same number of spatial dimensions, but "
+                    f"got input with spatial dimensions of {list(input.shape[2:])} and "
+                    f"scale_factor of shape {scale_factor}. "
+                    "Please provide input tensor in (N, C, d1, d2, ...,dK) format and "
+                    "scale_factor in (s1, s2, ...,sK) format."
+                )
+            scale_factors = scale_factor
+        else:
+            scale_factors = [scale_factor for _ in range(dim)]
+    else:
+        raise ValueError("either size or scale_factor should be defined")
+
+    if (
+        recompute_scale_factor is not None
+        and recompute_scale_factor
+        and size is not None
+    ):
+        raise ValueError(
+            "recompute_scale_factor is not meaningful with an explicit size."
+        )
+
+    # "area" mode always requires an explicit size rather than scale factor.
+    # Re-use the recompute_scale_factor code path.
+    if mode == "area" and output_size is None:
+        recompute_scale_factor = True
+
+    if recompute_scale_factor is not None and recompute_scale_factor:
+        # We compute output_size here, then un-set scale_factors.
+        # The C++ code will recompute it based on the (integer) output size.
+        assert scale_factors is not None
+        if not torch.jit.is_scripting() and torch._C._get_tracing_state():
+            # make scale_factor a tensor in tracing so constant doesn't get baked in
+            output_size = [
+                (
+                    torch.floor(
+                        (
+                            input.size(i + 2).float()
+                            * torch.tensor(scale_factors[i], dtype=torch.float32)
+                        ).float()
+                    )
+                )
+                for i in range(dim)
+            ]
+        elif torch.jit.is_scripting():
+            output_size = [
+                int(math.floor(float(input.size(i + 2)) * scale_factors[i]))
+                for i in range(dim)
+            ]
+        else:
+            output_size = [
+                _sym_int(input.size(i + 2) * scale_factors[i]) for i in range(dim)
+            ]
+        scale_factors = None
+
+    if antialias and not (mode in ("bilinear", "bicubic") and input.ndim == 4):
+        raise ValueError(
+            "Anti-alias option is restricted to bilinear and bicubic modes and requires a 4-D tensor as input"
+        )
+
+    if input.dim() == 3 and mode == "nearest":
+        return torch._C._nn.upsample_nearest1d(input, output_size, scale_factors)
+    if input.dim() == 4 and mode == "nearest":
+        return torch._C._nn.upsample_nearest2d(input, output_size, scale_factors)
+    if input.dim() == 5 and mode == "nearest":
+        return torch._C._nn.upsample_nearest3d(input, output_size, scale_factors)
+
+    if input.dim() == 3 and mode == "nearest-exact":
+        return torch._C._nn._upsample_nearest_exact1d(input, output_size, scale_factors)
+    if input.dim() == 4 and mode == "nearest-exact":
+        return torch._C._nn._upsample_nearest_exact2d(input, output_size, scale_factors)
+    if input.dim() == 5 and mode == "nearest-exact":
+        return torch._C._nn._upsample_nearest_exact3d(input, output_size, scale_factors)
+
+    if input.dim() == 3 and mode == "area":
+        assert output_size is not None
+        return adaptive_avg_pool1d(input, output_size)
+    if input.dim() == 4 and mode == "area":
+        assert output_size is not None
+        return adaptive_avg_pool2d(input, output_size)
+    if input.dim() == 5 and mode == "area":
+        assert output_size is not None
+        return adaptive_avg_pool3d(input, output_size)
+
+    if input.dim() == 3 and mode == "linear":
+        assert align_corners is not None
+        return torch._C._nn.upsample_linear1d(
+            input, output_size, align_corners, scale_factors
+        )
+    if input.dim() == 4 and mode == "bilinear":
+        assert align_corners is not None
+        if antialias:
+            return torch._C._nn._upsample_bilinear2d_aa(
+                input, output_size, align_corners, scale_factors
+            )
+        # Two levels are necessary to prevent TorchScript from touching
+        # are_deterministic_algorithms_enabled.
+        if not torch.jit.is_scripting():
+            if torch.are_deterministic_algorithms_enabled() and (
+                input.is_cuda or input.is_xpu
+            ):
+                # Use slow decomp whose backward will be in terms of index_put
+                # importlib is required because the import cannot be top level
+                # (cycle) and cannot be nested (TS doesn't support)
+                return importlib.import_module(
+                    "torch._decomp.decompositions"
+                )._upsample_linear_vec(input, output_size, align_corners, scale_factors)
+        return torch._C._nn.upsample_bilinear2d(
+            input, output_size, align_corners, scale_factors
+        )
+    if input.dim() == 5 and mode == "trilinear":
+        assert align_corners is not None
+        return torch._C._nn.upsample_trilinear3d(
+            input, output_size, align_corners, scale_factors
+        )
+    if input.dim() == 4 and mode == "bicubic":
+        assert align_corners is not None
+        if antialias:
+            return torch._C._nn._upsample_bicubic2d_aa(
+                input, output_size, align_corners, scale_factors
+            )
+        return torch._C._nn.upsample_bicubic2d(
+            input, output_size, align_corners, scale_factors
+        )
+
+    if input.dim() == 3 and mode == "bilinear":
+        raise NotImplementedError("Got 3D input, but bilinear mode needs 4D input")
+    if input.dim() == 3 and mode == "trilinear":
+        raise NotImplementedError("Got 3D input, but trilinear mode needs 5D input")
+    if input.dim() == 4 and mode == "linear":
+        raise NotImplementedError("Got 4D input, but linear mode needs 3D input")
+    if input.dim() == 4 and mode == "trilinear":
+        raise NotImplementedError("Got 4D input, but trilinear mode needs 5D input")
+    if input.dim() == 5 and mode == "linear":
+        raise NotImplementedError("Got 5D input, but linear mode needs 3D input")
+    if input.dim() == 5 and mode == "bilinear":
+        raise NotImplementedError("Got 5D input, but bilinear mode needs 4D input")
+
+    raise NotImplementedError(
+        "Input Error: Only 3D, 4D and 5D input Tensors supported"
+        f" (got {input.dim()}D) for the modes: nearest | linear | bilinear | bicubic | trilinear | area | nearest-exact"
+        f" (got {mode})"
+    )
+
+
+if interpolate.__doc__:
+    interpolate.__doc__ = interpolate.__doc__.format(**reproducibility_notes)
+
+
+@_overload
+def upsample_nearest(  # noqa: F811
+    input: Tensor,
+    size: Optional[int] = None,
+    scale_factor: Optional[float] = None,
+) -> Tensor:
+    pass
+
+
+@_overload
+def upsample_nearest(  # noqa: F811
+    input: Tensor,
+    size: Optional[List[int]] = None,
+    scale_factor: Optional[float] = None,
+) -> Tensor:
+    pass
+
+
+def upsample_nearest(input, size=None, scale_factor=None):  # noqa: F811
+    r"""Upsamples the input, using nearest neighbours' pixel values.
+
+    .. warning::
+        This function is deprecated in favor of :func:`torch.nn.functional.interpolate`.
+        This is equivalent with ``nn.functional.interpolate(..., mode='nearest')``.
+
+    Currently spatial and volumetric upsampling are supported (i.e. expected
+    inputs are 4 or 5 dimensional).
+
+    Args:
+        input (Tensor): input
+        size (int or Tuple[int, int] or Tuple[int, int, int]): output spatia
+            size.
+        scale_factor (int): multiplier for spatial size. Has to be an integer.
+
+    Note:
+        {backward_reproducibility_note}
+    """
+    # DeprecationWarning is ignored by default
+    warnings.warn(
+        "`nn.functional.upsample_nearest` is deprecated. "
+        "Use `nn.functional.interpolate` instead.",
+        stacklevel=2,
+    )
+    return interpolate(input, size, scale_factor, mode="nearest")
+
+
+if upsample_nearest.__doc__:
+    upsample_nearest.__doc__ = upsample_nearest.__doc__.format(**reproducibility_notes)
+
+
+@_overload
+def upsample_bilinear(  # noqa: F811
+    input: Tensor,
+    size: Optional[int] = None,
+    scale_factor: Optional[float] = None,
+) -> Tensor:
+    pass
+
+
+@_overload
+def upsample_bilinear(  # noqa: F811
+    input: Tensor,
+    size: Optional[List[int]] = None,
+    scale_factor: Optional[float] = None,
+) -> Tensor:
+    pass
+
+
+@_overload
+def upsample_bilinear(  # noqa: F811
+    input: Tensor,
+    size: Optional[int] = None,
+    scale_factor: Optional[List[float]] = None,
+) -> Tensor:
+    pass
+
+
+@_overload
+def upsample_bilinear(  # noqa: F811
+    input: Tensor,
+    size: Optional[List[int]] = None,
+    scale_factor: Optional[List[float]] = None,
+) -> Tensor:
+    pass
+
+
+def upsample_bilinear(input, size=None, scale_factor=None):  # noqa: F811
+    r"""Upsamples the input, using bilinear upsampling.
+
+    .. warning::
+        This function is deprecated in favor of :func:`torch.nn.functional.interpolate`.
+        This is equivalent with
+        ``nn.functional.interpolate(..., mode='bilinear', align_corners=True)``.
+
+    Expected inputs are spatial (4 dimensional). Use `upsample_trilinear` fo
+    volumetric (5 dimensional) inputs.
+
+    Args:
+        input (Tensor): input
+        size (int or Tuple[int, int]): output spatial size.
+        scale_factor (int or Tuple[int, int]): multiplier for spatial size
+
+    Note:
+        {backward_reproducibility_note}
+    """
+    # DeprecationWarning is ignored by default
+    warnings.warn(
+        "`nn.functional.upsample_bilinear` is deprecated. "
+        "Use `nn.functional.interpolate` instead.",
+        stacklevel=2,
+    )
+    return interpolate(input, size, scale_factor, mode="bilinear", align_corners=True)
+
+
+if upsample_bilinear.__doc__:
+    upsample_bilinear.__doc__ = upsample_bilinear.__doc__.format(
+        **reproducibility_notes
+    )
+
+GRID_SAMPLE_INTERPOLATION_MODES = {
+    "bilinear": 0,
+    "nearest": 1,
+    "bicubic": 2,
+}
+
+GRID_SAMPLE_PADDING_MODES = {
+    "zeros": 0,
+    "border": 1,
+    "reflection": 2,
+}
+
+
+def grid_sample(
+    input: Tensor,
+    grid: Tensor,
+    mode: str = "bilinear",
+    padding_mode: str = "zeros",
+    align_corners: Optional[bool] = None,
+) -> Tensor:
+    r"""Compute grid sample.
+
+    Given an :attr:`input` and a flow-field :attr:`grid`, computes the
+    ``output`` using :attr:`input` values and pixel locations from :attr:`grid`.
+
+    Currently, only spatial (4-D) and volumetric (5-D) :attr:`input` are
+    supported.
+
+    In the spatial (4-D) case, for :attr:`input` with shape
+    :math:`(N, C, H_\text{in}, W_\text{in})` and :attr:`grid` with shape
+    :math:`(N, H_\text{out}, W_\text{out}, 2)`, the output will have shape
+    :math:`(N, C, H_\text{out}, W_\text{out})`.
+
+    For each output location ``output[n, :, h, w]``, the size-2 vector
+    ``grid[n, h, w]`` specifies :attr:`input` pixel locations ``x`` and ``y``,
+    which are used to interpolate the output value ``output[n, :, h, w]``.
+    In the case of 5D inputs, ``grid[n, d, h, w]`` specifies the
+    ``x``, ``y``, ``z`` pixel locations for interpolating
+    ``output[n, :, d, h, w]``. :attr:`mode` argument specifies ``nearest`` or
+    ``bilinear`` interpolation method to sample the input pixels.
+
+    :attr:`grid` specifies the sampling pixel locations normalized by the
+    :attr:`input` spatial dimensions. Therefore, it should have most values in
+    the range of ``[-1, 1]``. For example, values ``x = -1, y = -1`` is the
+    left-top pixel of :attr:`input`, and values  ``x = 1, y = 1`` is the
+    right-bottom pixel of :attr:`input`.
+
+    If :attr:`grid` has values outside the range of ``[-1, 1]``, the corresponding
+    outputs are handled as defined by :attr:`padding_mode`. Options are
+
+        * ``padding_mode="zeros"``: use ``0`` for out-of-bound grid locations,
+        * ``padding_mode="border"``: use border values for out-of-bound grid locations,
+        * ``padding_mode="reflection"``: use values at locations reflected by
+          the border for out-of-bound grid locations. For location far away
+          from the border, it will keep being reflected until becoming in bound,
+          e.g., (normalized) pixel location ``x = -3.5`` reflects by border ``-1``
+          and becomes ``x' = 1.5``, then reflects by border ``1`` and becomes
+          ``x'' = -0.5``.
+
+    Note:
+        This function is often used in conjunction with :func:`affine_grid`
+        to build `Spatial Transformer Networks`_ .
+
+    Note:
+        When using the CUDA backend, this operation may induce nondeterministic
+        behaviour in its backward pass that is not easily switched off.
+        Please see the notes on :doc:`/notes/randomness` for background.
+
+    Note:
+        NaN values in :attr:`grid` would be interpreted as ``-1``.
+
+    Args:
+        input (Tensor): input of shape :math:`(N, C, H_\text{in}, W_\text{in})` (4-D case)
+                        or :math:`(N, C, D_\text{in}, H_\text{in}, W_\text{in})` (5-D case)
+        grid (Tensor): flow-field of shape :math:`(N, H_\text{out}, W_\text{out}, 2)` (4-D case)
+                       or :math:`(N, D_\text{out}, H_\text{out}, W_\text{out}, 3)` (5-D case)
+        mode (str): interpolation mode to calculate output values
+            ``'bilinear'`` | ``'nearest'`` | ``'bicubic'``. Default: ``'bilinear'``
+            Note: ``mode='bicubic'`` supports only 4-D input.
+            When ``mode='bilinear'`` and the input is 5-D, the interpolation mode
+            used internally will actually be trilinear. However, when the input is 4-D,
+            the interpolation mode will legitimately be bilinear.
+        padding_mode (str): padding mode for outside grid values
+            ``'zeros'`` | ``'border'`` | ``'reflection'``. Default: ``'zeros'``
+        align_corners (bool, optional): Geometrically, we consider the pixels of the
+            input  as squares rather than points.
+            If set to ``True``, the extrema (``-1`` and ``1``) are considered as referring
+            to the center points of the input's corner pixels. If set to ``False``, they
+            are instead considered as referring to the corner points of the input's corner
+            pixels, making the sampling more resolution agnostic.
+            This option parallels the ``align_corners`` option in
+            :func:`interpolate`, and so whichever option is used here
+            should also be used there to resize the input image before grid sampling.
+            Default: ``False``
+
+    Returns:
+        output (Tensor): output Tensor
+
+    .. _`Spatial Transformer Networks`:
+        https://arxiv.org/abs/1506.02025
+
+    .. warning::
+        When ``align_corners = True``, the grid positions depend on the pixel
+        size relative to the input image size, and so the locations sampled by
+        :func:`grid_sample` will differ for the same input given at different
+        resolutions (that is, after being upsampled or downsampled).
+        The default behavior up to version 1.2.0 was ``align_corners = True``.
+        Since then, the default behavior has been changed to ``align_corners = False``,
+        in order to bring it in line with the default for :func:`interpolate`.
+
+    .. note::
+        ``mode='bicubic'`` is implemented using the `cubic convolution algorithm`_ with :math:`\alpha=-0.75`.
+        The constant :math:`\alpha` might be different from packages to packages.
+        For example, `PIL`_ and `OpenCV`_ use -0.5 and -0.75 respectively.
+        This algorithm may "overshoot" the range of values it's interpolating.
+        For example, it may produce negative values or values greater than 255 when interpolating input in [0, 255].
+        Clamp the results with :func:`torch.clamp` to ensure they are within the valid range.
+    .. _`cubic convolution algorithm`: https://en.wikipedia.org/wiki/Bicubic_interpolation
+    .. _`PIL`: https://github.com/python-pillow/Pillow/blob/4634eafe3c695a014267eefdce830b4a825beed7/src/libImaging/Resample.c#L51
+    .. _`OpenCV`: https://github.com/opencv/opencv/blob/f345ed564a06178670750bad59526cfa4033be55/modules/imgproc/src/resize.cpp#L908
+    """
+    if has_torch_function_variadic(input, grid):
+        return handle_torch_function(
+            grid_sample,
+            (input, grid),
+            input,
+            grid,
+            mode=mode,
+            padding_mode=padding_mode,
+            align_corners=align_corners,
+        )
+    if mode != "bilinear" and mode != "nearest" and mode != "bicubic":
+        raise ValueError(
+            f"nn.functional.grid_sample(): expected mode to be 'bilinear', 'nearest' or 'bicubic', but got: '{mode}'"
+        )
+    if (
+        padding_mode != "zeros"
+        and padding_mode != "border"
+        and padding_mode != "reflection"
+    ):
+        raise ValueError(
+            "nn.functional.grid_sample(): expected padding_mode "
+            "to be 'zeros', 'border', or 'reflection', "
+            f"but got: '{padding_mode}'"
+        )
+
+    if mode == "bilinear":
+        mode_enum = 0
+    elif mode == "nearest":
+        mode_enum = 1
+    else:  # mode == 'bicubic'
+        mode_enum = 2
+
+    if padding_mode == "zeros":
+        padding_mode_enum = 0
+    elif padding_mode == "border":
+        padding_mode_enum = 1
+    else:  # padding_mode == 'reflection'
+        padding_mode_enum = 2
+
+    if align_corners is None:
+        warnings.warn(
+            "Default grid_sample and affine_grid behavior has changed "
+            "to align_corners=False since 1.3.0. Please specify "
+            "align_corners=True if the old behavior is desired. "
+            "See the documentation of grid_sample for details."
+        )
+        align_corners = False
+
+    return torch.grid_sampler(input, grid, mode_enum, padding_mode_enum, align_corners)
+
+
+def affine_grid(
+    theta: Tensor,
+    size: List[int],
+    align_corners: Optional[bool] = None,
+) -> Tensor:
+    r"""Generate 2D or 3D flow field (sampling grid), given a batch of affine matrices :attr:`theta`.
+
+    .. note::
+        This function is often used in conjunction with :func:`grid_sample`
+        to build `Spatial Transformer Networks`_ .
+
+    Args:
+        theta (Tensor): input batch of affine matrices with shape
+            (:math:`N \times 2 \times 3`) for 2D or
+            (:math:`N \times 3 \times 4`) for 3D
+        size (torch.Size): the target output image size.
+            (:math:`N \times C \times H \times W` for 2D or
+            :math:`N \times C \times D \times H \times W` for 3D)
+            Example: torch.Size((32, 3, 24, 24))
+        align_corners (bool, optional): if ``True``, consider ``-1`` and ``1``
+            to refer to the centers of the corner pixels rather than the image corners.
+            Refer to :func:`grid_sample` for a more complete description.
+            A grid generated by :func:`affine_grid` should be passed to :func:`grid_sample`
+            with the same setting for this option.
+            Default: ``False``
+
+    Returns:
+        output (Tensor): output Tensor of size (:math:`N \times H \times W \times 2`)
+
+    .. _`Spatial Transformer Networks`:
+        https://arxiv.org/abs/1506.02025
+
+    .. warning::
+        When ``align_corners = True``, the grid positions depend on the pixel
+        size relative to the input image size, and so the locations sampled by
+        :func:`grid_sample` will differ for the same input given at different
+        resolutions (that is, after being upsampled or downsampled).
+        The default behavior up to version 1.2.0 was ``align_corners = True``.
+        Since then, the default behavior has been changed to ``align_corners = False``,
+        in order to bring it in line with the default for :func:`interpolate`.
+    .. warning::
+        When ``align_corners = True``, 2D affine transforms on 1D data and
+        3D affine transforms on 2D data (that is, when one of the spatial
+        dimensions has unit size) are ill-defined, and not an intended use case.
+        This is not a problem when ``align_corners = False``.
+        Up to version 1.2.0, all grid points along a unit dimension were
+        considered arbitrarily to be at ``-1``.
+        From version 1.3.0, under ``align_corners = True`` all grid points
+        along a unit dimension are considered to be at ``0``
+        (the center of the input image).
+    """
+    if has_torch_function_unary(theta):
+        return handle_torch_function(
+            affine_grid, (theta,), theta, size, align_corners=align_corners
+        )
+    if align_corners is None:
+        warnings.warn(
+            "Default grid_sample and affine_grid behavior has changed "
+            "to align_corners=False since 1.3.0. Please specify "
+            "align_corners=True if the old behavior is desired. "
+            "See the documentation of grid_sample for details."
+        )
+        align_corners = False
+
+    # enforce floating point dtype on theta
+    if not theta.is_floating_point():
+        raise ValueError(
+            f"Expected theta to have floating point type, but got {theta.dtype}"
+        )
+    # check that shapes and sizes match
+    if len(size) == 4:
+        if theta.dim() != 3 or theta.shape[-2] != 2 or theta.shape[-1] != 3:
+            raise ValueError(
+                f"Expected a batch of 2D affine matrices of shape Nx2x3 for size {size}. Got {theta.shape}."
+            )
+        spatial_size = size[-2:]  # spatial dimension sizes
+    elif len(size) == 5:
+        if theta.dim() != 3 or theta.shape[-2] != 3 or theta.shape[-1] != 4:
+            raise ValueError(
+                f"Expected a batch of 3D affine matrices of shape Nx3x4 for size {size}. Got {theta.shape}."
+            )
+        spatial_size = size[-3:]  # spatial dimension sizes
+    else:
+        raise NotImplementedError(
+            "affine_grid only supports 4D and 5D sizes, "
+            "for 2D and 3D affine transforms, respectively. "
+            f"Got size {size}."
+        )
+    # check for empty span
+    if align_corners and min(spatial_size) == 1:
+        warnings.warn(
+            "Since version 1.3.0, affine_grid behavior has changed "
+            "for unit-size grids when align_corners=True. "
+            "This is not an intended use case of affine_grid. "
+            "See the documentation of affine_grid for details."
+        )
+    elif min(size) <= 0:
+        raise ValueError(f"Expected non-zero, positive output size. Got {size}")
+
+    return torch.affine_grid_generator(theta, size, align_corners)
+
+
+def pad(
+    input: Tensor,
+    pad: List[int],
+    mode: str = "constant",
+    value: Optional[float] = None,
+) -> Tensor:
+    r"""
+    pad(input, pad, mode="constant", value=None) -> Tensor
+
+    Pads tensor.
+
+    Padding size:
+        The padding size by which to pad some dimensions of :attr:`input`
+        are described starting from the last dimension and moving forward.
+        :math:`\left\lfloor\frac{\text{len(pad)}}{2}\right\rfloor` dimensions
+        of ``input`` will be padded.
+        For example, to pad only the last dimension of the input tensor, then
+        :attr:`pad` has the form
+        :math:`(\text{padding\_left}, \text{padding\_right})`;
+        to pad the last 2 dimensions of the input tensor, then use
+        :math:`(\text{padding\_left}, \text{padding\_right},`
+        :math:`\text{padding\_top}, \text{padding\_bottom})`;
+        to pad the last 3 dimensions, use
+        :math:`(\text{padding\_left}, \text{padding\_right},`
+        :math:`\text{padding\_top}, \text{padding\_bottom}`
+        :math:`\text{padding\_front}, \text{padding\_back})`.
+
+    Padding mode:
+        See :class:`torch.nn.CircularPad2d`, :class:`torch.nn.ConstantPad2d`,
+        :class:`torch.nn.ReflectionPad2d`, and :class:`torch.nn.ReplicationPad2d`
+        for concrete examples on how each of the padding modes works. Constant
+        padding is implemented for arbitrary dimensions. Circular, replicate and
+        reflection padding are implemented for padding the last 3 dimensions of a
+        4D or 5D input tensor, the last 2 dimensions of a 3D or 4D input tensor,
+        or the last dimension of a 2D or 3D input tensor.
+
+    Note:
+        When using the CUDA backend, this operation may induce nondeterministic
+        behaviour in its backward pass that is not easily switched off.
+        Please see the notes on :doc:`/notes/randomness` for background.
+
+    Args:
+        input (Tensor): N-dimensional tensor
+        pad (tuple): m-elements tuple, where
+            :math:`\frac{m}{2} \leq` input dimensions and :math:`m` is even.
+        mode: ``'constant'``, ``'reflect'``, ``'replicate'`` or ``'circular'``.
+            Default: ``'constant'``
+        value: fill value for ``'constant'`` padding. Default: ``0``
+
+    Examples::
+
+        >>> t4d = torch.empty(3, 3, 4, 2)
+        >>> p1d = (1, 1) # pad last dim by 1 on each side
+        >>> out = F.pad(t4d, p1d, "constant", 0)  # effectively zero padding
+        >>> print(out.size())
+        torch.Size([3, 3, 4, 4])
+        >>> p2d = (1, 1, 2, 2) # pad last dim by (1, 1) and 2nd to last by (2, 2)
+        >>> out = F.pad(t4d, p2d, "constant", 0)
+        >>> print(out.size())
+        torch.Size([3, 3, 8, 4])
+        >>> t4d = torch.empty(3, 3, 4, 2)
+        >>> p3d = (0, 1, 2, 1, 3, 3) # pad by (0, 1), (2, 1), and (3, 3)
+        >>> out = F.pad(t4d, p3d, "constant", 0)
+        >>> print(out.size())
+        torch.Size([3, 9, 7, 3])
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            torch.nn.functional.pad, (input,), input, pad, mode=mode, value=value
+        )
+    if not torch.jit.is_scripting():
+        if torch.are_deterministic_algorithms_enabled() and (
+            input.is_cuda or input.is_xpu
+        ):
+            if mode == "replicate":
+                # Use slow decomp whose backward will be in terms of index_put.
+                # importlib is required because the import cannot be top level
+                # (cycle) and cannot be nested (TS doesn't support)
+                return importlib.import_module(
+                    "torch._decomp.decompositions"
+                )._replication_pad(input, pad)
+    return torch._C._nn.pad(input, pad, mode, value)
+
+
+# TODO: Fix via https://github.com/pytorch/pytorch/issues/75798
+pad.__module__ = "torch.nn.functional"
+
+# distance
+
+
+pairwise_distance = _add_docstr(
+    torch.pairwise_distance,
+    r"""
+pairwise_distance(x1, x2, p=2.0, eps=1e-6, keepdim=False) -> Tensor
+
+See :class:`torch.nn.PairwiseDistance` for details
+""",
+)
+
+
+pdist = _add_docstr(
+    torch.pdist,
+    r"""
+pdist(input, p=2) -> Tensor
+
+Computes the p-norm distance between every pair of row vectors in the input.
+This is identical to the upper triangular portion, excluding the diagonal, of
+`torch.norm(input[:, None] - input, dim=2, p=p)`. This function will be faster
+if the rows are contiguous.
+
+If input has shape :math:`N \times M` then the output will have shape
+:math:`\frac{1}{2} N (N - 1)`.
+
+This function is equivalent to ``scipy.spatial.distance.pdist(input,
+'minkowski', p=p)`` if :math:`p \in (0, \infty)`. When :math:`p = 0` it is
+equivalent to ``scipy.spatial.distance.pdist(input, 'hamming') * M``.
+When :math:`p = \infty`, the closest scipy function is
+``scipy.spatial.distance.pdist(xn, lambda x, y: np.abs(x - y).max())``.
+
+Args:
+    input: input tensor of shape :math:`N \times M`.
+    p: p value for the p-norm distance to calculate between each vector pair
+        :math:`\in [0, \infty]`.
+""",
+)
+
+
+cosine_similarity = _add_docstr(
+    torch.cosine_similarity,
+    r"""
+cosine_similarity(x1, x2, dim=1, eps=1e-8) -> Tensor
+
+Returns cosine similarity between ``x1`` and ``x2``, computed along dim. ``x1`` and ``x2`` must be broadcastable
+to a common shape. ``dim`` refers to the dimension in this common shape. Dimension ``dim`` of the output is
+squeezed (see :func:`torch.squeeze`), resulting in the
+output tensor having 1 fewer dimension.
+
+.. math ::
+    \text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2, \epsilon) \cdot \max(\Vert x_2 \Vert _2, \epsilon)}
+
+Supports :ref:`type promotion `.
+
+Args:
+    x1 (Tensor): First input.
+    x2 (Tensor): Second input.
+    dim (int, optional): Dimension along which cosine similarity is computed. Default: 1
+    eps (float, optional): Small value to avoid division by zero.
+        Default: 1e-8
+
+Example::
+
+    >>> input1 = torch.randn(100, 128)
+    >>> input2 = torch.randn(100, 128)
+    >>> output = F.cosine_similarity(input1, input2)
+    >>> print(output)
+""",
+)
+
+
+one_hot = _add_docstr(
+    torch._C._nn.one_hot,
+    r"""
+one_hot(tensor, num_classes=-1) -> LongTensor
+
+Takes LongTensor with index values of shape ``(*)`` and returns a tensor
+of shape ``(*, num_classes)`` that have zeros everywhere except where the
+index of last dimension matches the corresponding value of the input tensor,
+in which case it will be 1.
+
+See also `One-hot on Wikipedia`_ .
+
+.. _One-hot on Wikipedia:
+    https://en.wikipedia.org/wiki/One-hot
+
+Arguments:
+    tensor (LongTensor): class values of any shape.
+    num_classes (int):  Total number of classes. If set to -1, the number
+        of classes will be inferred as one greater than the largest class
+        value in the input tensor.
+
+Returns:
+    LongTensor that has one more dimension with 1 values at the
+    index of last dimension indicated by the input, and 0 everywhere
+    else.
+
+Examples:
+    >>> F.one_hot(torch.arange(0, 5) % 3)
+    tensor([[1, 0, 0],
+            [0, 1, 0],
+            [0, 0, 1],
+            [1, 0, 0],
+            [0, 1, 0]])
+    >>> F.one_hot(torch.arange(0, 5) % 3, num_classes=5)
+    tensor([[1, 0, 0, 0, 0],
+            [0, 1, 0, 0, 0],
+            [0, 0, 1, 0, 0],
+            [1, 0, 0, 0, 0],
+            [0, 1, 0, 0, 0]])
+    >>> F.one_hot(torch.arange(0, 6).view(3,2) % 3)
+    tensor([[[1, 0, 0],
+             [0, 1, 0]],
+            [[0, 0, 1],
+             [1, 0, 0]],
+            [[0, 1, 0],
+             [0, 0, 1]]])
+""",
+)
+
+
+def triplet_margin_loss(
+    anchor: Tensor,
+    positive: Tensor,
+    negative: Tensor,
+    margin: float = 1.0,
+    p: float = 2,
+    eps: float = 1e-6,
+    swap: bool = False,
+    size_average: Optional[bool] = None,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+) -> Tensor:
+    r"""Compute the triplet loss between given input tensors and a margin greater than 0.
+
+    See :class:`~torch.nn.TripletMarginLoss` for details.
+    """
+    if has_torch_function_variadic(anchor, positive, negative):
+        return handle_torch_function(
+            triplet_margin_loss,
+            (anchor, positive, negative),
+            anchor,
+            positive,
+            negative,
+            margin=margin,
+            p=p,
+            eps=eps,
+            swap=swap,
+            size_average=size_average,
+            reduce=reduce,
+            reduction=reduction,
+        )
+    if size_average is not None or reduce is not None:
+        reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
+    else:
+        reduction_enum = _Reduction.get_enum(reduction)
+    if margin <= 0:
+        raise ValueError(f"margin must be greater than 0, got {margin}")
+    return torch.triplet_margin_loss(
+        anchor, positive, negative, margin, p, eps, swap, reduction_enum
+    )
+
+
+def triplet_margin_with_distance_loss(
+    anchor: Tensor,
+    positive: Tensor,
+    negative: Tensor,
+    *,
+    distance_function: Optional[Callable[[Tensor, Tensor], Tensor]] = None,
+    margin: float = 1.0,
+    swap: bool = False,
+    reduction: str = "mean",
+) -> Tensor:
+    r"""Compute the triplet margin loss for input tensors using a custom distance function.
+
+    See :class:`~torch.nn.TripletMarginWithDistanceLoss` for details.
+    """
+    if torch.jit.is_scripting():
+        raise NotImplementedError(
+            "F.triplet_margin_with_distance_loss does not support JIT scripting: "
+            "functions requiring Callables cannot be scripted."
+        )
+
+    if has_torch_function_variadic(anchor, positive, negative):
+        return handle_torch_function(
+            triplet_margin_with_distance_loss,
+            (anchor, positive, negative),
+            anchor,
+            positive,
+            negative,
+            distance_function=distance_function,
+            margin=margin,
+            swap=swap,
+            reduction=reduction,
+        )
+
+    # Check validity of reduction mode
+    if reduction not in ("mean", "sum", "none"):
+        raise ValueError(f"{reduction} is not a valid value for reduction")
+
+    # Check validity of margin
+    if margin <= 0:
+        raise ValueError(f"margin must be greater than 0, got {margin}")
+
+    # Check dimensions
+    a_dim = anchor.ndim
+    p_dim = positive.ndim
+    n_dim = negative.ndim
+    if not (a_dim == p_dim and p_dim == n_dim):
+        raise RuntimeError(
+            f"The anchor, positive, and negative tensors are expected to have "
+            f"the same number of dimensions, but got: anchor {a_dim}D, "
+            f"positive {p_dim}D, and negative {n_dim}D inputs"
+        )
+
+    # Calculate loss
+    if distance_function is None:
+        distance_function = torch.pairwise_distance
+
+    dist_pos = distance_function(anchor, positive)
+    dist_neg = distance_function(anchor, negative)
+    # The distance swap is described in the paper "Learning shallow
+    # convolutional feature descriptors with triplet losses" by V. Balntas, E.
+    # Riba et al.  If True, and if the positive example is closer to the
+    # negative example than the anchor is, swaps the positive example and the
+    # anchor in the loss computation.
+    if swap:
+        dist_swap = distance_function(positive, negative)
+        dist_neg = torch.minimum(dist_neg, dist_swap)
+    loss = torch.clamp_min(margin + dist_pos - dist_neg, 0)
+
+    # Apply reduction
+    if reduction == "sum":
+        return torch.sum(loss)
+    elif reduction == "mean":
+        return torch.mean(loss)
+    else:  # reduction == "none"
+        return loss
+
+
+def normalize(
+    input: Tensor,
+    p: float = 2.0,
+    dim: int = 1,
+    eps: float = 1e-12,
+    out: Optional[Tensor] = None,
+) -> Tensor:
+    r"""Perform :math:`L_p` normalization of inputs over specified dimension.
+
+    For a tensor :attr:`input` of sizes :math:`(n_0, ..., n_{dim}, ..., n_k)`, each
+    :math:`n_{dim}` -element vector :math:`v` along dimension :attr:`dim` is transformed as
+
+    .. math::
+        v = \frac{v}{\max(\lVert v \rVert_p, \epsilon)}.
+
+    With the default arguments it uses the Euclidean norm over vectors along dimension :math:`1` for normalization.
+
+    Args:
+        input: input tensor of any shape
+        p (float): the exponent value in the norm formulation. Default: 2
+        dim (int or tuple of ints): the dimension to reduce. Default: 1
+        eps (float): small value to avoid division by zero. Default: 1e-12
+        out (Tensor, optional): the output tensor. If :attr:`out` is used, this
+                                operation won't be differentiable.
+    """
+    if has_torch_function_variadic(input, out):
+        return handle_torch_function(
+            normalize, (input, out), input, p=p, dim=dim, eps=eps, out=out
+        )
+    if out is None:
+        denom = input.norm(p, dim, keepdim=True).clamp_min(eps).expand_as(input)
+        return input / denom
+    else:
+        denom = input.norm(p, dim, keepdim=True).clamp_min_(eps).expand_as(input)
+        return torch.div(input, denom, out=out)
+
+
+def assert_int_or_pair(arg: List[int], arg_name: str, message: str) -> None:
+    assert isinstance(arg, int) or len(arg) == 2, message.format(arg_name)
+
+
+def unfold(
+    input: Tensor,
+    kernel_size: BroadcastingList2[int],
+    dilation: BroadcastingList2[int] = 1,
+    padding: BroadcastingList2[int] = 0,
+    stride: BroadcastingList2[int] = 1,
+) -> Tensor:
+    r"""Extract sliding local blocks from a batched input tensor.
+
+    .. warning::
+        Currently, only 4-D input tensors (batched image-like tensors) are
+        supported.
+
+    .. warning::
+
+        More than one element of the unfolded tensor may refer to a single
+        memory location. As a result, in-place operations (especially ones that
+        are vectorized) may result in incorrect behavior. If you need to write
+        to the tensor, please clone it first.
+
+
+    See :class:`torch.nn.Unfold` for details
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            unfold,
+            (input,),
+            input,
+            kernel_size,
+            dilation=dilation,
+            padding=padding,
+            stride=stride,
+        )
+    return torch._C._nn.im2col(
+        input, _pair(kernel_size), _pair(dilation), _pair(padding), _pair(stride)
+    )
+
+
+def fold(
+    input: Tensor,
+    output_size: BroadcastingList2[int],
+    kernel_size: BroadcastingList2[int],
+    dilation: BroadcastingList2[int] = 1,
+    padding: BroadcastingList2[int] = 0,
+    stride: BroadcastingList2[int] = 1,
+) -> Tensor:
+    r"""Combine an array of sliding local blocks into a large containing tensor.
+
+    .. warning::
+        Currently, only unbatched (3D) or batched (4D) image-like output tensors are supported.
+
+    See :class:`torch.nn.Fold` for details
+    """
+    if has_torch_function_unary(input):
+        return handle_torch_function(
+            fold,
+            (input,),
+            input,
+            output_size,
+            kernel_size,
+            dilation=dilation,
+            padding=padding,
+            stride=stride,
+        )
+    return torch._C._nn.col2im(
+        input,
+        _pair(output_size),
+        _pair(kernel_size),
+        _pair(dilation),
+        _pair(padding),
+        _pair(stride),
+    )
+
+
+#
+# multihead attention
+#
+
+
+def _in_projection_packed(
+    q: Tensor,
+    k: Tensor,
+    v: Tensor,
+    w: Tensor,
+    b: Optional[Tensor] = None,
+) -> List[Tensor]:
+    r"""Perform the in-projection step of the attention operation, using packed weights.
+
+    Output is a triple containing projection tensors for query, key and value.
+
+    Args:
+        q, k, v: query, key and value tensors to be projected. For self-attention,
+            these are typically the same tensor; for encoder-decoder attention,
+            k and v are typically the same tensor. (We take advantage of these
+            identities for performance if they are present.) Regardless, q, k and v
+            must share a common embedding dimension; otherwise their shapes may vary.
+        w: projection weights for q, k and v, packed into a single tensor. Weights
+            are packed along dimension 0, in q, k, v order.
+        b: optional projection biases for q, k and v, packed into a single tensor
+            in q, k, v order.
+
+    Shape:
+        Inputs:
+        - q: :math:`(..., E)` where E is the embedding dimension
+        - k: :math:`(..., E)` where E is the embedding dimension
+        - v: :math:`(..., E)` where E is the embedding dimension
+        - w: :math:`(E * 3, E)` where E is the embedding dimension
+        - b: :math:`E * 3` where E is the embedding dimension
+
+        Output:
+        - in output list :math:`[q', k', v']`, each output tensor will have the
+            same shape as the corresponding input tensor.
+    """
+    E = q.size(-1)
+    if k is v:
+        if q is k:
+            # self-attention
+            proj = linear(q, w, b)
+            # reshape to 3, E and not E, 3 is deliberate for better memory coalescing and keeping same order as chunk()
+            proj = (
+                proj.unflatten(-1, (3, E))
+                .unsqueeze(0)
+                .transpose(0, -2)
+                .squeeze(-2)
+                .contiguous()
+            )
+            return proj[0], proj[1], proj[2]
+        else:
+            # encoder-decoder attention
+            w_q, w_kv = w.split([E, E * 2])
+            if b is None:
+                b_q = b_kv = None
+            else:
+                b_q, b_kv = b.split([E, E * 2])
+            q_proj = linear(q, w_q, b_q)
+            kv_proj = linear(k, w_kv, b_kv)
+            # reshape to 2, E and not E, 2 is deliberate for better memory coalescing and keeping same order as chunk()
+            kv_proj = (
+                kv_proj.unflatten(-1, (2, E))
+                .unsqueeze(0)
+                .transpose(0, -2)
+                .squeeze(-2)
+                .contiguous()
+            )
+            return (q_proj, kv_proj[0], kv_proj[1])
+    else:
+        w_q, w_k, w_v = w.chunk(3)
+        if b is None:
+            b_q = b_k = b_v = None
+        else:
+            b_q, b_k, b_v = b.chunk(3)
+        return linear(q, w_q, b_q), linear(k, w_k, b_k), linear(v, w_v, b_v)
+
+
+def _in_projection(
+    q: Tensor,
+    k: Tensor,
+    v: Tensor,
+    w_q: Tensor,
+    w_k: Tensor,
+    w_v: Tensor,
+    b_q: Optional[Tensor] = None,
+    b_k: Optional[Tensor] = None,
+    b_v: Optional[Tensor] = None,
+) -> Tuple[Tensor, Tensor, Tensor]:
+    r"""Perform the in-projection step of the attention operation.
+
+    This is simply a triple of linear projections,
+    with shape constraints on the weights which
+    ensure embedding dimension uniformity in the projected outputs.
+    Output is a triple containing projection tensors for query, key and value.
+
+    Args:
+        q, k, v: query, key and value tensors to be projected.
+        w_q, w_k, w_v: weights for q, k and v, respectively.
+        b_q, b_k, b_v: optional biases for q, k and v, respectively.
+
+    Shape:
+        Inputs:
+        - q: :math:`(Qdims..., Eq)` where Eq is the query embedding dimension and Qdims are any
+            number of leading dimensions.
+        - k: :math:`(Kdims..., Ek)` where Ek is the key embedding dimension and Kdims are any
+            number of leading dimensions.
+        - v: :math:`(Vdims..., Ev)` where Ev is the value embedding dimension and Vdims are any
+            number of leading dimensions.
+        - w_q: :math:`(Eq, Eq)`
+        - w_k: :math:`(Eq, Ek)`
+        - w_v: :math:`(Eq, Ev)`
+        - b_q: :math:`(Eq)`
+        - b_k: :math:`(Eq)`
+        - b_v: :math:`(Eq)`
+
+        Output: in output triple :math:`(q', k', v')`,
+         - q': :math:`[Qdims..., Eq]`
+         - k': :math:`[Kdims..., Eq]`
+         - v': :math:`[Vdims..., Eq]`
+
+    """
+    Eq, Ek, Ev = q.size(-1), k.size(-1), v.size(-1)
+    assert w_q.shape == (
+        Eq,
+        Eq,
+    ), f"expecting query weights shape of {(Eq, Eq)}, but got {w_q.shape}"
+    assert w_k.shape == (
+        Eq,
+        Ek,
+    ), f"expecting key weights shape of {(Eq, Ek)}, but got {w_k.shape}"
+    assert w_v.shape == (
+        Eq,
+        Ev,
+    ), f"expecting value weights shape of {(Eq, Ev)}, but got {w_v.shape}"
+    assert b_q is None or b_q.shape == (
+        Eq,
+    ), f"expecting query bias shape of {(Eq,)}, but got {b_q.shape}"
+    assert b_k is None or b_k.shape == (
+        Eq,
+    ), f"expecting key bias shape of {(Eq,)}, but got {b_k.shape}"
+    assert b_v is None or b_v.shape == (
+        Eq,
+    ), f"expecting value bias shape of {(Eq,)}, but got {b_v.shape}"
+    return linear(q, w_q, b_q), linear(k, w_k, b_k), linear(v, w_v, b_v)
+
+
+scaled_dot_product_attention = _add_docstr(
+    torch._C._nn.scaled_dot_product_attention,
+    r"""scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0,
+        is_causal=False, scale=None, enable_gqa=False) -> Tensor:
+
+    Computes scaled dot product attention on query, key and value tensors, using an optional attention mask if passed,
+    and applying dropout if a probability greater than 0.0 is specified. The optional scale argument can only be
+    specified as a keyword argument.
+
+    .. code-block:: python
+
+        # Efficient implementation equivalent to the following:
+        def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0,
+                is_causal=False, scale=None, enable_gqa=False) -> torch.Tensor:
+            L, S = query.size(-2), key.size(-2)
+            scale_factor = 1 / math.sqrt(query.size(-1)) if scale is None else scale
+            attn_bias = torch.zeros(L, S, dtype=query.dtype)
+            if is_causal:
+                assert attn_mask is None
+                temp_mask = torch.ones(L, S, dtype=torch.bool).tril(diagonal=0)
+                attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf"))
+                attn_bias.to(query.dtype)
+
+            if attn_mask is not None:
+                if attn_mask.dtype == torch.bool:
+                    attn_bias.masked_fill_(attn_mask.logical_not(), float("-inf"))
+                else:
+                    attn_bias += attn_mask
+
+            if enable_gqa:
+                key = key.repeat_interleave(query.size(-3)//key.size(-3), -3)
+                value = value.repeat_interleave(query.size(-3)//value.size(-3), -3)
+
+            attn_weight = query @ key.transpose(-2, -1) * scale_factor
+            attn_weight += attn_bias
+            attn_weight = torch.softmax(attn_weight, dim=-1)
+            attn_weight = torch.dropout(attn_weight, dropout_p, train=True)
+            return attn_weight @ value
+
+    .. warning::
+        This function is beta and subject to change.
+
+    .. warning::
+        This function always applies dropout according to the specified ``dropout_p`` argument.
+        To disable dropout during evaluation, be sure to pass a value of ``0.0`` when the module
+        that makes the function call is not in training mode.
+
+        For example:
+
+        .. code-block:: python
+
+            class MyModel(nn.Module):
+                def __init__(self, p=0.5):
+                    super().__init__()
+                    self.p = p
+
+                def forward(self, ...):
+                    return F.scaled_dot_product_attention(...,
+                        dropout_p=(self.p if self.training else 0.0))
+
+    Note:
+
+        There are currently three supported implementations of scaled dot product attention:
+
+            - `FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning`_
+            - `Memory-Efficient Attention`_
+            - A PyTorch implementation defined in C++ matching the above formulation
+
+        The function may call optimized kernels for improved performance when using the CUDA backend.
+        For all other backends, the PyTorch implementation will be used.
+
+        All implementations are enabled by default. Scaled dot product attention attempts to automatically select the
+        most optimal implementation based on the inputs. In order to provide more fine-grained control over what implementation
+        is used, the following functions are provided for enabling and disabling implementations.
+        The context manager is the preferred mechanism:
+
+            - :func:`torch.nn.attention.sdpa_kernel`: A context manager used to enable or disable any of the implementations.
+            - :func:`torch.backends.cuda.enable_flash_sdp`: Globally enables or disables FlashAttention.
+            - :func:`torch.backends.cuda.enable_mem_efficient_sdp`: Globally enables or disables  Memory-Efficient Attention.
+            - :func:`torch.backends.cuda.enable_math_sdp`: Globally enables or disables  the PyTorch C++ implementation.
+
+        Each of the fused kernels has specific input limitations. If the user requires the use of a specific fused implementation,
+        disable the PyTorch C++ implementation using :func:`torch.nn.attention.sdpa_kernel`.
+        In the event that a fused implementation is not available, a warning will be raised with the
+        reasons why the fused implementation cannot run.
+
+        Due to the nature of fusing floating point operations, the output of this function may be different
+        depending on what backend kernel is chosen.
+        The c++ implementation supports torch.float64 and can be used when higher precision is required.
+        For math backend, all intermediates are kept in torch.float if inputs are in torch.half or torch.bfloat16.
+    For more information please see :doc:`/notes/numerical_accuracy`
+
+        Grouped Query Attention (GQA) is an experimental feature. It currently works only for Flash_attention
+        and math kernel on CUDA tensor, and does not support Nested tensor.
+        Constraints for GQA:
+
+            - number_of_heads_query % number_of_heads_key_value == 0 and,
+            - number_of_heads_key == number_of_heads_value
+
+    Note:
+
+        {cudnn_reproducibility_note}
+    """.format(
+        **reproducibility_notes
+    )
+    + r"""
+    Args:
+        query (Tensor): Query tensor; shape :math:`(N, ..., Hq, L, E)`.
+        key (Tensor): Key tensor; shape :math:`(N, ..., H, S, E)`.
+        value (Tensor): Value tensor; shape :math:`(N, ..., H, S, Ev)`.
+        attn_mask (optional Tensor): Attention mask; shape must be broadcastable to the shape of attention weights,
+            which is :math:`(N,..., L, S)`. Two types of masks are supported.
+            A boolean mask where a value of True indicates that the element *should* take part in attention.
+            A float mask of the same type as query, key, value that is added to the attention score.
+        dropout_p (float): Dropout probability; if greater than 0.0, dropout is applied
+        is_causal (bool): If set to true, the attention masking is a lower triangular matrix when the mask is a
+            square matrix. The attention masking has the form of the upper left causal bias due to the alignment
+            (see :class:`torch.nn.attention.bias.CausalBias`) when the mask is a non-square matrix.
+            An error is thrown if both attn_mask and is_causal are set.
+        scale (optional float, keyword-only): Scaling factor applied prior to softmax. If None, the default value is set
+            to :math:`\frac{1}{\sqrt{E}}`.
+        enable_gqa (bool): If set to True, Grouped Query Attention (GQA) is enabled, by default it is set to False.
+
+    Returns:
+        output (Tensor): Attention output; shape :math:`(N, ..., Hq, L, Ev)`.
+
+    Shape legend:
+        - :math:`N: \text{Batch size} ... : \text{Any number of other batch dimensions (optional)}`
+        - :math:`S: \text{Source sequence length}`
+        - :math:`L: \text{Target sequence length}`
+        - :math:`E: \text{Embedding dimension of the query and key}`
+        - :math:`Ev: \text{Embedding dimension of the value}`
+        - :math:`Hq: \text{Number of heads of query}`
+        - :math:`H: \text{Number of heads of key and value}`
+
+    Examples:
+
+        >>> # Optionally use the context manager to ensure one of the fused kernels is run
+        >>> query = torch.rand(32, 8, 128, 64, dtype=torch.float16, device="cuda")
+        >>> key = torch.rand(32, 8, 128, 64, dtype=torch.float16, device="cuda")
+        >>> value = torch.rand(32, 8, 128, 64, dtype=torch.float16, device="cuda")
+        >>> with sdpa_kernel(backends=[SDPBackend.FLASH_ATTENTION]):
+        >>>     F.scaled_dot_product_attention(query,key,value)
+
+
+        >>> # Sample for GQA for llama3
+        >>> query = torch.rand(32, 32, 128, 64, dtype=torch.float16, device="cuda")
+        >>> key = torch.rand(32, 8, 128, 64, dtype=torch.float16, device="cuda")
+        >>> value = torch.rand(32, 8, 128, 64, dtype=torch.float16, device="cuda")
+        >>> with sdpa_kernel(backends=[SDPBackend.MATH]):
+        >>>     F.scaled_dot_product_attention(query,key,value,enable_gqa=True)
+
+
+    .. _FlashAttention-2\: Faster Attention with Better Parallelism and Work Partitioning:
+        https://arxiv.org/abs/2307.08691
+    .. _Memory-Efficient Attention:
+        https://github.com/facebookresearch/xformers
+    .. _Grouped-Query Attention:
+        https://arxiv.org/pdf/2305.13245
+    """,
+)
+
+
+def _mha_shape_check(
+    query: Tensor,
+    key: Tensor,
+    value: Tensor,
+    key_padding_mask: Optional[Tensor],
+    attn_mask: Optional[Tensor],
+    num_heads: int,
+):
+    # Verifies the expected shape for `query, `key`, `value`, `key_padding_mask` and `attn_mask`
+    # and returns if the input is batched or not.
+    # Raises an error if `query` is not 2-D (unbatched) or 3-D (batched) tensor.
+
+    # Shape check.
+    if query.dim() == 3:
+        # Batched Inputs
+        is_batched = True
+        assert key.dim() == 3 and value.dim() == 3, (
+            "For batched (3-D) `query`, expected `key` and `value` to be 3-D"
+            f" but found {key.dim()}-D and {value.dim()}-D tensors respectively"
+        )
+        if key_padding_mask is not None:
+            assert key_padding_mask.dim() == 2, (
+                "For batched (3-D) `query`, expected `key_padding_mask` to be `None` or 2-D"
+                f" but found {key_padding_mask.dim()}-D tensor instead"
+            )
+        if attn_mask is not None:
+            assert attn_mask.dim() in (2, 3), (
+                "For batched (3-D) `query`, expected `attn_mask` to be `None`, 2-D or 3-D"
+                f" but found {attn_mask.dim()}-D tensor instead"
+            )
+    elif query.dim() == 2:
+        # Unbatched Inputs
+        is_batched = False
+        assert key.dim() == 2 and value.dim() == 2, (
+            "For unbatched (2-D) `query`, expected `key` and `value` to be 2-D"
+            f" but found {key.dim()}-D and {value.dim()}-D tensors respectively"
+        )
+
+        if key_padding_mask is not None:
+            assert key_padding_mask.dim() == 1, (
+                "For unbatched (2-D) `query`, expected `key_padding_mask` to be `None` or 1-D"
+                f" but found {key_padding_mask.dim()}-D tensor instead"
+            )
+
+        if attn_mask is not None:
+            assert attn_mask.dim() in (2, 3), (
+                "For unbatched (2-D) `query`, expected `attn_mask` to be `None`, 2-D or 3-D"
+                f" but found {attn_mask.dim()}-D tensor instead"
+            )
+            if attn_mask.dim() == 3:
+                expected_shape = (num_heads, query.shape[0], key.shape[0])
+                assert (
+                    attn_mask.shape == expected_shape
+                ), f"Expected `attn_mask` shape to be {expected_shape} but got {attn_mask.shape}"
+    else:
+        raise AssertionError(
+            f"query should be unbatched 2D or batched 3D tensor but received {query.dim()}-D query tensor"
+        )
+
+    return is_batched
+
+
+def _canonical_mask(
+    mask: Optional[Tensor],
+    mask_name: str,
+    other_type: Optional[DType],
+    other_name: str,
+    target_type: DType,
+    check_other: bool = True,
+) -> Optional[Tensor]:
+    if mask is not None:
+        _mask_dtype = mask.dtype
+        _mask_is_float = torch.is_floating_point(mask)
+        if _mask_dtype != torch.bool and not _mask_is_float:
+            raise AssertionError(
+                f"only bool and floating types of {mask_name} are supported"
+            )
+        if check_other and other_type is not None:
+            if _mask_dtype != other_type:
+                warnings.warn(
+                    f"Support for mismatched {mask_name} and {other_name} "
+                    "is deprecated. Use same type for both instead."
+                )
+        if not _mask_is_float:
+            mask = torch.zeros_like(mask, dtype=target_type).masked_fill_(
+                mask, float("-inf")
+            )
+    return mask
+
+
+def _none_or_dtype(input: Optional[Tensor]) -> Optional[DType]:
+    if input is None:
+        return None
+    elif isinstance(input, torch.Tensor):
+        return input.dtype
+    raise RuntimeError("input to _none_or_dtype() must be None or torch.Tensor")
+
+
+def multi_head_attention_forward(
+    query: Tensor,
+    key: Tensor,
+    value: Tensor,
+    embed_dim_to_check: int,
+    num_heads: int,
+    in_proj_weight: Optional[Tensor],
+    in_proj_bias: Optional[Tensor],
+    bias_k: Optional[Tensor],
+    bias_v: Optional[Tensor],
+    add_zero_attn: bool,
+    dropout_p: float,
+    out_proj_weight: Tensor,
+    out_proj_bias: Optional[Tensor],
+    training: bool = True,
+    key_padding_mask: Optional[Tensor] = None,
+    need_weights: bool = True,
+    attn_mask: Optional[Tensor] = None,
+    use_separate_proj_weight: bool = False,
+    q_proj_weight: Optional[Tensor] = None,
+    k_proj_weight: Optional[Tensor] = None,
+    v_proj_weight: Optional[Tensor] = None,
+    static_k: Optional[Tensor] = None,
+    static_v: Optional[Tensor] = None,
+    average_attn_weights: bool = True,
+    is_causal: bool = False,
+) -> Tuple[Tensor, Optional[Tensor]]:
+    r"""Forward method for MultiHeadAttention.
+
+    See :class:`torch.nn.MultiheadAttention` for details.
+
+    Args:
+        query, key, value: map a query and a set of key-value pairs to an output.
+            See "Attention Is All You Need" for more details.
+        embed_dim_to_check: total dimension of the model.
+        num_heads: parallel attention heads.
+        in_proj_weight, in_proj_bias: input projection weight and bias.
+        bias_k, bias_v: bias of the key and value sequences to be added at dim=0.
+        add_zero_attn: add a new batch of zeros to the key and
+                       value sequences at dim=1.
+        dropout_p: probability of an element to be zeroed.
+        out_proj_weight, out_proj_bias: the output projection weight and bias.
+        training: apply dropout if is ``True``.
+        key_padding_mask: if provided, specified padding elements in the key will
+            be ignored by the attention. This is an binary mask. When the value is True,
+            the corresponding value on the attention layer will be filled with -inf.
+        need_weights: output attn_output_weights.
+            Default: `True`
+            Note: `needs_weight` defaults to `True`, but should be set to `False`
+            For best performance when attention weights are not needed.
+            *Setting needs_weights to `True`
+            leads to a significant performance degradation.*
+        attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all
+            the batches while a 3D mask allows to specify a different mask for the entries of each batch.
+        is_causal: If specified, applies a causal mask as attention mask, and ignores
+            attn_mask for computing scaled dot product attention.
+            Default: ``False``.
+            .. warning::
+                is_causal is provides a hint that the attn_mask is the
+                causal mask.Providing incorrect hints can result in
+                incorrect execution, including forward and backward
+                compatibility.
+        use_separate_proj_weight: the function accept the proj. weights for query, key,
+            and value in different forms. If false, in_proj_weight will be used, which is
+            a combination of q_proj_weight, k_proj_weight, v_proj_weight.
+        q_proj_weight, k_proj_weight, v_proj_weight, in_proj_bias: input projection weight and bias.
+        static_k, static_v: static key and value used for attention operators.
+        average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across heads.
+            Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an effect
+            when ``need_weights=True.``. Default: True
+
+
+    Shape:
+        Inputs:
+        - query: :math:`(L, E)` or :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is
+          the embedding dimension.
+        - key: :math:`(S, E)` or :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is
+          the embedding dimension.
+        - value: :math:`(S, E)` or :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is
+          the embedding dimension.
+        - key_padding_mask: :math:`(S)` or :math:`(N, S)` where N is the batch size, S is the source sequence length.
+          If a FloatTensor is provided, it will be directly added to the value.
+          If a BoolTensor is provided, the positions with the
+          value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.
+        - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.
+          3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,
+          S is the source sequence length. attn_mask ensures that position i is allowed to attend the unmasked
+          positions. If a BoolTensor is provided, positions with ``True``
+          are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
+          is provided, it will be added to the attention weight.
+        - static_k: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length,
+          N is the batch size, E is the embedding dimension. E/num_heads is the head dimension.
+        - static_v: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length,
+          N is the batch size, E is the embedding dimension. E/num_heads is the head dimension.
+
+        Outputs:
+        - attn_output: :math:`(L, E)` or :math:`(L, N, E)` where L is the target sequence length, N is the batch size,
+          E is the embedding dimension.
+        - attn_output_weights: Only returned when ``need_weights=True``. If ``average_attn_weights=True``, returns
+          attention weights averaged across heads of shape :math:`(L, S)` when input is unbatched or
+          :math:`(N, L, S)`, where :math:`N` is the batch size, :math:`L` is the target sequence length, and
+          :math:`S` is the source sequence length. If ``average_attn_weights=False``, returns attention weights per
+          head of shape :math:`(num_heads, L, S)` when input is unbatched or :math:`(N, num_heads, L, S)`.
+    """
+    tens_ops = (
+        query,
+        key,
+        value,
+        in_proj_weight,
+        in_proj_bias,
+        bias_k,
+        bias_v,
+        out_proj_weight,
+        out_proj_bias,
+    )
+    if has_torch_function(tens_ops):
+        return handle_torch_function(
+            multi_head_attention_forward,
+            tens_ops,
+            query,
+            key,
+            value,
+            embed_dim_to_check,
+            num_heads,
+            in_proj_weight,
+            in_proj_bias,
+            bias_k,
+            bias_v,
+            add_zero_attn,
+            dropout_p,
+            out_proj_weight,
+            out_proj_bias,
+            training=training,
+            key_padding_mask=key_padding_mask,
+            need_weights=need_weights,
+            attn_mask=attn_mask,
+            is_causal=is_causal,
+            use_separate_proj_weight=use_separate_proj_weight,
+            q_proj_weight=q_proj_weight,
+            k_proj_weight=k_proj_weight,
+            v_proj_weight=v_proj_weight,
+            static_k=static_k,
+            static_v=static_v,
+            average_attn_weights=average_attn_weights,
+        )
+
+    is_batched = _mha_shape_check(
+        query, key, value, key_padding_mask, attn_mask, num_heads
+    )
+
+    # For unbatched input, we unsqueeze at the expected batch-dim to pretend that the input
+    # is batched, run the computation and before returning squeeze the
+    # batch dimension so that the output doesn't carry this temporary batch dimension.
+    if not is_batched:
+        # unsqueeze if the input is unbatched
+        query = query.unsqueeze(1)
+        key = key.unsqueeze(1)
+        value = value.unsqueeze(1)
+        if key_padding_mask is not None:
+            key_padding_mask = key_padding_mask.unsqueeze(0)
+
+    # set up shape vars
+    tgt_len, bsz, embed_dim = query.shape
+    src_len, _, _ = key.shape
+
+    key_padding_mask = _canonical_mask(
+        mask=key_padding_mask,
+        mask_name="key_padding_mask",
+        other_type=_none_or_dtype(attn_mask),
+        other_name="attn_mask",
+        target_type=query.dtype,
+    )
+
+    if is_causal and attn_mask is None:
+        raise RuntimeError(
+            "Need attn_mask if specifying the is_causal hint. "
+            "You may use the Transformer module method "
+            "`generate_square_subsequent_mask` to create this mask."
+        )
+
+    if is_causal and key_padding_mask is None and not need_weights:
+        # when we have a kpm or need weights, we need attn_mask
+        # Otherwise, we use the is_causal hint go as is_causal
+        # indicator to SDPA.
+        attn_mask = None
+    else:
+        attn_mask = _canonical_mask(
+            mask=attn_mask,
+            mask_name="attn_mask",
+            other_type=None,
+            other_name="",
+            target_type=query.dtype,
+            check_other=False,
+        )
+
+        if key_padding_mask is not None:
+            # We have the attn_mask, and use that to merge kpm into it.
+            # Turn off use of is_causal hint, as the merged mask is no
+            # longer causal.
+            is_causal = False
+
+    assert (
+        embed_dim == embed_dim_to_check
+    ), f"was expecting embedding dimension of {embed_dim_to_check}, but got {embed_dim}"
+    if isinstance(embed_dim, torch.Tensor):
+        # embed_dim can be a tensor when JIT tracing
+        head_dim = embed_dim.div(num_heads, rounding_mode="trunc")
+    else:
+        head_dim = embed_dim // num_heads
+    assert (
+        head_dim * num_heads == embed_dim
+    ), f"embed_dim {embed_dim} not divisible by num_heads {num_heads}"
+    if use_separate_proj_weight:
+        # allow MHA to have different embedding dimensions when separate projection weights are used
+        assert (
+            key.shape[:2] == value.shape[:2]
+        ), f"key's sequence and batch dims {key.shape[:2]} do not match value's {value.shape[:2]}"
+    else:
+        assert (
+            key.shape == value.shape
+        ), f"key shape {key.shape} does not match value shape {value.shape}"
+
+    #
+    # compute in-projection
+    #
+    if not use_separate_proj_weight:
+        assert (
+            in_proj_weight is not None
+        ), "use_separate_proj_weight is False but in_proj_weight is None"
+        q, k, v = _in_projection_packed(query, key, value, in_proj_weight, in_proj_bias)
+    else:
+        assert (
+            q_proj_weight is not None
+        ), "use_separate_proj_weight is True but q_proj_weight is None"
+        assert (
+            k_proj_weight is not None
+        ), "use_separate_proj_weight is True but k_proj_weight is None"
+        assert (
+            v_proj_weight is not None
+        ), "use_separate_proj_weight is True but v_proj_weight is None"
+        if in_proj_bias is None:
+            b_q = b_k = b_v = None
+        else:
+            b_q, b_k, b_v = in_proj_bias.chunk(3)
+        q, k, v = _in_projection(
+            query,
+            key,
+            value,
+            q_proj_weight,
+            k_proj_weight,
+            v_proj_weight,
+            b_q,
+            b_k,
+            b_v,
+        )
+
+    # prep attention mask
+
+    if attn_mask is not None:
+        # ensure attn_mask's dim is 3
+        if attn_mask.dim() == 2:
+            correct_2d_size = (tgt_len, src_len)
+            if attn_mask.shape != correct_2d_size:
+                raise RuntimeError(
+                    f"The shape of the 2D attn_mask is {attn_mask.shape}, but should be {correct_2d_size}."
+                )
+            attn_mask = attn_mask.unsqueeze(0)
+        elif attn_mask.dim() == 3:
+            correct_3d_size = (bsz * num_heads, tgt_len, src_len)
+            if attn_mask.shape != correct_3d_size:
+                raise RuntimeError(
+                    f"The shape of the 3D attn_mask is {attn_mask.shape}, but should be {correct_3d_size}."
+                )
+        else:
+            raise RuntimeError(
+                f"attn_mask's dimension {attn_mask.dim()} is not supported"
+            )
+
+    # add bias along batch dimension (currently second)
+    if bias_k is not None and bias_v is not None:
+        assert static_k is None, "bias cannot be added to static key."
+        assert static_v is None, "bias cannot be added to static value."
+        k = torch.cat([k, bias_k.repeat(1, bsz, 1)])
+        v = torch.cat([v, bias_v.repeat(1, bsz, 1)])
+        if attn_mask is not None:
+            attn_mask = pad(attn_mask, (0, 1))
+        if key_padding_mask is not None:
+            key_padding_mask = pad(key_padding_mask, (0, 1))
+    else:
+        assert bias_k is None
+        assert bias_v is None
+
+    #
+    # reshape q, k, v for multihead attention and make them batch first
+    #
+    q = q.view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1)
+    if static_k is None:
+        k = k.view(k.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
+    else:
+        # TODO finish disentangling control flow so we don't do in-projections when statics are passed
+        assert (
+            static_k.size(0) == bsz * num_heads
+        ), f"expecting static_k.size(0) of {bsz * num_heads}, but got {static_k.size(0)}"
+        assert (
+            static_k.size(2) == head_dim
+        ), f"expecting static_k.size(2) of {head_dim}, but got {static_k.size(2)}"
+        k = static_k
+    if static_v is None:
+        v = v.view(v.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
+    else:
+        # TODO finish disentangling control flow so we don't do in-projections when statics are passed
+        assert (
+            static_v.size(0) == bsz * num_heads
+        ), f"expecting static_v.size(0) of {bsz * num_heads}, but got {static_v.size(0)}"
+        assert (
+            static_v.size(2) == head_dim
+        ), f"expecting static_v.size(2) of {head_dim}, but got {static_v.size(2)}"
+        v = static_v
+
+    # add zero attention along batch dimension (now first)
+    if add_zero_attn:
+        zero_attn_shape = (bsz * num_heads, 1, head_dim)
+        k = torch.cat(
+            [k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=1
+        )
+        v = torch.cat(
+            [v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=1
+        )
+        if attn_mask is not None:
+            attn_mask = pad(attn_mask, (0, 1))
+        if key_padding_mask is not None:
+            key_padding_mask = pad(key_padding_mask, (0, 1))
+
+    # update source sequence length after adjustments
+    src_len = k.size(1)
+
+    # merge key padding and attention masks
+    if key_padding_mask is not None:
+        assert key_padding_mask.shape == (
+            bsz,
+            src_len,
+        ), f"expecting key_padding_mask shape of {(bsz, src_len)}, but got {key_padding_mask.shape}"
+        key_padding_mask = (
+            key_padding_mask.view(bsz, 1, 1, src_len)
+            .expand(-1, num_heads, -1, -1)
+            .reshape(bsz * num_heads, 1, src_len)
+        )
+        if attn_mask is None:
+            attn_mask = key_padding_mask
+        else:
+            attn_mask = attn_mask + key_padding_mask
+
+    # adjust dropout probability
+    if not training:
+        dropout_p = 0.0
+
+    #
+    # (deep breath) calculate attention and out projection
+    #
+
+    if need_weights:
+        B, Nt, E = q.shape
+        q_scaled = q * math.sqrt(1.0 / float(E))
+
+        assert not (
+            is_causal and attn_mask is None
+        ), "FIXME: is_causal not implemented for need_weights"
+
+        if attn_mask is not None:
+            attn_output_weights = torch.baddbmm(
+                attn_mask, q_scaled, k.transpose(-2, -1)
+            )
+        else:
+            attn_output_weights = torch.bmm(q_scaled, k.transpose(-2, -1))
+        attn_output_weights = softmax(attn_output_weights, dim=-1)
+        if dropout_p > 0.0:
+            attn_output_weights = dropout(attn_output_weights, p=dropout_p)
+
+        attn_output = torch.bmm(attn_output_weights, v)
+
+        attn_output = (
+            attn_output.transpose(0, 1).contiguous().view(tgt_len * bsz, embed_dim)
+        )
+        attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
+        attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1))
+
+        # optionally average attention weights over heads
+        attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)
+        if average_attn_weights:
+            attn_output_weights = attn_output_weights.mean(dim=1)
+
+        if not is_batched:
+            # squeeze the output if input was unbatched
+            attn_output = attn_output.squeeze(1)
+            attn_output_weights = attn_output_weights.squeeze(0)
+        return attn_output, attn_output_weights
+    else:
+        # attn_mask can be either (L,S) or (N*num_heads, L, S)
+        # if attn_mask's shape is (1, L, S) we need to unsqueeze to (1, 1, L, S)
+        # in order to match the input for SDPA of (N, num_heads, L, S)
+        if attn_mask is not None:
+            if attn_mask.size(0) == 1 and attn_mask.dim() == 3:
+                attn_mask = attn_mask.unsqueeze(0)
+            else:
+                attn_mask = attn_mask.view(bsz, num_heads, -1, src_len)
+
+        q = q.view(bsz, num_heads, tgt_len, head_dim)
+        k = k.view(bsz, num_heads, src_len, head_dim)
+        v = v.view(bsz, num_heads, src_len, head_dim)
+
+        attn_output = scaled_dot_product_attention(
+            q, k, v, attn_mask, dropout_p, is_causal
+        )
+        attn_output = (
+            attn_output.permute(2, 0, 1, 3).contiguous().view(bsz * tgt_len, embed_dim)
+        )
+
+        attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
+        attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1))
+        if not is_batched:
+            # squeeze the output if input was unbatched
+            attn_output = attn_output.squeeze(1)
+        return attn_output, None
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/functional.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/functional.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..97186614cff810f7df1c4128ac586323fad87861
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/functional.pyi
@@ -0,0 +1,691 @@
+# @generated by tools/pyi/gen_pyi.py from torch/nn/functional.pyi.in
+# mypy: allow-untyped-defs
+
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Literal,
+    Optional,
+    overload,
+    Sequence,
+    Tuple,
+    Union,
+)
+
+from torch import Tensor
+from torch.types import _dtype, _int, _size
+
+from .common_types import (
+    _ratio_any_t,
+    _size_1_t,
+    _size_2_opt_t,
+    _size_2_t,
+    _size_3_opt_t,
+    _size_3_t,
+    _size_any_t,
+)
+
+# 'TypedDict' is a new accepted type that represents a dictionary with a fixed set of allowed keys.
+# It is standards-track but not in `typing` yet. We leave this hear to be uncommented once the feature
+# is wide-spread.
+
+# from mypy_extensions import TypedDict
+
+# GRID_SAMPLE_INTERPOLATION_MODES = TypedDict('GRID_SAMPLE_INTERPOLATION_MODES', {'bilinear': int, 'nearest': int})
+# GRID_SAMPLE_PADDING_MODES = TypedDict('GRID_SAMPLE_PADDING_MODES', {'zeros': int, 'border': int, 'reflection': int})
+
+GRID_SAMPLE_INTERPOLATION_MODES = Dict[str, int]
+GRID_SAMPLE_PADDING_MODES = Dict[str, int]
+
+# These stubs were generated by running stubgen (`stubgen --parse-only functional.py`), followed by manual cleaning.
+#
+# The 'BroadcastingList{1,2,3}' types were replaced by `_size` or _output_ratio, as appropriate.
+# This was necessary since the JIT uses BroadcastingList* types but static checking with mypy etc requires a `Sequence`
+# type. There is no way to express the expected lengths of these lists in the current Python typing system.
+#
+# Functions created via `_add_docstr` in `functional.py` where merely typed as `Any` by `stubgen`, so those were
+# deleted from the stub and replaced by generated declarations. See `gen_pyi` for the implementation of the code
+# generation logic for those functions. In the future, it might be worth looking into using the mypy plugin system
+# to encode the type semantics of `_add_docstr`, should that system ever become widespread.
+def fractional_max_pool2d_with_indices(
+    input: Tensor,
+    kernel_size: _size,
+    output_size: Optional[_size] = ...,
+    output_ratio: Optional[_ratio_any_t] = ...,
+    return_indices: bool = ...,
+    _random_samples: Optional[Tensor] = ...,
+) -> Tuple[Tensor, Tensor]: ...
+def fractional_max_pool3d_with_indices(
+    input: Tensor,
+    kernel_size: _size,
+    output_size: Optional[_size] = ...,
+    output_ratio: Optional[_ratio_any_t] = ...,
+    return_indices: bool = ...,
+    _random_samples: Optional[Tensor] = ...,
+) -> Tuple[Tensor, Tensor]: ...
+def max_pool1d_with_indices(
+    input: Tensor,
+    kernel_size: _size,
+    stride: Optional[_size] = ...,
+    padding: _size = ...,
+    dilation: _size = ...,
+    ceil_mode: bool = ...,
+    return_indices: bool = ...,
+) -> Tuple[Tensor, Tensor]: ...
+def max_pool2d_with_indices(
+    input: Tensor,
+    kernel_size: _size,
+    stride: Optional[_size] = ...,
+    padding: _size = ...,
+    dilation: _size = ...,
+    ceil_mode: bool = ...,
+    return_indices: bool = ...,
+) -> Tuple[Tensor, Tensor]: ...
+def max_pool3d_with_indices(
+    input: Tensor,
+    kernel_size: _size,
+    stride: Optional[_size] = ...,
+    padding: _size = ...,
+    dilation: _size = ...,
+    ceil_mode: bool = ...,
+    return_indices: bool = ...,
+) -> Tuple[Tensor, Tensor]: ...
+def max_unpool1d(
+    input: Tensor,
+    indices: Tensor,
+    kernel_size: _size,
+    stride: Optional[_size] = ...,
+    padding: _size = ...,
+    output_size: Optional[_size] = ...,
+) -> Tensor: ...
+def max_unpool2d(
+    input: Tensor,
+    indices: Tensor,
+    kernel_size: _size,
+    stride: Optional[_size] = ...,
+    padding: _size = ...,
+    output_size: Optional[_size] = ...,
+) -> Tensor: ...
+def max_unpool3d(
+    input: Tensor,
+    indices: Tensor,
+    kernel_size: _size,
+    stride: Optional[_size] = ...,
+    padding: _size = ...,
+    output_size: Optional[_size] = ...,
+) -> Tensor: ...
+def lp_pool1d(
+    input: Tensor,
+    norm_type: float,
+    kernel_size: _size_1_t,
+    stride: Union[Optional[_size], Optional[int]] = ...,
+    ceil_mode: bool = ...,
+) -> Tensor: ...
+def lp_pool2d(
+    input: Tensor,
+    norm_type: float,
+    kernel_size: _size_2_t,
+    stride: Union[Optional[_size], Optional[int]] = ...,
+    ceil_mode: bool = ...,
+) -> Tensor: ...
+def lp_pool3d(
+    input: Tensor,
+    norm_type: float,
+    kernel_size: _size_3_t,
+    stride: Union[Optional[_size], Optional[int]] = ...,
+    ceil_mode: bool = ...,
+) -> Tensor: ...
+def adaptive_max_pool1d_with_indices(
+    input: Tensor,
+    output_size: _size,
+    return_indices: bool = ...,
+) -> Tuple[Tensor, Tensor]: ...
+def adaptive_max_pool2d_with_indices(
+    input: Tensor,
+    output_size: _size_2_opt_t,
+    return_indices: bool = ...,
+) -> Tuple[Tensor, Tensor]: ...
+def adaptive_max_pool3d_with_indices(
+    input: Tensor,
+    output_size: _size_3_opt_t,
+    return_indices: bool = ...,
+) -> Tuple[Tensor, Tensor]: ...
+def adaptive_avg_pool2d(input: Tensor, output_size: _size_2_opt_t) -> Tensor: ...
+def adaptive_avg_pool3d(input: Tensor, output_size: _size_3_opt_t) -> Tensor: ...
+def dropout(
+    input: Tensor,
+    p: float = ...,
+    training: bool = ...,
+    inplace: bool = ...,
+) -> Tensor: ...
+def alpha_dropout(
+    input: Tensor,
+    p: float = ...,
+    training: bool = ...,
+    inplace: bool = ...,
+) -> Tensor: ...
+def dropout1d(
+    input: Tensor,
+    p: float = ...,
+    training: bool = ...,
+    inplace: bool = ...,
+) -> Tensor: ...
+def dropout2d(
+    input: Tensor,
+    p: float = ...,
+    training: bool = ...,
+    inplace: bool = ...,
+) -> Tensor: ...
+def dropout3d(
+    input: Tensor,
+    p: float = ...,
+    training: bool = ...,
+    inplace: bool = ...,
+) -> Tensor: ...
+def feature_alpha_dropout(
+    input: Tensor,
+    p: float = ...,
+    training: bool = ...,
+    inplace: bool = ...,
+) -> Tensor: ...
+def threshold(
+    input: Tensor,
+    threshold: float,
+    value: float,
+    inplace: bool = ...,
+) -> Tensor: ...
+def relu(input: Tensor, inplace: bool = ...) -> Tensor: ...
+def glu(input: Tensor, dim: int = ...) -> Tensor: ...
+def hardtanh(
+    input: Tensor,
+    min_val: float = ...,
+    max_val: float = ...,
+    inplace: bool = ...,
+) -> Tensor: ...
+def relu6(input: Tensor, inplace: bool = ...) -> Tensor: ...
+def elu(input: Tensor, alpha: float = ..., inplace: bool = ...) -> Tensor: ...
+def selu(input: Tensor, inplace: bool = ...) -> Tensor: ...
+def celu(input: Tensor, alpha: float = ..., inplace: bool = ...) -> Tensor: ...
+def leaky_relu(
+    input: Tensor,
+    negative_slope: float = ...,
+    inplace: bool = ...,
+) -> Tensor: ...
+def rrelu(
+    input: Tensor,
+    lower: float = ...,
+    upper: float = ...,
+    training: bool = ...,
+    inplace: bool = ...,
+) -> Tensor: ...
+def tanhshrink(input: Any): ...
+def softsign(input: Any): ...
+def softmin(
+    input: Tensor,
+    dim: Optional[int] = ...,
+    _stacklevel: int = ...,
+    dtype: Optional[_dtype] = ...,
+) -> Tensor: ...
+def softmax(
+    input: Tensor,
+    dim: Optional[int] = ...,
+    _stacklevel: int = ...,
+    dtype: Optional[_dtype] = ...,
+) -> Tensor: ...
+def gumbel_softmax(
+    logits: Tensor,
+    tau: float = ...,
+    hard: bool = ...,
+    eps: float = ...,
+    dim: int = ...,
+) -> Tensor: ...
+def log_softmax(
+    input: Tensor,
+    dim: Optional[int] = ...,
+    _stacklevel: int = ...,
+    dtype: Optional[_dtype] = ...,
+) -> Tensor: ...
+def tanh(input: Any): ...
+def sigmoid(input: Any) -> Tensor: ...
+def hardsigmoid(input: Tensor, inplace: bool = False) -> Tensor: ...
+def silu(input: Tensor, inplace: bool = False) -> Tensor: ...
+def mish(input: Tensor, inplace: bool = False) -> Tensor: ...
+def hardswish(input: Tensor, inplace: bool = False) -> Tensor: ...
+def embedding(
+    input: Tensor,
+    weight: Tensor,
+    padding_idx: Optional[int] = ...,
+    max_norm: Optional[float] = ...,
+    norm_type: float = ...,
+    scale_grad_by_freq: bool = ...,
+    sparse: bool = ...,
+) -> Tensor: ...
+def embedding_bag(
+    input: Tensor,
+    weight: Tensor,
+    offsets: Optional[Tensor] = ...,
+    max_norm: Optional[float] = ...,
+    norm_type: float = ...,
+    scale_grad_by_freq: bool = ...,
+    mode: str = ...,
+    sparse: bool = ...,
+    per_sample_weights: Optional[Tensor] = ...,
+    include_last_offset: bool = ...,
+    padding_idx: Optional[int] = ...,
+) -> Tensor: ...
+def batch_norm(
+    input: Tensor,
+    running_mean: Optional[Tensor],
+    running_var: Optional[Tensor],
+    weight: Optional[Tensor] = ...,
+    bias: Optional[Tensor] = ...,
+    training: bool = ...,
+    momentum: float = ...,
+    eps: float = ...,
+) -> Tensor: ...
+def instance_norm(
+    input: Tensor,
+    running_mean: Optional[Tensor] = ...,
+    running_var: Optional[Tensor] = ...,
+    weight: Optional[Tensor] = ...,
+    bias: Optional[Tensor] = ...,
+    use_input_stats: bool = ...,
+    momentum: float = ...,
+    eps: float = ...,
+) -> Tensor: ...
+def layer_norm(
+    input: Tensor,
+    normalized_shape: Sequence[int],
+    weight: Optional[Tensor] = ...,
+    bias: Optional[Tensor] = ...,
+    eps: float = ...,
+) -> Tensor: ...
+def rms_norm(
+    input: Tensor,
+    normalized_shape: Sequence[int],
+    weight: Optional[Tensor] = ...,
+    eps: Optional[float] = ...,
+) -> Tensor: ...
+def group_norm(
+    input: Tensor,
+    num_groups: int,
+    weight: Optional[Tensor] = ...,
+    bias: Optional[Tensor] = ...,
+    eps: float = ...,
+) -> Tensor: ...
+def local_response_norm(
+    input: Tensor,
+    size: int,
+    alpha: float = ...,
+    beta: float = ...,
+    k: float = ...,
+) -> Tensor: ...
+def ctc_loss(
+    log_probs: Tensor,
+    targets: Tensor,
+    input_lengths: Tensor,
+    target_lengths: Tensor,
+    blank: int = ...,
+    reduction: str = ...,
+    zero_infinity: bool = ...,
+) -> Tensor: ...
+def nll_loss(
+    input: Tensor,
+    target: Tensor,
+    weight: Optional[Tensor] = ...,
+    size_average: Optional[bool] = ...,
+    ignore_index: int = ...,
+    reduce: Optional[bool] = ...,
+    reduction: str = ...,
+) -> Tensor: ...
+def poisson_nll_loss(
+    input: Tensor,
+    target: Tensor,
+    log_input: bool = ...,
+    full: bool = ...,
+    size_average: Optional[bool] = ...,
+    eps: float = ...,
+    reduce: Optional[bool] = ...,
+    reduction: str = ...,
+) -> Tensor: ...
+def gaussian_nll_loss(
+    input: Tensor,
+    target: Tensor,
+    var: Tensor,
+    full: Optional[bool] = ...,
+    eps: Optional[float] = ...,
+    reduction: Optional[str] = ...,
+) -> Tensor: ...
+def kl_div(
+    input: Tensor,
+    target: Tensor,
+    size_average: Optional[bool] = ...,
+    reduce: Optional[bool] = ...,
+    reduction: str = ...,
+    log_target: bool = ...,
+) -> Tensor: ...
+def cross_entropy(
+    input: Tensor,
+    target: Tensor,
+    weight: Optional[Tensor] = ...,
+    size_average: Optional[bool] = ...,
+    ignore_index: int = ...,
+    reduce: Optional[bool] = ...,
+    reduction: str = ...,
+    label_smoothing: float = ...,
+) -> Tensor: ...
+def binary_cross_entropy(
+    input: Tensor,
+    target: Tensor,
+    weight: Optional[Tensor] = ...,
+    size_average: Optional[bool] = ...,
+    reduce: Optional[bool] = ...,
+    reduction: str = ...,
+) -> Tensor: ...
+def binary_cross_entropy_with_logits(
+    input: Tensor,
+    target: Tensor,
+    weight: Optional[Tensor] = ...,
+    size_average: Optional[bool] = ...,
+    reduce: Optional[bool] = ...,
+    reduction: str = ...,
+    pos_weight: Optional[Tensor] = ...,
+) -> Tensor: ...
+def smooth_l1_loss(
+    input: Tensor,
+    target: Tensor,
+    size_average: Optional[bool] = ...,
+    reduce: Optional[bool] = ...,
+    reduction: str = ...,
+    beta: float = ...,
+) -> Tensor: ...
+def huber_loss(
+    input: Tensor,
+    target: Tensor,
+    reduction: str = ...,
+    delta: float = ...,
+) -> Tensor: ...
+def l1_loss(
+    input: Tensor,
+    target: Tensor,
+    size_average: Optional[bool] = ...,
+    reduce: Optional[bool] = ...,
+    reduction: str = ...,
+) -> Tensor: ...
+def mse_loss(
+    input: Tensor,
+    target: Tensor,
+    size_average: Optional[bool] = ...,
+    reduce: Optional[bool] = ...,
+    reduction: str = ...,
+) -> Tensor: ...
+def margin_ranking_loss(
+    input1: Tensor,
+    input2: Tensor,
+    target: Tensor,
+    margin: float = ...,
+    size_average: Optional[bool] = ...,
+    reduce: Optional[bool] = ...,
+    reduction: str = ...,
+) -> Tensor: ...
+def hinge_embedding_loss(
+    input: Tensor,
+    target: Tensor,
+    margin: float = ...,
+    size_average: Optional[bool] = ...,
+    reduce: Optional[bool] = ...,
+    reduction: str = ...,
+) -> Tensor: ...
+def multilabel_margin_loss(
+    input: Tensor,
+    target: Tensor,
+    size_average: Optional[bool] = ...,
+    reduce: Optional[bool] = ...,
+    reduction: str = ...,
+) -> Tensor: ...
+def soft_margin_loss(
+    input: Tensor,
+    target: Tensor,
+    size_average: Optional[bool] = ...,
+    reduce: Optional[bool] = ...,
+    reduction: str = ...,
+) -> Tensor: ...
+def multilabel_soft_margin_loss(
+    input: Tensor,
+    target: Tensor,
+    weight: Optional[Tensor] = ...,
+    size_average: Optional[bool] = ...,
+    reduce: Optional[bool] = ...,
+    reduction: str = ...,
+) -> Tensor: ...
+def cosine_embedding_loss(
+    input1: Tensor,
+    input2: Tensor,
+    target: Tensor,
+    margin: float = ...,
+    size_average: Optional[bool] = ...,
+    reduce: Optional[bool] = ...,
+    reduction: str = ...,
+) -> Tensor: ...
+def multi_margin_loss(
+    input: Tensor,
+    target: Tensor,
+    p: int = ...,
+    margin: float = ...,
+    weight: Optional[Tensor] = ...,
+    size_average: Optional[bool] = ...,
+    reduce: Optional[bool] = ...,
+    reduction: str = ...,
+) -> Tensor: ...
+def upsample(
+    input: Any,
+    size: Optional[Any] = ...,
+    scale_factor: Optional[Any] = ...,
+    mode: str = ...,
+    align_corners: Optional[Any] = ...,
+): ...
+def interpolate(
+    input: Any,
+    size: Optional[Any] = ...,
+    scale_factor: Optional[Any] = ...,
+    mode: str = ...,
+    align_corners: Optional[Any] = ...,
+    recompute_scale_factor: Optional[Any] = ...,
+    antialias: bool = ...,
+): ...
+def upsample_nearest(
+    input: Any,
+    size: Optional[Any] = ...,
+    scale_factor: Optional[Any] = ...,
+): ...
+def upsample_bilinear(
+    input: Any,
+    size: Optional[Any] = ...,
+    scale_factor: Optional[Any] = ...,
+): ...
+def grid_sample(
+    input: Tensor,
+    grid: Tensor,
+    mode: str = ...,
+    padding_mode: str = ...,
+    align_corners: Optional[Any] = ...,
+) -> Tensor: ...
+def affine_grid(
+    theta: Tensor,
+    size: List[int],
+    align_corners: Optional[Any] = ...,
+) -> Tensor: ...
+def triplet_margin_loss(
+    anchor: Tensor,
+    positive: Tensor,
+    negative: Tensor,
+    margin: float = ...,
+    p: float = ...,
+    eps: float = ...,
+    swap: bool = ...,
+    size_average: Optional[bool] = ...,
+    reduce: Optional[bool] = ...,
+    reduction: str = ...,
+) -> Tensor: ...
+def triplet_margin_with_distance_loss(
+    anchor: Tensor,
+    positive: Tensor,
+    negative: Tensor,
+    *,
+    distance_function: Optional[Callable[[Tensor, Tensor], Tensor]] = ...,
+    margin: float = ...,
+    swap: bool = ...,
+    reduction: str = ...,
+) -> Tensor: ...
+def normalize(
+    input: Tensor,
+    p: float = ...,
+    dim: int = ...,
+    eps: float = ...,
+    out: Optional[Tensor] = ...,
+) -> Tensor: ...
+def assert_int_or_pair(
+    arg: Any,
+    arg_name: Any,
+    message: Any,
+) -> None: ...
+def unfold(
+    input: Tensor,
+    kernel_size: _size_any_t,
+    dilation: _size_any_t = ...,
+    padding: _size_any_t = ...,
+    stride: _size_any_t = ...,
+) -> Tensor: ...
+def fold(
+    input: Tensor,
+    output_size: _size_any_t,
+    kernel_size: _size_any_t,
+    dilation: _size_any_t = ...,
+    padding: _size_any_t = ...,
+    stride: _size_any_t = ...,
+) -> Tensor: ...
+def _canonical_mask(
+    mask: Optional[Tensor],
+    mask_name: str,
+    other_type: Optional[_dtype],
+    other_name: str,
+    target_type: _dtype,
+    check_other: bool = True,
+) -> Optional[Tensor]: ...
+def _none_or_dtype(input: Optional[Tensor]) -> Optional[_dtype]: ...
+def multi_head_attention_forward(
+    query: Tensor,
+    key: Tensor,
+    value: Tensor,
+    embed_dim_to_check: int,
+    num_heads: int,
+    in_proj_weight: Optional[Tensor],
+    in_proj_bias: Optional[Tensor],
+    bias_k: Optional[Tensor],
+    bias_v: Optional[Tensor],
+    add_zero_attn: bool,
+    dropout_p: float,
+    out_proj_weight: Tensor,
+    out_proj_bias: Optional[Tensor],
+    training: bool = True,
+    key_padding_mask: Optional[Tensor] = None,
+    need_weights: bool = True,
+    attn_mask: Optional[Tensor] = None,
+    use_separate_proj_weight: bool = False,
+    q_proj_weight: Optional[Tensor] = None,
+    k_proj_weight: Optional[Tensor] = None,
+    v_proj_weight: Optional[Tensor] = None,
+    static_k: Optional[Tensor] = None,
+    static_v: Optional[Tensor] = None,
+    average_attn_weights: bool = True,
+    is_causal: bool = False,
+) -> Tuple[Tensor, Optional[Tensor]]: ...
+
+from torch import conv1d as conv1d
+from torch import conv2d as conv2d
+from torch import conv3d as conv3d
+from torch import conv_transpose1d as conv_transpose1d
+from torch import conv_transpose2d as conv_transpose2d
+from torch import conv_transpose3d as conv_transpose3d
+from torch import conv_tbc as conv_tbc
+from torch import avg_pool1d as avg_pool1d
+from torch import adaptive_avg_pool1d as adaptive_avg_pool1d
+from torch import relu_ as relu_
+from torch import selu_ as selu_
+from torch import celu_ as celu_
+from torch import prelu as prelu
+from torch import rrelu_ as rrelu_
+from torch import hardshrink as hardshrink
+from torch import bilinear as bilinear
+from torch import pixel_shuffle as pixel_shuffle
+from torch import pixel_unshuffle as pixel_unshuffle
+from torch import channel_shuffle as channel_shuffle
+from torch import native_channel_shuffle as native_channel_shuffle
+from torch import pairwise_distance as pairwise_distance
+from torch import pdist as pdist
+from torch import cosine_similarity as cosine_similarity
+from torch._C._nn import avg_pool2d as avg_pool2d
+from torch._C._nn import avg_pool3d as avg_pool3d
+from torch._C._nn import hardtanh_ as hardtanh_
+from torch._C._nn import elu_ as elu_
+from torch._C._nn import leaky_relu_ as leaky_relu_
+from torch._C._nn import gelu as gelu
+from torch._C._nn import softplus as softplus
+from torch._C._nn import softshrink as softshrink
+from torch._C._nn import linear as linear
+from torch._C._nn import pad as pad
+from torch._C._nn import one_hot as one_hot
+from torch._C._nn import scaled_dot_product_attention as scaled_dot_product_attention
+from torch._C._nn import log_sigmoid
+logsigmoid = log_sigmoid
+
+@overload
+def adaptive_max_pool1d(input: Tensor, output_size: Union[_int, _size], return_indices: Literal[False] = False) -> Tensor: ...
+@overload
+def adaptive_max_pool1d(input: Tensor, output_size: Union[_int, _size], return_indices: Literal[True], /) -> Tuple[Tensor, Tensor]: ...
+@overload
+def adaptive_max_pool1d(input: Tensor, output_size: Union[_int, _size], *, return_indices: Literal[True]) -> Tuple[Tensor, Tensor]: ...
+@overload
+def adaptive_max_pool2d(input: Tensor, output_size: Union[_int, _size], return_indices: Literal[False] = False) -> Tensor: ...
+@overload
+def adaptive_max_pool2d(input: Tensor, output_size: Union[_int, _size], return_indices: Literal[True], /) -> Tuple[Tensor, Tensor]: ...
+@overload
+def adaptive_max_pool2d(input: Tensor, output_size: Union[_int, _size], *, return_indices: Literal[True]) -> Tuple[Tensor, Tensor]: ...
+@overload
+def adaptive_max_pool3d(input: Tensor, output_size: Union[_int, _size], return_indices: Literal[False] = False) -> Tensor: ...
+@overload
+def adaptive_max_pool3d(input: Tensor, output_size: Union[_int, _size], return_indices: Literal[True], /) -> Tuple[Tensor, Tensor]: ...
+@overload
+def adaptive_max_pool3d(input: Tensor, output_size: Union[_int, _size], *, return_indices: Literal[True]) -> Tuple[Tensor, Tensor]: ...
+@overload
+def fractional_max_pool2d(input: Tensor, kernel_size: Union[_int, _size], output_size: Optional[Union[_int, _size]] = None, output_ratio: Optional[_ratio_any_t] = None, return_indices: Literal[False] = False, _random_samples: Optional[Tensor] = None) -> Tensor: ...
+@overload
+def fractional_max_pool2d(input: Tensor, kernel_size: Union[_int, _size], output_size: Optional[Union[_int, _size]], output_ratio: Optional[_ratio_any_t], return_indices: Literal[True], /, _random_samples: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]: ...
+@overload
+def fractional_max_pool2d(input: Tensor, kernel_size: Union[_int, _size], output_size: Optional[Union[_int, _size]] = None, output_ratio: Optional[_ratio_any_t] = None, *, return_indices: Literal[True], _random_samples: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]: ...
+@overload
+def fractional_max_pool3d(input: Tensor, kernel_size: Union[_int, _size], output_size: Optional[Union[_int, _size]] = None, output_ratio: Optional[_ratio_any_t] = None, return_indices: Literal[False] = False, _random_samples: Optional[Tensor] = None) -> Tensor: ...
+@overload
+def fractional_max_pool3d(input: Tensor, kernel_size: Union[_int, _size], output_size: Optional[Union[_int, _size]], output_ratio: Optional[_ratio_any_t], return_indices: Literal[True], /, _random_samples: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]: ...
+@overload
+def fractional_max_pool3d(input: Tensor, kernel_size: Union[_int, _size], output_size: Optional[Union[_int, _size]] = None, output_ratio: Optional[_ratio_any_t] = None, *, return_indices: Literal[True], _random_samples: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]: ...
+@overload
+def max_pool1d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]] = None, padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: bool = False, return_indices: Literal[False] = False) -> Tensor: ...
+@overload
+def max_pool1d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]], padding: Union[_int, _size], dilation: Union[_int, _size], ceil_mode: bool, return_indices: Literal[True], /) -> Tuple[Tensor, Tensor]: ...
+@overload
+def max_pool1d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]] = None, padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: bool = False, *, return_indices: Literal[True]) -> Tuple[Tensor, Tensor]: ...
+@overload
+def max_pool2d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]] = None, padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: bool = False, return_indices: Literal[False] = False) -> Tensor: ...
+@overload
+def max_pool2d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]], padding: Union[_int, _size], dilation: Union[_int, _size], ceil_mode: bool, return_indices: Literal[True], /) -> Tuple[Tensor, Tensor]: ...
+@overload
+def max_pool2d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]] = None, padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: bool = False, *, return_indices: Literal[True]) -> Tuple[Tensor, Tensor]: ...
+@overload
+def max_pool3d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]] = None, padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: bool = False, return_indices: Literal[False] = False) -> Tensor: ...
+@overload
+def max_pool3d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]], padding: Union[_int, _size], dilation: Union[_int, _size], ceil_mode: bool, return_indices: Literal[True], /) -> Tuple[Tensor, Tensor]: ...
+@overload
+def max_pool3d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]] = None, padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: bool = False, *, return_indices: Literal[True]) -> Tuple[Tensor, Tensor]: ...
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/grad.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/grad.py
new file mode 100644
index 0000000000000000000000000000000000000000..61e817dbed612e51f1ff426373fbef1d192e331e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/grad.py
@@ -0,0 +1,298 @@
+# mypy: allow-untyped-defs
+"""Gradient interface."""
+
+import torch
+from torch.nn.modules.utils import _pair, _single, _triple
+
+
+def conv1d_input(
+    input_size,
+    weight,
+    grad_output,
+    stride=1,
+    padding=0,
+    dilation=1,
+    groups=1,
+):
+    r"""Compute the gradient of conv1d with respect to the input of the convolution.
+
+    This is same as the 1D transposed convolution operator under the hood but requires
+    the shape of the gradient w.r.t. input to be specified explicitly.
+
+    Args:
+        input_size : Shape of the input gradient tensor
+        weight: weight tensor (out_channels x in_channels/groups x kW)
+        grad_output : output gradient tensor (minibatch x out_channels x oW)
+        stride (int or tuple, optional): Stride of the convolution. Default: 1
+        padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0
+        dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
+        groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
+
+    Examples::
+
+        >>> input = torch.randn(1, 1, 3, requires_grad=True)
+        >>> weight = torch.randn(1, 1, 1, requires_grad=True)
+        >>> output = F.conv1d(input, weight)
+        >>> grad_output = torch.randn(output.shape)
+        >>> grad_input = torch.autograd.grad(output, input, grad_output)
+        >>> F.grad.conv1d_input(input.shape, weight, grad_output)
+
+    """
+    input = grad_output.new_empty(1).expand(input_size)
+
+    return torch.ops.aten.convolution_backward(
+        grad_output,
+        input,
+        weight,
+        None,
+        _single(stride),
+        _single(padding),
+        _single(dilation),
+        False,
+        [0],
+        groups,
+        (True, False, False),
+    )[0]
+
+
+def conv1d_weight(
+    input,
+    weight_size,
+    grad_output,
+    stride=1,
+    padding=0,
+    dilation=1,
+    groups=1,
+):
+    r"""Compute the gradient of conv1d with respect to the weight of the convolution.
+
+    Args:
+        input: input tensor of shape (minibatch x in_channels x iW)
+        weight_size : Shape of the weight gradient tensor
+        grad_output : output gradient tensor (minibatch x out_channels x oW)
+        stride (int or tuple, optional): Stride of the convolution. Default: 1
+        padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0
+        dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
+        groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
+
+    Examples::
+
+        >>> input = torch.randn(1, 1, 3, requires_grad=True)
+        >>> weight = torch.randn(1, 1, 1, requires_grad=True)
+        >>> output = F.conv1d(input, weight)
+        >>> grad_output = torch.randn(output.shape)
+        >>> # xdoctest: +SKIP
+        >>> grad_weight = torch.autograd.grad(output, filter, grad_output)
+        >>> F.grad.conv1d_weight(input, weight.shape, grad_output)
+
+    """
+    weight = grad_output.new_empty(1).expand(weight_size)
+
+    return torch.ops.aten.convolution_backward(
+        grad_output,
+        input,
+        weight,
+        None,
+        _single(stride),
+        _single(padding),
+        _single(dilation),
+        False,
+        [0],
+        groups,
+        (False, True, False),
+    )[1]
+
+
+def conv2d_input(
+    input_size,
+    weight,
+    grad_output,
+    stride=1,
+    padding=0,
+    dilation=1,
+    groups=1,
+):
+    r"""Compute the gradient of conv2d with respect to the input of the convolution.
+
+    This is same as the 2D transposed convolution operator under the hood but requires
+    the shape of the gradient w.r.t. input to be specified explicitly.
+
+    Args:
+        input_size : Shape of the input gradient tensor
+        weight: weight tensor (out_channels x in_channels/groups x kH x kW)
+        grad_output : output gradient tensor (minibatch x out_channels x oH x oW)
+        stride (int or tuple, optional): Stride of the convolution. Default: 1
+        padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0
+        dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
+        groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
+
+    Examples::
+
+        >>> input = torch.randn(1, 1, 3, 3, requires_grad=True)
+        >>> weight = torch.randn(1, 1, 1, 2, requires_grad=True)
+        >>> output = F.conv2d(input, weight)
+        >>> grad_output = torch.randn(output.shape)
+        >>> grad_input = torch.autograd.grad(output, input, grad_output)
+        >>> F.grad.conv2d_input(input.shape, weight, grad_output)
+
+    """
+    input = grad_output.new_empty(1).expand(input_size)
+
+    return torch.ops.aten.convolution_backward(
+        grad_output,
+        input,
+        weight,
+        None,
+        _pair(stride),
+        _pair(padding),
+        _pair(dilation),
+        False,
+        [0],
+        groups,
+        (True, False, False),
+    )[0]
+
+
+def conv2d_weight(
+    input,
+    weight_size,
+    grad_output,
+    stride=1,
+    padding=0,
+    dilation=1,
+    groups=1,
+):
+    r"""Compute the gradient of conv2d with respect to the weight of the convolution.
+
+    Args:
+        input: input tensor of shape (minibatch x in_channels x iH x iW)
+        weight_size : Shape of the weight gradient tensor
+        grad_output : output gradient tensor (minibatch x out_channels x oH x oW)
+        stride (int or tuple, optional): Stride of the convolution. Default: 1
+        padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0
+        dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
+        groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
+
+    Examples::
+
+        >>> input = torch.randn(1, 1, 3, 3, requires_grad=True)
+        >>> weight = torch.randn(1, 1, 1, 2, requires_grad=True)
+        >>> output = F.conv2d(input, weight)
+        >>> grad_output = torch.randn(output.shape)
+        >>> # xdoctest: +SKIP
+        >>> grad_weight = torch.autograd.grad(output, filter, grad_output)
+        >>> F.grad.conv2d_weight(input, weight.shape, grad_output)
+
+    """
+    weight = grad_output.new_empty(1).expand(weight_size)
+
+    return torch.ops.aten.convolution_backward(
+        grad_output,
+        input,
+        weight,
+        None,
+        _pair(stride),
+        _pair(padding),
+        _pair(dilation),
+        False,
+        [0],
+        groups,
+        (False, True, False),
+    )[1]
+
+
+def conv3d_input(
+    input_size,
+    weight,
+    grad_output,
+    stride=1,
+    padding=0,
+    dilation=1,
+    groups=1,
+):
+    r"""Compute the gradient of conv3d with respect to the input of the convolution.
+
+    This is same as the 3D transposed convolution operator under the hood but requires
+    the shape of the gradient w.r.t. input to be specified explicitly.
+
+    Args:
+        input_size : Shape of the input gradient tensor
+        weight: weights tensor (out_channels x in_channels/groups x kT x kH x kW)
+        grad_output : output gradient tensor (minibatch x out_channels x oT x oH x oW)
+        stride (int or tuple, optional): Stride of the convolution. Default: 1
+        padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0
+        dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
+        groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
+
+    Examples::
+
+        >>> input = torch.randn(2, 8, 10, 10, 20, requires_grad=True)
+        >>> weight = torch.randn(4, 8, 2, 3, 3, requires_grad=True)
+        >>> output = F.conv3d(input, weight)
+        >>> grad_output = torch.randn(output.shape)
+        >>> grad_input = torch.autograd.grad(output, input, grad_output)
+        >>> F.grad.conv3d_input(input.shape, weight, grad_output)
+
+    """
+    input = grad_output.new_empty(1).expand(input_size)
+
+    return torch.ops.aten.convolution_backward(
+        grad_output,
+        input,
+        weight,
+        None,
+        _triple(stride),
+        _triple(padding),
+        _triple(dilation),
+        False,
+        [0],
+        groups,
+        (True, False, False),
+    )[0]
+
+
+def conv3d_weight(
+    input,
+    weight_size,
+    grad_output,
+    stride=1,
+    padding=0,
+    dilation=1,
+    groups=1,
+):
+    r"""Compute the gradient of conv3d with respect to the weight of the convolution.
+
+    Args:
+        input: input tensor of shape (minibatch x in_channels x iT x iH x iW)
+        weight_size : Shape of the weight gradient tensor
+        grad_output : output gradient tensor (minibatch x out_channels x oT x oH x oW)
+        stride (int or tuple, optional): Stride of the convolution. Default: 1
+        padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0
+        dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
+        groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
+
+    Examples::
+
+        >>> input = torch.randn(2, 8, 10, 10, 20, requires_grad=True)
+        >>> weight = torch.randn(4, 8, 2, 3, 3, requires_grad=True)
+        >>> output = F.conv3d(input, weight)
+        >>> grad_output = torch.randn(output.shape)
+        >>> grad_weight = torch.autograd.grad(output, weight, grad_output)
+        >>> F.grad.conv3d_weight(input, weight.shape, grad_output)
+
+    """
+    weight = grad_output.new_empty(1).expand(weight_size)
+
+    return torch.ops.aten.convolution_backward(
+        grad_output,
+        input,
+        weight,
+        None,
+        _triple(stride),
+        _triple(padding),
+        _triple(dilation),
+        False,
+        [0],
+        groups,
+        (False, True, False),
+    )[1]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/init.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/init.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d0600b43b68f96e0db09dd330d93582c7fb0ff0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/init.py
@@ -0,0 +1,697 @@
+# mypy: allow-untyped-defs
+"""This file contains utilities for initializing neural network parameters."""
+import math
+import warnings
+from typing import Optional as _Optional
+
+import torch
+from torch import Tensor
+
+
+# These no_grad_* functions are necessary as wrappers around the parts of these
+# functions that use `with torch.no_grad()`. The JIT doesn't support context
+# managers, so these need to be implemented as builtins. Using these wrappers
+# lets us keep those builtins small and re-usable.
+def _no_grad_uniform_(tensor, a, b, generator=None):
+    with torch.no_grad():
+        return tensor.uniform_(a, b, generator=generator)
+
+
+def _no_grad_normal_(tensor, mean, std, generator=None):
+    with torch.no_grad():
+        return tensor.normal_(mean, std, generator=generator)
+
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b, generator=None):
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn(
+            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+            "The distribution of values may be incorrect.",
+            stacklevel=2,
+        )
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        l = norm_cdf((a - mean) / std)
+        u = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [l, u], then translate to
+        # [2l-1, 2u-1].
+        tensor.uniform_(2 * l - 1, 2 * u - 1, generator=generator)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.0))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def _no_grad_fill_(tensor, val):
+    with torch.no_grad():
+        return tensor.fill_(val)
+
+
+def _no_grad_zero_(tensor):
+    with torch.no_grad():
+        return tensor.zero_()
+
+
+def calculate_gain(nonlinearity, param=None):
+    r"""Return the recommended gain value for the given nonlinearity function.
+
+    The values are as follows:
+
+    ================= ====================================================
+    nonlinearity      gain
+    ================= ====================================================
+    Linear / Identity :math:`1`
+    Conv{1,2,3}D      :math:`1`
+    Sigmoid           :math:`1`
+    Tanh              :math:`\frac{5}{3}`
+    ReLU              :math:`\sqrt{2}`
+    Leaky Relu        :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}`
+    SELU              :math:`\frac{3}{4}`
+    ================= ====================================================
+
+    .. warning::
+        In order to implement `Self-Normalizing Neural Networks`_ ,
+        you should use ``nonlinearity='linear'`` instead of ``nonlinearity='selu'``.
+        This gives the initial weights a variance of ``1 / N``,
+        which is necessary to induce a stable fixed point in the forward pass.
+        In contrast, the default gain for ``SELU`` sacrifices the normalization
+        effect for more stable gradient flow in rectangular layers.
+
+    Args:
+        nonlinearity: the non-linear function (`nn.functional` name)
+        param: optional parameter for the non-linear function
+
+    Examples:
+        >>> gain = nn.init.calculate_gain('leaky_relu', 0.2)  # leaky_relu with negative_slope=0.2
+
+    .. _Self-Normalizing Neural Networks: https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html
+    """
+    linear_fns = [
+        "linear",
+        "conv1d",
+        "conv2d",
+        "conv3d",
+        "conv_transpose1d",
+        "conv_transpose2d",
+        "conv_transpose3d",
+    ]
+    if nonlinearity in linear_fns or nonlinearity == "sigmoid":
+        return 1
+    elif nonlinearity == "tanh":
+        return 5.0 / 3
+    elif nonlinearity == "relu":
+        return math.sqrt(2.0)
+    elif nonlinearity == "leaky_relu":
+        if param is None:
+            negative_slope = 0.01
+        elif (
+            not isinstance(param, bool)
+            and isinstance(param, int)
+            or isinstance(param, float)
+        ):
+            # True/False are instances of int, hence check above
+            negative_slope = param
+        else:
+            raise ValueError(f"negative_slope {param} not a valid number")
+        return math.sqrt(2.0 / (1 + negative_slope**2))
+    elif nonlinearity == "selu":
+        return (
+            3.0 / 4
+        )  # Value found empirically (https://github.com/pytorch/pytorch/pull/50664)
+    else:
+        raise ValueError(f"Unsupported nonlinearity {nonlinearity}")
+
+
+def uniform_(
+    tensor: Tensor,
+    a: float = 0.0,
+    b: float = 1.0,
+    generator: _Optional[torch.Generator] = None,
+) -> Tensor:
+    r"""Fill the input Tensor with values drawn from the uniform distribution.
+
+    :math:`\mathcal{U}(a, b)`.
+
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        a: the lower bound of the uniform distribution
+        b: the upper bound of the uniform distribution
+        generator: the torch Generator to sample from (default: None)
+
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.uniform_(w)
+    """
+    if torch.overrides.has_torch_function_variadic(tensor):
+        return torch.overrides.handle_torch_function(
+            uniform_, (tensor,), tensor=tensor, a=a, b=b, generator=generator
+        )
+    return _no_grad_uniform_(tensor, a, b, generator)
+
+
+def normal_(
+    tensor: Tensor,
+    mean: float = 0.0,
+    std: float = 1.0,
+    generator: _Optional[torch.Generator] = None,
+) -> Tensor:
+    r"""Fill the input Tensor with values drawn from the normal distribution.
+
+    :math:`\mathcal{N}(\text{mean}, \text{std}^2)`.
+
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        generator: the torch Generator to sample from (default: None)
+
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.normal_(w)
+    """
+    if torch.overrides.has_torch_function_variadic(tensor):
+        return torch.overrides.handle_torch_function(
+            normal_, (tensor,), tensor=tensor, mean=mean, std=std, generator=generator
+        )
+    return _no_grad_normal_(tensor, mean, std, generator)
+
+
+def trunc_normal_(
+    tensor: Tensor,
+    mean: float = 0.0,
+    std: float = 1.0,
+    a: float = -2.0,
+    b: float = 2.0,
+    generator: _Optional[torch.Generator] = None,
+) -> Tensor:
+    r"""Fill the input Tensor with values drawn from a truncated normal distribution.
+
+    The values are effectively drawn from the
+    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \leq \text{mean} \leq b`.
+
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        a: the minimum cutoff value
+        b: the maximum cutoff value
+        generator: the torch Generator to sample from (default: None)
+
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.trunc_normal_(w)
+    """
+    return _no_grad_trunc_normal_(tensor, mean, std, a, b, generator=generator)
+
+
+def constant_(tensor: Tensor, val: float) -> Tensor:
+    r"""Fill the input Tensor with the value :math:`\text{val}`.
+
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        val: the value to fill the tensor with
+
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.constant_(w, 0.3)
+    """
+    if torch.overrides.has_torch_function_variadic(tensor):
+        return torch.overrides.handle_torch_function(
+            constant_, (tensor,), tensor=tensor, val=val
+        )
+    return _no_grad_fill_(tensor, val)
+
+
+def ones_(tensor: Tensor) -> Tensor:
+    r"""Fill the input Tensor with the scalar value `1`.
+
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.ones_(w)
+    """
+    return _no_grad_fill_(tensor, 1.0)
+
+
+def zeros_(tensor: Tensor) -> Tensor:
+    r"""Fill the input Tensor with the scalar value `0`.
+
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.zeros_(w)
+    """
+    return _no_grad_zero_(tensor)
+
+
+def eye_(tensor):
+    r"""Fill the 2-dimensional input `Tensor` with the identity matrix.
+
+    Preserves the identity of the inputs in `Linear` layers, where as
+    many inputs are preserved as possible.
+
+    Args:
+        tensor: a 2-dimensional `torch.Tensor`
+
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.eye_(w)
+    """
+    if tensor.ndimension() != 2:
+        raise ValueError("Only tensors with 2 dimensions are supported")
+
+    with torch.no_grad():
+        torch.eye(*tensor.shape, out=tensor, requires_grad=tensor.requires_grad)
+    return tensor
+
+
+def dirac_(tensor, groups=1):
+    r"""Fill the {3, 4, 5}-dimensional input `Tensor` with the Dirac delta function.
+
+    Preserves the identity of the inputs in `Convolutional`
+    layers, where as many input channels are preserved as possible. In case
+    of groups>1, each group of channels preserves identity
+
+    Args:
+        tensor: a {3, 4, 5}-dimensional `torch.Tensor`
+        groups (int, optional): number of groups in the conv layer (default: 1)
+    Examples:
+        >>> w = torch.empty(3, 16, 5, 5)
+        >>> nn.init.dirac_(w)
+        >>> w = torch.empty(3, 24, 5, 5)
+        >>> nn.init.dirac_(w, 3)
+    """
+    dimensions = tensor.ndimension()
+    if dimensions not in [3, 4, 5]:
+        raise ValueError("Only tensors with 3, 4, or 5 dimensions are supported")
+
+    sizes = tensor.size()
+
+    if sizes[0] % groups != 0:
+        raise ValueError("dim 0 must be divisible by groups")
+
+    out_chans_per_grp = sizes[0] // groups
+    min_dim = min(out_chans_per_grp, sizes[1])
+
+    with torch.no_grad():
+        tensor.zero_()
+
+        for g in range(groups):
+            for d in range(min_dim):
+                if dimensions == 3:  # Temporal convolution
+                    tensor[g * out_chans_per_grp + d, d, tensor.size(2) // 2] = 1
+                elif dimensions == 4:  # Spatial convolution
+                    tensor[
+                        g * out_chans_per_grp + d,
+                        d,
+                        tensor.size(2) // 2,
+                        tensor.size(3) // 2,
+                    ] = 1
+                else:  # Volumetric convolution
+                    tensor[
+                        g * out_chans_per_grp + d,
+                        d,
+                        tensor.size(2) // 2,
+                        tensor.size(3) // 2,
+                        tensor.size(4) // 2,
+                    ] = 1
+    return tensor
+
+
+def _calculate_fan_in_and_fan_out(tensor):
+    dimensions = tensor.dim()
+    if dimensions < 2:
+        raise ValueError(
+            "Fan in and fan out can not be computed for tensor with fewer than 2 dimensions"
+        )
+
+    num_input_fmaps = tensor.size(1)
+    num_output_fmaps = tensor.size(0)
+    receptive_field_size = 1
+    if tensor.dim() > 2:
+        # math.prod is not always available, accumulate the product manually
+        # we could use functools.reduce but that is not supported by TorchScript
+        for s in tensor.shape[2:]:
+            receptive_field_size *= s
+    fan_in = num_input_fmaps * receptive_field_size
+    fan_out = num_output_fmaps * receptive_field_size
+
+    return fan_in, fan_out
+
+
+def xavier_uniform_(
+    tensor: Tensor,
+    gain: float = 1.0,
+    generator: _Optional[torch.Generator] = None,
+) -> Tensor:
+    r"""Fill the input `Tensor` with values using a Xavier uniform distribution.
+
+    The method is described in `Understanding the difficulty of training
+    deep feedforward neural networks` - Glorot, X. & Bengio, Y. (2010).
+    The resulting tensor will have values sampled from
+    :math:`\mathcal{U}(-a, a)` where
+
+    .. math::
+        a = \text{gain} \times \sqrt{\frac{6}{\text{fan\_in} + \text{fan\_out}}}
+
+    Also known as Glorot initialization.
+
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        gain: an optional scaling factor
+        generator: the torch Generator to sample from (default: None)
+
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.xavier_uniform_(w, gain=nn.init.calculate_gain('relu'))
+
+    Note:
+        Be aware that ``fan_in`` and ``fan_out`` are calculated assuming
+        that the weight matrix is used in a transposed manner,
+        (i.e., ``x @ w.T`` in ``Linear`` layers, where ``w.shape = [fan_out, fan_in]``).
+        This is important for correct initialization.
+        If you plan to use ``x @ w``, where ``w.shape = [fan_in, fan_out]``,
+        pass in a transposed weight matrix, i.e. ``nn.init.xavier_uniform_(w.T, ...)``.
+    """
+    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
+    std = gain * math.sqrt(2.0 / float(fan_in + fan_out))
+    a = math.sqrt(3.0) * std  # Calculate uniform bounds from standard deviation
+
+    return _no_grad_uniform_(tensor, -a, a, generator)
+
+
+def xavier_normal_(
+    tensor: Tensor,
+    gain: float = 1.0,
+    generator: _Optional[torch.Generator] = None,
+) -> Tensor:
+    r"""Fill the input `Tensor` with values using a Xavier normal distribution.
+
+    The method is described in `Understanding the difficulty of training deep feedforward
+    neural networks` - Glorot, X. & Bengio, Y. (2010). The resulting tensor
+    will have values sampled from :math:`\mathcal{N}(0, \text{std}^2)` where
+
+    .. math::
+        \text{std} = \text{gain} \times \sqrt{\frac{2}{\text{fan\_in} + \text{fan\_out}}}
+
+    Also known as Glorot initialization.
+
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        gain: an optional scaling factor
+        generator: the torch Generator to sample from (default: None)
+
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.xavier_normal_(w)
+
+    Note:
+        Be aware that ``fan_in`` and ``fan_out`` are calculated assuming
+        that the weight matrix is used in a transposed manner,
+        (i.e., ``x @ w.T`` in ``Linear`` layers, where ``w.shape = [fan_out, fan_in]``).
+        This is important for correct initialization.
+        If you plan to use ``x @ w``, where ``w.shape = [fan_in, fan_out]``,
+        pass in a transposed weight matrix, i.e. ``nn.init.xavier_normal_(w.T, ...)``.
+    """
+    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
+    std = gain * math.sqrt(2.0 / float(fan_in + fan_out))
+
+    return _no_grad_normal_(tensor, 0.0, std, generator)
+
+
+def _calculate_correct_fan(tensor, mode):
+    mode = mode.lower()
+    valid_modes = ["fan_in", "fan_out"]
+    if mode not in valid_modes:
+        raise ValueError(f"Mode {mode} not supported, please use one of {valid_modes}")
+
+    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
+    return fan_in if mode == "fan_in" else fan_out
+
+
+def kaiming_uniform_(
+    tensor: Tensor,
+    a: float = 0,
+    mode: str = "fan_in",
+    nonlinearity: str = "leaky_relu",
+    generator: _Optional[torch.Generator] = None,
+):
+    r"""Fill the input `Tensor` with values using a Kaiming uniform distribution.
+
+    The method is described in `Delving deep into rectifiers: Surpassing
+    human-level performance on ImageNet classification` - He, K. et al. (2015).
+    The resulting tensor will have values sampled from
+    :math:`\mathcal{U}(-\text{bound}, \text{bound})` where
+
+    .. math::
+        \text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}}
+
+    Also known as He initialization.
+
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        a: the negative slope of the rectifier used after this layer (only
+            used with ``'leaky_relu'``)
+        mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
+            preserves the magnitude of the variance of the weights in the
+            forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
+            backwards pass.
+        nonlinearity: the non-linear function (`nn.functional` name),
+            recommended to use only with ``'relu'`` or ``'leaky_relu'`` (default).
+        generator: the torch Generator to sample from (default: None)
+
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.kaiming_uniform_(w, mode='fan_in', nonlinearity='relu')
+
+    Note:
+        Be aware that ``fan_in`` and ``fan_out`` are calculated assuming
+        that the weight matrix is used in a transposed manner,
+        (i.e., ``x @ w.T`` in ``Linear`` layers, where ``w.shape = [fan_out, fan_in]``).
+        This is important for correct initialization.
+        If you plan to use ``x @ w``, where ``w.shape = [fan_in, fan_out]``,
+        pass in a transposed weight matrix, i.e. ``nn.init.kaiming_uniform_(w.T, ...)``.
+    """
+    if torch.overrides.has_torch_function_variadic(tensor):
+        return torch.overrides.handle_torch_function(
+            kaiming_uniform_,
+            (tensor,),
+            tensor=tensor,
+            a=a,
+            mode=mode,
+            nonlinearity=nonlinearity,
+            generator=generator,
+        )
+
+    if 0 in tensor.shape:
+        warnings.warn("Initializing zero-element tensors is a no-op")
+        return tensor
+    fan = _calculate_correct_fan(tensor, mode)
+    gain = calculate_gain(nonlinearity, a)
+    std = gain / math.sqrt(fan)
+    bound = math.sqrt(3.0) * std  # Calculate uniform bounds from standard deviation
+    with torch.no_grad():
+        return tensor.uniform_(-bound, bound, generator=generator)
+
+
+def kaiming_normal_(
+    tensor: Tensor,
+    a: float = 0,
+    mode: str = "fan_in",
+    nonlinearity: str = "leaky_relu",
+    generator: _Optional[torch.Generator] = None,
+):
+    r"""Fill the input `Tensor` with values using a Kaiming normal distribution.
+
+    The method is described in `Delving deep into rectifiers: Surpassing
+    human-level performance on ImageNet classification` - He, K. et al. (2015).
+    The resulting tensor will have values sampled from
+    :math:`\mathcal{N}(0, \text{std}^2)` where
+
+    .. math::
+        \text{std} = \frac{\text{gain}}{\sqrt{\text{fan\_mode}}}
+
+    Also known as He initialization.
+
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        a: the negative slope of the rectifier used after this layer (only
+            used with ``'leaky_relu'``)
+        mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
+            preserves the magnitude of the variance of the weights in the
+            forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
+            backwards pass.
+        nonlinearity: the non-linear function (`nn.functional` name),
+            recommended to use only with ``'relu'`` or ``'leaky_relu'`` (default).
+        generator: the torch Generator to sample from (default: None)
+
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.kaiming_normal_(w, mode='fan_out', nonlinearity='relu')
+
+    Note:
+        Be aware that ``fan_in`` and ``fan_out`` are calculated assuming
+        that the weight matrix is used in a transposed manner,
+        (i.e., ``x @ w.T`` in ``Linear`` layers, where ``w.shape = [fan_out, fan_in]``).
+        This is important for correct initialization.
+        If you plan to use ``x @ w``, where ``w.shape = [fan_in, fan_out]``,
+        pass in a transposed weight matrix, i.e. ``nn.init.kaiming_normal_(w.T, ...)``.
+    """
+    if 0 in tensor.shape:
+        warnings.warn("Initializing zero-element tensors is a no-op")
+        return tensor
+    fan = _calculate_correct_fan(tensor, mode)
+    gain = calculate_gain(nonlinearity, a)
+    std = gain / math.sqrt(fan)
+    with torch.no_grad():
+        return tensor.normal_(0, std, generator=generator)
+
+
+def orthogonal_(
+    tensor,
+    gain=1,
+    generator: _Optional[torch.Generator] = None,
+):
+    r"""Fill the input `Tensor` with a (semi) orthogonal matrix.
+
+    Described in `Exact solutions to the nonlinear dynamics of learning in deep
+    linear neural networks` - Saxe, A. et al. (2013). The input tensor must have
+    at least 2 dimensions, and for tensors with more than 2 dimensions the
+    trailing dimensions are flattened.
+
+    Args:
+        tensor: an n-dimensional `torch.Tensor`, where :math:`n \geq 2`
+        gain: optional scaling factor
+        generator: the torch Generator to sample from (default: None)
+
+    Examples:
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK)
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.orthogonal_(w)
+    """
+    if tensor.ndimension() < 2:
+        raise ValueError("Only tensors with 2 or more dimensions are supported")
+
+    if tensor.numel() == 0:
+        # no-op
+        return tensor
+    rows = tensor.size(0)
+    cols = tensor.numel() // rows
+    flattened = tensor.new_empty((rows, cols)).normal_(0, 1, generator=generator)
+
+    if rows < cols:
+        flattened.t_()
+
+    # Compute the qr factorization
+    q, r = torch.linalg.qr(flattened)
+    # Make Q uniform according to https://arxiv.org/pdf/math-ph/0609050.pdf
+    d = torch.diag(r, 0)
+    ph = d.sign()
+    q *= ph
+
+    if rows < cols:
+        q.t_()
+
+    with torch.no_grad():
+        tensor.view_as(q).copy_(q)
+        tensor.mul_(gain)
+    return tensor
+
+
+def sparse_(
+    tensor,
+    sparsity,
+    std=0.01,
+    generator: _Optional[torch.Generator] = None,
+):
+    r"""Fill the 2D input `Tensor` as a sparse matrix.
+
+    The non-zero elements will be drawn from the normal distribution
+    :math:`\mathcal{N}(0, 0.01)`, as described in `Deep learning via
+    Hessian-free optimization` - Martens, J. (2010).
+
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        sparsity: The fraction of elements in each column to be set to zero
+        std: the standard deviation of the normal distribution used to generate
+            the non-zero values
+        generator: the torch Generator to sample from (default: None)
+
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.sparse_(w, sparsity=0.1)
+    """
+    if tensor.ndimension() != 2:
+        raise ValueError("Only tensors with 2 dimensions are supported")
+
+    rows, cols = tensor.shape
+    num_zeros = int(math.ceil(sparsity * rows))
+
+    with torch.no_grad():
+        tensor.normal_(0, std, generator=generator)
+        for col_idx in range(cols):
+            row_indices = torch.randperm(rows)
+            zero_indices = row_indices[:num_zeros]
+            tensor[zero_indices, col_idx] = 0
+    return tensor
+
+
+# for backward compatibility
+def _make_deprecate(meth):
+    new_name = meth.__name__
+    old_name = new_name[:-1]
+
+    def deprecated_init(*args, **kwargs):
+        warnings.warn(
+            f"`nn.init.{old_name}` is now deprecated in favor of `nn.init.{new_name}`.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        return meth(*args, **kwargs)
+
+    deprecated_init.__doc__ = rf"""
+    {old_name}(...)
+
+    .. warning::
+        This method is now deprecated in favor of :func:`torch.nn.init.{new_name}`.
+
+    See :func:`~torch.nn.init.{new_name}` for details."""
+    deprecated_init.__name__ = old_name
+    return deprecated_init
+
+
+uniform = _make_deprecate(uniform_)
+normal = _make_deprecate(normal_)
+constant = _make_deprecate(constant_)
+eye = _make_deprecate(eye_)
+dirac = _make_deprecate(dirac_)
+xavier_uniform = _make_deprecate(xavier_uniform_)
+xavier_normal = _make_deprecate(xavier_normal_)
+kaiming_uniform = _make_deprecate(kaiming_uniform_)
+kaiming_normal = _make_deprecate(kaiming_normal_)
+orthogonal = _make_deprecate(orthogonal_)
+sparse = _make_deprecate(sparse_)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/parameter.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/parameter.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d2ad36aeb589cad23e6c78dfa1482d8ad0d02ea
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/parameter.py
@@ -0,0 +1,280 @@
+from collections import OrderedDict
+
+import torch
+from torch._C import _disabled_torch_function_impl
+
+
+# Metaclass to combine _TensorMeta and the instance check override for Parameter.
+class _ParameterMeta(torch._C._TensorMeta):
+    # Make `isinstance(t, Parameter)` return True for custom tensor instances that have the _is_param flag.
+    def __instancecheck__(self, instance):
+        if self is Parameter:
+            if isinstance(instance, torch.Tensor) and getattr(
+                instance, "_is_param", False
+            ):
+                return True
+        return super().__instancecheck__(instance)
+
+
+class Parameter(torch.Tensor, metaclass=_ParameterMeta):
+    r"""A kind of Tensor that is to be considered a module parameter.
+
+    Parameters are :class:`~torch.Tensor` subclasses, that have a
+    very special property when used with :class:`Module` s - when they're
+    assigned as Module attributes they are automatically added to the list of
+    its parameters, and will appear e.g. in :meth:`~Module.parameters` iterator.
+    Assigning a Tensor doesn't have such effect. This is because one might
+    want to cache some temporary state, like last hidden state of the RNN, in
+    the model. If there was no such class as :class:`Parameter`, these
+    temporaries would get registered too.
+
+    Args:
+        data (Tensor): parameter tensor.
+        requires_grad (bool, optional): if the parameter requires gradient. Note that
+            the torch.no_grad() context does NOT affect the default behavior of
+            Parameter creation--the Parameter will still have `requires_grad=True` in
+            :class:`~no_grad` mode. See :ref:`locally-disable-grad-doc` for more
+            details. Default: `True`
+    """
+
+    def __new__(cls, data=None, requires_grad=True):
+        if data is None:
+            data = torch.empty(0)
+        if type(data) is torch.Tensor or type(data) is Parameter:
+            # For ease of BC maintenance, keep this path for standard Tensor.
+            # Eventually (tm), we should change the behavior for standard Tensor to match.
+            return torch.Tensor._make_subclass(cls, data, requires_grad)
+
+        # Path for custom tensors: set a flag on the instance to indicate parameter-ness.
+        t = data.detach().requires_grad_(requires_grad)
+        if type(t) is not type(data):
+            raise RuntimeError(
+                f"Creating a Parameter from an instance of type {type(data).__name__} "
+                "requires that detach() returns an instance of the same type, but return "
+                f"type {type(t).__name__} was found instead. To use the type as a "
+                "Parameter, please correct the detach() semantics defined by "
+                "its __torch_dispatch__() implementation."
+            )
+        t._is_param = True
+        return t
+
+    # Note: the 3 methods below only apply to standard Tensor. Parameters of custom tensor types
+    # are still considered that custom tensor type and these methods will not be called for them.
+    def __deepcopy__(self, memo):
+        if id(self) in memo:
+            return memo[id(self)]
+        else:
+            result = type(self)(
+                self.data.clone(memory_format=torch.preserve_format), self.requires_grad
+            )
+            memo[id(self)] = result
+            return result
+
+    def __repr__(self):
+        return "Parameter containing:\n" + super().__repr__()
+
+    def __reduce_ex__(self, proto):
+        state = torch._utils._get_obj_state(self)
+
+        # See Note [Don't serialize hooks]
+        hooks = OrderedDict()
+        if not state:
+            return (
+                torch._utils._rebuild_parameter,
+                (self.data, self.requires_grad, hooks),
+            )
+
+        return (
+            torch._utils._rebuild_parameter_with_state,
+            (self.data, self.requires_grad, hooks, state),
+        )
+
+    __torch_function__ = _disabled_torch_function_impl
+
+
+class UninitializedTensorMixin:
+    _allowed_methods = [
+        torch.Tensor.__hash__,
+        torch.Tensor.size,
+        torch.Tensor.copy_,
+        torch.Tensor.is_complex,
+        torch.Tensor.is_floating_point,
+        torch.Tensor.half,
+        torch.Tensor.float,
+        torch.Tensor.double,
+        torch.Tensor.char,
+        torch.Tensor.short,
+        torch.Tensor.int,
+        torch.Tensor.long,
+        torch.Tensor.cuda,
+        torch.Tensor.cpu,
+        torch.Tensor.to,
+        torch.Tensor.get_device,
+        torch._has_compatible_shallow_copy_type,
+    ]
+
+    def materialize(self, shape, device=None, dtype=None):
+        r"""Create a Parameter or Tensor with the same properties of the uninitialized one.
+
+        Given a shape, it materializes a parameter in the same device
+        and with the same `dtype` as the current one or the specified ones in the
+        arguments.
+
+        Args:
+            shape : (tuple): the shape for the materialized tensor.
+            device (:class:`torch.device`): the desired device of the parameters
+                and buffers in this module. Optional.
+            dtype (:class:`torch.dtype`): the desired floating point type of
+                the floating point parameters and buffers in this module. Optional.
+        """
+        if device is None:
+            device = self.data.device
+        if dtype is None:
+            dtype = self.data.dtype
+        self.data = torch.empty(shape, device=device, dtype=dtype)
+        self.__class__ = self.cls_to_become
+
+    @property
+    def shape(self):
+        raise RuntimeError(
+            "Can't access the shape of an uninitialized parameter or buffer. "
+            "This error usually happens in `load_state_dict` when trying to load "
+            "an uninitialized parameter into an initialized one. "
+            "Call `forward` to initialize the parameters before accessing their attributes."
+        )
+
+    def share_memory_(self):
+        raise RuntimeError(
+            "Can't share memory on an uninitialized parameter or buffer. "
+            "Call `forward` to initialize the parameters before calling "
+            "`module.share_memory()`."
+        )
+
+    def __repr__(self):
+        return f"<{self.__class__.__name__}>"
+
+    def __reduce_ex__(self, proto):
+        # See Note [Don't serialize hooks]
+        return (self.__class__, (self.requires_grad,))
+
+    @classmethod
+    def __torch_function__(cls, func, types, args=(), kwargs=None):
+        # method-wrapper is to detect access to Tensor properties that are
+        # wrapped in descriptors
+        if func in cls._allowed_methods or func.__class__.__name__ == "method-wrapper":
+            if kwargs is None:
+                kwargs = {}
+            return super().__torch_function__(func, types, args, kwargs)
+        raise ValueError(
+            f"Attempted to use an uninitialized parameter in {func}. "
+            "This error happens when you are using a `LazyModule` or "
+            f"explicitly manipulating `torch.nn.parameter.{cls.__name__}` "
+            "objects. When using LazyModules Call `forward` with a dummy batch "
+            "to initialize the parameters before calling torch functions"
+        )
+
+
+def is_lazy(param):
+    return isinstance(param, UninitializedTensorMixin)
+
+
+class UninitializedParameter(UninitializedTensorMixin, Parameter):
+    r"""A parameter that is not initialized.
+
+    Uninitialized Parameters are a a special case of :class:`torch.nn.Parameter`
+    where the shape of the data is still unknown.
+
+    Unlike a :class:`torch.nn.Parameter`, uninitialized parameters
+    hold no data and attempting to access some properties, like their shape,
+    will throw a runtime error. The only operations that can be performed on a uninitialized
+    parameter are changing its datatype, moving it to a different device and
+    converting it to a regular :class:`torch.nn.Parameter`.
+
+    The default device or dtype to use when the parameter is materialized can be set
+    during construction using e.g. ``device='cuda'``.
+    """
+
+    cls_to_become = Parameter
+
+    def __new__(cls, requires_grad=True, device=None, dtype=None) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        data = torch.empty(0, **factory_kwargs)
+        return torch.Tensor._make_subclass(cls, data, requires_grad)
+
+    def __deepcopy__(self, memo):
+        if id(self) in memo:
+            return memo[id(self)]
+        else:
+            result = type(self)(self.requires_grad, self.data.device, self.data.dtype)
+            memo[id(self)] = result
+            return result
+
+
+# Metaclass to combine _TensorMeta and the instance check override for Buffer.
+class _BufferMeta(torch._C._TensorMeta):
+    # Make `isinstance(t, Buffer)` return True for custom tensor instances that have the _is_buffer flag.
+    def __instancecheck__(self, instance):
+        if self is Buffer:
+            if isinstance(instance, torch.Tensor) and getattr(
+                instance, "_is_buffer", False
+            ):
+                return True
+        return super().__instancecheck__(instance)
+
+
+class Buffer(torch.Tensor, metaclass=_BufferMeta):
+    r"""A kind of Tensor that should not be considered a model
+    parameter. For example, BatchNorm's ``running_mean`` is not a parameter, but is part of the module's state.
+
+    Buffers are :class:`~torch.Tensor` subclasses, that have a
+    very special property when used with :class:`Module` s -- when they're
+    assigned as Module attributes they are automatically added to the list of
+    its buffers, and will appear e.g. in :meth:`~torch.nn.Module.buffers` iterator.
+    Assigning a Tensor doesn't have such effect. One can still assign a Tensor as explicitly by using
+    the :meth:`~torch.nn.Module.register_buffer` function.
+
+    Args:
+        data (Tensor): buffer tensor.
+        persistent (bool, optional): whether the buffer is part of the module's
+            :attr:`state_dict`. Default: ``True``
+    """
+
+    def __new__(cls, data=None, *, persistent=True):
+        if data is None:
+            data = torch.empty(0)
+
+        t = data.detach().requires_grad_(data.requires_grad)
+        t.persistent = persistent
+        t._is_buffer = True
+        return t
+
+    __torch_function__ = _disabled_torch_function_impl
+
+
+class UninitializedBuffer(UninitializedTensorMixin, torch.Tensor):
+    r"""A buffer that is not initialized.
+
+    Uninitialized Buffer is a a special case of :class:`torch.Tensor`
+    where the shape of the data is still unknown.
+
+    Unlike a :class:`torch.Tensor`, uninitialized parameters
+    hold no data and attempting to access some properties, like their shape,
+    will throw a runtime error. The only operations that can be performed on a uninitialized
+    parameter are changing its datatype, moving it to a different device and
+    converting it to a regular :class:`torch.Tensor`.
+
+    The default device or dtype to use when the buffer is materialized can be set
+    during construction using e.g. ``device='cuda'``.
+    """
+
+    cls_to_become = torch.Tensor
+
+    def __new__(
+        cls, requires_grad=False, device=None, dtype=None, persistent=True
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        data = torch.empty(0, **factory_kwargs)
+        ret = torch.Tensor._make_subclass(cls, data, requires_grad)
+        ret.persistent = persistent
+        ret._is_buffer = True
+        return ret
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/parameter.pyi b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/parameter.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..9c998fb07f2c185c1ad2f59556486a95d7c6a9e5
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/nn/parameter.pyi
@@ -0,0 +1,44 @@
+# mypy: allow-untyped-defs
+from typing_extensions import TypeGuard
+
+from torch import device, dtype, Tensor
+
+class Parameter(Tensor):
+    def __init__(self, data: Tensor = ..., requires_grad: bool = ...) -> None: ...
+
+def is_lazy(
+    param: Tensor,
+) -> TypeGuard[UninitializedParameter | UninitializedBuffer]: ...
+
+class UninitializedParameter(Tensor):
+    def __init__(self, data: Tensor = ..., requires_grad: bool = ...) -> None: ...
+    def materialize(
+        self,
+        shape: tuple[int, ...],
+        device: device | None = None,
+        dtype: dtype | None = None,
+    ) -> None: ...
+
+class Buffer(Tensor):
+    persistent: bool
+    def __init__(
+        self,
+        data: Tensor = ...,
+        requires_grad: bool = ...,
+        persistent: bool = ...,
+    ): ...
+
+class UninitializedBuffer(Tensor):
+    persistent: bool
+    def __init__(
+        self,
+        data: Tensor = ...,
+        requires_grad: bool = ...,
+        persistent: bool = ...,
+    ): ...
+    def materialize(
+        self,
+        shape: tuple[int, ...],
+        device: device | None = None,
+        dtype: dtype | None = None,
+    ) -> None: ...
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..27c8e2c6240c18fe550b9a383c41a3ab2a33182c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/__init__.py
@@ -0,0 +1,553 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+
+__all__ = [
+    # Modules
+    "symbolic_helper",
+    "utils",
+    "errors",
+    # All opsets
+    "symbolic_caffe2",
+    "symbolic_opset7",
+    "symbolic_opset8",
+    "symbolic_opset9",
+    "symbolic_opset10",
+    "symbolic_opset11",
+    "symbolic_opset12",
+    "symbolic_opset13",
+    "symbolic_opset14",
+    "symbolic_opset15",
+    "symbolic_opset16",
+    "symbolic_opset17",
+    "symbolic_opset18",
+    "symbolic_opset19",
+    "symbolic_opset20",
+    # Enums
+    "ExportTypes",
+    "OperatorExportTypes",
+    "TrainingMode",
+    "TensorProtoDataType",
+    "JitScalarType",
+    # Public functions
+    "export",
+    "export_to_pretty_string",
+    "is_in_onnx_export",
+    "select_model_mode_for_export",
+    "register_custom_op_symbolic",
+    "unregister_custom_op_symbolic",
+    "disable_log",
+    "enable_log",
+    # Base error
+    "OnnxExporterError",
+    # Dynamo Exporter
+    "DiagnosticOptions",
+    "ExportOptions",
+    "ONNXProgram",
+    "ONNXRuntimeOptions",
+    "OnnxRegistry",
+    "dynamo_export",
+    "enable_fake_mode",
+    # DORT / torch.compile
+    "is_onnxrt_backend_supported",
+]
+
+from typing import Any, Callable, Collection, Mapping, Sequence, TYPE_CHECKING
+
+import torch
+from torch import _C
+from torch._C import _onnx as _C_onnx
+from torch._C._onnx import OperatorExportTypes, TensorProtoDataType, TrainingMode
+
+from ._exporter_states import ExportTypes
+from ._internal.onnxruntime import (
+    is_onnxrt_backend_supported,
+    OrtBackend as _OrtBackend,
+    OrtBackendOptions as _OrtBackendOptions,
+    OrtExecutionProvider as _OrtExecutionProvider,
+)
+from ._type_utils import JitScalarType
+from .errors import OnnxExporterError
+from .utils import (
+    _optimize_graph,
+    _run_symbolic_function,
+    _run_symbolic_method,
+    export_to_pretty_string,
+    is_in_onnx_export,
+    register_custom_op_symbolic,
+    select_model_mode_for_export,
+    unregister_custom_op_symbolic,
+)
+
+
+from . import (  # usort: skip. Keep the order instead of sorting lexicographically
+    errors,
+    symbolic_caffe2,
+    symbolic_helper,
+    symbolic_opset7,
+    symbolic_opset8,
+    symbolic_opset9,
+    symbolic_opset10,
+    symbolic_opset11,
+    symbolic_opset12,
+    symbolic_opset13,
+    symbolic_opset14,
+    symbolic_opset15,
+    symbolic_opset16,
+    symbolic_opset17,
+    symbolic_opset18,
+    symbolic_opset19,
+    symbolic_opset20,
+    utils,
+)
+
+
+from ._internal._exporter_legacy import (  # usort: skip. needs to be last to avoid circular import
+    DiagnosticOptions,
+    ExportOptions,
+    ONNXProgram,
+    ONNXRuntimeOptions,
+    OnnxRegistry,
+    enable_fake_mode,
+)
+
+
+if TYPE_CHECKING:
+    import os
+
+# Set namespace for exposed private names
+DiagnosticOptions.__module__ = "torch.onnx"
+ExportOptions.__module__ = "torch.onnx"
+ExportTypes.__module__ = "torch.onnx"
+JitScalarType.__module__ = "torch.onnx"
+ONNXProgram.__module__ = "torch.onnx"
+ONNXRuntimeOptions.__module__ = "torch.onnx"
+OnnxExporterError.__module__ = "torch.onnx"
+OnnxRegistry.__module__ = "torch.onnx"
+_OrtBackend.__module__ = "torch.onnx"
+_OrtBackendOptions.__module__ = "torch.onnx"
+_OrtExecutionProvider.__module__ = "torch.onnx"
+enable_fake_mode.__module__ = "torch.onnx"
+is_onnxrt_backend_supported.__module__ = "torch.onnx"
+
+producer_name = "pytorch"
+producer_version = _C_onnx.PRODUCER_VERSION
+
+
+def export(
+    model: torch.nn.Module
+    | torch.export.ExportedProgram
+    | torch.jit.ScriptModule
+    | torch.jit.ScriptFunction,
+    args: tuple[Any, ...] = (),
+    f: str | os.PathLike | None = None,
+    *,
+    kwargs: dict[str, Any] | None = None,
+    export_params: bool = True,
+    verbose: bool | None = None,
+    input_names: Sequence[str] | None = None,
+    output_names: Sequence[str] | None = None,
+    opset_version: int | None = None,
+    dynamic_axes: Mapping[str, Mapping[int, str]]
+    | Mapping[str, Sequence[int]]
+    | None = None,
+    keep_initializers_as_inputs: bool = False,
+    dynamo: bool = False,
+    # Dynamo only options
+    external_data: bool = True,
+    dynamic_shapes: dict[str, Any] | tuple[Any, ...] | list[Any] | None = None,
+    report: bool = False,
+    verify: bool = False,
+    profile: bool = False,
+    dump_exported_program: bool = False,
+    artifacts_dir: str | os.PathLike = ".",
+    fallback: bool = False,
+    # Deprecated options
+    training: _C_onnx.TrainingMode = _C_onnx.TrainingMode.EVAL,
+    operator_export_type: _C_onnx.OperatorExportTypes = _C_onnx.OperatorExportTypes.ONNX,
+    do_constant_folding: bool = True,
+    custom_opsets: Mapping[str, int] | None = None,
+    export_modules_as_functions: bool | Collection[type[torch.nn.Module]] = False,
+    autograd_inlining: bool = True,
+    **_: Any,  # ignored options
+) -> Any | None:
+    r"""Exports a model into ONNX format.
+
+    Args:
+        model: The model to be exported.
+        args: Example positional inputs. Any non-Tensor arguments will be hard-coded into the
+            exported model; any Tensor arguments will become inputs of the exported model,
+            in the order they occur in the tuple.
+        f: Path to the output ONNX model file. E.g. "model.onnx".
+        kwargs: Optional example keyword inputs.
+        export_params: If false, parameters (weights) will not be exported.
+        verbose: Whether to enable verbose logging.
+        input_names: names to assign to the input nodes of the graph, in order.
+        output_names: names to assign to the output nodes of the graph, in order.
+        opset_version: The version of the
+            `default (ai.onnx) opset `_
+            to target. Must be >= 7.
+        dynamic_axes:
+
+            By default the exported model will have the shapes of all input and output tensors
+            set to exactly match those given in ``args``. To specify axes of tensors as
+            dynamic (i.e. known only at run-time), set ``dynamic_axes`` to a dict with schema:
+
+            * KEY (str): an input or output name. Each name must also be provided in ``input_names`` or
+                ``output_names``.
+            * VALUE (dict or list): If a dict, keys are axis indices and values are axis names. If a
+                list, each element is an axis index.
+
+            For example::
+
+                class SumModule(torch.nn.Module):
+                    def forward(self, x):
+                        return torch.sum(x, dim=1)
+
+
+                torch.onnx.export(
+                    SumModule(),
+                    (torch.ones(2, 2),),
+                    "onnx.pb",
+                    input_names=["x"],
+                    output_names=["sum"],
+                )
+
+            Produces::
+
+                input {
+                  name: "x"
+                  ...
+                      shape {
+                        dim {
+                          dim_value: 2  # axis 0
+                        }
+                        dim {
+                          dim_value: 2  # axis 1
+                ...
+                output {
+                  name: "sum"
+                  ...
+                      shape {
+                        dim {
+                          dim_value: 2  # axis 0
+                ...
+
+            While::
+
+                torch.onnx.export(
+                    SumModule(),
+                    (torch.ones(2, 2),),
+                    "onnx.pb",
+                    input_names=["x"],
+                    output_names=["sum"],
+                    dynamic_axes={
+                        # dict value: manually named axes
+                        "x": {0: "my_custom_axis_name"},
+                        # list value: automatic names
+                        "sum": [0],
+                    },
+                )
+
+            Produces::
+
+                input {
+                  name: "x"
+                  ...
+                      shape {
+                        dim {
+                          dim_param: "my_custom_axis_name"  # axis 0
+                        }
+                        dim {
+                          dim_value: 2  # axis 1
+                ...
+                output {
+                  name: "sum"
+                  ...
+                      shape {
+                        dim {
+                          dim_param: "sum_dynamic_axes_1"  # axis 0
+                ...
+
+        keep_initializers_as_inputs: If True, all the
+            initializers (typically corresponding to model weights) in the
+            exported graph will also be added as inputs to the graph. If False,
+            then initializers are not added as inputs to the graph, and only
+            the user inputs are added as inputs.
+
+            Set this to True if you intend to supply model weights at runtime.
+            Set it to False if the weights are static to allow for better optimizations
+            (e.g. constant folding) by backends/runtimes.
+
+        dynamo: Whether to export the model with ``torch.export`` ExportedProgram instead of TorchScript.
+        external_data: Whether to save the model weights as an external data file.
+            This is required for models with large weights that exceed the ONNX file size limit (2GB).
+            When False, the weights are saved in the ONNX file with the model architecture.
+        dynamic_shapes: A dictionary of dynamic shapes for the model inputs. Refer to
+            :func:`torch.export.export` for more details. This is only used (and preferred) when dynamo is True.
+            Only one parameter `dynamic_axes` or `dynamic_shapes` should be set
+            at the same time.
+        report: Whether to generate a markdown report for the export process.
+        verify: Whether to verify the exported model using ONNX Runtime.
+        profile: Whether to profile the export process.
+        dump_exported_program: Whether to dump the :class:`torch.export.ExportedProgram` to a file.
+            This is useful for debugging the exporter.
+        artifacts_dir: The directory to save the debugging artifacts like the report and the serialized
+            exported program.
+        fallback: Whether to fallback to the TorchScript exporter if the dynamo exporter fails.
+
+        training: Deprecated option. Instead, set the training mode of the model before exporting.
+        operator_export_type: Deprecated option. Only ONNX is supported.
+        do_constant_folding: Deprecated option. The exported graph is always optimized.
+        custom_opsets: Deprecated.
+            A dictionary:
+
+            * KEY (str): opset domain name
+            * VALUE (int): opset version
+
+            If a custom opset is referenced by ``model`` but not mentioned in this dictionary,
+            the opset version is set to 1. Only custom opset domain name and version should be
+            indicated through this argument.
+        export_modules_as_functions: Deprecated option.
+
+            Flag to enable
+            exporting all ``nn.Module`` forward calls as local functions in ONNX. Or a set to indicate the
+            particular types of modules to export as local functions in ONNX.
+            This feature requires ``opset_version`` >= 15, otherwise the export will fail. This is because
+            ``opset_version`` < 15 implies IR version < 8, which means no local function support.
+            Module variables will be exported as function attributes. There are two categories of function
+            attributes.
+
+            1. Annotated attributes: class variables that have type annotations via
+            `PEP 526-style `_
+            will be exported as attributes.
+            Annotated attributes are not used inside the subgraph of ONNX local function because
+            they are not created by PyTorch JIT tracing, but they may be used by consumers
+            to determine whether or not to replace the function with a particular fused kernel.
+
+            2. Inferred attributes: variables that are used by operators inside the module. Attribute names
+            will have prefix "inferred::". This is to differentiate from predefined attributes retrieved from
+            python module annotations. Inferred attributes are used inside the subgraph of ONNX local function.
+
+            * ``False`` (default): export ``nn.Module`` forward calls as fine grained nodes.
+            * ``True``: export all ``nn.Module`` forward calls as local function nodes.
+            * Set of type of nn.Module: export ``nn.Module`` forward calls as local function nodes,
+                only if the type of the ``nn.Module`` is found in the set.
+        autograd_inlining: Deprecated.
+            Flag used to control whether to inline autograd functions.
+            Refer to https://github.com/pytorch/pytorch/pull/74765 for more details.
+    """
+    if dynamo is True or isinstance(model, torch.export.ExportedProgram):
+        from torch.onnx._internal import exporter
+
+        if isinstance(args, torch.Tensor):
+            args = (args,)
+        return exporter.export_compat(
+            model,
+            args,
+            f,
+            kwargs=kwargs,
+            export_params=export_params,
+            verbose=verbose,
+            input_names=input_names,
+            output_names=output_names,
+            opset_version=opset_version,
+            dynamic_axes=dynamic_axes,
+            keep_initializers_as_inputs=keep_initializers_as_inputs,
+            external_data=external_data,
+            dynamic_shapes=dynamic_shapes,
+            report=report,
+            verify=verify,
+            profile=profile,
+            dump_exported_program=dump_exported_program,
+            artifacts_dir=artifacts_dir,
+            fallback=fallback,
+        )
+    else:
+        from torch.onnx.utils import export
+
+        if dynamic_shapes:
+            raise ValueError(
+                "The exporter only supports dynamic shapes "
+                "through parameter dynamic_axes when dynamo=False."
+            )
+
+        export(
+            model,
+            args,
+            f,  # type: ignore[arg-type]
+            kwargs=kwargs,
+            export_params=export_params,
+            verbose=verbose is True,
+            input_names=input_names,
+            output_names=output_names,
+            opset_version=opset_version,
+            dynamic_axes=dynamic_axes,
+            keep_initializers_as_inputs=keep_initializers_as_inputs,
+            training=training,
+            operator_export_type=operator_export_type,
+            do_constant_folding=do_constant_folding,
+            custom_opsets=custom_opsets,
+            export_modules_as_functions=export_modules_as_functions,
+            autograd_inlining=autograd_inlining,
+        )
+        return None
+
+
+def dynamo_export(
+    model: torch.nn.Module | Callable | torch.export.ExportedProgram,  # type: ignore[name-defined]
+    /,
+    *model_args,
+    export_options: ExportOptions | None = None,
+    **model_kwargs,
+) -> ONNXProgram | Any:
+    """Export a torch.nn.Module to an ONNX graph.
+
+    Args:
+        model: The PyTorch model to be exported to ONNX.
+        model_args: Positional inputs to ``model``.
+        model_kwargs: Keyword inputs to ``model``.
+        export_options: Options to influence the export to ONNX.
+
+    Returns:
+        An in-memory representation of the exported ONNX model.
+
+    **Example 1 - Simplest export**
+    ::
+
+        class MyModel(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.linear = torch.nn.Linear(2, 2)
+
+            def forward(self, x, bias=None):
+                out = self.linear(x)
+                out = out + bias
+                return out
+
+
+        model = MyModel()
+        kwargs = {"bias": 3.0}
+        args = (torch.randn(2, 2, 2),)
+        onnx_program = torch.onnx.dynamo_export(model, *args, **kwargs).save(
+            "my_simple_model.onnx"
+        )
+
+    **Example 2 - Exporting with dynamic shapes**
+    ::
+
+        # The previous model can be exported with dynamic shapes
+        export_options = torch.onnx.ExportOptions(dynamic_shapes=True)
+        onnx_program = torch.onnx.dynamo_export(
+            model, *args, **kwargs, export_options=export_options
+        )
+        onnx_program.save("my_dynamic_model.onnx")
+    """
+
+    # NOTE: The new exporter is experimental and is not enabled by default.
+    import warnings
+
+    from torch.onnx import _flags
+    from torch.onnx._internal import exporter
+    from torch.utils import _pytree
+
+    if isinstance(model, torch.export.ExportedProgram):
+        return exporter.export_compat(
+            model,  # type: ignore[arg-type]
+            model_args,
+            f=None,
+            kwargs=model_kwargs,
+            opset_version=18,
+            external_data=True,
+            export_params=True,
+            fallback=True,
+        )
+    elif _flags.USE_EXPERIMENTAL_LOGIC:
+        if export_options is not None:
+            warnings.warn(
+                "You are using an experimental ONNX export logic, which currently only supports dynamic shapes. "
+                "For a more comprehensive set of export options, including advanced features, please consider using "
+                "`torch.onnx.export(..., dynamo=True)`. ",
+                category=FutureWarning,
+            )
+
+        if export_options is not None and export_options.dynamic_shapes:
+            # Make all shapes dynamic
+            def _to_dynamic_shapes_mapper():
+                arg_order = 0
+
+                def _to_dynamic_shape(x):
+                    nonlocal arg_order
+                    if isinstance(x, torch.Tensor):
+                        rank = len(x.shape)
+                        dynamic_shape = {}
+                        for i in range(rank):
+                            dynamic_shape[i] = torch.export.Dim(
+                                f"arg_{arg_order}_dim_{i}"
+                            )
+                        arg_order += 1
+                        return dynamic_shape
+                    else:
+                        return None
+
+                return _to_dynamic_shape
+
+            # model_args could be nested
+            dynamic_shapes = _pytree.tree_map(
+                _to_dynamic_shapes_mapper(),
+                model_args,
+            )
+        else:
+            dynamic_shapes = None
+
+        return exporter.export_compat(
+            model,  # type: ignore[arg-type]
+            model_args,
+            f=None,
+            kwargs=model_kwargs,
+            dynamic_shapes=dynamic_shapes,
+            opset_version=18,
+            external_data=True,
+            export_params=True,
+            fallback=True,
+        )
+    else:
+        from torch.onnx._internal._exporter_legacy import dynamo_export
+
+        return dynamo_export(
+            model, *model_args, export_options=export_options, **model_kwargs
+        )
+
+
+# TODO(justinchuby): Deprecate these logging functions in favor of the new diagnostic module.
+
+# Returns True iff ONNX logging is turned on.
+is_onnx_log_enabled = _C._jit_is_onnx_log_enabled
+
+
+def enable_log() -> None:
+    r"""Enables ONNX logging."""
+    _C._jit_set_onnx_log_enabled(True)
+
+
+def disable_log() -> None:
+    r"""Disables ONNX logging."""
+    _C._jit_set_onnx_log_enabled(False)
+
+
+"""Sets output stream for ONNX logging.
+
+Args:
+    stream_name (str, default "stdout"): Only 'stdout' and 'stderr' are supported
+        as ``stream_name``.
+"""
+set_log_stream = _C._jit_set_onnx_log_output_stream
+
+
+"""A simple logging facility for ONNX exporter.
+
+Args:
+    args: Arguments are converted to string, concatenated together with a newline
+        character appended to the end, and flushed to output stream.
+"""
+log = _C._jit_onnx_log
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_constants.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c91b245ed703f3b539b3baff36c25e278f40134
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_constants.py
@@ -0,0 +1,25 @@
+"""Constant values used in ONNX."""
+
+ONNX_ARCHIVE_MODEL_PROTO_NAME = "__MODEL_PROTO"
+
+ONNX_BASE_OPSET = 9
+ONNX_MIN_OPSET = 7
+ONNX_MAX_OPSET = 20
+ONNX_TORCHSCRIPT_EXPORTER_MAX_OPSET = 20
+# ONNX_DEFAULT_OPSET generated by tools/onnx/update_default_opset_version.py
+ONNX_DEFAULT_OPSET = 17
+ONNX_CONSTANT_FOLDING_MIN_OPSET = 9
+
+PYTORCH_GITHUB_ISSUES_URL = "https://github.com/pytorch/pytorch/issues"
+
+INT64_MAX = 9223372036854775807
+INT32_MAX = 2147483647
+INT16_MAX = 32767
+INT8_MAX = 127
+UINT8_MAX = 255
+
+INT64_MIN = -9223372036854775808
+INT32_MIN = -2147483648
+INT16_MIN = -32768
+INT8_MIN = -128
+UINT8_MIN = 0
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_deprecation.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_deprecation.py
new file mode 100644
index 0000000000000000000000000000000000000000..24fe4ccc54fcdbe2dc8cd99ab802b8fdf25e9821
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_deprecation.py
@@ -0,0 +1,72 @@
+"""Utility for deprecating functions."""
+
+import functools
+import textwrap
+import warnings
+from typing import Callable, TypeVar
+from typing_extensions import ParamSpec
+
+
+_T = TypeVar("_T")
+_P = ParamSpec("_P")
+
+
+def deprecated(
+    since: str, removed_in: str, instructions: str
+) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]:
+    """Marks functions as deprecated.
+
+    It will result in a warning when the function is called and a note in the
+    docstring.
+
+    Args:
+        since: The version when the function was first deprecated.
+        removed_in: The version when the function will be removed.
+        instructions: The action users should take.
+    """
+
+    def decorator(function: Callable[_P, _T]) -> Callable[_P, _T]:
+        @functools.wraps(function)
+        def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _T:
+            warnings.warn(
+                f"'{function.__module__}.{function.__name__}' "
+                f"is deprecated in version {since} and will be "
+                f"removed in {removed_in}. Please {instructions}.",
+                category=FutureWarning,
+                stacklevel=2,
+            )
+            return function(*args, **kwargs)
+
+        # Add a deprecation note to the docstring.
+        docstring = function.__doc__ or ""
+
+        # Add a note to the docstring.
+        deprecation_note = textwrap.dedent(
+            f"""\
+            .. deprecated:: {since}
+                Deprecated and will be removed in version {removed_in}.
+                Please {instructions}.
+            """
+        )
+
+        # Split docstring at first occurrence of newline
+        summary_and_body = docstring.split("\n\n", 1)
+
+        if len(summary_and_body) > 1:
+            summary, body = summary_and_body
+
+            # Dedent the body. We cannot do this with the presence of the summary because
+            # the body contains leading whitespaces when the summary does not.
+            body = textwrap.dedent(body)
+
+            new_docstring_parts = [deprecation_note, "\n\n", summary, body]
+        else:
+            summary = summary_and_body[0]
+
+            new_docstring_parts = [deprecation_note, "\n\n", summary]
+
+        wrapper.__doc__ = "".join(new_docstring_parts)
+
+        return wrapper
+
+    return decorator
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_experimental.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_experimental.py
new file mode 100644
index 0000000000000000000000000000000000000000..86c035d412fd7da2cd7b03a6e187dfca8a3a6061
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_experimental.py
@@ -0,0 +1,27 @@
+"""Experimental classes and functions used by ONNX export."""
+
+import dataclasses
+from typing import Mapping, Optional, Sequence, Set, Type, Union
+
+import torch
+import torch._C._onnx as _C_onnx
+
+
+@dataclasses.dataclass
+class ExportOptions:
+    """Arguments used by :func:`torch.onnx.export`."""
+
+    # TODO(justinchuby): Deprecate and remove this class.
+
+    export_params: bool = True
+    verbose: bool = False
+    training: _C_onnx.TrainingMode = _C_onnx.TrainingMode.EVAL
+    input_names: Optional[Sequence[str]] = None
+    output_names: Optional[Sequence[str]] = None
+    operator_export_type: _C_onnx.OperatorExportTypes = _C_onnx.OperatorExportTypes.ONNX
+    opset_version: Optional[int] = None
+    do_constant_folding: bool = True
+    dynamic_axes: Optional[Mapping[str, Union[Mapping[int, str], Sequence[int]]]] = None
+    keep_initializers_as_inputs: Optional[bool] = None
+    custom_opsets: Optional[Mapping[str, int]] = None
+    export_modules_as_functions: Union[bool, Set[Type[torch.nn.Module]]] = False
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_exporter_states.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_exporter_states.py
new file mode 100644
index 0000000000000000000000000000000000000000..2fdf7a7ac95c7b58a5e21145bb11cedc3ad29a88
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_exporter_states.py
@@ -0,0 +1,12 @@
+from __future__ import annotations
+
+
+class ExportTypes:
+    """Specifies how the ONNX model is stored."""
+
+    # TODO(justinchuby): Deprecate and remove this class.
+
+    PROTOBUF_FILE = "Saves model in the specified protobuf file."
+    ZIP_ARCHIVE = "Saves model in the specified ZIP file (uncompressed)."
+    COMPRESSED_ZIP_ARCHIVE = "Saves model in the specified ZIP file (compressed)."
+    DIRECTORY = "Saves model in the specified folder."
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_flags.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_flags.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bbabef61870255fffd4be5ca26782e109a77597
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_flags.py
@@ -0,0 +1,49 @@
+"""Internal feature flags for torch.onnx.
+
+NOTE: These flags are experimental only. Any flag here can be removed at any
+time without notice.
+"""
+
+import logging
+import os
+
+
+logger = logging.getLogger(__name__)
+
+
+def _load_boolean_flag(
+    name: str,
+    *,
+    this_will: str,
+    deprecated: bool = False,
+    default: bool = False,
+) -> bool:
+    """Load a boolean flag from environment variable.
+
+    Args:
+        name: The name of the environment variable.
+        this_will: A string that describes what this flag will do.
+        deprecated: Whether this flag is deprecated.
+        default: The default value if envvar not defined.
+    """
+    undefined = os.getenv(name) is None
+    state = os.getenv(name) == "1"
+    if state:
+        if deprecated:
+            logger.error(
+                "Experimental flag %s is deprecated. Please remove it from your environment.",
+                name,
+            )
+        else:
+            logger.warning(
+                "Experimental flag %s is enabled. This will %s.", name, this_will
+            )
+    if undefined:
+        state = default
+    return state
+
+
+USE_EXPERIMENTAL_LOGIC: bool = _load_boolean_flag(
+    "TORCH_ONNX_USE_EXPERIMENTAL_LOGIC",
+    this_will="use ExportedProgram and the new torch.onnx export logic",
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_globals.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_globals.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3dd273386f8f06edd41a1a1b53771c101f2902a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_globals.py
@@ -0,0 +1,87 @@
+# mypy: allow-untyped-defs
+"""Globals used internally by the ONNX exporter.
+
+Do not use this module outside of `torch.onnx` and its tests.
+
+Be very judicious when adding any new global variables. Do not create new global
+variables unless they are absolutely necessary.
+"""
+
+import torch._C._onnx as _C_onnx
+
+# This module should only depend on _constants and nothing else in torch.onnx to keep
+# dependency direction clean.
+from torch.onnx import _constants
+
+
+class _InternalGlobals:
+    """Globals used internally by ONNX exporter.
+
+    NOTE: Be very judicious when adding any new variables. Do not create new
+    global variables unless they are absolutely necessary.
+    """
+
+    def __init__(self) -> None:
+        self._export_onnx_opset_version = _constants.ONNX_DEFAULT_OPSET
+        self._training_mode: _C_onnx.TrainingMode = _C_onnx.TrainingMode.EVAL
+        self._in_onnx_export: bool = False
+        # Whether the user's model is training during export
+        self.export_training: bool = False
+        self.operator_export_type: _C_onnx.OperatorExportTypes = (
+            _C_onnx.OperatorExportTypes.ONNX
+        )
+        self.onnx_shape_inference: bool = True
+        self._autograd_inlining: bool = True
+
+    @property
+    def training_mode(self):
+        """The training mode for the exporter."""
+        return self._training_mode
+
+    @training_mode.setter
+    def training_mode(self, training_mode: _C_onnx.TrainingMode):
+        if not isinstance(training_mode, _C_onnx.TrainingMode):
+            raise TypeError(
+                "training_mode must be of type 'torch.onnx.TrainingMode'. This is "
+                "likely a bug in torch.onnx."
+            )
+        self._training_mode = training_mode
+
+    @property
+    def export_onnx_opset_version(self) -> int:
+        """Opset version used during export."""
+        return self._export_onnx_opset_version
+
+    @export_onnx_opset_version.setter
+    def export_onnx_opset_version(self, value: int):
+        supported_versions = range(
+            _constants.ONNX_MIN_OPSET, _constants.ONNX_MAX_OPSET + 1
+        )
+        if value not in supported_versions:
+            raise ValueError(f"Unsupported ONNX opset version: {value}")
+        self._export_onnx_opset_version = value
+
+    @property
+    def in_onnx_export(self) -> bool:
+        """Whether it is in the middle of ONNX export."""
+        return self._in_onnx_export
+
+    @in_onnx_export.setter
+    def in_onnx_export(self, value: bool):
+        if type(value) is not bool:
+            raise TypeError("in_onnx_export must be a boolean")
+        self._in_onnx_export = value
+
+    @property
+    def autograd_inlining(self) -> bool:
+        """Whether Autograd must be inlined."""
+        return self._autograd_inlining
+
+    @autograd_inlining.setter
+    def autograd_inlining(self, value: bool):
+        if type(value) is not bool:
+            raise TypeError("autograd_inlining must be a boolean")
+        self._autograd_inlining = value
+
+
+GLOBALS = _InternalGlobals()
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+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/_exporter_legacy.py
@@ -0,0 +1,1310 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+
+__all__ = [
+    "DiagnosticOptions",
+    "ExportOptions",
+    "ONNXProgram",
+    "ONNXRuntimeOptions",
+    "InvalidExportOptionsError",
+    "OnnxRegistry",
+    "UnsatisfiedDependencyError",
+    "dynamo_export",
+    "enable_fake_mode",
+]
+
+
+import abc
+import contextlib
+import dataclasses
+import logging
+import os
+import tempfile
+import warnings
+from collections import defaultdict
+from typing import Any, Callable, Final, Mapping, Sequence, TYPE_CHECKING, TypeVar
+from typing_extensions import Self
+
+import torch
+import torch._ops
+import torch.utils._pytree as pytree
+from torch.onnx import errors
+from torch.onnx._internal import io_adapter
+from torch.onnx._internal.diagnostics import infra
+from torch.onnx._internal.fx import (
+    decomposition_table,
+    patcher as patcher,
+    registration,
+    serialization as fx_serialization,
+)
+
+
+# We can only import onnx from this module in a type-checking context to ensure that
+# 'import torch.onnx' continues to work without having 'onnx' installed. We fully
+# 'import onnx' inside of dynamo_export (by way of _assert_dependencies).
+if TYPE_CHECKING:
+    import io
+
+    import onnx
+
+    import onnxruntime
+    import onnxscript
+
+    from torch._subclasses import fake_tensor
+    from torch.onnx._internal.fx import diagnostics
+
+_DEFAULT_OPSET_VERSION: Final[int] = 18
+"""The default ONNX opset version the exporter will use if one is not specified explicitly
+through :class:`ExportOptions`. This should NEVER be accessed outside of this module! Users
+should reference :attr:`ExportOptions.opset_version`."""
+
+_PYTORCH_GITHUB_ISSUES_URL = "https://github.com/pytorch/pytorch/issues"
+"""The URL to the PyTorch GitHub issues page."""
+
+_DEFAULT_FAILED_EXPORT_SARIF_LOG_PATH = "report_dynamo_export.sarif"
+"""The default path to write the SARIF log to if the export fails."""
+
+_PROTOBUF_SIZE_MAX_LIMIT = 2 * 1024 * 1024 * 1024
+"""The maximum size of a Protobuf file in bytes. This is used to determine whether to
+serialize the model with external data or not."""
+
+log = logging.getLogger(__name__)
+
+
+DiagnosticOptions = infra.DiagnosticOptions
+
+
+@dataclasses.dataclass
+class ONNXFakeContext:
+    """A dataclass used to store context for model export using FakeTensor.
+
+    This dataclass stores the FakeTensorMode instance used to convert
+    real tensors and model parameters into fake tensors. This :attr:`ONNXFakeContext.fake_mode` is
+    reused internally during tracing of a :class:`torch.nn.Module` into a FX :class:`GraphModule`.
+    """
+
+    fake_mode: fake_tensor.FakeTensorMode
+    """The fake tensor mode used for tracing model using fake tensors and parameters."""
+
+    state_dict_paths: tuple[str | io.BytesIO | dict[str, Any]] | None = None
+    """List of paths of files that contain the model :meth:`state_dict`"""
+
+
+class OnnxRegistry:
+    """Registry for ONNX functions.
+
+    The registry maintains a mapping from qualified names to symbolic functions under a
+    fixed opset version. It supports registering custom onnx-script functions and for
+    dispatcher to dispatch calls to the appropriate function.
+
+    """
+
+    def __init__(self) -> None:
+        """Initializes the registry"""
+
+        # NOTE: _registry is the registry maps OpNameto a list of ONNXFunctions. It is important
+        # not to directly modify this variable. Instead, access to it should be done through
+        # the public methods: register_custom_op, get_ops, and is_registered_op.
+        self._registry: dict[registration.OpName, list[registration.ONNXFunction]] = (
+            defaultdict(list)
+        )
+
+        # opset_version is unused for now, since torchlib only supports opset18.
+        # TODO: get opset version from torchlib
+        self._opset_version = _DEFAULT_OPSET_VERSION
+        warnings.warn(
+            f"torch.onnx.dynamo_export only implements opset version {self._opset_version} for now. If you need to use a "
+            "different opset version, please register them with register_custom_op."
+        )
+
+        self._initiate_registry_from_torchlib()
+
+    @property
+    def opset_version(self) -> int:
+        """The ONNX opset version the exporter should target. Defaults to the latest
+        supported ONNX opset version: 18. The default version will increment over time as
+        ONNX continues to evolve."""
+
+        return self._opset_version
+
+    def _initiate_registry_from_torchlib(self) -> None:
+        """Populates the registry with ATen functions from torchlib.
+
+        Args:
+            torchlib_registry: The torchlib registry to use for populating the registry.
+        """
+        import onnxscript._framework_apis.torch_2_5 as onnxscript_apis
+
+        for meta in onnxscript_apis.get_torchlib_ops():
+            internal_name_instance = registration.OpName.from_qualified_name(
+                meta.qualified_name
+            )
+            symbolic_function = registration.ONNXFunction(
+                onnx_function=meta.function,  # type: ignore[arg-type]
+                op_full_name=internal_name_instance.qualified_name(),
+                is_custom=False,
+                is_complex=meta.is_complex,
+            )
+            self._register(internal_name_instance, symbolic_function)
+
+    def _register(
+        self,
+        internal_qualified_name: registration.OpName,
+        symbolic_function: registration.ONNXFunction,
+    ) -> None:
+        """Registers a ONNXFunction to an operator.
+
+        Args:
+            internal_qualified_name: The qualified name of the operator to register: OpName.
+            symbolic_function: The ONNXFunction to register.
+        """
+        self._registry[internal_qualified_name].append(symbolic_function)
+
+    def register_op(
+        self,
+        function: onnxscript.OnnxFunction | onnxscript.TracedOnnxFunction,
+        namespace: str,
+        op_name: str,
+        overload: str | None = None,
+        is_complex: bool = False,
+    ) -> None:
+        """Registers a custom operator: torch.ops....
+
+        Args:
+            function: The onnx-sctip function to register.
+            namespace: The namespace of the operator to register.
+            op_name: The name of the operator to register.
+            overload: The overload of the operator to register. If it's default overload,
+                leave it to None.
+            is_complex: Whether the function is a function that handles complex valued inputs.
+
+        Raises:
+            ValueError: If the name is not in the form of 'namespace::op'.
+        """
+        internal_name_instance = registration.OpName.from_name_parts(
+            namespace=namespace, op_name=op_name, overload=overload
+        )
+        symbolic_function = registration.ONNXFunction(
+            onnx_function=function,
+            op_full_name=internal_name_instance.qualified_name(),
+            is_custom=True,
+            is_complex=is_complex,
+        )
+        self._register(internal_name_instance, symbolic_function)
+
+    def get_op_functions(
+        self, namespace: str, op_name: str, overload: str | None = None
+    ) -> list[registration.ONNXFunction] | None:
+        """Returns a list of ONNXFunctions for the given op: torch.ops....
+
+        The list is ordered by the time of registration. The custom operators should be
+        in the second half of the list.
+
+        Args:
+            namespace: The namespace of the operator to get.
+            op_name: The name of the operator to get.
+            overload: The overload of the operator to get. If it's default overload,
+                leave it to None.
+        Returns:
+            A list of ONNXFunctions corresponding to the given name, or None if
+            the name is not in the registry.
+        """
+        internal_name_instance = registration.OpName.from_name_parts(
+            namespace=namespace, op_name=op_name, overload=overload
+        )
+        return self._registry.get(internal_name_instance)
+
+    def is_registered_op(
+        self, namespace: str, op_name: str, overload: str | None = None
+    ) -> bool:
+        """Returns whether the given op is registered: torch.ops....
+
+        Args:
+            namespace: The namespace of the operator to check.
+            op_name: The name of the operator to check.
+            overload: The overload of the operator to check. If it's default overload,
+                leave it to None.
+
+        Returns:
+            True if the given op is registered, otherwise False.
+        """
+        functions = self.get_op_functions(
+            namespace=namespace, op_name=op_name, overload=overload
+        )
+        return functions is not None
+
+    def _all_registered_ops(self) -> set[str]:
+        """Returns the set of all registered function names."""
+        return {
+            op_name_class.qualified_name() for op_name_class in self._registry.keys()
+        }
+
+
+class ExportOptions:
+    """Options to influence the TorchDynamo ONNX exporter.
+
+    Attributes:
+        dynamic_shapes: Shape information hint for input/output tensors.
+            When ``None``, the exporter determines the most compatible setting.
+            When ``True``, all input shapes are considered dynamic.
+            When ``False``, all input shapes are considered static.
+        diagnostic_options: The diagnostic options for the exporter.
+        fake_context: The fake context used for symbolic tracing.
+        onnx_registry: The ONNX registry used to register ATen operators to ONNX functions.
+    """
+
+    dynamic_shapes: bool | None = None
+    """Shape information hint for input/output tensors.
+
+    - ``None``: the exporter determines the most compatible setting.
+    - ``True``: all input shapes are considered dynamic.
+    - ``False``: all input shapes are considered static.
+    """
+
+    diagnostic_options: DiagnosticOptions
+    """The diagnostic options for the exporter."""
+
+    fake_context: ONNXFakeContext | None = None
+    """The fake context used for symbolic tracing."""
+
+    onnx_registry: OnnxRegistry | None = None
+    """The ONNX registry used to register ATen operators to ONNX functions."""
+
+    def __init__(
+        self,
+        *,
+        dynamic_shapes: bool | None = None,
+        fake_context: ONNXFakeContext | None = None,
+        onnx_registry: OnnxRegistry | None = None,
+        diagnostic_options: DiagnosticOptions | None = None,
+    ):
+        self.dynamic_shapes = dynamic_shapes
+        self.fake_context = fake_context
+        self.onnx_registry = onnx_registry
+        self.diagnostic_options = diagnostic_options or DiagnosticOptions()
+
+
+class ResolvedExportOptions(ExportOptions):
+    """Consolidates :class:`ExportOptions` with default values.
+    All unspecified options from :class:`ExportOptions` are assigned a default value.
+    This is an internal class and its API may be changed at any time without notice.
+    """
+
+    # Public attributes MUST be redefined below without ``Optional[]`` from ``ExportOptions``
+    dynamic_shapes: bool
+    diagnostic_options: DiagnosticOptions
+    fake_context: ONNXFakeContext
+    onnx_registry: OnnxRegistry
+
+    # Private only attributes
+    decomposition_table: dict[torch._ops.OpOverload, Callable]
+    """A dictionary that maps operators to their decomposition functions."""
+
+    onnxfunction_dispatcher: (
+        torch.onnx._internal.fx.onnxfunction_dispatcher.OnnxFunctionDispatcher
+    )
+    """The ONNX dispatcher used to dispatch ATen operators to ONNX functions."""
+
+    fx_tracer: FXGraphExtractor
+    """The FXGraphExtractor instance used to extract the FX graph from the model."""
+
+    diagnostic_context: diagnostics.DiagnosticContext
+    """The diagnostics context for the export. Responsible for recording diagnostics,
+    logging diagnostics, and generating the SARIF log."""
+
+    def __init__(
+        self,
+        options: ExportOptions | ResolvedExportOptions,
+        model: torch.nn.Module | Callable | None = None,  # type: ignore[name-defined]
+    ):
+        from torch.onnx._internal.fx import (  # TODO: Prevent circular dep
+            diagnostics,
+            dynamo_graph_extractor,
+        )
+
+        if isinstance(options, ResolvedExportOptions):
+            self.dynamic_shapes = options.dynamic_shapes
+            self.diagnostic_options = options.diagnostic_options
+            self.fake_context = options.fake_context
+            self.fx_tracer = options.fx_tracer
+            self.onnx_registry = options.onnx_registry
+            self.onnxfunction_dispatcher = options.onnxfunction_dispatcher
+            self.decomposition_table = options.decomposition_table
+            self.diagnostic_context = options.diagnostic_context
+        else:
+            T = TypeVar("T")
+
+            def resolve(value: T | None, fallback: T | Callable[[], T]) -> T:
+                if value is not None:
+                    return value
+                if callable(fallback):
+                    return fallback()
+                return fallback
+
+            self.dynamic_shapes = resolve(options.dynamic_shapes, False)
+
+            self.diagnostic_options = resolve(
+                options.diagnostic_options, DiagnosticOptions()
+            )
+
+            self.fx_tracer = dynamo_graph_extractor.DynamoExport()
+
+            self.fake_context = resolve(options.fake_context, None)  # type: ignore[arg-type]
+            self.diagnostic_context = diagnostics.DiagnosticContext(
+                "torch.onnx.dynamo_export",
+                torch.__version__,
+                self.diagnostic_options,
+            )
+
+            self.onnx_registry = resolve(options.onnx_registry, OnnxRegistry())
+            self.decomposition_table = (
+                decomposition_table.create_onnx_friendly_decomposition_table(  # type: ignore[assignment]
+                    self.onnx_registry
+                )
+            )
+
+            from torch.onnx._internal.fx import onnxfunction_dispatcher
+
+            self.onnxfunction_dispatcher = (
+                onnxfunction_dispatcher.OnnxFunctionDispatcher(
+                    self.onnx_registry,
+                    self.diagnostic_context,
+                )
+            )
+
+            for key in dir(options):
+                if not key.startswith("_"):  # skip private attributes
+                    assert hasattr(self, key), f"Unresolved option '{key}'"
+
+
+@contextlib.contextmanager
+def enable_fake_mode():
+    """Enable fake mode for the duration of the context.
+
+    Internally it instantiates a :class:`torch._subclasses.fake_tensor.FakeTensorMode` context manager
+    that converts user input and model parameters into :class:`torch._subclasses.fake_tensor.FakeTensor`.
+
+    A :class:`torch._subclasses.fake_tensor.FakeTensor`
+    is a :class:`torch.Tensor` with the ability to run PyTorch code without having to
+    actually do computation through tensors allocated on a ``meta`` device. Because
+    there is no actual data being allocated on the device, this API allows for
+    exporting large models without the actual memory footprint needed for executing it.
+
+    It is highly recommended to enable fake mode when exporting models that
+    are too large to fit into memory.
+
+    Returns:
+        A :class:`ONNXFakeContext` object.
+
+    Example::
+
+        # xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)
+        >>> import torch
+        >>> import torch.onnx
+        >>> class MyModel(torch.nn.Module):  # Dummy model
+        ...     def __init__(self) -> None:
+        ...         super().__init__()
+        ...         self.linear = torch.nn.Linear(2, 2)
+        ...     def forward(self, x):
+        ...         out = self.linear(x)
+        ...         return out
+        >>> with torch.onnx.enable_fake_mode() as fake_context:
+        ...     my_nn_module = MyModel()
+        ...     arg1 = torch.randn(2, 2, 2)  # positional input 1
+        >>> export_options = torch.onnx.ExportOptions(fake_context=fake_context)
+        >>> onnx_program = torch.onnx.export(my_nn_module, (arg1,), dynamo=True)
+        >>> onnx_program.apply_weights(MyModel().state_dict())
+        >>> # Saving model WITHOUT initializers
+        >>> onnx_program.save(
+        ...     "my_model_without_initializers.onnx",
+        ...     include_initializers=False,
+        ...     keep_initializers_as_inputs=True,
+        ... )
+        >>> # Saving model WITH initializers
+        >>> onnx_program.save("my_model_with_initializers.onnx")
+
+    .. warning::
+        This API is experimental and is *NOT* backward-compatible.
+
+    """
+    from torch._subclasses import fake_tensor
+    from torch.fx.experimental.symbolic_shapes import ShapeEnv
+
+    # This overrides the internal `FakeTensorMode` instance created by `torch._dynamo.export`[1].
+    # It is a good idea to keep them in sync (constructor args) to maintain the same default behavior
+    # [1] `torch/_dynamo/output_graph.py::InstructionTranslator::OutputGraph.__init__`
+    # Mixed fake/real tensors are only allowed when `torch.onnx.dynamo_export` is not called within `FakeTensorMode`
+    # This is needed because models can create new parameters during `forward(self, *args, **kwargs)` run
+    fake_mode = fake_tensor.FakeTensorMode(
+        allow_non_fake_inputs=not torch._guards.detect_fake_mode(),
+        shape_env=ShapeEnv(
+            allow_scalar_outputs=False, allow_dynamic_output_shape_ops=False
+        ),
+    )
+    # The patcher is needed for when user calls `fake_model.load_state_dict(...)` within fake mode
+    patcher_context = patcher.ONNXTorchPatcher()
+    fake_context = ONNXFakeContext(fake_mode=fake_mode)
+    with fake_mode, patcher_context:
+        yield fake_context
+    fake_context.state_dict_paths = tuple(
+        patcher_context.paths,
+    )  # type: ignore[assignment]
+
+
+class ONNXRuntimeOptions:
+    """Options to influence the execution of the ONNX model through ONNX Runtime.
+
+    Attributes:
+        session_options: ONNX Runtime session options.
+        execution_providers: ONNX Runtime execution providers to use during model execution.
+        execution_provider_options: ONNX Runtime execution provider options.
+    """
+
+    session_options: Sequence[onnxruntime.SessionOptions] | None = None
+    """ONNX Runtime session options."""
+
+    execution_providers: Sequence[str | tuple[str, dict[Any, Any]]] | None = None
+    """ONNX Runtime execution providers to use during model execution."""
+
+    execution_provider_options: Sequence[dict[Any, Any]] | None = None
+    """ONNX Runtime execution provider options."""
+
+    def __init__(
+        self,
+        *,
+        session_options: Sequence[onnxruntime.SessionOptions] | None = None,
+        execution_providers: Sequence[str | tuple[str, dict[Any, Any]]] | None = None,
+        execution_provider_options: Sequence[dict[Any, Any]] | None = None,
+    ):
+        self.session_options = session_options
+        self.execution_providers = execution_providers
+        self.execution_provider_options = execution_provider_options
+
+
+class ONNXProgram:
+    """An in-memory representation of a PyTorch model that has been exported to ONNX.
+
+    Args:
+        model_proto: The exported ONNX model as an :py:obj:`onnx.ModelProto`.
+        input_adapter: The input adapter used to convert PyTorch inputs into ONNX inputs.
+        output_adapter: The output adapter used to convert PyTorch outputs into ONNX outputs.
+        diagnostic_context: Context object for the SARIF diagnostic system responsible for logging errors and metadata.
+        fake_context: The fake context used for symbolic tracing.
+        export_exception: The exception that occurred during export, if any.
+    """
+
+    _model_proto: Final[onnx.ModelProto]  # type: ignore[name-defined, misc]
+    _input_adapter: Final[io_adapter.InputAdapter]  # type: ignore[misc]
+    _output_adapter: Final[io_adapter.OutputAdapter]  # type: ignore[misc]
+    _diagnostic_context: Final[diagnostics.DiagnosticContext]  # type: ignore[misc]
+    _fake_context: Final[ONNXFakeContext | None]  # type: ignore[misc]
+    _export_exception: Final[Exception | None]  # type: ignore[misc]
+    _model_torch: Final[  # type: ignore[misc]
+        torch.nn.Module | Callable | None
+    ]
+
+    def __init__(
+        self,
+        model_proto: onnx.ModelProto,  # type: ignore[name-defined]
+        input_adapter: io_adapter.InputAdapter,
+        output_adapter: io_adapter.OutputAdapter,
+        diagnostic_context: diagnostics.DiagnosticContext,
+        *,
+        fake_context: ONNXFakeContext | None = None,
+        export_exception: Exception | None = None,
+        model_torch: torch.nn.Module | Callable | None = None,
+    ):
+        self._model_proto = model_proto
+        self._model_torch = model_torch
+        self._input_adapter = input_adapter
+        self._output_adapter = output_adapter
+        self._diagnostic_context = diagnostic_context
+        self._fake_context = fake_context
+        self._export_exception = export_exception
+        self._state_dict: dict[str, torch.Tensor] = {}
+
+    def __call__(
+        self,
+        *args: Any,
+        model_with_state_dict: torch.nn.Module | Callable | None = None,
+        options: ONNXRuntimeOptions | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        """Runs the ONNX model using ONNX Runtime
+
+        Args:
+            args: The positional inputs to the model.
+            kwargs: The keyword inputs to the model.
+            model_with_state_dict: The PyTorch model to fetch state from.
+                Required when :func:`enable_fake_mode` is used to extract real initializers as needed by the ONNX graph.
+            options: The options to use for running the model with ONNX Runtime.
+
+        Returns:
+            The model output as computed by ONNX Runtime
+        """
+
+        # TODO: If ONNX used absolute paths on the initializers external data files,
+        # users could call ONNXProgram.save and use ONNXProgram.__call__ without the internal save below
+        with contextlib.ExitStack() as stack:
+            # model specified by the user has precedence, when specified
+            model_with_state_dict = model_with_state_dict or self._model_torch
+
+            if self.fake_context:
+                tmpdir_path = stack.enter_context(tempfile.TemporaryDirectory())
+                warnings.warn(
+                    "Cannot run model directly from `ONNXProgram` because"
+                    " the model was exported using `enable_fake_mode`."
+                    " The model will be serialized to disk using a temporary folder ({tmpdir_path})"
+                    " to populate the model with initializers before being execution."
+                )
+                # TODO: Revisit the need of `model_with_state_dict` being a real model and not just its state
+                onnx_model = os.path.join(tmpdir_path, "model.onnx")
+                if isinstance(model_with_state_dict, torch.nn.Module):
+                    model_state = model_with_state_dict.state_dict()
+                else:
+                    model_state = self._state_dict
+                self.save(
+                    onnx_model,
+                    model_state=model_state,
+                )
+            else:
+                onnx_model = self.model_proto.SerializeToString()  # type: ignore[assignment]
+
+            import onnxruntime  # type: ignore[import]
+
+            onnx_input = self.adapt_torch_inputs_to_onnx(
+                *args, model_with_state_dict=model_with_state_dict, **kwargs
+            )
+            options = options or ONNXRuntimeOptions()
+            providers = (
+                options.execution_providers or onnxruntime.get_available_providers()
+            )
+            ort_session = onnxruntime.InferenceSession(onnx_model, providers=providers)
+
+            onnxruntime_input = {
+                k.name: v.numpy(force=True)  # type: ignore[union-attr]
+                for k, v in zip(ort_session.get_inputs(), onnx_input)
+            }
+
+            return ort_session.run(None, onnxruntime_input)
+
+    @property
+    def model_proto(self) -> onnx.ModelProto:  # type: ignore[name-defined]
+        """The exported ONNX model as an :py:obj:`onnx.ModelProto`."""
+
+        if self._export_exception is not None:
+            raise self._export_exception
+        return self._model_proto
+
+    @property
+    def diagnostic_context(self) -> diagnostics.DiagnosticContext:
+        """The diagnostic context associated with the export."""
+
+        return self._diagnostic_context
+
+    @property
+    def fake_context(self) -> ONNXFakeContext | None:
+        """The fake context associated with the export."""
+
+        return self._fake_context
+
+    def adapt_torch_inputs_to_onnx(
+        self,
+        *model_args,
+        model_with_state_dict: torch.nn.Module | Callable | None = None,
+        **model_kwargs,
+    ) -> Sequence[torch.Tensor | int | float | bool | torch.dtype]:
+        """Converts the PyTorch model inputs to exported ONNX model inputs format.
+
+        Due to design differences, input/output format between PyTorch model and exported
+        ONNX model are often not the same. E.g., None is allowed for PyTorch model, but are
+        not supported by ONNX. Nested constructs of tensors are allowed for PyTorch model,
+        but only flattened tensors are supported by ONNX, etc.
+
+        The actual adapting steps are associated with each individual export. It
+        depends on the PyTorch model, the particular set of model_args and model_kwargs
+        used for the export, and export options.
+
+        This method replays the adapting steps recorded during export.
+
+        Args:
+            model_args: The PyTorch model inputs.
+            model_with_state_dict: The PyTorch model to get extra state from.
+                If not specified, the model used during export is used.
+                Required when :func:`enable_fake_mode` is used to extract real initializers as needed by the ONNX graph.
+            model_kwargs: The PyTorch model keyword inputs.
+
+        Returns:
+            A sequence of tensors converted from PyTorch model inputs.
+
+        Example::
+
+            # xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)
+            >>> import torch
+            >>> import torch.onnx
+            >>> from typing import Dict, Tuple
+            >>> def func_nested_input(
+            ...     x_dict: Dict[str, torch.Tensor],
+            ...     y_tuple: Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]
+            ... ):
+            ...     if "a" in x_dict:
+            ...         x = x_dict["a"]
+            ...     elif "b" in x_dict:
+            ...         x = x_dict["b"]
+            ...     else:
+            ...         x = torch.randn(3)
+            ...
+            ...     y1, (y2, y3) = y_tuple
+            ...
+            ...     return x + y1 + y2 + y3
+            >>> x_dict = {"a": torch.tensor(1.)}
+            >>> y_tuple = (torch.tensor(2.), (torch.tensor(3.), torch.tensor(4.)))
+            >>> onnx_program = torch.onnx.dynamo_export(func_nested_input, x_dict, y_tuple)
+            >>> print(x_dict, y_tuple)
+            {'a': tensor(1.)} (tensor(2.), (tensor(3.), tensor(4.)))
+            >>> print(onnx_program.adapt_torch_inputs_to_onnx(x_dict, y_tuple, model_with_state_dict=func_nested_input))
+            (tensor(1.), tensor(2.), tensor(3.), tensor(4.))
+
+        .. warning::
+            This API is experimental and is *NOT* backward-compatible.
+
+        """
+        # model specified by the user has precedence, when specified
+        model_with_state_dict = model_with_state_dict or self._model_torch
+        assert (
+            model_with_state_dict is not None
+        ), "model_with_state_dict must be specified."
+        return self._input_adapter.apply(  # type: ignore[return-value]
+            *model_args, model=model_with_state_dict, **model_kwargs
+        )
+
+    def adapt_torch_outputs_to_onnx(
+        self,
+        model_outputs: Any,
+        model_with_state_dict: torch.nn.Module | Callable | None = None,
+    ) -> Sequence[torch.Tensor | int | float | bool]:
+        """Converts the PyTorch model outputs to exported ONNX model outputs format.
+
+        Due to design differences, input/output format between PyTorch model and exported
+        ONNX model are often not the same. E.g., None is allowed for PyTorch model, but are
+        not supported by ONNX. Nested constructs of tensors are allowed for PyTorch model,
+        but only flattened tensors are supported by ONNX, etc.
+
+        The actual adapting steps are associated with each individual export. It
+        depends on the PyTorch model, the particular set of model_args and model_kwargs
+        used for the export, and export options.
+
+        This method replays the adapting steps recorded during export.
+
+        Args:
+            model_outputs: The PyTorch model outputs.
+            model_with_state_dict: The PyTorch model to get extra state from.
+                If not specified, the model used during export is used.
+                Required when :func:`enable_fake_mode` is used to extract real initializers as needed by the ONNX graph.
+
+        Returns:
+            PyTorch model outputs in exported ONNX model outputs format.
+
+        Example::
+
+            # xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)
+            >>> import torch
+            >>> import torch.onnx
+            >>> def func_returning_tuples(x, y, z):
+            ...     x = x + y
+            ...     y = y + z
+            ...     z = x + y
+            ...     return (x, (y, z))
+            >>> x = torch.tensor(1.)
+            >>> y = torch.tensor(2.)
+            >>> z = torch.tensor(3.)
+            >>> onnx_program = torch.onnx.dynamo_export(func_returning_tuples, x, y, z)
+            >>> pt_output = func_returning_tuples(x, y, z)
+            >>> print(pt_output)
+            (tensor(3.), (tensor(5.), tensor(8.)))
+            >>> print(onnx_program.adapt_torch_outputs_to_onnx(pt_output, model_with_state_dict=func_returning_tuples))
+            [tensor(3.), tensor(5.), tensor(8.)]
+
+        .. warning::
+            This API is experimental and is *NOT* backward-compatible.
+
+        """
+        # model specified by the user has precedence, when specified
+        model_with_state_dict = model_with_state_dict or self._model_torch
+        assert (
+            model_with_state_dict is not None
+        ), "model_with_state_dict must be specified."
+        return self._output_adapter.apply(model_outputs, model=model_with_state_dict)  # type: ignore[return-value]
+
+    def apply_weights(self, state_dict: dict[str, torch.Tensor]) -> None:
+        """Apply the weights from the specified state dict to the ONNX model.
+        Args:
+            state_dict: The state dict containing the weights to apply to the ONNX model.
+        """
+        self._state_dict = state_dict
+
+    def save(
+        self,
+        destination: str | io.BufferedIOBase,
+        *,
+        include_initializers: bool = True,
+        model_state: dict[str, Any] | str | None = None,
+    ) -> None:
+        """Saves the in-memory ONNX model to ``destination`` using specified ``serializer``.
+
+        Args:
+            destination: The destination to save the ONNX model. It can be either a string or a file-like object.
+                When used with ``model_state``, it must be a string with a full path to the destination.
+                If `destination` is a string, besides saving the ONNX model into a file, model weights are also stored
+                in separate files in the same directory as the ONNX model. E.g. for `destination="/path/model.onnx"`,
+                the initializers are saved in "/path/" folder along with "onnx.model".
+            include_initializers: Whether to include initializers in the ONNX graph as external data.
+                Cannot be combined with `model_state_dict`.
+            model_state: The state_dict of the PyTorch model containing all weights on it.
+                It can be either a string with the path to a checkpoint or a dictionary with the actual model state.
+                The supported file formats are the same as those supported by `torch.load` and `safetensors.safe_open`.
+                Required when :func:`enable_fake_mode` is used but real initializers are needed on the ONNX graph.
+        """
+        import onnx
+
+        assert (
+            include_initializers is True or model_state is None
+        ), "Cannot specify both `include_initializers=False` and `model_state`."
+
+        if self._state_dict and model_state is None:
+            model_state = self._state_dict
+
+        # Add initializers when symbolic tracing is enabled
+        _model_state_files: list[str | io.BytesIO | dict[str, Any]] = []
+        if include_initializers:
+            if model_state is not None:
+                assert isinstance(
+                    model_state, (dict, str)
+                ), "model_state must be a path to the model's state_dict or the actual state_dict"
+                # NOTE: For dict, there can be performance penalty or high memory usage that might lead to OOM
+                #       if the dict wasn't loaded with torch.load(..., mmap=True, map_location="cpu")
+                _model_state_files.append(model_state)
+            elif self._fake_context and self._fake_context.state_dict_paths:
+                # Load state from previous model.load_state_dict() call within enable_fake_mode() context
+                for path in self._fake_context.state_dict_paths:
+                    if path in _model_state_files:
+                        # ignore duplicate
+                        continue
+                    if os.path.exists(path):  # type: ignore[arg-type]
+                        _model_state_files.append(path)
+        else:
+            # self.model_proto.graph.initializer.clear() not available in older protobuf versions
+            initializer_count = len(self.model_proto.graph.initializer)
+            for _ in range(initializer_count):
+                del self.model_proto.graph.initializer[0]
+
+        if _model_state_files:
+            if not isinstance(destination, str):
+                raise RuntimeError(
+                    "`destination` must be a string with a path when `model_state` is specified."
+                )
+            destination_path, destination_filename = os.path.split(destination)
+            destination_path = destination_path or os.getcwd()
+            onnx_model_location = destination_filename
+
+            # TODO: Should this be part of the serializer?
+            fx_serialization.save_model_with_external_data(
+                destination_path,
+                onnx_model_location,
+                "",  # When initializers >2GB, must be in the same folder as the model
+                tuple(_model_state_files),
+                self.model_proto,
+            )
+        else:
+            if isinstance(destination, str):
+                with open(destination, "wb") as f:
+                    if self.model_proto.ByteSize() < _PROTOBUF_SIZE_MAX_LIMIT:
+                        onnx.save_model(self.model_proto, destination)  # type: ignore[attr-defined]
+                    else:
+                        # ValueError: Message onnx.ModelProto exceeds maximum protobuf size of 2GB
+                        # Fallback to serializing the model with external data.
+                        onnx.save_model(  # type: ignore[attr-defined]
+                            self.model_proto,
+                            destination,
+                            save_as_external_data=True,
+                            all_tensors_to_one_file=True,
+                        )
+            else:
+                try:
+                    destination.write(self.model_proto.SerializeToString())
+                except ValueError as exc:
+                    raise ValueError(
+                        "'destination' should be provided as a path-like string when saving a model larger than 2GB. "
+                        "External tensor data will be saved alongside the model on disk."
+                    ) from exc
+
+    def save_diagnostics(self, destination: str) -> None:
+        """Saves the export diagnostics as a SARIF log to the specified destination path.
+
+        Args:
+            destination: The destination to save the diagnostics SARIF log.
+                It must have a `.sarif` extension.
+
+        Raises:
+            ValueError: If the destination path does not end with `.sarif` extension.
+        """
+        if not destination.endswith(".sarif"):
+            message = f"'destination' must have a .sarif extension, got {destination}"
+            log.fatal(message)
+            raise ValueError(message)
+
+        self.diagnostic_context.dump(destination)
+
+    @classmethod
+    def _from_failure(
+        cls,
+        export_exception: Exception,
+        diagnostic_context: diagnostics.DiagnosticContext,
+    ) -> Self:
+        """
+        Creates an instance of :class:`ONNXProgram` when the export process encounters a failure.
+
+        In case of a failed export, this method is used to encapsulate the exception
+        and associated diagnostic context within an :class:`ONNXProgram` instance for
+        easier handling and debugging.
+
+        Args:
+            export_exception: The exception raised during the export process.
+            diagnostic_context: The context associated with diagnostics during export.
+
+        Returns:
+            An instance of :class:`ONNXProgram` representing the failed ONNX program.
+        """
+        # Defer `import onnx` out of `import torch` path
+        # https://github.com/pytorch/pytorch/issues/103764
+        import onnx
+
+        return cls(
+            onnx.ModelProto(),  # type: ignore[attr-defined]
+            io_adapter.InputAdapter(),
+            io_adapter.OutputAdapter(),
+            diagnostic_context,
+            export_exception=export_exception,
+        )
+
+
+class FXGraphExtractor(abc.ABC):
+    """Abstract interface for FX graph extractor engines.
+    This class isolates FX extraction logic from the rest of the export logic.
+    That allows a single ONNX exporter that can leverage different FX graphs."""
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.input_adapter: io_adapter.InputAdapter = io_adapter.InputAdapter()
+        self.output_adapter: io_adapter.OutputAdapter = io_adapter.OutputAdapter()
+
+    @abc.abstractmethod
+    def generate_fx(
+        self,
+        options: ResolvedExportOptions,
+        model: torch.nn.Module | Callable,
+        model_args: Sequence[Any],
+        model_kwargs: Mapping[str, Any],
+    ) -> torch.fx.GraphModule:
+        """Analyzes user ``model`` and generates a FX graph.
+        Args:
+            options: The export options.
+            model: The user model.
+            model_args: The model's positional input arguments.
+            model_kwargs: The model's keyword input arguments.
+        Returns:
+            The generated FX Graph.
+        """
+        ...
+
+    # TODO: Design the passes API
+    @abc.abstractmethod
+    def pre_export_passes(
+        self,
+        options: ResolvedExportOptions,
+        original_model: torch.nn.Module | Callable,
+        fx_module: torch.fx.GraphModule,
+        fx_module_args: Sequence[Any],
+    ):
+        """Applies pre-export passes to the FX graph.
+
+        Pre-export passes are FX-to-FX graph transformations that make the graph
+        more palatable for the FX-to-ONNX conversion.
+        For example, it can be used to flatten model input/output, add explicit
+        casts to the graph, replace/decompose operators, functionalize the graph, etc.
+        """
+        ...
+
+
+class Exporter:
+    def __init__(
+        self,
+        options: ResolvedExportOptions,
+        model: torch.nn.Module | Callable,
+        model_args: Sequence[Any],
+        model_kwargs: Mapping[str, Any],
+    ):
+        self.options = options
+        assert self.options is not None
+
+        self.model = model
+        self.model_args = model_args
+        self.model_kwargs = model_kwargs
+
+        # TODO: https://github.com/pytorch/pytorch/issues/107714
+        # NOTE: FXSymbolicTracer would fail in this assert, as it does not use `enable_fake_mode`
+        from torch.onnx._internal.fx import fx_symbolic_graph_extractor
+
+        if not isinstance(
+            self.options.fx_tracer, fx_symbolic_graph_extractor.FXSymbolicTracer
+        ):
+            self._assert_fake_tensor_mode()
+
+    def export(self) -> ONNXProgram:
+        from torch.export._trace import (  # TODO: Prevent circular dependency
+            DEFAULT_EXPORT_DYNAMO_CONFIG,
+        )
+
+        # TODO: Defer `import onnxscript` out of `import torch` path
+        # https://github.com/pytorch/pytorch/issues/103764
+        from torch.onnx._internal.fx import decomposition_skip
+
+        with self.options.diagnostic_context, decomposition_skip.enable_decomposition_skips(
+            self.options
+        ), torch._dynamo.config.patch(dataclasses.asdict(DEFAULT_EXPORT_DYNAMO_CONFIG)):
+            graph_module = self.options.fx_tracer.generate_fx(
+                self.options, self.model, self.model_args, self.model_kwargs
+            )
+            # TODO: Defer `import onnxscript` out of `import torch` path
+            # https://github.com/pytorch/pytorch/issues/103764
+            from torch.onnx._internal.fx import fx_onnx_interpreter
+
+            fx_interpreter = fx_onnx_interpreter.FxOnnxInterpreter(
+                diagnostic_context=self.options.diagnostic_context
+            )
+            onnxscript_graph = fx_interpreter.run(
+                fx_graph_module=graph_module,
+                onnxfunction_dispatcher=self.options.onnxfunction_dispatcher,
+            )
+
+            # NOTE: Filter out the initializers with fake tensors when it's fake_mode exporting.
+            # Otherwise, the ONNX exporter will fail: RuntimeError: basic_string::_M_construct null
+            # not valid.
+            # Concrete data is expected to be filled for those initializers later during `ONNXProgram.save`.
+            if self.options.fake_context is not None:
+                initializers_with_real_tensors: dict[str, torch.Tensor] = {}
+                for (
+                    initializer_name,
+                    initializer,
+                ) in onnxscript_graph.initializers.items():
+                    if not isinstance(initializer, torch._subclasses.FakeTensor):
+                        initializers_with_real_tensors[initializer_name] = initializer
+                onnxscript_graph.initializers = initializers_with_real_tensors
+
+            # Export TorchScript graph to ONNX ModelProto.
+            onnx_model = onnxscript_graph.to_model_proto(
+                self.options.onnx_registry.opset_version,
+            )
+
+            try:
+                from onnxscript import optimizer
+
+                onnx_model = optimizer.optimize(onnx_model)
+            except ImportError:
+                warnings.warn(
+                    "ONNXScript optimizer is not available. Skipping optimization. "
+                    "Please `pip install onnxscript -U` to enable post-export optimization."
+                )
+            except Exception as e:
+                warnings.warn(
+                    "ONNXScript optimizer failed. Skipping optimization. "
+                    "\n\nPLEASE REPORT A BUG AT https://github.com/microsoft/onnxscript/issues "
+                    f"\n\nDetail:\n{e}"
+                )
+
+            return torch.onnx.ONNXProgram(
+                onnx_model,
+                self.options.fx_tracer.input_adapter,
+                self.options.fx_tracer.output_adapter,
+                self.options.diagnostic_context,
+                fake_context=self.options.fake_context,
+                model_torch=self.model,
+            )
+
+    def _assert_fake_tensor_mode(self):
+        """Asserts that the model and its input do not contain fake tensors."""
+
+        # Case 1: Model with fake inputs/weights and without enabling fake mode
+        has_any_fake_tensor = pytree.tree_any(
+            lambda x: isinstance(x, torch._subclasses.FakeTensor),
+            (self.model_args, self.model_kwargs),
+        )
+        has_any_fake_param_or_buffer = False
+        if isinstance(self.model, torch.nn.Module):
+            has_any_fake_param_or_buffer = pytree.tree_any(
+                lambda x: isinstance(x, torch._subclasses.FakeTensor),
+                (self.model.parameters(), self.model.buffers()),
+            )
+        if (
+            has_any_fake_tensor or has_any_fake_param_or_buffer
+        ) and not self.options.fake_context:
+            raise RuntimeError(
+                "Cannot export a model with fake inputs/weights without enabling fake mode.",
+            )
+        # Case 2: Model with non fake inputs/weights and enabled fake mode
+        has_any_non_fake_tensors = pytree.tree_any(
+            lambda x: isinstance(x, torch.Tensor)
+            and not isinstance(x, torch._subclasses.FakeTensor),
+            (self.model_args, self.model_kwargs),
+        )
+        has_any_non_fake_param_or_buffer = False
+        if isinstance(self.model, torch.nn.Module):
+            has_any_non_fake_param_or_buffer = pytree.tree_any(
+                lambda x: isinstance(x, torch.Tensor)
+                and not isinstance(x, torch._subclasses.FakeTensor),
+                (self.model.parameters(), self.model.buffers()),
+            )
+        if (
+            has_any_non_fake_tensors or has_any_non_fake_param_or_buffer
+        ) and self.options.fake_context:
+            raise RuntimeError(
+                "Cannot export a model with non fake inputs/weights and enabled fake mode.",
+            )
+
+
+class UnsatisfiedDependencyError(RuntimeError):
+    """Raised when an ONNX exporter dependency cannot be satisfied."""
+
+    def __init__(self, package_name: str, message: str):
+        super().__init__(message)
+        self.package_name = package_name
+
+
+class InvalidExportOptionsError(RuntimeError):
+    """Raised when user specified an invalid value for the :class:`ExportOptions`."""
+
+
+def _assert_dependencies(export_options: ResolvedExportOptions):
+    opset_version = export_options.onnx_registry.opset_version
+
+    def missing_package(package_name: str, exc_info: logging._ExcInfoType):
+        message = (
+            f"Please install the `{package_name}` package "
+            f"(e.g. `python -m pip install {package_name}`)."
+        )
+        log.fatal(message, exc_info=exc_info)
+        return UnsatisfiedDependencyError(package_name, message)
+
+    def missing_opset(package_name: str):
+        message = (
+            f"The installed `{package_name}` does not support the specified ONNX opset "
+            f"version {opset_version}. Install a newer `{package_name}` package or "
+            f"specify an older opset version."
+        )
+        log.fatal(message)
+        return UnsatisfiedDependencyError(package_name, message)
+
+    try:
+        import onnx
+    except ImportError as e:
+        raise missing_package("onnx", e) from e
+
+    if onnx.defs.onnx_opset_version() < opset_version:
+        raise missing_opset("onnx")
+
+    try:
+        # PyTorch runs lintrunner in CI without onnxscript installed
+        import onnxscript  # type: ignore[import]
+    except ImportError as e:
+        raise missing_package("onnxscript", e) from e
+
+    if not isinstance(
+        onnxscript.onnx_opset.all_opsets[("", opset_version)],
+        onnxscript.values.Opset,
+    ):
+        raise missing_opset("onnxscript")
+
+
+def dynamo_export(
+    model: torch.nn.Module | Callable,
+    /,
+    *model_args,
+    export_options: ExportOptions | None = None,
+    **model_kwargs,
+) -> ONNXProgram | Any:
+    """Export a torch.nn.Module to an ONNX graph.
+
+    Args:
+        model: The PyTorch model to be exported to ONNX.
+        model_args: Positional inputs to ``model``.
+        model_kwargs: Keyword inputs to ``model``.
+        export_options: Options to influence the export to ONNX.
+
+    Returns:
+        An in-memory representation of the exported ONNX model.
+
+    **Example 1 - Simplest export**
+    ::
+
+        class MyModel(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.linear = torch.nn.Linear(2, 2)
+
+            def forward(self, x, bias=None):
+                out = self.linear(x)
+                out = out + bias
+                return out
+
+
+        model = MyModel()
+        kwargs = {"bias": 3.0}
+        args = (torch.randn(2, 2, 2),)
+        onnx_program = torch.onnx.dynamo_export(model, *args, **kwargs).save(
+            "my_simple_model.onnx"
+        )
+
+    **Example 2 - Exporting with dynamic shapes**
+    ::
+
+        # The previous model can be exported with dynamic shapes
+        export_options = torch.onnx.ExportOptions(dynamic_shapes=True)
+        onnx_program = torch.onnx.dynamo_export(
+            model, *args, **kwargs, export_options=export_options
+        )
+        onnx_program.save("my_dynamic_model.onnx")
+
+
+    By printing input dynamic dimensions we can see the input shape is no longer (2,2,2)
+    ::
+
+        >>> print(onnx_program.model_proto.graph.input[0])
+        name: "arg0"
+        type {
+          tensor_type {
+            elem_type: 1
+            shape {
+              dim {
+                dim_param: "arg0_dim_0"
+              }
+              dim {
+                dim_param: "arg0_dim_1"
+              }
+              dim {
+                dim_param: "arg0_dim_2"
+              }
+            }
+          }
+        }
+    """
+
+    if export_options is not None:
+        resolved_export_options = (
+            export_options
+            if isinstance(export_options, ResolvedExportOptions)
+            else ResolvedExportOptions(export_options, model=model)
+        )
+    else:
+        resolved_export_options = ResolvedExportOptions(ExportOptions(), model=model)
+
+    _assert_dependencies(resolved_export_options)
+
+    try:
+        from torch._dynamo import config as _dynamo_config
+
+        with _dynamo_config.patch(do_not_emit_runtime_asserts=True):
+            return Exporter(
+                options=resolved_export_options,
+                model=model,
+                model_args=model_args,
+                model_kwargs=model_kwargs,
+            ).export()
+    except Exception as e:
+        sarif_report_path = _DEFAULT_FAILED_EXPORT_SARIF_LOG_PATH
+        resolved_export_options.diagnostic_context.dump(sarif_report_path)
+        message = (
+            f"Failed to export the model to ONNX. Generating SARIF report at '{sarif_report_path}'. "
+            "SARIF is a standard format for the output of static analysis tools. "
+            "SARIF logs can be loaded in VS Code SARIF viewer extension, "
+            "or SARIF web viewer (https://microsoft.github.io/sarif-web-component/). "
+            f"Please report a bug on PyTorch Github: {_PYTORCH_GITHUB_ISSUES_URL}"
+        )
+        raise errors.OnnxExporterError(message) from e
+
+
+def common_pre_export_passes(
+    options: ResolvedExportOptions,
+    original_model: torch.nn.Module | Callable,
+    fx_module: torch.fx.GraphModule,
+    fx_module_args: Sequence[Any],
+):
+    # TODO: Import here to prevent circular dependency
+    from torch.onnx._internal.fx import analysis, passes
+
+    diagnostic_context = options.diagnostic_context
+
+    # Apply decomposition table to the input graph.
+    module = passes.Decompose(
+        diagnostic_context,
+        fx_module,
+        options.decomposition_table,
+        enable_dynamic_axes=options.dynamic_shapes,
+        allow_fake_constant=options.fake_context is not None,
+    ).run(*fx_module_args)
+
+    # ONNX does not support views and mutations.
+    # Functionalize to get a semantically equivalent graph without mutations.
+    module = passes.Functionalize(
+        diagnostic_context,
+        module,
+        enable_dynamic_axes=options.dynamic_shapes,
+        allow_fake_constant=options.fake_context is not None,
+    ).run(*fx_module_args)
+
+    # Input mutations are detected and distilled after `Functionalize` pass.
+    # Remove them since ONNX inference does not need them.
+    module = passes.RemoveInputMutation(diagnostic_context, module).run(*fx_module_args)
+
+    # ONNX does not support concept of (implicit) type promotion.
+    # Insert type casts explicitly where needed.
+    module = passes.InsertTypePromotion(diagnostic_context, module).run()
+
+    analysis.UnsupportedFxNodesAnalysis(
+        diagnostic_context, module, options.onnxfunction_dispatcher
+    ).analyze(infra.levels.ERROR)
+
+    if isinstance(original_model, torch.nn.Module):
+        module = passes.RestoreParameterAndBufferNames(
+            diagnostic_context, module, original_model
+        ).run()
+
+    # This operation should be invoked as the last pre export pass.
+    # See [NOTE: Modularize pass ordering]
+    module = passes.Modularize(diagnostic_context, module).run()
+
+    # ONNX does not support None inputs. During graph building, all None inputs
+    # are removed. Here we register this step to input adapter.
+    options.fx_tracer.input_adapter.append_step(io_adapter.RemoveNoneInputStep())
+
+    # NOTE: temp workaround for https://github.com/pytorch/pytorch/issues/99534
+    # Dynamo doesn't support non-tensor inputs.
+    options.fx_tracer.input_adapter.append_step(io_adapter.RemoveNonTensorInputStep())
+
+    # ONNX does not support complex inputs. During graph building, all complex inputs
+    # are converted to real representation inputs. Here we register this step to
+    # input/output adapter.
+    options.fx_tracer.input_adapter.append_step(
+        io_adapter.ConvertComplexToRealRepresentationInputStep()
+    )
+
+    # ONNX can't represent collection types (e.g., dictionary, tuple of tuple of
+    # tensor, etc), we flatten the collection and register each element as output.
+    options.fx_tracer.output_adapter.append_step(io_adapter.FlattenOutputStep())
+
+    # Output post-processing steps should happen after `FlattenOutputStep`.
+    options.fx_tracer.output_adapter.append_step(
+        io_adapter.ConvertComplexToRealRepresentationOutputStep()
+    )
+
+    return module
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/_lazy_import.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/_lazy_import.py
new file mode 100644
index 0000000000000000000000000000000000000000..b12e53ef292624d4b705b95a6d6e8a97bb8bbcd2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/_lazy_import.py
@@ -0,0 +1,41 @@
+"""Utility to lazily import modules."""
+
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import importlib
+from typing import Any, TYPE_CHECKING
+
+
+class _LazyModule:
+    """Lazily import a module."""
+
+    def __init__(self, module_name: str) -> None:
+        self._name = module_name
+        self._module: Any = None
+
+    def __repr__(self) -> str:
+        return f""
+
+    def __getattr__(self, attr):
+        if self._module is None:
+            self._module = importlib.import_module(".", self._name)
+        return getattr(self._module, attr)
+
+
+# Import the following modules during type checking to enable code intelligence features,
+# such as auto-completion in tools like pylance, even when these modules are not explicitly
+# imported in user code.
+# NOTE: Add additional used imports here.
+if TYPE_CHECKING:
+    import onnx
+    import onnxscript
+    import onnxscript._framework_apis.torch_2_5 as onnxscript_apis
+
+    onnxscript_ir = onnxscript.ir
+
+else:
+    onnx = _LazyModule("onnx")
+    onnxscript = _LazyModule("onnxscript")
+    onnxscript_ir = _LazyModule("onnxscript.ir")
+    onnxscript_apis = _LazyModule("onnxscript._framework_apis.torch_2_5")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3eab565a20e8581b4c2d30287a2a6f4868daeb7
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/__init__.py
@@ -0,0 +1,22 @@
+from ._diagnostic import (
+    create_export_diagnostic_context,
+    diagnose,
+    engine,
+    export_context,
+    ExportDiagnosticEngine,
+    TorchScriptOnnxExportDiagnostic,
+)
+from ._rules import rules
+from .infra import levels
+
+
+__all__ = [
+    "TorchScriptOnnxExportDiagnostic",
+    "ExportDiagnosticEngine",
+    "rules",
+    "levels",
+    "engine",
+    "export_context",
+    "create_export_diagnostic_context",
+    "diagnose",
+]
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/_diagnostic.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/_diagnostic.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8110ccf4b0a0129f6cd3d4c6408f895a2384a60
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/_diagnostic.py
@@ -0,0 +1,211 @@
+# mypy: allow-untyped-defs
+"""Diagnostic components for TorchScript based ONNX export, i.e. `torch.onnx.export`."""
+
+from __future__ import annotations
+
+import contextlib
+import gzip
+from typing import TYPE_CHECKING
+
+import torch
+from torch.onnx._internal.diagnostics import infra
+from torch.onnx._internal.diagnostics.infra import formatter, sarif
+from torch.onnx._internal.diagnostics.infra.sarif import version as sarif_version
+from torch.utils import cpp_backtrace
+
+
+if TYPE_CHECKING:
+    from collections.abc import Generator
+
+
+def _cpp_call_stack(frames_to_skip: int = 0, frames_to_log: int = 32) -> infra.Stack:
+    """Returns the current C++ call stack.
+
+    This function utilizes `torch.utils.cpp_backtrace` to get the current C++ call stack.
+    The returned C++ call stack is a concatenated string of the C++ call stack frames.
+    Each frame is separated by a newline character, in the same format of
+    r"frame #[0-9]+: (?P.*)". More info at `c10/util/Backtrace.cpp`.
+
+    """
+    frames = cpp_backtrace.get_cpp_backtrace(frames_to_skip, frames_to_log).split("\n")
+    frame_messages = []
+    for frame in frames:
+        segments = frame.split(":", 1)
+        if len(segments) == 2:
+            frame_messages.append(segments[1].strip())
+        else:
+            frame_messages.append("")
+    return infra.Stack(
+        frames=[
+            infra.StackFrame(location=infra.Location(message=message))
+            for message in frame_messages
+        ]
+    )
+
+
+class TorchScriptOnnxExportDiagnostic(infra.Diagnostic):
+    """Base class for all export diagnostics.
+
+    This class is used to represent all export diagnostics. It is a subclass of
+    infra.Diagnostic, and adds additional methods to add more information to the
+    diagnostic.
+    """
+
+    python_call_stack: infra.Stack | None = None
+    cpp_call_stack: infra.Stack | None = None
+
+    def __init__(
+        self,
+        *args,
+        frames_to_skip: int = 1,
+        cpp_stack: bool = False,
+        **kwargs,
+    ) -> None:
+        super().__init__(*args, **kwargs)
+        self.python_call_stack = self.record_python_call_stack(
+            frames_to_skip=frames_to_skip
+        )
+        if cpp_stack:
+            self.cpp_call_stack = self.record_cpp_call_stack(
+                frames_to_skip=frames_to_skip
+            )
+
+    def record_cpp_call_stack(self, frames_to_skip: int) -> infra.Stack:
+        """Records the current C++ call stack in the diagnostic."""
+        stack = _cpp_call_stack(frames_to_skip=frames_to_skip)
+        stack.message = "C++ call stack"
+        self.with_stack(stack)
+        return stack
+
+
+class ExportDiagnosticEngine:
+    """PyTorch ONNX Export diagnostic engine.
+
+    The only purpose of creating this class instead of using `DiagnosticContext` directly
+    is to provide a background context for `diagnose` calls inside exporter.
+
+    By design, one `torch.onnx.export` call should initialize one diagnostic context.
+    All `diagnose` calls inside exporter should be made in the context of that export.
+    However, since diagnostic context is currently being accessed via a global variable,
+    there is no guarantee that the context is properly initialized. Therefore, we need
+    to provide a default background context to fallback to, otherwise any invocation of
+    exporter internals, e.g. unit tests, will fail due to missing diagnostic context.
+    This can be removed once the pipeline for context to flow through the exporter is
+    established.
+    """
+
+    contexts: list[infra.DiagnosticContext]
+    _background_context: infra.DiagnosticContext
+
+    def __init__(self) -> None:
+        self.contexts = []
+        self._background_context = infra.DiagnosticContext(
+            name="torch.onnx",
+            version=torch.__version__,
+        )
+
+    @property
+    def background_context(self) -> infra.DiagnosticContext:
+        return self._background_context
+
+    def create_diagnostic_context(
+        self,
+        name: str,
+        version: str,
+        options: infra.DiagnosticOptions | None = None,
+    ) -> infra.DiagnosticContext:
+        """Creates a new diagnostic context.
+
+        Args:
+            name: The subject name for the diagnostic context.
+            version: The subject version for the diagnostic context.
+            options: The options for the diagnostic context.
+
+        Returns:
+            A new diagnostic context.
+        """
+        if options is None:
+            options = infra.DiagnosticOptions()
+        context: infra.DiagnosticContext[infra.Diagnostic] = infra.DiagnosticContext(
+            name, version, options
+        )
+        self.contexts.append(context)
+        return context
+
+    def clear(self):
+        """Clears all diagnostic contexts."""
+        self.contexts.clear()
+        self._background_context.diagnostics.clear()
+
+    def to_json(self) -> str:
+        return formatter.sarif_to_json(self.sarif_log())
+
+    def dump(self, file_path: str, compress: bool = False) -> None:
+        """Dumps the SARIF log to a file."""
+        if compress:
+            with gzip.open(file_path, "wt") as f:
+                f.write(self.to_json())
+        else:
+            with open(file_path, "w") as f:
+                f.write(self.to_json())
+
+    def sarif_log(self):
+        log = sarif.SarifLog(
+            version=sarif_version.SARIF_VERSION,
+            schema_uri=sarif_version.SARIF_SCHEMA_LINK,
+            runs=[context.sarif() for context in self.contexts],
+        )
+
+        log.runs.append(self._background_context.sarif())
+        return log
+
+
+engine = ExportDiagnosticEngine()
+_context = engine.background_context
+
+
+@contextlib.contextmanager
+def create_export_diagnostic_context() -> (
+    Generator[infra.DiagnosticContext, None, None]
+):
+    """Create a diagnostic context for export.
+
+    This is a workaround for code robustness since diagnostic context is accessed by
+    export internals via global variable. See `ExportDiagnosticEngine` for more details.
+    """
+    global _context
+    assert (
+        _context == engine.background_context
+    ), "Export context is already set. Nested export is not supported."
+    _context = engine.create_diagnostic_context(
+        "torch.onnx.export",
+        torch.__version__,
+    )
+    try:
+        yield _context
+    finally:
+        _context = engine.background_context
+
+
+def diagnose(
+    rule: infra.Rule,
+    level: infra.Level,
+    message: str | None = None,
+    frames_to_skip: int = 2,
+    **kwargs,
+) -> TorchScriptOnnxExportDiagnostic:
+    """Creates a diagnostic and record it in the global diagnostic context.
+
+    This is a wrapper around `context.log` that uses the global diagnostic
+    context.
+    """
+    diagnostic = TorchScriptOnnxExportDiagnostic(
+        rule, level, message, frames_to_skip=frames_to_skip, **kwargs
+    )
+    export_context().log(diagnostic)
+    return diagnostic
+
+
+def export_context() -> infra.DiagnosticContext:
+    global _context
+    return _context
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/_rules.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/_rules.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d353d2e88b3433fd8286da2c45dc529342fc5f8
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/_rules.py
@@ -0,0 +1,636 @@
+# mypy: allow-untyped-defs
+"""
+GENERATED CODE - DO NOT EDIT DIRECTLY
+This file is generated by gen_diagnostics.py.
+See tools/onnx/gen_diagnostics.py for more information.
+
+Diagnostic rules for PyTorch ONNX export.
+"""
+
+import dataclasses
+from typing import Tuple
+
+# flake8: noqa
+from torch.onnx._internal.diagnostics import infra
+
+
+"""
+GENERATED CODE - DO NOT EDIT DIRECTLY
+The purpose of generating a class for each rule is to override the `format_message`
+method to provide more details in the signature about the format arguments.
+"""
+
+
+class _NodeMissingOnnxShapeInference(infra.Rule):
+    """Node is missing ONNX shape inference."""
+
+    def format_message(self, op_name) -> str:  # type: ignore[override]
+        """Returns the formatted default message of this Rule.
+
+        Message template: 'The shape inference of {op_name} type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.'
+        """
+        return self.message_default_template.format(op_name=op_name)
+
+    def format(  # type: ignore[override]
+        self, level: infra.Level, op_name
+    ) -> Tuple[infra.Rule, infra.Level, str]:
+        """Returns a tuple of (Rule, Level, message) for this Rule.
+
+        Message template: 'The shape inference of {op_name} type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.'
+        """
+        return self, level, self.format_message(op_name=op_name)
+
+
+class _MissingCustomSymbolicFunction(infra.Rule):
+    """Missing symbolic function for custom PyTorch operator, cannot translate node to ONNX."""
+
+    def format_message(self, op_name) -> str:  # type: ignore[override]
+        """Returns the formatted default message of this Rule.
+
+        Message template: 'ONNX export failed on an operator with unrecognized namespace {op_name}. If you are trying to export a custom operator, make sure you registered it with the right domain and version.'
+        """
+        return self.message_default_template.format(op_name=op_name)
+
+    def format(  # type: ignore[override]
+        self, level: infra.Level, op_name
+    ) -> Tuple[infra.Rule, infra.Level, str]:
+        """Returns a tuple of (Rule, Level, message) for this Rule.
+
+        Message template: 'ONNX export failed on an operator with unrecognized namespace {op_name}. If you are trying to export a custom operator, make sure you registered it with the right domain and version.'
+        """
+        return self, level, self.format_message(op_name=op_name)
+
+
+class _MissingStandardSymbolicFunction(infra.Rule):
+    """Missing symbolic function for standard PyTorch operator, cannot translate node to ONNX."""
+
+    def format_message(  # type: ignore[override]
+        self, op_name, opset_version, issue_url
+    ) -> str:
+        """Returns the formatted default message of this Rule.
+
+        Message template: "Exporting the operator '{op_name}' to ONNX opset version {opset_version} is not supported. Please feel free to request support or submit a pull request on PyTorch GitHub: {issue_url}."
+        """
+        return self.message_default_template.format(
+            op_name=op_name, opset_version=opset_version, issue_url=issue_url
+        )
+
+    def format(  # type: ignore[override]
+        self, level: infra.Level, op_name, opset_version, issue_url
+    ) -> Tuple[infra.Rule, infra.Level, str]:
+        """Returns a tuple of (Rule, Level, message) for this Rule.
+
+        Message template: "Exporting the operator '{op_name}' to ONNX opset version {opset_version} is not supported. Please feel free to request support or submit a pull request on PyTorch GitHub: {issue_url}."
+        """
+        return (
+            self,
+            level,
+            self.format_message(
+                op_name=op_name, opset_version=opset_version, issue_url=issue_url
+            ),
+        )
+
+
+class _OperatorSupportedInNewerOpsetVersion(infra.Rule):
+    """Operator is supported in newer opset version."""
+
+    def format_message(  # type: ignore[override]
+        self, op_name, opset_version, supported_opset_version
+    ) -> str:
+        """Returns the formatted default message of this Rule.
+
+        Message template: "Exporting the operator '{op_name}' to ONNX opset version {opset_version} is not supported. Support for this operator was added in version {supported_opset_version}, try exporting with this version."
+        """
+        return self.message_default_template.format(
+            op_name=op_name,
+            opset_version=opset_version,
+            supported_opset_version=supported_opset_version,
+        )
+
+    def format(  # type: ignore[override]
+        self, level: infra.Level, op_name, opset_version, supported_opset_version
+    ) -> Tuple[infra.Rule, infra.Level, str]:
+        """Returns a tuple of (Rule, Level, message) for this Rule.
+
+        Message template: "Exporting the operator '{op_name}' to ONNX opset version {opset_version} is not supported. Support for this operator was added in version {supported_opset_version}, try exporting with this version."
+        """
+        return (
+            self,
+            level,
+            self.format_message(
+                op_name=op_name,
+                opset_version=opset_version,
+                supported_opset_version=supported_opset_version,
+            ),
+        )
+
+
+class _FxGraphToOnnx(infra.Rule):
+    """Transforms graph from FX IR to ONNX IR."""
+
+    def format_message(self, graph_name) -> str:  # type: ignore[override]
+        """Returns the formatted default message of this Rule.
+
+        Message template: 'Transforming FX graph {graph_name} to ONNX graph.'
+        """
+        return self.message_default_template.format(graph_name=graph_name)
+
+    def format(  # type: ignore[override]
+        self, level: infra.Level, graph_name
+    ) -> Tuple[infra.Rule, infra.Level, str]:
+        """Returns a tuple of (Rule, Level, message) for this Rule.
+
+        Message template: 'Transforming FX graph {graph_name} to ONNX graph.'
+        """
+        return self, level, self.format_message(graph_name=graph_name)
+
+
+class _FxNodeToOnnx(infra.Rule):
+    """Transforms an FX node to an ONNX node."""
+
+    def format_message(self, node_repr) -> str:  # type: ignore[override]
+        """Returns the formatted default message of this Rule.
+
+        Message template: 'Transforming FX node {node_repr} to ONNX node.'
+        """
+        return self.message_default_template.format(node_repr=node_repr)
+
+    def format(  # type: ignore[override]
+        self, level: infra.Level, node_repr
+    ) -> Tuple[infra.Rule, infra.Level, str]:
+        """Returns a tuple of (Rule, Level, message) for this Rule.
+
+        Message template: 'Transforming FX node {node_repr} to ONNX node.'
+        """
+        return self, level, self.format_message(node_repr=node_repr)
+
+
+class _FxPass(infra.Rule):
+    """FX graph transformation during ONNX export before converting from FX IR to ONNX IR."""
+
+    def format_message(self, pass_name) -> str:  # type: ignore[override]
+        """Returns the formatted default message of this Rule.
+
+        Message template: 'Running {pass_name} pass.'
+        """
+        return self.message_default_template.format(pass_name=pass_name)
+
+    def format(  # type: ignore[override]
+        self, level: infra.Level, pass_name
+    ) -> Tuple[infra.Rule, infra.Level, str]:
+        """Returns a tuple of (Rule, Level, message) for this Rule.
+
+        Message template: 'Running {pass_name} pass.'
+        """
+        return self, level, self.format_message(pass_name=pass_name)
+
+
+class _NoSymbolicFunctionForCallFunction(infra.Rule):
+    """Cannot find symbolic function to convert the "call_function" FX node to ONNX."""
+
+    def format_message(self, target) -> str:  # type: ignore[override]
+        """Returns the formatted default message of this Rule.
+
+        Message template: 'No symbolic function to convert the "call_function" node {target} to ONNX. '
+        """
+        return self.message_default_template.format(target=target)
+
+    def format(  # type: ignore[override]
+        self, level: infra.Level, target
+    ) -> Tuple[infra.Rule, infra.Level, str]:
+        """Returns a tuple of (Rule, Level, message) for this Rule.
+
+        Message template: 'No symbolic function to convert the "call_function" node {target} to ONNX. '
+        """
+        return self, level, self.format_message(target=target)
+
+
+class _UnsupportedFxNodeAnalysis(infra.Rule):
+    """Result from FX graph analysis to reveal unsupported FX nodes."""
+
+    def format_message(  # type: ignore[override]
+        self, node_op_to_target_mapping
+    ) -> str:
+        """Returns the formatted default message of this Rule.
+
+        Message template: 'Unsupported FX nodes: {node_op_to_target_mapping}. '
+        """
+        return self.message_default_template.format(
+            node_op_to_target_mapping=node_op_to_target_mapping
+        )
+
+    def format(  # type: ignore[override]
+        self, level: infra.Level, node_op_to_target_mapping
+    ) -> Tuple[infra.Rule, infra.Level, str]:
+        """Returns a tuple of (Rule, Level, message) for this Rule.
+
+        Message template: 'Unsupported FX nodes: {node_op_to_target_mapping}. '
+        """
+        return (
+            self,
+            level,
+            self.format_message(node_op_to_target_mapping=node_op_to_target_mapping),
+        )
+
+
+class _OpLevelDebugging(infra.Rule):
+    """Report any op level validation failure in warnings."""
+
+    def format_message(self, node, symbolic_fn) -> str:  # type: ignore[override]
+        """Returns the formatted default message of this Rule.
+
+        Message template: 'FX node: {node} and its onnx function: {symbolic_fn} fails on op level validation.'
+        """
+        return self.message_default_template.format(node=node, symbolic_fn=symbolic_fn)
+
+    def format(  # type: ignore[override]
+        self, level: infra.Level, node, symbolic_fn
+    ) -> Tuple[infra.Rule, infra.Level, str]:
+        """Returns a tuple of (Rule, Level, message) for this Rule.
+
+        Message template: 'FX node: {node} and its onnx function: {symbolic_fn} fails on op level validation.'
+        """
+        return self, level, self.format_message(node=node, symbolic_fn=symbolic_fn)
+
+
+class _FindOpschemaMatchedSymbolicFunction(infra.Rule):
+    """Find the OnnxFunction that matches the input/attribute dtypes by comparing them with their opschemas."""
+
+    def format_message(self, symbolic_fn, node) -> str:  # type: ignore[override]
+        """Returns the formatted default message of this Rule.
+
+        Message template: 'The OnnxFunction: {symbolic_fn} is the nearest match of the node {node}.'
+        """
+        return self.message_default_template.format(symbolic_fn=symbolic_fn, node=node)
+
+    def format(  # type: ignore[override]
+        self, level: infra.Level, symbolic_fn, node
+    ) -> Tuple[infra.Rule, infra.Level, str]:
+        """Returns a tuple of (Rule, Level, message) for this Rule.
+
+        Message template: 'The OnnxFunction: {symbolic_fn} is the nearest match of the node {node}.'
+        """
+        return self, level, self.format_message(symbolic_fn=symbolic_fn, node=node)
+
+
+class _FxNodeInsertTypePromotion(infra.Rule):
+    """Determine if type promotion is required for the FX node. Insert cast nodes if needed."""
+
+    def format_message(self, target) -> str:  # type: ignore[override]
+        """Returns the formatted default message of this Rule.
+
+        Message template: 'Performing explicit type promotion for node {target}. '
+        """
+        return self.message_default_template.format(target=target)
+
+    def format(  # type: ignore[override]
+        self, level: infra.Level, target
+    ) -> Tuple[infra.Rule, infra.Level, str]:
+        """Returns a tuple of (Rule, Level, message) for this Rule.
+
+        Message template: 'Performing explicit type promotion for node {target}. '
+        """
+        return self, level, self.format_message(target=target)
+
+
+class _FindOperatorOverloadsInOnnxRegistry(infra.Rule):
+    """Find the list of OnnxFunction of the PyTorch operator in onnx registry."""
+
+    def format_message(self, node) -> str:  # type: ignore[override]
+        """Returns the formatted default message of this Rule.
+
+        Message template: 'Checking if the FX node: {node} is supported in onnx registry.'
+        """
+        return self.message_default_template.format(node=node)
+
+    def format(  # type: ignore[override]
+        self, level: infra.Level, node
+    ) -> Tuple[infra.Rule, infra.Level, str]:
+        """Returns a tuple of (Rule, Level, message) for this Rule.
+
+        Message template: 'Checking if the FX node: {node} is supported in onnx registry.'
+        """
+        return self, level, self.format_message(node=node)
+
+
+@dataclasses.dataclass
+class _POERules(infra.RuleCollection):
+    node_missing_onnx_shape_inference: _NodeMissingOnnxShapeInference = dataclasses.field(
+        default=_NodeMissingOnnxShapeInference.from_sarif(
+            **{
+                "id": "POE0001",
+                "name": "node-missing-onnx-shape-inference",
+                "short_description": {"text": "Node is missing ONNX shape inference."},
+                "full_description": {
+                    "text": "Node is missing ONNX shape inference. This usually happens when the node is not valid under standard ONNX operator spec.",
+                    "markdown": "Node is missing ONNX shape inference.\nThis usually happens when the node is not valid under standard ONNX operator spec.\n",
+                },
+                "message_strings": {
+                    "default": {
+                        "text": "The shape inference of {op_name} type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function."
+                    }
+                },
+                "help_uri": None,
+                "properties": {"deprecated": False, "tags": []},
+            }
+        ),
+        init=False,
+    )
+    """Node is missing ONNX shape inference."""
+
+    missing_custom_symbolic_function: _MissingCustomSymbolicFunction = dataclasses.field(
+        default=_MissingCustomSymbolicFunction.from_sarif(
+            **{
+                "id": "POE0002",
+                "name": "missing-custom-symbolic-function",
+                "short_description": {
+                    "text": "Missing symbolic function for custom PyTorch operator, cannot translate node to ONNX."
+                },
+                "full_description": {
+                    "text": "Missing symbolic function for custom PyTorch operator, cannot translate node to ONNX.",
+                    "markdown": "Missing symbolic function for custom PyTorch operator, cannot translate node to ONNX.\n",
+                },
+                "message_strings": {
+                    "default": {
+                        "text": "ONNX export failed on an operator with unrecognized namespace {op_name}. If you are trying to export a custom operator, make sure you registered it with the right domain and version."
+                    }
+                },
+                "help_uri": None,
+                "properties": {"deprecated": False, "tags": []},
+            }
+        ),
+        init=False,
+    )
+    """Missing symbolic function for custom PyTorch operator, cannot translate node to ONNX."""
+
+    missing_standard_symbolic_function: _MissingStandardSymbolicFunction = dataclasses.field(
+        default=_MissingStandardSymbolicFunction.from_sarif(
+            **{
+                "id": "POE0003",
+                "name": "missing-standard-symbolic-function",
+                "short_description": {
+                    "text": "Missing symbolic function for standard PyTorch operator, cannot translate node to ONNX."
+                },
+                "full_description": {
+                    "text": "Missing symbolic function for standard PyTorch operator, cannot translate node to ONNX.",
+                    "markdown": "Missing symbolic function for standard PyTorch operator, cannot translate node to ONNX.\n",
+                },
+                "message_strings": {
+                    "default": {
+                        "text": "Exporting the operator '{op_name}' to ONNX opset version {opset_version} is not supported. Please feel free to request support or submit a pull request on PyTorch GitHub: {issue_url}."
+                    }
+                },
+                "help_uri": None,
+                "properties": {"deprecated": False, "tags": []},
+            }
+        ),
+        init=False,
+    )
+    """Missing symbolic function for standard PyTorch operator, cannot translate node to ONNX."""
+
+    operator_supported_in_newer_opset_version: _OperatorSupportedInNewerOpsetVersion = dataclasses.field(
+        default=_OperatorSupportedInNewerOpsetVersion.from_sarif(
+            **{
+                "id": "POE0004",
+                "name": "operator-supported-in-newer-opset-version",
+                "short_description": {
+                    "text": "Operator is supported in newer opset version."
+                },
+                "full_description": {
+                    "text": "Operator is supported in newer opset version.",
+                    "markdown": "Operator is supported in newer opset version.\n\nExample:\n```python\ntorch.onnx.export(model, args, ..., opset_version=9)\n```\n",
+                },
+                "message_strings": {
+                    "default": {
+                        "text": "Exporting the operator '{op_name}' to ONNX opset version {opset_version} is not supported. Support for this operator was added in version {supported_opset_version}, try exporting with this version."
+                    }
+                },
+                "help_uri": None,
+                "properties": {"deprecated": False, "tags": []},
+            }
+        ),
+        init=False,
+    )
+    """Operator is supported in newer opset version."""
+
+    fx_graph_to_onnx: _FxGraphToOnnx = dataclasses.field(
+        default=_FxGraphToOnnx.from_sarif(
+            **{
+                "id": "FXE0007",
+                "name": "fx-graph-to-onnx",
+                "short_description": {
+                    "text": "Transforms graph from FX IR to ONNX IR."
+                },
+                "full_description": {
+                    "text": "Transforms graph from FX IR to ONNX IR.",
+                    "markdown": "This diagnostic tracks the transformation process from an FX Graph (in FX IR) to an ONNX Graph (in ONNX IR).\n\n## Key Representations:\n\n- **FX Graph**: The graph in FX IR produced by dynamo or symbolic tracing.\n- **ONNX Graph**: The graph in ONNX IR and [operators](https://onnx.ai/onnx/operators/).\n\n## Additional Notes:\n\n- Prior to this transformation step, the FX graph undergoes preprocessing through multiple FX passes.\n  To gain insight into these transformations, refer to diagnostic `FXE0010`.\n- To enable a detailed view of the graph transformation in progress within this diagnostic, switch to the DEBUG mode.\n\n  - Set DiagnosticOptions.verbosity_level to logging.DEBUG.\n  - Activate the environment variable TORCH_LOGS='onnx_diagnostics'.\n\n- For specific information related to node-level FX to ONNX transformations, explore the diagnostic `FXE0008`.\n",
+                },
+                "message_strings": {
+                    "default": {
+                        "text": "Transforming FX graph {graph_name} to ONNX graph."
+                    }
+                },
+                "help_uri": None,
+                "properties": {"deprecated": False, "tags": []},
+            }
+        ),
+        init=False,
+    )
+    """Transforms graph from FX IR to ONNX IR."""
+
+    fx_node_to_onnx: _FxNodeToOnnx = dataclasses.field(
+        default=_FxNodeToOnnx.from_sarif(
+            **{
+                "id": "FXE0008",
+                "name": "fx-node-to-onnx",
+                "short_description": {"text": "Transforms an FX node to an ONNX node."},
+                "full_description": {
+                    "text": "Transforms an FX node to an ONNX node.",
+                    "markdown": "This diagnostic tracks the transformation process from an FX Node to ONNX [Operators](https://onnx.ai/onnx/operators/).\n\nThe process of converting FX Node to ONNX Node involves dealing with six distinct node types:\n  1. `placeholder`: Represents a module input, maps to an ONNX graph input.\n  2. `call_module`: Symbolizes a call to a submodule, maps to an ONNX\n  3. `call_method`: Symbolizes a method call. Not yet implemented.\n  4. `call_function`: Symbolizes a function call. [Core ATen](https://pytorch.org/docs/stable/ir.html#core-aten-ir) is expected\n    as the function call target. The mapping from ATen to ONNX is implemented by [ONNXScript torchlib](https://github.com/microsoft/onnxscript/tree/main/onnxscript/function_libs/torch_lib/ops).\n    This [guide](https://pytorch.org/docs/stable/onnx.html#onnx-script-functions) shows how to write and register a custom symbolic function for call_function FX node.\n  5. `get_attr`: Indicates an attribute access within the current module. Maps to an ONNX graph initializer.\n  6. `output`: Represents the module's output. Maps to an ONNX graph output.\n\nFor a granular understanding of how each node type is transformed, refer to the implementation details in `FxOnnxInterpreter`.\n",
+                },
+                "message_strings": {
+                    "default": {
+                        "text": "Transforming FX node {node_repr} to ONNX node."
+                    }
+                },
+                "help_uri": None,
+                "properties": {"deprecated": False, "tags": []},
+            }
+        ),
+        init=False,
+    )
+    """Transforms an FX node to an ONNX node."""
+
+    fx_pass: _FxPass = dataclasses.field(
+        default=_FxPass.from_sarif(
+            **{
+                "id": "FXE0010",
+                "name": "fx-pass",
+                "short_description": {
+                    "text": "FX graph transformation during ONNX export before converting from FX IR to ONNX IR."
+                },
+                "full_description": {
+                    "text": "FX graph transformation during ONNX export before converting from FX IR to ONNX IR.",
+                    "markdown": "This diagnostic tracks the FX passes executed during the ONNX export process prior\nto converting from FX IR (Intermediate Representation) to ONNX IR.\n\nUnder the scope of ONNX export, an FX pass refers to a specific transformation applied to the FX GraphModule.\nThe primary aim of these passes is to streamline the graph into a format that aligns more with the ONNX IR.\nMoreover, these passes work to substitute unsupported FX IR features with those recognized and endorsed by\nONNX IR. Common transformations include, but aren't limited to, decomposition, functionalization and\ntype promotion.\n\nFor those who are interested in a comprehensive log detailing the modifications made during these passes,\nthere are a couple of options:\n\n- Set DiagnosticOptions.verbosity_level to logging.DEBUG.\n- Activate the environment variable TORCH_LOGS='onnx_diagnostics'.\n\nHowever, it's noteworthy that by default, such detailed logging is turned off. The primary reason being\nits considerable impact on performance.\n\nFor an in-depth understanding of each specific pass, please refer to the directory: torch/onnx/_internal/fx/passes.\n",
+                },
+                "message_strings": {"default": {"text": "Running {pass_name} pass."}},
+                "help_uri": None,
+                "properties": {"deprecated": False, "tags": []},
+            }
+        ),
+        init=False,
+    )
+    """FX graph transformation during ONNX export before converting from FX IR to ONNX IR."""
+
+    no_symbolic_function_for_call_function: _NoSymbolicFunctionForCallFunction = dataclasses.field(
+        default=_NoSymbolicFunctionForCallFunction.from_sarif(
+            **{
+                "id": "FXE0011",
+                "name": "no-symbolic-function-for-call-function",
+                "short_description": {
+                    "text": 'Cannot find symbolic function to convert the "call_function" FX node to ONNX.'
+                },
+                "full_description": {
+                    "text": 'Cannot find symbolic function to convert the "call_function" FX node to ONNX. ',
+                    "markdown": 'This error occurs when the ONNX converter is unable to find a corresponding symbolic function\nto convert a "call_function" node in the input graph to its equivalence in ONNX. The "call_function"\nnode represents a normalized function call in PyTorch, such as "torch.aten.ops.add".\n\nTo resolve this error, you can try one of the following:\n\n- If exists, apply the auto-fix suggested by the diagnostic. TODO: this part is not available yet.\n- Rewrite the model using only supported PyTorch operators or functions.\n- Follow this [guide](https://pytorch.org/tutorials/beginner/onnx/onnx_registry_tutorial.html#overview) to write and\n  register a custom symbolic function for the unsupported call_function FX node.\n',
+                },
+                "message_strings": {
+                    "default": {
+                        "text": 'No symbolic function to convert the "call_function" node {target} to ONNX. '
+                    }
+                },
+                "help_uri": None,
+                "properties": {"deprecated": False, "tags": []},
+            }
+        ),
+        init=False,
+    )
+    """Cannot find symbolic function to convert the "call_function" FX node to ONNX."""
+
+    unsupported_fx_node_analysis: _UnsupportedFxNodeAnalysis = dataclasses.field(
+        default=_UnsupportedFxNodeAnalysis.from_sarif(
+            **{
+                "id": "FXE0012",
+                "name": "unsupported-fx-node-analysis",
+                "short_description": {
+                    "text": "Result from FX graph analysis to reveal unsupported FX nodes."
+                },
+                "full_description": {
+                    "text": "Result from FX graph analysis to reveal unsupported FX nodes.",
+                    "markdown": "This error indicates that an FX graph contains one or more unsupported nodes. The error message\nis typically accompanied by a list of the unsupported nodes found during analysis.\n\nTo resolve this error, you can try resolving each individual unsupported node error by following\nthe suggestions by its diagnostic. Typically, options include:\n\n- If exists, apply the auto-fix suggested by the diagnostic. TODO: this part is not available yet.\n- Rewrite the model using only supported PyTorch operators or functions.\n- Follow this [guide](https://pytorch.org/docs/stable/onnx.html#onnx-script-functions) to write and\n  register a custom symbolic function for the unsupported call_function FX node.\n",
+                },
+                "message_strings": {
+                    "default": {
+                        "text": "Unsupported FX nodes: {node_op_to_target_mapping}. "
+                    }
+                },
+                "help_uri": None,
+                "properties": {"deprecated": False, "tags": []},
+            }
+        ),
+        init=False,
+    )
+    """Result from FX graph analysis to reveal unsupported FX nodes."""
+
+    op_level_debugging: _OpLevelDebugging = dataclasses.field(
+        default=_OpLevelDebugging.from_sarif(
+            **{
+                "id": "FXE0013",
+                "name": "op-level-debugging",
+                "short_description": {
+                    "text": "Report any op level validation failure in warnings."
+                },
+                "full_description": {
+                    "text": "Report any op level validation failure in warnings.",
+                    "markdown": "This warning message indicates that during op level debugging, certain symbolic functions\nhave failed to match the results of torch ops when using real tensors generated from fake\ntensors. It is important to note that the symbolic functions may not necessarily be\nincorrect, as the validation process is non-deterministic and should only be used as a\nreference.\n\nThere are two categories of warnings that can be triggered:\n\n1. Non-validated operators:\n  If the warnings are caused by the following errors, they can be disregarded by users,\n  as these errors occur due to the non-deterministic nature of the validation. However,\n  it is important to be aware that the operators have not been validated.\n\n  - IndexError: Unsupported input arguments of randomized dimensions/indices(INT64).\n  - RuntimeError: Unsupported input arguments for torch ops are generated.\n  - ValueError: Arguments/keyword arguments do not match the signature of the symbolic function.\n\n2. Potentially wrong torchlib operators:\n  If the warnings are triggered by the following error, users should be aware that the symbolic functions\n  may be incorrect in dispatching or implementation. In such cases, it is recommended to report\n  the issue to the PyTorch-ONNX team, or create/register a custom symbolic function to replace the default one.\n\n  - AssertionError: The symbolic function is potentially wrong as the results do not match the results of torch ops.\n  - TypeError: The symbolic function is potentially wrong as the opschema doesn't match inputs.\n",
+                },
+                "message_strings": {
+                    "default": {
+                        "text": "FX node: {node} and its onnx function: {symbolic_fn} fails on op level validation."
+                    }
+                },
+                "help_uri": None,
+                "properties": {"deprecated": False, "tags": []},
+            }
+        ),
+        init=False,
+    )
+    """Report any op level validation failure in warnings."""
+
+    find_opschema_matched_symbolic_function: _FindOpschemaMatchedSymbolicFunction = dataclasses.field(
+        default=_FindOpschemaMatchedSymbolicFunction.from_sarif(
+            **{
+                "id": "FXE0014",
+                "name": "find-opschema-matched-symbolic-function",
+                "short_description": {
+                    "text": "Find the OnnxFunction that matches the input/attribute dtypes by comparing them with their opschemas."
+                },
+                "full_description": {
+                    "text": "Find the OnnxFunction that matches the input dtypes by comparing them with their opschemas. A warning will be issued if the matched OnnxFunction is not an exact match.",
+                    "markdown": "When an ATen/Custom operator is registered and needs to be dispatched to an OnnxFunction, the input/attribute\ndtypes of the ATen/Custom operator are compared with the input/attribute dtypes of the OnnxFunction opschemas\nto find a match. However, if a perfect/exact match is not found, the dispatcher will attempt to find\nthe nearest match with the highest number of input/attribute dtypes matching the OnnxFunction opschemas, while\nissuing a warning.\n\nThere are two types of level that can be triggered in this rule:\n\n1. NOTE: A perfect match is found, and no warning is issued.\n2. WARNING: The matched OnnxFunction is not a perfect/exact match.\n\nHere are some suggestions based on the WARNING situation:\n\n1. If there are NO errors or mismatches in the results, it is safe to disregard this warning,\n  as the definition of OnnxFunction schema is usually more stringent.\n2. If there are errors or mismatches in the results, it is recommended to:\n  (a) Enable op_level_debugging to determine if the OnnxFunction might be incorrect.\n  (b) Report the issue to the PyTorch-ONNX team.\n  (c) Create/register a custom symbolic function to replace the default one.\n",
+                },
+                "message_strings": {
+                    "default": {
+                        "text": "The OnnxFunction: {symbolic_fn} is the nearest match of the node {node}."
+                    }
+                },
+                "help_uri": None,
+                "properties": {"deprecated": False, "tags": []},
+            }
+        ),
+        init=False,
+    )
+    """Find the OnnxFunction that matches the input/attribute dtypes by comparing them with their opschemas."""
+
+    fx_node_insert_type_promotion: _FxNodeInsertTypePromotion = dataclasses.field(
+        default=_FxNodeInsertTypePromotion.from_sarif(
+            **{
+                "id": "FXE0015",
+                "name": "fx-node-insert-type-promotion",
+                "short_description": {
+                    "text": "Determine if type promotion is required for the FX node. Insert cast nodes if needed."
+                },
+                "full_description": {
+                    "text": "Determine if type promotion is required for the FX node. Insert cast nodes if needed.",
+                    "markdown": "This diagnostic monitors the node-level type promotion insertion process. In PyTorch, there is an automatic process called implicit type promotion,\nwhere the input types of an operator are promoted to a common type. The determination of the common type is based on the type promotion rule specific to each operator.\nTo learn more about PyTorch's type promotion rules, refer to the [elementwise_dtypes doc](https://github.com/pytorch/pytorch/blob/f044613f78df713fb57f70c608483c9f10ad332e/torch/_prims_common/__init__.py#L1252-L1335)\nand [torch._refs ops](https://github.com/pytorch/pytorch/blob/a475ea4542dfe961c9d097e33ab5041f61c8c17f/torch/_refs/__init__.py#L484).\n\nHowever, implicit type promotion is not supported in ONNX. Therefore, to replicate the PyTorch behavior, we need to explicitly insert cast nodes.\nThis diagnostic tracks the process of node-level type promotion insertion.\n\nThe type promotion rules used by this process can be found in `torch/onnx/_internal/fx/passes/type_promotion.py.`\nTo update or add new type promotion rules, please refer to the [Note: Update type promotion rule] section.\n",
+                },
+                "message_strings": {
+                    "default": {
+                        "text": "Performing explicit type promotion for node {target}. "
+                    }
+                },
+                "help_uri": None,
+                "properties": {"deprecated": False, "tags": []},
+            }
+        ),
+        init=False,
+    )
+    """Determine if type promotion is required for the FX node. Insert cast nodes if needed."""
+
+    find_operator_overloads_in_onnx_registry: _FindOperatorOverloadsInOnnxRegistry = dataclasses.field(
+        default=_FindOperatorOverloadsInOnnxRegistry.from_sarif(
+            **{
+                "id": "FXE0016",
+                "name": "find-operator-overloads-in-onnx-registry",
+                "short_description": {
+                    "text": "Find the list of OnnxFunction of the PyTorch operator in onnx registry."
+                },
+                "full_description": {
+                    "text": "This rule involves finding the list of OnnxFunction for the PyTorch operator overload in the ONNX registry. If the operator overload is not supported but its default overload is, a warning will be issued. If both the operator overload and its default overload are not supported, an error will be issued.",
+                    "markdown": "The operator overload name serves the purpose of verifying whether a PyTorch operator is registered in the ONNX registry.\nIf it's not found, the dispatcher takes a fallback approach and tries to locate the default overload of the PyTorch\noperator in the registry. If even the default overload is absent, it signifies that the operator is officially unsupported.\n\nThere are three types of level that can be triggered in this rule:\n\n1. NOTE: The op overload is supported.\n2. WARNING: The op overload is not supported, but it's default overload is supported.\n3. ERROR: The op overload is not supported, and it's default overload is also not supported.\n\nHere are some suggestions based on the WARNING situation:\n\n1. If there are NO errors or mismatches in the results, it is safe to disregard this warning.\n2. If there are errors or mismatches in the results, it is recommended to:\n  (a) Enable op_level_debugging to determine if the OnnxFunction might be incorrect.\n  (b) Report the unsupported overload to the PyTorch-ONNX team.\n  (c) Create/register a custom symbolic function to replace the default one.\n\nHere are some suggestions based on the ERROR situation:\n\n1. Report the unsupported operator to the PyTorch-ONNX team.\n2. Create/register a custom symbolic function to replace the default one.\n",
+                },
+                "message_strings": {
+                    "default": {
+                        "text": "Checking if the FX node: {node} is supported in onnx registry."
+                    }
+                },
+                "help_uri": None,
+                "properties": {"deprecated": False, "tags": []},
+            }
+        ),
+        init=False,
+    )
+    """Find the list of OnnxFunction of the PyTorch operator in onnx registry."""
+
+
+rules = _POERules()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddcd4891643a4db5bee207c4ce510192ad10e32e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/__init__.py
@@ -0,0 +1,34 @@
+from ._infra import (
+    DiagnosticOptions,
+    Graph,
+    Invocation,
+    Level,
+    levels,
+    Location,
+    Rule,
+    RuleCollection,
+    Stack,
+    StackFrame,
+    Tag,
+    ThreadFlowLocation,
+)
+from .context import Diagnostic, DiagnosticContext, RuntimeErrorWithDiagnostic
+
+
+__all__ = [
+    "Diagnostic",
+    "DiagnosticContext",
+    "DiagnosticOptions",
+    "Graph",
+    "Invocation",
+    "Level",
+    "levels",
+    "Location",
+    "Rule",
+    "RuleCollection",
+    "RuntimeErrorWithDiagnostic",
+    "Stack",
+    "StackFrame",
+    "Tag",
+    "ThreadFlowLocation",
+]
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/_infra.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/_infra.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7d174d8af2e3b3f05987da9cdbc2d7c827c3b7d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/_infra.py
@@ -0,0 +1,285 @@
+# mypy: allow-untyped-defs
+"""This file defines an additional layer of abstraction on top of the SARIF OM."""
+
+from __future__ import annotations
+
+import dataclasses
+import enum
+import logging
+from typing import Mapping, Sequence
+
+from torch.onnx._internal.diagnostics.infra import formatter, sarif
+
+
+class Level(enum.IntEnum):
+    """The level of a diagnostic.
+
+    This class is used to represent the level of a diagnostic. The levels are defined
+    by the SARIF specification, and are not modifiable. For alternative categories,
+    please use infra.Tag instead. When selecting a level, please consider the following
+    guidelines:
+
+    - NONE: Informational result that does not indicate the presence of a problem.
+    - NOTE: An opportunity for improvement was found.
+    - WARNING: A potential problem was found.
+    - ERROR: A serious problem was found.
+
+    This level is a subclass of enum.IntEnum, and can be used as an integer. Its integer
+    value maps to the logging levels in Python's logging module. The mapping is as
+    follows:
+
+        Level.NONE = logging.DEBUG = 10
+        Level.NOTE = logging.INFO = 20
+        Level.WARNING = logging.WARNING = 30
+        Level.ERROR = logging.ERROR = 40
+    """
+
+    NONE = 10
+    NOTE = 20
+    WARNING = 30
+    ERROR = 40
+
+
+levels = Level
+
+
+class Tag(enum.Enum):
+    """The tag of a diagnostic. This class can be inherited to define custom tags."""
+
+
+class PatchedPropertyBag(sarif.PropertyBag):
+    """Key/value pairs that provide additional information about the object.
+
+    The definition of PropertyBag via SARIF spec is "A property bag is an object (section 3.6)
+    containing an unordered set of properties with arbitrary names." However it is not
+    reflected in the json file, and therefore not captured by the python representation.
+    This patch adds additional **kwargs to the `__init__` method to allow recording
+    arbitrary key/value pairs.
+    """
+
+    def __init__(self, tags: list[str] | None = None, **kwargs):
+        super().__init__(tags=tags)
+        self.__dict__.update(kwargs)
+
+
+@dataclasses.dataclass(frozen=True)
+class Rule:
+    id: str
+    name: str
+    message_default_template: str
+    short_description: str | None = None
+    full_description: str | None = None
+    full_description_markdown: str | None = None
+    help_uri: str | None = None
+
+    @classmethod
+    def from_sarif(cls, **kwargs):
+        """Returns a rule from the SARIF reporting descriptor."""
+        short_description = kwargs.get("short_description", {}).get("text")
+        full_description = kwargs.get("full_description", {}).get("text")
+        full_description_markdown = kwargs.get("full_description", {}).get("markdown")
+        help_uri = kwargs.get("help_uri")
+
+        rule = cls(
+            id=kwargs["id"],
+            name=kwargs["name"],
+            message_default_template=kwargs["message_strings"]["default"]["text"],
+            short_description=short_description,
+            full_description=full_description,
+            full_description_markdown=full_description_markdown,
+            help_uri=help_uri,
+        )
+        return rule
+
+    def sarif(self) -> sarif.ReportingDescriptor:
+        """Returns a SARIF reporting descriptor of this Rule."""
+        short_description = (
+            sarif.MultiformatMessageString(text=self.short_description)
+            if self.short_description is not None
+            else None
+        )
+        full_description = (
+            sarif.MultiformatMessageString(
+                text=self.full_description, markdown=self.full_description_markdown
+            )
+            if self.full_description is not None
+            else None
+        )
+        return sarif.ReportingDescriptor(
+            id=self.id,
+            name=self.name,
+            short_description=short_description,
+            full_description=full_description,
+            help_uri=self.help_uri,
+        )
+
+    def format(self, level: Level, *args, **kwargs) -> tuple[Rule, Level, str]:
+        """Returns a tuple of (rule, level, message) for a diagnostic.
+
+        This method is used to format the message of a diagnostic. The message is
+        formatted using the default template of this rule, and the arguments passed in
+        as `*args` and `**kwargs`. The level is used to override the default level of
+        this rule.
+        """
+        return (self, level, self.format_message(*args, **kwargs))
+
+    def format_message(self, *args, **kwargs) -> str:
+        """Returns the formatted default message of this Rule.
+
+        This method should be overridden (with code generation) by subclasses to reflect
+        the exact arguments needed by the message template. This is a helper method to
+        create the default message for a diagnostic.
+        """
+        return self.message_default_template.format(*args, **kwargs)
+
+
+@dataclasses.dataclass
+class Location:
+    uri: str | None = None
+    line: int | None = None
+    message: str | None = None
+    start_column: int | None = None
+    end_column: int | None = None
+    snippet: str | None = None
+    function: str | None = None
+
+    def sarif(self) -> sarif.Location:
+        """Returns the SARIF representation of this location."""
+        return sarif.Location(
+            physical_location=sarif.PhysicalLocation(
+                artifact_location=sarif.ArtifactLocation(uri=self.uri),
+                region=sarif.Region(
+                    start_line=self.line,
+                    start_column=self.start_column,
+                    end_column=self.end_column,
+                    snippet=sarif.ArtifactContent(text=self.snippet),
+                ),
+            ),
+            message=sarif.Message(text=self.message)
+            if self.message is not None
+            else None,
+        )
+
+
+@dataclasses.dataclass
+class StackFrame:
+    location: Location
+
+    def sarif(self) -> sarif.StackFrame:
+        """Returns the SARIF representation of this stack frame."""
+        return sarif.StackFrame(location=self.location.sarif())
+
+
+@dataclasses.dataclass
+class Stack:
+    """Records a stack trace. The frames are in order from newest to oldest stack frame."""
+
+    frames: list[StackFrame] = dataclasses.field(default_factory=list)
+    message: str | None = None
+
+    def sarif(self) -> sarif.Stack:
+        """Returns the SARIF representation of this stack."""
+        return sarif.Stack(
+            frames=[frame.sarif() for frame in self.frames],
+            message=sarif.Message(text=self.message)
+            if self.message is not None
+            else None,
+        )
+
+
+@dataclasses.dataclass
+class ThreadFlowLocation:
+    """Records code location and the initial state."""
+
+    location: Location
+    state: Mapping[str, str]
+    index: int
+    stack: Stack | None = None
+
+    def sarif(self) -> sarif.ThreadFlowLocation:
+        """Returns the SARIF representation of this thread flow location."""
+        return sarif.ThreadFlowLocation(
+            location=self.location.sarif(),
+            state=self.state,
+            stack=self.stack.sarif() if self.stack is not None else None,
+        )
+
+
+@dataclasses.dataclass
+class Graph:
+    """A graph of diagnostics.
+
+    This class stores the string representation of a model graph.
+    The `nodes` and `edges` fields are unused in the current implementation.
+    """
+
+    graph: str
+    name: str
+    description: str | None = None
+
+    def sarif(self) -> sarif.Graph:
+        """Returns the SARIF representation of this graph."""
+        return sarif.Graph(
+            description=sarif.Message(text=self.graph),
+            properties=PatchedPropertyBag(name=self.name, description=self.description),
+        )
+
+
+@dataclasses.dataclass
+class RuleCollection:
+    _rule_id_name_set: frozenset[tuple[str, str]] = dataclasses.field(init=False)
+
+    def __post_init__(self) -> None:
+        self._rule_id_name_set = frozenset(
+            {
+                (field.default.id, field.default.name)
+                for field in dataclasses.fields(self)
+                if isinstance(field.default, Rule)
+            }
+        )
+
+    def __contains__(self, rule: Rule) -> bool:
+        """Checks if the rule is in the collection."""
+        return (rule.id, rule.name) in self._rule_id_name_set
+
+    @classmethod
+    def custom_collection_from_list(
+        cls, new_collection_class_name: str, rules: Sequence[Rule]
+    ) -> RuleCollection:
+        """Creates a custom class inherited from RuleCollection with the list of rules."""
+        return dataclasses.make_dataclass(
+            new_collection_class_name,
+            [
+                (
+                    formatter.kebab_case_to_snake_case(rule.name),
+                    type(rule),
+                    dataclasses.field(default=rule),
+                )
+                for rule in rules
+            ],
+            bases=(cls,),
+        )()
+
+
+class Invocation:
+    # TODO: Implement this.
+    # Tracks top level call arguments and diagnostic options.
+    def __init__(self) -> None:
+        raise NotImplementedError
+
+
+@dataclasses.dataclass
+class DiagnosticOptions:
+    """Options for diagnostic context.
+
+    Attributes:
+        verbosity_level: Set the amount of information logged for each diagnostics,
+            equivalent to the 'level' in Python logging module.
+        warnings_as_errors: When True, warning diagnostics are treated as error diagnostics.
+    """
+
+    verbosity_level: int = dataclasses.field(default=logging.INFO)
+    """Set the amount of information logged for each diagnostics, equivalent to the 'level' in Python logging module."""
+
+    warnings_as_errors: bool = dataclasses.field(default=False)
+    """If True, warning diagnostics are treated as error diagnostics."""
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/context.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/context.py
new file mode 100644
index 0000000000000000000000000000000000000000..36ae4207b427376eb4e82ac67c746d4c6d376dee
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/context.py
@@ -0,0 +1,404 @@
+# mypy: allow-untyped-defs
+"""A diagnostic context based on SARIF."""
+
+from __future__ import annotations
+
+import contextlib
+import dataclasses
+import gzip
+import logging
+from typing import Callable, Generator, Generic, Literal, Mapping, TypeVar
+from typing_extensions import Self
+
+from torch.onnx._internal.diagnostics import infra
+from torch.onnx._internal.diagnostics.infra import formatter, sarif, utils
+from torch.onnx._internal.diagnostics.infra.sarif import version as sarif_version
+
+
+# This is a workaround for mypy not supporting Self from typing_extensions.
+_Diagnostic = TypeVar("_Diagnostic", bound="Diagnostic")
+diagnostic_logger: logging.Logger = logging.getLogger(__name__)
+
+
+@dataclasses.dataclass
+class Diagnostic:
+    rule: infra.Rule
+    level: infra.Level
+    message: str | None = None
+    locations: list[infra.Location] = dataclasses.field(default_factory=list)
+    stacks: list[infra.Stack] = dataclasses.field(default_factory=list)
+    graphs: list[infra.Graph] = dataclasses.field(default_factory=list)
+    thread_flow_locations: list[infra.ThreadFlowLocation] = dataclasses.field(
+        default_factory=list
+    )
+    additional_messages: list[str] = dataclasses.field(default_factory=list)
+    tags: list[infra.Tag] = dataclasses.field(default_factory=list)
+    source_exception: Exception | None = None
+    """The exception that caused this diagnostic to be created."""
+    logger: logging.Logger = dataclasses.field(init=False, default=diagnostic_logger)
+    """The logger for this diagnostic. Defaults to 'diagnostic_logger' which has the same
+    log level setting with `DiagnosticOptions.verbosity_level`."""
+    _current_log_section_depth: int = 0
+
+    def __post_init__(self) -> None:
+        pass
+
+    def sarif(self) -> sarif.Result:
+        """Returns the SARIF Result representation of this diagnostic."""
+        message = self.message or self.rule.message_default_template
+        if self.additional_messages:
+            additional_message = "\n".join(self.additional_messages)
+            message_markdown = (
+                f"{message}\n\n## Additional Message:\n\n{additional_message}"
+            )
+        else:
+            message_markdown = message
+
+        kind: Literal["informational", "fail"] = (
+            "informational" if self.level == infra.Level.NONE else "fail"
+        )
+
+        sarif_result = sarif.Result(
+            message=sarif.Message(text=message, markdown=message_markdown),
+            level=self.level.name.lower(),  # type: ignore[arg-type]
+            rule_id=self.rule.id,
+            kind=kind,
+        )
+        sarif_result.locations = [location.sarif() for location in self.locations]
+        sarif_result.stacks = [stack.sarif() for stack in self.stacks]
+        sarif_result.graphs = [graph.sarif() for graph in self.graphs]
+        sarif_result.code_flows = [
+            sarif.CodeFlow(
+                thread_flows=[
+                    sarif.ThreadFlow(
+                        locations=[loc.sarif() for loc in self.thread_flow_locations]
+                    )
+                ]
+            )
+        ]
+        sarif_result.properties = sarif.PropertyBag(
+            tags=[tag.value for tag in self.tags]
+        )
+        return sarif_result
+
+    def with_location(self: Self, location: infra.Location) -> Self:
+        """Adds a location to the diagnostic."""
+        self.locations.append(location)
+        return self
+
+    def with_thread_flow_location(
+        self: Self, location: infra.ThreadFlowLocation
+    ) -> Self:
+        """Adds a thread flow location to the diagnostic."""
+        self.thread_flow_locations.append(location)
+        return self
+
+    def with_stack(self: Self, stack: infra.Stack) -> Self:
+        """Adds a stack to the diagnostic."""
+        self.stacks.append(stack)
+        return self
+
+    def with_graph(self: Self, graph: infra.Graph) -> Self:
+        """Adds a graph to the diagnostic."""
+        self.graphs.append(graph)
+        return self
+
+    @contextlib.contextmanager
+    def log_section(
+        self, level: int, message: str, *args, **kwargs
+    ) -> Generator[None, None, None]:
+        """
+        Context manager for a section of log messages, denoted by a title message and increased indentation.
+
+        Same api as `logging.Logger.log`.
+
+        This context manager logs the given title at the specified log level, increases the current
+        section depth for subsequent log messages, and ensures that the section depth is decreased
+        again when exiting the context.
+
+        Args:
+            level: The log level.
+            message: The title message to log.
+            *args: The arguments to the message. Use `LazyString` to defer the
+                expensive evaluation of the arguments until the message is actually logged.
+            **kwargs: The keyword arguments for `logging.Logger.log`.
+
+        Yields:
+            None: This context manager does not yield any value.
+
+        Example:
+            >>> with DiagnosticContext("DummyContext", "1.0"):
+            ...     rule = infra.Rule("RuleID", "DummyRule", "Rule message")
+            ...     diagnostic = Diagnostic(rule, infra.Level.WARNING)
+            ...     with diagnostic.log_section(logging.INFO, "My Section"):
+            ...         diagnostic.log(logging.INFO, "My Message")
+            ...         with diagnostic.log_section(logging.INFO, "My Subsection"):
+            ...             diagnostic.log(logging.INFO, "My Submessage")
+            ...     diagnostic.additional_messages
+            ['## My Section', 'My Message', '### My Subsection', 'My Submessage']
+        """
+        if self.logger.isEnabledFor(level):
+            indented_format_message = (
+                f"##{'#' * self._current_log_section_depth } {message}"
+            )
+            self.log(
+                level,
+                indented_format_message,
+                *args,
+                **kwargs,
+            )
+        self._current_log_section_depth += 1
+        try:
+            yield
+        finally:
+            self._current_log_section_depth -= 1
+
+    def log(self, level: int, message: str, *args, **kwargs) -> None:
+        """Logs a message within the diagnostic. Same api as `logging.Logger.log`.
+
+        If logger is not enabled for the given level, the message will not be logged.
+        Otherwise, the message will be logged and also added to the diagnostic's additional_messages.
+
+        The default setting for `DiagnosticOptions.verbosity_level` is `logging.INFO`. Based on this default,
+        the log level recommendations are as follows. If you've set a different default verbosity level in your
+        application, please adjust accordingly:
+
+        - logging.ERROR: Log any events leading to application failure.
+        - logging.WARNING: Log events that might result in application issues or failures, although not guaranteed.
+        - logging.INFO: Log general useful information, ensuring minimal performance overhead.
+        - logging.DEBUG: Log detailed debug information, which might affect performance when logged.
+
+        Args:
+            level: The log level.
+            message: The message to log.
+            *args: The arguments to the message. Use `LazyString` to defer the
+                expensive evaluation of the arguments until the message is actually logged.
+            **kwargs: The keyword arguments for `logging.Logger.log`.
+        """
+        if self.logger.isEnabledFor(level):
+            formatted_message = message % args
+            self.logger.log(level, formatted_message, **kwargs)
+            self.additional_messages.append(formatted_message)
+
+    def debug(self, message: str, *args, **kwargs) -> None:
+        """Logs a debug message within the diagnostic. Same api as logging.Logger.debug.
+
+        Checkout `log` for more details.
+        """
+        self.log(logging.DEBUG, message, *args, **kwargs)
+
+    def info(self, message: str, *args, **kwargs) -> None:
+        """Logs an info message within the diagnostic. Same api as logging.Logger.info.
+
+        Checkout `log` for more details.
+        """
+        self.log(logging.INFO, message, *args, **kwargs)
+
+    def warning(self, message: str, *args, **kwargs) -> None:
+        """Logs a warning message within the diagnostic. Same api as logging.Logger.warning.
+
+        Checkout `log` for more details.
+        """
+        self.log(logging.WARNING, message, *args, **kwargs)
+
+    def error(self, message: str, *args, **kwargs) -> None:
+        """Logs an error message within the diagnostic. Same api as logging.Logger.error.
+
+        Checkout `log` for more details.
+        """
+        self.log(logging.ERROR, message, *args, **kwargs)
+
+    def log_source_exception(self, level: int, exception: Exception) -> None:
+        """Logs a source exception within the diagnostic.
+
+        Invokes `log_section` and `log` to log the exception in markdown section format.
+        """
+        self.source_exception = exception
+        with self.log_section(level, "Exception log"):
+            self.log(level, "%s", formatter.lazy_format_exception(exception))
+
+    def record_python_call_stack(self, frames_to_skip: int) -> infra.Stack:
+        """Records the current Python call stack."""
+        frames_to_skip += 1  # Skip this function.
+        stack = utils.python_call_stack(frames_to_skip=frames_to_skip)
+        self.with_stack(stack)
+        if len(stack.frames) > 0:
+            self.with_location(stack.frames[0].location)
+        return stack
+
+    def record_python_call(
+        self,
+        fn: Callable,
+        state: Mapping[str, str],
+        message: str | None = None,
+        frames_to_skip: int = 0,
+    ) -> infra.ThreadFlowLocation:
+        """Records a python call as one thread flow step."""
+        frames_to_skip += 1  # Skip this function.
+        stack = utils.python_call_stack(frames_to_skip=frames_to_skip, frames_to_log=5)
+        location = utils.function_location(fn)
+        location.message = message
+        # Add function location to the top of the stack.
+        stack.frames.insert(0, infra.StackFrame(location=location))
+        thread_flow_location = infra.ThreadFlowLocation(
+            location=location,
+            state=state,
+            index=len(self.thread_flow_locations),
+            stack=stack,
+        )
+        self.with_thread_flow_location(thread_flow_location)
+        return thread_flow_location
+
+
+class RuntimeErrorWithDiagnostic(RuntimeError):
+    """Runtime error with enclosed diagnostic information."""
+
+    def __init__(self, diagnostic: Diagnostic):
+        super().__init__(diagnostic.message)
+        self.diagnostic = diagnostic
+
+
+@dataclasses.dataclass
+class DiagnosticContext(Generic[_Diagnostic]):
+    name: str
+    version: str
+    options: infra.DiagnosticOptions = dataclasses.field(
+        default_factory=infra.DiagnosticOptions
+    )
+    diagnostics: list[_Diagnostic] = dataclasses.field(init=False, default_factory=list)
+    # TODO(bowbao): Implement this.
+    # _invocation: infra.Invocation = dataclasses.field(init=False)
+    _inflight_diagnostics: list[_Diagnostic] = dataclasses.field(
+        init=False, default_factory=list
+    )
+    _previous_log_level: int = dataclasses.field(init=False, default=logging.WARNING)
+    logger: logging.Logger = dataclasses.field(init=False, default=diagnostic_logger)
+    _bound_diagnostic_type: type = dataclasses.field(init=False, default=Diagnostic)
+
+    def __enter__(self):
+        self._previous_log_level = self.logger.level
+        self.logger.setLevel(self.options.verbosity_level)
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.logger.setLevel(self._previous_log_level)
+        return None
+
+    def sarif(self) -> sarif.Run:
+        """Returns the SARIF Run object."""
+        unique_rules = {diagnostic.rule for diagnostic in self.diagnostics}
+        return sarif.Run(
+            sarif.Tool(
+                driver=sarif.ToolComponent(
+                    name=self.name,
+                    version=self.version,
+                    rules=[rule.sarif() for rule in unique_rules],
+                )
+            ),
+            results=[diagnostic.sarif() for diagnostic in self.diagnostics],
+        )
+
+    def sarif_log(self) -> sarif.SarifLog:  # type: ignore[name-defined]
+        """Returns the SARIF Log object."""
+        return sarif.SarifLog(
+            version=sarif_version.SARIF_VERSION,
+            schema_uri=sarif_version.SARIF_SCHEMA_LINK,
+            runs=[self.sarif()],
+        )
+
+    def to_json(self) -> str:
+        return formatter.sarif_to_json(self.sarif_log())
+
+    def dump(self, file_path: str, compress: bool = False) -> None:
+        """Dumps the SARIF log to a file."""
+        if compress:
+            with gzip.open(file_path, "wt") as f:
+                f.write(self.to_json())
+        else:
+            with open(file_path, "w") as f:
+                f.write(self.to_json())
+
+    def log(self, diagnostic: _Diagnostic) -> None:
+        """Logs a diagnostic.
+
+        This method should be used only after all the necessary information for the diagnostic
+        has been collected.
+
+        Args:
+            diagnostic: The diagnostic to add.
+        """
+        if not isinstance(diagnostic, self._bound_diagnostic_type):
+            raise TypeError(
+                f"Expected diagnostic of type {self._bound_diagnostic_type}, got {type(diagnostic)}"
+            )
+        if self.options.warnings_as_errors and diagnostic.level == infra.Level.WARNING:  # type: ignore[attr-defined]
+            diagnostic.level = infra.Level.ERROR  # type: ignore[attr-defined]
+        self.diagnostics.append(diagnostic)  # type: ignore[arg-type]
+
+    def log_and_raise_if_error(self, diagnostic: _Diagnostic) -> None:
+        """Logs a diagnostic and raises an exception if it is an error.
+
+        Use this method for logging non inflight diagnostics where diagnostic level is not known or
+        lower than ERROR. If it is always expected raise, use `log` and explicit
+        `raise` instead. Otherwise there is no way to convey the message that it always
+        raises to Python intellisense and type checking tools.
+
+        This method should be used only after all the necessary information for the diagnostic
+        has been collected.
+
+        Args:
+            diagnostic: The diagnostic to add.
+        """
+        self.log(diagnostic)
+        if diagnostic.level == infra.Level.ERROR:
+            if diagnostic.source_exception is not None:
+                raise diagnostic.source_exception
+            raise RuntimeErrorWithDiagnostic(diagnostic)
+
+    @contextlib.contextmanager
+    def add_inflight_diagnostic(
+        self, diagnostic: _Diagnostic
+    ) -> Generator[_Diagnostic, None, None]:
+        """Adds a diagnostic to the context.
+
+        Use this method to add diagnostics that are not created by the context.
+        Args:
+            diagnostic: The diagnostic to add.
+        """
+        self._inflight_diagnostics.append(diagnostic)
+        try:
+            yield diagnostic
+        finally:
+            self._inflight_diagnostics.pop()
+
+    def push_inflight_diagnostic(self, diagnostic: _Diagnostic) -> None:
+        """Pushes a diagnostic to the inflight diagnostics stack.
+
+        Args:
+            diagnostic: The diagnostic to push.
+
+        Raises:
+            ValueError: If the rule is not supported by the tool.
+        """
+        self._inflight_diagnostics.append(diagnostic)
+
+    def pop_inflight_diagnostic(self) -> _Diagnostic:
+        """Pops the last diagnostic from the inflight diagnostics stack.
+
+        Returns:
+            The popped diagnostic.
+        """
+        return self._inflight_diagnostics.pop()
+
+    def inflight_diagnostic(self, rule: infra.Rule | None = None) -> _Diagnostic:
+        if rule is None:
+            # TODO(bowbao): Create builtin-rules and create diagnostic using that.
+            if len(self._inflight_diagnostics) <= 0:
+                raise AssertionError("No inflight diagnostics")
+
+            return self._inflight_diagnostics[-1]
+        else:
+            for diagnostic in reversed(self._inflight_diagnostics):
+                if diagnostic.rule == rule:
+                    return diagnostic
+            raise AssertionError(f"No inflight diagnostic for rule {rule.name}")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/decorator.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/decorator.py
new file mode 100644
index 0000000000000000000000000000000000000000..1445f47d4cd79735932dedcde5f1e28eb6dff4e2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/decorator.py
@@ -0,0 +1,153 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import functools
+import logging
+import traceback
+from typing import Any, Callable, Dict, Tuple
+
+from torch.onnx._internal.diagnostics import infra
+from torch.onnx._internal.diagnostics.infra import formatter, utils
+
+
+MessageFormatterType = Callable[..., str]
+
+
+def format_message_in_text(fn: Callable, *args: Any, **kwargs: Any) -> str:
+    return f"{formatter.display_name(fn)}. "
+
+
+def format_exception_in_markdown(exception: Exception) -> str:
+    msg_list = ["### Exception log", "```"]
+    msg_list.extend(
+        traceback.format_exception(type(exception), exception, exception.__traceback__)
+    )
+    msg_list.append("```")
+    return "\n".join(msg_list)
+
+
+def format_function_signature_in_markdown(
+    fn: Callable,
+    args: tuple[Any, ...],
+    kwargs: dict[str, Any],
+    format_argument: Callable[[Any], str] = formatter.format_argument,
+) -> str:
+    msg_list = [f"### Function Signature {formatter.display_name(fn)}"]
+
+    state = utils.function_state(fn, args, kwargs)
+
+    for k, v in state.items():
+        msg_list.append(f"- {k}: {format_argument(v)}")
+
+    return "\n".join(msg_list)
+
+
+def format_return_values_in_markdown(
+    return_values: Any,
+    format_argument: Callable[[Any], str] = formatter.format_argument,
+) -> str:
+    return f"{format_argument(return_values)}"
+
+
+ModifierCallableType = Callable[
+    [infra.Diagnostic, Callable, Tuple[Any, ...], Dict[str, Any], Any], None
+]
+
+
+def diagnose_call(
+    rule: infra.Rule,
+    *,
+    level: infra.Level = infra.Level.NONE,
+    diagnostic_type: type[infra.Diagnostic] = infra.Diagnostic,
+    format_argument: Callable[[Any], str] = formatter.format_argument,
+    diagnostic_message_formatter: MessageFormatterType = format_message_in_text,
+) -> Callable:
+    def decorator(fn):
+        @functools.wraps(fn)
+        def wrapper(*args, **kwargs):
+            common_error_message = "diagnose_call can only be applied to callables"
+            if not callable(fn):
+                raise AssertionError(
+                    f"{common_error_message}. Got {type(fn)} instead of callable."
+                )
+            arg0 = args[0] if len(args) > 0 else None
+            if isinstance(ctx := arg0, infra.DiagnosticContext):
+                pass
+            elif isinstance(
+                ctx := getattr(arg0, "diagnostic_context", None),
+                infra.DiagnosticContext,
+            ):
+                pass
+            else:
+                # NOTE: At decorate time, it can't tell if a callable is function or method.
+                # Technically both are regarded as function at that time.
+                raise AssertionError(
+                    f"{common_error_message}. For {fn}, "
+                    f"If it is a function, a DiagnosticContext instance must be present as "
+                    f"the first argument. "
+                    f"If it is a method, a DiagnosticContext instance must be present as "
+                    f"the attribute 'diagnostic_context' of the 'self' argument."
+                )
+
+            diag = diagnostic_type(
+                rule,
+                level,
+                diagnostic_message_formatter(fn, *args, **kwargs),
+            )
+
+            # pop the decorator frame
+            # TODO(bowbao): by default diagnostic doesn't have stack.
+            # So need to check before doing this. Make the code cleaner.
+            # Option: do not capture stack by default in diagnostic initialization.
+            stack: infra.Stack | None = None
+            if len(diag.stacks) > 0:
+                stack = diag.stacks[0]
+                stack.frames.pop(0)
+
+            # set function location
+            fn_location = utils.function_location(fn)
+            diag.locations.insert(0, fn_location)
+            # Add function location to the top of the stack.
+            if stack is not None:
+                stack.frames.insert(0, infra.StackFrame(location=fn_location))
+
+            with diag.log_section(logging.INFO, "Function Signature"):
+                diag.log(
+                    logging.INFO,
+                    "%s",
+                    formatter.LazyString(
+                        format_function_signature_in_markdown,
+                        fn,
+                        args,
+                        kwargs,
+                        format_argument,
+                    ),
+                )
+
+            return_values: Any = None
+            with ctx.add_inflight_diagnostic(diag) as diag:
+                try:
+                    return_values = fn(*args, **kwargs)
+                    with diag.log_section(logging.INFO, "Return values"):
+                        diag.log(
+                            logging.INFO,
+                            "%s",
+                            formatter.LazyString(
+                                format_return_values_in_markdown,
+                                return_values,
+                                format_argument,
+                            ),
+                        )
+                    return return_values
+                except Exception as e:
+                    diag.log_source_exception(logging.ERROR, e)
+                    diag.level = infra.Level.ERROR
+                finally:
+                    ctx.log_and_raise_if_error(diag)
+
+        return wrapper
+
+    return decorator
+
+
+# TODO(bowbao): decorator to report only when failed.
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/formatter.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/formatter.py
new file mode 100644
index 0000000000000000000000000000000000000000..5abb698dafce6a87112465ade70591f39d1302ef
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/formatter.py
@@ -0,0 +1,106 @@
+from __future__ import annotations
+
+import dataclasses
+import json
+import re
+import traceback
+from typing import Any, Callable, Union
+
+from torch._logging import LazyString
+from torch.onnx._internal.diagnostics.infra import sarif
+
+
+# A list of types in the SARIF module to support pretty printing.
+# This is solely for type annotation for the functions below.
+_SarifClass = Union[
+    sarif.SarifLog,
+    sarif.Run,
+    sarif.ReportingDescriptor,
+    sarif.Result,
+]
+
+
+def lazy_format_exception(exception: Exception) -> LazyString:
+    return LazyString(
+        lambda: "\n".join(
+            (
+                "```",
+                *traceback.format_exception(
+                    type(exception), exception, exception.__traceback__
+                ),
+                "```",
+            )
+        ),
+    )
+
+
+def snake_case_to_camel_case(s: str) -> str:
+    splits = s.split("_")
+    if len(splits) <= 1:
+        return s
+    return "".join([splits[0], *map(str.capitalize, splits[1:])])
+
+
+def camel_case_to_snake_case(s: str) -> str:
+    return re.sub(r"([A-Z])", r"_\1", s).lower()
+
+
+def kebab_case_to_snake_case(s: str) -> str:
+    return s.replace("-", "_")
+
+
+def _convert_key(
+    object: dict[str, Any] | Any, convert: Callable[[str], str]
+) -> dict[str, Any] | Any:
+    """Convert and update keys in a dictionary with "convert".
+
+    Any value that is a dictionary will be recursively updated.
+    Any value that is a list will be recursively searched.
+
+    Args:
+        object: The object to update.
+        convert: The function to convert the keys, e.g. `kebab_case_to_snake_case`.
+
+    Returns:
+        The updated object.
+    """
+    if not isinstance(object, dict):
+        return object
+    new_dict = {}
+    for k, v in object.items():
+        new_k = convert(k)
+        if isinstance(v, dict):
+            new_v = _convert_key(v, convert)
+        elif isinstance(v, list):
+            new_v = [_convert_key(elem, convert) for elem in v]
+        else:
+            new_v = v
+        if new_v is None:
+            # Otherwise unnecessarily bloated sarif log with "null"s.
+            continue
+        if new_v == -1:
+            # WAR: -1 as default value shouldn't be logged into sarif.
+            continue
+
+        new_dict[new_k] = new_v
+
+    return new_dict
+
+
+def sarif_to_json(attr_cls_obj: _SarifClass, indent: str | None = " ") -> str:
+    dict = dataclasses.asdict(attr_cls_obj)
+    dict = _convert_key(dict, snake_case_to_camel_case)
+    return json.dumps(dict, indent=indent, separators=(",", ":"))
+
+
+def format_argument(obj: Any) -> str:
+    return f"{type(obj)}"
+
+
+def display_name(fn: Callable) -> str:
+    if hasattr(fn, "__qualname__"):
+        return fn.__qualname__
+    elif hasattr(fn, "__name__"):
+        return fn.__name__
+    else:
+        return str(fn)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a01b2abeef9b0f47e8f99250f6e8c124e2f09480
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/__init__.py
@@ -0,0 +1,101 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from torch.onnx._internal.diagnostics.infra.sarif._address import Address
+from torch.onnx._internal.diagnostics.infra.sarif._artifact import Artifact
+from torch.onnx._internal.diagnostics.infra.sarif._artifact_change import ArtifactChange
+from torch.onnx._internal.diagnostics.infra.sarif._artifact_content import (
+    ArtifactContent,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._artifact_location import (
+    ArtifactLocation,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._attachment import Attachment
+from torch.onnx._internal.diagnostics.infra.sarif._code_flow import CodeFlow
+from torch.onnx._internal.diagnostics.infra.sarif._configuration_override import (
+    ConfigurationOverride,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._conversion import Conversion
+from torch.onnx._internal.diagnostics.infra.sarif._edge import Edge
+from torch.onnx._internal.diagnostics.infra.sarif._edge_traversal import EdgeTraversal
+from torch.onnx._internal.diagnostics.infra.sarif._exception import Exception
+from torch.onnx._internal.diagnostics.infra.sarif._external_properties import (
+    ExternalProperties,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._external_property_file_reference import (
+    ExternalPropertyFileReference,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._external_property_file_references import (
+    ExternalPropertyFileReferences,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._fix import Fix
+from torch.onnx._internal.diagnostics.infra.sarif._graph import Graph
+from torch.onnx._internal.diagnostics.infra.sarif._graph_traversal import GraphTraversal
+from torch.onnx._internal.diagnostics.infra.sarif._invocation import Invocation
+from torch.onnx._internal.diagnostics.infra.sarif._location import Location
+from torch.onnx._internal.diagnostics.infra.sarif._location_relationship import (
+    LocationRelationship,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._logical_location import (
+    LogicalLocation,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._message import Message
+from torch.onnx._internal.diagnostics.infra.sarif._multiformat_message_string import (
+    MultiformatMessageString,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._node import Node
+from torch.onnx._internal.diagnostics.infra.sarif._notification import Notification
+from torch.onnx._internal.diagnostics.infra.sarif._physical_location import (
+    PhysicalLocation,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._property_bag import PropertyBag
+from torch.onnx._internal.diagnostics.infra.sarif._rectangle import Rectangle
+from torch.onnx._internal.diagnostics.infra.sarif._region import Region
+from torch.onnx._internal.diagnostics.infra.sarif._replacement import Replacement
+from torch.onnx._internal.diagnostics.infra.sarif._reporting_configuration import (
+    ReportingConfiguration,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._reporting_descriptor import (
+    ReportingDescriptor,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._reporting_descriptor_reference import (
+    ReportingDescriptorReference,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._reporting_descriptor_relationship import (
+    ReportingDescriptorRelationship,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._result import Result
+from torch.onnx._internal.diagnostics.infra.sarif._result_provenance import (
+    ResultProvenance,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._run import Run
+from torch.onnx._internal.diagnostics.infra.sarif._run_automation_details import (
+    RunAutomationDetails,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._sarif_log import SarifLog
+from torch.onnx._internal.diagnostics.infra.sarif._special_locations import (
+    SpecialLocations,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._stack import Stack
+from torch.onnx._internal.diagnostics.infra.sarif._stack_frame import StackFrame
+from torch.onnx._internal.diagnostics.infra.sarif._suppression import Suppression
+from torch.onnx._internal.diagnostics.infra.sarif._thread_flow import ThreadFlow
+from torch.onnx._internal.diagnostics.infra.sarif._thread_flow_location import (
+    ThreadFlowLocation,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._tool import Tool
+from torch.onnx._internal.diagnostics.infra.sarif._tool_component import ToolComponent
+from torch.onnx._internal.diagnostics.infra.sarif._tool_component_reference import (
+    ToolComponentReference,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._translation_metadata import (
+    TranslationMetadata,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._version_control_details import (
+    VersionControlDetails,
+)
+from torch.onnx._internal.diagnostics.infra.sarif._web_request import WebRequest
+from torch.onnx._internal.diagnostics.infra.sarif._web_response import WebResponse
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_address.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_address.py
new file mode 100644
index 0000000000000000000000000000000000000000..df68b103374ae9af848c44ea41030967951c9be8
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_address.py
@@ -0,0 +1,48 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import _property_bag
+
+
+@dataclasses.dataclass
+class Address(object):
+    """A physical or virtual address, or a range of addresses, in an 'addressable region' (memory or a binary file)."""
+
+    absolute_address: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "absoluteAddress"}
+    )
+    fully_qualified_name: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "fullyQualifiedName"}
+    )
+    index: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "index"}
+    )
+    kind: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "kind"}
+    )
+    length: Optional[int] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "length"}
+    )
+    name: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "name"}
+    )
+    offset_from_parent: Optional[int] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "offsetFromParent"}
+    )
+    parent_index: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "parentIndex"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    relative_address: Optional[int] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "relativeAddress"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_artifact.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_artifact.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f66167772488624c6fba14c0de36a0ffb846f0a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_artifact.py
@@ -0,0 +1,88 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Any, List, Literal, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _artifact_content,
+    _artifact_location,
+    _message,
+    _property_bag,
+)
+
+
+@dataclasses.dataclass
+class Artifact(object):
+    """A single artifact. In some cases, this artifact might be nested within another artifact."""
+
+    contents: Optional[_artifact_content.ArtifactContent] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "contents"}
+    )
+    description: Optional[_message.Message] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "description"}
+    )
+    encoding: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "encoding"}
+    )
+    hashes: Any = dataclasses.field(
+        default=None, metadata={"schema_property_name": "hashes"}
+    )
+    last_modified_time_utc: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "lastModifiedTimeUtc"}
+    )
+    length: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "length"}
+    )
+    location: Optional[_artifact_location.ArtifactLocation] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "location"}
+    )
+    mime_type: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "mimeType"}
+    )
+    offset: Optional[int] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "offset"}
+    )
+    parent_index: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "parentIndex"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    roles: Optional[
+        List[
+            Literal[
+                "analysisTarget",
+                "attachment",
+                "responseFile",
+                "resultFile",
+                "standardStream",
+                "tracedFile",
+                "unmodified",
+                "modified",
+                "added",
+                "deleted",
+                "renamed",
+                "uncontrolled",
+                "driver",
+                "extension",
+                "translation",
+                "taxonomy",
+                "policy",
+                "referencedOnCommandLine",
+                "memoryContents",
+                "directory",
+                "userSpecifiedConfiguration",
+                "toolSpecifiedConfiguration",
+                "debugOutputFile",
+            ]
+        ]
+    ] = dataclasses.field(default=None, metadata={"schema_property_name": "roles"})
+    source_language: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "sourceLanguage"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_artifact_change.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_artifact_change.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8cca329f25b13bceb6469e5a59321bcc29c643e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_artifact_change.py
@@ -0,0 +1,31 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _artifact_location,
+    _property_bag,
+    _replacement,
+)
+
+
+@dataclasses.dataclass
+class ArtifactChange(object):
+    """A change to a single artifact."""
+
+    artifact_location: _artifact_location.ArtifactLocation = dataclasses.field(
+        metadata={"schema_property_name": "artifactLocation"}
+    )
+    replacements: List[_replacement.Replacement] = dataclasses.field(
+        metadata={"schema_property_name": "replacements"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_artifact_content.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_artifact_content.py
new file mode 100644
index 0000000000000000000000000000000000000000..e11aeddce77d23ebbc1a72602e2115e41b105ba8
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_artifact_content.py
@@ -0,0 +1,33 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _multiformat_message_string,
+    _property_bag,
+)
+
+
+@dataclasses.dataclass
+class ArtifactContent(object):
+    """Represents the contents of an artifact."""
+
+    binary: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "binary"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    rendered: Optional[_multiformat_message_string.MultiformatMessageString] = (
+        dataclasses.field(default=None, metadata={"schema_property_name": "rendered"})
+    )
+    text: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "text"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_artifact_location.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_artifact_location.py
new file mode 100644
index 0000000000000000000000000000000000000000..96e6dbba31599933fb054b95eb862a534d912996
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_artifact_location.py
@@ -0,0 +1,33 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import _message, _property_bag
+
+
+@dataclasses.dataclass
+class ArtifactLocation(object):
+    """Specifies the location of an artifact."""
+
+    description: Optional[_message.Message] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "description"}
+    )
+    index: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "index"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    uri: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "uri"}
+    )
+    uri_base_id: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "uriBaseId"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_attachment.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_attachment.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e5ee6d13fadca1d7028b148d877a29a7e94c69e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_attachment.py
@@ -0,0 +1,39 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _artifact_location,
+    _message,
+    _property_bag,
+    _rectangle,
+    _region,
+)
+
+
+@dataclasses.dataclass
+class Attachment(object):
+    """An artifact relevant to a result."""
+
+    artifact_location: _artifact_location.ArtifactLocation = dataclasses.field(
+        metadata={"schema_property_name": "artifactLocation"}
+    )
+    description: Optional[_message.Message] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "description"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    rectangles: Optional[List[_rectangle.Rectangle]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "rectangles"}
+    )
+    regions: Optional[List[_region.Region]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "regions"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_code_flow.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_code_flow.py
new file mode 100644
index 0000000000000000000000000000000000000000..5515ef78bee12bc129aeb41da6d1a8db2c3e0810
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_code_flow.py
@@ -0,0 +1,31 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _message,
+    _property_bag,
+    _thread_flow,
+)
+
+
+@dataclasses.dataclass
+class CodeFlow(object):
+    """A set of threadFlows which together describe a pattern of code execution relevant to detecting a result."""
+
+    thread_flows: List[_thread_flow.ThreadFlow] = dataclasses.field(
+        metadata={"schema_property_name": "threadFlows"}
+    )
+    message: Optional[_message.Message] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "message"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_configuration_override.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_configuration_override.py
new file mode 100644
index 0000000000000000000000000000000000000000..be32e77ff4e18c10b9a8576c857594133c9d3613
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_configuration_override.py
@@ -0,0 +1,31 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _property_bag,
+    _reporting_configuration,
+    _reporting_descriptor_reference,
+)
+
+
+@dataclasses.dataclass
+class ConfigurationOverride(object):
+    """Information about how a specific rule or notification was reconfigured at runtime."""
+
+    configuration: _reporting_configuration.ReportingConfiguration = dataclasses.field(
+        metadata={"schema_property_name": "configuration"}
+    )
+    descriptor: _reporting_descriptor_reference.ReportingDescriptorReference = (
+        dataclasses.field(metadata={"schema_property_name": "descriptor"})
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_conversion.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_conversion.py
new file mode 100644
index 0000000000000000000000000000000000000000..fad4807046a6441f3fb2c75b6815bc7b6fc23213
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_conversion.py
@@ -0,0 +1,35 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _artifact_location,
+    _invocation,
+    _property_bag,
+    _tool,
+)
+
+
+@dataclasses.dataclass
+class Conversion(object):
+    """Describes how a converter transformed the output of a static analysis tool from the analysis tool's native output format into the SARIF format."""
+
+    tool: _tool.Tool = dataclasses.field(metadata={"schema_property_name": "tool"})
+    analysis_tool_log_files: Optional[List[_artifact_location.ArtifactLocation]] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "analysisToolLogFiles"}
+        )
+    )
+    invocation: Optional[_invocation.Invocation] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "invocation"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_edge.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_edge.py
new file mode 100644
index 0000000000000000000000000000000000000000..f85ec8dd99c424419dbaa1ea6cb2ebb1690b4f92
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_edge.py
@@ -0,0 +1,31 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import _message, _property_bag
+
+
+@dataclasses.dataclass
+class Edge(object):
+    """Represents a directed edge in a graph."""
+
+    id: str = dataclasses.field(metadata={"schema_property_name": "id"})
+    source_node_id: str = dataclasses.field(
+        metadata={"schema_property_name": "sourceNodeId"}
+    )
+    target_node_id: str = dataclasses.field(
+        metadata={"schema_property_name": "targetNodeId"}
+    )
+    label: Optional[_message.Message] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "label"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_edge_traversal.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_edge_traversal.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8f12d921704b55654991fb0ff7e3ca73aa63f4b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_edge_traversal.py
@@ -0,0 +1,31 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Any, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import _message, _property_bag
+
+
+@dataclasses.dataclass
+class EdgeTraversal(object):
+    """Represents the traversal of a single edge during a graph traversal."""
+
+    edge_id: str = dataclasses.field(metadata={"schema_property_name": "edgeId"})
+    final_state: Any = dataclasses.field(
+        default=None, metadata={"schema_property_name": "finalState"}
+    )
+    message: Optional[_message.Message] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "message"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    step_over_edge_count: Optional[int] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "stepOverEdgeCount"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_exception.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_exception.py
new file mode 100644
index 0000000000000000000000000000000000000000..9afa806332413726547a5d71d86d45e5a20c18a9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_exception.py
@@ -0,0 +1,37 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _exception,
+    _property_bag,
+    _stack,
+)
+
+
+@dataclasses.dataclass
+class Exception(object):
+    """Describes a runtime exception encountered during the execution of an analysis tool."""
+
+    inner_exceptions: Optional[List[_exception.Exception]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "innerExceptions"}
+    )
+    kind: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "kind"}
+    )
+    message: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "message"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    stack: Optional[_stack.Stack] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "stack"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_external_properties.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_external_properties.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ac43f6db9b1fc7f62d52cff9f4556764cdc4784
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_external_properties.py
@@ -0,0 +1,98 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Literal, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _address,
+    _artifact,
+    _conversion,
+    _graph,
+    _invocation,
+    _logical_location,
+    _property_bag,
+    _result,
+    _thread_flow_location,
+    _tool_component,
+    _web_request,
+    _web_response,
+)
+
+
+@dataclasses.dataclass
+class ExternalProperties(object):
+    """The top-level element of an external property file."""
+
+    addresses: Optional[List[_address.Address]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "addresses"}
+    )
+    artifacts: Optional[List[_artifact.Artifact]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "artifacts"}
+    )
+    conversion: Optional[_conversion.Conversion] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "conversion"}
+    )
+    driver: Optional[_tool_component.ToolComponent] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "driver"}
+    )
+    extensions: Optional[List[_tool_component.ToolComponent]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "extensions"}
+    )
+    externalized_properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "externalizedProperties"}
+    )
+    graphs: Optional[List[_graph.Graph]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "graphs"}
+    )
+    guid: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "guid"}
+    )
+    invocations: Optional[List[_invocation.Invocation]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "invocations"}
+    )
+    logical_locations: Optional[List[_logical_location.LogicalLocation]] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "logicalLocations"}
+        )
+    )
+    policies: Optional[List[_tool_component.ToolComponent]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "policies"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    results: Optional[List[_result.Result]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "results"}
+    )
+    run_guid: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "runGuid"}
+    )
+    schema: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "schema"}
+    )
+    taxonomies: Optional[List[_tool_component.ToolComponent]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "taxonomies"}
+    )
+    thread_flow_locations: Optional[List[_thread_flow_location.ThreadFlowLocation]] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "threadFlowLocations"}
+        )
+    )
+    translations: Optional[List[_tool_component.ToolComponent]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "translations"}
+    )
+    version: Optional[Literal["2.1.0"]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "version"}
+    )
+    web_requests: Optional[List[_web_request.WebRequest]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "webRequests"}
+    )
+    web_responses: Optional[List[_web_response.WebResponse]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "webResponses"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_external_property_file_reference.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_external_property_file_reference.py
new file mode 100644
index 0000000000000000000000000000000000000000..13a472fec9a34f640331ed2310b6a4c933fdbc9f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_external_property_file_reference.py
@@ -0,0 +1,33 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _artifact_location,
+    _property_bag,
+)
+
+
+@dataclasses.dataclass
+class ExternalPropertyFileReference(object):
+    """Contains information that enables a SARIF consumer to locate the external property file that contains the value of an externalized property associated with the run."""
+
+    guid: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "guid"}
+    )
+    item_count: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "itemCount"}
+    )
+    location: Optional[_artifact_location.ArtifactLocation] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "location"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_external_property_file_references.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_external_property_file_references.py
new file mode 100644
index 0000000000000000000000000000000000000000..78ae2db62708adb20fd8e9246854c56d7db8a3b7
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_external_property_file_references.py
@@ -0,0 +1,86 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _external_property_file_reference,
+    _property_bag,
+)
+
+
+@dataclasses.dataclass
+class ExternalPropertyFileReferences(object):
+    """References to external property files that should be inlined with the content of a root log file."""
+
+    addresses: Optional[
+        List[_external_property_file_reference.ExternalPropertyFileReference]
+    ] = dataclasses.field(default=None, metadata={"schema_property_name": "addresses"})
+    artifacts: Optional[
+        List[_external_property_file_reference.ExternalPropertyFileReference]
+    ] = dataclasses.field(default=None, metadata={"schema_property_name": "artifacts"})
+    conversion: Optional[
+        _external_property_file_reference.ExternalPropertyFileReference
+    ] = dataclasses.field(default=None, metadata={"schema_property_name": "conversion"})
+    driver: Optional[
+        _external_property_file_reference.ExternalPropertyFileReference
+    ] = dataclasses.field(default=None, metadata={"schema_property_name": "driver"})
+    extensions: Optional[
+        List[_external_property_file_reference.ExternalPropertyFileReference]
+    ] = dataclasses.field(default=None, metadata={"schema_property_name": "extensions"})
+    externalized_properties: Optional[
+        _external_property_file_reference.ExternalPropertyFileReference
+    ] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "externalizedProperties"}
+    )
+    graphs: Optional[
+        List[_external_property_file_reference.ExternalPropertyFileReference]
+    ] = dataclasses.field(default=None, metadata={"schema_property_name": "graphs"})
+    invocations: Optional[
+        List[_external_property_file_reference.ExternalPropertyFileReference]
+    ] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "invocations"}
+    )
+    logical_locations: Optional[
+        List[_external_property_file_reference.ExternalPropertyFileReference]
+    ] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "logicalLocations"}
+    )
+    policies: Optional[
+        List[_external_property_file_reference.ExternalPropertyFileReference]
+    ] = dataclasses.field(default=None, metadata={"schema_property_name": "policies"})
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    results: Optional[
+        List[_external_property_file_reference.ExternalPropertyFileReference]
+    ] = dataclasses.field(default=None, metadata={"schema_property_name": "results"})
+    taxonomies: Optional[
+        List[_external_property_file_reference.ExternalPropertyFileReference]
+    ] = dataclasses.field(default=None, metadata={"schema_property_name": "taxonomies"})
+    thread_flow_locations: Optional[
+        List[_external_property_file_reference.ExternalPropertyFileReference]
+    ] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "threadFlowLocations"}
+    )
+    translations: Optional[
+        List[_external_property_file_reference.ExternalPropertyFileReference]
+    ] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "translations"}
+    )
+    web_requests: Optional[
+        List[_external_property_file_reference.ExternalPropertyFileReference]
+    ] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "webRequests"}
+    )
+    web_responses: Optional[
+        List[_external_property_file_reference.ExternalPropertyFileReference]
+    ] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "webResponses"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_fix.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_fix.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e3b944aa23983b9fbaa1e7015921124ae0f7c75
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_fix.py
@@ -0,0 +1,31 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _artifact_change,
+    _message,
+    _property_bag,
+)
+
+
+@dataclasses.dataclass
+class Fix(object):
+    """A proposed fix for the problem represented by a result object. A fix specifies a set of artifacts to modify. For each artifact, it specifies a set of bytes to remove, and provides a set of new bytes to replace them."""
+
+    artifact_changes: List[_artifact_change.ArtifactChange] = dataclasses.field(
+        metadata={"schema_property_name": "artifactChanges"}
+    )
+    description: Optional[_message.Message] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "description"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_graph.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..306d6b305126a796e9f67c0b69d81ec0f22fad40
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_graph.py
@@ -0,0 +1,35 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _edge,
+    _message,
+    _node,
+    _property_bag,
+)
+
+
+@dataclasses.dataclass
+class Graph(object):
+    """A network of nodes and directed edges that describes some aspect of the structure of the code (for example, a call graph)."""
+
+    description: Optional[_message.Message] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "description"}
+    )
+    edges: Optional[List[_edge.Edge]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "edges"}
+    )
+    nodes: Optional[List[_node.Node]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "nodes"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_graph_traversal.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_graph_traversal.py
new file mode 100644
index 0000000000000000000000000000000000000000..bdc25c4591a2da71cdde040731ee1a472a4cca7a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_graph_traversal.py
@@ -0,0 +1,43 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Any, List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _edge_traversal,
+    _message,
+    _property_bag,
+)
+
+
+@dataclasses.dataclass
+class GraphTraversal(object):
+    """Represents a path through a graph."""
+
+    description: Optional[_message.Message] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "description"}
+    )
+    edge_traversals: Optional[List[_edge_traversal.EdgeTraversal]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "edgeTraversals"}
+    )
+    immutable_state: Any = dataclasses.field(
+        default=None, metadata={"schema_property_name": "immutableState"}
+    )
+    initial_state: Any = dataclasses.field(
+        default=None, metadata={"schema_property_name": "initialState"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    result_graph_index: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "resultGraphIndex"}
+    )
+    run_graph_index: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "runGraphIndex"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_invocation.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_invocation.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9fae0d550c46ab3768372ef738922abd242955b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_invocation.py
@@ -0,0 +1,118 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Any, List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _artifact_location,
+    _configuration_override,
+    _notification,
+    _property_bag,
+)
+
+
+@dataclasses.dataclass
+class Invocation(object):
+    """The runtime environment of the analysis tool run."""
+
+    execution_successful: bool = dataclasses.field(
+        metadata={"schema_property_name": "executionSuccessful"}
+    )
+    account: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "account"}
+    )
+    arguments: Optional[List[str]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "arguments"}
+    )
+    command_line: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "commandLine"}
+    )
+    end_time_utc: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "endTimeUtc"}
+    )
+    environment_variables: Any = dataclasses.field(
+        default=None, metadata={"schema_property_name": "environmentVariables"}
+    )
+    executable_location: Optional[_artifact_location.ArtifactLocation] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "executableLocation"}
+        )
+    )
+    exit_code: Optional[int] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "exitCode"}
+    )
+    exit_code_description: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "exitCodeDescription"}
+    )
+    exit_signal_name: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "exitSignalName"}
+    )
+    exit_signal_number: Optional[int] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "exitSignalNumber"}
+    )
+    machine: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "machine"}
+    )
+    notification_configuration_overrides: Optional[
+        List[_configuration_override.ConfigurationOverride]
+    ] = dataclasses.field(
+        default=None,
+        metadata={"schema_property_name": "notificationConfigurationOverrides"},
+    )
+    process_id: Optional[int] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "processId"}
+    )
+    process_start_failure_message: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "processStartFailureMessage"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    response_files: Optional[List[_artifact_location.ArtifactLocation]] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "responseFiles"}
+        )
+    )
+    rule_configuration_overrides: Optional[
+        List[_configuration_override.ConfigurationOverride]
+    ] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "ruleConfigurationOverrides"}
+    )
+    start_time_utc: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "startTimeUtc"}
+    )
+    stderr: Optional[_artifact_location.ArtifactLocation] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "stderr"}
+    )
+    stdin: Optional[_artifact_location.ArtifactLocation] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "stdin"}
+    )
+    stdout: Optional[_artifact_location.ArtifactLocation] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "stdout"}
+    )
+    stdout_stderr: Optional[_artifact_location.ArtifactLocation] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "stdoutStderr"}
+    )
+    tool_configuration_notifications: Optional[List[_notification.Notification]] = (
+        dataclasses.field(
+            default=None,
+            metadata={"schema_property_name": "toolConfigurationNotifications"},
+        )
+    )
+    tool_execution_notifications: Optional[List[_notification.Notification]] = (
+        dataclasses.field(
+            default=None,
+            metadata={"schema_property_name": "toolExecutionNotifications"},
+        )
+    )
+    working_directory: Optional[_artifact_location.ArtifactLocation] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "workingDirectory"}
+        )
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_location.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_location.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc5635ac8c34fbb6ae9eb6d495851b8cb32ec06f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_location.py
@@ -0,0 +1,50 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _location_relationship,
+    _logical_location,
+    _message,
+    _physical_location,
+    _property_bag,
+    _region,
+)
+
+
+@dataclasses.dataclass
+class Location(object):
+    """A location within a programming artifact."""
+
+    annotations: Optional[List[_region.Region]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "annotations"}
+    )
+    id: int = dataclasses.field(default=-1, metadata={"schema_property_name": "id"})
+    logical_locations: Optional[List[_logical_location.LogicalLocation]] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "logicalLocations"}
+        )
+    )
+    message: Optional[_message.Message] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "message"}
+    )
+    physical_location: Optional[_physical_location.PhysicalLocation] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "physicalLocation"}
+        )
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    relationships: Optional[List[_location_relationship.LocationRelationship]] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "relationships"}
+        )
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_location_relationship.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_location_relationship.py
new file mode 100644
index 0000000000000000000000000000000000000000..92f6c4128e04b8e51e832deaeb82debf48ee02f6
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_location_relationship.py
@@ -0,0 +1,28 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import _message, _property_bag
+
+
+@dataclasses.dataclass
+class LocationRelationship(object):
+    """Information about the relation of one location to another."""
+
+    target: int = dataclasses.field(metadata={"schema_property_name": "target"})
+    description: Optional[_message.Message] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "description"}
+    )
+    kinds: List[str] = dataclasses.field(
+        default_factory=lambda: ["relevant"], metadata={"schema_property_name": "kinds"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_logical_location.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_logical_location.py
new file mode 100644
index 0000000000000000000000000000000000000000..70e67528a95a811e0b2d1ce9a83171728c88d758
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_logical_location.py
@@ -0,0 +1,39 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import _property_bag
+
+
+@dataclasses.dataclass
+class LogicalLocation(object):
+    """A logical location of a construct that produced a result."""
+
+    decorated_name: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "decoratedName"}
+    )
+    fully_qualified_name: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "fullyQualifiedName"}
+    )
+    index: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "index"}
+    )
+    kind: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "kind"}
+    )
+    name: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "name"}
+    )
+    parent_index: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "parentIndex"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_message.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_message.py
new file mode 100644
index 0000000000000000000000000000000000000000..03528747fa5abbb2aa0a62cbefdd2af4158bc9b7
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_message.py
@@ -0,0 +1,33 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import _property_bag
+
+
+@dataclasses.dataclass
+class Message(object):
+    """Encapsulates a message intended to be read by the end user."""
+
+    arguments: Optional[List[str]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "arguments"}
+    )
+    id: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "id"}
+    )
+    markdown: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "markdown"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    text: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "text"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_multiformat_message_string.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_multiformat_message_string.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1dede2923f4bf01a913a3920dc3f5d2f12befc9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_multiformat_message_string.py
@@ -0,0 +1,25 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import _property_bag
+
+
+@dataclasses.dataclass
+class MultiformatMessageString(object):
+    """A message string or message format string rendered in multiple formats."""
+
+    text: str = dataclasses.field(metadata={"schema_property_name": "text"})
+    markdown: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "markdown"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_node.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_node.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4b3f74db3a2930a21db7da4c9e23469d63e4ca5
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_node.py
@@ -0,0 +1,36 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _location,
+    _message,
+    _node,
+    _property_bag,
+)
+
+
+@dataclasses.dataclass
+class Node(object):
+    """Represents a node in a graph."""
+
+    id: str = dataclasses.field(metadata={"schema_property_name": "id"})
+    children: Optional[List[_node.Node]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "children"}
+    )
+    label: Optional[_message.Message] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "label"}
+    )
+    location: Optional[_location.Location] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "location"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_notification.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_notification.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ffb40b4d19bb1c2e1d4311dadee56573b471ac8
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_notification.py
@@ -0,0 +1,53 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Literal, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _exception,
+    _location,
+    _message,
+    _property_bag,
+    _reporting_descriptor_reference,
+)
+
+
+@dataclasses.dataclass
+class Notification(object):
+    """Describes a condition relevant to the tool itself, as opposed to being relevant to a target being analyzed by the tool."""
+
+    message: _message.Message = dataclasses.field(
+        metadata={"schema_property_name": "message"}
+    )
+    associated_rule: Optional[
+        _reporting_descriptor_reference.ReportingDescriptorReference
+    ] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "associatedRule"}
+    )
+    descriptor: Optional[
+        _reporting_descriptor_reference.ReportingDescriptorReference
+    ] = dataclasses.field(default=None, metadata={"schema_property_name": "descriptor"})
+    exception: Optional[_exception.Exception] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "exception"}
+    )
+    level: Literal["none", "note", "warning", "error"] = dataclasses.field(
+        default="warning", metadata={"schema_property_name": "level"}
+    )
+    locations: Optional[List[_location.Location]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "locations"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    thread_id: Optional[int] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "threadId"}
+    )
+    time_utc: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "timeUtc"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_physical_location.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_physical_location.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3a932f12a32c7f6cde9f77e80bcb12cb0e11133
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_physical_location.py
@@ -0,0 +1,40 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _address,
+    _artifact_location,
+    _property_bag,
+    _region,
+)
+
+
+@dataclasses.dataclass
+class PhysicalLocation(object):
+    """A physical location relevant to a result. Specifies a reference to a programming artifact together with a range of bytes or characters within that artifact."""
+
+    address: Optional[_address.Address] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "address"}
+    )
+    artifact_location: Optional[_artifact_location.ArtifactLocation] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "artifactLocation"}
+        )
+    )
+    context_region: Optional[_region.Region] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "contextRegion"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    region: Optional[_region.Region] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "region"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_property_bag.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_property_bag.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb576b4dbd11c45e9588d79681f0d3e97f93f965
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_property_bag.py
@@ -0,0 +1,19 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Optional
+
+
+@dataclasses.dataclass
+class PropertyBag(object):
+    """Key/value pairs that provide additional information about the object."""
+
+    tags: Optional[List[str]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "tags"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_rectangle.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_rectangle.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf24d758252657d2e415cbe39dd2bc1a9aa51684
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_rectangle.py
@@ -0,0 +1,36 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import _message, _property_bag
+
+
+@dataclasses.dataclass
+class Rectangle(object):
+    """An area within an image."""
+
+    bottom: Optional[float] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "bottom"}
+    )
+    left: Optional[float] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "left"}
+    )
+    message: Optional[_message.Message] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "message"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    right: Optional[float] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "right"}
+    )
+    top: Optional[float] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "top"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_region.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_region.py
new file mode 100644
index 0000000000000000000000000000000000000000..658fdb121734ee7f3af7a8fbc1da82ac2553a461
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_region.py
@@ -0,0 +1,58 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _artifact_content,
+    _message,
+    _property_bag,
+)
+
+
+@dataclasses.dataclass
+class Region(object):
+    """A region within an artifact where a result was detected."""
+
+    byte_length: Optional[int] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "byteLength"}
+    )
+    byte_offset: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "byteOffset"}
+    )
+    char_length: Optional[int] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "charLength"}
+    )
+    char_offset: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "charOffset"}
+    )
+    end_column: Optional[int] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "endColumn"}
+    )
+    end_line: Optional[int] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "endLine"}
+    )
+    message: Optional[_message.Message] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "message"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    snippet: Optional[_artifact_content.ArtifactContent] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "snippet"}
+    )
+    source_language: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "sourceLanguage"}
+    )
+    start_column: Optional[int] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "startColumn"}
+    )
+    start_line: Optional[int] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "startLine"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_replacement.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_replacement.py
new file mode 100644
index 0000000000000000000000000000000000000000..9acbc9d8133a118c3aa215ee42f7767178f6d19b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_replacement.py
@@ -0,0 +1,31 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _artifact_content,
+    _property_bag,
+    _region,
+)
+
+
+@dataclasses.dataclass
+class Replacement(object):
+    """The replacement of a single region of an artifact."""
+
+    deleted_region: _region.Region = dataclasses.field(
+        metadata={"schema_property_name": "deletedRegion"}
+    )
+    inserted_content: Optional[_artifact_content.ArtifactContent] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "insertedContent"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_reporting_configuration.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_reporting_configuration.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbc74a9fb35b7c7ba80d7534f1dd0b9dfca765ca
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_reporting_configuration.py
@@ -0,0 +1,33 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Literal, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import _property_bag
+
+
+@dataclasses.dataclass
+class ReportingConfiguration(object):
+    """Information about a rule or notification that can be configured at runtime."""
+
+    enabled: bool = dataclasses.field(
+        default=True, metadata={"schema_property_name": "enabled"}
+    )
+    level: Literal["none", "note", "warning", "error"] = dataclasses.field(
+        default="warning", metadata={"schema_property_name": "level"}
+    )
+    parameters: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "parameters"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    rank: float = dataclasses.field(
+        default=-1.0, metadata={"schema_property_name": "rank"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_reporting_descriptor.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_reporting_descriptor.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca3e0c998971b53e24193b2a1b3e5e93505922af
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_reporting_descriptor.py
@@ -0,0 +1,71 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Any, List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _multiformat_message_string,
+    _property_bag,
+    _reporting_configuration,
+    _reporting_descriptor_relationship,
+)
+
+
+@dataclasses.dataclass
+class ReportingDescriptor(object):
+    """Metadata that describes a specific report produced by the tool, as part of the analysis it provides or its runtime reporting."""
+
+    id: str = dataclasses.field(metadata={"schema_property_name": "id"})
+    default_configuration: Optional[_reporting_configuration.ReportingConfiguration] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "defaultConfiguration"}
+        )
+    )
+    deprecated_guids: Optional[List[str]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "deprecatedGuids"}
+    )
+    deprecated_ids: Optional[List[str]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "deprecatedIds"}
+    )
+    deprecated_names: Optional[List[str]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "deprecatedNames"}
+    )
+    full_description: Optional[_multiformat_message_string.MultiformatMessageString] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "fullDescription"}
+        )
+    )
+    guid: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "guid"}
+    )
+    help: Optional[_multiformat_message_string.MultiformatMessageString] = (
+        dataclasses.field(default=None, metadata={"schema_property_name": "help"})
+    )
+    help_uri: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "helpUri"}
+    )
+    message_strings: Any = dataclasses.field(
+        default=None, metadata={"schema_property_name": "messageStrings"}
+    )
+    name: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "name"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    relationships: Optional[
+        List[_reporting_descriptor_relationship.ReportingDescriptorRelationship]
+    ] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "relationships"}
+    )
+    short_description: Optional[
+        _multiformat_message_string.MultiformatMessageString
+    ] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "shortDescription"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_reporting_descriptor_reference.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_reporting_descriptor_reference.py
new file mode 100644
index 0000000000000000000000000000000000000000..c29406269ac80e207b69c2cb0879de505dfccf12
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_reporting_descriptor_reference.py
@@ -0,0 +1,38 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _property_bag,
+    _tool_component_reference,
+)
+
+
+@dataclasses.dataclass
+class ReportingDescriptorReference(object):
+    """Information about how to locate a relevant reporting descriptor."""
+
+    guid: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "guid"}
+    )
+    id: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "id"}
+    )
+    index: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "index"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    tool_component: Optional[_tool_component_reference.ToolComponentReference] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "toolComponent"}
+        )
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_reporting_descriptor_relationship.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_reporting_descriptor_relationship.py
new file mode 100644
index 0000000000000000000000000000000000000000..b66bd1bb4c0f359c038df836c8f4f55516e64a9d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_reporting_descriptor_relationship.py
@@ -0,0 +1,34 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _message,
+    _property_bag,
+    _reporting_descriptor_reference,
+)
+
+
+@dataclasses.dataclass
+class ReportingDescriptorRelationship(object):
+    """Information about the relation of one reporting descriptor to another."""
+
+    target: _reporting_descriptor_reference.ReportingDescriptorReference = (
+        dataclasses.field(metadata={"schema_property_name": "target"})
+    )
+    description: Optional[_message.Message] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "description"}
+    )
+    kinds: List[str] = dataclasses.field(
+        default_factory=lambda: ["relevant"], metadata={"schema_property_name": "kinds"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_result.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_result.py
new file mode 100644
index 0000000000000000000000000000000000000000..44503e1e39898c3de38b0694208d75df861fac7b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_result.py
@@ -0,0 +1,128 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Any, List, Literal, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _artifact_location,
+    _attachment,
+    _code_flow,
+    _fix,
+    _graph,
+    _graph_traversal,
+    _location,
+    _message,
+    _property_bag,
+    _reporting_descriptor_reference,
+    _result_provenance,
+    _stack,
+    _suppression,
+    _web_request,
+    _web_response,
+)
+
+
+@dataclasses.dataclass
+class Result(object):
+    """A result produced by an analysis tool."""
+
+    message: _message.Message = dataclasses.field(
+        metadata={"schema_property_name": "message"}
+    )
+    analysis_target: Optional[_artifact_location.ArtifactLocation] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "analysisTarget"}
+    )
+    attachments: Optional[List[_attachment.Attachment]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "attachments"}
+    )
+    baseline_state: Optional[Literal["new", "unchanged", "updated", "absent"]] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "baselineState"}
+        )
+    )
+    code_flows: Optional[List[_code_flow.CodeFlow]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "codeFlows"}
+    )
+    correlation_guid: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "correlationGuid"}
+    )
+    fingerprints: Any = dataclasses.field(
+        default=None, metadata={"schema_property_name": "fingerprints"}
+    )
+    fixes: Optional[List[_fix.Fix]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "fixes"}
+    )
+    graph_traversals: Optional[List[_graph_traversal.GraphTraversal]] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "graphTraversals"}
+        )
+    )
+    graphs: Optional[List[_graph.Graph]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "graphs"}
+    )
+    guid: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "guid"}
+    )
+    hosted_viewer_uri: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "hostedViewerUri"}
+    )
+    kind: Literal[
+        "notApplicable", "pass", "fail", "review", "open", "informational"
+    ] = dataclasses.field(default="fail", metadata={"schema_property_name": "kind"})
+    level: Literal["none", "note", "warning", "error"] = dataclasses.field(
+        default="warning", metadata={"schema_property_name": "level"}
+    )
+    locations: Optional[List[_location.Location]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "locations"}
+    )
+    occurrence_count: Optional[int] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "occurrenceCount"}
+    )
+    partial_fingerprints: Any = dataclasses.field(
+        default=None, metadata={"schema_property_name": "partialFingerprints"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    provenance: Optional[_result_provenance.ResultProvenance] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "provenance"}
+    )
+    rank: float = dataclasses.field(
+        default=-1.0, metadata={"schema_property_name": "rank"}
+    )
+    related_locations: Optional[List[_location.Location]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "relatedLocations"}
+    )
+    rule: Optional[_reporting_descriptor_reference.ReportingDescriptorReference] = (
+        dataclasses.field(default=None, metadata={"schema_property_name": "rule"})
+    )
+    rule_id: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "ruleId"}
+    )
+    rule_index: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "ruleIndex"}
+    )
+    stacks: Optional[List[_stack.Stack]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "stacks"}
+    )
+    suppressions: Optional[List[_suppression.Suppression]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "suppressions"}
+    )
+    taxa: Optional[
+        List[_reporting_descriptor_reference.ReportingDescriptorReference]
+    ] = dataclasses.field(default=None, metadata={"schema_property_name": "taxa"})
+    web_request: Optional[_web_request.WebRequest] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "webRequest"}
+    )
+    web_response: Optional[_web_response.WebResponse] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "webResponse"}
+    )
+    work_item_uris: Optional[List[str]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "workItemUris"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_result_provenance.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_result_provenance.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f3e6919939a50dd6c4852191f534484564084f7
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_result_provenance.py
@@ -0,0 +1,44 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _physical_location,
+    _property_bag,
+)
+
+
+@dataclasses.dataclass
+class ResultProvenance(object):
+    """Contains information about how and when a result was detected."""
+
+    conversion_sources: Optional[List[_physical_location.PhysicalLocation]] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "conversionSources"}
+        )
+    )
+    first_detection_run_guid: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "firstDetectionRunGuid"}
+    )
+    first_detection_time_utc: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "firstDetectionTimeUtc"}
+    )
+    invocation_index: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "invocationIndex"}
+    )
+    last_detection_run_guid: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "lastDetectionRunGuid"}
+    )
+    last_detection_time_utc: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "lastDetectionTimeUtc"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_run.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_run.py
new file mode 100644
index 0000000000000000000000000000000000000000..76d38f35bbb4a9e650b114938f713d2d00940e11
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_run.py
@@ -0,0 +1,134 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Any, List, Literal, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _address,
+    _artifact,
+    _conversion,
+    _external_property_file_references,
+    _graph,
+    _invocation,
+    _logical_location,
+    _property_bag,
+    _result,
+    _run_automation_details,
+    _special_locations,
+    _thread_flow_location,
+    _tool,
+    _tool_component,
+    _version_control_details,
+    _web_request,
+    _web_response,
+)
+
+
+@dataclasses.dataclass
+class Run(object):
+    """Describes a single run of an analysis tool, and contains the reported output of that run."""
+
+    tool: _tool.Tool = dataclasses.field(metadata={"schema_property_name": "tool"})
+    addresses: Optional[List[_address.Address]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "addresses"}
+    )
+    artifacts: Optional[List[_artifact.Artifact]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "artifacts"}
+    )
+    automation_details: Optional[_run_automation_details.RunAutomationDetails] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "automationDetails"}
+        )
+    )
+    baseline_guid: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "baselineGuid"}
+    )
+    column_kind: Optional[Literal["utf16CodeUnits", "unicodeCodePoints"]] = (
+        dataclasses.field(default=None, metadata={"schema_property_name": "columnKind"})
+    )
+    conversion: Optional[_conversion.Conversion] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "conversion"}
+    )
+    default_encoding: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "defaultEncoding"}
+    )
+    default_source_language: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "defaultSourceLanguage"}
+    )
+    external_property_file_references: Optional[
+        _external_property_file_references.ExternalPropertyFileReferences
+    ] = dataclasses.field(
+        default=None,
+        metadata={"schema_property_name": "externalPropertyFileReferences"},
+    )
+    graphs: Optional[List[_graph.Graph]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "graphs"}
+    )
+    invocations: Optional[List[_invocation.Invocation]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "invocations"}
+    )
+    language: str = dataclasses.field(
+        default="en-US", metadata={"schema_property_name": "language"}
+    )
+    logical_locations: Optional[List[_logical_location.LogicalLocation]] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "logicalLocations"}
+        )
+    )
+    newline_sequences: List[str] = dataclasses.field(
+        default_factory=lambda: ["\r\n", "\n"],
+        metadata={"schema_property_name": "newlineSequences"},
+    )
+    original_uri_base_ids: Any = dataclasses.field(
+        default=None, metadata={"schema_property_name": "originalUriBaseIds"}
+    )
+    policies: Optional[List[_tool_component.ToolComponent]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "policies"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    redaction_tokens: Optional[List[str]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "redactionTokens"}
+    )
+    results: Optional[List[_result.Result]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "results"}
+    )
+    run_aggregates: Optional[List[_run_automation_details.RunAutomationDetails]] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "runAggregates"}
+        )
+    )
+    special_locations: Optional[_special_locations.SpecialLocations] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "specialLocations"}
+        )
+    )
+    taxonomies: Optional[List[_tool_component.ToolComponent]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "taxonomies"}
+    )
+    thread_flow_locations: Optional[List[_thread_flow_location.ThreadFlowLocation]] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "threadFlowLocations"}
+        )
+    )
+    translations: Optional[List[_tool_component.ToolComponent]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "translations"}
+    )
+    version_control_provenance: Optional[
+        List[_version_control_details.VersionControlDetails]
+    ] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "versionControlProvenance"}
+    )
+    web_requests: Optional[List[_web_request.WebRequest]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "webRequests"}
+    )
+    web_responses: Optional[List[_web_response.WebResponse]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "webResponses"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_run_automation_details.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_run_automation_details.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae63959240b34fd8d71d3eef87d40c240717431d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_run_automation_details.py
@@ -0,0 +1,33 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import _message, _property_bag
+
+
+@dataclasses.dataclass
+class RunAutomationDetails(object):
+    """Information that describes a run's identity and role within an engineering system process."""
+
+    correlation_guid: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "correlationGuid"}
+    )
+    description: Optional[_message.Message] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "description"}
+    )
+    guid: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "guid"}
+    )
+    id: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "id"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_sarif_log.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_sarif_log.py
new file mode 100644
index 0000000000000000000000000000000000000000..c738222981e59eb4fc528d0b74088bd3134024dd
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_sarif_log.py
@@ -0,0 +1,37 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Literal, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _external_properties,
+    _property_bag,
+    _run,
+)
+
+
+@dataclasses.dataclass
+class SarifLog(object):
+    """Static Analysis Results Format (SARIF) Version 2.1.0 JSON Schema: a standard format for the output of static analysis tools."""
+
+    runs: List[_run.Run] = dataclasses.field(metadata={"schema_property_name": "runs"})
+    version: Literal["2.1.0"] = dataclasses.field(
+        metadata={"schema_property_name": "version"}
+    )
+    schema_uri: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "$schema"}
+    )
+    inline_external_properties: Optional[
+        List[_external_properties.ExternalProperties]
+    ] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "inlineExternalProperties"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_special_locations.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_special_locations.py
new file mode 100644
index 0000000000000000000000000000000000000000..77bd3ccff59d47f815ac215b7b2f8f08f3521a7c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_special_locations.py
@@ -0,0 +1,27 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _artifact_location,
+    _property_bag,
+)
+
+
+@dataclasses.dataclass
+class SpecialLocations(object):
+    """Defines locations of special significance to SARIF consumers."""
+
+    display_base: Optional[_artifact_location.ArtifactLocation] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "displayBase"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_stack.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_stack.py
new file mode 100644
index 0000000000000000000000000000000000000000..cad11f5ec38c0e265953b0dafd8de86cbaaada34
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_stack.py
@@ -0,0 +1,31 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _message,
+    _property_bag,
+    _stack_frame,
+)
+
+
+@dataclasses.dataclass
+class Stack(object):
+    """A call stack that is relevant to a result."""
+
+    frames: List[_stack_frame.StackFrame] = dataclasses.field(
+        metadata={"schema_property_name": "frames"}
+    )
+    message: Optional[_message.Message] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "message"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_stack_frame.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_stack_frame.py
new file mode 100644
index 0000000000000000000000000000000000000000..509756f3374ab84c2277206cadc9d881d1fcc9ec
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_stack_frame.py
@@ -0,0 +1,33 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import _location, _property_bag
+
+
+@dataclasses.dataclass
+class StackFrame(object):
+    """A function call within a stack trace."""
+
+    location: Optional[_location.Location] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "location"}
+    )
+    module: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "module"}
+    )
+    parameters: Optional[List[str]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "parameters"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    thread_id: Optional[int] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "threadId"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_suppression.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_suppression.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1dcb014809d994a4777917e5e1764388b48dff5
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_suppression.py
@@ -0,0 +1,36 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Literal, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import _location, _property_bag
+
+
+@dataclasses.dataclass
+class Suppression(object):
+    """A suppression that is relevant to a result."""
+
+    kind: Literal["inSource", "external"] = dataclasses.field(
+        metadata={"schema_property_name": "kind"}
+    )
+    guid: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "guid"}
+    )
+    justification: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "justification"}
+    )
+    location: Optional[_location.Location] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "location"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    state: Optional[Literal["accepted", "underReview", "rejected"]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "state"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_thread_flow.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_thread_flow.py
new file mode 100644
index 0000000000000000000000000000000000000000..b107ee774c9241bb049f3068bf6dc500b21f1575
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_thread_flow.py
@@ -0,0 +1,40 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Any, List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _message,
+    _property_bag,
+    _thread_flow_location,
+)
+
+
+@dataclasses.dataclass
+class ThreadFlow(object):
+    """Describes a sequence of code locations that specify a path through a single thread of execution such as an operating system or fiber."""
+
+    locations: List[_thread_flow_location.ThreadFlowLocation] = dataclasses.field(
+        metadata={"schema_property_name": "locations"}
+    )
+    id: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "id"}
+    )
+    immutable_state: Any = dataclasses.field(
+        default=None, metadata={"schema_property_name": "immutableState"}
+    )
+    initial_state: Any = dataclasses.field(
+        default=None, metadata={"schema_property_name": "initialState"}
+    )
+    message: Optional[_message.Message] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "message"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_thread_flow_location.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_thread_flow_location.py
new file mode 100644
index 0000000000000000000000000000000000000000..43c67cf62ccf79a75d5227d0b2018bc29d416640
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_thread_flow_location.py
@@ -0,0 +1,67 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Any, List, Literal, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _location,
+    _property_bag,
+    _reporting_descriptor_reference,
+    _stack,
+    _web_request,
+    _web_response,
+)
+
+
+@dataclasses.dataclass
+class ThreadFlowLocation(object):
+    """A location visited by an analysis tool while simulating or monitoring the execution of a program."""
+
+    execution_order: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "executionOrder"}
+    )
+    execution_time_utc: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "executionTimeUtc"}
+    )
+    importance: Literal["important", "essential", "unimportant"] = dataclasses.field(
+        default="important", metadata={"schema_property_name": "importance"}
+    )
+    index: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "index"}
+    )
+    kinds: Optional[List[str]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "kinds"}
+    )
+    location: Optional[_location.Location] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "location"}
+    )
+    module: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "module"}
+    )
+    nesting_level: Optional[int] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "nestingLevel"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    stack: Optional[_stack.Stack] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "stack"}
+    )
+    state: Any = dataclasses.field(
+        default=None, metadata={"schema_property_name": "state"}
+    )
+    taxa: Optional[
+        List[_reporting_descriptor_reference.ReportingDescriptorReference]
+    ] = dataclasses.field(default=None, metadata={"schema_property_name": "taxa"})
+    web_request: Optional[_web_request.WebRequest] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "webRequest"}
+    )
+    web_response: Optional[_web_response.WebResponse] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "webResponse"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_tool.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6cfa87b05d7599cdc4cefe203180b38dc39c1dc
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_tool.py
@@ -0,0 +1,27 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import List, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import _property_bag, _tool_component
+
+
+@dataclasses.dataclass
+class Tool(object):
+    """The analysis tool that was run."""
+
+    driver: _tool_component.ToolComponent = dataclasses.field(
+        metadata={"schema_property_name": "driver"}
+    )
+    extensions: Optional[List[_tool_component.ToolComponent]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "extensions"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_tool_component.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_tool_component.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e2ddd20454870faa3657d7bb15934a19093aaee
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_tool_component.py
@@ -0,0 +1,123 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Any, List, Literal, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _artifact_location,
+    _multiformat_message_string,
+    _property_bag,
+    _reporting_descriptor,
+    _tool_component_reference,
+    _translation_metadata,
+)
+
+
+@dataclasses.dataclass
+class ToolComponent(object):
+    """A component, such as a plug-in or the driver, of the analysis tool that was run."""
+
+    name: str = dataclasses.field(metadata={"schema_property_name": "name"})
+    associated_component: Optional[_tool_component_reference.ToolComponentReference] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "associatedComponent"}
+        )
+    )
+    contents: List[Literal["localizedData", "nonLocalizedData"]] = dataclasses.field(
+        default_factory=lambda: ["localizedData", "nonLocalizedData"],
+        metadata={"schema_property_name": "contents"},
+    )
+    dotted_quad_file_version: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "dottedQuadFileVersion"}
+    )
+    download_uri: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "downloadUri"}
+    )
+    full_description: Optional[_multiformat_message_string.MultiformatMessageString] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "fullDescription"}
+        )
+    )
+    full_name: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "fullName"}
+    )
+    global_message_strings: Any = dataclasses.field(
+        default=None, metadata={"schema_property_name": "globalMessageStrings"}
+    )
+    guid: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "guid"}
+    )
+    information_uri: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "informationUri"}
+    )
+    is_comprehensive: Optional[bool] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "isComprehensive"}
+    )
+    language: str = dataclasses.field(
+        default="en-US", metadata={"schema_property_name": "language"}
+    )
+    localized_data_semantic_version: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "localizedDataSemanticVersion"}
+    )
+    locations: Optional[List[_artifact_location.ArtifactLocation]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "locations"}
+    )
+    minimum_required_localized_data_semantic_version: Optional[str] = dataclasses.field(
+        default=None,
+        metadata={
+            "schema_property_name": "minimumRequiredLocalizedDataSemanticVersion"
+        },
+    )
+    notifications: Optional[List[_reporting_descriptor.ReportingDescriptor]] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "notifications"}
+        )
+    )
+    organization: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "organization"}
+    )
+    product: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "product"}
+    )
+    product_suite: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "productSuite"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    release_date_utc: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "releaseDateUtc"}
+    )
+    rules: Optional[List[_reporting_descriptor.ReportingDescriptor]] = (
+        dataclasses.field(default=None, metadata={"schema_property_name": "rules"})
+    )
+    semantic_version: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "semanticVersion"}
+    )
+    short_description: Optional[
+        _multiformat_message_string.MultiformatMessageString
+    ] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "shortDescription"}
+    )
+    supported_taxonomies: Optional[
+        List[_tool_component_reference.ToolComponentReference]
+    ] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "supportedTaxonomies"}
+    )
+    taxa: Optional[List[_reporting_descriptor.ReportingDescriptor]] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "taxa"}
+    )
+    translation_metadata: Optional[_translation_metadata.TranslationMetadata] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "translationMetadata"}
+        )
+    )
+    version: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "version"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_tool_component_reference.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_tool_component_reference.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7929e12bc80de9d1f6db7d7810ff22ba5f51802
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_tool_component_reference.py
@@ -0,0 +1,30 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import _property_bag
+
+
+@dataclasses.dataclass
+class ToolComponentReference(object):
+    """Identifies a particular toolComponent object, either the driver or an extension."""
+
+    guid: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "guid"}
+    )
+    index: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "index"}
+    )
+    name: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "name"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_translation_metadata.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_translation_metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad6e1ae6ddb8a6d288df9b662ef5b8a7faede9c8
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_translation_metadata.py
@@ -0,0 +1,44 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _multiformat_message_string,
+    _property_bag,
+)
+
+
+@dataclasses.dataclass
+class TranslationMetadata(object):
+    """Provides additional metadata related to translation."""
+
+    name: str = dataclasses.field(metadata={"schema_property_name": "name"})
+    download_uri: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "downloadUri"}
+    )
+    full_description: Optional[_multiformat_message_string.MultiformatMessageString] = (
+        dataclasses.field(
+            default=None, metadata={"schema_property_name": "fullDescription"}
+        )
+    )
+    full_name: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "fullName"}
+    )
+    information_uri: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "informationUri"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    short_description: Optional[
+        _multiformat_message_string.MultiformatMessageString
+    ] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "shortDescription"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_version_control_details.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_version_control_details.py
new file mode 100644
index 0000000000000000000000000000000000000000..870222e7a5ddeee87945c570d70899461375bf9b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_version_control_details.py
@@ -0,0 +1,42 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _artifact_location,
+    _property_bag,
+)
+
+
+@dataclasses.dataclass
+class VersionControlDetails(object):
+    """Specifies the information necessary to retrieve a desired revision from a version control system."""
+
+    repository_uri: str = dataclasses.field(
+        metadata={"schema_property_name": "repositoryUri"}
+    )
+    as_of_time_utc: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "asOfTimeUtc"}
+    )
+    branch: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "branch"}
+    )
+    mapped_to: Optional[_artifact_location.ArtifactLocation] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "mappedTo"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    revision_id: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "revisionId"}
+    )
+    revision_tag: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "revisionTag"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_web_request.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_web_request.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8d4e34756a48e5f0cf81b80d6c1b8d18871ab09
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_web_request.py
@@ -0,0 +1,48 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Any, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _artifact_content,
+    _property_bag,
+)
+
+
+@dataclasses.dataclass
+class WebRequest(object):
+    """Describes an HTTP request."""
+
+    body: Optional[_artifact_content.ArtifactContent] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "body"}
+    )
+    headers: Any = dataclasses.field(
+        default=None, metadata={"schema_property_name": "headers"}
+    )
+    index: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "index"}
+    )
+    method: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "method"}
+    )
+    parameters: Any = dataclasses.field(
+        default=None, metadata={"schema_property_name": "parameters"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    protocol: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "protocol"}
+    )
+    target: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "target"}
+    )
+    version: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "version"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_web_response.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_web_response.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e86deedb3c6319448868a2a355d4f0721912635
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_web_response.py
@@ -0,0 +1,48 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Any, Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _artifact_content,
+    _property_bag,
+)
+
+
+@dataclasses.dataclass
+class WebResponse(object):
+    """Describes the response to an HTTP request."""
+
+    body: Optional[_artifact_content.ArtifactContent] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "body"}
+    )
+    headers: Any = dataclasses.field(
+        default=None, metadata={"schema_property_name": "headers"}
+    )
+    index: int = dataclasses.field(
+        default=-1, metadata={"schema_property_name": "index"}
+    )
+    no_response_received: Optional[bool] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "noResponseReceived"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+    protocol: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "protocol"}
+    )
+    reason_phrase: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "reasonPhrase"}
+    )
+    status_code: Optional[int] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "statusCode"}
+    )
+    version: Optional[str] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "version"}
+    )
+
+
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/version.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/version.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9b2c9d8fa0760103675c79e10c9548837d96683
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/version.py
@@ -0,0 +1,6 @@
+from typing import Final
+
+
+SARIF_VERSION: Final = "2.1.0"
+SARIF_SCHEMA_LINK: Final = "https://docs.oasis-open.org/sarif/sarif/v2.1.0/cs01/schemas/sarif-schema-2.1.0.json"
+# flake8: noqa
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5d49c38968f83695e015f7caa10fc4418b6d28e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/diagnostics/infra/utils.py
@@ -0,0 +1,69 @@
+from __future__ import annotations
+
+import functools
+import inspect
+import traceback
+from typing import Any, Callable, Mapping, Sequence
+
+from torch.onnx._internal.diagnostics.infra import _infra, formatter
+
+
+def python_frame(frame: traceback.FrameSummary) -> _infra.StackFrame:
+    """Returns a StackFrame for the given traceback.FrameSummary."""
+    snippet = frame.line
+
+    return _infra.StackFrame(
+        location=_infra.Location(
+            uri=frame.filename,
+            line=frame.lineno,
+            snippet=snippet,
+            function=frame.name,
+            message=snippet,
+        )
+    )
+
+
+def python_call_stack(frames_to_skip: int = 0, frames_to_log: int = 16) -> _infra.Stack:
+    """Returns the current Python call stack."""
+    if frames_to_skip < 0:
+        raise ValueError("frames_to_skip must be non-negative")
+    if frames_to_log < 0:
+        raise ValueError("frames_to_log must be non-negative")
+    frames_to_skip += 1  # Skip this function.
+    stack = _infra.Stack()
+    # Frames are returned in order of oldest to newest.
+    frames = traceback.extract_stack(limit=frames_to_skip + frames_to_log)
+    frames.reverse()
+    stack.frames = [python_frame(frame) for frame in frames[frames_to_skip:]]
+    stack.message = "Python call stack"
+    return stack
+
+
+@functools.lru_cache
+def _function_source_info(fn: Callable) -> tuple[Sequence[str], int, str | None]:
+    """Returns the source lines, line number, and source file path for the given function.
+
+    Essentially, inspect.getsourcelines() and inspect.getsourcefile() combined.
+    Caching is applied to reduce the performance impact of this function.
+    """
+    source_lines, lineno = inspect.getsourcelines(fn)
+    return source_lines, lineno, inspect.getsourcefile(fn)
+
+
+def function_location(fn: Callable) -> _infra.Location:
+    """Returns a Location for the given function."""
+    source_lines, lineno, uri = _function_source_info(fn)
+    snippet = source_lines[0].strip() if len(source_lines) > 0 else ""
+    return _infra.Location(
+        uri=uri,
+        line=lineno,
+        snippet=snippet,
+        message=formatter.display_name(fn),
+    )
+
+
+def function_state(
+    fn: Callable, args: tuple[Any, ...], kwargs: dict[str, Any]
+) -> Mapping[str, Any]:
+    bind = inspect.signature(fn).bind(*args, **kwargs)
+    return bind.arguments
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f8bc9517e61b4f48c6c943ab5d3dccdf413cee6
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/__init__.py
@@ -0,0 +1,17 @@
+__all__ = [
+    "ONNXRegistry",
+    "ONNXProgram",
+    "analyze",
+    "export",
+    "exported_program_to_ir",
+    "export_compat",
+    "testing",
+    "verification",
+]
+
+from . import _testing as testing, _verification as verification
+from ._analysis import analyze
+from ._compat import export_compat
+from ._core import export, exported_program_to_ir
+from ._onnx_program import ONNXProgram
+from ._registration import ONNXRegistry
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--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_analysis.py
@@ -0,0 +1,242 @@
+"""Compatibility analyzer for PyTorch models."""
+
+# mypy: allow-untyped-defs
+# flake8: noqa: B950 We do not need flake8 as it complains line length
+from __future__ import annotations
+
+import dataclasses
+import textwrap
+import traceback
+from collections import defaultdict
+from typing import TYPE_CHECKING
+
+import torch
+import torch._export.serde.schema
+from torch.export import graph_signature
+from torch.onnx._internal.exporter import _dispatching, _registration
+
+
+if TYPE_CHECKING:
+    import torch.fx
+
+
+@dataclasses.dataclass
+class ModelInfo:
+    """Information about the model."""
+
+    parameter_count: defaultdict[torch.dtype, int] = dataclasses.field(
+        default_factory=lambda: defaultdict(int)
+    )
+    buffer_count: defaultdict[torch.dtype, int] = dataclasses.field(
+        default_factory=lambda: defaultdict(int)
+    )
+    fx_node_count: int = 0
+    fx_node_op_count: defaultdict[str, int] = dataclasses.field(
+        default_factory=lambda: defaultdict(int)
+    )
+    fx_node_target_count: defaultdict[str, int] = dataclasses.field(
+        default_factory=lambda: defaultdict(int)
+    )
+    dispatch_failures: list[tuple[torch.fx.Node, str]] = dataclasses.field(
+        default_factory=list
+    )
+    inputs: dict[str, torch._export.serde.schema.TensorMeta] = dataclasses.field(
+        default_factory=dict
+    )
+    outputs: dict[str, torch._export.serde.schema.TensorMeta] = dataclasses.field(
+        default_factory=dict
+    )
+
+
+def _count_weights(
+    exported_program: torch.export.ExportedProgram,
+) -> tuple[defaultdict[torch.dtype, int], defaultdict[torch.dtype, int]]:
+    """Count the size of the parameters in the exported program."""
+
+    parameter_count: defaultdict[torch.dtype, int] = defaultdict(int)
+    buffer_count: defaultdict[torch.dtype, int] = defaultdict(int)
+    for parameter in exported_program.parameters():
+        dtype = parameter.dtype
+        parameter_count[dtype] += parameter.numel()
+
+    for buffer in exported_program.buffers():
+        dtype = buffer.dtype
+        buffer_count[dtype] += buffer.numel()
+
+    return parameter_count, buffer_count
+
+
+def _format_model_info(model_info: ModelInfo) -> str:
+    """Format the information about the model."""
+    lines = [
+        textwrap.dedent(
+            f"""\
+            PyTorch ONNX Conversion Analysis
+
+            ## Model Information
+
+            The model has {sum(model_info.parameter_count.values())} parameters and {sum(model_info.buffer_count.values())} buffers (non-trainable parameters).
+            Number of parameters per dtype:
+            ```python
+            {model_info.parameter_count}
+            ```
+            Number of buffers per dtype:
+            ```python
+            {model_info.buffer_count}
+            ```
+            """
+        ),
+        "Inputs:",
+        *[f"- `{name}`: `{meta}`" for name, meta in model_info.inputs.items()],
+        "",
+        "Outputs:",
+        *[f"- `{name}`: `{meta}`" for name, meta in model_info.outputs.items()],
+        "",
+        f"The FX graph has {model_info.fx_node_count} nodes in total. Number of FX nodes per op:",
+    ]
+    for op, count in model_info.fx_node_op_count.items():
+        lines.append(f"- `{op}`: {count}")
+    lines.append("\n")
+    lines.append("Of the call_function nodes, the counts of operators used are:\n")
+    sorted_targets = sorted(
+        model_info.fx_node_target_count.items(), key=lambda x: x[1], reverse=True
+    )
+    for target, count in sorted_targets:
+        lines.append(f"- `{target}`: {count}")
+
+    lines.append("")
+    lines.append("## ONNX Conversion Information")
+    lines.append("")
+
+    if model_info.dispatch_failures:
+        lines.append(
+            "The model contains operators the dispatcher could not find registered ONNX decompositions for. "
+            "This may be due to missing implementations, decompositions not registered "
+            "correctly, or a bug in the dispatcher."
+        )
+        lines.append("")
+        lines.append("Errors grouped by operator:\n")
+
+        target_to_nodes = defaultdict(list)
+        for node, _ in model_info.dispatch_failures:
+            target_to_nodes[str(node.target)].append(node)
+
+        target_to_messages = {}
+        for node, message in model_info.dispatch_failures:
+            if str(node.target) not in target_to_messages:
+                target_to_messages[str(node.target)] = message
+
+        for target, nodes in sorted(
+            target_to_nodes.items(), key=lambda x: x[0], reverse=True
+        ):
+            message = textwrap.indent(
+                f"{target_to_messages[target]}. Example node: `{nodes[0].format_node()}`. All nodes: `{nodes}`",
+                "    ",
+            )
+            lines.append(f"- `{target}`: {message}")
+    else:
+        lines.append("All operators in the model have registered ONNX decompositions.")
+
+    return "\n".join(lines)
+
+
+def _get_io_specs(exported_program: torch.export.ExportedProgram) -> tuple[dict, dict]:
+    """Get the input and output specs of the exported program."""
+
+    nodes: dict[str, torch.fx.Node] = {
+        node.name: node for node in exported_program.graph.nodes
+    }
+    user_inputs = [
+        spec
+        for spec in exported_program.graph_signature.input_specs
+        if spec.kind == graph_signature.InputKind.USER_INPUT
+    ]
+    user_outputs = [
+        spec
+        for spec in exported_program.graph_signature.output_specs
+        if spec.kind == graph_signature.OutputKind.USER_OUTPUT
+    ]
+    inputs: dict[str, torch._export.serde.schema.TensorMeta] = {}
+    outputs: dict[str, torch._export.serde.schema.TensorMeta] = {}
+    for spec in user_inputs:
+        if isinstance(spec.arg, graph_signature.ConstantArgument):
+            continue
+        name = spec.arg.name
+        # FIXME: tensor_meta is None sometimes when the exported program still knows the shape/type
+        inputs[name] = nodes[name].meta["tensor_meta"]
+    for spec in user_outputs:
+        if isinstance(spec.arg, graph_signature.ConstantArgument):
+            continue
+        name = spec.arg.name
+        outputs[name] = nodes[name].meta["tensor_meta"]
+    return inputs, outputs
+
+
+def _count_fx_targets(
+    exported_program: torch.export.ExportedProgram,
+) -> defaultdict[str, int]:
+    """Count the number of targets for each node in the exported program."""
+    fx_node_target_count: defaultdict[str, int] = defaultdict(int)
+    for node in exported_program.graph.nodes:
+        if node.op == "call_function":
+            fx_node_target_count[str(node.target)] += 1
+    return fx_node_target_count
+
+
+def analyze(
+    exported_program: torch.export.ExportedProgram,
+    registry: _registration.ONNXRegistry | None = None,
+    file=None,
+) -> None:
+    """Analyze the compatibility of the exported program."""
+    # Get basic information about the model
+    model_info = ModelInfo()
+    model_info.parameter_count, model_info.buffer_count = _count_weights(
+        exported_program
+    )
+    model_info.fx_node_count = len(exported_program.graph.nodes)
+    model_info.fx_node_target_count = _count_fx_targets(exported_program)
+    inputs, outputs = _get_io_specs(exported_program)
+    model_info.inputs = inputs
+    model_info.outputs = outputs
+
+    if registry is None:
+        registry = _registration.ONNXRegistry.from_torchlib()
+
+    # Try to find ops for every node in the graph
+    for node in exported_program.graph.nodes:
+        model_info.fx_node_op_count[node.op] += 1
+        if node.op == "call_function":
+            try:
+                onnx_function, message = _dispatching.dispatch(node, registry)
+            except Exception as e:
+                message = "Critical Error in dispatcher:\n"
+                formatted_exception = "\n".join(
+                    traceback.format_exception(type(e), e, e.__traceback__)
+                )
+                message += f"```pytb\n{formatted_exception}\n```\n"
+                onnx_function = None
+            if onnx_function is None:
+                model_info.dispatch_failures.append((node, message))
+
+    # Print the results
+    report = _format_model_info(model_info)
+    print(report, file=file, flush=True)
+
+
+def compare_ops(
+    program_a: torch.export.ExportedProgram, program_b: torch.export.ExportedProgram
+) -> tuple[set[str], set[str]]:
+    """Compare and get unique ops in two exported programs.
+
+    Args:
+        program_a: The first exported program.
+        program_b: The second exported program.
+
+    Returns:
+        A tuple of two sets, where the first set contains the unique ops in the first program
+        and the second set contains the unique ops in the second program.
+    """
+    program_a_ops = set(_count_fx_targets(program_a))
+    program_b_ops = set(_count_fx_targets(program_b))
+    return program_a_ops - program_b_ops, program_b_ops - program_a_ops
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_building.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_building.py
new file mode 100644
index 0000000000000000000000000000000000000000..7729aa000b281e99d6e7d6e791973868cb495d82
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_building.py
@@ -0,0 +1,674 @@
+"""NOTES:
+
+We need a typing module that will handling Python to ONNX type promotion for use.
+For example, if we have torch.ops.aten.add(Tensor, 1.0), we need to promote 1.0
+to the same type as Tensor. The same thing needs to work for
+torch.ops.aten.add(1.0, Tensor) as well, which means we need a mechanism to`
+"""
+
+# mypy: allow-untyped-defs
+# mypy: disable-error-code=union-attr
+from __future__ import annotations
+
+import copy
+import inspect
+import logging
+from typing import Any, Iterable, Mapping, Sequence, TYPE_CHECKING, Union
+
+import onnxscript
+from onnxscript import evaluator, ir
+from onnxscript.ir import convenience as ir_convenience
+
+import torch
+from torch.onnx._internal.exporter import _errors, _schemas, _tensors
+
+
+if TYPE_CHECKING:
+    import onnx
+
+
+logger = logging.getLogger(__name__)
+
+ValidAttributeType = Union[
+    ir.TensorProtocol, int, float, bool, str, Sequence[int], Sequence[float], None
+]
+
+AllowedArgType = Union[
+    ir.Value, Sequence[Union[ir.Value, ValidAttributeType]], ValidAttributeType
+]
+
+
+# Logic for adapting inputs from general Python or PyTorch inputs to ONNX ir.Value
+def _construct_named_inputs_and_attrs(
+    signature: _schemas.OpSignature,
+    args: Sequence[AllowedArgType],
+    kwargs: Mapping[str, AllowedArgType],
+) -> tuple[dict[str, AllowedArgType], dict[str, ValidAttributeType]]:
+    """Construct two mappings: name to inputs and named to attributes based on the signature and args/kwargs.
+
+    This function uses the OpSignature to determine which argument in args and kwargs corresponds to
+    which parameter in the signature. ONNX node inputs are stored in named_inputs, and attributes are
+    stored in named_attrs. If an _optional input_ is not provided, it is filled with None.
+
+    Args:
+        signature: The OpSignature for the node.
+        args: The positional arguments for the node.
+        kwargs: The keyword arguments for the node.
+
+    Returns:
+        A tuple of two mappings: named_inputs and named_attrs.
+
+    Raises:
+        ValueError: If a required parameter is not provided.
+    """
+    # 1. Construct the (named_inputs, named_attrs) mapping based on (args, kwargs) and the signature.
+    #   a. Loop over all parameters in the signature and args together
+    #   b. Depending on param.is_input, Record named_inputs[param.name] = arg or named_attrs[param.name] = arg
+    #   c. Handle kwargs as well
+    #   d. Fill in None if the input is not provided
+    named_inputs: dict[str, Any] = {}
+    named_attrs: dict[str, Any] = {}
+    reversed_args_stack = list(reversed(args))
+    for param in signature.params:
+        if isinstance(param, _schemas.Parameter):
+            # Handle inputs
+            if reversed_args_stack:
+                # First exhaust the positional arguments
+                if param.variadic:
+                    # Handle variadic arguments
+                    named_inputs[param.name] = tuple(args)
+                    reversed_args_stack.clear()
+                else:
+                    named_inputs[param.name] = reversed_args_stack.pop()  # type: ignore[assignment]
+            elif param.name in kwargs:
+                named_inputs[param.name] = kwargs[param.name]  # type: ignore[assignment]
+            elif param.required:
+                raise ValueError(
+                    f"Required parameter '{param.name}' is not provided. "
+                    f"Signature: {signature}. Args: {args}. Kwargs: {kwargs}."
+                )
+            else:
+                logger.debug(
+                    "Optional parameter '%s' is not provided. Added as None. Signature: %s",
+                    param.name,
+                    signature,
+                )
+                named_inputs[param.name] = None  # type: ignore[assignment]
+        else:
+            # Handle attributes
+            attribute: ValidAttributeType | ir.Attr
+            assert isinstance(
+                param, _schemas.AttributeParameter
+            ), f"Expected AttributeParameter, got {type(param)}"
+            if reversed_args_stack:
+                # First exhaust the positional arguments
+                attribute = reversed_args_stack.pop()  # type: ignore[assignment]
+            elif param.name in kwargs:
+                attribute = kwargs[param.name]  # type: ignore[assignment]
+            elif param.default is not None:
+                attribute = param.default
+            else:
+                attribute = None
+
+            if attribute is None:
+                if param.required:
+                    raise ValueError(
+                        f"Required attribute '{param.name}' is not provided. "
+                        f"Signature: {signature}. Args: {args}. Kwargs: {kwargs}."
+                    )
+                else:
+                    logger.debug(
+                        "Optional attribute '%s' is None. Dropped. Signature: %s",
+                        param.name,
+                        signature,
+                    )
+                    continue
+
+            if isinstance(attribute, ir.Attr):
+                # Turn the attribute from an default value into an actual parameter for the node
+                attr_copied = copy.copy(attribute)
+                # Make sure the name is the same as the parameter name and not the name of the default parameter
+                attr_copied.name = param.name
+                attribute = attr_copied
+
+            if isinstance(attribute, int) and param.type == ir.AttributeType.FLOAT:
+                # Convert the attribute to float if needed. This happens in PyTorch
+                # where an attribute marked as float can be passed as an int.
+                attribute = float(attribute)
+            named_attrs[param.name] = attribute
+    return named_inputs, named_attrs  # type: ignore[return-value]
+
+
+def _resolve_parameter_dtypes(
+    signature: _schemas.OpSignature, named_inputs: Mapping[str, AllowedArgType]
+) -> Mapping[_schemas.TypeConstraintParam, ir.TypeProtocol]:
+    """Determine which parameter takes which type.
+
+    Handle non-tensor input corner cases and type promotion.
+
+    Requires:
+        All ir.Value in name_inputs should have type set. Their type should be
+        compatible with the type_constraint of the corresponding parameter in the signature.
+
+    Args:
+        signature: The OpSignature for the node.
+        named_inputs: The mapping of parameter names to their arguments.
+
+    Returns:
+        A mapping of Constraint names to ir.TypeProtocol.
+    """
+    #   a. Create type_binding: dict[str, ir.TypeProtocol]
+    #   b. Iterate over all named_inputs
+    #   b0. Find the corresponding parameter in the signature
+    #   b1. If the argument is a Python constant, skip.
+    #   b2. If the argument is a ir.Value, Bind {constraint: arg.type}.
+    type_binding = {}
+    for name, arg in named_inputs.items():
+        param = signature.params_map[name]
+        assert isinstance(
+            param, _schemas.Parameter
+        ), f"Expected Parameter, got {type(param)}"
+        if isinstance(arg, (int, float, bool, str, Sequence, torch.Tensor)):
+            # Skip the Python constants because we do not know what dtype they should take yet
+            continue
+        elif isinstance(arg, ir.Value):
+            if arg.type is None:
+                # Skip the ir.Value if the type is not set
+                continue
+            # NOTE: We assume arg.type is compatible with the type_constraint
+            assert arg.type is not None, f"Expected type to be set for {arg}"
+            # TODO(justinchuby): Implement type promotion logic here.
+            type_binding[param.type_constraint] = arg.type
+    return type_binding
+
+
+def _determine_input_dtype(
+    param: _schemas.Parameter,
+    arg: AllowedArgType,
+    type_binding: Mapping[_schemas.TypeConstraintParam, ir.TypeProtocol],
+) -> ir.DataType:
+    """Determine the dtype of the input that is a mix of Python constants and ir.Value."""
+    if param.type_constraint in type_binding:
+        # A known dtype is available because it was resolved
+        return type_binding[param.type_constraint].dtype
+    if len(param.type_constraint.allowed_types) == 1:
+        # Only one type is allowed by the type constraint
+        return next(iter(param.type_constraint.allowed_types)).dtype
+
+    # No dtype information available. Infer from the Python constant or (in the Sequence case)
+    # from a mix of Python constants and ir.Value
+    if isinstance(arg, bool):
+        return ir.DataType.BOOL
+    if isinstance(arg, float):
+        return ir.DataType.FLOAT
+    if isinstance(arg, int):
+        return ir.DataType.INT64
+    if isinstance(arg, str):
+        return ir.DataType.STRING
+    if isinstance(arg, (ir.Tensor, ir.TensorProtocol)):
+        return arg.dtype
+    if arg is None:
+        return ir.DataType.UNDEFINED
+
+    # Handle sequences
+    if isinstance(arg, (tuple, list)):
+        if len(arg) == 0:
+            # Special case: Treat empty sequence as INT64 as they are typically used for shape
+            return ir.DataType.INT64
+
+        # Try to obtain the dtype from one of the values
+        for val in arg:
+            if isinstance(val, ir.Value) and val.dtype is not None:
+                return val.dtype
+
+        if any(isinstance(val, float) for val in arg):
+            # If any float is present, the dtype is float
+            return ir.DataType.FLOAT
+        elif any(isinstance(val, int) for val in arg):
+            # Otherwise if any int is present, the dtype is int
+            return ir.DataType.INT64
+
+    raise ValueError(
+        f"Could not determine the dtype for the input '{param.name}'. "
+        f"param={param}, arg={arg}, param_type_constraint={param.type_constraint}, "
+        f"type_binding={type_binding}"
+    )
+
+
+def _allowed_types_are_sequence_types(allowed_types: Iterable[ir.TypeProtocol]) -> bool:
+    """Check if all allowed types are Sequence types."""
+    return all(isinstance(t, ir.SequenceType) for t in allowed_types)
+
+
+def _get_or_create_constant(
+    constant_farm: dict[
+        tuple[
+            bool | int | float | str | tuple[int] | tuple[float],
+            ir.DataType,
+        ],
+        ir.Value,
+    ],
+    arg: bool
+    | int
+    | float
+    | str
+    | tuple[int]
+    | tuple[float]
+    | tuple[bool]
+    | list[int]
+    | list[float]
+    | list[bool],
+    dtype: ir.DataType,
+    opset: onnxscript.values.Opset,
+) -> ir.Value:
+    if isinstance(arg, list):
+        # Make the arg hashable
+        arg = tuple(arg)  # type: ignore[assignment]
+    constant_value = constant_farm.get((arg, dtype))  # type: ignore[arg-type]
+    if constant_value is None:
+        constant_tensor = ir.tensor(value=arg, dtype=dtype)  # type: ignore[arg-type]
+        constant_value = opset.Constant(value=constant_tensor)
+        constant_farm[(arg, dtype)] = constant_value  # type: ignore[arg-type,index]
+    return constant_value
+
+
+def _process_python_constants(
+    signature: _schemas.OpSignature,
+    named_inputs: dict[str, AllowedArgType],
+    type_binding: Mapping[_schemas.TypeConstraintParam, ir.TypeProtocol],
+    constant_farm: dict[
+        tuple[
+            bool | int | float | str | tuple[int] | tuple[float],
+            ir.DataType,
+        ],
+        ir.Value,
+    ],
+    opset: onnxscript.values.Opset,
+) -> dict[str, ir.Value | None]:
+    """Convert Python constants to Constant nodes and list to Sequence nodes based on the dtype information.
+
+    The added constants will be replacing values in named_inputs in place.
+
+    Args:
+        signature: The OpSignature for the node.
+        named_inputs: The mapping of parameter names to their arguments.
+        type_binding: A mapping of Constraint names to ir.DataType.
+        constant_farm: A dictionary of {(py_value, ir.DataType): ir.Value} to store the deduplicated constants.
+        opset: The Opset to use for creating Constant nodes.
+
+    Returns:
+        A mapping of parameter names to Python constants converted to constant Nodes.
+    """
+    # 3. Convert Python constants to Constant nodes based on the dtype information;
+    #    construct sequences
+    #   a. Iterate over all parameters in the signature the second time
+    #   b. If the parameter is in to_resolve_type:
+    #       - If param.constraint in type_binding,
+    #         Get the constant from constant_farm (deduplicated);
+    #            otherwise set named_inputs[param.name] = Constant(value, dtype=type_binding[param.constraint])
+    #       - Otherwise, set named_inputs[param.name] = Constant(value)
+    for name, arg in named_inputs.items():
+        param = signature.params_map[name]
+        assert isinstance(
+            param, _schemas.Parameter
+        ), f"Expected Parameter, got {type(param)}"
+
+        if isinstance(arg, ir.Value):
+            # TODO(justinchuby): Cast the ir.Value here if needed
+            continue
+
+        if (
+            isinstance(arg, Sequence)
+            and len(arg) > 0
+            and any(isinstance(val, ir.Value) for val in arg)
+        ):
+            # Skip the sequence of ir.Value. This is a variadic input or a Sequence input
+            # It will be handled by _process_python_sequences
+            continue
+        if param.variadic:
+            # Handled by _process_python_sequences
+            continue
+        if _allowed_types_are_sequence_types(param.type_constraint.allowed_types):
+            # Handled by _process_python_sequences
+            continue
+
+        dtype = _determine_input_dtype(param, arg, type_binding)
+
+        if arg is None:
+            constant_value = None
+        elif isinstance(arg, (ir.Tensor, ir.TensorProtocol)):
+            constant_value = opset.Constant(value=arg)
+        else:
+            # Deduplicate the constants
+            constant_value = _get_or_create_constant(constant_farm, arg, dtype, opset)  # type: ignore[arg-type]
+
+        named_inputs[param.name] = constant_value
+    return named_inputs  # type: ignore[return-value]
+
+
+def _process_python_sequences(
+    signature: _schemas.OpSignature,
+    named_inputs: dict[str, AllowedArgType],
+    type_binding: Mapping[_schemas.TypeConstraintParam, ir.TypeProtocol],
+    constant_farm: dict[
+        tuple[
+            bool | int | float | str | ir.TensorProtocol | tuple[int] | tuple[float],
+            ir.DataType,
+        ],
+        ir.Value,
+    ],
+    opset: onnxscript.values.Opset,
+):
+    """Handle three types of sequences.
+
+    1. Variadic inputs
+    2. Sequence input of ir.Value,
+    3. Sequence of Python constants that contains ir.Value
+    """
+    for name, arg in named_inputs.items():
+        param = signature.params_map[name]
+        assert isinstance(
+            param, _schemas.Parameter
+        ), f"Expected Parameter, got {type(param)}"
+
+        if not isinstance(arg, (tuple, list)):
+            continue
+
+        if len(arg) == 0:
+            # Skip empty sequences
+            continue
+
+        # 1. Sequence input of ir.Value
+        if _allowed_types_are_sequence_types(param.type_constraint.allowed_types):
+            # Turn the list into a Sequence node
+            # Constant op creation will be handled by the variadic case below when calling
+            # the SequenceConstruct op.
+            named_inputs[name] = opset.SequenceConstruct(*arg)
+            continue
+
+        # 2. Variadic inputs
+        # NOTE: Variadic operators like Max can be called with mixed ir.Value and Python constants
+        # like `Max(0, ir.Value())`
+        # We need to convert the Python constants to Constant nodes
+        if param.variadic:
+            if all(isinstance(val, ir.Value) for val in arg):
+                # Skip the variadic input if all values are ir.Value
+                continue
+
+            dtype = _determine_input_dtype(param, arg, type_binding)
+            new_args = []
+            for val in arg:
+                if isinstance(val, ir.Value):
+                    new_args.append(val)
+                else:
+                    constant_tensor = ir.tensor(value=val, dtype=dtype)  # type: ignore[arg-type]
+                    constant_value = opset.Constant(value=constant_tensor)
+                    new_args.append(constant_value)
+            named_inputs[name] = new_args
+            continue
+        else:
+            # 3. Concat the list as a single input
+            # E.g. [Value, 42] should be converted to op.Concat(Value, Constant(42))
+            # when the expected input type is INT64
+            # We assume this only happens for 1D cases
+            if all(isinstance(val, ir.Value) for val in arg):
+                named_inputs[name] = opset.Concat(*arg)
+                continue
+
+            dtype = _determine_input_dtype(param, arg, type_binding)
+            new_args = []
+            for val in arg:
+                if isinstance(val, ir.Value):
+                    new_args.append(val)
+                elif val is None:
+                    # Skip None values
+                    continue
+                elif isinstance(arg, (ir.Tensor, ir.TensorProtocol)):
+                    new_args.append(opset.Constant(value=val))
+                else:
+                    # Turn the Python constant into 1D tensor for the constant
+                    assert isinstance(
+                        val, (bool, int, float)
+                    ), f"Expected int or float, got {type(val)}"
+                    new_args.append(
+                        _get_or_create_constant(constant_farm, [arg], dtype, opset)  # type: ignore[arg-type]
+                    )
+            named_inputs[name] = opset.Concat(*new_args)
+            continue
+    return named_inputs
+
+
+def _construct_node(
+    signature: _schemas.OpSignature,
+    named_inputs: Mapping[str, ir.Value | None],
+    named_attrs: Mapping[str, ValidAttributeType],
+    opset: onnxscript.values.Opset,
+) -> ir.Node:
+    """Construct the node with the inputs and attributes.
+
+    Variadic inputs are flattened.
+
+    Args:
+        signature: The OpSignature for the node.
+        named_inputs: The mapping of parameter names to their arguments. When we
+            do not have the schema of an operator, we do not know the names of
+            the inputs, in which case the names can be anything because they
+            are not used in this function. The data structure is passed in for
+            consistency with the other functions.
+        named_attrs: The mapping of attribute names to their values.
+    """
+    inputs: list[ir.Value | None] = []
+    # Flatten variadic inputs
+    for value in named_inputs.values():
+        if isinstance(value, Sequence):
+            inputs.extend(value)
+        else:
+            inputs.append(value)
+
+    # If final inputs are None, strip them from the node inputs
+    for input in reversed(inputs):
+        if input is not None:
+            break
+        inputs.pop()
+
+    # Construct and filter out None attributes
+    attributes = [
+        attr
+        for attr in ir_convenience.convert_attributes(named_attrs)
+        if attr.value is not None
+    ]
+    outputs = [_tensors.SymbolicTensor(opset) for _ in signature.outputs]
+    return ir.Node(
+        signature.domain,
+        signature.name,
+        inputs=inputs,
+        attributes=attributes,
+        outputs=outputs,
+    )
+
+
+class OpRecorder(evaluator.Evaluator):
+    """An onnxscript Evaluator that captures the graph into torchscript."""
+
+    def __init__(
+        self, opset: onnxscript.values.Opset, constant_farm: dict[Any, ir.Value]
+    ):
+        self.nodes: list[ir.Node] = []
+        self.opset = opset
+        self.functions: dict[ir.OperatorIdentifier, onnxscript.OnnxFunction] = {}
+        self.constant_farm = constant_farm
+
+    def _call_op(
+        self,
+        op_signature: _schemas.OpSignature,
+        named_inputs: dict[str, AllowedArgType],
+        named_attrs: dict[str, ValidAttributeType],
+    ) -> Sequence[_tensors.SymbolicTensor]:
+        """Record nodes for the given opschema and arguments.
+
+        Args:
+            op_signature: The OpSchema containing the node signature.
+            named_inputs: The mapping of parameter names to their arguments.
+            named_attrs: The mapping of attribute names to their values.
+        """
+        type_binding = _resolve_parameter_dtypes(op_signature, named_inputs)
+        try:
+            converted_named_inputs = _process_python_constants(
+                op_signature, named_inputs, type_binding, self.constant_farm, self.opset
+            )
+            converted_named_inputs = _process_python_sequences(
+                op_signature,
+                converted_named_inputs,  # type: ignore[arg-type]
+                type_binding,
+                self.constant_farm,
+                self.opset,
+            )
+
+        except Exception as e:
+            raise _errors.GraphConstructionError(
+                f"Error processing Python constants for operator '{op_signature.domain}::{op_signature.name}'. "
+                f"named_inputs={named_inputs}, named_attrs={named_attrs}, opset={self.opset}, op_signature={op_signature}."
+            ) from e
+
+        try:
+            self.nodes.append(
+                node := _construct_node(
+                    op_signature, converted_named_inputs, named_attrs, self.opset
+                )
+            )
+        except Exception as e:
+            raise _errors.GraphConstructionError(
+                f"Error constructing node for operator '{op_signature.domain}::{op_signature.name}'. "
+                f"named_inputs={named_inputs}, converted_named_inputs={converted_named_inputs}, "
+                f"named_attrs={named_attrs}, opset={self.opset}, op_signature={op_signature}."
+            ) from e
+        return node.outputs  # type: ignore[return-value]
+
+    def eval(
+        self,
+        schema: onnx.defs.OpSchema,
+        args: Sequence[AllowedArgType],  # type: ignore[override]
+        kwargs: Mapping[str, AllowedArgType],
+    ) -> _tensors.SymbolicTensor | Sequence[_tensors.SymbolicTensor]:
+        try:
+            op_signature = _schemas.OpSignature.from_opschema(schema)
+            named_inputs, named_attrs = _construct_named_inputs_and_attrs(
+                op_signature, args, kwargs
+            )
+            # TODO(justinchuby): Handle cast
+            if schema.name == "CastLike":
+                assert len(named_inputs) == 2
+                # Skip CastLike if the input and output types are the same
+                src_input = named_inputs["input"]
+                target_type = named_inputs["target_type"]
+
+                if (
+                    isinstance(src_input, ir.Value)
+                    and isinstance(target_type, ir.Value)
+                    and src_input.dtype is not None
+                    and target_type.dtype is not None
+                ):
+                    # dtypes are available
+                    if src_input.dtype == target_type.dtype:
+                        # Same type. No cast needed
+                        return src_input  # type: ignore[return-value]
+                    else:
+                        # Create a Cast node
+                        return self.opset.Cast(src_input, to=target_type.dtype)  # type: ignore[union-attr,return-value]
+
+            outputs = self._call_op(op_signature, named_inputs, named_attrs)
+            if len(outputs) == 1:
+                return outputs[0]
+            return outputs
+        except Exception as e:
+            raise _errors.GraphConstructionError(
+                f"Error calling operator '{schema.name}' with args {args} and kwargs {kwargs}."
+            ) from e
+
+    def eval_function(  # type: ignore[override]
+        self,
+        function: onnxscript.OnnxFunction,
+        args: Sequence[AllowedArgType],
+        kwargs: Mapping[str, AllowedArgType],
+    ) -> _tensors.SymbolicTensor | Sequence[_tensors.SymbolicTensor] | bool | int:
+        try:
+            # Special cases for handling IsScalar and Rank
+            if function.name == "IsScalar":
+                if len(args) != 1:
+                    raise TypeError(
+                        f"Expected 1 positional argument for function '{function}', got {len(args)}."
+                    )
+                if isinstance(args[0], _tensors.SymbolicTensor):
+                    if args[0].rank is not None:
+                        return args[0].rank == 0
+                    else:
+                        # Fall to call add_function_call
+                        pass
+                elif isinstance(args[0], Sequence):
+                    return False
+                else:
+                    # Python constants are scalars
+                    return True
+            if function.name == "Rank":
+                if len(args) != 1:
+                    raise TypeError(
+                        f"Expected 1 positional argument for function '{function}', got {len(args)}."
+                    )
+                if isinstance(args[0], _tensors.SymbolicTensor):
+                    if args[0].rank is not None:
+                        return args[0].rank
+                    else:
+                        # Fall to call add_function_call
+                        pass
+                elif isinstance(args[0], Sequence):
+                    if all(isinstance(arg, (int, float)) for arg in args[0]):
+                        return 1
+                    else:
+                        # Fall to call add_function_call
+                        pass
+                else:
+                    # Python constants are scalars
+                    return 0
+
+            # NOTE: signature is written to function in the registration process
+            # TODO: Upstream signature to ONNX Function
+            if hasattr(function, "signature"):
+                op_signature = function.signature
+            else:
+                op_signature = _schemas.OpSignature.from_function(
+                    function, function.function_ir.domain, function.name
+                )
+
+            named_inputs, named_attrs = _construct_named_inputs_and_attrs(
+                op_signature, args, kwargs
+            )
+
+            # NOTE: We need to call traceable functions after the _construct_named_inputs_and_attrs
+            # call because it will filter out the unexpected kwargs for us.
+            if function.traceable:
+                # Trace the function call instead of adding the function as a node
+                # Turn the ir.Attr objects into Python constants first
+                named_attrs = {
+                    name: attr.value if isinstance(attr, ir.Attr) else attr
+                    for name, attr in named_attrs.items()
+                }
+                return function.function(**named_inputs, **named_attrs)
+
+            outputs = self._call_op(op_signature, named_inputs, named_attrs)
+
+            self.functions[(function.function_ir.domain, function.name, "")] = function
+            if len(outputs) == 1:
+                return outputs[0]
+            return outputs
+        except Exception as e:
+            try:
+                source_file = inspect.getsourcefile(function.function)
+                _, lineno = inspect.getsourcelines(function.function)
+            except Exception:
+                source_file = lineno = None
+            raise _errors.GraphConstructionError(
+                f"Error calling function '{function.name}' with args {args} and kwargs {kwargs}."
+                + f" The function is defined at '{source_file}:{lineno}'."
+                if source_file
+                else ""
+            ) from e
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_capture_strategies.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_capture_strategies.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cec92854ea8584e10d3ff83b9c4c7a66c8f98da
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_capture_strategies.py
@@ -0,0 +1,361 @@
+"""Strategies for capturing ExportedPrograms."""
+
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import abc
+import dataclasses
+import datetime
+import pathlib
+from typing import Any, Callable, TYPE_CHECKING
+
+import torch
+from torch._export import converter as _torchscript_converter
+from torch.utils import _pytree
+
+
+if TYPE_CHECKING:
+    import os
+
+
+def _verbose_printer(verbose: bool | None) -> Callable[..., None]:
+    """Prints messages based on `verbose`."""
+    if verbose is False:
+        return lambda *_, **__: None
+    return lambda *args, **kwargs: print("[torch.onnx]", *args, **kwargs)
+
+
+def _take_first_line(text: str) -> str:
+    """Take the first line of a text."""
+    lines = text.split("\n", maxsplit=1)
+    first_line = lines[0]
+    if len(lines) > 1:
+        first_line += "[...]"
+    return first_line
+
+
+@dataclasses.dataclass
+class Result:
+    exported_program: torch.export.ExportedProgram | None
+    strategy: str
+    exception: Exception | None = None
+
+    @property
+    def success(self) -> bool:
+        return self.exported_program is not None
+
+
+class CaptureStrategy(abc.ABC):
+    """Strategy for capturing a module as ExportedProgram.
+
+    To use a strategy, create an instance and call it with the model, args, kwargs, and dynamic_shapes.
+    Example::
+
+        strategy = TorchExportStrategy(verbose=True)
+        result = strategy(model, args, kwargs, dynamic_shapes)
+    """
+
+    def __init__(
+        self,
+        *,
+        verbose: bool = False,
+        dump: bool = False,
+        artifacts_dir: str | os.PathLike = ".",
+        timestamp: str | None = None,
+    ):
+        """Initialize the strategy.
+
+        Args:
+            verbose: Whether to print verbose messages.
+            dump: Whether to dump the intermediate artifacts to a file.
+        """
+        self._verbose_print = _verbose_printer(verbose)
+        self._dump = dump
+        self._artifacts_dir = pathlib.Path(artifacts_dir)
+        self._timestamp = timestamp or datetime.datetime.now().strftime(
+            "%Y-%m-%d_%H-%M-%S-%f"
+        )
+
+    def __call__(
+        self,
+        model: torch.nn.Module | torch.jit.ScriptFunction,
+        args: tuple[Any, ...],
+        kwargs: dict[str, Any] | None,
+        dynamic_shapes,
+    ) -> Result:
+        self._enter(model)
+        if kwargs is None:
+            kwargs = {}
+        try:
+            exported_program = self._capture(model, args, kwargs, dynamic_shapes)
+        except Exception as e:
+            self._failure(model, e)
+            return Result(
+                exported_program=None,
+                strategy=self.__class__.__name__,
+                exception=e,
+            )
+        self._success(model)
+        return Result(exported_program, strategy=self.__call__.__name__)
+
+    @abc.abstractmethod
+    def _capture(
+        self, model, args, kwargs, dynamic_shapes
+    ) -> torch.export.ExportedProgram:
+        raise NotImplementedError
+
+    def _enter(self, model: torch.nn.Module | torch.jit.ScriptFunction) -> None:
+        return
+
+    def _success(self, model: torch.nn.Module | torch.jit.ScriptFunction) -> None:
+        return
+
+    def _failure(
+        self, model: torch.nn.Module | torch.jit.ScriptFunction, e: Exception
+    ) -> None:
+        return
+
+
+class TorchExportStrategy(CaptureStrategy):
+    def _capture(
+        self, model, args, kwargs, dynamic_shapes
+    ) -> torch.export.ExportedProgram:
+        try:
+            return torch.export.export(
+                model, args, kwargs=kwargs, dynamic_shapes=dynamic_shapes
+            )
+        except torch._dynamo.exc.UserError as exc:
+            # Refine the dynamic shapes based on the suggested fixes.
+            try:
+                new_shapes = torch.export.dynamic_shapes.refine_dynamic_shapes_from_suggested_fixes(
+                    exc.msg, dynamic_shapes
+                )
+            except Exception:
+                # If the dynamic shapes cannot be refined, re-raise the exception.
+                raise exc from None
+            return torch.export.export(
+                model, args, kwargs=kwargs, dynamic_shapes=new_shapes
+            )
+
+    def _enter(self, model) -> None:
+        model_repr = _take_first_line(repr(model))
+        self._verbose_print(
+            f"Obtain model graph for `{model_repr}` with `torch.export.export`..."
+        )
+
+    def _success(self, model) -> None:
+        model_repr = _take_first_line(repr(model))
+        self._verbose_print(
+            f"Obtain model graph for `{model_repr}` with `torch.export.export`... ✅"
+        )
+
+    def _failure(self, model, e) -> None:
+        del e  # Unused
+        model_repr = _take_first_line(repr(model))
+        self._verbose_print(
+            f"Obtain model graph for `{model_repr}` with `torch.export.export`... ❌"
+        )
+
+
+class TorchExportNonStrictStrategy(CaptureStrategy):
+    def _capture(
+        self, model, args, kwargs, dynamic_shapes
+    ) -> torch.export.ExportedProgram:
+        try:
+            return torch.export.export(
+                model, args, kwargs=kwargs, dynamic_shapes=dynamic_shapes, strict=False
+            )
+        except torch._dynamo.exc.UserError as exc:
+            # Refine the dynamic shapes based on the suggested fixes.
+            try:
+                new_shapes = torch.export.dynamic_shapes.refine_dynamic_shapes_from_suggested_fixes(
+                    exc.msg, dynamic_shapes
+                )
+            except Exception:
+                # If the dynamic shapes cannot be refined, re-raise the exception.
+                raise exc from None
+            return torch.export.export(
+                model, args, kwargs=kwargs, dynamic_shapes=new_shapes, strict=False
+            )
+
+    def _enter(self, model) -> None:
+        model_repr = _take_first_line(repr(model))
+        self._verbose_print(
+            f"Obtain model graph for `{model_repr}` with `torch.export.export(..., strict=False)`..."
+        )
+
+    def _success(self, model) -> None:
+        model_repr = _take_first_line(repr(model))
+        self._verbose_print(
+            f"Obtain model graph for `{model_repr}` with `torch.export.export(..., strict=False)`... ✅"
+        )
+
+    def _failure(self, model, e) -> None:
+        del e  # Unused
+        model_repr = _take_first_line(repr(model))
+        self._verbose_print(
+            f"Obtain model graph for `{model_repr}` with `torch.export.export(..., strict=False)`... ❌"
+        )
+
+
+class JitTraceConvertStrategy(CaptureStrategy):
+    def _capture(
+        self, model, args, kwargs, dynamic_shapes
+    ) -> torch.export.ExportedProgram:
+        del dynamic_shapes  # Unused
+
+        flattened_args, spec = _pytree.tree_flatten((args, kwargs))
+        flattened_args = tuple(flattened_args)
+
+        # Since torch.jit.trace only accepts Tensors as inputs, we filter
+        # out non-Tensor arguments and reconstruct the arguments after entering
+        # the WrappedModel.
+        tensor_placeholder = object()
+        non_tensor_args = [
+            arg if not isinstance(arg, torch.Tensor) else tensor_placeholder
+            for arg in flattened_args
+        ]
+        tensor_args = tuple(
+            arg for arg in flattened_args if isinstance(arg, torch.Tensor)
+        )
+
+        class WrappedModel(torch.nn.Module):
+            """Wrap the model so that it takes flattened arguments."""
+
+            def __init__(self, m):
+                super().__init__()
+                self.model = m
+
+            def forward(self, *_args):
+                # Take the non-Tensor arguments list as a starting point and
+                # replace the tensor_placeholder with the actual tensor arguments
+                # from _args.
+                reconstructed_flattened_args = non_tensor_args.copy()
+                _args_iter = iter(_args)
+                for i, arg in enumerate(reconstructed_flattened_args):
+                    if arg is tensor_placeholder:
+                        reconstructed_flattened_args[i] = next(_args_iter)
+                # Unflatten the arguments and kwargs to pass to the model.
+                unflattened_args, unflattened_kwargs = _pytree.tree_unflatten(
+                    reconstructed_flattened_args, spec
+                )
+                results = self.model(*unflattened_args, **unflattened_kwargs)
+                if not isinstance(results, tuple):
+                    results = (results,)
+                flattened_results, _ = _pytree.tree_flatten(results)
+                if len(flattened_results) == 1:
+                    return flattened_results[0]
+                return tuple(flattened_results)
+
+        jit_model = torch.jit.trace(
+            WrappedModel(model),
+            example_inputs=tensor_args,
+            check_trace=False,
+            strict=False,
+        )
+        if self._dump:
+            program_path = self._artifacts_dir / f"onnx_export_{self._timestamp}.pt"
+            try:
+                torch.jit.save(jit_model, program_path)
+            except Exception as e:
+                self._verbose_print(
+                    f"Failed to save Torch Script model due to an error: {e}"
+                )
+            else:
+                self._verbose_print(
+                    f"Torch Script model has been saved to '{program_path}'."
+                )
+        return _torchscript_converter.TS2EPConverter(
+            jit_model, flattened_args
+        ).convert()
+
+    def _enter(self, model) -> None:
+        model_repr = _take_first_line(repr(model))
+        self._verbose_print(
+            f"Obtain model graph for `{model_repr}` with Torch Script..."
+        )
+
+    def _success(self, model) -> None:
+        model_repr = _take_first_line(repr(model))
+        self._verbose_print(
+            f"Obtain model graph for `{model_repr}` with Torch Script... ✅"
+        )
+
+    def _failure(self, model, e) -> None:
+        del e  # Unused
+        model_repr = _take_first_line(repr(model))
+        self._verbose_print(
+            f"Obtain model graph for `{model_repr}` with Torch Script... ❌"
+        )
+
+
+class LegacyDynamoStrategy(CaptureStrategy):
+    """Strategy implemented by the ONNX team using internal dynamo APIs and custom fx passes."""
+
+    def _capture(
+        self, model, args, kwargs, dynamic_shapes
+    ) -> torch.export.ExportedProgram:
+        # NOTE: Import here to prevent circular dependency
+        from torch.onnx._internal.fx import diagnostics, passes
+
+        graph_module, _ = torch._dynamo.export(
+            model,
+            tracing_mode="symbolic",
+            dynamic_shapes=dynamic_shapes,
+        )(
+            *args,
+            **kwargs,
+        )
+        torch._dynamo.reset()
+
+        diagnostic_context = diagnostics.DiagnosticContext(
+            "torch.onnx.export",
+            torch.__version__,
+        )
+
+        flattened_args, _ = _pytree.tree_flatten((args, kwargs))
+        flattened_args = tuple(flattened_args)
+
+        # ONNX does not support views and mutations.
+        # Functionalize to get a semantically equivalent graph without mutations.
+        graph_module = passes.Functionalize(
+            diagnostic_context,
+            graph_module,
+            enable_dynamic_axes=bool(dynamic_shapes),
+        ).run(*flattened_args)
+
+        # Input mutations are detected and distilled after `Functionalize` pass.
+        # Remove them since ONNX inference does not need them.
+        graph_module = passes.RemoveInputMutation(diagnostic_context, graph_module).run(
+            *flattened_args
+        )
+
+        # Use torch.export to recapture the GraphModule into an ExportedProgram.
+        return torch.export.export(graph_module, flattened_args)
+
+    def _enter(self, model) -> None:
+        model_repr = _take_first_line(repr(model))
+        self._verbose_print(
+            f"Obtain model graph for `{model_repr}` with internal Dynamo apis..."
+        )
+
+    def _success(self, model) -> None:
+        model_repr = _take_first_line(repr(model))
+        self._verbose_print(
+            f"Obtain model graph for `{model_repr}` with internal Dynamo apis... ✅"
+        )
+
+    def _failure(self, model, e) -> None:
+        del e  # Unused
+        model_repr = _take_first_line(repr(model))
+        self._verbose_print(
+            f"Obtain model graph for `{model_repr}` with internal Dynamo apis... ❌"
+        )
+
+
+CAPTURE_STRATEGIES = (
+    TorchExportStrategy,
+    TorchExportNonStrictStrategy,
+    JitTraceConvertStrategy,
+    LegacyDynamoStrategy,
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_compat.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_compat.py
new file mode 100644
index 0000000000000000000000000000000000000000..3fddef36b8b428f671dc1e2c88932097378fa661
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_compat.py
@@ -0,0 +1,216 @@
+"""Compatibility functions for the torch.onnx.export API."""
+
+# mypy: allow-untyped-defs
+# mypy: disable-error-code=attr-defined
+from __future__ import annotations
+
+import inspect
+import logging
+from typing import Any, Mapping, Sequence, TYPE_CHECKING
+
+import torch
+from torch.onnx._internal._lazy_import import onnxscript_apis, onnxscript_ir as ir
+from torch.onnx._internal.exporter import _core, _onnx_program
+
+
+if TYPE_CHECKING:
+    import os
+
+logger = logging.getLogger(__name__)
+
+
+def _signature(model) -> inspect.Signature:
+    should_be_callable = getattr(model, "forward", model)
+    if callable(should_be_callable):
+        return inspect.signature(should_be_callable)
+    raise ValueError("model has no forward method and is not callable")
+
+
+def _from_dynamic_axes_to_dynamic_shapes(
+    model,
+    *,
+    dynamic_axes=None,
+    output_names: set[str],
+    input_names: Sequence[str] | None = None,
+) -> dict[str, Any] | None:
+    """
+
+    dynamic_axes examples:
+    (1) dynamic_axes = {"x": {0: "my_custom_axis_name_1"}, "y": {1: "my_custom_axis_name_2"}}
+    (2) dynamic_axes = {"x": [0], "y": [1]}
+
+    these will be converted to dynamic_shapes respectively:
+    (1) dynamic_shapes = {"x": {0: Dim("my_custom_axis_name_1")}, "y": {1: Dim("my_custom_axis_name_2")}}
+    (2) dynamic_shapes = {"x": {0: Dim("x_dim_0")}, "y": {1: Dim("y_dim_1")}}  # auto-generated dim names
+
+    """
+    # https://github.com/pytorch/pytorch/pull/128371
+    # 1. The function does not need to provide dynamic_shapes to torch.export.export
+    if dynamic_axes is None:
+        return None
+
+    if input_names is None:
+        input_names = []
+
+    sig = _signature(model)
+    if len(input_names) > len(sig.parameters):
+        raise ValueError(
+            f"Number of input names ({len(input_names)}) should not be greater than "
+            f"the number of model inputs ({len(sig.parameters)})"
+        )
+    input_names_to_model_inputs = {}
+    for idx, param_name in enumerate(sig.parameters):
+        if idx < len(input_names):
+            input_names_to_model_inputs[input_names[idx]] = param_name
+        else:
+            input_names_to_model_inputs[param_name] = param_name
+
+    # NOTE: torch.export.export does not support input names assignment,
+    # so we need to map input names to model inputs to create dynamic_shapes
+    # for the exported program
+    dynamic_shapes_to_exported_program = {}
+    for input_name, axes in dynamic_axes.items():
+        if input_name in output_names:
+            # User specified an output name as a dynamic axis, so we skip it
+            continue
+        # input_name can be either from input_names or from the model inputs
+        if input_name not in input_names_to_model_inputs:
+            raise ValueError(
+                f"dynamic axis: {input_name} is not found in the input names: {input_names}"
+            )
+        model_input_name = input_names_to_model_inputs[input_name]
+        if isinstance(axes, dict):
+            dynamic_shapes_to_exported_program[model_input_name] = {
+                k: torch.export.Dim(v) for k, v in axes.items()
+            }
+        elif isinstance(axes, list):
+            dynamic_shapes_to_exported_program[model_input_name] = {
+                k: torch.export.Dim(f"{model_input_name}_dim_{k}") for k in axes
+            }
+        else:
+            raise TypeError(
+                f"dynamic_axes value must be either a dict or a list, but got {type(axes)}"
+            )
+    # torch.export.export needs static dim to present in dynamic_shapes
+    # for all input tensors, so we need to add them with None
+    for input_name in sig.parameters:
+        if input_name not in dynamic_shapes_to_exported_program:
+            dynamic_shapes_to_exported_program[input_name] = None  # type: ignore[assignment]
+
+    return dynamic_shapes_to_exported_program
+
+
+def _get_torch_export_args(
+    args: tuple[Any, ...],
+    kwargs: dict[str, Any] | None,
+) -> tuple[tuple[Any, ...], dict[str, Any] | None]:
+    """Obtain the arguments for torch.onnx.export from the model and the input arguments."""
+    if not kwargs and args and isinstance(args[-1], dict):
+        kwargs = args[-1]
+        args = args[:-1]
+    return args, kwargs
+
+
+def export_compat(
+    model: torch.nn.Module
+    | torch.export.ExportedProgram
+    | torch.jit.ScriptModule
+    | torch.jit.ScriptFunction,
+    args: tuple[Any, ...],
+    f: str | os.PathLike | None = None,
+    *,
+    kwargs: dict[str, Any] | None = None,
+    export_params: bool = True,
+    verbose: bool | None = None,
+    input_names: Sequence[str] | None = None,
+    output_names: Sequence[str] | None = None,
+    opset_version: int | None = None,
+    dynamic_axes: Mapping[str, Mapping[int, str]]
+    | Mapping[str, Sequence[int]]
+    | None = None,
+    dynamic_shapes: dict[str, Any] | tuple[Any, ...] | list[Any] | None = None,
+    keep_initializers_as_inputs: bool = False,
+    external_data: bool = True,
+    report: bool = False,
+    verify: bool = False,
+    profile: bool = False,
+    dump_exported_program: bool = False,
+    artifacts_dir: str | os.PathLike = ".",
+    fallback: bool = False,
+    **_,
+) -> _onnx_program.ONNXProgram:
+    if opset_version is None:
+        # TODO(justinchuby): Change the hardcoded opset version for it to be flexible
+        opset_version = 18
+
+    if isinstance(model, torch.export.ExportedProgram):
+        # We know the model is already exported program, so the args, kwargs, and dynamic_shapes
+        # are not used
+        dynamic_shapes = dynamic_shapes or {}
+    else:
+        args, kwargs = _get_torch_export_args(args, kwargs)
+        if dynamic_shapes is None and dynamic_axes is not None:
+            dynamic_shapes = _from_dynamic_axes_to_dynamic_shapes(
+                model,
+                dynamic_axes=dynamic_axes,
+                input_names=input_names,
+                output_names=set(output_names or ()),
+            )
+
+    try:
+        onnx_program = _core.export(
+            model,
+            args,
+            kwargs,
+            registry=None,
+            dynamic_shapes=dynamic_shapes,
+            input_names=input_names,
+            output_names=output_names,
+            profile=profile,
+            report=report,
+            verify=verify,
+            dump_exported_program=dump_exported_program,
+            artifacts_dir=artifacts_dir,
+            verbose=verbose,
+        )
+
+    except Exception as e:
+        if fallback:
+            if verbose is not False:
+                print(
+                    "[torch.onnx] Falling back to legacy torch.onnx.export due "
+                    f"to the following error: {e}",
+                )
+            if f is None:
+                raise TypeError("f must be provided when fallback is enabled") from e
+            torch.onnx.utils.export(
+                model,  # type: ignore[arg-type]
+                args,
+                f,  # type: ignore[arg-type]
+                kwargs=kwargs,
+                export_params=export_params,
+                input_names=input_names,
+                output_names=output_names,
+                opset_version=17,  # TODO(justinchuby): Hard coded to 17 for now
+                dynamic_axes=dynamic_axes,
+                keep_initializers_as_inputs=keep_initializers_as_inputs,
+            )
+            onnx_program = _onnx_program.ONNXProgram(ir.load(f), None)
+        else:
+            raise
+
+    # Converter opset version and optimize
+    onnx_program.model = onnxscript_apis.convert_version(
+        onnx_program.model, opset_version
+    )
+    onnx_program.model = onnxscript_apis.optimize(onnx_program.model)
+
+    if f is not None:
+        onnx_program.save(
+            f,
+            include_initializers=export_params,
+            keep_initializers_as_inputs=keep_initializers_as_inputs,
+            external_data=external_data,
+        )
+
+    return onnx_program
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_core.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_core.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d49a654a9c00c9301992e3a9afd33c4af5a35ef
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_core.py
@@ -0,0 +1,1341 @@
+# mypy: allow-untyped-defs
+# flake8: noqa: B950 We do not need flake8 as it complains line length
+from __future__ import annotations
+
+import ctypes
+import datetime
+import inspect
+import itertools
+import logging
+import operator
+import pathlib
+import textwrap
+import traceback
+import typing
+from typing import Any, Callable, Literal, Sequence
+
+import onnxscript
+import onnxscript.evaluator
+from onnxscript import ir
+from onnxscript.ir import convenience as ir_convenience
+
+import torch
+import torch.fx
+from torch.export import graph_signature
+from torch.onnx._internal._lazy_import import onnxscript_apis
+from torch.onnx._internal.exporter import (
+    _analysis,
+    _building,
+    _capture_strategies,
+    _dispatching,
+    _errors,
+    _fx_passes,
+    _ir_passes,
+    _onnx_program,
+    _registration,
+    _reporting,
+    _tensors,
+    _verification,
+)
+
+
+if typing.TYPE_CHECKING:
+    import os
+
+    import numpy as np
+
+
+# Define utilities to convert PyTorch data types so users do not need to specify manually
+_TORCH_DTYPE_TO_ONNX: dict[torch.dtype, ir.DataType] = {
+    torch.bfloat16: ir.DataType.BFLOAT16,
+    torch.bool: ir.DataType.BOOL,
+    torch.complex128: ir.DataType.COMPLEX128,
+    torch.complex64: ir.DataType.COMPLEX64,
+    torch.float16: ir.DataType.FLOAT16,
+    torch.float32: ir.DataType.FLOAT,
+    torch.float64: ir.DataType.DOUBLE,
+    torch.float8_e4m3fn: ir.DataType.FLOAT8E4M3FN,
+    torch.float8_e4m3fnuz: ir.DataType.FLOAT8E4M3FNUZ,
+    torch.float8_e5m2: ir.DataType.FLOAT8E5M2,
+    torch.float8_e5m2fnuz: ir.DataType.FLOAT8E5M2FNUZ,
+    torch.int16: ir.DataType.INT16,
+    torch.int32: ir.DataType.INT32,
+    torch.int64: ir.DataType.INT64,
+    torch.int8: ir.DataType.INT8,
+    torch.uint8: ir.DataType.UINT8,
+    torch.uint16: ir.DataType.UINT16,
+    torch.uint32: ir.DataType.UINT32,
+    torch.uint64: ir.DataType.UINT64,
+}
+_BLUE = "\033[96m"
+_END = "\033[0m"
+
+_STEP_ONE_ERROR_MESSAGE = textwrap.dedent(
+    f"""\
+    Failed to export the model with torch.export. {_BLUE}This is step 1/2{_END} of exporting the model to ONNX. Next steps:
+    - Modify the model code for `torch.export.export` to succeed. Refer to https://pytorch.org/docs/stable/generated/exportdb/index.html for more information.
+    - Debug `torch.export.export` and summit a PR to PyTorch.
+    - Create an issue in the PyTorch GitHub repository against the {_BLUE}*torch.export*{_END} component and attach the full error stack as well as reproduction scripts."""
+)
+
+_STEP_TWO_ERROR_MESSAGE = textwrap.dedent(
+    f"""\
+    Failed to convert the exported program to an ONNX model. {_BLUE}This is step 2/2{_END} of exporting the model to ONNX. Next steps:
+    - If there is a missing ONNX function, implement it and register it to the registry.
+    - If there is an internal error during ONNX conversion, debug the error and summit a PR to PyTorch.
+    - Save the ExportedProgram as a pt2 file and create an error report with `export(..., report=True)`. Create an issue in the PyTorch GitHub repository against the {_BLUE}*onnx*{_END} component. Attach the pt2 model and the error report."""
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _torch_dtype_to_onnx_dtype(dtype: torch.dtype) -> ir.DataType:
+    return _TORCH_DTYPE_TO_ONNX[dtype]
+
+
+class TorchTensor(ir.Tensor):
+    def __init__(self, tensor: torch.Tensor, name: str | None = None):
+        # Pass the tensor as the raw data to ir.Tensor's constructor
+        super().__init__(
+            tensor, dtype=_torch_dtype_to_onnx_dtype(tensor.dtype), name=name
+        )
+
+    def numpy(self) -> np.ndarray:
+        self.raw: torch.Tensor
+        if self.dtype == ir.DataType.BFLOAT16:
+            return self.raw.view(torch.uint16).numpy(force=True)
+        if self.dtype in {
+            ir.DataType.FLOAT8E4M3FN,
+            ir.DataType.FLOAT8E4M3FNUZ,
+            ir.DataType.FLOAT8E5M2,
+            ir.DataType.FLOAT8E5M2FNUZ,
+        }:
+            # TODO: Use ml_dtypes
+            return self.raw.view(torch.uint8).numpy(force=True)
+        return self.raw.numpy(force=True)
+
+    def __array__(self, dtype: Any = None, copy: bool | None = None) -> np.ndarray:
+        del copy  # Unused, but needed for the signature
+        if dtype is None:
+            return self.numpy()
+        return self.numpy().__array__(dtype)
+
+    def tobytes(self) -> bytes:
+        # Implement tobytes to support native PyTorch types so we can use types like bloat16
+        # Reading from memory directly is also more efficient because
+        # it avoids copying to a NumPy array
+        import torch._subclasses.fake_tensor
+
+        if isinstance(self.raw, torch._subclasses.fake_tensor.FakeTensor):
+            raise TypeError(
+                f"Cannot take content out from the FakeTensor ('{self.name}'). Please replace the tensor "
+                "with a tensor backed by real data using ONNXProgram.apply_weights() "
+                "or save the model without initializers by setting include_initializers=False."
+            )
+        tensor = self.raw.detach().cpu().contiguous()
+        return bytes(
+            (ctypes.c_ubyte * tensor.element_size() * tensor.numel()).from_address(
+                tensor.data_ptr()
+            )
+        )
+
+
+# https://github.com/pytorch/pytorch/blob/ee6cb6daa173896f8ea1876266a19775aaa4f610/torch/export/graph_signature.py#L56C1-L62C19
+# class InputKind(Enum):
+#     USER_INPUT = auto()
+#     PARAMETER = auto()
+#     BUFFER = auto()
+#     CONSTANT_TENSOR = auto()
+#     CUSTOM_OBJ = auto()
+#     TOKEN = auto()
+
+# https://github.com/pytorch/pytorch/blob/ee6cb6daa173896f8ea1876266a19775aaa4f610/torch/export/graph_signature.py#L89C1-L96C19
+# class OutputKind(Enum):
+#     USER_OUTPUT = auto()
+#     LOSS_OUTPUT = auto()
+#     BUFFER_MUTATION = auto()
+#     GRADIENT_TO_PARAMETER = auto()
+#     GRADIENT_TO_USER_INPUT = auto()
+#     USER_INPUT_MUTATION = auto()
+#     TOKEN = auto()
+
+
+def _set_shape_types(
+    values: Sequence[ir.Value],
+    meta_vals: Sequence[torch.Tensor],
+    complex_to_float: bool = True,
+) -> None:
+    if not isinstance(meta_vals, Sequence):
+        logger.warning(
+            "Expected meta_vals to be a sequence, but got %s. There may be an internal error.",
+            meta_vals,
+        )
+        meta_vals = (meta_vals,)
+    for value, meta_val in zip(values, meta_vals):
+        _set_shape_type(value, meta_val, complex_to_float=complex_to_float)
+
+
+def _set_shape_type(
+    value: ir.Value,
+    meta_val: torch.Tensor
+    | torch.SymBool
+    | torch.SymInt
+    | torch.SymFloat
+    | tuple[torch.Tensor],
+    complex_to_float: bool,
+) -> None:
+    # TODO: Consider using meta["tensor_meta"] for this? Would it be faster?
+    if isinstance(meta_val, tuple):
+        logger.warning("Setting shape and type of tensors is not supported yet")
+    if isinstance(meta_val, torch.Tensor):
+        # FIXME: Consider shape for complex values
+        dims = []
+        for dim in meta_val.shape:
+            if isinstance(dim, int):
+                dims.append(dim)
+            else:
+                dims.append(str(dim.node))
+        value.dtype = _torch_dtype_to_onnx_dtype(meta_val.dtype)
+        if complex_to_float:
+            if meta_val.dtype == torch.complex64:
+                value.dtype = ir.DataType.FLOAT
+                # Add 2 as the last dimension if the tensor is complex to hold the real/imag parts
+                dims.append(2)
+            elif meta_val.dtype == torch.complex128:
+                value.dtype = ir.DataType.DOUBLE
+                # Add 2 as the last dimension if the tensor is complex to hold the real/imag parts
+                dims.append(2)
+
+        value.shape = ir.Shape(dims)
+    elif isinstance(meta_val, (int, torch.SymInt)):
+        # aten::sym_size output is a int, not a tensor, which stands
+        # for the size of one dim. We treat it as a scalar.
+        value.dtype = ir.DataType.INT64
+        value.shape = ir.Shape([])
+    elif isinstance(meta_val, (bool, torch.SymBool)):
+        value.dtype = ir.DataType.BOOL
+        value.shape = ir.Shape([])
+    elif isinstance(meta_val, (float, torch.SymFloat)):
+        value.dtype = ir.DataType.FLOAT
+        value.shape = ir.Shape([])
+    else:
+        pass
+
+
+def _get_qualified_module_name(cls: Any) -> str:
+    if isinstance(cls, str):
+        return cls
+    module = cls.__module__
+    if module is None or module == str.__class__.__module__:
+        return cls.__name__
+    return module + "." + cls.__name__
+
+
+def _get_node_namespace(node: torch.fx.Node) -> tuple[str, list[str], list[str]]:
+    """Get the namespace and scope of the node.
+
+    Example::
+
+        {
+            'L__self__': ('', ),
+            'L__self___avgpool': ('avgpool', )
+        }
+
+    Will yield
+
+    namespace: ": torchvision.models.resnet.ResNet/avgpool: torch.nn.modules.pooling.AdaptiveAvgPool2d/node_name: node_target"
+    class_hierarchy: ["torchvision.models.resnet.ResNet", "torch.nn.modules.pooling.AdaptiveAvgPool2d", ]
+    name_scopes: ["", "avgpool", ]
+
+    Args:
+        node: The node to get the namespace and scope of.
+
+    Returns:
+        (namespace, class_hierarchy, name_scope)
+    """
+    nn_module_stack = node.meta.get("nn_module_stack")
+    logger.debug("%s", nn_module_stack)
+    if nn_module_stack is None:
+        logger.warning(
+            "nn_module_stack not found for node '%s'. Skip adding metadata...",
+            node.name,
+        )
+        return f"{node.name}: {node.target}", [str(node.target)], [node.name]
+    namespaces = []
+    class_hierarchy = []
+    name_scopes = []
+    for name, nn_module in nn_module_stack.values():
+        name_scopes.append(name)
+        nn_module_name = _get_qualified_module_name(nn_module)
+        class_hierarchy.append(nn_module_name)
+        namespaces.append(f"{name}: {_get_qualified_module_name(nn_module)}")
+    namespaces.append(f"{node.name}: {node.target}")
+    class_hierarchy.append(str(node.target))
+    name_scopes.append(node.name)
+
+    return "/".join(namespaces), class_hierarchy, name_scopes
+
+
+def _set_node_metadata(fx_node: torch.fx.Node, ir_node: ir.Node) -> None:
+    """Adds namespace and other node metadata to the ONNX node."""
+    namespace, class_hierarchy, name_scopes = _get_node_namespace(fx_node)
+    ir_node.metadata_props["namespace"] = namespace
+    ir_node.metadata_props["pkg.torch.onnx.class_hierarchy"] = repr(class_hierarchy)
+    ir_node.metadata_props["pkg.torch.onnx.name_scopes"] = repr(name_scopes)
+    ir_node.metadata_props["pkg.torch.onnx.fx_node"] = str(fx_node.format_node())
+    ir_node.metadata_props["pkg.torch.onnx.stack_trace"] = fx_node.meta.get(
+        "stack_trace", ""
+    )
+
+
+def _handle_getitem_node(
+    node: torch.fx.Node, node_name_to_values: dict[str, ir.Value | Sequence[ir.Value]]
+) -> ir.Value:
+    """Handle a getitem node.
+
+    Add the input value it is getting to the mapping, then return the value.
+
+    There are two cases for this node:
+    1. The output is a Sequence (traced), we can simply get the value from the sequence
+    2. The output is produced by a SplitToSequence node, we need to get the value from the sequence value
+    This function only handles the first case
+    """
+    assert len(node.all_input_nodes) == 1
+    source = node.all_input_nodes[0]
+    source_outputs = node_name_to_values[source.name]
+    assert isinstance(
+        source_outputs, Sequence
+    ), f"Expected {source.name} to output sequence, got {node_name_to_values[source.name]}"
+    index = typing.cast(int, node.args[1])
+    value = source_outputs[index]
+    # Save the getitem value to the values mapping to in case
+    # it is one of the graph outputs
+    node_name_to_values[node.name] = value
+    # Rename the name of value with the getitem name.
+    value.name = node.name
+    return value
+
+
+def _handle_call_function_node(
+    graph: ir.Graph,
+    node: torch.fx.Node,
+    node_name_to_values: dict[str, ir.Value | Sequence[ir.Value]],
+) -> None:
+    """Handle a call_function node.
+
+    Args:
+        graph: The ONNX graph at construction.
+        node: The FX node to translate.
+        node_name_to_values: A mapping of FX node names to their produced ir.Value.
+    """
+    if node.target == operator.getitem:
+        _handle_getitem_node(node, node_name_to_values)
+    # Add op to the graph
+    op = str(node.target)
+    fx_inputs, attributes, input_names, output_names = _get_inputs_and_attributes(node)
+    inputs: list[ir.Value | None] = []
+    for i, input_ in enumerate(fx_inputs):
+        if input_ is None:
+            inputs.append(None)
+        elif hasattr(input_, "name"):
+            if isinstance(input_, torch.fx.Node) and input_.target == operator.getitem:
+                actual_input = _handle_getitem_node(input_, node_name_to_values)
+                inputs.append(actual_input)
+            else:
+                value = node_name_to_values[input_.name]
+                assert not isinstance(value, Sequence)
+                inputs.append(value)
+        else:
+            attributes[f"arg_{i}"] = input_
+
+    outputs = [ir.Value(name=name) for name in output_names]
+    if len(outputs) > 1:
+        _set_shape_types(outputs, node.meta["val"], complex_to_float=False)
+        node_name_to_values[node.name] = outputs
+    else:
+        _set_shape_type(outputs[0], node.meta["val"], complex_to_float=False)
+        node_name_to_values[node.name] = outputs[0]
+    ir_node = ir.Node(
+        "pkg.torch.ops",
+        op,
+        inputs,
+        attributes=ir_convenience.convert_attributes(attributes),
+        outputs=outputs,
+        name=node.name,
+    )
+    ir_node.meta["node"] = node
+    ir_node.metadata_props["pkg.torch.onnx.input_names"] = repr(input_names)
+    # Record the nn.Module stack for the node
+    _set_node_metadata(node, ir_node)
+
+    graph.append(ir_node)
+
+
+def _convert_fx_arg_to_onnx_arg(
+    arg, node_name_to_values: dict[str, ir.Value | Sequence[ir.Value]]
+) -> Any:
+    """Convert an FX argument to an ONNX compatible argument.
+
+    This function
+    - Converts a torch dtype to an integer
+    - Converts a torch device/memory_format/layout to a string
+    - Converts a torch.fx.Node to an ir.Value
+    - Converts a sequence of torch.fx.Node to a sequence of ir.Value
+    """
+    if arg is None:
+        # None arguments are not modified because when the arg is an ONNX input
+        # we need to preserve the None value; when the arg is an ONNX attribute,
+        # we want to drop the value.
+        # The actual dropping of a None attribute value is done by OpRecorder
+        return None
+    if hasattr(arg, "name"):
+        if isinstance(arg, torch.fx.Node) and arg.target == operator.getitem:
+            source = arg.all_input_nodes[0]
+            source_outputs = node_name_to_values[source.name]
+            if isinstance(source_outputs, Sequence):
+                # If the node is getting an input from another node, get the actual value the node is retrieving
+                return _handle_getitem_node(arg, node_name_to_values)
+            else:
+                # `source_outputs` is a sequence(tensor()) value and we need to
+                # use SequenceAt to get the value. This is handled by torchlib
+                pass
+        # If the input is a node, get the value from the mapping
+        return node_name_to_values[arg.name]
+    if isinstance(arg, (list, tuple)):
+        return [_convert_fx_arg_to_onnx_arg(elem, node_name_to_values) for elem in arg]
+    if isinstance(arg, (torch.device, torch.memory_format, torch.layout)):
+        return str(arg)
+    if isinstance(arg, torch.dtype):
+        return _torch_dtype_to_onnx_dtype(arg)
+    # Maybe a Python value
+    return arg
+
+
+def _get_onnxscript_opset(opset_version: int) -> onnxscript.values.Opset:
+    return onnxscript.values.Opset("", opset_version)
+
+
+def _handle_call_function_node_with_lowering(
+    model: ir.Model,
+    node: torch.fx.Node,
+    node_name_to_values: dict[str, ir.Value | Sequence[ir.Value]],
+    constant_farm: dict[Any, ir.Value],
+    registry: _registration.ONNXRegistry,
+    opset: onnxscript.values.Opset,
+) -> None:
+    if node.target == operator.getitem:
+        source = node.all_input_nodes[0]
+        source_outputs = node_name_to_values[source.name]
+        if isinstance(source_outputs, Sequence):
+            _handle_getitem_node(node, node_name_to_values)
+            return
+        else:
+            # `source_outputs` is a sequence(tensor()) value and we need to
+            # use SequenceAt to get the value. This is handled by torchlib
+            pass
+
+    # Find the matching ONNX overload for the node
+    # NOTE: Create different registries for different ONNX opset versions
+    # TODO: Log the message here to expose false positives
+    onnx_function, message = _dispatching.dispatch(node, registry)
+
+    if onnx_function is None:
+        # TODO(justinchuby): Fall back to ATen op or do something else?
+        raise _errors.DispatchError(
+            f"No ONNX function found for {node.target!r}. Failure message: {message}"
+        )
+
+    # Map FX inputs to ONNX inputs and fill optional inputs.
+    # torch_args and torch_kwargs are for op-level validation
+    fx_args = node.args
+    fx_kwargs = node.kwargs
+
+    # Replace the input FX nodes with ONNX values
+    onnx_args = [
+        _convert_fx_arg_to_onnx_arg(input_, node_name_to_values) for input_ in fx_args
+    ]
+
+    onnx_kwargs = {}
+    for key, value in fx_kwargs.items():
+        onnx_kwargs[key] = _convert_fx_arg_to_onnx_arg(value, node_name_to_values)
+        if key == "dtype" and onnx_kwargs[key] is None:
+            # Set dtype to -1 if it is None
+            onnx_kwargs[key] = -1
+
+    with onnxscript.evaluator.default_as(
+        tracer := _building.OpRecorder(opset, constant_farm)
+    ):
+        try:
+            outputs = onnx_function(*onnx_args, **onnx_kwargs)
+        except Exception as e:
+            raise _errors.GraphConstructionError(
+                f"Error when calling function '{onnx_function}' with args '{onnx_args}' and kwargs '{onnx_kwargs}'"
+            ) from e
+
+    # NOTE: Instead of using the output names from node.target._schema,
+    # we always use the index if there are more than one outputs so the
+    # names can be programmatically reconstructed. This is useful for
+    # comparing values from the ONNX graph with those from the FX graph.
+    #
+    # When there are multiple outputs, the output names will be
+    # node_name__0, node_name__1, etc.
+    if isinstance(outputs, Sequence):
+        _set_shape_types(outputs, node.meta["val"], complex_to_float=True)
+        node_name_to_values[node.name] = outputs
+        for i, output in enumerate(outputs):
+            output.name = f"{node.name}__{i}"
+    else:
+        _set_shape_type(outputs, node.meta["val"], complex_to_float=True)
+        node_name_to_values[node.name] = outputs
+        outputs.name = node.name
+
+    for ir_node in tracer.nodes:
+        ir_node.meta["node"] = node
+        # Record the nn.Module stack for the node
+        _set_node_metadata(node, ir_node)
+
+    # Add the traced nodes to the graph
+    model.graph.extend(tracer.nodes)
+    # Add the defined functions to the model
+    for identifier, onnxscript_function in tracer.functions.items():
+        if identifier in model.functions:
+            continue
+        # TODO: Get IR function directly when onnxscript is updated
+        proto = onnxscript_function.to_function_proto()
+        ir_function = ir.serde.deserialize_function(proto)
+        model.functions[identifier] = ir_function
+        if ir_function.domain not in model.opset_imports:
+            # FIXME: Record the correct opset version of the function
+            model.opset_imports[ir_function.domain] = 1
+
+
+def _handle_placeholder_node(
+    node: torch.fx.Node,
+    node_name_to_values: dict[str, ir.Value | Sequence[ir.Value]],
+    *,
+    lower: str,
+    opset: onnxscript.values.Opset,
+) -> None:
+    # Placeholder nodes are user inputs
+    # We need to create a new tensor for each user input
+    # and add it to the graph's inputs
+    name = node.name
+    input_ = _tensors.SymbolicTensor(opset, name=name)
+    input_.meta["node"] = node
+    _set_shape_type(input_, node.meta["val"], complex_to_float=lower != "none")
+    node_name_to_values[name] = input_
+    # The inputs will be added to the graph later
+
+
+def _add_nodes(
+    exported_program: torch.export.ExportedProgram,
+    model: ir.Model,
+    lower: Literal["at_conversion", "post_conversion", "none"],
+    registry: _registration.ONNXRegistry,
+) -> dict[str, ir.Value | Sequence[ir.Value]]:
+    node_name_to_values: dict[str, ir.Value | Sequence[ir.Value]] = {}
+    constant_farm: dict[Any, ir.Value] = {}
+    opset = _get_onnxscript_opset(registry.opset_version)
+    for node in exported_program.graph.nodes:
+        logger.debug(
+            "%s", (node.name, node.args, node.target, node.op, node.type, node.kwargs)
+        )
+        try:
+            if node.op == "placeholder":
+                _handle_placeholder_node(
+                    node,
+                    node_name_to_values,
+                    lower=lower,
+                    opset=opset,
+                )
+            elif node.op == "call_function":
+                if lower == "at_conversion":
+                    _handle_call_function_node_with_lowering(
+                        model,
+                        node,
+                        node_name_to_values,
+                        constant_farm,
+                        registry=registry,
+                        opset=opset,
+                    )
+                else:
+                    # No lowering
+                    _handle_call_function_node(model.graph, node, node_name_to_values)
+        except Exception as e:
+            raise _errors.ConversionError(
+                f"Error when translating node {node.format_node()}. See the stack trace for more information."
+            ) from e
+    return node_name_to_values
+
+
+def _torch_version_integer() -> int:
+    return int(torch.__version__.replace(".", "").split("dev")[0])
+
+
+def _get_inputs_and_attributes(
+    node: torch.fx.Node,
+) -> tuple[list[torch.fx.Node | None], dict[str, Any], list[str], list[str]]:
+    """Find and Fill in the not provided kwargs with default values.
+
+    Returns:
+        (inputs, attributes, input_names, output_names)
+    """
+    if inspect.isbuiltin(node.target) or isinstance(node.target, str):
+        inputs = list(node.args)
+        return inputs, {}, [], [node.name]  # type: ignore[return-value]
+
+    # The target should be an ATen operator now
+    assert hasattr(
+        node.target, "_schema"
+    ), f"The target should be an ATen operator now, but node target {node.target} has no schema"
+    node_schema: torch.FunctionSchema = node.target._schema
+
+    # This function assumes the order of arguments in FX op is the
+    # same as the order of arguments in TorchScript op.
+    inputs: list[Any] = []  # type: ignore[no-redef]
+    input_names: list[str] = []
+    attributes: dict[str, Any] = {}
+
+    if inspect.isbuiltin(node.target):
+        inputs = list(node.args)
+    else:
+        for arg, schema_arg in zip(node.args, node_schema.arguments):
+            if arg is None or isinstance(arg, torch.fx.Node):
+                inputs.append(arg)
+                input_names.append(schema_arg.name)
+            elif isinstance(arg, Sequence) and all(
+                elem is None or isinstance(elem, torch.fx.Node) for elem in arg
+            ):
+                inputs.extend(arg)
+                input_names.extend([schema_arg.name] * len(arg))
+            elif isinstance(arg, torch.device):
+                attributes[schema_arg.name] = str(arg)
+            elif isinstance(arg, torch.dtype):
+                attributes[schema_arg.name] = _torch_dtype_to_onnx_dtype(arg)
+            else:
+                attributes[schema_arg.name] = arg
+        for schema_arg in node_schema.arguments:
+            if schema_arg.name not in node.kwargs:
+                continue
+            kwarg = node.kwargs[schema_arg.name]
+            if schema_arg.name in {
+                "layout",
+                "device",
+                "requires_grad",
+                "memory_format",
+                "implicit",
+            } or isinstance(kwarg, torch.device):
+                attr = str(kwarg)
+            elif isinstance(kwarg, torch.dtype):
+                attr = _torch_dtype_to_onnx_dtype(kwarg)  # type: ignore[assignment]
+            else:
+                attr = kwarg  # type: ignore[assignment]
+
+            attributes[schema_arg.name] = attr
+
+    output_names = [f"{node.name}_{output.name}" for output in node_schema.returns]
+
+    return inputs, attributes, input_names, output_names  # type: ignore[return-value]
+
+
+def _maybe_start_profiler(should_profile: bool) -> Any:
+    if should_profile:
+        import pyinstrument  # type: ignore[import-not-found]
+
+        profiler = pyinstrument.Profiler(async_mode="disabled")
+        profiler.start()
+        return profiler
+    return None
+
+
+def _maybe_stop_profiler_and_get_result(profiler) -> str | None:
+    if profiler is None:
+        return None
+    profiler.stop()
+    return profiler.output_text(unicode=True)
+
+
+def _format_exception(e: Exception) -> str:
+    """Format the full traceback as Python would show it."""
+    return "\n".join(traceback.format_exception(type(e), e, e.__traceback__))
+
+
+def _summarize_exception_stack(e: BaseException) -> str:
+    """Format the exception stack by showing the text of each exception."""
+    causes = [e]
+    while e.__cause__ is not None:
+        causes.append(e.__cause__)
+        e = e.__cause__
+    return (
+        "\n\n## Exception summary\n\n"
+        + "⬆️\n".join([f"{type(e)}: {e}\n" for e in reversed(causes)])
+        + "\n(Refer to the full stack trace above for more information.)"
+    )
+
+
+def _format_exceptions_for_all_strategies(
+    results: list[_capture_strategies.Result],
+) -> str:
+    """Format all the exceptions from the capture strategies."""
+    return "\n".join(
+        [
+            f"# ⚠️ Errors from strategy '{result.strategy}': -----------------------\n\n"
+            f"{_format_exception(result.exception)}\n"
+            for result in results
+            if result.exception is not None
+        ]
+    )
+
+
+def exported_program_to_ir(
+    exported_program: torch.export.ExportedProgram,
+    *,
+    registry: _registration.ONNXRegistry | None = None,
+    lower: Literal["at_conversion", "post_conversion", "none"] = "at_conversion",
+) -> ir.Model:
+    """Convert an exported program to an ONNX IR model.
+
+    Reference:
+        - ExportedProgram spec: https://pytorch.org/docs/stable/export.ir_spec.html
+
+    Args:
+        exported_program: The exported program to convert.
+        lower: Whether to lower the graph to core ONNX operators.
+            at_conversion: Lower whe translating the FX graph to ONNX IR.
+            post_conversion: Use an IR pass to lower the graph.
+            none: Do not lower the graph.
+        registry: The registry of all ONNX Script decomposition.
+    """
+    if registry is None:
+        registry = _registration.ONNXRegistry.from_torchlib()
+    if lower != "none":
+        exported_program = _prepare_exported_program_for_export(
+            exported_program, registry=registry
+        )
+    return _exported_program_to_onnx_program(
+        exported_program, registry=registry, lower=lower
+    ).model
+
+
+def _prepare_exported_program_for_export(
+    exported_program: torch.export.ExportedProgram,
+    *,
+    registry: _registration.ONNXRegistry,
+) -> torch.export.ExportedProgram:
+    """Decompose and apply pre-export transformations to the exported program."""
+    # Decompose the graph given the implemented torch ops in ONNX
+    exported_program = _fx_passes.decompose_with_registry(exported_program, registry)
+
+    graph_module = exported_program.graph_module
+    # Include explicit type promotion nodes
+    graph_module = _fx_passes.insert_type_promotion_nodes(graph_module)
+    graph_module = _fx_passes.remove_assertion_nodes(graph_module)
+    # TODO(justinchuby): Reassigning the graph module to save some runtime.
+    # If this does not work, we need to retrace the module with torch.export
+    exported_program._graph_module = graph_module
+    return exported_program
+
+
+def _exported_program_to_onnx_program(
+    exported_program: torch.export.ExportedProgram,
+    *,
+    registry: _registration.ONNXRegistry,
+    lower: Literal["at_conversion", "post_conversion", "none"] = "at_conversion",
+) -> _onnx_program.ONNXProgram:
+    """Convert an exported program to an ONNX Program.
+
+    The exported_program field in the returned ONNXProgram is one that is after
+    decompositions have been applied.
+
+    Reference:
+        - ExportedProgram spec: https://pytorch.org/docs/stable/export.ir_spec.html
+
+    Args:
+        exported_program: The exported program to convert. The exported program
+            should be the one that is after decompositions have been applied.
+        lower: Whether to lower the graph to core ONNX operators.
+            at_conversion: Lower whe translating the FX graph to ONNX IR.
+            post_conversion: Use an IR pass to lower the graph.
+            none: Do not lower the graph.
+        registry: The registry of all ONNX Script decomposition.
+    """
+    model = ir.Model(
+        graph=ir.Graph(
+            [],
+            [],
+            nodes=[],
+            opset_imports={
+                "": registry.opset_version,
+            },
+            name="main_graph",
+            metadata_props={
+                "pkg.torch.export.ExportedProgram.graph_signature": str(
+                    exported_program.graph_signature
+                ),
+                "pkg.torch.export.ExportedProgram.range_constraints": str(
+                    exported_program.range_constraints
+                ),
+            },
+        ),
+        ir_version=9,
+        producer_name="pytorch",
+        producer_version=torch.__version__,
+    )
+
+    if lower == "none":
+        # Add the opset import for the torch ops
+        model.opset_imports["pkg.torch.ops"] = _torch_version_integer()
+    # NOTE: Function domains are added when translating nodes when lower="at_conversion"
+
+    # 1. Add all nodes to the graph and create a dictionary of values
+    values = _add_nodes(exported_program, model, lower=lower, registry=registry)
+
+    # 2. Add user inputs and all parameters/buffers to the graph.
+    # Since the node names and the tensor names are different, we need to rename
+    # the nodes to match the tensor names later. For now we will just use the node names.
+    user_inputs = [
+        spec
+        for spec in exported_program.graph_signature.input_specs
+        if spec.kind == graph_signature.InputKind.USER_INPUT
+    ]
+    non_user_inputs = [
+        spec
+        for spec in exported_program.graph_signature.input_specs
+        if spec.kind != graph_signature.InputKind.USER_INPUT
+    ]
+
+    for spec in itertools.chain(user_inputs, non_user_inputs):
+        # Put the user inputs first and then the parameters/buffers
+        if isinstance(spec.arg, graph_signature.ConstantArgument):
+            logger.debug("Skipping constant argument %s", spec.arg)
+            continue
+        value_name = spec.arg.name
+        input_kind = spec.kind
+        persistent = spec.persistent
+        value = values[value_name]
+
+        assert not isinstance(
+            value, Sequence
+        ), f"Input '{value_name}' should not be a sequence. This is unexpected."
+
+        value.metadata_props["pkg.torch.export.graph_signature.InputSpec.kind"] = (
+            input_kind.name
+        )
+        value.metadata_props[
+            "pkg.torch.export.graph_signature.InputSpec.persistent"
+        ] = str(persistent)
+
+        if input_kind == graph_signature.InputKind.USER_INPUT:
+            # Add only user inputs to the graph
+            # Subsequent passes can decide if they want to add initializers as inputs
+            model.graph.inputs.append(value)
+        else:
+            model.graph.initializers[value_name] = value
+
+    # 3. Add user outputs to the graph and assign metadata to all outputs
+    user_outputs = [
+        spec
+        for spec in exported_program.graph_signature.output_specs
+        if spec.kind == graph_signature.OutputKind.USER_OUTPUT
+    ]
+    non_user_outputs = [
+        spec
+        for spec in exported_program.graph_signature.output_specs
+        if spec.kind != graph_signature.OutputKind.USER_OUTPUT
+    ]
+    for spec in itertools.chain(user_outputs, non_user_outputs):
+        if isinstance(spec.arg, graph_signature.ConstantArgument):
+            logger.warning("Skipping constant argument %s", spec.arg)
+            continue
+        value_name = spec.arg.name
+        output_kind = spec.kind
+        value = values[value_name]
+
+        if not isinstance(value, (ir.Value, Sequence)):
+            raise TypeError(
+                f"Output '{value_name}' should be an ir.Value. Actual type is '{type(value)}': {value!r}. "
+                "This may be due to an incorrect implementation of the ONNX function that produced this output."
+            )
+
+        # The output value may be a sequence, meaning the operator has multiple outputs
+        _values = (value,) if not isinstance(value, Sequence) else value
+
+        if len(_values) > 1:
+            logger.warning(
+                "Model output '%s' has multiple values: %s (output spec: %s). Please make sure this is expected.",
+                value_name,
+                _values,
+                spec,
+            )
+
+        for value in _values:
+            value.metadata_props["pkg.torch.export.graph_signature.OutputSpec.kind"] = (
+                output_kind.name
+            )
+            if output_kind == graph_signature.OutputKind.USER_OUTPUT:
+                model.graph.outputs.append(value)
+
+    # 4. Rename the initializers to match the tensor names
+    for name, param_name in itertools.chain(
+        exported_program.graph_signature.inputs_to_parameters.items(),
+        exported_program.graph_signature.inputs_to_buffers.items(),
+        exported_program.graph_signature.inputs_to_lifted_tensor_constants.items(),
+    ):
+        initializer = model.graph.initializers.pop(name)
+        initializer.name = param_name
+        # Record the original name so users can search the metadata and correspond
+        # with the FX graph
+        initializer.metadata_props["pkg.torch.onnx.original_node_name"] = name
+        model.graph.initializers[param_name] = initializer
+
+    # 5. Add initializers to the graph
+    # ExportedProgram stores parameters and buffers in state_dict,
+    # but non_persistent_buffers and lifted_tensor_constants are not there
+    # so we need to get them from the name_* apis.
+    for name, torch_tensor in itertools.chain(
+        exported_program.named_parameters(),
+        exported_program.named_buffers(),
+        exported_program.constants.items(),
+    ):
+        initializer = model.graph.initializers.get(name)  # type: ignore[assignment]
+        if initializer is None:
+            logger.warning("Tensor '%s' is not one of the initializers", name)
+            continue
+        if not isinstance(torch_tensor, torch.Tensor):
+            raise NotImplementedError(
+                f"Tensor '{name}' should be a torch.Tensor. Actual type is '{type(torch_tensor)}': {torch_tensor!r}. "
+                "This is unexpected and not yet supported."
+            )
+        ir_tensor = TorchTensor(torch_tensor, name=name)
+        initializer.const_value = ir_tensor
+        _set_shape_type(
+            initializer,
+            torch_tensor,
+            complex_to_float=lower != "none",
+        )
+
+    # TODO: Decide if we should keep mutated buffers as inputs/outputs
+
+    # TODO(justinchuby): Remove the hack
+    _ir_passes.add_torchlib_common_imports(model)
+
+    return _onnx_program.ONNXProgram(model, exported_program)
+
+
+def _verbose_printer(verbose: bool | None) -> Callable[..., None]:
+    """Prints messages based on `verbose`."""
+    if verbose is False:
+        return lambda *_, **__: None
+    return lambda *args, **kwargs: print("[torch.onnx]", *args, **kwargs)
+
+
+def export(
+    model: torch.nn.Module
+    | torch.export.ExportedProgram
+    | torch.fx.GraphModule
+    | torch.jit.ScriptModule
+    | torch.jit.ScriptFunction,
+    args: tuple[Any, ...] = (),
+    kwargs: dict[str, Any] | None = None,
+    *,
+    registry: _registration.ONNXRegistry | None = None,
+    dynamic_shapes: dict[str, Any] | tuple[Any, ...] | list[Any] | None = None,
+    input_names: Sequence[str] | None = None,
+    output_names: Sequence[str] | None = None,
+    report: bool = False,
+    verify: bool = False,
+    profile: bool = False,
+    dump_exported_program: bool = False,
+    artifacts_dir: str | os.PathLike = ".",
+    verbose: bool | None = None,
+) -> _onnx_program.ONNXProgram:
+    """Export a PyTorch model to ONNXProgram.
+
+    Args:
+        model: The model to export. This can be a PyTorch nn.Module or an ExportedProgram.
+        args: The arguments to pass to the model.
+        kwargs: The keyword arguments to pass to the model.
+        registry: The registry of all ONNX decompositions.
+        dynamic_shapes: Dynamic shapes in the graph.
+        input_names: If provided, rename the inputs.
+        output_names: If provided, rename the outputs.
+        report: Whether to generate an error report if the export fails.
+        verify: Whether to verify the ONNX model after exporting.
+        profile: Whether to profile the export process. When report is True,
+            the profile result will be saved in the report. Otherwise, the profile
+            result will be printed.
+        dump_exported_program: Whether to save the exported program to a file.
+        artifacts_dir: The directory to save the exported program and error reports.
+        verbose: Whether to print verbose messages. If None (default), some messages will be printed.
+
+    Returns:
+        The ONNXProgram with the exported IR graph.
+
+    Raises:
+        TorchExportError: If the export process fails with torch.export.
+        ConversionError: If the ExportedProgram to ONNX translation fails.
+    """
+    # Set up the error reporting facilities
+    timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S-%f")
+    profiler = _maybe_start_profiler(profile)
+
+    # Create the artifacts directory if it does not exist
+    artifacts_dir = pathlib.Path(artifacts_dir)
+    if report or profile or dump_exported_program:
+        artifacts_dir.mkdir(parents=True, exist_ok=True)
+
+    verbose_print = _verbose_printer(verbose)
+    export_status = _reporting.ExportStatus()
+    failed_results: list[_capture_strategies.Result] = []
+
+    program: torch.export.ExportedProgram | None = None
+    # Step 1: Export the model with torch.export.export if the model is not already an ExportedProgram
+    if isinstance(model, torch.export.ExportedProgram):
+        # We know the model is already exported program, so the args, kwargs, and dynamic_shapes
+        # are not used.
+        program = model
+        export_status.torch_export = True
+    else:
+        # Convert an nn.Module to an ExportedProgram
+        # Try everything 🐰 (all paths for getting an ExportedProgram)
+        # When input is a JIT module, the last strategy will succeed so it is handled
+        result: _capture_strategies.Result | None = None
+        for strategy_class in _capture_strategies.CAPTURE_STRATEGIES:
+            strategy = strategy_class(  # type: ignore[abstract]
+                verbose=verbose is not False,  # Treat None as verbose
+                dump=dump_exported_program,
+                artifacts_dir=artifacts_dir,
+                timestamp=timestamp,
+            )
+            result = strategy(model, args, kwargs, dynamic_shapes=dynamic_shapes)
+
+            # Record the status
+            if strategy_class is _capture_strategies.TorchExportStrategy:
+                export_status.torch_export = result.success
+            elif strategy_class is _capture_strategies.TorchExportNonStrictStrategy:
+                export_status.torch_export_non_strict = result.success
+            elif strategy_class is _capture_strategies.JitTraceConvertStrategy:
+                export_status.torch_jit = result.success
+
+            if result.exported_program is not None:
+                program = result.exported_program
+                break
+            else:
+                failed_results.append(result)
+
+        assert result is not None
+        if result.exported_program is None:
+            # If all strategies fail, produce an error report and raise the first error
+            profile_result = _maybe_stop_profiler_and_get_result(profiler)
+
+            if report:
+                report_path = artifacts_dir / _reporting.construct_report_file_name(
+                    timestamp, export_status
+                )
+
+                try:
+                    _reporting.create_torch_export_error_report(
+                        report_path,
+                        _format_exceptions_for_all_strategies(failed_results),
+                        export_status=export_status,
+                        profile_result=profile_result,
+                    )
+                except Exception as e_report:
+                    verbose_print(
+                        f"Failed to save error report due to an error: {e_report}"
+                    )
+            else:
+                report_path = None
+
+            first_error = failed_results[0].exception
+            assert first_error is not None
+
+            # NOTE: We only throw the torch.export (first) exception because we want to
+            # focus on the torch.export.export error. Errors from other strategies like
+            # torch.jit.trace is due to the fallback and can be confusing to users.
+            # We save all errors in the error report.
+            raise _errors.TorchExportError(
+                _STEP_ONE_ERROR_MESSAGE
+                + (
+                    f"\nError report has been saved to '{report_path}'."
+                    if report
+                    else ""
+                )
+                + _summarize_exception_stack(first_error)
+            ) from first_error
+
+    assert program is not None
+
+    if dump_exported_program:
+        verbose_print("Dumping ExportedProgram because `dump_exported_program=True`...")
+        program_path = artifacts_dir / f"onnx_export_{timestamp}.pt2"
+        try:
+            torch.export.save(program, program_path)
+        except Exception as e:
+            verbose_print(f"Failed to save ExportedProgram due to an error: {e}")
+        else:
+            verbose_print(f"ExportedProgram has been saved to '{program_path}'.")
+
+    # Step 2: Convert the exported program to an ONNX model
+    verbose_print("Translate the graph into ONNX...")
+
+    # Step 2a: Decompose the exported program and insert type promotion nodes
+    try:
+        # Build the ONNX function registry
+        if registry is None:
+            registry = _registration.ONNXRegistry.from_torchlib()
+
+        # Process the exported program to run decompositions and type promotions etc.
+        decomposed_program = _prepare_exported_program_for_export(
+            program, registry=registry
+        )
+    except Exception as e:
+        export_status.onnx_translation = False
+        verbose_print("Translate the graph into ONNX... ❌")
+        profile_result = _maybe_stop_profiler_and_get_result(profiler)
+
+        if report:
+            report_path = artifacts_dir / _reporting.construct_report_file_name(
+                timestamp, export_status
+            )
+
+            # Run the analysis to get the error report
+            try:
+                _reporting.create_onnx_export_report(
+                    report_path,
+                    f"{_format_exceptions_for_all_strategies(failed_results)}\n\n{_format_exception(e)}",
+                    program,
+                    export_status=export_status,
+                    profile_result=profile_result,
+                    registry=registry,
+                )
+            except Exception:
+                logger.exception("Failed to save report due to an error.")
+        else:
+            report_path = None
+
+        raise _errors.ConversionError(
+            _STEP_TWO_ERROR_MESSAGE
+            + (f"\nError report has been saved to '{report_path}'." if report else "")
+            + _summarize_exception_stack(e)
+        ) from e
+
+    # Step 2b: Translate the decomposed program to ONNX and produce ONNXProgram
+    if report or profile:
+        pre_decomp_unique_ops, post_decomp_unique_ops = _analysis.compare_ops(
+            program, decomposed_program
+        )
+    else:
+        pre_decomp_unique_ops = None
+        post_decomp_unique_ops = None
+
+    try:
+        # Convert the exported program to an ONNX model
+        onnx_program = _exported_program_to_onnx_program(
+            decomposed_program, registry=registry
+        )
+
+        # Run the ONNX passes
+        if input_names:
+            _ir_passes.rename_inputs(onnx_program.model, input_names)
+        if output_names:
+            _ir_passes.rename_outputs(onnx_program.model, output_names)
+
+        # TODO(justinchuby): Remove the hack
+        _ir_passes.add_torchlib_common_imports(onnx_program.model)
+
+        export_status.onnx_translation = True
+        verbose_print("Translate the graph into ONNX... ✅")
+    except Exception as e:
+        export_status.onnx_translation = False
+        verbose_print("Translate the graph into ONNX... ❌")
+        profile_result = _maybe_stop_profiler_and_get_result(profiler)
+
+        if report:
+            report_path = artifacts_dir / _reporting.construct_report_file_name(
+                timestamp, export_status
+            )
+
+            try:
+                assert pre_decomp_unique_ops is not None
+                assert post_decomp_unique_ops is not None
+
+                # Run the analysis to get the error report
+                _reporting.create_onnx_export_report(
+                    report_path,
+                    f"{_format_exceptions_for_all_strategies(failed_results)}\n\n{_format_exception(e)}",
+                    program,
+                    decomp_comparison=_reporting.format_decomp_comparison(
+                        pre_decomp_unique_ops, post_decomp_unique_ops
+                    ),
+                    export_status=export_status,
+                    profile_result=profile_result,
+                    registry=registry,
+                )
+                verbose_print(f"Export report has been saved to '{report_path}'.")
+            except Exception:
+                logger.exception("Failed to save report due to an error.")
+        else:
+            report_path = None
+
+        raise _errors.ConversionError(
+            _STEP_TWO_ERROR_MESSAGE
+            + (f"\nError report has been saved to '{report_path}'." if report else "")
+            + _summarize_exception_stack(e)
+        ) from e
+
+    profile_result = _maybe_stop_profiler_and_get_result(profiler)
+
+    assert onnx_program.exported_program is not None
+
+    if not verify:
+        # Return if verification is not requested
+        if report:
+            try:
+                assert pre_decomp_unique_ops is not None
+                assert post_decomp_unique_ops is not None
+                report_path = artifacts_dir / _reporting.construct_report_file_name(
+                    timestamp, export_status
+                )
+                _reporting.create_onnx_export_report(
+                    report_path,
+                    "No errors"
+                    if not failed_results
+                    else _format_exceptions_for_all_strategies(failed_results),
+                    onnx_program.exported_program,
+                    decomp_comparison=_reporting.format_decomp_comparison(
+                        pre_decomp_unique_ops, post_decomp_unique_ops
+                    ),
+                    export_status=export_status,
+                    profile_result=profile_result,
+                    model=onnx_program.model,
+                    registry=registry,
+                )
+                verbose_print(f"Export report has been saved to '{report_path}'.")
+            except Exception:
+                logger.exception("Failed to save report due to an error.")
+        elif profile and profile_result is not None:
+            verbose_print("Profile result:")
+            verbose_print(profile_result)
+        return onnx_program
+
+    # Step 3: (verify=True) Check the ONNX model with ONNX checker
+    try:
+        verbose_print("Check the ONNX model...")
+        onnxscript_apis.check_model(onnx_program.model)
+        export_status.onnx_checker = True
+        verbose_print("Check the ONNX model... ✅")
+    except Exception as e:
+        export_status.onnx_checker = False
+        verbose_print("Check the ONNX model... ❌")
+        if report:
+            try:
+                assert pre_decomp_unique_ops is not None
+                assert post_decomp_unique_ops is not None
+                report_path = artifacts_dir / _reporting.construct_report_file_name(
+                    timestamp, export_status
+                )
+                _reporting.create_onnx_export_report(
+                    report_path,
+                    f"{_format_exceptions_for_all_strategies(failed_results)}\n\n{_format_exception(e)}",
+                    onnx_program.exported_program,
+                    decomp_comparison=_reporting.format_decomp_comparison(
+                        pre_decomp_unique_ops, post_decomp_unique_ops
+                    ),
+                    export_status=export_status,
+                    profile_result=profile_result,
+                    model=onnx_program.model,
+                    registry=registry,
+                )
+                verbose_print(f"Export report has been saved to '{report_path}'.")
+            except Exception:
+                logger.exception("Failed to save report due to an error.")
+        logger.warning(
+            "Conversion successful but the ONNX model fails ONNX checker. "  # noqa: G004
+            "Please create an issue "
+            f"in the PyTorch GitHub repository against the {_BLUE}*onnx*{_END} component and "
+            "attach the full error stack as well as reproduction scripts. ",
+            exc_info=e,
+        )
+        return onnx_program
+
+    # Step 4: (verify=True) Execute the model with ONNX Runtime
+    try:
+        verbose_print("Execute the model with ONNX Runtime...")
+        verification_results = _verification.verify_onnx_program(onnx_program)
+        verbose_print("Execute the model with ONNX Runtime... ✅")
+        export_status.onnx_runtime = True
+        onnx_runtime_error_message = None
+    except Exception as e:
+        verbose_print("Execute the model with ONNX Runtime... ❌")
+        export_status.onnx_runtime = False
+        onnx_runtime_error_message = _format_exception(e)
+        verification_message = None
+
+    else:
+        # Step 5: (verify=True) Validate the output values
+        verbose_print("Verify output accuracy...")
+        export_status.output_accuracy = True
+        for verification_result in verification_results:
+            # TODO(justinchuby): The threshold is arbitrary right now
+            if verification_result.max_abs_diff >= 5e-3:
+                logger.warning(
+                    "Output '%s' has a large absolute difference of %f. ",
+                    verification_result.name,
+                    verification_result.max_abs_diff,
+                )
+                export_status.output_accuracy = False
+            if verification_result.max_rel_diff >= 1e-1:
+                logger.warning(
+                    "Output '%s' has a large relative difference of %f. ",
+                    verification_result.name,
+                    verification_result.max_rel_diff,
+                )
+                export_status.output_accuracy = False
+        if export_status.output_accuracy:
+            verbose_print("Verify output accuracy... ✅")
+        else:
+            verbose_print("Verify output accuracy... ❌")
+        verification_message = _reporting.format_verification_infos(
+            verification_results
+        )
+
+    if report:
+        try:
+            assert pre_decomp_unique_ops is not None
+            assert post_decomp_unique_ops is not None
+
+            traceback_lines = []
+            if failed_results:
+                traceback_lines.append(
+                    _format_exceptions_for_all_strategies(failed_results)
+                )
+            if onnx_runtime_error_message:
+                traceback_lines.append("# ⚠️ ONNX Runtime error -----------------------")
+                traceback_lines.append(onnx_runtime_error_message)
+            if not traceback_lines:
+                traceback_lines.append("No errors")
+
+            report_path = artifacts_dir / _reporting.construct_report_file_name(
+                timestamp, export_status
+            )
+            _reporting.create_onnx_export_report(
+                report_path,
+                "\n\n".join(traceback_lines),
+                onnx_program.exported_program,
+                profile_result=profile_result,
+                export_status=export_status,
+                decomp_comparison=_reporting.format_decomp_comparison(
+                    pre_decomp_unique_ops, post_decomp_unique_ops
+                ),
+                model=onnx_program.model,
+                registry=registry,
+                verification_result=verification_message,
+            )
+            verbose_print(f"Export report has been saved to '{report_path}'.")
+        except Exception:
+            logger.exception("Failed to save report due to an error.")
+
+    # Release the inference session created during verification
+    onnx_program.release()
+    return onnx_program
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_decomp.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_decomp.py
new file mode 100644
index 0000000000000000000000000000000000000000..3bbff757e92ef20987b5314ad9a69e73594f6766
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_decomp.py
@@ -0,0 +1,100 @@
+"""Build decomp table from PyTorch."""
+
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+from typing import Callable, TYPE_CHECKING
+
+import torch
+import torch._ops
+
+
+if TYPE_CHECKING:
+    from torch.onnx._internal.exporter import _registration
+
+
+def get_onnx_implemented_overloads(
+    registry: _registration.ONNXRegistry,
+) -> list[torch._ops.OperatorBase]:
+    """
+    Creates a set of OperatorBase and Callable objects that represent ONNX-supported PyTorch operations.
+
+    Args:
+        registry: The ONNX registry for PyTorch.
+
+    Returns:
+        A collection of OperatorBase and Callable objects representing ONNX-supported PyTorch operations.
+    """
+    registered_ops: list[torch._ops.OperatorBase] = []
+    for op_namespace in (torch.ops.aten, torch.ops.prims):
+        op_names = dir(op_namespace)
+        for op_name in op_names:
+            op_overload_packet = getattr(op_namespace, op_name)
+            if not isinstance(op_overload_packet, torch._ops.OpOverloadPacket):
+                continue
+
+            for overload_name in op_overload_packet.overloads():
+                op_overload = getattr(op_overload_packet, overload_name)
+                if registry.is_registered(op_overload):
+                    registered_ops.append(op_overload)
+    return registered_ops
+
+
+def get_preserve_ops() -> set[torch._ops.OpOverload]:
+    """Return a set of CompositeImplicitAutograd ops that should be preserved."""
+    aten = torch.ops.aten
+    # NOTE: Keep this list sorted
+    # NOTE: Do _not_ retain aten.linear as its decomposition is addmm, which is Gemm and is preferable for accuracy
+    return {
+        aten._upsample_bilinear2d_aa.default,
+        aten._upsample_nearest_exact1d.vec,
+        aten._upsample_nearest_exact2d.vec,
+        aten._upsample_nearest_exact3d.vec,
+        aten.group_norm.default,
+        aten.instance_norm.default,
+        aten.upsample_bilinear2d.default,
+        aten.upsample_bilinear2d.vec,
+        aten.upsample_linear1d.default,
+        aten.upsample_linear1d.vec,
+        aten.upsample_nearest1d.default,
+        aten.upsample_nearest1d.vec,
+        aten.upsample_nearest2d.default,
+        aten.upsample_nearest2d.vec,
+        aten.upsample_nearest3d.default,
+        aten.upsample_nearest3d.vec,
+        aten.upsample_trilinear3d.default,
+        aten.upsample_trilinear3d.vec,
+    }
+
+
+def create_onnx_friendly_decomposition_table(
+    onnx_registered_ops: set[torch._ops.OperatorBase],
+) -> dict[torch._ops.OperatorBase, Callable]:
+    """
+    This function creates a dictionary of op overloads and their decomposition functions
+    for ops that do not have ONNX symbolic functions. If an op already has an ONNX symbolic function,
+    its decomposition function is excluded from the table. The decomposition table is a subset of PyTorch's
+    built-in aten-to-aten decomposition.
+
+    Args:
+        onnx_registered_ops: All ops that have an ONNX decomposition implemented.
+
+    Returns:
+        Dict[torch._ops.OperatorBase, Callable]: A dictionary that maps op overloads to their corresponding
+        decomposition functions.
+    """
+    decomposition_table: dict[torch._ops.OperatorBase, Callable] = {}
+
+    # NOTE: If we import torch._decomp, we will get RuntimeError: Only a single
+    # TORCH_LIBRARY can be used to register the namespace nvprims; please put all of your
+    # definitions in a single TORCH_LIBRARY block.
+    for op_overload, decomp_fn in torch._decomp.decomposition_table.items():  # type: ignore[attr-defined]
+        # Skip decomposition for op_overload as long as that op_overload has a corresponding ONNX
+        # symbolic function.
+        # NOTE: Do not skip torch._refs decomps. They are fine because otherwise the model is
+        # not exportable anyways.
+        if op_overload in onnx_registered_ops:
+            continue
+        decomposition_table[op_overload] = decomp_fn
+
+    return decomposition_table
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_dispatching.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_dispatching.py
new file mode 100644
index 0000000000000000000000000000000000000000..11ed1af17aaade760a265daae33e88df0fbaee05
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_dispatching.py
@@ -0,0 +1,362 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import logging
+from typing import Callable, Sequence
+
+from onnxscript import ir
+
+import torch
+import torch.fx
+from torch.onnx._internal.exporter import _registration, _schemas
+
+
+logger = logging.getLogger(__name__)
+
+# Define utilities to convert PyTorch data types so users do not need to specify manually
+_TORCH_DTYPE_TO_ONNX_COMPATIBLE: dict[torch.dtype, ir.DataType] = {
+    torch.bfloat16: ir.DataType.BFLOAT16,
+    torch.bool: ir.DataType.BOOL,
+    torch.complex128: ir.DataType.DOUBLE,
+    torch.complex64: ir.DataType.FLOAT,
+    torch.float16: ir.DataType.FLOAT16,
+    torch.float32: ir.DataType.FLOAT,
+    torch.float64: ir.DataType.DOUBLE,
+    torch.float8_e4m3fn: ir.DataType.FLOAT8E4M3FN,
+    torch.float8_e4m3fnuz: ir.DataType.FLOAT8E4M3FNUZ,
+    torch.float8_e5m2: ir.DataType.FLOAT8E5M2,
+    torch.float8_e5m2fnuz: ir.DataType.FLOAT8E5M2FNUZ,
+    torch.int16: ir.DataType.INT16,
+    torch.int32: ir.DataType.INT32,
+    torch.int64: ir.DataType.INT64,
+    torch.int8: ir.DataType.INT8,
+    torch.uint8: ir.DataType.UINT8,
+}
+
+
+def _torch_dtype_to_onnx_compatible_dtype(dtype: torch.dtype) -> ir.DataType:
+    return _TORCH_DTYPE_TO_ONNX_COMPATIBLE[dtype]
+
+
+def _attribute_type_compatible_with_arg(
+    attr: _schemas.AttributeParameter,
+    value: ir.Value | int | float | bool | Sequence[int] | Sequence[float] | None,
+) -> bool:
+    """Check if the attribute type is compatible with the argument."""
+    if isinstance(value, bool):
+        return attr.type is ir.AttributeType.INT
+    if isinstance(value, str):
+        return attr.type is ir.AttributeType.STRING
+    if isinstance(value, int):
+        return attr.type in {ir.AttributeType.INT, ir.AttributeType.FLOAT}
+    if isinstance(value, float):
+        return attr.type is ir.AttributeType.FLOAT
+    if isinstance(value, complex):
+        return False
+    if isinstance(value, Sequence):
+        if attr.type is ir.AttributeType.INTS:
+            return all(isinstance(i, int) for i in value)
+        if attr.type is ir.AttributeType.FLOATS:
+            return all(isinstance(i, (int, float)) for i in value)
+    if isinstance(value, torch.dtype):
+        return attr.type is ir.AttributeType.INT
+    if isinstance(value, (torch.device, torch.memory_format, torch.layout)):
+        return attr.type is ir.AttributeType.STRING
+    if value is None and not attr.required:
+        # An optional attribute is not supplied
+        return True
+    return False
+
+
+def _param_type_compatible_with_arg(
+    param: _schemas.Parameter,
+    value: ir.TypeProtocol
+    | str
+    | int
+    | float
+    | complex
+    | Sequence[int]
+    | Sequence[float]
+    | None,
+    assigned_types: dict[str, ir.TypeProtocol],
+) -> bool:
+    # Handle Python types first
+    if isinstance(value, bool):  # noqa: SIM102
+        if param.type_constraint.allowed_types & {ir.TensorType(ir.DataType.BOOL)}:
+            return True
+    if isinstance(value, int) and param.type_constraint.allowed_types & {
+        ir.TensorType(ir.DataType.INT4),
+        ir.TensorType(ir.DataType.INT8),
+        ir.TensorType(ir.DataType.INT16),
+        ir.TensorType(ir.DataType.INT32),
+        ir.TensorType(ir.DataType.INT64),
+        # Int inputs can be casted to a float too
+        ir.TensorType(ir.DataType.FLOAT8E4M3FN),
+        ir.TensorType(ir.DataType.FLOAT8E4M3FNUZ),
+        ir.TensorType(ir.DataType.FLOAT8E5M2),
+        ir.TensorType(ir.DataType.FLOAT8E5M2FNUZ),
+        ir.TensorType(ir.DataType.FLOAT16),
+        ir.TensorType(ir.DataType.FLOAT),
+        ir.TensorType(ir.DataType.DOUBLE),
+    }:
+        return True
+    if isinstance(value, float) and param.type_constraint.allowed_types & {
+        ir.TensorType(ir.DataType.FLOAT8E4M3FN),
+        ir.TensorType(ir.DataType.FLOAT8E4M3FNUZ),
+        ir.TensorType(ir.DataType.FLOAT8E5M2),
+        ir.TensorType(ir.DataType.FLOAT8E5M2FNUZ),
+        ir.TensorType(ir.DataType.FLOAT16),
+        ir.TensorType(ir.DataType.FLOAT),
+        ir.TensorType(ir.DataType.DOUBLE),
+    }:
+        return True
+    if isinstance(value, complex) and param.type_constraint.allowed_types & {
+        ir.TensorType(ir.DataType.FLOAT),
+        ir.TensorType(ir.DataType.DOUBLE),
+        ir.TensorType(ir.DataType.COMPLEX64),
+        ir.TensorType(ir.DataType.COMPLEX128),
+    }:
+        return True
+    if isinstance(value, str):  # noqa: SIM102
+        if param.type_constraint.allowed_types & {ir.TensorType(ir.DataType.STRING)}:
+            return True
+    if isinstance(value, (list, tuple)):
+        if param.type_constraint.allowed_types & {
+            ir.TensorType(ir.DataType.INT32),
+            ir.TensorType(ir.DataType.INT64),
+            ir.TensorType(ir.DataType.FLOAT),
+            ir.TensorType(ir.DataType.DOUBLE),
+            ir.SequenceType(ir.TensorType(ir.DataType.INT32)),
+            ir.SequenceType(ir.TensorType(ir.DataType.INT64)),
+            ir.SequenceType(ir.TensorType(ir.DataType.FLOAT)),
+            ir.SequenceType(ir.TensorType(ir.DataType.DOUBLE)),
+        } and all(isinstance(i, (int)) for i in value):
+            # We will just allow any fx node and trust that the overload handles it
+            return True
+        if param.type_constraint.allowed_types & {
+            ir.TensorType(ir.DataType.FLOAT),
+            ir.TensorType(ir.DataType.DOUBLE),
+            ir.SequenceType(ir.TensorType(ir.DataType.FLOAT)),
+            ir.SequenceType(ir.TensorType(ir.DataType.DOUBLE)),
+        } and all(isinstance(i, (int, float)) for i in value):
+            # We will just allow any fx node and trust that the overload handles it
+            return True
+    if value is None and not param.required:
+        # An optional parameter is not supplied
+        return True
+
+    if not isinstance(value, ir.TypeProtocol):
+        return False
+
+    # Then check tensor types
+    if param.type_constraint.name in assigned_types:
+        # If a typevar is already bound, check if the value has the same type
+        assigned_type = assigned_types[param.type_constraint.name]
+        return assigned_type == value
+    # If the typevar is not bound, bind it to the value type
+    if value in param.type_constraint.allowed_types:
+        # TODO: Maybe just check dtype? Being more strict here for now
+        assigned_types[param.type_constraint.name] = value
+        return True
+    return False
+
+
+def _get_type_from_tensor(
+    tensor: torch.Tensor
+    | torch.SymBool
+    | torch.SymInt
+    | torch.SymFloat
+    | Sequence[torch.Tensor],
+) -> ir.TypeProtocol:
+    if isinstance(tensor, torch.Tensor):
+        return ir.TensorType(_torch_dtype_to_onnx_compatible_dtype(tensor.dtype))
+    if isinstance(tensor, torch.SymBool):
+        return ir.TensorType(ir.DataType.BOOL)
+    if isinstance(tensor, torch.SymInt):
+        return ir.TensorType(ir.DataType.INT64)
+    if isinstance(tensor, torch.SymFloat):
+        return ir.TensorType(ir.DataType.FLOAT)
+
+    # Handle sequences
+    first_tensor = next((item for item in tensor if item is not None), None)
+    if first_tensor is None:
+        return ir.SequenceType(ir.TensorType(ir.DataType.UNDEFINED))
+    return ir.SequenceType(
+        ir.TensorType(_torch_dtype_to_onnx_compatible_dtype(first_tensor.dtype))
+    )
+
+
+def _get_first_tensor_in_node_list(
+    nodes: Sequence[torch.fx.Node | None],
+) -> torch.Tensor | None:
+    for node in nodes:
+        if (
+            node is not None
+            and "val" in node.meta
+            and isinstance(node.meta["val"], torch.Tensor)
+        ):
+            return node.meta["val"]
+    return None
+
+
+def _get_named_fx_node_args(node: torch.fx.Node) -> dict[str, torch.fx.node.Argument]:
+    assert hasattr(node.target, "_schema")
+    torch_schema: torch.FunctionSchema = node.target._schema  # type: ignore[union-attr]
+    node_args = {}
+    for arg, schema_arg in zip(node.args, torch_schema.arguments):
+        node_args[schema_arg.name] = arg
+
+    node_args.update(node.kwargs)
+    return node_args
+
+
+def get_matching_overload(
+    node: torch.fx.Node,
+    overloads: Sequence[Callable],
+) -> tuple[Callable | None, str]:
+    """Get the overload that matches the node's arguments.
+
+    Args:
+        node: The node to match.
+        overloads: The overloads to match against.
+
+    Returns:
+        A tuple containing the matched overload and a string describing the reason for failure or success.
+    """
+    if not hasattr(node.target, "_schema"):
+        # FIXME(justinchuby): When the target is a builtin, we should instead
+        # Match only the inputs positionally. Figure out how to do that as right
+        # now we assume all inputs are named.
+        return overloads[
+            0
+        ], "The node target does not have a schema. Return the first one."
+    named_args = _get_named_fx_node_args(node)
+    # FIXME: Handle when we don't know the names of the arguments
+    schema_args: dict[str, torch.Argument] = {
+        arg.name: arg
+        for arg in node.target._schema.arguments  # type: ignore[union-attr]
+    }
+    failure_messages: list[str] = []
+    for overload in overloads:
+        assigned_types: dict[str, ir.TypeProtocol] = {}
+        fail_reason = ""
+        if not hasattr(overload, "signature"):
+            # When an overload does not have a signature, we assume it is a custom op and should be matched
+            return (
+                overload,
+                "The overload does not have a signature. Assuming it is a custom op and matching it.",
+            )
+        for param in overload.signature:
+            if param.name not in schema_args and param.required:
+                # We don't need to handle variadic inputs as there is none.
+                # A required parameter is not supplied.
+                fail_reason = "Required parameter not supplied"
+                break
+
+            # Get the argument
+            if param.name in named_args:
+                # Provided in Node args
+                arg = named_args[param.name]
+            elif (
+                param.name in schema_args
+                and schema_args[param.name].has_default_value()
+            ):
+                # Provided in schema args
+                arg = schema_args[param.name].default_value
+            elif param.has_default():
+                # Provided in the ONNX op definition
+                arg = param.default
+            else:
+                fail_reason = "Parameter not provided"
+                break
+
+            if isinstance(param, _schemas.Parameter):
+                if isinstance(arg, torch.Tensor):
+                    arg = _get_type_from_tensor(arg)  # type: ignore[assignment]
+                if isinstance(arg, (list, tuple)) and any(
+                    isinstance(t, torch.fx.Node) for t in arg
+                ):
+                    first_tensor = _get_first_tensor_in_node_list(arg)
+                    assert first_tensor is not None
+                    # FIXME: Handle symfloat here
+                    arg = ir.SequenceType(_get_type_from_tensor(first_tensor))  # type: ignore[assignment]
+                elif isinstance(arg, torch.fx.Node):
+                    meta_val = arg.meta["val"]
+                    arg = _get_type_from_tensor(meta_val)  # type: ignore[assignment]
+                # TODO: Handle None attributes
+                # FIXME: Handle symfloat etc.
+                # Handle tensors and Python values
+                if not _param_type_compatible_with_arg(param, arg, assigned_types):  # type: ignore[arg-type]
+                    fail_reason = (
+                        f"Parameter type not compatible with argument: param=`{param}`, "
+                        f"assigned_types=`{assigned_types}`, arg=`{arg}`"
+                    )
+                    break
+            elif isinstance(param, _schemas.AttributeParameter):
+                if not _attribute_type_compatible_with_arg(param, arg):  # type: ignore[arg-type]
+                    fail_reason = f"Attribute type not compatible with argument: param=`{param}`, arg=`{arg}`"
+                    break
+        if not fail_reason:
+            return overload, "Successfully matched overload"
+        else:
+            failure_messages.append(
+                f"- Failed to match overload `{overload}`: {fail_reason}"
+            )
+    return (
+        None,
+        f"All overloads did not match the node `{node.format_node()}`.\n"
+        + "\n".join(failure_messages),
+    )
+
+
+def _arg_has_complex_dtype(arg) -> bool:
+    """Check if the node has complex dtype recursively."""
+    if (
+        isinstance(arg, torch.fx.Node)
+        and "val" in arg.meta
+        and isinstance(arg.meta["val"], torch.Tensor)
+        and torch.is_complex(arg.meta["val"])
+    ):
+        return True
+    elif isinstance(arg, list):
+        return any(_arg_has_complex_dtype(item) for item in arg)
+    return False
+
+
+def dispatch(
+    node: torch.fx.Node, registry: _registration.ONNXRegistry
+) -> tuple[Callable | None, str]:
+    """Dispatch a node to an ONNX function based on the node's target and the ONNX registry.
+
+    Args:
+        node: The node to dispatch.
+        registry: The ONNX registry to use for dispatching.
+
+    Returns:
+        A tuple containing the matched ONNX function and a string describing the reason for failure or success.
+    """
+    # TODO: Handle when node does not have a target
+    decomp_metas = registry.get_decomps(node.target)  # type: ignore[arg-type]
+    # Determine if the node has complex inputs.
+    is_complex = any(_arg_has_complex_dtype(arg) for arg in node.args) or any(
+        _arg_has_complex_dtype(arg) for arg in node.kwargs.values()
+    )
+    if is_complex:
+        decomp_metas = [decomp for decomp in decomp_metas if decomp.is_complex]
+        if not decomp_metas:
+            return None, "No decompositions registered for the complex-valued input"
+    else:
+        decomp_metas = [decomp for decomp in decomp_metas if not decomp.is_complex]
+        if not decomp_metas:
+            return None, "No decompositions registered for the real-valued input"
+
+    if len(decomp_metas) == 1:
+        return (
+            decomp_metas[0].onnx_function,
+            "Fast path: Only one decomposition is defined",
+        )
+
+    overload, message = get_matching_overload(
+        node, [decomp.onnx_function for decomp in decomp_metas]
+    )
+    return overload, message
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_errors.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_errors.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff41bbe695fe7d0ebe60c40014332abc36430d0f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_errors.py
@@ -0,0 +1,21 @@
+"""Error classes for the ONNX exporter."""
+
+from __future__ import annotations
+
+import torch.onnx.errors
+
+
+class TorchExportError(torch.onnx.errors.OnnxExporterError):
+    """Error during graph capturing using torch.export."""
+
+
+class ConversionError(torch.onnx.errors.OnnxExporterError):
+    """Error during ExportedProgram to ONNX conversion."""
+
+
+class DispatchError(ConversionError):
+    """Error during ONNX Function dispatching."""
+
+
+class GraphConstructionError(ConversionError):
+    """Error during ONNX graph construction."""
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_fx_passes.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_fx_passes.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c19b552d0e4f07e98814839fa1b07be23d22801
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_fx_passes.py
@@ -0,0 +1,51 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import torch
+import torch.export
+import torch.fx
+from torch.onnx._internal.exporter import _decomp, _registration
+from torch.onnx._internal.fx import diagnostics, passes
+
+
+def decompose_with_registry(
+    exported_program: torch.export.ExportedProgram, registry: _registration.ONNXRegistry
+) -> torch.export.ExportedProgram:
+    """Decompose the exported program with the given registry.
+
+    This function is needed so it shows clearly on the profiler results.
+    """
+    onnx_registered_ops = set(_decomp.get_onnx_implemented_overloads(registry))
+    decomp_table = _decomp.create_onnx_friendly_decomposition_table(onnx_registered_ops)
+    # Try to preserve some known CompositeImplicitAutograd ops
+    to_preserve = _decomp.get_preserve_ops()
+    # We can only preserve implemented ops
+    can_preserve = tuple(to_preserve.intersection(onnx_registered_ops))
+    return exported_program.run_decompositions(decomp_table, _preserve_ops=can_preserve)
+
+
+def insert_type_promotion_nodes(
+    graph_module: torch.fx.GraphModule,
+) -> torch.fx.GraphModule:
+    """Inplace pass to insert explicit type promotion nodes."""
+    diagnostic_context = diagnostics.DiagnosticContext(
+        "torch.onnx.export",
+        torch.__version__,
+    )
+    return passes.InsertTypePromotion(diagnostic_context, graph_module).run()
+
+
+def remove_assertion_nodes(graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
+    """Remove all assertion and check nodes from the FX graph"""
+    aten_assertion_targets = {
+        torch.ops.aten.sym_constrain_range_for_size.default,
+        torch.ops.aten._assert_async.default,
+        torch.ops.aten._assert_async.msg,
+        torch.ops.aten._assert_scalar.default,
+        torch.ops.aten._assert_tensor_metadata.default,
+    }
+    for node in graph_module.graph.nodes:
+        if node.op == "call_function" and node.target in aten_assertion_targets:
+            graph_module.graph.erase_node(node)
+    graph_module.recompile()
+    return graph_module
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_ir_passes.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_ir_passes.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e8748443e2b1318f4d57d2e034897581c90f24a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_ir_passes.py
@@ -0,0 +1,41 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import logging
+from typing import Sequence
+
+from onnxscript import ir
+
+
+logger = logging.getLogger(__name__)
+
+
+def rename_inputs(model: ir.Model, new_names: Sequence[str]) -> None:
+    # TODO: Ensure the names do not have duplicates
+    for input, new_name in zip(model.graph.inputs, new_names):
+        input.metadata_props["pkg.torch.onnx.original_node_name"] = str(input.name)
+        input.name = new_name
+
+
+def rename_outputs(model: ir.Model, new_names: Sequence[str]) -> None:
+    for output, new_name in zip(model.graph.outputs, new_names):
+        output.metadata_props["pkg.torch.onnx.original_node_name"] = str(output.name)
+        output.name = new_name
+
+
+def add_torchlib_common_imports(model: ir.Model) -> None:
+    """Hack to add torchlib common imports to the model."""
+
+    try:
+        # TODO(justinchuby): Remove this hack and improved onnxscript
+        from onnxscript.function_libs.torch_lib.ops import common as common_ops
+
+        model.opset_imports["pkg.onnxscript.torch_lib.common"] = 1
+        rank_func = ir.serde.deserialize_function(common_ops.Rank.to_function_proto())
+        is_scalar_func = ir.serde.deserialize_function(
+            common_ops.IsScalar.to_function_proto()
+        )
+        model.functions[rank_func.identifier()] = rank_func
+        model.functions[is_scalar_func.identifier()] = is_scalar_func
+    except Exception:
+        logger.exception("Failed to add torchlib common imports to the model.")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_isolated.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_isolated.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a5c5fcdf793c0df4662c1e2c11597b09351d3d0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_isolated.py
@@ -0,0 +1,55 @@
+"""Isolated calls to methods that may segfault."""
+
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import multiprocessing
+import os
+import warnings
+from typing import Callable
+
+
+_IS_WINDOWS = os.name == "nt"
+
+
+def _call_function_and_return_exception(func, args, kwargs):
+    """Call function and return a exception if there is one."""
+
+    try:
+        return func(*args, **kwargs)
+    except Exception as e:
+        return e
+
+
+def safe_call(func: Callable, *args, **kwargs):
+    """Call a function in a separate process.
+
+    Args:
+        func: The function to call.
+        args: The positional arguments to pass to the function.
+        kwargs: The keyword arguments to pass to the function.
+
+    Returns:
+        The return value of the function.
+
+    Raises:
+        Exception: If the function raised an exception.
+    """
+    if _IS_WINDOWS:
+        # On Windows, we cannot create a new process with fork.
+        warnings.warn(
+            f"A new process is not created for {func} on Windows.", stacklevel=1
+        )
+        return func(*args, **kwargs)
+
+    with multiprocessing.get_context("fork").Pool(1) as pool:
+        # It is important to fork a process here to prevent the main logic from
+        # running again when the user does not place it under a `if __name__ == "__main__":`
+        # block.
+        result = pool.apply_async(
+            _call_function_and_return_exception, (func, args, kwargs)
+        )
+        result = result.get(timeout=5)
+    if isinstance(result, Exception):
+        raise result
+    return result
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_onnx_program.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_onnx_program.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fb62a5961f87516a12657a41fc777f0b7ddd8b1
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_onnx_program.py
@@ -0,0 +1,317 @@
+# mypy: allow-untyped-defs
+# mypy: disable-error-code="attr-defined,name-defined"
+from __future__ import annotations
+
+
+__all__ = ["ONNXProgram"]
+
+import copy
+import gc
+import logging
+import os
+import tempfile
+import textwrap
+import warnings
+from typing import Callable, Sequence, TYPE_CHECKING
+
+import torch
+from torch.onnx._internal._lazy_import import onnx, onnxscript_apis, onnxscript_ir as ir
+from torch.utils import _pytree
+
+
+# NOTE: DO NOT import module from torch.onnx._internal to this module in the global scope
+# because ONNXProgram is exposed to the public API
+
+if TYPE_CHECKING:
+    import onnxruntime as ort
+
+_LARGE_MODEL_THRESHOLD = 1536 * 1024 * 1024  # 1536MB
+
+logger = logging.getLogger(__name__)
+
+
+def _ort_session_initializer(model: str | bytes) -> ort.InferenceSession:
+    """Initialize an ONNX Runtime inference session with the specified model."""
+    import onnxruntime as ort
+
+    session_options = ort.SessionOptions()
+    session_options.log_severity_level = 3  # 3: Error
+    possible_providers = (
+        "CUDAExecutionProvider",
+        "CPUExecutionProvider",
+    )
+    available_providers = set(ort.get_available_providers())
+    providers = [
+        provider for provider in possible_providers if provider in available_providers
+    ]
+    return ort.InferenceSession(
+        model, providers=providers, sess_options=session_options
+    )
+
+
+def _count_initializer_size(graph: ir.Graph) -> int:
+    """Count the total size of the initializers in bytes."""
+    return sum(
+        v.const_value.nbytes
+        for v in graph.initializers.values()
+        if v.const_value is not None
+    )
+
+
+class ONNXProgram:
+    """A class to represent an ONNX program that is callable with torch tensors."""
+
+    def __init__(
+        self, model: ir.Model, exported_program: torch.export.ExportedProgram | None
+    ):
+        """Initialize the ONNX program with the specified model and exported program.
+        Args:
+            model: The ONNX model.
+            exported_program: The exported program that produced the ONNX model. Optional.
+        """
+        self.model: ir.Model = model
+        self.exported_program = exported_program
+        self._inference_session: ort.InferenceSession | None = None
+        self._tempdir: tempfile.TemporaryDirectory | None = None
+
+    def __repr__(self) -> str:
+        return f"""\
+ONNXProgram(
+    model=
+{textwrap.indent(str(self.model), ' ' * 8)}
+    ,
+    exported_program=
+{textwrap.indent(str(self.exported_program), ' ' * 8)}
+)
+"""
+
+    def __call__(self, *args, **kwargs) -> Sequence[torch.Tensor]:
+        """Run the ONNX model with the same arguments you would provide to the GraphModule."""
+        import onnxruntime as ort
+
+        flatten_args = _process_args(args, kwargs)
+
+        if self._inference_session is None:
+            self.initialize_inference_session()
+
+        assert self._inference_session is not None
+
+        # We don't expect non-tensor as inputs
+        ort_input = {
+            k.name: v.numpy(force=True)
+            for k, v in zip(self.model.graph.inputs, flatten_args)
+        }
+        run_options = ort.RunOptions()
+        run_options.log_severity_level = 3  # 3: Error
+        logger.debug("Running the inference session with %s arguments.", len(ort_input))
+        outputs = self._inference_session.run(None, ort_input, run_options=run_options)
+        logger.debug("Inference session run completed.")
+        # TODO(justinchuby): Maybe output complex tensors as needed
+        return tuple(torch.from_numpy(output) for output in outputs)
+
+    @property
+    def model_proto(self) -> onnx.ModelProto:
+        """Compatibility property for `torch.onnx.ONNXProgram.model_proto`."""
+        return ir.serde.serialize_model(self.model)
+
+    def save(
+        self,
+        destination: str | os.PathLike,
+        *,
+        include_initializers: bool = True,
+        keep_initializers_as_inputs: bool = False,
+        external_data: bool | None = None,
+        **_,
+    ):
+        """Save the ONNX model to the specified destination.
+
+        When `external_data` is `True` or the model is larger than 2GB,
+        the weights are saved as external data in a separate file.
+
+        Initializer (model weights) serialization behaviors:
+        - include_initializers=True, keep_initializers_as_inputs=False (default):
+            The initializers are included in the saved model.
+        - include_initializers=True, keep_initializers_as_inputs=True:
+            The initializers are included in the saved model and kept as model inputs.
+            Choose this option if you want the ability to override the model weights
+            during inference.
+        - include_initializers=False, keep_initializers_as_inputs=False:
+            The initializers are not included in the saved model and are not listed
+            as model inputs. Choose this option if you want to attach the initializers
+            to the ONNX model in a separate, post-processing, step.
+        - include_initializers=False, keep_initializers_as_inputs=True:
+            The initializers are not included in the saved model but are listed as model
+            inputs. Choose this option if you want to supply the initializers during
+            inference and want to minimize the size of the saved model.
+
+        Args:
+            destination: The path to save the ONNX model to.
+            include_initializers: Whether to include the initializers in the saved model.
+            keep_initializers_as_inputs: Whether to keep the initializers as inputs in the saved model.
+                If `True`, the initializers are added as inputs to the model which means they can be overwritten.
+                by providing the initializers as model inputs.
+            external_data: Whether to save the weights as external data in a separate file.
+
+        Raises:
+            TypeError: If `external_data` is `True` and `destination` is not a file path.
+        """
+        original_initializers = copy.copy(self.model.graph.initializers)
+        original_inputs = copy.copy(self.model.graph.inputs)
+
+        # Adjust the model based on options
+        if not include_initializers:
+            self.model.graph.initializers.clear()
+        if keep_initializers_as_inputs:
+            self.model.graph.inputs.extend(original_initializers.values())  # type: ignore[arg-type]
+
+        # Save the model to disk
+        if (
+            external_data
+            or _count_initializer_size(self.model.graph) > _LARGE_MODEL_THRESHOLD
+        ):
+            onnxscript_apis.save_model_with_external_data(self.model, destination)
+        else:
+            ir.save(self.model, destination)
+
+        # Revert the changes to the model
+        if not include_initializers:
+            self.model.graph.initializers.update(original_initializers)
+        if keep_initializers_as_inputs:
+            self.model.graph.inputs.clear()
+            self.model.graph.inputs.extend(original_inputs)
+
+    def apply_weights(self, state_dict: dict[str, torch.Tensor]) -> None:
+        """Apply the weights from the specified state dict to the ONNX model.
+        Args:
+            state_dict: The state dict containing the weights to apply to the ONNX model.
+        """
+        from torch.onnx._internal.exporter import _core
+
+        for name, tensor in state_dict.items():
+            if name in self.model.graph.initializers:
+                self.model.graph.initializers[name].const_value = _core.TorchTensor(
+                    tensor, name
+                )
+            else:
+                warnings.warn(
+                    f"Weight '{name}' not found in the model. Skipped applying.",
+                    category=torch.onnx.errors.OnnxExporterWarning,
+                    stacklevel=1,
+                )
+
+    def initialize_inference_session(
+        self,
+        initializer: Callable[
+            [str | bytes], ort.InferenceSession
+        ] = _ort_session_initializer,
+    ) -> None:
+        """Initialize the ONNX Runtime inference session.
+
+        Args:
+            initializer: The function to initialize the ONNX Runtime inference
+                session with the specified model. By default, it uses the
+                :func:`_ort_session_initializer` function.
+        """
+        # TODO(justinchuby): Allow different inference options
+        logger.debug("Initializing the inference session.")
+        if (
+            byte_size := _count_initializer_size(self.model.graph)
+        ) > _LARGE_MODEL_THRESHOLD:
+            logger.debug("The model initializers is larger than 1.5GB (%s).", byte_size)
+            # Save the model to a temporary file if too large
+            self._tempdir = tempfile.TemporaryDirectory(ignore_cleanup_errors=True)
+            model_path = os.path.join(self._tempdir.name, "model.onnx")
+            self.save(model_path, external_data=True)
+            model = model_path
+        else:
+            model = self.model_proto.SerializeToString()  # type: ignore[assignment]
+
+        self._inference_session = initializer(model)
+        logger.debug("Inference session initialized.")
+
+    def release(self) -> None:
+        """Release the inference session.
+
+        You may call this method to release the resources used by the inference session.
+        """
+        # Release the inference session first so that the model file can be deleted
+        if self._inference_session is not None:
+            self._inference_session = None
+        gc.collect()
+        if self._tempdir is not None:
+            self._tempdir.cleanup()
+            self._tempdir = None
+
+
+def _process_args(args, kwargs) -> tuple[torch.Tensor, ...]:
+    """Process input arguments for the ONNX model."""
+    args = _flatten_inputs(args, kwargs)
+    args = _remove_none_from_inputs(args)
+    args = _remove_non_tensor(args)
+    args = _convert_complex_to_real_representation(args)
+    return args
+
+
+def _flatten_inputs(model_args, model_kwargs):
+    flattened_args, _ = _pytree.tree_flatten((model_args, model_kwargs))
+    return flattened_args
+
+
+def _remove_none_from_inputs(model_args):
+    return tuple(arg for arg in model_args if arg is not None)
+
+
+def _remove_non_tensor(model_args):
+    """Remove the non-tensor input arguments.
+
+    Dynamo does not support non-tensor input arguments (https://github.com/pytorch/pytorch/issues/99534).
+
+    Specifically, it does put the input into graph with an empty node, but consumed by no ones.
+    The concrete value is embedded into the graph as a constant arg of a target node. Meta
+    suggests in this case that one should rewrite the model code to make it tensor if the
+    input value is supposed to change at runtime. We might need to further investigate
+    the feasibility of that suggestion.
+
+    For example,
+
+        def func(x, b=1.0):
+            y = x + b
+            z = y.relu()
+            return (y, z)
+
+        x = torch.randn(1, 1, 2, dtype=torch.float32)
+        gm_fun, _ = dynamo.export(func, x, b=8.0, aten_graph=True, tracing_mode="real")
+
+        # class GraphModule(torch.nn.Module):
+        #     def forward(self, x, b):
+        #         arg0: f32[1, 1, 2], arg1, = fx_pytree.tree_flatten_spec(([x, b], {}), self._in_spec)
+        #         # File: path/to/pytorch/test_constant_input.py:5, code: y = x + b
+        #         add_tensor: f32[1, 1, 2] = torch.ops.aten.add.Tensor(arg0, 8.0);  arg0 = None
+
+        #         # File: path/to/pytorch/test_constant_input.py:6, code: z = y.relu()
+        #         relu_default: f32[1, 1, 2] = torch.ops.aten.relu.default(add_tensor)
+        #         return pytree.tree_unflatten([add_tensor, relu_default], self._out_spec)
+
+    Empty torch.fx.Node input leading to a mismatched number of input with PyTorch, as
+    it's ignored in ONNX graph. Thus, we delete the useless input here.
+
+    """
+
+    return tuple(
+        arg for arg in model_args if not isinstance(arg, (int, float, bool, str))
+    )
+
+
+def _convert_complex_to_real_representation(model_args):
+    """Convert complex dtype tensors to real representation tensors.
+
+    ONNX does not support complex dtype tensors. Thus, we convert complex dtype tensors
+    to real representation tensors (i.e., float dtype tensors with an extra dimension
+    representing the real and imaginary parts of the complex number).
+    """
+    return tuple(
+        torch.view_as_real(arg.resolve_conj())
+        if isinstance(arg, torch.Tensor) and arg.is_complex()
+        else arg
+        for arg in model_args
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_registration.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_registration.py
new file mode 100644
index 0000000000000000000000000000000000000000..0afa084b06c80dac628e956d24a403e1f9bbd0ff
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_registration.py
@@ -0,0 +1,258 @@
+"""Module for handling ATen to ONNX functions registration.
+
+https://github.com/pytorch/pytorch/blob/6aa5bb1a76dee8112f1a9e7c194c790b5cdc6462/torch/onnx/_internal/fx/registration.py
+"""
+
+# NOTE: Why do we need a different registry than the one in torchlib?
+# The registry in torchlib is used to register functions that are already implemented in
+# torchlib, and is designed to be a static singleton. It does not take into account custom ops or different
+# opsets etc. The registry implemented for the exporter is designed to be modifiable at
+# export time by users, and is designed with dispatching in mind.
+
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import dataclasses
+import logging
+import math
+import operator
+import types
+import typing
+from typing import Callable, Literal, Union
+from typing_extensions import TypeAlias
+
+import torch
+import torch._ops
+from torch.onnx._internal._lazy_import import onnxscript_apis
+from torch.onnx._internal.exporter import _schemas
+
+
+if typing.TYPE_CHECKING:
+    import onnxscript
+
+_DEFAULT_OPSET_VERSION = 18
+
+
+TorchOp: TypeAlias = Union[torch._ops.OpOverload, types.BuiltinFunctionType, Callable]
+
+logger = logging.getLogger(__name__)
+
+
+@dataclasses.dataclass(frozen=True)
+class OnnxDecompMeta:
+    """A wrapper of onnx-script function with additional metadata.
+
+    onnx_function: The onnx-script function from torchlib.
+    fx_target: The PyTorch node callable target.
+    is_custom: Whether the function is a custom function.
+    is_complex: Whether the function is a function that handles complex valued inputs.
+    device: The device the function is registered to. If None, it is registered to all devices.
+    """
+
+    onnx_function: Callable
+    fx_target: TorchOp
+    is_custom: bool = False
+    is_complex: bool = False
+    device: Literal["cuda", "cpu"] | str | None = None  # noqa: PYI051
+
+
+def _get_overload(qualified_name: str) -> torch._ops.OpOverload | None:
+    """Obtain the torch op from ::[.]"""
+    # TODO(justinchuby): Handle arbitrary custom ops
+    namespace, opname_overload = qualified_name.split("::")
+    op_name, *maybe_overload = opname_overload.split(".", 1)
+    if namespace == "_operator":
+        # Builtin functions
+        return getattr(operator, op_name)
+    if namespace == "math":
+        return getattr(math, op_name)
+    if namespace == "torchvision":
+        try:
+            import torchvision.ops  # type: ignore[import-untyped]
+        except ImportError:
+            logger.warning("torchvision is not installed. Skipping %s", qualified_name)
+            return None
+        try:
+            return getattr(torchvision.ops, op_name)
+        except AttributeError:
+            logger.warning("Failed to find torchvision op '%s'", qualified_name)
+            return None
+        except Exception:
+            logger.exception("Failed to find torchvision op '%s'", qualified_name)
+    try:
+        op_packet = getattr(getattr(torch.ops, namespace), op_name)
+        if maybe_overload:
+            overload = maybe_overload[0]
+        elif "default" in op_packet._overload_names or "" in op_packet._overload_names:
+            # Has a default overload
+            overload = "default"
+        else:
+            logger.warning(
+                "'%s' does not have a 'default' overload. This could be an error in specifying the op name. Ignoring.",
+                qualified_name,
+                stacklevel=1,
+            )
+            return None
+
+        return getattr(op_packet, overload)  # type: ignore[call-overload]
+    except AttributeError:
+        if qualified_name.endswith("getitem"):
+            # This is a special case where we registered the function incorrectly,
+            # but for BC reasons (pt<=2.4) we need to keep it.
+            return None
+        logger.info("'%s' is not found in this version of PyTorch.", qualified_name)
+        return None
+    except Exception:
+        logger.exception("Failed to find torch op '%s'", qualified_name)
+        return None
+
+
+class ONNXRegistry:
+    """Registry for ONNX functions.
+
+    The registry maintains a mapping from qualified names to symbolic functions under a
+    fixed opset version. It supports registering custom onnx-script functions and for
+    dispatcher to dispatch calls to the appropriate function.
+
+    """
+
+    def __init__(self) -> None:
+        """Initializes the registry"""
+
+        # TODO: Design multi-opset version support
+        self._opset_version = _DEFAULT_OPSET_VERSION
+
+        self.functions: dict[TorchOp | str, list[OnnxDecompMeta]] = {}
+
+    @property
+    def opset_version(self) -> int:
+        """The ONNX opset version the exporter should target.
+
+        Defaults to the latest supported ONNX opset version: 18.
+        The default version will increment over time as ONNX continues to evolve.
+        """
+
+        return self._opset_version
+
+    @classmethod
+    def from_torchlib(cls) -> ONNXRegistry:
+        """Populates the registry with ATen functions from torchlib.
+
+        Args:
+            torchlib_registry: The torchlib registry to use for populating the registry.
+        """
+        registry = cls()
+
+        torchlib_ops = onnxscript_apis.get_torchlib_ops()
+
+        for meta in torchlib_ops:
+            qualified_name = meta.qualified_name
+            overload_func = meta.function
+            domain = meta.domain
+            name = meta.name
+            try:
+                # NOTE: This is heavily guarded with try-except because we don't want
+                # to fail the entire registry population if one function fails.
+                if qualified_name.startswith("internal::"):
+                    # Skip the custom defined internal functions
+                    continue
+                target = _get_overload(qualified_name)
+                if target is None:
+                    continue
+
+                overload_func.signature = _schemas.OpSignature.from_function(  # type: ignore[attr-defined]
+                    overload_func,
+                    domain,
+                    name,
+                )
+                onnx_decomposition = OnnxDecompMeta(
+                    onnx_function=overload_func,
+                    fx_target=target,
+                    is_custom=False,
+                    is_complex=meta.is_complex,
+                )
+                registry._register(target, onnx_decomposition)
+            except Exception:
+                logger.exception("Failed to register '%s'. Skipped", qualified_name)
+                continue
+        return registry
+
+    def _register(
+        self,
+        target: TorchOp,
+        onnx_decomposition: OnnxDecompMeta,
+    ) -> None:
+        """Registers a OnnxDecompMeta to an operator.
+
+        Args:
+            target: The PyTorch node callable target.
+            onnx_decomposition: The OnnxDecompMeta to register.
+        """
+        target_or_name: str | TorchOp
+        if isinstance(target, torch._ops.OpOverload):
+            # Get the qualified name of the aten op because torch._ops.OpOverload lookup in
+            # a dictionary is unreliable for some reason.
+            target_or_name = target.name()
+        else:
+            target_or_name = target
+        if onnx_decomposition.is_custom:
+            self.functions.setdefault(target_or_name, []).insert(0, onnx_decomposition)
+        else:
+            self.functions.setdefault(target_or_name, []).append(onnx_decomposition)
+
+    def register_op(
+        self,
+        target: TorchOp,
+        function: onnxscript.OnnxFunction | onnxscript.TracedOnnxFunction,
+        is_complex: bool = False,
+    ) -> None:
+        """Registers a custom operator: torch.ops....
+
+        Args:
+            target: The PyTorch node callable target.
+            function: The onnx-script function to register.
+            is_complex: Whether the function is a function that handles complex valued inputs.
+        """
+        onnx_decomposition = OnnxDecompMeta(
+            onnx_function=function,
+            fx_target=target,
+            is_custom=True,
+            is_complex=is_complex,
+        )
+        self._register(target, onnx_decomposition)
+
+    def get_decomps(self, target: TorchOp) -> list[OnnxDecompMeta]:
+        """Returns a list of OnnxDecompMeta for the given op: torch.ops....
+
+        The list is ordered by the time of registration. The custom operators should come
+        first in the list.
+
+        Args:
+            target: The PyTorch node callable target.
+        Returns:
+            A list of OnnxDecompMeta corresponding to the given name, or None if
+            the name is not in the registry.
+        """
+        target_or_name: str | TorchOp
+        if isinstance(target, torch._ops.OpOverload):
+            # Get the qualified name of the aten op because torch._ops.OpOverload lookup in
+            # a dictionary is unreliable for some reason.
+            target_or_name = target.name()
+        else:
+            target_or_name = target
+        decomps = self.functions.get(target_or_name, [])
+        return sorted(decomps, key=lambda x: x.is_custom, reverse=True)
+
+    def is_registered(self, target: TorchOp) -> bool:
+        """Returns whether the given op is registered: torch.ops....
+
+        Args:
+            target: The PyTorch node callable target.
+
+        Returns:
+            True if the given op is registered, otherwise False.
+        """
+        return bool(self.get_decomps(target))
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}(functions={self.functions})"
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_reporting.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_reporting.py
new file mode 100644
index 0000000000000000000000000000000000000000..86c79df97430a6b91faf41bf8f40637020d8779f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_reporting.py
@@ -0,0 +1,194 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import dataclasses
+import re
+from typing import TYPE_CHECKING
+
+from torch.onnx._internal.exporter import _analysis, _registration, _verification
+
+
+if TYPE_CHECKING:
+    import os
+
+    from onnxscript import ir
+
+    import torch
+
+
+@dataclasses.dataclass
+class ExportStatus:
+    # Whether torch.export.export.export() succeeds
+    torch_export: bool | None = None
+    # Whether torch.export.export.export(..., strict=False) succeeds
+    torch_export_non_strict: bool | None = None
+    # Whether torch.jit.trace succeeds
+    torch_jit: bool | None = None
+    # Whether ONNX translation succeeds
+    onnx_translation: bool | None = None
+    # Whether ONNX model passes onnx.checker.check_model
+    onnx_checker: bool | None = None
+    # Whether ONNX model runs successfully with ONNX Runtime
+    onnx_runtime: bool | None = None
+    # Whether the output of the ONNX model is accurate
+    output_accuracy: bool | None = None
+
+
+def _status_emoji(status: bool | None) -> str:
+    if status is None:
+        return "⚪"
+    return "✅" if status else "❌"
+
+
+def _format_export_status(status: ExportStatus) -> str:
+    return (
+        f"```\n"
+        f"{_status_emoji(status.torch_export)} Obtain model graph with `torch.export.export`\n"
+        f"{_status_emoji(status.torch_export_non_strict)} Obtain model graph with `torch.export.export(..., strict=False)`\n"
+        f"{_status_emoji(status.torch_jit)} Obtain model graph with `torch.jit.trace`\n"
+        f"{_status_emoji(status.onnx_translation)} Translate the graph into ONNX\n"
+        f"{_status_emoji(status.onnx_checker)} Run `onnx.checker` on the ONNX model\n"
+        f"{_status_emoji(status.onnx_runtime)} Execute the model with ONNX Runtime\n"
+        f"{_status_emoji(status.output_accuracy)} Validate model output accuracy\n"
+        f"```\n\n"
+    )
+
+
+def _strip_color_from_string(text: str) -> str:
+    # This regular expression matches ANSI escape codes
+    # https://github.com/pytorch/pytorch/blob/9554a9af8788c57e1c5222c39076a5afcf0998ae/torch/_dynamo/utils.py#L2785-L2788
+    ansi_escape = re.compile(r"\x1B[@-_][0-?]*[ -/]*[@-~]")
+    return ansi_escape.sub("", text)
+
+
+def _format_exported_program(exported_program: torch.export.ExportedProgram) -> str:
+    # Adapted from https://github.com/pytorch/pytorch/pull/128476
+    # to remove colors
+    # Even though we can call graph_module.print_readable directly, since the
+    # colored option was added only recently, we can't guarantee that the
+    # version of PyTorch used by the user has this option. Therefore, we
+    # still call str(ExportedProgram)
+    text = f"```python\n{_strip_color_from_string(str(exported_program))}\n```\n\n"
+    return text
+
+
+def construct_report_file_name(timestamp: str, status: ExportStatus) -> str:
+    # Status could be None. So we need to check for False explicitly.
+    if not (status.torch_export or status.torch_export_non_strict or status.torch_jit):
+        # All strategies failed
+        postfix = "pt_export"
+    elif status.onnx_translation is False:
+        postfix = "conversion"
+    elif status.onnx_checker is False:
+        postfix = "checker"
+    elif status.onnx_runtime is False:
+        postfix = "runtime"
+    elif status.output_accuracy is False:
+        postfix = "accuracy"
+    elif status.torch_export is False or status.torch_export_non_strict is False:
+        # Some strategies failed
+        postfix = "strategies"
+    else:
+        postfix = "success"
+    return f"onnx_export_{timestamp}_{postfix}.md"
+
+
+def format_decomp_comparison(
+    pre_decomp_unique_ops: set[str],
+    post_decomp_unique_ops: set[str],
+) -> str:
+    """Format the decomposition comparison result.
+
+    Args:
+        unique_ops_in_a: The unique ops in the first program.
+        unique_ops_in_b: The unique ops in the second program.
+
+    Returns:
+        The formatted comparison result.
+    """
+    return (
+        f"Ops exist only in the ExportedProgram before decomposition: `{sorted(pre_decomp_unique_ops)}`\n\n"
+        f"Ops exist only in the ExportedProgram after decomposition: `{sorted(post_decomp_unique_ops)}`\n"
+    )
+
+
+def format_verification_infos(
+    verification_infos: list[_verification.VerificationInfo],
+) -> str:
+    """Format the verification result.
+
+    Args:
+        verification_infos: The verification result.
+
+    Returns:
+        The formatted verification result.
+    """
+    return "\n".join(
+        f"`{info.name}`: `max_abs_diff={info.max_abs_diff:e}`, `max_rel_diff={info.max_rel_diff:e}`, "
+        f"`abs_diff_hist={info.abs_diff_hist}`, `rel_diff_hist={info.rel_diff_hist}`"
+        for info in verification_infos
+    )
+
+
+def create_torch_export_error_report(
+    filename: str | os.PathLike,
+    formatted_traceback: str,
+    *,
+    export_status: ExportStatus,
+    profile_result: str | None,
+):
+    with open(filename, "w", encoding="utf-8") as f:
+        f.write("# PyTorch ONNX Conversion Error Report\n\n")
+        f.write(_format_export_status(export_status))
+        f.write("Error message:\n\n")
+        f.write("```pytb\n")
+        f.write(formatted_traceback)
+        f.write("```\n\n")
+        if profile_result is not None:
+            f.write("## Profiling result\n\n")
+            f.write("```\n")
+            f.write(profile_result)
+            f.write("```\n")
+
+
+def create_onnx_export_report(
+    filename: str | os.PathLike,
+    formatted_traceback: str,
+    program: torch.export.ExportedProgram,
+    *,
+    decomp_comparison: str | None = None,
+    export_status: ExportStatus,
+    profile_result: str | None,
+    model: ir.Model | None = None,
+    registry: _registration.ONNXRegistry | None = None,
+    verification_result: str | None = None,
+):
+    with open(filename, "w", encoding="utf-8") as f:
+        f.write("# PyTorch ONNX Conversion Report\n\n")
+        f.write(_format_export_status(export_status))
+        f.write("## Error messages\n\n")
+        f.write("```pytb\n")
+        f.write(formatted_traceback)
+        f.write("\n```\n\n")
+        f.write("## Exported program\n\n")
+        f.write(_format_exported_program(program))
+        if model is not None:
+            f.write("## ONNX model\n\n")
+            f.write("```python\n")
+            f.write(str(model))
+            f.write("\n```\n\n")
+        f.write("## Analysis\n\n")
+        _analysis.analyze(program, file=f, registry=registry)
+        if decomp_comparison is not None:
+            f.write("\n## Decomposition comparison\n\n")
+            f.write(decomp_comparison)
+            f.write("\n")
+        if verification_result is not None:
+            f.write("\n## Verification results\n\n")
+            f.write(verification_result)
+            f.write("\n")
+        if profile_result is not None:
+            f.write("\n## Profiling result\n\n")
+            f.write("```\n")
+            f.write(profile_result)
+            f.write("```\n")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_schemas.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_schemas.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbc406053fbee88eec2d6685a4423d862634a68f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_schemas.py
@@ -0,0 +1,561 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import collections.abc
+import dataclasses
+import inspect
+import logging
+import types
+import typing
+from typing import Any, Iterator, Mapping, Optional, Sequence, TypeVar, Union
+
+import onnx
+
+import onnxscript
+from onnxscript import ir
+
+
+logger = logging.getLogger(__name__)
+
+
+# A special value to indicate that the default value is not specified
+class _Empty:
+    def __repr__(self):
+        return "_EMPTY_DEFAULT"
+
+
+_EMPTY_DEFAULT = _Empty()
+
+# Map from python type to corresponding ONNX AttributeProto type
+_PY_TYPE_TO_ATTR_TYPE = {
+    float: ir.AttributeType.FLOAT,
+    int: ir.AttributeType.INT,
+    str: ir.AttributeType.STRING,
+    bool: ir.AttributeType.INT,
+    ir.Tensor: ir.AttributeType.TENSOR,
+    ir.TensorProtocol: ir.AttributeType.TENSOR,
+    ir.Graph: ir.AttributeType.GRAPH,
+    ir.GraphProtocol: ir.AttributeType.GRAPH,
+}
+
+# Map from python type to corresponding ONNX AttributeProto type,
+# for repeated (i.e., list of) values
+_LIST_TYPE_TO_ATTR_TYPE = {
+    float: ir.AttributeType.FLOATS,
+    int: ir.AttributeType.INTS,
+    str: ir.AttributeType.STRINGS,
+    bool: ir.AttributeType.INTS,
+    ir.Tensor: ir.AttributeType.TENSORS,
+    ir.TensorProtocol: ir.AttributeType.TENSORS,
+    ir.Graph: ir.AttributeType.GRAPHS,
+    ir.GraphProtocol: ir.AttributeType.GRAPHS,
+}
+
+_ALL_VALUE_TYPES = (
+    {ir.TensorType(dtype) for dtype in ir.DataType}
+    | {ir.SequenceType(ir.TensorType(dtype)) for dtype in ir.DataType}
+    | {ir.OptionalType(ir.TensorType(dtype)) for dtype in ir.DataType}
+)
+
+# TypeAnnotationValue represents the (value of) valid type-annotations recognized
+# by ONNX Script. Currently, it supports
+# - float, int, str (primitive attribute types)
+# - Sequence[float], Sequence[int], Sequence[str] (attribute types)
+# - Tensor types
+# - Sequence[Tensor] types
+# - Union of above 2
+# - TypeVars with above bounds
+# - Above types with annotation attached
+TypeAnnotationValue = Any
+
+
+@dataclasses.dataclass(frozen=True)
+class TypeConstraintParam:
+    """Type constraint for a parameter.
+
+    Attributes:
+        name: Name of the parameter. E.g. "TFloat"
+        allowed_types: Allowed types for the parameter.
+    """
+
+    name: str
+    allowed_types: set[ir.TypeProtocol]
+    description: str = ""
+
+    def __hash__(self) -> int:
+        return hash((self.name, tuple(self.allowed_types)))
+
+    def __str__(self) -> str:
+        allowed_types_str = " | ".join(str(t) for t in self.allowed_types)
+        return f"{self.name}={allowed_types_str}"
+
+    @classmethod
+    def any_tensor(cls, name: str, description: str = "") -> TypeConstraintParam:
+        return cls(name, {ir.TensorType(dtype) for dtype in ir.DataType}, description)
+
+    @classmethod
+    def any_value(cls, name: str, description: str = "") -> TypeConstraintParam:
+        return cls(name, _ALL_VALUE_TYPES, description)  # type: ignore[arg-type]
+
+
+@dataclasses.dataclass(frozen=True)
+class Parameter:
+    """A formal parameter of an operator."""
+
+    name: str
+    type_constraint: TypeConstraintParam
+    required: bool
+    variadic: bool
+    default: Any = _EMPTY_DEFAULT
+    # TODO: Add other properties too
+
+    def __str__(self) -> str:
+        type_str = self.type_constraint.name
+        if self.has_default():
+            return f"{self.name}: {type_str} = {self.default}"
+        return f"{self.name}: {type_str}"
+
+    def has_default(self) -> bool:
+        return self.default is not _EMPTY_DEFAULT
+
+
+@dataclasses.dataclass(frozen=True)
+class AttributeParameter:
+    """A parameter in the function signature that represents an ONNX attribute."""
+
+    name: str
+    type: ir.AttributeType
+    required: bool
+    default: ir.Attr | None = None
+
+    def __str__(self) -> str:
+        type_str = self.type.name
+        if self.has_default():
+            return f"{self.name}: {type_str} = {self.default}"
+        return f"{self.name}: {type_str}"
+
+    def has_default(self) -> bool:
+        return self.default is not None
+
+
+def _get_type_from_str(
+    type_str: str,
+) -> ir.TensorType | ir.SequenceType | ir.OptionalType:
+    """Converter a type_str from ONNX Opschema to ir.TypeProtocol.
+
+    A type str has the form of "tensor(float)" or composite type like "seq(tensor(float))".
+    """
+
+    # TODO: Upstream this to IR
+
+    # Split the type_str a sequence types and dtypes
+    # 1. Remove the ending ")"
+    striped = type_str.rstrip(")")
+    # 2. Split the type_str by "("
+    type_parts = striped.split("(")
+
+    # Convert the dtype to ir.DataType
+    dtype = ir.DataType[type_parts[-1].upper()]
+
+    # Create a place holder type first
+    type_: ir.TypeProtocol = ir.TensorType(ir.DataType.UNDEFINED)
+
+    # Construct the type
+    for type_part in reversed(type_parts[:-1]):
+        if type_part == "tensor":
+            type_ = ir.TensorType(dtype)
+        elif type_part == "seq":
+            type_ = ir.SequenceType(type_)
+        elif type_part == "optional":
+            type_ = ir.OptionalType(type_)
+        else:
+            raise ValueError(f"Unknown type part: '{type_part}' in type '{type_str}'")
+    return type_  # type: ignore[return-value]
+
+
+def _convert_formal_parameter(
+    param: onnx.defs.OpSchema.FormalParameter,
+    type_constraints: Mapping[str, TypeConstraintParam],
+) -> Parameter:
+    """Convert a formal parameter from ONNX Opschema to Parameter."""
+    if param.type_str in type_constraints:
+        type_constraint = type_constraints[param.type_str]
+    else:
+        # param.type_str can be a plain type like 'int64'.
+        type_constraint = TypeConstraintParam(
+            name=param.name,
+            allowed_types={_get_type_from_str(param.type_str)},
+        )
+    return Parameter(
+        name=param.name,
+        type_constraint=type_constraint,
+        required=param.option != onnx.defs.OpSchema.FormalParameterOption.Optional,
+        variadic=param.option == onnx.defs.OpSchema.FormalParameterOption.Variadic,
+    )
+
+
+def _is_optional(type_: type) -> bool:
+    """Returns whether a type_ is an Optional."""
+    origin_type = typing.get_origin(type_)
+    if origin_type is Union and type(None) in typing.get_args(type_):
+        # Python < 3.10
+        return True
+    if origin_type is Optional:
+        # Python >= 3.10
+        return True
+    if (
+        hasattr(types, "UnionType")
+        and origin_type is types.UnionType
+        and type(None) in typing.get_args(type_)
+    ):
+        # Python >= 3.10
+        return True
+    return False
+
+
+def _get_attr_type(type_: type) -> ir.AttributeType:
+    """Obtain the type of the attribute from a Python class."""
+    try:
+        if type_ in _PY_TYPE_TO_ATTR_TYPE:
+            return _PY_TYPE_TO_ATTR_TYPE[type_]
+        origin_type = typing.get_origin(type_)
+        if origin_type is None:
+            return ir.AttributeType.UNDEFINED
+        if origin_type in (
+            collections.abc.Sequence,
+            Sequence,
+            typing.List,
+            list,
+            typing.Tuple,
+            tuple,
+        ):
+            inner_type = typing.get_args(type_)[0]
+            if inner_type in _LIST_TYPE_TO_ATTR_TYPE:
+                return _LIST_TYPE_TO_ATTR_TYPE[inner_type]
+    except TypeError:
+        logger.warning("TypeError when checking %s.", type_, exc_info=True)
+    return ir.AttributeType.UNDEFINED
+
+
+def _get_type_constraint_name(type_: TypeAnnotationValue) -> str | None:
+    """Returns the name of the type constraint for a given type annotation.
+
+    Args:
+        type_: A Python type.
+
+    Returns:
+        The name of the type constraint if it is a TypeVar.
+        - Prefixes the name with "Sequence_" if the type annotation is a Sequence[].
+    """
+    if isinstance(type_, TypeVar):
+        return type_.__name__
+    if _is_optional(type_):
+        subtypes = typing.get_args(type_)
+        for subtype in subtypes:
+            if subtype is type(None):
+                continue
+            type_param_name = _get_type_constraint_name(subtype)
+            return type_param_name if type_param_name else None
+    origin_type = typing.get_origin(type_)
+    if isinstance(origin_type, type) and issubclass(origin_type, Sequence):
+        subtypes = typing.get_args(type_)
+        type_param_name = _get_type_constraint_name(subtypes[0])
+        return f"Sequence_{type_param_name}" if type_param_name else None
+    return None
+
+
+def _get_allowed_types_from_type_annotation(
+    type_: TypeAnnotationValue,
+) -> set[ir.TypeProtocol]:
+    """Obtain the allowed types from a type annotation."""
+    if type_ is onnxscript.onnx_types.TensorType:
+        # Any tensor type
+        return {ir.TensorType(dtype) for dtype in ir.DataType}
+
+    allowed_types: set[ir.TypeProtocol]
+
+    if isinstance(type_, TypeVar):
+        allowed_types = set()
+        if constraints := type_.__constraints__:
+            for constraint in constraints:
+                allowed_types.update(
+                    _get_allowed_types_from_type_annotation(constraint)
+                )
+        else:
+            bound = type_.__bound__
+            if bound is None:
+                allowed_types = _ALL_VALUE_TYPES  # type: ignore[assignment]
+            else:
+                allowed_types.update(_get_allowed_types_from_type_annotation(bound))
+        return allowed_types
+    if hasattr(type_, "dtype"):
+        # A single tensor type like INT64, FLOAT, etc.
+        return {ir.TensorType(ir.DataType(type_.dtype))}
+    if _is_optional(type_):
+        allowed_types = set()
+        subtypes = typing.get_args(type_)
+        for subtype in subtypes:
+            if subtype is type(None):
+                continue
+            allowed_types.update(_get_allowed_types_from_type_annotation(subtype))
+        # NOTE: We do not consider dynamic optional types like optional(float) because they are not very useful.
+        return allowed_types
+
+    origin_type = typing.get_origin(type_)
+    if origin_type is Union:
+        allowed_types = set()
+        subtypes = typing.get_args(type_)
+        for subtype in subtypes:
+            assert (
+                subtype is not type(None)
+            ), "Union should not contain None type because it is handled by _is_optional."
+            allowed_types.update(_get_allowed_types_from_type_annotation(subtype))
+        return allowed_types
+
+    if isinstance(origin_type, type) and issubclass(origin_type, Sequence):
+        subtypes = typing.get_args(type_)
+        return {
+            ir.SequenceType(t)
+            for t in _get_allowed_types_from_type_annotation(subtypes[0])
+        }
+
+    # Allow everything by default
+    return _ALL_VALUE_TYPES  # type: ignore[return-value]
+
+
+@dataclasses.dataclass
+class OpSignature:
+    """Schema for an operator.
+
+    Attributes:
+        domain: Domain of the operator. E.g. "".
+        name: Name of the operator. E.g. "Add".
+        overload: Overload name of the operator.
+        params: Input parameters. When the op is an ONNX function definition,
+          the order is according to the function signature. This mean we can
+          interleave ONNX inputs and ONNX attributes in the list.
+        outputs: Output parameters.
+    """
+
+    domain: str
+    name: str
+    overload: str
+    params: Sequence[Parameter | AttributeParameter]
+    outputs: Sequence[Parameter]
+    params_map: Mapping[str, Parameter | AttributeParameter] = dataclasses.field(
+        init=False, repr=False
+    )
+
+    def __post_init__(self):
+        self.params_map = {param.name: param for param in self.params}
+
+    def get(self, name: str) -> Parameter | AttributeParameter:
+        return self.params_map[name]
+
+    def __contains__(self, name: str) -> bool:
+        return name in self.params_map
+
+    def __iter__(self) -> Iterator[Parameter | AttributeParameter]:
+        return iter(self.params)
+
+    def __str__(self) -> str:
+        domain = self.domain or "''"
+        # TODO: Double check the separator for overload
+        overload = f"::{self.overload}" if self.overload else ""
+        params = ", ".join(str(param) for param in self.params)
+        outputs = ", ".join(str(param.type_constraint.name) for param in self.outputs)
+        type_constraints = {}
+        for param in self.params:
+            if isinstance(param, Parameter):
+                type_constraints[param.type_constraint.name] = param.type_constraint
+        for param in self.outputs:
+            type_constraints[param.type_constraint.name] = param.type_constraint
+        type_constraints_str = ", ".join(
+            str(type_constraint) for type_constraint in type_constraints.values()
+        )
+        return f"{domain}::{self.name}{overload}({params}) -> ({outputs}) where {type_constraints_str}"
+
+    @classmethod
+    def from_opschema(cls, opschema: onnx.defs.OpSchema) -> OpSignature:
+        """Produce an OpSignature from an ONNX Opschema."""
+        type_constraints = {
+            constraint.type_param_str: TypeConstraintParam(
+                name=constraint.type_param_str,
+                allowed_types={
+                    _get_type_from_str(type_str)
+                    for type_str in constraint.allowed_type_strs
+                },
+                description=constraint.description,
+            )
+            for constraint in opschema.type_constraints
+        }
+
+        params = [
+            _convert_formal_parameter(param, type_constraints)
+            for param in opschema.inputs
+        ]
+
+        for param in opschema.attributes.values():
+            default_attr = (
+                ir.serde.deserialize_attribute(param.default_value)
+                if param.default_value is not None
+                else None
+            )
+            if default_attr is not None:
+                # Set the name of the default attribute because it may have a different name from the parameter
+                default_attr.name = param.name
+            params.append(
+                AttributeParameter(
+                    name=param.name,
+                    type=ir.AttributeType(param.type),  # type: ignore[arg-type]
+                    required=param.required,
+                    default=default_attr,  # type: ignore[arg-type]
+                )
+            )
+
+        outputs = [
+            _convert_formal_parameter(param, type_constraints)
+            for param in opschema.outputs
+        ]
+
+        return cls(
+            domain=opschema.domain,
+            name=opschema.name,
+            overload="",
+            params=params,
+            outputs=outputs,
+        )
+
+    @classmethod
+    def from_function(
+        cls, func, domain: str, name: str | None = None, overload: str = ""
+    ) -> OpSignature:
+        """Produce an OpSignature from a function using type annotation."""
+
+        py_signature = inspect.signature(func)
+        # Not using inspect.get_annotations because typing.get_type_hints seems to handle more cases
+        # https://github.com/python/cpython/issues/102405
+        type_hints = typing.get_type_hints(func)
+
+        params: list[Parameter | AttributeParameter] = []
+        # Create a mapping from type to a unique name
+        type_constraints: dict[str, TypeConstraintParam] = {}
+
+        for param in py_signature.parameters.values():
+            if param.name not in type_hints:
+                logger.warning(
+                    "Missing annotation for parameter '%s' from %s. Treating as an Input.",
+                    param.name,
+                    py_signature,
+                )
+                type_constraint = TypeConstraintParam.any_value(f"T_{param.name}")
+                type_constraints[param.name] = type_constraint
+                params.append(
+                    Parameter(
+                        name=param.name,
+                        type_constraint=type_constraint,
+                        required=param.default is inspect.Parameter.empty,
+                        # TODO: Handle variadic
+                        variadic=False,
+                        default=param.default
+                        if param.default is not inspect.Parameter.empty
+                        else _EMPTY_DEFAULT,
+                    )
+                )
+            else:
+                type_ = type_hints[param.name]
+                if (attr_type := _get_attr_type(type_)) != ir.AttributeType.UNDEFINED:
+                    # Construct the default attribute
+                    if param.default is not inspect.Parameter.empty:
+                        # TODO: Use ir_convenience instead to handle int as float
+                        default = ir.Attr(param.name, attr_type, param.default)
+                    else:
+                        default = None
+                    params.append(
+                        AttributeParameter(
+                            name=param.name,
+                            type=attr_type,
+                            required=param.default is inspect.Parameter.empty,
+                            default=default,
+                        )
+                    )
+                else:
+                    # Obtain the type constraint from the type annotation
+
+                    # 1. Get a type constraint name from the type annotation
+                    # If the type annotation is a TypeVar or Optional[TypeVar], get its name
+                    # Otherwise, name it T_{param.name}
+                    type_constraint_name = _get_type_constraint_name(type_)
+                    if type_constraint_name is None:
+                        type_constraint_name = f"T_{param.name}"
+
+                    # 2. If the type constraint param is already initialized, use it
+                    if type_constraint_name in type_constraints:
+                        type_constraint = type_constraints[type_constraint_name]
+                    else:
+                        # 3. Otherwise, create a new TypeConstraintParam
+                        type_constraint = TypeConstraintParam(
+                            name=type_constraint_name,
+                            allowed_types=_get_allowed_types_from_type_annotation(
+                                type_
+                            ),
+                        )
+                        type_constraints[type_constraint_name] = type_constraint
+                    # 4. Create Parameter
+                    params.append(
+                        Parameter(
+                            name=param.name,
+                            type_constraint=type_constraint,
+                            required=param.default is inspect.Parameter.empty,
+                            # TODO: Handle variadic
+                            variadic=False,
+                            default=param.default
+                            if param.default is not inspect.Parameter.empty
+                            else _EMPTY_DEFAULT,
+                        )
+                    )
+
+        return_type = type_hints.get("return")
+
+        outputs = []
+        if return_type is None:
+            # No returns
+            pass
+        else:
+            if typing.get_origin(return_type) is tuple:
+                # Multiple returns
+                return_types = typing.get_args(return_type)
+            else:
+                return_types = [return_type]  # type: ignore[assignment]
+
+            for i, return_type_i in enumerate(return_types):
+                if (
+                    return_param_name := _get_type_constraint_name(return_type_i)
+                ) in type_constraints:
+                    type_constraint = type_constraints[return_param_name]
+                else:
+                    return_param_name = f"TReturn{i}"
+                    type_constraint = TypeConstraintParam(
+                        name=return_param_name,
+                        allowed_types=_get_allowed_types_from_type_annotation(
+                            return_type_i
+                        ),
+                    )
+                    type_constraints[return_param_name] = type_constraint
+                outputs.append(
+                    Parameter(
+                        name=return_param_name,
+                        type_constraint=type_constraint,
+                        required=True,
+                        variadic=False,
+                        default=_EMPTY_DEFAULT,
+                    )
+                )
+
+        return cls(
+            domain=domain,
+            name=name or func.__name__,
+            overload=overload,
+            params=params,
+            outputs=outputs,
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_tensors.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_tensors.py
new file mode 100644
index 0000000000000000000000000000000000000000..2fdafacbe06f4b811d21d6de7720d95f278c0c92
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_tensors.py
@@ -0,0 +1,95 @@
+"""Subclass of ir.Value that supports Python operators."""
+
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import onnxscript
+from onnxscript import ir
+
+
+class SymbolicTensor(ir.Value):
+    """A subclass of ir.Value that supports Python operators."""
+
+    def __init__(
+        self,
+        opset: onnxscript.values.Opset,
+        name: str | None = None,
+        shape: ir.Shape | None = None,
+        type: ir.TypeProtocol | None = None,
+        doc_string: str | None = None,
+        const_value: ir.TensorProtocol | None = None,
+    ):
+        super().__init__(
+            name=name,
+            shape=shape,
+            type=type,
+            doc_string=doc_string,
+            const_value=const_value,
+        )
+        self._opset = opset
+
+    @property
+    def rank(self) -> int | None:
+        if self.shape is None:
+            return None
+        return len(self.shape)
+
+    # TODO: Implement indexing
+
+    def __mod__(self, other):
+        if self.dtype in {
+            ir.DataType.FLOAT,
+            ir.DataType.DOUBLE,
+            ir.DataType.FLOAT16,
+            ir.DataType.BFLOAT16,
+        }:
+            return self._opset.Mod(self, other, fmod=1)
+        return self._opset.Mod(self, other)
+
+    def __ne__(self, other):
+        return self._opset.Not(self._opset.Equal(self, other))
+
+    def __neg__(self):
+        return self._opset.Neg(self)
+
+    def __add__(self, other):
+        return self._opset.Add(self, other)
+
+    def __radd__(self, other):
+        return self._opset.Add(other, self)
+
+    def __rand__(self, other):
+        return self._opset.And(other, self)
+
+    def __mul__(self, other):
+        return self._opset.Mul(self, other)
+
+    def __rmul__(self, other):
+        return self._opset.Mul(other, self)
+
+    def __matmul__(self, other):
+        return self._opset.MatMul(self, other)
+
+    def __pow__(self, other):
+        return self._opset.Pow(self, other)
+
+    def __sub__(self, other):
+        return self._opset.Sub(self, other)
+
+    def __rsub__(self, other):
+        return self._opset.Sub(other, self)
+
+    def __truediv__(self, other):
+        return self._opset.Div(self, other)
+
+    def __lt__(self, other):
+        return self._opset.Less(self, other)
+
+    def __le__(self, other):
+        return self._opset.LessOrEqual(self, other)
+
+    def __ge__(self, other):
+        return self._opset.GreaterOrEqual(self, other)
+
+    def __gt__(self, other):
+        return self._opset.Greater(self, other)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_testing.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_testing.py
new file mode 100644
index 0000000000000000000000000000000000000000..19f0c73734839d9a5abe5ee5d4bdc945d45dd64e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_testing.py
@@ -0,0 +1,66 @@
+"""Test utilities for ONNX export."""
+
+from __future__ import annotations
+
+
+__all__ = ["assert_onnx_program"]
+
+from typing import Any, TYPE_CHECKING
+
+import torch
+from torch.utils import _pytree
+
+
+if TYPE_CHECKING:
+    from torch.onnx._internal.exporter import _onnx_program
+
+
+def assert_onnx_program(
+    program: _onnx_program.ONNXProgram,
+    *,
+    rtol: float | None = None,
+    atol: float | None = None,
+    args: tuple[Any, ...] | None = None,
+    kwargs: dict[str, Any] | None = None,
+) -> None:
+    """Assert that the ONNX model produces the same output as the PyTorch ExportedProgram.
+    Args:
+        program: The ``ONNXProgram`` to verify.
+        rtol: Relative tolerance.
+        atol: Absolute tolerance.
+        args: The positional arguments to pass to the program.
+            If None, the default example inputs in the ExportedProgram will be used.
+        kwargs: The keyword arguments to pass to the program.
+            If None, the default example inputs in the ExportedProgram will be used.
+    """
+    exported_program = program.exported_program
+    if exported_program is None:
+        raise ValueError(
+            "The ONNXProgram does not contain an ExportedProgram. "
+            "To verify the ONNX program, initialize ONNXProgram with an ExportedProgram, "
+            "or assign the ExportedProgram to the ONNXProgram.exported_program attribute."
+        )
+    if args is None and kwargs is None:
+        # User did not provide example inputs, use the default example inputs
+        if exported_program.example_inputs is None:
+            raise ValueError(
+                "No example inputs provided and the exported_program does not contain example inputs. "
+                "Please provide arguments to verify the ONNX program."
+            )
+        args, kwargs = exported_program.example_inputs
+    if args is None:
+        args = ()
+    if kwargs is None:
+        kwargs = {}
+    torch_module = exported_program.module()
+    torch_outputs, _ = _pytree.tree_flatten(torch_module(*args, **kwargs))
+    onnx_outputs = program(*args, **kwargs)
+    # TODO(justinchuby): Include output names in the error message
+    torch.testing.assert_close(
+        tuple(onnx_outputs),
+        tuple(torch_outputs),
+        rtol=rtol,
+        atol=atol,
+        equal_nan=True,
+        check_device=False,
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_verification.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_verification.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4eec16da4990f135d3c5958bd9ebcc04fce743f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/exporter/_verification.py
@@ -0,0 +1,111 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+
+__all__ = [
+    "VerificationInfo",
+    "verify_onnx_program",
+]
+
+import dataclasses
+import math
+from typing import Any, TYPE_CHECKING
+
+import torch
+from torch.utils import _pytree
+
+
+if TYPE_CHECKING:
+    from torch.onnx._internal.exporter import _onnx_program
+
+
+@dataclasses.dataclass
+class VerificationInfo:
+    name: str
+    max_abs_diff: float
+    max_rel_diff: float
+    abs_diff_hist: tuple[torch.Tensor, torch.Tensor]
+    rel_diff_hist: tuple[torch.Tensor, torch.Tensor]
+    expected_dtype: torch.dtype
+    actual_dtype: torch.dtype
+    # NOTE: We don't need to include shape because the expected shape is already known
+    # and checked by the runtime
+
+
+def _compare_tensors(
+    expected: torch.Tensor,
+    actual: torch.Tensor,
+) -> tuple[float, float, torch.Tensor, torch.Tensor]:
+    # Move tensors to the same device
+    expected = expected.detach().cpu()
+    actual = actual.detach().cpu()
+    if expected.numel() == 0 or actual.numel() == 0:
+        return math.inf, math.inf, torch.tensor(math.inf), torch.tensor(math.inf)
+    if expected.dtype == torch.bool:
+        expected = expected.to(torch.float32)
+        actual = actual.to(torch.float32)
+    abs_diff = torch.abs(expected - actual)
+    eps = 1e-7
+    normalizer = torch.abs(expected) + eps
+    rel_diff = abs_diff / normalizer
+
+    max_absolute_difference = abs_diff.max().item()
+    max_relative_difference = rel_diff.max().item()
+
+    return max_absolute_difference, max_relative_difference, abs_diff, rel_diff
+
+
+def verify_onnx_program(
+    onnx_program: _onnx_program.ONNXProgram,
+    args: tuple[Any, ...] | None = None,
+    kwargs: dict[str, Any] | None = None,
+) -> list[VerificationInfo]:
+    exported_program = onnx_program.exported_program
+    if exported_program is None:
+        raise ValueError(
+            "The ONNX program does not contain an exported_program. "
+            "Please provide an exported_program to verify the ONNX program."
+        )
+    if args is None and kwargs is None:
+        # User did not provide example inputs, use the default example inputs
+        if exported_program.example_inputs is None:
+            raise ValueError(
+                "No example inputs provided and the exported_program does not contain example inputs. "
+                "Please provide arguments to verify the ONNX program."
+            )
+        args, kwargs = exported_program.example_inputs
+    if args is None:
+        args = ()
+    if kwargs is None:
+        kwargs = {}
+    torch_module = exported_program.module()
+    torch_outputs, _ = _pytree.tree_flatten(torch_module(*args, **kwargs))
+    onnx_outputs = onnx_program(*args, **kwargs)
+    results = []
+    for torch_output, onnx_output, output_val in zip(
+        torch_outputs, onnx_outputs, onnx_program.model.graph.outputs
+    ):
+        name = output_val.name
+        max_abs_diff, max_rel_diff, abs_diff, rel_diff = _compare_tensors(
+            torch_output, onnx_output
+        )
+        abs_diff = abs_diff.flatten()
+        rel_diff = rel_diff.flatten()
+        bins = torch.tensor(
+            [0.0, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1.0, 10, 1000000],
+            dtype=abs_diff.dtype,
+        )
+        abs_diff_hist = torch.histogram(abs_diff, bins=bins)
+        rel_diff_hist = torch.histogram(rel_diff, bins=bins)
+        results.append(
+            VerificationInfo(
+                name=str(name),
+                max_abs_diff=max_abs_diff,
+                max_rel_diff=max_rel_diff,
+                abs_diff_hist=abs_diff_hist,
+                rel_diff_hist=rel_diff_hist,
+                expected_dtype=torch_output.dtype,
+                actual_dtype=onnx_output.dtype,
+            )
+        )
+    return results
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5716bdafced78fc8e6eb9aa07c3799b6893cfe1
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/__init__.py
@@ -0,0 +1,8 @@
+from .patcher import ONNXTorchPatcher
+from .serialization import save_model_with_external_data
+
+
+__all__ = [
+    "save_model_with_external_data",
+    "ONNXTorchPatcher",
+]
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/_pass.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/_pass.py
new file mode 100644
index 0000000000000000000000000000000000000000..5246788756f3ed67cd697b489d89045767ffde5e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/_pass.py
@@ -0,0 +1,323 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import abc
+import contextlib
+import dataclasses
+import difflib
+import io
+import logging
+import sys
+from typing import Any, Callable, TYPE_CHECKING
+
+import torch
+import torch.fx
+from torch._subclasses.fake_tensor import unset_fake_temporarily
+from torch.onnx._internal.fx import diagnostics, onnxfunction_dispatcher
+
+
+if TYPE_CHECKING:
+    from torch._subclasses import fake_tensor
+
+
+@dataclasses.dataclass
+class PackageInfo:
+    package_name: str
+    version: str | None
+    commit_hash: str | None
+
+    def to_onnx_domain_string(self) -> str:
+        return ".".join(
+            filter(None, ("pkg", self.package_name, self.version, self.commit_hash))
+        )
+
+    @classmethod
+    def from_python_class(cls, python_class_name: type | str) -> PackageInfo:
+        if isinstance(python_class_name, type):
+            python_class_name = python_class_name.__module__
+        package_name = python_class_name.split(".")[0]
+        package = __import__(package_name)
+        version = getattr(package, "__version__", None)
+        # TODO: Figure out how to retrieve commit hash.
+        commit_hash = None
+        return cls(package_name, version, commit_hash)
+
+
+@dataclasses.dataclass
+class GraphModuleOnnxMeta:
+    package_info: PackageInfo
+
+
+@contextlib.contextmanager
+def _patch_difflib_sequence_matcher_init():
+    """Context patching `difflib.SequenceMatcher` for fx readable graph.
+
+    Under this context, the `autojunk` argument of `difflib.SequenceMatcher` will always
+    be considered as `False`. This is to prevent `difflib.SequenceMatcher` recognizing
+    stacktrace messages in fx readable graph as junk, as these messages tend to be long (>200)
+    and repeat multiple times, which falls under the junk filter criteria.
+
+    `difflib.SequenceMatcher` is used underneath by all sorts of diffing functions
+    in `difflib`, including `difflib.unified_diff`, `difflib.ndiff`, `difflib.context_diff`.
+    Unfortunately, there is no way to pass `autojunk` argument to these functions, and
+    they all default to `True`. This context patching will affect all of them.
+
+    `Reference: Automatic junk heuristic `_
+    """
+    original_init = difflib.SequenceMatcher.__init__
+
+    def patched_init(self, isjunk=None, a="", b="", autojunk=True):
+        original_init(self, isjunk, a, b, autojunk=False)
+
+    difflib.SequenceMatcher.__init__ = patched_init  # type: ignore[assignment]
+    try:
+        yield
+    finally:
+        difflib.SequenceMatcher.__init__ = original_init  # type: ignore[assignment]
+
+
+def _unified_diff(a: str, b: str) -> str:
+    """Return a string containing the unified diff of two strings.
+
+    This function calls a patched version of `difflib.unified_diff` with `autojunk` set
+    to `False` for `difflib.SequenceMatcher` class. More details can be found in
+    `_patch_difflib_sequence_matcher_init` function.
+
+    Args:
+        a: The first string.
+        b: The second string.
+
+    Returns:
+        The unified diff of the two strings. If there is no diff, return "".
+
+    Example::
+
+        >>> a = '''class GraphModule(torch.nn.Module):
+        ...     def forward(self, input_ids : torch.Tensor, attention_mask : torch.Tensor):
+        ...         # File: /modeling.py:770, code: input_ids = input_ids.view(-1, input_shape[-1])
+        ...         view = input_ids.view(-1, 3);  input_ids = None
+        ... '''
+        >>> b = '''class (torch.nn.Module):
+        ...     def forward(self, input_ids: i64[1, 3], attention_mask: i64[1, 3]):
+        ...         # File: /modeling.py:770, code: input_ids = input_ids.view(-1, input_shape[-1])
+        ...         view: i64[1, 3] = torch.ops.aten.view.default(input_ids, [-1, 3]);  input_ids = None
+        ... '''
+        >>> print(_unified_diff(a, b))
+        ---
+        +++
+        @@ -1,4 +1,4 @@
+        -class GraphModule(torch.nn.Module):
+        -    def forward(self, input_ids : torch.Tensor, attention_mask : torch.Tensor):
+        +class (torch.nn.Module):
+        +    def forward(self, input_ids: i64[1, 3], attention_mask: i64[1, 3]):
+                # File: /modeling.py:770, code: input_ids = input_ids.view(-1, input_shape[-1])
+        -        view = input_ids.view(-1, 3);  input_ids = None
+        +        view: i64[1, 3] = torch.ops.aten.view.default(input_ids, [-1, 3]);  input_ids = None
+    """
+
+    a_list = a.splitlines(keepends=True)
+    b_list = b.splitlines(keepends=True)
+
+    with _patch_difflib_sequence_matcher_init():
+        # Set `n` to `sys.maxsize` to show entire graph when there is a diff.
+        diff = "".join(difflib.unified_diff(a_list, b_list, n=sys.maxsize))
+
+    if not diff:
+        return ""
+    return diff
+
+
+def _transform_diagnose_call_message_formatter(
+    run: Callable,
+    self: Transform,
+    *args: Any,
+    **kwargs: Any,
+) -> str:
+    return f"Running {self.__class__.__name__} pass. "
+
+
+def maybe_fx_graph_tabular(graph: torch.fx.Graph) -> str | None:
+    """Return the Graph nodes in tabular format. Equivalent to stdout of `graph.print_tabular()`.
+    If `tabulate` is not installed, return `None`.
+
+    Args:
+        graph: The Graph to print.
+
+    Returns:
+        The Graph printed in a tabular format. None if `tabulate` is not installed.
+    """
+    f = io.StringIO()
+    with contextlib.redirect_stdout(f):
+        try:
+            graph.print_tabular()
+        except ImportError:
+            return None
+    return f.getvalue()
+
+
+class Transform(abc.ABC):
+    """Base class for FX graph transformations to be used by FX-ONNX exporter.
+
+    Similar to `FX Interpreter `_,
+    specializations of this class execute the FX graph Node-by-Node.
+    Methods in the `Transform` class can be overridden to customize the behavior of the model.
+    This pattern can be useful for many things, including writing code transformations as well as analysis passes.
+
+    The following methods can be overridden::
+
+        _run()
+            +-- run_node()
+                +-- placeholder()
+                +-- get_attr()
+                +-- call_function()
+                +-- call_method()
+                +-- call_module()
+                +-- output()
+
+    One important aspect to note is that if the transformation modifies the model input and/or output signature,
+    (e.g. additional inputs/outputs are added to the model), :class:`InputAdaptStep` and/or :class:`OutputAdaptStep`
+    are needed to reconcile :attr:`ONNXProgram.model_proto`.
+    That is, the model signature and the model representation must match.
+
+    As an additional feature, this class provides builtin support for transformation recording using the diagnostics.
+    The granularity of overriding is up to the user. And it affects the granularity of
+    the diagnostics information. For example, if `_run()` is overridden, the
+    diagnostics information will only contain graph level transformation. Instead,
+    if `call_function()` is overridden, the diagnostics information will additionally
+    contain the node level information of `call_function()`.
+
+    TODO(bowbao): Add more overridable methods in call hierarchy
+    TODO(bowbao): Create an example once more overridable methods are added.
+    """
+
+    diagnostic_context: diagnostics.DiagnosticContext
+    """The diagnostic context for recording diagnostics."""
+
+    module: torch.fx.GraphModule
+    """The module to be transformed."""
+
+    fake_mode: fake_tensor.FakeTensorMode | None
+    """The existing fake mode detected from `self.module`."""
+
+    def __init__(
+        self,
+        diagnostic_context: diagnostics.DiagnosticContext,
+        module: torch.fx.GraphModule,
+    ):
+        """Initialize the transform.
+
+        Args:
+            diagnostic_context: The diagnostic context for recording diagnostics.
+            module: The module to be transformed.
+        """
+        self.diagnostic_context = diagnostic_context
+        self.module = module
+        self.fake_mode = self._detect_fake_mode()
+
+    def _detect_fake_mode(self) -> fake_tensor.FakeTensorMode | None:
+        """Detect fake mode from the graph.
+
+        Scan through all nodes in graph and their meta['val'] to detect fake mode.
+        """
+        fake_tensors = [node.meta.get("val") for node in self.module.graph.nodes]
+        with unset_fake_temporarily():
+            return torch._dynamo.utils.detect_fake_mode(fake_tensors)
+
+    def _maybe_fakefy_args(
+        self, fake_mode: fake_tensor.FakeTensorMode | None, *args: Any
+    ) -> tuple[Any, ...]:
+        if fake_mode is None:
+            return args
+        # NB: This should hit the cache if tensors were fakefied before.
+        # E.g., when the fx graph is produced by Dynamo.
+        return tuple(
+            fake_mode.from_tensor(t) if isinstance(t, torch.Tensor) else t for t in args
+        )
+
+    @abc.abstractmethod
+    def _run(self, *args, **kwargs) -> torch.fx.GraphModule: ...
+
+    @diagnostics.diagnose_call(
+        diagnostics.rules.fx_pass,
+        diagnostic_message_formatter=_transform_diagnose_call_message_formatter,
+    )
+    def run(self, *args, **kwargs) -> torch.fx.GraphModule:
+        """Run the transform on `self.module`.
+
+        Note that this method may or may not mutate `self.module`, and the returned
+        `GraphModule` could be either `self.module` or a new `GraphModule`.
+
+        Args:
+            *args: Positional arguments for `self.module` to run.
+            **kwargs: Keyword arguments for `self.module` to run.
+        """
+        diagnostic = self.diagnostic_context.inflight_diagnostic(
+            rule=diagnostics.rules.fx_pass
+        )
+        diagnostic.info(
+            "For detailed logging of graph modifications by this pass, either set "
+            "`DiagnosticOptions.verbosity_level` to `logging.DEBUG` or use the environment variable "
+            "`TORCH_LOGS='onnx_diagnostics'`."
+        )
+
+        # Gather graph information before transform.
+        graph_diff_log_level = logging.DEBUG
+        if diagnostic.logger.isEnabledFor(graph_diff_log_level):
+            # Cannot use LazyString because the graph may have been mutated at evaluation time.
+            old_readable_graph = self.module.print_readable(print_output=False)
+            old_tabular = maybe_fx_graph_tabular(self.module.graph)
+        else:
+            # Set to empty string to avoid unbound warning. This value should never be
+            # used since the log level is not enabled.
+            old_readable_graph = ""
+            old_tabular = ""
+
+        module = self._run(*args, **kwargs)
+
+        # Gather graph information after transform.
+        if diagnostic.logger.isEnabledFor(graph_diff_log_level):
+            new_readable_graph = module.print_readable(print_output=False)
+            new_tabular = maybe_fx_graph_tabular(module.graph)
+
+            with diagnostic.log_section(graph_diff_log_level, "Graph diff:"):
+                diagnostic.log(
+                    graph_diff_log_level,
+                    "```\n%s\n```",
+                    diagnostics.LazyString(
+                        _unified_diff, old_readable_graph, new_readable_graph
+                    ),
+                )
+
+            with diagnostic.log_section(graph_diff_log_level, "Tabular diff:"):
+                if old_tabular is None or new_tabular is None:
+                    diagnostic.log(
+                        graph_diff_log_level,
+                        "Tabular diff is not available because `tabulate` is not installed.",
+                    )
+                else:
+                    diagnostic.log(
+                        graph_diff_log_level,
+                        "```\n%s\n```",
+                        diagnostics.LazyString(_unified_diff, old_tabular, new_tabular),
+                    )
+
+        return module
+
+
+class AnalysisResult(abc.ABC):  # noqa: B024
+    ...
+
+
+class Analysis(abc.ABC):
+    def __init__(
+        self,
+        diagnostic_context: diagnostics.DiagnosticContext,
+        module: torch.fx.GraphModule,
+        onnxfunction_dispatcher: onnxfunction_dispatcher.OnnxFunctionDispatcher,
+    ):
+        self.diagnostic_context = diagnostic_context
+        self.module = module
+        self.onnxfunction_dispatcher = onnxfunction_dispatcher
+
+    @abc.abstractmethod
+    def analyze(self, diagnostic_level: diagnostics.infra.Level) -> AnalysisResult: ...
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/analysis/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/analysis/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4440cf66939bb642016ac49f97d7243a1ad82987
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/analysis/__init__.py
@@ -0,0 +1,6 @@
+from .unsupported_nodes import UnsupportedFxNodesAnalysis
+
+
+__all__ = [
+    "UnsupportedFxNodesAnalysis",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/analysis/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/analysis/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8d4bc4817107435c99df992b0a91b9456e2ab868
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/analysis/__pycache__/unsupported_nodes.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/analysis/__pycache__/unsupported_nodes.cpython-311.pyc
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index 0000000000000000000000000000000000000000..37aaa8a7c6056e286e71ad19ea1edf4be27a7b8c
Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/analysis/__pycache__/unsupported_nodes.cpython-311.pyc differ
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/analysis/unsupported_nodes.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/analysis/unsupported_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..172a051cabca7a16d8591075db9ec2a48ba1121a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/analysis/unsupported_nodes.py
@@ -0,0 +1,86 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import dataclasses
+
+from torch.onnx._internal.fx import _pass, diagnostics, registration
+
+
+@dataclasses.dataclass
+class UnsupportedFxNodesAnalysisResult(_pass.AnalysisResult):
+    unsupported_op_to_target_mapping: dict[str, dict[str, None]]
+
+
+class UnsupportedFxNodesAnalysis(_pass.Analysis):
+    """An analysis that detects unsupported FX nodes in the graph."""
+
+    def _lint(
+        self,
+        analysis_result: UnsupportedFxNodesAnalysisResult,
+        diagnostic_level: diagnostics.infra.Level,
+    ):
+        """Lint the graph and emit diagnostics if unsupported FX nodes are found."""
+        if not analysis_result.unsupported_op_to_target_mapping:
+            return
+
+        normalized_op_targets_map = {
+            op: list(targets.keys())
+            for op, targets in analysis_result.unsupported_op_to_target_mapping.items()
+        }
+
+        rule = diagnostics.rules.unsupported_fx_node_analysis
+        diagnostic = diagnostics.Diagnostic(
+            rule,
+            level=diagnostic_level,
+            message=rule.format_message(normalized_op_targets_map),
+        )
+        self.diagnostic_context.log_and_raise_if_error(diagnostic)
+
+    def analyze(
+        self, diagnostic_level: diagnostics.infra.Level
+    ) -> UnsupportedFxNodesAnalysisResult:
+        """Analyze the graph, emit diagnostics and return a result that contains unsupported FX nodes.
+
+        Args:
+            diagnostic_level: The diagnostic level to use when emitting diagnostics.
+
+        Returns:
+            An analysis result that contains unsupported FX nodes.
+
+        Raises:
+            RuntimeErrorWithDiagnostic: If diagnostics are emitted and the diagnostic
+                level is `ERROR`.
+        """
+
+        op_to_target_mapping: dict[str, dict[str, None]] = {}
+        for node in self.module.graph.nodes:
+            if node.op == "call_function":
+                # NOTE: OPSchema matcher is not in this analysis scope.
+                internal_opname: registration.OpName = (
+                    self.onnxfunction_dispatcher._get_aten_name(
+                        node=node, diagnostic_context=self.diagnostic_context
+                    )
+                )
+                overload_registration = (
+                    self.onnxfunction_dispatcher.onnx_registry.is_registered_op(
+                        namespace=internal_opname.namespace,
+                        op_name=internal_opname.op_name,
+                        overload=internal_opname.overload,
+                    )
+                )
+                # NOTE: Fall back to default overload if the ONNX registry doesn't have the overload.
+                default_registration = (
+                    self.onnxfunction_dispatcher.onnx_registry.is_registered_op(
+                        namespace=internal_opname.namespace,
+                        op_name=internal_opname.op_name,
+                        overload=None,
+                    )
+                )
+                if not overload_registration and not default_registration:
+                    op_to_target_mapping.setdefault(node.op, {}).setdefault(
+                        str(node.target), None
+                    )
+
+        analysis_result = UnsupportedFxNodesAnalysisResult(op_to_target_mapping)
+        self._lint(analysis_result, diagnostic_level)
+        return analysis_result
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/decomposition_skip.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/decomposition_skip.py
new file mode 100644
index 0000000000000000000000000000000000000000..4849616e412aaa1736db2754bc4093d6c05e66f6
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/decomposition_skip.py
@@ -0,0 +1,238 @@
+# mypy: allow-untyped-defs
+"""A context manager that disables the decomposition of certain ops during dynamo tracing.
+
+The approach is to temporarily hijack the operator callable with PT2 custom operator.
+The custom operator will not be decomposed and will show up as a single node to be exported to ONNX.
+
+For the time being the decomposition of these ops is otherwise unavoidable.
+
+https://github.com/pytorch/pytorch/issues/116684
+https://github.com/pytorch/pytorch/issues/115883
+
+This solution will no longer be required once the issue is resolved.
+"""
+
+from __future__ import annotations
+
+import abc
+import contextlib
+from typing import Callable, Sequence
+
+from onnxscript.function_libs.torch_lib.ops import (  # type: ignore[import-not-found]
+    core as torchlib_core,
+    nn as torchlib_nn,
+)
+
+import torch
+from torch._decomp import decompositions
+
+
+_NEW_OP_NAMESPACE: str = "onnx_export"
+"""The namespace for the custom operator."""
+
+
+class DecompSkip(abc.ABC):
+    op_callable: Callable
+    """The original operator callable to skip decomposition."""
+    onnxscript_function: Callable
+    """The ONNXScript function to be registered for exporting the custom operator."""
+
+    new_op_name: str
+    """The name for the custom operator."""
+    new_op_schema: str
+    """The schema for the custom operator. This should match with the signature of the original operator."""
+
+    @classmethod
+    @abc.abstractmethod
+    def register(cls, export_options: torch.onnx.ExportOptions):
+        """Registers the custom operator and overrides the original operator.
+
+        It should do the following steps in order:
+
+        1. Register the custom operator.
+        2. Override the original operator with the replacement callable.
+        3. Register the ONNXScript function for exporting the custom operator.
+        """
+        ...
+
+    @classmethod
+    @abc.abstractmethod
+    def unregister(cls):
+        """Restores the original operator callable."""
+        ...
+
+    @classmethod
+    @abc.abstractmethod
+    def abstract(cls, *args, **kwargs):
+        """An abstract impl (meta kernel) for the operator."""
+        ...
+
+    @classmethod
+    def register_custom_op(cls):
+        """Registers the custom operator."""
+        new_op_qualname = f"{_NEW_OP_NAMESPACE}::{cls.new_op_name}"
+        torch.library.define(new_op_qualname, cls.new_op_schema)
+        torch.library.impl(new_op_qualname, "default", cls.replacement)
+        torch.library.register_fake(new_op_qualname, cls.abstract)
+
+    @classmethod
+    def replacement(cls, *args, **kwargs):
+        """A replacement callable for the operator to be hijacked.
+
+        This has the same signature and eager behavior as the original operator.
+        """
+        return cls.op_callable(*args, **kwargs)
+
+
+class UpsampleBilinear2DDecompSkip(DecompSkip):
+    op_callable = torch._C._nn.upsample_bilinear2d  # type: ignore[attr-defined]
+    onnxscript_function = torchlib_nn.aten_upsample_bilinear2d_vec  # type: ignore[attr-defined]
+    new_op_name = "upsample_bilinear2d"
+    new_op_schema = "(Tensor self, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> (Tensor)"
+
+    @classmethod
+    def register(cls, export_options: torch.onnx.ExportOptions):
+        if not hasattr(torch.ops, _NEW_OP_NAMESPACE) or not hasattr(
+            torch.ops.onnx_export, cls.new_op_name
+        ):
+            cls.register_custom_op()
+        torch._C._nn.upsample_bilinear2d = torch.ops.onnx_export.upsample_bilinear2d  # type: ignore[attr-defined]
+        if export_options.onnx_registry is None:
+            export_options.onnx_registry = torch.onnx.OnnxRegistry()
+        registry = export_options.onnx_registry
+        registry.register_op(
+            function=cls.onnxscript_function,
+            namespace=_NEW_OP_NAMESPACE,
+            op_name=cls.new_op_name,
+        )
+
+    @classmethod
+    def unregister(cls):
+        torch._C._nn.upsample_bilinear2d = cls.op_callable  # type: ignore[attr-defined]
+
+    @classmethod
+    def abstract(cls, input, output_size, align_corners, scale_factors):
+        osize = decompositions.upsample_compute_output_size(
+            input.size(), output_size, scale_factors
+        )
+        return torch.empty(
+            (input.size(0), input.size(1), *osize),
+            dtype=input.dtype,
+            device=input.device,
+        )
+
+
+class UpsampleTrilinear3DDecompSkip(DecompSkip):
+    op_callable = torch._C._nn.upsample_trilinear3d  # type: ignore[attr-defined]
+    onnxscript_function = torchlib_nn.aten_upsample_trilinear3d_vec  # type: ignore[attr-defined]
+    new_op_name = "upsample_trilinear3d"
+    new_op_schema = "(Tensor self, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> (Tensor)"
+
+    @classmethod
+    def register(cls, export_options: torch.onnx.ExportOptions):
+        if not hasattr(torch.ops, _NEW_OP_NAMESPACE) or not hasattr(
+            torch.ops.onnx_export, cls.new_op_name
+        ):
+            cls.register_custom_op()
+        torch._C._nn.upsample_trilinear3d = torch.ops.onnx_export.upsample_trilinear3d  # type: ignore[attr-defined]
+        if export_options.onnx_registry is None:
+            export_options.onnx_registry = torch.onnx.OnnxRegistry()
+        registry = export_options.onnx_registry
+        registry.register_op(
+            function=cls.onnxscript_function,
+            namespace=_NEW_OP_NAMESPACE,
+            op_name=cls.new_op_name,
+        )
+
+    @classmethod
+    def unregister(cls):
+        torch._C._nn.upsample_trilinear3d = cls.op_callable  # type: ignore[attr-defined]
+
+    @classmethod
+    def abstract(cls, input, output_size, align_corners, scale_factors):
+        osize = decompositions.upsample_compute_output_size(
+            input.size(), output_size, scale_factors
+        )
+        return torch.empty(
+            (input.size(0), input.size(1), input.size(2), *osize),
+            dtype=input.dtype,
+            device=input.device,
+        )
+
+
+class InstanceNormDecompSkip(DecompSkip):
+    op_callable = torch.instance_norm  # type: ignore[attr-defined]
+    onnxscript_function = torchlib_core.aten_instance_norm  # type: ignore[attr-defined]
+    new_op_name = "instance_norm"
+    new_op_schema = (
+        "(Tensor input, Tensor? weight, Tensor? bias, "
+        "Tensor? running_mean, Tensor? running_var, "
+        "bool use_input_stats, float momentum, float eps, "
+        "bool cudnn_enabled) -> Tensor"
+    )
+
+    @classmethod
+    def register(cls, export_options: torch.onnx.ExportOptions):
+        if not hasattr(torch.ops, _NEW_OP_NAMESPACE) or not hasattr(
+            torch.ops.onnx_export, cls.new_op_name
+        ):
+            cls.register_custom_op()
+
+        torch.instance_norm = torch.ops.onnx_export.instance_norm  # type: ignore[attr-defined]
+        if export_options.onnx_registry is None:
+            export_options.onnx_registry = torch.onnx.OnnxRegistry()
+        registry = export_options.onnx_registry
+        registry.register_op(
+            function=cls.onnxscript_function,
+            namespace=_NEW_OP_NAMESPACE,
+            op_name=cls.new_op_name,
+        )
+
+    @classmethod
+    def unregister(cls):
+        torch.instance_norm = cls.op_callable  # type: ignore[attr-defined]
+
+    @classmethod
+    def abstract(
+        cls,
+        input,
+        weight,
+        bias,
+        running_mean,
+        running_var,
+        use_input_stats: bool,
+        momentum: float,
+        eps: float,
+        cudnn_enabled: bool,
+    ):
+        return torch.empty(
+            input.size(),
+            dtype=input.dtype,
+            device=input.device,
+        )
+
+
+_DEFAULT_SKIP_LIST = [
+    UpsampleBilinear2DDecompSkip,
+    InstanceNormDecompSkip,
+    UpsampleTrilinear3DDecompSkip,
+]
+
+
+@contextlib.contextmanager
+def enable_decomposition_skips(
+    export_options: torch.onnx.ExportOptions,
+    skips: Sequence[type[DecompSkip]] = _DEFAULT_SKIP_LIST,
+):
+    """A context manager that enables the decomposition skips.
+
+    The original operator callables that are otherwise decomposed are replaced with custom operators.
+    The ONNXScript functions for exporting the custom operators are added to the ONNX registry inside export_options.
+    """
+    try:
+        for skip in skips:
+            skip.register(export_options)
+        yield
+    finally:
+        for skip in skips:
+            skip.unregister()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/decomposition_table.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/decomposition_table.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ca128da11e4955ff900d806ed4f7b4c6d349c5c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/decomposition_table.py
@@ -0,0 +1,116 @@
+# mypy: allow-untyped-defs
+"""Dispatcher for AtenLib functions from onnx-script."""
+
+from __future__ import annotations
+
+from typing import Callable
+
+import torch
+import torch._ops
+import torch.fx
+from torch.onnx._internal.fx import registration
+
+
+def _create_onnx_supports_op_overload_table(
+    registry,
+) -> set[torch._ops.OperatorBase | Callable]:
+    """
+    Creates a set of OperatorBase and Callable objects that represent ONNX-supported PyTorch operations.
+
+    Args:
+        registry (OnnxRegistry): The ONNX registry for PyTorch.
+
+    Returns:
+        A collection of OperatorBase and Callable objects representing ONNX-supported PyTorch operations.
+    """
+    table: set[torch._ops.OperatorBase | Callable] = set()
+
+    # Some ops in `torch.ops.aten` are not discoverable through `dir(torch.ops.aten)`,
+    # but retrievable via explicit lookup.
+    # https://github.com/pytorch/pytorch/issues/99681
+    # This is a workaround to make sure we register ONNX symbolic functions for these.
+    onnx_supported_aten_lookup_table = [
+        k.split("::")[1].split(".")[0]
+        for k in registry._all_registered_ops()
+        if k.startswith("aten::")
+    ]
+
+    for op_namespace in (torch.ops.aten, torch.ops.prims):
+        attr_names = dir(op_namespace)
+        if op_namespace is torch.ops.aten:
+            attr_names += onnx_supported_aten_lookup_table
+        for attr_name in attr_names:
+            if not hasattr(op_namespace, attr_name):
+                # torchlib owns some attributes that are not aten ops.
+                continue
+            op_overload_packet = getattr(op_namespace, attr_name)
+            if not isinstance(op_overload_packet, torch._ops.OpOverloadPacket):
+                continue
+
+            for overload_name in op_overload_packet.overloads():
+                op_overload = getattr(op_overload_packet, overload_name)
+                internal_op_name = registration.OpName.from_qualified_name(
+                    qualified_name=op_overload.name()
+                )
+                # NOTE: If the overload is supported in registry or it's default overload is supported in registry,
+                # we add it to the table.
+                if registry.is_registered_op(
+                    namespace=internal_op_name.namespace,
+                    op_name=internal_op_name.op_name,
+                    overload=internal_op_name.overload,
+                ) or registry.is_registered_op(
+                    namespace=internal_op_name.namespace,
+                    op_name=internal_op_name.op_name,
+                    overload=None,
+                ):
+                    # This line maps torch.ops.aten.add.Tensor, torch.ops.aten.add.Scalar, torch.ops.aten.add.out, etc
+                    # to "aten::add". This means the exporter for "aten::add" is used for all overloads of "aten::add".
+                    # This is applied to all ops under torch.ops.aten.
+                    table.add(op_overload)
+    return table
+
+
+def create_onnx_friendly_decomposition_table(
+    registry,
+) -> dict[torch._ops.OperatorBase, Callable]:
+    """
+    This function creates a dictionary of op overloads and their decomposition functions
+    for ops that do not have ONNX symbolic functions. If an op already has an ONNX symbolic function,
+    its decomposition function is excluded from the table. The decomposition table is a subset of PyTorch's
+    built-in aten-to-aten decomposition.
+
+    Args:
+        registry (torch.onnx.OnnxRegistry): The ONNX registry for PyTorch.
+
+    Returns:
+        Dict[torch._ops.OperatorBase, Callable]: A dictionary that maps op overloads to their corresponding
+        decomposition functions.
+    """
+    decomposition_table: dict[torch._ops.OperatorBase, Callable] = {}
+    # Dictionary that maps torch.ops.aten.* to exporter look up key; e.g.,
+    # _OP_OVERLOAD_TO_EXPORTER_KEY_TABLE[torch.add.Tensor] is "aten::add".
+    _ONNX_SUPPORT_OP_OVERLOADS = _create_onnx_supports_op_overload_table(registry)
+
+    # NOTE: If we import torch._decomp, we will get RuntimeError: Only a single
+    # TORCH_LIBRARY can be used to register the namespace nvprims; please put all of your
+    # definitions in a single TORCH_LIBRARY block.
+    for op_overload, decomp_fn in torch._decomp.decomposition_table.items():  # type: ignore[attr-defined]
+        # Skip decomposition into "prim::*" ops (defined in 'torch._refs'), because they
+        # are not generally supported by ONNX.
+        # Skip decomposition for op_overload as long as that op_overload has a corresponding ONNX
+        # symbolic function.
+        if (
+            "torch._refs" in decomp_fn.__module__
+            or op_overload in _ONNX_SUPPORT_OP_OVERLOADS
+        ):
+            continue
+        decomposition_table[op_overload] = decomp_fn
+
+    # NOTE: There are ops in core ATen and under torch._refs,
+    # that are not decomposed to prim::ops. We need to pick them
+    # back
+    for op_overload, decomp_fn in torch._decomp.core_aten_decompositions().items():
+        if op_overload in _ONNX_SUPPORT_OP_OVERLOADS:
+            continue
+        decomposition_table[op_overload] = decomp_fn
+    return decomposition_table
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/diagnostics.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/diagnostics.py
new file mode 100644
index 0000000000000000000000000000000000000000..8617afa5f440a37ab300706b6ee8edc8ebd4ccdf
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/diagnostics.py
@@ -0,0 +1,261 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import dataclasses
+import functools
+from typing import Any, TYPE_CHECKING
+
+import onnxscript  # type: ignore[import]
+from onnxscript.function_libs.torch_lib import graph_building  # type: ignore[import]
+
+import torch
+import torch.fx
+from torch.onnx._internal import diagnostics
+from torch.onnx._internal.diagnostics import infra
+from torch.onnx._internal.diagnostics.infra import decorator, formatter
+from torch.onnx._internal.fx import registration, type_utils as fx_type_utils
+
+
+if TYPE_CHECKING:
+    import logging
+
+# NOTE: The following limits are for the number of items to display in diagnostics for
+# a list, tuple or dict. The limit is picked such that common useful scenarios such as
+# operator arguments are covered, while preventing excessive processing loads on considerably
+# large containers such as the dictionary mapping from fx to onnx nodes.
+_CONTAINER_ITEM_LIMIT: int = 10
+
+# NOTE(bowbao): This is a shim over `torch.onnx._internal.diagnostics`, which is
+# used in `torch.onnx`, and loaded with `torch`. Hence anything related to `onnxscript`
+# cannot be put there.
+
+# [NOTE: `dynamo_export` diagnostics logging]
+# The 'dynamo_export' diagnostics leverages the PT2 artifact logger to handle the verbosity
+# level of logs that are recorded in each SARIF log diagnostic. In addition to SARIF log,
+# terminal logging is by default disabled. Terminal logging can be activated by setting
+# the environment variable `TORCH_LOGS="onnx_diagnostics"`. When the environment variable
+# is set, it also fixes logging level to `logging.DEBUG`, overriding the verbosity level
+# specified in the diagnostic options.
+# See `torch/_logging/__init__.py` for more on PT2 logging.
+_ONNX_DIAGNOSTICS_ARTIFACT_LOGGER_NAME = "onnx_diagnostics"
+diagnostic_logger = torch._logging.getArtifactLogger(
+    "torch.onnx", _ONNX_DIAGNOSTICS_ARTIFACT_LOGGER_NAME
+)
+
+
+def is_onnx_diagnostics_log_artifact_enabled() -> bool:
+    return torch._logging._internal.log_state.is_artifact_enabled(
+        _ONNX_DIAGNOSTICS_ARTIFACT_LOGGER_NAME
+    )
+
+
+@functools.singledispatch
+def _format_argument(obj: Any) -> str:
+    return formatter.format_argument(obj)
+
+
+def format_argument(obj: Any) -> str:
+    formatter = _format_argument.dispatch(type(obj))
+    return formatter(obj)
+
+
+# NOTE: EDITING BELOW? READ THIS FIRST!
+#
+# The below functions register the `format_argument` function for different types via
+# `functools.singledispatch` registry. These are invoked by the diagnostics system
+# when recording function arguments and return values as part of a diagnostic.
+# Hence, code with heavy workload should be avoided. Things to avoid for example:
+# `torch.fx.GraphModule.print_readable()`.
+
+
+@_format_argument.register
+def _torch_nn_module(obj: torch.nn.Module) -> str:
+    return f"torch.nn.Module({obj.__class__.__name__})"
+
+
+@_format_argument.register
+def _torch_fx_graph_module(obj: torch.fx.GraphModule) -> str:
+    return f"torch.fx.GraphModule({obj.__class__.__name__})"
+
+
+@_format_argument.register
+def _torch_fx_node(obj: torch.fx.Node) -> str:
+    node_string = f"fx.Node({obj.target})[{obj.op}]:"
+    if "val" not in obj.meta:
+        return node_string + "None"
+    return node_string + format_argument(obj.meta["val"])
+
+
+@_format_argument.register
+def _torch_fx_symbolic_bool(obj: torch.SymBool) -> str:
+    return f"SymBool({obj})"
+
+
+@_format_argument.register
+def _torch_fx_symbolic_int(obj: torch.SymInt) -> str:
+    return f"SymInt({obj})"
+
+
+@_format_argument.register
+def _torch_fx_symbolic_float(obj: torch.SymFloat) -> str:
+    return f"SymFloat({obj})"
+
+
+@_format_argument.register
+def _torch_tensor(obj: torch.Tensor) -> str:
+    return f"Tensor({fx_type_utils.from_torch_dtype_to_abbr(obj.dtype)}{_stringify_shape(obj.shape)})"
+
+
+@_format_argument.register
+def _int(obj: int) -> str:
+    return str(obj)
+
+
+@_format_argument.register
+def _float(obj: float) -> str:
+    return str(obj)
+
+
+@_format_argument.register
+def _bool(obj: bool) -> str:
+    return str(obj)
+
+
+@_format_argument.register
+def _str(obj: str) -> str:
+    return obj
+
+
+@_format_argument.register
+def _registration_onnx_function(obj: registration.ONNXFunction) -> str:
+    # TODO: Compact display of `param_schema`.
+    return f"registration.ONNXFunction({obj.op_full_name}, is_custom={obj.is_custom}, is_complex={obj.is_complex})"
+
+
+@_format_argument.register
+def _list(obj: list) -> str:
+    list_string = f"List[length={len(obj)}](\n"
+    if not obj:
+        return list_string + "None)"
+    for i, item in enumerate(obj):
+        if i >= _CONTAINER_ITEM_LIMIT:
+            # NOTE: Print only first _CONTAINER_ITEM_LIMIT items.
+            list_string += "...,\n"
+            break
+        list_string += f"{format_argument(item)},\n"
+    return list_string + ")"
+
+
+@_format_argument.register
+def _tuple(obj: tuple) -> str:
+    tuple_string = f"Tuple[length={len(obj)}](\n"
+    if not obj:
+        return tuple_string + "None)"
+    for i, item in enumerate(obj):
+        if i >= _CONTAINER_ITEM_LIMIT:
+            # NOTE: Print only first _CONTAINER_ITEM_LIMIT items.
+            tuple_string += "...,\n"
+            break
+        tuple_string += f"{format_argument(item)},\n"
+    return tuple_string + ")"
+
+
+@_format_argument.register
+def _dict(obj: dict) -> str:
+    dict_string = f"Dict[length={len(obj)}](\n"
+    if not obj:
+        return dict_string + "None)"
+    for i, (key, value) in enumerate(obj.items()):
+        if i >= _CONTAINER_ITEM_LIMIT:
+            # NOTE: Print only first _CONTAINER_ITEM_LIMIT items.
+            dict_string += "...\n"
+            break
+        dict_string += f"{key}: {format_argument(value)},\n"
+    return dict_string + ")"
+
+
+@_format_argument.register
+def _torch_nn_parameter(obj: torch.nn.Parameter) -> str:
+    return f"Parameter({format_argument(obj.data)})"
+
+
+@_format_argument.register
+def _onnxscript_torch_script_tensor(obj: graph_building.TorchScriptTensor) -> str:
+    return f"`TorchScriptTensor({fx_type_utils.from_torch_dtype_to_abbr(obj.dtype)}{_stringify_shape(obj.shape)})`"  # type: ignore[arg-type]  # noqa: B950
+
+
+@_format_argument.register
+def _onnxscript_onnx_function(obj: onnxscript.OnnxFunction) -> str:
+    return f"`OnnxFunction({obj.name})`"
+
+
+@_format_argument.register
+def _onnxscript_traced_onnx_function(obj: onnxscript.TracedOnnxFunction) -> str:
+    return f"`TracedOnnxFunction({obj.name})`"
+
+
+# from torch/fx/graph.py to follow torch format
+def _stringify_shape(shape: torch.Size | None) -> str:
+    if shape is None:
+        return ""
+    return f"[{', '.join(str(x) for x in shape)}]"
+
+
+rules = diagnostics.rules
+levels = diagnostics.levels
+RuntimeErrorWithDiagnostic = infra.RuntimeErrorWithDiagnostic
+LazyString = formatter.LazyString
+DiagnosticOptions = infra.DiagnosticOptions
+
+
+@dataclasses.dataclass
+class Diagnostic(infra.Diagnostic):
+    logger: logging.Logger = dataclasses.field(init=False, default=diagnostic_logger)
+
+    def log(self, level: int, message: str, *args, **kwargs) -> None:
+        if self.logger.isEnabledFor(level):
+            formatted_message = message % args
+            if is_onnx_diagnostics_log_artifact_enabled():
+                # Only log to terminal if artifact is enabled.
+                # See [NOTE: `dynamo_export` diagnostics logging] for details.
+                self.logger.log(level, formatted_message, **kwargs)
+
+            self.additional_messages.append(formatted_message)
+
+
+@dataclasses.dataclass
+class DiagnosticContext(infra.DiagnosticContext[Diagnostic]):
+    logger: logging.Logger = dataclasses.field(init=False, default=diagnostic_logger)
+    _bound_diagnostic_type: type[Diagnostic] = dataclasses.field(
+        init=False, default=Diagnostic
+    )
+
+    def __enter__(self):
+        self._previous_log_level = self.logger.level
+        # Adjust the logger level based on `options.verbosity_level` and the environment
+        # variable `TORCH_LOGS`. See [NOTE: `dynamo_export` diagnostics logging] for details.
+        if not is_onnx_diagnostics_log_artifact_enabled():
+            return super().__enter__()
+        else:
+            return self
+
+
+diagnose_call = functools.partial(
+    decorator.diagnose_call,
+    diagnostic_type=Diagnostic,
+    format_argument=format_argument,
+)
+
+
+@dataclasses.dataclass
+class UnsupportedFxNodeDiagnostic(Diagnostic):
+    unsupported_fx_node: torch.fx.Node | None = None
+
+    def __post_init__(self) -> None:
+        super().__post_init__()
+        # NOTE: This is a hack to make sure that the additional fields must be set and
+        # not None. Ideally they should not be set as optional. But this is a known
+        # limitation with `dataclasses`. Resolvable in Python 3.10 with `kw_only=True`.
+        # https://stackoverflow.com/questions/69711886/python-dataclasses-inheritance-and-default-values
+        if self.unsupported_fx_node is None:
+            raise ValueError("unsupported_fx_node must be specified.")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/dynamo_graph_extractor.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/dynamo_graph_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..54af3142cc230dd09a3c3b4805ff0f98bd50a485
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/dynamo_graph_extractor.py
@@ -0,0 +1,228 @@
+# mypy: allow-untyped-defs
+# NOTE: This file is referenced by name at
+#       /opt/pytorch/torch/_dynamo/eval_frame.py::DONT_WRAP_FILES.
+#       introduced by https://github.com/pytorch/pytorch/pull/98894.
+#       If this file is renamed, moved, etc please update the reference there!
+
+from __future__ import annotations
+
+import contextlib
+import functools
+import inspect
+from typing import Any, Callable, Mapping, Sequence
+
+import torch._dynamo
+import torch.export as torch_export
+import torch.fx
+import torch.onnx
+from torch.onnx._internal import _exporter_legacy, io_adapter
+from torch.utils import _pytree as pytree
+
+
+class _PyTreeExtensionContext:
+    """Context manager to register PyTree extension."""
+
+    _extensions: dict[type, tuple[pytree.FlattenFunc, pytree.UnflattenFunc]]
+
+    def __init__(self) -> None:
+        self._extensions = {}
+        # Register PyTree extension for HuggingFace model output.
+        self._register_huggingface_model_output_extension()
+
+    def __enter__(self):
+        for class_type, (flatten_func, unflatten_func) in self._extensions.items():
+            pytree._private_register_pytree_node(
+                class_type,
+                flatten_func,
+                unflatten_func,
+            )
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        for class_type in self._extensions:
+            pytree.SUPPORTED_NODES.pop(class_type)
+
+    def register_pytree_node(
+        self,
+        class_type: type,
+        flatten_func: pytree.FlattenFunc,
+        unflatten_func: pytree.UnflattenFunc,
+    ):
+        """Register PyTree extension for a custom python type.
+
+        Args:
+            class_type: The custom python type.
+            flatten_func: The flatten function.
+            unflatten_func: The unflatten function.
+
+        Raises:
+            AssertionError: If the custom python type is already registered.
+        """
+        if class_type in pytree.SUPPORTED_NODES or class_type in self._extensions:
+            # PyTree node already registered.
+            # E.g., `huggingface/transformer` registers `ModelOutput` as PyTree node after
+            # https://github.com/huggingface/transformers/pull/25358.
+            return
+        self._extensions[class_type] = (flatten_func, unflatten_func)
+
+    def _register_huggingface_model_output_extension(self):
+        try:
+            from transformers import modeling_outputs  # type: ignore[import]
+        except ImportError as e:
+            return
+
+        def model_output_flatten(
+            output: modeling_outputs.ModelOutput,
+        ) -> tuple[list[Any], pytree.Context]:
+            return list(output.values()), (type(output), list(output.keys()))
+
+        def model_output_unflatten(
+            values: list[Any], context: pytree.Context
+        ) -> modeling_outputs.ModelOutput:
+            output_type, keys = context
+            return output_type(**dict(zip(keys, values)))
+
+        # All 'ModelOutput' subclasses are defined under module 'modeling_outputs'.
+        named_model_output_classes = inspect.getmembers(
+            modeling_outputs,
+            lambda x: (
+                inspect.isclass(x)
+                and issubclass(x, modeling_outputs.ModelOutput)
+                and x is not modeling_outputs.ModelOutput
+            ),
+        )
+
+        for _, class_type in named_model_output_classes:
+            self.register_pytree_node(
+                class_type,
+                model_output_flatten,
+                model_output_unflatten,  # type: ignore[arg-type ]
+            )
+
+
+class DynamoFlattenOutputStep(io_adapter.FlattenOutputStep):
+    """Flatten nested collection and custom python types and return a flat list of elements.
+
+    Extended from :class:`io_adapter.FlattenOutputStep` to support flattening arbitrary
+    types via pytree extension. By default this supports many common user defined python
+    types such as :class:`ModelOutput` from HuggingFace transformers.
+
+    The pytree extension can be customized by passing in a ``_PyTreeExtensionContext``
+    object. See :meth:`_PyTreeExtensionContext.register_pytree_node`.
+    """
+
+    def __init__(self, pytree_extension_context: _PyTreeExtensionContext | None = None):
+        super().__init__()
+        self._pytree_extension_context = (
+            pytree_extension_context or _PyTreeExtensionContext()
+        )
+
+    def apply(
+        self,
+        model_outputs: Any,
+        model: torch.nn.Module | Callable | torch_export.ExportedProgram | None = None,
+    ) -> Sequence[Any]:
+        """Flatten the model outputs, under the context of pytree extension."""
+        with self._pytree_extension_context:
+            return super().apply(model_outputs, model=model)
+
+
+def _wrap_model_with_output_adapter(
+    model: torch.nn.Module | Callable,
+    output_adapter: DynamoFlattenOutputStep,
+) -> Callable:
+    """Wrap model with output adapter.
+
+    This is a helper function to enable :func:`dynamo.export` on models that produce
+    custom user defined types outputs. It wraps the model with an output adapter to
+    convert the outputs to :func:`dynamo.export` compatible types, i.e. :class:`torch.Tensor`.
+
+    The adapting logic is controlled by ``output_adapter``.
+
+    Args:
+        model: PyTorch model or function.
+        output_adapter: Output adapter to apply to model output.
+    Returns:
+        Wrapped model.
+    """
+    model_func = model.forward if isinstance(model, torch.nn.Module) else model
+
+    # Preserve original function signature.
+    @functools.wraps(model_func)
+    def wrapped(*args, **kwargs):
+        return output_adapter.apply(model_func(*args, **kwargs), model=model)
+
+    return wrapped
+
+
+class DynamoExport(_exporter_legacy.FXGraphExtractor):
+    """Generates a FX GraphModule using torch.dynamo.export API
+    Args:
+        aten_graph: If True, exports a graph with ATen operators.
+                    If False, exports a graph with Python operators.
+    """
+
+    def __init__(
+        self,
+        aten_graph: bool | None = None,
+    ):
+        super().__init__()
+        self.aten_graph = aten_graph or True
+
+    def generate_fx(
+        self,
+        options: _exporter_legacy.ResolvedExportOptions,
+        model: torch.nn.Module | Callable,
+        model_args: Sequence[Any],
+        model_kwargs: Mapping[str, Any],
+    ) -> torch.fx.GraphModule:
+        # `dynamo.export` does not recognize custom user defined classes as output type.
+        # Apply wrapper to adapt the outputs back to `dynamo.export` compatible types,
+        # i.e. :class:`torch.Tensor`.
+        dynamo_flatten_output_step = DynamoFlattenOutputStep()
+        wrapped_model = _wrap_model_with_output_adapter(
+            model, dynamo_flatten_output_step
+        )
+        # Record the output adapter step.
+        self.output_adapter.append_step(dynamo_flatten_output_step)
+
+        # Translate callable to FX graph.
+        #
+        fake_mode = (
+            options.fake_context.fake_mode
+            if options.fake_context
+            else contextlib.nullcontext()
+        )
+        fx_mode = "symbolic" if options.dynamic_shapes else "fake"
+        with fake_mode:  # type: ignore[attr-defined]
+            graph_module, graph_guard = torch._dynamo.export(
+                wrapped_model,
+                tracing_mode=fx_mode,
+            )(
+                *model_args,
+                **model_kwargs,
+            )
+        del graph_guard  # Unused
+        torch._dynamo.reset()
+
+        # Export FX graph to ONNX ModelProto.
+        self.input_adapter.append_step(
+            io_adapter.FlattenInputWithTreeSpecValidationInputStep()
+        )
+
+        updated_model_args = self.input_adapter.apply(
+            *model_args, model=model, **model_kwargs
+        )
+
+        return self.pre_export_passes(options, model, graph_module, updated_model_args)  # type: ignore[return-value]
+
+    def pre_export_passes(
+        self,
+        options: _exporter_legacy.ResolvedExportOptions,
+        original_model: torch.nn.Module | Callable,
+        fx_module: torch.fx.GraphModule,
+        fx_module_args: Sequence[Any],
+    ):
+        return _exporter_legacy.common_pre_export_passes(
+            options, original_model, fx_module, fx_module_args
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/fx_onnx_interpreter.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/fx_onnx_interpreter.py
new file mode 100644
index 0000000000000000000000000000000000000000..b81c7254751ba8e7587c63926f746eefb453ec00
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/fx_onnx_interpreter.py
@@ -0,0 +1,794 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import inspect
+import logging
+import operator
+import re
+from typing import Callable, Sequence
+
+import onnxscript  # type: ignore[import]
+from onnxscript.function_libs.torch_lib import (  # type: ignore[import]
+    graph_building as onnxscript_graph_building,
+)
+
+import torch
+import torch.fx
+from torch.onnx import _type_utils as jit_type_utils
+from torch.onnx._internal.fx import (
+    _pass,
+    diagnostics,
+    onnxfunction_dispatcher,
+    type_utils as fx_type_utils,
+)
+from torch.utils import _pytree
+
+
+def _fx_node_to_onnx_message_formatter(
+    fn: Callable,
+    self,
+    node: torch.fx.Node,
+    *args,
+    **kwargs,
+) -> str:
+    return f"FX Node: {node.op}:{node.target}[name={node.name}]. "
+
+
+def _fx_graph_to_onnx_message_formatter(
+    fn: Callable,
+    self,
+    fx_graph_module: torch.fx.GraphModule,
+    *args,
+    **kwargs,
+) -> str:
+    return f"FX Graph: {fx_graph_module._get_name()}. "
+
+
+def _location_from_fx_stack_trace(
+    node_stack_trace: str,
+) -> diagnostics.infra.Location | None:
+    """Extract location from FX node stack trace.
+
+    TODO(bowbao): Create fx utils module and move this function there.
+
+    Args:
+        node_stack_trace: The stack trace of the FX node. Example:
+
+            File "path/file.py", line 311, in 
+                
+            |   File "path/file2.py", line 389, in 
+                
+
+    Returns:
+        location: The location of the FX node.
+    """
+    if "File" not in node_stack_trace:
+        return None
+
+    lines = node_stack_trace.strip().split("\n")
+    idx = 0
+    while idx < len(lines) and "File" not in lines[idx]:
+        idx += 1
+    if idx + 1 >= len(lines):
+        return None
+
+    pattern = re.compile(r"^File \"(.+)\", line (\d+), in (.+)$")
+    matches = pattern.match(lines[idx].strip())
+    if matches:
+        uri = matches.group(1)
+        line_number = int(matches.group(2))
+        snippet = lines[idx + 1].strip()
+        return diagnostics.infra.Location(uri=uri, line=line_number, snippet=snippet)
+    return None
+
+
+def _retrieve_or_adapt_input_to_graph_set(
+    fx_node_arg: fx_type_utils.Argument,
+    fx_name_to_onnxscript_value: dict[
+        str,
+        onnxscript_graph_building.TorchScriptTensor
+        | tuple[onnxscript_graph_building.TorchScriptTensor, ...],
+    ],
+    tracer: onnxscript_graph_building.TorchScriptTracingEvaluator,
+):
+    """Map FX value to TorchScript value.
+
+    When creating TorchScript graph from FX graph, we need a mapping from FX variable
+    to TorchScript variable. This function maps FX variable, fx_node_arg, to torch.jit.Value.
+    """
+
+    onnx_tensor = fx_node_arg
+    if isinstance(onnx_tensor, torch.fx.Node):
+        # 1. fx_node_arg is a torch.fx.Node, which means
+        #    fx_node_arg stands for the output of that torch.fx.Node.
+        # 2. fx_node_arg (variable in torch.fx.Graph) is be mapped to
+        #    torch.jit.Value, fx_name_to_onnxscript_value[fx_node_arg.name],
+        #    in TorchScript graph.
+        return fx_name_to_onnxscript_value[onnx_tensor.name]
+    elif isinstance(onnx_tensor, (tuple, list)) and any(
+        isinstance(node, torch.fx.Node)
+        and fx_type_utils.is_torch_symbolic_type(node.meta.get("val"))
+        for node in onnx_tensor
+    ):
+        # This intends to handle dynamic axes. for example, if the input size of op.Expand
+        # is dynamic, each dimension would be variable (i.e., sym variable in Pytorch
+        # FX graph. Note that sym variable is mapped to tensor in ONNX Script world)
+        # calculated by other operators.
+        sequence_mixed_elements: list[
+            onnxscript_graph_building.TorchScriptTensor
+            | tuple[onnxscript_graph_building.TorchScriptTensor, ...]
+            | list[int]
+        ] = []
+        # onnx_tensor contains a list of scalars which could be one of
+        #   - tensor with empty shape,
+        #   - tensor with tensor with shape (1,),
+        #   - torch.SymInt,
+        #   - int
+        #   - ...
+        # They should all be promoted to tensor with shape (1,)
+        # in order to call ONNX's Concat.
+        for tensor in onnx_tensor:
+            # Prepare `tensor` as input of ONNX's Concat.
+
+            if isinstance(
+                tensor, torch.fx.Node
+            ) and fx_type_utils.is_torch_symbolic_type(tensor.meta.get("val")):
+                # In this case, tensor is a torch.SymInt from Dynamo's perspective.
+                # It might be mapped to tensor with shape () or (1,) in ONNX.
+                element_value = fx_name_to_onnxscript_value[tensor.name]
+                if isinstance(
+                    element_value, onnxscript_graph_building.TorchScriptTensor
+                ):
+                    # All elements sequence_mixed_elements will be send to onnx's Concat
+                    # as inputs. Therefore, they are required to have the same rank.
+                    # Since tensors with rank=0 (i.e., scalar) cannot be concated, all
+                    # scalars are promoted to tensors with shape (1,).
+                    with onnxscript.evaluator.default_as(tracer):
+                        element_value = onnxscript.opset18.Reshape(element_value, [1])  # type: ignore[arg-type, type-var]
+                sequence_mixed_elements.append(element_value)
+            elif isinstance(tensor, int):
+                # NOTE: op.Concat doesn't support scalar, so we need to wrap it with
+                # dim, and onnx-script will promote it to tensor(int64)
+                sequence_mixed_elements.append([tensor])
+            else:
+                raise RuntimeError(
+                    f"Unsupported type in sequence_mixed_elements: {type(tensor)}"
+                )
+        # Concat all the elements in the sequence.
+        # shapes are mapped to tensors in ONNX graph (TorchScriptGraph),
+        # so list of sym_ints is concatenated to a tensor before calling ONNX op.
+
+        # For example:
+        #    inputs: [[2], [4], fx.Node(SymIntA), [1], fx.Node(SymIntB)]
+        #    outputs: op.Concat([op.Constant(2), op.Constant(4), TorchScriptTensor(A), op.Constant(1), TorchScriptTensor(B)])
+
+        # onnx-script auto wraps python number with op.Constants,
+        # so we don't need to specifically process them.
+        with onnxscript.evaluator.default_as(tracer):
+            output = onnxscript.opset18.Concat(*sequence_mixed_elements, axis=0)  # type: ignore[type-var]
+        output.dtype = torch.int64  # type: ignore[union-attr]
+        output.shape = [len(sequence_mixed_elements)]  # type: ignore[union-attr]
+        return output
+    elif isinstance(onnx_tensor, (tuple, list)) and all(
+        isinstance(node, torch.fx.Node) or node is None for node in onnx_tensor
+    ):
+        sequence_elements: list[
+            onnxscript_graph_building.TorchScriptTensor
+            | None
+            | tuple[onnxscript_graph_building.TorchScriptTensor, ...]
+        ] = []
+        for tensor in onnx_tensor:
+            sequence_elements.append(
+                fx_name_to_onnxscript_value[tensor.name] if tensor is not None else None
+            )
+        return sequence_elements
+    if isinstance(onnx_tensor, torch.dtype):
+        onnx_tensor = int(  # type: ignore[call-overload]
+            jit_type_utils.JitScalarType.from_dtype(onnx_tensor).onnx_type()
+        )
+    # NOTE: if device is specified in kwargs (not consumed), it's free to ignored. But
+    # if it's in args, we need to set it to string for dispatcher to match schema.
+    if isinstance(onnx_tensor, torch.device):
+        # torch.device is not supported by onnxscript (no op). We turn it into
+        # a string.
+        return str(onnx_tensor)
+    # all other cases, we do nothing.
+    return onnx_tensor
+
+
+def filter_incompatible_and_dtype_convert_kwargs(kwargs):
+    """Filter out kwargs that are not supported by onnxscript."""
+    filtered = {}
+    for key, value in kwargs.items():
+        if key in {
+            "layout",
+            "device",
+            "requires_grad",
+            "pin_memory",
+            "memory_format",
+            "implicit",
+        }:
+            continue
+        if key == "dtype":
+            if value is None:
+                # We omit if dtype is not provided, because onnxscript handles the
+                # default case.
+                continue
+            else:
+                value = int(jit_type_utils.JitScalarType.from_dtype(value).onnx_type())  # type: ignore[call-overload]
+        filtered[key] = value
+    return filtered
+
+
+def _fill_tensor_shape_type(
+    onnxscript_values: onnxscript_graph_building.TorchScriptTensor
+    | tuple[onnxscript_graph_building.TorchScriptTensor, ...],
+    name: str,
+    expected_values: fx_type_utils.META_VALUE_TYPE
+    | list[fx_type_utils.META_VALUE_TYPE]
+    | tuple[fx_type_utils.META_VALUE_TYPE | None, ...],
+):
+    """Fill the meta information of onnxscript_values with that from the fx FakeTensor."""
+
+    if isinstance(expected_values, (list, tuple)) and not isinstance(
+        onnxscript_values, (list, tuple)
+    ):
+        # ex: aten::split - in onnx_dtype: seq(tensor)
+        # onnxscript_values is a single tensor, but expected_values is a list of tensors.
+        return
+
+    flat_onnxscript_values, _ = _pytree.tree_flatten(onnxscript_values)
+    flat_expected_values, _ = _pytree.tree_flatten(expected_values)
+    for i, (onnxscript_value, expected_value) in enumerate(
+        zip(flat_onnxscript_values, flat_expected_values)
+    ):
+        if expected_value is None:
+            # There is no shape/type from None.
+            # NOTE: according to https://github.com/pytorch/pytorch/blob/main/torch/_meta_registrations.py,
+            # None could be a valid value for return type, so we need to handle it.
+            # e.g. the function: meta__scaled_dot_product_flash() in cpu mode.
+            continue
+        elif fx_type_utils.is_torch_symbolic_type(expected_value):
+            # aten::sym_size output is a int, not a tensor, which stands
+            # for the size of one dim. We treat it as 1-D tensor.
+            onnxscript_value.dtype = fx_type_utils.from_sym_value_to_torch_dtype(
+                expected_value
+            )
+            onnxscript_value.shape = torch.Size([1])
+        elif isinstance(expected_value, (int, float, bool)):
+            onnxscript_value.dtype = fx_type_utils.from_scalar_type_to_torch_dtype(
+                type(expected_value)
+            )
+            onnxscript_value.shape = torch.Size([])
+        elif isinstance(expected_value, complex):
+            # From complex scalar to real representation
+            onnxscript_value_to_torch_dtype = (
+                fx_type_utils.from_scalar_type_to_torch_dtype(type(expected_value))
+            )
+            onnxscript_value.dtype = (
+                fx_type_utils.from_complex_to_float(onnxscript_value_to_torch_dtype)
+                if onnxscript_value_to_torch_dtype is not None
+                else None
+            )
+            onnxscript_value.shape = torch.Size([2])
+        elif fx_type_utils.is_torch_complex_dtype(expected_value.dtype):
+            # Like torch.view_as_real, we flatten complex tensors to real tensors with
+            # additional last dimension of 2
+            onnxscript_value.shape = torch.Size((*expected_value.size(), 2))
+            # complex64 -> float32, complex128 -> float64, etc.
+            onnxscript_value.dtype = fx_type_utils.from_complex_to_float(
+                expected_value.dtype
+            )
+            # Dispatcher needs to know the value is complex
+            onnxscript_value.is_complex = True
+        else:
+            # We set node output sizes to be dynamic to continue the model conversion,
+            # and inputs are also set to be dynamic in add_input().
+            onnxscript_value.shape = expected_value.size()
+            onnxscript_value.dtype = expected_value.dtype
+
+        # naming
+        if i > 0:
+            onnxscript_value.name = f"{name}_{i}"
+        else:
+            onnxscript_value.name = name
+
+
+def _fill_in_default_kwargs(
+    node: torch.fx.Node,
+) -> tuple[list[fx_type_utils.Argument], dict[str, fx_type_utils.Argument]]:
+    """Find and Fill in the not provided kwargs with default values."""
+
+    # TODO: aten::sym_size has overload, but fx graph is using
+    # overloadpacket for some reasons.
+    # https://github.com/pytorch/pytorch/issues/97201
+    # We manually assigned overload for aten::sym_size.
+    if hasattr(node.target, "_schema"):
+        node_schema = node.target._schema  # type: ignore[union-attr]
+    else:
+        node_schema = torch.ops.aten.sym_size.int._schema  # type: ignore[union-attr]
+
+    # This function assumes the order of arguments in FX op is the
+    # same as the order of arguments in TorchScript op.
+    complete_args: list[fx_type_utils.Argument] = []
+    complete_kwargs: dict[str, fx_type_utils.Argument] = {}
+
+    if inspect.isbuiltin(node.target):
+        complete_args = list(node.args)
+    else:
+        for i, expected_arg in enumerate(node_schema.arguments):
+            if i < len(node.args):
+                complete_args.append(node.args[i])
+            elif expected_arg.name in node.kwargs:
+                complete_kwargs[expected_arg.name] = node.kwargs[expected_arg.name]
+            else:
+                # Get default from schema.
+                complete_kwargs[expected_arg.name] = expected_arg.default_value
+
+    return complete_args, complete_kwargs
+
+
+def _wrap_fx_args_as_onnxscript_args(
+    complete_args: list[fx_type_utils.Argument],
+    complete_kwargs: dict[str, fx_type_utils.Argument],
+    fx_name_to_onnxscript_value: dict[
+        str,
+        onnxscript_graph_building.TorchScriptTensor
+        | tuple[onnxscript_graph_building.TorchScriptTensor, ...],
+    ],
+    tracer: onnxscript_graph_building.TorchScriptTracingEvaluator,
+) -> tuple[
+    Sequence[
+        onnxscript_graph_building.TorchScriptTensor
+        | str
+        | int
+        | float
+        | bool
+        | list
+        | complex
+        | None
+    ],
+    dict[str, fx_type_utils.Argument],
+]:
+    """Map all FX arguments of a node to arguments in TorchScript graph."""
+
+    onnxscript_args = tuple(
+        _retrieve_or_adapt_input_to_graph_set(arg, fx_name_to_onnxscript_value, tracer)
+        for arg in complete_args
+    )
+    onnxscript_kwargs = filter_incompatible_and_dtype_convert_kwargs(complete_kwargs)
+
+    return onnxscript_args, onnxscript_kwargs
+
+
+class FxOnnxInterpreter:
+    """Stateless class to process FX graph Nodes and translate them into their ONNX counterparts.
+
+    All FX nodes described by [FX Graph](https://pytorch.org/docs/stable/fx.html#torch.fx.Graph) are supported.
+    Similarly to [FX Interpreter pattern](https://pytorch.org/docs/stable/fx.html#torch.fx.Interpreter), each FX node
+    must be implemented on its own method in this class.
+
+    Each operator's implementation returns either an `onnxscript.OnnxFunction` or
+    `onnxscript.TracedOnnxFunction` instance based on the dispatch algorithm. They can
+    also raise RuntimeError: If there are no overloaded functions available for the given FX node.
+
+    TODO: Convert methods to @staticmethod when the diagnostic system supports it
+          DO NOT ADD NEW ATTRIBUTES TO THIS CLASS!
+    """
+
+    def __init__(
+        self,
+        diagnostic_context: diagnostics.DiagnosticContext,
+    ):
+        # THIS SHOULD BE THE ONLY STATE IN THIS CLASS (constraint from diagnosticS API)
+        # TODO: Diagnostics API should be revised to get rid of this attribute.
+        # DO NOT add other class-level attributes.
+        self.diagnostic_context = diagnostic_context
+
+    @diagnostics.diagnose_call(
+        diagnostics.rules.fx_node_to_onnx,
+        diagnostic_message_formatter=_fx_node_to_onnx_message_formatter,
+    )
+    def run_node(
+        self,
+        node,
+        fx_graph_module: torch.fx.GraphModule,
+        onnxfunction_dispatcher: onnxfunction_dispatcher.OnnxFunctionDispatcher,
+        onnxscript_graph: onnxscript_graph_building.TorchScriptGraph,
+        onnxscript_tracer: onnxscript_graph_building.TorchScriptTracingEvaluator,
+        fx_name_to_onnxscript_value: dict[
+            str,
+            onnxscript_graph_building.TorchScriptTensor
+            | tuple[onnxscript_graph_building.TorchScriptTensor, ...],
+        ],
+    ):
+        """Execute a single FX node to produce its ONNX counterpart.
+
+        Args:
+            node: The FX node to be translated.
+            fx_graph_module: The FX graph module containing the node.
+            onnxfunction_dispatcher: The dispatcher to find the best matched ONNX op.
+            onnxscript_graph: The ONNX graph to be populated.
+            onnxscript_tracer: The tracer to trace the ONNX graph.
+            fx_name_to_onnxscript_value: The mapping from FX node name to ONNX Script value.
+
+        Raises:
+            RuntimeError: When a node.op is not supported.
+        """
+        # Record stack trace of node in diagnostic.
+        node_stack_trace = node.stack_trace
+        if node_stack_trace:
+            diagnostic = self.diagnostic_context.inflight_diagnostic(
+                rule=diagnostics.rules.fx_node_to_onnx
+            )
+            with diagnostic.log_section(logging.INFO, "PyTorch source information"):
+                diagnostic.info("```\n%s\n```", node_stack_trace)
+            location = _location_from_fx_stack_trace(node_stack_trace)
+            if location is not None:
+                diagnostic.with_location(location)
+
+        if node.op == "placeholder":
+            self.placeholder(node, onnxscript_graph, fx_name_to_onnxscript_value)
+        elif node.op == "get_attr":
+            self.get_attr(
+                node,
+                onnxscript_graph,
+                fx_name_to_onnxscript_value,
+                fx_graph_module,
+            )
+        elif node.op == "call_function":
+            self.call_function(
+                node,
+                onnxscript_tracer,
+                fx_name_to_onnxscript_value,
+                onnxfunction_dispatcher,
+                fx_graph_module,
+            )
+        elif node.op == "call_method":
+            self.call_method(node)
+        elif node.op == "call_module":
+            self.call_module(
+                node,
+                onnxscript_graph,
+                fx_name_to_onnxscript_value,
+                onnxscript_tracer,
+                fx_graph_module,
+                onnxfunction_dispatcher,
+            )
+        elif node.op == "output":
+            self.output(node, onnxscript_graph, fx_name_to_onnxscript_value)
+        else:
+            raise RuntimeError(f"Found node type not defined in torch.fx: {node.op}")
+
+    @diagnostics.diagnose_call(
+        diagnostics.rules.fx_graph_to_onnx,
+        diagnostic_message_formatter=_fx_graph_to_onnx_message_formatter,
+    )
+    def run(
+        self,
+        fx_graph_module: torch.fx.GraphModule,
+        onnxfunction_dispatcher: onnxfunction_dispatcher.OnnxFunctionDispatcher,
+        parent_onnxscript_graph: onnxscript_graph_building.TorchScriptGraph
+        | None = None,
+    ) -> onnxscript_graph_building.TorchScriptGraph:
+        """Analyze all FX nodes and trigger their ONNX translation.
+
+        Args:
+            fx_graph_module: FX graph module to be translated.
+            onnxfunction_dispatcher: ONNX function dispatcher.
+            parent_onnxscript_graph: The parent TorchScript graph. Must be provided if
+                `fx_graph_module` is a submodule. If not provided,
+                `fx_graph_module` is assumed to be the root module.
+        """
+        diagnostic = self.diagnostic_context.inflight_diagnostic()
+        with diagnostic.log_section(logging.DEBUG, "FX Graph:"):
+            diagnostic.debug(
+                "```\n%s\n```",
+                diagnostics.LazyString(fx_graph_module.print_readable, False),
+            )
+
+        if parent_onnxscript_graph is not None:
+            # If parent_onnxscript_graph is provided, we assume fx_graph_module is a
+            # submodule representing a forward call of an nn.Module.
+            # Compose package and version where the nn.Module is defined as domain name
+            # for the local function.
+
+            onnx_meta: _pass.GraphModuleOnnxMeta | None = fx_graph_module.meta.get(
+                "onnx"
+            )
+            if onnx_meta is None:
+                raise RuntimeError(
+                    f"ONNX meta is not found in submodule {fx_graph_module._get_name()}. "
+                    f"Only submodules produced by `Modularize` pass is supported in ONNX export."
+                )
+
+            onnx_domain = onnx_meta.package_info.to_onnx_domain_string()
+        else:
+            # Leave as default domain name for the root module.
+            onnx_domain = None
+
+        onnxscript_graph = onnxscript_graph_building.TorchScriptGraph(
+            parent_onnxscript_graph, domain_name=onnx_domain
+        )
+        onnxscript_tracer = onnxscript_graph_building.TorchScriptTracingEvaluator(
+            onnxscript_graph
+        )
+        # In the following loop, a TorchScript graph is created to
+        # represent the input FX graph with ONNX symbols (e.g., onnx::add).
+        # To connect the values to nodes in the TorchScript graph, we maintain
+        # fx_name_to_onnxscript_value. Basically, we want to translate
+        #   fx_tensor_x (type: torch.fx.Node) -> fx_node_1 -> fx_tensor_y (type: torch.fx.Node)
+        # to
+        #   fx_name_to_onnxscript_value[fx_tensor_x.name] -> onnx_node_1 -> fx_name_to_onnxscript_value[fx_tensor_y.name]
+        fx_name_to_onnxscript_value: dict[
+            str,
+            onnxscript_graph_building.TorchScriptTensor
+            | tuple[onnxscript_graph_building.TorchScriptTensor, ...],
+        ] = {}
+
+        # TODO: Fix FakeTensorMode limitation asap
+        # We want to pass list of ints and floats to TorchScript graph correctly
+        # in _export_fx_to_ts, so we must disable FakeTensorMode. Otherwise, graph may
+        # receive FakeTensor and results runtime error. In addition, TorchScript-based
+        # ONNX exporter used in _ts_graph_to_onnx_model_in_protobuf is not compatible
+        # with FakeTensorMode.
+        with torch.utils._mode_utils.no_dispatch():
+            for node in fx_graph_module.graph.nodes:
+                self.run_node(
+                    node,
+                    fx_graph_module,
+                    onnxfunction_dispatcher,
+                    onnxscript_graph,
+                    onnxscript_tracer,
+                    fx_name_to_onnxscript_value,
+                )
+
+        with diagnostic.log_section(logging.DEBUG, "ONNX Graph:"):
+            diagnostic.debug("```\n%s\n```", onnxscript_graph.torch_graph)  # type: ignore[attr-defined]
+
+        return onnxscript_graph
+
+    def placeholder(
+        self,
+        node: torch.fx.Node,
+        onnxscript_graph: onnxscript_graph_building.TorchScriptGraph,
+        fx_name_to_onnxscript_value: dict[
+            str,
+            onnxscript_graph_building.TorchScriptTensor
+            | tuple[onnxscript_graph_building.TorchScriptTensor, ...],
+        ],
+    ):
+        # Input of graph.
+        # The node.meta["val"] is generated by FakeTensorProp.
+        # NOTE: add_input() intends to create nodes with shape/type
+        fake_tensor = node.meta.get("val", None)
+        # NOTE: During the tracing, when inputs are constants, they are represented
+        # by nodes with node.meta['val'] being None (nn.Module to dynamo_export)
+        # or nodes with node.meta['val'] being a builtin value (ExportedProgram to dynamo_export).
+        # Nonethless, the nodes are not consumed by others, so we don't need to
+        # create a TorchScriptTensor for them.
+        if fake_tensor is None or isinstance(fake_tensor, (int, float, bool, str)):
+            output = onnxscript_graph.add_input(
+                input_name=None,
+            )
+        elif isinstance(fake_tensor, torch.Tensor):
+            # NOTE: ONNX doesn't support tensor of complex64/complex128, so we
+            # convert them to float32/float64 with real representation.
+            if fx_type_utils.is_torch_complex_dtype(fake_tensor.dtype):
+                fake_tensor = torch.view_as_real(fake_tensor.resolve_conj())
+            output = onnxscript_graph.add_input(
+                input_name=node.name,
+                shape=fake_tensor.shape,
+                dtype=fake_tensor.dtype,
+            )
+
+        elif fx_type_utils.is_torch_symbolic_type(fake_tensor):
+            output = onnxscript_graph.add_input(
+                input_name=node.name,
+                shape=torch.Size([]),
+                dtype=fx_type_utils.from_sym_value_to_torch_dtype(fake_tensor),
+            )
+        else:
+            raise RuntimeError(
+                f"Unsupported type(node.meta['val']) for placeholder: {type(fake_tensor)}"
+            )
+        assert (
+            output is not None
+        ), f"Node creates None with target={node.target} and name={node.name}"
+
+        assert isinstance(output, onnxscript_graph_building.TorchScriptTensor)
+        assert isinstance(output, onnxscript.tensor.Tensor)
+
+        fx_name_to_onnxscript_value[node.name] = output
+
+    def call_function(
+        self,
+        node: torch.fx.Node,
+        onnxscript_tracer: onnxscript_graph_building.TorchScriptTracingEvaluator,
+        fx_name_to_onnxscript_value: dict[
+            str,
+            onnxscript_graph_building.TorchScriptTensor
+            | tuple[onnxscript_graph_building.TorchScriptTensor, ...],
+        ],
+        onnxfunction_dispatcher: onnxfunction_dispatcher.OnnxFunctionDispatcher,
+        fx_graph_module: torch.fx.GraphModule,
+    ):
+        # aten ops and other stateless functions.
+        if node.target == operator.getitem and isinstance(
+            fx_name_to_onnxscript_value[node.args[0].name],  # type: ignore[union-attr,index]
+            tuple,
+        ):
+            onnx_tensor_tuple = fx_name_to_onnxscript_value[node.args[0].name]  # type: ignore[union-attr,index]
+            index = node.args[1]
+            value = onnx_tensor_tuple[index]  # type: ignore[index]
+            assert (
+                value is not None
+            ), f"Node creates None with target={node.target} and name={node.name}"
+            assert isinstance(
+                value, (onnxscript_graph_building.TorchScriptTensor, tuple)
+            ), type(value)
+
+            fx_name_to_onnxscript_value[node.name] = value
+            return
+
+        # Map FX inputs to ONNX inputs and fill optional inputs with default values.
+        # torch_args and torch_kwargs are for op-level validation
+        fx_args, fx_kwargs = _fill_in_default_kwargs(node)
+
+        onnx_args, onnx_kwargs = _wrap_fx_args_as_onnxscript_args(
+            fx_args,
+            fx_kwargs,
+            fx_name_to_onnxscript_value,
+            onnxscript_tracer,
+        )
+        # Dispatch to ONNX op through OpShema. The input argument dtypes are compared to
+        # function signature in OpSchema, and find the best matched overload.
+        symbolic_fn = onnxfunction_dispatcher.dispatch(
+            node=node,
+            onnx_args=onnx_args,  # type: ignore[arg-type]
+            onnx_kwargs=onnx_kwargs,
+            diagnostic_context=self.diagnostic_context,
+        )
+        with onnxscript.evaluator.default_as(onnxscript_tracer):
+            output: (
+                onnxscript_graph_building.TorchScriptTensor
+                | tuple[onnxscript_graph_building.TorchScriptTensor, ...]
+            ) = symbolic_fn(*onnx_args, **onnx_kwargs)
+        assert (
+            output is not None
+        ), f"Node creates None with target={node.target}, name={node.name}, args={onnx_args}, kwargs={onnx_kwargs}"
+        # Assign type and shape from fx graph.
+        _fill_tensor_shape_type(output, node.name, node.meta["val"])
+        # One fx node could produce multiple outputs (e.g., tuple of tensors); in
+        # that case, v is a tuple of TorchScriptTensors.
+        assert isinstance(
+            output, (onnxscript_graph_building.TorchScriptTensor, tuple)
+        ), type(output)
+        fx_name_to_onnxscript_value[node.name] = output
+
+    def output(
+        self,
+        node: torch.fx.Node,
+        onnxscript_graph: onnxscript_graph_building.TorchScriptGraph,
+        fx_name_to_onnxscript_value: dict[
+            str,
+            onnxscript_graph_building.TorchScriptTensor
+            | tuple[onnxscript_graph_building.TorchScriptTensor, ...],
+        ],
+    ):
+        if isinstance(node.args[0], torch.fx.Node):
+            onnx_tensor_or_tensor_tuple = fx_name_to_onnxscript_value[node.args[0].name]
+            onnxscript_graph.register_outputs(onnx_tensor_or_tensor_tuple)
+        else:
+            # ONNX can't represent collection types (e.g., dictionary, tuple of tuple of
+            # tensor, etc), we flatten the collection and register each element as output.
+            flat_args, _ = _pytree.tree_flatten(node.args[0])
+            for arg in flat_args:
+                assert isinstance(
+                    arg, torch.fx.Node
+                ), f"arg must be a torch.fx.Node, not {type(arg)}"
+                onnx_tensor_or_tensor_tuple = fx_name_to_onnxscript_value[arg.name]
+                onnxscript_graph.register_outputs(onnx_tensor_or_tensor_tuple)
+
+    def call_method(self, node: torch.fx.Node):
+        # TODO(wechi): Support call_method.
+        raise RuntimeError("call_method is not supported yet.")
+
+    def call_module(
+        self,
+        node: torch.fx.Node,
+        parent_onnxscript_graph: onnxscript_graph_building.TorchScriptGraph,
+        fx_name_to_onnxscript_value: dict[
+            str,
+            onnxscript_graph_building.TorchScriptTensor
+            | tuple[onnxscript_graph_building.TorchScriptTensor, ...],
+        ],
+        tracer: onnxscript_graph_building.TorchScriptTracingEvaluator,
+        root_fx_graph_module: torch.fx.GraphModule,
+        onnxfunction_dispatcher: onnxfunction_dispatcher.OnnxFunctionDispatcher,
+    ) -> None:
+        """Export a fx.GraphModule submodule to ONNXScript graph.
+
+        The export process specifically targets `call_module` nodes that are created by
+        the exporter's `Modularize` pass. Each `call_module` node has an associated fx.GraphModule
+        by `node.target` underneath the root fx.GraphModule. These `call_module` nodes are exported as ONNX
+        function nodes. The related `sub_module` is then exported as an ONNX model local function,
+        which is represented by another `TorchScriptGraph`. This `TorchScriptGraph` sets the current
+        `onnxscript_graph` as its parent.
+
+        Args:
+            node: The call_module node in the FX graph that represents the submodule call.
+            parent_onnxscript_graph: The parent ONNXScript graph to which the ONNX function and
+                function node belong.
+            fx_name_to_onnxscript_value: The mapping from FX node name to ONNXScript value.
+            tracer: The tracer used to trace the ONNXScript graph.
+            root_fx_graph_module: The root FX module.
+            onnxfunction_dispatcher: The dispatcher.
+        """
+        assert isinstance(
+            node.target, str
+        ), f"node.target must be a str, not {type(node.target)} for node {node}."
+
+        sub_module = root_fx_graph_module.get_submodule(node.target)
+
+        assert isinstance(
+            sub_module, torch.fx.GraphModule
+        ), f"sub_module must be a torch.fx.GraphModule, not {type(sub_module)} for node {node}."
+
+        sub_onnxscript_graph = self.run(
+            sub_module, onnxfunction_dispatcher, parent_onnxscript_graph
+        )
+
+        onnx_args, _ = _wrap_fx_args_as_onnxscript_args(
+            list(node.args), {}, fx_name_to_onnxscript_value, tracer
+        )
+
+        # TODO: We may want to consider other naming styles. The goal is to be stable and
+        # unique such that it can be easily identified in case of kernel substitution.
+        # Example for current style is combination of qualified module class name and
+        # module attribute name: `torch_nn_modules_conv_Conv2d_conv1`.
+        # Other naming styles such as qualified module class name made unique can also
+        # be considered.
+        unique_module_name = f"{sub_module._get_name()}_{node.target}"
+
+        outputs: (
+            onnxscript_graph_building.TorchScriptTensor
+            | tuple[onnxscript_graph_building.TorchScriptTensor, ...]
+        ) = parent_onnxscript_graph.add_module_call(  # type: ignore[assignment]
+            unique_module_name, sub_onnxscript_graph, onnx_args
+        )
+
+        assert isinstance(
+            outputs, (onnxscript_graph_building.TorchScriptTensor, tuple)
+        ), f"Unexpected outputs type {type(outputs)} for node {node}."
+
+        _fill_tensor_shape_type(outputs, node.name, node.meta["val"])
+        fx_name_to_onnxscript_value[node.name] = outputs
+
+        # Skip op_level_validation for call_module. Subgraph nodes are validated individually.
+
+    def get_attr(
+        self,
+        node: torch.fx.Node,
+        onnxscript_graph: onnxscript_graph_building.TorchScriptGraph,
+        fx_name_to_onnxscript_value: dict[
+            str,
+            onnxscript_graph_building.TorchScriptTensor
+            | tuple[onnxscript_graph_building.TorchScriptTensor, ...],
+        ],
+        fx_graph_module: torch.fx.GraphModule,
+    ):
+        assert isinstance(node.target, str), f"node.target {node.target} is not a str."
+        attr_tensor = getattr(fx_graph_module, node.target)
+        assert isinstance(attr_tensor, torch.Tensor), f"{attr_tensor} is not a tensor."
+
+        # Parameter/buffer name cannot contain "."
+        # Revert from "/" to restore namespace formatting.
+        input_ = onnxscript_graph.add_initializer(
+            name=node.target.replace("/", "."),
+            value=attr_tensor,
+        )
+
+        assert isinstance(input_, onnxscript_graph_building.TorchScriptTensor)
+        assert isinstance(input_, onnxscript.tensor.Tensor)
+        fx_name_to_onnxscript_value[node.name] = input_
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/fx_symbolic_graph_extractor.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/fx_symbolic_graph_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..140fceb64d69de4b2b651bced7e481db2c66a12d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/fx_symbolic_graph_extractor.py
@@ -0,0 +1,247 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import functools
+from typing import Any, Callable, Mapping, Sequence
+
+import torch
+import torch.fx
+import torch.onnx
+import torch.onnx._internal.fx.passes as passes
+from torch.onnx._internal import _exporter_legacy, io_adapter
+
+
+# Functions directly wrapped to produce torch.fx.Proxy so that symbolic
+# data can flow through those functions. Python functions (e.g., `torch.arange`)
+# not defined by pybind11 in C++ do not go though Python dispatcher, so
+# they are not automatically patched by FX's Python dispatcher.
+# The list below means `torch.arange`, `torch.tensor`, and so on will be
+# patched.
+_TORCH_METHODS_TO_PATCH: tuple[str, ...] = (
+    "arange",
+    "tensor",
+    "finfo",
+    "full",
+    "empty",
+)
+
+
+class ModuleExpansionTracer(torch.fx._symbolic_trace.Tracer):
+    """Tracer to create ONNX-exporting friendly FX graph.
+
+    This tracer traces models into operators. That is,
+    the traced graph mostly contains call_function nodes and
+    has no call_module nodes. The call_module nodes
+    are problematic to the use of make_fx(...) in ONNX
+    exporter.
+    """
+
+    def is_leaf_module(
+        self, module: torch.nn.Module, module_qualified_name: str
+    ) -> bool:
+        # This returns False so that all sub-modules are considered as not leaves
+        # and therefore expanded into operators in
+        # torch.fx._symbolic_trace.Tracer.call_module.
+        return False
+
+    def to_bool(self, obj: torch.fx.Proxy) -> bool:
+        # FIXME: This is a hack to tracing through if-else Python blocks.
+        # It may generate incorrect ONNX graphs if the if-else block
+        return False
+
+
+def _wrap_for_symbolic_trace(target: Callable) -> tuple[Callable, Callable]:
+    """This function wraps ```target`` for symbolic tracing.
+
+    This function wraps ```target``` so that its wrapper produces
+    torch.fx.Proxy in symbolic computation. The returned values are
+    the wrapper and then the original function. Per `_TORCH_METHODS_TO_PATCH`,
+    this function shall receive `torch.arange`, `torch.tensor`, etc. as inputs.
+    """
+
+    @functools.wraps(target)
+    def wrapper(*args, **kwargs):
+        proxy = None
+
+        def check_has_proxy(v):
+            if isinstance(v, torch.fx.Proxy):
+                nonlocal proxy
+                proxy = v
+
+        torch.fx.node.map_aggregate(args, check_has_proxy)
+        torch.fx.node.map_aggregate(kwargs, check_has_proxy)
+
+        if proxy is not None:
+            return proxy.tracer.create_proxy("call_function", target, args, kwargs)
+        else:
+            return target(*args, **kwargs)
+
+    return wrapper, target
+
+
+def _module_expansion_symbolic_trace(
+    root: torch.nn.Module | Callable[..., Any],
+    concrete_args: dict[str, Any] | None = None,
+) -> torch.fx.GraphModule:
+    """Trace a callable into FX graph.
+
+    When "root" is torch.nn.Module, calls to its submodule (type: torch.nn.Module) will be
+    expanded into operators (e.g., torch.matmul, torch.add, +, and -) to simplify graph
+    structure.
+    """
+    # For functions doesn't support symbolic tracing, create wrappers
+    # which produce symbolic results during tracing.
+    patched_torch_methods = {
+        target_name: _wrap_for_symbolic_trace(getattr(torch, target_name))
+        for target_name in _TORCH_METHODS_TO_PATCH
+    }
+
+    # Set the symbolic-tracing friendly functions so that `tracer.trace` below
+    # can work.
+    for name, (wrapper, _) in patched_torch_methods.items():
+        setattr(torch, name, wrapper)
+
+    try:
+        # Set up a tracer.
+        tracer = ModuleExpansionTracer()
+        # Trace the model.
+        graph = tracer.trace(root, concrete_args)
+        name = (
+            root.__class__.__name__
+            if isinstance(root, torch.nn.Module)
+            else root.__name__
+        )
+        return torch.fx.GraphModule(tracer.root, graph, name)
+    finally:
+        # Revert the patches for symbolic tracing.
+        for name, (_, wrapped) in patched_torch_methods.items():
+            # wrapped is the original version of `torch.name`.
+            setattr(torch, name, wrapped)
+
+
+# TODO: Migrate to `DynamoExporter` after fake model tracing is supported.
+# Proposal at https://github.com/pytorch/pytorch/issues/95900.
+class FXSymbolicTracer(_exporter_legacy.FXGraphExtractor):
+    """Generates a FX GraphModule using torch.fx.symbolic_trace API
+    Args:
+        concrete_args: Inputs to be partially specialized
+            It can be used to remove control flow or data structures.
+            For example::
+                def f(a, b):
+                    if b == True:
+                        return a
+                    else:
+                        return a*2
+            FX can typically not trace through this due to the presence of control
+            flow. However, we can use `concrete_args` to specialize on the value of
+            `b` to trace through this::
+                f = fx.symbolic_trace(f, concrete_args={'b': False})
+                assert f(3, False)  == 6
+            Note that although you can still pass in different values of `b`, they will be ignored.
+            It can also be used to eliminate data-structure handling from
+            our function. This will use pytrees to flatten your input. To avoid
+            overspecializing, pass in `fx.PH` for values that shouldn't be
+            specialized. For example::
+                def f(x):
+                    out = 0
+                    for v in x.values():
+                        out += v
+                    return out
+
+
+                f = fx.symbolic_trace(f, concrete_args={"x": {"a": fx.PH, "b": fx.PH, "c": fx.PH}})
+                assert f({"a": 1, "b": 2, "c": 4}) == 7
+    """
+
+    def __init__(self, concrete_args: dict[str, Any] | None = None):
+        super().__init__()
+        # TODO: plumb ``concrete_args`` to symbolic_trace call at ``generate_fx``
+        self.concrete_args = concrete_args
+
+    def _trace_into_fx_graph_via_fx_symbolic_trace(
+        self, model, model_args, model_kwargs
+    ) -> torch.fx.GraphModule:
+        # Bind model args and kwargs with model signature to retrieve default values
+        # of unprovided arguments. These are then used to construct ``concrete_args``.
+        bind_input_step = io_adapter.BindInputStep(
+            torch.onnx.utils.model_signature(model)
+        )
+        self.input_adapter.append_step(bind_input_step)
+        _, named_args = bind_input_step.apply(model_args, model_kwargs, model=model)
+
+        # Create inputs to call symbolic trace (torch.fx.symbolic_trace)
+        # Example content of concrete_args:
+        #  concrete_args["x"] = torch.fx._symbolic_trace.PH
+        #  concrete_args["b"] = 1
+        # where "x" and "b" are argument names in "signature".
+        concrete_args = {}
+        for param_name, param_value in named_args.items():
+            if isinstance(param_value, torch.Tensor):
+                # param_value can be, e.g., a real tensor or a fake tensor.
+                # param_value is treated as substitutable tensor symbol (aka placeholder).
+                concrete_args[param_name] = torch.fx._symbolic_trace.PH
+            else:
+                concrete_args[param_name] = param_value
+
+        # Merge kwargs back into args since that is the format FX graph expects.
+        merge_kwargs_step = io_adapter.MergeKwargsIntoArgsInputStep()
+        self.input_adapter.append_step(merge_kwargs_step)
+        return _module_expansion_symbolic_trace(model, concrete_args=concrete_args)
+
+    def generate_fx(
+        self,
+        options: _exporter_legacy.ResolvedExportOptions,
+        model: torch.nn.Module | Callable,
+        model_args: Sequence[Any],
+        model_kwargs: Mapping[str, Any],
+    ) -> torch.fx.GraphModule:
+        diagnostic_context = options.diagnostic_context
+        graph_module = self._trace_into_fx_graph_via_fx_symbolic_trace(
+            model, model_args, model_kwargs
+        )
+
+        # Make sure all placeholder nodes are executed before get_attr nodes.
+        # Otherwise, inputs can interleave with initializers in the final ModeoProto.graph.input.
+        # Basically, we want
+        #  ModeoProto.graph.input =
+        #   [input_0, input_1, ..., input_n, weight_0, weight_1, ..., weight_m]
+        # and we don't want
+        #  ModeoProto.graph.input =
+        #   [input_0, weight_0, input_1, weight_1, ..., input_n, weight_0, weight_1, ..., weight_m]
+        graph_module = passes.MovePlaceholderToFront(
+            diagnostic_context, graph_module
+        ).run()
+        # To save memory, move get_attr to input so that the generated model doesn't
+        # have weigh tensors. "replaced_attrs" are a tuple of replaced weight tensors.
+        replace_get_attr_with_placeholder_pass = passes.ReplaceGetAttrWithPlaceholder(
+            diagnostic_context, graph_module
+        )
+        graph_module = replace_get_attr_with_placeholder_pass.run()
+        replaced_attrs = replace_get_attr_with_placeholder_pass.replaced_attrs
+        append_extra_input_step = io_adapter.LiftParametersAndBuffersIntoArgsInputStep(
+            replaced_attrs
+        )
+        self.input_adapter.append_step(append_extra_input_step)
+        # Move all newly created placeholder nodes to the front of the graph.
+        graph_module = passes.MovePlaceholderToFront(
+            diagnostic_context, graph_module
+        ).run()
+        # Finalize the graph editing.
+        graph_module.recompile()
+
+        updated_model_args = self.input_adapter.apply(
+            *model_args, model=model, **model_kwargs
+        )
+
+        return self.pre_export_passes(options, model, graph_module, updated_model_args)  # type: ignore[return-value]
+
+    def pre_export_passes(
+        self,
+        options: _exporter_legacy.ResolvedExportOptions,
+        original_model: torch.nn.Module | Callable,
+        fx_module: torch.fx.GraphModule,
+        fx_module_args: Sequence[Any],
+    ):
+        return _exporter_legacy.common_pre_export_passes(
+            options, original_model, fx_module, fx_module_args
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/onnxfunction_dispatcher.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/onnxfunction_dispatcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..964afcee9ae4657fcba9a90073fae41be94a443f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/onnxfunction_dispatcher.py
@@ -0,0 +1,874 @@
+# mypy: allow-untyped-defs
+"""Dispatcher for AtenLib functions from onnx-script."""
+
+from __future__ import annotations
+
+import logging
+import operator
+import types
+from typing import Any, Callable, Sequence, TYPE_CHECKING
+
+import torch
+import torch._ops
+import torch.fx
+from torch.onnx._internal.fx import (
+    diagnostics,
+    registration,
+    type_utils as fx_type_utils,
+)
+
+
+if TYPE_CHECKING:
+    import onnxscript  # type: ignore[import]
+    from onnxscript.function_libs.torch_lib import (  # type: ignore[import]
+        graph_building as onnxscript_graph_building,
+    )
+
+    from torch.onnx import OnnxRegistry
+
+
+def _find_opschema_matched_symbolic_function_disagnostic_message_formatter(
+    fn: Callable,
+    self,
+    node: torch.fx.Node,
+    default_and_custom_functions: list[registration.ONNXFunction],
+    *args,
+    **kwargs,
+) -> str:
+    """Format the diagnostic message for the nearest match warning."""
+    all_function_overload_names = ""
+    for symbolic_func in default_and_custom_functions:
+        overload_func = symbolic_func.onnx_function
+        all_function_overload_names += f"ONNX Node: {overload_func.name}[opset={overload_func.opset};is_custom={symbolic_func.is_custom}]. \n"  # noqa: B950
+    return f"FX Node: {node.target}. \n" f"{all_function_overload_names}"
+
+
+def _find_operator_overloads_in_onnx_registry_disagnostic_message_formatter(
+    fn: Callable,
+    self,
+    node: torch.fx.Node,
+    *args,
+    **kwargs,
+) -> str:
+    """Format the diagnostic message for the nearest match warning."""
+    return f"Searching operator overload: '{node.target}' in onnx registry...\n"
+
+
+class OnnxFunctionDispatcher:
+    """A dispatcher that finds the best ONNX Function for ATen/Custom operators.
+
+    It uses the `torch.ops` name to find the function. If not found, it falls back to default.
+    Otherwise, the best match is found among all function overloads. An exact match has
+    higher precedence over the closest ones.
+
+    Below is a breakdown on how the dispatch mechanism works:
+
+    1. Use the torch.ops name to find the function:
+        a. Check if the ATen overload exists in the registry.
+        b. If not, check if the default overload exists in the registry.
+
+    2. Find the nearest match among all overloaded functions:
+        a. If the types match perfectly, select the function.
+        b. Otherwise, find the nearest one with the highest matching score. Because of
+            the potential wrongly annotated dtypes and attributes matching, we use
+            nearest match to find the best function once the aten name is targeted.
+
+    3. Tie-breaker: If there are multiple nearest matches, we will select the one with
+        the highest matching score.
+
+    NOTE: The nearest match `doesn't guarantee` a correct match, and a warning message is logged.
+    """
+
+    def __init__(
+        self,
+        onnx_registry: OnnxRegistry,
+        diagnostic_context: diagnostics.DiagnosticContext,
+    ):
+        """Initialize the ONNX Function dispatcher.
+
+        Args:
+            onnx_registry: The ONNX registry.
+            diagnostic_context: The diagnostic context to use for reporting errors.
+        """
+        self.onnx_registry = onnx_registry
+        self.diagnostic_context = diagnostic_context
+
+    def dispatch(
+        self,
+        node: torch.fx.Node,
+        onnx_args: Sequence[
+            fx_type_utils.TensorLike | str | int | float | bool | list | complex | None
+        ],
+        onnx_kwargs: dict[str, fx_type_utils.Argument],
+        diagnostic_context: diagnostics.DiagnosticContext,
+    ) -> onnxscript.OnnxFunction | onnxscript.TracedOnnxFunction:
+        """Dispatches an ONNX function based on the given FX node, arguments, and keyword arguments.
+        Args:
+            node: The TorchFX node to dispatch the function for.
+            onnx_args: The arguments of the ONNX function.
+            onnx_kwargs: The keyword arguments of the ONNX function.
+            diagnostic_context: The diagnostic context to use for reporting errors.
+        Returns:
+            Either an `onnxscript.OnnxFunction` or `onnxscript.TracedOnnxFunction` instance based on the dispatch algorithm.
+        Raises:
+            RuntimeError: If there are no overloaded functions available for the given FX node.
+        """
+        # If there are no overloaded functions available for the given FX node, raise an
+        # unsupported error
+        default_and_custom_functions = self.get_function_overloads(
+            node, diagnostic_context
+        )
+
+        # If there are overloaded functions available, we will find one that perfect or
+        # nearest matches the given arguments and keyword arguments
+        return self._find_the_perfect_or_nearest_match_onnxfunction(
+            node,
+            default_and_custom_functions,
+            onnx_args,
+            onnx_kwargs,
+            diagnostic_context,
+        )
+
+    def _filter_or_keep_complex(
+        self,
+        node,
+        default_and_custom_functions: list[registration.ONNXFunction],
+        diagnostic_context: diagnostics.DiagnosticContext,
+    ) -> list[registration.ONNXFunction]:
+        """Filter the complex functions if the input has complex dtype."""
+
+        args_with_complex_dtype = [_is_arg_with_complex_dtype(arg) for arg in node.args]
+        if any(args_with_complex_dtype):
+            default_and_custom_functions = [
+                func for func in default_and_custom_functions if func.is_complex
+            ]
+            # If we can't find the complex function group, raise error.
+            if not default_and_custom_functions:
+                op_full_name = self._get_aten_name(
+                    node, diagnostic_context
+                ).qualified_name()
+                diagnostic = diagnostics.UnsupportedFxNodeDiagnostic(
+                    diagnostics.rules.no_symbolic_function_for_call_function,
+                    diagnostics.levels.ERROR,
+                    f"Cannot find any COMPLEX symbolic function for {op_full_name}, "
+                    f"which should be registered under {node.target}.",
+                    unsupported_fx_node=node,
+                )
+                diagnostic_context.log(diagnostic)
+                raise diagnostics.RuntimeErrorWithDiagnostic(diagnostic)
+        else:
+            default_and_custom_functions = [
+                func for func in default_and_custom_functions if not func.is_complex
+            ]
+            # If we can't find the complex function group, raise error.
+            if not default_and_custom_functions:
+                op_full_name = self._get_aten_name(
+                    node, diagnostic_context
+                ).qualified_name()
+                diagnostic = diagnostics.UnsupportedFxNodeDiagnostic(
+                    diagnostics.rules.no_symbolic_function_for_call_function,
+                    diagnostics.levels.ERROR,
+                    f"Can ONLY find COMPLEX symbolic function for {op_full_name}, "
+                    f"which should be registered under {node.target}.",
+                    unsupported_fx_node=node,
+                )
+                diagnostic_context.log(diagnostic)
+                raise diagnostics.RuntimeErrorWithDiagnostic(diagnostic)
+        return default_and_custom_functions
+
+    @diagnostics.diagnose_call(
+        diagnostics.rules.find_opschema_matched_symbolic_function,
+        diagnostic_message_formatter=_find_opschema_matched_symbolic_function_disagnostic_message_formatter,
+    )
+    def _find_the_perfect_or_nearest_match_onnxfunction(
+        self,
+        node: torch.fx.Node,  # this is used in diagnostic_message_formatter
+        default_and_custom_functions: list[registration.ONNXFunction],
+        onnx_args: Sequence[
+            fx_type_utils.TensorLike | str | int | float | bool | list | complex | None
+        ],
+        onnx_kwargs: dict[str, fx_type_utils.Argument],
+        diagnostic_context: diagnostics.DiagnosticContext,
+    ):
+        """Find the perfect/nearest matched OnnxFunction for the given FX node, arguments, and keyword arguments.
+
+        Args:
+            default_and_custom_functions: The list includes overloaded functions, with
+                custom ones appearing after the default ones.
+            onnx_args: Arguments organized in PyTorch inputs way.
+            onnx_kwargs: Keyword arguments organized in PyTorch inputs way.
+            diagnostic_context: The diagnostic context to use for reporting errors.
+
+            Returns:
+                Either an `onnxscript.OnnxFunction` or `onnxscript.TracedOnnxFunction` instance based on the dispatch algorithm.
+            Raises:
+                RuntimeError: If there are no overloaded functions available for the given FX node.
+        """
+        overload_match_ranking: dict[registration.ONNXFunction, int | None] = {}
+        diagnostic = diagnostic_context.inflight_diagnostic()
+
+        # Iterate the overloaded functions in reverse order to prioritize the custom ones
+        # over the default ones, and find the perfect match.
+        for symbolic_function in reversed(default_and_custom_functions):
+            function_opschema = _OnnxSchemaChecker(symbolic_function.onnx_function)
+
+            # NOTE: 1. If the perfect match is found, return the function
+            if function_opschema.perfect_match_inputs(
+                diagnostic, onnx_args, onnx_kwargs
+            ):
+                return symbolic_function.onnx_function
+            # Record the match score for the nearest match if it's not the perfect match
+            overload_match_ranking[symbolic_function] = function_opschema.match_score
+
+        # NOTE: 2. If there is no perfect match, find the nearest match among the nearest matche candidates
+        # If there is no nearest match, raise an error
+        overload_match_ranking = {
+            k: v for k, v in overload_match_ranking.items() if v is not None
+        }
+        if not overload_match_ranking:
+            # If there are no overloaded functions available for the given FX node, raise an
+            # unsupported error
+            op_full_name = self._get_aten_name(
+                node, diagnostic_context
+            ).qualified_name()
+            diagnostic = diagnostics.UnsupportedFxNodeDiagnostic(
+                diagnostics.rules.no_symbolic_function_for_call_function,
+                diagnostics.levels.ERROR,
+                f"Cannot find any perfect/nearest match of symbolic function for {op_full_name},"
+                f"which should be registered under {node.target}.",
+                unsupported_fx_node=node,
+            )
+            diagnostic_context.log(diagnostic)
+            raise diagnostics.RuntimeErrorWithDiagnostic(diagnostic)
+
+        diagnostic.warning(
+            "### Exact match is not found!\n"
+            "Cannot find a perfect match of symbolic overload, "
+            "a nearest match is found. Please check the ONNX output carefully. \n",
+        )
+        diagnostic.level = diagnostics.levels.WARNING
+        # NOTE: 3. Tie breaker: if there are multiple nearest matches, we will choose the one
+        # that is custom first. If there are multiple custom ones, we will choose the one
+        # that is added lastly in the list.
+        symbolic_function_list: list[registration.ONNXFunction] = sorted(
+            overload_match_ranking,
+            key=lambda k: (
+                overload_match_ranking[k],
+                k.is_custom,
+                default_and_custom_functions.index(k),
+            ),
+            reverse=True,
+        )
+        return symbolic_function_list[0].onnx_function
+
+    def _get_aten_name(
+        self, node: torch.fx.Node, diagnostic_context: diagnostics.DiagnosticContext
+    ) -> registration.OpName:
+        """Get the OpName from the target.
+
+        Args:
+            node: The TorchFX node to get the aten name for.
+            diagnostic_context: The diagnostic context to use for reporting errors.
+
+        Returns:
+            The internal op name within dataclass: registration.OpName.
+        """
+        if node.target == operator.getitem:
+            return registration.OpName.from_name_parts(
+                namespace="aten", op_name="getitem"
+            )
+        if isinstance(node.target, torch._ops.OpOverloadPacket):
+            # aten::sym_size is the only OverloadPacket that we support.
+            # schema: aten::sym_size(Tensor self, int dim) -> Tensor
+            if node.target != torch.ops.aten.sym_size:
+                diagnostic = diagnostics.UnsupportedFxNodeDiagnostic(
+                    diagnostics.rules.no_symbolic_function_for_call_function,
+                    diagnostics.levels.ERROR,
+                    f"Unsupported OverloadPacket: {node.target}, aten.sym_size is the only allowed OverloadPacket!",
+                    unsupported_fx_node=node,
+                )
+                diagnostic_context.log(diagnostic)
+                raise diagnostics.RuntimeErrorWithDiagnostic(diagnostic)
+            # TODO(titaiwang): aten::sym_size has overload, but fx graph is using
+            # overloadpacket for some reasons.
+            # https://github.com/pytorch/pytorch/issues/97201
+            aten_op_default = node.target.default
+            return registration.OpName.from_op_overload(op_overload=aten_op_default)  # type: ignore[no-any-return]
+
+        if isinstance(node.target, types.BuiltinFunctionType):
+            # Make sure it's symint/symfloat consuming builtin ops.
+            for node_arg in node.args:
+                if (not isinstance(node_arg, (torch.fx.Node, int, float))) or (
+                    isinstance(node_arg, torch.fx.Node)
+                    and not fx_type_utils.is_torch_symbolic_type(node_arg.meta["val"])
+                ):
+                    diagnostic = diagnostics.UnsupportedFxNodeDiagnostic(
+                        diagnostics.rules.no_symbolic_function_for_call_function,
+                        diagnostics.levels.ERROR,
+                        f"Unsupported node arg: {node_arg} (type {type(node_arg)}) with builtin function: {node.target},"
+                        " only int/float/SymInt/SymFloat is supported with built-in ops!",
+                        unsupported_fx_node=node,
+                    )
+                    diagnostic_context.log(diagnostic)
+                    raise diagnostics.RuntimeErrorWithDiagnostic(diagnostic)
+            return registration.OpName.from_builtin_function(node.target)
+
+        if isinstance(node.target, torch._ops.OpOverload):
+            return registration.OpName.from_op_overload(op_overload=node.target)
+
+        # Unexpected target, raise error.
+        diagnostic = diagnostics.UnsupportedFxNodeDiagnostic(
+            diagnostics.rules.no_symbolic_function_for_call_function,
+            diagnostics.levels.ERROR,
+            f"Unknown call_function target: {node.target}",
+            unsupported_fx_node=node,
+        )
+        diagnostic_context.log(diagnostic)
+        raise diagnostics.RuntimeErrorWithDiagnostic(diagnostic)
+
+    @diagnostics.diagnose_call(
+        diagnostics.rules.find_operator_overloads_in_onnx_registry,
+        diagnostic_message_formatter=_find_operator_overloads_in_onnx_registry_disagnostic_message_formatter,
+    )
+    def get_function_overloads(
+        self,
+        node: torch.fx.Node,
+        diagnostic_context: diagnostics.DiagnosticContext,
+    ) -> list[registration.ONNXFunction]:
+        """Get the function overloads from the registry.
+
+        Args:
+            node: The node to get the function overloads for.
+            diagnostic_context: The diagnostic context to use for reporting errors.
+
+        Returns:
+            The list contains ONNXFunctions, starting with the default ones and
+            followed by any custom ones.
+        """
+
+        internal_opname: registration.OpName = self._get_aten_name(
+            node=node, diagnostic_context=diagnostic_context
+        )
+
+        # If the ATen/Custom operators are not registered, the group will be None.
+        # And non-registered ATen/Custom operators will trigger error in the next step.
+        function_group: list[registration.ONNXFunction] | None = None
+
+        function_group = self.onnx_registry.get_op_functions(
+            namespace=internal_opname.namespace,
+            op_name=internal_opname.op_name,
+            overload=internal_opname.overload,
+        )
+
+        # NOTE: Fall back to default overload if the ONNX registry doesn't have the overload.
+        if function_group is None:
+            function_group = self.onnx_registry.get_op_functions(
+                namespace=internal_opname.namespace,
+                op_name=internal_opname.op_name,
+                overload=None,
+            )
+            if function_group is not None:
+                op_full_name = internal_opname.qualified_name()
+                diagnostic = diagnostic_context.inflight_diagnostic()
+                diagnostic.warning(
+                    "### The operator overload is not found in onnx registry!\n"
+                    "Cannot find the operator overload in onnx registry, but "
+                    "the default overload is found. Please check the ONNX output carefully. \n",
+                )
+                diagnostic.level = diagnostics.levels.WARNING
+
+        if function_group is not None:
+            # NOTE: If the input has complex dtype, we will only dispatch to the complex functions.
+            function_group = self._filter_or_keep_complex(
+                node, function_group, diagnostic_context
+            )
+            return function_group  # type: ignore[return-value]
+
+        op_full_name = internal_opname.qualified_name()
+        diagnostic = diagnostics.UnsupportedFxNodeDiagnostic(
+            diagnostics.rules.no_symbolic_function_for_call_function,
+            diagnostics.levels.ERROR,
+            f"Cannot find symbolic function for {op_full_name}, "
+            f"which should be registered under {node.target}.",
+            unsupported_fx_node=node,
+        )
+        diagnostic_context.log(diagnostic)
+        raise diagnostics.RuntimeErrorWithDiagnostic(diagnostic)
+
+
+class _OnnxSchemaChecker:
+    """
+    The OnnxSchemaChecker class is a checker for ONNX OpSchema and param schema.
+
+    It provides methods to check for input compatibility based on the OpSchema. It also
+    provides a matching score to indicate how well the OpSchema matches the input and
+    kwargs types. A function will be evaluated as perfect match, nearest match eligible,
+    or no match.
+
+    Here are some common examples in categories:
+
+    1. [NOTE: Perfect match]: The number of inputs and attributes are exactly the same as
+        the OpSchema. The types of inputs and attributes are exactly the same as the
+        OpSchema.
+
+        ```python
+        inputs = (Tensor[2, 3], Tensor[2, 3])
+        attributes = {"alpha": 1.0}
+
+
+        @torch_op("aten::op")
+        def aten_op(self: TReal, other: TReal, alpha: float = 1) -> TReal: ...
+        ```
+        Result: Perfect match.
+
+    2. [NOTE: Optional input]: The dispatcher recognizes optional inputs. However,
+        the input can't be ignored. None must be provided.
+
+        ```python
+        inputs = (Tensor([2, 3]), None)
+        attributes = {}
+
+        aten_op(X: TTensor, Y: Optional[INT64]):
+            ...
+        ```
+        Result: Perfect match.
+        Real example: `aten::convolution`.
+
+    3. [NOTE: Different attributes]: If an attribute is provided with value, it's
+        a must to match the attribute in function signature.
+        ```python
+        inputs = (Tensor([2, 3]),)
+        attributes = {"a":1, "b":2}
+
+        aten_op(X: TTensor, a: int):
+            ...
+        ```
+        Result: No match.
+        Real example: `aten::div` vs `aten::div.Tensor_mode`.
+
+    4. [NOTE: Default attributes]: Default attribute will fill in the value into
+        inputs/attributes.
+        ```python
+        inputs = (Tensor([2, 3]),)
+        attributes = {}
+
+        aten_op(X: TTensor, a: int = 3):
+            ...
+        ```
+        Result: Perfect match.
+        Real example: `aten::clone`
+
+    5. [NOTE: Ignore attribute with None value]: The attributes with None value
+        will be ignored in matching.
+        ```python
+        inputs = (Tensor([2, 3]),)
+        attributes = {"a": None}
+
+        aten_op(X: TTensor):
+            ...
+        ```
+        Result: Perfect match.
+
+        ```python
+        inputs = (Tensor([2, 3]),)
+        attributes = {"a": None}
+
+        aten_op(X: TTensor, a: int = 3):
+            ...
+        ```
+        Result: Nearest match eligible.
+
+        Real example: `aten::div` vs `aten::div.Tensor_mode`.
+
+    Attributes:
+        onnxfunction: The OnnxFunction.
+        param_schema: The parameter schema defined in the OnnxFunction.
+        op_schema: The ONNX OpSchema.
+        type_constraints: The type constraints defined in the OpSchema.
+        attributes: The attributes defined in the OpSchema.
+        _matching_score: The matching score of the OnnxSchemaChecker .
+
+    """
+
+    def __init__(
+        self,
+        onnxfunction: onnxscript.OnnxFunction | onnxscript.TracedOnnxFunction,
+    ):
+        """Initialize the OnnxSchemaChecker .
+
+        Args:
+            onnxfunction: The OnnxFunction.
+        """
+        self.onnxfunction = onnxfunction
+        self.param_schema = self.onnxfunction.param_schemas()
+        op_schema = self.onnxfunction.op_schema
+        # Both `OnnxFunction` and `TracedOnnxFunction` never return None for `op_schema`.
+        # However their base class would. Hence return type is annotated as Optional[OpSchema].
+        assert op_schema is not None
+        self.op_schema = op_schema
+        self.type_constraints = {
+            # "T": {"tensor(int64)"}
+            constraint.type_param_str: set(constraint.allowed_type_strs)
+            for constraint in self.op_schema.type_constraints
+        }
+        self.attributes = self.op_schema.attributes
+        self._matching_score: int | None = None
+
+    @property
+    def match_score(self) -> int | None:
+        """The matching score of the OnnxSchemaChecker .
+
+        If this remains None, it means the matching score has not been calculated,
+        and it's not a nearest match candidate.
+
+        Returns:
+            The matching score of the OnnxSchemaChecker .
+        """
+        return self._matching_score
+
+    def perfect_match_inputs(
+        self,
+        diagnostic: diagnostics.Diagnostic,
+        args: Sequence[
+            fx_type_utils.TensorLike | str | int | float | bool | list | complex | None
+        ],
+        kwargs: dict[str, fx_type_utils.Argument],
+    ) -> bool:
+        """Check if the inputs perfectly match the OpSchema requirements.
+
+        The definition of perfect match is that the input types are all in the type
+        constraints and the number of inputs matches the number of inputs in the
+        OpSchema.
+
+        Checking steps:
+        1. The function signature matches the inputs number, and attribute names.
+        2. The input/attribute types are all in the type constraints.
+
+        A function should at least pass the first step to be eligible for the
+        nearest matching.
+
+        Args:
+            diagnostic: The diagnostic to use for logging detailed info.
+            args: The input arguments organized in PyTorch inputs way.
+            kwargs: The input keyword arguments organized in PyTorch inputs way.
+
+        Returns:
+            True if the inputs match the requirements, False otherwise.
+        """
+
+        # NOTE: OnnxFunction does not have the same function signature as the original
+        # PyTorch operator. We need to separate the input/attributes from the arguments.
+        (
+            function_inputs,
+            function_attributes,
+        ) = self._separate_input_attributes_from_arguments(
+            self.param_schema,
+            args,
+            kwargs,
+            fill_defaults=True,  # fill defaults for optional arguments to match
+        )
+        with diagnostic.log_section(logging.INFO, "Checking perfect match..."):
+            diagnostic.info(
+                "%s",
+                diagnostics.LazyString(diagnostics.format_argument, self.onnxfunction),
+            )
+            # NOTE: 1. Check if the input number and attribute names match the
+            # OpSchema. If it's not, we know the function is not eligible to be a perfect
+            # match, nor a nearest match.
+            # We use is_perfect_match to postpone the return value to the end
+            # of the function, as we want to log all the mismatch info.
+            is_perfect_match = True
+            if len(function_inputs) != len(self.op_schema.inputs):
+                with diagnostic.log_section(
+                    logging.INFO, "Failed: input number mismatch!"
+                ):
+                    diagnostic.info(
+                        "Actual %d vs expected %d",
+                        len(function_inputs),
+                        len(self.op_schema.inputs),
+                    )
+                diagnostic.info("The function is not a nearest match candidate.")
+                is_perfect_match = False
+
+            if set(function_attributes) != set(self.attributes):
+                with diagnostic.log_section(
+                    logging.INFO, "Failed: attribute mismatch!"
+                ):
+                    diagnostic.info(
+                        "%s",
+                        diagnostics.LazyString(
+                            lambda: f"Actual {set(function_attributes)} vs expected {set(self.attributes)}",
+                        ),
+                    )
+                diagnostic.info("The function is not a nearest match candidate.")
+                is_perfect_match = False
+
+            # If it's already not a perfect match, we can return False directly. Further
+            # checking is only for the functions that are eligible for nearest match.
+            if not is_perfect_match:
+                return False
+
+            # NOTE: 2. The dtypes of inputs and attributes should be in the
+            # type constraints of the OpSchema. If they are not, we know the function is not
+            # eligible to be a perfect match, but can be a nearest match candidate.
+            for schema_input, torch_input in zip(
+                self.op_schema.inputs, function_inputs
+            ):
+                torch_input_compatible_types = _find_onnx_data_type(torch_input)
+                allowed_types = self.type_constraints[schema_input.type_str]
+                if not allowed_types.intersection(
+                    torch_input_compatible_types
+                ) and not any(
+                    fx_type_utils.is_optional_onnx_dtype_str(onnx_type_str)
+                    for onnx_type_str in allowed_types
+                ):
+                    # If torch_input_compatible_types isn't in allowed_types
+                    # of this input defined in the OpSchema, we know the function
+                    # and the input are not compatible
+                    with diagnostic.log_section(
+                        logging.INFO,
+                        "Failed: input type mismatch for input '%s'!",
+                        schema_input.name,
+                    ):
+                        diagnostic.info(
+                            "Actual %s vs\nExpected %s",
+                            torch_input_compatible_types,
+                            allowed_types,
+                        )
+                    is_perfect_match = False
+
+            for attribute_name, attribute in function_attributes.items():
+                if not self._match_onnx_attribute_type(attribute_name, attribute):
+                    # If the attribute type of the OpSchema and the attribute type don't match,
+                    # we know the function and the input are not compatible
+                    with diagnostic.log_section(
+                        logging.INFO,
+                        "Failed: attribute '%s' type mismatch!",
+                        attribute_name,
+                    ):
+                        diagnostic.info(
+                            "Actual %s vs\nExpected %s",
+                            type(attribute),
+                            self.attributes[attribute_name].type,
+                        )
+                    is_perfect_match = False
+
+            # NOTE: This is still a candidate for nearest match, as it only mismatches attributes on dtype.
+            self._record_matching_score(function_inputs, function_attributes)
+            diagnostic.info("match score: %d", self.match_score)
+            return is_perfect_match
+
+    def _match_onnx_attribute_type(
+        self,
+        attribute_name: str,
+        attribute: fx_type_utils.Argument | onnxscript_graph_building.TorchScriptTensor,
+        is_sequence: bool = False,
+    ) -> bool:
+        if isinstance(attribute, (int, float, bool, str)):
+            attribute_onnx_type = fx_type_utils.from_python_type_to_onnx_attribute_type(
+                type(attribute), is_sequence=is_sequence
+            )
+            if attribute_onnx_type != self.attributes[attribute_name].type:
+                return False
+        # If the attribute is an empty list, we don't know the type of the list
+        # so it's a mismatch
+        elif isinstance(attribute, (list, tuple)) and attribute:
+            return self._match_onnx_attribute_type(
+                attribute_name, attribute[0], is_sequence=True
+            )
+        else:
+            # NOTE: Unrecognized attribute type
+            return False
+        return True
+
+    def _record_matching_score(
+        self,
+        inputs: Sequence[
+            fx_type_utils.TensorLike | str | int | float | bool | list | complex | None
+        ],
+        attributes: dict[str, fx_type_utils.Argument],
+    ):
+        """Calculate the inputs matching score of the OpSchema requirements to find the nearest match.
+
+        Only the functions which have the same number of inputs and attributes as the
+        OpSchema are eligible to be a nearest match candidate. Thus, we don't need to
+        check the length of inputs and attributes here, and only check the types of
+        inputs and attributes.
+
+        How the matchsing score is calculated:
+            score += 1 if one input/attribute type is in the type constraints.
+
+        Limitations:
+            None/NoeType/[] could result in zero matches, and the same score of overloads,
+            which will be recorded in SARIF.
+
+        Args:
+            inputs: The input arguments.
+            attributes: The input keyword arguments.
+
+        Returns:
+            True if the inputs match the requirements, False otherwise.
+        """
+        self._matching_score = 0
+        # If they have different length of arguments, the score would be lower to those
+        # functions which have the same length of arguments.
+        for schema_input, torch_input in zip(self.op_schema.inputs, inputs):
+            torch_input_compatible_types = _find_onnx_data_type(torch_input)
+            allowed_types = self.type_constraints[schema_input.type_str]
+            if allowed_types.intersection(torch_input_compatible_types):
+                # If torch_input_compatible_types is in allowed_types
+                # of this input defined in the OpSchema, we know the function
+                # and the input are compatible
+                self._matching_score += 1
+        # NOTE: The penalty is applied to those functions which have different attributes.
+        for attribute_name, attribute_proto in self.attributes.items():
+            attribute = attributes[attribute_name]
+            attribute_onnx_type = fx_type_utils.from_python_type_to_onnx_attribute_type(
+                type(attribute)
+            )
+            if attribute_onnx_type != attribute_proto.type:
+                # If the attribute type of the OpSchema and the attribute type don't match,
+                # we know the function and the input are not compatible
+                self._matching_score -= 1
+
+    # NOTE: Referenced from onnxscript internal function.
+    # Importing this function makes the code less robust, as it is not a public API.
+
+    def _separate_input_attributes_from_arguments(
+        self,
+        param_schemas: Sequence[onnxscript.values.ParamSchema],
+        args: Sequence[
+            fx_type_utils.TensorLike | str | int | float | bool | list | complex | None
+        ],
+        kwargs: dict[str, fx_type_utils.Argument],
+        fill_defaults: bool = True,
+    ) -> tuple[list[Any], dict[str, Any]]:
+        """Separate Python args and kwargs into ONNX inputs and attributes.
+
+        Extra_kwargs are ignored if their values are None. For example, if the
+        OpSchema has an attribute "rounding_mode" and the caller provides
+        "rounding_mode=None", the attribute "rounding_mode" will not be included
+        in the returned attributes when the OnnxFunction signature doesn't have
+        "rounding_mode" as an attribute.
+
+        Args:
+            param_schemas: The parameter schemas of an Op or a OnnxFunction.
+            args: The Python positional arguments supplied by the caller.
+            kwargs: The Python keyword arguments supplied by the caller.
+            fill_defaults: Whether to fill the default values for attributes.
+
+        Returns:
+            A tuple of two elements:
+            - A list of ONNX inputs.
+            - An dictionary of ONNX attribute names and values.
+
+        Raises:
+            TypeError: When allow_extra_kwargs is False and there are unknown kwargs.
+            TypeError: When a required input is not provided.
+        """
+        # args, kwargs and param_schemas should be all in order
+        # user may not specify all inputs or attributes
+
+        import onnx
+
+        onnx_inputs: list[Any] = []
+        onnx_attributes: dict[str, Any] = {}
+        # NOTE: We need to copy kwargs because we will mutate it
+        copy_kwargs = kwargs.copy()
+        for i, param in enumerate(param_schemas):
+            if param.is_variadic_input:
+                # Exhaust all remaining args
+                onnx_inputs.extend(args[i:])
+                args = []
+                continue
+            if i < len(args):
+                if param.is_input:
+                    onnx_inputs.append(args[i])
+                else:
+                    onnx_attributes[param.name] = args[i]
+            elif param.name in copy_kwargs:
+                if param.is_input:
+                    # Move the input from kwargs to inputs
+                    onnx_inputs.append(copy_kwargs[param.name])
+                    copy_kwargs.pop(param.name)
+                else:
+                    onnx_attributes[param.name] = copy_kwargs[param.name]
+            elif (
+                param.is_attribute
+                and self.attributes[param.name].default_value.type
+                != onnx.AttributeProto.UNDEFINED  # type: ignore[attr-defined]
+            ):
+                # User did not provide the attribute
+                if fill_defaults:
+                    onnx_attributes[param.name] = param.default
+            # optional input
+            elif param.is_input:
+                if fill_defaults:
+                    onnx_inputs.append(None)
+
+        # NOTE: Pick up extra kwargs if it's not None. None is not expected
+        # as an attribute value in torchlib.
+        for k, v in copy_kwargs.items():
+            if k not in onnx_attributes and v is not None:
+                onnx_attributes[k] = v
+        return onnx_inputs, onnx_attributes
+
+
+def _is_arg_with_complex_dtype(arg: fx_type_utils.Argument) -> bool:
+    """Check if the node has complex dtype recursively."""
+    if (
+        isinstance(arg, torch.fx.Node)
+        and "val" in arg.meta
+        and isinstance(arg.meta["val"], torch.Tensor)
+        and torch.is_complex(arg.meta["val"])
+    ):
+        return True
+    elif isinstance(arg, list):
+        for item in arg:
+            return _is_arg_with_complex_dtype(item)
+    return False
+
+
+def _find_onnx_data_type(
+    torch_input: fx_type_utils.TensorLike
+    | str
+    | int
+    | float
+    | bool
+    | list
+    | tuple
+    | complex
+    | None,
+) -> set[str]:
+    """Convert inputs data type from torch acceptable dtype to the compatible onnx dtype string."""
+    if (
+        isinstance(torch_input, fx_type_utils.TensorLike)
+        and torch_input.dtype is not None
+    ):
+        return fx_type_utils.from_torch_dtype_to_onnx_dtype_str(torch_input.dtype)
+    if isinstance(torch_input, (int, float, bool, str, complex)):
+        return fx_type_utils.from_torch_dtype_to_onnx_dtype_str(type(torch_input))
+    if isinstance(torch_input, (list, tuple)) and torch_input:  # [Tensor, Tensor]
+        the_first_non_none_item = next(
+            (item for item in torch_input if item is not None), None
+        )
+        set_dtype = _find_onnx_data_type(the_first_non_none_item)
+        if any(isinstance(input, fx_type_utils.TensorLike) for input in torch_input):
+            # NOTE: Any Tensor involved in a list would make it a seq(tensor(onnx_type))
+            return {f"seq({dtype})" for dtype in set_dtype}
+        else:
+            # constant list of non-tensor type
+            return set_dtype
+    if (
+        torch_input is None
+        or (
+            isinstance(torch_input, fx_type_utils.TensorLike)
+            and torch_input.dtype is None
+        )
+        or (isinstance(torch_input, (list, tuple)) and not torch_input)
+    ):
+        # NOTE: None, No dtype, and empty list are edge cases, we allow it to be any type to relax the type check
+        # seq(tensor) also goes to here, as it is not supported in torchscript, and it would be None in this case.
+        return set()
+
+    raise RuntimeError(f"Unknown input type from input: {torch_input}")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa04e6beb5f127a7685e9f4d7b4d1ac5842159de
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/__init__.py
@@ -0,0 +1,18 @@
+from .decomp import Decompose
+from .functionalization import Functionalize, RemoveInputMutation
+from .modularization import Modularize
+from .readability import RestoreParameterAndBufferNames
+from .type_promotion import InsertTypePromotion
+from .virtualization import MovePlaceholderToFront, ReplaceGetAttrWithPlaceholder
+
+
+__all__ = [
+    "Decompose",
+    "InsertTypePromotion",
+    "Functionalize",
+    "Modularize",
+    "MovePlaceholderToFront",
+    "RemoveInputMutation",
+    "RestoreParameterAndBufferNames",
+    "ReplaceGetAttrWithPlaceholder",
+]
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..853557362e049536d4c3ec67d8ba6dc1198bda73
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/_utils.py
@@ -0,0 +1,115 @@
+# mypy: allow-untyped-defs
+"""Common utility functions for FX passes.
+
+These functions should NOT be directly invoked outside of `passes` package.
+"""
+
+from __future__ import annotations
+
+import collections
+import re
+from typing import Callable
+
+import torch.fx
+import torch.fx.traceback as fx_traceback
+
+
+def wrap_graph_module_for_node_meta_preservation(
+    graph_module: torch.fx.GraphModule,
+) -> Callable:
+    """Wrap a GraphModule with contexts to preserve node meta information, such as stacktrace info.
+
+    This is typically useful before calling `make_fx`. Without this wrapper, the
+    stacktrace information will be lost afterwards.
+    """
+
+    def wrapped(*args):
+        with fx_traceback.preserve_node_meta():
+            return torch.fx.Interpreter(graph_module).run(*args)
+
+    return wrapped
+
+
+def _get_node_base_name(node_name: str) -> tuple[str, int | None]:
+    pattern = r"(.*)\.(\d+)"
+    match = re.match(pattern, node_name)
+    if match is not None:
+        base_name, count_str = match.groups()
+        return base_name, int(count_str)
+    return node_name, None
+
+
+def set_node_name(
+    node: torch.fx.Node,
+    new_name: str,
+    name_to_node_cache: dict[str, torch.fx.Node],
+):
+    """Safely set the unique name of a node.
+
+    If the new name is already taken by another node, the name of the other node will be
+    updated. If `new_name` is a string of format f"{base_name}.{count}", where `count`
+    is an integer, the other node will be renamed as f"{base_name}.{count+1}". If not,
+    the other node will be renamed as "{new_name}.1". This function will iteratively
+    update the names until there is no conflict.
+
+    ``name_to_node_cache`` is required as an argument to avoid recomputation. The caller
+    is responsible for ensuring the cache is accurate and in sync with the owning module
+    of the node. The values in the cache will be updated accordingly.
+
+    Args:
+        node: The node to update.
+        new_name: The new name to use.
+        name_to_node_cache: A cache of node names to nodes.
+    """
+    module = node.graph.owning_module
+    node_name_to_set = collections.deque([(node, new_name)])
+
+    while node_name_to_set:
+        node, new_name = node_name_to_set.pop()
+        if new_name in name_to_node_cache and name_to_node_cache[new_name] != node:
+            base_name, postfix_count = _get_node_base_name(new_name)
+            if postfix_count is None:
+                postfix_count = 0
+            node_name_to_set.append(
+                (name_to_node_cache[new_name], f"{base_name}.{postfix_count + 1}")
+            )
+        node.name = new_name
+        name_to_node_cache[new_name] = node
+
+
+def replace_placeholder_name_and_target(
+    module: torch.fx.GraphModule, reference_module: torch.fx.GraphModule
+):
+    """Replace the argument names in module with those in reference_module.
+
+    This function assumes the two modules have the same signature structure.
+    The caller is responsible for ensuring this. Otherwise, the behavior of this
+    function is undefined. This function only does minimal sanity check that the two
+    modules have the same number of arguments.
+
+    Name conflicts between new names and existing node names in the graph are handled.
+    Check the documentation of :func:`set_node_name` for more details.
+
+    Raises:
+        RuntimeError: If the two modules have different number of arguments.
+    """
+    placeholders = [node for node in module.graph.nodes if node.op == "placeholder"]
+    reference_placeholders = [
+        node for node in reference_module.graph.nodes if node.op == "placeholder"
+    ]
+
+    if len(placeholders) != len(reference_placeholders):
+        raise RuntimeError(
+            "The two modules have different number of arguments. "
+            f"module: {len(placeholders)}, reference_module: {len(reference_placeholders)}"
+        )
+
+    name_to_node: dict[str, torch.fx.Node] = {}
+    for node in module.graph.nodes:
+        name_to_node[node.name] = node
+
+    for placeholder, reference_placeholder in zip(placeholders, reference_placeholders):
+        placeholder.target = reference_placeholder.target
+        set_node_name(placeholder, reference_placeholder.name, name_to_node)
+
+    module.recompile()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/decomp.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/decomp.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7352f67b1492c54bfaad14a08baa51888cb1003
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/decomp.py
@@ -0,0 +1,82 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import contextlib
+from typing import Callable, Mapping, TYPE_CHECKING
+
+import torch
+import torch._ops
+from torch._dispatch import python as python_dispatch
+from torch._subclasses import fake_tensor
+from torch.fx.experimental import proxy_tensor
+from torch.onnx._internal.fx import _pass, diagnostics
+from torch.onnx._internal.fx.passes import _utils
+
+
+if TYPE_CHECKING:
+    import torch.fx
+
+
+class Decompose(_pass.Transform):
+    def __init__(
+        self,
+        diagnostic_context: diagnostics.DiagnosticContext,
+        module: torch.fx.GraphModule,
+        decomposition_table: Mapping[torch._ops.OpOverload, Callable],
+        enable_dynamic_axes: bool,
+        allow_fake_constant: bool | None = False,
+    ):
+        super().__init__(diagnostic_context, module)
+        self.decomposition_table = decomposition_table
+        self.enable_dynamic_axes = enable_dynamic_axes
+        self.allow_fake_constant = allow_fake_constant
+
+    def _run(self, *args, **kwargs) -> torch.fx.GraphModule:
+        assert not kwargs, "kwargs is not supported in Decompose."
+
+        # To preserve stack trace info after `make_fx`.
+        module = _utils.wrap_graph_module_for_node_meta_preservation(self.module)
+
+        # fake mode use static size to trace the size of tensors. while symbolic
+        # mode generates aten::sym_size to dynamically trace the size of tensors.
+
+        # e.g. fake mode:
+        #  view: f32[3, 5, 20] = torch.ops.aten.view.default(x, [3, 5, 20])
+
+        # e.g. symbolic mode:
+        #  sym_size = torch.ops.aten.sym_size(x, 0)
+        #  sym_size_1 = torch.ops.aten.sym_size(x, 1)
+        #  sym_size_2 = torch.ops.aten.sym_size(x, 2)
+        #  sym_size_3 = torch.ops.aten.sym_size(x, 3)
+        #  mul = sym_size_2 * sym_size_3;  sym_size_2 = sym_size_3 = None
+        #  view: f32[3, 5, 20] = torch.ops.aten.view.default(x, [sym_size, sym_size_1, mul])
+
+        # Mimic `torch._dynamo.export(aten_graph=True)` behavior in invoking `make_fx`.
+        # TODO: May need revisit for user fake mode export + dynamic shape scenario.
+        fake_mode: fake_tensor.FakeTensorMode | None = self.fake_mode
+        maybe_fake_args = self._maybe_fakefy_args(fake_mode, *args)
+        if fake_mode is not None:
+            # Using existing fake mode as context, signal `make_fx` that it does not need
+            # to create a new fake mode by passing tracing_mode as "real".
+            tracing_mode = "real"
+        else:
+            # Existing fake mode not found, signal `make_fx` to create one.
+            fake_mode = contextlib.nullcontext()  # type: ignore[assignment]
+            tracing_mode = "symbolic" if self.enable_dynamic_axes else "fake"
+
+        # Apply decomposition table to the input graph.
+        assert fake_mode is not None  # for mypy
+        with fake_tensor.unset_fake_temporarily(), python_dispatch.enable_python_dispatcher(), fake_mode:
+            decomposed_module = proxy_tensor.make_fx(
+                module,
+                decomposition_table=self.decomposition_table,
+                tracing_mode=tracing_mode,
+                _allow_non_fake_inputs=True,
+                _allow_fake_constant=bool(self.allow_fake_constant),
+            )(*maybe_fake_args)
+
+        # Rename placeholder targets to match the original module's signature since
+        # We don't want to map forward(x, y, z) to forward(arg0, arg1, arg2).
+        _utils.replace_placeholder_name_and_target(decomposed_module, self.module)
+
+        return decomposed_module
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/functionalization.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/functionalization.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b68de48080c6938cacf8186d42b1d3b1e32eac2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/functionalization.py
@@ -0,0 +1,154 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import contextlib
+from typing import Callable
+
+import torch
+import torch._ops
+import torch.func
+import torch.fx
+from torch._subclasses import fake_tensor
+from torch.fx.experimental import proxy_tensor
+from torch.onnx._internal.fx import _pass, diagnostics
+from torch.onnx._internal.fx.passes import _utils
+from torch.utils import _pytree as pytree
+
+
+class Functionalize(_pass.Transform):
+    """Functionalize a GraphModule.
+
+    This pass utilizes ``functionalization`` utility of ``torch._functorch`` to convert
+    a GraphModule into a functional form. The two main functionalities are (copied from
+    its documentations):
+
+    * ``functionalization`` removes (intermediate) mutations and aliasing from a
+    function, while preserving the function's semantics.
+
+    * ``functionalization`` also removes mutations (and views) that were performed
+    on function inputs. However to preserve semantics, functionalize will "fix up" the
+    mutations after the transform has finished running, by detecting if any tensor inputs
+    "should have" been mutated, and copying the new data back to the inputs if necessary.
+    For example, consider::
+
+        def fn(a, b):
+            a.add_(b)
+            return a
+
+      For a call like `fn(x, y)`, the variable `x` outside is also mutated. Hence just
+      functionalizing is not enough for preserving the original semantics. A "special"
+      input mutation step needs to be inserted at the end.::
+
+        # After functionalization, without input mutation "fix up".
+        # This is not semantically the same. The variable outside the function call that
+        # was passed in as `a` is not mutated.
+        def fn(a, b):
+            new_a = a + b
+            return new_a
+
+        # Functionalization with input mutation "fix up" that preserves semantics.
+        def fn(a, b):
+            new_a = a + b
+
+            # Copying the new data back to the inputs
+            a.copy_(new_a)
+
+            return new_a
+
+    For ONNX inference, it is recommended to run ``RemoveInputMutation`` after this pass.
+    ``RemoveInputMutation`` removes the "fix up" nodes that were added by ``Functionalize``,
+    which are not needed for ONNX inference.
+    """
+
+    def __init__(
+        self,
+        diagnostic_context: diagnostics.DiagnosticContext,
+        module: torch.fx.GraphModule,
+        enable_dynamic_axes: bool,
+        allow_fake_constant: bool | None = False,
+    ):
+        super().__init__(diagnostic_context, module)
+        self.enable_dynamic_axes = enable_dynamic_axes
+        self.allow_fake_constant = allow_fake_constant
+
+    def _functionalize(self, function: Callable) -> Callable:
+        # Working around a dispatcher issue with `torch.func.functionalize` when used
+        # together with `make_fx`.
+        # Ref: https://github.com/pytorch/pytorch/issues/99774#issuecomment-1527949391
+        def wrapped(*inputs):
+            inputs_functional = pytree.tree_map_only(
+                torch.Tensor, torch._to_functional_tensor, inputs
+            )
+            torch._enable_functionalization(reapply_views=True)
+            try:
+                out = function(*inputs_functional)
+            finally:
+                torch._disable_functionalization()
+            flat_inputs = pytree.tree_leaves(inputs)
+            flat_inputs_functional = pytree.tree_leaves(inputs_functional)
+            for inpt, input_functional in zip(flat_inputs, flat_inputs_functional):
+                if isinstance(input_functional, torch.Tensor):
+                    torch._sync(input_functional)
+                    inpt_new = torch._from_functional_tensor(input_functional)
+            pytree.tree_map(torch._sync, out)
+            out_unwrapped = pytree.tree_map(torch._from_functional_tensor, out)
+            return out_unwrapped
+
+        return wrapped
+
+    def _run(self, *args) -> torch.fx.GraphModule:
+        # To preserve stack trace info after `make_fx`.
+        module = _utils.wrap_graph_module_for_node_meta_preservation(self.module)
+
+        functionalized_callable = self._functionalize(module)
+
+        # Mimic `torch._dynamo.export(aten_graph=True)` behavior in invoking `make_fx`.
+        # TODO: May need revisit for user fake mode export + dynamic shape scenario.
+        fake_mode: fake_tensor.FakeTensorMode | None = self.fake_mode
+        maybe_fake_args = self._maybe_fakefy_args(fake_mode, *args)
+        if fake_mode is not None:
+            # Using existing fake mode as context, signal `make_fx` that it does not need
+            # to create a new fake mode by passing tracing_mode as "real".
+            tracing_mode = "real"
+        else:
+            # Existing fake mode not found, signal `make_fx` to create one.
+            fake_mode = contextlib.nullcontext()  # type: ignore[assignment]
+            tracing_mode = "symbolic" if self.enable_dynamic_axes else "fake"
+
+        assert fake_mode is not None  # for mypy
+        with fake_tensor.unset_fake_temporarily(), fake_mode:
+            graph_module = proxy_tensor.make_fx(
+                functionalized_callable,
+                decomposition_table={},
+                tracing_mode=tracing_mode,
+                _allow_non_fake_inputs=True,
+                _allow_fake_constant=bool(self.allow_fake_constant),
+            )(*maybe_fake_args)
+
+        # Rename placeholder targets to match the original module's signature since
+        # We don't want to map forward(x, y, z) to forward(arg0, arg1, arg2).
+        _utils.replace_placeholder_name_and_target(graph_module, self.module)
+
+        return graph_module
+
+
+class RemoveInputMutation(_pass.Transform):
+    """Remove `aten.copy_.default` nodes that mutate module inputs.
+
+    This pass is recommended to be used after ``Functionalization`` pass.
+    ``Functionalization`` pass adds `aten.copy_.default` nodes to the graph
+    when it detects mutations to inputs. These nodes are not needed for ONNX export
+    for inference. They could be useful for training.
+    """
+
+    def _run(self, *args) -> torch.fx.GraphModule:
+        for node in reversed(self.module.graph.nodes):
+            if (
+                node.op == "call_function"
+                and node.target == torch.ops.aten.copy_.default
+                and len(node.users) == 0
+                and isinstance(node.args[0], torch.fx.Node)
+                and node.args[0].op == "placeholder"
+            ):
+                self.module.graph.erase_node(node)
+        return self.module
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/modularization.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/modularization.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1ec411aea19ec94fb4b06408a4e400426df8619
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/modularization.py
@@ -0,0 +1,854 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import abc
+import collections
+import copy
+import operator
+from typing import Any, Dict, Final, Generator, Iterator, Sequence, Tuple
+
+import torch
+import torch.fx
+from torch.onnx._internal.fx import _pass, diagnostics
+from torch.utils import _pytree as pytree
+
+
+_FX_TRACER_NN_MODULE_META_TYPE = Tuple[str, type]
+"""Legacy type of item from `node.meta["nn_module_stack"].items()` produced by FX symbolic tracer."""
+_FX_TRACER_NN_MODULE_STACK_META_TYPE = collections.OrderedDict
+"""Legacy type of `node.meta["nn_module_stack"]` produced by FX symbolic tracer."""
+
+_DYNAMO_NN_MODULE_META_TYPE = Tuple[str, Tuple[str, type]]
+"""Type of item from `node.meta["nn_module_stack"].items()` produced by FX dynamo tracer."""
+_DYNAMO_NN_MODULE_STACK_META_TYPE = Dict[str, _DYNAMO_NN_MODULE_META_TYPE]
+"""Type of `node.meta["nn_module_stack"]` produced by FX dynamo tracer."""
+
+
+class _ModuleMeta:
+    """Meta information about a module.
+
+    This class is used to represent the module information in a more structured way.
+    It parses raw module information from a single item from
+    `node.meta["nn_module_stack"].items()`.
+
+    See the uses of `from_raw_meta`, `from_fx_tracer_produced_raw_meta`, and
+    `from_dynamo_produced_raw_meta` for how to create an instance.
+
+    Attributes:
+        _module_class: The class of the module. E.g. `torch.nn.module.sparse.Embedding`.
+        _module_name: The name of the module. E.g. `L__self___h_1_mlp_c_proj`.
+        _raw_meta: The raw meta '(module_name, node.meta["nn_module_stack"][module_name])'.
+    """
+
+    _module_class: Final[type | str | None]  # type: ignore[misc]
+    _module_name: Final[str]  # type: ignore[misc]
+    _raw_meta: Final[tuple[Any, Any]]  # type: ignore[misc]
+
+    def __init__(
+        self,
+        module_name: str,
+        module_class: type | str | None,
+        raw_meta: tuple[Any, Any],
+    ):
+        self._module_name = module_name
+        self._module_class = module_class
+        self._raw_meta = raw_meta
+
+    @property
+    def module_display_name(self) -> str:
+        """The display name of the module.
+
+        E.g. `h_1_mlp_c_proj`.
+        """
+        # E.g., from 'L__self___h_1_mlp_c_proj' to 'h_1_mlp_c_proj'.
+        name = self.module_name
+        if name.startswith("L__self___"):
+            name = name[len("L__self___") :]
+        return name
+
+    @property
+    def qualified_module_class_name(self) -> str:
+        """Qualified name of the module class.
+
+        E.g. `torch_nn_module_sparse_Embedding`.
+        """
+        if self._module_class is None:
+            return ""
+        mod_cls = self._module_class
+        if isinstance(mod_cls, type):
+            mod_cls = mod_cls.__module__ + "." + mod_cls.__qualname__
+        return mod_cls.replace(".", "_")
+
+    @property
+    def module_class_name(self) -> str:
+        """Name of the module class.
+
+        E.g. `Embedding`.
+        """
+        if self._module_class is None:
+            return ""
+        if isinstance(self._module_class, type):
+            return self._module_class.__name__
+        return self._module_class
+
+    @property
+    def module_name(self) -> str:
+        """Name of the module.
+
+        E.g. `L__self___h_1_mlp_c_proj`.
+        """
+        return self._module_name
+
+    @property
+    def raw_meta(self) -> tuple[Any, Any]:
+        """Returns the raw module meta data.
+
+        I.e. (module_name, node.meta['nn_module_stack'][module_name]).
+        """
+        return self._raw_meta
+
+    def __eq__(self, __value: object) -> bool:
+        if not isinstance(__value, _ModuleMeta):
+            return False
+        return (
+            self._module_name == __value._module_name
+            and self._module_class == __value._module_class
+        )
+
+    def __hash__(self) -> int:
+        return hash((self._module_name, self._module_class))
+
+    def __repr__(self) -> str:
+        return f"ModuleMeta(name={self._module_name}, class={self._module_class})"
+
+    @classmethod
+    def create_root(cls) -> _ModuleMeta:
+        """Create an empty module meta representing root module."""
+        return _ModuleMeta("", None, ("", None))
+
+    @classmethod
+    def from_fx_tracer_produced_raw_meta(
+        cls, raw_meta: _FX_TRACER_NN_MODULE_META_TYPE
+    ) -> _ModuleMeta:
+        """Create a module meta from raw meta produced by FX symbolic tracer."""
+        module_name, module_class = raw_meta
+        return _ModuleMeta(module_name, module_class, raw_meta)
+
+    @classmethod
+    def from_dynamo_produced_raw_meta(
+        cls, raw_meta: _DYNAMO_NN_MODULE_META_TYPE
+    ) -> _ModuleMeta:
+        """Create a module meta from raw meta produced by FX dynamo tracer."""
+        module_name, (qualified_name, module_class) = raw_meta
+        return _ModuleMeta(module_name, module_class, raw_meta)
+
+    @classmethod
+    def from_raw_meta(
+        cls,
+        raw_meta: _FX_TRACER_NN_MODULE_META_TYPE | _DYNAMO_NN_MODULE_META_TYPE,
+    ) -> _ModuleMeta:
+        if (
+            isinstance(raw_meta, tuple)
+            and len(raw_meta) == 2
+            and isinstance(raw_meta[1], type)
+        ):
+            # Trying to do `instance(raw_meta, _FX_TRACER_NN_MODULE_META_TYPE)`
+            return _ModuleMeta.from_fx_tracer_produced_raw_meta(raw_meta)
+        if (
+            isinstance(raw_meta, tuple)
+            and len(raw_meta) == 2
+            and isinstance(raw_meta[1], tuple)
+        ):
+            # Trying to do `instance(raw_meta, _DYNAMO_NN_MODULE_META_TYPE)`
+            return _ModuleMeta.from_dynamo_produced_raw_meta(raw_meta)
+        raise TypeError(
+            f"Unknown type of raw meta item from node.meta['nn_module_stack'].items(): {type(raw_meta)}"
+        )
+
+
+class _ModuleStackMeta:
+    """Meta information about the module call stack.
+
+    This class is used to represent the module call stack information in a more
+    structured way. It parses raw module stack information from `node.meta["nn_module_stack"]`.
+
+    Example of raw module stack information:
+
+        If produced by dynamo:
+
+            {
+                'L__self___h_1': (
+                    "L['self'].h[1]",
+                    
+                ),
+                'L__self___h_1_attn': (
+                    "L['self'].h[1].attn",
+                    
+                )
+            }
+
+        If produced by fx.symbolic_trace:
+
+            {
+                'h.1': ,
+                'h.1.attn': 
+            }
+    """
+
+    _module_stack: Final[list[_ModuleMeta]]  # type: ignore[misc]
+
+    def __init__(
+        self,
+        nn_module_stack_meta: _FX_TRACER_NN_MODULE_STACK_META_TYPE
+        | _DYNAMO_NN_MODULE_STACK_META_TYPE
+        | None,
+        is_exported_program: bool = True,
+    ):
+        self._module_stack = []
+        if nn_module_stack_meta is None:
+            return
+        raw_meta = copy.copy(nn_module_stack_meta)
+        for item in raw_meta.items():
+            # If produced by torch.export.export, there is another call stack layer
+            # that we need to skip
+            if is_exported_program:
+                is_exported_program = False
+                continue
+            self.push(_ModuleMeta.from_raw_meta(item))  # type: ignore[arg-type]
+
+    def __len__(self) -> int:
+        return len(self._module_stack)
+
+    def __getitem__(self, index: int) -> _ModuleMeta:
+        return self._module_stack[index]
+
+    def __iter__(self) -> Iterator[_ModuleMeta]:
+        return iter(self._module_stack)
+
+    def is_empty_or_root(self) -> bool:
+        return len(self._module_stack) == 0
+
+    def top(self) -> _ModuleMeta:
+        """Returns the top module meta in the stack. I.e., the meta for leaf module.
+
+        Example:
+
+            Consider the following module stack:
+
+            stack = [GPT, block1, Attention_1, MLP]
+
+            stack.top() == MLP
+        """
+        if self.is_empty_or_root():
+            return _ModuleMeta.create_root()
+        return self._module_stack[-1]
+
+    def is_superset_of(
+        self,
+        module_stack: _ModuleStackMeta,
+    ) -> bool:
+        """Determines if self is a superset of the provided module stack.
+
+        I.e., If self includes all elements from the provided module stack, plus additional
+        elements on top. If self is empty or root, this method always return False.
+
+        Example:
+
+            Consider the following module stack:
+
+            stack_1 = [GPT, block1, Attention_1, MLP]
+            stack_2 = [GPT, block1]
+
+            stack_1.is_superset_of(stack_2) == True
+            stack_2.is_superset_of(stack_1) == False
+
+            stack_3 = [GPT, block2, Attention_1]
+
+            stack_1.is_superset_of(stack_3) == False
+            stack_3.is_superset_of(stack_1) == False
+        """
+        if self.is_empty_or_root():
+            return False
+
+        if module_stack.is_empty_or_root() is None:
+            return True
+
+        if len(self) <= len(module_stack):
+            return False
+
+        for i, parent_key in enumerate(module_stack):
+            if self[i] != parent_key:
+                return False
+
+        return True
+
+    def push(self, module_meta: _ModuleMeta) -> None:
+        """Pushes a module meta to the stack."""
+        self._module_stack.append(module_meta)
+
+    def __eq__(self, __value: object) -> bool:
+        if not isinstance(__value, _ModuleStackMeta):
+            return False
+        return self._module_stack == __value._module_stack
+
+    @property
+    def raw_meta(self) -> dict[str, tuple[str, type]] | None:
+        """Returns the raw module stack meta data, i.e. node.meta['nn_module_stack']."""
+        return {
+            module_meta.raw_meta[0]: module_meta.raw_meta[1]
+            for module_meta in self._module_stack
+        }
+
+    def __repr__(self) -> str:
+        return f"ModuleStackMeta({self._module_stack})"
+
+    @property
+    def module_display_name(self) -> str:
+        """Returns the module display name of the top module."""
+        return self.top().module_display_name
+
+    @property
+    def qualified_module_class_name(self) -> str:
+        """Returns the qualified module class name of the top module."""
+        return self.top().qualified_module_class_name
+
+    @property
+    def module_class(self) -> type | str | None:
+        """Returns the module class of the top module."""
+        return self.top()._module_class
+
+
+def _module_stack_meta_from_node(
+    node: torch.fx.Node, is_exported_program: bool = False
+) -> _ModuleStackMeta:
+    return _ModuleStackMeta(
+        node.meta.get("nn_module_stack"), is_exported_program=is_exported_program
+    )
+
+
+def _get_unique_module_name(module_names: dict[str, int], module_name: str) -> str:
+    module_names.setdefault(module_name, 0)
+    module_names[module_name] += 1
+    return f"{module_name}_{module_names[module_name]}"
+
+
+class _IRNode(abc.ABC):
+    """Base class for IR nodes.
+
+    IR nodes are used for Modularize pass only. They add a layer of abstraction on top of
+    torch.fx.Node.
+
+    [NOTE: Modularize Pass Implementation]
+    The main job of the pass is to group `fx.Node`s that belong to the same `nn.Module`
+    forward call, and then create `call_module` node and sub `fx.GraphModule` from them.
+    Each `fx.Node` possesses an `nn_module_stack` meta data that contains information
+    about the module call stack. See `_ModuleStackMeta` for examples.
+
+    Analysis step
+    -------------
+
+    Each module call is identified by a set of base stack layers. For each module call,
+    the pass creates a `_ModuleNode` and groups the sequence of nodes that shares the
+    same base stack layers.
+
+    For example,
+
+        stack_of_node_0 = [GPT, block0]
+        stack_of_node_1 = [GPT, block1]
+        stack_of_node_2 = [GPT, block1, Attention1, MLP]
+        stack_of_node_3 = [GPT, block1, Attention1]
+        stack_of_node_4 = [GPT, block2]
+
+    All nodes belong to the `GPT` module call, since they share the base stack layers [GPT].
+    [node_1, node_2, node_3] are grouped for `GPT.block1`, because they share the base
+    stack layers [GPT, block1]. And [node_2, node_3] for `GPT.block1.Attention1`, [node_0]
+    for `GPT.block0`, and [node_4] for `GPT.block2` respectfully.
+
+    After the analysis step, a hierarchical representation is generated.
+
+    For above example, the representation is:
+
+        _ModuleNode(GPT)
+            _ModuleNode(block0)
+                _LeafNode(node_0)
+            _ModuleNode(block1)
+                _LeafNode(node_1)
+                _ModuleNode(Attention1)
+                    _ModuleNode(MLP)
+                        _LeafNode(node_2)
+                _LeafNode(node_3)
+            _ModuleNode(block2)
+                _LeafNode(node_4)
+
+    Construction step
+    -----------------
+
+    The second step is to build the actual `call_module` node and the sub `fx.GraphModule`.
+    This is done recursively from the leaf `_ModuleNode` to the root.
+
+    For example, the first submodule to be built is `GPT.block1.Attention1.MLP`. Below pair
+    is generated from `_ModuleNode(MLP)`.
+
+        fx.GraphModule(GPT.block1.Attention1.MLP)
+            graph:
+                node_2
+
+        new_mlp_node = `call_module[GPT.block1.Attention1.MLP](...)`
+
+    Next, the `GPT.block1.Attention1` submodule is built. Below is generated from
+    `_ModuleNode(Attention1)`.
+
+        fx.GraphModule(GPT.block1.Attention1)
+            graph:
+                new_mlp_node
+                node_3
+
+        new_attention1_node = `call_module[GPT.block1.Attention1](...)`
+
+    Until every submodule is built, the new modularized `fx.GraphModule` is generated.
+
+    Alternatives
+    ------------
+
+    The current algorithm adopts a top down approach. A bottom up approach is similar.
+    In contrast to these two, an alternative flat order approach is also possible, where
+    each node is traversed and copied to the corresponding submodule.
+
+    The advantage of the current approach lies in the encapsulation of the fx.GraphModule
+    construction for each individual submodule within a single `build_module` method, which
+    can be called separately once the analysis phase is completed, making debugging more
+    convenient.
+
+    Regarding construction step, an alternative implementation is to utilize `fx.Interpreter`
+    for traversing all the nodes under the flattened root module and copying the nodes
+    into their respective submodule under construction. This approach is not adopted because
+
+        1. It uses the flat order approach discussed above. This means one cannot individually
+    construct a submodule and examine it while debugging.
+
+        2. The graph execution functionality of `fx.Interpreter` is not necessary for the
+    purpose of this pass. Ignoring that, `fx.Interpreter.run` achieves the same effect
+    as a for loop over all the nodes.
+    """
+
+    @property
+    @abc.abstractmethod
+    def stack_meta(self) -> _ModuleStackMeta:
+        """The module stack meta data associated with this node."""
+        ...
+
+    @property
+    @abc.abstractmethod
+    def stack_trace(self) -> str | None:
+        """The stack trace associated with this node."""
+        ...
+
+
+class _ModuleNode(_IRNode):
+    """Representing a sequence of fx.Nodes to be formed into a fx.GraphModule.
+
+    This class encapsulates metadata and provides building block methods to construct this
+    layered abstraction from a sequence of flat fx.Nodes.
+
+    Attributes:
+    - _stack_meta: Metadata of the module stack.
+    - _nodes: List of IR nodes in the module.
+    - _reference_root_module: Reference to the root flat fx.GraphModule instance.
+    """
+
+    def __init__(
+        self, reference_root_module: torch.fx.GraphModule, stack_meta: _ModuleStackMeta
+    ):
+        self._stack_meta = stack_meta
+        self._nodes: list[_IRNode] = []
+        self._reference_module = reference_root_module
+
+    @property
+    def stack_meta(self) -> _ModuleStackMeta:
+        return self._stack_meta
+
+    @property
+    def stack_trace(self) -> str | None:
+        assert self._nodes
+        return self._nodes[0].stack_trace
+
+    def __str__(self) -> str:
+        return f"ModuleNode({self._stack_meta})"
+
+    def is_same_module_as(self, node: _IRNode) -> bool:
+        """Determines if the provided node pertains to the same module as this node."""
+        return self.stack_meta == node.stack_meta
+
+    def is_parent_module_of(self, node: _IRNode) -> bool:
+        """Determines if this node represents a parent module of the provided node."""
+        return node.stack_meta.is_superset_of(self.stack_meta)
+
+    def add_leaf_node(self, leaf_node: _LeafNode) -> None:
+        """Adds a leaf node to the module.
+
+        The leaf node must belong to the same or a child module. This method will recursively
+        construct _ModuleNode instance based on the stack_meta information of the leaf node.
+        """
+        if self.is_same_module_as(leaf_node) or leaf_node.fx_op == "call_module":
+            self._nodes.append(leaf_node)
+        elif leaf_node.fx_op == "placeholder":
+            # Although the original placeholder has empty nn_module_stack, the placeholder lifted
+            # from get_attr nodes by exported program has their original nn_module_stack. Here
+            # we need to avoid them building submodule.
+            self._nodes.append(leaf_node)
+        elif self.is_parent_module_of(leaf_node):
+            # This node belongs in a submodule.
+            # Check if the last node is a submodule and if it is the parent of this node.
+            last_node = self._nodes[-1] if self._nodes else None
+            if isinstance(last_node, _ModuleNode) and (
+                last_node.is_parent_module_of(leaf_node)
+                or last_node.is_same_module_as(leaf_node)
+            ):
+                # This node belongs to the last_node.
+                last_node.add_leaf_node(leaf_node)
+            else:
+                # Create a new SubmoduleNode for the immediate child module of the current
+                # module. The leaf node may be a grandchild of the current module.
+                # Example:
+                #   self.stack_meta = [A, B, C]
+                #   leaf_node.stack_meta = [A, B, C, D, E, F]
+                # Create a new ModuleNode with stack_meta = [A, B, C, D] and add leaf_node to it.
+                stack_meta = copy.deepcopy(self.stack_meta)
+                stack_meta.push(leaf_node.stack_meta[len(self.stack_meta)])
+                last_node = _ModuleNode(
+                    self._reference_module,
+                    stack_meta,
+                )
+                self._nodes.append(last_node)
+                last_node.add_leaf_node(leaf_node)
+        else:
+            raise AssertionError(
+                f"Node {leaf_node} ({leaf_node.stack_meta}) does not belong to module "
+                f"{self._stack_meta}."
+            )
+
+    def fx_nodes(self) -> Generator[torch.fx.Node, None, None]:
+        """Returns an iterator for the sequence of fx nodes this instance holds."""
+        for node in self._nodes:
+            if isinstance(node, _ModuleNode):
+                yield from node.fx_nodes()
+            else:
+                assert isinstance(node, _LeafNode)
+                yield node.fx_node
+
+    def module_inputs(self) -> Sequence[torch.fx.Node]:
+        """Extract module inputs from the sequence of fx nodes this instance holds.
+
+        All node args that are produced by nodes outside of the module are considered module
+        inputs. The order of returned module inputs is the same as the their use order.
+
+        ### Known limitations
+
+        The original ordering of module inputs is not preserved. There is no meta information
+        to be found from the `fx.GraphModule` that can be used to recover the original ordering.
+
+        Returns:
+            Sequence of module inputs.
+        """
+        nodes = list(self.fx_nodes())
+        assert len(nodes) > 0, "Cannot extract module inputs from empty nodes."
+        module_inputs: dict[torch.fx.Node, None] = {}
+        node_set: set[torch.fx.Node] = set(nodes)
+
+        def _extract_arg_if_node_outside_module(arg: Any):
+            if isinstance(arg, torch.fx.Node) and arg not in node_set:
+                module_inputs[arg] = None
+
+        for node in nodes:
+            pytree.tree_map(_extract_arg_if_node_outside_module, node.args)
+            pytree.tree_map(_extract_arg_if_node_outside_module, node.kwargs)
+        return list(module_inputs.keys())
+
+    def module_outputs(self) -> Sequence[torch.fx.Node]:
+        """Extract module outputs from the sequence of fx nodes this instance holds.
+
+        All nodes that are used by nodes outside of the module are considered module
+        outputs. The order of returned module outputs is the same as the their creation order.
+
+        ### Known limitations
+
+        The original ordering of module outputs is not preserved. There is no meta information
+        to be found from the `fx.GraphModule` that can be used to recover the original ordering.
+
+        Returns:
+            Sequence of module outputs.
+        """
+        nodes = list(self.fx_nodes())
+        assert len(nodes) > 0, "Cannot extract module inputs from empty nodes."
+        # Need ordered set. Emulate with dict.
+        module_outputs: dict[torch.fx.Node, None] = {}
+        node_set: set[torch.fx.Node] = set(nodes)
+
+        for node in nodes:
+            if any(user not in node_set for user in node.users):
+                module_outputs[node] = None
+        return list(module_outputs.keys())
+
+    def build_module(self, module_names: dict[str, int]) -> torch.fx.GraphModule:
+        """
+        Constructs the fx.GraphModule for this node, registering submodules as necessary.
+
+        Args:
+            module_names: A dictionary of module names and their counts. This is used to
+                generate unique module names for submodules. This should be an empty
+                dictionary when the method is called on a root module.
+        """
+        module_class_name = self._stack_meta.qualified_module_class_name
+        fx_graph = torch.fx.Graph()
+        copy_env: dict[torch.fx.Node, torch.fx.Node] = {}
+
+        def _arg_transform(node: torch.fx.Node) -> torch.fx.Node:
+            return copy_env[node]
+
+        ref_inputs = self.module_inputs()
+        for node in ref_inputs:
+            copy_env[node] = fx_graph.placeholder(node.name, node.type)
+            copy_env[node].meta = copy.copy(node.meta)
+
+        for ir_node in self._nodes:
+            if isinstance(ir_node, _LeafNode):
+                fx_node = ir_node.fx_node
+                copy_env[fx_node] = fx_graph.node_copy(
+                    fx_node, arg_transform=_arg_transform
+                )
+                continue
+
+            assert isinstance(ir_node, _ModuleNode)
+            # Create fx.GraphModule for child submodule.
+            submodule = ir_node.build_module(module_names)
+            ref_submodule_inputs = ir_node.module_inputs()
+            ref_submodule_outputs = ir_node.module_outputs()
+            unique_submodule_name = _get_unique_module_name(
+                module_names, ir_node.stack_meta.module_display_name
+            )
+            # Link the newly generated sub fx.GraphModule with the root reference module.
+            # This step is essential to meet the needs of the subsequent fx.GraphModule initialization
+            # for the fx.GraphModule being created by this method.
+            # The initialization of fx.GraphModule will replicate all necessary attributes from a reference
+            # fx.GraphModule for the fx.Graph. While the root reference module possesses all
+            # parameters and buffers, it does not include the newly created sub fx.GraphModule.
+            # Therefore, it's necessary to register it under the root reference at this stage.
+            self._reference_module.add_submodule(unique_submodule_name, submodule)
+
+            # create call_module fx.Node
+            submodule_node = fx_graph.call_module(
+                unique_submodule_name,
+                tuple(_arg_transform(node) for node in ref_submodule_inputs),
+            )
+            if len(ref_submodule_outputs) > 1:
+                # Module node has multiple output. Create 'getitem' node for each output.
+                submodule_node.meta["val"] = tuple(
+                    ref_output.meta.get("val") for ref_output in ref_submodule_outputs
+                )
+                for i, ref_output in enumerate(ref_submodule_outputs):
+                    getitem_node = fx_graph.call_function(
+                        operator.getitem,
+                        args=(submodule_node, i),
+                        type_expr=ref_output.type,
+                    )
+                    getitem_node.meta = copy.copy(ref_output.meta)
+                    # Make a copy for "nn_module_stack" since the current module will be
+                    # popped from the stack for this 'getitem' node.
+                    getitem_node.meta["nn_module_stack"] = copy.copy(
+                        ref_output.meta["nn_module_stack"]
+                    )
+                    # The node is associated with the parent module.
+                    getitem_node.meta["nn_module_stack"].popitem()
+                    copy_env[ref_output] = getitem_node
+            else:
+                # Module node has single output. Use module node directly.
+                copy_env[ref_submodule_outputs[0]] = submodule_node
+                submodule_node.meta = copy.copy(ref_submodule_outputs[0].meta)
+
+            # Update meta for new call_module node.
+            if (stack_trace := ir_node.stack_trace) is not None:
+                submodule_node.meta["stack_trace"] = stack_trace
+            raw_module_stack_meta = ir_node.stack_meta.raw_meta
+            assert raw_module_stack_meta is not None
+            submodule_node.meta["nn_module_stack"] = copy.copy(raw_module_stack_meta)
+            # The node is associated with the parent module.
+            submodule_node.meta["nn_module_stack"].popitem()
+
+        new_nodes = fx_graph.nodes
+        # Skip if the last node is already 'output'. This is the case for root module.
+        # Otherwise create an 'output' node for the inferred outputs.
+        if next(iter(reversed(new_nodes))).op != "output":
+            ref_submodule_outputs = self.module_outputs()
+            new_outputs = [copy_env[ref_output] for ref_output in self.module_outputs()]
+            node = fx_graph.output(
+                new_outputs[0] if len(new_outputs) == 1 else new_outputs
+            )
+
+        graph_module = torch.fx.GraphModule(
+            self._reference_module, fx_graph, module_class_name
+        )
+        if (module_class := self._stack_meta.module_class) is not None:
+            graph_module.meta["onnx"] = _pass.GraphModuleOnnxMeta(
+                _pass.PackageInfo.from_python_class(module_class)
+            )
+        return graph_module
+
+
+class _LeafNode(_IRNode):
+    """Representing a single fx.Node."""
+
+    def __init__(self, node: torch.fx.Node, is_exported_program: bool = False):
+        self._node = node
+        self._stack_meta = _module_stack_meta_from_node(
+            node, is_exported_program=is_exported_program
+        )
+
+    @property
+    def fx_op(self) -> str:
+        """Syntax sugar for self.fx_node.op."""
+        return self._node.op
+
+    @property
+    def fx_node(self) -> torch.fx.Node:
+        """Returns the fx.Node this instance represents."""
+        return self._node
+
+    @property
+    def stack_meta(self) -> _ModuleStackMeta:
+        """Returns the module stack meta data associated with this node."""
+        return self._stack_meta
+
+    @property
+    def stack_trace(self) -> str | None:
+        """Returns the stack trace associated with this node."""
+        return self.fx_node.meta.get("stack_trace")
+
+    def __str__(self) -> str:
+        return f"LeafNode({self._node})"
+
+
+class Modularize(_pass.Transform):
+    """Transforms a flattened `fx.GraphModule` into a modular structure.
+
+    In the flattened `fx.GraphModule`, each `nn.Module` forward call has been traced as
+    a sequence of `fx.Node`s. All these `fx.Node`s are flattened and reside in the same
+    `fx.GraphModule`. `fx.GraphModule` could be from `torch.export.ExportedProgram` or
+    directly generated by `torch._dynamo.export` with torch.nn.Module.
+
+    This pass generates a new `fx.GraphModule`. It groups the flattened `fx.Node`s that belong
+    to the same `nn.Module` forward call into a sub `fx.GraphModule`. It then replaces the
+    sequence of flattened `fx.Node`s with a single `call_module` node, which is linked with
+    the sub `fx.GraphModule` by `node.target`. The sub `fx.GraphModule` is registered as a
+    submodule of the new `fx.GraphModule`.
+
+    The process is done based on information from the `nn_module_stack` metadata of each node, i.e.
+    `node.meta["nn_module_stack"]`. For more implementation details, see [NOTE: Modularize Pass Implementation].
+
+    An fx submodule under this context can typically be interpreted in three different ways:
+
+        1. As an embodiment of an nn.Module class, which is considered stateless.
+        Its execution path can vary depending on the configuration of module initialization,
+        which should also be part of the inputs.
+
+        2. As a representation of an nn.Module instance. It maintains the state initialized in the module.
+        The execution path can vary based on actual input data.
+
+        3. As a captured call of an nn.Module instance, where the execution path
+        is set.
+
+    The generality decreases along this list. Within the scope of this function, the pass
+    creates fx submodules according to the third interpretation.
+
+    The first interpretation is the most general case. It requires complex analysis and additional
+    metadata and code information to construct its general form. Consider an example nn.Module
+    that generates arbitrary submodules based on an initialization configuration file. It's impractical
+    to extract this logic for the generated fx submodule to function with arbitrary configuration.
+
+    The second interpretation demands less analysis and is sturdier than the
+    first. In most use cases, it's equivalent to the third. It only differs in exceptional situations
+    where a complex nn.Module instance is called multiple times, each with a different set of inputs
+    leading to a unique execution branching path.
+
+    The third interpretation is the most specific scenario. It necessitates the minimum
+    analysis and creates the most stable representation. The drawback is that it
+    generates more redundancy than the other two methods. If needed, a subsequent post-processing
+    pass can be applied to consolidate completely identical functions and reduce duplication.
+
+    ### Known constraints
+    Two successive calls to the same module instance will be conflated. They are indistinguishable.
+    This is due to limitations of the current fx metadata "nn_module_stack".
+
+    [NOTE: Modularize pass ordering]
+    This pass groups fx nodes into subgraphs that reside within the `call_module` fx node.
+    Other fx passes (including some outside the exporter) might not recognize `call_module`.
+    They may assume that all nodes are flattened. Hence it is recommended to invoke this pass
+    as the last pre onnx export fx pass. If not for this consideration, this operation could
+    potentially be relocated anywhere earlier in the pipeline.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)
+        >>> import torch
+        >>> from torch.onnx._internal.fx import passes
+        >>> from torch.onnx._internal.diagnostics import infra
+        >>>
+        >>> class CustomModule(torch.nn.Module):
+        >>>     def __init__(self) -> None:
+        >>>         super().__init__()
+        >>>         self.embedding = torch.nn.Embedding(10, 32)
+        >>>         self.relu = torch.nn.ReLU()
+        >>>
+        >>>     def forward(self, x):
+        >>>         out = self.embedding(x)
+        >>>         out = self.relu(out)
+        >>>         return out
+        >>>
+        >>> class TestModule(torch.nn.Module):
+        >>>     def __init__(self) -> None:
+        >>>         super().__init__()
+        >>>         self.layer = CustomModule()
+        >>>         self.linear = torch.nn.Linear(32, 10)
+        >>>
+        >>>     def forward(self, x):
+        >>>         out = self.layer(x)
+        >>>         out = self.linear(out)
+        >>>         return out
+        >>>
+        >>> gm, _ = torch._dynamo.export(TestModule(), aten_graph=True)(
+        ...     torch.tensor([0, 1, 2])
+        ... )
+        >>> gm.print_readable()
+
+        >>> gm = passes.Modularize(infra.DiagnosticContext("test_context", "1.0"), gm).run()
+        >>> gm.print_readable()
+
+    """
+
+    def __init__(
+        self,
+        diagnostic_context: diagnostics.DiagnosticContext,
+        module: torch.fx.GraphModule,
+        is_exported_program: bool = False,
+    ):
+        super().__init__(diagnostic_context, module)
+        self.module = module
+        self.is_exported_program = is_exported_program
+
+    def _run(self) -> torch.fx.GraphModule:
+        # DCE to remove unused nodes.
+        # If a submodule is unused, it is hard to analyze which nodes constitutes the submodule
+        # outputs. But since it is unused, we can just remove it.
+        self.module.graph.eliminate_dead_code()
+
+        reference_module = torch.fx.GraphModule(self.module, self.module.graph)
+        root_module_node = _ModuleNode(
+            reference_module,
+            _ModuleStackMeta(
+                nn_module_stack_meta=None, is_exported_program=self.is_exported_program
+            ),
+        )
+        for fx_node in self.module.graph.nodes:
+            root_module_node.add_leaf_node(
+                _LeafNode(fx_node, is_exported_program=self.is_exported_program)
+            )
+        return root_module_node.build_module({})
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/readability.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/readability.py
new file mode 100644
index 0000000000000000000000000000000000000000..50221f47f64fffd94cd3acbb2955f864ecfca5c6
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/readability.py
@@ -0,0 +1,130 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+from typing import Sequence
+
+import torch
+from torch.onnx._internal.fx import _pass, diagnostics
+
+
+class RestoreParameterAndBufferNames(_pass.Transform):
+    """Restore parameter and buffer names from original nn.module.
+
+    This pass is useful for readability of the exported ONNX graph. It restores the
+    parameter and buffer names from the original nn.module. For example, if the original
+    nn.module has a parameter named `root.linear.0.weight`, and the parameter is renamed to
+    `_param_constant9` by FX, this pass will rename it back.
+
+    This pass must be run after `Decompose` pass. Because this pass is expected to be called on
+    `fx.GraphModule` produced by `proxy_tensor.make_fx`, where all parameters and buffers
+    are registered at root level.
+    """
+
+    def __init__(
+        self,
+        diagnostic_context: diagnostics.DiagnosticContext,
+        fx_module: torch.fx.GraphModule,
+        original_nn_module: torch.nn.Module,
+    ):
+        super().__init__(diagnostic_context, fx_module)
+        self.original_nn_module = original_nn_module
+
+    def _rename_param_and_buffer(
+        self,
+        diagnostic: diagnostics.Diagnostic,
+        nodes: Sequence[torch.fx.Node],
+        new_name: str,
+    ) -> None:
+        """Rename the parameter/buffer and replace corresponding nodes with new nodes of updated target."""
+        assert len(nodes) > 0, "`nodes` cannot be empty"
+        assert (
+            len({node.target for node in nodes}) == 1
+        ), "`nodes` must all have same `target`"
+        old_name = nodes[0].target
+        assert isinstance(old_name, str), f"Expected str, got type({old_name})"
+        # Parameter/buffer name cannot contain "."
+        normalized_name = new_name.replace(".", "/")
+        attr_value = getattr(self.module, old_name)
+        setattr(self.module, normalized_name, attr_value)
+        delattr(self.module, old_name)
+        for node in nodes:
+            with self.module.graph.inserting_before(node):
+                new_node = self.module.graph.get_attr(normalized_name)
+                new_node.meta = node.meta
+                node.replace_all_uses_with(new_node)
+                self.module.graph.erase_node(node)
+        diagnostic.info(
+            "Renamed 'self.%s' to 'self.%s', "
+            "normalized from original parameter name '%s'.",
+            old_name,
+            normalized_name,
+            new_name,
+        )
+
+    def _run(self, *args, **kwargs) -> torch.fx.GraphModule:
+        """Restore parameter and buffer names from original module.
+
+        For each `get_attr` node, if the target is a str representing a parameter or buffer
+        under `self.module`, we rename the parameter or buffer to its original name.
+        The parameters and buffers between `self.module` and `self.original_nn_module` refer
+        to the same objects, allowing us to use it as key to retrieve the original name.
+        """
+        assert len(args) == 0, "RestoreParameterAndBufferNames does not take any args"
+        assert (
+            len(kwargs) == 0
+        ), "RestoreParameterAndBufferNames does not take any kwargs"
+        # state_to_readable_name[parameter/buffer] returns the original readable name of
+        # the parameter/buffer. E.g., "self.linear.weight".
+        state_to_readable_name: dict[torch.nn.Parameter | torch.Tensor, str] = {}
+        state_to_readable_name.update(
+            {v: k for k, v in self.original_nn_module.named_parameters()}
+        )
+        state_to_readable_name.update(
+            {v: k for k, v in self.original_nn_module.named_buffers()}
+        )
+        diagnostic = self.diagnostic_context.inflight_diagnostic()
+
+        # old_name_to_nodes[old_name] returns a tuple of (nodes, new_name)
+        # where `nodes` is a list of `get_attr` nodes with `old_name` as `target` and
+        # `new_name` is the new readable name.
+        old_name_to_nodes: dict[str, tuple[list[torch.fx.Node], str]] = {}
+
+        for node in self.module.graph.nodes:
+            if node.op == "get_attr":
+                assert isinstance(
+                    node.target, str
+                ), f"Expected str, got type({node.target})"
+                if node.target.find(".") != -1:
+                    raise RuntimeError(
+                        f"Unexpected target {node.target} in get_attr, found '.' in target. "
+                        f"All parameters and buffers are expected to be registered at root level, "
+                        f"i.e., self.module. "
+                    )
+                if node.target in old_name_to_nodes:
+                    # We have already processed this parameter/buffer.
+                    old_name_to_nodes[node.target][0].append(node)
+                    continue
+                attr_value = getattr(self.module, node.target)
+                if (
+                    isinstance(attr_value, (torch.nn.Parameter, torch.Tensor))
+                    and attr_value in state_to_readable_name
+                ):
+                    readable_name = state_to_readable_name[attr_value]
+                    old_name_to_nodes[node.target] = ([node], readable_name)
+                    continue
+
+                diagnostic.info(
+                    "Cannot find readable name for self.%s: %s. The name is unchanged.",
+                    node.target,
+                    type(attr_value),
+                )
+                if isinstance(attr_value, torch.nn.Parameter):
+                    # If it is a parameter we treat it more seriously.
+                    diagnostic.level = diagnostics.levels.WARNING
+                else:
+                    diagnostic.level = diagnostics.levels.NONE
+
+        for nodes, new_name in old_name_to_nodes.values():
+            self._rename_param_and_buffer(diagnostic, nodes, new_name)
+
+        return self.module
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/type_promotion.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/type_promotion.py
new file mode 100644
index 0000000000000000000000000000000000000000..6397beb5f089a4968cf520a48fa4f900fffcb29e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/type_promotion.py
@@ -0,0 +1,1720 @@
+# mypy: allow-untyped-defs
+# Owner(s): ["module: onnx"]
+from __future__ import annotations
+
+import abc
+import dataclasses
+import inspect
+import logging
+from typing import Any, Callable, Mapping, Sequence, TYPE_CHECKING
+
+import torch
+import torch._ops
+import torch.fx
+import torch.fx.traceback as fx_traceback
+from torch import _prims_common, _refs
+from torch._prims_common import (
+    ELEMENTWISE_TYPE_PROMOTION_KIND,
+    wrappers as _prims_common_wrappers,
+)
+from torch._refs import linalg as _linalg_refs, nn as _nn_refs, special as _special_refs
+from torch._refs.nn import functional as _functional_refs
+from torch._subclasses import fake_tensor
+from torch.fx.experimental import proxy_tensor
+from torch.onnx._internal.fx import _pass, diagnostics, type_utils as fx_type_utils
+from torch.utils import _python_dispatch, _pytree
+
+
+if TYPE_CHECKING:
+    from types import ModuleType
+
+
+logger = logging.getLogger(__name__)
+
+# TODO(bowbao): move to type utils.
+_SCALAR_TYPE_TENSOR_DTYPE_MAP: Mapping[type, torch.dtype] = {
+    bool: torch.bool,
+    int: torch.int64,
+    float: torch.float32,
+    complex: torch.complex32,
+}
+
+
+def _try_getclosurevars(func):
+    try:
+        return inspect.getclosurevars(func)
+    except TypeError as e:
+        return None
+
+
+@dataclasses.dataclass
+class TypePromotionSnapshot:
+    """Type promotion snapshot for a fx node and its inputs.
+
+    Contains the promoted dtype for args and kwargs that needs promoting.
+    Contains the expected node output dtype.
+    """
+
+    args_dtypes: Mapping[int, torch.dtype]
+    """Mapping from arg position to dtype to promote to."""
+
+    kwargs_dtypes: Mapping[str, torch.dtype]
+    """Mapping from kwarg name to dtype to promote to."""
+
+    out_dtype: torch.dtype
+    """Expected output dtype of the node."""
+
+
+class TypePromotionRule(abc.ABC):
+    """Base class for type promotion rule per 'torch.ops.{namespace}.{op_name}'."""
+
+    def __init__(self, namespace: str, op_name: str):
+        self.namespace = namespace
+        self.op_name = op_name
+
+    # Make this abstract as well because subclass needs to override __eq__().
+    # A class that overrides __eq__() and does not define __hash__() will have its __hash__() implicitly set to None.
+    # Ref: https://docs.python.org/3/reference/datamodel.html#object.__hash__
+    @abc.abstractmethod
+    def __hash__(self) -> int: ...
+
+    @abc.abstractmethod
+    def __repr__(self): ...
+
+    @abc.abstractmethod
+    def __eq__(self, other: object) -> bool: ...
+
+    def is_valid(self) -> bool:
+        """Check if the rule is valid."""
+        # This always returns a module. If the module does not exist it will be created.
+        module = getattr(torch.ops, self.namespace)
+        py_op = getattr(module, self.op_name, None)
+        if py_op is None:
+            logger.warning(
+                "Cannot find op: %s in module: %s", self.op_name, self.namespace
+            )
+            return False
+        if not isinstance(py_op, torch._ops.OpOverloadPacket):
+            logger.warning(
+                "Op: torch.ops.%s.%s is not an OpOverloadPacket, got: %s",
+                self.namespace,
+                self.op_name,
+                type(py_op),
+            )
+            return False
+
+        return True
+
+    @abc.abstractmethod
+    def preview_type_promotion(
+        self, args: tuple, kwargs: dict
+    ) -> TypePromotionSnapshot:
+        """Preview type promotion results for provided set of args and kwargs.
+
+        Returns a TypePromotionSnapshot object that contains the promoted dtypes for
+        the arguments and the expected output dtype.
+        """
+        ...
+
+
+class ElementwiseTypePromotionRule(TypePromotionRule):
+    """Defines how to perform elementwise type promotion for 'torch.ops.{namespace}.{op_name}'."""
+
+    _USE_OPMATH: bool = False
+    """Whether to use opmath to compute the promoted input dtype.
+    If used, upcasts will be inserted everywhere for lower precision models.
+    Set to False and have torchlib handle upcasts in op implementation internally.
+    """
+
+    def __init__(
+        self,
+        namespace: str,
+        op_name: str,
+        promote_args_positions: Sequence[int],
+        promote_kwargs_names: Sequence[str],
+        promotion_kind: _prims_common.ELEMENTWISE_TYPE_PROMOTION_KIND,
+    ):
+        """Constructs a TypePromotionRule for elementwise operators.
+
+        Args:
+            namespace: Namespace of the op. E.g. 'aten' in 'torch.ops.aten.add'.
+            op_name: Name of the op. E.g. 'add' in 'torch.ops.aten.add'.
+            promote_args_positions: Positions of args to promote.
+            promote_kwargs_names: Names of kwargs to promote.
+            promotion_kind: Type promotion kind. Refer to [_prims_common.elementwise_dtypes](https://github.com/pytorch/pytorch/blob/main/torch/_prims_common/__init__.py) for detail.  # noqa: B950
+        """
+        super().__init__(namespace, op_name)
+        self.promote_args_positions = promote_args_positions
+        self.promote_kwargs_names = promote_kwargs_names
+        self.promotion_kind = promotion_kind
+
+    def __repr__(self):
+        return (
+            f"ElementwiseTypePromotionRule('{self.namespace}', '{self.op_name}', "
+            f"{self.promote_args_positions}, {self.promote_kwargs_names}, {self.promotion_kind})"
+        )
+
+    def __eq__(self, __value: object) -> bool:
+        if not isinstance(__value, ElementwiseTypePromotionRule):
+            return False
+        return (
+            self.namespace == __value.namespace
+            and self.op_name == __value.op_name
+            and self.promote_args_positions == __value.promote_args_positions
+            and self.promote_kwargs_names == __value.promote_kwargs_names
+            and self.promotion_kind == __value.promotion_kind
+        )
+
+    def __hash__(self) -> int:
+        return f"{type(self)}:{self.namespace}.{self.op_name}".__hash__()
+
+    def _consolidate_input_dtype(
+        self, computed_dtype: torch.dtype, result_dtype: torch.dtype
+    ) -> torch.dtype:
+        """
+        Although opmath is the right thing to do to retain on-par precision, it inserts
+        upcasts everywhere in the graph. This is particularly hard for backend to optimize
+        since there is no way to differentiate between inserted upcasts and model code
+        casts. Hence we consolidate the input dtype to the result dtype to avoid this.
+        """
+        if not self._USE_OPMATH and self.promotion_kind in (
+            _prims_common.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+            _prims_common.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+        ):
+            return result_dtype
+        return computed_dtype
+
+    def preview_type_promotion(
+        self, args: tuple, kwargs: dict
+    ) -> TypePromotionSnapshot:
+        candidate_args = {
+            i: args[i]
+            for i in self.promote_args_positions
+            if i < len(args) and args[i] is not None
+        }
+        candidate_kwargs = {
+            name: kwargs[name]
+            for name in self.promote_kwargs_names
+            if name in kwargs and kwargs[name] is not None
+        }
+
+        computed_dtype, result_dtype = _prims_common.elementwise_dtypes(
+            *_pytree.arg_tree_leaves(*candidate_args.values(), **candidate_kwargs),
+            type_promotion_kind=self.promotion_kind,
+        )
+
+        consolidated_input_dtype = self._consolidate_input_dtype(
+            computed_dtype, result_dtype
+        )
+
+        return TypePromotionSnapshot(
+            dict.fromkeys(candidate_args.keys(), consolidated_input_dtype),
+            dict.fromkeys(candidate_kwargs.keys(), consolidated_input_dtype),
+            result_dtype,
+        )
+
+
+class DivElementwiseTypePromotionRule(ElementwiseTypePromotionRule):
+    """Reference type promotion rule from torch._refs.div.
+
+    Rule depends on the value of the `rounding_mode` argument.
+    """
+
+    def __init__(self):
+        super().__init__(
+            "aten",
+            "div",
+            promote_args_positions=(0, 1),
+            promote_kwargs_names=(),
+            promotion_kind=_prims_common.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+        )
+
+    def preview_type_promotion(
+        self, args: tuple, kwargs: dict
+    ) -> TypePromotionSnapshot:
+        rounding_mode = kwargs.get("rounding_mode", None)
+        if rounding_mode is None:
+            # true_divide
+            self.promotion_kind = (
+                _prims_common.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+            )
+            return super().preview_type_promotion(args, kwargs)
+        if rounding_mode == "trunc":
+            # trunc_divide
+            self.promotion_kind = _prims_common.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+            return super().preview_type_promotion(args, kwargs)
+        if rounding_mode == "floor":
+            # floor_divide
+            self.promotion_kind = _prims_common.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+            return super().preview_type_promotion(args, kwargs)
+        raise ValueError(f"Unknown rounding_mode: {rounding_mode}")
+
+
+class ReductionTypePromotionRule(TypePromotionRule):
+    def __init__(
+        self,
+        namespace: str,
+        op_name: str,
+        promotion_kind: _prims_common.REDUCTION_OUTPUT_TYPE_KIND,
+    ):
+        """Constructs a TypePromotionRule for reduction operators.
+
+        Args:
+            namespace: Namespace of the op. E.g. 'aten' in 'torch.ops.aten.sum'.
+            op_name: Name of the op. E.g. 'sum' in 'torch.ops.aten.sum'.
+            promotion_kind: Type promotion kind. Refer to [_prims_common.reduction_dtypes]((https://github.com/pytorch/pytorch/blob/main/torch/_prims_common/__init__.py)) for detail.  # noqa: B950
+        """
+        super().__init__(namespace, op_name)
+        self.promotion_kind = promotion_kind
+
+    def __repr__(self):
+        return f"ReductionTypePromotionRule('{self.namespace}', '{self.op_name}', {self.promotion_kind})"
+
+    def __eq__(self, __value: object) -> bool:
+        if not isinstance(__value, ElementwiseTypePromotionRule):
+            return False
+        return (
+            self.namespace == __value.namespace
+            and self.op_name == __value.op_name
+            and self.promotion_kind == __value.promotion_kind
+        )
+
+    def __hash__(self) -> int:
+        return f"{type(self)}:{self.namespace}.{self.op_name}".__hash__()
+
+    def preview_type_promotion(
+        self, args: tuple, kwargs: dict
+    ) -> TypePromotionSnapshot:
+        assert (
+            len(args) >= 1
+        ), f"Reduction op torch.ops.{self.namespace}.{self.op_name} expects at least one argument"
+        arg = args[0]
+        assert isinstance(arg, torch.Tensor), f"{type(arg)=} is not torch.Tensor"
+        dtype: torch.dtype | None = kwargs.get("dtype", None)
+
+        computation_dtype, result_dtype = _prims_common.reduction_dtypes(
+            arg, self.promotion_kind, dtype
+        )
+        if result_dtype is None:
+            # Inspecting code, this can only happen when `promotion_kind` is `KEEP_PROMOTED_TYPE`.
+            # Hence set same as computation_dtype.
+            result_dtype = computation_dtype
+
+        return TypePromotionSnapshot(
+            {0: computation_dtype},
+            {},
+            result_dtype,
+        )
+
+
+class AllOrAnyReductionTypePromotionRule(ReductionTypePromotionRule):
+    """Reference type promotion rule from torch.ops.aten.all or torch.ops.aten.any.
+
+    This is a special case where computation dtype is always torch.bool.
+    The result dtype is always uint8 if `dtype` kwarg is uint8, otherwise torch.bool.
+    """
+
+    def __init__(self, op_name: str):
+        super().__init__(
+            "aten",
+            op_name,
+            _prims_common.REDUCTION_OUTPUT_TYPE_KIND.ALWAYS_BOOL,
+        )
+
+    def preview_type_promotion(
+        self, args: tuple, kwargs: dict
+    ) -> TypePromotionSnapshot:
+        assert (
+            len(args) >= 1
+        ), f"Reduction op torch.ops.{self.namespace}.{self.op_name} expects at least one argument"
+        arg = args[0]
+        assert isinstance(arg, torch.Tensor), f"{type(arg)=} is not torch.Tensor"
+        computation_dtype = torch.bool
+        # Preserves uint8 -- probably a legacy mask thing
+        result_dtype = torch.uint8 if arg.dtype == torch.uint8 else torch.bool
+        return TypePromotionSnapshot(
+            {0: computation_dtype},
+            {},
+            result_dtype,
+        )
+
+
+class SumLikeReductionTypePromotionRule(ReductionTypePromotionRule):
+    """Reference type promotion rule from torch.ops.aten.sum.
+
+    This is a special case where computation dtype is always torch.int64 for integral arg,
+    unless overridden by `dtype` kwarg.
+    """
+
+    def preview_type_promotion(
+        self, args: tuple, kwargs: dict
+    ) -> TypePromotionSnapshot:
+        assert (
+            len(args) >= 1
+        ), f"Reduction op torch.ops.{self.namespace}.{self.op_name} expects at least one argument"
+        arg = args[0]
+        assert isinstance(arg, torch.Tensor), f"{type(arg)=} is not torch.Tensor"
+        dtype: torch.dtype | None = kwargs.get("dtype", None)
+        # The below logic is copied from `torch/_refs/__init__.py` reduction ops impl.
+        if dtype is None:
+            if _prims_common.is_boolean_dtype(
+                arg.dtype
+            ) or _prims_common.is_integer_dtype(arg.dtype):
+                dtype = torch.int64
+            else:
+                dtype = arg.dtype
+        return super().preview_type_promotion(args, {"dtype": dtype})
+
+
+# NOTE: [Update type promotion rule]
+# BELOW TABLE IS GENERATED FROM `TypePromotionRuleSetGenerator.generate_from_torch_refs`.
+# DO NOT EDIT MANUALLY !!!
+# For missing rules or discrepancies, please
+# 1. Run `pytest test/onnx/test_fx_type_promotion.py` to validate if the generated rule set is current.
+#    If it is not, update with new generated set.
+# 2. If discrepancies still exist, consider debugging torch._refs or report a bug.
+# 3. If rules are still missing, add them to `_EXTRA_TYPE_PROMOTION_RULE_SET` or report a bug.
+# Check `TypePromotionRule` class for how each rule is defined and used.
+_GENERATED_ATEN_TYPE_PROMOTION_RULE_SET = {
+    ElementwiseTypePromotionRule(
+        "aten", "abs", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "abs_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "acos", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "acos_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "acosh", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "acosh_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "add", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "add_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "addcdiv", [0, 1, 2], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "addcdiv_", [0, 1, 2], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "addcmul", [0, 1, 2], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "addcmul_", [0, 1, 2], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "addr", [0, 1, 2], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "asin", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "asin_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "asinh", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "asinh_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "atan", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "atan2", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "atan2_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "atan_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "atanh", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "atanh_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "bitwise_and", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "bitwise_and_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten",
+        "bitwise_left_shift",
+        [0, 1],
+        [],
+        ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+    ),
+    ElementwiseTypePromotionRule(
+        "aten",
+        "bitwise_left_shift_",
+        [0, 1],
+        [],
+        ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "bitwise_not", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "bitwise_not_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "bitwise_or", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "bitwise_or_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten",
+        "bitwise_right_shift",
+        [0, 1],
+        [],
+        ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+    ),
+    ElementwiseTypePromotionRule(
+        "aten",
+        "bitwise_right_shift_",
+        [0, 1],
+        [],
+        ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "bitwise_xor", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "bitwise_xor_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "cat", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.NO_OPMATH
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "cauchy", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "cauchy_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "ceil", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "ceil_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "celu", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "celu_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "clamp", [0, 1, 2], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "clamp_", [0, 1, 2], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "copysign", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "copysign_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "cos", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "cos_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "cosh", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "cosh_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "deg2rad", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "deg2rad_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "digamma", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "digamma_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "elu", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "elu_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "eq", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "eq_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "erf", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "erf_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "erfc", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "erfc_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "erfinv", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "erfinv_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "exp", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "exp2", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "exp2_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "exp_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "expm1", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "expm1_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "exponential", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "exponential_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "fill", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.NO_OPMATH
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "floor", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "floor_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "floor_divide", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "floor_divide_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "fmax", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "fmin", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "fmod", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "fmod_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "frac", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "frac_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "gcd", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "gcd_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "ge", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "ge_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "gelu", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "geometric", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "geometric_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "glu", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "gt", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "gt_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "hardtanh", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "heaviside", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "heaviside_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "huber_loss", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "hypot", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "hypot_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "i0", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "i0_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "igamma", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "igamma_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "igammac", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "igammac_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "isfinite", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "isinf", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "isnan", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "isneginf", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "isposinf", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "isreal", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "l1_loss", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "lcm", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "lcm_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "le", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "le_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "leaky_relu", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "lerp", [0, 1, 2], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "lerp_", [0, 1, 2], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "lgamma", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "lgamma_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "log", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "log10", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "log10_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "log1p", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "log1p_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "log2", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "log2_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "log_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "log_normal", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "log_normal_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "logaddexp", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "logaddexp2", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "logical_and", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "logical_and_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "logical_not", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "logical_not_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "logical_or", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "logical_or_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "logical_xor", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "logical_xor_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "logit", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "logsumexp", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "lt", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "lt_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "maximum", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "minimum", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "mish", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "mish_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "mse_loss", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "mul", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "mul_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "ne", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "ne_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "neg", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "neg_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "nextafter", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.NO_OPMATH
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "nextafter_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.NO_OPMATH
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "nll_loss", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "normal", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "normal_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "pdist", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten",
+        "poisson_nll_loss",
+        [0, 1],
+        [],
+        ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "pow", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.BOOL_TO_LONG
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "pow_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.BOOL_TO_LONG
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "prelu", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "rad2deg", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "rad2deg_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "reciprocal", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "reciprocal_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "relu", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "remainder", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "remainder_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "round", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "rsqrt", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "rsqrt_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "rsub", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "selu", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "selu_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "sgn", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "sgn_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "sigmoid", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "sigmoid_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "sign", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "sign_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "signbit", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.ALWAYS_BOOL
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "sin", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "sin_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "sinc", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "sinc_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "sinh", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "sinh_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten",
+        "smooth_l1_loss",
+        [0, 1],
+        [],
+        ELEMENTWISE_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT,
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "softplus", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "sqrt", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "sqrt_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "square", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.BOOL_TO_LONG
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "square_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.BOOL_TO_LONG
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "sub", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "sub_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "tan", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "tan_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "tanh", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "tanh_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "threshold", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "threshold_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "true_divide", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "true_divide_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "trunc", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "trunc_", [0], [], ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "where", [1, 2], [], ELEMENTWISE_TYPE_PROMOTION_KIND.NO_OPMATH
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "xlogy", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+    ElementwiseTypePromotionRule(
+        "aten", "xlogy_", [0, 1], [], ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
+    ),
+}
+
+# Manually curated extra type promotion rules. Please see NOTE [Update type promotion rule]
+# before adding new rules.
+_EXTRA_TYPE_PROMOTION_RULE_SET = {
+    # torch._refs skips type promotion decoration for `clamp_min` and `clamp_max` since
+    # the call is routed to the decorated `aten.clamp` op.
+    ElementwiseTypePromotionRule(
+        "aten",
+        "clamp_max",
+        promote_args_positions=(0, 1),
+        promote_kwargs_names=(),
+        promotion_kind=_prims_common.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+    ),
+    ElementwiseTypePromotionRule(
+        "aten",
+        "clamp_min",
+        promote_args_positions=(0, 1),
+        promote_kwargs_names=(),
+        promotion_kind=_prims_common.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+    ),
+    # torch.ops.aten.div.Tensor_mode applies different type promotion rules
+    # depending on the value of the `mode` argument.
+    DivElementwiseTypePromotionRule(),
+    # Manually curating reduction ops since the logic is written inside the op reference
+    # implementation.
+    AllOrAnyReductionTypePromotionRule("all"),
+    AllOrAnyReductionTypePromotionRule("any"),
+    ReductionTypePromotionRule(
+        "aten",
+        "amax",
+        promotion_kind=_prims_common.REDUCTION_OUTPUT_TYPE_KIND.SAME,
+    ),
+    ReductionTypePromotionRule(
+        "aten",
+        "amin",
+        promotion_kind=_prims_common.REDUCTION_OUTPUT_TYPE_KIND.SAME,
+    ),
+    # torch.ops.aten.mean is a special case that does not need type promotion.
+    ReductionTypePromotionRule(
+        "aten",
+        "std",
+        promotion_kind=_prims_common.REDUCTION_OUTPUT_TYPE_KIND.COMPLEX_TO_FLOAT,
+    ),
+    ReductionTypePromotionRule(
+        "aten",
+        "std_mean",
+        promotion_kind=_prims_common.REDUCTION_OUTPUT_TYPE_KIND.COMPLEX_TO_FLOAT,
+    ),
+    ReductionTypePromotionRule(
+        "aten",
+        "var",
+        promotion_kind=_prims_common.REDUCTION_OUTPUT_TYPE_KIND.COMPLEX_TO_FLOAT,
+    ),
+    SumLikeReductionTypePromotionRule(
+        "aten",
+        "cumprod",
+        promotion_kind=_prims_common.REDUCTION_OUTPUT_TYPE_KIND.SAME,
+    ),
+    SumLikeReductionTypePromotionRule(
+        "aten",
+        "cumsum",
+        promotion_kind=_prims_common.REDUCTION_OUTPUT_TYPE_KIND.SAME,
+    ),
+    SumLikeReductionTypePromotionRule(
+        "aten",
+        "prod",
+        promotion_kind=_prims_common.REDUCTION_OUTPUT_TYPE_KIND.SAME,
+    ),
+    SumLikeReductionTypePromotionRule(
+        "aten",
+        "sum",
+        promotion_kind=_prims_common.REDUCTION_OUTPUT_TYPE_KIND.SAME,
+    ),
+}
+
+
+class ElementwiseTypePromotionRuleSetGenerator:
+    """Hackly distilling info from reference ops decorated with elementwise type promotion rule.
+
+    The goal is to retrieve the decorator
+
+    ```python
+        @elementwise_type_promotion_wrapper(
+            type_promoting_args=("a", "b"),
+            type_promotion_kind=type_promotion_kind,
+        )
+    ```
+
+    from the reference ops. It provides info as for which arguments are promoted
+    and what kind of promotion is applied.
+    """
+
+    @classmethod
+    def generate_from_torch_refs(cls) -> set[ElementwiseTypePromotionRule]:
+        """Parse type promotion rules from reference ops under torch._C._refs."""
+        rule_set = set()
+        rule_set.update(cls._parse_torch_refs(_refs))
+        rule_set.update(cls._parse_torch_refs(_nn_refs))
+        rule_set.update(cls._parse_torch_refs(_linalg_refs))
+        rule_set.update(cls._parse_torch_refs(_special_refs))
+        rule_set.update(cls._parse_torch_refs(_functional_refs))
+        return rule_set
+
+    @classmethod
+    def _parse_torch_refs(
+        cls, ref_module: ModuleType
+    ) -> set[ElementwiseTypePromotionRule]:
+        logger.info("Processing module: %s", ref_module.__name__)
+        rule_set = set()
+        for name in ref_module.__all__:
+            decorated_op = getattr(ref_module, name)
+            rule = cls._parse_type_promotion_rule_from_refs_op(decorated_op)
+            if rule is not None and rule.is_valid():
+                rule_set.add(rule)
+
+        return rule_set
+
+    @classmethod
+    def _parse_type_promotion_rule_from_refs_op(
+        cls,
+        decorated_op: Callable,
+    ) -> ElementwiseTypePromotionRule | None:
+        """Retrieve and parse type promotion decorator from op under torch._refs."""
+        fn = decorated_op
+        type_promo_wrapper = None
+        while fn_closure_vars := _try_getclosurevars(fn):
+            if "fn" not in fn_closure_vars.nonlocals:
+                break
+            if "self" in fn_closure_vars.nonlocals and isinstance(
+                fn_closure_vars.nonlocals["self"],
+                _prims_common_wrappers.elementwise_type_promotion_wrapper,
+            ):
+                type_promo_wrapper = fn_closure_vars.nonlocals["self"]
+                break
+            fn = fn_closure_vars.nonlocals["fn"]
+
+        if type_promo_wrapper is not None:
+            signature = inspect.signature(decorated_op)
+
+            pos = 0
+            promote_args_positions = []
+            promote_kwargs_names = []
+
+            if type_promo_wrapper.type_promoting_arg_names is not None:
+                for name, param in signature.parameters.items():
+                    if name in type_promo_wrapper.type_promoting_arg_names:
+                        if param.kind in (
+                            param.POSITIONAL_OR_KEYWORD,
+                            param.POSITIONAL_ONLY,
+                        ):
+                            promote_args_positions.append(pos)
+                        elif param.kind == param.KEYWORD_ONLY:
+                            promote_kwargs_names.append(name)
+                    pos += 1
+
+            return ElementwiseTypePromotionRule(
+                "aten",
+                decorated_op.__name__,
+                promote_args_positions=promote_args_positions,
+                promote_kwargs_names=promote_kwargs_names,
+                promotion_kind=type_promo_wrapper.type_promotion_kind,
+            )
+
+        logger.warning(
+            "Cannot find type promotion rule for: %s.%s",
+            decorated_op.__module__,
+            decorated_op.__name__,
+        )
+        return None
+
+
+class TypePromotionTable:
+    """Type promotion table for torch.ops."""
+
+    def __init__(self):
+        self._rule_table = {}
+        for rule in _GENERATED_ATEN_TYPE_PROMOTION_RULE_SET:
+            self.add_rule(rule)
+        for rule in _EXTRA_TYPE_PROMOTION_RULE_SET:
+            self.add_rule(rule)
+
+    def add_rule(self, rule: TypePromotionRule) -> None:
+        """Add a type promotion rule for a python op in a torch.ops module.
+
+        Args:
+            rule: Type promotion rule.
+            module: Module containing the op. E.g. torch.ops.aten.
+
+        Raises:
+            ValueError: If the rule is invalid.
+        """
+        if not rule.is_valid():
+            raise ValueError(f"Invalid type promotion rule: {rule}")
+        self._rule_table[f"{rule.namespace}.{rule.op_name}"] = rule
+
+    def get_rule(self, py_op: torch._ops.OpOverloadPacket) -> TypePromotionRule | None:
+        """Get type promotion rule for a python op under 'torch.ops.'."""
+        return self._rule_table.get(str(py_op), None)
+
+
+def get_type_promotion_rule(
+    diagnostic: diagnostics.Diagnostic,
+    node: torch.fx.Node,
+    type_promotion_table: TypePromotionTable,
+) -> TypePromotionRule | None:
+    """Get type promotion rule for a node.
+
+    Args:
+        diagnostic: Diagnostic object.
+        node: Node to get type promotion rule for.
+        type_promotion_table: Type promotion table.
+
+    Returns:
+        Type promotion rule for the node. None if no rule is found or if the node is not
+        representing a torch operator.
+    """
+    op = node.target
+    if not isinstance(op, torch._ops.OpOverload):
+        # TODO(bowbao): diagnostic.emit and diagnostic.set_message api.
+        diagnostic.message = (
+            f"Skipped for {diagnostics.format_argument(node)}: "
+            f"node.target is not OpOverload. Got type: {type(op)}"
+        )
+        return None
+    if (rule := type_promotion_table.get_rule(op.overloadpacket)) is None:
+        diagnostic.message = (
+            f"Skipped for {diagnostics.format_argument(node)}: "
+            f"Cannot find type promotion rule for op: {op}"
+        )
+        return None
+
+    diagnostic.info("Found type promotion rule: %s", rule)
+    return rule
+
+
+class _OpTraceDispatchMode(_python_dispatch.TorchDispatchMode):
+    """Trace ops that were dispatched.
+
+    Utilize the dispatch mechanism in [`__torch_dispatch__`](https://dev-discuss.pytorch.org/t/what-and-why-is-torch-dispatch/557)
+    to trace op overloads that were dispatched to. This is used to find the compatible
+    op overload for a given op overload packet for different set of args and kwargs.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.traced_ops = []
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        self.traced_ops.append(func)
+        return func(*args, **kwargs)
+
+
+def find_compatible_op_overload(
+    op: torch._ops.OpOverloadPacket, args: tuple, kwargs: dict
+) -> torch._ops.OpOverload:
+    """Find compatible OpOverload for an OpOverloadPacket using provided args and kwargs.
+
+    Each "call_function" fx.Node in the fx.GraphModule has a target that represents a torch._ops.OpOverload.
+    The OpOverload contains an OpOverloadPacket that holds all the available overloads for the operation.
+
+    During the type promotion pass, there are cases where the types of the args and kwargs may change,
+    such as promoting Python numbers to tensors. Consequently, the original OpOverload might not be
+    compatible with the updated args and kwargs. This function is used to identify the compatible
+    OpOverload for the given args and kwargs.
+
+    Args:
+        op: OpOverloadPacket to find compatible OpOverload for.
+        args: The positional arguments to consider for compatibility.
+        kwargs: The keyword arguments to consider for compatibility.
+
+    Returns:
+        torch._ops.OpOverload: The compatible OpOverload found for the given args and kwargs.
+
+    Raises:
+        RuntimeError: If no compatible op overload is found.
+
+    Examples:
+        >>> import torch
+        >>> packet = torch.ops.aten.pow
+        >>> args = (torch.tensor([1.0, 2.0]), 2)
+        >>> find_compatible_op_overload(packet, args, {})._overloadname
+        'Tensor_Scalar'
+        >>> args = (torch.tensor([1.0, 2.0]), torch.tensor(2.0))
+        >>> find_compatible_op_overload(packet, args, {})._overloadname
+        'Tensor_Tensor'
+    """
+    # Utilize the dispatch mechanism to find the compatible op overload.
+    op_trace_dispatch_mode = _OpTraceDispatchMode()
+    with op_trace_dispatch_mode:
+        op(*args, **kwargs)
+    assert (
+        len(op_trace_dispatch_mode.traced_ops) >= 1
+    ), "Expected at least 1 traced op, got 0"
+
+    new_op_overload = op_trace_dispatch_mode.traced_ops[0]
+    assert isinstance(
+        new_op_overload, torch._ops.OpOverload
+    ), f"Expected OpOverload, got {type(new_op_overload)}"
+    assert (
+        new_op_overload.overloadpacket == op
+    ), f"Expected same OpOverload packet, got {new_op_overload.overloadpacket} != {op}"
+
+    return new_op_overload
+
+
+class _TypePromotionInterpreter(torch.fx.Interpreter):
+    """Interpreter that inserts type promotion for each node."""
+
+    def __init__(
+        self,
+        diagnostic_context: diagnostics.DiagnosticContext,
+        module: torch.fx.GraphModule,
+        type_promotion_table: TypePromotionTable,
+    ):
+        super().__init__(module)
+        self.diagnostic_context = diagnostic_context
+        self.type_promotion_table = type_promotion_table
+
+    def _run_node_and_set_meta(self, node) -> Any:
+        """Run node and set meta according to `fx_traceback.get_current_meta()`.
+
+        This should be used on new nodes or nodes that have been modified.
+        By default `Interpreter.run_node` does not update `node.meta`.
+        Set `node.meta` to the current meta, except for `node.meta["val"]`, which is
+        recomputed.
+        """
+        out = super().run_node(node)
+        # Update interpreter env state with new output value.
+        self.env[node] = out
+        node.meta.update(
+            (k, v)
+            for k, v in fx_traceback.get_current_meta().items()
+            if k not in node.meta
+        )
+        node.meta["val"] = proxy_tensor.extract_val(out)
+        return out
+
+    def _create_node(
+        self,
+        graph: torch.fx.Graph,
+        op_type: str,
+        target: torch.fx.node.Target,
+        args: tuple,
+        kwargs: dict,
+    ) -> torch.fx.Node:
+        """Create a node and set its metadata."""
+        assert op_type in (
+            "call_function",
+            "call_method",
+            "get_attr",
+            "call_module",
+            "placeholder",
+            "output",
+        ), f"Unexpected op_type: {op_type}"
+        node = getattr(graph, op_type)(target, args, kwargs)
+        self._run_node_and_set_meta(node)
+        return node
+
+    def _rerun_node_after_type_promotion(
+        self,
+        diagnostic: diagnostics.Diagnostic,
+        node: torch.fx.Node,
+        expected_out_dtype: torch.dtype,
+    ) -> None:
+        """Rerun a node after type promotion and update node.meta["val"] with the output value."""
+        node_val = node.meta.get("val", None)
+        assert node_val is not None, f"Node {node} node.meta['val'] is not set."
+        args, kwargs = self.fetch_args_kwargs_from_env(node)
+        target = node.target
+        assert isinstance(
+            target, torch._ops.OpOverload
+        ), f"Expected OpOverload, got {type(target)}"
+        node.target = find_compatible_op_overload(target.overloadpacket, args, kwargs)
+
+        new_node_val = self._run_node_and_set_meta(node)
+        assert isinstance(new_node_val, type(node_val)), (
+            f"run_node output type should not change between runs. "
+            f"Got {type(new_node_val)}, expect {type(node_val)}."
+        )
+
+        if isinstance(node_val, torch.Tensor):
+            prev_node_dtype = node_val.dtype
+
+            assert prev_node_dtype == expected_out_dtype, (
+                f"node.meta['val'].dtype({prev_node_dtype}) does not agree with "
+                f"type promotion rule({expected_out_dtype})."
+            )
+
+            if new_node_val.dtype != expected_out_dtype:
+                # With explicit type promotion, the expected result dtype may not be
+                # the same as the computation dtype. This is referred to as "op math".
+                # We need to explicitly cast the output back to the expected dtype.
+                # See more about "op math" topic at `_prims_common.elementwise_dtypes`.
+                graph = node.graph
+                with graph.inserting_after(node):
+                    output_cast_node = self._create_node(
+                        graph,
+                        "call_function",
+                        torch.ops.prims.convert_element_type.default,
+                        (node,),
+                        {"dtype": expected_out_dtype},
+                    )
+                    node.replace_all_uses_with(output_cast_node)
+                    output_cast_node.args = (node,)
+                    diagnostic.info(
+                        "Node '%s' output dtype becomes %s due to op math. "
+                        "Cast back to %s.",
+                        node,
+                        new_node_val.dtype,
+                        expected_out_dtype,
+                    )
+
+        elif fx_type_utils.is_torch_symbolic_type(node_val):
+            raise NotImplementedError(
+                "Type promotion does not support node output of sym types."
+            )
+        elif isinstance(node_val, (list, tuple)):
+            raise NotImplementedError(
+                "Type promotion does not support node output of list or tuple."
+            )
+        else:
+            raise RuntimeError(f"Unexpected node output type: {type(node_val)}.")
+
+    def _maybe_promote_arg(
+        self,
+        diagnostic: diagnostics.Diagnostic,
+        node: torch.fx.Node,
+        fx_arg: torch.fx.node.Argument,
+        dtype: torch.dtype | None,
+    ) -> torch.fx.node.Argument:
+        """Promote fx_arg to dtype if necessary."""
+        if dtype is None:
+            diagnostic.info(
+                "Argument %s is not promoted. Not mentioned by type promotion rule.",
+                fx_arg,
+            )
+            return fx_arg
+
+        if isinstance(fx_arg, torch.fx.Node):
+            arg_val = self.env[fx_arg]
+            if isinstance(arg_val, torch.Tensor):
+                if (old_dtype := arg_val.dtype) != dtype:
+                    # Promote tensor to dtype.
+                    graph = node.graph
+                    with graph.inserting_before(node):
+                        diagnostic.info(
+                            "Argument %s(%s) is promoted to %s.",
+                            fx_arg,
+                            old_dtype,
+                            dtype,
+                        )
+                        return self._create_node(
+                            graph,
+                            "call_function",
+                            torch.ops.prims.convert_element_type.default,
+                            (fx_arg,),
+                            {"dtype": dtype},
+                        )
+                diagnostic.info(
+                    "Argument %s is not promoted. Already %s.", fx_arg, dtype
+                )
+                return fx_arg
+            elif fx_type_utils.is_torch_symbolic_type(arg_val):
+                arg_type = type(arg_val)
+                equivalent_dtype = fx_type_utils.from_scalar_type_to_torch_dtype(
+                    arg_type
+                )
+                assert equivalent_dtype is not None, f"Unexpected arg_type: {arg_type}"
+                if equivalent_dtype != dtype:
+                    # Promote Sym number to tensor of dtype.
+                    graph = node.graph
+                    with graph.inserting_before(node):
+                        diagnostic.info(
+                            "Argument %s(Scalar of equivalent dtype: %s) "
+                            "is promoted to %s.",
+                            fx_arg,
+                            equivalent_dtype,
+                            dtype,
+                        )
+                        return self._create_node(
+                            graph,
+                            "call_function",
+                            torch.ops.aten.scalar_tensor.default,
+                            (fx_arg,),
+                            {"dtype": dtype},
+                        )
+                diagnostic.info(
+                    "Argument %s is not promoted. Already %s.", fx_arg, dtype
+                )
+                return fx_arg
+        elif (
+            equivalent_dtype := fx_type_utils.from_scalar_type_to_torch_dtype(
+                type(fx_arg)
+            )
+        ) is not None:
+            if equivalent_dtype != dtype:
+                # Promote number to tensor of dtype.
+                # The op should have overload that supports tensor for this arg, otherwise
+                # the type promotion rule should not suggest promoting this arg.
+                graph = node.graph
+                with graph.inserting_before(node):
+                    diagnostic.info(
+                        "Argument %s(Scalar of equivalent dtype: %s) "
+                        "is promoted to %s.",
+                        fx_arg,
+                        equivalent_dtype,
+                        dtype,
+                    )
+                    return self._create_node(
+                        graph,
+                        "call_function",
+                        torch.ops.aten.scalar_tensor.default,
+                        (fx_arg,),
+                        {"dtype": dtype},
+                    )
+            diagnostic.info("Argument %s is not promoted. Already %s.", fx_arg, dtype)
+            return fx_arg
+        elif isinstance(fx_arg, (tuple, list)):
+            diagnostic.info(
+                "Argument %s is a tuple/list. Promoting each element.", fx_arg
+            )
+            return type(fx_arg)(
+                self._maybe_promote_arg(diagnostic, node, fx_arg_elem, dtype)
+                for fx_arg_elem in fx_arg
+            )
+
+        raise NotImplementedError(f"Unknown fx arg type: {type(fx_arg)}")
+
+    def _maybe_promote_node(
+        self,
+        diagnostic: diagnostics.Diagnostic,
+        node: torch.fx.Node,
+        rule: TypePromotionRule,
+    ) -> torch.fx.Node:
+        """Promote node inputs and outputs according to type promotion rule."""
+        args, kwargs = self.fetch_args_kwargs_from_env(node)
+        type_promotion_info = rule.preview_type_promotion(args, kwargs)
+        new_args = []
+        new_kwargs = {}
+        for i, arg in enumerate(node.args):
+            new_args.append(
+                self._maybe_promote_arg(
+                    diagnostic, node, arg, type_promotion_info.args_dtypes.get(i, None)
+                )
+            )
+
+        for name, arg in node.kwargs.items():
+            new_kwargs[name] = self._maybe_promote_arg(
+                diagnostic, node, arg, type_promotion_info.kwargs_dtypes.get(name, None)
+            )
+        new_args = tuple(new_args)
+
+        if node.args != new_args or node.kwargs != new_kwargs:
+            diagnostic.message = f"Applied type promotion for {node}. "
+            node.args = new_args
+            node.kwargs = new_kwargs
+            self._rerun_node_after_type_promotion(
+                diagnostic, node, type_promotion_info.out_dtype
+            )
+        else:
+            diagnostic.message = f"Type promotion not needed for {node}. "
+
+        return node
+
+    @diagnostics.diagnose_call(
+        rule=diagnostics.rules.fx_node_insert_type_promotion,
+        level=diagnostics.levels.NONE,
+    )
+    def run_node(self, node: torch.fx.Node) -> Any:
+        """This method is an override which inserts type promotion nodes as needed.
+
+        For each `call_function` node, an initial check is conducted to determine if a type
+        promotion rule is applicable. If a relevant rule exists, type casting nodes are
+        introduced for the corresponding arguments. The OpOverload of the node is updated
+        to one that accommodates the promoted types. Should the output type be different,
+        type casting node is inserted for this output.
+
+        The call `super().run_node(node)` is guaranteed to be invoked for each node.
+        In the case of new or modified nodes, the result of `super().run_node(node)` is
+        used to update its `node.meta["val"]` value.
+        """
+        diagnostic = self.diagnostic_context.inflight_diagnostic()
+        with self._set_current_node(node):
+            if node.op != "call_function":
+                diagnostic.message = f"Skipped {node}: not a call_function."
+            elif rule := get_type_promotion_rule(
+                diagnostic, node, self.type_promotion_table
+            ):
+                self._maybe_promote_node(diagnostic, node, rule)
+
+        return super().run_node(node)
+
+
+class InsertTypePromotion(_pass.Transform):
+    """Explicitly insert type promotion ops to the graph.
+
+    This class subclasses `_pass.Transform` to provide graph level diagnostic tracking.
+    Underneath, the main pass is driven by `_TypePromotionInterpreter`, which is a subclass
+    of `torch.fx.Interpreter` to interpret the fx.Graph and perform the insertion of type
+    promotion operations.
+
+    The interpreter is extended with ability to track diagnostic information for each node.
+
+    By re-running the new and modified nodes using the interpreter, we can update the
+    metadata, specifically the fake tensor stored under node.meta["val"], and ensure it
+    reflects the latest changes.
+
+    See [FXE0015: fx_node_insert_type_promotion](https://pytorch.org/docs/main/generated/onnx_dynamo_diagnostics_rules/FXE0015%3Afx-node-insert-type-promotion.html) for more details.  # noqa: B950
+    """
+
+    def __init__(
+        self,
+        diagnostic_context: diagnostics.DiagnosticContext,
+        module: torch.fx.GraphModule,
+        type_promotion_table: TypePromotionTable | None = None,
+    ):
+        super().__init__(diagnostic_context, module)
+        self.interpreter = _TypePromotionInterpreter(
+            diagnostic_context, module, type_promotion_table or TypePromotionTable()
+        )
+
+    def _fetch_fake_args(
+        self,
+    ) -> Sequence[
+        fake_tensor.FakeTensor
+        | float
+        | int
+        | bool
+        | torch.SymInt
+        | torch.SymFloat
+        | torch.SymBool
+        | None
+    ]:
+        """Fetch fake args from fx graph.
+
+        For each argument, try to fetch fake tensor from the matching placeholder node.
+        """
+        fake_args = []
+        for node in self.module.graph.nodes:
+            if node.op == "placeholder":
+                try:
+                    # Meta value can be torch.Tensor, int, float, bool,
+                    # torch.SymInt, torch.SymFloat, torch.SymBool.
+                    meta_value = _val = node.meta.get("val", None)
+                except RuntimeError as e:
+                    if not node.users:
+                        # If the placeholder is not used, we can safely ignore it and put
+                        # None as placeholder.
+                        meta_value = None
+                    else:
+                        raise RuntimeError(
+                            "Cannot fetch symbolic fake args from fx graph. "
+                            "InsertTypePromotion pass needs to run with pre-existing fake args, "
+                            "Otherwise the pass will produce inaccurate dynamic shape. "
+                        ) from e
+
+                fake_args.append(meta_value)
+        return fake_args
+
+    def _run(self, *args, **kwargs) -> torch.fx.GraphModule:
+        assert not args, (
+            "`InsertTypePromotion` deduces symbolic fake arguments from the graph. "
+            "It does not accept concrete arguments as input because this pass requires "
+            "re-running the graph. When executed with newly faked concrete arguments, "
+            "the pass loses the symbolic dynamic shape information."
+        )
+        assert not kwargs, "`kwargs` is not supported"
+
+        fake_args = self._fetch_fake_args()
+        fake_mode = self.fake_mode
+        assert fake_mode is not None, "Cannot detect fake_mode."
+
+        with fake_tensor.unset_fake_temporarily(), (
+            fake_mode
+        ), fx_traceback.preserve_node_meta():
+            self.interpreter.run(*fake_args)
+
+        return self.module
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/virtualization.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/virtualization.py
new file mode 100644
index 0000000000000000000000000000000000000000..456c25fee777c81efa3580facca3b093c9471dab
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/passes/virtualization.py
@@ -0,0 +1,96 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import torch
+from torch.onnx._internal.fx import _pass
+
+
+if TYPE_CHECKING:
+    import torch.fx
+
+
+class MovePlaceholderToFront(_pass.Transform):
+    """This pass move all placeholder nodes to the front of the graph node list.
+
+    In torch.fx.Graph, placeholder is a special assignment node. If it's not
+    executed in the beginning, it could overwrite values computed by upstream
+    nodes.
+    """
+
+    def _run(self, *args, **kwargs) -> torch.fx.GraphModule:
+        graph_module = self.module
+        graph = graph_module.graph
+        placeholders = []
+        first_not_placeholder = None
+        for node in graph.nodes:
+            if node.op == "placeholder":
+                placeholders.append(node)
+            if first_not_placeholder is None and node.op != "placeholder":
+                first_not_placeholder = node
+        if first_not_placeholder is None:
+            return graph_module
+        for placeholder in placeholders:
+            first_not_placeholder.prepend(placeholder)
+        return graph_module
+
+
+class ReplaceGetAttrWithPlaceholder(_pass.Transform):
+    """Replace get_attr with placeholder.
+
+    The parameters and buffers accessed by the original get_attr are returned;
+    they are useful when creating random inputs for the modified graph_module.
+    """
+
+    _replaced_attrs: tuple[torch.Tensor, ...] | None
+
+    @property
+    def replaced_attrs(self) -> tuple[torch.Tensor, ...]:
+        """The list of replaced weight tensors."""
+        assert (
+            self._replaced_attrs is not None
+        ), "Must run ReplaceGetAttrWithPlaceholder first"
+        return self._replaced_attrs
+
+    def _run(self, *args, **kwargs) -> torch.fx.GraphModule:
+        graph_module = self.module
+        graph = graph_module.graph
+        replaced_attrs: list[torch.Tensor] = []
+        for node in graph.nodes:
+            if node.op == "get_attr":
+                replaced_attr: torch.Tensor | None = None
+                # get_attr could retrieve either parameter or buffer, so
+                # we need to try both.
+                try:
+                    replaced_attr = graph_module.get_parameter(node.target)
+                except AttributeError:
+                    # It's possible that model author use buffer instead of
+                    # parameter to store trainable weights. In this case,
+                    # 1. get_parameter will throw something like
+                    #    AttributeError: `bias` is not an nn.Parameter.
+                    # 2. get_buffer should work.
+                    replaced_attr = graph_module.get_buffer(node.target)
+
+                # Reassign op type so that get_attr node becomes placeholder node.
+                node.op = "placeholder"
+                # The target name in placeholder must be a valid Python identifier.
+                # Thus, we replace, e.g., "module.submodule.weight" with
+                # "module_submodule_weight".
+                node.target = node.target.replace(".", "_")
+                # Default value is None. This is needed as long as the "graph_module"
+                # has optional inputs. Assume the original forward signature is
+                #  def forward(self, x, y=None)
+                # and the replaced get_attr node has target "z". Then, the modified
+                # signature should be
+                #  def forward(self, x, y=None, z=None)
+                # Without the following line, the signature will be
+                #  def forward(self, x, y=None, z)
+                # , which is not valid Python code.
+                node.args = (None,)
+
+                replaced_attrs.append(replaced_attr)
+
+        self._replaced_attrs = tuple(replaced_attrs)
+
+        return graph_module
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/patcher.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/patcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..04298159deb0622d08065f538d1d76af1496d72e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/patcher.py
@@ -0,0 +1,143 @@
+# mypy: allow-untyped-defs
+import copy
+import functools
+from typing import List, TYPE_CHECKING, Union
+
+import torch
+
+
+if TYPE_CHECKING:
+    import io
+
+
+# TODO: Remove after https://github.com/huggingface/safetensors/pull/318
+@functools.lru_cache(None)
+def has_safetensors_and_transformers():
+    try:
+        # safetensors is not an exporter requirement, but needed for some huggingface models
+        import safetensors  # type: ignore[import]  # noqa: F401
+        import transformers  # type: ignore[import]  # noqa: F401
+        from safetensors import torch as safetensors_torch  # noqa: F401
+
+        return True
+    except ImportError:
+        return False
+
+
+class ONNXTorchPatcher:
+    """Context manager to temporarily patch PyTorch during FX-to-ONNX export.
+
+    This class is a collection of "patches" required by FX-to-ONNX exporter.
+
+    This context overrides several torch functions to support symbolic
+    export of large scale models.
+
+    torch.load:
+        This function is patched to record the files PyTorch stores model
+        parameters and buffers. Downstream FX-to-ONNX exporter can create
+        initializers from these files.
+    torch.fx._symbolic_trace._wrapped_methods_to_patch:
+        This list is extended with (torch.Tensor, "__getitem__") so that
+        weight[x, :, y] becomes exportable with torch.fx.symbolic_trace.
+    safetensors.torch.load_file:
+        This function is patched to allow safetensors to be loaded within
+        FakeTensorMode. Remove after https://github.com/huggingface/safetensors/pull/318
+
+    Search for ONNXTorchPatcher in test_fx_to_onnx_with_onnxruntime.py for
+    example usage.
+
+    TODO: Should this really be a global patcher? Can we make it a local patcher?
+        A reason for splitting this into several patchers is to patch one part of the code
+        as a collateral damage of patching another part of the code. For example, we
+        for tracing model with torch._dynamo.export, we don't need to patch
+        `torch.fx._symbolic_trace._wrapped_methods_to_patch`
+    """
+
+    def __init__(self) -> None:
+        # List of file paths processed by torch.load.
+        self.paths: List[Union[str, io.BufferedIOBase]] = []
+
+        def torch_load_wrapper(f, *args, **kwargs):
+            # Record path for later serialization into ONNX proto
+            self.paths.append(f)
+            # Then, call the original torch.load.
+            return self.torch_load(f, *args, **kwargs)
+
+        # Original version of torch.load.
+        self.torch_load = torch.load
+
+        # Wrapper or modified version of torch functions.
+        self.torch_load_wrapper = torch_load_wrapper
+
+        if has_safetensors_and_transformers():
+            import safetensors
+            import transformers
+
+            def safetensors_load_file_wrapper(filename, device="cpu"):
+                # Record path for later serialization into ONNX proto
+                self.paths.append(filename)
+                result = {}
+                with safetensors.torch.safe_open(  # type: ignore[attr-defined]
+                    filename, framework="pt", device=device
+                ) as f:
+                    for k in f.keys():
+                        fake_mode = torch._guards.detect_fake_mode()
+                        if not fake_mode:
+                            result[k] = f.get_tensor(k)
+                        else:
+                            empty_tensor = f.get_slice(k)
+                            result[k] = torch.empty(
+                                tuple(empty_tensor.get_shape()),
+                                dtype=safetensors.torch._getdtype(
+                                    empty_tensor.get_dtype()
+                                ),
+                            )
+                return result
+
+            self.safetensors_torch_load_file = safetensors.torch.load_file
+            self.safetensors_torch_load_file_wrapper = safetensors_load_file_wrapper
+            self.transformers_modeling_utils_safe_load_file = (
+                transformers.modeling_utils.safe_load_file
+            )
+
+    def __enter__(self):
+        torch.load = self.torch_load_wrapper
+
+        self.torch_fx__symbolic_trace__wrapped_methods_to_patch = (
+            torch.fx._symbolic_trace._wrapped_methods_to_patch
+        )
+        desired_wrapped_methods = copy.deepcopy(
+            torch.fx._symbolic_trace._wrapped_methods_to_patch
+        )
+        if (torch.Tensor, "__getitem__") not in desired_wrapped_methods:
+            # Adding `__getitem__` to the patching list will make tensor indexing traceable via
+            # torch.fx.symbolic_trace. Otherwise, `tensor[x, :, y]` cannot be traced.
+            # This happens because `__getitem__` is neither under torch domain nor an aten operator,
+            # so the patching (or similar Proxy-generating mechanism) doesn't happen automatically.
+            # Note that torch.fx.symbolic_trace defines FX_PATCH_GETITEM environment variable for
+            # enabling the line below for patching.
+            desired_wrapped_methods.append((torch.Tensor, "__getitem__"))
+        torch.fx._symbolic_trace._wrapped_methods_to_patch = desired_wrapped_methods
+
+        if has_safetensors_and_transformers():
+            import safetensors
+            import transformers
+
+            safetensors.torch.load_file = self.safetensors_torch_load_file_wrapper
+            transformers.modeling_utils.safe_load_file = (
+                self.safetensors_torch_load_file_wrapper
+            )
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        torch.load = self.torch_load
+        torch.fx._symbolic_trace._wrapped_methods_to_patch = (
+            self.torch_fx__symbolic_trace__wrapped_methods_to_patch
+        )
+        if has_safetensors_and_transformers():
+            import safetensors
+            import transformers
+
+            safetensors.torch.load_file = self.safetensors_torch_load_file
+            transformers.modeling_utils.safe_load_file = (
+                self.transformers_modeling_utils_safe_load_file
+            )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/registration.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/registration.py
new file mode 100644
index 0000000000000000000000000000000000000000..e855f98f044f6a3cc65d114c8a302bbdea993948
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/registration.py
@@ -0,0 +1,87 @@
+"""Module for handling ATen to ONNX functions registration."""
+
+from __future__ import annotations
+
+import dataclasses
+from typing import TYPE_CHECKING
+
+
+# We can only import onnx from this module in a type-checking context to ensure that
+# 'import torch.onnx' continues to work without having 'onnx' installed. We fully
+# 'import onnx' inside of dynamo_export (by way of _assert_dependencies).
+if TYPE_CHECKING:
+    import types
+
+    import onnxscript  # type: ignore[import]
+
+    import torch._ops
+
+
+@dataclasses.dataclass(frozen=True, eq=True)
+class ONNXFunction:
+    """A wrapper of onnx-script function.
+
+    op_full_name: The qualified name of the function. In the form of '::.'.
+    onnx_function: The onnx-script function from torchlib.
+    is_custom: Whether the function is a custom function.
+    is_complex: Whether the function is a function that handles complex valued inputs.
+
+    """
+
+    onnx_function: onnxscript.OnnxFunction | onnxscript.TracedOnnxFunction
+    op_full_name: str
+    is_custom: bool = False
+    is_complex: bool = False
+
+
+@dataclasses.dataclass(frozen=True, eq=True)
+class OpName:
+    """A class representing an operator name in internal ONNX converter."""
+
+    namespace: str
+    op_name: str
+    overload: str
+
+    @classmethod
+    def from_name_parts(
+        cls, namespace: str, op_name: str, overload: str | None = None
+    ) -> OpName:
+        # NOTE: in PyTorch, the overload could be unprovided to indicate the
+        # default overload
+        if overload is None or overload == "":
+            overload = "default"
+        return cls(namespace, op_name, overload)
+
+    @classmethod
+    def from_qualified_name(cls, qualified_name: str) -> OpName:
+        """When the name is ::[.]"""
+        namespace, opname_overload = qualified_name.split("::")
+        op_name, *overload = opname_overload.split(".", 1)
+        overload = overload[0] if overload else "default"
+        return cls(namespace, op_name, overload)
+
+    @classmethod
+    def from_op_overload(cls, op_overload: torch._ops.OpOverload) -> OpName:
+        return cls.from_qualified_name(op_overload.name())
+
+    @classmethod
+    def from_builtin_function(
+        cls, builtin_function: types.BuiltinFunctionType
+    ) -> OpName:
+        """From a builtin function, e.g. operator.add, math.ceil, etc, get the OpName.
+
+        FX graph uses built-in functions to caculate sympy expression. This function
+        is used to get the OpName from a builtin function.
+
+        Args:
+            builtin_function (types.BuiltinFunctionType): operator.add, math.ceil, etc.
+
+        Returns:
+            OpName: _description_
+        """
+        op = builtin_function.__name__  # add, sub, etc.
+        module = builtin_function.__module__  # _operators or math
+        return cls.from_qualified_name(module + "::" + op)
+
+    def qualified_name(self) -> str:
+        return f"{self.namespace}::{self.op_name}.{self.overload}"
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/serialization.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/serialization.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d01cf01c4ef18b474a7cfe880eecd18df9b92c4
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/serialization.py
@@ -0,0 +1,244 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import io
+import logging
+import os
+from typing import TYPE_CHECKING
+
+import torch
+from torch.onnx import _type_utils as jit_type_utils
+
+
+if TYPE_CHECKING:
+    import onnx
+
+log = logging.getLogger(__name__)
+
+
+def _create_tensor_proto_with_external_data(
+    tensor: torch.Tensor,
+    name: str,
+    location: str,
+    basepath: str,
+    dtype_override: onnx.TypeProto | None = None,  # type: ignore[name-defined]
+) -> onnx.TensorProto:  # type: ignore[name-defined]
+    """Create a TensorProto with external data from a PyTorch tensor.
+    The external data is saved to os.path.join(basepath, location).
+
+    Args:
+        tensor: Tensor to be saved.
+        name: Name of the tensor (i.e., initializer name in ONNX graph).
+        location: Relative location of the external data file
+            (e.g., "/tmp/initializers/weight_0" when model is "/tmp/model_name.onnx").
+        basepath: Base path of the external data file (e.g., "/tmp/external_data" while model must be in "/tmp").
+
+
+    Reference for ONNX's external data format:
+        How to load?
+        https://github.com/onnx/onnx/blob/5dac81ac0707bdf88f56c35c0a5e8855d3534673/onnx/external_data_helper.py#L187
+        How to save?
+        https://github.com/onnx/onnx/blob/5dac81ac0707bdf88f56c35c0a5e8855d3534673/onnx/external_data_helper.py#L43
+        How to set ONNX fields?
+        https://github.com/onnx/onnx/blob/5dac81ac0707bdf88f56c35c0a5e8855d3534673/onnx/external_data_helper.py#L88
+    """
+    # FIXME: Avoid importing onnx into torch.onnx.
+    import onnx
+
+    scalar_type = (
+        jit_type_utils.JitScalarType.from_onnx_type(
+            dtype_override.tensor_type.elem_type
+        )
+        if dtype_override is not None
+        else jit_type_utils.JitScalarType.from_dtype(tensor.dtype)
+    )
+
+    # Checkpoints can be stored with a different dtype as the model expects because
+    # the user script can explicitly cast the original type to something or maybe
+    # PyTorch's type promotion might do it
+    if dtype_override is not None and scalar_type.dtype() != tensor.dtype:
+        tensor = tensor.to(scalar_type.dtype())
+
+    tensor_proto = onnx.TensorProto()  # type: ignore[attr-defined]
+    tensor_proto.name = name
+    tensor_proto.data_type = scalar_type.onnx_type()  # type: ignore[assignment]
+
+    tensor_proto.dims.extend(tensor.shape)
+    tensor_proto.data_location = onnx.TensorProto.EXTERNAL  # type: ignore[attr-defined]
+
+    # Settings for saving one tensor per file.
+    # Offset is zero because there is no other tensor in the same file.
+    key_value_pairs = {
+        "location": location,
+        "offset": 0,
+        "length": tensor.untyped_storage().nbytes(),
+    }
+    for k, v in key_value_pairs.items():
+        entry = tensor_proto.external_data.add()
+        entry.key = k
+        entry.value = str(v)
+
+    # Actual path to write content of tensor.
+    external_data_file_path = os.path.join(basepath, location)
+    if os.path.exists(external_data_file_path):
+        os.remove(external_data_file_path)
+
+    # Create external data's folder if not exists.
+    external_data_dir_path = os.path.dirname(external_data_file_path)
+    if not os.path.exists(external_data_dir_path):
+        # if the demo_folder directory is not present
+        # then create it.
+        os.makedirs(external_data_dir_path)
+
+    # Create a fresh file.
+    with open(external_data_file_path, "xb") as data_file:
+        # No need to call "seek" because offset is 0.
+        # data_file.seek(0)
+        # Write tensor content to the file.
+        data_file.write(tensor.numpy(force=True).tobytes())
+
+    return tensor_proto
+
+
+def _convert_safetensors_to_torch_format(safetensors_file):
+    # It this function is called, safetensors is guaranteed to exist
+    # because the HF model with safetensors was already loaded and exported to ONNX
+    from safetensors import safe_open  # type: ignore[import-not-found]
+
+    tensors = {}
+    with safe_open(safetensors_file, framework="pt", device="cpu") as f:  # type: ignore[attr-defined]
+        for k in f.keys():
+            tensors[k] = f.get_tensor(k).cpu()
+    return tensors
+
+
+# TODO: generalize to allow more checkpoints formats (torch or gguf)
+def save_model_with_external_data(
+    basepath: str,
+    model_location: str,
+    initializer_location: str,
+    torch_state_dicts: tuple[dict | str | io.BytesIO, ...],
+    onnx_model: onnx.ModelProto,  # type: ignore[name-defined]
+    rename_initializer: bool = False,
+) -> None:
+    """Load PyTorch tensors from files and add to "onnx_model" as external initializers.
+
+    Output files:
+        ONNX model file path:
+        ONNX initializer folder: os.path.join(basepath, initializer_location)
+
+    After running this function, you can do
+        ort_sess = onnxruntime.InferenceSession(os.path.join(basepath, model_location))
+    to execute the model.
+
+    Arguments:
+        basepath: Base path of the ONNX external data file (e.g., "/path/to/large_model/").
+        model_location: Relative location of the ONNX model file.
+            E.g., "model.onnx" so that the model file is saved to
+            "/model.onnx".
+        initializer_location: Relative location of the ONNX initializer folder.
+            E.g., "initializers" so that the initializers are saved to
+            "/initializers/".
+            Note: When initializers are >2GB, must be the same as `model_location`.
+        torch_state_dicts: Dictionaries or files which contain PyTorch tensors to be saved
+            as ONNX initializers. For non-dict arguments, `torch.load` will be used to load them from file-like objects.
+        onnx_model: ONNX model to be saved with external initializers.
+            If an input name matches a tensor loaded from "torch_state_dicts",
+            the tensor will be saved as that input's external initializer.
+        rename_initializer: Replaces "." by "_" for all ONNX initializer names.
+            Not needed by the official torch.onnx.dynamo_export. This is a hack
+            for supporting `FXSymbolicTracer` tracer with fake tensor mode.
+            In short, `FXSymbolicTracer` lifts FX parameters (self.linear_weight)
+            as inputs (`def forward(self, linear_weight)`) and therefore, `.` cannot be used.
+    """
+    # FIXME: Avoid importing onnx into torch.onnx.
+    import onnx
+
+    initializers_to_be_deleted = {}  # Using dict because it is **ordered**
+    existing_initializers = {
+        k.name: idx for idx, k in enumerate(onnx_model.graph.initializer)
+    }
+    onnx_input_names = {input.name for input in onnx_model.graph.input}
+    for el in torch_state_dicts:
+        if isinstance(el, dict):
+            # Useful for when state_dict is loaded with torch.load(..., mmap=True, map_location="cpu") by the user
+            # Using torch.save wouldn't leverage mmap, leading to higher memory usage
+            state_dict = el
+        else:
+            if isinstance(el, str) and el.endswith(".safetensors"):
+                state_dict = _convert_safetensors_to_torch_format(el)
+            else:
+                try:
+                    # Loads checkpoint using memory-map on CPU to support really large models
+                    # The underlying torch.UntypedStorage is memory mapped, so state_dict is lazy loaded
+                    state_dict = torch.load(el, map_location="cpu", mmap=True)
+                except (RuntimeError, ValueError) as e:
+                    if "mmap can only be used with files saved with" in str(
+                        e
+                    ) or isinstance(el, io.BytesIO):
+                        log.warning(
+                            "Failed to load the checkpoint with memory-map enabled, retrying without memory-map."
+                            "Consider updating the checkpoint with mmap by using torch.save() on PyTorch version >= 1.6."
+                        )
+                        if isinstance(el, io.BytesIO):
+                            el.seek(0)  # torch.load from `try:` has read the file.
+                        state_dict = torch.load(el, map_location="cpu")
+                    else:
+                        raise e
+
+        for name, tensor in state_dict.items():
+            if rename_initializer:
+                # Basically, "transformer.attention.self.query.weight" is mapped
+                # to "transformer_attention_self_query_weight" for mimicking the
+                # name-modifying code in FX-to-ONNX exporter.
+                # See function _replace_get_attr_with_placeholder for details.
+                name = name.replace(".", "_")
+
+            # This block tries to match the onnx initializer name with torch parameter/buffer
+            #  e.g. A pytorch buffer 'transformer.h.0.attn.bias' can be named 'h.0.attn.bias' in a ONNX initializer
+            # For each PyTorch tensor name loaded by torch.load,
+            #  1.  Search its best match in ONNX model. E.g., the match of
+            #       "transformer_attention_weight" could be "attention_weight".
+            #  2.  Set "tensor" as the initializer of the matched ONNX input.
+            #      E.g., "tensor" is stored as the initializer of "attention_weight".
+            # Step 1 is required because sometimes, tensor names are stored with prefix the dictionary
+            # loaded by torch.load.
+            if name in onnx_input_names:
+                # Same input name shouldn't be matched again
+                onnx_input_names.remove(name)
+            else:
+                for onnx_input_name in onnx_input_names:
+                    if onnx_input_name.endswith(name) or name.endswith(onnx_input_name):
+                        # Find a match. Change name to the matched ONNX input name, so that we
+                        # create initializer with the right ONNX name.
+                        name = onnx_input_name
+                        onnx_input_names.remove(onnx_input_name)
+                        break
+
+            relative_tensor_file_path = os.path.join(initializer_location, name)
+            # Create one file per tensor.
+            # tensor_proto.raw_data is stored to external file at
+            # os.path.join(basepath, relative_tensor_file_path).
+            model_input_types = {k.name: k.type for k in onnx_model.graph.input}
+
+            # Mark for deletion - a replacement will be appended next
+            if name in existing_initializers:
+                initializers_to_be_deleted[existing_initializers[name]] = name
+            tensor_proto = _create_tensor_proto_with_external_data(
+                tensor,
+                name,
+                relative_tensor_file_path,
+                basepath,
+                model_input_types.pop(name, None),
+            )
+            # Add the tensor_proto to the ONNX model as an initializer with external data.
+            onnx_model.graph.initializer.append(tensor_proto)
+    # Remove old duplicated initializers, if any. delete in desc order to not invalidate deletion indices
+    initializers_to_be_deleted = dict(
+        sorted(initializers_to_be_deleted.items(), reverse=True)
+    )
+    for idx in initializers_to_be_deleted.keys():
+        del onnx_model.graph.initializer[idx]
+
+    # model_location should be a pure file name such as "file_name.onnx", not "folder/file_name.onnx".
+    onnx.save(onnx_model, os.path.join(basepath, model_location))  # type: ignore[attr-defined]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/type_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/type_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..74501e865c797120afcc951b47a95c8bdf2ba7c9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/fx/type_utils.py
@@ -0,0 +1,256 @@
+# mypy: allow-untyped-defs
+"""Utilities for converting and operating on ONNX, JIT and torch types."""
+
+from __future__ import annotations
+
+from typing import (
+    Any,
+    Dict,
+    List,
+    Optional,
+    Protocol,
+    runtime_checkable,
+    Tuple,
+    TYPE_CHECKING,
+    Union,
+)
+
+import numpy
+
+import onnx
+
+import torch
+from torch._subclasses import fake_tensor
+
+
+if TYPE_CHECKING:
+    import onnx.defs.OpSchema.AttrType  # type: ignore[import]  # noqa: TCH004
+
+
+# Enable both TorchScriptTensor and torch.Tensor to be tested
+# for dtype in OpSchemaWrapper.
+@runtime_checkable
+class TensorLike(Protocol):
+    @property
+    def dtype(self) -> torch.dtype | None: ...
+
+
+def is_torch_complex_dtype(tensor_dtype: torch.dtype) -> bool:
+    # NOTE: This is needed as TorchScriptTensor is nor supported by torch.is_complex()
+    return tensor_dtype in _COMPLEX_TO_FLOAT
+
+
+def from_complex_to_float(dtype: torch.dtype) -> torch.dtype:
+    return _COMPLEX_TO_FLOAT[dtype]
+
+
+def from_sym_value_to_torch_dtype(sym_value: SYM_VALUE_TYPE) -> torch.dtype:
+    return _SYM_TYPE_TO_TORCH_DTYPE[type(sym_value)]
+
+
+def is_optional_onnx_dtype_str(onnx_type_str: str) -> bool:
+    return onnx_type_str in _OPTIONAL_ONNX_DTYPE_STR
+
+
+def from_torch_dtype_to_onnx_dtype_str(dtype: torch.dtype | type) -> set[str]:
+    return _TORCH_DTYPE_TO_COMPATIBLE_ONNX_TYPE_STRINGS[dtype]
+
+
+def from_python_type_to_onnx_attribute_type(
+    dtype: type, is_sequence: bool = False
+) -> onnx.defs.OpSchema.AttrType | None:
+    import onnx.defs  # type: ignore[import]
+
+    _PYTHON_TYPE_TO_ONNX_ATTRIBUTE_TYPE = {
+        float: onnx.defs.OpSchema.AttrType.FLOAT,
+        int: onnx.defs.OpSchema.AttrType.INT,
+        str: onnx.defs.OpSchema.AttrType.STRING,
+        bool: onnx.defs.OpSchema.AttrType.INT,
+    }
+
+    _SEQUENCE_TYPE_TO_ONNX_ATTRIBUTE_TYPE = {
+        float: onnx.defs.OpSchema.AttrType.FLOATS,
+        int: onnx.defs.OpSchema.AttrType.INTS,
+        str: onnx.defs.OpSchema.AttrType.STRINGS,
+        bool: onnx.defs.OpSchema.AttrType.INTS,
+    }
+
+    if is_sequence:
+        return _SEQUENCE_TYPE_TO_ONNX_ATTRIBUTE_TYPE.get(dtype)
+    return _PYTHON_TYPE_TO_ONNX_ATTRIBUTE_TYPE.get(dtype)
+
+
+def from_python_type_to_onnx_tensor_element_type(type: type):
+    """
+    Converts a Python type to the corresponding ONNX tensor element type.
+    For example, `from_python_type_to_onnx_tensor_element_type(float)` returns
+    `onnx.TensorProto.FLOAT`.
+
+    Args:
+      type (type): The Python type to convert.
+
+    Returns:
+      int: The corresponding ONNX tensor element type.
+
+    """
+    _PYTHON_TYPE_TO_ONNX_TENSOR_ELEMENT_TYPE = {
+        float: onnx.TensorProto.FLOAT,  # type: ignore[attr-defined]
+        int: onnx.TensorProto.INT64,  # type: ignore[attr-defined]
+        bool: onnx.TensorProto.BOOL,  # type: ignore[attr-defined]
+    }
+    return _PYTHON_TYPE_TO_ONNX_TENSOR_ELEMENT_TYPE.get(type)
+
+
+def is_torch_symbolic_type(value: Any) -> bool:
+    return isinstance(value, (torch.SymBool, torch.SymInt, torch.SymFloat))
+
+
+def from_torch_dtype_to_abbr(dtype: torch.dtype | None) -> str:
+    if dtype is None:
+        return ""
+    return _TORCH_DTYPE_TO_ABBREVIATION.get(dtype, "")
+
+
+def from_scalar_type_to_torch_dtype(scalar_type: type) -> torch.dtype | None:
+    return _SCALAR_TYPE_TO_TORCH_DTYPE.get(scalar_type)
+
+
+# NOTE: this is a mapping from torch dtype to a set of compatible onnx types
+# It's used in dispatcher to find the best match overload for the input dtypes
+_TORCH_DTYPE_TO_COMPATIBLE_ONNX_TYPE_STRINGS: dict[torch.dtype | type, set[str]] = {
+    torch.bfloat16: {"tensor(bfloat16)"},
+    torch.bool: {"tensor(bool)"},
+    torch.float64: {"tensor(double)"},
+    torch.float32: {"tensor(float)"},
+    torch.float16: {"tensor(float16)"},
+    torch.float8_e4m3fn: {"tensor(float8_e4m3fn)"},
+    torch.float8_e4m3fnuz: {"tensor(float8_e4m3fnuz)"},
+    torch.float8_e5m2: {"tensor(float8_e5m2)"},
+    torch.float8_e5m2fnuz: {"tensor(float8_e5m2fnuz)"},
+    torch.int16: {"tensor(int16)"},
+    torch.int32: {"tensor(int32)"},
+    torch.int64: {"tensor(int64)"},
+    torch.int8: {"tensor(int8)"},
+    torch.uint8: {"tensor(uint8)"},
+    str: {"tensor(string)"},
+    int: {"tensor(int16)", "tensor(int32)", "tensor(int64)"},
+    float: {"tensor(float16)", "tensor(float)", "tensor(double)"},
+    bool: {"tensor(int32)", "tensor(int64)", "tensor(bool)"},
+    complex: {"tensor(float)", "tensor(double)"},
+    torch.complex32: {"tensor(float16)"},
+    torch.complex64: {"tensor(float)"},
+    torch.complex128: {"tensor(double)"},
+}
+
+_OPTIONAL_ONNX_DTYPE_STR: set[str] = {
+    f"optional({value})"
+    for value_set in _TORCH_DTYPE_TO_COMPATIBLE_ONNX_TYPE_STRINGS.values()
+    for value in value_set
+}
+
+_PYTHON_TYPE_TO_TORCH_DTYPE = {
+    bool: torch.bool,
+    int: torch.int64,
+    float: torch.float32,
+    complex: torch.complex64,
+}
+
+_COMPLEX_TO_FLOAT: dict[torch.dtype, torch.dtype] = {
+    torch.complex32: torch.float16,
+    torch.complex64: torch.float32,
+    torch.complex128: torch.float64,  # NOTE: ORT doesn't support torch.float64
+}
+
+_SYM_TYPE_TO_TORCH_DTYPE = {
+    torch.SymInt: torch.int64,
+    torch.SymFloat: torch.float32,
+    torch.SymBool: torch.bool,
+}
+
+_SCALAR_TYPE_TO_TORCH_DTYPE: dict[type, torch.dtype] = {
+    **_PYTHON_TYPE_TO_TORCH_DTYPE,
+    **_SYM_TYPE_TO_TORCH_DTYPE,  # type: ignore[dict-item]
+}
+
+_TORCH_DTYPE_TO_ABBREVIATION = {
+    torch.bfloat16: "bf16",
+    torch.float64: "f64",
+    torch.float32: "f32",
+    torch.float16: "f16",
+    torch.float8_e4m3fn: "e4m3fn",
+    torch.float8_e4m3fnuz: "e4m3fnuz",
+    torch.float8_e5m2: "f8e5m2",
+    torch.float8_e5m2fnuz: "e5m2fnuz",
+    torch.complex32: "c32",
+    torch.complex64: "c64",
+    torch.complex128: "c128",
+    torch.int8: "i8",
+    torch.int16: "i16",
+    torch.int32: "i32",
+    torch.int64: "i64",
+    torch.bool: "b8",
+    torch.uint8: "u8",
+}
+
+_TORCH_DTYPE_TO_NUMPY_DTYPE = {
+    torch.float16: numpy.float16,
+    torch.float32: numpy.float32,
+    torch.float64: numpy.float64,
+    torch.uint8: numpy.uint8,
+    torch.int8: numpy.int8,
+    torch.int16: numpy.int16,
+    torch.int32: numpy.int32,
+    torch.int64: numpy.longlong,
+    torch.bool: numpy.bool_,
+}
+
+_ONNX_TENSOR_ELEMENT_TYPE_TO_TORCH_DTYPE = {
+    onnx.TensorProto.FLOAT: torch.float32,  # type: ignore[attr-defined]
+    onnx.TensorProto.FLOAT16: torch.float16,  # type: ignore[attr-defined]
+    onnx.TensorProto.FLOAT8E5M2: torch.float8_e5m2,  # type: ignore[attr-defined]
+    onnx.TensorProto.FLOAT8E5M2FNUZ: torch.float8_e5m2fnuz,  # type: ignore[attr-defined]
+    onnx.TensorProto.FLOAT8E4M3FN: torch.float8_e4m3fn,  # type: ignore[attr-defined]
+    onnx.TensorProto.FLOAT8E4M3FNUZ: torch.float8_e4m3fnuz,  # type: ignore[attr-defined]
+    onnx.TensorProto.DOUBLE: torch.float64,  # type: ignore[attr-defined]
+    onnx.TensorProto.BOOL: torch.bool,  # type: ignore[attr-defined]
+    onnx.TensorProto.UINT8: torch.uint8,  # type: ignore[attr-defined]
+    onnx.TensorProto.INT8: torch.int8,  # type: ignore[attr-defined]
+    onnx.TensorProto.INT16: torch.int16,  # type: ignore[attr-defined]
+    onnx.TensorProto.INT32: torch.int32,  # type: ignore[attr-defined]
+    onnx.TensorProto.INT64: torch.int64,  # type: ignore[attr-defined]
+}
+
+_TORCH_DTYPE_TO_ONNX_TENSOR_ELEMENT_TYPE = {
+    value: key for key, value in _ONNX_TENSOR_ELEMENT_TYPE_TO_TORCH_DTYPE.items()
+}
+
+SYM_VALUE_TYPE = Union[torch.SymInt, torch.SymFloat, torch.SymBool]
+META_VALUE_TYPE = Union[fake_tensor.FakeTensor, SYM_VALUE_TYPE, int, float, bool]
+# NOTE: Belows are from torch/fx/node.py
+BaseArgumentTypes = Union[
+    str,
+    int,
+    float,
+    bool,
+    complex,
+    torch.dtype,
+    torch.Tensor,
+    torch.device,
+    torch.memory_format,
+    torch.layout,
+    torch._ops.OpOverload,
+    torch.SymInt,
+    torch.SymFloat,
+    torch.SymBool,
+]
+Argument = Optional[
+    Union[
+        Tuple[Any, ...],  # actually Argument, but mypy can't represent recursive types
+        List[Any],  # actually Argument
+        Dict[str, Any],  # actually Argument
+        slice,  # Slice[Argument, Argument, Argument], but slice is not a templated type in typing
+        range,
+        "torch.fx.Node",
+        BaseArgumentTypes,
+    ]
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/io_adapter.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/io_adapter.py
new file mode 100644
index 0000000000000000000000000000000000000000..16c1313a2d5a2ee19a99c8694de0412935a00ce5
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/io_adapter.py
@@ -0,0 +1,641 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+from typing import (
+    Any,
+    Callable,
+    Mapping,
+    Protocol,
+    runtime_checkable,
+    Sequence,
+    TYPE_CHECKING,
+)
+
+import torch
+import torch.export as torch_export
+from torch.utils import _pytree as pytree
+
+
+if TYPE_CHECKING:
+    import inspect
+
+# TODO(bowbao): Add diagnostics for IO adapters.
+
+
+@runtime_checkable
+class InputAdaptStep(Protocol):
+    """A protocol that defines a step in the input adapting process.
+
+    The input adapting process is a sequence of steps that are applied to the
+    PyTorch model inputs to transform them into the inputs format expected by the
+    exported ONNX model. Each step takes the PyTorch model inputs as arguments and
+    returns the transformed inputs.
+
+    This serves as a base formalized construct for the transformation done to model
+    input signature by any individual component in the exporter.
+    """
+
+    def apply(
+        self,
+        model_args: Sequence[Any],
+        model_kwargs: Mapping[str, Any],
+        model: torch.nn.Module | Callable | torch_export.ExportedProgram | None = None,
+    ) -> tuple[Sequence[Any], Mapping[str, Any]]: ...
+
+
+class InputAdapter:
+    """A class that adapts the PyTorch model inputs to exported ONNX model inputs format."""
+
+    def __init__(self, steps: list[InputAdaptStep] | None = None):
+        self._steps = steps or []
+
+    def append_step(self, step: InputAdaptStep) -> None:
+        """Appends a step to the input adapt steps.
+
+        Args:
+            step: The step to append.
+        """
+        self._steps.append(step)
+
+    def apply(
+        self,
+        *model_args,
+        model: torch.nn.Module | Callable | torch_export.ExportedProgram | None = None,
+        **model_kwargs,
+    ) -> Sequence[int | float | bool | str | torch.Tensor | torch.dtype | None]:
+        """Converts the PyTorch model inputs to exported ONNX model inputs format.
+
+        Args:
+            model_args: The PyTorch model inputs.
+            model: The PyTorch model.
+            model_kwargs: The PyTorch model keyword inputs.
+        Returns:
+            A sequence of tensors converted from PyTorch model inputs.
+        """
+        args: Sequence[Any] = model_args
+        kwargs: Mapping[str, Any] = model_kwargs
+        for step in self._steps:
+            args, kwargs = step.apply(args, kwargs, model=model)
+        assert not kwargs
+        return args
+
+
+@runtime_checkable
+class OutputAdaptStep(Protocol):
+    """A protocol that defines a step in the output adapting process.
+
+    The output adapting process is a sequence of steps that are applied to the
+    PyTorch model outputs to transform them into the outputs format produced by the
+    exported ONNX model. Each step takes the PyTorch model outputs as arguments and
+    returns the transformed outputs.
+
+    This serves as a base formalized construct for the transformation done to model
+    output signature by any individual component in the exporter.
+    """
+
+    def apply(
+        self,
+        model_outputs: Any,
+        model: torch.nn.Module | Callable | torch_export.ExportedProgram | None = None,
+    ) -> Any: ...
+
+
+class OutputAdapter:
+    """A class that adapts the PyTorch model outputs to exported ONNX model outputs format."""
+
+    def __init__(self, steps: list[OutputAdaptStep] | None = None):
+        self._steps = steps or []
+
+    def append_step(self, step: OutputAdaptStep) -> None:
+        """Appends a step to the output format steps.
+
+        Args:
+            step: The step to append.
+        """
+        self._steps.append(step)
+
+    def apply(
+        self,
+        model_outputs: Any,
+        model: torch.nn.Module | Callable | torch_export.ExportedProgram | None = None,
+    ) -> Sequence[torch.Tensor | int | float | bool | str]:
+        """Converts the PyTorch model outputs to exported ONNX model outputs format.
+
+        Args:
+            model_outputs: The PyTorch model outputs.
+            model: The PyTorch model.
+
+        Returns:
+            PyTorch model outputs in exported ONNX model outputs format.
+        """
+        for step in self._steps:
+            model_outputs = step.apply(model_outputs, model=model)
+        return model_outputs
+
+
+# TODO: make_fx lose stack info https://github.com/pytorch/pytorch/issues/90276
+
+
+def _replace_tuple_with_list(spec: pytree.TreeSpec) -> pytree.TreeSpec:
+    _type = list if spec.type == tuple else spec.type
+    return pytree.TreeSpec(
+        _type, spec.context, list(map(_replace_tuple_with_list, spec.children_specs))
+    )
+
+
+def _open_top_level_list_if_single_element(spec: pytree.TreeSpec) -> pytree.TreeSpec:
+    if spec.type == list and spec.num_children == 1:
+        return spec.children_specs[0]
+    return spec
+
+
+def _assert_identical_pytree_spec(
+    spec1: pytree.TreeSpec, spec2: pytree.TreeSpec, error_message: str
+) -> None:
+    """Assert the two `TreeSpec` objects are identical.
+
+    Args:
+        spec1: The first `TreeSpec` object.
+        spec2: The second `TreeSpec` object.
+        error_message: The error message to raise if the two `TreeSpec` objects are not
+            identical.
+
+    Raises:
+        ValueError: If the two `TreeSpec` objects are not identical.
+    """
+    # TODO(bowbao): Turn this check into diagnostic. Consider warning instead of error.
+    pass_if_any_checks: Sequence[Callable[[], bool]] = [
+        lambda: spec1 == spec2,
+        # FIXME: Bug in `dynamo.export`. Sometimes outputs returned in 'list' instead of 'tuple'.
+        lambda: _replace_tuple_with_list(spec1) == _replace_tuple_with_list(spec2),
+        # FIXME: Bug in `dynamo.export`. Sometimes single function return is wrapped in list.
+        lambda: _open_top_level_list_if_single_element(spec1) == spec2,
+        lambda: spec1 == _open_top_level_list_if_single_element(spec2),
+    ]
+
+    if not any(check() for check in pass_if_any_checks):
+        raise ValueError(f"{error_message}\nExpect {spec1}.\nActual {spec2}.")
+
+
+class BindInputStep(InputAdaptStep):
+    """Bind the input arguments to the model signature."""
+
+    def __init__(self, model_signature: inspect.Signature):
+        self._model_signature = model_signature
+
+    def apply(
+        self,
+        model_args: Sequence[Any],
+        model_kwargs: Mapping[str, Any],
+        model: torch.nn.Module | Callable | torch_export.ExportedProgram | None = None,
+    ) -> tuple[Sequence[Any], Mapping[str, Any]]:
+        """Bind the input arguments to the model signature.
+
+        We hope the input kwargs will be mapped to bound.args after binding.
+        If not, we will raise an error.
+
+        Args:
+            model_args: The model args.
+            model_kwargs: The model kwargs.
+            model: The PyTorch model.
+
+        Returns:
+            A tuple of the model args and kwargs. args is always empty.
+
+        Raises:
+            ValueError: If there are keyword-only arguments left after binding args and
+                kwargs to model signature.
+        """
+        bound = self._model_signature.bind(*model_args, **model_kwargs)
+        bound.apply_defaults()
+
+        # keyword-only arguments are not handled.
+        # bound.kwargs only contains keyword-only arguments after calling
+        # bind & apply_defaults, so we raise if it's not empty.
+        if bound.kwargs:
+            raise ValueError("Keyword-only arguments are not supported.")
+        return (), bound.arguments
+
+
+class MergeKwargsIntoArgsInputStep(InputAdaptStep):
+    """Merge the input kwargs into the input args."""
+
+    def apply(
+        self,
+        model_args: Sequence[Any],
+        model_kwargs: Mapping[str, Any],
+        model: torch.nn.Module | Callable | torch_export.ExportedProgram | None = None,
+    ) -> tuple[Sequence[Any], Mapping[str, Any]]:
+        """Merge the input kwargs into the input args.
+
+        Args:
+            model_args: The model args.
+            model_kwargs: The model kwargs.
+            model: The PyTorch model.
+
+        Returns:
+            A tuple of the model args and kwargs. kwargs is always empty.
+        """
+        return tuple(model_args) + tuple(model_kwargs.values()), {}
+
+
+class LiftParametersAndBuffersIntoArgsInputStep(InputAdaptStep):
+    """Append parameters and buffers to model's positional argument list."""
+
+    def __init__(self, inputs: tuple[torch.Tensor, ...]) -> None:
+        self.inputs = inputs
+
+    def apply(
+        self,
+        model_args: Sequence[Any],
+        model_kwargs: Mapping[str, Any],
+        model: torch.nn.Module | Callable | torch_export.ExportedProgram | None = None,
+    ) -> tuple[Sequence[Any], Mapping[str, Any]]:
+        """Append model's parameters and buffers into its input.
+
+        Args:
+            model_args: The model args.
+            model_kwargs: The model kwargs.
+            model: The PyTorch model.
+
+        Returns:
+            A tuple of the model args + appended inputs and kwargs.
+        """
+        return (*model_args, *self.inputs), model_kwargs
+
+
+class ConvertComplexToRealRepresentationInputStep(InputAdaptStep):
+    """Convert complex dtype tensors to real representation tensors.
+
+    ONNX does not support complex dtype tensors. Thus, we convert complex dtype tensors
+    to real representation tensors (i.e., float dtype tensors with an extra dimension
+    representing the real and imaginary parts of the complex number).
+
+    """
+
+    def apply(
+        self,
+        model_args: Sequence[Any],
+        model_kwargs: Mapping[str, Any],
+        model: torch.nn.Module | Callable | torch_export.ExportedProgram | None = None,
+    ) -> tuple[Sequence[Any], Mapping[str, Any]]:
+        """Convert complex tensors to float tensors.
+
+        Args:
+            model_args: The model args.
+            model_kwargs: The model kwargs.
+            model: The PyTorch model.
+
+        Returns:
+            A tuple of the model args and kwargs.
+        """
+        return (
+            tuple(
+                torch.view_as_real(arg.resolve_conj())
+                if isinstance(arg, torch.Tensor) and arg.is_complex()
+                else arg
+                for arg in model_args
+            ),
+            model_kwargs,
+        )
+
+
+class RemoveNoneInputStep(InputAdaptStep):
+    """Remove `None` from arguments.
+
+    This adapt step assumes ``model_kwargs`` is empty. It also assumes ``model_args``
+    is flattened, i.e. it does not check `None` inside nested collections.
+    """
+
+    def apply(
+        self,
+        model_args: Sequence[Any],
+        model_kwargs: Mapping[str, Any],
+        model: torch.nn.Module | Callable | torch_export.ExportedProgram | None = None,
+    ) -> tuple[Sequence[Any], Mapping[str, Any]]:
+        """Remove `None` from arguments.
+
+        Args:
+            model_args: The model args.
+            model_kwargs: The model kwargs.
+            model: The PyTorch model.
+
+        Returns:
+            A tuple of the model args and kwargs.
+
+        Raises:
+            ValueError: If `model_kwargs` is not empty.
+        """
+        assert not model_kwargs
+        return tuple(arg for arg in model_args if arg is not None), {}
+
+
+class RemoveNonTensorInputStep(InputAdaptStep):
+    """Remove the non-tensor input arguments.
+
+    Dynamo does not support non-tensor input arguments (https://github.com/pytorch/pytorch/issues/99534).
+
+    Specifically, it does put the input into graph with an empty node, but consumed by no ones.
+    The concrete value is embedded into the graph as a constant arg of a target node. Meta
+    suggests in this case that one should rewrite the model code to make it tensor if the
+    input value is supposed to change at runtime. We might need to further investigate
+    the feasibility of that suggestion.
+
+    For example,
+
+        def func(x, b=1.0):
+            y = x + b
+            z = y.relu()
+            return (y, z)
+
+        x = torch.randn(1, 1, 2, dtype=torch.float32)
+        gm_fun, _ = dynamo.export(func, x, b=8.0, aten_graph=True, tracing_mode="real")
+
+        # class GraphModule(torch.nn.Module):
+        #     def forward(self, x, b):
+        #         arg0: f32[1, 1, 2], arg1, = fx_pytree.tree_flatten_spec(([x, b], {}), self._in_spec)
+        #         # File: path/to/pytorch/test_constant_input.py:5, code: y = x + b
+        #         add_tensor: f32[1, 1, 2] = torch.ops.aten.add.Tensor(arg0, 8.0);  arg0 = None
+
+        #         # File: path/to/pytorch/test_constant_input.py:6, code: z = y.relu()
+        #         relu_default: f32[1, 1, 2] = torch.ops.aten.relu.default(add_tensor)
+        #         return pytree.tree_unflatten([add_tensor, relu_default], self._out_spec)
+
+    Empty torch.fx.Node input leading to a mismatched number of input with PyTorch, as
+    it's ignored in ONNX graph. Thus, we delete the useless input here.
+
+    """
+
+    def apply(
+        self,
+        model_args: Sequence[Any],
+        model_kwargs: Mapping[str, Any],
+        model: torch.nn.Module | Callable | torch_export.ExportedProgram | None = None,
+    ) -> tuple[Sequence[Any], Mapping[str, Any]]:
+        """Remove Constant from arguments.
+
+        Args:
+            model_args: The model args.
+            model_kwargs: The model kwargs.
+            model: The PyTorch model.
+
+        Returns:
+            A tuple of the model args and kwargs.
+
+        Raises:
+            ValueError: If `model_kwargs` is not empty.
+        """
+        assert not model_kwargs
+        return (
+            tuple(
+                arg
+                for arg in model_args
+                if not isinstance(arg, (int, float, bool, str))
+            ),
+            {},
+        )
+
+
+class FlattenInputWithTreeSpecValidationInputStep(InputAdaptStep):
+    """Flatten nested collection types and return a flat list of elements.
+
+    ONNX can't represent collection types (e.g., dictionary, tuple of tuple of tensor,
+    etc).
+
+    This class stores the `SpecTree` output produced when `adapt` was called the first
+    time. It then validates the `SpecTree` output produced from later `adapt` calls.
+    """
+
+    _spec: pytree.TreeSpec | None = None
+
+    def apply(
+        self,
+        model_args: Sequence[Any],
+        model_kwargs: Mapping[str, Any],
+        model: torch.nn.Module | Callable | torch_export.ExportedProgram | None = None,
+    ) -> tuple[Sequence[Any], Mapping[str, Any]]:
+        """Flatten the model args and kwargs and validate the `SpecTree` output.
+
+        Args:
+            model_args: The model args.
+            model_kwargs: The model kwargs.
+            model: The PyTorch model.
+
+        Returns:
+            A tuple of the flattened model args and kwargs. The kwargs is empty, because
+            they are flattened and merged into the args.
+
+        Raises:
+            ValueError: If the `SpecTree` output produced from the current `model_outputs`
+                is not identical to the `SpecTree` output produced from the first
+                `model_outputs` that was passed to this method.
+        """
+        flattened_args, spec = pytree.tree_flatten((model_args, model_kwargs))
+        if self._spec is None:
+            self._spec = spec
+        else:
+            _assert_identical_pytree_spec(
+                self._spec,
+                spec,
+                error_message="Model inputs incompatible with the format that was exported. ",
+            )
+        return flattened_args, {}
+
+
+class FlattenOutputStep(OutputAdaptStep):
+    """Flatten nested collection types and return a flat list of elements.
+
+    ONNX can't represent collection types (e.g., dictionary, tuple of tuple of tensor,
+    etc).
+
+    NOTE: Ideally we would want to use ``FlattenOutputWithTreeSpecValidationOutputStep``, such
+    that `SpecTree` can be validate for new model outputs. However, this is not possible
+    currently because we never have access to real PyTorch model outputs during export.
+    Only traced outputs may be available, but they are not an accurate reflection of the
+    original PyTorch model outputs format as they are typically in their own unique format,
+    depending on the tracing strategy.
+    """
+
+    def apply(
+        self,
+        model_outputs: Any,
+        model: torch.nn.Module | Callable | torch_export.ExportedProgram | None = None,
+    ) -> Sequence[Any]:
+        """Flatten the model outputs.
+
+        Args:
+            model_outputs: The model outputs to flatten.
+            model: The PyTorch model.
+
+        Returns:
+            A tuple of the flattened model outputs.
+        """
+        return pytree.tree_leaves(model_outputs)
+
+
+class ConvertComplexToRealRepresentationOutputStep(OutputAdaptStep):
+    """Convert complex dtype tensors to real representation tensors.
+
+    ONNX does not support complex dtype tensors. Thus, we convert complex dtype tensors
+    to real representation tensors (i.e., float dtype tensors with an extra dimension
+    representing the real and imaginary parts of the complex number).
+
+    """
+
+    def apply(
+        self,
+        model_outputs: Any,
+        model: torch.nn.Module | Callable | torch_export.ExportedProgram | None = None,
+    ) -> Any:
+        """Convert float tensors to complex tensors.
+
+        Args:
+            model_output: The model output.
+            model: The PyTorch model.
+
+        Returns:
+            A tuple of the model output.
+        """
+        return [
+            torch.view_as_real(output.resolve_conj())
+            if isinstance(output, torch.Tensor) and torch.is_complex(output)
+            else output
+            for output in model_outputs
+        ]
+
+
+class FlattenOutputWithTreeSpecValidationOutputStep(OutputAdaptStep):
+    """Same as ``FlattenOutputStep``, with additional `TreeSpec` validation.
+
+    This class stores the `SpecTree` output produced when `adapt` was called the first
+    time. It then validates the `SpecTree` output produced from later `adapt` calls.
+    """
+
+    _spec: pytree.TreeSpec | None = None
+
+    def apply(
+        self,
+        model_outputs: Any,
+        model: torch.nn.Module | Callable | torch_export.ExportedProgram | None = None,
+    ) -> Sequence[Any]:
+        """Flatten the model outputs and validate the `SpecTree` output.
+
+        Args:
+            model_outputs: The model outputs to flatten.
+            model: The PyTorch model.
+
+        Returns:
+            flattened_outputs: The flattened model outputs.
+
+        Raises:
+            ValueError: If the `SpecTree` output produced from the current `model_outputs`
+                is not identical to the `SpecTree` output produced from the first
+                `model_outputs` that was passed to this method.
+        """
+        flattened_outputs, spec = pytree.tree_flatten(model_outputs)
+        if self._spec is None:
+            self._spec = spec
+        else:
+            _assert_identical_pytree_spec(
+                self._spec,
+                spec,
+                error_message="Model outputs incompatible with the format that was exported. ",
+            )
+        return flattened_outputs
+
+
+class PrependParamsBuffersConstantAotAutogradInputStep(InputAdaptStep):
+    """Prepend model parameters, buffers and constants to the user input.
+
+    :func:`torch.export.export` lifts model parameters, buffers and constants as model input, thus, they
+    must be added to the user input before the model is executed.
+
+    Args:
+        model: The PyTorch model with embedded parameters and buffers.
+    """
+
+    def apply(
+        self,
+        model_args: Sequence[Any],
+        model_kwargs: Mapping[str, Any],
+        model: torch.nn.Module | Callable | torch_export.ExportedProgram | None = None,
+    ) -> tuple[Sequence[Any], Mapping[str, Any]]:
+        """Convert complex tensors to float tensors.
+
+        Args:
+            model_args: The model args.
+            model_kwargs: The model kwargs.
+            model: The PyTorch model.
+
+        Returns:
+            A tuple of the model args and kwargs.
+        """
+        ordered_params = tuple(
+            model.state_dict[name]  # type: ignore[union-attr,index]
+            for name in model.graph_signature.parameters  # type: ignore[union-attr]
+        )
+        non_persistent_buffers = set(model.graph_signature.non_persistent_buffers)  # type: ignore[union-attr]
+        ordered_buffers = []
+        for name in model.graph_signature.buffers:  # type: ignore[union-attr]
+            if name in non_persistent_buffers:
+                ordered_buffers.append(model.constants[name])  # type: ignore[union-attr]
+            else:
+                ordered_buffers.append(model.state_dict[name])  # type: ignore[union-attr,index]
+        ordered_constant_tensors = tuple(
+            model.constants[fqn]  # type: ignore[union-attr,index]
+            for fqn in model.graph_signature.lifted_tensor_constants  # type: ignore[union-attr]
+        )
+
+        # NOTE: calling convention is first params, then buffers, then args as user supplied them.
+        # See: torch/_functorch/aot_autograd.py#L1034
+        updated_args = (
+            *ordered_params,
+            *ordered_buffers,
+            *ordered_constant_tensors,
+            *model_args,
+        )
+        if model_kwargs:
+            return MergeKwargsIntoArgsInputStep().apply(
+                updated_args, model_kwargs, model=model
+            )
+        return updated_args, {}
+
+
+class PrependParamsAndBuffersAotAutogradOutputStep(OutputAdaptStep):
+    """Prepend model's mutated buffers to the user output.
+
+    :func:`torch.export.export` lifts model's mutated buffers as outputs, thus, they
+    must be added to the user output after the model is executed.
+
+    Args:
+        model: The PyTorch model with mutated buffers.
+    """
+
+    def apply(
+        self,
+        model_outputs: Any,
+        model: torch.nn.Module | Callable | torch_export.ExportedProgram | None = None,
+    ) -> Sequence[Any]:
+        """Flatten the model outputs and validate the `SpecTree` output.
+
+        Args:
+            model_outputs: The model outputs to flatten.
+            model: The PyTorch model.
+
+        Returns:
+            flattened_outputs: The flattened model outputs.
+        """
+
+        assert isinstance(
+            model, torch_export.ExportedProgram
+        ), "'model' must be torch_export.ExportedProgram"
+        ordered_buffers = tuple(
+            model.state_dict[name]
+            if name in model.state_dict
+            else model.constants[name]
+            for name in model.graph_signature.buffers_to_mutate.values()
+        )
+
+        # NOTE: calling convention is first mutated buffers, then outputs args as model returned them.
+        updated_outputs = (*ordered_buffers, *model_outputs)
+        return updated_outputs
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/jit_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/jit_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec5dc4b96200364fc84e832c365676560cee6400
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/jit_utils.py
@@ -0,0 +1,373 @@
+# mypy: allow-untyped-defs
+"""Utilities for manipulating the torch.Graph object and the torchscript."""
+
+# TODO(justinchuby): Move more of the symbolic helper functions here and expose
+# them to the user.
+
+from __future__ import annotations
+
+import dataclasses
+import re
+import typing
+from typing import Any, Iterable, Sequence
+
+import torch
+from torch import _C
+from torch.onnx._globals import GLOBALS
+from torch.onnx._internal import registration
+
+
+_ATTR_PATTERN = re.compile("^(.+)_(([ifstgz])|(ty))$")
+_SKIP_NODE_ATTRIBUTES = {"inplace", "aten"}
+
+
+@dataclasses.dataclass
+class GraphContext:
+    """Extra context for symbolic functions with all methods from torch.Graph.
+
+    NOTE: This class is not meant for external consumption. Please do not depend on
+    it outside of torch.onnx as the interface may evolve.
+
+    Attributes:
+        graph: The _C.Graph being constructed.
+        block: The current _C.Block being constructed.
+        opset: The opset version.
+        original_node: Current node that is being converted from.
+        params_dict: Mapping from graph initializer name to IValue.
+        env: Mapping from Torch domain graph Value to ONNX domain graph Value.
+        values_in_env: Set of all values in env, for constant-time lookups.
+        new_nodes: List that tracks all new nodes that are added (used to make
+            sure metadata is propagated to all new nodes).
+    """
+
+    graph: _C.Graph
+    block: _C.Block
+    opset: int
+    original_node: _C.Node
+    params_dict: dict[str, _C.IValue]
+    env: dict[_C.Value, _C.Value]
+    values_in_env: set[_C.Value]
+    new_nodes: list[_C.Node] = dataclasses.field(default_factory=list)
+
+    # Relay methods from _C.Graph for compatibility with symbolic functions that expect
+    # a _C.Graph
+    def __getattr__(self, name: str) -> Any:
+        return getattr(self.graph, name)
+
+    def op(
+        self,
+        opname: str,
+        *raw_args: torch.Tensor | _C.Value,
+        outputs: int = 1,
+        **kwargs,
+    ):
+        """Creates an ONNX operator "opname", taking "raw_args" as inputs and "kwargs" as attributes.
+
+        The set of operators and the inputs/attributes they take
+        is documented at https://github.com/onnx/onnx/blob/master/docs/Operators.md
+
+        Args:
+            opname: The ONNX operator name, e.g., `Abs` or `Add`, or an operator qualified
+                with a namespace, e.g., `aten::add`.
+            raw_args: The inputs to the operator; usually provided
+                as arguments to the `symbolic` definition.
+            outputs: The number of outputs this operator returns.
+                By default an operator is assumed to return a single output.
+                If `outputs` is greater than one, this functions returns a tuple
+                of output `Value`, representing each output of the ONNX operator
+                in order.
+            kwargs: The attributes of the ONNX operator, whose keys are named
+                according to the following convention: `alpha_f` indicates
+                the `alpha` attribute with type `f`.  The valid type specifiers are
+                `f` (float), `i` (int), `s` (string) or `t` (Tensor).  An attribute
+                specified with type float accepts either a single float, or a
+                list of floats (e.g., you would say `dims_i` for a `dims` attribute
+                that takes a list of integers).
+
+        Returns:
+            The value representing the single output of this operator (see the `outputs`
+            keyword argument for multi-return nodes).
+        """
+        # FIXME(justinchuby): Add the return type back once we know how to handle mypy
+        return _add_op(self, opname, *raw_args, outputs=outputs, **kwargs)
+
+    def aten_op(self, operator: str, *args, overload_name: str = "", **kwargs):
+        """Generates an ONNX ATen op node.
+
+        This function is for backward compatibility with the old symbolic functions.
+        """
+        return self.op(
+            "aten::ATen",
+            *args,
+            operator_s=operator,
+            overload_name_s=overload_name,
+            **kwargs,
+        )
+
+    # NOTE: For backward compatibility with the old symbolic functions.
+    # We are probably going to remove this only after the fx exporter is established.
+    at = aten_op
+
+    def onnxscript_op(
+        self,
+        onnx_fn,
+        *raw_args: torch.Tensor | _C.Value,
+        outputs: int = 1,
+        **kwargs,
+    ):
+        """Creates an ONNX operator from onnx-script function, taking "raw_args" as inputs and "kwargs" as attributes.
+
+        onnx-script repository: https://github.com/microsoft/onnx-script
+
+        Args:
+            onnx_fn: ONNXFunction from onnx-script; An example can be found at
+                https://github.com/microsoft/onnx-script#example
+            raw_args: The inputs to the operator; usually provided
+                as arguments to the `symbolic` definition.
+            outputs: The number of outputs this operator returns.
+                By default an operator is assumed to return a single output.
+                If `outputs` is greater than one, this functions returns a tuple
+                of output `Value`, representing each output of the ONNX operator
+                in order.
+            kwargs: The attributes of the ONNX operator, whose keys are named
+                according to the following convention: `alpha_f` indicates
+                the `alpha` attribute with type `f`.  The valid type specifiers are
+                `f` (float), `i` (int), `s` (string) or `t` (Tensor).  An attribute
+                specified with type float accepts either a single float, or a
+                list of floats (e.g., you would say `dims_i` for a `dims` attribute
+                that takes a list of integers).
+
+        Returns:
+            The value representing the single output of this operator (see the `outputs`
+            keyword argument for multi-return nodes).
+        """
+        # NOTE(titaiwang): This is using class attributes, and it needs to be updated
+        # if onnx-script makes any change on these.
+        symbolic_name = f"{onnx_fn.opset.domain}::{onnx_fn.name}"
+        opset_version = onnx_fn.opset.version
+
+        registration.custom_onnx_symbolic(symbolic_name, opset_version)(onnx_fn)
+
+        return _add_op(self, symbolic_name, *raw_args, outputs=outputs, **kwargs)
+
+
+def add_op_with_blocks(
+    graph_context: GraphContext,
+    opname: str,
+    *inputs: _C.Value,
+    outputs: int = 1,
+    n_blocks: int = 1,
+    **attributes,
+) -> tuple[Any, tuple[GraphContext, ...], _C.Node]:
+    """Creates an ONNX operator "opname", taking inputs and attributes.
+
+    Args:
+        graph_context: The context for the current graph.
+        opname: The ONNX operator name, e.g., `Abs` or `Add`, or an operator qualified
+            with a namespace, e.g., `aten::add`.
+        inputs: The inputs to the operator.
+        outputs: The number of outputs this operator returns.
+            By default an operator is assumed to return a single output.
+            If `outputs` is greater than one, this functions returns a tuple
+            of output `Value`, representing each output of the ONNX operator
+            in order.
+        n_blocks: The number of sub-blocks to create in the node.
+        attributes: The attributes of the ONNX operator.
+
+    Returns:
+        A tuple of (output_values, new_contexts, node) where:
+            output_values: One or more output value of this operator
+                (see the `outputs` keyword argument for multi-return nodes).
+            new_contexts: A tuple of new graph contexts for each sub-block.
+            node: The node representing the operator.
+    """
+
+    output_values = graph_context.op(opname, *inputs, outputs=outputs, **attributes)
+    if isinstance(output_values, Sequence):
+        node = output_values[0].node()
+    else:
+        node = output_values.node()
+
+    new_contexts = []
+    for _ in range(n_blocks):
+        new_block = node.addBlock()
+        # Create shallow copy of the graph context and update the block
+        new_context = dataclasses.replace(graph_context, block=new_block)
+        new_contexts.append(new_context)
+
+    return output_values, tuple(new_contexts), node
+
+
+def _add_op(
+    graph_context: GraphContext,
+    opname: str,
+    *args: torch.Tensor | _C.Value,
+    outputs: int = 1,
+    **kwargs,
+):
+    """Creates an ONNX operator "opname", taking "args" as inputs and attributes "kwargs".
+
+    The set of operators and the inputs/attributes they take
+    is documented at https://github.com/onnx/onnx/blob/master/docs/Operators.md
+
+    This function is monkey-patched onto Graph.
+
+    Args:
+        graph_context: The Torch Graph or Block.
+        opname: The ONNX operator name, e.g., `Abs` or `Add`, or an operator qualified
+            with a namespace, e.g., `aten::add`.
+        args: The inputs to the operator; usually provided
+            as arguments to the `symbolic` definition.
+        outputs: The number of outputs this operator returns.
+            By default an operator is assumed to return a single output.
+            If `outputs` is greater than one, this functions returns a tuple
+            of output `Value`, representing each output of the ONNX operator
+            in order.
+        kwargs: The attributes of the ONNX operator, whose keys are named
+            according to the following convention: `alpha_f` indicates
+            the `alpha` attribute with type `f`.  The valid type specifiers are
+            `f` (float), `i` (int), `s` (string) or `t` (Tensor).  An attribute
+            specified with type float accepts either a single float, or a
+            list of floats (e.g., you would say `dims_i` for a `dims` attribute
+            that takes a list of integers).
+
+    Returns:
+        (Union[_C.Value, Tuple[_C.Value, ...]])
+        The value representing the single output of this operator (see the `outputs`
+        keyword argument for multi-return nodes).
+    """
+    inputs = [_const_if_tensor(graph_context, arg) for arg in args]
+    # Filter out None attributes, this can be convenient client side because
+    # now they can pass through None attributes, and have them not show up
+    attributes = {k: v for k, v in kwargs.items() if v is not None}
+
+    if "::" not in opname:
+        opname = "onnx::" + opname
+
+    node = _create_node(
+        graph_context.block,
+        opname,
+        inputs,
+        attributes,
+        params_dict=graph_context.params_dict,
+        opset_version=graph_context.opset,
+        n_outputs=outputs,
+        shape_inference=GLOBALS.onnx_shape_inference,
+    )
+    graph_context.new_nodes.append(node)
+
+    if outputs == 1:
+        return node.output()
+    return tuple(node.outputs())
+
+
+def _const_if_tensor(graph_context: GraphContext, arg):
+    if arg is None:
+        return arg
+    if isinstance(arg, _C.Value):
+        return arg
+
+    return _add_op(graph_context, "onnx::Constant", value_z=arg)
+
+
+def _create_node(
+    graph_or_block: _C.Graph | _C.Block,
+    domain_op: str,
+    inputs: Sequence,
+    attributes: dict,
+    params_dict: dict,
+    opset_version: int,
+    n_outputs: int,
+    shape_inference: bool = True,
+) -> _C.Node:
+    """Creates an node 'domain_op', taking inputs and attributes."""
+    if isinstance(graph_or_block, _C.Graph):
+        graph = graph_or_block
+        node = graph.create(domain_op, inputs, n_outputs)
+        node = graph.insertNode(node)
+    elif isinstance(graph_or_block, _C.Block):
+        block = graph_or_block
+        node = block.addNode(domain_op, inputs)
+
+        # Block does not have create defined, so we need to add outputs manually
+        if n_outputs > 1:
+            for _ in range(1, n_outputs):
+                node.addOutput()
+
+    node_outputs = tuple(node.outputs())  # type: ignore[possibly-undefined]
+    assert len(node_outputs) == n_outputs
+
+    aten = domain_op.startswith("aten::")
+
+    # Add all attributes
+    for key, value in sorted(attributes.items()):
+        if key in _SKIP_NODE_ATTRIBUTES:
+            continue
+        _add_attribute(node, key, value, aten=aten)
+    if shape_inference:
+        _C._jit_pass_onnx_node_shape_type_inference(node, params_dict, opset_version)
+    return node
+
+
+def _is_onnx_list(value):
+    return isinstance(value, Iterable) and not isinstance(
+        value, (str, bytes, torch.Tensor)
+    )
+
+
+def _scalar(x: torch.Tensor):
+    """Convert a scalar tensor into a Python value."""
+    assert x.numel() == 1
+    return x[0]
+
+
+def _add_attribute(node: _C.Node, key: str, value: Any, aten: bool):
+    r"""Initializes the right attribute based on type of value."""
+    m = _ATTR_PATTERN.match(key)
+    if m is None:
+        raise ValueError(
+            f"Invalid attribute specifier '{key}' names "
+            "must be suffixed with type, e.g. 'dim_i' or 'dims_i'"
+        )
+    name, kind = m.group(1), m.group(2)
+    if _is_onnx_list(value):
+        kind += "s"
+
+    return getattr(node, f"{kind}_")(name, value)
+
+
+# TODO: Expose this to user when migrating symbolic helper functions to here.
+def _is_tensor(x: _C.Value) -> bool:
+    return x.type().isSubtypeOf(_C.TensorType.get())
+
+
+def get_device_from_value(value: _C.Value) -> torch.device | None:
+    if not _is_tensor(value):
+        return None
+    tensor_type = typing.cast(_C.TensorType, value.type())
+    return tensor_type.device()
+
+
+def parse_node_kind(kind: str) -> tuple[str, str]:
+    """Parse node kind into domain and Op name."""
+    if "::" not in kind:
+        raise ValueError(f"Node kind: {kind} is invalid. '::' is not in node kind.")
+    domain, opname = kind.split("::", 1)
+    if "::" in opname:
+        raise ValueError(f"Node kind: {kind} is invalid. '::' should only apear once.")
+    return domain, opname
+
+
+def is_aten(domain: str) -> bool:
+    """Check if the domain is official."""
+    return domain == "aten"
+
+
+def is_prim(domain: str) -> bool:
+    """Check if the domain is official."""
+    return domain == "prim"
+
+
+def is_onnx(domain: str) -> bool:
+    """Check if the domain is official."""
+    return domain == "onnx"
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/onnx_proto_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/onnx_proto_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5fc181b180824a28ad8516d56e3a09512cae3031
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/onnx_proto_utils.py
@@ -0,0 +1,283 @@
+# mypy: allow-untyped-defs
+"""Utilities for manipulating the onnx and onnx-script dependencies and ONNX proto."""
+
+from __future__ import annotations
+
+import glob
+import io
+import os
+import shutil
+import zipfile
+from typing import Any, Mapping
+
+import torch
+import torch.jit._trace
+import torch.serialization
+from torch.onnx import _constants, _exporter_states, errors
+from torch.onnx._internal import jit_utils, registration
+
+
+def export_as_test_case(
+    model_bytes: bytes, inputs_data, outputs_data, name: str, dir: str
+) -> str:
+    """Export an ONNX model as a self contained ONNX test case.
+
+    The test case contains the model and the inputs/outputs data. The directory structure
+    is as follows:
+
+    dir
+    \u251c\u2500\u2500 test_
+    \u2502   \u251c\u2500\u2500 model.onnx
+    \u2502   \u2514\u2500\u2500 test_data_set_0
+    \u2502       \u251c\u2500\u2500 input_0.pb
+    \u2502       \u251c\u2500\u2500 input_1.pb
+    \u2502       \u251c\u2500\u2500 output_0.pb
+    \u2502       \u2514\u2500\u2500 output_1.pb
+
+    Args:
+        model_bytes: The ONNX model in bytes.
+        inputs_data: The inputs data, nested data structure of numpy.ndarray.
+        outputs_data: The outputs data, nested data structure of numpy.ndarray.
+
+    Returns:
+        The path to the test case directory.
+    """
+    try:
+        import onnx
+    except ImportError as exc:
+        raise ImportError(
+            "Export test case to ONNX format failed: Please install ONNX."
+        ) from exc
+
+    test_case_dir = os.path.join(dir, "test_" + name)
+    os.makedirs(test_case_dir, exist_ok=True)
+    _export_file(
+        model_bytes,
+        os.path.join(test_case_dir, "model.onnx"),
+        _exporter_states.ExportTypes.PROTOBUF_FILE,
+        {},
+    )
+    data_set_dir = os.path.join(test_case_dir, "test_data_set_0")
+    if os.path.exists(data_set_dir):
+        shutil.rmtree(data_set_dir)
+    os.makedirs(data_set_dir)
+
+    proto = onnx.load_model_from_string(model_bytes)  # type: ignore[attr-defined]
+
+    for i, (input_proto, input) in enumerate(zip(proto.graph.input, inputs_data)):
+        export_data(input, input_proto, os.path.join(data_set_dir, f"input_{i}.pb"))
+    for i, (output_proto, output) in enumerate(zip(proto.graph.output, outputs_data)):
+        export_data(output, output_proto, os.path.join(data_set_dir, f"output_{i}.pb"))
+
+    return test_case_dir
+
+
+def load_test_case(dir: str) -> tuple[bytes, Any, Any]:
+    """Load a self contained ONNX test case from a directory.
+
+    The test case must contain the model and the inputs/outputs data. The directory structure
+    should be as follows:
+
+    dir
+    \u251c\u2500\u2500 test_
+    \u2502   \u251c\u2500\u2500 model.onnx
+    \u2502   \u2514\u2500\u2500 test_data_set_0
+    \u2502       \u251c\u2500\u2500 input_0.pb
+    \u2502       \u251c\u2500\u2500 input_1.pb
+    \u2502       \u251c\u2500\u2500 output_0.pb
+    \u2502       \u2514\u2500\u2500 output_1.pb
+
+    Args:
+        dir: The directory containing the test case.
+
+    Returns:
+        model_bytes: The ONNX model in bytes.
+        inputs: the inputs data, mapping from input name to numpy.ndarray.
+        outputs: the outputs data, mapping from output name to numpy.ndarray.
+    """
+    try:
+        import onnx
+        from onnx import numpy_helper  # type: ignore[attr-defined]
+    except ImportError as exc:
+        raise ImportError(
+            "Load test case from ONNX format failed: Please install ONNX."
+        ) from exc
+
+    with open(os.path.join(dir, "model.onnx"), "rb") as f:
+        model_bytes = f.read()
+
+    test_data_dir = os.path.join(dir, "test_data_set_0")
+
+    inputs = {}
+    input_files = glob.glob(os.path.join(test_data_dir, "input_*.pb"))
+    for input_file in input_files:
+        tensor = onnx.load_tensor(input_file)  # type: ignore[attr-defined]
+        inputs[tensor.name] = numpy_helper.to_array(tensor)
+    outputs = {}
+    output_files = glob.glob(os.path.join(test_data_dir, "output_*.pb"))
+    for output_file in output_files:
+        tensor = onnx.load_tensor(output_file)  # type: ignore[attr-defined]
+        outputs[tensor.name] = numpy_helper.to_array(tensor)
+
+    return model_bytes, inputs, outputs
+
+
+def export_data(data, value_info_proto, f: str) -> None:
+    """Export data to ONNX protobuf format.
+
+    Args:
+        data: The data to export, nested data structure of numpy.ndarray.
+        value_info_proto: The ValueInfoProto of the data. The type of the ValueInfoProto
+            determines how the data is stored.
+        f: The file to write the data to.
+    """
+    try:
+        from onnx import numpy_helper  # type: ignore[attr-defined]
+    except ImportError as exc:
+        raise ImportError(
+            "Export data to ONNX format failed: Please install ONNX."
+        ) from exc
+
+    with open(f, "wb") as opened_file:
+        if value_info_proto.type.HasField("map_type"):
+            opened_file.write(
+                numpy_helper.from_dict(data, value_info_proto.name).SerializeToString()
+            )
+        elif value_info_proto.type.HasField("sequence_type"):
+            opened_file.write(
+                numpy_helper.from_list(data, value_info_proto.name).SerializeToString()
+            )
+        elif value_info_proto.type.HasField("optional_type"):
+            opened_file.write(
+                numpy_helper.from_optional(
+                    data, value_info_proto.name
+                ).SerializeToString()
+            )
+        else:
+            assert value_info_proto.type.HasField("tensor_type")
+            opened_file.write(
+                numpy_helper.from_array(data, value_info_proto.name).SerializeToString()
+            )
+
+
+def _export_file(
+    model_bytes: bytes,
+    f: io.BytesIO | str,
+    export_type: str,
+    export_map: Mapping[str, bytes],
+) -> None:
+    """export/write model bytes into directory/protobuf/zip"""
+    if export_type == _exporter_states.ExportTypes.PROTOBUF_FILE:
+        assert len(export_map) == 0
+        with torch.serialization._open_file_like(f, "wb") as opened_file:
+            opened_file.write(model_bytes)
+    elif export_type in {
+        _exporter_states.ExportTypes.ZIP_ARCHIVE,
+        _exporter_states.ExportTypes.COMPRESSED_ZIP_ARCHIVE,
+    }:
+        compression = (
+            zipfile.ZIP_DEFLATED
+            if export_type == _exporter_states.ExportTypes.COMPRESSED_ZIP_ARCHIVE
+            else zipfile.ZIP_STORED
+        )
+        with zipfile.ZipFile(f, "w", compression=compression) as z:
+            z.writestr(_constants.ONNX_ARCHIVE_MODEL_PROTO_NAME, model_bytes)
+            for k, v in export_map.items():
+                z.writestr(k, v)
+    elif export_type == _exporter_states.ExportTypes.DIRECTORY:
+        if isinstance(f, io.BytesIO) or not os.path.isdir(f):  # type: ignore[arg-type]
+            raise ValueError(
+                f"f should be directory when export_type is set to DIRECTORY, instead get type(f): {type(f)}"
+            )
+        if not os.path.exists(f):  # type: ignore[arg-type]
+            os.makedirs(f)  # type: ignore[arg-type]
+
+        model_proto_file = os.path.join(f, _constants.ONNX_ARCHIVE_MODEL_PROTO_NAME)  # type: ignore[arg-type]
+        with torch.serialization._open_file_like(model_proto_file, "wb") as opened_file:
+            opened_file.write(model_bytes)
+
+        for k, v in export_map.items():
+            weight_proto_file = os.path.join(f, k)  # type: ignore[arg-type]
+            with torch.serialization._open_file_like(
+                weight_proto_file, "wb"
+            ) as opened_file:
+                opened_file.write(v)
+    else:
+        raise ValueError("Unknown export type")
+
+
+def _add_onnxscript_fn(
+    model_bytes: bytes,
+    custom_opsets: Mapping[str, int],
+) -> bytes:
+    """Insert model-included custom onnx-script function into ModelProto"""
+    try:
+        import onnx
+    except ImportError as e:
+        raise errors.OnnxExporterError("Module onnx is not installed!") from e
+
+    # For > 2GB model, onnx.load_fromstring would fail. However, because
+    # in _export_onnx, the tensors should be saved separately if the proto
+    # size > 2GB, and if it for some reason did not, the model would fail on
+    # serialization anyway in terms of the protobuf limitation. So we don't
+    # need to worry about > 2GB model getting here.
+    model_proto = onnx.load_model_from_string(model_bytes)  # type: ignore[attr-defined]
+
+    # Iterate graph nodes to insert only the included custom
+    # function_proto into model_proto
+    onnx_function_list = []  # type: ignore[var-annotated]
+    included_node_func: set[str] = set()
+    # onnx_function_list and included_node_func are expanded in-place
+    _find_onnxscript_op(
+        model_proto.graph, included_node_func, custom_opsets, onnx_function_list
+    )
+
+    if onnx_function_list:
+        model_proto.functions.extend(onnx_function_list)
+        model_bytes = model_proto.SerializeToString()
+    return model_bytes
+
+
+def _find_onnxscript_op(
+    graph_proto,
+    included_node_func: set[str],
+    custom_opsets: Mapping[str, int],
+    onnx_function_list: list,
+):
+    """Recursively iterate ModelProto to find ONNXFunction op as it may contain control flow Op."""
+    for node in graph_proto.node:
+        node_kind = node.domain + "::" + node.op_type
+        # Recursive needed for control flow nodes: IF/Loop which has inner graph_proto
+        for attr in node.attribute:
+            if attr.g is not None:
+                _find_onnxscript_op(
+                    attr.g, included_node_func, custom_opsets, onnx_function_list
+                )
+        # Only custom Op with ONNX function and aten with symbolic_fn should be found in registry
+        onnx_function_group = registration.registry.get_function_group(node_kind)
+        # Ruled out corner cases: onnx/prim in registry
+        if (
+            node.domain
+            and not jit_utils.is_aten(node.domain)
+            and not jit_utils.is_prim(node.domain)
+            and not jit_utils.is_onnx(node.domain)
+            and onnx_function_group is not None
+            and node_kind not in included_node_func
+        ):
+            specified_version = custom_opsets.get(node.domain, 1)
+            onnx_fn = onnx_function_group.get(specified_version)
+            if onnx_fn is not None:
+                if hasattr(onnx_fn, "to_function_proto"):
+                    onnx_function_proto = onnx_fn.to_function_proto()  # type: ignore[attr-defined]
+                    onnx_function_list.append(onnx_function_proto)
+                    included_node_func.add(node_kind)
+                continue
+
+            raise errors.UnsupportedOperatorError(
+                node_kind,
+                specified_version,
+                onnx_function_group.get_min_supported()
+                if onnx_function_group
+                else None,
+            )
+    return onnx_function_list, included_node_func
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/onnxruntime.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/onnxruntime.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a8fd4d4fefaf371964613adcd2e0cc4f231e8a0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/onnxruntime.py
@@ -0,0 +1,1260 @@
+# mypy: allow-untyped-defs
+import dataclasses
+import importlib
+import logging
+import os
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Final,
+    List,
+    Mapping,
+    Optional,
+    Sequence,
+    Set,
+    Tuple,
+    TYPE_CHECKING,
+    Union,
+)
+from typing_extensions import TypeAlias
+
+import torch
+import torch._C
+import torch._ops
+import torch._prims.executor
+import torch.fx
+from torch._subclasses.fake_tensor import FakeTensor
+from torch.fx._compatibility import compatibility
+from torch.fx.passes.fake_tensor_prop import FakeTensorProp
+from torch.fx.passes.operator_support import OperatorSupport
+from torch.fx.passes.tools_common import CALLABLE_NODE_OPS
+from torch.utils import _pytree
+
+
+if TYPE_CHECKING:
+    import onnx
+    import onnxruntime
+    from onnxruntime.capi import _pybind_state as ORTC
+
+    import torch.onnx
+    import torch.onnx._internal
+    import torch.onnx._internal._exporter_legacy
+    import torch.onnx._internal.diagnostics
+    import torch.onnx._internal.fx.decomposition_table
+    import torch.onnx._internal.fx.passes  # noqa: TCH004
+
+
+_SUPPORT_ONNXRT: Optional[bool] = None
+
+__all__ = [
+    "is_onnxrt_backend_supported",
+    "torch_compile_backend",
+    "OrtExecutionProvider",
+    "OrtBackendOptions",
+    "OrtBackend",
+]
+
+
+def is_onnxrt_backend_supported() -> bool:
+    """Returns ``True`` if ONNX Runtime dependencies are installed and usable
+    to support TorchDynamo backend integration; ``False`` otherwise.
+
+    Example::
+
+        # xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)
+        >>> import torch
+        >>> if torch.onnx.is_onnxrt_backend_supported():
+        ...     @torch.compile(backend="onnxrt")
+        ...     def f(x):
+        ...             return x * x
+        ...     print(f(torch.randn(10)))
+        ... else:
+        ...     print("pip install onnx onnxscript onnxruntime")
+        ...
+    """
+    global _SUPPORT_ONNXRT
+
+    if _SUPPORT_ONNXRT is None:
+        # `onnxruntime` might import a lot of other runtime packages,
+        # e.g. apex, deepspeed, transformers.
+        # So lazy-importing onnxruntime to avoid possible circular import.
+        try:
+            importlib.import_module("onnxruntime")
+            importlib.import_module("onnxruntime.capi._pybind_state")
+
+            # This is not use directly in DORT but needed by underlying exporter,
+            # so we still need to check if it exists.
+            importlib.import_module("onnxscript")
+
+            import torch.onnx  # noqa: F401
+            import torch.onnx._internal  # noqa: F401
+            import torch.onnx._internal._exporter_legacy  # noqa: F401
+            import torch.onnx._internal.diagnostics  # noqa: F401
+            from torch.onnx._internal.fx import (  # noqa: F401
+                decomposition_table,
+                fx_onnx_interpreter,
+                passes,
+                type_utils,
+            )
+
+            _SUPPORT_ONNXRT = True
+        except ImportError:
+            _SUPPORT_ONNXRT = False
+
+    return _SUPPORT_ONNXRT
+
+
+_dumped_onnx_model: Dict[str, int] = {}
+
+
+def _dump_onnx_model(
+    model_string: bytes, graph_module: Optional[torch.fx.GraphModule] = None
+) -> str:
+    """Stores the onnx model into a file.
+    The name is "{ONNXRT_DUMP_PATH}{N}.onnx"
+    where *N* is the number of files already stored with
+    this prefix.
+    If graph_module is not None, the graph is stored as a string with
+    the same filename except the extension (.txt).
+    """
+    prefix = os.environ.get("ONNXRT_DUMP_PATH", None)
+    if not prefix:
+        return ""
+    n = _dumped_onnx_model.get(prefix, -1) + 1
+    filename = f"{prefix}{n}.onnx"
+    with open(filename, "wb") as f:
+        f.write(model_string)
+    _dumped_onnx_model[prefix] = n
+    if graph_module is not None:
+        filename_txt = f"{prefix}{n}.txt"
+        with open(filename_txt, "w", encoding="utf-8") as f:
+            f.write(str(graph_module.graph))
+    return filename
+
+
+def _infer_default_eps() -> Sequence[str]:
+    # TODO: select a good default based on the capabilities of the host
+    # e.g. DML on Windows, etc.
+    return ["CPUExecutionProvider"]
+
+
+def _nvtx_range_push(name: str):
+    """If PyTorch is installed with CUDA support, this starts NVTX range.
+
+    Check torch.cuda.nvtx.range_push's document for more details.
+    """
+    if torch.cuda.is_available():
+        torch.cuda.nvtx.range_push(name)
+
+
+def _nvtx_range_pop():
+    """If PyTorch is installed with CUDA support, this terminates NVTX range.
+
+    Check torch.cuda.nvtx.range_pop's document for more details.
+    """
+    if torch.cuda.is_available():
+        torch.cuda.nvtx.range_pop()
+
+
+def _get_ort_device_type(device_type: str):
+    from onnxruntime.capi import _pybind_state as ORTC
+
+    if device_type == "cuda":
+        return ORTC.OrtDevice.cuda()
+    if device_type == "cpu":
+        return ORTC.OrtDevice.cpu()
+    # ort pytorch device is mapped to NPU OrtDevice type
+    if device_type == "maia":
+        return ORTC.OrtDevice.npu()
+    raise ValueError("Unsupported device type: " + device_type)
+
+
+logger = logging.getLogger(__name__)
+# Uncomment the following lines to print out development info.
+# logging.basicConfig(level=logging.WARNING)
+# logger.setLevel(logging.WARNING)
+
+
+class OrtOperatorSupport(OperatorSupport):
+    """Operator support for ONNXRuntime backend.
+
+    It has two-level of support decision. One is via support_dict and the other one
+    is via extra_support_dict. The logic of using support_dict is implemented in
+    OrtOperatorSupport and extra_support_dict is used by OperatorSupport.is_node_supported.
+    """
+
+    def __init__(self, support_dict: Set[Any], extra_support_dict: Dict[str, Any]):
+        # Use extra_support_dict[op_name] = None to indicate
+        # we support op_name with all input types. Otherwise,
+        # see support_dict (type: SupportDict) in operator_support.py
+        # for specifying supported types.
+        super().__init__(extra_support_dict)
+        self._onnx_support_dict = support_dict
+
+    def is_node_supported(
+        self, submodules: Mapping[str, torch.nn.Module], node: torch.fx.Node
+    ) -> bool:
+        # OperatorSupport.is_node_supported returns True for non-callable nodes.
+        # Since ORT can't execute them, we return False here to override the base
+        # behavior.
+        if node.op not in CALLABLE_NODE_OPS:
+            return False
+        # This is the and the only place to decide if aten op is supported.
+        if node.op == "call_function" and node.target in self._onnx_support_dict:
+            logger.info(
+                "support_dict supports node.target: %s (type: %s)",
+                node.target,
+                type(node.target),
+            )
+            return True
+        # If node.target is not in support_dict, we still want to check if torch.jit.script
+        # can convert it to ONNX equivalence. Let's use base mechanism to do this.
+        # See extra_support_dict  for supported ops.
+        if super().is_node_supported(submodules, node):
+            logger.info(
+                "extra_support_dict supports node.target: %s (type: %s)",
+                node.target,
+                type(node.target),
+            )
+            return True
+        logger.warning(
+            "support_dict and extra_support_dict don't support node.target: %s (type: %s)",
+            node.target,
+            type(node.target),
+        )
+        return False
+
+
+def _move_placeholder_to_front(graph_module: torch.fx.GraphModule) -> None:
+    """
+    In torch.fx.Graph, placeholder is a special assignment node. If it's not
+    executed in the beginning, it could overwrite values computed by upstream
+    nodes.
+    """
+
+    graph = graph_module.graph
+    placeholders = []
+    first_not_placeholder = None
+    for node in graph.nodes:
+        if node.op == "placeholder":
+            placeholders.append(node)
+        if first_not_placeholder is None and node.op != "placeholder":
+            first_not_placeholder = node
+    if first_not_placeholder is None:
+        return
+    for placeholder in placeholders:
+        first_not_placeholder.prepend(placeholder)
+
+
+def _infer_ep_from_device(*args) -> Tuple[str, ...]:
+    """Return the first valid device (i.e., GPU or CPU) in argument list."""
+    eps = []
+    for arg in args:
+        if hasattr(arg, "device"):
+            device = arg.device
+            if device.type == "cuda":
+                eps.append("CUDAExecutionProvider")
+            elif device.type == "cpu":
+                eps.append("CPUExecutionProvider")
+    return tuple(eps)
+
+
+def _extract_graph_module_inputs(graph_module: torch.fx.GraphModule) -> Tuple[Any, ...]:
+    placeholders = []
+    for node in graph_module.graph.nodes:
+        if node.op == "placeholder":
+            if hasattr(node, "meta") and "val" in node.meta:
+                assert isinstance(node.meta["val"], torch.Tensor)
+            placeholders.append(node)
+    return tuple(placeholders)
+
+
+def _extract_graph_module_outputs(graph_module: torch.fx.GraphModule) -> Any:
+    """Collect "val" fields from outputs metadata in this torch.fx.GraphModule."""
+    for node in graph_module.graph.nodes:
+        if node.op == "output":
+            # Output node is unique. Let's retrieve output values from
+            # this node's input list. And then just return.
+            return node.args[0]
+    raise ValueError("No output node found in this torch.fx.GraphModule.")
+
+
+def _infer_ep_from_graph_module(graph_module: torch.fx.GraphModule) -> Tuple[str, ...]:
+    """Return the all valid devices (i.e., GPU or CPU) among outputs of this torch.fx.GraphModule."""
+    flattened_output_args, _ = _pytree.tree_flatten(
+        _extract_graph_module_outputs(graph_module)
+    )
+    # Output arguments with example value (type: torch.Tensor) in the `graph_module`.
+    selected_output_args = [
+        output_arg.meta["val"]
+        for output_arg in flattened_output_args
+        # output_arg must have tensor for its device information.
+        # Otherwise, skip it.
+        if (hasattr(output_arg, "meta") and "val" in output_arg.meta)
+    ]
+    return _infer_ep_from_device(*selected_output_args)
+
+
+def _sort_eps(eps: Tuple[str, ...]) -> Tuple[str, ...]:
+    """Sort execution providers in eps based on pre-set priority."""
+
+    def get_execution_provider_priority(ep: str) -> int:
+        if ep == "CPUExecutionProvider":
+            # Lowest priority.
+            return 2
+        if ep == "CUDAExecutionProvider":
+            # Higher priority than CPU but lower than
+            # other specialized EPs.
+            return 1
+        # Highest priority.
+        return 0
+
+    unique_eps = set(eps)
+    return tuple(sorted(unique_eps, key=get_execution_provider_priority, reverse=True))
+
+
+def _get_onnx_devices(
+    values: Tuple[
+        Union[
+            torch.Tensor, torch.SymInt, int, torch.SymFloat, float, torch.SymBool, bool
+        ],
+        ...,
+    ],
+) -> Tuple["ORTC.OrtDevice", ...]:
+    from onnxruntime.capi import _pybind_state as ORTC
+
+    def _device_id_or_zero(device_id: int) -> int:
+        return device_id or 0
+
+    def _map_tensor_or_sym_to_device(
+        value: Union[
+            torch.Tensor, torch.SymInt, int, torch.SymFloat, float, torch.SymBool, bool
+        ],
+    ) -> int:
+        if isinstance(value, torch.Tensor):
+            return ORTC.OrtDevice(
+                _get_ort_device_type(value.device.type),
+                ORTC.OrtDevice.default_memory(),
+                _device_id_or_zero(value.device.index),
+            )
+        elif isinstance(
+            value, (torch.SymInt, int, torch.SymFloat, float, torch.SymBool, bool)
+        ):
+            return ORTC.OrtDevice(
+                _get_ort_device_type("cpu"), ORTC.OrtDevice.default_memory(), 0
+            )
+        else:
+            raise ValueError("Unsupported value type: " + str(type(value)))
+
+    if len(values) > 0:
+        ort_devices = tuple(_map_tensor_or_sym_to_device(value) for value in values)
+        return ort_devices
+    else:
+        return (_map_tensor_or_sym_to_device(1),)
+
+
+def _get_ortvalues_from_torch_tensors(
+    tensors: Tuple[torch.Tensor, ...], devices: Tuple["ORTC.OrtDevice", ...]
+) -> Tuple[torch.Tensor, ...]:
+    from onnxruntime.capi import _pybind_state as ORTC
+
+    from torch.onnx._internal.fx.type_utils import _TORCH_DTYPE_TO_NUMPY_DTYPE
+
+    ortvalues = ORTC.OrtValueVector()
+    ortvalues.reserve(len(tensors))
+    dtypes = []
+    shapes = []
+    data_ptrs = []
+
+    for tensor in tensors:
+        dtypes.append(_TORCH_DTYPE_TO_NUMPY_DTYPE[tensor.dtype])
+        shapes.append(tensor.size())
+        data_ptrs.append(tensor.data_ptr())
+    ortvalues.push_back_batch(tensors, data_ptrs, dtypes, shapes, devices)
+    return ortvalues
+
+
+def _to_real_tensor(tensor: FakeTensor) -> torch.Tensor:
+    if tensor.is_sparse:
+        raise ValueError("sparse tensor is not yet supported.")
+    out = torch.empty(tensor.size(), dtype=tensor.dtype, device=tensor.device)
+    return out
+
+
+def _adjust_scalar_from_fx_to_onnx(
+    dynamo_value: Union[
+        torch.Tensor,
+        int,
+        float,
+        bool,
+    ],
+    value_info: "onnx.ValueInfoProto",  # type: ignore[name-defined]
+) -> torch.Tensor:
+    """Helper function to wrap PyTorch variables as torch.Tensor"""
+    if (
+        isinstance(dynamo_value, torch.Tensor)
+        and len(value_info.type.tensor_type.shape.dim) == 0
+        and dynamo_value.shape == (1,)
+    ):
+        # ONNX expect a scalar with empty shape.
+        # In contrast, PyTorch usually allows implicit
+        # conversion between shape=() and shape=(1,).
+        #
+        # Below, PyTorch's shape (1,) is reshaped to ().
+        return torch.squeeze(dynamo_value)
+    elif isinstance(dynamo_value, int):
+        return torch.tensor(dynamo_value, dtype=torch.int64)
+    elif isinstance(dynamo_value, float):
+        return torch.tensor(dynamo_value, dtype=torch.float32)
+    elif isinstance(dynamo_value, bool):
+        return torch.tensor(dynamo_value, dtype=torch.bool)
+    else:
+        assert isinstance(dynamo_value, torch.Tensor)
+        return dynamo_value.contiguous()
+
+
+def _adjust_scalar_from_onnx_to_fx(
+    tensor: torch.Tensor,
+    prim_value: Union[
+        torch.Tensor,
+        torch.SymInt,
+        int,
+        torch.SymFloat,
+        float,
+        torch.SymBool,
+        bool,
+    ],
+) -> Union[
+    torch.Tensor,
+    int,
+    float,
+    bool,
+]:
+    """Helper function to wrap ORT-produced torch.Tensor as PyTorch variables"""
+    assert isinstance(tensor, torch.Tensor), "ORT's output must be tensor."
+    if isinstance(
+        prim_value,
+        (torch.SymInt, int, torch.SymFloat, float, torch.SymBool, bool),
+    ):
+        # Convert tensor back to scalar to match Dynamo's expectation.
+        return tensor.item()
+    return tensor
+
+
+def _run_onnx_session_with_ortvaluevector(
+    sess: "onnxruntime.InferenceSession",
+    input_names: Tuple[str, ...],
+    inputs: Tuple[torch.Tensor, ...],
+    input_devices: Tuple["ORTC.OrtDevice", ...],
+    output_names: Tuple[str, ...],
+    outputs: Tuple[torch.Tensor, ...],
+    output_devices: Tuple["ORTC.OrtDevice", ...],
+    preallocate_output: bool,
+    input_value_infos: Tuple["onnx.ValueInfoProto", ...],  # type: ignore[name-defined]
+    normalized_prim_outputs: Tuple[
+        Union[
+            torch.Tensor, torch.SymInt, int, torch.SymFloat, float, torch.SymBool, bool
+        ],
+        ...,
+    ],
+) -> Tuple[Union[torch.Tensor, int, float, bool], ...]:
+    import onnxruntime
+    from onnxruntime.capi import _pybind_state as ORTC
+
+    _nvtx_range_push("contiguous")
+    inputs = tuple(
+        _adjust_scalar_from_fx_to_onnx(arg, value_info)
+        for arg, value_info in zip(inputs, input_value_infos)
+    )
+    _nvtx_range_pop()
+
+    _nvtx_range_push("push_back_batch")
+    ort_inputs = _get_ortvalues_from_torch_tensors(inputs, input_devices)
+
+    # preallocate output pytorch Tensors and use the buffers affined to the torch device for the output ortvalue.
+    # Because the output ortvalue is not allocated and owned by ort, it does not need to convert the output ortvalue
+    # to torch Tensor transferring the ownership.
+    if preallocate_output:
+        pth_outputs = tuple(
+            _to_real_tensor(t) if isinstance(t, FakeTensor) else t for t in outputs
+        )
+        ort_outputs = _get_ortvalues_from_torch_tensors(pth_outputs, output_devices)
+    else:
+        ort_outputs = ORTC.OrtValueVector()
+    _nvtx_range_pop()
+
+    _nvtx_range_push("run_with_ortvaluevector")
+    run_options = onnxruntime.RunOptions()
+    run_options.add_run_config_entry("disable_synchronize_execution_providers", "1")
+    sess.run_with_ortvaluevector(
+        run_options, input_names, ort_inputs, output_names, ort_outputs, output_devices
+    )
+    _nvtx_range_pop()
+
+    # Post-processing step:
+    #  wrap ORT's outputs to the schema represented by
+    #  `prim_output` (obtained by running the original
+    #  torch.fx.GraphModule).
+    if preallocate_output:
+        # Profile the ORT-to-PyTorch type cast below
+        _nvtx_range_push("after run_with_ortvaluevector")
+        # Outputs are stored on pre-allocated torch.Tensors' memory,
+        # so this case doesn't need to convert ORTValue to torch.Tensor.
+        pth_outputs = tuple(
+            _adjust_scalar_from_onnx_to_fx(onnx_output, prim_output)  # type: ignore[misc]
+            for onnx_output, prim_output in zip(pth_outputs, normalized_prim_outputs)
+        )
+        _nvtx_range_pop()
+        return pth_outputs
+    else:
+        # Profile the two ORT-to-PyTorch type casts below
+        _nvtx_range_push("after run_with_ortvaluevector")
+        # Map ORTValue to torch.Tensor.
+        pth_outputs = onnxruntime.training.ortmodule._utils._ortvalues_to_torch_tensor(
+            ort_outputs
+        )
+        # Change some torch.Tensor to int, float, bool.
+        pth_outputs = tuple(
+            _adjust_scalar_from_onnx_to_fx(onnx_output, prim_output)  # type: ignore[misc]
+            for onnx_output, prim_output in zip(pth_outputs, normalized_prim_outputs)
+        )
+        _nvtx_range_pop()
+        return pth_outputs
+
+
+def _run_onnx_session_with_fetch(
+    sess: "onnxruntime.InferenceSession",
+    input_names: Tuple[str, ...],
+    inputs: Tuple[torch.Tensor, ...],
+    input_devices: Tuple["ORTC.OrtDevice", ...],
+    output_names: Tuple[str, ...],
+    outputs: Tuple[torch.Tensor, ...],
+    output_devices: Tuple["ORTC.OrtDevice", ...],
+    preallocate_output: bool,
+    input_value_infos: Tuple["onnx.ValueInfoProto", ...],  # type: ignore[name-defined]
+    normalized_prim_outputs: Tuple[
+        Union[
+            torch.Tensor, torch.SymInt, int, torch.SymFloat, float, torch.SymBool, bool
+        ],
+        ...,
+    ],
+) -> Tuple[Union[torch.Tensor, int, float, bool], ...]:
+    import onnxruntime
+
+    inputs = tuple(
+        _adjust_scalar_from_fx_to_onnx(arg, value_info)
+        for arg, value_info in zip(inputs, input_value_infos)
+    )
+    feed = {
+        name: onnxruntime.OrtValue.ortvalue_from_numpy(tensor.cpu().numpy())
+        for name, tensor in zip(input_names, inputs)
+    }
+    ort_outputs = sess.run(output_names, feed)
+    pth_outputs = tuple(
+        _adjust_scalar_from_onnx_to_fx(
+            torch.from_numpy(value),
+            prim_output,
+        )
+        for value, prim_output in zip(ort_outputs, normalized_prim_outputs)
+    )
+    return pth_outputs
+
+
+class OrtExecutionInfoPerSession:
+    """Information required to execute torch.fx.GraphModule using onnxruntime.InferenceSession"""
+
+    def __init__(
+        self,
+        session: "onnxruntime.InferenceSession",
+        input_names: Tuple[str, ...],
+        input_value_infos: Tuple["onnx.ValueInfoProto", ...],  # type: ignore[name-defined]
+        output_names: Tuple[str, ...],
+        output_value_infos: Tuple["onnx.ValueInfoProto", ...],  # type: ignore[name-defined]
+        input_devices: Tuple["ORTC.OrtDevice", ...],
+        output_devices: Tuple["ORTC.OrtDevice", ...],
+        example_outputs: Union[Tuple[torch.Tensor, ...], torch.Tensor],
+    ):
+        # Carrier of ONNX model and its executor.
+        self.session: onnxruntime.InferenceSession = session
+        # For the ONNX model stored in self.session, self.input_names[i] is the
+        # name of the i-th positional input.
+        self.input_names: Tuple[str, ...] = input_names
+        # self.input_name[i]'s type information is stored in self.input_value_infos[i].
+        self.input_value_infos: Tuple[onnx.ValueInfoProto, ...] = input_value_infos  # type: ignore[name-defined]
+        # Similar to self.input_names, but for outputs.
+        self.output_names: Tuple[str, ...] = output_names
+        # Similar to self.input_value_infos but for outputs.
+        self.output_value_infos: Tuple[onnx.ValueInfoProto, ...] = output_value_infos  # type: ignore[name-defined]
+        # For the ONNX model stored in self.session, self.input_devices[i] is the
+        # i-th positional input's device.
+        self.input_devices: Tuple[ORTC.OrtDevice, ...] = input_devices
+        # Similar to self.input_devices, but for outputs.
+        self.output_devices: Tuple[ORTC.OrtDevice, ...] = output_devices
+        # This is the outputs of executing the original torch.fx.GraphModule with example inputs
+        # (i.e., args passed into OrtBackend._ort_acclerated_call).
+        self.example_outputs: Union[Tuple[torch.Tensor, ...], torch.Tensor] = (
+            example_outputs
+        )
+
+    def is_supported(self, *args):
+        from torch.onnx._internal.fx.type_utils import (
+            _TORCH_DTYPE_TO_ONNX_TENSOR_ELEMENT_TYPE,
+            from_python_type_to_onnx_tensor_element_type,
+        )
+
+        # Compare the args and the input schema in ONNX model and
+        # return the first match.
+        if len(args) != len(self.input_value_infos):
+            return False
+        for arg, value_info in zip(args, self.input_value_infos):
+            if not isinstance(arg, (torch.Tensor, float, int)):
+                return False
+
+            # Check Python scalars such as int, float, and bool.
+            if isinstance(arg, (int, float, bool)):
+                # Map, e.g., float to onnx.TensorProto.FLOAT.
+                onnx_dtype = from_python_type_to_onnx_tensor_element_type(type(arg))
+                if onnx_dtype != value_info.type.tensor_type.elem_type:
+                    return False
+                if len(value_info.type.tensor_type.shape.dim) != 0:
+                    return False
+                continue
+
+            # Check tensor.
+            onnx_dtype = _TORCH_DTYPE_TO_ONNX_TENSOR_ELEMENT_TYPE[arg.dtype]
+            if onnx_dtype != value_info.type.tensor_type.elem_type:
+                return False
+            for dim, onnx_dim in zip(arg.shape, value_info.type.tensor_type.shape.dim):
+                if isinstance(dim, int) and (
+                    onnx_dim.dim_value == dim or onnx_dim.dim_param
+                ):
+                    continue
+                elif isinstance(dim, torch.SymInt) and onnx_dim.dim_param:
+                    continue
+                else:
+                    return False
+        return True
+
+
+@dataclasses.dataclass
+class OrtExecutionInfoForAllGraphModules:
+    def __init__(self) -> None:
+        # All sessions (and their related information) created by exporting the same GraphModule
+        # with different inputs.
+        self.execution_info_per_graph_module: Dict[
+            torch.fx.GraphModule, List[OrtExecutionInfoPerSession]
+        ] = {}
+
+    def search_reusable_session_execution_info(
+        self, graph_module: torch.fx.GraphModule, *args
+    ):
+        if graph_module not in self.execution_info_per_graph_module:
+            return None
+        # All execution information for ONNX models exported from the same `graph_module`
+        # with different inputs.
+        candidates = self.execution_info_per_graph_module[graph_module]
+
+        for candidate in candidates:
+            if candidate.is_supported(*args):
+                # Returns the first session that accepts this input schema.
+                return candidate
+        # No reusable session found.
+        return None
+
+    def cache_session_execution_info(
+        self, graph_module: torch.fx.GraphModule, info: OrtExecutionInfoPerSession
+    ):
+        if graph_module not in self.execution_info_per_graph_module:
+            self.execution_info_per_graph_module[graph_module] = [info]
+        else:
+            self.execution_info_per_graph_module[graph_module].append(info)
+
+
+OrtExecutionProvider: TypeAlias = Union[str, Tuple[str, Mapping[str, Any]]]
+"""Either the name of an ONNX Runtime execution provider as a string or
+a 2-tuple of the name and a dictionary of execution provider options.
+
+Examples::
+
+    >>> "CPUExecutionProvider"
+
+    >>> ("CUDAExecutionProvider", {"device_id": 3})
+
+"""
+
+
+@dataclasses.dataclass(frozen=True)
+@compatibility(is_backward_compatible=False)
+class OrtBackendOptions:
+    """Options for constructing an ``OrtBackend``, the ONNX Runtime
+    backend (``"onnxrt"``) for ``torch.compile``.
+
+    Example::
+
+        >>> @torch.compile(
+        ...     backend="onnxrt",
+        ...     options=torch.onnx._OrtBackendOptions(...),
+        ... )
+        ... def ort_function(x):
+        ...     return x ** x
+    """
+
+    preferred_execution_providers: Optional[Sequence[OrtExecutionProvider]] = None
+    """An optional sequence of execution providers to be prioritized ahead of any
+    execution providers that may be inferred (see ``infer_execution_providers``).
+    """
+
+    infer_execution_providers: bool = True
+    """Whether to infer an execution provider from ``torch.device`` bound to inputs or found in the graph."""
+
+    default_execution_providers: Optional[Sequence[OrtExecutionProvider]] = None
+    """The default fallback execution providers. If not specified, one will be
+    be selected based on the host environment (most likely ``"CPUExecutionProvider"``).
+    """
+
+    # preallocate_output allows for allocating output torch Tensor buffers and feeding them to InferenceSession
+    # in order to avoid internal allocation of output buffers in InferenceSession.
+    # If output ortvalue returned from InferenceSession is allocated internally,
+    # it needs to be converted to torch Tensor for return, and the torch Tensor should hold the ownership.
+    # When a custom torch device is used with a custom aten allocator, the conversion from ortvalue to torch Tensor
+    # should be supported, which is currently done through dlpack. Note that dlpack might not support a custom torch device.
+    # It can be avoided by allowing for preallocation for output buffers allocated by a custom aten allocator,
+    # and use the preallocated output buffers for InferenceSession not holding any ownership for them.
+    # TODO(wschin): Make it to inference session level flag.
+    # See https://github.com/pytorch/pytorch/issues/106869.
+    preallocate_output: bool = False
+    """If ``True``, allocate memory for ONNX Runtime's outputs on the PyTorch side."""
+
+    use_aot_autograd: bool = True
+    """Whether to wrap the ``OrtBackend`` with TorchDynamo's aot_autograd backend
+    to support training (i.e., backward graphs are also sent to ``OrtBackend``).
+
+    Symbolic execution is used to capture the forward pass and backward passes as a single graph.
+    Then, a selected graph partition algorithm (``min_cut_rematerialization_partition``) is used
+    to split the entire graph into forward sub-graph and backward sub-graph. Finally, both
+    sub-graphs are compiled by ``OrtBackend``.
+    """
+
+    export_options: Optional["torch.onnx.ExportOptions"] = None
+    """Options for the TorchDynamo-based ONNX exporter used by the ``OrtBackend``."""
+
+    ort_session_options: Optional["onnxruntime.SessionOptions"] = None
+    """Options for the ``onnxruntime.InferenceSession`` used by the ``OrtBackend``."""
+
+    pre_ort_model_transforms: Optional[  # type: ignore[name-defined]
+        Sequence[Callable[["onnx.ModelProto"], None]]
+    ] = None
+    """A list of graph transforms to be applied to the ONNX model before it
+    is fed to ONNXRuntime's InferenceSession."""
+
+
+@compatibility(is_backward_compatible=False)
+class OrtBackend:
+    """A backend compiles (sub-)graphs in torch.fx.GraphModule to onnxruntime.InferenceSession calls.
+
+    The compiler entry point is OrtBackend.compile, which
+        1. partitions the original graph into supported sub-graphs (type: torch.fx.GraphModule) and unsupported
+           sub-graphs.
+        2. For each supported sub-graph, it replaces its _wrapped_call function with _ort_accelerated_call.
+        3. Inside _ort_accelerated_call, it creates onnxruntime.InferenceSession and calls it to execute the sub-graph.
+    """
+
+    def __init__(self, options: Optional[OrtBackendOptions] = None):
+        from onnxruntime.capi import _pybind_state as ORTC
+
+        import torch.onnx
+        import torch.onnx._internal._exporter_legacy
+        import torch.onnx._internal.fx.decomposition_table
+
+        self._options: Final = OrtBackendOptions() if options is None else options
+
+        # options.export_options contains information shared between exporter and DORT.
+        # For example, they should use the same decomposition table when
+        #  1. capturing FX graph in torch.compile (see how we create aot_ort in register_backend.py)
+        #  2. call exporter's API to convert `torch.fx.GraphModule` to ONNX model
+        #     (see onnxfunction_dispatcher passed to FxOnnxInterpreter.run below).
+        #
+        # Convert user-facing option to internal option used by ONNX exporter
+        # to access required information.
+        # Some useful fields:
+        # - Decomposition table for decomposing FX operators in exporter is
+        #   self._resolved_onnx_exporter_options.decomposition_table.
+        # - self._resolved_onnx_exporter_options.onnx_registry records what
+        #   aten/prim ops are supported by exporter and their exporters (type: callable).
+        self._resolved_onnx_exporter_options = (
+            torch.onnx._internal._exporter_legacy.ResolvedExportOptions(
+                torch.onnx.ExportOptions()
+                if self._options.export_options is None
+                else self._options.export_options
+            )
+        )
+
+        #  Given DORT's computation flow:
+        #   1. OrtOperatorSupport uses support_dict and extra_support_dict to select operators
+        #      and send them to DORT.
+        #   2. Then, DORT exports the selected sub-graphs into ONNX.
+        #   3. Finally DORT calls ORT to do the computation.
+        #  OrtOperatorSupport and create_onnx_friendly_decomposition_table(...)
+        #  must use the same support_dict. If the support_dict here contains something not
+        #  supported by exporter, exporter will fails in step 2 since the selected graphs may
+        #  contains unsupported operators such as aten::_who_you_are.
+        #  This restriction is automatically done since DORT and exporter shares the same
+        #  self._resolved_onnx_exporter_options.
+        support_dict = torch.onnx._internal.fx.decomposition_table._create_onnx_supports_op_overload_table(
+            self._resolved_onnx_exporter_options.onnx_registry
+        )
+
+        extra_support_dict: Dict[str, Any] = {
+            "getattr": None,
+            # To send operator.getitem to ORT, add the corresponding string
+            # recognized by PyTorch's OperatorSupport class.
+            "_operator.getitem": None,
+            # To send operator.mul to ORT, add the corresponding string
+            # recognized by PyTorch's OperatorSupport class.
+            "_operator.mul": None,
+            "_operator.add": None,
+            "_operator.sub": None,
+        }
+
+        self._supported_ops = OrtOperatorSupport(support_dict, extra_support_dict)
+        # TODO(wschin): this is a naive implementation of cache without proper guard
+        # See https://github.com/pytorch/pytorch/issues/106868.
+        self._partitioner_cache: Dict[torch.fx.GraphModule, torch.fx.GraphModule] = {}
+        # Conceptually, this filed is a 2-layer dictionary
+        #   GraphModule 0
+        #     ONNX Model 0 (with ORT InferenceSession and related information. type: OrtExecutionInfoPerSession)
+        #     ONNX Model 1
+        #     ...
+        #   GraphModule 1
+        #     ONNX Model 2 (with ORT InferenceSession and related information. type: OrtExecutionInfoPerSession)
+        #     ONNX Model 3
+        #     ...
+        #   ...
+        # , which caches all previous compilation result so that we can reuse them.
+        # ONNX Model 0 and 1 are exported from the same GraphModule 0 but with different inputs
+        # (e.g., tensors with different ranks). GraphModule 0 and GraphModule 1 are different
+        # graphs captured by Dynamo and sent to OrtBackend.compile.
+        self._all_ort_execution_info = OrtExecutionInfoForAllGraphModules()
+
+        self._assert_allclose_to_baseline = False
+
+        self.execution_count = 0
+
+        # Function which invokes ORT do to the real computation.
+        self.run = (
+            _run_onnx_session_with_ortvaluevector
+            if hasattr(ORTC.OrtValueVector, "push_back_batch")
+            else _run_onnx_session_with_fetch
+        )
+
+    def _select_eps(
+        self, graph_module: torch.fx.GraphModule, *args
+    ) -> Sequence[Tuple[str, Mapping[str, Any]]]:
+        inferred_eps: Tuple[str, ...] = ()
+        if self._options.infer_execution_providers:
+            if eps_from_args := _infer_ep_from_device(*args):
+                # If user feeds CUDA tensor as input argument,
+                # we want to use CUDA EP.
+                # Thus, `eps_from_args` (deduced from input arguments)
+                # has highest priority.
+                inferred_eps = eps_from_args
+            elif eps_from_graph_module := _infer_ep_from_graph_module(graph_module):
+                # If there is no EP in input arguments, we deduce EP from
+                # graph_module's outputs. Those outputs may come from
+                # FakeTensorProp or Dynamo's built-in symbolic shape inference.
+                inferred_eps = eps_from_graph_module
+
+        selected_eps = []
+
+        for ep in (
+            *(self._options.preferred_execution_providers or []),
+            *_sort_eps(inferred_eps),
+            *(self._options.default_execution_providers or _infer_default_eps()),
+        ):
+            if isinstance(ep, str):
+                ep = (ep, {})
+            elif isinstance(ep, tuple) and ep[1] is None:
+                ep = (ep[0], {})
+            if ep is not None and ep not in selected_eps:
+                selected_eps.append(ep)
+
+        return selected_eps
+
+    def _ort_acclerated_call(self, graph_module: torch.fx.GraphModule, *args, **kwargs):
+        """This function replaces GraphModule._wrapped_call in compiled model.
+
+        The _wrapped_call is the underlying implementation of forward method. Replacing
+        it means we delegate the computation to _ort_acclerated_call and therefore
+        onnxruntime.InferenceSession.
+        """
+        import onnxruntime
+
+        from torch.onnx._internal.fx import fx_onnx_interpreter, passes
+
+        cached_execution_info_per_session = (
+            self._all_ort_execution_info.search_reusable_session_execution_info(
+                graph_module, *args
+            )
+        )
+        if cached_execution_info_per_session:
+            onnx_session = cached_execution_info_per_session.session
+            input_names = cached_execution_info_per_session.input_names
+            output_names = cached_execution_info_per_session.output_names
+            input_value_infos = cached_execution_info_per_session.input_value_infos
+            output_value_infos = cached_execution_info_per_session.output_value_infos
+            input_devices = cached_execution_info_per_session.input_devices
+            output_devices = cached_execution_info_per_session.output_devices
+            prim_outputs = cached_execution_info_per_session.example_outputs
+        else:
+            # It's first time seeing such as graph. Let's make a new session
+            # (type: onnxruntime.InferenceSession) for it.
+
+            graph_module = passes.MovePlaceholderToFront(
+                self._resolved_onnx_exporter_options.diagnostic_context,
+                graph_module,
+            ).run()
+            # Generate reference outputs. They are used to indicate output
+            # tensors' types and devices when calling ORT.
+            #
+            # WARNING: The downstream code should not change prim_outputs and
+            # this backend should always produces output with schema identical to prim_outputs'.
+
+            if self._resolved_onnx_exporter_options.dynamic_shapes:
+                # No pre-allocation when dynamic shape is enabled.
+                self.preallocate_output = False
+                extracted_outputs = _extract_graph_module_outputs(graph_module)
+
+                def maybe_map_to_meta_val(value):
+                    if hasattr(value, "meta") and "val" in value.meta:
+                        # Select outputs with "val" information. Without "val",
+                        # it's not possible access output_arg.meta["val"].device.
+                        return value.meta["val"]
+                    else:
+                        return value
+
+                prim_outputs = _pytree.tree_map(
+                    maybe_map_to_meta_val, extracted_outputs
+                )
+            else:
+                try:
+                    prim_outputs = FakeTensorProp(graph_module).propagate(
+                        *args, **kwargs
+                    )
+                except Exception:
+                    logger.warning("FakeTensorProb failed for %s", graph_module)
+                    # When FakeTensorProp fails, it is not possible to preallocate output buffers
+                    # because the output shapes are not inferred.
+                    self.preallocate_output = False
+
+                    # rethrow FakeTensorProb failure because it is not yet currently handled.
+                    raise
+
+            # Create the object to iterate through the nodes in graph one-by-one
+            # and calls the corresponding ONNX exporter for each node.
+            fx_interpreter = fx_onnx_interpreter.FxOnnxInterpreter(
+                diagnostic_context=self._resolved_onnx_exporter_options.diagnostic_context
+            )
+            # Cast FX variables if they will result schema-mismatch when searching
+            # for ONNX operator. E.g., add(double_tensor, int_tensor) is fine in PyTorch,
+            # but ONNX expects add(double_tensor, double_tensor).
+            graph_module = passes.InsertTypePromotion(
+                self._resolved_onnx_exporter_options.diagnostic_context, graph_module
+            ).run()
+            # Start the per-node exporting process. It's conceptually a for loop
+            # scanning through the nodes in the graph.
+            exported = fx_interpreter.run(
+                fx_graph_module=graph_module,
+                onnxfunction_dispatcher=self._resolved_onnx_exporter_options.onnxfunction_dispatcher,
+            )
+            # Convert the exported result to ONNX ModelProto.
+            onnx_model = exported.to_model_proto(
+                opset_version=self._resolved_onnx_exporter_options.onnx_registry.opset_version,
+            )
+
+            try:
+                from onnxscript import optimizer  # type: ignore[import]
+                from onnxscript.rewriter import (  # type: ignore[import]
+                    onnxruntime as ort_rewriter,
+                )
+
+                onnx_model = optimizer.optimize(onnx_model)
+                onnx_model = ort_rewriter.rewrite(onnx_model)
+            except ImportError:
+                logger.warning(
+                    "ONNXScript optimizer is not available. Skipping optimization. "
+                    "Please `pip install onnxscript -U` to enable post-export optimization."
+                )
+
+            # Modify ONNX model using pre-registered graph transforms.
+            # They are in-place modifications for avoiding unnecessary
+            # copy of ONNX initializers.
+            if self._options.pre_ort_model_transforms:
+                for transform in self._options.pre_ort_model_transforms:
+                    transform(onnx_model)
+
+            onnx_model_bytes = onnx_model.SerializeToString()
+            if os.environ.get("ONNXRT_DUMP_PATH", None):
+                # If not empty, environment variable ONNXRT_DUMP_PATH defined the path
+                # where generated onnx files should be stored.
+                # This module keeps a global variables keeping track of the
+                # stored models.
+                # If ONNXRT_DUMP_PATH="dumped/dumped_model_"
+                # The first file name will be 'dumped/dumped_model_0.onnx'.
+                # For every dumped model, a text file 'dumped/dumped_model_0.txt'
+                # is created as well to contain the string representing the graph_module.
+                _dump_onnx_model(onnx_model_bytes, graph_module=graph_module)
+
+            # Initialize a ORT session to execute this ONNX model.
+            # Note that TorchDynamo assumes all inputs/outputs are on the
+            # same device, but it's subject to change (very likely with
+            # dynamic shape support), so we add execution providers
+            # based on the logic in _select_eps: (explicitly preferred EPs,
+            # EPs inferred from inputs or graph, and the fallback default EP)/
+            #
+            # TODO(wschin): enable external allocators.
+            # See https://github.com/pytorch/pytorch/issues/106867
+            onnx_session = onnxruntime.InferenceSession(
+                path_or_bytes=onnx_model_bytes,
+                sess_options=self._options.ort_session_options,
+                providers=self._select_eps(graph_module, *args),
+            )
+
+            # Cache ORT session. It's reused for the same "graph_module".
+            # Generate ONNX model and extract its input and output names.
+            input_names = tuple(input.name for input in onnx_model.graph.input)
+            output_names = tuple(output.name for output in onnx_model.graph.output)
+            input_devices = _get_onnx_devices(args)
+            # Cache devices for inputs and outputs. They are used to invoke
+            # ORT session. Output devices indicate where (e.g., GPU or CPU)
+            # to store outputs
+            if isinstance(prim_outputs, tuple):
+                output_devices = _get_onnx_devices(prim_outputs)
+            else:
+                output_devices = _get_onnx_devices((prim_outputs,))
+
+            input_value_infos = tuple(input for input in onnx_model.graph.input)
+            output_value_infos = tuple(output for output in onnx_model.graph.output)
+
+            execution_info_per_session = OrtExecutionInfoPerSession(
+                session=onnx_session,
+                input_names=input_names,
+                input_value_infos=input_value_infos,
+                output_names=output_names,
+                output_value_infos=output_value_infos,
+                input_devices=input_devices,
+                output_devices=output_devices,
+                example_outputs=prim_outputs,
+            )
+
+            self._all_ort_execution_info.cache_session_execution_info(
+                graph_module, execution_info_per_session
+            )
+
+        self.execution_count += 1
+
+        # ORT always returns a tuple of outputs. If the original output is a tensor,
+        # ORT output's first element must be extracted and returned. Otherwise, type
+        # mismatch may happen in downstream computation.
+        is_single_tensor_output = isinstance(prim_outputs, torch.Tensor)
+        normalized_prim_outputs = (
+            (prim_outputs,) if is_single_tensor_output else prim_outputs
+        )
+        assert isinstance(normalized_prim_outputs, tuple)
+        assert all(
+            isinstance(elem, (torch.Tensor, torch.SymInt, int))
+            for elem in normalized_prim_outputs
+        )
+
+        _nvtx_range_push("run_onnx_session_with_ortvaluevector")
+        onnx_outputs = self.run(
+            onnx_session,
+            input_names,
+            args,
+            input_devices,
+            output_names,
+            normalized_prim_outputs,
+            output_devices,
+            self._options.preallocate_output,
+            input_value_infos,
+            normalized_prim_outputs,
+        )
+        _nvtx_range_pop()
+
+        if self._assert_allclose_to_baseline:
+            # Compute baseline.
+            baseline_outputs = torch._prims.executor.execute(
+                graph_module, *args, executor="aten"
+            )
+            normalized_baseline_ouptuts = (
+                (baseline_outputs,) if is_single_tensor_output else baseline_outputs
+            )
+            # Ensure every output tensor is close to the corresponding baseline.
+            for onnx_output, baseline_output in zip(
+                onnx_outputs, normalized_baseline_ouptuts
+            ):
+                torch.testing.assert_close(onnx_output, baseline_output)
+        return onnx_outputs[0] if is_single_tensor_output else onnx_outputs
+
+    def compile(self, graph_module: torch.fx.GraphModule, args) -> torch.fx.GraphModule:
+        # Deferred import since CapabilityBasedPartitioner is not decorated with
+        # @compatibility; importing it at the module level will result in the test
+        # failing: pytest test/test_fx.py -k test_public_api_surface
+        # because this module is imported into torch.onnx.
+        from torch.fx.passes.infra.partitioner import CapabilityBasedPartitioner
+
+        # FX graph based partitioning based on ONNX supported ops.
+        # Given a graph module
+        #  GraphModule0
+        #   node_0
+        #   node_1
+        #   node_2
+        #   node_3
+        #   node_4
+        # If only node_2 is not supported by ONNX, this graph module will be partitioned into
+        #  GraphModule0
+        #   GraphModule1
+        #    node_0
+        #    node_1
+        #   node_2
+        #   GraphModule2
+        #    node_3
+        #    node_4
+        # by calling CapabilityBasedPartitioner.partition_and_fuse.
+        # Then, GraphModule1's and GraphModule2's forward method (GraphModule._wrapped_call)
+        # will be replaced by OrtBackend._ort_accelerated_call to delegate computation to ORT.
+        if graph_module in self._partitioner_cache:
+            partitioned_prim_graph_module = self._partitioner_cache[graph_module]
+        else:
+            prim_graph_module = graph_module
+            partitioner = CapabilityBasedPartitioner(
+                prim_graph_module,
+                self._supported_ops,
+                allows_single_node_partition=True,
+            )
+            partitioned_prim_graph_module = partitioner.partition_and_fuse()
+            self._partitioner_cache[graph_module] = partitioned_prim_graph_module
+
+            # Overriding fused_module's __call__() function with ort_acclerated_call()
+            # This loop goes through all graph partitions (each of them is an ONNX-representable graph)
+            # and override their _wrapped_call function with _ort_accelerated_call.
+            # Inside _ort_accelerated_call, the partition's graph is exported into ONNX and executed by ORT.
+            for node in partitioned_prim_graph_module.graph.nodes:
+                # TODO(wschin): use a better way to identify fused submodule
+                # See https://github.com/pytorch/pytorch/issues/106872.
+                if node.op == "call_module" and "fused_" in node.name:
+                    fused_module = getattr(partitioned_prim_graph_module, node.name)
+                    # self.ort_acclerated_call is responsible for exporting graph to ONNX,
+                    # creating ORT session, and running ORT session.
+                    fused_module._wrapped_call = self._ort_acclerated_call
+
+        return partitioned_prim_graph_module
+
+    def __call__(
+        self, graph_module: torch.fx.GraphModule, args
+    ) -> torch.fx.GraphModule:
+        """If ``OrtBackendOptions.use_aot_autograd`` is ``True``, the `auto_autograd` compiler
+        will be invoked, wrapping this ``OrtBackend`` instance's ``compile`` method. Otherwise,
+        the ``compile`` method is invoked directly."""
+        if self._options.use_aot_autograd:
+            from functorch.compile import min_cut_rematerialization_partition
+            from torch._dynamo.backends.common import aot_autograd
+
+            return aot_autograd(
+                fw_compiler=self.compile,
+                partition_fn=min_cut_rematerialization_partition,
+                decompositions=self._resolved_onnx_exporter_options.decomposition_table,
+            )(graph_module, args)
+
+        return self.compile(graph_module, args)
+
+    __instance_cache_max_count: Final = 8
+    __instance_cache: Final[List["OrtBackend"]] = []
+
+    @staticmethod
+    def get_cached_instance_for_options(
+        options: Optional[Union[OrtBackendOptions, Mapping[str, Any]]] = None,
+    ) -> "OrtBackend":
+        """Returns a possibly cached instance of an ``OrtBackend``. If an existing
+        backend was created previously through this function with the same options,
+        it will be returned. Otherwise a new backend will be created, cached, and
+        returned.
+
+        Note: if ``options`` sets ``ort_session_options``, a new ``OrtBackend``
+        will always be returned, since ``onnxruntime.SessionOptions`` cannot
+        participate in caching."""
+
+        def reusable(a: OrtBackendOptions, b: OrtBackendOptions):
+            if (
+                a.preferred_execution_providers != b.preferred_execution_providers
+                or a.infer_execution_providers != b.infer_execution_providers
+                or a.default_execution_providers != b.default_execution_providers
+                or a.preallocate_output != b.preallocate_output
+                or a.use_aot_autograd != b.use_aot_autograd
+                or a.pre_ort_model_transforms != b.pre_ort_model_transforms
+            ):
+                return False
+
+            # onnxruntime.SessionOptions is a pybind11 object, cannot be pickled,
+            # and holds too much potential state to reasonably check manually;
+            # ort_session_options is provided at all, the backend does not participate
+            # in caching.
+            if a.ort_session_options is not None or b.ort_session_options is not None:
+                return False
+
+            if a.export_options is b.export_options:
+                return True
+
+            # Similarly, some objects in ExportOptions are too stateful to use for
+            # caching. We should revisit this.
+            if a.export_options is not None and b.export_options is not None:
+                return (
+                    a.export_options.dynamic_shapes == b.export_options.dynamic_shapes
+                    and a.export_options.diagnostic_options
+                    == b.export_options.diagnostic_options
+                    and a.export_options.onnx_registry is b.export_options.onnx_registry
+                    and a.export_options.fake_context is b.export_options.fake_context
+                )
+
+            # We can't account for how the two option sets may differ, so it's not safe to reuse.
+            return False
+
+        if not isinstance(options, OrtBackendOptions):
+            options = OrtBackendOptions(**(options or {}))
+
+        backend = next(
+            (b for b in OrtBackend.__instance_cache if reusable(b._options, options)),
+            None,
+        )
+
+        if backend is None:
+            assert (
+                len(OrtBackend.__instance_cache) < OrtBackend.__instance_cache_max_count
+            ), (
+                f"No more than {OrtBackend.__instance_cache_max_count} instances of "
+                f"{OrtBackend} allowed. Please instantiate `{OrtBackend}` explicitly "
+                "to pass to `torch.compile`. "
+                "See https://github.com/pytorch/pytorch/pull/107973#discussion_r1306144795 "
+                "for discussion."
+            )
+            OrtBackend.__instance_cache.append(backend := OrtBackend(options))
+
+        return backend
+
+    @staticmethod
+    def clear_cached_instances():
+        OrtBackend.__instance_cache.clear()
+
+    @staticmethod
+    def get_cached_instances():
+        return tuple(OrtBackend.__instance_cache)
+
+
+@compatibility(is_backward_compatible=False)
+def torch_compile_backend(
+    graph_module: torch.fx.GraphModule,
+    args,
+    *,
+    options: Optional[Union[OrtBackendOptions, Mapping[str, Any]]] = None,
+):
+    return OrtBackend.get_cached_instance_for_options(options)(graph_module, args)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/registration.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/registration.py
new file mode 100644
index 0000000000000000000000000000000000000000..b655a1087da550b02eeb0166a4406525126ca9f4
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_internal/registration.py
@@ -0,0 +1,341 @@
+# mypy: allow-untyped-defs
+"""Module for handling symbolic function registration."""
+
+import warnings
+from typing import (
+    Callable,
+    Collection,
+    Dict,
+    Generic,
+    Optional,
+    Sequence,
+    Set,
+    TypeVar,
+    Union,
+)
+
+from torch.onnx import _constants, errors
+
+
+OpsetVersion = int
+
+
+def _dispatch_opset_version(
+    target: OpsetVersion, registered_opsets: Collection[OpsetVersion]
+) -> Optional[OpsetVersion]:
+    """Finds the registered opset given a target opset version and the available opsets.
+
+    Args:
+        target: The target opset version.
+        registered_opsets: The available opsets.
+
+    Returns:
+        The registered opset version.
+    """
+    if not registered_opsets:
+        return None
+
+    descending_registered_versions = sorted(registered_opsets, reverse=True)
+    # Linear search for the opset version, which is fine since the number of opset
+    # versions is small.
+
+    if target >= _constants.ONNX_BASE_OPSET:
+        # Always look down toward opset 1 when the target is >= ONNX_BASE_OPSET (opset 9).
+        # When a custom op is register at opset 1, we want to be able to discover it as a
+        # fallback for all opsets >= ONNX_BASE_OPSET.
+        for version in descending_registered_versions:
+            if version <= target:
+                return version
+        return None
+
+    # target < opset 9. This is the legacy behavior to support opset 7 and opset 8.
+    # for caffe2 support. We search up toward opset 9.
+    for version in reversed(descending_registered_versions):
+        # Count back up until _constants.ONNX_BASE_OPSET
+        if target <= version <= _constants.ONNX_BASE_OPSET:
+            return version
+
+    return None
+
+
+_K = TypeVar("_K")
+_V = TypeVar("_V")
+
+
+class OverrideDict(Collection[_K], Generic[_K, _V]):
+    """A dictionary that merges built-in and custom symbolic functions.
+
+    It supports overriding and un-overriding built-in symbolic functions with custom
+    ones.
+    """
+
+    def __init__(self) -> None:
+        self._base: Dict[_K, _V] = {}
+        self._overrides: Dict[_K, _V] = {}
+        self._merged: Dict[_K, _V] = {}
+
+    def set_base(self, key: _K, value: _V) -> None:
+        self._base[key] = value
+        if key not in self._overrides:
+            self._merged[key] = value
+
+    def in_base(self, key: _K) -> bool:
+        """Checks if a key is in the base dictionary."""
+        return key in self._base
+
+    def override(self, key: _K, value: _V) -> None:
+        """Overrides a base key-value with a new pair."""
+        self._overrides[key] = value
+        self._merged[key] = value
+
+    def remove_override(self, key: _K) -> None:
+        """Un-overrides a key-value pair."""
+        self._overrides.pop(key, None)  # type: ignore[arg-type]
+        self._merged.pop(key, None)  # type: ignore[arg-type]
+        if key in self._base:
+            self._merged[key] = self._base[key]
+
+    def overridden(self, key: _K) -> bool:
+        """Checks if a key-value pair is overridden."""
+        return key in self._overrides
+
+    def __getitem__(self, key: _K) -> _V:
+        return self._merged[key]
+
+    def get(self, key: _K, default: Optional[_V] = None):
+        return self._merged.get(key, default)
+
+    def __contains__(self, key: object) -> bool:
+        return key in self._merged
+
+    def __iter__(self):
+        return iter(self._merged)
+
+    def __len__(self) -> int:
+        return len(self._merged)
+
+    def __repr__(self) -> str:
+        return f"OverrideDict(base={self._base}, overrides={self._overrides})"
+
+    def __bool__(self) -> bool:
+        return bool(self._merged)
+
+
+class _SymbolicFunctionGroup:
+    """Different versions of symbolic functions registered to the same name.
+
+    O(number of registered versions of an op) search is performed to find the most
+    recent version of the op.
+
+    The registration is delayed until op is used to improve startup time.
+
+    Function overloads with different arguments are not allowed.
+    Custom op overrides are supported.
+    """
+
+    def __init__(self, name: str) -> None:
+        self._name = name
+        # A dictionary of functions, keyed by the opset version.
+        self._functions: OverrideDict[OpsetVersion, Callable] = OverrideDict()
+
+    def __repr__(self) -> str:
+        return f"_SymbolicFunctionGroup({self._name}, registered={self._functions})"
+
+    def __getitem__(self, key: OpsetVersion) -> Callable:
+        result = self.get(key)
+        if result is None:
+            raise KeyError(key)
+        return result
+
+    # TODO(justinchuby): Add @functools.lru_cache(maxsize=None) if lookup time becomes
+    # a problem.
+    def get(self, opset: OpsetVersion) -> Optional[Callable]:
+        """Find the most recent version of the function."""
+        version = _dispatch_opset_version(opset, self._functions)
+        if version is None:
+            return None
+
+        return self._functions[version]
+
+    def add(self, func: Callable, opset: OpsetVersion) -> None:
+        """Adds a symbolic function.
+
+        Args:
+            func: The function to add.
+            opset: The opset version of the function to add.
+        """
+        if self._functions.in_base(opset):
+            warnings.warn(
+                f"Symbolic function '{self._name}' already registered for opset {opset}. "
+                f"Replacing the existing function with new function. This is unexpected. "
+                f"Please report it on {_constants.PYTORCH_GITHUB_ISSUES_URL}.",
+                errors.OnnxExporterWarning,
+            )
+        self._functions.set_base(opset, func)
+
+    def add_custom(self, func: Callable, opset: OpsetVersion) -> None:
+        """Adds a custom symbolic function.
+
+        Args:
+            func: The symbolic function to register.
+            opset: The corresponding opset version.
+        """
+        self._functions.override(opset, func)
+
+    def remove_custom(self, opset: OpsetVersion) -> None:
+        """Removes a custom symbolic function.
+
+        Args:
+            opset: The opset version of the custom function to remove.
+        """
+        if not self._functions.overridden(opset):
+            warnings.warn(
+                f"No custom function registered for '{self._name}' opset {opset}"
+            )
+            return
+        self._functions.remove_override(opset)
+
+    def get_min_supported(self) -> OpsetVersion:
+        """Returns the lowest built-in opset version supported by the function."""
+        return min(self._functions)
+
+
+class SymbolicRegistry:
+    """Registry for symbolic functions.
+
+    The registry maintains a mapping from qualified names to symbolic functions.
+    It is used to register new symbolic functions and to dispatch calls to
+    the appropriate function.
+    """
+
+    def __init__(self) -> None:
+        self._registry: Dict[str, _SymbolicFunctionGroup] = {}
+
+    def register(
+        self, name: str, opset: OpsetVersion, func: Callable, custom: bool = False
+    ) -> None:
+        """Registers a symbolic function.
+
+        Args:
+            name: The qualified name of the function to register. In the form of 'domain::op'.
+                E.g. 'aten::add'.
+            opset: The opset version of the function to register.
+            func: The symbolic function to register.
+            custom: Whether the function is a custom function that overrides existing ones.
+
+        Raises:
+            ValueError: If the separator '::' is not in the name.
+        """
+        if "::" not in name:
+            raise ValueError(
+                f"The name must be in the form of 'domain::op', not '{name}'"
+            )
+        symbolic_functions = self._registry.setdefault(
+            name, _SymbolicFunctionGroup(name)
+        )
+        if custom:
+            symbolic_functions.add_custom(func, opset)
+        else:
+            symbolic_functions.add(func, opset)
+
+    def unregister(self, name: str, opset: OpsetVersion) -> None:
+        """Unregisters a symbolic function.
+
+        Args:
+            name: The qualified name of the function to unregister.
+            opset: The opset version of the function to unregister.
+        """
+        if name not in self._registry:
+            return
+        self._registry[name].remove_custom(opset)
+
+    def get_function_group(self, name: str) -> Optional[_SymbolicFunctionGroup]:
+        """Returns the function group for the given name."""
+        return self._registry.get(name)
+
+    def is_registered_op(self, name: str, version: int) -> bool:
+        """Returns whether the given op is registered for the given opset version."""
+        functions = self.get_function_group(name)
+        if functions is None:
+            return False
+        return functions.get(version) is not None
+
+    def all_functions(self) -> Set[str]:
+        """Returns the set of all registered function names."""
+        return set(self._registry)
+
+
+def onnx_symbolic(
+    name: str,
+    opset: Union[OpsetVersion, Sequence[OpsetVersion]],
+    decorate: Optional[Sequence[Callable]] = None,
+    custom: bool = False,
+) -> Callable:
+    """Registers a symbolic function.
+
+    Usage::
+
+    ```
+    @onnx_symbolic(
+        "aten::symbolic_b",
+        opset=10,
+        decorate=[quantized_aten_handler(scale=1 / 128, zero_point=0)],
+    )
+    @symbolic_helper.parse_args("v", "v", "b")
+    def symbolic_b(g: _C.Graph, x: _C.Value, y: _C.Value, arg1: bool) -> _C.Value: ...
+    ```
+
+    Args:
+        name: The qualified name of the function in the form of 'domain::op'.
+            E.g. 'aten::add'.
+        opset: The opset versions of the function to register at.
+        decorate: A sequence of decorators to apply to the function.
+        custom: Whether the function is a custom symbolic function.
+
+    Raises:
+        ValueError: If the separator '::' is not in the name.
+    """
+
+    def wrapper(func: Callable) -> Callable:
+        decorated = func
+        if decorate is not None:
+            for decorate_func in decorate:
+                decorated = decorate_func(decorated)
+
+        global registry
+        nonlocal opset
+        if isinstance(opset, OpsetVersion):
+            opset = (opset,)
+        for opset_version in opset:
+            registry.register(name, opset_version, decorated, custom=custom)
+
+        # Return the original function because the decorators in "decorate" are only
+        # specific to the instance being registered.
+        return func
+
+    return wrapper
+
+
+def custom_onnx_symbolic(
+    name: str,
+    opset: Union[OpsetVersion, Sequence[OpsetVersion]],
+    decorate: Optional[Sequence[Callable]] = None,
+) -> Callable:
+    """Registers a custom symbolic function.
+
+    Args:
+        name: the qualified name of the function.
+        opset: the opset version of the function.
+        decorate: a sequence of decorators to apply to the function.
+
+    Returns:
+        The decorator.
+
+    Raises:
+        ValueError: If the separator '::' is not in the name.
+    """
+    return onnx_symbolic(name, opset, decorate, custom=True)
+
+
+# The registry for all symbolic functions.
+registry = SymbolicRegistry()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_onnx_supported_ops.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_onnx_supported_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2707298d6d908639104ffcc36846f2e2430ee7b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_onnx_supported_ops.py
@@ -0,0 +1,98 @@
+# mypy: allow-untyped-defs
+import inspect
+from typing import Dict, List, Union
+
+from torch import _C
+from torch.onnx import _constants
+from torch.onnx._internal import registration
+
+
+class _TorchSchema:
+    def __init__(self, schema: Union[_C.FunctionSchema, str]) -> None:
+        if isinstance(schema, _C.FunctionSchema):
+            self.name: str = schema.name
+            self.overload_name: str = schema.overload_name
+            self.arguments: List[str] = [arg.name for arg in schema.arguments]
+            self.optional_arguments: List[str] = []
+            self.returns: List[str] = [ret.name for ret in schema.returns]
+            self.opsets: List[int] = []
+        else:
+            self.name = schema
+            self.overload_name = ""
+            self.arguments = []
+            self.optional_arguments = []
+            self.returns = []
+            self.opsets = []
+
+    def __str__(self) -> str:
+        s = (
+            f"{self.name}.{self.overload_name}("
+            + ", ".join(self.arguments)
+            + ") -> ("
+            + ", ".join(self.returns)
+            + ")"
+            + " in opsets "
+            + ", ".join(str(opset) for opset in self.opsets)
+        )
+        return s
+
+    def __hash__(self):
+        # TODO(thiagocrepaldi): handle overload_name?
+        return hash(self.name)
+
+    def __eq__(self, other) -> bool:
+        if not isinstance(other, _TorchSchema):
+            return False
+        # TODO(thiagocrepaldi): handle overload_name?
+        return self.name == other.name
+
+    def is_aten(self) -> bool:
+        return self.name.startswith("aten::")
+
+    def is_backward(self) -> bool:
+        return "backward" in self.name
+
+
+def _symbolic_argument_count(func):
+    params = []
+    signature = inspect.signature(func)
+    optional_params = []
+    for name, parameter in signature.parameters.items():
+        if name in {"_outputs", "g"}:
+            continue
+        if parameter.default is parameter.empty:
+            optional_params.append(parameter)
+        else:
+            params.append(str(parameter))
+    return params
+
+
+def all_forward_schemas() -> Dict[str, _TorchSchema]:
+    """Returns schemas for all TorchScript forward ops."""
+    torch_schemas = [_TorchSchema(s) for s in _C._jit_get_all_schemas()]
+    return {schema.name: schema for schema in torch_schemas if not schema.is_backward()}
+
+
+def all_symbolics_schemas() -> Dict[str, _TorchSchema]:
+    """Returns schemas for all onnx supported ops."""
+    symbolics_schemas = {}
+
+    for name in registration.registry.all_functions():
+        func_group = registration.registry.get_function_group(name)
+        assert func_group is not None
+        symbolics_schema = _TorchSchema(name)
+        func = func_group.get(_constants.ONNX_MAX_OPSET)
+        if func is not None:
+            symbolics_schema.arguments = _symbolic_argument_count(func)
+            symbolics_schema.opsets = list(
+                range(func_group.get_min_supported(), _constants.ONNX_MAX_OPSET + 1)
+            )
+        else:
+            # Only support opset < 9
+            func = func_group.get(7)
+            symbolics_schema.arguments = _symbolic_argument_count(func)
+            symbolics_schema.opsets = list(range(7, _constants.ONNX_BASE_OPSET))
+
+        symbolics_schemas[name] = symbolics_schema
+
+    return symbolics_schemas
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_type_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_type_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..81bcaeef1107a1ca3291a4468e78c28ed0a906c8
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/_type_utils.py
@@ -0,0 +1,391 @@
+# mypy: allow-untyped-defs
+"""Utilities for converting and operating on ONNX, JIT and torch types."""
+
+from __future__ import annotations
+
+import enum
+import typing
+from typing import Literal
+
+import torch
+from torch._C import _onnx as _C_onnx
+from torch.onnx import errors
+
+
+if typing.TYPE_CHECKING:
+    # Hack to help mypy to recognize torch._C.Value
+    from torch import _C  # noqa: F401
+
+ScalarName = Literal[
+    "Byte",
+    "Char",
+    "Double",
+    "Float",
+    "Half",
+    "Int",
+    "Long",
+    "Short",
+    "Bool",
+    "ComplexHalf",
+    "ComplexFloat",
+    "ComplexDouble",
+    "QInt8",
+    "QUInt8",
+    "QInt32",
+    "BFloat16",
+    "Float8E5M2",
+    "Float8E4M3FN",
+    "Float8E5M2FNUZ",
+    "Float8E4M3FNUZ",
+    "Undefined",
+]
+
+TorchName = Literal[
+    "bool",
+    "uint8_t",
+    "int8_t",
+    "double",
+    "float",
+    "half",
+    "int",
+    "int64_t",
+    "int16_t",
+    "complex32",
+    "complex64",
+    "complex128",
+    "qint8",
+    "quint8",
+    "qint32",
+    "bfloat16",
+    "float8_e5m2",
+    "float8_e4m3fn",
+    "float8_e5m2fnuz",
+    "float8_e4m3fnuz",
+]
+
+
+class JitScalarType(enum.IntEnum):
+    """Scalar types defined in torch.
+
+    Use ``JitScalarType`` to convert from torch and JIT scalar types to ONNX scalar types.
+
+    Examples:
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)
+        >>> # xdoctest: +IGNORE_WANT("win32 has different output")
+        >>> JitScalarType.from_value(torch.ones(1, 2)).onnx_type()
+        TensorProtoDataType.FLOAT
+
+        >>> JitScalarType.from_value(torch_c_value_with_type_float).onnx_type()
+        TensorProtoDataType.FLOAT
+
+        >>> JitScalarType.from_dtype(torch.get_default_dtype).onnx_type()
+        TensorProtoDataType.FLOAT
+
+    """
+
+    # Order defined in https://github.com/pytorch/pytorch/blob/344defc9733a45fee8d0c4d3f5530f631e823196/c10/core/ScalarType.h
+    UINT8 = 0
+    INT8 = enum.auto()  # 1
+    INT16 = enum.auto()  # 2
+    INT = enum.auto()  # 3
+    INT64 = enum.auto()  # 4
+    HALF = enum.auto()  # 5
+    FLOAT = enum.auto()  # 6
+    DOUBLE = enum.auto()  # 7
+    COMPLEX32 = enum.auto()  # 8
+    COMPLEX64 = enum.auto()  # 9
+    COMPLEX128 = enum.auto()  # 10
+    BOOL = enum.auto()  # 11
+    QINT8 = enum.auto()  # 12
+    QUINT8 = enum.auto()  # 13
+    QINT32 = enum.auto()  # 14
+    BFLOAT16 = enum.auto()  # 15
+    FLOAT8E5M2 = enum.auto()  # 16
+    FLOAT8E4M3FN = enum.auto()  # 17
+    FLOAT8E5M2FNUZ = enum.auto()  # 18
+    FLOAT8E4M3FNUZ = enum.auto()  # 19
+    UNDEFINED = enum.auto()  # 20
+
+    @classmethod
+    def _from_name(cls, name: ScalarName | TorchName | str | None) -> JitScalarType:
+        """Convert a JIT scalar type or torch type name to ScalarType.
+
+        Note: DO NOT USE this API when `name` comes from a `torch._C.Value.type()` calls.
+            A "RuntimeError: INTERNAL ASSERT FAILED at "../aten/src/ATen/core/jit_type_base.h" can
+            be raised in several scenarios where shape info is not present.
+            Instead use `from_value` API which is safer.
+
+        Args:
+            name: JIT scalar type name (Byte) or torch type name (uint8_t).
+
+        Returns:
+            JitScalarType
+
+        Raises:
+           OnnxExporterError: if name is not a valid scalar type name or if it is None.
+        """
+        if name is None:
+            raise errors.OnnxExporterError("Scalar type name cannot be None")
+        if valid_scalar_name(name):
+            return _SCALAR_NAME_TO_TYPE[name]  # type: ignore[index]
+        if valid_torch_name(name):
+            return _TORCH_NAME_TO_SCALAR_TYPE[name]  # type: ignore[index]
+
+        raise errors.OnnxExporterError(f"Unknown torch or scalar type: '{name}'")
+
+    @classmethod
+    def from_dtype(cls, dtype: torch.dtype | None) -> JitScalarType:
+        """Convert a torch dtype to JitScalarType.
+
+        Note: DO NOT USE this API when `dtype` comes from a `torch._C.Value.type()` calls.
+            A "RuntimeError: INTERNAL ASSERT FAILED at "../aten/src/ATen/core/jit_type_base.h" can
+            be raised in several scenarios where shape info is not present.
+            Instead use `from_value` API which is safer.
+
+        Args:
+            dtype: A torch.dtype to create a JitScalarType from
+
+        Returns:
+            JitScalarType
+
+        Raises:
+            OnnxExporterError: if dtype is not a valid torch.dtype or if it is None.
+        """
+        if dtype not in _DTYPE_TO_SCALAR_TYPE:
+            raise errors.OnnxExporterError(f"Unknown dtype: {dtype}")
+        return _DTYPE_TO_SCALAR_TYPE[dtype]
+
+    @classmethod
+    def from_onnx_type(
+        cls, onnx_type: int | _C_onnx.TensorProtoDataType | None
+    ) -> JitScalarType:
+        """Convert a ONNX data type to JitScalarType.
+
+        Args:
+            onnx_type: A torch._C._onnx.TensorProtoDataType to create a JitScalarType from
+
+        Returns:
+            JitScalarType
+
+        Raises:
+            OnnxExporterError: if dtype is not a valid torch.dtype or if it is None.
+        """
+        if onnx_type not in _ONNX_TO_SCALAR_TYPE:
+            raise errors.OnnxExporterError(f"Unknown onnx_type: {onnx_type}")
+        return _ONNX_TO_SCALAR_TYPE[typing.cast(_C_onnx.TensorProtoDataType, onnx_type)]
+
+    @classmethod
+    def from_value(
+        cls, value: None | torch._C.Value | torch.Tensor, default=None
+    ) -> JitScalarType:
+        """Create a JitScalarType from an value's scalar type.
+
+        Args:
+            value: An object to fetch scalar type from.
+            default: The JitScalarType to return if a valid scalar cannot be fetched from value
+
+        Returns:
+            JitScalarType.
+
+        Raises:
+            OnnxExporterError: if value does not have a valid scalar type and default is None.
+            SymbolicValueError: when value.type()'s info are empty and default is None
+        """
+
+        if not isinstance(value, (torch._C.Value, torch.Tensor)) or (
+            isinstance(value, torch._C.Value) and value.node().mustBeNone()
+        ):
+            # default value of type JitScalarType is returned when value is not valid
+            if default is None:
+                raise errors.OnnxExporterError(
+                    "value must be either torch._C.Value or torch.Tensor objects."
+                )
+            elif not isinstance(default, JitScalarType):
+                raise errors.OnnxExporterError(
+                    "default value must be a JitScalarType object."
+                )
+            return default
+
+        # Each value type has their own way of storing scalar type
+        if isinstance(value, torch.Tensor):
+            return cls.from_dtype(value.dtype)
+        if isinstance(value.type(), torch.ListType):
+            try:
+                return cls.from_dtype(value.type().getElementType().dtype())
+            except RuntimeError:
+                return cls._from_name(str(value.type().getElementType()))
+        if isinstance(value.type(), torch._C.OptionalType):
+            if value.type().getElementType().dtype() is None:
+                if isinstance(default, JitScalarType):
+                    return default
+                raise errors.OnnxExporterError(
+                    "default value must be a JitScalarType object."
+                )
+            return cls.from_dtype(value.type().getElementType().dtype())
+
+        scalar_type = None
+        if value.node().kind() != "prim::Constant" or not isinstance(
+            value.type(), torch._C.NoneType
+        ):
+            # value must be a non-list torch._C.Value scalar
+            scalar_type = value.type().scalarType()
+
+        if scalar_type is not None:
+            return cls._from_name(scalar_type)
+
+        # When everything fails... try to default
+        if default is not None:
+            return default
+        raise errors.SymbolicValueError(
+            f"Cannot determine scalar type for this '{type(value.type())}' instance and "
+            "a default value was not provided.",
+            value,
+        )
+
+    def scalar_name(self) -> ScalarName:
+        """Convert a JitScalarType to a JIT scalar type name."""
+        return _SCALAR_TYPE_TO_NAME[self]
+
+    def torch_name(self) -> TorchName:
+        """Convert a JitScalarType to a torch type name."""
+        return _SCALAR_TYPE_TO_TORCH_NAME[self]
+
+    def dtype(self) -> torch.dtype:
+        """Convert a JitScalarType to a torch dtype."""
+        return _SCALAR_TYPE_TO_DTYPE[self]
+
+    def onnx_type(self) -> _C_onnx.TensorProtoDataType:
+        """Convert a JitScalarType to an ONNX data type."""
+        if self not in _SCALAR_TYPE_TO_ONNX:
+            raise errors.OnnxExporterError(
+                f"Scalar type {self} cannot be converted to ONNX"
+            )
+        return _SCALAR_TYPE_TO_ONNX[self]
+
+    def onnx_compatible(self) -> bool:
+        """Return whether this JitScalarType is compatible with ONNX."""
+        return (
+            self in _SCALAR_TYPE_TO_ONNX
+            and self != JitScalarType.UNDEFINED
+            and self != JitScalarType.COMPLEX32
+        )
+
+
+def valid_scalar_name(scalar_name: ScalarName | str) -> bool:
+    """Return whether the given scalar name is a valid JIT scalar type name."""
+    return scalar_name in _SCALAR_NAME_TO_TYPE
+
+
+def valid_torch_name(torch_name: TorchName | str) -> bool:
+    """Return whether the given torch name is a valid torch type name."""
+    return torch_name in _TORCH_NAME_TO_SCALAR_TYPE
+
+
+# https://github.com/pytorch/pytorch/blob/344defc9733a45fee8d0c4d3f5530f631e823196/c10/core/ScalarType.h
+_SCALAR_TYPE_TO_NAME: dict[JitScalarType, ScalarName] = {
+    JitScalarType.BOOL: "Bool",
+    JitScalarType.UINT8: "Byte",
+    JitScalarType.INT8: "Char",
+    JitScalarType.INT16: "Short",
+    JitScalarType.INT: "Int",
+    JitScalarType.INT64: "Long",
+    JitScalarType.HALF: "Half",
+    JitScalarType.FLOAT: "Float",
+    JitScalarType.DOUBLE: "Double",
+    JitScalarType.COMPLEX32: "ComplexHalf",
+    JitScalarType.COMPLEX64: "ComplexFloat",
+    JitScalarType.COMPLEX128: "ComplexDouble",
+    JitScalarType.QINT8: "QInt8",
+    JitScalarType.QUINT8: "QUInt8",
+    JitScalarType.QINT32: "QInt32",
+    JitScalarType.BFLOAT16: "BFloat16",
+    JitScalarType.FLOAT8E5M2: "Float8E5M2",
+    JitScalarType.FLOAT8E4M3FN: "Float8E4M3FN",
+    JitScalarType.FLOAT8E5M2FNUZ: "Float8E5M2FNUZ",
+    JitScalarType.FLOAT8E4M3FNUZ: "Float8E4M3FNUZ",
+    JitScalarType.UNDEFINED: "Undefined",
+}
+
+_SCALAR_NAME_TO_TYPE: dict[ScalarName, JitScalarType] = {
+    v: k for k, v in _SCALAR_TYPE_TO_NAME.items()
+}
+
+_SCALAR_TYPE_TO_TORCH_NAME: dict[JitScalarType, TorchName] = {
+    JitScalarType.BOOL: "bool",
+    JitScalarType.UINT8: "uint8_t",
+    JitScalarType.INT8: "int8_t",
+    JitScalarType.INT16: "int16_t",
+    JitScalarType.INT: "int",
+    JitScalarType.INT64: "int64_t",
+    JitScalarType.HALF: "half",
+    JitScalarType.FLOAT: "float",
+    JitScalarType.DOUBLE: "double",
+    JitScalarType.COMPLEX32: "complex32",
+    JitScalarType.COMPLEX64: "complex64",
+    JitScalarType.COMPLEX128: "complex128",
+    JitScalarType.QINT8: "qint8",
+    JitScalarType.QUINT8: "quint8",
+    JitScalarType.QINT32: "qint32",
+    JitScalarType.BFLOAT16: "bfloat16",
+    JitScalarType.FLOAT8E5M2: "float8_e5m2",
+    JitScalarType.FLOAT8E4M3FN: "float8_e4m3fn",
+    JitScalarType.FLOAT8E5M2FNUZ: "float8_e5m2fnuz",
+    JitScalarType.FLOAT8E4M3FNUZ: "float8_e4m3fnuz",
+}
+
+_TORCH_NAME_TO_SCALAR_TYPE: dict[TorchName, JitScalarType] = {
+    v: k for k, v in _SCALAR_TYPE_TO_TORCH_NAME.items()
+}
+
+_SCALAR_TYPE_TO_ONNX = {
+    JitScalarType.BOOL: _C_onnx.TensorProtoDataType.BOOL,
+    JitScalarType.UINT8: _C_onnx.TensorProtoDataType.UINT8,
+    JitScalarType.INT8: _C_onnx.TensorProtoDataType.INT8,
+    JitScalarType.INT16: _C_onnx.TensorProtoDataType.INT16,
+    JitScalarType.INT: _C_onnx.TensorProtoDataType.INT32,
+    JitScalarType.INT64: _C_onnx.TensorProtoDataType.INT64,
+    JitScalarType.HALF: _C_onnx.TensorProtoDataType.FLOAT16,
+    JitScalarType.FLOAT: _C_onnx.TensorProtoDataType.FLOAT,
+    JitScalarType.DOUBLE: _C_onnx.TensorProtoDataType.DOUBLE,
+    JitScalarType.COMPLEX64: _C_onnx.TensorProtoDataType.COMPLEX64,
+    JitScalarType.COMPLEX128: _C_onnx.TensorProtoDataType.COMPLEX128,
+    JitScalarType.BFLOAT16: _C_onnx.TensorProtoDataType.BFLOAT16,
+    JitScalarType.UNDEFINED: _C_onnx.TensorProtoDataType.UNDEFINED,
+    JitScalarType.COMPLEX32: _C_onnx.TensorProtoDataType.UNDEFINED,
+    JitScalarType.QINT8: _C_onnx.TensorProtoDataType.INT8,
+    JitScalarType.QUINT8: _C_onnx.TensorProtoDataType.UINT8,
+    JitScalarType.QINT32: _C_onnx.TensorProtoDataType.INT32,
+    JitScalarType.FLOAT8E5M2: _C_onnx.TensorProtoDataType.FLOAT8E5M2,
+    JitScalarType.FLOAT8E4M3FN: _C_onnx.TensorProtoDataType.FLOAT8E4M3FN,
+    JitScalarType.FLOAT8E5M2FNUZ: _C_onnx.TensorProtoDataType.FLOAT8E5M2FNUZ,
+    JitScalarType.FLOAT8E4M3FNUZ: _C_onnx.TensorProtoDataType.FLOAT8E4M3FNUZ,
+}
+
+_ONNX_TO_SCALAR_TYPE = {v: k for k, v in _SCALAR_TYPE_TO_ONNX.items()}
+
+# source of truth is
+# https://github.com/pytorch/pytorch/blob/master/torch/csrc/utils/tensor_dtypes.cpp
+_SCALAR_TYPE_TO_DTYPE = {
+    JitScalarType.BOOL: torch.bool,
+    JitScalarType.UINT8: torch.uint8,
+    JitScalarType.INT8: torch.int8,
+    JitScalarType.INT16: torch.short,
+    JitScalarType.INT: torch.int,
+    JitScalarType.INT64: torch.int64,
+    JitScalarType.HALF: torch.half,
+    JitScalarType.FLOAT: torch.float,
+    JitScalarType.DOUBLE: torch.double,
+    JitScalarType.COMPLEX32: torch.complex32,
+    JitScalarType.COMPLEX64: torch.complex64,
+    JitScalarType.COMPLEX128: torch.complex128,
+    JitScalarType.QINT8: torch.qint8,
+    JitScalarType.QUINT8: torch.quint8,
+    JitScalarType.QINT32: torch.qint32,
+    JitScalarType.BFLOAT16: torch.bfloat16,
+    JitScalarType.FLOAT8E5M2: torch.float8_e5m2,
+    JitScalarType.FLOAT8E4M3FN: torch.float8_e4m3fn,
+    JitScalarType.FLOAT8E5M2FNUZ: torch.float8_e5m2fnuz,
+    JitScalarType.FLOAT8E4M3FNUZ: torch.float8_e4m3fnuz,
+}
+
+_DTYPE_TO_SCALAR_TYPE = {v: k for k, v in _SCALAR_TYPE_TO_DTYPE.items()}
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/errors.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/errors.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6e035a8a85f1d4786e2b08f1a73231c8aec9696
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/errors.py
@@ -0,0 +1,103 @@
+"""ONNX exporter exceptions."""
+
+from __future__ import annotations
+
+
+__all__ = [
+    "OnnxExporterWarning",
+    "SymbolicValueError",
+    "UnsupportedOperatorError",
+]
+
+import textwrap
+from typing import TYPE_CHECKING
+
+
+if TYPE_CHECKING:
+    from torch import _C
+
+
+class OnnxExporterWarning(UserWarning):
+    """Warnings in the ONNX exporter."""
+
+
+class OnnxExporterError(RuntimeError):
+    """Errors raised by the ONNX exporter. This is the base class for all exporter errors."""
+
+
+class UnsupportedOperatorError(OnnxExporterError):
+    """Raised when an operator is unsupported by the exporter."""
+
+    # NOTE: This is legacy and is only used by the torchscript exporter
+    # Clean up when the torchscript exporter is removed
+    def __init__(self, name: str, version: int, supported_version: int | None):
+        from torch.onnx import _constants
+        from torch.onnx._internal import diagnostics
+
+        if supported_version is not None:
+            diagnostic_rule: diagnostics.infra.Rule = (
+                diagnostics.rules.operator_supported_in_newer_opset_version
+            )
+            msg = diagnostic_rule.format_message(name, version, supported_version)
+            diagnostics.diagnose(diagnostic_rule, diagnostics.levels.ERROR, msg)
+        else:
+            if name.startswith(("aten::", "prim::", "quantized::")):
+                diagnostic_rule = diagnostics.rules.missing_standard_symbolic_function
+                msg = diagnostic_rule.format_message(
+                    name, version, _constants.PYTORCH_GITHUB_ISSUES_URL
+                )
+                diagnostics.diagnose(diagnostic_rule, diagnostics.levels.ERROR, msg)
+            else:
+                diagnostic_rule = diagnostics.rules.missing_custom_symbolic_function
+                msg = diagnostic_rule.format_message(name)
+                diagnostics.diagnose(diagnostic_rule, diagnostics.levels.ERROR, msg)
+        super().__init__(msg)
+
+
+class SymbolicValueError(OnnxExporterError):
+    """Errors around TorchScript values and nodes."""
+
+    # NOTE: This is legacy and is only used by the torchscript exporter
+    # Clean up when the torchscript exporter is removed
+    def __init__(self, msg: str, value: _C.Value):
+        message = (
+            f"{msg}  [Caused by the value '{value}' (type '{value.type()}') in the "
+            f"TorchScript graph. The containing node has kind '{value.node().kind()}'.] "
+        )
+
+        code_location = value.node().sourceRange()
+        if code_location:
+            message += f"\n    (node defined in {code_location})"
+
+        try:
+            # Add its input and output to the message.
+            message += "\n\n"
+            message += textwrap.indent(
+                (
+                    "Inputs:\n"
+                    + (
+                        "\n".join(
+                            f"    #{i}: {input_}  (type '{input_.type()}')"
+                            for i, input_ in enumerate(value.node().inputs())
+                        )
+                        or "    Empty"
+                    )
+                    + "\n"
+                    + "Outputs:\n"
+                    + (
+                        "\n".join(
+                            f"    #{i}: {output}  (type '{output.type()}')"
+                            for i, output in enumerate(value.node().outputs())
+                        )
+                        or "    Empty"
+                    )
+                ),
+                "    ",
+            )
+        except AttributeError:
+            message += (
+                " Failed to obtain its input and output for debugging. "
+                "Please refer to the TorchScript graph for debugging information."
+            )
+
+        super().__init__(message)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/operators.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/operators.py
new file mode 100644
index 0000000000000000000000000000000000000000..88ac6779f91ccec30518b5fa302f4d3f16e6b77a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/operators.py
@@ -0,0 +1,47 @@
+# mypy: allow-untyped-defs
+r"""This file provides a location for operators that help exporting models via onnx.
+
+E.g. `shape_as_tensor` and `reshape_from_tensor_shape`
+are to make all dynamic sizes operations traceable.
+
+NOTE: at one point these functions were implemented differently.
+Since then we have implemented these directly in ATen, so this
+file is kept purely for backward-compatibility.
+"""
+
+import torch
+import torch.onnx
+
+
+def shape_as_tensor(x):
+    """Get the shape of a tensor as a tensor.
+
+    Args:
+        x (Tensor): The input tensor.
+
+    Returns:
+        Tensor: A tensor of shape [len(x.shape)] containing the size of each dimension of x.
+
+    Example:
+        >>> x = torch.randn(2, 3)
+        >>> shape_as_tensor(x)
+        tensor([2, 3])
+
+    """
+    return torch._shape_as_tensor(x)
+
+
+def reshape_from_tensor_shape(x, shape):
+    """Reshape a tensor to the given shape.
+
+    This function is used to make dynamic size operations traceable when exporting models via ONNX.
+    This function is kept for backward-compatibility. It is implemented directly in ATen.
+
+    Parameters:
+        x (Tensor): the tensor to be reshaped.
+        shape (Tensor): the target shape.
+
+    Returns:
+        Tensor: the reshaped tensor.
+    """
+    return torch._reshape_from_tensor(x, shape)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_caffe2.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_caffe2.py
new file mode 100644
index 0000000000000000000000000000000000000000..83a2ff6c32ec9511a86bf42b9ad76c7a24a5b2cf
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_caffe2.py
@@ -0,0 +1,361 @@
+# mypy: allow-untyped-defs
+# mypy: disable-error-code=arg-type
+import importlib
+import inspect
+
+from torch.onnx import symbolic_helper, symbolic_opset9 as opset9
+from torch.onnx._internal import jit_utils, registration
+
+
+def register_quantized_ops(domain: str, version: int):
+    # Register all quantized ops
+    module = importlib.import_module("torch.onnx.symbolic_caffe2")
+    quant_version_ops = inspect.getmembers(module)
+    aten_q_ops = {
+        "relu",
+        "_empty_affine_quantized",
+        "dequantize",
+        "quantize_per_tensor",
+        "upsample_nearest2d",
+        "avg_pool2d",
+        "reshape",
+        "slice",
+        "cat",
+        "max_pool2d",
+        "sigmoid",
+    }
+    for op, func in quant_version_ops:
+        name = f"{domain}::{op}"
+        if inspect.isfunction(func) and not registration.registry.is_registered_op(
+            name, version
+        ):
+            if op in aten_q_ops:
+                # Override the builtin aten ops
+                registration.registry.register(
+                    f"aten::{op}", version, func, custom=True
+                )
+            registration.registry.register(name, version, func)
+
+
+def _permute_helper(g: jit_utils.GraphContext, input, axes):
+    quant_args = {
+        "axes_i": axes,
+        "Y_scale_f": symbolic_helper._node_get(input.node(), "Y_scale"),
+        "Y_zero_point_i": symbolic_helper._node_get(input.node(), "Y_zero_point"),
+    }
+    output = g.op("_caffe2::Int8Transpose", input, **quant_args)
+    symbolic_helper._quantized_ops.add(output)
+    return output
+
+
+def nchw2nhwc(g: jit_utils.GraphContext, input):
+    axes = [0, 2, 3, 1]
+    return _permute_helper(g, input, axes)
+
+
+def nhwc2nchw(g: jit_utils.GraphContext, input):
+    axes = [0, 3, 1, 2]
+    return _permute_helper(g, input, axes)
+
+
+def linear_prepack(g: jit_utils.GraphContext, weight, bias):
+    # Mapping to a dummy caffe2 prepack node.
+    # During the onnx -> c2 conversion we can look up original weight and bias
+    # from this node
+    output = g.op("_caffe2::WeightPrepack", weight, bias)
+    symbolic_helper._quantized_ops.add(output)
+    return output
+
+
+@symbolic_helper.parse_args("v", "v", "v", "f", "i")
+def linear(g: jit_utils.GraphContext, input, weight, bias, scale, zero_point):
+    kwargs = {
+        "Y_scale_f": scale,
+        "Y_zero_point_i": zero_point,
+    }
+    output = g.op("_caffe2::Int8FC", input, weight, bias, **kwargs)
+    symbolic_helper._quantized_ops.add(output)
+    return output
+
+
+def conv_prepack(
+    g: jit_utils.GraphContext, input, weight, bias, stride, padding, dilation, groups
+):
+    # Mapping to a dummy caffe2 prepack node.
+    # During the onnx -> c2 conversion we can look up original weight and bias
+    # from this node
+    output = g.op("_caffe2::WeightPrepack", input, weight, bias)
+    symbolic_helper._quantized_ops.add(output)
+    return output
+
+
+@symbolic_helper.parse_args("v", "v", "v", "is", "is", "is", "i", "f", "i")
+def conv2d(
+    g: jit_utils.GraphContext,
+    input,
+    weight,
+    bias,
+    stride,
+    padding,
+    dilation,
+    groups,
+    scale,
+    zero_point,
+):
+    kernel_size = weight.node()["shape"][1:3]
+    kwargs = {
+        "strides_i": stride,
+        "pads_i": padding + padding,
+        "dilations_i": dilation,
+        "group_i": groups,
+        "kernels_i": kernel_size,
+        "order_s": "NHWC",
+        "Y_scale_f": scale,
+        "Y_zero_point_i": zero_point,
+    }
+    output = g.op("_caffe2::Int8Conv", input, weight, bias, **kwargs)
+    symbolic_helper._quantized_ops.add(output)
+    return output
+
+
+@symbolic_helper.parse_args("v", "v", "v", "is", "is", "is", "i", "f", "i")
+def conv2d_relu(
+    g: jit_utils.GraphContext,
+    input,
+    weight,
+    bias,
+    stride,
+    padding,
+    dilation,
+    groups,
+    scale,
+    zero_point,
+):
+    kernel_size = weight.node()["shape"][1:3]
+    kwargs = {
+        "strides_i": stride,
+        "pads_i": padding + padding,
+        "dilations_i": dilation,
+        "group_i": groups,
+        "kernels_i": kernel_size,
+        "order_s": "NHWC",
+        "Y_scale_f": scale,
+        "Y_zero_point_i": zero_point,
+    }
+    output = g.op("_caffe2::Int8ConvRelu", input, weight, bias, **kwargs)
+    symbolic_helper._quantized_ops.add(output)
+    return output
+
+
+@symbolic_helper.parse_args("v", "v", "f", "i")
+def add(g: jit_utils.GraphContext, input_a, input_b, scale, zero_point):
+    kwargs = {
+        "Y_scale_f": scale,
+        "Y_zero_point_i": zero_point,
+    }
+    output = g.op("_caffe2::Int8Add", input_a, input_b, **kwargs)
+    symbolic_helper._quantized_ops.add(output)
+    return output
+
+
+@symbolic_helper.parse_args("v")
+def relu(g: jit_utils.GraphContext, input):
+    if input not in symbolic_helper._quantized_ops:
+        return opset9.relu(g, input)
+    kwargs = {
+        "Y_scale_f": symbolic_helper._node_get(input.node(), "Y_scale"),
+        "Y_zero_point_i": symbolic_helper._node_get(input.node(), "Y_zero_point"),
+    }
+    output = g.op("_caffe2::Int8Relu", input, **kwargs)
+    symbolic_helper._quantized_ops.add(output)
+    return output
+
+
+@symbolic_helper.parse_args("v", "f", "i", "t")
+def quantize_per_tensor(g: jit_utils.GraphContext, input, scale, zero_point, dtype):
+    kwargs = {
+        "Y_scale_f": scale,
+        "Y_zero_point_i": zero_point,
+    }
+    output = g.op("_caffe2::Int8Quantize", input, **kwargs)
+    symbolic_helper._quantized_ops.add(output)
+    return output
+
+
+@symbolic_helper.parse_args("v")
+def dequantize(g: jit_utils.GraphContext, input):
+    return g.op("_caffe2::Int8Dequantize", input)
+
+
+@symbolic_helper.parse_args("v", "t", "t", "t", "t", "t", "t", "t")
+def _empty_affine_quantized(
+    g: jit_utils.GraphContext,
+    input,
+    shape,
+    scale,
+    zero_point,
+    dtype,
+    pin_memory,
+    memory_format,
+    layout,
+):
+    return input
+
+
+def upsample_nearest2d(
+    g: jit_utils.GraphContext,
+    input,
+    output_size,
+    align_corners=None,
+    scales_h=None,
+    scales_w=None,
+):
+    if input not in symbolic_helper._quantized_ops:
+        return opset9.upsample_nearest2d(g, input, output_size, align_corners)  # type: ignore[attr-defined]
+
+    output_size = symbolic_helper._parse_arg(output_size, "is")
+    kwargs = {
+        "output_size_i": output_size,
+        "Y_scale_f": symbolic_helper._node_get(input.node(), "Y_scale"),
+        "Y_zero_point_i": symbolic_helper._node_get(input.node(), "Y_zero_point"),
+    }
+    input = nchw2nhwc(g, input)
+    output = g.op("_caffe2::Int8ResizeNearest", input, **kwargs)
+    output = nhwc2nchw(g, output)
+    symbolic_helper._quantized_ops.add(output)
+    return output
+
+
+@symbolic_helper.parse_args("v", "is", "is", "is", "is", "i")
+def max_pool2d(
+    g: jit_utils.GraphContext,
+    input,
+    kernel_size,
+    stride,
+    padding,
+    dilation,
+    ceil_mode,
+):
+    if input not in symbolic_helper._quantized_ops:
+        return opset9.max_pool2d(  # type: ignore[attr-defined]
+            g, input, kernel_size, stride, padding, dilation, ceil_mode
+        )
+    kwargs = {
+        "strides_i": stride,
+        "pads_i": padding + padding,
+        "kernel_i": kernel_size[0],
+        "order_s": "NHWC",
+        "Y_scale_f": symbolic_helper._node_get(input.node(), "Y_scale"),
+        "Y_zero_point_i": symbolic_helper._node_get(input.node(), "Y_zero_point"),
+    }
+    input = nchw2nhwc(g, input)
+    output = g.op("_caffe2::Int8MaxPool", input, **kwargs)
+    output = nhwc2nchw(g, output)
+    symbolic_helper._quantized_ops.add(output)
+    return output
+
+
+@symbolic_helper.parse_args("v", "is", "is", "is", "i", "i", "none")
+def avg_pool2d(
+    g: jit_utils.GraphContext,
+    input,
+    kernel_size,
+    stride,
+    padding,
+    ceil_mode,
+    count_include_pad,
+    divisor_override=None,
+):
+    if input not in symbolic_helper._quantized_ops:
+        return opset9.avg_pool2d(  # type: ignore[attr-defined]
+            g,
+            input,
+            kernel_size,
+            stride,
+            padding,
+            ceil_mode,
+            count_include_pad,
+            divisor_override,
+        )
+    kwargs = {
+        "strides_i": stride,
+        "pads_i": padding + padding,
+        "kernel_i": kernel_size[0],
+        "order_s": "NHWC",
+        "Y_scale_f": symbolic_helper._node_get(input.node(), "Y_scale"),
+        "Y_zero_point_i": symbolic_helper._node_get(input.node(), "Y_zero_point"),
+    }
+    input = nchw2nhwc(g, input)
+    output = g.op("_caffe2::Int8AveragePool", input, **kwargs)
+    output = nhwc2nchw(g, output)
+    symbolic_helper._quantized_ops.add(output)
+    return output
+
+
+def reshape(g: jit_utils.GraphContext, input, shape):
+    if input not in symbolic_helper._quantized_ops:
+        return opset9.reshape(g, input, shape)
+
+    kwargs = {
+        "Y_scale_f": symbolic_helper._node_get(input.node(), "Y_scale"),
+        "Y_zero_point_i": symbolic_helper._node_get(input.node(), "Y_zero_point"),
+    }
+    output = g.op("_caffe2::Int8Reshape", input, shape, **kwargs)
+    symbolic_helper._quantized_ops.add(output)
+    return output
+
+
+@symbolic_helper.parse_args("v", "v", "v", "v", "i")
+def slice(g: jit_utils.GraphContext, input, dim, start, end, step):
+    if input not in symbolic_helper._quantized_ops:
+        return opset9.slice(g, input, dim, start, end, step)
+
+    if step != 1:
+        raise RuntimeError("ONNX quantized slice export only works for step 1.")
+    start = symbolic_helper._parse_arg(start, "i")
+    end = symbolic_helper._parse_arg(end, "i")
+    dim = symbolic_helper._parse_arg(dim, "i")
+
+    kwargs = {
+        "start_idx_i": start,
+        "end_idx_i": end,
+        "dim_i": dim,
+        "Y_scale_f": symbolic_helper._node_get(input.node(), "Y_scale"),
+        "Y_zero_point_i": symbolic_helper._node_get(input.node(), "Y_zero_point"),
+    }
+    output = g.op("_caffe2::Int8Slice", input, **kwargs)
+    symbolic_helper._quantized_ops.add(output)
+    return output
+
+
+def cat(g: jit_utils.GraphContext, tensor_list, dim, scale=None, zero_point=None):
+    tensors = symbolic_helper._unpack_list(tensor_list)
+    input = tensors[0]
+    if input not in symbolic_helper._quantized_ops:
+        return opset9.cat(g, tensor_list, dim)
+
+    dim = symbolic_helper._parse_arg(dim, "i")
+    kwargs = {
+        "Y_scale_f": tensors[0].node()["Y_scale"],
+        "Y_zero_point_i": tensors[0].node()["Y_zero_point"],
+    }
+    output = g.op("_caffe2::Int8Concat", *tensors, axis_i=dim, **kwargs)
+    symbolic_helper._quantized_ops.add(output)
+    return output
+
+
+@symbolic_helper.parse_args("v")
+def sigmoid(g: jit_utils.GraphContext, input):
+    if input not in symbolic_helper._quantized_ops:
+        return opset9.sigmoid(g, input)
+    # Caffe2 expects the output scale to be 1/2^8
+    # and output zero_point to be 0 (quint8 type)
+    out_scale = 1.0 / 256
+    zero_point = 0
+    kwargs = {
+        "Y_scale_f": out_scale,
+        "Y_zero_point_i": zero_point,
+    }
+    output = g.op("_caffe2::Int8Sigmoid", input, **kwargs)
+    symbolic_helper._quantized_ops.add(output)
+    return output
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_helper.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_helper.py
new file mode 100644
index 0000000000000000000000000000000000000000..799f2d6f81a568345fa876dde6cb15faad50053b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_helper.py
@@ -0,0 +1,2261 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import functools
+import inspect
+import math
+import sys
+import typing
+import warnings
+from typing import Any, Callable, Literal, NoReturn, Sequence, TypeVar as _TypeVar
+from typing_extensions import Concatenate as _Concatenate, ParamSpec as _ParamSpec
+
+import torch
+import torch._C._onnx as _C_onnx
+from torch import _C
+
+# Monkey-patch graph manipulation methods on Graph, used for the ONNX symbolics
+from torch.onnx import _constants, _type_utils, errors, utils
+from torch.onnx._globals import GLOBALS
+from torch.onnx._internal import jit_utils
+
+
+if typing.TYPE_CHECKING:
+    from torch.types import Number
+
+_T = _TypeVar("_T")
+_U = _TypeVar("_U")
+_P = _ParamSpec("_P")
+
+# ---------------------------------------------------------------------------------
+# Helper functions
+# ---------------------------------------------------------------------------------
+
+_ValueDescriptor = Literal[
+    "v",
+    "i",
+    "is",
+    "f",
+    "fs",
+    "b",
+    "s",
+    "t",
+    "none",
+]
+
+
+def _parse_arg(
+    value,
+    desc: _ValueDescriptor,
+    arg_name: str | None = None,
+    node_name: str | None = None,
+):
+    if desc == "none":
+        return value
+    if desc == "v" or not _is_value(value):
+        return value
+
+    node = value.node()
+    if node.mustBeNone():
+        return None
+    if node.kind() == "onnx::Constant":
+        node_val = _node_get(node, "value")
+        if desc == "i":
+            return int(node_val)
+        elif desc == "f":
+            return float(node_val)
+        elif desc == "b":
+            return bool(node_val)
+        elif desc == "s":
+            return str(node_val)
+        elif desc == "t":
+            return node_val
+        elif desc == "is":
+            return [int(v) for v in node_val]
+        elif desc == "fs":
+            return [float(v) for v in node_val]
+        else:
+            raise errors.SymbolicValueError(
+                f"ONNX symbolic does not understand the Constant node '{node}' "
+                f"specified with descriptor '{desc}'.",
+                value,
+            )
+    elif node.kind() == "prim::ListConstruct":
+        if desc == "is":
+            for v in node.inputs():
+                element_node = v.node()
+                if element_node.kind() != "onnx::Constant":
+                    raise errors.SymbolicValueError(
+                        f"Failed to export a node '{element_node}' "
+                        f"(in list node {node}) "
+                        f"because it is not constant. "
+                        f"Please try to make things (e.g. kernel sizes) static if possible.",
+                        value,
+                    )
+            return [int(_node_get(v.node(), "value")) for v in value.node().inputs()]
+        else:
+            raise errors.SymbolicValueError(
+                f"ONNX symbolic does not know how to unpack the ListConstruct node that "
+                f"is not a list of integers: '{node}'",
+                value,
+            )
+
+    if arg_name is None or node_name is None:
+        raise errors.SymbolicValueError(
+            f"Expected node type 'onnx::Constant', got '{node.kind()}'.",
+            value,
+        )
+
+    raise errors.SymbolicValueError(
+        "Expected node type 'onnx::Constant' "
+        f"for argument '{arg_name}' of node '{node_name}', got '{node.kind()}'.",
+        value,
+    )
+
+
+def _node_get(node: _C.Node, key: str):
+    """Gets attributes of a node which is polymorphic over return type."""
+    assert isinstance(node, _C.Node)
+    sel = node.kindOf(key)
+    return getattr(node, sel)(key)
+
+
+def _is_onnx_constant(value: _C.Value):
+    """Whether a Value is an ONNX constant."""
+    return value.node().kind() == "onnx::Constant"
+
+
+def _maybe_get_const(
+    value: _C.Value | torch.Tensor | Number | Sequence | None,
+    descriptor: _ValueDescriptor,
+):
+    # NOTE: prim::Constant at this stage usually means something not compatible in ONNX,
+    # otherwise it'd be converted to onnx::Constant
+    # TODO(justinchuby): Replace insinstance with _is_value once we figure out mypy
+    if isinstance(value, _C.Value) and _is_onnx_constant(value):
+        return _parse_arg(value, descriptor)
+    return value
+
+
+def _maybe_get_scalar(value):
+    value_t = _maybe_get_const(value, "t")
+    if isinstance(value_t, torch.Tensor) and value_t.shape == ():
+        return value_t
+    return value
+
+
+def _get_const(value, desc, arg_name):
+    if not _is_constant(value):
+        raise errors.SymbolicValueError(
+            f"ONNX symbolic expected a constant value of the '{arg_name}' argument, "
+            f"got '{value}'",
+            value,
+        )
+    return _parse_arg(value, desc)
+
+
+def _unpack_list(list_value: _C.Value) -> list[_C.Value]:
+    list_node = list_value.node()
+    if list_node.kind() != "prim::ListConstruct":
+        raise errors.SymbolicValueError(
+            f"ONNX symbolic expected node type prim::ListConstruct, "
+            f"got '{list_node}'.",
+            list_value,
+        )
+    return list(list_node.inputs())
+
+
+def _unpack_tuple(tuple_value: _C.Value) -> tuple[_C.Value, ...]:
+    tuple_node = tuple_value.node()
+    if not _is_tuple_construct(tuple_value):
+        raise errors.SymbolicValueError(
+            f"ONNX symbolic expected node type 'prim::TupleConstruct', "
+            f"got '{tuple_node.kind()}'.",
+            tuple_value,
+        )
+    return tuple(tuple_node.inputs())
+
+
+def _unpack_quantized_tensor(tuple_value: _C.Value) -> tuple[_C.Value, ...]:
+    """Unpacks a quantized tensor into a tuple of tensor and scale/zero_point.
+    Args:
+        tuple_value: A tuple of tensor, scale, zero_point, and optionally axis.
+    Returns:
+        A tuple of tensor, scale, zero_point, and optionally axis.
+    """
+    tuple_node = tuple_value.node()
+    # A quantized tensor is represented as tuple of the form (tensor, scale, zero_point, )
+    if not _is_tuple_construct(tuple_value):
+        raise errors.SymbolicValueError(
+            f"ONNX symbolic expected the output of `{tuple_node}` to be a quantized "
+            f"tensor. Is this likely due to missing support for quantized "
+            f"`{tuple_node.kind()}`. Please create an issue on {_constants.PYTORCH_GITHUB_ISSUES_URL}",
+            tuple_value,
+        )
+    unpacked = tuple(tuple_node.inputs())
+    assert len(unpacked) == 3 or len(unpacked) == 4
+    return unpacked
+
+
+# Check if list_value is output from prim::ListConstruct
+# This is usually called before _unpack_list to ensure the list can be unpacked.
+def _is_packed_list(list_value: Any) -> bool:
+    return _is_value(list_value) and list_value.node().kind() == "prim::ListConstruct"
+
+
+def parse_args(
+    *arg_descriptors: _ValueDescriptor,
+) -> Callable[[Callable[_Concatenate[_U, _P], _T]], Callable[_Concatenate[_U, _P], _T]]:
+    """A decorator which converts args from torch._C.Value to built-in types.
+
+    For example:
+
+    ```
+    @parse_args('v', 'i', 'fs')
+    foo(g, a, b, c):
+        assert isinstance(a, torch._C.Value)
+        assert isinstance(b, int)
+        assert isinstance(c, list)
+        assert isinstance(c[0], float)
+    ```
+
+    Args:
+        arg_descriptors: list of str, where each element is
+            a string that specifies the type to convert to. Valid descriptors:
+            "v": no conversion, keep torch._C.Value.
+            "i": int
+            "is": list of int
+            "f": float
+            "fs": list of float
+            "b": bool
+            "s": str
+            "t": torch.Tensor
+            "none": the variable is unused
+    """
+
+    def decorator(
+        fn: Callable[_Concatenate[_U, _P], _T],
+    ) -> Callable[_Concatenate[_U, _P], _T]:
+        fn._arg_descriptors = arg_descriptors  # type: ignore[attr-defined]
+
+        @functools.wraps(fn)
+        def wrapper(g: _U, *args: _P.args, **kwargs: _P.kwargs) -> _T:
+            # some args may be optional, so the length may be smaller
+            FILE_BUG_MSG = (
+                "If you believe this is not due to custom symbolic implementation within your code or "
+                "an external library, please file an issue at "
+                "https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml to report this bug."
+            )
+            assert len(arg_descriptors) >= len(args), (
+                f"A mismatch between the number of arguments ({len(args)}) and "
+                f"their descriptors ({len(arg_descriptors)}) was found at symbolic function '{fn.__name__}'. "
+                f"{FILE_BUG_MSG}"
+            )
+
+            try:
+                sig = inspect.signature(fn)
+                arg_names = list(sig.parameters.keys())[1:]
+                fn_name = fn.__name__
+            except Exception:
+                # FIXME(justinchuby): Avoid catching Exception.
+                # Catch a more specific exception instead.
+                arg_names = [None] * len(args)  # type: ignore[list-item]
+                fn_name = None
+            args = [
+                _parse_arg(arg, arg_desc, arg_name, fn_name)  # type: ignore[method-assign]
+                for arg, arg_desc, arg_name in zip(args, arg_descriptors, arg_names)
+            ]
+            # only support _outputs in kwargs
+            assert len(kwargs) <= 1, (
+                f"Symbolic function {fn.__name__}'s '**kwargs' can contain a single "
+                f"key/value entry. "
+                f"{FILE_BUG_MSG}"
+            )
+
+            if len(kwargs) == 1:
+                assert "_outputs" in kwargs, (
+                    f"Symbolic function {fn.__name__}'s '**kwargs' can only contain "
+                    f"'_outputs' key at '**kwargs'. "
+                    f"{FILE_BUG_MSG}"
+                )
+            return fn(g, *args, **kwargs)
+
+        return wrapper
+
+    return decorator
+
+
+def quantized_args(
+    *arg_q_descriptors: bool,
+    scale: float | None = None,
+    zero_point: int | None = None,
+    quantize_output: bool = True,
+) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]:
+    """A decorator which extends support for quantized version of the base operator.
+
+    Quantization is detected by examining the arguments that are annotated by
+    `arg_q_descriptors`.
+
+    If quantization is detected, the base operator symbolic function will be wrapped with
+    argument de-quantization and output quantization.
+
+    Otherwise, only the base symbolic function will be invoked.
+
+    For example:
+
+    ```
+    @quantized_args(True, False)
+    def foo(g, x, y):
+        return x + y
+    ```
+
+    is equivalent to
+
+    ```
+    def q_foo(g, x, y):
+        if is_quantized_tensor(x):
+            x = dequantize(x)
+            out = foo(g, x, y)
+            return quantize(out)
+        else:
+            return foo(g, x, y)
+    ```
+
+    Args:
+        arg_q_descriptors: A sequence of bool, where each element represents if the
+          argument is QTensor for quantized version of this operator. It defaults
+          to False for unspecified (variable length) arguments.
+        scale: Quantized output scale. If None, derive from
+          the first quantized input scale.
+        zero_point: Quantized output zero point. If None,
+          derive from the first quantized input zero point.
+        quantize_output: If True, quantize the output of the base operator. Default is True
+    """
+
+    def decorator(fn):
+        @functools.wraps(fn)
+        def wrapper(g, *args, **kwargs):
+            nonlocal scale
+            nonlocal zero_point
+            if scale is not None:
+                _scale = g.op("Constant", value_t=torch.tensor(scale))
+            else:
+                _scale = None
+            if zero_point is not None:
+                _zero_point = g.op("Constant", value_t=torch.tensor(zero_point))
+            else:
+                _zero_point = None
+
+            # Support variable length arguments by marking unspecified ones as non-quantized
+            arg_q_descriptors_extended = arg_q_descriptors + (False,) * (
+                len(args) - len(arg_q_descriptors)
+            )
+            descriptor_args = tuple(zip(arg_q_descriptors_extended, args))
+
+            def _is_arg_quantized(descriptor, arg):
+                return descriptor and _is_value(arg) and _is_tuple_construct(arg)
+
+            # Run regular symbolic function if none of the argument is QTensor.
+            is_quantized = []
+            for descriptor, arg in descriptor_args:
+                # ListConstruct
+                if _is_packed_list(arg):
+                    for arg_input in arg.node().inputs():
+                        is_quantized.append(_is_arg_quantized(descriptor, arg_input))
+                else:
+                    is_quantized.append(_is_arg_quantized(descriptor, arg))
+
+            if not any(is_quantized):
+                return fn(g, *args, **kwargs)
+
+            # Dequantize arguments that are quantized
+            non_quantized_args = []
+            for descriptor, arg in descriptor_args:
+                if _is_arg_quantized(descriptor, arg):
+                    # Quantized arg is a tuple of (value, scale, zero_point)
+                    dequantized_arg, arg_scale, arg_zero_point, _ = dequantize_helper(
+                        g, arg
+                    )
+                    non_quantized_args.append(dequantized_arg)
+                    # Set scale and zero_point to the first quantized input if not already set
+                    if _scale is None:
+                        _scale = arg_scale
+                    if _zero_point is None:
+                        _zero_point = arg_zero_point
+                # ListConstruct
+                elif _is_packed_list(arg):
+                    for arg_input in arg.node().inputs():
+                        if _is_arg_quantized(descriptor, arg_input):
+                            # Quantized arg is a tuple of (value, scale, zero_point)
+                            (
+                                dequantized_arg,
+                                arg_scale,
+                                arg_zero_point,
+                                _,
+                            ) = dequantize_helper(g, arg_input)
+                            # Set scale and zero_point to the first quantized input if not already set
+                            if _scale is None:
+                                _scale = arg_scale
+                            if _zero_point is None:
+                                _zero_point = arg_zero_point
+                            arg_input.replaceAllUsesWith(dequantized_arg)
+                    non_quantized_args.append(arg)
+                else:
+                    # Non-quantized arg
+                    non_quantized_args.append(arg)
+            # TODO(justinchuby): Only single output is supported for now. We may want to
+            # support multiple outputs in the future.
+            output = fn(g, *non_quantized_args, **kwargs)
+
+            assert _scale is not None, "Bug: Scale must be set for quantized operator"
+            assert (
+                _zero_point is not None
+            ), "Bug: Zero point must be set for quantized operator"
+
+            if quantize_output:
+                return quantize_helper(g, output, _scale, _zero_point)
+            return output
+
+        return wrapper
+
+    return decorator
+
+
+def _scalar(x: Any) -> Number | None:
+    """Convert a scalar tensor into a Python value."""
+    if isinstance(x, torch.Tensor) and x.shape == ():
+        return x.item()
+    return None
+
+
+def _if_scalar_type_as(self, tensor):
+    """
+    Convert self into the same type of tensor, as necessary.
+    We only support implicit casting for scalars, so we never
+    actually need to insert an ONNX cast operator here; just
+    fix up the scalar.
+    """
+    if isinstance(self, _C.Value):
+        return self
+
+    scalar_type = _type_utils.JitScalarType.from_value(
+        tensor, _type_utils.JitScalarType.UNDEFINED
+    )
+    if scalar_type != _type_utils.JitScalarType.UNDEFINED:
+        ty = scalar_type.scalar_name().lower()
+        return getattr(self, ty)()
+    return self
+
+
+def _is_none(x: Any) -> bool:
+    return x is None or (x.node().mustBeNone() if isinstance(x, _C.Value) else False)
+
+
+def _is_value(x: Any) -> bool:
+    return isinstance(x, _C.Value)
+
+
+def _is_constant(value: Any) -> bool:
+    return not _is_value(value) or value.node().kind() in {
+        "onnx::Constant",
+        "prim::Constant",
+    }
+
+
+def _is_tensor(x: _C.Value) -> bool:
+    return x.type().isSubtypeOf(_C.TensorType.get())
+
+
+# Note: _C.JitType is not exposed to Python and cannot be checked in runtime.
+def _as_list_type(jit_type: _C.JitType) -> _C.ListType | None:
+    if isinstance(jit_type, _C.ListType):
+        return jit_type
+    return None
+
+
+def _is_list(x: _C.Value) -> bool:
+    return _as_list_type(x.type()) is not None
+
+
+def _is_tensor_list(x: _C.Value) -> bool:
+    x_type = _as_list_type(x.type())
+    if x_type is None:
+        return False
+    return isinstance(x_type.getElementType(), _C.TensorType)
+
+
+def _is_scalar_list(x: _C.Value) -> bool:
+    """Checks if x is a scalar list, for example: List[float], List[int].
+
+    Besides checking the type is ListType, we also check if the data type is
+    a valid ONNX data type.
+    """
+    x_type = _as_list_type(x.type())
+    if x_type is None:
+        return False
+    scalar_type = _type_utils.JitScalarType.from_value(x)
+    return scalar_type.onnx_compatible()
+
+
+def _is_tuple_construct(x: _C.Value) -> bool:
+    return x.node().kind() == "prim::TupleConstruct"
+
+
+def is_complex_value(x: _C.Value) -> bool:
+    assert _is_value(x)
+    return _type_utils.JitScalarType.from_value(
+        x, _type_utils.JitScalarType.UNDEFINED
+    ) in {
+        _type_utils.JitScalarType.COMPLEX32,
+        _type_utils.JitScalarType.COMPLEX64,
+        _type_utils.JitScalarType.COMPLEX128,
+    }
+
+
+def _get_tensor_rank(x: _C.Value) -> int | None:
+    if not _is_tensor(x) or x.type() is None:
+        return None
+    x_type = x.type()
+    x_type = typing.cast(_C.TensorType, x_type)
+    return x_type.dim()
+
+
+def _get_tensor_sizes(x: _C.Value, allow_nonstatic: bool = True):
+    if not _is_tensor(x) or x.type() is None:
+        return None
+    x_type = x.type()
+    x_type = typing.cast(_C.TensorType, x_type)
+    if allow_nonstatic:
+        # Each individual symbol is returned as None.
+        # e.g. [1, "a", "b"] -> [1, None, None]
+        return x_type.varyingSizes()
+    # returns None, if exists any symbol in sizes.
+    # e.g. [1, "a", "b"] -> None
+    return x_type.sizes()
+
+
+def _get_tensor_dim_size(x: _C.Value, dim: int) -> int | None:
+    sizes = _get_tensor_sizes(x)
+    return sizes[dim] if sizes else None
+
+
+def _get_dim_for_cross(x: _C.Value, dim: int | None):
+    if dim == -1:
+        tensor_rank = _get_tensor_rank(x)
+        assert tensor_rank is not None
+        return dim + tensor_rank
+    # If dim is not given, it defaults to the first dimension found with the size 3
+    if dim is None:
+        sizes = _get_tensor_sizes(x)
+        assert sizes is not None
+        for index, size in enumerate(sizes):
+            if size is not None and size == 3:
+                return index
+    return dim
+
+
+def _unimplemented(op: str, msg: str, value: _C.Value | None = None) -> None:
+    # For BC reasons, the behavior for Caffe2 does not raise exception for unimplemented operators
+    if GLOBALS.operator_export_type == _C_onnx.OperatorExportTypes.ONNX:
+        _onnx_unsupported(f"{op}, {msg}", value)
+
+
+def _onnx_unsupported(op_name: str, value: _C.Value | None = None) -> NoReturn:
+    message = (
+        f"Unsupported: ONNX export of operator {op_name}. "
+        f"Please feel free to request support or submit a pull request "
+        f"on PyTorch GitHub: {_constants.PYTORCH_GITHUB_ISSUES_URL}"
+    )
+    if isinstance(value, _C.Value):
+        raise errors.SymbolicValueError(
+            message,
+            value,
+        )
+    raise errors.OnnxExporterError(message)
+
+
+def _onnx_opset_unsupported(
+    op_name: str,
+    current_opset: int,
+    supported_opset: int,
+    value: _C.Value | None = None,
+) -> NoReturn:
+    message = (
+        f"Unsupported: ONNX export of {op_name} in opset {current_opset}. "
+        f"Please try opset version {supported_opset}."
+    )
+    if isinstance(value, _C.Value):
+        raise errors.SymbolicValueError(
+            message,
+            value,
+        )
+    raise errors.OnnxExporterError(message)
+
+
+def _onnx_opset_unsupported_detailed(
+    op_name: str,
+    current_opset: int,
+    supported_opset: int,
+    reason: str,
+    value: _C.Value | None = None,
+) -> NoReturn:
+    message = (
+        f"Unsupported: ONNX export of {op_name} in "
+        f"opset {current_opset}. {reason}. Please try opset version {supported_opset}."
+    )
+    if isinstance(value, _C.Value):
+        raise errors.SymbolicValueError(
+            message,
+            value,
+        )
+    raise errors.OnnxExporterError(message)
+
+
+def _block_list_in_opset(name: str):
+    def symbolic_fn(*args, **kwargs):
+        raise errors.OnnxExporterError(
+            f"ONNX export failed on {name}, which is not implemented for opset "
+            f"{GLOBALS.export_onnx_opset_version}. "
+            "Try exporting with other opset versions."
+        )
+
+    return symbolic_fn
+
+
+def _try_get_scalar_type(*args) -> _type_utils.JitScalarType | None:
+    for arg in args:
+        scalar_type = _type_utils.JitScalarType.from_value(
+            arg, _type_utils.JitScalarType.UNDEFINED
+        )
+        if scalar_type != _type_utils.JitScalarType.UNDEFINED:
+            return scalar_type
+    return None
+
+
+def _type_promote_from_values(*args) -> _type_utils.JitScalarType:
+    undef = _type_utils.JitScalarType.UNDEFINED
+    jit_types = [_try_get_scalar_type(arg) for arg in args]
+    if len(jit_types) == 0:
+        return undef
+    if len(jit_types) == 1:
+        return jit_types[0]  # type: ignore[return-value]
+    new_dtype = jit_types[0].dtype()  # type: ignore[union-attr]
+    for t in jit_types:
+        new_dtype = torch.promote_types(new_dtype, t.dtype())  # type: ignore[union-attr]
+    return _type_utils.JitScalarType.from_dtype(new_dtype)
+
+
+def _maybe_cast_to_type(
+    g: jit_utils.GraphContext, value, jit_type: _type_utils.JitScalarType
+):
+    if (
+        _type_utils.JitScalarType.from_value(value, _type_utils.JitScalarType.UNDEFINED)
+        != jit_type
+    ):
+        return g.op(
+            "Cast",
+            value,
+            to_i=jit_type.onnx_type(),
+        )
+    return value
+
+
+def _select_helper(g: jit_utils.GraphContext, self, dim, index, apply_reshape=True):
+    index_const = _maybe_get_scalar(index)
+    index_dim = _get_tensor_rank(index)
+    if not _is_value(index_const):
+        # Index is a constant scalar. Make it a size 1 constant tensor.
+        index = g.op("Constant", value_t=torch.LongTensor([index_const]))
+    elif index_dim is not None and apply_reshape:
+        if index_dim == 0:
+            # Index is a scalar. Reshape it to a size 1 tensor.
+            index = _reshape_helper(
+                g, index, g.op("Constant", value_t=torch.LongTensor([1]))
+            )
+
+    index_scalar_type = _type_utils.JitScalarType.from_value(
+        index, _type_utils.JitScalarType.UNDEFINED
+    )
+    if index_scalar_type not in {
+        _type_utils.JitScalarType.INT64,
+        _type_utils.JitScalarType.INT,
+    }:
+        index = g.op("Cast", index, to_i=_C_onnx.TensorProtoDataType.INT64)
+    return g.op("Gather", self, index, axis_i=dim)
+
+
+def _slice_helper(
+    g: jit_utils.GraphContext,
+    input,
+    axes,
+    starts,
+    ends,
+    steps=None,
+):
+    if g.opset <= 9:
+        from torch.onnx.symbolic_opset9 import _slice as _slice9
+
+        return _slice9(g, input, axes, starts, ends)
+    else:
+        from torch.onnx.symbolic_opset10 import _slice as _slice10
+
+        return _slice10(g, input, axes, starts, ends, steps)
+
+
+def _is_fp(value) -> bool:
+    return _type_utils.JitScalarType.from_value(
+        value, _type_utils.JitScalarType.UNDEFINED
+    ) in {
+        _type_utils.JitScalarType.FLOAT,
+        _type_utils.JitScalarType.DOUBLE,
+        _type_utils.JitScalarType.HALF,
+        _type_utils.JitScalarType.BFLOAT16,
+    }
+
+
+def _is_bool(value) -> bool:
+    return _type_utils.JitScalarType.from_value(
+        value, _type_utils.JitScalarType.UNDEFINED
+    ) in {_type_utils.JitScalarType.BOOL}
+
+
+def _generate_wrapped_number(g: jit_utils.GraphContext, scalar):
+    """Creates a wrapped number based on https://github.com/pytorch/pytorch/issues/9515.
+
+    A Tensor is a considered a "wrapped number" if it is
+    auto-wrapped from a C++ or Python number type. Integer types are
+    wrapped as 0-dim int64 tensors and floating-point types are
+    wrapped as 0-dim double tensors.
+
+    The input to this function is constant value. If the data type
+    is a floating point type, it is converted to a 0-dim double
+    tensor, else it is converted to a 0-dim tensor of its original type
+    """
+    assert not isinstance(scalar, torch.Tensor)
+    if isinstance(scalar, float):
+        return g.op("Constant", value_t=torch.tensor(scalar, dtype=torch.double))
+    return g.op("Constant", value_t=torch.tensor(scalar))
+
+
+def _sort_helper(g: jit_utils.GraphContext, input, dim, decending=True, out=None):
+    if out is not None:
+        _unimplemented("Sort", "Out parameter is not supported")
+    shape_ = g.op("Shape", input)
+    dim_size_ = g.op(
+        "Gather",
+        shape_,
+        g.op("Constant", value_t=torch.tensor([dim], dtype=torch.int64)),
+    )
+    if g.opset <= 10:
+        if not decending:
+            _unimplemented("Sort", "Ascending is not supported")
+        return g.op("TopK", input, dim_size_, axis_i=dim, outputs=2)
+    else:
+        return g.op(
+            "TopK", input, dim_size_, axis_i=dim, largest_i=decending, outputs=2
+        )
+
+
+def _topk_helper(
+    g: jit_utils.GraphContext, input, k, dim, largest=True, sorted=False, out=None
+):
+    if out is not None:
+        _unimplemented("TopK", "Out parameter is not supported")
+    if not _is_value(k):
+        k = g.op("Constant", value_t=torch.tensor([k], dtype=torch.int64))
+    else:
+        k = _reshape_helper(g, k, g.op("Constant", value_t=torch.tensor([1])))
+        if _try_get_scalar_type(k) != _type_utils.JitScalarType.INT64:
+            k = g.op("Cast", k, to_i=_C_onnx.TensorProtoDataType.INT64)
+    if g.opset <= 10:
+        if not largest:
+            _unimplemented("TopK", "Ascending is not supported")
+        return g.op("TopK", input, k, axis_i=dim, outputs=2)
+    else:
+        return g.op(
+            "TopK", input, k, axis_i=dim, largest_i=largest, sorted_i=sorted, outputs=2
+        )
+
+
+def _lt_helper(g: jit_utils.GraphContext, input, other):
+    if g.opset <= 8:
+        from torch.onnx.symbolic_opset8 import lt as _lt8
+
+        return _lt8(g, input, other)
+    else:
+        from torch.onnx.symbolic_opset9 import lt as _lt9
+
+        return _lt9(g, input, other)
+
+
+def _interpolate_warning(interpolate_mode):
+    onnx_op = (
+        "onnx:Resize" if GLOBALS.export_onnx_opset_version >= 10 else "onnx:Upsample"
+    )
+    warnings.warn(
+        "You are trying to export the model with "
+        + onnx_op
+        + " for ONNX opset version "
+        "" + str(GLOBALS.export_onnx_opset_version) + ". "
+        "This operator might cause results to not match the expected results by PyTorch.\n"
+        "ONNX's Upsample/Resize operator did not match Pytorch's Interpolation until opset 11. "
+        "Attributes to determine how to transform the input were added in onnx:Resize in opset 11 "
+        "to support Pytorch's behavior (like coordinate_transformation_mode and nearest_mode).\n"
+        "We recommend using opset 11 and above for models using this operator."
+    )
+
+
+def _unsqueeze_helper(g: jit_utils.GraphContext, input, axes_i):
+    if _is_constant(axes_i[0]):
+        if g.opset >= 13:
+            axes = g.op("Constant", value_t=torch.tensor(axes_i, dtype=torch.long))
+            return g.op("Unsqueeze", input, axes)
+        return g.op("Unsqueeze", input, axes_i=axes_i)
+    # Tensor type
+    if g.opset < 13:
+        raise errors.SymbolicValueError(
+            "Opset version must be >= 13 for Unsqueeze with dynamic axes.", input
+        )
+    return g.op("Unsqueeze", input, axes_i[0])
+
+
+def _squeeze_helper(g: jit_utils.GraphContext, input, axes_i):
+    if _is_constant(axes_i[0]):
+        if g.opset >= 13:
+            axes = g.op("Constant", value_t=torch.tensor(axes_i, dtype=torch.long))
+            return g.op("Squeeze", input, axes)
+        return g.op("Squeeze", input, axes_i=axes_i)
+    # Tensor type
+    if g.opset < 13:
+        raise errors.SymbolicValueError(
+            "Opset version must be >= 13 for Squeeze with dynamic axes.", input
+        )
+    axes_t = axes_i[0]
+    axes_rank = _get_tensor_rank(axes_t)
+    assert axes_rank is not None
+    if axes_rank > 1:
+        raise errors.SymbolicValueError(
+            "For Squeeze axses as input, the axes rank must be one in ONNX spec.", input
+        )
+    elif axes_rank == 0:
+        # The axes is a scalar. Unsqueeze it to a rank 1 tensor.
+        axes_t = _unsqueeze_helper(g, axes_t, [0])
+        return g.op("Squeeze", input, axes_t)
+    return g.op("Squeeze", input, axes_t)
+
+
+def _reducesum_helper(
+    g: jit_utils.GraphContext,
+    input,
+    axes_i=None,
+    keepdims_i=1,
+    noop_with_empty_axes_i=0,
+):
+    keepdims_i = _maybe_get_const(keepdims_i, "i")
+    if g.opset >= 13:
+        if axes_i:
+            if not _is_value(axes_i):
+                axes_i = g.op(
+                    "Constant", value_t=torch.tensor(axes_i, dtype=torch.long)
+                )
+            return g.op(
+                "ReduceSum",
+                input,
+                axes_i,
+                keepdims_i=keepdims_i,
+                noop_with_empty_axes_i=noop_with_empty_axes_i,
+            )
+        return g.op(
+            "ReduceSum",
+            input,
+            keepdims_i=keepdims_i,
+            noop_with_empty_axes_i=noop_with_empty_axes_i,
+        )
+    else:
+        return g.op("ReduceSum", input, axes_i=axes_i, keepdims_i=keepdims_i)
+
+
+def _interpolate_size_to_scales(g: jit_utils.GraphContext, input, output_size, dim):
+    output_size = _maybe_get_const(output_size, "is")
+    if _is_value(output_size):
+        offset = 2
+        offsets = g.op("Constant", value_t=torch.ones(offset, dtype=torch.float32))
+        dividend = g.op("Cast", output_size, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+        divisor = _slice_helper(
+            g, g.op("Shape", input), axes=[0], ends=[sys.maxsize], starts=[offset]
+        )
+        divisor = g.op("Cast", divisor, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+        scale_dims = g.op("Div", dividend, divisor)
+        scales = g.op("Concat", offsets, scale_dims, axis_i=0)
+    else:
+        scales_constant = [
+            1.0
+            if i < 2
+            else float(output_size[-(dim - i)])
+            / float(input.type().sizes()[-(dim - i)])
+            for i in range(0, dim)
+        ]
+        scales = g.op(
+            "Constant", value_t=torch.tensor(scales_constant, dtype=torch.float32)
+        )
+    return scales
+
+
+def _interpolate_get_scales_if_available(g: jit_utils.GraphContext, scales):
+    available_scales = _maybe_get_const(scales[0], "fs") != -1 and not _is_none(
+        scales[0]
+    )
+
+    if not available_scales:
+        return None
+
+    offsets = g.op("Constant", value_t=torch.ones(2, dtype=torch.float32))
+    scales_list = g.op(
+        "Constant", value_t=torch.tensor(_maybe_get_const(scales[0], "fs"))
+    )
+    scales = g.op("Concat", offsets, scales_list, axis_i=0)
+    return scales
+
+
+def _get_interpolate_attributes(g: jit_utils.GraphContext, mode, args):
+    if mode == "nearest":
+        align_corners = None
+        scales = args[0:]
+    else:
+        align_corners = args[0]
+        scales = args[1:]
+    scales = _interpolate_get_scales_if_available(g, scales)
+    return scales, align_corners
+
+
+def _interpolate_get_scales(g: jit_utils.GraphContext, scale_factor, dim):
+    offsets = g.op("Constant", value_t=torch.ones(2, dtype=torch.float32))
+    scale_factor_rank = _get_tensor_rank(scale_factor)
+    if isinstance(scale_factor.type(), _C.ListType) or (
+        scale_factor_rank is not None and scale_factor_rank > 0
+    ):
+        return g.op("Concat", offsets, scale_factor, axis_i=0)
+    else:
+        scale_factor = _unsqueeze_helper(g, scale_factor, [0])
+        scale_factor = g.op(
+            "Cast", scale_factor, to_i=_C_onnx.TensorProtoDataType.FLOAT
+        )
+        scales = [scale_factor for i in range(dim - 2)]
+    scale_factor = g.op("Concat", offsets, *scales, axis_i=0)
+    return scale_factor
+
+
+def _interpolate_get_scales_and_mode(
+    g: jit_utils.GraphContext, input, size, scale_factor, mode, align_corners
+):
+    mode = _maybe_get_const(mode, "s")
+    if "linear" in mode:
+        mode = "linear"
+    if "cubic" in mode:
+        mode = "cubic"
+    _interpolate_warning(mode)
+
+    align_corners = _maybe_get_const(align_corners, "b")
+    if isinstance(align_corners, bool) and align_corners:
+        return _unimplemented("interpolate", "align_corners == True")
+
+    if not input.type().dim():
+        return _unimplemented("interpolate", "missing input shape")
+    dim = input.type().dim()
+
+    if not _is_none(scale_factor):
+        scale_factor = _interpolate_get_scales(g, scale_factor, dim)
+    elif not _is_none(size):
+        if not _is_packed_list(size):
+            is_scalar = _maybe_get_const(size, "t").dim() == 0
+            if is_scalar:
+                size = _unsqueeze_helper(g, size, [0])
+                size = [size for i in range(dim - 2)]
+                size = g.op("Concat", *size, axis_i=0)
+        scale_factor = _interpolate_size_to_scales(g, input, size, dim)
+    else:
+        return _unimplemented(
+            "interpolate", "Both size and scales are None in __interpolate"
+        )
+    return scale_factor, mode
+
+
+def _argmin_argmax_helper(
+    g: jit_utils.GraphContext,
+    input: torch._C.Value,
+    dim: torch._C.Value,
+    keepdim: bool,
+    op_name: str,
+):
+    def op_wrapper(input, axis_i, keepdims_i):
+        if g.opset >= 12:
+            return g.op(
+                op_name,
+                input,
+                axis_i=axis_i,
+                keepdims_i=keepdims_i,
+                select_last_index_i=False,
+            )
+        return g.op(op_name, input, axis_i=axis_i, keepdims_i=keepdims_i)
+
+    if _is_none(dim):
+        flattened = _reshape_helper(
+            g, input, g.op("Constant", value_t=torch.tensor([-1]))
+        )
+        output = op_wrapper(flattened, axis_i=0, keepdims_i=False)
+        if keepdim:
+            input_shape = g.op("Shape", input)
+            input_shape_shape = g.op("Shape", input_shape)
+            new_shape = g.op(
+                "ConstantOfShape",
+                input_shape_shape,
+                value_t=torch.tensor([1], dtype=torch.int64),
+            )
+            output = g.op("Reshape", output, new_shape)
+        return output
+
+    dim = _parse_arg(dim, "i")
+    return op_wrapper(input, axis_i=dim, keepdims_i=keepdim)
+
+
+def _interpolate_helper(name, dim, interpolate_mode):
+    @quantized_args(True, False, False)
+    def symbolic_fn(g, input, output_size, *args):
+        scales, align_corners = _get_interpolate_attributes(g, interpolate_mode, args)
+        align_corners = _maybe_get_scalar(align_corners)
+        coordinate_transformation_mode = (
+            "asymmetric"
+            if interpolate_mode == "nearest"
+            else "align_corners"
+            if align_corners
+            else "half_pixel"
+        )
+
+        if scales is None:
+            input_size = g.op("Shape", input)
+            input_size_beg = _slice_helper(
+                g, input_size, axes=[0], ends=[2], starts=[0]
+            )
+            output_size = g.op(
+                "Cast", output_size, to_i=_C_onnx.TensorProtoDataType.INT64
+            )
+            output_size = g.op("Concat", input_size_beg, output_size, axis_i=0)
+
+            if g.opset >= 13:
+                empty_roi = _optional_input_placeholder_tensor(g)
+                empty_scales = _optional_input_placeholder_tensor(g)
+            else:
+                empty_roi = g.op(
+                    "Constant", value_t=torch.tensor([], dtype=torch.float32)
+                )
+                empty_scales = g.op(
+                    "Constant", value_t=torch.tensor([], dtype=torch.float32)
+                )
+
+            return g.op(
+                "Resize",
+                input,
+                empty_roi,
+                empty_scales,
+                output_size,
+                coordinate_transformation_mode_s=coordinate_transformation_mode,
+                cubic_coeff_a_f=-0.75,  # only valid when mode="cubic"
+                mode_s=interpolate_mode,  # nearest, linear, or cubic
+                nearest_mode_s="floor",
+            )  # only valid when mode="nearest"
+        else:
+            if g.opset >= 13:
+                empty_roi = _optional_input_placeholder_tensor(g)
+            else:
+                empty_roi = g.op(
+                    "Constant", value_t=torch.tensor([], dtype=torch.float32)
+                )
+
+            return g.op(
+                "Resize",
+                input,
+                empty_roi,
+                scales,
+                coordinate_transformation_mode_s=coordinate_transformation_mode,
+                cubic_coeff_a_f=-0.75,  # only valid when mode="cubic"
+                mode_s=interpolate_mode,  # nearest, linear, or cubic
+                nearest_mode_s="floor",
+            )  # only valid when mode="nearest"
+
+    return symbolic_fn
+
+
+def __interpolate_helper(
+    g: jit_utils.GraphContext,
+    input,
+    size,
+    scale_factor,
+    mode,
+    align_corners,
+    recompute_scale_factor,
+):
+    mode = _maybe_get_const(mode, "s")
+    if "linear" in mode:
+        mode = "linear"
+    if "cubic" in mode:
+        mode = "cubic"
+    align_corners = _maybe_get_const(align_corners, "b")
+    align_corners = False if not isinstance(align_corners, bool) else align_corners
+    coordinate_transformation_mode = (
+        "asymmetric"
+        if mode == "nearest"
+        else "align_corners"
+        if align_corners
+        else "half_pixel"
+    )
+
+    if not _is_none(size):
+        input_size = g.op("Shape", input)
+        input_size = _slice_helper(g, input_size, axes=[0], ends=[2], starts=[0])
+        # in some cases size is not a packed list but size is a scalar
+        # We need to also verify that (_maybe_get_const(size, "t").dim() == 0)
+        # but this information is not always available. Try to get the dim,
+        # and if not assume that it is not a scalar.
+        try:
+            is_scalar = not _is_packed_list(size) and (
+                _maybe_get_const(size, "t").dim() == 0
+            )
+        except AttributeError:
+            is_scalar = not _is_packed_list(size)
+            if not is_scalar:
+                warnings.warn(
+                    "Cannot verify if the output_size is a scalar "
+                    "while exporting interpolate. Assuming that it is not a scalar."
+                )
+
+        if is_scalar:
+            rank = _get_tensor_rank(input)
+            if rank is None:
+                return _unimplemented(
+                    "interpolate (with a scalar output_size)",
+                    "missing input shape (try giving an array of output_size values)",
+                )
+            size = _unsqueeze_helper(g, size, [0])
+            size = [size for i in range(rank - 2)]
+            size = g.op("Concat", *size, axis_i=0)
+        size = g.op("Cast", size, to_i=_C_onnx.TensorProtoDataType.INT64)
+        size = g.op("Concat", input_size, size, axis_i=0)
+
+        if g.opset >= 13:
+            empty_roi = _optional_input_placeholder_tensor(g)
+            empty_scales = _optional_input_placeholder_tensor(g)
+        else:
+            empty_roi = g.op("Constant", value_t=torch.tensor([], dtype=torch.float32))
+            empty_scales = g.op(
+                "Constant", value_t=torch.tensor([], dtype=torch.float32)
+            )
+
+        return g.op(
+            "Resize",
+            input,
+            empty_roi,
+            empty_scales,
+            size,
+            coordinate_transformation_mode_s=coordinate_transformation_mode,
+            cubic_coeff_a_f=-0.75,  # only valid when mode="cubic"
+            mode_s=mode,  # nearest, linear, or cubic
+            nearest_mode_s="floor",
+        )
+    else:  # if not _is_none(scales)
+        rank = _get_tensor_rank(input)
+        if rank is None:
+            return _unimplemented("interpolate (with scales)", "missing input shape")
+
+        if g.opset >= 13:
+            empty_roi = _optional_input_placeholder_tensor(g)
+        else:
+            empty_roi = g.op("Constant", value_t=torch.tensor([], dtype=torch.float32))
+
+        scales = _interpolate_get_scales(g, scale_factor, rank)
+        return g.op(
+            "Resize",
+            input,
+            empty_roi,
+            scales,
+            coordinate_transformation_mode_s=coordinate_transformation_mode,
+            cubic_coeff_a_f=-0.75,  # only valid when mode="cubic"
+            mode_s=mode,  # nearest, linear, or cubic
+            nearest_mode_s="floor",
+        )  # only valid when mode="nearest"
+
+
+def _unbind_helper(g: jit_utils.GraphContext, self, dim, _outputs):
+    if g.opset < 11:
+        from torch.onnx.symbolic_opset9 import unbind
+    elif g.opset <= 12:
+        from torch.onnx.symbolic_opset11 import unbind  # type: ignore[no-redef]
+    else:
+        from torch.onnx.symbolic_opset13 import unbind  # type: ignore[no-redef]
+    return unbind(g, self, dim, _outputs)
+
+
+def _scatter_helper(g: jit_utils.GraphContext, self, dim, index, src):
+    if g.opset <= 10:
+        from torch.onnx.symbolic_opset9 import scatter
+    else:
+        # for mypy, scatter was imported two lines above
+        from torch.onnx.symbolic_opset11 import scatter  # type: ignore[no-redef]
+    return scatter(g, self, dim, index, src)
+
+
+def _repeat_interleave_split_helper(g: jit_utils.GraphContext, self, reps, dim):
+    if g.opset <= 12:
+        split_out = g.op("Split", self, split_i=[1] * reps, axis_i=dim, outputs=reps)
+    else:
+        from torch.onnx.symbolic_opset13 import split
+
+        repeats = g.op("Constant", value_t=torch.tensor([1] * reps))
+        split_out = split(g, self, repeats, dim, _outputs=reps)
+    return split_out if reps > 1 else [split_out]
+
+
+def _repeat_interleave_single_value_repeat_helper(
+    g: jit_utils.GraphContext, self, repeats, dim
+):
+    from torch.onnx.symbolic_opset9 import flatten, unsqueeze
+
+    if not _is_tensor(repeats):
+        repeats = g.op("Constant", value_t=torch.LongTensor(repeats))
+
+    const_repeats: bool = _is_constant(repeats)
+    reps = _maybe_get_const(repeats, "t")
+
+    # Convert 'repeats' to 1-d if it is 0-d.
+    if _get_tensor_rank(repeats) == 0:
+        repeats = g.op("Reshape", repeats, g.op("Constant", value_t=torch.tensor([1])))
+
+    # Create a new dim of size 1, then expand it to be 'repeats' long, and finally collapse it.
+    unsqueezed = unsqueeze(g, self, dim + 1)
+
+    # repeats_per_dim is 1 for all dims except for the new unsqueezed dim, where it has value 'repeats'.
+    if const_repeats:
+        # 'Repeats' is a constant, 'repeats_per_dim' can be a constant.
+        onehot = torch.ones(_get_tensor_rank(unsqueezed), dtype=torch.int64)  # type: ignore[arg-type]
+        onehot[dim + 1] = reps
+        repeats_per_dim = g.op("Constant", value_t=onehot)
+    else:
+        # 'Repeats' is a variable, 'repeats_per_dim' cannot be a constant.
+        onehot = g.op(
+            "OneHot",
+            unsqueeze(g, dim + 1, 0),  # indices, must be >= 1-dimensional
+            g.op(
+                "Constant", value_t=torch.tensor(_get_tensor_rank(unsqueezed))
+            ),  # depth
+            g.op(
+                "Concat", g.op("Constant", value_t=torch.tensor([1])), repeats, axis_i=0
+            ),  # on/off values
+        )
+        repeats_per_dim = flatten(g, onehot, 0, 1)
+
+    tiled = g.op("Tile", unsqueezed, repeats_per_dim)
+    return flatten(g, tiled, dim, dim + 1)
+
+
+def _arange_cast_helper(
+    g: jit_utils.GraphContext, end, start=None, step=None, dtype=None
+) -> tuple[
+    _type_utils.JitScalarType,
+    _C.Value | None,
+    _C.Value | None,
+    _C.Value | None,
+]:
+    def _is_all_integral(scalars):
+        for scalar in scalars:
+            scalar_type = _type_utils.JitScalarType.from_value(
+                scalar, _type_utils.JitScalarType.UNDEFINED
+            )
+            if (
+                scalar_type != _type_utils.JitScalarType.INT64
+                and scalar_type != _type_utils.JitScalarType.UNDEFINED
+            ):
+                return False
+        return True
+
+    # This logic is based on torch.arange docs. If "dtype" is provided,
+    # infer input types from dtype. If not, then check if any of start, stop,
+    # or step are floating point, and infer the type from get_default.
+    # Otherwise, the dtype is inferred to be torch.int64.
+    if dtype is None or (_is_value(dtype) and _is_none(dtype)):
+        if _is_all_integral([start, end, step]):
+            scalar_type = _type_utils.JitScalarType.INT64
+        else:
+            scalar_type = _type_utils.JitScalarType.from_dtype(
+                torch.get_default_dtype()
+            )
+    else:
+        assert isinstance(dtype, int)
+        # TODO(justinchuby): Check if dtype is indeed a int.
+        scalar_type = _type_utils.JitScalarType(dtype)
+
+    start = g.op("Cast", start, to_i=scalar_type.onnx_type()) if start else None
+    end = g.op("Cast", end, to_i=scalar_type.onnx_type()) if end else None
+    step = g.op("Cast", step, to_i=scalar_type.onnx_type()) if step else None
+    return scalar_type, end, start, step
+
+
+def _arange_helper(g: jit_utils.GraphContext, *args):
+    if g.opset <= 10:
+        from torch.onnx.symbolic_opset9 import arange
+    else:
+        from torch.onnx.symbolic_opset11 import arange  # type: ignore[no-redef]
+    return arange(g, *args)
+
+
+def _size_helper(g: jit_utils.GraphContext, self, dim):
+    full_shape = g.op("Shape", self)
+    from torch.onnx.symbolic_opset9 import select
+
+    return select(g, full_shape, g.op("Constant", value_t=torch.tensor([0])), dim)
+
+
+def _index_fill_reshape_helper(g: jit_utils.GraphContext, self, dim, index):
+    # 1. reshape index => [1, ..., 1, dim, 1, ..., 1]
+    # 2. expand index => [..., dim, ...], same shape as self except for dim.
+    # 3. expand value as well.
+    # 4. apply onnx::scatter.
+
+    from torch.onnx.symbolic_opset9 import expand
+
+    if g.opset <= 10:
+        from torch.onnx.symbolic_opset9 import scatter
+    else:
+        # for mypy, scatter was imported two lines above
+        from torch.onnx.symbolic_opset11 import scatter  # type: ignore[no-redef]
+
+    if self.type().dim() is None:
+        return _unimplemented("index_fill", "input rank not accessible")
+    self_dim = self.type().dim()
+    dim_value = _parse_arg(dim, "i")
+    if dim_value < 0:
+        dim_value += self_dim
+    unsqueezed_index = _unsqueeze_helper(
+        g, index, [i for i in range(self_dim) if i != dim_value]
+    )
+    expanded_index_shape = scatter(
+        g, g.op("Shape", self), 0, _unsqueeze_helper(g, dim, [0]), g.op("Shape", index)
+    )
+    expanded_index = expand(g, unsqueezed_index, expanded_index_shape, None)
+    return expanded_index_shape, expanded_index
+
+
+# By default, when any value in the 'shape' input is equal to zero
+# the corresponding dimension value is copied from the input tensor dynamically.
+# allowzero=1 indicates that if any value in the 'shape' input is set to zero,
+# the zero value is honored, similar to NumPy.
+# allowzero=1 is only supported for opset version >= 14.
+def _reshape_helper(g: jit_utils.GraphContext, input, shape, allowzero=0):
+    shape = _maybe_get_const(shape, "is")
+    if not _is_value(shape):
+        shape = g.op("Constant", value_t=torch.LongTensor(shape))
+    if g.opset <= 13:
+        if allowzero == 1:
+            _onnx_opset_unsupported(
+                "Reshape with allowzero=1", GLOBALS.export_onnx_opset_version, 14, input
+            )
+        return g.op("Reshape", input, shape)
+    else:
+        return g.op("Reshape", input, shape, allowzero_i=allowzero)
+
+
+def _batchnorm_helper(
+    g: jit_utils.GraphContext, input, weight, bias, running_mean, running_var
+):
+    from torch.onnx.symbolic_opset9 import _var_mean
+
+    batch_size = _get_tensor_dim_size(input, 0)
+    channel_size = _get_tensor_dim_size(input, 1)
+
+    if weight is None or _is_none(weight):
+        if channel_size is None:
+            raise errors.SymbolicValueError(
+                "Unsupported: ONNX export of batch_norm for unknown channel size.",
+                input,
+            )
+        weight_value = torch.tensor(
+            [1.0] * channel_size,
+            dtype=_type_utils.JitScalarType.from_value(input).dtype(),
+        )
+        weight = g.op("Constant", value_t=weight_value)
+    if bias is None or _is_none(bias):
+        if channel_size is None:
+            raise errors.SymbolicValueError(
+                "Unsupported: ONNX export of batch_norm for unknown channel size.",
+                input,
+            )
+        bias_value = torch.tensor(
+            [0.0] * channel_size,
+            dtype=_type_utils.JitScalarType.from_value(input).dtype(),
+        )
+        bias = g.op("Constant", value_t=bias_value)
+    # If track_running_stats is set to False batch statistics are instead used during evaluation time
+    if (
+        running_mean is None
+        or _is_none(running_mean)
+        or running_var is None
+        or _is_none(running_var)
+    ):
+        assert batch_size is not None and channel_size is not None
+        reshape_in = _reshape_helper(
+            g,
+            input,
+            g.op(
+                "Constant",
+                value_t=torch.tensor([batch_size, channel_size, -1], dtype=torch.int64),
+            ),
+        )
+        trans_in = g.op("Transpose", reshape_in, perm_i=[0, 2, 1])
+        running_var, running_mean = _var_mean(
+            g,
+            trans_in,
+            g.op("Constant", value_t=torch.tensor([0, 1], dtype=torch.int64)),
+            False,
+            False,
+        )
+    return weight, bias, running_mean, running_var
+
+
+def _avgpool_helper(
+    tuple_fn: Callable[[Any], Sequence[int]],
+    padding: int | Sequence[int],
+    kernel_size,
+    stride,
+    divisor_override,
+    name,
+) -> tuple[int, ...]:
+    if divisor_override and divisor_override.node().kind() != "prim::Constant":
+        _unimplemented(name, "divisor_override")
+    return tuple(tuple_fn(padding))
+
+
+def check_training_mode(op_train_mode: int, op_name: str) -> None:
+    """Warns the user if the model's training mode and the export mode do not agree."""
+    if GLOBALS.training_mode == _C_onnx.TrainingMode.PRESERVE:
+        return
+
+    if op_train_mode:
+        op_mode_enum = _C_onnx.TrainingMode.TRAINING
+    else:
+        op_mode_enum = _C_onnx.TrainingMode.EVAL
+    if op_mode_enum == GLOBALS.training_mode:
+        # The modes agree. Do nothing
+        return
+
+    op_mode_text = f"train={bool(op_train_mode)}"
+    # Setting the model mode could result in op_mode != GLOBALS.training_mode
+    # if the model is a FuncModule. In this case we warn the user of
+    # the state and export depending on op_mode
+    # This is to support use-cases of fixing certain layer weights
+    # in training.
+    warnings.warn(
+        f"ONNX export mode is set to {GLOBALS.training_mode}, but operator '{op_name}' "
+        f"is set to {op_mode_text}. Exporting with {op_mode_text}."
+    )
+
+
+def _flatten_helper(g: jit_utils.GraphContext, input, start_dim, end_dim, dim):
+    input_size = g.op("Shape", input)
+    slice1 = _slice_helper(g, input_size, axes=[0], starts=[0], ends=[start_dim])
+    slices = [slice1, g.op("Constant", value_t=torch.tensor([-1], dtype=torch.long))]
+    if end_dim < dim - 1:
+        slice3 = _slice_helper(
+            g, input_size, axes=[0], starts=[end_dim + 1], ends=[dim]
+        )
+        slices = [
+            slice1,
+            g.op("Constant", value_t=torch.tensor([-1], dtype=torch.long)),
+            slice3,
+        ]
+
+    final_shape = g.op("Concat", *slices, axis_i=0)
+    from torch.onnx.symbolic_opset9 import _reshape_from_tensor
+
+    return _reshape_from_tensor(g, input, final_shape)
+
+
+def _is_split_static(split_size_or_sizes, _outputs):
+    if _outputs is None:
+        return False
+    if (
+        _is_value(split_size_or_sizes)
+        and split_size_or_sizes.node().kind() != "onnx::Constant"
+    ):
+        return False
+    return True
+
+
+def _optional_input_placeholder_tensor(g):
+    n = g.op("prim::Constant")
+    n.setType(_C.OptionalType.ofTensor())
+    return n
+
+
+def _handle_reduce_dim_none(g: jit_utils.GraphContext, self, op_name):
+    rank = _get_tensor_rank(self)
+    if rank is not None and any(
+        _get_tensor_dim_size(self, i) == 0 for i in range(rank)
+    ):
+        # If input tensor is empty, according to ONNX ReduceSum definition,
+        # set keepdims=1 so that the resulted tensor has the same rank as the input.
+        return g.op(op_name, self, keepdims_i=1)
+    return g.op(op_name, self, keepdims_i=0)
+
+
+def dequantize_helper(
+    g: jit_utils.GraphContext,
+    qtensor: _C.Value,
+    qdtype: _C_onnx.TensorProtoDataType | None = None,
+) -> tuple[_C.Value, _C.Value, _C.Value, _C.Value | None]:
+    """Appends to graph `g` ONNX nodes that dequantizes `qtensor` into `tensor`.
+
+    Args:
+        g: Graph, the ONNX IR graph that is under construction.
+        qtensor: torch._C.Value, either a tuple of (quantized_tensor, scale, zero_point)
+            for per tensor quantization, or
+            (quantized_tensor, scale, zero_point, axis) for per channel quantization,
+            representing the quantized tensor.
+        qdtype: torch.onnx.TensorProtoDataType default None, if not None, represents the
+            data type of quantized tensor. It must be either
+            torch.onnx.TensorProtoDataType.UINT8 or torch.onnx.TensorProtoDataType.INT8.
+    """
+    unpacked_qtensors = _unpack_quantized_tensor(qtensor)
+    tensor, scale, zero_point = unpacked_qtensors[:3]
+    axis = unpacked_qtensors[3] if len(unpacked_qtensors) >= 4 else None
+    axis_i = _get_const(axis, "i", "axis")
+    input_qdtype = _type_utils.JitScalarType.from_value(tensor)
+    if qdtype is None:
+        if input_qdtype is not None:
+            qdtype = input_qdtype.onnx_type()
+        else:
+            qdtype = _C_onnx.TensorProtoDataType.UINT8
+    value = g.op("Cast", tensor, to_i=qdtype)
+    scale = g.op("Cast", scale, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+    zero_point = g.op("Cast", zero_point, to_i=qdtype)
+
+    if axis_i is not None and GLOBALS.export_onnx_opset_version < 13:
+        _onnx_opset_unsupported_detailed(
+            "DequantizeLinear",
+            GLOBALS.export_onnx_opset_version,
+            13,
+            "Attribute axis is not supported.",
+            qtensor,
+        )
+
+    return (
+        g.op("DequantizeLinear", value, scale, zero_point, axis_i=axis_i),
+        scale,
+        zero_point,
+        axis,
+    )
+
+
+def quantize_helper(
+    g: jit_utils.GraphContext,
+    tensor: _C.Value,
+    scale: _C.Value,
+    zero_point: _C.Value,
+    axis: _C.Value | None = None,
+) -> _C.Value:
+    """Appends to graph `g` ONNX nodes that quantizes `tensor` based on `scale`, `zero_point` and `axis`.
+
+    Args:
+        g: Graph, the ONNX IR graph that is under construction.
+        tensor: torch._C.Value, representing the tensor to be quantized.
+        scale: torch._C.Value, quantized scale.
+        zero_point: torch._C.Value, quantized zero point.
+        axis: Optional[torch._C.Value] default None, if None, represents per tensor quantization.
+            Otherwise, represents per channel quantization, along given axis.
+
+    Returns:
+        A TupleConstruct storing information of the quantized tensor.
+    """
+    if (
+        axis is not None
+        and not _is_none(axis)
+        and GLOBALS.export_onnx_opset_version < 13
+    ):
+        _onnx_opset_unsupported_detailed(
+            "QuantizeLinear",
+            GLOBALS.export_onnx_opset_version,
+            13,
+            "Attribute axis is not supported.",
+            tensor,
+        )
+
+    assert scale is not None
+    if (
+        _type_utils.JitScalarType.from_value(scale, _type_utils.JitScalarType.UNDEFINED)
+        != _type_utils.JitScalarType.FLOAT
+    ):
+        scale = g.op("Cast", scale, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+
+    assert zero_point is not None
+    if _type_utils.JitScalarType.from_value(
+        zero_point, _type_utils.JitScalarType.UNDEFINED
+    ) not in {
+        _type_utils.JitScalarType.UINT8,
+        _type_utils.JitScalarType.INT8,
+    }:
+        zero_point = g.op("Cast", zero_point, to_i=_C_onnx.TensorProtoDataType.UINT8)
+    output = g.op(
+        "QuantizeLinear",
+        tensor,
+        scale,
+        zero_point,
+        axis_i=_get_const(axis, "i", "axis"),
+    )
+    args = [output, scale, zero_point]
+    if axis is not None and not _is_none(axis):
+        args.append(axis)
+    return g.op("prim::TupleConstruct", *args)
+
+
+def requantize_bias_helper(
+    g: jit_utils.GraphContext, bias, input_scale, weight_scale, axis=None
+):
+    """In PyTorch, bias is float and is quantized to int32 implicitly inside the quantized ATen op kernel.
+    In ONNX we need to make the quantization explicit because operators expect all of their inputs to be quantized.
+    Since int32 is not a supported output type by ONNX operator `QuantizeLinear`, quantization is exported using
+    regular operators.
+    """
+    bias_scale = g.op("Mul", weight_scale, input_scale)
+    bias_scale_shape = g.op("Shape", bias_scale)
+    bias_zero_point = g.op(
+        "ConstantOfShape", bias_scale_shape, value_t=torch.tensor([0], dtype=torch.int)
+    )
+    q_bias = g.op(
+        "Cast", g.op("Div", bias, bias_scale), to_i=_C_onnx.TensorProtoDataType.INT32
+    )
+    axis_args = []
+    if axis is not None and not _is_none(axis):
+        axis_args.append(axis)
+    return g.op("prim::TupleConstruct", q_bias, bias_scale, bias_zero_point, *axis_args)
+
+
+def args_have_same_dtype(args):
+    assert args
+    base_dtype = _type_utils.JitScalarType.from_value(args[0])
+    has_same_dtype = all(
+        _type_utils.JitScalarType.from_value(elem) == base_dtype for elem in args
+    )
+    return has_same_dtype
+
+
+def _op_with_optional_float_cast(g: jit_utils.GraphContext, op_name, *args, **kwargs):
+    """Some PyTorch operators (e.g., Clip/Min/ReLU/Pad) are super set of ONNX in terms of data types.
+    This function maximizes the exportability of PyTorch-ONNX by allowing ONNX-unsupported PyTorch
+    operator data type. For example, `Cast(Clip(Cast(INPUT)))` can be used to mimic
+    `Clip(INPUT)` (opset version < 12).
+
+    Args:
+        g (torch._C.Graph): graph to write the ONNX representation into.
+        op_name (str): operator name in ONNX.
+        *args (tuple): operands to the operator.
+        **kwargs (dict): attributes to the operator along with "opset_before" (optional, None by default)
+            indicating the smallest opset version to trigger such casting behavior and "target_float_t"
+            (optional, torch.onnx.JitScalarType.FLOAT by default) indicating the data type of internal operator.
+
+    Returns:
+        Optional[torch._C.Value, Tuple[torch._C.Value, ...]]: output(s) of the operator.
+    """
+    opset_before = kwargs.pop("opset_before", None)
+    target_float_t = kwargs.pop("target_float_t", _type_utils.JitScalarType.FLOAT)
+
+    inputs = list(args)
+    dtype_0 = _type_utils.JitScalarType.from_value(inputs[0])
+
+    require_cast = not _is_fp(inputs[0]) and (
+        opset_before is None or GLOBALS.export_onnx_opset_version < opset_before
+    )
+
+    if require_cast:
+        for input in inputs:
+            if input.isCompleteTensor():
+                input_scalar_type = _type_utils.JitScalarType.from_value(input)
+                if input_scalar_type != dtype_0:
+                    raise errors.SymbolicValueError(
+                        f"Inputs of {op_name} must have same dtype."
+                        f"Got {dtype_0.scalar_name()} and {input_scalar_type.scalar_name()}",
+                        input,
+                    )
+        for i, input in enumerate(inputs):
+            if input.isCompleteTensor() and not _is_fp(input):
+                inputs[i] = g.op(
+                    "Cast",
+                    input,
+                    to_i=target_float_t.onnx_type(),
+                )
+
+    self = g.op(op_name, *inputs, **kwargs)
+
+    if require_cast:
+        self = g.op("Cast", self, to_i=dtype_0.onnx_type())
+
+    return self
+
+
+def _maybe_cast_reduce_op_input(g: jit_utils.GraphContext, self):
+    scalar_type = _type_utils.JitScalarType.from_value(
+        self, _type_utils.JitScalarType.UNDEFINED
+    )
+    if scalar_type != _type_utils.JitScalarType.UNDEFINED:
+        # This check only covers traced modules where dtype is present
+        # pytorch reduce-ops cast all other integral types to int64
+        if not _is_fp(self) and scalar_type != _type_utils.JitScalarType.INT64:
+            self = g.op("Cast", self, to_i=_C_onnx.TensorProtoDataType.INT64)
+    return self
+
+
+def _apply_params(*args, **kwargs):
+    """Returns a decorator that calls the decorated (higher-order) function with the given parameters."""
+
+    def _apply(fn):
+        return fn(*args, **kwargs)
+
+    return _apply
+
+
+def _reduce_op_symbolic_helper(onnx_op_name, allow_multi_dim_support=True):
+    def symbolic(g, self, dim=None, keepdim=None):
+        self = _maybe_cast_reduce_op_input(g, self)
+        if dim is None or dim == ():
+            # Dim can be 0, which will cause (not dim) == True. So we don't want to do
+            # (not dim)
+            # all-reduce path
+            return _handle_reduce_dim_none(g, self, onnx_op_name)
+        else:
+            # dim-reduce path
+            keepdim = _get_const(keepdim, "i", "keepdim")
+            if g.opset < 18:
+                desc = "is" if allow_multi_dim_support else "i"
+                dim = _get_const(dim, desc, "dim")
+                dim_list = dim if allow_multi_dim_support else [dim]
+                return g.op(onnx_op_name, self, axes_i=dim_list, keepdims_i=keepdim)
+            else:
+                if _is_value(dim):
+                    axes = dim
+                else:
+                    if allow_multi_dim_support:
+                        axes = g.op(
+                            "Constant", value_t=torch.tensor(dim, dtype=torch.long)
+                        )
+                    else:
+                        axes = g.op(
+                            "Constant", value_t=torch.tensor([dim], dtype=torch.long)
+                        )
+                return g.op(onnx_op_name, self, axes, keepdims_i=keepdim)
+
+    return symbolic
+
+
+def _overload_by_arg_count(fn):
+    @functools.wraps(fn)
+    def wrapper(g, *args):
+        overloads = fn(g, *args)
+        for overload in overloads:
+            arg_descriptors = overload._arg_descriptors
+            if len(arg_descriptors) == len(args):
+                return overload(g, *args)
+        return _unimplemented(f"aten::{fn.__name__}", f"with {len(args)} arguments")
+
+    return wrapper
+
+
+def _reduce_with_dtype_helper(
+    onnx_op: str, name: str, allow_multi_dim_support: bool = True
+):
+    symbolic = _reduce_op_symbolic_helper(
+        onnx_op, allow_multi_dim_support=allow_multi_dim_support
+    )
+
+    @_overload_by_arg_count
+    def reduce(g, *args, **kwargs):
+        @quantized_args(True)
+        @parse_args("v", "none")
+        def reduce_nodim(g, self, dtype):
+            dtype_onnx = None
+            if dtype.node().kind() == "onnx::Constant":
+                dtype = _get_const(dtype, "i", "dtype")
+                dtype_onnx = _type_utils.JitScalarType(dtype).onnx_type()
+                self = g.op("Cast", self, to_i=dtype_onnx)
+            elif dtype.node().kind() != "prim::Constant":
+                return _unimplemented(name, "dtype", dtype)
+            result = symbolic(g, self)
+            if dtype_onnx is not None:
+                result_dtype_onnx = _type_utils.JitScalarType.from_value(
+                    result
+                ).onnx_type()
+                if result_dtype_onnx != dtype_onnx:
+                    result = g.op("Cast", result, to_i=dtype_onnx)
+            return result
+
+        dim_desc = "is" if allow_multi_dim_support else "i"
+
+        @quantized_args(True)
+        @parse_args("v", dim_desc, "i", "none")  # type: ignore[arg-type]
+        def reduce_dim(g, self, dim, keepdim, dtype):
+            dtype_onnx = None
+            if dtype.node().kind() == "onnx::Constant":
+                dtype = _get_const(dtype, "i", "dtype")
+                dtype_onnx = _type_utils.JitScalarType(dtype).onnx_type()
+                self = g.op("Cast", self, to_i=dtype_onnx)
+            elif dtype.node().kind() != "prim::Constant":
+                return _unimplemented(name, "dtype", dtype)
+            result = symbolic(g, self, dim, keepdim)
+            if dtype_onnx is not None:
+                result_dtype_onnx = _type_utils.JitScalarType.from_value(
+                    result
+                ).onnx_type()
+                if result_dtype_onnx != dtype_onnx:
+                    result = g.op("Cast", result, to_i=dtype_onnx)
+            return result
+
+        return reduce_nodim, reduce_dim
+
+    return reduce
+
+
+def _max_helper(g: jit_utils.GraphContext, self, dim_or_y=None, keepdim=None):
+    # torch.max(input)
+    if dim_or_y is None and keepdim is None:
+        return g.op("ReduceMax", self, keepdims_i=0)
+    # torch.max(input, other)
+    if keepdim is None:
+        return _op_with_optional_float_cast(g, "Max", self, dim_or_y, opset_before=12)
+    # torch.max(input, dim, keepdim)
+    else:
+        keepdim = _get_const(keepdim, "i", "keepdim")
+        dim = _get_const(dim_or_y, "i", "dim")
+        if g.opset < 18:
+            max = g.op("ReduceMax", self, axes_i=[dim], keepdims_i=keepdim)
+        else:
+            axes = g.op("Constant", value_t=torch.tensor([dim], dtype=torch.long))
+            max = g.op("ReduceMax", self, axes, keepdims_i=keepdim)
+        indices = g.op("ArgMax", self, axis_i=dim, keepdims_i=keepdim)
+        return max, indices
+
+
+def _min_helper(g: jit_utils.GraphContext, self, dim_or_y=None, keepdim=None):
+    # torch.min(input)
+    if dim_or_y is None and keepdim is None:
+        return g.op("ReduceMin", self, keepdims_i=0)
+    # torch.min(input, other)
+    if keepdim is None:
+        return _op_with_optional_float_cast(g, "Min", self, dim_or_y, opset_before=12)
+    # torch.min(input, dim, keepdim)
+    else:
+        keepdim = _get_const(keepdim, "i", "keepdim")
+        dim = _get_const(dim_or_y, "i", "dim")
+        if g.opset < 18:
+            min = g.op("ReduceMin", self, axes_i=[dim], keepdims_i=keepdim)
+        else:
+            axes = g.op("Constant", value_t=torch.tensor([dim], dtype=torch.long))
+            min = g.op("ReduceMin", self, axes, keepdims_i=keepdim)
+        indices = g.op("ArgMin", self, axis_i=dim, keepdims_i=keepdim)
+        return min, indices
+
+
+def _numel_helper(g: jit_utils.GraphContext, self):
+    shape = g.op("Shape", self)
+    return g.op("ReduceProd", shape, keepdims_i=0)
+
+
+@parse_args("v", "is", "i", "i")
+def _var_mean_helper(g: jit_utils.GraphContext, input, dim, correction, keepdim):
+    if g.opset < 18:
+        if dim is None:
+            mean = g.op("ReduceMean", input, keepdims_i=0)
+            t_mean = mean
+            num_elements = _numel_helper(g, input)
+        else:
+            mean = g.op("ReduceMean", input, axes_i=dim, keepdims_i=keepdim)
+            t_mean = g.op("ReduceMean", input, axes_i=dim, keepdims_i=1)
+            redudced_dims = g.op("Shape", input)
+            # dim could contain one or multiple dimensions
+            redudced_dims = g.op(
+                "Gather",
+                redudced_dims,
+                g.op("Constant", value_t=torch.tensor(dim)),
+                axis_i=0,
+            )
+            num_elements = g.op("ReduceProd", redudced_dims, keepdims_i=0)
+        sub_v = g.op("Sub", input, t_mean)
+        sqr_sub = g.op("Mul", sub_v, sub_v)
+        keepdim_mean = 0 if dim is None else keepdim
+        var = g.op("ReduceMean", sqr_sub, axes_i=dim, keepdims_i=keepdim_mean)
+        # Correct bias in calculating variance, by dividing it over (N - correction) instead on N
+        if correction is None:
+            correction = 1
+        if correction != 0:
+            num_elements = g.op(
+                "Cast", num_elements, to_i=_C_onnx.TensorProtoDataType.FLOAT
+            )
+            one = g.op("Constant", value_t=torch.tensor(correction, dtype=torch.float))
+            mul = g.op("Mul", var, num_elements)
+            var = g.op("Div", mul, g.op("Sub", num_elements, one))
+        return var, mean
+    else:
+        axes = None
+        if dim is None:
+            mean = g.op("ReduceMean", input, keepdims_i=0)
+            t_mean = mean
+            num_elements = _numel_helper(g, input)
+        else:
+            axes = g.op("Constant", value_t=torch.tensor(dim, dtype=torch.long))
+            mean = g.op("ReduceMean", input, axes, keepdims_i=keepdim)
+            t_mean = g.op("ReduceMean", input, axes, keepdims_i=1)
+            redudced_dims = g.op("Shape", input)
+            # dim could contain one or multiple dimensions
+            redudced_dims = g.op(
+                "Gather",
+                redudced_dims,
+                g.op("Constant", value_t=torch.tensor(dim)),
+                axis_i=0,
+            )
+            num_elements = g.op("ReduceProd", redudced_dims, keepdims_i=0)
+        sub_v = g.op("Sub", input, t_mean)
+        sqr_sub = g.op("Mul", sub_v, sub_v)
+        keepdim_mean = 0 if dim is None else keepdim
+        if axes is None:
+            var = g.op("ReduceMean", sqr_sub, keepdims_i=keepdim_mean)
+        else:
+            var = g.op("ReduceMean", sqr_sub, axes, keepdims_i=keepdim_mean)
+        # Correct bias in calculating variance, by dividing it over (N - correction) instead on N
+        if correction is None:
+            correction = 1
+        if correction != 0:
+            num_elements = g.op(
+                "Cast", num_elements, to_i=_C_onnx.TensorProtoDataType.FLOAT
+            )
+            one = g.op("Constant", value_t=torch.tensor(correction, dtype=torch.float))
+            mul = g.op("Mul", var, num_elements)
+            var = g.op("Div", mul, g.op("Sub", num_elements, one))
+        return var, mean
+
+
+def _embedding_bag_helper(
+    g: jit_utils.GraphContext,
+    embedding_matrix,
+    indices,
+    offsets,
+    scale_grad_by_freq,
+    mode,
+    sparse,
+    per_sample_weights,
+    include_last_offset,
+    padding_idx,
+):
+    if scale_grad_by_freq and GLOBALS.export_training:
+        return _onnx_unsupported(
+            "embedding_bag with scale_grad_by_freq for training mode"
+        )
+    if padding_idx is not None and padding_idx >= 0:
+        raise RuntimeError("embedding_bag with padding_idx")
+
+    loop_condition = g.op("Constant", value_t=torch.tensor(1))
+    loop_condition = g.op("Cast", loop_condition, to_i=_C_onnx.TensorProtoDataType.BOOL)
+    zero = g.op("Constant", value_t=torch.tensor([0]))
+
+    indices_len = _unsqueeze_helper(
+        g,
+        _size_helper(g, indices, g.op("Constant", value_t=torch.tensor(0))),
+        [0],
+    )
+    if not include_last_offset:
+        offsets = [offsets, indices_len]
+        offsets = g.op("Concat", *offsets, axis_i=0)
+
+    # Offsets holds the starting index position of each bag. So we create a list of the indices slices (determined by
+    # offsets) and gather those indices in indices_row. Then we use this subset of indices to gather from embeddings.
+    # The embeddings output is a loop scan output, so we can avoid creating a sequence and inserting elements in.
+    offsets_starts = _slice_helper(
+        g, offsets, axes=[0], starts=[0], ends=[sys.maxsize], steps=[1]
+    )
+    offsets_ends = _slice_helper(
+        g, offsets, axes=[0], starts=[1], ends=[sys.maxsize], steps=[1]
+    )
+
+    loop_len = _size_helper(g, offsets_ends, g.op("Constant", value_t=torch.tensor(0)))
+
+    loop, (loop_context,), _ = jit_utils.add_op_with_blocks(
+        g, "Loop", loop_len, loop_condition, n_blocks=1
+    )
+    loop_block = loop_context.block
+
+    # FIXME(justinchuby): We need to handle what happens when we call b.op on a node return
+    block_input_iter = utils._add_input_to_block(loop_block)
+    cond = utils._add_input_to_block(loop_block)
+
+    indices_start = loop_context.op(
+        "Gather", offsets_starts, block_input_iter, axis_i=0
+    )
+    indices_end = loop_context.op("Gather", offsets_ends, block_input_iter, axis_i=0)
+    indices_start = _unsqueeze_helper(loop_context, indices_start, [0])
+    indices_end = _unsqueeze_helper(loop_context, indices_end, [0])
+
+    indices_row = loop_context.op("Slice", indices, indices_start, indices_end, zero)
+    embeddings = loop_context.op("Gather", embedding_matrix, indices_row, axis_i=0)
+    if not _is_none(per_sample_weights):
+        per_sample_weights_row = loop_context.op(
+            "Slice", per_sample_weights, indices_start, indices_end, zero
+        )
+        per_sample_weights_row = _unsqueeze_helper(
+            loop_context, per_sample_weights_row, [1]
+        )
+        embeddings = loop_context.op("Mul", embeddings, per_sample_weights_row)
+    if mode == 0:
+        embeddings = _reducesum_helper(
+            loop_context, embeddings, axes_i=[0], keepdims_i=0
+        )
+    elif mode == 1:
+        if loop_context.opset < 18:
+            embeddings = loop_context.op(
+                "ReduceMean", embeddings, axes_i=[0], keepdims_i=0
+            )
+        else:
+            axes = loop_context.op(
+                "Constant", value_t=torch.tensor([0], dtype=torch.long)
+            )
+            embeddings = loop_context.op("ReduceMean", embeddings, axes, keepdims_i=0)
+    else:
+        if loop_context.opset < 18:
+            embeddings = loop_context.op(
+                "ReduceMax", embeddings, axes_i=[0], keepdims_i=0
+            )
+        else:
+            axes = loop_context.op(
+                "Constant", value_t=torch.tensor([0], dtype=torch.long)
+            )
+            embeddings = loop_context.op("ReduceMax", embeddings, axes, keepdims_i=0)
+
+    cond_out = loop_context.op(
+        "Cast", loop_condition, to_i=_C_onnx.TensorProtoDataType.BOOL
+    )
+    utils._add_output_to_block(loop_block, cond_out)
+    utils._add_output_to_block(loop_block, embeddings)
+
+    # aten::embedding_bag returns a tuple of 4 elements: output, offset2bag, bag_size, max_indices.
+    # But the last three outputs are not used in torch.nn.EmbeddingBag or torch.nn.functional.embedding_bag.
+    return loop.node().output(), None, None, None
+
+
+def _linalg_vector_norm_helper(
+    g: jit_utils.GraphContext,
+    self: torch._C.Value,
+    ord: float,
+    dim: Sequence[int] | None,
+    keepdim: bool,
+    dtype: torch._C.Value,
+):
+    axes = None
+    # Conditions based on https://pytorch.org/docs/stable/generated/torch.linalg.vector_norm.html
+    if _is_none(dim):
+        self = _reshape_helper(g, self, [-1])
+        keepdim = False
+    elif g.opset >= 18:
+        axes = g.op("Constant", value_t=torch.tensor(dim, dtype=torch.long))
+
+    if ord == math.inf:
+        if g.opset < 18:
+            result = g.op(
+                "ReduceMax", g.op("Abs", self), axes_i=dim, keepdims_i=keepdim
+            )
+        else:
+            if axes is None:
+                result = g.op("ReduceMax", g.op("Abs", self), keepdims_i=keepdim)
+            else:
+                result = g.op("ReduceMax", g.op("Abs", self), axes, keepdims_i=keepdim)
+    elif ord == -math.inf:
+        if g.opset < 18:
+            result = g.op(
+                "ReduceMin", g.op("Abs", self), axes_i=dim, keepdims_i=keepdim
+            )
+        else:
+            if axes is None:
+                result = g.op("ReduceMin", g.op("Abs", self), keepdims_i=keepdim)
+            else:
+                result = g.op("ReduceMin", g.op("Abs", self), axes, keepdims_i=keepdim)
+    elif ord == 0:
+        if g.opset < 11:
+            return _onnx_opset_unsupported_detailed(
+                "linalg_vector_norm", 9, 11, "ord=0 not supported", self
+            )
+        else:
+            if dim is None:
+                self = _reshape_helper(
+                    g,
+                    self,
+                    g.op("Constant", value_t=torch.tensor([-1], dtype=torch.int64)),
+                )
+                keepdim = False
+
+            cond_op = g.op(
+                "Not",
+                g.op("Equal", self, g.op("Constant", value_t=torch.LongTensor([0]))),
+            )
+            cond_op = g.op(
+                "Cast",
+                cond_op,
+                to_i=_type_utils.JitScalarType.from_value(self).onnx_type(),
+            )
+            return _reducesum_helper(g, cond_op, axes_i=dim, keepdims_i=keepdim)
+    elif ord == 1:
+        if g.opset < 18:
+            result = _reduce_op_symbolic_helper("ReduceL1")(
+                g, self, dim=dim, keepdim=keepdim
+            )
+        else:
+            if axes is None:
+                result = _reduce_op_symbolic_helper("ReduceL1")(
+                    g, self, keepdim=keepdim
+                )
+            else:
+                result = _reduce_op_symbolic_helper("ReduceL1")(
+                    g, self, axes, keepdim=keepdim
+                )
+    elif ord == 2:
+        if g.opset < 18:
+            result = _reduce_op_symbolic_helper("ReduceL2")(
+                g, self, dim=dim, keepdim=keepdim
+            )
+        else:
+            if axes is None:
+                result = _reduce_op_symbolic_helper("ReduceL2")(
+                    g, self, keepdim=keepdim
+                )
+            else:
+                result = _reduce_op_symbolic_helper("ReduceL2")(
+                    g, self, axes, keepdim=keepdim
+                )
+    else:
+        ord_op = g.op("Constant", value_t=torch.tensor(ord, dtype=torch.float32))
+        result = _reducesum_helper(
+            g, g.op("Pow", g.op("Abs", self), ord_op), axes_i=dim, keepdims_i=keepdim
+        )
+        result = g.op(
+            "Pow",
+            result,
+            g.op(
+                "Div",
+                g.op("Constant", value_t=torch.tensor(1, dtype=torch.float32)),
+                ord_op,
+            ),
+        )
+
+    if not _is_none(dtype):
+        dtype = _get_const(dtype, "i", "dtype")
+        result = g.op("Cast", result, to_i=_type_utils.JitScalarType(dtype).onnx_type())  # type: ignore[arg-type]
+    return result
+
+
+# Deprecated. Internally use _type_utils.ScalarType
+# TODO: remove these once we support Type's in the JIT IR and we can once again
+# use the unified toType operator
+cast_pytorch_to_onnx = {
+    "Byte": _C_onnx.TensorProtoDataType.UINT8,
+    "Char": _C_onnx.TensorProtoDataType.INT8,
+    "Double": _C_onnx.TensorProtoDataType.DOUBLE,
+    "Float": _C_onnx.TensorProtoDataType.FLOAT,
+    "Half": _C_onnx.TensorProtoDataType.FLOAT16,
+    "Int": _C_onnx.TensorProtoDataType.INT32,
+    "Long": _C_onnx.TensorProtoDataType.INT64,
+    "Short": _C_onnx.TensorProtoDataType.INT16,
+    "Bool": _C_onnx.TensorProtoDataType.BOOL,
+    "ComplexFloat": _C_onnx.TensorProtoDataType.COMPLEX64,
+    "ComplexDouble": _C_onnx.TensorProtoDataType.COMPLEX128,
+    "BFloat16": _C_onnx.TensorProtoDataType.BFLOAT16,
+    "Undefined": _C_onnx.TensorProtoDataType.UNDEFINED,
+}
+
+# Deprecated. Internally use _type_utils.ScalarType
+scalar_name_to_pytorch = {
+    "uint8_t": "Byte",
+    "int8_t": "Char",
+    "double": "Double",
+    "float": "Float",
+    "half": "Half",
+    "int": "Int",
+    "int64_t": "Long",
+    "int16_t": "Short",
+    "bool": "Bool",
+    "complex64": "ComplexFloat",
+    "complex128": "ComplexDouble",
+    "qint8": "QInt8",
+    "quint8": "QUInt8",
+    "qint32": "QInt32",
+    "bfloat16": "BFloat16",
+}
+
+
+# Deprecated. Internally use _type_utils.ScalarType
+# This indicates each scalar type's corresponding
+# torch type. Related source:
+# https://github.com/pytorch/pytorch/blob/344defc9733a45fee8d0c4d3f5530f631e823196/c10/core/ScalarType.h
+scalar_type_to_pytorch_type = [
+    torch.uint8,  # 0
+    torch.int8,  # 1
+    torch.short,  # 2
+    torch.int,  # 3
+    torch.int64,  # 4
+    torch.half,  # 5
+    torch.float,  # 6
+    torch.double,  # 7
+    torch.complex32,  # 8
+    torch.complex64,  # 9
+    torch.complex128,  # 10
+    torch.bool,  # 11
+    torch.qint8,  # 12
+    torch.quint8,  # 13
+    torch.qint32,  # 14
+    torch.bfloat16,  # 15
+]
+
+# Deprecated. Internally use _type_utils.ScalarType
+# source of truth is
+# https://github.com/pytorch/pytorch/blob/master/torch/csrc/utils/tensor_dtypes.cpp
+pytorch_name_to_type = {
+    "Byte": torch.uint8,
+    "Char": torch.int8,
+    "Double": torch.double,
+    "Float": torch.float,
+    "Half": torch.half,
+    "Int": torch.int,
+    "Long": torch.int64,
+    "Short": torch.short,
+    "Bool": torch.bool,
+    "ComplexFloat": torch.complex64,
+    "ComplexDouble": torch.complex128,
+    "QInt8": torch.qint8,
+    "QUInt8": torch.quint8,
+    "QInt32": torch.qint32,
+    "BFloat16": torch.bfloat16,
+}
+
+
+# Deprecated. Internally use _type_utils.ScalarType
+scalar_type_to_onnx = [
+    cast_pytorch_to_onnx["Byte"],  # 0
+    cast_pytorch_to_onnx["Char"],  # 1
+    cast_pytorch_to_onnx["Short"],  # 2
+    cast_pytorch_to_onnx["Int"],  # 3
+    cast_pytorch_to_onnx["Long"],  # 4
+    cast_pytorch_to_onnx["Half"],  # 5
+    cast_pytorch_to_onnx["Float"],  # 6
+    cast_pytorch_to_onnx["Double"],  # 7
+    cast_pytorch_to_onnx["Undefined"],  # 8
+    cast_pytorch_to_onnx["ComplexFloat"],  # 9
+    cast_pytorch_to_onnx["ComplexDouble"],  # 10
+    cast_pytorch_to_onnx["Bool"],  # 11
+    cast_pytorch_to_onnx["Char"],  # 12
+    cast_pytorch_to_onnx["Byte"],  # 13
+    cast_pytorch_to_onnx["Int"],  # 14
+    cast_pytorch_to_onnx["BFloat16"],  # 15
+]
+
+# Global set to store the list of quantized operators in the network.
+# This is currently only used in the conversion of quantized ops from PT -> C2 via ONNX.
+_quantized_ops: set[int] = set()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset10.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset10.py
new file mode 100644
index 0000000000000000000000000000000000000000..975b6bdbe7d85f78843dfa5d55de643ca01b44fd
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset10.py
@@ -0,0 +1,1184 @@
+# mypy: allow-untyped-defs
+# mypy: disable-error-code=arg-type
+from __future__ import annotations
+
+import functools
+import sys
+import warnings
+from typing import Sequence
+
+import torch
+import torch._C._onnx as _C_onnx
+import torch.onnx
+from torch import _C
+
+# Monkey-patch graph manipulation methods on Graph, used for the ONNX symbolics
+from torch.onnx import (
+    _constants,
+    _type_utils,
+    errors,
+    symbolic_helper,
+    symbolic_opset9 as opset9,
+)
+from torch.onnx._globals import GLOBALS
+from torch.onnx._internal import jit_utils, registration
+
+
+# EDITING THIS FILE? READ THIS FIRST!
+# see Note [Edit Symbolic Files] in README.md
+
+# This file exports ONNX ops for opset 10
+# Opset 10 is supported by ONNX release 1.5.0
+# release on 04/24/19
+
+
+__all__ = [
+    "dequantize",
+    "div",
+    "embedding_bag",
+    "fake_quantize_per_tensor_affine",
+    "flip",
+    "fmod",
+    "isfinite",
+    "isinf",
+    "nan_to_num",
+    "quantize_per_tensor",
+    "quantized_add_relu",
+    "quantized_add",
+    "quantized_cat",
+    "quantized_conv1d_relu",
+    "quantized_conv2d_relu",
+    "quantized_conv3d_relu",
+    "quantized_conv1d",
+    "quantized_conv2d",
+    "quantized_conv3d",
+    "quantized_conv_transpose1d",
+    "quantized_conv_transpose2d",
+    "quantized_conv_transpose3d",
+    "quantized_group_norm",
+    "quantized_hardswish",
+    "quantized_instance_norm",
+    "quantized_layer_norm",
+    "quantized_leaky_relu",
+    "quantized_linear",
+    "quantized_linear_relu",
+    "quantized_mul",
+    "quantized_sigmoid",
+    "slice",
+    "sort",
+    "topk",
+]
+
+
+_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=10)
+
+
+@_onnx_symbolic("aten::div")
+def div(g: jit_utils.GraphContext, self, other, *args):
+    if len(args) == 0:
+        return opset9.true_divide(g, self, other)
+    else:
+        return _div_rounding_mode(g, self, other, *args)
+
+
+@symbolic_helper.parse_args("v", "v", "s")
+def _div_rounding_mode(g: jit_utils.GraphContext, self, other, rounding_mode):
+    if rounding_mode == "floor":
+        return _floor_divide(g, self, other)
+    else:
+        return opset9._div_rounding_mode(g, self, other, rounding_mode)
+
+
+@_onnx_symbolic("aten::_floor_divide")
+def _floor_divide(g: jit_utils.GraphContext, self, other):
+    if symbolic_helper._is_fp(self) or symbolic_helper._is_fp(other):
+        out = opset9.true_divide(g, self, other)
+        return g.op("Floor", out)
+    else:
+        # Integer division does trunction rounding
+        div = g.op("Div", self, other)
+        # Division is negative if: self < 0 != other < 0
+        zero = g.op("Constant", value_t=torch.tensor(0, dtype=torch.int64))
+        negative = g.op("Xor", g.op("Less", self, zero), g.op("Less", other, zero))
+
+        # For negative numbers with self % other != 0, subtract 1 to round down instead of up
+        mod = g.op("Mod", self, other, fmod_i=0)
+        fixup_mask = g.op("And", negative, g.op("Not", g.op("Equal", mod, zero)))
+
+        one = g.op("Constant", value_t=torch.tensor(1, dtype=torch.int64))
+        fixup = g.op("Sub", div, one)
+        return g.op("Where", fixup_mask, fixup, div)
+
+
+@_onnx_symbolic("aten::sort")
+@symbolic_helper.parse_args("v", "i", "i", "none")
+def sort(g: jit_utils.GraphContext, self, dim, decending, out=None):
+    return symbolic_helper._sort_helper(g, self, dim, decending=decending, out=out)
+
+
+@_onnx_symbolic("aten::topk")
+@symbolic_helper.parse_args("v", "v", "i", "i", "i", "none")
+def topk(g: jit_utils.GraphContext, self, k, dim, largest, sorted, out=None):
+    return symbolic_helper._topk_helper(
+        g, self, k, dim, largest=largest, sorted=sorted, out=out
+    )
+
+
+def _aten_max_pool_onnx(
+    g: jit_utils.GraphContext,
+    self: _C.Value,
+    kernel_shape: Sequence[int],
+    strides: Sequence[int],
+    pads: Sequence[int],
+    dilations: Sequence[int],
+    ceil_mode: bool,
+    unbatched_rank: int,
+) -> _C.Value:
+    self_rank = g.op("Size", g.op("Shape", self))
+    if self_rank == unbatched_rank:  # C,H,W -> N,C,H,W and N=1
+        self = g.op(
+            "Unsqueeze",
+            self,
+            g.op("Constant", value_t=torch.tensor([0], dtype=torch.int64)),
+        )
+
+    pool_result, _ = g.op(
+        "MaxPool",
+        self,
+        outputs=2,
+        ceil_mode_i=ceil_mode,
+        dilations_i=dilations,
+        kernel_shape_i=kernel_shape,
+        pads_i=pads,
+        strides_i=strides,
+    )
+
+    if self_rank == unbatched_rank:
+        pool_result = g.op(
+            "Squeeze",
+            pool_result,
+            g.op("Constant", value_t=torch.tensor([0], dtype=torch.int64)),
+        )
+
+    return pool_result
+
+
+# For MaxPool
+def _adjust_attributes_of_max_pool(
+    expand_size: int,
+    kernel_size: Sequence[int] | int,
+    stride: Sequence[int] | int,
+    padding: Sequence[int] | int,
+    dilation: Sequence[int] | int,
+) -> tuple[Sequence[int], Sequence[int], Sequence[int], Sequence[int]]:
+    """Adjust attributes of avg_pool to match ONNX specification."""
+
+    if isinstance(dilation, int):
+        dilation = [dilation] * expand_size
+
+    if isinstance(kernel_size, int):
+        kernel_shape = [kernel_size] * expand_size
+    else:
+        kernel_shape = kernel_size  # type: ignore[assignment]
+
+    if isinstance(padding, int):
+        pads = [padding] * expand_size * 2  # type: ignore[operator, assignment]
+    elif len(padding) == 1:
+        pads = padding * expand_size * 2  # type: ignore[operator, assignment]
+    elif len(padding) == 2:
+        # 2D padding
+        pads = padding * 2  # type: ignore[operator, assignment]
+    elif len(padding) == 3:
+        # 3D padding
+        pads = padding * 2  # type: ignore[operator, assignment]
+    else:
+        # When padding is already done for all dimensions,
+        # we don't need to double it
+        # eg: (1, 1, 1, 1, 1, 1)
+        pads = padding  # type: ignore[assignment]
+
+    if isinstance(stride, int):
+        strides = [stride] * expand_size
+    elif not stride:
+        strides = kernel_shape
+    else:
+        strides = stride  # type: ignore[assignment]
+
+    return (kernel_shape, strides, pads, dilation)
+
+
+def _aten_max_pool_with_indices_onnx(
+    g: jit_utils.GraphContext,
+    self: _C.Value,
+    kernel_shape: Sequence[int],
+    strides: Sequence[int],
+    pads: Sequence[int],
+    dilations: Sequence[int],
+    ceil_mode: bool,
+    unbatched_rank: int,
+    n_dims_one: Sequence[int],
+    n_dims_zero: Sequence[int],
+    n_dims_axes: Sequence[int],
+) -> tuple[_C.Value, Sequence[int]]:
+    self_rank = g.op("Size", g.op("Shape", self))
+    if self_rank == unbatched_rank:  # C,H,W -> N,C,H,W and N=1
+        self = g.op(
+            "Unsqueeze",
+            self,
+            g.op("Constant", value_t=torch.tensor([0], dtype=torch.int64)),
+        )
+
+    pool_result, indices = g.op(
+        "MaxPool",
+        self,
+        outputs=2,
+        ceil_mode_i=ceil_mode,
+        dilations_i=dilations,
+        kernel_shape_i=kernel_shape,
+        pads_i=pads,
+        strides_i=strides,
+    )
+    _, flatten_indices = g.op(
+        "MaxPool",
+        self,
+        outputs=2,
+        dilations_i=dilations,
+        kernel_shape_i=n_dims_one,
+        strides_i=n_dims_one,
+    )
+
+    ends = g.op("Constant", value_t=torch.tensor(n_dims_one))
+    starts = g.op("Constant", value_t=torch.tensor(n_dims_zero))
+    axes = g.op("Constant", value_t=torch.tensor(n_dims_axes))
+
+    delta = g.op("Slice", flatten_indices, starts, ends, axes)
+    indices = g.op("Sub", indices, delta)
+
+    if self_rank == unbatched_rank:
+        pool_result = g.op(
+            "Squeeze", pool_result, value_t=torch.tensor([0], dtype=torch.int64)
+        )
+        indices = g.op("Squeeze", indices, value_t=torch.tensor([0], dtype=torch.int64))
+
+    return (pool_result, indices)
+
+
+@_onnx_symbolic(
+    "aten::max_pool1d",
+    decorate=[symbolic_helper._apply_params("max_pool1d", 1, return_indices=False)],
+)
+@_onnx_symbolic(
+    "aten::max_pool2d",
+    decorate=[symbolic_helper._apply_params("max_pool2d", 2, return_indices=False)],
+)
+@_onnx_symbolic(
+    "aten::max_pool3d",
+    decorate=[symbolic_helper._apply_params("max_pool3d", 3, return_indices=False)],
+)
+@_onnx_symbolic(
+    "aten::max_pool1d_with_indices",
+    decorate=[
+        symbolic_helper._apply_params(
+            "max_pool1d_with_indices",
+            1,
+            return_indices=True,
+        )
+    ],
+)
+@_onnx_symbolic(
+    "aten::max_pool2d_with_indices",
+    decorate=[
+        symbolic_helper._apply_params(
+            "max_pool2d_with_indices",
+            2,
+            return_indices=True,
+        )
+    ],
+)
+@_onnx_symbolic(
+    "aten::max_pool3d_with_indices",
+    decorate=[
+        symbolic_helper._apply_params(
+            "max_pool3d_with_indices",
+            3,
+            return_indices=True,
+        )
+    ],
+)
+def _max_pool(name: str, expand_size: int, return_indices: bool):
+    @symbolic_helper.quantized_args(True, False, False, False, False, False)
+    @symbolic_helper.parse_args("v", "is", "is", "is", "is", "i")
+    def symbolic_fn(
+        g: jit_utils.GraphContext,
+        input: _C.Value,
+        kernel_size: Sequence[int],
+        stride: Sequence[int],
+        padding: int | Sequence[int],
+        dilation: Sequence[int],
+        ceil_mode: bool,
+    ):
+        kernel_shape, strides, pads, dilations = _adjust_attributes_of_max_pool(
+            expand_size, kernel_size, stride, padding, dilation
+        )
+
+        if return_indices:
+            return _aten_max_pool_with_indices_onnx(
+                g,
+                input,
+                kernel_shape,
+                strides,
+                pads,
+                dilations,
+                ceil_mode,
+                expand_size + 1,
+                ([1] * expand_size),
+                ([0] * expand_size),
+                ([2 + i for i in range(expand_size)]),
+            )
+        else:
+            return _aten_max_pool_onnx(
+                g,
+                input,
+                kernel_shape,
+                strides,
+                pads,
+                dilations,
+                ceil_mode,
+                expand_size + 1,
+            )
+
+    return symbolic_fn
+
+
+# For AvgPool
+def _adjust_attributes_of_avg_pool(
+    expand_size: int,
+    kernel_size: Sequence[int] | int,
+    stride: Sequence[int] | int,
+    padding: Sequence[int] | int,
+) -> tuple[Sequence[int], Sequence[int], Sequence[int]]:
+    """Adjust attributes of avg_pool to match ONNX specification."""
+
+    if isinstance(kernel_size, int):
+        kernel_shape = [kernel_size] * expand_size
+    else:
+        kernel_shape = kernel_size  # type: ignore[assignment]
+
+    if isinstance(padding, int):
+        pads = [padding] * expand_size * 2
+    elif len(padding) == 1:
+        pads = padding * expand_size * 2  # type: ignore[operator, assignment]
+    elif len(padding) == 2:
+        pads = padding * expand_size  # type: ignore[operator, assignment]
+    else:
+        pads = padding * 2  # type: ignore[operator, assignment]
+
+    if isinstance(stride, int):
+        strides = [stride] * expand_size
+    elif not stride:
+        strides = kernel_shape
+    else:
+        strides = stride  # type: ignore[assignment]
+
+    return (kernel_shape, strides, pads)
+
+
+@_onnx_symbolic(
+    "aten::avg_pool1d",
+    decorate=[symbolic_helper._apply_params("avg_pool1d", 1)],
+)
+@_onnx_symbolic(
+    "aten::avg_pool2d",
+    decorate=[symbolic_helper._apply_params("avg_pool2d", 2)],
+)
+@_onnx_symbolic(
+    "aten::avg_pool3d",
+    decorate=[symbolic_helper._apply_params("avg_pool3d", 3)],
+)
+def _avg_pool(name, expand_size):
+    @symbolic_helper.quantized_args(True, False, False, False, False, False, False)
+    @symbolic_helper.parse_args("v", "is", "is", "is", "i", "i", "none")
+    def symbolic_fn(
+        g,
+        input: _C.Value,
+        kernel_size: Sequence[int],
+        stride: Sequence[int],
+        padding: int | Sequence[int],
+        ceil_mode: int,
+        count_include_pad: int,
+        divisor_override=None,
+    ):
+        kernel_shape, strides, pads = _adjust_attributes_of_avg_pool(
+            expand_size, kernel_size, stride, padding
+        )
+
+        result = g.op(
+            "AveragePool",
+            input,
+            ceil_mode_i=ceil_mode,
+            count_include_pad_i=count_include_pad,
+            kernel_shape_i=kernel_shape,
+            pads_i=pads,
+            strides_i=strides,
+        )
+
+        return result
+
+    return symbolic_fn
+
+
+@_onnx_symbolic(
+    "aten::upsample_nearest1d",
+    decorate=[symbolic_helper._apply_params("upsample_nearest1d", 3, "nearest")],
+)
+@_onnx_symbolic(
+    "aten::upsample_nearest2d",
+    decorate=[symbolic_helper._apply_params("upsample_nearest2d", 4, "nearest")],
+)
+@_onnx_symbolic(
+    "aten::upsample_nearest3d",
+    decorate=[symbolic_helper._apply_params("upsample_nearest3d", 5, "nearest")],
+)
+@_onnx_symbolic(
+    "aten::upsample_linear1d",
+    decorate=[symbolic_helper._apply_params("upsample_linear1d", 3, "linear")],
+)
+@_onnx_symbolic(
+    "aten::upsample_bilinear2d",
+    decorate=[symbolic_helper._apply_params("upsample_bilinear2d", 4, "linear")],
+)
+@_onnx_symbolic(
+    "aten::upsample_trilinear3d",
+    decorate=[symbolic_helper._apply_params("upsample_trilinear3d", 5, "linear")],
+)
+def _interpolate(name, dim, interpolate_mode):
+    @symbolic_helper.quantized_args(True, False, False)
+    def symbolic_fn(g, input, output_size, *args):
+        scales, align_corners = symbolic_helper._get_interpolate_attributes(
+            g, interpolate_mode, args
+        )
+        symbolic_helper._interpolate_warning(interpolate_mode)
+        align_corners = symbolic_helper._maybe_get_scalar(align_corners)
+        if align_corners:
+            return symbolic_helper._unimplemented(name, "align_corners == True", input)
+        if scales is None:
+            scales = symbolic_helper._interpolate_size_to_scales(
+                g, input, output_size, dim
+            )
+        return g.op("Resize", input, scales, mode_s=interpolate_mode)
+
+    return symbolic_fn
+
+
+@_onnx_symbolic("aten::__interpolate")
+def __interpolate(
+    g: jit_utils.GraphContext,
+    input,
+    size,
+    scale_factor,
+    mode,
+    align_corners,
+    recompute_scale_factor,
+    antialias,
+):
+    scales, mode = symbolic_helper._interpolate_get_scales_and_mode(
+        g, input, size, scale_factor, mode, align_corners
+    )
+    return g.op("Resize", input, scales, mode_s=mode)
+
+
+def _slice(
+    g: jit_utils.GraphContext,
+    input: torch._C.Value,
+    axes: list | torch.Tensor | torch._C.Value,
+    starts: list | torch.Tensor | torch._C.Value,
+    ends: list | torch.Tensor | torch._C.Value,
+    steps: list | torch.Tensor | torch._C.Value | None = None,
+):
+    def is_none_value(value):
+        if value is None:
+            return True
+        return (
+            isinstance(value, torch._C.Value)
+            and value.node().kind() == "prim::Constant"
+            and isinstance(value.type(), _C.NoneType)
+        )
+
+    def to_slice_input(list_or_value, default_value=None):
+        # Convert input param into a 1D torch.Value.
+        if is_none_value(list_or_value) and default_value is not None:
+            list_or_value = [default_value]
+
+        if isinstance(list_or_value, (list, torch.Tensor)):
+            return g.op("Constant", value_t=torch.tensor(list_or_value))
+
+        rank = symbolic_helper._get_tensor_rank(list_or_value)
+        if rank == 0:
+            return symbolic_helper._unsqueeze_helper(g, list_or_value, [0])
+        if rank == 1:
+            return list_or_value
+        raise errors.SymbolicValueError(
+            f"Rank must be 0 or 1, not {rank}", list_or_value
+        )
+
+    def get_const_value(list_or_value):
+        if isinstance(list_or_value, (list, torch.Tensor)):
+            if len(list_or_value) == 1:
+                return list_or_value[0]
+            return None
+        return symbolic_helper._maybe_get_const(list_or_value, "i")
+
+    # Check if slice is a no-op
+    if (
+        get_const_value(starts) == 0
+        and get_const_value(ends) == _constants.INT64_MAX
+        and (steps is None or get_const_value(steps) == 1)
+    ):
+        return input
+
+    axes = to_slice_input(axes)
+    starts = to_slice_input(starts, default_value=0)
+    ends = to_slice_input(ends, default_value=_constants.INT64_MAX)
+    if steps is None:
+        return g.op("Slice", input, starts, ends, axes)
+    steps = to_slice_input(steps, default_value=1)
+    return g.op("Slice", input, starts, ends, axes, steps)
+
+
+@_onnx_symbolic("aten::slice")
+def slice(g: jit_utils.GraphContext, self, *args):
+    if len(args) == 4:
+        # aten::slice(Tensor self, int dim, int? start=None, int? end=None, int step=1) -> Tensor
+        dims, start, end, step = args
+    elif len(args) == 3:
+        # aten::slice(t[] l, int? start=None, int? end=None, int step=1) -> t[]
+        start, end, step = args
+        dims = [0]
+    else:
+        raise errors.SymbolicValueError("Unknown aten::slice signature", self)
+
+    return symbolic_helper._slice_helper(
+        g,
+        self,
+        axes=dims,
+        starts=start,
+        ends=end,
+        steps=step,
+    )
+
+
+@_onnx_symbolic("aten::flip")
+@symbolic_helper.parse_args("v", "is")
+def flip(g: jit_utils.GraphContext, input, dims):
+    return symbolic_helper._slice_helper(
+        g,
+        input,
+        axes=dims,
+        starts=[-1] * len(dims),
+        ends=[-_constants.INT64_MAX] * len(dims),
+        steps=[-1] * len(dims),
+    )
+
+
+@_onnx_symbolic("aten::fmod")
+def fmod(g: jit_utils.GraphContext, input, other):
+    return g.op("Mod", input, other, fmod_i=1)
+
+
+@_onnx_symbolic("aten::embedding_bag")
+@symbolic_helper.parse_args("v", "v", "v", "i", "i", "i", "v", "i", "i")
+def embedding_bag(
+    g: jit_utils.GraphContext,
+    embedding_matrix,
+    indices,
+    offsets,
+    scale_grad_by_freq,
+    mode,
+    sparse,
+    per_sample_weights,
+    include_last_offset,
+    padding_idx,
+):
+    if scale_grad_by_freq and GLOBALS.export_training:
+        return symbolic_helper._onnx_unsupported(
+            "embedding_bag with scale_grad_by_freq for training mode"
+        )
+    if padding_idx is not None and padding_idx >= 0:
+        raise RuntimeError("embedding_bag with padding_idx")
+
+    warnings.warn(
+        "Export of embedding_bag with dynamic input/offsets shape is not supported in opset 10. "
+        "Please use opset 11 or higher to export model for dynamic input shape.'"
+    )
+    offsets_dim_0 = symbolic_helper._get_tensor_dim_size(offsets, 0)
+    if offsets_dim_0 is not None:
+        if include_last_offset:
+            offset_len = offsets_dim_0 - 1
+            offsets_extended = offsets
+        else:
+            offset_len = offsets_dim_0
+            offsets_extended = [
+                offsets,
+                g.op("Constant", value_t=torch.tensor([sys.maxsize])),
+            ]
+            offsets_extended = g.op("Concat", *offsets_extended, axis_i=0)
+        list_ = []
+        for i in range(offset_len):
+            start_ = symbolic_helper._unsqueeze_helper(
+                g,
+                opset9.select(g, offsets_extended, torch.tensor(0), torch.tensor(i)),
+                [0],
+            )
+            end_ = symbolic_helper._unsqueeze_helper(
+                g,
+                opset9.select(
+                    g, offsets_extended, torch.tensor(0), torch.tensor(i + 1)
+                ),
+                [0],
+            )
+            axes_ = g.op("Constant", value_t=torch.tensor([0]))
+            indices_row = g.op("Slice", indices, start_, end_, axes_)
+
+            embeddings = g.op("Gather", embedding_matrix, indices_row)
+            if not symbolic_helper._is_none(per_sample_weights):
+                per_sample_weights_row = g.op(
+                    "Slice", per_sample_weights, start_, end_, axes_
+                )
+                per_sample_weights_row = symbolic_helper._unsqueeze_helper(
+                    g, per_sample_weights_row, [1]
+                )
+                embeddings = g.op("Mul", embeddings, per_sample_weights_row)
+            if mode == 0:
+                embeddings = symbolic_helper._reducesum_helper(
+                    g, embeddings, axes_i=[0], keepdims_i=0
+                )
+            elif mode == 1:
+                embeddings = g.op("ReduceMean", embeddings, axes_i=[0], keepdims_i=0)
+            else:
+                embeddings = g.op("ReduceMax", embeddings, axes_i=[0], keepdims_i=0)
+
+            embeddings = symbolic_helper._unsqueeze_helper(g, embeddings, [0])
+            list_.append(embeddings)
+
+        output = g.op("Concat", *list_, axis_i=0)
+        # aten::embedding_bag returns a tuple of 4 elements: output, offset2bag, bag_size, max_indices.
+        # But the last three outputs are not used in torch.nn.EmbeddingBag or torch.nn.functional.embedding_bag.
+        return output, None, None, None
+    else:
+        return symbolic_helper._onnx_unsupported(
+            "embedding_bag with unknown shape of offsets for opset 10 is not supported. "
+            "please use opset 11 or higher."
+        )
+
+
+@_onnx_symbolic("aten::fake_quantize_per_tensor_affine")
+@symbolic_helper.parse_args("v", "v", "v", "i", "i")
+def fake_quantize_per_tensor_affine(
+    g: jit_utils.GraphContext,
+    inputs,
+    scale,
+    zero_point,
+    quant_min=-128,
+    quant_max=127,
+):
+    # NOTE: (0, 127) is a special case. PyTorch restricts activations to be in the range (0, 127).
+    #   https://github.com/pytorch/pytorch/blob/b34b192d6b97325c9f78e5995c48c8498ede34bd/torch/ao/quantization/observer.py#L1422
+    if (quant_min, quant_max) == (0, 127):
+        symbolic_helper._onnx_opset_unsupported_detailed(
+            "fake_quantize_per_tensor_affine",
+            10,
+            13,
+            "Quantize range (0, 127) not supported, requires opset 13 Clip",
+            inputs,
+        )
+    if (quant_min, quant_max) not in [(0, 255), (-128, 127)]:
+        raise errors.SymbolicValueError(
+            f"For (quant_min, quant_max), ONNX allows only (0, 255) and (-128, 127). "
+            f"Got ({quant_min}, {quant_max})",
+            inputs,
+        )
+    scale = symbolic_helper._maybe_get_scalar(scale)
+    if scale is None:
+        symbolic_helper._onnx_opset_unsupported_detailed(
+            "fake_quantize_per_tensor_affine",
+            10,
+            13,
+            "Non-constant scale not supported",
+            inputs,
+        )
+    scale = scale.float().data  # Avoid exporter generating double type
+    if quant_min == 0:
+        zero_point = g.op("Cast", zero_point, to_i=_C_onnx.TensorProtoDataType.UINT8)
+    else:
+        zero_point = g.op("Cast", zero_point, to_i=_C_onnx.TensorProtoDataType.INT8)
+    return g.op(
+        "DequantizeLinear",
+        g.op("QuantizeLinear", inputs, scale, zero_point),
+        scale,
+        zero_point,
+    )
+
+
+@_onnx_symbolic("aten::isinf")
+def isinf(g: jit_utils.GraphContext, input):
+    return g.op("IsInf", g.op("Cast", input, to_i=_C_onnx.TensorProtoDataType.DOUBLE))
+
+
+@_onnx_symbolic("aten::isfinite")
+def isfinite(g: jit_utils.GraphContext, input):
+    inf_node = isinf(g, input)
+    nan_node = opset9.isnan(g, input)
+    return opset9.__not_(g, opset9.__or_(g, inf_node, nan_node))
+
+
+@_onnx_symbolic("aten::quantize_per_tensor")
+def quantize_per_tensor(g: jit_utils.GraphContext, input, scale, zero_point, dtype):
+    dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+    # TODO(justinchuby): Extract all the cast ops into a helper function.
+    zero_point = g.op(
+        "Cast", zero_point, to_i=_type_utils.JitScalarType(dtype).onnx_type()
+    )
+    scale = g.op("Cast", scale, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+    return symbolic_helper.quantize_helper(g, input, scale, zero_point)
+
+
+@_onnx_symbolic("aten::dequantize")
+def dequantize(g: jit_utils.GraphContext, input):
+    return symbolic_helper.dequantize_helper(g, input)[0]
+
+
+@_onnx_symbolic("aten::nan_to_num")
+@symbolic_helper.parse_args("v", "f", "f", "f")
+def nan_to_num(g: jit_utils.GraphContext, input, nan, posinf, neginf):
+    # Cannot create a int type tensor with inf/nan values, so we simply
+    # return the original tensor
+    if not symbolic_helper._is_fp(input):
+        return input
+    input_dtype = _type_utils.JitScalarType.from_value(input).dtype()
+    if nan is None:
+        nan = 0.0
+    nan_cond = opset9.isnan(g, input)
+    nan_result = g.op(
+        "Where",
+        nan_cond,
+        g.op("Constant", value_t=torch.tensor([nan], dtype=input_dtype)),
+        input,
+    )
+
+    # For None values of posinf, neginf we use the greatest/lowest finite
+    # value representable by input's dtype.
+    finfo = torch.finfo(input_dtype)
+    if posinf is None:
+        posinf = finfo.max
+    posinf_cond = opset9.logical_and(
+        g,
+        isinf(g, nan_result),
+        opset9.gt(g, nan_result, g.op("Constant", value_t=torch.LongTensor([0]))),
+    )
+    nan_posinf_result = g.op(
+        "Where",
+        posinf_cond,
+        g.op("Constant", value_t=torch.tensor([posinf], dtype=input_dtype)),
+        nan_result,
+    )
+
+    if neginf is None:
+        neginf = finfo.min
+    neginf_cond = opset9.logical_and(
+        g,
+        isinf(g, nan_posinf_result),
+        opset9.lt(
+            g, nan_posinf_result, g.op("Constant", value_t=torch.LongTensor([0]))
+        ),
+    )
+    return g.op(
+        "Where",
+        neginf_cond,
+        g.op("Constant", value_t=torch.tensor([neginf], dtype=input_dtype)),
+        nan_posinf_result,
+    )
+
+
+# Quantized symbolics ---------------------------------------------------------
+# https://github.com/pytorch/pytorch/wiki/PyTorch-ONNX-exporter#quantized-model-export
+# Support starts from opset 10 because `DequantizeLinear` and `QuantizeLinear` were
+# introduced in opset version 10.
+@_onnx_symbolic("quantized::linear")
+def quantized_linear(
+    g: jit_utils.GraphContext, q_input, q_weight, bias, op_scale, op_zero_point
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, _ = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(g, bias, input_scale, weight_scale)
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.linear(g, input, weight, bias)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::linear_relu")
+def quantized_linear_relu(
+    g: jit_utils.GraphContext, q_input, q_weight, bias, op_scale, op_zero_point
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, _ = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(g, bias, input_scale, weight_scale)
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.linear(g, input, weight, bias)
+    output = opset9.relu(g, output)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::add")
+def quantized_add(g: jit_utils.GraphContext, x, y, op_scale, op_zero_point):
+    x, _, _, _ = symbolic_helper.dequantize_helper(g, x)
+    y, _, _, _ = symbolic_helper.dequantize_helper(g, y)
+
+    output = opset9.add(g, x, y)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::add_relu")
+def quantized_add_relu(g: jit_utils.GraphContext, x, y, op_scale, op_zero_point):
+    x, _, _, _ = symbolic_helper.dequantize_helper(g, x)
+    y, _, _, _ = symbolic_helper.dequantize_helper(g, y)
+
+    output = opset9.add(g, x, y)
+    output = opset9.relu(g, output)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::mul")
+def quantized_mul(g: jit_utils.GraphContext, x, y, op_scale, op_zero_point):
+    x, _, _, _ = symbolic_helper.dequantize_helper(g, x)
+    y, _, _, _ = symbolic_helper.dequantize_helper(g, y)
+
+    output = opset9.mul(g, x, y)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::hardswish")
+def quantized_hardswish(g: jit_utils.GraphContext, x, op_scale, op_zero_point):
+    x, _, _, _ = symbolic_helper.dequantize_helper(g, x)
+
+    output = opset9.hardswish(g, x)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::sigmoid")
+def quantized_sigmoid(g: jit_utils.GraphContext, x, op_scale, op_zero_point):
+    x, _, _, _ = symbolic_helper.dequantize_helper(g, x)
+
+    output = opset9.sigmoid(g, x)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::leaky_relu")
+def quantized_leaky_relu(
+    g: jit_utils.GraphContext, x, negative_slope, inplace, op_scale, op_zero_point
+):
+    x, _, _, _ = symbolic_helper.dequantize_helper(g, x)
+
+    output = opset9.leaky_relu(g, x, negative_slope, inplace)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::layer_norm")
+def quantized_layer_norm(
+    g: jit_utils.GraphContext,
+    x,
+    normalized_shape,
+    weight,
+    bias,
+    eps,
+    op_scale,
+    op_zero_point,
+):
+    x, _, _, _ = symbolic_helper.dequantize_helper(g, x)
+
+    output = opset9.layer_norm(g, x, normalized_shape, weight, bias, eps, False)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::group_norm")
+def quantized_group_norm(
+    g: jit_utils.GraphContext,
+    x,
+    num_groups,
+    weight,
+    bias,
+    eps,
+    op_scale,
+    op_zero_point,
+):
+    x, _, _, _ = symbolic_helper.dequantize_helper(g, x)
+
+    output = opset9.group_norm(g, x, num_groups, weight, bias, eps, False)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::instance_norm")
+@symbolic_helper.parse_args("v", "v", "v", "f", "v", "v")
+def quantized_instance_norm(
+    g: jit_utils.GraphContext,
+    q_input,
+    weight,
+    bias,
+    eps,
+    op_scale,
+    op_zero_point,
+):
+    input, _, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+
+    output = opset9.instance_norm(
+        g, input, weight, bias, None, None, False, 0.0, eps, False
+    )
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::conv1d_relu")
+def quantized_conv1d_relu(
+    g: jit_utils.GraphContext,
+    q_input,
+    q_weight,
+    bias,
+    stride,
+    padding,
+    dilation,
+    groups,
+    op_scale,
+    op_zero_point,
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, _ = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(g, bias, input_scale, weight_scale)
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.conv1d(g, input, weight, bias, stride, padding, dilation, groups)
+    output = opset9.relu(g, output)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::conv2d_relu")
+def quantized_conv2d_relu(
+    g: jit_utils.GraphContext,
+    q_input,
+    q_weight,
+    bias,
+    stride,
+    padding,
+    dilation,
+    groups,
+    op_scale,
+    op_zero_point,
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, _ = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(g, bias, input_scale, weight_scale)
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.conv2d(g, input, weight, bias, stride, padding, dilation, groups)
+    output = opset9.relu(g, output)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::conv3d_relu")
+def quantized_conv3d_relu(
+    g: jit_utils.GraphContext,
+    q_input,
+    q_weight,
+    bias,
+    stride,
+    padding,
+    dilation,
+    groups,
+    op_scale,
+    op_zero_point,
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, _ = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(g, bias, input_scale, weight_scale)
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.conv3d(g, input, weight, bias, stride, padding, dilation, groups)
+    output = opset9.relu(g, output)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::conv1d")
+def quantized_conv1d(
+    g: jit_utils.GraphContext,
+    q_input,
+    q_weight,
+    bias,
+    stride,
+    padding,
+    dilation,
+    groups,
+    op_scale,
+    op_zero_point,
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, _ = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(g, bias, input_scale, weight_scale)
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.conv1d(g, input, weight, bias, stride, padding, dilation, groups)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::conv2d")
+def quantized_conv2d(
+    g: jit_utils.GraphContext,
+    q_input,
+    q_weight,
+    bias,
+    stride,
+    padding,
+    dilation,
+    groups,
+    op_scale,
+    op_zero_point,
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, _ = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(g, bias, input_scale, weight_scale)
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.conv2d(g, input, weight, bias, stride, padding, dilation, groups)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::conv3d")
+def quantized_conv3d(
+    g: jit_utils.GraphContext,
+    q_input,
+    q_weight,
+    bias,
+    stride,
+    padding,
+    dilation,
+    groups,
+    op_scale,
+    op_zero_point,
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, _ = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(g, bias, input_scale, weight_scale)
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.conv3d(g, input, weight, bias, stride, padding, dilation, groups)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::conv_transpose1d")
+def quantized_conv_transpose1d(
+    g: jit_utils.GraphContext,
+    q_input,
+    q_weight,
+    bias,
+    stride,
+    padding,
+    output_padding,
+    dilation,
+    groups,
+    op_scale,
+    op_zero_point,
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, _ = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(g, bias, input_scale, weight_scale)
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.conv_transpose2d(
+        g, input, weight, bias, stride, padding, output_padding, groups, dilation
+    )
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::conv_transpose2d")
+def quantized_conv_transpose2d(
+    g: jit_utils.GraphContext,
+    q_input,
+    q_weight,
+    bias,
+    stride,
+    padding,
+    output_padding,
+    dilation,
+    groups,
+    op_scale,
+    op_zero_point,
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, _ = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(g, bias, input_scale, weight_scale)
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.conv_transpose2d(
+        g, input, weight, bias, stride, padding, output_padding, groups, dilation
+    )
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::conv_transpose3d")
+def quantized_conv_transpose3d(
+    g: jit_utils.GraphContext,
+    q_input,
+    q_weight,
+    bias,
+    stride,
+    padding,
+    output_padding,
+    dilation,
+    groups,
+    op_scale,
+    op_zero_point,
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, _ = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(g, bias, input_scale, weight_scale)
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.conv_transpose3d(
+        g, input, weight, bias, stride, padding, output_padding, groups, dilation
+    )
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::cat")
+@symbolic_helper.parse_args("v", "i", "v", "v")
+def quantized_cat(
+    g: jit_utils.GraphContext,
+    q_inputs: _C.Value,
+    dim: int,
+    op_scale: _C.Value,
+    op_zero_point: _C.Value,
+) -> _C.Value:
+    unpacked_inputs = symbolic_helper._unpack_list(q_inputs)
+    dequantized = [
+        symbolic_helper.dequantize_helper(g, input)[0] for input in unpacked_inputs
+    ]
+    concatenated = g.op("Concat", *dequantized, axis_i=dim)
+    return symbolic_helper.quantize_helper(g, concatenated, op_scale, op_zero_point)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset11.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset11.py
new file mode 100644
index 0000000000000000000000000000000000000000..7bf27b273832f2f12293f565b4598ee1b9d2868a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset11.py
@@ -0,0 +1,1467 @@
+# mypy: allow-untyped-defs
+# mypy: disable-error-code=arg-type
+"""This file exports ONNX ops for opset 11."""
+
+from __future__ import annotations
+
+import functools
+import sys
+import warnings
+from typing import Sequence
+
+import torch
+from torch import _C
+from torch._C import _onnx as _C_onnx
+from torch.onnx import (
+    _type_utils,
+    errors,
+    symbolic_helper,
+    symbolic_opset10 as opset10,
+    symbolic_opset9 as opset9,
+    utils,
+)
+from torch.onnx._internal import jit_utils, registration
+
+
+# EDITING THIS FILE? READ THIS FIRST!
+# see Note [Edit Symbolic Files] in README.md
+
+__all__ = [
+    "add",
+    "append",
+    "arange",
+    "argsort",
+    "atleast_1d",
+    "atleast_2d",
+    "atleast_3d",
+    "cat",
+    "chunk",
+    "clamp_max",
+    "clamp_min",
+    "clamp",
+    "constant_pad_nd",
+    "cumsum",
+    "Delete",
+    "embedding_bag",
+    "embedding_renorm",
+    "flatten",
+    "gather",
+    "hardtanh",
+    "hstack",
+    "im2col",
+    "index_fill",
+    "index",
+    "index_copy",
+    "index_put",
+    "insert",
+    "linalg_det",
+    "linalg_vector_norm",
+    "logdet",
+    "masked_scatter",
+    "masked_select",
+    "mm",
+    "narrow",
+    "normal",
+    "pad",
+    "pixel_shuffle",
+    "pop",
+    "prim_constant_chunk",
+    "reflection_pad",
+    "relu6",
+    "remainder",
+    "replication_pad",
+    "round",
+    "scatter",
+    "select",
+    "size",
+    "sort",
+    "split_with_sizes",
+    "split",
+    "squeeze",
+    "stack",
+    "topk",
+    "unbind",
+    "unique_dim",
+    "unsqueeze",
+    "vstack",
+]
+
+_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=11)
+
+
+@_onnx_symbolic("aten::hardtanh")
+@symbolic_helper.quantized_args(True)
+@symbolic_helper.parse_args("v", "f", "f")
+def hardtanh(g: jit_utils.GraphContext, self: _C.Value, min_val: float, max_val: float):
+    scalar_type = _type_utils.JitScalarType.from_value(
+        self, _type_utils.JitScalarType.FLOAT
+    )
+    min_val = g.op(
+        "Constant",
+        value_t=torch.tensor(min_val, dtype=scalar_type.dtype()),
+    )
+    max_val = g.op(
+        "Constant",
+        value_t=torch.tensor(max_val, dtype=scalar_type.dtype()),
+    )
+    return symbolic_helper._op_with_optional_float_cast(
+        g, "Clip", self, min_val, max_val, opset_before=12
+    )
+
+
+@_onnx_symbolic("aten::clamp")
+def clamp(g: jit_utils.GraphContext, self, min, max):
+    def _cast_if_not_none(tensor, dtype):
+        if tensor is not None and not symbolic_helper._is_none(tensor):
+            return g.op(
+                "Cast",
+                tensor,
+                to_i=dtype.onnx_type(),
+            )
+        else:
+            return tensor
+
+    scalar_type = _type_utils.JitScalarType.from_value(
+        self, _type_utils.JitScalarType.UNDEFINED
+    )
+    if scalar_type != _type_utils.JitScalarType.UNDEFINED:
+        min = _cast_if_not_none(min, scalar_type)
+        max = _cast_if_not_none(max, scalar_type)
+
+    if symbolic_helper._is_none(min):
+        return clamp_max(g, self, max)
+    elif symbolic_helper._is_none(max):
+        return clamp_min(g, self, min)
+    else:
+        if (
+            symbolic_helper._get_tensor_rank(min) == 0
+            and symbolic_helper._get_tensor_rank(max) == 0
+        ):
+            return symbolic_helper._op_with_optional_float_cast(
+                g, "Clip", self, min, max, opset_before=12
+            )
+        else:
+            return clamp_max(g, clamp_min(g, self, min), max)
+
+
+@_onnx_symbolic("aten::clamp_min")
+@symbolic_helper.parse_args("v", "v")
+def clamp_min(g: jit_utils.GraphContext, self, min):
+    min = g.op("Cast", min, to_i=_type_utils.JitScalarType.from_value(self).onnx_type())
+    if symbolic_helper._get_tensor_rank(min) == 0:
+        max = opset9.unused(g)
+        return symbolic_helper._op_with_optional_float_cast(
+            g, "Clip", self, min, max, opset_before=12
+        )
+    else:
+        return symbolic_helper._op_with_optional_float_cast(
+            g, "Max", self, min, opset_before=12
+        )
+
+
+@_onnx_symbolic("aten::clamp_max")
+@symbolic_helper.parse_args("v", "v")
+def clamp_max(g: jit_utils.GraphContext, self, max):
+    max = g.op("Cast", max, to_i=_type_utils.JitScalarType.from_value(self).onnx_type())
+    if symbolic_helper._get_tensor_rank(max) == 0:
+        min = opset9.unused(g)
+        return symbolic_helper._op_with_optional_float_cast(
+            g, "Clip", self, min, max, opset_before=12
+        )
+    else:
+        return symbolic_helper._op_with_optional_float_cast(
+            g, "Min", self, max, opset_before=12
+        )
+
+
+@_onnx_symbolic("aten::relu6")
+def relu6(g: jit_utils.GraphContext, input):
+    scalar_type = _type_utils.JitScalarType.from_value(
+        input, _type_utils.JitScalarType.FLOAT
+    )
+    min_val = g.op(
+        "Constant",
+        value_t=torch.tensor(0, dtype=scalar_type.dtype()),
+    )
+    max_val = g.op(
+        "Constant",
+        value_t=torch.tensor(6, dtype=scalar_type.dtype()),
+    )
+    return clamp(g, input, min_val, max_val)
+
+
+@_onnx_symbolic("aten::select")
+# Opset 11 gather accepts negative indices
+@symbolic_helper.quantized_args(True)
+@symbolic_helper.parse_args("v", "i", "v")
+def select(g: jit_utils.GraphContext, self, dim, index):
+    return g.op("Gather", self, index, axis_i=dim)
+
+
+@_onnx_symbolic("aten::index_put")
+def index_put(
+    g: jit_utils.GraphContext, self, indices_list_value, values, accumulate=False
+):
+    if symbolic_helper._is_packed_list(indices_list_value):
+        indices_list = symbolic_helper._unpack_list(indices_list_value)
+    else:
+        indices_list = [indices_list_value]
+    accumulate = symbolic_helper._parse_arg(accumulate, "b")
+
+    if len(indices_list) == 0:
+        return values
+
+    if len(indices_list) > 1:
+        for idx_ in range(len(indices_list)):
+            if symbolic_helper._is_bool(indices_list[idx_]):
+                indices_list[idx_] = g.op("NonZero", indices_list[idx_])
+        index = indices_list[0]
+
+        for ind in indices_list[1:]:
+            index = opset9.add(g, index, ind)
+        broadcast_index_shape = g.op("Shape", index)
+        indices_list = [
+            symbolic_helper._unsqueeze_helper(
+                g, opset9.expand(g, ind, broadcast_index_shape, None), [-1]
+            )
+            for ind in indices_list
+        ]
+        index = g.op("Concat", *indices_list, axis_i=-1)
+    else:
+        # Replace index_put node with masked_scatter or masked_fill
+        # when inputs to the index_put node contains a single boolean input.
+        #
+        # index_put -> masked_fill
+        #   * input index contains single tensor of Bool type (e.g.: %24 <- %23).
+        #   * input value contains single element (e.g.: %18).
+        #
+        # Torch IR
+        #   %mask : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) = aten::clone(%0, %6)
+        #   %16 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) =
+        #               aten::to(%8, %26, %27, %11, %12, %28, %29, %15)
+        #   %18 : Float(requires_grad=0, device=cpu) = prim::Constant[value={1}]()
+        #   %23 : Bool(8, strides=[1], device=cpu) = aten::view(%16, %22)
+        #   %24 : Tensor?[] = prim::ListConstruct(%23)
+        #   %25 : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) =
+        #                aten::index_put(%mask, %24, %18, %30)
+        #   return (%25)
+        #
+        #
+        # index_put -> masked_scatter
+        #   * input index contains single tensor of Bool type (e.g.: %32 <- %31).
+        #   * input value contains multiple elements (e.g.: %28).
+        #
+        # Torch IR
+        #   %mask : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) = aten::clone(%0, %6)
+        #   %28 : Float(8, strides=[1], requires_grad=0, device=cpu)
+        #                = prim::Constant[value= 1  1  1  1  1  1  1  1 [ CPUFloatType{8} ]]()
+        #   %15 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu)
+        #                = aten::ne(%mask, %some_const)
+        #   %23 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu)
+        #                = aten::to(%15, %34, %35, %18, %19, %36, %37, %22)
+        #   %38 : Long(requires_grad=0, device=cpu) = prim::Constant[value={0}]()
+        #   %30 : int[] = prim::Constant[value=[-1]]()
+        #   %31 : Bool(8, strides=[1], device=cpu) = aten::view(%23, %30)
+        #   %32 : Tensor?[] = prim::ListConstruct(%31)
+        #   %33 : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu)
+        #               = aten::index_put(%mask, %32, %28, %38)
+        #   return (%33)
+        index = indices_list[0]
+        bool_inp = index
+        if symbolic_helper._is_bool(bool_inp):
+            rank = symbolic_helper._get_tensor_rank(values)
+            if rank is not None and rank == 0:
+                return opset9.masked_fill(g, self, bool_inp, values)
+            mask_rank = symbolic_helper._get_tensor_rank(bool_inp)
+            self_rank = symbolic_helper._get_tensor_rank(self)
+            if (
+                mask_rank is not None
+                and self_rank is not None
+                and self_rank > mask_rank
+            ):
+                # Unsqueeze 'bool_inp' to be broadcastable to shape of 'self'.
+                bool_inp = symbolic_helper._unsqueeze_helper(
+                    g, bool_inp, list(range(mask_rank, self_rank))
+                )
+            return masked_scatter(g, self, bool_inp, values)
+        broadcast_index_shape = g.op("Shape", index)
+        index = symbolic_helper._unsqueeze_helper(g, index, [-1])
+    sub_data_shape = symbolic_helper._slice_helper(
+        g, g.op("Shape", self), axes=[0], starts=[len(indices_list)], ends=[sys.maxsize]
+    )
+    values_shape = g.op("Concat", broadcast_index_shape, sub_data_shape, axis_i=0)
+    # Check if values is a singular value and expand accordingly
+    rank = symbolic_helper._get_tensor_rank(values)
+    if rank is not None and rank == 0:
+        values = opset9.expand(g, values, values_shape, None)
+    values = symbolic_helper._reshape_helper(g, values, values_shape)
+
+    self_scalar_type = _type_utils.JitScalarType.from_value(
+        self, _type_utils.JitScalarType.UNDEFINED
+    )
+    if self_scalar_type != _type_utils.JitScalarType.UNDEFINED:
+        values_scalar_type = _type_utils.JitScalarType.from_value(
+            values, _type_utils.JitScalarType.UNDEFINED
+        )
+        if self_scalar_type != values_scalar_type:
+            values = g.op("Cast", values, to_i=self_scalar_type.onnx_type())
+    elif accumulate:
+        raise errors.SymbolicValueError("self does not have a valid scalar type.", self)
+
+    if accumulate:
+        zeros = g.op(
+            "ConstantOfShape",
+            g.op("Shape", self),
+            value_t=torch.tensor([0], dtype=self_scalar_type.dtype()),
+        )
+        result = g.op("ScatterND", zeros, index, values)
+        result = add(g, self, result)
+    else:
+        result = g.op("ScatterND", self, index, values)
+
+    return result
+
+
+@_onnx_symbolic("aten::pixel_shuffle")
+@symbolic_helper.parse_args("v", "i")
+def pixel_shuffle(g: jit_utils.GraphContext, self, upscale_factor):
+    rank = symbolic_helper._get_tensor_rank(self)
+    if rank is not None and rank != 4:
+        return symbolic_helper._unimplemented("pixel_shuffle", "only support 4d input")
+    return g.op("DepthToSpace", self, blocksize_i=upscale_factor, mode_s="CRD")
+
+
+@_onnx_symbolic(
+    "aten::upsample_nearest1d",
+    decorate=[symbolic_helper._apply_params("upsample_nearest1d", 3, "nearest")],
+)
+@_onnx_symbolic(
+    "aten::upsample_nearest2d",
+    decorate=[symbolic_helper._apply_params("upsample_nearest2d", 4, "nearest")],
+)
+@_onnx_symbolic(
+    "aten::upsample_nearest3d",
+    decorate=[symbolic_helper._apply_params("upsample_nearest3d", 5, "nearest")],
+)
+@_onnx_symbolic(
+    "aten::upsample_linear1d",
+    decorate=[symbolic_helper._apply_params("upsample_linear1d", 3, "linear")],
+)
+@_onnx_symbolic(
+    "aten::upsample_bilinear2d",
+    decorate=[symbolic_helper._apply_params("upsample_bilinear2d", 4, "linear")],
+)
+@_onnx_symbolic(
+    "aten::upsample_trilinear3d",
+    decorate=[symbolic_helper._apply_params("upsample_trilinear3d", 5, "linear")],
+)
+@_onnx_symbolic(
+    "aten::upsample_bicubic2d",
+    decorate=[symbolic_helper._apply_params("upsample_bicubic2d", 4, "cubic")],
+)
+def _interpolate(name: str, dim: int, interpolate_mode: str):
+    return symbolic_helper._interpolate_helper(name, dim, interpolate_mode)
+
+
+@_onnx_symbolic("aten::__interpolate")
+@symbolic_helper.quantized_args(True, False, False, False, False, False, False)
+def __interpolate(
+    g: jit_utils.GraphContext,
+    input,
+    size,
+    scale_factor,
+    mode,
+    align_corners,
+    recompute_scale_factor,
+    antialias,
+):
+    return symbolic_helper.__interpolate_helper(
+        g, input, size, scale_factor, mode, align_corners, recompute_scale_factor
+    )
+
+
+@_onnx_symbolic("aten::gather")
+@symbolic_helper.parse_args("v", "i", "v", "v")
+def gather(g: jit_utils.GraphContext, self, dim, index, sparse_grad=False):
+    if symbolic_helper._maybe_get_const(sparse_grad, "i"):
+        return symbolic_helper._unimplemented("gather", "sparse_grad == True")
+    return g.op("GatherElements", self, index, axis_i=dim)
+
+
+@_onnx_symbolic("aten::scatter")
+@symbolic_helper.parse_args("v", "i", "v", "v")
+def scatter(g: jit_utils.GraphContext, self, dim, index, src):
+    src_type = _type_utils.JitScalarType.from_value(src)
+    src = symbolic_helper._maybe_get_scalar(src)
+    if symbolic_helper._is_value(src):
+        return g.op("ScatterElements", self, index, src, axis_i=dim)
+    else:
+        # Check if scalar "src" has same type as self (PyTorch allows different
+        # type for scalar src (but not when src is tensor)). If not, insert Cast node.
+        if _type_utils.JitScalarType.from_value(self) != src_type:
+            src = g.op(
+                "Cast",
+                src,
+                to_i=_type_utils.JitScalarType.from_value(self).onnx_type(),
+            )
+        return g.op(
+            "ScatterElements", self, index, opset9.expand_as(g, src, index), axis_i=dim
+        )
+
+
+@_onnx_symbolic("aten::cumsum")
+@symbolic_helper.parse_args("v", "i", "none")
+def cumsum(g: jit_utils.GraphContext, self, dim, dtype=None):
+    dim_tensor = g.op("Constant", value_t=torch.tensor(dim, dtype=torch.int))
+    if dtype and dtype.node().kind() != "prim::Constant":
+        parsed_dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+        cast = g.op(
+            "Cast", self, to_i=_type_utils.JitScalarType(parsed_dtype).onnx_type()
+        )
+    else:
+        cast = self
+    csum = g.op("CumSum", cast, dim_tensor)
+    return csum
+
+
+@_onnx_symbolic("aten::masked_select")
+def masked_select(g: jit_utils.GraphContext, self, mask):
+    index = opset9.nonzero(g, opset9.expand_as(g, mask, self))
+    return g.op("GatherND", self, index)
+
+
+@_onnx_symbolic("aten::masked_scatter")
+def masked_scatter(g: jit_utils.GraphContext, self, mask, source):
+    index = opset9.nonzero(g, opset9.expand_as(g, mask, self))
+    # NOTE: source can have more elements than needed.
+    # It could also have arbitrary shape.
+    # This is not supported by ONNX::ScatterND, so we need to flatten and slice source tensor.
+    source = symbolic_helper._reshape_helper(g, source, torch.LongTensor([-1]))
+    source = symbolic_helper._slice_helper(
+        g,
+        source,
+        axes=torch.LongTensor([0]),
+        starts=torch.LongTensor([0]),
+        ends=opset9.size(g, index, torch.LongTensor([0])),
+    )
+    return g.op("ScatterND", self, index, source)
+
+
+@_onnx_symbolic("aten::len")
+def _len(g: jit_utils.GraphContext, self):
+    if (
+        symbolic_helper._is_tensor_list(self)
+        or self.node().kind() == "onnx::SplitToSequence"
+    ):
+        return g.op("SequenceLength", self)
+    sz_0 = size(g, self, g.op("Constant", value_t=torch.LongTensor([0])))
+    return symbolic_helper._squeeze_helper(g, sz_0, [0])
+
+
+@_onnx_symbolic("aten::__getitem_")
+def __getitem_(g: jit_utils.GraphContext, self, i):
+    if symbolic_helper._is_tensor_list(self):
+        # SequenceAt requires that the input be a List of Tensors
+        return g.op("SequenceAt", self, i)
+    else:
+        from torch.onnx.symbolic_opset9 import __getitem_ as getitem
+
+        return getitem(g, self, i)
+
+
+@_onnx_symbolic("aten::_set_item")
+def _set_item(g: jit_utils.GraphContext, tensor_list, i, v):
+    tensor_list = g.op("SequenceErase", tensor_list, i)
+    return g.op("SequenceInsert", tensor_list, v, i)
+
+
+@_onnx_symbolic("aten::append")
+def append(g: jit_utils.GraphContext, self, tensor):
+    return g.op("SequenceInsert", self, tensor)
+
+
+@_onnx_symbolic("aten::add")
+def add(g: jit_utils.GraphContext, self, other, alpha=None):
+    if symbolic_helper._is_value(self) and symbolic_helper._is_tensor_list(self):
+        tensor_list_node = other.node()
+        if tensor_list_node.kind() != "prim::ListConstruct":
+            return symbolic_helper._unimplemented(
+                "add", "does not support adding dynamic tensor list to another"
+            )
+        tensors = symbolic_helper._unpack_list(other)
+        l = self
+        for t in tensors:
+            l = g.op("SequenceInsert", l, t)
+        return l
+
+    return opset9.add(g, self, other, alpha)
+
+
+@_onnx_symbolic("aten::insert")
+def insert(g: jit_utils.GraphContext, self, pos, tensor):
+    return g.op("SequenceInsert", self, tensor, pos)
+
+
+@_onnx_symbolic("aten::pop")
+def pop(g: jit_utils.GraphContext, tensor_list, dim):
+    return g.op("SequenceErase", tensor_list, dim)
+
+
+@_onnx_symbolic("aten::Delete")
+def Delete(g: jit_utils.GraphContext, tensor_list, dim):
+    return g.op("SequenceErase", tensor_list, dim)
+
+
+@_onnx_symbolic("aten::cat")
+@symbolic_helper.quantized_args(True)
+def cat(g: jit_utils.GraphContext, tensor_list, dim):
+    if symbolic_helper._is_packed_list(tensor_list):
+        return opset9.cat(g, tensor_list, dim)
+    else:
+        dim = symbolic_helper._get_const(dim, "i", "dim")
+        return g.op("ConcatFromSequence", tensor_list, axis_i=dim)
+
+
+@_onnx_symbolic("aten::stack")
+def stack(g: jit_utils.GraphContext, tensor_list, dim):
+    if symbolic_helper._is_packed_list(tensor_list):
+        return opset9.stack(g, tensor_list, dim)
+    else:
+        dim = symbolic_helper._get_const(dim, "i", "dim")
+        return g.op("ConcatFromSequence", tensor_list, axis_i=dim, new_axis_i=1)
+
+
+@_onnx_symbolic("aten::_unique2")
+@symbolic_helper.parse_args("v", "i", "i", "i")
+def _unique2(g: jit_utils.GraphContext, self, sorted, return_inverse, return_counts):
+    u, indices, inverse_indices, counts = g.op(
+        "Unique", self, sorted_i=sorted, outputs=4
+    )
+    return u, inverse_indices, counts
+
+
+@_onnx_symbolic("aten::unique_dim")
+@symbolic_helper.parse_args("v", "i", "i", "i", "i")
+def unique_dim(
+    g: jit_utils.GraphContext, self, dim, sorted, return_inverse, return_counts
+):
+    u, indices, inverse_indices, counts = g.op(
+        "Unique", self, axis_i=dim, sorted_i=sorted, outputs=4
+    )
+    return u, inverse_indices, counts
+
+
+@_onnx_symbolic("aten::topk")
+@symbolic_helper.parse_args("v", "v", "i", "i", "i", "none")
+def topk(g: jit_utils.GraphContext, self, k, dim, largest, sorted, out=None):
+    return symbolic_helper._topk_helper(
+        g, self, k, dim, largest=largest, sorted=sorted, out=out
+    )
+
+
+@_onnx_symbolic("aten::sort")
+@symbolic_helper.parse_args("v", "i", "i", "none")
+def sort(g: jit_utils.GraphContext, self, dim, decending, out=None):
+    return symbolic_helper._sort_helper(g, self, dim, decending=decending, out=out)
+
+
+@_onnx_symbolic("aten::argsort")
+@symbolic_helper.parse_args("v", "i", "i", "none")
+def argsort(g: jit_utils.GraphContext, self, dim, decending, out=None):
+    _, indices = symbolic_helper._sort_helper(
+        g, self, dim, decending=decending, out=out
+    )
+    return indices
+
+
+@_onnx_symbolic("aten::round")
+@symbolic_helper.parse_args("v", "i")
+def round(g: jit_utils.GraphContext, self, decimals=0):
+    if not symbolic_helper._is_fp(self):
+        return self
+    if decimals == 0:
+        return g.op("Round", self)
+    mul = g.op("Mul", self, g.op("Constant", value_t=torch.tensor(pow(10, decimals))))
+    round = g.op("Round", mul)
+    return g.op(
+        "Mul", round, g.op("Constant", value_t=torch.tensor(pow(10, -1 * decimals)))
+    )
+
+
+@_onnx_symbolic("aten::remainder")
+def remainder(g: jit_utils.GraphContext, input, other):
+    if symbolic_helper._is_fp(input) or symbolic_helper._is_fp(other):
+        return opset9.remainder(g, input, other)
+    return g.op("Mod", input, other, fmod_i=0)
+
+
+@_onnx_symbolic("aten::split")
+@symbolic_helper.parse_args("v", "v", "i", "i")
+def split(g: jit_utils.GraphContext, self, split_size_or_sizes, dim, _outputs=None):
+    if not symbolic_helper._is_split_static(split_size_or_sizes, _outputs):
+        split_out = g.op("SplitToSequence", self, split_size_or_sizes, axis_i=dim)
+        if _outputs is None:
+            return split_out
+        # Convert to multiple slice nodes iff number of splits and number of outputs are statically known.
+        if (
+            symbolic_helper._is_packed_list(split_size_or_sizes)
+            and len(symbolic_helper._unpack_list(split_size_or_sizes)) == _outputs
+        ):
+            split_sizes = [
+                symbolic_helper._unsqueeze_helper(g, v, [0])
+                for v in symbolic_helper._unpack_list(split_size_or_sizes)
+            ]
+            start = g.op("Constant", value_t=torch.tensor([0], dtype=torch.long))
+            axis = g.op("Constant", value_t=torch.tensor([dim], dtype=torch.long))
+            res = []
+            for i in range(_outputs):
+                end = g.op(
+                    "Add", start, split_sizes[i]
+                )  # split_sizes is a list of same length as _outputs
+                res.append(g.op("Slice", self, start, end, axis))
+                start = end
+            return res
+        return [
+            g.op(
+                "SequenceAt",
+                split_out,
+                g.op("Constant", value_t=torch.tensor([i], dtype=torch.long)),
+            )
+            for i in range(_outputs)
+        ]
+    else:
+        return opset9.split(g, self, split_size_or_sizes, dim, _outputs)
+
+
+@_onnx_symbolic("aten::split_with_sizes")
+@symbolic_helper.parse_args("v", "v", "i", "i")
+def split_with_sizes(g: jit_utils.GraphContext, self, split_sizes, dim, _outputs=None):
+    return split(g, self, split_sizes, dim, _outputs)
+
+
+@_onnx_symbolic("aten::unbind")
+@symbolic_helper.parse_args("v", "i", "i")
+def unbind(g: jit_utils.GraphContext, self, dim=0, _outputs=None):
+    if _outputs is None:
+        return g.op(
+            "SplitToSequence",
+            self,
+            g.op("Constant", value_t=torch.tensor(1, dtype=torch.long)),
+            axis_i=dim,
+            keepdims_i=0,
+        )
+    else:
+        return opset9.unbind(g, self, dim, _outputs)
+
+
+def _prepare_onnx_paddings(g: jit_utils.GraphContext, input, pad):
+    """Generate paddings in ONNX order based on pad in pytorch.
+
+    Args:
+        input: the input tensor.
+        pad: the paddings in pytorch.
+            The order is dim_n_begin, dim_n_end, dim_n-1_begin, dim_n-1_end, ..., dim_m_begin, dim_m_end,
+            where m is in range [0, n].
+    """
+    if (
+        not symbolic_helper._is_packed_list(pad)
+        and symbolic_helper._is_list(pad)
+        and symbolic_helper._is_scalar_list(pad)
+    ):
+        pad = g.op("ConcatFromSequence", pad, axis_i=0, new_axis_i=1)
+    # The desired order of paddings is
+    # dim_0_begin, dim_1_begin, ... , dim_0_end, ..., dim_n_end.
+    # n is the dimension of input.
+    # Assume zero-dimensions in the beginning, pad the "pad" sequence with zeros in the beginning
+    pad_len = opset9.size(g, pad, g.op("Constant", value_t=torch.tensor([0])))
+    # Set extension = [0] * (dim * 2 - len(pad))
+    rank = symbolic_helper._get_tensor_rank(input)
+    if rank is None:
+        rank = g.op("Size", g.op("Shape", input))
+    else:
+        rank = g.op("Constant", value_t=torch.tensor(rank, dtype=torch.int64))
+    extension = g.op(
+        "Sub",
+        g.op("Mul", rank, g.op("Constant", value_t=torch.tensor(2, dtype=torch.int64))),
+        pad_len,
+    )
+    # Concat pad with extension: paddings = [dim_n_begin, dim_n_end, dim_n-1_begin, dim_n-1_end, 0, 0, ... ]
+    # Currently ONNX only supports int64 type for Pad
+    pad = g.op("Cast", pad, to_i=_C_onnx.TensorProtoDataType.INT64)
+    paddings = g.op(
+        "Concat",
+        pad,
+        g.op(
+            "ConstantOfShape", extension, value_t=torch.tensor([0], dtype=torch.int64)
+        ),
+        axis_i=0,
+    )
+    # Reshape and reverse order and collate first beginnings and then ends
+    # paddings = [[..., 0, dim_n-1_begin, dim_n_begin],
+    #               [..., 0, dim_n-1_end, dim_n_end]]
+    # Reshape back to 1-D paddings = [..., 0, dim_n - 1_begin, dim_n_begin, ..., 0, dim_n - 1_end, dim_n_end]
+    paddings = symbolic_helper._reshape_helper(
+        g, paddings, g.op("Constant", value_t=torch.tensor([-1, 2]))
+    )
+    paddings = g.op("Transpose", opset10.flip(g, paddings, [0]), perm_i=[1, 0])
+    paddings = symbolic_helper._reshape_helper(
+        g, paddings, g.op("Constant", value_t=torch.tensor([-1]))
+    )
+    padding_c = g.op("Cast", paddings, to_i=_C_onnx.TensorProtoDataType.INT64)
+    return padding_c
+
+
+@_onnx_symbolic("aten::constant_pad_nd")
+def constant_pad_nd(g: jit_utils.GraphContext, input, padding, value=None):
+    mode = "constant"
+    value = symbolic_helper._maybe_get_scalar(value)
+    value = symbolic_helper._if_scalar_type_as(value, input)
+    pad = _prepare_onnx_paddings(g, input, padding)
+    return g.op("Pad", input, pad, value, mode_s=mode)
+
+
+@_onnx_symbolic("aten::reflection_pad1d")
+@_onnx_symbolic("aten::reflection_pad2d")
+@_onnx_symbolic("aten::reflection_pad3d")
+def reflection_pad(g: jit_utils.GraphContext, input, padding):
+    mode = "reflect"
+    paddings = _prepare_onnx_paddings(g, input, padding)
+    return g.op("Pad", input, paddings, mode_s=mode)
+
+
+@_onnx_symbolic("aten::replication_pad1d")
+@_onnx_symbolic("aten::replication_pad2d")
+@_onnx_symbolic("aten::replication_pad3d")
+def replication_pad(g: jit_utils.GraphContext, input, padding):
+    mode = "edge"
+    paddings = _prepare_onnx_paddings(g, input, padding)
+    return g.op("Pad", input, paddings, mode_s=mode)
+
+
+@_onnx_symbolic("aten::pad")
+def pad(
+    g: jit_utils.GraphContext,
+    input: _C.Value,
+    pad: _C.Value,
+    mode: _C.Value,
+    value: _C.Value,
+):
+    mode = symbolic_helper._parse_arg(mode, "s")
+    if mode == "replicate":
+        return replication_pad(g, input, pad)
+    elif mode == "reflect":
+        return reflection_pad(g, input, pad)
+    elif mode == "constant":
+        return constant_pad_nd(g, input, pad, value)
+    elif mode == "circular":
+        return opset9._pad_circular(g, input, pad)
+    else:
+        raise errors.SymbolicValueError(f"Unrecognized padding mode {mode}", input)
+
+
+@_onnx_symbolic("aten::linalg_det")
+def linalg_det(g: jit_utils.GraphContext, self):
+    return g.op("Det", self)
+
+
+@_onnx_symbolic("aten::logdet")
+def logdet(g: jit_utils.GraphContext, input):
+    return opset9.log(g, linalg_det(g, input))
+
+
+@_onnx_symbolic("aten::arange")
+def arange(g: jit_utils.GraphContext, *args):
+    def _get_arange_dtype(dtype):
+        dtype = symbolic_helper._maybe_get_const(dtype, "i")
+        return dtype
+
+    if len(args) == 2 and all(isinstance(val, int) for val in args):
+        # aten::arange(Scalar start, Scalar end)
+        dtype = torch.int64
+        # Start index.
+        start = g.op(
+            "Constant",
+            value_t=torch.tensor(args[0], dtype=dtype),
+        )
+        # End (exclusive) index.
+        end = g.op(
+            "Constant",
+            value_t=torch.tensor(args[1], dtype=dtype),
+        )
+        # Step size from start to end indexes.
+        delta_default = g.op(
+            "Constant",
+            value_t=torch.tensor(1, dtype=dtype),
+        )
+        return g.op("Range", start, end, delta_default)
+    elif len(args) == 2 or len(args) == 5:
+        if len(args) == 2:
+            # aten::arange(Scalar end, Tensor out)
+            dtype = None
+        else:
+            # aten::arange(Scalar end, ScalarType dtype, Layout, Device, bool pin_memory)
+            dtype = _get_arange_dtype(args[1])
+        type_, end, start, step = symbolic_helper._arange_cast_helper(
+            g, end=args[0], dtype=dtype
+        )
+        start_default = g.op(
+            "Constant",
+            value_t=torch.tensor(0, dtype=type_.dtype()),
+        )
+        delta_default = g.op(
+            "Constant",
+            value_t=torch.tensor(1, dtype=type_.dtype()),
+        )
+        return g.op("Range", start_default, end, delta_default)
+    elif len(args) == 4 or len(args) == 7:
+        if len(args) == 4:
+            # aten::arange(Scalar start, Scalar end, Scalar step, Tensor out)
+            dtype = None
+        else:
+            # aten::arange(Scalar start, Scalar end, Scalar step, ScalarType dtype, Layout, Device, bool pin_memory)
+            dtype = _get_arange_dtype(args[3])
+        _, end, start, step = symbolic_helper._arange_cast_helper(
+            g, start=args[0], end=args[1], step=args[2], dtype=dtype
+        )
+        return g.op("Range", start, end, step)
+    elif len(args) == 6:
+        # aten::arange(Scalar start, Scalar end, ScalarType dtype, Layout, Device, bool pin_memory)
+        dtype = _get_arange_dtype(args[2])
+        type_, end, start, step = symbolic_helper._arange_cast_helper(
+            g, start=args[0], end=args[1], dtype=dtype
+        )
+        delta_default = g.op(
+            "Constant",
+            value_t=torch.tensor(1, dtype=type_.dtype()),
+        )
+        return g.op("Range", start, end, delta_default)
+    else:
+        return symbolic_helper._unimplemented(
+            "aten::arange", f"with {len(args)} arguments"
+        )
+
+
+@_onnx_symbolic("aten::_dim_arange")
+@symbolic_helper.parse_args("v", "i")
+def _dim_arange(g: jit_utils.GraphContext, like, dim):
+    like_shape = g.op("Shape", like)
+    stop = g.op(
+        "Gather", like_shape, g.op("Constant", value_t=torch.tensor(dim)), axis_i=0
+    )
+    return arange(g, stop, 4, None, None, None)
+
+
+@_onnx_symbolic("aten::size")
+@symbolic_helper.quantized_args(True, quantize_output=False)
+def size(g: jit_utils.GraphContext, self, dim=None):
+    if dim is None:
+        return g.op("Shape", self)
+    return symbolic_helper._size_helper(g, self, dim)
+
+
+@_onnx_symbolic("aten::squeeze")
+def squeeze(g: jit_utils.GraphContext, self, dim=None):
+    if dim is None:
+        return g.op("Squeeze", self)
+
+    # dim as a tensor
+    if not symbolic_helper._is_constant(dim):
+        return symbolic_helper._squeeze_helper(g, self, [dim])
+
+    dim = symbolic_helper._get_const(dim, "i", "dim")
+
+    input_rank = symbolic_helper._get_tensor_rank(self)
+    adjusted_dim = dim
+    if input_rank is not None and dim < 0:
+        adjusted_dim += input_rank
+    dim_size = symbolic_helper._get_tensor_dim_size(self, adjusted_dim)
+    if (dim < 0 and input_rank is None) or dim_size is None:
+        # If onnx shape inference is not on, export always as dynamic.
+        # Because we cannot tell if observed static shape is also static at runtime.
+        # create "cond" node (condition is shape[i]==1)
+        dim_constant = g.op("Constant", value_t=torch.tensor([dim]))
+        size = symbolic_helper._size_helper(g, self, dim_constant)
+        const_one = g.op("Constant", value_t=torch.ones(1, dtype=torch.int64))
+        cond = g.op("Equal", size, const_one)
+        # create the "If" node and add the "then" and "else" blocks to it.
+        if_op, (if_context, else_context), _ = jit_utils.add_op_with_blocks(
+            g, "If", cond, n_blocks=2
+        )
+        squeeze_ = symbolic_helper._squeeze_helper(if_context, self, [dim])
+        utils._add_output_to_block(if_context.block, squeeze_)
+        identity_ = else_context.op("Identity", self)
+        utils._add_output_to_block(else_context.block, identity_)
+        return if_op
+
+    # For static input shape
+    dim = adjusted_dim
+    if dim_size > 1:
+        warnings.warn(
+            "This model contains a squeeze operation on dimension "
+            + str(dim)
+            + ". The size of "
+            + "this dimension in the given input is "
+            + str(dim_size)
+            + ". The model will "
+            + "be exported without the squeeze node. If the model is intended to be used with dynamic "
+            + "input shapes, please export with dynamic_axes argument."
+        )
+        return self
+    return symbolic_helper._squeeze_helper(g, self, [dim])
+
+
+@_onnx_symbolic("aten::unsqueeze")
+def unsqueeze(g: jit_utils.GraphContext, self, dim):
+    if symbolic_helper._is_constant(dim):
+        dim = symbolic_helper._get_const(dim, "i", "dim")
+
+    return symbolic_helper._unsqueeze_helper(g, self, [dim])
+
+
+@_onnx_symbolic("aten::mm")
+def mm(g: jit_utils.GraphContext, self, other):
+    return g.op("Gemm", self, other, beta_f=0.0, alpha_f=1.0)
+
+
+@_onnx_symbolic("aten::index")
+def index(g: jit_utils.GraphContext, self, index):
+    if symbolic_helper._is_packed_list(index):
+        indices = symbolic_helper._unpack_list(index)
+    else:
+        indices = [index]
+
+    # Handle single mask index.
+    if len(indices) == 1:
+        index = indices[0]
+        if not symbolic_helper._is_none(index) and (
+            symbolic_helper._is_bool(index)
+            or _type_utils.JitScalarType.from_value(index)
+            == _type_utils.JitScalarType.UINT8
+        ):
+            index = opset9.nonzero(g, index)
+            return g.op("GatherND", self, index)
+    return opset9.index(g, self, index)
+
+
+@_onnx_symbolic("aten::index_fill")
+def index_fill(g: jit_utils.GraphContext, self, dim, index, value):
+    dim_value = symbolic_helper._parse_arg(dim, "i")
+    expanded_index_shape, expanded_index = symbolic_helper._index_fill_reshape_helper(
+        g, self, dim, index
+    )
+    value = symbolic_helper._maybe_get_scalar(value)
+    value = symbolic_helper._if_scalar_type_as(value, self)
+    expanded_value = opset9.expand(g, value, expanded_index_shape, None)
+    return scatter(g, self, dim, expanded_index, expanded_value)
+
+
+@_onnx_symbolic("aten::index_copy")
+def index_copy(g: jit_utils.GraphContext, self, dim, index, source):
+    dim_value = symbolic_helper._parse_arg(dim, "i")
+    expanded_index_shape, expanded_index = symbolic_helper._index_fill_reshape_helper(
+        g, self, dim, index
+    )
+    return scatter(g, self, dim, expanded_index, source)
+
+
+@_onnx_symbolic("aten::bitwise_right_shift")
+@_onnx_symbolic("aten::__rshift_")
+def __rshift_(g: jit_utils.GraphContext, self, other):
+    # make sure to cast other to self's type
+    # (when self is long, make sure that other is not float)
+    if _type_utils.JitScalarType.from_value(
+        other, _type_utils.JitScalarType.UNDEFINED
+    ) != _type_utils.JitScalarType.from_value(self):
+        other = g.op(
+            "Cast",
+            other,
+            to_i=_type_utils.JitScalarType.from_value(self).onnx_type(),
+        )
+
+    if (
+        _type_utils.JitScalarType.from_value(self, _type_utils.JitScalarType.UNDEFINED)
+        == _type_utils.JitScalarType.UINT8
+    ):
+        return g.op("BitShift", self, other, direction_s="RIGHT")
+
+    two = g.op("Constant", value_t=torch.tensor(2, dtype=torch.float32))
+    # exponent (same type as self) has to be float or double in onnx::Pow
+    if not symbolic_helper._is_fp(self):
+        other = g.op("Cast", other, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+    two_pow = g.op("Pow", two, other)
+    two_pow = g.op(
+        "Cast",
+        two_pow,
+        to_i=_type_utils.JitScalarType.from_value(self).onnx_type(),
+    )
+    rshift = g.op("Div", self, two_pow)
+    return rshift
+
+
+@_onnx_symbolic("aten::bitwise_left_shift")
+@_onnx_symbolic("aten::__lshift_")
+def __lshift_(g: jit_utils.GraphContext, self, other):
+    # make sure to cast other to self's type
+    # (when self is long, make sure that other is not float)
+    if _type_utils.JitScalarType.from_value(
+        other, _type_utils.JitScalarType.UNDEFINED
+    ) != _type_utils.JitScalarType.from_value(self):
+        other = g.op(
+            "Cast",
+            other,
+            to_i=_type_utils.JitScalarType.from_value(self).onnx_type(),
+        )
+
+    if (
+        _type_utils.JitScalarType.from_value(self, _type_utils.JitScalarType.UNDEFINED)
+        == _type_utils.JitScalarType.UINT8
+    ):
+        return g.op("BitShift", self, other, direction_s="LEFT")
+
+    two = g.op("Constant", value_t=torch.tensor(2, dtype=torch.float32))
+    # exponent (same type as self) has to be float or double in onnx::Pow
+    if not symbolic_helper._is_fp(self):
+        other = g.op("Cast", other, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+    two_pow = g.op("Pow", two, other)
+    two_pow = g.op(
+        "Cast",
+        two_pow,
+        to_i=_type_utils.JitScalarType.from_value(self).onnx_type(),
+    )
+    lshift = g.op("Mul", self, two_pow)
+    return lshift
+
+
+def _get_im2col_indices_along_dim(
+    g: jit_utils.GraphContext, input_d, kernel_size_d, dilation_d, padding_d, stride_d
+):
+    # Input is always 4-D (N, C, H, W)
+    # Calculate indices of sliding blocks along spatial dimension
+    # Slide kernel over input each dim d:
+    # each dimension d ranges from 0 to input[d]+2xpadding[d]-dilation[d]x(kernel_size[d]-1)
+    # with steps = stride
+
+    blocks_d = g.op(
+        "Add", input_d, g.op("Constant", value_t=torch.tensor(padding_d * 2))
+    )
+    blocks_d = g.op(
+        "Sub",
+        blocks_d,
+        g.op("Constant", value_t=torch.tensor(dilation_d * (kernel_size_d - 1))),
+    )
+
+    # Stride kernel over input and find starting indices along dim d
+    blocks_d_indices = g.op(
+        "Range",
+        g.op("Constant", value_t=torch.tensor(0)),
+        blocks_d,
+        g.op("Constant", value_t=torch.tensor(stride_d)),
+    )
+
+    # Apply dilation on kernel and find its indices along dim d
+    kernel_grid = torch.arange(0, kernel_size_d * dilation_d, dilation_d)
+    kernel_grid = g.op("Constant", value_t=kernel_grid.unsqueeze(0))
+
+    # Broadcast and add kernel staring positions (indices) with
+    # kernel_grid along dim d, to get block indices along dim d
+    blocks_d_indices = symbolic_helper._unsqueeze_helper(
+        g, blocks_d_indices, [0]
+    )  # Reshape to [1, -1]
+    kernel_mask = symbolic_helper._reshape_helper(
+        g, kernel_grid, g.op("Constant", value_t=torch.tensor([-1, 1]))
+    )
+    block_mask = g.op("Add", blocks_d_indices, kernel_mask)
+
+    return block_mask
+
+
+def _get_im2col_padded_input(g: jit_utils.GraphContext, input, padding_h, padding_w):
+    # Input is always 4-D tensor (N, C, H, W)
+    # Padding tensor has the following format: (padding_h, padding_w)
+    # Reshape the padding to follow ONNX format: (dim1_begin, dim2_begin,...,dim1_end, dim2_end,...)
+    pad = g.op("Constant", value_t=torch.LongTensor([0, 0, padding_h, padding_w] * 2))
+    return g.op("Pad", input, pad)
+
+
+def _get_im2col_output_shape(g: jit_utils.GraphContext, input, kernel_h, kernel_w):
+    batch_dim = size(g, input, g.op("Constant", value_t=torch.tensor(0)))
+    channel_dim = size(g, input, g.op("Constant", value_t=torch.tensor(1)))
+    channel_unfolded = g.op(
+        "Mul", channel_dim, g.op("Constant", value_t=torch.tensor(kernel_h * kernel_w))
+    )
+
+    return g.op(
+        "Concat",
+        symbolic_helper._unsqueeze_helper(g, batch_dim, [0]),
+        symbolic_helper._unsqueeze_helper(g, channel_unfolded, [0]),
+        g.op("Constant", value_t=torch.tensor([-1])),
+        axis_i=0,
+    )
+
+
+@_onnx_symbolic("aten::im2col")
+@symbolic_helper.parse_args("v", "is", "is", "is", "is")
+def im2col(g: jit_utils.GraphContext, input, kernel_size, dilation, padding, stride):
+    # Input is always 4-D tensor (N, C, H, W)
+    # All other args are int[2]
+
+    input_h = size(g, input, g.op("Constant", value_t=torch.tensor(2)))
+    input_w = size(g, input, g.op("Constant", value_t=torch.tensor(3)))
+
+    stride_h, stride_w = stride[0], stride[1]
+    padding_h, padding_w = padding[0], padding[1]
+    dilation_h, dilation_w = dilation[0], dilation[1]
+    kernel_h, kernel_w = kernel_size[0], kernel_size[1]
+
+    blocks_row_indices = _get_im2col_indices_along_dim(
+        g, input_h, kernel_h, dilation_h, padding_h, stride_h
+    )
+    blocks_col_indices = _get_im2col_indices_along_dim(
+        g, input_w, kernel_w, dilation_w, padding_w, stride_w
+    )
+
+    output_shape = _get_im2col_output_shape(g, input, kernel_h, kernel_w)
+    padded_input = _get_im2col_padded_input(g, input, padding_h, padding_w)
+
+    # For a 4D matrix of size (1, 1, 3, 3) as below with kernel_size=2, stride=1, and dilation=1
+    # [[[[1., 2., 3.,],
+    #    [4., 5., 6.,],
+    #    [7., 8., 9.,]]]]
+    # First gather indices along rows (dim=2) with blocks_row_indices = [[0,1], [1,2]] to get:
+    # [[[[[1., 2., 3.],
+    #     [4., 5., 6.]],
+    #    [[4., 5., 6.],
+    #     [7., 8., 9.]]]]]
+    # And then gather along cols (dim=4) with blocks_row_indices = [[0,1], [1,2]] to get:
+    # [[[[[[1., 2.],
+    #      [4., 5.]],
+    #     [[2., 3.],
+    #      [5., 6]]],
+    #    [[[4., 5.],
+    #      [7., 8.]],
+    #     [[5., 6.],
+    #      [8., 9.]]]]]]
+    # Transpose dims 3 (depth) and 4 (rows), and then reshape to output shape (1, 1, 4, 4) to get:
+    #  [[[1., 2., 4., 5.],
+    #    [2., 3., 5., 6.],
+    #    [4., 5., 7., 8.],
+    #    [5., 6., 8., 9.]]]
+    output = g.op("Gather", padded_input, blocks_row_indices, axis_i=2)
+    output = g.op("Gather", output, blocks_col_indices, axis_i=4)
+    output = g.op("Transpose", output, perm_i=[0, 1, 2, 4, 3, 5])
+    return symbolic_helper._reshape_helper(g, output, output_shape)
+
+
+@_onnx_symbolic("aten::narrow")
+def narrow(g: jit_utils.GraphContext, input, dim, start, length):
+    end = g.op("Add", start, length)
+    return symbolic_helper._slice_helper(g, input, axes=dim, starts=start, ends=end)
+
+
+@_onnx_symbolic("aten::flatten")
+@symbolic_helper.quantized_args(True, False, False)
+@symbolic_helper.parse_args("v", "i", "i")
+def flatten(g: jit_utils.GraphContext, input, start_dim, end_dim):
+    dim = symbolic_helper._get_tensor_rank(input)
+    if dim == 1:
+        return input
+    # use ONNX's Flatten operator for cases where the output shape is 2D
+    if start_dim == 1:
+        if end_dim == -1 or (dim is not None and end_dim == dim - 1):
+            return g.op("Flatten", input, axis_i=start_dim)
+    elif start_dim == 0:
+        if end_dim == -2 or (dim is not None and end_dim == dim - 2):
+            return g.op("Flatten", input, axis_i=end_dim + 1)
+    if dim is None:
+        return symbolic_helper._unimplemented(
+            "dim",
+            "ONNX and PyTorch use different strategies to split the input. "
+            "Input rank must be known at export time.",
+        )
+    # if end_dim is negative add dim
+    if end_dim < 0:
+        end_dim = dim + end_dim
+
+    return symbolic_helper._flatten_helper(g, input, start_dim, end_dim, dim)
+
+
+@_onnx_symbolic("aten::linalg_vector_norm")
+@symbolic_helper.parse_args("v", "f", "is", "b", "v")
+def linalg_vector_norm(
+    g: jit_utils.GraphContext,
+    self,
+    ord,
+    dim: Sequence[int] | None,
+    keepdim: bool,
+    dtype,
+):
+    return symbolic_helper._linalg_vector_norm_helper(g, self, ord, dim, keepdim, dtype)
+
+
+@_onnx_symbolic("aten::embedding_bag")
+@symbolic_helper.parse_args("v", "v", "v", "i", "i", "i", "v", "i", "i")
+def embedding_bag(
+    g: jit_utils.GraphContext,
+    embedding_matrix,
+    indices,
+    offsets,
+    scale_grad_by_freq,
+    mode,
+    sparse,
+    per_sample_weights,
+    include_last_offset,
+    padding_idx,
+):
+    return symbolic_helper._embedding_bag_helper(
+        g,
+        embedding_matrix,
+        indices,
+        offsets,
+        scale_grad_by_freq,
+        mode,
+        sparse,
+        per_sample_weights,
+        include_last_offset,
+        padding_idx,
+    )
+
+
+@_onnx_symbolic("aten::embedding_renorm")
+@symbolic_helper.parse_args("v", "v", "f", "f")
+def embedding_renorm(g: jit_utils.GraphContext, weight, indices, max_norm, norm_type):
+    unique_indices = g.op("Unique", indices)
+    partial_weight = g.op("Gather", weight, unique_indices)
+    norm_i = int(norm_type)
+    if norm_i == 1:
+        norm_type = "ReduceL1"
+    elif norm_i == 2:
+        norm_type = "ReduceL2"
+    else:
+        raise errors.SymbolicValueError(
+            f"Unsupported: ONNX export of embedding_renorm with norm: {norm_i}. "
+            "Only 1. and 2. are supported.",
+            weight,
+        )
+    partial_weight_norm = g.op(norm_type, partial_weight, axes_i=[1], keepdims_i=1)
+    # https://github.com/pytorch/pytorch/blob/0a07488ed2c47765e337e290bd138c0e6e459cbd/aten/src/ATen/native/Embedding.cpp#L177
+    # Add 1e-7 to prevent division by zero.
+    partial_weight_norm_ = g.op(
+        "Add", partial_weight_norm, g.op("Constant", value_t=torch.tensor(1e-7))
+    )
+    max_norm = torch.tensor(max_norm)
+    scales = g.op("Div", max_norm, partial_weight_norm_)
+    partial_weight_renorm = g.op("Mul", partial_weight, scales)
+    partial_weight_renorm = g.op(
+        "Where",
+        g.op("Greater", partial_weight_norm, max_norm),
+        partial_weight_renorm,
+        partial_weight,
+    )
+    return g.op(
+        "ScatterND",
+        weight,
+        symbolic_helper._unsqueeze_helper(g, unique_indices, [1]),
+        partial_weight_renorm,
+    )
+
+
+@_onnx_symbolic("aten::chunk")
+def chunk(g: jit_utils.GraphContext, self, chunks, dim):
+    # Calculate chunk size for dynamic chunk
+    dim_size = g.op("Gather", g.op("Shape", self), dim, axis_i=0)
+    chunk_size_s = g.op(
+        "Sub", chunks, g.op("Constant", value_t=torch.tensor([1], dtype=torch.long))
+    )
+    chunk_size = g.op("Div", g.op("Add", dim_size, chunk_size_s), chunks)
+    # Create splits vector
+    chunk_vec = [
+        opset9.expand(g, chunk_size, chunk_size_s, None),
+        g.op("Sub", dim_size, g.op("Mul", chunk_size, chunk_size_s)),
+    ]
+    chunk_vec = g.op("Concat", *chunk_vec, axis_i=0)
+    return split(g, self, chunk_vec, dim)
+
+
+@_onnx_symbolic("aten::normal")
+def normal(
+    g: jit_utils.GraphContext,
+    mean,
+    std,
+    sizes=None,
+    generator=None,
+    dtype=None,
+    layout=None,
+    device=None,
+    pin_memory=None,
+):
+    # If you can sample from a given distribution with mean 0 and variance 1, then you can easily sample from a
+    # scale-location transformation of that distribution, which has mean mu and variance sigma's square. If x is a sample
+    # from a mean 0 and variance 1 distribution then
+    #       sigma x+mu
+    # is a sample with mean mu and variance sigma's square.
+    if sizes is not None and not symbolic_helper._is_none(sizes):
+        mean = opset9.expand(g, mean, sizes, None)
+    result = opset9.mul(g, std, g.op("RandomNormalLike", mean))
+    return add(g, result, mean)
+
+
+@_onnx_symbolic("aten::atleast_1d")
+def atleast_1d(g: jit_utils.GraphContext, self: torch._C.Value):
+    # NOTE: If it's 0D, reshape to 1D
+
+    # NOTE: self could be a packed list or a tensor
+    if symbolic_helper._is_value(self) and symbolic_helper._is_packed_list(self):
+        tensor_list = symbolic_helper._unpack_list(self)
+        new_tensor_list = []
+        for tensor in tensor_list:
+            new_tensor = tensor
+            tensor_rank = symbolic_helper._get_tensor_rank(tensor)
+            if tensor_rank == 0:
+                new_tensor = symbolic_helper._reshape_helper(
+                    g, new_tensor, g.op("Constant", value_t=torch.tensor([1]))
+                )
+            new_tensor_list.append(new_tensor)
+        return g.op("SequenceConstruct", *new_tensor_list)
+
+    tensor_rank = symbolic_helper._get_tensor_rank(self)
+    if tensor_rank == 0:
+        self = symbolic_helper._reshape_helper(
+            g, self, g.op("Constant", value_t=torch.tensor([1]))
+        )
+    return self
+
+
+@_onnx_symbolic("aten::atleast_2d")
+def atleast_2d(g: jit_utils.GraphContext, self: torch._C.Value):
+    # NOTE: If it's 0D, reshape to 2D
+    #       If it's 1D, unsqueeze to 2D
+
+    # NOTE: self could be a packed list or a tensor
+    if symbolic_helper._is_value(self) and symbolic_helper._is_packed_list(self):
+        tensor_list = symbolic_helper._unpack_list(self)
+        new_tensor_list = []
+        for tensor in tensor_list:
+            new_tensor = tensor
+            tensor_rank = symbolic_helper._get_tensor_rank(tensor)
+            if tensor_rank == 0:
+                new_tensor = symbolic_helper._reshape_helper(
+                    g, new_tensor, g.op("Constant", value_t=torch.tensor([1, 1]))
+                )
+            elif tensor_rank == 1:
+                new_tensor = symbolic_helper._unsqueeze_helper(
+                    g, new_tensor, axes_i=[0]
+                )
+            new_tensor_list.append(new_tensor)
+        return g.op("SequenceConstruct", *new_tensor_list)
+
+    tensor_rank = symbolic_helper._get_tensor_rank(self)
+    if tensor_rank == 0:
+        self = symbolic_helper._reshape_helper(
+            g, self, g.op("Constant", value_t=torch.tensor([1, 1]))
+        )
+    elif tensor_rank == 1:
+        self = symbolic_helper._unsqueeze_helper(g, self, axes_i=[0])
+    return self
+
+
+@_onnx_symbolic("aten::atleast_3d")
+def atleast_3d(g: jit_utils.GraphContext, self: torch._C.Value):
+    # NOTE: If it's 0D, reshape to 3D
+    #       If it's 1D, unsqueeze to 3D
+    #       If it's 2D, unsqueeze to 3D
+
+    # NOTE: self could be a packed list or a tensor
+    if symbolic_helper._is_value(self) and symbolic_helper._is_packed_list(self):
+        tensor_list = symbolic_helper._unpack_list(self)
+        new_tensor_list = []
+        for tensor in tensor_list:
+            new_tensor = tensor
+            tensor_rank = symbolic_helper._get_tensor_rank(tensor)
+            if tensor_rank == 0:
+                new_tensor = symbolic_helper._reshape_helper(
+                    g, new_tensor, g.op("Constant", value_t=torch.tensor([1, 1, 1]))
+                )
+            elif tensor_rank == 1:
+                new_tensor = symbolic_helper._unsqueeze_helper(
+                    g, new_tensor, axes_i=[0]
+                )
+                new_tensor = symbolic_helper._unsqueeze_helper(
+                    g, new_tensor, axes_i=[-1]
+                )
+            elif tensor_rank == 2:
+                new_tensor = symbolic_helper._unsqueeze_helper(
+                    g, new_tensor, axes_i=[-1]
+                )
+            new_tensor_list.append(new_tensor)
+        return g.op("SequenceConstruct", *new_tensor_list)
+
+    tensor_rank = symbolic_helper._get_tensor_rank(self)
+    if tensor_rank == 0:
+        self = symbolic_helper._reshape_helper(
+            g, self, g.op("Constant", value_t=torch.tensor([1, 1, 1]))
+        )
+    elif tensor_rank == 1:
+        self = symbolic_helper._unsqueeze_helper(g, self, axes_i=[0])
+        self = symbolic_helper._unsqueeze_helper(g, self, axes_i=[-1])
+    elif tensor_rank == 2:
+        self = symbolic_helper._unsqueeze_helper(g, self, axes_i=[-1])
+    return self
+
+
+@_onnx_symbolic("prim::ConstantChunk")
+def prim_constant_chunk(g: jit_utils.GraphContext, self, chunks, dim):
+    input_shape = g.op("Shape", self)
+    axis = g.op("Constant", value_t=torch.tensor([dim], dtype=torch.long))
+    input_shape_dim = g.op("Gather", input_shape, axis, axis_i=0)
+    start = g.op("Constant", value_t=torch.tensor([0], dtype=torch.long))
+    chunk_size = g.op("Constant", value_t=torch.tensor([chunks], dtype=torch.long))
+    chunk_size_minus_1 = g.op(
+        "Constant", value_t=torch.tensor([chunks - 1], dtype=torch.long)
+    )
+    input_shape_dim_shift = g.op("Add", input_shape_dim, chunk_size_minus_1)
+    chunk_dim = g.op("Div", input_shape_dim_shift, chunk_size)
+    res = []
+    for i in range(chunks):
+        index = g.op("Constant", value_t=torch.tensor([i + 1], dtype=torch.long))
+        end = g.op("Mul", chunk_dim, index)
+        res.append(g.op("Slice", self, start, end, axis))
+        start = end
+    return res
+
+
+@_onnx_symbolic("aten::hstack")
+def hstack(g: jit_utils.GraphContext, tensor_list: _C.Value):
+    tensor_list = atleast_1d(g, tensor_list)
+    first_tensor = g.op(
+        "SequenceAt",
+        tensor_list,
+        g.op("Constant", value_t=torch.tensor(0, dtype=torch.long)),
+    )
+    first_tensor_shape = g.op("Shape", first_tensor)
+    first_tensor_dim = g.op("Size", first_tensor_shape)
+
+    const_one = g.op("Constant", value_t=torch.tensor(1, dtype=torch.long))
+    equal_to_one = g.op("Equal", first_tensor_dim, const_one)
+
+    (
+        if_op_greater,
+        (if_context_equal, else_context_equal),
+        _,
+    ) = jit_utils.add_op_with_blocks(g, "If", equal_to_one, n_blocks=2, outputs=1)
+    result_if = if_context_equal.op(
+        "ConcatFromSequence", tensor_list, axis_i=0, new_axis_i=0
+    )
+    utils._add_output_to_block(if_context_equal.block, result_if)
+    result_else = else_context_equal.op(
+        "ConcatFromSequence", tensor_list, axis_i=1, new_axis_i=0
+    )
+    utils._add_output_to_block(else_context_equal.block, result_else)
+    result = if_op_greater.node().output()
+
+    return result
+
+
+@_onnx_symbolic("aten::vstack")
+def vstack(g: jit_utils.GraphContext, tensor_list: _C.Value):
+    tensor_list = atleast_2d(g, tensor_list)
+    return g.op("ConcatFromSequence", tensor_list, axis_i=0, new_axis_i=0)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset12.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset12.py
new file mode 100644
index 0000000000000000000000000000000000000000..7aaefd37201dd4541c520fb138f3e85c436a9ebe
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset12.py
@@ -0,0 +1,465 @@
+# mypy: allow-untyped-defs
+# mypy: disable-error-code=arg-type
+from __future__ import annotations
+
+import functools
+import sys
+
+import torch
+from torch._C import _onnx as _C_onnx
+from torch.onnx import (
+    _type_utils,
+    errors,
+    symbolic_helper,
+    symbolic_opset9 as opset9,
+    utils,
+)
+from torch.onnx._internal import jit_utils, registration
+
+
+# EDITING THIS FILE? READ THIS FIRST!
+# see Note [Edit Symbolic Files] in README.md
+
+# This file exports ONNX ops for opset 12
+
+__all__ = [
+    "argmax",
+    "argmin",
+    "binary_cross_entropy_with_logits",
+    "celu",
+    "cross_entropy_loss",
+    "dropout",
+    "einsum",
+    "ge",
+    "le",
+    "native_dropout",
+    "nll_loss",
+    "nll_loss2d",
+    "nll_loss_nd",
+    "outer",
+    "pow",
+    "tensordot",
+    "unfold",
+]
+
+_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=12)
+
+
+def _einsum_helper(g: jit_utils.GraphContext, equation, tensors):
+    if not tensors:
+        raise RuntimeError("Einsum inputs are empty.")
+    # ONNX does not support bool for Einsum inputs.
+    if symbolic_helper._is_bool(tensors[0]):
+        tensors = [
+            g.op("Cast", tensor, to_i=_C_onnx.TensorProtoDataType.INT64)
+            for tensor in tensors
+        ]
+        return g.op(
+            "Cast",
+            g.op("Einsum", *tensors, equation_s=equation),
+            to_i=_C_onnx.TensorProtoDataType.BOOL,
+        )
+    else:
+        return g.op("Einsum", *tensors, equation_s=equation)
+
+
+@_onnx_symbolic("aten::einsum")
+@symbolic_helper.parse_args("s", "v", "is")
+def einsum(g: jit_utils.GraphContext, equation, tensor_list, path=None):
+    tensors = symbolic_helper._unpack_list(tensor_list)
+    return _einsum_helper(g, equation, tensors)
+
+
+@_onnx_symbolic("aten::outer")
+@symbolic_helper.parse_args("v", "v")
+def outer(g: jit_utils.GraphContext, input, other):
+    # make sure to cast other to self's type
+    if _type_utils.JitScalarType.from_value(
+        other, _type_utils.JitScalarType.UNDEFINED
+    ) != _type_utils.JitScalarType.from_value(input):
+        other = g.op(
+            "Cast",
+            other,
+            to_i=_type_utils.JitScalarType.from_value(input).onnx_type(),
+        )
+    return _einsum_helper(g, "i,j->ij", [input, other])
+
+
+def _dropout_returns_masked_input_and_mask(
+    g: jit_utils.GraphContext, input: torch._C.Value, p: float, train: bool
+) -> tuple[torch._C.Value, torch._C.Value | None]:
+    symbolic_helper.check_training_mode(train, "dropout")
+    # In eval mode, dropout is non-op. That is, if the node's
+    # train param is set to False, dropout just returns its inputs.
+    if not train:
+        return input, None
+    p = g.op("Constant", value_t=torch.tensor(p))
+    t = g.op("Constant", value_t=torch.tensor(train, dtype=torch.bool))
+    r, mask = g.op("Dropout", input, p, t, outputs=2)
+    return r, mask
+
+
+@_onnx_symbolic("aten::dropout")
+@symbolic_helper.parse_args("v", "f", "b")
+def dropout(g: jit_utils.GraphContext, input, p, train):
+    masked, _ = _dropout_returns_masked_input_and_mask(g, input, p, train)
+    return masked
+
+
+@_onnx_symbolic("aten::native_dropout")
+@symbolic_helper.parse_args("v", "f", "b")
+def native_dropout(g: jit_utils.GraphContext, input, p, train):
+    return _dropout_returns_masked_input_and_mask(g, input, p, train)
+
+
+@_onnx_symbolic("aten::nll_loss")
+def nll_loss(g: jit_utils.GraphContext, self, target, weight, reduction, ignore_index):
+    # none reduction : onnx::Constant[value={0}]
+    # mean reduction : onnx::Constant[value={1}]
+    # sum reduction : onnx::Constant[value={2}]
+    reduction = symbolic_helper._maybe_get_const(reduction, "i")
+    reduction_vals = ["none", "mean", "sum"]
+    reduction = reduction_vals[reduction]
+
+    # in onnx NegativeLogLikelihoodLoss specification, ignore_index is optional without default value.
+    # therefore we need to set ignore_index attribute even if it is not specified (e.g. ignore_index=-100).
+    ignore_index = symbolic_helper._maybe_get_const(ignore_index, "i")
+    if weight.node().mustBeNone():
+        nllloss = g.op(
+            "NegativeLogLikelihoodLoss",
+            self,
+            target,
+            reduction_s=reduction,
+            ignore_index_i=ignore_index,
+        )
+    else:
+        nllloss = g.op(
+            "NegativeLogLikelihoodLoss",
+            self,
+            target,
+            weight,
+            reduction_s=reduction,
+            ignore_index_i=ignore_index,
+        )
+
+    return nllloss
+
+
+@_onnx_symbolic("aten::nll_loss2d")
+def nll_loss2d(
+    g: jit_utils.GraphContext, self, target, weight, reduction, ignore_index
+):
+    return nll_loss(g, self, target, weight, reduction, ignore_index)
+
+
+@_onnx_symbolic("aten::nll_loss_nd")
+def nll_loss_nd(
+    g: jit_utils.GraphContext, self, target, weight, reduction, ignore_index
+):
+    return nll_loss(g, self, target, weight, reduction, ignore_index)
+
+
+@_onnx_symbolic("aten::cross_entropy_loss")
+def cross_entropy_loss(
+    g: jit_utils.GraphContext,
+    self,
+    target,
+    weight,
+    reduction,
+    ignore_index,
+    label_smoothing,
+):
+    # none reduction : onnx::Constant[value={0}]
+    # mean reduction : onnx::Constant[value={1}]
+    # sum reduction : onnx::Constant[value={2}]
+    reduction = symbolic_helper._maybe_get_const(reduction, "i")
+    reduction_vals = ["none", "mean", "sum"]
+    reduction = reduction_vals[reduction]
+
+    label_smoothing = symbolic_helper._maybe_get_const(label_smoothing, "f")
+    if label_smoothing is not None and label_smoothing > 0.0:
+        raise errors.SymbolicValueError(
+            "Unsupported: ONNX does not support label_smoothing", self
+        )
+
+    # in onnx SoftmaxCrossEntropyLoss specification, ignore_index is optional without default value.
+    # therefore we need to set ignore_index attribute even if it is not specified (e.g. ignore_index=-100).
+    ignore_index = symbolic_helper._maybe_get_const(ignore_index, "i")
+    if weight.node().mustBeNone():
+        celoss = g.op(
+            "SoftmaxCrossEntropyLoss",
+            self,
+            target,
+            reduction_s=reduction,
+            ignore_index_i=ignore_index,
+        )
+    else:
+        celoss = g.op(
+            "SoftmaxCrossEntropyLoss",
+            self,
+            target,
+            weight,
+            reduction_s=reduction,
+            ignore_index_i=ignore_index,
+        )
+
+    return celoss
+
+
+@_onnx_symbolic("aten::binary_cross_entropy_with_logits")
+@symbolic_helper.parse_args("v", "v", "v", "v", "i")
+def binary_cross_entropy_with_logits(
+    g: jit_utils.GraphContext, input, target, weight, pos_weight, reduction
+):
+    p = g.op("Constant", value_t=torch.tensor([1]))
+    sig_x = opset9.sigmoid(g, input)
+    log_sig_x = opset9.log(g, sig_x)
+    sub_1_x = opset9.sub(g, p, sig_x)
+    sub_1_y = opset9.sub(g, p, target)
+    log_1_x = opset9.log(g, sub_1_x)
+    if pos_weight is None or symbolic_helper._is_none(pos_weight):
+        output = opset9.neg(
+            g,
+            opset9.add(
+                g, opset9.mul(g, target, log_sig_x), opset9.mul(g, sub_1_y, log_1_x)
+            ),
+        )
+    else:
+        output = opset9.neg(
+            g,
+            opset9.add(
+                g,
+                opset9.mul(g, opset9.mul(g, target, log_sig_x), pos_weight),
+                opset9.mul(g, sub_1_y, log_1_x),
+            ),
+        )
+
+    if weight is not None and not symbolic_helper._is_none(weight):
+        output = opset9.mul(g, weight, output)
+
+    reduction = symbolic_helper._maybe_get_const(reduction, "i")
+    if reduction == 0:
+        return output
+    elif reduction == 1:
+        return g.op("ReduceMean", output, keepdims_i=0)
+    elif reduction == 2:
+        return g.op("ReduceSum", output, keepdims_i=0)
+    else:
+        return symbolic_helper._onnx_unsupported(
+            "binary_cross_entropy_with_logits with reduction other than none, mean, or sum",
+            input,
+        )
+
+
+@_onnx_symbolic("aten::celu")
+def celu(g: jit_utils.GraphContext, self, alpha):
+    alpha = symbolic_helper._maybe_get_const(alpha, "f")
+    # if the input is of type double cast it to float
+    if (
+        _type_utils.JitScalarType.from_value(self, _type_utils.JitScalarType.UNDEFINED)
+        == _type_utils.JitScalarType.DOUBLE
+    ):
+        self = g.op("Cast", self, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+        out = g.op("Celu", self, alpha_f=alpha)
+        return g.op("Cast", out, to_i=_C_onnx.TensorProtoDataType.DOUBLE)
+
+    return g.op("Celu", self, alpha_f=alpha)
+
+
+@_onnx_symbolic("aten::argmax")
+@symbolic_helper.parse_args("v", "v", "b")
+def argmax(
+    g: jit_utils.GraphContext,
+    input: torch._C.Value,
+    dim: torch._C.Value,
+    keepdim: bool,
+):
+    return symbolic_helper._argmin_argmax_helper(g, input, dim, keepdim, "ArgMax")
+
+
+@_onnx_symbolic("aten::argmin")
+@symbolic_helper.parse_args("v", "v", "b")
+def argmin(
+    g: jit_utils.GraphContext,
+    input: torch._C.Value,
+    dim: torch._C.Value,
+    keepdim: bool,
+):
+    return symbolic_helper._argmin_argmax_helper(g, input, dim, keepdim, "ArgMin")
+
+
+@_onnx_symbolic("aten::pow")
+def pow(g: jit_utils.GraphContext, self, exponent):
+    return g.op("Pow", self, exponent)
+
+
+@_onnx_symbolic("aten::ge")
+def ge(g: jit_utils.GraphContext, input, other):
+    return g.op("GreaterOrEqual", input, other)
+
+
+@_onnx_symbolic("aten::le")
+def le(g: jit_utils.GraphContext, input, other):
+    return g.op("LessOrEqual", input, other)
+
+
+@_onnx_symbolic("aten::unfold")
+@symbolic_helper.parse_args("v", "i", "v", "v")
+def unfold(g: jit_utils.GraphContext, input, dimension, size, step):
+    const_size = symbolic_helper._maybe_get_const(size, "i")
+    const_step = symbolic_helper._maybe_get_const(step, "i")
+    if not symbolic_helper._is_value(const_size) and not symbolic_helper._is_value(
+        const_step
+    ):
+        return opset9.unfold(g, input, dimension, const_size, const_step)
+
+    sizedim = symbolic_helper._get_tensor_dim_size(input, dimension)
+    if sizedim is not None:
+        low_start = g.op("Constant", value_t=torch.tensor(0))
+        low_end = g.op("Constant", value_t=torch.tensor(sizedim))
+        hi_end = g.op("Constant", value_t=torch.tensor(sizedim + 1))
+        low_indices = g.op("Range", low_start, low_end, step)
+        hi_indices = g.op("Range", size, hi_end, step)
+
+        low_size = symbolic_helper._size_helper(
+            g, low_indices, g.op("Constant", value_t=torch.tensor(0))
+        )
+        hi_size = symbolic_helper._size_helper(
+            g, hi_indices, g.op("Constant", value_t=torch.tensor(0))
+        )
+
+        ndim = symbolic_helper._get_tensor_rank(input)
+        assert ndim is not None
+        perm = list(range(0, ndim))
+        perm.append(perm.pop(dimension))
+
+        unsqueeze_list = []
+        loop_condition = g.op("Constant", value_t=torch.tensor(1))
+        loop_condition = g.op(
+            "Cast", loop_condition, to_i=_C_onnx.TensorProtoDataType.BOOL
+        )
+        loop_len = g.op("Min", low_size, hi_size)
+
+        loop, (loop_context,), _ = jit_utils.add_op_with_blocks(
+            g, "Loop", loop_len, loop_condition, n_blocks=1
+        )
+
+        loop_block = loop_context.block
+        block_input_iter = utils._add_input_to_block(loop_block)
+        # FIXME(justinchuby): cond is unused?
+        cond = utils._add_input_to_block(loop_block)
+
+        starts = loop_context.op("Gather", low_indices, block_input_iter)
+        ends = loop_context.op("Gather", hi_indices, block_input_iter)
+        axes = loop_context.op("Constant", value_t=torch.tensor([2]))
+        starts = symbolic_helper._unsqueeze_helper(loop_context, starts, [0])
+        ends = symbolic_helper._unsqueeze_helper(loop_context, ends, [0])
+        stack = loop_context.op("Slice", input, starts, ends, axes)
+
+        unsqueeze = symbolic_helper._unsqueeze_helper(
+            loop_context, loop_context.op("Transpose", stack, perm_i=perm), [dimension]
+        )
+        unsqueeze_list.append(unsqueeze)
+        concat = loop_context.op("Concat", *unsqueeze_list, axis_i=0)
+
+        cond_out = loop_context.op(
+            "Cast", loop_condition, _C_onnx.TensorProtoDataType.BOOL
+        )
+        utils._add_output_to_block(loop_block, cond_out)
+        utils._add_output_to_block(loop_block, concat)
+
+        loop_output = loop.node().output()
+        perm = [0, 1, 2, 3, 4]
+        perm[0], perm[dimension + 1] = perm[dimension + 1], perm[0]
+        transpose = g.op("Transpose", loop_output, perm_i=perm)
+        squeeze = symbolic_helper._squeeze_helper(g, transpose, [0])
+
+        return squeeze
+
+    return symbolic_helper._unimplemented("Unfold", "input size not accessible")
+
+
+@_onnx_symbolic("aten::tensordot")
+@symbolic_helper.parse_args("v", "v", "is", "is", "v")
+def tensordot(g: jit_utils.GraphContext, input_a, input_b, dims_a, dims_b, out=None):
+    if out is not None:
+        symbolic_helper._unimplemented(
+            "Tensordot", "Out parameter is not supported for tensordot."
+        )
+
+    dim_count_a = symbolic_helper._get_tensor_rank(input_a)
+    if dim_count_a is None:
+        raise errors.SymbolicValueError(
+            "Unsupported: ONNX export of tensordot for tensor(input_a) of unknown rank.",
+            input_a,
+        )
+
+    dim_count_b = symbolic_helper._get_tensor_rank(input_b)
+    if dim_count_b is None:
+        raise errors.SymbolicValueError(
+            "Unsupported: ONNX export of tensordot for tensor(input_b) of unknown rank.",
+            input_b,
+        )
+
+    dims_a = [
+        (dims_a[i] + dim_count_a) if (dims_a[i] < 0) else dims_a[i]
+        for i in range(len(dims_a))
+    ]
+    dims_b = [
+        (dims_b[i] + dim_count_b) if (dims_b[i] < 0) else dims_b[i]
+        for i in range(len(dims_b))
+    ]
+
+    left_dims_a = [i for i in range(dim_count_a) if (i not in dims_a)]
+    left_dims_b = [i for i in range(dim_count_b) if (i not in dims_b)]
+
+    new_input_a = opset9.permute(g, input_a, left_dims_a + dims_a)
+    new_input_b = opset9.permute(g, input_b, dims_b + left_dims_b)
+
+    input_shape = g.op("Shape", new_input_a)
+    left_sizes_a = symbolic_helper._slice_helper(
+        g, input_shape, axes=[0], starts=[0], ends=[len(left_dims_a)]
+    )
+    shape_sizes = [
+        left_sizes_a,
+        g.op("Constant", value_t=torch.tensor([-1], dtype=torch.long)),
+    ]
+    output_a = opset9._reshape_from_tensor(g, new_input_a, shape_sizes)
+
+    input_shape = g.op("Shape", output_a)
+    slices = symbolic_helper._slice_helper(
+        g, input_shape, axes=[0], starts=[-1], ends=[sys.maxsize]
+    )
+    shape_sizes = [
+        g.op("Constant", value_t=torch.tensor([-1], dtype=torch.long)),
+        slices,
+    ]
+    output_a = opset9._reshape_from_tensor(g, new_input_a, shape_sizes)
+
+    input_shape = g.op("Shape", new_input_b)
+    left_sizes_b = symbolic_helper._slice_helper(
+        g, input_shape, axes=[0], starts=[len(dims_b)], ends=[sys.maxsize]
+    )
+    slices = symbolic_helper._slice_helper(
+        g, input_shape, axes=[0], starts=[0], ends=[len(dims_b)]
+    )
+    shape_sizes = [
+        slices,
+        g.op("Constant", value_t=torch.tensor([-1], dtype=torch.long)),
+    ]
+    output_b = opset9._reshape_from_tensor(g, new_input_b, shape_sizes)
+
+    input_shape = g.op("Shape", output_b)
+    slices = symbolic_helper._slice_helper(
+        g, input_shape, axes=[0], starts=[-1], ends=[sys.maxsize]
+    )
+    shape_sizes = [
+        g.op("Constant", value_t=torch.tensor([-1], dtype=torch.long)),
+        slices,
+    ]
+    output_b = opset9._reshape_from_tensor(g, new_input_b, shape_sizes)
+
+    output = einsum(g, "ij,jk->ik", g.op("prim::ListConstruct", *[output_a, output_b]))
+
+    shape_sizes = [left_sizes_a, left_sizes_b]
+    return opset9._reshape_from_tensor(g, output, shape_sizes)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset13.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset13.py
new file mode 100644
index 0000000000000000000000000000000000000000..e31416ae2bc9060e57f6edaefdb271fc5b40f2f6
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset13.py
@@ -0,0 +1,1113 @@
+# mypy: allow-untyped-defs
+# EDITING THIS FILE? READ THIS FIRST!
+# see Note [Edit Symbolic Files] in README.md
+
+# This file exports ONNX ops for opset 13
+import functools
+
+import torch
+import torch._C._onnx as _C_onnx
+from torch.onnx import (
+    _constants,
+    _type_utils,
+    errors,
+    symbolic_helper,
+    symbolic_opset11 as opset11,
+    symbolic_opset9 as opset9,
+    utils,
+)
+from torch.onnx._internal import jit_utils, registration
+
+
+_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=13)
+
+
+@_onnx_symbolic("aten::softmax")
+@symbolic_helper.parse_args("v", "i", "none")
+def softmax(g: jit_utils.GraphContext, input, dim, dtype=None):
+    softmax = g.op("Softmax", input, axis_i=dim)
+    if dtype and dtype.node().kind() != "prim::Constant":
+        parsed_dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+        softmax = g.op(
+            "Cast", softmax, to_i=_type_utils.JitScalarType(parsed_dtype).onnx_type()
+        )
+
+    return softmax
+
+
+@_onnx_symbolic("aten::log_softmax")
+@symbolic_helper.parse_args("v", "i", "none")
+def log_softmax(g: jit_utils.GraphContext, input, dim, dtype=None):
+    return_op = g.op("LogSoftmax", input, axis_i=dim)
+    if dtype and dtype.node().kind() != "prim::Constant":
+        parsed_dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+        return_op = g.op(
+            "Cast", return_op, to_i=_type_utils.JitScalarType(parsed_dtype).onnx_type()
+        )
+    return return_op
+
+
+@_onnx_symbolic("aten::frobenius_norm")
+@symbolic_helper.parse_args("v", "v", "i")
+def frobenius_norm(g: jit_utils.GraphContext, self, dim=None, keepdim=False):
+    dim_val = symbolic_helper._maybe_get_const(dim, "is")
+    if not symbolic_helper._is_value(dim_val) and len(dim_val) == 0:
+        return g.op("ReduceL2", self, keepdims_i=0)
+    sqr = g.op("Mul", self, self)
+    sumsqr = symbolic_helper._reducesum_helper(g, sqr, dim, keepdims_i=keepdim)
+    return g.op("Sqrt", sumsqr)
+
+
+@_onnx_symbolic("aten::split")
+@symbolic_helper.parse_args("v", "v", "i", "i")
+def split(g: jit_utils.GraphContext, self, split_size_or_sizes, dim, _outputs=None):
+    if not symbolic_helper._is_split_static(split_size_or_sizes, _outputs):
+        split_out = g.op("SplitToSequence", self, split_size_or_sizes, axis_i=dim)
+        if _outputs is None:
+            return split_out
+        # Convert to multiple slice nodes iff number of splits and number of outputs are statically known.
+        if (
+            symbolic_helper._is_packed_list(split_size_or_sizes)
+            and len(symbolic_helper._unpack_list(split_size_or_sizes)) == _outputs
+        ):
+            split_sizes = [
+                symbolic_helper._unsqueeze_helper(g, v, [0])
+                for v in symbolic_helper._unpack_list(split_size_or_sizes)
+            ]
+
+            start = g.op("Constant", value_t=torch.tensor([0], dtype=torch.long))
+            axis = g.op("Constant", value_t=torch.tensor([dim], dtype=torch.long))
+            res = []
+            for i in range(_outputs):
+                end = g.op(
+                    "Add", start, split_sizes[i]
+                )  # split_sizes is a list of same length as _outputs
+                res.append(g.op("Slice", self, start, end, axis))
+                start = end
+            return res
+        return [
+            g.op(
+                "SequenceAt",
+                split_out,
+                g.op("Constant", value_t=torch.tensor([i], dtype=torch.long)),
+            )
+            for i in range(_outputs)
+        ]
+
+    split_val = symbolic_helper._node_get(split_size_or_sizes.node(), "value")
+    if split_val.dim() > 0:
+        return g.op("Split", self, split_size_or_sizes, axis_i=dim, outputs=_outputs)
+    split_size = symbolic_helper._get_const(split_size_or_sizes, "i", "split_size")
+
+    size = symbolic_helper._get_tensor_dim_size(self, dim)
+    if size is None:
+        if _outputs is not None:
+            size = split_size * _outputs
+        else:
+            raise errors.SymbolicValueError(
+                "Unknown dimension size not supported", self
+            )
+    splits = [split_size] * (size // split_size)
+    leftover = size % split_size
+    if leftover:
+        splits.append(leftover)
+    splits = g.op("Constant", value_t=torch.tensor(splits))
+    return g.op("Split", self, splits, axis_i=dim, outputs=_outputs)
+
+
+@_onnx_symbolic("aten::split_with_sizes")
+def split_with_sizes(g: jit_utils.GraphContext, self, split_sizes, dim, _outputs=None):
+    return split(g, self, split_sizes, dim, _outputs)
+
+
+@_onnx_symbolic("aten::unsafe_split")
+def unsafe_split(
+    g: jit_utils.GraphContext, self, split_size_or_sizes, dim, _outputs=None
+):
+    return split(g, self, split_size_or_sizes, dim, _outputs)
+
+
+@_onnx_symbolic("aten::unsafe_split_with_sizes")
+def unsafe_split_with_sizes(
+    g: jit_utils.GraphContext, self, split_sizes, dim, _outputs=None
+):
+    return split_with_sizes(g, self, split_sizes, dim, _outputs)
+
+
+@_onnx_symbolic("aten::tensor_split")
+@symbolic_helper.parse_args("v", "v", "i", "i")
+def tensor_split(
+    g: jit_utils.GraphContext, self, indices_or_sections, dim, _outputs=None
+):
+    axis = g.op("Constant", value_t=torch.tensor(dim, dtype=torch.long))
+    axis = opset11.unsqueeze(g, axis, 0)
+    const_1 = g.op("Constant", value_t=torch.tensor(1, dtype=torch.long))
+
+    if symbolic_helper._is_split_static(indices_or_sections, _outputs):
+        split_val = symbolic_helper._node_get(indices_or_sections.node(), "value")
+
+        if split_val.dim() > 0:
+            start = g.op("Constant", value_t=torch.tensor([0], dtype=torch.long))
+            res = []
+            assert _outputs is not None
+            for i in range(_outputs - 1):
+                end = g.op(
+                    "Gather",
+                    indices_or_sections,
+                    g.op("Constant", value_t=torch.tensor([i], dtype=torch.long)),
+                    axis_i=0,
+                )
+                res.append(g.op("Slice", self, start, end, axis))
+                start = end
+
+            end = symbolic_helper._size_helper(g, self, axis)
+            res.append(g.op("Slice", self, start, end, axis))
+            return res
+
+        split_size = symbolic_helper._get_const(
+            indices_or_sections, "i", "indices_or_sections"
+        )
+
+        size = symbolic_helper._get_tensor_dim_size(self, dim)
+        if size is None:
+            if _outputs is not None:
+                size = split_size * _outputs
+            else:
+                raise errors.SymbolicValueError(
+                    "Unknown dimension size not supported", self
+                )
+
+        min_split_size = size // split_size
+        num_splits_one_extra = size % split_size
+
+        splits = num_splits_one_extra * [min_split_size + 1]
+        leftover = (split_size - num_splits_one_extra) * [min_split_size]
+
+        splits = g.op(
+            "Constant", value_t=torch.tensor(splits + leftover, dtype=torch.long)
+        )
+        return g.op("Split", self, splits, axis_i=dim, outputs=_outputs)
+
+    if (
+        symbolic_helper._is_tensor(indices_or_sections)
+        and symbolic_helper._get_tensor_rank(indices_or_sections) == 1
+    ):
+        loop_len = symbolic_helper._size_helper(
+            g, indices_or_sections, g.op("Constant", value_t=torch.tensor(0))
+        )
+        loop_len = opset11.unsqueeze(g, loop_len, 0)
+        loop_condition = g.op("Cast", const_1, to_i=_C_onnx.TensorProtoDataType.BOOL)
+
+        # To make the first slice in the below loop work,
+        # we pad a zero to the first position so that it will be the initial start of slice.
+        padding_0 = g.op("Constant", value_t=torch.tensor([0], dtype=torch.long))
+        indices_or_sections = g.op("Concat", padding_0, indices_or_sections, axis_i=0)
+
+        final_splits = g.op("SequenceEmpty")
+        # Loop inputs
+        loop, (loop_context,), _ = jit_utils.add_op_with_blocks(
+            g, "Loop", loop_len, loop_condition, final_splits, outputs=1, n_blocks=1
+        )
+
+        loop_block = loop_context.block
+        block_input_iter = utils._add_input_to_block(loop_block)
+        cond = utils._add_input_to_block(loop_block)
+        final_splits = utils._add_input_to_block(loop_block)
+
+        start = loop_context.op(
+            "Gather", indices_or_sections, block_input_iter, axis_i=0
+        )
+        end = loop_context.op(
+            "Gather",
+            indices_or_sections,
+            loop_context.op("Add", block_input_iter, const_1),
+            axis_i=0,
+        )
+
+        slice = loop_context.op("Slice", self, start, end, axis)
+        final_splits = loop_context.op("SequenceInsert", final_splits, slice)
+
+        # Loop outputs
+        cond_out = loop_context.op("Identity", loop_condition)
+        utils._add_output_to_block(loop_block, cond_out)
+        utils._add_output_to_block(loop_block, final_splits)
+
+        loop_out = loop.node().output()
+        start = g.op(
+            "Gather",
+            indices_or_sections,
+            g.op("Constant", value_t=torch.tensor(-1, dtype=torch.long)),
+            axis_i=0,
+        )
+        start = opset11.unsqueeze(g, start, 0)
+        end = symbolic_helper._size_helper(g, self, axis)
+
+        last_slice = g.op("Slice", self, start, end, axis)
+
+        return g.op("SequenceInsert", loop_out, last_slice)
+
+    else:  # scalar tensor
+        dim_size = symbolic_helper._size_helper(g, self, axis)
+        min_split_size = g.op("Div", dim_size, indices_or_sections)
+        min_split_size_plus_1 = g.op(
+            "Add",
+            min_split_size,
+            const_1,
+        )
+        num_splits_one_extra = g.op("Mod", dim_size, indices_or_sections)
+        splits = g.op("Tile", min_split_size_plus_1, num_splits_one_extra)
+        leftover = g.op(
+            "Tile",
+            min_split_size,
+            g.op(
+                "Sub",
+                opset11.unsqueeze(g, indices_or_sections, 0),
+                num_splits_one_extra,
+            ),
+        )
+
+        splits = g.op("Concat", splits, leftover, axis_i=0)
+        if _outputs is None:
+            return g.op("SplitToSequence", self, splits, axis_i=dim)
+        return g.op("Split", self, splits, axis_i=dim, outputs=_outputs)
+
+
+@_onnx_symbolic("aten::unbind")
+@symbolic_helper.parse_args("v", "i", "i")
+def unbind(g: jit_utils.GraphContext, self, dim=0, _outputs=None):
+    if _outputs is None:
+        return g.op(
+            "SplitToSequence",
+            self,
+            g.op("Constant", value_t=torch.tensor(1, dtype=torch.long)),
+            axis_i=dim,
+            keepdims_i=0,
+        )
+
+    splits = g.op("Constant", value_t=torch.tensor([1] * _outputs))
+    outputs = g.op("Split", self, splits, axis_i=dim, outputs=_outputs)
+    outputs = [outputs] if _outputs == 1 else outputs
+    squeezed_outputs = [
+        g.op("Squeeze", out, g.op("Constant", value_t=torch.tensor([dim])))
+        for out in outputs
+    ]
+    return squeezed_outputs
+
+
+@_onnx_symbolic("aten::nonzero_numpy")
+# Emitted from `torch.nonzero(x, as_tuple=True)`
+def nonzero_numpy(g: jit_utils.GraphContext, input, _outputs=None):
+    return unbind(g, opset9.nonzero(g, input), 1, _outputs=_outputs)
+
+
+@_onnx_symbolic("aten::where")
+@symbolic_helper.parse_args("v", "v", "v", "i")
+def where(g: jit_utils.GraphContext, condition, self=None, other=None, _outputs=None):
+    # Assumes that torch.where's first argument takes only Bool and Byte tensors.
+    if not symbolic_helper._is_bool(condition):
+        condition = g.op("Cast", condition, to_i=_C_onnx.TensorProtoDataType.BOOL)
+    if self is None:
+        condition = opset9.nonzero(g, condition)
+        return symbolic_helper._unbind_helper(
+            g, condition, g.op("Constant", value_t=torch.tensor(1)), _outputs
+        )
+    return g.op("Where", condition, self, other)
+
+
+@_onnx_symbolic("aten::fake_quantize_per_channel_affine")
+@symbolic_helper.parse_args("v", "v", "v", "i", "i", "i")
+def fake_quantize_per_channel_affine(
+    g: jit_utils.GraphContext,
+    inputs,
+    scale,
+    zero_point,
+    axis,
+    quant_min=-128,
+    quant_max=127,
+):
+    # NOTE: (0, 127) is allowed as special case. PyTorch restricts activations to be in the range (0, 127).
+    #   https://github.com/pytorch/pytorch/blob/b34b192d6b97325c9f78e5995c48c8498ede34bd/torch/ao/quantization/observer.py#L1422
+    if (quant_min, quant_max) not in [(0, 255), (-128, 127), (0, 127)]:
+        raise errors.SymbolicValueError(
+            "For (quant_min, quant_max), ONNX allows only (0, 127), (0, 255) and (-128, 127). "
+            f"Got ({quant_min}, {quant_max})",
+            inputs,
+        )
+    # ONNX defines zero_point to be int8 or uint8
+    if quant_min == 0:
+        zero_point = g.op("Cast", zero_point, to_i=_C_onnx.TensorProtoDataType.UINT8)
+    else:
+        zero_point = g.op("Cast", zero_point, to_i=_C_onnx.TensorProtoDataType.INT8)
+    quantized = g.op("QuantizeLinear", inputs, scale, zero_point, axis_i=axis)
+    if (quant_min, quant_max) == (0, 127):
+        quantized = g.op(
+            "Clip",
+            quantized,
+            opset9.unused(g),
+            g.op("Constant", value_t=torch.tensor(127, dtype=torch.uint8)),
+        )
+    return g.op("DequantizeLinear", quantized, scale, zero_point, axis_i=axis)
+
+
+@_onnx_symbolic("aten::fake_quantize_per_tensor_affine")
+@symbolic_helper.parse_args("v", "v", "v", "i", "i")
+def fake_quantize_per_tensor_affine(
+    g: jit_utils.GraphContext,
+    inputs,
+    scale,
+    zero_point,
+    quant_min=-128,
+    quant_max=127,
+):
+    # NOTE: (0, 127) is allowed as special case. PyTorch restricts activations to be in the range (0, 127).
+    #   https://github.com/pytorch/pytorch/blob/b34b192d6b97325c9f78e5995c48c8498ede34bd/torch/ao/quantization/observer.py#L1422
+    if (quant_min, quant_max) not in [(0, 255), (-128, 127), (0, 127)]:
+        raise errors.SymbolicValueError(
+            "For (quant_min, quant_max), ONNX allows only (0, 127), (0, 255) and (-128, 127). "
+            f"Got ({quant_min}, {quant_max})",
+            inputs,
+        )
+    if quant_min == 0:
+        zero_point = g.op("Cast", zero_point, to_i=_C_onnx.TensorProtoDataType.UINT8)
+    else:
+        zero_point = g.op("Cast", zero_point, to_i=_C_onnx.TensorProtoDataType.INT8)
+    if (
+        _type_utils.JitScalarType.from_value(scale, _type_utils.JitScalarType.UNDEFINED)
+        != _type_utils.JitScalarType.FLOAT
+    ):
+        scale = g.op("Cast", scale, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+    quantized = g.op("QuantizeLinear", inputs, scale, zero_point)
+    if (quant_min, quant_max) == (0, 127):
+        quantized = g.op(
+            "Clip",
+            quantized,
+            opset9.unused(g),
+            g.op("Constant", value_t=torch.tensor(127, dtype=torch.uint8)),
+        )
+    return g.op("DequantizeLinear", quantized, scale, zero_point)
+
+
+def _reduce_op_symbolic(onnx_op_name):
+    def symbolic(g, self, dim=None, keepdim=None):
+        self = symbolic_helper._maybe_cast_reduce_op_input(g, self)
+        if dim is None:
+            # all-reduce path
+            return symbolic_helper._handle_reduce_dim_none(g, self, onnx_op_name)
+        else:
+            keepdim = symbolic_helper._get_const(keepdim, "i", "keepdim")
+            return g.op(onnx_op_name, self, dim, keepdims_i=keepdim)
+
+    return symbolic
+
+
+@_onnx_symbolic(
+    "aten::sum",
+    decorate=[symbolic_helper._apply_params("ReduceSum", "sum")],
+)
+def _reduce_with_dtype(onnx_op, name):
+    symbolic = _reduce_op_symbolic(onnx_op)
+
+    @symbolic_helper._overload_by_arg_count
+    def reduce(g, *args, **kwargs):
+        @symbolic_helper.parse_args("v", "none")
+        def reduce_nodim(g, self, dtype):
+            dtype_onnx = None
+            if dtype.node().kind() == "onnx::Constant":
+                dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+                dtype_onnx = _type_utils.JitScalarType(dtype).onnx_type()
+                self = g.op("Cast", self, to_i=dtype_onnx)
+            elif dtype.node().kind() != "prim::Constant":
+                return symbolic_helper._unimplemented(name, "dtype", dtype)
+            result = symbolic(g, self)
+            if dtype_onnx is not None:
+                result_dtype_onnx = _type_utils.JitScalarType.from_value(
+                    result
+                ).onnx_type()
+                if result_dtype_onnx != dtype_onnx:
+                    result = g.op("Cast", result, to_i=dtype_onnx)
+            return result
+
+        @symbolic_helper.parse_args("v", "v", "i", "none")
+        def reduce_dim(g, self, dim, keepdim, dtype):
+            dtype_onnx = None
+            if dtype.node().kind() == "onnx::Constant":
+                dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+                dtype_onnx = _type_utils.JitScalarType(dtype).onnx_type()
+                self = g.op("Cast", self, to_i=dtype_onnx)
+            elif dtype.node().kind() != "prim::Constant":
+                return symbolic_helper._unimplemented(name, "dtype", dtype)
+            result = symbolic(g, self, dim, keepdim)
+            if dtype_onnx is not None:
+                result_dtype_onnx = _type_utils.JitScalarType.from_value(
+                    result
+                ).onnx_type()
+                if result_dtype_onnx != dtype_onnx:
+                    result = g.op("Cast", result, to_i=dtype_onnx)
+            return result
+
+        return reduce_nodim, reduce_dim
+
+    return reduce
+
+
+# Ported from
+# https://github.com/microsoft/onnxscript/blob/6b1b81700b4523f31d8c6d3321e5d8ef5d42b764/onnxscript/function_libs/torch_aten/ops/core.py#L6097
+# NOTE: Supporting aten::unflatten before opset13 needs helper function to adjust ONNX op changes in Concat, Slice, ...
+@_onnx_symbolic("aten::unflatten")
+def unflatten(g: jit_utils.GraphContext, input, dim, unflattened_size):
+    input_dim = symbolic_helper._get_tensor_rank(input)
+    if input_dim is None:
+        return symbolic_helper._unimplemented(
+            "dim",
+            "ONNX and PyTorch use different strategies to split the input. "
+            "Input rank must be known at export time.",
+        )
+
+    # dim could be negative
+    input_dim = g.op("Constant", value_t=torch.tensor([input_dim], dtype=torch.int64))
+    dim = g.op("Add", input_dim, dim)
+    dim = g.op("Mod", dim, input_dim)
+
+    input_size = g.op("Shape", input)
+
+    head_start_idx = g.op("Constant", value_t=torch.tensor([0], dtype=torch.int64))
+    head_end_idx = g.op(
+        "Reshape", dim, g.op("Constant", value_t=torch.tensor([1], dtype=torch.int64))
+    )
+    head_part_rank = g.op("Slice", input_size, head_start_idx, head_end_idx)
+
+    dim_plus_one = g.op(
+        "Add", dim, g.op("Constant", value_t=torch.tensor([1], dtype=torch.int64))
+    )
+    tail_start_idx = g.op(
+        "Reshape",
+        dim_plus_one,
+        g.op("Constant", value_t=torch.tensor([1], dtype=torch.int64)),
+    )
+    tail_end_idx = g.op(
+        "Constant", value_t=torch.tensor([_constants.INT64_MAX], dtype=torch.int64)
+    )
+    tail_part_rank = g.op("Slice", input_size, tail_start_idx, tail_end_idx)
+
+    final_shape = g.op(
+        "Concat", head_part_rank, unflattened_size, tail_part_rank, axis_i=0
+    )
+
+    return symbolic_helper._reshape_helper(g, input, final_shape)
+
+
+@_onnx_symbolic("aten::unsafe_chunk")
+@symbolic_helper.parse_args("v", "i", "i", "i")
+def unsafe_chunk(g: jit_utils.GraphContext, self, chunks, dim, _outputs=None):
+    if _outputs is None:
+        return g.op(
+            "SplitToSequence",
+            self,
+            g.op("Constant", value_t=torch.tensor(1, dtype=torch.long)),
+            axis_i=dim,
+            keepdims_i=0,
+        )
+
+    size = symbolic_helper._get_tensor_dim_size(self, dim)
+    if size is None:
+        return symbolic_helper._unimplemented("unsafe_chunk", "unknown dimension size")
+    split_size = (size + chunks - 1) // chunks
+    splits = [split_size] * (size // split_size)
+    leftover = size % split_size
+    if leftover:
+        splits.append(leftover)
+
+    # TODO: So far we don"t have a module using this method. We"ll keep
+    # this as a constant unless we see a request of dynamics in any
+    # user's modules.
+    splits = g.op("Constant", value_t=torch.tensor(splits, dtype=torch.long))
+    return g.op("Split", self, splits, axis_i=dim, outputs=_outputs)
+
+
+@_onnx_symbolic("aten::tile")
+def tile(g: jit_utils.GraphContext, self, dims):
+    self_shape = g.op("Shape", self)
+    self_rank = g.op("Size", self_shape)
+    dims_rank = g.op("Size", dims)
+    diff = g.op("Sub", self_rank, dims_rank)
+    const_zero = g.op("Constant", value_t=torch.tensor([0]))
+
+    # 1. If dims is shorter than self.shape pad dims with 1
+    dims_shorter_than_self_shape = g.op("Greater", diff, const_zero)
+    (
+        if_op_greater,
+        (if_context_greater, else_context_greater),
+        _,
+    ) = jit_utils.add_op_with_blocks(
+        g, "If", dims_shorter_than_self_shape, n_blocks=2, outputs=1
+    )
+    const_one = if_context_greater.op("Constant", value_t=torch.LongTensor([1]))
+    diff_1d_greater = if_context_greater.op("Reshape", diff, const_one)
+    exapnd_ones_greater = if_context_greater.op("Expand", const_one, diff_1d_greater)
+    dims_ = if_context_greater.op("Concat", exapnd_ones_greater, dims, axis_i=0)
+    utils._add_output_to_block(if_context_greater.block, dims_)
+    identity_dim = else_context_greater.op("Identity", dims)
+    utils._add_output_to_block(else_context_greater.block, identity_dim)
+    dims_final = if_op_greater.node().output()
+
+    # 2. If dims is longer than self.shape pad self.shape with 1
+    dims_longer_than_self_shape = g.op("Less", diff, const_zero)
+    (
+        if_op_less,
+        (if_context_less, else_context_less),
+        _,
+    ) = jit_utils.add_op_with_blocks(
+        g, "If", dims_longer_than_self_shape, n_blocks=2, outputs=1
+    )
+    const_one = if_context_less.op("Constant", value_t=torch.LongTensor([1]))
+    diff_1d_less = if_context_less.op(
+        "Reshape",
+        if_context_less.op("Abs", diff),
+        const_one,
+    )
+    exapnd_ones_less = if_context_less.op("Expand", const_one, diff_1d_less)
+    self_final_shape = if_context_less.op(
+        "Concat", exapnd_ones_less, self_shape, axis_i=0
+    )
+    self_ = if_context_less.op("Reshape", self, self_final_shape)
+    utils._add_output_to_block(if_context_less.block, self_)
+    identity_self = else_context_less.op("Identity", self)
+    utils._add_output_to_block(else_context_less.block, identity_self)
+    self_final = if_op_less.node().output()
+
+    dims_final = g.op("Cast", dims_final, to_i=_C_onnx.TensorProtoDataType.INT64)
+    return g.op("Tile", self_final, dims_final)
+
+
+@_onnx_symbolic("aten::repeat_interleave")
+def repeat_interleave(
+    g: jit_utils.GraphContext, self, repeats, dim=None, output_size=None
+):
+    repeats_dim = symbolic_helper._get_tensor_rank(repeats)
+    repeats_sizes = symbolic_helper._get_tensor_sizes(repeats)
+    input_sizes = symbolic_helper._get_tensor_sizes(self)
+    if repeats_dim is None:
+        raise errors.SymbolicValueError(
+            "Unsupported: ONNX export of repeat_interleave for unknown repeats rank.",
+            self,
+        )
+    if repeats_sizes is None:
+        raise errors.SymbolicValueError(
+            "Unsupported: ONNX export of repeat_interleave for unknown repeats size.",
+            self,
+        )
+    if input_sizes is None:
+        raise errors.SymbolicValueError(
+            "Unsupported: ONNX export of repeat_interleave for unknown input size.",
+            self,
+        )
+
+    final_dim = dim
+    # if dim is None flatten
+    # By default, use the flattened input array, and return a flat output array
+    if symbolic_helper._is_none(dim):
+        self = symbolic_helper._reshape_helper(
+            g, self, g.op("Constant", value_t=torch.tensor([-1]))
+        )
+        dim = torch.tensor(0, dtype=torch.int64)
+    else:
+        dim = symbolic_helper._maybe_get_scalar(dim)
+
+    # Handle cases where dim is negative
+    if dim < 0:
+        dim += len(input_sizes)
+
+    output_sizes = input_sizes.copy()
+    for idx, input_size in enumerate(input_sizes):
+        if input_size is None:
+            output_sizes[idx], input_sizes[idx] = 0, -1
+
+    # Check if all indices should be repeated the same number of times.
+    if repeats_dim == 0 or (repeats_dim == 1 and repeats_sizes[0] == 1):
+        return symbolic_helper._repeat_interleave_single_value_repeat_helper(
+            g, self, repeats, dim
+        )
+
+    cond_dynamic_repeats = repeats_dim == 1 and repeats_sizes[0] is None
+    # If input size is dynamic or repeats vector is dynamic
+    if output_sizes[dim] == 0 or cond_dynamic_repeats:
+        reps = symbolic_helper._size_helper(g, self, dim)
+        reps = opset11.unsqueeze(g, reps, 0)
+
+        # Check if repeats is dynamic
+        # As repeats is dynamic, we use a where node as a substitute for the if statement
+        # If repests_dim = 1, expand repeats otherwise use original tensor
+        if cond_dynamic_repeats:
+            repeat_dim = symbolic_helper._size_helper(
+                g, repeats, g.op("Constant", value_t=torch.LongTensor([0]))
+            )
+            repeat_cond = g.op(
+                "Equal", repeat_dim, g.op("Constant", value_t=torch.LongTensor([1]))
+            )
+            repeats = where(g, repeat_cond, g.op("Expand", repeats, reps), repeats)
+    # There are cases when the repeats are 1-d tensor with multiple repeats, but dim
+    # provided along one of the dynamic axes provided. A simple example would be
+    # input.shape -> [1, 1, *] where * represents the dynamic axes, and dim = 2
+    # Now, repeat interleaving can be performed in pytorch when the value of * matches
+    # with the number of elements in repeat, for example if * -> 2, number of repeats
+    # should be 2 as well.
+    else:
+        return opset9.repeat_interleave(g, self, repeats, final_dim)
+
+    reps_like = g.op(
+        "ConstantOfShape",
+        g.op("Shape", repeats),
+        value_t=torch.tensor([1], dtype=torch.long),
+    )
+    r_splits = split(g, repeats, reps_like, 0)
+    i_splits = split(g, self, reps_like, dim)
+
+    output_sizes[dim], input_sizes[dim] = -1, 1
+
+    # Create a loop to iterate over each value along the dimension
+    # and perform individual interleaving using the repeats tensor
+    # Loop is of the following pattern
+    # input (trip_count, cond)
+    #   int trip_count = ...;
+    #   bool cond = ...;
+    #   for (int i=0; i < trip_count && cond; ++i) {
+    #     cond = ...;
+    #   }
+
+    # Loop conditions
+    loop_condition = g.op("Constant", value_t=torch.tensor(1))
+    loop_condition = g.op("Cast", loop_condition, to_i=_C_onnx.TensorProtoDataType.BOOL)
+    loop_len = reps
+
+    # Create an empty sequence to store final expansions
+    final_splits = g.op("SequenceEmpty")
+
+    # Loop inputs
+    loop, (loop_context,), _ = jit_utils.add_op_with_blocks(
+        g, "Loop", loop_len, loop_condition, final_splits, n_blocks=1
+    )
+
+    loop_block = loop_context.block
+    block_input_iter = utils._add_input_to_block(loop_block)
+    cond = utils._add_input_to_block(loop_block)
+    final_splits = utils._add_input_to_block(loop_block)
+
+    r_split = loop_context.op("SequenceAt", r_splits, block_input_iter)
+    i_split = loop_context.op("SequenceAt", i_splits, block_input_iter)
+
+    i_split = opset11.unsqueeze(loop_context, i_split, dim + 1)
+    r_concat = [
+        loop_context.op("Constant", value_t=torch.LongTensor(input_sizes[: dim + 1])),
+        r_split,
+        loop_context.op("Constant", value_t=torch.LongTensor(input_sizes[dim + 1 :])),
+    ]
+    r_concat = loop_context.op("Concat", *r_concat, axis_i=0)
+    i_split = opset9.expand(loop_context, i_split, r_concat, None)
+    i_split = symbolic_helper._reshape_helper(
+        loop_context, i_split, g.op("Constant", value_t=torch.LongTensor(output_sizes))
+    )
+    final_splits = loop_context.op("SequenceInsert", final_splits, i_split)
+
+    # Loop outputs
+    cond_out = loop_context.op(
+        "Cast", loop_condition, to_i=_C_onnx.TensorProtoDataType.BOOL
+    )
+    utils._add_output_to_block(loop_block, cond_out)
+    utils._add_output_to_block(loop_block, final_splits)
+
+    loop_out = loop.node().output()
+    loop_out = g.op("ConcatFromSequence", loop_out, axis_i=dim)
+    return loop_out
+
+
+@_onnx_symbolic("aten::diagonal")
+@symbolic_helper.parse_args("v", "i", "i", "i")
+def diagonal(g: jit_utils.GraphContext, self, offset, dim1, dim2):
+    rank = symbolic_helper._get_tensor_rank(self)
+    # Replace negative indexing when rank is known
+    if rank is not None:
+        dim1 = dim1 if dim1 >= 0 else dim1 + rank
+        dim2 = dim2 if dim2 >= 0 else dim2 + rank
+
+    dim1_size = opset9.size(
+        g, self, dim=g.op("Constant", value_t=torch.LongTensor([dim1]))
+    )
+    dim2_size = opset9.size(
+        g, self, dim=g.op("Constant", value_t=torch.LongTensor([dim2]))
+    )
+    # Create appropriate mask
+    mask_shape = g.op("Concat", dim1_size, dim2_size, axis_i=0)
+    mask = opset9.zeros(g, mask_shape, None, None, None)
+    mask = g.op("EyeLike", mask, k_i=offset)
+    # dim1 and dim2 appended as a dimension at the end of the shape
+
+    if rank is not None:
+        axes = list(range(rank))
+        axes.remove(dim1)
+        axes.remove(dim2)
+        self = g.op("Transpose", self, perm_i=axes + [dim1, dim2])
+    else:
+        return symbolic_helper._unimplemented("diagonal", "unknown input rank")
+
+    # Multiply input and mask to calculate values along diagonal
+    # The mask consists of one values where diagonal values are to be calculated
+    # For example:
+    # [[1.1, 1.2, 1.3],   *    [[1, 0, 0]   =   [[1.1, 0, 0],
+    #  [2.1, 2.2, 2.3],         [0, 1, 0]        [0, 2.2, 0],
+    #  [3.1, 3.2, 3.3]]         [0, 0, 1]]       [0, 0, 3.3]]
+    result = g.op("Mul", self, mask)
+    result = symbolic_helper._reducesum_helper(g, result, axes_i=[-1], keepdims_i=0)
+
+    # Calculate gather indices based on offset and dims
+    # If offset is greater than zero, set offset to zero as this aids in
+    # calculation of selection window
+    offset_op = g.op("Constant", value_t=torch.LongTensor([offset]))
+    if offset >= 0:
+        diag_size = g.op(
+            "Max",
+            g.op("Min", dim1_size, g.op("Sub", dim2_size, offset_op)),
+            g.op("Constant", value_t=torch.LongTensor([0])),
+        )
+        offset = 0
+    else:
+        diag_size = g.op(
+            "Max",
+            g.op("Min", g.op("Add", dim1_size, offset_op), dim2_size),
+            g.op("Constant", value_t=torch.LongTensor([0])),
+        )
+    diag_size = g.op("Concat", diag_size, axis_i=0)
+
+    # Calculate which diagonal values to select
+    # For example, in cases with offsets:
+    # [[0, 1.1, 0]
+    #  [0, 0, 2.2]]
+    # we need to select the last two columns, so we create a tensor
+    # with all columns that are to be selected
+    # So in this example, it is [1, 2]
+    select_window_ones_fill = opset9.ones(g, diag_size, 4, None, None)
+    select_window = g.op(
+        "CumSum",
+        select_window_ones_fill,
+        g.op("Constant", value_t=torch.LongTensor([0])),
+    )
+    select_window = g.op(
+        "Add",
+        select_window,
+        g.op("Constant", value_t=torch.LongTensor([abs(offset) - 1])),
+    )
+
+    gather_shape = [
+        opset9.size(g, result, dim=g.op("Constant", value_t=torch.LongTensor([axis])))
+        for axis in list(range(rank))[:-2]
+    ]
+    gather_shape.append(diag_size)
+    gather_shape = g.op("Concat", *gather_shape, axis_i=0)
+    gather_indices = opset9.zeros(g, gather_shape, 4, None, None)
+
+    # There might be cases where offset value is greater than number of rows/columns
+    # and might cause the diagonal to overrun and as a result of this, diag_size would be zero.
+    # For example, if
+    #       offset = 9, dim1_size = 2 (columns), dim2_size = 4 (rows)
+    #       diag_size = max(min(2, (4-9)), 0) = 0, based on calculation above
+    # Cases with diagonal overrun always result in diag_size = max(0, -ve value) = 0
+    # In cases without diagonal overrun, we select the appropriate rows/columns along which we
+    # are calculating diagonal values. In cases with diagonal overrun, we return a tensor which has
+    # the dimension of the row/column where overrun occurred as 0-dim, as we are essentially
+    # returning an empty tensor
+    overrun_cond = g.op(
+        "Not",
+        g.op(
+            "Equal",
+            diag_size,
+            g.op("Constant", value_t=torch.tensor(0, dtype=torch.int64)),
+        ),
+    )
+
+    if_op, (if_context, else_context), _ = jit_utils.add_op_with_blocks(
+        g, "If", overrun_cond, n_blocks=2
+    )
+
+    gather_indices_if_block = if_context.op("Add", gather_indices, select_window)
+    gather_indices_if_block = symbolic_helper._unsqueeze_helper(
+        if_context, gather_indices_if_block, [rank - 1]
+    )
+    final_non_overrun = if_context.op(
+        "GatherND", result, gather_indices_if_block, batch_dims_i=rank - 2
+    )
+    final_overrun = opset9.zeros(else_context, gather_shape, 6, None, None)
+    utils._add_output_to_block(if_context.block, final_non_overrun)
+    utils._add_output_to_block(else_context.block, final_overrun)
+    return if_op
+
+
+# Quantized ops
+
+
+@_onnx_symbolic("quantized::linear")
+def quantized_linear(
+    g: jit_utils.GraphContext, q_input, q_weight, bias, op_scale, op_zero_point
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, axis = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(
+        g, bias, input_scale, weight_scale, axis
+    )
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.linear(g, input, weight, bias)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::linear_relu")
+def quantized_linear_relu(
+    g: jit_utils.GraphContext, q_input, q_weight, bias, op_scale, op_zero_point
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, axis = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(
+        g, bias, input_scale, weight_scale, axis
+    )
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.linear(g, input, weight, bias)
+    output = opset9.relu(g, output)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::conv1d_relu")
+def quantized_conv1d_relu(
+    g: jit_utils.GraphContext,
+    q_input,
+    q_weight,
+    bias,
+    stride,
+    padding,
+    dilation,
+    groups,
+    op_scale,
+    op_zero_point,
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, axis = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(
+        g, bias, input_scale, weight_scale, axis
+    )
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.conv1d(g, input, weight, bias, stride, padding, dilation, groups)
+    output = opset9.relu(g, output)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::conv2d_relu")
+def quantized_conv2d_relu(
+    g: jit_utils.GraphContext,
+    q_input,
+    q_weight,
+    bias,
+    stride,
+    padding,
+    dilation,
+    groups,
+    op_scale,
+    op_zero_point,
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, axis = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(
+        g, bias, input_scale, weight_scale, axis
+    )
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.conv2d(g, input, weight, bias, stride, padding, dilation, groups)
+    output = opset9.relu(g, output)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::conv3d_relu")
+def quantized_conv3d_relu(
+    g: jit_utils.GraphContext,
+    q_input,
+    q_weight,
+    bias,
+    stride,
+    padding,
+    dilation,
+    groups,
+    op_scale,
+    op_zero_point,
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, axis = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(
+        g, bias, input_scale, weight_scale, axis
+    )
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.conv3d(g, input, weight, bias, stride, padding, dilation, groups)
+    output = opset9.relu(g, output)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::conv1d")
+def quantized_conv1d(
+    g: jit_utils.GraphContext,
+    q_input,
+    q_weight,
+    bias,
+    stride,
+    padding,
+    dilation,
+    groups,
+    op_scale,
+    op_zero_point,
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, axis = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(
+        g, bias, input_scale, weight_scale, axis
+    )
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.conv1d(g, input, weight, bias, stride, padding, dilation, groups)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::conv2d")
+def quantized_conv2d(
+    g: jit_utils.GraphContext,
+    q_input,
+    q_weight,
+    bias,
+    stride,
+    padding,
+    dilation,
+    groups,
+    op_scale,
+    op_zero_point,
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, axis = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(
+        g, bias, input_scale, weight_scale, axis
+    )
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.conv2d(g, input, weight, bias, stride, padding, dilation, groups)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::conv3d")
+def quantized_conv3d(
+    g: jit_utils.GraphContext,
+    q_input,
+    q_weight,
+    bias,
+    stride,
+    padding,
+    dilation,
+    groups,
+    op_scale,
+    op_zero_point,
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, axis = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(
+        g, bias, input_scale, weight_scale, axis
+    )
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.conv3d(g, input, weight, bias, stride, padding, dilation, groups)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::conv_transpose1d")
+def quantized_conv_transpose1d(
+    g: jit_utils.GraphContext,
+    q_input,
+    q_weight,
+    bias,
+    stride,
+    padding,
+    output_padding,
+    dilation,
+    groups,
+    op_scale,
+    op_zero_point,
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, axis = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(
+        g, bias, input_scale, weight_scale, axis
+    )
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.conv_transpose2d(
+        g, input, weight, bias, stride, padding, output_padding, groups, dilation
+    )
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::conv_transpose2d")
+def quantized_conv_transpose2d(
+    g: jit_utils.GraphContext,
+    q_input,
+    q_weight,
+    bias,
+    stride,
+    padding,
+    output_padding,
+    dilation,
+    groups,
+    op_scale,
+    op_zero_point,
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, axis = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(
+        g, bias, input_scale, weight_scale, axis
+    )
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.conv_transpose2d(
+        g, input, weight, bias, stride, padding, output_padding, groups, dilation
+    )
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+@_onnx_symbolic("quantized::conv_transpose3d")
+def quantized_conv_transpose3d(
+    g: jit_utils.GraphContext,
+    q_input,
+    q_weight,
+    bias,
+    stride,
+    padding,
+    output_padding,
+    dilation,
+    groups,
+    op_scale,
+    op_zero_point,
+):
+    input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
+    weight, weight_scale, _, axis = symbolic_helper.dequantize_helper(g, q_weight)
+    q_bias = symbolic_helper.requantize_bias_helper(
+        g, bias, input_scale, weight_scale, axis
+    )
+    bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
+
+    output = opset9.conv_transpose3d(
+        g, input, weight, bias, stride, padding, output_padding, groups, dilation
+    )
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset14.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset14.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae33ddf58c6e09245d1419b6d3f5bb749cd61198
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset14.py
@@ -0,0 +1,283 @@
+# mypy: allow-untyped-defs
+# mypy: disable-error-code=arg-type
+"""This file exports ONNX ops for opset 14.
+
+Note [ONNX operators that are added/updated in opset 14]
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+New operators:
+    HardSwish, Trilu
+
+Updated operators:
+    Reshape
+    Add, Sub, Mul, Div
+    GRU, LSTM, RNN
+    BatchNorm, Cumsum, Relu
+"""
+
+# EDITING THIS FILE? READ THIS FIRST!
+# see Note [Edit Symbolic Files] in README.md
+from __future__ import annotations
+
+import functools
+
+import torch
+from torch.onnx import _constants, _type_utils, symbolic_helper
+from torch.onnx._globals import GLOBALS
+from torch.onnx._internal import jit_utils, registration
+
+
+__all__ = [
+    "hardswish",
+    "tril",
+    "triu",
+    "reshape",
+    "batch_norm",
+    "quantized_hardswish",
+    "scaled_dot_product_attention",
+]
+
+_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=14)
+
+
+@_onnx_symbolic("aten::hardswish")
+@symbolic_helper.parse_args("v")
+def hardswish(g: jit_utils.GraphContext, self):
+    return g.op("HardSwish", self)
+
+
+@_onnx_symbolic("aten::tril")
+def tril(g: jit_utils.GraphContext, self, diagonal, out=None):
+    return g.op("Trilu", self, diagonal, upper_i=0)
+
+
+@_onnx_symbolic("aten::triu")
+def triu(g: jit_utils.GraphContext, self, diagonal, out=None):
+    return g.op("Trilu", self, diagonal, upper_i=1)
+
+
+@_onnx_symbolic("aten::reshape")
+@symbolic_helper.quantized_args(True)
+@symbolic_helper.parse_args("v", "v")
+def reshape(g: jit_utils.GraphContext, self, shape):
+    # NOTE: Due to bug in ORT https://github.com/microsoft/onnxruntime/issues/10664
+    #       Reshape export cannot utilize the new allowzero attribute introduced in opset 14.
+    return symbolic_helper._reshape_helper(g, self, shape, allowzero=0)
+
+
+@_onnx_symbolic("aten::batch_norm")
+@symbolic_helper.parse_args("v", "v", "v", "v", "v", "i", "f", "f", "i")
+def batch_norm(
+    g: jit_utils.GraphContext,
+    input,
+    weight,
+    bias,
+    running_mean,
+    running_var,
+    training,
+    momentum,
+    eps,
+    cudnn_enabled,
+):
+    if (
+        torch.is_autocast_enabled()
+        and not symbolic_helper.args_have_same_dtype(
+            [input, weight, bias, running_mean, running_var]
+        )
+        and GLOBALS.export_onnx_opset_version < 15
+    ):
+        return symbolic_helper._onnx_opset_unsupported_detailed(
+            "BatchNormalization",
+            14,
+            15,
+            "All input tensors must have the same `dtype`."
+            " Turn off Autocast or export using opset version 15.",
+            input,
+        )
+
+    symbolic_helper.check_training_mode(training, "batch_norm")
+    weight, bias, running_mean, running_var = symbolic_helper._batchnorm_helper(
+        g, input, weight, bias, running_mean, running_var
+    )
+    out = g.op(
+        "BatchNormalization",
+        input,
+        weight,
+        bias,
+        running_mean,
+        running_var,
+        epsilon_f=eps,
+        momentum_f=1 - momentum,
+        training_mode_i=0 if not training else 1,
+        outputs=1 if not training else 3,
+    )
+    if not training:
+        return out
+    else:
+        res, new_running_mean, new_running_var = out
+        new_running_mean.setType(running_mean.type())
+        new_running_var.setType(running_var.type())
+        return res
+
+
+@_onnx_symbolic("quantized::hardswish")
+def quantized_hardswish(g: jit_utils.GraphContext, x, op_scale, op_zero_point):
+    x, _, _, _ = symbolic_helper.dequantize_helper(g, x)
+
+    output = hardswish(g, x)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+# Ported from
+# https://github.com/microsoft/onnxscript/blob/6b1b81700b4523f31d8c6d3321e5d8ef5d42b764/onnxscript/function_libs/torch_aten/ops/nn.py#L1504
+# aten_scaled_dot_product_attention
+# NOTE: Need op.Trilu
+@_onnx_symbolic("aten::scaled_dot_product_attention")
+@symbolic_helper.parse_args("v", "v", "v", "v", "f", "b", "v", "b")
+def scaled_dot_product_attention(
+    g: jit_utils.GraphContext,
+    query: torch._C.Value,
+    key: torch._C.Value,
+    value: torch._C.Value,
+    attn_mask: torch._C.Value | None = None,
+    dropout_p: float = 0.0,
+    is_causal: bool = False,
+    scale: torch._C.Value | None = None,
+    enable_gqa: bool = False,
+):
+    assert (not is_causal) or (
+        is_causal and symbolic_helper._is_none(attn_mask)
+    ), "is_causal and attn_mask cannot be set at the same time"
+    assert not enable_gqa, "conversion of scaled_dot_product_attention not implemented if enable_gqa is True"
+
+    if symbolic_helper._is_none(scale):
+        scale = _attention_scale(g, query)
+
+    if is_causal:
+        attn_mask = _causal_attention_mask(g, query, key)
+
+    # Swap the last two axes of key
+    # NOTE: onnx-script has different logic here, because the attribute perms in
+    # transpose needs list of ints
+    key_shape_builtin = symbolic_helper._get_tensor_rank(key)
+    key_transposed_axes = list(range(key_shape_builtin))
+    key_transposed_axes[-1], key_transposed_axes[-2] = (
+        key_transposed_axes[-2],
+        key_transposed_axes[-1],
+    )
+    key_transposed = g.op("Transpose", key, perm_i=key_transposed_axes)
+
+    # https://github.com/pytorch/pytorch/blob/12da0c70378b5be9135c6fda62a9863bce4a4818/aten/src/ATen/native/transformers/attention.cpp#L653
+    # Scale q, k before matmul for stability see https://tinyurl.com/sudb9s96 for math
+    query_scaled = g.op("Mul", query, g.op("Sqrt", scale))
+    key_transposed_scaled = g.op("Mul", key_transposed, g.op("Sqrt", scale))
+    mul_qk = g.op("MatMul", query_scaled, key_transposed_scaled)
+
+    if symbolic_helper._is_none(attn_mask):
+        mul_qk_add = mul_qk
+    elif (
+        _type_utils.JitScalarType.from_value(attn_mask)
+        == _type_utils.JitScalarType.BOOL
+    ):
+        # Turn the Boolean mask to float: attn_mask.masked_fill(not attn_mask, -float('inf'))
+        const_zero = g.op("Constant", value_t=torch.tensor([0.0]))
+        const_neg_inf = g.op("Constant", value_t=torch.tensor([-float("inf")]))
+        attn_mask = g.op("Where", attn_mask, const_zero, const_neg_inf)
+        mul_qk_add = g.op("Add", mul_qk, attn_mask)
+    elif _type_utils.JitScalarType.from_value(attn_mask) in (
+        _type_utils.JitScalarType.FLOAT,
+        _type_utils.JitScalarType.HALF,
+        _type_utils.JitScalarType.BFLOAT16,
+    ):
+        mul_qk_add = g.op("Add", mul_qk, attn_mask)
+    else:
+        raise ValueError(
+            f"Unsupported type for attn_mask: {_type_utils.JitScalarType.from_value(attn_mask)}"
+        )
+
+    attn_weight = g.op("Softmax", mul_qk_add, axis_i=-1)
+
+    if dropout_p != 0:
+        attn_weight = g.op(
+            "Dropout",
+            attn_weight,
+            g.op("Constant", value_t=torch.tensor(dropout_p, dtype=torch.float)),
+        )
+
+    return g.op("MatMul", attn_weight, value)
+
+
+def _attention_scale(
+    g: jit_utils.GraphContext, query: torch._C.Value
+) -> torch._C.Value:
+    """Calculate the scale factor for the attention result.
+
+    Args:
+        query: Tensor of shape [..., L, E]
+
+    Returns:
+        Scalar scale factor := 1 / math.sqrt(query.size(-1))
+    """
+    query_shape = g.op("Shape", query)
+    query_shape_last = g.op(
+        "Slice",
+        query_shape,
+        g.op("Constant", value_t=torch.tensor([-1], dtype=torch.int64)),
+        g.op(
+            "Constant", value_t=torch.tensor([_constants.INT64_MAX], dtype=torch.int64)
+        ),
+    )
+    embedding_size = g.op(
+        "Cast",
+        query_shape_last,
+        to_i=_type_utils.JitScalarType.from_value(query).onnx_type(),
+    )
+    const_one = g.op("Constant", value_t=torch.tensor([1.0], dtype=torch.float))
+    scale = g.op("Div", const_one, g.op("Sqrt", embedding_size))
+    # Add a Cast to convert the scale back to original type
+    scale = g.op(
+        "Cast",
+        scale,
+        to_i=_type_utils.JitScalarType.from_value(query).onnx_type(),
+    )
+    return scale
+
+
+def _causal_attention_mask(
+    g: jit_utils.GraphContext, query: torch._C.Value, key: torch._C.Value
+) -> torch._C.Value:
+    """Create a causal mask for the given query and key tensors.
+
+    Equivalent to::
+        mask = torch.ones(L, S, dtype=torch.bool).tril(diagonal=0)
+        attn_mask = torch.zeros(L, S, dtype=torch.float)
+        attn_mask = attn_mask.masked_fill(not mask, -float("inf"))
+
+    Args:
+        query: Tensor of shape [..., L, E]
+        key: Tensor of shape [..., S, E]
+
+    Returns:
+        Tensor of shape [L, S]
+    """
+
+    query_shape = g.op("Shape", query)
+    key_shape = g.op("Shape", key)
+
+    last_idx = g.op("Constant", value_t=torch.tensor([-1], dtype=torch.int64))
+    second_last_idx = g.op("Constant", value_t=torch.tensor([-2], dtype=torch.int64))
+    target_length = g.op("Slice", query_shape, second_last_idx, last_idx)
+    source_length = g.op("Slice", key_shape, second_last_idx, last_idx)
+    # attn_mask = torch.ones(L, S) := {
+    size = g.op("Concat", target_length, source_length, axis_i=0)
+    const_one = g.op("Constant", value_t=torch.tensor([1.0]))
+    attn_mask = g.op("Expand", const_one, size)
+    # }
+    attn_mask = g.op("Trilu", attn_mask, upper_i=0)
+    # The causal mask has 0s in the lower triangle and -inf in the upper triangle.
+    const_zero = g.op("Constant", value_t=torch.tensor([0.0]))
+    const_neg_inf = g.op("Constant", value_t=torch.tensor([-float("inf")]))
+    attn_mask = g.op(
+        "Where", g.op("Equal", attn_mask, const_zero), const_neg_inf, const_zero
+    )
+    return attn_mask
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset15.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset15.py
new file mode 100644
index 0000000000000000000000000000000000000000..08f8dcbf5a2266774f878bf2e91693e280da4228
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset15.py
@@ -0,0 +1,80 @@
+# mypy: allow-untyped-defs
+"""This file exports ONNX ops for opset 15.
+
+Note [ONNX operators that are added/updated in opset 15]
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+https://github.com/onnx/onnx/blob/master/docs/Changelog.md#version-15-of-the-default-onnx-operator-set
+New operators:
+    Bernoulli
+    CastLike
+    Optional
+    OptionalGetElement
+    OptionalHasElement
+
+Updated operators:
+    BatchNormalization https://github.com/onnx/onnx/pull/3545
+                        Backwards compatible
+                        TODO: test coverage for mixed types inputs.
+    Pow                https://github.com/onnx/onnx/pull/3412
+                        Backwards compatible
+                        TODO: bfloat16 support.
+    Shape              https://github.com/onnx/onnx/pull/3580
+                        Backwards compatible
+                        TODO: optional start/end attribute.
+"""
+
+# EDITING THIS FILE? READ THIS FIRST!
+# see Note [Edit Symbolic Files] in README.md
+
+import functools
+
+import torch
+from torch import _C
+from torch.onnx import symbolic_helper, symbolic_opset9 as opset9
+from torch.onnx._internal import jit_utils, registration
+
+
+_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=15)
+
+
+@_onnx_symbolic("aten::__is_")
+def aten__is_(g: jit_utils.GraphContext, self, other):
+    if symbolic_helper._is_none(other):
+        if isinstance(self.type(), _C.OptionalType):
+            none = g.op("OptionalHasElement", self)
+            return g.op("Not", none)
+        else:
+            return g.op("Constant", value_t=torch.BoolTensor([0]))
+    return opset9.eq(g, self, other)
+
+
+@_onnx_symbolic("aten::__isnot_")
+@opset9.wrap_logical_op_with_negation  # type: ignore[has-type]
+def aten__isnot_(g: jit_utils.GraphContext, self, other):
+    return aten__is_(g, self, other)
+
+
+@_onnx_symbolic("aten::bernoulli")
+def bernoulli(g: jit_utils.GraphContext, input, p=None, generator=None, out=None):
+    if out is not None and not symbolic_helper._is_none(out):
+        symbolic_helper._unimplemented(
+            "Bernoulli", "out parameter is not supported for bernoulli", input
+        )
+    if generator is not None and not symbolic_helper._is_none(generator):
+        symbolic_helper._unimplemented(
+            "Bernoulli", "generator is not supported for bernoulli", input
+        )
+    if p is None or symbolic_helper._is_none(p):
+        return g.op("Bernoulli", input)
+    return opset9.bernoulli(g, input, p, generator, out)
+
+
+@_onnx_symbolic("prim::unchecked_cast")
+def prim_unchecked_cast(g: jit_utils.GraphContext, self):
+    # exists to refine the type of the Value
+    # if x is Optional[Tensor], unchecked_cast will cast
+    # x to Tensor, so the rest of the graph knows that x is a Tensor.
+    if isinstance(self.type(), _C.OptionalType):
+        return g.op("OptionalGetElement", self)
+
+    return self
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset16.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset16.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4a7baa78c2d574be7bd12869d9dc6b6a3ca1e87
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset16.py
@@ -0,0 +1,185 @@
+# mypy: allow-untyped-defs
+"""This file exports ONNX ops for opset 16.
+
+Note [ONNX Operators that are added/updated in opset 16]
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+https://github.com/onnx/onnx/blob/main/docs/Changelog.md#version-16-of-the-default-onnx-operator-set
+New operators:
+    GridSample https://github.com/onnx/onnx/pull/3557
+
+Updated operators:
+    Identity
+    If
+    LeakyRelu
+    Loop
+    PRelu
+    RoiAlign
+    Scan
+    ScatterElements
+    ScatterND
+    Where
+    GreaterOrEqual
+    LessOrEqual
+"""
+
+# EDITING THIS FILE? READ THIS FIRST!
+# see Note [Edit Symbolic Files] in README.md
+
+import functools
+
+import torch
+from torch.nn.functional import (
+    GRID_SAMPLE_INTERPOLATION_MODES,
+    GRID_SAMPLE_PADDING_MODES,
+)
+from torch.onnx import _type_utils, errors, symbolic_helper, utils
+from torch.onnx._internal import jit_utils, registration
+
+
+_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=16)
+
+
+# note (mkozuki): Why `grid_sampler` instead of `grid_sample`?
+# Because `torch.nn.functional.grid_sample` calls `torch.grid_sampler`.
+@_onnx_symbolic("aten::grid_sampler")
+@symbolic_helper.parse_args("v", "v", "i", "i", "b")
+def grid_sampler(
+    g: jit_utils.GraphContext,
+    input,
+    grid,
+    mode_enum,
+    padding_mode_enum,
+    align_corners,
+):
+    # Check the input and grid tensor rank beforehand.
+    if symbolic_helper._get_tensor_rank(input) == 5:
+        return symbolic_helper._onnx_unsupported("GridSample with 5D volumetric input")
+    mode_s = {v: k for k, v in GRID_SAMPLE_INTERPOLATION_MODES.items()}[mode_enum]  # type: ignore[call-arg]
+    padding_mode_s = {v: k for k, v in GRID_SAMPLE_PADDING_MODES.items()}[  # type: ignore[call-arg]
+        padding_mode_enum
+    ]
+    return g.op(
+        "GridSample",
+        input,
+        grid,
+        align_corners_i=int(align_corners),
+        mode_s=mode_s,
+        padding_mode_s=padding_mode_s,
+    )
+
+
+@_onnx_symbolic("aten::scatter_add")
+@symbolic_helper.parse_args("v", "i", "v", "v")
+def scatter_add(g: jit_utils.GraphContext, self, dim, index, src):
+    src_type = _type_utils.JitScalarType.from_value(
+        src, _type_utils.JitScalarType.UNDEFINED
+    )
+    src_sizes = symbolic_helper._get_tensor_sizes(src)
+    index_sizes = symbolic_helper._get_tensor_sizes(index)
+
+    if len(src_sizes) != len(index_sizes):
+        return symbolic_helper._unimplemented(
+            "scatter_add",
+            f"`index` ({index_sizes}) should have the same dimensionality as `src` ({src_sizes})",
+        )
+
+    # PyTorch only allows index shape <= src shape, so we can only consider
+    # taking index as subset size to src, like PyTorch does. When sizes for src
+    # and index are not matched or there are dynamic axes, we take index shape to
+    # slice src to accommodate.
+    if src_sizes != index_sizes or None in index_sizes:
+        adjusted_shape = g.op("Shape", index)
+        starts = g.op("Constant", value_t=torch.tensor([0] * len(index_sizes)))
+        src = g.op("Slice", src, starts, adjusted_shape)
+
+    src = symbolic_helper._maybe_get_scalar(src)
+    if symbolic_helper._is_value(src):
+        return g.op("ScatterElements", self, index, src, axis_i=dim, reduction_s="add")
+    else:
+        # Check if scalar "src" has same type as self (PyTorch allows different
+        # type for scalar src (but not when src is tensor)). If not, insert Cast node.
+        if _type_utils.JitScalarType.from_value(self) != src_type:
+            src = g.op(
+                "Cast",
+                src,
+                to_i=_type_utils.JitScalarType.from_value(self).onnx_type(),
+            )
+
+        return g.op(
+            "ScatterElements",
+            self,
+            index,
+            src,
+            axis_i=dim,
+            reduction_s="add",
+        )
+
+
+@_onnx_symbolic("aten::scatter_reduce")
+@symbolic_helper.parse_args("v", "i", "v", "v", "s", "b")
+def scatter_reduce(
+    g: jit_utils.GraphContext,
+    self: torch._C.Value,
+    dim: int,
+    index: torch._C.Value,
+    src: torch._C.Value,
+    reduce: str,
+    include_self: bool,
+):
+    if reduce == "mean":
+        raise errors.OnnxExporterError(
+            "ONNX does not support mean reduction for scatter_reduce"
+        )
+    if not include_self:
+        raise errors.OnnxExporterError(
+            "ONNX does not support include_self=False for scatter_reduce"
+        )
+
+    reduce_mode = {  # convert torch string name to onnx string name
+        "mean": "none",  # 'mean' doesn't support in ONNX 1.14 definition
+        "sum": "add",
+        "prod": "mul",
+        "amin": "min",
+        "amax": "max",
+    }
+    onnx_reduce = reduce_mode[reduce]
+
+    self_rank = g.op("Size", g.op("Shape", self))
+
+    # if self_rank == 0:  # assert (index_rank == 0 and rank_src == 0)
+    self_rank_is_zero = g.op(
+        "Equal", self_rank, g.op("Constant", value_t=torch.tensor(0, dtype=torch.int64))
+    )
+    if_op, (if_context, else_context), _ = jit_utils.add_op_with_blocks(
+        g, "If", self_rank_is_zero, n_blocks=2, outputs=3
+    )
+    neg_1 = if_context.op("Constant", value_t=torch.tensor([-1], dtype=torch.int64))
+
+    self_reshape = if_context.op("Reshape", self, neg_1)
+    utils._add_output_to_block(if_context.block, self_reshape)
+    index_reshape = if_context.op("Reshape", index, neg_1)
+    utils._add_output_to_block(if_context.block, index_reshape)
+    src_reshape = if_context.op("Reshape", src, neg_1)
+    utils._add_output_to_block(if_context.block, src_reshape)
+
+    self_identity = else_context.op("Identity", self)
+    utils._add_output_to_block(else_context.block, self_identity)
+    index_identitye = else_context.op("Identity", index)
+    utils._add_output_to_block(else_context.block, index_identitye)
+    src_identity = else_context.op("Identity", src)
+    utils._add_output_to_block(else_context.block, src_identity)
+
+    result = g.op("ScatterElements", *if_op, axis_i=dim, reduction_s=onnx_reduce)
+
+    # if self_rank == 0:
+    if_op, (if_context, else_context), _ = jit_utils.add_op_with_blocks(
+        g, "If", self_rank_is_zero, n_blocks=2, outputs=1
+    )
+    result_squeezed = if_context.op("Squeeze", result)
+    utils._add_output_to_block(if_context.block, result_squeezed)
+    result_identity = else_context.op("Identity", result)
+    utils._add_output_to_block(else_context.block, result_identity)
+    result_final = if_op.node().output()
+
+    return result_final
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset17.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset17.py
new file mode 100644
index 0000000000000000000000000000000000000000..0aca6634d2f69be0033fe71af678158a8c7b6130
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset17.py
@@ -0,0 +1,231 @@
+# mypy: allow-untyped-defs
+# mypy: disable-error-code=arg-type
+"""This file exports ONNX ops for opset 17.
+
+Note [ONNX Operators that are added/updated in opset 17]
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+https://github.com/onnx/onnx/blob/main/docs/Changelog.md#version-17-of-the-default-onnx-operator-set
+New operators:
+    BlackmanWindow
+    DFT
+    HammingWindow
+    HannWindow
+    LayerNormalization
+    MelWeightMatrix
+    STFT
+    SequenceMap
+"""
+
+import functools
+from typing import Optional, Sequence
+
+import torch
+from torch import _C
+from torch.onnx import _type_utils, errors, symbolic_helper
+from torch.onnx._internal import jit_utils, registration
+
+
+# EDITING THIS FILE? READ THIS FIRST!
+# see Note [Edit Symbolic Files] in README.md
+
+__all__ = ["layer_norm", "stft", "quantized_layer_norm"]
+
+_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=17)
+
+
+@_onnx_symbolic("aten::layer_norm")
+@symbolic_helper.parse_args("v", "is", "v", "v", "f", "none")
+def layer_norm(
+    g: jit_utils.GraphContext,
+    input: _C.Value,
+    normalized_shape: Sequence[int],
+    weight: _C.Value,
+    bias: _C.Value,
+    eps: float,
+    cudnn_enable: bool,
+):
+    # normalized_shape: input shape from an expected input of size
+    # axis: The first normalization dimension.
+    # layer_norm normalizes on the last D dimensions,
+    # where D is the size of normalized_shape
+    axis = -len(normalized_shape)
+    scalar_type = _type_utils.JitScalarType.from_value(
+        input, _type_utils.JitScalarType.FLOAT
+    )
+    dtype = scalar_type.dtype()
+    if symbolic_helper._is_none(weight):
+        weight_value = torch.ones(normalized_shape, dtype=dtype)
+        weight = g.op("Constant", value_t=weight_value)
+    if symbolic_helper._is_none(bias):
+        bias_value = torch.zeros(normalized_shape, dtype=dtype)
+        bias = g.op("Constant", value_t=bias_value)
+    return g.op(
+        "LayerNormalization",
+        input,
+        weight,
+        bias,
+        epsilon_f=eps,
+        axis_i=axis,
+    )
+
+
+@_onnx_symbolic("quantized::layer_norm")
+def quantized_layer_norm(
+    g: jit_utils.GraphContext,
+    x,
+    normalized_shape,
+    weight,
+    bias,
+    eps,
+    op_scale,
+    op_zero_point,
+):
+    x, _, _, _ = symbolic_helper.dequantize_helper(g, x)
+
+    output = layer_norm(g, x, normalized_shape, weight, bias, eps, False)
+
+    return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)
+
+
+def _compute_edge_sizes(n_fft, window_size):
+    """Helper function to compute the sizes of the edges (left and right)
+    of a given window centered within an FFT size."""
+    left = (n_fft - window_size) // 2
+    right = n_fft - left - window_size
+    return left, right
+
+
+@_onnx_symbolic("aten::stft")
+@symbolic_helper.parse_args("v", "i", "i", "i", "v", "b", "b", "b")
+def stft(
+    g: jit_utils.GraphContext,
+    input: _C.Value,
+    n_fft: int,
+    hop_length: Optional[int] = None,
+    win_length: Optional[int] = None,
+    window: Optional[_C.Value] = None,
+    normalized: bool = False,
+    onesided: Optional[bool] = True,
+    return_complex: Optional[bool] = False,
+) -> _C.Value:
+    """Associates `torch.stft` with the `STFT` ONNX operator.
+    Note that torch.stft calls _VF.stft, without centering or padding options.
+    Hence, this function does not contain these two arguments.
+    See torch.stft source code for more info.
+
+    Args:
+        g: Graph to write the ONNX representation into
+        input: Input tensor for the transformation
+        n_fft: FFT size
+        hop_length: Size of the hop. Defaults to `floot(n_fft // 4)`
+        win_length: Size of the analysis window. Defaults to `n_fft`
+        window: Analysis window. Defaults to a window of all ones
+        normalized: Whether to return a normalized STFT
+        onesided: Whether to return only half (+1) of the results, given the
+            symmetry of the STFT
+        return_complex: Whether to return the complex value (Note: Must be
+            `False` or `None`)
+
+    Returns:
+        op: Operator for torch.stft associated with STFT (ONNX)
+    """
+    # Checks
+    if return_complex:
+        raise errors.SymbolicValueError(
+            msg="STFT does not currently support complex types", value=input
+        )
+
+    # Get STFT sizes
+    frame_step_value = hop_length if hop_length is not None else n_fft // 4
+    frame_step_const = g.op(
+        "Constant", value_t=torch.tensor(frame_step_value, dtype=torch.int64)
+    )
+    frame_length_const = g.op(
+        "Constant", value_t=torch.tensor(n_fft, dtype=torch.int64)
+    )
+
+    # Pre-process input if needed
+    signal = input
+    signal_rank = symbolic_helper._get_tensor_rank(signal)
+    if signal_rank == 1:
+        # Add batch dimension
+        signal = g.op(
+            "Unsqueeze",
+            signal,
+            g.op("Constant", value_t=torch.tensor([0], dtype=torch.int64)),
+        )
+    elif signal_rank is None or signal_rank > 2:
+        raise errors.SymbolicValueError(
+            msg="STFT can only take inputs of 1 [signal] or 2 [batch, signal] dimensions. "
+            f"Current rank of signal is {signal_rank}, please reduce it.",
+            value=input,
+        )
+
+    # Get window and make sure it's the same size as `win_length` or `n_fft`
+    n_win = symbolic_helper._get_tensor_dim_size(window, dim=0)
+    if n_win is not None:
+        win_length_default = win_length if win_length else n_fft
+        assert n_win == win_length_default, (
+            "Analysis window size must equal `win_length` or `n_fft`. "
+            f"Please, set `win_length` or `n_fft` to match `window` size ({n_win})",
+        )
+
+        # Center window around zeros if needed (required by ONNX's STFT)
+        if n_win < n_fft:
+            left, right = _compute_edge_sizes(n_fft, n_win)
+            left_win = g.op("Constant", value_t=torch.zeros(left))
+            right_win = g.op("Constant", value_t=torch.zeros(right))
+            window = g.op("Concat", left_win, window, right_win, axis_i=0)
+
+    # Create window, if needed
+    if symbolic_helper._is_none(window):
+        if win_length:
+            if win_length > n_fft:
+                raise errors.SymbolicValueError(
+                    msg="The analysis window can't be longer than the size of the FFT. "
+                    f"Please set `win_length` ({win_length}) to `n_fft` ({n_fft}) or less.",
+                    value=input,
+                )
+
+            # Center window, if needed
+            left, right = _compute_edge_sizes(n_fft, win_length)
+            torch_window = torch.hstack(
+                (torch.zeros(left), torch.ones(win_length), torch.zeros(right))
+            )
+        else:
+            # Rectangle window
+            torch_window = torch.ones(n_fft)
+        assert torch_window.shape[0] == n_fft
+        window = g.op("Constant", value_t=torch_window)
+    window = g.op(
+        "Cast", window, to_i=_type_utils.JitScalarType.from_value(signal).onnx_type()
+    )
+
+    # Run STFT
+    result = g.op(
+        "STFT",
+        signal,
+        frame_step_const,
+        window,
+        frame_length_const,
+        onesided_i=1 if onesided is None or onesided else 0,
+    )
+
+    # Transpose to mimic torch.stft's behavior
+    result = g.op("Transpose", result, perm_i=[0, 2, 1, 3])
+
+    # Remove batch dimension, if needed
+    if signal_rank == 1:
+        result = g.op(
+            "Squeeze",
+            result,
+            g.op("Constant", value_t=torch.tensor([0], dtype=torch.int64)),
+        )
+
+    # Normalize, if needed
+    if normalized:
+        sqrt_nfft = torch.sqrt(torch.tensor(n_fft, dtype=signal.type().dtype()))
+        result = g.op("Div", result, g.op("Constant", value_t=sqrt_nfft))
+
+    return result
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset18.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset18.py
new file mode 100644
index 0000000000000000000000000000000000000000..d28fadc1bab1de5b21366c4bd5aa855cb33938b8
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset18.py
@@ -0,0 +1,265 @@
+# mypy: allow-untyped-defs
+"""This file exports ONNX ops for opset 18.
+
+Note [ONNX Operators that are added/updated in opset 18]
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+https://github.com/onnx/onnx/blob/main/docs/Changelog.md#version-18-of-the-default-onnx-operator-set
+New operators:
+    BitwiseAnd
+    CenterCropPad
+    Col2Im
+    Mish
+    OptionalGetElement
+    OptionalHasElement
+    Pad
+    Resize
+    ScatterElements
+    ScatterND
+    Split
+"""
+
+import functools
+from typing import List, Optional, Sequence, Tuple
+
+import torch
+from torch import _C
+from torch.onnx import _type_utils, symbolic_helper, symbolic_opset9 as opset9
+from torch.onnx._internal import jit_utils, registration
+
+
+# EDITING THIS FILE? READ THIS FIRST!
+# see Note [Edit Symbolic Files] in symbolic_helper.py
+
+__all__ = [
+    "col2im",
+]
+
+_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=18)
+
+
+@_onnx_symbolic("aten::__and_")
+@_onnx_symbolic("aten::bitwise_and")
+def __and_(g: jit_utils.GraphContext, self, other):
+    # do type promotion (scalars don't seem to apply)
+    args = [self, other]
+    # type promotion doesn't happen with torch.bitwise_and(tensor, scalar)
+    prom_args = [arg for arg in args if symbolic_helper._get_tensor_rank(arg)]
+    if len(prom_args) == 0:
+        prom_args = args
+    promotion_jit_type = symbolic_helper._type_promote_from_values(*prom_args)
+    self = symbolic_helper._maybe_cast_to_type(g, self, promotion_jit_type)
+    other = symbolic_helper._maybe_cast_to_type(g, other, promotion_jit_type)
+    if promotion_jit_type == _type_utils.JitScalarType.BOOL:
+        return g.op("And", self, other)
+    return g.op("BitwiseAnd", self, other)
+
+
+@_onnx_symbolic("aten::col2im")
+@symbolic_helper.parse_args("v", "v", "v", "is", "is", "is")
+def col2im(
+    g,
+    input: _C.Value,
+    output_size: _C.Value,
+    kernel_size: _C.Value,
+    dilation: Sequence[int],
+    padding: Sequence[int],
+    stride: Sequence[int],
+):
+    # convert [i0, i1, ..., in] into [i0, i0, i1, i1, ..., in, in]
+    adjusted_padding = []
+    for pad in padding:
+        for _ in range(2):
+            adjusted_padding.append(pad)
+
+    num_dimensional_axis = symbolic_helper._get_tensor_sizes(output_size)[0]
+    if not adjusted_padding:
+        adjusted_padding = [0, 0] * num_dimensional_axis
+
+    if not dilation:
+        dilation = [1] * num_dimensional_axis
+
+    if not stride:
+        stride = [1] * num_dimensional_axis
+
+    return g.op(
+        "Col2Im",
+        input,
+        output_size,
+        kernel_size,
+        dilations_i=dilation,
+        pads_i=adjusted_padding,
+        strides_i=stride,
+    )
+
+
+@_onnx_symbolic(
+    "aten::mean", decorate=[symbolic_helper._apply_params("ReduceMean", "mean")]
+)
+@_onnx_symbolic(
+    "aten::prod",
+    decorate=[
+        symbolic_helper._apply_params(
+            "ReduceProd", "prod", allow_multi_dim_support=False
+        )
+    ],
+)
+def _reduce_with_dtype(onnx_op: str, name: str, allow_multi_dim_support: bool = True):
+    return symbolic_helper._reduce_with_dtype_helper(
+        onnx_op, name, allow_multi_dim_support
+    )
+
+
+@_onnx_symbolic("aten::native_layer_norm")
+@symbolic_helper.quantized_args(True, False, False, False)
+@symbolic_helper.parse_args("v", "is", "v", "v", "f")
+def _native_layer_norm(
+    g: jit_utils.GraphContext,
+    input: _C.Value,
+    normalized_shape: Sequence[int],
+    weight: _C.Value,
+    bias: _C.Value,
+    eps: float,
+) -> Tuple[_C.Value, _C.Value, _C.Value]:
+    return opset9.native_layer_norm(g, input, normalized_shape, weight, bias, eps)
+
+
+@_onnx_symbolic("aten::glu")
+@symbolic_helper.parse_args("v", "i")
+def _glu(g: jit_utils.GraphContext, input, dim):
+    dim_size = symbolic_helper._get_tensor_dim_size(input, dim)
+    if dim_size is not None:
+        assert dim_size % 2 == 0
+
+    first, second = g.op("Split", input, axis_i=dim, num_outputs_i=2, outputs=2)
+    return g.op("Mul", first, g.op("Sigmoid", second))
+
+
+@_onnx_symbolic("aten::max")
+# torch.max (same for torch.min) actually has two interfaces smashed together:
+# torch.max(x, dim, keepdim) and torch.max(x, y)
+# TODO(justinchuby): Support multiple quantized args in output
+def max(g: jit_utils.GraphContext, self, dim_or_y=None, keepdim=None):
+    return symbolic_helper._max_helper(g, self, dim_or_y, keepdim)
+
+
+@_onnx_symbolic("aten::maximum")
+@symbolic_helper.quantized_args(True, True)
+def maximum(g: jit_utils.GraphContext, input, other):
+    return max(g, input, dim_or_y=other)
+
+
+@_onnx_symbolic("aten::min")
+# TODO(justinchuby): Support multiple quantized args in output
+def min(g: jit_utils.GraphContext, self, dim_or_y=None, keepdim=None):
+    return symbolic_helper._min_helper(g, self, dim_or_y, keepdim)
+
+
+@_onnx_symbolic("aten::minimum")
+@symbolic_helper.quantized_args(True, True)
+def minimum(g: jit_utils.GraphContext, input, other):
+    return min(g, input, dim_or_y=other)
+
+
+@_onnx_symbolic("aten::amax")
+@symbolic_helper.quantized_args(True)
+@symbolic_helper.parse_args("v", "is", "i")
+def amax(g: jit_utils.GraphContext, self, dim, keepdim):
+    axes = g.op("Constant", value_t=torch.tensor(dim, dtype=torch.long))
+    return g.op("ReduceMax", self, axes, keepdims_i=keepdim)
+
+
+@_onnx_symbolic("aten::amin")
+@symbolic_helper.quantized_args(True)
+@symbolic_helper.parse_args("v", "is", "i")
+def amin(g: jit_utils.GraphContext, self, dim, keepdim):
+    axes = g.op("Constant", value_t=torch.tensor(dim, dtype=torch.long))
+    return g.op("ReduceMin", self, axes, keepdims_i=keepdim)
+
+
+@_onnx_symbolic("aten::aminmax")
+@symbolic_helper.quantized_args(True)
+@symbolic_helper.parse_args("v", "v", "i")
+def aminmax(g: jit_utils.GraphContext, self, dim, keepdim):
+    if not symbolic_helper._is_none(dim):
+        dim = symbolic_helper._get_const(dim, "i", "dim")
+        axes = g.op("Constant", value_t=torch.tensor([dim], dtype=torch.long))
+        return g.op("ReduceMin", self, axes, keepdims_i=keepdim), g.op(
+            "ReduceMax", self, axes, keepdims_i=keepdim
+        )
+    else:
+        return g.op("ReduceMin", self, keepdims_i=keepdim), g.op(
+            "ReduceMax", self, keepdims_i=keepdim
+        )
+
+
+@_onnx_symbolic("aten::var_mean")
+def _var_mean(g: jit_utils.GraphContext, input, *args):
+    if len(args) == 1:
+        return symbolic_helper._var_mean_helper(g, input, None, args[0], None)
+    else:
+        return symbolic_helper._var_mean_helper(g, input, *args)
+
+
+@_onnx_symbolic("aten::logsumexp")
+@symbolic_helper.parse_args("v", "is", "i")
+def _logsumexp(g: jit_utils.GraphContext, input, dim, keepdim):
+    if dim is None:
+        return g.op("ReduceLogSumExp", input, keepdims_i=0)
+    else:
+        axes = g.op("Constant", value_t=torch.tensor(dim, dtype=torch.long))
+        return g.op("ReduceLogSumExp", input, axes, keepdims_i=keepdim)
+
+
+@_onnx_symbolic("aten::linalg_matrix_norm")
+@symbolic_helper.parse_args("v", "v", "is", "b", "v")
+def _linalg_matrix_norm(
+    g: jit_utils.GraphContext,
+    self: torch._C.Value,
+    ord: torch._C.Value,
+    dim: List[int],
+    keepdim: bool,
+    dtype: torch._C.Value,
+):
+    return opset9.linalg_matrix_norm(g, self, ord, dim, keepdim, dtype)
+
+
+@_onnx_symbolic("aten::embedding_bag")
+@symbolic_helper.parse_args("v", "v", "v", "i", "i", "i", "v", "i", "i")
+def embedding_bag(
+    g: jit_utils.GraphContext,
+    embedding_matrix,
+    indices,
+    offsets,
+    scale_grad_by_freq,
+    mode,
+    sparse,
+    per_sample_weights,
+    include_last_offset,
+    padding_idx,
+):
+    return symbolic_helper._embedding_bag_helper(
+        g,
+        embedding_matrix,
+        indices,
+        offsets,
+        scale_grad_by_freq,
+        mode,
+        sparse,
+        per_sample_weights,
+        include_last_offset,
+        padding_idx,
+    )
+
+
+@_onnx_symbolic("aten::linalg_vector_norm")
+@symbolic_helper.parse_args("v", "f", "is", "b", "v")
+def linalg_vector_norm(
+    g: jit_utils.GraphContext,
+    self: torch._C.Value,
+    ord: float,
+    dim: Optional[Sequence[int]],
+    keepdim: bool,
+    dtype: torch._C.Value,
+):
+    return symbolic_helper._linalg_vector_norm_helper(g, self, ord, dim, keepdim, dtype)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset19.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset19.py
new file mode 100644
index 0000000000000000000000000000000000000000..a97dda26f81f5562fc26a1d4f8b54e6460695113
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset19.py
@@ -0,0 +1,33 @@
+"""This file exports ONNX ops for opset 19.
+
+Note [ONNX Operators that are added/updated in opset 19]
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+https://github.com/onnx/onnx/blob/main/docs/Changelog.md#version-19-of-the-default-onnx-operator-set
+New operators:
+AveragePool
+Cast
+CastLike
+Constant
+DeformConv
+DequantizeLinear
+Equal
+Identity
+If
+Loop
+Pad
+QuantizeLinear
+Reshape
+Resize
+Scan
+Shape
+Size
+"""
+
+from typing import List
+
+
+# EDITING THIS FILE? READ THIS FIRST!
+# see Note [Edit Symbolic Files] in symbolic_helper.py
+
+__all__: List[str] = []
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset20.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset20.py
new file mode 100644
index 0000000000000000000000000000000000000000..d96f770ca11e26db582768332b0860c7c94558ae
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset20.py
@@ -0,0 +1,92 @@
+# mypy: allow-untyped-defs
+"""This file exports ONNX ops for opset 20.
+
+Note [ONNX Operators that are added/updated in opset 20]
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+https://github.com/onnx/onnx/blob/main/docs/Changelog.md#version-20-of-the-default-onnx-operator-set
+New operators:
+    AffineGrid
+    ConstantOfShape
+    DFT
+    Gelu
+    GridSample
+    ImageDecoder
+    IsInf
+    IsNaN
+    ReduceMax
+    ReduceMin
+    RegexFullMatch
+    StringConcat
+    StringSplit
+"""
+
+import functools
+
+import torch.nn.functional as F
+from torch import _C
+from torch.onnx import symbolic_helper
+from torch.onnx._internal import jit_utils, registration
+
+
+# EDITING THIS FILE? READ THIS FIRST!
+# see Note [Edit Symbolic Files] in symbolic_helper.py
+
+__all__ = ["_grid_sampler", "_affine_grid_generator", "gelu"]
+
+
+def convert_grid_sample_mode(mode_s):
+    return (
+        "linear" if mode_s == "bilinear" else "cubic" if mode_s == "bicubic" else mode_s
+    )
+
+
+_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=20)
+
+
+@_onnx_symbolic("aten::grid_sampler")
+@symbolic_helper.parse_args("v", "v", "i", "i", "b")
+def _grid_sampler(
+    g: jit_utils.GraphContext,
+    input: _C.Value,
+    grid: _C.Value,
+    mode_enum: int,
+    padding_mode_enum: int,
+    align_corners: bool,
+):
+    mode_s = {v: k for k, v in F.GRID_SAMPLE_INTERPOLATION_MODES.items()}[mode_enum]  # type: ignore[call-arg, index]
+    # mode string changes at https://onnx.ai/onnx/operators/text_diff_GridSample_16_20.html
+    mode_s = convert_grid_sample_mode(mode_s)
+    padding_mode_s = {v: k for k, v in F.GRID_SAMPLE_PADDING_MODES.items()}[  # type: ignore[call-arg, index]
+        padding_mode_enum  # type: ignore[index]
+    ]
+    return g.op(
+        "GridSample",
+        input,
+        grid,
+        align_corners_i=int(align_corners),
+        mode_s=mode_s,
+        padding_mode_s=padding_mode_s,
+    )
+
+
+@_onnx_symbolic("aten::affine_grid_generator")
+@symbolic_helper.parse_args("v", "v", "b")
+def _affine_grid_generator(
+    g: jit_utils.GraphContext,
+    theta: _C.Value,
+    size: _C.Value,
+    align_corners: bool,
+):
+    return g.op(
+        "AffineGrid",
+        theta,
+        size,
+        align_corners_i=int(align_corners),
+    )
+
+
+@_onnx_symbolic("aten::gelu")
+@symbolic_helper.parse_args("v", "s")
+def gelu(g: jit_utils.GraphContext, self: _C.Value, approximate: str = "none"):
+    return g.op("Gelu", self, approximate_s=approximate)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset7.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset7.py
new file mode 100644
index 0000000000000000000000000000000000000000..c647ead4e2975e0497a7de9b8b5801617443ab27
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset7.py
@@ -0,0 +1,67 @@
+# mypy: allow-untyped-defs
+"""
+Note [ONNX operators that are added/updated from opset 7 to opset 8]
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+New operators:
+  Expand
+
+Updated operators:
+  Min, Max, Sum, Mean: supports multidirectional broadcasting.
+  MaxPool: added optional indices output.
+  Scan
+"""
+
+import functools
+import warnings
+
+from torch.onnx import symbolic_helper, symbolic_opset9 as opset9
+from torch.onnx._internal import jit_utils, registration
+
+
+_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=7)
+
+block_listed_operators = (
+    "scan",
+    "expand",
+    "expand_as",
+    "meshgrid",
+    "adaptive_max_pool1d",
+    "adaptive_max_pool2d",
+    "adaptive_max_pool3d",
+    "max_pool1d_with_indices",
+    "max_pool2d_with_indices",
+    "max_pool3d_with_indices",
+)
+
+
+# NOTE: max, min, sum, mean: broadcasting is not supported in opset 7.
+# torch.max (same for torch.min) actually has two interfaces smashed together:
+# torch.max(x, dim, keepdim) and torch.max(x, y)
+@_onnx_symbolic("aten::max")
+def max(g: jit_utils.GraphContext, self, dim_or_y=None, keepdim=None):
+    # torch.max(input, other)
+    if keepdim is None and dim_or_y is not None:
+        warnings.warn(
+            "Multidirectional broadcasting is not supported in opset 7. "
+            "This might cause the onnx model to be incorrect, if inputs to max operators "
+            "have different shapes"
+        )
+    return opset9.max(g, self, dim_or_y, keepdim)
+
+
+@_onnx_symbolic("aten::min")
+def min(g: jit_utils.GraphContext, self, dim_or_y=None, keepdim=None):
+    # torch.min(input, other)
+    if keepdim is None and dim_or_y is not None:
+        warnings.warn(
+            "Multidirectional broadcasting is not supported in opset 7. "
+            "This might cause the onnx model to be incorrect, if inputs to min operators "
+            "have different shapes"
+        )
+    return opset9.min(g, self, dim_or_y, keepdim)
+
+
+for block_listed_op in block_listed_operators:
+    _onnx_symbolic(f"aten::{block_listed_op}")(
+        symbolic_helper._block_list_in_opset(block_listed_op)
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset8.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset8.py
new file mode 100644
index 0000000000000000000000000000000000000000..41abf46be2a0af9facde773d026efacb32ac51d7
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset8.py
@@ -0,0 +1,463 @@
+# mypy: allow-untyped-defs
+"""
+Note [ONNX operators that are added/updated from opset 8 to opset 9]
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+New operators:
+    Compress
+    ConstantOfShape
+    EyeLike
+    MaxUnpool
+    OneHot
+    Sinh
+    Cosh
+    Asinh
+    Acosh
+    Atanh
+    Shrink
+    IsNaN
+    Sign
+    Erf
+    Scatter
+    Where
+    NonZero
+    TfIdfVectorizer
+    MeanVarianceNormalization
+
+Updated operators:
+    BatchNormalization: removed spatial attribute.
+    Greater, Less, Constant, MatMul, PRelu, Gemm, Flatten: more data types{integers} supported.
+    Cast: more data types{string} supported.
+    Upsample: moved scales from attribute to input.
+    Scan
+"""
+
+import functools
+import warnings
+
+import torch
+from torch._C import _onnx as _C_onnx
+from torch.onnx import _type_utils, errors, symbolic_helper, symbolic_opset9 as opset9
+from torch.onnx._internal import jit_utils, registration
+
+
+_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=8)
+
+block_listed_operators = (
+    "nonzero",
+    "where",
+    "scatter",
+    "scatter_add",
+    "erf",
+    "sign",
+    "isnan",
+    "gather",
+    "arange",
+    "masked_fill",
+    "index_fill",
+    "index_copy",
+    "repeat_interleave",
+    "any",
+    "all",
+)
+
+for block_listed_op in block_listed_operators:
+    _onnx_symbolic(f"aten::{block_listed_op}")(
+        symbolic_helper._block_list_in_opset(block_listed_op)
+    )
+
+
+@_onnx_symbolic(
+    "aten::upsample_nearest1d",
+    decorate=[symbolic_helper._apply_params("upsample_nearest1d", 3, "nearest")],
+)
+@_onnx_symbolic(
+    "aten::upsample_nearest2d",
+    decorate=[symbolic_helper._apply_params("upsample_nearest2d", 4, "nearest")],
+)
+@_onnx_symbolic(
+    "aten::upsample_nearest3d",
+    decorate=[symbolic_helper._apply_params("upsample_nearest3d", 5, "nearest")],
+)
+@_onnx_symbolic(
+    "aten::upsample_linear1d",
+    decorate=[symbolic_helper._apply_params("upsample_linear1d", 3, "linear")],
+)
+@_onnx_symbolic(
+    "aten::upsample_bilinear2d",
+    decorate=[symbolic_helper._apply_params("upsample_bilinear2d", 4, "linear")],
+)
+@_onnx_symbolic(
+    "aten::upsample_trilinear3d",
+    decorate=[symbolic_helper._apply_params("upsample_trilinear3d", 5, "linear")],
+)
+def _interpolate(name, dim, interpolate_mode):
+    def symbolic_fn(g, input, output_size, *args):
+        scales, align_corners = symbolic_helper._get_interpolate_attributes(
+            g, interpolate_mode, args
+        )
+        symbolic_helper._interpolate_warning(interpolate_mode)
+        align_corners = symbolic_helper._maybe_get_scalar(align_corners)
+        if align_corners:
+            return symbolic_helper._unimplemented(name, "align_corners == True", input)
+        output_size = symbolic_helper._maybe_get_const(output_size, "is")
+        if symbolic_helper._is_value(output_size):
+            return symbolic_helper._unimplemented(
+                name, "torch._C.Value (output_size) indexing"
+            )
+        if scales is None:
+            scales = [
+                1.0
+                if i < 2
+                else float(output_size[-(dim - i)])
+                / float(input.type().sizes()[-(dim - i)])
+                for i in range(0, dim)
+            ]
+        return g.op("Upsample", input, mode_s=interpolate_mode, scales_f=scales)
+
+    return symbolic_fn
+
+
+@_onnx_symbolic("aten::__interpolate")
+def __interpolate(
+    g: jit_utils.GraphContext,
+    input,
+    size,
+    scale_factor,
+    mode,
+    align_corners,
+    recompute_scale_factor,
+    antialias,
+):
+    align_corners = symbolic_helper._maybe_get_const(align_corners, "b")
+    if not symbolic_helper._is_none(align_corners) and align_corners:
+        return symbolic_helper._unimplemented("interpolate", "align_corners == True")
+
+    if not symbolic_helper._is_none(scale_factor) and symbolic_helper._is_value(
+        scale_factor
+    ):
+        return symbolic_helper._unimplemented(
+            "interpolate", "dynamic scales in opset 8"
+        )
+
+    if not symbolic_helper._is_none(size) and symbolic_helper._is_value(size):
+        return symbolic_helper._unimplemented("interpolate", "dynamic size in opset 8")
+
+    scales, mode = symbolic_helper._interpolate_get_scales_and_mode(
+        g, input, size, scale_factor, mode, align_corners
+    )
+    return g.op("Upsample", input, mode_s=mode, scales_f=scales)
+
+
+# NOTE: We should create a wrapper for this kind of operation, after resolving the shape/type propagation
+#       issue for "cast" operators. Some symbolic functions depend on shape information of input tensor, which
+#       is lost after casting.
+def _try_cast_integer_to_float(g: jit_utils.GraphContext, *args):
+    floating_scalar_types = {
+        _type_utils.JitScalarType.HALF,
+        _type_utils.JitScalarType.FLOAT,
+        _type_utils.JitScalarType.DOUBLE,
+    }
+    old_type = None
+    # Cast the input tensor to Float if its scalarType is known and is not floating number.
+    # If casting is performed, return the old scalarType, otherwise return None.
+    arg0_type = _type_utils.JitScalarType.from_value(
+        args[0], _type_utils.JitScalarType.UNDEFINED
+    )
+    if arg0_type != _type_utils.JitScalarType.UNDEFINED:
+        old_type = arg0_type
+        if old_type not in floating_scalar_types:
+            old_type = old_type.scalar_name()  # type: ignore[assignment]
+            args = tuple(
+                g.op("Cast", arg, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+                for arg in args
+            )
+        else:
+            return (None,) + args
+    else:
+        warnings.warn(
+            "Only floating datatype is supported for these operators: "
+            "{Greater, Less, MatMul, PRelu, Gemm, Flatten}. This might cause "
+            "the onnx model to be incorrect, if inputs have integer datatypes."
+        )
+    return (old_type,) + args
+
+
+def _cast_to_type(g: jit_utils.GraphContext, input, to_type):
+    if to_type is None:
+        return input
+    return getattr(opset9, f"_cast_{to_type}")(g, input, False)
+
+
+def _comparison_operator(g: jit_utils.GraphContext, input, other, op_name):
+    other = symbolic_helper._maybe_get_scalar(other)
+    other = symbolic_helper._if_scalar_type_as(other, input)
+    _, input, other = _try_cast_integer_to_float(g, input, other)
+    return g.op(op_name, input, other)
+
+
+# NOTE: For symbolics {gt, lt, bmm, matmul, prelu, mm, addmm, view, flatten},
+#       integer input type not supported in opset8. Cast to float if possible.
+@_onnx_symbolic("aten::gt")
+def gt(g: jit_utils.GraphContext, input, other):
+    return _comparison_operator(g, input, other, "Greater")
+
+
+@_onnx_symbolic("aten::lt")
+def lt(g: jit_utils.GraphContext, input, other):
+    return _comparison_operator(g, input, other, "Less")
+
+
+@_onnx_symbolic("aten::bmm")
+def bmm(g: jit_utils.GraphContext, self, other):
+    if symbolic_helper._try_get_scalar_type(self):
+        old_type, self, other = _try_cast_integer_to_float(g, self, other)
+        return _cast_to_type(g, g.op("MatMul", self, other), old_type)
+    else:
+        return g.op("MatMul", self, other)
+
+
+@_onnx_symbolic("aten::matmul")
+def matmul(g: jit_utils.GraphContext, self, other):
+    return bmm(g, self, other)
+
+
+@_onnx_symbolic("aten::prelu")
+def prelu(g: jit_utils.GraphContext, self, weight):
+    self_rank = symbolic_helper._get_tensor_rank(self)
+    weight_sizes = symbolic_helper._get_tensor_sizes(weight)
+    if self_rank is not None and self_rank > 2:
+        weight = g.op("Unsqueeze", weight, axes_i=list(range(1, self_rank - 1)))
+    elif self_rank == 0 and weight_sizes == [1]:
+        # self and weight are both scalar but weight has rank == 1, squeeze weight.
+        weight = symbolic_helper._squeeze_helper(g, weight, [0])
+    if symbolic_helper._try_get_scalar_type(self):
+        old_type, self, weight = _try_cast_integer_to_float(g, self, weight)
+        return _cast_to_type(g, g.op("PRelu", self, weight), old_type)
+    else:
+        return g.op("PRelu", self, weight)
+
+
+@_onnx_symbolic("aten::mm")
+def mm(g: jit_utils.GraphContext, self, other):
+    # Create a dummy C tensor. Only needed for API purposes, the value is
+    # since beta = 0
+    scalar_type = symbolic_helper._try_get_scalar_type(self, other)
+    if scalar_type is None:
+        raise errors.SymbolicValueError(
+            "mm can only operate on tensors with known types", self
+        )
+    zero_constant = g.op(
+        "Constant",
+        value_t=torch.tensor([0], dtype=scalar_type.dtype()),
+    )
+
+    if symbolic_helper._try_get_scalar_type(self):
+        old_type, self, other, zero_constant = _try_cast_integer_to_float(
+            g, self, other, zero_constant
+        )
+        return _cast_to_type(
+            g,
+            g.op("Gemm", self, other, zero_constant, beta_f=0.0, alpha_f=1.0),
+            old_type,
+        )
+    return g.op("Gemm", self, other, zero_constant, beta_f=0.0, alpha_f=1.0)
+
+
+@_onnx_symbolic("aten::addmm")
+@symbolic_helper.parse_args("v", "v", "v", "t", "t")
+def addmm(g: jit_utils.GraphContext, self, mat1, mat2, beta, alpha):
+    if symbolic_helper._try_get_scalar_type(self):
+        old_type, self, mat1, mat2 = _try_cast_integer_to_float(g, self, mat1, mat2)
+        return _cast_to_type(
+            g,
+            g.op(
+                "Gemm",
+                mat1,
+                mat2,
+                self,
+                beta_f=symbolic_helper._scalar(beta),
+                alpha_f=symbolic_helper._scalar(alpha),
+            ),
+            old_type,
+        )
+    else:
+        return g.op(
+            "Gemm",
+            mat1,
+            mat2,
+            self,
+            beta_f=symbolic_helper._scalar(beta),
+            alpha_f=symbolic_helper._scalar(alpha),
+        )
+
+
+@_onnx_symbolic("aten::flatten")
+def flatten(g: jit_utils.GraphContext, input, start_dim, end_dim):
+    start_dim_i = symbolic_helper._get_const(start_dim, "i", "start_dim")
+    end_dim_i = symbolic_helper._get_const(end_dim, "i", "end_dim")
+
+    dim = input.type().dim()
+    if end_dim_i < 0:
+        end_dim_i = dim + end_dim_i
+    # use ONNX's Flatten operator for cases where the output shape is 2D
+    if start_dim_i == 1 and end_dim_i == dim - 1:
+        if symbolic_helper._try_get_scalar_type(input):
+            old_type, input = _try_cast_integer_to_float(g, input)
+            return _cast_to_type(
+                g, g.op("Flatten", input, axis_i=start_dim_i), old_type
+            )
+        else:
+            return g.op("Flatten", input, axis_i=start_dim_i)
+    if start_dim_i == 0 and end_dim_i == dim - 2:
+        if symbolic_helper._try_get_scalar_type(input):
+            old_type, input = _try_cast_integer_to_float(g, input)
+            return _cast_to_type(
+                g, g.op("Flatten", input, axis_i=end_dim_i + 1), old_type
+            )
+        else:
+            return g.op("Flatten", input, axis_i=end_dim_i + 1)
+
+    return opset9.flatten(g, input, start_dim, end_dim)
+
+
+def _constant_fill(g: jit_utils.GraphContext, sizes, dtype: int, const_value):
+    if dtype is None:
+        scalar_type = _type_utils.JitScalarType.FLOAT
+    else:
+        scalar_type = _type_utils.JitScalarType(dtype)
+    if not scalar_type.dtype().is_floating_point:
+        result = g.op(
+            "ConstantFill",
+            sizes,
+            dtype_i=_type_utils.JitScalarType.FLOAT.onnx_type(),
+            input_as_shape_i=1,
+            value_f=const_value,
+        )
+        return g.op("Cast", result, to_i=scalar_type.onnx_type())
+    else:
+        return g.op(
+            "ConstantFill",
+            sizes,
+            dtype_i=scalar_type.onnx_type(),
+            input_as_shape_i=1,
+            value_f=const_value,
+        )
+
+
+@_onnx_symbolic("aten::empty")
+@symbolic_helper.parse_args("v", "i", "v", "v", "v", "v")
+def empty(
+    g: jit_utils.GraphContext,
+    sizes,
+    dtype,
+    layout,
+    device,
+    pin_memory=False,
+    memory_format=None,
+):
+    return zeros(g, sizes, dtype, layout, device, pin_memory)
+
+
+@_onnx_symbolic("aten::empty_like")
+@symbolic_helper.parse_args("v", "i", "v", "v", "v", "v")
+def empty_like(
+    g: jit_utils.GraphContext,
+    input,
+    dtype,
+    layout,
+    device,
+    pin_memory=False,
+    memory_format=None,
+):
+    return zeros_like(g, input, dtype, layout, device, pin_memory)
+
+
+@_onnx_symbolic("aten::zeros")
+@symbolic_helper.parse_args("v", "i", "v", "v", "v")
+def zeros(g: jit_utils.GraphContext, sizes, dtype, layout, device, pin_memory=False):
+    # NOTE: no way to set device and layout in ONNX, so we ignore it
+    return _constant_fill(g, sizes, dtype, 0)
+
+
+@_onnx_symbolic("aten::zeros_like")
+@symbolic_helper.parse_args("v", "i", "v", "v", "v", "v")
+def zeros_like(
+    g: jit_utils.GraphContext,
+    input,
+    dtype,
+    layout,
+    device,
+    pin_memory=False,
+    memory_format=None,
+):
+    shape = g.op("Shape", input)
+    return _constant_fill(g, shape, dtype, 0)
+
+
+@_onnx_symbolic("aten::ones")
+@symbolic_helper.parse_args("v", "i", "v", "v", "v")
+def ones(g: jit_utils.GraphContext, sizes, dtype, layout, device, pin_memory=False):
+    return _constant_fill(g, sizes, dtype, 1)
+
+
+@_onnx_symbolic("aten::ones_like")
+@symbolic_helper.parse_args("v", "i", "v", "v", "v", "v")
+def ones_like(
+    g: jit_utils.GraphContext,
+    input,
+    dtype,
+    layout,
+    device,
+    pin_memory=False,
+    memory_format=None,
+):
+    shape = g.op("Shape", input)
+    return _constant_fill(g, shape, dtype, 1)
+
+
+@_onnx_symbolic("aten::full")
+def full(
+    g: jit_utils.GraphContext, sizes, value, dtype, layout, device, pin_memory=False
+):
+    const_value = symbolic_helper._maybe_get_const(value, "t")
+    if symbolic_helper._is_value(const_value):
+        tmp = zeros(g, sizes, dtype, layout, device)
+        return opset9.add(g, tmp, value, g.op("Constant", value_t=torch.tensor(1)))
+    else:
+        dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+        return _constant_fill(g, sizes, dtype, const_value)
+
+
+@_onnx_symbolic("aten::full_like")
+@symbolic_helper.parse_args("v", "f", "i", "v", "v", "v", "v")
+def full_like(
+    g: jit_utils.GraphContext,
+    input,
+    fill_value,
+    dtype,
+    layout,
+    device,
+    pin_memory=False,
+    memory_format=None,
+):
+    shape = g.op("Shape", input)
+    return _constant_fill(g, shape, dtype, fill_value)
+
+
+@_onnx_symbolic("aten::repeat")
+def repeat(g: jit_utils.GraphContext, self, repeats):
+    if not symbolic_helper._is_value(repeats):
+        repeats = g.op("Constant", value_t=torch.LongTensor(repeats))
+    if symbolic_helper._is_packed_list(repeats):
+        repeat_size_len = len(symbolic_helper._unpack_list(repeats))
+    else:
+        const_repeats = symbolic_helper._maybe_get_const(repeats, "is")
+        repeat_size_len = len(const_repeats)
+    if self.isCompleteTensor():
+        sizes = self.type().sizes()
+        diff_dims = repeat_size_len - len(sizes)
+        if diff_dims > 0:
+            self = opset9.view(
+                g, self, g.op("Constant", value_t=torch.tensor([1] * diff_dims + sizes))
+            )
+    return g.op("Tile", self, repeats)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset9.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset9.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bcba2f93d04ae27709f7f0acc67f846acd0a01d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/symbolic_opset9.py
@@ -0,0 +1,6637 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+# mypy: disable-error-code=arg-type
+"""This file exports ONNX ops for opset 9.
+
+Opset 9 is supported by ONNX release 1.4.1
+release on 01/23/19
+"""
+
+from __future__ import annotations
+
+import builtins
+import functools
+import math
+import sys
+import warnings
+from typing import Callable, Sequence, TYPE_CHECKING
+
+import torch
+import torch._C._onnx as _C_onnx
+import torch.nn.modules.utils
+import torch.onnx
+from torch import _C
+
+# Monkey-patch graph manipulation methods on Graph, used for the ONNX symbolics
+from torch.onnx import _constants, _deprecation, _type_utils, errors, symbolic_helper
+from torch.onnx._globals import GLOBALS
+from torch.onnx._internal import jit_utils, registration
+
+
+if TYPE_CHECKING:
+    from torch.types import Number
+
+# EDITING THIS FILE? READ THIS FIRST!
+# see Note [Edit Symbolic Files] in README.md
+
+__all__ = [
+    "abs",
+    "acos",
+    "add",
+    "addcmul",
+    "addmm",
+    "alias",
+    "amax",
+    "amin",
+    "aminmax",
+    "arange",
+    "argmax",
+    "argmin",
+    "as_strided",
+    "as_tensor",
+    "asin",
+    "atan",
+    "atan2",
+    "baddbmm",
+    "batch_norm",
+    "bernoulli",
+    "bitwise_not",
+    "bitwise_or",
+    "bmm",
+    "broadcast_tensors",
+    "broadcast_to",
+    "bucketize",
+    "cat",
+    "cdist",
+    "ceil",
+    "clamp_max",
+    "clamp_min",
+    "clamp",
+    "clone",
+    "constant_pad_nd",
+    "contiguous",
+    "conv_tbc",
+    "conv_transpose1d",
+    "conv_transpose2d",
+    "conv_transpose3d",
+    "conv1d",
+    "conv2d",
+    "conv3d",
+    "convert_element_type",
+    "convolution",
+    "cos",
+    "cosine_similarity",
+    "cross",
+    "cumsum",
+    "detach",
+    "dim",
+    "div",
+    "dot",
+    "dropout",
+    "elu",
+    "embedding_bag",
+    "embedding",
+    "empty_like",
+    "empty",
+    "eq",
+    "erf",
+    "exp",
+    "expand_as",
+    "expand",
+    "eye",
+    "fill",
+    "flatten",
+    "floor_divide",
+    "floor",
+    "floordiv",
+    "frobenius_norm",
+    "full_like",
+    "full",
+    "gather",
+    "ge",
+    "gelu",
+    "get_pool_ceil_padding",
+    "glu",
+    "group_norm",
+    "gt",
+    "hann_window",
+    "hardshrink",
+    "hardsigmoid",
+    "hardswish",
+    "hardtanh",
+    "index_add",
+    "index_copy",
+    "index_fill",
+    "index_put",
+    "index_select",
+    "index",
+    "instance_norm",
+    "is_floating_point",
+    "is_pinned",
+    "isnan",
+    "item",
+    "kl_div",
+    "layer_norm",
+    "le",
+    "leaky_relu",
+    "lerp",
+    "lift",
+    "linalg_cross",
+    "linalg_matrix_norm",
+    "linalg_norm",
+    "linalg_vector_norm",
+    "linear",
+    "linspace",
+    "log_sigmoid",
+    "log_softmax",
+    "log",
+    "log10",
+    "log1p",
+    "log2",
+    "logical_and",
+    "logical_not",
+    "logical_or",
+    "logical_xor",
+    "logit",
+    "logsumexp",
+    "lstm_cell",
+    "lstm",
+    "lt",
+    "masked_fill",
+    "masked_fill_",
+    "matmul",
+    "max_pool1d_with_indices",
+    "max_pool2d_with_indices",
+    "max_pool3d_with_indices",
+    "max",
+    "maximum",
+    "meshgrid",
+    "min",
+    "minimum",
+    "mish",
+    "mm",
+    "movedim",
+    "mse_loss",
+    "mul",
+    "multinomial",
+    "mv",
+    "narrow",
+    "native_layer_norm",
+    "ne",
+    "neg",
+    "new_empty",
+    "new_full",
+    "new_ones",
+    "new_zeros",
+    "nonzero_numpy",
+    "nonzero",
+    "norm",
+    "numel",
+    "numpy_T",
+    "one_hot",
+    "ones_like",
+    "ones",
+    "onnx_placeholder",
+    "pad",
+    "pairwise_distance",
+    "permute",
+    "pixel_shuffle",
+    "pixel_unshuffle",
+    "pow",
+    "prelu",
+    "prim_constant_chunk",
+    "prim_constant_split",
+    "prim_constant",
+    "prim_data",
+    "prim_device",
+    "prim_dtype",
+    "prim_if",
+    "prim_layout",
+    "prim_list_construct",
+    "prim_list_unpack",
+    "prim_loop",
+    "prim_max",
+    "prim_min",
+    "prim_shape",
+    "prim_tolist",
+    "prim_tuple_construct",
+    "prim_type",
+    "prim_unchecked_cast",
+    "prim_uninitialized",
+    "rand_like",
+    "rand",
+    "randint_like",
+    "randint",
+    "randn_like",
+    "randn",
+    "reciprocal",
+    "reflection_pad",
+    "relu",
+    "relu6",
+    "remainder",
+    "repeat_interleave",
+    "repeat",
+    "replication_pad",
+    "reshape_as",
+    "reshape",
+    "roll",
+    "rrelu",
+    "rsqrt",
+    "rsub",
+    "scalar_tensor",
+    "scatter_add",
+    "scatter",
+    "select",
+    "selu",
+    "sigmoid",
+    "sign",
+    "silu",
+    "sin",
+    "size",
+    "slice",
+    "softmax",
+    "softplus",
+    "softshrink",
+    "sort",
+    "split_with_sizes",
+    "split",
+    "sqrt",
+    "square",
+    "squeeze",
+    "stack",
+    "std_mean",
+    "std",
+    "sub",
+    "t",
+    "take",
+    "tan",
+    "tanh",
+    "tanhshrink",
+    "tensor",
+    "threshold",
+    "to",
+    "topk",
+    "transpose",
+    "true_divide",
+    "type_as",
+    "unbind",
+    "unfold",
+    "unsafe_chunk",
+    "unsafe_split_with_sizes",
+    "unsafe_split",
+    "unsqueeze",
+    "unsupported_complex_operators",
+    "noop_complex_operators",
+    "unused",
+    "var_mean",
+    "var",
+    "view_as",
+    "view",
+    "where",
+    "wrap_logical_op_with_cast_to",
+    "wrap_logical_op_with_negation",
+    "zeros_like",
+    "zeros",
+    "zero",
+]
+
+
+_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=9)
+
+
+def _export(name: str):
+    """Exports the function in the current global namespace."""
+
+    def wrapper(func):
+        globals()[name] = func
+        __all__.append(name)
+        return func
+
+    return wrapper
+
+
+def unused(g):
+    """Represents "missing" optional inputs."""
+    n = g.op("prim::Constant")
+    n.setType(_C.OptionalType.ofTensor())
+    return n
+
+
+@_onnx_symbolic("aten::_shape_as_tensor")
+def _shape_as_tensor(g: jit_utils.GraphContext, input):
+    return g.op("Shape", input)
+
+
+@_onnx_symbolic("aten::_reshape_from_tensor")
+def _reshape_from_tensor(g: jit_utils.GraphContext, input, shape):
+    if isinstance(shape, list):
+        shape = g.op("Concat", *shape, axis_i=0)
+    return reshape(g, input, shape)
+
+
+@_onnx_symbolic("aten::reshape")
+@symbolic_helper.quantized_args(True)
+def reshape(g: jit_utils.GraphContext, self, shape):
+    return symbolic_helper._reshape_helper(g, self, shape)
+
+
+@_onnx_symbolic("aten::reshape_as")
+@symbolic_helper.quantized_args(True)
+def reshape_as(g: jit_utils.GraphContext, self, other):
+    shape = g.op("Shape", other)
+    return reshape(g, self, shape)
+
+
+@_onnx_symbolic("aten::add")
+def add(g: jit_utils.GraphContext, self, other, alpha=None):
+    """
+    This function takes the add function and returns the corresponding ONNX operator.
+
+    This function is not meant to be called directly by the user.
+
+    Args:
+        g (GraphContext): The graph context.
+        self (Tensor): The first operand.
+        other (Tensor): The second operand.
+        alpha (float, optional): The scaling factor for the second operand. Defaults to None.
+
+    Returns:
+        ONNX operator.
+    """
+    if symbolic_helper._is_value(self) and symbolic_helper._is_tensor_list(self):
+        return symbolic_helper._onnx_opset_unsupported_detailed(
+            "Add", 9, 11, "Add between list of tensors not supported", self
+        )
+    if alpha and symbolic_helper._scalar(symbolic_helper._maybe_get_scalar(alpha)) != 1:
+        other = g.op("Mul", other, alpha)
+    return g.op("Add", self, other)
+
+
+@_onnx_symbolic("aten::sub")
+def sub(g: jit_utils.GraphContext, self, other, alpha=None):
+    """
+    Consumes sub function and returns the corresponding ONNX operator.
+
+    This function is not meant to be called directly by the user.
+
+    Args:
+        g (GraphContext): The graph context.
+        self (Tensor): The first operand.
+        other (Tensor): The second operand.
+        alpha (Optional[Tensor]): A scaling factor to apply to the second operand.
+            If `alpha` is not provided, it defaults to 1.
+
+    Returns:
+        ONNX operator
+    """
+    if alpha and symbolic_helper._scalar(symbolic_helper._maybe_get_scalar(alpha)) != 1:
+        other = g.op("Mul", other, alpha)
+    return g.op("Sub", self, other)
+
+
+@_onnx_symbolic("aten::rsub")
+def rsub(g: jit_utils.GraphContext, self, other, alpha=None):
+    return sub(g, other, self, alpha=alpha)
+
+
+@_onnx_symbolic("aten::mul")
+def mul(g: jit_utils.GraphContext, self, other):
+    if symbolic_helper._is_bool(self) and symbolic_helper._is_bool(other):
+        # ONNX Mul doesn't support Boolean, so use And as an equivalent operator.
+        return g.op("And", self, other)
+    else:
+        return g.op("Mul", self, other)
+
+
+@_onnx_symbolic("aten::div")
+def div(g: jit_utils.GraphContext, self, other, *args):
+    if len(args) == 0:
+        return true_divide(g, self, other)
+    else:
+        return _div_rounding_mode(g, self, other, *args)
+
+
+@_onnx_symbolic("aten::addcmul")
+@symbolic_helper.parse_args("v", "v", "v", "f")
+def addcmul(g: jit_utils.GraphContext, self, tensor1, tensor2, value=1.0):
+    value_tens = g.op("Constant", value_t=torch.tensor([value]))
+    return add(g, self, mul(g, mul(g, tensor1, tensor2), value_tens))
+
+
+@symbolic_helper.parse_args("v", "v", "s")
+def _div_rounding_mode(g: jit_utils.GraphContext, self, other, rounding_mode):
+    if rounding_mode is None:
+        return true_divide(g, self, other)
+    elif rounding_mode == "floor":
+        return _floor_divide(g, self, other)
+    elif rounding_mode == "trunc":
+        return _trunc_divide(g, self, other)
+    else:
+        raise errors.SymbolicValueError(
+            f'Unsupported rounding mode: "{rounding_mode}". Expected None, "floor" or "trunc"',
+            self,
+        )
+
+
+def _trunc_divide(g: jit_utils.GraphContext, self, other):
+    out = g.op("Div", self, other)
+    # the correct operation is truncate, which is not supported in ONNX,
+    # we cannot call floor since it will behave differently for negative numbers
+    # (eg. -0.1 should become -0 )
+    # - if scalar_type information are not available, assume that
+    # we need to call floor (treat as float)
+    out = g.op("Cast", out, to_i=_C_onnx.TensorProtoDataType.INT64)
+
+    # Matching PyTorch's behavior:
+    # - if self is fp the output's type is self's type
+    # - if self is not fp and other is fp, the output is of type JitScalarType.FLOAT
+    # - self is not fp and other is not fp, the output's type is self's output type
+    # - the output type defaults to Float
+    scalar_type = _type_utils.JitScalarType.from_value(
+        self, _type_utils.JitScalarType.UNDEFINED
+    )
+    if scalar_type != _type_utils.JitScalarType.UNDEFINED:
+        if not symbolic_helper._is_fp(self) and symbolic_helper._is_fp(other):
+            out = g.op("Cast", out, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+        else:
+            out = g.op(
+                "Cast",
+                out,
+                to_i=scalar_type.onnx_type(),
+            )
+    else:
+        out = g.op("Cast", out, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+    return out
+
+
+def _floor_divide(g: jit_utils.GraphContext, self, other):
+    if symbolic_helper._is_fp(self) or symbolic_helper._is_fp(other):
+        out = true_divide(g, self, other)
+        return g.op("Floor", out)
+    else:
+        # Integer division does trunction rounding
+        div = g.op("Div", self, other)
+        # Division is negative if: self < 0 != other < 0
+        zero = g.op("Constant", value_t=torch.tensor(0, dtype=torch.int64))
+        negative = g.op(
+            "Xor",
+            symbolic_helper._lt_helper(g, self, zero),
+            symbolic_helper._lt_helper(g, other, zero),
+        )
+
+        # For negative numbers with self % other != 0, subtract 1 to round down instead of up
+        mod = g.op("Sub", self, g.op("Mul", div, other))
+        fixup_mask = g.op("And", negative, g.op("Not", g.op("Equal", mod, zero)))
+
+        one = g.op("Constant", value_t=torch.tensor(1, dtype=torch.int64))
+        fixup = g.op("Mul", fixup_mask, one)
+        return g.op("Sub", div, fixup)
+
+
+@_onnx_symbolic("aten::floor_divide")
+def floor_divide(g: jit_utils.GraphContext, self, other):
+    # Deprecated behavior, floor_divide actually truncates
+    return _trunc_divide(g, self, other)
+
+
+@_onnx_symbolic("aten::floordiv")
+def floordiv(g: jit_utils.GraphContext, self, other):
+    return floor_divide(g, self, other)
+
+
+@_onnx_symbolic("aten::true_divide")
+def true_divide(g: jit_utils.GraphContext, self, other):
+    """Division where both inputs are cast to floating types
+
+    If both inputs are floating, performs div as usual
+    If only one input is a floating type, the other input is cast to its type
+    If neither input is a floating type, both inputs are cast to the default scalar type
+    """
+
+    # Case 1: either values are floating
+    # Performs div as usual.
+    # Implicit casting will be handled in scalar type analysis pass.
+    if symbolic_helper._is_fp(self) or symbolic_helper._is_fp(other):
+        return g.op("Div", self, other)
+
+    # Case 2: neither is floating
+    # Casts both inputs to the default scalar type
+    scalar_type = torch.get_default_dtype()
+    onnx_scalar_type = _C_onnx.TensorProtoDataType.FLOAT
+    assert scalar_type is torch.float or scalar_type is torch.double
+    if torch.get_default_dtype() is torch.double:
+        onnx_scalar_type = _C_onnx.TensorProtoDataType.DOUBLE
+
+    self = g.op("Cast", self, to_i=onnx_scalar_type)
+    other = g.op("Cast", other, to_i=onnx_scalar_type)
+    return g.op("Div", self, other)
+
+
+@_onnx_symbolic("aten::reciprocal")
+def reciprocal(g: jit_utils.GraphContext, self):
+    # torch.reciprocal implicitly casts to float, so we do the same.
+    if not symbolic_helper._is_fp(self):
+        self = g.op("Cast", self, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+    return g.op("Reciprocal", self)
+
+
+@_onnx_symbolic("aten::cat")
+@symbolic_helper.parse_args("v", "i")
+def cat(g: jit_utils.GraphContext, tensor_list, dim):
+    """Implement concatenation of pytorch tensors in ONNX along the specified `dim` dimension.
+
+    Parameters:
+        g (jit_utils.GraphContext): Graph context.
+        tensor_list (List[torch.Tensor]): List of tensors to concatenate.
+        dim (int): Dimension along which to concatenate the tensors.
+
+    Returns:
+        ONNX graph node representing the concatenated tensor.
+    """
+    tensors = symbolic_helper._unpack_list(tensor_list)
+    # torch.cat ignores empty tensors such as `torch.Tensor([])`
+    # These needs to be removed as input from ONNX's concat too, otherwise shape inference
+    # will likely fail due to inputs with different ranks (0 for empty tensor, > 0 for anything else)
+    nonempty_tensors = []
+    for t in tensors:
+        if symbolic_helper._is_constant(t) and not symbolic_helper._get_tensor_dim_size(
+            t, 0
+        ):
+            continue
+        nonempty_tensors.append(t)
+    assert len(nonempty_tensors) > 0
+    assert all(
+        symbolic_helper._get_tensor_rank(nonempty_tensors[0]) is None
+        or symbolic_helper._get_tensor_rank(t) is None
+        or symbolic_helper._get_tensor_rank(t)
+        == symbolic_helper._get_tensor_rank(nonempty_tensors[0])
+        for t in nonempty_tensors
+    )
+    tensor_list.node().removeAllInputs()
+    for t in nonempty_tensors:
+        tensor_list.node().addInput(t)
+
+    tensors = symbolic_helper._unpack_list(tensor_list)
+    return g.op("Concat", *tensors, axis_i=dim)
+
+
+@_onnx_symbolic("aten::stack")
+@symbolic_helper.parse_args("v", "i")
+def stack(g: jit_utils.GraphContext, tensor_list, dim):
+    unsqueezed = [
+        symbolic_helper._unsqueeze_helper(g, t, [dim])
+        for t in symbolic_helper._unpack_list(tensor_list)
+    ]
+    return g.op("Concat", *unsqueezed, axis_i=dim)
+
+
+@_onnx_symbolic("aten::list")
+def _list(g: jit_utils.GraphContext, self):
+    return self
+
+
+@_onnx_symbolic("aten::mm")
+def mm(g: jit_utils.GraphContext, self, other):
+    # Create a dummy C tensor. Only needed for API purposes, the value is
+    # since beta = 0
+    C = g.op("Constant", value_t=torch.tensor([1]))
+    return g.op("Gemm", self, other, C, beta_f=0.0, alpha_f=1.0)
+
+
+@_onnx_symbolic("aten::bmm")
+def bmm(g: jit_utils.GraphContext, self, other):
+    return g.op("MatMul", self, other)
+
+
+@_onnx_symbolic("aten::matmul")
+def matmul(g: jit_utils.GraphContext, self, other):
+    return g.op("MatMul", self, other)
+
+
+@_onnx_symbolic("aten::addmm")
+@symbolic_helper.parse_args("v", "v", "v", "t", "t")
+def addmm(g: jit_utils.GraphContext, self, mat1, mat2, beta, alpha):
+    scalar_type = None
+    self_scalar_type = symbolic_helper._try_get_scalar_type(self)
+    mat1_scalar_type = symbolic_helper._try_get_scalar_type(mat1)
+    mat2_scalar_type = symbolic_helper._try_get_scalar_type(mat2)
+    if self_scalar_type is not None:
+        scalar_type = self_scalar_type
+    elif mat1_scalar_type is not None:
+        scalar_type = mat1_scalar_type
+    elif mat2_scalar_type is not None:
+        scalar_type = mat2_scalar_type
+
+    mat1_rank = symbolic_helper._get_tensor_rank(mat1)
+    mat2_rank = symbolic_helper._get_tensor_rank(mat2)
+
+    def is_not_none_nor(v, u):
+        return v is not None and v != u
+
+    if scalar_type is not None and (
+        is_not_none_nor(mat1_rank, 2) or is_not_none_nor(mat2_rank, 2)
+    ):
+        res1 = g.op("MatMul", mat1, mat2)
+        res2 = self
+
+        alpha = symbolic_helper._scalar(alpha)
+        beta = symbolic_helper._scalar(beta)
+
+        if alpha != 1:
+            alpha = g.op(
+                "Constant", value_t=torch.tensor(alpha, dtype=scalar_type.dtype())
+            )
+            res1 = g.op("Mul", res1, alpha)
+        if beta != 1:
+            beta = g.op(
+                "Constant",
+                value_t=torch.tensor(
+                    symbolic_helper._scalar(beta), dtype=scalar_type.dtype()
+                ),
+            )
+            res2 = g.op("Mul", res2, beta)
+
+        return g.op("Add", res1, res2)
+
+    return g.op(
+        "Gemm",
+        mat1,
+        mat2,
+        self,
+        beta_f=symbolic_helper._scalar(beta),
+        alpha_f=symbolic_helper._scalar(alpha),
+    )
+
+
+@_onnx_symbolic("aten::neg")
+def neg(g: jit_utils.GraphContext, self):
+    return g.op("Neg", self)
+
+
+@_onnx_symbolic("aten::sqrt")
+def sqrt(g: jit_utils.GraphContext, self):
+    if _type_utils.JitScalarType.from_value(
+        self, _type_utils.JitScalarType.UNDEFINED
+    ) in {
+        _type_utils.JitScalarType.UINT8,
+        _type_utils.JitScalarType.INT8,
+        _type_utils.JitScalarType.INT16,
+        _type_utils.JitScalarType.INT,
+        _type_utils.JitScalarType.INT64,
+    }:
+        # torch converts all int inputs to sqrt to float
+        self = g.op("Cast", self, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+
+    return g.op("Sqrt", self)
+
+
+@_onnx_symbolic("aten::rsqrt")
+def rsqrt(g: jit_utils.GraphContext, self):
+    return g.op(
+        "Div", symbolic_helper._if_scalar_type_as(torch.ones(1), self), sqrt(g, self)
+    )
+
+
+@_onnx_symbolic("aten::tanh")
+# Fixed scale and zero_point, discovered from aten/src/ATen/native/quantized/cpu/qtanh.cpp
+@symbolic_helper.quantized_args(True, scale=2.0 / 256.0, zero_point=128)
+def tanh(g: jit_utils.GraphContext, self):
+    return g.op("Tanh", self)
+
+
+@_onnx_symbolic("aten::sin")
+def sin(g: jit_utils.GraphContext, self):
+    return g.op("Sin", self)
+
+
+@_onnx_symbolic("aten::cos")
+def cos(g: jit_utils.GraphContext, self):
+    return g.op("Cos", self)
+
+
+@_onnx_symbolic("aten::tan")
+def tan(g: jit_utils.GraphContext, self):
+    return g.op("Tan", self)
+
+
+@_onnx_symbolic("aten::asin")
+def asin(g: jit_utils.GraphContext, self):
+    return g.op("Asin", self)
+
+
+@_onnx_symbolic("aten::acos")
+def acos(g: jit_utils.GraphContext, self):
+    return g.op("Acos", self)
+
+
+@_onnx_symbolic("aten::atan")
+def atan(g: jit_utils.GraphContext, self):
+    return g.op("Atan", self)
+
+
+@_onnx_symbolic("aten::atan2")
+def atan2(g: jit_utils.GraphContext, self, other):
+    # self is y, and other is x on coordinate
+    slope = g.op("Div", self, other)
+    atan = g.op("Atan", slope)
+    const_zero = g.op("Constant", value_t=torch.tensor(0))
+    const_pi = g.op("Constant", value_t=torch.tensor(math.pi))
+
+    condition_second_or_third_quadrant = g.op("Greater", self, const_zero)
+    second_third_quadrant = g.op(
+        "Where",
+        condition_second_or_third_quadrant,
+        g.op("Add", atan, const_pi),
+        g.op("Sub", atan, const_pi),
+    )
+
+    condition_14_or_23_quadrant = g.op("Less", other, const_zero)
+    result = g.op("Where", condition_14_or_23_quadrant, second_third_quadrant, atan)
+
+    return result
+
+
+@_onnx_symbolic("aten::sigmoid")
+# Fixed scale and zero_point, discovered from aten/src/ATen/native/quantized/cpu/qsigmoid.cpp
+@symbolic_helper.quantized_args(True, scale=1.0 / 256.0, zero_point=0)
+def sigmoid(g: jit_utils.GraphContext, self):
+    """Converts the corresponding PyTorch function into ONNX operators.
+
+    It is not meant to be called directly by a user.
+
+    Args:
+        g (jit_utils.GraphContext): Graph context.
+        self (Tensor): the input tensor.
+    Returns:
+        ONNX operator
+    """
+    return g.op("Sigmoid", self)
+
+
+@_onnx_symbolic("aten::sign")
+def sign(g: jit_utils.GraphContext, self):
+    return g.op("Sign", self)
+
+
+@symbolic_helper.quantized_args(True)
+def _slice(g: jit_utils.GraphContext, input, axes, starts, ends):
+    assert len(starts) == len(ends)
+    if len(starts) == 1 and starts[0] == 0 and ends[0] == _constants.INT64_MAX:
+        return input
+    return g.op("Slice", input, axes_i=axes, starts_i=starts, ends_i=ends)
+
+
+@_onnx_symbolic(
+    "aten::sum", decorate=[symbolic_helper._apply_params("ReduceSum", "sum")]
+)
+@_onnx_symbolic(
+    "aten::mean", decorate=[symbolic_helper._apply_params("ReduceMean", "mean")]
+)
+# torch.prod does not support multidimensional "dim"
+@_onnx_symbolic(
+    "aten::prod",
+    decorate=[
+        symbolic_helper._apply_params(
+            "ReduceProd", "prod", allow_multi_dim_support=False
+        )
+    ],
+)
+def _reduce_with_dtype(onnx_op: str, name: str, allow_multi_dim_support: bool = True):
+    return symbolic_helper._reduce_with_dtype_helper(
+        onnx_op, name, allow_multi_dim_support
+    )
+
+
+@_onnx_symbolic("aten::cumsum")
+@symbolic_helper.parse_args("v", "i", "none")
+def cumsum(g: jit_utils.GraphContext, input, dim, dtype):
+    symbolic_helper._onnx_opset_unsupported("cumsum", 9, 11, input)
+
+
+@_onnx_symbolic("aten::_sample_dirichlet")
+def _sample_dirichlet(g: jit_utils.GraphContext, self, generator):
+    return symbolic_helper._onnx_unsupported("_sample_dirichlet", self)
+
+
+@_onnx_symbolic("aten::_standard_gamma")
+def _standard_gamma(g: jit_utils.GraphContext, self, generator):
+    return symbolic_helper._onnx_unsupported("_standard_gamma", self)
+
+
+@_onnx_symbolic("aten::t")
+def t(g: jit_utils.GraphContext, self):
+    rank = symbolic_helper._get_tensor_rank(self)
+    if rank is None or rank < 2:
+        # The transpose of a 1d or 0d tensor is itself. ONNX does not define the behavior
+        # clearly and onnxruntime fails on these cases. So we add an Identity node to
+        # mirror the behavior of eager mode.
+        return g.op("Identity", self)
+    return g.op("Transpose", self, perm_i=(1, 0))
+
+
+@_onnx_symbolic("aten::numpy_T")
+@symbolic_helper.quantized_args(True)
+def numpy_T(g: jit_utils.GraphContext, input):
+    ndim = symbolic_helper._get_tensor_rank(input)
+    assert ndim is not None
+    perm = list(reversed(range(0, ndim)))
+    return g.op("Transpose", input, perm_i=perm)
+
+
+@_onnx_symbolic("aten::expand")
+@symbolic_helper.quantized_args(True)
+def expand(g: jit_utils.GraphContext, self, size, implicit):
+    """Implement the expand function for a pytorch tensor in ONNX according to specified `size`"""
+    size = symbolic_helper._maybe_get_const(size, "is")
+    if not symbolic_helper._is_value(size):
+        size = g.op("Constant", value_t=torch.LongTensor(size))
+    elif symbolic_helper._is_packed_list(size):
+        # Expand with -1 dim value means dim is unchanged.
+        # Since onnx::expand supports two-way broadcasting,
+        # -1 dim value can be exported to onnx as 1
+        size = symbolic_helper._reshape_helper(
+            g, stack(g, size, 0), g.op("Constant", value_t=torch.tensor([-1]))
+        )
+    dtype = _type_utils.JitScalarType.INT64
+    ones = ones_like(g, size, dtype)
+    neg_ones = mul(g, ones, g.op("Constant", value_t=torch.tensor(-1)))
+    size = where(g, g.op("Equal", size, neg_ones), ones, size)
+    return g.op("Expand", self, size)
+
+
+@_onnx_symbolic("aten::broadcast_to")
+@symbolic_helper.quantized_args(True)
+def broadcast_to(g: jit_utils.GraphContext, self, size):
+    size = symbolic_helper._maybe_get_const(size, "is")
+    if not symbolic_helper._is_value(size):
+        size = g.op("Constant", value_t=torch.LongTensor(size))
+    elif symbolic_helper._is_packed_list(size):
+        # Expand with -1 dim value means dim is unchanged.
+        # Since onnx::expand supports two-way broadcasting,
+        # -1 dim value can be exported to onnx as 1
+        size = symbolic_helper._reshape_helper(
+            g, stack(g, size, 0), g.op("Constant", value_t=torch.tensor([-1]))
+        )
+    dtype = _type_utils.JitScalarType.INT64
+    ones = ones_like(g, size, dtype)
+    neg_ones = mul(g, ones, g.op("Constant", value_t=torch.tensor(-1)))
+    size = where(g, g.op("Equal", size, neg_ones), ones, size)
+    return g.op("Expand", self, size)
+
+
+@_onnx_symbolic("aten::expand_as")
+@symbolic_helper.quantized_args(True, True)
+def expand_as(g: jit_utils.GraphContext, self, other):
+    self_t = symbolic_helper._maybe_get_const(self, "t")
+    if isinstance(self_t, torch.Tensor):
+        orig_type = self_t.dtype
+        self_t = self_t.to(torch.double)
+        dims = []
+        for d in range(self_t.dim()):
+            if torch.equal(self_t.mean(d).unsqueeze(d).expand_as(self_t), self_t):
+                dims.append(d)
+                self = g.op(
+                    "Constant", value_t=self_t.mean(dims, keepdim=True).to(orig_type)
+                )
+
+    shape = g.op("Shape", other)
+    return g.op("Expand", self, shape)
+
+
+@_onnx_symbolic("aten::embedding")
+@symbolic_helper.quantized_args(True)
+@symbolic_helper.parse_args("v", "v", "i", "b", "v")
+def embedding(
+    g: jit_utils.GraphContext,
+    weight,
+    indices,
+    padding_idx,
+    scale_grad_by_freq,
+    sparse,
+):
+    if scale_grad_by_freq and GLOBALS.export_training:
+        raise errors.SymbolicValueError(
+            "Unsupported: ONNX export of embedding with scale_grad_by_freq=True "
+            "for training mode. ONNX does not support scaling the gradients.",
+            weight,
+        )
+    if padding_idx >= 0 and GLOBALS.export_training:
+        warnings.warn(
+            "Warning: ONNX export of embedding with padding_idx >= 0 "
+            "for training mode. "
+            "ONNX does not support not updating the embedding vector at padding_idx during training."
+        )
+
+    return g.op("Gather", weight, indices)
+
+
+@_onnx_symbolic("aten::embedding_bag")
+@symbolic_helper.quantized_args(True)
+@symbolic_helper.parse_args("v", "v", "v", "i", "i", "i", "v", "i", "i")
+def embedding_bag(
+    g: jit_utils.GraphContext,
+    embedding_matrix,
+    indices,
+    offsets,
+    scale_grad_by_freq,
+    mode,
+    sparse,
+    per_sample_weights,
+    include_last_offset,
+    padding_idx,
+):
+    if not symbolic_helper._is_none(per_sample_weights):
+        return symbolic_helper._onnx_unsupported(
+            "embedding_bag with per_sample_weights"
+        )
+
+    return symbolic_helper._onnx_unsupported("embedding_bag", embedding_matrix)
+
+
+@_onnx_symbolic("aten::size")
+@symbolic_helper.quantized_args(True, quantize_output=False)
+def size(g: jit_utils.GraphContext, self, dim=None):
+    if dim is None:
+        return g.op("Shape", self)
+    if symbolic_helper._maybe_get_const(dim, "i") < 0:
+        rank = symbolic_helper._get_tensor_rank(self)
+        if rank is not None:
+            dim = symbolic_helper._maybe_get_const(dim, "i") + rank
+            dim = g.op("Constant", value_t=torch.tensor(dim))
+    return symbolic_helper._size_helper(g, self, dim)
+
+
+@_onnx_symbolic("aten::transpose")
+@symbolic_helper.quantized_args(True)
+@symbolic_helper.parse_args("v", "i", "i")
+def transpose(g: jit_utils.GraphContext, self, dim0, dim1):
+    if dim0 == dim1:  # micro-optimization
+        return self
+
+    # NB: Transpose in ONNX is actually a Permute
+    rank = symbolic_helper._get_tensor_rank(self)
+    if rank is not None:
+        axes = list(range(rank))
+        axes[dim0], axes[dim1] = axes[dim1], axes[dim0]
+        return g.op("Transpose", self, perm_i=axes)
+    else:
+        raise errors.SymbolicValueError(
+            "Unsupported: ONNX export of transpose for tensor of unknown rank.",
+            self,
+        )
+
+
+@_onnx_symbolic("aten::permute")
+@symbolic_helper.parse_args("v", "is")
+def permute(g: jit_utils.GraphContext, self, dims):
+    if dims == list(range(0, len(dims))):
+        return self
+    return g.op("Transpose", self, perm_i=dims)
+
+
+@_onnx_symbolic("aten::view")
+@symbolic_helper.quantized_args(True)
+def view(g: jit_utils.GraphContext, self, size):
+    return reshape(g, self, size)
+
+
+@_onnx_symbolic("aten::view_as")
+def view_as(g: jit_utils.GraphContext, self, other):
+    shape = g.op("Shape", other)
+    return reshape(g, self, shape)
+
+
+@_onnx_symbolic("aten::unsafe_chunk")
+@symbolic_helper.parse_args("v", "i", "i", "i")
+def unsafe_chunk(g: jit_utils.GraphContext, self, chunks, dim, _outputs=None):
+    if _outputs is None:
+        return symbolic_helper._onnx_opset_unsupported_detailed(
+            "unsafe_chunk", 9, 11, "Dynamic number of outputs not supported", self
+        )
+    size = symbolic_helper._get_tensor_dim_size(self, dim)
+    if size is None:
+        return symbolic_helper._unimplemented(
+            "unsafe_chunk", "unknown dimension size", self
+        )
+    split_size = (size + chunks - 1) // chunks
+    splits = [split_size] * (size // split_size)
+    leftover = size % split_size
+    if leftover:
+        splits.append(leftover)
+    return g.op("Split", self, split_i=splits, axis_i=dim, outputs=_outputs)
+
+
+@_onnx_symbolic("aten::split")
+@symbolic_helper.parse_args("v", "v", "i", "i")
+def split(g: jit_utils.GraphContext, self, split_size_or_sizes, dim, _outputs=None):
+    if not symbolic_helper._is_split_static(split_size_or_sizes, _outputs):
+        return symbolic_helper._onnx_opset_unsupported_detailed(
+            "split", 9, 11, "Dynamic number of outputs not supported", self
+        )
+    split_val = symbolic_helper._node_get(split_size_or_sizes.node(), "value")
+    if split_val.dim() > 0:
+        return split_with_sizes(g, self, split_size_or_sizes, dim, _outputs)
+    split_size = symbolic_helper._get_const(split_size_or_sizes, "i", "split_size")
+
+    size = symbolic_helper._get_tensor_dim_size(self, dim)
+    if size is None:
+        if _outputs is not None:
+            size = split_size * _outputs
+        else:
+            return symbolic_helper._onnx_opset_unsupported_detailed(
+                "split", 9, 11, "Unknown dimension size not supported", self
+            )
+    splits = [split_size] * (size // split_size)
+    leftover = size % split_size
+    if leftover:
+        splits.append(leftover)
+    return g.op("Split", self, split_i=splits, axis_i=dim, outputs=_outputs)
+
+
+@_onnx_symbolic("aten::unsafe_split")
+def unsafe_split(
+    g: jit_utils.GraphContext, self, split_size_or_sizes, dim, _outputs=None
+):
+    return split(g, self, split_size_or_sizes, dim, _outputs)
+
+
+@_onnx_symbolic("aten::split_with_sizes")
+@symbolic_helper.parse_args("v", "is", "i", "i")
+def split_with_sizes(g: jit_utils.GraphContext, self, split_sizes, dim, _outputs=None):
+    if not symbolic_helper._is_split_static(split_sizes, _outputs):
+        return symbolic_helper._onnx_opset_unsupported_detailed(
+            "split_with_sizes", 9, 11, "Dynamic number of outputs not supported", self
+        )
+    return g.op("Split", self, split_i=split_sizes, axis_i=dim, outputs=_outputs)
+
+
+@_onnx_symbolic("aten::unsafe_split_with_sizes")
+def unsafe_split_with_sizes(
+    g: jit_utils.GraphContext, self, split_sizes, dim, _outputs=None
+):
+    return split_with_sizes(g, self, split_sizes, dim, _outputs)
+
+
+@_onnx_symbolic("aten::unbind")
+@symbolic_helper.parse_args("v", "i", "i")
+def unbind(g: jit_utils.GraphContext, self, dim=0, _outputs=None):
+    if _outputs is None:
+        return symbolic_helper._onnx_opset_unsupported_detailed(
+            "unbind", 9, 11, "Dynamic number of outputs not supported", self
+        )
+
+    outputs = g.op("Split", self, split_i=[1] * _outputs, axis_i=dim, outputs=_outputs)
+    outputs = [outputs] if _outputs == 1 else outputs
+    squeezed_outputs = [
+        symbolic_helper._squeeze_helper(g, out, [dim]) for out in outputs
+    ]
+    return squeezed_outputs
+
+
+@_onnx_symbolic("aten::select")
+@symbolic_helper.quantized_args(True)
+@symbolic_helper.parse_args("v", "i", "v")
+def select(g: jit_utils.GraphContext, self, dim, index):
+    """Implement the select functionality for a pytorch tensor in ONNX.
+
+    Selects elements from the input tensor along the specified `dim` dimension based on the `index` tensor.
+    """
+    index = symbolic_helper._maybe_get_scalar(index)
+    if (not symbolic_helper._is_value(index)) and (index < 0):
+        if index == -1:
+            end_index = _constants.INT64_MAX
+        else:
+            end_index = index + 1
+        slice_node = symbolic_helper._slice_helper(
+            g, self, axes=[dim], starts=[index], ends=[end_index]
+        )
+        return symbolic_helper._squeeze_helper(g, slice_node, [dim])
+    else:
+        # FIXME(justinchuby): can index be an int and not a value?
+        return g.op("Gather", self, index, axis_i=dim)
+
+
+@_onnx_symbolic("aten::square")
+def square(g: jit_utils.GraphContext, self):
+    return g.op("Mul", self, self)
+
+
+@_onnx_symbolic("aten::squeeze")
+def squeeze(g: jit_utils.GraphContext, self, dim=None):
+    if dim is None:
+        return g.op("Squeeze", self)
+
+    squeeze_dim = symbolic_helper._get_const(dim, "i", "dim")
+    # Handle negative dims
+    if squeeze_dim < 0:
+        rank = symbolic_helper._get_tensor_rank(self)
+        if rank is not None:
+            warnings.warn(
+                "ONNX export squeeze with negative axis "
+                + str(squeeze_dim)
+                + " might cause the onnx model to be incorrect. "
+                + "Negative axis is not supported in ONNX. "
+                + "Axis is converted to "
+                + str(squeeze_dim + rank)
+                + " based on input shape at export time. "
+                + "Passing an tensor of different rank in execution will be incorrect."
+            )
+            squeeze_dim += rank
+        else:
+            return symbolic_helper._unimplemented(
+                "squeeze", "negative axis with unknown input rank", self
+            )
+
+    dim_size = symbolic_helper._get_tensor_dim_size(self, squeeze_dim)
+    if dim_size is None:
+        warnings.warn(
+            "This model contains a squeeze operation on dimension "
+            + str(squeeze_dim)
+            + " on an input "
+            + "with unknown shape. Note that if the size of dimension "
+            + str(squeeze_dim)
+            + " of the input "
+            + "is not 1, the ONNX model will return an error. Opset version 11 supports squeezing on "
+            + "non-singleton dimensions, it is recommended to export this model using opset "
+            + "version 11 or higher."
+        )
+        return symbolic_helper._squeeze_helper(g, self, axes_i=[squeeze_dim])
+    if dim_size > 1:
+        warnings.warn(
+            "This model contains a squeeze operation on dimension "
+            + str(squeeze_dim)
+            + ". The size of "
+            + "this dimension in the given input is "
+            + str(dim_size)
+            + ". The model will "
+            + "be exported without the squeeze node. If the model is intended to be used with dynamic "
+            + "input shapes, please use opset version 11 to "
+            + "export the model."
+        )
+        return self
+
+    warnings.warn(
+        "This model contains a squeeze operation on dimension "
+        + str(squeeze_dim)
+        + ". If the model is "
+        + "intended to be used with dynamic input shapes, please use opset version 11 to export the model."
+    )
+    return symbolic_helper._squeeze_helper(g, self, axes_i=[squeeze_dim])
+
+
+@_onnx_symbolic("aten::prelu")
+def prelu(g: jit_utils.GraphContext, self, weight):
+    self_rank = symbolic_helper._get_tensor_rank(self)
+    weight_sizes = symbolic_helper._get_tensor_sizes(weight)
+    weight_rank = len(weight_sizes)
+    if self_rank is not None:
+        if self_rank > 2:
+            # make weight unidirectional broadcastable
+            weight = symbolic_helper._unsqueeze_helper(
+                g, weight, list(range(1, self_rank - 1))
+            )
+        elif self_rank == 0 and weight_sizes == [1]:
+            # self and weight are both scalar but weight has rank == 1, squeeze weight.
+            weight = symbolic_helper._squeeze_helper(g, weight, [0])
+            weight_rank = 0
+
+    if self_rank is not None and weight_rank is not None:
+        assert (
+            self_rank >= weight_rank
+        ), f"rank(x) should be >= rank(slope) but got {self_rank} < {weight_rank}"
+    return g.op("PRelu", self, weight)
+
+
+@_onnx_symbolic("aten::silu")
+def silu(g: jit_utils.GraphContext, input):
+    return g.op("Mul", input, g.op("Sigmoid", input))
+
+
+@_onnx_symbolic("aten::mish")
+def mish(g: jit_utils.GraphContext, input):
+    return g.op("Mul", input, g.op("Tanh", g.op("Softplus", input)))
+
+
+@_onnx_symbolic("aten::relu")
+@symbolic_helper.quantized_args(True)
+def relu(g: jit_utils.GraphContext, input):
+    return symbolic_helper._op_with_optional_float_cast(
+        g, "Relu", input, opset_before=14
+    )
+
+
+@_onnx_symbolic("aten::relu6")
+@symbolic_helper.quantized_args(True)
+def relu6(g: jit_utils.GraphContext, input):
+    return clamp(g, input, 0, 6)
+
+
+@_onnx_symbolic("aten::ceil")
+def ceil(g: jit_utils.GraphContext, input):
+    return g.op("Ceil", input)
+
+
+@_onnx_symbolic("aten::floor")
+def floor(g: jit_utils.GraphContext, input):
+    return g.op("Floor", input)
+
+
+@_onnx_symbolic("aten::len")
+def _len(g: jit_utils.GraphContext, self):
+    sz_0 = size(g, self, g.op("Constant", value_t=torch.LongTensor([0])))
+    return symbolic_helper._squeeze_helper(g, sz_0, [0])
+
+
+@_onnx_symbolic("aten::threshold")
+@symbolic_helper.parse_args("v", "t", "t")
+def threshold(g: jit_utils.GraphContext, self, threshold, value):
+    # See Note [Export inplace]
+    if symbolic_helper._scalar(threshold) != 0:
+        return symbolic_helper._unimplemented("threshold", "non-zero threshold", self)
+    if symbolic_helper._scalar(value) != 0:
+        return symbolic_helper._unimplemented("threshold", "non-zero value", self)
+    return g.op("Relu", self)
+
+
+@_onnx_symbolic("aten::leaky_relu")
+@symbolic_helper.quantized_args(True)
+@symbolic_helper.parse_args("v", "f", "b")
+def leaky_relu(
+    g: jit_utils.GraphContext,
+    input: _C.Value,
+    negative_slope: float,
+    inplace: bool = False,
+):
+    # See Note [Export inplace]
+    return g.op("LeakyRelu", input, alpha_f=negative_slope)
+
+
+@_onnx_symbolic("aten::glu")
+@symbolic_helper.parse_args("v", "i")
+def glu(g: jit_utils.GraphContext, input, dim):
+    dim_size = symbolic_helper._get_tensor_dim_size(input, dim)
+    if dim_size is not None:
+        assert dim_size % 2 == 0
+
+    first, second = g.op("Split", input, axis_i=dim, outputs=2)
+    return g.op("Mul", first, g.op("Sigmoid", second))
+
+
+@_onnx_symbolic("aten::softmax")
+@symbolic_helper.parse_args("v", "i", "none")
+def softmax(g: jit_utils.GraphContext, input, dim, dtype=None):
+    # Softmax does normalization at vector level.
+    # PyTorch and ONNX use different strategies to split the input tensor into vectors.
+    # Thus dim and axis have different meanings.
+    # PyTorch slices the input tensor into vectors along the `dim`-th dimension.
+    # ONNX reshapes the input into a 2-D tensor, and `axis` indicates where the input is coerced.
+    # If input is a 2 x 3 tensor:
+    # input = [[1.0, 1.0, 1.0],
+    #          [1.0, 1,0, 1,0]]
+    # with dim = 0, the result is:
+    # result = [[0.5, 0.5, 0.5],
+    #           [0.5, 0.5, 0.5]]
+    # with axis = 0, the result is:
+    # result = [[0.167, 0.167, 0.167],
+    #           [0.167, 0.167, 0.167]]
+    # So only when dim and axis both equal to ndim - 1 (the last dimension),
+    # their semantics are equivalent.
+    # So use softmax when dim and axis both equal to ndim - 1,
+    # otherwise transpose the input to put the vectors to be normalized to the last dimension.
+    # When input rank is not known at export time we compute softmax using a subgraph
+    # with other operators
+    input_dim = symbolic_helper._get_tensor_rank(input)
+    if input_dim is not None:
+        # TODO: remove this as onnx opset 11 spec allows negative axes
+        if dim < 0:
+            dim = input_dim + dim
+
+        is_transpose_required = input_dim != dim + 1
+
+        if is_transpose_required:
+            axes = list(range(input_dim))
+            axes[dim], axes[-1] = axes[-1], axes[dim]
+            input = g.op("Transpose", input, perm_i=axes)
+            dim = input_dim - 1
+
+        softmax = g.op("Softmax", input, axis_i=dim)
+        if dtype and dtype.node().kind() != "prim::Constant":
+            parsed_dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+            softmax = g.op(
+                "Cast",
+                softmax,
+                to_i=_type_utils.JitScalarType(parsed_dtype).onnx_type(),
+            )
+
+        if is_transpose_required:
+            softmax = g.op("Transpose", softmax, perm_i=axes)  # type: ignore[possibly-undefined]
+        return softmax
+
+    # Apply max normalization.
+    input = g.op("Sub", input, g.op("ReduceMax", input, axes_i=[dim], keepdims_i=1))
+
+    exp = g.op("Exp", input)
+    sum = symbolic_helper._reducesum_helper(g, exp, axes_i=[dim])
+    softmax = g.op("Div", exp, sum)
+    if dtype and dtype.node().kind() != "prim::Constant":
+        parsed_dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+        softmax = g.op(
+            "Cast", softmax, to_i=_type_utils.JitScalarType(parsed_dtype).onnx_type()
+        )
+    return softmax
+
+
+@_onnx_symbolic("aten::softplus")
+def softplus(g: jit_utils.GraphContext, self, beta, threshold):
+    beta_const = symbolic_helper._maybe_get_const(beta, "f")
+    if beta_const != 1:
+        return g.op("Div", g.op("Softplus", g.op("Mul", self, beta)), beta)
+    return g.op("Softplus", self)
+
+
+@_onnx_symbolic("aten::get_pool_ceil_padding")
+def get_pool_ceil_padding(input, kernel_size, stride, padding):
+    # TODO(justinchuby): Looks like this op is deprecated in torch
+    sizes = symbolic_helper._get_tensor_sizes(input)
+    dim = sizes[-len(padding) :] if sizes is not None else None
+    if dim is None or any(i is None for i in dim):
+        return symbolic_helper._unimplemented(
+            "get_pool_ceil_padding", "input size not accessible", input
+        )
+    ceiled_output_dim = [
+        int(math.ceil((dim[i] + 2 * padding[i] - kernel_size[i]) / float(stride[i])))
+        + 1
+        for i in range(0, len(padding))
+    ]
+    # ensure last pooling starts inside
+    ceiled_output_dim = [
+        (
+            ceiled_output_dim[i] - 1
+            if (((ceiled_output_dim[i] - 1) * stride[i]) >= (dim[i] + padding[i]))
+            else ceiled_output_dim[i]
+        )
+        for i in range(0, len(ceiled_output_dim))
+    ]
+    padding_ceil = [
+        (
+            0
+            if (stride[i] == 1)
+            else (
+                kernel_size[i]
+                - (
+                    dim[i]
+                    + 2 * padding[i]
+                    - ((ceiled_output_dim[i] - 1) * stride[i] + 1)
+                )
+            )
+        )
+        for i in range(0, len(padding))
+    ]
+    # ensure padding is not > kernel_size
+    padding_ceil = [
+        (
+            (
+                int(padding_ceil[i])
+                if padding_ceil[i] < kernel_size[i] - 1
+                else int(kernel_size[i] - 1)
+            )
+            if ((padding_ceil[i] + 2 * padding[i]) >= (kernel_size[i]))
+            else int(padding_ceil[i])
+        )
+        for i in range(0, len(padding_ceil))
+    ]
+    return padding_ceil
+
+
+@_onnx_symbolic(
+    "aten::max_pool1d",
+    decorate=[
+        symbolic_helper._apply_params(
+            "max_pool1d", torch.nn.modules.utils._single, 1, return_indices=False
+        ),
+        _export("max_pool1d"),
+    ],
+)
+@_onnx_symbolic(
+    "aten::max_pool2d",
+    decorate=[
+        symbolic_helper._apply_params(
+            "max_pool2d", torch.nn.modules.utils._pair, 2, return_indices=False
+        ),
+        _export("max_pool2d"),
+    ],
+)
+@_onnx_symbolic(
+    "aten::max_pool3d",
+    decorate=[
+        symbolic_helper._apply_params(
+            "max_pool3d", torch.nn.modules.utils._triple, 3, return_indices=False
+        ),
+        _export("max_pool3d"),
+    ],
+)
+def _max_pool(name, tuple_fn, ndims, return_indices):
+    @symbolic_helper.quantized_args(True, False, False, False, False, False)
+    @symbolic_helper.parse_args("v", "is", "is", "is", "is", "i")
+    def symbolic_fn(g, input, kernel_size, stride, padding, dilation, ceil_mode):
+        if set(tuple_fn(dilation)) != {1}:
+            return symbolic_helper._unimplemented(name, "dilation", input)
+        if not stride:
+            stride = kernel_size
+        padding = tuple(tuple_fn(padding))
+        if ceil_mode:
+            padding_ceil = get_pool_ceil_padding(input, kernel_size, stride, padding)
+            padding = padding + tuple(a + b for (a, b) in zip(padding_ceil, padding))
+        else:
+            padding = padding * 2
+        kwargs = {
+            "kernel_shape_i": tuple_fn(kernel_size),
+            "pads_i": padding,
+            "strides_i": tuple_fn(stride),
+        }
+        # easy but hacky way to get flattened indices values
+        # to be used to convert the indices values to non-flattened.
+        # In ONNX the indices are computed as a flatten 1-D tensor,
+        # so the values in indices are in [0, N x C x D1 x ... x Dn).
+        # To convert the indices to the same format used by Pytorch,
+        # we first execute a maxpool with a kernel and stride of 1 on the same input.
+        # This will result in a tensor of indices in which each index will have it's own value.
+        # Using this tensor as a reference, we extract the first index of each axis and subtract
+        # it from each index of this axis in the indices to convert.
+        # This step will result in a tensor were each dimension has values of indices within
+        # the dimension it is in.
+        # For more information :
+        # https://github.com/pytorch/pytorch/pull/16455#issuecomment-460776407
+        if return_indices:
+            r, indices = g.op("MaxPool", input, outputs=2, **kwargs)
+            _, flattened_indices = g.op(
+                "MaxPool",
+                input,
+                outputs=2,
+                kernel_shape_i=[1 for _ in range(ndims)],
+                strides_i=[1 for _ in range(ndims)],
+            )
+            # convert indices to have non-flattened indices values
+            s = symbolic_helper._slice_helper(
+                g,
+                flattened_indices,
+                axes=[2 + i for i in range(ndims)],
+                starts=list(tuple_fn(0)),
+                ends=list(tuple_fn(1)),
+            )
+            indices = sub(g, indices, s)
+            return r, indices
+        else:
+            r = g.op("MaxPool", input, outputs=1, **kwargs)
+            return r
+
+    return symbolic_fn
+
+
+max_pool1d_with_indices = _onnx_symbolic("aten::max_pool1d_with_indices")(
+    _max_pool(
+        "max_pool1d_with_indices",
+        torch.nn.modules.utils._single,
+        1,
+        return_indices=True,
+    )
+)
+max_pool2d_with_indices = _onnx_symbolic("aten::max_pool2d_with_indices")(
+    _max_pool(
+        "max_pool2d_with_indices",
+        torch.nn.modules.utils._pair,
+        2,
+        return_indices=True,
+    )
+)
+max_pool3d_with_indices = _onnx_symbolic("aten::max_pool3d_with_indices")(
+    _max_pool(
+        "max_pool3d_with_indices",
+        torch.nn.modules.utils._triple,
+        3,
+        return_indices=True,
+    )
+)
+
+
+@_onnx_symbolic(
+    "aten::avg_pool1d",
+    decorate=[
+        symbolic_helper._apply_params("avg_pool1d", torch.nn.modules.utils._single),
+        _export("avg_pool1d"),
+    ],
+)
+@_onnx_symbolic(
+    "aten::avg_pool2d",
+    decorate=[
+        symbolic_helper._apply_params("avg_pool2d", torch.nn.modules.utils._pair),
+        _export("avg_pool2d"),
+    ],
+)
+@_onnx_symbolic(
+    "aten::avg_pool3d",
+    decorate=[
+        symbolic_helper._apply_params("avg_pool3d", torch.nn.modules.utils._triple),
+        _export("avg_pool3d"),
+    ],
+)
+def _avg_pool(name, tuple_fn):
+    @symbolic_helper.quantized_args(True)
+    @symbolic_helper.parse_args("v", "is", "is", "is", "i", "i", "none")
+    def symbolic_fn(
+        g,
+        input: _C.Value,
+        kernel_size: Sequence[int],
+        stride: Sequence[int],
+        padding: int | Sequence[int],
+        ceil_mode: int,
+        count_include_pad: int,
+        divisor_override=None,
+    ):
+        if not stride:
+            stride = kernel_size
+        padding = symbolic_helper._avgpool_helper(
+            tuple_fn, padding, kernel_size, stride, divisor_override, name
+        )
+        assert isinstance(padding, tuple)
+        adjusted_padding = padding
+        # Although onnx::AvgPool provides count_include_pad,
+        # The corner case of Average Pooling with ceil_mode on
+        # PyTorch allows sliding window go off bound, which leads to
+        # this accommodation.
+        # More detail on https://github.com/pytorch/pytorch/issues/57178
+        if count_include_pad:
+            input = symbolic_helper._op_with_optional_float_cast(
+                g,
+                "Pad",
+                input,
+                pads_i=((0,) * 2 + padding) * 2,
+                mode_s="constant",
+                value_f=0.0,
+                opset_before=11,
+            )
+            adjusted_padding = (0,) * len(padding)
+        if ceil_mode:
+            padding_ceil = get_pool_ceil_padding(input, kernel_size, stride, padding)
+            adjusted_padding = adjusted_padding + tuple(
+                a + b for (a, b) in zip(padding_ceil, adjusted_padding)
+            )
+        else:
+            adjusted_padding = adjusted_padding * 2
+        output = g.op(
+            "AveragePool",
+            input,
+            kernel_shape_i=tuple_fn(kernel_size),
+            strides_i=tuple_fn(stride),
+            pads_i=adjusted_padding,
+        )
+        return output
+
+    return symbolic_fn
+
+
+@_onnx_symbolic(
+    "aten::adaptive_avg_pool1d",
+    decorate=[
+        symbolic_helper._apply_params(
+            "adaptive_avg_pool1d", "AveragePool", torch.nn.modules.utils._single
+        ),
+        _export("adaptive_avg_pool1d"),
+    ],
+)
+@_onnx_symbolic(
+    "aten::adaptive_avg_pool2d",
+    decorate=[
+        symbolic_helper._apply_params(
+            "adaptive_avg_pool2d", "AveragePool", torch.nn.modules.utils._pair
+        ),
+        _export("adaptive_avg_pool2d"),
+    ],
+)
+@_onnx_symbolic(
+    "aten::adaptive_avg_pool3d",
+    decorate=[
+        symbolic_helper._apply_params(
+            "adaptive_avg_pool3d", "AveragePool", torch.nn.modules.utils._triple
+        ),
+        _export("adaptive_avg_pool3d"),
+    ],
+)
+@_onnx_symbolic(
+    "aten::adaptive_max_pool1d",
+    decorate=[
+        symbolic_helper._apply_params(
+            "adaptive_max_pool1d",
+            "MaxPool",
+            torch.nn.modules.utils._single,
+            max_pool1d_with_indices,
+        ),
+        _export("adaptive_max_pool1d"),
+    ],
+)
+@_onnx_symbolic(
+    "aten::adaptive_max_pool2d",
+    decorate=[
+        symbolic_helper._apply_params(
+            "adaptive_max_pool2d",
+            "MaxPool",
+            torch.nn.modules.utils._pair,
+            max_pool2d_with_indices,
+        ),
+        _export("adaptive_max_pool2d"),
+    ],
+)
+@_onnx_symbolic(
+    "aten::adaptive_max_pool3d",
+    decorate=[
+        symbolic_helper._apply_params(
+            "adaptive_max_pool3d",
+            "MaxPool",
+            torch.nn.modules.utils._triple,
+            max_pool3d_with_indices,
+        ),
+        _export("adaptive_max_pool3d"),
+    ],
+)
+def _adaptive_pool(name, type, tuple_fn, fn=None):
+    @symbolic_helper.quantized_args(True, False)
+    def symbolic_fn(g, input, output_size):
+        # _adaptive_pool is supported for cases where output_size is 1 for all dimensions,
+        # by executing a GlobalPool.
+        # It is also supported for cases where the output size is a factor of the input size.
+        # For these cases the stride and kernel size are uniform along all the indices of
+        # the same dimension, which makes it possible to export it to ONNX.
+        # for MaxPool, GlobalMaxPool does not return indices,
+        # so we try using max_poolxd_with_indices, and if it is not possible
+        # (input is not a complete tensor or output size not factor of input size)
+        # then we call GlobalAveragePool and return None for the indices
+        output_size_value = output_size
+        try:
+            output_size = symbolic_helper._parse_arg(output_size, "is")
+        except Exception:
+            # FIXME(justinchuby): Avoid catching Exception.
+            # Catch a more specific exception instead.
+            return symbolic_helper._onnx_unsupported(
+                "adaptive pooling, since output_size is not constant.", input
+            )
+        if output_size == [1] * len(output_size) and type == "AveragePool":
+            return g.op("GlobalAveragePool", input)
+        sizes = symbolic_helper._get_tensor_sizes(input)
+        try:
+            dim = sizes[2:]
+        except Exception:
+            # FIXME(justinchuby): Avoid catching Exception.
+            # Catch a more specific exception instead.
+            dim = None
+        if dim is None or any(i is None for i in dim):
+            if output_size == [1] * len(output_size):
+                return g.op("GlobalMaxPool", input), None
+            return symbolic_helper._unimplemented(
+                name, "input size not accessible", input
+            )
+        # verify if output size % input size = 0 for all dim
+        mod = [dim[i] % output_size[i] for i in range(0, len(dim))]
+        if mod != [0] * len(mod):
+            if output_size == [1] * len(output_size):
+                return g.op("GlobalMaxPool", input), None
+            return symbolic_helper._unimplemented(
+                name, "output size that are not factor of input size", output_size_value
+            )
+        k = [int(dim[i] / output_size[i]) for i in range(0, len(dim))]
+        # call max_poolxd_with_indices to get indices in the output
+        if type == "MaxPool":
+            return fn(g, input, k, k, (0,) * len(dim), (1,) * len(dim), False)
+        output = g.op(type, input, kernel_shape_i=tuple_fn(k), strides_i=tuple_fn(k))
+        return output
+
+    return symbolic_fn
+
+
+def _prepare_onnx_paddings(dim: int, pad):
+    """Generate paddings in ONNX order based on pad in pytorch.
+    Args:
+        dim: the dimension of the tensor.
+        pad: the paddings in pytorch.
+            The order is dim_n_begin, dim_n_end, dim_n-1_begin, dim_n-1_end, ...
+    """
+    # The desired order of paddings is
+    # dim_0_begin, dim_1_begin, ... , dim_0_end, ..., dim_n_end.
+    # n is the dimension of input.
+    # assume zero-dimensions in the beginning
+    paddings = list(pad[:]) + [0] * (dim * 2 - len(pad))
+    # reverse order and collate first beginnings and then ends
+    paddings = paddings[-2::-2] + paddings[-1::-2]
+    return paddings
+
+
+def _convert_padding_node(input):
+    padding = symbolic_helper._maybe_get_const(input, "is")
+    if symbolic_helper._is_value(padding) and symbolic_helper._is_packed_list(padding):
+        input_list = symbolic_helper._unpack_list(padding)
+        try:
+            padding = [
+                symbolic_helper._get_const(v, "i", "padding") for v in input_list
+            ]
+        except Exception:
+            # FIXME(justinchuby): Avoid catching Exception.
+            # Catch a more specific exception instead.
+            return symbolic_helper._onnx_opset_unsupported_detailed(
+                "Pad", 9, 11, "The sizes of the padding must be constant", input
+            )
+    return padding
+
+
+@_onnx_symbolic("aten::constant_pad_nd")
+def constant_pad_nd(g: jit_utils.GraphContext, input, padding, value):
+    mode = "constant"
+    try:
+        value = symbolic_helper._get_const(value, "f", "value")
+    except Exception:
+        # FIXME(justinchuby): Avoid catching Exception.
+        # Catch a more specific exception instead.
+        return symbolic_helper._onnx_opset_unsupported_detailed(
+            "Pad", 9, 11, "The value for the padding must be constant", value
+        )
+
+    padding = _convert_padding_node(padding)
+    paddings = _prepare_onnx_paddings(symbolic_helper._get_tensor_rank(input), padding)
+    return symbolic_helper._op_with_optional_float_cast(
+        g, "Pad", input, pads_i=paddings, mode_s=mode, value_f=value, opset_before=11
+    )
+
+
+def _pad_circular(g: jit_utils.GraphContext, input: _C.Value, pad: _C.Value):
+    padding = _convert_padding_node(pad)
+    assert len(padding) % 2 == 0
+    ndim = len(padding) // 2
+
+    cur = input
+    for idx in range(ndim):
+        pad_r = padding[-(2 * idx + 1)]
+        pad_l = padding[-(2 * idx + 2)]
+        tensors = []
+        if pad_l > 0:
+            left = symbolic_helper._slice_helper(
+                g, cur, axes=[2 + idx], starts=[-(pad_l)], ends=[_constants.INT64_MAX]
+            )
+            tensors.append(left)
+
+        if pad_l < 0 or pad_r < 0:
+            start = builtins.max(0, -pad_l)
+            end = -(builtins.max(0, -pad_r))
+            middle = symbolic_helper._slice_helper(
+                g,
+                cur,
+                axes=[2 + idx],
+                starts=[start],
+                ends=[end],
+            )
+            tensors.append(middle)
+        else:
+            tensors.append(cur)
+
+        if pad_r > 0:
+            right = symbolic_helper._slice_helper(
+                g, cur, axes=[2 + idx], starts=[0], ends=[pad_r]
+            )
+            tensors.append(right)
+
+        cur = g.op("Concat", *tensors, axis_i=(2 + idx))
+
+    return cur
+
+
+@_onnx_symbolic("aten::reflection_pad1d")
+@_onnx_symbolic("aten::reflection_pad2d")
+@_onnx_symbolic("aten::reflection_pad3d")
+def reflection_pad(g: jit_utils.GraphContext, input, padding):
+    mode = "reflect"
+    padding = _convert_padding_node(padding)
+    paddings = _prepare_onnx_paddings(symbolic_helper._get_tensor_rank(input), padding)
+    return symbolic_helper._op_with_optional_float_cast(
+        g, "Pad", input, pads_i=paddings, mode_s=mode, opset_before=11
+    )
+
+
+@_onnx_symbolic("aten::replication_pad1d")
+@_onnx_symbolic("aten::replication_pad2d")
+@_onnx_symbolic("aten::replication_pad3d")
+def replication_pad(g: jit_utils.GraphContext, input, padding):
+    mode = "edge"
+    padding = _convert_padding_node(padding)
+    paddings = _prepare_onnx_paddings(symbolic_helper._get_tensor_rank(input), padding)
+    return symbolic_helper._op_with_optional_float_cast(
+        g, "Pad", input, pads_i=paddings, mode_s=mode, opset_before=11
+    )
+
+
+@_onnx_symbolic("aten::pad")
+def pad(
+    g: jit_utils.GraphContext,
+    input: _C.Value,
+    pad: _C.Value,
+    mode: _C.Value,
+    value: _C.Value,
+):
+    mode = symbolic_helper._parse_arg(mode, "s")
+    if mode == "replicate":
+        return replication_pad(g, input, pad)
+    elif mode == "reflect":
+        return reflection_pad(g, input, pad)
+    elif mode == "constant":
+        return constant_pad_nd(g, input, pad, value)
+    elif mode == "circular":
+        return _pad_circular(g, input, pad)
+    else:
+        raise errors.SymbolicValueError(f"Unrecognized padding mode {mode}", input)
+
+
+@_onnx_symbolic(
+    "aten::upsample_nearest1d",
+    decorate=[
+        symbolic_helper._apply_params("upsample_nearest1d", 3, "nearest"),
+        _export("upsample_nearest1d"),
+    ],
+)
+@_onnx_symbolic(
+    "aten::upsample_nearest2d",
+    decorate=[
+        symbolic_helper._apply_params("upsample_nearest2d", 4, "nearest"),
+        _export("upsample_nearest2d"),
+    ],
+)
+@_onnx_symbolic(
+    "aten::upsample_nearest3d",
+    decorate=[
+        symbolic_helper._apply_params("upsample_nearest3d", 5, "nearest"),
+        _export("upsample_nearest3d"),
+    ],
+)
+@_onnx_symbolic(
+    "aten::upsample_linear1d",
+    decorate=[
+        symbolic_helper._apply_params("upsample_linear1d", 3, "linear"),
+        _export("upsample_linear1d"),
+    ],
+)
+@_onnx_symbolic(
+    "aten::upsample_bilinear2d",
+    decorate=[
+        symbolic_helper._apply_params("upsample_bilinear2d", 4, "linear"),
+        _export("upsample_bilinear2d"),
+    ],
+)
+@_onnx_symbolic(
+    "aten::upsample_trilinear3d",
+    decorate=[
+        symbolic_helper._apply_params("upsample_trilinear3d", 5, "linear"),
+        _export("upsample_trilinear3d"),
+    ],
+)
+def _interpolate(name: str, dim: int, interpolate_mode: str):
+    def symbolic_fn(g, input, output_size, *args):
+        scales, align_corners = symbolic_helper._get_interpolate_attributes(
+            g, interpolate_mode, args
+        )
+        symbolic_helper._interpolate_warning(interpolate_mode)
+        align_corners = symbolic_helper._maybe_get_scalar(align_corners)
+        if align_corners:
+            return symbolic_helper._unimplemented(name, "align_corners == True", input)
+        if scales is None:
+            scales = symbolic_helper._interpolate_size_to_scales(
+                g, input, output_size, dim
+            )
+        return g.op("Upsample", input, scales, mode_s=interpolate_mode)
+
+    return symbolic_fn
+
+
+@_onnx_symbolic("aten::__interpolate")
+def __interpolate(
+    g: jit_utils.GraphContext,
+    input,
+    size,
+    scale_factor,
+    mode,
+    align_corners,
+    recompute_scale_factor,
+    antialias,
+):
+    scales, mode = symbolic_helper._interpolate_get_scales_and_mode(
+        g, input, size, scale_factor, mode, align_corners
+    )
+    return g.op("Upsample", input, scales, mode_s=mode)
+
+
+@_onnx_symbolic("aten::bitwise_not")
+def bitwise_not(g: jit_utils.GraphContext, input):
+    if not symbolic_helper._is_bool(input):
+        raise errors.SymbolicValueError(
+            "ONNX export does NOT support exporting bitwise Not "
+            "for non-boolean input values",
+            input,
+        )
+    return g.op("Not", input)
+
+
+@_onnx_symbolic("aten::bitwise_or")
+def bitwise_or(g, self, other):
+    if not symbolic_helper._is_bool(self):
+        raise errors.SymbolicValueError(
+            "ONNX export does NOT support exporting bitwise OR "
+            "for non-boolean input values. self: ",
+            self,
+        )
+    if not symbolic_helper._is_bool(other):
+        raise errors.SymbolicValueError(
+            "ONNX export does NOT support exporting bitwise OR "
+            "for non-boolean input values. other: ",
+            other,
+        )
+    return g.op("Or", self, other)
+
+
+def wrap_logical_op_with_cast_to(to_type):
+    def decorator(fn):
+        @functools.wraps(fn)
+        def wrap_with_cast(g, input, other):
+            to_cast_func = globals()[f"_cast_{to_type}"]
+            return fn(g, to_cast_func(g, input, False), to_cast_func(g, other, False))
+
+        return wrap_with_cast
+
+    return decorator
+
+
+def wrap_logical_op_with_negation(func: Callable) -> Callable:
+    @functools.wraps(func)
+    def wrap_with_not(g, input, other):
+        return g.op("Not", func(g, input, other))
+
+    return wrap_with_not
+
+
+@_onnx_symbolic("aten::__not_")
+def __not_(g: jit_utils.GraphContext, self):
+    if not symbolic_helper._is_bool(self):
+        raise errors.SymbolicValueError(
+            "ONNX export does NOT support exporting bitwise Not "
+            "for non-boolean input values",
+            self,
+        )
+    return g.op("Not", self)
+
+
+@_onnx_symbolic("aten::eq")
+@symbolic_helper.quantized_args(True, True)
+def eq(g: jit_utils.GraphContext, self, other):
+    if isinstance(self.type(), _C.DeviceObjType) and isinstance(
+        other.type(), _C.DeviceObjType
+    ):
+        # ONNX doesn't have devices, so consider them all to be equal.
+        # The no-op check for equality will get constant-folded.
+        return g.op("Constant", value_t=torch.tensor(True, dtype=torch.bool))
+    self_node = self.node()
+    other_node = other.node()
+    if self_node.kind() == other_node.kind() == "onnx::Constant":
+        if self_node.kindOf("value") == other_node.kindOf("value") == "s":
+            # Exporting strings to ONNX is not supported.
+            # If both strings are constant, we can compare them directly.
+            # The no-op check for equality will get constant-folded.
+            return g.op(
+                "Constant",
+                value_t=torch.tensor(
+                    self_node.s("value") == other_node.s("value"),
+                    dtype=torch.bool,
+                ),
+            )
+
+    return g.op("Equal", self, other)
+
+
+@_onnx_symbolic("aten::ne")
+@symbolic_helper.quantized_args(True, True)
+@wrap_logical_op_with_negation
+def ne(g: jit_utils.GraphContext, self, other):
+    return eq(g, self, other)
+
+
+@_onnx_symbolic("aten::gt")
+@symbolic_helper.quantized_args(True, True)
+def gt(g: jit_utils.GraphContext, input, other):
+    return _gt_impl(g, input, other)
+
+
+def _gt_impl(g: jit_utils.GraphContext, input, other):
+    if symbolic_helper._is_bool(input) and symbolic_helper._is_bool(other):
+        input = g.op("Cast", input, to_i=_C_onnx.TensorProtoDataType.INT32)
+        other = g.op("Cast", other, to_i=_C_onnx.TensorProtoDataType.INT32)
+    return g.op("Greater", input, other)
+
+
+@_onnx_symbolic("aten::lt")
+@symbolic_helper.quantized_args(True, True)
+def lt(g: jit_utils.GraphContext, input, other):
+    return _lt_impl(g, input, other)
+
+
+def _lt_impl(g: jit_utils.GraphContext, input, other):
+    if symbolic_helper._is_bool(input) and symbolic_helper._is_bool(other):
+        input = g.op("Cast", input, to_i=_C_onnx.TensorProtoDataType.INT32)
+        other = g.op("Cast", other, to_i=_C_onnx.TensorProtoDataType.INT32)
+    return g.op("Less", input, other)
+
+
+@_onnx_symbolic("aten::ge")
+@symbolic_helper.quantized_args(True, True)
+@wrap_logical_op_with_negation
+def ge(g: jit_utils.GraphContext, input, other):
+    return _lt_impl(g, input, other)
+
+
+@_onnx_symbolic("aten::le")
+@symbolic_helper.quantized_args(True, True)
+@wrap_logical_op_with_negation
+def le(g: jit_utils.GraphContext, input, other):
+    return _gt_impl(g, input, other)
+
+
+@_onnx_symbolic("aten::__and_")
+def __and_(g: jit_utils.GraphContext, input, other):
+    if not symbolic_helper._is_bool(input):
+        raise errors.SymbolicValueError(
+            "ONNX export does NOT support exporting bitwise AND "
+            "for non-boolean input values",
+            input,
+        )
+    if not symbolic_helper._is_bool(other):
+        raise errors.SymbolicValueError(
+            "ONNX export does NOT support exporting bitwise AND "
+            "for non-boolean input values",
+            other,
+        )
+    return g.op("And", input, other)
+
+
+@_onnx_symbolic("aten::__or_")
+def __or_(g: jit_utils.GraphContext, input, other):
+    if not symbolic_helper._is_bool(input):
+        raise errors.SymbolicValueError(
+            "ONNX export does NOT support exporting bitwise OR "
+            "for non-boolean input values",
+            input,
+        )
+    if not symbolic_helper._is_bool(other):
+        raise errors.SymbolicValueError(
+            "ONNX export does NOT support exporting bitwise OR "
+            "for non-boolean input values",
+            other,
+        )
+    return g.op("Or", input, other)
+
+
+@_onnx_symbolic("aten::__xor_")
+def __xor_(g: jit_utils.GraphContext, input, other):
+    if not symbolic_helper._is_bool(input):
+        raise errors.SymbolicValueError(
+            "ONNX export does NOT support exporting bitwise XOR "
+            "for non-boolean input values",
+            input,
+        )
+    if not symbolic_helper._is_bool(other):
+        raise errors.SymbolicValueError(
+            "ONNX export does NOT support exporting bitwise XOR "
+            "for non-boolean input values",
+            other,
+        )
+    return g.op("Xor", input, other)
+
+
+@_onnx_symbolic("aten::logical_and")
+@wrap_logical_op_with_cast_to("Bool")
+def logical_and(g: jit_utils.GraphContext, input, other):
+    return g.op("And", input, other)
+
+
+@_onnx_symbolic("aten::logical_or")
+@wrap_logical_op_with_cast_to("Bool")
+def logical_or(g: jit_utils.GraphContext, input, other):
+    return g.op("Or", input, other)
+
+
+@_onnx_symbolic("aten::logical_xor")
+@wrap_logical_op_with_cast_to("Bool")
+def logical_xor(g: jit_utils.GraphContext, input, other):
+    return g.op("Xor", input, other)
+
+
+@_onnx_symbolic("aten::logical_not")
+def logical_not(g: jit_utils.GraphContext, input):
+    return g.op("Not", g.op("Cast", input, to_i=_C_onnx.TensorProtoDataType.BOOL))
+
+
+@_onnx_symbolic("aten::__rshift_")
+def __rshift_(g: jit_utils.GraphContext, self, other):
+    # make sure to cast other to self's type
+    # (when self is long, make sure that other is not float)
+    self_scalar_type = _type_utils.JitScalarType.from_value(self)
+    if (
+        _type_utils.JitScalarType.from_value(other, _type_utils.JitScalarType.UNDEFINED)
+        != self_scalar_type
+    ):
+        other = g.op(
+            "Cast",
+            other,
+            to_i=self_scalar_type.onnx_type(),
+        )
+
+    two = g.op("Constant", value_t=torch.tensor(2, dtype=torch.float32))
+    # exponent (same type as self) has to be float or double in onnx::Pow
+    if not symbolic_helper._is_fp(self):
+        other = g.op("Cast", other, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+    two_pow = g.op("Pow", two, other)
+    two_pow = g.op(
+        "Cast",
+        two_pow,
+        to_i=self_scalar_type.onnx_type(),
+    )
+    rshift = g.op("Div", self, two_pow)
+    return rshift
+
+
+@_onnx_symbolic("aten::__lshift_")
+def __lshift_(g: jit_utils.GraphContext, self, other):
+    # make sure to cast other to self's type
+    # (when self is long, make sure that other is not float)
+    self_scalar_type = _type_utils.JitScalarType.from_value(self)
+    if (
+        _type_utils.JitScalarType.from_value(other, _type_utils.JitScalarType.UNDEFINED)
+        != self_scalar_type
+    ):
+        other = g.op(
+            "Cast",
+            other,
+            to_i=self_scalar_type.onnx_type(),
+        )
+
+    two = g.op("Constant", value_t=torch.tensor(2, dtype=torch.float32))
+    # exponent (same type as self) has to be float or double in onnx::Pow
+    if not symbolic_helper._is_fp(self):
+        other = g.op("Cast", other, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+    two_pow = g.op("Pow", two, other)
+    two_pow = g.op(
+        "Cast",
+        two_pow,
+        to_i=self_scalar_type.onnx_type(),
+    )
+    lshift = g.op("Mul", self, two_pow)
+    return lshift
+
+
+@_onnx_symbolic("aten::where")
+@symbolic_helper.parse_args("v", "v", "v", "i")
+def where(g: jit_utils.GraphContext, condition, self=None, other=None, _outputs=None):
+    # Assumes that torch.where's first argument takes only Bool and Byte tensors.
+    if not symbolic_helper._is_bool(condition):
+        condition = g.op("Cast", condition, to_i=_C_onnx.TensorProtoDataType.BOOL)
+    if self is None:
+        condition = nonzero(g, condition)
+        return symbolic_helper._unbind_helper(
+            g, condition, g.op("Constant", value_t=torch.tensor(1)), _outputs
+        )
+    return g.op("Where", condition, self, other)
+
+
+@_onnx_symbolic("aten::log_softmax")
+@symbolic_helper.parse_args("v", "i", "none")
+def log_softmax(g: jit_utils.GraphContext, input, dim, dtype=None):
+    # PyTorch dim and ONNX axis have different meanings.
+    # See Softmax comment for details.
+    # TODO: remove this as onnx opset 11 spec allows negative axes
+    input_dim = symbolic_helper._get_tensor_rank(input)
+    if input_dim is None:
+        return symbolic_helper._unimplemented(
+            "dim",
+            "ONNX and PyTorch use different strategies to split the input. "
+            "Input rank must be known at export time.",
+        )
+    if dim < 0:
+        dim = input_dim + dim
+    is_transpose_required = input_dim != dim + 1
+    # ONNX only supports log_softmax with dim = -1. Transpose must be added before and after log_softmax to support other cases.
+    if is_transpose_required:
+        axes = list(range(input_dim))
+        axes[dim], axes[-1] = axes[-1], axes[dim]
+        input = g.op("Transpose", input, perm_i=axes)
+        dim = input_dim - 1
+    return_op = g.op("LogSoftmax", input, axis_i=dim)
+    if dtype and dtype.node().kind() != "prim::Constant":
+        parsed_dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+        return_op = g.op(
+            "Cast", return_op, to_i=_type_utils.JitScalarType(parsed_dtype).onnx_type()
+        )
+    if is_transpose_required:
+        return_op = g.op("Transpose", return_op, perm_i=axes)  # type: ignore[possibly-undefined]
+    return return_op
+
+
+@_onnx_symbolic("aten::_log_softmax")
+@symbolic_helper.parse_args("v", "i", "i")
+def _log_softmax(g: jit_utils.GraphContext, input, dim, half_to_float):
+    if (
+        half_to_float
+        and _type_utils.JitScalarType.from_value(
+            input, _type_utils.JitScalarType.UNDEFINED
+        )
+        == _type_utils.JitScalarType.HALF
+    ):
+        input = g.op("Cast", input, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+    return log_softmax(g, input, dim)
+
+
+@_onnx_symbolic("aten::_convolution")
+@symbolic_helper.parse_args(
+    "v", "v", "v", "is", "is", "is", "i", "is", "i", "i", "i", "i", "i"
+)
+def _convolution(
+    g: jit_utils.GraphContext,
+    input,
+    weight,
+    bias,
+    stride,
+    padding,
+    dilation,
+    transposed,
+    output_padding,
+    groups,
+    benchmark,
+    deterministic,
+    cudnn_enabled,
+    allow_tf32=None,
+):
+    weight_size = symbolic_helper._get_tensor_sizes(weight)
+    try:
+        kernel_shape = weight_size[2:]
+    except Exception:
+        # FIXME(justinchuby): Avoid catching Exception.
+        # Catch a more specific exception instead.
+        kernel_shape = None
+
+    if kernel_shape is None or any(i is None for i in kernel_shape):
+        raise errors.SymbolicValueError(
+            "Unsupported: ONNX export of convolution for kernel of unknown shape.",
+            input,
+        )
+
+    args = [input, weight]
+    # ONNX only supports 1D bias
+    if (
+        not symbolic_helper._is_none(bias)
+        and symbolic_helper._get_tensor_rank(bias) == 1
+    ):
+        args.append(bias)
+
+    kwargs = {
+        "kernel_shape_i": weight_size[2:],
+        "strides_i": stride,
+        # NB: ONNX supports asymmetric padding, whereas PyTorch supports only
+        # symmetric padding
+        "pads_i": padding + padding,
+        "dilations_i": dilation,
+        "group_i": groups,
+    }
+
+    if any(o != 0 for o in output_padding):
+        # ONNX supports both output_shape and output_padding. they are equivalent expressive.
+        # output_padding is more straightforward, so we use it here.
+        # output_shape = stride * (input_shape - 1) + output_padding + kernel_shape - padding * 2
+        assert transposed
+        assert len(stride) == len(output_padding)
+        kwargs["output_padding_i"] = output_padding
+
+    n = g.op("ConvTranspose" if transposed else "Conv", *args, **kwargs)
+
+    if (
+        not symbolic_helper._is_none(bias)
+        and symbolic_helper._get_tensor_rank(bias) != 1
+    ):
+        return g.op("Add", n, bias)
+    else:
+        return n
+
+
+@_onnx_symbolic("aten::_convolution_mode")
+@symbolic_helper.parse_args(
+    "v",
+    "v",
+    "v",
+    "is",
+    "s",
+    "is",
+    "i",
+)
+def _convolution_mode(
+    g: jit_utils.GraphContext,
+    input,
+    weight,
+    bias,
+    stride,
+    padding,
+    dilation,
+    groups,
+):
+    weight_size = symbolic_helper._get_tensor_sizes(weight)
+    try:
+        kernel_shape = weight_size[2:]
+    except Exception:
+        # FIXME(justinchuby): Avoid catching Exception.
+        # Catch a more specific exception instead.
+        kernel_shape = None
+
+    if kernel_shape is None or any(i is None for i in kernel_shape):
+        raise errors.SymbolicValueError(
+            "Unsupported: ONNX export of convolution for kernel of unknown shape.",
+            input,
+        )
+
+    args = [input, weight]
+    # ONNX only supports 1D bias
+    if (
+        not symbolic_helper._is_none(bias)
+        and symbolic_helper._get_tensor_rank(bias) == 1
+    ):
+        args.append(bias)
+
+    if padding == "valid":
+        padding = "VALID"
+    elif padding == "same":
+        padding = "SAME_UPPER"
+    kwargs = {
+        "kernel_shape_i": weight_size[2:],
+        "strides_i": stride,
+        "auto_pad_s": padding,
+        "dilations_i": dilation,
+        "group_i": groups,
+    }
+
+    n = g.op("Conv", *args, **kwargs)
+
+    if (
+        not symbolic_helper._is_none(bias)
+        and symbolic_helper._get_tensor_rank(bias) != 1
+    ):
+        return g.op("Add", n, bias)
+    else:
+        return n
+
+
+@_onnx_symbolic("aten::convolution")
+@symbolic_helper.parse_args("v", "v", "v", "is", "is", "is", "i", "is", "i")
+def convolution(
+    g: jit_utils.GraphContext,
+    input,
+    weight,
+    bias,
+    stride,
+    padding,
+    dilation,
+    transposed,
+    output_padding,
+    groups,
+):
+    return _convolution(
+        g,
+        input,
+        weight,
+        bias,
+        stride,
+        padding,
+        dilation,
+        transposed,
+        output_padding,
+        groups,
+        None,
+        None,
+        None,
+        None,
+    )
+
+
+@_onnx_symbolic("aten::conv1d")
+@symbolic_helper.parse_args("v", "v", "v", "is", "v", "is", "i")
+def conv1d(
+    g: jit_utils.GraphContext, input, weight, bias, stride, padding, dilation, groups
+):
+    str_padding = symbolic_helper._parse_arg(padding, "s")
+    if str_padding in ["valid", "same"]:
+        return _convolution_mode(
+            g,
+            input,
+            weight,
+            bias,
+            stride,
+            str_padding,
+            dilation,
+            groups,
+        )
+    else:
+        padding = symbolic_helper._parse_arg(padding, "is")
+        return _convolution(
+            g,
+            input,
+            weight,
+            bias,
+            stride,
+            padding,
+            dilation,
+            False,
+            (),
+            groups,
+            None,
+            None,
+            None,
+            None,
+        )
+
+
+@_onnx_symbolic("aten::conv2d")
+@symbolic_helper.parse_args("v", "v", "v", "is", "v", "is", "i")
+def conv2d(
+    g: jit_utils.GraphContext, input, weight, bias, stride, padding, dilation, groups
+):
+    str_padding = symbolic_helper._parse_arg(padding, "s")
+    if str_padding in ["valid", "same"]:
+        return _convolution_mode(
+            g,
+            input,
+            weight,
+            bias,
+            stride,
+            str_padding,
+            dilation,
+            groups,
+        )
+    else:
+        padding = symbolic_helper._parse_arg(padding, "is")
+        return _convolution(
+            g,
+            input,
+            weight,
+            bias,
+            stride,
+            padding,
+            dilation,
+            False,
+            (),
+            groups,
+            None,
+            None,
+            None,
+            None,
+        )
+
+
+@_onnx_symbolic("aten::conv3d")
+@symbolic_helper.parse_args("v", "v", "v", "is", "v", "is", "i")
+def conv3d(
+    g: jit_utils.GraphContext, input, weight, bias, stride, padding, dilation, groups
+):
+    str_padding = symbolic_helper._parse_arg(padding, "s")
+    if str_padding in ["valid", "same"]:
+        return _convolution_mode(
+            g,
+            input,
+            weight,
+            bias,
+            stride,
+            str_padding,
+            dilation,
+            groups,
+        )
+    else:
+        padding = symbolic_helper._parse_arg(padding, "is")
+        return _convolution(
+            g,
+            input,
+            weight,
+            bias,
+            stride,
+            padding,
+            dilation,
+            False,
+            (),
+            groups,
+            None,
+            None,
+            None,
+            None,
+        )
+
+
+@_onnx_symbolic("aten::conv_transpose1d")
+@symbolic_helper.parse_args("v", "v", "v", "is", "is", "is", "i", "is")
+def conv_transpose1d(
+    g: jit_utils.GraphContext,
+    input,
+    weight,
+    bias,
+    stride,
+    padding,
+    output_padding,
+    groups,
+    dilation,
+):
+    return _convolution(
+        g,
+        input,
+        weight,
+        bias,
+        stride,
+        padding,
+        dilation,
+        True,
+        output_padding,
+        groups,
+        None,
+        None,
+        None,
+        None,
+    )
+
+
+@_onnx_symbolic("aten::conv_transpose2d")
+@symbolic_helper.parse_args("v", "v", "v", "is", "is", "is", "i", "is")
+def conv_transpose2d(
+    g: jit_utils.GraphContext,
+    input,
+    weight,
+    bias,
+    stride,
+    padding,
+    output_padding,
+    groups,
+    dilation,
+):
+    return _convolution(
+        g,
+        input,
+        weight,
+        bias,
+        stride,
+        padding,
+        dilation,
+        True,
+        output_padding,
+        groups,
+        None,
+        None,
+        None,
+        None,
+    )
+
+
+@_onnx_symbolic("aten::conv_transpose3d")
+@symbolic_helper.parse_args("v", "v", "v", "is", "is", "is", "i", "is")
+def conv_transpose3d(
+    g: jit_utils.GraphContext,
+    input,
+    weight,
+    bias,
+    stride,
+    padding,
+    output_padding,
+    groups,
+    dilation,
+):
+    return _convolution(
+        g,
+        input,
+        weight,
+        bias,
+        stride,
+        padding,
+        dilation,
+        True,
+        output_padding,
+        groups,
+        None,
+        None,
+        None,
+        None,
+    )
+
+
+@_onnx_symbolic("aten::batch_norm")
+@symbolic_helper.parse_args("v", "v", "v", "v", "v", "i", "f", "f", "i")
+def batch_norm(
+    g: jit_utils.GraphContext,
+    input,
+    weight,
+    bias,
+    running_mean,
+    running_var,
+    training,
+    momentum,
+    eps,
+    cudnn_enabled,
+):
+    symbolic_helper.check_training_mode(training, "batch_norm")
+
+    if (
+        torch.is_autocast_enabled()
+        and not symbolic_helper.args_have_same_dtype(
+            [input, weight, bias, running_mean, running_var]
+        )
+        and GLOBALS.export_onnx_opset_version < 15
+    ):
+        return symbolic_helper._onnx_opset_unsupported_detailed(
+            "BatchNormalization",
+            9,
+            15,
+            "All input tensors must have the same `dtype`."
+            " Turn off Autocast or export using opset version 15.",
+            input,
+        )
+
+    weight, bias, running_mean, running_var = symbolic_helper._batchnorm_helper(
+        g, input, weight, bias, running_mean, running_var
+    )
+    out = g.op(
+        "BatchNormalization",
+        input,
+        weight,
+        bias,
+        running_mean,
+        running_var,
+        epsilon_f=eps,
+        momentum_f=1 - momentum,
+        outputs=1 if not training else 5,
+    )
+    if not training:
+        return out
+    else:
+        res, new_running_mean, new_running_var, saved_mean, saved_var = out
+        new_running_mean.setType(running_mean.type())
+        new_running_var.setType(running_var.type())
+        saved_mean.setDebugName("batch_norm_dead_output-" + saved_mean.debugName())
+        saved_var.setDebugName("batch_norm_dead_output-" + saved_var.debugName())
+        return res
+
+
+@_onnx_symbolic("aten::native_layer_norm")
+@symbolic_helper.quantized_args(True, False, False, False)
+@symbolic_helper.parse_args("v", "is", "v", "v", "f")
+def native_layer_norm(
+    g: jit_utils.GraphContext,
+    input: _C.Value,
+    normalized_shape: Sequence[int],
+    weight: _C.Value,
+    bias: _C.Value,
+    eps: float,
+) -> tuple[_C.Value, _C.Value, _C.Value]:
+    axes = [-i for i in range(len(normalized_shape), 0, -1)]
+
+    two_cst = symbolic_helper._generate_wrapped_number(g, 2.0)
+    eps_cst = symbolic_helper._generate_wrapped_number(g, eps)
+
+    if g.opset < 18:
+        mean = g.op("ReduceMean", input, axes_i=axes)
+    else:
+        mean = g.op(
+            "ReduceMean",
+            input,
+            g.op("Constant", value_t=torch.tensor(axes, dtype=torch.long)),
+        )
+
+    numerator = sub(g, input, mean)
+
+    # Cast it to eps dtype to avoid precision loss
+    is_type_half = (
+        _type_utils.JitScalarType.from_value(numerator)
+        == _type_utils.JitScalarType.HALF
+    )
+    if is_type_half:
+        eps_dtype = _type_utils.JitScalarType.from_value(eps_cst)
+        numerator = g.op(
+            "Cast", numerator, to_i=_type_utils.JitScalarType(eps_dtype).onnx_type()
+        )
+
+    # variance = e((x - e(x))^2), and (x - e(x)) is the numerator in the layer_norm formula
+    if g.opset < 18:
+        variance = g.op("ReduceMean", pow(g, numerator, two_cst), axes_i=axes)
+    else:
+        variance = g.op(
+            "ReduceMean",
+            pow(g, numerator, two_cst),
+            g.op("Constant", value_t=torch.tensor(axes, dtype=torch.long)),
+        )
+
+    denominator = sqrt(g, g.op("Add", variance, eps_cst))
+    normalized = g.op("Div", numerator, denominator)
+
+    # Cast back to input type as eps related ops are all done
+    if is_type_half:
+        input_dtype = _type_utils.JitScalarType.from_value(input)
+        normalized = g.op(
+            "Cast", normalized, to_i=_type_utils.JitScalarType(input_dtype).onnx_type()
+        )
+
+    if not (weight is None or symbolic_helper._is_none(weight)):
+        normalized = mul(g, normalized, weight)
+    if not (bias is None or symbolic_helper._is_none(bias)):
+        normalized = add(g, normalized, bias)
+
+    # rdenominator := 1 / sqrt(variance + eps)
+    # According to aten::native_layer_norm, rdenominator should have the same dtype as input,
+    # mean and normalized, so we need to Cast it back
+    if is_type_half:
+        denominator = g.op(
+            "Cast",
+            denominator,
+            to_i=_type_utils.JitScalarType(input_dtype).onnx_type(),  # type: ignore[possibly-undefined]
+        )
+        rdenominator = g.op("Reciprocal", denominator)
+    else:
+        rdenominator = reciprocal(g, denominator)
+
+    return normalized, mean, rdenominator
+
+
+@_onnx_symbolic("aten::layer_norm")
+@symbolic_helper.quantized_args(True, False, False, False)
+@symbolic_helper.parse_args("v", "is", "v", "v", "f", "b")
+def layer_norm(
+    g: jit_utils.GraphContext,
+    input: _C.Value,
+    normalized_shape: Sequence[int],
+    weight: _C.Value,
+    bias: _C.Value,
+    eps: float,
+    cudnn_enable: bool,
+) -> _C.Value:
+    normalized, _, _ = native_layer_norm(g, input, normalized_shape, weight, bias, eps)
+    return normalized
+
+
+@_onnx_symbolic("aten::instance_norm")
+@symbolic_helper.parse_args("v", "v", "v", "v", "v", "b", "f", "f", "b")
+def instance_norm(
+    g: jit_utils.GraphContext,
+    input,
+    weight,
+    bias,
+    running_mean,
+    running_var,
+    use_input_stats: bool,
+    momentum: Number,
+    eps: Number,
+    cudnn_enabled: bool,
+):
+    symbolic_helper.check_training_mode(use_input_stats, "instance_norm")
+    channel_size = symbolic_helper._get_tensor_dim_size(input, 1)
+    if weight is None or symbolic_helper._is_none(weight):
+        if channel_size is None:
+            raise errors.SymbolicValueError(
+                "Unsupported: ONNX export of instance_norm for unknown channel size.",
+                input,
+            )
+        weight_value = torch.tensor(
+            [1.0] * channel_size,
+            dtype=_type_utils.JitScalarType.from_value(input).dtype(),
+        )
+        weight = g.op("Constant", value_t=weight_value)
+    if bias is None or symbolic_helper._is_none(bias):
+        if channel_size is None:
+            raise errors.SymbolicValueError(
+                "Unsupported: ONNX export of instance_norm for unknown channel size.",
+                input,
+            )
+        bias_value = torch.tensor(
+            [0.0] * channel_size,
+            dtype=_type_utils.JitScalarType.from_value(input).dtype(),
+        )
+        bias = g.op("Constant", value_t=bias_value)
+    if (
+        running_mean is None
+        or symbolic_helper._is_none(running_mean)
+        or running_var is None
+        or symbolic_helper._is_none(running_var)
+    ):
+        return g.op("InstanceNormalization", input, weight, bias, epsilon_f=eps)
+    else:
+        input_size = symbolic_helper._get_tensor_sizes(input)
+        # If input shape is [N, C, H, W], reshape to [1, N * C, H, W] and call batch_norm.
+        # For more information instance_norm():
+        # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Normalization.cpp#L542
+        input_size_reshape = input_size.copy()
+        n = input_size[0]
+        if n is None:
+            raise errors.SymbolicValueError(
+                "Unsupported: ONNX export of instance_norm training for unknown "
+                "batch size.",
+                input,
+            )
+        c = input_size[1]
+        input_size_reshape[0] = 1
+        input_size_reshape[1] = n * c
+        weight_ = repeat(
+            g, weight, g.op("Constant", value_t=torch.tensor([n], dtype=torch.int64))
+        )
+        bias_ = repeat(
+            g, bias, g.op("Constant", value_t=torch.tensor([n], dtype=torch.int64))
+        )
+        running_mean_ = repeat(
+            g,
+            running_mean,
+            g.op("Constant", value_t=torch.tensor([n], dtype=torch.int64)),
+        )
+        running_var_ = repeat(
+            g,
+            running_var,
+            g.op("Constant", value_t=torch.tensor([n], dtype=torch.int64)),
+        )
+        input_reshaped = g.op(
+            "Reshape",
+            input,
+            g.op("Constant", value_t=torch.LongTensor(input_size_reshape)),
+        )
+        out = batch_norm(
+            g,
+            input_reshaped,
+            weight_,
+            bias_,
+            running_mean_,
+            running_var_,
+            use_input_stats,
+            momentum,
+            eps,
+            cudnn_enabled,
+        )
+        return view(g, out, g.op("Constant", value_t=torch.tensor(input_size)))
+
+
+@_onnx_symbolic("aten::unfold")
+@symbolic_helper.parse_args("v", "i", "i", "i")
+def unfold(g: jit_utils.GraphContext, input, dimension, size, step):
+    sizes = symbolic_helper._get_tensor_sizes(input)
+    # FIXME(justinchuby): Get rid of the try catch here to improve readability
+    try:
+        sizedim = sizes[dimension]
+    except Exception:
+        # FIXME(justinchuby): Avoid catching Exception.
+        # Catch a more specific exception instead.
+        sizedim = None
+    if sizedim is not None:
+        low_indices = range(0, sizedim, step)
+        hi_indices = range(size, sizedim + 1, step)
+        stack = [
+            symbolic_helper._slice_helper(
+                g, input, axes=[dimension], starts=[low], ends=[hi]
+            )
+            for low, hi in zip(low_indices, hi_indices)
+        ]
+        ndim = len(sizes)
+        perm = list(range(0, ndim))
+        perm.append(perm.pop(dimension))
+        unsqueeze = [
+            symbolic_helper._unsqueeze_helper(
+                g, g.op("Transpose", t, perm_i=perm), [dimension]
+            )
+            for t in stack
+        ]
+        return g.op("Concat", *unsqueeze, axis_i=dimension)
+    else:
+        return symbolic_helper._unimplemented(
+            "Unfold", "input size not accessible", input
+        )
+
+
+@_onnx_symbolic("aten::elu")
+@symbolic_helper.quantized_args(True)
+@symbolic_helper.parse_args("v", "t", "t", "t")
+def elu(g: jit_utils.GraphContext, input, alpha, scale, input_scale):
+    if scale and scale != 1.0:
+        return symbolic_helper._unimplemented(
+            "scale", "does not support scale in Elu", scale
+        )
+    if input_scale and input_scale != 1.0:
+        return symbolic_helper._unimplemented(
+            "input_scale", "does not support input_scale in Elu", input_scale
+        )
+    # See Note [Export inplace]
+    return g.op("Elu", input, alpha_f=symbolic_helper._scalar(alpha))
+
+
+@_onnx_symbolic("aten::selu")
+@symbolic_helper.quantized_args(True)
+def selu(g: jit_utils.GraphContext, input):
+    return g.op("Selu", input)
+
+
+@_onnx_symbolic("aten::index_select")
+@symbolic_helper.parse_args("v", "i", "v")
+def index_select(g: jit_utils.GraphContext, self, dim, index):
+    # In case of a scalar index, index_select returns a tensor with the same rank as the input.
+    # To match this behavior in ONNX, we make index a 1D tensor so that the following gather
+    # also produces a tensor with the same rank as the input.
+    return symbolic_helper._select_helper(g, self, dim, index)
+
+
+@_onnx_symbolic("aten::index_put")
+def index_put(g: jit_utils.GraphContext, self, indices_list_value, values, accumulate):
+    if symbolic_helper._is_packed_list(indices_list_value):
+        indices_list = symbolic_helper._unpack_list(indices_list_value)
+    else:
+        indices_list = [indices_list_value]
+
+    accumulate = symbolic_helper._parse_arg(accumulate, "b")
+
+    if len(indices_list) == 0:
+        if accumulate:
+            return add(g, self, values)
+        return values
+    symbolic_helper._onnx_opset_unsupported("index_put", 9, 11, self)
+
+
+@_onnx_symbolic("aten::index_fill")
+def index_fill(g: jit_utils.GraphContext, self, dim, index, value):
+    dim_value = symbolic_helper._parse_arg(dim, "i")
+    expanded_index_shape, expanded_index = symbolic_helper._index_fill_reshape_helper(
+        g, self, dim, index
+    )
+    value = symbolic_helper._maybe_get_scalar(value)
+    value = symbolic_helper._if_scalar_type_as(value, self)
+    expanded_value = expand(g, value, expanded_index_shape, None)
+
+    return scatter(g, self, dim, expanded_index, expanded_value)
+
+
+@_onnx_symbolic("aten::index_copy")
+def index_copy(g: jit_utils.GraphContext, self, dim, index, source):
+    dim_value = symbolic_helper._parse_arg(dim, "i")
+    expanded_index_shape, expanded_index = symbolic_helper._index_fill_reshape_helper(
+        g, self, dim, index
+    )
+    return scatter(g, self, dim, expanded_index, source)
+
+
+@_onnx_symbolic("aten::bucketize")
+@symbolic_helper.parse_args("v", "v", "b", "b")
+def bucketize(
+    g: jit_utils.GraphContext, self, boundaries, out_int32=False, right=False
+):
+    out_type = _C_onnx.TensorProtoDataType.INT64
+    if out_int32:
+        out_type = _C_onnx.TensorProtoDataType.INT32
+    # A tensor expanded_boundaries is created such that it
+    # contains a copy of boundaries for each element of self.
+    new_shape = g.op("Concat", g.op("Shape", boundaries), g.op("Shape", self), axis_i=0)
+    # Unsqueeze step is performed to respect ONNX's numpy style broadcasting for comparison ops
+    # https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md
+    tensor_rank = symbolic_helper._get_tensor_rank(self)
+    assert tensor_rank is not None
+    unsqueeze_axes = list(range(1, tensor_rank + 1))
+    expanded_boundaries = expand(
+        g,
+        symbolic_helper._unsqueeze_helper(g, boundaries, unsqueeze_axes),
+        new_shape,
+        None,
+    )
+    # Compare each element of self to boundaries to get a tensor
+    # with leading 1s and trailing 0s.
+    # e.g., 4 > [1, 3, 4] = [1, 1, 0]
+    # The index of the last 1 is the bucket where the element should go.
+    if right:
+        cond = ge(g, self, expanded_boundaries)
+    else:
+        cond = gt(g, self, expanded_boundaries)
+    cond_out = g.op("Cast", cond, to_i=out_type)
+    # Sum to get the number of 1s corresponding to each element,
+    # which is the same as the bucket index.
+    # e.g., sum(4 > [1, 3, 4]) = sum([1, 1, 0]) = 2
+    return symbolic_helper._reducesum_helper(g, cond_out, axes_i=[0], keepdims_i=0)
+
+
+@_onnx_symbolic("aten::type_as")
+def type_as(g: jit_utils.GraphContext, self, other):
+    self_dtype = symbolic_helper._try_get_scalar_type(self)
+    other_dtype = symbolic_helper._try_get_scalar_type(other)
+    if self_dtype == other_dtype and self_dtype is not None:
+        return self
+    if other_dtype is not None:
+        return g.op(
+            "Cast",
+            self,
+            to_i=other_dtype.onnx_type(),
+        )
+
+    raise errors.SymbolicValueError(
+        "Unsupported: ONNX export of type_as for tensor "
+        "of unknown dtype. Please check if the dtype of the "
+        "parameter passed to the type_as function is correct.",
+        other,
+    )
+
+
+@_onnx_symbolic("aten::cosine_similarity")
+@symbolic_helper.parse_args("v", "v", "i", "f")
+def cosine_similarity(g: jit_utils.GraphContext, x1, x2, dim, eps):
+    cross = symbolic_helper._reducesum_helper(
+        g, mul(g, x1, x2), axes_i=[dim], keepdims_i=0
+    )
+    x1_l2 = symbolic_helper._reducesum_helper(
+        g, mul(g, x1, x1), axes_i=[dim], keepdims_i=0
+    )
+    x2_l2 = symbolic_helper._reducesum_helper(
+        g, mul(g, x2, x2), axes_i=[dim], keepdims_i=0
+    )
+    div_tens = max(
+        g, sqrt(g, mul(g, x1_l2, x2_l2)), g.op("Constant", value_t=torch.tensor([eps]))
+    )
+    return div(g, cross, div_tens)
+
+
+@_onnx_symbolic("aten::pairwise_distance")
+def pairwise_distance(g: jit_utils.GraphContext, input1, input2, p, eps, keepdim):
+    if not symbolic_helper._is_value(eps):
+        eps = g.op("Constant", value_t=torch.tensor([eps]))
+    inv_p = div(
+        g,
+        g.op("Constant", value_t=torch.tensor([1], dtype=torch.float)),
+        add(g, p, eps),
+    )
+    summation = symbolic_helper._reducesum_helper(
+        g,
+        pow(g, sub(g, input1, input2), p),
+        axes_i=[-1],
+        keepdims_i=symbolic_helper._parse_arg(keepdim, "i"),
+    )
+    return pow(g, summation, inv_p)
+
+
+@_onnx_symbolic("aten::clone")
+# ignore clone operators that are inserted by PyTorch autograd
+def clone(g: jit_utils.GraphContext, input, unused_memory_format):
+    return input
+
+
+@_onnx_symbolic("aten::abs")
+def abs(g: jit_utils.GraphContext, self):
+    return g.op("Abs", self)
+
+
+@_onnx_symbolic("aten::log")
+def log(g: jit_utils.GraphContext, self):
+    return g.op("Log", self)
+
+
+@_onnx_symbolic("aten::log1p")
+def log1p(g: jit_utils.GraphContext, self):
+    return log(g, add(g, symbolic_helper._if_scalar_type_as(torch.ones(1), self), self))
+
+
+@_onnx_symbolic("aten::log10")
+def log10(g: jit_utils.GraphContext, self):
+    _ln10 = 2.30258509299404568401
+    return g.op("Div", log(g, self), g.op("Constant", value_t=torch.tensor([_ln10])))
+
+
+@_onnx_symbolic("aten::pow")
+def pow(g: jit_utils.GraphContext, self, exponent):
+    f_dtype = _type_utils.JitScalarType.from_value(self)
+    if not symbolic_helper._is_fp(self):
+        f_dtype = _type_utils.JitScalarType.FLOAT
+        self = g.op("Cast", self, to_i=f_dtype.onnx_type())
+    if not symbolic_helper._is_fp(exponent):
+        exponent = g.op(
+            "Cast",
+            exponent,
+            to_i=f_dtype.onnx_type(),
+        )
+    pow = g.op("Pow", self, exponent)
+    return pow
+
+
+@_onnx_symbolic("aten::clamp")
+def clamp(g: jit_utils.GraphContext, self, min, max):
+    # min or max may be None that we need to dispatch to
+    # Clip separately, as ONNX does not have None syntax
+    if symbolic_helper._is_none(min):
+        return clamp_max(g, self, max)
+    elif symbolic_helper._is_none(max):
+        return clamp_min(g, self, min)
+    else:
+        if symbolic_helper._is_constant(min) and symbolic_helper._is_constant(max):
+            return symbolic_helper._op_with_optional_float_cast(
+                g,
+                "Clip",
+                self,
+                min_f=symbolic_helper._parse_arg(min, "f"),
+                max_f=symbolic_helper._parse_arg(max, "f"),
+                opset_before=12,
+            )
+        else:
+            return clamp_max(g, clamp_min(g, self, min), max)
+
+
+@_onnx_symbolic("aten::clamp_min")
+@symbolic_helper.parse_args("v", "v")
+def clamp_min(g: jit_utils.GraphContext, self, min):
+    if symbolic_helper._is_constant(min):
+        return symbolic_helper._op_with_optional_float_cast(
+            g, "Clip", self, min_f=symbolic_helper._parse_arg(min, "f"), opset_before=12
+        )
+    else:
+        dtype = _type_utils.JitScalarType.from_value(self)
+        min = g.op("Cast", min, to_i=dtype.onnx_type())
+        return symbolic_helper._op_with_optional_float_cast(
+            g, "Max", self, min, opset_before=12
+        )
+
+
+@_onnx_symbolic("aten::clamp_max")
+@symbolic_helper.parse_args("v", "v")
+def clamp_max(g: jit_utils.GraphContext, self, max):
+    if symbolic_helper._is_constant(max):
+        return symbolic_helper._op_with_optional_float_cast(
+            g, "Clip", self, max_f=symbolic_helper._parse_arg(max, "f"), opset_before=12
+        )
+    else:
+        dtype = _type_utils.JitScalarType.from_value(self)
+        max = g.op("Cast", max, to_i=dtype.onnx_type())
+        return symbolic_helper._op_with_optional_float_cast(
+            g, "Min", self, max, opset_before=12
+        )
+
+
+@_onnx_symbolic("aten::max")
+# torch.max (same for torch.min) actually has two interfaces smashed together:
+# torch.max(x, dim, keepdim) and torch.max(x, y)
+# TODO(justinchuby): Support multiple quantized args in output
+def max(g: jit_utils.GraphContext, self, dim_or_y=None, keepdim=None):
+    return symbolic_helper._max_helper(g, self, dim_or_y, keepdim)
+
+
+@_onnx_symbolic("aten::maximum")
+@symbolic_helper.quantized_args(True, True)
+def maximum(g: jit_utils.GraphContext, input, other):
+    return max(g, input, dim_or_y=other)
+
+
+@_onnx_symbolic("aten::min")
+# TODO(justinchuby): Support multiple quantized args in output
+def min(g: jit_utils.GraphContext, self, dim_or_y=None, keepdim=None):
+    return symbolic_helper._min_helper(g, self, dim_or_y, keepdim)
+
+
+@_onnx_symbolic("aten::minimum")
+@symbolic_helper.quantized_args(True, True)
+def minimum(g: jit_utils.GraphContext, input, other):
+    return min(g, input, dim_or_y=other)
+
+
+@_onnx_symbolic("aten::amax")
+@symbolic_helper.quantized_args(True)
+@symbolic_helper.parse_args("v", "is", "i")
+def amax(g: jit_utils.GraphContext, self, dim, keepdim):
+    return g.op("ReduceMax", self, axes_i=dim, keepdims_i=keepdim)
+
+
+@_onnx_symbolic("aten::amin")
+@symbolic_helper.quantized_args(True)
+@symbolic_helper.parse_args("v", "is", "i")
+def amin(g: jit_utils.GraphContext, self, dim, keepdim):
+    return g.op("ReduceMin", self, axes_i=dim, keepdims_i=keepdim)
+
+
+@_onnx_symbolic("aten::aminmax")
+@symbolic_helper.quantized_args(True)
+@symbolic_helper.parse_args("v", "v", "i")
+def aminmax(g: jit_utils.GraphContext, self, dim, keepdim):
+    reduce_kwargs = {"keepdims_i": keepdim}
+    if not symbolic_helper._is_none(dim):
+        dim = symbolic_helper._get_const(dim, "i", "dim")
+        reduce_kwargs["axes_i"] = [dim]
+
+    return g.op("ReduceMin", self, **reduce_kwargs), g.op(
+        "ReduceMax", self, **reduce_kwargs
+    )
+
+
+@_onnx_symbolic("aten::exp")
+def exp(g: jit_utils.GraphContext, self):
+    return g.op("Exp", self)
+
+
+@_onnx_symbolic("aten::dropout_")
+@_onnx_symbolic("aten::dropout")
+@symbolic_helper.parse_args("v", "f", "i")
+def dropout(g: jit_utils.GraphContext, input, p, train):
+    symbolic_helper.check_training_mode(train, "dropout")
+    # if train is False, dropout is no-op
+    if not train:
+        return input
+    r, _ = g.op("Dropout", input, ratio_f=p, outputs=2)
+    return r
+
+
+@_onnx_symbolic(
+    "aten::alpha_dropout_",
+    decorate=[symbolic_helper._apply_params("aten::alpha_dropout_")],
+)  # See Note [Export inplace]
+@_onnx_symbolic(
+    "aten::feature_alpha_dropout_",
+    decorate=[symbolic_helper._apply_params("aten::feature_alpha_dropout_")],
+)
+@_onnx_symbolic(
+    "aten::feature_dropout_",
+    decorate=[symbolic_helper._apply_params("aten::feature_dropout_")],
+)
+@_onnx_symbolic(
+    "aten::feature_alpha_dropout",
+    decorate=[symbolic_helper._apply_params("aten::feature_alpha_dropout")],
+)
+@_onnx_symbolic(
+    "aten::alpha_dropout",
+    decorate=[symbolic_helper._apply_params("aten::alpha_dropout")],
+)
+@_onnx_symbolic(
+    "aten::feature_dropout",
+    decorate=[symbolic_helper._apply_params("aten::feature_dropout")],
+)
+def _unsupported_dropout(name: str):
+    @symbolic_helper.parse_args("v", "none", "b")
+    def feature_dropout(g, input, p, train):
+        # NB: In inference mode, FeatureDropout is exported as an identity op.
+        if train:
+            return symbolic_helper._unimplemented(name, "training mode", input)
+        return input
+
+    return feature_dropout
+
+
+@_onnx_symbolic("aten::norm")
+@symbolic_helper.parse_args("v", "t", "is", "i", "v")
+def norm(g: jit_utils.GraphContext, self, p, dim, keepdim, dtype=None):
+    if p == 1:
+        f = symbolic_helper._reduce_op_symbolic_helper("ReduceL1")
+    elif p == 2:
+        f = symbolic_helper._reduce_op_symbolic_helper("ReduceL2")
+    else:
+        raise errors.SymbolicValueError(
+            "ONNX export only p-norms with p of 1 or 2", self
+        )
+    result = f(g, self, dim=dim, keepdim=keepdim)
+    if dtype is not None:
+        dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+        result = g.op("Cast", result, to_i=_type_utils.JitScalarType(dtype).onnx_type())
+    return result
+
+
+@_onnx_symbolic("aten::conv_tbc")
+@symbolic_helper.parse_args("v", "v", "v", "i")
+def conv_tbc(g: jit_utils.GraphContext, input, weight, bias, pad):
+    # input must have 3 dimensions, see:
+    # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/ConvolutionTBC.cpp#L8-L10
+    # input = (time, batch, in_channels)
+    # weight = (kernel_width, in_channels, out_channels)
+    # bias = (out_channels,)
+    input = g.op("Transpose", input, perm_i=[1, 2, 0])
+    weight = g.op("Transpose", weight, perm_i=[2, 1, 0])
+    conv = conv1d(g, input, weight, bias, [1], [pad], [1], 1)
+    return g.op("Transpose", conv, perm_i=[2, 0, 1])
+
+
+@_onnx_symbolic("aten::_unique")
+@symbolic_helper.parse_args("v", "i", "i")
+def _unique(g: jit_utils.GraphContext, input, sorted, return_inverse):
+    return symbolic_helper._onnx_unsupported("_unique", input)
+
+
+@_onnx_symbolic("aten::_unique2")
+@symbolic_helper.parse_args("v", "i", "i", "i")
+def _unique2(g: jit_utils.GraphContext, input, sorted, return_inverse, return_counts):
+    symbolic_helper._onnx_opset_unsupported("_unique2", 9, 11, input)
+
+
+@_onnx_symbolic("aten::_cast_Byte")
+@_deprecation.deprecated(
+    "2.0",
+    "the future",
+    "Avoid using this function and create a Cast node instead",
+)
+def _cast_Byte(g: jit_utils.GraphContext, input, non_blocking):
+    return g.op("Cast", input, to_i=_C_onnx.TensorProtoDataType.UINT8)
+
+
+@_onnx_symbolic("aten::_cast_Char")
+@_deprecation.deprecated(
+    "2.0",
+    "the future",
+    "Avoid using this function and create a Cast node instead",
+)
+def _cast_Char(g: jit_utils.GraphContext, input, non_blocking):
+    return g.op("Cast", input, to_i=_C_onnx.TensorProtoDataType.INT8)
+
+
+@_onnx_symbolic("aten::_cast_Short")
+@_deprecation.deprecated(
+    "2.0",
+    "the future",
+    "Avoid using this function and create a Cast node instead",
+)
+def _cast_Short(g: jit_utils.GraphContext, input, non_blocking):
+    return g.op("Cast", input, to_i=_C_onnx.TensorProtoDataType.INT16)
+
+
+@_onnx_symbolic("aten::_cast_Int")
+@_deprecation.deprecated(
+    "2.0",
+    "the future",
+    "Avoid using this function and create a Cast node instead",
+)
+def _cast_Int(g: jit_utils.GraphContext, input, non_blocking):
+    return g.op("Cast", input, to_i=_C_onnx.TensorProtoDataType.INT32)
+
+
+@_onnx_symbolic("aten::_cast_Long")
+@_deprecation.deprecated(
+    "2.0",
+    "the future",
+    "Avoid using this function and create a Cast node instead",
+)
+def _cast_Long(g: jit_utils.GraphContext, input, non_blocking):
+    return g.op("Cast", input, to_i=_C_onnx.TensorProtoDataType.INT64)
+
+
+@_onnx_symbolic("aten::_cast_Half")
+@_deprecation.deprecated(
+    "2.0",
+    "the future",
+    "Avoid using this function and create a Cast node instead",
+)
+def _cast_Half(g: jit_utils.GraphContext, input, non_blocking):
+    return g.op("Cast", input, to_i=_C_onnx.TensorProtoDataType.FLOAT16)
+
+
+@_onnx_symbolic("aten::_cast_Float")
+@_deprecation.deprecated(
+    "2.0",
+    "the future",
+    "Avoid using this function and create a Cast node instead",
+)
+def _cast_Float(g: jit_utils.GraphContext, input, non_blocking):
+    return g.op("Cast", input, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+
+
+@_onnx_symbolic("aten::_cast_Double")
+@_deprecation.deprecated(
+    "2.0",
+    "the future",
+    "Avoid using this function and create a Cast node instead",
+)
+def _cast_Double(g: jit_utils.GraphContext, input, non_blocking):
+    return g.op("Cast", input, to_i=_C_onnx.TensorProtoDataType.DOUBLE)
+
+
+@_onnx_symbolic("aten::_cast_Bool")
+@_deprecation.deprecated(
+    "2.0",
+    "the future",
+    "Avoid using this function and create a Cast node instead",
+)
+def _cast_Bool(g: jit_utils.GraphContext, input, non_blocking):
+    return g.op("Cast", input, to_i=_C_onnx.TensorProtoDataType.BOOL)
+
+
+@_onnx_symbolic("aten::empty")
+@symbolic_helper.parse_args("v", "i", "v", "v", "v", "v")
+def empty(
+    g: jit_utils.GraphContext,
+    sizes,
+    dtype,
+    layout,
+    device,
+    pin_memory=False,
+    memory_format=None,
+):
+    return zeros(g, sizes, dtype, layout, device, pin_memory)
+
+
+@_onnx_symbolic("aten::empty_like")
+@symbolic_helper.parse_args("v", "i", "v", "v", "v", "v")
+def empty_like(
+    g: jit_utils.GraphContext,
+    input,
+    dtype=None,
+    layout=None,
+    device=None,
+    pin_memory=False,
+    memory_format=None,
+):
+    return zeros_like(g, input, dtype, layout, device, pin_memory)
+
+
+@_onnx_symbolic("aten::new_empty")
+def new_empty(
+    g: jit_utils.GraphContext, self, sizes, dtype, layout, device, pin_memory=False
+):
+    self_dtype = symbolic_helper._try_get_scalar_type(self)
+    if symbolic_helper._is_none(dtype) and self_dtype is not None:
+        dtype = self_dtype
+    return empty(g, sizes, dtype, layout, device, pin_memory)
+
+
+@_onnx_symbolic("aten::scalar_tensor")
+def scalar_tensor(g: jit_utils.GraphContext, scalar, dtype, *options):
+    dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+    if dtype is None:
+        dtype = _type_utils.JitScalarType.FLOAT
+    scalar = g.op("Cast", scalar, to_i=_type_utils.JitScalarType(dtype).onnx_type())
+    return scalar
+
+
+@_onnx_symbolic("aten::tensor")
+def tensor(
+    g: jit_utils.GraphContext, data, dtype=None, device=None, requires_grad=False
+):
+    dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+    if symbolic_helper._is_packed_list(data):
+        if dtype is None:
+            dtype = _type_utils.JitScalarType.from_value(
+                symbolic_helper._unpack_list(data)[0]
+            )
+        input_list = []
+        for t in symbolic_helper._unpack_list(data):
+            shape_reference = g.op("Constant", value_t=torch.LongTensor([1]))
+            t = symbolic_helper._reshape_helper(g, t, shape_reference)
+            t = g.op("Cast", t, to_i=_type_utils.JitScalarType(dtype).onnx_type())
+            input_list.append(t)
+        return g.op("Concat", *input_list, axis_i=0)
+    else:
+        if dtype is None:
+            dtype = _type_utils.JitScalarType.from_value(data)
+        if symbolic_helper._is_list(data) and (
+            symbolic_helper._is_tensor_list(data)
+            or symbolic_helper._is_scalar_list(data)
+        ):
+            data = g.op("ConcatFromSequence", data, axis_i=0, new_axis_i=1)
+    return g.op("Cast", data, to_i=_type_utils.JitScalarType(dtype).onnx_type())
+
+
+@_onnx_symbolic("aten::as_tensor")
+def as_tensor(g: jit_utils.GraphContext, data, dtype=None, device=None):
+    return tensor(g, data, dtype, device)
+
+
+@_onnx_symbolic("aten::zeros")
+@symbolic_helper.parse_args("v", "i", "v", "v", "v")
+def zeros(g: jit_utils.GraphContext, sizes, dtype, layout, device, pin_memory=False):
+    # NOTE: no way to set device, layout and pin_memory in ONNX, so we ignore it
+    if dtype is None:
+        scalar_type = _type_utils.JitScalarType.FLOAT
+    else:
+        scalar_type = _type_utils.JitScalarType(dtype)
+    sizes_ = symbolic_helper._maybe_get_const(sizes, "is")
+    if isinstance(sizes_, list) and len(sizes_) == 0:
+        sizes = g.op("Constant", value_t=torch.tensor([]).to(torch.int64))
+    return g.op(
+        "ConstantOfShape",
+        sizes,
+        value_t=torch.tensor([0], dtype=scalar_type.dtype()),
+    )
+
+
+@_onnx_symbolic("aten::zeros_like")
+@symbolic_helper.parse_args("v", "i", "v", "v", "v", "v")
+def zeros_like(
+    g: jit_utils.GraphContext,
+    input,
+    dtype=None,
+    layout=None,
+    device=None,
+    pin_memory=False,
+    memory_format=None,
+):
+    shape = g.op("Shape", input)
+    if symbolic_helper._is_none(dtype):
+        scalar_type = _type_utils.JitScalarType.from_value(
+            input, _type_utils.JitScalarType.FLOAT
+        )
+    else:
+        scalar_type = _type_utils.JitScalarType(dtype)
+    return g.op(
+        "ConstantOfShape",
+        shape,
+        value_t=torch.tensor([0], dtype=scalar_type.dtype()),
+    )
+
+
+@_onnx_symbolic("aten::new_zeros")
+def new_zeros(
+    g: jit_utils.GraphContext, self, sizes, dtype, layout, device, pin_memory=False
+):
+    self_dtype = symbolic_helper._try_get_scalar_type(self)
+
+    if symbolic_helper._is_none(dtype) and self_dtype is not None:
+        dtype = self_dtype
+    return zeros(g, sizes, dtype, layout, device, pin_memory)
+
+
+@_onnx_symbolic("aten::zero")
+def zero(g: jit_utils.GraphContext, self):
+    self_dtype = symbolic_helper._try_get_scalar_type(self)
+    return zeros_like(g, self, self_dtype)
+
+
+@_onnx_symbolic("aten::ones")
+@symbolic_helper.parse_args("v", "i", "v", "v", "v")
+def ones(g: jit_utils.GraphContext, sizes, dtype, layout, device, pin_memory=False):
+    if dtype is None:
+        scalar_type = _type_utils.JitScalarType.FLOAT
+    else:
+        scalar_type = _type_utils.JitScalarType(dtype)
+    sizes_ = symbolic_helper._maybe_get_const(sizes, "is")
+    if isinstance(sizes_, list) and len(sizes_) == 0:
+        sizes = g.op("Constant", value_t=torch.tensor([]).to(torch.int64))
+    return g.op(
+        "ConstantOfShape",
+        sizes,
+        value_t=torch.tensor([1], dtype=scalar_type.dtype()),
+    )
+
+
+@_onnx_symbolic("aten::ones_like")
+@symbolic_helper.parse_args("v", "i", "v", "v", "v", "v")
+def ones_like(
+    g: jit_utils.GraphContext,
+    input,
+    dtype=None,
+    layout=None,
+    device=None,
+    pin_memory=False,
+    memory_format=None,
+):
+    shape = g.op("Shape", input)
+    if symbolic_helper._is_none(dtype):
+        scalar_type = _type_utils.JitScalarType.from_value(
+            input, _type_utils.JitScalarType.FLOAT
+        )
+    else:
+        scalar_type = _type_utils.JitScalarType(dtype)
+    return g.op(
+        "ConstantOfShape",
+        shape,
+        value_t=torch.tensor([1], dtype=scalar_type.dtype()),
+    )
+
+
+@_onnx_symbolic("aten::new_ones")
+def new_ones(
+    g: jit_utils.GraphContext, self, sizes, dtype, layout, device, pin_memory=False
+):
+    self_dtype = symbolic_helper._try_get_scalar_type(self)
+    if symbolic_helper._is_none(dtype) and self_dtype is not None:
+        dtype = self_dtype
+    return ones(g, sizes, dtype, layout, device, pin_memory)
+
+
+@_onnx_symbolic("aten::full")
+def full(
+    g: jit_utils.GraphContext, sizes, value, dtype, layout, device, pin_memory=False
+):
+    const_value = symbolic_helper._maybe_get_const(value, "t")
+    if symbolic_helper._is_value(const_value):
+        dtype = _type_utils.JitScalarType.FLOAT if dtype is None else dtype
+        tmp = zeros(g, sizes, dtype, layout, device)
+        return add(g, tmp, value, g.op("Constant", value_t=torch.tensor(1)))
+    else:
+        dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+        if dtype is None:
+            scalar_type = _type_utils.JitScalarType.FLOAT
+        else:
+            scalar_type = _type_utils.JitScalarType(dtype)
+        sizes_ = symbolic_helper._maybe_get_const(sizes, "is")
+        if isinstance(sizes_, list) and len(sizes_) == 0:
+            sizes = g.op("Constant", value_t=torch.tensor([]).to(torch.int64))
+        return g.op(
+            "ConstantOfShape",
+            sizes,
+            value_t=const_value.view(1).to(scalar_type.dtype()),
+        )
+
+
+@_onnx_symbolic("aten::full_like")
+def full_like(
+    g: jit_utils.GraphContext,
+    input,
+    fill_value,
+    dtype=None,
+    layout=None,
+    device=None,
+    pin_memory=False,
+    memory_format=None,
+):
+    fill_value = symbolic_helper._maybe_get_const(fill_value, "f")
+    dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+    if dtype is None:
+        scalar_type = _type_utils.JitScalarType.from_value(
+            input, _type_utils.JitScalarType.FLOAT
+        )
+    else:
+        scalar_type = _type_utils.JitScalarType(dtype)
+    if symbolic_helper._is_value(fill_value):
+        tmp = zeros_like(g, input, dtype, layout, device)
+        fill_value = g.op("Cast", fill_value, to_i=scalar_type.onnx_type())
+        return add(g, tmp, fill_value, g.op("Constant", value_t=torch.tensor(1)))
+    else:
+        shape = g.op("Shape", input)
+        return g.op(
+            "ConstantOfShape",
+            shape,
+            value_t=torch.tensor([fill_value], dtype=scalar_type.dtype()),
+        )
+
+
+@_onnx_symbolic("aten::new_full")
+def new_full(
+    g: jit_utils.GraphContext,
+    self,
+    size,
+    fill_value,
+    dtype,
+    layout,
+    device,
+    pin_memory=False,
+):
+    self_dtype = symbolic_helper._try_get_scalar_type(self)
+    if symbolic_helper._is_none(dtype) and self_dtype is not None:
+        dtype = self_dtype
+    return full(g, size, fill_value, dtype, layout, device, pin_memory)
+
+
+@_onnx_symbolic("aten::eye")
+def eye(g: jit_utils.GraphContext, *args):
+    if len(args) == 5:
+        # aten::eye(n, dtype, layout, device, pin_memory)
+        n, dtype, layout, device, pin_memory = args
+        dim_size = symbolic_helper._unsqueeze_helper(g, n, [0])
+        shape = g.op("Concat", dim_size, dim_size, axis_i=0)
+        tensor = zeros(g, shape, dtype, layout, device)
+        return g.op("EyeLike", tensor)
+    if len(args) == 6:
+        # aten::eye(n, m, dtype, layout, device, pin_memory)
+        n, m, dtype, layout, device, pin_memory = args
+        shape = g.op(
+            "Concat",
+            symbolic_helper._unsqueeze_helper(g, n, [0]),
+            symbolic_helper._unsqueeze_helper(g, m, [0]),
+            axis_i=0,
+        )
+        tensor = zeros(g, shape, dtype, layout, device)
+        return g.op("EyeLike", tensor)
+
+    return symbolic_helper._unimplemented("aten::eye", f"with {len(args)} arguments")
+
+
+@_onnx_symbolic("aten::slice")
+def slice(g: jit_utils.GraphContext, self, *args):
+    if len(args) == 4:
+        # aten::slice(Tensor self, int dim, int start, int end, int step) -> Tensor
+        dim, start, end, step = args
+        step = symbolic_helper._parse_arg(step, "i")
+        if step != 1:
+            raise errors.SymbolicValueError("step!=1 is currently not supported", self)
+        is_start_none = start.node().kind() == "prim::Constant" and isinstance(
+            start.type(), _C.NoneType
+        )
+        is_end_none = end.node().kind() == "prim::Constant" and isinstance(
+            end.type(), _C.NoneType
+        )
+        is_start_onnx_const = start.node().kind() == "onnx::Constant"
+        is_end_onnx_const = end.node().kind() == "onnx::Constant"
+        if (
+            ((not is_start_none) and (not is_start_onnx_const))
+            or ((not is_end_none) and (not is_end_onnx_const))
+            or dim.node().kind() != "onnx::Constant"
+        ):
+            if GLOBALS.operator_export_type == _C_onnx.OperatorExportTypes.ONNX:
+                raise errors.SymbolicValueError(
+                    "Unsupported: ONNX export of Slice with dynamic inputs. DynamicSlice "
+                    "is a deprecated experimental op. Please use statically allocated "
+                    "variables or export to a higher opset version.",
+                    self,
+                )
+            else:
+                start_unsqueezed = symbolic_helper._unsqueeze_helper(g, start, [0])
+                end_unsqueezed = symbolic_helper._unsqueeze_helper(g, end, [0])
+                dim_unsqueezed = symbolic_helper._unsqueeze_helper(g, dim, [0])
+                return g.op(
+                    "DynamicSlice",
+                    self,
+                    start_unsqueezed,
+                    end_unsqueezed,
+                    dim_unsqueezed,
+                )
+        else:
+            start = 0 if is_start_none else symbolic_helper._parse_arg(start, "i")
+            end = (
+                _constants.INT64_MAX
+                if is_end_none
+                else symbolic_helper._parse_arg(end, "i")
+            )
+            dim = symbolic_helper._parse_arg(dim, "i")
+            return symbolic_helper._slice_helper(
+                g, self, axes=[dim], starts=[start], ends=[end]
+            )
+    elif len(args) == 3:
+        # aten::slice(t[] l, int start, int end, int step) -> t[]
+        start, end, step = args
+        dim = 0
+        is_start_none = start.node().kind() == "prim::Constant" and isinstance(
+            start.type(), _C.NoneType
+        )
+        is_end_none = end.node().kind() == "prim::Constant" and isinstance(
+            end.type(), _C.NoneType
+        )
+        start = 0 if is_start_none else symbolic_helper._parse_arg(start, "i")
+        end = (
+            _constants.INT64_MAX
+            if is_end_none
+            else symbolic_helper._parse_arg(end, "i")
+        )
+        return symbolic_helper._slice_helper(
+            g, self, axes=[dim], starts=[start], ends=[end]
+        )
+
+    return symbolic_helper._unimplemented("aten::slice", f"with {len(args)} arguments")
+
+
+@_onnx_symbolic("aten::hardtanh")
+@symbolic_helper.quantized_args(True)
+@symbolic_helper.parse_args("v", "f", "f")
+def hardtanh(g: jit_utils.GraphContext, self: _C.Value, min_val: float, max_val: float):
+    return symbolic_helper._op_with_optional_float_cast(
+        g, "Clip", self, min_f=min_val, max_f=max_val, opset_before=12
+    )
+
+
+@_onnx_symbolic("aten::hardswish")
+@symbolic_helper.quantized_args(True)
+@symbolic_helper.parse_args("v")
+def hardswish(g: jit_utils.GraphContext, self):
+    hs = hardsigmoid(g, self)
+    return g.op("Mul", self, hs)
+
+
+@_onnx_symbolic("aten::hardsigmoid")
+# Fixed scale and zero_point, discovered from aten/src/ATen/native/quantized/cpu/qhardsigmoid.cpp
+@symbolic_helper.quantized_args(True, scale=1.0 / 256.0, zero_point=0)
+@symbolic_helper.parse_args("v")
+def hardsigmoid(g: jit_utils.GraphContext, self):
+    # Set alpha_f to 1 / 6 to make op equivalent to PyTorch's definition of Hardsigmoid.
+    # See https://pytorch.org/docs/stable/generated/torch.nn.Hardsigmoid.html
+    return g.op("HardSigmoid", self, alpha_f=1 / 6)
+
+
+@_onnx_symbolic("aten::tanhshrink")
+@symbolic_helper.parse_args("v")
+def tanhshrink(g: jit_utils.GraphContext, self):
+    return g.op("Sub", self, tanh(g, self))
+
+
+@_onnx_symbolic("aten::hardshrink")
+@symbolic_helper.parse_args("v", "f")
+def hardshrink(g: jit_utils.GraphContext, self, lambd):
+    scalar_type = _type_utils.JitScalarType.from_value(
+        self, _type_utils.JitScalarType.FLOAT
+    )
+    lambd_op = g.op(
+        "Constant",
+        value_t=torch.tensor(lambd, dtype=scalar_type.dtype()),
+    )
+    cond = logical_or(g, gt(g, self, lambd_op), lt(g, self, neg(g, lambd_op)))
+    return g.op(
+        "Where",
+        cond,
+        self,
+        g.op(
+            "Constant",
+            value_t=torch.tensor(0, dtype=scalar_type.dtype()),
+        ),
+    )
+
+
+@_onnx_symbolic("aten::softshrink")
+@symbolic_helper.parse_args("v", "f")
+def softshrink(g: jit_utils.GraphContext, self, lambd):
+    scalar_type = _type_utils.JitScalarType.from_value(
+        self, _type_utils.JitScalarType.FLOAT
+    )
+    lambd_op = g.op(
+        "Constant",
+        value_t=torch.tensor(lambd, dtype=scalar_type.dtype()),
+    )
+    gt_cond = gt(g, self, lambd_op)
+    gt_out = g.op(
+        "Where",
+        gt_cond,
+        sub(g, self, lambd_op),
+        g.op(
+            "Constant",
+            value_t=torch.tensor(0, dtype=scalar_type.dtype()),
+        ),
+    )
+    lt_cond = lt(g, self, neg(g, lambd_op))
+    lt_out = g.op(
+        "Where",
+        lt_cond,
+        add(g, self, lambd_op),
+        g.op(
+            "Constant",
+            value_t=torch.tensor(0, dtype=scalar_type.dtype()),
+        ),
+    )
+    return add(g, gt_out, lt_out)
+
+
+@_onnx_symbolic("aten::alias")
+def alias(g: jit_utils.GraphContext, self):
+    return self
+
+
+@_onnx_symbolic("aten::unsqueeze")
+@symbolic_helper.parse_args("v", "i")
+def unsqueeze(g: jit_utils.GraphContext, self, dim):
+    """Implement unsqueezing a pytorch tensor in ONNX by inserting a new dimension at the specified `dim`"""
+    # Handle negative dim
+    if dim < 0:
+        rank = symbolic_helper._get_tensor_rank(self)
+        if rank is not None:
+            warnings.warn(
+                "ONNX export unsqueeze with negative axis "
+                + str(dim)
+                + " might cause the onnx model to be incorrect. "
+                + "Negative axis is not supported in ONNX. "
+                + "Axis is converted to "
+                + str(dim + rank + 1)
+                + " based on input shape at export time. "
+                + "Passing an tensor of different rank in execution will be incorrect."
+            )
+            dim = dim + rank + 1
+        else:
+            return symbolic_helper._unimplemented(
+                "unsqueeze", "negative axis with unknown input rank", self
+            )
+
+    return symbolic_helper._unsqueeze_helper(g, self, axes_i=[dim])
+
+
+@_onnx_symbolic("aten::sort")
+# TODO(justinchuby): Support multiple quantized args in output
+@symbolic_helper.parse_args("v", "i", "i", "none")
+def sort(g: jit_utils.GraphContext, self, dim, decending, out=None):
+    if out is not None:
+        symbolic_helper._unimplemented(
+            "Sort", "Out parameter is not supported for sort", self
+        )
+    self_sizes = symbolic_helper._get_tensor_sizes(self)
+    try:
+        dim_size = self_sizes[dim]
+    except Exception:
+        # FIXME(justinchuby): Avoid catching Exception.
+        # Catch a more specific exception instead.
+        dim_size = None
+
+    if dim_size is None:
+        return symbolic_helper._unimplemented("Sort", "input size not accessible", self)
+
+    return g.op("TopK", self, k_i=dim_size, axis_i=dim, outputs=2)
+
+
+@_onnx_symbolic("aten::numel")
+def numel(g: jit_utils.GraphContext, self):
+    return symbolic_helper._numel_helper(g, self)
+
+
+@_onnx_symbolic("aten::topk")
+# TODO(justinchuby): Support multiple quantized args in output
+@symbolic_helper.parse_args("v", "i", "i", "i", "i", "none")
+def topk(g: jit_utils.GraphContext, self, k, dim, largest, sorted, out=None):
+    if out is not None:
+        symbolic_helper._unimplemented(
+            "TopK", "Out parameter is not supported for topk", self
+        )
+    if not largest:
+        symbolic_helper._unimplemented("TopK", "Ascending TopK is not supported", self)
+
+    return g.op("TopK", self, k_i=k, axis_i=dim, outputs=2)
+
+
+@_onnx_symbolic("prim::convert_element_type")
+def convert_element_type(g: jit_utils.GraphContext, self, *args):
+    dtype = symbolic_helper._get_const(args[0], "i", "dtype")
+    return g.op("Cast", self, to_i=_type_utils.JitScalarType(dtype).onnx_type())
+
+
+@_onnx_symbolic("aten::to")
+def to(g: jit_utils.GraphContext, self, *args):
+    def is_aten_to_device_only(args):
+        if len(args) == 4:
+            # aten::to(Tensor, Device, bool, bool, memory_format)
+            return (
+                args[0].node().kind() == "prim::device"
+                or args[0].type().isSubtypeOf(_C.ListType.ofInts())
+                or isinstance(args[0].type(), _C.DeviceObjType)
+            )
+        elif len(args) == 5:
+            # aten::to(Tensor, Device, ScalarType, bool, bool, memory_format)
+            # When dtype is None, this is a aten::to(device) call
+            dtype = symbolic_helper._get_const(args[1], "i", "dtype")
+            return dtype is None
+        elif len(args) in (6, 7):
+            # aten::to(Tensor, ScalarType, Layout, Device, bool, bool, memory_format) -> Tensor
+            # aten::to(Tensor, ScalarType, Layout, Device, bool, bool, bool, memory_format) -> Tensor
+            # When dtype is None, this is a aten::to(device) call
+            dtype = symbolic_helper._get_const(args[0], "i", "dtype")
+            return dtype is None
+        return False
+
+    # ONNX doesn't have a concept of a device, so we ignore device-only casts
+    if is_aten_to_device_only(args):
+        return self
+
+    if len(args) == 4:
+        # TestONNXRuntime::test_ones_bool shows args[0] of aten::to() can be onnx::Constant[value=]()
+        # In this case, the constant value is a tensor not int,
+        # so symbolic_helper._maybe_get_const(args[0], 'i') would not work.
+        dtype = args[0]
+        if (
+            symbolic_helper._is_value(args[0])
+            and args[0].node().kind() == "onnx::Constant"
+        ):
+            tval = symbolic_helper._node_get(args[0].node(), "value")
+            if isinstance(tval, torch.Tensor):
+                if len(tval.shape) == 0:
+                    tval = tval.item()
+                    dtype = int(tval)
+                else:
+                    dtype = tval
+
+        if symbolic_helper._is_value(dtype) or isinstance(dtype, torch.Tensor):
+            # aten::to(Tensor, Tensor, bool, bool, memory_format)
+            dtype = _type_utils.JitScalarType.from_value(args[0])
+            return g.op(
+                "Cast",
+                self,
+                to_i=dtype.onnx_type(),
+            )
+        else:
+            # aten::to(Tensor, ScalarType, bool, bool, memory_format)
+            # memory_format is ignored
+            return g.op("Cast", self, to_i=_type_utils.JitScalarType(dtype).onnx_type())
+    elif len(args) == 5:
+        # aten::to(Tensor, Device, ScalarType, bool, bool, memory_format)
+        dtype = symbolic_helper._get_const(args[1], "i", "dtype")
+        # memory_format is ignored
+        return g.op("Cast", self, to_i=_type_utils.JitScalarType(dtype).onnx_type())
+    elif len(args) == 6:
+        # aten::to(Tensor, ScalarType, Layout, Device, bool, bool, memory_format) -> Tensor
+        dtype = symbolic_helper._get_const(args[0], "i", "dtype")
+        # Layout, device and memory_format are ignored
+        return g.op("Cast", self, to_i=_type_utils.JitScalarType(dtype).onnx_type())
+    elif len(args) == 7:
+        # aten::to(Tensor, ScalarType, Layout, Device, bool, bool, bool, memory_format) -> Tensor
+        dtype = symbolic_helper._get_const(args[0], "i", "dtype")
+        # Layout, device and memory_format are ignored
+        return g.op("Cast", self, to_i=_type_utils.JitScalarType(dtype).onnx_type())
+
+    return symbolic_helper._onnx_unsupported("Unknown aten::to signature", self)
+
+
+@_onnx_symbolic("aten::repeat")
+def repeat(g: jit_utils.GraphContext, self, repeats):
+    dtype = _type_utils.JitScalarType.INT64
+    shape_ = ones_like(g, repeats, dtype)
+    self = g.op("Expand", self, shape_)
+    return g.op("Tile", self, repeats)
+
+
+@_onnx_symbolic("aten::repeat_interleave")
+def repeat_interleave(
+    g: jit_utils.GraphContext, self, repeats, dim=None, output_size=None
+):
+    repeats_dim = symbolic_helper._get_tensor_rank(repeats)
+    repeats_sizes = symbolic_helper._get_tensor_sizes(repeats)
+    input_sizes = symbolic_helper._get_tensor_sizes(self)
+    if repeats_dim is None:
+        raise errors.SymbolicValueError(
+            "Unsupported: ONNX export of repeat_interleave for unknown repeats rank.",
+            self,
+        )
+    if repeats_sizes is None:
+        raise errors.SymbolicValueError(
+            "Unsupported: ONNX export of repeat_interleave for unknown repeats size.",
+            self,
+        )
+    if input_sizes is None:
+        raise errors.SymbolicValueError(
+            "Unsupported: ONNX export of repeat_interleave for unknown input size.",
+            self,
+        )
+
+    # if dim is None flatten
+    # By default, use the flattened input array, and return a flat output array
+    if symbolic_helper._is_none(dim):
+        self = symbolic_helper._reshape_helper(
+            g, self, g.op("Constant", value_t=torch.tensor([-1]))
+        )
+        dim = torch.tensor(0, dtype=torch.int64)
+    else:
+        dim = symbolic_helper._maybe_get_scalar(dim)
+
+    # Handle cases where dim is negative
+    if dim < 0:
+        dim += len(input_sizes)
+
+    input_sizes_temp = input_sizes.copy()
+    for idx, input_size in enumerate(input_sizes):
+        if input_size is None:
+            input_sizes[idx], input_sizes_temp[idx] = 0, -1
+
+    # Cases where repeats is an int or single value tensor
+    if repeats_dim == 0 or (repeats_dim == 1 and repeats_sizes[0] == 1):
+        if input_sizes[dim] == 0:
+            return symbolic_helper._onnx_opset_unsupported_detailed(
+                "repeat_interleave",
+                9,
+                13,
+                "Unsupported along dimension with unknown input size",
+                self,
+            )
+        return symbolic_helper._repeat_interleave_single_value_repeat_helper(
+            g, self, repeats, dim
+        )
+
+    # Cases where repeats is a 1 dim Tensor
+    elif repeats_dim == 1:
+        if input_sizes[dim] == 0:
+            return symbolic_helper._onnx_opset_unsupported_detailed(
+                "repeat_interleave",
+                9,
+                13,
+                "Unsupported along dimension with unknown input size",
+                self,
+            )
+        if repeats_sizes[0] is None:
+            return symbolic_helper._onnx_opset_unsupported_detailed(
+                "repeat_interleave",
+                9,
+                13,
+                "Unsupported for cases with dynamic repeats",
+                self,
+            )
+        assert (
+            repeats_sizes[0] == input_sizes[dim]
+        ), "repeats must have the same size as input along dim"
+        reps = repeats_sizes[0]
+    else:
+        raise errors.SymbolicValueError("repeats must be 0-dim or 1-dim tensor", self)
+
+    final_splits = []
+    r_splits = symbolic_helper._repeat_interleave_split_helper(g, repeats, reps, 0)
+    i_splits = symbolic_helper._repeat_interleave_split_helper(g, self, reps, dim)
+    input_sizes[dim], input_sizes_temp[dim] = -1, 1
+    for idx, r_split in enumerate(r_splits):
+        i_split = unsqueeze(g, i_splits[idx], dim + 1)
+        r_concat = [
+            g.op("Constant", value_t=torch.LongTensor(input_sizes_temp[: dim + 1])),
+            r_split,
+            g.op("Constant", value_t=torch.LongTensor(input_sizes_temp[dim + 1 :])),
+        ]
+        r_concat = g.op("Concat", *r_concat, axis_i=0)
+        i_split = expand(g, i_split, r_concat, None)
+        i_split = symbolic_helper._reshape_helper(
+            g,
+            i_split,
+            g.op("Constant", value_t=torch.LongTensor(input_sizes)),
+            allowzero=0,
+        )
+        final_splits.append(i_split)
+    return g.op("Concat", *final_splits, axis_i=dim)
+
+
+@_onnx_symbolic("aten::pixel_shuffle")
+@symbolic_helper.parse_args("v", "i")
+def pixel_shuffle(g: jit_utils.GraphContext, self, upscale_factor):
+    dims = symbolic_helper._get_tensor_sizes(self)
+    if len(dims) != 4:
+        return symbolic_helper._unimplemented(
+            "pixel_shuffle", "only support 4d input", self
+        )
+    if any(i is None for i in dims[1:]):
+        after_view = symbolic_helper._reshape_helper(
+            g,
+            symbolic_helper._unsqueeze_helper(g, self, [2, 3]),
+            g.op(
+                "Constant",
+                value_t=torch.tensor([0, -1, upscale_factor, upscale_factor, 0, 0]),
+            ),
+            allowzero=0,
+        )
+        after_transpose = g.op("Transpose", after_view, perm_i=[0, 1, 4, 2, 5, 3])
+        # For dynamic input shapes, two reshapes are performed
+        reshape_h = symbolic_helper._reshape_helper(
+            g,
+            after_transpose,
+            g.op("Constant", value_t=torch.tensor([0, 0, -1, 1, 0, 0])),
+            allowzero=0,
+        )
+        reshape_w = symbolic_helper._reshape_helper(
+            g,
+            reshape_h,
+            g.op("Constant", value_t=torch.tensor([0, 0, 0, 0, -1, 1])),
+            allowzero=0,
+        )
+        return symbolic_helper._squeeze_helper(g, reshape_w, [3, 5])
+    else:
+        output_channel = dims[1] // upscale_factor // upscale_factor
+        after_view = symbolic_helper._reshape_helper(
+            g,
+            self,
+            g.op(
+                "Constant",
+                value_t=torch.tensor(
+                    [
+                        -1,
+                        output_channel,
+                        upscale_factor,
+                        upscale_factor,
+                        dims[2],
+                        dims[3],
+                    ]
+                ),
+            ),
+            allowzero=0,
+        )
+        after_transpose = g.op("Transpose", after_view, perm_i=[0, 1, 4, 2, 5, 3])
+        return symbolic_helper._reshape_helper(
+            g,
+            after_transpose,
+            g.op(
+                "Constant",
+                value_t=torch.tensor(
+                    [
+                        -1,
+                        output_channel,
+                        dims[2] * upscale_factor,
+                        dims[3] * upscale_factor,
+                    ]
+                ),
+            ),
+            allowzero=0,
+        )
+
+
+@_onnx_symbolic("aten::pixel_unshuffle")
+@symbolic_helper.parse_args("v", "i")
+def pixel_unshuffle(g: jit_utils.GraphContext, self, downscale_factor):
+    dims = symbolic_helper._get_tensor_sizes(self)
+    if len(dims) != 4:
+        return symbolic_helper._unimplemented(
+            "pixel_shuffle", "only support 4d input", self
+        )
+    if any(i is None for i in dims[1:]):
+        # For dynamic input shapes, two reshapes are performed
+        reshape_h = symbolic_helper._reshape_helper(
+            g,
+            symbolic_helper._unsqueeze_helper(g, self, [3]),
+            g.op("Constant", value_t=torch.tensor([0, 0, -1, downscale_factor, 0])),
+            allowzero=0,
+        )
+        reshape_w = symbolic_helper._reshape_helper(
+            g,
+            reshape_h,
+            g.op("Constant", value_t=torch.tensor([0, 0, 0, 0, -1, downscale_factor])),
+            allowzero=0,
+        )
+        after_transpose = g.op("Transpose", reshape_w, perm_i=[0, 1, 3, 5, 2, 4])
+        final_reshape = symbolic_helper._reshape_helper(
+            g,
+            after_transpose,
+            g.op("Constant", value_t=torch.tensor([0, -1, 1, 1, 0, 0])),
+            allowzero=0,
+        )
+        return symbolic_helper._squeeze_helper(g, final_reshape, [2, 3])
+    else:
+        output_channel = dims[1] * downscale_factor * downscale_factor
+        after_view = symbolic_helper._reshape_helper(
+            g,
+            self,
+            g.op(
+                "Constant",
+                value_t=torch.tensor(
+                    [
+                        -1,
+                        dims[1],
+                        dims[2] // downscale_factor,
+                        downscale_factor,
+                        dims[3] // downscale_factor,
+                        downscale_factor,
+                    ]
+                ),
+            ),
+            allowzero=0,
+        )
+        after_transpose = g.op("Transpose", after_view, perm_i=[0, 1, 3, 5, 2, 4])
+        return symbolic_helper._reshape_helper(
+            g,
+            after_transpose,
+            g.op(
+                "Constant",
+                value_t=torch.tensor(
+                    [
+                        -1,
+                        output_channel,
+                        dims[2] // downscale_factor,
+                        dims[3] // downscale_factor,
+                    ]
+                ),
+            ),
+            allowzero=0,
+        )
+
+
+def _generic_rnn(
+    g: jit_utils.GraphContext,
+    variant,
+    input,
+    initial_states,
+    all_weights,
+    has_biases,
+    num_layers,
+    dropout,
+    train,
+    bidirectional,
+    batch_first=None,
+    batch_sizes=None,
+):
+    warnings.warn(
+        "Exporting a model to ONNX with a batch_size other than 1, "
+        + "with a variable length with "
+        + variant
+        + " can cause an error "
+        + "when running the ONNX model with a different batch size. "
+        + "Make sure to save the model with a batch size of 1, "
+        + "or define the initial states (h0/c0) as inputs of the model. "
+    )
+
+    onnxActivations = [
+        "Relu",
+        "Tanh",
+        "Sigmoid",
+        "Affine",
+        "LeakyRelu",
+        "ThresholdedRelu",
+        "ScaledTanh",
+        "HardSigmoid",
+        "Elu",
+        "Softsign",
+        "Softplus",
+    ]
+    variantToOnnxActivationMap = dict(
+        zip([act_fun.lower() for act_fun in onnxActivations], onnxActivations)
+    )
+    weights_per_layer = 4 if has_biases else 2
+    # this means that projections are used inside LSTM, so need to tell user that it's not supported
+    if variant == "LSTM" and len(all_weights) != num_layers * weights_per_layer * (
+        1 + bidirectional
+    ):
+        return symbolic_helper._unimplemented("LSTM", "LSTMs with projections", input)
+    assert len(all_weights) == num_layers * weights_per_layer * (1 + bidirectional)
+    layer_weights = [
+        all_weights[i : i + weights_per_layer]
+        for i in range(0, len(all_weights), weights_per_layer)
+    ]
+    if batch_first:
+        # batch, seq, feat -> seq, batch, feat
+        input = g.op("Transpose", input, perm_i=[1, 0, 2])
+    if dropout and train:
+        return symbolic_helper._unimplemented(
+            "RNN/GRU/LSTM", "dropout in training mode", input
+        )
+
+    if variant.startswith("RNN"):
+        nonlinearity = variantToOnnxActivationMap[variant[4:].lower()]
+        variant = "RNN"
+
+    w_hh = all_weights[1]
+    hidden_size = symbolic_helper._get_tensor_dim_size(w_hh, 1)
+    if hidden_size is None:
+        return symbolic_helper._unimplemented(
+            "RNN/GRU/LSTM", "unknown hidden size", input
+        )
+
+    unidirectional = not bidirectional
+
+    prev_output = input
+
+    h_outs = []
+    if variant == "RNN" or variant == "GRU":
+        h0 = initial_states
+    elif variant == "LSTM":
+        h0, c0 = initial_states
+        c_outs = []
+
+    sequence_lens = unused(g) if batch_sizes is None else batch_sizes
+
+    if variant == "GRU":
+        # pytorch is reset, input, hidden
+        # onnx is    input, reset, hidden
+        reform_permutation = [(1, 2), (0, 1), (2, 3)]
+    elif variant == "LSTM":
+        # pytorch is input, forget, cell, output.
+        # onnx is    input, output, forget, cell.
+        reform_permutation = [(0, 1), (3, 4), (1, 3)]
+
+    def reform_weights(g, w, n, intervals):
+        slices = [
+            symbolic_helper._slice_helper(g, w, axes=[0], starts=[x * n], ends=[y * n])
+            for x, y in intervals
+        ]
+        return g.op("Concat", *slices, axis_i=0)
+
+    def transform_weights_no_bias(layer_index):
+        weights = layer_weights[layer_index]
+        if variant == "RNN":
+            weight_ih, weight_hh = weights
+        elif variant == "GRU" or variant == "LSTM":
+            weight_ih, weight_hh = (
+                reform_weights(g, w, hidden_size, reform_permutation) for w in weights
+            )
+        return tuple(
+            symbolic_helper._unsqueeze_helper(g, x, [0])
+            for x in (weight_ih, weight_hh)  # type: ignore[possibly-undefined]
+        )
+
+    def transform_weights(layer_index):
+        weights = layer_weights[layer_index]
+        if variant == "RNN":
+            weight_ih, weight_hh, bias_ih, bias_hh = weights
+        elif variant == "GRU" or variant == "LSTM":
+            weight_ih, weight_hh, bias_ih, bias_hh = (
+                reform_weights(g, w, hidden_size, reform_permutation) for w in weights
+            )
+        bias_concat = g.op("Concat", bias_ih, bias_hh, axis_i=0)  # type: ignore[possibly-undefined]
+        return tuple(
+            symbolic_helper._unsqueeze_helper(g, x, [0])
+            for x in (weight_ih, weight_hh, bias_concat)  # type: ignore[possibly-undefined]
+        )
+
+    def retrieve_state(x, start, end):
+        return (
+            x
+            if num_layers == 1
+            else symbolic_helper._slice_helper(
+                g, x, axes=[0], starts=[start], ends=[end]
+            )
+        )
+
+    for i in range(num_layers):
+        if unidirectional:
+            if weights_per_layer == 4:
+                weight_ih, weight_hh, bias_concat = transform_weights(i)
+            else:
+                weight_ih, weight_hh = transform_weights_no_bias(i)
+                bias_concat = unused(g)
+
+            state_indices = i, i + 1
+        else:
+            if weights_per_layer == 4:
+                weight_ih_f, weight_hh_f, bias_f = transform_weights(2 * i)
+                weight_ih_b, weight_hh_b, bias_b = transform_weights(2 * i + 1)
+                bias_concat = g.op("Concat", bias_f, bias_b, axis_i=0)
+            else:
+                weight_ih_f, weight_hh_f = transform_weights_no_bias(2 * i)
+                weight_ih_b, weight_hh_b = transform_weights_no_bias(2 * i + 1)
+                bias_concat = unused(g)
+
+            weight_ih = g.op("Concat", weight_ih_f, weight_ih_b, axis_i=0)
+            weight_hh = g.op("Concat", weight_hh_f, weight_hh_b, axis_i=0)
+
+            state_indices = 2 * i, 2 * i + 2
+
+        inputs = [prev_output, weight_ih, weight_hh, bias_concat, sequence_lens]
+
+        inputs.append(retrieve_state(h0, *state_indices))  # type: ignore[possibly-undefined]
+        if variant == "LSTM":
+            inputs.append(retrieve_state(c0, *state_indices))  # type: ignore[possibly-undefined]
+
+        extra_kwargs = {} if unidirectional else {"direction_s": "bidirectional"}
+        if variant == "RNN":
+            if bidirectional:
+                activation = [nonlinearity, nonlinearity]  # type: ignore[possibly-undefined]
+            else:
+                activation = [nonlinearity]  # type: ignore[possibly-undefined]
+
+            prev_output, h_out = g.op(
+                "RNN",
+                *inputs,
+                outputs=2,
+                hidden_size_i=hidden_size,
+                activations_s=activation,
+                **extra_kwargs,
+            )
+        elif variant == "GRU":
+            prev_output, h_out = g.op(
+                "GRU",
+                *inputs,
+                outputs=2,
+                hidden_size_i=hidden_size,
+                linear_before_reset_i=1,
+                **extra_kwargs,
+            )
+        elif variant == "LSTM":
+            prev_output, h_out, c_out = g.op(
+                "LSTM", *inputs, outputs=3, hidden_size_i=hidden_size, **extra_kwargs
+            )
+
+        if bidirectional:
+            # The ONNX RNN/GRU/LSTM produce an output of dimensions
+            #   seq_len, num_directions, batch, hidden_size
+            # We have to convert to match pytorch's expected
+            #   seq_len, batch, num_directions * hidden_size
+            # by first moving num_directions before hidden_size with
+            # Transpose, and then combining it with hidden_size
+            # with Reshape.
+            prev_output = g.op("Transpose", prev_output, perm_i=[0, 2, 1, 3])
+            prev_output = symbolic_helper._reshape_helper(
+                g,
+                prev_output,
+                g.op("Constant", value_t=torch.LongTensor([0, 0, -1])),
+                allowzero=0,
+            )
+        else:
+            prev_output = symbolic_helper._squeeze_helper(g, prev_output, [1])
+
+        h_outs.append(h_out)  # type: ignore[possibly-undefined]
+        if variant == "LSTM":
+            c_outs.append(c_out)  # type: ignore[possibly-undefined]
+    if batch_first:
+        # seq, batch, num_directions * hidden_size -> batch, seq, num_directions * hidden_size
+        prev_output = g.op("Transpose", prev_output, perm_i=[1, 0, 2])
+    h_outs = h_out if num_layers == 1 else g.op("Concat", *h_outs, axis_i=0)  # type: ignore[possibly-undefined]
+    if variant == "RNN" or variant == "GRU":
+        return prev_output, h_outs
+    elif variant == "LSTM":
+        c_outs = c_out if num_layers == 1 else g.op("Concat", *c_outs, axis_i=0)  # type: ignore[possibly-undefined]
+        return prev_output, h_outs, c_outs
+
+
+@symbolic_helper.parse_args("v", "v", "v", "i", "i", "f", "i", "i", "i")
+def _lstm_full(
+    g: jit_utils.GraphContext,
+    input,
+    hidden_v,
+    weight_v,
+    has_biases,
+    num_layers,
+    dropout,
+    train,
+    bidirectional,
+    batch_first,
+):
+    hidden, weight = (
+        symbolic_helper._unpack_list(hidden_v),
+        symbolic_helper._unpack_list(weight_v),
+    )
+    return _generic_rnn(
+        g,
+        "LSTM",
+        input,
+        hidden,
+        weight,
+        has_biases,
+        num_layers,
+        dropout,
+        train,
+        bidirectional,
+        batch_first,
+    )
+
+
+@symbolic_helper.parse_args("v", "v", "v", "v", "i", "i", "f", "i", "i")
+def _lstm_packed(
+    g: jit_utils.GraphContext,
+    input,
+    batch_sizes,
+    hidden_v,
+    weight_v,
+    has_biases,
+    num_layers,
+    dropout,
+    train,
+    bidirectional,
+):
+    hidden, weight = (
+        symbolic_helper._unpack_list(hidden_v),
+        symbolic_helper._unpack_list(weight_v),
+    )
+    return _generic_rnn(
+        g,
+        "LSTM",
+        input,
+        hidden,
+        weight,
+        has_biases,
+        num_layers,
+        dropout,
+        train,
+        bidirectional,
+        batch_sizes=batch_sizes,
+    )
+
+
+@_onnx_symbolic("aten::lstm")
+def lstm(g: jit_utils.GraphContext, *args):
+    if symbolic_helper._is_tensor_list(args[3]):
+        return _lstm_packed(g, *args)
+    else:
+        return _lstm_full(g, *args)
+
+
+@_onnx_symbolic("aten::lstm_cell")
+def lstm_cell(g: jit_utils.GraphContext, self, hidden, w_ih, w_hh, b_ih, b_hh):
+    input = symbolic_helper._unsqueeze_helper(g, self, [0])
+    hidden = symbolic_helper._unpack_list(hidden)
+    hidden = [symbolic_helper._unsqueeze_helper(g, x, [0]) for x in hidden]
+    weight = (
+        (w_ih, w_hh, b_ih, b_hh) if symbolic_helper._is_tensor(b_ih) else (w_ih, w_hh)
+    )
+    has_biases = True if symbolic_helper._is_tensor(b_ih) else False
+    _, h_outs, c_outs = _generic_rnn(
+        g,
+        "LSTM",
+        input,
+        hidden,
+        weight,
+        has_biases,
+        num_layers=1,
+        dropout=0,
+        train=0,
+        bidirectional=False,
+        batch_first=False,
+    )
+    return symbolic_helper._squeeze_helper(
+        g, h_outs, [0]
+    ), symbolic_helper._squeeze_helper(g, c_outs, [0])
+
+
+@_onnx_symbolic(
+    "aten::gru", decorate=[symbolic_helper._apply_params("GRU"), _export("gru")]
+)
+@_onnx_symbolic(
+    "aten::rnn_tanh",
+    decorate=[symbolic_helper._apply_params("RNN_TANH"), _export("rnn_tanh")],
+)
+@_onnx_symbolic(
+    "aten::rnn_relu",
+    decorate=[symbolic_helper._apply_params("RNN_RELU"), _export("rnn_relu")],
+)
+def _one_hidden_rnn(kind: str):
+    @symbolic_helper.parse_args("v", "v", "v", "i", "i", "f", "i", "i", "i")
+    def _rnn_full(
+        g,
+        input,
+        hidden,
+        weight_v,
+        has_biases,
+        num_layers,
+        dropout,
+        train,
+        bidirectional,
+        batch_first,
+    ):
+        weight = symbolic_helper._unpack_list(weight_v)
+        return _generic_rnn(
+            g,
+            kind,
+            input,
+            hidden,
+            weight,
+            has_biases,
+            num_layers,
+            dropout,
+            train,
+            bidirectional,
+            batch_first,
+        )
+
+    @symbolic_helper.parse_args("v", "v", "v", "v", "i", "i", "f", "i", "i")
+    def _rnn_packed(
+        g,
+        input,
+        batch_sizes,
+        hidden,
+        weight_v,
+        has_biases,
+        num_layers,
+        dropout,
+        train,
+        bidirectional,
+    ):
+        weight = symbolic_helper._unpack_list(weight_v)
+        return _generic_rnn(
+            g,
+            kind,
+            input,
+            hidden,
+            weight,
+            has_biases,
+            num_layers,
+            dropout,
+            train,
+            bidirectional,
+            batch_sizes=batch_sizes,
+        )
+
+    def symbolic(g, *args):
+        if symbolic_helper._is_tensor_list(args[3]):
+            return _rnn_packed(g, *args)
+        else:
+            return _rnn_full(g, *args)
+
+    return symbolic
+
+
+@_onnx_symbolic("aten::_dim_arange")
+@symbolic_helper.parse_args("v", "i")
+def _dim_arange(g: jit_utils.GraphContext, like, dim):
+    like_shape = g.op("Shape", like)
+    stop = g.op(
+        "Gather", like_shape, g.op("Constant", value_t=torch.tensor(dim)), axis_i=0
+    )
+    # aten::arange(Scalar end, ScalarType dtype, Layout, Device, bool pin_memory)
+    return arange(g, stop, 4, None, None, None)
+
+
+@_onnx_symbolic("aten::detach")
+def detach(g: jit_utils.GraphContext, input):
+    # Erase aten::detach nodes because ONNX is inference only
+    return input
+
+
+@_onnx_symbolic("aten::contiguous")
+@symbolic_helper.parse_args("v", "i")
+def contiguous(g: jit_utils.GraphContext, input, memory_format):
+    if memory_format > 2:  # allower values are any, preserve and contiguous_format
+        raise errors.SymbolicValueError(
+            "onnx memory_format support is not implemented", input
+        )
+    return input
+
+
+@_onnx_symbolic("aten::_pack_padded_sequence")
+@symbolic_helper.parse_args("v", "v", "i")
+def _pack_padded_sequence(g: jit_utils.GraphContext, input, lengths, batch_first):
+    # Currently there is no PackPadded operator in ONNX. We rely on an
+    # optimization pass to remove this later. It is an error if all
+    # PackPadded operators cannot be optimized out.
+    if batch_first:
+        input = g.op("Transpose", input, perm_i=[1, 0, 2])
+    if not lengths.type().isSubtypeOf(torch._C.TensorType.get()):
+        raise errors.SymbolicValueError(
+            "'lengths' must be a Tensor for ONNX export", input
+        )
+    # We know it's a TensorType so this check is now safe.
+    # It's really only necessary because those operators expand to something that
+    # only works with int32 types in Caffe2...
+    if (
+        _type_utils.JitScalarType.from_value(
+            lengths, _type_utils.JitScalarType.UNDEFINED
+        )
+        != _type_utils.JitScalarType.INT
+    ):
+        lengths = g.op("Cast", lengths, to_i=_C_onnx.TensorProtoDataType.INT32)
+    return g.op("prim::PackPadded", input, lengths, outputs=2)
+
+
+@_onnx_symbolic("aten::_pad_packed_sequence")
+@symbolic_helper.parse_args("v", "v", "i", "t", "v")
+def _pad_packed_sequence(
+    g: jit_utils.GraphContext,
+    data,
+    batch_sizes,
+    batch_first,
+    padding_value,
+    total_length,
+):
+    # Ignore total_length as it is not supported in _symbolic_pad_packed_sequence
+    # It is only useful/used when training using data_parallel model, so
+    # It shouldn't be relevant for ONNX anyway
+    data, lengths = g.op("prim::PadPacked", data, batch_sizes, outputs=2)
+    if batch_first:
+        data = g.op("Transpose", data, perm_i=[1, 0, 2])
+    return data, lengths
+
+
+@_onnx_symbolic("aten::randint")
+def randint(g: jit_utils.GraphContext, low, high, shapes, dtype, *options):
+    dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+    low_i = symbolic_helper._get_const(low, "i", "low")
+    high_i = symbolic_helper._get_const(high, "i", "high")
+    if dtype is None:
+        scalar_type = _type_utils.JitScalarType.INT64
+    else:
+        scalar_type = _type_utils.JitScalarType(dtype)
+    if low_i is None:
+        raise symbolic_helper._onnx_unsupported("randint", low)
+    if high_i is None:
+        raise symbolic_helper._onnx_unsupported("randint", high)
+
+    shape = symbolic_helper._maybe_get_const(shapes, "is")
+    if symbolic_helper._is_value(shape):
+        shape_const = g.op(
+            "ConstantOfShape",
+            shapes,
+            value_t=torch.tensor([0], dtype=torch.float),
+        )
+        randn = g.op(
+            "RandomUniformLike",
+            shape_const,
+            low_f=low_i,
+            high_f=high_i,
+        )
+    else:
+        randn = g.op(
+            "RandomUniform",
+            shape_i=shape,
+            low_f=low_i,
+            high_f=high_i,
+        )
+
+    # cast to integer type
+    int_dtype = _type_utils.JitScalarType.INT64
+    randint = g.op("Cast", randn, to_i=int_dtype.onnx_type())
+    if int_dtype != scalar_type:
+        randint = g.op("Cast", randint, to_i=scalar_type.onnx_type())
+    return randint
+
+
+@_onnx_symbolic("aten::randint_like")
+def randint_like(g: jit_utils.GraphContext, self, low, high, dtype, *options):
+    dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+    low_i = symbolic_helper._get_const(low, "i", "low")
+    high_i = symbolic_helper._get_const(high, "i", "high")
+    if dtype is None:
+        scalar_type = _type_utils.JitScalarType.INT64
+    else:
+        scalar_type = _type_utils.JitScalarType(dtype)
+    if low_i is None:
+        raise symbolic_helper._onnx_unsupported("randint", low)
+    if high_i is None:
+        raise symbolic_helper._onnx_unsupported("randint", high)
+
+    randn = g.op(
+        "RandomUniformLike",
+        self,
+        low_f=low_i,
+        high_f=high_i,
+    )
+
+    # cast to integer type
+    int_dtype = _type_utils.JitScalarType.INT64
+    randint = g.op("Cast", randn, to_i=int_dtype.onnx_type())
+    if int_dtype != scalar_type:
+        randint = g.op("Cast", randint, to_i=scalar_type.onnx_type())
+    return randint
+
+
+@_onnx_symbolic("aten::randn")
+def randn(g: jit_utils.GraphContext, shapes, dtype, *options):
+    dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+    if dtype is None:
+        scalar_type = _type_utils.JitScalarType.FLOAT
+    else:
+        scalar_type = _type_utils.JitScalarType(dtype)
+    shape = symbolic_helper._maybe_get_const(shapes, "is")
+    if symbolic_helper._is_value(shape):
+        shape_const = g.op(
+            "ConstantOfShape",
+            shapes,
+            value_t=torch.tensor([0], dtype=torch.float),
+        )
+        return g.op(
+            "RandomNormalLike",
+            shape_const,
+            dtype_i=scalar_type.onnx_type(),
+        )
+    return g.op(
+        "RandomNormal",
+        shape_i=shape,
+        dtype_i=scalar_type.onnx_type(),
+    )
+
+
+@_onnx_symbolic("aten::rand")
+def rand(g: jit_utils.GraphContext, shapes, dtype, *options):
+    dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+    if dtype is None:
+        scalar_type = _type_utils.JitScalarType.FLOAT
+    else:
+        scalar_type = _type_utils.JitScalarType(dtype)
+    shape = symbolic_helper._maybe_get_const(shapes, "is")
+    if symbolic_helper._is_value(shape):
+        shape_const = g.op(
+            "ConstantOfShape",
+            shapes,
+            value_t=torch.tensor([0], dtype=torch.float),
+        )
+        return g.op(
+            "RandomUniformLike",
+            shape_const,
+            dtype_i=scalar_type.onnx_type(),
+        )
+    return g.op(
+        "RandomUniform",
+        shape_i=shape,
+        dtype_i=scalar_type.onnx_type(),
+    )
+
+
+@_onnx_symbolic("aten::randn_like")
+def randn_like(
+    g: jit_utils.GraphContext,
+    self,
+    dtype,
+    layout=None,
+    device=None,
+    pin_memory=False,
+    memory_format=None,
+):
+    dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+    if dtype is None:
+        scalar_type = _type_utils.JitScalarType.from_value(
+            self, _type_utils.JitScalarType.FLOAT
+        )
+    else:
+        scalar_type = _type_utils.JitScalarType(dtype)
+    return g.op("RandomNormalLike", self, dtype_i=scalar_type.onnx_type())
+
+
+@_onnx_symbolic("aten::rand_like")
+def rand_like(
+    g: jit_utils.GraphContext,
+    self,
+    dtype,
+    layout=None,
+    device=None,
+    pin_memory=False,
+    memory_format=None,
+):
+    dtype = symbolic_helper._get_const(dtype, "i", "dtype")
+    if dtype is None:
+        dtype = _type_utils.JitScalarType.from_value(
+            self, _type_utils.JitScalarType.FLOAT
+        )
+    return g.op(
+        "RandomUniformLike", self, dtype_i=_type_utils.JitScalarType(dtype).onnx_type()
+    )
+
+
+@_onnx_symbolic("aten::rrelu")
+@symbolic_helper.parse_args("v", "f", "f", "i", "none")
+def rrelu(g: jit_utils.GraphContext, input, lower, upper, training, generator):
+    if not training:
+        slope = (upper + lower) / 2.0
+        return g.op("LeakyRelu", input, alpha_f=slope)
+    p = g.op("RandomUniformLike", input, high_f=upper, low_f=lower)
+    return g.op("PRelu", input, p)
+
+
+@_onnx_symbolic("aten::bernoulli")
+def bernoulli(g: jit_utils.GraphContext, input, p=None, generator=None, out=None):
+    if out is not None and not symbolic_helper._is_none(out):
+        symbolic_helper._unimplemented(
+            "Bernoulli", "out parameter is not supported for bernoulli", input
+        )
+    if generator is not None and not symbolic_helper._is_none(generator):
+        symbolic_helper._unimplemented(
+            "Bernoulli", "generator is not supported for bernoulli", input
+        )
+
+    dtype = _type_utils.JitScalarType.from_value(
+        input, _type_utils.JitScalarType.UNDEFINED
+    )
+    if dtype == _type_utils.JitScalarType.UNDEFINED:
+        return symbolic_helper._unimplemented(
+            "Bernoulli", "input dtype not accessible", input
+        )
+
+    rands = g.op(
+        "RandomUniformLike",
+        input,
+        high_f=1.0,
+        low_f=0.0,
+        dtype_i=dtype.onnx_type(),
+    )
+    prob = p if p is not None and not symbolic_helper._is_none(p) else input
+    output = g.op("Less", rands, prob)
+    return g.op("Cast", output, to_i=dtype.onnx_type())
+
+
+@_onnx_symbolic("aten::log_sigmoid")
+@symbolic_helper.parse_args("v")
+def log_sigmoid(g: jit_utils.GraphContext, input):
+    p = g.op("Sigmoid", input)
+    return g.op("Log", p)
+
+
+@_onnx_symbolic("aten::erf")
+@symbolic_helper.parse_args("v")
+def erf(g: jit_utils.GraphContext, input):
+    return g.op("Erf", input)
+
+
+@_onnx_symbolic("aten::flatten")
+@symbolic_helper.quantized_args(True, False, False)
+@symbolic_helper.parse_args("v", "i", "i")
+def flatten(g: jit_utils.GraphContext, input, start_dim, end_dim):
+    dim = symbolic_helper._get_tensor_rank(input)
+    if dim is None:
+        return symbolic_helper._unimplemented(
+            "dim",
+            "ONNX and PyTorch use different strategies to split the input. "
+            "Input rank must be known at export time.",
+            input,
+        )
+
+    if dim == 0:
+        return symbolic_helper._reshape_helper(g, input, [1])
+    if dim == 1:
+        return g.op("Identity", input)
+    # TODO: remove this as onnx opset 11 spec allows negative axes
+    if end_dim < 0:
+        end_dim = dim + end_dim
+    # use ONNX's Flatten operator for cases where the output shape is 2D
+    if start_dim == 1 and end_dim == dim - 1:
+        return g.op("Flatten", input, axis_i=start_dim)
+    if start_dim == 0 and end_dim == dim - 2:
+        return g.op("Flatten", input, axis_i=end_dim + 1)
+
+    return symbolic_helper._flatten_helper(g, input, start_dim, end_dim, dim)
+
+
+@_onnx_symbolic("aten::nonzero")
+@symbolic_helper.parse_args("v")
+def nonzero(g: jit_utils.GraphContext, input):
+    """Emitted from `torch.nonzero(x, as_tuple=False)`"""
+    return t(g, g.op("NonZero", input))
+
+
+@_onnx_symbolic("aten::nonzero_numpy")
+# Emitted from `torch.nonzero(x, as_tuple=True)`
+def nonzero_numpy(g: jit_utils.GraphContext, input, _outputs=None):
+    return unbind(g, nonzero(g, input), 1, _outputs=_outputs)
+
+
+@_onnx_symbolic("aten::isnan")
+@symbolic_helper.parse_args("v")
+def isnan(g: jit_utils.GraphContext, input):
+    output = g.op("IsNaN", input)
+    return output
+
+
+@_onnx_symbolic("aten::any")
+def _any(g: jit_utils.GraphContext, *args):
+    # aten::any(Tensor self)
+    if len(args) == 1:
+        input = args[0]
+        dim, keepdim = None, 0
+    # aten::any(Tensor self, int[]? dim, bool keepdim)
+    else:
+        input, dim, keepdim = args
+        # Can be int list or single int
+        dim = symbolic_helper._parse_arg(dim, "t")
+        dim = [int(d) for d in dim.view(-1)]
+        keepdim = symbolic_helper._parse_arg(keepdim, "i")
+    input = g.op("Cast", input, to_i=_C_onnx.TensorProtoDataType.INT64)
+    input_sum = symbolic_helper._reducesum_helper(
+        g, input, axes_i=dim, keepdims_i=keepdim
+    )
+    return gt(g, input_sum, g.op("Constant", value_t=torch.tensor(0, dtype=torch.long)))
+
+
+@_onnx_symbolic("aten::all")
+def _all(g: jit_utils.GraphContext, *args):
+    input = g.op("Not", args[0])
+    # aten::all(Tensor self)
+    if len(args) == 1:
+        return g.op("Not", _any(g, input))
+    # aten::all(Tensor self, int[]? dim, bool keepdim)
+    else:
+        return g.op("Not", _any(g, input, args[1], args[2]))
+
+
+@_onnx_symbolic("aten::narrow")
+@symbolic_helper.parse_args("v", "i", "i", "i")
+def narrow(g: jit_utils.GraphContext, input, dim, start, length):
+    return symbolic_helper._slice_helper(
+        g, input, axes=[dim], starts=[start], ends=[start + length]
+    )
+
+
+@_onnx_symbolic("aten::argmax")
+@symbolic_helper.parse_args("v", "v", "b")
+def argmax(
+    g: jit_utils.GraphContext,
+    input: torch._C.Value,
+    dim: torch._C.Value,
+    keepdim: bool,
+):
+    return symbolic_helper._argmin_argmax_helper(g, input, dim, keepdim, "ArgMax")
+
+
+@_onnx_symbolic("aten::argmin")
+@symbolic_helper.parse_args("v", "v", "b")
+def argmin(
+    g: jit_utils.GraphContext,
+    input: torch._C.Value,
+    dim: torch._C.Value,
+    keepdim: bool,
+):
+    return symbolic_helper._argmin_argmax_helper(g, input, dim, keepdim, "ArgMin")
+
+
+@_onnx_symbolic("aten::scatter")
+@symbolic_helper.parse_args("v", "i", "v", "v")
+def scatter(g: jit_utils.GraphContext, self, dim, index, src):
+    src_type = _type_utils.JitScalarType.from_value(
+        src, _type_utils.JitScalarType.UNDEFINED
+    )
+    src = symbolic_helper._maybe_get_scalar(src)
+    if symbolic_helper._is_value(src):
+        return g.op("Scatter", self, index, src, axis_i=dim)
+    else:
+        # Check if scalar "src" has same type as self (PyTorch allows different
+        # type for scalar src (but not when src is tensor)). If not, insert Cast node.
+        self_scalar_type = _type_utils.JitScalarType.from_value(self)
+        if self_scalar_type != src_type:
+            src = g.op("Cast", src, to_i=self_scalar_type.onnx_type())
+        return g.op("Scatter", self, index, expand_as(g, src, index), axis_i=dim)
+
+
+@_onnx_symbolic("aten::scatter_add")
+@symbolic_helper.parse_args("v", "i", "v", "v")
+def scatter_add(g: jit_utils.GraphContext, self, dim, index, src):
+    scalar_type = symbolic_helper._try_get_scalar_type(self)
+    if scalar_type is None:
+        return symbolic_helper._unimplemented(
+            "scatter_add", "input dtype not accessible", self
+        )
+    sizes = symbolic_helper._get_tensor_sizes(self, allow_nonstatic=False)
+    if sizes:
+        to_add = g.op("Constant", value_t=torch.zeros(sizes, dtype=scalar_type.dtype()))
+    else:
+        to_add = zeros_like(g, self, scalar_type)
+    to_add = symbolic_helper._scatter_helper(g, to_add, dim, index, src)
+    return add(g, self, to_add)
+
+
+@_onnx_symbolic("aten::log2")
+def log2(g: jit_utils.GraphContext, self):
+    _ln2 = 0.693147180559945309
+    return g.op("Div", log(g, self), g.op("Constant", value_t=torch.tensor(_ln2)))
+
+
+@_onnx_symbolic("aten::is_floating_point")
+def is_floating_point(g: jit_utils.GraphContext, self):
+    if symbolic_helper._is_fp(self):
+        return g.op("Constant", value_t=torch.BoolTensor([1]))
+    return g.op("Constant", value_t=torch.BoolTensor([0]))
+
+
+@_onnx_symbolic("aten::__is_")
+def __is_(g: jit_utils.GraphContext, self, other):
+    if symbolic_helper._is_none(other):
+        if symbolic_helper._is_none(self):
+            return g.op("Constant", value_t=torch.BoolTensor([1]))
+        return g.op("Constant", value_t=torch.BoolTensor([0]))
+    return eq(g, self, other)
+
+
+@_onnx_symbolic("aten::__isnot_")
+@wrap_logical_op_with_negation
+def __isnot_(g: jit_utils.GraphContext, self, other):
+    return __is_(g, self, other)
+
+
+@_onnx_symbolic("aten::one_hot")
+def one_hot(g: jit_utils.GraphContext, self, num_classes):
+    values = g.op("Constant", value_t=torch.LongTensor([0, 1]))
+    # onnxruntime supports limited type combinations for OneHot.
+    if _type_utils.JitScalarType.from_value(
+        num_classes, _type_utils.JitScalarType.UNDEFINED
+    ) in {
+        _type_utils.JitScalarType.UINT8,
+        _type_utils.JitScalarType.INT8,
+        _type_utils.JitScalarType.INT,
+        _type_utils.JitScalarType.INT16,
+    }:
+        num_classes = g.op("Cast", num_classes, to_i=_C_onnx.TensorProtoDataType.INT64)
+    return g.op("OneHot", self, num_classes, values, axis_i=-1)
+
+
+@_onnx_symbolic("aten::gather")
+@symbolic_helper.parse_args("v", "i", "v", "v")
+def gather(g: jit_utils.GraphContext, self, dim, index, sparse_grad=False):
+    if symbolic_helper._maybe_get_const(sparse_grad, "i"):
+        return symbolic_helper._unimplemented("gather", "sparse_grad == True", self)
+    # NOTE: This workaround is needed since GatherElement is only supported
+    #       since opset 11, and Gather in ONNX is not the same as torch.gather.
+    scalar_type = _type_utils.JitScalarType.from_value(self)
+    values = g.op("Constant", value_t=torch.LongTensor([0, 1]))
+    depth = size(g, self, g.op("Constant", value_t=torch.LongTensor([dim])))
+    index = g.op(
+        "Cast",
+        g.op("OneHot", index, depth, values, axis_i=dim),
+        to_i=scalar_type.onnx_type(),
+    )
+    mul = g.op("Mul", symbolic_helper._unsqueeze_helper(g, self, [dim + 1]), index)
+    return symbolic_helper._reducesum_helper(g, mul, axes_i=[dim], keepdims_i=0)
+
+
+@symbolic_helper.parse_args("v", "is", "i", "i")
+def _var_mean(g: jit_utils.GraphContext, input, dim, correction, keepdim):
+    return symbolic_helper._var_mean_helper(g, input, dim, correction, keepdim)
+
+
+@_onnx_symbolic("aten::std")
+def std(g: jit_utils.GraphContext, input, *args):
+    var, _ = var_mean(g, input, *args)
+    return g.op("Sqrt", var)
+
+
+@_onnx_symbolic("aten::var")
+def var(g: jit_utils.GraphContext, input, *args):
+    var, _ = var_mean(g, input, *args)
+    return var
+
+
+@_onnx_symbolic("aten::var_mean")
+def var_mean(g: jit_utils.GraphContext, input, *args):
+    if len(args) == 1:
+        return _var_mean(g, input, None, args[0], None)
+    else:
+        return _var_mean(g, input, *args)
+
+
+@_onnx_symbolic("aten::std_mean")
+def std_mean(g: jit_utils.GraphContext, input, *args):
+    var, mean = var_mean(g, input, *args)
+    return g.op("Sqrt", var), mean
+
+
+@_onnx_symbolic("aten::logsumexp")
+@symbolic_helper.parse_args("v", "is", "i")
+def logsumexp(g: jit_utils.GraphContext, input, dim, keepdim):
+    return g.op("ReduceLogSumExp", input, axes_i=dim, keepdims_i=keepdim)
+
+
+@_onnx_symbolic("aten::arange")
+def arange(g: jit_utils.GraphContext, *args):
+    def _get_arange_dtype(dtype):
+        dtype = symbolic_helper._maybe_get_const(dtype, "i")
+        return dtype
+
+    def _float_step_convert(range_tensor):
+        if symbolic_helper._is_fp(range_tensor):
+            range_tensor = g.op(
+                "Cast",
+                g.op("Ceil", range_tensor),
+                to_i=_type_utils.JitScalarType.INT64.onnx_type(),
+            )
+        return range_tensor
+
+    if len(args) == 2 or len(args) == 5:
+        if len(args) == 2:
+            # aten::arange(Scalar end, Tensor out)
+            dtype = None
+        else:
+            # aten::arange(Scalar end, ScalarType dtype, Layout, Device, bool pin_memory)
+            dtype = _get_arange_dtype(args[1])
+        dtype, end, start, step = symbolic_helper._arange_cast_helper(
+            g, end=args[0], dtype=dtype
+        )
+        end = symbolic_helper._unsqueeze_helper(g, end, [0])
+        range_tensor = _float_step_convert(end)
+        arange_tensor = symbolic_helper._squeeze_helper(
+            g, nonzero(g, ones(g, range_tensor, dtype, None, None)), [1]
+        )
+        return g.op(
+            "Cast", arange_tensor, to_i=_type_utils.JitScalarType(dtype).onnx_type()
+        )
+    elif len(args) == 4 or len(args) == 7:
+        if len(args) == 4:
+            # aten::arange(Scalar start, Scalar end, Scalar step, Tensor out)
+            dtype = None
+        else:
+            # aten::arange(Scalar start, Scalar end, Scalar step, ScalarType dtype, Layout, Device, bool pin_memory)
+            dtype = _get_arange_dtype(args[3])
+        dtype, end, start, step = symbolic_helper._arange_cast_helper(
+            g, start=args[0], end=args[1], step=args[2], dtype=dtype
+        )
+        step = symbolic_helper._unsqueeze_helper(g, step, [0])
+        end = symbolic_helper._unsqueeze_helper(g, end, [0])
+        start = symbolic_helper._unsqueeze_helper(g, start, [0])
+        range_tensor = _float_step_convert(g.op("Div", g.op("Sub", end, start), step))
+        arange_tensor = symbolic_helper._squeeze_helper(
+            g, nonzero(g, ones(g, range_tensor, None, None, None)), [1]
+        )
+        arange_tensor = g.op("Add", g.op("Mul", arange_tensor, step), start)
+        return g.op(
+            "Cast", arange_tensor, to_i=_type_utils.JitScalarType(dtype).onnx_type()
+        )
+    elif len(args) == 6:
+        # aten::arange(Scalar start, Scalar end, ScalarType dtype, Layout, Device, bool pin_memory)
+        dtype = _get_arange_dtype(args[2])
+        dtype, end, start, step = symbolic_helper._arange_cast_helper(
+            g, start=args[0], end=args[1], dtype=dtype
+        )
+        end = symbolic_helper._unsqueeze_helper(g, end, [0])
+        start = symbolic_helper._unsqueeze_helper(g, start, [0])
+        range_tensor = _float_step_convert(g.op("Sub", end, start))
+        arange_tensor = g.op(
+            "Add",
+            symbolic_helper._squeeze_helper(
+                g, nonzero(g, ones(g, range_tensor, dtype, *(args[3:]))), [1]
+            ),
+            start,
+        )
+        return g.op(
+            "Cast", arange_tensor, to_i=_type_utils.JitScalarType(dtype).onnx_type()
+        )
+
+    return symbolic_helper._unimplemented("aten::arange", f"with {len(args)} arguments")
+
+
+@_onnx_symbolic("aten::linspace")
+def linspace(
+    g: jit_utils.GraphContext, start, end, steps, dtype, layout, device, pin_memory
+):
+    range_tensor = symbolic_helper._arange_helper(g, steps, None)
+    step = div(
+        g,
+        sub(g, end, start),
+        sub(g, steps, g.op("Constant", value_t=torch.tensor(1, dtype=torch.int64))),
+    )
+    return add(g, mul(g, range_tensor, step), start)
+
+
+@_onnx_symbolic("aten::lift")
+def lift(g: jit_utils.GraphContext, self):
+    # at::lift() is a no-op from the perspective of tracing for onnx
+    return self
+
+
+@_onnx_symbolic("aten::masked_fill")
+def masked_fill(g: jit_utils.GraphContext, self, mask, value):
+    """Implement the masked_fill functionality available for a pytorch tensor in ONNX.
+
+    Fills elements of the input tensor with `value` where `mask` is True.
+    """
+    mask = g.op("Cast", mask, to_i=_C_onnx.TensorProtoDataType.BOOL)
+    value = symbolic_helper._maybe_get_scalar(value)
+    return g.op("Where", mask, symbolic_helper._if_scalar_type_as(value, self), self)
+
+
+@_onnx_symbolic("aten::masked_fill_")
+def masked_fill_(g: jit_utils.GraphContext, self, mask, value):
+    return masked_fill(g, self, mask, value)
+
+
+@_onnx_symbolic("aten::index")
+def index(g: jit_utils.GraphContext, self, index):
+    if symbolic_helper._is_packed_list(index):
+        indices = symbolic_helper._unpack_list(index)
+    else:
+        indices = [index]
+
+    def try_mask_to_index(index):
+        if not symbolic_helper._is_none(index) and (
+            _type_utils.JitScalarType.from_value(
+                index, _type_utils.JitScalarType.UNDEFINED
+            )
+            == _type_utils.JitScalarType.UINT8
+            or symbolic_helper._is_bool(index)
+        ):
+            if g.opset < 9:
+                raise errors.SymbolicValueError(
+                    "Exporting masked indices are only supported after ONNX opset 9.",
+                    self,
+                )
+            warnings.warn(
+                "Exporting aten::index operator with indices of type Byte. "
+                "Only 1-D indices are supported. In any other case, "
+                "this will produce an incorrect ONNX graph."
+            )
+            index = symbolic_helper._squeeze_helper(g, nonzero(g, index), [1])
+        return index
+
+    indices = [try_mask_to_index(idx) for idx in indices]
+    if len(indices) == 1:
+        return symbolic_helper._select_helper(
+            g, self, 0, indices[0], apply_reshape=False
+        )
+    else:
+        # Multiple tensors as indices. Each tensor could either be
+        #   1. prim::Constant()
+        #           representing ":" in python indexing. E.g. tensor[:, :]
+        #   2. prim::Constant[value=...] or tensor output
+        #           representing advanced indexing. E.g. tensor[[0, 1], [2, 0]].
+        # For more info on advanced indexing,
+        # check https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing
+
+        # Consider a general case of
+        #       t: [x_1, y_1, y_2, ..., x_m, ..., y_n]
+        # where t is a tensor of rank m+n, {x_i} are axes where tensor index is provided, and {y_i} are axes for ":".
+        # Same results can be achieved through transposing t into
+        #       t: [x_1, x_2, ..., x_m, y_1, y_2, ..., y_n]
+        # and use gatherND. However ONNX does not have gatherND, to use 1d gather we'll need to flatten t
+        # and process the tensor indices.
+        #       t: [x_1 * x_2 * ... * x_m, y_1 * y_2 * ... * y_n]
+        #       tensor index = \sum_{i=1}^m (ind_i * \prod_{j=i+1}^m (x_j))
+        # After gather, reshape and transpose back.
+        adv_idx_indices = [
+            i for i, idx in enumerate(indices) if not symbolic_helper._is_none(idx)
+        ]
+
+        if len(adv_idx_indices) == 0:
+            return self
+        elif len(adv_idx_indices) == 1:
+            return index_select(
+                g, self, adv_idx_indices[0], indices[adv_idx_indices[0]]
+            )
+        else:
+            rank = symbolic_helper._get_tensor_rank(self)
+            if rank is None:
+                return symbolic_helper._unimplemented(
+                    "aten::index",
+                    "operator of advanced indexing on tensor of unknown rank. ",
+                    self,
+                )
+            # TODO: If indexing is supported natively in ONNX in future opsets,
+            #       update the warning to recommend exporting with higher opset version.
+            warnings.warn(
+                "Exporting aten::index operator of advanced indexing in opset "
+                f"{GLOBALS.export_onnx_opset_version}"
+                " is achieved by combination of multiple ONNX operators, "
+                "including Reshape, Transpose, Concat, and Gather. "
+                "If indices include negative values, the exported graph will produce incorrect results."
+            )
+            adv_idx_count = len(adv_idx_indices)
+            shape_tensor = _shape_as_tensor(g, self)
+            dim_tensor_list = [
+                g.op(
+                    "Gather",
+                    shape_tensor,
+                    g.op("Constant", value_t=torch.LongTensor([dim])),
+                    axis_i=0,
+                )
+                for dim in range(rank)
+            ]
+
+            self = g.op(
+                "Transpose",
+                self,
+                perm_i=adv_idx_indices
+                + [i for i in range(rank) if i not in adv_idx_indices],
+            )
+            self = g.op("Flatten", self, axis_i=adv_idx_count)
+
+            # Note that tensor indices will be broadcasted while accumulating. Thus we get the final subarray shape as well.
+            cum_adv_index = indices[adv_idx_indices[-1]]
+            multiplier = dim_tensor_list[adv_idx_indices[-1]]
+            for i in range(adv_idx_count - 2, -1, -1):
+                adv_index = g.op("Mul", indices[adv_idx_indices[i]], multiplier)
+                cum_adv_index = g.op("Add", cum_adv_index, adv_index)
+                multiplier = g.op(
+                    "Mul", multiplier, dim_tensor_list[adv_idx_indices[i]]
+                )
+
+            # perform gather
+            self = index_select(g, self, 0, cum_adv_index)
+
+            cum_adv_index_shape_tensor = _shape_as_tensor(g, cum_adv_index)
+            # check if all advanced indices are consecutive.
+            # Refer to https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#combining-advanced-and-basic-indexing
+            # to understand how the subarray position is decided.
+            if adv_idx_indices == list(
+                range(adv_idx_indices[0], adv_idx_indices[-1] + 1)
+            ):
+                # unfold regular index axes
+                folded_adv_idx_shape_list = [
+                    g.op("Constant", value_t=torch.LongTensor([-1]))
+                ] + [
+                    dim_tensor_list[i] for i in range(rank) if i not in adv_idx_indices
+                ]
+                folded_adv_idx_shape = g.op(
+                    "Concat", *folded_adv_idx_shape_list, axis_i=0
+                )
+                self = symbolic_helper._reshape_helper(g, self, folded_adv_idx_shape)
+
+                # Transpose folded advanced indexed axis to its original location.
+                adv_idx_permute = (
+                    list(range(1, adv_idx_indices[0] + 1))
+                    + [0]
+                    + list(range(adv_idx_indices[0] + 1, rank - adv_idx_count + 1))
+                )
+                self = g.op("Transpose", self, perm_i=adv_idx_permute)
+
+                # unfold advanced index axes
+                final_shape_list = (
+                    [dim_tensor_list[i] for i in range(adv_idx_indices[0])]
+                    + [cum_adv_index_shape_tensor]
+                    + [
+                        dim_tensor_list[i]
+                        for i in range(adv_idx_indices[0], rank)
+                        if i not in adv_idx_indices
+                    ]
+                )
+                final_shape = g.op("Concat", *final_shape_list, axis_i=0)
+            else:
+                final_shape = g.op(
+                    "Concat",
+                    cum_adv_index_shape_tensor,
+                    *[
+                        dim_tensor_list[i]
+                        for i in range(rank)
+                        if i not in adv_idx_indices
+                    ],
+                    axis_i=0,
+                )
+
+            return symbolic_helper._reshape_helper(g, self, final_shape)
+
+
+@_onnx_symbolic("aten::linalg_norm")
+@symbolic_helper.parse_args("v", "v", "is", "b", "v")
+def linalg_norm(
+    g: jit_utils.GraphContext,
+    self: torch._C.Value,
+    ord: torch._C.Value,
+    dim: Sequence[int] | None,
+    keepdim: bool,
+    dtype: torch._C.Value,
+):
+    # Conditions based on https://pytorch.org/docs/stable/generated/torch.linalg.norm.html
+    ord_value = None
+    if dim is None:
+        if symbolic_helper._is_none(ord):
+            self = symbolic_helper._reshape_helper(g, self, [-1])
+            ord = g.op("Constant", value_t=torch.LongTensor([2]))
+        self_dim = symbolic_helper._get_tensor_rank(self)
+        if self_dim is None:
+            return symbolic_helper._unimplemented(
+                "dim", "Input rank must be known at export time.", self
+            )
+        if self_dim == 1:
+            ord_value = symbolic_helper._parse_arg(ord, "f")
+        else:
+            dim = [0, 1]
+    else:
+        if len(dim) == 1:
+            if symbolic_helper._is_none(ord):
+                ord = g.op("Constant", value_t=torch.LongTensor([2]))
+            ord_value = symbolic_helper._parse_arg(ord, "f")
+    if ord_value:
+        return linalg_vector_norm(g, self, ord_value, dim, keepdim, dtype)
+    return linalg_matrix_norm(g, self, ord, dim, keepdim, dtype)
+
+
+@_onnx_symbolic("aten::linalg_vector_norm")
+@symbolic_helper.parse_args("v", "f", "is", "b", "v")
+def linalg_vector_norm(
+    g: jit_utils.GraphContext,
+    self: torch._C.Value,
+    ord: float,
+    dim: Sequence[int] | None,
+    keepdim: bool,
+    dtype: torch._C.Value,
+):
+    return symbolic_helper._linalg_vector_norm_helper(g, self, ord, dim, keepdim, dtype)
+
+
+@_onnx_symbolic("aten::linalg_matrix_norm")
+@symbolic_helper.parse_args("v", "v", "is", "b", "v")
+def linalg_matrix_norm(
+    g: jit_utils.GraphContext,
+    self: torch._C.Value,
+    ord: torch._C.Value,
+    dim: list[int],
+    keepdim: bool,
+    dtype: torch._C.Value,
+):
+    # Conditions based on https://pytorch.org/docs/stable/generated/torch.linalg.matrix_norm.html
+    ord_value = symbolic_helper._parse_arg(ord, "s")
+    if ord_value == "fro":
+        return frobenius_norm(g, self, dim, keepdim)
+    elif ord_value == "nuc":
+        return symbolic_helper._unimplemented("linalg.matrix_norm", "ord==nuc", self)
+    else:
+        ord_value = symbolic_helper._parse_arg(ord, "f")
+        if ord_value is None:
+            return frobenius_norm(g, self, dim, keepdim)
+        if ord_value == 2 or ord_value == -2:
+            # ord = 2/-2 unimplemented due to lack of operators
+            # used to calculate singular values
+            return symbolic_helper._unimplemented("linalg.matrix_norm", "ord==2", self)
+        # Wrap the dim vector to handle negative dim values
+        self_dim = symbolic_helper._get_tensor_rank(self)
+        if self_dim is None:
+            return symbolic_helper._unimplemented(
+                "linalg.matrix_norm", "Input rank must be known at export time.", self
+            )
+        # Common implementation for cases with
+        # ord = 1/-1 and ord = inf/-inf
+        if dim[0] < 0:
+            dim[0] += self_dim
+        if dim[1] < 0:
+            dim[1] += self_dim
+
+        if ord_value == math.inf or ord_value == -math.inf:
+            dim[0], dim[1] = dim[1], dim[0]
+        if dim[1] > dim[0] and not keepdim:
+            dim[1] -= 1
+        sum = symbolic_helper._reducesum_helper(
+            g, g.op("Abs", self), axes_i=[dim[0]], keepdims_i=keepdim
+        )
+        if ord_value > 0:
+            result, indices = max(
+                g,
+                sum,
+                dim_or_y=g.op("Constant", value_t=torch.LongTensor([dim[1]])),
+                keepdim=keepdim,
+            )
+        else:
+            result, indices = min(
+                g,
+                sum,
+                dim_or_y=g.op("Constant", value_t=torch.LongTensor([dim[1]])),
+                keepdim=keepdim,
+            )
+        return result
+
+
+@_onnx_symbolic("aten::linalg_cross")
+@symbolic_helper.parse_args("v", "v", "i")
+def linalg_cross(g: jit_utils.GraphContext, input, other, dim=-1):
+    return cross(g, input, other, dim)
+
+
+@_onnx_symbolic("aten::frobenius_norm")
+@symbolic_helper.parse_args("v", "is", "b")
+def frobenius_norm(g: jit_utils.GraphContext, self, dim=None, keepdim=False):
+    sqr = g.op("Mul", self, self)
+    sumsqr = symbolic_helper._reducesum_helper(g, sqr, axes_i=dim, keepdims_i=keepdim)
+    return g.op("Sqrt", sumsqr)
+
+
+@_onnx_symbolic("aten::multinomial")
+@symbolic_helper.parse_args("v", "i", "b", "v")
+def multinomial(
+    g: jit_utils.GraphContext, input, num_samples, replacement=False, generator=None
+):
+    if generator is not None and not symbolic_helper._is_none(generator):
+        symbolic_helper._unimplemented(
+            "Multinomial", "generator is not supported for multinomial", input
+        )
+    if not replacement and num_samples > 1:
+        symbolic_helper._unimplemented(
+            "Multinomial",
+            "replacement=False when num_samples > 1 is not supported for multinomial",
+            input,
+        )
+
+    log_input = log(g, input)
+    return g.op(
+        "Multinomial",
+        log_input,
+        dtype_i=_C_onnx.TensorProtoDataType.INT64,
+        sample_size_i=num_samples,
+    )
+
+
+@_onnx_symbolic("aten::baddbmm")
+def baddbmm(g: jit_utils.GraphContext, self, batch1, batch2, beta, alpha):
+    scalar_type = _type_utils.JitScalarType.from_value(self)
+    batch_mul = matmul(g, batch1, batch2)
+    mul_a = mul(
+        g,
+        batch_mul,
+        g.op("Cast", alpha, to_i=scalar_type.onnx_type()),
+    )
+    mul_b = mul(
+        g,
+        self,
+        g.op("Cast", beta, to_i=scalar_type.onnx_type()),
+    )
+    return add(g, mul_a, mul_b)
+
+
+@_onnx_symbolic("aten::meshgrid")
+@symbolic_helper.parse_args("v", "s")
+def meshgrid(g: jit_utils.GraphContext, tensor_list, indexing: str | None = None):
+    if indexing is None:
+        indexing = "ij"
+    elif indexing not in {"ij", "xy"}:
+        raise errors.SymbolicValueError(
+            f"Unsupported indexing: {indexing}", tensor_list
+        )
+    unpacked_tensor_list = symbolic_helper._unpack_list(tensor_list)
+    if indexing == "xy":
+        unpacked_tensor_list[:2] = unpacked_tensor_list[1::-1]
+    tensors = [
+        symbolic_helper._reshape_helper(
+            g, t, g.op("Constant", value_t=torch.LongTensor([-1]))
+        )
+        for t in unpacked_tensor_list
+    ]
+    tensors_shape = [g.op("Shape", t) for t in tensors]
+    out_shape = g.op("Concat", *tensors_shape, axis_i=0)
+    out = []
+    for i, t in enumerate(tensors):
+        shape_i = [g.op("Constant", value_t=torch.ones(1, dtype=torch.int64))] * len(
+            tensors
+        )
+        shape_i[i] = tensors_shape[i]
+        t_reshaped = _reshape_from_tensor(g, t, g.op("Concat", *shape_i, axis_i=0))
+        out.append(g.op("Expand", t_reshaped, out_shape))
+    if indexing == "xy":
+        out[0], out[1] = out[1], out[0]
+    return g.op("prim::ListConstruct", *out)
+
+
+@_onnx_symbolic("aten::remainder")
+def remainder(g: jit_utils.GraphContext, input, other):
+    div = _floor_divide(g, input, other)
+    quo = g.op("Mul", div, other)
+    return g.op("Sub", input, quo)
+
+
+@_onnx_symbolic("aten::gelu")
+@symbolic_helper.parse_args("v", "s")
+def gelu(g: jit_utils.GraphContext, self: torch._C.Value, approximate: str = "none"):
+    if approximate == "tanh":
+        kBeta = math.sqrt(2 / math.pi)
+        kKappa = 0.044715
+
+        beta = torch.tensor(kBeta, dtype=torch.double)
+        kappa = torch.tensor(kKappa, dtype=torch.double)
+        one = torch.tensor(1.0, dtype=torch.double)
+        half = torch.tensor(0.5, dtype=torch.double)
+
+        self_cube = mul(g, self, mul(g, self, self))
+        inner = mul(g, beta, add(g, self, mul(g, kappa, self_cube)))
+        return mul(g, half, mul(g, self, add(g, one, g.op("Tanh", inner))))
+    else:
+        _sqrt2 = 1.4142135623730951
+        erf = g.op("Erf", g.op("Div", self, torch.tensor(_sqrt2, dtype=torch.double)))
+        erf_plusone = add(
+            g, erf, g.op("Constant", value_t=torch.tensor(1, dtype=torch.double))
+        )
+        return mul(
+            g,
+            mul(g, self, erf_plusone),
+            g.op("Constant", value_t=torch.tensor(0.5, dtype=torch.double)),
+        )
+
+
+@_onnx_symbolic("aten::group_norm")
+@symbolic_helper.quantized_args(True, False, False, False)
+@symbolic_helper.parse_args("v", "i", "v", "v", "f", "i")
+def group_norm(
+    g: jit_utils.GraphContext, input, num_groups, weight, bias, eps, cudnn_enabled
+):
+    channel_size = symbolic_helper._get_tensor_dim_size(input, 1)
+    if channel_size is not None:
+        assert channel_size % num_groups == 0
+    input_rank = symbolic_helper._get_tensor_rank(input)
+    if input_rank is None:
+        return symbolic_helper._unimplemented("group_norm", "unknown input rank", input)
+    # 0 in the shape list keeps dimension value unchanged.
+    shape = [0, num_groups, -1]
+    input_reshaped = symbolic_helper._reshape_helper(
+        g, input, g.op("Constant", value_t=torch.LongTensor(shape))
+    )
+
+    # C is always divisible by num_groups
+    # Due to shape difference. we need to apply weight and bias after
+    # instance norm computation and reshape
+    weight_ = g.op(
+        "Constant",
+        value_t=torch.tensor(
+            [1.0] * num_groups,
+            dtype=_type_utils.JitScalarType.from_value(input).dtype(),
+        ),
+    )
+    bias_ = g.op(
+        "Constant",
+        value_t=torch.tensor(
+            [0.0] * num_groups,
+            dtype=_type_utils.JitScalarType.from_value(input).dtype(),
+        ),
+    )
+
+    norm_reshaped = g.op(
+        "InstanceNormalization", input_reshaped, weight_, bias_, epsilon_f=eps
+    )
+    norm = symbolic_helper._reshape_helper(g, norm_reshaped, g.op("Shape", input))
+
+    if weight is None or weight.node().mustBeNone():
+        weight_value = torch.tensor(
+            [1.0], dtype=_type_utils.JitScalarType.from_value(input).dtype()
+        )
+        weight = g.op("Constant", value_t=weight_value)
+    if bias is None or bias.node().mustBeNone():
+        bias_value = torch.tensor(
+            [0.0], dtype=_type_utils.JitScalarType.from_value(input).dtype()
+        )
+        bias = g.op("Constant", value_t=bias_value)
+
+    # Norm has shape [N, C, *] so we reshape weight and bias to [C, *]
+    axes = list(range(1, input_rank - 1))
+    return add(
+        g,
+        mul(g, norm, symbolic_helper._unsqueeze_helper(g, weight, axes)),
+        symbolic_helper._unsqueeze_helper(g, bias, axes),
+    )
+
+
+@_onnx_symbolic("aten::_weight_norm")
+@symbolic_helper.parse_args("v", "v", "i")
+def _weight_norm(g: jit_utils.GraphContext, weight_v, weight_g, dim):
+    rank = symbolic_helper._get_tensor_rank(weight_v)
+    if rank is not None:
+        # W = g * ((v) / ||v||)
+        # Compute norm_except_dim for l2 norm. dim = None means over all dims
+        # torch's weight_norm module sets dim = -1 if it's None.
+        # This conflicts the logic for negative axes to access dims backwards
+        # TODO: Might need a fix in torch group_norm module
+        axes = list(range(rank))
+        if dim is not None:
+            if dim < -1:
+                dim += rank
+            if dim != -1:
+                axes.remove(dim)
+        norm_v = norm(g, weight_v, 2, axes, 1)
+        div = g.op("Div", weight_v, norm_v)
+        return g.op("Mul", div, weight_g)
+    raise errors.SymbolicValueError(
+        "Unsupported: ONNX export of _weight_norm for tensor of unknown rank.",
+        weight_v,
+    )
+
+
+@_onnx_symbolic("aten::dim")
+def dim(g: jit_utils.GraphContext, self):
+    """Implement the dim functionality available for a pytorch tensor in ONNX"""
+    # ONNX does not support dim directly in this opset so we can use 2 ops to get the info
+    shape = g.op("Shape", self)
+    return g.op("Size", shape)
+
+
+@_onnx_symbolic("aten::__contains_")
+def __contains_(g: jit_utils.GraphContext, self, element):
+    unpacked_list = symbolic_helper._unpack_list(self)
+    if all(
+        symbolic_helper._is_constant(x) for x in unpacked_list
+    ) and symbolic_helper._is_constant(element):
+        return g.op(
+            "Constant",
+            value_t=torch.tensor(
+                symbolic_helper._node_get(element.node(), "value")
+                in (symbolic_helper._node_get(x.node(), "value") for x in unpacked_list)
+            ),
+        )
+
+    raise errors.SymbolicValueError(
+        "Unsupported: ONNX export of __contains__ for non-constant list or element.",
+        self,
+    )
+
+
+@_onnx_symbolic("aten::__getitem_")
+def __getitem_(g: jit_utils.GraphContext, self, i):
+    return select(g, self, g.op("Constant", value_t=torch.tensor([0])), i)
+
+
+@_onnx_symbolic("aten::item")
+def item(g: jit_utils.GraphContext, self):
+    return self
+
+
+@_onnx_symbolic("aten::take")
+def take(g: jit_utils.GraphContext, self, index):
+    self_flattened = symbolic_helper._reshape_helper(
+        g, self, g.op("Constant", value_t=torch.tensor([-1], dtype=torch.int64))
+    )
+    out = index_select(g, self_flattened, 0, index)
+    out = reshape_as(g, out, index)
+    return out
+
+
+def _kl_div_log_target_impl(g: jit_utils.GraphContext, input, target):
+    diff_ = sub(g, target, input)
+    exp_ = exp(g, target)
+    output = mul(g, exp_, diff_)
+    return output
+
+
+def _kl_div_non_log_target_impl(g: jit_utils.GraphContext, input, target):
+    log_ = log(g, target)
+    diff_ = sub(g, log_, input)
+    output_pos = mul(g, target, diff_)
+    zeros_ = zeros_like(g, output_pos)
+    mask_ = gt(g, target, g.op("Constant", value_t=torch.tensor(0)))
+    output = where(g, mask_, output_pos, zeros_)
+    return output
+
+
+@_onnx_symbolic("aten::kl_div")
+@symbolic_helper.parse_args("v", "v", "i", "b")
+def kl_div(g: jit_utils.GraphContext, input, target, reduction, log_target):
+    if log_target:
+        output = _kl_div_log_target_impl(g, input, target)
+    else:
+        output = _kl_div_non_log_target_impl(g, input, target)
+
+    if reduction == 0:
+        return output
+    elif reduction == 1:
+        return g.op("ReduceMean", output, keepdims_i=0)
+    elif reduction == 2:
+        return symbolic_helper._reducesum_helper(g, output, keepdims_i=0)
+    else:
+        return symbolic_helper._onnx_unsupported(
+            "kl_div with reduction other than none, mean, or sum.", input
+        )
+
+
+@_onnx_symbolic("aten::mse_loss")
+@symbolic_helper.parse_args("v", "v", "i")
+def mse_loss(g: jit_utils.GraphContext, input, target, reduction):
+    output = mul(g, sub(g, input, target), sub(g, input, target))
+    if reduction == 0:
+        return output
+    elif reduction == 1:
+        return g.op("ReduceMean", output, keepdims_i=0)
+    elif reduction == 2:
+        return symbolic_helper._reducesum_helper(g, output, keepdims_i=0)
+    else:
+        return symbolic_helper._onnx_unsupported(
+            "mse_loss with reduction other than none, mean, or sum.", input
+        )
+
+
+@_onnx_symbolic("aten::as_strided")
+@symbolic_helper.quantized_args(True)
+@symbolic_helper.parse_args("v", "v", "is", "i")
+def as_strided(g: jit_utils.GraphContext, self, sizes, strides, offset=None):
+    sizes = symbolic_helper._maybe_get_const(sizes, "is")
+    rank = len(strides)
+    self_1d = symbolic_helper._reshape_helper(
+        g, self, g.op("Constant", value_t=torch.tensor([-1], dtype=torch.int64))
+    )
+    ind: torch.Tensor | None
+    if not symbolic_helper._is_value(sizes):
+        ind = torch.tensor([0], dtype=torch.long)
+        for i, (size, stride) in enumerate(zip(sizes, strides)):
+            r_size = [1] * rank
+            r_size[i] = -1
+            ind = ind + torch.arange(size).view(r_size) * stride
+        if offset:
+            ind = ind + offset
+        return g.op("Gather", self_1d, g.op("Constant", value_t=ind))
+    else:
+        ind = None
+        for i, stride in enumerate(strides):
+            r_size = [1] * rank
+            r_size[i] = -1
+            size = select(
+                g,
+                sizes,
+                g.op("Constant", value_t=torch.tensor([0])),
+                g.op("Constant", value_t=torch.tensor(i)),
+            )
+            tmp_ind = symbolic_helper._reshape_helper(
+                g,
+                arange(g, size, 4, None, None, None),
+                g.op("Constant", value_t=torch.tensor(r_size)),
+            )
+            tmp_ind = g.op(
+                "Mul", tmp_ind, g.op("Constant", value_t=torch.tensor([stride]))
+            )
+            if ind is None:
+                ind = tmp_ind
+            else:
+                ind = g.op("Add", ind, tmp_ind)
+        if offset:
+            ind = g.op("Add", ind, g.op("Constant", torch.tensor([offset])))
+        return g.op("Gather", self_1d, ind)
+
+
+@_onnx_symbolic("aten::__derive_index")
+def __derive_index(g: jit_utils.GraphContext, index, start, step):
+    return g.op("Add", start, g.op("Mul", index, step))
+
+
+@_onnx_symbolic("aten::__range_length")
+# Source code for aten op can be found here: pytorch/torch/csrc/jit/runtime/register_prim_ops.cpp
+# if (step > 0 && lo < hi) {
+#   push(stack, 1 + (hi - 1 - lo) / step);
+# } else if (step < 0 && lo > hi) {
+#   push(stack, 1 + (lo - 1 - hi) / (0 - step));
+# } else {
+#  push(stack, 0);
+# }
+def __range_length(g: jit_utils.GraphContext, lo, hi, step):
+    sub = g.op("Sub", hi, lo)
+    div = g.op("Ceil", true_divide(g, sub, step))
+    return g.op("Cast", div, to_i=_C_onnx.TensorProtoDataType.INT64)
+
+
+@_onnx_symbolic("aten::linear")
+def linear(g: jit_utils.GraphContext, input, weight, bias):
+    rank = symbolic_helper._get_tensor_rank(input)
+    weight = t(g, weight)
+    if rank == 2 and not bias.node().mustBeNone():
+        alpha = g.op("Constant", value_t=torch.tensor(1, dtype=torch.int64))
+        beta = g.op("Constant", value_t=torch.tensor(1, dtype=torch.int64))
+        output = addmm(g, bias, input, weight, alpha, beta)
+    else:
+        output = matmul(g, input, weight)
+        if not bias.node().mustBeNone():
+            output = add(g, bias, output)
+
+    return output
+
+
+@_onnx_symbolic("aten::hann_window")
+@symbolic_helper.parse_args("v", "b", "i", "v", "v", "v", "v")
+def hann_window(
+    g: jit_utils.GraphContext,
+    window_length,
+    periodic=True,
+    dtype: int | None = None,
+    layout=None,
+    device=None,
+    pin_memory=None,
+    requires_grad=False,
+):
+    if dtype is None:
+        dtype_ = torch.get_default_dtype()
+        if not dtype_ or not dtype_.is_floating_point:
+            dtype_ = torch.float
+        scalar_type = _type_utils.JitScalarType.from_dtype(dtype_)
+    else:
+        scalar_type = _type_utils.JitScalarType(dtype)
+
+    n_array = arange(g, window_length, 4, None, None, None)
+    output = g.op("Cast", n_array, to_i=_C_onnx.TensorProtoDataType.FLOAT)
+    output = mul(
+        g, g.op("Constant", value_t=torch.tensor(math.pi, dtype=torch.float)), output
+    )
+
+    if periodic is False:
+        window_length = sub(
+            g, window_length, g.op("Constant", value_t=torch.tensor(1, dtype=torch.int))
+        )
+    output = div(g, output, window_length)
+    output = g.op(
+        "Cast",
+        square(g, sin(g, output)),
+        to_i=scalar_type.onnx_type(),
+    )
+
+    return output
+
+
+@_onnx_symbolic("aten::mv")
+def mv(g: jit_utils.GraphContext, self, vec):
+    return matmul(g, self, vec)
+
+
+@_onnx_symbolic("aten::dot")
+def dot(g: jit_utils.GraphContext, self, other):
+    return matmul(g, self, other)
+
+
+@_onnx_symbolic("aten::movedim")
+@symbolic_helper.parse_args("v", "t", "t")
+def movedim(g: jit_utils.GraphContext, self, source, destination):
+    # This is a pythonic implementation mostly taken from aten/src/ATen/native/TensorShape.cpp::movedim
+    source = source.view(-1)
+    destination = destination.view(-1)
+
+    assert source.size() == destination.size()
+
+    if (source == destination).all():
+        return self
+
+    self_rank = symbolic_helper._get_tensor_rank(self)
+    assert self_rank is not None
+
+    perm = list(range(self_rank))
+
+    src_dims = perm.copy()
+    dst_dims = perm.copy()
+
+    for src, dst in zip(source.tolist(), destination.tolist()):
+        perm[dst] = src
+        src_dims[src] = -1
+        dst_dims[dst] = -1
+
+    src_dims = [dim for dim in src_dims if dim != -1]
+    dst_dims = [dim for dim in dst_dims if dim != -1]
+
+    for src, dst in zip(src_dims, dst_dims):
+        perm[dst] = src
+
+    return g.op("Transpose", self, perm_i=perm)
+
+
+@_onnx_symbolic("aten::fill")
+@symbolic_helper.parse_args("v", "v")
+def fill(g: jit_utils.GraphContext, self, value):
+    scalar_type = _type_utils.JitScalarType.from_value(
+        self, _type_utils.JitScalarType.FLOAT
+    )
+    return full_like(g, self, value, scalar_type)
+
+
+@_onnx_symbolic("aten::index_add")
+def index_add(g: jit_utils.GraphContext, self, dim, index, other, alpha=None):
+    warnings.warn(
+        "Warning: ONNX export does not support duplicated values in 'index' field, "
+        + "this will cause the ONNX model to be incorrect."
+    )
+
+    # ONNX does not support "alpha" argument, unlike aten index_add
+    # See: https://github.com/pytorch/pytorch/pull/65993#issuecomment-953151102 for more context
+    if alpha and symbolic_helper._scalar(symbolic_helper._maybe_get_scalar(alpha)) != 1:
+        return symbolic_helper._unimplemented("index_add", "alpha != 1", self)
+
+    dim = symbolic_helper._maybe_get_const(dim, "i")
+    if dim is None:
+        raise errors.SymbolicValueError(
+            "ONNX export does NOT support exporting 'index_add_()' function with "
+            "unknown 'dim' value.",
+            self,
+        )
+
+    self_dim_rank = symbolic_helper._get_tensor_rank(self)
+    other_dim_rank = symbolic_helper._get_tensor_rank(other)
+
+    if self_dim_rank is None or other_dim_rank is None:
+        raise errors.SymbolicValueError(
+            "ONNX export does NOT support exporting 'index_add_()' function while "
+            "the rank of self tensor or tensor to be added is unknown.",
+            self,
+        )
+
+    if other_dim_rank != self_dim_rank:
+        delta = self_dim_rank - other_dim_rank
+        for i in range(delta):
+            other = symbolic_helper._unsqueeze_helper(
+                g, other, [symbolic_helper._get_tensor_rank(other)]
+            )
+
+    other_dim_size = symbolic_helper._get_tensor_dim_size(other, dim)
+    self_dim_size = symbolic_helper._get_tensor_dim_size(self, dim)
+
+    if (other_dim_size is not None) and (self_dim_size is not None):
+        if other_dim_size > self_dim_size:
+            raise errors.SymbolicValueError(
+                "ONNX export does not support exporting 'index_add_()' function with "
+                "duplicated values in 'index' parameter yet.",
+                self,
+            )
+
+    # Construct a new shape. It's almost as same as self except the size of the 'dim'
+    # dimension is 1, so that we can expand other dimensions as expected.
+    new_shape_axes = list(range(self_dim_rank))
+    new_shape_starts = [0 for i in range(self_dim_rank)]
+    new_shape_ends = [sys.maxsize if (i != dim) else 1 for i in range(self_dim_rank)]
+
+    new_shape = symbolic_helper._slice_helper(
+        g, self, axes=new_shape_axes, starts=new_shape_starts, ends=new_shape_ends
+    )
+    other = expand_as(g, other, new_shape)
+
+    for i in range(dim):
+        index = symbolic_helper._unsqueeze_helper(g, index, [0])
+
+    for i in range(self_dim_rank - dim - 1):
+        index = symbolic_helper._unsqueeze_helper(
+            g, index, [symbolic_helper._get_tensor_rank(index)]
+        )
+
+    return scatter_add(g, self, dim, expand_as(g, index, other), other)
+
+
+@_onnx_symbolic("aten::roll")
+@symbolic_helper.parse_args("v", "is", "is")
+def roll(g: jit_utils.GraphContext, self, shifts, dims):
+    assert len(shifts) == len(dims)
+
+    result = self
+    for i in range(len(shifts)):
+        shapes = []
+        shape = symbolic_helper._slice_helper(
+            g, result, axes=[dims[i]], starts=[-shifts[i]], ends=[sys.maxsize]
+        )
+        shapes.append(shape)
+        shape = symbolic_helper._slice_helper(
+            g, result, axes=[dims[i]], starts=[0], ends=[-shifts[i]]
+        )
+        shapes.append(shape)
+        result = g.op("Concat", *shapes, axis_i=dims[i])
+
+    return result
+
+
+@_onnx_symbolic("aten::cross")
+@symbolic_helper.parse_args("v", "v", "i")
+def cross(g: jit_utils.GraphContext, input, other, dim=None):
+    dim = symbolic_helper._get_dim_for_cross(input, dim)
+    # If we have two tensors such that
+    # A = [a, b, c], B = [d, e, f], we permute the tensor such that we have
+    # After first roll,
+    # A' = [b, c, a], B' = [f, d, e], so that we calculate (b*f, c*d, a*e)
+    roll_x_1 = roll(g, input, [2], [dim])
+    roll_y_1 = roll(g, other, [1], [dim])
+    # After second roll,
+    # A' = [c, a, b], B' = [e, f, d], so that we calculate (c*e, a*f, b*d)
+    roll_x_2 = roll(g, input, [1], [dim])
+    roll_y_2 = roll(g, other, [2], [dim])
+    # cross product is calculated as
+    # result = [(b*f - c*e), (c*d - a*f), (a*e - b*d)]
+    return sub(g, mul(g, roll_x_1, roll_y_1), mul(g, roll_x_2, roll_y_2))
+
+
+@_onnx_symbolic("aten::cdist")
+def cdist(
+    g: jit_utils.GraphContext,
+    x1,
+    x2,
+    p=2.0,
+    compute_mode="use_mm_for_euclid_dist_if_necessary",
+):
+    # X1.shape = (B * P * D), X2.shape = (B * R * D)
+    # In order to respect numpy style broadcasting as demonstrated in
+    # https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md
+    # we unsqueeze both input tensors
+    # Currently we ignore the 'compute_mode' variable as we use default to
+    # using matrix multiplication to calculate the euclidean distance
+    rank = symbolic_helper._get_tensor_rank(x1)
+    assert rank is not None
+    broadcasted_x1 = symbolic_helper._unsqueeze_helper(g, x1, [rank - 1])
+    broadcasted_x2 = symbolic_helper._unsqueeze_helper(g, x2, [rank - 2])
+    return pairwise_distance(
+        g, broadcasted_x1, broadcasted_x2, p, eps=1e-06, keepdim=False
+    )
+
+
+@_onnx_symbolic("aten::lerp")
+def lerp(g: jit_utils.GraphContext, self, end, weight):
+    # Conditional for better numeric. This has been discussed in
+    # https://github.com/pytorch/pytorch/pull/18871
+    diff = g.op("Sub", end, self)
+    return where(
+        g,
+        g.op("Less", weight, g.op("Constant", value_t=torch.tensor(0.5))),
+        g.op("Add", self, g.op("Mul", weight, diff)),
+        g.op(
+            "Sub",
+            end,
+            g.op(
+                "Mul",
+                diff,
+                g.op("Sub", g.op("Constant", value_t=torch.tensor(1.0)), weight),
+            ),
+        ),
+    )
+
+
+@_onnx_symbolic("aten::broadcast_tensors")
+def broadcast_tensors(g: jit_utils.GraphContext, self):
+    all_tensors = symbolic_helper._unpack_list(self)
+    t_with_final_shape = zeros_like(g, all_tensors[0])
+
+    # Add operator supports multidirectional broadcasting. So we leverage this function
+    # to infer the final shape generated by the broadcast.
+    for t in all_tensors:
+        t_with_final_shape = add(g, t_with_final_shape, t)
+
+    t_list = [expand_as(g, t, t_with_final_shape) for t in all_tensors]
+    return g.op("prim::ListConstruct", *t_list)
+
+
+@_onnx_symbolic("aten::is_pinned")
+def is_pinned(g: jit_utils.GraphContext, self, device=None):
+    # Unused by ONNX.
+    return None
+
+
+@_onnx_symbolic("prim::ConstantSplit")
+def prim_constant_split(g: jit_utils.GraphContext, self, split_size, dim):
+    size = symbolic_helper._get_tensor_dim_size(self, dim)
+    if size is None:
+        return symbolic_helper._unimplemented(
+            "prim::ConstantSplit", "unknown dimension size", self
+        )
+    splits = [split_size] * (size // split_size)
+    leftover = size % split_size
+    if leftover:
+        splits.append(leftover)
+    return g.op("Split", self, split_i=splits, axis_i=dim, outputs=len(splits))
+
+
+# TODO: It would be better to export this as a chunk directly, as this is
+# less sensitive to changes in input size.
+# TODO: Once we have proper scoping, stop reimplementing chunk, delete this
+# method, and use the desugared version
+@_onnx_symbolic("prim::ConstantChunk")
+def prim_constant_chunk(g: jit_utils.GraphContext, self, chunks, dim):
+    dim_size = symbolic_helper._get_tensor_dim_size(self, dim)
+    if dim_size is None:
+        return symbolic_helper._unimplemented(
+            "prim::ConstantChunk", "unknown dimension size", self
+        )
+    split_size = (dim_size + chunks - 1) // chunks
+    return prim_constant_split(g, self, split_size, dim)
+
+
+@_onnx_symbolic("prim::shape")
+def prim_shape(g: jit_utils.GraphContext, self):
+    return g.op("Shape", self)
+
+
+@_onnx_symbolic("prim::max")
+def prim_max(g: jit_utils.GraphContext, self, other):
+    return symbolic_helper._op_with_optional_float_cast(
+        g, "Max", self, other, opset_before=12
+    )
+
+
+@_onnx_symbolic("prim::min")
+def prim_min(g: jit_utils.GraphContext, self, other=None):
+    if not other:
+        if symbolic_helper._is_packed_list(self):
+            self = stack(g, self, g.op("Constant", value_t=torch.tensor([0])))
+        return min(g, self)
+    return min(g, self, other)
+
+
+@_onnx_symbolic("prim::data")
+def prim_data(g: jit_utils.GraphContext, self):
+    return self
+
+
+@_onnx_symbolic("prim::layout")
+def prim_layout(g: jit_utils.GraphContext, self):
+    # Always return 'torch.strided'. Other layout types are not supported by JIT 'TensorType'.
+    # Layout class defined in 'c10/core/Layout.h'.
+    return g.op("Constant", value_t=torch.tensor(0))
+
+
+@_onnx_symbolic("prim::ListConstruct")
+def prim_list_construct(g: jit_utils.GraphContext, *inputs, **kwargs):
+    return None
+
+
+@_onnx_symbolic("prim::ListUnpack")
+def prim_list_unpack(
+    g: jit_utils.GraphContext, *inputs, **kwargs
+) -> list[_C.Value] | None:
+    if len(inputs) == 1 and inputs[0].node().kind() == "prim::ListConstruct":
+        # Cancel the previous node if it is ListConstruct by returning its inputs
+        # TODO(justinchuby): Use a public method in the helper module
+        return symbolic_helper._unpack_list(inputs[0])
+
+    return None
+
+
+@_onnx_symbolic("prim::TupleConstruct")
+def prim_tuple_construct(g: jit_utils.GraphContext, *inputs, **kwargs):
+    return None
+
+
+@_onnx_symbolic("prim::Uninitialized")
+def prim_uninitialized(g: jit_utils.GraphContext, *inputs, **kwargs):
+    return None
+
+
+# exists to refine the type of the Value
+# if x is an optional Tensor, unchecked_cast will cast
+# x to Tensor, so the rest of the graph knows that x is a Tensor
+# this doesn't do anything in runtime and is a noop in ONNX
+@_onnx_symbolic("prim::unchecked_cast")
+def prim_unchecked_cast(g: jit_utils.GraphContext, self):
+    return self
+
+
+@_onnx_symbolic("prim::dtype")
+def prim_dtype(g: jit_utils.GraphContext, self):
+    scalar_type = symbolic_helper._try_get_scalar_type(self)
+    if scalar_type is None:
+        scalar_type = _type_utils.JitScalarType.FLOAT
+    # This node records a torch dtype as int
+    return g.op("Constant", value_t=torch.tensor(scalar_type))
+
+
+@_onnx_symbolic("prim::tolist")
+def prim_tolist(g: jit_utils.GraphContext, input, dim_val, elem_ty_val):
+    """tolist is currently supported only for 1D input tensors.
+
+    dim_val and elem_ty_val represent dimension and type annotations
+    that need to match dimension and type of the input tensor.
+    """
+    dim = symbolic_helper._maybe_get_const(dim_val, "i")
+    if dim > 1:
+        return symbolic_helper._unimplemented("prim::tolist", "dim_val > 1", input)
+    return input
+
+
+# -----------------------------------------------------------------------------
+# Symbolic functions that need extra context
+# -----------------------------------------------------------------------------
+@_onnx_symbolic("prim::device")
+def prim_device(g: jit_utils.GraphContext, *inputs, **kwargs) -> None:
+    output_type = g.original_node.output().type()
+    if isinstance(output_type, _C.DeviceObjType):
+        return None
+
+    return symbolic_helper._unimplemented(
+        "prim::device",
+        f"output type should be 'DeviceObjType', not '{output_type.kind()}'",
+        g.original_node.output(),
+    )
+
+
+@_onnx_symbolic("prim::Loop")
+def prim_loop(g: jit_utils.GraphContext, *inputs, **attrs) -> list[_C.Value]:
+    node = g.original_node
+    env = g.env
+    values_in_env = g.values_in_env
+    params_dict = g.params_dict
+
+    operator_export_type = GLOBALS.operator_export_type
+    opset_version = GLOBALS.export_onnx_opset_version
+
+    old_blocks = tuple(node.blocks())
+    new_op_outputs, new_block_contexts, new_node = jit_utils.add_op_with_blocks(
+        g, "Loop", *inputs, outputs=node.outputsSize(), n_blocks=len(old_blocks)
+    )
+
+    for old_block, new_block_context in zip(old_blocks, new_block_contexts):
+        # Copy input metadata to subblock
+        #
+        #   prim::Loop(iter, cond, input_1, ..., input_n)
+        #     block0(iter, input_1, ..., input_n)
+        #
+        # For `Loop` node, copy metadata for `iter`, `input_1`, ..., `input_n`.
+        for i, b_in in enumerate(old_block.inputs()):
+            if i == 0 and i < len(inputs):
+                b_in.setType(inputs[i].type())
+            # For optional block inputs, they may switch between None not-None inside
+            # the loop body, so if the loop input is not optional, the block input may
+            # still need to be optional.
+            if (
+                i > 0
+                and (i + 1) < len(inputs)
+                and not isinstance(b_in.type(), _C.OptionalType)
+            ):
+                b_in.setType(inputs[i + 1].type())
+        torch._C._jit_pass_onnx_block(
+            old_block,
+            new_block_context.block,
+            operator_export_type,
+            env,
+            values_in_env,
+            False,
+        )
+    fixed_outputs = torch._C._jit_pass_fixup_onnx_controlflow_node(
+        new_node, opset_version
+    )
+    # Run shape type inference for Loop after subblock is converted.
+    if GLOBALS.onnx_shape_inference:
+        torch._C._jit_pass_onnx_node_shape_type_inference(
+            new_node, params_dict, opset_version
+        )
+    return fixed_outputs
+
+
+@_onnx_symbolic("prim::If")
+def prim_if(g: jit_utils.GraphContext, *inputs, **attrs) -> list[_C.Value]:
+    n = g.original_node
+    block = g.block
+    env = g.env
+    values_in_env = g.values_in_env
+    params_dict = g.params_dict
+
+    operator_export_type = GLOBALS.operator_export_type
+    opset_version = GLOBALS.export_onnx_opset_version
+
+    static_if = inputs[0].node().kind() == "onnx::Constant"
+    if static_if:
+        # Fold static if
+        #
+        # The torch IR
+        # graph(%embedding_matrix.1 : Float(10, 15, strides=[15, 1], requires_grad=0, device=cpu),
+        #    %input.1 : Long(6, strides=[1], requires_grad=0, device=cpu), ...
+        # %65 : Bool(requires_grad=0, device=cpu) = prim::Constant[value={0}]()
+        # %21 : Long(device=cpu) = aten::eq(%20, %64)
+        # %22 : Long(device=cpu) = prim::If(%21)
+        #     block0():
+        #     %23 : Long(device=cpu) = aten::is_floating_point(%input.1)
+        #     -> (%23)
+        #     block1():
+        #     -> (%65)
+        # %input.53 : Tensor, %weight : Tensor = prim::If(%22)
+        #     block0():
+        #     -> (%embedding_matrix.1, %input.1)
+        #     block1():
+        #     -> (%input.1, %embedding_matrix.1)
+        # %26 : int[] = aten::size(%input.53)
+        #
+        # The converted ONNX graph
+        # %10 : Bool(device=cpu) = onnx::Constant[value={0}]()
+        # %14 : Bool(device=cpu) = onnx::Equal(%13, %8)
+        # %15 : Bool(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()
+        # %16 : Long(1, strides=[1], device=cpu) = onnx::Shape(%input.1)
+        input_flag = symbolic_helper._node_get(inputs[0].node(), "value").tolist()
+        const_value = (
+            all(input_flag) if isinstance(input_flag, list) else bool(input_flag)
+        )
+        block_idx = 0 if const_value else 1
+        current_b = list(n.blocks())[block_idx]
+        env = torch._C._jit_pass_onnx_block(
+            current_b,
+            block,
+            operator_export_type,
+            env,
+            values_in_env,
+            True,
+        )
+        if_output_list = list(n.outputs())
+        current_b_list = list(current_b.outputs())
+
+        final_b_list = []
+        for idx in range(len(if_output_list)):
+            if current_b_list[idx] not in env:
+                raise errors.SymbolicValueError(
+                    f"The sub block ATen output {current_b_list[idx]} is not in env.",
+                    current_b_list[idx],
+                )  # type:ignore[operator]
+            onnx_b = env[current_b_list[idx]]
+            final_b_list.append(onnx_b)
+        return final_b_list
+    else:
+        old_blocks = tuple(n.blocks())
+        new_op_outputs, new_block_contexts, new_node = jit_utils.add_op_with_blocks(
+            g, "If", *inputs, outputs=n.outputsSize(), n_blocks=len(old_blocks)
+        )
+
+        for old_block, new_block_context in zip(old_blocks, new_block_contexts):
+            torch._C._jit_pass_onnx_block(
+                old_block,
+                new_block_context.block,
+                operator_export_type,
+                env,
+                values_in_env,
+                False,
+            )
+        fixed_outputs = torch._C._jit_pass_fixup_onnx_controlflow_node(
+            new_node, opset_version
+        )
+        # Run shape type inference for If after subblock is converted.
+        if GLOBALS.onnx_shape_inference:
+            torch._C._jit_pass_onnx_node_shape_type_inference(
+                new_node, params_dict, opset_version
+            )
+        return fixed_outputs
+
+
+@_onnx_symbolic("prim::Constant")
+def prim_constant(g: jit_utils.GraphContext, *inputs, **attrs):
+    node = g.original_node
+
+    if node.mustBeNone():
+        return None
+    # This must go before checking for string values, because some device constants
+    # have string values, but we want to keep them as unconverted Device types so
+    # that eq() can work on them.
+    if isinstance(node.output().type(), _C.DeviceObjType):
+        return None
+    if node.kindOf("value") == "t":
+        return g.op("Constant", value_t=symbolic_helper._node_get(node, "value"))
+    if node.kindOf("value") == "s":
+        return g.op("Constant", value_s=symbolic_helper._node_get(node, "value"))
+    if node.output().type().isSubtypeOf(
+        _C.ListType.ofInts()
+    ) or node.output().type().isSubtypeOf(_C.ListType.ofFloats()):
+        return g.op(
+            "Constant", value_t=torch.tensor(symbolic_helper._node_get(node, "value"))
+        )
+    if node.output().type().isSubtypeOf(_C.ListType.ofStrings()):
+        str_constants = [
+            g.op("Constant", value_s=s)
+            for s in symbolic_helper._node_get(node, "value")
+        ]
+        return g.op("prim::ListConstruct", *str_constants)
+
+    raise errors.SymbolicValueError(
+        f"Unsupported prim::Constant kind: '{node.kindOf('value')}'. "
+        f"Please send a bug report at {_constants.PYTORCH_GITHUB_ISSUES_URL}.",
+        node.output(),
+    )
+
+
+@_onnx_symbolic("prim::type")
+def prim_type(g: jit_utils.GraphContext, device_value: _C.Value, *args, **kwargs):
+    if device_value.node().kind() == "prim::device":
+        device = jit_utils.get_device_from_value(device_value.node().input())
+        if device is not None:
+            return g.op("Constant", value_s=str(device))
+
+    return symbolic_helper._unimplemented(
+        "prim::type",
+        "Device type cannot be statically determined.",
+        device_value,
+    )
+
+
+@_onnx_symbolic("onnx::Placeholder")
+def onnx_placeholder(g: jit_utils.GraphContext, *inputs, **attrs):
+    node = g.original_node
+    block = g.block
+    env = g.env
+    values_in_env = g.values_in_env
+
+    return torch._C._jit_onnx_convert_pattern_from_subblock(
+        block, node, env, values_in_env
+    )
+
+
+@_onnx_symbolic("aten::resolve_conj")
+@_onnx_symbolic("aten::resolve_neg")
+def noop_complex_operators(g: jit_utils.GraphContext, input: _C.Value):
+    # ONNX does not have operators to *directly* manipulate real/imaginary components
+    # However, a few torch APIs (e.g. .tolist()) use complex operations when input is real,
+    # which results in failures due to missing operators for complex numbers
+
+    # `aten::resolve_conj` and `aten::resolve_neg` can safely be implemented as no-op
+    return input
+
+
+@_onnx_symbolic("aten::_conj")
+@_onnx_symbolic("aten::conj_physical")
+def unsupported_complex_operators(g: jit_utils.GraphContext, input: _C.Value):
+    # ONNX does not have operators to *directly* manipulate real/imaginary components
+    # However, a few torch APIs (e.g. .tolist()) use complex operations when input is real,
+    # which results in failures due to missing operators for complex numbers
+
+    # While `aten::_conj` and `aten::conj_physical` raise exception when input is complex
+    if symbolic_helper.is_complex_value(input):
+        # FIXME(justinchuby): report correct name for symbolic being executed
+        return symbolic_helper._onnx_unsupported(
+            "aten::_conj, aten::conj_physical",
+            input,
+        )
+
+    # they can safely be implemented as no-op for real numbers only
+    return noop_complex_operators(g, input)
+
+
+@_onnx_symbolic("aten::logit")
+def logit(g: jit_utils.GraphContext, self: torch._C.Value, eps: torch._C.Value):
+    one = g.op("Constant", value_t=torch.tensor(1.0))
+
+    if not symbolic_helper._is_none(eps):
+        eps = g.op(
+            "Cast", eps, to_i=_type_utils.JitScalarType.from_value(self).onnx_type()
+        )
+        one_sub_eps = g.op("Sub", one, eps)
+        self_less_equal_one_sub_eps = g.op("Greater", one_sub_eps, self)
+        temporary_self = g.op("Where", self_less_equal_one_sub_eps, self, one_sub_eps)
+
+        temporary_self_less_eps = g.op("Less", temporary_self, eps)
+        z = g.op("Where", temporary_self_less_eps, eps, temporary_self)
+    else:
+        z = self
+
+    sub = g.op("Sub", one, z)
+    div = g.op("Div", z, sub)
+    return g.op("Log", div)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b7848e580584519505df117128d5a5a6fff9708
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/utils.py
@@ -0,0 +1,1990 @@
+# mypy: allow-untyped-defs
+"""Functions to export models into the ONNX IR format.
+
+These models can be loaded with the ONNX library and then
+converted to models which run on other deep learning frameworks.
+"""
+
+from __future__ import annotations
+
+import contextlib
+import copy
+import inspect
+import re
+import typing
+import warnings
+from typing import Any, Callable, cast, Collection, Mapping, Sequence
+
+import torch
+import torch._C._onnx as _C_onnx
+import torch.jit._trace
+import torch.serialization
+from torch import _C
+from torch.onnx import (  # noqa: F401
+    _constants,
+    _deprecation,
+    _exporter_states,
+    errors,
+    symbolic_helper,
+)
+from torch.onnx._globals import GLOBALS
+from torch.onnx._internal import diagnostics, jit_utils, onnx_proto_utils, registration
+
+
+__all__ = [
+    "is_in_onnx_export",
+    "select_model_mode_for_export",
+    "disable_apex_o2_state_dict_hook",
+    "setup_onnx_logging",
+    "exporter_context",
+    "export",
+    "model_signature",
+    "warn_on_static_input_change",
+    "unpack_quantized_tensor",
+    "export_to_pretty_string",
+    "unconvertible_ops",
+    "register_custom_op_symbolic",
+    "unregister_custom_op_symbolic",
+]
+
+
+def is_in_onnx_export() -> bool:
+    """Returns whether it is in the middle of ONNX export."""
+    return GLOBALS.in_onnx_export
+
+
+# TODO(justinchuby): Remove dependency to this global variable from constant_fold.cpp
+# Skip check due to cannot import IValue from torch._C
+_params_dict = {}  # type: ignore[var-annotated]
+
+
+@contextlib.contextmanager
+def select_model_mode_for_export(model, mode: _C_onnx.TrainingMode):
+    r"""A context manager to temporarily set the training mode of ``model``
+    to ``mode``, resetting it when we exit the with-block.
+
+    Args:
+        model: Same type and meaning as ``model`` arg to :func:`export`.
+        mode: Same type and meaning as ``training`` arg to :func:`export`.
+    """
+    if not isinstance(mode, _C_onnx.TrainingMode):
+        raise TypeError(
+            f"'mode' should be a torch.onnx.TrainingMode enum, but got '{type(mode)}'."
+        )
+    originally_training: bool = False
+
+    if hasattr(model, "training"):
+        originally_training = model.training
+
+        # ONNX opset 12 has better support for training amenable models, with updated
+        # versions of the dropout and batch_norm operators
+        if mode == _C_onnx.TrainingMode.TRAINING or (
+            mode == _C_onnx.TrainingMode.PRESERVE and originally_training
+        ):
+            GLOBALS.export_training = True
+            if GLOBALS.export_onnx_opset_version < 12:
+                warnings.warn(
+                    "You are exporting the model in training mode with onnx opset "
+                    f"version {GLOBALS.export_onnx_opset_version}. "
+                    "Opset versions lower than opset 12 will not be able to export "
+                    "nodes such as Dropout and BatchNorm correctly."
+                )
+        else:
+            GLOBALS.export_training = False
+
+        GLOBALS.training_mode = mode
+        if mode == _C_onnx.TrainingMode.TRAINING:
+            model.train(True)
+        elif mode == _C_onnx.TrainingMode.EVAL:
+            model.train(False)
+        # else mode == _C_onnx.TrainingMode.PRESERVE, do nothing
+
+    try:
+        yield
+    finally:
+        if hasattr(model, "training") and not mode == _C_onnx.TrainingMode.PRESERVE:
+            model.train(originally_training)
+
+
+@contextlib.contextmanager
+def disable_apex_o2_state_dict_hook(model: torch.nn.Module | torch.jit.ScriptFunction):
+    # Apex O2 hook state_dict to return fp16 weights as fp32.
+    # Exporter cannot identify them as same tensors.
+    # Since this hook is only used by optimizer, it is safe to
+    # remove this hook while exporting.
+    if not isinstance(model, torch.jit.ScriptFunction):
+        model_hooks = {}  # type: ignore[var-annotated]
+        for module in model.modules():
+            for key, hook in module._state_dict_hooks.items():
+                if type(hook).__name__ == "O2StateDictHook":
+                    if module not in model_hooks:
+                        model_hooks[module] = {}
+                    model_hooks[module][key] = hook
+            if module in model_hooks:
+                for key in model_hooks[module]:
+                    module._state_dict_hooks.pop(key)
+        try:
+            yield
+        finally:
+            # Add the hooks back
+            for module, m_map in model_hooks.items():
+                for key, hook in m_map.items():
+                    module._state_dict_hooks[key] = hook
+    else:
+        try:
+            yield
+        finally:
+            pass
+
+
+@contextlib.contextmanager
+def setup_onnx_logging(verbose: bool):
+    is_originally_enabled = torch.onnx.is_onnx_log_enabled()
+    if is_originally_enabled or verbose:
+        torch.onnx.enable_log()
+    try:
+        yield
+    finally:
+        if not is_originally_enabled:
+            torch.onnx.disable_log()
+
+
+@contextlib.contextmanager
+def exporter_context(model, mode: _C_onnx.TrainingMode, verbose: bool):
+    with select_model_mode_for_export(
+        model, mode
+    ) as mode_ctx, disable_apex_o2_state_dict_hook(
+        model
+    ) as apex_ctx, setup_onnx_logging(
+        verbose
+    ) as log_ctx, diagnostics.create_export_diagnostic_context() as diagnostic_ctx:
+        yield (mode_ctx, apex_ctx, log_ctx, diagnostic_ctx)
+
+
+def _get_torch_export_args(
+    args: tuple[Any, ...],
+    kwargs: dict[str, Any] | None,
+) -> tuple[tuple[Any, ...], dict[str, Any] | None]:
+    """Obtain the arguments for torch.onnx.export from the model and the input arguments."""
+    if not kwargs and args and isinstance(args[-1], dict):
+        kwargs = args[-1]
+        args = args[:-1]
+    return args, kwargs
+
+
+def export(
+    model: torch.nn.Module | torch.jit.ScriptModule | torch.jit.ScriptFunction,
+    args: tuple[Any, ...] | torch.Tensor,
+    f: str,
+    *,
+    kwargs: dict[str, Any] | None = None,
+    export_params: bool = True,
+    verbose: bool = False,
+    training: _C_onnx.TrainingMode = _C_onnx.TrainingMode.EVAL,
+    input_names: Sequence[str] | None = None,
+    output_names: Sequence[str] | None = None,
+    operator_export_type: _C_onnx.OperatorExportTypes = _C_onnx.OperatorExportTypes.ONNX,
+    opset_version: int | None = None,
+    do_constant_folding: bool = True,
+    dynamic_axes: Mapping[str, Mapping[int, str]]
+    | Mapping[str, Sequence[int]]
+    | None = None,
+    keep_initializers_as_inputs: bool | None = None,
+    custom_opsets: Mapping[str, int] | None = None,
+    export_modules_as_functions: bool | Collection[type[torch.nn.Module]] = False,
+    autograd_inlining: bool = True,
+) -> None:
+    r"""Exports a model into ONNX format.
+
+    If ``model`` is not a :class:`torch.jit.ScriptModule` nor a
+    :class:`torch.jit.ScriptFunction`, this runs
+    ``model`` once in order to convert it to a TorchScript graph to be exported
+    (the equivalent of :func:`torch.jit.trace`). Thus this has the same limited support
+    for dynamic control flow as :func:`torch.jit.trace`.
+
+    Args:
+        model: The model to be exported.
+        args:
+
+            args can be structured either as:
+
+            1. ONLY A TUPLE OF ARGUMENTS::
+
+                args = (x, y, z)
+
+            The tuple should contain model inputs such that ``model(*args)`` is a valid
+            invocation of the model. Any non-Tensor arguments will be hard-coded into the
+            exported model; any Tensor arguments will become inputs of the exported model,
+            in the order they occur in the tuple.
+
+            2. A TENSOR::
+
+                args = torch.Tensor([1])
+
+            This is equivalent to a 1-ary tuple of that Tensor.
+
+            3. A TUPLE OF ARGUMENTS ENDING WITH A DICTIONARY OF NAMED ARGUMENTS::
+
+                args = (x, {"y": input_y, "z": input_z})
+
+            All but the last element of the tuple will be passed as non-keyword arguments,
+            and named arguments will be set from the last element. If a named argument is
+            not present in the dictionary, it is assigned the default value, or None if a
+            default value is not provided.
+
+            .. warning::
+                This behavior will be deprecated in a future release. Please use the
+                kwargs argument instead.
+
+            .. note::
+                If a dictionary is the last element of the args tuple, it will be
+                interpreted as containing named arguments. In order to pass a dict as the
+                last non-keyword arg, provide an empty dict as the last element of the args
+                tuple. For example, instead of::
+
+                    torch.onnx.export(
+                        model,
+                        (
+                            x,
+                            # WRONG: will be interpreted as named arguments
+                            {y: z},
+                        ),
+                        "test.onnx.pb",
+                    )
+
+                Write::
+
+                    torch.onnx.export(model, (x, {y: z}, {}), "test.onnx.pb")
+
+        f: Path to the output ONNX model file. E.g. "model.onnx".
+        kwargs: Named arguments to the model.
+        export_params: If True, all parameters will
+            be exported. Set this to False if you want to export an untrained model.
+            In this case, the exported model will first take all of its parameters
+            as arguments, with the ordering as specified by ``model.state_dict().values()``
+        verbose: if True, prints a description of the
+            model being exported to stdout. In addition, the final ONNX graph will include the
+            field ``doc_string``` from the exported model which mentions the source code locations
+            for ``model``. If True, ONNX exporter logging will be turned on.
+        training:
+            * ``TrainingMode.EVAL``: export the model in inference mode.
+            * ``TrainingMode.PRESERVE``: export the model in inference mode if model.training is
+                False and in training mode if model.training is True.
+            * ``TrainingMode.TRAINING``: export the model in training mode. Disables optimizations
+                which might interfere with training.
+        input_names (list of str, default empty list): names to assign to the
+            input nodes of the graph, in order.
+        output_names (list of str, default empty list): names to assign to the
+            output nodes of the graph, in order.
+        operator_export_type (enum, default OperatorExportTypes.ONNX):
+
+            .. warning::
+                This option will be deprecated in a future release. Future exported
+                graphs will always use the default opset domain.
+
+            * ``OperatorExportTypes.ONNX``: Export all ops as regular ONNX ops
+                (in the default opset domain).
+            * ``OperatorExportTypes.ONNX_FALLTHROUGH``: Try to convert all ops
+                to standard ONNX ops in the default opset domain. If unable to do so
+                (e.g. because support has not been added to convert a particular torch op to ONNX),
+                fall back to exporting the op into a custom opset domain without conversion. Applies
+                to `custom ops `_
+                as well as ATen ops. For the exported model to be usable, the runtime must support
+                these non-standard ops.
+            * ``OperatorExportTypes.ONNX_ATEN``: All ATen ops (in the TorchScript namespace "aten")
+                are exported as ATen ops (in opset domain "org.pytorch.aten").
+                `ATen `_ is PyTorch's built-in tensor library, so
+                this instructs the runtime to use PyTorch's implementation of these ops.
+
+                .. warning::
+
+                    Models exported this way are probably runnable only by Caffe2.
+
+                    This may be useful if the numeric differences in implementations of operators are
+                    causing large differences in behavior between PyTorch and Caffe2 (which is more
+                    common on untrained models).
+
+            * ``OperatorExportTypes.ONNX_ATEN_FALLBACK``: Try to export each ATen op
+                (in the TorchScript namespace "aten") as a regular ONNX op. If we are unable to do so
+                (e.g. because support has not been added to convert a particular torch op to ONNX),
+                fall back to exporting an ATen op. See documentation on OperatorExportTypes.ONNX_ATEN for
+                context.
+                For example::
+
+                    graph(%0 : Float):
+                    %3 : int = prim::Constant[value=0]()
+                    # conversion unsupported
+                    %4 : Float = aten::triu(%0, %3)
+                    # conversion supported
+                    %5 : Float = aten::mul(%4, %0)
+                    return (%5)
+
+                Assuming ``aten::triu`` is not supported in ONNX, this will be exported as::
+
+                    graph(%0 : Float):
+                    %1 : Long() = onnx::Constant[value={0}]()
+                    # not converted
+                    %2 : Float = aten::ATen[operator="triu"](%0, %1)
+                    # converted
+                    %3 : Float = onnx::Mul(%2, %0)
+                    return (%3)
+
+                .. warning::
+
+                    Models exported this way are probably runnable only by Caffe2.
+
+        opset_version (int, default 17): The version of the
+            `default (ai.onnx) opset `_
+            to target. Must be >= 7 and <= 17.
+        do_constant_folding: Apply the constant-folding optimization.
+            Constant-folding will replace some of the ops that have all constant inputs
+            with pre-computed constant nodes.
+        dynamic_axes:
+
+            By default the exported model will have the shapes of all input and output tensors
+            set to exactly match those given in ``args``. To specify axes of tensors as
+            dynamic (i.e. known only at run-time), set ``dynamic_axes`` to a dict with schema:
+
+            * KEY (str): an input or output name. Each name must also be provided in ``input_names`` or
+                ``output_names``.
+            * VALUE (dict or list): If a dict, keys are axis indices and values are axis names. If a
+                list, each element is an axis index.
+
+            For example::
+
+                class SumModule(torch.nn.Module):
+                    def forward(self, x):
+                        return torch.sum(x, dim=1)
+
+
+                torch.onnx.export(
+                    SumModule(),
+                    (torch.ones(2, 2),),
+                    "onnx.pb",
+                    input_names=["x"],
+                    output_names=["sum"],
+                )
+
+            Produces::
+
+                input {
+                  name: "x"
+                  ...
+                      shape {
+                        dim {
+                          dim_value: 2  # axis 0
+                        }
+                        dim {
+                          dim_value: 2  # axis 1
+                ...
+                output {
+                  name: "sum"
+                  ...
+                      shape {
+                        dim {
+                          dim_value: 2  # axis 0
+                ...
+
+            While::
+
+                torch.onnx.export(
+                    SumModule(),
+                    (torch.ones(2, 2),),
+                    "onnx.pb",
+                    input_names=["x"],
+                    output_names=["sum"],
+                    dynamic_axes={
+                        # dict value: manually named axes
+                        "x": {0: "my_custom_axis_name"},
+                        # list value: automatic names
+                        "sum": [0],
+                    },
+                )
+
+            Produces::
+
+                input {
+                  name: "x"
+                  ...
+                      shape {
+                        dim {
+                          dim_param: "my_custom_axis_name"  # axis 0
+                        }
+                        dim {
+                          dim_value: 2  # axis 1
+                ...
+                output {
+                  name: "sum"
+                  ...
+                      shape {
+                        dim {
+                          dim_param: "sum_dynamic_axes_1"  # axis 0
+                ...
+
+        keep_initializers_as_inputs: If True, all the
+            initializers (typically corresponding to parameters) in the
+            exported graph will also be added as inputs to the graph. If False,
+            then initializers are not added as inputs to the graph, and only
+            the non-parameter inputs are added as inputs.
+            This may allow for better optimizations (e.g. constant folding) by
+            backends/runtimes.
+
+            If True, `deduplicate_initializers` pass will not be executed. This means
+            initializers with duplicated values will not be deduplicated and
+            will be treated as distinct inputs to the graph. This allows different
+            input initializers to be supplied at the runtime following export.
+
+            If ``opset_version < 9``, initializers MUST be part of graph
+            inputs and this argument will be ignored and the behavior will be
+            equivalent to setting this argument to True.
+
+        custom_opsets (dict[str, int], default empty dict): A dict with schema:
+
+            * KEY (str): opset domain name
+            * VALUE (int): opset version
+
+            If a custom opset is referenced by ``model`` but not mentioned in this dictionary,
+            the opset version is set to 1. Only custom opset domain name and version should be
+            indicated through this argument.
+
+        export_modules_as_functions: Flag to enable
+            exporting all ``nn.Module`` forward calls as local functions in ONNX. Or a set to indicate the
+            particular types of modules to export as local functions in ONNX.
+            This feature requires ``opset_version`` >= 15, otherwise the export will fail. This is because
+            ``opset_version`` < 15 implies IR version < 8, which means no local function support.
+            Module variables will be exported as function attributes. There are two categories of function
+            attributes.
+
+            1. Annotated attributes: class variables that have type annotations via
+            `PEP 526-style `_
+            will be exported as attributes.
+            Annotated attributes are not used inside the subgraph of ONNX local function because
+            they are not created by PyTorch JIT tracing, but they may be used by consumers
+            to determine whether or not to replace the function with a particular fused kernel.
+
+            2. Inferred attributes: variables that are used by operators inside the module. Attribute names
+            will have prefix "inferred::". This is to differentiate from predefined attributes retrieved from
+            python module annotations. Inferred attributes are used inside the subgraph of ONNX local function.
+
+            * ``False`` (default): export ``nn.Module`` forward calls as fine grained nodes.
+            * ``True``: export all ``nn.Module`` forward calls as local function nodes.
+            * Set of type of nn.Module: export ``nn.Module`` forward calls as local function nodes,
+                only if the type of the ``nn.Module`` is found in the set.
+
+        autograd_inlining: Flag used to control whether to inline autograd functions.
+            Refer to https://github.com/pytorch/pytorch/pull/74765 for more details.
+
+    Raises:
+        :class:`torch.onnx.errors.CheckerError`: If the ONNX checker detects an invalid ONNX graph.
+        :class:`torch.onnx.errors.UnsupportedOperatorError`: If the ONNX graph cannot be exported because it
+            uses an operator that is not supported by the exporter.
+        :class:`torch.onnx.errors.OnnxExporterError`: Other errors that can occur during export.
+            All errors are subclasses of :class:`errors.OnnxExporterError`.
+    """
+    if operator_export_type != _C_onnx.OperatorExportTypes.ONNX:
+        warnings.warn(
+            "Setting `operator_export_type` to something other than default is deprecated. "
+            "The option will be removed in a future release.",
+            category=FutureWarning,
+        )
+    if training == _C_onnx.TrainingMode.TRAINING:
+        warnings.warn(
+            "Setting `training` to something other than default is deprecated. "
+            "The option will be removed in a future release. Please set the training mode "
+            "before exporting the model.",
+            category=FutureWarning,
+        )
+
+    args = (args,) if isinstance(args, torch.Tensor) else args
+    if kwargs is not None:
+        args = args + (kwargs,)
+
+    _export(
+        model,
+        args,
+        f,
+        export_params,
+        verbose,
+        training,
+        input_names,
+        output_names,
+        operator_export_type=operator_export_type,
+        opset_version=opset_version,
+        do_constant_folding=do_constant_folding,
+        dynamic_axes=dynamic_axes,
+        keep_initializers_as_inputs=keep_initializers_as_inputs,
+        custom_opsets=custom_opsets,
+        export_modules_as_functions=export_modules_as_functions,
+        autograd_inlining=autograd_inlining,
+    )
+
+    return None
+
+
+def _is_constant_tensor_list(node):
+    if node.kind() != "prim::Constant":
+        return False
+    output_type = node.output().type()
+    if output_type.isSubtypeOf(_C.ListType.ofTensors()):
+        return True
+    if output_type.isSubtypeOf(_C.ListType(_C.OptionalType.ofTensor())):
+        return True
+
+
+# ONNX can't handle constants that are lists of tensors, which can
+# get generated in constant prop. So we split them back into prim::ListConstructs
+
+
+def _split_tensor_list_constants(g, block):
+    for node in block.nodes():
+        for subblock in node.blocks():
+            _split_tensor_list_constants(g, subblock)
+        if _is_constant_tensor_list(node):
+            inputs = []
+            for val in node.output().toIValue():
+                input = g.insertConstant(val)
+                input.node().moveBefore(node)
+                input.node().copyMetadata(node)
+                inputs.append(input)
+
+            lc = (
+                g.create("prim::ListConstruct", inputs)
+                .insertBefore(node)
+                .output()
+                .setType(_C.ListType.ofTensors())
+            )
+            lc.node().copyMetadata(node)
+            node.output().replaceAllUsesWith(lc)
+
+
+def _optimize_graph(
+    graph: _C.Graph,
+    operator_export_type: _C_onnx.OperatorExportTypes,
+    _disable_torch_constant_prop: bool = False,
+    fixed_batch_size: bool = False,
+    params_dict=None,
+    dynamic_axes=None,
+    input_names=None,
+    module=None,
+):
+    if params_dict is None:
+        params_dict = {}
+
+    # Inline everything
+    _C._jit_pass_inline(graph)
+
+    # Remove fork/wait nodes
+    _C._jit_pass_inline_fork_wait(graph)
+    _C._jit_pass_lint(graph)
+    if GLOBALS.autograd_inlining:
+        _C._jit_pass_onnx_autograd_function_process(graph)
+    _C._jit_pass_lower_all_tuples(graph)
+
+    # we now record some ops like ones/zeros
+    # into a trace where we previously recorded constants.
+    # use constant prop to maintain our current level of onnx support
+    # without implementing symbolics for all of them
+    if _disable_torch_constant_prop is False:
+        _C._jit_pass_constant_propagation(graph)
+
+    _split_tensor_list_constants(graph, graph)
+    # run dce to eliminate dead parts of the graph that might have been
+    # left behind by things like symbolic_override
+    _C._jit_pass_dce(graph)
+    _C._jit_pass_lint(graph)
+
+    # CSE should improve perf when Autocast is used with disabled cache
+    # Autocast is disabled due to a limitation on tracer as described at https://github.com/pytorch/pytorch/issues/84092
+    # Must run before _C._jit_pass_erase_number_types to prevent type substitution
+    if _C._jit_pass_cse(graph):
+        _C._jit_pass_onnx_lint(graph)
+
+    _C._jit_pass_canonicalize_graph_fuser_ops(graph)
+    _C._jit_pass_lint(graph)
+    _C._jit_pass_peephole(graph, True)
+    _C._jit_pass_fuse_addmm(graph)
+    _C._jit_pass_lint(graph)
+
+    _C._jit_pass_peephole(graph, True)
+    _C._jit_pass_lower_all_tuples(graph)
+    # in _jit_pass_onnx, symbolic functions are called for each node for conversion.
+    # However, there are nodes that cannot be converted without additional context.
+    # For example, the number of outputs from split (and whether it is static or dynamic) is unknown
+    # until the point where it is unpacked by listUnpack node.
+    # This pass does a preprocess, and prepares the nodes such that enough context can be received
+    # by the symbolic function.
+    _C._jit_pass_onnx_remove_inplace_ops_for_onnx(graph, module)
+    _C._jit_pass_onnx_preprocess(graph)
+
+    # onnx does not support tuples, so try to remove them
+    _C._jit_pass_lint(graph)
+
+    # onnx only supports tensors, but 1 / 2 = 0.5 and tensor(1) / tensor(2) = 0
+    _C._jit_pass_prepare_division_for_onnx(graph)
+
+    _C._jit_pass_onnx_remove_print(graph)
+    _C._jit_pass_onnx_preprocess_caffe2(graph)
+
+    symbolic_helper._quantized_ops.clear()
+    # Unpack quantized weights for conv and linear ops and insert into graph.
+    _C._jit_pass_onnx_unpack_quantized_weights(graph, params_dict)
+    # onnx only supports tensors, so we turn all out number types into tensors
+    _C._jit_pass_erase_number_types(graph)
+    if GLOBALS.onnx_shape_inference:
+        input_names = [] if input_names is None else input_names
+        dynamic_axes = {} if dynamic_axes is None else dynamic_axes
+        _C._jit_pass_onnx_set_dynamic_input_shape(graph, dynamic_axes, input_names)
+    _C._jit_pass_onnx_lint(graph)
+
+    graph = _C._jit_pass_onnx(graph, operator_export_type)
+    _C._jit_pass_onnx_lint(graph)
+    _C._jit_pass_lint(graph)
+
+    _C._jit_pass_onnx_scalar_type_analysis(
+        graph, True, GLOBALS.export_onnx_opset_version
+    )
+    _C._jit_pass_lint(graph)
+
+    _C._jit_pass_onnx_peephole(
+        graph, GLOBALS.export_onnx_opset_version, fixed_batch_size
+    )
+    _C._jit_pass_lint(graph)
+
+    # graph is not a valid jit graph anymore because types have been replaced
+    # (e.g. int with Tensor), so it now contains operators that don't actually
+    # exist. We can't run normal dead code elimination because it'd fail trying
+    # to look up if an operator has side effects, but we can run a dead code
+    # elimination variant that doesn't need to look up if an op has side effects.
+    _C._jit_pass_dce_allow_deleting_nodes_with_side_effects(graph)
+    _C._jit_pass_lint(graph)
+    graph = _C._jit_pass_canonicalize(graph)
+    _C._jit_pass_lint(graph)
+    if GLOBALS.onnx_shape_inference:
+        _C._jit_pass_onnx_graph_shape_type_inference(
+            graph, params_dict, GLOBALS.export_onnx_opset_version
+        )
+
+    return graph
+
+
+def warn_on_static_input_change(input_states):
+    """Warns that changes to input dictionaries and strings won't take effect in the traced ONNX graph.
+
+    We accept dictionaries and strings as ONNX inputs, but they should be only for
+    configuration use. we detect here if these inputs are modified, and if so we warn
+    the user that the changes won't take effect in the traced ONNX graph.
+    """
+    for input, traced_input in zip(input_states[0], input_states[1]):
+        if isinstance(input, dict):
+            if list(input.keys()) != list(traced_input.keys()):
+                warning = (
+                    "We detected that you are modifying a dictionary that is an input to your "
+                    "model. "
+                    "Note that dictionaries are allowed as inputs in ONNX but they should be "
+                    "handled with care. "
+                    "Usages of dictionaries is not recommended, and should not be used except "
+                    "for configuration use. "
+                    "Also note that the order and values of the keys must remain the same. "
+                )
+                warnings.warn(warning)
+        elif isinstance(input, str):
+            if input != traced_input:
+                warning = (
+                    "The model seems to have string inputs/outputs. "
+                    "Note that strings will not appear as inputs/outputs of the ONNX graph. "
+                )
+                warnings.warn(warning)
+
+
+def _resolve_args_by_export_type(arg_name, arg_value, operator_export_type):
+    """Resolves the arguments that are ignored when export_type != operator_export_type.ONNX."""
+    return arg_value
+
+
+def _decide_keep_init_as_input(
+    keep_initializers_as_inputs: bool | None,
+    operator_export_type: _C_onnx.OperatorExportTypes,
+    opset_version: int,
+):
+    """Decides whether the initializers in the graph should be listed as ONNX graph inputs.
+
+    This method encapsulates the logic to decide whether the initializers in the graph
+    should be listed as ONNX graph inputs (i.e., whether to choose ONNX IR v3 or v4).
+    If keep_initializers_as_inputs is not specified (None), then we decide whether to keep
+    initializers as graph inputs (val_keep_init_as_ip) based on export type. If export type
+    is ONNX, then do not keep initializers as input (val_keep_init_as_ip=False). For all other
+    export types keep initializers as input (val_keep_init_as_ip=True).
+    If keep_initializers_as_inputs is specified, then respect it. Unless opset version <= 8,
+    in which case it must be ignored because for opset version <= 8, all initializers MUST be
+    part of graph input (only ONNX IR v3 is allowed), i.e. val_keep_init_as_ip=True.
+
+    Special handling is needed for opset version 8 or lower, because irrespective
+    of user input for keep_initializers_as_inputs, the graph must follow ONNX IR v3
+    semantics, i.e. all initializers must be listed as ONNX graph input.
+    """
+
+    if opset_version < 9:
+        if keep_initializers_as_inputs is False:
+            warnings.warn(
+                "Setting 'keep_initializers_as_inputs=False' for opset version"
+                "8 or lower would lead to an invalid ONNX graph. Therefore, "
+                "'keep_initializers_as_inputs=False' is ignored during export."
+                "Exported model will have initializers as graph inputs (compliant "
+                " to ONNX IR v3)."
+            )
+        return True  # i.e. True == initializers are part of graph input (ONNX IR v3)
+    val_keep_init_as_ip = (
+        True if keep_initializers_as_inputs is None else keep_initializers_as_inputs
+    )
+    if (
+        keep_initializers_as_inputs is None
+        and operator_export_type is _C_onnx.OperatorExportTypes.ONNX
+    ):
+        val_keep_init_as_ip = False
+    return val_keep_init_as_ip
+
+
+def _decide_add_node_names(add_node_names, operator_export_type):
+    return _resolve_args_by_export_type(
+        "add_node_names", add_node_names, operator_export_type
+    )
+
+
+def _decide_constant_folding(do_constant_folding, operator_export_type, training):
+    do_constant_folding = _resolve_args_by_export_type(
+        "do_constant_folding", do_constant_folding, operator_export_type
+    )
+    if do_constant_folding and (
+        training is not None and training is not _C_onnx.TrainingMode.EVAL
+    ):
+        warnings.warn(
+            "It is recommended that constant folding be turned off ('do_constant_folding=False') "
+            "when exporting the model in training-amenable mode, i.e. with 'training=TrainingMode.TRAIN' "
+            "or 'training=TrainingMode.PRESERVE' (when model is in training mode). Otherwise, some "
+            "learnable model parameters may not translate correctly in the exported ONNX model "
+            "because constant folding mutates model parameters. Please consider "
+            "turning off constant folding or setting the training=TrainingMode.EVAL."
+        )
+    return do_constant_folding
+
+
+def _signature(model) -> inspect.Signature:
+    should_be_callable = getattr(model, "forward", model)
+    if callable(should_be_callable):
+        return inspect.signature(should_be_callable)
+    raise ValueError("model has no forward method and is not callable")
+
+
+def _decide_input_format(model, args):
+    try:
+        sig = _signature(model)
+    except ValueError as e:
+        warnings.warn(f"{e}, skipping _decide_input_format")
+        return args
+    try:
+        ordered_list_keys = list(sig.parameters.keys())
+        if ordered_list_keys[0] == "self":
+            ordered_list_keys = ordered_list_keys[1:]
+        args_dict: dict = {}
+        if isinstance(args, list):
+            args_list = args
+        elif isinstance(args, tuple):
+            args_list = list(args)
+        else:
+            args_list = [args]
+        if isinstance(args_list[-1], dict):
+            args_dict = args_list[-1]
+            args_list = args_list[:-1]
+        n_nonkeyword = len(args_list)
+        for optional_arg in ordered_list_keys[n_nonkeyword:]:
+            if optional_arg in args_dict:
+                args_list.append(args_dict[optional_arg])
+            # Check if this arg has a default value
+            else:
+                param = sig.parameters[optional_arg]
+                if param.default != param.empty:
+                    args_list.append(param.default)
+        args = args_list if isinstance(args, list) else tuple(args_list)
+    # Cases of models with no input args
+    except IndexError:
+        warnings.warn("No input args, skipping _decide_input_format")
+    except Exception as e:
+        warnings.warn(f"Skipping _decide_input_format\n {e.args[0]}")
+    return args
+
+
+def _from_dynamic_axes_to_dynamic_shapes(
+    model,
+    dynamic_axes: Mapping[str, Mapping[int, str]]
+    | Mapping[str, Sequence[int]]
+    | None = None,
+    input_names: Sequence[str] | None = None,
+) -> dict[str, Any] | None:
+    """
+
+    dynamic_axes examples:
+    (1) dynamic_axes = {"x": {0: "my_custom_axis_name_1"}, "y": {1: "my_custom_axis_name_2"}}
+    (2) dynamic_axes = {"x": [0], "y": [1]}
+
+    these will be converted to dynamic_shapes respectively:
+    (1) dynamic_shapes = {"x": {0: Dim("my_custom_axis_name_1")}, "y": {1: Dim("my_custom_axis_name_2")}}
+    (2) dynamic_shapes = {"x": {0: Dim("x_dim_0")}, "y": {1: Dim("y_dim_1")}}  # auto-generated dim names
+
+    """
+    if dynamic_axes is None:
+        return None
+
+    if input_names is None:
+        input_names_set = set()
+    else:
+        input_names_set = set(input_names)
+
+    dynamic_shapes: dict[str, Any | None] = {}
+    for input_name, axes in dynamic_axes.items():
+        if input_name in input_names_set:
+            raise ValueError(
+                "Assinging new input names is not supported yet. Please use model forward signature "
+                "to specify input names in dynamix_axes."
+            )
+        if isinstance(axes, dict):
+            dynamic_shapes[input_name] = {
+                k: torch.export.Dim(v) for k, v in axes.items()
+            }
+        elif isinstance(axes, list):
+            dynamic_shapes[input_name] = {
+                k: torch.export.Dim(f"{input_name}_dim_{k}") for k in axes
+            }
+        else:
+            raise TypeError(
+                f"dynamic_axes value must be either a dict or a list, but got {type(axes)}"
+            )
+    # torch.export.export needs static dim to present in dynamic_shapes
+    # for all input tensors, so we need to add them with None
+    try:
+        sig = _signature(model)
+    except ValueError as e:
+        warnings.warn(f"{e}, skipping auto filling None on static axes...")
+        return dynamic_shapes
+    for input_name in sig.parameters.keys():
+        if input_name not in dynamic_shapes:
+            dynamic_shapes[input_name] = None
+    return dynamic_shapes
+
+
+def _trace(func, args, operator_export_type, return_outs=False):
+    # Special case for common case of passing a single Tensor
+    if isinstance(args, torch.Tensor):
+        args = (args,)
+
+    trace_graph, torch_out, inputs_states = torch.jit._get_trace_graph(
+        func,
+        args,
+        strict=False,
+        _force_outplace=False,
+        _return_inputs_states=True,
+    )
+    warn_on_static_input_change(inputs_states)
+
+    trace_graph = _optimize_graph(trace_graph, operator_export_type, params_dict={})
+    if return_outs:
+        return trace_graph, torch_out
+    return trace_graph
+
+
+def _trace_and_get_graph_from_model(model, args):
+    # A basic sanity check: make sure the state_dict keys are the same
+    # before and after running the model.  Fail fast!
+    orig_state_dict_keys = torch.jit._unique_state_dict(model).keys()
+
+    # Disable Autocast cache because it replaces kernel's weight and bias
+    # by (undesired) constants.
+    # No perf impact for when there are reused weights since https://github.com/pytorch/pytorch/pull/85665
+    prev_autocast_cache_enabled = torch.is_autocast_cache_enabled()
+    torch.set_autocast_cache_enabled(False)
+    trace_graph, torch_out, inputs_states = torch.jit._get_trace_graph(
+        model,
+        args,
+        strict=False,
+        _force_outplace=False,
+        _return_inputs_states=True,
+    )
+    torch.set_autocast_cache_enabled(prev_autocast_cache_enabled)
+
+    warn_on_static_input_change(inputs_states)
+
+    if orig_state_dict_keys != torch.jit._unique_state_dict(model).keys():
+        raise RuntimeError(
+            "state_dict changed after running the tracer; "
+            "something weird is happening in your model!"
+        )
+
+    return trace_graph, torch_out
+
+
+def _get_param_count_list(method_graph, args_params):
+    param_count_list = []
+    for input_, arg_params_ in zip(method_graph.inputs(), args_params):
+        if "PackedParams" in str(input_.type()):
+            in_vars, _ = torch.jit._flatten(arg_params_)
+            param_count_list.append(len(in_vars))
+        else:
+            param_count_list.append(arg_params_ is not None)
+
+    return param_count_list
+
+
+def _check_flatten_did_not_remove(original, jit_flattened):
+    """torch.jit._flatten removes None. Check if it did so in this case."""
+
+    def flatten(x):
+        if isinstance(x, (list, tuple)):
+            for inner in x:
+                yield from flatten(inner)
+        elif isinstance(x, dict):
+            for inner in x.values():
+                yield from flatten(inner)
+        else:
+            yield x
+
+    flattened_with_none = list(flatten(original))
+    num_none = len(flattened_with_none) - len(jit_flattened)
+    assert num_none >= 0
+    if num_none:
+        raise ValueError(
+            f"args contained {num_none} None's after flattening. "
+            "When exporting a ScriptModule or ScriptFunction, no args may "
+            "be None because that breaks type propagation."
+        )
+
+
+def _create_jit_graph(
+    model: torch.nn.Module | torch.jit.ScriptFunction, args: Sequence[Any]
+) -> tuple[_C.Graph, list[_C.IValue], Any | None, _C.ScriptModule | None]:
+    if isinstance(model, (torch.jit.ScriptFunction, torch.jit.ScriptModule)):
+        flattened_args = tuple(torch.jit._flatten(tuple(args))[0])
+        _check_flatten_did_not_remove(args, flattened_args)
+        torch_out = None
+
+        if isinstance(model, torch.jit.ScriptModule):
+            try:
+                graph = model.forward.graph  # type: ignore[attr-defined]
+            except AttributeError as e:
+                raise RuntimeError("'forward' method must be a script method") from e
+            _C._jit_pass_onnx_function_substitution(graph)
+            freezed_module = _C._freeze_module(
+                cast(_C.ScriptModule, model._c), preserveParameters=True
+            )
+            module, params = _C._jit_onnx_list_model_parameters(freezed_module)
+            method_graph = module._get_method("forward").graph
+            args_params = tuple(args) + tuple(params)
+            param_count_list = _get_param_count_list(method_graph, args_params)
+            in_vars, _ = torch.jit._flatten(args_params)
+            graph = _C._propagate_and_assign_input_shapes(
+                method_graph, tuple(in_vars), param_count_list, False, False
+            )
+            return graph, params, torch_out, module
+
+        # torch.jit.ScriptFunction
+        params = []
+        graph = model.graph
+        _C._jit_pass_onnx_function_substitution(graph)
+        param_count_list = _get_param_count_list(graph, args)
+        graph = _C._propagate_and_assign_input_shapes(
+            graph, flattened_args, param_count_list, False, False
+        )
+        return graph, params, torch_out, None
+
+    graph, torch_out = _trace_and_get_graph_from_model(model, args)
+    _C._jit_pass_onnx_lint(graph)
+    state_dict = torch.jit._unique_state_dict(model)
+    params = list(state_dict.values())
+    graph_inputs = list(graph.inputs())
+    user_input_num = len(graph_inputs) - len(state_dict)
+    param_names = list(state_dict.keys())
+    for i, inp in enumerate(graph_inputs):
+        if i >= user_input_num:
+            inp.setDebugName(param_names[i - user_input_num])
+    _C._jit_pass_onnx_function_substitution(graph)
+    return graph, params, torch_out, None
+
+
+def _get_named_param_dict(graph, params):
+    input_and_param_names = [val.debugName() for val in graph.inputs()]
+    param_names = input_and_param_names[len(input_and_param_names) - len(params) :]
+    _params_dict = dict(zip(param_names, params))
+    return _params_dict
+
+
+def _get_example_outputs(model, args):
+    input_args = copy.deepcopy(args)
+    input_kwargs = {}
+    if input_args and isinstance(input_args[-1], dict):
+        input_kwargs = input_args[-1]
+        input_args = input_args[:-1]
+
+    example_outputs = model(*input_args, **input_kwargs)
+    if isinstance(example_outputs, list):
+        example_outputs = [example_outputs]
+    elif not isinstance(example_outputs, tuple):
+        example_outputs = (example_outputs,)
+
+    return example_outputs
+
+
+_qtype_vtype_map = {
+    torch.quint8: torch.uint8,
+    torch.qint8: torch.int8,
+    torch.qint32: torch.int32,
+    torch.quint4x2: torch.int8,
+}
+
+
+def unpack_quantized_tensor(value, cast_onnx_accepted=True):
+    if isinstance(value, torch.Tensor) and value.dtype in _qtype_vtype_map:
+        q_value_dequantize = value.dequantize()
+        q_scale = (
+            torch.tensor(value.q_scale(), dtype=torch.double)
+            if cast_onnx_accepted
+            else torch.tensor(value.q_scale(), dtype=torch.float32)
+        )
+        q_zero_point = (
+            torch.tensor(value.q_zero_point(), dtype=torch.int64)
+            if cast_onnx_accepted
+            else torch.tensor(value.q_zero_point(), dtype=_qtype_vtype_map[value.dtype])
+        )
+        q_value = q_value_dequantize / q_scale + q_zero_point
+        q_value = q_value.to(dtype=_qtype_vtype_map[value.dtype])
+        return q_value, q_scale, q_zero_point
+    else:
+        return (value,)
+
+
+def _pre_trace_quant_model(model, args):
+    r"""Returns `torch.jit.trace(model, args)` if model is quantized. Otherwise do nothing and return
+    original model.
+
+    This is due to https://github.com/pytorch/pytorch/issues/75761.
+    """
+    if any(
+        hasattr(m, "_packed_params") for m in getattr(model, "modules", list)()
+    ) or any(getattr(arg, "is_quantized", False) for arg in args):
+        return torch.jit.trace(model, args)
+    return model
+
+
+def _model_to_graph(
+    model,
+    args,
+    verbose=False,
+    input_names=None,
+    output_names=None,
+    operator_export_type=_C_onnx.OperatorExportTypes.ONNX,
+    do_constant_folding=True,
+    _disable_torch_constant_prop=False,
+    fixed_batch_size=False,
+    training=_C_onnx.TrainingMode.EVAL,
+    dynamic_axes=None,
+) -> tuple[
+    _C.Graph,
+    dict[str, torch.Tensor],
+    torch.Tensor
+    | tuple[torch.Tensor, ...]
+    | list[torch.Tensor]
+    | dict[str, torch.Tensor]
+    | Any
+    | None,
+]:
+    """Converts model into an ONNX graph.
+
+    Returns:
+        graph: A TorchScript IR Graph with ONNX nodes.
+        params_dict: Dict from input param name to param value.
+        torch_out: The output tensors resulting from the trace of ``model``.
+            If ``model`` is a :class:`torch.jit.ScriptModule` or :class:`torch.jit.ScriptFunction`,
+            this will be None, since we are not doing any tracing.
+    """
+    # TODO: can we simplify this to always return a tuple of Tensor or None?
+
+    # Special case for common case of passing a single Tensor
+    if isinstance(args, (torch.Tensor, int, float, bool)):
+        args = (args,)
+
+    model = _pre_trace_quant_model(model, args)
+    graph, params, torch_out, module = _create_jit_graph(model, args)
+    params_dict = _get_named_param_dict(graph, params)
+
+    try:
+        graph = _optimize_graph(
+            graph,
+            operator_export_type,
+            _disable_torch_constant_prop=_disable_torch_constant_prop,
+            fixed_batch_size=fixed_batch_size,
+            params_dict=params_dict,
+            dynamic_axes=dynamic_axes,
+            input_names=input_names,
+            module=module,
+        )
+    except Exception as e:
+        torch.onnx.log("Torch IR graph at exception: ", graph)
+        raise
+
+    is_script = isinstance(model, (torch.jit.ScriptFunction, torch.jit.ScriptModule))
+    if is_script:
+        example_outputs = _get_example_outputs(model, args)
+        example_outputs_final = ()
+        for example_output in example_outputs:
+            example_outputs_final += unpack_quantized_tensor(example_output)
+        out_vars, desc = torch.jit._flatten(example_outputs_final)
+        _C._jit_pass_onnx_assign_output_shape(
+            graph,
+            out_vars,
+            desc,
+            GLOBALS.onnx_shape_inference,
+            is_script,
+            GLOBALS.export_onnx_opset_version,
+        )
+
+    # NB: ONNX requires complete information about output types, which might be
+    # erased by some optimizations, so we need to set it explicitly again.
+    else:
+        if not isinstance(torch_out, (list, tuple)):
+            output_wrapped = [torch_out]
+        else:
+            output_wrapped = torch_out  # type: ignore[assignment]
+
+        output_tensors, out_desc = torch.jit._flatten(tuple(output_wrapped))
+        # assign_output_shape pass is not compatible with quantized outputs.
+        # Quantized outputs are flattened to 3 values in ONNX, while packed as
+        # single value in PyTorch.
+        if not any(getattr(out, "is_quantized", False) for out in output_tensors):
+            _C._jit_pass_onnx_assign_output_shape(
+                graph,
+                output_tensors,
+                out_desc,
+                GLOBALS.onnx_shape_inference,
+                is_script,
+                GLOBALS.export_onnx_opset_version,
+            )
+
+    _set_input_and_output_names(graph, input_names, output_names)
+    params_dict = _get_named_param_dict(graph, params)
+
+    if (
+        do_constant_folding
+        and GLOBALS.export_onnx_opset_version
+        >= _constants.ONNX_CONSTANT_FOLDING_MIN_OPSET
+    ):
+        if training is None or training == _C_onnx.TrainingMode.EVAL:
+            params_dict = _C._jit_pass_onnx_eval_peephole(graph, params_dict)
+
+        params_dict = _C._jit_pass_onnx_constant_fold(
+            graph, params_dict, GLOBALS.export_onnx_opset_version
+        )
+        _C._jit_pass_dce_allow_deleting_nodes_with_side_effects(graph)
+
+    if GLOBALS.onnx_shape_inference:
+        _C._jit_pass_onnx_graph_shape_type_inference(
+            graph, params_dict, GLOBALS.export_onnx_opset_version
+        )
+
+    params_dict = _C._jit_pass_onnx_eliminate_unused_items(graph, params_dict)
+
+    # For ONNX opset < 9, constants only have three data types: float16, float, double.
+    # In this pass transform constants of other data types to float/double + cast operator.
+    if GLOBALS.export_onnx_opset_version < 9:
+        _C._jit_pass_onnx_cast_all_constant_to_floating(graph)
+
+    params_dict = _C._jit_pass_filter_non_tensor_arguments(params_dict)
+    _C._jit_decay_packed_param_input_types(graph)
+
+    # If output names lack a proper name and are identified only by their unique
+    # give them a legible name for debugging purposes
+    _apply_friendly_debug_names(graph, params_dict)
+
+    return graph, params_dict, torch_out
+
+
+@torch._disable_dynamo
+@_deprecation.deprecated("2.5", "the future", "use onnx.printer.to_text() instead")
+def export_to_pretty_string(
+    model,
+    args,
+    export_params=True,
+    verbose=False,
+    training=_C_onnx.TrainingMode.EVAL,
+    input_names=None,
+    output_names=None,
+    operator_export_type=_C_onnx.OperatorExportTypes.ONNX,
+    export_type=None,
+    google_printer=False,
+    opset_version=None,
+    keep_initializers_as_inputs=None,
+    custom_opsets=None,
+    add_node_names=True,
+    do_constant_folding=True,
+    dynamic_axes=None,
+):
+    """Similar to :func:`export`, but returns a text representation of the ONNX model.
+
+    Only differences in args listed below. All other args are the same
+    as :func:`export`.
+
+    Args:
+        add_node_names (bool, default True): Whether or not to set
+            NodeProto.name. This makes no difference unless
+            ``google_printer=True``.
+        google_printer (bool, default False): If False, will return a custom,
+            compact representation of the model. If True will return the
+            protobuf's `Message::DebugString()`, which is more verbose.
+
+    Returns:
+        A UTF-8 str containing a human-readable representation of the ONNX model.
+    """
+    if opset_version is None:
+        opset_version = _constants.ONNX_DEFAULT_OPSET
+    if custom_opsets is None:
+        custom_opsets = {}
+    GLOBALS.export_onnx_opset_version = opset_version
+    GLOBALS.operator_export_type = operator_export_type
+
+    with exporter_context(model, training, verbose):
+        val_keep_init_as_ip = _decide_keep_init_as_input(
+            keep_initializers_as_inputs, operator_export_type, opset_version
+        )
+        val_add_node_names = _decide_add_node_names(
+            add_node_names, operator_export_type
+        )
+        val_do_constant_folding = _decide_constant_folding(
+            do_constant_folding, operator_export_type, training
+        )
+        args = _decide_input_format(model, args)
+        graph, params_dict, torch_out = _model_to_graph(
+            model,
+            args,
+            verbose,
+            input_names,
+            output_names,
+            operator_export_type,
+            val_do_constant_folding,
+            training=training,
+            dynamic_axes=dynamic_axes,
+        )
+
+        return graph._pretty_print_onnx(  # type: ignore[attr-defined]
+            params_dict,
+            opset_version,
+            False,
+            operator_export_type,
+            google_printer,
+            val_keep_init_as_ip,
+            custom_opsets,
+            val_add_node_names,
+        )
+
+
+@_deprecation.deprecated("2.5", "the future", "avoid using this function")
+def unconvertible_ops(
+    model,
+    args,
+    training: _C_onnx.TrainingMode = _C_onnx.TrainingMode.EVAL,
+    opset_version: int | None = None,
+) -> tuple[_C.Graph, list[str]]:
+    """Returns an approximated list of all ops that are yet supported by :mod:`torch.onnx`.
+
+    The list is approximated because some ops may be removed during the conversion
+    process and don't need to be converted. Some other ops may have partial support
+    that will fail conversion with particular inputs. Please open a Github Issue
+    for op support requests.
+
+    Args:
+        model: Same as the `model` parameter in :func:`torch.onnx.export`.
+        args: Same as the `args` parameter in :func:`torch.onnx.export`.
+        training: Same as the `training` parameter in :func:`torch.onnx.export`.
+        opset_version: Same as the `opset_version` parameter in :func:`torch.onnx.export`.
+
+    Returns:
+        The JIT graph and a list of unconvertible ops in the format of "domain::op".
+    """
+
+    opset_version = opset_version or _constants.ONNX_DEFAULT_OPSET
+    GLOBALS.export_onnx_opset_version = opset_version
+
+    try:
+        with exporter_context(model, training, verbose=False):
+            # Create a mostly clean JIT graph that contains the plain aten and
+            # other ops we can check with the symbolic registry.
+            # NOTE: We don't want to actually convert any ops to ONNX or run any
+            # symbolic functions because there is a higher chance that a pass
+            # fails or an unconvertible op messes up the graph during ONNX conversion.
+            # This way we can always generate a list just by looking at the names
+            # of the ops in the graph.
+            args = _decide_input_format(model, args)
+            model = _pre_trace_quant_model(model, args)
+            graph, _, _, module = _create_jit_graph(model, args)
+            _C._jit_pass_inline(graph)
+            _C._jit_pass_onnx_remove_inplace_ops_for_onnx(graph, module)
+            _C._jit_pass_erase_number_types(graph)
+            _C._jit_pass_dce_allow_deleting_nodes_with_side_effects(graph)
+    except Exception as e:
+        raise errors.OnnxExporterError(
+            "Failed to discover unconvertible ops because of errors during the JIT graph "
+            "generation process."
+        ) from e
+
+    unsupported_ops = []
+    for node in graph.nodes():
+        domain_op = node.kind()
+        if domain_op.startswith(("onnx::", "prim::")):
+            # We consider onnx and prim ops as supported ops, even though some "prim"
+            # ops are not implemented as symbolic functions, because they may be
+            # eliminated in the conversion passes. Users may still see errors caused
+            # by prim ops even though they don't show up in the list.
+            continue
+        if not registration.registry.is_registered_op(
+            domain_op.rstrip("_"), opset_version
+        ):
+            # We consider all registered ops supported, even though some of them are
+            # only partially supported, because there is not yet a good way to check
+            # if an op is fully supported.
+            # TODO(justinchuby): Create a way to check if an op is fully supported.
+            unsupported_ops.append(domain_op)
+    return graph, unsupported_ops
+
+
+def _setup_trace_module_map(
+    model: torch.nn.Module | torch.jit.ScriptModule,
+    export_modules_as_functions: bool | Collection[type[torch.nn.Module]],
+) -> set[str]:
+    def __register_attribute_hook():
+        attr_name = "_onnx_attrs"
+
+        def _track_module_attributes_forward_pre_hook(module, input):
+            setattr(module, attr_name, _get_module_attributes(module))
+
+        def _track_module_attributes_forward_hook(module, input, output):
+            tracing_state = _C._get_tracing_state()
+            if not tracing_state:
+                return
+
+            graph = tracing_state.graph()
+            onnx_attrs = {}
+            if hasattr(module, attr_name):
+                onnx_attrs = getattr(module, attr_name)
+                delattr(module, attr_name)
+
+            _C._jit_pass_onnx_track_scope_attributes(graph, onnx_attrs)
+
+        for m in model.modules():
+            m.register_forward_hook(_track_module_attributes_forward_hook)
+            m.register_forward_pre_hook(_track_module_attributes_forward_pre_hook)
+
+    def _unqualified_variable_name(qualified_name: str) -> str:
+        """
+        Parse qualified variable name and return the unqualified version.
+
+        Pure numeric atoms are considered inadequate, so this function will look past them,
+        and start from the first non-numeric atom.
+
+        Example:
+            >>> _unqualified_variable_name("__main__.Foo.bar")
+            'bar'
+            >>> _unqualified_variable_name("__main__.Foo.bar.0")
+            'bar.0'
+        """
+        name_atoms = qualified_name.split(".")
+        for i, atom in reversed(list(enumerate(name_atoms))):
+            if not atom.isnumeric():
+                return ".".join(name_atoms[i:])
+        return qualified_name
+
+    trace_module_map = {
+        _m: torch._C._jit_onnx_create_full_scope_name(
+            torch.typename(type(_m)), _unqualified_variable_name(_n)
+        )
+        for _n, _m in model.named_modules()
+    }
+    torch.jit._trace._trace_module_map = trace_module_map
+    if isinstance(export_modules_as_functions, bool) and export_modules_as_functions:
+        module_typenames = {torch.typename(type(module)) for module in trace_module_map}
+    elif isinstance(export_modules_as_functions, set) and export_modules_as_functions:
+
+        def _find_typename(v):
+            if isinstance(v, type):
+                return torch.typename(v)
+            else:
+                raise RuntimeError(
+                    "Only type of the `nn.Module` should be "
+                    "passed in the set for argument `export_modules_as_functions`. "
+                    f"Got `{type(v).__name__}`."
+                )
+
+        module_typenames = {_find_typename(v) for v in export_modules_as_functions}
+    else:
+        module_typenames = set()
+
+    if module_typenames:
+        __register_attribute_hook()
+
+    return module_typenames
+
+
+def _reset_trace_module_map():
+    torch.jit._trace._trace_module_map = None
+    _C._jit_pass_onnx_clear_scope_records()
+
+
+def _get_module_attributes(module):
+    annotations = typing.get_type_hints(type(module))
+    base_m_annotations = typing.get_type_hints(torch.nn.Module)
+    [annotations.pop(k, None) for k in base_m_annotations]
+    # Check whether module attributes can be accessed. Some classes
+    # define attributes but don't provide access to them in their
+    # constructor.
+    #
+    # For example, torch.nn.Embedding has the `freeze` variable and its
+    # type specified in the class but the attribute is not created in the
+    # constructor. In other words, there is no `self.freeze = `
+    # in the constructor.
+    #
+    # Reference: https://github.com/pytorch/pytorch/blob/92de1d322223fb5584e384971b32c46b93bc2f4b/torch/nn/modules/sparse.py#L120
+    attrs = {}
+    for k in annotations:
+        try:
+            attrs[k] = getattr(module, k)
+        except AttributeError:
+            torch.onnx.log(f"Skipping module attribute '{k}'")
+            continue
+    return attrs
+
+
+def _export(
+    model,
+    args,
+    f,
+    export_params=True,
+    verbose=False,
+    training=_C_onnx.TrainingMode.EVAL,
+    input_names=None,
+    output_names=None,
+    operator_export_type=_C_onnx.OperatorExportTypes.ONNX,
+    export_type=None,
+    opset_version=None,
+    do_constant_folding=True,
+    dynamic_axes=None,
+    keep_initializers_as_inputs=None,
+    fixed_batch_size=False,
+    custom_opsets=None,
+    add_node_names=True,
+    onnx_shape_inference=True,
+    export_modules_as_functions: Any = False,
+    autograd_inlining=True,
+):
+    assert GLOBALS.in_onnx_export is False
+
+    if export_type is None:
+        export_type = _exporter_states.ExportTypes.PROTOBUF_FILE
+
+    if isinstance(model, torch.nn.DataParallel):
+        raise ValueError(
+            "torch.nn.DataParallel is not supported by ONNX "
+            "exporter, please use 'attribute' module to "
+            "unwrap model from torch.nn.DataParallel. Try "
+            "torch.onnx.export(model.module, ...)"
+        )
+
+    GLOBALS.onnx_shape_inference = onnx_shape_inference
+
+    if opset_version is None:
+        opset_version = _constants.ONNX_DEFAULT_OPSET
+
+    # torch.onnx.export does not support opset versions >=18
+    if opset_version > _constants.ONNX_TORCHSCRIPT_EXPORTER_MAX_OPSET:
+        # We do not want to fail because we should still allow users to create
+        # custom symbolic functions for opset>17
+        warnings.warn(
+            f"Exporting to ONNX opset version {opset_version} is not supported. "
+            f"by 'torch.onnx.export()'. "
+            f"The highest opset version supported is {_constants.ONNX_TORCHSCRIPT_EXPORTER_MAX_OPSET}. "
+            f"To use a newer opset version, consider 'torch.onnx.export(..., dynamo=True)'. ",
+            category=errors.OnnxExporterWarning,
+        )
+
+    if export_modules_as_functions and opset_version < 15:
+        raise ValueError(
+            "`export_modules_as_functions` is not supported for `opset_version` < 15."
+            "This is because `opset_version` < 15 implies IR version < 8, which means "
+            "no local function support. "
+        )
+    if not operator_export_type:
+        operator_export_type = _C_onnx.OperatorExportTypes.ONNX
+
+    # By default, training=TrainingMode.EVAL,
+    # which is good because running a model in training mode could result in
+    # internal buffers getting updated, dropout getting applied, etc.
+    # If you really know what you're doing, you can turn
+    # training=TrainingMode.TRAINING or training=TrainingMode.PRESERVE,
+    # (to preserve whatever the original training mode was.)
+    GLOBALS.export_onnx_opset_version = opset_version
+    GLOBALS.operator_export_type = operator_export_type
+
+    try:
+        GLOBALS.in_onnx_export = True
+        _autograd_inlining_previous = GLOBALS.autograd_inlining
+        GLOBALS.autograd_inlining = autograd_inlining
+
+        module_typenames_to_export_as_functions: set[str] = set()
+        if isinstance(model, (torch.nn.Module, torch.jit.ScriptModule)):
+            module_typenames_to_export_as_functions = _setup_trace_module_map(
+                model, export_modules_as_functions
+            )
+
+        with exporter_context(model, training, verbose):
+            val_keep_init_as_ip = _decide_keep_init_as_input(
+                keep_initializers_as_inputs,
+                operator_export_type,
+                opset_version,
+            )
+            val_add_node_names = _decide_add_node_names(
+                add_node_names, operator_export_type
+            )
+            val_do_constant_folding = _decide_constant_folding(
+                do_constant_folding, operator_export_type, training
+            )
+            # Normally f can be a file-like object, but for large models, the external data format requires a
+            # valid `model_file_location`. Code in export.cpp will enforce this.
+            if isinstance(f, str):
+                model_file_location = f
+            else:
+                model_file_location = ""
+            args = _decide_input_format(model, args)
+            if dynamic_axes is None:
+                dynamic_axes = {}
+            _validate_dynamic_axes(dynamic_axes, model, input_names, output_names)
+
+            graph, params_dict, torch_out = _model_to_graph(
+                model,
+                args,
+                verbose,
+                input_names,
+                output_names,
+                operator_export_type,
+                val_do_constant_folding,
+                fixed_batch_size=fixed_batch_size,
+                training=training,
+                dynamic_axes=dynamic_axes,
+            )
+
+            # TODO: Don't allocate a in-memory string for the protobuf
+            defer_weight_export = (
+                export_type is not _exporter_states.ExportTypes.PROTOBUF_FILE
+            )
+            if custom_opsets is None:
+                custom_opsets = {}
+
+            _C._jit_pass_dce_allow_deleting_nodes_with_side_effects(graph)
+            node_attr_to_name = {}  # type: ignore[var-annotated]
+            if module_typenames_to_export_as_functions:
+                # NOTE: cannot call DCE after this pass. DCE will remove function definition nodes.
+                node_attr_to_name = _C._jit_pass_onnx_function_extraction(
+                    graph,
+                    module_typenames_to_export_as_functions,
+                    list(params_dict.keys()),
+                )
+
+            if keep_initializers_as_inputs is not True:
+                params_dict = _C._jit_pass_onnx_deduplicate_initializers(  # type: ignore[assignment]
+                    graph,
+                    params_dict,  # type: ignore[arg-type]
+                    getattr(model, "training", False),  # type: ignore[arg-type]
+                )
+            _C._jit_pass_onnx_assign_scoped_names_for_node_and_value(graph)
+            if export_params:
+                (
+                    proto,
+                    export_map,
+                    val_use_external_data_format,
+                    node_names,
+                ) = graph._export_onnx(  # type: ignore[attr-defined]
+                    params_dict,
+                    opset_version,
+                    dynamic_axes,
+                    defer_weight_export,
+                    operator_export_type,
+                    not verbose,
+                    val_keep_init_as_ip,
+                    custom_opsets,
+                    val_add_node_names,
+                    model_file_location,
+                    node_attr_to_name,
+                )
+            else:
+                (
+                    proto,
+                    export_map,
+                    val_use_external_data_format,
+                    node_names,
+                ) = graph._export_onnx(  # type: ignore[attr-defined]
+                    {},
+                    opset_version,
+                    dynamic_axes,
+                    False,
+                    operator_export_type,
+                    not verbose,
+                    val_keep_init_as_ip,
+                    custom_opsets,
+                    val_add_node_names,
+                    model_file_location,
+                    node_attr_to_name,
+                )
+            # insert function_proto into model_proto.
+            proto = onnx_proto_utils._add_onnxscript_fn(
+                proto,
+                custom_opsets,
+            )
+            if verbose:
+                torch.onnx.log("Exported graph: ", graph)
+            onnx_proto_utils._export_file(proto, f, export_type, export_map)
+    finally:
+        assert GLOBALS.in_onnx_export
+        GLOBALS.in_onnx_export = False
+        GLOBALS.autograd_inlining = _autograd_inlining_previous
+        _reset_trace_module_map()
+
+    return torch_out
+
+
+def _apply_friendly_debug_names(graph, params):
+    for n in graph.nodes():
+        for v in n.inputs():
+            old_name = v.debugName()
+            if old_name != str(v.unique()):
+                continue
+            new_name = f"{n.kind()}_{v.unique()}"
+            v.setDebugName(new_name)
+            if old_name in params:
+                params[new_name] = params.pop(old_name)
+
+
+def _set_input_and_output_names(graph, input_names, output_names):
+    def set_names(node_list, name_list, descriptor):
+        if name_list is None:
+            return
+        if len(name_list) > len(node_list):
+            raise RuntimeError(
+                "number of %s names provided (%d) exceeded number of %ss (%d)"
+                % (descriptor, len(name_list), descriptor, len(node_list))
+            )
+
+        # Mark if the output node DebugName is set before.
+        output_node_set = set()
+        for i, (name, node) in enumerate(zip(name_list, node_list)):
+            # Duplicated output node, insert onnx::Identity to avoid setting the same DebugName after setDebugName().
+            if descriptor == "output":
+                if node in output_node_set:
+                    identity_node = graph.create("onnx::Identity")
+                    identity_node.insertAfter(node.node())
+                    identity_node.addInput(node)
+                    identity_node.output().setType(node.type())
+                    graph.return_node().replaceInput(i, identity_node.output())
+                    node = identity_node.output()
+                output_node_set.add(node)
+
+            if node.debugName() != name:
+                node.setDebugName(name)
+
+    set_names(list(graph.inputs()), input_names, "input")
+    set_names(list(graph.outputs()), output_names, "output")
+
+
+def _run_symbolic_method(g, op_name, symbolic_fn, args):
+    r"""
+    This trampoline function gets invoked for every symbolic method
+    call from C++.
+    """
+    try:
+        graph_context = jit_utils.GraphContext(
+            graph=g,
+            block=g.block(),
+            opset=GLOBALS.export_onnx_opset_version,
+            original_node=None,  # type: ignore[arg-type]
+            params_dict=_params_dict,
+            env={},
+            values_in_env=set(),
+            new_nodes=[],
+        )
+        return symbolic_fn(graph_context, *args)
+    except TypeError as e:
+        # Handle the specific case where we didn't successfully dispatch
+        # to symbolic_fn.  Otherwise, the backtrace will have the clues
+        # you need.
+        e.args = (f"{e.args[0]} (occurred when translating {op_name})",)
+        raise
+
+
+def _add_block(node: _C.Node) -> _C.Block:
+    return node.addBlock()
+
+
+def _add_input_to_block(block: _C.Block):
+    return block.addInputToBlock()  # type: ignore[attr-defined]
+
+
+def _add_output_to_block(block: _C.Block, value: _C.Value) -> int:
+    return block.registerOutput(value)
+
+
+def _should_aten_fallback(
+    name: str, opset_version: int, operator_export_type: _C_onnx.OperatorExportTypes
+):
+    # For all builds, if domain=="aten" and operator_export_type==ONNX_ATEN,
+    #   an aten::ATen operator is created regardless of symbolics existence
+
+    is_exportable_aten_op = registration.registry.is_registered_op(name, opset_version)
+    is_onnx_aten_export = operator_export_type == _C_onnx.OperatorExportTypes.ONNX_ATEN
+    is_aten_fallback_export = (
+        operator_export_type == _C_onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK
+    )
+
+    if not name.startswith("aten::"):
+        return False
+
+    if is_onnx_aten_export or (is_aten_fallback_export and not is_exportable_aten_op):
+        return True
+
+    return False
+
+
+def _get_aten_op_overload_name(n: _C.Node) -> str:
+    # Returns `overload_name` attribute to ATen ops on non-Caffe2 builds
+    schema = n.schema()
+    if not schema.startswith("aten::"):
+        return ""
+    return _C.parse_schema(schema).overload_name
+
+
+def _run_symbolic_function(
+    graph: _C.Graph,
+    block: _C.Block,
+    node: _C.Node,
+    inputs: Any,
+    env: dict[_C.Value, _C.Value],
+    values_in_env: set[_C.Value],
+    new_nodes: list[_C.Node],
+    operator_export_type=_C_onnx.OperatorExportTypes.ONNX,
+) -> _C.Value | Sequence[_C.Value | None] | None:
+    """Runs a symbolic function.
+
+    The function is used in C++ to export the node to ONNX.
+
+    Returns:
+        A single or a tuple of Values.
+        None when the node gets cloned as is into the new graph.
+    """
+
+    opset_version = GLOBALS.export_onnx_opset_version
+
+    # See Note [Export inplace]
+    node_kind = node.kind()
+    if node_kind.endswith("_"):
+        # Treat relu_ -> relu; add_ -> add etc.
+        ns_op_name = node_kind[:-1]
+    else:
+        ns_op_name = node_kind
+
+    namespace, op_name = jit_utils.parse_node_kind(ns_op_name)
+
+    graph_context = jit_utils.GraphContext(
+        graph=graph,
+        block=block,
+        opset=opset_version,
+        original_node=node,
+        params_dict=_params_dict,
+        env=env,
+        values_in_env=values_in_env,
+        new_nodes=new_nodes,
+    )
+
+    # Direct ATen export requested
+    if _should_aten_fallback(ns_op_name, opset_version, operator_export_type):
+        attrs = {
+            k + "_" + node.kindOf(k)[0]: symbolic_helper._node_get(node, k)
+            for k in node.attributeNames()
+        }
+        outputs = node.outputsSize()
+        attrs["outputs"] = outputs
+        return graph_context.aten_op(
+            op_name,
+            *inputs,
+            overload_name=_get_aten_op_overload_name(node),
+            **attrs,
+        )
+
+    try:
+        domain = namespace
+        symbolic_function_name = f"{domain}::{op_name}"
+
+        symbolic_function_group = registration.registry.get_function_group(
+            symbolic_function_name
+        )
+        if symbolic_function_group is not None:
+            symbolic_fn = symbolic_function_group.get(opset_version)
+            if symbolic_fn is not None:
+                # TODO Wrap almost identical attrs assignment or comment the difference.
+                attrs = {
+                    k: symbolic_helper._node_get(node, k) for k in node.attributeNames()
+                }
+                return symbolic_fn(graph_context, *inputs, **attrs)
+
+        attrs = {
+            k + "_" + node.kindOf(k)[0]: symbolic_helper._node_get(node, k)
+            for k in node.attributeNames()
+        }
+        if namespace == "onnx":
+            # Clone node to trigger ONNX shape inference
+            return graph_context.op(
+                op_name, *inputs, **attrs, outputs=node.outputsSize()
+            )  # type: ignore[attr-defined]
+
+        raise errors.UnsupportedOperatorError(
+            symbolic_function_name,
+            opset_version,
+            symbolic_function_group.get_min_supported()
+            if symbolic_function_group
+            else None,
+        )
+
+    except RuntimeError:
+        if operator_export_type == _C_onnx.OperatorExportTypes.ONNX_FALLTHROUGH:
+            return None
+        elif operator_export_type == _C_onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK:
+            # Emit ATen op for non-Caffe2 builds when `operator_export_type==ONNX_ATEN_FALLBACK`
+            attrs = {
+                k + "_" + node.kindOf(k)[0]: symbolic_helper._node_get(node, k)
+                for k in node.attributeNames()
+            }
+            return graph_context.aten_op(
+                op_name,
+                *inputs,
+                overload_name=_get_aten_op_overload_name(node),
+                **attrs,
+            )
+        raise
+    except TypeError as e:
+        # Handle the specific case where we didn't successfully dispatch.
+        # Otherwise, the backtrace will have the clues you need.
+        e.args = (f"{e.args[0]} \n(Occurred when translating {op_name}).",)
+        raise
+
+
+def _verify_custom_op_name(symbolic_name: str):
+    if not re.match(r"^[a-zA-Z0-9-_]+::[a-zA-Z-_]+[a-zA-Z0-9-_]*$", symbolic_name):
+        raise errors.OnnxExporterError(
+            f"Failed to register operator {symbolic_name}. "
+            "The symbolic name must match the format domain::name, "
+            "and should start with a letter and contain only "
+            "alphanumerical characters"
+        )
+
+    ns, _ = jit_utils.parse_node_kind(symbolic_name)
+    if ns == "onnx":
+        raise ValueError(
+            f"Failed to register operator {symbolic_name}. {ns} domain cannot be modified."
+        )
+
+
+def register_custom_op_symbolic(
+    symbolic_name: str,
+    symbolic_fn: Callable,
+    opset_version: int,
+):
+    """Registers a symbolic function for a custom operator.
+
+    When the user registers symbolic for custom/contrib ops,
+    it is highly recommended to add shape inference for that operator via setType API,
+    otherwise the exported graph may have incorrect shape inference in some extreme cases.
+    An example of setType is `test_aten_embedding_2` in `test_operators.py`.
+
+    See "Custom Operators" in the module documentation for an example usage.
+
+    Args:
+        symbolic_name (str): The name of the custom operator in "::"
+            format.
+        symbolic_fn (Callable): A function that takes in the ONNX graph and
+            the input arguments to the current operator, and returns new
+            operator nodes to add to the graph.
+        opset_version (int): The ONNX opset version in which to register.
+    """
+    if symbolic_name.startswith("::"):
+        symbolic_name = f"aten{symbolic_name}"
+
+    _verify_custom_op_name(symbolic_name)
+
+    registration.custom_onnx_symbolic(symbolic_name, opset_version)(symbolic_fn)
+
+
+def unregister_custom_op_symbolic(symbolic_name: str, opset_version: int):
+    """Unregisters ``symbolic_name``.
+
+    See "Custom Operators" in the module documentation for an example usage.
+
+    Args:
+        symbolic_name (str): The name of the custom operator in "::"
+            format.
+        opset_version (int): The ONNX opset version in which to unregister.
+    """
+    if symbolic_name.startswith("::"):
+        symbolic_name = f"aten{symbolic_name}"
+
+    _verify_custom_op_name(symbolic_name)
+
+    registration.registry.unregister(symbolic_name, opset_version)
+
+
+def _validate_dynamic_axes(dynamic_axes, model, input_names, output_names):
+    """Ensures dynamic axes argument is follows the expected format."""
+    if len(dynamic_axes) == 0:
+        return
+
+    if hasattr(model, "graph"):
+        # Extracting set of valid input/output names that shall be used for dynamic_axes
+        if (input_names is None) or len(input_names) == 0:
+            input_names = [x.debugName() for x in model.graph.inputs()]
+        if (output_names is None) or len(output_names) == 0:
+            output_names = [y.debugName() for y in model.graph.outputs()]
+
+    valid_names = set((input_names or []) + (output_names or []))
+
+    # If dynamic axes are provided as a list rather than dictionary, they should
+    # first get converted to a dictionary in expected format. If desired axes names
+    # are not provided for dynamic axes, automatic names shall be generated for
+    # provided dynamic axes of specified input/output
+    for key, value in dynamic_axes.items():
+        if key not in valid_names:
+            warnings.warn(
+                f"Provided key {key} for dynamic axes is not a valid input/output name"
+            )
+        if isinstance(value, list):
+            warnings.warn(
+                "No names were found for specified dynamic axes of provided input."
+                f"Automatically generated names will be applied to each dynamic axes of input {key}"
+            )
+
+            value_dict = {}
+            for i, x in enumerate(value):
+                if not isinstance(x, int):
+                    raise ValueError(
+                        "The type of axis index is expected to be an integer"
+                    )
+                if x in value_dict:
+                    warnings.warn(
+                        f"Duplicate dynamic axis index {x} was provided for input {key}."
+                    )
+                else:
+                    value_dict[x] = str(key) + "_dynamic_axes_" + str(i + 1)
+            dynamic_axes[key] = value_dict
+
+
+def model_signature(model: torch.nn.Module | Callable) -> inspect.Signature:
+    return inspect.signature(
+        model.forward if isinstance(model, torch.nn.Module) else model
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/verification.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/verification.py
new file mode 100644
index 0000000000000000000000000000000000000000..a21f1ffbba778301570b87fc0eb929583ac0f9b0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/onnx/verification.py
@@ -0,0 +1,1806 @@
+# mypy: allow-untyped-defs
+"""Functions to verify exported ONNX model is functionally equivalent to original PyTorch model.
+
+ONNX Runtime is required, and is used as the ONNX backend for export verification.
+"""
+
+from __future__ import annotations
+
+import contextlib
+import copy
+import dataclasses
+import datetime
+import difflib
+import enum
+import functools
+import io
+import itertools
+import os
+import tempfile
+import warnings
+from typing import Any, Callable, Collection, Mapping, Sequence, Tuple, Union
+
+import numpy as np
+
+import torch
+import torch._C._onnx as _C_onnx
+from torch import _C
+from torch.onnx import _constants, _experimental, _exporter_states, utils
+from torch.onnx._globals import GLOBALS
+from torch.onnx._internal import onnx_proto_utils
+from torch.types import Number
+
+
+_ORT_PROVIDERS = ("CPUExecutionProvider",)
+
+_NumericType = Union[Number, torch.Tensor, np.ndarray]
+_ModelType = Union[torch.nn.Module, torch.jit.ScriptModule]
+_InputArgsType = Union[torch.Tensor, Tuple[Any, ...]]
+_InputKwargsType = Mapping[str, Any]
+_OutputsType = Union[Sequence[_NumericType], Sequence]
+
+
+class OnnxBackend(enum.Enum):
+    """Enum class for ONNX backend used for export verification."""
+
+    REFERENCE = "ONNXReferenceEvaluator"
+    ONNX_RUNTIME_CPU = "CPUExecutionProvider"
+    ONNX_RUNTIME_CUDA = "CUDAExecutionProvider"
+
+
+@dataclasses.dataclass
+class VerificationOptions:
+    """Options for ONNX export verification.
+
+    Attributes:
+        flatten: If True, unpack nested list/tuple/dict inputs into a flattened list of
+            Tensors for ONNX. Set this to False if nested structures are to be preserved
+            for ONNX, which is usually the case with exporting ScriptModules. Default True.
+        ignore_none: Whether to ignore None type in torch output, which is usually the
+            case with tracing. Set this to False, if torch output should keep None type,
+            which is usually the case with exporting ScriptModules. Default to True.
+        check_shape: Whether to check the shapes between PyTorch and ONNX Runtime outputs
+            are exactly the same. Set this to False to allow output shape broadcasting.
+            Default to True.
+        check_dtype: Whether to check the dtypes between PyTorch and ONNX Runtime outputs
+            are consistent. Default to True.
+        backend: ONNX backend for verification. Default to OnnxBackend.ONNX_RUNTIME_CPU.
+        rtol: relative tolerance in comparison between ONNX and PyTorch outputs.
+        atol: absolute tolerance in comparison between ONNX and PyTorch outputs.
+        remained_onnx_input_idx: If provided, only the specified inputs will be passed
+            to the ONNX model. Supply a list when there are unused inputs in the model.
+            Since unused inputs will be removed in the exported ONNX model, supplying
+            all inputs will cause an error on unexpected inputs. This parameter tells
+            the verifier which inputs to pass into the ONNX model.
+        acceptable_error_percentage: acceptable percentage of element mismatches in comparison.
+            It should be a float of value between 0.0 and 1.0.
+    """
+
+    flatten: bool = True
+    ignore_none: bool = True
+    check_shape: bool = True
+    check_dtype: bool = True
+    backend: OnnxBackend = OnnxBackend.ONNX_RUNTIME_CPU
+    rtol: float = 1e-3
+    atol: float = 1e-7
+    remained_onnx_input_idx: Sequence[int] | None = None
+    acceptable_error_percentage: float | None = None
+
+
+def _flatten_tuples(elem):
+    flattened = []
+    for t in elem:
+        if isinstance(t, tuple):
+            flattened.extend(_flatten_tuples(t))
+        else:
+            flattened.append(t)
+    return flattened
+
+
+# TODO(justinchuby): Add type checking by narrowing down the return type when input is None
+def _to_numpy(elem) -> list | np.ndarray:
+    if isinstance(elem, torch.Tensor):
+        if elem.requires_grad:
+            return elem.detach().cpu().numpy()
+        else:
+            return elem.cpu().numpy()
+    elif isinstance(elem, (list, tuple)):
+        return [_to_numpy(inp) for inp in elem]
+    elif isinstance(elem, (bool, int, float)):
+        return np.array(elem)
+    elif isinstance(elem, dict):
+        flattened = []
+        for k in elem:
+            flattened.extend([_to_numpy(k), _to_numpy(elem[k])])
+        return flattened
+    return elem
+
+
+def _inline_flatten_list(inputs, res_list) -> list:
+    for i in inputs:
+        res_list.append(i) if not isinstance(
+            i, (list, tuple)
+        ) else _inline_flatten_list(i, res_list)
+    return res_list
+
+
+def _unpack_to_numpy(values, cast_onnx_accepted=True) -> list:
+    value_unpacked = []
+    for value in values:
+        value_unpacked.extend(
+            utils.unpack_quantized_tensor(value, cast_onnx_accepted=cast_onnx_accepted)
+        )
+    return [_to_numpy(v) for v in value_unpacked]
+
+
+def _run_onnx(onnx_session, inputs) -> _OutputsType:
+    kw_inputs = {}
+    if inputs and isinstance(inputs[-1], dict):
+        kw_inputs = inputs[-1]
+        inputs = inputs[:-1]
+    inputs = _unpack_to_numpy(_flatten_tuples(inputs))
+    ort_inputs = {}
+    for input_name, input in kw_inputs.items():
+        ort_inputs[input_name] = _to_numpy(input)
+    inputs = _to_numpy(inputs)
+    if hasattr(onnx_session, "get_inputs"):
+        # onnxruntime.InferenceSession
+        input_names = [i.name for i in onnx_session.get_inputs()]
+    elif hasattr(onnx_session, "input_names"):
+        # onnx.reference.ReferenceEvaluator
+        input_names = onnx_session.input_names
+    else:
+        raise ValueError(f"Unknown ONNX backend type: {type(onnx_session)}.")
+
+    for i, input in enumerate(inputs):
+        if i == len(input_names) or input_names[i] in ort_inputs:
+            raise ValueError(
+                f"got too many positional inputs. inputs: {inputs}. kw_inputs: {kw_inputs}. "
+                f"input names: {input_names}."
+            )
+        ort_inputs[input_names[i]] = input
+    onnx_outs = onnx_session.run(None, ort_inputs)
+    return onnx_outs
+
+
+def _ort_session(
+    model: str | io.BytesIO, ort_providers: Sequence[str] = _ORT_PROVIDERS
+):
+    try:
+        import onnxruntime  # type: ignore[import]
+    except ImportError as e:
+        raise ImportError("onnxruntime is required for export verification.") from e
+
+    if ort_providers is None:
+        ort_providers = _ORT_PROVIDERS
+
+    session_options = onnxruntime.SessionOptions()
+    # suppress ort warnings.
+    # 0:Verbose, 1:Info, 2:Warning. 3:Error, 4:Fatal. Default is 2.
+    session_options.log_severity_level = 3
+    ort_session = onnxruntime.InferenceSession(
+        model if isinstance(model, str) else model.getvalue(),
+        session_options,
+        providers=ort_providers,
+    )
+    return ort_session
+
+
+def _onnx_reference_evaluator_session(model: str | io.BytesIO):
+    try:
+        import onnx
+        from onnx import reference as onnx_reference  # type: ignore[attr-defined]
+    except ImportError as exc:
+        raise ImportError("onnx >= 1.13 is required for reference evaluator.") from exc
+
+    proto = (
+        onnx.load(model)  # type: ignore[attr-defined]
+        if isinstance(model, str)
+        else onnx.load_model_from_string(model.getvalue())  # type: ignore[attr-defined]
+    )
+    onnx_session = onnx_reference.ReferenceEvaluator(proto)
+    return onnx_session
+
+
+def _onnx_backend_session(model: str | io.BytesIO, backend: OnnxBackend):
+    if backend == OnnxBackend.REFERENCE:
+        onnx_session = _onnx_reference_evaluator_session(model)
+    elif backend in {OnnxBackend.ONNX_RUNTIME_CPU, OnnxBackend.ONNX_RUNTIME_CUDA}:
+        onnx_session = _ort_session(model, (backend.value,))
+    else:
+        raise ValueError(f"Unsupported backend: {backend}")
+    return onnx_session
+
+
+def _compare_onnx_pytorch_outputs_in_np(
+    onnx_outs: _OutputsType,
+    pt_outs: _OutputsType,
+    options: VerificationOptions,
+):
+    assert (
+        len(onnx_outs) == len(pt_outs)
+    ), f"Number of outputs differ ONNX runtime: ({len(onnx_outs)}) PyTorch: ({len(pt_outs)})"
+    acceptable_error_percentage = options.acceptable_error_percentage
+    if acceptable_error_percentage and (
+        acceptable_error_percentage > 1.0 or acceptable_error_percentage < 0.0
+    ):
+        raise ValueError(
+            "If set, acceptable_error_percentage should be between 0.0 and 1.0"
+        )
+
+    for ort_out, pt_out in zip(onnx_outs, pt_outs):
+        try:
+            # TODO: Remove `check_shape` option once every shape inconsistent issue is addressed.
+            if not options.check_shape:
+                # Allow different but broadcastable output shapes.
+                ort_out, pt_out = np.broadcast_arrays(ort_out, pt_out)
+            torch.testing.assert_close(
+                ort_out,
+                pt_out,
+                rtol=options.rtol,
+                atol=options.atol,
+                check_dtype=options.check_dtype,
+                equal_nan=True,
+            )
+        except AssertionError as e:
+            if acceptable_error_percentage:
+                error_percentage = 1 - np.sum(
+                    np.isclose(ort_out, pt_out, rtol=options.rtol, atol=options.atol)
+                ) / np.prod(ort_out.shape)
+                if error_percentage <= acceptable_error_percentage:
+                    warnings.warn(
+                        f"Suppressed AssertionError:\n{e}.\n"
+                        f"Error percentage {error_percentage} "
+                        f"within acceptable range {acceptable_error_percentage}."
+                    )
+                    continue
+            if ort_out.dtype == np.uint8 or ort_out.dtype == np.int8:
+                warnings.warn("ONNX output is quantized")
+            if pt_out.dtype == np.uint8 or pt_out.dtype == np.int8:
+                warnings.warn("PyTorch output is quantized")
+            raise
+
+
+def _compare_onnx_pytorch_outputs(
+    onnx_outs: _OutputsType,
+    pt_outs: Any,
+    options: VerificationOptions,
+):
+    """
+    Compare ONNX and PyTorch outputs.
+
+    Args:
+        onnx_outs: outputs from ONNX backend.
+        pt_outs: outputs from PyTorch.
+        options: options for verification.
+
+    Raises:
+        AssertionError: if outputs from ONNX model and PyTorch model are not
+            equal up to specified precision.
+        ValueError: if arguments provided are invalid.
+    """
+    if options.ignore_none:
+        # torch.jit._flatten filters None type
+        pt_outs, _ = torch.jit._flatten(pt_outs)
+    else:
+        pt_outs = _inline_flatten_list([pt_outs], [])
+    pt_outs_np = _unpack_to_numpy(pt_outs, cast_onnx_accepted=False)
+    onnx_outs = _inline_flatten_list(onnx_outs, [])
+    _compare_onnx_pytorch_outputs_in_np(onnx_outs, pt_outs_np, options)
+
+
+def _prepare_input_for_pytorch(args, kwargs):
+    """Prepare input for PyTorch model execution.
+
+    Any future changes/formatting to the input before dispatching to the PyTorch
+    model should be made in this function.
+
+    Args:
+        args: positional arguments for PyTorch model forward method.
+        kwargs: keyword arguments for PyTorch model forward method.
+
+    Returns:
+        args: positional arguments for PyTorch model forward method.
+        kwargs: keyword arguments for PyTorch model forward method.
+    """
+    if isinstance(args, (torch.Tensor, dict)):
+        args = (args,)
+    # In-place operators will update input tensor data as well.
+    # Thus inputs are replicated before every forward call.
+    args = copy.deepcopy(args)
+    if kwargs:
+        kwargs = copy.deepcopy(kwargs)
+    else:
+        kwargs = {}
+    return args, kwargs
+
+
+def _prepare_input_for_export(args, kwargs):
+    """Prepare input for ONNX model export.
+
+    Any future changes/formatting to the input before dispatching to the
+    :func:`torch.onnx.export` api should be made in this function.
+
+    Args:
+        args: positional arguments for PyTorch model forward method.
+        kwargs: keyword arguments for PyTorch model forward method.
+
+    Returns:
+        onnx_inputs: positional arguments for ONNX model export, as `args` in
+            :func:`torch.onnx.export`.
+    """
+    args, kwargs = _prepare_input_for_pytorch(args, kwargs)
+    if not kwargs and len(args) > 0 and isinstance(args[-1], dict):
+        onnx_inputs = args + ({},)
+    elif kwargs:
+        onnx_inputs = args + (kwargs,)
+    else:
+        onnx_inputs = args
+    return onnx_inputs
+
+
+def _prepare_input_for_onnx(
+    args, kwargs, remained_onnx_input_idx: Sequence[int] | None, flatten: bool
+):
+    """Prepare input for ONNX model execution in ONNX backend.
+
+    Any future changes/formatting to the input before dispatching to the ONNX backend
+    run should be made in this function.
+
+    Args:
+        args: positional arguments for PyTorch model forward method.
+        kwargs: keyword arguments for PyTorch model forward method.
+        remained_onnx_input_idx: indices of inputs to be used for ONNX model execution.
+        flatten: whether to flatten the input before dispatching to the ONNX model execution.
+
+    Returns:
+        onnx_inputs: positional arguments for ONNX model execution in ONNX backend.
+    """
+    onnx_inputs = _prepare_input_for_export(args, kwargs)
+    if flatten:
+        onnx_inputs, _ = torch.jit._flatten(onnx_inputs)
+    elif onnx_inputs and onnx_inputs[-1] == {}:
+        # Handle empty kwargs (normally removed by flatten).
+        onnx_inputs = onnx_inputs[:-1]
+    if remained_onnx_input_idx is not None:
+        return [onnx_inputs[i] for i in remained_onnx_input_idx]
+    else:
+        return onnx_inputs
+
+
+def _try_clone_model(model):
+    """Used for preserving original model in case forward mutates model states."""
+    try:
+        return copy.deepcopy(model)
+    except Exception:
+        warnings.warn(
+            "Failed to clone model. Model state might be mutated during verification."
+        )
+        return model
+
+
+def _compare_onnx_pytorch_model(
+    pt_model: _ModelType,
+    onnx_model_f: str | io.BytesIO,
+    input_args: _InputArgsType,
+    input_kwargs: _InputKwargsType | None,
+    additional_test_inputs: Sequence[_InputArgsType] | None,
+    options: VerificationOptions,
+):
+    """Compare outputs from ONNX model runs with outputs from PyTorch model runs.
+
+    Args:
+        pt_model: PyTorch model.
+        onnx_model_f: ONNX model file path or file-like object.
+        input_args: positional arguments for PyTorch model forward method.
+        input_kwargs: keyword arguments for PyTorch model forward method.
+        additional_test_inputs: additional positional arguments for PyTorch model
+            forward method.
+        options: options for verification.
+
+    Raises:
+        AssertionError: if outputs from ONNX model and PyTorch model are not
+            equal up to specified precision.
+    """
+    onnx_session = _onnx_backend_session(onnx_model_f, options.backend)
+
+    def compare_onnx_pytorch_model_with_input(input_args, input_kwargs):
+        pt_args, pt_kwargs = _prepare_input_for_pytorch(input_args, input_kwargs)
+        # TODO: remove this and treat mutating model separately. See #77679
+        pt_model_copy = _try_clone_model(pt_model)
+        pt_outs = pt_model_copy(*pt_args, **pt_kwargs)
+
+        onnx_inputs = _prepare_input_for_onnx(
+            input_args, input_kwargs, options.remained_onnx_input_idx, options.flatten
+        )
+
+        onnx_outs = _run_onnx(onnx_session, onnx_inputs)
+
+        _compare_onnx_pytorch_outputs(
+            onnx_outs=onnx_outs,
+            pt_outs=pt_outs,
+            options=options,
+        )
+
+    compare_onnx_pytorch_model_with_input(input_args, input_kwargs)
+
+    if additional_test_inputs:
+        for test_input_args in additional_test_inputs:
+            compare_onnx_pytorch_model_with_input(test_input_args, {})
+
+
+class _GraphDiff:
+    """A class to represent the difference between two graphs."""
+
+    def __init__(self, graph_a: _C.Graph, graph_b: _C.Graph):
+        """Construct a _GraphDiff object.
+
+        Args:
+            graph_a (_C.Graph): First graph to compare.
+            graph_b (_C.Graph): Second graph to compare.
+        """
+        self.graph_a = graph_a
+        self.graph_b = graph_b
+
+    def __str__(self):
+        """See function :func:`diff_report`."""
+        return self.diff_report()
+
+    def _indent(self, lines: str) -> str:
+        return "\n".join(["\t" + line for line in lines.splitlines()])
+
+    def diff_report(self) -> str:
+        """Return a string representation of the graph difference.
+
+        The report shows the first pair of nodes that diverges. It also shows the source
+        location of the pair of nodes.
+
+        Returns:
+            graph_diff_report (str): A string representation of the graph difference.
+        """
+        graph_a = self.graph_a
+        graph_b = self.graph_b
+
+        graph_a_str = str(graph_a)
+        graph_b_str = str(graph_b)
+
+        if graph_a_str == graph_b_str:
+            return ""
+
+        graph_diff = difflib.ndiff(
+            graph_a_str.splitlines(True), graph_b_str.splitlines(True)
+        )
+        graph_diff_report = ["Graph diff:", self._indent("".join(graph_diff))]
+
+        for node_a, node_b in itertools.zip_longest(graph_a.nodes(), graph_b.nodes()):
+            if str(node_a) != str(node_b):
+                graph_diff_report.append("First diverging operator:")
+                node_diff = difflib.ndiff(
+                    str(node_a).splitlines(True), str(node_b).splitlines(True)
+                )
+                source_printout = ["node diff:", self._indent("".join(node_diff))]
+
+                stack_a = node_a.sourceRange() if node_a else None
+                if stack_a:
+                    source_printout.extend(
+                        ["Former source location:", self._indent(str(stack_a))]
+                    )
+                stack_b = node_b.sourceRange() if node_b else None
+                if stack_b:
+                    source_printout.extend(
+                        ["Latter source location:", self._indent(str(stack_b))]
+                    )
+
+                graph_diff_report.extend(source_printout)
+
+                break
+
+        return "\n".join(graph_diff_report)
+
+
+def _check_graph_diff(
+    model: torch.nn.Module | torch.jit.ScriptModule,
+    test_input_groups: Sequence[tuple[tuple[Any, ...], Mapping[str, Any]]],
+    export_options: _experimental.ExportOptions,
+    model_to_graph_func: Callable[
+        [
+            torch.nn.Module,
+            tuple[Any, ...],
+            Mapping[str, Any],
+            _experimental.ExportOptions,
+        ],
+        _C.Graph,
+    ],
+) -> str:
+    """Check if graph produced by `model_to_graph_func` is the same across `test_input_groups`.
+
+    Args:
+        model: See :func:`check_export_model_diff`.
+        test_input_groups: See :func:`check_export_model_diff`.
+        export_options: See :func:`check_export_model_diff`.
+        model_to_graph_func: A function to convert a PyTorch model to a JIT IR graph.
+
+    Returns:
+        graph_diff_report (str): A string representation of the graph difference.
+    """
+    if len(test_input_groups) < 2:
+        raise ValueError("Need at least two groups of test inputs to compare.")
+
+    ref_jit_graph = None
+    for args, kwargs in test_input_groups:
+        jit_graph = model_to_graph_func(model, args, kwargs, export_options)
+        if ref_jit_graph is None:
+            ref_jit_graph = jit_graph
+            continue
+
+        graph_diff_report = _GraphDiff(ref_jit_graph, jit_graph).diff_report()
+        if graph_diff_report:
+            return graph_diff_report
+    return ""
+
+
+def _traced_graph_from_model(
+    model: torch.nn.Module | torch.jit.ScriptModule,
+    args: tuple[Any, ...],
+    kwargs: Mapping[str, Any],
+    export_options: _experimental.ExportOptions,
+) -> _C.Graph:
+    """As part of the ONNX export steps, create a traced JIT graph from a PyTorch model.
+
+    Args:
+        model: See :func:`check_export_model_diff`.
+        args: See :func:`check_export_model_diff`.
+        kwargs: See :func:`check_export_model_diff`.
+        export_options: See :func:`check_export_model_diff`.
+
+    Returns:
+        jit_graph (_C.Graph): A traced JIT graph.
+    """
+    training = export_options.training
+    verbose = export_options.verbose
+
+    with utils.exporter_context(model, training, verbose):
+        export_inputs = _prepare_input_for_export(args, kwargs)
+        model = utils._pre_trace_quant_model(model, export_inputs)
+        jit_graph, _, _, _ = utils._create_jit_graph(model, export_inputs)
+        return jit_graph
+
+
+def _onnx_graph_from_model(
+    model: torch.nn.Module | torch.jit.ScriptModule,
+    args: tuple[Any, ...],
+    kwargs: Mapping[str, Any],
+    export_options: _experimental.ExportOptions,
+) -> _C.Graph:
+    """As part of the ONNX export steps, export an ONNX JIT graph from a PyTorch model.
+
+    Args:
+        model: See :func:`check_export_model_diff`.
+        args: See :func:`check_export_model_diff`.
+        kwargs: See :func:`check_export_model_diff`.
+        export_options: See :func:`check_export_model_diff`.
+
+    Returns:
+        onnx_graph (_C.Graph): An ONNX JIT graph.
+    """
+    # TODO: refactor utils.py to remove duplicated code of context setup. See #78834
+    opset_version = export_options.opset_version
+    operator_export_type = export_options.operator_export_type
+    export_modules_as_functions = export_options.export_modules_as_functions
+    training = export_options.training
+    verbose = export_options.verbose
+    dynamic_axes = export_options.dynamic_axes
+    input_names = export_options.input_names
+    output_names = export_options.output_names
+
+    if opset_version is None:
+        opset_version = _constants.ONNX_DEFAULT_OPSET
+
+    utils._setup_trace_module_map(model, export_modules_as_functions)
+
+    if not operator_export_type:
+        operator_export_type = _C_onnx.OperatorExportTypes.ONNX
+
+    GLOBALS.export_onnx_opset_version = opset_version
+    GLOBALS.operator_export_type = operator_export_type
+
+    with utils.exporter_context(model, training, verbose):
+        do_constant_folding = utils._decide_constant_folding(
+            export_options.do_constant_folding, operator_export_type, training
+        )
+
+        if dynamic_axes is None:
+            dynamic_axes = {}
+        utils._validate_dynamic_axes(dynamic_axes, model, input_names, output_names)
+
+        export_inputs = _prepare_input_for_export(args, kwargs)
+        export_inputs = utils._decide_input_format(model, export_inputs)
+        onnx_graph, _, _ = utils._model_to_graph(
+            model,
+            export_inputs,
+            verbose,
+            input_names,
+            output_names,
+            operator_export_type,
+            do_constant_folding,
+            training=training,
+            dynamic_axes=dynamic_axes,
+        )
+
+        return onnx_graph
+
+
+def _onnx_graph_from_aten_graph(
+    graph: torch.Graph,
+    export_options: _experimental.ExportOptions,
+    params_dict: dict[str, Any] | None = None,
+) -> tuple[torch.Graph, dict[str, Any]]:
+    if params_dict is None:
+        params_dict = {}
+    operator_export_type = export_options.operator_export_type
+    dynamic_axes = export_options.dynamic_axes or {}
+    input_names = export_options.input_names
+    training = export_options.training
+    do_constant_folding = export_options.do_constant_folding
+    opset_version = export_options.opset_version or _constants.ONNX_DEFAULT_OPSET
+
+    GLOBALS.export_onnx_opset_version = opset_version
+    GLOBALS.operator_export_type = operator_export_type
+
+    do_constant_folding = utils._decide_constant_folding(
+        do_constant_folding, operator_export_type, training
+    )
+
+    # TODO: Below is doing aten graph to onnx. It should be abstracted as a
+    # function in torch/onnx/utils.py.
+    graph = graph.copy()
+    graph = utils._optimize_graph(
+        graph,
+        operator_export_type,
+        params_dict=params_dict,
+        dynamic_axes=dynamic_axes,
+        input_names=input_names,
+    )
+
+    if training is None or training == _C_onnx.TrainingMode.EVAL:
+        params_dict = torch._C._jit_pass_onnx_eval_peephole(graph, params_dict)
+
+    if (
+        do_constant_folding
+        and opset_version >= _constants.ONNX_CONSTANT_FOLDING_MIN_OPSET
+    ):
+        params_dict = _C._jit_pass_onnx_constant_fold(graph, params_dict, opset_version)
+        _C._jit_pass_dce_allow_deleting_nodes_with_side_effects(graph)
+
+    if GLOBALS.onnx_shape_inference:
+        _C._jit_pass_onnx_graph_shape_type_inference(graph, params_dict, opset_version)
+
+    params_dict = _C._jit_pass_onnx_eliminate_unused_items(graph, params_dict)
+
+    # For ONNX opset < 9, constants only have three data types: float16, float, double.
+    # In this pass transform constants of other data types to float/double + cast operator.
+    if opset_version < 9:
+        _C._jit_pass_onnx_cast_all_constant_to_floating(graph)
+
+    params_dict = _C._jit_pass_filter_non_tensor_arguments(params_dict)
+    _C._jit_decay_packed_param_input_types(graph)
+
+    _C._jit_pass_dce_allow_deleting_nodes_with_side_effects(graph)
+
+    if export_options.verbose:
+        print("ONNX graph: ", graph)
+
+    return graph, params_dict
+
+
+def _onnx_proto_from_onnx_graph(
+    onnx_graph: torch.Graph,
+    export_options: _experimental.ExportOptions,
+    params_dict: dict[str, Any],
+) -> tuple[bytes, Mapping[str, bytes]]:
+    opset_version = export_options.opset_version or _constants.ONNX_DEFAULT_OPSET
+    dynamic_axes = export_options.dynamic_axes or {}
+    operator_export_type = export_options.operator_export_type
+    val_keep_init_as_ip = utils._decide_keep_init_as_input(
+        export_options.keep_initializers_as_inputs,
+        operator_export_type,
+        opset_version,
+    )
+    val_add_node_names = utils._decide_add_node_names(True, operator_export_type)
+    custom_opsets = export_options.custom_opsets or {}
+
+    proto, export_map, _, _ = onnx_graph._export_onnx(  # type: ignore[attr-defined]
+        params_dict,
+        opset_version,
+        dynamic_axes,
+        False,
+        operator_export_type,
+        not export_options.verbose,
+        val_keep_init_as_ip,
+        custom_opsets,
+        val_add_node_names,
+        "",
+        {},
+    )
+
+    return proto, export_map
+
+
+def check_export_model_diff(
+    model: torch.nn.Module | torch.jit.ScriptModule,
+    test_input_groups: Sequence[tuple[tuple[Any, ...], Mapping[str, Any]]],
+    export_options: _experimental.ExportOptions | None = None,
+) -> str:
+    """Verify exported model discrepancy between different groups of inputs.
+
+    A graph is exported for each group of inputs. The exported graphs are then compared
+    to each other, and discrepancies of first pair of nodes are reported. This function
+    first checks the jit graph. If no discrepancies were found, it then checks the onnx
+    graph.
+
+    Unless otherwise specified, the jit/ONNX graph is expected to be the same, regardless
+    of the inputs used for exporting. A discrepancy implies the graph exported is
+    not accurate when run on other groups of inputs, which will typically results in
+    runtime errors or mismatching output.
+
+    Args:
+        model (torch.nn.Module or torch.jit.ScriptModule): The model to be exported.
+        test_input_groups (Sequence[Tuple[Tuple[Any, ...], Mapping[str, Any]]]): A sequence
+            of input groups to be used to export the model. Each input group is a pair of
+            (args, kwargs).
+        export_options (_experimental.ExportOptions, optional): An _experimental.ExportOptions
+            object that controls the export behavior.
+
+    Returns:
+        str: A string containing the diff of the exported models.
+    """
+    export_options = (
+        _experimental.ExportOptions() if export_options is None else export_options
+    )
+
+    jit_diff_report = _check_graph_diff(
+        model, test_input_groups, export_options, _traced_graph_from_model
+    )
+    if jit_diff_report:
+        return jit_diff_report
+
+    return _check_graph_diff(
+        model, test_input_groups, export_options, _onnx_graph_from_model
+    )
+
+
+def verify(
+    model: _ModelType,
+    input_args: _InputArgsType,
+    input_kwargs: _InputKwargsType | None = None,
+    do_constant_folding: bool = True,
+    dynamic_axes: Mapping[str, Mapping[int, str] | Mapping[str, Sequence[int]]]
+    | None = None,
+    input_names: Sequence[str] | None = None,
+    output_names: Sequence[str] | None = None,
+    training: _C_onnx.TrainingMode = _C_onnx.TrainingMode.EVAL,
+    opset_version: int | None = None,
+    keep_initializers_as_inputs: bool = True,
+    verbose: bool = False,
+    fixed_batch_size: bool = False,
+    use_external_data: bool = False,
+    additional_test_inputs: Sequence[_InputArgsType] | None = None,
+    options: VerificationOptions | None = None,
+):
+    """Verify model export to ONNX against original PyTorch model.
+
+    Args:
+        model (torch.nn.Module or torch.jit.ScriptModule): See :func:`torch.onnx.export`.
+        input_args (tuple): See :func:`torch.onnx.export`.
+        input_kwargs (dict): See :func:`torch.onnx.export`.
+        do_constant_folding (bool, optional): See :func:`torch.onnx.export`.
+        dynamic_axes (dict, optional): See :func:`torch.onnx.export`.
+        input_names (list, optional): See :func:`torch.onnx.export`.
+        output_names (list, optional): See :func:`torch.onnx.export`.
+        training (torch.onnx.TrainingMode): See :func:`torch.onnx.export`.
+        opset_version (int, optional): See :func:`torch.onnx.export`.
+        keep_initializers_as_inputs (bool, optional): See :func:`torch.onnx.export`.
+        verbose (bool, optional): See :func:`torch.onnx.export`.
+        fixed_batch_size (bool, optional): Legacy argument, used only by rnn test cases.
+        use_external_data (bool, optional): Explicitly specify whether to export the
+            model with external data.
+        additional_test_inputs (list, optional): List of tuples. Each tuple is a group of
+            input arguments to test. Currently only *args are supported.
+        options (_VerificationOptions, optional): A _VerificationOptions object that
+            controls the verification behavior.
+
+    Raises:
+        AssertionError: if outputs from ONNX model and PyTorch model are not
+            equal up to specified precision.
+        ValueError: if arguments provided are invalid.
+    """
+    if options is None:
+        options = VerificationOptions()
+
+    if training == torch.onnx.TrainingMode.TRAINING:
+        model.train()
+    elif training == torch.onnx.TrainingMode.EVAL:
+        model.eval()
+    with torch.no_grad(), contextlib.ExitStack() as stack:
+        model_f: str | io.BytesIO = io.BytesIO()
+        if use_external_data:
+            tmpdir_path = stack.enter_context(tempfile.TemporaryDirectory())
+            model_f = os.path.join(tmpdir_path, "model.onnx")
+
+        inputs_for_export = _prepare_input_for_export(input_args, input_kwargs)
+
+        # TODO(#77679): remove this and treat mutating model separately.
+        model_copy = _try_clone_model(model)
+        utils._export(
+            model,
+            inputs_for_export,
+            model_f,
+            opset_version=opset_version,
+            do_constant_folding=do_constant_folding,
+            keep_initializers_as_inputs=keep_initializers_as_inputs,
+            dynamic_axes=dynamic_axes,
+            input_names=input_names,
+            output_names=output_names,
+            fixed_batch_size=fixed_batch_size,
+            training=training,
+            verbose=verbose,
+        )
+
+        _compare_onnx_pytorch_model(
+            pt_model=model_copy,
+            onnx_model_f=model_f,
+            input_args=input_args,
+            input_kwargs=input_kwargs,
+            additional_test_inputs=additional_test_inputs,
+            options=options,
+        )
+
+
+def verify_aten_graph(
+    graph: torch.Graph,
+    input_args: tuple[Any, ...],
+    export_options: _experimental.ExportOptions,
+    params_dict: dict[str, Any] | None = None,
+    verification_options: VerificationOptions | None = None,
+) -> tuple[AssertionError | None, torch.Graph, _OutputsType, _OutputsType]:
+    if verification_options is None:
+        verification_options = VerificationOptions()
+    if params_dict is None:
+        params_dict = {}
+
+    original_jit_graph = graph
+    graph = graph.copy()
+
+    # Execute aten graph and get reference torch jit outputs.
+    graph_inputs = list(graph.inputs())
+    jit_inputs = tuple([arg for arg in input_args if arg is not None])
+    weights = [params_dict[v.debugName()] for v in graph_inputs[len(jit_inputs) :]]
+    assert all(w is not None for w in weights)
+    # TODO: Only copy the argument if mutation is detected in Graph.
+    jit_inputs = copy.deepcopy(jit_inputs)
+    jit_input_and_parameters = jit_inputs + tuple(weights)
+    jit_outs = torch._C._jit_interpret_graph(graph, jit_input_and_parameters)  # type: ignore[attr-defined]
+    if not isinstance(jit_outs, (list, tuple)):
+        jit_outs = [jit_outs]
+
+    # Convert aten graph to onnx graph.
+    graph, onnx_params_dict = _onnx_graph_from_aten_graph(
+        graph, export_options, params_dict
+    )
+
+    proto, export_map = _onnx_proto_from_onnx_graph(
+        graph, export_options, onnx_params_dict
+    )
+    model_f: str | io.BytesIO = io.BytesIO()
+    export_type = _exporter_states.ExportTypes.PROTOBUF_FILE
+    onnx_proto_utils._export_file(proto, model_f, export_type, export_map)
+
+    # NOTE: Verification is unstable. Try catch to emit information for debugging.
+    try:
+        # NOTE: Input might be dce'ed, so we need to remove those from the input args.
+        new_input_names = {v.debugName() for v in graph.inputs()}
+        new_input_args = []
+        for v, arg in zip(original_jit_graph.inputs(), input_args):
+            if v.debugName() in new_input_names:
+                new_input_args.append(arg)
+        input_args = tuple(new_input_args)
+
+        onnx_inputs = _prepare_input_for_onnx(
+            input_args,
+            {},
+            verification_options.remained_onnx_input_idx,
+            verification_options.flatten,
+        )
+
+        onnx_session = _onnx_backend_session(model_f, verification_options.backend)
+        onnx_outs = _run_onnx(onnx_session, onnx_inputs)
+        del onnx_session  # To free device memory
+
+        try:
+            _compare_onnx_pytorch_outputs(
+                onnx_outs=onnx_outs,
+                pt_outs=jit_outs,
+                options=verification_options,
+            )
+        except AssertionError as e:
+            return e, graph, jit_outs, onnx_outs
+
+        return None, graph, jit_outs, onnx_outs
+
+    except Exception as e:
+        print("Unexpected error during verification.")
+        print("jit graph: ", original_jit_graph)
+        print("onnx graph: ", graph)
+        raise e
+
+
+class GraphInfoPrettyPrinter:
+    graph_info: GraphInfo | None
+    upper_printer: GraphInfoPrettyPrinter | None
+    lower_printer: GraphInfoPrettyPrinter | None
+
+    graph_str_lambdas: Mapping[int, str]
+    connector_str_lambdas: Mapping[int, str]
+    children_str_lambdas: Mapping[int, str]
+
+    def __init__(self, graph_info: GraphInfo | None):
+        self.graph_info = graph_info
+        if (
+            graph_info is not None
+            and graph_info.upper_graph_info is not None
+            and graph_info.lower_graph_info is not None
+        ):
+            self.upper_printer = GraphInfoPrettyPrinter(graph_info.upper_graph_info)
+            self.lower_printer = GraphInfoPrettyPrinter(graph_info.lower_graph_info)
+        else:
+            self.upper_printer = None
+            self.lower_printer = None
+
+    def _total_rows(self) -> int:
+        if self.graph_info is None:
+            return 1
+        if self.upper_printer and self.lower_printer:
+            return (
+                self.upper_printer._total_rows() + self.lower_printer._total_rows() + 1
+            )
+        return 2  # Two lines: node count + id.
+
+    def _node_count_segment_str(self) -> str:
+        if self.graph_info is None:
+            return "..."
+        node_count = self.graph_info.essential_node_count()
+        has_mismatch = self.graph_info.has_mismatch()
+        error_node_kind = (
+            f"({self.graph_info.essential_node_kinds().pop()})"
+            if node_count == 1 and has_mismatch
+            else ""
+        )
+
+        return f"{node_count} {'X' if has_mismatch else chr(0x2713)} {error_node_kind}"
+
+    def _graph_id_segment_str(self) -> str:
+        if self.graph_info is None:
+            return ""
+        return f"id: {self.graph_info.id}"
+
+    def _max_segment_columns(self) -> int:
+        return max(
+            map(len, (self._node_count_segment_str(), self._graph_id_segment_str()))
+        )
+
+    def _graph_segment_str_at_line(self, line: int) -> str:
+        """Get the string representation of the graph segment at the given line."""
+        if line == 0:
+            result_str = self._node_count_segment_str()
+            result_str += " " * (self._max_segment_columns() - len(result_str))
+            return result_str
+        if line == 1:
+            result_str = self._graph_id_segment_str()
+            result_str += " " * (self._max_segment_columns() - len(result_str))
+            return result_str
+        if 0 <= line < self._total_rows():
+            return " " * self._max_segment_columns()
+        return ""
+
+    def _connector_segment_str_at_line(self, line: int) -> str:
+        """Get the connector segment string at the given line."""
+        if self.upper_printer is None and self.lower_printer is None:
+            return ""
+        upper_total_rows = self.upper_printer._total_rows() if self.upper_printer else 1
+        lower_total_rows = self.lower_printer._total_rows() if self.lower_printer else 1
+        if line == 0:
+            return "  __"
+        elif line < upper_total_rows + 1:
+            return " |  "
+        elif line == upper_total_rows + 1:
+            return " |__"
+        elif line < upper_total_rows + lower_total_rows + 1:
+            return "    "
+        return ""
+
+    def _children_str_at_line(self, line: int) -> str:
+        """Get the string representation of the children at the given line.
+
+        Recursively calls `_str_at_line` on children nodes.
+        """
+        if self.upper_printer is None and self.lower_printer is None:
+            return ""
+        upper_total_rows = self.upper_printer._total_rows() if self.upper_printer else 1
+        lower_total_rows = self.lower_printer._total_rows() if self.lower_printer else 1
+        if 0 <= line < upper_total_rows:
+            return (
+                self.upper_printer._str_at_line(line) if self.upper_printer else "..."
+            )
+        elif upper_total_rows < line < upper_total_rows + lower_total_rows + 1:
+            return (
+                self.lower_printer._str_at_line(line - upper_total_rows - 1)
+                if self.lower_printer
+                else "..."
+            )
+        return ""
+
+    def _str_at_line(self, line: int) -> str:
+        """Get the string representation of the graph at the given line."""
+        return (
+            self._graph_segment_str_at_line(line)
+            + self._connector_segment_str_at_line(line)
+            + self._children_str_at_line(line)
+        )
+
+    def pretty_print(self):
+        if self.graph_info is None:
+            print(None)
+            return
+        # Print tree.
+        print(" Tree: ".center(80, "="))
+        total_rows = self._total_rows()
+        for line in range(total_rows):
+            print(self._str_at_line(line).rstrip())
+        if self.graph_info.has_mismatch():
+            # Summarize leaf subgraphs with mismatch.
+            print(" Mismatch leaf subgraphs: ".center(80, "="))
+            print(
+                [
+                    graph_info.id
+                    for graph_info in self.graph_info.all_mismatch_leaf_graph_info()
+                ]
+            )
+            # Summarize node kinds with mismatch.
+            mismatch_node_kinds: dict[str, int] = {}
+            for graph_info in self.graph_info.all_mismatch_leaf_graph_info():
+                node_kinds = graph_info.essential_node_kinds()
+                if len(node_kinds) == 1:
+                    node_kind = node_kinds.pop()
+                    mismatch_node_kinds[node_kind] = (
+                        mismatch_node_kinds.get(node_kind, 0) + 1
+                    )
+            print(" Mismatch node kinds: ".center(80, "="))
+            print(mismatch_node_kinds)
+        else:
+            print(" No mismatch found. ".center(80, "="))
+
+
+class OnnxTestCaseRepro:
+    def __init__(self, repro_dir):
+        self.repro_dir = repro_dir
+        self.proto, self.inputs, self.outputs = onnx_proto_utils.load_test_case(
+            repro_dir
+        )
+
+    @classmethod
+    def create_test_case_repro(
+        cls, proto: bytes, inputs, outputs, dir: str, name: str | None = None
+    ):
+        """Create a repro under "{dir}/test_{name}" for an ONNX test case.
+
+        The test case contains the model and the inputs/outputs data. The directory
+        structure is as follows:
+
+        dir
+        \u251c\u2500\u2500 test_
+        \u2502   \u251c\u2500\u2500 model.onnx
+        \u2502   \u2514\u2500\u2500 test_data_set_0
+        \u2502       \u251c\u2500\u2500 input_0.pb
+        \u2502       \u251c\u2500\u2500 input_1.pb
+        \u2502       \u251c\u2500\u2500 output_0.pb
+        \u2502       \u2514\u2500\u2500 output_1.pb
+
+        Args:
+            proto: ONNX model proto.
+            inputs: Inputs to the model.
+            outputs: Outputs of the model.
+            dir: Directory to save the repro.
+            name: Name of the test case. If not specified, a name based on current time
+                will be generated.
+        Returns:
+            Path to the repro.
+        """
+        if name is None:
+            name = datetime.datetime.now().strftime("%Y_%m_%d_%H_%M_%S_%f")
+        return onnx_proto_utils.export_as_test_case(
+            proto,
+            _to_numpy(inputs),
+            _to_numpy(outputs),
+            name,
+            dir,
+        )
+
+    def validate(self, options: VerificationOptions):
+        """Run the ONNX test case with options.backend, and compare with the expected outputs.
+
+        Args:
+            options: Options for validation.
+
+        Raise:
+            AssertionError: if outputs from options.backend and expected outputs are not
+                equal up to specified precision.
+        """
+        onnx_session = _onnx_backend_session(io.BytesIO(self.proto), options.backend)
+        run_outputs = onnx_session.run(None, self.inputs)
+        if hasattr(onnx_session, "get_outputs"):
+            output_names = [o.name for o in onnx_session.get_outputs()]
+        elif hasattr(onnx_session, "output_names"):
+            output_names = onnx_session.output_names
+        else:
+            raise ValueError(f"Unknown onnx session type: {type(onnx_session)}")
+        expected_outs = [self.outputs[name] for name in output_names]
+        _compare_onnx_pytorch_outputs_in_np(run_outputs, expected_outs, options)
+
+
+@dataclasses.dataclass
+class GraphInfo:
+    """GraphInfo contains validation information of a TorchScript graph and its converted ONNX graph."""
+
+    graph: torch.Graph
+    input_args: tuple[Any, ...]
+    params_dict: dict[str, Any]
+    export_options: _experimental.ExportOptions = dataclasses.field(
+        default_factory=_experimental.ExportOptions
+    )
+    mismatch_error: AssertionError | None = dataclasses.field(default=None, init=False)
+    pt_outs: Sequence[_NumericType] | None = dataclasses.field(default=None, init=False)
+    upper_graph_info: GraphInfo | None = dataclasses.field(default=None, init=False)
+    lower_graph_info: GraphInfo | None = dataclasses.field(default=None, init=False)
+    id: str = dataclasses.field(default="")
+    _onnx_graph: torch.Graph | None = dataclasses.field(init=False, default=None)
+
+    _EXCLUDED_NODE_KINDS: frozenset[str] = frozenset(
+        {"prim::Constant", "prim::ListConstruct", "aten::ScalarImplicit"}
+    )
+
+    def clear(self):
+        """Clear states and results of previous verification."""
+        self.mismatch_error = None
+        self.pt_outs = None
+        self._onnx_graph = None
+        self.upper_graph_info = None
+        self.lower_graph_info = None
+
+    def pretty_print_tree(self):
+        """Pretty print `GraphInfo` tree.
+
+        Each node represents a subgraph, showing the number of nodes in the subgraph and
+        a check mark if the subgraph has output mismatch between torch and ONNX.
+
+        The id of the subgraph is shown under the node. The `GraphInfo` object for any
+        subgraph can be retrieved by calling `graph_info.find_partition(id)`.
+
+        Example::
+
+            ==================================== Tree: =====================================
+            5 X   __2 X    __1 \u2713
+            id:  |  id: 0 |  id: 00
+                 |        |
+                 |        |__1 X (aten::relu)
+                 |           id: 01
+                 |
+                 |__3 X    __1 \u2713
+                    id: 1 |  id: 10
+                          |
+                          |__2 X     __1 X (aten::relu)
+                             id: 11 |  id: 110
+                                    |
+                                    |__1 \u2713
+                                       id: 111
+            =========================== Mismatch leaf subgraphs: ===========================
+            ['01', '110']
+            ============================= Mismatch node kinds: =============================
+            {'aten::relu': 2}
+
+        """
+        GraphInfoPrettyPrinter(self).pretty_print()
+
+    def pretty_print_mismatch(self, graph: bool = False):
+        """Pretty print details of the mismatch between torch and ONNX.
+
+        Args:
+            graph: If True, print the ATen JIT graph and ONNX graph.
+        """
+        print(f" Mismatch info for graph partition {self.id}: ".center(80, "="))
+        if graph:
+            print(" ATen JIT graph ".center(80, "="))
+            # TODO: A more compact graph printer.
+            #   * Drop stride, grad, device information.
+            #   * Show source location on a separate line.
+            print(self.graph)
+            if self._onnx_graph is not None:
+                print(" ONNX graph ".center(80, "="))
+                print(self._onnx_graph)
+        if self.has_mismatch():
+            print(" Mismatch error ".center(80, "="))
+            print(self.mismatch_error)
+        else:
+            print(" No mismatch ".center(80, "="))
+
+    def has_mismatch(self) -> bool:
+        """Return True if the subgraph has output mismatch between torch and ONNX."""
+        return self.mismatch_error is not None
+
+    def essential_node_count(self) -> int:
+        """Return the number of nodes in the subgraph excluding those in `_EXCLUDED_NODE_KINDS`."""
+        return sum(
+            1 for n in self.graph.nodes() if n.kind() not in self._EXCLUDED_NODE_KINDS
+        )
+
+    def essential_node_kinds(self) -> set[str]:
+        """Return the set of node kinds in the subgraph excluding those in `_EXCLUDED_NODE_KINDS`."""
+        return {
+            n.kind()
+            for n in self.graph.nodes()
+            if n.kind() not in self._EXCLUDED_NODE_KINDS
+        }
+
+    def all_mismatch_leaf_graph_info(self) -> list[GraphInfo]:
+        """Return a list of all leaf `GraphInfo` objects that have mismatch."""
+        if not self.has_mismatch():
+            return []
+
+        no_mismatch_children = (
+            self.upper_graph_info is None or not self.upper_graph_info.has_mismatch()
+        ) and (
+            self.lower_graph_info is None or not self.lower_graph_info.has_mismatch()
+        )
+
+        if no_mismatch_children:
+            return [self]
+
+        results = []
+        if self.upper_graph_info is not None:
+            results += self.upper_graph_info.all_mismatch_leaf_graph_info()
+        if self.lower_graph_info is not None:
+            results += self.lower_graph_info.all_mismatch_leaf_graph_info()
+
+        return results
+
+    def find_partition(self, id: str) -> GraphInfo | None:
+        """Find the `GraphInfo` object with the given id."""
+        if id == self.id:
+            return self
+        current_length = len(self.id)
+        if len(id) > current_length:
+            if id[current_length] == "0" and self.upper_graph_info is not None:
+                return self.upper_graph_info.find_partition(id)
+            elif id[current_length] == "1" and self.lower_graph_info is not None:
+                return self.lower_graph_info.find_partition(id)
+        return None
+
+    def export_repro(
+        self, repro_dir: str | None = None, name: str | None = None
+    ) -> str:
+        """Export the subgraph to ONNX along with the input/output data for repro.
+
+        The repro directory will contain the following files::
+
+            dir
+            \u251c\u2500\u2500 test_
+            \u2502   \u251c\u2500\u2500 model.onnx
+            \u2502   \u2514\u2500\u2500 test_data_set_0
+            \u2502       \u251c\u2500\u2500 input_0.pb
+            \u2502       \u251c\u2500\u2500 input_1.pb
+            \u2502       \u251c\u2500\u2500 output_0.pb
+            \u2502       \u2514\u2500\u2500 output_1.pb
+
+        Args:
+            repro_dir: The directory to export the repro files to. Defaults to current
+                working directory if None.
+            name: An optional name for the test case folder: "test_{name}".
+
+        Returns:
+            The path to the exported repro directory.
+        """
+
+        if repro_dir is None:
+            repro_dir = os.getcwd()
+        repro_dir = os.path.join(repro_dir, "onnx_debug")
+
+        onnx_graph, onnx_params_dict = _onnx_graph_from_aten_graph(
+            self.graph, self.export_options, self.params_dict
+        )
+
+        proto, _ = _onnx_proto_from_onnx_graph(
+            onnx_graph, self.export_options, onnx_params_dict
+        )
+        return OnnxTestCaseRepro.create_test_case_repro(
+            proto, self.input_args, self.pt_outs, repro_dir, name
+        )
+
+    def _graph_partition_pivot(self) -> int:
+        """Find the pivot index to partition the graph.
+
+        The pivot is the node that splits the graph into two parts. Each part should
+        have the similar amount of nodes, excluding non essential ops, defined in
+        `_EXCLUDED_NODE_KINDS`, such as `prim::Constant`.
+        If the graph has an odd number of nodes, the upper part will have one more node.
+        If the graph does not have any node that can be partitioned, return -1.
+
+        Returns:
+            The index of the pivot node.
+        """
+        included_node_indices = [
+            i
+            for i, n in enumerate(self.graph.nodes())
+            if n.kind() not in self._EXCLUDED_NODE_KINDS
+        ]
+        half_idx = len(included_node_indices) // 2 - 1
+        if half_idx >= 0 and len(included_node_indices) > half_idx:
+            return included_node_indices[half_idx] + 1
+        return -1
+
+    def _partition_upper_graph(self) -> torch.Graph:
+        pivot = self._graph_partition_pivot()
+        if pivot == -1:
+            return torch.Graph()
+        graph = self.graph.copy()  # Copy to not mutate parent graph.
+        original_outputs = list(graph.outputs())
+
+        def _process_bridge_value_for_upper(
+            new_outputs: list[torch.Value], bridge_value: torch.Value
+        ) -> torch.Value:
+            # Add bridge values as upper graph outputs.
+            new_outputs.append(bridge_value)
+            return bridge_value
+
+        new_outputs: list[torch.Value] = []
+        process_bridge_value_for_upper = functools.partial(
+            _process_bridge_value_for_upper, new_outputs
+        )
+        _, dropped_nodes, complete_upper_nodes_set, _ = self._partition_nodes(
+            graph, pivot, process_bridge_value_for_upper
+        )
+
+        for _ in enumerate(original_outputs):
+            graph.eraseOutput(0)
+        for output in new_outputs:
+            graph.registerOutput(output)
+
+        for node in reversed(dropped_nodes):
+            node.destroy()
+
+        for i, input in reversed(list(enumerate(list(graph.inputs())))):
+            if (
+                not _has_uses_by_nodes(input, complete_upper_nodes_set)
+                and input not in new_outputs
+            ):
+                try:
+                    graph.eraseInput(i)
+                except RuntimeError as e:
+                    print(input, graph)
+                    raise e
+
+        return graph
+
+    def _partition_lower_graph(self) -> torch.Graph:
+        pivot = self._graph_partition_pivot()
+        if pivot == -1:
+            return torch.Graph()
+        graph = self.graph.copy()  # Copy to not mutate parent graph.
+        original_outputs = list(graph.outputs())
+        original_inputs = list(graph.inputs())
+
+        new_outputs = []
+
+        def _process_bridge_value_for_lower(
+            graph: torch.Graph, bridge_value: torch.Value
+        ) -> torch.Value:
+            # Add bridge values as lower graph inputs.
+            new_input = graph.addInput()
+            bridge_value.replaceAllUsesWith(new_input)
+            new_input.copyMetadata(bridge_value)
+            return new_input
+
+        process_bridge_value_for_lower = functools.partial(
+            _process_bridge_value_for_lower, graph
+        )
+
+        upper_nodes, lower_nodes, _, complete_lower_nodes_set = self._partition_nodes(
+            graph, pivot, process_bridge_value_for_lower
+        )
+
+        for output in original_outputs:
+            if _produced_by(output, lower_nodes):
+                new_outputs.append(output)
+        for _ in enumerate(original_outputs):
+            graph.eraseOutput(0)
+        for output in new_outputs:
+            graph.registerOutput(output)
+
+        for input in original_inputs:
+            if _has_uses_by_nodes(input, complete_lower_nodes_set):
+                new_input = graph.addInput()
+                input.replaceAllUsesWith(new_input)
+                new_input.copyMetadata(input)
+
+        for node in reversed(upper_nodes):
+            if node not in complete_lower_nodes_set:
+                try:
+                    node.destroy()
+                except RuntimeError as e:
+                    print(node, graph)
+                    raise e
+
+        for _ in original_inputs:
+            graph.eraseInput(0)
+
+        return graph
+
+    def _partition_node(
+        self,
+        node: torch.Node,
+        complete_upper_nodes_set: set[torch.Node],
+        complete_lower_nodes_set: set[torch.Node],
+        original_graph_outputs: set[torch.Value],
+        covered_bridge_values: set[torch.Value],
+        process_bridge_value: Callable[[torch.Value], torch.Value],
+    ):
+        if node in complete_lower_nodes_set:
+            return
+
+        if (
+            _node_has_uses_by(node, complete_lower_nodes_set)
+            and node.kind() in self._EXCLUDED_NODE_KINDS
+        ):
+            complete_lower_nodes_set.update(_all_nodes([node]))
+            for input in node.inputs():
+                if input in covered_bridge_values:
+                    continue
+                self._partition_node(
+                    input.node(),
+                    complete_upper_nodes_set,
+                    complete_lower_nodes_set,
+                    original_graph_outputs,
+                    covered_bridge_values,
+                    process_bridge_value,
+                )
+        else:
+            for output in node.outputs():
+                if output in covered_bridge_values:
+                    continue
+                if (
+                    _has_uses_by_nodes(output, complete_lower_nodes_set)
+                    or output in original_graph_outputs
+                ):
+                    covered_bridge_values.add(process_bridge_value(output))
+
+    def _partition_nodes(
+        self,
+        graph: torch.Graph,
+        pivot: int,
+        process_bridge_value: Callable[[torch.Value], torch.Value],
+    ) -> tuple[list[torch.Node], list[torch.Node], set[torch.Node], set[torch.Node]]:
+        nodes = list(graph.nodes())
+        upper_nodes = nodes[:pivot]
+        lower_nodes = nodes[pivot:]
+        # `upper_nodes` and `complete_upper_nodes_set` differs in that the latter
+        # recursively contains nodes in subblock of `upper_nodes`.
+        # The same applies for `lower_nodes` and `complete_lower_nodes_set`.
+        # With addition that `complete_lower_nodes_set` will include nodes that
+        # are determined to be copied from `upper_nodes` to `lower_nodes`.
+        complete_upper_nodes_set = _all_nodes(upper_nodes)
+        complete_lower_nodes_set = _all_nodes(lower_nodes)
+        original_graph_outputs = set(graph.outputs())
+        # Bridge values are values produced from upper graph, and consumed
+        # by lower graph. These values need to be become upper graph outputs
+        # and lower graph inputs, to bridge the interaction.
+        # Start with all graph inputs marked as covered. If any graph input is
+        # needed by lower graph, just keep it in lower graph inputs later.
+        covered_bridge_values = set(graph.inputs())
+        for node in upper_nodes:
+            self._partition_node(
+                node,
+                complete_upper_nodes_set,
+                complete_lower_nodes_set,
+                original_graph_outputs,
+                covered_bridge_values,
+                process_bridge_value,
+            )
+        return (
+            upper_nodes,
+            lower_nodes,
+            complete_upper_nodes_set,
+            complete_lower_nodes_set,
+        )
+
+    def _bridge_kwargs(self):
+        pt_outs = self.pt_outs
+        graph_outputs = list(self.graph.outputs())
+        assert pt_outs is not None
+        assert len(graph_outputs) == len(
+            pt_outs
+        ), f"{len(graph_outputs)} vs {len(pt_outs)}\nGraph: {self.graph}"
+        return {v.debugName(): o for v, o in zip(graph_outputs, pt_outs)}
+
+    def _args_and_params_for_partition_graph(
+        self,
+        graph: torch.Graph,
+        bridge_kwargs: Mapping[str, _NumericType | Sequence[_NumericType]],
+        full_kwargs: Mapping[str, torch.Tensor],
+        full_params: Mapping[str, torch.Tensor],
+    ):
+        input_names = [input.debugName() for input in graph.inputs()]
+        args = tuple(bridge_kwargs[k] for k in input_names if k in bridge_kwargs)
+        args += tuple(full_kwargs[k] for k in input_names if k in full_kwargs)
+        params = {k: full_params[k] for k in input_names if k in full_params}
+        assert len(args) + len(params) == len(
+            input_names
+        ), f"{len(args)} + {len(params)} vs {len(input_names)}: {input_names}"
+        return args, params
+
+    def verify_export(
+        self, options: VerificationOptions
+    ) -> tuple[AssertionError | None, torch.Graph, _OutputsType, _OutputsType]:
+        """
+        Verify the export from TorchScript IR graph to ONNX.
+
+        Export the TorchScript IR graph to ONNX, with the inputs, parameters and export
+        options recorded in this object. Then verify the exported ONNX graph against
+        the original TorchScript IR graph under the provided verification options.
+
+        Args:
+            options: The verification options.
+
+        Returns:
+            error: The AssertionError raised during the verification. Returns None if no
+            error is raised.
+            onnx_graph: The exported ONNX graph in TorchScript IR format.
+            onnx_outs: The outputs from running exported ONNX model under the onnx
+            backend in `options`.
+            pt_outs: The outputs from running the TorchScript IR graph.
+        """
+        return verify_aten_graph(
+            self.graph,
+            input_args=self.input_args,
+            params_dict=self.params_dict,
+            export_options=self.export_options,
+            verification_options=options,
+        )
+
+    def find_mismatch(
+        self,
+        options: VerificationOptions | None = None,
+    ):
+        """
+        Find all mismatches between the TorchScript IR graph and the exported onnx model.
+
+        Binary searches the model graph to find the minimal subgraph that exhibits the
+        mismatch. A `GraphInfo` object is created for each subgraph, recording the test
+        inputs and export options, as well as the validation results.
+
+        Args:
+            options: The verification options.
+        """
+        self.clear()
+
+        if options is None:
+            options = VerificationOptions()
+
+        if self.export_options.verbose:
+            print(self.graph)
+
+        if len(list(self.graph.outputs())) == 0:
+            return
+
+        assert len(self.input_args) + len(self.params_dict) == len(
+            list(self.graph.inputs())
+        ), (
+            f"Number of graph inputs({len(list(self.graph.inputs()))}) does not match "
+            f"the provided tensor arguments({len(self.input_args)} + {len(self.params_dict)})."
+        )
+
+        self.mismatch_error, self._onnx_graph, self.pt_outs, _ = self.verify_export(
+            options
+        )
+
+        if self.mismatch_error is None:
+            # No mismatch found in graph.
+            return
+
+        if self.essential_node_count() <= 1:
+            # Reached leaf node, no more partitioning.
+            return
+
+        full_kwargs = {
+            k.debugName(): v for k, v in zip(self.graph.inputs(), self.input_args)
+        }
+        full_params = self.params_dict
+
+        upper_graph = self._partition_upper_graph()
+        upper_args, upper_params = self._args_and_params_for_partition_graph(
+            upper_graph, {}, full_kwargs, full_params
+        )
+        self.upper_graph_info = GraphInfo(
+            upper_graph,
+            upper_args,
+            upper_params,
+            self.export_options,
+            id=self.id + "0",
+        )
+
+        self.upper_graph_info.find_mismatch(options)
+
+        bridge_kwargs = self.upper_graph_info._bridge_kwargs()
+        lower_graph = self._partition_lower_graph()
+        lower_args, lower_params = self._args_and_params_for_partition_graph(
+            lower_graph, bridge_kwargs, full_kwargs, full_params
+        )
+        self.lower_graph_info = GraphInfo(
+            lower_graph,
+            lower_args,
+            lower_params,
+            self.export_options,
+            id=self.id + "1",
+        )
+
+        self.lower_graph_info.find_mismatch(options)
+
+
+def _all_nodes(nodes: Collection[torch.Node]) -> set[torch.Node]:
+    all_nodes = set(nodes)
+    for n in nodes:
+        for b in n.blocks():
+            all_nodes.update(_all_nodes(list(b.nodes())))
+    return all_nodes
+
+
+def _has_uses_by_nodes(value: torch.Value, nodes: Collection[torch.Node]) -> bool:
+    return any(use.user in nodes for use in value.uses())
+
+
+def _node_has_uses_by(node: torch.Node, nodes: Collection[torch.Node]) -> bool:
+    for output in node.outputs():
+        if _has_uses_by_nodes(output, nodes):
+            return True
+    return False
+
+
+def _produced_by(value: torch.Value, nodes: Collection[torch.Node]) -> bool:
+    return value.node() in nodes
+
+
+def find_mismatch(
+    model: torch.nn.Module | torch.jit.ScriptModule,
+    input_args: tuple[Any, ...],
+    do_constant_folding: bool = True,
+    training: _C_onnx.TrainingMode = _C_onnx.TrainingMode.EVAL,
+    opset_version: int | None = None,
+    keep_initializers_as_inputs: bool = True,
+    verbose: bool = False,
+    options: VerificationOptions | None = None,
+) -> GraphInfo:
+    r"""Find all mismatches between the original model and the exported model.
+
+    Experimental. The API is subject to change.
+
+    This tool helps debug the mismatch between the original PyTorch model and exported
+    ONNX model. It binary searches the model graph to find the minimal subgraph that
+    exhibits the mismatch.
+
+    Args:
+        model: The model to be exported.
+        input_args: The input arguments to the model.
+        do_constant_folding: Same as `do_constant_folding` in :func:`torch.onnx.export`.
+        training: Same as `training` in :func:`torch.onnx.export`.
+        opset_version: Same as `opset_version` in :func:`torch.onnx.export`.
+        keep_initializers_as_inputs: Same as `keep_initializers_as_inputs` in :func:`torch.onnx.export`.
+        verbose: Same as `verbose` in :func:`torch.onnx.export`.
+        options: The options for the mismatch verification.
+
+    Returns:
+        A GraphInfo object that contains the mismatch information.
+
+    Example::
+
+        >>> import torch
+        >>> import torch.onnx.verification
+        >>> torch.manual_seed(0)
+        >>> opset_version = 15
+        >>> # Define a custom symbolic function for aten::relu.
+        >>> # The custom symbolic function is incorrect, which will result in mismatches.
+        >>> def incorrect_relu_symbolic_function(g, self):
+        ...     return self
+        >>> torch.onnx.register_custom_op_symbolic(
+        ...     "aten::relu",
+        ...     incorrect_relu_symbolic_function,
+        ...     opset_version=opset_version,
+        ... )
+        >>> class Model(torch.nn.Module):
+        ...     def __init__(self) -> None:
+        ...         super().__init__()
+        ...         self.layers = torch.nn.Sequential(
+        ...             torch.nn.Linear(3, 4),
+        ...             torch.nn.ReLU(),
+        ...             torch.nn.Linear(4, 5),
+        ...             torch.nn.ReLU(),
+        ...             torch.nn.Linear(5, 6),
+        ...         )
+        ...     def forward(self, x):
+        ...         return self.layers(x)
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)
+        >>> graph_info = torch.onnx.verification.find_mismatch(
+        ...     Model(),
+        ...     (torch.randn(2, 3),),
+        ...     opset_version=opset_version,
+        ... )
+        ===================== Mismatch info for graph partition : ======================
+        ================================ Mismatch error ================================
+        Tensor-likes are not close!
+        Mismatched elements: 12 / 12 (100.0%)
+        Greatest absolute difference: 0.2328854203224182 at index (1, 2) (up to 1e-07 allowed)
+        Greatest relative difference: 0.699536174352349 at index (1, 3) (up to 0.001 allowed)
+        ==================================== Tree: =====================================
+        5 X   __2 X    __1 \u2713
+        id:  |  id: 0 |  id: 00
+             |        |
+             |        |__1 X (aten::relu)
+             |           id: 01
+             |
+             |__3 X    __1 \u2713
+                id: 1 |  id: 10
+                      |
+                      |__2 X     __1 X (aten::relu)
+                         id: 11 |  id: 110
+                                |
+                                |__1 \u2713
+                                   id: 111
+        =========================== Mismatch leaf subgraphs: ===========================
+        ['01', '110']
+        ============================= Mismatch node kinds: =============================
+        {'aten::relu': 2}
+
+    """
+    if options is None:
+        options = VerificationOptions()
+    if opset_version is None:
+        opset_version = _constants.ONNX_DEFAULT_OPSET
+    """From aten graph, do binary search on graph partition to find operator export discrepancy."""
+    # TODO: Copied from utils.py `export` until `_optimize_graph`.
+    if training == torch.onnx.TrainingMode.TRAINING:
+        model.train()
+    elif training == torch.onnx.TrainingMode.EVAL:
+        model.eval()
+    with torch.no_grad():
+        inputs_for_export = _prepare_input_for_export(input_args, {})
+        args = utils._decide_input_format(model, inputs_for_export)
+
+        model = utils._pre_trace_quant_model(model, args)
+        graph, params, torch_out, module = utils._create_jit_graph(model, args)
+        params_dict = utils._get_named_param_dict(graph, params)
+
+        utils._apply_friendly_debug_names(graph, params_dict)
+
+        graph_info = GraphInfo(
+            graph,
+            input_args,
+            params_dict,
+            _experimental.ExportOptions(
+                do_constant_folding=do_constant_folding,
+                training=training,
+                opset_version=opset_version,
+                keep_initializers_as_inputs=keep_initializers_as_inputs,
+                verbose=verbose,
+            ),
+        )
+        graph_info.find_mismatch(options)
+        graph_info.pretty_print_mismatch()
+        graph_info.pretty_print_tree()
+
+        return graph_info
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7354092dda4e02bfa05dd8c71ebd1e0f8408a87d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/__init__.py
@@ -0,0 +1,63 @@
+"""
+:mod:`torch.optim` is a package implementing various optimization algorithms.
+
+Most commonly used methods are already supported, and the interface is general
+enough, so that more sophisticated ones can also be easily integrated in the
+future.
+"""
+
+from torch.optim import lr_scheduler as lr_scheduler, swa_utils as swa_utils
+from torch.optim._adafactor import Adafactor as Adafactor
+from torch.optim.adadelta import Adadelta as Adadelta
+from torch.optim.adagrad import Adagrad as Adagrad
+from torch.optim.adam import Adam as Adam
+from torch.optim.adamax import Adamax as Adamax
+from torch.optim.adamw import AdamW as AdamW
+from torch.optim.asgd import ASGD as ASGD
+from torch.optim.lbfgs import LBFGS as LBFGS
+from torch.optim.nadam import NAdam as NAdam
+from torch.optim.optimizer import Optimizer as Optimizer
+from torch.optim.radam import RAdam as RAdam
+from torch.optim.rmsprop import RMSprop as RMSprop
+from torch.optim.rprop import Rprop as Rprop
+from torch.optim.sgd import SGD as SGD
+from torch.optim.sparse_adam import SparseAdam as SparseAdam
+
+
+Adafactor.__module__ = "torch.optim"
+
+
+del adadelta  # type: ignore[name-defined] # noqa: F821
+del adagrad  # type: ignore[name-defined] # noqa: F821
+del adam  # type: ignore[name-defined] # noqa: F821
+del adamw  # type: ignore[name-defined] # noqa: F821
+del sparse_adam  # type: ignore[name-defined] # noqa: F821
+del adamax  # type: ignore[name-defined] # noqa: F821
+del asgd  # type: ignore[name-defined] # noqa: F821
+del sgd  # type: ignore[name-defined] # noqa: F821
+del radam  # type: ignore[name-defined] # noqa: F821
+del rprop  # type: ignore[name-defined] # noqa: F821
+del rmsprop  # type: ignore[name-defined] # noqa: F821
+del optimizer  # type: ignore[name-defined] # noqa: F821
+del nadam  # type: ignore[name-defined] # noqa: F821
+del lbfgs  # type: ignore[name-defined] # noqa: F821
+
+__all__ = [
+    "Adafactor",
+    "Adadelta",
+    "Adagrad",
+    "Adam",
+    "Adamax",
+    "AdamW",
+    "ASGD",
+    "LBFGS",
+    "lr_scheduler",
+    "NAdam",
+    "Optimizer",
+    "RAdam",
+    "RMSprop",
+    "Rprop",
+    "SGD",
+    "SparseAdam",
+    "swa_utils",
+]
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/_adafactor.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/_adafactor.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc3941008ab8a850ccfa2e01a2f2784ea63b4e92
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/_adafactor.py
@@ -0,0 +1,656 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+from typing import cast, Dict, List, Optional, Tuple, TYPE_CHECKING, Union
+
+import torch
+from torch import Tensor
+
+from .optimizer import (
+    _disable_dynamo_if_unsupported,
+    _get_scalar_dtype,
+    _maximize_doc,
+    Optimizer,
+    ParamsT,
+    TensorListList,
+)
+
+
+__all__ = ["Adafactor", "adafactor"]
+
+
+class Adafactor(Optimizer):
+    def __init__(
+        self,
+        params: ParamsT,
+        lr: Union[float, Tensor] = 1e-2,
+        beta2_decay: float = -0.8,
+        eps: Tuple[Optional[float], float] = (None, 1e-3),
+        d: float = 1.0,
+        weight_decay: float = 0.0,
+        *,
+        foreach: Optional[bool] = None,
+        maximize: bool = False,
+    ):
+        if isinstance(lr, Tensor) and lr.numel() != 1:
+            raise ValueError("Tensor lr must be 1-element")
+        if not 0.0 <= lr:
+            raise ValueError(f"Learning rate should be >= 0 but is: {lr}")
+        if not 0.0 >= beta2_decay:
+            raise ValueError(f"beta2_decay should be <= 0 but is: {beta2_decay}")
+        if eps[0] is not None and not 0.0 <= eps[0]:
+            raise ValueError(f"epsilon1 should be >= 0 but is: {eps[0]}")
+        if not 0.0 <= eps[1]:
+            raise ValueError(f"epsilon2 should be >= 0 but is: {eps[1]}")
+        if not 1.0 <= d:
+            raise ValueError(f"Clipping threshold d should be >= 1 but is: {d}")
+        if not 0.0 <= weight_decay:
+            raise ValueError(f"weight_decay should be >= 0 but is: {weight_decay}")
+        defaults = dict(
+            lr=lr,
+            beta2_decay=beta2_decay,
+            eps=eps,
+            d=d,
+            weight_decay=weight_decay,
+            foreach=foreach,
+            maximize=maximize,
+        )
+        super().__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super().__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault("foreach", None)
+            for p in group["params"]:
+                p_state = self.state.get(p, [])
+                if len(p_state) != 0 and not torch.is_tensor(p_state["step"]):
+                    step_val = float(p_state["step"])
+                    p_state["step"] = torch.tensor(step_val, dtype=_get_scalar_dtype())
+
+    def _init_group(
+        self,
+        group,
+        params_with_grad,
+        grads,
+        row_vars,
+        col_vars,
+        variances,
+        state_steps,
+    ):
+        for p in group["params"]:
+            if p.grad is None:
+                continue
+            if torch.is_complex(p):
+                raise RuntimeError("Adafactor does not support complex parameters")
+            if p.grad.is_sparse:
+                raise RuntimeError("Adafactor does not support sparse gradients")
+
+            params_with_grad.append(p)
+            grads.append(p.grad)
+
+            state = self.state[p]
+
+            # State initialization
+            if len(state) == 0:
+                # note(crcrpar): Deliberately host `step` on CPU if both capturable and fused are off.
+                # This is because kernel launches are costly on CUDA and XLA.
+                state["step"] = torch.tensor(0.0, dtype=_get_scalar_dtype())
+
+                if p.grad.dim() > 1:
+                    row_shape = list(p.grad.shape)
+                    row_shape[-1] = 1
+                    # Row factor of variance, NOT the same shape as grads (will be reduced along last dim)
+                    state["row_var"] = p.grad.new_zeros(row_shape)
+
+                    col_shape = list(p.grad.shape)
+                    col_shape[-2] = 1
+                    # Col factor of variance, NOT the same shape as grads (will be reduced along penultimate dim)
+                    state["col_var"] = p.grad.new_zeros(col_shape)
+                else:
+                    state["variance"] = torch.zeros_like(
+                        p.grad, memory_format=torch.preserve_format
+                    )
+
+            row_vars.append(state.get("row_var", None))
+            col_vars.append(state.get("col_var", None))
+            variances.append(state.get("variance", None))
+            state_steps.append(state["step"])
+        return False  # has_complex
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        r"""Perform a single optimization step.
+
+        Args:
+            closure (Callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        self._cuda_graph_capture_health_check()
+
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            params_with_grad: List[Tensor] = []
+            grads: List[Tensor] = []
+            row_vars: List[Optional[Tensor]] = []
+            col_vars: List[Optional[Tensor]] = []
+            variances: List[Optional[Tensor]] = []
+            state_steps: List[Tensor] = []
+            eps1, eps2 = group["eps"]
+
+            has_complex = self._init_group(
+                group,
+                params_with_grad,
+                grads,
+                row_vars,
+                col_vars,
+                variances,
+                state_steps,
+            )
+
+            adafactor(
+                params_with_grad,
+                grads,
+                row_vars,
+                col_vars,
+                variances,
+                state_steps,
+                d=group["d"],
+                lr=group["lr"],
+                beta2_decay=group["beta2_decay"],
+                weight_decay=group["weight_decay"],
+                eps1=eps1,
+                eps2=eps2,
+                foreach=group["foreach"],
+                maximize=group["maximize"],
+                grad_scale=getattr(self, "grad_scale", None),
+                found_inf=getattr(self, "found_inf", None),
+                has_complex=has_complex,
+            )
+
+        return loss
+
+
+Adafactor.__doc__ = (
+    r"""Implements Adafactor algorithm.
+
+    .. math::
+        \begin{aligned}
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{input}      : \gamma \text{(lr)}, \: \tau
+                \text{(}\beta_2\text{ decay)}, \: \theta_0 \text{(params)}, \: f(\theta) \text{(objective)},    \\
+            &\hspace{15mm}      \: \epsilon_1, \epsilon_2 \text{ (epsilons)}, \: d \text{(clipping threshold)}, \\
+            &\hspace{15mm}      \: \lambda \text{(weight decay)},
+                \: \textit{maximize}                                                             \\
+            &\textbf{initialize} : \: R_0 \leftarrow 0 \text{ (second moment row factor)},       \\
+            &\hspace{23mm} \: C_0 \leftarrow 0 \text{ (second moment col factor)},               \\
+            &\hspace{23mm} \: \widehat{V}_0 \leftarrow 0 \text{ (second moment for vectors)}     \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do}                         \\
+
+            &\hspace{5mm}\textbf{if} \: \textit{maximize}:                                       \\
+            &\hspace{10mm}G_t           \leftarrow   -\nabla_{\theta} f_t (\theta_{t-1})         \\
+            &\hspace{5mm}\textbf{else}                                                           \\
+            &\hspace{10mm}G_t           \leftarrow   \nabla_{\theta} f_t (\theta_{t-1})          \\
+            &\hspace{5mm}\widehat{\beta}_{2_t} \leftarrow 1 - t^{\tau}                           \\
+            &\hspace{5mm}\rho_t         \leftarrow min(lr, \frac{1}{\sqrt{t}})                   \\
+            &\hspace{5mm}\alpha_t       \leftarrow max(\epsilon_2,
+                \text{RMS}(\theta_{t-1}))\rho_t                                                  \\
+            &\hspace{5mm}\theta_t       \leftarrow \theta_{t-1} - \gamma \lambda \theta_{t-1}    \\
+            &\hspace{5mm}\textbf{if} \: \text{dim}(G_t) > 1:                                     \\
+            &\hspace{10mm}R_t           \leftarrow \widehat{\beta}_{2_t}R_{t-1}+
+                (1-\widehat{\beta}_{2_t})(G_t \odot G_t) \cdot 1_m                               \\
+            &\hspace{10mm}C_t           \leftarrow \widehat{\beta}_{2_t}C_{t-1}+
+                (1-\widehat{\beta}_{2_t}) 1^\top_n \cdot (G_t \odot G_t)                         \\
+            &\hspace{10mm}\widehat{V}_t \leftarrow
+                \frac{R_t \cdot C_t}{max(1^\top_n \cdot R_t, \epsilon_1)}                        \\
+            &\hspace{5mm}\textbf{else}                                                           \\
+            &\hspace{10mm}\widehat{V}_t \leftarrow \widehat{\beta}_{2_t}\widehat{V}_{t-1}+
+                (1-\widehat{\beta}_{2_t}) \cdot (G_t \odot G_t)                                  \\
+            &\hspace{5mm}U_t            \leftarrow
+                \frac{G_t}{max(\sqrt{\widehat{V}_t}, \epsilon_1)}                                \\
+            &\hspace{5mm}\widehat{U}_t  \leftarrow \frac{U_t}{max(1, \frac{\text{RMS}(U_t)}{d})} \\
+            &\hspace{5mm}\theta_t       \leftarrow \theta_{t-1} - \alpha_t \widehat{U}_t         \\
+
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+            &\bf{return} \:  \theta_t                                                     \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+        \end{aligned}
+
+    For further details regarding the algorithm we refer to `Adafactor: Adaptive Learning Rates with Sublinear Memory Cost`_.
+    """
+    + rf"""
+    Args:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, Tensor, optional): unlike other optimizers, Adafactor does not require a
+            learning rate, and Shazeer, Noam, and Mitchell Stern do not use lr at all.
+            Deviating from the paper, this implementation uses lr for applying weight
+            decay and as the maximum value for relative step size rho_t. Note that in
+            the paper, a constant of 0.01 is used as the maximum value for relative
+            step size, and so we set 0.01 as the default value. (default: 1e-2)
+        beta2_decay (float, optional): the decay rate of beta2. beta2 standardly refers
+            to the coefficient used for computing the running average of the gradient
+            squared. (default: -0.8)
+        eps (Tuple[float, float], optional): epsilon1 is the term added to the denominator
+            of the update calculation to improve numerical stability. This use of epsilon1
+            deviates from the algorithm written in the paper! See note below for more details.
+            epsilon2 is the term used to avoid having too small a weight update when applying
+            parameter scaling. (default: (None, 1e-3))
+        d (float, optional): the clipping threshold, used to avoid larger-than-desired
+            updates.
+        weight_decay (float, optional): weight decay coefficient (default: 1e-2)
+        foreach (bool, optional): whether foreach implementation of optimizer is used. Note
+            that the foreach implementation uses ~ sizeof(params) more peak memory than the
+            for-loop version due to the intermediates being a tensorlist vs just one tensor.
+            As Adafactor is commonly used when memory is prohibitive, Adafactor will default
+            to the slower single tensor for-loop implementation unless this flag is explicitly
+            True. This behavior is contrary to other optimizers, which will attempt defaulting
+            to foreach on CUDA for faster runtime. (default: None)
+        {_maximize_doc}"""
+    + r"""
+    .. Note::
+        The implementation of Adafactor subtly differs from Shazeer, Noam, and Mitchell Stern
+        and implementations in some other frameworks with its use of learning rate and
+        :math:`\epsilon_1`.
+
+        Regarding the learning rate hyperparameter: Shazeer, Noam, and Mitchell Stern do not
+        use lr at all, as the stated algorithm uses :math:`\rho_t` and update clipping to
+        affect the step size.
+
+        This implementation allows `lr` to influence the maximum value for :math:`\rho_t`:
+
+        .. math::
+            \begin{aligned}
+                &\hspace{5mm}\rho_t \leftarrow min(lr, \frac{1}{\sqrt{t}})
+            \end{aligned}
+
+        This differs from Shazeer, Noam, and Mitchell Stern, who use a constant of 0.01 as
+        the maximum value of :math:`\rho_t`
+
+        .. math::
+            \begin{aligned}
+                &\hspace{5mm}\rho_t \leftarrow min(0.01, \frac{1}{\sqrt{t}})
+            \end{aligned}
+
+        Shazeer, Noam, and Mitchell Stern do not enforce an opinion on how weight decay should
+        be computed, and so we use the learning rate as a coefficient for decoupled weight
+        decay, similar to what is suggested in `Decoupled Weight Decay Regularization`_.
+
+        Regarding the use of :math:`\epsilon_1`: The implementation attempts to replicate the
+        presumed intention of Shazeer, Noam, and Mitchell Stern to use :math:`\epsilon_1` as
+        a stabilizing term when the squared gradient becomes small.
+
+        This stabilization can be written as
+
+        .. math::
+            \begin{aligned}
+                &\hspace{5mm}R_t \leftarrow \widehat{\beta}_{2_t}R_{t-1}+
+                    (1-\widehat{\beta}_{2_t})(G_t \odot G_t + 1_n \cdot 1^\top_m) \cdot 1_m          \\
+                &\hspace{5mm}C_t \leftarrow \widehat{\beta}_{2_t}C_{t-1}+
+                    (1-\widehat{\beta}_{2_t}) 1^\top_n \cdot (G_t \odot G_t + 1_n \cdot 1^\top_m)    \\
+                &\hspace{5mm}\widehat{V}_t \leftarrow
+                    \frac{R_t \cdot C_t}{max(1^\top_n \cdot R_t, \epsilon_1)}                        \\
+                &\hspace{5mm}U_t \leftarrow \frac{G_t}{max(\sqrt{\widehat{V}_t}, \epsilon_1)}        \\
+            \end{aligned}
+
+        where the row and column factors of gradient squared :math:`R_t` and :math:`C_t`
+        are left alone, and we apply :math:`\epsilon_1` at the final calculation of
+        the variance estimate :math:`\widehat{V}_t` and for the update :math:`U_t`.
+
+        This is in contrast to Shazeer, Noam, and Mitchell Stern and other frameworks which
+        apply :math:`\epsilon_1` to both row and column factors of the squared gradient, but
+        not in the calculations after:
+
+        .. math::
+            \begin{aligned}
+                &\hspace{5mm}R_t \leftarrow \widehat{\beta}_{2_t}R_{t-1}+
+                            (1-\widehat{\beta}_{2_t})(G_t \odot G_t + \epsilon_1 1_n \cdot 1^\top_m) \cdot 1_m          \\
+                &\hspace{5mm}C_t \leftarrow \widehat{\beta}_{2_t}C_{t-1}+
+                            (1-\widehat{\beta}_{2_t}) 1^\top_n \cdot (G_t \odot G_t + \epsilon_1 1_n \cdot 1^\top_m)    \\
+                &\hspace{5mm}\widehat{V}_t \leftarrow \frac{R_t \cdot C_t}{1^\top_n \cdot R_t}                          \\
+                &\hspace{5mm}U_t \leftarrow \frac{G_t}{\sqrt{\widehat{V}_t}}                                            \\
+            \end{aligned}
+
+
+    .. _Adafactor\: Adaptive Learning Rates with Sublinear Memory Cost:
+        https://arxiv.org/pdf/1804.04235
+    .. _Decoupled Weight Decay Regularization:
+        https://arxiv.org/abs/1711.05101
+    """
+)
+
+
+def _single_tensor_adafactor(
+    params: List[Tensor],
+    grads: List[Tensor],
+    # If grad is 1-dimensional (aka a vector), there is no factorization necessary
+    # so row_var and col_var will be None while variance will be filled.
+    # Contrarily, for a grad with multiple dimensions, we will factor along the last
+    # 2 dimensions, and so row_var and col_var will be filled and variance will be None.
+    row_vars: List[Optional[Tensor]],
+    col_vars: List[Optional[Tensor]],
+    variances: List[Optional[Tensor]],
+    state_steps: List[Tensor],
+    grad_scale: Optional[Tensor],
+    found_inf: Optional[Tensor],
+    *,
+    d: float,
+    lr: Union[Tensor, float],
+    beta2_decay: float,
+    weight_decay: float,
+    eps1: Optional[float],
+    eps2: float,
+    maximize: bool,
+    has_complex: bool,
+):
+    assert (
+        grad_scale is None and found_inf is None
+    ), "Grad scaling should occur outside of optimizer.step()"
+
+    if torch.jit.is_scripting():
+        # this assert is due to JIT being dumb and not realizing that the ops below
+        # have overloads to handle both float and Tensor lrs, so we just assert it's
+        # a float since most people using JIT are using floats
+        assert isinstance(lr, float)
+
+    for i, param in enumerate(params):
+        grad = grads[i] if not maximize else -grads[i]
+        step_t = state_steps[i]
+        row_var = row_vars[i]
+        col_var = col_vars[i]
+        variance = variances[i]
+        if eps1 is None:
+            eps1 = torch.finfo(param.dtype).eps
+
+        # update step
+        step_t += 1
+        step_float = step_t.item()
+
+        one_minus_beta2_t = step_float**beta2_decay
+        rho_t = min(lr, 1 / (step_float**0.5))
+        alpha = max(eps2, param.norm(2).item() / (param.numel() ** 0.5)) * rho_t
+
+        # Perform stepweight decay
+        if weight_decay != 0:
+            param.mul_(1 - lr * weight_decay)
+
+        if grad.dim() > 1:
+            assert (
+                row_var is not None and col_var is not None
+            ), "row_var and col_var should be defined when grad is multidimensional"
+            # same as (g * g).mean(dim=-1) w/o materializing an intermediate size g
+            row_mean = (
+                torch.norm(grad, dim=-1, keepdim=True).square_().div_(grad.size(-1))
+            )
+            row_var.lerp_(row_mean, one_minus_beta2_t)
+            # same as (g * g).mean(dim=-2) w/o materializing an intermediate size g
+            col_mean = (
+                torch.norm(grad, dim=-2, keepdim=True).square_().div_(grad.size(-2))
+            )
+            col_var.lerp_(col_mean, one_minus_beta2_t)
+            var_estimate = row_var @ col_var
+            var_estimate.div_(row_var.mean(dim=-2, keepdim=True).clamp_(min=eps1))
+        else:
+            assert (
+                variance is not None
+            ), "variance should be defined when grad is a vector"
+            grad_squared = grad * grad
+            variance.lerp_(grad_squared, one_minus_beta2_t)
+            # avoid writing into variance during update
+            var_estimate = variance.clone()
+
+        # square the eps1 as we sqrt after to keep eps1's magnitude
+        update = var_estimate.clamp_(min=eps1 * eps1).rsqrt_()
+        update.mul_(grad)
+        denom = max(1.0, update.norm(2).item() / ((update.numel() ** 0.5) * d))
+        param.add_(update, alpha=-alpha / denom)
+
+
+def _group_tensors_by_device_dtype_and_is_multidim(
+    tensorlists: TensorListList,
+) -> Dict[
+    Tuple[Optional[torch.device], Optional[torch.dtype], bool],
+    List[List[Optional[Tensor]]],
+]:
+    """Groups tensors by device, dtype, AND multidimensionality -- whether the tensor
+    has multiple dims or just one dim (is a vector). This allows the foreach impl of
+    Adafactor to assume that every group of params will either be factored or not."""
+    grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(tensorlists)
+    ultra_grouped_tensors: Dict[
+        Tuple[Optional[torch.device], Optional[torch.dtype], bool],
+        List[List[Optional[Tensor]]],
+    ] = {}
+    for (device, dtype), (tensorlists, _) in grouped_tensors.items():
+        matrix_key = (device, dtype, True)
+        vector_key = (device, dtype, False)
+
+        # assumes grad is the second tensorlist
+        for j, tensor in enumerate(tensorlists[1]):
+            assert tensor is not None, "grad should not be None"
+            if tensor.dim() > 1:
+                if matrix_key not in ultra_grouped_tensors:
+                    ultra_grouped_tensors[matrix_key] = [[] for _ in tensorlists]
+                for i in range(len(tensorlists)):
+                    ultra_grouped_tensors[matrix_key][i].append(tensorlists[i][j])
+            else:
+                if vector_key not in ultra_grouped_tensors:
+                    ultra_grouped_tensors[vector_key] = [[] for _ in tensorlists]
+                for i in range(len(tensorlists)):
+                    ultra_grouped_tensors[vector_key][i].append(tensorlists[i][j])
+    return ultra_grouped_tensors
+
+
+def _multi_tensor_adafactor(
+    params: List[Tensor],
+    grads: List[Tensor],
+    # If grad is 1-dimensional (aka a vector), there is no factorization necessary
+    # so row_var and col_var will be None while variance will be filled.
+    # Contrarily, for a grad with multiple dimensions, we will factor along the last
+    # 2 dimensions, and so row_var and col_var will be filled and variance will be None.
+    row_vars: List[Optional[Tensor]],
+    col_vars: List[Optional[Tensor]],
+    variances: List[Optional[Tensor]],
+    state_steps: List[Tensor],
+    grad_scale: Optional[Tensor],
+    found_inf: Optional[Tensor],
+    *,
+    d: float,
+    lr: Union[Tensor, float],
+    beta2_decay: float,
+    weight_decay: float,
+    eps1: Optional[float],
+    eps2: float,
+    maximize: bool,
+    has_complex: bool,
+):
+    if len(params) == 0:
+        return
+
+    assert (
+        grad_scale is None and found_inf is None
+    ), "Grad scaling should occur outside of optimizer.step()"
+
+    grouped_tensors = _group_tensors_by_device_dtype_and_is_multidim(
+        [params, grads, row_vars, col_vars, variances, state_steps]  # type: ignore[list-item]
+    )
+    for (_, dtype, is_multidim), (
+        (
+            device_params_,
+            device_grads_,
+            device_row_vars_,
+            device_col_vars_,
+            device_variances_,
+            device_state_steps_,
+        )
+    ) in grouped_tensors.items():
+        device_params = cast(List[Tensor], device_params_)
+        device_grads = cast(List[Tensor], device_grads_)
+        device_state_steps = cast(List[Tensor], device_state_steps_)
+        if eps1 is None:
+            assert (
+                dtype is not None
+            ), "dtype is needed to compute eps1 when eps1 is unset"
+            eps1 = torch.finfo(dtype).eps
+
+        if TYPE_CHECKING:
+            assert device_state_steps[0] is not None
+
+        if maximize:
+            device_grads = torch._foreach_neg(device_grads)  # type: ignore[assignment]
+
+        # Update steps
+        # If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
+        # and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
+        # wrapped it once now. The alpha is required to assure we go to the right overload.
+        if not torch._utils.is_compiling() and device_state_steps[0].is_cpu:
+            torch._foreach_add_(
+                device_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
+            )
+        else:
+            torch._foreach_add_(device_state_steps, 1.0)
+
+        one_minus_beta2_ts = []
+        beta2_ts = []
+        rho_ts = []
+        for s in device_state_steps:
+            one_minus_beta2_ts.append(s.item() ** beta2_decay)
+            beta2_ts.append(1 - s.item() ** beta2_decay)
+            rho_ts.append(min(lr, 1 / (s.item() ** 0.5)))
+
+        alphas = [
+            max(eps2, p.norm(2).item() / (p.numel() ** 0.5)) * r
+            for p, r in zip(device_params, rho_ts)
+        ]
+
+        # Perform stepweight decay
+        if weight_decay != 0:
+            torch._foreach_mul_(device_params, 1 - lr * weight_decay)
+
+        if is_multidim:
+            device_row_vars = cast(List[Tensor], device_row_vars_)
+            device_col_vars = cast(List[Tensor], device_col_vars_)
+            assert (
+                device_row_vars[0] is not None and device_col_vars[0] is not None
+            ), "row_var and col_var should be defined when grad is multidimensional"
+            # same as (g * g).mean(dim=-1) w/o materializing an intermediate size g
+            row_means = [
+                torch.norm(grad, dim=-1, keepdim=True) for grad in device_grads
+            ]
+            torch._foreach_mul_(row_means, row_means)
+            torch._foreach_div_(row_means, [grad.size(-1) for grad in device_grads])
+            torch._foreach_mul_(device_row_vars, beta2_ts)
+            torch._foreach_mul_(row_means, one_minus_beta2_ts)
+            torch._foreach_add_(device_row_vars, row_means)
+            del row_means
+
+            # same as (g * g).mean(dim=-2) w/o materializing an intermediate size g
+            col_means = [
+                torch.norm(grad, dim=-2, keepdim=True) for grad in device_grads
+            ]
+            torch._foreach_mul_(col_means, col_means)
+            torch._foreach_div_(col_means, [grad.size(-2) for grad in device_grads])
+            torch._foreach_mul_(device_col_vars, beta2_ts)
+            torch._foreach_mul_(col_means, one_minus_beta2_ts)
+            torch._foreach_add_(device_col_vars, col_means)
+            del col_means
+
+            var_estimates = [
+                row_var @ col_var
+                for row_var, col_var in zip(device_row_vars, device_col_vars)
+            ]
+            row_var_means = [
+                row_var.mean(dim=-2, keepdim=True) for row_var in device_row_vars
+            ]
+            torch._foreach_clamp_min_(row_var_means, eps1)
+            torch._foreach_div_(var_estimates, row_var_means)
+            del row_var_means
+        else:
+            device_variances = cast(List[Tensor], device_variances_)
+            assert (
+                device_variances[0] is not None
+            ), "variance should be defined when grad is a vector"
+
+            grads_squared = torch._foreach_mul(device_grads, device_grads)
+            torch._foreach_mul_(device_variances, beta2_ts)
+            torch._foreach_mul_(grads_squared, one_minus_beta2_ts)
+            torch._foreach_add_(device_variances, grads_squared)
+            del grads_squared
+
+            # avoid writing into variance during update
+            var_estimates = [v.clone() for v in device_variances]
+
+        # square the eps1 as we sqrt after to keep eps1's magnitude
+        torch._foreach_clamp_min_(var_estimates, eps1 * eps1)
+        torch._foreach_sqrt_(var_estimates)
+        torch._foreach_reciprocal_(var_estimates)
+        torch._foreach_mul_(var_estimates, device_grads)
+        updates = var_estimates
+
+        alphas = [
+            -a / (max(1.0, update.norm(2).item() / ((update.numel() ** 0.5) * d)))
+            for a, update in zip(alphas, updates)
+        ]
+        torch._foreach_mul_(updates, alphas)
+        torch._foreach_add_(device_params, updates)
+
+
+@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_adafactor)
+def adafactor(
+    params: List[Tensor],
+    grads: List[Tensor],
+    row_vars: List[Optional[Tensor]],
+    col_vars: List[Optional[Tensor]],
+    variances: List[Optional[Tensor]],
+    state_steps: List[Tensor],
+    # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
+    # setting this as kwarg for now as functional API is compiled by torch/distributed/optim
+    foreach: Optional[bool] = None,
+    grad_scale: Optional[Tensor] = None,
+    found_inf: Optional[Tensor] = None,
+    has_complex: bool = False,
+    *,
+    d: float,
+    lr: Union[float, Tensor],
+    beta2_decay: float,
+    weight_decay: float,
+    eps1: float,
+    eps2: float,
+    maximize: bool,
+):
+    r"""Functional API that performs Adafactor algorithm computation.
+
+    See :class:`~torch.optim.Adafactor` for details.
+    """
+    if not torch._utils.is_compiling() and not all(
+        isinstance(t, torch.Tensor) for t in state_steps
+    ):
+        raise RuntimeError(
+            "`state_steps` argument must contain a list of singleton tensors"
+        )
+
+    if foreach:
+        func = _multi_tensor_adafactor
+    else:
+        func = _single_tensor_adafactor
+
+    func(
+        params,
+        grads,
+        row_vars,
+        col_vars,
+        variances,
+        state_steps,
+        d=d,
+        lr=lr,
+        beta2_decay=beta2_decay,
+        weight_decay=weight_decay,
+        eps1=eps1,
+        eps2=eps2,
+        maximize=maximize,
+        grad_scale=grad_scale,
+        found_inf=found_inf,
+        has_complex=has_complex,
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/_functional.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/_functional.py
new file mode 100644
index 0000000000000000000000000000000000000000..a307cc76846dc2be51a47a1b5b4e70c29aafffc4
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/_functional.py
@@ -0,0 +1,84 @@
+# mypy: allow-untyped-defs
+r"""Functional interface."""
+import math
+from typing import List
+
+from torch import Tensor
+
+from .adadelta import adadelta  # type: ignore[attr-defined]  # noqa: F401
+from .adagrad import _make_sparse, adagrad  # type: ignore[attr-defined]  # noqa: F401
+from .adam import adam  # type: ignore[attr-defined]  # noqa: F401
+from .adamax import adamax  # type: ignore[attr-defined]  # noqa: F401
+from .adamw import adamw  # type: ignore[attr-defined]  # noqa: F401
+from .asgd import asgd  # type: ignore[attr-defined]  # noqa: F401
+from .nadam import nadam  # type: ignore[attr-defined]  # noqa: F401
+from .radam import radam  # type: ignore[attr-defined]  # noqa: F401
+from .rmsprop import rmsprop  # type: ignore[attr-defined]  # noqa: F401
+from .rprop import rprop  # type: ignore[attr-defined]  # noqa: F401
+from .sgd import sgd  # type: ignore[attr-defined]  # noqa: F401
+
+
+# TODO: use foreach API in optim._functional to do all the computation
+
+
+def sparse_adam(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    state_steps: List[int],
+    *,
+    eps: float,
+    beta1: float,
+    beta2: float,
+    lr: float,
+    maximize: bool,
+):
+    r"""Functional API that performs Sparse Adam algorithm computation.
+
+    See :class:`~torch.optim.SparseAdam` for details.
+    """
+    for i, param in enumerate(params):
+        grad = grads[i]
+        grad = grad if not maximize else -grad
+        grad = grad.coalesce()  # the update is non-linear so indices must be unique
+        grad_indices = grad._indices()
+        grad_values = grad._values()
+        if grad_values.numel() == 0:
+            # Skip update for empty grad
+            continue
+        size = grad.size()
+
+        exp_avg = exp_avgs[i]
+        exp_avg_sq = exp_avg_sqs[i]
+        step = state_steps[i]
+
+        def make_sparse(values):
+            constructor = grad.new
+            if grad_indices.dim() == 0 or values.dim() == 0:
+                return constructor().resize_as_(grad)
+            return constructor(grad_indices, values, size)
+
+        # Decay the first and second moment running average coefficient
+        #      old <- b * old + (1 - b) * new
+        # <==> old += (1 - b) * (new - old)
+        old_exp_avg_values = exp_avg.sparse_mask(grad)._values()
+        exp_avg_update_values = grad_values.sub(old_exp_avg_values).mul_(1 - beta1)
+        exp_avg.add_(make_sparse(exp_avg_update_values))
+        old_exp_avg_sq_values = exp_avg_sq.sparse_mask(grad)._values()
+        exp_avg_sq_update_values = (
+            grad_values.pow(2).sub_(old_exp_avg_sq_values).mul_(1 - beta2)
+        )
+        exp_avg_sq.add_(make_sparse(exp_avg_sq_update_values))
+
+        # Dense addition again is intended, avoiding another sparse_mask
+        numer = exp_avg_update_values.add_(old_exp_avg_values)
+        exp_avg_sq_update_values.add_(old_exp_avg_sq_values)
+        denom = exp_avg_sq_update_values.sqrt_().add_(eps)
+        del exp_avg_update_values, exp_avg_sq_update_values
+
+        bias_correction1 = 1 - beta1**step
+        bias_correction2 = 1 - beta2**step
+        step_size = lr * math.sqrt(bias_correction2) / bias_correction1
+
+        param.add_(make_sparse(-step_size * numer.div_(denom)))
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/_multi_tensor/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/_multi_tensor/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..41a195713b927efd74f2bd7cfadde646d1d7a704
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/_multi_tensor/__init__.py
@@ -0,0 +1,30 @@
+"""
+:mod:`torch.optim._multi_tensor` is a package implementing various optimization algorithms.
+
+Most commonly used methods are already supported, and the interface is general
+enough, so that more sophisticated ones can be also easily integrated in the
+future.
+"""
+from functools import partialmethod
+
+from torch import optim
+
+
+def partialclass(cls, *args, **kwargs):  # noqa: D103
+    class NewCls(cls):
+        __init__ = partialmethod(cls.__init__, *args, **kwargs)
+
+    return NewCls
+
+
+Adam = partialclass(optim.Adam, foreach=True)
+AdamW = partialclass(optim.AdamW, foreach=True)
+NAdam = partialclass(optim.NAdam, foreach=True)
+SGD = partialclass(optim.SGD, foreach=True)
+RAdam = partialclass(optim.RAdam, foreach=True)
+RMSprop = partialclass(optim.RMSprop, foreach=True)
+Rprop = partialclass(optim.Rprop, foreach=True)
+ASGD = partialclass(optim.ASGD, foreach=True)
+Adamax = partialclass(optim.Adamax, foreach=True)
+Adadelta = partialclass(optim.Adadelta, foreach=True)
+Adagrad = partialclass(optim.Adagrad, foreach=True)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/_multi_tensor/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/_multi_tensor/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..489e335faa89d43aa10890379881b22c0cb04dc4
Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/_multi_tensor/__pycache__/__init__.cpython-311.pyc differ
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/adadelta.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/adadelta.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef45706176a347d2f987a4c6d45eb5bf6b8aebc4
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/adadelta.py
@@ -0,0 +1,461 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+from typing import Any, cast, Dict, List, Optional, Union
+
+import torch
+from torch import Tensor
+
+from .optimizer import (
+    _capturable_doc,
+    _default_to_fused_or_foreach,
+    _differentiable_doc,
+    _disable_dynamo_if_unsupported,
+    _foreach_doc,
+    _get_capturable_supported_devices,
+    _get_scalar_dtype,
+    _maximize_doc,
+    _use_grad_for_differentiable,
+    _view_as_real,
+    Optimizer,
+    ParamsT,
+)
+
+
+__all__ = ["Adadelta", "adadelta"]
+
+
+class Adadelta(Optimizer):
+    def __init__(
+        self,
+        params: ParamsT,
+        lr: Union[float, Tensor] = 1.0,
+        rho: float = 0.9,
+        eps: float = 1e-6,
+        weight_decay: float = 0,
+        foreach: Optional[bool] = None,
+        *,
+        capturable: bool = False,
+        maximize: bool = False,
+        differentiable: bool = False,
+    ):
+        if isinstance(lr, Tensor) and lr.numel() != 1:
+            raise ValueError("Tensor lr must be 1-element")
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 <= rho <= 1.0:
+            raise ValueError(f"Invalid rho value: {rho}")
+        if not 0.0 <= eps:
+            raise ValueError(f"Invalid epsilon value: {eps}")
+        if not 0.0 <= weight_decay:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+
+        defaults = dict(
+            lr=lr,
+            rho=rho,
+            eps=eps,
+            weight_decay=weight_decay,
+            maximize=maximize,
+            capturable=capturable,
+            foreach=foreach,
+            differentiable=differentiable,
+        )
+        super().__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super().__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault("foreach", None)
+            group.setdefault("maximize", False)
+            group.setdefault("differentiable", False)
+            group.setdefault("capturable", False)
+            for p in group["params"]:
+                p_state = self.state.get(p, [])
+                if len(p_state) != 0 and not torch.is_tensor(p_state["step"]):
+                    step_val = float(p_state["step"])
+                    p_state["step"] = (
+                        torch.tensor(
+                            step_val, dtype=_get_scalar_dtype(), device=p.device
+                        )
+                        if group["capturable"]
+                        else torch.tensor(step_val, dtype=_get_scalar_dtype())
+                    )
+
+    def _init_group(
+        self,
+        group: Dict[str, Any],
+        params_with_grad: List[Tensor],
+        grads: List[Tensor],
+        square_avgs: List[Tensor],
+        acc_deltas: List[Tensor],
+        state_steps: List[Tensor],
+    ):
+        has_complex = False
+        p: Tensor
+        for p in group["params"]:
+            if p.grad is None:
+                continue
+            has_complex |= torch.is_complex(p)
+            params_with_grad.append(p)
+            if p.grad.is_sparse:
+                raise RuntimeError("Adadelta does not support sparse gradients")
+            grads.append(p.grad)
+
+            state = self.state[p]
+
+            # Lazy state initialization
+            if len(state) == 0:
+                state["step"] = (
+                    torch.zeros((), dtype=_get_scalar_dtype(), device=p.device)
+                    if group["capturable"]
+                    else torch.zeros((), dtype=_get_scalar_dtype())
+                )
+
+                state["square_avg"] = torch.zeros_like(
+                    p, memory_format=torch.preserve_format
+                )
+                state["acc_delta"] = torch.zeros_like(
+                    p, memory_format=torch.preserve_format
+                )
+
+            square_avgs.append(state["square_avg"])
+            acc_deltas.append(state["acc_delta"])
+            state_steps.append(state["step"])
+
+        return has_complex
+
+    @_use_grad_for_differentiable
+    def step(self, closure=None):
+        """Perform a single optimization step.
+
+        Args:
+            closure (Callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        self._cuda_graph_capture_health_check()
+
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            params_with_grad: List[Tensor] = []
+            grads: List[Tensor] = []
+            square_avgs: List[Tensor] = []
+            acc_deltas: List[Tensor] = []
+            state_steps: List[Tensor] = []
+            (
+                lr,
+                rho,
+                eps,
+                weight_decay,
+                foreach,
+                maximize,
+                differentiable,
+                capturable,
+            ) = (
+                group["lr"],
+                group["rho"],
+                group["eps"],
+                group["weight_decay"],
+                group["foreach"],
+                group["maximize"],
+                group["differentiable"],
+                group["capturable"],
+            )
+
+            has_complex = self._init_group(
+                group, params_with_grad, grads, square_avgs, acc_deltas, state_steps
+            )
+
+            adadelta(
+                params_with_grad,
+                grads,
+                square_avgs,
+                acc_deltas,
+                state_steps,
+                lr=lr,
+                rho=rho,
+                eps=eps,
+                weight_decay=weight_decay,
+                foreach=foreach,
+                maximize=maximize,
+                differentiable=differentiable,
+                capturable=capturable,
+                has_complex=has_complex,
+            )
+
+        return loss
+
+
+Adadelta.__doc__ = (
+    r"""Implements Adadelta algorithm.
+
+    .. math::
+       \begin{aligned}
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{input}      : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)},
+                \: f(\theta) \text{ (objective)}, \: \rho \text{ (decay)},
+                \: \lambda \text{ (weight decay)}                                                \\
+            &\textbf{initialize} :  v_0  \leftarrow 0 \: \text{ (square avg)},
+                \: u_0 \leftarrow 0 \: \text{ (accumulate variables)}                     \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do}                         \\
+            &\hspace{5mm}g_t           \leftarrow   \nabla_{\theta} f_t (\theta_{t-1})           \\
+            &\hspace{5mm}if \: \lambda \neq 0                                                    \\
+            &\hspace{10mm} g_t \leftarrow g_t + \lambda  \theta_{t-1}                            \\
+            &\hspace{5mm} v_t      \leftarrow v_{t-1} \rho + g^2_t (1 - \rho)                    \\
+            &\hspace{5mm}\Delta x_t    \leftarrow   \frac{\sqrt{u_{t-1} +
+                \epsilon }}{ \sqrt{v_t + \epsilon}  }g_t \hspace{21mm}                           \\
+            &\hspace{5mm} u_t  \leftarrow   u_{t-1}  \rho +
+                 \Delta x^2_t  (1 - \rho)                                                        \\
+            &\hspace{5mm}\theta_t      \leftarrow   \theta_{t-1} - \gamma  \Delta x_t            \\
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+            &\bf{return} \:  \theta_t                                                     \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+       \end{aligned}
+
+    For further details regarding the algorithm we refer to `ADADELTA: An Adaptive Learning Rate Method`_.
+    """
+    + rf"""
+    Args:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        rho (float, optional): coefficient used for computing a running average
+            of squared gradients (default: 0.9). A higher value of `rho` will
+            result in a slower average, which can be helpful for preventing
+            oscillations in the learning process.
+        eps (float, optional): term added to the denominator to improve
+            numerical stability (default: 1e-6).
+        lr (float, Tensor, optional): coefficient that scale delta before it is applied
+            to the parameters (default: 1.0)
+        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+        {_foreach_doc}
+        {_capturable_doc}
+        {_maximize_doc}
+        {_differentiable_doc}
+
+    .. _ADADELTA\: An Adaptive Learning Rate Method:
+        https://arxiv.org/abs/1212.5701
+
+    """
+)
+
+
+def _single_tensor_adadelta(
+    params: List[Tensor],
+    grads: List[Tensor],
+    square_avgs: List[Tensor],
+    acc_deltas: List[Tensor],
+    state_steps: List[Tensor],
+    *,
+    lr: float,
+    rho: float,
+    eps: float,
+    weight_decay: float,
+    maximize: bool,
+    differentiable: bool,
+    capturable: bool,
+    has_complex: bool,
+):
+    # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+    if not torch._utils.is_compiling() and capturable:
+        capturable_supported_devices = _get_capturable_supported_devices(
+            supports_xla=False
+        )
+        assert all(
+            p.device.type == step.device.type
+            and p.device.type in capturable_supported_devices
+            for p, step in zip(params, state_steps)
+        ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
+
+    for param, grad, square_avg, acc_delta, step in zip(
+        params, grads, square_avgs, acc_deltas, state_steps
+    ):
+        step += 1
+        grad = grad if not maximize else -grad
+
+        if weight_decay != 0:
+            grad = grad.add(param, alpha=weight_decay)
+
+        if torch.is_complex(param):
+            square_avg = torch.view_as_real(square_avg)
+            acc_delta = torch.view_as_real(acc_delta)
+            grad = torch.view_as_real(grad)
+
+        square_avg.mul_(rho).addcmul_(grad, grad, value=1 - rho)
+        std = square_avg.add(eps).sqrt_()
+        delta = acc_delta.add(eps).sqrt_()
+        if differentiable:
+            delta = delta.clone()
+        delta.div_(std).mul_(grad)
+        acc_delta.mul_(rho).addcmul_(delta, delta, value=1 - rho)
+
+        if torch.is_complex(param):
+            delta = torch.view_as_complex(delta)
+        param.add_(delta, alpha=-lr)
+
+
+def _multi_tensor_adadelta(
+    params: List[Tensor],
+    grads: List[Tensor],
+    square_avgs: List[Tensor],
+    acc_deltas: List[Tensor],
+    state_steps: List[Tensor],
+    *,
+    lr: float,
+    rho: float,
+    eps: float,
+    weight_decay: float,
+    maximize: bool,
+    differentiable: bool,
+    capturable: bool,
+    has_complex: bool,
+):
+    assert not differentiable, "_foreach ops don't support autograd"
+
+    # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+    if not torch._utils.is_compiling() and capturable:
+        capturable_supported_devices = _get_capturable_supported_devices(
+            supports_xla=False
+        )
+        assert all(
+            p.device.type == step.device.type
+            and p.device.type in capturable_supported_devices
+            for p, step in zip(params, state_steps)
+        ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
+
+    if len(params) == 0:
+        return
+
+    grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
+        [params, grads, square_avgs, acc_deltas, state_steps]  # type: ignore[list-item]
+    )
+    for (
+        device_params_,
+        device_grads_,
+        device_square_avgs_,
+        device_acc_deltas_,
+        device_state_steps_,
+    ), _ in grouped_tensors.values():
+        device_params = cast(List[Tensor], device_params_)
+        device_grads = cast(List[Tensor], device_grads_)
+        device_square_avgs = cast(List[Tensor], device_square_avgs_)
+        device_acc_deltas = cast(List[Tensor], device_acc_deltas_)
+        device_state_steps = cast(List[Tensor], device_state_steps_)
+        if has_complex:
+            _view_as_real(
+                device_params, device_grads, device_square_avgs, device_acc_deltas
+            )
+
+        # Update steps
+        # If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
+        # and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
+        # wrapped it once now. The alpha is required to assure we go to the right overload.
+        if not torch._utils.is_compiling() and device_state_steps[0].is_cpu:
+            torch._foreach_add_(
+                device_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
+            )
+        else:
+            torch._foreach_add_(device_state_steps, 1)
+
+        if maximize:
+            device_grads = torch._foreach_neg(device_grads)  # type: ignore[assignment]
+
+        if weight_decay != 0:
+            # Re-use the intermediate memory (device_grads) already allocated for maximize
+            if maximize:
+                torch._foreach_add_(device_grads, device_params, alpha=weight_decay)
+            else:
+                device_grads = torch._foreach_add(  # type: ignore[assignment]
+                    device_grads, device_params, alpha=weight_decay
+                )
+
+        torch._foreach_mul_(device_square_avgs, rho)
+        torch._foreach_addcmul_(
+            device_square_avgs, device_grads, device_grads, value=1 - rho
+        )
+
+        std = torch._foreach_add(device_square_avgs, eps)
+        torch._foreach_sqrt_(std)
+
+        deltas = torch._foreach_add(device_acc_deltas, eps)
+        torch._foreach_sqrt_(deltas)
+        torch._foreach_div_(deltas, std)
+        torch._foreach_mul_(deltas, device_grads)
+
+        torch._foreach_mul_(device_acc_deltas, rho)
+        torch._foreach_addcmul_(device_acc_deltas, deltas, deltas, value=1 - rho)
+
+        # If LR is a tensor, the else branch will internally call item()
+        # which will cause silent incorrectness if we are capturing
+        if capturable and isinstance(lr, torch.Tensor):
+            torch._foreach_mul_(deltas, -lr)
+            torch._foreach_add_(device_params, deltas)
+        else:
+            torch._foreach_add_(device_params, deltas, alpha=-lr)
+
+
+@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_adadelta)
+def adadelta(
+    params: List[Tensor],
+    grads: List[Tensor],
+    square_avgs: List[Tensor],
+    acc_deltas: List[Tensor],
+    state_steps: List[Tensor],
+    # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
+    # setting this as kwarg for now as functional API is compiled by torch/distributed/optim
+    capturable: bool = False,
+    foreach: Optional[bool] = None,
+    differentiable: bool = False,
+    has_complex: bool = False,
+    *,
+    lr: float,
+    rho: float,
+    eps: float,
+    weight_decay: float,
+    maximize: bool,
+):
+    r"""Functional API that performs Adadelta algorithm computation.
+
+    See :class:`~torch.optim.Adadelta` for details.
+    """
+
+    # this check is slow during compilation, so we skip it
+    # if it's strictly needed we can add this check back in dynamo
+    if not torch._utils.is_compiling() and not all(
+        isinstance(t, torch.Tensor) for t in state_steps
+    ):
+        raise RuntimeError(
+            "API has changed, `state_steps` argument must contain a list of singleton tensors"
+        )
+
+    # We still respect when the user inputs False for foreach.
+    if foreach is None:
+        _, foreach = _default_to_fused_or_foreach(
+            params, differentiable, use_fused=False
+        )
+
+    if foreach and torch.jit.is_scripting():
+        raise RuntimeError("torch.jit.script not supported with foreach optimizers")
+
+    if foreach and not torch.jit.is_scripting():
+        func = _multi_tensor_adadelta
+    else:
+        func = _single_tensor_adadelta
+
+    func(
+        params,
+        grads,
+        square_avgs,
+        acc_deltas,
+        state_steps,
+        lr=lr,
+        rho=rho,
+        eps=eps,
+        weight_decay=weight_decay,
+        maximize=maximize,
+        differentiable=differentiable,
+        capturable=capturable,
+        has_complex=has_complex,
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/adagrad.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/adagrad.py
new file mode 100644
index 0000000000000000000000000000000000000000..7427471c1bfd49ea31b1505612245c349f39cf0e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/adagrad.py
@@ -0,0 +1,564 @@
+# mypy: allow-untyped-defs
+from typing import cast, List, Optional, Union
+
+import torch
+from torch import Tensor
+
+from .optimizer import (
+    _default_to_fused_or_foreach,
+    _device_dtype_check_for_fused,
+    _differentiable_doc,
+    _foreach_doc,
+    _get_scalar_dtype,
+    _get_value,
+    _maximize_doc,
+    _use_grad_for_differentiable,
+    _view_as_real,
+    Optimizer,
+    ParamsT,
+)
+
+
+__all__ = ["Adagrad", "adagrad"]
+
+
+class Adagrad(Optimizer):
+    def __init__(
+        self,
+        params: ParamsT,
+        lr: Union[float, Tensor] = 1e-2,
+        lr_decay: float = 0,
+        weight_decay: float = 0,
+        initial_accumulator_value: float = 0,
+        eps: float = 1e-10,
+        foreach: Optional[bool] = None,
+        *,
+        maximize: bool = False,
+        differentiable: bool = False,
+        fused: Optional[bool] = None,
+    ):
+        if isinstance(lr, Tensor) and lr.numel() != 1:
+            raise ValueError("Tensor lr must be 1-element")
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 <= lr_decay:
+            raise ValueError(f"Invalid lr_decay value: {lr_decay}")
+        if not 0.0 <= weight_decay:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+        if not 0.0 <= initial_accumulator_value:
+            raise ValueError(
+                f"Invalid initial_accumulator_value value: {initial_accumulator_value}"
+            )
+        if not 0.0 <= eps:
+            raise ValueError(f"Invalid epsilon value: {eps}")
+
+        defaults = dict(
+            lr=lr,
+            lr_decay=lr_decay,
+            eps=eps,
+            weight_decay=weight_decay,
+            initial_accumulator_value=initial_accumulator_value,
+            foreach=foreach,
+            maximize=maximize,
+            differentiable=differentiable,
+            fused=fused,
+        )
+        super().__init__(params, defaults)
+
+        if fused:
+            if differentiable:
+                raise RuntimeError("`fused` does not support `differentiable`")
+            if foreach:
+                raise RuntimeError("`fused` and `foreach` cannot be `True` together.")
+            self._need_device_dtype_check_for_fused = True
+
+        for group in self.param_groups:
+            for p in group["params"]:
+                state = self.state[p]
+                state["step"] = (
+                    torch.zeros(
+                        (),
+                        dtype=_get_scalar_dtype(is_fused=group["fused"]),
+                        device=p.device,
+                    )
+                    if group["fused"]
+                    else torch.tensor(0.0, dtype=_get_scalar_dtype())
+                )
+                init_value = (
+                    complex(initial_accumulator_value, initial_accumulator_value)
+                    if torch.is_complex(p)
+                    else initial_accumulator_value
+                )
+                state["sum"] = torch.full_like(
+                    p, init_value, memory_format=torch.preserve_format
+                )
+
+    def __setstate__(self, state):
+        super().__setstate__(state)
+        #  define "fused" for
+        #  MYPY error: Name "fused" may be undefined
+        fused = None
+        for group in self.param_groups:
+            group.setdefault("foreach", None)
+            group.setdefault("maximize", False)
+            group.setdefault("differentiable", False)
+            fused = group.setdefault("fused", None)
+
+        state_values = list(self.state.values())
+        step_is_tensor = (len(state_values) != 0) and torch.is_tensor(
+            state_values[0]["step"]
+        )
+        if not step_is_tensor:
+            for s in state_values:
+                s["step"] = torch.tensor(
+                    float(s["step"]), dtype=_get_scalar_dtype(is_fused=fused)
+                )
+
+    def share_memory(self):
+        for group in self.param_groups:
+            for p in group["params"]:
+                state = self.state[p]
+                state["sum"].share_memory_()
+
+    def _init_group(self, group, params_with_grad, grads, state_sums, state_steps):
+        has_sparse_grad, has_complex = False, False
+        for p in group["params"]:
+            if p.grad is not None:
+                if group["fused"] and getattr(
+                    self,
+                    "_need_device_dtype_check_for_fused",
+                    True,
+                ):
+                    _device_dtype_check_for_fused(p, cuda_unsupported=True)
+                    self._need_device_dtype_check_for_fused = False
+                has_sparse_grad |= p.grad.is_sparse
+                has_complex |= torch.is_complex(p)
+                params_with_grad.append(p)
+                grads.append(p.grad)
+                state = self.state[p]
+                state_sums.append(state["sum"])
+                state_steps.append(state["step"])
+
+        return has_sparse_grad, has_complex
+
+    @_use_grad_for_differentiable
+    def step(self, closure=None):
+        """Perform a single optimization step.
+
+        Args:
+            closure (Callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            params_with_grad: List[Tensor] = []
+            grads: List[Tensor] = []
+            state_sums: List[Tensor] = []
+            state_steps: List[Tensor] = []
+
+            has_sparse_grad, has_complex = self._init_group(
+                group, params_with_grad, grads, state_sums, state_steps
+            )
+
+            adagrad(
+                params_with_grad,
+                grads,
+                state_sums,
+                state_steps,
+                lr=group["lr"],
+                weight_decay=group["weight_decay"],
+                lr_decay=group["lr_decay"],
+                eps=group["eps"],
+                has_sparse_grad=has_sparse_grad,
+                foreach=group["foreach"],
+                maximize=group["maximize"],
+                differentiable=group["differentiable"],
+                has_complex=has_complex,
+                fused=group["fused"],
+                grad_scale=getattr(self, "grad_scale", None),
+                found_inf=getattr(self, "found_inf", None),
+            )
+
+        return loss
+
+
+Adagrad.__doc__ = (
+    r"""Implements Adagrad algorithm.
+
+    .. math::
+       \begin{aligned}
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{input}      : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta)
+                \text{ (objective)}, \: \lambda \text{ (weight decay)},                          \\
+            &\hspace{12mm}    \tau \text{ (initial accumulator value)}, \: \eta\text{ (lr decay)}\\
+            &\textbf{initialize} :  state\_sum_0 \leftarrow \tau                          \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do}                         \\
+            &\hspace{5mm}g_t           \leftarrow   \nabla_{\theta} f_t (\theta_{t-1})           \\
+            &\hspace{5mm} \tilde{\gamma}    \leftarrow \gamma / (1 +(t-1) \eta)                  \\
+            &\hspace{5mm} \textbf{if} \: \lambda \neq 0                                          \\
+            &\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1}                             \\
+            &\hspace{5mm}state\_sum_t  \leftarrow  state\_sum_{t-1} + g^2_t                      \\
+            &\hspace{5mm}\theta_t \leftarrow
+                \theta_{t-1}- \tilde{\gamma} \frac{g_t}{\sqrt{state\_sum_t}+\epsilon}            \\
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+            &\bf{return} \:  \theta_t                                                     \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+       \end{aligned}
+
+    For further details regarding the algorithm we refer to `Adaptive Subgradient Methods for Online Learning
+    and Stochastic Optimization`_.
+    """
+    + rf"""
+    Args:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, Tensor, optional): learning rate (default: 1e-2)
+        lr_decay (float, optional): learning rate decay (default: 0)
+        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+        initial_accumulator_value (float, optional): initial value of the
+            sum of squares of gradients (default: 0)
+        eps (float, optional): term added to the denominator to improve
+            numerical stability (default: 1e-10)
+        {_foreach_doc}
+        {_maximize_doc}
+        {_differentiable_doc}
+        fused (bool, optional): whether the fused implementation (CPU only) is used.
+            Currently, `torch.float64`, `torch.float32`, `torch.float16`, and `torch.bfloat16`
+            are supported. (default: None). Please note that the fused implementations does not
+            support sparse or complex gradients.
+    .. _Adaptive Subgradient Methods for Online Learning and Stochastic
+        Optimization: http://jmlr.org/papers/v12/duchi11a.html
+
+    """
+)
+
+
+def adagrad(
+    params: List[Tensor],
+    grads: List[Tensor],
+    state_sums: List[Tensor],
+    state_steps: List[Tensor],
+    fused: Optional[bool] = None,
+    grad_scale: Optional[Tensor] = None,
+    found_inf: Optional[Tensor] = None,
+    # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
+    # setting these as kwargs for now as functional API is compiled by torch/distributed/optim
+    has_sparse_grad: bool = False,
+    foreach: Optional[bool] = None,
+    differentiable: bool = False,
+    has_complex: bool = False,
+    *,
+    lr: float,
+    weight_decay: float,
+    lr_decay: float,
+    eps: float,
+    maximize: bool,
+):
+    r"""Functional API that performs Adagrad algorithm computation.
+
+    See :class:`~torch.optim.Adagrad` for details.
+    """
+    if not all(isinstance(t, torch.Tensor) for t in state_steps):
+        raise RuntimeError(
+            "API has changed, `state_steps` argument must contain a list of singleton tensors"
+        )
+
+    # Respect when the user inputs False/True for foreach or fused. We only want to change
+    # the default when neither have been user-specified. Note that we default to foreach
+    # and pass False to use_fused. This is not a mistake--we want to give the fused impl
+    # bake-in time before making it the default, even if it is typically faster.
+    if fused is None and foreach is None:
+        _, foreach = _default_to_fused_or_foreach(
+            params, differentiable, use_fused=False
+        )
+
+    if fused is None:
+        fused = False
+    if foreach is None:
+        foreach = False
+
+    if foreach and torch.jit.is_scripting():
+        raise RuntimeError("torch.jit.script not supported with foreach optimizers")
+    if fused and torch.jit.is_scripting():
+        raise RuntimeError("torch.jit.script not supported with fused optimizers")
+
+    if fused and not torch.jit.is_scripting():
+        func = _fused_adagrad
+    elif foreach and not torch.jit.is_scripting():
+        func = _multi_tensor_adagrad
+    else:
+        func = _single_tensor_adagrad
+
+    func(
+        params,
+        grads,
+        state_sums,
+        state_steps,
+        lr=lr,
+        weight_decay=weight_decay,
+        lr_decay=lr_decay,
+        eps=eps,
+        has_sparse_grad=has_sparse_grad,
+        maximize=maximize,
+        differentiable=differentiable,
+        has_complex=has_complex,
+        grad_scale=grad_scale,
+        found_inf=found_inf,
+    )
+
+
+def _make_sparse(grad, grad_indices, values):
+    size = grad.size()
+    return torch.sparse_coo_tensor(grad_indices, values, size)
+
+
+def _single_tensor_adagrad(
+    params: List[Tensor],
+    grads: List[Tensor],
+    state_sums: List[Tensor],
+    state_steps: List[Tensor],
+    grad_scale: Optional[Tensor],
+    found_inf: Optional[Tensor],
+    *,
+    lr: float,
+    weight_decay: float,
+    lr_decay: float,
+    eps: float,
+    has_sparse_grad: bool,
+    maximize: bool,
+    differentiable: bool,
+    has_complex: bool,
+):
+    assert grad_scale is None and found_inf is None
+    for param, grad, state_sum, step_t in zip(params, grads, state_sums, state_steps):
+        # update step
+        step_t += 1
+        step = _get_value(step_t)
+        grad = grad if not maximize else -grad
+
+        if weight_decay != 0:
+            if grad.is_sparse:
+                raise RuntimeError(
+                    "weight_decay option is not compatible with sparse gradients"
+                )
+            grad = grad.add(param, alpha=weight_decay)
+
+        clr = lr / (1 + (step - 1) * lr_decay)
+
+        if grad.is_sparse:
+            grad = grad.coalesce()  # the update is non-linear so indices must be unique
+            grad_indices = grad._indices()
+            grad_values = grad._values()
+
+            state_sum.add_(_make_sparse(grad, grad_indices, grad_values.pow(2)))
+            std = state_sum.sparse_mask(grad)
+            std_values = std._values().sqrt_().add_(eps)
+            param.add_(
+                _make_sparse(grad, grad_indices, grad_values / std_values), alpha=-clr
+            )
+        else:
+            is_complex = torch.is_complex(param)
+            if is_complex:
+                grad = torch.view_as_real(grad)
+                state_sum = torch.view_as_real(state_sum)
+                param = torch.view_as_real(param)
+            state_sum.addcmul_(grad, grad, value=1)
+            if differentiable:
+                std = state_sum.sqrt() + eps
+            else:
+                std = state_sum.sqrt().add_(eps)
+            param.addcdiv_(grad, std, value=-clr)
+            if is_complex:
+                param = torch.view_as_complex(param)
+                state_sum = torch.view_as_complex(state_sum)
+
+
+def _multi_tensor_adagrad(
+    params: List[Tensor],
+    grads: List[Tensor],
+    state_sums: List[Tensor],
+    state_steps: List[Tensor],
+    grad_scale: Optional[Tensor],
+    found_inf: Optional[Tensor],
+    *,
+    lr: float,
+    weight_decay: float,
+    lr_decay: float,
+    eps: float,
+    has_sparse_grad: bool,
+    maximize: bool,
+    differentiable: bool,
+    has_complex: bool,
+):
+    assert not differentiable, "_foreach ops don't support autograd"
+    assert grad_scale is None and found_inf is None
+
+    # Foreach functions will throw errors if given empty lists
+    if len(params) == 0:
+        return
+
+    grouped_tensorlists = Optimizer._group_tensors_by_device_and_dtype(
+        [params, grads, state_sums, state_steps]  # type: ignore[list-item]
+    )
+    for (
+        device_params_,
+        device_grads_,
+        device_state_sums_,
+        device_state_steps_,
+    ), _ in grouped_tensorlists.values():
+        device_params = cast(List[Tensor], device_params_)
+        device_grads = cast(List[Tensor], device_grads_)
+        device_state_sums = cast(List[Tensor], device_state_sums_)
+        device_state_steps = cast(List[Tensor], device_state_steps_)
+
+        device_has_sparse_grad = has_sparse_grad and any(
+            grad.is_sparse for grad in device_grads
+        )
+
+        if device_has_sparse_grad:
+            _single_tensor_adagrad(
+                device_params,
+                device_grads,
+                device_state_sums,
+                device_state_steps,
+                lr=lr,
+                weight_decay=weight_decay,
+                lr_decay=lr_decay,
+                eps=eps,
+                has_sparse_grad=True,
+                maximize=maximize,
+                differentiable=differentiable,
+                has_complex=has_complex,
+                grad_scale=grad_scale,
+                found_inf=found_inf,
+            )
+            continue
+
+        # Handle complex parameters
+        if has_complex:
+            _view_as_real(device_params, device_grads, device_state_sums)
+
+        if maximize:
+            device_grads = torch._foreach_neg(device_grads)  # type: ignore[assignment]
+
+        # Update steps
+        # If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
+        # and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
+        # wrapped it once now. The alpha is required to assure we go to the right overload.
+        if not torch._utils.is_compiling() and device_state_steps[0].is_cpu:
+            torch._foreach_add_(
+                device_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
+            )
+        else:
+            torch._foreach_add_(device_state_steps, 1)
+
+        if weight_decay != 0:
+            # Re-use the intermediate memory (device_grads) already allocated for maximize
+            if maximize:
+                torch._foreach_add_(device_grads, device_params, alpha=weight_decay)
+            else:
+                device_grads = torch._foreach_add(  # type: ignore[assignment]
+                    device_grads, device_params, alpha=weight_decay
+                )
+
+        minus_clr = [
+            -lr / (1 + (_get_value(step) - 1) * lr_decay) for step in device_state_steps
+        ]
+
+        torch._foreach_addcmul_(device_state_sums, device_grads, device_grads, value=1)
+
+        std = torch._foreach_sqrt(device_state_sums)
+        torch._foreach_add_(std, eps)
+
+        if weight_decay != 0 or maximize:
+            # Again, re-use the intermediate memory (device_grads) already allocated
+            torch._foreach_mul_(device_grads, minus_clr)
+            numerator = device_grads
+        else:
+            numerator = torch._foreach_mul(device_grads, minus_clr)  # type: ignore[assignment]
+
+        torch._foreach_addcdiv_(device_params, numerator, std)
+
+
+def _fused_adagrad(
+    params: List[Tensor],
+    grads: List[Tensor],
+    state_sums: List[Tensor],
+    state_steps: List[Tensor],
+    grad_scale: Optional[Tensor],
+    found_inf: Optional[Tensor],
+    *,
+    lr: float,
+    weight_decay: float,
+    lr_decay: float,
+    eps: float,
+    has_sparse_grad: bool,
+    maximize: bool,
+    differentiable: bool,
+    has_complex: bool,
+) -> None:
+    if not params:
+        return
+    if has_sparse_grad or has_complex:
+        raise RuntimeError("`fused` does not support sparse grad or complex param")
+
+    if differentiable:
+        raise RuntimeError(
+            "adagrad with fused=True does not support differentiable=True"
+        )
+
+    grad_scale_dict = (
+        {grad_scale.device: grad_scale} if grad_scale is not None else None
+    )
+    found_inf_dict = {found_inf.device: found_inf} if found_inf is not None else None
+
+    grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
+        [params, grads, state_sums, state_steps]  # type: ignore[list-item]
+    )
+    for (device, _), (
+        (
+            device_params_,
+            device_grads_,
+            device_state_sums_,
+            device_state_steps_,
+        ),
+        _,
+    ) in grouped_tensors.items():
+        device_params = cast(List[Tensor], device_params_)
+        device_grads = cast(List[Tensor], device_grads_)
+        device_state_sums = cast(List[Tensor], device_state_sums_)
+        device_state_steps = cast(List[Tensor], device_state_steps_)
+
+        device_grad_scale, device_found_inf = None, None
+        if grad_scale is not None and grad_scale_dict is not None:
+            if device not in grad_scale_dict:
+                grad_scale_dict[device] = grad_scale.to(device, non_blocking=True)  # type: ignore[index]
+            device_grad_scale = grad_scale_dict[device]  # type: ignore[index]
+        if found_inf is not None and found_inf_dict is not None:
+            if found_inf not in found_inf_dict:
+                found_inf_dict[device] = found_inf.to(device, non_blocking=True)  # type: ignore[index]
+            device_found_inf = found_inf_dict[device]  # type: ignore[index]
+        torch._foreach_add_(device_state_steps, 1)
+        torch._fused_adagrad_(
+            device_params,
+            device_grads,
+            device_state_sums,
+            device_state_steps,
+            lr=lr,
+            lr_decay=lr_decay,
+            weight_decay=weight_decay,
+            eps=eps,
+            maximize=maximize,
+            grad_scale=device_grad_scale,
+            found_inf=device_found_inf,
+        )
+        if device_found_inf is not None:
+            torch._foreach_sub_(
+                device_state_steps, [device_found_inf] * len(device_state_steps)
+            )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/adam.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/adam.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf8c5809ea3c36eca80639b703d6794a9171498f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/adam.py
@@ -0,0 +1,803 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+from typing import cast, List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+
+from .optimizer import (
+    _capturable_doc,
+    _default_to_fused_or_foreach,
+    _device_dtype_check_for_fused,
+    _differentiable_doc,
+    _disable_dynamo_if_unsupported,
+    _foreach_doc,
+    _fused_doc,
+    _get_capturable_supported_devices,
+    _get_scalar_dtype,
+    _get_value,
+    _maximize_doc,
+    _stack_if_compiling,
+    _use_grad_for_differentiable,
+    _view_as_real,
+    DeviceDict,
+    Optimizer,
+    ParamsT,
+)
+
+
+__all__ = ["Adam", "adam"]
+
+
+class Adam(Optimizer):
+    def __init__(
+        self,
+        params: ParamsT,
+        lr: Union[float, Tensor] = 1e-3,
+        betas: Tuple[float, float] = (0.9, 0.999),
+        eps: float = 1e-8,
+        weight_decay: float = 0,
+        amsgrad: bool = False,
+        *,
+        foreach: Optional[bool] = None,
+        maximize: bool = False,
+        capturable: bool = False,
+        differentiable: bool = False,
+        fused: Optional[bool] = None,
+    ):
+        if isinstance(lr, Tensor):
+            if foreach and not capturable:
+                raise ValueError(
+                    "lr as a Tensor is not supported for capturable=False and foreach=True"
+                )
+            if lr.numel() != 1:
+                raise ValueError("Tensor lr must be 1-element")
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 <= eps:
+            raise ValueError(f"Invalid epsilon value: {eps}")
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
+        if not 0.0 <= weight_decay:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+
+        defaults = dict(
+            lr=lr,
+            betas=betas,
+            eps=eps,
+            weight_decay=weight_decay,
+            amsgrad=amsgrad,
+            maximize=maximize,
+            foreach=foreach,
+            capturable=capturable,
+            differentiable=differentiable,
+            fused=fused,
+        )
+        super().__init__(params, defaults)
+
+        if fused:
+            if differentiable:
+                raise RuntimeError("`fused` does not support `differentiable`")
+            self._step_supports_amp_scaling = True
+            # TODO(crcrpar): [low prec params & their higher prec copy]
+            # Support AMP with FP16/BF16 model params which would need
+            # higher prec copy of params to do update math in higher prec to
+            # alleviate the loss of information.
+            if foreach:
+                raise RuntimeError("`fused` and `foreach` cannot be `True` together.")
+
+    def __setstate__(self, state):
+        super().__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault("amsgrad", False)
+            group.setdefault("maximize", False)
+            group.setdefault("foreach", None)
+            group.setdefault("capturable", False)
+            group.setdefault("differentiable", False)
+            fused = group.setdefault("fused", None)
+            for p in group["params"]:
+                p_state = self.state.get(p, [])
+                if len(p_state) != 0 and not torch.is_tensor(p_state["step"]):
+                    step_val = float(p_state["step"])
+                    p_state["step"] = (
+                        torch.tensor(
+                            step_val,
+                            dtype=_get_scalar_dtype(is_fused=fused),
+                            device=p.device,
+                        )
+                        if group["capturable"] or group["fused"]
+                        else torch.tensor(step_val, dtype=_get_scalar_dtype())
+                    )
+
+    def _init_group(
+        self,
+        group,
+        params_with_grad,
+        grads,
+        exp_avgs,
+        exp_avg_sqs,
+        max_exp_avg_sqs,
+        state_steps,
+    ):
+        has_complex = False
+        for p in group["params"]:
+            if p.grad is not None:
+                has_complex |= torch.is_complex(p)
+                params_with_grad.append(p)
+                if p.grad.is_sparse:
+                    raise RuntimeError(
+                        "Adam does not support sparse gradients, please consider SparseAdam instead"
+                    )
+                grads.append(p.grad)
+
+                state = self.state[p]
+                # Lazy state initialization
+                if len(state) == 0:
+                    if group["fused"]:
+                        _device_dtype_check_for_fused(p)
+                    # note(crcrpar): [special device hosting for step]
+                    # Deliberately host `step` on CPU if both capturable and fused are off.
+                    # This is because kernel launches are costly on CUDA and XLA.
+                    state["step"] = (
+                        torch.zeros(
+                            (),
+                            dtype=_get_scalar_dtype(is_fused=group["fused"]),
+                            device=p.device,
+                        )
+                        if group["capturable"] or group["fused"]
+                        else torch.tensor(0.0, dtype=_get_scalar_dtype())
+                    )
+                    # Exponential moving average of gradient values
+                    state["exp_avg"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+                    # Exponential moving average of squared gradient values
+                    state["exp_avg_sq"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+                    if group["amsgrad"]:
+                        # Maintains max of all exp. moving avg. of sq. grad. values
+                        state["max_exp_avg_sq"] = torch.zeros_like(
+                            p, memory_format=torch.preserve_format
+                        )
+
+                exp_avgs.append(state["exp_avg"])
+                exp_avg_sqs.append(state["exp_avg_sq"])
+
+                if group["amsgrad"]:
+                    max_exp_avg_sqs.append(state["max_exp_avg_sq"])
+                if group["differentiable"] and state["step"].requires_grad:
+                    raise RuntimeError(
+                        "`requires_grad` is not supported for `step` in differentiable mode"
+                    )
+
+                # Foreach without capturable does not support a tensor lr
+                if (
+                    group["foreach"]
+                    and torch.is_tensor(group["lr"])
+                    and not group["capturable"]
+                ):
+                    raise RuntimeError(
+                        "lr as a Tensor is not supported for capturable=False and foreach=True"
+                    )
+
+                state_steps.append(state["step"])
+        return has_complex
+
+    @_use_grad_for_differentiable
+    def step(self, closure=None):
+        """Perform a single optimization step.
+
+        Args:
+            closure (Callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        self._cuda_graph_capture_health_check()
+
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            params_with_grad: List[Tensor] = []
+            grads: List[Tensor] = []
+            exp_avgs: List[Tensor] = []
+            exp_avg_sqs: List[Tensor] = []
+            max_exp_avg_sqs: List[Tensor] = []
+            state_steps: List[Tensor] = []
+            beta1, beta2 = group["betas"]
+
+            has_complex = self._init_group(
+                group,
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                max_exp_avg_sqs,
+                state_steps,
+            )
+
+            adam(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                max_exp_avg_sqs,
+                state_steps,
+                amsgrad=group["amsgrad"],
+                has_complex=has_complex,
+                beta1=beta1,
+                beta2=beta2,
+                lr=group["lr"],
+                weight_decay=group["weight_decay"],
+                eps=group["eps"],
+                maximize=group["maximize"],
+                foreach=group["foreach"],
+                capturable=group["capturable"],
+                differentiable=group["differentiable"],
+                fused=group["fused"],
+                grad_scale=getattr(self, "grad_scale", None),
+                found_inf=getattr(self, "found_inf", None),
+            )
+
+        return loss
+
+
+Adam.__doc__ = (
+    r"""Implements Adam algorithm.
+
+    .. math::
+       \begin{aligned}
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{input}      : \gamma \text{ (lr)}, \beta_1, \beta_2
+                \text{ (betas)},\theta_0 \text{ (params)},f(\theta) \text{ (objective)}          \\
+            &\hspace{13mm}      \lambda \text{ (weight decay)},  \: \textit{amsgrad},
+                \:\textit{maximize}                                                              \\
+            &\textbf{initialize} :  m_0 \leftarrow 0 \text{ ( first moment)},
+                v_0\leftarrow 0 \text{ (second moment)},\: \widehat{v_0}^{max}\leftarrow 0\\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do}                         \\
+
+            &\hspace{5mm}\textbf{if} \: \textit{maximize}:                                       \\
+            &\hspace{10mm}g_t           \leftarrow   -\nabla_{\theta} f_t (\theta_{t-1})         \\
+            &\hspace{5mm}\textbf{else}                                                           \\
+            &\hspace{10mm}g_t           \leftarrow   \nabla_{\theta} f_t (\theta_{t-1})          \\
+            &\hspace{5mm}\textbf{if} \: \lambda \neq 0                                           \\
+            &\hspace{10mm} g_t \leftarrow g_t + \lambda  \theta_{t-1}                            \\
+            &\hspace{5mm}m_t           \leftarrow   \beta_1 m_{t-1} + (1 - \beta_1) g_t          \\
+            &\hspace{5mm}v_t           \leftarrow   \beta_2 v_{t-1} + (1-\beta_2) g^2_t          \\
+            &\hspace{5mm}\widehat{m_t} \leftarrow   m_t/\big(1-\beta_1^t \big)                   \\
+            &\hspace{5mm}\widehat{v_t} \leftarrow   v_t/\big(1-\beta_2^t \big)                   \\
+            &\hspace{5mm}\textbf{if} \: amsgrad                                                  \\
+            &\hspace{10mm}\widehat{v_t}^{max} \leftarrow \mathrm{max}(\widehat{v_t}^{max},
+                \widehat{v_t})                                                                   \\
+            &\hspace{10mm}\theta_t \leftarrow \theta_{t-1} - \gamma \widehat{m_t}/
+                \big(\sqrt{\widehat{v_t}^{max}} + \epsilon \big)                                 \\
+            &\hspace{5mm}\textbf{else}                                                           \\
+            &\hspace{10mm}\theta_t \leftarrow \theta_{t-1} - \gamma \widehat{m_t}/
+                \big(\sqrt{\widehat{v_t}} + \epsilon \big)                                       \\
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+            &\bf{return} \:  \theta_t                                                     \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+       \end{aligned}
+
+    For further details regarding the algorithm we refer to `Adam: A Method for Stochastic Optimization`_.
+    """
+    + rf"""
+    Args:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, Tensor, optional): learning rate (default: 1e-3). A tensor LR
+            is not yet supported for all our implementations. Please use a float
+            LR if you are not also specifying fused=True or capturable=True.
+        betas (Tuple[float, float], optional): coefficients used for computing
+            running averages of gradient and its square (default: (0.9, 0.999))
+        eps (float, optional): term added to the denominator to improve
+            numerical stability (default: 1e-8)
+        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+        amsgrad (bool, optional): whether to use the AMSGrad variant of this
+            algorithm from the paper `On the Convergence of Adam and Beyond`_
+            (default: False)
+        {_foreach_doc}
+        {_maximize_doc}
+        {_capturable_doc}
+        {_differentiable_doc}
+        {_fused_doc}
+    .. Note::
+        A prototype implementation of Adam and AdamW for MPS supports `torch.float32` and `torch.float16`.
+    .. _Adam\: A Method for Stochastic Optimization:
+        https://arxiv.org/abs/1412.6980
+    .. _On the Convergence of Adam and Beyond:
+        https://openreview.net/forum?id=ryQu7f-RZ
+
+    """
+)
+
+
+def _single_tensor_adam(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    max_exp_avg_sqs: List[Tensor],
+    state_steps: List[Tensor],
+    grad_scale: Optional[Tensor],
+    found_inf: Optional[Tensor],
+    *,
+    amsgrad: bool,
+    has_complex: bool,
+    beta1: float,
+    beta2: float,
+    lr: Union[float, Tensor],
+    weight_decay: float,
+    eps: float,
+    maximize: bool,
+    capturable: bool,
+    differentiable: bool,
+):
+    assert grad_scale is None and found_inf is None
+
+    if torch.jit.is_scripting():
+        # this assert is due to JIT being dumb and not realizing that the ops below
+        # have overloads to handle both float and Tensor lrs, so we just assert it's
+        # a float since most people using JIT are using floats
+        assert isinstance(lr, float)
+
+    for i, param in enumerate(params):
+        grad = grads[i] if not maximize else -grads[i]
+        exp_avg = exp_avgs[i]
+        exp_avg_sq = exp_avg_sqs[i]
+        step_t = state_steps[i]
+
+        # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+        if not torch._utils.is_compiling() and capturable:
+            capturable_supported_devices = _get_capturable_supported_devices()
+            assert (
+                param.device.type == step_t.device.type
+                and param.device.type in capturable_supported_devices
+            ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
+
+        # update step
+        step_t += 1
+
+        if weight_decay != 0:
+            grad = grad.add(param, alpha=weight_decay)
+
+        if torch.is_complex(param):
+            grad = torch.view_as_real(grad)
+            exp_avg = torch.view_as_real(exp_avg)
+            exp_avg_sq = torch.view_as_real(exp_avg_sq)
+            if amsgrad:
+                max_exp_avg_sqs[i] = torch.view_as_real(max_exp_avg_sqs[i])
+            param = torch.view_as_real(param)
+
+        # Decay the first and second moment running average coefficient
+        exp_avg.lerp_(grad, 1 - beta1)
+        exp_avg_sq.mul_(beta2).addcmul_(grad, grad.conj(), value=1 - beta2)
+
+        if capturable or differentiable:
+            step = step_t
+
+            bias_correction1 = 1 - beta1**step
+            bias_correction2 = 1 - beta2**step
+
+            step_size = lr / bias_correction1
+            step_size_neg = step_size.neg()
+
+            bias_correction2_sqrt = bias_correction2.sqrt()
+
+            if amsgrad:
+                # Maintains the maximum of all 2nd moment running avg. till now
+                if differentiable:
+                    max_exp_avg_sq = max_exp_avg_sqs[i].clone()
+                else:
+                    max_exp_avg_sq = max_exp_avg_sqs[i]
+
+                max_exp_avg_sqs[i].copy_(torch.maximum(max_exp_avg_sq, exp_avg_sq))
+
+                # Uses the max. for normalizing running avg. of gradient
+                # Folds in (admittedly ugly) 1-elem step_size math here to avoid extra param-set-sized read+write
+                # (can't fold it into addcdiv_ below because addcdiv_ requires value is a Number, not a Tensor)
+                denom = (
+                    max_exp_avg_sqs[i].sqrt() / (bias_correction2_sqrt * step_size_neg)
+                ).add_(eps / step_size_neg)
+            else:
+                denom = (
+                    exp_avg_sq.sqrt() / (bias_correction2_sqrt * step_size_neg)
+                ).add_(eps / step_size_neg)
+
+            param.addcdiv_(exp_avg, denom)
+        else:
+            step = _get_value(step_t)
+
+            bias_correction1 = 1 - beta1**step
+            bias_correction2 = 1 - beta2**step
+
+            step_size = lr / bias_correction1
+
+            bias_correction2_sqrt = bias_correction2**0.5
+
+            if amsgrad:
+                # Maintains the maximum of all 2nd moment running avg. till now
+                torch.maximum(max_exp_avg_sqs[i], exp_avg_sq, out=max_exp_avg_sqs[i])
+
+                # Use the max. for normalizing running avg. of gradient
+                denom = (max_exp_avg_sqs[i].sqrt() / bias_correction2_sqrt).add_(eps)
+            else:
+                denom = (exp_avg_sq.sqrt() / bias_correction2_sqrt).add_(eps)
+
+            param.addcdiv_(exp_avg, denom, value=-step_size)
+
+        # Lastly, switch back to complex view
+        if amsgrad and torch.is_complex(params[i]):
+            max_exp_avg_sqs[i] = torch.view_as_complex(max_exp_avg_sqs[i])
+
+
+def _multi_tensor_adam(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    max_exp_avg_sqs: List[Tensor],
+    state_steps: List[Tensor],
+    grad_scale: Optional[Tensor],
+    found_inf: Optional[Tensor],
+    *,
+    amsgrad: bool,
+    has_complex: bool,
+    beta1: float,
+    beta2: float,
+    lr: Union[float, Tensor],
+    weight_decay: float,
+    eps: float,
+    maximize: bool,
+    capturable: bool,
+    differentiable: bool,
+):
+    if len(params) == 0:
+        return
+
+    if isinstance(lr, Tensor) and not capturable:
+        raise RuntimeError(
+            "lr as a Tensor is not supported for capturable=False and foreach=True"
+        )
+
+    # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+    if not torch._utils.is_compiling() and capturable:
+        capturable_supported_devices = _get_capturable_supported_devices(
+            supports_xla=False
+        )
+        assert all(
+            p.device.type == step.device.type
+            and p.device.type in capturable_supported_devices
+            for p, step in zip(params, state_steps)
+        ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
+
+    assert grad_scale is None and found_inf is None
+
+    assert not differentiable, "_foreach ops don't support autograd"
+
+    grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
+        [params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps]  # type: ignore[list-item]
+    )
+    for (
+        device_params_,
+        device_grads_,
+        device_exp_avgs_,
+        device_exp_avg_sqs_,
+        device_max_exp_avg_sqs_,
+        device_state_steps_,
+    ), _ in grouped_tensors.values():
+        device_params = cast(List[Tensor], device_params_)
+        device_grads = cast(List[Tensor], device_grads_)
+        device_exp_avgs = cast(List[Tensor], device_exp_avgs_)
+        device_exp_avg_sqs = cast(List[Tensor], device_exp_avg_sqs_)
+        device_state_steps = cast(List[Tensor], device_state_steps_)
+
+        # Handle complex parameters
+        if has_complex:
+            if amsgrad:
+                device_max_exp_avg_sqs = cast(List[Tensor], device_max_exp_avg_sqs_)
+                _view_as_real(
+                    device_params,
+                    device_grads,
+                    device_exp_avgs,
+                    device_exp_avg_sqs,
+                    device_max_exp_avg_sqs,
+                )
+            else:
+                _view_as_real(
+                    device_params, device_grads, device_exp_avgs, device_exp_avg_sqs
+                )
+
+        if maximize:
+            device_grads = torch._foreach_neg(device_grads)  # type: ignore[assignment]
+
+        # Update steps
+        # If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
+        # and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
+        # wrapped it once now. The alpha is required to assure we go to the right overload.
+        if not torch._utils.is_compiling() and device_state_steps[0].is_cpu:
+            torch._foreach_add_(
+                device_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
+            )
+        else:
+            torch._foreach_add_(device_state_steps, 1)
+
+        if weight_decay != 0:
+            # Re-use the intermediate memory (device_grads) already allocated for maximize
+            if maximize:
+                torch._foreach_add_(device_grads, device_params, alpha=weight_decay)
+            else:
+                device_grads = torch._foreach_add(  # type: ignore[assignment]
+                    device_grads, device_params, alpha=weight_decay
+                )
+
+        # Decay the first and second moment running average coefficient
+        torch._foreach_lerp_(device_exp_avgs, device_grads, 1 - beta1)
+
+        torch._foreach_mul_(device_exp_avg_sqs, beta2)
+        torch._foreach_addcmul_(
+            device_exp_avg_sqs, device_grads, device_grads, 1 - beta2
+        )
+
+        # Delete the local intermediate since it won't be used anymore to save on peak memory
+        del device_grads
+
+        bias_correction1: Union[Tuple[Tensor, ...], List[Tensor]]
+        bias_correction2: Union[Tuple[Tensor, ...], List[Tensor]]
+        bias_correction2_sqrt: Union[Tuple[Tensor, ...], List[Tensor]]
+
+        if capturable:
+            bias_correction1 = torch._foreach_pow(beta1, device_state_steps)
+            bias_correction2 = torch._foreach_pow(beta2, device_state_steps)
+            # foreach_sub doesn't allow a scalar as the first arg
+            torch._foreach_sub_(bias_correction1, 1)
+            torch._foreach_sub_(bias_correction2, 1)
+            # we do not negate bias_correction1 as it'll need to be negated later anyway
+            torch._foreach_neg_(bias_correction2)
+
+            # foreach_div doesn't allow a scalar as the first arg
+            torch._foreach_div_(bias_correction1, lr)
+            torch._foreach_reciprocal_(bias_correction1)
+
+            torch._foreach_sqrt_(bias_correction2)
+
+            # Re-assign for clarity as we maintain minimal intermediates: we'll have
+            # step_size = - lr / (1 - beta1 ^ t) where t = num_steps
+            # bias_correction2_sqrt = sqrt(1 - beta2 ^ t)
+            step_size = bias_correction1
+            bias_correction2_sqrt = bias_correction2
+
+            if amsgrad:
+                device_max_exp_avg_sqs = cast(List[Tensor], device_max_exp_avg_sqs_)
+                # Maintains the maximum of all 2nd moment running avg. till now
+                torch._foreach_maximum_(device_max_exp_avg_sqs, device_exp_avg_sqs)  # type: ignore[assignment]
+
+                # Set intermediate to the max. for normalizing running avg. of gradient when amsgrad
+                exp_avg_sq_sqrt = torch._foreach_sqrt(device_max_exp_avg_sqs)
+            else:
+                exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs)
+
+            torch._foreach_div_(exp_avg_sq_sqrt, bias_correction2_sqrt)
+            torch._foreach_add_(exp_avg_sq_sqrt, eps)
+            torch._foreach_div_(exp_avg_sq_sqrt, step_size)
+
+            # at this point, exp_avg_sq_sqrt = - (1 - beta^t) * [sqrt(exp_avg_sq / (1 - beta2^t)) + eps] / lr
+            torch._foreach_addcdiv_(device_params, device_exp_avgs, exp_avg_sq_sqrt)
+        else:
+            bias_correction1 = [
+                1 - beta1 ** _get_value(step) for step in device_state_steps
+            ]
+            bias_correction2 = [
+                1 - beta2 ** _get_value(step) for step in device_state_steps
+            ]
+
+            step_size = _stack_if_compiling([(lr / bc) * -1 for bc in bias_correction1])
+
+            bias_correction2_sqrt = [bc**0.5 for bc in bias_correction2]  # type: ignore[arg-type]
+
+            if amsgrad:
+                device_max_exp_avg_sqs = cast(List[Tensor], device_max_exp_avg_sqs_)
+                # Maintains the maximum of all 2nd moment running avg. till now
+                torch._foreach_maximum_(device_max_exp_avg_sqs, device_exp_avg_sqs)
+
+                # Use the max. for normalizing running avg. of gradient
+                exp_avg_sq_sqrt = torch._foreach_sqrt(device_max_exp_avg_sqs)
+            else:
+                exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs)
+
+            torch._foreach_div_(exp_avg_sq_sqrt, bias_correction2_sqrt)
+            torch._foreach_add_(exp_avg_sq_sqrt, eps)
+            torch._foreach_addcdiv_(
+                device_params, device_exp_avgs, exp_avg_sq_sqrt, step_size  # type: ignore[arg-type]
+            )
+
+
+def _fused_adam(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    max_exp_avg_sqs: List[Tensor],
+    state_steps: List[Tensor],
+    grad_scale: Optional[Tensor],
+    found_inf: Optional[Tensor],
+    *,
+    amsgrad: bool,
+    has_complex: bool,  # Needed for consistency.
+    beta1: float,
+    beta2: float,
+    lr: Union[float, Tensor],
+    weight_decay: float,
+    eps: float,
+    maximize: bool,
+    capturable: bool,  # Needed for consistency.
+    differentiable: bool,
+) -> None:
+    if not params:
+        return
+    if differentiable:
+        raise RuntimeError("Adam with fused=True does not support differentiable=True")
+
+    grad_scale_dict: DeviceDict = (
+        {grad_scale.device: grad_scale} if grad_scale is not None else {}
+    )
+    found_inf_dict: DeviceDict = (
+        {found_inf.device: found_inf} if found_inf is not None else {}
+    )
+
+    # We only shuffle around the lr when it is a Tensor and on CUDA, otherwise, we prefer
+    # treating it as a scalar.
+    lr_dict: Optional[DeviceDict] = (
+        {lr.device: lr} if isinstance(lr, Tensor) and str(lr.device) != "cpu" else None
+    )
+    grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
+        [params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps]  # type: ignore[list-item]
+    )
+    for (device, _), (
+        (
+            device_params_,
+            device_grads_,
+            device_exp_avgs_,
+            device_exp_avg_sqs_,
+            device_max_exp_avg_sqs,
+            device_state_steps_,
+        ),
+        _,
+    ) in grouped_tensors.items():
+        device_params = cast(List[Tensor], device_params_)
+        device_grads = cast(List[Tensor], device_grads_)
+        device_exp_avgs = cast(List[Tensor], device_exp_avgs_)
+        device_exp_avg_sqs = cast(List[Tensor], device_exp_avg_sqs_)
+        device_state_steps = cast(List[Tensor], device_state_steps_)
+
+        if device.type == "mps":  # type: ignore[union-attr]
+            assert found_inf is None and grad_scale is None
+
+        device_grad_scale, device_found_inf = None, None
+        if grad_scale is not None:
+            device_grad_scale = grad_scale_dict.setdefault(
+                device, grad_scale.to(device, non_blocking=True)
+            )
+        if found_inf is not None:
+            device_found_inf = found_inf_dict.setdefault(
+                device, found_inf.to(device, non_blocking=True)
+            )
+        if lr_dict is not None and device not in lr_dict:
+            lr_dict[device] = lr.to(device=device, non_blocking=True)  # type: ignore[union-attr]
+            lr = lr_dict[device]
+        torch._foreach_add_(device_state_steps, 1)
+        torch._fused_adam_(
+            device_params,
+            device_grads,
+            device_exp_avgs,
+            device_exp_avg_sqs,
+            device_max_exp_avg_sqs,  # type: ignore[arg-type]
+            device_state_steps,
+            amsgrad=amsgrad,
+            lr=lr,  # type: ignore[arg-type]
+            beta1=beta1,
+            beta2=beta2,
+            weight_decay=weight_decay,
+            eps=eps,
+            maximize=maximize,
+            grad_scale=device_grad_scale,
+            found_inf=device_found_inf,
+        )
+        if device_found_inf is not None:
+            torch._foreach_sub_(
+                device_state_steps, [device_found_inf] * len(device_state_steps)
+            )
+
+
+@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_adam)
+def adam(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    max_exp_avg_sqs: List[Tensor],
+    state_steps: List[Tensor],
+    # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
+    # setting this as kwarg for now as functional API is compiled by torch/distributed/optim
+    foreach: Optional[bool] = None,
+    capturable: bool = False,
+    differentiable: bool = False,
+    fused: Optional[bool] = None,
+    grad_scale: Optional[Tensor] = None,
+    found_inf: Optional[Tensor] = None,
+    has_complex: bool = False,
+    *,
+    amsgrad: bool,
+    beta1: float,
+    beta2: float,
+    lr: Union[float, Tensor],
+    weight_decay: float,
+    eps: float,
+    maximize: bool,
+):
+    r"""Functional API that performs Adam algorithm computation.
+
+    See :class:`~torch.optim.Adam` for details.
+    """
+    # Respect when the user inputs False/True for foreach or fused. We only want to change
+    # the default when neither have been user-specified. Note that we default to foreach
+    # and pass False to use_fused. This is not a mistake--we want to give the fused impl
+    # bake-in time before making it the default, even if it is typically faster.
+    if fused is None and foreach is None:
+        _, foreach = _default_to_fused_or_foreach(
+            params, differentiable, use_fused=False
+        )
+        # Do not flip on foreach for the unsupported case where lr is a Tensor and capturable=False.
+        if foreach and isinstance(lr, Tensor) and not capturable:
+            foreach = False
+    if fused is None:
+        fused = False
+    if foreach is None:
+        foreach = False
+
+    # this check is slow during compilation, so we skip it
+    # if it's strictly needed we can add this check back in dynamo
+    if not torch._utils.is_compiling() and not all(
+        isinstance(t, torch.Tensor) for t in state_steps
+    ):
+        raise RuntimeError(
+            "API has changed, `state_steps` argument must contain a list of singleton tensors"
+        )
+
+    if foreach and torch.jit.is_scripting():
+        raise RuntimeError("torch.jit.script not supported with foreach optimizers")
+    if fused and torch.jit.is_scripting():
+        raise RuntimeError("torch.jit.script not supported with fused optimizers")
+
+    if fused and not torch.jit.is_scripting():
+        func = _fused_adam
+    elif foreach and not torch.jit.is_scripting():
+        func = _multi_tensor_adam
+    else:
+        func = _single_tensor_adam
+
+    func(
+        params,
+        grads,
+        exp_avgs,
+        exp_avg_sqs,
+        max_exp_avg_sqs,
+        state_steps,
+        amsgrad=amsgrad,
+        has_complex=has_complex,
+        beta1=beta1,
+        beta2=beta2,
+        lr=lr,
+        weight_decay=weight_decay,
+        eps=eps,
+        maximize=maximize,
+        capturable=capturable,
+        differentiable=differentiable,
+        grad_scale=grad_scale,
+        found_inf=found_inf,
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/adamax.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/adamax.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1c80a2ae3dca957eacc010f80380554735de873
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/adamax.py
@@ -0,0 +1,473 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+from typing import cast, List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+
+from .optimizer import (
+    _capturable_doc,
+    _default_to_fused_or_foreach,
+    _differentiable_doc,
+    _disable_dynamo_if_unsupported,
+    _foreach_doc,
+    _get_capturable_supported_devices,
+    _get_scalar_dtype,
+    _get_value,
+    _maximize_doc,
+    _use_grad_for_differentiable,
+    _view_as_real,
+    Optimizer,
+    ParamsT,
+)
+
+
+__all__ = ["Adamax", "adamax"]
+
+
+class Adamax(Optimizer):
+    def __init__(
+        self,
+        params: ParamsT,
+        lr: Union[float, Tensor] = 2e-3,
+        betas: Tuple[float, float] = (0.9, 0.999),
+        eps: float = 1e-8,
+        weight_decay: float = 0,
+        foreach: Optional[bool] = None,
+        *,
+        maximize: bool = False,
+        differentiable: bool = False,
+        capturable: bool = False,
+    ):
+        if isinstance(lr, Tensor) and lr.numel() != 1:
+            raise ValueError("Tensor lr must be 1-element")
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 <= eps:
+            raise ValueError(f"Invalid epsilon value: {eps}")
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
+        if not 0.0 <= weight_decay:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+
+        defaults = dict(
+            lr=lr,
+            betas=betas,
+            eps=eps,
+            weight_decay=weight_decay,
+            foreach=foreach,
+            maximize=maximize,
+            differentiable=differentiable,
+            capturable=capturable,
+        )
+        super().__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super().__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault("foreach", None)
+            group.setdefault("maximize", False)
+            group.setdefault("differentiable", False)
+            group.setdefault("capturable", False)
+            for p in group["params"]:
+                p_state = self.state.get(p, [])
+                if len(p_state) != 0 and not torch.is_tensor(p_state["step"]):
+                    step_val = float(p_state["step"])
+                    p_state["step"] = (
+                        torch.tensor(
+                            step_val, dtype=_get_scalar_dtype(), device=p.device
+                        )
+                        if group["capturable"]
+                        else torch.tensor(step_val, dtype=_get_scalar_dtype())
+                    )
+
+    def _init_group(
+        self, group, params_with_grad, grads, exp_avgs, exp_infs, state_steps
+    ):
+        has_complex = False
+        for p in group["params"]:
+            if p.grad is None:
+                continue
+            has_complex |= torch.is_complex(p)
+            params_with_grad.append(p)
+            if p.grad.is_sparse:
+                raise RuntimeError("Adamax does not support sparse gradients")
+            grads.append(p.grad)
+
+            state = self.state[p]
+
+            # State initialization
+            if len(state) == 0:
+                state["step"] = (
+                    torch.zeros((), dtype=_get_scalar_dtype(), device=p.device)
+                    if group["capturable"]
+                    else torch.tensor(0.0, dtype=_get_scalar_dtype())
+                )
+                state["exp_avg"] = torch.zeros_like(
+                    p, memory_format=torch.preserve_format
+                )
+                state["exp_inf"] = torch.zeros_like(
+                    p, memory_format=torch.preserve_format
+                )
+
+            exp_avgs.append(state["exp_avg"])
+            exp_infs.append(state["exp_inf"])
+            state_steps.append(state["step"])
+
+        return has_complex
+
+    @_use_grad_for_differentiable
+    def step(self, closure=None):
+        """Performs a single optimization step.
+
+        Args:
+            closure (Callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        self._cuda_graph_capture_health_check()
+
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            params_with_grad: List[Tensor] = []
+            grads: List[Tensor] = []
+            exp_avgs: List[Tensor] = []
+            exp_infs: List[Tensor] = []
+            state_steps: List[Tensor] = []
+
+            beta1, beta2 = group["betas"]
+            eps = group["eps"]
+            lr = group["lr"]
+            weight_decay = group["weight_decay"]
+            foreach = group["foreach"]
+            maximize = group["maximize"]
+            differentiable = group["differentiable"]
+            capturable = group["capturable"]
+
+            has_complex = self._init_group(
+                group, params_with_grad, grads, exp_avgs, exp_infs, state_steps
+            )
+
+            adamax(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_infs,
+                state_steps,
+                eps=eps,
+                beta1=beta1,
+                beta2=beta2,
+                lr=lr,
+                weight_decay=weight_decay,
+                foreach=foreach,
+                maximize=maximize,
+                differentiable=differentiable,
+                capturable=capturable,
+                has_complex=has_complex,
+            )
+
+        return loss
+
+
+Adamax.__doc__ = (
+    r"""Implements Adamax algorithm (a variant of Adam based on infinity norm).
+
+    .. math::
+       \begin{aligned}
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{input}      : \gamma \text{ (lr)}, \beta_1, \beta_2
+                \text{ (betas)},\theta_0 \text{ (params)},f(\theta) \text{ (objective)},
+                \: \lambda \text{ (weight decay)},                                                \\
+            &\hspace{13mm}    \epsilon \text{ (epsilon)}                                          \\
+            &\textbf{initialize} :  m_0 \leftarrow 0 \text{ ( first moment)},
+                u_0 \leftarrow 0 \text{ ( infinity norm)}                                 \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do}                         \\
+            &\hspace{5mm}g_t           \leftarrow   \nabla_{\theta} f_t (\theta_{t-1})           \\
+            &\hspace{5mm}if \: \lambda \neq 0                                                    \\
+            &\hspace{10mm} g_t \leftarrow g_t + \lambda  \theta_{t-1}                            \\
+            &\hspace{5mm}m_t      \leftarrow   \beta_1 m_{t-1} + (1 - \beta_1) g_t               \\
+            &\hspace{5mm}u_t      \leftarrow   \mathrm{max}(\beta_2 u_{t-1}, |g_{t}|+\epsilon)   \\
+            &\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \frac{\gamma m_t}{(1-\beta^t_1) u_t} \\
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+            &\bf{return} \:  \theta_t                                                     \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+       \end{aligned}
+
+    For further details regarding the algorithm we refer to `Adam: A Method for Stochastic Optimization`_.
+    """
+    + rf"""
+    Args:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, Tensor, optional): learning rate (default: 2e-3)
+        betas (Tuple[float, float], optional): coefficients used for computing
+            running averages of gradient and its square
+        eps (float, optional): term added to the denominator to improve
+            numerical stability (default: 1e-8)
+        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+        {_foreach_doc}
+        {_maximize_doc}
+        {_differentiable_doc}
+        {_capturable_doc}
+
+    .. _Adam\: A Method for Stochastic Optimization:
+        https://arxiv.org/abs/1412.6980
+
+    """
+)
+
+
+def _single_tensor_adamax(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_infs: List[Tensor],
+    state_steps: List[Tensor],
+    *,
+    eps: float,
+    beta1: float,
+    beta2: float,
+    lr: float,
+    weight_decay: float,
+    maximize: bool,
+    differentiable: bool,
+    capturable: bool,
+    has_complex: bool,
+):
+    for i, param in enumerate(params):
+        grad = grads[i]
+        grad = grad if not maximize else -grad
+        exp_avg = exp_avgs[i]
+        exp_inf = exp_infs[i]
+        step_t = state_steps[i]
+
+        # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+        if not torch._utils.is_compiling() and capturable:
+            capturable_supported_devices = _get_capturable_supported_devices()
+            assert (
+                param.device.type == step_t.device.type
+                and param.device.type in capturable_supported_devices
+            ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
+
+        # update step
+        step_t += 1
+
+        if weight_decay != 0:
+            grad = grad.add(param, alpha=weight_decay)
+
+        if torch.is_complex(param):
+            param = torch.view_as_real(param)
+            grad = torch.view_as_real(grad)
+            exp_avg = torch.view_as_real(exp_avg)
+            exp_inf = torch.view_as_real(exp_inf)
+
+        # Update biased first moment estimate.
+        exp_avg.lerp_(grad, 1 - beta1)
+        # Update the exponentially weighted infinity norm.
+        if not differentiable:
+            torch.maximum(
+                exp_inf.mul_(beta2),
+                grad.abs().add_(eps),
+                out=exp_inf,
+            )
+        else:
+            norm_buf = torch.cat(
+                [exp_inf.mul_(beta2).unsqueeze(0), grad.abs().add_(eps).unsqueeze_(0)],
+                0,
+            )
+            exp_inf.copy_(torch.amax(norm_buf, 0, keepdim=False))
+
+        if capturable:
+            # why jump through extra hoops and negate bias_correction? check out #121238
+            # once fixed, we should use bias_correction with addcdiv value=-1 for readability
+            neg_bias_correction = beta1**step_t - 1
+            neg_bias_correction.div_(lr)
+            denom = exp_inf * neg_bias_correction
+            param.addcdiv_(exp_avg, denom)
+        else:
+            bias_correction = 1 - beta1 ** _get_value(step_t)
+            clr = lr / bias_correction
+
+            param.addcdiv_(exp_avg, exp_inf, value=-clr)
+
+
+def _multi_tensor_adamax(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_infs: List[Tensor],
+    state_steps: List[Tensor],
+    *,
+    eps: float,
+    beta1: float,
+    beta2: float,
+    lr: float,
+    weight_decay: float,
+    maximize: bool,
+    differentiable: bool,
+    capturable: bool,
+    has_complex: bool,
+):
+    assert not differentiable, "_foreach ops don't support autograd"
+
+    if len(params) == 0:
+        return
+
+    # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+    if not torch._utils.is_compiling() and capturable:
+        capturable_supported_devices = _get_capturable_supported_devices(
+            supports_xla=False
+        )
+        assert all(
+            p.device.type == step.device.type
+            and p.device.type in capturable_supported_devices
+            for p, step in zip(params, state_steps)
+        ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
+
+    grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
+        [params, grads, exp_avgs, exp_infs, state_steps]  # type: ignore[list-item]
+    )
+    for (
+        grouped_params_,
+        grouped_grads_,
+        grouped_exp_avgs_,
+        grouped_exp_infs_,
+        grouped_state_steps_,
+    ), _ in grouped_tensors.values():
+        grouped_params = cast(List[Tensor], grouped_params_)
+        grouped_grads = cast(List[Tensor], grouped_grads_)
+        grouped_exp_avgs = cast(List[Tensor], grouped_exp_avgs_)
+        grouped_exp_infs = cast(List[Tensor], grouped_exp_infs_)
+        grouped_state_steps = cast(List[Tensor], grouped_state_steps_)
+
+        if has_complex:
+            _view_as_real(
+                grouped_params, grouped_grads, grouped_exp_avgs, grouped_exp_infs
+            )
+
+        if maximize:
+            grouped_grads = torch._foreach_neg(grouped_grads)  # type: ignore[assignment]
+
+        # Update steps
+        # If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
+        # and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
+        # wrapped it once now. The alpha is required to assure we go to the right overload.
+        if not torch._utils.is_compiling() and grouped_state_steps[0].is_cpu:
+            torch._foreach_add_(
+                grouped_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
+            )
+        else:
+            torch._foreach_add_(grouped_state_steps, 1)
+
+        if weight_decay != 0:
+            if maximize:
+                # Re-use the intermediate memory (grouped_grads) already allocated for maximize
+                torch._foreach_add_(grouped_grads, grouped_params, alpha=weight_decay)
+            else:
+                grouped_grads = torch._foreach_add(  # type: ignore[assignment]
+                    grouped_grads, grouped_params, alpha=weight_decay
+                )
+
+        # Update biased first moment estimate.
+        torch._foreach_lerp_(grouped_exp_avgs, grouped_grads, 1 - beta1)
+
+        # Update the exponentially weighted infinity norm.
+        torch._foreach_mul_(grouped_exp_infs, beta2)
+
+        # in this case, we need to introduce a copy of the grads
+        # since one has not been introduced previously
+        if not maximize and weight_decay == 0:
+            grouped_grads = torch._foreach_abs(grouped_grads)  # type: ignore[assignment]
+        else:
+            torch._foreach_abs_(grouped_grads)
+
+        torch._foreach_add_(grouped_grads, eps)
+        torch._foreach_maximum_(grouped_exp_infs, grouped_grads)
+
+        bias_corrections: Union[Tuple[Tensor, ...], List[Tensor]]
+        if capturable:
+            bias_corrections = torch._foreach_pow(beta1, grouped_state_steps)
+            # foreach_sub doesn't allow a scalar as the first arg
+            torch._foreach_sub_(bias_corrections, 1)
+            torch._foreach_div_(bias_corrections, lr)
+
+            denom = torch._foreach_mul(grouped_exp_infs, bias_corrections)
+            torch._foreach_addcdiv_(grouped_params, grouped_exp_avgs, denom)
+        else:
+            bias_corrections = [
+                1 - beta1 ** _get_value(step) for step in grouped_state_steps
+            ]
+            step_size = [(_get_value(lr) / bc) * -1 for bc in bias_corrections]
+            torch._foreach_addcdiv_(
+                grouped_params, grouped_exp_avgs, grouped_exp_infs, step_size
+            )
+
+
+@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_adamax)
+def adamax(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_infs: List[Tensor],
+    state_steps: List[Tensor],
+    # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
+    # setting this as kwarg for now as functional API is compiled by torch/distributed/optim
+    foreach: Optional[bool] = None,
+    maximize: bool = False,
+    differentiable: bool = False,
+    capturable: bool = False,
+    has_complex: bool = False,
+    *,
+    eps: float,
+    beta1: float,
+    beta2: float,
+    lr: float,
+    weight_decay: float,
+):
+    r"""Functional API that performs adamax algorithm computation.
+
+    See :class:`~torch.optim.Adamax` for details.
+    """
+
+    if not torch._utils.is_compiling() and not all(
+        isinstance(t, torch.Tensor) for t in state_steps
+    ):
+        raise RuntimeError(
+            "API has changed, `state_steps` argument must contain a list of singleton tensors"
+        )
+
+    if foreach is None:
+        _, foreach = _default_to_fused_or_foreach(
+            params, differentiable, use_fused=False
+        )
+
+    if foreach and torch.jit.is_scripting():
+        raise RuntimeError("torch.jit.script not supported with foreach optimizers")
+
+    if foreach and not torch.jit.is_scripting():
+        func = _multi_tensor_adamax
+    else:
+        func = _single_tensor_adamax
+
+    func(
+        params,
+        grads,
+        exp_avgs,
+        exp_infs,
+        state_steps,
+        eps=eps,
+        beta1=beta1,
+        beta2=beta2,
+        lr=lr,
+        weight_decay=weight_decay,
+        maximize=maximize,
+        differentiable=differentiable,
+        has_complex=has_complex,
+        capturable=capturable,
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/adamw.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/adamw.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c49f528e8f136efa12d6b50709276ff5288be2f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/adamw.py
@@ -0,0 +1,801 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+from typing import cast, List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+
+from .optimizer import (
+    _capturable_doc,
+    _default_to_fused_or_foreach,
+    _device_dtype_check_for_fused,
+    _differentiable_doc,
+    _disable_dynamo_if_unsupported,
+    _foreach_doc,
+    _fused_doc,
+    _get_capturable_supported_devices,
+    _get_scalar_dtype,
+    _get_value,
+    _maximize_doc,
+    _stack_if_compiling,
+    _use_grad_for_differentiable,
+    _view_as_real,
+    DeviceDict,
+    Optimizer,
+    ParamsT,
+)
+
+
+__all__ = ["AdamW", "adamw"]
+
+
+class AdamW(Optimizer):
+    def __init__(
+        self,
+        params: ParamsT,
+        lr: Union[float, Tensor] = 1e-3,
+        betas: Tuple[float, float] = (0.9, 0.999),
+        eps: float = 1e-8,
+        weight_decay: float = 1e-2,
+        amsgrad: bool = False,
+        *,
+        maximize: bool = False,
+        foreach: Optional[bool] = None,
+        capturable: bool = False,
+        differentiable: bool = False,
+        fused: Optional[bool] = None,
+    ):
+        if isinstance(lr, Tensor):
+            if foreach and not capturable:
+                raise ValueError(
+                    "lr as a Tensor is not supported for capturable=False and foreach=True"
+                )
+            if lr.numel() != 1:
+                raise ValueError("Tensor lr must be 1-element")
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 <= eps:
+            raise ValueError(f"Invalid epsilon value: {eps}")
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
+        if not 0.0 <= weight_decay:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+        defaults = dict(
+            lr=lr,
+            betas=betas,
+            eps=eps,
+            weight_decay=weight_decay,
+            amsgrad=amsgrad,
+            foreach=foreach,
+            maximize=maximize,
+            capturable=capturable,
+            differentiable=differentiable,
+            fused=fused,
+        )
+        super().__init__(params, defaults)
+
+        if fused:
+            if differentiable:
+                raise RuntimeError("`fused` does not support `differentiable`")
+            self._step_supports_amp_scaling = True
+            if foreach:
+                raise RuntimeError("`fused` and `foreach` cannot be `True` together.")
+
+    def __setstate__(self, state):
+        super().__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault("amsgrad", False)
+            group.setdefault("maximize", False)
+            group.setdefault("foreach", None)
+            group.setdefault("capturable", False)
+            group.setdefault("differentiable", False)
+            fused = group.setdefault("fused", None)
+            for p in group["params"]:
+                p_state = self.state.get(p, [])
+                if len(p_state) != 0 and not torch.is_tensor(p_state["step"]):
+                    step_val = float(p_state["step"])
+                    p_state["step"] = (
+                        torch.tensor(
+                            step_val,
+                            dtype=_get_scalar_dtype(is_fused=fused),
+                            device=p.device,
+                        )
+                        if group["capturable"] or group["fused"]
+                        else torch.tensor(step_val, dtype=_get_scalar_dtype())
+                    )
+
+    def _init_group(
+        self,
+        group,
+        params_with_grad,
+        grads,
+        amsgrad,
+        exp_avgs,
+        exp_avg_sqs,
+        max_exp_avg_sqs,
+        state_steps,
+    ):
+        has_complex = False
+        for p in group["params"]:
+            if p.grad is None:
+                continue
+            has_complex |= torch.is_complex(p)
+            params_with_grad.append(p)
+            if p.grad.is_sparse:
+                raise RuntimeError("AdamW does not support sparse gradients")
+            grads.append(p.grad)
+
+            state = self.state[p]
+
+            # State initialization
+            if len(state) == 0:
+                if group["fused"]:
+                    _device_dtype_check_for_fused(p)
+                # note(crcrpar): Deliberately host `step` on CPU if both capturable and fused are off.
+                # This is because kernel launches are costly on CUDA and XLA.
+                state["step"] = (
+                    torch.zeros(
+                        (),
+                        dtype=_get_scalar_dtype(is_fused=group["fused"]),
+                        device=p.device,
+                    )
+                    if group["capturable"] or group["fused"]
+                    else torch.tensor(0.0, dtype=_get_scalar_dtype())
+                )
+                # Exponential moving average of gradient values
+                state["exp_avg"] = torch.zeros_like(
+                    p, memory_format=torch.preserve_format
+                )
+                # Exponential moving average of squared gradient values
+                state["exp_avg_sq"] = torch.zeros_like(
+                    p, memory_format=torch.preserve_format
+                )
+                if amsgrad:
+                    # Maintains max of all exp. moving avg. of sq. grad. values
+                    state["max_exp_avg_sq"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+
+            exp_avgs.append(state["exp_avg"])
+            exp_avg_sqs.append(state["exp_avg_sq"])
+
+            if group["amsgrad"]:
+                max_exp_avg_sqs.append(state["max_exp_avg_sq"])
+            if group["differentiable"] and state["step"].requires_grad:
+                raise RuntimeError(
+                    "`requires_grad` is not supported for `step` in differentiable mode"
+                )
+
+            # Foreach without capturable does not support a tensor lr
+            if (
+                group["foreach"]
+                and isinstance(group["lr"], Tensor)
+                and not group["capturable"]
+            ):
+                raise RuntimeError(
+                    "lr as a Tensor is not supported for capturable=False and foreach=True"
+                )
+
+            state_steps.append(state["step"])
+        return has_complex
+
+    @_use_grad_for_differentiable
+    def step(self, closure=None):
+        """Perform a single optimization step.
+
+        Args:
+            closure (Callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        self._cuda_graph_capture_health_check()
+
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            params_with_grad: List[Tensor] = []
+            grads: List[Tensor] = []
+            exp_avgs: List[Tensor] = []
+            exp_avg_sqs: List[Tensor] = []
+            max_exp_avg_sqs: List[Tensor] = []
+            state_steps: List[Tensor] = []
+            amsgrad: bool = group["amsgrad"]
+            beta1, beta2 = cast(Tuple[float, float], group["betas"])
+
+            has_complex = self._init_group(
+                group,
+                params_with_grad,
+                grads,
+                amsgrad,
+                exp_avgs,
+                exp_avg_sqs,
+                max_exp_avg_sqs,
+                state_steps,
+            )
+
+            adamw(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                max_exp_avg_sqs,
+                state_steps,
+                amsgrad=amsgrad,
+                beta1=beta1,
+                beta2=beta2,
+                lr=group["lr"],
+                weight_decay=group["weight_decay"],
+                eps=group["eps"],
+                maximize=group["maximize"],
+                foreach=group["foreach"],
+                capturable=group["capturable"],
+                differentiable=group["differentiable"],
+                fused=group["fused"],
+                grad_scale=getattr(self, "grad_scale", None),
+                found_inf=getattr(self, "found_inf", None),
+                has_complex=has_complex,
+            )
+
+        return loss
+
+
+AdamW.__doc__ = (
+    r"""Implements AdamW algorithm.
+
+    .. math::
+       \begin{aligned}
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{input}      : \gamma \text{(lr)}, \: \beta_1, \beta_2
+                \text{(betas)}, \: \theta_0 \text{(params)}, \: f(\theta) \text{(objective)},
+                \: \epsilon \text{ (epsilon)}                                                    \\
+            &\hspace{13mm}      \lambda \text{(weight decay)},  \: \textit{amsgrad},
+                \: \textit{maximize}                                                             \\
+            &\textbf{initialize} : m_0 \leftarrow 0 \text{ (first moment)}, v_0 \leftarrow 0
+                \text{ ( second moment)}, \: \widehat{v_0}^{max}\leftarrow 0              \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do}                         \\
+
+            &\hspace{5mm}\textbf{if} \: \textit{maximize}:                                       \\
+            &\hspace{10mm}g_t           \leftarrow   -\nabla_{\theta} f_t (\theta_{t-1})          \\
+            &\hspace{5mm}\textbf{else}                                                           \\
+            &\hspace{10mm}g_t           \leftarrow   \nabla_{\theta} f_t (\theta_{t-1})           \\
+            &\hspace{5mm} \theta_t \leftarrow \theta_{t-1} - \gamma \lambda \theta_{t-1}         \\
+            &\hspace{5mm}m_t           \leftarrow   \beta_1 m_{t-1} + (1 - \beta_1) g_t          \\
+            &\hspace{5mm}v_t           \leftarrow   \beta_2 v_{t-1} + (1-\beta_2) g^2_t          \\
+            &\hspace{5mm}\widehat{m_t} \leftarrow   m_t/\big(1-\beta_1^t \big)                   \\
+            &\hspace{5mm}\widehat{v_t} \leftarrow   v_t/\big(1-\beta_2^t \big)                   \\
+            &\hspace{5mm}\textbf{if} \: amsgrad                                                  \\
+            &\hspace{10mm}\widehat{v_t}^{max} \leftarrow \mathrm{max}(\widehat{v_t}^{max},
+                \widehat{v_t})                                                                   \\
+            &\hspace{10mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t}/
+                \big(\sqrt{\widehat{v_t}^{max}} + \epsilon \big)                                 \\
+            &\hspace{5mm}\textbf{else}                                                           \\
+            &\hspace{10mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t}/
+                \big(\sqrt{\widehat{v_t}} + \epsilon \big)                                       \\
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+            &\bf{return} \:  \theta_t                                                     \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+       \end{aligned}
+
+    For further details regarding the algorithm we refer to `Decoupled Weight Decay Regularization`_.
+    """
+    + rf"""
+    Args:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, Tensor, optional): learning rate (default: 1e-3). A tensor LR
+            is not yet supported for all our implementations. Please use a float
+            LR if you are not also specifying fused=True or capturable=True.
+        betas (Tuple[float, float], optional): coefficients used for computing
+            running averages of gradient and its square (default: (0.9, 0.999))
+        eps (float, optional): term added to the denominator to improve
+            numerical stability (default: 1e-8)
+        weight_decay (float, optional): weight decay coefficient (default: 1e-2)
+        amsgrad (bool, optional): whether to use the AMSGrad variant of this
+            algorithm from the paper `On the Convergence of Adam and Beyond`_
+            (default: False)
+        {_maximize_doc}
+        {_foreach_doc}
+        {_capturable_doc}
+        {_differentiable_doc}
+        {_fused_doc}
+    .. Note::
+        A prototype implementation of Adam and AdamW for MPS supports `torch.float32` and `torch.float16`.
+    .. _Decoupled Weight Decay Regularization:
+        https://arxiv.org/abs/1711.05101
+    .. _On the Convergence of Adam and Beyond:
+        https://openreview.net/forum?id=ryQu7f-RZ
+
+    """
+)
+
+
+def _single_tensor_adamw(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    max_exp_avg_sqs: List[Tensor],
+    state_steps: List[Tensor],
+    grad_scale: Optional[Tensor],
+    found_inf: Optional[Tensor],
+    *,
+    amsgrad: bool,
+    beta1: float,
+    beta2: float,
+    lr: Union[Tensor, float],
+    weight_decay: float,
+    eps: float,
+    maximize: bool,
+    capturable: bool,
+    differentiable: bool,
+    has_complex: bool,
+):
+    assert grad_scale is None and found_inf is None
+
+    if torch.jit.is_scripting():
+        # this assert is due to JIT being dumb and not realizing that the ops below
+        # have overloads to handle both float and Tensor lrs, so we just assert it's
+        # a float since most people using JIT are using floats
+        assert isinstance(lr, float)
+
+    for i, param in enumerate(params):
+        grad = grads[i] if not maximize else -grads[i]
+        exp_avg = exp_avgs[i]
+        exp_avg_sq = exp_avg_sqs[i]
+        step_t = state_steps[i]
+
+        # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+        if not torch._utils.is_compiling() and capturable:
+            capturable_supported_devices = _get_capturable_supported_devices()
+            assert (
+                param.device.type == step_t.device.type
+                and param.device.type in capturable_supported_devices
+            ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
+
+        if torch.is_complex(param):
+            grad = torch.view_as_real(grad)
+            exp_avg = torch.view_as_real(exp_avg)
+            exp_avg_sq = torch.view_as_real(exp_avg_sq)
+            if amsgrad:
+                max_exp_avg_sqs[i] = torch.view_as_real(max_exp_avg_sqs[i])
+            param = torch.view_as_real(param)
+
+        # update step
+        step_t += 1
+
+        # Perform stepweight decay
+        param.mul_(1 - lr * weight_decay)
+
+        # Decay the first and second moment running average coefficient
+        exp_avg.lerp_(grad, 1 - beta1)
+        exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+
+        if capturable or differentiable:
+            step = step_t
+
+            bias_correction1 = 1 - beta1**step
+            bias_correction2 = 1 - beta2**step
+
+            step_size = lr / bias_correction1
+            step_size_neg = step_size.neg()
+
+            bias_correction2_sqrt = bias_correction2.sqrt()
+
+            if amsgrad:
+                # Maintains the maximum of all 2nd moment running avg. till now
+                if differentiable:
+                    max_exp_avg_sq = max_exp_avg_sqs[i].clone()
+                else:
+                    max_exp_avg_sq = max_exp_avg_sqs[i]
+
+                max_exp_avg_sqs[i].copy_(torch.maximum(max_exp_avg_sq, exp_avg_sq))
+
+                # Uses the max. for normalizing running avg. of gradient
+                # Folds in (admittedly ugly) 1-elem step_size math here to avoid extra param-set-sized read+write
+                # (can't fold it into addcdiv_ below because addcdiv_ requires value is a Number, not a Tensor)
+                denom = (
+                    max_exp_avg_sqs[i].sqrt() / (bias_correction2_sqrt * step_size_neg)
+                ).add_(eps / step_size_neg)
+            else:
+                denom = (
+                    exp_avg_sq.sqrt() / (bias_correction2_sqrt * step_size_neg)
+                ).add_(eps / step_size_neg)
+
+            param.addcdiv_(exp_avg, denom)
+        else:
+            step = _get_value(step_t)
+
+            bias_correction1 = 1 - beta1**step
+            bias_correction2 = 1 - beta2**step
+
+            step_size = lr / bias_correction1
+
+            bias_correction2_sqrt = bias_correction2**0.5
+
+            if amsgrad:
+                # Maintains the maximum of all 2nd moment running avg. till now
+                torch.maximum(max_exp_avg_sqs[i], exp_avg_sq, out=max_exp_avg_sqs[i])
+
+                # Use the max. for normalizing running avg. of gradient
+                denom = (max_exp_avg_sqs[i].sqrt() / bias_correction2_sqrt).add_(eps)
+            else:
+                denom = (exp_avg_sq.sqrt() / bias_correction2_sqrt).add_(eps)
+
+            param.addcdiv_(exp_avg, denom, value=-step_size)
+
+        # Lastly, switch back to complex view
+        if amsgrad and torch.is_complex(params[i]):
+            max_exp_avg_sqs[i] = torch.view_as_complex(max_exp_avg_sqs[i])
+
+
+def _multi_tensor_adamw(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    max_exp_avg_sqs: List[Tensor],
+    state_steps: List[Tensor],
+    grad_scale: Optional[Tensor],
+    found_inf: Optional[Tensor],
+    *,
+    amsgrad: bool,
+    beta1: float,
+    beta2: float,
+    lr: Union[Tensor, float],
+    weight_decay: float,
+    eps: float,
+    maximize: bool,
+    capturable: bool,
+    differentiable: bool,
+    has_complex: bool,
+):
+    if len(params) == 0:
+        return
+
+    if isinstance(lr, Tensor) and not capturable:
+        raise RuntimeError(
+            "lr as a Tensor is not supported for capturable=False and foreach=True"
+        )
+
+    # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+    if not torch._utils.is_compiling() and capturable:
+        capturable_supported_devices = _get_capturable_supported_devices(
+            supports_xla=False
+        )
+        assert all(
+            p.device.type == step.device.type
+            and p.device.type in capturable_supported_devices
+            for p, step in zip(params, state_steps)
+        ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
+
+    assert not differentiable, "_foreach ops don't support autograd"
+
+    assert grad_scale is None and found_inf is None
+
+    grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
+        [params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps]  # type: ignore[list-item]
+    )
+    for (
+        device_params_,
+        device_grads_,
+        device_exp_avgs_,
+        device_exp_avg_sqs_,
+        device_max_exp_avg_sqs_,
+        device_state_steps_,
+    ), _ in grouped_tensors.values():
+        device_params = cast(List[Tensor], device_params_)
+        device_grads = cast(List[Tensor], device_grads_)
+        device_exp_avgs = cast(List[Tensor], device_exp_avgs_)
+        device_exp_avg_sqs = cast(List[Tensor], device_exp_avg_sqs_)
+        device_state_steps = cast(List[Tensor], device_state_steps_)
+
+        if has_complex:
+            if amsgrad:
+                device_max_exp_avg_sqs = cast(List[Tensor], device_max_exp_avg_sqs_)
+                _view_as_real(
+                    device_params,
+                    device_grads,
+                    device_exp_avgs,
+                    device_exp_avg_sqs,
+                    device_max_exp_avg_sqs,
+                )
+            else:
+                _view_as_real(
+                    device_params, device_grads, device_exp_avgs, device_exp_avg_sqs
+                )
+
+        if maximize:
+            device_grads = torch._foreach_neg(device_grads)  # type: ignore[assignment]
+
+        # Update steps
+        # If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
+        # and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
+        # wrapped it once now. The alpha is required to assure we go to the right overload.
+        if not torch._utils.is_compiling() and device_state_steps[0].is_cpu:
+            torch._foreach_add_(
+                device_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
+            )
+        else:
+            torch._foreach_add_(device_state_steps, 1)
+
+        # Perform stepweight decay
+        if weight_decay != 0:
+            torch._foreach_mul_(device_params, 1 - lr * weight_decay)
+
+        # Decay the first and second moment running average coefficient
+        torch._foreach_lerp_(device_exp_avgs, device_grads, 1 - beta1)
+
+        torch._foreach_mul_(device_exp_avg_sqs, beta2)
+        torch._foreach_addcmul_(
+            device_exp_avg_sqs, device_grads, device_grads, 1 - beta2
+        )
+
+        # Delete the local intermediate since it won't be used anymore to save on peak memory
+        del device_grads
+
+        bias_correction1: Union[Tuple[Tensor, ...], List[Tensor]]
+        bias_correction2: Union[Tuple[Tensor, ...], List[Tensor]]
+        bias_correction2_sqrt: Union[Tuple[Tensor, ...], List[Tensor]]
+
+        if capturable:
+            bias_correction1 = torch._foreach_pow(beta1, device_state_steps)
+            bias_correction2 = torch._foreach_pow(beta2, device_state_steps)
+            # foreach_sub doesn't allow a scalar as the first arg
+            torch._foreach_sub_(bias_correction1, 1)
+            torch._foreach_sub_(bias_correction2, 1)
+            # we do not negate bias_correction1 as it'll need to be negated later anyway
+            torch._foreach_neg_(bias_correction2)
+
+            # foreach_div doesn't allow a scalar as the first arg
+            torch._foreach_div_(bias_correction1, lr)
+            torch._foreach_reciprocal_(bias_correction1)
+
+            torch._foreach_sqrt_(bias_correction2)
+
+            # Re-assign for clarity as we maintain minimal intermediates: we'll have
+            # step_size = - lr / (1 - beta1 ^ t) where t = num_steps
+            # bias_correction2_sqrt = sqrt(1 - beta2 ^ t)
+            step_size = bias_correction1
+            bias_correction2_sqrt = bias_correction2
+
+            if amsgrad:
+                device_max_exp_avg_sqs = cast(List[Tensor], device_max_exp_avg_sqs_)
+
+                # Maintains the maximum of all 2nd moment running avg. till now
+                torch._foreach_maximum_(device_max_exp_avg_sqs, device_exp_avg_sqs)
+
+                # Use the max. for normalizing running avg. of gradient
+                exp_avg_sq_sqrt = torch._foreach_sqrt(device_max_exp_avg_sqs)
+            else:
+                exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs)
+
+            torch._foreach_div_(exp_avg_sq_sqrt, bias_correction2_sqrt)
+            torch._foreach_add_(exp_avg_sq_sqrt, eps)
+            torch._foreach_div_(exp_avg_sq_sqrt, step_size)
+
+            # at this point, exp_avg_sq_sqrt = - (1 - beta^t) * [sqrt(exp_avg_sq / (1 - beta2^t)) + eps] / lr
+            torch._foreach_addcdiv_(device_params, device_exp_avgs, exp_avg_sq_sqrt)
+        else:
+            bias_correction1 = [
+                1 - beta1 ** _get_value(step) for step in device_state_steps
+            ]
+            bias_correction2 = [
+                1 - beta2 ** _get_value(step) for step in device_state_steps
+            ]
+
+            step_size = _stack_if_compiling([(lr / bc) * -1 for bc in bias_correction1])
+
+            bias_correction2_sqrt = [
+                bc**0.5 for bc in bias_correction2  # type: ignore[arg-type]
+            ]
+
+            if amsgrad:
+                device_max_exp_avg_sqs = cast(List[Tensor], device_max_exp_avg_sqs_)
+
+                # Maintains the maximum of all 2nd moment running avg. till now
+                torch._foreach_maximum_(device_max_exp_avg_sqs, device_exp_avg_sqs)
+
+                # Use the max. for normalizing running avg. of gradient
+                exp_avg_sq_sqrt = torch._foreach_sqrt(device_max_exp_avg_sqs)
+            else:
+                exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs)
+
+            torch._foreach_div_(exp_avg_sq_sqrt, bias_correction2_sqrt)
+            torch._foreach_add_(exp_avg_sq_sqrt, eps)
+            torch._foreach_addcdiv_(
+                device_params,
+                device_exp_avgs,
+                exp_avg_sq_sqrt,
+                step_size,  # type: ignore[arg-type]
+            )
+
+
+def _fused_adamw(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    max_exp_avg_sqs: List[Tensor],
+    state_steps: List[Tensor],
+    grad_scale: Optional[Tensor],
+    found_inf: Optional[Tensor],
+    *,
+    amsgrad: bool,
+    beta1: float,
+    beta2: float,
+    lr: Union[Tensor, float],
+    weight_decay: float,
+    eps: float,
+    maximize: bool,
+    capturable: bool,  # Needed for consistency.
+    differentiable: bool,
+    has_complex: bool,  # Needed for consistency.
+) -> None:
+    if not params:
+        return
+    if differentiable:
+        raise RuntimeError("Adam with fused=True does not support differentiable=True")
+
+    grad_scale_dict: DeviceDict = (
+        {grad_scale.device: grad_scale} if grad_scale is not None else {}
+    )
+    found_inf_dict: DeviceDict = (
+        {found_inf.device: found_inf} if found_inf is not None else {}
+    )
+
+    # We only shuffle around the lr when it is a Tensor and on CUDA, otherwise, we prefer
+    # treating it as a scalar.
+    lr_dict: Optional[DeviceDict] = (
+        {lr.device: lr} if isinstance(lr, Tensor) and str(lr.device) != "cpu" else None
+    )
+
+    grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
+        [params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps]  # type: ignore[list-item]
+    )
+    for (device, _), (
+        (
+            device_params_,
+            device_grads_,
+            device_exp_avgs_,
+            device_exp_avg_sqs_,
+            device_max_exp_avg_sqs,
+            device_state_steps_,
+        ),
+        _,
+    ) in grouped_tensors.items():
+        device_params = cast(List[Tensor], device_params_)
+        device_grads = cast(List[Tensor], device_grads_)
+        device_exp_avgs = cast(List[Tensor], device_exp_avgs_)
+        device_exp_avg_sqs = cast(List[Tensor], device_exp_avg_sqs_)
+        device_state_steps = cast(List[Tensor], device_state_steps_)
+
+        if device.type == "mps":  # type: ignore[union-attr]
+            assert found_inf is None and grad_scale is None
+
+        device_grad_scale, device_found_inf = None, None
+        if grad_scale is not None:
+            device_grad_scale = grad_scale_dict.setdefault(
+                device, grad_scale.to(device, non_blocking=True)
+            )
+        if found_inf is not None:
+            device_found_inf = found_inf_dict.setdefault(
+                device, found_inf.to(device, non_blocking=True)
+            )
+        if lr_dict is not None and device not in lr_dict:
+            lr = lr_dict.setdefault(
+                device, lr.to(device=device, non_blocking=True)  # type: ignore[union-attr]
+            )
+        torch._foreach_add_(device_state_steps, 1)
+        torch._fused_adamw_(
+            device_params,
+            device_grads,
+            device_exp_avgs,
+            device_exp_avg_sqs,
+            device_max_exp_avg_sqs,  # type: ignore[arg-type]
+            device_state_steps,
+            amsgrad=amsgrad,
+            lr=lr,  # type: ignore[arg-type]
+            beta1=beta1,
+            beta2=beta2,
+            weight_decay=weight_decay,
+            eps=eps,
+            maximize=maximize,
+            grad_scale=device_grad_scale,
+            found_inf=device_found_inf,
+        )
+        if device_found_inf is not None:
+            torch._foreach_sub_(
+                device_state_steps, [device_found_inf] * len(device_state_steps)
+            )
+
+
+@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_adamw)
+def adamw(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    max_exp_avg_sqs: List[Tensor],
+    state_steps: List[Tensor],
+    # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
+    # setting this as kwarg for now as functional API is compiled by torch/distributed/optim
+    foreach: Optional[bool] = None,
+    capturable: bool = False,
+    differentiable: bool = False,
+    fused: Optional[bool] = None,
+    grad_scale: Optional[Tensor] = None,
+    found_inf: Optional[Tensor] = None,
+    has_complex: bool = False,
+    *,
+    amsgrad: bool,
+    beta1: float,
+    beta2: float,
+    lr: Union[float, Tensor],
+    weight_decay: float,
+    eps: float,
+    maximize: bool,
+):
+    r"""Functional API that performs AdamW algorithm computation.
+
+    See :class:`~torch.optim.AdamW` for details.
+    """
+    if not torch._utils.is_compiling() and not all(
+        isinstance(t, torch.Tensor) for t in state_steps
+    ):
+        raise RuntimeError(
+            "API has changed, `state_steps` argument must contain a list of singleton tensors"
+        )
+
+    # Respect when the user inputs False/True for foreach or fused. We only want to change
+    # the default when neither have been user-specified. Note that we default to foreach
+    # and pass False to use_fused. This is not a mistake--we want to give the fused impl
+    # bake-in time before making it the default, even if it is typically faster.
+    if fused is None and foreach is None:
+        _, foreach = _default_to_fused_or_foreach(
+            params, differentiable, use_fused=False
+        )
+        # Do not flip on foreach for the unsupported case where lr is a Tensor and capturable=False.
+        if foreach and isinstance(lr, Tensor) and not capturable:
+            foreach = False
+    if fused is None:
+        fused = False
+    if foreach is None:
+        foreach = False
+
+    if foreach and torch.jit.is_scripting():
+        raise RuntimeError("torch.jit.script not supported with foreach optimizers")
+    if fused and torch.jit.is_scripting():
+        raise RuntimeError("torch.jit.script not supported with fused optimizers")
+
+    if fused and not torch.jit.is_scripting():
+        func = _fused_adamw
+    elif foreach and not torch.jit.is_scripting():
+        func = _multi_tensor_adamw
+    else:
+        func = _single_tensor_adamw
+
+    func(
+        params,
+        grads,
+        exp_avgs,
+        exp_avg_sqs,
+        max_exp_avg_sqs,
+        state_steps,
+        amsgrad=amsgrad,
+        beta1=beta1,
+        beta2=beta2,
+        lr=lr,
+        weight_decay=weight_decay,
+        eps=eps,
+        maximize=maximize,
+        capturable=capturable,
+        differentiable=differentiable,
+        grad_scale=grad_scale,
+        found_inf=found_inf,
+        has_complex=has_complex,
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/asgd.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/asgd.py
new file mode 100644
index 0000000000000000000000000000000000000000..79de96aa86cd2f5b28ad2ed36d57578ec520c1e0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/asgd.py
@@ -0,0 +1,465 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+from typing import cast, List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+
+from .optimizer import (
+    _capturable_doc,
+    _default_to_fused_or_foreach,
+    _differentiable_doc,
+    _disable_dynamo_if_unsupported,
+    _foreach_doc,
+    _get_capturable_supported_devices,
+    _get_scalar_dtype,
+    _get_value,
+    _maximize_doc,
+    _use_grad_for_differentiable,
+    _view_as_real,
+    Optimizer,
+    ParamsT,
+)
+
+
+__all__ = ["ASGD", "asgd"]
+
+
+class ASGD(Optimizer):
+    def __init__(
+        self,
+        params: ParamsT,
+        lr: Union[float, Tensor] = 1e-2,
+        lambd: float = 1e-4,
+        alpha: float = 0.75,
+        t0: float = 1e6,
+        weight_decay: float = 0,
+        foreach: Optional[bool] = None,
+        maximize: bool = False,
+        differentiable: bool = False,
+        capturable: bool = False,
+    ):
+        if isinstance(lr, Tensor) and lr.numel() != 1:
+            raise ValueError("Tensor lr must be 1-element")
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 <= weight_decay:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+
+        defaults = dict(
+            lr=lr,
+            lambd=lambd,
+            alpha=alpha,
+            t0=t0,
+            weight_decay=weight_decay,
+            foreach=foreach,
+            maximize=maximize,
+            differentiable=differentiable,
+            capturable=capturable,
+        )
+        super().__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super().__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault("foreach", None)
+            group.setdefault("maximize", False)
+            group.setdefault("differentiable", False)
+            group.setdefault("capturable", False)
+            for p in group["params"]:
+                p_state = self.state.get(p, [])
+                if len(p_state) != 0:
+                    if not torch.is_tensor(p_state["step"]):
+                        step_val = float(p_state["step"])
+                        p_state["step"] = torch.tensor(
+                            step_val, dtype=_get_scalar_dtype(), device=p.device
+                        )
+                    if not torch.is_tensor(p_state["eta"]):
+                        p_state["eta"] = torch.tensor(
+                            p_state["eta"], dtype=_get_scalar_dtype(), device=p.device
+                        )
+                    if not torch.is_tensor(p_state["mu"]):
+                        p_state["mu"] = torch.tensor(
+                            p_state["mu"], dtype=_get_scalar_dtype(), device=p.device
+                        )
+
+    def _init_group(self, group, params_with_grad, grads, mus, axs, etas, state_steps):
+        has_complex = False
+        for p in group["params"]:
+            if p.grad is not None:
+                has_complex |= torch.is_complex(p)
+                params_with_grad.append(p)
+                if p.grad.is_sparse:
+                    raise RuntimeError("ASGD does not support sparse gradients")
+                grads.append(p.grad)
+
+                state = self.state[p]
+                # State initialization
+                if len(state) == 0:
+                    state["step"] = torch.zeros(
+                        (), device=p.device, dtype=_get_scalar_dtype()
+                    )
+                    state["eta"] = (
+                        torch.as_tensor(
+                            group["lr"], device=p.device, dtype=_get_scalar_dtype()
+                        )
+                        .clone()
+                        .detach()
+                    )
+                    state["mu"] = torch.ones(
+                        (), device=p.device, dtype=_get_scalar_dtype()
+                    )
+                    state["ax"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+
+                mus.append(state["mu"])
+                axs.append(state["ax"])
+                etas.append(state["eta"])
+                state_steps.append(state["step"])
+        return has_complex
+
+    @_use_grad_for_differentiable
+    def step(self, closure=None):
+        """Perform a single optimization step.
+
+        Args:
+            closure (Callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        self._cuda_graph_capture_health_check()
+
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            params_with_grad: List[Tensor] = []
+            grads: List[Tensor] = []
+            mus: List[Tensor] = []
+            axs: List[Tensor] = []
+            etas: List[Tensor] = []
+            state_steps: List[Tensor] = []
+
+            has_complex = self._init_group(
+                group, params_with_grad, grads, mus, axs, etas, state_steps
+            )
+
+            asgd(
+                params_with_grad,
+                grads,
+                axs,
+                mus,
+                etas,
+                state_steps,
+                lambd=group["lambd"],
+                lr=group["lr"],
+                t0=group["t0"],
+                alpha=group["alpha"],
+                weight_decay=group["weight_decay"],
+                foreach=group["foreach"],
+                maximize=group["maximize"],
+                differentiable=group["differentiable"],
+                capturable=group["capturable"],
+                has_complex=has_complex,
+            )
+
+        return loss
+
+
+ASGD.__doc__ = rf"""Implements Averaged Stochastic Gradient Descent.
+
+    It has been proposed in `Acceleration of stochastic approximation by
+    averaging`_.
+
+    Args:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, Tensor, optional): learning rate (default: 1e-2)
+        lambd (float, optional): decay term (default: 1e-4)
+        alpha (float, optional): power for eta update (default: 0.75)
+        t0 (float, optional): point at which to start averaging (default: 1e6)
+        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+        {_foreach_doc}
+        {_maximize_doc}
+        {_differentiable_doc}
+        {_capturable_doc}
+
+    .. _Acceleration of stochastic approximation by averaging:
+        https://dl.acm.org/citation.cfm?id=131098
+
+    """
+
+
+def _single_tensor_asgd(
+    params: List[Tensor],
+    grads: List[Tensor],
+    axs: List[Tensor],
+    mus: List[Tensor],
+    etas: List[Tensor],
+    state_steps: List[Tensor],
+    *,
+    lambd: float,
+    lr: float,
+    t0: float,
+    alpha: float,
+    weight_decay: float,
+    maximize: bool,
+    differentiable: bool,
+    capturable: bool,
+    has_complex: bool,
+):
+    for i, param in enumerate(params):
+        grad = grads[i]
+        grad = grad if not maximize else -grad
+        mu = mus[i]
+        ax = axs[i]
+        eta = etas[i]
+        step_t = state_steps[i]
+
+        # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+        if not torch._utils.is_compiling() and capturable:
+            capturable_supported_devices = _get_capturable_supported_devices()
+            assert (
+                param.device.type
+                == mu.device.type
+                == eta.device.type
+                == step_t.device.type
+                and param.device.type in capturable_supported_devices
+            ), (
+                f"If capturable=True, params, mus, etas, and state_steps must be "
+                f"on supported devices: {capturable_supported_devices}."
+            )
+
+        if torch.is_complex(param):
+            grad = torch.view_as_real(grad)
+            param = torch.view_as_real(param)
+            ax = torch.view_as_real(ax)
+
+        # update step
+        step_t += 1
+
+        if weight_decay != 0:
+            grad = grad.add(param, alpha=weight_decay)
+
+        if capturable:
+            param.mul_(1 - lambd * eta)
+            param.addcmul_(grad, eta, value=-1)  # update parameter
+        else:
+            eta_value = _get_value(eta)
+            param.mul_(1 - lambd * eta_value)  # decay term
+            param.add_(grad, alpha=-eta_value)  # update parameter
+
+        # averaging
+        if capturable or mu.item() != 1:
+            ax.add_(param.sub(ax).mul_(mu))
+        else:
+            ax.copy_(param)
+
+        if capturable:
+            eta.copy_(lr / ((1 + lambd * lr * step_t) ** alpha))
+            mu.copy_(1 / torch.maximum(step_t - t0, torch.ones_like(step_t)))
+        else:
+            step = _get_value(step_t)
+            new_eta = torch.as_tensor(lr / ((1 + lambd * lr * step) ** alpha))
+            eta.copy_(new_eta)
+            new_mu = torch.as_tensor(1 / max(1, step - t0))
+            mu.copy_(new_mu)
+
+
+def _multi_tensor_asgd(
+    params: List[Tensor],
+    grads: List[Tensor],
+    axs: List[Tensor],
+    mus: List[Tensor],
+    etas: List[Tensor],
+    state_steps: List[Tensor],
+    *,
+    lambd: float,
+    lr: float,
+    t0: float,
+    alpha: float,
+    weight_decay: float,
+    maximize: bool,
+    differentiable: bool,
+    capturable: bool,
+    has_complex: bool,
+):
+    if len(params) == 0:
+        return
+
+    assert not differentiable, "_foreach ops don't support autograd"
+
+    # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+    if not torch._utils.is_compiling() and capturable:
+        capturable_supported_devices = _get_capturable_supported_devices(
+            supports_xla=False
+        )
+        assert all(
+            p.device.type == mu.device.type == eta.device.type == step.device.type
+            and p.device.type in capturable_supported_devices
+            for p, mu, eta, step in zip(params, mus, etas, state_steps)
+        ), f"If capturable=True, params, mus, etas, and state_steps must be on supported devices: {capturable_supported_devices}."
+
+    grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
+        [params, grads, axs, mus, etas, state_steps]  # type: ignore[list-item]
+    )
+    for (device, _), (
+        (
+            grouped_params_,
+            grouped_grads_,
+            grouped_axs_,
+            grouped_mus_,
+            grouped_etas_,
+            grouped_state_steps_,
+        ),
+        _,
+    ) in grouped_tensors.items():
+        grouped_params = cast(List[Tensor], grouped_params_)
+        grouped_grads = cast(List[Tensor], grouped_grads_)
+        grouped_axs = cast(List[Tensor], grouped_axs_)
+        grouped_mus = cast(List[Tensor], grouped_mus_)
+        grouped_etas = cast(List[Tensor], grouped_etas_)
+        grouped_state_steps = cast(List[Tensor], grouped_state_steps_)
+
+        if has_complex:
+            _view_as_real(grouped_params, grouped_grads, grouped_axs)
+
+        if maximize:
+            grouped_grads = torch._foreach_neg(grouped_grads)  # type: ignore[assignment]
+
+        # Update steps
+        # If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
+        # and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
+        # wrapped it once now. The alpha is required to assure we go to the right overload.
+        if not torch._utils.is_compiling() and grouped_state_steps[0].is_cpu:
+            torch._foreach_add_(
+                grouped_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
+            )
+        else:
+            torch._foreach_add_(grouped_state_steps, 1)
+
+        # intermediate = grad + param * lambd
+        intermediate: Union[Tuple[Tensor, ...], List[Tensor]]
+        if weight_decay != 0:
+            if maximize:
+                torch._foreach_add_(grouped_grads, grouped_params, alpha=weight_decay)
+                intermediate = grouped_grads
+            else:
+                intermediate = torch._foreach_add(
+                    grouped_grads, grouped_params, alpha=weight_decay
+                )
+
+            torch._foreach_add_(intermediate, grouped_params, alpha=lambd)
+        else:
+            intermediate = torch._foreach_add(
+                grouped_grads, grouped_params, alpha=lambd
+            )
+
+        # update param
+        # param * (1 - lambd * eta) - eta * grad
+        # => param - param * lambd * eta - eta * grad
+        # => param - eta * intermediate
+        torch._foreach_addcmul_(grouped_params, intermediate, grouped_etas, value=-1)
+        del intermediate
+
+        # update grouped_axs
+        # averaging: ax = ax + mu * (param - ax)
+        # Note (mlazos): We can't use lerp here since it requires weight to be float64
+        # and our grouping code requires dtypes to match for all tensors in a group (and it should, since
+        # we use the mus in other places)
+        # all dtypes need to match, so we could introduce a cast in a loop
+        # but since this only adds one additional kernel launch, this looks like the cleaner
+        # and faster solution
+        intermediate = torch._foreach_sub(grouped_params, grouped_axs)
+        torch._foreach_addcmul_(grouped_axs, intermediate, grouped_mus)
+        del intermediate
+
+        new_etas: Union[Tuple[Tensor, ...], List[Tensor]]
+        new_mus: Union[Tuple[Tensor, ...], List[Tensor]]
+        if capturable:
+            # update grouped_mus
+            new_mus = torch._foreach_sub(grouped_state_steps, t0)
+            torch._foreach_maximum_(new_mus, 1.0)
+            torch._foreach_reciprocal_(new_mus)
+            torch._foreach_copy_(grouped_mus, new_mus)
+            del new_mus
+
+            # update eta = lr / ((1 + lambd * lr * step)^alpha)
+            new_etas = torch._foreach_mul(grouped_state_steps, lambd)
+            torch._foreach_mul_(new_etas, lr)
+            torch._foreach_add_(new_etas, 1)
+            torch._foreach_pow_(new_etas, alpha)
+            torch._foreach_reciprocal_(new_etas)
+            torch._foreach_mul_(new_etas, lr)
+            torch._foreach_copy_(grouped_etas, new_etas)
+        else:
+            new_etas = [
+                torch.as_tensor(lr / ((1 + lambd * lr * step) ** alpha), device=device)
+                for step in grouped_state_steps
+            ]
+            new_mus = [
+                torch.as_tensor(1 / max(1, _get_value(step) - t0), device=device)
+                for step in grouped_state_steps
+            ]
+            torch._foreach_copy_(grouped_etas, new_etas)
+            torch._foreach_copy_(grouped_mus, new_mus)
+
+
+@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_asgd)
+def asgd(
+    params: List[Tensor],
+    grads: List[Tensor],
+    axs: List[Tensor],
+    mus: List[Tensor],
+    etas: List[Tensor],
+    state_steps: List[Tensor],
+    # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
+    # setting this as kwarg for now as functional API is compiled by torch/distributed/optim
+    foreach: Optional[bool] = None,
+    maximize: bool = False,
+    differentiable: bool = False,
+    capturable: bool = False,
+    has_complex: bool = False,
+    *,
+    lambd: float,
+    lr: float,
+    t0: float,
+    alpha: float,
+    weight_decay: float,
+):
+    r"""Functional API that performs asgd algorithm computation.
+
+    See :class:`~torch.optim.ASGD` for details.
+    """
+    if foreach is None:
+        _, foreach = _default_to_fused_or_foreach(
+            params, differentiable, use_fused=False
+        )
+
+    if foreach and torch.jit.is_scripting():
+        raise RuntimeError("torch.jit.script not supported with foreach optimizers")
+
+    if foreach and not torch.jit.is_scripting():
+        func = _multi_tensor_asgd
+    else:
+        func = _single_tensor_asgd
+
+    func(
+        params,
+        grads,
+        axs,
+        mus,
+        etas,
+        state_steps,
+        lambd=lambd,
+        lr=lr,
+        t0=t0,
+        alpha=alpha,
+        weight_decay=weight_decay,
+        maximize=maximize,
+        differentiable=differentiable,
+        capturable=capturable,
+        has_complex=has_complex,
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/lbfgs.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/lbfgs.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9c2e13077e3b8409f0048d60971619d2b4da588
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/lbfgs.py
@@ -0,0 +1,495 @@
+# mypy: allow-untyped-defs
+from typing import Optional, Union
+
+import torch
+from torch import Tensor
+
+from .optimizer import Optimizer, ParamsT
+
+
+__all__ = ["LBFGS"]
+
+
+def _cubic_interpolate(x1, f1, g1, x2, f2, g2, bounds=None):
+    # ported from https://github.com/torch/optim/blob/master/polyinterp.lua
+    # Compute bounds of interpolation area
+    if bounds is not None:
+        xmin_bound, xmax_bound = bounds
+    else:
+        xmin_bound, xmax_bound = (x1, x2) if x1 <= x2 else (x2, x1)
+
+    # Code for most common case: cubic interpolation of 2 points
+    #   w/ function and derivative values for both
+    # Solution in this case (where x2 is the farthest point):
+    #   d1 = g1 + g2 - 3*(f1-f2)/(x1-x2);
+    #   d2 = sqrt(d1^2 - g1*g2);
+    #   min_pos = x2 - (x2 - x1)*((g2 + d2 - d1)/(g2 - g1 + 2*d2));
+    #   t_new = min(max(min_pos,xmin_bound),xmax_bound);
+    d1 = g1 + g2 - 3 * (f1 - f2) / (x1 - x2)
+    d2_square = d1**2 - g1 * g2
+    if d2_square >= 0:
+        d2 = d2_square.sqrt()
+        if x1 <= x2:
+            min_pos = x2 - (x2 - x1) * ((g2 + d2 - d1) / (g2 - g1 + 2 * d2))
+        else:
+            min_pos = x1 - (x1 - x2) * ((g1 + d2 - d1) / (g1 - g2 + 2 * d2))
+        return min(max(min_pos, xmin_bound), xmax_bound)
+    else:
+        return (xmin_bound + xmax_bound) / 2.0
+
+
+def _strong_wolfe(
+    obj_func, x, t, d, f, g, gtd, c1=1e-4, c2=0.9, tolerance_change=1e-9, max_ls=25
+):
+    # ported from https://github.com/torch/optim/blob/master/lswolfe.lua
+    d_norm = d.abs().max()
+    g = g.clone(memory_format=torch.contiguous_format)
+    # evaluate objective and gradient using initial step
+    f_new, g_new = obj_func(x, t, d)
+    ls_func_evals = 1
+    gtd_new = g_new.dot(d)
+
+    # bracket an interval containing a point satisfying the Wolfe criteria
+    t_prev, f_prev, g_prev, gtd_prev = 0, f, g, gtd
+    done = False
+    ls_iter = 0
+    while ls_iter < max_ls:
+        # check conditions
+        if f_new > (f + c1 * t * gtd) or (ls_iter > 1 and f_new >= f_prev):
+            bracket = [t_prev, t]
+            bracket_f = [f_prev, f_new]
+            bracket_g = [g_prev, g_new.clone(memory_format=torch.contiguous_format)]
+            bracket_gtd = [gtd_prev, gtd_new]
+            break
+
+        if abs(gtd_new) <= -c2 * gtd:
+            bracket = [t]
+            bracket_f = [f_new]
+            bracket_g = [g_new]
+            done = True
+            break
+
+        if gtd_new >= 0:
+            bracket = [t_prev, t]
+            bracket_f = [f_prev, f_new]
+            bracket_g = [g_prev, g_new.clone(memory_format=torch.contiguous_format)]
+            bracket_gtd = [gtd_prev, gtd_new]
+            break
+
+        # interpolate
+        min_step = t + 0.01 * (t - t_prev)
+        max_step = t * 10
+        tmp = t
+        t = _cubic_interpolate(
+            t_prev, f_prev, gtd_prev, t, f_new, gtd_new, bounds=(min_step, max_step)
+        )
+
+        # next step
+        t_prev = tmp
+        f_prev = f_new
+        g_prev = g_new.clone(memory_format=torch.contiguous_format)
+        gtd_prev = gtd_new
+        f_new, g_new = obj_func(x, t, d)
+        ls_func_evals += 1
+        gtd_new = g_new.dot(d)
+        ls_iter += 1
+
+    # reached max number of iterations?
+    if ls_iter == max_ls:
+        bracket = [0, t]
+        bracket_f = [f, f_new]
+        bracket_g = [g, g_new]
+
+    # zoom phase: we now have a point satisfying the criteria, or
+    # a bracket around it. We refine the bracket until we find the
+    # exact point satisfying the criteria
+    insuf_progress = False
+    # find high and low points in bracket
+    low_pos, high_pos = (0, 1) if bracket_f[0] <= bracket_f[-1] else (1, 0)  # type: ignore[possibly-undefined]
+    while not done and ls_iter < max_ls:
+        # line-search bracket is so small
+        if abs(bracket[1] - bracket[0]) * d_norm < tolerance_change:  # type: ignore[possibly-undefined]
+            break
+
+        # compute new trial value
+        t = _cubic_interpolate(
+            bracket[0],
+            bracket_f[0],
+            bracket_gtd[0],  # type: ignore[possibly-undefined]
+            bracket[1],
+            bracket_f[1],
+            bracket_gtd[1],
+        )
+
+        # test that we are making sufficient progress:
+        # in case `t` is so close to boundary, we mark that we are making
+        # insufficient progress, and if
+        #   + we have made insufficient progress in the last step, or
+        #   + `t` is at one of the boundary,
+        # we will move `t` to a position which is `0.1 * len(bracket)`
+        # away from the nearest boundary point.
+        eps = 0.1 * (max(bracket) - min(bracket))
+        if min(max(bracket) - t, t - min(bracket)) < eps:
+            # interpolation close to boundary
+            if insuf_progress or t >= max(bracket) or t <= min(bracket):
+                # evaluate at 0.1 away from boundary
+                if abs(t - max(bracket)) < abs(t - min(bracket)):
+                    t = max(bracket) - eps
+                else:
+                    t = min(bracket) + eps
+                insuf_progress = False
+            else:
+                insuf_progress = True
+        else:
+            insuf_progress = False
+
+        # Evaluate new point
+        f_new, g_new = obj_func(x, t, d)
+        ls_func_evals += 1
+        gtd_new = g_new.dot(d)
+        ls_iter += 1
+
+        if f_new > (f + c1 * t * gtd) or f_new >= bracket_f[low_pos]:
+            # Armijo condition not satisfied or not lower than lowest point
+            bracket[high_pos] = t
+            bracket_f[high_pos] = f_new
+            bracket_g[high_pos] = g_new.clone(memory_format=torch.contiguous_format)  # type: ignore[possibly-undefined]
+            bracket_gtd[high_pos] = gtd_new
+            low_pos, high_pos = (0, 1) if bracket_f[0] <= bracket_f[1] else (1, 0)
+        else:
+            if abs(gtd_new) <= -c2 * gtd:
+                # Wolfe conditions satisfied
+                done = True
+            elif gtd_new * (bracket[high_pos] - bracket[low_pos]) >= 0:
+                # old high becomes new low
+                bracket[high_pos] = bracket[low_pos]
+                bracket_f[high_pos] = bracket_f[low_pos]
+                bracket_g[high_pos] = bracket_g[low_pos]  # type: ignore[possibly-undefined]
+                bracket_gtd[high_pos] = bracket_gtd[low_pos]
+
+            # new point becomes new low
+            bracket[low_pos] = t
+            bracket_f[low_pos] = f_new
+            bracket_g[low_pos] = g_new.clone(memory_format=torch.contiguous_format)  # type: ignore[possibly-undefined]
+            bracket_gtd[low_pos] = gtd_new
+
+    # return stuff
+    t = bracket[low_pos]  # type: ignore[possibly-undefined]
+    f_new = bracket_f[low_pos]
+    g_new = bracket_g[low_pos]  # type: ignore[possibly-undefined]
+    return f_new, g_new, t, ls_func_evals
+
+
+class LBFGS(Optimizer):
+    """Implements L-BFGS algorithm.
+
+    Heavily inspired by `minFunc
+    `_.
+
+    .. warning::
+        This optimizer doesn't support per-parameter options and parameter
+        groups (there can be only one).
+
+    .. warning::
+        Right now all parameters have to be on a single device. This will be
+        improved in the future.
+
+    .. note::
+        This is a very memory intensive optimizer (it requires additional
+        ``param_bytes * (history_size + 1)`` bytes). If it doesn't fit in memory
+        try reducing the history size, or use a different algorithm.
+
+    Args:
+        params (iterable): iterable of parameters to optimize. Parameters must be real.
+        lr (float): learning rate (default: 1)
+        max_iter (int): maximal number of iterations per optimization step
+            (default: 20)
+        max_eval (int): maximal number of function evaluations per optimization
+            step (default: max_iter * 1.25).
+        tolerance_grad (float): termination tolerance on first order optimality
+            (default: 1e-7).
+        tolerance_change (float): termination tolerance on function
+            value/parameter changes (default: 1e-9).
+        history_size (int): update history size (default: 100).
+        line_search_fn (str): either 'strong_wolfe' or None (default: None).
+    """
+
+    def __init__(
+        self,
+        params: ParamsT,
+        lr: Union[float, Tensor] = 1,
+        max_iter: int = 20,
+        max_eval: Optional[int] = None,
+        tolerance_grad: float = 1e-7,
+        tolerance_change: float = 1e-9,
+        history_size: int = 100,
+        line_search_fn: Optional[str] = None,
+    ):
+        if isinstance(lr, Tensor) and lr.numel() != 1:
+            raise ValueError("Tensor lr must be 1-element")
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if max_eval is None:
+            max_eval = max_iter * 5 // 4
+        defaults = dict(
+            lr=lr,
+            max_iter=max_iter,
+            max_eval=max_eval,
+            tolerance_grad=tolerance_grad,
+            tolerance_change=tolerance_change,
+            history_size=history_size,
+            line_search_fn=line_search_fn,
+        )
+        super().__init__(params, defaults)
+
+        if len(self.param_groups) != 1:
+            raise ValueError(
+                "LBFGS doesn't support per-parameter options " "(parameter groups)"
+            )
+
+        self._params = self.param_groups[0]["params"]
+        self._numel_cache = None
+
+    def _numel(self):
+        if self._numel_cache is None:
+            self._numel_cache = sum(
+                2 * p.numel() if torch.is_complex(p) else p.numel()
+                for p in self._params
+            )
+
+        return self._numel_cache
+
+    def _gather_flat_grad(self):
+        views = []
+        for p in self._params:
+            if p.grad is None:
+                view = p.new(p.numel()).zero_()
+            elif p.grad.is_sparse:
+                view = p.grad.to_dense().view(-1)
+            else:
+                view = p.grad.view(-1)
+            if torch.is_complex(view):
+                view = torch.view_as_real(view).view(-1)
+            views.append(view)
+        return torch.cat(views, 0)
+
+    def _add_grad(self, step_size, update):
+        offset = 0
+        for p in self._params:
+            if torch.is_complex(p):
+                p = torch.view_as_real(p)
+            numel = p.numel()
+            # view as to avoid deprecated pointwise semantics
+            p.add_(update[offset : offset + numel].view_as(p), alpha=step_size)
+            offset += numel
+        assert offset == self._numel()
+
+    def _clone_param(self):
+        return [p.clone(memory_format=torch.contiguous_format) for p in self._params]
+
+    def _set_param(self, params_data):
+        for p, pdata in zip(self._params, params_data):
+            p.copy_(pdata)
+
+    def _directional_evaluate(self, closure, x, t, d):
+        self._add_grad(t, d)
+        loss = float(closure())
+        flat_grad = self._gather_flat_grad()
+        self._set_param(x)
+        return loss, flat_grad
+
+    @torch.no_grad()
+    def step(self, closure):
+        """Perform a single optimization step.
+
+        Args:
+            closure (Callable): A closure that reevaluates the model
+                and returns the loss.
+        """
+        assert len(self.param_groups) == 1
+
+        # Make sure the closure is always called with grad enabled
+        closure = torch.enable_grad()(closure)
+
+        group = self.param_groups[0]
+        lr = group["lr"]
+        max_iter = group["max_iter"]
+        max_eval = group["max_eval"]
+        tolerance_grad = group["tolerance_grad"]
+        tolerance_change = group["tolerance_change"]
+        line_search_fn = group["line_search_fn"]
+        history_size = group["history_size"]
+
+        # NOTE: LBFGS has only global state, but we register it as state for
+        # the first param, because this helps with casting in load_state_dict
+        state = self.state[self._params[0]]
+        state.setdefault("func_evals", 0)
+        state.setdefault("n_iter", 0)
+
+        # evaluate initial f(x) and df/dx
+        orig_loss = closure()
+        loss = float(orig_loss)
+        current_evals = 1
+        state["func_evals"] += 1
+
+        flat_grad = self._gather_flat_grad()
+        opt_cond = flat_grad.abs().max() <= tolerance_grad
+
+        # optimal condition
+        if opt_cond:
+            return orig_loss
+
+        # tensors cached in state (for tracing)
+        d = state.get("d")
+        t = state.get("t")
+        old_dirs = state.get("old_dirs")
+        old_stps = state.get("old_stps")
+        ro = state.get("ro")
+        H_diag = state.get("H_diag")
+        prev_flat_grad = state.get("prev_flat_grad")
+        prev_loss = state.get("prev_loss")
+
+        n_iter = 0
+        # optimize for a max of max_iter iterations
+        while n_iter < max_iter:
+            # keep track of nb of iterations
+            n_iter += 1
+            state["n_iter"] += 1
+
+            ############################################################
+            # compute gradient descent direction
+            ############################################################
+            if state["n_iter"] == 1:
+                d = flat_grad.neg()
+                old_dirs = []
+                old_stps = []
+                ro = []
+                H_diag = 1
+            else:
+                # do lbfgs update (update memory)
+                y = flat_grad.sub(prev_flat_grad)
+                s = d.mul(t)
+                ys = y.dot(s)  # y*s
+                if ys > 1e-10:
+                    # updating memory
+                    if len(old_dirs) == history_size:
+                        # shift history by one (limited-memory)
+                        old_dirs.pop(0)
+                        old_stps.pop(0)
+                        ro.pop(0)
+
+                    # store new direction/step
+                    old_dirs.append(y)
+                    old_stps.append(s)
+                    ro.append(1.0 / ys)
+
+                    # update scale of initial Hessian approximation
+                    H_diag = ys / y.dot(y)  # (y*y)
+
+                # compute the approximate (L-BFGS) inverse Hessian
+                # multiplied by the gradient
+                num_old = len(old_dirs)
+
+                if "al" not in state:
+                    state["al"] = [None] * history_size
+                al = state["al"]
+
+                # iteration in L-BFGS loop collapsed to use just one buffer
+                q = flat_grad.neg()
+                for i in range(num_old - 1, -1, -1):
+                    al[i] = old_stps[i].dot(q) * ro[i]
+                    q.add_(old_dirs[i], alpha=-al[i])
+
+                # multiply by initial Hessian
+                # r/d is the final direction
+                d = r = torch.mul(q, H_diag)
+                for i in range(num_old):
+                    be_i = old_dirs[i].dot(r) * ro[i]
+                    r.add_(old_stps[i], alpha=al[i] - be_i)
+
+            if prev_flat_grad is None:
+                prev_flat_grad = flat_grad.clone(memory_format=torch.contiguous_format)
+            else:
+                prev_flat_grad.copy_(flat_grad)
+            prev_loss = loss
+
+            ############################################################
+            # compute step length
+            ############################################################
+            # reset initial guess for step size
+            if state["n_iter"] == 1:
+                t = min(1.0, 1.0 / flat_grad.abs().sum()) * lr
+            else:
+                t = lr
+
+            # directional derivative
+            gtd = flat_grad.dot(d)  # g * d
+
+            # directional derivative is below tolerance
+            if gtd > -tolerance_change:
+                break
+
+            # optional line search: user function
+            ls_func_evals = 0
+            if line_search_fn is not None:
+                # perform line search, using user function
+                if line_search_fn != "strong_wolfe":
+                    raise RuntimeError("only 'strong_wolfe' is supported")
+                else:
+                    x_init = self._clone_param()
+
+                    def obj_func(x, t, d):
+                        return self._directional_evaluate(closure, x, t, d)
+
+                    loss, flat_grad, t, ls_func_evals = _strong_wolfe(
+                        obj_func, x_init, t, d, loss, flat_grad, gtd
+                    )
+                self._add_grad(t, d)
+                opt_cond = flat_grad.abs().max() <= tolerance_grad
+            else:
+                # no line search, simply move with fixed-step
+                self._add_grad(t, d)
+                if n_iter != max_iter:
+                    # re-evaluate function only if not in last iteration
+                    # the reason we do this: in a stochastic setting,
+                    # no use to re-evaluate that function here
+                    with torch.enable_grad():
+                        loss = float(closure())
+                    flat_grad = self._gather_flat_grad()
+                    opt_cond = flat_grad.abs().max() <= tolerance_grad
+                    ls_func_evals = 1
+
+            # update func eval
+            current_evals += ls_func_evals
+            state["func_evals"] += ls_func_evals
+
+            ############################################################
+            # check conditions
+            ############################################################
+            if n_iter == max_iter:
+                break
+
+            if current_evals >= max_eval:
+                break
+
+            # optimal condition
+            if opt_cond:
+                break
+
+            # lack of progress
+            if d.mul(t).abs().max() <= tolerance_change:
+                break
+
+            if abs(loss - prev_loss) < tolerance_change:
+                break
+
+        state["d"] = d
+        state["t"] = t
+        state["old_dirs"] = old_dirs
+        state["old_stps"] = old_stps
+        state["ro"] = ro
+        state["H_diag"] = H_diag
+        state["prev_flat_grad"] = prev_flat_grad
+        state["prev_loss"] = prev_loss
+
+        return orig_loss
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/lr_scheduler.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/lr_scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..57dcbd85a8316444ef59ee6e12d692fdc6f657d9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/lr_scheduler.py
@@ -0,0 +1,2151 @@
+# mypy: allow-untyped-defs
+r"""Learning Rate Scheduler."""
+import math
+import types
+import warnings
+from bisect import bisect_right
+from collections import Counter
+from functools import partial, wraps
+from typing import (
+    Any,
+    Callable,
+    cast,
+    Dict,
+    Iterable,
+    List,
+    Literal,
+    Optional,
+    Sequence,
+    SupportsFloat,
+    TypedDict,
+    Union,
+)
+from weakref import ref
+
+from torch import inf, Tensor
+
+from .optimizer import Optimizer
+
+
+__all__ = [
+    "LambdaLR",
+    "MultiplicativeLR",
+    "StepLR",
+    "MultiStepLR",
+    "ConstantLR",
+    "LinearLR",
+    "ExponentialLR",
+    "SequentialLR",
+    "CosineAnnealingLR",
+    "ChainedScheduler",
+    "ReduceLROnPlateau",
+    "CyclicLR",
+    "CosineAnnealingWarmRestarts",
+    "OneCycleLR",
+    "PolynomialLR",
+    "LRScheduler",
+]
+
+EPOCH_DEPRECATION_WARNING = (
+    "The epoch parameter in `scheduler.step()` was not necessary and is being "
+    "deprecated where possible. Please use `scheduler.step()` to step the "
+    "scheduler. During the deprecation, if epoch is different from None, the "
+    "closed form is used instead of the new chainable form, where available. "
+    "Please open an issue if you are unable to replicate your use case: "
+    "https://github.com/pytorch/pytorch/issues/new/choose."
+)
+
+
+def _check_verbose_deprecated_warning(verbose):
+    """Raise a warning when verbose is not the default value."""
+    if verbose != "deprecated":
+        warnings.warn(
+            "The verbose parameter is deprecated. Please use get_last_lr() "
+            "to access the learning rate.",
+            UserWarning,
+        )
+        return verbose
+    return False
+
+
+def _format_param(name: str, optimizer: Optimizer, param):
+    """Return correctly formatted lr/momentum for each param group."""
+
+    def _copy(_param):
+        return _param.clone() if isinstance(_param, Tensor) else _param
+
+    if isinstance(param, (list, tuple)):
+        if len(param) != len(optimizer.param_groups):
+            raise ValueError(
+                f"{name} must have the same length as optimizer.param_groups. "
+                f"{name} has {len(param)} values, param_groups has {len(optimizer.param_groups)}."
+            )
+    else:
+        param = [param] * len(optimizer.param_groups)
+
+    return list(map(_copy, param))
+
+
+class LRScheduler:
+    r"""Adjusts the learning rate during optimization."""
+
+    _get_lr_called_within_step: bool = False
+
+    def __init__(
+        self, optimizer: Optimizer, last_epoch=-1, verbose="deprecated"
+    ):  # noqa: D107
+        # Attach optimizer
+        if not isinstance(optimizer, Optimizer):
+            raise TypeError(f"{type(optimizer).__name__} is not an Optimizer")
+        self.optimizer = optimizer
+
+        # Initialize epoch and base learning rates
+        if last_epoch == -1:
+            for group in optimizer.param_groups:
+                initial_lr = group["lr"]
+                if isinstance(initial_lr, Tensor):
+                    initial_lr = initial_lr.clone()
+                group.setdefault("initial_lr", initial_lr)
+        else:
+            for i, group in enumerate(optimizer.param_groups):
+                if "initial_lr" not in group:
+                    raise KeyError(
+                        "param 'initial_lr' is not specified "
+                        f"in param_groups[{i}] when resuming an optimizer"
+                    )
+        self.base_lrs: List[float] = [
+            group["initial_lr"] for group in optimizer.param_groups
+        ]
+        self.last_epoch = last_epoch
+
+        # Following https://github.com/pytorch/pytorch/issues/20124
+        # We would like to ensure that `lr_scheduler.step()` is called after
+        # `optimizer.step()`
+        def patch_track_step_called(opt: Optimizer):
+            if hasattr(opt.step, "_wrapped_by_lr_sched"):
+                # we've already patched
+                return opt.step
+
+            def wrap_step(step_fn):
+                opt_ref = ref(self.optimizer)
+                func = step_fn.__func__
+
+                @wraps(func)
+                def wrapper(*args, **kwargs):
+                    opt = opt_ref()
+                    opt._opt_called = True  # type: ignore[union-attr]
+                    return func.__get__(opt, opt.__class__)(*args, **kwargs)
+
+                wrapper._wrapped_by_lr_sched = True  # type: ignore[attr-defined]
+                return wrapper
+
+            opt.step = wrap_step(opt.step)  # type: ignore[method-assign]
+
+        patch_track_step_called(self.optimizer)
+        self.verbose = _check_verbose_deprecated_warning(verbose)
+        self._initial_step()
+
+    def _initial_step(self):
+        """Initialize step counts and perform a step."""
+        self._step_count = 0
+        self.step()
+
+    def state_dict(self):
+        """Return the state of the scheduler as a :class:`dict`.
+
+        It contains an entry for every variable in self.__dict__ which
+        is not the optimizer.
+        """
+        return {
+            key: value for key, value in self.__dict__.items() if key != "optimizer"
+        }
+
+    def load_state_dict(self, state_dict: Dict[str, Any]):
+        """Load the scheduler's state.
+
+        Args:
+            state_dict (dict): scheduler state. Should be an object returned
+                from a call to :meth:`state_dict`.
+        """
+        self.__dict__.update(state_dict)
+
+    def get_last_lr(self) -> List[float]:
+        """Return last computed learning rate by current scheduler."""
+        return self._last_lr
+
+    def get_lr(self) -> List[float]:
+        """Compute learning rate using chainable form of the scheduler."""
+        raise NotImplementedError
+
+    def print_lr(
+        self,
+        is_verbose: bool,
+        group: Dict[str, Any],
+        lr: float,
+        epoch: Optional[int] = None,
+    ):
+        """Display the current learning rate.
+
+        .. deprecated:: 2.4
+            ``print_lr()`` is deprecated. Please use ``get_last_lr()`` to access the
+            learning rate.
+        """
+        warnings.warn(
+            "`LRScheduler.print_lr()` is being deprecated. To fetch the learning rate, "
+            "please use `get_last_lr()` instead. For more details, "
+            "see https://github.com/pytorch/pytorch/issues/99270.",
+            UserWarning,
+        )
+        if is_verbose:
+            if epoch is None:
+                print(f"Adjusting learning rate of group {group} to {lr:.4e}.")
+            else:
+                epoch_str = ("%.2f" if isinstance(epoch, float) else "%.5d") % epoch
+                print(
+                    f"Epoch {epoch_str}: adjusting learning rate of group {group} to {lr:.4e}."
+                )
+
+    def step(self, epoch: Optional[int] = None):
+        """Perform a step."""
+        # Raise a warning if old pattern is detected
+        # https://github.com/pytorch/pytorch/issues/20124
+        if self._step_count == 1:
+            if not hasattr(self.optimizer.step, "_wrapped_by_lr_sched"):
+                warnings.warn(
+                    "Seems like `optimizer.step()` has been overridden after learning rate scheduler "
+                    "initialization. Please, make sure to call `optimizer.step()` before "
+                    "`lr_scheduler.step()`. See more details at "
+                    "https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate",
+                    UserWarning,
+                )
+
+            # Just check if there were two first lr_scheduler.step() calls before optimizer.step()
+            elif not getattr(self.optimizer, "_opt_called", False):
+                warnings.warn(
+                    "Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
+                    "In PyTorch 1.1.0 and later, you should call them in the opposite order: "
+                    "`optimizer.step()` before `lr_scheduler.step()`.  Failure to do this "
+                    "will result in PyTorch skipping the first value of the learning rate schedule. "
+                    "See more details at "
+                    "https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate",
+                    UserWarning,
+                )
+        self._step_count += 1
+
+        with _enable_get_lr_call(self):
+            if epoch is None:
+                self.last_epoch += 1
+                values = self.get_lr()
+            else:
+                warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
+                self.last_epoch = epoch
+                if hasattr(self, "_get_closed_form_lr"):
+                    values = cast(List[float], self._get_closed_form_lr())
+                else:
+                    values = self.get_lr()
+
+        for i, data in enumerate(zip(self.optimizer.param_groups, values)):
+            param_group, lr = data
+            if isinstance(param_group["lr"], Tensor):
+                param_group["lr"].fill_(lr)
+            else:
+                param_group["lr"] = lr
+
+        self._last_lr: List[float] = [
+            group["lr"] for group in self.optimizer.param_groups
+        ]
+
+
+def _warn_get_lr_called_within_step(lr_scheduler: LRScheduler):
+    if not lr_scheduler._get_lr_called_within_step:
+        warnings.warn(
+            "To get the last learning rate computed by the scheduler, "
+            "please use `get_last_lr()`.",
+            UserWarning,
+            stacklevel=2,
+        )
+
+
+# Including _LRScheduler for backwards compatibility
+# Subclass instead of assign because we want __name__ of _LRScheduler to be _LRScheduler (assigning would make it LRScheduler).
+class _LRScheduler(LRScheduler):
+    pass
+
+
+class _enable_get_lr_call:
+    def __init__(self, o: LRScheduler):
+        self.o = o
+
+    def __enter__(self):
+        self.o._get_lr_called_within_step = True
+        return self
+
+    def __exit__(self, type, value, traceback):
+        self.o._get_lr_called_within_step = False
+
+
+class LambdaLR(LRScheduler):
+    """Sets the initial learning rate.
+
+    The learning rate of each parameter group is set to the initial lr
+    times a given function. When last_epoch=-1, sets initial lr as lr.
+
+    Args:
+        optimizer (Optimizer): Wrapped optimizer.
+        lr_lambda (function or list): A function which computes a multiplicative
+            factor given an integer parameter epoch, or a list of such
+            functions, one for each group in optimizer.param_groups.
+        last_epoch (int): The index of last epoch. Default: -1.
+        verbose (bool | str): If ``True``, prints a message to stdout for
+            each update. Default: ``False``.
+
+            .. deprecated:: 2.2
+                ``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
+                learning rate.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> # Assuming optimizer has two groups.
+        >>> lambda1 = lambda epoch: epoch // 30
+        >>> lambda2 = lambda epoch: 0.95 ** epoch
+        >>> scheduler = LambdaLR(optimizer, lr_lambda=[lambda1, lambda2])
+        >>> for epoch in range(100):
+        >>>     train(...)
+        >>>     validate(...)
+        >>>     scheduler.step()
+    """
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        lr_lambda: Union[Callable[[int], float], List[Callable[[int], float]]],
+        last_epoch=-1,
+        verbose="deprecated",
+    ):  # noqa: D107
+        self.optimizer = optimizer
+
+        self.lr_lambdas: List[Callable[[int], float]]
+        if not isinstance(lr_lambda, list) and not isinstance(lr_lambda, tuple):
+            self.lr_lambdas = [lr_lambda] * len(optimizer.param_groups)
+        else:
+            if len(lr_lambda) != len(optimizer.param_groups):
+                raise ValueError(
+                    f"Expected {len(optimizer.param_groups)} lr_lambdas, but got {len(lr_lambda)}"
+                )
+            self.lr_lambdas = list(lr_lambda)
+        super().__init__(optimizer, last_epoch, verbose)
+
+    def state_dict(self):
+        """Return the state of the scheduler as a :class:`dict`.
+
+        It contains an entry for every variable in self.__dict__ which
+        is not the optimizer.
+        The learning rate lambda functions will only be saved if they are callable objects
+        and not if they are functions or lambdas.
+
+        When saving or loading the scheduler, please make sure to also save or load the state of the optimizer.
+        """
+        state_dict = {
+            key: value
+            for key, value in self.__dict__.items()
+            if key not in ("optimizer", "lr_lambdas")
+        }
+        state_dict["lr_lambdas"] = [None] * len(self.lr_lambdas)
+
+        for idx, fn in enumerate(self.lr_lambdas):
+            if not isinstance(fn, types.FunctionType):
+                state_dict["lr_lambdas"][idx] = fn.__dict__.copy()
+
+        return state_dict
+
+    def load_state_dict(self, state_dict):
+        """Load the scheduler's state.
+
+        When saving or loading the scheduler, please make sure to also save or load the state of the optimizer.
+
+        Args:
+            state_dict (dict): scheduler state. Should be an object returned
+                from a call to :meth:`state_dict`.
+        """
+        lr_lambdas = state_dict.pop("lr_lambdas")
+        self.__dict__.update(state_dict)
+        # Restore state_dict keys in order to prevent side effects
+        # https://github.com/pytorch/pytorch/issues/32756
+        state_dict["lr_lambdas"] = lr_lambdas
+
+        for idx, fn in enumerate(lr_lambdas):
+            if fn is not None:
+                self.lr_lambdas[idx].__dict__.update(fn)
+
+    def get_lr(self):
+        """Compute learning rate."""
+        _warn_get_lr_called_within_step(self)
+
+        return [
+            base_lr * lmbda(self.last_epoch)
+            for lmbda, base_lr in zip(self.lr_lambdas, self.base_lrs)
+        ]
+
+
+class MultiplicativeLR(LRScheduler):
+    """Multiply the learning rate of each parameter group by the factor given in the specified function.
+
+    When last_epoch=-1, set initial lr as lr.
+
+    Args:
+        optimizer (Optimizer): Wrapped optimizer.
+        lr_lambda (function or list): A function which computes a multiplicative
+            factor given an integer parameter epoch, or a list of such
+            functions, one for each group in optimizer.param_groups.
+        last_epoch (int): The index of last epoch. Default: -1.
+        verbose (bool | str): If ``True``, prints a message to stdout for
+            each update. Default: ``False``.
+
+            .. deprecated:: 2.2
+                ``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
+                learning rate.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> lmbda = lambda epoch: 0.95
+        >>> scheduler = MultiplicativeLR(optimizer, lr_lambda=lmbda)
+        >>> for epoch in range(100):
+        >>>     train(...)
+        >>>     validate(...)
+        >>>     scheduler.step()
+    """
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        lr_lambda: Union[Callable[[int], float], List[Callable[[int], float]]],
+        last_epoch=-1,
+        verbose="deprecated",
+    ):  # noqa: D107
+        self.optimizer = optimizer
+
+        self.lr_lambdas: List[Callable[[int], float]]
+        if not isinstance(lr_lambda, list) and not isinstance(lr_lambda, tuple):
+            self.lr_lambdas = [lr_lambda] * len(optimizer.param_groups)
+        else:
+            if len(lr_lambda) != len(optimizer.param_groups):
+                raise ValueError(
+                    f"Expected {len(optimizer.param_groups)} lr_lambdas, but got {len(lr_lambda)}"
+                )
+            self.lr_lambdas = list(lr_lambda)
+        super().__init__(optimizer, last_epoch, verbose)
+
+    def state_dict(self):
+        """Return the state of the scheduler as a :class:`dict`.
+
+        It contains an entry for every variable in self.__dict__ which
+        is not the optimizer.
+        The learning rate lambda functions will only be saved if they are callable objects
+        and not if they are functions or lambdas.
+        """
+        state_dict = {
+            key: value
+            for key, value in self.__dict__.items()
+            if key not in ("optimizer", "lr_lambdas")
+        }
+        state_dict["lr_lambdas"] = [None] * len(self.lr_lambdas)
+
+        for idx, fn in enumerate(self.lr_lambdas):
+            if not isinstance(fn, types.FunctionType):
+                state_dict["lr_lambdas"][idx] = fn.__dict__.copy()
+
+        return state_dict
+
+    def load_state_dict(self, state_dict):
+        """Load the scheduler's state.
+
+        Args:
+            state_dict (dict): scheduler state. Should be an object returned
+                from a call to :meth:`state_dict`.
+        """
+        lr_lambdas = state_dict.pop("lr_lambdas")
+        self.__dict__.update(state_dict)
+        # Restore state_dict keys in order to prevent side effects
+        # https://github.com/pytorch/pytorch/issues/32756
+        state_dict["lr_lambdas"] = lr_lambdas
+
+        for idx, fn in enumerate(lr_lambdas):
+            if fn is not None:
+                self.lr_lambdas[idx].__dict__.update(fn)
+
+    def get_lr(self):
+        """Compute the learning rate of each parameter group."""
+        _warn_get_lr_called_within_step(self)
+
+        if self.last_epoch > 0:
+            return [
+                group["lr"] * lmbda(self.last_epoch)
+                for lmbda, group in zip(self.lr_lambdas, self.optimizer.param_groups)
+            ]
+        else:
+            return [group["lr"] for group in self.optimizer.param_groups]
+
+
+class StepLR(LRScheduler):
+    """Decays the learning rate of each parameter group by gamma every step_size epochs.
+
+    Notice that such decay can happen simultaneously with other changes to the learning rate
+    from outside this scheduler. When last_epoch=-1, sets initial lr as lr.
+
+    Args:
+        optimizer (Optimizer): Wrapped optimizer.
+        step_size (int): Period of learning rate decay.
+        gamma (float): Multiplicative factor of learning rate decay.
+            Default: 0.1.
+        last_epoch (int): The index of last epoch. Default: -1.
+        verbose (bool | str): If ``True``, prints a message to stdout for
+            each update. Default: ``False``.
+
+            .. deprecated:: 2.2
+                ``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
+                learning rate.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> # Assuming optimizer uses lr = 0.05 for all groups
+        >>> # lr = 0.05     if epoch < 30
+        >>> # lr = 0.005    if 30 <= epoch < 60
+        >>> # lr = 0.0005   if 60 <= epoch < 90
+        >>> # ...
+        >>> scheduler = StepLR(optimizer, step_size=30, gamma=0.1)
+        >>> for epoch in range(100):
+        >>>     train(...)
+        >>>     validate(...)
+        >>>     scheduler.step()
+    """
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        step_size: int,
+        gamma=0.1,
+        last_epoch=-1,
+        verbose="deprecated",
+    ):  # noqa: D107
+        self.step_size = step_size
+        self.gamma = gamma
+        super().__init__(optimizer, last_epoch, verbose)
+
+    def get_lr(self):
+        """Compute the learning rate of each parameter group."""
+        _warn_get_lr_called_within_step(self)
+
+        if (self.last_epoch == 0) or (self.last_epoch % self.step_size != 0):
+            return [group["lr"] for group in self.optimizer.param_groups]
+        return [group["lr"] * self.gamma for group in self.optimizer.param_groups]
+
+    def _get_closed_form_lr(self):
+        return [
+            base_lr * self.gamma ** (self.last_epoch // self.step_size)
+            for base_lr in self.base_lrs
+        ]
+
+
+class MultiStepLR(LRScheduler):
+    """Decays the learning rate of each parameter group by gamma once the number of epoch reaches one of the milestones.
+
+    Notice that such decay can happen simultaneously with other changes to the learning rate
+    from outside this scheduler. When last_epoch=-1, sets initial lr as lr.
+
+    Args:
+        optimizer (Optimizer): Wrapped optimizer.
+        milestones (list): List of epoch indices. Must be increasing.
+        gamma (float): Multiplicative factor of learning rate decay.
+            Default: 0.1.
+        last_epoch (int): The index of last epoch. Default: -1.
+        verbose (bool | str): If ``True``, prints a message to stdout for
+            each update. Default: ``False``.
+
+            .. deprecated:: 2.2
+                ``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
+                learning rate.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> # Assuming optimizer uses lr = 0.05 for all groups
+        >>> # lr = 0.05     if epoch < 30
+        >>> # lr = 0.005    if 30 <= epoch < 80
+        >>> # lr = 0.0005   if epoch >= 80
+        >>> scheduler = MultiStepLR(optimizer, milestones=[30,80], gamma=0.1)
+        >>> for epoch in range(100):
+        >>>     train(...)
+        >>>     validate(...)
+        >>>     scheduler.step()
+    """
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        milestones: Iterable[int],
+        gamma=0.1,
+        last_epoch=-1,
+        verbose="deprecated",
+    ):  # noqa: D107
+        self.milestones = Counter(milestones)
+        self.gamma = gamma
+        super().__init__(optimizer, last_epoch, verbose)
+
+    def get_lr(self):
+        """Compute the learning rate of each parameter group."""
+        _warn_get_lr_called_within_step(self)
+
+        if self.last_epoch not in self.milestones:
+            return [group["lr"] for group in self.optimizer.param_groups]
+        return [
+            group["lr"] * self.gamma ** self.milestones[self.last_epoch]
+            for group in self.optimizer.param_groups
+        ]
+
+    def _get_closed_form_lr(self):
+        milestones = sorted(self.milestones.elements())
+        return [
+            base_lr * self.gamma ** bisect_right(milestones, self.last_epoch)
+            for base_lr in self.base_lrs
+        ]
+
+
+class ConstantLR(LRScheduler):
+    """Multiply the learning rate of each parameter group by a small constant factor.
+
+    The multiplication is done until the number of epoch reaches a pre-defined milestone: total_iters.
+    Notice that such multiplication of the small constant factor can
+    happen simultaneously with other changes to the learning rate from outside this scheduler.
+    When last_epoch=-1, sets initial lr as lr.
+
+    Args:
+        optimizer (Optimizer): Wrapped optimizer.
+        factor (float): The number we multiply learning rate until the milestone. Default: 1./3.
+        total_iters (int): The number of steps that the scheduler multiplies the learning rate by the factor.
+            Default: 5.
+        last_epoch (int): The index of the last epoch. Default: -1.
+        verbose (bool | str): If ``True``, prints a message to stdout for
+            each update. Default: ``False``.
+
+            .. deprecated:: 2.2
+                ``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
+                learning rate.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> # Assuming optimizer uses lr = 0.05 for all groups
+        >>> # lr = 0.025   if epoch == 0
+        >>> # lr = 0.025   if epoch == 1
+        >>> # lr = 0.025   if epoch == 2
+        >>> # lr = 0.025   if epoch == 3
+        >>> # lr = 0.05    if epoch >= 4
+        >>> scheduler = ConstantLR(optimizer, factor=0.5, total_iters=4)
+        >>> for epoch in range(100):
+        >>>     train(...)
+        >>>     validate(...)
+        >>>     scheduler.step()
+    """
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        factor=1.0 / 3,
+        total_iters=5,
+        last_epoch=-1,
+        verbose="deprecated",
+    ):  # noqa: D107
+        if factor > 1.0 or factor < 0:
+            raise ValueError(
+                "Constant multiplicative factor expected to be between 0 and 1."
+            )
+
+        self.factor = factor
+        self.total_iters = total_iters
+        super().__init__(optimizer, last_epoch, verbose)
+
+    def get_lr(self):
+        """Compute the learning rate of each parameter group."""
+        _warn_get_lr_called_within_step(self)
+
+        if self.last_epoch == 0:
+            return [group["lr"] * self.factor for group in self.optimizer.param_groups]
+
+        if self.last_epoch != self.total_iters:
+            return [group["lr"] for group in self.optimizer.param_groups]
+
+        return [
+            group["lr"] * (1.0 / self.factor) for group in self.optimizer.param_groups
+        ]
+
+    def _get_closed_form_lr(self):
+        return [
+            base_lr
+            * (self.factor + (self.last_epoch >= self.total_iters) * (1 - self.factor))
+            for base_lr in self.base_lrs
+        ]
+
+
+class LinearLR(LRScheduler):
+    """Decays the learning rate of each parameter group by linearly changing small multiplicative factor.
+
+    The multiplication is done until the number of epoch reaches a pre-defined milestone: total_iters.
+    Notice that such decay can happen simultaneously with other changes to the learning rate
+    from outside this scheduler. When last_epoch=-1, sets initial lr as lr.
+
+    Args:
+        optimizer (Optimizer): Wrapped optimizer.
+        start_factor (float): The number we multiply learning rate in the first epoch.
+            The multiplication factor changes towards end_factor in the following epochs.
+            Default: 1./3.
+        end_factor (float): The number we multiply learning rate at the end of linear changing
+            process. Default: 1.0.
+        total_iters (int): The number of iterations that multiplicative factor reaches to 1.
+            Default: 5.
+        last_epoch (int): The index of the last epoch. Default: -1.
+        verbose (bool | str): If ``True``, prints a message to stdout for
+            each update. Default: ``False``.
+
+            .. deprecated:: 2.2
+                ``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
+                learning rate.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> # Assuming optimizer uses lr = 0.05 for all groups
+        >>> # lr = 0.025    if epoch == 0
+        >>> # lr = 0.03125  if epoch == 1
+        >>> # lr = 0.0375   if epoch == 2
+        >>> # lr = 0.04375  if epoch == 3
+        >>> # lr = 0.05    if epoch >= 4
+        >>> scheduler = LinearLR(optimizer, start_factor=0.5, total_iters=4)
+        >>> for epoch in range(100):
+        >>>     train(...)
+        >>>     validate(...)
+        >>>     scheduler.step()
+    """
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        start_factor=1.0 / 3,
+        end_factor=1.0,
+        total_iters=5,
+        last_epoch=-1,
+        verbose="deprecated",
+    ):  # noqa: D107
+        if start_factor > 1.0 or start_factor <= 0:
+            raise ValueError(
+                "Starting multiplicative factor expected to be greater than 0 and less or equal to 1."
+            )
+
+        if end_factor > 1.0 or end_factor < 0:
+            raise ValueError(
+                "Ending multiplicative factor expected to be between 0 and 1."
+            )
+
+        self.start_factor = start_factor
+        self.end_factor = end_factor
+        self.total_iters = total_iters
+        super().__init__(optimizer, last_epoch, verbose)
+
+    def get_lr(self):
+        """Compute the learning rate."""
+        _warn_get_lr_called_within_step(self)
+
+        if self.last_epoch == 0:
+            return [
+                group["lr"] * self.start_factor for group in self.optimizer.param_groups
+            ]
+
+        if self.last_epoch > self.total_iters:
+            return [group["lr"] for group in self.optimizer.param_groups]
+
+        return [
+            group["lr"]
+            * (
+                1.0
+                + (self.end_factor - self.start_factor)
+                / (
+                    self.total_iters * self.start_factor
+                    + (self.last_epoch - 1) * (self.end_factor - self.start_factor)
+                )
+            )
+            for group in self.optimizer.param_groups
+        ]
+
+    def _get_closed_form_lr(self):
+        return [
+            base_lr
+            * (
+                self.start_factor
+                + (self.end_factor - self.start_factor)
+                * min(self.total_iters, self.last_epoch)
+                / self.total_iters
+            )
+            for base_lr in self.base_lrs
+        ]
+
+
+class ExponentialLR(LRScheduler):
+    """Decays the learning rate of each parameter group by gamma every epoch.
+
+    When last_epoch=-1, sets initial lr as lr.
+
+    Args:
+        optimizer (Optimizer): Wrapped optimizer.
+        gamma (float): Multiplicative factor of learning rate decay.
+        last_epoch (int): The index of last epoch. Default: -1.
+        verbose (bool | str): If ``True``, prints a message to stdout for
+            each update. Default: ``False``.
+
+            .. deprecated:: 2.2
+                ``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
+                learning rate.
+    """
+
+    def __init__(
+        self, optimizer: Optimizer, gamma: float, last_epoch=-1, verbose="deprecated"
+    ):  # noqa: D107
+        self.gamma = gamma
+        super().__init__(optimizer, last_epoch, verbose)
+
+    def get_lr(self):
+        """Compute the learning rate of each parameter group."""
+        _warn_get_lr_called_within_step(self)
+
+        if self.last_epoch == 0:
+            return [group["lr"] for group in self.optimizer.param_groups]
+        return [group["lr"] * self.gamma for group in self.optimizer.param_groups]
+
+    def _get_closed_form_lr(self):
+        return [base_lr * self.gamma**self.last_epoch for base_lr in self.base_lrs]
+
+
+class SequentialLR(LRScheduler):
+    """Contains a list of schedulers expected to be called sequentially during the optimization process.
+
+    Specifically, the schedulers will be called according to the milestone points, which should provide exact
+    intervals by which each scheduler should be called at a given epoch.
+
+    Args:
+        optimizer (Optimizer): Wrapped optimizer.
+        schedulers (list): List of chained schedulers.
+        milestones (list): List of integers that reflects milestone points.
+        last_epoch (int): The index of last epoch. Default: -1.
+        verbose (bool | str): Does nothing.
+
+            .. deprecated:: 2.2
+                ``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
+                learning rate.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> # Assuming optimizer uses lr = 1. for all groups
+        >>> # lr = 0.1     if epoch == 0
+        >>> # lr = 0.1     if epoch == 1
+        >>> # lr = 0.9     if epoch == 2
+        >>> # lr = 0.81    if epoch == 3
+        >>> # lr = 0.729   if epoch == 4
+        >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=2)
+        >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9)
+        >>> scheduler = SequentialLR(optimizer, schedulers=[scheduler1, scheduler2], milestones=[2])
+        >>> for epoch in range(100):
+        >>>     train(...)
+        >>>     validate(...)
+        >>>     scheduler.step()
+    """
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        schedulers: List[LRScheduler],
+        milestones: List[int],
+        last_epoch=-1,
+        verbose="deprecated",
+    ):  # noqa: D107
+        if len(schedulers) < 1:
+            raise ValueError(
+                f"{self.__class__.__name__} expects at least one scheduler, but got no scheduler."
+            )
+
+        for scheduler_idx, scheduler in enumerate(schedulers):
+            if not hasattr(scheduler, "optimizer"):
+                raise TypeError(
+                    f"{self.__class__.__name__} at index {scheduler_idx} should have `optimizer` as its attribute."
+                )
+            if isinstance(scheduler, ReduceLROnPlateau):
+                raise ValueError(
+                    f"{self.__class__.__name__} does not support `ReduceLROnPlateau` scheduler as it "
+                    "requires additional kwargs to be specified when calling `step`, "
+                    f"but got one at index {scheduler_idx} in the given schedulers sequence."
+                )
+            if optimizer != scheduler.optimizer:
+                raise ValueError(
+                    f"{self.__class__.__name__} expects all schedulers to belong to the same optimizer, but "
+                    f"got scheduler {scheduler.__class__.__name__} at index {scheduler_idx} has {scheduler.optimizer}, "
+                    f"which is different from {optimizer.__class__.__name__}."
+                )
+
+        if len(milestones) != len(schedulers) - 1:
+            raise ValueError(
+                "Sequential Schedulers expects number of schedulers provided to be one more "
+                f"than the number of milestone points, but got number of schedulers {len(schedulers)} and the "
+                f"number of milestones to be equal to {len(milestones)}"
+            )
+        _check_verbose_deprecated_warning(verbose)
+        self._schedulers = schedulers
+        self._milestones = milestones
+        self.last_epoch = last_epoch + 1
+        self.optimizer = optimizer
+
+        # Reset learning rates back to initial values
+        for group in self.optimizer.param_groups:
+            group["lr"] = group["initial_lr"]
+
+        # "Undo" the step performed by other schedulers
+        for scheduler in self._schedulers:
+            scheduler.last_epoch -= 1
+
+        # Perform the initial step for only the first scheduler
+        self._schedulers[0]._initial_step()
+
+        self._last_lr = schedulers[0].get_last_lr()
+
+    def step(self):
+        """Perform a step."""
+        self.last_epoch += 1
+        idx = bisect_right(self._milestones, self.last_epoch)
+        scheduler = self._schedulers[idx]
+        if idx > 0 and self._milestones[idx - 1] == self.last_epoch:
+            scheduler.step(0)
+        else:
+            scheduler.step()
+
+        self._last_lr = scheduler.get_last_lr()
+
+    def state_dict(self):
+        """Return the state of the scheduler as a :class:`dict`.
+
+        It contains an entry for every variable in self.__dict__ which
+        is not the optimizer.
+        The wrapped scheduler states will also be saved.
+        """
+        state_dict = {
+            key: value
+            for key, value in self.__dict__.items()
+            if key not in ("optimizer", "_schedulers")
+        }
+        state_dict["_schedulers"] = [None] * len(self._schedulers)
+
+        for idx, s in enumerate(self._schedulers):
+            state_dict["_schedulers"][idx] = s.state_dict()
+
+        return state_dict
+
+    def load_state_dict(self, state_dict):
+        """Load the scheduler's state.
+
+        Args:
+            state_dict (dict): scheduler state. Should be an object returned
+                from a call to :meth:`state_dict`.
+        """
+        _schedulers = state_dict.pop("_schedulers")
+        self.__dict__.update(state_dict)
+        # Restore state_dict keys in order to prevent side effects
+        # https://github.com/pytorch/pytorch/issues/32756
+        state_dict["_schedulers"] = _schedulers
+
+        for idx, s in enumerate(_schedulers):
+            self._schedulers[idx].load_state_dict(s)
+
+
+class PolynomialLR(LRScheduler):
+    """Decays the learning rate of each parameter group using a polynomial function in the given total_iters.
+
+    When last_epoch=-1, sets initial lr as lr.
+
+    Args:
+        optimizer (Optimizer): Wrapped optimizer.
+        total_iters (int): The number of steps that the scheduler decays the learning rate. Default: 5.
+        power (float): The power of the polynomial. Default: 1.0.
+        verbose (bool | str): If ``True``, prints a message to stdout for
+            each update. Default: ``False``.
+
+            .. deprecated:: 2.2
+                ``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
+                learning rate.
+
+    Example:
+        >>> # xdoctest: +SKIP("undefined vars")
+        >>> # Assuming optimizer uses lr = 0.001 for all groups
+        >>> # lr = 0.001     if epoch == 0
+        >>> # lr = 0.00075   if epoch == 1
+        >>> # lr = 0.00050   if epoch == 2
+        >>> # lr = 0.00025   if epoch == 3
+        >>> # lr = 0.0       if epoch >= 4
+        >>> scheduler = PolynomialLR(optimizer, total_iters=4, power=1.0)
+        >>> for epoch in range(100):
+        >>>     train(...)
+        >>>     validate(...)
+        >>>     scheduler.step()
+    """
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        total_iters=5,
+        power=1.0,
+        last_epoch=-1,
+        verbose="deprecated",
+    ):  # noqa: D107
+        self.total_iters = total_iters
+        self.power = power
+        super().__init__(optimizer, last_epoch, verbose)
+
+    def get_lr(self):
+        """Compute the learning rate."""
+        _warn_get_lr_called_within_step(self)
+
+        if self.last_epoch == 0 or self.last_epoch > self.total_iters:
+            return [group["lr"] for group in self.optimizer.param_groups]
+
+        decay_factor = (
+            (1.0 - self.last_epoch / self.total_iters)
+            / (1.0 - (self.last_epoch - 1) / self.total_iters)
+        ) ** self.power
+        return [group["lr"] * decay_factor for group in self.optimizer.param_groups]
+
+    def _get_closed_form_lr(self):
+        return [
+            (
+                base_lr
+                * (1.0 - min(self.total_iters, self.last_epoch) / self.total_iters)
+                ** self.power
+            )
+            for base_lr in self.base_lrs
+        ]
+
+
+class CosineAnnealingLR(LRScheduler):
+    r"""Set the learning rate of each parameter group using a cosine annealing schedule.
+
+    The :math:`\eta_{max}` is set to the initial lr and
+    :math:`T_{cur}` is the number of epochs since the last restart in SGDR:
+
+    .. math::
+        \begin{aligned}
+            \eta_t & = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1
+            + \cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right),
+            & T_{cur} \neq (2k+1)T_{max}; \\
+            \eta_{t+1} & = \eta_{t} + \frac{1}{2}(\eta_{max} - \eta_{min})
+            \left(1 - \cos\left(\frac{1}{T_{max}}\pi\right)\right),
+            & T_{cur} = (2k+1)T_{max}.
+        \end{aligned}
+
+    When last_epoch=-1, sets initial lr as lr. Notice that because the schedule
+    is defined recursively, the learning rate can be simultaneously modified
+    outside this scheduler by other operators. If the learning rate is set
+    solely by this scheduler, the learning rate at each step becomes:
+
+    .. math::
+        \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 +
+        \cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right)
+
+    It has been proposed in
+    `SGDR: Stochastic Gradient Descent with Warm Restarts`_. Note that this only
+    implements the cosine annealing part of SGDR, and not the restarts.
+
+    Args:
+        optimizer (Optimizer): Wrapped optimizer.
+        T_max (int): Maximum number of iterations.
+        eta_min (float): Minimum learning rate. Default: 0.
+        last_epoch (int): The index of last epoch. Default: -1.
+        verbose (bool | str): If ``True``, prints a message to stdout for
+            each update. Default: ``False``.
+
+            .. deprecated:: 2.2
+                ``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
+                learning rate.
+
+    .. _SGDR\: Stochastic Gradient Descent with Warm Restarts:
+        https://arxiv.org/abs/1608.03983
+    """
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        T_max: int,
+        eta_min=0.0,
+        last_epoch=-1,
+        verbose="deprecated",
+    ):  # noqa: D107
+        self.T_max = T_max
+        self.eta_min = eta_min
+        super().__init__(optimizer, last_epoch, verbose)
+
+    def get_lr(self):
+        """Retrieve the learning rate of each parameter group."""
+        _warn_get_lr_called_within_step(self)
+
+        if self.last_epoch == 0:
+            return [group["lr"] for group in self.optimizer.param_groups]
+        elif self._step_count == 1 and self.last_epoch > 0:
+            return [
+                self.eta_min
+                + (base_lr - self.eta_min)
+                * (1 + math.cos((self.last_epoch) * math.pi / self.T_max))
+                / 2
+                for base_lr, group in zip(self.base_lrs, self.optimizer.param_groups)
+            ]
+        elif (self.last_epoch - 1 - self.T_max) % (2 * self.T_max) == 0:
+            return [
+                group["lr"]
+                + (base_lr - self.eta_min) * (1 - math.cos(math.pi / self.T_max)) / 2
+                for base_lr, group in zip(self.base_lrs, self.optimizer.param_groups)
+            ]
+        return [
+            (1 + math.cos(math.pi * self.last_epoch / self.T_max))
+            / (1 + math.cos(math.pi * (self.last_epoch - 1) / self.T_max))
+            * (group["lr"] - self.eta_min)
+            + self.eta_min
+            for group in self.optimizer.param_groups
+        ]
+
+    def _get_closed_form_lr(self):
+        return [
+            self.eta_min
+            + (base_lr - self.eta_min)
+            * (1 + math.cos(math.pi * self.last_epoch / self.T_max))
+            / 2
+            for base_lr in self.base_lrs
+        ]
+
+
+class ChainedScheduler(LRScheduler):
+    """Chains a list of learning rate schedulers.
+
+    Takes in a sequence of chainable learning rate schedulers and calls their
+    step() functions consecutively in just one call to step().
+
+    Args:
+        schedulers (sequence): sequence of chained schedulers.
+        optimizer (Optimizer, optional): Wrapped optimizer. Default: None.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> # Assuming optimizer uses lr = 1. for all groups
+        >>> # lr = 0.09     if epoch == 0
+        >>> # lr = 0.081    if epoch == 1
+        >>> # lr = 0.729    if epoch == 2
+        >>> # lr = 0.6561   if epoch == 3
+        >>> # lr = 0.59049  if epoch >= 4
+        >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=2)
+        >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9)
+        >>> scheduler = ChainedScheduler([scheduler1, scheduler2], optimizer=optimizer)
+        >>> for epoch in range(100):
+        >>>     train(...)
+        >>>     validate(...)
+        >>>     scheduler.step()
+    """
+
+    def __init__(
+        self, schedulers: Sequence[LRScheduler], optimizer: Optional[Optimizer] = None
+    ):  # noqa: D107
+        if len(schedulers) < 1:
+            raise ValueError(
+                f"{self.__class__.__name__} expects at least one scheduler to be chained, but got no scheduler."
+            )
+
+        optimizer = optimizer or schedulers[0].optimizer
+        for scheduler_idx, scheduler in enumerate(schedulers):
+            if not hasattr(scheduler, "optimizer"):
+                raise TypeError(
+                    f"{self.__class__.__name__} at index {scheduler_idx} should have `optimizer` as its attribute."
+                )
+            if isinstance(scheduler, ReduceLROnPlateau):
+                raise ValueError(
+                    f"{self.__class__.__name__} does not support `ReduceLROnPlateau` scheduler as it "
+                    "requires additional kwargs to be specified when calling `step`, "
+                    f"but got one at index {scheduler_idx} in the given schedulers sequence."
+                )
+            if optimizer != scheduler.optimizer:
+                raise ValueError(
+                    f"{self.__class__.__name__} expects all schedulers to belong to the same optimizer, but "
+                    f"got scheduler {scheduler.__class__.__name__} at index {scheduler_idx} has {scheduler.optimizer}, "
+                    f"which is different from {optimizer.__class__.__name__}."
+                )
+        self._schedulers = schedulers
+        self.optimizer = optimizer
+        self._last_lr = [
+            group["lr"] for group in self._schedulers[-1].optimizer.param_groups
+        ]
+
+    def step(self):
+        """Perform a step."""
+        for scheduler in self._schedulers:
+            scheduler.step()
+        self._last_lr = [
+            group["lr"] for group in self._schedulers[-1].optimizer.param_groups
+        ]
+
+    def state_dict(self):
+        """Return the state of the scheduler as a :class:`dict`.
+
+        It contains an entry for every variable in self.__dict__ which
+        is not the optimizer.
+        The wrapped scheduler states will also be saved.
+        """
+        state_dict = {
+            key: value
+            for key, value in self.__dict__.items()
+            if key not in ("optimizer", "_schedulers")
+        }
+        state_dict["_schedulers"] = [None] * len(self._schedulers)
+
+        for idx, s in enumerate(self._schedulers):
+            state_dict["_schedulers"][idx] = s.state_dict()
+
+        return state_dict
+
+    def load_state_dict(self, state_dict):
+        """Load the scheduler's state.
+
+        Args:
+            state_dict (dict): scheduler state. Should be an object returned
+                from a call to :meth:`state_dict`.
+        """
+        _schedulers = state_dict.pop("_schedulers")
+        self.__dict__.update(state_dict)
+        # Restore state_dict keys in order to prevent side effects
+        # https://github.com/pytorch/pytorch/issues/32756
+        state_dict["_schedulers"] = _schedulers
+
+        for idx, s in enumerate(_schedulers):
+            self._schedulers[idx].load_state_dict(s)
+
+
+class ReduceLROnPlateau(LRScheduler):
+    """Reduce learning rate when a metric has stopped improving.
+
+    Models often benefit from reducing the learning rate by a factor
+    of 2-10 once learning stagnates. This scheduler reads a metrics
+    quantity and if no improvement is seen for a 'patience' number
+    of epochs, the learning rate is reduced.
+
+    Args:
+        optimizer (Optimizer): Wrapped optimizer.
+        mode (str): One of `min`, `max`. In `min` mode, lr will
+            be reduced when the quantity monitored has stopped
+            decreasing; in `max` mode it will be reduced when the
+            quantity monitored has stopped increasing. Default: 'min'.
+        factor (float): Factor by which the learning rate will be
+            reduced. new_lr = lr * factor. Default: 0.1.
+        patience (int): The number of allowed epochs with no improvement after
+            which the learning rate will be reduced.
+            For example, consider the case of having no patience (`patience = 0`).
+            In the first epoch, a baseline is established and is always considered good as there's no previous baseline.
+            In the second epoch, if the performance is worse than the baseline,
+            we have what is considered an intolerable epoch.
+            Since the count of intolerable epochs (1) is greater than the patience level (0),
+            the learning rate is reduced at the end of this epoch.
+            From the third epoch onwards, the learning rate continues to be reduced at the end of each epoch
+            if the performance is worse than the baseline. If the performance improves or remains the same,
+            the learning rate is not adjusted.
+            Default: 10.
+        threshold (float): Threshold for measuring the new optimum,
+            to only focus on significant changes. Default: 1e-4.
+        threshold_mode (str): One of `rel`, `abs`. In `rel` mode,
+            dynamic_threshold = best * ( 1 + threshold ) in 'max'
+            mode or best * ( 1 - threshold ) in `min` mode.
+            In `abs` mode, dynamic_threshold = best + threshold in
+            `max` mode or best - threshold in `min` mode. Default: 'rel'.
+        cooldown (int): Number of epochs to wait before resuming
+            normal operation after lr has been reduced. Default: 0.
+        min_lr (float or list): A scalar or a list of scalars. A
+            lower bound on the learning rate of all param groups
+            or each group respectively. Default: 0.
+        eps (float): Minimal decay applied to lr. If the difference
+            between new and old lr is smaller than eps, the update is
+            ignored. Default: 1e-8.
+        verbose (bool | str): If ``True``, prints a message to stdout for
+            each update. Default: ``False``.
+
+            .. deprecated:: 2.2
+                ``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
+                learning rate.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
+        >>> scheduler = ReduceLROnPlateau(optimizer, 'min')
+        >>> for epoch in range(10):
+        >>>     train(...)
+        >>>     val_loss = validate(...)
+        >>>     # Note that step should be called after validate()
+        >>>     scheduler.step(val_loss)
+    """
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        mode: Literal["min", "max"] = "min",
+        factor=0.1,
+        patience=10,
+        threshold=1e-4,
+        threshold_mode: Literal["rel", "abs"] = "rel",
+        cooldown=0,
+        min_lr: Union[List[float], float] = 0,
+        eps=1e-8,
+        verbose="deprecated",
+    ):  # noqa: D107
+        if factor >= 1.0:
+            raise ValueError("Factor should be < 1.0.")
+        self.factor = factor
+
+        # Attach optimizer
+        if not isinstance(optimizer, Optimizer):
+            raise TypeError(f"{type(optimizer).__name__} is not an Optimizer")
+        self.optimizer = optimizer
+
+        if isinstance(min_lr, (list, tuple)):
+            if len(min_lr) != len(optimizer.param_groups):
+                raise ValueError(
+                    f"expected {len(optimizer.param_groups)} min_lrs, got {len(min_lr)}"
+                )
+            self.min_lrs = list(min_lr)
+        else:
+            self.min_lrs = [min_lr] * len(optimizer.param_groups)
+
+        self.patience = patience
+
+        self.verbose = _check_verbose_deprecated_warning(verbose)
+        self.cooldown = cooldown
+        self.cooldown_counter = 0
+        self.mode = mode
+        self.threshold = threshold
+        self.threshold_mode = threshold_mode
+        self.best: float
+        self.num_bad_epochs: int
+        self.mode_worse: float  # the worse value for the chosen mode
+        self.eps = eps
+        self.last_epoch = 0
+        self._last_lr = [group["lr"] for group in self.optimizer.param_groups]
+        self._init_is_better(
+            mode=mode, threshold=threshold, threshold_mode=threshold_mode
+        )
+        self._reset()
+
+    def _reset(self):
+        """Reset num_bad_epochs counter and cooldown counter."""
+        self.best = self.mode_worse
+        self.cooldown_counter = 0
+        self.num_bad_epochs = 0
+
+    def step(self, metrics: SupportsFloat, epoch=None):  # type: ignore[override]
+        """Perform a step."""
+        # convert `metrics` to float, in case it's a zero-dim Tensor
+        current = float(metrics)
+        if epoch is None:
+            epoch = self.last_epoch + 1
+        else:
+            warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
+        self.last_epoch = epoch
+
+        if self.is_better(current, self.best):
+            self.best = current
+            self.num_bad_epochs = 0
+        else:
+            self.num_bad_epochs += 1
+
+        if self.in_cooldown:
+            self.cooldown_counter -= 1
+            self.num_bad_epochs = 0  # ignore any bad epochs in cooldown
+
+        if self.num_bad_epochs > self.patience:
+            self._reduce_lr(epoch)
+            self.cooldown_counter = self.cooldown
+            self.num_bad_epochs = 0
+
+        self._last_lr = [group["lr"] for group in self.optimizer.param_groups]
+
+    def _reduce_lr(self, epoch):
+        for i, param_group in enumerate(self.optimizer.param_groups):
+            old_lr = float(param_group["lr"])
+            new_lr = max(old_lr * self.factor, self.min_lrs[i])
+            if old_lr - new_lr > self.eps:
+                param_group["lr"] = new_lr
+
+    @property
+    def in_cooldown(self):  # noqa: D102
+        return self.cooldown_counter > 0
+
+    def is_better(self, a, best):  # noqa: D102
+        if self.mode == "min" and self.threshold_mode == "rel":
+            rel_epsilon = 1.0 - self.threshold
+            return a < best * rel_epsilon
+
+        elif self.mode == "min" and self.threshold_mode == "abs":
+            return a < best - self.threshold
+
+        elif self.mode == "max" and self.threshold_mode == "rel":
+            rel_epsilon = self.threshold + 1.0
+            return a > best * rel_epsilon
+
+        else:  # mode == 'max' and epsilon_mode == 'abs':
+            return a > best + self.threshold
+
+    def _init_is_better(self, mode, threshold, threshold_mode):
+        if mode not in {"min", "max"}:
+            raise ValueError("mode " + mode + " is unknown!")
+        if threshold_mode not in {"rel", "abs"}:
+            raise ValueError("threshold mode " + threshold_mode + " is unknown!")
+
+        if mode == "min":
+            self.mode_worse = inf
+        else:  # mode == 'max':
+            self.mode_worse = -inf
+
+        self.mode = mode
+        self.threshold = threshold
+        self.threshold_mode = threshold_mode
+
+    def state_dict(self):  # noqa: D102
+        return {
+            key: value for key, value in self.__dict__.items() if key != "optimizer"
+        }
+
+    def load_state_dict(self, state_dict):
+        """Load the scheduler's state."""
+        self.__dict__.update(state_dict)
+        self._init_is_better(
+            mode=self.mode, threshold=self.threshold, threshold_mode=self.threshold_mode
+        )
+
+
+class CyclicLR(LRScheduler):
+    r"""Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR).
+
+    The policy cycles the learning rate between two boundaries with a constant frequency,
+    as detailed in the paper `Cyclical Learning Rates for Training Neural Networks`_.
+    The distance between the two boundaries can be scaled on a per-iteration
+    or per-cycle basis.
+
+    Cyclical learning rate policy changes the learning rate after every batch.
+    `step` should be called after a batch has been used for training.
+
+    This class has three built-in policies, as put forth in the paper:
+
+    * "triangular": A basic triangular cycle without amplitude scaling.
+    * "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle.
+    * "exp_range": A cycle that scales initial amplitude by :math:`\text{gamma}^{\text{cycle iterations}}`
+      at each cycle iteration.
+
+    This implementation was adapted from the github repo: `bckenstler/CLR`_
+
+    Args:
+        optimizer (Optimizer): Wrapped optimizer.
+        base_lr (float or list): Initial learning rate which is the
+            lower boundary in the cycle for each parameter group.
+        max_lr (float or list): Upper learning rate boundaries in the cycle
+            for each parameter group. Functionally,
+            it defines the cycle amplitude (max_lr - base_lr).
+            The lr at any cycle is the sum of base_lr
+            and some scaling of the amplitude; therefore
+            max_lr may not actually be reached depending on
+            scaling function.
+        step_size_up (int): Number of training iterations in the
+            increasing half of a cycle. Default: 2000
+        step_size_down (int): Number of training iterations in the
+            decreasing half of a cycle. If step_size_down is None,
+            it is set to step_size_up. Default: None
+        mode (str): One of {triangular, triangular2, exp_range}.
+            Values correspond to policies detailed above.
+            If scale_fn is not None, this argument is ignored.
+            Default: 'triangular'
+        gamma (float): Constant in 'exp_range' scaling function:
+            gamma**(cycle iterations)
+            Default: 1.0
+        scale_fn (function): Custom scaling policy defined by a single
+            argument lambda function, where
+            0 <= scale_fn(x) <= 1 for all x >= 0.
+            If specified, then 'mode' is ignored.
+            Default: None
+        scale_mode (str): {'cycle', 'iterations'}.
+            Defines whether scale_fn is evaluated on
+            cycle number or cycle iterations (training
+            iterations since start of cycle).
+            Default: 'cycle'
+        cycle_momentum (bool): If ``True``, momentum is cycled inversely
+            to learning rate between 'base_momentum' and 'max_momentum'.
+            Default: True
+        base_momentum (float or list): Lower momentum boundaries in the cycle
+            for each parameter group. Note that momentum is cycled inversely
+            to learning rate; at the peak of a cycle, momentum is
+            'base_momentum' and learning rate is 'max_lr'.
+            Default: 0.8
+        max_momentum (float or list): Upper momentum boundaries in the cycle
+            for each parameter group. Functionally,
+            it defines the cycle amplitude (max_momentum - base_momentum).
+            The momentum at any cycle is the difference of max_momentum
+            and some scaling of the amplitude; therefore
+            base_momentum may not actually be reached depending on
+            scaling function. Note that momentum is cycled inversely
+            to learning rate; at the start of a cycle, momentum is 'max_momentum'
+            and learning rate is 'base_lr'
+            Default: 0.9
+        last_epoch (int): The index of the last batch. This parameter is used when
+            resuming a training job. Since `step()` should be invoked after each
+            batch instead of after each epoch, this number represents the total
+            number of *batches* computed, not the total number of epochs computed.
+            When last_epoch=-1, the schedule is started from the beginning.
+            Default: -1
+        verbose (bool | str): If ``True``, prints a message to stdout for
+            each update. Default: ``False``.
+
+            .. deprecated:: 2.2
+                ``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
+                learning rate.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
+        >>> scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.01, max_lr=0.1)
+        >>> data_loader = torch.utils.data.DataLoader(...)
+        >>> for epoch in range(10):
+        >>>     for batch in data_loader:
+        >>>         train_batch(...)
+        >>>         scheduler.step()
+
+
+    .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186
+    .. _bckenstler/CLR: https://github.com/bckenstler/CLR
+    """
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        base_lr: Union[float, List[float]],
+        max_lr: Union[float, List[float]],
+        step_size_up=2000,
+        step_size_down: Optional[int] = None,
+        mode: Literal["triangular", "triangular2", "exp_range"] = "triangular",
+        gamma=1.0,
+        scale_fn: Optional[Callable[[float], float]] = None,
+        scale_mode: Literal["cycle", "iterations"] = "cycle",
+        cycle_momentum=True,
+        base_momentum=0.8,
+        max_momentum=0.9,
+        last_epoch=-1,
+        verbose="deprecated",
+    ):  # noqa: D107
+        # Attach optimizer
+        if not isinstance(optimizer, Optimizer):
+            raise TypeError(f"{type(optimizer).__name__} is not an Optimizer")
+        self.optimizer = optimizer
+
+        base_lrs = _format_param("base_lr", optimizer, base_lr)
+        if last_epoch == -1:
+            for lr, group in zip(base_lrs, optimizer.param_groups):
+                if isinstance(group["lr"], Tensor):
+                    lr_val = lr.item() if isinstance(lr, Tensor) else lr
+                    group["lr"].fill_(lr_val)
+                else:
+                    group["lr"] = lr
+
+        self.max_lrs = _format_param("max_lr", optimizer, max_lr)
+
+        step_size_up = float(step_size_up)
+        step_size_down = (
+            float(step_size_down) if step_size_down is not None else step_size_up
+        )
+        self.total_size = step_size_up + step_size_down
+        self.step_ratio = step_size_up / self.total_size
+
+        if mode not in ["triangular", "triangular2", "exp_range"] and scale_fn is None:
+            raise ValueError("mode is invalid and scale_fn is None")
+
+        self.mode = mode
+        self.gamma = gamma
+
+        self._scale_fn_ref: Callable[[float], float]
+        self._scale_fn_custom = scale_fn
+        self.scale_mode = scale_mode
+        self._init_scale_fn()
+
+        self.cycle_momentum = cycle_momentum
+        if cycle_momentum:
+            if (
+                "momentum" not in optimizer.defaults
+                and "betas" not in optimizer.defaults
+            ):
+                raise ValueError(
+                    "optimizer must support momentum or beta1 with `cycle_momentum` option enabled"
+                )
+
+            self.use_beta1 = "betas" in self.optimizer.defaults
+            self.base_momentums = _format_param(
+                "base_momentum", optimizer, base_momentum
+            )
+            self.max_momentums = _format_param("max_momentum", optimizer, max_momentum)
+            if last_epoch == -1:
+                for m_momentum, b_momentum, group in zip(
+                    self.max_momentums, self.base_momentums, optimizer.param_groups
+                ):
+                    if self.use_beta1:
+                        group["betas"] = (m_momentum, *group["betas"][1:])
+                    else:
+                        group["momentum"] = m_momentum
+                    group["max_momentum"] = m_momentum
+                    group["base_momentum"] = b_momentum
+
+        super().__init__(optimizer, last_epoch, verbose)
+        self.base_lrs = base_lrs
+
+    def _init_scale_fn(self):
+        if self._scale_fn_custom is not None:
+            return
+        if self.mode == "triangular":
+            self._scale_fn_ref = self._triangular_scale_fn
+            self.scale_mode = "cycle"
+        elif self.mode == "triangular2":
+            self._scale_fn_ref = self._triangular2_scale_fn
+            self.scale_mode = "cycle"
+        elif self.mode == "exp_range":
+            self._scale_fn_ref = partial(self._exp_range_scale_fn, self.gamma)
+            self.scale_mode = "iterations"
+
+    def scale_fn(self, x) -> float:
+        """Get the scaling policy."""
+        if self._scale_fn_custom is not None:
+            return self._scale_fn_custom(x)
+        else:
+            return self._scale_fn_ref(x)  # static method
+
+    @staticmethod
+    def _triangular_scale_fn(x: float) -> float:
+        return 1.0
+
+    @staticmethod
+    def _triangular2_scale_fn(x: float) -> float:
+        return 1 / (2.0 ** (x - 1))
+
+    @staticmethod
+    def _exp_range_scale_fn(gamma: float, x: float) -> float:
+        return gamma**x
+
+    def get_lr(self):
+        """Calculate the learning rate at batch index.
+
+        This function treats `self.last_epoch` as the last batch index.
+
+        If `self.cycle_momentum` is ``True``, this function has a side effect of
+        updating the optimizer's momentum.
+        """
+        _warn_get_lr_called_within_step(self)
+
+        cycle = math.floor(1 + self.last_epoch / self.total_size)
+        x = 1.0 + self.last_epoch / self.total_size - cycle
+        if x <= self.step_ratio:
+            scale_factor = x / self.step_ratio
+        else:
+            scale_factor = (x - 1) / (self.step_ratio - 1)
+
+        lrs = []
+        for base_lr, max_lr in zip(self.base_lrs, self.max_lrs):
+            base_height = (max_lr - base_lr) * scale_factor
+            if self.scale_mode == "cycle":
+                lr = base_lr + base_height * self.scale_fn(cycle)
+            else:
+                lr = base_lr + base_height * self.scale_fn(self.last_epoch)
+            lrs.append(lr)
+
+        if self.cycle_momentum:
+            momentums = []
+            for base_momentum, max_momentum in zip(
+                self.base_momentums, self.max_momentums
+            ):
+                base_height = (max_momentum - base_momentum) * scale_factor
+                if self.scale_mode == "cycle":
+                    momentum = max_momentum - base_height * self.scale_fn(cycle)
+                else:
+                    momentum = max_momentum - base_height * self.scale_fn(
+                        self.last_epoch
+                    )
+                momentums.append(momentum)
+            for param_group, momentum in zip(self.optimizer.param_groups, momentums):
+                if self.use_beta1:
+                    param_group["betas"] = (momentum, *param_group["betas"][1:])
+                else:
+                    param_group["momentum"] = momentum
+
+        return lrs
+
+    def state_dict(self):  # noqa: D102
+        state = super().state_dict()
+        # We are dropping the `_scale_fn_ref` attribute because it is a
+        # `weakref.WeakMethod` and can't be pickled.
+        state.pop("_scale_fn_ref", None)
+        fn = state.pop("_scale_fn_custom")
+        state["_scale_fn_custom"] = None
+        if fn is not None and not isinstance(fn, types.FunctionType):
+            # The _scale_fn_custom will only be saved if it is a callable object
+            # and not if it is a function or lambda.
+            state["_scale_fn_custom"] = fn.__dict__.copy()
+
+        return state
+
+    def load_state_dict(self, state_dict):
+        """Load the scheduler's state."""
+        fn = state_dict.pop("_scale_fn_custom")
+        super().load_state_dict(state_dict)
+        if fn is not None:
+            self._scale_fn_custom.__dict__.update(fn)
+        self._init_scale_fn()
+
+
+class CosineAnnealingWarmRestarts(LRScheduler):
+    r"""Set the learning rate of each parameter group using a cosine annealing schedule.
+
+    The :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}`
+    is the number of epochs since the last restart and :math:`T_{i}` is the number
+    of epochs between two warm restarts in SGDR:
+
+    .. math::
+        \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 +
+        \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right)
+
+    When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`.
+    When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`.
+
+    It has been proposed in
+    `SGDR: Stochastic Gradient Descent with Warm Restarts`_.
+
+    Args:
+        optimizer (Optimizer): Wrapped optimizer.
+        T_0 (int): Number of iterations until the first restart.
+        T_mult (int, optional): A factor by which :math:`T_{i}` increases after a restart. Default: 1.
+        eta_min (float, optional): Minimum learning rate. Default: 0.
+        last_epoch (int, optional): The index of the last epoch. Default: -1.
+        verbose (bool | str): If ``True``, prints a message to stdout for
+            each update. Default: ``False``.
+
+            .. deprecated:: 2.2
+                ``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
+                learning rate.
+
+    .. _SGDR\: Stochastic Gradient Descent with Warm Restarts:
+        https://arxiv.org/abs/1608.03983
+    """
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        T_0: int,
+        T_mult=1,
+        eta_min=0.0,
+        last_epoch=-1,
+        verbose="deprecated",
+    ):  # noqa: D107
+        if T_0 <= 0 or not isinstance(T_0, int):
+            raise ValueError(f"Expected positive integer T_0, but got {T_0}")
+        if T_mult < 1 or not isinstance(T_mult, int):
+            raise ValueError(f"Expected integer T_mult >= 1, but got {T_mult}")
+        if not isinstance(eta_min, (float, int)):
+            raise ValueError(
+                f"Expected float or int eta_min, but got {eta_min} of type {type(eta_min)}"
+            )
+        self.T_0 = T_0
+        self.T_i = T_0
+        self.T_mult = T_mult
+        self.eta_min = eta_min
+        self.T_cur = last_epoch
+        super().__init__(optimizer, last_epoch, verbose)
+
+    def get_lr(self):
+        """Compute the initial learning rate."""
+        _warn_get_lr_called_within_step(self)
+
+        return [
+            self.eta_min
+            + (base_lr - self.eta_min)
+            * (1 + math.cos(math.pi * self.T_cur / self.T_i))
+            / 2
+            for base_lr in self.base_lrs
+        ]
+
+    def step(self, epoch=None):
+        """Step could be called after every batch update.
+
+        Example:
+            >>> # xdoctest: +SKIP("Undefined vars")
+            >>> scheduler = CosineAnnealingWarmRestarts(optimizer, T_0, T_mult)
+            >>> iters = len(dataloader)
+            >>> for epoch in range(20):
+            >>>     for i, sample in enumerate(dataloader):
+            >>>         inputs, labels = sample['inputs'], sample['labels']
+            >>>         optimizer.zero_grad()
+            >>>         outputs = net(inputs)
+            >>>         loss = criterion(outputs, labels)
+            >>>         loss.backward()
+            >>>         optimizer.step()
+            >>>         scheduler.step(epoch + i / iters)
+
+        This function can be called in an interleaved way.
+
+        Example:
+            >>> # xdoctest: +SKIP("Undefined vars")
+            >>> scheduler = CosineAnnealingWarmRestarts(optimizer, T_0, T_mult)
+            >>> for epoch in range(20):
+            >>>     scheduler.step()
+            >>> scheduler.step(26)
+            >>> scheduler.step() # scheduler.step(27), instead of scheduler(20)
+        """
+        if epoch is None and self.last_epoch < 0:
+            epoch = 0
+
+        if epoch is None:
+            epoch = self.last_epoch + 1
+            self.T_cur = self.T_cur + 1
+            if self.T_cur >= self.T_i:
+                self.T_cur = self.T_cur - self.T_i
+                self.T_i = self.T_i * self.T_mult
+        else:
+            if epoch < 0:
+                raise ValueError(f"Expected non-negative epoch, but got {epoch}")
+            if epoch >= self.T_0:
+                if self.T_mult == 1:
+                    self.T_cur = epoch % self.T_0
+                else:
+                    n = int(
+                        math.log(
+                            (epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult
+                        )
+                    )
+                    self.T_cur = epoch - self.T_0 * (self.T_mult**n - 1) / (
+                        self.T_mult - 1
+                    )
+                    self.T_i = self.T_0 * self.T_mult ** (n)
+            else:
+                self.T_i = self.T_0
+                self.T_cur = epoch
+        self.last_epoch = math.floor(epoch)
+
+        with _enable_get_lr_call(self):
+            for i, data in enumerate(zip(self.optimizer.param_groups, self.get_lr())):
+                param_group, lr = data
+                param_group["lr"] = lr
+
+        self._last_lr = [group["lr"] for group in self.optimizer.param_groups]
+
+
+class _SchedulePhase(TypedDict):
+    end_step: float
+    start_lr: str
+    end_lr: str
+    start_momentum: str
+    end_momentum: str
+
+
+class OneCycleLR(LRScheduler):
+    r"""Sets the learning rate of each parameter group according to the 1cycle learning rate policy.
+
+    The 1cycle policy anneals the learning rate from an initial learning rate to some maximum
+    learning rate and then from that maximum learning rate to some minimum learning rate much
+    lower than the initial learning rate.
+    This policy was initially described in the paper `Super-Convergence:
+    Very Fast Training of Neural Networks Using Large Learning Rates`_.
+
+    The 1cycle learning rate policy changes the learning rate after every batch.
+    `step` should be called after a batch has been used for training.
+
+    This scheduler is not chainable.
+
+    Note also that the total number of steps in the cycle can be determined in one
+    of two ways (listed in order of precedence):
+
+    #. A value for total_steps is explicitly provided.
+    #. A number of epochs (epochs) and a number of steps per epoch
+       (steps_per_epoch) are provided.
+       In this case, the number of total steps is inferred by
+       total_steps = epochs * steps_per_epoch
+
+    You must either provide a value for total_steps or provide a value for both
+    epochs and steps_per_epoch.
+
+    The default behaviour of this scheduler follows the fastai implementation of 1cycle, which
+    claims that "unpublished work has shown even better results by using only two phases". To
+    mimic the behaviour of the original paper instead, set ``three_phase=True``.
+
+    Args:
+        optimizer (Optimizer): Wrapped optimizer.
+        max_lr (float or list): Upper learning rate boundaries in the cycle
+            for each parameter group.
+        total_steps (int): The total number of steps in the cycle. Note that
+            if a value is not provided here, then it must be inferred by providing
+            a value for epochs and steps_per_epoch.
+            Default: None
+        epochs (int): The number of epochs to train for. This is used along
+            with steps_per_epoch in order to infer the total number of steps in the cycle
+            if a value for total_steps is not provided.
+            Default: None
+        steps_per_epoch (int): The number of steps per epoch to train for. This is
+            used along with epochs in order to infer the total number of steps in the
+            cycle if a value for total_steps is not provided.
+            Default: None
+        pct_start (float): The percentage of the cycle (in number of steps) spent
+            increasing the learning rate.
+            Default: 0.3
+        anneal_strategy (str): {'cos', 'linear'}
+            Specifies the annealing strategy: "cos" for cosine annealing, "linear" for
+            linear annealing.
+            Default: 'cos'
+        cycle_momentum (bool): If ``True``, momentum is cycled inversely
+            to learning rate between 'base_momentum' and 'max_momentum'.
+            Default: True
+        base_momentum (float or list): Lower momentum boundaries in the cycle
+            for each parameter group. Note that momentum is cycled inversely
+            to learning rate; at the peak of a cycle, momentum is
+            'base_momentum' and learning rate is 'max_lr'.
+            Default: 0.85
+        max_momentum (float or list): Upper momentum boundaries in the cycle
+            for each parameter group. Functionally,
+            it defines the cycle amplitude (max_momentum - base_momentum).
+            Note that momentum is cycled inversely
+            to learning rate; at the start of a cycle, momentum is 'max_momentum'
+            and learning rate is 'base_lr'
+            Default: 0.95
+        div_factor (float): Determines the initial learning rate via
+            initial_lr = max_lr/div_factor
+            Default: 25
+        final_div_factor (float): Determines the minimum learning rate via
+            min_lr = initial_lr/final_div_factor
+            Default: 1e4
+        three_phase (bool): If ``True``, use a third phase of the schedule to annihilate the
+            learning rate according to 'final_div_factor' instead of modifying the second
+            phase (the first two phases will be symmetrical about the step indicated by
+            'pct_start').
+        last_epoch (int): The index of the last batch. This parameter is used when
+            resuming a training job. Since `step()` should be invoked after each
+            batch instead of after each epoch, this number represents the total
+            number of *batches* computed, not the total number of epochs computed.
+            When last_epoch=-1, the schedule is started from the beginning.
+            Default: -1
+        verbose (bool | str): If ``True``, prints a message to stdout for
+            each update. Default: ``False``.
+
+            .. deprecated:: 2.2
+                ``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
+                learning rate.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> data_loader = torch.utils.data.DataLoader(...)
+        >>> optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9)
+        >>> scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10)
+        >>> for epoch in range(10):
+        >>>     for batch in data_loader:
+        >>>         train_batch(...)
+        >>>         optimizer.step()
+        >>>         scheduler.step()
+
+
+    .. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates:
+        https://arxiv.org/abs/1708.07120
+    """
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        max_lr: Union[float, List[float]],
+        total_steps: Optional[int] = None,
+        epochs: Optional[int] = None,
+        steps_per_epoch: Optional[int] = None,
+        pct_start=0.3,
+        anneal_strategy: Literal["cos", "linear"] = "cos",
+        cycle_momentum=True,
+        base_momentum: Union[float, List[float]] = 0.85,
+        max_momentum: Union[float, List[float]] = 0.95,
+        div_factor=25.0,
+        final_div_factor=1e4,
+        three_phase=False,
+        last_epoch=-1,
+        verbose="deprecated",
+    ):  # noqa: D107
+        # Validate optimizer
+        if not isinstance(optimizer, Optimizer):
+            raise TypeError(f"{type(optimizer).__name__} is not an Optimizer")
+        self.optimizer = optimizer
+
+        # Validate total_steps
+        if total_steps is not None:
+            if total_steps <= 0 or not isinstance(total_steps, int):
+                raise ValueError(
+                    f"Expected positive integer total_steps, but got {total_steps}"
+                )
+            self.total_steps = total_steps
+        elif epochs is not None and steps_per_epoch is not None:
+            if not isinstance(epochs, int) or epochs <= 0:
+                raise ValueError(f"Expected positive integer epochs, but got {epochs}")
+            if not isinstance(steps_per_epoch, int) or steps_per_epoch <= 0:
+                raise ValueError(
+                    f"Expected positive integer steps_per_epoch, but got {steps_per_epoch}"
+                )
+            self.total_steps = epochs * steps_per_epoch
+        else:
+            raise ValueError(
+                "You must define either total_steps OR (epochs AND steps_per_epoch)"
+            )
+
+        self._schedule_phases: List[_SchedulePhase]
+        if three_phase:
+            self._schedule_phases = [
+                {
+                    "end_step": float(pct_start * self.total_steps) - 1,
+                    "start_lr": "initial_lr",
+                    "end_lr": "max_lr",
+                    "start_momentum": "max_momentum",
+                    "end_momentum": "base_momentum",
+                },
+                {
+                    "end_step": float(2 * pct_start * self.total_steps) - 2,
+                    "start_lr": "max_lr",
+                    "end_lr": "initial_lr",
+                    "start_momentum": "base_momentum",
+                    "end_momentum": "max_momentum",
+                },
+                {
+                    "end_step": self.total_steps - 1,
+                    "start_lr": "initial_lr",
+                    "end_lr": "min_lr",
+                    "start_momentum": "max_momentum",
+                    "end_momentum": "max_momentum",
+                },
+            ]
+        else:
+            self._schedule_phases = [
+                {
+                    "end_step": float(pct_start * self.total_steps) - 1,
+                    "start_lr": "initial_lr",
+                    "end_lr": "max_lr",
+                    "start_momentum": "max_momentum",
+                    "end_momentum": "base_momentum",
+                },
+                {
+                    "end_step": self.total_steps - 1,
+                    "start_lr": "max_lr",
+                    "end_lr": "min_lr",
+                    "start_momentum": "base_momentum",
+                    "end_momentum": "max_momentum",
+                },
+            ]
+
+        # Validate pct_start
+        if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float):
+            raise ValueError(
+                f"Expected float between 0 and 1 pct_start, but got {pct_start}"
+            )
+
+        # Validate anneal_strategy
+        if anneal_strategy not in ["cos", "linear"]:
+            raise ValueError(
+                f"anneal_strategy must be one of 'cos' or 'linear', instead got {anneal_strategy}"
+            )
+        else:
+            self._anneal_func_type = anneal_strategy
+
+        # Initialize learning rate variables
+        max_lrs = _format_param("max_lr", self.optimizer, max_lr)
+        if last_epoch == -1:
+            for idx, group in enumerate(self.optimizer.param_groups):
+                group["initial_lr"] = max_lrs[idx] / div_factor
+                group["max_lr"] = max_lrs[idx]
+                group["min_lr"] = group["initial_lr"] / final_div_factor
+
+        # Initialize momentum variables
+        self.cycle_momentum = cycle_momentum
+        if self.cycle_momentum:
+            if (
+                "momentum" not in self.optimizer.defaults
+                and "betas" not in self.optimizer.defaults
+            ):
+                raise ValueError(
+                    "optimizer must support momentum or beta1 with `cycle_momentum` option enabled"
+                )
+            self.use_beta1 = "betas" in self.optimizer.defaults
+            max_momentums = _format_param("max_momentum", optimizer, max_momentum)
+            base_momentums = _format_param("base_momentum", optimizer, base_momentum)
+            if last_epoch == -1:
+                for m_momentum, b_momentum, group in zip(
+                    max_momentums, base_momentums, optimizer.param_groups
+                ):
+                    if self.use_beta1:
+                        group["betas"] = (m_momentum, *group["betas"][1:])
+                    else:
+                        group["momentum"] = m_momentum
+                    group["max_momentum"] = m_momentum
+                    group["base_momentum"] = b_momentum
+
+        super().__init__(optimizer, last_epoch, verbose)
+
+    def _anneal_func(self, *args, **kwargs):
+        if hasattr(self, "_anneal_func_type"):
+            if self._anneal_func_type == "cos":
+                return self._annealing_cos(*args, **kwargs)
+            elif self._anneal_func_type == "linear":
+                return self._annealing_linear(*args, **kwargs)
+            else:
+                raise ValueError(f"Unknown _anneal_func_type: {self._anneal_func_type}")
+        else:
+            # For BC
+            return self.anneal_func(*args, **kwargs)  # type: ignore[attr-defined]
+
+    @staticmethod
+    def _annealing_cos(start, end, pct):
+        """Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0."""
+        cos_out = math.cos(math.pi * pct) + 1
+        return end + (start - end) / 2.0 * cos_out
+
+    @staticmethod
+    def _annealing_linear(start, end, pct):
+        """Linearly anneal from `start` to `end` as pct goes from 0.0 to 1.0."""
+        return (end - start) * pct + start
+
+    def get_lr(self):
+        """Compute the learning rate of each parameter group."""
+        _warn_get_lr_called_within_step(self)
+
+        lrs = []
+        step_num = self.last_epoch
+
+        if step_num > self.total_steps:
+            raise ValueError(
+                f"Tried to step {step_num} times. The specified number of total steps is {self.total_steps}"  # noqa: UP032
+            )
+
+        for group in self.optimizer.param_groups:
+            start_step = 0.0
+            for i, phase in enumerate(self._schedule_phases):
+                end_step = phase["end_step"]
+                if step_num <= end_step or i == len(self._schedule_phases) - 1:
+                    pct = (step_num - start_step) / (end_step - start_step)
+                    computed_lr = self._anneal_func(
+                        group[phase["start_lr"]], group[phase["end_lr"]], pct
+                    )
+                    if self.cycle_momentum:
+                        computed_momentum = self._anneal_func(
+                            group[phase["start_momentum"]],
+                            group[phase["end_momentum"]],
+                            pct,
+                        )
+                    break
+                start_step = phase["end_step"]
+
+            lrs.append(computed_lr)  # type: ignore[possibly-undefined]
+            if self.cycle_momentum:
+                if self.use_beta1:
+                    group["betas"] = (computed_momentum, *group["betas"][1:])  # type: ignore[possibly-undefined]
+                else:
+                    group[
+                        "momentum"
+                    ] = computed_momentum  # type: ignore[possibly-undefined]
+
+        return lrs
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/nadam.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/nadam.py
new file mode 100644
index 0000000000000000000000000000000000000000..e26b3bf302587fcdf7df9c1616551cf1687b740c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/nadam.py
@@ -0,0 +1,649 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+r"""Implementation for the NAdam algorithm."""
+from typing import cast, List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+
+from .optimizer import (
+    _capturable_doc,
+    _default_to_fused_or_foreach,
+    _differentiable_doc,
+    _disable_dynamo_if_unsupported,
+    _foreach_doc,
+    _get_capturable_supported_devices,
+    _get_scalar_dtype,
+    _get_value,
+    _maximize_doc,
+    _stack_if_compiling,
+    _use_grad_for_differentiable,
+    _view_as_real,
+    Optimizer,
+    ParamsT,
+)
+
+
+__all__ = ["NAdam", "nadam"]
+
+
+class NAdam(Optimizer):  # noqa: D101
+    def __init__(
+        self,
+        params: ParamsT,
+        lr: Union[float, Tensor] = 2e-3,
+        betas: Tuple[float, float] = (0.9, 0.999),
+        eps: float = 1e-8,
+        weight_decay: float = 0,
+        momentum_decay: float = 4e-3,
+        decoupled_weight_decay: bool = False,
+        *,
+        foreach: Optional[bool] = None,
+        maximize: bool = False,
+        capturable: bool = False,
+        differentiable: bool = False,
+    ):  # noqa: D107
+        if isinstance(lr, Tensor) and lr.numel() != 1:
+            raise ValueError("Tensor lr must be 1-element")
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 <= eps:
+            raise ValueError(f"Invalid epsilon value: {eps}")
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
+        if not 0.0 <= weight_decay:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+        if not 0.0 <= momentum_decay:
+            raise ValueError(f"Invalid momentum_decay value: {momentum_decay}")
+        defaults = dict(
+            lr=lr,
+            betas=betas,
+            eps=eps,
+            weight_decay=weight_decay,
+            momentum_decay=momentum_decay,
+            decoupled_weight_decay=decoupled_weight_decay,
+            maximize=maximize,
+            foreach=foreach,
+            capturable=capturable,
+            differentiable=differentiable,
+        )
+        super().__init__(params, defaults)
+
+    def __setstate__(self, state):  # noqa: D105
+        super().__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault("maximize", False)
+            group.setdefault("foreach", None)
+            group.setdefault("capturable", False)
+            group.setdefault("differentiable", False)
+            group.setdefault("decoupled_weight_decay", False)
+            for p in group["params"]:
+                p_state = self.state.get(p, [])
+                if len(p_state) != 0:
+                    if not torch.is_tensor(p_state["step"]):
+                        step_val = float(p_state["step"])
+                        p_state["step"] = (
+                            torch.tensor(
+                                step_val, dtype=_get_scalar_dtype(), device=p.device
+                            )
+                            if group["capturable"]
+                            else torch.tensor(step_val, dtype=_get_scalar_dtype())
+                        )
+                    if not torch.is_tensor(p_state["mu_product"]):
+                        mu_prod_val = p_state["mu_product"]
+                        p_state["mu_product"] = (
+                            torch.tensor(
+                                mu_prod_val, dtype=_get_scalar_dtype(), device=p.device
+                            )
+                            if group["capturable"]
+                            else torch.tensor(mu_prod_val, dtype=_get_scalar_dtype())
+                        )
+
+    def _init_group(
+        self,
+        group,
+        params_with_grad,
+        grads,
+        exp_avgs,
+        exp_avg_sqs,
+        mu_products,
+        state_steps,
+    ):
+        has_complex = False
+        for p in group["params"]:
+            if p.grad is not None:
+                has_complex |= torch.is_complex(p)
+                params_with_grad.append(p)
+                if p.grad.is_sparse:
+                    raise RuntimeError("NAdam does not support sparse gradients")
+                grads.append(p.grad)
+
+                state = self.state[p]
+                # Lazy state initialization
+                if len(state) == 0:
+                    # note(crcrpar): [special device hosting for step]
+                    # Deliberately host `step` and `mu_product` on CPU if capturable is False.
+                    # This is because kernel launches are costly on CUDA and XLA.
+                    state["step"] = (
+                        torch.zeros((), dtype=_get_scalar_dtype(), device=p.device)
+                        if group["capturable"]
+                        else torch.tensor(0.0, dtype=_get_scalar_dtype())
+                    )
+                    state["mu_product"] = (
+                        torch.ones((), dtype=_get_scalar_dtype(), device=p.device)
+                        if group["capturable"]
+                        else torch.tensor(1.0, dtype=_get_scalar_dtype())
+                    )
+                    # Exponential moving average of gradient values
+                    state["exp_avg"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+                    # Exponential moving average of squared gradient values
+                    state["exp_avg_sq"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+
+                exp_avgs.append(state["exp_avg"])
+                exp_avg_sqs.append(state["exp_avg_sq"])
+                mu_products.append(state["mu_product"])
+                state_steps.append(state["step"])
+        return has_complex
+
+    @_use_grad_for_differentiable
+    def step(self, closure=None):
+        """Perform a single optimization step.
+
+        Args:
+            closure (Callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        self._cuda_graph_capture_health_check()
+
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            params_with_grad: List[Tensor] = []
+            grads: List[Tensor] = []
+            exp_avgs: List[Tensor] = []
+            exp_avg_sqs: List[Tensor] = []
+            mu_products: List[Tensor] = []
+            state_steps: List[Tensor] = []
+            beta1, beta2 = cast(Tuple[float, float], group["betas"])
+
+            has_complex = self._init_group(
+                group,
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                mu_products,
+                state_steps,
+            )
+
+            nadam(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                mu_products,
+                state_steps,
+                beta1=beta1,
+                beta2=beta2,
+                lr=group["lr"],
+                weight_decay=group["weight_decay"],
+                momentum_decay=group["momentum_decay"],
+                eps=group["eps"],
+                maximize=group["maximize"],
+                decoupled_weight_decay=group["decoupled_weight_decay"],
+                foreach=group["foreach"],
+                capturable=group["capturable"],
+                differentiable=group["differentiable"],
+                has_complex=has_complex,
+            )
+
+        return loss
+
+
+NAdam.__doc__ = (
+    r"""Implements NAdam algorithm.
+
+    .. math::
+       \begin{aligned}
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{input}      : \gamma_t \text{ (lr)}, \: \beta_1,\beta_2 \text{ (betas)},
+                \: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)}                   \\
+            &\hspace{13mm} \: \lambda \text{ (weight decay)}, \:\psi \text{ (momentum decay)}    \\
+            &\hspace{13mm} \: \textit{decoupled\_weight\_decay}, \:\textit{maximize}             \\
+            &\textbf{initialize} :  m_0 \leftarrow 0 \text{ ( first moment)},
+                v_0 \leftarrow 0 \text{ ( second moment)}                                 \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do}                         \\
+            &\hspace{5mm}\textbf{if} \: \textit{maximize}:                                       \\
+            &\hspace{10mm}g_t           \leftarrow   -\nabla_{\theta} f_t (\theta_{t-1})         \\
+            &\hspace{5mm}\textbf{else}                                                           \\
+            &\hspace{10mm}g_t           \leftarrow   \nabla_{\theta} f_t (\theta_{t-1})          \\
+            &\hspace{5mm} \theta_t \leftarrow \theta_{t-1}                                       \\
+            &\hspace{5mm} \textbf{if} \: \lambda \neq 0                                          \\
+            &\hspace{10mm}\textbf{if} \: \textit{decoupled\_weight\_decay}                       \\
+            &\hspace{15mm} \theta_t \leftarrow \theta_{t-1} - \gamma \lambda \theta_{t-1}                    \\
+            &\hspace{10mm}\textbf{else}                                                          \\
+            &\hspace{15mm} g_t \leftarrow g_t + \lambda \theta_{t-1}                             \\
+            &\hspace{5mm} \mu_t \leftarrow \beta_1 \big(1 - \frac{1}{2}  0.96^{t \psi} \big)     \\
+            &\hspace{5mm} \mu_{t+1} \leftarrow \beta_1 \big(1 - \frac{1}{2} 0.96^{(t+1)\psi}\big)\\
+            &\hspace{5mm}m_t           \leftarrow   \beta_1 m_{t-1} + (1 - \beta_1) g_t          \\
+            &\hspace{5mm}v_t           \leftarrow   \beta_2 v_{t-1} + (1-\beta_2) g^2_t          \\
+            &\hspace{5mm}\widehat{m_t} \leftarrow \mu_{t+1} m_t/(1-\prod_{i=1}^{t+1}\mu_i)\\[-1.ex]
+            & \hspace{11mm} + (1-\mu_t) g_t /(1-\prod_{i=1}^{t} \mu_{i})                         \\
+            &\hspace{5mm}\widehat{v_t} \leftarrow   v_t/\big(1-\beta_2^t \big)                   \\
+            &\hspace{5mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t}/
+                \big(\sqrt{\widehat{v_t}} + \epsilon \big)                                       \\
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+            &\bf{return} \:  \theta_t                                                     \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+       \end{aligned}
+
+    For further details regarding the algorithm we refer to `Incorporating Nesterov Momentum into Adam`_.
+    """
+    + rf"""
+    Args:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, Tensor, optional): learning rate (default: 2e-3)
+        betas (Tuple[float, float], optional): coefficients used for computing
+            running averages of gradient and its square (default: (0.9, 0.999))
+        eps (float, optional): term added to the denominator to improve
+            numerical stability (default: 1e-8)
+        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+        momentum_decay (float, optional): momentum momentum_decay (default: 4e-3)
+        decoupled_weight_decay (bool, optional): whether to use decoupled weight
+            decay as in AdamW to obtain NAdamW (default: False)
+        {_foreach_doc}
+        {_maximize_doc}
+        {_capturable_doc}
+        {_differentiable_doc}
+
+    .. _Incorporating Nesterov Momentum into Adam:
+        https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ
+    .. _Decoupled Weight Decay Regularization:
+        https://arxiv.org/abs/1711.05101
+
+    """
+)
+
+
+def _single_tensor_nadam(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    mu_products: List[Tensor],
+    state_steps: List[Tensor],
+    *,
+    beta1: float,
+    beta2: float,
+    lr: float,
+    weight_decay: float,
+    momentum_decay: float,
+    eps: float,
+    decoupled_weight_decay: bool,
+    maximize: bool,
+    capturable: bool,
+    differentiable: bool,
+    has_complex: bool,
+):
+    for i, param in enumerate(params):
+        grad = grads[i] if not maximize else -grads[i]
+        exp_avg = exp_avgs[i]
+        exp_avg_sq = exp_avg_sqs[i]
+        mu_product = mu_products[i]
+        step_t = state_steps[i]
+
+        if torch.is_complex(param):
+            param = torch.view_as_real(param)
+            grad = torch.view_as_real(grad)
+            exp_avg = torch.view_as_real(exp_avg)
+            exp_avg_sq = torch.view_as_real(exp_avg_sq)
+
+        # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+        if not torch._utils.is_compiling() and capturable:
+            capturable_supported_devices = _get_capturable_supported_devices()
+            assert (
+                param.device.type == mu_product.device.type == step_t.device.type
+                and param.device.type in capturable_supported_devices
+            ), (
+                f"If capturable=True, params, mu_products and state_steps must be "
+                f"on supported devices: {capturable_supported_devices}."
+            )
+
+        # update step
+        step_t += 1
+
+        if capturable:
+            step = step_t
+        else:
+            step = _get_value(step_t)
+
+        bias_correction2 = 1 - beta2**step
+
+        if weight_decay != 0:
+            if decoupled_weight_decay:
+                # Perform stepweight decay
+                param.mul_(1 - lr * weight_decay)
+            else:
+                grad = grad.add(param, alpha=weight_decay)
+
+        # calculate the momentum cache \mu^{t} and \mu^{t+1}
+        mu = beta1 * (1.0 - 0.5 * (0.96 ** (step * momentum_decay)))
+        mu_next = beta1 * (1.0 - 0.5 * (0.96 ** ((step + 1) * momentum_decay)))
+
+        # update mu_product
+        mu_product *= mu
+
+        # decay the first and second moment running average coefficient
+        exp_avg.lerp_(grad, 1 - beta1)
+        exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+        denom = exp_avg_sq.div(bias_correction2).sqrt()
+
+        if differentiable or capturable:
+            denom = denom.add(eps)
+            # Make autograd track the operations
+            # by updating the grad and exp_avg directly and not using the
+            # scalar "value" argument of addcdiv.
+            mu_product_next = mu_product * mu_next
+            grad = grad * (-lr * (1.0 - mu) / (1.0 - mu_product))
+            exp_avg = exp_avg * (-lr * mu_next / (1.0 - mu_product_next))
+            param.addcdiv_(grad, denom)
+            param.addcdiv_(exp_avg, denom)
+        else:
+            mu_product_next = _get_value(mu_product) * mu_next
+            denom.add_(eps)
+            param.addcdiv_(
+                grad, denom, value=(-lr * (1.0 - mu) / (1.0 - _get_value(mu_product)))
+            )
+            param.addcdiv_(
+                exp_avg, denom, value=(-lr * mu_next) / (1.0 - mu_product_next)
+            )
+
+
+def _multi_tensor_nadam(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    mu_products: List[Tensor],
+    state_steps: List[Tensor],
+    *,
+    beta1: float,
+    beta2: float,
+    lr: float,
+    weight_decay: float,
+    momentum_decay: float,
+    eps: float,
+    decoupled_weight_decay: bool,
+    maximize: bool,
+    capturable: bool,
+    differentiable: bool,
+    has_complex: bool,
+):
+    if len(params) == 0:
+        return
+
+    assert not differentiable, "_foreach ops don't support autograd"
+
+    # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+    if not torch._utils.is_compiling() and capturable:
+        capturable_supported_devices = _get_capturable_supported_devices(
+            supports_xla=False
+        )
+        assert all(
+            p.device.type == mp.device.type == step.device.type
+            and p.device.type in capturable_supported_devices
+            for p, mp, step in zip(params, mu_products, state_steps)
+        ), f"If capturable=True, params, mu_products, and state_steps must be on supported devices: {capturable_supported_devices}."
+
+    grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
+        [params, grads, exp_avgs, exp_avg_sqs, mu_products, state_steps]  # type: ignore[list-item]
+    )
+    for (
+        grouped_params_,
+        grouped_grads_,
+        grouped_exp_avgs_,
+        grouped_exp_avg_sqs_,
+        grouped_mu_products_,
+        grouped_state_steps_,
+    ), _ in grouped_tensors.values():
+        grouped_params = cast(List[Tensor], grouped_params_)
+        grouped_grads = cast(List[Tensor], grouped_grads_)
+        grouped_exp_avgs = cast(List[Tensor], grouped_exp_avgs_)
+        grouped_exp_avg_sqs = cast(List[Tensor], grouped_exp_avg_sqs_)
+        grouped_mu_products = cast(List[Tensor], grouped_mu_products_)
+        grouped_state_steps = cast(List[Tensor], grouped_state_steps_)
+
+        # handle complex
+        if has_complex:
+            _view_as_real(
+                grouped_params, grouped_grads, grouped_exp_avgs, grouped_exp_avg_sqs
+            )
+
+        if maximize:
+            grouped_grads = torch._foreach_neg(grouped_grads)  # type: ignore[assignment]
+
+        # Update steps
+        # If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
+        # and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
+        # wrapped it once now. The alpha is required to assure we go to the right overload.
+        if not torch._utils.is_compiling() and grouped_state_steps[0].is_cpu:
+            torch._foreach_add_(
+                grouped_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
+            )
+        else:
+            torch._foreach_add_(grouped_state_steps, 1)
+
+        if weight_decay != 0:
+            if decoupled_weight_decay:
+                # Perform stepweight decay
+                torch._foreach_mul_(grouped_params, 1 - lr * weight_decay)
+            else:
+                # Re-use the intermediate memory (grouped_grads) already allocated for maximize
+                if maximize:
+                    torch._foreach_add_(
+                        grouped_grads, grouped_params, alpha=weight_decay
+                    )
+                else:
+                    grouped_grads = torch._foreach_add(  # type: ignore[assignment]
+                        grouped_grads, grouped_params, alpha=weight_decay
+                    )
+
+        # Decay the first and second moment running average coefficient
+        torch._foreach_lerp_(grouped_exp_avgs, grouped_grads, 1 - beta1)
+
+        torch._foreach_mul_(grouped_exp_avg_sqs, beta2)
+        torch._foreach_addcmul_(
+            grouped_exp_avg_sqs, grouped_grads, grouped_grads, 1 - beta2
+        )
+
+        exp_avg_sq_sqrt = torch._foreach_sqrt(grouped_exp_avg_sqs)
+
+        bias_correction_sqrt: Union[Tuple[Tensor, ...], List[Tensor]]
+        mus: Union[Tuple[Tensor, ...], List[Tensor]]
+        mu_nexts: Union[Tuple[Tensor, ...], List[Tensor]]
+        if capturable:
+            # mus will be beta1 * (1 - 0.5 * 0.96 ** (step * momentum_decay))
+            exponent = torch._foreach_mul(grouped_state_steps, momentum_decay)
+            mus = torch._foreach_pow(0.96, exponent)
+            torch._foreach_mul_(mus, -0.5)
+            torch._foreach_add_(mus, 1.0)
+            torch._foreach_mul_(mus, beta1)
+
+            # mu_nexts will be beta1 * (1 - 0.5 * 0.96 ** ((step + 1) * momentum_decay))
+            torch._foreach_add_(exponent, momentum_decay)
+            mu_nexts = torch._foreach_pow(0.96, exponent)
+            torch._foreach_mul_(mu_nexts, -0.5)
+            torch._foreach_add_(mu_nexts, 1.0)
+            torch._foreach_mul_(mu_nexts, beta1)
+
+            # save peak memory as we don't need exponent anymore
+            del exponent
+
+            bias_correction_sqrt = torch._foreach_pow(beta2, grouped_state_steps)
+            # foreach_sub doesn't allow a scalar as the first arg
+            torch._foreach_sub_(bias_correction_sqrt, 1.0)
+            torch._foreach_neg_(bias_correction_sqrt)
+            torch._foreach_sqrt_(bias_correction_sqrt)
+        else:
+            bias_correction_sqrt = [
+                (1 - beta2 ** _get_value(step)) ** 0.5 for step in grouped_state_steps
+            ]
+            mus = [
+                beta1 * (1.0 - 0.5 * (0.96 ** (_get_value(step) * momentum_decay)))
+                for step in grouped_state_steps
+            ]
+            mu_nexts = [
+                beta1
+                * (1.0 - 0.5 * (0.96 ** ((_get_value(step) + 1) * momentum_decay)))
+                for step in grouped_state_steps
+            ]
+
+        # update mu_products
+        torch._foreach_mul_(grouped_mu_products, mus)
+
+        torch._foreach_div_(exp_avg_sq_sqrt, bias_correction_sqrt)
+        torch._foreach_add_(exp_avg_sq_sqrt, eps)
+
+        # explicitly delete bias_correction refs to save memory
+        del bias_correction_sqrt
+
+        if capturable:
+            # Build up the step_size multiplier for grad, reusing mus' memory
+            torch._foreach_sub_(mus, 1.0)
+            torch._foreach_mul_(mus, lr)
+            # foreach_sub doesn't allow a scalar as the first arg
+            denom = torch._foreach_sub(grouped_mu_products, 1.0)
+            torch._foreach_neg_(denom)
+            torch._foreach_div_(mus, denom)
+            # - lr * (1 - mu) / (1 - mu_product)
+            step_size_grads = mus
+            # explicitly delete denom to save memory
+            del denom
+
+            # Build up the step_size multiplier for exp_avg, reusing mu_nexts' memory
+            denom = torch._foreach_mul(grouped_mu_products, mu_nexts)
+            torch._foreach_mul_(mu_nexts, lr)
+            # foreach_sub doesn't allow a scalar as the first arg, but it's okay because
+            # we need a negative here anyway
+            torch._foreach_sub_(denom, 1.0)
+            torch._foreach_div_(mu_nexts, denom)
+            # - lr * mu_next / (1 - mu_product * mu_next)
+            step_size_expavg = mu_nexts
+            # explicitly delete denom to save memory
+            del denom
+
+            # we cannot inplace into step_size_grads cuz it is a list of ScalarTensors
+            # and mul'ing with grouped_grads will result in a list of bigger Tensors
+            numerator = torch._foreach_mul(step_size_grads, grouped_grads)
+            torch._foreach_addcmul_(numerator, step_size_expavg, grouped_exp_avgs)
+
+            # finally, update params
+            torch._foreach_addcdiv_(grouped_params, numerator, exp_avg_sq_sqrt)
+        else:
+            step_size_grads = _stack_if_compiling(
+                [
+                    (_get_value(lr) * (1.0 - mu) / (1.0 - _get_value(mu_product))) * -1
+                    for mu_product, mu in zip(grouped_mu_products, mus)
+                ]
+            )
+            step_size_expavg = _stack_if_compiling(
+                [
+                    (
+                        _get_value(lr)
+                        * mu_next
+                        / (1.0 - _get_value(mu_product) * mu_next)
+                    )
+                    * -1
+                    for mu_product, mu_next in zip(grouped_mu_products, mu_nexts)
+                ]
+            )
+
+            torch._foreach_addcdiv_(
+                grouped_params, grouped_grads, exp_avg_sq_sqrt, step_size_grads  # type: ignore[arg-type]
+            )
+            torch._foreach_addcdiv_(
+                grouped_params, grouped_exp_avgs, exp_avg_sq_sqrt, step_size_expavg  # type: ignore[arg-type]
+            )
+
+
+@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_nadam)
+def nadam(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    mu_products: List[Tensor],
+    state_steps: List[Tensor],
+    # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
+    # setting this as kwarg for now as functional API is compiled by torch/distributed/optim
+    decoupled_weight_decay: bool = False,
+    foreach: Optional[bool] = None,
+    capturable: bool = False,
+    differentiable: bool = False,
+    has_complex: bool = False,
+    maximize: bool = False,
+    *,
+    beta1: float,
+    beta2: float,
+    lr: float,
+    weight_decay: float,
+    momentum_decay: float,
+    eps: float,
+):
+    r"""Functional API that performs NAdam algorithm computation.
+
+    See :class:`~torch.optim.NAdam` for details.
+    """
+    if not all(isinstance(t, torch.Tensor) for t in state_steps):
+        raise RuntimeError(
+            "API has changed, `state_steps` argument must contain a list of singleton tensors"
+        )
+
+    if not all(isinstance(t, torch.Tensor) for t in mu_products):
+        raise RuntimeError(
+            "API has changed, `mu_products` argument must contain a list of singleton tensors"
+        )
+
+    if foreach is None:
+        _, foreach = _default_to_fused_or_foreach(
+            params, differentiable, use_fused=False
+        )
+
+    if foreach and torch.jit.is_scripting():
+        raise RuntimeError("torch.jit.script not supported with foreach optimizers")
+
+    if foreach and not torch.jit.is_scripting():
+        func = _multi_tensor_nadam
+    else:
+        func = _single_tensor_nadam
+
+    func(
+        params,
+        grads,
+        exp_avgs,
+        exp_avg_sqs,
+        mu_products,
+        state_steps,
+        beta1=beta1,
+        beta2=beta2,
+        lr=lr,
+        weight_decay=weight_decay,
+        momentum_decay=momentum_decay,
+        maximize=maximize,
+        decoupled_weight_decay=decoupled_weight_decay,
+        eps=eps,
+        capturable=capturable,
+        differentiable=differentiable,
+        has_complex=has_complex,
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/optimizer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f7993842c1009d0565d94a8d6d3187694fa236b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/optimizer.py
@@ -0,0 +1,1052 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+"""Base optimizer."""
+import functools
+import warnings
+from collections import defaultdict, OrderedDict
+from copy import deepcopy
+from itertools import chain
+from typing import (
+    Any,
+    Callable,
+    cast,
+    DefaultDict,
+    Dict,
+    Hashable,
+    Iterable,
+    List,
+    Optional,
+    overload,
+    Set,
+    Tuple,
+    TypeVar,
+    Union,
+)
+from typing_extensions import ParamSpec, Self, TypeAlias
+
+import torch
+import torch.utils.hooks as hooks
+from torch._utils import is_compiling
+from torch.utils._foreach_utils import (
+    _get_foreach_kernels_supported_devices,
+    _get_fused_kernels_supported_devices,
+    _group_tensors_by_device_and_dtype,
+    Indices,
+    TensorListList,
+)
+from torch.utils.hooks import RemovableHandle
+
+
+Args: TypeAlias = Tuple[Any, ...]
+Kwargs: TypeAlias = Dict[str, Any]
+StateDict: TypeAlias = Dict[str, Any]
+DeviceDict = Dict[Optional[torch.device], torch.Tensor]
+
+
+GlobalOptimizerPreHook: TypeAlias = Callable[
+    ["Optimizer", Args, Kwargs], Optional[Tuple[Args, Kwargs]]
+]
+GlobalOptimizerPostHook: TypeAlias = Callable[["Optimizer", Args, Kwargs], None]
+
+__all__ = [
+    "Optimizer",
+    "register_optimizer_step_pre_hook",
+    "register_optimizer_step_post_hook",
+]
+_global_optimizer_pre_hooks: Dict[int, GlobalOptimizerPreHook] = OrderedDict()
+_global_optimizer_post_hooks: Dict[int, GlobalOptimizerPostHook] = OrderedDict()
+_foreach_supported_types = [torch.Tensor, torch.nn.parameter.Parameter]
+
+
+class _RequiredParameter:
+    """Singleton class representing a required parameter for an Optimizer."""
+
+    def __repr__(self) -> str:
+        return ""
+
+
+required = _RequiredParameter()
+
+
+def _use_grad_for_differentiable(func):
+    def _use_grad(self, *args, **kwargs):
+        import torch._dynamo
+
+        prev_grad = torch.is_grad_enabled()
+        try:
+            # Note on graph break below:
+            # we need to graph break to ensure that aot respects the no_grad annotation.
+            # This is important for perf because without this, functionalization will generate an epilogue
+            # which updates the mutated parameters of the optimizer which is *not* visible to inductor, as a result,
+            # inductor will allocate for every parameter in the model, which is horrible.
+            # With this, aot correctly sees that this is an inference graph, and functionalization will generate
+            # an epilogue which is appended to the graph, which *is* visible to inductor, as a result, inductor sees that
+            # step is in place and is able to avoid the extra allocation.
+            # In the future, we will either 1) continue to graph break on backward, so this graph break does not matter
+            # or 2) have a fully fused forward and backward graph, which will have no_grad by default, and we can remove this
+            # graph break to allow the fully fused fwd-bwd-optimizer graph to be compiled.
+            # see https://github.com/pytorch/pytorch/issues/104053
+            torch.set_grad_enabled(self.defaults["differentiable"])
+            torch._dynamo.graph_break()
+            ret = func(self, *args, **kwargs)
+        finally:
+            torch._dynamo.graph_break()
+            torch.set_grad_enabled(prev_grad)
+        return ret
+
+    functools.update_wrapper(_use_grad, func)
+    return _use_grad
+
+
+def _get_value(x):
+    # item is significantly faster than a cpu tensor in eager mode
+    if not torch.jit.is_scripting() and is_compiling():
+        return x
+    else:
+        return x.item() if isinstance(x, torch.Tensor) else x
+
+
+def _stack_if_compiling(x):
+    if not torch.jit.is_scripting() and is_compiling():
+        return torch.stack(x)
+    else:
+        return x
+
+
+def _disable_dynamo_if_unsupported(single_tensor_fn=None):
+    # workaround for torchscript BC
+    # it requires all called functions to be in the
+    # global environment at the site at which the
+    # maybe_fallback closure is created
+    if single_tensor_fn:
+        globals()[single_tensor_fn.__name__] = single_tensor_fn
+
+    def wrapper(func):
+        import inspect
+
+        disabled_func = torch._disable_dynamo(func)
+        ps = inspect.signature(func).parameters
+        has_state_steps = True
+        try:
+            state_steps_ind = list(ps.keys()).index("state_steps")
+        except ValueError:
+            has_state_steps = False
+
+        # Today, there are cases where we stack state steps
+        # and pass them as the value arg of foreach ops.
+        # Having state steps on cuda as the value arg is not supported in eager,
+        # but this only occurs in the rare case that the user explicitly deletes
+        # the capturable flag. If capturable=True, this is not a problem.
+        @functools.wraps(func)
+        def maybe_fallback(*args, **kwargs):
+            if is_compiling() and (
+                not kwargs.get("capturable", False)
+                and has_state_steps
+                and (args[state_steps_ind] and args[state_steps_ind][0].is_cuda)
+                or (
+                    "state_steps" in kwargs
+                    and kwargs["state_steps"]
+                    and kwargs["state_steps"][0].is_cuda
+                )
+            ):
+                return disabled_func(*args, **kwargs)
+            else:
+                return func(*args, **kwargs)
+
+        return maybe_fallback
+
+    return wrapper
+
+
+# For any optimizer with a faster implementation, we attempt to default to the
+# fastest + stablest whenever possible. For foreach, the requirements are to have
+# native params all on CUDA. For fused, there's currently the additional requirement
+# that the tensors' dtypes must be floating point. Neither alternative supports
+# torch.jit.script nor differentiable, so we fall back to the single tensor
+# implementation in those cases.
+def _default_to_fused_or_foreach(
+    params: List[torch.Tensor], differentiable: bool, use_fused: bool = False
+) -> Tuple[bool, bool]:
+    if torch.jit.is_scripting() or differentiable:
+        return False, False
+
+    fused_supported_devices = _get_fused_kernels_supported_devices()
+    foreach_supported_devices = _get_foreach_kernels_supported_devices()
+    fused = use_fused and all(
+        p is None
+        or (
+            type(p) in _foreach_supported_types
+            and p.device.type in fused_supported_devices
+            and torch.is_floating_point(p)
+        )
+        for p in params
+    )
+    foreach = not fused and all(
+        p is None
+        or (
+            type(p) in _foreach_supported_types
+            and p.device.type in foreach_supported_devices
+        )
+        for p in params
+    )
+    return fused, foreach
+
+
+def _device_dtype_check_for_fused(
+    p: torch.Tensor, cuda_unsupported: bool = False
+) -> None:
+    fused_supported_devices = _get_fused_kernels_supported_devices()
+    if cuda_unsupported:
+        fused_supported_devices.remove("cuda")
+    if not (p.device.type in fused_supported_devices and torch.is_floating_point(p)):
+        raise RuntimeError(
+            "`fused=True` requires all the params to be floating point Tensors of "
+            f"supported devices: {fused_supported_devices} but {p.dtype} and {p.device.type}"
+        )
+
+
+def _view_as_real(params, *state_and_grads):
+    for i, p in enumerate(params):
+        if torch.is_complex(p):
+            params[i] = torch.view_as_real(params[i])
+            for s in state_and_grads:
+                s[i] = torch.view_as_real(s[i])
+
+
+def _get_scalar_dtype(is_fused=None):
+    if is_fused:
+        return torch.float32
+    return (
+        torch.float64 if torch.get_default_dtype() == torch.float64 else torch.float32
+    )
+
+
+def _get_capturable_supported_devices(supports_xla: bool = True) -> List[str]:
+    r"""Return the device type list that supports capturable optimizer."""
+    capturable_supported_devices = ["cuda", "xpu", "hpu"]
+    if not torch.jit.is_scripting():
+        capturable_supported_devices.append(torch._C._get_privateuse1_backend_name())
+    if supports_xla:
+        capturable_supported_devices.append("xla")
+    return capturable_supported_devices
+
+
+# Common doc strings among optimizers
+_foreach_doc = r"""foreach (bool, optional): whether foreach implementation of optimizer
+            is used. If unspecified by the user (so foreach is None), we will try to use
+            foreach over the for-loop implementation on CUDA, since it is usually
+            significantly more performant. Note that the foreach implementation uses
+            ~ sizeof(params) more peak memory than the for-loop version due to the intermediates
+            being a tensorlist vs just one tensor. If memory is prohibitive, batch fewer
+            parameters through the optimizer at a time or switch this flag to False (default: None)"""
+
+_fused_doc = r"""fused (bool, optional): whether the fused implementation is used.
+            Currently, `torch.float64`, `torch.float32`, `torch.float16`, and `torch.bfloat16`
+            are supported. (default: None)
+
+    .. note:: The foreach and fused implementations are typically faster than the for-loop,
+              single-tensor implementation, with fused being theoretically fastest with both
+              vertical and horizontal fusion. As such, if the user has not specified either
+              flag (i.e., when foreach = fused = None), we will attempt defaulting to the foreach
+              implementation when the tensors are all on CUDA. Why not fused? Since the fused
+              implementation is relatively new, we want to give it sufficient bake-in time.
+              To specify fused, pass True for fused. To force running the for-loop
+              implementation, pass False for either foreach or fused. """
+
+_capturable_doc = r"""capturable (bool, optional): whether this instance is safe to
+            capture in a CUDA graph. Passing True can impair ungraphed performance,
+            so if you don't intend to graph capture this instance, leave it False
+            (default: False)"""
+
+_differentiable_doc = r"""differentiable (bool, optional): whether autograd should
+            occur through the optimizer step in training. Otherwise, the step()
+            function runs in a torch.no_grad() context. Setting to True can impair
+            performance, so leave it False if you don't intend to run autograd
+            through this instance (default: False)"""
+
+_maximize_doc = r"""maximize (bool, optional): maximize the objective with respect to the
+            params, instead of minimizing (default: False)"""
+
+
+def register_optimizer_step_pre_hook(hook: GlobalOptimizerPreHook) -> RemovableHandle:
+    r"""Register a pre hook common to all optimizers.
+
+    The hook should have the following signature::
+
+        hook(optimizer, args, kwargs) -> None or modified args and kwargs
+
+    Args:
+        hook (Callable): A user defined hook which is registered on all optimizers.
+
+    Returns:
+        :class:`torch.utils.hooks.RemovableHandle`:
+            a handle that can be used to remove the added hook by calling
+            ``handle.remove()``
+    """
+    handle = hooks.RemovableHandle(_global_optimizer_pre_hooks)
+    _global_optimizer_pre_hooks[handle.id] = hook
+    return handle
+
+
+def register_optimizer_step_post_hook(hook: GlobalOptimizerPostHook) -> RemovableHandle:
+    r"""Register a post hook common to all optimizers.
+
+    The hook should have the following signature::
+
+        hook(optimizer, args, kwargs) -> None
+
+    Args:
+        hook (Callable): A user defined hook which is registered on all optimizers.
+
+    Returns:
+        :class:`torch.utils.hooks.RemovableHandle`:
+            a handle that can be used to remove the added hook by calling
+            ``handle.remove()``
+    """
+    handle = hooks.RemovableHandle(_global_optimizer_post_hooks)
+    _global_optimizer_post_hooks[handle.id] = hook
+    return handle
+
+
+ParamsT: TypeAlias = Union[Iterable[torch.Tensor], Iterable[Dict[str, Any]]]
+
+_P = ParamSpec("_P")
+R = TypeVar("R")
+T = TypeVar("T")
+
+
+class Optimizer:
+    r"""Base class for all optimizers.
+
+    .. warning::
+        Parameters need to be specified as collections that have a deterministic
+        ordering that is consistent between runs. Examples of objects that don't
+        satisfy those properties are sets and iterators over values of dictionaries.
+
+    Args:
+        params (iterable): an iterable of :class:`torch.Tensor` s or
+            :class:`dict` s. Specifies what Tensors should be optimized.
+        defaults: (dict): a dict containing default values of optimization
+            options (used when a parameter group doesn't specify them).
+    """
+
+    OptimizerPreHook: TypeAlias = Callable[[Self, Args, Kwargs], Optional[Tuple[Args, Kwargs]]]  # type: ignore[misc]
+    OptimizerPostHook: TypeAlias = Callable[[Self, Args, Kwargs], None]  # type: ignore[misc]
+
+    _optimizer_step_pre_hooks: Dict[int, OptimizerPreHook]
+    _optimizer_step_post_hooks: Dict[int, OptimizerPostHook]
+    _optimizer_state_dict_pre_hooks: 'OrderedDict[int, Callable[["Optimizer"], None]]'
+    _optimizer_state_dict_post_hooks: 'OrderedDict[int, Callable[["Optimizer", StateDict], Optional[StateDict]]]'
+    _optimizer_load_state_dict_pre_hooks: 'OrderedDict[int, Callable[["Optimizer", StateDict], Optional[StateDict]]]'
+    _optimizer_load_state_dict_post_hooks: 'OrderedDict[int, Callable[["Optimizer"], None]]'
+
+    def __init__(self, params: ParamsT, defaults: Dict[str, Any]) -> None:  # noqa: D107
+        torch._C._log_api_usage_once("python.optimizer")
+        self.defaults = defaults
+        self._optimizer_step_pre_hooks = OrderedDict()
+        self._optimizer_step_post_hooks = OrderedDict()
+        self._optimizer_state_dict_pre_hooks = OrderedDict()
+        self._optimizer_state_dict_post_hooks = OrderedDict()
+        self._optimizer_load_state_dict_pre_hooks = OrderedDict()
+        self._optimizer_load_state_dict_post_hooks = OrderedDict()
+
+        self._patch_step_function()
+
+        if isinstance(params, torch.Tensor):
+            raise TypeError(
+                "params argument given to the optimizer should be "
+                "an iterable of Tensors or dicts, but got " + torch.typename(params)
+            )
+
+        self.state: DefaultDict[torch.Tensor, Any] = defaultdict(dict)
+        self.param_groups: List[Dict[str, Any]] = []
+
+        param_groups = list(params)
+        if len(param_groups) == 0:
+            raise ValueError("optimizer got an empty parameter list")
+        if not isinstance(param_groups[0], dict):
+            param_groups = [{"params": param_groups}]
+
+        for param_group in param_groups:
+            self.add_param_group(cast(dict, param_group))
+
+        # Allows _cuda_graph_capture_health_check to rig a poor man's TORCH_WARN_ONCE in python,
+        # which I don't think exists
+        # https://github.com/pytorch/pytorch/issues/72948
+        self._warned_capturable_if_run_uncaptured = True
+
+    def __getstate__(self) -> Dict[str, Any]:  # noqa: D105
+        return {
+            "defaults": self.defaults,
+            "state": self.state,
+            "param_groups": self.param_groups,
+        }
+
+    def __setstate__(self, state: Dict[str, Any]) -> None:  # noqa: D105
+        self.__dict__.update(state)
+        if "_optimizer_step_pre_hooks" not in self.__dict__:
+            self._optimizer_step_pre_hooks = OrderedDict()
+        if "_optimizer_step_post_hooks" not in self.__dict__:
+            self._optimizer_step_post_hooks = OrderedDict()
+        if "_optimizer_state_dict_pre_hooks" not in self.__dict__:
+            self._optimizer_state_dict_pre_hooks = OrderedDict()
+        if "_optimizer_state_dict_post_hooks" not in self.__dict__:
+            self._optimizer_state_dict_post_hooks = OrderedDict()
+        if "_optimizer_load_state_dict_pre_hooks" not in self.__dict__:
+            self._optimizer_load_state_dict_pre_hooks = OrderedDict()
+        if "_optimizer_load_state_dict_post_hooks" not in self.__dict__:
+            self._optimizer_load_state_dict_post_hooks = OrderedDict()
+        self._patch_step_function()  # To support multiprocessing pickle/unpickle
+        self.defaults.setdefault("differentiable", False)
+
+    def __repr__(self) -> str:  # noqa: D105
+        format_string = self.__class__.__name__ + " ("
+        for i, group in enumerate(self.param_groups):
+            format_string += "\n"
+            format_string += f"Parameter Group {i}\n"
+            for key in sorted(group.keys()):
+                if key != "params":
+                    format_string += f"    {key}: {group[key]}\n"
+        format_string += ")"
+        return format_string
+
+    # Currently needed by Adam and AdamW
+    def _cuda_graph_capture_health_check(self) -> None:
+        # Note [torch.compile x capturable]
+        # If we are compiling, we try to take the capturable path automatically by
+        # setting the flag to True during tracing. Due to this, we skip all the checks
+        # normally required for determining whether we can use CUDA graphs and
+        # shunt the responsibility to torch.inductor. This saves time during tracing
+        # since the checks are slow without sacrificing UX since inductor will warn
+        # later if CUDA graphs cannot be enabled, e.g.,
+        # https://github.com/pytorch/pytorch/blob/d3ba8901d8640eb16f88b2bfef9df7fa383d4b47/torch/_inductor/compile_fx.py#L390.
+        # Thus, when compiling, inductor will determine if cudagraphs
+        # can be enabled based on whether there is input mutation or CPU tensors.
+        if (
+            not is_compiling()
+            and torch.backends.cuda.is_built()
+            and torch.cuda.is_available()
+        ):
+            capturing = torch.cuda.is_current_stream_capturing()
+
+            if capturing and not all(
+                group["capturable"] for group in self.param_groups
+            ):
+                raise RuntimeError(
+                    "Attempting CUDA graph capture of step() for an instance of "
+                    + self.__class__.__name__
+                    + " but param_groups' capturable is False."
+                )
+
+            if (
+                (not getattr(self, "_warned_capturable_if_run_uncaptured", False))
+                and all(group["capturable"] for group in self.param_groups)
+                and (not capturing)
+            ):
+                warnings.warn(
+                    "This instance was constructed with capturable=True or some of all the param_groups came with capturable=True, "
+                    "but step() is running without CUDA graph capture. If you never intend to graph-capture this "
+                    "instance, capturable=True can impair performance, and you should set capturable=False."
+                )
+                self._warned_capturable_if_run_uncaptured = True
+
+    def _optimizer_step_code(self) -> None:
+        """Entry point for `torch.profile.profiler`.
+
+        When python tracing is enabled the profiler will hook into this
+        function at the CPython level to inspect the optimizer's parameters and
+        param groups. It is called it after `step()` since many optimizers
+        lazily initialize state.
+
+        This is a workaround due to lack of a proper step hook on the optimizer,
+        and will be removed if it exists.
+        """
+
+    @staticmethod
+    def profile_hook_step(func: Callable[_P, R]) -> Callable[_P, R]:  # noqa: D102
+        @functools.wraps(func)
+        def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> R:
+            self, *_ = args
+            self = cast(Optimizer, self)
+            profile_name = f"Optimizer.step#{self.__class__.__name__}.step"
+            with torch.autograd.profiler.record_function(profile_name):
+                # call optimizer step pre hooks
+                for pre_hook in chain(
+                    _global_optimizer_pre_hooks.values(),
+                    self._optimizer_step_pre_hooks.values(),
+                ):
+                    result = pre_hook(self, args, kwargs)
+                    if result is not None:
+                        if isinstance(result, tuple) and len(result) == 2:
+                            args, kwargs = result  # type: ignore[assignment]
+                        else:
+                            raise RuntimeError(
+                                f"{func} must return None or a tuple of (new_args, new_kwargs), but got {result}."
+                            )
+
+                out = func(*args, **kwargs)
+                self._optimizer_step_code()
+
+                # call optimizer step post hooks
+                for post_hook in chain(
+                    self._optimizer_step_post_hooks.values(),
+                    _global_optimizer_post_hooks.values(),
+                ):
+                    post_hook(self, args, kwargs)
+
+                return out
+
+        return wrapper
+
+    @staticmethod
+    def _group_tensors_by_device_and_dtype(
+        tensorlistlist: TensorListList,
+        with_indices: bool = False,
+    ) -> Union[
+        Dict[Tuple[None, None], Tuple[TensorListList, Indices]],
+        Dict[Tuple[torch.device, torch.dtype], Tuple[TensorListList, Indices]],
+    ]:
+        """Group a list of lists of tensors by device and dtype.
+
+        Skips this step if we are compiling since this will occur during inductor lowering.
+        """
+        if is_compiling():
+            return {(None, None): (tensorlistlist, list(range(len(tensorlistlist[0]))))}
+        else:
+            return _group_tensors_by_device_and_dtype(tensorlistlist, with_indices)  # type: ignore[return-value, arg-type]
+
+    def _patch_step_function(self) -> None:
+        self._zero_grad_profile_name = (
+            f"Optimizer.zero_grad#{self.__class__.__name__}.zero_grad"
+        )
+        hooked = getattr(self.__class__.step, "hooked", None)
+        if not hooked:
+            self.__class__.step = self.profile_hook_step(self.__class__.step)  # type: ignore[assignment]
+            self.__class__.step.hooked = True  # type: ignore[attr-defined]
+
+    def register_step_pre_hook(self, hook: OptimizerPreHook) -> RemovableHandle:
+        r"""Register an optimizer step pre hook which will be called before optimizer step.
+
+        It should have the following signature::
+
+            hook(optimizer, args, kwargs) -> None or modified args and kwargs
+
+        The ``optimizer`` argument is the optimizer instance being used. If
+        args and kwargs are modified by the pre-hook, then the transformed
+        values are returned as a tuple containing the new_args and new_kwargs.
+
+        Args:
+            hook (Callable): The user defined hook to be registered.
+
+        Returns:
+            :class:`torch.utils.hooks.RemovableHandle`:
+                a handle that can be used to remove the added hook by calling
+                ``handle.remove()``
+        """
+        handle = hooks.RemovableHandle(self._optimizer_step_pre_hooks)
+        self._optimizer_step_pre_hooks[handle.id] = hook
+        return handle
+
+    def register_step_post_hook(self, hook: OptimizerPostHook) -> RemovableHandle:
+        r"""Register an optimizer step post hook which will be called after optimizer step.
+
+        It should have the following signature::
+
+            hook(optimizer, args, kwargs) -> None
+
+        The ``optimizer`` argument is the optimizer instance being used.
+
+        Args:
+            hook (Callable): The user defined hook to be registered.
+
+        Returns:
+            :class:`torch.utils.hooks.RemovableHandle`:
+                a handle that can be used to remove the added hook by calling
+                ``handle.remove()``
+        """
+        handle = hooks.RemovableHandle(self._optimizer_step_post_hooks)
+        self._optimizer_step_post_hooks[handle.id] = hook
+        return handle
+
+    def register_state_dict_pre_hook(
+        self, hook: Callable[["Optimizer"], None], prepend: bool = False
+    ) -> RemovableHandle:  # noqa: D101
+        r"""Register a state dict pre-hook which will be called before :meth:`~torch.optim.Optimizer.state_dict` is called.
+
+        It should have the following signature::
+
+            hook(optimizer) -> None
+
+        The ``optimizer`` argument is the optimizer instance being used.
+        The hook will be called with argument ``self`` before calling ``state_dict`` on ``self``.
+        The registered hook can be used to perform pre-processing before the ``state_dict``
+        call is made.
+
+        Args:
+            hook (Callable): The user defined hook to be registered.
+            prepend (bool): If True, the provided pre ``hook`` will be fired before
+                all the already registered pre-hooks on ``state_dict``. Otherwise,
+                the provided ``hook`` will be fired after all the already registered
+                pre-hooks. (default: False)
+
+        Returns:
+            :class:`torch.utils.hooks.RemoveableHandle`:
+                a handle that can be used to remove the added hook by calling
+                ``handle.remove()``
+        """
+        handle = hooks.RemovableHandle(self._optimizer_state_dict_pre_hooks)
+        self._optimizer_state_dict_pre_hooks[handle.id] = hook
+        if prepend:
+            self._optimizer_state_dict_pre_hooks.move_to_end(handle.id, last=False)
+        return handle
+
+    def register_state_dict_post_hook(
+        self,
+        hook: Callable[["Optimizer", StateDict], Optional[StateDict]],
+        prepend: bool = False,
+    ) -> RemovableHandle:
+        r"""Register a state dict post-hook which will be called after :meth:`~torch.optim.Optimizer.state_dict` is called.
+
+        It should have the following signature::
+
+            hook(optimizer, state_dict) -> state_dict or None
+
+        The hook will be called with arguments ``self`` and ``state_dict`` after generating
+        a ``state_dict`` on ``self``. The hook may modify the state_dict inplace or optionally
+        return a new one. The registered hook can be used to perform post-processing
+        on the ``state_dict`` before it is returned.
+
+        Args:
+            hook (Callable): The user defined hook to be registered.
+            prepend (bool): If True, the provided post ``hook`` will be fired before
+                all the already registered post-hooks on ``state_dict``. Otherwise,
+                the provided ``hook`` will be fired after all the already registered
+                post-hooks. (default: False)
+
+        Returns:
+            :class:`torch.utils.hooks.RemoveableHandle`:
+                a handle that can be used to remove the added hook by calling
+                ``handle.remove()``
+        """
+        handle = hooks.RemovableHandle(self._optimizer_state_dict_post_hooks)
+        self._optimizer_state_dict_post_hooks[handle.id] = hook
+        if prepend:
+            self._optimizer_state_dict_post_hooks.move_to_end(handle.id, last=False)
+        return handle
+
+    @torch._disable_dynamo
+    def state_dict(self) -> StateDict:
+        r"""Return the state of the optimizer as a :class:`dict`.
+
+        It contains two entries:
+
+        * ``state``: a Dict holding current optimization state. Its content
+            differs between optimizer classes, but some common characteristics
+            hold. For example, state is saved per parameter, and the parameter
+            itself is NOT saved. ``state`` is a Dictionary mapping parameter ids
+            to a Dict with state corresponding to each parameter.
+        * ``param_groups``: a List containing all parameter groups where each
+            parameter group is a Dict. Each parameter group contains metadata
+            specific to the optimizer, such as learning rate and weight decay,
+            as well as a List of parameter IDs of the parameters in the group.
+
+        NOTE: The parameter IDs may look like indices but they are just IDs
+        associating state with param_group. When loading from a state_dict,
+        the optimizer will zip the param_group ``params`` (int IDs) and the
+        optimizer ``param_groups`` (actual ``nn.Parameter`` s) in order to
+        match state WITHOUT additional verification.
+
+        A returned state dict might look something like:
+
+        .. code-block:: text
+
+            {
+                'state': {
+                    0: {'momentum_buffer': tensor(...), ...},
+                    1: {'momentum_buffer': tensor(...), ...},
+                    2: {'momentum_buffer': tensor(...), ...},
+                    3: {'momentum_buffer': tensor(...), ...}
+                },
+                'param_groups': [
+                    {
+                        'lr': 0.01,
+                        'weight_decay': 0,
+                        ...
+                        'params': [0]
+                    },
+                    {
+                        'lr': 0.001,
+                        'weight_decay': 0.5,
+                        ...
+                        'params': [1, 2, 3]
+                    }
+                ]
+            }
+
+        """
+        for pre_hook in self._optimizer_state_dict_pre_hooks.values():
+            pre_hook(self)
+
+        # Save order indices instead of Tensors
+        param_mappings: Dict[int, int] = {}
+        start_index = 0
+
+        def pack_group(group: Dict[str, Any]) -> Dict[str, Any]:
+            nonlocal start_index
+            packed = {k: v for k, v in group.items() if k != "params"}
+            param_mappings.update(
+                {
+                    id(p): i
+                    for i, p in enumerate(group["params"], start_index)
+                    if id(p) not in param_mappings
+                }
+            )
+            packed["params"] = [param_mappings[id(p)] for p in group["params"]]
+            start_index += len(packed["params"])
+            return packed
+
+        param_groups = [pack_group(g) for g in self.param_groups]
+        # Remap state to use order indices as keys
+        packed_state = {
+            (param_mappings[id(k)] if isinstance(k, torch.Tensor) else k): v
+            for k, v in self.state.items()
+        }
+
+        state_dict = {
+            "state": packed_state,
+            "param_groups": param_groups,
+        }
+
+        for post_hook in self._optimizer_state_dict_post_hooks.values():
+            hook_result = post_hook(self, state_dict)
+            if hook_result is not None:
+                state_dict = hook_result
+        return state_dict
+
+    @staticmethod
+    def _process_value_according_to_param_policy(
+        param: torch.Tensor,
+        value: torch.Tensor,
+        param_id: int,
+        param_groups: List[Dict[Any, Any]],
+        key: Hashable = None,
+    ) -> torch.Tensor:
+        # Floating-point types are a bit special here. They are the only ones
+        # that are assumed to always match the type of params.
+        # Make sure state['step'] is not casted https://github.com/pytorch/pytorch/issues/74424
+        # UNLESS fused or capturable, see note [special device hosting for step]
+        fused = False
+        capturable = False
+        assert param_groups is not None
+        for pg in param_groups:
+            if param_id in pg["params"]:
+                fused = pg["fused"] if "fused" in pg else False
+                capturable = pg["capturable"] if "capturable" in pg else False
+                break
+        if key == "step":
+            if capturable or fused:
+                return value.to(dtype=torch.float32, device=param.device)
+            else:
+                return value
+        else:
+            if param.is_floating_point():
+                return value.to(dtype=param.dtype, device=param.device)
+            else:
+                return value.to(device=param.device)
+
+    def register_load_state_dict_pre_hook(
+        self,
+        hook: Callable[["Optimizer", StateDict], Optional[StateDict]],
+        prepend: bool = False,
+    ) -> RemovableHandle:  # noqa: D205 D400
+        r"""Register a load_state_dict pre-hook which will be called before
+        :meth:`~torch.optim.Optimizer.load_state_dict` is called. It should have the
+        following signature::
+
+            hook(optimizer, state_dict) -> state_dict or None
+
+        The ``optimizer`` argument is the optimizer instance being used and the
+        ``state_dict`` argument is a shallow copy of the ``state_dict`` the user
+        passed in to ``load_state_dict``. The hook may modify the state_dict inplace
+        or optionally return a new one. If a state_dict is returned, it will be used
+        to be loaded into the optimizer.
+
+        The hook will be called with argument ``self`` and ``state_dict`` before
+        calling ``load_state_dict`` on ``self``. The registered hook can be used to
+        perform pre-processing before the ``load_state_dict`` call is made.
+
+        Args:
+            hook (Callable): The user defined hook to be registered.
+            prepend (bool): If True, the provided pre ``hook`` will be fired before
+                all the already registered pre-hooks on ``load_state_dict``. Otherwise,
+                the provided ``hook`` will be fired after all the already registered
+                pre-hooks. (default: False)
+
+        Returns:
+            :class:`torch.utils.hooks.RemoveableHandle`:
+                a handle that can be used to remove the added hook by calling
+                ``handle.remove()``
+        """
+        handle = hooks.RemovableHandle(self._optimizer_load_state_dict_pre_hooks)
+        self._optimizer_load_state_dict_pre_hooks[handle.id] = hook
+        if prepend:
+            self._optimizer_load_state_dict_pre_hooks.move_to_end(handle.id, last=False)
+        return handle
+
+    def register_load_state_dict_post_hook(
+        self, hook: Callable[["Optimizer"], None], prepend: bool = False
+    ) -> RemovableHandle:  # noqa: D205 D400
+        r"""Register a load_state_dict post-hook which will be called after
+        :meth:`~torch.optim.Optimizer.load_state_dict` is called. It should have the
+        following signature::
+
+            hook(optimizer) -> None
+
+        The ``optimizer`` argument is the optimizer instance being used.
+
+        The hook will be called with argument ``self`` after calling
+        ``load_state_dict`` on ``self``. The registered hook can be used to
+        perform post-processing after ``load_state_dict`` has loaded the
+        ``state_dict``.
+
+        Args:
+            hook (Callable): The user defined hook to be registered.
+            prepend (bool): If True, the provided post ``hook`` will be fired before
+                all the already registered post-hooks on ``load_state_dict``. Otherwise,
+                the provided ``hook`` will be fired after all the already registered
+                post-hooks. (default: False)
+
+        Returns:
+            :class:`torch.utils.hooks.RemoveableHandle`:
+                a handle that can be used to remove the added hook by calling
+                ``handle.remove()``
+        """
+        handle = hooks.RemovableHandle(self._optimizer_load_state_dict_post_hooks)
+        self._optimizer_load_state_dict_post_hooks[handle.id] = hook
+        if prepend:
+            self._optimizer_load_state_dict_post_hooks.move_to_end(handle.id, last=False)  # type: ignore[attr-defined]
+        return handle
+
+    @torch._disable_dynamo
+    def load_state_dict(self, state_dict: StateDict) -> None:
+        r"""Load the optimizer state.
+
+        Args:
+            state_dict (dict): optimizer state. Should be an object returned
+                from a call to :meth:`state_dict`.
+        """
+        # shallow copy, to be consistent with module API
+        state_dict = state_dict.copy()
+
+        for pre_hook in self._optimizer_load_state_dict_pre_hooks.values():
+            hook_result = pre_hook(self, state_dict)
+            if hook_result is not None:
+                state_dict = hook_result
+
+        # Validate the state_dict
+        groups = self.param_groups
+
+        # Deepcopy as we write into saved_groups later to update state
+        saved_groups = deepcopy(state_dict["param_groups"])
+
+        if len(groups) != len(saved_groups):
+            raise ValueError(
+                "loaded state dict has a different number of " "parameter groups"
+            )
+        param_lens = (len(g["params"]) for g in groups)
+        saved_lens = (len(g["params"]) for g in saved_groups)
+        if any(p_len != s_len for p_len, s_len in zip(param_lens, saved_lens)):
+            raise ValueError(
+                "loaded state dict contains a parameter group "
+                "that doesn't match the size of optimizer's group"
+            )
+
+        # Update the state
+        id_map = dict(
+            zip(
+                chain.from_iterable(g["params"] for g in saved_groups),
+                chain.from_iterable(g["params"] for g in groups),
+            )
+        )
+
+        def _cast(param, value, param_id=None, param_groups=None, key=None):
+            r"""Make a deep copy of value, casting all tensors to device of param."""
+            if isinstance(value, torch.Tensor):
+                return Optimizer._process_value_according_to_param_policy(
+                    param, value, param_id, param_groups, key
+                )
+            elif isinstance(value, dict):
+                return {
+                    k: _cast(
+                        param, v, param_id=param_id, param_groups=param_groups, key=k
+                    )
+                    for k, v in value.items()
+                }
+            elif isinstance(value, Iterable):
+                return type(value)(_cast(param, v, param_id=param_id, param_groups=param_groups) for v in value)  # type: ignore[call-arg]
+            else:
+                return value
+
+        # Copy state assigned to params (and cast tensors to appropriate types).
+        # State that is not assigned to params is copied as is (needed for
+        # backward compatibility).
+        state: DefaultDict[torch.Tensor, Dict[Any, Any]] = defaultdict(dict)
+        for k, v in state_dict["state"].items():
+            if k in id_map:
+                param = id_map[k]
+                state[param] = _cast(
+                    param, v, param_id=k, param_groups=state_dict["param_groups"]
+                )
+            else:
+                state[k] = v
+
+        # Update parameter groups, setting their 'params' value
+        def update_group(
+            group: Dict[str, Any], new_group: Dict[str, Any]
+        ) -> Dict[str, Any]:
+            new_group["params"] = group["params"]
+            return new_group
+
+        param_groups = [update_group(g, ng) for g, ng in zip(groups, saved_groups)]
+        self.__setstate__({"state": state, "param_groups": param_groups})
+
+        for post_hook in self._optimizer_load_state_dict_post_hooks.values():
+            post_hook(self)
+
+    @torch._disable_dynamo
+    def zero_grad(self, set_to_none: bool = True) -> None:
+        r"""Reset the gradients of all optimized :class:`torch.Tensor` s.
+
+        Args:
+            set_to_none (bool): instead of setting to zero, set the grads to None.
+                This will in general have lower memory footprint, and can modestly improve performance.
+                However, it changes certain behaviors. For example:
+                1. When the user tries to access a gradient and perform manual ops on it,
+                a None attribute or a Tensor full of 0s will behave differently.
+                2. If the user requests ``zero_grad(set_to_none=True)`` followed by a backward pass, ``.grad``\ s
+                are guaranteed to be None for params that did not receive a gradient.
+                3. ``torch.optim`` optimizers have a different behavior if the gradient is 0 or None
+                (in one case it does the step with a gradient of 0 and in the other it skips
+                the step altogether).
+        """
+        foreach = self.defaults.get("foreach", False) or self.defaults.get(
+            "fused", False
+        )
+
+        if not hasattr(self, "_zero_grad_profile_name"):
+            self._patch_step_function()
+
+        per_device_and_dtype_grads: Optional[
+            DefaultDict[torch.device, DefaultDict[torch.dtype, List[torch.Tensor]]]
+        ]
+        if foreach:
+            per_device_and_dtype_grads = defaultdict(lambda: defaultdict(list))
+        else:
+            per_device_and_dtype_grads = None
+
+        with torch.autograd.profiler.record_function(self._zero_grad_profile_name):
+            for group in self.param_groups:
+                for p in group["params"]:
+                    if p.grad is not None:
+                        if set_to_none:
+                            p.grad = None
+                        else:
+                            if p.grad.grad_fn is not None:
+                                p.grad.detach_()
+                            else:
+                                p.grad.requires_grad_(False)
+                            if not foreach or p.grad.is_sparse:
+                                p.grad.zero_()
+                            else:
+                                assert per_device_and_dtype_grads is not None
+                                per_device_and_dtype_grads[p.grad.device][
+                                    p.grad.dtype
+                                ].append(p.grad)
+            if foreach:
+                assert per_device_and_dtype_grads is not None
+                for per_dtype_grads in per_device_and_dtype_grads.values():
+                    for grads in per_dtype_grads.values():
+                        torch._foreach_zero_(grads)
+
+    @overload
+    def step(self, closure: None = ...) -> None:
+        ...
+
+    @overload
+    def step(self, closure: Callable[[], float]) -> float:
+        ...
+
+    def step(self, closure: Optional[Callable[[], float]] = None) -> Optional[float]:
+        r"""Perform a single optimization step to update parameter.
+
+        Args:
+            closure (Callable): A closure that reevaluates the model and
+                returns the loss. Optional for most optimizers.
+
+        .. note::
+            Unless otherwise specified, this function should not modify the
+            ``.grad`` field of the parameters.
+        """
+        raise NotImplementedError
+
+    @torch._disable_dynamo
+    def add_param_group(self, param_group: Dict[str, Any]) -> None:
+        r"""Add a param group to the :class:`Optimizer` s `param_groups`.
+
+        This can be useful when fine tuning a pre-trained network as frozen layers can be made
+        trainable and added to the :class:`Optimizer` as training progresses.
+
+        Args:
+            param_group (dict): Specifies what Tensors should be optimized along with group
+                specific optimization options.
+        """
+        if not isinstance(param_group, dict):
+            raise TypeError(f"param_group must be a dict, but got {type(param_group)}")
+
+        params = param_group["params"]
+        if isinstance(params, torch.Tensor):
+            param_group["params"] = [params]
+        elif isinstance(params, set):
+            raise TypeError(
+                "optimizer parameters need to be organized in ordered collections, but "
+                "the ordering of tensors in sets will change between runs. Please use a list instead."
+            )
+        else:
+            param_group["params"] = list(params)
+
+        for param in param_group["params"]:
+            if not isinstance(param, torch.Tensor):
+                raise TypeError(
+                    "optimizer can only optimize Tensors, "
+                    "but one of the params is " + torch.typename(param)
+                )
+            if not self.defaults.get("differentiable", None) and not (
+                param.is_leaf or param.retains_grad
+            ):
+                raise ValueError("can't optimize a non-leaf Tensor")
+
+        for name, default in self.defaults.items():
+            if default is required and name not in param_group:
+                raise ValueError(
+                    f"parameter group didn't specify a value of required optimization parameter {name}"
+                )
+            else:
+                param_group.setdefault(name, default)
+
+        params = param_group["params"]
+        if len(params) != len(set(params)):
+            warnings.warn(
+                "optimizer contains a parameter group with duplicate parameters; "
+                "in future, this will cause an error; "
+                "see github.com/pytorch/pytorch/issues/40967 for more information",
+                stacklevel=3,
+            )
+
+        param_set: Set[torch.Tensor] = set()
+        for group in self.param_groups:
+            param_set.update(set(group["params"]))
+
+        if not param_set.isdisjoint(set(param_group["params"])):
+            raise ValueError("some parameters appear in more than one parameter group")
+
+        self.param_groups.append(param_group)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/radam.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/radam.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2d0c31a91736554e72c0664f5bc1ffbd85c3b75
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/radam.py
@@ -0,0 +1,608 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+r"""Implementation for the RAdam algorithm."""
+from typing import cast, List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+
+from .optimizer import (
+    _capturable_doc,
+    _default_to_fused_or_foreach,
+    _differentiable_doc,
+    _disable_dynamo_if_unsupported,
+    _foreach_doc,
+    _get_capturable_supported_devices,
+    _get_scalar_dtype,
+    _get_value,
+    _maximize_doc,
+    _use_grad_for_differentiable,
+    _view_as_real,
+    Optimizer,
+    ParamsT,
+)
+
+
+__all__ = ["RAdam", "radam"]
+
+
+class RAdam(Optimizer):  # noqa: D101
+    def __init__(
+        self,
+        params: ParamsT,
+        lr: Union[float, Tensor] = 1e-3,
+        betas: Tuple[float, float] = (0.9, 0.999),
+        eps: float = 1e-8,
+        weight_decay: float = 0,
+        decoupled_weight_decay: bool = False,
+        *,
+        foreach: Optional[bool] = None,
+        maximize: bool = False,
+        capturable: bool = False,
+        differentiable: bool = False,
+    ):  # noqa: D107
+        if isinstance(lr, Tensor) and lr.numel() != 1:
+            raise ValueError("Tensor lr must be 1-element")
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 <= eps:
+            raise ValueError(f"Invalid epsilon value: {eps}")
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
+        if not 0.0 <= weight_decay:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+
+        defaults = dict(
+            lr=lr,
+            betas=betas,
+            eps=eps,
+            weight_decay=weight_decay,
+            maximize=maximize,
+            foreach=foreach,
+            capturable=capturable,
+            decoupled_weight_decay=decoupled_weight_decay,
+            differentiable=differentiable,
+        )
+        super().__init__(params, defaults)
+
+    def __setstate__(self, state):  # noqa: D105
+        super().__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault("foreach", None)
+            group.setdefault("maximize", False)
+            group.setdefault("differentiable", False)
+            group.setdefault("decoupled_weight_decay", False)
+            group.setdefault("capturable", False)
+            for p in group["params"]:
+                p_state = self.state.get(p, [])
+                if len(p_state) != 0 and not torch.is_tensor(p_state["step"]):
+                    step_val = float(p_state["step"])
+                    p_state["step"] = (
+                        torch.tensor(
+                            step_val, dtype=_get_scalar_dtype(), device=p.device
+                        )
+                        if group["capturable"]
+                        else torch.tensor(step_val, dtype=_get_scalar_dtype())
+                    )
+
+    def _init_group(
+        self, group, params_with_grad, grads, exp_avgs, exp_avg_sqs, state_steps
+    ):
+        has_complex = False
+        for p in group["params"]:
+            if p.grad is not None:
+                has_complex |= torch.is_complex(p)
+                params_with_grad.append(p)
+                if p.grad.is_sparse:
+                    raise RuntimeError("RAdam does not support sparse gradients")
+                grads.append(p.grad)
+
+                state = self.state[p]
+                # Lazy state initialization
+                if len(state) == 0:
+                    state["step"] = (
+                        torch.zeros((), dtype=_get_scalar_dtype(), device=p.device)
+                        if group["capturable"]
+                        else torch.tensor(0.0, dtype=_get_scalar_dtype())
+                    )
+                    # Exponential moving average of gradient values
+                    state["exp_avg"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+                    # Exponential moving average of squared gradient values
+                    state["exp_avg_sq"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+
+                exp_avgs.append(state["exp_avg"])
+                exp_avg_sqs.append(state["exp_avg_sq"])
+                state_steps.append(state["step"])
+
+        return has_complex
+
+    @_use_grad_for_differentiable
+    def step(self, closure=None):
+        """Perform a single optimization step.
+
+        Args:
+            closure (Callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        self._cuda_graph_capture_health_check()
+
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            params_with_grad: List[Tensor] = []
+            grads: List[Tensor] = []
+            exp_avgs: List[Tensor] = []
+            exp_avg_sqs: List[Tensor] = []
+            state_steps: List[Tensor] = []
+            beta1, beta2 = cast(Tuple[float, float], group["betas"])
+
+            has_complex = self._init_group(
+                group, params_with_grad, grads, exp_avgs, exp_avg_sqs, state_steps
+            )
+
+            radam(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                state_steps,
+                beta1=beta1,
+                beta2=beta2,
+                lr=group["lr"],
+                weight_decay=group["weight_decay"],
+                eps=group["eps"],
+                maximize=group["maximize"],
+                foreach=group["foreach"],
+                capturable=group["capturable"],
+                differentiable=group["differentiable"],
+                decoupled_weight_decay=group["decoupled_weight_decay"],
+                has_complex=has_complex,
+            )
+
+        return loss
+
+
+RAdam.__doc__ = (
+    r"""Implements RAdam algorithm.
+
+    .. math::
+       \begin{aligned}
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{input}      : \gamma \text{ (lr)}, \: \beta_1, \beta_2
+                \text{ (betas)}, \: \theta_0 \text{ (params)}, \:f(\theta) \text{ (objective)}, \:
+                \lambda \text{ (weightdecay)}, \:\textit{maximize}                               \\
+            &\hspace{13mm} \epsilon \text{ (epsilon)}, \textit{decoupled\_weight\_decay}         \\
+            &\textbf{initialize} :  m_0 \leftarrow 0 \text{ ( first moment)},
+                v_0 \leftarrow 0 \text{ ( second moment)},                                       \\
+            &\hspace{18mm} \rho_{\infty} \leftarrow 2/(1-\beta_2) -1                      \\[-1.ex]
+            &\rule{110mm}{0.4pt}  \\
+            &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do}                         \\
+            &\hspace{6mm}\textbf{if} \: \textit{maximize}:                                       \\
+            &\hspace{12mm}g_t           \leftarrow   -\nabla_{\theta} f_t (\theta_{t-1})         \\
+            &\hspace{6mm}\textbf{else}                                                           \\
+            &\hspace{12mm}g_t           \leftarrow   \nabla_{\theta} f_t (\theta_{t-1})          \\
+            &\hspace{6mm} \theta_t \leftarrow \theta_{t-1}                                       \\
+            &\hspace{6mm} \textbf{if} \: \lambda \neq 0                                          \\
+            &\hspace{12mm}\textbf{if} \: \textit{decoupled\_weight\_decay}                       \\
+            &\hspace{18mm} \theta_t \leftarrow \theta_{t} - \gamma \lambda \theta_{t}            \\
+            &\hspace{12mm}\textbf{else}                                                          \\
+            &\hspace{18mm} g_t \leftarrow g_t + \lambda \theta_{t}                               \\
+            &\hspace{6mm}m_t           \leftarrow   \beta_1 m_{t-1} + (1 - \beta_1) g_t          \\
+            &\hspace{6mm}v_t           \leftarrow   \beta_2 v_{t-1} + (1-\beta_2) g^2_t          \\
+            &\hspace{6mm}\widehat{m_t} \leftarrow   m_t/\big(1-\beta_1^t \big)                   \\
+            &\hspace{6mm}\rho_t \leftarrow \rho_{\infty} -
+                2 t \beta^t_2 /\big(1-\beta_2^t \big)                                    \\[0.1.ex]
+            &\hspace{6mm}\textbf{if} \: \rho_t > 5                                               \\
+            &\hspace{12mm} l_t \leftarrow \frac{\sqrt{ (1-\beta^t_2) }}{ \sqrt{v_t} +\epsilon  } \\
+            &\hspace{12mm} r_t \leftarrow
+      \sqrt{\frac{(\rho_t-4)(\rho_t-2)\rho_{\infty}}{(\rho_{\infty}-4)(\rho_{\infty}-2) \rho_t}} \\
+            &\hspace{12mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t} r_t l_t        \\
+            &\hspace{6mm}\textbf{else}                                                           \\
+            &\hspace{12mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t}                \\
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+            &\bf{return} \:  \theta_t                                                     \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+       \end{aligned}
+
+    For further details regarding the algorithm we refer to `On the variance of the adaptive learning rate and beyond`_.
+
+    This implementation provides an option to use either the original weight_decay implementation as in Adam
+    (where the weight_decay is applied to the gradient) or the one from AdamW (where weight_decay is applied
+    to the weight) through the decoupled_weight_decay option. When decoupled_weight_decay is set to False
+    (default), it uses the original Adam style weight decay, otherwise, it uses the AdamW style which
+    corresponds more closely to the `author's implementation`_ in the RAdam paper. Further information
+    about decoupled weight decay can be found in `Decoupled Weight Decay Regularization`_.
+
+    """
+    + rf"""
+    Args:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, Tensor, optional): learning rate (default: 1e-3)
+        betas (Tuple[float, float], optional): coefficients used for computing
+            running averages of gradient and its square (default: (0.9, 0.999))
+        eps (float, optional): term added to the denominator to improve
+            numerical stability (default: 1e-8)
+        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+        decoupled_weight_decay (bool, optional): whether to use decoupled weight
+            decay as in AdamW to obtain RAdamW (default: False)
+        {_foreach_doc}
+        {_maximize_doc}
+        {_differentiable_doc}
+        {_capturable_doc}
+
+    .. _On the variance of the adaptive learning rate and beyond:
+        https://arxiv.org/abs/1908.03265
+    .. _author's implementation:
+        https://github.com/LiyuanLucasLiu/RAdam
+    .. _Decoupled Weight Decay Regularization:
+        https://arxiv.org/abs/1711.05101
+
+    """
+)
+
+
+def _single_tensor_radam(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    state_steps: List[Tensor],
+    *,
+    beta1: float,
+    beta2: float,
+    lr: float,
+    weight_decay: float,
+    eps: float,
+    decoupled_weight_decay: bool,
+    differentiable: bool,
+    maximize: bool,
+    capturable: bool,
+    has_complex: bool,
+):
+    for i, param in enumerate(params):
+        grad = grads[i] if not maximize else -grads[i]
+        exp_avg = exp_avgs[i]
+        exp_avg_sq = exp_avg_sqs[i]
+        step_t = state_steps[i]
+
+        # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+        if not torch._utils.is_compiling() and capturable:
+            capturable_supported_devices = _get_capturable_supported_devices()
+            assert (
+                param.device.type == step_t.device.type
+                and param.device.type in capturable_supported_devices
+            ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
+
+        if torch.is_complex(param):
+            param = torch.view_as_real(param)
+            grad = torch.view_as_real(grad)
+            exp_avg = torch.view_as_real(exp_avg)
+            exp_avg_sq = torch.view_as_real(exp_avg_sq)
+
+        # update step
+        step_t += 1
+        step = step_t if capturable else _get_value(step_t)
+
+        if weight_decay != 0:
+            if decoupled_weight_decay:
+                param.mul_(1 - lr * weight_decay)
+            else:
+                grad = grad.add(param, alpha=weight_decay)
+
+        # Decay the first and second moment running average coefficient
+        exp_avg.lerp_(grad, 1 - beta1)
+        exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+
+        bias_correction1 = 1 - beta1**step
+        bias_correction2 = 1 - beta2**step
+
+        # correcting bias for the first moving moment
+        bias_corrected_exp_avg = exp_avg / bias_correction1
+
+        # maximum length of the approximated SMA
+        rho_inf = 2 / (1 - beta2) - 1
+        # compute the length of the approximated SMA
+        rho_t = rho_inf - 2 * step * (beta2**step) / bias_correction2
+
+        def _compute_rect():
+            return (
+                (rho_t - 4)
+                * (rho_t - 2)
+                * rho_inf
+                / ((rho_inf - 4) * (rho_inf - 2) * rho_t)
+            ) ** 0.5
+
+        def _compute_adaptive_lr():
+            exp_avg_sq_sqrt = exp_avg_sq.sqrt()
+            if differentiable:
+                exp_avg_sq_sqrt = exp_avg_sq_sqrt.add(eps)
+            else:
+                exp_avg_sq_sqrt = exp_avg_sq_sqrt.add_(eps)
+
+            return (bias_correction2**0.5) / exp_avg_sq_sqrt
+
+        # Compute the variance rectification term and update parameters accordingly
+        if capturable:
+            update = torch.where(
+                rho_t > 5.0, _compute_rect() * _compute_adaptive_lr(), 1.0
+            )
+            param.add_(bias_corrected_exp_avg * lr * update, alpha=-1.0)
+        else:
+            if rho_t > 5.0:
+                param.add_(
+                    bias_corrected_exp_avg
+                    * lr
+                    * _compute_adaptive_lr()
+                    * _compute_rect(),
+                    alpha=-1.0,
+                )
+            else:
+                param.add_(bias_corrected_exp_avg * lr, alpha=-1.0)
+
+
+def _multi_tensor_radam(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    state_steps: List[Tensor],
+    *,
+    beta1: float,
+    beta2: float,
+    lr: float,
+    weight_decay: float,
+    eps: float,
+    decoupled_weight_decay: bool,
+    differentiable: bool,
+    maximize: bool,
+    capturable: bool,
+    has_complex: bool,
+):
+    if len(params) == 0:
+        return
+
+    assert not differentiable, "_foreach ops don't support autograd"
+
+    # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+    if not torch._utils.is_compiling() and capturable:
+        capturable_supported_devices = _get_capturable_supported_devices(
+            supports_xla=False
+        )
+        assert all(
+            p.device.type == step.device.type
+            and p.device.type in capturable_supported_devices
+            for p, step in zip(params, state_steps)
+        ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
+
+    grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
+        [params, grads, exp_avgs, exp_avg_sqs, state_steps]  # type: ignore[list-item]
+    )
+    for (
+        grouped_params_,
+        grouped_grads_,
+        grouped_exp_avgs_,
+        grouped_exp_avg_sqs_,
+        grouped_state_steps_,
+    ), _ in grouped_tensors.values():
+        grouped_params = cast(List[Tensor], grouped_params_)
+        grouped_grads = cast(List[Tensor], grouped_grads_)
+        grouped_exp_avgs = cast(List[Tensor], grouped_exp_avgs_)
+        grouped_exp_avg_sqs = cast(List[Tensor], grouped_exp_avg_sqs_)
+        grouped_state_steps = cast(List[Tensor], grouped_state_steps_)
+
+        # Update steps
+        # If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
+        # and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
+        # wrapped it once now. The alpha is required to assure we go to the right overload.
+        if not torch._utils.is_compiling() and grouped_state_steps[0].is_cpu:
+            torch._foreach_add_(
+                grouped_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
+            )
+        else:
+            torch._foreach_add_(grouped_state_steps, 1)
+
+        if has_complex:
+            _view_as_real(
+                grouped_params, grouped_grads, grouped_exp_avgs, grouped_exp_avg_sqs
+            )
+
+        if maximize:
+            grouped_grads = torch._foreach_neg(grouped_grads)  # type: ignore[assignment]
+
+        # maximum length of the approximated SMA
+        rho_inf = 2 / (1 - beta2) - 1
+        # compute the length of the approximated SMA
+        bias_correction1: Union[Tuple[Tensor, ...], List[Tensor]]
+        bias_correction2: Union[Tuple[Tensor, ...], List[Tensor]]
+        rho_t_list: Union[Tuple[Tensor, ...], List[Tensor]]
+        if capturable:
+            bias_correction1 = torch._foreach_pow(beta2, grouped_state_steps)
+            torch._foreach_neg_(bias_correction1)
+            torch._foreach_add_(bias_correction1, 1)
+            bias_correction2 = torch._foreach_pow(beta2, grouped_state_steps)
+            torch._foreach_mul_(bias_correction2, grouped_state_steps)
+            torch._foreach_mul_(bias_correction2, 2)
+            torch._foreach_div_(bias_correction2, bias_correction1)
+            torch._foreach_neg_(bias_correction2)
+            torch._foreach_add_(bias_correction2, rho_inf)
+            rho_t_list = bias_correction2
+        else:
+            rho_t_list = [
+                rho_inf
+                - 2
+                * _get_value(step)
+                * (beta2 ** _get_value(step))
+                / (1 - beta2 ** _get_value(step))
+                for step in grouped_state_steps
+            ]
+
+        if weight_decay != 0:
+            if decoupled_weight_decay:
+                torch._foreach_mul_(grouped_params, 1 - lr * weight_decay)
+            else:
+                # Re-use the intermediate memory (grouped_grads) already allocated for maximize
+                if maximize:
+                    torch._foreach_add_(
+                        grouped_grads, grouped_params, alpha=weight_decay
+                    )
+                else:
+                    grouped_grads = torch._foreach_add(  # type: ignore[assignment]
+                        grouped_grads, grouped_params, alpha=weight_decay
+                    )
+
+        # Decay the first and second moment running average coefficient
+        torch._foreach_lerp_(grouped_exp_avgs, grouped_grads, 1 - beta1)
+
+        torch._foreach_mul_(grouped_exp_avg_sqs, beta2)
+        torch._foreach_addcmul_(
+            grouped_exp_avg_sqs, grouped_grads, grouped_grads, 1 - beta2
+        )
+
+        # Delete the local intermediate since it won't be used anymore to save on peak memory
+        del grouped_grads
+
+        if capturable:
+            num = torch._foreach_sub(rho_t_list, 4)
+            sub2 = torch._foreach_sub(rho_t_list, 2)
+            torch._foreach_mul_(num, sub2)
+            del sub2
+            torch._foreach_mul_(num, rho_inf)
+            rho_inf = (rho_inf - 4) * (rho_inf - 2)
+            denom = torch._foreach_mul(rho_t_list, rho_inf)
+            torch._foreach_div_(num, denom)
+            del denom
+            torch._foreach_sqrt_(num)
+
+            # TODO(mlazos): we should try and get a foreach_where op https://github.com/pytorch/pytorch/issues/117884
+            rect = [
+                torch.where(rho_t > 5.0, n, 0.0) for n, rho_t in zip(num, rho_t_list)
+            ]
+            del num
+            del rho_t_list
+            unrect_step_size = [torch.where(rect > 0, 0.0, 1.0) for rect in rect]
+            torch._foreach_mul_(unrect_step_size, lr)
+
+            bias_correction1 = torch._foreach_pow(beta1, grouped_state_steps)
+            torch._foreach_neg_(bias_correction1)
+            torch._foreach_add_(bias_correction1, 1)
+
+            torch._foreach_div_(unrect_step_size, bias_correction1)
+            torch._foreach_neg_(unrect_step_size)
+
+            bias_correction2 = torch._foreach_pow(beta2, grouped_state_steps)
+            torch._foreach_neg_(bias_correction2)
+            torch._foreach_add_(bias_correction2, 1)
+            torch._foreach_sqrt_(bias_correction2)
+            torch._foreach_mul_(bias_correction2, lr)
+            torch._foreach_mul_(bias_correction2, rect)
+            del rect
+            torch._foreach_neg_(bias_correction2)
+            torch._foreach_div_(bias_correction2, bias_correction1)
+            del bias_correction1
+        else:
+            rect = [
+                (
+                    (rho_t - 4)  # type: ignore[arg-type]
+                    * (rho_t - 2)
+                    * rho_inf
+                    / ((rho_inf - 4) * (rho_inf - 2) * rho_t)
+                )
+                ** 0.5
+                if rho_t > 5
+                else 0
+                for rho_t in rho_t_list
+            ]
+            unrectified = [0 if rect > 0 else 1.0 for rect in rect]
+
+            bias_correction1 = [
+                1 - beta1 ** _get_value(step) for step in grouped_state_steps
+            ]
+            unrect_step_size = [
+                (lr * rect / bc) * -1 for rect, bc in zip(unrectified, bias_correction1)
+            ]
+            bias_correction2 = [
+                ((1 - beta2 ** _get_value(step)) ** 0.5) * (lr * rect / bc) * -1
+                for step, rect, bc in zip(grouped_state_steps, rect, bias_correction1)
+            ]
+
+        buffer = torch._foreach_sqrt(grouped_exp_avg_sqs)
+        torch._foreach_add_(buffer, eps)
+        torch._foreach_div_(buffer, bias_correction2)
+        torch._foreach_reciprocal_(buffer)
+        torch._foreach_add_(buffer, unrect_step_size)
+
+        # Here, buffer = sqrt(1 - beta2^t) * rect_step_size / (sqrt(v) + eps) + unrect_step_size
+        torch._foreach_addcmul_(grouped_params, grouped_exp_avgs, buffer)
+
+
+@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_radam)
+def radam(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    state_steps: List[Tensor],
+    # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
+    # setting this as kwarg for now as functional API is compiled by torch/distributed/optim
+    decoupled_weight_decay: bool = False,
+    foreach: Optional[bool] = None,
+    differentiable: bool = False,
+    capturable: bool = False,
+    has_complex: bool = False,
+    maximize: bool = False,
+    *,
+    beta1: float,
+    beta2: float,
+    lr: float,
+    weight_decay: float,
+    eps: float,
+):
+    r"""Functional API that performs RAdam algorithm computation.
+
+    See :class:`~torch.optim.RAdam` for details.
+    """
+    if not all(isinstance(t, torch.Tensor) for t in state_steps):
+        raise RuntimeError(
+            "API has changed, `state_steps` argument must contain a list of singleton tensors"
+        )
+
+    if foreach is None:
+        _, foreach = _default_to_fused_or_foreach(
+            params, differentiable, use_fused=False
+        )
+
+    if foreach and torch.jit.is_scripting():
+        raise RuntimeError("torch.jit.script not supported with foreach optimizers")
+
+    if foreach and not torch.jit.is_scripting():
+        func = _multi_tensor_radam
+    else:
+        func = _single_tensor_radam
+
+    func(
+        params,
+        grads,
+        exp_avgs,
+        exp_avg_sqs,
+        state_steps,
+        beta1=beta1,
+        beta2=beta2,
+        lr=lr,
+        weight_decay=weight_decay,
+        eps=eps,
+        maximize=maximize,
+        decoupled_weight_decay=decoupled_weight_decay,
+        differentiable=differentiable,
+        capturable=capturable,
+        has_complex=has_complex,
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/rmsprop.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/rmsprop.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b77ad7fe3eeafcac5afa14c5127afc26d3c1a2d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/rmsprop.py
@@ -0,0 +1,528 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+r"""Implementation for the RMSprop algorithm."""
+from typing import cast, List, Optional, Union
+
+import torch
+from torch import Tensor
+
+from .optimizer import (
+    _capturable_doc,
+    _default_to_fused_or_foreach,
+    _differentiable_doc,
+    _disable_dynamo_if_unsupported,
+    _foreach_doc,
+    _get_capturable_supported_devices,
+    _get_scalar_dtype,
+    _maximize_doc,
+    _use_grad_for_differentiable,
+    _view_as_real,
+    Optimizer,
+    ParamsT,
+)
+
+
+__all__ = ["RMSprop", "rmsprop"]
+
+
+class RMSprop(Optimizer):  # noqa: D101
+    def __init__(
+        self,
+        params: ParamsT,
+        lr: Union[float, Tensor] = 1e-2,
+        alpha: float = 0.99,
+        eps: float = 1e-8,
+        weight_decay: float = 0,
+        momentum: float = 0,
+        centered=False,
+        capturable=False,
+        foreach: Optional[bool] = None,
+        maximize: bool = False,
+        differentiable: bool = False,
+    ):  # noqa: D107
+        if isinstance(lr, Tensor) and lr.numel() != 1:
+            raise ValueError("Tensor lr must be 1-element")
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 <= eps:
+            raise ValueError(f"Invalid epsilon value: {eps}")
+        if not 0.0 <= momentum:
+            raise ValueError(f"Invalid momentum value: {momentum}")
+        if not 0.0 <= weight_decay:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+        if not 0.0 <= alpha:
+            raise ValueError(f"Invalid alpha value: {alpha}")
+
+        defaults = dict(
+            lr=lr,
+            momentum=momentum,
+            alpha=alpha,
+            eps=eps,
+            centered=centered,
+            weight_decay=weight_decay,
+            capturable=capturable,
+            foreach=foreach,
+            maximize=maximize,
+            differentiable=differentiable,
+        )
+        super().__init__(params, defaults)
+
+    def __setstate__(self, state):  # noqa: D105
+        super().__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault("momentum", 0)
+            group.setdefault("centered", False)
+            group.setdefault("foreach", None)
+            group.setdefault("maximize", False)
+            group.setdefault("differentiable", False)
+            group.setdefault("capturable", False)
+            for p in group["params"]:
+                p_state = self.state.get(p, [])
+                if len(p_state) != 0 and not torch.is_tensor(p_state["step"]):
+                    step_val = float(p_state["step"])
+                    p_state["step"] = (
+                        torch.tensor(
+                            step_val, dtype=_get_scalar_dtype(), device=p.device
+                        )
+                        if group["capturable"]
+                        else torch.tensor(step_val, dtype=_get_scalar_dtype())
+                    )
+
+    def _init_group(
+        self,
+        group,
+        params_with_grad,
+        grads,
+        square_avgs,
+        momentum_buffer_list,
+        grad_avgs,
+        state_steps,
+    ):
+        has_complex = False
+        for p in group["params"]:
+            if p.grad is None:
+                continue
+            has_complex |= torch.is_complex(p)
+            params_with_grad.append(p)
+
+            if p.grad.is_sparse:
+                raise RuntimeError("RMSprop does not support sparse gradients")
+            grads.append(p.grad)
+
+            state = self.state[p]
+
+            # State initialization
+            if len(state) == 0:
+                state["step"] = (
+                    torch.zeros((), dtype=_get_scalar_dtype(), device=p.device)
+                    if group["capturable"]
+                    else torch.zeros((), dtype=_get_scalar_dtype())
+                )
+                state["square_avg"] = torch.zeros_like(
+                    p, memory_format=torch.preserve_format
+                )
+                if group["momentum"] > 0:
+                    state["momentum_buffer"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+                if group["centered"]:
+                    state["grad_avg"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+            square_avgs.append(state["square_avg"])
+            state_steps.append(state["step"])
+
+            if group["momentum"] > 0:
+                momentum_buffer_list.append(state["momentum_buffer"])
+            if group["centered"]:
+                grad_avgs.append(state["grad_avg"])
+
+        return has_complex
+
+    @_use_grad_for_differentiable
+    def step(self, closure=None):
+        """Perform a single optimization step.
+
+        Args:
+            closure (Callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        self._cuda_graph_capture_health_check()
+
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            params_with_grad: List[Tensor] = []
+            grads: List[Tensor] = []
+            square_avgs: List[Tensor] = []
+            grad_avgs: List[Tensor] = []
+            momentum_buffer_list: List[Tensor] = []
+            state_steps: List[Tensor] = []
+
+            has_complex = self._init_group(
+                group,
+                params_with_grad,
+                grads,
+                square_avgs,
+                momentum_buffer_list,
+                grad_avgs,
+                state_steps,
+            )
+
+            rmsprop(
+                params_with_grad,
+                grads,
+                square_avgs,
+                grad_avgs,
+                momentum_buffer_list,
+                state_steps,
+                lr=group["lr"],
+                alpha=group["alpha"],
+                eps=group["eps"],
+                weight_decay=group["weight_decay"],
+                momentum=group["momentum"],
+                centered=group["centered"],
+                foreach=group["foreach"],
+                maximize=group["maximize"],
+                differentiable=group["differentiable"],
+                capturable=group["capturable"],
+                has_complex=has_complex,
+            )
+
+        return loss
+
+
+RMSprop.__doc__ = (
+    r"""Implements RMSprop algorithm.
+
+    .. math::
+       \begin{aligned}
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{input}      : \alpha \text{ (alpha)},\: \gamma \text{ (lr)},
+                \: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)}                   \\
+            &\hspace{13mm}   \lambda \text{ (weight decay)},\: \mu \text{ (momentum)},\: centered\\
+            &\textbf{initialize} : v_0 \leftarrow 0 \text{ (square average)}, \:
+                \textbf{b}_0 \leftarrow 0 \text{ (buffer)}, \: g^{ave}_0 \leftarrow 0     \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do}                         \\
+            &\hspace{5mm}g_t           \leftarrow   \nabla_{\theta} f_t (\theta_{t-1})           \\
+            &\hspace{5mm}if \: \lambda \neq 0                                                    \\
+            &\hspace{10mm} g_t \leftarrow g_t + \lambda  \theta_{t-1}                            \\
+            &\hspace{5mm}v_t           \leftarrow   \alpha v_{t-1} + (1 - \alpha) g^2_t
+                \hspace{8mm}                                                                     \\
+            &\hspace{5mm} \tilde{v_t} \leftarrow v_t                                             \\
+            &\hspace{5mm}if \: centered                                                          \\
+            &\hspace{10mm} g^{ave}_t \leftarrow g^{ave}_{t-1} \alpha + (1-\alpha) g_t            \\
+            &\hspace{10mm} \tilde{v_t} \leftarrow \tilde{v_t} -  \big(g^{ave}_{t} \big)^2        \\
+            &\hspace{5mm}if \: \mu > 0                                                           \\
+            &\hspace{10mm} \textbf{b}_t\leftarrow \mu \textbf{b}_{t-1} +
+                g_t/ \big(\sqrt{\tilde{v_t}} +  \epsilon \big)                                   \\
+            &\hspace{10mm} \theta_t \leftarrow \theta_{t-1} - \gamma \textbf{b}_t                \\
+            &\hspace{5mm} else                                                                   \\
+            &\hspace{10mm}\theta_t      \leftarrow   \theta_{t-1} -
+                \gamma  g_t/ \big(\sqrt{\tilde{v_t}} + \epsilon \big)  \hspace{3mm}              \\
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+            &\bf{return} \:  \theta_t                                                     \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+       \end{aligned}
+
+    For further details regarding the algorithm we refer to
+    `lecture notes `_ by G. Hinton.
+    and centered version `Generating Sequences
+    With Recurrent Neural Networks `_.
+    The implementation here takes the square root of the gradient average before
+    adding epsilon (note that TensorFlow interchanges these two operations). The effective
+    learning rate is thus :math:`\gamma/(\sqrt{v} + \epsilon)` where :math:`\gamma`
+    is the scheduled learning rate and :math:`v` is the weighted moving average
+    of the squared gradient.
+    """
+    + rf"""
+    Args:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, Tensor, optional): learning rate (default: 1e-2)
+        momentum (float, optional): momentum factor (default: 0)
+        alpha (float, optional): smoothing constant (default: 0.99)
+        eps (float, optional): term added to the denominator to improve
+            numerical stability (default: 1e-8)
+        centered (bool, optional) : if ``True``, compute the centered RMSProp,
+            the gradient is normalized by an estimation of its variance
+        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+        {_foreach_doc}
+        {_maximize_doc}
+        {_capturable_doc}
+        {_differentiable_doc}
+
+    """
+)
+
+
+def _single_tensor_rmsprop(
+    params: List[Tensor],
+    grads: List[Tensor],
+    square_avgs: List[Tensor],
+    grad_avgs: List[Tensor],
+    momentum_buffer_list: List[Tensor],
+    state_steps: List[Tensor],
+    *,
+    lr: float,
+    alpha: float,
+    eps: float,
+    weight_decay: float,
+    momentum: float,
+    centered: bool,
+    maximize: bool,
+    differentiable: bool,
+    capturable: bool,
+    has_complex: bool,
+):
+    for i, param in enumerate(params):
+        step = state_steps[i]
+
+        # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+        if not torch._utils.is_compiling() and capturable:
+            capturable_supported_devices = _get_capturable_supported_devices()
+            assert (
+                param.device.type == step.device.type
+                and param.device.type in capturable_supported_devices
+            ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
+
+        grad = grads[i]
+        grad = grad if not maximize else -grad
+        square_avg = square_avgs[i]
+
+        step += 1
+
+        if weight_decay != 0:
+            grad = grad.add(param, alpha=weight_decay)
+
+        is_complex_param = torch.is_complex(param)
+        if is_complex_param:
+            param = torch.view_as_real(param)
+            grad = torch.view_as_real(grad)
+            square_avg = torch.view_as_real(square_avg)
+
+        square_avg.mul_(alpha).addcmul_(grad, grad, value=1 - alpha)
+
+        if centered:
+            grad_avg = grad_avgs[i]
+            if is_complex_param:
+                grad_avg = torch.view_as_real(grad_avg)
+            grad_avg.lerp_(grad, 1 - alpha)
+            avg = square_avg.addcmul(grad_avg, grad_avg, value=-1).sqrt_()
+        else:
+            avg = square_avg.sqrt()
+
+        if differentiable:
+            avg = avg.add(eps)
+        else:
+            avg = avg.add_(eps)
+
+        if momentum > 0:
+            buf = momentum_buffer_list[i]
+            if is_complex_param:
+                buf = torch.view_as_real(buf)
+            buf.mul_(momentum).addcdiv_(grad, avg)
+            param.add_(buf, alpha=-lr)
+        else:
+            param.addcdiv_(grad, avg, value=-lr)
+
+
+def _multi_tensor_rmsprop(
+    params: List[Tensor],
+    grads: List[Tensor],
+    square_avgs: List[Tensor],
+    grad_avgs: List[Tensor],
+    momentum_buffer_list: List[Tensor],
+    state_steps: List[Tensor],
+    *,
+    lr: float,
+    alpha: float,
+    eps: float,
+    weight_decay: float,
+    momentum: float,
+    centered: bool,
+    maximize: bool,
+    differentiable: bool,
+    capturable: bool,
+    has_complex: bool,
+):
+    if len(params) == 0:
+        return
+
+    assert not differentiable, "_foreach ops don't support autograd"
+
+    # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+    if not torch._utils.is_compiling() and capturable:
+        capturable_supported_devices = _get_capturable_supported_devices()
+        assert all(
+            p.device.type == step.device.type
+            and p.device.type in capturable_supported_devices
+            for p, step in zip(params, state_steps)
+        ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
+
+    grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
+        [params, grads, square_avgs, grad_avgs, momentum_buffer_list, state_steps]  # type: ignore[list-item]
+    )
+    for (
+        (
+            grouped_params_,
+            grouped_grads_,
+            grouped_square_avgs_,
+            grouped_grad_avgs_,
+            grouped_momentum_buffer_list_,
+            grouped_state_steps_,
+        )
+    ), _ in grouped_tensors.values():
+        grouped_params = cast(List[Tensor], grouped_params_)
+        grouped_grads = cast(List[Tensor], grouped_grads_)
+        grouped_square_avgs = cast(List[Tensor], grouped_square_avgs_)
+        grouped_state_steps = cast(List[Tensor], grouped_state_steps_)
+
+        if has_complex:
+            state_and_grads = [grouped_grads, grouped_square_avgs]
+            if momentum > 0:
+                grouped_momentum_buffer_list = cast(
+                    List[Tensor], grouped_momentum_buffer_list_
+                )
+                state_and_grads.append(grouped_momentum_buffer_list)
+            if centered:
+                grouped_grad_avgs = cast(List[Tensor], grouped_grad_avgs_)
+                state_and_grads.append(grouped_grad_avgs)
+            _view_as_real(grouped_params, *state_and_grads)
+
+        if maximize:
+            grouped_grads = torch._foreach_neg(grouped_grads)  # type: ignore[assignment]
+
+        # Update steps
+        # If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
+        # and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
+        # wrapped it once now. The alpha is required to assure we go to the right overload.
+        if not torch._utils.is_compiling() and grouped_state_steps[0].is_cpu:
+            torch._foreach_add_(
+                grouped_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
+            )
+        else:
+            torch._foreach_add_(grouped_state_steps, 1)
+
+        if weight_decay != 0:
+            # Re-use the intermediate memory (grouped_grads) already allocated for maximize
+            if maximize:
+                torch._foreach_add_(grouped_grads, grouped_params, alpha=weight_decay)
+            else:
+                grouped_grads = torch._foreach_add(  # type: ignore[assignment]
+                    grouped_grads, grouped_params, alpha=weight_decay
+                )
+
+        torch._foreach_mul_(grouped_square_avgs, alpha)
+        torch._foreach_addcmul_(
+            grouped_square_avgs, grouped_grads, grouped_grads, value=1 - alpha
+        )
+
+        if centered:
+            grouped_grad_avgs = cast(List[Tensor], grouped_grad_avgs_)
+            torch._foreach_lerp_(grouped_grad_avgs, grouped_grads, 1 - alpha)
+            avg = torch._foreach_addcmul(
+                grouped_square_avgs, grouped_grad_avgs, grouped_grad_avgs, value=-1
+            )
+            torch._foreach_sqrt_(avg)
+            torch._foreach_add_(avg, eps)
+        else:
+            avg = torch._foreach_sqrt(grouped_square_avgs)
+            torch._foreach_add_(avg, eps)
+
+        if momentum > 0:
+            grouped_momentum_buffer_list = cast(
+                List[Tensor], grouped_momentum_buffer_list_
+            )
+            torch._foreach_mul_(grouped_momentum_buffer_list, momentum)
+            torch._foreach_addcdiv_(grouped_momentum_buffer_list, grouped_grads, avg)
+            # If LR is a tensor, the else branch will internally call item()
+            # which will cause silent incorrectness if we are capturing
+            if capturable and isinstance(lr, torch.Tensor):
+                momentum_lr = torch._foreach_mul(grouped_momentum_buffer_list, -lr)
+                torch._foreach_add_(grouped_params, momentum_lr)
+            else:
+                torch._foreach_add_(
+                    grouped_params, grouped_momentum_buffer_list, alpha=-lr
+                )
+        else:
+            # If LR is a tensor, the else branch will internally call item()
+            # which will cause silent incorrectness if we are capturing
+            if capturable and isinstance(lr, torch.Tensor):
+                torch._foreach_div_(avg, -lr)
+                torch._foreach_addcdiv_(grouped_params, grouped_grads, avg)
+            else:
+                torch._foreach_addcdiv_(grouped_params, grouped_grads, avg, value=-lr)
+
+
+@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_rmsprop)
+def rmsprop(
+    params: List[Tensor],
+    grads: List[Tensor],
+    square_avgs: List[Tensor],
+    grad_avgs: List[Tensor],
+    momentum_buffer_list: List[Tensor],
+    state_steps: List[Tensor],
+    # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
+    # setting this as kwarg for now as functional API is compiled by torch/distributed/optim
+    foreach: Optional[bool] = None,
+    maximize: bool = False,
+    differentiable: bool = False,
+    capturable: bool = False,
+    has_complex: bool = False,
+    *,
+    lr: float,
+    alpha: float,
+    eps: float,
+    weight_decay: float,
+    momentum: float,
+    centered: bool,
+):
+    r"""Functional API that performs rmsprop algorithm computation.
+
+    See :class:`~torch.optim.RMSProp` for details.
+    """
+    # this check is slow during compilation, so we skip it
+    # if it's strictly needed we can add this check back in dynamo
+    if not torch._utils.is_compiling() and not all(
+        isinstance(t, torch.Tensor) for t in state_steps
+    ):
+        raise RuntimeError(
+            "API has changed, `state_steps` argument must contain a list of singleton tensors"
+        )
+
+    if foreach is None:
+        _, foreach = _default_to_fused_or_foreach(
+            params, differentiable, use_fused=False
+        )
+
+    if foreach and torch.jit.is_scripting():
+        raise RuntimeError("torch.jit.script not supported with foreach optimizers")
+
+    if foreach and not torch.jit.is_scripting():
+        func = _multi_tensor_rmsprop
+    else:
+        func = _single_tensor_rmsprop
+
+    func(
+        params,
+        grads,
+        square_avgs,
+        grad_avgs,
+        momentum_buffer_list,
+        state_steps,
+        lr=lr,
+        alpha=alpha,
+        eps=eps,
+        weight_decay=weight_decay,
+        momentum=momentum,
+        centered=centered,
+        maximize=maximize,
+        capturable=capturable,
+        differentiable=differentiable,
+        has_complex=has_complex,
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/rprop.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/rprop.py
new file mode 100644
index 0000000000000000000000000000000000000000..e28f3535a0b99561c15ed3030f72d1ef3dd1616c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/rprop.py
@@ -0,0 +1,464 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+r"""Implementation for the Resilient backpropagation."""
+from typing import cast, List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+
+from .optimizer import (
+    _capturable_doc,
+    _default_to_fused_or_foreach,
+    _differentiable_doc,
+    _disable_dynamo_if_unsupported,
+    _foreach_doc,
+    _get_capturable_supported_devices,
+    _get_scalar_dtype,
+    _maximize_doc,
+    _use_grad_for_differentiable,
+    _view_as_real,
+    Optimizer,
+    ParamsT,
+)
+
+
+__all__ = ["Rprop", "rprop"]
+
+
+class Rprop(Optimizer):  # noqa: D101
+    def __init__(
+        self,
+        params: ParamsT,
+        lr: Union[float, Tensor] = 1e-2,
+        etas: Tuple[float, float] = (0.5, 1.2),
+        step_sizes: Tuple[float, float] = (1e-6, 50),
+        *,
+        capturable: bool = False,
+        foreach: Optional[bool] = None,
+        maximize: bool = False,
+        differentiable: bool = False,
+    ):  # noqa: D107
+        if isinstance(lr, Tensor) and lr.numel() != 1:
+            raise ValueError("Tensor lr must be 1-element")
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 < etas[0] < 1.0 < etas[1]:
+            raise ValueError(f"Invalid eta values: {etas[0]}, {etas[1]}")
+
+        defaults = dict(
+            lr=lr,
+            etas=etas,
+            step_sizes=step_sizes,
+            foreach=foreach,
+            maximize=maximize,
+            differentiable=differentiable,
+            capturable=capturable,
+        )
+        super().__init__(params, defaults)
+
+    def __setstate__(self, state):  # noqa: D105
+        super().__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault("foreach", None)
+            group.setdefault("maximize", False)
+            group.setdefault("differentiable", False)
+            group.setdefault("capturable", False)
+            for p in group["params"]:
+                p_state = self.state.get(p, [])
+                if len(p_state) != 0 and not torch.is_tensor(p_state["step"]):
+                    step_val = float(p_state["step"])
+                    p_state["step"] = (
+                        torch.tensor(
+                            step_val, dtype=_get_scalar_dtype(), device=p.device
+                        )
+                        if group["capturable"]
+                        else torch.tensor(step_val, dtype=_get_scalar_dtype())
+                    )
+
+    def _init_group(self, group, params, grads, prevs, step_sizes, state_steps):
+        has_complex = False
+        for p in group["params"]:
+            if p.grad is None:
+                continue
+            has_complex |= torch.is_complex(p)
+            params.append(p)
+            grad = p.grad
+            if grad.is_sparse:
+                raise RuntimeError("Rprop does not support sparse gradients")
+
+            grads.append(grad)
+            state = self.state[p]
+
+            # State initialization
+            if len(state) == 0:
+                state["step"] = (
+                    torch.zeros((), dtype=_get_scalar_dtype(), device=p.device)
+                    if group["capturable"]
+                    else torch.zeros((), dtype=_get_scalar_dtype())
+                )
+
+                state["prev"] = torch.zeros_like(p, memory_format=torch.preserve_format)
+                if p.dtype.is_complex:
+                    # Complex Number should be as if they are two independent real numbers.
+                    # Hence the step_size shouldn't be zero for imaginary part.
+                    state["step_size"] = torch.full_like(
+                        grad, complex(group["lr"], group["lr"])
+                    )
+                else:
+                    state["step_size"] = torch.full_like(grad, group["lr"])
+
+            prevs.append(state["prev"])
+            step_sizes.append(state["step_size"])
+            state_steps.append(state["step"])
+
+        return has_complex
+
+    @_use_grad_for_differentiable
+    def step(self, closure=None):
+        """Perform a single optimization step.
+
+        Args:
+            closure (Callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        self._cuda_graph_capture_health_check()
+
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            params: List[Tensor] = []
+            grads: List[Tensor] = []
+            prevs: List[Tensor] = []
+            step_sizes: List[Tensor] = []
+            state_steps: List[Tensor] = []
+
+            etaminus, etaplus = group["etas"]
+            step_size_min, step_size_max = group["step_sizes"]
+            foreach = group["foreach"]
+            maximize = group["maximize"]
+
+            has_complex = self._init_group(
+                group, params, grads, prevs, step_sizes, state_steps
+            )
+
+            rprop(
+                params,
+                grads,
+                prevs,
+                step_sizes,
+                state_steps,
+                step_size_min=step_size_min,
+                step_size_max=step_size_max,
+                etaminus=etaminus,
+                etaplus=etaplus,
+                foreach=foreach,
+                maximize=maximize,
+                differentiable=group["differentiable"],
+                capturable=group["capturable"],
+                has_complex=has_complex,
+            )
+
+        return loss
+
+
+Rprop.__doc__ = (
+    r"""Implements the resilient backpropagation algorithm.
+
+    .. math::
+       \begin{aligned}
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{input}      : \theta_0 \in \mathbf{R}^d \text{ (params)},f(\theta)
+                \text{ (objective)},                                                             \\
+            &\hspace{13mm}      \eta_{+/-} \text{ (etaplus, etaminus)}, \Gamma_{max/min}
+                \text{ (step sizes)}                                                             \\
+            &\textbf{initialize} :   g^0_{prev} \leftarrow 0,
+                \: \eta_0 \leftarrow \text{lr (learning rate)}                                   \\
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do}                         \\
+            &\hspace{5mm}g_t           \leftarrow   \nabla_{\theta} f_t (\theta_{t-1})           \\
+            &\hspace{5mm} \textbf{for} \text{  } i = 0, 1, \ldots, d-1 \: \mathbf{do}            \\
+            &\hspace{10mm}  \textbf{if} \:   g^i_{prev} g^i_t  > 0                               \\
+            &\hspace{15mm}  \eta^i_t \leftarrow \mathrm{min}(\eta^i_{t-1} \eta_{+},
+                \Gamma_{max})                                                                    \\
+            &\hspace{10mm}  \textbf{else if}  \:  g^i_{prev} g^i_t < 0                           \\
+            &\hspace{15mm}  \eta^i_t \leftarrow \mathrm{max}(\eta^i_{t-1} \eta_{-},
+                \Gamma_{min})                                                                    \\
+            &\hspace{15mm}  g^i_t \leftarrow 0                                                   \\
+            &\hspace{10mm}  \textbf{else}  \:                                                    \\
+            &\hspace{15mm}  \eta^i_t \leftarrow \eta^i_{t-1}                                     \\
+            &\hspace{5mm}\theta_t \leftarrow \theta_{t-1}- \eta_t \mathrm{sign}(g_t)             \\
+            &\hspace{5mm}g_{prev} \leftarrow  g_t                                                \\
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+            &\bf{return} \:  \theta_t                                                     \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+       \end{aligned}
+
+    For further details regarding the algorithm we refer to the paper
+    `A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm
+    `_.
+    """
+    + rf"""
+    Args:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, optional): learning rate (default: 1e-2)
+        etas (Tuple[float, float], optional): pair of (etaminus, etaplus), that
+            are multiplicative increase and decrease factors
+            (default: (0.5, 1.2))
+        step_sizes (Tuple[float, float], optional): a pair of minimal and
+            maximal allowed step sizes (default: (1e-6, 50))
+        {_foreach_doc}
+        {_capturable_doc}
+        {_maximize_doc}
+        {_differentiable_doc}
+
+    """
+)
+
+
+def _single_tensor_rprop(
+    params: List[Tensor],
+    grads: List[Tensor],
+    prevs: List[Tensor],
+    step_sizes: List[Tensor],
+    state_steps: List[Tensor],
+    *,
+    step_size_min: float,
+    step_size_max: float,
+    etaminus: float,
+    etaplus: float,
+    maximize: bool,
+    capturable: bool,
+    differentiable: bool,
+    has_complex: bool,
+):
+    for i, param in enumerate(params):
+        grad = grads[i]
+        grad = grad if not maximize else -grad
+        prev = prevs[i]
+        step_size = step_sizes[i]
+        step = state_steps[i]
+
+        # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+        if not torch._utils.is_compiling() and capturable:
+            capturable_supported_devices = _get_capturable_supported_devices()
+            assert (
+                param.device.type == step.device.type
+                and param.device.type in capturable_supported_devices
+            ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
+
+        step += 1
+
+        if torch.is_complex(param):
+            grad = torch.view_as_real(grad)
+            prev = torch.view_as_real(prev)
+            param = torch.view_as_real(param)
+            step_size = torch.view_as_real(step_size)
+        if differentiable:
+            sign = grad.mul(prev.clone()).sign()
+        else:
+            sign = grad.mul(prev).sign()
+
+        if capturable:
+            sign.copy_(torch.where(sign.gt(0), etaplus, sign))
+            sign.copy_(torch.where(sign.lt(0), etaminus, sign))
+            sign.copy_(torch.where(sign.eq(0), 1, sign))
+        else:
+            sign[sign.gt(0)] = etaplus
+            sign[sign.lt(0)] = etaminus
+            sign[sign.eq(0)] = 1
+
+        # update stepsizes with step size updates
+        step_size.mul_(sign).clamp_(step_size_min, step_size_max)
+
+        # for dir<0, dfdx=0
+        # for dir>=0 dfdx=dfdx
+        grad = grad.clone(memory_format=torch.preserve_format)
+        if capturable:
+            grad.copy_(torch.where(sign.eq(etaminus), 0, grad))
+        else:
+            grad[sign.eq(etaminus)] = 0
+
+        # update parameters
+        param.addcmul_(grad.sign(), step_size, value=-1)
+        prev.copy_(grad)
+
+
+def _multi_tensor_rprop(
+    params: List[Tensor],
+    grads: List[Tensor],
+    prevs: List[Tensor],
+    step_sizes: List[Tensor],
+    state_steps: List[Tensor],
+    *,
+    step_size_min: float,
+    step_size_max: float,
+    etaminus: float,
+    etaplus: float,
+    maximize: bool,
+    capturable: bool,
+    differentiable: bool,
+    has_complex: bool,
+):
+    if len(params) == 0:
+        return
+
+    assert not differentiable, "_foreach ops don't support autograd"
+
+    # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+    if not torch._utils.is_compiling() and capturable:
+        capturable_supported_devices = _get_capturable_supported_devices()
+        assert all(
+            p.device.type == step.device.type
+            and p.device.type in capturable_supported_devices
+            for p, step in zip(params, state_steps)
+        ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
+
+    grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
+        [params, grads, prevs, step_sizes, state_steps]  # type: ignore[list-item]
+    )
+    for (
+        grouped_params_,
+        grouped_grads_,
+        grouped_prevs_,
+        grouped_step_sizes_,
+        grouped_state_steps_,
+    ), _ in grouped_tensors.values():
+        grouped_params = cast(List[Tensor], grouped_params_)
+        grouped_grads = cast(List[Tensor], grouped_grads_)
+        grouped_prevs = cast(List[Tensor], grouped_prevs_)
+        grouped_step_sizes = cast(List[Tensor], grouped_step_sizes_)
+        grouped_state_steps = cast(List[Tensor], grouped_state_steps_)
+
+        # Update steps
+        # If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
+        # and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
+        # wrapped it once now. The alpha is required to assure we go to the right overload.
+        if not torch._utils.is_compiling() and grouped_state_steps[0].is_cpu:
+            torch._foreach_add_(
+                grouped_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
+            )
+        else:
+            torch._foreach_add_(grouped_state_steps, 1)
+
+        # Handle complex params
+        if has_complex:
+            _view_as_real(
+                grouped_params, grouped_grads, grouped_prevs, grouped_step_sizes
+            )
+
+        signs = torch._foreach_mul(grouped_grads, grouped_prevs)
+        if maximize:
+            torch._foreach_neg_(signs)
+
+        # At the end of the step, grouped_prevs will contain the current grads, so we reuse
+        # grouped_prevs memory instead of creating a new buffer, but, for clarity, we reassign
+        # to keep referring to the buffer as grouped_grads.
+        torch._foreach_copy_(grouped_prevs, grouped_grads)
+        if maximize:
+            torch._foreach_neg_(grouped_prevs)
+        grouped_grads = grouped_prevs
+
+        torch._foreach_sign_(signs)
+        if capturable:
+            for sign in signs:
+                sign.copy_(torch.where(sign.gt(0), etaplus, sign))
+                sign.copy_(torch.where(sign.lt(0), etaminus, sign))
+                sign.copy_(torch.where(sign.eq(0), 1, sign))
+        else:
+            for sign in signs:
+                sign[sign.gt(0)] = etaplus
+                sign[sign.lt(0)] = etaminus
+                sign[sign.eq(0)] = 1
+
+        # update stepsizes with step size updates
+        torch._foreach_mul_(grouped_step_sizes, signs)
+        for step_size in grouped_step_sizes:
+            step_size.clamp_(step_size_min, step_size_max)
+
+        # for dir<0, dfdx=0
+        # for dir>=0 dfdx=dfdx
+        grouped_grads = list(grouped_grads)
+        for i in range(len(grouped_grads)):
+            grouped_grads[i].copy_(
+                torch.where(signs[i].eq(etaminus), 0, grouped_grads[i])
+            )
+
+        # explicitly del signs as it's not used after here to save memory
+        del signs
+
+        # update parameters
+        grad_signs = [grad.sign() for grad in grouped_grads]
+        torch._foreach_addcmul_(
+            grouped_params, grad_signs, grouped_step_sizes, value=-1
+        )
+
+        # Logically, you may expect grouped_prevs to get updated to grouped_grads, but that's
+        # basically already happened since we've been using grouped_prevs' memory to store
+        # updated grouped_grads!
+
+
+@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_rprop)
+def rprop(
+    params: List[Tensor],
+    grads: List[Tensor],
+    prevs: List[Tensor],
+    step_sizes: List[Tensor],
+    state_steps: List[Tensor],
+    # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
+    # setting this as kwarg for now as functional API is compiled by torch/distributed/optim
+    foreach: Optional[bool] = None,
+    capturable: bool = False,
+    maximize: bool = False,
+    differentiable: bool = False,
+    has_complex: bool = False,
+    *,
+    step_size_min: float,
+    step_size_max: float,
+    etaminus: float,
+    etaplus: float,
+):
+    r"""Functional API that performs rprop algorithm computation.
+
+    See :class:`~torch.optim.Rprop` for details.
+    """
+    # this check is slow during compilation, so we skip it
+    # if it's strictly needed we can add this check back in dynamo
+    if not torch._utils.is_compiling() and not all(
+        isinstance(t, torch.Tensor) for t in state_steps
+    ):
+        raise RuntimeError(
+            "API has changed, `state_steps` argument must contain a list of singleton tensors"
+        )
+
+    if foreach is None:
+        _, foreach = _default_to_fused_or_foreach(
+            params, differentiable, use_fused=False
+        )
+
+    if foreach and torch.jit.is_scripting():
+        raise RuntimeError("torch.jit.script not supported with foreach optimizers")
+
+    if foreach and not torch.jit.is_scripting():
+        func = _multi_tensor_rprop
+    else:
+        func = _single_tensor_rprop
+
+    func(
+        params,
+        grads,
+        prevs,
+        step_sizes,
+        state_steps,
+        step_size_min=step_size_min,
+        step_size_max=step_size_max,
+        etaminus=etaminus,
+        etaplus=etaplus,
+        capturable=capturable,
+        maximize=maximize,
+        differentiable=differentiable,
+        has_complex=has_complex,
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/sgd.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/sgd.py
new file mode 100644
index 0000000000000000000000000000000000000000..b270afce4d60a2b4ae478f062ea7d481e6985cb2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/sgd.py
@@ -0,0 +1,511 @@
+# mypy: allow-untyped-defs
+r"""Implementation for Stochastic Gradient Descent optimizer."""
+from typing import cast, List, Optional, Union
+
+import torch
+from torch import Tensor
+
+from .optimizer import (
+    _default_to_fused_or_foreach,
+    _device_dtype_check_for_fused,
+    _differentiable_doc,
+    _foreach_doc,
+    _fused_doc,
+    _maximize_doc,
+    _use_grad_for_differentiable,
+    DeviceDict,
+    Optimizer,
+)
+
+
+__all__ = ["SGD", "sgd"]
+
+
+class SGD(Optimizer):  # noqa: D101
+    def __init__(
+        self,
+        params,
+        lr: Union[float, Tensor] = 1e-3,
+        momentum: float = 0,
+        dampening: float = 0,
+        weight_decay: float = 0,
+        nesterov=False,
+        *,
+        maximize: bool = False,
+        foreach: Optional[bool] = None,
+        differentiable: bool = False,
+        fused: Optional[bool] = None,
+    ):  # noqa: D107
+        if isinstance(lr, Tensor) and lr.numel() != 1:
+            raise ValueError("Tensor lr must be 1-element")
+        if lr < 0.0:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if momentum < 0.0:
+            raise ValueError(f"Invalid momentum value: {momentum}")
+        if weight_decay < 0.0:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+
+        defaults = dict(
+            lr=lr,
+            momentum=momentum,
+            dampening=dampening,
+            weight_decay=weight_decay,
+            nesterov=nesterov,
+            maximize=maximize,
+            foreach=foreach,
+            differentiable=differentiable,
+            fused=fused,
+        )
+        if nesterov and (momentum <= 0 or dampening != 0):
+            raise ValueError("Nesterov momentum requires a momentum and zero dampening")
+        super().__init__(params, defaults)
+
+        if fused:
+            self._step_supports_amp_scaling = True
+            self._need_device_dtype_check_for_fused = True
+            if differentiable:
+                raise RuntimeError("`fused` does not support `differentiable`")
+            if foreach:
+                raise RuntimeError("`fused` and `foreach` cannot be `True` together.")
+
+    def __setstate__(self, state):  # noqa: D105
+        super().__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault("nesterov", False)
+            group.setdefault("maximize", False)
+            group.setdefault("foreach", None)
+            group.setdefault("differentiable", False)
+            group.setdefault("fused", False)
+
+    def _init_group(self, group, params, grads, momentum_buffer_list):
+        has_sparse_grad = False
+
+        for p in group["params"]:
+            if p.grad is not None:
+                if group["fused"] and getattr(
+                    self, "_need_device_dtype_check_for_fused", True
+                ):
+                    _device_dtype_check_for_fused(p)
+                    self._need_device_dtype_check_for_fused = False
+                params.append(p)
+                grads.append(p.grad)
+                if p.grad.is_sparse:
+                    has_sparse_grad = True
+
+                if group["momentum"] != 0:
+                    state = self.state[p]
+                    momentum_buffer_list.append(state.get("momentum_buffer"))
+
+        return has_sparse_grad
+
+    @_use_grad_for_differentiable
+    def step(self, closure=None):
+        """Perform a single optimization step.
+
+        Args:
+            closure (Callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            params: List[Tensor] = []
+            grads: List[Tensor] = []
+            momentum_buffer_list: List[Optional[Tensor]] = []
+
+            has_sparse_grad = self._init_group(
+                group, params, grads, momentum_buffer_list
+            )
+
+            sgd(
+                params,
+                grads,
+                momentum_buffer_list,
+                weight_decay=group["weight_decay"],
+                momentum=group["momentum"],
+                lr=group["lr"],
+                dampening=group["dampening"],
+                nesterov=group["nesterov"],
+                maximize=group["maximize"],
+                has_sparse_grad=has_sparse_grad,
+                foreach=group["foreach"],
+                fused=group["fused"],
+                grad_scale=getattr(self, "grad_scale", None),
+                found_inf=getattr(self, "found_inf", None),
+            )
+
+            if group["momentum"] != 0:
+                # update momentum_buffers in state
+                for p, momentum_buffer in zip(params, momentum_buffer_list):
+                    state = self.state[p]
+                    state["momentum_buffer"] = momentum_buffer
+
+        return loss
+
+
+SGD.__doc__ = (
+    r"""Implements stochastic gradient descent (optionally with momentum).
+
+    .. math::
+       \begin{aligned}
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{input}      : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta)
+                \text{ (objective)}, \: \lambda \text{ (weight decay)},                          \\
+            &\hspace{13mm} \:\mu \text{ (momentum)}, \:\tau \text{ (dampening)},
+            \:\textit{ nesterov,}\:\textit{ maximize}                                     \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                                 \\
+            &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do}                         \\
+            &\hspace{5mm}g_t           \leftarrow   \nabla_{\theta} f_t (\theta_{t-1})           \\
+            &\hspace{5mm}\textbf{if} \: \lambda \neq 0                                           \\
+            &\hspace{10mm} g_t \leftarrow g_t + \lambda  \theta_{t-1}                            \\
+            &\hspace{5mm}\textbf{if} \: \mu \neq 0                                               \\
+            &\hspace{10mm}\textbf{if} \: t > 1                                                   \\
+            &\hspace{15mm} \textbf{b}_t \leftarrow \mu \textbf{b}_{t-1} + (1-\tau) g_t           \\
+            &\hspace{10mm}\textbf{else}                                                          \\
+            &\hspace{15mm} \textbf{b}_t \leftarrow g_t                                           \\
+            &\hspace{10mm}\textbf{if} \: \textit{nesterov}                                       \\
+            &\hspace{15mm} g_t \leftarrow g_{t} + \mu \textbf{b}_t                             \\
+            &\hspace{10mm}\textbf{else}                                                   \\[-1.ex]
+            &\hspace{15mm} g_t  \leftarrow  \textbf{b}_t                                         \\
+            &\hspace{5mm}\textbf{if} \: \textit{maximize}                                          \\
+            &\hspace{10mm}\theta_t \leftarrow \theta_{t-1} + \gamma g_t                   \\[-1.ex]
+            &\hspace{5mm}\textbf{else}                                                    \\[-1.ex]
+            &\hspace{10mm}\theta_t \leftarrow \theta_{t-1} - \gamma g_t                   \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+            &\bf{return} \:  \theta_t                                                     \\[-1.ex]
+            &\rule{110mm}{0.4pt}                                                          \\[-1.ex]
+       \end{aligned}
+
+    Nesterov momentum is based on the formula from
+    `On the importance of initialization and momentum in deep learning`__.
+    """
+    + rf"""
+    Args:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, Tensor, optional): learning rate (default: 1e-3)
+        momentum (float, optional): momentum factor (default: 0)
+        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+        dampening (float, optional): dampening for momentum (default: 0)
+        nesterov (bool, optional): enables Nesterov momentum (default: False)
+        {_maximize_doc}
+        {_foreach_doc}
+        {_differentiable_doc}
+        {_fused_doc}
+    """
+    + r"""
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
+        >>> optimizer.zero_grad()
+        >>> loss_fn(model(input), target).backward()
+        >>> optimizer.step()
+
+    __ http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf
+
+    .. note::
+        The implementation of SGD with Momentum/Nesterov subtly differs from
+        Sutskever et al. and implementations in some other frameworks.
+
+        Considering the specific case of Momentum, the update can be written as
+
+        .. math::
+            \begin{aligned}
+                v_{t+1} & = \mu * v_{t} + g_{t+1}, \\
+                p_{t+1} & = p_{t} - \text{lr} * v_{t+1},
+            \end{aligned}
+
+        where :math:`p`, :math:`g`, :math:`v` and :math:`\mu` denote the
+        parameters, gradient, velocity, and momentum respectively.
+
+        This is in contrast to Sutskever et al. and
+        other frameworks which employ an update of the form
+
+        .. math::
+            \begin{aligned}
+                v_{t+1} & = \mu * v_{t} + \text{lr} * g_{t+1}, \\
+                p_{t+1} & = p_{t} - v_{t+1}.
+            \end{aligned}
+
+        The Nesterov version is analogously modified.
+
+        Moreover, the initial value of the momentum buffer is set to the
+        gradient value at the first step. This is in contrast to some other
+        frameworks that initialize it to all zeros.
+
+    """
+)
+
+
+def sgd(
+    params: List[Tensor],
+    d_p_list: List[Tensor],
+    momentum_buffer_list: List[Optional[Tensor]],
+    # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
+    # setting this as kwarg for now as functional API is compiled by torch/distributed/optim
+    has_sparse_grad: bool = False,
+    foreach: Optional[bool] = None,
+    fused: Optional[bool] = None,
+    grad_scale: Optional[Tensor] = None,
+    found_inf: Optional[Tensor] = None,
+    *,
+    weight_decay: float,
+    momentum: float,
+    lr: float,
+    dampening: float,
+    nesterov: bool,
+    maximize: bool,
+):
+    r"""Functional API that performs SGD algorithm computation.
+
+    See :class:`~torch.optim.SGD` for details.
+    """
+    # Respect when the user inputs False/True for foreach or fused. We only want to change
+    # the default when neither have been user-specified. Note that we default to foreach
+    # and pass False to use_fused. This is not a mistake--we want to give the fused impl
+    # bake-in time before making it the default, even if it is typically faster.
+    if foreach is None and fused is None:
+        # why must we be explicit about an if statement for torch.jit.is_scripting here?
+        # because JIT can't handle Optionals nor fancy conditionals when scripting
+        if not torch.jit.is_scripting():
+            fused, foreach = _default_to_fused_or_foreach(
+                params, differentiable=False, use_fused=False
+            )
+        else:
+            foreach = False
+            fused = False
+    if foreach is None:
+        foreach = False
+    if fused is None:
+        fused = False
+
+    if foreach and torch.jit.is_scripting():
+        raise RuntimeError("torch.jit.script not supported with foreach optimizers")
+    if fused and torch.jit.is_scripting():
+        raise RuntimeError("torch.jit.script not supported with fused optimizers")
+
+    if foreach and not torch.jit.is_scripting():
+        func = _multi_tensor_sgd
+    elif fused and not torch.jit.is_scripting():
+        func = _fused_sgd
+    else:
+        func = _single_tensor_sgd
+
+    func(
+        params,
+        d_p_list,
+        momentum_buffer_list,
+        weight_decay=weight_decay,
+        momentum=momentum,
+        lr=lr,
+        dampening=dampening,
+        nesterov=nesterov,
+        has_sparse_grad=has_sparse_grad,
+        maximize=maximize,
+        grad_scale=grad_scale,
+        found_inf=found_inf,
+    )
+
+
+def _single_tensor_sgd(
+    params: List[Tensor],
+    grads: List[Tensor],
+    momentum_buffer_list: List[Optional[Tensor]],
+    grad_scale: Optional[Tensor],
+    found_inf: Optional[Tensor],
+    *,
+    weight_decay: float,
+    momentum: float,
+    lr: float,
+    dampening: float,
+    nesterov: bool,
+    maximize: bool,
+    has_sparse_grad: bool,
+):
+    assert grad_scale is None and found_inf is None
+
+    for i, param in enumerate(params):
+        grad = grads[i] if not maximize else -grads[i]
+
+        if weight_decay != 0:
+            grad = grad.add(param, alpha=weight_decay)
+
+        if momentum != 0:
+            buf = momentum_buffer_list[i]
+
+            if buf is None:
+                buf = torch.clone(grad).detach()
+                momentum_buffer_list[i] = buf
+            else:
+                buf.mul_(momentum).add_(grad, alpha=1 - dampening)
+
+            if nesterov:
+                grad = grad.add(buf, alpha=momentum)
+            else:
+                grad = buf
+
+        param.add_(grad, alpha=-lr)
+
+
+def _multi_tensor_sgd(
+    params: List[Tensor],
+    grads: List[Tensor],
+    momentum_buffer_list: List[Optional[Tensor]],
+    grad_scale: Optional[Tensor],
+    found_inf: Optional[Tensor],
+    *,
+    weight_decay: float,
+    momentum: float,
+    lr: float,
+    dampening: float,
+    nesterov: bool,
+    maximize: bool,
+    has_sparse_grad: bool,
+):
+    assert grad_scale is None and found_inf is None
+
+    if len(params) == 0:
+        return
+
+    grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
+        [params, grads, momentum_buffer_list], with_indices=True  # type: ignore[list-item]
+    )
+
+    for (
+        device_params_,
+        device_grads_,
+        device_momentum_buffer_list,
+    ), indices in grouped_tensors.values():
+        device_params: List[Tensor] = cast(List[Tensor], device_params_)
+        device_grads: List[Tensor] = cast(List[Tensor], device_grads_)
+
+        device_has_sparse_grad = has_sparse_grad and any(
+            grad.is_sparse for grad in device_grads
+        )
+
+        if maximize:
+            device_grads = torch._foreach_neg(device_grads)  # type: ignore[assignment]
+
+        if weight_decay != 0:
+            # Re-use the intermediate memory (device_grads) already allocated for maximize
+            if maximize:
+                torch._foreach_add_(device_grads, device_params, alpha=weight_decay)
+            else:
+                device_grads = torch._foreach_add(  # type: ignore[assignment]
+                    device_grads, device_params, alpha=weight_decay
+                )
+
+        if momentum != 0:
+            bufs: List[Tensor] = []
+
+            all_states_with_momentum_buffer = True
+            for i in range(len(device_momentum_buffer_list)):
+                if device_momentum_buffer_list[i] is None:
+                    all_states_with_momentum_buffer = False
+                    break
+                else:
+                    bufs.append(cast(Tensor, device_momentum_buffer_list[i]))
+
+            if all_states_with_momentum_buffer:
+                torch._foreach_mul_(bufs, momentum)
+                torch._foreach_add_(bufs, device_grads, alpha=1 - dampening)
+            else:
+                bufs = []
+                for i in range(len(device_momentum_buffer_list)):
+                    if device_momentum_buffer_list[i] is None:
+                        buf = device_momentum_buffer_list[i] = momentum_buffer_list[
+                            indices[i]
+                        ] = torch.clone(device_grads[i]).detach()
+                    else:
+                        buf = cast(Tensor, device_momentum_buffer_list[i])
+                        buf.mul_(momentum).add_(device_grads[i], alpha=1 - dampening)
+
+                    bufs.append(buf)
+
+            if nesterov:
+                torch._foreach_add_(device_grads, bufs, alpha=momentum)
+            else:
+                device_grads = bufs
+
+        if not device_has_sparse_grad:
+            # handle internal item() call if lr is a tensor
+            if isinstance(lr, torch.Tensor) and torch._utils.is_compiling():
+                grads_x_lr = torch._foreach_mul(device_grads, -lr)
+                torch._foreach_add_(device_params, grads_x_lr)
+            else:
+                torch._foreach_add_(device_params, device_grads, alpha=-lr)
+        else:
+            # foreach APIs don't support sparse
+            for i in range(len(device_params)):
+                device_params[i].add_(device_grads[i], alpha=-lr)
+
+
+def _fused_sgd(
+    params: List[Tensor],
+    grads: List[Tensor],
+    momentum_buffer_list: List[Optional[Tensor]],
+    grad_scale: Optional[Tensor],
+    found_inf: Optional[Tensor],
+    *,
+    weight_decay: float,
+    momentum: float,
+    lr: float,
+    dampening: float,
+    nesterov: bool,
+    maximize: bool,
+    has_sparse_grad: bool,
+) -> None:
+    if not params:
+        return
+    if has_sparse_grad:
+        raise RuntimeError("`_fused_sgd` does not support sparse gradients")
+    grad_scale_dict: DeviceDict = (
+        {grad_scale.device: grad_scale} if grad_scale is not None else {}
+    )
+    found_inf_dict: DeviceDict = (
+        {found_inf.device: found_inf} if found_inf is not None else {}
+    )
+
+    no_momentum_buffer = momentum == 0
+    is_first_step = (
+        all(t is None for t in momentum_buffer_list) and not no_momentum_buffer
+    )
+    if is_first_step:
+        for i, g in enumerate(grads):
+            momentum_buffer_list[i] = torch.empty_like(g)
+    grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
+        [params, grads, momentum_buffer_list], with_indices=False  # type: ignore[list-item]
+    )
+    for (device, _), (
+        (device_params_, device_grads_, device_momentum_buffer_list),
+        _,
+    ) in grouped_tensors.items():
+        device_params: List[Tensor] = cast(List[Tensor], device_params_)
+        device_grads: List[Tensor] = cast(List[Tensor], device_grads_)
+        device_grad_scale, device_found_inf = None, None
+        if grad_scale is not None:
+            device_grad_scale = grad_scale_dict.setdefault(
+                device, grad_scale.to(device)
+            )
+        if found_inf_dict is not None and found_inf is not None:
+            device_found_inf = found_inf_dict.setdefault(device, found_inf.to(device))
+        torch._fused_sgd_(
+            device_params,
+            device_grads,
+            []
+            if no_momentum_buffer
+            else cast(List[Tensor], device_momentum_buffer_list),
+            weight_decay=weight_decay,
+            momentum=momentum,
+            lr=lr,
+            dampening=dampening,
+            nesterov=nesterov,
+            maximize=maximize,
+            is_first_step=is_first_step,
+            grad_scale=device_grad_scale,
+            found_inf=device_found_inf,
+        )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/sparse_adam.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/sparse_adam.py
new file mode 100644
index 0000000000000000000000000000000000000000..22ef7841270f6907f04d31a0d47edc1d02e42ff6
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/sparse_adam.py
@@ -0,0 +1,185 @@
+# mypy: allow-untyped-defs
+from typing import List, Tuple, Union
+
+import torch
+from torch import Tensor
+
+from . import _functional as F
+from .optimizer import _maximize_doc, Optimizer, ParamsT
+
+
+__all__ = ["SparseAdam"]
+
+
+class SparseAdam(Optimizer):
+    def __init__(
+        self,
+        params: ParamsT,
+        lr: Union[float, Tensor] = 1e-3,
+        betas: Tuple[float, float] = (0.9, 0.999),
+        eps: float = 1e-8,
+        maximize: bool = False,
+    ):
+        if isinstance(lr, Tensor) and lr.numel() != 1:
+            raise ValueError("Tensor lr must be 1-element")
+        if not 0.0 < lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 < eps:
+            raise ValueError(f"Invalid epsilon value: {eps}")
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
+
+        defaults = dict(lr=lr, betas=betas, eps=eps, maximize=maximize)
+        super().__init__(params, defaults)
+
+        sparse_params = []
+        complex_params = []
+        for index, param_group in enumerate(self.param_groups):
+            assert isinstance(
+                param_group, dict
+            ), f"param_groups must be a list of dicts, but got {type(param_group)}"
+            # given param group, convert given params to a list first before iterating
+            for d_index, d_param in enumerate(param_group["params"]):
+                if d_param.is_sparse:
+                    sparse_params.append([index, d_index])
+                if d_param.is_complex():
+                    complex_params.append([index, d_index])
+        if sparse_params:
+            raise ValueError(
+                f"Sparse params at indices {sparse_params}: SparseAdam requires dense parameter tensors"
+            )
+        if complex_params:
+            raise ValueError(
+                f"Complex params at indices {complex_params}: SparseAdam does not support complex parameters"
+            )
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        """Perform a single optimization step.
+
+        Args:
+            closure (Callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            params_with_grad: List[Tensor] = []
+            grads: List[Tensor] = []
+            exp_avgs: List[Tensor] = []
+            exp_avg_sqs: List[Tensor] = []
+            state_steps: List[int] = []
+            beta1, beta2 = group["betas"]
+            maximize = group.get("maximize", False)
+
+            for p in group["params"]:
+                if p.grad is not None:
+                    params_with_grad.append(p)
+                    if not p.grad.is_sparse:
+                        raise RuntimeError(
+                            "SparseAdam does not support dense gradients, please consider Adam instead"
+                        )
+                    grads.append(p.grad)
+
+                    state = self.state[p]
+
+                    # State initialization
+                    if len(state) == 0:
+                        state["step"] = 0
+                        # Exponential moving average of gradient values
+                        state["exp_avg"] = torch.zeros_like(
+                            p, memory_format=torch.preserve_format
+                        )
+                        # Exponential moving average of squared gradient values
+                        state["exp_avg_sq"] = torch.zeros_like(
+                            p, memory_format=torch.preserve_format
+                        )
+
+                    exp_avgs.append(state["exp_avg"])
+                    exp_avg_sqs.append(state["exp_avg_sq"])
+
+                    # update the steps for each param group update
+                    state["step"] += 1
+                    # record the step after step update
+                    state_steps.append(state["step"])
+
+            F.sparse_adam(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                state_steps,
+                eps=group["eps"],
+                beta1=beta1,
+                beta2=beta2,
+                lr=group["lr"],
+                maximize=maximize,
+            )
+
+        return loss
+
+
+SparseAdam.__doc__ = rf"""SparseAdam implements a masked version of the Adam algorithm
+    suitable for sparse gradients. Currently, due to implementation constraints (explained
+    below), SparseAdam is only intended for a narrow subset of use cases, specifically
+    parameters of a dense layout with gradients of a sparse layout. This occurs in a
+    special case where the module backwards produces grads already in a sparse layout.
+    One example NN module that behaves as such is ``nn.Embedding(sparse=True)``.
+
+    SparseAdam approximates the Adam algorithm by masking out the parameter and moment
+    updates corresponding to the zero values in the gradients. Whereas the Adam algorithm
+    will update the first moment, the second moment, and the parameters based on all values
+    of the gradients, SparseAdam only updates the moments and parameters corresponding
+    to the non-zero values of the gradients.
+
+    A simplified way of thinking about the `intended` implementation is as such:
+
+    1. Create a mask of the non-zero values in the sparse gradients. For example,
+       if your gradient looks like [0, 5, 0, 0, 9], the mask would be [0, 1, 0, 0, 1].
+    2. Apply this mask over the running moments and do computation on only the
+       non-zero values.
+    3. Apply this mask over the parameters and only apply an update on non-zero values.
+
+    In actuality, we use sparse layout Tensors to optimize this approximation, which means the
+    more gradients that are masked by not being materialized, the more performant the optimization.
+    Since we rely on using sparse layout tensors, we infer that any materialized value in the
+    sparse layout is non-zero and we do NOT actually verify that all values are not zero!
+    It is important to not conflate a semantically sparse tensor (a tensor where many
+    of its values are zeros) with a sparse layout tensor (a tensor where ``.is_sparse``
+    returns ``True``). The SparseAdam approximation is intended for `semantically` sparse
+    tensors and the sparse layout is only a implementation detail. A clearer implementation
+    would be to use MaskedTensors, but those are experimental.
+
+
+    .. note::
+
+        If you suspect your gradients are semantically sparse (but do not have sparse
+        layout), this variant may not be the best for you. Ideally, you want to avoid
+        materializing anything that is suspected to be sparse in the first place, since
+        needing to convert all your grads from dense layout to sparse layout may outweigh
+        the performance gain. Here, using Adam may be the best alternative, unless you
+        can easily rig up your module to output sparse grads similar to
+        ``nn.Embedding(sparse=True)``. If you insist on converting your grads, you can do
+        so by manually overriding your parameters' ``.grad`` fields with their sparse
+        equivalents before calling ``.step()``.
+
+
+    Args:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, Tensor, optional): learning rate (default: 1e-3)
+        betas (Tuple[float, float], optional): coefficients used for computing
+            running averages of gradient and its square (default: (0.9, 0.999))
+        eps (float, optional): term added to the denominator to improve
+            numerical stability (default: 1e-8)
+        {_maximize_doc}
+
+    .. _Adam\: A Method for Stochastic Optimization:
+        https://arxiv.org/abs/1412.6980
+
+    """
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/swa_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/swa_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7bea0d355bea34fbbf3bedea1cdcbbcbb6dc4e46
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/optim/swa_utils.py
@@ -0,0 +1,467 @@
+# mypy: allow-untyped-defs
+r"""Implementation for Stochastic Weight Averaging implementation."""
+import itertools
+import math
+import warnings
+from copy import deepcopy
+from typing import Any, Callable, Iterable, List, Literal, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+from torch.nn import Module
+from torch.optim.lr_scheduler import _format_param, LRScheduler
+from torch.utils._foreach_utils import _get_foreach_kernels_supported_devices
+
+from .optimizer import Optimizer
+
+
+__all__ = [
+    "AveragedModel",
+    "update_bn",
+    "SWALR",
+    "get_ema_multi_avg_fn",
+    "get_swa_multi_avg_fn",
+    "get_ema_avg_fn",
+    "get_swa_avg_fn",
+]
+
+from torch.utils._foreach_utils import _group_tensors_by_device_and_dtype
+
+
+PARAM_LIST = Union[Tuple[Tensor, ...], List[Tensor]]
+
+
+def get_ema_multi_avg_fn(decay=0.999):
+    """Get the function applying exponential moving average (EMA) across multiple params."""
+
+    @torch.no_grad()
+    def ema_update(ema_param_list: PARAM_LIST, current_param_list: PARAM_LIST, _):
+        # foreach lerp only handles float and complex
+        if torch.is_floating_point(ema_param_list[0]) or torch.is_complex(
+            ema_param_list[0]
+        ):
+            torch._foreach_lerp_(ema_param_list, current_param_list, 1 - decay)
+        else:
+            for p_ema, p_model in zip(ema_param_list, current_param_list):
+                p_ema.copy_(p_ema * decay + p_model * (1 - decay))
+
+    return ema_update
+
+
+def get_swa_multi_avg_fn():
+    """Get the function applying stochastic weight average (SWA) across multiple params."""
+
+    @torch.no_grad()
+    def swa_update(
+        averaged_param_list: PARAM_LIST,
+        current_param_list: PARAM_LIST,
+        num_averaged: Union[Tensor, int],
+    ):
+        # foreach lerp only handles float and complex
+        if torch.is_floating_point(averaged_param_list[0]) or torch.is_complex(
+            averaged_param_list[0]
+        ):
+            torch._foreach_lerp_(
+                averaged_param_list, current_param_list, 1 / (num_averaged + 1)
+            )
+        else:
+            diffs = torch._foreach_sub(current_param_list, averaged_param_list)
+            if isinstance(num_averaged, Tensor):
+                torch._foreach_addcdiv_(
+                    averaged_param_list,
+                    diffs,
+                    [num_averaged + 1] * len(averaged_param_list),
+                )
+            else:
+                torch._foreach_add_(
+                    averaged_param_list, diffs, alpha=1.0 / (num_averaged + 1)
+                )
+
+    return swa_update
+
+
+def get_ema_avg_fn(decay=0.999):
+    """Get the function applying exponential moving average (EMA) across a single param."""
+
+    @torch.no_grad()
+    def ema_update(ema_param: Tensor, current_param: Tensor, num_averaged):
+        return decay * ema_param + (1 - decay) * current_param
+
+    return ema_update
+
+
+def get_swa_avg_fn():
+    """Get the function applying stochastic weight average (SWA) across a single param."""
+
+    @torch.no_grad()
+    def swa_update(
+        averaged_param: Tensor, current_param: Tensor, num_averaged: Union[Tensor, int]
+    ):
+        return averaged_param + (current_param - averaged_param) / (num_averaged + 1)
+
+    return swa_update
+
+
+class AveragedModel(Module):
+    r"""Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA).
+
+    Stochastic Weight Averaging was proposed in `Averaging Weights Leads to
+    Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii
+    Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson
+    (UAI 2018).
+
+    Exponential Moving Average is a variation of `Polyak averaging`_,
+    but using exponential weights instead of equal weights across iterations.
+
+    AveragedModel class creates a copy of the provided module :attr:`model`
+    on the device :attr:`device` and allows to compute running averages of the
+    parameters of the :attr:`model`.
+
+    Args:
+        model (torch.nn.Module): model to use with SWA/EMA
+        device (torch.device, optional): if provided, the averaged model will be
+            stored on the :attr:`device`
+        avg_fn (function, optional): the averaging function used to update
+            parameters; the function must take in the current value of the
+            :class:`AveragedModel` parameter, the current value of :attr:`model`
+            parameter, and the number of models already averaged; if None,
+            an equally weighted average is used (default: None)
+        multi_avg_fn (function, optional): the averaging function used to update
+            parameters inplace; the function must take in the current values of the
+            :class:`AveragedModel` parameters as a list, the current values of :attr:`model`
+            parameters as a list, and the number of models already averaged; if None,
+            an equally weighted average is used (default: None)
+        use_buffers (bool): if ``True``, it will compute running averages for
+            both the parameters and the buffers of the model. (default: ``False``)
+
+    Example:
+        >>> # xdoctest: +SKIP("undefined variables")
+        >>> loader, optimizer, model, loss_fn = ...
+        >>> swa_model = torch.optim.swa_utils.AveragedModel(model)
+        >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
+        >>>                                     T_max=300)
+        >>> swa_start = 160
+        >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05)
+        >>> for i in range(300):
+        >>>      for input, target in loader:
+        >>>          optimizer.zero_grad()
+        >>>          loss_fn(model(input), target).backward()
+        >>>          optimizer.step()
+        >>>      if i > swa_start:
+        >>>          swa_model.update_parameters(model)
+        >>>          swa_scheduler.step()
+        >>>      else:
+        >>>          scheduler.step()
+        >>>
+        >>> # Update bn statistics for the swa_model at the end
+        >>> torch.optim.swa_utils.update_bn(loader, swa_model)
+
+    You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters.
+    If no averaging function is provided, the default is to compute
+    equally-weighted average of the weights (SWA).
+
+    Example:
+        >>> # xdoctest: +SKIP("undefined variables")
+        >>> # Compute exponential moving averages of the weights and buffers
+        >>> ema_model = torch.optim.swa_utils.AveragedModel(model,
+        >>>             torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True)
+
+    .. note::
+        When using SWA/EMA with models containing Batch Normalization you may
+        need to update the activation statistics for Batch Normalization.
+        This can be done either by using the :meth:`torch.optim.swa_utils.update_bn`
+        or by setting :attr:`use_buffers` to `True`. The first approach updates the
+        statistics in a post-training step by passing data through the model. The
+        second does it during the parameter update phase by averaging all buffers.
+        Empirical evidence has shown that updating the statistics in normalization
+        layers increases accuracy, but you may wish to empirically test which
+        approach yields the best results in your problem.
+
+    .. note::
+        :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model.
+
+    .. note::
+        When :meth:`update_parameters` is called for the first time (i.e.
+        :attr:`n_averaged` is `0`) the parameters of `model` are copied
+        to the parameters of :class:`AveragedModel`. For every subsequent
+        call of :meth:`update_parameters` the function `avg_fn` is used
+        to update the parameters.
+
+    .. _Averaging Weights Leads to Wider Optima and Better Generalization:
+        https://arxiv.org/abs/1803.05407
+    .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should
+        Average:
+        https://arxiv.org/abs/1806.05594
+    .. _SWALP: Stochastic Weight Averaging in Low-Precision Training:
+        https://arxiv.org/abs/1904.11943
+    .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That
+        Generalizes Well:
+        https://arxiv.org/abs/2001.02312
+    .. _Polyak averaging:
+        https://paperswithcode.com/method/polyak-averaging
+    """
+
+    n_averaged: Tensor
+
+    def __init__(
+        self,
+        model: Module,
+        device: Optional[Union[int, torch.device]] = None,
+        avg_fn: Optional[Callable[[Tensor, Tensor, Union[Tensor, int]], Tensor]] = None,
+        multi_avg_fn: Optional[
+            Callable[[PARAM_LIST, PARAM_LIST, Union[Tensor, int]], None]
+        ] = None,
+        use_buffers=False,
+    ):  # noqa: D107
+        super().__init__()
+        assert (
+            avg_fn is None or multi_avg_fn is None
+        ), "Only one of avg_fn and multi_avg_fn should be provided"
+        self.module = deepcopy(model)
+        if device is not None:
+            self.module = self.module.to(device)
+        self.register_buffer(
+            "n_averaged", torch.tensor(0, dtype=torch.long, device=device)
+        )
+        self.avg_fn = avg_fn
+        self.multi_avg_fn = multi_avg_fn
+        self.use_buffers = use_buffers
+
+    def forward(self, *args, **kwargs):
+        """Forward pass."""
+        return self.module(*args, **kwargs)
+
+    def update_parameters(self, model: Module):
+        """Update model parameters."""
+        self_param = (
+            itertools.chain(self.module.parameters(), self.module.buffers())
+            if self.use_buffers
+            else self.parameters()
+        )
+        model_param = (
+            itertools.chain(model.parameters(), model.buffers())
+            if self.use_buffers
+            else model.parameters()
+        )
+        self_param_detached: List[Optional[Tensor]] = []
+        model_param_detached: List[Optional[Tensor]] = []
+        for p_averaged, p_model in zip(self_param, model_param):
+            p_model_ = p_model.detach().to(p_averaged.device)
+            self_param_detached.append(p_averaged.detach())
+            model_param_detached.append(p_model_)
+            if self.n_averaged == 0:
+                p_averaged.detach().copy_(p_model_)
+
+        if self.n_averaged > 0:
+            if self.multi_avg_fn is not None or self.avg_fn is None:
+                grouped_tensors = _group_tensors_by_device_and_dtype(
+                    [self_param_detached, model_param_detached]
+                )
+                for (device, _), (
+                    [self_params, model_params],
+                    _,
+                ) in grouped_tensors.items():
+                    if self.multi_avg_fn:
+                        self.multi_avg_fn(
+                            self_params, model_params, self.n_averaged.to(device)  # type: ignore[arg-type]
+                        )
+                    elif (
+                        device is not None
+                        and device.type in _get_foreach_kernels_supported_devices()
+                    ):
+                        multi_avg_fn = get_swa_multi_avg_fn()
+                        multi_avg_fn(
+                            self_params, model_params, self.n_averaged.to(device)
+                        )
+                    else:
+                        avg_fn = get_swa_avg_fn()
+                        n_averaged = self.n_averaged.to(device)
+                        for p_averaged, p_model in zip(self_params, model_params):  # type: ignore[assignment]
+                            p_averaged.copy_(avg_fn(p_averaged, p_model, n_averaged))
+            else:
+                for p_averaged, p_model in zip(  # type: ignore[assignment]
+                    self_param_detached, model_param_detached
+                ):
+                    n_averaged = self.n_averaged.to(p_averaged.device)
+                    p_averaged.detach().copy_(
+                        self.avg_fn(p_averaged.detach(), p_model, n_averaged)
+                    )
+
+        if not self.use_buffers:
+            # If not apply running averages to the buffers,
+            # keep the buffers in sync with the source model.
+            for b_swa, b_model in zip(self.module.buffers(), model.buffers()):
+                b_swa.detach().copy_(b_model.detach().to(b_swa.device))
+        self.n_averaged += 1
+
+
+@torch.no_grad()
+def update_bn(
+    loader: Iterable[Any],
+    model: Module,
+    device: Optional[Union[int, torch.device]] = None,
+):
+    r"""Update BatchNorm running_mean, running_var buffers in the model.
+
+    It performs one pass over data in `loader` to estimate the activation
+    statistics for BatchNorm layers in the model.
+
+    Args:
+        loader (torch.utils.data.DataLoader): dataset loader to compute the
+            activation statistics on. Each data batch should be either a
+            tensor, or a list/tuple whose first element is a tensor
+            containing data.
+        model (torch.nn.Module): model for which we seek to update BatchNorm
+            statistics.
+        device (torch.device, optional): If set, data will be transferred to
+            :attr:`device` before being passed into :attr:`model`.
+
+    Example:
+        >>> # xdoctest: +SKIP("Undefined variables")
+        >>> loader, model = ...
+        >>> torch.optim.swa_utils.update_bn(loader, model)
+
+    .. note::
+        The `update_bn` utility assumes that each data batch in :attr:`loader`
+        is either a tensor or a list or tuple of tensors; in the latter case it
+        is assumed that :meth:`model.forward()` should be called on the first
+        element of the list or tuple corresponding to the data batch.
+    """
+    momenta = {}
+    for module in model.modules():
+        if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
+            module.reset_running_stats()
+            momenta[module] = module.momentum
+
+    if not momenta:
+        return
+
+    was_training = model.training
+    model.train()
+    for module in momenta.keys():
+        module.momentum = None
+
+    for input in loader:
+        if isinstance(input, (list, tuple)):
+            input = input[0]
+        if device is not None:
+            input = input.to(device)
+
+        model(input)
+
+    for bn_module in momenta.keys():
+        bn_module.momentum = momenta[bn_module]
+    model.train(was_training)
+
+
+class SWALR(LRScheduler):
+    r"""Anneals the learning rate in each parameter group to a fixed value.
+
+    This learning rate scheduler is meant to be used with Stochastic Weight
+    Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`).
+
+    Args:
+        optimizer (torch.optim.Optimizer): wrapped optimizer
+        swa_lrs (float or list): the learning rate value for all param groups
+            together or separately for each group.
+        annealing_epochs (int): number of epochs in the annealing phase
+            (default: 10)
+        annealing_strategy (str): "cos" or "linear"; specifies the annealing
+            strategy: "cos" for cosine annealing, "linear" for linear annealing
+            (default: "cos")
+        last_epoch (int): the index of the last epoch (default: -1)
+
+    The :class:`SWALR` scheduler can be used together with other
+    schedulers to switch to a constant learning rate late in the training
+    as in the example below.
+
+    Example:
+        >>> # xdoctest: +SKIP("Undefined variables")
+        >>> loader, optimizer, model = ...
+        >>> lr_lambda = lambda epoch: 0.9
+        >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer,
+        >>>        lr_lambda=lr_lambda)
+        >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer,
+        >>>        anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05)
+        >>> swa_start = 160
+        >>> for i in range(300):
+        >>>      for input, target in loader:
+        >>>          optimizer.zero_grad()
+        >>>          loss_fn(model(input), target).backward()
+        >>>          optimizer.step()
+        >>>      if i > swa_start:
+        >>>          swa_scheduler.step()
+        >>>      else:
+        >>>          scheduler.step()
+
+    .. _Averaging Weights Leads to Wider Optima and Better Generalization:
+        https://arxiv.org/abs/1803.05407
+    """
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        swa_lr: float,
+        anneal_epochs=10,
+        anneal_strategy: Literal["cos", "linear"] = "cos",
+        last_epoch=-1,
+    ):  # noqa: D107
+        swa_lrs = _format_param("swa_lr", optimizer, swa_lr)
+        for swa_lr, group in zip(swa_lrs, optimizer.param_groups):
+            group["swa_lr"] = swa_lr
+        if anneal_strategy not in ["cos", "linear"]:
+            raise ValueError(
+                "anneal_strategy must by one of 'cos' or 'linear', "
+                f"instead got {anneal_strategy}"
+            )
+        elif anneal_strategy == "cos":
+            self.anneal_func = self._cosine_anneal
+        elif anneal_strategy == "linear":
+            self.anneal_func = self._linear_anneal
+        if not isinstance(anneal_epochs, int) or anneal_epochs < 0:
+            raise ValueError(
+                f"anneal_epochs must be equal or greater than 0, got {anneal_epochs}"
+            )
+        self.anneal_epochs = anneal_epochs
+        super().__init__(optimizer, last_epoch)
+
+    @staticmethod
+    def _linear_anneal(t):
+        return t
+
+    @staticmethod
+    def _cosine_anneal(t):
+        return (1 - math.cos(math.pi * t)) / 2
+
+    @staticmethod
+    def _get_initial_lr(lr, swa_lr, alpha):
+        if alpha == 1:
+            return swa_lr
+        return (lr - alpha * swa_lr) / (1 - alpha)
+
+    def get_lr(self):
+        """Get learning rate."""
+        # `_get_lr_called_within_step` is only available `_enable_get_lr_call`,
+        # so we ignore the type error here. See `LRScheduler.step()` for more details.
+        if not self._get_lr_called_within_step:  # type: ignore[attr-defined]
+            warnings.warn(
+                "To get the last learning rate computed by the scheduler, "
+                "please use `get_last_lr()`.",
+                UserWarning,
+            )
+        # Set in `LRScheduler._initial_step()`
+        step = self._step_count - 1  # type: ignore[attr-defined]
+        if self.anneal_epochs == 0:
+            step = max(1, step)
+        prev_t = max(0, min(1, (step - 1) / max(1, self.anneal_epochs)))
+        prev_alpha = self.anneal_func(prev_t)
+        prev_lrs = [
+            self._get_initial_lr(group["lr"], group["swa_lr"], prev_alpha)
+            for group in self.optimizer.param_groups
+        ]
+        t = max(0, min(1, step / max(1, self.anneal_epochs)))
+        alpha = self.anneal_func(t)
+        return [
+            group["swa_lr"] * alpha + lr * (1 - alpha)
+            for group, lr in zip(self.optimizer.param_groups, prev_lrs)
+        ]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..66cace5931ac17c548becfddbb0e56dbbdac3d38
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/__init__.py
@@ -0,0 +1,12 @@
+from .analyze.is_from_package import is_from_package
+from .file_structure_representation import Directory
+from .glob_group import GlobGroup
+from .importer import (
+    Importer,
+    ObjMismatchError,
+    ObjNotFoundError,
+    OrderedImporter,
+    sys_importer,
+)
+from .package_exporter import EmptyMatchError, PackageExporter, PackagingError
+from .package_importer import PackageImporter
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_digraph.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_digraph.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b753f7ebdc4b162be565eb4e923c4cfa333c597
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_digraph.py
@@ -0,0 +1,174 @@
+# mypy: allow-untyped-defs
+from collections import deque
+from typing import List, Set
+
+
+class DiGraph:
+    """Really simple unweighted directed graph data structure to track dependencies.
+
+    The API is pretty much the same as networkx so if you add something just
+    copy their API.
+    """
+
+    def __init__(self):
+        # Dict of node -> dict of arbitrary attributes
+        self._node = {}
+        # Nested dict of node -> successor node -> nothing.
+        # (didn't implement edge data)
+        self._succ = {}
+        # Nested dict of node -> predecessor node -> nothing.
+        self._pred = {}
+
+        # Keep track of the order in which nodes are added to
+        # the graph.
+        self._node_order = {}
+        self._insertion_idx = 0
+
+    def add_node(self, n, **kwargs):
+        """Add a node to the graph.
+
+        Args:
+            n: the node. Can we any object that is a valid dict key.
+            **kwargs: any attributes you want to attach to the node.
+        """
+        if n not in self._node:
+            self._node[n] = kwargs
+            self._succ[n] = {}
+            self._pred[n] = {}
+            self._node_order[n] = self._insertion_idx
+            self._insertion_idx += 1
+        else:
+            self._node[n].update(kwargs)
+
+    def add_edge(self, u, v):
+        """Add an edge to graph between nodes ``u`` and ``v``
+
+        ``u`` and ``v`` will be created if they do not already exist.
+        """
+        # add nodes
+        self.add_node(u)
+        self.add_node(v)
+
+        # add the edge
+        self._succ[u][v] = True
+        self._pred[v][u] = True
+
+    def successors(self, n):
+        """Returns an iterator over successor nodes of n."""
+        try:
+            return iter(self._succ[n])
+        except KeyError as e:
+            raise ValueError(f"The node {n} is not in the digraph.") from e
+
+    def predecessors(self, n):
+        """Returns an iterator over predecessors nodes of n."""
+        try:
+            return iter(self._pred[n])
+        except KeyError as e:
+            raise ValueError(f"The node {n} is not in the digraph.") from e
+
+    @property
+    def edges(self):
+        """Returns an iterator over all edges (u, v) in the graph"""
+        for n, successors in self._succ.items():
+            for succ in successors:
+                yield n, succ
+
+    @property
+    def nodes(self):
+        """Returns a dictionary of all nodes to their attributes."""
+        return self._node
+
+    def __iter__(self):
+        """Iterate over the nodes."""
+        return iter(self._node)
+
+    def __contains__(self, n):
+        """Returns True if ``n`` is a node in the graph, False otherwise."""
+        try:
+            return n in self._node
+        except TypeError:
+            return False
+
+    def forward_transitive_closure(self, src: str) -> Set[str]:
+        """Returns a set of nodes that are reachable from src"""
+
+        result = set(src)
+        working_set = deque(src)
+        while len(working_set) > 0:
+            cur = working_set.popleft()
+            for n in self.successors(cur):
+                if n not in result:
+                    result.add(n)
+                    working_set.append(n)
+        return result
+
+    def backward_transitive_closure(self, src: str) -> Set[str]:
+        """Returns a set of nodes that are reachable from src in reverse direction"""
+
+        result = set(src)
+        working_set = deque(src)
+        while len(working_set) > 0:
+            cur = working_set.popleft()
+            for n in self.predecessors(cur):
+                if n not in result:
+                    result.add(n)
+                    working_set.append(n)
+        return result
+
+    def all_paths(self, src: str, dst: str):
+        """Returns a subgraph rooted at src that shows all the paths to dst."""
+
+        result_graph = DiGraph()
+        # First compute forward transitive closure of src (all things reachable from src).
+        forward_reachable_from_src = self.forward_transitive_closure(src)
+
+        if dst not in forward_reachable_from_src:
+            return result_graph
+
+        # Second walk the reverse dependencies of dst, adding each node to
+        # the output graph iff it is also present in forward_reachable_from_src.
+        # we don't use backward_transitive_closures for optimization purposes
+        working_set = deque(dst)
+        while len(working_set) > 0:
+            cur = working_set.popleft()
+            for n in self.predecessors(cur):
+                if n in forward_reachable_from_src:
+                    result_graph.add_edge(n, cur)
+                    # only explore further if its reachable from src
+                    working_set.append(n)
+
+        return result_graph.to_dot()
+
+    def first_path(self, dst: str) -> List[str]:
+        """Returns a list of nodes that show the first path that resulted in dst being added to the graph."""
+        path = []
+
+        while dst:
+            path.append(dst)
+            candidates = self._pred[dst].keys()
+            dst, min_idx = "", None
+            for candidate in candidates:
+                idx = self._node_order.get(candidate, None)
+                if idx is None:
+                    break
+                if min_idx is None or idx < min_idx:
+                    min_idx = idx
+                    dst = candidate
+
+        return list(reversed(path))
+
+    def to_dot(self) -> str:
+        """Returns the dot representation of the graph.
+
+        Returns:
+            A dot representation of the graph.
+        """
+        edges = "\n".join(f'"{f}" -> "{t}";' for f, t in self.edges)
+        return f"""\
+digraph G {{
+rankdir = LR;
+node [shape=box];
+{edges}
+}}
+"""
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_directory_reader.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_directory_reader.py
new file mode 100644
index 0000000000000000000000000000000000000000..f58065f47dc4374689abc7ac1c6f1ca8048ab5c7
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_directory_reader.py
@@ -0,0 +1,65 @@
+# mypy: allow-untyped-defs
+import os.path
+from glob import glob
+from typing import cast
+
+import torch
+from torch.types import Storage
+
+
+__serialization_id_record_name__ = ".data/serialization_id"
+
+
+# because get_storage_from_record returns a tensor!?
+class _HasStorage:
+    def __init__(self, storage):
+        self._storage = storage
+
+    def storage(self):
+        return self._storage
+
+
+class DirectoryReader:
+    """
+    Class to allow PackageImporter to operate on unzipped packages. Methods
+    copy the behavior of the internal PyTorchFileReader class (which is used for
+    accessing packages in all other cases).
+
+    N.B.: ScriptObjects are not depickleable or accessible via this DirectoryReader
+    class due to ScriptObjects requiring an actual PyTorchFileReader instance.
+    """
+
+    def __init__(self, directory):
+        self.directory = directory
+
+    def get_record(self, name):
+        filename = f"{self.directory}/{name}"
+        with open(filename, "rb") as f:
+            return f.read()
+
+    def get_storage_from_record(self, name, numel, dtype):
+        filename = f"{self.directory}/{name}"
+        nbytes = torch._utils._element_size(dtype) * numel
+        storage = cast(Storage, torch.UntypedStorage)
+        return _HasStorage(storage.from_file(filename=filename, nbytes=nbytes))
+
+    def has_record(self, path):
+        full_path = os.path.join(self.directory, path)
+        return os.path.isfile(full_path)
+
+    def get_all_records(
+        self,
+    ):
+        files = []
+        for filename in glob(f"{self.directory}/**", recursive=True):
+            if not os.path.isdir(filename):
+                files.append(filename[len(self.directory) + 1 :])
+        return files
+
+    def serialization_id(
+        self,
+    ):
+        if self.has_record(__serialization_id_record_name__):
+            return self.get_record(__serialization_id_record_name__)
+        else:
+            return ""
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_importlib.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_importlib.py
new file mode 100644
index 0000000000000000000000000000000000000000..609efd294c4c9650d890fd36aafc9f521068ce8b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_importlib.py
@@ -0,0 +1,95 @@
+# mypy: allow-untyped-defs
+import _warnings
+import os.path
+
+
+# note: implementations
+# copied from cpython's import code
+
+
+# _zip_searchorder defines how we search for a module in the Zip
+# archive: we first search for a package __init__, then for
+# non-package .pyc, and .py entries. The .pyc entries
+# are swapped by initzipimport() if we run in optimized mode. Also,
+# '/' is replaced by path_sep there.
+
+_zip_searchorder = (
+    ("/__init__.py", True),
+    (".py", False),
+)
+
+
+# Replace any occurrences of '\r\n?' in the input string with '\n'.
+# This converts DOS and Mac line endings to Unix line endings.
+def _normalize_line_endings(source):
+    source = source.replace(b"\r\n", b"\n")
+    source = source.replace(b"\r", b"\n")
+    return source
+
+
+def _resolve_name(name, package, level):
+    """Resolve a relative module name to an absolute one."""
+    bits = package.rsplit(".", level - 1)
+    if len(bits) < level:
+        raise ValueError("attempted relative import beyond top-level package")
+    base = bits[0]
+    return f"{base}.{name}" if name else base
+
+
+def _sanity_check(name, package, level):
+    """Verify arguments are "sane"."""
+    if not isinstance(name, str):
+        raise TypeError(f"module name must be str, not {type(name)}")
+    if level < 0:
+        raise ValueError("level must be >= 0")
+    if level > 0:
+        if not isinstance(package, str):
+            raise TypeError("__package__ not set to a string")
+        elif not package:
+            raise ImportError("attempted relative import with no known parent package")
+    if not name and level == 0:
+        raise ValueError("Empty module name")
+
+
+def _calc___package__(globals):
+    """Calculate what __package__ should be.
+
+    __package__ is not guaranteed to be defined or could be set to None
+    to represent that its proper value is unknown.
+
+    """
+    package = globals.get("__package__")
+    spec = globals.get("__spec__")
+    if package is not None:
+        if spec is not None and package != spec.parent:
+            _warnings.warn(  # noqa: G010
+                f"__package__ != __spec__.parent ({package!r} != {spec.parent!r})",  # noqa: G004
+                ImportWarning,
+                stacklevel=3,
+            )
+        return package
+    elif spec is not None:
+        return spec.parent
+    else:
+        _warnings.warn(  # noqa: G010
+            "can't resolve package from __spec__ or __package__, "
+            "falling back on __name__ and __path__",
+            ImportWarning,
+            stacklevel=3,
+        )
+        package = globals["__name__"]
+        if "__path__" not in globals:
+            package = package.rpartition(".")[0]
+    return package
+
+
+def _normalize_path(path):
+    """Normalize a path by ensuring it is a string.
+
+    If the resulting string contains path separators, an exception is raised.
+    """
+    parent, file_name = os.path.split(path)
+    if parent:
+        raise ValueError(f"{path!r} must be only a file name")
+    else:
+        return file_name
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_mangling.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_mangling.py
new file mode 100644
index 0000000000000000000000000000000000000000..0cf3791d160444e5c6ae397b7fccf6cadca96d0a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_mangling.py
@@ -0,0 +1,64 @@
+# mypy: allow-untyped-defs
+"""Import mangling.
+See mangling.md for details.
+"""
+import re
+
+
+_mangle_index = 0
+
+
+class PackageMangler:
+    """
+    Used on import, to ensure that all modules imported have a shared mangle parent.
+    """
+
+    def __init__(self) -> None:
+        global _mangle_index
+        self._mangle_index = _mangle_index
+        # Increment the global index
+        _mangle_index += 1
+        # Angle brackets are used so that there is almost no chance of
+        # confusing this module for a real module. Plus, it is Python's
+        # preferred way of denoting special modules.
+        self._mangle_parent = f""
+
+    def mangle(self, name) -> str:
+        assert len(name) != 0
+        return self._mangle_parent + "." + name
+
+    def demangle(self, mangled: str) -> str:
+        """
+        Note: This only demangles names that were mangled by this specific
+        PackageMangler. It will pass through names created by a different
+        PackageMangler instance.
+        """
+        if mangled.startswith(self._mangle_parent + "."):
+            return mangled.partition(".")[2]
+
+        # wasn't a mangled name
+        return mangled
+
+    def parent_name(self):
+        return self._mangle_parent
+
+
+def is_mangled(name: str) -> bool:
+    return bool(re.match(r"", name))
+
+
+def demangle(name: str) -> str:
+    """
+    Note: Unlike PackageMangler.demangle, this version works on any
+    mangled name, irrespective of which PackageMangler created it.
+    """
+    if is_mangled(name):
+        first, sep, last = name.partition(".")
+        # If there is only a base mangle prefix, e.g. '',
+        # then return an empty string.
+        return last if len(sep) != 0 else ""
+    return name
+
+
+def get_mangle_prefix(name: str) -> str:
+    return name.partition(".")[0] if is_mangled(name) else name
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_mock.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_mock.py
new file mode 100644
index 0000000000000000000000000000000000000000..44876b1a1d3fb3ef4a485eaf16f26755d5bb00f2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_mock.py
@@ -0,0 +1,123 @@
+# mypy: allow-untyped-defs
+_magic_methods = [
+    "__subclasscheck__",
+    "__hex__",
+    "__rmul__",
+    "__float__",
+    "__idiv__",
+    "__setattr__",
+    "__div__",
+    "__invert__",
+    "__nonzero__",
+    "__rshift__",
+    "__eq__",
+    "__pos__",
+    "__round__",
+    "__rand__",
+    "__or__",
+    "__complex__",
+    "__divmod__",
+    "__len__",
+    "__reversed__",
+    "__copy__",
+    "__reduce__",
+    "__deepcopy__",
+    "__rdivmod__",
+    "__rrshift__",
+    "__ifloordiv__",
+    "__hash__",
+    "__iand__",
+    "__xor__",
+    "__isub__",
+    "__oct__",
+    "__ceil__",
+    "__imod__",
+    "__add__",
+    "__truediv__",
+    "__unicode__",
+    "__le__",
+    "__delitem__",
+    "__sizeof__",
+    "__sub__",
+    "__ne__",
+    "__pow__",
+    "__bytes__",
+    "__mul__",
+    "__itruediv__",
+    "__bool__",
+    "__iter__",
+    "__abs__",
+    "__gt__",
+    "__iadd__",
+    "__enter__",
+    "__floordiv__",
+    "__call__",
+    "__neg__",
+    "__and__",
+    "__ixor__",
+    "__getitem__",
+    "__exit__",
+    "__cmp__",
+    "__getstate__",
+    "__index__",
+    "__contains__",
+    "__floor__",
+    "__lt__",
+    "__getattr__",
+    "__mod__",
+    "__trunc__",
+    "__delattr__",
+    "__instancecheck__",
+    "__setitem__",
+    "__ipow__",
+    "__ilshift__",
+    "__long__",
+    "__irshift__",
+    "__imul__",
+    "__lshift__",
+    "__dir__",
+    "__ge__",
+    "__int__",
+    "__ior__",
+]
+
+
+class MockedObject:
+    _name: str
+
+    def __new__(cls, *args, **kwargs):
+        # _suppress_err is set by us in the mocked module impl, so that we can
+        # construct instances of MockedObject to hand out to people looking up
+        # module attributes.
+
+        # Any other attempt to construct a MockedObject instance (say, in the
+        # unpickling process) should give an error.
+        if not kwargs.get("_suppress_err"):
+            raise NotImplementedError(
+                f"Object '{cls._name}' was mocked out during packaging "
+                f"but it is being used in '__new__'. If this error is "
+                "happening during 'load_pickle', please ensure that your "
+                "pickled object doesn't contain any mocked objects."
+            )
+        # Otherwise, this is just a regular object creation
+        # (e.g. `x = MockedObject("foo")`), so pass it through normally.
+        return super().__new__(cls)
+
+    def __init__(self, name: str, _suppress_err: bool):
+        self.__dict__["_name"] = name
+
+    def __repr__(self):
+        return f"MockedObject({self._name})"
+
+
+def install_method(method_name):
+    def _not_implemented(self, *args, **kwargs):
+        raise NotImplementedError(
+            f"Object '{self._name}' was mocked out during packaging but it is being used in {method_name}"
+        )
+
+    setattr(MockedObject, method_name, _not_implemented)
+
+
+for method_name in _magic_methods:
+    install_method(method_name)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_package_pickler.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_package_pickler.py
new file mode 100644
index 0000000000000000000000000000000000000000..7845ffe39a2a2efbb830eebaa24ccb8ef98ad06c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_package_pickler.py
@@ -0,0 +1,118 @@
+# mypy: allow-untyped-defs
+from pickle import (  # type: ignore[attr-defined]
+    _compat_pickle,
+    _extension_registry,
+    _getattribute,
+    _Pickler,
+    EXT1,
+    EXT2,
+    EXT4,
+    GLOBAL,
+    Pickler,
+    PicklingError,
+    STACK_GLOBAL,
+)
+from struct import pack
+from types import FunctionType
+
+from .importer import Importer, ObjMismatchError, ObjNotFoundError, sys_importer
+
+
+class PackagePickler(_Pickler):
+    """Package-aware pickler.
+
+    This behaves the same as a normal pickler, except it uses an `Importer`
+    to find objects and modules to save.
+    """
+
+    def __init__(self, importer: Importer, *args, **kwargs):
+        self.importer = importer
+        super().__init__(*args, **kwargs)
+
+        # Make sure the dispatch table copied from _Pickler is up-to-date.
+        # Previous issues have been encountered where a library (e.g. dill)
+        # mutate _Pickler.dispatch, PackagePickler makes a copy when this lib
+        # is imported, then the offending library removes its dispatch entries,
+        # leaving PackagePickler with a stale dispatch table that may cause
+        # unwanted behavior.
+        self.dispatch = _Pickler.dispatch.copy()  # type: ignore[misc]
+        self.dispatch[FunctionType] = PackagePickler.save_global  # type: ignore[assignment]
+
+    def save_global(self, obj, name=None):
+        # unfortunately the pickler code is factored in a way that
+        # forces us to copy/paste this function. The only change is marked
+        # CHANGED below.
+        write = self.write  # type: ignore[attr-defined]
+        memo = self.memo  # type: ignore[attr-defined]
+
+        # CHANGED: import module from module environment instead of __import__
+        try:
+            module_name, name = self.importer.get_name(obj, name)
+        except (ObjNotFoundError, ObjMismatchError) as err:
+            raise PicklingError(f"Can't pickle {obj}: {str(err)}") from None
+
+        module = self.importer.import_module(module_name)
+        _, parent = _getattribute(module, name)
+        # END CHANGED
+
+        if self.proto >= 2:  # type: ignore[attr-defined]
+            code = _extension_registry.get((module_name, name))
+            if code:
+                assert code > 0
+                if code <= 0xFF:
+                    write(EXT1 + pack("= 3.
+        if self.proto >= 4:  # type: ignore[attr-defined]
+            self.save(module_name)  # type: ignore[attr-defined]
+            self.save(name)  # type: ignore[attr-defined]
+            write(STACK_GLOBAL)
+        elif parent is not module:
+            self.save_reduce(getattr, (parent, lastname))  # type: ignore[attr-defined]
+        elif self.proto >= 3:  # type: ignore[attr-defined]
+            write(
+                GLOBAL
+                + bytes(module_name, "utf-8")
+                + b"\n"
+                + bytes(name, "utf-8")
+                + b"\n"
+            )
+        else:
+            if self.fix_imports:  # type: ignore[attr-defined]
+                r_name_mapping = _compat_pickle.REVERSE_NAME_MAPPING
+                r_import_mapping = _compat_pickle.REVERSE_IMPORT_MAPPING
+                if (module_name, name) in r_name_mapping:
+                    module_name, name = r_name_mapping[(module_name, name)]
+                elif module_name in r_import_mapping:
+                    module_name = r_import_mapping[module_name]
+            try:
+                write(
+                    GLOBAL
+                    + bytes(module_name, "ascii")
+                    + b"\n"
+                    + bytes(name, "ascii")
+                    + b"\n"
+                )
+            except UnicodeEncodeError:
+                raise PicklingError(
+                    "can't pickle global identifier '%s.%s' using "
+                    "pickle protocol %i" % (module, name, self.proto)  # type: ignore[attr-defined]
+                ) from None
+
+        self.memoize(obj)  # type: ignore[attr-defined]
+
+
+def create_pickler(data_buf, importer, protocol=4):
+    if importer is sys_importer:
+        # if we are using the normal import library system, then
+        # we can use the C implementation of pickle which is faster
+        return Pickler(data_buf, protocol=protocol)
+    else:
+        return PackagePickler(importer, data_buf, protocol=protocol)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_package_unpickler.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_package_unpickler.py
new file mode 100644
index 0000000000000000000000000000000000000000..890e6b4e03ba076e30512712d57c4bf715c4c8bb
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_package_unpickler.py
@@ -0,0 +1,27 @@
+# mypy: allow-untyped-defs
+import _compat_pickle
+import pickle
+
+from .importer import Importer
+
+
+class PackageUnpickler(pickle._Unpickler):  # type: ignore[name-defined]
+    """Package-aware unpickler.
+
+    This behaves the same as a normal unpickler, except it uses `importer` to
+    find any global names that it encounters while unpickling.
+    """
+
+    def __init__(self, importer: Importer, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._importer = importer
+
+    def find_class(self, module, name):
+        # Subclasses may override this.
+        if self.proto < 3 and self.fix_imports:  # type: ignore[attr-defined]
+            if (module, name) in _compat_pickle.NAME_MAPPING:
+                module, name = _compat_pickle.NAME_MAPPING[(module, name)]
+            elif module in _compat_pickle.IMPORT_MAPPING:
+                module = _compat_pickle.IMPORT_MAPPING[module]
+        mod = self._importer.import_module(module)
+        return getattr(mod, name)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_stdlib.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_stdlib.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d5145b40aa701043badad77889ff50da06ad497
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/_stdlib.py
@@ -0,0 +1,465 @@
+# mypy: allow-untyped-defs
+"""List of Python standard library modules.
+
+Sadly, there is no reliable way to tell whether a module is part of the
+standard library except by comparing to a canonical list.
+
+This is taken from https://github.com/PyCQA/isort/tree/develop/isort/stdlibs,
+which itself is sourced from the Python documentation.
+"""
+
+import sys
+
+
+def is_stdlib_module(module: str) -> bool:
+    base_module = module.partition(".")[0]
+    return base_module in _get_stdlib_modules()
+
+
+def _get_stdlib_modules():
+    if sys.version_info.major == 3:
+        if sys.version_info.minor == 8:
+            return stdlib3_8
+        if sys.version_info.minor == 9:
+            return stdlib3_9
+        if sys.version_info.minor >= 10:
+            return sys.stdlib_module_names  # type: ignore[attr-defined]
+    elif sys.version_info.major > 3:
+        return sys.stdlib_module_names  # type: ignore[attr-defined]
+
+    raise RuntimeError(f"Unsupported Python version: {sys.version_info}")
+
+
+stdlib3_8 = {
+    "_dummy_thread",
+    "_thread",
+    "abc",
+    "aifc",
+    "argparse",
+    "array",
+    "ast",
+    "asynchat",
+    "asyncio",
+    "asyncore",
+    "atexit",
+    "audioop",
+    "base64",
+    "bdb",
+    "binascii",
+    "binhex",
+    "bisect",
+    "builtins",
+    "bz2",
+    "cProfile",
+    "calendar",
+    "cgi",
+    "cgitb",
+    "chunk",
+    "cmath",
+    "cmd",
+    "code",
+    "codecs",
+    "codeop",
+    "collections",
+    "colorsys",
+    "compileall",
+    "concurrent",
+    "configparser",
+    "contextlib",
+    "contextvars",
+    "copy",
+    "copyreg",
+    "crypt",
+    "csv",
+    "ctypes",
+    "curses",
+    "dataclasses",
+    "datetime",
+    "dbm",
+    "decimal",
+    "difflib",
+    "dis",
+    "distutils",
+    "doctest",
+    "dummy_threading",
+    "email",
+    "encodings",
+    "ensurepip",
+    "enum",
+    "errno",
+    "faulthandler",
+    "fcntl",
+    "filecmp",
+    "fileinput",
+    "fnmatch",
+    "formatter",
+    "fractions",
+    "ftplib",
+    "functools",
+    "gc",
+    "getopt",
+    "getpass",
+    "gettext",
+    "glob",
+    "grp",
+    "gzip",
+    "hashlib",
+    "heapq",
+    "hmac",
+    "html",
+    "http",
+    "imaplib",
+    "imghdr",
+    "imp",
+    "importlib",
+    "inspect",
+    "io",
+    "ipaddress",
+    "itertools",
+    "json",
+    "keyword",
+    "lib2to3",
+    "linecache",
+    "locale",
+    "logging",
+    "lzma",
+    "mailbox",
+    "mailcap",
+    "marshal",
+    "math",
+    "mimetypes",
+    "mmap",
+    "modulefinder",
+    "msilib",
+    "msvcrt",
+    "multiprocessing",
+    "netrc",
+    "nis",
+    "nntplib",
+    "ntpath",
+    "numbers",
+    "operator",
+    "optparse",
+    "os",
+    "ossaudiodev",
+    "parser",
+    "pathlib",
+    "pdb",
+    "pickle",
+    "pickletools",
+    "pipes",
+    "pkgutil",
+    "platform",
+    "plistlib",
+    "poplib",
+    "posix",
+    "posixpath",
+    "pprint",
+    "profile",
+    "pstats",
+    "pty",
+    "pwd",
+    "py_compile",
+    "pyclbr",
+    "pydoc",
+    "queue",
+    "quopri",
+    "random",
+    "re",
+    "readline",
+    "reprlib",
+    "resource",
+    "rlcompleter",
+    "runpy",
+    "sched",
+    "secrets",
+    "select",
+    "selectors",
+    "shelve",
+    "shlex",
+    "shutil",
+    "signal",
+    "site",
+    "smtpd",
+    "smtplib",
+    "sndhdr",
+    "socket",
+    "socketserver",
+    "spwd",
+    "sqlite3",
+    "sre",
+    "sre_compile",
+    "sre_constants",
+    "sre_parse",
+    "ssl",
+    "stat",
+    "statistics",
+    "string",
+    "stringprep",
+    "struct",
+    "subprocess",
+    "sunau",
+    "symbol",
+    "symtable",
+    "sys",
+    "sysconfig",
+    "syslog",
+    "tabnanny",
+    "tarfile",
+    "telnetlib",
+    "tempfile",
+    "termios",
+    "test",
+    "textwrap",
+    "threading",
+    "time",
+    "timeit",
+    "tkinter",
+    "token",
+    "tokenize",
+    "trace",
+    "traceback",
+    "tracemalloc",
+    "tty",
+    "turtle",
+    "turtledemo",
+    "types",
+    "typing",
+    "unicodedata",
+    "unittest",
+    "urllib",
+    "uu",
+    "uuid",
+    "venv",
+    "warnings",
+    "wave",
+    "weakref",
+    "webbrowser",
+    "winreg",
+    "winsound",
+    "wsgiref",
+    "xdrlib",
+    "xml",
+    "xmlrpc",
+    "zipapp",
+    "zipfile",
+    "zipimport",
+    "zlib",
+}
+
+stdlib3_9 = {
+    "_thread",
+    "abc",
+    "aifc",
+    "argparse",
+    "array",
+    "ast",
+    "asynchat",
+    "asyncio",
+    "asyncore",
+    "atexit",
+    "audioop",
+    "base64",
+    "bdb",
+    "binascii",
+    "binhex",
+    "bisect",
+    "builtins",
+    "bz2",
+    "cProfile",
+    "calendar",
+    "cgi",
+    "cgitb",
+    "chunk",
+    "cmath",
+    "cmd",
+    "code",
+    "codecs",
+    "codeop",
+    "collections",
+    "colorsys",
+    "compileall",
+    "concurrent",
+    "configparser",
+    "contextlib",
+    "contextvars",
+    "copy",
+    "copyreg",
+    "crypt",
+    "csv",
+    "ctypes",
+    "curses",
+    "dataclasses",
+    "datetime",
+    "dbm",
+    "decimal",
+    "difflib",
+    "dis",
+    "distutils",
+    "doctest",
+    "email",
+    "encodings",
+    "ensurepip",
+    "enum",
+    "errno",
+    "faulthandler",
+    "fcntl",
+    "filecmp",
+    "fileinput",
+    "fnmatch",
+    "formatter",
+    "fractions",
+    "ftplib",
+    "functools",
+    "gc",
+    "getopt",
+    "getpass",
+    "gettext",
+    "glob",
+    "graphlib",
+    "grp",
+    "gzip",
+    "hashlib",
+    "heapq",
+    "hmac",
+    "html",
+    "http",
+    "imaplib",
+    "imghdr",
+    "imp",
+    "importlib",
+    "inspect",
+    "io",
+    "ipaddress",
+    "itertools",
+    "json",
+    "keyword",
+    "lib2to3",
+    "linecache",
+    "locale",
+    "logging",
+    "lzma",
+    "mailbox",
+    "mailcap",
+    "marshal",
+    "math",
+    "mimetypes",
+    "mmap",
+    "modulefinder",
+    "msilib",
+    "msvcrt",
+    "multiprocessing",
+    "netrc",
+    "nis",
+    "nntplib",
+    "ntpath",
+    "numbers",
+    "operator",
+    "optparse",
+    "os",
+    "ossaudiodev",
+    "parser",
+    "pathlib",
+    "pdb",
+    "pickle",
+    "pickletools",
+    "pipes",
+    "pkgutil",
+    "platform",
+    "plistlib",
+    "poplib",
+    "posix",
+    "posixpath",
+    "pprint",
+    "profile",
+    "pstats",
+    "pty",
+    "pwd",
+    "py_compile",
+    "pyclbr",
+    "pydoc",
+    "queue",
+    "quopri",
+    "random",
+    "re",
+    "readline",
+    "reprlib",
+    "resource",
+    "rlcompleter",
+    "runpy",
+    "sched",
+    "secrets",
+    "select",
+    "selectors",
+    "shelve",
+    "shlex",
+    "shutil",
+    "signal",
+    "site",
+    "smtpd",
+    "smtplib",
+    "sndhdr",
+    "socket",
+    "socketserver",
+    "spwd",
+    "sqlite3",
+    "sre",
+    "sre_compile",
+    "sre_constants",
+    "sre_parse",
+    "ssl",
+    "stat",
+    "statistics",
+    "string",
+    "stringprep",
+    "struct",
+    "subprocess",
+    "sunau",
+    "symbol",
+    "symtable",
+    "sys",
+    "sysconfig",
+    "syslog",
+    "tabnanny",
+    "tarfile",
+    "telnetlib",
+    "tempfile",
+    "termios",
+    "test",
+    "textwrap",
+    "threading",
+    "time",
+    "timeit",
+    "tkinter",
+    "token",
+    "tokenize",
+    "trace",
+    "traceback",
+    "tracemalloc",
+    "tty",
+    "turtle",
+    "turtledemo",
+    "types",
+    "typing",
+    "unicodedata",
+    "unittest",
+    "urllib",
+    "uu",
+    "uuid",
+    "venv",
+    "warnings",
+    "wave",
+    "weakref",
+    "webbrowser",
+    "winreg",
+    "winsound",
+    "wsgiref",
+    "xdrlib",
+    "xml",
+    "xmlrpc",
+    "zipapp",
+    "zipfile",
+    "zipimport",
+    "zlib",
+    "zoneinfo",
+}
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/file_structure_representation.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/file_structure_representation.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1137234ab739b480ae7d5686c7dc313147ba314
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/file_structure_representation.py
@@ -0,0 +1,138 @@
+# mypy: allow-untyped-defs
+from typing import Dict, List
+
+from .glob_group import GlobGroup, GlobPattern
+
+
+__all__ = ["Directory"]
+
+
+class Directory:
+    """A file structure representation. Organized as Directory nodes that have lists of
+    their Directory children. Directories for a package are created by calling
+    :meth:`PackageImporter.file_structure`."""
+
+    def __init__(self, name: str, is_dir: bool):
+        self.name = name
+        self.is_dir = is_dir
+        self.children: Dict[str, Directory] = {}
+
+    def _get_dir(self, dirs: List[str]) -> "Directory":
+        """Builds path of Directories if not yet built and returns last directory
+        in list.
+
+        Args:
+            dirs (List[str]): List of directory names that are treated like a path.
+
+        Returns:
+            :class:`Directory`: The last Directory specified in the dirs list.
+        """
+        if len(dirs) == 0:
+            return self
+        dir_name = dirs[0]
+        if dir_name not in self.children:
+            self.children[dir_name] = Directory(dir_name, True)
+        return self.children[dir_name]._get_dir(dirs[1:])
+
+    def _add_file(self, file_path: str):
+        """Adds a file to a Directory.
+
+        Args:
+            file_path (str): Path of file to add. Last element is added as a file while
+                other paths items are added as directories.
+        """
+        *dirs, file = file_path.split("/")
+        dir = self._get_dir(dirs)
+        dir.children[file] = Directory(file, False)
+
+    def has_file(self, filename: str) -> bool:
+        """Checks if a file is present in a :class:`Directory`.
+
+        Args:
+            filename (str): Path of file to search for.
+        Returns:
+            bool: If a :class:`Directory` contains the specified file.
+        """
+        lineage = filename.split("/", maxsplit=1)
+        child = lineage[0]
+        grandchildren = lineage[1] if len(lineage) > 1 else None
+        if child in self.children.keys():
+            if grandchildren is None:
+                return True
+            else:
+                return self.children[child].has_file(grandchildren)
+        return False
+
+    def __str__(self):
+        str_list: List[str] = []
+        self._stringify_tree(str_list)
+        return "".join(str_list)
+
+    def _stringify_tree(
+        self,
+        str_list: List[str],
+        preamble: str = "",
+        dir_ptr: str = "\u2500\u2500\u2500 ",
+    ):
+        """Recursive method to generate print-friendly version of a Directory."""
+        space = "    "
+        branch = "\u2502   "
+        tee = "\u251c\u2500\u2500 "
+        last = "\u2514\u2500\u2500 "
+
+        # add this directory's representation
+        str_list.append(f"{preamble}{dir_ptr}{self.name}\n")
+
+        # add directory's children representations
+        if dir_ptr == tee:
+            preamble = preamble + branch
+        else:
+            preamble = preamble + space
+
+        file_keys: List[str] = []
+        dir_keys: List[str] = []
+        for key, val in self.children.items():
+            if val.is_dir:
+                dir_keys.append(key)
+            else:
+                file_keys.append(key)
+
+        for index, key in enumerate(sorted(dir_keys)):
+            if (index == len(dir_keys) - 1) and len(file_keys) == 0:
+                self.children[key]._stringify_tree(str_list, preamble, last)
+            else:
+                self.children[key]._stringify_tree(str_list, preamble, tee)
+        for index, file in enumerate(sorted(file_keys)):
+            pointer = last if (index == len(file_keys) - 1) else tee
+            str_list.append(f"{preamble}{pointer}{file}\n")
+
+
+def _create_directory_from_file_list(
+    filename: str,
+    file_list: List[str],
+    include: "GlobPattern" = "**",
+    exclude: "GlobPattern" = (),
+) -> Directory:
+    """Return a :class:`Directory` file structure representation created from a list of files.
+
+    Args:
+        filename (str): The name given to the top-level directory that will be the
+            relative root for all file paths found in the file_list.
+
+        file_list (List[str]): List of files to add to the top-level directory.
+
+        include (Union[List[str], str]): An optional pattern that limits what is included from the file_list to
+            files whose name matches the pattern.
+
+        exclude (Union[List[str], str]): An optional pattern that excludes files whose name match the pattern.
+
+    Returns:
+            :class:`Directory`: a :class:`Directory` file structure representation created from a list of files.
+    """
+    glob_pattern = GlobGroup(include, exclude=exclude, separator="/")
+
+    top_dir = Directory(filename, True)
+    for file in file_list:
+        if glob_pattern.matches(file):
+            top_dir._add_file(file)
+    return top_dir
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/find_file_dependencies.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/find_file_dependencies.py
new file mode 100644
index 0000000000000000000000000000000000000000..80cfccbec50a6c5f4cb3839d1afc5e4ea404efd2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/find_file_dependencies.py
@@ -0,0 +1,96 @@
+# mypy: allow-untyped-defs
+import ast
+from typing import List, Optional, Tuple
+
+from ._importlib import _resolve_name
+
+
+class _ExtractModuleReferences(ast.NodeVisitor):
+    """
+    Extract the list of global variables a block of code will read and write
+    """
+
+    @classmethod
+    def run(cls, src: str, package: str) -> List[Tuple[str, Optional[str]]]:
+        visitor = cls(package)
+        tree = ast.parse(src)
+        visitor.visit(tree)
+        return list(visitor.references.keys())
+
+    def __init__(self, package):
+        super().__init__()
+        self.package = package
+        self.references = {}
+
+    def _absmodule(self, module_name: str, level: int) -> str:
+        if level > 0:
+            return _resolve_name(module_name, self.package, level)
+        return module_name
+
+    def visit_Import(self, node):
+        for alias in node.names:
+            self.references[(alias.name, None)] = True
+
+    def visit_ImportFrom(self, node):
+        name = self._absmodule(node.module, 0 if node.level is None else node.level)
+        for alias in node.names:
+            # from my_package import foo
+            # foo may be a module, so we have to add it to the list of
+            # potential references, if import of it fails, we will ignore it
+            if alias.name != "*":
+                self.references[(name, alias.name)] = True
+            else:
+                self.references[(name, None)] = True
+
+    def _grab_node_int(self, node):
+        return node.value
+
+    def _grab_node_str(self, node):
+        return node.value
+
+    def visit_Call(self, node):
+        # __import__ calls aren't routed to the visit_Import/From nodes
+        if hasattr(node.func, "id") and node.func.id == "__import__":
+            try:
+                name = self._grab_node_str(node.args[0])
+                fromlist = []
+                level = 0
+                if len(node.args) > 3:
+                    for v in node.args[3].elts:
+                        fromlist.append(self._grab_node_str(v))
+                elif hasattr(node, "keywords"):
+                    for keyword in node.keywords:
+                        if keyword.arg == "fromlist":
+                            for v in keyword.value.elts:
+                                fromlist.append(self._grab_node_str(v))
+                if len(node.args) > 4:
+                    level = self._grab_node_int(node.args[4])
+                elif hasattr(node, "keywords"):
+                    for keyword in node.keywords:
+                        if keyword.arg == "level":
+                            level = self._grab_node_int(keyword.value)
+                if fromlist == []:
+                    # the top-level package (the name up till the first dot) is returned
+                    # when the fromlist argument is empty in normal import system,
+                    # we need to include top level package to match this behavior and last
+                    # level package to capture the intended dependency of user
+                    self.references[(name, None)] = True
+                    top_name = name.rsplit(".", maxsplit=1)[0]
+                    if top_name != name:
+                        top_name = self._absmodule(top_name, level)
+                        self.references[(top_name, None)] = True
+                else:
+                    name = self._absmodule(name, level)
+                    for alias in fromlist:
+                        # fromlist args may be submodules, so we have to add the fromlist args
+                        # to the list of potential references. If import of an arg fails we
+                        # will ignore it, similar to visit_ImportFrom
+                        if alias != "*":
+                            self.references[(name, alias)] = True
+                        else:
+                            self.references[(name, None)] = True
+            except Exception as e:
+                return
+
+
+find_files_source_depends_on = _ExtractModuleReferences.run
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/glob_group.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/glob_group.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c1d31930fd18fa5bbad453d2b55eac07fa20869
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/glob_group.py
@@ -0,0 +1,84 @@
+# mypy: allow-untyped-defs
+import re
+from typing import Iterable, Union
+
+
+GlobPattern = Union[str, Iterable[str]]
+
+
+class GlobGroup:
+    """A set of patterns that candidate strings will be matched against.
+
+    A candidate is composed of a list of segments separated by ``separator``, e.g. "foo.bar.baz".
+
+    A pattern contains one or more segments. Segments can be:
+        - A literal string (e.g. "foo"), which matches exactly.
+        - A string containing a wildcard (e.g. "torch*", or "foo*baz*"). The wildcard matches
+          any string, including the empty string.
+        - A double wildcard ("**"). This matches against zero or more complete segments.
+
+    Examples:
+        ``torch.**``: matches ``torch`` and all its submodules, e.g. ``torch.nn`` and ``torch.nn.functional``.
+        ``torch.*``: matches ``torch.nn`` or ``torch.functional``, but not ``torch.nn.functional``.
+        ``torch*.**``: matches ``torch``, ``torchvision``, and all their submodules.
+
+    A candidates will match the ``GlobGroup`` if it matches any of the ``include`` patterns and
+    none of the ``exclude`` patterns.
+
+    Args:
+        include (Union[str, Iterable[str]]): A string or list of strings,
+            each representing a pattern to be matched against. A candidate
+            will match if it matches *any* include pattern
+        exclude (Union[str, Iterable[str]]): A string or list of strings,
+            each representing a pattern to be matched against. A candidate
+            will be excluded from matching if it matches *any* exclude pattern.
+        separator (str): A string that delimits segments in candidates and
+            patterns. By default this is "." which corresponds to how modules are
+            named in Python. Another common value for this is "/", which is
+            the Unix path separator.
+    """
+
+    def __init__(
+        self, include: GlobPattern, *, exclude: GlobPattern = (), separator: str = "."
+    ):
+        self._dbg = f"GlobGroup(include={include}, exclude={exclude})"
+        self.include = GlobGroup._glob_list(include, separator)
+        self.exclude = GlobGroup._glob_list(exclude, separator)
+        self.separator = separator
+
+    def __str__(self):
+        return self._dbg
+
+    def __repr__(self):
+        return self._dbg
+
+    def matches(self, candidate: str) -> bool:
+        candidate = self.separator + candidate
+        return any(p.fullmatch(candidate) for p in self.include) and all(
+            not p.fullmatch(candidate) for p in self.exclude
+        )
+
+    @staticmethod
+    def _glob_list(elems: GlobPattern, separator: str = "."):
+        if isinstance(elems, str):
+            return [GlobGroup._glob_to_re(elems, separator)]
+        else:
+            return [GlobGroup._glob_to_re(e, separator) for e in elems]
+
+    @staticmethod
+    def _glob_to_re(pattern: str, separator: str = "."):
+        # to avoid corner cases for the first component, we prefix the candidate string
+        # with '.' so `import torch` will regex against `.torch`, assuming '.' is the separator
+        def component_to_re(component):
+            if "**" in component:
+                if component == "**":
+                    return "(" + re.escape(separator) + "[^" + separator + "]+)*"
+                else:
+                    raise ValueError("** can only appear as an entire path segment")
+            else:
+                return re.escape(separator) + ("[^" + separator + "]*").join(
+                    re.escape(x) for x in component.split("*")
+                )
+
+        result = "".join(component_to_re(c) for c in pattern.split(separator))
+        return re.compile(result)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/importer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/importer.py
new file mode 100644
index 0000000000000000000000000000000000000000..2fb2891e076c7baf9dda3e1f92c5527f0f54f71d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/importer.py
@@ -0,0 +1,234 @@
+# mypy: allow-untyped-defs
+import importlib
+from abc import ABC, abstractmethod
+from pickle import (  # type: ignore[attr-defined]
+    _getattribute,
+    _Pickler,
+    whichmodule as _pickle_whichmodule,
+)
+from types import ModuleType
+from typing import Any, Dict, List, Optional, Tuple
+
+from ._mangling import demangle, get_mangle_prefix, is_mangled
+
+
+__all__ = ["ObjNotFoundError", "ObjMismatchError", "Importer", "OrderedImporter"]
+
+
+class ObjNotFoundError(Exception):
+    """Raised when an importer cannot find an object by searching for its name."""
+
+
+class ObjMismatchError(Exception):
+    """Raised when an importer found a different object with the same name as the user-provided one."""
+
+
+class Importer(ABC):
+    """Represents an environment to import modules from.
+
+    By default, you can figure out what module an object belongs by checking
+    __module__ and importing the result using __import__ or importlib.import_module.
+
+    torch.package introduces module importers other than the default one.
+    Each PackageImporter introduces a new namespace. Potentially a single
+    name (e.g. 'foo.bar') is present in multiple namespaces.
+
+    It supports two main operations:
+        import_module: module_name -> module object
+        get_name: object -> (parent module name, name of obj within module)
+
+    The guarantee is that following round-trip will succeed or throw an ObjNotFoundError/ObjMisMatchError.
+        module_name, obj_name = env.get_name(obj)
+        module = env.import_module(module_name)
+        obj2 = getattr(module, obj_name)
+        assert obj1 is obj2
+    """
+
+    modules: Dict[str, ModuleType]
+
+    @abstractmethod
+    def import_module(self, module_name: str) -> ModuleType:
+        """Import `module_name` from this environment.
+
+        The contract is the same as for importlib.import_module.
+        """
+
+    def get_name(self, obj: Any, name: Optional[str] = None) -> Tuple[str, str]:
+        """Given an object, return a name that can be used to retrieve the
+        object from this environment.
+
+        Args:
+            obj: An object to get the module-environment-relative name for.
+            name: If set, use this name instead of looking up __name__ or __qualname__ on `obj`.
+                This is only here to match how Pickler handles __reduce__ functions that return a string,
+                don't use otherwise.
+        Returns:
+            A tuple (parent_module_name, attr_name) that can be used to retrieve `obj` from this environment.
+            Use it like:
+                mod = importer.import_module(parent_module_name)
+                obj = getattr(mod, attr_name)
+
+        Raises:
+            ObjNotFoundError: we couldn't retrieve `obj by name.
+            ObjMisMatchError: we found a different object with the same name as `obj`.
+        """
+        if name is None and obj and _Pickler.dispatch.get(type(obj)) is None:
+            # Honor the string return variant of __reduce__, which will give us
+            # a global name to search for in this environment.
+            # TODO: I guess we should do copyreg too?
+            reduce = getattr(obj, "__reduce__", None)
+            if reduce is not None:
+                try:
+                    rv = reduce()
+                    if isinstance(rv, str):
+                        name = rv
+                except Exception:
+                    pass
+        if name is None:
+            name = getattr(obj, "__qualname__", None)
+        if name is None:
+            name = obj.__name__
+
+        orig_module_name = self.whichmodule(obj, name)
+        # Demangle the module name before importing. If this obj came out of a
+        # PackageImporter, `__module__` will be mangled. See mangling.md for
+        # details.
+        module_name = demangle(orig_module_name)
+
+        # Check that this name will indeed return the correct object
+        try:
+            module = self.import_module(module_name)
+            obj2, _ = _getattribute(module, name)
+        except (ImportError, KeyError, AttributeError):
+            raise ObjNotFoundError(
+                f"{obj} was not found as {module_name}.{name}"
+            ) from None
+
+        if obj is obj2:
+            return module_name, name
+
+        def get_obj_info(obj):
+            assert name is not None
+            module_name = self.whichmodule(obj, name)
+            is_mangled_ = is_mangled(module_name)
+            location = (
+                get_mangle_prefix(module_name)
+                if is_mangled_
+                else "the current Python environment"
+            )
+            importer_name = (
+                f"the importer for {get_mangle_prefix(module_name)}"
+                if is_mangled_
+                else "'sys_importer'"
+            )
+            return module_name, location, importer_name
+
+        obj_module_name, obj_location, obj_importer_name = get_obj_info(obj)
+        obj2_module_name, obj2_location, obj2_importer_name = get_obj_info(obj2)
+        msg = (
+            f"\n\nThe object provided is from '{obj_module_name}', "
+            f"which is coming from {obj_location}."
+            f"\nHowever, when we import '{obj2_module_name}', it's coming from {obj2_location}."
+            "\nTo fix this, make sure this 'PackageExporter's importer lists "
+            f"{obj_importer_name} before {obj2_importer_name}."
+        )
+        raise ObjMismatchError(msg)
+
+    def whichmodule(self, obj: Any, name: str) -> str:
+        """Find the module name an object belongs to.
+
+        This should be considered internal for end-users, but developers of
+        an importer can override it to customize the behavior.
+
+        Taken from pickle.py, but modified to exclude the search into sys.modules
+        """
+        module_name = getattr(obj, "__module__", None)
+        if module_name is not None:
+            return module_name
+
+        # Protect the iteration by using a list copy of self.modules against dynamic
+        # modules that trigger imports of other modules upon calls to getattr.
+        for module_name, module in self.modules.copy().items():
+            if (
+                module_name == "__main__"
+                or module_name == "__mp_main__"  # bpo-42406
+                or module is None
+            ):
+                continue
+            try:
+                if _getattribute(module, name)[0] is obj:
+                    return module_name
+            except AttributeError:
+                pass
+
+        return "__main__"
+
+
+class _SysImporter(Importer):
+    """An importer that implements the default behavior of Python."""
+
+    def import_module(self, module_name: str):
+        return importlib.import_module(module_name)
+
+    def whichmodule(self, obj: Any, name: str) -> str:
+        return _pickle_whichmodule(obj, name)
+
+
+sys_importer = _SysImporter()
+
+
+class OrderedImporter(Importer):
+    """A compound importer that takes a list of importers and tries them one at a time.
+
+    The first importer in the list that returns a result "wins".
+    """
+
+    def __init__(self, *args):
+        self._importers: List[Importer] = list(args)
+
+    def _is_torchpackage_dummy(self, module):
+        """Returns true iff this module is an empty PackageNode in a torch.package.
+
+        If you intern `a.b` but never use `a` in your code, then `a` will be an
+        empty module with no source. This can break cases where we are trying to
+        re-package an object after adding a real dependency on `a`, since
+        OrderedImportere will resolve `a` to the dummy package and stop there.
+
+        See: https://github.com/pytorch/pytorch/pull/71520#issuecomment-1029603769
+        """
+        if not getattr(module, "__torch_package__", False):
+            return False
+        if not hasattr(module, "__path__"):
+            return False
+        if not hasattr(module, "__file__"):
+            return True
+        return module.__file__ is None
+
+    def import_module(self, module_name: str) -> ModuleType:
+        last_err = None
+        for importer in self._importers:
+            if not isinstance(importer, Importer):
+                raise TypeError(
+                    f"{importer} is not a Importer. "
+                    "All importers in OrderedImporter must inherit from Importer."
+                )
+            try:
+                module = importer.import_module(module_name)
+                if self._is_torchpackage_dummy(module):
+                    continue
+                return module
+            except ModuleNotFoundError as err:
+                last_err = err
+
+        if last_err is not None:
+            raise last_err
+        else:
+            raise ModuleNotFoundError(module_name)
+
+    def whichmodule(self, obj: Any, name: str) -> str:
+        for importer in self._importers:
+            module_name = importer.whichmodule(obj, name)
+            if module_name != "__main__":
+                return module_name
+
+        return "__main__"
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/package_exporter.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/package_exporter.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b377b95454da1492ef09e469c7813e55376c2e0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/package_exporter.py
@@ -0,0 +1,1199 @@
+# mypy: allow-untyped-defs
+import collections
+import importlib.machinery
+import io
+import linecache
+import pickletools
+import platform
+import types
+from collections import defaultdict, OrderedDict
+from dataclasses import dataclass
+from enum import Enum
+from importlib.machinery import SourceFileLoader
+from pathlib import Path
+from typing import (
+    Any,
+    BinaryIO,
+    Callable,
+    cast,
+    DefaultDict,
+    Dict,
+    List,
+    Optional,
+    Sequence,
+    Set,
+    Union,
+)
+
+import torch
+from torch.serialization import location_tag, normalize_storage_type
+from torch.types import Storage
+from torch.utils.hooks import RemovableHandle
+
+from ._digraph import DiGraph
+from ._importlib import _normalize_path
+from ._mangling import demangle, is_mangled
+from ._package_pickler import create_pickler
+from ._stdlib import is_stdlib_module
+from .find_file_dependencies import find_files_source_depends_on
+from .glob_group import GlobGroup, GlobPattern
+from .importer import Importer, OrderedImporter, sys_importer
+
+
+__all__ = [
+    "PackagingErrorReason",
+    "EmptyMatchError",
+    "PackagingError",
+    "PackageExporter",
+]
+
+_gate_torchscript_serialization = True
+
+ActionHook = Callable[["PackageExporter", str], None]
+
+
+class _ModuleProviderAction(Enum):
+    """Represents one of the actions that :class:`PackageExporter` can take on a module.
+
+    See :meth:`PackageExporter.extern` and friends for a description of what the actions do.
+    """
+
+    INTERN = 1
+    EXTERN = 2
+    MOCK = 3
+    DENY = 4
+    # Special case: when a module is mocked, PackageExporter writes out a
+    # `_mock` module that implements our mocking stubs. If we re-package code,
+    # we may encounter a `_mock` module from the original package. If we do,
+    # just ignore it and write a `_mock` module once.
+    REPACKAGED_MOCK_MODULE = 5
+    # Special case: PackageImporter adds a fake module
+    # (`torch_package_importer`) that allows packaged code to access it. Don't
+    # re-export this.
+    SKIP = 6
+
+
+class PackagingErrorReason(Enum):
+    """Listing of different reasons a dependency may fail to package.
+
+    This enum is used to provide good error messages when
+    :class:`PackagingError` is raised.
+    """
+
+    def __repr__(self):
+        return f"<{self.__class__.__name__}.{self.name}>"
+
+    IS_EXTENSION_MODULE = (
+        "Module is a C extension module. torch.package supports Python modules only."
+    )
+    NO_DUNDER_FILE = "Module had no __file__ defined."
+    SOURCE_FILE_NOT_FOUND = (
+        "Module had a __file__, but we could not find it in your filesystem."
+    )
+    DEPENDENCY_RESOLUTION_FAILED = "Dependency resolution failed."
+    NO_ACTION = (
+        "Module did not match against any action pattern. Extern, mock, or intern it."
+    )
+    DENIED = "Module was denied by a pattern."
+    MOCKED_BUT_STILL_USED = (
+        "Module was mocked out, but is still being used in the package. "
+        "Please intern or extern the mocked modules if objects are supposed to be in "
+        "the package."
+    )
+
+
+@dataclass
+class _PatternInfo:
+    """Holds :class:`PackageExporter`-specific info about how to execute matches against"""
+
+    # What action to take on a module that matches this pattern.
+    action: _ModuleProviderAction
+    # The value of `allow_empty` the user gave when specifying the pattern.
+    allow_empty: bool
+    # Whether this pattern has been matched during packaging.
+    was_matched: bool
+
+    def __init__(self, action, allow_empty):
+        self.action = action
+        self.allow_empty = allow_empty
+        self.was_matched = False
+
+
+class EmptyMatchError(Exception):
+    """This is an exception that is thrown when a mock or extern is marked as
+    ``allow_empty=False``, and is not matched with any module during packaging.
+    """
+
+
+class PackagingError(Exception):
+    """This exception is raised when there is an issue with exporting a package.
+    ``PackageExporter`` will attempt to gather up all the errors and present
+    them to you at once.
+    """
+
+    def __init__(self, dependency_graph: DiGraph, debug=False):
+        # Group errors by reason.
+        broken: Dict[PackagingErrorReason, List[str]] = defaultdict(list)
+        for module_name, attrs in dependency_graph.nodes.items():
+            error = attrs.get("error")
+            if error is None:
+                continue
+            if error == PackagingErrorReason.NO_ACTION:
+                assert "action" not in attrs
+            broken[error].append(module_name)
+
+        message = io.StringIO()
+        message.write("\n")
+
+        for reason, module_names in broken.items():
+            message.write(f"* {reason.value}\n")
+            for module_name in module_names:
+                message.write(f"    {module_name}\n")
+
+                # Print additional context if it's provided.
+                error_context = dependency_graph.nodes[module_name].get("error_context")
+                if error_context is not None:
+                    message.write(f"      Context: {error_context}\n")
+                if module_name in _DISALLOWED_MODULES:
+                    message.write(
+                        "      Note: While we usually use modules in the python standard library "
+                        f"from the local environment, `{module_name}` has a lot of system "
+                        "level access and therefore can pose a security risk. We heavily "
+                        f"recommend removing `{module_name}` from your packaged code. However, if that "
+                        "is not possible, add it to the extern list by calling "
+                        f'PackageExporter.extern("`{module_name}`")\n'
+                    )
+                if debug:
+                    module_path = dependency_graph.first_path(module_name)
+                    message.write(
+                        f"      A path to {module_name}: {' -> '.join(module_path)}\n"
+                    )
+        if not debug:
+            message.write("\n")
+            message.write(
+                "Set debug=True when invoking PackageExporter for a visualization of where "
+                "broken modules are coming from!\n"
+            )
+        # Save the dependency graph so that tooling can get at it.
+        self.dependency_graph = dependency_graph
+        super().__init__(message.getvalue())
+
+
+class PackageExporter:
+    """Exporters allow you to write packages of code, pickled Python data, and
+    arbitrary binary and text resources into a self-contained package.
+
+    Imports can load this code in a hermetic way, such that code is loaded
+    from the package rather than the normal Python import system. This allows
+    for the packaging of PyTorch model code and data so that it can be run
+    on a server or used in the future for transfer learning.
+
+    The code contained in packages is copied file-by-file from the original
+    source when it is created, and the file format is a specially organized
+    zip file. Future users of the package can unzip the package, and edit the code
+    in order to perform custom modifications to it.
+
+    The importer for packages ensures that code in the module can only be loaded from
+    within the package, except for modules explicitly listed as external using :meth:`extern`.
+    The file ``extern_modules`` in the zip archive lists all the modules that a package externally depends on.
+    This prevents "implicit" dependencies where the package runs locally because it is importing
+    a locally-installed package, but then fails when the package is copied to another machine.
+
+    When source code is added to the package, the exporter can optionally scan it
+    for further code dependencies (``dependencies=True``). It looks for import statements,
+    resolves relative references to qualified module names, and performs an action specified by the user
+    (See: :meth:`extern`, :meth:`mock`, and :meth:`intern`).
+    """
+
+    """A importer that will be searched in order to find the modules referenced by other modules or by
+    pickled objects. The default module environment just uses sys_importer, which searches the Python environment.
+    """
+    importer: Importer
+
+    def __init__(
+        self,
+        f: Union[str, Path, BinaryIO],
+        importer: Union[Importer, Sequence[Importer]] = sys_importer,
+        debug: bool = False,
+    ):
+        """
+        Create an exporter.
+
+        Args:
+            f: The location to export to. Can be a  ``string``/``Path`` object containing a filename
+                or a binary I/O object.
+            importer: If a single Importer is passed, use that to search for modules.
+                If a sequence of importers are passed, an ``OrderedImporter`` will be constructed out of them.
+            debug: If set to True, add path of broken modules to PackagingErrors.
+        """
+        torch._C._log_api_usage_once("torch.package.PackageExporter")
+        self.debug = debug
+        if isinstance(f, (Path, str)):
+            f = str(f)
+            self.buffer: Optional[BinaryIO] = None
+        else:  # is a byte buffer
+            self.buffer = f
+
+        self.zip_file = torch._C.PyTorchFileWriter(f)
+        self.zip_file.set_min_version(6)
+        self._written_files: Set[str] = set()
+
+        self.serialized_reduces: Dict[int, Any] = {}
+
+        # A graph tracking all the modules and pickle objects added to this
+        # package and the dependencies between them.
+        # - Each node is a module name (or a pickle name that looks like '')
+        # - Each directed edge (u, v) means u depends on v.
+        # - Nodes may contain metadata that describe how to write the thing to the zipfile.
+        self.dependency_graph = DiGraph()
+        self.script_module_serializer = torch._C.ScriptModuleSerializer(self.zip_file)
+        self.storage_context = self.script_module_serializer.storage_context()
+
+        # These are OrderedDicts for compatibility with RemovableHandle.
+        # Generic OrderedDict type annotations are not present until 3.7.
+        # The real type signature is OrderedDict[int, Callable[[PackageExporter, str], None]]
+        self._extern_hooks: OrderedDict = OrderedDict()
+        self._mock_hooks: OrderedDict = OrderedDict()
+        self._intern_hooks: OrderedDict = OrderedDict()
+
+        if isinstance(importer, Importer):
+            self.importer = importer
+        else:
+            if not isinstance(importer, collections.abc.Sequence):
+                raise TypeError(
+                    "importer arg should be an Importer or a sequence of Importers, "
+                    f"got {type(importer)} instead."
+                )
+            self.importer = OrderedImporter(*importer)
+
+        self.patterns: Dict[GlobGroup, _PatternInfo] = {}
+        self._unique_id = 0
+
+    def save_source_file(
+        self, module_name: str, file_or_directory: str, dependencies=True
+    ):
+        """Adds the local file system ``file_or_directory`` to the source package to provide the code
+        for ``module_name``.
+
+        Args:
+            module_name (str): e.g. ``"my_package.my_subpackage"``, code will be saved to provide code for this package.
+            file_or_directory (str): the path to a file or directory of code. When a directory, all python files in the directory
+                are recursively copied using :meth:`save_source_file`. If a file is named ``"/__init__.py"`` the code is treated
+                as a package.
+            dependencies (bool, optional): If ``True``, we scan the source for dependencies.
+        """
+        path = Path(file_or_directory)
+        if path.is_dir():
+            to_save = []  # list of tuples with arguments to save_source_string
+            module_path = module_name.replace(".", "/")
+            for filename in path.glob("**/*.py"):
+                relative_path = filename.relative_to(path).as_posix()
+                archivename = module_path + "/" + relative_path
+                submodule_name = None
+                if filename.name == "__init__.py":
+                    submodule_name = archivename[: -len("/__init__.py")].replace(
+                        "/", "."
+                    )
+                    is_package = True
+                else:
+                    submodule_name = archivename[: -len(".py")].replace("/", ".")
+                    is_package = False
+
+                # we delay the call to save_source_string so that we record all the source files
+                # being provided by this directory structure _before_ attempting to resolve the dependencies
+                # on the source. This makes sure we don't try to copy over modules that will just get
+                # overwritten by this directory blob
+                to_save.append(
+                    (
+                        submodule_name,
+                        _read_file(str(filename)),
+                        is_package,
+                        dependencies,
+                    )
+                )
+
+            for item in to_save:
+                self.save_source_string(*item)
+        else:
+            is_package = path.name == "__init__.py"
+            self.save_source_string(
+                module_name,
+                _read_file(file_or_directory),
+                is_package,
+                dependencies,
+            )
+
+    def get_unique_id(self) -> str:
+        """Get an id. This id is guaranteed to only be handed out once for this package."""
+        ret = str(self._unique_id)
+        self._unique_id += 1
+        return ret
+
+    def _get_dependencies(
+        self, src: str, module_name: str, is_package: bool
+    ) -> List[str]:
+        """Return all modules that this source code depends on.
+
+        Dependencies are found by scanning the source code for import-like statements.
+
+        Arguments:
+            src: The Python source code to analyze for dependencies.
+            module_name: The name of the module that ``src`` corresponds to.
+            is_package: Whether this module should be treated as a package.
+                See :py:meth:`save_source_string` for more info.
+
+        Returns:
+            A list containing modules detected as direct dependencies in
+            ``src``.  The items in the list are guaranteed to be unique.
+        """
+        package_name = (
+            module_name if is_package else module_name.rsplit(".", maxsplit=1)[0]
+        )
+        try:
+            dep_pairs = find_files_source_depends_on(src, package_name)
+        except Exception as e:
+            self.dependency_graph.add_node(
+                module_name,
+                error=PackagingErrorReason.DEPENDENCY_RESOLUTION_FAILED,
+                error_context=str(e),
+            )
+            return []
+
+        # Use a dict to get uniquing but also deterministic order
+        dependencies = {}
+        for dep_module_name, dep_module_obj in dep_pairs:
+            # handle the case where someone did something like `from pack import sub`
+            # where `sub` is a submodule. In this case we don't have to save pack, just sub.
+            # this ensures we don't pick up additional dependencies on pack.
+            # However, in the case where `sub` is not a submodule but an object, then we do have
+            # to save pack.
+            if dep_module_obj is not None:
+                possible_submodule = f"{dep_module_name}.{dep_module_obj}"
+                if self._module_exists(possible_submodule):
+                    dependencies[possible_submodule] = True
+                    # we don't need to save `pack`
+                    continue
+            if self._module_exists(dep_module_name):
+                dependencies[dep_module_name] = True
+
+        return list(dependencies.keys())
+
+    def save_source_string(
+        self,
+        module_name: str,
+        src: str,
+        is_package: bool = False,
+        dependencies: bool = True,
+    ):
+        """Adds ``src`` as the source code for ``module_name`` in the exported package.
+
+        Args:
+            module_name (str): e.g. ``my_package.my_subpackage``, code will be saved to provide code for this package.
+            src (str): The Python source code to save for this package.
+            is_package (bool, optional): If ``True``, this module is treated as a package. Packages are allowed to have submodules
+                (e.g. ``my_package.my_subpackage.my_subsubpackage``), and resources can be saved inside them. Defaults to ``False``.
+            dependencies (bool, optional): If ``True``, we scan the source for dependencies.
+        """
+        self.dependency_graph.add_node(
+            module_name,
+            source=src,
+            is_package=is_package,
+            provided=True,
+            action=_ModuleProviderAction.INTERN,
+        )
+
+        if dependencies:
+            deps = self._get_dependencies(src, module_name, is_package)
+
+            for dep in deps:
+                self.dependency_graph.add_edge(module_name, dep)
+                self.add_dependency(dep)
+
+    def _write_source_string(
+        self,
+        module_name: str,
+        src: str,
+        is_package: bool = False,
+    ):
+        """Write ``src`` as the source code for ``module_name`` in the zip archive.
+
+        Arguments are otherwise the same as for :meth:`save_source_string`.
+        """
+        extension = "/__init__.py" if is_package else ".py"
+        filename = module_name.replace(".", "/") + extension
+
+        self._write(filename, src)
+
+    def _import_module(self, module_name: str):
+        try:
+            return self.importer.import_module(module_name)
+        except ModuleNotFoundError as e:
+            if not is_mangled(module_name):
+                raise
+            msg = (
+                f"Module not found: '{module_name}'. Make sure the PackageImporter that "
+                "created this module is present in `self.importer`"
+            )
+            raise ModuleNotFoundError(msg) from None
+
+    def _module_exists(self, module_name: str) -> bool:
+        try:
+            self._import_module(module_name)
+            return True
+        except Exception:
+            return False
+
+    def _get_source_of_module(self, module: types.ModuleType) -> Optional[str]:
+        filename = None
+        spec = getattr(module, "__spec__", None)
+        if spec is not None:
+            loader = getattr(spec, "loader", None)
+            if loader is not None and isinstance(loader, SourceFileLoader):
+                try:
+                    filename = loader.get_filename(module.__name__)
+                except ImportError:
+                    pass
+        if filename is None:
+            filename = getattr(module, "__file__", None)
+        if isinstance(filename, str) and filename.endswith(".py"):
+            return "".join(linecache.getlines(filename, module.__dict__))
+        return None
+
+    def add_dependency(self, module_name: str, dependencies=True):
+        """Given a module, add it to the dependency graph according to patterns
+        specified by the user.
+        """
+        if (
+            module_name in self.dependency_graph
+            and self.dependency_graph.nodes[module_name].get("provided") is True
+        ):
+            return
+
+        # Special case: PackageImporter provides a special module called
+        # `torch_package_importer` that allows packaged modules to reference
+        # their PackageImporter. We don't want to re-export this.
+        if module_name == "torch_package_importer":
+            self.dependency_graph.add_node(
+                module_name,
+                action=_ModuleProviderAction.SKIP,
+                provided=True,
+            )
+            return
+
+        if module_name == "_mock":
+            self.dependency_graph.add_node(
+                module_name,
+                action=_ModuleProviderAction.REPACKAGED_MOCK_MODULE,
+                provided=True,
+            )
+            return
+
+        if self._can_implicitly_extern(module_name):
+            self.dependency_graph.add_node(
+                module_name, action=_ModuleProviderAction.EXTERN, provided=True
+            )
+            return
+
+        for pattern, pattern_info in self.patterns.items():
+            if pattern.matches(module_name):
+                pattern_info.was_matched = True
+                self.dependency_graph.add_node(
+                    module_name, action=pattern_info.action, provided=True
+                )
+
+                if pattern_info.action == _ModuleProviderAction.DENY:
+                    # Requiring a denied module just adds an error to the graph.
+                    self.dependency_graph.add_node(
+                        module_name, error=PackagingErrorReason.DENIED
+                    )
+
+                # If we are interning this module, we need to retrieve its
+                # dependencies and package those as well.
+                if pattern_info.action == _ModuleProviderAction.INTERN:
+                    self._intern_module(module_name, dependencies)
+                return
+
+        # No patterns have matched. Explicitly add this as an error.
+        self.dependency_graph.add_node(
+            module_name, error=PackagingErrorReason.NO_ACTION
+        )
+
+    def save_module(self, module_name: str, dependencies=True):
+        """Save the code for ``module`` into the package. Code for the module is resolved using the ``importers`` path to find the
+        module object, and then using its ``__file__`` attribute to find the source code.
+
+        Args:
+            module_name (str): e.g. ``my_package.my_subpackage``, code will be saved to provide code
+                for this package.
+            dependencies (bool, optional): If ``True``, we scan the source for dependencies.
+        """
+        if not isinstance(module_name, str):
+            raise TypeError(
+                "save_module() expects a string input, did you perhaps mean to pass `__name__`?"
+            )
+
+        self._intern_module(module_name, dependencies)
+
+    def _intern_module(
+        self,
+        module_name: str,
+        dependencies: bool,
+    ):
+        """Adds the module to the dependency graph as an interned module,
+        along with any metadata needed to write it out to the zipfile at serialization time.
+        """
+        module_obj = self._import_module(module_name)
+        # Subtle: if the import above succeeded, either:
+        #   1. The module name is not mangled, and this was just a regular import, or
+        #   2. The module name is mangled, but one of the importers was able to
+        #      recognize the mangling and import it.
+        # Either way, it is now safe to demangle this name so that we don't
+        # serialize the mangled version to the package.
+        module_name = demangle(module_name)
+
+        # Find dependencies of this module and require them as well.
+        is_package = hasattr(module_obj, "__path__")
+        source = self._get_source_of_module(module_obj)
+        if source is None:
+            # Couldn't find a source!  Add it to our dependency graph as broken
+            # and continue.
+            filename = getattr(module_obj, "__file__", None)
+            error_context = None
+            if filename is None:
+                packaging_error = PackagingErrorReason.NO_DUNDER_FILE
+            elif filename.endswith(tuple(importlib.machinery.EXTENSION_SUFFIXES)):
+                packaging_error = PackagingErrorReason.IS_EXTENSION_MODULE
+            else:
+                packaging_error = PackagingErrorReason.SOURCE_FILE_NOT_FOUND
+                error_context = f"filename: {filename}"
+            self.dependency_graph.add_node(
+                module_name,
+                action=_ModuleProviderAction.INTERN,
+                is_package=is_package,
+                error=packaging_error,
+                error_context=error_context,
+                provided=True,
+            )
+            return
+
+        self.dependency_graph.add_node(
+            module_name,
+            action=_ModuleProviderAction.INTERN,
+            is_package=is_package,
+            source=source,
+            provided=True,
+        )
+
+        if dependencies:
+            deps = self._get_dependencies(source, module_name, is_package)
+            for dep in deps:
+                self.dependency_graph.add_edge(module_name, dep)
+                self.add_dependency(dep)
+
+    def save_pickle(
+        self,
+        package: str,
+        resource: str,
+        obj: Any,
+        dependencies: bool = True,
+        pickle_protocol: int = 3,
+    ):
+        """Save a python object to the archive using pickle. Equivalent to :func:`torch.save` but saving into
+        the archive rather than a stand-alone file. Standard pickle does not save the code, only the objects.
+        If ``dependencies`` is true, this method will also scan the pickled objects for which modules are required
+        to reconstruct them and save the relevant code.
+
+        To be able to save an object where ``type(obj).__name__`` is ``my_module.MyObject``,
+        ``my_module.MyObject`` must resolve to the class of the object according to the ``importer`` order. When saving objects that
+        have previously been packaged, the importer's ``import_module`` method will need to be present in the ``importer`` list
+        for this to work.
+
+        Args:
+            package (str): The name of module package this resource should go in (e.g. ``"my_package.my_subpackage"``).
+            resource (str): A unique name for the resource, used to identify it to load.
+            obj (Any): The object to save, must be picklable.
+            dependencies (bool, optional): If ``True``, we scan the source for dependencies.
+        """
+
+        assert (pickle_protocol == 4) or (
+            pickle_protocol == 3
+        ), "torch.package only supports pickle protocols 3 and 4"
+
+        filename = self._filename(package, resource)
+        # Write the pickle data for `obj`
+        data_buf = io.BytesIO()
+        pickler = create_pickler(data_buf, self.importer, protocol=pickle_protocol)
+        pickler.persistent_id = self._persistent_id
+        pickler.dump(obj)
+        data_value = data_buf.getvalue()
+        mocked_modules = defaultdict(list)
+        name_in_dependency_graph = f"<{package}.{resource}>"
+        self.dependency_graph.add_node(
+            name_in_dependency_graph,
+            action=_ModuleProviderAction.INTERN,
+            provided=True,
+            is_pickle=True,
+        )
+
+        def _check_mocked_error(module: Optional[str], field: Optional[str]):
+            """
+            checks if an object (field) comes from a mocked module and then adds
+            the pair to mocked_modules which contains mocked modules paired with their
+            list of mocked objects present in the pickle.
+
+            We also hold the invariant that the first user defined rule that applies
+            to the module is the one we use.
+            """
+
+            assert isinstance(module, str)
+            assert isinstance(field, str)
+            if self._can_implicitly_extern(module):
+                return
+            for pattern, pattern_info in self.patterns.items():
+                if pattern.matches(module):
+                    if pattern_info.action == _ModuleProviderAction.MOCK:
+                        mocked_modules[module].append(field)
+                    return
+
+        if dependencies:
+            all_dependencies = []
+            module = None
+            field = None
+            memo: DefaultDict[int, str] = defaultdict(None)
+            memo_count = 0
+            # pickletools.dis(data_value)
+            for opcode, arg, pos in pickletools.genops(data_value):
+                if pickle_protocol == 4:
+                    if (
+                        opcode.name == "SHORT_BINUNICODE"
+                        or opcode.name == "BINUNICODE"
+                        or opcode.name == "BINUNICODE8"
+                    ):
+                        assert isinstance(arg, str)
+                        module = field
+                        field = arg
+                        memo[memo_count] = arg
+                    elif (
+                        opcode.name == "LONG_BINGET"
+                        or opcode.name == "BINGET"
+                        or opcode.name == "GET"
+                    ):
+                        assert isinstance(arg, int)
+                        module = field
+                        field = memo.get(arg, None)
+                    elif opcode.name == "MEMOIZE":
+                        memo_count += 1
+                    elif opcode.name == "STACK_GLOBAL":
+                        if module is None:
+                            # If not module was passed on in the entries preceeding this one, continue.
+                            continue
+                        assert isinstance(module, str)
+                        if module not in all_dependencies:
+                            all_dependencies.append(module)
+                        _check_mocked_error(module, field)
+                elif (
+                    pickle_protocol == 3 and opcode.name == "GLOBAL"
+                ):  # a global reference
+                    assert isinstance(arg, str)
+                    module, field = arg.split(" ")
+                    if module not in all_dependencies:
+                        all_dependencies.append(module)
+                    _check_mocked_error(module, field)
+            for module_name in all_dependencies:
+                self.dependency_graph.add_edge(name_in_dependency_graph, module_name)
+
+                """ If an object happens to come from a mocked module, then we collect these errors and spit them
+                    out with the other errors found by package exporter.
+                """
+                if module_name in mocked_modules:
+                    assert isinstance(module_name, str)
+                    fields = mocked_modules[module_name]
+                    self.dependency_graph.add_node(
+                        module_name,
+                        action=_ModuleProviderAction.MOCK,
+                        error=PackagingErrorReason.MOCKED_BUT_STILL_USED,
+                        error_context=f"Object(s) '{fields}' from module `{module_name}` was mocked out during packaging "
+                        f"but is being used in resource - `{resource}` in package `{package}`. ",
+                        provided=True,
+                    )
+                else:
+                    self.add_dependency(module_name)
+
+        self._write(filename, data_value)
+
+    def save_text(self, package: str, resource: str, text: str):
+        """Save text data to the package.
+
+        Args:
+            package (str): The name of module package this resource should go it (e.g. ``"my_package.my_subpackage"``).
+            resource (str): A unique name for the resource, used to identify it to load.
+            text (str): The contents to save.
+        """
+        return self.save_binary(package, resource, text.encode("utf-8"))
+
+    def save_binary(self, package, resource, binary: bytes):
+        """Save raw bytes to the package.
+
+        Args:
+            package (str): The name of module package this resource should go it (e.g. ``"my_package.my_subpackage"``).
+            resource (str): A unique name for the resource, used to identify it to load.
+            binary (str): The data to save.
+        """
+        filename = self._filename(package, resource)
+        self._write(filename, binary)
+
+    def register_extern_hook(self, hook: ActionHook) -> RemovableHandle:
+        """Registers an extern hook on the exporter.
+
+        The hook will be called each time a module matches against an :meth:`extern` pattern.
+        It should have the following signature::
+
+            hook(exporter: PackageExporter, module_name: str) -> None
+
+        Hooks will be called in order of registration.
+
+        Returns:
+            :class:`torch.utils.hooks.RemovableHandle`:
+                A handle that can be used to remove the added hook by calling
+                ``handle.remove()``.
+        """
+        handle = RemovableHandle(self._extern_hooks)
+        self._extern_hooks[handle.id] = hook
+        return handle
+
+    def register_mock_hook(self, hook: ActionHook) -> RemovableHandle:
+        """Registers a mock hook on the exporter.
+
+        The hook will be called each time a module matches against a :meth:`mock` pattern.
+        It should have the following signature::
+
+            hook(exporter: PackageExporter, module_name: str) -> None
+
+        Hooks will be called in order of registration.
+
+        Returns:
+            :class:`torch.utils.hooks.RemovableHandle`:
+                A handle that can be used to remove the added hook by calling
+                ``handle.remove()``.
+        """
+        handle = RemovableHandle(self._mock_hooks)
+        self._mock_hooks[handle.id] = hook
+        return handle
+
+    def register_intern_hook(self, hook: ActionHook) -> RemovableHandle:
+        """Registers an intern hook on the exporter.
+
+        The hook will be called each time a module matches against an :meth:`intern` pattern.
+        It should have the following signature::
+
+            hook(exporter: PackageExporter, module_name: str) -> None
+
+        Hooks will be called in order of registration.
+
+        Returns:
+            :class:`torch.utils.hooks.RemovableHandle`:
+                A handle that can be used to remove the added hook by calling
+                ``handle.remove()``.
+        """
+        handle = RemovableHandle(self._intern_hooks)
+        self._intern_hooks[handle.id] = hook
+        return handle
+
+    def intern(
+        self,
+        include: "GlobPattern",
+        *,
+        exclude: "GlobPattern" = (),
+        allow_empty: bool = True,
+    ):
+        """Specify modules that should be packaged. A module must match some ``intern`` pattern in order to be
+        included in the package and have its dependencies processed recursively.
+
+        Args:
+            include (Union[List[str], str]): A string e.g. "my_package.my_subpackage", or list of strings
+                for the names of the modules to be externed. This can also be a glob-style pattern, as described in :meth:`mock`.
+
+            exclude (Union[List[str], str]): An optional pattern that excludes some patterns that match the include string.
+
+            allow_empty (bool): An optional flag that specifies whether the intern modules specified by this call
+                to the ``intern`` method must be matched to some module during packaging. If an ``intern`` module glob
+                pattern is added with ``allow_empty=False``, and :meth:`close` is called (either explicitly or via ``__exit__``)
+                before any modules match that pattern, an exception is thrown. If ``allow_empty=True``, no such exception is thrown.
+
+        """
+        self.patterns[GlobGroup(include, exclude=exclude)] = _PatternInfo(
+            _ModuleProviderAction.INTERN, allow_empty
+        )
+
+    def mock(
+        self,
+        include: "GlobPattern",
+        *,
+        exclude: "GlobPattern" = (),
+        allow_empty: bool = True,
+    ):
+        """Replace some required modules with a mock implementation.  Mocked modules will return a fake
+        object for any attribute accessed from it. Because we copy file-by-file, the dependency resolution will sometimes
+        find files that are imported by model files but whose functionality is never used
+        (e.g. custom serialization code or training helpers).
+        Use this function to mock this functionality out without having to modify the original code.
+
+        Args:
+            include (Union[List[str], str]): A string e.g. ``"my_package.my_subpackage"``, or list of strings
+                for the names of the modules to be mocked out. Strings can also be a glob-style pattern
+                string that may match multiple modules. Any required dependencies that match this pattern
+                string will be mocked out automatically.
+
+                Examples :
+                    ``'torch.**'`` -- matches ``torch`` and all submodules of torch, e.g. ``'torch.nn'``
+                    and ``'torch.nn.functional'``
+
+                    ``'torch.*'`` -- matches ``'torch.nn'`` or ``'torch.functional'``, but not
+                    ``'torch.nn.functional'``
+
+            exclude (Union[List[str], str]): An optional pattern that excludes some patterns that match the include string.
+                e.g. ``include='torch.**', exclude='torch.foo'`` will mock all torch packages except ``'torch.foo'``,
+                Default: is ``[]``.
+
+            allow_empty (bool): An optional flag that specifies whether the mock implementation(s) specified by this call
+                to the :meth:`mock` method must be matched to some module during packaging. If a mock is added with
+                ``allow_empty=False``, and :meth:`close` is called (either explicitly or via ``__exit__``) and the mock has
+                not been matched to a module used by the package being exported, an exception is thrown.
+                If ``allow_empty=True``, no such exception is thrown.
+
+        """
+        self.patterns[GlobGroup(include, exclude=exclude)] = _PatternInfo(
+            _ModuleProviderAction.MOCK, allow_empty
+        )
+
+    def extern(
+        self,
+        include: "GlobPattern",
+        *,
+        exclude: "GlobPattern" = (),
+        allow_empty: bool = True,
+    ):
+        """Include ``module`` in the list of external modules the package can import.
+        This will prevent dependency discovery from saving
+        it in the package. The importer will load an external module directly from the standard import system.
+        Code for extern modules must also exist in the process loading the package.
+
+        Args:
+            include (Union[List[str], str]): A string e.g. ``"my_package.my_subpackage"``, or list of strings
+                for the names of the modules to be externed. This can also be a glob-style pattern, as
+                described in :meth:`mock`.
+
+            exclude (Union[List[str], str]): An optional pattern that excludes some patterns that match the
+                include string.
+
+            allow_empty (bool): An optional flag that specifies whether the extern modules specified by this call
+                to the ``extern`` method must be matched to some module during packaging. If an extern module glob
+                pattern is added with ``allow_empty=False``, and :meth:`close` is called (either explicitly or via
+                ``__exit__``) before any modules match that pattern, an exception is thrown. If ``allow_empty=True``,
+                no such exception is thrown.
+
+        """
+        self.patterns[GlobGroup(include, exclude=exclude)] = _PatternInfo(
+            _ModuleProviderAction.EXTERN, allow_empty
+        )
+
+    def deny(self, include: "GlobPattern", *, exclude: "GlobPattern" = ()):
+        """Blocklist modules who names match the given glob patterns from the list of modules the package can import.
+        If a dependency on any matching packages is found, a :class:`PackagingError` is raised.
+
+        Args:
+            include (Union[List[str], str]): A string e.g. ``"my_package.my_subpackage"``, or list of strings
+                for the names of the modules to be externed. This can also be a glob-style pattern, as described in :meth:`mock`.
+
+            exclude (Union[List[str], str]): An optional pattern that excludes some patterns that match the include string.
+        """
+        self.patterns[GlobGroup(include, exclude=exclude)] = _PatternInfo(
+            _ModuleProviderAction.DENY, allow_empty=True
+        )
+
+    def _persistent_id(self, obj):
+        if torch.is_storage(obj) or isinstance(obj, torch.storage.TypedStorage):
+            storage: Storage
+            if isinstance(obj, torch.storage.TypedStorage):
+                # TODO: Once we decide to break serialization FC, we can
+                # remove this case
+                untyped_storage = obj._untyped_storage
+                storage_type_str = obj.pickle_storage_type()
+                storage_type = getattr(torch, storage_type_str)
+                storage = cast(Storage, untyped_storage)
+                storage_numel = obj.size()
+
+            elif isinstance(obj, torch.UntypedStorage):
+                untyped_storage = obj
+                storage = cast(Storage, untyped_storage)
+                storage_type = normalize_storage_type(type(storage))
+                storage_numel = storage.nbytes()
+            else:
+                raise RuntimeError(f"storage type not recognized: {type(obj)}")
+
+            location = location_tag(storage)
+
+            # serialize storage if not already written
+            storage_present = self.storage_context.has_storage(storage)
+            storage_id = self.storage_context.get_or_add_storage(storage)
+            if not storage_present:
+                if storage.device.type != "cpu":
+                    storage = storage.cpu()
+                num_bytes = storage.nbytes()
+                self.zip_file.write_record(
+                    f".data/{storage_id}.storage", storage, num_bytes
+                )
+            return ("storage", storage_type, storage_id, location, storage_numel)
+
+        if hasattr(obj, "__reduce_package__"):
+            if _gate_torchscript_serialization and isinstance(
+                obj, torch.jit.RecursiveScriptModule
+            ):
+                raise Exception(  # noqa: TRY002
+                    "Serializing ScriptModules directly into a package is a beta feature. "
+                    "To use, set global "
+                    "`torch.package.package_exporter._gate_torchscript_serialization` to `False`."
+                )
+            if self.serialized_reduces.get(id(obj)) is None:
+                self.serialized_reduces[id(obj)] = (
+                    "reduce_package",
+                    id(obj),
+                    *obj.__reduce_package__(self),
+                )
+
+            return self.serialized_reduces[id(obj)]
+
+        return None
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        # If __exit__ was called because an exception was raised, we do not
+        # attempt to finalize the package. Instead, control is returned to the
+        # caller to continue raising the exception.
+        if exc_type is not None:
+            # Do the bare minimum to leave the open buffer in a valid state.
+            self._finalize_zip()
+            return
+
+        self.close()
+
+    def _write(self, filename, str_or_bytes):
+        if filename in self._written_files:
+            raise AssertionError(
+                f"Tried to write file '{filename}', but it already exists in this archive. "
+                "Please file a bug."
+            )
+        self._written_files.add(filename)
+
+        if is_mangled(filename):
+            raise AssertionError(
+                f"Tried to save a torch.package'd module as '{filename}'. "
+                "Directly saving torch.package'd modules is not allowed."
+            )
+        if isinstance(str_or_bytes, str):
+            str_or_bytes = str_or_bytes.encode("utf-8")
+        self.zip_file.write_record(filename, str_or_bytes, len(str_or_bytes))
+
+    def _validate_dependency_graph(self):
+        # 1. Check the graph for any errors inserted during dependency analysis.
+        for attrs in self.dependency_graph.nodes.values():
+            if "error" in attrs:
+                raise PackagingError(self.dependency_graph, debug=self.debug)
+
+        # 2. Check that all patterns for which allow_empty=False have been matched at least once.
+        for pattern, pattern_info in self.patterns.items():
+            if not pattern_info.allow_empty and not pattern_info.was_matched:
+                raise EmptyMatchError(
+                    f"Exporter did not match any modules to {pattern}, which was marked as allow_empty=False"
+                )
+
+    def _write_mock_file(self):
+        if "_mock.py" not in self._written_files:
+            mock_file = str(Path(__file__).parent / "_mock.py")
+            self._write_source_string("_mock", _read_file(mock_file), is_package=False)
+
+    def _execute_dependency_graph(self):
+        """Takes a finalized dependency graph describing how to package all
+        modules and executes it, writing to the ZIP archive.
+        """
+        self._validate_dependency_graph()
+
+        extern_modules = []
+        for module_name, attrs in self.dependency_graph.nodes.items():
+            action = attrs["action"]
+
+            if action == _ModuleProviderAction.EXTERN:
+                for hook in self._extern_hooks.values():
+                    hook(self, module_name)
+
+                extern_modules.append(module_name)
+
+            elif action == _ModuleProviderAction.MOCK:
+                for hook in self._mock_hooks.values():
+                    hook(self, module_name)
+
+                self._write_mock_file()
+
+                is_package = hasattr(self._import_module(module_name), "__path__")
+                self._write_source_string(module_name, _MOCK_IMPL, is_package)
+
+            elif action == _ModuleProviderAction.INTERN:
+                for hook in self._intern_hooks.values():
+                    hook(self, module_name)
+
+                # The node in the dependency graph contains metadata that tells us
+                # how to intern the module.
+                if "provided" not in attrs:
+                    raise AssertionError(
+                        f"Module was marked `intern` but not provided: {module_name}"
+                    )
+
+                if attrs.get("is_pickle") is True:
+                    # This node came from save_pickle, we don't need to write any source for it.
+                    continue
+
+                is_package = attrs["is_package"]
+                source = attrs["source"]
+                self._write_source_string(module_name, source, is_package)
+
+            elif action == _ModuleProviderAction.REPACKAGED_MOCK_MODULE:
+                self._write_mock_file()
+            elif action == _ModuleProviderAction.SKIP:
+                continue
+            else:
+                raise AssertionError(
+                    f"Invalid action: {module_name}, {action}. Please report a bug to PyTorch."
+                )
+
+        extern_file_contents = "\n".join(extern_modules) + "\n"
+        self._write(".data/extern_modules", extern_file_contents)
+
+    def _write_python_version(self):
+        """Writes the python version that the package was created with to .data/python_version"""
+        self._write(".data/python_version", platform.python_version())
+
+    def close(self):
+        """Write the package to the filesystem. Any calls after :meth:`close` are now invalid.
+        It is preferable to use resource guard syntax instead::
+
+            with PackageExporter("file.zip") as e:
+                ...
+        """
+        self._execute_dependency_graph()
+        self._write_python_version()
+
+        self.script_module_serializer.write_files()
+        self._finalize_zip()
+
+    def _finalize_zip(self):
+        """Called at the very end of packaging to leave the zipfile in a closed but valid state."""
+        del self.zip_file
+        if self.buffer:
+            self.buffer.flush()
+
+    def _filename(self, package, resource):
+        package_path = package.replace(".", "/")
+        resource = _normalize_path(resource)
+        return f"{package_path}/{resource}"
+
+    def _can_implicitly_extern(self, module_name: str):
+        top_level_package_name = module_name.partition(".")[0]
+        return top_level_package_name == "torch" or (
+            top_level_package_name not in _DISALLOWED_MODULES
+            and is_stdlib_module(top_level_package_name)
+        )
+
+    def dependency_graph_string(self) -> str:
+        """Returns digraph string representation of dependencies in package.
+
+        Returns:
+            A string representation of dependencies in package.
+        """
+        return self.dependency_graph.to_dot()
+
+    def _nodes_with_action_type(
+        self, action: Optional[_ModuleProviderAction]
+    ) -> List[str]:
+        result = []
+        for name, node_dict in self.dependency_graph.nodes.items():
+            node_action = node_dict.get("action", None)
+            if node_action == action and "is_pickle" not in node_dict:
+                result.append(name)
+        result.sort()
+        return result
+
+    def externed_modules(self) -> List[str]:
+        """Return all modules that are currently externed.
+
+        Returns:
+            A list containing the names of modules which will be
+            externed in this package.
+        """
+        return self._nodes_with_action_type(_ModuleProviderAction.EXTERN)
+
+    def interned_modules(self) -> List[str]:
+        """Return all modules that are currently interned.
+
+        Returns:
+            A list containing the names of modules which will be
+            interned in this package.
+        """
+        return self._nodes_with_action_type(_ModuleProviderAction.INTERN)
+
+    def mocked_modules(self) -> List[str]:
+        """Return all modules that are currently mocked.
+
+        Returns:
+            A list containing the names of modules which will be
+            mocked in this package.
+        """
+        return self._nodes_with_action_type(_ModuleProviderAction.MOCK)
+
+    def denied_modules(self) -> List[str]:
+        """Return all modules that are currently denied.
+
+        Returns:
+            A list containing the names of modules which will be
+            denied in this package.
+        """
+        return self._nodes_with_action_type(_ModuleProviderAction.DENY)
+
+    def get_rdeps(self, module_name: str) -> List[str]:
+        """Return a list of all modules which depend on the module ``module_name``.
+
+        Returns:
+            A list containing the names of modules which depend on ``module_name``.
+        """
+        if module_name in self.dependency_graph._pred.keys():
+            return list(self.dependency_graph._pred[module_name].keys())
+        else:
+            return []
+
+    def all_paths(self, src: str, dst: str) -> str:
+        """Return a dot representation of the subgraph
+           that has all paths from src to dst.
+
+        Returns:
+            A dot representation containing all paths from src to dst.
+            (https://graphviz.org/doc/info/lang.html)
+        """
+        return self.dependency_graph.all_paths(src, dst)
+
+
+# even though these are in the standard library, we do not allow them to be
+# automatically externed since they offer a lot of system level access
+_DISALLOWED_MODULES = ["sys", "io"]
+
+_MOCK_IMPL = """\
+from _mock import MockedObject
+def __getattr__(attr: str):
+    return MockedObject(__name__ + '.' + attr, _suppress_err=True)
+"""
+
+
+def _read_file(filename: str) -> str:
+    with open(filename, "rb") as f:
+        b = f.read()
+        return b.decode("utf-8")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/package_importer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/package_importer.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf557d72bd4f7df0182e9db37b09089c724b291a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/package/package_importer.py
@@ -0,0 +1,791 @@
+# mypy: allow-untyped-defs
+import builtins
+import importlib
+import importlib.machinery
+import inspect
+import io
+import linecache
+import os
+import types
+from contextlib import contextmanager
+from typing import (
+    Any,
+    BinaryIO,
+    Callable,
+    cast,
+    Dict,
+    Iterable,
+    List,
+    Optional,
+    TYPE_CHECKING,
+    Union,
+)
+from weakref import WeakValueDictionary
+
+import torch
+from torch.serialization import _get_restore_location, _maybe_decode_ascii
+
+from ._directory_reader import DirectoryReader
+from ._importlib import (
+    _calc___package__,
+    _normalize_line_endings,
+    _normalize_path,
+    _resolve_name,
+    _sanity_check,
+)
+from ._mangling import demangle, PackageMangler
+from ._package_unpickler import PackageUnpickler
+from .file_structure_representation import _create_directory_from_file_list, Directory
+from .importer import Importer
+
+
+if TYPE_CHECKING:
+    from .glob_group import GlobPattern
+
+__all__ = ["PackageImporter"]
+
+
+# This is a list of imports that are implicitly allowed even if they haven't
+# been marked as extern. This is to work around the fact that Torch implicitly
+# depends on numpy and package can't track it.
+# https://github.com/pytorch/MultiPy/issues/46
+IMPLICIT_IMPORT_ALLOWLIST: Iterable[str] = [
+    "numpy",
+    "numpy.core",
+    "numpy.core._multiarray_umath",
+    # FX GraphModule might depend on builtins module and users usually
+    # don't extern builtins. Here we import it here by default.
+    "builtins",
+]
+
+
+# Compatibility name mapping to facilitate upgrade of external modules.
+# The primary motivation is to enable Numpy upgrade that many modules
+# depend on. The latest release of Numpy removed `numpy.str` and
+# `numpy.bool` breaking unpickling for many modules.
+EXTERN_IMPORT_COMPAT_NAME_MAPPING: Dict[str, Dict[str, Any]] = {
+    "numpy": {
+        "str": str,
+        "bool": bool,
+    },
+}
+
+
+class PackageImporter(Importer):
+    """Importers allow you to load code written to packages by :class:`PackageExporter`.
+    Code is loaded in a hermetic way, using files from the package
+    rather than the normal python import system. This allows
+    for the packaging of PyTorch model code and data so that it can be run
+    on a server or used in the future for transfer learning.
+
+    The importer for packages ensures that code in the module can only be loaded from
+    within the package, except for modules explicitly listed as external during export.
+    The file ``extern_modules`` in the zip archive lists all the modules that a package externally depends on.
+    This prevents "implicit" dependencies where the package runs locally because it is importing
+    a locally-installed package, but then fails when the package is copied to another machine.
+    """
+
+    """The dictionary of already loaded modules from this package, equivalent to ``sys.modules`` but
+    local to this importer.
+    """
+
+    modules: Dict[str, types.ModuleType]
+
+    def __init__(
+        self,
+        file_or_buffer: Union[str, torch._C.PyTorchFileReader, os.PathLike, BinaryIO],
+        module_allowed: Callable[[str], bool] = lambda module_name: True,
+    ):
+        """Open ``file_or_buffer`` for importing. This checks that the imported package only requires modules
+        allowed by ``module_allowed``
+
+        Args:
+            file_or_buffer: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`),
+                a string, or an ``os.PathLike`` object containing a filename.
+            module_allowed (Callable[[str], bool], optional): A method to determine if a externally provided module
+                should be allowed. Can be used to ensure packages loaded do not depend on modules that the server
+                does not support. Defaults to allowing anything.
+
+        Raises:
+            ImportError: If the package will use a disallowed module.
+        """
+        torch._C._log_api_usage_once("torch.package.PackageImporter")
+
+        self.zip_reader: Any
+        if isinstance(file_or_buffer, torch._C.PyTorchFileReader):
+            self.filename = ""
+            self.zip_reader = file_or_buffer
+        elif isinstance(file_or_buffer, (os.PathLike, str)):
+            self.filename = os.fspath(file_or_buffer)
+            if not os.path.isdir(self.filename):
+                self.zip_reader = torch._C.PyTorchFileReader(self.filename)
+            else:
+                self.zip_reader = DirectoryReader(self.filename)
+        else:
+            self.filename = ""
+            self.zip_reader = torch._C.PyTorchFileReader(file_or_buffer)
+
+        torch._C._log_api_usage_metadata(
+            "torch.package.PackageImporter.metadata",
+            {
+                "serialization_id": self.zip_reader.serialization_id(),
+                "file_name": self.filename,
+            },
+        )
+
+        self.root = _PackageNode(None)
+        self.modules = {}
+        self.extern_modules = self._read_extern()
+
+        for extern_module in self.extern_modules:
+            if not module_allowed(extern_module):
+                raise ImportError(
+                    f"package '{file_or_buffer}' needs the external module '{extern_module}' "
+                    f"but that module has been disallowed"
+                )
+            self._add_extern(extern_module)
+
+        for fname in self.zip_reader.get_all_records():
+            self._add_file(fname)
+
+        self.patched_builtins = builtins.__dict__.copy()
+        self.patched_builtins["__import__"] = self.__import__
+        # Allow packaged modules to reference their PackageImporter
+        self.modules["torch_package_importer"] = self  # type: ignore[assignment]
+
+        self._mangler = PackageMangler()
+
+        # used for reduce deserializaiton
+        self.storage_context: Any = None
+        self.last_map_location = None
+
+        # used for torch.serialization._load
+        self.Unpickler = lambda *args, **kwargs: PackageUnpickler(self, *args, **kwargs)
+
+    def import_module(self, name: str, package=None):
+        """Load a module from the package if it hasn't already been loaded, and then return
+        the module. Modules are loaded locally
+        to the importer and will appear in ``self.modules`` rather than ``sys.modules``.
+
+        Args:
+            name (str): Fully qualified name of the module to load.
+            package ([type], optional): Unused, but present to match the signature of importlib.import_module. Defaults to ``None``.
+
+        Returns:
+            types.ModuleType: The (possibly already) loaded module.
+        """
+        # We should always be able to support importing modules from this package.
+        # This is to support something like:
+        #   obj = importer.load_pickle(...)
+        #   importer.import_module(obj.__module__)  <- this string will be mangled
+        #
+        # Note that _mangler.demangle will not demangle any module names
+        # produced by a different PackageImporter instance.
+        name = self._mangler.demangle(name)
+
+        return self._gcd_import(name)
+
+    def load_binary(self, package: str, resource: str) -> bytes:
+        """Load raw bytes.
+
+        Args:
+            package (str): The name of module package (e.g. ``"my_package.my_subpackage"``).
+            resource (str): The unique name for the resource.
+
+        Returns:
+            bytes: The loaded data.
+        """
+
+        path = self._zipfile_path(package, resource)
+        return self.zip_reader.get_record(path)
+
+    def load_text(
+        self,
+        package: str,
+        resource: str,
+        encoding: str = "utf-8",
+        errors: str = "strict",
+    ) -> str:
+        """Load a string.
+
+        Args:
+            package (str): The name of module package (e.g. ``"my_package.my_subpackage"``).
+            resource (str): The unique name for the resource.
+            encoding (str, optional): Passed to ``decode``. Defaults to ``'utf-8'``.
+            errors (str, optional): Passed to ``decode``. Defaults to ``'strict'``.
+
+        Returns:
+            str: The loaded text.
+        """
+        data = self.load_binary(package, resource)
+        return data.decode(encoding, errors)
+
+    def load_pickle(self, package: str, resource: str, map_location=None) -> Any:
+        """Unpickles the resource from the package, loading any modules that are needed to construct the objects
+        using :meth:`import_module`.
+
+        Args:
+            package (str): The name of module package (e.g. ``"my_package.my_subpackage"``).
+            resource (str): The unique name for the resource.
+            map_location: Passed to `torch.load` to determine how tensors are mapped to devices. Defaults to ``None``.
+
+        Returns:
+            Any: The unpickled object.
+        """
+        pickle_file = self._zipfile_path(package, resource)
+        restore_location = _get_restore_location(map_location)
+        loaded_storages = {}
+        loaded_reduces = {}
+        storage_context = torch._C.DeserializationStorageContext()
+
+        def load_tensor(dtype, size, key, location, restore_location):
+            name = f"{key}.storage"
+
+            if storage_context.has_storage(name):
+                storage = storage_context.get_storage(name, dtype)._typed_storage()
+            else:
+                tensor = self.zip_reader.get_storage_from_record(
+                    ".data/" + name, size, dtype
+                )
+                if isinstance(self.zip_reader, torch._C.PyTorchFileReader):
+                    storage_context.add_storage(name, tensor)
+                storage = tensor._typed_storage()
+            loaded_storages[key] = restore_location(storage, location)
+
+        def persistent_load(saved_id):
+            assert isinstance(saved_id, tuple)
+            typename = _maybe_decode_ascii(saved_id[0])
+            data = saved_id[1:]
+
+            if typename == "storage":
+                storage_type, key, location, size = data
+                dtype = storage_type.dtype
+
+                if key not in loaded_storages:
+                    load_tensor(
+                        dtype,
+                        size,
+                        key,
+                        _maybe_decode_ascii(location),
+                        restore_location,
+                    )
+                storage = loaded_storages[key]
+                # TODO: Once we decide to break serialization FC, we can
+                # stop wrapping with TypedStorage
+                return torch.storage.TypedStorage(
+                    wrap_storage=storage._untyped_storage, dtype=dtype, _internal=True
+                )
+            elif typename == "reduce_package":
+                # to fix BC breaking change, objects on this load path
+                # will be loaded multiple times erroneously
+                if len(data) == 2:
+                    func, args = data
+                    return func(self, *args)
+                reduce_id, func, args = data
+                if reduce_id not in loaded_reduces:
+                    loaded_reduces[reduce_id] = func(self, *args)
+                return loaded_reduces[reduce_id]
+            else:
+                f"Unknown typename for persistent_load, expected 'storage' or 'reduce_package' but got '{typename}'"
+
+        # Load the data (which may in turn use `persistent_load` to load tensors)
+        data_file = io.BytesIO(self.zip_reader.get_record(pickle_file))
+        unpickler = self.Unpickler(data_file)
+        unpickler.persistent_load = persistent_load  # type: ignore[assignment]
+
+        @contextmanager
+        def set_deserialization_context():
+            # to let reduce_package access deserializaiton context
+            self.storage_context = storage_context
+            self.last_map_location = map_location
+            try:
+                yield
+            finally:
+                self.storage_context = None
+                self.last_map_location = None
+
+        with set_deserialization_context():
+            result = unpickler.load()
+
+        # TODO from zdevito:
+        #   This stateful weird function will need to be removed in our efforts
+        #   to unify the format. It has a race condition if multiple python
+        #   threads try to read independent files
+        torch._utils._validate_loaded_sparse_tensors()
+
+        return result
+
+    def id(self):
+        """
+        Returns internal identifier that torch.package uses to distinguish :class:`PackageImporter` instances.
+        Looks like::
+
+            
+        """
+        return self._mangler.parent_name()
+
+    def file_structure(
+        self, *, include: "GlobPattern" = "**", exclude: "GlobPattern" = ()
+    ) -> Directory:
+        """Returns a file structure representation of package's zipfile.
+
+        Args:
+            include (Union[List[str], str]): An optional string e.g. ``"my_package.my_subpackage"``, or optional list of strings
+                for the names of the files to be included in the zipfile representation. This can also be
+                a glob-style pattern, as described in :meth:`PackageExporter.mock`
+
+            exclude (Union[List[str], str]): An optional pattern that excludes files whose name match the pattern.
+
+        Returns:
+            :class:`Directory`
+        """
+        return _create_directory_from_file_list(
+            self.filename, self.zip_reader.get_all_records(), include, exclude
+        )
+
+    def python_version(self):
+        """Returns the version of python that was used to create this package.
+
+        Note: this function is experimental and not Forward Compatible. The plan is to move this into a lock
+        file later on.
+
+        Returns:
+            :class:`Optional[str]` a python version e.g. 3.8.9 or None if no version was stored with this package
+        """
+        python_version_path = ".data/python_version"
+        return (
+            self.zip_reader.get_record(python_version_path).decode("utf-8").strip()
+            if self.zip_reader.has_record(python_version_path)
+            else None
+        )
+
+    def _read_extern(self):
+        return (
+            self.zip_reader.get_record(".data/extern_modules")
+            .decode("utf-8")
+            .splitlines(keepends=False)
+        )
+
+    def _make_module(
+        self, name: str, filename: Optional[str], is_package: bool, parent: str
+    ):
+        mangled_filename = self._mangler.mangle(filename) if filename else None
+        spec = importlib.machinery.ModuleSpec(
+            name,
+            self,  # type: ignore[arg-type]
+            origin="",
+            is_package=is_package,
+        )
+        module = importlib.util.module_from_spec(spec)
+        self.modules[name] = module
+        module.__name__ = self._mangler.mangle(name)
+        ns = module.__dict__
+        ns["__spec__"] = spec
+        ns["__loader__"] = self
+        ns["__file__"] = mangled_filename
+        ns["__cached__"] = None
+        ns["__builtins__"] = self.patched_builtins
+        ns["__torch_package__"] = True
+
+        # Add this module to our private global registry. It should be unique due to mangling.
+        assert module.__name__ not in _package_imported_modules
+        _package_imported_modules[module.__name__] = module
+
+        # pre-emptively install on the parent to prevent IMPORT_FROM from trying to
+        # access sys.modules
+        self._install_on_parent(parent, name, module)
+
+        if filename is not None:
+            assert mangled_filename is not None
+            # pre-emptively install the source in `linecache` so that stack traces,
+            # `inspect`, etc. work.
+            assert filename not in linecache.cache  # type: ignore[attr-defined]
+            linecache.lazycache(mangled_filename, ns)
+
+            code = self._compile_source(filename, mangled_filename)
+            exec(code, ns)
+
+        return module
+
+    def _load_module(self, name: str, parent: str):
+        cur: _PathNode = self.root
+        for atom in name.split("."):
+            if not isinstance(cur, _PackageNode) or atom not in cur.children:
+                if name in IMPLICIT_IMPORT_ALLOWLIST:
+                    module = self.modules[name] = importlib.import_module(name)
+                    return module
+                raise ModuleNotFoundError(
+                    f'No module named "{name}" in self-contained archive "{self.filename}"'
+                    f" and the module is also not in the list of allowed external modules: {self.extern_modules}",
+                    name=name,
+                )
+            cur = cur.children[atom]
+            if isinstance(cur, _ExternNode):
+                module = self.modules[name] = importlib.import_module(name)
+
+                if compat_mapping := EXTERN_IMPORT_COMPAT_NAME_MAPPING.get(name):
+                    for old_name, new_name in compat_mapping.items():
+                        module.__dict__.setdefault(old_name, new_name)
+
+                return module
+        return self._make_module(name, cur.source_file, isinstance(cur, _PackageNode), parent)  # type: ignore[attr-defined]
+
+    def _compile_source(self, fullpath: str, mangled_filename: str):
+        source = self.zip_reader.get_record(fullpath)
+        source = _normalize_line_endings(source)
+        return compile(source, mangled_filename, "exec", dont_inherit=True)
+
+    # note: named `get_source` so that linecache can find the source
+    # when this is the __loader__ of a module.
+    def get_source(self, module_name) -> str:
+        # linecache calls `get_source` with the `module.__name__` as the argument, so we must demangle it here.
+        module = self.import_module(demangle(module_name))
+        return self.zip_reader.get_record(demangle(module.__file__)).decode("utf-8")
+
+    # note: named `get_resource_reader` so that importlib.resources can find it.
+    # This is otherwise considered an internal method.
+    def get_resource_reader(self, fullname):
+        try:
+            package = self._get_package(fullname)
+        except ImportError:
+            return None
+        if package.__loader__ is not self:
+            return None
+        return _PackageResourceReader(self, fullname)
+
+    def _install_on_parent(self, parent: str, name: str, module: types.ModuleType):
+        if not parent:
+            return
+        # Set the module as an attribute on its parent.
+        parent_module = self.modules[parent]
+        if parent_module.__loader__ is self:
+            setattr(parent_module, name.rpartition(".")[2], module)
+
+    # note: copied from cpython's import code, with call to create module replaced with _make_module
+    def _do_find_and_load(self, name):
+        path = None
+        parent = name.rpartition(".")[0]
+        module_name_no_parent = name.rpartition(".")[-1]
+        if parent:
+            if parent not in self.modules:
+                self._gcd_import(parent)
+            # Crazy side-effects!
+            if name in self.modules:
+                return self.modules[name]
+            parent_module = self.modules[parent]
+
+            try:
+                path = parent_module.__path__  # type: ignore[attr-defined]
+
+            except AttributeError:
+                # when we attempt to import a package only containing pybinded files,
+                # the parent directory isn't always a package as defined by python,
+                # so we search if the package is actually there or not before calling the error.
+                if isinstance(
+                    parent_module.__loader__,
+                    importlib.machinery.ExtensionFileLoader,
+                ):
+                    if name not in self.extern_modules:
+                        msg = (
+                            _ERR_MSG
+                            + "; {!r} is a c extension module which was not externed. C extension modules \
+                            need to be externed by the PackageExporter in order to be used as we do not support interning them.}."
+                        ).format(name, name)
+                        raise ModuleNotFoundError(msg, name=name) from None
+                    if not isinstance(
+                        parent_module.__dict__.get(module_name_no_parent),
+                        types.ModuleType,
+                    ):
+                        msg = (
+                            _ERR_MSG
+                            + "; {!r} is a c extension package which does not contain {!r}."
+                        ).format(name, parent, name)
+                        raise ModuleNotFoundError(msg, name=name) from None
+                else:
+                    msg = (_ERR_MSG + "; {!r} is not a package").format(name, parent)
+                    raise ModuleNotFoundError(msg, name=name) from None
+
+        module = self._load_module(name, parent)
+
+        self._install_on_parent(parent, name, module)
+
+        return module
+
+    # note: copied from cpython's import code
+    def _find_and_load(self, name):
+        module = self.modules.get(name, _NEEDS_LOADING)
+        if module is _NEEDS_LOADING:
+            return self._do_find_and_load(name)
+
+        if module is None:
+            message = f"import of {name} halted; None in sys.modules"
+            raise ModuleNotFoundError(message, name=name)
+
+        # To handle https://github.com/pytorch/pytorch/issues/57490, where std's
+        # creation of fake submodules via the hacking of sys.modules is not import
+        # friendly
+        if name == "os":
+            self.modules["os.path"] = cast(Any, module).path
+        elif name == "typing":
+            self.modules["typing.io"] = cast(Any, module).io
+            self.modules["typing.re"] = cast(Any, module).re
+
+        return module
+
+    def _gcd_import(self, name, package=None, level=0):
+        """Import and return the module based on its name, the package the call is
+        being made from, and the level adjustment.
+
+        This function represents the greatest common denominator of functionality
+        between import_module and __import__. This includes setting __package__ if
+        the loader did not.
+
+        """
+        _sanity_check(name, package, level)
+        if level > 0:
+            name = _resolve_name(name, package, level)
+
+        return self._find_and_load(name)
+
+    # note: copied from cpython's import code
+    def _handle_fromlist(self, module, fromlist, *, recursive=False):
+        """Figure out what __import__ should return.
+
+        The import_ parameter is a callable which takes the name of module to
+        import. It is required to decouple the function from assuming importlib's
+        import implementation is desired.
+
+        """
+        module_name = demangle(module.__name__)
+        # The hell that is fromlist ...
+        # If a package was imported, try to import stuff from fromlist.
+        if hasattr(module, "__path__"):
+            for x in fromlist:
+                if not isinstance(x, str):
+                    if recursive:
+                        where = module_name + ".__all__"
+                    else:
+                        where = "``from list''"
+                    raise TypeError(
+                        f"Item in {where} must be str, " f"not {type(x).__name__}"
+                    )
+                elif x == "*":
+                    if not recursive and hasattr(module, "__all__"):
+                        self._handle_fromlist(module, module.__all__, recursive=True)
+                elif not hasattr(module, x):
+                    from_name = f"{module_name}.{x}"
+                    try:
+                        self._gcd_import(from_name)
+                    except ModuleNotFoundError as exc:
+                        # Backwards-compatibility dictates we ignore failed
+                        # imports triggered by fromlist for modules that don't
+                        # exist.
+                        if (
+                            exc.name == from_name
+                            and self.modules.get(from_name, _NEEDS_LOADING) is not None
+                        ):
+                            continue
+                        raise
+        return module
+
+    def __import__(self, name, globals=None, locals=None, fromlist=(), level=0):
+        if level == 0:
+            module = self._gcd_import(name)
+        else:
+            globals_ = globals if globals is not None else {}
+            package = _calc___package__(globals_)
+            module = self._gcd_import(name, package, level)
+        if not fromlist:
+            # Return up to the first dot in 'name'. This is complicated by the fact
+            # that 'name' may be relative.
+            if level == 0:
+                return self._gcd_import(name.partition(".")[0])
+            elif not name:
+                return module
+            else:
+                # Figure out where to slice the module's name up to the first dot
+                # in 'name'.
+                cut_off = len(name) - len(name.partition(".")[0])
+                # Slice end needs to be positive to alleviate need to special-case
+                # when ``'.' not in name``.
+                module_name = demangle(module.__name__)
+                return self.modules[module_name[: len(module_name) - cut_off]]
+        else:
+            return self._handle_fromlist(module, fromlist)
+
+    def _get_package(self, package):
+        """Take a package name or module object and return the module.
+
+        If a name, the module is imported.  If the passed or imported module
+        object is not a package, raise an exception.
+        """
+        if hasattr(package, "__spec__"):
+            if package.__spec__.submodule_search_locations is None:
+                raise TypeError(f"{package.__spec__.name!r} is not a package")
+            else:
+                return package
+        else:
+            module = self.import_module(package)
+            if module.__spec__.submodule_search_locations is None:
+                raise TypeError(f"{package!r} is not a package")
+            else:
+                return module
+
+    def _zipfile_path(self, package, resource=None):
+        package = self._get_package(package)
+        assert package.__loader__ is self
+        name = demangle(package.__name__)
+        if resource is not None:
+            resource = _normalize_path(resource)
+            return f"{name.replace('.', '/')}/{resource}"
+        else:
+            return f"{name.replace('.', '/')}"
+
+    def _get_or_create_package(
+        self, atoms: List[str]
+    ) -> "Union[_PackageNode, _ExternNode]":
+        cur = self.root
+        for i, atom in enumerate(atoms):
+            node = cur.children.get(atom, None)
+            if node is None:
+                node = cur.children[atom] = _PackageNode(None)
+            if isinstance(node, _ExternNode):
+                return node
+            if isinstance(node, _ModuleNode):
+                name = ".".join(atoms[:i])
+                raise ImportError(
+                    f"inconsistent module structure. module {name} is not a package, but has submodules"
+                )
+            assert isinstance(node, _PackageNode)
+            cur = node
+        return cur
+
+    def _add_file(self, filename: str):
+        """Assembles a Python module out of the given file. Will ignore files in the .data directory.
+
+        Args:
+            filename (str): the name of the file inside of the package archive to be added
+        """
+        *prefix, last = filename.split("/")
+        if len(prefix) > 1 and prefix[0] == ".data":
+            return
+        package = self._get_or_create_package(prefix)
+        if isinstance(package, _ExternNode):
+            raise ImportError(
+                f"inconsistent module structure. package contains a module file {filename}"
+                f" that is a subpackage of a module marked external."
+            )
+        if last == "__init__.py":
+            package.source_file = filename
+        elif last.endswith(".py"):
+            package_name = last[: -len(".py")]
+            package.children[package_name] = _ModuleNode(filename)
+
+    def _add_extern(self, extern_name: str):
+        *prefix, last = extern_name.split(".")
+        package = self._get_or_create_package(prefix)
+        if isinstance(package, _ExternNode):
+            return  # the shorter extern covers this extern case
+        package.children[last] = _ExternNode()
+
+
+_NEEDS_LOADING = object()
+_ERR_MSG_PREFIX = "No module named "
+_ERR_MSG = _ERR_MSG_PREFIX + "{!r}"
+
+
+class _PathNode:
+    pass
+
+
+class _PackageNode(_PathNode):
+    def __init__(self, source_file: Optional[str]):
+        self.source_file = source_file
+        self.children: Dict[str, _PathNode] = {}
+
+
+class _ModuleNode(_PathNode):
+    __slots__ = ["source_file"]
+
+    def __init__(self, source_file: str):
+        self.source_file = source_file
+
+
+class _ExternNode(_PathNode):
+    pass
+
+
+# A private global registry of all modules that have been package-imported.
+_package_imported_modules: WeakValueDictionary = WeakValueDictionary()
+
+# `inspect` by default only looks in `sys.modules` to find source files for classes.
+# Patch it to check our private registry of package-imported modules as well.
+_orig_getfile = inspect.getfile
+
+
+def _patched_getfile(object):
+    if inspect.isclass(object):
+        if object.__module__ in _package_imported_modules:
+            return _package_imported_modules[object.__module__].__file__
+    return _orig_getfile(object)
+
+
+inspect.getfile = _patched_getfile
+
+
+class _PackageResourceReader:
+    """Private class used to support PackageImporter.get_resource_reader().
+
+    Confirms to the importlib.abc.ResourceReader interface. Allowed to access
+    the innards of PackageImporter.
+    """
+
+    def __init__(self, importer, fullname):
+        self.importer = importer
+        self.fullname = fullname
+
+    def open_resource(self, resource):
+        from io import BytesIO
+
+        return BytesIO(self.importer.load_binary(self.fullname, resource))
+
+    def resource_path(self, resource):
+        # The contract for resource_path is that it either returns a concrete
+        # file system path or raises FileNotFoundError.
+        if isinstance(
+            self.importer.zip_reader, DirectoryReader
+        ) and self.importer.zip_reader.has_record(
+            os.path.join(self.fullname, resource)
+        ):
+            return os.path.join(
+                self.importer.zip_reader.directory, self.fullname, resource
+            )
+        raise FileNotFoundError
+
+    def is_resource(self, name):
+        path = self.importer._zipfile_path(self.fullname, name)
+        return self.importer.zip_reader.has_record(path)
+
+    def contents(self):
+        from pathlib import Path
+
+        filename = self.fullname.replace(".", "/")
+
+        fullname_path = Path(self.importer._zipfile_path(self.fullname))
+        files = self.importer.zip_reader.get_all_records()
+        subdirs_seen = set()
+        for filename in files:
+            try:
+                relative = Path(filename).relative_to(fullname_path)
+            except ValueError:
+                continue
+            # If the path of the file (which is relative to the top of the zip
+            # namespace), relative to the package given when the resource
+            # reader was created, has a parent, then it's a name in a
+            # subdirectory and thus we skip it.
+            parent_name = relative.parent.name
+            if len(parent_name) == 0:
+                yield relative.name
+            elif parent_name not in subdirs_seen:
+                subdirs_seen.add(parent_name)
+                yield parent_name
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..073096607afe7ef44a9fe0896ff4b242b37e851b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/__init__.py
@@ -0,0 +1,50 @@
+# mypy: allow-untyped-defs
+r"""
+PyTorch Profiler is a tool that allows the collection of performance metrics during training and inference.
+Profiler's context manager API can be used to better understand what model operators are the most expensive,
+examine their input shapes and stack traces, study device kernel activity and visualize the execution trace.
+
+.. note::
+    An earlier version of the API in :mod:`torch.autograd` module is considered legacy and will be deprecated.
+
+"""
+import os
+
+from torch._C._autograd import _supported_activities, DeviceType, kineto_available
+from torch._C._profiler import _ExperimentalConfig, ProfilerActivity, RecordScope
+from torch.autograd.profiler import KinetoStepTracker, record_function
+from torch.optim.optimizer import register_optimizer_step_post_hook
+
+from .profiler import (
+    _KinetoProfile,
+    ExecutionTraceObserver,
+    profile,
+    ProfilerAction,
+    schedule,
+    supported_activities,
+    tensorboard_trace_handler,
+)
+
+
+__all__ = [
+    "profile",
+    "schedule",
+    "supported_activities",
+    "tensorboard_trace_handler",
+    "ProfilerAction",
+    "ProfilerActivity",
+    "kineto_available",
+    "DeviceType",
+    "record_function",
+    "ExecutionTraceObserver",
+]
+
+from . import itt
+
+
+def _optimizer_post_hook(optimizer, args, kwargs):
+    KinetoStepTracker.increment_step("Optimizer")
+
+
+if os.environ.get("KINETO_USE_DAEMON", None):
+    _ = register_optimizer_step_post_hook(_optimizer_post_hook)
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/_memory_profiler.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/_memory_profiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..2095b882f5de9d090ac161ae49d278643919837d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/_memory_profiler.py
@@ -0,0 +1,1204 @@
+# mypy: allow-untyped-defs
+import collections
+import dataclasses
+import enum
+import itertools as it
+import logging
+from typing import (
+    Any,
+    cast,
+    DefaultDict,
+    Dict,
+    Iterator,
+    List,
+    Optional,
+    Set,
+    Tuple,
+    Union,
+)
+from typing_extensions import Literal
+
+import torch
+from torch._C import FunctionSchema
+from torch._C._autograd import _ProfilerResult
+from torch._C._profiler import (
+    _EventType,
+    _ExtraFields_Allocation,
+    _ExtraFields_TorchOp,
+    _ProfilerEvent,
+    _TensorMetadata,
+    RecordScope,
+)
+from torch._utils import _element_size
+from torch.profiler import _utils
+
+
+KeyAndID = Tuple["Key", int]
+TensorAndID = Tuple["TensorKey", int]
+
+log = logging.getLogger(__name__)
+
+
+class Category(enum.Enum):
+    INPUT = enum.auto()
+    TEMPORARY = enum.auto()
+    ACTIVATION = enum.auto()
+    GRADIENT = enum.auto()
+    AUTOGRAD_DETAIL = enum.auto()
+    PARAMETER = enum.auto()
+    OPTIMIZER_STATE = enum.auto()
+
+
+_CATEGORY_TO_COLORS = {
+    Category.PARAMETER: "darkgreen",
+    Category.OPTIMIZER_STATE: "goldenrod",
+    Category.INPUT: "black",
+    Category.TEMPORARY: "mediumpurple",
+    Category.ACTIVATION: "red",
+    Category.GRADIENT: "mediumblue",
+    Category.AUTOGRAD_DETAIL: "royalblue",
+    None: "grey",
+}
+
+_CATEGORY_TO_INDEX = {c: i for i, c in enumerate(_CATEGORY_TO_COLORS)}
+
+
+class Action(enum.Enum):
+    PREEXISTING = enum.auto()
+    CREATE = enum.auto()
+    INCREMENT_VERSION = enum.auto()
+    DESTROY = enum.auto()
+
+
+_ACTION_TO_INDEX = {i: i.value for i in Action}
+
+
+@dataclasses.dataclass(eq=True, unsafe_hash=False, frozen=True)
+class Key:
+    device: torch.device
+
+
+@dataclasses.dataclass
+class _Storage:
+    """Bundle storage pointer and id.
+
+    All profiling logic should use `allocation_id`, however it is useful to
+    print storage pointers for debugging and unit tests sometimes look up
+    values using the storage data pointer of a live Tensor."""
+
+    ptr: int
+    allocation_id: int
+
+    def __repr__(self) -> str:
+        return f"{hex(self.ptr):>18} ({self.allocation_id})"
+
+    def __eq__(self, other: object) -> bool:
+        return isinstance(other, _Storage) and self.allocation_id == other.allocation_id
+
+    def __hash__(self) -> int:
+        return hash(self.allocation_id)
+
+
+@dataclasses.dataclass(eq=True, unsafe_hash=True, frozen=True)
+class TensorKey(Key):
+    """Hashable identifier for a storage which has been asigned an ID.
+
+    A detailed description of Tensor IDs and why they are needed is given in
+    `torch/csrc/profiler/collection.h` when `TensorID` is declared. To
+    summarize, multiple Storage buffers can map to the same logical Tensor.
+    This dataclass is used to refer to a concrete in-memory StorageImpl of
+    a Tensor.
+    """
+
+    id: int
+    storage: _Storage
+
+    def __repr__(self) -> str:
+        return f"id={self.id}: {repr(self.storage):<24} ({self.device})"
+
+    def __lt__(self, other: "TensorKey") -> bool:
+        return self._as_sortable < other._as_sortable
+
+    @staticmethod
+    def _make(
+        tensor_id: Optional[int],
+        storage_ptr: Optional[int],
+        allocation_id: Optional[int],
+        device: torch.device,
+    ) -> Optional["TensorKey"]:
+        if (
+            tensor_id is not None
+            and storage_ptr is not None
+            and allocation_id is not None
+        ):
+            return TensorKey(device, tensor_id, _Storage(storage_ptr, allocation_id))
+        return None
+
+    @classmethod
+    def from_allocation(cls, alloc: _ExtraFields_Allocation) -> Optional["TensorKey"]:
+        return cls._make(alloc.id, alloc.ptr, alloc.allocation_id, alloc.device)
+
+    @classmethod
+    def from_tensor(cls, t: Optional[_TensorMetadata]) -> Optional["TensorKey"]:
+        if t is not None:
+            return cls._make(t.id, t.storage_data_ptr, t.allocation_id, t.device)
+        return None
+
+    @property
+    def _as_sortable(self) -> Tuple[int, int, str, int]:
+        return self.id, self.storage.allocation_id, self.device.type, self.device.index
+
+
+def _extract_parameters_and_gradients(
+    node: _ProfilerEvent,
+) -> Iterator[Tuple[Optional[TensorKey], Optional[TensorKey]]]:
+    children = node.children
+
+    # AccumulateGrad is used in the Autograd engine to handle gradient updates.
+    # There are two possible cases:
+    # 1) This is a newly created gradient Tensor. In that case there is nothing
+    #    to accumulate, so autograd simply detaches the Tensor.
+    #
+    # 2) There is a preexisting gradient Tensor and we need to add the newly
+    #    computed update. This is done with an in-place add (aten::add_) op.
+    #    (The underscore suffix denotes "in-place".)
+    if (
+        node.typed[0] == _EventType.TorchOp
+        and node.typed[1].scope == RecordScope.BACKWARD_FUNCTION
+        # TODO(robieta): Move away from load bearing names
+        and node.name == "torch::autograd::AccumulateGrad"
+        and children
+        and children[0].typed[0] == _EventType.TorchOp
+        and children[0].name in ("aten::detach", "aten::add_")
+        and children[0].typed[1].inputs
+        and isinstance(children[0].typed[1].inputs[0], _TensorMetadata)
+    ):
+        yield None, TensorKey.from_tensor(children[0].typed[1].inputs[0])
+
+    # We directly instrument `torch.nn.Module` and `torch.optim.Optimizer`
+    # NOTE: The values captured by the python tracer are cached; they can be
+    #       used to build up labels but do not imply that a Tensor was live at
+    #       a particular time.
+    elif node.typed[0] == _EventType.PyCall:
+        typed_fields = node.typed[1]
+        assert typed_fields.module is None or typed_fields.optimizer is None
+        if typed_fields.module is not None:
+            for _, p, p_grad in typed_fields.module.parameters:
+                yield TensorKey.from_tensor(p), TensorKey.from_tensor(p_grad)
+
+        if typed_fields.optimizer is not None:
+            for p, p_grad, _ in typed_fields.optimizer.parameters:
+                yield TensorKey.from_tensor(p), TensorKey.from_tensor(p_grad)
+
+
+def extract_parameters(node: _ProfilerEvent) -> Iterator[TensorKey]:
+    for p, p_grad in _extract_parameters_and_gradients(node):
+        if p is not None:
+            yield p
+
+
+def extract_gradients(
+    node: _ProfilerEvent,
+) -> Iterator[Tuple[Optional[TensorKey], TensorKey]]:
+    for p, p_grad in _extract_parameters_and_gradients(node):
+        if p_grad is not None:
+            yield p, p_grad
+
+
+def get_scopes(event: Optional[_ProfilerEvent]) -> Tuple[RecordScope, ...]:
+    scopes = []
+    while event:
+        if event.typed[0] == _EventType.TorchOp:
+            scopes.append(event.typed[1].scope)
+        event = event.parent
+    return tuple(scopes)
+
+
+class SchemaMatcher:
+    """Lookup operator schema based on profiled name.
+
+    When profiling we record the operator's name but not the schema. However
+    some analysis requires that information. Fortunately we can look up
+    registered schema from the recorded name. We do not, however, record the
+    overload and so we must compare the profiled arguments with all overloads
+    to determine viable matches.
+
+    Note: Once https://github.com/pytorch/pytorch/issues/78871 is completed
+    this code will be obsolete.
+    """
+
+    @classmethod
+    def inputs_are_mutable(cls, t: _ExtraFields_TorchOp) -> Tuple[Optional[bool], ...]:
+        """Determine which inputs may have mutated based on function schema.
+
+        Note that we don't need to resolve down to a single schema to perform
+        this analysis. An input is mutable if it is mutable in any overload. In
+        practice, however, it is overwhelmingly common to match a single
+        overload. If we cannot find any valid schema then we must be
+        conservative and assume all inputs are mutable.
+        """
+        mutable: Optional[List[bool]] = None
+        for schema in cls.match_schemas(t):
+            mutable = mutable or [False for _ in schema.arguments]
+            for i, arg in enumerate(schema.arguments):
+                mutable[i] |= getattr(arg.alias_info, "is_write", False)
+
+        return tuple(mutable or (None for _ in t.inputs))
+
+    @classmethod
+    def match_schemas(cls, t: _ExtraFields_TorchOp) -> Tuple[FunctionSchema, ...]:
+        signature = tuple(
+            # Tensor
+            TensorKey.from_tensor(i) if isinstance(i, _TensorMetadata)
+            #
+            # TensorList
+            else [TensorKey.from_tensor(j) for j in i] if isinstance(i, list)
+            #
+            # Scalar and uncaptured inputs.
+            else i
+            for i in t.inputs
+        )
+
+        def matches(schema) -> bool:
+            return len(schema.arguments) == len(signature) and all(
+                cls._types_match(observed, schema_arg.type)
+                for observed, schema_arg in zip(signature, schema.arguments)
+            )
+
+        return tuple(s for s in cls.lookup_schemas(t.name) or () if matches(s))
+
+    @classmethod
+    def _types_match(cls, observed, schema_type) -> bool:
+        if isinstance(schema_type, torch._C.OptionalType):
+            schema_type = schema_type.getElementType()
+            return observed is None or cls._types_match(observed, schema_type)
+
+        if isinstance(schema_type, torch._C.AnyType):
+            return True
+
+        if schema_type.isSubtypeOf(torch._C.ListType.ofTensors()):
+            return isinstance(observed, list) and all(
+                isinstance(i, TensorKey) for i in observed
+            )
+
+        type_map: Tuple[Tuple[Any, Union[type, Tuple[type, ...]]], ...] = (
+            (torch._C.TensorType, TensorKey),
+            (torch._C.NoneType, type(None)),
+            (torch._C.BoolType, bool),
+            (torch._C.IntType, int),
+            (torch._C.FloatType, float),
+            (torch._C.ComplexType, complex),
+            (torch._C.NumberType, (bool, int, float, complex)),
+        )
+
+        for jit_type, py_types in type_map:
+            if isinstance(schema_type, jit_type):
+                return isinstance(observed, py_types)
+
+        # Profiler only records a subset of possible argument types. If we
+        # reach this point then the schema must call for a type that profiler
+        # does not record. Thus, the schema can only be a match if `observed`
+        # is also None.
+        return observed is None
+
+    @staticmethod
+    def lookup_schemas(name: str) -> Optional[Tuple[FunctionSchema, ...]]:
+        # TODO(robieta):
+        #   _jit_get_schemas_for_operator is quite expensive. (~100us / call)
+        #   Consider adding `functools.lru_cache` if that becomes an issue.
+
+        try:
+            # Schema lookup will throw if `name` is malformed. (For example,
+            # schemas must be namespaced and schema lookup will fail if name
+            # does not include "::".) We simply catch the exception and return
+            # `None` to denote that `name` cannot be an operator name.
+            #
+            # Note that record_function annotations also go through this path,
+            # so it is expected that some names will not correspond to PyTorch
+            # operators.
+            if "::" not in name:
+                return None
+            return tuple(torch._C._jit_get_schemas_for_operator(name))
+        except RuntimeError:
+            return None
+
+
+class OpTree:
+    def __init__(self, result: _ProfilerResult) -> None:
+        self._root_nodes = result.experimental_event_tree()
+        self._sorted_nodes = tuple(sorted(self.dfs(), key=lambda x: x.start_time_ns))
+
+    def dfs(self, *args, **kwargs) -> Iterator[_ProfilerEvent]:
+        yield from _utils.traverse_dfs(self._root_nodes, *args, **kwargs)
+
+    @property
+    def sorted_nodes(self) -> Tuple[_ProfilerEvent, ...]:
+        return self._sorted_nodes
+
+
+class SizeMap:
+    def __init__(self, op_tree: OpTree) -> None:
+        self._values: Dict[TensorKey, int] = {}
+
+        for node in op_tree.sorted_nodes:
+            if node.typed[0] == _EventType.TorchOp:
+                for t in self._flat_tensor_inputs(node.typed[1]):
+                    self._update_values(t)
+
+            elif node.typed[0] == _EventType.PyCall:
+                typed_fields = node.typed[1]
+                assert typed_fields.module is None or typed_fields.optimizer is None
+                if typed_fields.module is not None:
+                    for _, p, p_grad in typed_fields.module.parameters:
+                        self._update_values(p)
+                        self._update_values(p_grad)
+
+                if typed_fields.optimizer is not None:
+                    for p, p_grad, state in typed_fields.optimizer.parameters:
+                        self._update_values(p)
+                        self._update_values(p_grad)
+                        for _, t in state:
+                            self._update_values(t)
+
+        allocations: Dict[TensorKey, int] = {}
+        for node in op_tree.sorted_nodes:
+            if node.typed[0] == _EventType.Allocation:
+                alloc_fields = node.typed[1]
+                key = TensorKey.from_allocation(alloc_fields)
+                if key:
+                    new_size = abs(alloc_fields.alloc_size)
+                    prior_size = allocations.setdefault(key, new_size)
+
+                    # It is possible to resize Storage in PyTorch, however we
+                    # key on data pointer so most resizes will be treated as a
+                    # change in storage. The one corner case that cannot be
+                    # handled is `realloc` which successfully resizes the
+                    # storage. At time of writing this is not done anywhere in
+                    # the core PyTorch codebase.
+                    if prior_size != new_size:
+                        delta = f"{prior_size} vs. {new_size}"
+                        log.warning("Mismatch between allocation and free: %s", delta)
+
+        self._values.update(allocations)
+
+    def _update_values(self, t: Optional[_TensorMetadata]) -> None:
+        key = TensorKey.from_tensor(t)
+        if key is not None and t is not None and t.layout == torch.strided:
+            # Scalars are represented as zero dim Tensors
+            n = max(i[0] * i[1] for i in zip(t.sizes or [1], t.strides or [1]))
+
+            num_bytes = n * _element_size(t.dtype)
+            assert num_bytes >= 0, f"{num_bytes}"
+            self._values[key] = max(self._values.get(key, 0), num_bytes)
+
+    @staticmethod
+    def _flat_tensor_inputs(op: _ExtraFields_TorchOp) -> Iterator[_TensorMetadata]:
+        for i in op.inputs:
+            if isinstance(i, _TensorMetadata):
+                yield i
+            elif isinstance(i, list):
+                yield from i
+
+    def __getitem__(self, key: TensorKey):
+        return self._values[key]
+
+
+@dataclasses.dataclass()
+class DataFlowEdge:
+    input_version: Optional[int] = None
+    mutated: Optional[bool] = False
+
+    @property
+    def is_allocation(self) -> bool:
+        return self.input_version is None
+
+    @property
+    def is_deletion(self) -> bool:
+        return self.mutated is None
+
+
+class DataFlowNode:
+    def __init__(self, event: _ProfilerEvent, graph: "DataFlowGraph") -> None:
+        self._event = event
+        self._graph = graph
+        self._edges: Dict[TensorKey, DataFlowEdge] = self._determine_edges()
+
+        for key, edge in self._edges.items():
+            if edge.mutated and not edge.is_allocation:
+                self._graph.bump(key)
+
+        # Make sure the version bumping behavior matches what we expect.
+        versions = {k: (v, self._graph.lookup(k)) for k, v in self.outputs.items()}
+        assert all(i == j for i, j in versions.values()), f"{versions}, {self._edges}"
+
+    def _determine_edges(self) -> Dict[TensorKey, DataFlowEdge]:
+        subtree = tuple(_utils.traverse_dfs([self._event]))
+
+        # Start by populating edges from op inputs and outputs.
+        mutable_by_key: Dict[Optional[TensorKey], Set[Optional[bool]]] = {}
+        for op in (i.typed[1] for i in subtree if i.typed[0] == _EventType.TorchOp):
+            for op_input, mutable in zip(
+                op.inputs, SchemaMatcher.inputs_are_mutable(op)
+            ):
+                # Tensor
+                if isinstance(op_input, _TensorMetadata):
+                    key = TensorKey.from_tensor(op_input)
+                    mutable_by_key.setdefault(key, set()).add(mutable)
+
+                # TensorList
+                elif isinstance(op_input, list):
+                    for op_input_i in op_input:
+                        key = TensorKey.from_tensor(op_input_i)
+                        mutable_by_key.setdefault(key, set()).add(mutable)
+
+        edges: DefaultDict[Optional[TensorKey], DataFlowEdge]
+        edges = collections.defaultdict(DataFlowEdge)
+        for key, mutable_set in mutable_by_key.items():
+            if key is not None:
+                edges[key].input_version = self._graph.lookup(key) if key else -1
+
+                # We consider an op to be mutated if we encounter a schema where it
+                # is a mutable argument OR if it is ambiguous. (We never explicitly
+                # see it in any schema.)
+                mutated = (True in mutable_set) or (tuple(mutable_set) == (None,))
+                edges[key].mutated = mutated
+
+        # Then handle deletions. Note that deleting a Tensor implicitly adds
+        # it as an input edge.
+        for i in subtree:
+            if i.typed[0] == _EventType.Allocation and i.typed[1].alloc_size < 0:
+                key = TensorKey.from_allocation(i.typed[1])
+                edge = edges[key]
+                assert key is None or edge.mutated is not None, f"Double delete: {key}"
+                edge.mutated = None
+                edge.input_version = self._graph.lookup(key) if key else -1
+
+        # And finally handle allocations. This step must be last, because the
+        # previous two steps optimistically add input edges.
+        for i in subtree:
+            if i.typed[0] == _EventType.Allocation and i.typed[1].alloc_size > 0:
+                edges[TensorKey.from_allocation(i.typed[1])].input_version = None
+
+        # We don't need to sort the inputs, but it makes debugging and unit tests nicer.
+        return dict(sorted((k, v) for k, v in edges.items() if k is not None))
+
+    @property
+    def inputs(self) -> Dict[TensorKey, Tuple[bool, int]]:
+        return {
+            # MyPy can't see through `is_allocation` to know that
+            # `v.input_version` is not None.
+            k: (bool(v.mutated), cast(int, v.input_version))
+            for k, v in self._edges.items()
+            if not v.is_allocation
+        }
+
+    @property
+    def outputs(self) -> Dict[TensorKey, int]:
+        return {
+            k: 0 if v.input_version is None else v.input_version + 1
+            for k, v in self._edges.items()
+            if (v.is_allocation and not v.is_deletion) or v.mutated
+        }
+
+    @property
+    def intermediates(self) -> Tuple[TensorKey, ...]:
+        return tuple(
+            k for k, v in self._edges.items() if v.is_allocation and v.is_deletion
+        )
+
+    @property
+    def start_time(self) -> int:
+        return self._event.start_time_ns
+
+
+class DataFlowGraph:
+    def __init__(self, op_tree: OpTree) -> None:
+        self._op_tree = op_tree
+        self._leaf_events = self._extract_leaf_events(op_tree)
+        self._active_version: Dict[TensorKey, Optional[int]] = {}
+        self._flow_nodes = [DataFlowNode(e, self) for e in self.leaf_events]
+        self._flow_nodes.sort(key=lambda x: x.start_time)
+        self.validate()
+
+    @property
+    def flow_nodes(self) -> Tuple[DataFlowNode, ...]:
+        return tuple(self._flow_nodes)
+
+    def validate(self):
+        # Check that each (Tensor, version) pair has a unique creation node
+        outputs: Set[Tuple[TensorKey, int]] = set()
+        for node in self.flow_nodes:
+            node_outputs = set(node.outputs.items())
+            duplicates = outputs & node_outputs
+            assert not duplicates, f"{node._event.name} {node._edges} {duplicates}"
+            outputs |= node_outputs
+
+        # And check that `self._nodes` forms a valid topologically sorted DAG.
+        tensor_versions: Dict[TensorKey, int] = {}
+        for node in self.flow_nodes:
+            for key, (_, version) in node.inputs.items():
+                expected = tensor_versions.get(key, 0)
+                assert expected == version, (expected, version)
+
+            for key, version in node.outputs.items():
+                prior_version = tensor_versions.get(key, version)
+                assert version >= prior_version, (version, prior_version)
+                tensor_versions[key] = version
+
+    @property
+    def leaf_events(self) -> Tuple[_ProfilerEvent, ...]:
+        return self._leaf_events
+
+    @staticmethod
+    def _extract_leaf_events(op_tree: OpTree) -> Tuple[_ProfilerEvent, ...]:
+        """Partially traverse the op tree and extract top level ops.
+
+        Consider the following code:
+        ```
+        with record_function("My annotation"):
+            x.zero_()
+            y.zero_()
+        ```
+
+        The op tree (assuming no Autograd) will look like:
+          
+            TorchOp: "My annotation"
+              TorchOp: zero_
+                TorchOp: fill_
+              TorchOp: zero_
+                TorchOp: fill_
+
+        The recursive structure of operator calls makes data flow unwieldy.
+        In order to simplify analysis we would like to select the highest level
+        ops to represent in the graph. In this case those are the `zero_` ops;
+        the fact that `fill_` is called is an implementation detail. We also
+        do not want to group everything under "My annotation" as this could
+        create overly coarse bundles and lose critical semantics.
+
+        To address this issue we walk over the graph and select the topmost
+        torch ops ** which match at least one operator schema **. These form
+        the leaves of the first pass through the op tree. (As well as any
+        allocations or frees which do are not part of a kernel.) These events
+        form the logical nodes in our data flow graph.
+        """
+
+        leaf_events: List[_ProfilerEvent] = []
+
+        def leaf_op(e: _ProfilerEvent) -> bool:
+            return e.typed[0] == _EventType.TorchOp and (
+                e.typed[1].scope == RecordScope.BACKWARD_FUNCTION
+                or bool(SchemaMatcher.match_schemas(e.typed[1]))
+            )
+
+        def children_fn(e: _ProfilerEvent):
+            if leaf_op(e) or e.tag == _EventType.Allocation:
+                leaf_events.append(e)
+                return []
+
+            return e.children
+
+        for _ in op_tree.dfs(children_fn=children_fn):
+            pass
+
+        return tuple(sorted(leaf_events, key=lambda x: x.start_time_ns))
+
+    def lookup(self, key: TensorKey) -> int:
+        version = self._active_version.setdefault(key, 0)
+        assert version is not None
+        return version
+
+    def bump(self, key: TensorKey) -> None:
+        prior_version = self._active_version.get(key, None)
+        assert prior_version is not None
+        self._active_version[key] = prior_version + 1
+
+    def delete(self, key: TensorKey) -> None:
+        assert self._active_version.setdefault(key, 0) is not None
+        self._active_version[key] = None
+
+
+@dataclasses.dataclass
+class CategoryElement:
+    by_id: Optional[Category] = None
+    by_key: Dict[TensorKey, Category] = dataclasses.field(default_factory=dict)
+    by_version: Dict[TensorAndID, Category] = dataclasses.field(default_factory=dict)
+
+    # Used by unit tests to check internals. (And consequently by
+    # MemoryProfile.lookup) This should not be used in any other capacity.
+    _by_id_keyset: Set[TensorKey] = dataclasses.field(default_factory=set)
+
+
+@dataclasses.dataclass
+class CategoryDict:
+    _values: DefaultDict[int, CategoryElement] = dataclasses.field(
+        default_factory=lambda: collections.defaultdict(CategoryElement)
+    )
+
+    def set_by_id(self, key: TensorKey, category: Category) -> None:
+        self._values[key.id].by_id = category
+        self._values[key.id]._by_id_keyset.add(key)
+
+    def set_by_key(self, key: TensorKey, category: Category) -> None:
+        self._values[key.id].by_key[key] = category
+
+    def set_by_version(self, key: TensorKey, version: int, category: Category) -> None:
+        self._values[key.id].by_version[(key, version)] = category
+
+    def setdefault_by_version(
+        self, key: TensorKey, version: int, category: Category
+    ) -> None:
+        self._values[key.id].by_version.setdefault((key, version), category)
+
+    def get(self, key: Key, version: int) -> Optional[Category]:
+        if isinstance(key, Key) and not isinstance(key, TensorKey):
+            return None
+        element = self._values[key.id]
+        return (
+            element.by_id
+            or element.by_key.get(key, None)
+            or element.by_version.get((key, version), None)
+        )
+
+
+class MemoryProfile:
+    def __init__(self, result: _ProfilerResult) -> None:
+        self._op_tree = OpTree(result)
+        self._data_flow_graph = DataFlowGraph(self._op_tree)
+        self._size_map = SizeMap(self._op_tree)
+        self._categories = CategoryDict()
+
+        self._set_gradients_and_temporaries()
+        self._set_parameters_using_python_tracer()
+        self._set_inputs()
+        self._set_parameters_using_data_flow()
+        self._set_activations()
+        self._set_optimizer_state()
+        self._set_autograd_detail()
+
+    @property
+    def timeline(self) -> Tuple[Tuple[int, Action, KeyAndID, int], ...]:
+        output: List[Tuple[int, Action, KeyAndID, int]] = []
+        allocation_times: Dict[Tuple[TensorKey, bool], int] = {}
+        live_unknown: Dict[Tuple[int, torch.device], Literal[True]] = {}
+        for event in self._op_tree.dfs():
+            if event.typed[0] == _EventType.Allocation:
+                alloc_fields = event.typed[1]
+                alloc_size = alloc_fields.alloc_size
+                is_allocation = alloc_size > 0
+                t = event.start_time_ns
+
+                tkey = TensorKey.from_allocation(alloc_fields)
+                if tkey is not None:
+                    allocation_times[(tkey, is_allocation)] = t
+
+                else:
+                    key = Key(alloc_fields.device)
+                    ptr_and_device = (alloc_fields.ptr, key.device)
+                    if is_allocation:
+                        if ptr_and_device in live_unknown:
+                            output.append(
+                                (t, Action.INCREMENT_VERSION, (key, 0), alloc_size)
+                            )
+                        else:
+                            live_unknown[ptr_and_device] = True
+                            output.append((t, Action.CREATE, (key, 0), alloc_size))
+                    else:
+                        output.append((t, Action.DESTROY, (key, 0), -alloc_size))
+                        if not live_unknown.pop(ptr_and_device, False):
+                            output.append(
+                                (-1, Action.PREEXISTING, (key, 0), -alloc_size)
+                            )
+
+        snapshot = self._category_snapshot()
+        last_version = dict(sorted(snapshot.keys()))
+
+        events: List[Tuple[int, Action, TensorAndID]] = [
+            (-1, Action.PREEXISTING, (key, version))
+            for key, version in snapshot.keys()
+            if (key, True) not in allocation_times and version == 0
+        ]
+
+        for node in self._data_flow_graph.flow_nodes:
+            for key, edge in node._edges.items():
+                if edge.is_allocation:
+                    t = allocation_times[(key, True)]
+                    events.append((t, Action.CREATE, (key, 0)))
+
+                elif edge.mutated:
+                    t = node._event.start_time_ns
+                    version = edge.input_version
+                    assert version is not None
+                    events.append((t, Action.INCREMENT_VERSION, (key, version)))
+
+                if edge.is_deletion:
+                    t = allocation_times[(key, False)]
+                    events.append((t, Action.DESTROY, (key, last_version[key])))
+
+        output.extend(
+            (time, action, (key, version), self._size_map[key])
+            for time, action, (key, version) in events
+        )
+
+        output.sort(key=lambda x: (x[0], x[1].value))
+        return tuple(output)
+
+    def _is_gradient(self, *args, **kwargs) -> bool:
+        return self._categories.get(*args, **kwargs) == Category.GRADIENT
+
+    def _category_snapshot(self) -> Dict[TensorAndID, Optional[Category]]:
+        all_tensor_versions: Set[TensorAndID] = set()
+
+        for node in self._data_flow_graph.flow_nodes:
+            all_tensor_versions.update(((k, v) for k, (_, v) in node.inputs.items()))
+            all_tensor_versions.update((key, 0) for key in node.intermediates)
+            all_tensor_versions.update(node.outputs.items())
+
+        for i in self._categories._values.values():
+            all_tensor_versions.update((key, 0) for key in i._by_id_keyset)
+
+        return {
+            (key, version): self._categories.get(key, version)
+            for key, version in sorted(all_tensor_versions)
+        }
+
+    def _any_version_depends_on_gradient(self) -> Set[int]:
+        """Extract IDs of Tensors which depend or will depend on a gradient.
+
+        Note that this weakened definition of "depends" requires us to loop
+        over the data flow graph multiple times because it allows dependency
+        information to flow backward through edges and removes the guarantee
+        that nodes are topologically sorted. (Or indeed, even that a valid
+        topological order exists.) Put another way, we have converted an
+        acyclic data flow graph into a cyclic graph and we are attempting to
+        partition cycles involving a gradient from the rest of the graph.
+        """
+        depends_on_gradient: Set[int] = set()
+        while True:
+            start_size = len(depends_on_gradient)
+            for node in self._data_flow_graph.flow_nodes:
+                ids = tuple(
+                    key.id
+                    for key, (_, version) in node.inputs.items()
+                    if self._categories.get(key, version)
+                    in (Category.GRADIENT, Category.PARAMETER)
+                    or key.id in depends_on_gradient
+                )
+
+                if ids:
+                    depends_on_gradient.update(ids)
+                    depends_on_gradient.update(key.id for key in node.outputs)
+
+            # We are guaranteed to exit because there is a finite set of
+            # TensorAndID pairs. In practice we do not expect to loop more than
+            # three times: once to identify the core parameter update loop,
+            # once to fold the first step into that loop, and a third time
+            # where no new elements are added.
+            if len(depends_on_gradient) == start_size:
+                return depends_on_gradient
+
+    def _set_gradients_and_temporaries(self) -> None:
+        """Mark Tensors which are unambiguous and simple to reason about."""
+
+        # Gradients are straightforward to detect. We directly check the
+        # `.grad` property in the Python tracer, and we can detect any new
+        # gradient Tensors from `AccumulateGrad` ops.
+        for event in self._op_tree.dfs():
+            for _, p_grad in extract_gradients(event):
+                self._categories.set_by_id(p_grad, Category.GRADIENT)
+
+        # Similarly, temporary Tensors are easy to identify and are useful to
+        # flag since they can make memory use "spikier" than one would
+        # otherwise expect.
+        for node in self._data_flow_graph.flow_nodes:
+            for i in node.intermediates:
+                self._categories.set_by_key(i, Category.TEMPORARY)
+
+    def _set_parameters_using_python_tracer(self) -> None:
+        for event in self._op_tree.dfs():
+            for p in extract_parameters(event):
+                if p is not None:
+                    self._categories.set_by_id(p, Category.PARAMETER)
+
+    def _set_inputs(self) -> None:
+        """Mark inputs based on which Tensors are updated using gradients.
+
+        The process for differentiating between inputs and activations is more
+        involved. Most Tensors in a training loop depend on at least one
+        gradient: parameters depend on them through updates, and activations
+        and optimizer state depend on them transitively through parameters.
+        Critically, we do not need to know which Tensors are parameters to
+        apply this method; we can simply walk the data flow graph to build the
+        set of all values which depend on a gradient and then obtain the set
+        of inputs from the conjugate set.
+
+        There is, however, one hiccup. The first time we see a parameter is
+        generally on the forward pass of the first step. We know from
+        inspection of the data flow graph that v1 of that Tensor depends on
+        a gradient (provided we profile an optimizer step), but not v0. To
+        address this problem we weaken the definition of "depends on a
+        gradient" to "any version of this Tensor depends on a gradient",
+        which in turn strengthens the criteria for the input set enough to
+        filter the activations in the forward pass of the first step."""
+
+        # All of this analysis is predicated on using at least one training
+        # step (or parameters from the python tracer) to partition the graph.
+        # Absent that we cannot determine which Tensors are inputs and which
+        # ones are part of the model.
+        depends_on_gradient = self._any_version_depends_on_gradient()
+
+        # We only want to annotate Tensors which actually contribute to the
+        # model calculation.
+        produces_gradient: Set[TensorAndID] = set()
+        for node in reversed(self._data_flow_graph.flow_nodes):
+            tensors = {(key, version) for key, (_, version) in node.inputs.items()}
+            tensors |= node.outputs.items()
+            if any(
+                self._categories.get(*i) in (Category.GRADIENT, Category.PARAMETER)
+                or i in produces_gradient
+                for i in tensors
+            ):
+                produces_gradient |= tensors
+
+        # Don't include Tensors created in the backward pass, as these are
+        # generally Autograd implementation details rather than proper inputs.
+        input_candidates = produces_gradient.copy()
+        for node in self._data_flow_graph.flow_nodes:
+            if RecordScope.BACKWARD_FUNCTION in get_scopes(node._event):
+                input_candidates -= set(node.outputs.items())
+
+        for key, version in input_candidates:
+            if key.id not in depends_on_gradient:
+                self._categories.setdefault_by_version(key, version, Category.INPUT)
+
+    def _set_parameters_using_data_flow(self) -> None:
+        """Deduce which Tensors are parameters.
+
+        Consider the following code for the step of SGD with momentum
+        (nesterov=False), where `d_p` is the gradient of `param` and `buf` is
+        the momentum buffer.
+        ```
+          buf.mul_(momentum).add_(d_p, alpha=1 - dampening)
+          d_p = buf
+          param.add_(d_p, alpha=-lr)
+        ```
+        Both `param` and `buf` take a gradient and perform an in-place update.
+
+        The python tracer will inspect calls to `nn.Module.forward` and
+        `optim.Optimizer.step` to extract parameter and optimizer state
+        respectively (including parameters), so this is generally a non-issue.
+
+        However as a fallback we can also exploit several properties of
+        parameters to distinguish them from other model state.
+
+        First, they are directly used in the forward pass. (At this point we
+        haven't established which parts of the graph correspond to the forward
+        pass but we can deduce enough to suffice.) Some mutable state such as
+        batch norm moving averages also contribute to the forward pass, but
+        optimizer state does not.
+
+        Second, a parameter is by definition used to compute at least one
+        gradient and depends on at least one gradient.
+        """
+        snapshot = self._category_snapshot()
+
+        # Determine which Tensors might be parameters based on forward pass
+        # data flow. Note this these are only candidates; we filter nodes that
+        # we know are part of the backward pass but that doesn't guarantee that
+        # they are part of the forward pass.
+        candidate_parameters: Set[TensorAndID] = set()
+        candidate_fwd_tensors: Set[TensorAndID] = {
+            i for i, category in snapshot.items() if category == Category.INPUT
+        }
+
+        for node in self._data_flow_graph.flow_nodes:
+            inputs = {(key, value) for key, (_, value) in node.inputs.items()}
+            if (
+                # Don't check nodes in the backward pass.
+                RecordScope.BACKWARD_FUNCTION not in get_scopes(node._event)
+                and not any(self._is_gradient(*i) for i in inputs)
+                and not any(self._is_gradient(*i) for i in node.outputs.items())
+                #
+                # and only check nodes which depend on an input.
+                and candidate_fwd_tensors.intersection(inputs)
+            ):
+                candidate_fwd_tensors |= node.outputs.items()
+                candidate_parameters |= inputs.difference(candidate_fwd_tensors)
+
+        # Require that each parameter eventually contributes to the value of a gradient
+        used_for_gradient: Set[TensorAndID] = set()
+        for node in reversed(self._data_flow_graph.flow_nodes):
+            if any(
+                self._is_gradient(*i) or i in used_for_gradient
+                for i in node.outputs.items()
+            ):
+                used_for_gradient.update(
+                    (key, version) for key, (_, version) in node.inputs.items()
+                )
+        candidate_parameters.intersection_update(used_for_gradient)
+
+        # and depends on a gradient.
+        parameter_keys = {key.id for key, _ in candidate_parameters}
+        parameter_keys &= self._any_version_depends_on_gradient()
+
+        for key, _ in snapshot.keys():
+            if key.id in parameter_keys:
+                self._categories.set_by_id(key, Category.PARAMETER)
+
+    def _set_activations(self) -> None:
+        """Flood the graph to identify activations."""
+
+        required = {Category.INPUT, Category.ACTIVATION}
+        also_allowed = {Category.PARAMETER, Category.TEMPORARY}
+        for node in self._data_flow_graph.flow_nodes:
+            inputs = {(key, value) for key, (_, value) in node.inputs.items()}
+            input_categories = {self._categories.get(*i) for i in inputs}
+
+            if (
+                (input_categories & required)
+                and not (input_categories - (required | also_allowed))
+                #
+                # Stop filling when we reach the backward pass.
+                and RecordScope.BACKWARD_FUNCTION not in get_scopes(node._event)
+            ):
+                for i in node.outputs.items():
+                    self._categories.setdefault_by_version(*i, Category.ACTIVATION)
+
+    def _set_optimizer_state(self) -> None:
+        for event in self._op_tree.dfs():
+            if event.typed[0] == _EventType.PyCall and event.typed[1].optimizer:
+                parameters = event.typed[1].optimizer.parameters
+                for _, t in it.chain(*[state for _, _, state in parameters]):
+                    key = TensorKey.from_tensor(t)
+                    if key is not None:
+                        self._categories.set_by_id(key, Category.OPTIMIZER_STATE)
+
+    def _set_autograd_detail(self):
+        prior = {None, Category.AUTOGRAD_DETAIL}
+        for node in self._data_flow_graph.flow_nodes:
+            if RecordScope.BACKWARD_FUNCTION in get_scopes(node._event):
+                for key, version in node.outputs.items():
+                    if version == 0 or self._categories.get(key, version - 1) in prior:
+                        self._categories.setdefault_by_version(
+                            key, version, Category.AUTOGRAD_DETAIL
+                        )
+
+
+class MemoryProfileTimeline:
+    def __init__(self, memory_profile):
+        """The minimum representation of the memory profile timeline
+        includes the memory timeline and categories. The timeline
+        consists of [timestamp, action, (TensorKey, version), numbytes]
+        elements, to denote any actions (pre-existing, create, destroy,
+        or increment_version) that occurred to a specific Tensor for a
+        chunk of memory. The categories help map each (TensorKey,
+        version) pair into a category."""
+        self.timeline = memory_profile.timeline
+        self.categories = memory_profile._categories
+
+    def _coalesce_timeline(self, device_str):
+        """Convert the memory timeline and categories into a memory plot
+        consisting of timestamps and their respective sizes by category
+        for a given device.
+
+        Input: device
+        Output: [timestamps, sizes by category]
+        """
+        device = torch.device(device_str)
+        times: List[int] = []
+        sizes: List[List[int]] = []
+
+        def update(key, version, delta):
+            category = (
+                self.categories.get(key, version)
+                if isinstance(key, TensorKey)
+                else None
+            )
+            index = _CATEGORY_TO_INDEX[category] + 1
+            sizes[-1][index] += int(delta)
+
+        t_min = -1
+        for t, action, (key, version), numbytes in self.timeline:
+            if key.device != device:
+                continue
+
+            # Convert timestamps from ns to us, to match trace events.
+            if t != -1:
+                t = int(t / 1000)
+
+            # Save the smallest timestamp to populate pre-existing allocs.
+            if t_min == -1 or (t < t_min and t > 0):
+                t_min = t
+
+            # Handle timestep
+            if len(times) == 0:
+                times.append(t)
+                sizes.append([0] + [0 for _ in _CATEGORY_TO_INDEX])
+
+            elif t != times[-1]:
+                times.append(t)
+                sizes.append(sizes[-1].copy())
+
+            # Handle memory and categories
+            if action in (Action.PREEXISTING, Action.CREATE):
+                update(key, version, numbytes)
+
+            elif action == Action.INCREMENT_VERSION:
+                update(key, version, -numbytes)
+                update(key, version + 1, numbytes)
+
+            elif action == Action.DESTROY:
+                update(key, version, -numbytes)
+
+            else:
+                raise ValueError(f"Unknown action: {action}")
+
+        times = [t_min if t < 0 else t for t in times]
+        return times, sizes
+
+    def export_memory_timeline(self, path, device_str) -> None:
+        """Saves the memory timeline as [times, sizes by category]
+        as a JSON formatted file to the given path for the given
+        device."""
+        times, sizes = self._coalesce_timeline(device_str)
+        # TODO: Write a faster serialize (orjson not available in CI)
+        import json
+
+        with open(path, "w") as f:
+            json.dump([times, sizes], f)
+
+    def export_memory_timeline_raw(self, path, device_str) -> None:
+        """Saves the memory timeline as raw memory event tuples in the
+        form of (timestamp, action, numbytes, category)
+        as a JSON formatted file to the given path for the given
+        device."""
+        device = torch.device(device_str)
+        raw_events: List[Tuple[int, int, int, int]] = []
+
+        def get_category_index(key, version):
+            category = (
+                self.categories.get(key, version)
+                if isinstance(key, TensorKey)
+                else None
+            )
+            return _CATEGORY_TO_INDEX[category]
+
+        for t, action, (key, version), numbytes in self.timeline:
+            if key.device != device:
+                continue
+
+            if action in (Action.PREEXISTING, Action.CREATE):
+                raw_events.append(
+                    (
+                        t,
+                        _ACTION_TO_INDEX[action],
+                        numbytes,
+                        get_category_index(key, version),
+                    )
+                )
+
+            elif action == Action.INCREMENT_VERSION:
+                raw_events.append(
+                    (
+                        t,
+                        _ACTION_TO_INDEX[action],
+                        -numbytes,
+                        get_category_index(key, version),
+                    )
+                )
+                raw_events.append(
+                    (
+                        t,
+                        _ACTION_TO_INDEX[action],
+                        numbytes,
+                        get_category_index(key, version + 1),
+                    )
+                )
+
+            elif action == Action.DESTROY:
+                raw_events.append(
+                    (
+                        t,
+                        _ACTION_TO_INDEX[action],
+                        -numbytes,
+                        get_category_index(key, version),
+                    )
+                )
+
+            else:
+                raise ValueError(f"Unknown action: {action}")
+
+        import json
+
+        with open(path, "w") as f:
+            json.dump(raw_events, f)
+
+    def export_memory_timeline_html(
+        self, path, device_str, figsize=(20, 12), title=None
+    ) -> None:
+        """Exports the memory timeline as an HTML file which contains
+        the memory timeline plot embedded as a PNG file."""
+        # Check if user has matplotlib installed, return gracefully if not.
+        import importlib.util
+
+        matplotlib_spec = importlib.util.find_spec("matplotlib")
+        if matplotlib_spec is None:
+            print(
+                "export_memory_timeline_html failed because matplotlib was not found."
+            )
+            return
+
+        from base64 import b64encode
+        from os import remove
+        from tempfile import NamedTemporaryFile
+
+        import matplotlib.pyplot as plt
+        import numpy as np
+
+        mt = self._coalesce_timeline(device_str)
+        times, sizes = np.array(mt[0]), np.array(mt[1])
+        # For this timeline, start at 0 to match Chrome traces.
+        t_min = min(times)
+        times -= t_min
+        stacked = np.cumsum(sizes, axis=1) / 1024**3
+        device = torch.device(device_str)
+        max_memory_allocated = torch.cuda.max_memory_allocated(device)
+        max_memory_reserved = torch.cuda.max_memory_reserved(device)
+
+        # Plot memory timeline as stacked data
+        fig = plt.figure(figsize=figsize, dpi=80)
+        axes = fig.gca()
+        for category, color in _CATEGORY_TO_COLORS.items():
+            i = _CATEGORY_TO_INDEX[category]
+            axes.fill_between(
+                times / 1e3, stacked[:, i], stacked[:, i + 1], color=color, alpha=0.7
+            )
+        fig.legend(["Unknown" if i is None else i.name for i in _CATEGORY_TO_COLORS])
+        # Usually training steps are in magnitude of ms.
+        axes.set_xlabel("Time (ms)")
+        axes.set_ylabel("Memory (GB)")
+        title = "\n\n".join(
+            ([title] if title else [])
+            + [
+                f"Max memory allocated: {max_memory_allocated/(1024**3):.2f} GiB \n"
+                f"Max memory reserved: {max_memory_reserved/(1024**3):.2f} GiB"
+            ]
+        )
+        axes.set_title(title)
+
+        # Embed the memory timeline image into the HTML file
+        tmpfile = NamedTemporaryFile("wb", suffix=".png", delete=False)
+        tmpfile.close()
+        fig.savefig(tmpfile.name, format="png")
+
+        with open(tmpfile.name, "rb") as tmp:
+            encoded = b64encode(tmp.read()).decode("utf-8")
+            html = f"""
+GPU Memory Timeline HTML
+
+  
+
+"""
+
+            with open(path, "w") as f:
+                f.write(html)
+        remove(tmpfile.name)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/_pattern_matcher.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/_pattern_matcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7ec5d05dd68e672157d876e7a91cd2f0caf1f21
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/_pattern_matcher.py
@@ -0,0 +1,663 @@
+# mypy: allow-untyped-defs
+import json
+import math
+import os
+import re
+from typing import Dict, List, Optional, Set
+
+import torch
+import torch.utils.benchmark as benchmark
+from torch._C._profiler import (
+    _EventType,
+    _ExtraFields_PyCall,
+    _ExtraFields_PyCCall,
+    _ExtraFields_TorchOp,
+    _ProfilerEvent,
+)
+from torch.profiler import profile
+from torch.profiler._utils import index_of_first_match, traverse_bfs, traverse_dfs
+
+
+class Pattern:
+    """
+    Base class for all patterns, subclass this class and implement match()
+    to define custom patterns.
+
+    In subclass, define description and skip property.
+    """
+
+    def __init__(self, prof: profile, should_benchmark: bool = False):
+        self.prof = prof
+        self.should_benchmark = should_benchmark
+        self.name = "Please specify a name for pattern"
+        self.description = "Please specify a description for pattern"
+        self.url = ""
+        assert prof.profiler is not None and prof.profiler.kineto_results is not None
+        self.event_tree = prof.profiler.kineto_results.experimental_event_tree()
+        self.tid_root: Dict[int, List[_ProfilerEvent]] = {}
+        for event in self.event_tree:
+            self.tid_root.setdefault(event.start_tid, []).append(event)
+
+    @property
+    def skip(self):
+        return False
+
+    def report(self, event: _ProfilerEvent):
+        msg = (
+            f"{self.description}\n[Source Code Location] {source_code_location(event)}"
+        )
+        return msg
+
+    def eventTreeTraversal(self):
+        """
+        Traverse the event tree and yield all events.
+        Override this method in subclass to customize the traversal.
+        """
+        yield from traverse_dfs(self.event_tree)
+
+    def summary(self, events: List[_ProfilerEvent]):
+        default_summary = f"{self.name}: {len(events)} events matched."
+        if self.should_benchmark:
+            # If benchmark summary is not empty, use it.
+            return (
+                self.benchmark_summary(events)
+                if hasattr(self, "benchmark")  # type: ignore[attr-defined]
+                else default_summary
+            )
+        return default_summary
+
+    def benchmark_summary(self, events: List[_ProfilerEvent]):
+        def format_time(time_ns: int):
+            unit_lst = ["ns", "us", "ms"]
+            for unit in unit_lst:
+                if time_ns < 1000:
+                    return f"{time_ns:.2f} {unit}"
+                time_ns //= 1000
+            return f"{time_ns:.2f} s"
+
+        assert hasattr(self, "benchmark"), "Please implement benchmark()"
+        shapes_factor_map = self.benchmark(events)  # type: ignore[attr-defined]
+        original_time = sum(event.duration_time_ns for event in events)
+        new_time = sum(
+            shapes_factor_map[input_shapes(event)] * event.duration_time_ns
+            for event in events
+        )
+        return (
+            f"{self.name}: {len(events)} events matched. "
+            f"Total Estimated Speedup: {format_time(original_time - new_time)} ({round(original_time/new_time, 2)}X)"
+        )
+
+    def match(self, event: _ProfilerEvent):
+        """
+        Return True if the event matches the pattern.
+        This method should be overriden in subclass.
+        """
+        raise NotImplementedError
+
+    def matched_events(self):
+        if self.skip:
+            return []
+        matched_events = []
+        for event in self.eventTreeTraversal():
+            if self.match(event):
+                matched_events.append(event)
+        return matched_events
+
+    def root_of(self, event: _ProfilerEvent):
+        while event.parent:
+            event = event.parent
+        return event
+
+    def siblings_of(self, event: _ProfilerEvent):
+        if event.parent:
+            children = event.parent.children
+        else:
+            children = self.tid_root[event.start_tid]
+        index = children.index(event)
+        return children[:index], children[index + 1 :]
+
+    def next_of(self, event: _ProfilerEvent):
+        _, next_events = self.siblings_of(event)
+        return next_events[0] if next_events else None
+
+    def prev_of(self, event: _ProfilerEvent):
+        prev_events, _ = self.siblings_of(event)
+        return prev_events[-1] if prev_events else None
+
+    def go_up_until(self, event: _ProfilerEvent, predicate):
+        if not event:
+            return None
+        while event.parent and not predicate(event):
+            event = event.parent
+        return event
+
+
+# Patterns
+
+
+class NamePattern(Pattern):
+    def __init__(self, prof: profile, name: str, should_benchmark: bool = False):
+        super().__init__(prof, should_benchmark)
+        self.description = f"Matched Name Event: {name}"
+        self.name = name
+
+    def match(self, event: _ProfilerEvent):
+        return re.search(self.name, event.name) is not None
+
+
+class ExtraCUDACopyPattern(Pattern):
+    """
+    This pattern identifies if we creates a constant tensor on CPU and immediately moves it to GPU.
+    example: torch.zeros((100, 100)).to("cuda")
+
+    Pattern:
+    build-in method                 |build-in method
+        ...                         |    aten::to
+            aten::fill_/aten::zero_ |        aten::_to_copy
+
+    Algorithm:
+    We start at node aten::to, go parent events' previous events,
+    and check if we have a aten::fill_/aten::zero_ as we keep going down the tree.
+    We always select the last child in the children list when we go down the tree.
+    If at any step we failed, it is not a match.
+    """
+
+    def __init__(self, prof: profile, should_benchmark: bool = False):
+        super().__init__(prof, should_benchmark)
+        self.name = "Extra CUDA Copy Pattern"
+        self.description = "Filled a CPU tensor and immediately moved it to GPU. Please initialize it on GPU."
+        self.url = "https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html#create-tensors-directly-on-the-target-device"
+        self.init_ops = {
+            "aten::fill_",
+            "aten::zero_",
+            "aten::normal_",
+            "aten::uniform_",
+        }
+
+    @property
+    def skip(self):
+        return not self.prof.with_stack or not self.prof.record_shapes
+
+    def match(self, event):
+        # TODO: We should also check tensor identities
+        if event.name != "aten::to":
+            return False
+        to_event = event
+        if not event.children:
+            return False
+        event = event.children[-1]
+        if event.name != "aten::_to_copy":
+            return False
+        if not event.children:
+            return False
+        event = event.children[-1]
+        if event.name != "aten::copy_":
+            return False
+        # aten::copy_ should have the first 2 args dtype the same
+        dtypes = input_dtypes(event)
+        if len(dtypes) < 2:
+            return False
+        if dtypes[0] is None or dtypes[0] != dtypes[1]:
+            return False
+        event = to_event
+        # Up one level
+        event = event.parent
+        if event is None:
+            return False
+        # Check if we have a aten::fill_ in previous leaf
+        event = self.prev_of(event)
+        if event is None:
+            return False
+        while event.children:
+            event = event.children[-1]
+            # aten::zero_ is a special optimzation case where fill_ is not called
+            if event.name in self.init_ops:
+                return True
+        return event.name in self.init_ops
+        # TODO: Check if tensor is reused
+
+    def benchmark(self, events: List[_ProfilerEvent]):
+        shapes_factor_map = {input_shapes(event): 0.0 for event in events}
+        for shape in shapes_factor_map:
+            size = shape[0]
+            to_timer = benchmark.Timer(
+                stmt='torch.ones(size).to("cuda")', globals={"size": size}
+            )
+            de_timer = benchmark.Timer(
+                stmt='torch.ones(size, device="cuda")', globals={"size": size}
+            )
+            to_time = to_timer.timeit(10).mean
+            de_time = de_timer.timeit(10).mean
+            shapes_factor_map[shape] = de_time / to_time
+        return shapes_factor_map
+
+
+class ForLoopIndexingPattern(Pattern):
+    """
+    This pattern identifies if we use a for loop to index a tensor that
+    can be vectorized.
+    example:
+    tensor = torch.empty((100, 100))
+    for i in range(100):
+        tensor[i] = i
+
+    Pattern:
+    aten::select | ... | aten::select | ... (Repeat)
+
+    Algorithm:
+    We start at node aten::select, and we check if we can find this alternating patterns.
+    We also keep a dictionary to avoid duplicate match in the for loop.
+    """
+
+    def __init__(self, prof: profile, should_benchmark: bool = False):
+        super().__init__(prof, should_benchmark)
+        self.name = "For Loop Indexing Pattern"
+        self.description = "For loop indexing detected. Vectorization recommended."
+        self.visited: Set[int] = set()
+
+    def eventTreeTraversal(self):
+        """
+        We need to use BFS traversal order to avoid duplicate match.
+        """
+        yield from traverse_bfs(self.event_tree)
+
+    def match(self, event: _ProfilerEvent):
+        if event.name != "aten::select":
+            return False
+        if event.id in self.visited:
+            return False
+        repeat_count = 1
+        _, next = self.siblings_of(event)
+        if len(next) <= 1:
+            return False
+
+        # Custom event list matching
+        def same_ops(list1, list2):
+            if len(list1) != len(list2):
+                return False
+            for op1, op2 in zip(list1, list2):
+                if op1.name != op2.name:
+                    return False
+            return True
+
+        # Record the ops between two aten::select
+        next_select_idx = index_of_first_match(next, lambda e: e.name == "aten::select")
+        if next_select_idx is None:
+            return False
+        indexing_ops = [event] + next[:next_select_idx]
+        next = next[len(indexing_ops) - 1 :]
+        for i in range(0, len(next), len(indexing_ops)):
+            if same_ops(indexing_ops, next[i : i + len(indexing_ops)]):
+                repeat_count += 1
+                self.visited.add(next[i].id)
+            else:
+                break
+        return repeat_count >= 10
+
+
+class FP32MatMulPattern(Pattern):
+    def __init__(self, prof: profile, should_benchmark: bool = False):
+        super().__init__(prof, should_benchmark)
+        self.name = "FP32 MatMul Pattern"
+        self.description = (
+            "You are currently using GPU that supports TF32. "
+            "Please enable TF32 by setting 'torch.backends.cuda.matmul.allow_tf32 = True'"
+        )
+        self.url = "https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+
+    @property
+    def skip(self):
+        if torch.version.hip is not None:
+            has_tf32 = False
+        else:
+            # Anything less than sm_80 is not Ampere which doesn't support TF32
+            has_tf32 = all(int(arch[3:]) >= 80 for arch in torch.cuda.get_arch_list())
+        return has_tf32 is False or super().skip or not self.prof.record_shapes
+
+    def match(self, event: _ProfilerEvent):
+        # If we saw this pattern once, we don't need to match it again
+        if event.tag != _EventType.TorchOp:
+            return False
+        assert isinstance(event.extra_fields, _ExtraFields_TorchOp)
+        if event.name == "aten::mm":
+            if event.extra_fields.allow_tf32_cublas is False:
+                return True
+        return False
+
+    def report(self, event: _ProfilerEvent):
+        return self.description
+
+    def benchmark(self, events: List[_ProfilerEvent]):
+        shapes_factor_map = {input_shapes(event): 0.0 for event in events}
+        for shape in shapes_factor_map:
+            matrixA = torch.randn(shape[0], device="cuda", dtype=torch.float32)
+            matrixB = torch.randn(shape[1], device="cuda", dtype=torch.float32)
+            fp32_timer = benchmark.Timer(
+                stmt="torch.mm(matrixA, matrixB)",
+                globals={"matrixA": matrixA, "matrixB": matrixB},
+            )
+            tf32_timer = benchmark.Timer(
+                stmt="torch.mm(matrixA, matrixB)",
+                setup="torch.backends.cuda.matmul.allow_tf32 = True",
+                globals={"matrixA": matrixA, "matrixB": matrixB},
+            )
+            torch.backends.cuda.matmul.allow_tf32 = False
+            fp32_time = fp32_timer.timeit(10).mean
+            tf32_time = tf32_timer.timeit(10).mean
+            shapes_factor_map[shape] = tf32_time / fp32_time
+        return shapes_factor_map
+
+
+class OptimizerSingleTensorPattern(Pattern):
+    """
+    This pattern identifies if we are using the single-tensor version of an optimizer.
+    example:
+    optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
+    By adding foreach=True to enable multi-tensor optimizer, we can gain speedup when
+    the kernels are relatively small.
+
+    Pattern:
+    XXXXX: _single_tenser_
+
+    Algorithm:
+    String match
+    """
+
+    def __init__(self, prof: profile, should_benchmark: bool = False):
+        super().__init__(prof, should_benchmark)
+        self.name = "Optimizer Single Tensor Pattern"
+        self.optimizers_with_foreach = ["adam", "sgd", "adamw"]
+        self.description = (
+            "Deteced optimizer running with single tensor implementation. "
+            "Please enable multi tensor implementation by passing 'foreach=True' into optimizer."
+        )
+        self.url = ""
+
+    def match(self, event: _ProfilerEvent):
+        for optimizer in self.optimizers_with_foreach:
+            if event.name.endswith(f"_single_tensor_{optimizer}"):
+                return True
+        return False
+
+
+class SynchronizedDataLoaderPattern(Pattern):
+    """
+    This pattern identifies if we are using num_workers=0 in DataLoader.
+    example:
+    torch.utils.data.DataLoader(dataset, batch_size=batch_size)
+    Add num_workers=N to the arguments. N depends on system configuration.
+
+    Pattern:
+    dataloader.py(...): __iter__
+        dataloader.py(...): _get_iterator
+            NOT dataloader.py(...): check_worker_number_rationality
+
+    Algorithm:
+    If we don't see check_worker_number_rationality call in the dataloader __iter__,
+    It is not an asynchronous dataloader.
+
+    """
+
+    def __init__(self, prof: profile, should_benchmark: bool = False):
+        super().__init__(prof, should_benchmark)
+        self.name = "Synchronized DataLoader Pattern"
+        self.description = (
+            "Detected DataLoader running with synchronized implementation. "
+            "Please enable asynchronous dataloading by setting num_workers > 0 when initializing DataLoader."
+        )
+        self.url = (
+            "https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html"
+            "#enable-async-data-loading-and-augmentation"
+        )
+
+    def match(self, event: _ProfilerEvent):
+        def is_dataloader_function(name: str, function_name: str):
+            return name.startswith(
+                os.path.join("torch", "utils", "data", "dataloader.py")
+            ) and name.endswith(function_name)
+
+        # TODO: fixme! Due to lifetime issues of the function name, this field might
+        # actually point to an already freed string when the even is a PyCall.
+        # Just silently skip this to unblock testing.
+        try:
+            event.name
+        except UnicodeDecodeError:
+            return False
+
+        if not is_dataloader_function(event.name, "__iter__"):
+            return False
+        if not event.children:
+            return False
+        event = event.children[0]
+        if not is_dataloader_function(event.name, "_get_iterator"):
+            return False
+        if not event.children:
+            return False
+        event = event.children[0]
+        return not is_dataloader_function(event.name, "check_worker_number_rationality")
+        # TODO: We should also check if the loader is bottleneck.
+
+
+class GradNotSetToNonePattern(Pattern):
+    """
+    This pattern identifies if we are not setting grad to None in zero_grad.
+    example:
+    optimizer.zero_grad()
+    By setting set_to_none=True, we can gain speedup
+
+    Pattern:
+    XXXXX: _zero_grad
+        NOT aten::zeros
+            aten::zero_
+
+    aten::zero_ is called on each parameter in the model.
+    We also want to make sure it is not called by aten::zeros.
+
+    Algorithm:
+    String match
+    """
+
+    def __init__(self, prof: profile, should_benchmark: bool = False):
+        super().__init__(prof, should_benchmark)
+        self.name = "Gradient Set To Zero Instead of None Pattern"
+        self.description = (
+            "Detected gradient set to zero instead of None. "
+            "Please add 'set_to_none=True' when calling zero_grad()."
+        )
+        self.url = (
+            "https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html"
+            "#disable-gradient-calculation-for-validation-or-inference"
+        )
+
+    def match(self, event: _ProfilerEvent):
+        if not event.name.endswith(": zero_grad"):
+            return False
+        if not event.children:
+            return False
+
+        for sub_event in traverse_dfs(event.children):
+            if (
+                sub_event.name == "aten::zero_"
+                and sub_event.parent.name != "aten::zeros"
+            ):
+                return True
+        # TODO: We should also check if the optimizer's numerical behavior will change.
+        return False
+
+
+class Conv2dBiasFollowedByBatchNorm2dPattern(Pattern):
+    """
+    This pattern identifies if we are enabling bias in Conv2d which is followed by BatchNorm2d.
+    Bias doesn't do anything when followed by batchnorm.
+    Pattern:
+    nn.Module: Conv2d            | nn.Module: BatchNorm2d
+        ...
+            aten::conv2d AND dtype of third argument is not null
+    The third argument is the bias
+    Algorithm:
+    String match
+    """
+
+    def __init__(self, prof: profile, should_benchmark: bool = False):
+        super().__init__(prof, should_benchmark)
+        self.name = "Enabling Bias in Conv2d Followed By BatchNorm Pattern"
+        self.description = "Detected bias enabled in Conv2d that is followed by BatchNorm2d. Please set 'bias=False' in Conv2d."
+        self.url = (
+            "https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html"
+            "#disable-bias-for-convolutions-directly-followed-by-a-batch-norm"
+        )
+
+    @property
+    def skip(self):
+        return self.prof.record_shapes is False or super().skip
+
+    def match(self, event: _ProfilerEvent):
+        if event.name != "aten::conv2d":
+            return False
+        if len(input_dtypes(event)) < 3 or input_dtypes(event)[2] is None:
+            return False
+        # This means bias=True
+        event = self.go_up_until(
+            event, lambda e: e.name.startswith("nn.Module: Conv2d")
+        )
+        if not event:
+            return False
+        event = self.next_of(event)
+        if not event:
+            return False
+        return event.name.startswith("nn.Module: BatchNorm2d")
+
+
+class MatMulDimInFP16Pattern(Pattern):
+    def __init__(self, prof: profile, should_benchmark: bool = False):
+        super().__init__(prof, should_benchmark)
+        self.name = "Matrix Multiplication Dimension Not Aligned Pattern"
+        self.description = "Detected matmul with dimension not aligned. Please use matmul with aligned dimension."
+        self.url = "https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html#use-mixed-precision-and-amp"
+
+    @property
+    def skip(self):
+        return not self.prof.with_stack or not self.prof.record_shapes
+
+    def match(self, event: _ProfilerEvent):
+        def mutiple_of(shapes, multiple):
+            return all(dim % multiple == 0 for shape in shapes for dim in shape[-2:])
+
+        if event.name not in ("aten::mm", "aten::bmm", "aten::addmm"):
+            return False
+        if not input_dtypes(event):
+            return False
+        arg_dtype = input_dtypes(event)[0]
+        if arg_dtype in (torch.bfloat16, torch.half) and not mutiple_of(
+            input_shapes(event), 8
+        ):
+            return True
+        return False
+
+    def benchmark(self, events: List[_ProfilerEvent]):
+        def closest_multiple(shapes, multiple):
+            return [multiple * math.ceil(shape / multiple) for shape in shapes]
+
+        shapes_factor_map = {input_shapes(event): 0.0 for event in events}
+        for shape in shapes_factor_map:
+            matrixA = torch.randn(shape[0], device="cuda", dtype=torch.float16)
+            matrixB = torch.randn(shape[1], device="cuda", dtype=torch.float16)
+            not_aligned_dim_timer = benchmark.Timer(
+                stmt="torch.mm(matrixA, matrixB)",
+                globals={"matrixA": matrixA, "matrixB": matrixB},
+            )
+            matrixA = torch.randn(
+                closest_multiple(shape[0], 8), device="cuda", dtype=torch.float16
+            )
+            matrixB = torch.randn(
+                closest_multiple(shape[1], 8), device="cuda", dtype=torch.float16
+            )
+            aligned_dim_timer = benchmark.Timer(
+                stmt="torch.mm(matrixA, matrixB)",
+                globals={"matrixA": matrixA, "matrixB": matrixB},
+            )
+            not_aligned_dim_time = not_aligned_dim_timer.timeit(10).mean
+            aligned_dim_time = aligned_dim_timer.timeit(10).mean
+            shapes_factor_map[shape] = aligned_dim_time / not_aligned_dim_time
+        return shapes_factor_map
+
+
+def source_code_location(event: Optional[_ProfilerEvent]):
+    while event:
+        if event.tag == _EventType.PyCall or event.tag == _EventType.PyCCall:
+            assert isinstance(
+                event.extra_fields, (_ExtraFields_PyCall, _ExtraFields_PyCCall)
+            )
+            if not event.extra_fields.caller.file_name.startswith("torch" + os.sep):
+                return f"{event.extra_fields.caller.file_name}:{event.extra_fields.caller.line_number}"
+        event = event.parent
+    return "No source code location found"
+
+
+def input_shapes(event: _ProfilerEvent):
+    assert isinstance(event.extra_fields, _ExtraFields_TorchOp)
+    return tuple(tuple(getattr(i, "sizes", ())) for i in event.extra_fields.inputs)
+
+
+def input_dtypes(event: _ProfilerEvent):
+    assert isinstance(event.extra_fields, _ExtraFields_TorchOp)
+    return tuple(getattr(i, "dtype", None) for i in event.extra_fields.inputs)
+
+
+def report_all_anti_patterns(
+    prof,
+    should_benchmark: bool = False,
+    print_enable: bool = True,
+    json_report_dir: Optional[str] = None,
+):
+    report_dict: Dict = {}
+    anti_patterns = [
+        ExtraCUDACopyPattern(prof, should_benchmark),
+        # ForLoopIndexingPattern(prof, should_benchmark),
+        FP32MatMulPattern(prof, should_benchmark),
+        OptimizerSingleTensorPattern(prof, should_benchmark),
+        SynchronizedDataLoaderPattern(prof, should_benchmark),
+        GradNotSetToNonePattern(prof, should_benchmark),
+        Conv2dBiasFollowedByBatchNorm2dPattern(prof, should_benchmark),
+        MatMulDimInFP16Pattern(prof, should_benchmark),
+    ]
+    reported = set()
+    summaries = []
+    message_list = [f"{'-'*40}TorchTidy Report{'-'*40}"]
+    message_list.append("Matched Events:")
+
+    for anti_pattern in anti_patterns:
+        matched_events = anti_pattern.matched_events()
+        if not matched_events:
+            continue
+        summaries.append(anti_pattern.summary(matched_events))
+        for event in matched_events:
+            report_msg = anti_pattern.report(event)
+            if report_msg not in reported:
+                message_list.append(report_msg)
+                reported.add(report_msg)
+                src_location, line_no = source_code_location(event).split(":")
+                report_dict.setdefault(src_location, []).append(
+                    {
+                        "line_number": int(line_no),
+                        "name": anti_pattern.name,
+                        "url": anti_pattern.url,
+                        "message": anti_pattern.description,
+                    }
+                )
+
+    if json_report_dir is not None:
+        json_report_path = os.path.join(json_report_dir, "torchtidy_report.json")
+        if os.path.exists(json_report_path):
+            with open(json_report_path) as f:
+                exisiting_report = json.load(f)
+                exisiting_report.update(report_dict)
+                report_dict = exisiting_report
+        with open(json_report_path, "w") as f:
+            json.dump(report_dict, f, indent=4)
+
+    message_list.append("Summary:")
+    message_list += summaries
+    message_list.append(f"{'-'*40}TorchTidy Report{'-'*40}")
+    if print_enable:
+        print("\n".join(message_list))
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..20dfeb80adeb6f5ef583571f20d18d0d4867e7c5
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/_utils.py
@@ -0,0 +1,385 @@
+# mypy: allow-untyped-defs
+import functools
+import operator
+import re
+from collections import deque
+from dataclasses import dataclass
+from typing import Dict, List, TYPE_CHECKING
+
+from torch.autograd.profiler import profile
+from torch.profiler import DeviceType
+
+
+if TYPE_CHECKING:
+    from torch.autograd import _KinetoEvent
+
+
+def _traverse(tree, next_fn, children_fn=lambda x: x.children, reverse: bool = False):
+    order = reversed if reverse else lambda x: x
+    remaining = deque(order(tree))
+    while remaining:
+        curr_event = next_fn(remaining)
+        yield curr_event
+        for child_event in order(children_fn(curr_event)):
+            remaining.append(child_event)
+
+
+traverse_dfs = functools.partial(_traverse, next_fn=lambda x: x.pop(), reverse=True)
+traverse_bfs = functools.partial(
+    _traverse, next_fn=lambda x: x.popleft(), reverse=False
+)
+
+
+@dataclass
+class EventMetrics:
+    duration_time_ns: int = 0
+    self_time_ns: int = 0
+    idle_time_ns: int = 0
+    queue_depth: int = 0
+
+    @property
+    def fraction_idle_time(self):
+        if self.duration_time_ns == 0:
+            return 0.0
+        return self.idle_time_ns / self.duration_time_ns
+
+
+@dataclass
+class Interval:
+    start: int
+    end: int
+    queue_depth: int = 0
+
+
+class EventKey:
+    def __init__(self, event):
+        self.event = event
+
+    def __hash__(self):
+        return hash(self.event.id)
+
+    def __eq__(self, other):
+        return self.event.id == other.event.id
+
+    def __repr__(self):
+        return f"{self.event.name}"
+
+    def intervals_overlap(self, intervals: List[Interval]):
+        overlap_time = 0
+        intervals = sorted(intervals, key=lambda x: x.start)
+
+        if intervals:
+            overlap_start = max(self.event.start_time_ns, intervals[0].start)
+            overlap_end = min(self.event.end_time_ns, intervals[0].end)
+
+            if overlap_start < overlap_end:
+                overlap_time += overlap_end - overlap_start
+
+        i, j = 0, 1
+        while j < len(intervals):
+            prev_interval = intervals[i]
+            curr_interval = intervals[j]
+            j += 1
+            if prev_interval.end > curr_interval.start:
+                # Completely subsumed by previous interval
+                if prev_interval.end > curr_interval.end:
+                    j += 1
+                    continue
+                else:
+                    curr_interval.start = prev_interval.end
+                    i = j
+
+            overlap_start = max(self.event.start_time_ns, curr_interval.start)
+            overlap_end = min(self.event.end_time_ns, curr_interval.end)
+            if overlap_start < overlap_end:
+                overlap_time += overlap_end - overlap_start
+
+        return overlap_time
+
+
+class BasicEvaluation:
+    def __init__(self, prof: profile):
+        self.profile = prof
+        self.metrics: Dict[EventKey, EventMetrics] = {}
+        self.compute_self_time()
+        self.event_keys = sorted(
+            (e for e in self.metrics.keys()), key=lambda x: x.event.start_time_ns
+        )
+        self.events = [e.event for e in self.event_keys]
+        self.cuda_events: List[_KinetoEvent] = []
+        self.queue_depth_list = self.compute_queue_depth()
+        self.compute_idle_time()
+
+    def compute_self_time(self):
+        """
+        Computes event's self time(total time - time in child ops).
+        """
+        assert self.profile.kineto_results is not None
+        stack = deque(self.profile.kineto_results.experimental_event_tree())
+
+        # standard iterating dfs
+        while stack:
+            curr_event = stack.pop()
+            self_time = curr_event.duration_time_ns
+            for child_event in curr_event.children:
+                self_time -= child_event.duration_time_ns
+                stack.append(child_event)
+            assert (
+                EventKey(curr_event) not in self.metrics
+            ), f"Duplicate id: {curr_event.id}, {curr_event.name}"
+            self.metrics[EventKey(curr_event)] = EventMetrics(self_time_ns=self_time)
+            self.metrics[
+                EventKey(curr_event)
+            ].duration_time_ns = curr_event.duration_time_ns
+
+    def compute_queue_depth(self):
+        """
+        Computes queue_depth at each event. This will calculate the queue depth data for
+        All the events in the tree.
+        This will return a list of Interval of queue depth data of cuda launch and kernels.
+        """
+        assert self.profile.kineto_results is not None
+        cuda_event_list = self.profile.kineto_results.events()
+
+        def is_cuda_launch_kernel(e):
+            # TODO: find a better way to identify cudaLaunchKernel
+            return e.name == "cudaLaunchKernel"
+
+        def is_cuda_kernel(e):
+            # TODO: find a better way to identify CUDA Kernel
+            return e.device_type() == DeviceType.CUDA and "mem" not in e.name.lower()
+
+        cuda_launch_events = sorted(
+            (e for e in cuda_event_list if is_cuda_launch_kernel(e)),
+            key=lambda x: x.start_ns(),
+        )
+        cuda_kernel_events = sorted(
+            (e for e in cuda_event_list if is_cuda_kernel(e)),
+            key=lambda x: x.start_ns(),
+        )
+
+        self.cuda_events = sorted(
+            cuda_launch_events + cuda_kernel_events, key=lambda x: x.start_ns()
+        )
+
+        kernel_mapping: Dict[_KinetoEvent, int] = {}
+        last_mapped_kernel = 0
+        for cuda_launch_event in cuda_launch_events:
+            index = index_of_first_match(
+                cuda_kernel_events,
+                lambda x: x.linked_correlation_id()
+                == cuda_launch_event.linked_correlation_id(),
+                start=last_mapped_kernel,
+            )
+            kernel_mapping[cuda_launch_event] = index
+            last_mapped_kernel = index if index is not None else last_mapped_kernel
+
+        current_kernel_index = 0
+        spawned_kernel_index = -1
+
+        all_events = cuda_launch_events + cuda_kernel_events + self.events
+
+        def new_old_event_comparator(event):
+            if hasattr(event, "start_us"):
+                return event.start_us() * 1000
+            if hasattr(event, "start_ns"):
+                return event.start_ns()
+            if hasattr(event, "start_time_ns"):
+                return event.start_time_ns
+            raise Exception("Unknown Event Type")  # noqa: TRY002
+
+        queue_depth_list: List[Interval] = []
+        all_events.sort(key=new_old_event_comparator)
+        for event in all_events:
+            # Find latest cuda kernel event
+            if hasattr(event, "start_us"):
+                start_time = event.start_us() * 1000
+                end_time = (event.start_us() + event.duration_us()) * 1000
+                # Find current spawned cuda kernel event
+                if event in kernel_mapping and kernel_mapping[event] is not None:
+                    spawned_kernel_index = kernel_mapping[event]
+            if hasattr(event, "start_ns"):
+                start_time = event.start_ns()
+                end_time = event.start_ns() + event.duration_ns()
+                # Find current spawned cuda kernel event
+                if event in kernel_mapping and kernel_mapping[event] is not None:
+                    spawned_kernel_index = kernel_mapping[event]
+            elif hasattr(event, "start_time_ns"):
+                start_time = event.start_time_ns  # type: ignore[attr-defined]
+                end_time = event.end_time_ns  # type: ignore[attr-defined]
+
+            while (
+                current_kernel_index < len(cuda_kernel_events)
+                and (cuda_kernel_events[current_kernel_index].start_ns())
+                <= start_time  # type: ignore[possibly-undefined]
+            ):
+                current_kernel_index += 1
+            current_queue_depth = spawned_kernel_index - current_kernel_index + 1
+            current_queue_depth = max(current_queue_depth, 0)
+
+            if hasattr(event, "start_us") or hasattr(event, "start_ns"):
+                queue_depth_list.append(
+                    Interval(start_time, end_time, current_queue_depth)  # type: ignore[possibly-undefined]
+                )
+            elif hasattr(event, "start_time_ns"):
+                self.metrics[EventKey(event)].queue_depth = current_queue_depth
+
+        return queue_depth_list
+
+    def compute_idle_time(self):
+        """
+        Computes idle time of the profile.
+        """
+        # Based on queue_depth_list, we can calculate idle time for all the events
+        idle = False
+        idle_start = 0
+        idle_intervals: List[Interval] = []
+        if self.queue_depth_list and self.events:
+            idle_intervals += [
+                Interval(self.events[0].start_time_ns, self.queue_depth_list[0].start),
+                Interval(self.queue_depth_list[-1].end, self.events[-1].end_time_ns),
+            ]
+
+        for data_point in self.queue_depth_list:
+            if data_point.queue_depth == 0 and not idle:
+                idle_start = data_point.end
+                idle = True
+            if data_point.queue_depth > 0 and idle:
+                idle_intervals.append(Interval(idle_start, data_point.start))
+                idle = False
+
+        event_list = [e.event for e in self.metrics.keys()]
+        for event in event_list:
+            self.metrics[EventKey(event)].idle_time_ns = EventKey(
+                event
+            ).intervals_overlap(idle_intervals)
+
+    def rank_events(self, length):
+        """
+        Filter and Rank the events based on some heuristics:
+        1) Events that are in the falling phase of the queue depth.
+        2) Events that have a high idle_time, self_time difference.
+
+        Parameters:
+            length: The number of events to return.
+        """
+
+        # Find the interval when qd is falling to 0
+        import torch
+
+        queue_depth_list = list(reversed(self.queue_depth_list))
+        qd_values = [e.queue_depth for e in queue_depth_list]
+
+        bottom_threashold = 0
+        top_threashold = 4
+        decrease_interval = []
+        i = 0
+        while i < len(qd_values):
+            if qd_values[i] > bottom_threashold:
+                i += 1
+                continue
+            for j in range(i + 1, len(qd_values)):
+                # Find next zero and if the max value between them exceeds
+                # the threshold, then we have a falling interval
+                next_minimum_idx = index_of_first_match(
+                    qd_values, lambda x: x <= bottom_threashold, start=j
+                )
+                peak_idx = argmax(qd_values, start=j, end=next_minimum_idx)
+
+                # if is a valid peak, we add to list and continue
+                if peak_idx is not None and qd_values[peak_idx] >= top_threashold:
+                    decrease_interval.append(
+                        Interval(
+                            queue_depth_list[peak_idx].start, queue_depth_list[i].start
+                        )
+                    )
+                    i = next_minimum_idx if next_minimum_idx is not None else i
+                    break
+            i += 1
+        # Filter out events that are not in the decrease interval
+        event_list = [
+            event
+            for event in self.metrics.keys()
+            if event.intervals_overlap(decrease_interval)
+        ]
+        if event_list:
+            self_time = torch.tensor(
+                [self.metrics[event].self_time_ns for event in event_list],
+                dtype=torch.float32,
+            )
+            idle_time = torch.tensor(
+                [self.metrics[event].fraction_idle_time for event in event_list],
+                dtype=torch.float32,
+            )
+            normalized_gain = (idle_time - torch.mean(idle_time)) / torch.std(idle_time)
+            normalized_self = (self_time - torch.mean(self_time)) / torch.std(self_time)
+            heuristic_score_list = normalized_gain + 0.6 * normalized_self
+
+            # Sort events by heuristic
+            event_list = [
+                event
+                for _, event in sorted(
+                    zip(heuristic_score_list, event_list),
+                    key=operator.itemgetter(0),
+                    reverse=True,
+                )
+            ]
+            event_list = event_list[:length]
+        return event_list
+
+    def get_optimizable_events(self, length: int = 1, print_enable: bool = True):
+        event_list = self.rank_events(length)
+        if not print_enable:
+            return event_list
+        output = "Optimizable events:\n" if event_list else "No events to optimize\n"
+
+        output += "\n".join(
+            [
+                f"""{'-'*80}
+Event:                {event}
+Source code location: {source_code_location(event.event)}
+Percentage idle time: {self.metrics[event].fraction_idle_time * 100:.2f}%
+{'-'*80}"""
+                for event in event_list
+            ]
+        )
+        if print_enable:
+            print(output)
+        return event_list
+
+
+def index_of_first_match(seq, predicate, start=0, end=None):
+    if end is None or end >= len(seq):
+        end = len(seq)
+    for i in range(start, end):
+        if predicate(seq[i]):
+            return i
+    return None
+
+
+def argmax(seq, key=lambda x: x, start=0, end=None):
+    seq = seq[start:end]
+    if len(seq) == 0:
+        return None
+    return seq.index(max(seq, key=key)) + start
+
+
+def source_code_location(event):
+    while event is not None:
+        match = re.search(r"\.py\(.*\)", event.name)
+        if match is None:
+            event = event.parent
+            continue
+        return event.name
+    return "No source code location found"
+
+
+# Provide an OSS workaround for cudagraphs + CUPTI issue
+# https://github.com/pytorch/pytorch/issues/75504
+# TODO(dberard) - deprecate / remove workaround for CUDA >= 12, when
+# we stop supporting older CUDA versions.
+def _init_for_cuda_graphs():
+    from torch.autograd.profiler import profile
+
+    with profile():
+        pass
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/itt.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/itt.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d4bda2b3420bdb367033aba2c0ef426bdd2a59a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/itt.py
@@ -0,0 +1,80 @@
+# mypy: allow-untyped-defs
+from contextlib import contextmanager
+
+
+try:
+    from torch._C import _itt
+except ImportError:
+
+    class _ITTStub:
+        @staticmethod
+        def _fail(*args, **kwargs):
+            raise RuntimeError(
+                "ITT functions not installed. Are you sure you have a ITT build?"
+            )
+
+        @staticmethod
+        def is_available():
+            return False
+
+        rangePush = _fail
+        rangePop = _fail
+        mark = _fail
+
+    _itt = _ITTStub()  # type: ignore[assignment]
+
+
+__all__ = ["is_available", "range_push", "range_pop", "mark", "range"]
+
+
+def is_available():
+    """
+    Check if ITT feature is available or not
+    """
+    return _itt.is_available()
+
+
+def range_push(msg):
+    """
+    Pushes a range onto a stack of nested range span.  Returns zero-based
+    depth of the range that is started.
+
+    Arguments:
+        msg (str): ASCII message to associate with range
+    """
+    return _itt.rangePush(msg)
+
+
+def range_pop():
+    """
+    Pops a range off of a stack of nested range spans. Returns the
+    zero-based depth of the range that is ended.
+    """
+    return _itt.rangePop()
+
+
+def mark(msg):
+    """
+    Describe an instantaneous event that occurred at some point.
+
+    Arguments:
+        msg (str): ASCII message to associate with the event.
+    """
+    return _itt.mark(msg)
+
+
+@contextmanager
+def range(msg, *args, **kwargs):
+    """
+    Context manager / decorator that pushes an ITT range at the beginning
+    of its scope, and pops it at the end. If extra arguments are given,
+    they are passed as arguments to msg.format().
+
+    Args:
+        msg (str): message to associate with the range
+    """
+    range_push(msg.format(*args, **kwargs))
+    try:
+        yield
+    finally:
+        range_pop()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/profiler.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/profiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..939ae73a99afb4d2202aafcdc1209738e643f3df
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/profiler.py
@@ -0,0 +1,935 @@
+# mypy: allow-untyped-defs
+import gzip
+import json
+import os
+import shutil
+import tempfile
+from abc import ABC, abstractmethod
+from enum import Enum
+from functools import partial
+from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
+from typing_extensions import Self
+from warnings import warn
+
+import torch
+import torch.autograd.profiler as prof
+from torch._C import _get_privateuse1_backend_name
+from torch._C._profiler import (
+    _add_execution_trace_observer,
+    _disable_execution_trace_observer,
+    _enable_execution_trace_observer,
+    _ExperimentalConfig,
+    _remove_execution_trace_observer,
+)
+from torch.autograd import kineto_available, ProfilerActivity
+from torch.profiler._memory_profiler import MemoryProfile, MemoryProfileTimeline
+
+
+__all__ = [
+    "supported_activities",
+    "ProfilerAction",
+    "schedule",
+    "tensorboard_trace_handler",
+    "profile",
+    "ExecutionTraceObserver",
+]
+PROFILER_STEP_NAME = "ProfilerStep"
+
+
+def supported_activities():
+    """
+    Returns a set of supported profiler tracing activities.
+
+    Note: profiler uses CUPTI library to trace on-device CUDA kernels.
+    In case when CUDA is enabled but CUPTI is not available, passing
+    ``ProfilerActivity.CUDA`` to profiler results in using the legacy CUDA
+    profiling code (same as in the legacy ``torch.autograd.profiler``).
+    This, in turn, results in including CUDA time in the profiler table output,
+    but not in the JSON trace.
+    """
+    return torch.autograd._supported_activities()
+
+
+class _ITraceObserver(ABC):
+    """Abstract interface for a Trace observer.
+    This satisfies 3 methods: start, stop and cleanup"""
+
+    @abstractmethod
+    def start(self):
+        pass
+
+    @abstractmethod
+    def stop(self):
+        pass
+
+    @abstractmethod
+    def cleanup(self):
+        pass
+
+
+class _KinetoProfile:
+    """Low-level profiler wrap the autograd profile
+
+    Args:
+        activities (iterable): list of activity groups (CPU, CUDA) to use in profiling, supported values:
+            ``torch.profiler.ProfilerActivity.CPU``, ``torch.profiler.ProfilerActivity.CUDA``,
+            ``torch.profiler.ProfilerActivity.XPU``.
+            Default value: ProfilerActivity.CPU and (when available) ProfilerActivity.CUDA
+            or (when available) ProfilerActivity.XPU.
+        record_shapes (bool): save information about operator's input shapes.
+        profile_memory (bool): track tensor memory allocation/deallocation (see ``export_memory_timeline``
+            for more details).
+        with_stack (bool): record source information (file and line number) for the ops.
+        with_flops (bool): use formula to estimate the FLOPS of specific operators
+            (matrix multiplication and 2D convolution).
+        with_modules (bool): record module hierarchy (including function names)
+            corresponding to the callstack of the op. e.g. If module A's forward call's
+            module B's forward which contains an aten::add op,
+            then aten::add's module hierarchy is A.B
+            Note that this support exist, at the moment, only for TorchScript models
+            and not eager mode models.
+        experimental_config (_ExperimentalConfig) : A set of experimental options
+            used by profiler libraries like Kineto. Note, backward compatibility is not guaranteed.
+        execution_trace_observer (ExecutionTraceObserver) : A PyTorch Execution Trace Observer object.
+            `PyTorch Execution Traces `__ offer a graph based
+            representation of AI/ML workloads and enable replay benchmarks, simulators, and emulators.
+            When this argument is included the observer start() and stop() will be called for the
+            same time window as PyTorch profiler.
+        acc_events (bool): Enable the accumulation of FunctionEvents across multiple profiling cycles
+
+
+    .. note::
+        This API is experimental and subject to change in the future.
+
+        Enabling shape and stack tracing results in additional overhead.
+        When record_shapes=True is specified, profiler will temporarily hold references to the tensors;
+        that may further prevent certain optimizations that depend on the reference count and introduce
+        extra tensor copies.
+    """
+
+    def __init__(
+        self,
+        *,
+        activities: Optional[Iterable[ProfilerActivity]] = None,
+        record_shapes: bool = False,
+        profile_memory: bool = False,
+        with_stack: bool = False,
+        with_flops: bool = False,
+        with_modules: bool = False,
+        experimental_config: Optional[_ExperimentalConfig] = None,
+        execution_trace_observer: Optional[_ITraceObserver] = None,
+        acc_events: bool = False,
+    ):
+        self.activities = set(activities) if activities else supported_activities()
+        self.record_shapes = record_shapes
+        self.with_flops = with_flops
+        self.profile_memory = profile_memory
+        self.with_stack = with_stack
+        self.with_modules = with_modules
+        self.experimental_config = experimental_config
+        self.execution_trace_observer = execution_trace_observer
+        self.acc_events = acc_events
+        self.profiler: Optional[prof.profile] = None
+        self.mem_tl: Optional[MemoryProfileTimeline] = None
+        self.use_device = None
+        if ProfilerActivity.CUDA in self.activities:
+            self.use_device = "cuda"
+        elif ProfilerActivity.XPU in self.activities:
+            self.use_device = "xpu"
+        elif ProfilerActivity.MTIA in self.activities:
+            self.use_device = "mtia"
+        elif ProfilerActivity.PrivateUse1 in self.activities:
+            self.use_device = _get_privateuse1_backend_name()
+
+        # user-defined metadata to be amended to the trace
+        self.preset_metadata: Dict[str, str] = {}
+
+    def start(self):
+        self.prepare_trace()
+        self.start_trace()
+
+    def stop(self):
+        self.stop_trace()
+
+    def prepare_trace(self):
+        if (self.profiler is None) or (not self.acc_events):
+            self.profiler = prof.profile(
+                use_cpu=(ProfilerActivity.CPU in self.activities),
+                use_device=self.use_device,
+                record_shapes=self.record_shapes,
+                with_flops=self.with_flops,
+                profile_memory=self.profile_memory,
+                with_stack=self.with_stack,
+                with_modules=self.with_modules,
+                use_kineto=True,
+                experimental_config=self.experimental_config,
+                acc_events=self.acc_events,
+            )
+        self.profiler._prepare_trace()
+
+    def start_trace(self):
+        if self.execution_trace_observer:
+            self.execution_trace_observer.start()
+        assert self.profiler is not None
+        self.profiler._start_trace()
+
+        if self.profile_memory:
+            self.add_metadata_json("profile_memory", "1")
+        if self.with_stack:
+            self.add_metadata_json("with_stack", "1")
+        if self.record_shapes:
+            self.add_metadata_json("record_shapes", "1")
+        if self.with_modules:
+            self.add_metadata_json("with_modules", "1")
+        if self.with_flops:
+            self.add_metadata_json("with_flops", "1")
+
+        if kineto_available():
+            dist_info = self._get_distributed_info()
+            if dist_info:
+                self.add_metadata_json("distributedInfo", json.dumps(dist_info))
+
+            if hasattr(torch, "_inductor"):
+                import torch._inductor.config as inductor_config
+
+                if inductor_config.triton.cudagraphs:
+                    os.environ["DISABLE_CUPTI_LAZY_REINIT"] = "1"
+                    self.add_metadata_json("DISABLE_CUPTI_LAZY_REINIT", "1")
+                    # FIXME: CUDA Graph does not work well with CUPTI teardown.
+                    #   1) crashes on 1st lazy CUPTI re-init after teardown (CUDA 11)
+                    #   2) crashes on 2nd non-lazy CUPTI re-init after teardown (CUDA 12)
+                    # Workaround: turn off CUPTI teardown when using CUDA Graphs.
+                    os.environ["TEARDOWN_CUPTI"] = "0"
+
+            # Insert the preset user metadata to the trace
+            for k, v in self.preset_metadata.items():
+                self.add_metadata_json(k, v)
+
+    def stop_trace(self):
+        if self.execution_trace_observer:
+            self.execution_trace_observer.stop()
+        assert self.profiler is not None
+        self.profiler.__exit__(None, None, None)
+
+    def export_chrome_trace(self, path: str):
+        """
+        Exports the collected trace in Chrome JSON format. If kineto is enabled, only
+        last cycle in schedule is exported.
+        """
+        assert self.profiler
+        if path.endswith(".gz"):
+            fp = tempfile.NamedTemporaryFile("w+t", suffix=".json", delete=False)
+            fp.close()
+            retvalue = self.profiler.export_chrome_trace(fp.name)
+            with open(fp.name) as fin:
+                with gzip.open(path, "wt") as fout:
+                    fout.writelines(fin)
+            os.remove(fp.name)
+            return retvalue
+        else:
+            return self.profiler.export_chrome_trace(path)
+
+    def export_stacks(self, path: str, metric: str = "self_cpu_time_total"):
+        """Save stack traces to a file
+
+        Args:
+            path (str): save stacks file to this location;
+            metric (str): metric to use: "self_cpu_time_total" or "self_cuda_time_total"
+        """
+        assert self.profiler
+        return self.profiler.export_stacks(path, metric)
+
+    def toggle_collection_dynamic(
+        self, enable: bool, activities: Iterable[ProfilerActivity]
+    ):
+        """Toggle collection of activities on/off at any point of collection. Currently supports toggling Torch Ops
+        (CPU) and CUDA activity supported in Kineto
+
+        Args:
+            activities (iterable): list of activity groups to use in profiling, supported values:
+                ``torch.profiler.ProfilerActivity.CPU``, ``torch.profiler.ProfilerActivity.CUDA``
+        Examples:
+
+        .. code-block:: python
+
+            with torch.profiler.profile(
+                activities=[
+                    torch.profiler.ProfilerActivity.CPU,
+                    torch.profiler.ProfilerActivity.CUDA,
+                ]
+            ) as p:
+                code_to_profile_0()
+                // turn off collection of all CUDA activity
+                p.toggle_collection_dynamic(False, [torch.profiler.ProfilerActivity.CUDA])
+                code_to_profile_1()
+                // turn on collection of all CUDA activity
+                p.toggle_collection_dynamic(True, [torch.profiler.ProfilerActivity.CUDA])
+                code_to_profile_2()
+            print(p.key_averages().table(
+                sort_by="self_cuda_time_total", row_limit=-1))
+        """
+        if not self.profiler:
+            return
+        self.profiler.toggle_collection_dynamic(enable, activities)
+
+    def key_averages(
+        self, group_by_input_shape: bool = False, group_by_stack_n: int = 0
+    ):
+        """Averages events, grouping them by operator name and (optionally) input shapes and
+        stack.
+
+        .. note::
+            To use shape/stack functionality make sure to set record_shapes/with_stack
+            when creating profiler context manager.
+        """
+        assert self.profiler
+        return self.profiler.key_averages(group_by_input_shape, group_by_stack_n)
+
+    def events(self):
+        """
+        Returns the list of unaggregated profiler events,
+        to be used in the trace callback or after the profiling is finished
+        """
+        assert self.profiler
+        return self.profiler.function_events
+
+    def add_metadata(self, key: str, value: str):
+        """
+        Adds a user defined metadata with a string key and a string value
+        into the trace file
+        """
+        wrapped_value = '"' + value.replace('"', '\\"') + '"'
+        torch.autograd._add_metadata_json(key, wrapped_value)
+
+    def add_metadata_json(self, key: str, value: str):
+        """
+        Adds a user defined metadata with a string key and a valid json value
+        into the trace file
+        """
+        torch.autograd._add_metadata_json(key, value)
+
+    def preset_metadata_json(self, key: str, value: str):
+        """
+        Preset a user defined metadata when the profiler is not started
+        and added into the trace file later.
+        Metadata is in the format of a string key and a valid json value
+        """
+        self.preset_metadata[key] = value
+
+    def _get_distributed_info(self):
+        import torch.distributed as dist
+
+        if not dist.is_available() or not dist.is_initialized():
+            return None
+
+        backend = dist.get_backend()
+        dist_info = {
+            "backend": backend,
+            "rank": dist.get_rank(),
+            "world_size": dist.get_world_size(),
+            "pg_count": dist.get_pg_count(),
+            "pg_config": dist.distributed_c10d._get_all_pg_configs(),
+        }
+        if backend == "nccl":
+            nccl_version = torch.cuda.nccl.version()
+            dist_info["nccl_version"] = ".".join(str(v) for v in nccl_version)
+        return dist_info
+
+    def _memory_profile(self) -> MemoryProfile:
+        required = ("record_shapes", "profile_memory", "with_stack")
+        missing = [f"{i}=True" for i in required if not getattr(self, i)]
+        if missing:
+            raise ValueError(f"{', '.join(missing)} required for memory profiling.")
+
+        assert self.profiler is not None and self.profiler.kineto_results is not None
+        return MemoryProfile(self.profiler.kineto_results)
+
+    def export_memory_timeline(self, path: str, device: Optional[str] = None) -> None:
+        """Export memory event information from the profiler collected
+        tree for a given device, and export a timeline plot. There are 3
+        exportable files using ``export_memory_timeline``, each controlled by the
+        ``path``'s suffix.
+
+        - For an HTML compatible plot, use the suffix ``.html``, and a memory timeline
+          plot will be embedded as a PNG file in the HTML file.
+
+        - For plot points consisting of ``[times, [sizes by category]]``, where
+          ``times`` are timestamps and ``sizes`` are memory usage for each category.
+          The memory timeline plot will be saved a JSON (``.json``) or gzipped JSON
+          (``.json.gz``) depending on the suffix.
+
+        - For raw memory points, use the suffix ``.raw.json.gz``. Each raw memory
+          event will consist of ``(timestamp, action, numbytes, category)``, where
+          ``action`` is one of ``[PREEXISTING, CREATE, INCREMENT_VERSION, DESTROY]``,
+          and ``category`` is one of the enums from
+          ``torch.profiler._memory_profiler.Category``.
+
+        Output: Memory timeline written as gzipped JSON, JSON, or HTML.
+        """
+        # Default to device 0, if unset. Fallback on cpu.
+        if device is None and self.use_device and self.use_device != "cuda":
+            device = self.use_device + ":0"
+
+        if device is None:
+            device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+        # Construct the memory timeline plot data
+        self.mem_tl = MemoryProfileTimeline(self._memory_profile())
+
+        # Depending on the file suffix, save the data as json.gz or json.
+        # For html, we can embed the image into an HTML file.
+        if path.endswith(".html"):
+            self.mem_tl.export_memory_timeline_html(path, device)
+        elif path.endswith(".gz"):
+            fp = tempfile.NamedTemporaryFile("w+t", suffix=".json", delete=False)
+            fp.close()
+            if path.endswith("raw.json.gz"):
+                self.mem_tl.export_memory_timeline_raw(fp.name, device)
+            else:
+                self.mem_tl.export_memory_timeline(fp.name, device)
+            with open(fp.name) as fin:
+                with gzip.open(path, "wt") as fout:
+                    fout.writelines(fin)
+            os.remove(fp.name)
+        else:
+            self.mem_tl.export_memory_timeline(path, device)
+
+
+class ProfilerAction(Enum):
+    """
+    Profiler actions that can be taken at the specified intervals
+    """
+
+    NONE = 0
+    WARMUP = 1
+    RECORD = 2
+    RECORD_AND_SAVE = 3
+
+
+def schedule(
+    *, wait: int, warmup: int, active: int, repeat: int = 0, skip_first: int = 0
+) -> Callable:
+    """
+    Returns a callable that can be used as profiler ``schedule`` argument. The profiler will skip
+    the first ``skip_first`` steps, then wait for ``wait`` steps, then do the warmup for the next ``warmup`` steps,
+    then do the active recording for the next ``active`` steps and then repeat the cycle starting with ``wait`` steps.
+    The optional number of cycles is specified with the ``repeat`` parameter, the zero value means that
+    the cycles will continue until the profiling is finished.
+    """
+
+    def schedule_fn(step: int) -> ProfilerAction:
+        assert step >= 0
+        if step < skip_first:
+            return ProfilerAction.NONE
+        else:
+            step -= skip_first
+        num_steps = wait + warmup + active
+        if repeat > 0 and step / num_steps >= repeat:
+            return ProfilerAction.NONE
+        mod_step = step % num_steps
+        if mod_step < wait:
+            return ProfilerAction.NONE
+        elif mod_step < wait + warmup:
+            return ProfilerAction.WARMUP
+        else:
+            return (
+                ProfilerAction.RECORD
+                if mod_step < num_steps - 1
+                else ProfilerAction.RECORD_AND_SAVE
+            )
+
+    assert (
+        wait >= 0 and warmup >= 0 and active > 0 and repeat >= 0 and skip_first >= 0
+    ), "Invalid profiler schedule arguments"
+    if warmup == 0:
+        warn("Profiler won't be using warmup, this can skew profiler results")
+    return schedule_fn
+
+
+def _default_schedule_fn(_: int) -> ProfilerAction:
+    """
+    Default profiler behavior - immediately starts recording the events,
+    keeps doing it on every profiler step.
+    """
+    return ProfilerAction.RECORD
+
+
+def tensorboard_trace_handler(
+    dir_name: str, worker_name: Optional[str] = None, use_gzip: bool = False
+):
+    """
+    Outputs tracing files to directory of ``dir_name``, then that directory can be
+    directly delivered to tensorboard as logdir.
+    ``worker_name`` should be unique for each worker in distributed scenario,
+    it will be set to '[hostname]_[pid]' by default.
+    """
+    import os
+    import socket
+    import time
+
+    def handler_fn(prof) -> None:
+        nonlocal worker_name
+        if not os.path.isdir(dir_name):
+            try:
+                os.makedirs(dir_name, exist_ok=True)
+            except Exception as e:
+                raise RuntimeError("Can't create directory: " + dir_name) from e
+        if not worker_name:
+            worker_name = f"{socket.gethostname()}_{os.getpid()}"
+        # Use nanosecond here to avoid naming clash when exporting the trace
+        file_name = f"{worker_name}.{time.time_ns()}.pt.trace.json"
+        if use_gzip:
+            file_name = file_name + ".gz"
+        prof.export_chrome_trace(os.path.join(dir_name, file_name))
+
+    return handler_fn
+
+
+class profile(_KinetoProfile):
+    """Profiler context manager.
+
+    Args:
+        activities (iterable): list of activity groups (CPU, CUDA) to use in profiling, supported values:
+            ``torch.profiler.ProfilerActivity.CPU``, ``torch.profiler.ProfilerActivity.CUDA``,
+            ``torch.profiler.ProfilerActivity.XPU``.
+            Default value: ProfilerActivity.CPU and (when available) ProfilerActivity.CUDA
+            or (when available) ProfilerActivity.XPU.
+        schedule (Callable): callable that takes step (int) as a single parameter and returns
+            ``ProfilerAction`` value that specifies the profiler action to perform at each step.
+        on_trace_ready (Callable): callable that is called at each step when ``schedule``
+            returns ``ProfilerAction.RECORD_AND_SAVE`` during the profiling.
+        record_shapes (bool): save information about operator's input shapes.
+        profile_memory (bool): track tensor memory allocation/deallocation.
+        with_stack (bool): record source information (file and line number) for the ops.
+        with_flops (bool): use formula to estimate the FLOPs (floating point operations) of specific operators
+            (matrix multiplication and 2D convolution).
+        with_modules (bool): record module hierarchy (including function names)
+            corresponding to the callstack of the op. e.g. If module A's forward call's
+            module B's forward which contains an aten::add op,
+            then aten::add's module hierarchy is A.B
+            Note that this support exist, at the moment, only for TorchScript models
+            and not eager mode models.
+        experimental_config (_ExperimentalConfig) : A set of experimental options
+            used for Kineto library features. Note, backward compatibility is not guaranteed.
+        execution_trace_observer (ExecutionTraceObserver) : A PyTorch Execution Trace Observer object.
+            `PyTorch Execution Traces `__ offer a graph based
+            representation of AI/ML workloads and enable replay benchmarks, simulators, and emulators.
+            When this argument is included the observer start() and stop() will be called for the
+            same time window as PyTorch profiler. See the examples section below for a code sample.
+        acc_events (bool): Enable the accumulation of FunctionEvents across multiple profiling cycles
+        use_cuda (bool):
+            .. deprecated:: 1.8.1
+                use ``activities`` instead.
+
+    .. note::
+        Use :func:`~torch.profiler.schedule` to generate the callable schedule.
+        Non-default schedules are useful when profiling long training jobs
+        and allow the user to obtain multiple traces at the different iterations
+        of the training process.
+        The default schedule simply records all the events continuously for the
+        duration of the context manager.
+
+    .. note::
+        Use :func:`~torch.profiler.tensorboard_trace_handler` to generate result files for TensorBoard:
+
+        ``on_trace_ready=torch.profiler.tensorboard_trace_handler(dir_name)``
+
+        After profiling, result files can be found in the specified directory. Use the command:
+
+        ``tensorboard --logdir dir_name``
+
+        to see the results in TensorBoard.
+        For more information, see
+        `PyTorch Profiler TensorBoard Plugin `__
+
+    .. note::
+        Enabling shape and stack tracing results in additional overhead.
+        When record_shapes=True is specified, profiler will temporarily hold references to the tensors;
+        that may further prevent certain optimizations that depend on the reference count and introduce
+        extra tensor copies.
+
+
+    Examples:
+
+    .. code-block:: python
+
+        with torch.profiler.profile(
+            activities=[
+                torch.profiler.ProfilerActivity.CPU,
+                torch.profiler.ProfilerActivity.CUDA,
+            ]
+        ) as p:
+            code_to_profile()
+        print(p.key_averages().table(
+            sort_by="self_cuda_time_total", row_limit=-1))
+
+    Using the profiler's ``schedule``, ``on_trace_ready`` and ``step`` functions:
+
+    .. code-block:: python
+
+        # Non-default profiler schedule allows user to turn profiler on and off
+        # on different iterations of the training loop;
+        # trace_handler is called every time a new trace becomes available
+        def trace_handler(prof):
+            print(prof.key_averages().table(
+                sort_by="self_cuda_time_total", row_limit=-1))
+            # prof.export_chrome_trace("/tmp/test_trace_" + str(prof.step_num) + ".json")
+
+        with torch.profiler.profile(
+            activities=[
+                torch.profiler.ProfilerActivity.CPU,
+                torch.profiler.ProfilerActivity.CUDA,
+            ],
+
+            # In this example with wait=1, warmup=1, active=2, repeat=1,
+            # profiler will skip the first step/iteration,
+            # start warming up on the second, record
+            # the third and the forth iterations,
+            # after which the trace will become available
+            # and on_trace_ready (when set) is called;
+            # the cycle repeats starting with the next step
+
+            schedule=torch.profiler.schedule(
+                wait=1,
+                warmup=1,
+                active=2,
+                repeat=1),
+            on_trace_ready=trace_handler
+            # on_trace_ready=torch.profiler.tensorboard_trace_handler('./log')
+            # used when outputting for tensorboard
+            ) as p:
+                for iter in range(N):
+                    code_iteration_to_profile(iter)
+                    # send a signal to the profiler that the next iteration has started
+                    p.step()
+
+    The following sample shows how to setup up an Execution Trace Observer (`execution_trace_observer`)
+
+    .. code-block:: python
+
+        with torch.profiler.profile(
+            ...
+            execution_trace_observer=(
+                ExecutionTraceObserver().register_callback("./execution_trace.json")
+            ),
+        ) as p:
+            for iter in range(N):
+                code_iteration_to_profile(iter)
+                p.step()
+
+    You can also refer to test_execution_trace_with_kineto() in tests/profiler/test_profiler.py.
+    Note: One can also pass any object satisfying the _ITraceObserver interface.
+    """
+
+    def __init__(
+        self,
+        *,
+        activities: Optional[Iterable[ProfilerActivity]] = None,
+        schedule: Optional[Callable[[int], ProfilerAction]] = None,
+        on_trace_ready: Optional[Callable[..., Any]] = None,
+        record_shapes: bool = False,
+        profile_memory: bool = False,
+        with_stack: bool = False,
+        with_flops: bool = False,
+        with_modules: bool = False,
+        experimental_config: Optional[_ExperimentalConfig] = None,
+        execution_trace_observer: Optional[_ITraceObserver] = None,
+        acc_events: bool = False,
+        # deprecated:
+        use_cuda: Optional[bool] = None,
+    ):
+        activities_set = set(activities) if activities else supported_activities()
+        if use_cuda is not None:
+            warn(
+                "`use_cuda` is deprecated, use `activities` argument instead",
+                FutureWarning,
+                stacklevel=2,
+            )
+            if use_cuda:
+                activities_set.add(ProfilerActivity.CUDA)
+            elif ProfilerActivity.CUDA in activities_set:
+                activities_set.remove(ProfilerActivity.CUDA)
+        assert len(activities_set) > 0, "No valid profiler activities found"
+
+        super().__init__(
+            activities=activities,
+            record_shapes=record_shapes,
+            profile_memory=profile_memory,
+            with_stack=with_stack,
+            with_flops=with_flops,
+            with_modules=with_modules,
+            experimental_config=experimental_config,
+            execution_trace_observer=execution_trace_observer,
+            acc_events=acc_events,
+        )
+
+        if schedule:
+            self.schedule = schedule
+            # add step markers into the trace and table view
+            self.record_steps = True
+        else:
+            self.schedule = _default_schedule_fn
+            self.record_steps = False
+        self.on_trace_ready = on_trace_ready
+        self.step_num = 0
+        self.current_action = self.schedule(self.step_num)
+        self.step_rec_fn: Optional[prof.record_function] = None
+
+        self.action_map: Dict[
+            Tuple[ProfilerAction, Optional[ProfilerAction]], List[Any]
+        ] = {
+            # key is (prev_action, current_action), value is action list corresponding to the state pair.
+            (ProfilerAction.NONE, ProfilerAction.NONE): [],
+            (ProfilerAction.NONE, ProfilerAction.WARMUP): [self.prepare_trace],
+            (ProfilerAction.NONE, ProfilerAction.RECORD): [
+                self.prepare_trace,
+                self.start_trace,
+            ],
+            (ProfilerAction.NONE, ProfilerAction.RECORD_AND_SAVE): [
+                self.prepare_trace,
+                self.start_trace,
+            ],
+            (ProfilerAction.WARMUP, ProfilerAction.NONE): [
+                partial(warn, "Incorrect schedule: WARMUP followed by NONE"),
+                self.start_trace,
+                self.stop_trace,
+            ],
+            (ProfilerAction.WARMUP, ProfilerAction.WARMUP): [],
+            (ProfilerAction.WARMUP, ProfilerAction.RECORD): [self.start_trace],
+            (ProfilerAction.WARMUP, ProfilerAction.RECORD_AND_SAVE): [self.start_trace],
+            (ProfilerAction.RECORD, ProfilerAction.NONE): [
+                partial(warn, "Incorrect schedule: RECORD followed by NONE"),
+                self.stop_trace,
+            ],
+            (ProfilerAction.RECORD, ProfilerAction.WARMUP): [
+                partial(warn, "Incorrect schedule: RECORD followed by WARMUP"),
+                self.stop_trace,
+            ],
+            (ProfilerAction.RECORD, ProfilerAction.RECORD): [],
+            (ProfilerAction.RECORD, ProfilerAction.RECORD_AND_SAVE): [],
+            (ProfilerAction.RECORD_AND_SAVE, ProfilerAction.NONE): [
+                self.stop_trace,
+                self._trace_ready,
+            ],
+            (ProfilerAction.RECORD_AND_SAVE, ProfilerAction.WARMUP): [
+                self.stop_trace,
+                self._trace_ready,
+                self.prepare_trace,
+            ],
+            (ProfilerAction.RECORD_AND_SAVE, ProfilerAction.RECORD): [
+                self.stop_trace,
+                self._trace_ready,
+                self.prepare_trace,
+                self.start_trace,
+            ],
+            (ProfilerAction.RECORD_AND_SAVE, ProfilerAction.RECORD_AND_SAVE): [
+                self.stop_trace,
+                self._trace_ready,
+                self.prepare_trace,
+                self.start_trace,
+            ],
+            # used for exit action
+            (ProfilerAction.WARMUP, None): [self.start_trace, self.stop_trace],
+            (ProfilerAction.RECORD, None): [self.stop_trace, self._trace_ready],
+            (ProfilerAction.RECORD_AND_SAVE, None): [
+                self.stop_trace,
+                self._trace_ready,
+            ],
+        }
+        # Start tracking increments to profiler step, this will be used
+        # by Kineto
+        prof.KinetoStepTracker.init_step_count(PROFILER_STEP_NAME)
+
+    def __enter__(self):
+        self.start()
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.stop()
+        prof.KinetoStepTracker.erase_step_count(PROFILER_STEP_NAME)
+        if self.execution_trace_observer:
+            self.execution_trace_observer.cleanup()
+
+    def start(self):
+        self._transit_action(ProfilerAction.NONE, self.current_action)
+        if self.record_steps:
+            self.step_rec_fn = prof.record_function(
+                "ProfilerStep#" + str(self.step_num)
+            )
+            self.step_rec_fn.__enter__()
+
+    def stop(self):
+        if self.record_steps and self.step_rec_fn:
+            self.step_rec_fn.__exit__(None, None, None)
+        self._transit_action(self.current_action, None)
+
+    def step(self):
+        """
+        Signals the profiler that the next profiling step has started.
+        """
+        if self.record_steps and self.step_rec_fn:
+            self.step_rec_fn.__exit__(None, None, None)
+        prev_action = self.current_action
+        self.step_num += 1
+        self.current_action = self.schedule(self.step_num)
+
+        self._transit_action(prev_action, self.current_action)
+        prof.KinetoStepTracker.increment_step(PROFILER_STEP_NAME)
+
+        if self.record_steps:
+            self.step_rec_fn = prof.record_function(
+                "ProfilerStep#" + str(self.step_num)
+            )
+            self.step_rec_fn.__enter__()
+
+    def _trace_ready(self):
+        if self.on_trace_ready:
+            self.on_trace_ready(self)
+
+    def _transit_action(self, prev_action, current_action):
+        action_list = self.action_map.get((prev_action, current_action))
+        if action_list:
+            for action in action_list:
+                action()
+
+    def _stats(self) -> Optional[prof._ProfilerStats]:
+        if self.profiler is None:
+            return None
+        return self.profiler._stats
+
+
+class ExecutionTraceObserver(_ITraceObserver):
+    """Execution Trace Observer
+
+    Each process can have a single ExecutionTraceObserver instance. The observer
+    can be added to record function callbacks via calling register_callback()
+    explicitly. Without calling unregister_callback(), repeated calls to
+    register_callback() will not add additional observers to record function
+    callbacks. Once an ExecutionTraceObserver is created, the start() and stop()
+    methods control when the event data is recorded.
+
+    Deleting or calling unregister_callback() will remove the observer from the
+    record function callbacks, finalize the output file, and will stop
+    incurring any overheads.
+    """
+
+    def __init__(self) -> None:
+        """
+        Initializes the default states.
+        """
+        self._registered = False
+        self._execution_trace_running = False
+
+    def __del__(self):
+        """
+        Calls unregister_callback() to make sure to finalize outputs.
+        """
+        self.unregister_callback()
+
+    def register_callback(self, output_file_path: str) -> Self:
+        """
+        Adds ET observer to record function callbacks. The data will be
+        written to output_file_path.
+        """
+        if not self._registered:
+            self._output_file_path = output_file_path
+            self._registered = _add_execution_trace_observer(output_file_path)
+        return self
+
+    def unregister_callback(self):
+        """
+        Removes ET observer from record function callbacks.
+        """
+
+        def _save_triton_kernels():
+            # Save the kernel paths for the generated kernels
+            from torch._inductor.codecache import PyCodeCache as PyCodeCache
+
+            kernel_files = [
+                v.__file__
+                for v in PyCodeCache.cache.values()
+                if getattr(v, "__file__", None) is not None
+            ]
+            work_dir, file_name = os.path.split(self._output_file_path)
+            resource_dir = os.path.join(
+                work_dir, os.path.splitext(file_name)[0] + "_resources"
+            )
+            if not os.path.exists(resource_dir):
+                os.mkdir(resource_dir)
+
+            for kernel_file in kernel_files:
+                if kernel_file is None:
+                    continue
+                path, name = os.path.split(kernel_file)
+                dst = os.path.join(resource_dir, name)
+                shutil.copyfile(kernel_file, dst)
+
+        if self._registered:
+            self.stop()
+            try:
+                _save_triton_kernels()
+            except Exception as e:
+                warn(f"Execution trace failed to save kernels: {e}")
+            _remove_execution_trace_observer()
+            self._registered = False
+
+    @property
+    def is_registered(self):
+        """
+        Returns True if the execution trace observer is registered, otherwise False.
+        """
+        return self._registered
+
+    def is_running(self):
+        """
+        Returns True if the observer is running, otherwise False.
+        """
+        return self._execution_trace_running
+
+    def start(self):
+        """
+        Starts to capture.
+        """
+        if self._registered and not self._execution_trace_running:
+            _enable_execution_trace_observer()
+            self._execution_trace_running = True
+            self._record_pg_config()
+
+    def stop(self):
+        """
+        Stops to capture.
+        """
+        if self._execution_trace_running:
+            _disable_execution_trace_observer()
+            self._execution_trace_running = False
+
+    def cleanup(self):
+        """
+        Calls unregister_callback() to make sure to finalize outputs.
+        """
+        self.unregister_callback()
+
+    def get_output_file_path(self) -> str:
+        """
+        Returns the output file name.
+        """
+        if self.is_registered:
+            return self._output_file_path
+        else:
+            raise RuntimeError(
+                "A callback to the ET profiler needs to be registered "
+                "first before getting the output file path"
+            )
+
+    def _record_pg_config(self) -> None:
+        # Records the PG config info to the trace as node:
+        #  ## process_group:init ##
+        if (
+            self.is_registered
+            and torch.distributed.is_available()
+            and torch.distributed.is_initialized()
+        ):
+            pg_config_info = torch.distributed.distributed_c10d._world.pg_config_info
+            torch.autograd._record_function_with_args_enter(
+                "## process_group:init ##", json.dumps(pg_config_info)
+            )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/python_tracer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/python_tracer.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3e624911f95812a523d4dd927a74eec7fe5171b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/profiler/python_tracer.py
@@ -0,0 +1,20 @@
+import os
+import site
+import sys
+import typing
+
+import torch
+
+
+def _prefix_regex() -> typing.List[str]:
+    raw_paths = (
+        site.getsitepackages()
+        + sys.path
+        + [site.getuserbase()]
+        + [site.getusersitepackages()]
+        + [os.path.dirname(os.path.dirname(torch.__file__))]
+    )
+
+    path_prefixes = sorted({os.path.abspath(i) for i in raw_paths}, reverse=True)
+    assert all(isinstance(i, str) for i in path_prefixes)
+    return [i + os.sep for i in path_prefixes]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8789fea17a17ffa8e490a8d744892c5140a70ee2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/__init__.py
@@ -0,0 +1,86 @@
+# mypy: allow-untyped-defs
+from .fake_quantize import *  # noqa: F403
+from .fuse_modules import fuse_modules
+from .fuser_method_mappings import *  # noqa: F403
+from .observer import *  # noqa: F403
+from .qconfig import *  # noqa: F403
+from .quant_type import *  # noqa: F403
+from .quantization_mappings import *  # noqa: F403
+from .quantize import *  # noqa: F403
+from .quantize_jit import *  # noqa: F403
+from .stubs import *  # noqa: F403
+
+
+def default_eval_fn(model, calib_data):
+    r"""
+    Default evaluation function takes a torch.utils.data.Dataset or a list of
+    input Tensors and run the model on the dataset
+    """
+    for data, target in calib_data:
+        model(data)
+
+
+__all__ = [
+    "QuantWrapper",
+    "QuantStub",
+    "DeQuantStub",
+    # Top level API for eager mode quantization
+    "quantize",
+    "quantize_dynamic",
+    "quantize_qat",
+    "prepare",
+    "convert",
+    "prepare_qat",
+    # Top level API for graph mode quantization on TorchScript
+    "quantize_jit",
+    "quantize_dynamic_jit",
+    "_prepare_ondevice_dynamic_jit",
+    "_convert_ondevice_dynamic_jit",
+    "_quantize_ondevice_dynamic_jit",
+    # Top level API for graph mode quantization on GraphModule(torch.fx)
+    # 'fuse_fx', 'quantize_fx',  # TODO: add quantize_dynamic_fx
+    # 'prepare_fx', 'prepare_dynamic_fx', 'convert_fx',
+    "QuantType",  # quantization type
+    # custom module APIs
+    "get_default_static_quant_module_mappings",
+    "get_static_quant_module_class",
+    "get_default_dynamic_quant_module_mappings",
+    "get_default_qat_module_mappings",
+    "get_default_qconfig_propagation_list",
+    "get_default_compare_output_module_list",
+    "get_quantized_operator",
+    "get_fuser_method",
+    # Sub functions for `prepare` and `swap_module`
+    "propagate_qconfig_",
+    "add_quant_dequant",
+    "swap_module",
+    "default_eval_fn",
+    # Observers
+    "ObserverBase",
+    "WeightObserver",
+    "HistogramObserver",
+    "observer",
+    "default_observer",
+    "default_weight_observer",
+    "default_placeholder_observer",
+    "default_per_channel_weight_observer",
+    # FakeQuantize (for qat)
+    "default_fake_quant",
+    "default_weight_fake_quant",
+    "default_fixed_qparams_range_neg1to1_fake_quant",
+    "default_fixed_qparams_range_0to1_fake_quant",
+    "default_per_channel_weight_fake_quant",
+    "default_histogram_fake_quant",
+    # QConfig
+    "QConfig",
+    "default_qconfig",
+    "default_dynamic_qconfig",
+    "float16_dynamic_qconfig",
+    "float_qparams_weight_only_qconfig",
+    # QAT utilities
+    "default_qat_qconfig",
+    "prepare_qat",
+    "quantize_qat",
+    # module transformations
+    "fuse_modules",
+]
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/_numeric_suite.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/_numeric_suite.py
new file mode 100644
index 0000000000000000000000000000000000000000..49ccc8e69523f7dbee2335b788a2cb3a7db618a2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/_numeric_suite.py
@@ -0,0 +1,28 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/ns/_numeric_suite.py`, while adding an import statement
+here.
+"""
+
+from torch.ao.ns._numeric_suite import (
+    _convert_tuple_to_list,
+    _dequantize_tensor_list,
+    _find_match,
+    _get_logger_dict_helper,
+    _is_identical_module_type,
+    compare_model_outputs,
+    compare_model_stub,
+    compare_weights,
+    get_logger_dict,
+    get_matching_activations,
+    Logger,
+    NON_LEAF_MODULE_TO_ADD_OBSERVER_ALLOW_LIST,
+    OutputLogger,
+    prepare_model_outputs,
+    prepare_model_with_stubs,
+    Shadow,
+    ShadowLogger,
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/_numeric_suite_fx.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/_numeric_suite_fx.py
new file mode 100644
index 0000000000000000000000000000000000000000..55cd7085740d0ce8de79491acbfc4888ebba21f8
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/_numeric_suite_fx.py
@@ -0,0 +1,26 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/ns/_numeric_suite_fx.py`, while adding an import statement
+here.
+"""
+
+from torch.ao.ns._numeric_suite_fx import (
+    _add_loggers_impl,
+    _add_loggers_one_model,
+    _add_shadow_loggers_impl,
+    _extract_logger_info_one_model,
+    _extract_weights_impl,
+    _extract_weights_one_model,
+    add_loggers,
+    add_shadow_loggers,
+    extend_logger_results_with_comparison,
+    extract_logger_info,
+    extract_shadow_logger_info,
+    extract_weights,
+    NSTracer,
+    OutputLogger,
+    RNNReturnType,
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/_quantized_conversions.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/_quantized_conversions.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d930c366c0dd9857e463005474a2d59c04c4ae6
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/_quantized_conversions.py
@@ -0,0 +1,133 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+# Pack pairs of int4 values into int8, in row major order; first int4
+# value goes into lower order bits, and second int4 value into higher
+# order bits of resulting int8 value.
+def pack_int4_to_int8(weight):
+    assert weight.dim() == 2
+    assert weight.shape[1] % 2 == 0
+    assert weight.dtype == torch.int8
+    return ((weight[:, 1::2] & 0xF) << 4) | (weight[:, 0::2] & 0xF)
+
+
+# Unpack quandruples of bits in int8 values into int4 values, in row
+# major order; lower 4 bits go into first int4 value goes, and upper 4
+# bits go into second int4 value.
+def unpack_int8_to_int4(weight):
+    assert weight.dim() == 2
+    assert weight.dtype == torch.int8
+    return torch.stack((weight & 0xF, (weight >> 4) & 0xF), dim=2).view(
+        weight.shape[0], 2 * weight.shape[1]
+    )
+
+
+# Transpose the weight matrix, and then reorder its elements according
+# to underlying requirements of CUTLASS library, so that it could be
+# used for CUTLASS-based mixed datatypes linear operation.
+def quantized_weight_reorder_for_mixed_dtypes_linear_cutlass(
+    weight, dtypeq, transpose=False
+):
+    assert weight.dim() == 2
+    assert weight.dtype == torch.int8
+    assert dtypeq == torch.int8 or dtypeq == torch.quint4x2
+    assert weight.device.type == "cuda"
+
+    device = weight.device
+
+    # subbyte_transpose
+    if not transpose:
+        if dtypeq == torch.int8:
+            outp = weight.T
+        elif dtypeq == torch.quint4x2:
+            outp = pack_int4_to_int8(unpack_int8_to_int4(weight.view(torch.int8)).T)
+    else:
+        outp = weight
+
+    ncols, nrows = outp.shape  # type: ignore[possibly-undefined]
+    assert nrows % (32 if dtypeq == torch.quint4x2 else 64) == 0
+    assert ncols % 64 == 0
+
+    # permute_B_rows_for_mixed_gemm
+    # (permute cols actually, as transpose is applied first here)
+    if dtypeq == torch.quint4x2:
+        cols_permuted = (
+            torch.tensor(
+                [0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15],
+                device=device,
+            )
+            + (torch.arange(0, nrows // 16, device=device).reshape(-1, 1) * 16).expand(
+                nrows // 16, 16
+            )
+        ).view(-1)
+    else:
+        cols_permuted = (
+            torch.tensor(
+                [0, 1, 4, 5, 8, 9, 12, 13, 2, 3, 6, 7, 10, 11, 14, 15],
+                device=device,
+            )
+            + (torch.arange(0, nrows // 16, device=device).reshape(-1, 1) * 16).expand(
+                nrows // 16, 16
+            )
+        ).view(-1)
+    outp = outp.index_copy(1, cols_permuted, outp)
+
+    # interleave_column_major_tensor
+    magic0 = 4 if dtypeq == torch.quint4x2 else 2
+    magic1 = 32 // magic0
+
+    tmp0 = (
+        (torch.arange(0, ncols // magic0, device=device) * (nrows // 4 * magic0))
+        .view(-1, 1)
+        .repeat(1, nrows // 4 * magic0)
+        .view(-1)
+    )
+    tmp1 = (
+        (torch.arange(0, nrows // 4 // magic1, device=device) * (magic0 * magic1))
+        .view(-1, 1)
+        .repeat(1, magic1)
+        .view(-1)
+        .repeat(ncols)
+    )
+    tmp2 = (
+        (torch.arange(0, magic0, device=device) * magic1)
+        .view(-1, 1)
+        .repeat(1, nrows // 4)
+        .view(-1)
+        .repeat(ncols // magic0)
+    )
+    tmp3 = torch.arange(0, magic1, device=device).repeat(nrows // 4 * ncols // magic1)
+
+    outp_offsets = tmp0 + tmp1 + tmp2 + tmp3
+
+    tmp = outp.view(-1).view(torch.int32)
+    outp = torch.zeros_like(tmp)
+    outp.scatter_(0, outp_offsets, tmp)
+    outp = outp.view(weight.dtype)
+
+    # add_bias_and_interleave_quantized_tensor_inplace
+    tmp = outp.view(-1)
+
+    outp = torch.empty_like(tmp)
+    if dtypeq == torch.int8:
+        tmp = (tmp.to(torch.int) + 128).to(tmp.dtype)
+        outp[0::4] = tmp[0::4]
+        outp[1::4] = tmp[2::4]
+        outp[2::4] = tmp[1::4]
+        outp[3::4] = tmp[3::4]
+    elif dtypeq == torch.quint4x2:
+        tmp0 = ((tmp & 0xF) + 8) & 0xF
+        tmp0 = (tmp0[1::2] << 4) | tmp0[0::2]
+        tmp1 = (((tmp >> 4) & 0xF) + 8) & 0xF
+        tmp1 = (tmp1[1::2] << 4) | tmp1[0::2]
+        outp[0::4] = tmp0[0::2]
+        outp[1::4] = tmp0[1::2]
+        outp[2::4] = tmp1[0::2]
+        outp[3::4] = tmp1[1::2]
+
+    if dtypeq == torch.quint4x2:
+        nrows *= 2
+        ncols //= 2
+
+    return outp.view(nrows, ncols).view(torch.uint8)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fake_quantize.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fake_quantize.py
new file mode 100644
index 0000000000000000000000000000000000000000..69a5d730bfb68e89e24beb04ad13fd3fa5881ae9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fake_quantize.py
@@ -0,0 +1,32 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/fake_quantize.py`, while adding an import statement
+here.
+"""
+
+from torch.ao.quantization.fake_quantize import (
+    _is_fake_quant_script_module,
+    _is_per_channel,
+    _is_per_tensor,
+    _is_symmetric_quant,
+    default_fake_quant,
+    default_fixed_qparams_range_0to1_fake_quant,
+    default_fixed_qparams_range_neg1to1_fake_quant,
+    default_fused_act_fake_quant,
+    default_fused_per_channel_wt_fake_quant,
+    default_fused_wt_fake_quant,
+    default_histogram_fake_quant,
+    default_per_channel_weight_fake_quant,
+    default_weight_fake_quant,
+    disable_fake_quant,
+    disable_observer,
+    enable_fake_quant,
+    enable_observer,
+    FakeQuantize,
+    FakeQuantizeBase,
+    FixedQParamsFakeQuantize,
+    FusedMovingAvgObsFakeQuantize,
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fuse_modules.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fuse_modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b704fa8094e8b367e9eba47102863ba845415b9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fuse_modules.py
@@ -0,0 +1,22 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/fuse_modules.py`, while adding an import statement
+here.
+"""
+
+# TODO: These functions are not used outside the `fuse_modules.py`
+#       Keeping here for now, need to remove them later.
+from torch.ao.quantization.fuse_modules import (
+    _fuse_modules,
+    _get_module,
+    _set_module,
+    fuse_known_modules,
+    fuse_modules,
+    get_fuser_method,
+)
+
+# for backward compatiblity
+from torch.ao.quantization.fuser_method_mappings import fuse_conv_bn, fuse_conv_bn_relu
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fuser_method_mappings.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fuser_method_mappings.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfb13ac96271fa7b926cc703918984760e6ede15
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fuser_method_mappings.py
@@ -0,0 +1,15 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/fuser_method_mappings.py`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fuser_method_mappings import (
+    _DEFAULT_OP_LIST_TO_FUSER_METHOD,
+    fuse_conv_bn,
+    fuse_conv_bn_relu,
+    fuse_linear_bn,
+    get_fuser_method,
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c01cbd457374c27e40b07daca5ae1644a701767d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/__init__.py
@@ -0,0 +1,15 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+
+from torch.ao.quantization.fx.convert import convert
+from torch.ao.quantization.fx.fuse import fuse
+
+# omitting files that's unlikely to be used right now, for example
+# the newly added lower_to_fbgemm etc.
+from torch.ao.quantization.fx.prepare import prepare
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/_equalize.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/_equalize.py
new file mode 100644
index 0000000000000000000000000000000000000000..7acea4f84a2a0a82f134b6790e573f8f1cb677f2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/_equalize.py
@@ -0,0 +1,38 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx._equalize import (
+    _convert_equalization_ref,
+    _InputEqualizationObserver,
+    _WeightEqualizationObserver,
+    calculate_equalization_scale,
+    clear_weight_quant_obs_node,
+    convert_eq_obs,
+    CUSTOM_MODULE_SUPP_LIST,
+    custom_module_supports_equalization,
+    default_equalization_qconfig,
+    EqualizationQConfig,
+    fused_module_supports_equalization,
+    get_equalization_qconfig_dict,
+    get_layer_sqnr_dict,
+    get_op_node_and_weight_eq_obs,
+    input_equalization_observer,
+    is_equalization_observer,
+    maybe_get_next_equalization_scale,
+    maybe_get_next_input_eq_obs,
+    maybe_get_weight_eq_obs_node,
+    nn_module_supports_equalization,
+    node_supports_equalization,
+    remove_node,
+    reshape_scale,
+    scale_input_observer,
+    scale_weight_functional,
+    scale_weight_node,
+    update_obs_for_equalization,
+    weight_equalization_observer,
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/convert.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d6ac350602bb7a97c773a3a09fec0780483379f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/convert.py
@@ -0,0 +1,9 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.convert import convert
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/fuse.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/fuse.py
new file mode 100644
index 0000000000000000000000000000000000000000..67527080304fb31ddc54fe254533e2196f77a616
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/fuse.py
@@ -0,0 +1,9 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.fuse import fuse
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/fusion_patterns.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/fusion_patterns.py
new file mode 100644
index 0000000000000000000000000000000000000000..e29337b3f861e5b54dc9f37d39d12ad975ad1315
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/fusion_patterns.py
@@ -0,0 +1,9 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.fuse_handler import DefaultFuseHandler, FuseHandler
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/graph_module.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/graph_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..a71e980a57ba141bdc5bbe9b283d69582eb8fd82
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/graph_module.py
@@ -0,0 +1,17 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.graph_module import (
+    _is_observed_module,
+    _is_observed_standalone_module,
+    FusedGraphModule,
+    GraphModule,
+    ObservedGraphModule,
+    ObservedStandaloneGraphModule,
+    QuantizedGraphModule,
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/match_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/match_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b49f7c645d8d1bc3a154d62a1295a90b155f986
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/match_utils.py
@@ -0,0 +1,14 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.match_utils import (
+    _find_matches,
+    _is_match,
+    _MatchResult,
+    MatchAllNode,
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/pattern_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/pattern_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a83e180fc4dbaa28d1d41a10037684f0afa6610
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/pattern_utils.py
@@ -0,0 +1,35 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.pattern_utils import (
+    _register_fusion_pattern,
+    _register_quant_pattern,
+    get_default_fusion_patterns,
+    get_default_output_activation_post_process_map,
+    get_default_quant_patterns,
+    QuantizeHandler,
+)
+
+
+# QuantizeHandler.__module__ = _NAMESPACE
+_register_fusion_pattern.__module__ = "torch.ao.quantization.fx.pattern_utils"
+get_default_fusion_patterns.__module__ = "torch.ao.quantization.fx.pattern_utils"
+_register_quant_pattern.__module__ = "torch.ao.quantization.fx.pattern_utils"
+get_default_quant_patterns.__module__ = "torch.ao.quantization.fx.pattern_utils"
+get_default_output_activation_post_process_map.__module__ = (
+    "torch.ao.quantization.fx.pattern_utils"
+)
+
+# __all__ = [
+#     "QuantizeHandler",
+#     "_register_fusion_pattern",
+#     "get_default_fusion_patterns",
+#     "_register_quant_pattern",
+#     "get_default_quant_patterns",
+#     "get_default_output_activation_post_process_map",
+# ]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/prepare.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/prepare.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca65dcc04dd0021f0065892ca86e209a1c218473
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/prepare.py
@@ -0,0 +1,9 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.prepare import prepare
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/quantization_patterns.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/quantization_patterns.py
new file mode 100644
index 0000000000000000000000000000000000000000..20d8cc52ee4fb16843becec5487d9d4ee46681c9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/quantization_patterns.py
@@ -0,0 +1,48 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.quantize_handler import (
+    BatchNormQuantizeHandler,
+    BinaryOpQuantizeHandler,
+    CatQuantizeHandler,
+    ConvReluQuantizeHandler,
+    CopyNodeQuantizeHandler,
+    CustomModuleQuantizeHandler,
+    DefaultNodeQuantizeHandler,
+    EmbeddingQuantizeHandler,
+    FixedQParamsOpQuantizeHandler,
+    GeneralTensorShapeOpQuantizeHandler,
+    LinearReLUQuantizeHandler,
+    QuantizeHandler,
+    RNNDynamicQuantizeHandler,
+    StandaloneModuleQuantizeHandler,
+)
+
+
+QuantizeHandler.__module__ = "torch.ao.quantization.fx.quantization_patterns"
+BinaryOpQuantizeHandler.__module__ = "torch.ao.quantization.fx.quantization_patterns"
+CatQuantizeHandler.__module__ = "torch.ao.quantization.fx.quantization_patterns"
+ConvReluQuantizeHandler.__module__ = "torch.ao.quantization.fx.quantization_patterns"
+LinearReLUQuantizeHandler.__module__ = "torch.ao.quantization.fx.quantization_patterns"
+BatchNormQuantizeHandler.__module__ = "torch.ao.quantization.fx.quantization_patterns"
+EmbeddingQuantizeHandler.__module__ = "torch.ao.quantization.fx.quantization_patterns"
+RNNDynamicQuantizeHandler.__module__ = "torch.ao.quantization.fx.quantization_patterns"
+DefaultNodeQuantizeHandler.__module__ = "torch.ao.quantization.fx.quantization_patterns"
+FixedQParamsOpQuantizeHandler.__module__ = (
+    "torch.ao.quantization.fx.quantization_patterns"
+)
+CopyNodeQuantizeHandler.__module__ = "torch.ao.quantization.fx.quantization_patterns"
+CustomModuleQuantizeHandler.__module__ = (
+    "torch.ao.quantization.fx.quantization_patterns"
+)
+GeneralTensorShapeOpQuantizeHandler.__module__ = (
+    "torch.ao.quantization.fx.quantization_patterns"
+)
+StandaloneModuleQuantizeHandler.__module__ = (
+    "torch.ao.quantization.fx.quantization_patterns"
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/quantization_types.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/quantization_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..a422cdd3142e04c8d16f495cc6cd65823451810b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/quantization_types.py
@@ -0,0 +1,9 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.utils import Pattern, QuantizerCls
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef35559884b7c430f1d5c72b21f72979108469a5
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/fx/utils.py
@@ -0,0 +1,20 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.utils import (
+    all_node_args_have_no_tensors,
+    assert_and_get_unique_device,
+    create_getattr_from_value,
+    get_custom_module_class_keys,
+    get_linear_prepack_op_for_dtype,
+    get_new_attr_name_with_prefix,
+    get_non_observable_arg_indexes_and_types,
+    get_qconv_prepack_op,
+    graph_module_from_producer_nodes,
+    maybe_get_next_module,
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/observer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/observer.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e6c7c1917c83433fc19f016140b25d060284535
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/observer.py
@@ -0,0 +1,36 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/observer.py`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.observer import (
+    _is_activation_post_process,
+    _is_per_channel_script_obs_instance,
+    _ObserverBase,
+    _PartialWrapper,
+    _with_args,
+    _with_callable_args,
+    ABC,
+    default_debug_observer,
+    default_dynamic_quant_observer,
+    default_float_qparams_observer,
+    default_histogram_observer,
+    default_observer,
+    default_per_channel_weight_observer,
+    default_placeholder_observer,
+    default_weight_observer,
+    get_observer_state_dict,
+    HistogramObserver,
+    load_observer_state_dict,
+    MinMaxObserver,
+    MovingAverageMinMaxObserver,
+    MovingAveragePerChannelMinMaxObserver,
+    NoopObserver,
+    ObserverBase,
+    PerChannelMinMaxObserver,
+    PlaceholderObserver,
+    RecordingObserver,
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/qconfig.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/qconfig.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bb7e14110cb9cdc4e9c2c418c6776ea6445f0d3
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/qconfig.py
@@ -0,0 +1,30 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/qconfig.py`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.qconfig import (
+    _add_module_to_qconfig_obs_ctr,
+    _assert_valid_qconfig,
+    default_activation_only_qconfig,
+    default_debug_qconfig,
+    default_dynamic_qconfig,
+    default_per_channel_qconfig,
+    default_qat_qconfig,
+    default_qat_qconfig_v2,
+    default_qconfig,
+    default_weight_only_qconfig,
+    float16_dynamic_qconfig,
+    float16_static_qconfig,
+    float_qparams_weight_only_qconfig,
+    get_default_qat_qconfig,
+    get_default_qconfig,
+    per_channel_dynamic_qconfig,
+    QConfig,
+    qconfig_equals,
+    QConfigAny,
+    QConfigDynamic,
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/quant_type.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/quant_type.py
new file mode 100644
index 0000000000000000000000000000000000000000..8555f03792661f39c85c8facf3f911786cc25d0f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/quant_type.py
@@ -0,0 +1,10 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/quant_type.py`, while adding an import statement
+here.
+"""
+
+from torch.ao.quantization.quant_type import _get_quant_type_to_str, QuantType
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/quantization_mappings.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/quantization_mappings.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b44a980ce82fbfa5a81ad906499806cf99b876f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/quantization_mappings.py
@@ -0,0 +1,29 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/quantization_mappings.py`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.quantization_mappings import (
+    _get_special_act_post_process,
+    _has_special_act_post_process,
+    _INCLUDE_QCONFIG_PROPAGATE_LIST,
+    DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS,
+    DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS,
+    DEFAULT_MODULE_TO_ACT_POST_PROCESS,
+    DEFAULT_QAT_MODULE_MAPPINGS,
+    DEFAULT_REFERENCE_STATIC_QUANT_MODULE_MAPPINGS,
+    DEFAULT_STATIC_QUANT_MODULE_MAPPINGS,
+    get_default_compare_output_module_list,
+    get_default_dynamic_quant_module_mappings,
+    get_default_float_to_quantized_operator_mappings,
+    get_default_qat_module_mappings,
+    get_default_qconfig_propagation_list,
+    get_default_static_quant_module_mappings,
+    get_dynamic_quant_module_class,
+    get_quantized_operator,
+    get_static_quant_module_class,
+    no_observer_set,
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/quantize.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/quantize.py
new file mode 100644
index 0000000000000000000000000000000000000000..600d3a46fed0346e3ae8909872cd5bf3c733860c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/quantize.py
@@ -0,0 +1,30 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/quantize.py`, while adding an import statement
+here.
+"""
+
+from torch.ao.quantization.quantize import (
+    _add_observer_,
+    _convert,
+    _get_observer_dict,
+    _get_unique_devices_,
+    _is_activation_post_process,
+    _observer_forward_hook,
+    _propagate_qconfig_helper,
+    _register_activation_post_process_hook,
+    _remove_activation_post_process,
+    _remove_qconfig,
+    add_quant_dequant,
+    convert,
+    prepare,
+    prepare_qat,
+    propagate_qconfig_,
+    quantize,
+    quantize_dynamic,
+    quantize_qat,
+    swap_module,
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/quantize_fx.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/quantize_fx.py
new file mode 100644
index 0000000000000000000000000000000000000000..649142c7a7eee9885d96b37f70e582f3ea9a9f8d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/quantize_fx.py
@@ -0,0 +1,26 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/quantize_fx.py`, while adding an import statement
+here.
+"""
+
+from torch.ao.quantization.fx.graph_module import ObservedGraphModule
+from torch.ao.quantization.quantize_fx import (
+    _check_is_graph_module,
+    _convert_fx,
+    _convert_standalone_module_fx,
+    _fuse_fx,
+    _prepare_fx,
+    _prepare_standalone_module_fx,
+    _swap_ff_with_fxff,
+    convert_fx,
+    fuse_fx,
+    prepare_fx,
+    prepare_qat_fx,
+    QuantizationTracer,
+    Scope,
+    ScopeContextManager,
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/quantize_jit.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/quantize_jit.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa627dc7bb51ef7ea1fde7e2e5da283c9f6c8900
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/quantize_jit.py
@@ -0,0 +1,26 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/quantize_jit.py`, while adding an import statement
+here.
+"""
+
+from torch.ao.quantization.quantize_jit import (
+    _check_forward_method,
+    _check_is_script_module,
+    _convert_jit,
+    _prepare_jit,
+    _prepare_ondevice_dynamic_jit,
+    _quantize_jit,
+    convert_dynamic_jit,
+    convert_jit,
+    fuse_conv_bn_jit,
+    prepare_dynamic_jit,
+    prepare_jit,
+    quantize_dynamic_jit,
+    quantize_jit,
+    script_qconfig,
+    script_qconfig_dict,
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/stubs.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/stubs.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3fd5c63683dc572c35cabc202ee4ddb2b0053c6
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/stubs.py
@@ -0,0 +1,10 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/stubs.py`, while adding an import statement
+here.
+"""
+
+from torch.ao.quantization.stubs import DeQuantStub, QuantStub, QuantWrapper
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d51d58f38d7462713f84ab62427852c1dd8e52c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/quantization/utils.py
@@ -0,0 +1,29 @@
+# flake8: noqa: F401
+r"""
+Utils shared by different modes of quantization (eager/graph)
+
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/utils.py`, while adding an import statement
+here.
+"""
+
+from torch.ao.quantization.utils import (
+    activation_dtype,
+    activation_is_int8_quantized,
+    activation_is_statically_quantized,
+    calculate_qmin_qmax,
+    check_min_max_valid,
+    get_combined_dict,
+    get_qconfig_dtypes,
+    get_qparam_dict,
+    get_quant_type,
+    get_swapped_custom_module_class,
+    getattr_from_fqn,
+    is_per_channel,
+    is_per_tensor,
+    weight_dtype,
+    weight_is_quantized,
+    weight_is_statically_quantized,
+)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/ATen/ATenConfig.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/ATen/ATenConfig.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..0ce7803dbf78897298d81c2679f2cdb3c872bc15
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/ATen/ATenConfig.cmake
@@ -0,0 +1,9 @@
+# Find the TH includes and library
+#
+# ATEN_INCLUDE_DIR -- where to find the includes
+# ATEN_LIBRARIES -- list of libraries to link against
+# ATEN_FOUND -- set to 1 if found
+
+set(ATEN_FOUND 1)
+set(ATEN_INCLUDE_DIR "/pytorch/torch/include")
+set(ATEN_LIBRARIES "")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Caffe2Config.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Caffe2Config.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..b6c7c9dc16f480bcd727eb3615c96b80409eb96e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Caffe2Config.cmake
@@ -0,0 +1,133 @@
+# - Config file for the Caffe2 package
+# It defines the following variable(s)
+#   CAFFE2_INCLUDE_DIRS     - include directories for FooBar
+# as well as Caffe2 targets for other cmake libraries to use.
+
+# library version information
+
+# Utils functions.
+include("${CMAKE_CURRENT_LIST_DIR}/public/utils.cmake")
+
+# Depending on whether Caffe2 uses gflags during compile time or
+# not, invoke gflags.
+if(OFF)
+  include("${CMAKE_CURRENT_LIST_DIR}/public/gflags.cmake")
+  if(NOT TARGET gflags)
+    message(FATAL_ERROR
+        "Your installed Caffe2 version uses gflags but the gflags library "
+        "cannot be found. Did you accidentally remove it, or have you set "
+        "the right CMAKE_PREFIX_PATH and/or GFLAGS_ROOT_DIR? If you do not "
+        "have gflags, you will need to install gflags and set the library "
+        "path accordingly.")
+  endif()
+endif()
+
+# Depending on whether Caffe2 uses glog during compile time or
+# not, invoke glog.
+if(OFF)
+  include("${CMAKE_CURRENT_LIST_DIR}/public/glog.cmake")
+  if(NOT TARGET glog::glog)
+    message(FATAL_ERROR
+        "Your installed Caffe2 version uses glog but the glog library "
+        "cannot be found. Did you accidentally remove it, or have you set "
+        "the right CMAKE_PREFIX_PATH and/or GFLAGS_ROOT_DIR? If you do not "
+        "have glog, you will need to install glog and set the library "
+        "path accordingly.")
+  endif()
+endif()
+
+# Protobuf
+if(ON)
+  if(NOT TARGET protobuf::libprotobuf)
+    # Define protobuf::libprotobuf as a dummy target to resolve references to
+    # protobuf::libprotobuf in Caffe2Targets.cmake.
+    add_library(dummy INTERFACE)
+    add_library(protobuf::libprotobuf ALIAS dummy)
+  endif()
+else()
+  include("${CMAKE_CURRENT_LIST_DIR}/public/protobuf.cmake")
+  if(NOT TARGET protobuf::libprotobuf)
+    message(FATAL_ERROR
+        "Your installed Caffe2 version uses protobuf but the protobuf library "
+        "cannot be found. Did you accidentally remove it, or have you set "
+        "the right CMAKE_PREFIX_PATH? If you do not have protobuf, you will "
+        "need to install protobuf and set the library path accordingly.")
+  endif()
+  message(STATUS "Caffe2: Protobuf version " ${Protobuf_VERSION})
+  # If during build time we know the protobuf version, we will also do a sanity
+  # check to ensure that the protobuf library that Caffe2 found is consistent
+  # with the compiled version.
+  if(FALSE)
+    if(NOT (${Protobuf_VERSION} VERSION_EQUAL Protobuf_VERSION_NOTFOUND))
+      message(FATAL_ERROR
+          "Your installed Caffe2 is built with protobuf "
+          "Protobuf_VERSION_NOTFOUND"
+          ", while your current cmake setting discovers protobuf version "
+          ${Protobuf_VERSION}
+          ". Please specify a protobuf version that is the same as the built "
+          "version.")
+    endif()
+  endif()
+endif()
+
+if (OFF)
+  include("${CMAKE_CURRENT_LIST_DIR}/public/LoadHIP.cmake")
+endif()
+
+if(0)
+  # The file public/cuda.cmake exclusively uses CAFFE2_USE_*.
+  # If Caffe2 was compiled with the libraries below, they must
+  # be found again when including the Caffe2 target.
+  set(CAFFE2_USE_CUDA 0)
+
+  # Add current directory to module path so we pick up FindCUDAToolkit.cmake
+  set(old_CMAKE_MODULE_PATH "${CMAKE_MODULE_PATH}")
+  list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}")
+  include("${CMAKE_CURRENT_LIST_DIR}/public/cuda.cmake")
+  set(CMAKE_MODULE_PATH "${old_CMAKE_MODULE_PATH}")
+
+  if( AND NOT CAFFE2_USE_CUDA)
+    message(FATAL_ERROR
+      "Your installed Caffe2 version uses CUDA but I cannot find the CUDA "
+      "libraries. Please set the proper CUDA prefixes and / or install "
+      "CUDA.")
+  endif()
+endif()
+
+if(OFF)
+  # Add current directory to module path so we pick up FindSYCLToolkit.cmake
+  set(old_CMAKE_MODULE_PATH "${CMAKE_MODULE_PATH}")
+  list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}")
+  include("${CMAKE_CURRENT_LIST_DIR}/public/xpu.cmake")
+  set(CMAKE_MODULE_PATH "${old_CMAKE_MODULE_PATH}")
+endif()
+
+if(ON)
+  include("${CMAKE_CURRENT_LIST_DIR}/public/mkl.cmake")
+endif()
+
+if(ON)
+  include("${CMAKE_CURRENT_LIST_DIR}/public/mkldnn.cmake")
+endif()
+
+# import targets
+include ("${CMAKE_CURRENT_LIST_DIR}/Caffe2Targets.cmake")
+
+# Interface libraries, that allows one to build proper link flags.
+# We will also define a helper variable, Caffe2_MAIN_LIBS, that resolves to
+# the main caffe2 libraries in cases of cuda presence / absence.
+set(Caffe2_MAIN_LIBS torch_library)
+
+# include directory.
+#
+# Newer versions of CMake set the INTERFACE_INCLUDE_DIRECTORIES property
+# of the imported targets. It is hence not necessary to add this path
+# manually to the include search path for targets which link to gflags.
+# The following lines are here for backward compatibility, in case one
+# would like to use the old-style include path.
+get_filename_component(
+    CMAKE_CURRENT_LIST_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
+# Note: the current list dir is _INSTALL_PREFIX/share/cmake/Gloo.
+get_filename_component(
+    _INSTALL_PREFIX "${CMAKE_CURRENT_LIST_DIR}/../../../" ABSOLUTE)
+set(CAFFE2_INCLUDE_DIRS "${_INSTALL_PREFIX}/include")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Caffe2Targets-release.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Caffe2Targets-release.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..ba9fc252679c79ca76be8cc92bb6674d0e5e4332
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Caffe2Targets-release.cmake
@@ -0,0 +1,39 @@
+#----------------------------------------------------------------
+# Generated CMake target import file for configuration "Release".
+#----------------------------------------------------------------
+
+# Commands may need to know the format version.
+set(CMAKE_IMPORT_FILE_VERSION 1)
+
+# Import target "c10" for configuration "Release"
+set_property(TARGET c10 APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
+set_target_properties(c10 PROPERTIES
+  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libc10.so"
+  IMPORTED_SONAME_RELEASE "libc10.so"
+  )
+
+list(APPEND _IMPORT_CHECK_TARGETS c10 )
+list(APPEND _IMPORT_CHECK_FILES_FOR_c10 "${_IMPORT_PREFIX}/lib/libc10.so" )
+
+# Import target "torch_cpu" for configuration "Release"
+set_property(TARGET torch_cpu APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
+set_target_properties(torch_cpu PROPERTIES
+  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libtorch_cpu.so"
+  IMPORTED_SONAME_RELEASE "libtorch_cpu.so"
+  )
+
+list(APPEND _IMPORT_CHECK_TARGETS torch_cpu )
+list(APPEND _IMPORT_CHECK_FILES_FOR_torch_cpu "${_IMPORT_PREFIX}/lib/libtorch_cpu.so" )
+
+# Import target "torch" for configuration "Release"
+set_property(TARGET torch APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
+set_target_properties(torch PROPERTIES
+  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libtorch.so"
+  IMPORTED_SONAME_RELEASE "libtorch.so"
+  )
+
+list(APPEND _IMPORT_CHECK_TARGETS torch )
+list(APPEND _IMPORT_CHECK_FILES_FOR_torch "${_IMPORT_PREFIX}/lib/libtorch.so" )
+
+# Commands beyond this point should not need to know the version.
+set(CMAKE_IMPORT_FILE_VERSION)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Caffe2Targets.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Caffe2Targets.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..96faa622aab2a97f800ed919cf9d9a2f12112190
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Caffe2Targets.cmake
@@ -0,0 +1,152 @@
+# Generated by CMake
+
+if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.5)
+   message(FATAL_ERROR "CMake >= 2.6.0 required")
+endif()
+cmake_policy(PUSH)
+cmake_policy(VERSION 2.6...3.17)
+#----------------------------------------------------------------
+# Generated CMake target import file.
+#----------------------------------------------------------------
+
+# Commands may need to know the format version.
+set(CMAKE_IMPORT_FILE_VERSION 1)
+
+# Protect against multiple inclusion, which would fail when already imported targets are added once more.
+set(_targetsDefined)
+set(_targetsNotDefined)
+set(_expectedTargets)
+foreach(_expectedTarget c10 torch_cpu torch_cpu_library torch torch_library)
+  list(APPEND _expectedTargets ${_expectedTarget})
+  if(NOT TARGET ${_expectedTarget})
+    list(APPEND _targetsNotDefined ${_expectedTarget})
+  endif()
+  if(TARGET ${_expectedTarget})
+    list(APPEND _targetsDefined ${_expectedTarget})
+  endif()
+endforeach()
+if("${_targetsDefined}" STREQUAL "${_expectedTargets}")
+  unset(_targetsDefined)
+  unset(_targetsNotDefined)
+  unset(_expectedTargets)
+  set(CMAKE_IMPORT_FILE_VERSION)
+  cmake_policy(POP)
+  return()
+endif()
+if(NOT "${_targetsDefined}" STREQUAL "")
+  message(FATAL_ERROR "Some (but not all) targets in this export set were already defined.\nTargets Defined: ${_targetsDefined}\nTargets not yet defined: ${_targetsNotDefined}\n")
+endif()
+unset(_targetsDefined)
+unset(_targetsNotDefined)
+unset(_expectedTargets)
+
+
+# Compute the installation prefix relative to this file.
+get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+if(_IMPORT_PREFIX STREQUAL "/")
+  set(_IMPORT_PREFIX "")
+endif()
+
+# Create imported target c10
+add_library(c10 SHARED IMPORTED)
+
+set_target_properties(c10 PROPERTIES
+  INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include"
+)
+
+# Create imported target torch_cpu
+add_library(torch_cpu SHARED IMPORTED)
+
+set_target_properties(torch_cpu PROPERTIES
+  INTERFACE_COMPILE_DEFINITIONS "USE_DISTRIBUTED;USE_C10D_GLOO;USE_RPC;USE_TENSORPIPE"
+  INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include"
+  INTERFACE_LINK_LIBRARIES "protobuf::libprotobuf;c10;caffe2::mkl"
+)
+
+# Create imported target torch_cpu_library
+add_library(torch_cpu_library INTERFACE IMPORTED)
+
+set_target_properties(torch_cpu_library PROPERTIES
+  INTERFACE_COMPILE_DEFINITIONS "\$"
+  INTERFACE_COMPILE_OPTIONS "\$"
+  INTERFACE_INCLUDE_DIRECTORIES "\$"
+  INTERFACE_LINK_LIBRARIES "-Wl,--no-as-needed,\"\$\" -Wl,--as-needed;\$"
+  INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "\$"
+)
+
+# Create imported target torch
+add_library(torch SHARED IMPORTED)
+
+set_target_properties(torch PROPERTIES
+  INTERFACE_LINK_LIBRARIES "torch_cpu_library"
+)
+
+# Create imported target torch_library
+add_library(torch_library INTERFACE IMPORTED)
+
+set_target_properties(torch_library PROPERTIES
+  INTERFACE_COMPILE_DEFINITIONS "\$"
+  INTERFACE_COMPILE_OPTIONS "\$"
+  INTERFACE_INCLUDE_DIRECTORIES "\$"
+  INTERFACE_LINK_LIBRARIES "-Wl,--no-as-needed,\"\$\" -Wl,--as-needed;\$"
+  INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "\$"
+)
+
+if(CMAKE_VERSION VERSION_LESS 3.0.0)
+  message(FATAL_ERROR "This file relies on consumers using CMake 3.0.0 or greater.")
+endif()
+
+# Load information for each installed configuration.
+get_filename_component(_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
+file(GLOB CONFIG_FILES "${_DIR}/Caffe2Targets-*.cmake")
+foreach(f ${CONFIG_FILES})
+  include(${f})
+endforeach()
+
+# Cleanup temporary variables.
+set(_IMPORT_PREFIX)
+
+# Loop over all imported files and verify that they actually exist
+foreach(target ${_IMPORT_CHECK_TARGETS} )
+  foreach(file ${_IMPORT_CHECK_FILES_FOR_${target}} )
+    if(NOT EXISTS "${file}" )
+      message(FATAL_ERROR "The imported target \"${target}\" references the file
+   \"${file}\"
+but this file does not exist.  Possible reasons include:
+* The file was deleted, renamed, or moved to another location.
+* An install or uninstall procedure did not complete successfully.
+* The installation package was faulty and contained
+   \"${CMAKE_CURRENT_LIST_FILE}\"
+but not all the files it references.
+")
+    endif()
+  endforeach()
+  unset(_IMPORT_CHECK_FILES_FOR_${target})
+endforeach()
+unset(_IMPORT_CHECK_TARGETS)
+
+# Make sure the targets which have been exported in some other
+# export set exist.
+unset(${CMAKE_FIND_PACKAGE_NAME}_NOT_FOUND_MESSAGE_targets)
+foreach(_target "protobuf::libprotobuf" )
+  if(NOT TARGET "${_target}" )
+    set(${CMAKE_FIND_PACKAGE_NAME}_NOT_FOUND_MESSAGE_targets "${${CMAKE_FIND_PACKAGE_NAME}_NOT_FOUND_MESSAGE_targets} ${_target}")
+  endif()
+endforeach()
+
+if(DEFINED ${CMAKE_FIND_PACKAGE_NAME}_NOT_FOUND_MESSAGE_targets)
+  if(CMAKE_FIND_PACKAGE_NAME)
+    set( ${CMAKE_FIND_PACKAGE_NAME}_FOUND FALSE)
+    set( ${CMAKE_FIND_PACKAGE_NAME}_NOT_FOUND_MESSAGE "The following imported targets are referenced, but are missing: ${${CMAKE_FIND_PACKAGE_NAME}_NOT_FOUND_MESSAGE_targets}")
+  else()
+    message(FATAL_ERROR "The following imported targets are referenced, but are missing: ${${CMAKE_FIND_PACKAGE_NAME}_NOT_FOUND_MESSAGE_targets}")
+  endif()
+endif()
+unset(${CMAKE_FIND_PACKAGE_NAME}_NOT_FOUND_MESSAGE_targets)
+
+# Commands beyond this point should not need to know the version.
+set(CMAKE_IMPORT_FILE_VERSION)
+cmake_policy(POP)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/FindCUDAToolkit.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/FindCUDAToolkit.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..ec9ae530aa6b2bdceb87f966e706fb5c2a36349a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/FindCUDAToolkit.cmake
@@ -0,0 +1,1081 @@
+
+# This module is back-ported from CMake 3.17 and above to work with CMake 3.10
+
+# Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
+# file Copyright.txt or https://cmake.org/licensing for details.
+
+#[=======================================================================[.rst:
+FindCUDAToolkit
+---------------
+
+.. versionadded:: 3.17
+
+This script locates the NVIDIA CUDA toolkit and the associated libraries, but
+does not require the ``CUDA`` language be enabled for a given project. This
+module does not search for the NVIDIA CUDA Samples.
+
+.. versionadded:: 3.19
+  QNX support.
+
+Search Behavior
+^^^^^^^^^^^^^^^
+
+The CUDA Toolkit search behavior uses the following order:
+
+1. If the ``CUDA`` language has been enabled we will use the directory
+   containing the compiler as the first search location for ``nvcc``.
+
+2. If the ``CUDAToolkit_ROOT`` cmake configuration variable (e.g.,
+   ``-DCUDAToolkit_ROOT=/some/path``) *or* environment variable is defined, it
+   will be searched.  If both an environment variable **and** a
+   configuration variable are specified, the *configuration* variable takes
+   precedence.
+
+   The directory specified here must be such that the executable ``nvcc`` or
+   the appropriate ``version.txt`` file can be found underneath the specified
+   directory.
+
+3. If the CUDA_PATH environment variable is defined, it will be searched
+   for ``nvcc``.
+
+4. The user's path is searched for ``nvcc`` using :command:`find_program`.  If
+   this is found, no subsequent search attempts are performed.  Users are
+   responsible for ensuring that the first ``nvcc`` to show up in the path is
+   the desired path in the event that multiple CUDA Toolkits are installed.
+
+5. On Unix systems, if the symbolic link ``/usr/local/cuda`` exists, this is
+   used.  No subsequent search attempts are performed.  No default symbolic link
+   location exists for the Windows platform.
+
+6. The platform specific default install locations are searched.  If exactly one
+   candidate is found, this is used.  The default CUDA Toolkit install locations
+   searched are:
+
+   +-------------+-------------------------------------------------------------+
+   | Platform    | Search Pattern                                              |
+   +=============+=============================================================+
+   | macOS       | ``/Developer/NVIDIA/CUDA-X.Y``                              |
+   +-------------+-------------------------------------------------------------+
+   | Other Unix  | ``/usr/local/cuda-X.Y``                                     |
+   +-------------+-------------------------------------------------------------+
+   | Windows     | ``C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vX.Y`` |
+   +-------------+-------------------------------------------------------------+
+
+   Where ``X.Y`` would be a specific version of the CUDA Toolkit, such as
+   ``/usr/local/cuda-9.0`` or
+   ``C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0``
+
+   .. note::
+
+       When multiple CUDA Toolkits are installed in the default location of a
+       system(e.g., both ``/usr/local/cuda-9.0`` and ``/usr/local/cuda-10.0``
+       exist but the ``/usr/local/cuda`` symbolic link does **not** exist), this
+       package is marked as **not** found.
+
+       There are too many factors involved in making an automatic decision in
+       the presence of multiple CUDA Toolkits being installed.  In this
+       situation, users are encouraged to either (1) set ``CUDAToolkit_ROOT`` or
+       (2) ensure that the correct ``nvcc`` executable shows up in ``$PATH`` for
+       :command:`find_program` to find.
+
+Arguments
+^^^^^^^^^
+
+``[]``
+    The ``[]`` argument requests a version with which the package found
+    should be compatible. See :ref:`find_package version format `
+    for more details.
+
+Options
+^^^^^^^
+
+``REQUIRED``
+    If specified, configuration will error if a suitable CUDA Toolkit is not
+    found.
+
+``QUIET``
+    If specified, the search for a suitable CUDA Toolkit will not produce any
+    messages.
+
+``EXACT``
+    If specified, the CUDA Toolkit is considered found only if the exact
+    ``VERSION`` specified is recovered.
+
+Imported targets
+^^^^^^^^^^^^^^^^
+
+An :ref:`imported target ` named ``CUDA::toolkit`` is provided.
+
+This module defines :prop_tgt:`IMPORTED` targets for each
+of the following libraries that are part of the CUDAToolkit:
+
+- :ref:`CUDA Runtime Library`
+- :ref:`CUDA Driver Library`
+- :ref:`cuBLAS`
+- :ref:`cuFFT`
+- :ref:`cuRAND`
+- :ref:`cuSOLVER`
+- :ref:`cuSPARSE`
+- :ref:`cuPTI`
+- :ref:`NPP`
+- :ref:`nvBLAS`
+- :ref:`nvGRAPH`
+- :ref:`nvJPEG`
+- :ref:`nvidia-ML`
+- :ref:`nvRTC`
+- :ref:`nvToolsExt`
+- :ref:`OpenCL`
+- :ref:`cuLIBOS`
+
+.. _`cuda_toolkit_rt_lib`:
+
+CUDA Runtime Library
+""""""""""""""""""""
+
+The CUDA Runtime library (cudart) are what most applications will typically
+need to link against to make any calls such as `cudaMalloc`, and `cudaFree`.
+
+Targets Created:
+
+- ``CUDA::cudart``
+- ``CUDA::cudart_static``
+
+.. _`cuda_toolkit_driver_lib`:
+
+CUDA Driver Library
+""""""""""""""""""""
+
+The CUDA Driver library (cuda) are used by applications that use calls
+such as `cuMemAlloc`, and `cuMemFree`.
+
+Targets Created:
+
+- ``CUDA::cuda_driver``
+
+.. _`cuda_toolkit_cuBLAS`:
+
+cuBLAS
+""""""
+
+The `cuBLAS `_ library.
+
+Targets Created:
+
+- ``CUDA::cublas``
+- ``CUDA::cublas_static``
+- ``CUDA::cublasLt`` starting in CUDA 10.1
+- ``CUDA::cublasLt_static`` starting in CUDA 10.1
+
+.. _`cuda_toolkit_cuFFT`:
+
+cuFFT
+"""""
+
+The `cuFFT `_ library.
+
+Targets Created:
+
+- ``CUDA::cufft``
+- ``CUDA::cufftw``
+- ``CUDA::cufft_static``
+- ``CUDA::cufft_static_nocallback`` starting in CUDA 9.2, requires CMake 3.23+
+- ``CUDA::cufftw_static``
+
+cuRAND
+""""""
+
+The `cuRAND `_ library.
+
+Targets Created:
+
+- ``CUDA::curand``
+- ``CUDA::curand_static``
+
+.. _`cuda_toolkit_cuSOLVER`:
+
+cuSOLVER
+""""""""
+
+The `cuSOLVER `_ library.
+
+Targets Created:
+
+- ``CUDA::cusolver``
+- ``CUDA::cusolver_static``
+
+.. _`cuda_toolkit_cuSPARSE`:
+
+cuSPARSE
+""""""""
+
+The `cuSPARSE `_ library.
+
+Targets Created:
+
+- ``CUDA::cusparse``
+- ``CUDA::cusparse_static``
+
+.. _`cuda_toolkit_cupti`:
+
+cupti
+"""""
+
+The `NVIDIA CUDA Profiling Tools Interface `_.
+
+Targets Created:
+
+- ``CUDA::cupti``
+- ``CUDA::cupti_static``
+
+.. _`cuda_toolkit_NPP`:
+
+NPP
+"""
+
+The `NPP `_ libraries.
+
+Targets Created:
+
+- `nppc`:
+
+  - ``CUDA::nppc``
+  - ``CUDA::nppc_static``
+
+- `nppial`: Arithmetic and logical operation functions in `nppi_arithmetic_and_logical_operations.h`
+
+  - ``CUDA::nppial``
+  - ``CUDA::nppial_static``
+
+- `nppicc`: Color conversion and sampling functions in `nppi_color_conversion.h`
+
+  - ``CUDA::nppicc``
+  - ``CUDA::nppicc_static``
+
+- `nppicom`: JPEG compression and decompression functions in `nppi_compression_functions.h`
+  Removed starting in CUDA 11.0, use :ref:`nvJPEG` instead.
+
+  - ``CUDA::nppicom``
+  - ``CUDA::nppicom_static``
+
+- `nppidei`: Data exchange and initialization functions in `nppi_data_exchange_and_initialization.h`
+
+  - ``CUDA::nppidei``
+  - ``CUDA::nppidei_static``
+
+- `nppif`: Filtering and computer vision functions in `nppi_filter_functions.h`
+
+  - ``CUDA::nppif``
+  - ``CUDA::nppif_static``
+
+- `nppig`: Geometry transformation functions found in `nppi_geometry_transforms.h`
+
+  - ``CUDA::nppig``
+  - ``CUDA::nppig_static``
+
+- `nppim`: Morphological operation functions found in `nppi_morphological_operations.h`
+
+  - ``CUDA::nppim``
+  - ``CUDA::nppim_static``
+
+- `nppist`: Statistics and linear transform in `nppi_statistics_functions.h` and `nppi_linear_transforms.h`
+
+  - ``CUDA::nppist``
+  - ``CUDA::nppist_static``
+
+- `nppisu`: Memory support functions in `nppi_support_functions.h`
+
+  - ``CUDA::nppisu``
+  - ``CUDA::nppisu_static``
+
+- `nppitc`: Threshold and compare operation functions in `nppi_threshold_and_compare_operations.h`
+
+  - ``CUDA::nppitc``
+  - ``CUDA::nppitc_static``
+
+- `npps`:
+
+  - ``CUDA::npps``
+  - ``CUDA::npps_static``
+
+.. _`cuda_toolkit_nvBLAS`:
+
+nvBLAS
+""""""
+
+The `nvBLAS `_ libraries.
+This is a shared library only.
+
+Targets Created:
+
+- ``CUDA::nvblas``
+
+.. _`cuda_toolkit_nvGRAPH`:
+
+nvGRAPH
+"""""""
+
+The `nvGRAPH `_ library.
+Removed starting in CUDA 11.0
+
+Targets Created:
+
+- ``CUDA::nvgraph``
+- ``CUDA::nvgraph_static``
+
+
+.. _`cuda_toolkit_nvJPEG`:
+
+nvJPEG
+""""""
+
+The `nvJPEG `_ library.
+Introduced in CUDA 10.
+
+Targets Created:
+
+- ``CUDA::nvjpeg``
+- ``CUDA::nvjpeg_static``
+
+.. _`cuda_toolkit_nvRTC`:
+
+nvRTC
+"""""
+
+The `nvRTC `_ (Runtime Compilation) library.
+This is a shared library only.
+
+Targets Created:
+
+- ``CUDA::nvrtc``
+
+.. _`cuda_toolkit_nvml`:
+
+nvidia-ML
+"""""""""
+
+The `NVIDIA Management Library `_.
+This is a shared library only.
+
+Targets Created:
+
+- ``CUDA::nvml``
+
+.. _`cuda_toolkit_nvToolsExt`:
+
+nvToolsExt
+""""""""""
+
+The `NVIDIA Tools Extension `_.
+This is a shared library only.
+
+Targets Created:
+
+- ``CUDA::nvToolsExt``
+
+.. _`cuda_toolkit_opencl`:
+
+OpenCL
+""""""
+
+The `NVIDIA OpenCL Library `_.
+This is a shared library only.
+
+Targets Created:
+
+- ``CUDA::OpenCL``
+
+.. _`cuda_toolkit_cuLIBOS`:
+
+cuLIBOS
+"""""""
+
+The cuLIBOS library is a backend thread abstraction layer library which is
+static only.  The ``CUDA::cublas_static``, ``CUDA::cusparse_static``,
+``CUDA::cufft_static``, ``CUDA::curand_static``, and (when implemented) NPP
+libraries all automatically have this dependency linked.
+
+Target Created:
+
+- ``CUDA::culibos``
+
+**Note**: direct usage of this target by consumers should not be necessary.
+
+.. _`cuda_toolkit_cuRAND`:
+
+
+
+Result variables
+^^^^^^^^^^^^^^^^
+
+``CUDAToolkit_FOUND``
+    A boolean specifying whether or not the CUDA Toolkit was found.
+
+``CUDAToolkit_VERSION``
+    The exact version of the CUDA Toolkit found (as reported by
+    ``nvcc --version`` or ``version.txt``).
+
+``CUDAToolkit_VERSION_MAJOR``
+    The major version of the CUDA Toolkit.
+
+``CUDAToolkit_VERSION_MINOR``
+    The minor version of the CUDA Toolkit.
+
+``CUDAToolkit_VERSION_PATCH``
+    The patch version of the CUDA Toolkit.
+
+``CUDAToolkit_BIN_DIR``
+    The path to the CUDA Toolkit library directory that contains the CUDA
+    executable ``nvcc``.
+
+``CUDAToolkit_INCLUDE_DIRS``
+    The path to the CUDA Toolkit ``include`` folder containing the header files
+    required to compile a project linking against CUDA.
+
+``CUDAToolkit_LIBRARY_DIR``
+    The path to the CUDA Toolkit library directory that contains the CUDA
+    Runtime library ``cudart``.
+
+``CUDAToolkit_LIBRARY_ROOT``
+    .. versionadded:: 3.18
+
+    The path to the CUDA Toolkit directory containing the nvvm directory and
+    version.txt.
+
+``CUDAToolkit_TARGET_DIR``
+    The path to the CUDA Toolkit directory including the target architecture
+    when cross-compiling. When not cross-compiling this will be equivalent to
+    the parent directory of ``CUDAToolkit_BIN_DIR``.
+
+``CUDAToolkit_NVCC_EXECUTABLE``
+    The path to the NVIDIA CUDA compiler ``nvcc``.  Note that this path may
+    **not** be the same as
+    :variable:`CMAKE_CUDA_COMPILER _COMPILER>`.  ``nvcc`` must be
+    found to determine the CUDA Toolkit version as well as determining other
+    features of the Toolkit.  This variable is set for the convenience of
+    modules that depend on this one.
+
+
+#]=======================================================================]
+
+# NOTE: much of this was simply extracted from FindCUDA.cmake.
+
+#   James Bigler, NVIDIA Corp (nvidia.com - jbigler)
+#   Abe Stephens, SCI Institute -- http://www.sci.utah.edu/~abe/FindCuda.html
+#
+#   Copyright (c) 2008 - 2009 NVIDIA Corporation.  All rights reserved.
+#
+#   Copyright (c) 2007-2009
+#   Scientific Computing and Imaging Institute, University of Utah
+#
+#   This code is licensed under the MIT License.  See the FindCUDA.cmake script
+#   for the text of the license.
+
+# The MIT License
+#
+# License for the specific language governing rights and limitations under
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+# DEALINGS IN THE SOFTWARE.
+#
+###############################################################################
+
+# The toolkit is located during compiler detection for CUDA and stored in CMakeCUDACompiler.cmake as
+# CMAKE_CUDA_COMPILER_TOOLKIT_ROOT and CMAKE_CUDA_COMPILER_LIBRARY_ROOT.
+# We compute the rest based on those here to avoid re-searching and to avoid finding a possibly
+# different installation.
+if(CMAKE_CUDA_COMPILER_TOOLKIT_ROOT)
+  set(CUDAToolkit_ROOT_DIR "${CMAKE_CUDA_COMPILER_TOOLKIT_ROOT}")
+  set(CUDAToolkit_LIBRARY_ROOT "${CMAKE_CUDA_COMPILER_LIBRARY_ROOT}")
+  set(CUDAToolkit_VERSION "${CMAKE_CUDA_COMPILER_TOOLKIT_VERSION}")
+
+  if(CUDAToolkit_VERSION MATCHES [=[([0-9]+)\.([0-9]+)\.([0-9]+)]=])
+    set(CUDAToolkit_VERSION_MAJOR "${CMAKE_MATCH_1}")
+    set(CUDAToolkit_VERSION_MINOR "${CMAKE_MATCH_2}")
+    set(CUDAToolkit_VERSION_PATCH "${CMAKE_MATCH_3}")
+  endif()
+else()
+  function(_CUDAToolkit_find_root_dir )
+    cmake_parse_arguments(arg "" "" "SEARCH_PATHS;FIND_FLAGS" ${ARGN})
+
+    if(NOT CUDAToolkit_BIN_DIR)
+      if(NOT CUDAToolkit_SENTINEL_FILE)
+        find_program(CUDAToolkit_NVCC_EXECUTABLE
+          NAMES nvcc nvcc.exe
+          PATHS ${arg_SEARCH_PATHS}
+          ${arg_FIND_FLAGS}
+        )
+      endif()
+
+      if(NOT CUDAToolkit_NVCC_EXECUTABLE)
+        find_file(CUDAToolkit_SENTINEL_FILE
+          NAMES version.txt
+          PATHS ${arg_SEARCH_PATHS}
+          NO_DEFAULT_PATH
+        )
+      endif()
+
+      if(EXISTS "${CUDAToolkit_NVCC_EXECUTABLE}")
+        # If NVCC exists  then invoke it to find the toolkit location.
+        # This allows us to support wrapper scripts (e.g. ccache or colornvcc), CUDA Toolkit,
+        # NVIDIA HPC SDK, and distro's splayed layouts
+        execute_process(COMMAND ${CUDAToolkit_NVCC_EXECUTABLE} "-v" "__cmake_determine_cuda"
+          OUTPUT_VARIABLE _CUDA_NVCC_OUT ERROR_VARIABLE _CUDA_NVCC_OUT)
+        if(_CUDA_NVCC_OUT MATCHES "\\#\\$ TOP=([^\r\n]*)")
+          get_filename_component(CUDAToolkit_BIN_DIR "${CMAKE_MATCH_1}/bin" ABSOLUTE)
+        else()
+          get_filename_component(CUDAToolkit_BIN_DIR "${CUDAToolkit_NVCC_EXECUTABLE}" DIRECTORY)
+        endif()
+        unset(_CUDA_NVCC_OUT)
+
+        mark_as_advanced(CUDAToolkit_BIN_DIR)
+        set(CUDAToolkit_BIN_DIR "${CUDAToolkit_BIN_DIR}" CACHE PATH "" FORCE)
+      endif()
+
+      if(CUDAToolkit_SENTINEL_FILE)
+        get_filename_component(CUDAToolkit_BIN_DIR ${CUDAToolkit_SENTINEL_FILE} DIRECTORY ABSOLUTE)
+        set(CUDAToolkit_BIN_DIR "${CUDAToolkit_BIN_DIR}/bin")
+
+        set(CUDAToolkit_BIN_DIR "${CUDAToolkit_BIN_DIR}" CACHE PATH "" FORCE)
+        mark_as_advanced(CUDAToolkit_BIN_DIR)
+      endif()
+    endif()
+
+    if(CUDAToolkit_BIN_DIR)
+      get_filename_component(CUDAToolkit_ROOT_DIR ${CUDAToolkit_BIN_DIR} DIRECTORY ABSOLUTE)
+      set(CUDAToolkit_ROOT_DIR "${CUDAToolkit_ROOT_DIR}" PARENT_SCOPE)
+    endif()
+
+  endfunction()
+
+  # For NVCC we can easily deduce the SDK binary directory from the compiler path.
+  if(CMAKE_CUDA_COMPILER_LOADED AND NOT CUDAToolkit_BIN_DIR AND CMAKE_CUDA_COMPILER_ID STREQUAL "NVIDIA")
+    get_filename_component(CUDAToolkit_BIN_DIR "${CMAKE_CUDA_COMPILER}" DIRECTORY)
+    set(CUDAToolkit_BIN_DIR "${CUDAToolkit_BIN_DIR}" CACHE PATH "")
+    # Try language provided path first.
+    _CUDAToolkit_find_root_dir(SEARCH_PATHS "${CUDAToolkit_BIN_DIR}" FIND_FLAGS NO_DEFAULT_PATH)
+    mark_as_advanced(CUDAToolkit_BIN_DIR)
+  endif()
+
+  # Try user provided path
+  if(NOT CUDAToolkit_ROOT_DIR AND CUDAToolkit_ROOT)
+    _CUDAToolkit_find_root_dir(SEARCH_PATHS "${CUDAToolkit_ROOT}" FIND_FLAGS PATH_SUFFIXES bin NO_DEFAULT_PATH)
+  endif()
+  if(NOT CUDAToolkit_ROOT_DIR)
+    _CUDAToolkit_find_root_dir(FIND_FLAGS PATHS ENV CUDA_PATH PATH_SUFFIXES bin)
+  endif()
+
+  # If the user specified CUDAToolkit_ROOT but the toolkit could not be found, this is an error.
+  if(NOT CUDAToolkit_ROOT_DIR AND (DEFINED CUDAToolkit_ROOT OR DEFINED ENV{CUDAToolkit_ROOT}))
+    # Declare error messages now, print later depending on find_package args.
+    set(fail_base "Could not find nvcc executable in path specified by")
+    set(cuda_root_fail "${fail_base} CUDAToolkit_ROOT=${CUDAToolkit_ROOT}")
+    set(env_cuda_root_fail "${fail_base} environment variable CUDAToolkit_ROOT=$ENV{CUDAToolkit_ROOT}")
+
+    if(CUDAToolkit_FIND_REQUIRED)
+      if(DEFINED CUDAToolkit_ROOT)
+        message(FATAL_ERROR ${cuda_root_fail})
+      elseif(DEFINED ENV{CUDAToolkit_ROOT})
+        message(FATAL_ERROR ${env_cuda_root_fail})
+      endif()
+    else()
+      if(NOT CUDAToolkit_FIND_QUIETLY)
+        if(DEFINED CUDAToolkit_ROOT)
+          message(STATUS ${cuda_root_fail})
+        elseif(DEFINED ENV{CUDAToolkit_ROOT})
+          message(STATUS ${env_cuda_root_fail})
+        endif()
+      endif()
+      set(CUDAToolkit_FOUND FALSE)
+      unset(fail_base)
+      unset(cuda_root_fail)
+      unset(env_cuda_root_fail)
+      return()
+    endif()
+  endif()
+
+  # CUDAToolkit_ROOT cmake / env variable not specified, try platform defaults.
+  #
+  # - Linux: /usr/local/cuda-X.Y
+  # - macOS: /Developer/NVIDIA/CUDA-X.Y
+  # - Windows: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vX.Y
+  #
+  # We will also search the default symlink location /usr/local/cuda first since
+  # if CUDAToolkit_ROOT is not specified, it is assumed that the symlinked
+  # directory is the desired location.
+  if(NOT CUDAToolkit_ROOT_DIR)
+    if(UNIX)
+      if(NOT APPLE)
+        set(platform_base "/usr/local/cuda-")
+      else()
+        set(platform_base "/Developer/NVIDIA/CUDA-")
+      endif()
+    else()
+      set(platform_base "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v")
+    endif()
+
+    # Build out a descending list of possible cuda installations, e.g.
+    file(GLOB possible_paths "${platform_base}*")
+    # Iterate the glob results and create a descending list.
+    set(versions)
+    foreach(p ${possible_paths})
+      # Extract version number from end of string
+      string(REGEX MATCH "[0-9][0-9]?\\.[0-9]$" p_version ${p})
+      if(IS_DIRECTORY ${p} AND p_version)
+        list(APPEND versions ${p_version})
+      endif()
+    endforeach()
+
+    # Sort numerically in descending order, so we try the newest versions first.
+    if(CMAKE_VERSION VERSION_GREATER_EQUAL 3.18)
+      list(SORT versions COMPARE NATURAL ORDER DESCENDING)
+    elseif(versions)
+      # Alphabetical sort here is not ideal but better than nothing
+      list(SORT versions)
+      list(REVERSE versions)
+    endif()
+
+    # With a descending list of versions, populate possible paths to search.
+    set(search_paths)
+    foreach(v ${versions})
+      list(APPEND search_paths "${platform_base}${v}")
+    endforeach()
+
+    # Force the global default /usr/local/cuda to the front on Unix.
+    if(UNIX)
+      list(INSERT search_paths 0 "/usr/local/cuda")
+    endif()
+
+    # Now search for the toolkit again using the platform default search paths.
+    _CUDAToolkit_find_root_dir(SEARCH_PATHS "${search_paths}" FIND_FLAGS PATH_SUFFIXES bin)
+
+    # We are done with these variables now, cleanup for caller.
+    unset(platform_base)
+    unset(possible_paths)
+    unset(versions)
+    unset(search_paths)
+
+    if(NOT CUDAToolkit_ROOT_DIR)
+      if(CUDAToolkit_FIND_REQUIRED)
+        message(FATAL_ERROR "Could not find nvcc, please set CUDAToolkit_ROOT.")
+      elseif(NOT CUDAToolkit_FIND_QUIETLY)
+        message(STATUS "Could not find nvcc, please set CUDAToolkit_ROOT.")
+      endif()
+
+      set(CUDAToolkit_FOUND FALSE)
+      return()
+    endif()
+  endif()
+endif()
+
+if(NOT CUDAToolkit_BIN_DIR)
+  set(CUDAToolkit_BIN_DIR "${CUDAToolkit_ROOT_DIR}/bin")
+endif()
+
+if(NOT CUDAToolkit_NVCC_EXECUTABLE)
+  set(CUDAToolkit_NVCC_EXECUTABLE "${CUDAToolkit_BIN_DIR}/nvcc${CMAKE_EXECUTABLE_SUFFIX}")
+endif()
+
+if(CMAKE_CUDA_COMPILER_TOOLKIT_VERSION)
+  set(CUDAToolkit_VERSION "${CMAKE_CUDA_COMPILER_TOOLKIT_VERSION}")
+else()
+  function(_CUDAToolkit_find_version_file result_variable)
+    # We first check for a non-scattered installation to prefer it over a scattered installation.
+    if(CUDAToolkit_ROOT AND EXISTS "${CUDAToolkit_ROOT}/version.txt")
+      set(${result_variable} "${CUDAToolkit_ROOT}/version.txt" PARENT_SCOPE)
+    elseif(CUDAToolkit_ROOT_DIR AND EXISTS "${CUDAToolkit_ROOT_DIR}/version.txt")
+      set(${result_variable} "${CUDAToolkit_ROOT_DIR}/version.txt" PARENT_SCOPE)
+    elseif(CMAKE_SYSROOT_LINK AND EXISTS "${CMAKE_SYSROOT_LINK}/usr/lib/cuda/version.txt")
+      set(${result_variable} "${CMAKE_SYSROOT_LINK}/usr/lib/cuda/version.txt" PARENT_SCOPE)
+    elseif(EXISTS "${CMAKE_SYSROOT}/usr/lib/cuda/version.txt")
+      set(${result_variable} "${CMAKE_SYSROOT}/usr/lib/cuda/version.txt" PARENT_SCOPE)
+    endif()
+  endfunction()
+
+  _CUDAToolkit_find_version_file( _CUDAToolkit_version_file )
+  if(_CUDAToolkit_version_file)
+    # CUDAToolkit_LIBRARY_ROOT contains the device library and version file.
+    get_filename_component(CUDAToolkit_LIBRARY_ROOT "${_CUDAToolkit_version_file}" DIRECTORY ABSOLUTE)
+  endif()
+  unset(_CUDAToolkit_version_file)
+
+  if(CUDAToolkit_NVCC_EXECUTABLE AND
+     CMAKE_CUDA_COMPILER_VERSION AND
+     CUDAToolkit_NVCC_EXECUTABLE STREQUAL CMAKE_CUDA_COMPILER)
+    # Need to set these based off the already computed CMAKE_CUDA_COMPILER_VERSION value
+    # This if statement will always match, but is used to provide variables for MATCH 1,2,3...
+    if(CMAKE_CUDA_COMPILER_VERSION MATCHES [=[([0-9]+)\.([0-9]+)\.([0-9]+)]=])
+      set(CUDAToolkit_VERSION_MAJOR "${CMAKE_MATCH_1}")
+      set(CUDAToolkit_VERSION_MINOR "${CMAKE_MATCH_2}")
+      set(CUDAToolkit_VERSION_PATCH "${CMAKE_MATCH_3}")
+      set(CUDAToolkit_VERSION "${CMAKE_CUDA_COMPILER_VERSION}")
+    endif()
+  elseif(CUDAToolkit_NVCC_EXECUTABLE)
+    # Compute the version by invoking nvcc
+    execute_process(COMMAND ${CUDAToolkit_NVCC_EXECUTABLE} "--version" OUTPUT_VARIABLE NVCC_OUT)
+    if(NVCC_OUT MATCHES [=[ V([0-9]+)\.([0-9]+)\.([0-9]+)]=])
+      set(CUDAToolkit_VERSION_MAJOR "${CMAKE_MATCH_1}")
+      set(CUDAToolkit_VERSION_MINOR "${CMAKE_MATCH_2}")
+      set(CUDAToolkit_VERSION_PATCH "${CMAKE_MATCH_3}")
+      set(CUDAToolkit_VERSION "${CMAKE_MATCH_1}.${CMAKE_MATCH_2}.${CMAKE_MATCH_3}")
+    endif()
+    unset(NVCC_OUT)
+  else()
+    _CUDAToolkit_find_version_file(version_file)
+    if(version_file)
+      file(READ "${version_file}" VERSION_INFO)
+      if(VERSION_INFO MATCHES [=[CUDA Version ([0-9]+)\.([0-9]+)\.([0-9]+)]=])
+        set(CUDAToolkit_VERSION_MAJOR "${CMAKE_MATCH_1}")
+        set(CUDAToolkit_VERSION_MINOR "${CMAKE_MATCH_2}")
+        set(CUDAToolkit_VERSION_PATCH "${CMAKE_MATCH_3}")
+        set(CUDAToolkit_VERSION "${CMAKE_MATCH_1}.${CMAKE_MATCH_2}.${CMAKE_MATCH_3}")
+      endif()
+    endif()
+  endif()
+endif()
+
+# Find target directory when crosscompiling.
+if(CMAKE_CROSSCOMPILING)
+  if(CMAKE_SYSTEM_PROCESSOR STREQUAL "armv7-a")
+    # Support for NVPACK
+    set(CUDAToolkit_TARGET_NAME "armv7-linux-androideabi")
+  elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "arm")
+    set(CUDAToolkit_TARGET_NAME "armv7-linux-gnueabihf")
+  elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "aarch64")
+    if(ANDROID_ARCH_NAME STREQUAL "arm64")
+      set(CUDAToolkit_TARGET_NAME "aarch64-linux-androideabi")
+    elseif(CMAKE_SYSTEM_NAME STREQUAL "QNX")
+      set(CUDAToolkit_TARGET_NAME "aarch64-qnx")
+    else()
+      set(CUDAToolkit_TARGET_NAME "aarch64-linux")
+    endif(ANDROID_ARCH_NAME STREQUAL "arm64")
+  elseif(CMAKE_SYSTEM_PROCESSOR STREQUAL "x86_64")
+    set(CUDAToolkit_TARGET_NAME "x86_64-linux")
+  endif()
+
+  if(EXISTS "${CUDAToolkit_ROOT_DIR}/targets/${CUDAToolkit_TARGET_NAME}")
+    set(CUDAToolkit_TARGET_DIR "${CUDAToolkit_ROOT_DIR}/targets/${CUDAToolkit_TARGET_NAME}")
+    # add known CUDA target root path to the set of directories we search for programs, libraries and headers
+    list(PREPEND CMAKE_FIND_ROOT_PATH "${CUDAToolkit_TARGET_DIR}")
+
+    # Mark that we need to pop the root search path changes after we have
+    # found all cuda libraries so that searches for our cross-compilation
+    # libraries work when another cuda sdk is in CMAKE_PREFIX_PATH or
+    # PATh
+    set(_CUDAToolkit_Pop_ROOT_PATH True)
+  endif()
+endif()
+
+# If not already set we can simply use the toolkit root or it's a scattered installation.
+if(NOT CUDAToolkit_TARGET_DIR)
+  # Not cross compiling
+  set(CUDAToolkit_TARGET_DIR "${CUDAToolkit_ROOT_DIR}")
+  # Now that we have the real ROOT_DIR, find components inside it.
+  list(APPEND CMAKE_PREFIX_PATH ${CUDAToolkit_ROOT_DIR})
+
+  # Mark that we need to pop the prefix path changes after we have
+  # found the cudart library.
+  set(_CUDAToolkit_Pop_Prefix True)
+endif()
+
+# CUDAToolkit_TARGET_DIR always points to the directory containing the include directory.
+# On a scattered installation /usr, on a non-scattered something like /usr/local/cuda or /usr/local/cuda-10.2/targets/aarch64-linux.
+if(EXISTS "${CUDAToolkit_TARGET_DIR}/include/cuda_runtime.h")
+  set(CUDAToolkit_INCLUDE_DIR "${CUDAToolkit_TARGET_DIR}/include")
+elseif(NOT CUDAToolkit_FIND_QUIETLY)
+  message(STATUS "Unable to find cuda_runtime.h in \"${CUDAToolkit_TARGET_DIR}/include\" for CUDAToolkit_INCLUDE_DIR.")
+endif()
+
+# The NVHPC layout moves math library headers and libraries to a sibling directory.
+# Create a separate variable so this directory can be selectively added to math targets.
+if(NOT EXISTS "${CUDAToolkit_INCLUDE_DIR}/cublas_v2.h")
+  set(CUDAToolkit_MATH_INCLUDE_DIR "${CUDAToolkit_TARGET_DIR}/../../math_libs/include")
+  get_filename_component(CUDAToolkit_MATH_INCLUDE_DIR "${CUDAToolkit_MATH_INCLUDE_DIR}" ABSOLUTE)
+  if(NOT EXISTS "${CUDAToolkit_MATH_INCLUDE_DIR}/cublas_v2.h")
+    if(NOT CUDAToolkit_FIND_QUIETLY)
+      message(STATUS "Unable to find cublas_v2.h in either \"${CUDAToolkit_INCLUDE_DIR}\" or \"${CUDAToolkit_MATH_INCLUDE_DIR}\"")
+    endif()
+    unset(CUDAToolkit_MATH_INCLUDE_DIR)
+  endif()
+endif()
+
+# Find the CUDA Runtime Library libcudart
+find_library(CUDA_CUDART
+  NAMES cudart
+  PATH_SUFFIXES lib64 lib/x64
+)
+find_library(CUDA_CUDART
+  NAMES cudart
+  PATH_SUFFIXES lib64/stubs lib/x64/stubs
+)
+
+if(NOT CUDA_CUDART AND NOT CUDAToolkit_FIND_QUIETLY)
+  message(STATUS "Unable to find cudart library.")
+endif()
+
+if(_CUDAToolkit_Pop_Prefix)
+  list(REMOVE_AT CMAKE_PREFIX_PATH -1)
+  unset(_CUDAToolkit_Pop_Prefix)
+endif()
+
+#-----------------------------------------------------------------------------
+# Perform version comparison and validate all required variables are set.
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(CUDAToolkit
+  REQUIRED_VARS
+    CUDAToolkit_INCLUDE_DIR
+    CUDAToolkit_VERSION
+    CUDA_CUDART
+    CUDAToolkit_BIN_DIR
+  VERSION_VAR
+    CUDAToolkit_VERSION
+)
+
+mark_as_advanced(CUDA_CUDART
+                 CUDAToolkit_INCLUDE_DIR
+                 CUDAToolkit_NVCC_EXECUTABLE
+                 CUDAToolkit_SENTINEL_FILE
+                 )
+
+#-----------------------------------------------------------------------------
+# Construct result variables
+if(CUDAToolkit_FOUND)
+  set(CUDAToolkit_INCLUDE_DIRS ${CUDAToolkit_INCLUDE_DIR})
+  get_filename_component(CUDAToolkit_LIBRARY_DIR ${CUDA_CUDART} DIRECTORY ABSOLUTE)
+endif()
+
+#-----------------------------------------------------------------------------
+# Construct import targets
+if(CUDAToolkit_FOUND)
+
+  function(_CUDAToolkit_find_and_add_import_lib lib_name)
+    cmake_parse_arguments(arg "" "" "ALT;DEPS;EXTRA_HINTS;EXTRA_PATH_SUFFIXES;EXTRA_INCLUDE_DIRS" ${ARGN})
+
+    set(search_names ${lib_name} ${arg_ALT})
+
+    find_library(CUDA_${lib_name}_LIBRARY
+      NAMES ${search_names}
+      HINTS ${CUDAToolkit_LIBRARY_DIR}
+            ENV CUDA_PATH
+            ${arg_EXTRA_HINTS}
+      PATH_SUFFIXES nvidia/current lib64 lib/x64 lib
+                    ${arg_EXTRA_PATH_SUFFIXES}
+    )
+    # Don't try any stub directories until we have exhausted all other
+    # search locations.
+    find_library(CUDA_${lib_name}_LIBRARY
+      NAMES ${search_names}
+      HINTS ${CUDAToolkit_LIBRARY_DIR}
+            ENV CUDA_PATH
+            ${arg_EXTRA_HINTS}
+      PATH_SUFFIXES lib64/stubs lib/x64/stubs lib/stubs stubs
+                    # Support NVHPC splayed math library layout
+                    ../../math_libs/${CUDAToolkit_VERSION_MAJOR}.${CUDAToolkit_VERSION_MINOR}/lib64
+                    ../../math_libs/lib64
+    )
+
+    mark_as_advanced(CUDA_${lib_name}_LIBRARY)
+
+    if(NOT TARGET CUDA::${lib_name} AND CUDA_${lib_name}_LIBRARY)
+      add_library(CUDA::${lib_name} UNKNOWN IMPORTED)
+      set_property(TARGET CUDA::${lib_name} APPEND PROPERTY
+          INTERFACE_INCLUDE_DIRECTORIES "${CUDAToolkit_INCLUDE_DIRS}")
+      set_property(TARGET CUDA::${lib_name} APPEND PROPERTY
+          INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${CUDAToolkit_INCLUDE_DIRS}")
+      if(DEFINED CUDAToolkit_MATH_INCLUDE_DIR)
+        string(FIND ${CUDA_${lib_name}_LIBRARY} "math_libs" math_libs)
+        if(NOT ${math_libs} EQUAL -1)
+          set_property(TARGET CUDA::${lib_name} APPEND PROPERTY
+              INTERFACE_INCLUDE_DIRECTORIES "${CUDAToolkit_MATH_INCLUDE_DIRS}")
+          set_property(TARGET CUDA::${lib_name} APPEND PROPERTY
+              INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${CUDAToolkit_MATH_INCLUDE_DIRS}")
+        endif()
+      endif()
+      set_property(TARGET CUDA::${lib_name} PROPERTY IMPORTED_LOCATION "${CUDA_${lib_name}_LIBRARY}")
+      foreach(dep ${arg_DEPS})
+        if(TARGET CUDA::${dep})
+          set_property(TARGET CUDA::${lib_name} APPEND PROPERTY
+              INTERFACE_LINK_LIBRARIES CUDA::${dep})
+        endif()
+      endforeach()
+      if(arg_EXTRA_INCLUDE_DIRS)
+        set_property(TARGET CUDA::${lib_name} APPEND PROPERTY
+            INTERFACE_INCLUDE_DIRECTORIES "${arg_EXTRA_INCLUDE_DIRS}")
+        set_property(TARGET CUDA::${lib_name} APPEND PROPERTY
+            INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${arg_EXTRA_INCLUDE_DIRS}")
+      endif()
+    endif()
+  endfunction()
+
+  if(NOT TARGET CUDA::toolkit)
+    add_library(CUDA::toolkit IMPORTED INTERFACE)
+    set_property(TARGET CUDA::toolkit APPEND PROPERTY
+        INTERFACE_INCLUDE_DIRECTORIES "${CUDAToolkit_INCLUDE_DIRS}")
+    set_property(TARGET CUDA::toolkit APPEND PROPERTY
+        INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${CUDAToolkit_INCLUDE_DIRS}")
+  endif()
+
+  _CUDAToolkit_find_and_add_import_lib(cuda_driver ALT cuda)
+
+  _CUDAToolkit_find_and_add_import_lib(cudart)
+  _CUDAToolkit_find_and_add_import_lib(cudart_static)
+
+  # setup dependencies that are required for cudart_static when building
+  # on linux. These are generally only required when using the CUDA toolkit
+  # when CUDA language is disabled
+  if(NOT TARGET CUDA::cudart_static_deps
+     AND TARGET CUDA::cudart_static)
+
+    add_library(CUDA::cudart_static_deps IMPORTED INTERFACE)
+    set_property(TARGET CUDA::cudart_static APPEND PROPERTY
+        INTERFACE_LINK_LIBRARIES CUDA::cudart_static_deps)
+
+    if(UNIX AND (CMAKE_C_COMPILER OR CMAKE_CXX_COMPILER))
+      find_package(Threads REQUIRED)
+      set_property(TARGET CUDA::cudart_static_deps APPEND PROPERTY
+          INTERFACE_LINK_LIBRARIES Threads::Threads ${CMAKE_DL_LIBS})
+    endif()
+
+    if(UNIX AND NOT APPLE AND NOT (CMAKE_SYSTEM_NAME STREQUAL "QNX"))
+      # On Linux, you must link against librt when using the static cuda runtime.
+      find_library(CUDAToolkit_rt_LIBRARY rt)
+      mark_as_advanced(CUDAToolkit_rt_LIBRARY)
+      if(NOT CUDAToolkit_rt_LIBRARY)
+        message(WARNING "Could not find librt library, needed by CUDA::cudart_static")
+      else()
+        set_property(TARGET CUDA::cudart_static_deps APPEND PROPERTY
+            INTERFACE_LINK_LIBRARIES ${CUDAToolkit_rt_LIBRARY})
+      endif()
+    endif()
+  endif()
+
+  _CUDAToolkit_find_and_add_import_lib(culibos) # it's a static library
+  foreach(cuda_lib cublasLt cufft curand cusparse nppc nvjpeg)
+    _CUDAToolkit_find_and_add_import_lib(${cuda_lib})
+    _CUDAToolkit_find_and_add_import_lib(${cuda_lib}_static DEPS culibos)
+  endforeach()
+
+  if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL 11.0.0)
+    # cublas depends on cublasLt
+    # https://docs.nvidia.com/cuda/archive/11.0/cublas/index.html#static-library
+    _CUDAToolkit_find_and_add_import_lib(cublas DEPS cublasLt)
+    _CUDAToolkit_find_and_add_import_lib(cublas_static DEPS cublasLt_static)
+  else()
+    _CUDAToolkit_find_and_add_import_lib(cublas)
+    _CUDAToolkit_find_and_add_import_lib(cublas_static DEPS culibos)
+  endif()
+
+  if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL 11.4)
+    _CUDAToolkit_find_and_add_import_lib(cuFile ALT cufile DEPS culibos)
+    _CUDAToolkit_find_and_add_import_lib(cuFile_static ALT cufile_static DEPS culibos)
+
+    _CUDAToolkit_find_and_add_import_lib(cuFile_rdma ALT cufile_rdma DEPS cuFile culibos)
+    _CUDAToolkit_find_and_add_import_lib(cuFile_rdma_static ALT cufile_rdma_static DEPS cuFile_static culibos)
+  endif()
+
+  # cuFFTW depends on cuFFT
+  _CUDAToolkit_find_and_add_import_lib(cufftw DEPS cufft)
+  _CUDAToolkit_find_and_add_import_lib(cufftw_static DEPS cufft_static)
+  if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL 9.2)
+    _CUDAToolkit_find_and_add_import_lib(cufft_static_nocallback DEPS culibos)
+  endif()
+
+  # cuSOLVER depends on cuBLAS, and cuSPARSE
+  _CUDAToolkit_find_and_add_import_lib(cusolver DEPS cublas cusparse)
+  _CUDAToolkit_find_and_add_import_lib(cusolver_static DEPS cublas_static cusparse_static culibos)
+
+
+  if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL 10.1.2)
+    # cusolver depends on liblapack_static.a starting with CUDA 10.1 update 2,
+    # https://docs.nvidia.com/cuda/archive/11.5.0/cusolver/index.html#static-link-lapack
+    _CUDAToolkit_find_and_add_import_lib(cusolver_lapack_static ALT lapack_static) # implementation detail static lib
+    _CUDAToolkit_find_and_add_import_lib(cusolver_static DEPS cusolver_lapack_static)
+  endif()
+
+  if(CUDAToolkit_VERSION VERSION_GREATER 11.2.1)
+    # cusolver depends on libcusolver_metis and cublasLt
+    # https://docs.nvidia.com/cuda/archive/11.2.2/cusolver/index.html#link-dependency
+    _CUDAToolkit_find_and_add_import_lib(cusolver DEPS cublasLt)
+
+    _CUDAToolkit_find_and_add_import_lib(cusolver_metis_static ALT metis_static) # implementation detail static lib
+    _CUDAToolkit_find_and_add_import_lib(cusolver_static DEPS cusolver_metis_static cublasLt_static)
+  endif()
+
+  # nvGRAPH depends on cuRAND, and cuSOLVER.
+  _CUDAToolkit_find_and_add_import_lib(nvgraph DEPS curand cusolver)
+  _CUDAToolkit_find_and_add_import_lib(nvgraph_static DEPS curand_static cusolver_static)
+
+  # Process the majority of the NPP libraries.
+  foreach(cuda_lib nppial nppicc nppidei nppif nppig nppim nppist nppitc npps nppicom nppisu)
+    _CUDAToolkit_find_and_add_import_lib(${cuda_lib} DEPS nppc)
+    _CUDAToolkit_find_and_add_import_lib(${cuda_lib}_static DEPS nppc_static)
+  endforeach()
+
+  find_path(CUDAToolkit_CUPTI_INCLUDE_DIR cupti.h PATHS
+      "${CUDAToolkit_ROOT_DIR}/extras/CUPTI/include"
+      "${CUDAToolkit_INCLUDE_DIR}/../extras/CUPTI/include"
+      "${CUDAToolkit_INCLUDE_DIR}"
+      NO_DEFAULT_PATH)
+  mark_as_advanced(CUDAToolkit_CUPTI_INCLUDE_DIR)
+
+  if(CUDAToolkit_CUPTI_INCLUDE_DIR)
+    _CUDAToolkit_find_and_add_import_lib(cupti
+                                        EXTRA_PATH_SUFFIXES ../extras/CUPTI/lib64/
+                                                            ../extras/CUPTI/lib/
+                                        EXTRA_INCLUDE_DIRS "${CUDAToolkit_CUPTI_INCLUDE_DIR}")
+    _CUDAToolkit_find_and_add_import_lib(cupti_static
+                                        EXTRA_PATH_SUFFIXES ../extras/CUPTI/lib64/
+                                                            ../extras/CUPTI/lib/
+                                        EXTRA_INCLUDE_DIRS "${CUDAToolkit_CUPTI_INCLUDE_DIR}")
+  endif()
+
+  _CUDAToolkit_find_and_add_import_lib(nvrtc DEPS cuda_driver)
+
+  _CUDAToolkit_find_and_add_import_lib(nvml ALT nvidia-ml nvml)
+
+  # nvtools can be installed outside the CUDA toolkit directory,
+  # so search the NVTOOLSEXT_PATH windows only environment variable
+  set(nvToolsExt_EXTRA_PATH)
+  if(WIN32)
+     set(nvToolsExt_EXTRA_PATH "C:\\Program Files\\NVIDIA Corporation\\NvToolsExt")
+  endif()
+
+  find_path(CUDAToolkit_nvToolsExt_INCLUDE_DIR nvToolsExt.h
+      PATHS "${CUDAToolkit_INCLUDE_DIR}"
+            "${CUDAToolkit_ROOT_DIR}"
+            ENV NVTOOLSEXT_PATH
+            "${nvToolsExt_EXTRA_PATH}"
+      PATH_SUFFIXES include
+      NO_DEFAULT_PATH)
+  mark_as_advanced(CUDAToolkit_nvToolsExt_INCLUDE_DIR)
+
+  if(CUDAToolkit_nvToolsExt_INCLUDE_DIR)
+    _CUDAToolkit_find_and_add_import_lib(nvToolsExt
+        ALT nvToolsExt64 nvToolsExt64_1
+        EXTRA_HINTS ENV NVTOOLSEXT_PATH
+                    "${nvToolsExt_EXTRA_PATH}"
+        EXTRA_INCLUDE_DIRS "${CUDAToolkit_nvToolsExt_INCLUDE_DIR}")
+  endif()
+
+  _CUDAToolkit_find_and_add_import_lib(OpenCL)
+endif()
+
+unset(CUDAToolkit_ROOT_DIR)
+
+if(_CUDAToolkit_Pop_ROOT_PATH)
+  list(REMOVE_AT CMAKE_FIND_ROOT_PATH 0)
+  unset(_CUDAToolkit_Pop_ROOT_PATH)
+endif()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/FindCUDSS.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/FindCUDSS.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..b614e1c492b99f7b3adf456b0b88bdf5cd26fd0b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/FindCUDSS.cmake
@@ -0,0 +1,67 @@
+# Find the CUDSS library
+#
+# The following variables are optionally searched for defaults
+#  CUDSS_ROOT: Base directory where CUDSS is found
+#  CUDSS_INCLUDE_DIR: Directory where CUDSS header is searched for
+#  CUDSS_LIBRARY: Directory where CUDSS library is searched for
+#
+# The following are set after configuration is done:
+#  CUDSS_FOUND
+#  CUDSS_INCLUDE_PATH
+#  CUDSS_LIBRARY_PATH
+
+include(FindPackageHandleStandardArgs)
+
+set(CUDSS_ROOT $ENV{CUDSS_ROOT_DIR} CACHE PATH "Folder containing NVIDIA CUDSS")
+if (DEFINED $ENV{CUDSS_ROOT_DIR})
+  message(WARNING "CUDSS_ROOT_DIR is deprecated. Please set CUDSS_ROOT instead.")
+endif()
+list(APPEND CUDSS_ROOT $ENV{CUDSS_ROOT_DIR} ${CUDA_TOOLKIT_ROOT_DIR})
+
+# Compatible layer for CMake <3.12. CUDSS_ROOT will be accounted in for searching paths and libraries for CMake >=3.12.
+list(APPEND CMAKE_PREFIX_PATH ${CUDSS_ROOT})
+
+set(CUDSS_INCLUDE_DIR $ENV{CUDSS_INCLUDE_DIR} CACHE PATH "Folder containing NVIDIA CUDSS header files")
+
+find_path(CUDSS_INCLUDE_PATH cudss.h
+  HINTS ${CUDSS_INCLUDE_DIR}
+  PATH_SUFFIXES cuda/include cuda include)
+
+set(CUDSS_LIBRARY $ENV{CUDSS_LIBRARY} CACHE PATH "Path to the CUDSS library file (e.g., libcudss.so)")
+
+set(CUDSS_LIBRARY_NAME "libcudss.so")
+if(MSVC)
+  set(CUDSS_LIBRARY_NAME "cudss.lib")
+endif()
+
+find_library(CUDSS_LIBRARY_PATH ${CUDSS_LIBRARY_NAME}
+  PATHS ${CUDSS_LIBRARY}
+  PATH_SUFFIXES lib lib64 cuda/lib cuda/lib64 lib/x64)
+
+find_package_handle_standard_args(CUDSS DEFAULT_MSG CUDSS_LIBRARY_PATH CUDSS_INCLUDE_PATH)
+
+if(CUDSS_FOUND)
+  # Get CUDSS version
+  file(READ ${CUDSS_INCLUDE_PATH}/cudss.h CUDSS_HEADER_CONTENTS)
+  string(REGEX MATCH "define CUDSS_VER_MAJOR * +([0-9]+)"
+               CUDSS_VERSION_MAJOR "${CUDSS_HEADER_CONTENTS}")
+  string(REGEX REPLACE "define CUDSS_VER_MAJOR * +([0-9]+)" "\\1"
+               CUDSS_VERSION_MAJOR "${CUDSS_VERSION_MAJOR}")
+  string(REGEX MATCH "define CUDSS_VER_MINOR * +([0-9]+)"
+               CUDSS_VERSION_MINOR "${CUDSS_HEADER_CONTENTS}")
+  string(REGEX REPLACE "define CUDSS_VER_MINOR * +([0-9]+)" "\\1"
+               CUDSS_VERSION_MINOR "${CUDSS_VERSION_MINOR}")
+  string(REGEX MATCH "define CUDSS_VER_PATCH * +([0-9]+)"
+               CUDSS_VERSION_PATCH "${CUDSS_HEADER_CONTENTS}")
+  string(REGEX REPLACE "define CUDSS_VER_PATCH * +([0-9]+)" "\\1"
+               CUDSS_VERSION_PATCH "${CUDSS_VERSION_PATCH}")
+  # Assemble CUDSS version. Use minor version since current major version is 0.
+  if(NOT CUDSS_VERSION_MINOR)
+    set(CUDSS_VERSION "?")
+  else()
+    set(CUDSS_VERSION
+        "${CUDSS_VERSION_MAJOR}.${CUDSS_VERSION_MINOR}.${CUDSS_VERSION_PATCH}")
+  endif()
+endif()
+
+mark_as_advanced(CUDSS_ROOT CUDSS_INCLUDE_DIR CUDSS_LIBRARY CUDSS_VERSION)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/FindCUSPARSELT.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/FindCUSPARSELT.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..6c15bde147469ddc84980dca0c756e8f26e1ddb1
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/FindCUSPARSELT.cmake
@@ -0,0 +1,67 @@
+# Find the CUSPARSELT library
+#
+# The following variables are optionally searched for defaults
+#  CUSPARSELT_ROOT: Base directory where CUSPARSELT is found
+#  CUSPARSELT_INCLUDE_DIR: Directory where CUSPARSELT header is searched for
+#  CUSPARSELT_LIBRARY: Directory where CUSPARSELT library is searched for
+#
+# The following are set after configuration is done:
+#  CUSPARSELT_FOUND
+#  CUSPARSELT_INCLUDE_PATH
+#  CUSPARSELT_LIBRARY_PATH
+
+include(FindPackageHandleStandardArgs)
+
+set(CUSPARSELT_ROOT $ENV{CUSPARSELT_ROOT_DIR} CACHE PATH "Folder containing NVIDIA cuSPARSELt")
+if (DEFINED $ENV{CUSPARSELT_ROOT_DIR})
+  message(WARNING "CUSPARSELT_ROOT_DIR is deprecated. Please set CUSPARSELT_ROOT instead.")
+endif()
+list(APPEND CUSPARSELT_ROOT $ENV{CUSPARSELT_ROOT_DIR} ${CUDA_TOOLKIT_ROOT_DIR})
+
+# Compatible layer for CMake <3.12. CUSPARSELT_ROOT will be accounted in for searching paths and libraries for CMake >=3.12.
+list(APPEND CMAKE_PREFIX_PATH ${CUSPARSELT_ROOT})
+
+set(CUSPARSELT_INCLUDE_DIR $ENV{CUSPARSELT_INCLUDE_DIR} CACHE PATH "Folder containing NVIDIA cuSPARSELt header files")
+
+find_path(CUSPARSELT_INCLUDE_PATH cusparseLt.h
+  HINTS ${CUSPARSELT_INCLUDE_DIR}
+  PATH_SUFFIXES cuda/include cuda include)
+
+set(CUSPARSELT_LIBRARY $ENV{CUSPARSELT_LIBRARY} CACHE PATH "Path to the cusparselt library file (e.g., libcusparseLt.so)")
+
+set(CUSPARSELT_LIBRARY_NAME "libcusparseLt.so")
+if(MSVC)
+  set(CUSPARSELT_LIBRARY_NAME "cusparseLt.lib")
+endif()
+
+find_library(CUSPARSELT_LIBRARY_PATH ${CUSPARSELT_LIBRARY_NAME}
+  PATHS ${CUSPARSELT_LIBRARY}
+  PATH_SUFFIXES lib lib64 cuda/lib cuda/lib64 lib/x64)
+
+find_package_handle_standard_args(CUSPARSELT DEFAULT_MSG CUSPARSELT_LIBRARY_PATH CUSPARSELT_INCLUDE_PATH)
+
+if(CUSPARSELT_FOUND)
+  # Get cuSPARSELt version
+  file(READ ${CUSPARSELT_INCLUDE_PATH}/cusparseLt.h CUSPARSELT_HEADER_CONTENTS)
+  string(REGEX MATCH "define CUSPARSELT_VER_MAJOR * +([0-9]+)"
+               CUSPARSELT_VERSION_MAJOR "${CUSPARSELT_HEADER_CONTENTS}")
+  string(REGEX REPLACE "define CUSPARSELT_VER_MAJOR * +([0-9]+)" "\\1"
+               CUSPARSELT_VERSION_MAJOR "${CUSPARSELT_VERSION_MAJOR}")
+  string(REGEX MATCH "define CUSPARSELT_VER_MINOR * +([0-9]+)"
+               CUSPARSELT_VERSION_MINOR "${CUSPARSELT_HEADER_CONTENTS}")
+  string(REGEX REPLACE "define CUSPARSELT_VER_MINOR * +([0-9]+)" "\\1"
+               CUSPARSELT_VERSION_MINOR "${CUSPARSELT_VERSION_MINOR}")
+  string(REGEX MATCH "define CUSPARSELT_VER_PATCH * +([0-9]+)"
+               CUSPARSELT_VERSION_PATCH "${CUSPARSELT_HEADER_CONTENTS}")
+  string(REGEX REPLACE "define CUSPARSELT_VER_PATCH * +([0-9]+)" "\\1"
+               CUSPARSELT_VERSION_PATCH "${CUSPARSELT_VERSION_PATCH}")
+  # Assemble cuSPARSELt version. Use minor version since current major version is 0.
+  if(NOT CUSPARSELT_VERSION_MINOR)
+    set(CUSPARSELT_VERSION "?")
+  else()
+    set(CUSPARSELT_VERSION
+        "${CUSPARSELT_VERSION_MAJOR}.${CUSPARSELT_VERSION_MINOR}.${CUSPARSELT_VERSION_PATCH}")
+  endif()
+endif()
+
+mark_as_advanced(CUSPARSELT_ROOT CUSPARSELT_INCLUDE_DIR CUSPARSELT_LIBRARY CUSPARSELT_VERSION)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/FindSYCLToolkit.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/FindSYCLToolkit.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..d96b4c8d45cfef62fa0948f23bd7abf0640a44e2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/FindSYCLToolkit.cmake
@@ -0,0 +1,96 @@
+# This will define the following variables:
+# SYCL_FOUND               : True if the system has the SYCL library.
+# SYCL_INCLUDE_DIR         : Include directories needed to use SYCL.
+# SYCL_LIBRARY_DIR         ：The path to the SYCL library.
+# SYCL_LIBRARY             : SYCL library fullname.
+
+include(FindPackageHandleStandardArgs)
+
+set(SYCL_ROOT "")
+if(DEFINED ENV{SYCL_ROOT})
+  set(SYCL_ROOT $ENV{SYCL_ROOT})
+elseif(DEFINED ENV{CMPLR_ROOT})
+  set(SYCL_ROOT $ENV{CMPLR_ROOT})
+endif()
+
+string(COMPARE EQUAL "${SYCL_ROOT}" "" nosyclfound)
+if(nosyclfound)
+  set(SYCL_FOUND False)
+  set(SYCL_REASON_FAILURE "SYCL library not set!!")
+  set(SYCL_NOT_FOUND_MESSAGE "${SYCL_REASON_FAILURE}")
+  return()
+endif()
+
+# Find include path from binary.
+find_file(
+  SYCL_INCLUDE_DIR
+  NAMES include
+  HINTS ${SYCL_ROOT}
+  NO_DEFAULT_PATH
+  )
+
+# Find include/sycl path from include path.
+find_file(
+  SYCL_INCLUDE_SYCL_DIR
+  NAMES sycl
+  HINTS ${SYCL_ROOT}/include/
+  NO_DEFAULT_PATH
+  )
+
+# Due to the unrecognized compilation option `-fsycl` in other compiler.
+list(APPEND SYCL_INCLUDE_DIR ${SYCL_INCLUDE_SYCL_DIR})
+
+# Find library directory from binary.
+find_file(
+  SYCL_LIBRARY_DIR
+  NAMES lib lib64
+  HINTS ${SYCL_ROOT}
+  NO_DEFAULT_PATH
+  )
+
+# Find SYCL library fullname.
+if(LINUX)
+  find_library(
+    SYCL_LIBRARY
+    NAMES sycl-preview
+    HINTS ${SYCL_LIBRARY_DIR}
+    NO_DEFAULT_PATH
+  )
+endif()
+# On Windows, currently there's no sycl.lib. Only sycl7.lib with version suffix,
+# where the current version of the SYCL runtime is 7.
+# Until oneAPI adds support to sycl.lib without the version suffix,
+# sycl_runtime_version needs to be hardcoded and uplifted when SYCL runtime version uplifts.
+# TODO: remove this when sycl.lib is supported on Windows
+if(WIN32)
+  set(sycl_runtime_version 7)
+  find_library(
+    SYCL_LIBRARY
+    NAMES "sycl${sycl_runtime_version}"
+    HINTS ${SYCL_LIBRARY_DIR}
+    NO_DEFAULT_PATH
+  )
+  if(SYCL_LIBRARY STREQUAL "SYCL_LIBRARY-NOTFOUND")
+    message(FATAL_ERROR "Cannot find a SYCL library on Windows")
+  endif()
+endif()
+
+find_library(
+  OCL_LIBRARY
+  NAMES OpenCL
+  HINTS ${SYCL_LIBRARY_DIR}
+  NO_DEFAULT_PATH
+)
+
+if((NOT SYCL_INCLUDE_DIR) OR (NOT SYCL_LIBRARY_DIR) OR (NOT SYCL_LIBRARY))
+  set(SYCL_FOUND False)
+  set(SYCL_REASON_FAILURE "SYCL library is incomplete!!")
+  set(SYCL_NOT_FOUND_MESSAGE "${SYCL_REASON_FAILURE}")
+  return()
+endif()
+
+find_package_handle_standard_args(
+  SYCL
+  FOUND_VAR SYCL_FOUND
+  REQUIRED_VARS SYCL_INCLUDE_DIR SYCL_LIBRARY_DIR SYCL_LIBRARY
+  REASON_FAILURE_MESSAGE "${SYCL_REASON_FAILURE}")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/FindCUDA.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/FindCUDA.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..017ea59578fe34dbca4984d09862d2359361180a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/FindCUDA.cmake
@@ -0,0 +1,11 @@
+# This is a wrapper of the upstream `./upstream/FindCUDA.cmake` that
+# automatically includes `./upstream/CMakeInitializeConfigs.cmake` before
+# `./upstream/FindCUDA.cmake`. The `CMakeInitializeConfigs.cmake`, which is
+# absent in old CMake versions, creates some necessary variables for the later
+# to run.
+# See ./README.md for details.
+
+set(UPSTREAM_FIND_CUDA_DIR "${CMAKE_CURRENT_LIST_DIR}/upstream/")
+
+include("${UPSTREAM_FIND_CUDA_DIR}/CMakeInitializeConfigs.cmake")
+include("${UPSTREAM_FIND_CUDA_DIR}/FindCUDA.cmake")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/FindCUDNN.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/FindCUDNN.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..82134328c803dc87a89564638540a6cbcfa2d906
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/FindCUDNN.cmake
@@ -0,0 +1,78 @@
+# Find the CUDNN libraries
+#
+# The following variables are optionally searched for defaults
+#  CUDNN_ROOT: Base directory where CUDNN is found
+#  CUDNN_INCLUDE_DIR: Directory where CUDNN header is searched for
+#  CUDNN_LIBRARY: Directory where CUDNN library is searched for
+#  CUDNN_STATIC: Are we looking for a static library? (default: no)
+#
+# The following are set after configuration is done:
+#  CUDNN_FOUND
+#  CUDNN_INCLUDE_PATH
+#  CUDNN_LIBRARY_PATH
+#
+
+include(FindPackageHandleStandardArgs)
+
+set(CUDNN_ROOT $ENV{CUDNN_ROOT_DIR} CACHE PATH "Folder containing NVIDIA cuDNN")
+if (DEFINED $ENV{CUDNN_ROOT_DIR})
+  message(WARNING "CUDNN_ROOT_DIR is deprecated. Please set CUDNN_ROOT instead.")
+endif()
+list(APPEND CUDNN_ROOT $ENV{CUDNN_ROOT_DIR} ${CUDA_TOOLKIT_ROOT_DIR})
+
+# Compatible layer for CMake <3.12. CUDNN_ROOT will be accounted in for searching paths and libraries for CMake >=3.12.
+list(APPEND CMAKE_PREFIX_PATH ${CUDNN_ROOT})
+
+set(CUDNN_INCLUDE_DIR $ENV{CUDNN_INCLUDE_DIR} CACHE PATH "Folder containing NVIDIA cuDNN header files")
+
+find_path(CUDNN_INCLUDE_PATH cudnn.h
+  HINTS ${CUDNN_INCLUDE_DIR}
+  PATH_SUFFIXES cuda/include cuda include)
+
+option(CUDNN_STATIC "Look for static CUDNN" OFF)
+if (CUDNN_STATIC)
+  set(CUDNN_LIBNAME "libcudnn_static.a")
+else()
+  set(CUDNN_LIBNAME "cudnn")
+endif()
+
+set(CUDNN_LIBRARY $ENV{CUDNN_LIBRARY} CACHE PATH "Path to the cudnn library file (e.g., libcudnn.so)")
+if (CUDNN_LIBRARY MATCHES ".*cudnn_static.a" AND NOT CUDNN_STATIC)
+  message(WARNING "CUDNN_LIBRARY points to a static library (${CUDNN_LIBRARY}) but CUDNN_STATIC is OFF.")
+endif()
+
+find_library(CUDNN_LIBRARY_PATH ${CUDNN_LIBNAME}
+  PATHS ${CUDNN_LIBRARY}
+  PATH_SUFFIXES lib lib64 cuda/lib cuda/lib64 lib/x64)
+
+find_package_handle_standard_args(CUDNN DEFAULT_MSG CUDNN_LIBRARY_PATH CUDNN_INCLUDE_PATH)
+
+if(CUDNN_FOUND)
+  # Get cuDNN version
+  if(EXISTS ${CUDNN_INCLUDE_PATH}/cudnn_version.h)
+    file(READ ${CUDNN_INCLUDE_PATH}/cudnn_version.h CUDNN_HEADER_CONTENTS)
+  else()
+    file(READ ${CUDNN_INCLUDE_PATH}/cudnn.h CUDNN_HEADER_CONTENTS)
+  endif()
+  string(REGEX MATCH "define CUDNN_MAJOR * +([0-9]+)"
+               CUDNN_VERSION_MAJOR "${CUDNN_HEADER_CONTENTS}")
+  string(REGEX REPLACE "define CUDNN_MAJOR * +([0-9]+)" "\\1"
+               CUDNN_VERSION_MAJOR "${CUDNN_VERSION_MAJOR}")
+  string(REGEX MATCH "define CUDNN_MINOR * +([0-9]+)"
+               CUDNN_VERSION_MINOR "${CUDNN_HEADER_CONTENTS}")
+  string(REGEX REPLACE "define CUDNN_MINOR * +([0-9]+)" "\\1"
+               CUDNN_VERSION_MINOR "${CUDNN_VERSION_MINOR}")
+  string(REGEX MATCH "define CUDNN_PATCHLEVEL * +([0-9]+)"
+               CUDNN_VERSION_PATCH "${CUDNN_HEADER_CONTENTS}")
+  string(REGEX REPLACE "define CUDNN_PATCHLEVEL * +([0-9]+)" "\\1"
+               CUDNN_VERSION_PATCH "${CUDNN_VERSION_PATCH}")
+  # Assemble cuDNN version
+  if(NOT CUDNN_VERSION_MAJOR)
+    set(CUDNN_VERSION "?")
+  else()
+    set(CUDNN_VERSION
+        "${CUDNN_VERSION_MAJOR}.${CUDNN_VERSION_MINOR}.${CUDNN_VERSION_PATCH}")
+  endif()
+endif()
+
+mark_as_advanced(CUDNN_ROOT CUDNN_INCLUDE_DIR CUDNN_LIBRARY CUDNN_VERSION)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/CMakeInitializeConfigs.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/CMakeInitializeConfigs.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..5517e8f0624b1e5538b761e1f4891227007d0045
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/CMakeInitializeConfigs.cmake
@@ -0,0 +1,40 @@
+# Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
+# file Copyright.txt or https://cmake.org/licensing for details.
+
+# Present in upstream, but not supported on versions of cmake we need to support
+# include_guard(GLOBAL)
+
+# Initializes `<_PREFIX>_` variables from the corresponding
+# `<_PREFIX>__INIT`, for the configurations currently used.
+function(cmake_initialize_per_config_variable _PREFIX _DOCSTRING)
+  string(STRIP "${${_PREFIX}_INIT}" _INIT)
+  set("${_PREFIX}" "${_INIT}"
+    CACHE STRING "${_DOCSTRING} during all build types.")
+  mark_as_advanced("${_PREFIX}")
+
+  if (NOT CMAKE_NOT_USING_CONFIG_FLAGS)
+    set(_CONFIGS Debug Release MinSizeRel RelWithDebInfo)
+
+    get_property(_GENERATOR_IS_MULTI_CONFIG GLOBAL PROPERTY GENERATOR_IS_MULTI_CONFIG)
+    if (_GENERATOR_IS_MULTI_CONFIG)
+      list(APPEND _CONFIGS ${CMAKE_CONFIGURATION_TYPES})
+    else()
+      if (NOT CMAKE_NO_BUILD_TYPE)
+        set(CMAKE_BUILD_TYPE "${CMAKE_BUILD_TYPE_INIT}" CACHE STRING
+          "Choose the type of build, options are: None Debug Release RelWithDebInfo MinSizeRel ...")
+      endif()
+      list(APPEND _CONFIGS ${CMAKE_BUILD_TYPE})
+    endif()
+
+    list(REMOVE_DUPLICATES _CONFIGS)
+    foreach(_BUILD_TYPE IN LISTS _CONFIGS)
+      if (NOT "${_BUILD_TYPE}" STREQUAL "")
+        string(TOUPPER "${_BUILD_TYPE}" _BUILD_TYPE)
+        string(STRIP "${${_PREFIX}_${_BUILD_TYPE}_INIT}" _INIT)
+        set("${_PREFIX}_${_BUILD_TYPE}" "${_INIT}"
+          CACHE STRING "${_DOCSTRING} during ${_BUILD_TYPE} builds.")
+        mark_as_advanced("${_PREFIX}_${_BUILD_TYPE}")
+      endif()
+    endforeach()
+  endif()
+endfunction()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..f642072bdc51c8511592ae9c679ca94dd063cadc
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA.cmake
@@ -0,0 +1,1982 @@
+#.rst:
+# FindCUDA
+# --------
+#
+# .. note::
+#
+#   The FindCUDA module has been superseded by first-class support
+#   for the CUDA language in CMake.  It is no longer necessary to
+#   use this module or call ``find_package(CUDA)``.  This module
+#   now exists only for compatibility with projects that have not
+#   been ported.
+#
+#   Instead, list ``CUDA`` among the languages named in the top-level
+#   call to the :command:`project` command, or call the
+#   :command:`enable_language` command with ``CUDA``.
+#   Then one can add CUDA (``.cu``) sources to programs directly
+#   in calls to :command:`add_library` and :command:`add_executable`.
+#
+# Tools for building CUDA C files: libraries and build dependencies.
+#
+# This script locates the NVIDIA CUDA C tools.  It should work on Linux,
+# Windows, and macOS and should be reasonably up to date with CUDA C
+# releases.
+#
+# This script makes use of the standard :command:`find_package` arguments of
+# ````, ``REQUIRED`` and ``QUIET``.  ``CUDA_FOUND`` will report if an
+# acceptable version of CUDA was found.
+#
+# The script will prompt the user to specify ``CUDA_TOOLKIT_ROOT_DIR`` if
+# the prefix cannot be determined by the location of nvcc in the system
+# path and ``REQUIRED`` is specified to :command:`find_package`.  To use
+# a different installed version of the toolkit set the environment variable
+# ``CUDA_BIN_PATH`` before running cmake (e.g.
+# ``CUDA_BIN_PATH=/usr/local/cuda1.0`` instead of the default
+# ``/usr/local/cuda``) or set ``CUDA_TOOLKIT_ROOT_DIR`` after configuring.  If
+# you change the value of ``CUDA_TOOLKIT_ROOT_DIR``, various components that
+# depend on the path will be relocated.
+#
+# It might be necessary to set ``CUDA_TOOLKIT_ROOT_DIR`` manually on certain
+# platforms, or to use a CUDA runtime not installed in the default
+# location.  In newer versions of the toolkit the CUDA library is
+# included with the graphics driver -- be sure that the driver version
+# matches what is needed by the CUDA runtime version.
+#
+# The following variables affect the behavior of the macros in the
+# script (in alphebetical order).  Note that any of these flags can be
+# changed multiple times in the same directory before calling
+# ``CUDA_ADD_EXECUTABLE``, ``CUDA_ADD_LIBRARY``, ``CUDA_COMPILE``,
+# ``CUDA_COMPILE_PTX``, ``CUDA_COMPILE_FATBIN``, ``CUDA_COMPILE_CUBIN``
+# or ``CUDA_WRAP_SRCS``::
+#
+#   CUDA_64_BIT_DEVICE_CODE (Default matches host bit size)
+#   -- Set to ON to compile for 64 bit device code, OFF for 32 bit device code.
+#      Note that making this different from the host code when generating object
+#      or C files from CUDA code just won't work, because size_t gets defined by
+#      nvcc in the generated source.  If you compile to PTX and then load the
+#      file yourself, you can mix bit sizes between device and host.
+#
+#   CUDA_ATTACH_VS_BUILD_RULE_TO_CUDA_FILE (Default ON)
+#   -- Set to ON if you want the custom build rule to be attached to the source
+#      file in Visual Studio.  Turn OFF if you add the same cuda file to multiple
+#      targets.
+#
+#      This allows the user to build the target from the CUDA file; however, bad
+#      things can happen if the CUDA source file is added to multiple targets.
+#      When performing parallel builds it is possible for the custom build
+#      command to be run more than once and in parallel causing cryptic build
+#      errors.  VS runs the rules for every source file in the target, and a
+#      source can have only one rule no matter how many projects it is added to.
+#      When the rule is run from multiple targets race conditions can occur on
+#      the generated file.  Eventually everything will get built, but if the user
+#      is unaware of this behavior, there may be confusion.  It would be nice if
+#      this script could detect the reuse of source files across multiple targets
+#      and turn the option off for the user, but no good solution could be found.
+#
+#   CUDA_BUILD_CUBIN (Default OFF)
+#   -- Set to ON to enable and extra compilation pass with the -cubin option in
+#      Device mode. The output is parsed and register, shared memory usage is
+#      printed during build.
+#
+#   CUDA_BUILD_EMULATION (Default OFF for device mode)
+#   -- Set to ON for Emulation mode. -D_DEVICEEMU is defined for CUDA C files
+#      when CUDA_BUILD_EMULATION is TRUE.
+#
+#   CUDA_LINK_LIBRARIES_KEYWORD (Default "")
+#    -- The  keyword to use for internal
+#       target_link_libraries calls. The default is to use no keyword which
+#       uses the old "plain" form of target_link_libraries. Note that is matters
+#       because whatever is used inside the FindCUDA module must also be used
+#       outside - the two forms of target_link_libraries cannot be mixed.
+#
+#   CUDA_GENERATED_OUTPUT_DIR (Default CMAKE_CURRENT_BINARY_DIR)
+#   -- Set to the path you wish to have the generated files placed.  If it is
+#      blank output files will be placed in CMAKE_CURRENT_BINARY_DIR.
+#      Intermediate files will always be placed in
+#      CMAKE_CURRENT_BINARY_DIR/CMakeFiles.
+#
+#   CUDA_HOST_COMPILATION_CPP (Default ON)
+#   -- Set to OFF for C compilation of host code.
+#
+#   CUDA_HOST_COMPILER (Default CMAKE_C_COMPILER)
+#   -- Set the host compiler to be used by nvcc.  Ignored if -ccbin or
+#      --compiler-bindir is already present in the CUDA_NVCC_FLAGS or
+#      CUDA_NVCC_FLAGS_ variables.  For Visual Studio targets,
+#      the host compiler is constructed with one or more visual studio macros
+#      such as $(VCInstallDir), that expands out to the path when
+#      the command is run from within VS.
+#      If the CUDAHOSTCXX environment variable is set it will
+#      be used as the default.
+#
+#   CUDA_NVCC_FLAGS
+#   CUDA_NVCC_FLAGS_
+#   -- Additional NVCC command line arguments.  NOTE: multiple arguments must be
+#      semi-colon delimited (e.g. --compiler-options;-Wall)
+#
+#   CUDA_PROPAGATE_HOST_FLAGS (Default ON)
+#   -- Set to ON to propagate CMAKE_{C,CXX}_FLAGS and their configuration
+#      dependent counterparts (e.g. CMAKE_C_FLAGS_DEBUG) automatically to the
+#      host compiler through nvcc's -Xcompiler flag.  This helps make the
+#      generated host code match the rest of the system better.  Sometimes
+#      certain flags give nvcc problems, and this will help you turn the flag
+#      propagation off.  This does not affect the flags supplied directly to nvcc
+#      via CUDA_NVCC_FLAGS or through the OPTION flags specified through
+#      CUDA_ADD_LIBRARY, CUDA_ADD_EXECUTABLE, or CUDA_WRAP_SRCS.  Flags used for
+#      shared library compilation are not affected by this flag.
+#
+#   CUDA_PROPAGATE_HOST_FLAGS_BLACKLIST (Default "")
+#   -- A list containing the host flags that should not be propagated when
+#      CUDA_PROPAGATE_HOST_FLAGS is ON.
+#
+#   CUDA_SEPARABLE_COMPILATION (Default OFF)
+#   -- If set this will enable separable compilation for all CUDA runtime object
+#      files.  If used outside of CUDA_ADD_EXECUTABLE and CUDA_ADD_LIBRARY
+#      (e.g. calling CUDA_WRAP_SRCS directly),
+#      CUDA_COMPUTE_SEPARABLE_COMPILATION_OBJECT_FILE_NAME and
+#      CUDA_LINK_SEPARABLE_COMPILATION_OBJECTS should be called.
+#
+#   CUDA_SOURCE_PROPERTY_FORMAT
+#   -- If this source file property is set, it can override the format specified
+#      to CUDA_WRAP_SRCS (OBJ, PTX, CUBIN, or FATBIN).  If an input source file
+#      is not a .cu file, setting this file will cause it to be treated as a .cu
+#      file. See documentation for set_source_files_properties on how to set
+#      this property.
+#
+#   CUDA_USE_STATIC_CUDA_RUNTIME (Default ON)
+#   -- When enabled the static version of the CUDA runtime library will be used
+#      in CUDA_LIBRARIES.  If the version of CUDA configured doesn't support
+#      this option, then it will be silently disabled.
+#
+#   CUDA_VERBOSE_BUILD (Default OFF)
+#   -- Set to ON to see all the commands used when building the CUDA file.  When
+#      using a Makefile generator the value defaults to VERBOSE (run make
+#      VERBOSE=1 to see output), although setting CUDA_VERBOSE_BUILD to ON will
+#      always print the output.
+#
+# The script creates the following macros (in alphebetical order)::
+#
+#   CUDA_ADD_CUFFT_TO_TARGET( cuda_target )
+#   -- Adds the cufft library to the target (can be any target).  Handles whether
+#      you are in emulation mode or not.
+#
+#   CUDA_ADD_CUBLAS_TO_TARGET( cuda_target )
+#   -- Adds the cublas library to the target (can be any target).  Handles
+#      whether you are in emulation mode or not.
+#
+#   CUDA_ADD_EXECUTABLE( cuda_target file0 file1 ...
+#                        [WIN32] [MACOSX_BUNDLE] [EXCLUDE_FROM_ALL] [OPTIONS ...] )
+#   -- Creates an executable "cuda_target" which is made up of the files
+#      specified.  All of the non CUDA C files are compiled using the standard
+#      build rules specified by CMAKE and the cuda files are compiled to object
+#      files using nvcc and the host compiler.  In addition CUDA_INCLUDE_DIRS is
+#      added automatically to include_directories().  Some standard CMake target
+#      calls can be used on the target after calling this macro
+#      (e.g. set_target_properties and target_link_libraries), but setting
+#      properties that adjust compilation flags will not affect code compiled by
+#      nvcc.  Such flags should be modified before calling CUDA_ADD_EXECUTABLE,
+#      CUDA_ADD_LIBRARY or CUDA_WRAP_SRCS.
+#
+#   CUDA_ADD_LIBRARY( cuda_target file0 file1 ...
+#                     [STATIC | SHARED | MODULE] [EXCLUDE_FROM_ALL] [OPTIONS ...] )
+#   -- Same as CUDA_ADD_EXECUTABLE except that a library is created.
+#
+#   CUDA_BUILD_CLEAN_TARGET()
+#   -- Creates a convenience target that deletes all the dependency files
+#      generated.  You should make clean after running this target to ensure the
+#      dependency files get regenerated.
+#
+#   CUDA_COMPILE( generated_files file0 file1 ... [STATIC | SHARED | MODULE]
+#                 [OPTIONS ...] )
+#   -- Returns a list of generated files from the input source files to be used
+#      with ADD_LIBRARY or ADD_EXECUTABLE.
+#
+#   CUDA_COMPILE_PTX( generated_files file0 file1 ... [OPTIONS ...] )
+#   -- Returns a list of PTX files generated from the input source files.
+#
+#   CUDA_COMPILE_FATBIN( generated_files file0 file1 ... [OPTIONS ...] )
+#   -- Returns a list of FATBIN files generated from the input source files.
+#
+#   CUDA_COMPILE_CUBIN( generated_files file0 file1 ... [OPTIONS ...] )
+#   -- Returns a list of CUBIN files generated from the input source files.
+#
+#   CUDA_COMPUTE_SEPARABLE_COMPILATION_OBJECT_FILE_NAME( output_file_var
+#                                                        cuda_target
+#                                                        object_files )
+#   -- Compute the name of the intermediate link file used for separable
+#      compilation.  This file name is typically passed into
+#      CUDA_LINK_SEPARABLE_COMPILATION_OBJECTS.  output_file_var is produced
+#      based on cuda_target the list of objects files that need separable
+#      compilation as specified by object_files.  If the object_files list is
+#      empty, then output_file_var will be empty.  This function is called
+#      automatically for CUDA_ADD_LIBRARY and CUDA_ADD_EXECUTABLE.  Note that
+#      this is a function and not a macro.
+#
+#   CUDA_INCLUDE_DIRECTORIES( path0 path1 ... )
+#   -- Sets the directories that should be passed to nvcc
+#      (e.g. nvcc -Ipath0 -Ipath1 ... ). These paths usually contain other .cu
+#      files.
+#
+#
+#   CUDA_LINK_SEPARABLE_COMPILATION_OBJECTS( output_file_var cuda_target
+#                                            nvcc_flags object_files)
+#   -- Generates the link object required by separable compilation from the given
+#      object files.  This is called automatically for CUDA_ADD_EXECUTABLE and
+#      CUDA_ADD_LIBRARY, but can be called manually when using CUDA_WRAP_SRCS
+#      directly.  When called from CUDA_ADD_LIBRARY or CUDA_ADD_EXECUTABLE the
+#      nvcc_flags passed in are the same as the flags passed in via the OPTIONS
+#      argument.  The only nvcc flag added automatically is the bitness flag as
+#      specified by CUDA_64_BIT_DEVICE_CODE.  Note that this is a function
+#      instead of a macro.
+#
+#   CUDA_SELECT_NVCC_ARCH_FLAGS(out_variable [target_CUDA_architectures])
+#   -- Selects GPU arch flags for nvcc based on target_CUDA_architectures
+#      target_CUDA_architectures : Auto | Common | All | LIST(ARCH_AND_PTX ...)
+#       - "Auto" detects local machine GPU compute arch at runtime.
+#       - "Common" and "All" cover common and entire subsets of architectures
+#      ARCH_AND_PTX : NAME | NUM.NUM | NUM.NUM(NUM.NUM) | NUM.NUM+PTX
+#      NAME: Kepler Maxwell Kepler+Tesla Maxwell+Tegra Pascal Volta Turing
+#      NUM: Any number. Only those pairs are currently accepted by NVCC though:
+#            3.5 3.7 5.0 5.2 5.3 6.0 6.1 6.2 7.0 7.2 7.5
+#      Returns LIST of flags to be added to CUDA_NVCC_FLAGS in ${out_variable}
+#      Additionally, sets ${out_variable}_readable to the resulting numeric list
+#      Example:
+#       CUDA_SELECT_NVCC_ARCH_FLAGS(ARCH_FLAGS 3.0 3.5+PTX 5.2(5.0) Maxwell)
+#        LIST(APPEND CUDA_NVCC_FLAGS ${ARCH_FLAGS})
+#
+#      More info on CUDA architectures: https://en.wikipedia.org/wiki/CUDA
+#      Note that this is a function instead of a macro.
+#
+#   CUDA_WRAP_SRCS ( cuda_target format generated_files file0 file1 ...
+#                    [STATIC | SHARED | MODULE] [OPTIONS ...] )
+#   -- This is where all the magic happens.  CUDA_ADD_EXECUTABLE,
+#      CUDA_ADD_LIBRARY, CUDA_COMPILE, and CUDA_COMPILE_PTX all call this
+#      function under the hood.
+#
+#      Given the list of files (file0 file1 ... fileN) this macro generates
+#      custom commands that generate either PTX or linkable objects (use "PTX" or
+#      "OBJ" for the format argument to switch).  Files that don't end with .cu
+#      or have the HEADER_FILE_ONLY property are ignored.
+#
+#      The arguments passed in after OPTIONS are extra command line options to
+#      give to nvcc.  You can also specify per configuration options by
+#      specifying the name of the configuration followed by the options.  General
+#      options must precede configuration specific options.  Not all
+#      configurations need to be specified, only the ones provided will be used.
+#
+#         OPTIONS -DFLAG=2 "-DFLAG_OTHER=space in flag"
+#         DEBUG -g
+#         RELEASE --use_fast_math
+#         RELWITHDEBINFO --use_fast_math;-g
+#         MINSIZEREL --use_fast_math
+#
+#      For certain configurations (namely VS generating object files with
+#      CUDA_ATTACH_VS_BUILD_RULE_TO_CUDA_FILE set to ON), no generated file will
+#      be produced for the given cuda file.  This is because when you add the
+#      cuda file to Visual Studio it knows that this file produces an object file
+#      and will link in the resulting object file automatically.
+#
+#      This script will also generate a separate cmake script that is used at
+#      build time to invoke nvcc.  This is for several reasons.
+#
+#        1. nvcc can return negative numbers as return values which confuses
+#        Visual Studio into thinking that the command succeeded.  The script now
+#        checks the error codes and produces errors when there was a problem.
+#
+#        2. nvcc has been known to not delete incomplete results when it
+#        encounters problems.  This confuses build systems into thinking the
+#        target was generated when in fact an unusable file exists.  The script
+#        now deletes the output files if there was an error.
+#
+#        3. By putting all the options that affect the build into a file and then
+#        make the build rule dependent on the file, the output files will be
+#        regenerated when the options change.
+#
+#      This script also looks at optional arguments STATIC, SHARED, or MODULE to
+#      determine when to target the object compilation for a shared library.
+#      BUILD_SHARED_LIBS is ignored in CUDA_WRAP_SRCS, but it is respected in
+#      CUDA_ADD_LIBRARY.  On some systems special flags are added for building
+#      objects intended for shared libraries.  A preprocessor macro,
+#      _EXPORTS is defined when a shared library compilation is
+#      detected.
+#
+#      Flags passed into add_definitions with -D or /D are passed along to nvcc.
+#
+#
+#
+# The script defines the following variables::
+#
+#   CUDA_VERSION_MAJOR    -- The major version of cuda as reported by nvcc.
+#   CUDA_VERSION_MINOR    -- The minor version.
+#   CUDA_VERSION
+#   CUDA_VERSION_STRING   -- CUDA_VERSION_MAJOR.CUDA_VERSION_MINOR
+#   CUDA_HAS_FP16         -- Whether a short float (float16,fp16) is supported.
+#
+#   CUDA_TOOLKIT_ROOT_DIR -- Path to the CUDA Toolkit (defined if not set).
+#   CUDA_SDK_ROOT_DIR     -- Path to the CUDA SDK.  Use this to find files in the
+#                            SDK.  This script will not directly support finding
+#                            specific libraries or headers, as that isn't
+#                            supported by NVIDIA.  If you want to change
+#                            libraries when the path changes see the
+#                            FindCUDA.cmake script for an example of how to clear
+#                            these variables.  There are also examples of how to
+#                            use the CUDA_SDK_ROOT_DIR to locate headers or
+#                            libraries, if you so choose (at your own risk).
+#   CUDA_INCLUDE_DIRS     -- Include directory for cuda headers.  Added automatically
+#                            for CUDA_ADD_EXECUTABLE and CUDA_ADD_LIBRARY.
+#   CUDA_LIBRARIES        -- Cuda RT library.
+#   CUDA_CUFFT_LIBRARIES  -- Device or emulation library for the Cuda FFT
+#                            implementation (alternative to:
+#                            CUDA_ADD_CUFFT_TO_TARGET macro)
+#   CUDA_CUBLAS_LIBRARIES -- Device or emulation library for the Cuda BLAS
+#                            implementation (alternative to:
+#                            CUDA_ADD_CUBLAS_TO_TARGET macro).
+#   CUDA_cudart_static_LIBRARY -- Statically linkable cuda runtime library.
+#                                 Only available for CUDA version 5.5+
+#   CUDA_cudadevrt_LIBRARY -- Device runtime library.
+#                             Required for separable compilation.
+#   CUDA_cupti_LIBRARY    -- CUDA Profiling Tools Interface library.
+#                            Only available for CUDA version 4.0+.
+#   CUDA_curand_LIBRARY   -- CUDA Random Number Generation library.
+#                            Only available for CUDA version 3.2+.
+#   CUDA_cusolver_LIBRARY -- CUDA Direct Solver library.
+#                            Only available for CUDA version 7.0+.
+#   CUDA_cusparse_LIBRARY -- CUDA Sparse Matrix library.
+#                            Only available for CUDA version 3.2+.
+#   CUDA_npp_LIBRARY      -- NVIDIA Performance Primitives lib.
+#                            Only available for CUDA version 4.0+.
+#   CUDA_nppc_LIBRARY     -- NVIDIA Performance Primitives lib (core).
+#                            Only available for CUDA version 5.5+.
+#   CUDA_nppi_LIBRARY     -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 5.5 - 8.0.
+#   CUDA_nppial_LIBRARY   -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 9.0.
+#   CUDA_nppicc_LIBRARY   -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 9.0.
+#   CUDA_nppicom_LIBRARY  -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 9.0.
+#   CUDA_nppidei_LIBRARY  -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 9.0.
+#   CUDA_nppif_LIBRARY    -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 9.0.
+#   CUDA_nppig_LIBRARY    -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 9.0.
+#   CUDA_nppim_LIBRARY    -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 9.0.
+#   CUDA_nppist_LIBRARY   -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 9.0.
+#   CUDA_nppisu_LIBRARY   -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 9.0.
+#   CUDA_nppitc_LIBRARY   -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 9.0.
+#   CUDA_npps_LIBRARY     -- NVIDIA Performance Primitives lib (signal processing).
+#                            Only available for CUDA version 5.5+.
+#   CUDA_nvcuvenc_LIBRARY -- CUDA Video Encoder library.
+#                            Only available for CUDA version 3.2+.
+#                            Windows only.
+#   CUDA_nvcuvid_LIBRARY  -- CUDA Video Decoder library.
+#                            Only available for CUDA version 3.2+.
+#                            Windows only.
+#
+
+#   James Bigler, NVIDIA Corp (nvidia.com - jbigler)
+#   Abe Stephens, SCI Institute -- http://www.sci.utah.edu/~abe/FindCuda.html
+#
+#   Copyright (c) 2008 - 2009 NVIDIA Corporation.  All rights reserved.
+#
+#   Copyright (c) 2007-2009
+#   Scientific Computing and Imaging Institute, University of Utah
+#
+#   This code is licensed under the MIT License.  See the FindCUDA.cmake script
+#   for the text of the license.
+
+# The MIT License
+#
+# License for the specific language governing rights and limitations under
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+# DEALINGS IN THE SOFTWARE.
+#
+###############################################################################
+
+# FindCUDA.cmake
+
+# This macro helps us find the location of helper files we will need the full path to
+macro(CUDA_FIND_HELPER_FILE _name _extension)
+  set(_full_name "${_name}.${_extension}")
+  # CMAKE_CURRENT_LIST_FILE contains the full path to the file currently being
+  # processed.  Using this variable, we can pull out the current path, and
+  # provide a way to get access to the other files we need local to here.
+  get_filename_component(CMAKE_CURRENT_LIST_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
+  set(CUDA_${_name} "${CMAKE_CURRENT_LIST_DIR}/FindCUDA/${_full_name}")
+  if(NOT EXISTS "${CUDA_${_name}}")
+    set(error_message "${_full_name} not found in ${CMAKE_CURRENT_LIST_DIR}/FindCUDA")
+    if(CUDA_FIND_REQUIRED)
+      message(FATAL_ERROR "${error_message}")
+    else()
+      if(NOT CUDA_FIND_QUIETLY)
+        message(STATUS "${error_message}")
+      endif()
+    endif()
+  endif()
+  # Set this variable as internal, so the user isn't bugged with it.
+  set(CUDA_${_name} ${CUDA_${_name}} CACHE INTERNAL "Location of ${_full_name}" FORCE)
+endmacro()
+
+#####################################################################
+## CUDA_INCLUDE_NVCC_DEPENDENCIES
+##
+
+# So we want to try and include the dependency file if it exists.  If
+# it doesn't exist then we need to create an empty one, so we can
+# include it.
+
+# If it does exist, then we need to check to see if all the files it
+# depends on exist.  If they don't then we should clear the dependency
+# file and regenerate it later.  This covers the case where a header
+# file has disappeared or moved.
+
+macro(CUDA_INCLUDE_NVCC_DEPENDENCIES dependency_file)
+  set(CUDA_NVCC_DEPEND)
+  set(CUDA_NVCC_DEPEND_REGENERATE FALSE)
+
+
+  # Include the dependency file.  Create it first if it doesn't exist .  The
+  # INCLUDE puts a dependency that will force CMake to rerun and bring in the
+  # new info when it changes.  DO NOT REMOVE THIS (as I did and spent a few
+  # hours figuring out why it didn't work.
+  if(NOT EXISTS ${dependency_file})
+    file(WRITE ${dependency_file} "#FindCUDA.cmake generated file.  Do not edit.\n")
+  endif()
+  # Always include this file to force CMake to run again next
+  # invocation and rebuild the dependencies.
+  #message("including dependency_file = ${dependency_file}")
+  include(${dependency_file})
+
+  # Now we need to verify the existence of all the included files
+  # here.  If they aren't there we need to just blank this variable and
+  # make the file regenerate again.
+#   if(DEFINED CUDA_NVCC_DEPEND)
+#     message("CUDA_NVCC_DEPEND set")
+#   else()
+#     message("CUDA_NVCC_DEPEND NOT set")
+#   endif()
+  if(CUDA_NVCC_DEPEND)
+    #message("CUDA_NVCC_DEPEND found")
+    foreach(f ${CUDA_NVCC_DEPEND})
+      # message("searching for ${f}")
+      if(NOT EXISTS ${f})
+        #message("file ${f} not found")
+        set(CUDA_NVCC_DEPEND_REGENERATE TRUE)
+      endif()
+    endforeach()
+  else()
+    #message("CUDA_NVCC_DEPEND false")
+    # No dependencies, so regenerate the file.
+    set(CUDA_NVCC_DEPEND_REGENERATE TRUE)
+  endif()
+
+  #message("CUDA_NVCC_DEPEND_REGENERATE = ${CUDA_NVCC_DEPEND_REGENERATE}")
+  # No incoming dependencies, so we need to generate them.  Make the
+  # output depend on the dependency file itself, which should cause the
+  # rule to re-run.
+  if(CUDA_NVCC_DEPEND_REGENERATE)
+    set(CUDA_NVCC_DEPEND ${dependency_file})
+    #message("Generating an empty dependency_file: ${dependency_file}")
+    file(WRITE ${dependency_file} "#FindCUDA.cmake generated file.  Do not edit.\n")
+  endif()
+
+endmacro()
+
+###############################################################################
+###############################################################################
+# Setup variables' defaults
+###############################################################################
+###############################################################################
+
+# Allow the user to specify if the device code is supposed to be 32 or 64 bit.
+if(CMAKE_SIZEOF_VOID_P EQUAL 8)
+  set(CUDA_64_BIT_DEVICE_CODE_DEFAULT ON)
+else()
+  set(CUDA_64_BIT_DEVICE_CODE_DEFAULT OFF)
+endif()
+option(CUDA_64_BIT_DEVICE_CODE "Compile device code in 64 bit mode" ${CUDA_64_BIT_DEVICE_CODE_DEFAULT})
+
+# Attach the build rule to the source file in VS.  This option
+option(CUDA_ATTACH_VS_BUILD_RULE_TO_CUDA_FILE "Attach the build rule to the CUDA source file.  Enable only when the CUDA source file is added to at most one target." ON)
+
+# Prints out extra information about the cuda file during compilation
+option(CUDA_BUILD_CUBIN "Generate and parse .cubin files in Device mode." OFF)
+
+# Set whether we are using emulation or device mode.
+option(CUDA_BUILD_EMULATION "Build in Emulation mode" OFF)
+
+# Where to put the generated output.
+set(CUDA_GENERATED_OUTPUT_DIR "" CACHE PATH "Directory to put all the output files.  If blank it will default to the CMAKE_CURRENT_BINARY_DIR")
+
+# Parse HOST_COMPILATION mode.
+option(CUDA_HOST_COMPILATION_CPP "Generated file extension" ON)
+
+# Extra user settable flags
+cmake_initialize_per_config_variable(CUDA_NVCC_FLAGS "Semi-colon delimit multiple arguments.")
+
+if(DEFINED ENV{CUDAHOSTCXX})
+  set(CUDA_HOST_COMPILER "$ENV{CUDAHOSTCXX}" CACHE FILEPATH "Host side compiler used by NVCC")
+elseif(CMAKE_GENERATOR MATCHES "Visual Studio")
+  set(_CUDA_MSVC_HOST_COMPILER "$(VCInstallDir)Tools/MSVC/$(VCToolsVersion)/bin/Host$(Platform)/$(PlatformTarget)")
+  if(MSVC_VERSION LESS 1910)
+   set(_CUDA_MSVC_HOST_COMPILER "$(VCInstallDir)bin")
+  endif()
+
+  set(CUDA_HOST_COMPILER "${_CUDA_MSVC_HOST_COMPILER}" CACHE FILEPATH "Host side compiler used by NVCC")
+
+else()
+  if(APPLE
+      AND "${CMAKE_C_COMPILER_ID}" MATCHES "Clang"
+      AND "${CMAKE_C_COMPILER}" MATCHES "/cc$")
+    # Using cc which is symlink to clang may let NVCC think it is GCC and issue
+    # unhandled -dumpspecs option to clang. Also in case neither
+    # CMAKE_C_COMPILER is defined (project does not use C language) nor
+    # CUDA_HOST_COMPILER is specified manually we should skip -ccbin and let
+    # nvcc use its own default C compiler.
+    # Only care about this on APPLE with clang to avoid
+    # following symlinks to things like ccache
+    if(DEFINED CMAKE_C_COMPILER AND NOT DEFINED CUDA_HOST_COMPILER)
+      get_filename_component(c_compiler_realpath "${CMAKE_C_COMPILER}" REALPATH)
+      # if the real path does not end up being clang then
+      # go back to using CMAKE_C_COMPILER
+      if(NOT "${c_compiler_realpath}" MATCHES "/clang$")
+        set(c_compiler_realpath "${CMAKE_C_COMPILER}")
+      endif()
+    else()
+      set(c_compiler_realpath "")
+    endif()
+    set(CUDA_HOST_COMPILER "${c_compiler_realpath}" CACHE FILEPATH "Host side compiler used by NVCC")
+  elseif(MSVC AND "${CMAKE_C_COMPILER}" MATCHES "clcache|sccache")
+    # NVCC does not think it will work if it is passed clcache.exe or sccache.exe
+    # as the host compiler, which means that builds with CC=cl.exe won't work.
+    # Best to just feed it whatever the actual cl.exe is as the host compiler.
+    set(CUDA_HOST_COMPILER "cl.exe" CACHE FILEPATH "Host side compiler used by NVCC")
+  else()
+    set(CUDA_HOST_COMPILER "${CMAKE_C_COMPILER}"
+      CACHE FILEPATH "Host side compiler used by NVCC")
+  endif()
+endif()
+
+# Propagate the host flags to the host compiler via -Xcompiler
+option(CUDA_PROPAGATE_HOST_FLAGS "Propagate C/CXX_FLAGS and friends to the host compiler via -Xcompile" ON)
+
+# Blacklisted flags to prevent propagation
+set(CUDA_PROPAGATE_HOST_FLAGS_BLACKLIST  "" CACHE STRING "Blacklisted flags to prevent propagation")
+
+# Enable CUDA_SEPARABLE_COMPILATION
+option(CUDA_SEPARABLE_COMPILATION "Compile CUDA objects with separable compilation enabled.  Requires CUDA 5.0+" OFF)
+
+# Specifies whether the commands used when compiling the .cu file will be printed out.
+option(CUDA_VERBOSE_BUILD "Print out the commands run while compiling the CUDA source file.  With the Makefile generator this defaults to VERBOSE variable specified on the command line, but can be forced on with this option." OFF)
+
+mark_as_advanced(
+  CUDA_64_BIT_DEVICE_CODE
+  CUDA_ATTACH_VS_BUILD_RULE_TO_CUDA_FILE
+  CUDA_GENERATED_OUTPUT_DIR
+  CUDA_HOST_COMPILATION_CPP
+  CUDA_NVCC_FLAGS
+  CUDA_PROPAGATE_HOST_FLAGS
+  CUDA_PROPAGATE_HOST_FLAGS_BLACKLIST
+  CUDA_BUILD_CUBIN
+  CUDA_BUILD_EMULATION
+  CUDA_VERBOSE_BUILD
+  CUDA_SEPARABLE_COMPILATION
+  )
+
+# Single config generators like Makefiles or Ninja don't usually have
+# CMAKE_CONFIGURATION_TYPES defined (but note that it can be defined if set by
+# projects or developers). Even CMAKE_BUILD_TYPE might not be defined for
+# single config generators (and should not be defined for multi-config
+# generators). To ensure we get a complete superset of all possible
+# configurations, we combine CMAKE_CONFIGURATION_TYPES, CMAKE_BUILD_TYPE and
+# all of the standard configurations, then weed out duplicates with
+# list(REMOVE_DUPLICATES). Looping over the unique set then ensures we have
+# each configuration-specific set of nvcc flags defined and marked as advanced.
+set(CUDA_configuration_types ${CMAKE_CONFIGURATION_TYPES} ${CMAKE_BUILD_TYPE} Debug MinSizeRel Release RelWithDebInfo)
+list(REMOVE_DUPLICATES CUDA_configuration_types)
+
+###############################################################################
+###############################################################################
+# Locate CUDA, Set Build Type, etc.
+###############################################################################
+###############################################################################
+
+macro(cuda_unset_include_and_libraries)
+  unset(CUDA_TOOLKIT_INCLUDE CACHE)
+  unset(CUDA_CUDART_LIBRARY CACHE)
+  unset(CUDA_CUDA_LIBRARY CACHE)
+  # Make sure you run this before you unset CUDA_VERSION.
+  unset(CUDA_cudart_static_LIBRARY CACHE)
+  unset(CUDA_cudadevrt_LIBRARY CACHE)
+  unset(CUDA_cublas_LIBRARY CACHE)
+  unset(CUDA_cublas_device_LIBRARY CACHE)
+  unset(CUDA_cublasemu_LIBRARY CACHE)
+  unset(CUDA_cublasLt_LIBRARY CACHE)
+  unset(CUDA_cufft_LIBRARY CACHE)
+  unset(CUDA_cufftemu_LIBRARY CACHE)
+  unset(CUDA_cupti_LIBRARY CACHE)
+  unset(CUDA_curand_LIBRARY CACHE)
+  unset(CUDA_cusolver_LIBRARY CACHE)
+  unset(CUDA_cusparse_LIBRARY CACHE)
+  unset(CUDA_npp_LIBRARY CACHE)
+  unset(CUDA_nppc_LIBRARY CACHE)
+  unset(CUDA_nppi_LIBRARY CACHE)
+  unset(CUDA_npps_LIBRARY CACHE)
+  unset(CUDA_nvcuvenc_LIBRARY CACHE)
+  unset(CUDA_nvcuvid_LIBRARY CACHE)
+  unset(CUDA_GPU_DETECT_OUTPUT CACHE)
+endmacro()
+
+# Check to see if the CUDA_TOOLKIT_ROOT_DIR and CUDA_SDK_ROOT_DIR have changed,
+# if they have then clear the cache variables, so that will be detected again.
+if(NOT "${CUDA_TOOLKIT_ROOT_DIR}" STREQUAL "${CUDA_TOOLKIT_ROOT_DIR_INTERNAL}")
+  unset(CUDA_TOOLKIT_TARGET_DIR CACHE)
+  unset(CUDA_NVCC_EXECUTABLE CACHE)
+  cuda_unset_include_and_libraries()
+  unset(CUDA_VERSION CACHE)
+endif()
+
+if(NOT "${CUDA_TOOLKIT_TARGET_DIR}" STREQUAL "${CUDA_TOOLKIT_TARGET_DIR_INTERNAL}")
+  cuda_unset_include_and_libraries()
+endif()
+
+#
+#  End of unset()
+#
+
+#
+#  Start looking for things
+#
+
+# Search for the cuda distribution.
+if(NOT CUDA_TOOLKIT_ROOT_DIR AND NOT CMAKE_CROSSCOMPILING)
+  # Search in the CUDA_BIN_PATH first.
+  find_program(CUDA_TOOLKIT_ROOT_DIR_NVCC
+    NAMES nvcc nvcc.exe
+    PATHS
+      ENV CUDA_TOOLKIT_ROOT
+      ENV CUDA_PATH
+      ENV CUDA_BIN_PATH
+    PATH_SUFFIXES bin bin64
+    DOC "Toolkit location."
+    NO_DEFAULT_PATH
+    )
+
+  # Now search default paths
+  find_program(CUDA_TOOLKIT_ROOT_DIR_NVCC
+    NAMES nvcc nvcc.exe
+    PATHS /opt/cuda/bin
+    PATH_SUFFIXES cuda/bin
+    DOC "Toolkit location."
+    )
+
+  if (CUDA_TOOLKIT_ROOT_DIR_NVCC)
+    get_filename_component(CUDA_TOOLKIT_ROOT_DIR_NVCC_PAR "${CUDA_TOOLKIT_ROOT_DIR_NVCC}" DIRECTORY)
+    get_filename_component(CUDA_TOOLKIT_ROOT_DIR "${CUDA_TOOLKIT_ROOT_DIR_NVCC_PAR}" DIRECTORY CACHE)
+    string(REGEX REPLACE "[/\\\\]?bin[64]*[/\\\\]?$" "" CUDA_TOOLKIT_ROOT_DIR ${CUDA_TOOLKIT_ROOT_DIR})
+    # We need to force this back into the cache.
+    set(CUDA_TOOLKIT_ROOT_DIR ${CUDA_TOOLKIT_ROOT_DIR} CACHE PATH "Toolkit location." FORCE)
+    set(CUDA_TOOLKIT_TARGET_DIR ${CUDA_TOOLKIT_ROOT_DIR})
+  endif()
+  unset(CUDA_TOOLKIT_ROOT_DIR_NVCC CACHE)
+
+  if (NOT EXISTS ${CUDA_TOOLKIT_ROOT_DIR})
+    if(CUDA_FIND_REQUIRED)
+      message(FATAL_ERROR "Specify CUDA_TOOLKIT_ROOT_DIR")
+    elseif(NOT CUDA_FIND_QUIETLY)
+      message("CUDA_TOOLKIT_ROOT_DIR not found or specified")
+    endif()
+  endif ()
+endif ()
+
+if(CMAKE_CROSSCOMPILING)
+  SET (CUDA_TOOLKIT_ROOT $ENV{CUDA_TOOLKIT_ROOT})
+  if(CMAKE_SYSTEM_PROCESSOR STREQUAL "armv7-a")
+    # Support for NVPACK
+    set (CUDA_TOOLKIT_TARGET_NAMES "armv7-linux-androideabi")
+  elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "arm")
+    # Support for arm cross compilation
+    set(CUDA_TOOLKIT_TARGET_NAMES "armv7-linux-gnueabihf")
+  elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "aarch64")
+    # Support for aarch64 cross compilation
+    if (ANDROID_ARCH_NAME STREQUAL "arm64")
+      set(CUDA_TOOLKIT_TARGET_NAMES "aarch64-linux-androideabi")
+    else()
+      set(CUDA_TOOLKIT_TARGET_NAMES "aarch64-linux" "sbsa-linux")
+    endif (ANDROID_ARCH_NAME STREQUAL "arm64")
+  endif()
+
+  foreach(CUDA_TOOLKIT_TARGET_NAME IN LISTS CUDA_TOOLKIT_TARGET_NAMES)
+    if (EXISTS "${CUDA_TOOLKIT_ROOT}/targets/${CUDA_TOOLKIT_TARGET_NAME}")
+      set(CUDA_TOOLKIT_TARGET_DIR "${CUDA_TOOLKIT_ROOT}/targets/${CUDA_TOOLKIT_TARGET_NAME}" CACHE PATH "CUDA Toolkit target location.")
+      SET (CUDA_TOOLKIT_ROOT_DIR ${CUDA_TOOLKIT_ROOT} CACHE PATH "Toolkit location." FORCE)
+      mark_as_advanced(CUDA_TOOLKIT_TARGET_DIR)
+      break()
+    endif()
+  endforeach()
+
+  # add known CUDA targetr root path to the set of directories we search for programs, libraries and headers
+  set( CMAKE_FIND_ROOT_PATH "${CUDA_TOOLKIT_TARGET_DIR};${CMAKE_FIND_ROOT_PATH}")
+  macro( cuda_find_host_program )
+    if (COMMAND find_host_program)
+      find_host_program( ${ARGN} )
+    else()
+      find_program( ${ARGN} )
+    endif()
+  endmacro()
+else()
+  # for non-cross-compile, find_host_program == find_program and CUDA_TOOLKIT_TARGET_DIR == CUDA_TOOLKIT_ROOT_DIR
+  macro( cuda_find_host_program )
+    find_program( ${ARGN} )
+  endmacro()
+  SET (CUDA_TOOLKIT_TARGET_DIR ${CUDA_TOOLKIT_ROOT_DIR})
+endif()
+
+
+# CUDA_NVCC_EXECUTABLE
+if(DEFINED ENV{CUDA_NVCC_EXECUTABLE})
+  set(CUDA_NVCC_EXECUTABLE "$ENV{CUDA_NVCC_EXECUTABLE}" CACHE FILEPATH "The CUDA compiler")
+else()
+  cuda_find_host_program(CUDA_NVCC_EXECUTABLE
+    NAMES nvcc
+    PATHS "${CUDA_TOOLKIT_ROOT_DIR}"
+    ENV CUDA_PATH
+    ENV CUDA_BIN_PATH
+    PATH_SUFFIXES bin bin64
+    NO_DEFAULT_PATH
+    )
+  # Search default search paths, after we search our own set of paths.
+  cuda_find_host_program(CUDA_NVCC_EXECUTABLE nvcc)
+endif()
+
+if(CUDA_NVCC_EXECUTABLE AND NOT CUDA_VERSION)
+  # Compute the version.
+  execute_process(COMMAND ${CUDA_NVCC_EXECUTABLE} "--version"
+    OUTPUT_VARIABLE NVCC_OUT
+    RESULT_VARIABLE NVCC_RC)
+  if(NOT (${NVCC_RC} EQUAL 0))
+    message(WARNING "Failed to execute '${CUDA_NVCC_EXECUTABLE} --version'")
+    set(CUDA_FOUND FALSE)
+    return()
+  endif()
+  string(REGEX REPLACE ".*release ([0-9]+)\\.([0-9]+).*" "\\1" CUDA_VERSION_MAJOR ${NVCC_OUT})
+  string(REGEX REPLACE ".*release ([0-9]+)\\.([0-9]+).*" "\\2" CUDA_VERSION_MINOR ${NVCC_OUT})
+  set(CUDA_VERSION "${CUDA_VERSION_MAJOR}.${CUDA_VERSION_MINOR}" CACHE STRING "Version of CUDA as computed from nvcc.")
+  mark_as_advanced(CUDA_VERSION)
+else()
+  # Need to set these based off of the cached value
+  string(REGEX REPLACE "([0-9]+)\\.([0-9]+).*" "\\1" CUDA_VERSION_MAJOR "${CUDA_VERSION}")
+  string(REGEX REPLACE "([0-9]+)\\.([0-9]+).*" "\\2" CUDA_VERSION_MINOR "${CUDA_VERSION}")
+endif()
+
+# Always set this convenience variable
+set(CUDA_VERSION_STRING "${CUDA_VERSION}")
+
+# CUDA_TOOLKIT_INCLUDE
+find_path(CUDA_TOOLKIT_INCLUDE
+  device_functions.h # Header included in toolkit
+  PATHS ${CUDA_TOOLKIT_TARGET_DIR}
+  ENV CUDA_PATH
+  ENV CUDA_INC_PATH
+  PATH_SUFFIXES include
+  NO_DEFAULT_PATH
+  )
+# Search default search paths, after we search our own set of paths.
+find_path(CUDA_TOOLKIT_INCLUDE device_functions.h)
+mark_as_advanced(CUDA_TOOLKIT_INCLUDE)
+
+set(CUDA_HAS_FP16 TRUE)
+
+# Set the user list of include dir to nothing to initialize it.
+set (CUDA_NVCC_INCLUDE_DIRS_USER "")
+set (CUDA_INCLUDE_DIRS ${CUDA_TOOLKIT_INCLUDE})
+
+macro(cuda_find_library_local_first_with_path_ext _var _names _doc _path_ext )
+  if(CMAKE_SIZEOF_VOID_P EQUAL 8)
+    # CUDA 3.2+ on Windows moved the library directories, so we need the new
+    # and old paths.
+    set(_cuda_64bit_lib_dir "${_path_ext}lib/x64" "${_path_ext}lib64" "${_path_ext}libx64" )
+  endif()
+  # CUDA 3.2+ on Windows moved the library directories, so we need to new
+  # (lib/Win32) and the old path (lib).
+  find_library(${_var}
+    NAMES ${_names}
+    PATHS "${CUDA_TOOLKIT_TARGET_DIR}"
+    ENV CUDA_PATH
+    ENV CUDA_LIB_PATH
+    PATH_SUFFIXES ${_cuda_64bit_lib_dir} "${_path_ext}lib/Win32" "${_path_ext}lib" "${_path_ext}libWin32"
+    DOC ${_doc}
+    NO_DEFAULT_PATH
+    )
+  if (NOT CMAKE_CROSSCOMPILING)
+    # Search default search paths, after we search our own set of paths.
+    find_library(${_var}
+      NAMES ${_names}
+      PATHS "/usr/lib/nvidia-current"
+      DOC ${_doc}
+      )
+  endif()
+endmacro()
+
+macro(cuda_find_library_local_first _var _names _doc)
+  cuda_find_library_local_first_with_path_ext( "${_var}" "${_names}" "${_doc}" "" )
+endmacro()
+
+macro(find_library_local_first _var _names _doc )
+  cuda_find_library_local_first( "${_var}" "${_names}" "${_doc}" "" )
+endmacro()
+
+
+# CUDA_LIBRARIES
+cuda_find_library_local_first(CUDA_CUDART_LIBRARY cudart "\"cudart\" library")
+
+cuda_find_library_local_first(CUDA_cudart_static_LIBRARY cudart_static "static CUDA runtime library")
+mark_as_advanced(CUDA_cudart_static_LIBRARY)
+
+
+if(CUDA_cudart_static_LIBRARY)
+  # If static cudart available, use it by default, but provide a user-visible option to disable it.
+  option(CUDA_USE_STATIC_CUDA_RUNTIME "Use the static version of the CUDA runtime library if available" ON)
+else()
+  # If not available, silently disable the option.
+  set(CUDA_USE_STATIC_CUDA_RUNTIME OFF CACHE INTERNAL "")
+endif()
+
+if(CUDA_USE_STATIC_CUDA_RUNTIME)
+  set(CUDA_CUDART_LIBRARY_VAR CUDA_cudart_static_LIBRARY)
+else()
+  set(CUDA_CUDART_LIBRARY_VAR CUDA_CUDART_LIBRARY)
+endif()
+
+cuda_find_library_local_first(CUDA_cudadevrt_LIBRARY cudadevrt "\"cudadevrt\" library")
+mark_as_advanced(CUDA_cudadevrt_LIBRARY)
+
+if(CUDA_USE_STATIC_CUDA_RUNTIME)
+  if(UNIX)
+    # Check for the dependent libraries.  Here we look for pthreads.
+    if (DEFINED CMAKE_THREAD_PREFER_PTHREAD)
+      set(_cuda_cmake_thread_prefer_pthread ${CMAKE_THREAD_PREFER_PTHREAD})
+    endif()
+    set(CMAKE_THREAD_PREFER_PTHREAD 1)
+
+    # Many of the FindXYZ CMake comes with makes use of try_compile with int main(){return 0;}
+    # as the source file.  Unfortunately this causes a warning with -Wstrict-prototypes and
+    # -Werror causes the try_compile to fail.  We will just temporarily disable other flags
+    # when doing the find_package command here.
+    set(_cuda_cmake_c_flags ${CMAKE_C_FLAGS})
+    set(CMAKE_C_FLAGS "-fPIC")
+    find_package(Threads REQUIRED)
+    set(CMAKE_C_FLAGS ${_cuda_cmake_c_flags})
+
+    if (DEFINED _cuda_cmake_thread_prefer_pthread)
+      set(CMAKE_THREAD_PREFER_PTHREAD ${_cuda_cmake_thread_prefer_pthread})
+      unset(_cuda_cmake_thread_prefer_pthread)
+    else()
+      unset(CMAKE_THREAD_PREFER_PTHREAD)
+    endif()
+
+    if(NOT APPLE)
+      #On Linux, you must link against librt when using the static cuda runtime.
+      find_library(CUDA_rt_LIBRARY rt)
+      if (NOT CUDA_rt_LIBRARY)
+        message(WARNING "Expecting to find librt for libcudart_static, but didn't find it.")
+      endif()
+    endif()
+  endif()
+endif()
+
+cuda_find_library_local_first_with_path_ext(CUDA_cupti_LIBRARY cupti "\"cupti\" library" "extras/CUPTI/")
+mark_as_advanced(CUDA_cupti_LIBRARY)
+
+# Set the CUDA_LIBRARIES variable.  This is the set of stuff to link against if you are
+# using the CUDA runtime.  For the dynamic version of the runtime, most of the
+# dependencies are brough in, but for the static version there are additional libraries
+# and linker commands needed.
+# Initialize to empty
+set(CUDA_LIBRARIES)
+
+# If we are using emulation mode and we found the cudartemu library then use
+# that one instead of cudart.
+if(CUDA_BUILD_EMULATION AND CUDA_CUDARTEMU_LIBRARY)
+  list(APPEND CUDA_LIBRARIES ${CUDA_CUDARTEMU_LIBRARY})
+elseif(CUDA_USE_STATIC_CUDA_RUNTIME AND CUDA_cudart_static_LIBRARY)
+  list(APPEND CUDA_LIBRARIES ${CUDA_cudart_static_LIBRARY} ${CMAKE_THREAD_LIBS_INIT} ${CMAKE_DL_LIBS})
+  if (CUDA_rt_LIBRARY)
+    list(APPEND CUDA_LIBRARIES ${CUDA_rt_LIBRARY})
+  endif()
+  if(APPLE)
+    # We need to add the default path to the driver (libcuda.dylib) as an rpath, so that
+    # the static cuda runtime can find it at runtime.
+    list(APPEND CUDA_LIBRARIES -Wl,-rpath,/usr/local/cuda/lib)
+  endif()
+else()
+  list(APPEND CUDA_LIBRARIES ${CUDA_CUDART_LIBRARY})
+endif()
+
+# 1.1 toolkit on linux doesn't appear to have a separate library on
+# some platforms.
+cuda_find_library_local_first(CUDA_CUDA_LIBRARY cuda "\"cuda\" library (older versions only).")
+
+mark_as_advanced(
+  CUDA_CUDA_LIBRARY
+  CUDA_CUDART_LIBRARY
+  )
+
+#######################
+# Look for some of the toolkit helper libraries
+macro(FIND_CUDA_HELPER_LIBS _name)
+  cuda_find_library_local_first(CUDA_${_name}_LIBRARY ${_name} "\"${_name}\" library")
+  mark_as_advanced(CUDA_${_name}_LIBRARY)
+endmacro()
+
+if(CUDA_BUILD_EMULATION)
+  message(FATAL_ERROR "CUDA_BUILD_EMULATION is not supported in version 3.1 and onwards.  You must disable it to proceed.  You have version ${CUDA_VERSION}.")
+endif()
+
+find_cuda_helper_libs(cufft)
+find_cuda_helper_libs(cublas)
+find_cuda_helper_libs(cublasLt)
+# cusparse showed up in version 3.2
+find_cuda_helper_libs(cusparse)
+find_cuda_helper_libs(curand)
+if (WIN32)
+  find_cuda_helper_libs(nvcuvenc)
+  find_cuda_helper_libs(nvcuvid)
+endif()
+
+# In CUDA 9.0 NPP was nppi was removed
+find_cuda_helper_libs(nppc)
+find_cuda_helper_libs(nppial)
+find_cuda_helper_libs(nppicc)
+find_cuda_helper_libs(nppicom)
+find_cuda_helper_libs(nppidei)
+find_cuda_helper_libs(nppif)
+find_cuda_helper_libs(nppig)
+find_cuda_helper_libs(nppim)
+find_cuda_helper_libs(nppist)
+find_cuda_helper_libs(nppisu)
+find_cuda_helper_libs(nppitc)
+find_cuda_helper_libs(npps)
+set(CUDA_npp_LIBRARY "${CUDA_nppc_LIBRARY};${CUDA_nppial_LIBRARY};${CUDA_nppicc_LIBRARY};${CUDA_nppicom_LIBRARY};${CUDA_nppidei_LIBRARY};${CUDA_nppif_LIBRARY};${CUDA_nppig_LIBRARY};${CUDA_nppim_LIBRARY};${CUDA_nppist_LIBRARY};${CUDA_nppisu_LIBRARY};${CUDA_nppitc_LIBRARY};${CUDA_npps_LIBRARY}")
+# cusolver showed up in version 7.0
+find_cuda_helper_libs(cusolver)
+
+if (CUDA_BUILD_EMULATION)
+  set(CUDA_CUFFT_LIBRARIES ${CUDA_cufftemu_LIBRARY})
+  set(CUDA_CUBLAS_LIBRARIES ${CUDA_cublasemu_LIBRARY})
+else()
+  set(CUDA_CUFFT_LIBRARIES ${CUDA_cufft_LIBRARY})
+  set(CUDA_CUBLAS_LIBRARIES ${CUDA_cublas_LIBRARY} ${CUDA_cublas_device_LIBRARY} ${CUDA_cublasLt_LIBRARY})
+endif()
+
+########################
+# Look for the SDK stuff.  As of CUDA 3.0 NVSDKCUDA_ROOT has been replaced with
+# NVSDKCOMPUTE_ROOT with the old CUDA C contents moved into the C subdirectory
+find_path(CUDA_SDK_ROOT_DIR common/inc/cutil.h
+ HINTS
+  "$ENV{NVSDKCOMPUTE_ROOT}/C"
+  ENV NVSDKCUDA_ROOT
+  "[HKEY_LOCAL_MACHINE\\SOFTWARE\\NVIDIA Corporation\\Installed Products\\NVIDIA SDK 10\\Compute;InstallDir]"
+ PATHS
+  "/Developer/GPU\ Computing/C"
+  )
+
+# Keep the CUDA_SDK_ROOT_DIR first in order to be able to override the
+# environment variables.
+set(CUDA_SDK_SEARCH_PATH
+  "${CUDA_SDK_ROOT_DIR}"
+  "${CUDA_TOOLKIT_ROOT_DIR}/local/NVSDK0.2"
+  "${CUDA_TOOLKIT_ROOT_DIR}/NVSDK0.2"
+  "${CUDA_TOOLKIT_ROOT_DIR}/NV_CUDA_SDK"
+  "$ENV{HOME}/NVIDIA_CUDA_SDK"
+  "$ENV{HOME}/NVIDIA_CUDA_SDK_MACOSX"
+  "/Developer/CUDA"
+  )
+
+# Example of how to find an include file from the CUDA_SDK_ROOT_DIR
+
+# find_path(CUDA_CUT_INCLUDE_DIR
+#   cutil.h
+#   PATHS ${CUDA_SDK_SEARCH_PATH}
+#   PATH_SUFFIXES "common/inc"
+#   DOC "Location of cutil.h"
+#   NO_DEFAULT_PATH
+#   )
+# # Now search system paths
+# find_path(CUDA_CUT_INCLUDE_DIR cutil.h DOC "Location of cutil.h")
+
+# mark_as_advanced(CUDA_CUT_INCLUDE_DIR)
+
+
+# Example of how to find a library in the CUDA_SDK_ROOT_DIR
+
+# # cutil library is called cutil64 for 64 bit builds on windows.  We don't want
+# # to get these confused, so we are setting the name based on the word size of
+# # the build.
+
+# if(CMAKE_SIZEOF_VOID_P EQUAL 8)
+#   set(cuda_cutil_name cutil64)
+# else()
+#   set(cuda_cutil_name cutil32)
+# endif()
+
+# find_library(CUDA_CUT_LIBRARY
+#   NAMES cutil ${cuda_cutil_name}
+#   PATHS ${CUDA_SDK_SEARCH_PATH}
+#   # The new version of the sdk shows up in common/lib, but the old one is in lib
+#   PATH_SUFFIXES "common/lib" "lib"
+#   DOC "Location of cutil library"
+#   NO_DEFAULT_PATH
+#   )
+# # Now search system paths
+# find_library(CUDA_CUT_LIBRARY NAMES cutil ${cuda_cutil_name} DOC "Location of cutil library")
+# mark_as_advanced(CUDA_CUT_LIBRARY)
+# set(CUDA_CUT_LIBRARIES ${CUDA_CUT_LIBRARY})
+
+
+
+#############################
+# Check for required components
+set(CUDA_FOUND TRUE)
+
+set(CUDA_TOOLKIT_ROOT_DIR_INTERNAL "${CUDA_TOOLKIT_ROOT_DIR}" CACHE INTERNAL
+  "This is the value of the last time CUDA_TOOLKIT_ROOT_DIR was set successfully." FORCE)
+set(CUDA_TOOLKIT_TARGET_DIR_INTERNAL "${CUDA_TOOLKIT_TARGET_DIR}" CACHE INTERNAL
+  "This is the value of the last time CUDA_TOOLKIT_TARGET_DIR was set successfully." FORCE)
+set(CUDA_SDK_ROOT_DIR_INTERNAL "${CUDA_SDK_ROOT_DIR}" CACHE INTERNAL
+  "This is the value of the last time CUDA_SDK_ROOT_DIR was set successfully." FORCE)
+
+include(${CMAKE_CURRENT_LIST_DIR}/FindPackageHandleStandardArgs.cmake)
+
+find_package_handle_standard_args(CUDA
+  REQUIRED_VARS
+    CUDA_TOOLKIT_ROOT_DIR
+    CUDA_NVCC_EXECUTABLE
+    CUDA_INCLUDE_DIRS
+    ${CUDA_CUDART_LIBRARY_VAR}
+  VERSION_VAR
+    CUDA_VERSION
+  )
+
+
+
+###############################################################################
+###############################################################################
+# Macros
+###############################################################################
+###############################################################################
+
+###############################################################################
+# Add include directories to pass to the nvcc command.
+macro(CUDA_INCLUDE_DIRECTORIES)
+  foreach(dir ${ARGN})
+    list(APPEND CUDA_NVCC_INCLUDE_DIRS_USER ${dir})
+  endforeach()
+endmacro()
+
+
+##############################################################################
+cuda_find_helper_file(parse_cubin cmake)
+cuda_find_helper_file(make2cmake cmake)
+cuda_find_helper_file(run_nvcc cmake)
+include("${CMAKE_CURRENT_LIST_DIR}/FindCUDA/select_compute_arch.cmake")
+
+##############################################################################
+# Separate the OPTIONS out from the sources
+#
+macro(CUDA_GET_SOURCES_AND_OPTIONS _sources _cmake_options _options)
+  set( ${_sources} )
+  set( ${_cmake_options} )
+  set( ${_options} )
+  set( _found_options FALSE )
+  foreach(arg ${ARGN})
+    if("x${arg}" STREQUAL "xOPTIONS")
+      set( _found_options TRUE )
+    elseif(
+        "x${arg}" STREQUAL "xWIN32" OR
+        "x${arg}" STREQUAL "xMACOSX_BUNDLE" OR
+        "x${arg}" STREQUAL "xEXCLUDE_FROM_ALL" OR
+        "x${arg}" STREQUAL "xSTATIC" OR
+        "x${arg}" STREQUAL "xSHARED" OR
+        "x${arg}" STREQUAL "xMODULE"
+        )
+      list(APPEND ${_cmake_options} ${arg})
+    else()
+      if ( _found_options )
+        list(APPEND ${_options} ${arg})
+      else()
+        # Assume this is a file
+        list(APPEND ${_sources} ${arg})
+      endif()
+    endif()
+  endforeach()
+endmacro()
+
+##############################################################################
+# Parse the OPTIONS from ARGN and set the variables prefixed by _option_prefix
+#
+macro(CUDA_PARSE_NVCC_OPTIONS _option_prefix)
+  set( _found_config )
+  foreach(arg ${ARGN})
+    # Determine if we are dealing with a perconfiguration flag
+    foreach(config ${CUDA_configuration_types})
+      string(TOUPPER ${config} config_upper)
+      if (arg STREQUAL "${config_upper}")
+        set( _found_config _${arg})
+        # Set arg to nothing to keep it from being processed further
+        set( arg )
+      endif()
+    endforeach()
+
+    if ( arg )
+      list(APPEND ${_option_prefix}${_found_config} "${arg}")
+    endif()
+  endforeach()
+endmacro()
+
+##############################################################################
+# Helper to add the include directory for CUDA only once
+function(CUDA_ADD_CUDA_INCLUDE_ONCE)
+  get_directory_property(_include_directories INCLUDE_DIRECTORIES)
+  set(_add TRUE)
+  if(_include_directories)
+    foreach(dir ${_include_directories})
+      if("${dir}" STREQUAL "${CUDA_INCLUDE_DIRS}")
+        set(_add FALSE)
+      endif()
+    endforeach()
+  endif()
+  if(_add)
+    include_directories(${CUDA_INCLUDE_DIRS})
+  endif()
+endfunction()
+
+function(CUDA_BUILD_SHARED_LIBRARY shared_flag)
+  set(cmake_args ${ARGN})
+  # If SHARED, MODULE, or STATIC aren't already in the list of arguments, then
+  # add SHARED or STATIC based on the value of BUILD_SHARED_LIBS.
+  list(FIND cmake_args SHARED _cuda_found_SHARED)
+  list(FIND cmake_args MODULE _cuda_found_MODULE)
+  list(FIND cmake_args STATIC _cuda_found_STATIC)
+  if( _cuda_found_SHARED GREATER -1 OR
+      _cuda_found_MODULE GREATER -1 OR
+      _cuda_found_STATIC GREATER -1)
+    set(_cuda_build_shared_libs)
+  else()
+    if (BUILD_SHARED_LIBS)
+      set(_cuda_build_shared_libs SHARED)
+    else()
+      set(_cuda_build_shared_libs STATIC)
+    endif()
+  endif()
+  set(${shared_flag} ${_cuda_build_shared_libs} PARENT_SCOPE)
+endfunction()
+
+##############################################################################
+# Helper to avoid clashes of files with the same basename but different paths.
+# This doesn't attempt to do exactly what CMake internals do, which is to only
+# add this path when there is a conflict, since by the time a second collision
+# in names is detected it's already too late to fix the first one.  For
+# consistency sake the relative path will be added to all files.
+function(CUDA_COMPUTE_BUILD_PATH path build_path)
+  #message("CUDA_COMPUTE_BUILD_PATH([${path}] ${build_path})")
+  # Only deal with CMake style paths from here on out
+  file(TO_CMAKE_PATH "${path}" bpath)
+  if (IS_ABSOLUTE "${bpath}")
+    # Absolute paths are generally unnessary, especially if something like
+    # file(GLOB_RECURSE) is used to pick up the files.
+
+    string(FIND "${bpath}" "${CMAKE_CURRENT_BINARY_DIR}" _binary_dir_pos)
+    if (_binary_dir_pos EQUAL 0)
+      file(RELATIVE_PATH bpath "${CMAKE_CURRENT_BINARY_DIR}" "${bpath}")
+    else()
+      file(RELATIVE_PATH bpath "${CMAKE_CURRENT_SOURCE_DIR}" "${bpath}")
+    endif()
+  endif()
+
+  # This recipe is from cmLocalGenerator::CreateSafeUniqueObjectFileName in the
+  # CMake source.
+
+  # Remove leading /
+  string(REGEX REPLACE "^[/]+" "" bpath "${bpath}")
+  # Avoid absolute paths by removing ':'
+  string(REPLACE ":" "_" bpath "${bpath}")
+  # Avoid relative paths that go up the tree
+  string(REPLACE "../" "__/" bpath "${bpath}")
+  # Avoid spaces
+  string(REPLACE " " "_" bpath "${bpath}")
+
+  # Strip off the filename.  I wait until here to do it, since removin the
+  # basename can make a path that looked like path/../basename turn into
+  # path/.. (notice the trailing slash).
+  get_filename_component(bpath "${bpath}" PATH)
+
+  set(${build_path} "${bpath}" PARENT_SCOPE)
+  #message("${build_path} = ${bpath}")
+endfunction()
+
+##############################################################################
+# This helper macro populates the following variables and setups up custom
+# commands and targets to invoke the nvcc compiler to generate C or PTX source
+# dependent upon the format parameter.  The compiler is invoked once with -M
+# to generate a dependency file and a second time with -cuda or -ptx to generate
+# a .cpp or .ptx file.
+# INPUT:
+#   cuda_target         - Target name
+#   format              - PTX, CUBIN, FATBIN or OBJ
+#   FILE1 .. FILEN      - The remaining arguments are the sources to be wrapped.
+#   OPTIONS             - Extra options to NVCC
+# OUTPUT:
+#   generated_files     - List of generated files
+##############################################################################
+##############################################################################
+
+macro(CUDA_WRAP_SRCS cuda_target format generated_files)
+
+  # Put optional arguments in list.
+  set(_argn_list "${ARGN}")
+  # If one of the given optional arguments is "PHONY", make a note of it, then
+  # remove it from the list.
+  list(FIND _argn_list "PHONY" _phony_idx)
+  if("${_phony_idx}" GREATER "-1")
+    set(_target_is_phony true)
+    list(REMOVE_AT _argn_list ${_phony_idx})
+  else()
+    set(_target_is_phony false)
+  endif()
+
+  # If CMake doesn't support separable compilation, complain
+  if(CUDA_SEPARABLE_COMPILATION AND CMAKE_VERSION VERSION_LESS "2.8.10.1")
+    message(SEND_ERROR "CUDA_SEPARABLE_COMPILATION isn't supported for CMake versions less than 2.8.10.1")
+  endif()
+
+  # Set up all the command line flags here, so that they can be overridden on a per target basis.
+
+  set(nvcc_flags "")
+
+  # Emulation if the card isn't present.
+  if (CUDA_BUILD_EMULATION)
+    # Emulation.
+    set(nvcc_flags ${nvcc_flags} --device-emulation -D_DEVICEEMU -g)
+  else()
+    # Device mode.  No flags necessary.
+  endif()
+
+  if(CUDA_HOST_COMPILATION_CPP)
+    set(CUDA_C_OR_CXX CXX)
+  else()
+    message(WARNING "--host-compilation flag is deprecated in CUDA version >= 3.0.  Removing --host-compilation C flag" )
+    set(CUDA_C_OR_CXX C)
+  endif()
+
+  set(generated_extension ${CMAKE_${CUDA_C_OR_CXX}_OUTPUT_EXTENSION})
+
+  if(CUDA_64_BIT_DEVICE_CODE)
+    set(nvcc_flags ${nvcc_flags} -m64)
+  else()
+    set(nvcc_flags ${nvcc_flags} -m32)
+  endif()
+
+  if(CUDA_TARGET_CPU_ARCH)
+    set(nvcc_flags ${nvcc_flags} "--target-cpu-architecture=${CUDA_TARGET_CPU_ARCH}")
+  endif()
+
+  # This needs to be passed in at this stage, because VS needs to fill out the
+  # various macros from within VS.  Note that CCBIN is only used if
+  # -ccbin or --compiler-bindir isn't used and CUDA_HOST_COMPILER matches
+  # _CUDA_MSVC_HOST_COMPILER
+  if(CMAKE_GENERATOR MATCHES "Visual Studio")
+    set(ccbin_flags -D "\"CCBIN:PATH=${_CUDA_MSVC_HOST_COMPILER}\"" )
+  else()
+    set(ccbin_flags)
+  endif()
+
+  # Figure out which configure we will use and pass that in as an argument to
+  # the script.  We need to defer the decision until compilation time, because
+  # for VS projects we won't know if we are making a debug or release build
+  # until build time.
+  if(CMAKE_GENERATOR MATCHES "Visual Studio")
+    set( CUDA_build_configuration "$(ConfigurationName)" )
+  else()
+    set( CUDA_build_configuration "${CMAKE_BUILD_TYPE}")
+  endif()
+
+  # Initialize our list of includes with the user ones followed by the CUDA system ones.
+  set(CUDA_NVCC_INCLUDE_DIRS ${CUDA_NVCC_INCLUDE_DIRS_USER} "${CUDA_INCLUDE_DIRS}")
+  if(_target_is_phony)
+    # If the passed in target name isn't a real target (i.e., this is from a call to one of the
+    # cuda_compile_* functions), need to query directory properties to get include directories
+    # and compile definitions.
+    get_directory_property(_dir_include_dirs INCLUDE_DIRECTORIES)
+    get_directory_property(_dir_compile_defs COMPILE_DEFINITIONS)
+
+    list(APPEND CUDA_NVCC_INCLUDE_DIRS "${_dir_include_dirs}")
+    set(CUDA_NVCC_COMPILE_DEFINITIONS "${_dir_compile_defs}")
+  else()
+    # Append the include directories for this target via generator expression, which is
+    # expanded by the FILE(GENERATE) call below.  This generator expression captures all
+    # include dirs set by the user, whether via directory properties or target properties
+    list(APPEND CUDA_NVCC_INCLUDE_DIRS "$")
+
+    # Do the same thing with compile definitions
+    set(CUDA_NVCC_COMPILE_DEFINITIONS "$")
+  endif()
+
+
+  # Reset these variables
+  set(CUDA_WRAP_OPTION_NVCC_FLAGS)
+  foreach(config ${CUDA_configuration_types})
+    string(TOUPPER ${config} config_upper)
+    set(CUDA_WRAP_OPTION_NVCC_FLAGS_${config_upper})
+  endforeach()
+
+  CUDA_GET_SOURCES_AND_OPTIONS(_cuda_wrap_sources _cuda_wrap_cmake_options _cuda_wrap_options ${_argn_list})
+  CUDA_PARSE_NVCC_OPTIONS(CUDA_WRAP_OPTION_NVCC_FLAGS ${_cuda_wrap_options})
+
+  # Figure out if we are building a shared library.  BUILD_SHARED_LIBS is
+  # respected in CUDA_ADD_LIBRARY.
+  set(_cuda_build_shared_libs FALSE)
+  # SHARED, MODULE
+  list(FIND _cuda_wrap_cmake_options SHARED _cuda_found_SHARED)
+  list(FIND _cuda_wrap_cmake_options MODULE _cuda_found_MODULE)
+  if(_cuda_found_SHARED GREATER -1 OR _cuda_found_MODULE GREATER -1)
+    set(_cuda_build_shared_libs TRUE)
+  endif()
+  # STATIC
+  list(FIND _cuda_wrap_cmake_options STATIC _cuda_found_STATIC)
+  if(_cuda_found_STATIC GREATER -1)
+    set(_cuda_build_shared_libs FALSE)
+  endif()
+
+  # CUDA_HOST_FLAGS
+  if(_cuda_build_shared_libs)
+    # If we are setting up code for a shared library, then we need to add extra flags for
+    # compiling objects for shared libraries.
+    set(CUDA_HOST_SHARED_FLAGS ${CMAKE_SHARED_LIBRARY_${CUDA_C_OR_CXX}_FLAGS})
+  else()
+    set(CUDA_HOST_SHARED_FLAGS)
+  endif()
+
+  macro(_filter_blocklisted_host_flags CUDA_FLAGS)
+    string(REGEX REPLACE "[ \t]+" ";" ${CUDA_FLAGS} "${${CUDA_FLAGS}}")
+    foreach(_blacklisted ${CUDA_PROPAGATE_HOST_FLAGS_BLACKLIST})
+      list(REMOVE_ITEM ${CUDA_FLAGS} "${_blacklisted}")
+    endforeach()
+    string(REPLACE ";" " " ${CUDA_FLAGS} "${${CUDA_FLAGS}}")
+  endmacro()
+
+  # Only add the CMAKE_{C,CXX}_FLAGS if we are propagating host flags.  We
+  # always need to set the SHARED_FLAGS, though.
+  if(CUDA_PROPAGATE_HOST_FLAGS)
+    set(_cuda_C_FLAGS "${CMAKE_${CUDA_C_OR_CXX}_FLAGS}")
+    _filter_blocklisted_host_flags(_cuda_C_FLAGS)
+    set(_cuda_host_flags "set(CMAKE_HOST_FLAGS ${_cuda_C_FLAGS} ${CUDA_HOST_SHARED_FLAGS})")
+  else()
+    set(_cuda_host_flags "set(CMAKE_HOST_FLAGS ${CUDA_HOST_SHARED_FLAGS})")
+  endif()
+
+  set(_cuda_nvcc_flags_config "# Build specific configuration flags")
+  # Loop over all the configuration types to generate appropriate flags for run_nvcc.cmake
+  foreach(config ${CUDA_configuration_types})
+    string(TOUPPER ${config} config_upper)
+    # CMAKE_FLAGS are strings and not lists.  By not putting quotes around CMAKE_FLAGS
+    # we convert the strings to lists (like we want).
+
+    if(CUDA_PROPAGATE_HOST_FLAGS)
+      # nvcc chokes on -g3 in versions previous to 3.0, so replace it with -g
+      set(_cuda_fix_g3 FALSE)
+
+      set(_cuda_C_FLAGS "${CMAKE_${CUDA_C_OR_CXX}_FLAGS_${config_upper}}")
+      _filter_blocklisted_host_flags(_cuda_C_FLAGS)
+      if(_cuda_fix_g3)
+        string(REPLACE "-g3" "-g" _cuda_C_FLAGS "${_cuda_C_FLAGS}")
+      endif()
+
+      string(APPEND _cuda_host_flags "\nset(CMAKE_HOST_FLAGS_${config_upper} ${_cuda_C_FLAGS})")
+    endif()
+
+    # Note that if we ever want CUDA_NVCC_FLAGS_ to be string (instead of a list
+    # like it is currently), we can remove the quotes around the
+    # ${CUDA_NVCC_FLAGS_${config_upper}} variable like the CMAKE_HOST_FLAGS_ variable.
+    string(APPEND _cuda_nvcc_flags_config "\nset(CUDA_NVCC_FLAGS_${config_upper} ${CUDA_NVCC_FLAGS_${config_upper}} ;; ${CUDA_WRAP_OPTION_NVCC_FLAGS_${config_upper}})")
+  endforeach()
+
+  # Process the C++14 flag.  If the host sets the flag, we need to add it to nvcc and
+  # remove it from the host. This is because -Xcompile -std=c++ will choke nvcc (it uses
+  # the C preprocessor).  In order to get this to work correctly, we need to use nvcc's
+  # specific c++14 flag.
+  if( "${_cuda_host_flags}" MATCHES "-std=c\\+\\+11")
+    # Add the c++14 flag to nvcc if it isn't already present.  Note that we only look at
+    # the main flag instead of the configuration specific flags.
+    if( NOT "${CUDA_NVCC_FLAGS}" MATCHES "-std=c\\+\\+14" )
+      list(APPEND nvcc_flags --std c++14)
+    endif()
+    string(REGEX REPLACE "[-]+std=c\\+\\+14" "" _cuda_host_flags "${_cuda_host_flags}")
+  endif()
+
+  if(_cuda_build_shared_libs)
+    list(APPEND nvcc_flags "-D${cuda_target}_EXPORTS")
+  endif()
+
+  # Reset the output variable
+  set(_cuda_wrap_generated_files "")
+
+  # Iterate over the macro arguments and create custom
+  # commands for all the .cu files.
+  foreach(file ${_argn_list})
+    # Ignore any file marked as a HEADER_FILE_ONLY
+    get_source_file_property(_is_header ${file} HEADER_FILE_ONLY)
+    # Allow per source file overrides of the format.  Also allows compiling non-.cu files.
+    get_source_file_property(_cuda_source_format ${file} CUDA_SOURCE_PROPERTY_FORMAT)
+    if((${file} MATCHES "\\.cu$" OR _cuda_source_format) AND NOT _is_header)
+
+      if(NOT _cuda_source_format)
+        set(_cuda_source_format ${format})
+      endif()
+      # If file isn't a .cu file, we need to tell nvcc to treat it as such.
+      if(NOT file MATCHES "\\.cu$")
+        set(cuda_language_flag -x=cu)
+      else()
+        set(cuda_language_flag)
+      endif()
+
+      if( ${_cuda_source_format} MATCHES "OBJ")
+        set( cuda_compile_to_external_module OFF )
+      else()
+        set( cuda_compile_to_external_module ON )
+        if( ${_cuda_source_format} MATCHES "PTX" )
+          set( cuda_compile_to_external_module_type "ptx" )
+        elseif( ${_cuda_source_format} MATCHES "CUBIN")
+          set( cuda_compile_to_external_module_type "cubin" )
+        elseif( ${_cuda_source_format} MATCHES "FATBIN")
+          set( cuda_compile_to_external_module_type "fatbin" )
+        else()
+          message( FATAL_ERROR "Invalid format flag passed to CUDA_WRAP_SRCS or set with CUDA_SOURCE_PROPERTY_FORMAT file property for file '${file}': '${_cuda_source_format}'.  Use OBJ, PTX, CUBIN or FATBIN.")
+        endif()
+      endif()
+
+      if(cuda_compile_to_external_module)
+        # Don't use any of the host compilation flags for PTX targets.
+        set(CUDA_HOST_FLAGS)
+        set(CUDA_NVCC_FLAGS_CONFIG)
+      else()
+        set(CUDA_HOST_FLAGS ${_cuda_host_flags})
+        set(CUDA_NVCC_FLAGS_CONFIG ${_cuda_nvcc_flags_config})
+      endif()
+
+      # Determine output directory
+      cuda_compute_build_path("${file}" cuda_build_path)
+      set(cuda_compile_intermediate_directory "${CMAKE_CURRENT_BINARY_DIR}/CMakeFiles/${cuda_target}.dir/${cuda_build_path}")
+      if(CUDA_GENERATED_OUTPUT_DIR)
+        set(cuda_compile_output_dir "${CUDA_GENERATED_OUTPUT_DIR}")
+      else()
+        if ( cuda_compile_to_external_module )
+          set(cuda_compile_output_dir "${CMAKE_CURRENT_BINARY_DIR}")
+        else()
+          set(cuda_compile_output_dir "${cuda_compile_intermediate_directory}")
+        endif()
+      endif()
+
+      # Add a custom target to generate a c or ptx file. ######################
+
+      get_filename_component( basename ${file} NAME )
+      if( cuda_compile_to_external_module )
+        set(generated_file_path "${cuda_compile_output_dir}")
+        set(generated_file_basename "${cuda_target}_generated_${basename}.${cuda_compile_to_external_module_type}")
+        set(format_flag "-${cuda_compile_to_external_module_type}")
+        file(MAKE_DIRECTORY "${cuda_compile_output_dir}")
+      else()
+        set(generated_file_path "${cuda_compile_output_dir}/${CMAKE_CFG_INTDIR}")
+        set(generated_file_basename "${cuda_target}_generated_${basename}${generated_extension}")
+        if(CUDA_SEPARABLE_COMPILATION)
+          set(format_flag "-dc")
+        else()
+          set(format_flag "-c")
+        endif()
+      endif()
+
+      # Set all of our file names.  Make sure that whatever filenames that have
+      # generated_file_path in them get passed in through as a command line
+      # argument, so that the ${CMAKE_CFG_INTDIR} gets expanded at run time
+      # instead of configure time.
+      set(generated_file "${generated_file_path}/${generated_file_basename}")
+      set(cmake_dependency_file "${cuda_compile_intermediate_directory}/${generated_file_basename}.depend")
+      set(NVCC_generated_dependency_file "${cuda_compile_intermediate_directory}/${generated_file_basename}.NVCC-depend")
+      set(generated_cubin_file "${generated_file_path}/${generated_file_basename}.cubin.txt")
+      set(custom_target_script_pregen "${cuda_compile_intermediate_directory}/${generated_file_basename}.cmake.pre-gen")
+      set(custom_target_script "${cuda_compile_intermediate_directory}/${generated_file_basename}$<$>:.$>.cmake")
+
+      # Setup properties for obj files:
+      if( NOT cuda_compile_to_external_module )
+        set_source_files_properties("${generated_file}"
+          PROPERTIES
+          EXTERNAL_OBJECT true # This is an object file not to be compiled, but only be linked.
+          )
+      endif()
+
+      # Don't add CMAKE_CURRENT_SOURCE_DIR if the path is already an absolute path.
+      get_filename_component(file_path "${file}" PATH)
+      if(IS_ABSOLUTE "${file_path}")
+        set(source_file "${file}")
+      else()
+        set(source_file "${CMAKE_CURRENT_SOURCE_DIR}/${file}")
+      endif()
+
+      if( NOT cuda_compile_to_external_module AND CUDA_SEPARABLE_COMPILATION)
+        list(APPEND ${cuda_target}_SEPARABLE_COMPILATION_OBJECTS "${generated_file}")
+      endif()
+
+      # Bring in the dependencies.  Creates a variable CUDA_NVCC_DEPEND #######
+      cuda_include_nvcc_dependencies(${cmake_dependency_file})
+
+      # Convenience string for output #########################################
+      if(CUDA_BUILD_EMULATION)
+        set(cuda_build_type "Emulation")
+      else()
+        set(cuda_build_type "Device")
+      endif()
+
+      # Build the NVCC made dependency file ###################################
+      set(build_cubin OFF)
+      if ( NOT CUDA_BUILD_EMULATION AND CUDA_BUILD_CUBIN )
+         if ( NOT cuda_compile_to_external_module )
+           set ( build_cubin ON )
+         endif()
+      endif()
+
+      # Configure the build script
+      configure_file("${CUDA_run_nvcc}" "${custom_target_script_pregen}" @ONLY)
+      file(GENERATE
+        OUTPUT "${custom_target_script}"
+        INPUT "${custom_target_script_pregen}"
+        )
+
+      # So if a user specifies the same cuda file as input more than once, you
+      # can have bad things happen with dependencies.  Here we check an option
+      # to see if this is the behavior they want.
+      if(CUDA_ATTACH_VS_BUILD_RULE_TO_CUDA_FILE)
+        set(main_dep MAIN_DEPENDENCY ${source_file})
+      else()
+        set(main_dep DEPENDS ${source_file})
+      endif()
+
+      if(CUDA_VERBOSE_BUILD)
+        set(verbose_output ON)
+      elseif(CMAKE_GENERATOR MATCHES "Makefiles")
+        set(verbose_output "$(VERBOSE)")
+      # This condition lets us also turn on verbose output when someone
+      # specifies CMAKE_VERBOSE_MAKEFILE, even if the generator isn't
+      # the Makefiles generator (this is important for us, Ninja users.)
+      elseif(CMAKE_VERBOSE_MAKEFILE)
+        set(verbose_output ON)
+      else()
+        set(verbose_output OFF)
+      endif()
+
+      # Create up the comment string
+      file(RELATIVE_PATH generated_file_relative_path "${CMAKE_BINARY_DIR}" "${generated_file}")
+      if(cuda_compile_to_external_module)
+        set(cuda_build_comment_string "Building NVCC ${cuda_compile_to_external_module_type} file ${generated_file_relative_path}")
+      else()
+        set(cuda_build_comment_string "Building NVCC (${cuda_build_type}) object ${generated_file_relative_path}")
+      endif()
+
+      set(_verbatim VERBATIM)
+      if(ccbin_flags MATCHES "\\$\\(VCInstallDir\\)")
+        set(_verbatim "")
+      endif()
+
+      # Build the generated file and dependency file ##########################
+      add_custom_command(
+        OUTPUT ${generated_file}
+        # These output files depend on the source_file and the contents of cmake_dependency_file
+        ${main_dep}
+        DEPENDS ${CUDA_NVCC_DEPEND}
+        DEPENDS ${custom_target_script}
+        # Make sure the output directory exists before trying to write to it.
+        COMMAND ${CMAKE_COMMAND} -E make_directory "${generated_file_path}"
+        COMMAND ${CMAKE_COMMAND} ARGS
+          -D verbose:BOOL=${verbose_output}
+          ${ccbin_flags}
+          -D build_configuration:STRING=${CUDA_build_configuration}
+          -D "generated_file:STRING=${generated_file}"
+          -D "generated_cubin_file:STRING=${generated_cubin_file}"
+          -P "${custom_target_script}"
+        WORKING_DIRECTORY "${cuda_compile_intermediate_directory}"
+        COMMENT "${cuda_build_comment_string}"
+        ${_verbatim}
+        )
+
+      # Make sure the build system knows the file is generated.
+      set_source_files_properties(${generated_file} PROPERTIES GENERATED TRUE)
+
+      list(APPEND _cuda_wrap_generated_files ${generated_file})
+
+      # Add the other files that we want cmake to clean on a cleanup ##########
+      list(APPEND CUDA_ADDITIONAL_CLEAN_FILES "${cmake_dependency_file}")
+      list(REMOVE_DUPLICATES CUDA_ADDITIONAL_CLEAN_FILES)
+      set(CUDA_ADDITIONAL_CLEAN_FILES ${CUDA_ADDITIONAL_CLEAN_FILES} CACHE INTERNAL "List of intermediate files that are part of the cuda dependency scanning.")
+
+    endif()
+  endforeach()
+
+  # Set the return parameter
+  set(${generated_files} ${_cuda_wrap_generated_files})
+endmacro()
+
+function(_cuda_get_important_host_flags important_flags flag_string)
+  if(CMAKE_GENERATOR MATCHES "Visual Studio")
+    string(REGEX MATCHALL "/M[DT][d]?" flags "${flag_string}")
+    list(APPEND ${important_flags} ${flags})
+  else()
+    string(REGEX MATCHALL "-fPIC" flags "${flag_string}")
+    list(APPEND ${important_flags} ${flags})
+  endif()
+  set(${important_flags} ${${important_flags}} PARENT_SCOPE)
+endfunction()
+
+###############################################################################
+###############################################################################
+# Separable Compilation Link
+###############################################################################
+###############################################################################
+
+# Compute the filename to be used by CUDA_LINK_SEPARABLE_COMPILATION_OBJECTS
+function(CUDA_COMPUTE_SEPARABLE_COMPILATION_OBJECT_FILE_NAME output_file_var cuda_target object_files)
+  if (object_files)
+    set(generated_extension ${CMAKE_${CUDA_C_OR_CXX}_OUTPUT_EXTENSION})
+    set(output_file "${CMAKE_CURRENT_BINARY_DIR}/CMakeFiles/${cuda_target}.dir/${CMAKE_CFG_INTDIR}/${cuda_target}_intermediate_link${generated_extension}")
+  else()
+    set(output_file)
+  endif()
+
+  set(${output_file_var} "${output_file}" PARENT_SCOPE)
+endfunction()
+
+# Setup the build rule for the separable compilation intermediate link file.
+function(CUDA_LINK_SEPARABLE_COMPILATION_OBJECTS output_file cuda_target options object_files)
+  if (object_files)
+
+    set_source_files_properties("${output_file}"
+      PROPERTIES
+      EXTERNAL_OBJECT TRUE # This is an object file not to be compiled, but only
+                           # be linked.
+      GENERATED TRUE       # This file is generated during the build
+      )
+
+    # For now we are ignoring all the configuration specific flags.
+    set(nvcc_flags)
+    CUDA_PARSE_NVCC_OPTIONS(nvcc_flags ${options})
+    if(CUDA_64_BIT_DEVICE_CODE)
+      list(APPEND nvcc_flags -m64)
+    else()
+      list(APPEND nvcc_flags -m32)
+    endif()
+    # If -ccbin, --compiler-bindir has been specified, don't do anything.  Otherwise add it here.
+    list( FIND nvcc_flags "-ccbin" ccbin_found0 )
+    list( FIND nvcc_flags "--compiler-bindir" ccbin_found1 )
+    if( ccbin_found0 LESS 0 AND ccbin_found1 LESS 0 AND CUDA_HOST_COMPILER )
+      # Match VERBATIM check below.
+      if(CUDA_HOST_COMPILER MATCHES "\\$\\(VCInstallDir\\)")
+        list(APPEND nvcc_flags -ccbin "\"${CUDA_HOST_COMPILER}\"")
+      else()
+        list(APPEND nvcc_flags -ccbin "${CUDA_HOST_COMPILER}")
+      endif()
+    endif()
+
+    # Create a list of flags specified by CUDA_NVCC_FLAGS_${CONFIG} and CMAKE_${CUDA_C_OR_CXX}_FLAGS*
+    set(config_specific_flags)
+    set(flags)
+    foreach(config ${CUDA_configuration_types})
+      string(TOUPPER ${config} config_upper)
+      # Add config specific flags
+      foreach(f ${CUDA_NVCC_FLAGS_${config_upper}})
+        list(APPEND config_specific_flags $<$:${f}>)
+      endforeach()
+      set(important_host_flags)
+      _cuda_get_important_host_flags(important_host_flags "${CMAKE_${CUDA_C_OR_CXX}_FLAGS_${config_upper}}")
+      foreach(f ${important_host_flags})
+        list(APPEND flags $<$:-Xcompiler> $<$:${f}>)
+      endforeach()
+    endforeach()
+    # Add CMAKE_${CUDA_C_OR_CXX}_FLAGS
+    set(important_host_flags)
+    _cuda_get_important_host_flags(important_host_flags "${CMAKE_${CUDA_C_OR_CXX}_FLAGS}")
+    foreach(f ${important_host_flags})
+      list(APPEND flags -Xcompiler ${f})
+    endforeach()
+
+    # Add our general CUDA_NVCC_FLAGS with the configuration specifig flags
+    set(nvcc_flags ${CUDA_NVCC_FLAGS} ${config_specific_flags} ${nvcc_flags})
+
+    file(RELATIVE_PATH output_file_relative_path "${CMAKE_BINARY_DIR}" "${output_file}")
+
+    # Some generators don't handle the multiple levels of custom command
+    # dependencies correctly (obj1 depends on file1, obj2 depends on obj1), so
+    # we work around that issue by compiling the intermediate link object as a
+    # pre-link custom command in that situation.
+    set(do_obj_build_rule TRUE)
+    if (MSVC_VERSION GREATER 1599 AND MSVC_VERSION LESS 1800)
+      # VS 2010 and 2012 have this problem.
+      set(do_obj_build_rule FALSE)
+    endif()
+
+    set(_verbatim VERBATIM)
+    if(nvcc_flags MATCHES "\\$\\(VCInstallDir\\)")
+      set(_verbatim "")
+    endif()
+
+    if (do_obj_build_rule)
+      add_custom_command(
+        OUTPUT ${output_file}
+        DEPENDS ${object_files}
+        COMMAND ${CUDA_NVCC_EXECUTABLE} ${nvcc_flags} -dlink ${object_files} -o ${output_file}
+        ${flags}
+        COMMENT "Building NVCC intermediate link file ${output_file_relative_path}"
+        COMMAND_EXPAND_LISTS
+        ${_verbatim}
+        )
+    else()
+      get_filename_component(output_file_dir "${output_file}" DIRECTORY)
+      add_custom_command(
+        TARGET ${cuda_target}
+        PRE_LINK
+        COMMAND ${CMAKE_COMMAND} -E echo "Building NVCC intermediate link file ${output_file_relative_path}"
+        COMMAND ${CMAKE_COMMAND} -E make_directory "${output_file_dir}"
+        COMMAND ${CUDA_NVCC_EXECUTABLE} ${nvcc_flags} ${flags} -dlink ${object_files} -o "${output_file}"
+        COMMAND_EXPAND_LISTS
+        ${_verbatim}
+        )
+    endif()
+ endif()
+endfunction()
+
+###############################################################################
+###############################################################################
+# ADD LIBRARY
+###############################################################################
+###############################################################################
+macro(CUDA_ADD_LIBRARY cuda_target)
+
+  CUDA_ADD_CUDA_INCLUDE_ONCE()
+
+  # Separate the sources from the options
+  CUDA_GET_SOURCES_AND_OPTIONS(_sources _cmake_options _options ${ARGN})
+  CUDA_BUILD_SHARED_LIBRARY(_cuda_shared_flag ${ARGN})
+  # Create custom commands and targets for each file.
+  CUDA_WRAP_SRCS( ${cuda_target} OBJ _generated_files ${_sources}
+    ${_cmake_options} ${_cuda_shared_flag}
+    OPTIONS ${_options} )
+
+  # Compute the file name of the intermedate link file used for separable
+  # compilation.
+  CUDA_COMPUTE_SEPARABLE_COMPILATION_OBJECT_FILE_NAME(link_file ${cuda_target} "${${cuda_target}_SEPARABLE_COMPILATION_OBJECTS}")
+
+  # Add the library.
+  add_library(${cuda_target} ${_cmake_options}
+    ${_generated_files}
+    ${_sources}
+    ${link_file}
+    )
+
+  # Add a link phase for the separable compilation if it has been enabled.  If
+  # it has been enabled then the ${cuda_target}_SEPARABLE_COMPILATION_OBJECTS
+  # variable will have been defined.
+  CUDA_LINK_SEPARABLE_COMPILATION_OBJECTS("${link_file}" ${cuda_target} "${_options}" "${${cuda_target}_SEPARABLE_COMPILATION_OBJECTS}")
+
+  target_link_libraries(${cuda_target} ${CUDA_LINK_LIBRARIES_KEYWORD}
+    ${CUDA_LIBRARIES}
+    )
+
+  if(CUDA_SEPARABLE_COMPILATION)
+    target_link_libraries(${cuda_target} ${CUDA_LINK_LIBRARIES_KEYWORD}
+      ${CUDA_cudadevrt_LIBRARY}
+      )
+  endif()
+
+  # We need to set the linker language based on what the expected generated file
+  # would be. CUDA_C_OR_CXX is computed based on CUDA_HOST_COMPILATION_CPP.
+  set_target_properties(${cuda_target}
+    PROPERTIES
+    LINKER_LANGUAGE ${CUDA_C_OR_CXX}
+    )
+
+endmacro()
+
+
+###############################################################################
+###############################################################################
+# ADD EXECUTABLE
+###############################################################################
+###############################################################################
+macro(CUDA_ADD_EXECUTABLE cuda_target)
+
+  CUDA_ADD_CUDA_INCLUDE_ONCE()
+
+  # Separate the sources from the options
+  CUDA_GET_SOURCES_AND_OPTIONS(_sources _cmake_options _options ${ARGN})
+  # Create custom commands and targets for each file.
+  CUDA_WRAP_SRCS( ${cuda_target} OBJ _generated_files ${_sources} OPTIONS ${_options} )
+
+  # Compute the file name of the intermedate link file used for separable
+  # compilation.
+  CUDA_COMPUTE_SEPARABLE_COMPILATION_OBJECT_FILE_NAME(link_file ${cuda_target} "${${cuda_target}_SEPARABLE_COMPILATION_OBJECTS}")
+
+  # Add the library.
+  add_executable(${cuda_target} ${_cmake_options}
+    ${_generated_files}
+    ${_sources}
+    ${link_file}
+    )
+
+  # Add a link phase for the separable compilation if it has been enabled.  If
+  # it has been enabled then the ${cuda_target}_SEPARABLE_COMPILATION_OBJECTS
+  # variable will have been defined.
+  CUDA_LINK_SEPARABLE_COMPILATION_OBJECTS("${link_file}" ${cuda_target} "${_options}" "${${cuda_target}_SEPARABLE_COMPILATION_OBJECTS}")
+
+  target_link_libraries(${cuda_target} ${CUDA_LINK_LIBRARIES_KEYWORD}
+    ${CUDA_LIBRARIES}
+    )
+
+  # We need to set the linker language based on what the expected generated file
+  # would be. CUDA_C_OR_CXX is computed based on CUDA_HOST_COMPILATION_CPP.
+  set_target_properties(${cuda_target}
+    PROPERTIES
+    LINKER_LANGUAGE ${CUDA_C_OR_CXX}
+    )
+
+endmacro()
+
+
+###############################################################################
+###############################################################################
+# (Internal) helper for manually added cuda source files with specific targets
+###############################################################################
+###############################################################################
+macro(cuda_compile_base cuda_target format generated_files)
+  # Update a counter in this directory, to keep phony target names unique.
+  set(_cuda_target "${cuda_target}")
+  get_property(_counter DIRECTORY PROPERTY _cuda_internal_phony_counter)
+  if(_counter)
+    math(EXPR _counter "${_counter} + 1")
+  else()
+    set(_counter 1)
+  endif()
+  string(APPEND _cuda_target "_${_counter}")
+  set_property(DIRECTORY PROPERTY _cuda_internal_phony_counter ${_counter})
+
+  # Separate the sources from the options
+  CUDA_GET_SOURCES_AND_OPTIONS(_sources _cmake_options _options ${ARGN})
+
+  # Create custom commands and targets for each file.
+  CUDA_WRAP_SRCS( ${_cuda_target} ${format} _generated_files ${_sources}
+                  ${_cmake_options} OPTIONS ${_options} PHONY)
+
+  set( ${generated_files} ${_generated_files})
+
+endmacro()
+
+###############################################################################
+###############################################################################
+# CUDA COMPILE
+###############################################################################
+###############################################################################
+macro(CUDA_COMPILE generated_files)
+  cuda_compile_base(cuda_compile OBJ ${generated_files} ${ARGN})
+endmacro()
+
+###############################################################################
+###############################################################################
+# CUDA COMPILE PTX
+###############################################################################
+###############################################################################
+macro(CUDA_COMPILE_PTX generated_files)
+  cuda_compile_base(cuda_compile_ptx PTX ${generated_files} ${ARGN})
+endmacro()
+
+###############################################################################
+###############################################################################
+# CUDA COMPILE FATBIN
+###############################################################################
+###############################################################################
+macro(CUDA_COMPILE_FATBIN generated_files)
+  cuda_compile_base(cuda_compile_fatbin FATBIN ${generated_files} ${ARGN})
+endmacro()
+
+###############################################################################
+###############################################################################
+# CUDA COMPILE CUBIN
+###############################################################################
+###############################################################################
+macro(CUDA_COMPILE_CUBIN generated_files)
+  cuda_compile_base(cuda_compile_cubin CUBIN ${generated_files} ${ARGN})
+endmacro()
+
+
+###############################################################################
+###############################################################################
+# CUDA ADD CUFFT TO TARGET
+###############################################################################
+###############################################################################
+macro(CUDA_ADD_CUFFT_TO_TARGET target)
+  if (CUDA_BUILD_EMULATION)
+    target_link_libraries(${target} ${CUDA_LINK_LIBRARIES_KEYWORD} ${CUDA_cufftemu_LIBRARY})
+  else()
+    target_link_libraries(${target} ${CUDA_LINK_LIBRARIES_KEYWORD} ${CUDA_cufft_LIBRARY})
+  endif()
+endmacro()
+
+###############################################################################
+###############################################################################
+# CUDA ADD CUBLAS TO TARGET
+###############################################################################
+###############################################################################
+macro(CUDA_ADD_CUBLAS_TO_TARGET target)
+  if (CUDA_BUILD_EMULATION)
+    target_link_libraries(${target} ${CUDA_LINK_LIBRARIES_KEYWORD} ${CUDA_cublasemu_LIBRARY})
+  else()
+    target_link_libraries(${target} ${CUDA_LINK_LIBRARIES_KEYWORD} ${CUDA_cublas_LIBRARY} ${CUDA_cublas_device_LIBRARY} ${CUDA_cublasLt_LIBRARY})
+  endif()
+endmacro()
+
+###############################################################################
+###############################################################################
+# CUDA BUILD CLEAN TARGET
+###############################################################################
+###############################################################################
+macro(CUDA_BUILD_CLEAN_TARGET)
+  # Call this after you add all your CUDA targets, and you will get a
+  # convenience target.  You should also make clean after running this target
+  # to get the build system to generate all the code again.
+
+  set(cuda_clean_target_name clean_cuda_depends)
+  if (CMAKE_GENERATOR MATCHES "Visual Studio")
+    string(TOUPPER ${cuda_clean_target_name} cuda_clean_target_name)
+  endif()
+  add_custom_target(${cuda_clean_target_name}
+    COMMAND ${CMAKE_COMMAND} -E remove ${CUDA_ADDITIONAL_CLEAN_FILES})
+
+  # Clear out the variable, so the next time we configure it will be empty.
+  # This is useful so that the files won't persist in the list after targets
+  # have been removed.
+  set(CUDA_ADDITIONAL_CLEAN_FILES "" CACHE INTERNAL "List of intermediate files that are part of the cuda dependency scanning.")
+endmacro()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/make2cmake.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/make2cmake.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..580f24a400d8c5662ec572c4631db9e3e47645d9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/make2cmake.cmake
@@ -0,0 +1,106 @@
+#  James Bigler, NVIDIA Corp (nvidia.com - jbigler)
+#  Abe Stephens, SCI Institute -- http://www.sci.utah.edu/~abe/FindCuda.html
+#
+#  Copyright (c) 2008 - 2009 NVIDIA Corporation.  All rights reserved.
+#
+#  Copyright (c) 2007-2009
+#  Scientific Computing and Imaging Institute, University of Utah
+#
+#  This code is licensed under the MIT License.  See the FindCUDA.cmake script
+#  for the text of the license.
+
+# The MIT License
+#
+# License for the specific language governing rights and limitations under
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+# DEALINGS IN THE SOFTWARE.
+#
+
+#######################################################################
+# This converts a file written in makefile syntax into one that can be included
+# by CMake.
+
+# Input variables
+#
+# verbose:BOOL=<>          OFF: Be as quiet as possible (default)
+#                          ON : Extra output
+#
+# input_file:FILEPATH=<>   Path to dependency file in makefile format
+#
+# output_file:FILEPATH=<>  Path to file with dependencies in CMake readable variable
+#
+
+file(READ ${input_file} depend_text)
+
+if (NOT "${depend_text}" STREQUAL "")
+
+  # message("FOUND DEPENDS")
+
+  string(REPLACE "\\ " " " depend_text ${depend_text})
+
+  # This works for the nvcc -M generated dependency files.
+  string(REGEX REPLACE "^.* : " "" depend_text ${depend_text})
+  string(REGEX REPLACE "[ \\\\]*\n" ";" depend_text ${depend_text})
+
+  set(dependency_list "")
+
+  foreach(file ${depend_text})
+
+    string(REGEX REPLACE "^ +" "" file ${file})
+
+    # OK, now if we had a UNC path, nvcc has a tendency to only output the first '/'
+    # instead of '//'.  Here we will test to see if the file exists, if it doesn't then
+    # try to prepend another '/' to the path and test again.  If it still fails remove the
+    # path.
+
+    if(NOT EXISTS "${file}")
+      if (EXISTS "/${file}")
+        set(file "/${file}")
+      else()
+        if(verbose)
+          message(WARNING " Removing non-existent dependency file: ${file}")
+        endif()
+        set(file "")
+      endif()
+    endif()
+
+    # Make sure we check to see if we have a file, before asking if it is not a directory.
+    # if(NOT IS_DIRECTORY "") will return TRUE.
+    if(file AND NOT IS_DIRECTORY "${file}")
+      # If softlinks start to matter, we should change this to REALPATH.  For now we need
+      # to flatten paths, because nvcc can generate stuff like /bin/../include instead of
+      # just /include.
+      get_filename_component(file_absolute "${file}" ABSOLUTE)
+      list(APPEND dependency_list "${file_absolute}")
+    endif()
+
+  endforeach()
+
+else()
+  # message("FOUND NO DEPENDS")
+endif()
+
+# Remove the duplicate entries and sort them.
+list(REMOVE_DUPLICATES dependency_list)
+list(SORT dependency_list)
+
+foreach(file ${dependency_list})
+  string(APPEND cuda_nvcc_depend " \"${file}\"\n")
+endforeach()
+
+file(WRITE ${output_file} "# Generated by: make2cmake.cmake\nSET(CUDA_NVCC_DEPEND\n ${cuda_nvcc_depend})\n\n")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/parse_cubin.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/parse_cubin.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..25ceb49f3dd8e684e35cac49834c4db0aa5c338a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/parse_cubin.cmake
@@ -0,0 +1,109 @@
+#  James Bigler, NVIDIA Corp (nvidia.com - jbigler)
+#  Abe Stephens, SCI Institute -- http://www.sci.utah.edu/~abe/FindCuda.html
+#
+#  Copyright (c) 2008 - 2009 NVIDIA Corporation.  All rights reserved.
+#
+#  Copyright (c) 2007-2009
+#  Scientific Computing and Imaging Institute, University of Utah
+#
+#  This code is licensed under the MIT License.  See the FindCUDA.cmake script
+#  for the text of the license.
+
+# The MIT License
+#
+# License for the specific language governing rights and limitations under
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+# DEALINGS IN THE SOFTWARE.
+#
+
+#######################################################################
+# Parses a .cubin file produced by nvcc and reports statistics about the file.
+
+
+file(READ ${input_file} file_text)
+
+if (NOT "${file_text}" STREQUAL "")
+
+  string(REPLACE ";" "\\;" file_text ${file_text})
+  string(REPLACE "\ncode" ";code" file_text ${file_text})
+
+  list(LENGTH file_text len)
+
+  foreach(line ${file_text})
+
+    # Only look at "code { }" blocks.
+    if(line MATCHES "^code")
+
+      # Break into individual lines.
+      string(REGEX REPLACE "\n" ";" line ${line})
+
+      foreach(entry ${line})
+
+        # Extract kernel names.
+        if (${entry} MATCHES "[^g]name = ([^ ]+)")
+          set(entry "${CMAKE_MATCH_1}")
+
+          # Check to see if the kernel name starts with "_"
+          set(skip FALSE)
+          # if (${entry} MATCHES "^_")
+            # Skip the rest of this block.
+            # message("Skipping ${entry}")
+            # set(skip TRUE)
+          # else ()
+            message("Kernel:    ${entry}")
+          # endif ()
+
+        endif()
+
+        # Skip the rest of the block if necessary
+        if(NOT skip)
+
+          # Registers
+          if (${entry} MATCHES "reg([ ]+)=([ ]+)([^ ]+)")
+            set(entry "${CMAKE_MATCH_3}")
+            message("Registers: ${entry}")
+          endif()
+
+          # Local memory
+          if (${entry} MATCHES "lmem([ ]+)=([ ]+)([^ ]+)")
+            set(entry "${CMAKE_MATCH_3}")
+            message("Local:     ${entry}")
+          endif()
+
+          # Shared memory
+          if (${entry} MATCHES "smem([ ]+)=([ ]+)([^ ]+)")
+            set(entry "${CMAKE_MATCH_3}")
+            message("Shared:    ${entry}")
+          endif()
+
+          if (${entry} MATCHES "^}")
+            message("")
+          endif()
+
+        endif()
+
+
+      endforeach()
+
+    endif()
+
+  endforeach()
+
+else()
+  # message("FOUND NO DEPENDS")
+endif()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/run_nvcc.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/run_nvcc.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..9293df3aafbdefdd8664ae2860d1b5b7fc9bfbfb
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/run_nvcc.cmake
@@ -0,0 +1,303 @@
+#  James Bigler, NVIDIA Corp (nvidia.com - jbigler)
+#
+#  Copyright (c) 2008 - 2009 NVIDIA Corporation.  All rights reserved.
+#
+#  This code is licensed under the MIT License.  See the FindCUDA.cmake script
+#  for the text of the license.
+
+# The MIT License
+#
+# License for the specific language governing rights and limitations under
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+# DEALINGS IN THE SOFTWARE.
+
+
+##########################################################################
+# This file runs the nvcc commands to produce the desired output file along with
+# the dependency file needed by CMake to compute dependencies.  In addition the
+# file checks the output of each command and if the command fails it deletes the
+# output files.
+
+# Input variables
+#
+# verbose:BOOL=<>          OFF: Be as quiet as possible (default)
+#                          ON : Describe each step
+#
+# build_configuration:STRING=<> Typically one of Debug, MinSizeRel, Release, or
+#                               RelWithDebInfo, but it should match one of the
+#                               entries in CUDA_HOST_FLAGS. This is the build
+#                               configuration used when compiling the code.  If
+#                               blank or unspecified Debug is assumed as this is
+#                               what CMake does.
+#
+# generated_file:STRING=<> File to generate.  This argument must be passed in.
+#
+# generated_cubin_file:STRING=<> File to generate.  This argument must be passed
+#                                                   in if build_cubin is true.
+
+cmake_policy(PUSH)
+cmake_policy(SET CMP0007 NEW)
+cmake_policy(SET CMP0010 NEW)
+if(NOT generated_file)
+  message(FATAL_ERROR "You must specify generated_file on the command line")
+endif()
+
+# Set these up as variables to make reading the generated file easier
+set(CMAKE_COMMAND "@CMAKE_COMMAND@") # path
+set(source_file "@source_file@") # path
+set(NVCC_generated_dependency_file "@NVCC_generated_dependency_file@") # path
+set(cmake_dependency_file "@cmake_dependency_file@") # path
+set(CUDA_make2cmake "@CUDA_make2cmake@") # path
+set(CUDA_parse_cubin "@CUDA_parse_cubin@") # path
+set(build_cubin @build_cubin@) # bool
+set(CUDA_HOST_COMPILER "@CUDA_HOST_COMPILER@") # path
+# We won't actually use these variables for now, but we need to set this, in
+# order to force this file to be run again if it changes.
+set(generated_file_path "@generated_file_path@") # path
+set(generated_file_internal "@generated_file@") # path
+set(generated_cubin_file_internal "@generated_cubin_file@") # path
+
+set(CUDA_NVCC_EXECUTABLE "@CUDA_NVCC_EXECUTABLE@") # path
+set(CUDA_NVCC_FLAGS @CUDA_NVCC_FLAGS@ ;; @CUDA_WRAP_OPTION_NVCC_FLAGS@) # list
+@CUDA_NVCC_FLAGS_CONFIG@
+set(nvcc_flags @nvcc_flags@) # list
+set(CUDA_NVCC_INCLUDE_DIRS [==[@CUDA_NVCC_INCLUDE_DIRS@]==]) # list (needs to be in lua quotes to address backslashes)
+string(REPLACE "\\" "/" CUDA_NVCC_INCLUDE_DIRS "${CUDA_NVCC_INCLUDE_DIRS}")
+set(CUDA_NVCC_COMPILE_DEFINITIONS [==[@CUDA_NVCC_COMPILE_DEFINITIONS@]==]) # list (needs to be in lua quotes see #16510 ).
+set(format_flag "@format_flag@") # string
+set(cuda_language_flag @cuda_language_flag@) # list
+
+# Clean up list of include directories and add -I flags
+list(REMOVE_DUPLICATES CUDA_NVCC_INCLUDE_DIRS)
+set(CUDA_NVCC_INCLUDE_ARGS)
+foreach(dir ${CUDA_NVCC_INCLUDE_DIRS})
+  # Extra quotes are added around each flag to help nvcc parse out flags with spaces.
+  list(APPEND CUDA_NVCC_INCLUDE_ARGS "-I${dir}")
+endforeach()
+
+# Clean up list of compile definitions, add -D flags, and append to nvcc_flags
+list(REMOVE_DUPLICATES CUDA_NVCC_COMPILE_DEFINITIONS)
+foreach(def ${CUDA_NVCC_COMPILE_DEFINITIONS})
+  list(APPEND nvcc_flags "-D${def}")
+endforeach()
+
+if(build_cubin AND NOT generated_cubin_file)
+  message(FATAL_ERROR "You must specify generated_cubin_file on the command line")
+endif()
+
+# This is the list of host compilation flags.  It C or CXX should already have
+# been chosen by FindCUDA.cmake.
+@CUDA_HOST_FLAGS@
+
+# Take the compiler flags and package them up to be sent to the compiler via -Xcompiler
+set(nvcc_host_compiler_flags "")
+# If we weren't given a build_configuration, use Debug.
+if(NOT build_configuration)
+  set(build_configuration Debug)
+endif()
+string(TOUPPER "${build_configuration}" build_configuration)
+#message("CUDA_NVCC_HOST_COMPILER_FLAGS = ${CUDA_NVCC_HOST_COMPILER_FLAGS}")
+foreach(flag ${CMAKE_HOST_FLAGS} ${CMAKE_HOST_FLAGS_${build_configuration}})
+  # Extra quotes are added around each flag to help nvcc parse out flags with spaces.
+  string(APPEND nvcc_host_compiler_flags ",\"${flag}\"")
+endforeach()
+if (nvcc_host_compiler_flags)
+  set(nvcc_host_compiler_flags "-Xcompiler" ${nvcc_host_compiler_flags})
+endif()
+#message("nvcc_host_compiler_flags = \"${nvcc_host_compiler_flags}\"")
+# Add the build specific configuration flags
+list(APPEND CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS_${build_configuration}})
+
+# Any -ccbin existing in CUDA_NVCC_FLAGS gets highest priority
+list( FIND CUDA_NVCC_FLAGS "-ccbin" ccbin_found0 )
+list( FIND CUDA_NVCC_FLAGS "--compiler-bindir" ccbin_found1 )
+if( ccbin_found0 LESS 0 AND ccbin_found1 LESS 0 AND CUDA_HOST_COMPILER )
+  if (CUDA_HOST_COMPILER STREQUAL "@_CUDA_MSVC_HOST_COMPILER@" AND DEFINED CCBIN)
+    set(CCBIN -ccbin "${CCBIN}")
+  else()
+    set(CCBIN -ccbin "${CUDA_HOST_COMPILER}")
+  endif()
+endif()
+
+# cuda_execute_process - Executes a command with optional command echo and status message.
+#
+#   status  - Status message to print if verbose is true
+#   command - COMMAND argument from the usual execute_process argument structure
+#   ARGN    - Remaining arguments are the command with arguments
+#
+#   CUDA_result - return value from running the command
+#
+# Make this a macro instead of a function, so that things like RESULT_VARIABLE
+# and other return variables are present after executing the process.
+macro(cuda_execute_process status command)
+  set(_command ${command})
+  if(NOT "x${_command}" STREQUAL "xCOMMAND")
+    message(FATAL_ERROR "Malformed call to cuda_execute_process.  Missing COMMAND as second argument. (command = ${command})")
+  endif()
+  if(verbose)
+    execute_process(COMMAND "${CMAKE_COMMAND}" -E echo -- ${status})
+    # Now we need to build up our command string.  We are accounting for quotes
+    # and spaces, anything else is left up to the user to fix if they want to
+    # copy and paste a runnable command line.
+    set(cuda_execute_process_string)
+    foreach(arg ${ARGN})
+      # If there are quotes, excape them, so they come through.
+      string(REPLACE "\"" "\\\"" arg ${arg})
+      # Args with spaces need quotes around them to get them to be parsed as a single argument.
+      if(arg MATCHES " ")
+        list(APPEND cuda_execute_process_string "\"${arg}\"")
+      else()
+        list(APPEND cuda_execute_process_string ${arg})
+      endif()
+    endforeach()
+    # Echo the command
+    execute_process(COMMAND ${CMAKE_COMMAND} -E echo ${cuda_execute_process_string})
+  endif()
+  # Run the command
+  execute_process(COMMAND ${ARGN} RESULT_VARIABLE CUDA_result )
+endmacro()
+
+# Delete the target file
+cuda_execute_process(
+  "Removing ${generated_file}"
+  COMMAND "${CMAKE_COMMAND}" -E remove "${generated_file}"
+  )
+
+# For CUDA 2.3 and below, -G -M doesn't work, so remove the -G flag
+# for dependency generation and hope for the best.
+set(depends_CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS}")
+set(CUDA_VERSION @CUDA_VERSION@)
+
+# nvcc doesn't define __CUDACC__ for some reason when generating dependency files.  This
+# can cause incorrect dependencies when #including files based on this macro which is
+# defined in the generating passes of nvcc invocation.  We will go ahead and manually
+# define this for now until a future version fixes this bug.
+set(CUDACC_DEFINE -D__CUDACC__)
+
+# Generate the dependency file
+cuda_execute_process(
+  "Generating dependency file: ${NVCC_generated_dependency_file}"
+  COMMAND "${CUDA_NVCC_EXECUTABLE}"
+  -M
+  ${CUDACC_DEFINE}
+  "${source_file}"
+  -o "${NVCC_generated_dependency_file}"
+  ${CCBIN}
+  ${nvcc_flags}
+  ${nvcc_host_compiler_flags}
+  ${depends_CUDA_NVCC_FLAGS}
+  -DNVCC
+  ${CUDA_NVCC_INCLUDE_ARGS}
+  )
+
+if(CUDA_result)
+  message(FATAL_ERROR "Error generating ${generated_file}")
+endif()
+
+# Generate the cmake readable dependency file to a temp file.  Don't put the
+# quotes just around the filenames for the input_file and output_file variables.
+# CMake will pass the quotes through and not be able to find the file.
+cuda_execute_process(
+  "Generating temporary cmake readable file: ${cmake_dependency_file}.tmp"
+  COMMAND "${CMAKE_COMMAND}"
+  -D "input_file:FILEPATH=${NVCC_generated_dependency_file}"
+  -D "output_file:FILEPATH=${cmake_dependency_file}.tmp"
+  -D "verbose=${verbose}"
+  -P "${CUDA_make2cmake}"
+  )
+
+if(CUDA_result)
+  message(FATAL_ERROR "Error generating ${generated_file}")
+endif()
+
+# Copy the file if it is different
+cuda_execute_process(
+  "Copy if different ${cmake_dependency_file}.tmp to ${cmake_dependency_file}"
+  COMMAND "${CMAKE_COMMAND}" -E copy_if_different "${cmake_dependency_file}.tmp" "${cmake_dependency_file}"
+  )
+
+if(CUDA_result)
+  message(FATAL_ERROR "Error generating ${generated_file}")
+endif()
+
+# Delete the temporary file
+cuda_execute_process(
+  "Removing ${cmake_dependency_file}.tmp and ${NVCC_generated_dependency_file}"
+  COMMAND "${CMAKE_COMMAND}" -E remove "${cmake_dependency_file}.tmp" "${NVCC_generated_dependency_file}"
+  )
+
+if(CUDA_result)
+  message(FATAL_ERROR "Error generating ${generated_file}")
+endif()
+
+# Generate the code
+cuda_execute_process(
+  "Generating ${generated_file}"
+  COMMAND "${CUDA_NVCC_EXECUTABLE}"
+  "${source_file}"
+  ${cuda_language_flag}
+  ${format_flag} -o "${generated_file}"
+  ${CCBIN}
+  ${nvcc_flags}
+  ${nvcc_host_compiler_flags}
+  ${CUDA_NVCC_FLAGS}
+  -DNVCC
+  ${CUDA_NVCC_INCLUDE_ARGS}
+  )
+
+if(CUDA_result)
+  # Since nvcc can sometimes leave half done files make sure that we delete the output file.
+  cuda_execute_process(
+    "Removing ${generated_file}"
+    COMMAND "${CMAKE_COMMAND}" -E remove "${generated_file}"
+    )
+  message(FATAL_ERROR "Error generating file ${generated_file}")
+else()
+  if(verbose)
+    message("Generated ${generated_file} successfully.")
+  endif()
+endif()
+
+# Cubin resource report commands.
+if( build_cubin )
+  # Run with -cubin to produce resource usage report.
+  cuda_execute_process(
+    "Generating ${generated_cubin_file}"
+    COMMAND "${CUDA_NVCC_EXECUTABLE}"
+    "${source_file}"
+    ${CUDA_NVCC_FLAGS}
+    ${nvcc_flags}
+    ${CCBIN}
+    ${nvcc_host_compiler_flags}
+    -DNVCC
+    -cubin
+    -o "${generated_cubin_file}"
+    ${CUDA_NVCC_INCLUDE_ARGS}
+    )
+
+  # Execute the parser script.
+  cuda_execute_process(
+    "Executing the parser script"
+    COMMAND  "${CMAKE_COMMAND}"
+    -D "input_file:STRING=${generated_cubin_file}"
+    -P "${CUDA_parse_cubin}"
+    )
+
+endif()
+
+cmake_policy(POP)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/select_compute_arch.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/select_compute_arch.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..90de8fb0d84447eaab9aaf76600c8d228c2a842e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/select_compute_arch.cmake
@@ -0,0 +1,280 @@
+# Synopsis:
+#   CUDA_SELECT_NVCC_ARCH_FLAGS(out_variable [target_CUDA_architectures])
+#   -- Selects GPU arch flags for nvcc based on target_CUDA_architectures
+#      target_CUDA_architectures : Auto | Common | All | LIST(ARCH_AND_PTX ...)
+#       - "Auto" detects local machine GPU compute arch at runtime.
+#       - "Common" and "All" cover common and entire subsets of architectures
+#      ARCH_AND_PTX : NAME | NUM.NUM | NUM.NUM(NUM.NUM) | NUM.NUM+PTX
+#      NAME: Kepler Maxwell Kepler+Tegra Kepler+Tesla Maxwell+Tegra Pascal Volta Turing Ampere
+#      NUM: Any number. Only those pairs are currently accepted by NVCC though:
+#            3.5 3.7 5.0 5.2 5.3 6.0 6.2 7.0 7.2 7.5 8.0
+#      Returns LIST of flags to be added to CUDA_NVCC_FLAGS in ${out_variable}
+#      Additionally, sets ${out_variable}_readable to the resulting numeric list
+#      Example:
+#       CUDA_SELECT_NVCC_ARCH_FLAGS(ARCH_FLAGS 3.0 3.5+PTX 5.2(5.0) Maxwell)
+#        LIST(APPEND CUDA_NVCC_FLAGS ${ARCH_FLAGS})
+#
+#      More info on CUDA architectures: https://en.wikipedia.org/wiki/CUDA
+#
+
+if(CMAKE_CUDA_COMPILER_LOADED) # CUDA as a language
+  if(CMAKE_CUDA_COMPILER_ID STREQUAL "NVIDIA"
+      AND CMAKE_CUDA_COMPILER_VERSION MATCHES "^([0-9]+\\.[0-9]+)")
+    set(CUDA_VERSION "${CMAKE_MATCH_1}")
+  endif()
+endif()
+
+# See: https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#gpu-feature-list
+
+# This list will be used for CUDA_ARCH_NAME = All option
+set(CUDA_KNOWN_GPU_ARCHITECTURES  "Kepler" "Maxwell")
+
+# This list will be used for CUDA_ARCH_NAME = Common option (enabled by default)
+set(CUDA_COMMON_GPU_ARCHITECTURES "3.5" "5.0")
+
+# This list is used to filter CUDA archs when autodetecting
+set(CUDA_ALL_GPU_ARCHITECTURES "3.5" "5.0")
+
+if(CUDA_VERSION VERSION_GREATER "10.5")
+  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Ampere")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.0")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "8.0")
+
+  if(CUDA_VERSION VERSION_LESS "11.1")
+    set(CUDA_LIMIT_GPU_ARCHITECTURE "8.0")
+    list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.0+PTX")
+  endif()
+endif()
+
+if(NOT CUDA_VERSION VERSION_LESS "11.1")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.6")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "8.6")
+  set(CUDA_LIMIT_GPU_ARCHITECUTRE "8.6")
+
+  if(CUDA_VERSION VERSION_LESS "11.8")
+    set(CUDA_LIMIT_GPU_ARCHITECTURE "8.9")
+    list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.6+PTX")
+  endif()
+endif()
+
+if(NOT CUDA_VERSION VERSION_LESS "11.8")
+  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Ada")
+  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Hopper")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.9")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "9.0")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "8.9")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "9.0")
+
+  if(CUDA_VERSION VERSION_LESS "12.0")
+    set(CUDA_LIMIT_GPU_ARCHITECTURE "9.0")
+    list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.9+PTX")
+    list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "9.0+PTX")
+  endif()
+endif()
+
+if(NOT CUDA_VERSION VERSION_LESS "12.0")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "9.0a")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "9.0a")
+  list(REMOVE_ITEM CUDA_COMMON_GPU_ARCHITECTURES "3.5")
+  list(REMOVE_ITEM CUDA_ALL_GPU_ARCHITECTURES "3.5")
+endif()
+
+################################################################################################
+# A function for automatic detection of GPUs installed  (if autodetection is enabled)
+# Usage:
+#   CUDA_DETECT_INSTALLED_GPUS(OUT_VARIABLE)
+#
+function(CUDA_DETECT_INSTALLED_GPUS OUT_VARIABLE)
+  if(NOT CUDA_GPU_DETECT_OUTPUT)
+    if(CMAKE_CUDA_COMPILER_LOADED) # CUDA as a language
+      set(file "${PROJECT_BINARY_DIR}/detect_cuda_compute_capabilities.cu")
+    else()
+      set(file "${PROJECT_BINARY_DIR}/detect_cuda_compute_capabilities.cpp")
+    endif()
+
+    file(WRITE ${file} ""
+      "#include \n"
+      "#include \n"
+      "int main()\n"
+      "{\n"
+      "  int count = 0;\n"
+      "  if (cudaSuccess != cudaGetDeviceCount(&count)) return -1;\n"
+      "  if (count == 0) return -1;\n"
+      "  for (int device = 0; device < count; ++device)\n"
+      "  {\n"
+      "    cudaDeviceProp prop;\n"
+      "    if (cudaSuccess == cudaGetDeviceProperties(&prop, device))\n"
+      "      std::printf(\"%d.%d \", prop.major, prop.minor);\n"
+      "  }\n"
+      "  return 0;\n"
+      "}\n")
+
+    if(CMAKE_CUDA_COMPILER_LOADED) # CUDA as a language
+      try_run(run_result compile_result ${PROJECT_BINARY_DIR} ${file}
+              RUN_OUTPUT_VARIABLE compute_capabilities)
+    else()
+      try_run(run_result compile_result ${PROJECT_BINARY_DIR} ${file}
+              CMAKE_FLAGS "-DINCLUDE_DIRECTORIES=${CUDA_INCLUDE_DIRS}"
+              LINK_LIBRARIES ${CUDA_LIBRARIES}
+              RUN_OUTPUT_VARIABLE compute_capabilities)
+    endif()
+
+    # Filter unrelated content out of the output.
+    string(REGEX MATCHALL "[0-9]+\\.[0-9]+" compute_capabilities "${compute_capabilities}")
+
+    if(run_result EQUAL 0)
+      string(REPLACE "2.1" "2.1(2.0)" compute_capabilities "${compute_capabilities}")
+      set(CUDA_GPU_DETECT_OUTPUT ${compute_capabilities}
+        CACHE INTERNAL "Returned GPU architectures from detect_gpus tool" FORCE)
+    endif()
+  endif()
+
+  if(NOT CUDA_GPU_DETECT_OUTPUT)
+    message(STATUS "Automatic GPU detection failed. Building for common architectures.")
+    set(${OUT_VARIABLE} ${CUDA_COMMON_GPU_ARCHITECTURES} PARENT_SCOPE)
+  else()
+    # Filter based on CUDA version supported archs
+    set(CUDA_GPU_DETECT_OUTPUT_FILTERED "")
+    separate_arguments(CUDA_GPU_DETECT_OUTPUT)
+    foreach(ITEM IN ITEMS ${CUDA_GPU_DETECT_OUTPUT})
+        if(CUDA_LIMIT_GPU_ARCHITECTURE AND (ITEM VERSION_GREATER CUDA_LIMIT_GPU_ARCHITECTURE OR
+                                            ITEM VERSION_EQUAL CUDA_LIMIT_GPU_ARCHITECTURE))
+        list(GET CUDA_COMMON_GPU_ARCHITECTURES -1 NEWITEM)
+        string(APPEND CUDA_GPU_DETECT_OUTPUT_FILTERED " ${NEWITEM}")
+      else()
+        string(APPEND CUDA_GPU_DETECT_OUTPUT_FILTERED " ${ITEM}")
+      endif()
+    endforeach()
+
+    set(${OUT_VARIABLE} ${CUDA_GPU_DETECT_OUTPUT_FILTERED} PARENT_SCOPE)
+  endif()
+endfunction()
+
+
+################################################################################################
+# Function for selecting GPU arch flags for nvcc based on CUDA architectures from parameter list
+# Usage:
+#   SELECT_NVCC_ARCH_FLAGS(out_variable [list of CUDA compute archs])
+function(CUDA_SELECT_NVCC_ARCH_FLAGS out_variable)
+  set(CUDA_ARCH_LIST "${ARGN}")
+
+  if("X${CUDA_ARCH_LIST}" STREQUAL "X" )
+    set(CUDA_ARCH_LIST "Auto")
+  endif()
+
+  set(cuda_arch_bin)
+  set(cuda_arch_ptx)
+
+  if("${CUDA_ARCH_LIST}" STREQUAL "All")
+    set(CUDA_ARCH_LIST ${CUDA_KNOWN_GPU_ARCHITECTURES})
+  elseif("${CUDA_ARCH_LIST}" STREQUAL "Common")
+    set(CUDA_ARCH_LIST ${CUDA_COMMON_GPU_ARCHITECTURES})
+  elseif("${CUDA_ARCH_LIST}" STREQUAL "Auto")
+    CUDA_DETECT_INSTALLED_GPUS(CUDA_ARCH_LIST)
+    message(STATUS "Autodetected CUDA architecture(s): ${CUDA_ARCH_LIST}")
+  endif()
+
+  # Now process the list and look for names
+  string(REGEX REPLACE "[ \t]+" ";" CUDA_ARCH_LIST "${CUDA_ARCH_LIST}")
+  list(REMOVE_DUPLICATES CUDA_ARCH_LIST)
+  foreach(arch_name ${CUDA_ARCH_LIST})
+    set(arch_bin)
+    set(arch_ptx)
+    set(add_ptx FALSE)
+    # Check to see if we are compiling PTX
+    if(arch_name MATCHES "(.*)\\+PTX$")
+      set(add_ptx TRUE)
+      set(arch_name ${CMAKE_MATCH_1})
+    endif()
+    if(arch_name MATCHES "^([0-9]\\.[0-9]a?(\\([0-9]\\.[0-9]\\))?)$")
+      set(arch_bin ${CMAKE_MATCH_1})
+      set(arch_ptx ${arch_bin})
+    else()
+      # Look for it in our list of known architectures
+      if(${arch_name} STREQUAL "Kepler+Tesla")
+        set(arch_bin 3.7)
+      elseif(${arch_name} STREQUAL "Kepler")
+        set(arch_bin 3.5)
+        set(arch_ptx 3.5)
+      elseif(${arch_name} STREQUAL "Maxwell+Tegra")
+        set(arch_bin 5.3)
+      elseif(${arch_name} STREQUAL "Maxwell")
+        set(arch_bin 5.0 5.2)
+        set(arch_ptx 5.2)
+      elseif(${arch_name} STREQUAL "Pascal")
+        set(arch_bin 6.0 6.1)
+        set(arch_ptx 6.1)
+     elseif(${arch_name} STREQUAL "Volta+Tegra")
+        set(arch_bin 7.2)
+      elseif(${arch_name} STREQUAL "Volta")
+        set(arch_bin 7.0 7.0)
+        set(arch_ptx 7.0)
+      elseif(${arch_name} STREQUAL "Turing")
+        set(arch_bin 7.5)
+        set(arch_ptx 7.5)
+      elseif(${arch_name} STREQUAL "Ampere+Tegra")
+        set(arch_bin 8.7)
+      elseif(${arch_name} STREQUAL "Ampere")
+        set(arch_bin 8.0 8.6)
+        set(arch_ptx 8.0 8.6)
+      elseif(${arch_name} STREQUAL "Ada")
+        set(arch_bin 8.9)
+        set(arch_ptx 8.9)
+      elseif(${arch_name} STREQUAL "Hopper")
+        set(arch_bin 9.0)
+        set(arch_ptx 9.0)
+      else()
+        message(SEND_ERROR "Unknown CUDA Architecture Name ${arch_name} in CUDA_SELECT_NVCC_ARCH_FLAGS")
+      endif()
+    endif()
+    if(NOT arch_bin)
+      message(SEND_ERROR "arch_bin wasn't set for some reason")
+    endif()
+    list(APPEND cuda_arch_bin ${arch_bin})
+    if(add_ptx)
+      if (NOT arch_ptx)
+        set(arch_ptx ${arch_bin})
+      endif()
+      list(APPEND cuda_arch_ptx ${arch_ptx})
+    endif()
+  endforeach()
+
+  # remove dots and convert to lists
+  string(REGEX REPLACE "\\." "" cuda_arch_bin "${cuda_arch_bin}")
+  string(REGEX REPLACE "\\." "" cuda_arch_ptx "${cuda_arch_ptx}")
+  string(REGEX MATCHALL "[0-9()]+a?" cuda_arch_bin "${cuda_arch_bin}")
+  string(REGEX MATCHALL "[0-9]+a?"   cuda_arch_ptx "${cuda_arch_ptx}")
+
+  if(cuda_arch_bin)
+    list(REMOVE_DUPLICATES cuda_arch_bin)
+  endif()
+  if(cuda_arch_ptx)
+    list(REMOVE_DUPLICATES cuda_arch_ptx)
+  endif()
+
+  set(nvcc_flags "")
+  set(nvcc_archs_readable "")
+
+  # Tell NVCC to add binaries for the specified GPUs
+  foreach(arch ${cuda_arch_bin})
+    if(arch MATCHES "([0-9]+)\\(([0-9]+)\\)")
+      # User explicitly specified ARCH for the concrete CODE
+      list(APPEND nvcc_flags -gencode arch=compute_${CMAKE_MATCH_2},code=sm_${CMAKE_MATCH_1})
+      list(APPEND nvcc_archs_readable sm_${CMAKE_MATCH_1})
+    else()
+      # User didn't explicitly specify ARCH for the concrete CODE, we assume ARCH=CODE
+      list(APPEND nvcc_flags -gencode arch=compute_${arch},code=sm_${arch})
+      list(APPEND nvcc_archs_readable sm_${arch})
+    endif()
+  endforeach()
+
+  # Tell NVCC to add PTX intermediate code for the specified architectures
+  foreach(arch ${cuda_arch_ptx})
+    list(APPEND nvcc_flags -gencode arch=compute_${arch},code=compute_${arch})
+    list(APPEND nvcc_archs_readable compute_${arch})
+  endforeach()
+
+  string(REPLACE ";" " " nvcc_archs_readable "${nvcc_archs_readable}")
+  set(${out_variable}          ${nvcc_flags}          PARENT_SCOPE)
+  set(${out_variable}_readable ${nvcc_archs_readable} PARENT_SCOPE)
+endfunction()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindPackageHandleStandardArgs.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindPackageHandleStandardArgs.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..67f6bd6f2bcd1a0313078a28a07cc584df7b885b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindPackageHandleStandardArgs.cmake
@@ -0,0 +1,386 @@
+# Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
+# file Copyright.txt or https://cmake.org/licensing for details.
+
+#[=======================================================================[.rst:
+FindPackageHandleStandardArgs
+-----------------------------
+
+This module provides a function intended to be used in :ref:`Find Modules`
+implementing :command:`find_package()` calls.  It handles the
+``REQUIRED``, ``QUIET`` and version-related arguments of ``find_package``.
+It also sets the ``_FOUND`` variable.  The package is
+considered found if all variables listed contain valid results, e.g.
+valid filepaths.
+
+.. command:: find_package_handle_standard_args
+
+  There are two signatures::
+
+    find_package_handle_standard_args(
+      (DEFAULT_MSG|)
+      ...
+      )
+
+    find_package_handle_standard_args(
+      [FOUND_VAR ]
+      [REQUIRED_VARS ...]
+      [VERSION_VAR ]
+      [HANDLE_COMPONENTS]
+      [CONFIG_MODE]
+      [FAIL_MESSAGE ]
+      )
+
+  The ``_FOUND`` variable will be set to ``TRUE`` if all
+  the variables ``...`` are valid and any optional
+  constraints are satisfied, and ``FALSE`` otherwise.  A success or
+  failure message may be displayed based on the results and on
+  whether the ``REQUIRED`` and/or ``QUIET`` option was given to
+  the :command:`find_package` call.
+
+  The options are:
+
+  ``(DEFAULT_MSG|)``
+    In the simple signature this specifies the failure message.
+    Use ``DEFAULT_MSG`` to ask for a default message to be computed
+    (recommended).  Not valid in the full signature.
+
+  ``FOUND_VAR ``
+    Obsolete.  Specifies either ``_FOUND`` or
+    ``_FOUND`` as the result variable.  This exists only
+    for compatibility with older versions of CMake and is now ignored.
+    Result variables of both names are always set for compatibility.
+
+  ``REQUIRED_VARS ...``
+    Specify the variables which are required for this package.
+    These may be named in the generated failure message asking the
+    user to set the missing variable values.  Therefore these should
+    typically be cache entries such as ``FOO_LIBRARY`` and not output
+    variables like ``FOO_LIBRARIES``.
+
+  ``VERSION_VAR ``
+    Specify the name of a variable that holds the version of the package
+    that has been found.  This version will be checked against the
+    (potentially) specified required version given to the
+    :command:`find_package` call, including its ``EXACT`` option.
+    The default messages include information about the required
+    version and the version which has been actually found, both
+    if the version is ok or not.
+
+  ``HANDLE_COMPONENTS``
+    Enable handling of package components.  In this case, the command
+    will report which components have been found and which are missing,
+    and the ``_FOUND`` variable will be set to ``FALSE``
+    if any of the required components (i.e. not the ones listed after
+    the ``OPTIONAL_COMPONENTS`` option of :command:`find_package`) are
+    missing.
+
+  ``CONFIG_MODE``
+    Specify that the calling find module is a wrapper around a
+    call to ``find_package( NO_MODULE)``.  This implies
+    a ``VERSION_VAR`` value of ``_VERSION``.  The command
+    will automatically check whether the package configuration file
+    was found.
+
+  ``FAIL_MESSAGE ``
+    Specify a custom failure message instead of using the default
+    generated message.  Not recommended.
+
+Example for the simple signature:
+
+.. code-block:: cmake
+
+  find_package_handle_standard_args(LibXml2 DEFAULT_MSG
+    LIBXML2_LIBRARY LIBXML2_INCLUDE_DIR)
+
+The ``LibXml2`` package is considered to be found if both
+``LIBXML2_LIBRARY`` and ``LIBXML2_INCLUDE_DIR`` are valid.
+Then also ``LibXml2_FOUND`` is set to ``TRUE``.  If it is not found
+and ``REQUIRED`` was used, it fails with a
+:command:`message(FATAL_ERROR)`, independent whether ``QUIET`` was
+used or not.  If it is found, success will be reported, including
+the content of the first ````.  On repeated CMake runs,
+the same message will not be printed again.
+
+Example for the full signature:
+
+.. code-block:: cmake
+
+  find_package_handle_standard_args(LibArchive
+    REQUIRED_VARS LibArchive_LIBRARY LibArchive_INCLUDE_DIR
+    VERSION_VAR LibArchive_VERSION)
+
+In this case, the ``LibArchive`` package is considered to be found if
+both ``LibArchive_LIBRARY`` and ``LibArchive_INCLUDE_DIR`` are valid.
+Also the version of ``LibArchive`` will be checked by using the version
+contained in ``LibArchive_VERSION``.  Since no ``FAIL_MESSAGE`` is given,
+the default messages will be printed.
+
+Another example for the full signature:
+
+.. code-block:: cmake
+
+  find_package(Automoc4 QUIET NO_MODULE HINTS /opt/automoc4)
+  find_package_handle_standard_args(Automoc4  CONFIG_MODE)
+
+In this case, a ``FindAutmoc4.cmake`` module wraps a call to
+``find_package(Automoc4 NO_MODULE)`` and adds an additional search
+directory for ``automoc4``.  Then the call to
+``find_package_handle_standard_args`` produces a proper success/failure
+message.
+#]=======================================================================]
+
+include(${CMAKE_CURRENT_LIST_DIR}/FindPackageMessage.cmake)
+
+# internal helper macro
+macro(_FPHSA_FAILURE_MESSAGE _msg)
+  if (${_NAME}_FIND_REQUIRED)
+    message(FATAL_ERROR "${_msg}")
+  else ()
+    if (NOT ${_NAME}_FIND_QUIETLY)
+      message(STATUS "${_msg}")
+    endif ()
+  endif ()
+endmacro()
+
+
+# internal helper macro to generate the failure message when used in CONFIG_MODE:
+macro(_FPHSA_HANDLE_FAILURE_CONFIG_MODE)
+  # _CONFIG is set, but FOUND is false, this means that some other of the REQUIRED_VARS was not found:
+  if(${_NAME}_CONFIG)
+    _FPHSA_FAILURE_MESSAGE("${FPHSA_FAIL_MESSAGE}: missing:${MISSING_VARS} (found ${${_NAME}_CONFIG} ${VERSION_MSG})")
+  else()
+    # If _CONSIDERED_CONFIGS is set, the config-file has been found, but no suitable version.
+    # List them all in the error message:
+    if(${_NAME}_CONSIDERED_CONFIGS)
+      set(configsText "")
+      list(LENGTH ${_NAME}_CONSIDERED_CONFIGS configsCount)
+      math(EXPR configsCount "${configsCount} - 1")
+      foreach(currentConfigIndex RANGE ${configsCount})
+        list(GET ${_NAME}_CONSIDERED_CONFIGS ${currentConfigIndex} filename)
+        list(GET ${_NAME}_CONSIDERED_VERSIONS ${currentConfigIndex} version)
+        string(APPEND configsText "    ${filename} (version ${version})\n")
+      endforeach()
+      if (${_NAME}_NOT_FOUND_MESSAGE)
+        string(APPEND configsText "    Reason given by package: ${${_NAME}_NOT_FOUND_MESSAGE}\n")
+      endif()
+      _FPHSA_FAILURE_MESSAGE("${FPHSA_FAIL_MESSAGE} ${VERSION_MSG}, checked the following files:\n${configsText}")
+
+    else()
+      # Simple case: No Config-file was found at all:
+      _FPHSA_FAILURE_MESSAGE("${FPHSA_FAIL_MESSAGE}: found neither ${_NAME}Config.cmake nor ${_NAME_LOWER}-config.cmake ${VERSION_MSG}")
+    endif()
+  endif()
+endmacro()
+
+
+function(FIND_PACKAGE_HANDLE_STANDARD_ARGS _NAME _FIRST_ARG)
+
+# Set up the arguments for `cmake_parse_arguments`.
+  set(options  CONFIG_MODE  HANDLE_COMPONENTS)
+  set(oneValueArgs  FAIL_MESSAGE  VERSION_VAR  FOUND_VAR)
+  set(multiValueArgs REQUIRED_VARS)
+
+# Check whether we are in 'simple' or 'extended' mode:
+  set(_KEYWORDS_FOR_EXTENDED_MODE  ${options} ${oneValueArgs} ${multiValueArgs} )
+  list(FIND _KEYWORDS_FOR_EXTENDED_MODE "${_FIRST_ARG}" INDEX)
+
+  if(${INDEX} EQUAL -1)
+    set(FPHSA_FAIL_MESSAGE ${_FIRST_ARG})
+    set(FPHSA_REQUIRED_VARS ${ARGN})
+    set(FPHSA_VERSION_VAR)
+  else()
+    cmake_parse_arguments(FPHSA "${options}" "${oneValueArgs}" "${multiValueArgs}"  ${_FIRST_ARG} ${ARGN})
+
+    if(FPHSA_UNPARSED_ARGUMENTS)
+      message(FATAL_ERROR "Unknown keywords given to FIND_PACKAGE_HANDLE_STANDARD_ARGS(): \"${FPHSA_UNPARSED_ARGUMENTS}\"")
+    endif()
+
+    if(NOT FPHSA_FAIL_MESSAGE)
+      set(FPHSA_FAIL_MESSAGE  "DEFAULT_MSG")
+    endif()
+
+    # In config-mode, we rely on the variable _CONFIG, which is set by find_package()
+    # when it successfully found the config-file, including version checking:
+    if(FPHSA_CONFIG_MODE)
+      list(INSERT FPHSA_REQUIRED_VARS 0 ${_NAME}_CONFIG)
+      list(REMOVE_DUPLICATES FPHSA_REQUIRED_VARS)
+      set(FPHSA_VERSION_VAR ${_NAME}_VERSION)
+    endif()
+
+    if(NOT FPHSA_REQUIRED_VARS)
+      message(FATAL_ERROR "No REQUIRED_VARS specified for FIND_PACKAGE_HANDLE_STANDARD_ARGS()")
+    endif()
+  endif()
+
+# now that we collected all arguments, process them
+
+  if("x${FPHSA_FAIL_MESSAGE}" STREQUAL "xDEFAULT_MSG")
+    set(FPHSA_FAIL_MESSAGE "Could NOT find ${_NAME}")
+  endif()
+
+  list(GET FPHSA_REQUIRED_VARS 0 _FIRST_REQUIRED_VAR)
+
+  string(TOUPPER ${_NAME} _NAME_UPPER)
+  string(TOLOWER ${_NAME} _NAME_LOWER)
+
+  if(FPHSA_FOUND_VAR)
+    if(FPHSA_FOUND_VAR MATCHES "^${_NAME}_FOUND$"  OR  FPHSA_FOUND_VAR MATCHES "^${_NAME_UPPER}_FOUND$")
+      set(_FOUND_VAR ${FPHSA_FOUND_VAR})
+    else()
+      message(FATAL_ERROR "The argument for FOUND_VAR is \"${FPHSA_FOUND_VAR}\", but only \"${_NAME}_FOUND\" and \"${_NAME_UPPER}_FOUND\" are valid names.")
+    endif()
+  else()
+    set(_FOUND_VAR ${_NAME_UPPER}_FOUND)
+  endif()
+
+  # collect all variables which were not found, so they can be printed, so the
+  # user knows better what went wrong (#6375)
+  set(MISSING_VARS "")
+  set(DETAILS "")
+  # check if all passed variables are valid
+  set(FPHSA_FOUND_${_NAME} TRUE)
+  foreach(_CURRENT_VAR ${FPHSA_REQUIRED_VARS})
+    if(NOT ${_CURRENT_VAR})
+      set(FPHSA_FOUND_${_NAME} FALSE)
+      string(APPEND MISSING_VARS " ${_CURRENT_VAR}")
+    else()
+      string(APPEND DETAILS "[${${_CURRENT_VAR}}]")
+    endif()
+  endforeach()
+  if(FPHSA_FOUND_${_NAME})
+    set(${_NAME}_FOUND TRUE)
+    set(${_NAME_UPPER}_FOUND TRUE)
+  else()
+    set(${_NAME}_FOUND FALSE)
+    set(${_NAME_UPPER}_FOUND FALSE)
+  endif()
+
+  # component handling
+  unset(FOUND_COMPONENTS_MSG)
+  unset(MISSING_COMPONENTS_MSG)
+
+  if(FPHSA_HANDLE_COMPONENTS)
+    foreach(comp ${${_NAME}_FIND_COMPONENTS})
+      if(${_NAME}_${comp}_FOUND)
+
+        if(NOT DEFINED FOUND_COMPONENTS_MSG)
+          set(FOUND_COMPONENTS_MSG "found components: ")
+        endif()
+        string(APPEND FOUND_COMPONENTS_MSG " ${comp}")
+
+      else()
+
+        if(NOT DEFINED MISSING_COMPONENTS_MSG)
+          set(MISSING_COMPONENTS_MSG "missing components: ")
+        endif()
+        string(APPEND MISSING_COMPONENTS_MSG " ${comp}")
+
+        if(${_NAME}_FIND_REQUIRED_${comp})
+          set(${_NAME}_FOUND FALSE)
+          string(APPEND MISSING_VARS " ${comp}")
+        endif()
+
+      endif()
+    endforeach()
+    set(COMPONENT_MSG "${FOUND_COMPONENTS_MSG} ${MISSING_COMPONENTS_MSG}")
+    string(APPEND DETAILS "[c${COMPONENT_MSG}]")
+  endif()
+
+  # version handling:
+  set(VERSION_MSG "")
+  set(VERSION_OK TRUE)
+
+  # check with DEFINED here as the requested or found version may be "0"
+  if (DEFINED ${_NAME}_FIND_VERSION)
+    if(DEFINED ${FPHSA_VERSION_VAR})
+      set(_FOUND_VERSION ${${FPHSA_VERSION_VAR}})
+
+      if(${_NAME}_FIND_VERSION_EXACT)       # exact version required
+        # count the dots in the version string
+        string(REGEX REPLACE "[^.]" "" _VERSION_DOTS "${_FOUND_VERSION}")
+        # add one dot because there is one dot more than there are components
+        string(LENGTH "${_VERSION_DOTS}." _VERSION_DOTS)
+        if (_VERSION_DOTS GREATER ${_NAME}_FIND_VERSION_COUNT)
+          # Because of the C++ implementation of find_package() ${_NAME}_FIND_VERSION_COUNT
+          # is at most 4 here. Therefore a simple lookup table is used.
+          if (${_NAME}_FIND_VERSION_COUNT EQUAL 1)
+            set(_VERSION_REGEX "[^.]*")
+          elseif (${_NAME}_FIND_VERSION_COUNT EQUAL 2)
+            set(_VERSION_REGEX "[^.]*\\.[^.]*")
+          elseif (${_NAME}_FIND_VERSION_COUNT EQUAL 3)
+            set(_VERSION_REGEX "[^.]*\\.[^.]*\\.[^.]*")
+          else ()
+            set(_VERSION_REGEX "[^.]*\\.[^.]*\\.[^.]*\\.[^.]*")
+          endif ()
+          string(REGEX REPLACE "^(${_VERSION_REGEX})\\..*" "\\1" _VERSION_HEAD "${_FOUND_VERSION}")
+          unset(_VERSION_REGEX)
+          if (NOT ${_NAME}_FIND_VERSION VERSION_EQUAL _VERSION_HEAD)
+            set(VERSION_MSG "Found unsuitable version \"${_FOUND_VERSION}\", but required is exact version \"${${_NAME}_FIND_VERSION}\"")
+            set(VERSION_OK FALSE)
+          else ()
+            set(VERSION_MSG "(found suitable exact version \"${_FOUND_VERSION}\")")
+          endif ()
+          unset(_VERSION_HEAD)
+        else ()
+          if (NOT ${_NAME}_FIND_VERSION VERSION_EQUAL _FOUND_VERSION)
+            set(VERSION_MSG "Found unsuitable version \"${_FOUND_VERSION}\", but required is exact version \"${${_NAME}_FIND_VERSION}\"")
+            set(VERSION_OK FALSE)
+          else ()
+            set(VERSION_MSG "(found suitable exact version \"${_FOUND_VERSION}\")")
+          endif ()
+        endif ()
+        unset(_VERSION_DOTS)
+
+      else()     # minimum version specified:
+        if (${_NAME}_FIND_VERSION VERSION_GREATER _FOUND_VERSION)
+          set(VERSION_MSG "Found unsuitable version \"${_FOUND_VERSION}\", but required is at least \"${${_NAME}_FIND_VERSION}\"")
+          set(VERSION_OK FALSE)
+        else ()
+          set(VERSION_MSG "(found suitable version \"${_FOUND_VERSION}\", minimum required is \"${${_NAME}_FIND_VERSION}\")")
+        endif ()
+      endif()
+
+    else()
+
+      # if the package was not found, but a version was given, add that to the output:
+      if(${_NAME}_FIND_VERSION_EXACT)
+         set(VERSION_MSG "(Required is exact version \"${${_NAME}_FIND_VERSION}\")")
+      else()
+         set(VERSION_MSG "(Required is at least version \"${${_NAME}_FIND_VERSION}\")")
+      endif()
+
+    endif()
+  else ()
+    # Check with DEFINED as the found version may be 0.
+    if(DEFINED ${FPHSA_VERSION_VAR})
+      set(VERSION_MSG "(found version \"${${FPHSA_VERSION_VAR}}\")")
+    endif()
+  endif ()
+
+  if(VERSION_OK)
+    string(APPEND DETAILS "[v${${FPHSA_VERSION_VAR}}(${${_NAME}_FIND_VERSION})]")
+  else()
+    set(${_NAME}_FOUND FALSE)
+  endif()
+
+
+  # print the result:
+  if (${_NAME}_FOUND)
+    FIND_PACKAGE_MESSAGE(${_NAME} "Found ${_NAME}: ${${_FIRST_REQUIRED_VAR}} ${VERSION_MSG} ${COMPONENT_MSG}" "${DETAILS}")
+  else ()
+
+    if(FPHSA_CONFIG_MODE)
+      _FPHSA_HANDLE_FAILURE_CONFIG_MODE()
+    else()
+      if(NOT VERSION_OK)
+        _FPHSA_FAILURE_MESSAGE("${FPHSA_FAIL_MESSAGE}: ${VERSION_MSG} (found ${${_FIRST_REQUIRED_VAR}})")
+      else()
+        _FPHSA_FAILURE_MESSAGE("${FPHSA_FAIL_MESSAGE} (missing:${MISSING_VARS}) ${VERSION_MSG}")
+      endif()
+    endif()
+
+  endif ()
+
+  set(${_NAME}_FOUND ${${_NAME}_FOUND} PARENT_SCOPE)
+  set(${_NAME_UPPER}_FOUND ${${_NAME}_FOUND} PARENT_SCOPE)
+endfunction()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindPackageMessage.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindPackageMessage.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..6821cee4f77a9d84c74f2c140870a2163ae5a5f0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindPackageMessage.cmake
@@ -0,0 +1,47 @@
+# Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
+# file Copyright.txt or https://cmake.org/licensing for details.
+
+#.rst:
+# FindPackageMessage
+# ------------------
+#
+#
+#
+# FIND_PACKAGE_MESSAGE( "message for user" "find result details")
+#
+# This macro is intended to be used in FindXXX.cmake modules files.  It
+# will print a message once for each unique find result.  This is useful
+# for telling the user where a package was found.  The first argument
+# specifies the name (XXX) of the package.  The second argument
+# specifies the message to display.  The third argument lists details
+# about the find result so that if they change the message will be
+# displayed again.  The macro also obeys the QUIET argument to the
+# find_package command.
+#
+# Example:
+#
+# ::
+#
+#   if(X11_FOUND)
+#     FIND_PACKAGE_MESSAGE(X11 "Found X11: ${X11_X11_LIB}"
+#       "[${X11_X11_LIB}][${X11_INCLUDE_DIR}]")
+#   else()
+#    ...
+#   endif()
+
+function(FIND_PACKAGE_MESSAGE pkg msg details)
+  # Avoid printing a message repeatedly for the same find result.
+  if(NOT ${pkg}_FIND_QUIETLY)
+    string(REPLACE "\n" "" details "${details}")
+    set(DETAILS_VAR FIND_PACKAGE_MESSAGE_DETAILS_${pkg})
+    if(NOT "${details}" STREQUAL "${${DETAILS_VAR}}")
+      # The message has not yet been printed.
+      message(STATUS "${msg}")
+
+      # Save the find details in the cache to avoid printing the same
+      # message again.
+      set("${DETAILS_VAR}" "${details}"
+        CACHE INTERNAL "Details about finding ${pkg}")
+    endif()
+  endif()
+endfunction()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/LoadHIP.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/LoadHIP.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..1c0d3a203991c83da71ca74e7713a128c6632eb1
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/LoadHIP.cmake
@@ -0,0 +1,215 @@
+set(PYTORCH_FOUND_HIP FALSE)
+
+if(NOT DEFINED ENV{ROCM_PATH})
+  set(ROCM_PATH /opt/rocm)
+else()
+  set(ROCM_PATH $ENV{ROCM_PATH})
+endif()
+if(NOT DEFINED ENV{ROCM_INCLUDE_DIRS})
+  set(ROCM_INCLUDE_DIRS ${ROCM_PATH}/include)
+else()
+  set(ROCM_INCLUDE_DIRS $ENV{ROCM_INCLUDE_DIRS})
+endif()
+
+if(NOT EXISTS ${ROCM_PATH})
+  return()
+endif()
+
+# MAGMA_HOME
+if(NOT DEFINED ENV{MAGMA_HOME})
+  set(MAGMA_HOME ${ROCM_PATH}/magma)
+  set(ENV{MAGMA_HOME} ${ROCM_PATH}/magma)
+else()
+  set(MAGMA_HOME $ENV{MAGMA_HOME})
+endif()
+
+torch_hip_get_arch_list(PYTORCH_ROCM_ARCH)
+if(PYTORCH_ROCM_ARCH STREQUAL "")
+  message(FATAL_ERROR "No GPU arch specified for ROCm build. Please use PYTORCH_ROCM_ARCH environment variable to specify GPU archs to build for.")
+endif()
+message("Building PyTorch for GPU arch: ${PYTORCH_ROCM_ARCH}")
+
+# Add HIP to the CMAKE Module Path
+set(CMAKE_MODULE_PATH ${ROCM_PATH}/lib/cmake/hip ${CMAKE_MODULE_PATH})
+
+macro(find_package_and_print_version PACKAGE_NAME)
+  find_package("${PACKAGE_NAME}" ${ARGN})
+  message("${PACKAGE_NAME} VERSION: ${${PACKAGE_NAME}_VERSION}")
+endmacro()
+
+# Find the HIP Package
+find_package_and_print_version(HIP 1.0)
+
+if(HIP_FOUND)
+  set(PYTORCH_FOUND_HIP TRUE)
+  set(FOUND_ROCM_VERSION_H FALSE)
+
+  set(PROJECT_RANDOM_BINARY_DIR "${PROJECT_BINARY_DIR}")
+  set(file "${PROJECT_BINARY_DIR}/detect_rocm_version.cc")
+
+  # Find ROCM version for checks
+  # ROCM 5.0 and later will have header api for version management
+  if(EXISTS ${ROCM_INCLUDE_DIRS}/rocm_version.h)
+    set(FOUND_ROCM_VERSION_H TRUE)
+    file(WRITE ${file} ""
+      "#include \n"
+      )
+  elseif(EXISTS ${ROCM_INCLUDE_DIRS}/rocm-core/rocm_version.h)
+    set(FOUND_ROCM_VERSION_H TRUE)
+    file(WRITE ${file} ""
+      "#include \n"
+      )
+  else()
+    message("********************* rocm_version.h couldnt be found ******************\n")
+  endif()
+
+  if(FOUND_ROCM_VERSION_H)
+    file(APPEND ${file} ""
+      "#include \n"
+
+      "#ifndef ROCM_VERSION_PATCH\n"
+      "#define ROCM_VERSION_PATCH 0\n"
+      "#endif\n"
+      "#define STRINGIFYHELPER(x) #x\n"
+      "#define STRINGIFY(x) STRINGIFYHELPER(x)\n"
+      "int main() {\n"
+      "  printf(\"%d.%d.%s\", ROCM_VERSION_MAJOR, ROCM_VERSION_MINOR, STRINGIFY(ROCM_VERSION_PATCH));\n"
+      "  return 0;\n"
+      "}\n"
+      )
+
+    try_run(run_result compile_result ${PROJECT_RANDOM_BINARY_DIR} ${file}
+      CMAKE_FLAGS "-DINCLUDE_DIRECTORIES=${ROCM_INCLUDE_DIRS}"
+      RUN_OUTPUT_VARIABLE rocm_version_from_header
+      COMPILE_OUTPUT_VARIABLE output_var
+      )
+    # We expect the compile to be successful if the include directory exists.
+    if(NOT compile_result)
+      message(FATAL_ERROR "Caffe2: Couldn't determine version from header: " ${output_var})
+    endif()
+    message(STATUS "Caffe2: Header version is: " ${rocm_version_from_header})
+    set(ROCM_VERSION_DEV_RAW ${rocm_version_from_header})
+    message("\n***** ROCm version from rocm_version.h ****\n")
+  endif()
+
+  string(REGEX MATCH "^([0-9]+)\.([0-9]+)\.([0-9]+).*$" ROCM_VERSION_DEV_MATCH ${ROCM_VERSION_DEV_RAW})
+
+  if(ROCM_VERSION_DEV_MATCH)
+    set(ROCM_VERSION_DEV_MAJOR ${CMAKE_MATCH_1})
+    set(ROCM_VERSION_DEV_MINOR ${CMAKE_MATCH_2})
+    set(ROCM_VERSION_DEV_PATCH ${CMAKE_MATCH_3})
+    set(ROCM_VERSION_DEV "${ROCM_VERSION_DEV_MAJOR}.${ROCM_VERSION_DEV_MINOR}.${ROCM_VERSION_DEV_PATCH}")
+    math(EXPR ROCM_VERSION_DEV_INT "(${ROCM_VERSION_DEV_MAJOR}*10000) + (${ROCM_VERSION_DEV_MINOR}*100) + ${ROCM_VERSION_DEV_PATCH}")
+  endif()
+
+  message("ROCM_VERSION_DEV: ${ROCM_VERSION_DEV}")
+  message("ROCM_VERSION_DEV_MAJOR: ${ROCM_VERSION_DEV_MAJOR}")
+  message("ROCM_VERSION_DEV_MINOR: ${ROCM_VERSION_DEV_MINOR}")
+  message("ROCM_VERSION_DEV_PATCH: ${ROCM_VERSION_DEV_PATCH}")
+  message("ROCM_VERSION_DEV_INT:   ${ROCM_VERSION_DEV_INT}")
+
+  math(EXPR TORCH_HIP_VERSION "(${HIP_VERSION_MAJOR} * 100) + ${HIP_VERSION_MINOR}")
+  message("HIP_VERSION_MAJOR: ${HIP_VERSION_MAJOR}")
+  message("HIP_VERSION_MINOR: ${HIP_VERSION_MINOR}")
+  message("TORCH_HIP_VERSION: ${TORCH_HIP_VERSION}")
+
+  message("\n***** Library versions from dpkg *****\n")
+  execute_process(COMMAND dpkg -l COMMAND grep rocm-dev COMMAND awk "{print $2 \" VERSION: \" $3}")
+  execute_process(COMMAND dpkg -l COMMAND grep rocm-libs COMMAND awk "{print $2 \" VERSION: \" $3}")
+  execute_process(COMMAND dpkg -l COMMAND grep hsakmt-roct COMMAND awk "{print $2 \" VERSION: \" $3}")
+  execute_process(COMMAND dpkg -l COMMAND grep rocr-dev COMMAND awk "{print $2 \" VERSION: \" $3}")
+  execute_process(COMMAND dpkg -l COMMAND grep -w hcc COMMAND awk "{print $2 \" VERSION: \" $3}")
+  execute_process(COMMAND dpkg -l COMMAND grep hip-base COMMAND awk "{print $2 \" VERSION: \" $3}")
+  execute_process(COMMAND dpkg -l COMMAND grep hip_hcc COMMAND awk "{print $2 \" VERSION: \" $3}")
+
+  message("\n***** Library versions from cmake find_package *****\n")
+
+  set(CMAKE_HIP_CLANG_FLAGS_DEBUG ${CMAKE_CXX_FLAGS_DEBUG})
+  set(CMAKE_HIP_CLANG_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE})
+  ### Remove setting of Flags when FindHIP.CMake PR #558 is accepted.###
+
+  set(hip_DIR ${ROCM_PATH}/lib/cmake/hip)
+  set(hsa-runtime64_DIR ${ROCM_PATH}/lib/cmake/hsa-runtime64)
+  set(AMDDeviceLibs_DIR ${ROCM_PATH}/lib/cmake/AMDDeviceLibs)
+  set(amd_comgr_DIR ${ROCM_PATH}/lib/cmake/amd_comgr)
+  set(rocrand_DIR ${ROCM_PATH}/lib/cmake/rocrand)
+  set(hiprand_DIR ${ROCM_PATH}/lib/cmake/hiprand)
+  set(rocblas_DIR ${ROCM_PATH}/lib/cmake/rocblas)
+  set(hipblas_DIR ${ROCM_PATH}/lib/cmake/hipblas)
+  set(hipblaslt_DIR ${ROCM_PATH}/lib/cmake/hipblaslt)
+  set(miopen_DIR ${ROCM_PATH}/lib/cmake/miopen)
+  set(rocfft_DIR ${ROCM_PATH}/lib/cmake/rocfft)
+  set(hipfft_DIR ${ROCM_PATH}/lib/cmake/hipfft)
+  set(hipsparse_DIR ${ROCM_PATH}/lib/cmake/hipsparse)
+  set(rccl_DIR ${ROCM_PATH}/lib/cmake/rccl)
+  set(rocprim_DIR ${ROCM_PATH}/lib/cmake/rocprim)
+  set(hipcub_DIR ${ROCM_PATH}/lib/cmake/hipcub)
+  set(rocthrust_DIR ${ROCM_PATH}/lib/cmake/rocthrust)
+  set(hipsolver_DIR ${ROCM_PATH}/lib/cmake/hipsolver)
+  set(hiprtc_DIR ${ROCM_PATH}/lib/cmake/hiprtc)
+
+
+  find_package_and_print_version(hip REQUIRED)
+  find_package_and_print_version(hsa-runtime64 REQUIRED)
+  find_package_and_print_version(amd_comgr REQUIRED)
+  find_package_and_print_version(rocrand REQUIRED)
+  find_package_and_print_version(hiprand REQUIRED)
+  find_package_and_print_version(rocblas REQUIRED)
+  find_package_and_print_version(hipblas REQUIRED)
+  find_package_and_print_version(hipblaslt REQUIRED)
+  find_package_and_print_version(miopen REQUIRED)
+  find_package_and_print_version(hipfft REQUIRED)
+  find_package_and_print_version(hipsparse REQUIRED)
+  find_package_and_print_version(rccl)
+  find_package_and_print_version(rocprim REQUIRED)
+  find_package_and_print_version(hipcub REQUIRED)
+  find_package_and_print_version(rocthrust REQUIRED)
+  find_package_and_print_version(hipsolver REQUIRED)
+  find_package_and_print_version(hiprtc REQUIRED)
+
+
+  find_library(PYTORCH_HIP_LIBRARIES amdhip64 HINTS ${ROCM_PATH}/lib)
+  # TODO: miopen_LIBRARIES should return fullpath to the library file,
+  # however currently it's just the lib name
+  if(TARGET ${miopen_LIBRARIES})
+    set(PYTORCH_MIOPEN_LIBRARIES ${miopen_LIBRARIES})
+  else()
+    find_library(PYTORCH_MIOPEN_LIBRARIES ${miopen_LIBRARIES} HINTS ${ROCM_PATH}/lib)
+  endif()
+  # TODO: rccl_LIBRARIES should return fullpath to the library file,
+  # however currently it's just the lib name
+  if(TARGET ${rccl_LIBRARIES})
+    set(PYTORCH_RCCL_LIBRARIES ${rccl_LIBRARIES})
+  else()
+    find_library(PYTORCH_RCCL_LIBRARIES ${rccl_LIBRARIES} HINTS ${ROCM_PATH}/lib)
+  endif()
+  find_library(ROCM_HIPRTC_LIB hiprtc HINTS ${ROCM_PATH}/lib)
+  # roctx is part of roctracer
+  find_library(ROCM_ROCTX_LIB roctx64 HINTS ${ROCM_PATH}/lib)
+
+  # check whether HIP declares new types
+  set(file "${PROJECT_BINARY_DIR}/hip_new_types.cc")
+  file(WRITE ${file} ""
+    "#include \n"
+    "int main() {\n"
+    "    hipDataType baz = HIP_R_8F_E4M3_FNUZ;\n"
+    "    return 0;\n"
+    "}\n"
+    )
+
+  try_compile(hip_compile_result ${PROJECT_RANDOM_BINARY_DIR} ${file}
+    CMAKE_FLAGS "-DINCLUDE_DIRECTORIES=${ROCM_INCLUDE_DIRS}"
+    COMPILE_DEFINITIONS -D__HIP_PLATFORM_AMD__ -D__HIP_PLATFORM_HCC__
+    OUTPUT_VARIABLE hip_compile_output)
+
+  if(hip_compile_result)
+    set(HIP_NEW_TYPE_ENUMS ON)
+    #message("HIP is using new type enums: ${hip_compile_output}")
+    message("HIP is using new type enums")
+  else()
+    set(HIP_NEW_TYPE_ENUMS OFF)
+    #message("HIP is NOT using new type enums: ${hip_compile_output}")
+    message("HIP is NOT using new type enums")
+  endif()
+
+endif()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/cuda.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/cuda.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..afc1bc12abf7d2d4bafe6c56528a842488f160e7
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/cuda.cmake
@@ -0,0 +1,383 @@
+# ---[ cuda
+
+# Poor man's include guard
+if(TARGET torch::cudart)
+  return()
+endif()
+
+# sccache is only supported in CMake master and not in the newest official
+# release (3.11.3) yet. Hence we need our own Modules_CUDA_fix to enable sccache.
+list(APPEND CMAKE_MODULE_PATH ${CMAKE_CURRENT_LIST_DIR}/../Modules_CUDA_fix)
+
+# We don't want to statically link cudart, because we rely on it's dynamic linkage in
+# python (follow along torch/cuda/__init__.py and usage of cudaGetErrorName).
+# Technically, we can link cudart here statically, and link libtorch_python.so
+# to a dynamic libcudart.so, but that's just wasteful.
+# However, on Windows, if this one gets switched off, the error "cuda: unknown error"
+# will be raised when running the following code:
+# >>> import torch
+# >>> torch.cuda.is_available()
+# >>> torch.cuda.current_device()
+# More details can be found in the following links.
+# https://github.com/pytorch/pytorch/issues/20635
+# https://github.com/pytorch/pytorch/issues/17108
+if(NOT MSVC)
+  set(CUDA_USE_STATIC_CUDA_RUNTIME OFF CACHE INTERNAL "")
+endif()
+
+# Find CUDA.
+find_package(CUDA)
+if(NOT CUDA_FOUND)
+  message(WARNING
+    "Caffe2: CUDA cannot be found. Depending on whether you are building "
+    "Caffe2 or a Caffe2 dependent library, the next warning / error will "
+    "give you more info.")
+  set(CAFFE2_USE_CUDA OFF)
+  return()
+endif()
+
+# Enable CUDA language support
+set(CUDAToolkit_ROOT "${CUDA_TOOLKIT_ROOT_DIR}")
+# Pass clang as host compiler, which according to the docs
+# Must be done before CUDA language is enabled, see
+# https://cmake.org/cmake/help/v3.15/variable/CMAKE_CUDA_HOST_COMPILER.html
+if("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang")
+  set(CMAKE_CUDA_HOST_COMPILER "${CMAKE_C_COMPILER}")
+endif()
+enable_language(CUDA)
+if("X${CMAKE_CUDA_STANDARD}" STREQUAL "X" )
+  set(CMAKE_CUDA_STANDARD ${CMAKE_CXX_STANDARD})
+endif()
+set(CMAKE_CUDA_STANDARD_REQUIRED ON)
+
+# CMP0074 - find_package will respect _ROOT variables
+cmake_policy(PUSH)
+if(CMAKE_VERSION VERSION_GREATER_EQUAL 3.12.0)
+  cmake_policy(SET CMP0074 NEW)
+endif()
+
+find_package(CUDAToolkit REQUIRED)
+
+cmake_policy(POP)
+
+if(NOT CMAKE_CUDA_COMPILER_VERSION VERSION_EQUAL CUDAToolkit_VERSION)
+  message(FATAL_ERROR "Found two conflicting CUDA versions:\n"
+                      "V${CMAKE_CUDA_COMPILER_VERSION} in '${CUDA_INCLUDE_DIRS}' and\n"
+                      "V${CUDAToolkit_VERSION} in '${CUDAToolkit_INCLUDE_DIRS}'")
+endif()
+
+message(STATUS "Caffe2: CUDA detected: " ${CUDA_VERSION})
+message(STATUS "Caffe2: CUDA nvcc is: " ${CUDA_NVCC_EXECUTABLE})
+message(STATUS "Caffe2: CUDA toolkit directory: " ${CUDA_TOOLKIT_ROOT_DIR})
+if(CUDA_VERSION VERSION_LESS 11.0)
+  message(FATAL_ERROR "PyTorch requires CUDA 11.0 or above.")
+endif()
+
+if(CUDA_FOUND)
+  # Sometimes, we may mismatch nvcc with the CUDA headers we are
+  # compiling with, e.g., if a ccache nvcc is fed to us by CUDA_NVCC_EXECUTABLE
+  # but the PATH is not consistent with CUDA_HOME.  It's better safe
+  # than sorry: make sure everything is consistent.
+  if(MSVC AND CMAKE_GENERATOR MATCHES "Visual Studio")
+    # When using Visual Studio, it attempts to lock the whole binary dir when
+    # `try_run` is called, which will cause the build to fail.
+    string(RANDOM BUILD_SUFFIX)
+    set(PROJECT_RANDOM_BINARY_DIR "${PROJECT_BINARY_DIR}/${BUILD_SUFFIX}")
+  else()
+    set(PROJECT_RANDOM_BINARY_DIR "${PROJECT_BINARY_DIR}")
+  endif()
+  set(file "${PROJECT_BINARY_DIR}/detect_cuda_version.cc")
+  file(WRITE ${file} ""
+    "#include \n"
+    "#include \n"
+    "int main() {\n"
+    "  printf(\"%d.%d\", CUDA_VERSION / 1000, (CUDA_VERSION / 10) % 100);\n"
+    "  return 0;\n"
+    "}\n"
+    )
+  if(NOT CMAKE_CROSSCOMPILING)
+    try_run(run_result compile_result ${PROJECT_RANDOM_BINARY_DIR} ${file}
+      CMAKE_FLAGS "-DINCLUDE_DIRECTORIES=${CUDA_INCLUDE_DIRS}"
+      LINK_LIBRARIES ${CUDA_LIBRARIES}
+      RUN_OUTPUT_VARIABLE cuda_version_from_header
+      COMPILE_OUTPUT_VARIABLE output_var
+      )
+    if(NOT compile_result)
+      message(FATAL_ERROR "Caffe2: Couldn't determine version from header: " ${output_var})
+    endif()
+    message(STATUS "Caffe2: Header version is: " ${cuda_version_from_header})
+    if(NOT cuda_version_from_header STREQUAL ${CUDA_VERSION_STRING})
+      # Force CUDA to be processed for again next time
+      # TODO: I'm not sure if this counts as an implementation detail of
+      # FindCUDA
+      set(${cuda_version_from_findcuda} ${CUDA_VERSION_STRING})
+      unset(CUDA_TOOLKIT_ROOT_DIR_INTERNAL CACHE)
+      # Not strictly necessary, but for good luck.
+      unset(CUDA_VERSION CACHE)
+      # Error out
+      message(FATAL_ERROR "FindCUDA says CUDA version is ${cuda_version_from_findcuda} (usually determined by nvcc), "
+        "but the CUDA headers say the version is ${cuda_version_from_header}.  This often occurs "
+        "when you set both CUDA_HOME and CUDA_NVCC_EXECUTABLE to "
+        "non-standard locations, without also setting PATH to point to the correct nvcc.  "
+        "Perhaps, try re-running this command again with PATH=${CUDA_TOOLKIT_ROOT_DIR}/bin:$PATH.  "
+        "See above log messages for more diagnostics, and see https://github.com/pytorch/pytorch/issues/8092 for more details.")
+    endif()
+  endif()
+endif()
+
+# ---[ CUDA libraries wrapper
+
+# find lbnvrtc.so
+set(CUDA_NVRTC_LIB "${CUDA_nvrtc_LIBRARY}" CACHE FILEPATH "")
+if(CUDA_NVRTC_LIB AND NOT CUDA_NVRTC_SHORTHASH)
+  find_package(Python COMPONENTS Interpreter)
+  execute_process(
+    COMMAND Python::Interpreter -c
+    "import hashlib;hash=hashlib.sha256();hash.update(open('${CUDA_NVRTC_LIB}','rb').read());print(hash.hexdigest()[:8])"
+    RESULT_VARIABLE _retval
+    OUTPUT_VARIABLE CUDA_NVRTC_SHORTHASH)
+  if(NOT _retval EQUAL 0)
+    message(WARNING "Failed to compute shorthash for libnvrtc.so")
+    set(CUDA_NVRTC_SHORTHASH "XXXXXXXX")
+  else()
+    string(STRIP "${CUDA_NVRTC_SHORTHASH}" CUDA_NVRTC_SHORTHASH)
+    message(STATUS "${CUDA_NVRTC_LIB} shorthash is ${CUDA_NVRTC_SHORTHASH}")
+  endif()
+endif()
+
+# Create new style imported libraries.
+# Several of these libraries have a hardcoded path if CAFFE2_STATIC_LINK_CUDA
+# is set. This path is where sane CUDA installations have their static
+# libraries installed. This flag should only be used for binary builds, so
+# end-users should never have this flag set.
+
+# cuda
+add_library(caffe2::cuda INTERFACE IMPORTED)
+set_property(
+    TARGET caffe2::cuda PROPERTY INTERFACE_LINK_LIBRARIES
+    CUDA::cuda_driver)
+
+# cudart
+add_library(torch::cudart INTERFACE IMPORTED)
+if(CAFFE2_STATIC_LINK_CUDA)
+    set_property(
+        TARGET torch::cudart PROPERTY INTERFACE_LINK_LIBRARIES
+        CUDA::cudart_static)
+else()
+    set_property(
+        TARGET torch::cudart PROPERTY INTERFACE_LINK_LIBRARIES
+        CUDA::cudart)
+endif()
+
+# nvToolsExt
+find_path(nvtx3_dir NAMES nvtx3 PATHS "${PROJECT_SOURCE_DIR}/third_party/NVTX/c/include" NO_DEFAULT_PATH)
+find_package_handle_standard_args(nvtx3 DEFAULT_MSG nvtx3_dir)
+if(nvtx3_FOUND)
+  add_library(torch::nvtx3 INTERFACE IMPORTED)
+  target_include_directories(torch::nvtx3 INTERFACE "${nvtx3_dir}")
+  target_compile_definitions(torch::nvtx3 INTERFACE TORCH_CUDA_USE_NVTX3)
+else()
+  message(WARNING "Cannot find NVTX3, find old NVTX instead")
+  add_library(torch::nvtoolsext INTERFACE IMPORTED)
+  set_property(TARGET torch::nvtoolsext PROPERTY INTERFACE_LINK_LIBRARIES CUDA::nvToolsExt)
+endif()
+
+
+# cublas
+add_library(caffe2::cublas INTERFACE IMPORTED)
+if(CAFFE2_STATIC_LINK_CUDA AND NOT WIN32)
+    set_property(
+        TARGET caffe2::cublas PROPERTY INTERFACE_LINK_LIBRARIES
+        # NOTE: cublas is always linked dynamically
+        CUDA::cublas CUDA::cublasLt)
+    set_property(
+        TARGET caffe2::cublas APPEND PROPERTY INTERFACE_LINK_LIBRARIES
+        CUDA::cudart_static rt)
+else()
+    set_property(
+        TARGET caffe2::cublas PROPERTY INTERFACE_LINK_LIBRARIES
+        CUDA::cublas CUDA::cublasLt)
+endif()
+
+# cudnn interface
+# static linking is handled by USE_STATIC_CUDNN environment variable
+if(CAFFE2_USE_CUDNN)
+  if(USE_STATIC_CUDNN)
+    set(CUDNN_STATIC ON CACHE BOOL "")
+  else()
+    set(CUDNN_STATIC OFF CACHE BOOL "")
+  endif()
+
+  find_package(CUDNN)
+
+  if(NOT CUDNN_FOUND)
+    message(WARNING
+      "Cannot find cuDNN library. Turning the option off")
+    set(CAFFE2_USE_CUDNN OFF)
+  else()
+    if(CUDNN_VERSION VERSION_LESS "8.1.0")
+      message(FATAL_ERROR "PyTorch requires cuDNN 8.1 and above.")
+    endif()
+  endif()
+
+  add_library(torch::cudnn INTERFACE IMPORTED)
+  target_include_directories(torch::cudnn INTERFACE ${CUDNN_INCLUDE_PATH})
+  if(CUDNN_STATIC AND NOT WIN32)
+    target_link_options(torch::cudnn INTERFACE
+        "-Wl,--exclude-libs,libcudnn_static.a")
+  else()
+    target_link_libraries(torch::cudnn INTERFACE ${CUDNN_LIBRARY_PATH})
+  endif()
+else()
+  message(STATUS "USE_CUDNN is set to 0. Compiling without cuDNN support")
+endif()
+
+if(CAFFE2_USE_CUSPARSELT)
+  find_package(CUSPARSELT)
+
+  if(NOT CUSPARSELT_FOUND)
+    message(WARNING
+      "Cannot find cuSPARSELt library. Turning the option off")
+    set(CAFFE2_USE_CUSPARSELT OFF)
+  else()
+    add_library(torch::cusparselt INTERFACE IMPORTED)
+    target_include_directories(torch::cusparselt INTERFACE ${CUSPARSELT_INCLUDE_PATH})
+    target_link_libraries(torch::cusparselt INTERFACE ${CUSPARSELT_LIBRARY_PATH})
+  endif()
+else()
+  message(STATUS "USE_CUSPARSELT is set to 0. Compiling without cuSPARSELt support")
+endif()
+
+if(USE_CUDSS)
+  find_package(CUDSS)
+
+  if(NOT CUDSS_FOUND)
+    message(WARNING
+      "Cannot find CUDSS library. Turning the option off")
+    set(USE_CUDSS OFF)
+  else()
+    add_library(torch::cudss INTERFACE IMPORTED)
+    target_include_directories(torch::cudss INTERFACE ${CUDSS_INCLUDE_PATH})
+    target_link_libraries(torch::cudss INTERFACE ${CUDSS_LIBRARY_PATH})
+  endif()
+else()
+  message(STATUS "USE_CUDSS is set to 0. Compiling without cuDSS support")
+endif()
+
+# cufile
+if(CAFFE2_USE_CUFILE)
+  add_library(torch::cufile INTERFACE IMPORTED)
+  if(CAFFE2_STATIC_LINK_CUDA AND NOT WIN32)
+      set_property(
+          TARGET torch::cufile PROPERTY INTERFACE_LINK_LIBRARIES
+          CUDA::cuFile_static)
+  else()
+      set_property(
+          TARGET torch::cufile PROPERTY INTERFACE_LINK_LIBRARIES
+          CUDA::cuFile)
+  endif()
+else()
+  message(STATUS "USE_CUFILE is set to 0. Compiling without cuFile support")
+endif()
+
+# curand
+add_library(caffe2::curand INTERFACE IMPORTED)
+if(CAFFE2_STATIC_LINK_CUDA AND NOT WIN32)
+    set_property(
+        TARGET caffe2::curand PROPERTY INTERFACE_LINK_LIBRARIES
+        CUDA::curand_static)
+else()
+    set_property(
+        TARGET caffe2::curand PROPERTY INTERFACE_LINK_LIBRARIES
+        CUDA::curand)
+endif()
+
+# cufft
+add_library(caffe2::cufft INTERFACE IMPORTED)
+if(CAFFE2_STATIC_LINK_CUDA AND NOT WIN32)
+    set_property(
+        TARGET caffe2::cufft PROPERTY INTERFACE_LINK_LIBRARIES
+        CUDA::cufft_static_nocallback)
+else()
+    set_property(
+        TARGET caffe2::cufft PROPERTY INTERFACE_LINK_LIBRARIES
+        CUDA::cufft)
+endif()
+
+# nvrtc
+add_library(caffe2::nvrtc INTERFACE IMPORTED)
+set_property(
+    TARGET caffe2::nvrtc PROPERTY INTERFACE_LINK_LIBRARIES
+    CUDA::nvrtc caffe2::cuda)
+
+# Add onnx namepsace definition to nvcc
+if(ONNX_NAMESPACE)
+  list(APPEND CUDA_NVCC_FLAGS "-DONNX_NAMESPACE=${ONNX_NAMESPACE}")
+else()
+  list(APPEND CUDA_NVCC_FLAGS "-DONNX_NAMESPACE=onnx_c2")
+endif()
+
+# Don't activate VC env again for Ninja generators with MSVC on Windows if CUDAHOSTCXX is not defined
+# by adding --use-local-env.
+if(MSVC AND CMAKE_GENERATOR STREQUAL "Ninja" AND NOT DEFINED ENV{CUDAHOSTCXX})
+  list(APPEND CUDA_NVCC_FLAGS "--use-local-env")
+endif()
+
+# setting nvcc arch flags
+torch_cuda_get_nvcc_gencode_flag(NVCC_FLAGS_EXTRA)
+# CMake 3.18 adds integrated support for architecture selection, but we can't rely on it
+set(CMAKE_CUDA_ARCHITECTURES OFF)
+list(APPEND CUDA_NVCC_FLAGS ${NVCC_FLAGS_EXTRA})
+message(STATUS "Added CUDA NVCC flags for: ${NVCC_FLAGS_EXTRA}")
+
+# disable some nvcc diagnostic that appears in boost, glog, glags, opencv, etc.
+foreach(diag cc_clobber_ignored
+             field_without_dll_interface
+             base_class_has_different_dll_interface
+             dll_interface_conflict_none_assumed
+             dll_interface_conflict_dllexport_assumed
+             bad_friend_decl)
+  list(APPEND SUPPRESS_WARNING_FLAGS --diag_suppress=${diag})
+endforeach()
+string(REPLACE ";" "," SUPPRESS_WARNING_FLAGS "${SUPPRESS_WARNING_FLAGS}")
+list(APPEND CUDA_NVCC_FLAGS -Xcudafe ${SUPPRESS_WARNING_FLAGS})
+
+set(CUDA_PROPAGATE_HOST_FLAGS_BLOCKLIST "-Werror")
+if(MSVC)
+  list(APPEND CUDA_NVCC_FLAGS "--Werror" "cross-execution-space-call")
+  list(APPEND CUDA_NVCC_FLAGS "--no-host-device-move-forward")
+endif()
+
+# Debug and Release symbol support
+if(MSVC)
+  if(${CAFFE2_USE_MSVC_STATIC_RUNTIME})
+    string(APPEND CMAKE_CUDA_FLAGS_DEBUG " -Xcompiler /MTd")
+    string(APPEND CMAKE_CUDA_FLAGS_MINSIZEREL " -Xcompiler /MT")
+    string(APPEND CMAKE_CUDA_FLAGS_RELEASE " -Xcompiler /MT")
+    string(APPEND CMAKE_CUDA_FLAGS_RELWITHDEBINFO " -Xcompiler /MT")
+  else()
+    string(APPEND CMAKE_CUDA_FLAGS_DEBUG " -Xcompiler /MDd")
+    string(APPEND CMAKE_CUDA_FLAGS_MINSIZEREL " -Xcompiler /MD")
+    string(APPEND CMAKE_CUDA_FLAGS_RELEASE " -Xcompiler /MD")
+    string(APPEND CMAKE_CUDA_FLAGS_RELWITHDEBINFO " -Xcompiler /MD")
+  endif()
+  if(CUDA_NVCC_FLAGS MATCHES "Zi")
+    list(APPEND CUDA_NVCC_FLAGS "-Xcompiler" "-FS")
+  endif()
+elseif(CUDA_DEVICE_DEBUG)
+  list(APPEND CUDA_NVCC_FLAGS "-g" "-G")  # -G enables device code debugging symbols
+endif()
+
+# Set expt-relaxed-constexpr to suppress Eigen warnings
+list(APPEND CUDA_NVCC_FLAGS "--expt-relaxed-constexpr")
+
+# Set expt-extended-lambda to support lambda on device
+list(APPEND CUDA_NVCC_FLAGS "--expt-extended-lambda")
+
+foreach(FLAG ${CUDA_NVCC_FLAGS})
+  string(FIND "${FLAG}" " " flag_space_position)
+  if(NOT flag_space_position EQUAL -1)
+    message(FATAL_ERROR "Found spaces in CUDA_NVCC_FLAGS entry '${FLAG}'")
+  endif()
+  string(APPEND CMAKE_CUDA_FLAGS " ${FLAG}")
+endforeach()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/gflags.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/gflags.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..186cda1a909ab79431114d1c61de895069255389
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/gflags.cmake
@@ -0,0 +1,83 @@
+# ---[ gflags
+
+# We will try to use the config mode first, and then manual find.
+find_package(gflags CONFIG QUIET)
+if(NOT TARGET gflags)
+  find_package(gflags MODULE QUIET)
+endif()
+
+if(TARGET gflags)
+  message(STATUS "Caffe2: Found gflags with new-style gflags target.")
+elseif(GFLAGS_FOUND)
+  message(STATUS "Caffe2: Found gflags with old-style gflag starget.")
+  add_library(gflags UNKNOWN IMPORTED)
+  set_property(
+      TARGET gflags PROPERTY IMPORTED_LOCATION ${GFLAGS_LIBRARY})
+  set_property(
+      TARGET gflags PROPERTY INTERFACE_INCLUDE_DIRECTORIES
+      ${GFLAGS_INCLUDE_DIR})
+else()
+  message(STATUS
+      "Caffe2: Cannot find gflags automatically. Using legacy find.")
+
+  # - Try to find GFLAGS in the legacy way.
+  #
+  # The following variables are optionally searched for defaults
+  #  GFLAGS_ROOT_DIR: Base directory where all GFLAGS components are found
+  #
+  # The following are set after configuration is done:
+  #  GFLAGS_FOUND
+  #  GFLAGS_INCLUDE_DIRS
+  #  GFLAGS_LIBRARIES
+  #  GFLAGS_LIBRARYRARY_DIRS
+  include(FindPackageHandleStandardArgs)
+  set(GFLAGS_ROOT_DIR "" CACHE PATH "Folder contains Gflags")
+
+  # We are testing only a couple of files in the include directories
+  if(WIN32)
+    find_path(GFLAGS_INCLUDE_DIR gflags/gflags.h
+        PATHS ${GFLAGS_ROOT_DIR}/src/windows)
+  else()
+    find_path(GFLAGS_INCLUDE_DIR gflags/gflags.h
+        PATHS ${GFLAGS_ROOT_DIR})
+  endif()
+
+  if(WIN32)
+    find_library(GFLAGS_LIBRARY_RELEASE
+        NAMES libgflags
+        PATHS ${GFLAGS_ROOT_DIR}
+        PATH_SUFFIXES Release)
+
+    find_library(GFLAGS_LIBRARY_DEBUG
+        NAMES libgflags-debug
+        PATHS ${GFLAGS_ROOT_DIR}
+        PATH_SUFFIXES Debug)
+    set(GFLAGS_LIBRARY optimized ${GFLAGS_LIBRARY_RELEASE} debug ${GFLAGS_LIBRARY_DEBUG})
+  else()
+    find_library(GFLAGS_LIBRARY gflags)
+  endif()
+
+  find_package_handle_standard_args(
+      gflags DEFAULT_MSG GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY)
+
+  if(GFLAGS_FOUND)
+    message(
+        STATUS
+        "Caffe2: Found gflags  (include: ${GFLAGS_INCLUDE_DIR}, "
+        "library: ${GFLAGS_LIBRARY})")
+    add_library(gflags UNKNOWN IMPORTED)
+    set_property(
+        TARGET gflags PROPERTY IMPORTED_LOCATION ${GFLAGS_LIBRARY})
+    set_property(
+        TARGET gflags PROPERTY INTERFACE_INCLUDE_DIRECTORIES
+        ${GFLAGS_INCLUDE_DIR})
+  endif()
+endif()
+
+# After above, we should have the gflags target now.
+if(NOT TARGET gflags)
+  message(WARNING
+      "Caffe2: gflags cannot be found. Depending on whether you are building "
+      "Caffe2 or a Caffe2 dependent library, the next warning / error will "
+      "give you more info.")
+endif()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/glog.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/glog.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..bb03e81f29e3afed43ba95260cc5c298be881f72
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/glog.cmake
@@ -0,0 +1,70 @@
+# ---[ glog
+
+# We will try to use the config mode first, and then manual find.
+find_package(glog CONFIG QUIET)
+if(NOT TARGET glog::glog)
+  find_package(glog MODULE QUIET)
+endif()
+
+if(TARGET glog::glog)
+  message(STATUS "Caffe2: Found glog with new-style glog target.")
+elseif(GLOG_FOUND)
+  message(
+      STATUS
+      "Caffe2: Found glog with old-style glog starget. Glog never shipped "
+      "old style glog targets, so somewhere in your cmake path there might "
+      "be a custom Findglog.cmake file that got triggered. We will make a "
+      "best effort to create the new style glog target for you.")
+  add_library(glog::glog UNKNOWN IMPORTED)
+  set_property(
+      TARGET glog::glog PROPERTY IMPORTED_LOCATION ${GLOG_LIBRARY})
+  set_property(
+      TARGET glog::glog PROPERTY INTERFACE_INCLUDE_DIRECTORIES
+      ${GLOG_INCLUDE_DIR})
+else()
+  message(STATUS "Caffe2: Cannot find glog automatically. Using legacy find.")
+
+  # - Try to find Glog
+  #
+  # The following variables are optionally searched for defaults
+  #  GLOG_ROOT_DIR: Base directory where all GLOG components are found
+  #
+  # The following are set after configuration is done:
+  #  GLOG_FOUND
+  #  GLOG_INCLUDE_DIRS
+  #  GLOG_LIBRARIES
+  #  GLOG_LIBRARYRARY_DIRS
+
+  include(FindPackageHandleStandardArgs)
+  set(GLOG_ROOT_DIR "" CACHE PATH "Folder contains Google glog")
+  if(NOT WIN32)
+      find_path(GLOG_INCLUDE_DIR glog/logging.h
+          PATHS ${GLOG_ROOT_DIR})
+  endif()
+
+  find_library(GLOG_LIBRARY glog
+      PATHS ${GLOG_ROOT_DIR}
+      PATH_SUFFIXES lib lib64)
+
+  find_package_handle_standard_args(glog DEFAULT_MSG GLOG_INCLUDE_DIR GLOG_LIBRARY)
+
+  if(GLOG_FOUND)
+    message(STATUS
+        "Caffe2: Found glog (include: ${GLOG_INCLUDE_DIR}, "
+        "library: ${GLOG_LIBRARY})")
+    add_library(glog::glog UNKNOWN IMPORTED)
+    set_property(
+        TARGET glog::glog PROPERTY IMPORTED_LOCATION ${GLOG_LIBRARY})
+    set_property(
+        TARGET glog::glog PROPERTY INTERFACE_INCLUDE_DIRECTORIES
+        ${GLOG_INCLUDE_DIR})
+  endif()
+endif()
+
+# After above, we should have the glog::glog target now.
+if(NOT TARGET glog::glog)
+  message(WARNING
+      "Caffe2: glog cannot be found. Depending on whether you are building "
+      "Caffe2 or a Caffe2 dependent library, the next warning / error will "
+      "give you more info.")
+endif()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/mkl.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/mkl.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..2f6d1fd905aa303cc240b058318acdfb2483e9ad
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/mkl.cmake
@@ -0,0 +1,40 @@
+find_package(MKL QUIET)
+
+if(TARGET caffe2::mkl)
+  return()
+endif()
+
+add_library(caffe2::mkl INTERFACE IMPORTED)
+target_include_directories(caffe2::mkl INTERFACE ${MKL_INCLUDE_DIR})
+target_link_libraries(caffe2::mkl INTERFACE ${MKL_LIBRARIES})
+foreach(MKL_LIB IN LISTS MKL_LIBRARIES)
+  if(EXISTS "${MKL_LIB}")
+    get_filename_component(MKL_LINK_DIR "${MKL_LIB}" DIRECTORY)
+    if(IS_DIRECTORY "${MKL_LINK_DIR}")
+      target_link_directories(caffe2::mkl INTERFACE "${MKL_LINK_DIR}")
+    endif()
+  endif()
+endforeach()
+
+# TODO: This is a hack, it will not pick up architecture dependent
+# MKL libraries correctly; see https://github.com/pytorch/pytorch/issues/73008
+set_property(
+  TARGET caffe2::mkl PROPERTY INTERFACE_LINK_DIRECTORIES
+  ${MKL_ROOT}/lib ${MKL_ROOT}/lib/intel64 ${MKL_ROOT}/lib/intel64_win ${MKL_ROOT}/lib/win-x64)
+
+if(UNIX)
+  if(USE_STATIC_MKL)
+    foreach(MKL_LIB_PATH IN LISTS MKL_LIBRARIES)
+      if(NOT EXISTS "${MKL_LIB_PATH}")
+        continue()
+      endif()
+
+      get_filename_component(MKL_LIB_NAME "${MKL_LIB_PATH}" NAME)
+
+      # Match archive libraries starting with "libmkl_"
+      if(MKL_LIB_NAME MATCHES "^libmkl_" AND MKL_LIB_NAME MATCHES ".a$")
+        target_link_options(caffe2::mkl INTERFACE "-Wl,--exclude-libs,${MKL_LIB_NAME}")
+      endif()
+    endforeach()
+  endif()
+endif()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/mkldnn.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/mkldnn.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..87935625f9bfb543d1cdc7f2b59f11e8d4a709e7
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/mkldnn.cmake
@@ -0,0 +1,18 @@
+set(MKLDNN_USE_NATIVE_ARCH ${USE_NATIVE_ARCH})
+
+if(CPU_AARCH64)
+  include(${CMAKE_CURRENT_LIST_DIR}/ComputeLibrary.cmake)
+endif()
+
+find_package(MKLDNN QUIET)
+
+if(NOT TARGET caffe2::mkldnn)
+  add_library(caffe2::mkldnn INTERFACE IMPORTED)
+endif()
+
+set_property(
+  TARGET caffe2::mkldnn PROPERTY INTERFACE_INCLUDE_DIRECTORIES
+  ${MKLDNN_INCLUDE_DIR})
+set_property(
+  TARGET caffe2::mkldnn PROPERTY INTERFACE_LINK_LIBRARIES
+  ${MKLDNN_LIBRARIES})
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/protobuf.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/protobuf.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..77ec3622b132dc7a7817716dd24ef986e6ac030d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/protobuf.cmake
@@ -0,0 +1,92 @@
+# ---[ Protobuf
+
+# We will try to use the config mode first, and then manual find.
+find_package(Protobuf CONFIG QUIET)
+if(NOT Protobuf_FOUND)
+  find_package(Protobuf MODULE QUIET)
+endif()
+
+if((TARGET protobuf::libprotobuf OR TARGET protobuf::libprotobuf-lite) AND TARGET protobuf::protoc)
+  # Hooray. This is the most ideal situation, meaning that you either have a
+  # Protobuf config file installed (like on Windows), or you are using a
+  # modern CMake that ships with a FindProtobuf.cmake file that produces
+  # modern targets.
+  message(STATUS "Caffe2: Found protobuf with new-style protobuf targets.")
+elseif(Protobuf_FOUND OR PROTOBUF_FOUND)
+  # If the modern targets are not present, we will generate them for you for
+  # backward compatibility. This is backported from CMake's new FindProtobuf.cmake
+  # content.
+  if((NOT PROTOBUF_LIBRARY) AND (NOT PROTOBUF_LITE_LIBRARY))
+    message(FATAL_ERROR
+        "Caffe2: Found protobuf with old style targets, but could not find targets."
+        " PROTOBUF_LIBRARY: " ${PROTOBUF_LIBRARY}
+        " PROTOBUF_LITE_LIBRARY: " ${PROTOBUF_LITE_LIBRARY}
+        " Protobuf_LIBRARY: " ${Protobuf_LIBRARY}
+        " Protobuf_LITE_LIBRARY: " ${Protobuf_LITE_LIBRARY})
+  endif()
+  message(STATUS "Caffe2: Found protobuf with old-style protobuf targets.")
+
+  if(PROTOBUF_LIBRARY)
+    if(NOT TARGET protobuf::libprotobuf)
+      add_library(protobuf::libprotobuf UNKNOWN IMPORTED)
+      set_target_properties(protobuf::libprotobuf PROPERTIES
+          INTERFACE_INCLUDE_DIRECTORIES "${PROTOBUF_INCLUDE_DIRS}")
+    endif()
+    if(EXISTS "${PROTOBUF_LIBRARY}")
+      set_target_properties(protobuf::libprotobuf PROPERTIES
+          IMPORTED_LOCATION "${PROTOBUF_LIBRARY}")
+    endif()
+    if(EXISTS "${PROTOBUF_LIBRARY_RELEASE}")
+      set_property(TARGET protobuf::libprotobuf APPEND PROPERTY
+          IMPORTED_CONFIGURATIONS RELEASE)
+      set_target_properties(protobuf::libprotobuf PROPERTIES
+          IMPORTED_LOCATION_RELEASE "${PROTOBUF_LIBRARY_RELEASE}")
+    endif()
+    if(EXISTS "${PROTOBUF_LIBRARY_DEBUG}")
+      set_property(TARGET protobuf::libprotobuf APPEND PROPERTY
+          IMPORTED_CONFIGURATIONS DEBUG)
+      set_target_properties(protobuf::libprotobuf PROPERTIES
+          IMPORTED_LOCATION_DEBUG "${PROTOBUF_LIBRARY_DEBUG}")
+    endif()
+  endif()
+
+  if(PROTOBUF_LITE_LIBRARY)
+    if(NOT TARGET protobuf::libprotobuf-lite)
+      add_library(protobuf::libprotobuf-lite UNKNOWN IMPORTED)
+      set_target_properties(protobuf::libprotobuf-lite PROPERTIES
+          INTERFACE_INCLUDE_DIRECTORIES "${PROTOBUF_INCLUDE_DIRS}")
+    endif()
+    if(EXISTS "${PROTOBUF_LITE_LIBRARY}")
+      set_target_properties(protobuf::libprotobuf-lite PROPERTIES
+          IMPORTED_LOCATION "${PROTOBUF_LITE_LIBRARY}")
+    endif()
+    if(EXISTS "${PROTOBUF_LITE_LIBRARY_RELEASE}")
+      set_property(TARGET protobuf::libprotobuf-lite APPEND PROPERTY
+          IMPORTED_CONFIGURATIONS RELEASE)
+      set_target_properties(protobuf::libprotobuf-lite PROPERTIES
+          IMPORTED_LOCATION_RELEASE "${PROTOBUF_LITE_LIBRARY_RELEASE}")
+    endif()
+    if(EXISTS "${PROTOBUF_LITE_LIBRARY_DEBUG}")
+      set_property(TARGET protobuf::libprotobuf-lite APPEND PROPERTY
+          IMPORTED_CONFIGURATIONS DEBUG)
+      set_target_properties(protobuf::libprotobuf-lite PROPERTIES
+          IMPORTED_LOCATION_DEBUG "${PROTOBUF_LITE_LIBRARY_DEBUG}")
+    endif()
+  endif()
+
+  if(PROTOBUF_PROTOC_EXECUTABLE)
+    if(NOT TARGET protobuf::protoc)
+      add_executable(protobuf::protoc IMPORTED)
+    endif()
+    set_property(TARGET protobuf::protoc PROPERTY
+        IMPORTED_LOCATION ${PROTOBUF_PROTOC_EXECUTABLE})
+  endif()
+endif()
+
+# After above, we should have the protobuf related target now.
+if((NOT TARGET protobuf::libprotobuf) AND (NOT TARGET protobuf::libprotobuf-lite))
+  message(WARNING
+      "Protobuf cannot be found. Depending on whether you are building Caffe2 "
+      "or a Caffe2 dependent library, the next warning / error will give you "
+      "more info.")
+endif()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/utils.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/utils.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..c6647eb457c3b0352eb0d243c42874fed71fb681
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/utils.cmake
@@ -0,0 +1,495 @@
+################################################################################################
+# Exclude and prepend functionalities
+function(exclude OUTPUT INPUT)
+set(EXCLUDES ${ARGN})
+foreach(EXCLUDE ${EXCLUDES})
+        list(REMOVE_ITEM INPUT "${EXCLUDE}")
+endforeach()
+set(${OUTPUT} ${INPUT} PARENT_SCOPE)
+endfunction(exclude)
+
+function(prepend OUTPUT PREPEND)
+set(OUT "")
+foreach(ITEM ${ARGN})
+        list(APPEND OUT "${PREPEND}${ITEM}")
+endforeach()
+set(${OUTPUT} ${OUT} PARENT_SCOPE)
+endfunction(prepend)
+
+################################################################################################
+# Parses a version string that might have values beyond major, minor, and patch
+# and set version variables for the library.
+# Usage:
+#   caffe2_parse_version_str( )
+function(caffe2_parse_version_str LIBNAME VERSIONSTR)
+  string(REGEX REPLACE "^([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_MAJOR "${VERSIONSTR}")
+  string(REGEX REPLACE "^[0-9]+\\.([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_MINOR  "${VERSIONSTR}")
+  string(REGEX REPLACE "[0-9]+\\.[0-9]+\\.([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_PATCH "${VERSIONSTR}")
+  set(${LIBNAME}_VERSION_MAJOR ${${LIBNAME}_VERSION_MAJOR} ${ARGN} PARENT_SCOPE)
+  set(${LIBNAME}_VERSION_MINOR ${${LIBNAME}_VERSION_MINOR} ${ARGN} PARENT_SCOPE)
+  set(${LIBNAME}_VERSION_PATCH ${${LIBNAME}_VERSION_PATCH} ${ARGN} PARENT_SCOPE)
+  set(${LIBNAME}_VERSION "${${LIBNAME}_VERSION_MAJOR}.${${LIBNAME}_VERSION_MINOR}.${${LIBNAME}_VERSION_PATCH}" PARENT_SCOPE)
+endfunction()
+
+###
+# Removes common indentation from a block of text to produce code suitable for
+# setting to `python -c`, or using with pycmd. This allows multiline code to be
+# nested nicely in the surrounding code structure.
+#
+# This function respsects Python_EXECUTABLE if it defined, otherwise it uses
+# `python` and hopes for the best. An error will be thrown if it is not found.
+#
+# Args:
+#     outvar : variable that will hold the stdout of the python command
+#     text   : text to remove indentation from
+#
+function(dedent outvar text)
+  # Use Python_EXECUTABLE if it is defined, otherwise default to python
+  if("${Python_EXECUTABLE}" STREQUAL "")
+    set(_python_exe "python3")
+  else()
+    set(_python_exe "${Python_EXECUTABLE}")
+  endif()
+  set(_fixup_cmd "import sys; from textwrap import dedent; print(dedent(sys.stdin.read()))")
+  file(WRITE "${CMAKE_BINARY_DIR}/indented.txt" "${text}")
+  execute_process(
+    COMMAND "${_python_exe}" -c "${_fixup_cmd}"
+    INPUT_FILE "${CMAKE_BINARY_DIR}/indented.txt"
+    RESULT_VARIABLE _dedent_exitcode
+    OUTPUT_VARIABLE _dedent_text)
+  if(NOT _dedent_exitcode EQUAL 0)
+    message(ERROR " Failed to remove indentation from: \n\"\"\"\n${text}\n\"\"\"
+    Python dedent failed with error code: ${_dedent_exitcode}")
+    message(FATAL_ERROR " Python dedent failed with error code: ${_dedent_exitcode}")
+  endif()
+  # Remove supurflous newlines (artifacts of print)
+  string(STRIP "${_dedent_text}" _dedent_text)
+  set(${outvar} "${_dedent_text}" PARENT_SCOPE)
+endfunction()
+
+
+function(pycmd_no_exit outvar exitcode cmd)
+  # Use Python_EXECUTABLE if it is defined, otherwise default to python
+  if("${Python_EXECUTABLE}" STREQUAL "")
+    set(_python_exe "python")
+  else()
+    set(_python_exe "${Python_EXECUTABLE}")
+  endif()
+  # run the actual command
+  execute_process(
+    COMMAND "${_python_exe}" -c "${cmd}"
+    RESULT_VARIABLE _exitcode
+    OUTPUT_VARIABLE _output)
+  # Remove supurflous newlines (artifacts of print)
+  string(STRIP "${_output}" _output)
+  set(${outvar} "${_output}" PARENT_SCOPE)
+  set(${exitcode} "${_exitcode}" PARENT_SCOPE)
+endfunction()
+
+
+###
+# Helper function to run `python -c ""` and capture the results of stdout
+#
+# Runs a python command and populates an outvar with the result of stdout.
+# Common indentation in the text of `cmd` is removed before the command is
+# executed, so the caller does not need to worry about indentation issues.
+#
+# This function respsects Python_EXECUTABLE if it defined, otherwise it uses
+# `python` and hopes for the best. An error will be thrown if it is not found.
+#
+# Args:
+#     outvar : variable that will hold the stdout of the python command
+#     cmd    : text representing a (possibly multiline) block of python code
+#
+function(pycmd outvar cmd)
+  dedent(_dedent_cmd "${cmd}")
+  pycmd_no_exit(_output _exitcode "${_dedent_cmd}")
+
+  if(NOT _exitcode EQUAL 0)
+    message(ERROR " Failed when running python code: \"\"\"\n${_dedent_cmd}\n\"\"\"")
+    message(FATAL_ERROR " Python command failed with error code: ${_exitcode}")
+  endif()
+  # Remove supurflous newlines (artifacts of print)
+  string(STRIP "${_output}" _output)
+  set(${outvar} "${_output}" PARENT_SCOPE)
+endfunction()
+
+
+##############################################################################
+# Macro to update cached options.
+macro(caffe2_update_option variable value)
+  if(CAFFE2_CMAKE_BUILDING_WITH_MAIN_REPO)
+    get_property(__help_string CACHE ${variable} PROPERTY HELPSTRING)
+    set(${variable} ${value} CACHE BOOL ${__help_string} FORCE)
+  else()
+    set(${variable} ${value})
+  endif()
+endmacro()
+
+
+##############################################################################
+# Add an interface library definition that is dependent on the source.
+#
+# It's probably easiest to explain why this macro exists, by describing
+# what things would look like if we didn't have this macro.
+#
+# Let's suppose we want to statically link against torch.  We've defined
+# a library in cmake called torch, and we might think that we just
+# target_link_libraries(my-app PUBLIC torch).  This will result in a
+# linker argument 'libtorch.a' getting passed to the linker.
+#
+# Unfortunately, this link command is wrong!  We have static
+# initializers in libtorch.a that would get improperly pruned by
+# the default link settings.  What we actually need is for you
+# to do -Wl,--whole-archive,libtorch.a -Wl,--no-whole-archive to ensure
+# that we keep all symbols, even if they are (seemingly) not used.
+#
+# What caffe2_interface_library does is create an interface library
+# that indirectly depends on the real library, but sets up the link
+# arguments so that you get all of the extra link settings you need.
+# The result is not a "real" library, and so we have to manually
+# copy over necessary properties from the original target.
+#
+# (The discussion above is about static libraries, but a similar
+# situation occurs for dynamic libraries: if no symbols are used from
+# a dynamic library, it will be pruned unless you are --no-as-needed)
+macro(caffe2_interface_library SRC DST)
+  add_library(${DST} INTERFACE)
+  add_dependencies(${DST} ${SRC})
+  # Depending on the nature of the source library as well as the compiler,
+  # determine the needed compilation flags.
+  get_target_property(__src_target_type ${SRC} TYPE)
+  # Depending on the type of the source library, we will set up the
+  # link command for the specific SRC library.
+  if(${__src_target_type} STREQUAL "STATIC_LIBRARY")
+    # In the case of static library, we will need to add whole-static flags.
+    if(APPLE)
+      target_link_libraries(
+          ${DST} INTERFACE -Wl,-force_load,\"$\")
+    elseif(MSVC)
+      # In MSVC, we will add whole archive in default.
+      target_link_libraries(
+         ${DST} INTERFACE "$")
+      target_link_options(
+         ${DST} INTERFACE "-WHOLEARCHIVE:$")
+    else()
+      # Assume everything else is like gcc
+      target_link_libraries(${DST} INTERFACE
+          "-Wl,--whole-archive,\"$\" -Wl,--no-whole-archive")
+    endif()
+    # Link all interface link libraries of the src target as well.
+    # For static library, we need to explicitly depend on all the libraries
+    # that are the dependent library of the source library. Note that we cannot
+    # use the populated INTERFACE_LINK_LIBRARIES property, because if one of the
+    # dependent library is not a target, cmake creates a $ wrapper
+    # and then one is not able to find target "src". For more discussions, check
+    #   https://gitlab.kitware.com/cmake/cmake/issues/15415
+    #   https://cmake.org/pipermail/cmake-developers/2013-May/019019.html
+    # Specifically the following quote
+    #
+    # """
+    # For STATIC libraries we can define that the PUBLIC/PRIVATE/INTERFACE keys
+    # are ignored for linking and that it always populates both LINK_LIBRARIES
+    # LINK_INTERFACE_LIBRARIES.  Note that for STATIC libraries the
+    # LINK_LIBRARIES property will not be used for anything except build-order
+    # dependencies.
+    # """
+    target_link_libraries(${DST} INTERFACE
+        $)
+  elseif(${__src_target_type} STREQUAL "SHARED_LIBRARY")
+    if("${CMAKE_CXX_COMPILER_ID}" MATCHES "GNU")
+      target_link_libraries(${DST} INTERFACE
+          "-Wl,--no-as-needed,\"$\" -Wl,--as-needed")
+    else()
+      target_link_libraries(${DST} INTERFACE ${SRC})
+    endif()
+    # Link all interface link libraries of the src target as well.
+    # For shared libraries, we can simply depend on the INTERFACE_LINK_LIBRARIES
+    # property of the target.
+    target_link_libraries(${DST} INTERFACE
+        $)
+  else()
+    message(FATAL_ERROR
+        "You made a CMake build file error: target " ${SRC}
+        " must be of type either STATIC_LIBRARY or SHARED_LIBRARY. However, "
+        "I got " ${__src_target_type} ".")
+  endif()
+  # For all other interface properties, manually inherit from the source target.
+  set_target_properties(${DST} PROPERTIES
+    INTERFACE_COMPILE_DEFINITIONS
+    $
+    INTERFACE_COMPILE_OPTIONS
+    $
+    INTERFACE_INCLUDE_DIRECTORIES
+    $
+    INTERFACE_SYSTEM_INCLUDE_DIRECTORIES
+    $)
+endmacro()
+
+
+##############################################################################
+# Creating a Caffe2 binary target with sources specified with relative path.
+# Usage:
+#   caffe2_binary_target(target_name_or_src  [] [] ...)
+# If only target_name_or_src is specified, this target is build with one single
+# source file and the target name is autogen from the filename. Otherwise, the
+# target name is given by the first argument and the rest are the source files
+# to build the target.
+function(caffe2_binary_target target_name_or_src)
+  # https://cmake.org/cmake/help/latest/command/function.html
+  # Checking that ARGC is greater than # is the only way to ensure
+  # that ARGV# was passed to the function as an extra argument.
+  if(ARGC GREATER 1)
+    set(__target ${target_name_or_src})
+    prepend(__srcs "${CMAKE_CURRENT_SOURCE_DIR}/" "${ARGN}")
+  else()
+    get_filename_component(__target ${target_name_or_src} NAME_WE)
+    prepend(__srcs "${CMAKE_CURRENT_SOURCE_DIR}/" "${target_name_or_src}")
+  endif()
+  add_executable(${__target} ${__srcs})
+  target_link_libraries(${__target} torch_library)
+  # If we have Caffe2_MODULES defined, we will also link with the modules.
+  if(DEFINED Caffe2_MODULES)
+    target_link_libraries(${__target} ${Caffe2_MODULES})
+  endif()
+  install(TARGETS ${__target} DESTINATION bin)
+endfunction()
+
+function(caffe2_hip_binary_target target_name_or_src)
+  if(ARGC GREATER 1)
+    set(__target ${target_name_or_src})
+    prepend(__srcs "${CMAKE_CURRENT_SOURCE_DIR}/" "${ARGN}")
+  else()
+    get_filename_component(__target ${target_name_or_src} NAME_WE)
+    prepend(__srcs "${CMAKE_CURRENT_SOURCE_DIR}/" "${target_name_or_src}")
+  endif()
+
+  caffe2_binary_target(${target_name_or_src})
+
+  target_compile_options(${__target} PRIVATE ${HIP_CXX_FLAGS})
+  target_include_directories(${__target} PRIVATE ${Caffe2_HIP_INCLUDE})
+endfunction()
+
+
+##############################################################################
+# Multiplex between adding libraries for CUDA versus HIP (AMD Software Stack).
+# Usage:
+#   torch_cuda_based_add_library(cuda_target)
+#
+macro(torch_cuda_based_add_library cuda_target)
+  if(USE_ROCM)
+    hip_add_library(${cuda_target} ${ARGN})
+  elseif(USE_CUDA)
+    add_library(${cuda_target} ${ARGN})
+  else()
+  endif()
+endmacro()
+
+##############################################################################
+# Get the HIP arch flags specified by PYTORCH_ROCM_ARCH.
+# Usage:
+#   torch_hip_get_arch_list(variable_to_store_flags)
+#
+macro(torch_hip_get_arch_list store_var)
+  if(DEFINED ENV{PYTORCH_ROCM_ARCH})
+    set(_TMP $ENV{PYTORCH_ROCM_ARCH})
+  else()
+    # Use arch of installed GPUs as default
+    execute_process(COMMAND "rocm_agent_enumerator" COMMAND bash "-c" "grep -v gfx000 | sort -u | xargs | tr -d '\n'"
+                    RESULT_VARIABLE ROCM_AGENT_ENUMERATOR_RESULT
+                    OUTPUT_VARIABLE ROCM_ARCH_INSTALLED)
+    if(NOT ROCM_AGENT_ENUMERATOR_RESULT EQUAL 0)
+      message(FATAL_ERROR " Could not detect ROCm arch for GPUs on machine. Result: '${ROCM_AGENT_ENUMERATOR_RESULT}'")
+    endif()
+    set(_TMP ${ROCM_ARCH_INSTALLED})
+  endif()
+  string(REPLACE " " ";" ${store_var} "${_TMP}")
+endmacro()
+
+##############################################################################
+# Get the NVCC arch flags specified by TORCH_CUDA_ARCH_LIST and CUDA_ARCH_NAME.
+# Usage:
+#   torch_cuda_get_nvcc_gencode_flag(variable_to_store_flags)
+#
+macro(torch_cuda_get_nvcc_gencode_flag store_var)
+  # setting nvcc arch flags
+  if((NOT DEFINED TORCH_CUDA_ARCH_LIST) AND (DEFINED ENV{TORCH_CUDA_ARCH_LIST}))
+    message(WARNING
+        "In the future we will require one to explicitly pass "
+        "TORCH_CUDA_ARCH_LIST to cmake instead of implicitly setting it as an "
+        "env variable. This will become a FATAL_ERROR in future version of "
+        "pytorch.")
+    set(TORCH_CUDA_ARCH_LIST $ENV{TORCH_CUDA_ARCH_LIST})
+  endif()
+  if(DEFINED CUDA_ARCH_NAME)
+    message(WARNING
+        "CUDA_ARCH_NAME is no longer used. Use TORCH_CUDA_ARCH_LIST instead. "
+        "Right now, CUDA_ARCH_NAME is ${CUDA_ARCH_NAME} and "
+        "TORCH_CUDA_ARCH_LIST is ${TORCH_CUDA_ARCH_LIST}.")
+    set(TORCH_CUDA_ARCH_LIST TORCH_CUDA_ARCH_LIST ${CUDA_ARCH_NAME})
+  endif()
+
+  # Invoke cuda_select_nvcc_arch_flags from proper cmake FindCUDA.
+  cuda_select_nvcc_arch_flags(${store_var} ${TORCH_CUDA_ARCH_LIST})
+endmacro()
+
+
+##############################################################################
+# Add standard compile options.
+# Usage:
+#   torch_compile_options(lib_name)
+function(torch_compile_options libname)
+  set_property(TARGET ${libname} PROPERTY CXX_STANDARD 17)
+
+  # until they can be unified, keep these lists synced with setup.py
+  if(MSVC)
+
+    if(MSVC_Z7_OVERRIDE)
+      set(MSVC_DEBINFO_OPTION "/Z7")
+    else()
+      set(MSVC_DEBINFO_OPTION "/Zi")
+    endif()
+
+    target_compile_options(${libname} PUBLIC
+      $<$:
+        ${MSVC_RUNTIME_LIBRARY_OPTION}
+        $<$,$>:${MSVC_DEBINFO_OPTION}>
+        /EHsc
+        /bigobj>
+      )
+  else()
+    set(private_compile_options
+      -Wall
+      -Wextra
+      -Wdeprecated
+      -Wno-unused-parameter
+      -Wno-missing-field-initializers
+      -Wno-type-limits
+      -Wno-array-bounds
+      -Wno-unknown-pragmas
+      -Wno-strict-overflow
+      -Wno-strict-aliasing
+      )
+    list(APPEND private_compile_options -Wunused-function)
+    list(APPEND private_compile_options -Wunused-variable)
+    if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
+      list(APPEND private_compile_options -Wunused-but-set-variable)
+    endif()
+    if("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang")
+      list(APPEND private_compile_options -Wunused-private-field)
+    endif()
+    if(NOT "${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang")
+      list(APPEND private_compile_options
+        # Considered to be flaky.  See the discussion at
+        # https://github.com/pytorch/pytorch/pull/9608
+        -Wno-maybe-uninitialized)
+    endif()
+
+    if(WERROR)
+      list(APPEND private_compile_options
+        -Werror
+        -Werror=inconsistent-missing-override
+        -Werror=inconsistent-missing-destructor-override
+        -Werror=unused-function
+        -Werror=unused-variable
+        -Werror=pedantic
+      )
+      if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
+        list(APPEND private_compile_options -Werror=unused-but-set-variable)
+      endif()
+    endif()
+  endif()
+
+
+  target_compile_options(${libname} PRIVATE
+      $<$:${private_compile_options}>)
+  if(USE_CUDA)
+    foreach(option IN LISTS private_compile_options)
+      target_compile_options(${libname} PRIVATE $<$:-Xcompiler ${option}>)
+    endforeach()
+  endif()
+
+  if(NOT WIN32 AND NOT USE_ASAN)
+    # Enable hidden visibility by default to make it easier to debug issues with
+    # TORCH_API annotations. Hidden visibility with selective default visibility
+    # behaves close enough to Windows' dllimport/dllexport.
+    #
+    # Unfortunately, hidden visibility messes up some ubsan warnings because
+    # templated classes crossing library boundary get duplicated (but identical)
+    # definitions. It's easier to just disable it.
+    target_compile_options(${libname} PRIVATE
+        $<$: -fvisibility=hidden>)
+  endif()
+
+  # Use -O2 for release builds (-O3 doesn't improve perf, and -Os results in perf regression)
+  target_compile_options(${libname} PRIVATE
+      $<$,$,$>>:-O2>)
+
+endfunction()
+
+##############################################################################
+# Set old-style FindCuda.cmake compile flags from modern CMake cuda flags.
+# Usage:
+#   torch_update_find_cuda_flags()
+function(torch_update_find_cuda_flags)
+  # Convert -O2 -Xcompiler="-O2 -Wall" to "-O2;-Xcompiler=-O2,-Wall"
+  if(USE_CUDA)
+    separate_arguments(FLAGS UNIX_COMMAND "${CMAKE_CUDA_FLAGS}")
+    string(REPLACE " " "," FLAGS "${FLAGS}")
+    set(CUDA_NVCC_FLAGS ${FLAGS} PARENT_SCOPE)
+
+    separate_arguments(FLAGS_DEBUG UNIX_COMMAND "${CMAKE_CUDA_FLAGS_DEBUG}")
+    string(REPLACE " " "," FLAGS_DEBUG "${FLAGS_DEBUG}")
+    set(CUDA_NVCC_FLAGS_DEBUG "${FLAGS_DEBUG}" PARENT_SCOPE)
+
+    separate_arguments(FLAGS_RELEASE UNIX_COMMAND "${CMAKE_CUDA_FLAGS_RELEASE}")
+    string(REPLACE " " "," FLAGS_RELEASE "${FLAGS_RELEASE}")
+    set(CUDA_NVCC_FLAGS_RELEASE "${FLAGS_RELEASE}" PARENT_SCOPE)
+
+    separate_arguments(FLAGS_MINSIZEREL UNIX_COMMAND "${CMAKE_CUDA_FLAGS_MINSIZEREL}")
+    string(REPLACE " " "," FLAGS_MINSIZEREL "${FLAGS_MINSIZEREL}")
+    set(CUDA_NVCC_FLAGS_MINSIZEREL "${FLAGS_MINSIZEREL}" PARENT_SCOPE)
+
+    separate_arguments(FLAGS_RELWITHDEBINFO UNIX_COMMAND "${CMAKE_CUDA_FLAGS_RELWITHDEBINFO}")
+    string(REPLACE " " "," FLAGS_RELWITHDEBINFO "${FLAGS_RELWITHDEBINFO}")
+    set(CUDA_NVCC_FLAGS_RELWITHDEBINFO "${FLAGS_RELWITHDEBINFO}" PARENT_SCOPE)
+
+    message(STATUS "Converting CMAKE_CUDA_FLAGS to CUDA_NVCC_FLAGS:\n"
+                    "    CUDA_NVCC_FLAGS                = ${FLAGS}\n"
+                    "    CUDA_NVCC_FLAGS_DEBUG          = ${FLAGS_DEBUG}\n"
+                    "    CUDA_NVCC_FLAGS_RELEASE        = ${FLAGS_RELEASE}\n"
+                    "    CUDA_NVCC_FLAGS_RELWITHDEBINFO = ${FLAGS_RELWITHDEBINFO}\n"
+                    "    CUDA_NVCC_FLAGS_MINSIZEREL     = ${FLAGS_MINSIZEREL}")
+  endif()
+endfunction()
+
+include(CheckCXXCompilerFlag)
+
+##############################################################################
+# CHeck if given flag is supported and append it to provided outputvar
+# Also define HAS_UPPER_CASE_FLAG_NAME variable
+# Usage:
+#   append_cxx_flag_if_supported("-Werror" CMAKE_CXX_FLAGS)
+function(append_cxx_flag_if_supported flag outputvar)
+    string(TOUPPER "HAS${flag}" _FLAG_NAME)
+    string(REGEX REPLACE "[=-]" "_" _FLAG_NAME "${_FLAG_NAME}")
+    # GCC silents unknown -Wno-XXX flags, so we detect the corresponding -WXXX.
+    if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
+      string(REGEX REPLACE "Wno-" "W" new_flag "${flag}")
+    else()
+      set(new_flag ${flag})
+    endif()
+    check_cxx_compiler_flag("${new_flag}" ${_FLAG_NAME})
+    if(${_FLAG_NAME})
+        string(APPEND ${outputvar} " ${flag}")
+        set(${outputvar} "${${outputvar}}" PARENT_SCOPE)
+    endif()
+endfunction()
+
+function(target_compile_options_if_supported target flag)
+  set(_compile_options "")
+  append_cxx_flag_if_supported("${flag}" _compile_options)
+  if(NOT "${_compile_options}" STREQUAL "")
+    target_compile_options(${target} PRIVATE ${flag})
+  endif()
+endfunction()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/xpu.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/xpu.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..d1a442f8efd419afb8c8236cf8c9b880cb1d8b0b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Caffe2/public/xpu.cmake
@@ -0,0 +1,30 @@
+# ---[ xpu
+
+# Poor man's include guard
+if(TARGET torch::xpurt)
+  return()
+endif()
+
+# Find SYCL library.
+find_package(SYCLToolkit REQUIRED)
+if(NOT SYCL_FOUND)
+  set(PYTORCH_FOUND_XPU FALSE)
+  return()
+endif()
+set(PYTORCH_FOUND_XPU TRUE)
+
+# SYCL library interface
+add_library(torch::sycl INTERFACE IMPORTED)
+
+set_property(
+    TARGET torch::sycl PROPERTY INTERFACE_INCLUDE_DIRECTORIES
+    ${SYCL_INCLUDE_DIR})
+set_property(
+    TARGET torch::sycl PROPERTY INTERFACE_LINK_LIBRARIES
+    ${SYCL_LIBRARY})
+
+# xpurt
+add_library(torch::xpurt INTERFACE IMPORTED)
+set_property(
+    TARGET torch::xpurt PROPERTY INTERFACE_LINK_LIBRARIES
+    torch::sycl)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Tensorpipe/TensorpipeTargets-release.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Tensorpipe/TensorpipeTargets-release.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..59676e3daddafc530eaef7e4753fcd74fe353e36
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Tensorpipe/TensorpipeTargets-release.cmake
@@ -0,0 +1,29 @@
+#----------------------------------------------------------------
+# Generated CMake target import file for configuration "Release".
+#----------------------------------------------------------------
+
+# Commands may need to know the format version.
+set(CMAKE_IMPORT_FILE_VERSION 1)
+
+# Import target "tensorpipe_uv" for configuration "Release"
+set_property(TARGET tensorpipe_uv APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
+set_target_properties(tensorpipe_uv PROPERTIES
+  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C"
+  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib64/libtensorpipe_uv.a"
+  )
+
+list(APPEND _IMPORT_CHECK_TARGETS tensorpipe_uv )
+list(APPEND _IMPORT_CHECK_FILES_FOR_tensorpipe_uv "${_IMPORT_PREFIX}/lib64/libtensorpipe_uv.a" )
+
+# Import target "tensorpipe" for configuration "Release"
+set_property(TARGET tensorpipe APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
+set_target_properties(tensorpipe PROPERTIES
+  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
+  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib64/libtensorpipe.a"
+  )
+
+list(APPEND _IMPORT_CHECK_TARGETS tensorpipe )
+list(APPEND _IMPORT_CHECK_FILES_FOR_tensorpipe "${_IMPORT_PREFIX}/lib64/libtensorpipe.a" )
+
+# Commands beyond this point should not need to know the version.
+set(CMAKE_IMPORT_FILE_VERSION)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Tensorpipe/TensorpipeTargets.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Tensorpipe/TensorpipeTargets.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..515e43df6057dcb9a650fb6d76bdd79c8afb3cb9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Tensorpipe/TensorpipeTargets.cmake
@@ -0,0 +1,106 @@
+# Generated by CMake
+
+if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.5)
+   message(FATAL_ERROR "CMake >= 2.6.0 required")
+endif()
+cmake_policy(PUSH)
+cmake_policy(VERSION 2.6...3.17)
+#----------------------------------------------------------------
+# Generated CMake target import file.
+#----------------------------------------------------------------
+
+# Commands may need to know the format version.
+set(CMAKE_IMPORT_FILE_VERSION 1)
+
+# Protect against multiple inclusion, which would fail when already imported targets are added once more.
+set(_targetsDefined)
+set(_targetsNotDefined)
+set(_expectedTargets)
+foreach(_expectedTarget tensorpipe_uv tensorpipe)
+  list(APPEND _expectedTargets ${_expectedTarget})
+  if(NOT TARGET ${_expectedTarget})
+    list(APPEND _targetsNotDefined ${_expectedTarget})
+  endif()
+  if(TARGET ${_expectedTarget})
+    list(APPEND _targetsDefined ${_expectedTarget})
+  endif()
+endforeach()
+if("${_targetsDefined}" STREQUAL "${_expectedTargets}")
+  unset(_targetsDefined)
+  unset(_targetsNotDefined)
+  unset(_expectedTargets)
+  set(CMAKE_IMPORT_FILE_VERSION)
+  cmake_policy(POP)
+  return()
+endif()
+if(NOT "${_targetsDefined}" STREQUAL "")
+  message(FATAL_ERROR "Some (but not all) targets in this export set were already defined.\nTargets Defined: ${_targetsDefined}\nTargets not yet defined: ${_targetsNotDefined}\n")
+endif()
+unset(_targetsDefined)
+unset(_targetsNotDefined)
+unset(_expectedTargets)
+
+
+# Compute the installation prefix relative to this file.
+get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+if(_IMPORT_PREFIX STREQUAL "/")
+  set(_IMPORT_PREFIX "")
+endif()
+
+# Create imported target tensorpipe_uv
+add_library(tensorpipe_uv STATIC IMPORTED)
+
+set_target_properties(tensorpipe_uv PROPERTIES
+  INTERFACE_LINK_LIBRARIES "\$;\$;\$"
+)
+
+# Create imported target tensorpipe
+add_library(tensorpipe STATIC IMPORTED)
+
+set_target_properties(tensorpipe PROPERTIES
+  INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include"
+  INTERFACE_LINK_LIBRARIES "\$"
+)
+
+if(CMAKE_VERSION VERSION_LESS 2.8.12)
+  message(FATAL_ERROR "This file relies on consumers using CMake 2.8.12 or greater.")
+endif()
+
+# Load information for each installed configuration.
+get_filename_component(_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
+file(GLOB CONFIG_FILES "${_DIR}/TensorpipeTargets-*.cmake")
+foreach(f ${CONFIG_FILES})
+  include(${f})
+endforeach()
+
+# Cleanup temporary variables.
+set(_IMPORT_PREFIX)
+
+# Loop over all imported files and verify that they actually exist
+foreach(target ${_IMPORT_CHECK_TARGETS} )
+  foreach(file ${_IMPORT_CHECK_FILES_FOR_${target}} )
+    if(NOT EXISTS "${file}" )
+      message(FATAL_ERROR "The imported target \"${target}\" references the file
+   \"${file}\"
+but this file does not exist.  Possible reasons include:
+* The file was deleted, renamed, or moved to another location.
+* An install or uninstall procedure did not complete successfully.
+* The installation package was faulty and contained
+   \"${CMAKE_CURRENT_LIST_FILE}\"
+but not all the files it references.
+")
+    endif()
+  endforeach()
+  unset(_IMPORT_CHECK_FILES_FOR_${target})
+endforeach()
+unset(_IMPORT_CHECK_TARGETS)
+
+# This file does not depend on other imported targets which have
+# been exported from the same project but in a separate export set.
+
+# Commands beyond this point should not need to know the version.
+set(CMAKE_IMPORT_FILE_VERSION)
+cmake_policy(POP)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Torch/TorchConfig.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Torch/TorchConfig.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..c29c6cc532aea41f355d8580d6856b953de61a83
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Torch/TorchConfig.cmake
@@ -0,0 +1,166 @@
+# FindTorch
+# -------
+#
+# Finds the Torch library
+#
+# This will define the following variables:
+#
+#   TORCH_FOUND        -- True if the system has the Torch library
+#   TORCH_INCLUDE_DIRS -- The include directories for torch
+#   TORCH_LIBRARIES    -- Libraries to link against
+#   TORCH_CXX_FLAGS    -- Additional (required) compiler flags
+#
+# and the following imported targets:
+#
+#   torch
+macro(append_torchlib_if_found)
+  foreach (_arg ${ARGN})
+    find_library(${_arg}_LIBRARY ${_arg} PATHS "${TORCH_INSTALL_PREFIX}/lib")
+    if(${_arg}_LIBRARY)
+      list(APPEND TORCH_LIBRARIES ${${_arg}_LIBRARY})
+    else()
+      message(WARNING "static library ${${_arg}_LIBRARY} not found.")
+    endif()
+  endforeach()
+endmacro()
+
+macro(append_wholearchive_lib_if_found)
+  foreach (_arg ${ARGN})
+    find_library(${_arg}_LIBRARY ${_arg} PATHS "${TORCH_INSTALL_PREFIX}/lib")
+    if(${_arg}_LIBRARY)
+      if(APPLE)
+        list(APPEND TORCH_LIBRARIES "-Wl,-force_load,${${_arg}_LIBRARY}")
+      elseif(MSVC)
+        list(APPEND TORCH_LIBRARIES "-WHOLEARCHIVE:${${_arg}_LIBRARY}")
+      else()
+        # Linux
+        list(APPEND TORCH_LIBRARIES "-Wl,--whole-archive ${${_arg}_LIBRARY} -Wl,--no-whole-archive")
+      endif()
+    else()
+      message(WARNING "static library ${${_arg}_LIBRARY} not found.")
+    endif()
+  endforeach()
+endmacro()
+
+include(FindPackageHandleStandardArgs)
+
+if(DEFINED ENV{TORCH_INSTALL_PREFIX})
+  set(TORCH_INSTALL_PREFIX $ENV{TORCH_INSTALL_PREFIX})
+else()
+  # Assume we are in /share/cmake/Torch/TorchConfig.cmake
+  get_filename_component(CMAKE_CURRENT_LIST_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
+  get_filename_component(TORCH_INSTALL_PREFIX "${CMAKE_CURRENT_LIST_DIR}/../../../" ABSOLUTE)
+endif()
+
+# Include directories.
+if(EXISTS "${TORCH_INSTALL_PREFIX}/include")
+  set(TORCH_INCLUDE_DIRS
+    ${TORCH_INSTALL_PREFIX}/include
+    ${TORCH_INSTALL_PREFIX}/include/torch/csrc/api/include)
+else()
+  set(TORCH_INCLUDE_DIRS
+    ${TORCH_INSTALL_PREFIX}/include
+    ${TORCH_INSTALL_PREFIX}/include/torch/csrc/api/include)
+endif()
+
+# Library dependencies.
+if(ON)
+  find_package(Caffe2 REQUIRED PATHS ${CMAKE_CURRENT_LIST_DIR}/../Caffe2)
+  set(TORCH_LIBRARIES torch ${Caffe2_MAIN_LIBS})
+  append_torchlib_if_found(c10)
+else()
+  add_library(torch STATIC IMPORTED) # set imported_location at the bottom
+  #library need whole archive
+  append_wholearchive_lib_if_found(torch torch_cpu)
+  if(0)
+    append_wholearchive_lib_if_found(torch_cuda c10_cuda)
+  endif()
+
+  # We need manually add dependent libraries when they are not linked into the
+  # shared library.
+  # TODO: this list might be incomplete.
+  append_torchlib_if_found(c10)
+
+  if(ON)
+    append_torchlib_if_found(nnpack)
+  endif()
+
+  if(ON)
+    append_torchlib_if_found(pytorch_qnnpack)
+  endif()
+
+  if(ON)
+    append_torchlib_if_found(XNNPACK)
+  endif()
+
+  append_torchlib_if_found(caffe2_protos protobuf-lite protobuf protoc)
+  append_torchlib_if_found(onnx onnx_proto)
+
+  append_torchlib_if_found(fmt)
+  append_torchlib_if_found(cpuinfo clog)
+
+  if(NOT OFF)
+    append_torchlib_if_found(pthreadpool)
+  endif()
+
+  append_torchlib_if_found(eigen_blas)
+
+  if(ON)
+    append_torchlib_if_found(fbgemm)
+  endif()
+
+  if(ON)
+    append_torchlib_if_found(dnnl mkldnn)
+  endif()
+
+  append_torchlib_if_found(sleef asmjit)
+endif()
+
+if(1)
+  append_torchlib_if_found(kineto)
+endif()
+
+if(0)
+  if(MSVC)
+    find_library(CAFFE2_NVRTC_LIBRARY caffe2_nvrtc PATHS "${TORCH_INSTALL_PREFIX}/lib")
+    list(APPEND TORCH_CUDA_LIBRARIES ${CAFFE2_NVRTC_LIBRARY})
+  else()
+    set(TORCH_CUDA_LIBRARIES ${CUDA_NVRTC_LIB})
+  endif()
+  if(TARGET torch::nvtoolsext)
+    list(APPEND TORCH_CUDA_LIBRARIES torch::nvtoolsext)
+  endif()
+
+  if(ON)
+    find_library(C10_CUDA_LIBRARY c10_cuda PATHS "${TORCH_INSTALL_PREFIX}/lib")
+    list(APPEND TORCH_CUDA_LIBRARIES ${C10_CUDA_LIBRARY} ${Caffe2_PUBLIC_CUDA_DEPENDENCY_LIBS})
+  endif()
+  list(APPEND TORCH_LIBRARIES ${TORCH_CUDA_LIBRARIES})
+endif()
+
+# When we build libtorch with the old libstdc++ ABI, dependent libraries must too.
+if(CMAKE_SYSTEM_NAME STREQUAL "Linux")
+  set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=0")
+endif()
+
+find_library(TORCH_LIBRARY torch PATHS "${TORCH_INSTALL_PREFIX}/lib")
+# the statements below changes target properties on
+# - the imported target from Caffe2Targets.cmake in shared library mode (see the find_package above)
+#    - this is untested whether it is the correct (or desired) methodology in CMake
+# - the imported target created in this file in static library mode
+if(NOT ON)
+  # do not set this property on the shared library target, as it will cause confusion in some builds
+  # as the configuration specific property is set in the Caffe2Targets.cmake file
+  set_target_properties(torch PROPERTIES
+      IMPORTED_LOCATION "${TORCH_LIBRARY}"
+  )
+endif()
+set_target_properties(torch PROPERTIES
+    INTERFACE_INCLUDE_DIRECTORIES "${TORCH_INCLUDE_DIRS}"
+    CXX_STANDARD 17
+)
+if(TORCH_CXX_FLAGS)
+  set_property(TARGET torch PROPERTY INTERFACE_COMPILE_OPTIONS "${TORCH_CXX_FLAGS}")
+endif()
+
+find_package_handle_standard_args(Torch DEFAULT_MSG TORCH_LIBRARY TORCH_INCLUDE_DIRS)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Torch/TorchConfigVersion.cmake b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Torch/TorchConfigVersion.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..fd2c4b47afe8938b21cb6ffa22fb743399252ff9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/share/cmake/Torch/TorchConfigVersion.cmake
@@ -0,0 +1,11 @@
+set(PACKAGE_VERSION "2.5.1")
+
+# Check whether the requested PACKAGE_FIND_VERSION is compatible
+if("${PACKAGE_VERSION}" VERSION_LESS "${PACKAGE_FIND_VERSION}")
+  set(PACKAGE_VERSION_COMPATIBLE FALSE)
+else()
+  set(PACKAGE_VERSION_COMPATIBLE TRUE)
+  if("${PACKAGE_VERSION}" VERSION_EQUAL "${PACKAGE_FIND_VERSION}")
+    set(PACKAGE_VERSION_EXACT TRUE)
+  endif()
+endif()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/signal/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/signal/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3684eabe71215fe96689181065a9432f127aa8e7
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/signal/__init__.py
@@ -0,0 +1,5 @@
+from . import windows
+
+__all__ = [
+    'windows'
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/signal/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/signal/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..36b91d202b5d055b7507396964c86ebd04d31da0
Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/signal/__pycache__/__init__.cpython-311.pyc differ
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/signal/windows/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/signal/windows/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d04cde6e5b9280a465fb5db739f102337de74717
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/signal/windows/__init__.py
@@ -0,0 +1,28 @@
+from .windows import (
+    bartlett,
+    blackman,
+    cosine,
+    exponential,
+    gaussian,
+    general_cosine,
+    general_hamming,
+    hamming,
+    hann,
+    kaiser,
+    nuttall,
+)
+
+
+__all__ = [
+    'bartlett',
+    'blackman',
+    'cosine',
+    'exponential',
+    'gaussian',
+    'general_cosine',
+    'general_hamming',
+    'hamming',
+    'hann',
+    'kaiser',
+    'nuttall',
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/signal/windows/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/signal/windows/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c84b0e8ca73286eaa30e1517db0eac729d68975f
Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/signal/windows/__pycache__/__init__.cpython-311.pyc differ
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/signal/windows/__pycache__/windows.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/signal/windows/__pycache__/windows.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5e857fec6e5a73aa007c47238ee598b45bd6306c
Binary files /dev/null and b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/signal/windows/__pycache__/windows.cpython-311.pyc differ
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/signal/windows/windows.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/signal/windows/windows.py
new file mode 100644
index 0000000000000000000000000000000000000000..6626b6d1f3aaf245bd84588872b0aa3f03bb8221
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/signal/windows/windows.py
@@ -0,0 +1,807 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+from typing import Optional, Iterable
+
+import torch
+from math import sqrt
+
+from torch import Tensor
+from torch._torch_docs import factory_common_args, parse_kwargs, merge_dicts
+
+__all__ = [
+    'bartlett',
+    'blackman',
+    'cosine',
+    'exponential',
+    'gaussian',
+    'general_cosine',
+    'general_hamming',
+    'hamming',
+    'hann',
+    'kaiser',
+    'nuttall',
+]
+
+window_common_args = merge_dicts(
+    parse_kwargs(
+        """
+    M (int): the length of the window.
+        In other words, the number of points of the returned window.
+    sym (bool, optional): If `False`, returns a periodic window suitable for use in spectral analysis.
+        If `True`, returns a symmetric window suitable for use in filter design. Default: `True`.
+"""
+    ),
+    factory_common_args,
+    {
+        "normalization": "The window is normalized to 1 (maximum value is 1). However, the 1 doesn't appear if "
+                         ":attr:`M` is even and :attr:`sym` is `True`.",
+    }
+)
+
+
+def _add_docstr(*args):
+    r"""Adds docstrings to a given decorated function.
+
+    Specially useful when then docstrings needs string interpolation, e.g., with
+    str.format().
+    REMARK: Do not use this function if the docstring doesn't need string
+    interpolation, just write a conventional docstring.
+
+    Args:
+        args (str):
+    """
+
+    def decorator(o):
+        o.__doc__ = "".join(args)
+        return o
+
+    return decorator
+
+
+def _window_function_checks(function_name: str, M: int, dtype: torch.dtype, layout: torch.layout) -> None:
+    r"""Performs common checks for all the defined windows.
+     This function should be called before computing any window.
+
+     Args:
+         function_name (str): name of the window function.
+         M (int): length of the window.
+         dtype (:class:`torch.dtype`): the desired data type of returned tensor.
+         layout (:class:`torch.layout`): the desired layout of returned tensor.
+     """
+    if M < 0:
+        raise ValueError(f'{function_name} requires non-negative window length, got M={M}')
+    if layout is not torch.strided:
+        raise ValueError(f'{function_name} is implemented for strided tensors only, got: {layout}')
+    if dtype not in [torch.float32, torch.float64]:
+        raise ValueError(f'{function_name} expects float32 or float64 dtypes, got: {dtype}')
+
+
+@_add_docstr(
+    r"""
+Computes a window with an exponential waveform.
+Also known as Poisson window.
+
+The exponential window is defined as follows:
+
+.. math::
+    w_n = \exp{\left(-\frac{|n - c|}{\tau}\right)}
+
+where `c` is the ``center`` of the window.
+    """,
+    r"""
+
+{normalization}
+
+Args:
+    {M}
+
+Keyword args:
+    center (float, optional): where the center of the window will be located.
+        Default: `M / 2` if `sym` is `False`, else `(M - 1) / 2`.
+    tau (float, optional): the decay value.
+        Tau is generally associated with a percentage, that means, that the value should
+        vary within the interval (0, 100]. If tau is 100, it is considered the uniform window.
+        Default: 1.0.
+    {sym}
+    {dtype}
+    {layout}
+    {device}
+    {requires_grad}
+
+Examples::
+
+    >>> # Generates a symmetric exponential window of size 10 and with a decay value of 1.0.
+    >>> # The center will be at (M - 1) / 2, where M is 10.
+    >>> torch.signal.windows.exponential(10)
+    tensor([0.0111, 0.0302, 0.0821, 0.2231, 0.6065, 0.6065, 0.2231, 0.0821, 0.0302, 0.0111])
+
+    >>> # Generates a periodic exponential window and decay factor equal to .5
+    >>> torch.signal.windows.exponential(10, sym=False,tau=.5)
+    tensor([4.5400e-05, 3.3546e-04, 2.4788e-03, 1.8316e-02, 1.3534e-01, 1.0000e+00, 1.3534e-01, 1.8316e-02, 2.4788e-03, 3.3546e-04])
+    """.format(
+        **window_common_args
+    ),
+)
+def exponential(
+        M: int,
+        *,
+        center: Optional[float] = None,
+        tau: float = 1.0,
+        sym: bool = True,
+        dtype: Optional[torch.dtype] = None,
+        layout: torch.layout = torch.strided,
+        device: Optional[torch.device] = None,
+        requires_grad: bool = False
+) -> Tensor:
+    if dtype is None:
+        dtype = torch.get_default_dtype()
+
+    _window_function_checks('exponential', M, dtype, layout)
+
+    if tau <= 0:
+        raise ValueError(f'Tau must be positive, got: {tau} instead.')
+
+    if sym and center is not None:
+        raise ValueError('Center must be None for symmetric windows')
+
+    if M == 0:
+        return torch.empty((0,), dtype=dtype, layout=layout, device=device, requires_grad=requires_grad)
+
+    if center is None:
+        center = (M if not sym and M > 1 else M - 1) / 2.0
+
+    constant = 1 / tau
+
+    k = torch.linspace(start=-center * constant,
+                       end=(-center + (M - 1)) * constant,
+                       steps=M,
+                       dtype=dtype,
+                       layout=layout,
+                       device=device,
+                       requires_grad=requires_grad)
+
+    return torch.exp(-torch.abs(k))
+
+
+@_add_docstr(
+    r"""
+Computes a window with a simple cosine waveform, following the same implementation as SciPy.
+This window is also known as the sine window.
+
+The cosine window is defined as follows:
+
+.. math::
+    w_n = \sin\left(\frac{\pi (n + 0.5)}{M}\right)
+
+This formula differs from the typical cosine window formula by incorporating a 0.5 term in the numerator,
+which shifts the sample positions. This adjustment results in a window that starts and ends with non-zero values.
+
+""",
+    r"""
+
+{normalization}
+
+Args:
+    {M}
+
+Keyword args:
+    {sym}
+    {dtype}
+    {layout}
+    {device}
+    {requires_grad}
+
+Examples::
+
+    >>> # Generates a symmetric cosine window.
+    >>> torch.signal.windows.cosine(10)
+    tensor([0.1564, 0.4540, 0.7071, 0.8910, 0.9877, 0.9877, 0.8910, 0.7071, 0.4540, 0.1564])
+
+    >>> # Generates a periodic cosine window.
+    >>> torch.signal.windows.cosine(10, sym=False)
+    tensor([0.1423, 0.4154, 0.6549, 0.8413, 0.9595, 1.0000, 0.9595, 0.8413, 0.6549, 0.4154])
+""".format(
+        **window_common_args,
+    ),
+)
+def cosine(
+        M: int,
+        *,
+        sym: bool = True,
+        dtype: Optional[torch.dtype] = None,
+        layout: torch.layout = torch.strided,
+        device: Optional[torch.device] = None,
+        requires_grad: bool = False
+) -> Tensor:
+    if dtype is None:
+        dtype = torch.get_default_dtype()
+
+    _window_function_checks('cosine', M, dtype, layout)
+
+    if M == 0:
+        return torch.empty((0,), dtype=dtype, layout=layout, device=device, requires_grad=requires_grad)
+
+    start = 0.5
+    constant = torch.pi / (M + 1 if not sym and M > 1 else M)
+
+    k = torch.linspace(start=start * constant,
+                       end=(start + (M - 1)) * constant,
+                       steps=M,
+                       dtype=dtype,
+                       layout=layout,
+                       device=device,
+                       requires_grad=requires_grad)
+
+    return torch.sin(k)
+
+
+@_add_docstr(
+    r"""
+Computes a window with a gaussian waveform.
+
+The gaussian window is defined as follows:
+
+.. math::
+    w_n = \exp{\left(-\left(\frac{n}{2\sigma}\right)^2\right)}
+    """,
+    r"""
+
+{normalization}
+
+Args:
+    {M}
+
+Keyword args:
+    std (float, optional): the standard deviation of the gaussian. It controls how narrow or wide the window is.
+        Default: 1.0.
+    {sym}
+    {dtype}
+    {layout}
+    {device}
+    {requires_grad}
+
+Examples::
+
+    >>> # Generates a symmetric gaussian window with a standard deviation of 1.0.
+    >>> torch.signal.windows.gaussian(10)
+    tensor([4.0065e-05, 2.1875e-03, 4.3937e-02, 3.2465e-01, 8.8250e-01, 8.8250e-01, 3.2465e-01, 4.3937e-02, 2.1875e-03, 4.0065e-05])
+
+    >>> # Generates a periodic gaussian window and standard deviation equal to 0.9.
+    >>> torch.signal.windows.gaussian(10, sym=False,std=0.9)
+    tensor([1.9858e-07, 5.1365e-05, 3.8659e-03, 8.4658e-02, 5.3941e-01, 1.0000e+00, 5.3941e-01, 8.4658e-02, 3.8659e-03, 5.1365e-05])
+""".format(
+        **window_common_args,
+    ),
+)
+def gaussian(
+        M: int,
+        *,
+        std: float = 1.0,
+        sym: bool = True,
+        dtype: Optional[torch.dtype] = None,
+        layout: torch.layout = torch.strided,
+        device: Optional[torch.device] = None,
+        requires_grad: bool = False
+) -> Tensor:
+    if dtype is None:
+        dtype = torch.get_default_dtype()
+
+    _window_function_checks('gaussian', M, dtype, layout)
+
+    if std <= 0:
+        raise ValueError(f'Standard deviation must be positive, got: {std} instead.')
+
+    if M == 0:
+        return torch.empty((0,), dtype=dtype, layout=layout, device=device, requires_grad=requires_grad)
+
+    start = -(M if not sym and M > 1 else M - 1) / 2.0
+
+    constant = 1 / (std * sqrt(2))
+
+    k = torch.linspace(start=start * constant,
+                       end=(start + (M - 1)) * constant,
+                       steps=M,
+                       dtype=dtype,
+                       layout=layout,
+                       device=device,
+                       requires_grad=requires_grad)
+
+    return torch.exp(-k ** 2)
+
+
+@_add_docstr(
+    r"""
+Computes the Kaiser window.
+
+The Kaiser window is defined as follows:
+
+.. math::
+    w_n = I_0 \left( \beta \sqrt{1 - \left( {\frac{n - N/2}{N/2}} \right) ^2 } \right) / I_0( \beta )
+
+where ``I_0`` is the zeroth order modified Bessel function of the first kind (see :func:`torch.special.i0`), and
+``N = M - 1 if sym else M``.
+    """,
+    r"""
+
+{normalization}
+
+Args:
+    {M}
+
+Keyword args:
+    beta (float, optional): shape parameter for the window. Must be non-negative. Default: 12.0
+    {sym}
+    {dtype}
+    {layout}
+    {device}
+    {requires_grad}
+
+Examples::
+
+    >>> # Generates a symmetric gaussian window with a standard deviation of 1.0.
+    >>> torch.signal.windows.kaiser(5)
+    tensor([4.0065e-05, 2.1875e-03, 4.3937e-02, 3.2465e-01, 8.8250e-01, 8.8250e-01, 3.2465e-01, 4.3937e-02, 2.1875e-03, 4.0065e-05])
+    >>> # Generates a periodic gaussian window and standard deviation equal to 0.9.
+    >>> torch.signal.windows.kaiser(5, sym=False,std=0.9)
+    tensor([1.9858e-07, 5.1365e-05, 3.8659e-03, 8.4658e-02, 5.3941e-01, 1.0000e+00, 5.3941e-01, 8.4658e-02, 3.8659e-03, 5.1365e-05])
+""".format(
+        **window_common_args,
+    ),
+)
+def kaiser(
+        M: int,
+        *,
+        beta: float = 12.0,
+        sym: bool = True,
+        dtype: Optional[torch.dtype] = None,
+        layout: torch.layout = torch.strided,
+        device: Optional[torch.device] = None,
+        requires_grad: bool = False
+) -> Tensor:
+    if dtype is None:
+        dtype = torch.get_default_dtype()
+
+    _window_function_checks('kaiser', M, dtype, layout)
+
+    if beta < 0:
+        raise ValueError(f'beta must be non-negative, got: {beta} instead.')
+
+    if M == 0:
+        return torch.empty((0,), dtype=dtype, layout=layout, device=device, requires_grad=requires_grad)
+
+    if M == 1:
+        return torch.ones((1,), dtype=dtype, layout=layout, device=device, requires_grad=requires_grad)
+
+    # Avoid NaNs by casting `beta` to the appropriate dtype.
+    beta = torch.tensor(beta, dtype=dtype, device=device)
+
+    start = -beta
+    constant = 2.0 * beta / (M if not sym else M - 1)
+    end = torch.minimum(beta, start + (M - 1) * constant)
+
+    k = torch.linspace(start=start,
+                       end=end,
+                       steps=M,
+                       dtype=dtype,
+                       layout=layout,
+                       device=device,
+                       requires_grad=requires_grad)
+
+    return torch.i0(torch.sqrt(beta * beta - torch.pow(k, 2))) / torch.i0(beta)
+
+
+@_add_docstr(
+    r"""
+Computes the Hamming window.
+
+The Hamming window is defined as follows:
+
+.. math::
+    w_n = \alpha - \beta\ \cos \left( \frac{2 \pi n}{M - 1} \right)
+    """,
+    r"""
+
+{normalization}
+
+Arguments:
+    {M}
+
+Keyword args:
+    {sym}
+    alpha (float, optional): The coefficient :math:`\alpha` in the equation above.
+    beta (float, optional): The coefficient :math:`\beta` in the equation above.
+    {dtype}
+    {layout}
+    {device}
+    {requires_grad}
+
+Examples::
+
+    >>> # Generates a symmetric Hamming window.
+    >>> torch.signal.windows.hamming(10)
+    tensor([0.0800, 0.1876, 0.4601, 0.7700, 0.9723, 0.9723, 0.7700, 0.4601, 0.1876, 0.0800])
+
+    >>> # Generates a periodic Hamming window.
+    >>> torch.signal.windows.hamming(10, sym=False)
+    tensor([0.0800, 0.1679, 0.3979, 0.6821, 0.9121, 1.0000, 0.9121, 0.6821, 0.3979, 0.1679])
+""".format(
+        **window_common_args
+    ),
+)
+def hamming(M: int,
+            *,
+            sym: bool = True,
+            dtype: Optional[torch.dtype] = None,
+            layout: torch.layout = torch.strided,
+            device: Optional[torch.device] = None,
+            requires_grad: bool = False) -> Tensor:
+    return general_hamming(M, sym=sym, dtype=dtype, layout=layout, device=device, requires_grad=requires_grad)
+
+
+@_add_docstr(
+    r"""
+Computes the Hann window.
+
+The Hann window is defined as follows:
+
+.. math::
+    w_n = \frac{1}{2}\ \left[1 - \cos \left( \frac{2 \pi n}{M - 1} \right)\right] =
+    \sin^2 \left( \frac{\pi n}{M - 1} \right)
+    """,
+    r"""
+
+{normalization}
+
+Arguments:
+    {M}
+
+Keyword args:
+    {sym}
+    {dtype}
+    {layout}
+    {device}
+    {requires_grad}
+
+Examples::
+
+    >>> # Generates a symmetric Hann window.
+    >>> torch.signal.windows.hann(10)
+    tensor([0.0000, 0.1170, 0.4132, 0.7500, 0.9698, 0.9698, 0.7500, 0.4132, 0.1170, 0.0000])
+
+    >>> # Generates a periodic Hann window.
+    >>> torch.signal.windows.hann(10, sym=False)
+    tensor([0.0000, 0.0955, 0.3455, 0.6545, 0.9045, 1.0000, 0.9045, 0.6545, 0.3455, 0.0955])
+""".format(
+        **window_common_args
+    ),
+)
+def hann(M: int,
+         *,
+         sym: bool = True,
+         dtype: Optional[torch.dtype] = None,
+         layout: torch.layout = torch.strided,
+         device: Optional[torch.device] = None,
+         requires_grad: bool = False) -> Tensor:
+    return general_hamming(M,
+                           alpha=0.5,
+                           sym=sym,
+                           dtype=dtype,
+                           layout=layout,
+                           device=device,
+                           requires_grad=requires_grad)
+
+
+@_add_docstr(
+    r"""
+Computes the Blackman window.
+
+The Blackman window is defined as follows:
+
+.. math::
+    w_n = 0.42 - 0.5 \cos \left( \frac{2 \pi n}{M - 1} \right) + 0.08 \cos \left( \frac{4 \pi n}{M - 1} \right)
+    """,
+    r"""
+
+{normalization}
+
+Arguments:
+    {M}
+
+Keyword args:
+    {sym}
+    {dtype}
+    {layout}
+    {device}
+    {requires_grad}
+
+Examples::
+
+    >>> # Generates a symmetric Blackman window.
+    >>> torch.signal.windows.blackman(5)
+    tensor([-1.4901e-08,  3.4000e-01,  1.0000e+00,  3.4000e-01, -1.4901e-08])
+
+    >>> # Generates a periodic Blackman window.
+    >>> torch.signal.windows.blackman(5, sym=False)
+    tensor([-1.4901e-08,  2.0077e-01,  8.4923e-01,  8.4923e-01,  2.0077e-01])
+""".format(
+        **window_common_args
+    ),
+)
+def blackman(M: int,
+             *,
+             sym: bool = True,
+             dtype: Optional[torch.dtype] = None,
+             layout: torch.layout = torch.strided,
+             device: Optional[torch.device] = None,
+             requires_grad: bool = False) -> Tensor:
+    if dtype is None:
+        dtype = torch.get_default_dtype()
+
+    _window_function_checks('blackman', M, dtype, layout)
+
+    return general_cosine(M, a=[0.42, 0.5, 0.08], sym=sym, dtype=dtype, layout=layout, device=device,
+                          requires_grad=requires_grad)
+
+
+@_add_docstr(
+    r"""
+Computes the Bartlett window.
+
+The Bartlett window is defined as follows:
+
+.. math::
+    w_n = 1 - \left| \frac{2n}{M - 1} - 1 \right| = \begin{cases}
+        \frac{2n}{M - 1} & \text{if } 0 \leq n \leq \frac{M - 1}{2} \\
+        2 - \frac{2n}{M - 1} & \text{if } \frac{M - 1}{2} < n < M \\ \end{cases}
+    """,
+    r"""
+
+{normalization}
+
+Arguments:
+    {M}
+
+Keyword args:
+    {sym}
+    {dtype}
+    {layout}
+    {device}
+    {requires_grad}
+
+Examples::
+
+    >>> # Generates a symmetric Bartlett window.
+    >>> torch.signal.windows.bartlett(10)
+    tensor([0.0000, 0.2222, 0.4444, 0.6667, 0.8889, 0.8889, 0.6667, 0.4444, 0.2222, 0.0000])
+
+    >>> # Generates a periodic Bartlett window.
+    >>> torch.signal.windows.bartlett(10, sym=False)
+    tensor([0.0000, 0.2000, 0.4000, 0.6000, 0.8000, 1.0000, 0.8000, 0.6000, 0.4000, 0.2000])
+""".format(
+        **window_common_args
+    ),
+)
+def bartlett(M: int,
+             *,
+             sym: bool = True,
+             dtype: Optional[torch.dtype] = None,
+             layout: torch.layout = torch.strided,
+             device: Optional[torch.device] = None,
+             requires_grad: bool = False) -> Tensor:
+    if dtype is None:
+        dtype = torch.get_default_dtype()
+
+    _window_function_checks('bartlett', M, dtype, layout)
+
+    if M == 0:
+        return torch.empty((0,), dtype=dtype, layout=layout, device=device, requires_grad=requires_grad)
+
+    if M == 1:
+        return torch.ones((1,), dtype=dtype, layout=layout, device=device, requires_grad=requires_grad)
+
+    start = -1
+    constant = 2 / (M if not sym else M - 1)
+
+    k = torch.linspace(start=start,
+                       end=start + (M - 1) * constant,
+                       steps=M,
+                       dtype=dtype,
+                       layout=layout,
+                       device=device,
+                       requires_grad=requires_grad)
+
+    return 1 - torch.abs(k)
+
+
+@_add_docstr(
+    r"""
+Computes the general cosine window.
+
+The general cosine window is defined as follows:
+
+.. math::
+    w_n = \sum^{M-1}_{i=0} (-1)^i a_i \cos{ \left( \frac{2 \pi i n}{M - 1}\right)}
+    """,
+    r"""
+
+{normalization}
+
+Arguments:
+    {M}
+
+Keyword args:
+    a (Iterable): the coefficients associated to each of the cosine functions.
+    {sym}
+    {dtype}
+    {layout}
+    {device}
+    {requires_grad}
+
+Examples::
+
+    >>> # Generates a symmetric general cosine window with 3 coefficients.
+    >>> torch.signal.windows.general_cosine(10, a=[0.46, 0.23, 0.31], sym=True)
+    tensor([0.5400, 0.3376, 0.1288, 0.4200, 0.9136, 0.9136, 0.4200, 0.1288, 0.3376, 0.5400])
+
+    >>> # Generates a periodic general cosine window wit 2 coefficients.
+    >>> torch.signal.windows.general_cosine(10, a=[0.5, 1 - 0.5], sym=False)
+    tensor([0.0000, 0.0955, 0.3455, 0.6545, 0.9045, 1.0000, 0.9045, 0.6545, 0.3455, 0.0955])
+""".format(
+        **window_common_args
+    ),
+)
+def general_cosine(M, *,
+                   a: Iterable,
+                   sym: bool = True,
+                   dtype: Optional[torch.dtype] = None,
+                   layout: torch.layout = torch.strided,
+                   device: Optional[torch.device] = None,
+                   requires_grad: bool = False) -> Tensor:
+    if dtype is None:
+        dtype = torch.get_default_dtype()
+
+    _window_function_checks('general_cosine', M, dtype, layout)
+
+    if M == 0:
+        return torch.empty((0,), dtype=dtype, layout=layout, device=device, requires_grad=requires_grad)
+
+    if M == 1:
+        return torch.ones((1,), dtype=dtype, layout=layout, device=device, requires_grad=requires_grad)
+
+    if not isinstance(a, Iterable):
+        raise TypeError("Coefficients must be a list/tuple")
+
+    if not a:
+        raise ValueError("Coefficients cannot be empty")
+
+    constant = 2 * torch.pi / (M if not sym else M - 1)
+
+    k = torch.linspace(start=0,
+                       end=(M - 1) * constant,
+                       steps=M,
+                       dtype=dtype,
+                       layout=layout,
+                       device=device,
+                       requires_grad=requires_grad)
+
+    a_i = torch.tensor([(-1) ** i * w for i, w in enumerate(a)], device=device, dtype=dtype, requires_grad=requires_grad)
+    i = torch.arange(a_i.shape[0], dtype=a_i.dtype, device=a_i.device, requires_grad=a_i.requires_grad)
+    return (a_i.unsqueeze(-1) * torch.cos(i.unsqueeze(-1) * k)).sum(0)
+
+
+@_add_docstr(
+    r"""
+Computes the general Hamming window.
+
+The general Hamming window is defined as follows:
+
+.. math::
+    w_n = \alpha - (1 - \alpha) \cos{ \left( \frac{2 \pi n}{M-1} \right)}
+    """,
+    r"""
+
+{normalization}
+
+Arguments:
+    {M}
+
+Keyword args:
+    alpha (float, optional): the window coefficient. Default: 0.54.
+    {sym}
+    {dtype}
+    {layout}
+    {device}
+    {requires_grad}
+
+Examples::
+
+    >>> # Generates a symmetric Hamming window with the general Hamming window.
+    >>> torch.signal.windows.general_hamming(10, sym=True)
+    tensor([0.0800, 0.1876, 0.4601, 0.7700, 0.9723, 0.9723, 0.7700, 0.4601, 0.1876, 0.0800])
+
+    >>> # Generates a periodic Hann window with the general Hamming window.
+    >>> torch.signal.windows.general_hamming(10, alpha=0.5, sym=False)
+    tensor([0.0000, 0.0955, 0.3455, 0.6545, 0.9045, 1.0000, 0.9045, 0.6545, 0.3455, 0.0955])
+""".format(
+        **window_common_args
+    ),
+)
+def general_hamming(M,
+                    *,
+                    alpha: float = 0.54,
+                    sym: bool = True,
+                    dtype: Optional[torch.dtype] = None,
+                    layout: torch.layout = torch.strided,
+                    device: Optional[torch.device] = None,
+                    requires_grad: bool = False) -> Tensor:
+    return general_cosine(M,
+                          a=[alpha, 1. - alpha],
+                          sym=sym,
+                          dtype=dtype,
+                          layout=layout,
+                          device=device,
+                          requires_grad=requires_grad)
+
+
+@_add_docstr(
+    r"""
+Computes the minimum 4-term Blackman-Harris window according to Nuttall.
+
+.. math::
+    w_n = 1 - 0.36358 \cos{(z_n)} + 0.48917 \cos{(2z_n)} - 0.13659 \cos{(3z_n)} + 0.01064 \cos{(4z_n)}
+
+where :math:`z_n = \frac{2 \pi n}{M}`.
+    """,
+    """
+
+{normalization}
+
+Arguments:
+    {M}
+
+Keyword args:
+    {sym}
+    {dtype}
+    {layout}
+    {device}
+    {requires_grad}
+
+References::
+
+    - A. Nuttall, "Some windows with very good sidelobe behavior,"
+      IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 29, no. 1, pp. 84-91,
+      Feb 1981. https://doi.org/10.1109/TASSP.1981.1163506
+
+    - Heinzel G. et al., "Spectrum and spectral density estimation by the Discrete Fourier transform (DFT),
+      including a comprehensive list of window functions and some new flat-top windows",
+      February 15, 2002 https://holometer.fnal.gov/GH_FFT.pdf
+
+Examples::
+
+    >>> # Generates a symmetric Nutall window.
+    >>> torch.signal.windows.general_hamming(5, sym=True)
+    tensor([3.6280e-04, 2.2698e-01, 1.0000e+00, 2.2698e-01, 3.6280e-04])
+
+    >>> # Generates a periodic Nuttall window.
+    >>> torch.signal.windows.general_hamming(5, sym=False)
+    tensor([3.6280e-04, 1.1052e-01, 7.9826e-01, 7.9826e-01, 1.1052e-01])
+""".format(
+        **window_common_args
+    ),
+)
+def nuttall(
+        M: int,
+        *,
+        sym: bool = True,
+        dtype: Optional[torch.dtype] = None,
+        layout: torch.layout = torch.strided,
+        device: Optional[torch.device] = None,
+        requires_grad: bool = False
+) -> Tensor:
+    return general_cosine(M,
+                          a=[0.3635819, 0.4891775, 0.1365995, 0.0106411],
+                          sym=sym,
+                          dtype=dtype,
+                          layout=layout,
+                          device=device,
+                          requires_grad=requires_grad)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/sparse/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/sparse/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7203bf6a6fa4ad7f403abdc98fc2495db4411f32
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/sparse/__init__.py
@@ -0,0 +1,703 @@
+# mypy: allow-untyped-defs
+# The Tensor classes are added to this module by python_tensor.cpp
+# A workaround to support both TorchScript and MyPy:
+from typing import Any, List, Optional, Tuple, TYPE_CHECKING, Union
+
+import torch
+from torch import Tensor
+from torch._C import _add_docstr, _sparse  # type: ignore[attr-defined]
+
+# Semi structured sparsity support
+from .semi_structured import (
+    SparseSemiStructuredTensor,
+    SparseSemiStructuredTensorCUSPARSELT,
+    SparseSemiStructuredTensorCUTLASS,
+    to_sparse_semi_structured,
+)
+
+
+if TYPE_CHECKING:
+    from torch.types import _dtype as DType
+
+    DimOrDims = Optional[Union[int, Tuple[int, ...], List[int]]]
+else:
+    # The JIT doesn't understand Union, nor torch.dtype here
+    DType = int
+    DimOrDims = Optional[Tuple[int]]
+
+
+__all__ = [
+    "addmm",
+    "check_sparse_tensor_invariants",
+    "mm",
+    "sum",
+    "softmax",
+    "solve",
+    "log_softmax",
+    "SparseSemiStructuredTensor",
+    "SparseSemiStructuredTensorCUTLASS",
+    "SparseSemiStructuredTensorCUSPARSELT",
+    "to_sparse_semi_structured",
+    "as_sparse_gradcheck",
+]
+
+addmm = _add_docstr(
+    _sparse._sparse_addmm,
+    r"""
+sparse.addmm(mat, mat1, mat2, *, beta=1., alpha=1.) -> Tensor
+
+This function does exact same thing as :func:`torch.addmm` in the forward,
+except that it supports backward for sparse COO matrix :attr:`mat1`.
+When :attr:`mat1` is a COO tensor it must have `sparse_dim = 2`.
+When inputs are COO tensors, this function also supports backward for both inputs.
+
+Supports both CSR and COO storage formats.
+
+.. note::
+    This function doesn't support computing derivaties with respect to CSR matrices.
+
+Args:
+    mat (Tensor): a dense matrix to be added
+    mat1 (Tensor): a sparse matrix to be multiplied
+    mat2 (Tensor): a dense matrix to be multiplied
+    beta (Number, optional): multiplier for :attr:`mat` (:math:`\beta`)
+    alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`)
+""",
+)
+
+
+mm = _add_docstr(
+    _sparse._sparse_mm,
+    r"""
+    Performs a matrix multiplication of the sparse matrix :attr:`mat1`
+    and the (sparse or strided) matrix :attr:`mat2`. Similar to :func:`torch.mm`, if :attr:`mat1` is a
+    :math:`(n \times m)` tensor, :attr:`mat2` is a :math:`(m \times p)` tensor, out will be a
+    :math:`(n \times p)` tensor.
+    When :attr:`mat1` is a COO tensor it must have `sparse_dim = 2`.
+    When inputs are COO tensors, this function also supports backward for both inputs.
+
+    Supports both CSR and COO storage formats.
+
+.. note::
+    This function doesn't support computing derivaties with respect to CSR matrices.
+
+    This function also additionally accepts an optional :attr:`reduce` argument that allows
+    specification of an optional reduction operation, mathematically performs the following operation:
+
+.. math::
+
+    z_{ij} = \bigoplus_{k = 0}^{K - 1} x_{ik} y_{kj}
+
+where :math:`\bigoplus` defines the reduce operator. :attr:`reduce` is implemented only for
+CSR storage format on CPU device.
+
+Args:
+    mat1 (Tensor): the first sparse matrix to be multiplied
+    mat2 (Tensor): the second matrix to be multiplied, which could be sparse or dense
+    reduce (str, optional): the reduction operation to apply for non-unique indices
+        (:obj:`"sum"`, :obj:`"mean"`, :obj:`"amax"`, :obj:`"amin"`). Default :obj:`"sum"`.
+
+Shape:
+    The format of the output tensor of this function follows:
+    - sparse x sparse -> sparse
+    - sparse x dense -> dense
+
+Example::
+
+    >>> a = torch.tensor([[1., 0, 2], [0, 3, 0]]).to_sparse().requires_grad_()
+    >>> a
+    tensor(indices=tensor([[0, 0, 1],
+                           [0, 2, 1]]),
+           values=tensor([1., 2., 3.]),
+           size=(2, 3), nnz=3, layout=torch.sparse_coo, requires_grad=True)
+    >>> b = torch.tensor([[0, 1.], [2, 0], [0, 0]], requires_grad=True)
+    >>> b
+    tensor([[0., 1.],
+            [2., 0.],
+            [0., 0.]], requires_grad=True)
+    >>> y = torch.sparse.mm(a, b)
+    >>> y
+    tensor([[0., 1.],
+            [6., 0.]], grad_fn=)
+    >>> y.sum().backward()
+    >>> a.grad
+    tensor(indices=tensor([[0, 0, 1],
+                           [0, 2, 1]]),
+           values=tensor([1., 0., 2.]),
+           size=(2, 3), nnz=3, layout=torch.sparse_coo)
+    >>> c = a.detach().to_sparse_csr()
+    >>> c
+    tensor(crow_indices=tensor([0, 2, 3]),
+           col_indices=tensor([0, 2, 1]),
+           values=tensor([1., 2., 3.]), size=(2, 3), nnz=3,
+           layout=torch.sparse_csr)
+    >>> y1 = torch.sparse.mm(c, b, 'sum')
+    >>> y1
+    tensor([[0., 1.],
+            [6., 0.]], grad_fn=)
+    >>> y2 = torch.sparse.mm(c, b, 'max')
+    >>> y2
+    tensor([[0., 1.],
+            [6., 0.]], grad_fn=)
+""",
+)
+
+
+sampled_addmm = _add_docstr(
+    _sparse.sparse_sampled_addmm,
+    r"""
+sparse.sampled_addmm(input, mat1, mat2, *, beta=1., alpha=1., out=None) -> Tensor
+
+Performs a matrix multiplication of the dense matrices :attr:`mat1` and :attr:`mat2` at the locations
+specified by the sparsity pattern of :attr:`input`. The matrix :attr:`input` is added to the final result.
+
+Mathematically this performs the following operation:
+
+.. math::
+
+    \text{out} = \alpha\ (\text{mat1} \mathbin{@} \text{mat2})*\text{spy}(\text{input}) + \beta\ \text{input}
+
+where :math:`\text{spy}(\text{input})` is the sparsity pattern matrix of :attr:`input`, :attr:`alpha`
+and :attr:`beta` are the scaling factors.
+:math:`\text{spy}(\text{input})` has value 1 at the positions where :attr:`input` has non-zero values, and 0 elsewhere.
+
+.. note::
+    :attr:`input` must be a sparse CSR tensor. :attr:`mat1` and :attr:`mat2` must be dense tensors.
+
+Args:
+    input (Tensor): a sparse CSR matrix of shape `(m, n)` to be added and used to compute
+        the sampled matrix multiplication
+    mat1 (Tensor): a dense matrix of shape `(m, k)` to be multiplied
+    mat2 (Tensor): a dense matrix of shape `(k, n)` to be multiplied
+
+Keyword args:
+    beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`)
+    alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`)
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> input = torch.eye(3, device='cuda').to_sparse_csr()
+    >>> mat1 = torch.randn(3, 5, device='cuda')
+    >>> mat2 = torch.randn(5, 3, device='cuda')
+    >>> torch.sparse.sampled_addmm(input, mat1, mat2)
+    tensor(crow_indices=tensor([0, 1, 2, 3]),
+        col_indices=tensor([0, 1, 2]),
+        values=tensor([ 0.2847, -0.7805, -0.1900]), device='cuda:0',
+        size=(3, 3), nnz=3, layout=torch.sparse_csr)
+    >>> torch.sparse.sampled_addmm(input, mat1, mat2).to_dense()
+    tensor([[ 0.2847,  0.0000,  0.0000],
+        [ 0.0000, -0.7805,  0.0000],
+        [ 0.0000,  0.0000, -0.1900]], device='cuda:0')
+    >>> torch.sparse.sampled_addmm(input, mat1, mat2, beta=0.5, alpha=0.5)
+    tensor(crow_indices=tensor([0, 1, 2, 3]),
+        col_indices=tensor([0, 1, 2]),
+        values=tensor([ 0.1423, -0.3903, -0.0950]), device='cuda:0',
+        size=(3, 3), nnz=3, layout=torch.sparse_csr)
+""",
+)
+
+
+def sum(input: Tensor, dim: DimOrDims = None, dtype: Optional[DType] = None) -> Tensor:
+    r"""Return the sum of each row of the given sparse tensor.
+
+    Returns the sum of each row of the sparse tensor :attr:`input` in the given
+    dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions,
+    reduce over all of them. When sum over all ``sparse_dim``, this method
+    returns a dense tensor instead of a sparse tensor.
+
+    All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output
+    tensor having :attr:`dim` fewer dimensions than :attr:`input`.
+
+    During backward, only gradients at ``nnz`` locations of :attr:`input`
+    will propagate back. Note that the gradients of :attr:`input` is coalesced.
+
+    Args:
+        input (Tensor): the input sparse tensor
+        dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce
+            over all dims.
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor.
+            Default: dtype of :attr:`input`.
+
+    Example::
+
+        >>> nnz = 3
+        >>> dims = [5, 5, 2, 3]
+        >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)),
+                           torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz)
+        >>> V = torch.randn(nnz, dims[2], dims[3])
+        >>> size = torch.Size(dims)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> S = torch.sparse_coo_tensor(I, V, size)
+        >>> S
+        tensor(indices=tensor([[2, 0, 3],
+                               [2, 4, 1]]),
+               values=tensor([[[-0.6438, -1.6467,  1.4004],
+                               [ 0.3411,  0.0918, -0.2312]],
+
+                              [[ 0.5348,  0.0634, -2.0494],
+                               [-0.7125, -1.0646,  2.1844]],
+
+                              [[ 0.1276,  0.1874, -0.6334],
+                               [-1.9682, -0.5340,  0.7483]]]),
+               size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo)
+
+        # when sum over only part of sparse_dims, return a sparse tensor
+        >>> torch.sparse.sum(S, [1, 3])
+        tensor(indices=tensor([[0, 2, 3]]),
+               values=tensor([[-1.4512,  0.4073],
+                              [-0.8901,  0.2017],
+                              [-0.3183, -1.7539]]),
+               size=(5, 2), nnz=3, layout=torch.sparse_coo)
+
+        # when sum over all sparse dim, return a dense tensor
+        # with summed dims squeezed
+        >>> torch.sparse.sum(S, [0, 1, 3])
+        tensor([-2.6596, -1.1450])
+    """
+    if dtype is None:
+        if dim is not None:
+            return torch._sparse_sum(input, dim)
+        else:
+            return torch._sparse_sum(input)
+    else:
+        if dim is not None:
+            return torch._sparse_sum(input, dim, dtype=dtype)
+        else:
+            return torch._sparse_sum(input, dtype=dtype)
+
+
+softmax = _add_docstr(
+    _sparse._sparse_softmax,
+    r"""
+sparse.softmax(input, dim, *, dtype=None) -> Tensor
+
+Applies a softmax function.
+
+Softmax is defined as:
+
+:math:`\text{Softmax}(x_{i}) = \frac{exp(x_i)}{\sum_j exp(x_j)}`
+
+where :math:`i, j` run over sparse tensor indices and unspecified
+entries are ignores. This is equivalent to defining unspecified
+entries as negative infinity so that :math:`exp(x_k) = 0` when the
+entry with index :math:`k` has not specified.
+
+It is applied to all slices along `dim`, and will re-scale them so
+that the elements lie in the range `[0, 1]` and sum to 1.
+
+Args:
+    input (Tensor): input
+    dim (int): A dimension along which softmax will be computed.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+        of returned tensor.  If specified, the input tensor is
+        casted to :attr:`dtype` before the operation is
+        performed. This is useful for preventing data type
+        overflows. Default: None
+""",
+)
+
+
+spsolve = _add_docstr(
+    _sparse._spsolve,
+    r"""
+sparse.spsolve(input, other, *, left=True) -> Tensor
+
+Computes the solution of a square system of linear equations with
+a unique solution. Its purpose is similar to :func:`torch.linalg.solve`,
+except that the system is defined by a sparse CSR matrix with layout
+`sparse_csr`.
+
+Args:
+    input (Tensor): a sparse CSR matrix of shape `(n, n)` representing the
+        coefficients of the linear system.
+    other (Tensor): a dense matrix of shape `(n, )` representing the right-hand
+        side of the linear system.
+    left (bool, optional): whether to solve the system for `input @ out = other`
+        (default) or `out @ input = other`. Only `left=True` is supported.
+""",
+)
+
+log_softmax = _add_docstr(
+    _sparse._sparse_log_softmax,
+    r"""
+sparse.log_softmax(input, dim, *, dtype=None) -> Tensor
+
+Applies a softmax function followed by logarithm.
+
+See :class:`~torch.sparse.softmax` for more details.
+
+Args:
+    input (Tensor): input
+    dim (int): A dimension along which softmax will be computed.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+        of returned tensor.  If specified, the input tensor is
+        casted to :attr:`dtype` before the operation is
+        performed. This is useful for preventing data type
+        overflows. Default: None
+""",
+)
+
+
+spdiags = _add_docstr(
+    _sparse._spdiags,
+    r"""
+sparse.spdiags(diagonals, offsets, shape, layout=None) -> Tensor
+
+Creates a sparse 2D tensor by placing the values from rows of
+:attr:`diagonals` along specified diagonals of the output
+
+The :attr:`offsets` tensor controls which diagonals are set.
+
+- If :attr:`offsets[i]` = 0, it is the main diagonal
+- If :attr:`offsets[i]` < 0, it is below the main diagonal
+- If :attr:`offsets[i]` > 0, it is above the main diagonal
+
+The number of rows in :attr:`diagonals` must match the length of :attr:`offsets`,
+and an offset may not be repeated.
+
+Args:
+    diagonals (Tensor): Matrix storing diagonals row-wise
+    offsets (Tensor): The diagonals to be set, stored as a vector
+    shape (2-tuple of ints): The desired shape of the result
+Keyword args:
+    layout (:class:`torch.layout`, optional): The desired layout of the
+        returned tensor. ``torch.sparse_coo``, ``torch.sparse_csc`` and ``torch.sparse_csr``
+        are supported. Default: ``torch.sparse_coo``
+
+Examples:
+
+Set the main and first two lower diagonals of a matrix::
+
+    >>> diags = torch.arange(9).reshape(3, 3)
+    >>> diags
+    tensor([[0, 1, 2],
+            [3, 4, 5],
+            [6, 7, 8]])
+    >>> s = torch.sparse.spdiags(diags, torch.tensor([0, -1, -2]), (3, 3))
+    >>> s
+    tensor(indices=tensor([[0, 1, 2, 1, 2, 2],
+                           [0, 1, 2, 0, 1, 0]]),
+           values=tensor([0, 1, 2, 3, 4, 6]),
+           size=(3, 3), nnz=6, layout=torch.sparse_coo)
+    >>> s.to_dense()
+    tensor([[0, 0, 0],
+            [3, 1, 0],
+            [6, 4, 2]])
+
+
+Change the output layout::
+
+    >>> diags = torch.arange(9).reshape(3, 3)
+    >>> diags
+    tensor([[0, 1, 2],[3, 4, 5], [6, 7, 8])
+    >>> s = torch.sparse.spdiags(diags, torch.tensor([0, -1, -2]), (3, 3), layout=torch.sparse_csr)
+    >>> s
+    tensor(crow_indices=tensor([0, 1, 3, 6]),
+           col_indices=tensor([0, 0, 1, 0, 1, 2]),
+           values=tensor([0, 3, 1, 6, 4, 2]), size=(3, 3), nnz=6,
+           layout=torch.sparse_csr)
+    >>> s.to_dense()
+    tensor([[0, 0, 0],
+            [3, 1, 0],
+            [6, 4, 2]])
+
+Set partial diagonals of a large output::
+
+    >>> diags = torch.tensor([[1, 2], [3, 4]])
+    >>> offsets = torch.tensor([0, -1])
+    >>> torch.sparse.spdiags(diags, offsets, (5, 5)).to_dense()
+    tensor([[1, 0, 0, 0, 0],
+            [3, 2, 0, 0, 0],
+            [0, 4, 0, 0, 0],
+            [0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0]])
+
+.. note::
+
+    When setting the values along a given diagonal the index into the diagonal
+    and the index into the row of :attr:`diagonals` is taken as the
+    column index in the output. This has the effect that when setting a diagonal
+    with a positive offset `k` the first value along that diagonal will be
+    the value in position `k` of the row of :attr:`diagonals`
+
+Specifying a positive offset::
+
+    >>> diags = torch.tensor([[1, 2, 3], [1, 2, 3], [1, 2, 3]])
+    >>> torch.sparse.spdiags(diags, torch.tensor([0, 1, 2]), (5, 5)).to_dense()
+    tensor([[1, 2, 3, 0, 0],
+            [0, 2, 3, 0, 0],
+            [0, 0, 3, 0, 0],
+            [0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0]])
+""",
+)
+
+
+class check_sparse_tensor_invariants:
+    """A tool to control checking sparse tensor invariants.
+
+    The following options exists to manage sparsr tensor invariants
+    checking in sparse tensor construction:
+
+    1. Using a context manager:
+
+       .. code:: python
+
+           with torch.sparse.check_sparse_tensor_invariants():
+               run_my_model()
+
+    2. Using a procedural approach:
+
+       .. code:: python
+
+           prev_checks_enabled = torch.sparse.check_sparse_tensor_invariants.is_enabled()
+           torch.sparse.check_sparse_tensor_invariants.enable()
+
+           run_my_model()
+
+           if not prev_checks_enabled:
+               torch.sparse.check_sparse_tensor_invariants.disable()
+
+    3. Using function decoration:
+
+       .. code:: python
+
+           @torch.sparse.check_sparse_tensor_invariants()
+           def run_my_model():
+               ...
+
+           run_my_model()
+
+    4. Using ``check_invariants`` keyword argument in sparse tensor constructor call.
+       For example:
+
+       >>> torch.sparse_csr_tensor([0, 1, 3], [0, 1], [1, 2], check_invariants=True)
+       Traceback (most recent call last):
+         File "", line 1, in 
+       RuntimeError: `crow_indices[..., -1] == nnz` is not satisfied.
+    """
+
+    @staticmethod
+    def is_enabled():
+        r"""Return True if the sparse tensor invariants checking is enabled.
+
+        .. note::
+
+            Use :func:`torch.sparse.check_sparse_tensor_invariants.enable` or
+            :func:`torch.sparse.check_sparse_tensor_invariants.disable` to
+            manage the state of the sparse tensor invariants checks.
+        """
+        return torch._C._check_sparse_tensor_invariants()
+
+    @staticmethod
+    def enable():
+        r"""Enable sparse tensor invariants checking in sparse tensor constructors.
+
+        .. note::
+
+            By default, the sparse tensor invariants checks are disabled. Use
+            :func:`torch.sparse.check_sparse_tensor_invariants.is_enabled` to
+            retrieve the current state of sparse tensor invariants checking.
+
+        .. note::
+
+            The sparse tensor invariants check flag is effective to all sparse
+            tensor constructors, both in Python and ATen.
+
+        The flag can be locally overridden by the ``check_invariants``
+        optional argument of the sparse tensor constructor functions.
+        """
+        torch._C._set_check_sparse_tensor_invariants(True)
+
+    @staticmethod
+    def disable():
+        r"""Disable sparse tensor invariants checking in sparse tensor constructors.
+
+        See :func:`torch.sparse.check_sparse_tensor_invariants.enable` for more information.
+        """
+        torch._C._set_check_sparse_tensor_invariants(False)
+
+    # context manager support
+    def __init__(self, enable=True):
+        self.state = enable
+        self.saved_state: Optional[bool] = None
+
+    def __enter__(self):
+        if self.saved_state is not None:
+            raise RuntimeError(
+                "This context manager instance is already activated."
+                " Use a different context manager instance for context nesting."
+            )
+        self.saved_state = self.is_enabled()
+        torch._C._set_check_sparse_tensor_invariants(self.state)
+
+    def __exit__(self, type, value, traceback):
+        assert self.saved_state is not None
+        torch._C._set_check_sparse_tensor_invariants(self.saved_state)
+        self.saved_state = None
+
+    # decorator support
+    def __call__(self, mth):
+        def test_mth(*args, **kwargs):
+            with type(self)(self.state):
+                return mth(*args, **kwargs)
+
+        return test_mth
+
+
+def as_sparse_gradcheck(gradcheck):
+    """Decorate function, to extend gradcheck for sparse tensors.
+
+    Decorator for torch.autograd.gradcheck or its functools.partial
+    variants that extends the gradcheck function with support to input
+    functions that operate on or/and return sparse tensors.
+
+    The specified gradcheck function itself is guaranteed to operate
+    on strided tensors only.
+
+    For example:
+
+    >>> gradcheck = torch.sparse.as_sparse_gradcheck(torch.autograd.gradcheck)
+    >>> x = torch.tensor([[0, 1], [2, 3]], dtype=torch.float64).to_sparse_coo().requires_grad_(True)
+    >>> gradcheck(lambda x: x.to_sparse_csr(), x)
+    True
+    """
+
+    def gradcheck_with_sparse_support(func, inputs, **kwargs):
+        """
+        Create gradcheck with support for sparse tensors.
+
+        Same as :func:`torch.autograd.gradcheck` but with sparse tensors inputs and outputs support.
+        """
+        masked = kwargs.pop("masked", False)
+        sparse_layouts = {
+            torch.sparse_coo,
+            torch.sparse_csr,
+            torch.sparse_csc,
+            torch.sparse_bsr,
+            torch.sparse_bsc,
+        }
+        sparse_compressed_layouts = {
+            torch.sparse_csr,
+            torch.sparse_csc,
+            torch.sparse_bsr,
+            torch.sparse_bsc,
+        }
+        sparse_block_layouts = {torch.sparse_bsr, torch.sparse_bsc}
+        STRIDED_REPRESENTATION = "__STRIDED_REPRESENTATION__"
+
+        def convert_to_strided_representation(args):
+            """Convert differentiable non-strided tensors to a representation containing differentiable strided tensors."""
+            if not isinstance(args, (list, tuple)):
+                args = (args,)
+            new_args: List[Any] = []
+            for obj in args:
+                if (
+                    isinstance(obj, torch.Tensor)
+                    and obj.requires_grad
+                    and obj.layout in sparse_layouts
+                ):
+                    d = dict(layout=obj.layout, shape=obj.shape)
+                    if not masked:
+                        # Materialize unspecified elements with zero values
+                        batch_dim = obj.ndim - obj.dense_dim() - obj.sparse_dim()
+                        blocksize = (
+                            obj.values().shape[batch_dim + 1 : batch_dim + 3]
+                            if obj.layout in sparse_block_layouts
+                            else None
+                        )
+                        full_mask = torch.ones(
+                            obj.shape, device=obj.device, dtype=torch.bool
+                        ).to_sparse(
+                            layout=obj.layout,
+                            blocksize=blocksize,
+                            dense_dim=obj.dense_dim(),
+                        )
+                        obj = obj.to_dense().sparse_mask(full_mask)
+                    if obj.layout is torch.sparse_coo:
+                        d.update(
+                            indices=obj._indices(), is_coalesced=obj.is_coalesced()
+                        )
+                        values = obj._values()
+                    elif obj.layout in {torch.sparse_csr, torch.sparse_bsr}:
+                        d.update(
+                            compressed_indices=obj.crow_indices(),
+                            plain_indices=obj.col_indices(),
+                        )
+                        values = obj.values()
+                    else:
+                        d.update(
+                            compressed_indices=obj.ccol_indices(),
+                            plain_indices=obj.row_indices(),
+                        )
+                        values = obj.values()
+                    new_args.extend(
+                        (STRIDED_REPRESENTATION, d, values.requires_grad_(True))
+                    )
+                else:
+                    new_args.append(obj)
+            return tuple(new_args)
+
+        def restore_from_strided_representation(args):
+            """Restore non-strided differentiable tensosr from their strided representations."""
+            new_args = []
+            args = list(args)
+            while args:
+                a = args.pop(0)
+                if a == STRIDED_REPRESENTATION:
+                    d, values = args.pop(0), args.pop(0)
+                    if d["layout"] is torch.sparse_coo:
+                        a = torch.sparse_coo_tensor(
+                            d["indices"],
+                            values,
+                            size=d["shape"],
+                            is_coalesced=d["is_coalesced"],
+                        )
+                    elif d["layout"] in sparse_compressed_layouts:
+                        a = torch.sparse_compressed_tensor(
+                            d["compressed_indices"],
+                            d["plain_indices"],
+                            values,
+                            size=d["shape"],
+                            layout=d["layout"],
+                        )
+                    else:
+                        raise NotImplementedError(
+                            f'conversion of {d["layout"]} strided representation to tensor'
+                        )
+                new_args.append(a)
+            return tuple(new_args)
+
+        def func_wrapper(*args, **kwargs):
+            restored_args = restore_from_strided_representation(args)
+
+            # convert differentiable output sparse tensors to strided
+            # tensors:
+            outputs = func(*restored_args, **kwargs)
+
+            strided_outputs = (
+                tuple(outputs) if isinstance(outputs, (list, tuple)) else (outputs,)
+            )
+            strided_outputs = tuple(
+                (
+                    o.to_dense(masked_grad=masked)
+                    if isinstance(o, torch.Tensor)
+                    and o.requires_grad
+                    and o.layout in sparse_layouts
+                    else o
+                )
+                for o in strided_outputs
+            )
+
+            return (
+                strided_outputs
+                if isinstance(outputs, (list, tuple))
+                else strided_outputs[0]
+            )
+
+        args = (func_wrapper, convert_to_strided_representation(inputs))
+
+        return gradcheck(*args, **kwargs)
+
+    return gradcheck_with_sparse_support
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/sparse/_semi_structured_conversions.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/sparse/_semi_structured_conversions.py
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index 0000000000000000000000000000000000000000..0828355202b576afb37584b8cb2e3b2c02e54367
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/sparse/_semi_structured_conversions.py
@@ -0,0 +1,356 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+def _calculate_meta_reordering_scatter_offsets(m, meta_ncols, meta_dtype, device):
+    """
+    This is PyTorch implementation of main part of reorder_meta()
+    function, from tools/util/include/cutlass/util/host_reorder.h file
+    of CUTLASS source tree.  Furthermore, CUTLASS template for sparse
+    GEMM decides upon layout of this matrix, and at the moment for the
+    sparse GEMM executed on tensor cores, this is layout described by
+    ColumnMajorInterleaved<2> data structure, in
+    include/cutlass/layout/matrix.h of CUTLASS source tree.  The
+    reordering of meta matrix into meta_reordered matrix calculated
+    according to these segments of CUTLASS code is re-implemented here.
+    Note that this calculation produces offsets for scattering metadata
+    matrix elements into reordered metadata matrix elements (or,
+    equivalently, for gathering reordered metadata matrix element back
+    into metadata matrix elements).
+    """
+    dst_rows = torch.arange(0, m, device=device)[:, None].repeat(1, meta_ncols)
+    dst_cols = torch.arange(0, meta_ncols, device=device).repeat(m, 1)
+
+    # Reorder the rows, then swizzle the 2x2 blocks.
+    group = 32 if meta_dtype.itemsize == 2 else 16
+    interweave = 4 if meta_dtype.itemsize == 2 else 2
+    dst_rows = (
+        dst_rows // group * group
+        + (dst_rows % 8) * interweave
+        + (dst_rows % group) // 8
+    )
+
+    topright = ((dst_rows % 2 == 0) & (dst_cols % 2 == 1)).to(torch.int8)
+    bottomleft = ((dst_rows % 2 == 1) & (dst_cols % 2 == 0)).to(torch.int8)
+    dst_rows += topright - bottomleft
+    dst_cols -= topright - bottomleft
+
+    # Assumed that meta tensor is to be stored in CUTLASS
+    # InterleavedColumnMajor layout, and reverse engineered
+    # corresponding code to store values into this tensor.
+    interleave = 2
+    cols_maj = dst_cols // interleave
+    cols_min = dst_cols % interleave
+    return (cols_maj * m * interleave + dst_rows * interleave + cols_min).view(-1)
+
+
+def sparse_semi_structured_from_dense_cutlass(dense):
+    """
+    This function converts dense matrix into sparse semi-structured
+    representation, producing "compressed" matrix, in the layout used by
+    CUTLASS backend, and corresponding metadata matrix.
+    """
+    if dense.dim() != 2:
+        raise RuntimeError(
+            f"Expected 2-dimensional dense tensor, got {dense.dim()}-dimensional tensor"
+        )
+
+    m, k = dense.shape
+    device = dense.device
+
+    meta_dtype = torch.int8
+    if dense.dtype == torch.int8:
+        meta_dtype = torch.int32
+    elif dense.dtype in [torch.half, torch.bfloat16, torch.float]:
+        meta_dtype = torch.int16
+    else:
+        raise RuntimeError(f"Invalid datatype {dense.dtype} of dense matrix")
+    quadbits_per_meta_elem = meta_dtype.itemsize * 8 // 4
+    if quadbits_per_meta_elem not in (4, 8):
+        raise RuntimeError("Invalid number of elements per meta element calculated")
+
+    if meta_dtype == torch.int32:
+        if m % 16 != 0:
+            raise RuntimeError(
+                f"Number of rows of dense matrix {m} must be divisible by 16"
+            )
+    else:
+        if m % 32 != 0:
+            raise RuntimeError(
+                f"Number of rows of dense matrix {m} must be divisible by 32"
+            )
+    if k % (4 * quadbits_per_meta_elem) != 0:
+        raise RuntimeError(
+            f"Number of columns of dense matrix {k} must be divisible by {4 * quadbits_per_meta_elem}"
+        )
+
+    if dense.dtype != torch.float:
+        ksparse = 4
+        dense_4 = dense.view(-1, k // ksparse, ksparse)
+        m0, m1, m2, m3 = (dense_4 != 0).unbind(-1)
+    else:
+        ksparse = 2
+        dense_2 = dense.view(-1, k // ksparse, ksparse)
+        m0, m2 = m1, m3 = (dense_2 != 0).unbind(-1)
+    meta_ncols = k // (ksparse * quadbits_per_meta_elem)
+
+    # Encoding quadruples of True/False values as follows:
+    #     [True,  True,  False, False] -> 0b0100
+    #     [True,  False, True,  False] -> 0b1000
+    #     [False, True,  True,  False] -> 0b1001
+    #     [True,  False, False, True ] -> 0b1100
+    #     [False, True,  False, True ] -> 0b1101
+    #     [False, False, True,  True ] -> 0b1110
+    # Thus, lower two bits in the encoding are index of the True value
+    # at the lowest index in the quadruple, and the higher two bits in
+    # the encoding are index of the other True value in the quadruple.
+    # In case there are less than two True values, than False value or
+    # values at some index or indices are considered True for the
+    # encoding.  In case there are more than two True values, then the
+    # excess True value(s) at some indices are considered False for
+    # the encoding.  The exact encodings used for these cases are as
+    # follows:
+    #     [False, False, False, False] -> 0b1110
+    #     [False, False, False, True ] -> 0b1110
+    #     [False, False, True,  False] -> 0b1110
+    #     [False, True,  False, False] -> 0b1001
+    #     [False, True,  True,  True ] -> 0b1101
+    #     [True,  False, False, False] -> 0b1000
+    #     [True,  False, True,  True ] -> 0b1100
+    #     [True,  True,  False, True ] -> 0b0100
+    #     [True,  True,  True,  False] -> 0b0100
+    #     [True,  True,  True,  True ] -> 0b0100
+    # These particular encodings are chosen, with the help of Espresso
+    # logic minimizer software, for the purpose of minimization of
+    # corresponding Boolean functions, that translate non-zero flags
+    # into encoding bits.  Note also possible choices for the first
+    # and last of these encodings were limited only to (0b0100,
+    # 0b1110), in order to produce valid encodings for 1:2 sparsity
+    # case.
+
+    expr0 = m0 & m1
+    expr1 = ~m0 & m1
+    expr2 = ~m0 & ~m1
+    bit0 = expr1
+    bit1 = expr2
+    bit2 = expr0 | expr2 | m3
+    bit3 = expr1 | ~m1
+    idxs0 = bit0 | (bit1.to(torch.int64) << 1)
+    idxs1 = bit2 | (bit3.to(torch.int64) << 1)
+
+    if dense.dtype != torch.float:
+        sparse0 = dense_4.gather(-1, idxs0.unsqueeze(-1))  # type: ignore[possibly-undefined]
+        sparse1 = dense_4.gather(-1, idxs1.unsqueeze(-1))
+        sparse = torch.stack((sparse0, sparse1), dim=-1).view(m, k // 2)
+    else:
+        sparse = dense_2.gather(-1, idxs0.unsqueeze(-1) // 2).view(m, k // 2)  # type: ignore[possibly-undefined]
+
+    meta_4 = idxs0 | (idxs1 << 2)
+    meta_n = meta_4.view((-1, meta_ncols, quadbits_per_meta_elem)).to(meta_dtype)
+
+    if quadbits_per_meta_elem == 4:
+        meta = (
+            meta_n[:, :, 0]
+            | (meta_n[:, :, 1] << 4)
+            | (meta_n[:, :, 2] << 8)
+            | (meta_n[:, :, 3] << 12)
+        )
+    elif quadbits_per_meta_elem == 8:
+        meta = (
+            meta_n[:, :, 0]
+            | (meta_n[:, :, 1] << 4)
+            | (meta_n[:, :, 2] << 8)
+            | (meta_n[:, :, 3] << 12)
+            | (meta_n[:, :, 4] << 16)
+            | (meta_n[:, :, 5] << 20)
+            | (meta_n[:, :, 6] << 24)
+            | (meta_n[:, :, 7] << 28)
+        )
+
+    # Reorder meta tensor elements.
+    meta_reordered = meta.new_empty((m * meta_ncols,))  # type: ignore[possibly-undefined]
+    meta_offsets = _calculate_meta_reordering_scatter_offsets(
+        m, meta_ncols, meta_dtype, device
+    )
+    meta_reordered.scatter_(0, meta_offsets, meta.view(-1))
+
+    return (sparse, meta_reordered.view(m, meta_ncols))
+
+
+def sparse_semi_structured_to_dense_cutlass(sparse, meta_reordered):
+    """
+    This function performs reverse of the function above - it
+    reconstructs dense matrix from a pair of "compressed" matrix, given
+    in the layout used by CUTLASS backend, and accompanying metadata
+    matrix.
+    """
+    if sparse.dim() != 2:
+        raise RuntimeError(
+            f"Expected 2-dimensional sparse tensor, got {sparse.dim()}-dimensional tensor"
+        )
+
+    m, k = sparse.shape
+    device = sparse.device
+
+    if meta_reordered.dim() != 2:
+        raise RuntimeError(
+            f"Expected 2-dimensional meta tensor, got {meta_reordered.dim()}-dimensional tensor"
+        )
+    if meta_reordered.device != device:
+        raise RuntimeError(
+            f"Expected meta matrix to be on {device} device, got matrix on {meta_reordered.device} device"
+        )
+
+    meta_dtype = meta_reordered.dtype
+    if meta_dtype not in (torch.int16, torch.int32):
+        raise RuntimeError(f"Invalid datatype {meta_dtype} of meta matrix")
+    quadbits_per_meta_elem = meta_dtype.itemsize * 8 // 4
+
+    if sparse.dtype != torch.float:
+        ksparse = 4
+    else:
+        ksparse = 2
+
+    meta_nrows, meta_ncols = meta_reordered.shape
+    if meta_nrows != m:
+        raise RuntimeError(
+            f"Number of rows of meta matrix {meta_nrows} must be equal to number of columns of spase matrix {m}"
+        )
+    if meta_ncols * ksparse * quadbits_per_meta_elem != 2 * k:
+        raise RuntimeError(
+            f"Number of columns of sparse matrix {k} different from the {meta_ncols * ksparse * quadbits_per_meta_elem // 2}, "
+            "expected according to the number of columns of meta matrix"
+        )
+
+    # Undo meta tensor elements reordering.
+    meta_offsets = _calculate_meta_reordering_scatter_offsets(
+        m, meta_ncols, meta_dtype, device
+    )
+    meta = torch.gather(meta_reordered.view(-1), 0, meta_offsets).view(m, meta_ncols)
+
+    # Unpack sparse tensor back to original dense tensor, using
+    # information provided by meta tensor.  Note that torch.float
+    # datatype is handled pretty much the same as
+    # torch.half/torch.bfloat16, as metadata for a pair of torch.float
+    # value is encoded as if underlying 8 bytes contain four
+    # torch.half/torch.bfloat16 values, where either first two or last
+    # two are zeros.
+    meta_2 = torch.empty(
+        (m, meta_ncols, 2 * quadbits_per_meta_elem),
+        dtype=meta_dtype,
+        device=device,
+    )
+    if quadbits_per_meta_elem == 4:
+        meta_2[:, :, 0] = meta & 0b11
+        meta_2[:, :, 1] = (meta >> 2) & 0b11
+        meta_2[:, :, 2] = (meta >> 4) & 0b11
+        meta_2[:, :, 3] = (meta >> 6) & 0b11
+        meta_2[:, :, 4] = (meta >> 8) & 0b11
+        meta_2[:, :, 5] = (meta >> 10) & 0b11
+        meta_2[:, :, 6] = (meta >> 12) & 0b11
+        meta_2[:, :, 7] = (meta >> 14) & 0b11
+    elif quadbits_per_meta_elem == 8:
+        meta_2[:, :, 0] = meta & 0b11
+        meta_2[:, :, 1] = (meta >> 2) & 0b11
+        meta_2[:, :, 2] = (meta >> 4) & 0b11
+        meta_2[:, :, 3] = (meta >> 6) & 0b11
+        meta_2[:, :, 4] = (meta >> 8) & 0b11
+        meta_2[:, :, 5] = (meta >> 10) & 0b11
+        meta_2[:, :, 6] = (meta >> 12) & 0b11
+        meta_2[:, :, 7] = (meta >> 14) & 0b11
+        meta_2[:, :, 8] = (meta >> 16) & 0b11
+        meta_2[:, :, 9] = (meta >> 18) & 0b11
+        meta_2[:, :, 10] = (meta >> 20) & 0b11
+        meta_2[:, :, 11] = (meta >> 22) & 0b11
+        meta_2[:, :, 12] = (meta >> 24) & 0b11
+        meta_2[:, :, 13] = (meta >> 26) & 0b11
+        meta_2[:, :, 14] = (meta >> 28) & 0b11
+        meta_2[:, :, 15] = (meta >> 30) & 0b11
+
+    dense_offsets = meta_2.view(-1) + (
+        torch.arange(0, 2 * m * k // ksparse, device=device) * 4
+    ).view(-1, 1).repeat(1, 2).view(-1)
+
+    dense = torch.zeros((m * 2 * k,), dtype=sparse.dtype, device=device)
+    if sparse.dtype != torch.float:
+        dense.scatter_(0, dense_offsets, sparse.view(-1))
+    else:
+        dense.view(torch.half).scatter_(
+            0, dense_offsets, sparse.view(torch.half).view(-1)
+        )
+
+    return dense.view(m, 2 * k)
+
+
+def _sparse_semi_structured_tile(dense):
+    """
+    This function computes a 2:4 sparse tile by greedily taking the largest values.
+
+    Since we take the largest values greedily, how the sorting algorithm handles duplicates affects
+    the ultimate sparsity pattern.
+
+    Note that this function does not have the same sorting semantics as our CUDA backend,
+    which is exposed via `torch._sparse_semi_structured_tile` and thus returns a different pattern.
+    """
+
+    def greedy_prune_tile(tile):
+        num_kept_row = [0, 0, 0, 0]
+        num_kept_col = [0, 0, 0, 0]
+
+        for x in tile.flatten().sort(descending=True, stable=True).indices:
+            r, c = x // 4, x % 4
+            if num_kept_row[r] < 2 and num_kept_col[c] < 2:
+                num_kept_row[r] += 1
+                num_kept_col[c] += 1
+            else:
+                tile[r, c] = 0
+
+    for batch in dense.unfold(0, 4, 4).unfold(1, 4, 4):
+        for tile in batch:
+            greedy_prune_tile(tile)
+
+    return dense
+
+
+def _compute_compressed_swizzled_bitmask(dense):
+    """
+    Calculates the compressed swizzled bitmask from a dense tensor
+    """
+
+    # first we need to convert the dense tensor to a bitmask
+    int_bitmask = dense.bool().to(torch.uint8)
+
+    # Each thread is responsible for an 8x8 tile, which contains 4 4x4 tiles:
+    # A, B, C and D, as displayed in the following schema:
+    # +---+---+
+    # | A | B |
+    # +---+---+
+    # | C | D |
+    # +---+---+
+
+    # we first need to split into the 8x8 tiles
+    bitmask_8x8_chunks = int_bitmask.unfold(0, 8, 8).unfold(1, 8, 8)
+
+    # then we unfold again to get our indivdual 4x4 tiles
+    bitmask_4x4_chunks = bitmask_8x8_chunks.unfold(2, 4, 4).unfold(3, 4, 4)
+
+    # Each 4x4 bitmask defines two 8-bit integers, which encode the sparsity pattern
+    # of that tile. Note that the least siginificant bit is stored first.
+    # [1 1 0 0]
+    # [1 1 0 0]  ->  0011 0011 ->   51
+    # [0 0 1 1]      1100 1100      204
+    # [0 0 1 1]
+
+    # reshape tensor to expand tiles into 8-bit vectors
+    bitmask_binary_representation = bitmask_4x4_chunks.reshape(
+        *bitmask_4x4_chunks.shape[:2], 4, 2, 8
+    )
+
+    # to convert from binary representaiton, we can do a matmul with powers of two
+    powers_of_two = 2 ** torch.arange(8, dtype=torch.float, device="cuda")
+    # To run on GPU: cast to float to do matmul and then cast back
+    compressed_swizzled_bitmask = (
+        bitmask_binary_representation.to(torch.float) @ powers_of_two
+    ).to(torch.uint8)
+
+    return compressed_swizzled_bitmask
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/sparse/_semi_structured_ops.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/sparse/_semi_structured_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7c76d8d8be8a9566a8b98fc38a414d61f1171d3
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/sparse/_semi_structured_ops.py
@@ -0,0 +1,168 @@
+# mypy: allow-untyped-defs
+import contextlib
+
+import torch
+
+
+__all__ = [
+    "fallback_dispatcher",
+    "semi_sparse_values",
+    "semi_sparse_indices",
+    "semi_sparse_t",
+    "semi_sparse_view",
+    "semi_sparse_detach",
+    "semi_sparse_mm",
+    "semi_sparse_addmm",
+    "semi_sparse_linear",
+]
+
+
+@contextlib.contextmanager
+def no_dispatch():
+    guard = torch._C._DisableTorchDispatch()
+    try:
+        yield
+    finally:
+        del guard
+
+
+def fallback_dispatcher(func, types, args, kwargs):
+    with no_dispatch():
+        return func(*args)
+
+
+def semi_sparse_values(func, types, args=(), kwargs=None) -> torch.Tensor:
+    assert len(args) == 1
+    A = args[0]
+    assert isinstance(A, torch.sparse.SparseSemiStructuredTensor)
+    assert A.packed is not None
+    if A.meta is None:
+        m, k = A.shape
+        num_kept_elements = m * k // 2
+        return A.packed[:num_kept_elements:].view(m, -1)
+    else:
+        return A.packed.detach()
+
+
+def semi_sparse_indices(func, types, args=(), kwargs=None) -> torch.Tensor:
+    assert len(args) == 1
+    A = args[0]
+    assert isinstance(A, torch.sparse.SparseSemiStructuredTensor)
+    assert A.packed is not None
+    if A.meta is None:
+        m, k = A.shape
+        num_kept_elements = m * k // 2
+        metadata = A.packed[num_kept_elements:].view(m, -1)
+        return metadata.view(torch.int32 if A.dtype == torch.int32 else torch.int16)
+    else:
+        return A.meta
+
+
+def semi_sparse_t(func, types, args=(), kwargs=None) -> torch.Tensor:
+    assert len(args) == 1
+    self = args[0]
+    assert isinstance(self, torch.sparse.SparseSemiStructuredTensor)
+    assert len(self.shape) == 2
+    # Because we cannot go from the compressed representation back to the dense representation currently,
+    # we just keep track of how many times we have been transposed. Depending on whether the sparse matrix
+    # is the first or second argument, we expect an even / odd number of calls to transpose respectively.
+    return self.__class__(
+        torch.Size([self.shape[-1], self.shape[0]]),
+        packed=self.packed_t,
+        meta=self.meta_t,
+        packed_t=self.packed,
+        meta_t=self.meta,
+        compressed_swizzled_bitmask=self.compressed_swizzled_bitmask.transpose(0, 1)
+        if self.compressed_swizzled_bitmask is not None
+        else None,
+        fuse_transpose_cusparselt=args[0].fuse_transpose_cusparselt,
+        alg_id_cusparselt=args[0].alg_id_cusparselt,
+    )
+
+
+def semi_sparse_view(func, types, args=(), kwargs=None) -> torch.Tensor:
+    assert len(args) == 2
+    self, shape = args
+    if tuple(shape) != self.shape:
+        raise NotImplementedError(
+            f"`view` is not implemented for SparseSemiStructuredTensor, except for the dummy case (shape={shape})"
+        )
+    return self
+
+
+def semi_sparse_detach(func, types, args, kwargs) -> torch.Tensor:
+    assert len(args) == 1
+    self = args[0]
+    return self.__class__(
+        shape=self.shape,
+        packed=self.packed,
+        meta=self.meta,
+        packed_t=self.packed_t,
+        meta_t=self.meta_t,
+        compressed_swizzled_bitmask=self.compressed_swizzled_bitmask,
+        requires_grad=False,
+    )
+
+
+def semi_sparse_mm(func, types, args=(), kwargs=None) -> torch.Tensor:
+    assert len(args) == 2
+    A, B = args
+    if A.ndim != 2 or B.ndim != 2:
+        raise NotImplementedError(
+            "`SparseSemiStructuredTensor` matmul: Broadcasting is not implemented"
+        )
+    if isinstance(A, torch.sparse.SparseSemiStructuredTensor):
+        row, col = B.shape
+        B_padded = A._pad_dense_input(B)
+        res = A._mm(B_padded)
+        return res[:, :col]
+    else:
+        B_t = B.t()
+        assert isinstance(B_t, torch.sparse.SparseSemiStructuredTensor)
+        row, col = A.shape
+        A_padded = B._pad_dense_input(A)
+        res = B_t._mm(A_padded.t()).t()
+        return res[:row, :]
+
+
+def semi_sparse_addmm(func, types, args=(), kwargs=None) -> torch.Tensor:
+    assert len(args) == 3
+    bias, A, B = args
+    if A.ndim != 2 or B.ndim != 2:
+        raise NotImplementedError(
+            "`SparseSemiStructuredTensor` matmul: Broadcasting is not implemented"
+        )
+    if bias.ndim != 1:
+        raise NotImplementedError(
+            f"`SparseSemiStructuredTensor` matmul: only bias dim=1 supported. Shape={bias.shape}"
+        )
+    if isinstance(A, torch.sparse.SparseSemiStructuredTensor):
+        raise NotImplementedError(
+            "`SparseSemiStructuredTensor` matmul: only operand B of `addmm` can be sparse"
+        )
+    B_t = B.t()
+    assert isinstance(B_t, torch.sparse.SparseSemiStructuredTensor)
+    row, col = A.shape
+    A_padded = B_t._pad_dense_input(A)
+    result = B_t._mm(A_padded.t(), bias=bias).t()
+    return result[:row, :]
+
+
+def semi_sparse_linear(func, types, args=(), kwargs=None) -> torch.Tensor:
+    assert len(args) in [2, 3]
+    A, B = args[:2]
+    bias = args[2] if len(args) == 3 else None
+
+    shape = A.shape
+    A_2d = A.view(-1, shape[-1])
+
+    if bias is None:
+        res = A_2d @ B.t()
+    else:
+        res = semi_sparse_addmm(
+            func=None,
+            types=None,
+            args=[bias, A_2d, B.t()],
+        )
+
+    return res.view(*shape[:-1], -1)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/sparse/_triton_ops.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/sparse/_triton_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..091e91d37f604a7435eb2a59e4a2ab88416a8824
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/sparse/_triton_ops.py
@@ -0,0 +1,2415 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import math
+import os
+import weakref
+from functools import lru_cache
+from typing import Optional, Tuple
+
+import torch
+from torch._dynamo.utils import warn_once
+from torch.utils._triton import has_triton
+
+from ._triton_ops_meta import get_meta
+
+
+TORCH_SPARSE_BSR_SCATTER_MM_LRU_CACHE_SIZE = int(
+    os.getenv("TORCH_SPARSE_BSR_SCATTER_MM_LRU_CACHE_SIZE", 2)
+)
+
+
+def check(cond, msg):
+    if not cond:
+        raise ValueError(msg)
+
+
+def check_bsr_layout(f_name, t):
+    check(
+        t.layout == torch.sparse_bsr,
+        f"{f_name}(): only BSR sparse format is supported for the sparse argument.",
+    )
+
+
+def check_device(f_name, t, device):
+    check(
+        t.device == device and t.device.type == "cuda",
+        f"{f_name}(): all inputs are expected to be on the same GPU device.",
+    )
+
+
+def check_mm_compatible_shapes(f_name, lhs, rhs):
+    check(
+        lhs.dim() >= 2 and rhs.dim() >= 2,
+        f"{f_name}(): all inputs involved in the matrix product are expected to be at least 2D, "
+        f"but got lhs.dim() == {lhs.dim()} and rhs.dim() == {rhs.dim()}.",
+    )
+
+    m, kl = lhs.shape[-2:]
+    kr, n = rhs.shape[-2:]
+
+    check(
+        kl == kr,
+        f"{f_name}(): arguments' sizes involved in the matrix product are not compatible for matrix multiplication, "
+        f"got lhs.shape[-1] == {kl} which is not equal to rhs.shape[-2] == {kr}.",
+    )
+
+
+def check_dtype(f_name, t, dtype, *additional_dtypes):
+    check(
+        t.dtype == dtype
+        and t.dtype
+        in ((torch.half, torch.bfloat16, torch.float) + tuple(*additional_dtypes)),
+        f"{f_name}(): all inputs are expected to be of the same dtype "
+        f"and one of (half, bfloat16, float32) or {additional_dtypes}, "
+        f"but got dtype == {t.dtype}.",
+    )
+
+
+def check_blocksize(f_name, blocksize):
+    assert len(blocksize) == 2
+
+    def is_power_of_two(v):
+        return not (v & (v - 1))
+
+    def is_compatible_blocksize(b):
+        res = True
+        for blocksize in b:
+            # Triton loads only blocks which are at least 16 and powers of 2.
+            res = (blocksize >= 16 and is_power_of_two(blocksize)) and res
+        return res
+
+    check(
+        is_compatible_blocksize(blocksize),
+        f"{f_name}(): sparse inputs' blocksize ({blocksize[0]}, {blocksize[1]}) "
+        "should be at least 16 and a power of 2 in each dimension.",
+    )
+
+
+def make_triton_contiguous(t):
+    """Return input as a triton-contiguous tensor.
+
+    A triton-contiguous tensor is defined as a tensor that has strides
+    with minimal value equal to 1.
+
+    While triton kernels support triton-non-contiguous tensors (all
+    strides being greater than 1 or having 0 strides) arguments, a
+    considerable slow-down occurs because tensor data is copied
+    element-wise rather than chunk-wise.
+    """
+    if min(t.stride()) != 1:
+        # TODO: investigate if contiguity along other axes than the
+        # last one can be beneficial for performance
+        return t.contiguous()
+    else:
+        return t
+
+
+def broadcast_batch_dims(f_name, *tensors):
+    try:
+        return torch.broadcast_shapes(*(t.shape[:-2] for t in tensors))
+    except Exception:
+        check(False, f"{f_name}(): inputs' batch dimensions are not broadcastable!")
+
+
+def slicer(dim, slice_range, *tensors):
+    for t in tensors:
+        slices = [slice(None)] * t.dim()
+        slices[dim] = slice_range
+        yield t[slices]
+
+
+def multidim_slicer(dims, slices, *tensors):
+    for t in tensors:
+        s = [slice(None)] * t.dim()
+        for d, d_slice in zip(dims, slices):
+            if d is not None:
+                s[d] = d_slice
+        yield t[s]
+
+
+def ptr_stride_extractor(*tensors):
+    for t in tensors:
+        yield t
+        yield from t.stride()
+
+
+def grid_partitioner(full_grid, grid_blocks, tensor_dims_map):
+    assert 0 <= len(full_grid) <= 3
+    assert 0 <= len(grid_blocks) <= 3
+
+    import itertools
+
+    def generate_grid_points():
+        for fg, mg in zip(full_grid, grid_blocks):
+            yield range(0, fg, mg)
+
+    def generate_sliced_tensors(slices):
+        for t, t_dims in tensor_dims_map.items():
+            yield next(multidim_slicer(t_dims, slices, t))
+
+    for grid_point in itertools.product(*generate_grid_points()):
+        grid = [
+            min(fg - gp, mg) for fg, gp, mg in zip(full_grid, grid_point, grid_blocks)
+        ]
+        slices = [slice(gp, gp + g) for gp, g in zip(grid_point, grid)]
+        # grid_points are iterated in a "contiguous" order, i.e.
+        # left dimensions traversed slower than right dimensions.
+        # This order is reversed for CUDA grids.
+        yield grid[::-1], *generate_sliced_tensors(slices)
+
+
+def launch_kernel(kernel, tensor_dims_map, full_grid, grid_blocks=None):
+    # cuda_max_grid = (2 ** 31 - 1, 2 ** 16 - 1, 2 ** 16 - 1)
+    cuda_max_grid = (2147483647, 65535, 65535)[::-1]
+    if grid_blocks is None:
+        grid_blocks = cuda_max_grid
+    else:
+
+        def valid_grid_dim(g, mg):
+            if g is None:
+                return mg
+            else:
+                # grid must be at least 1 and no greater than mg
+                return max(1, min(g, mg))
+
+        grid_blocks = tuple(
+            valid_grid_dim(g, mg) for g, mg in zip(grid_blocks, cuda_max_grid)
+        )  # type: ignore[assignment]
+
+    for grid, *sliced_tensors in grid_partitioner(
+        full_grid, grid_blocks, tensor_dims_map
+    ):
+        kernel(grid, *sliced_tensors)
+
+
+def prepare_inputs(bsr, *dense_tensors):
+    # Introduce fake batch dimension if not present for convenience.
+    crow_indices = bsr.crow_indices().unsqueeze(0)
+    col_indices = bsr.col_indices().unsqueeze(0)
+    values = make_triton_contiguous(bsr.values().unsqueeze(0))
+    tensors = [make_triton_contiguous(t.unsqueeze(0)) for t in dense_tensors]
+
+    # Compute broadcasted batch dimension
+    batch_dims_broadcasted = torch.broadcast_shapes(
+        values.shape[:-3], *(t.shape[:-2] for t in tensors)
+    )
+
+    # Broadcast batch dimensions and squash.
+    # The result can be either a view or a copy.
+    def batch_broadcast_and_squash(t, batch_dims, invariant_dims):
+        return t.broadcast_to(batch_dims + invariant_dims).flatten(
+            0, len(batch_dims) - 1
+        )
+
+    crow_indices = batch_broadcast_and_squash(
+        crow_indices, batch_dims_broadcasted, (-1,)
+    )
+
+    col_indices = batch_broadcast_and_squash(col_indices, batch_dims_broadcasted, (-1,))
+    values = batch_broadcast_and_squash(
+        values, batch_dims_broadcasted, values.shape[-3:]
+    )
+    tensors = [
+        batch_broadcast_and_squash(t, batch_dims_broadcasted, t.shape[-2:])
+        for t in tensors
+    ]
+
+    return crow_indices, col_indices, values, *tensors
+
+
+def broadcast_batch_dims_bsr(f_name, bsr, *tensors):
+    batch_shape = broadcast_batch_dims(f_name, bsr, *tensors)
+
+    crow_indices = bsr.crow_indices().broadcast_to(batch_shape + (-1,))
+    col_indices = bsr.col_indices().broadcast_to(batch_shape + (-1,))
+    values = bsr.values().broadcast_to(batch_shape + bsr.values().shape[-3:])
+    size = batch_shape + bsr.shape[-2:]
+    return torch.sparse_compressed_tensor(
+        crow_indices, col_indices, values, size=size, layout=bsr.layout
+    )
+
+
+# NOTE: this function will ALWAYS create a view
+def tile_to_blocksize(t, blocksize):
+    *rest, m, n = t.shape
+    new_shape = rest + [
+        m // blocksize[0],
+        blocksize[0],
+        n // blocksize[1],
+        blocksize[1],
+    ]
+    # using .view instead of .reshape to ensure that the result is
+    # indeed a view:
+    return t.view(new_shape).transpose(-3, -2)
+
+
+def as1Dbatch(tensor):
+    """Return tensor as 3D tensor by either prepending new dimensions to
+    the tensor shape (when ``tensor.ndim < 3``), or by collapsing
+    starting dimensions into the first dimension (when ``tensor.ndim >
+    3``).
+    """
+    while tensor.ndim < 3:
+        tensor = tensor.unsqueeze(0)
+    if tensor.ndim > 3:
+        tensor = tensor.flatten(0, tensor.ndim - 3)
+    assert tensor.ndim == 3, tensor.shape
+    return tensor
+
+
+def scatter_mm(blocks, others, indices_data, *, accumulators=None):
+    """Scattered matrix multiplication of tensors.
+
+    A scattered matrix multiplication is defined as a series of matrix
+    multiplications applied to input tensors according to the input
+    and output mappings specified by indices data.
+
+    The following indices data formats are supported for defining a
+    scattered matrix multiplication operation (:attr:`indices_data[0]`
+    holds the name of the indices data format as specified below):
+
+    - ``"scatter_mm"`` - matrix multiplications scattered in batches
+      of tensors.
+
+      If :attr:`blocks` is a :math:`(* \times M \times K) tensor,
+      :attr:`others` is a :math:`(* \times K \times N)` tensor,
+      :attr:`accumulators` is a :math:`(* \times M \times N)` tensor,
+      and :attr:`indices = indices_data['indices']` is a :math:`(*
+      \times 3)` tensor, then the operation is equivalent to the
+      following code::
+
+        c_offsets, pq = indices_data[1:]
+        for r in range(len(c_offsets) - 1):
+            for g in range(c_offsets[r], c_offsets[r + 1]):
+                p, q = pq[g]
+                accumulators[r] += blocks[p] @ others[q]
+
+    - ``"bsr_strided_mm"`` - matrix multiplications scattered in
+      batches of tensors and a tensor.
+
+      If :attr:`blocks` is a :math:`(Ms \times Ks) tensor,
+      :attr:`others` is a :math:`(* \times K \times N)` tensor,
+      :attr:`accumulators` is a :math:`(* \times M \times N)` tensor, then
+      the operation is equivalent to the following code::
+
+        c_indices, r_offsets, p_offsets, q_offsets, meta = indices_data[1:]
+        for b in range(nbatches):
+            for i, r in enumerate(r_offsets):
+                r0, r1 = divmod(r, N)
+                acc = accumulators[b, r0:r0 + Ms, r1:r1 + Ns]
+                for g in range(c_indices[i], c_indices[i+1]):
+                    p = p_offsets[g]
+                    q0, q1 = divmod(q_offsets[g], N)
+                    acc += blocks[p] @ others[b, q0:q0 + Ks, q1:q1 + Ns]
+
+      where ``Ns = N // meta['SPLIT_N']``, and ``M`` and ``K`` are
+      integer multiples of ``Ms`` and ``Ks``, respectively.
+
+    - ``"bsr_strided_mm_compressed"`` - matrix multiplications
+      scattered in batches of tensors and a tensor. A memory and
+      processor efficient version of ``"bsr_strided_mm"`` format.  If
+      :attr:`blocks` is a :math:`(Ms \times Ks) tensor, :attr:`others`
+      is a :math:`(* \times K \times N)` tensor, :attr:`accumulators`
+      is a :math:`(* \times M \times N)` tensor, then the operation is
+      equivalent to the following code::
+
+        c_indices, r_offsets, q_offsets, meta = indices_data[1:]
+        for b in range(nbatches):
+            for r in r_offsets:
+                m = (r // N) // Ms
+                n = (r % N) // Ns
+                r0, r1 = divmod(r, N)
+                c0, c1 = c_indices[m], c_indices[m + 1]
+                acc = accumulators[b, r0:r0 + Ms, r1:r1 + Ns]
+                for i, p in enumerate(range(c0, c1)):
+                    q = q_offsets[n * c1 + (SPLIT_N - n) * c0 + i]
+                    q0, q1 = divmod(q, N)
+                    acc += blocks[p] @ others[b, q0:q0 + Ks, q1:q1 + Ns]
+
+      where ``Ns = N // meta['SPLIT_N']``, and ``M`` and ``K`` are
+      integer multiples of ``Ms`` and ``Ks``, respectively.
+
+      Notice that the order of ``r_offsets`` items can be arbitrary;
+      this property enables defining swizzle operators via
+      rearrangements of ``r_offsets`` items..
+
+    Auxilary functions are provided for pre-computing
+    :attr:`indices_data`. For example,
+    :func:`bsr_scatter_mm_indices_data` is used to define indices data
+    for matrix multiplication of BSR and strided tensors.
+
+    Parameters
+    ----------
+    blocks (Tensor): a 3-D tensor of first matrices to be multiplied
+
+    others (Tensor): a tensor of second matrices to be multiplied. If
+      ``indices_data[0]=="scatter_mm"``, the tensor is a 1-D batch
+      tensor of second input matrices to be multiplied. Otherwise, the
+      second input matrices are slices of the :attr:`others` tensor.
+    indices_data (tuple): a format data that defines the inputs and
+      outputs of scattered matrix multiplications.
+
+    Keyword arguments
+    -----------------
+
+    accumulators (Tensor, optional): a tensor of matrix product
+      accumulators. If ``indices_data[0]=="scatter_mm"``, the tensor
+      is a 1-D batch tensor of output matrices. Otherwise, output
+      matrices are slices of the :attr:`accumulators` tensor.
+    """
+    indices_format = indices_data[0]
+
+    assert blocks.ndim == 3
+    P, Ms, Ks = blocks.shape
+
+    if indices_format == "scatter_mm":
+        c_offsets, pq = indices_data[1:]
+
+        assert others.ndim == 3
+        Q, Ks_, Ns = others.shape
+        assert Ks == Ks_
+
+        if accumulators is None:
+            R = c_offsets.shape[0] - 1
+            accumulators = torch.zeros(
+                (R, Ms, Ns), dtype=blocks.dtype, device=blocks.device
+            )
+        else:
+            R, Ms_, Ns_ = accumulators.shape
+            assert Ms_ == Ms
+            assert Ns_ == Ns
+
+        if Ms % 16 or Ks % 16 or Ns % 16 or _scatter_mm2 is None:
+            for r in range(c_offsets.shape[0] - 1):
+                g0 = c_offsets[r]
+                g1 = c_offsets[r + 1]
+                for g in range(g0, g1):
+                    p, q = pq[g]
+                    accumulators[r] += blocks[p] @ others[q]
+        else:
+            _scatter_mm2(blocks, others, c_offsets, pq, accumulators)
+        return accumulators
+
+    elif indices_format == "bsr_strided_mm":
+        others_shape = others.shape
+        others = as1Dbatch(others)
+
+        B, K, N = others.shape
+        assert K % Ks == 0
+
+        c_indices, r_offsets, p_offsets, q_offsets, meta = indices_data[1:]
+        SPLIT_N = meta["SPLIT_N"]
+
+        if accumulators is None:
+            M = Ms + (r_offsets.max().item() + 1) // N
+            accumulators = torch.zeros(
+                (*others_shape[:-2], M, N), dtype=blocks.dtype, device=blocks.device
+            )
+        else:
+            M, N_ = accumulators.shape[-2:]
+            assert N_ == N
+
+        accumulators_shape = accumulators.shape
+        accumulators = as1Dbatch(accumulators)
+
+        Ns = N // SPLIT_N
+
+        if Ms % 16 or Ks % 16 or Ns % 16 or _scatter_mm6 is None:
+            accumulators.zero_()
+            for b in range(B):
+                for r in range(r_offsets.shape[0]):
+                    r_ = r_offsets[r].item()
+                    g0 = c_indices[r].item()
+                    g1 = c_indices[r + 1].item()
+                    r0, r1 = divmod(r_, N)
+                    acc = accumulators[b, r0 : r0 + Ms, r1 : r1 + Ns]
+                    for g in range(g0, g1):
+                        p, q = p_offsets[g], q_offsets[g]
+                        q0, q1 = divmod(q.item(), N)
+                        acc += blocks[p] @ others[b, q0 : q0 + Ks, q1 : q1 + Ns]
+        else:
+            _scatter_mm6(
+                blocks,
+                others,
+                c_indices,
+                r_offsets,
+                p_offsets,
+                q_offsets,
+                meta,
+                accumulators,
+            )
+        return accumulators.view(accumulators_shape)
+
+    elif indices_format == "bsr_strided_mm_compressed":
+        others_shape = others.shape
+        others = as1Dbatch(others)
+
+        B, K, N = others.shape
+        assert K % Ks == 0
+
+        c_indices, r_offsets, q_offsets, meta = indices_data[1:]
+        SPLIT_N = meta["SPLIT_N"]
+
+        if accumulators is None:
+            M = Ms + (r_offsets.max().item() + 1) // N
+            accumulators = torch.zeros(
+                (*others_shape[:-2], M, N), dtype=blocks.dtype, device=blocks.device
+            )
+        else:
+            M, N_ = accumulators.shape[-2:]
+            assert N_ == N
+
+        accumulators_shape = accumulators.shape
+        accumulators = as1Dbatch(accumulators)
+
+        Ns = N // SPLIT_N
+
+        if Ms % 16 or Ks % 16 or Ns % 16 or _scatter_mm6 is None:
+            for b in range(B):
+                for j in range(len(r_offsets)):
+                    r0, r1 = divmod(r_offsets[j].item(), N)
+                    m = r0 // Ms
+                    n = r1 // Ns
+                    c0 = c_indices[m].item()
+                    c1 = c_indices[m + 1].item()
+                    acc = accumulators[b, r0 : r0 + Ms, r1 : r1 + Ns]
+                    for i, p in enumerate(range(c0, c1)):
+                        q = q_offsets[n * c1 + (SPLIT_N - n) * c0 + i].item()
+                        q0, q1 = divmod(q, N)
+                        acc += blocks[p] @ others[b, q0 : q0 + Ks, q1 : q1 + Ns]
+        else:
+            p_offsets = torch.empty(
+                (0,), dtype=q_offsets.dtype, device=q_offsets.device
+            )
+            _scatter_mm6(
+                blocks,
+                others,
+                c_indices,
+                r_offsets,
+                p_offsets,
+                q_offsets,
+                meta,
+                accumulators,
+            )
+        return accumulators.view(accumulators_shape)
+
+    else:
+        raise NotImplementedError(indices_format)
+
+
+def scatter_mm_meta(
+    M,
+    K,
+    N,
+    Ms,
+    Ks,
+    GROUP_SIZE=None,
+    TILE_M=None,
+    TILE_N=None,
+    SPLIT_N=None,
+    num_warps=None,
+    num_stages=None,
+    **extra,
+):
+    if {TILE_M, TILE_N, SPLIT_N, num_warps, num_stages, GROUP_SIZE} == {None}:
+        device_name = torch.cuda.get_device_name()
+        meta = get_meta(
+            "scatter_mm",
+            (M, K, N, Ms, Ks),
+            device_name,
+            version=(0, torch.float16, 0.5),
+        )
+        if meta is not None:
+            meta.update(**extra)
+            return meta
+        # The following parameters are optimized for the performance
+        # equilibrium points of bsr-dense and dense-dense matrix
+        # multiplications when using GPU card NVIDIA GeForce RTX 2060
+        # SUPER. For points far from the performance equilibrium
+        # points as well as for other GPU cards, the optimal
+        # parameters are likely different from what specified below.
+        if (M, K, N) == (256,) * 3:
+            if (Ms, Ks) == (16, 16):
+                SPLIT_N = 1
+                TILE_M = 16
+                TILE_N = 16
+                GROUP_SIZE = 4
+                num_stages = 1
+                num_warps = 4  # noqa: E225,E231,E702
+            elif (Ms, Ks) == (32, 32):
+                SPLIT_N = 2
+                TILE_M = 32
+                TILE_N = 16
+                GROUP_SIZE = 4
+                num_stages = 1
+                num_warps = 4  # noqa: E225,E231,E702
+            elif (Ms, Ks) == (64, 64):
+                SPLIT_N = 1
+                TILE_M = 32
+                TILE_N = 32
+                GROUP_SIZE = 4
+                num_stages = 1
+                num_warps = 4  # noqa: E225,E231,E702
+            elif (Ms, Ks) == (128, 128):
+                SPLIT_N = 1
+                TILE_M = 32
+                TILE_N = 32
+                GROUP_SIZE = 2
+                num_stages = 1
+                num_warps = 4  # noqa: E225,E231,E702
+        elif (M, K, N) == (512,) * 3:
+            if (Ms, Ks) == (16, 16):
+                SPLIT_N = 8
+                TILE_M = 16
+                TILE_N = 64
+                GROUP_SIZE = 2
+                num_stages = 1
+                num_warps = 2  # noqa: E225,E231,E702
+            elif (Ms, Ks) == (32, 32):
+                SPLIT_N = 8
+                TILE_M = 32
+                TILE_N = 64
+                GROUP_SIZE = 4
+                num_stages = 1
+                num_warps = 2  # noqa: E225,E231,E702
+            elif (Ms, Ks) == (64, 64):
+                SPLIT_N = 4
+                TILE_M = 32
+                TILE_N = 128
+                GROUP_SIZE = 4
+                num_stages = 1
+                num_warps = 4  # noqa: E225,E231,E702
+            elif (Ms, Ks) == (128, 128):
+                SPLIT_N = 8
+                TILE_M = 64
+                TILE_N = 64
+                GROUP_SIZE = 4
+                num_stages = 1
+                num_warps = 4  # noqa: E225,E231,E702
+        elif (M, K, N) == (1024,) * 3:
+            if (Ms, Ks) == (16, 16):
+                SPLIT_N = 4
+                TILE_M = 16
+                TILE_N = 128
+                GROUP_SIZE = 2
+                num_stages = 1
+                num_warps = 1  # noqa: E225,E231,E702
+            elif (Ms, Ks) == (32, 32):
+                SPLIT_N = 8
+                TILE_M = 32
+                TILE_N = 64
+                GROUP_SIZE = 2
+                num_stages = 1
+                num_warps = 1  # noqa: E225,E231,E702
+            elif (Ms, Ks) == (64, 64):
+                SPLIT_N = 16
+                TILE_M = 64
+                TILE_N = 64
+                GROUP_SIZE = 4
+                num_stages = 1
+                num_warps = 2  # noqa: E225,E231,E702
+            elif (Ms, Ks) == (128, 128):
+                SPLIT_N = 16
+                TILE_M = 64
+                TILE_N = 64
+                GROUP_SIZE = 4
+                num_stages = 1
+                num_warps = 4  # noqa: E225,E231,E702
+            elif (Ms, Ks) == (256, 256):
+                SPLIT_N = 16
+                TILE_M = 64
+                TILE_N = 64
+                GROUP_SIZE = 2
+                num_stages = 1
+                num_warps = 4  # noqa: E225,E231,E702
+        elif (M, K, N) == (2048,) * 3:
+            if (Ms, Ks) == (16, 16):
+                SPLIT_N = 4
+                TILE_M = 16
+                TILE_N = 128
+                GROUP_SIZE = 8
+                num_stages = 1
+                num_warps = 1  # noqa: E225,E231,E702
+            elif (Ms, Ks) == (32, 32):
+                SPLIT_N = 4
+                TILE_M = 32
+                TILE_N = 64
+                GROUP_SIZE = 4
+                num_stages = 1
+                num_warps = 1  # noqa: E225,E231,E702
+            elif (Ms, Ks) == (64, 64):
+                SPLIT_N = 4
+                TILE_M = 64
+                TILE_N = 128
+                GROUP_SIZE = 4
+                num_stages = 1
+                num_warps = 4  # noqa: E225,E231,E702
+            elif (Ms, Ks) == (128, 128):
+                SPLIT_N = 8
+                TILE_M = 64
+                TILE_N = 64
+                GROUP_SIZE = 4
+                num_stages = 1
+                num_warps = 4  # noqa: E225,E231,E702
+            elif (Ms, Ks) == (256, 256):
+                SPLIT_N = 4
+                TILE_M = 64
+                TILE_N = 64
+                GROUP_SIZE = 2
+                num_stages = 1
+                num_warps = 4  # noqa: E225,E231,E702
+        elif (M, K, N) == (4096,) * 3:
+            if (Ms, Ks) == (16, 16):
+                SPLIT_N = 2
+                TILE_M = 16
+                TILE_N = 256
+                GROUP_SIZE = 2
+                num_stages = 1
+                num_warps = 2  # noqa: E225,E231,E702
+            elif (Ms, Ks) == (32, 32):
+                SPLIT_N = 2
+                TILE_M = 32
+                TILE_N = 64
+                GROUP_SIZE = 2
+                num_stages = 1
+                num_warps = 1  # noqa: E225,E231,E702
+            elif (Ms, Ks) == (64, 64):
+                SPLIT_N = 2
+                TILE_M = 64
+                TILE_N = 128
+                GROUP_SIZE = 2
+                num_stages = 1
+                num_warps = 4  # noqa: E225,E231,E702
+
+    if SPLIT_N is None:
+        # Assume NVIDIA GeForce RTX 2060 SUPER:
+        # With the probality of 92% (99.9% when N > 512), the
+        # performance will not be worse more than 2% from the
+        # performance when using an optimal value.  Otherwise, when N
+        # <= 512, using the following heuristics may give upto 15%
+        # lower performance.
+        SPLIT_N = {
+            16: 1,
+            32: 2,
+            64: 4,
+            128: 8,
+            256: 16,
+            512: 8,
+            1024: 16,
+            4096: 32,
+            8192: 64,
+        }.get(N, 16)
+        if Ms >= 512 and N >= 2048:
+            SPLIT_N = 1
+    Ns = N // SPLIT_N
+    if TILE_M is None:
+        TILE_M = min(64 if Ns < 512 else 32, Ms)
+    if TILE_N is None:
+        TILE_N = min(64 if Ns < 512 else 32, Ns)
+    num_stages = num_stages or 1
+    if num_warps is None:
+        if min(M, N) > 1024:
+            num_warps = {16: 1, 32: 1, 64: 2}.get(Ms, 4)
+        elif min(M, N) == 1024:
+            num_warps = {16: 1, 32: 1, 64: 2}.get(Ms, 4)
+        elif min(M, N) == 256:
+            num_warps = {16: 1, 32: 4}.get(Ms, 4)
+        else:
+            num_warps = {16: 1, 32: 2}.get(Ms, 4)
+    GROUP_SIZE = GROUP_SIZE or 4
+
+    assert TILE_M <= Ms, dict(TILE_M=TILE_M, Ms=Ms)
+    assert TILE_N <= Ns, dict(TILE_N=TILE_N, Ns=Ns)
+    assert Ms <= M, dict(M=M, Ms=Ms)
+    assert Ns <= N, dict(N=N, Ns=Ns)
+    assert Ks <= K, dict(K=K, Ks=Ks)
+
+    return dict(
+        TILE_M=TILE_M,
+        TILE_N=TILE_N,
+        GROUP_SIZE=GROUP_SIZE,
+        num_stages=num_stages,
+        num_warps=num_warps,
+        SPLIT_N=SPLIT_N,
+        **extra,
+    )
+
+
+def bsr_dense_addmm_meta(
+    M,
+    K,
+    N,
+    Ms,
+    Ks,
+    beta,
+    alpha,
+    SPLIT_N=None,
+    GROUP_SIZE_ROW=None,
+    num_warps=None,
+    num_stages=None,
+    sparsity=None,
+    dtype=None,
+    _version=0,
+    **extra,
+):
+    # Specifying _version is useful for situations when one wants to
+    # discard existing triton kernel tuning results, say, in testing
+    # bsr_dense_addmm_meta functionality.
+    if dtype is None:
+        dtype = torch.float16
+    if sparsity is None:
+        sparsity = 0.5
+    if {SPLIT_N, num_warps, num_stages, GROUP_SIZE_ROW} == {None}:
+        device_name = torch.cuda.get_device_name()
+        key = (M, K, N, Ms, Ks, beta == 0, beta == 1, alpha == 1)
+        meta = get_meta(
+            "bsr_dense_addmm", key, device_name, version=(_version, dtype, sparsity)
+        )
+        if meta is None and sparsity != 0.5:
+            meta = get_meta(
+                "bsr_dense_addmm", key, device_name, version=(_version, dtype, 0.5)
+            )
+        if meta is None:
+            # find approximate meta such that N % SPLIT_N == 0.
+            matching_meta = get_meta(
+                "bsr_dense_addmm",
+                (*key[:2], "*", *key[3:]),
+                device_name,
+                version=(_version, dtype, 0.5),
+            )
+            for mkey in sorted(matching_meta or {}):
+                meta_ = matching_meta[mkey]
+                n = mkey[2]
+                split_n = meta_["SPLIT_N"]
+                c = n // split_n
+                if N % c == 0 and n <= N:
+                    meta = dict(meta_)
+                    meta["SPLIT_N"] = N // c
+        if meta is not None:
+            meta.update(**extra)
+            return meta
+        else:
+            # see [Computing optimal kernel parameters] in
+            # _triton_ops_meta.py for ways to avoid this warning
+            # message
+            warn_once(
+                f"bsr_dense_addmm uses non-optimal triton kernel parameters for {M=} {K=} {N=} {Ms=}, {Ks=} {beta=} {alpha=}"
+            )
+
+    SPLIT_N = SPLIT_N or max(N // Ms, 1)
+    GROUP_SIZE_ROW = GROUP_SIZE_ROW or 4
+    num_stages = num_stages or 1
+    num_warps = num_warps or 4
+    return dict(
+        SPLIT_N=SPLIT_N,
+        GROUP_SIZE_ROW=GROUP_SIZE_ROW,
+        num_stages=num_stages,
+        num_warps=num_warps,
+        **extra,
+    )
+
+
+class TensorAsKey:
+    """A light-weight wrapper of a tensor that enables storing tensors as
+    keys with efficient memory reference based comparision as an
+    approximation to data equality based keys.
+
+    Motivation: the hash value of a torch tensor is tensor instance
+    based that does not use data equality and makes the usage of
+    tensors as keys less useful. For instance, the result of
+    ``len({a.crow_indices(), a.crow_indices()})`` is `2`, although,
+    the tensor results from `crow_indices` method call are equal, in
+    fact, these share the same data storage.
+    On the other hand, for efficient caching of tensors we want to
+    avoid calling torch.equal that compares tensors item-wise.
+
+    TensorAsKey offers a compromise in that it guarantees key equality
+    of tensors that references data in the same storage in the same
+    manner and without accessing underlying data. However, this
+    approach does not always guarantee correctness. For instance, for
+    a complex tensor ``x``, we have ``TensorAsKey(x) ==
+    TensorAsKey(x.conj())`` while ``torch.equal(x, x.conj())`` would
+    return False.
+    """
+
+    def __init__(self, obj):
+        def get_tensor_key(obj):
+            # Warning: TensorAsKey does not track negative nor
+            # conjugate bits of its input object because in the use
+            # case of wrapping compressed/plain indices of compressed
+            # sparse tensors (that are always integer tensors with
+            # non-negative items) these bits are never set. However,
+            # when extending the use of TensorAsKey to float or
+            # complex tensors, the values of these bits (see is_neg
+            # and is_conj methods) must be included in the key as
+            # well.
+            assert not (obj.dtype.is_floating_point or obj.dtype.is_complex), obj.dtype
+            return (
+                obj.data_ptr(),
+                obj.storage_offset(),
+                obj.shape,
+                obj.stride(),
+                obj.dtype,
+            )
+
+        self._obj_ref = weakref.ref(obj)
+        if obj.layout is torch.strided:
+            self.key = get_tensor_key(obj)
+        elif obj.layout in {torch.sparse_csr, torch.sparse_bsr}:
+            self.key = (
+                get_tensor_key(obj.crow_indices()),
+                get_tensor_key(obj.col_indices()),
+            )
+        elif obj.layout in {torch.sparse_csc, torch.sparse_bsc}:
+            self.key = (
+                get_tensor_key(obj.ccol_indices()),
+                get_tensor_key(obj.row_indices()),
+            )
+        else:
+            raise NotImplementedError(obj.layout)
+        self._hash = hash(self.key)
+
+    def __hash__(self):
+        return self._hash
+
+    def __eq__(self, other):
+        if not isinstance(other, TensorAsKey):
+            return False
+        if self.obj is None or other.obj is None:
+            # dead objects always compare unequal unless these are
+            # same objects
+            return self is other
+        return self.key == other.key
+
+    @property
+    def obj(self):
+        """Return object if alive, otherwise None."""
+        return self._obj_ref()
+
+
+@lru_cache(maxsize=TORCH_SPARSE_BSR_SCATTER_MM_LRU_CACHE_SIZE)
+def _bsr_scatter_mm_indices_data(
+    indices_format, M, K, N, Ms, Ks, nbatches, SPLIT_N, compressed_sparse_tensor_as_key
+):
+    bsr = compressed_sparse_tensor_as_key.obj
+    assert bsr is not None
+    crow_indices, col_indices = bsr.crow_indices(), bsr.col_indices()
+    device = crow_indices.device
+    indices_dtype = torch.int32
+
+    if indices_format == "bsr_strided_mm_compressed":
+        Ns = N // SPLIT_N
+        q_offsets_lst = []
+        b = torch.arange(SPLIT_N, dtype=indices_dtype, device=device) * Ns
+        for m in range(M // Ms):
+            r0 = crow_indices[m].item()
+            r1 = crow_indices[m + 1].item()
+            if r1 == r0:
+                continue
+            q_offsets_lst.append(
+                (col_indices[r0:r1] * (Ks * N)).repeat(SPLIT_N)
+                + b.repeat_interleave(r1 - r0)
+            )
+        q_offsets = torch.cat(q_offsets_lst)
+        crow_indices_diff = crow_indices.diff()
+        non_zero_row_indices = crow_indices_diff.nonzero()
+        a = non_zero_row_indices * (Ms * N)
+        r_offsets = (a + b).view(-1)
+        c_indices = crow_indices
+        # swizzle operation: mm elements with longer sums are computed first:
+        nnz_per_row = crow_indices_diff[non_zero_row_indices].repeat_interleave(SPLIT_N)
+        nnz_per_row, indices = nnz_per_row.sort(descending=True, stable=True)
+        r_offsets = r_offsets[indices]
+        return (indices_format, c_indices, r_offsets, q_offsets)
+
+    elif indices_format == "bsr_strided_mm":
+        Ns = N // SPLIT_N
+        p_offsets_lst = []
+        q_offsets_lst = []
+        b = torch.arange(SPLIT_N, dtype=indices_dtype, device=device) * Ns
+        for m in range(M // Ms):
+            r0 = crow_indices[m].item()
+            r1 = crow_indices[m + 1].item()
+            if r1 == r0:
+                continue
+            p_offsets_lst.append(
+                torch.arange(r0, r1, dtype=indices_dtype, device=device).repeat(SPLIT_N)
+            )
+            q_offsets_lst.append(
+                (col_indices[r0:r1] * (Ks * N)).repeat(SPLIT_N)
+                + b.repeat_interleave(r1 - r0)
+            )
+        q_offsets = torch.cat(q_offsets_lst)
+        crow_indices_diff = crow_indices.diff()
+        non_zero_row_indices = crow_indices_diff.nonzero()
+        a = non_zero_row_indices * (Ms * N)
+        r_offsets = (a + b).view(-1)
+        c_indices = torch.cat(
+            (
+                crow_indices[:1],
+                torch.cumsum(
+                    crow_indices_diff[non_zero_row_indices].repeat_interleave(SPLIT_N),
+                    0,
+                ),
+            )
+        )
+        p_offsets = torch.cat(p_offsets_lst)
+        return (indices_format, c_indices, r_offsets, p_offsets, q_offsets)
+
+    elif indices_format == "scatter_mm":
+        Ns = Ms
+        c_indices = [0]
+        pq_offsets = []
+        # todo: eliminate inner for-loops for efficiency
+        for b in range(nbatches):
+            for m in range(M // Ms):
+                r0 = crow_indices[m].item()
+                r1 = crow_indices[m + 1].item()
+                for n in range(N // Ns):
+                    c_indices.append(c_indices[-1] + r1 - r0)
+                    for t in range(r1 - r0):
+                        p = r0 + t
+                        q = (col_indices[p].item() + b * (K // Ks)) * (N // Ns) + n
+                        pq_offsets.append([p, q])
+
+        return (
+            indices_format,
+            torch.tensor(c_indices, dtype=indices_dtype, device=device),
+            torch.tensor(pq_offsets, dtype=indices_dtype, device=device),
+        )
+
+    else:
+        raise ValueError(
+            f"Invalid {indices_format=}. Expected bsr_strided_mm_compressed|bsr_strided_mm|scatter_mm"
+        )
+
+
+def bsr_scatter_mm_indices_data(
+    bsr, other, indices_format="bsr_strided_mm_compressed", **meta_input
+):
+    """Computes indices data for :func:`scatter_mm` used in BSR and
+    strided tensor matrix multiplication.
+    """
+    assert bsr.dense_dim() == 0
+    assert bsr.ndim == 2  # no batch dims
+    crow_indices = bsr.crow_indices()
+    col_indices = bsr.col_indices()
+    blocksize = bsr.values().shape[-2:]
+    M, K = bsr.shape
+    Ms, Ks = blocksize
+    K_, N = other.shape[-2:]
+    assert K_ == K
+    nbatches = other.shape[:-2].numel()
+
+    meta = scatter_mm_meta(M, K, N, Ms, Ks, **meta_input)
+    if "allow_tf32" not in meta_input:
+        meta.update(allow_tf32=bsr.dtype in {torch.float16, torch.bfloat16})
+    SPLIT_N = meta["SPLIT_N"]
+    indices_data = _bsr_scatter_mm_indices_data(
+        indices_format, M, K, N, Ms, Ks, nbatches, SPLIT_N, TensorAsKey(bsr)
+    )
+
+    if indices_format == "bsr_strided_mm_compressed":
+        meta.update(is_compressed=True)
+        return indices_data + (meta,)
+    elif indices_format == "bsr_strided_mm":
+        meta.update(is_compressed=False)
+        return indices_data + (meta,)
+    else:
+        return indices_data
+
+
+def bsr_scatter_mm(bsr, other, indices_data=None, out=None):
+    """BSR @ strided -> strided"""
+
+    assert bsr.ndim == 2
+    assert other.ndim >= 2
+
+    Ms, Ks, Ns = bsr.shape[-2], bsr.shape[-1], other.shape[-1]
+    blocksize = bsr.values().shape[-2:]
+
+    if indices_data is None:
+        indices_data = bsr_scatter_mm_indices_data(
+            bsr, other, indices_format="bsr_strided_mm_compressed"
+        )
+
+    indices_format = indices_data[0]
+
+    if out is None:
+        out = torch.empty(
+            (*other.shape[:-2], Ms, Ns), dtype=bsr.dtype, device=bsr.device
+        )
+    out_shape = out.shape
+    out = as1Dbatch(out)
+
+    if bsr._nnz() == 0:
+        out.zero_()
+    elif indices_format in {"bsr_strided_mm_compressed", "bsr_strided_mm"}:
+        out.zero_()
+        scatter_mm(bsr.values(), other, indices_data, accumulators=out)
+    elif indices_format == "scatter_mm":
+        nbatches = other.shape[:-2].numel()
+        accumulators = torch.zeros(
+            (
+                nbatches * Ms // blocksize[0] * Ns // blocksize[0],
+                blocksize[0],
+                blocksize[0],
+            ),
+            dtype=bsr.dtype,
+            device=bsr.device,
+        )
+        others = (
+            as1Dbatch(other)
+            .transpose(-2, -1)
+            .view(
+                nbatches,
+                Ns // blocksize[0],
+                blocksize[0],
+                Ks // blocksize[1],
+                blocksize[1],
+            )
+            .movedim(
+                (3, 1, 4, 2), (1, 2, 3, 4)
+            )  # equivalent to .transpose(-3, -2).transpose(-2, -1).transpose(-4, -3)
+            .flatten(0, 2)
+        )
+        scatter_mm(bsr.values(), others, indices_data, accumulators=accumulators)
+        out.copy_(
+            accumulators.unflatten(
+                0, (nbatches, Ms // blocksize[0], Ns // blocksize[0])
+            )
+            .movedim(
+                (1, 2, 3, 4), (3, 1, 4, 2)
+            )  # equivalent to .transpose(-4, -3).transpose(-2, -1).transpose(-3, -2)
+            .reshape(nbatches, Ns, Ms)
+            .transpose(-2, -1)
+        )
+    else:
+        raise NotImplementedError(indices_format)
+
+    return out.view(out_shape)
+
+
+def _int_bsr_dense_addmm(
+    input: torch.Tensor,
+    bsr: torch.Tensor,
+    dense: torch.Tensor,
+    *,
+    beta=1,
+    alpha=1,
+    out: Optional[torch.Tensor] = None,
+    skip_checks: bool = False,
+    max_grid: Optional[Tuple[Optional[int], Optional[int], Optional[int]]] = None,
+    meta: Optional[dict] = None,
+):
+    if out is None and dense.dtype is torch.int8:
+        f_name = "_int_bsr_dense_addmm"
+        crow_indices = bsr.crow_indices()
+        batch_ndim = crow_indices.dim() - 1
+        M = bsr.shape[batch_ndim]
+        N = dense.shape[-1]
+        original_batch_dims_broadcasted = broadcast_batch_dims(f_name, bsr, dense)
+        out = torch.empty(
+            original_batch_dims_broadcasted + (M, N),
+            dtype=torch.int32,
+            device=dense.device,
+        )
+    return bsr_dense_addmm(
+        input,
+        bsr,
+        dense,
+        beta=beta,
+        alpha=alpha,
+        out=out,
+        skip_checks=skip_checks,
+        max_grid=max_grid,
+        meta=meta,
+    )
+
+
+def bsr_dense_addmm(
+    input: torch.Tensor,
+    bsr: torch.Tensor,
+    dense: torch.Tensor,
+    *,
+    beta=1,
+    alpha=1,
+    out: Optional[torch.Tensor] = None,
+    skip_checks: bool = False,
+    max_grid: Optional[Tuple[Optional[int], Optional[int], Optional[int]]] = None,
+    meta: Optional[dict] = None,
+):
+    f_name = "bsr_dense_addmm"
+    values = bsr.values()
+    crow_indices = bsr.crow_indices()
+    col_indices = bsr.col_indices()
+    batch_ndim = crow_indices.dim() - 1
+    M, K = bsr.shape[batch_ndim : batch_ndim + 2]
+    blocksize = values.shape[batch_ndim + 1 : batch_ndim + 3]
+    N = dense.shape[-1]
+
+    # todo: implement checks
+
+    if out is None:
+        original_batch_dims_broadcasted = broadcast_batch_dims(f_name, bsr, dense)
+        out = dense.new_empty(original_batch_dims_broadcasted + (M, N))
+
+    if bsr._nnz() == 0 or alpha == 0 or N == 0 or M == 0 or K == 0:
+        if beta == 0:
+            out.zero_()
+        else:
+            out.copy_(input)
+            if beta != 1:
+                out.mul_(beta)
+        return out
+
+    if meta is None:
+        sparsity = round(1 - bsr._nnz() * blocksize[0] * blocksize[1] / (M * K), 2)
+        meta = bsr_dense_addmm_meta(
+            M,
+            K,
+            N,
+            blocksize[0],
+            blocksize[1],
+            beta,
+            alpha,
+            sparsity=sparsity,
+            dtype=out.dtype,
+        )
+    out_backup = out
+
+    crow_indices, col_indices, values, input, dense, out = prepare_inputs(
+        bsr, input, dense, out
+    )
+
+    BM, BK = blocksize
+    SPLIT_N = meta.get("SPLIT_N", N // BM)
+    BN = N // SPLIT_N
+
+    out_untiled = out
+    out = tile_to_blocksize(out, (BM, BN))
+    dense = tile_to_blocksize(dense, (BK, BN))
+    input = tile_to_blocksize(input, (BM, BN))
+
+    dot_out_dtype = {
+        torch.float16: tl.float32,
+        torch.bfloat16: tl.float32,
+        torch.float32: tl.float64,
+        torch.float64: tl.float64,
+        torch.int8: tl.int32,
+        torch.int32: tl.int32,
+    }[out.dtype]
+
+    n_batches = dense.size(0)
+    n_block_rows = crow_indices.size(-1) - 1
+    n_block_cols = dense.size(-3)
+
+    full_grid = (n_batches, n_block_cols, n_block_rows)
+    if max_grid is not None:
+        grid_blocks = tuple(max_grid[:3][::-1]) + (None,) * (3 - len(max_grid[:3]))
+    else:
+        grid_blocks = None
+
+    tensor_dims_map = {
+        values: (0, None, None),
+        crow_indices: (0, None, -1),
+        col_indices: (0, None, None),
+        input: (0, -3, -4),
+        dense: (0, -3, None),
+        out: (0, -3, -4),
+    }
+
+    assert alpha != 0
+
+    def kernel(grid, *sliced_tensors):
+        _bsr_strided_addmm_kernel[grid](
+            *ptr_stride_extractor(*sliced_tensors),
+            beta,
+            alpha,
+            beta_is_one=beta == 1,
+            beta_is_nonzero=beta != 0,
+            alpha_is_one=alpha == 1,
+            BLOCKSIZE_ROW=BM,
+            BLOCKSIZE_INNER=BK,
+            BLOCKSIZE_COL=BN,
+            allow_tf32=dot_out_dtype == tl.float32,
+            acc_dtype=dot_out_dtype,
+            **meta,
+        )
+
+    launch_kernel(kernel, tensor_dims_map, full_grid, grid_blocks)
+
+    if out.data_ptr() != out_backup.data_ptr():
+        # prepare_inputs has made a copy of out, copy its content back
+        # to out_backup:
+        out_backup.copy_(out_untiled.view(out_backup.shape))
+
+    return out_backup
+
+
+if has_triton():
+    import triton
+    import triton.language as tl
+
+    @triton.jit
+    def _sampled_addmm_kernel(
+        alpha,
+        beta,
+        IS_BETA_ZERO: tl.constexpr,
+        BLOCKSIZE_ROW: tl.constexpr,
+        BLOCKSIZE_COL: tl.constexpr,
+        k,
+        TILE_K: tl.constexpr,
+        values_ptr,
+        values_batch_stride,
+        values_nnz_stride,
+        values_row_block_stride,
+        values_col_block_stride,
+        crow_indices_ptr,
+        crow_indices_batch_stride,
+        crow_indices_stride,
+        col_indices_ptr,
+        col_indices_batch_stride,
+        col_indices_stride,
+        mat1_ptr,
+        mat1_batch_stride,
+        mat1_tiled_row_stride,
+        mat1_tiled_col_stride,
+        mat1_row_block_stride,
+        mat1_col_block_stride,
+        mat2_ptr,
+        mat2_batch_stride,
+        mat2_tiled_row_stride,
+        mat2_tiled_col_stride,
+        mat2_row_block_stride,
+        mat2_col_block_stride,
+        acc_dtype: tl.constexpr,
+        allow_tf32: tl.constexpr,
+    ):
+        batch_pid = tl.program_id(axis=1)
+        row_block_pid = tl.program_id(axis=0)
+
+        crow_indices_offset_ptr = (
+            crow_indices_ptr
+            + crow_indices_batch_stride * batch_pid
+            + crow_indices_stride * row_block_pid
+        )
+        nnz_offset = tl.load(crow_indices_offset_ptr)
+        nnz_offset_next = tl.load(crow_indices_offset_ptr + crow_indices_stride)
+
+        # Compute nnz for the row with number row_block_pid.
+        # If it is zero, skip the row.
+        row_nnz = nnz_offset_next - nnz_offset
+        if row_nnz == 0:
+            return
+
+        row_block_arange = tl.arange(0, BLOCKSIZE_ROW)
+        col_block_arange = tl.arange(0, BLOCKSIZE_COL)
+
+        # Pointers are set to the first block of the current row.
+        values_block_ptrs = (
+            values_ptr
+            + values_batch_stride * batch_pid
+            + values_nnz_stride * nnz_offset
+            + values_row_block_stride * row_block_arange[:, None]
+            + values_col_block_stride * col_block_arange[None, :]
+        )
+
+        col_index_nnz_ptr = (
+            col_indices_ptr
+            + col_indices_batch_stride * batch_pid
+            + col_indices_stride * nnz_offset
+        )
+
+        # Advance mat1 to the current tiled row, ignore columns.
+        mat1_block_ptrs = (
+            mat1_ptr
+            + mat1_batch_stride * batch_pid
+            + mat1_tiled_row_stride * row_block_pid
+            + mat1_row_block_stride * row_block_arange[:, None]
+        )
+
+        # Advance mat2 in batch and block col dimension.
+        mat2_block_ptrs = (
+            mat2_ptr
+            + mat2_batch_stride * batch_pid
+            + mat2_col_block_stride * col_block_arange[None, :]
+        )
+
+        k_tile_arange = tl.arange(0, TILE_K)
+        for _ in range(row_nnz):
+            acc_block = tl.zeros((BLOCKSIZE_ROW, BLOCKSIZE_COL), dtype=acc_dtype)
+
+            # find column block index
+            col_block = tl.load(col_index_nnz_ptr)
+
+            for k_tile in range(0, k, TILE_K):
+                k_offsets = k_tile + k_tile_arange
+                mask_k = k_offsets < k
+
+                mat1_block = tl.load(
+                    mat1_block_ptrs + mat1_col_block_stride * k_offsets[None, :],
+                    mask=mask_k[None, :],
+                    other=0.0,
+                )
+
+                mat2_block = tl.load(
+                    mat2_block_ptrs
+                    + mat2_tiled_col_stride * col_block
+                    + mat2_row_block_stride * k_offsets[:, None],
+                    mask=mask_k[:, None],
+                    other=0.0,
+                )
+
+                acc_block += tl.dot(
+                    mat1_block, mat2_block, allow_tf32=allow_tf32, out_dtype=acc_dtype
+                )
+
+            if IS_BETA_ZERO:
+                acc_block *= alpha
+            else:
+                acc_block = alpha * acc_block + beta * tl.load(values_block_ptrs)
+
+            # write result
+            tl.store(values_block_ptrs, acc_block.to(values_ptr.dtype.element_ty))
+
+            # advance val/col_index ptrs to the next block in the row.
+            values_block_ptrs += values_nnz_stride
+            col_index_nnz_ptr += col_indices_stride
+
+    @triton.jit
+    def _bsr_strided_dense_rowspace_kernel(
+        # values prologue
+        values_ptr,
+        values_batch_stride,
+        values_nnz_stride,
+        values_row_block_stride,
+        values_col_block_stride,
+        # values epilogue
+        # crow_indices prologue
+        crow_indices_ptr,
+        crow_indices_batch_stride,
+        crow_indices_stride,
+        # crow_indices epilogue
+        # col_indices prologue
+        col_indices_ptr,
+        col_indices_batch_stride,
+        col_indices_stride,
+        # col_indices epilogue
+        # dense prologue
+        dense_ptr,
+        dense_batch_stride,
+        dense_tiled_row_stride,
+        dense_tiled_col_stride,
+        dense_row_block_stride,
+        dense_col_block_stride,
+        # dense epilogue
+        # output prologue
+        output_ptr,
+        output_batch_stride,
+        output_tiled_row_stride,
+        output_tiled_col_stride,
+        output_row_block_stride,
+        output_col_block_stride,
+        # output epilogue
+        #
+        # gh-113754: Always keep all constexpr arguments at the end of
+        # triton kernel arguments list because with triton 2.1 or
+        # earlier non-contiguous outputs will corrupt CUDA state due
+        # to a triton bug (fixed in openai/triton#2262).
+        BLOCKSIZE_ROW: tl.constexpr,
+        BLOCKSIZE_COL: tl.constexpr,
+        acc_dtype: tl.constexpr,
+        allow_tf32: tl.constexpr,
+        GROUP_SIZE_ROW: tl.constexpr,
+    ):
+        batch_pid = tl.program_id(axis=2)
+        row_block_pid = tl.program_id(axis=0)
+        col_block_pid = tl.program_id(axis=1)
+        n_block_rows = tl.num_programs(axis=0)
+        n_block_cols = tl.num_programs(axis=1)
+
+        row_block_pid, col_block_pid = tl.swizzle2d(
+            row_block_pid, col_block_pid, n_block_rows, n_block_cols, GROUP_SIZE_ROW
+        )
+
+        crow_indices_offset_ptr = (
+            crow_indices_ptr
+            + crow_indices_batch_stride * batch_pid
+            + crow_indices_stride * row_block_pid
+        )
+        nnz_offset = tl.load(crow_indices_offset_ptr)
+        nnz_offset_next = tl.load(crow_indices_offset_ptr + crow_indices_stride)
+
+        # Compute nnz for the row with number row_block_pid.
+        # If it is zero, skip the row.
+        row_nnz = nnz_offset_next - nnz_offset
+        if row_nnz == 0:
+            return
+
+        row_block_arange = tl.arange(0, BLOCKSIZE_ROW)
+        col_block_arange = tl.arange(0, BLOCKSIZE_COL)
+
+        # Pointers are set to the first block of the current row.
+        values_block_ptrs = (
+            values_ptr
+            + values_batch_stride * batch_pid
+            + values_nnz_stride * nnz_offset
+            + values_row_block_stride * row_block_arange[:, None]
+            + values_col_block_stride * col_block_arange[None, :]
+        )
+
+        # NOTE: dense is advanced into all dimensions but the tiled row one.
+        # That will be advanced in the loop according to values in col_indices.
+        dense_block_ptrs = (
+            dense_ptr
+            + dense_batch_stride * batch_pid
+            + dense_tiled_col_stride * col_block_pid
+            + dense_row_block_stride * col_block_arange[:, None]
+            + dense_col_block_stride * row_block_arange[None, :]
+        )
+
+        # Pointers are set to exact write-to locations
+        output_ptrs = (
+            output_ptr
+            + output_batch_stride * batch_pid
+            + output_tiled_row_stride * row_block_pid
+            + output_tiled_col_stride * col_block_pid
+            + output_row_block_stride * row_block_arange[:, None]
+            + output_col_block_stride * row_block_arange[None, :]
+        )
+
+        # Set pointer to the first nonzero element in the current row
+        col_index_nnz_ptr = (
+            col_indices_ptr
+            + col_indices_batch_stride * batch_pid
+            + col_indices_stride * nnz_offset
+        )
+
+        output_acc_block = tl.zeros((BLOCKSIZE_ROW, BLOCKSIZE_COL), dtype=acc_dtype)
+        for _ in range(row_nnz):
+            values_block = tl.load(values_block_ptrs)
+
+            # find which row of dense needs to get loaded
+            # for multiplication with values_block.
+            dense_row_idx = tl.load(col_index_nnz_ptr)
+            dense_block = tl.load(
+                dense_block_ptrs + dense_tiled_row_stride * dense_row_idx
+            )
+
+            # do block mm
+            output_acc_block += tl.dot(
+                values_block, dense_block, allow_tf32=allow_tf32, out_dtype=acc_dtype
+            )
+
+            # move val/col_index ptrs to the next block in the row
+            values_block_ptrs += values_nnz_stride
+            col_index_nnz_ptr += col_indices_stride
+
+        # write back the result
+        tl.store(output_ptrs, output_acc_block.to(output_ptr.dtype.element_ty))
+
+    def _run_sampled_addmm_kernel(
+        alpha,
+        beta,
+        is_beta_zero,
+        blocksize,
+        k,
+        tile_k,
+        values,
+        crow_indices,
+        col_indices,
+        mat1,
+        mat2,
+        max_grid,
+    ):
+        n_batches = values.size(0)
+        n_block_rows = crow_indices.size(-1) - 1
+
+        full_grid = (n_batches, n_block_rows)
+        if max_grid is not None:
+            grid_blocks = tuple(max_grid[:2][::-1]) + (None,) * (2 - len(max_grid[:2]))
+        else:
+            grid_blocks = None
+        tensor_dims_map = {
+            values: (0, None),
+            crow_indices: (0, -1),
+            col_indices: (0, None),
+            mat1: (0, -4),
+            mat2: (0, None),
+        }
+        if values.dtype in (torch.half, torch.bfloat16):
+            acc_dtype = tl.float32
+            allow_tf32 = True
+        else:
+            acc_dtype = tl.float64
+            allow_tf32 = False
+
+        def kernel(grid, *sliced_tensors):
+            _sampled_addmm_kernel[grid](
+                alpha,
+                beta,
+                is_beta_zero,
+                *blocksize,
+                k,
+                tile_k,
+                *ptr_stride_extractor(*sliced_tensors),
+                acc_dtype=acc_dtype,
+                allow_tf32=allow_tf32,
+                num_stages=1,
+                num_warps=4,
+            )
+
+        launch_kernel(kernel, tensor_dims_map, full_grid, grid_blocks)
+
+    def sampled_addmm(
+        input: torch.Tensor,
+        mat1: torch.Tensor,
+        mat2: torch.Tensor,
+        *,
+        beta=1.0,
+        alpha=1.0,
+        out: Optional[torch.Tensor] = None,
+        skip_checks: bool = False,
+        max_grid: Optional[Tuple[Optional[int], Optional[int], Optional[int]]] = None,
+    ):
+        f_name = "sampled_addmm"
+
+        check_bsr_layout(f_name, input)
+        input_broadcasted = broadcast_batch_dims_bsr(f_name, input, mat1, mat2)
+
+        if not skip_checks:
+            check_device(f_name, mat1, input.device)
+            check_device(f_name, mat2, input.device)
+            if beta != 0.0 and input.dtype is torch.bool:
+                check(
+                    False,
+                    f"{f_name}(): having beta == {beta} not equal to 0.0 with boolean mask is not allowed.",
+                )
+            if input.dtype is not torch.bool:
+                check_dtype(f_name, mat1, input.dtype)
+                check_dtype(f_name, mat2, input.dtype)
+            else:
+                check_dtype(f_name, mat1, mat2.dtype)
+            check_mm_compatible_shapes(f_name, mat1, mat2)
+            if out is not None:
+                check_bsr_layout(f_name, out)
+                check_device(f_name, out, mat1.device)
+                check_dtype(f_name, out, input.dtype)
+                check(
+                    out.shape == input_broadcasted.shape and out._nnz() == input._nnz(),
+                    f"{f_name}(): Expects `out` to be of shape {input_broadcasted.shape} "
+                    f"and with nnz equal to {input_broadcasted._nnz()} "
+                    f"but got out.shape = {out.shape} and out.nnz = {out._nnz()}",
+                )
+
+        if out is None:
+            out = input_broadcasted.to(mat1.dtype, copy=True)
+        else:
+            out.copy_(input_broadcasted)
+
+        if out.numel() == 0 or out._nnz() == 0:
+            return out
+
+        blocksize = out.values().shape[-2:]
+        m = mat1.size(-2)
+        n = mat2.size(-1)
+        k = mat1.size(-1)
+
+        # NOTE: (m, 0) @ (0, n) == zeros(m, n)
+        if alpha == 0.0 or k == 0:
+            out.values().mul_(beta)
+            return out
+
+        # prepare inputs by reshaping them to be kernel-compatible
+        out_backup = out
+        crow_indices, col_indices, values, mat1, mat2 = prepare_inputs(out, mat1, mat2)
+
+        mat1 = tile_to_blocksize(mat1, (blocksize[0], k))
+        mat2 = tile_to_blocksize(mat2, (k, blocksize[1]))
+        tile_k = max(*blocksize)
+
+        _run_sampled_addmm_kernel(
+            alpha,
+            beta,
+            beta == 0.0,
+            blocksize,
+            k,
+            tile_k,
+            values,
+            crow_indices,
+            col_indices,
+            mat1,
+            mat2,
+            max_grid,
+        )
+
+        # If nnz x block strides are not the same in out_backup.values and values,
+        # it means that out_backup.values and values are not the views of each other,
+        # so we have to copy.
+        if out_backup.values().stride()[-3:] != values.stride()[-3:]:
+            out_backup.values().copy_(values.reshape(out_backup.values().shape))
+        return out_backup
+
+    def bsr_dense_mm(
+        bsr: torch.Tensor,
+        dense: torch.Tensor,
+        *,
+        out: Optional[torch.Tensor] = None,
+        skip_checks: bool = False,
+        max_grid: Optional[Tuple[Optional[int], Optional[int], Optional[int]]] = None,
+        meta: Optional[dict] = None,
+    ):
+        f_name = "bsr_dense_mm"
+        m, kl = bsr.shape[-2:]
+        if not skip_checks:
+            check_bsr_layout(f_name, bsr)
+            check_device(f_name, bsr, dense.device)
+            check_dtype(f_name, bsr, dense.dtype, (torch.int8,))
+            check_mm_compatible_shapes(f_name, bsr, dense)
+
+            n = dense.size(-1)
+            row_block, col_block = bsr.values().shape[-2:]
+            check_blocksize(f_name, (row_block, col_block))
+            check(
+                not n % 16,
+                f"{f_name}(): dense.size(-1) == {n} should be divisible by 16",
+            )
+        else:
+            kr, n = dense.shape[-2:]
+
+        original_batch_dims_broadcasted = broadcast_batch_dims(f_name, bsr, dense)
+
+        if out is not None and not skip_checks:
+            expected_out_shape = original_batch_dims_broadcasted + (m, n)
+            check(
+                out.shape == expected_out_shape,
+                "bsr_dense_mm(): `out` argument has wrong shape, "
+                f"expected {expected_out_shape}, but got {out.shape}.",
+            )
+            check(
+                out.is_contiguous() or out.transpose(-2, -1).is_contiguous(),
+                "bsr_dense_mm(): only row-major/col-major `out` arguments are supported, "
+                "i.e. (out.is_contiguous() or out.transpose(-2, -1).is_contiguous()) "
+                "should be True.",
+            )
+
+        # Allocate out
+        if out is None:
+            out = dense.new_empty(original_batch_dims_broadcasted + (m, n))
+
+        # Short circuit if lhs is zero
+        if bsr._nnz() == 0:
+            return out.zero_()
+
+        # with beta==0, addmm ignores input content, so we can use out
+        # as a placeholder for input because their shapes match:
+        return bsr_dense_addmm(out, bsr, dense, alpha=1, beta=0, out=out)
+
+    @triton.jit
+    def _bsr_softmax_kernel(
+        crow_indices_ptr,
+        crow_indices_batch_stride,
+        crow_indices_stride,
+        values_ptr,
+        values_batch_stride,
+        values_row_block_stride,
+        values_nnz_col_block_stride,
+        row_block,
+        col_block,
+        MAX_ROW_NNZ: tl.constexpr,
+        TILE: tl.constexpr,
+    ):
+        batch_pid = tl.program_id(axis=2)
+        row_block_offset_pid = tl.program_id(axis=1)
+        row_block_pid = tl.program_id(axis=0)
+
+        crow_indices_offset_ptr = (
+            crow_indices_ptr
+            + crow_indices_batch_stride * batch_pid
+            + crow_indices_stride * row_block_pid
+        )
+        nnz_offset = tl.load(crow_indices_offset_ptr)
+        nnz_offset_next = tl.load(crow_indices_offset_ptr + crow_indices_stride)
+
+        # Compute nnz for the row with number row_block_pid.
+        # If it is zero, skip the row.
+        row_nnz = nnz_offset_next - nnz_offset
+        if row_nnz == 0:
+            return
+
+        row_arange = tl.arange(0, TILE)
+        mask = row_arange < row_nnz * col_block
+
+        curr_row_values_ptrs = (
+            values_ptr
+            + values_batch_stride * batch_pid
+            + values_row_block_stride * row_block_offset_pid
+            + nnz_offset * col_block
+        )
+
+        # find max in the row
+        row_tile = tl.load(
+            curr_row_values_ptrs + row_arange, mask=mask, other=-float("inf")
+        ).to(tl.float32)
+        max_row_value = tl.max(row_tile, axis=0)
+        for _ in range(TILE, MAX_ROW_NNZ, TILE):
+            row_arange += TILE
+            mask = row_arange < row_nnz * col_block
+            row_tile = tl.load(
+                curr_row_values_ptrs + row_arange, mask=mask, other=-float("inf")
+            ).to(tl.float32)
+            curr_max_row_value = tl.max(row_tile, axis=0)
+            max_row_value = tl.where(
+                max_row_value > curr_max_row_value, max_row_value, curr_max_row_value
+            )
+
+        # find denominator for stable softmax
+        num = tl.exp(row_tile - max_row_value)
+        denom = tl.sum(num, axis=0)
+        for _ in range(TILE, MAX_ROW_NNZ, TILE):
+            row_arange -= TILE
+            mask = row_arange < row_nnz * col_block
+            row_tile = tl.load(
+                curr_row_values_ptrs + row_arange, mask=mask, other=-float("inf")
+            ).to(tl.float32)
+            num = tl.exp(row_tile - max_row_value)
+            denom += tl.sum(num, axis=0)
+
+        # populate output
+        tl.store(
+            curr_row_values_ptrs + row_arange,
+            (num / denom).to(values_ptr.dtype.element_ty),
+            mask=mask,
+        )
+        for _ in range(TILE, MAX_ROW_NNZ, TILE):
+            row_arange += TILE
+            mask = row_arange < row_nnz * col_block
+            row_tile = tl.load(
+                curr_row_values_ptrs + row_arange, mask=mask, other=-float("inf")
+            ).to(tl.float32)
+            num = tl.exp(row_tile - max_row_value)
+            tl.store(
+                curr_row_values_ptrs + row_arange,
+                (num / denom).to(values_ptr.dtype.element_ty),
+                mask=mask,
+            )
+
+    def bsr_softmax(input, max_row_nnz=None):
+        f_name = "bsr_softmax"
+
+        check_bsr_layout(f_name, input)
+        check_dtype(f_name, input, input.dtype)
+
+        if input._nnz() == 0 or input.numel() == 0:
+            return input.clone()
+
+        m, n = input.shape[-2:]
+        nnz = input._nnz()
+        row_block, col_block = input.values().shape[-2:]
+
+        if max_row_nnz is None:
+            max_row_nnz = triton.next_power_of_2(n)
+        else:
+            max_row_nnz = triton.next_power_of_2(max_row_nnz)
+
+        crow_indices = input.crow_indices().unsqueeze(0).flatten(0, -2)
+        # reshape values from
+        # (b1, ..., bn, nnz, row_block, col_block) to
+        # (b1 * ... * bn, row_block, nnz * col_block).
+        # This simplifies batch dim manipulation and unlocks
+        # the possibility to access all nnzs in any given row.
+        if input.values().transpose(-3, -2).is_contiguous():
+            # Need to clone to avoid `contiguous` returning a view.
+            values = input.values().clone()
+        else:
+            values = input.values()
+        values = (
+            values.transpose(-3, -2)
+            .contiguous()
+            .unsqueeze(0)
+            .flatten(0, -4)
+            .reshape(-1, row_block, nnz * col_block)
+        )
+        full_grid = (values.shape[0], row_block, m // row_block)
+        grid_blocks = None
+        tensor_dims_map = {
+            # We span nnz number of blocks, not nnz + 1,
+            # hence crow_indices[..., :-1]
+            crow_indices[..., :-1]: (0, None, -1),
+            values: (0, None, None),
+        }
+
+        def kernel(grid, *sliced_tensors):
+            _bsr_softmax_kernel[grid](
+                *ptr_stride_extractor(*sliced_tensors),
+                row_block,
+                col_block,
+                max_row_nnz,
+                # Triton's max numel is bounded by 2 ** 17.
+                min(2**17, max_row_nnz),
+            )
+
+        launch_kernel(kernel, tensor_dims_map, full_grid, grid_blocks)
+
+        values = (
+            values.reshape(-1, row_block, nnz, col_block)
+            .transpose(-3, -2)
+            .reshape(*input.values().shape)
+        )
+
+        return torch.sparse_compressed_tensor(
+            input.crow_indices().clone(),
+            input.col_indices().clone(),
+            values,
+            size=input.shape,
+            layout=input.layout,
+        )
+
+    def _scaled_dot_product_attention(
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        attn_mask: Optional[torch.Tensor],
+        dropout_p: float = 0.0,
+        is_causal: bool = False,
+        scale: Optional[float] = None,
+    ):
+        f_name = "_scaled_dot_product_attention"
+        check(not is_causal, f"{f_name}(): is_causal == True is not supported.")
+        check(attn_mask is not None, f"{f_name}(): attn_mask == None is not supported.")
+        assert attn_mask is not None
+
+        check(
+            attn_mask.layout == torch.sparse_bsr,
+            f"{f_name}(): "
+            f"attn_mask.layout must be {torch.sparse_bsr}, but got "
+            f"attn_mask.layout == {attn_mask.layout}.",
+        )
+
+        check_device(f_name, key, query.device)
+        check_device(f_name, value, query.device)
+        check_device(f_name, attn_mask, query.device)
+
+        check_dtype(f_name, key, query.dtype)
+        check_dtype(f_name, value, query.dtype)
+        if attn_mask.dtype is not torch.bool:
+            check_dtype(f_name, attn_mask, query.dtype)
+
+        sdpa = sampled_addmm(
+            attn_mask, query, key.transpose(-2, -1), beta=0.0, skip_checks=False
+        )
+        if scale is None and query.size(-1) == 0 or scale == 0.0:
+            check(
+                False,
+                f"{f_name}(): current value of scale == {scale} "
+                "results in division by zero.",
+            )
+        scale_factor = 1 / math.sqrt(query.size(-1)) if scale is None else scale
+        sdpa.values().mul_(scale_factor)
+        sdpa = bsr_softmax(sdpa)
+        torch.nn.functional.dropout(sdpa.values(), p=dropout_p, inplace=True)
+        sdpa = bsr_dense_mm(sdpa, value)
+        return sdpa
+
+    @triton.jit
+    def _scatter_mm2_kernel(
+        M: tl.constexpr,
+        K: tl.constexpr,
+        N: tl.constexpr,
+        blocks_ptr,
+        blocks_stride_P,
+        blocks_stride_M,
+        blocks_stride_K,
+        others_ptr,
+        others_stride_Q,
+        others_stride_K,
+        others_stride_N,
+        accumulators_ptr,
+        accumulators_stride_R,
+        accumulators_stride_M,
+        accumulators_stride_N,
+        pq_offsets_ptr,
+        pq_offsets_stride,
+        pq_ptr,
+        pq_stride_T,
+        pq_stride_1,
+        dot_out_dtype: tl.constexpr,
+        TILE_M: tl.constexpr,
+        TILE_N: tl.constexpr,
+        allow_tf32: tl.constexpr,
+    ):
+        Ms = M // TILE_M
+        Ns = N // TILE_N
+
+        pid_t = tl.program_id(axis=0)
+
+        pid = tl.program_id(axis=1)
+        pid_m = pid // Ms
+        pid_n = pid % Ms
+
+        rm = pid_m * TILE_M + tl.arange(0, TILE_M)
+        rn = pid_n * TILE_N + tl.arange(0, TILE_N)
+        rk = tl.arange(0, K)
+
+        A_ptr = blocks_ptr + (
+            rm[:, None] * blocks_stride_M + rk[None, :] * blocks_stride_K
+        )
+        B_ptr = others_ptr + (
+            rk[:, None] * others_stride_K + rn[None, :] * others_stride_N
+        )
+
+        g0 = tl.load(pq_offsets_ptr + pid_t * pq_offsets_stride)
+        g1 = tl.load(pq_offsets_ptr + (pid_t + 1) * pq_offsets_stride)
+
+        if g0 == g1:
+            return
+
+        acc_block = tl.zeros((TILE_M, TILE_N), dtype=dot_out_dtype)
+
+        for i in range(g0, g1):
+            p = tl.load(pq_ptr + i * pq_stride_T)
+            q = tl.load(pq_ptr + i * pq_stride_T + pq_stride_1)
+            A = tl.load(A_ptr + p * blocks_stride_P)
+            B = tl.load(B_ptr + q * others_stride_Q)
+            acc_block += tl.dot(A, B, out_dtype=dot_out_dtype, allow_tf32=allow_tf32)
+
+        C_ptr = (
+            accumulators_ptr
+            + pid_t * accumulators_stride_R
+            + (
+                rm[:, None] * accumulators_stride_M
+                + rn[None, :] * accumulators_stride_N
+            )
+        )
+        tl.store(C_ptr, acc_block.to(accumulators_ptr.dtype.element_ty))
+
+    def _scatter_mm2(
+        blocks: torch.Tensor,
+        others: torch.Tensor,
+        pq_offsets: torch.Tensor,
+        pq_indices: torch.Tensor,
+        accumulators: torch.Tensor,
+    ):
+        P, M, K = blocks.shape
+        Q, _, N = others.shape
+        R, _, _ = accumulators.shape
+
+        meta = dict(
+            TILE_M=max(16, M // 4), TILE_N=max(16, N // 4), num_stages=1, num_warps=2
+        )
+
+        def grid(META):
+            return (
+                pq_offsets.shape[0] - 1,
+                triton.cdiv(M, META["TILE_M"]) * triton.cdiv(N, META["TILE_N"]),
+                1,
+            )
+
+        dot_out_dtype = {
+            torch.float16: tl.float32,
+            torch.bfloat16: tl.float32,
+            torch.float32: tl.float64,
+            torch.float64: tl.float64,
+        }[accumulators.dtype]
+        if "allow_tf32" not in meta:
+            meta.update(allow_tf32=dot_out_dtype == tl.float32)
+        _scatter_mm2_kernel[grid](
+            M,
+            K,
+            N,
+            blocks,
+            blocks.stride(0),
+            blocks.stride(1),
+            blocks.stride(2),
+            others,
+            others.stride(0),
+            others.stride(1),
+            others.stride(2),
+            accumulators,
+            accumulators.stride(0),
+            accumulators.stride(1),
+            accumulators.stride(2),
+            pq_offsets,
+            pq_offsets.stride(0),
+            pq_indices,
+            pq_indices.stride(0),
+            pq_indices.stride(1),
+            dot_out_dtype=dot_out_dtype,
+            **meta,
+        )
+
+    @triton.jit
+    def _scatter_mm6_kernel(
+        nbatches,
+        Ms,
+        Ks: tl.constexpr,
+        N,
+        blocks_ptr,
+        blocks_stride_P,
+        blocks_stride_M,
+        blocks_stride_K,
+        others_ptr,
+        others_stride_B,
+        others_stride_K,
+        others_stride_N,
+        accumulators_ptr,
+        accumulators_stride_B,
+        accumulators_stride_M,
+        accumulators_stride_N,
+        c_indices_ptr,
+        r_offsets_ptr,
+        p_offsets_ptr,
+        q_offsets_ptr,
+        is_compressed: tl.constexpr,
+        dot_out_dtype: tl.constexpr,
+        SPLIT_N: tl.constexpr,
+        TILE_M: tl.constexpr,
+        TILE_N: tl.constexpr,
+        GROUP_SIZE: tl.constexpr,
+        allow_tf32: tl.constexpr,
+    ):
+        Ns = N // SPLIT_N
+        BLOCKS_M = Ms // TILE_M
+        BLOCKS_N = Ns // TILE_N
+
+        pid_t_ = tl.program_id(axis=0)
+        pid = tl.program_id(axis=1)
+        pid_b = pid_t_ % nbatches
+        pid_t = pid_t_ // nbatches
+
+        num_pid_in_group = GROUP_SIZE * BLOCKS_N
+        group_id = pid // num_pid_in_group
+        first_pid_m = group_id * GROUP_SIZE
+        group_size_m = min(BLOCKS_M - first_pid_m, GROUP_SIZE)
+        pid_m = first_pid_m + (pid % group_size_m)
+        pid_n = (pid % num_pid_in_group) // group_size_m
+
+        rm = pid_m * TILE_M + tl.arange(0, TILE_M)
+        rn = pid_n * TILE_N + tl.arange(0, TILE_N)
+        rk = tl.arange(0, Ks)
+        A_ptr = blocks_ptr + (
+            rm[:, None] * blocks_stride_M + rk[None, :] * blocks_stride_K
+        )
+        B_ptr = (
+            others_ptr
+            + pid_b * others_stride_B
+            + (rk[:, None] * others_stride_K + rn[None, :] * others_stride_N)
+        )
+
+        # When is_compressed is True, r is the only variable that
+        # depends on pid_t. This property allows sorting r values
+        # before calling the kernel. The sorting of r is equivalent to
+        # defining swizzle operator outside of the kernel.
+        r = tl.load(r_offsets_ptr + pid_t)
+
+        if is_compressed:
+            m = (r // N) // Ms
+            n = (r % N) // Ns
+            r0 = tl.load(c_indices_ptr + m)
+            r1 = tl.load(c_indices_ptr + m + 1)
+            g0 = n * r1 + (SPLIT_N - n) * r0
+            nnz = r1 - r0
+        else:
+            g0 = tl.load(c_indices_ptr + pid_t)
+            g1 = tl.load(c_indices_ptr + pid_t + 1)
+            nnz = g1 - g0
+
+        q_ptr = q_offsets_ptr + g0
+        acc_block = tl.zeros((TILE_M, TILE_N), dtype=dot_out_dtype)
+
+        if is_compressed:
+            A_ptr += r0 * blocks_stride_P  # type: ignore[possibly-undefined]
+            for _ in range(nnz):
+                q = tl.load(q_ptr)
+                B = tl.load(B_ptr + q)
+                A = tl.load(A_ptr)
+                acc_block += tl.dot(
+                    A, B, out_dtype=dot_out_dtype, allow_tf32=allow_tf32
+                )
+                A_ptr += blocks_stride_P
+                q_ptr += 1
+        else:
+            p_ptr = p_offsets_ptr + g0
+            for _ in range(nnz):
+                q = tl.load(q_ptr)
+                B = tl.load(B_ptr + q)
+                p = tl.load(p_ptr)
+                A = tl.load(A_ptr + p * blocks_stride_P)
+                p_ptr += 1
+                q_ptr += 1
+                acc_block += tl.dot(
+                    A, B, out_dtype=dot_out_dtype, allow_tf32=allow_tf32
+                )
+
+        C_ptr = (
+            accumulators_ptr
+            + r
+            + pid_b * accumulators_stride_B
+            + (
+                rm[:, None] * accumulators_stride_M
+                + rn[None, :] * accumulators_stride_N
+            )
+        )
+        tl.store(C_ptr, acc_block.to(accumulators_ptr.dtype.element_ty))
+
+    def _scatter_mm6(
+        blocks: torch.Tensor,
+        others: torch.Tensor,
+        c_indices: torch.Tensor,
+        r_offsets: torch.Tensor,
+        p_offsets: torch.Tensor,
+        q_offsets: torch.Tensor,
+        meta: dict,
+        accumulators: torch.Tensor,
+        force_contiguous: bool = True,
+    ):
+        SPLIT_N = meta["SPLIT_N"]
+        P, Ms, Ks = blocks.shape
+        B, K_, N = others.shape
+        B_, M, N_ = accumulators.shape
+        assert N_ == N
+        Ns = N // SPLIT_N
+        assert B_ == B
+
+        def grid(META):
+            return (
+                r_offsets.shape[0] * B,
+                triton.cdiv(Ms, META["TILE_M"]) * triton.cdiv(Ns, META["TILE_N"]),
+            )
+
+        dot_out_dtype = {
+            torch.float16: tl.float32,
+            torch.bfloat16: tl.float32,
+            torch.float32: tl.float64,
+            torch.float64: tl.float64,
+        }[accumulators.dtype]
+        if "allow_tf32" not in meta:
+            meta.update(allow_tf32=dot_out_dtype == tl.float32)
+
+        assert c_indices.stride(0) == 1
+        assert r_offsets.stride(0) == 1
+        assert p_offsets.stride(0) == 1
+        assert q_offsets.stride(0) == 1
+
+        # Re non-contiguous tensor arguments. Sometimes triton kernel
+        # launches may fail with
+        #
+        #   RuntimeError: Triton Error [CUDA]: an illegal memory access was encountered
+        #
+        # that appears to be case when the size of a non-contiguous
+        # tensor argument is larger than a certain threshold. Could
+        # this be related to shared memory or L1 cache size of a GPU
+        # card? In anycase, ensuring that tensor arguments are
+        # contiguous seems to avoid the above exception. So, in the
+        # following we'll always convert tensor arguments to
+        # C-contiguous tensors.
+
+        if force_contiguous:
+            blocks = blocks.contiguous()
+            others = others.contiguous()
+            if not accumulators.is_contiguous():
+                accumulators_ = accumulators.contiguous()
+            else:
+                accumulators_ = accumulators
+        else:
+            accumulators_ = accumulators
+
+        _scatter_mm6_kernel[grid](
+            B,
+            Ms,
+            Ks,
+            N,
+            blocks,
+            blocks.stride(0),
+            blocks.stride(1),
+            blocks.stride(2),
+            others,
+            others.stride(0),
+            others.stride(1),
+            others.stride(2),
+            accumulators_,
+            accumulators_.stride(0),
+            accumulators_.stride(1),
+            accumulators_.stride(2),
+            c_indices,
+            r_offsets,
+            p_offsets,
+            q_offsets,
+            dot_out_dtype=dot_out_dtype,
+            **meta,
+        )
+
+        if force_contiguous and not accumulators.is_contiguous():
+            accumulators.copy_(accumulators_)
+
+    @triton.jit
+    def _bsr_strided_addmm_kernel(
+        # values prologue
+        values_ptr,
+        values_batch_stride,
+        values_nnz_stride,
+        values_row_block_stride,
+        values_col_block_stride,
+        # values epilogue
+        # crow_indices prologue
+        crow_indices_ptr,
+        crow_indices_batch_stride,
+        crow_indices_stride,
+        # crow_indices epilogue
+        # col_indices prologue
+        col_indices_ptr,
+        col_indices_batch_stride,
+        col_indices_stride,
+        # col_indices epilogue
+        # input prologue
+        input_ptr,
+        input_batch_stride,
+        input_tiled_row_stride,
+        input_tiled_col_stride,
+        input_row_block_stride,
+        input_col_block_stride,
+        # input epilogue
+        # dense prologue
+        dense_ptr,
+        dense_batch_stride,
+        dense_tiled_row_stride,
+        dense_tiled_col_stride,
+        dense_row_block_stride,
+        dense_col_block_stride,
+        # dense epilogue
+        # output prologue
+        output_ptr,
+        output_batch_stride,
+        output_tiled_row_stride,
+        output_tiled_col_stride,
+        output_row_block_stride,
+        output_col_block_stride,
+        # output epilogue
+        beta,
+        alpha,
+        beta_is_one: tl.constexpr,
+        beta_is_nonzero: tl.constexpr,
+        alpha_is_one: tl.constexpr,
+        BLOCKSIZE_ROW: tl.constexpr,
+        BLOCKSIZE_COL: tl.constexpr,
+        BLOCKSIZE_INNER: tl.constexpr,
+        acc_dtype: tl.constexpr,
+        allow_tf32: tl.constexpr,
+        GROUP_SIZE_ROW: tl.constexpr,
+        SPLIT_N: tl.constexpr,
+    ):
+        batch_pid = tl.program_id(axis=2)
+        row_block_pid = tl.program_id(axis=0)
+        col_block_pid = tl.program_id(axis=1)
+        n_block_rows = tl.num_programs(axis=0)
+        n_block_cols = tl.num_programs(axis=1)
+
+        row_block_pid, col_block_pid = tl.swizzle2d(
+            row_block_pid, col_block_pid, n_block_rows, n_block_cols, GROUP_SIZE_ROW
+        )
+
+        crow_indices_offset_ptr = (
+            crow_indices_ptr
+            + crow_indices_batch_stride * batch_pid
+            + crow_indices_stride * row_block_pid
+        )
+        nnz_offset = tl.load(crow_indices_offset_ptr)
+        nnz_offset_next = tl.load(crow_indices_offset_ptr + crow_indices_stride)
+
+        # Compute nnz for the row with number row_block_pid.
+        row_nnz = nnz_offset_next - nnz_offset
+
+        row_block_arange = tl.arange(0, BLOCKSIZE_ROW)
+        inner_block_arange = tl.arange(0, BLOCKSIZE_INNER)
+        col_block_arange = tl.arange(0, BLOCKSIZE_COL)
+
+        if beta_is_nonzero:
+            # Pointers are set to exact write-to locations
+            input_ptrs = (
+                input_ptr
+                + input_batch_stride * batch_pid
+                + input_tiled_row_stride * row_block_pid
+                + input_tiled_col_stride * col_block_pid
+                + input_row_block_stride * row_block_arange[:, None]
+                + input_col_block_stride * col_block_arange[None, :]
+            )
+
+        # Pointers are set to the first block of the current row.
+        values_block_ptrs = (
+            values_ptr
+            + values_batch_stride * batch_pid
+            + values_nnz_stride * nnz_offset
+            + values_row_block_stride * row_block_arange[:, None]
+            + values_col_block_stride * inner_block_arange[None, :]
+        )
+
+        # NOTE: dense is advanced into all dimensions but the tiled row one.
+        # That will be advanced in the loop according to values in col_indices.
+        dense_block_ptrs = (
+            dense_ptr
+            + dense_batch_stride * batch_pid
+            + dense_tiled_col_stride * col_block_pid
+            + dense_row_block_stride * inner_block_arange[:, None]
+            + dense_col_block_stride * col_block_arange[None, :]
+        )
+
+        # Pointers are set to exact write-to locations
+        output_ptrs = (
+            output_ptr
+            + output_batch_stride * batch_pid
+            + output_tiled_row_stride * row_block_pid
+            + output_tiled_col_stride * col_block_pid
+            + output_row_block_stride * row_block_arange[:, None]
+            + output_col_block_stride * col_block_arange[None, :]
+        )
+
+        # Set pointer to the first nonzero element in the current row
+        col_index_nnz_ptr = (
+            col_indices_ptr
+            + col_indices_batch_stride * batch_pid
+            + col_indices_stride * nnz_offset
+        )
+
+        # alpha is never 0
+        if beta_is_nonzero:
+            output_acc_block = tl.load(input_ptrs).to(acc_dtype)  # type: ignore[possibly-undefined]
+            if not (beta_is_one and alpha_is_one):
+                beta_alpha = beta / alpha
+                output_acc_block *= beta_alpha
+        else:
+            output_acc_block = tl.zeros((BLOCKSIZE_ROW, BLOCKSIZE_COL), dtype=acc_dtype)
+
+        for _ in range(row_nnz):
+            values_block = tl.load(values_block_ptrs)
+
+            # find which row of dense needs to get loaded
+            # for multiplication with values_block.
+            dense_row_idx = tl.load(col_index_nnz_ptr)
+            dense_block = tl.load(
+                dense_block_ptrs + dense_tiled_row_stride * dense_row_idx
+            )
+
+            # do block mm
+            output_acc_block += tl.dot(
+                values_block, dense_block, allow_tf32=allow_tf32, out_dtype=acc_dtype
+            )
+
+            # move val/col_index ptrs to the next block in the row
+            values_block_ptrs += values_nnz_stride
+            col_index_nnz_ptr += col_indices_stride
+
+        if not alpha_is_one:
+            output_acc_block *= alpha
+
+        # write back the result
+        tl.store(output_ptrs, output_acc_block.to(output_ptr.dtype.element_ty))
+
+else:
+    bsr_softmax = None  # type: ignore[assignment]
+    bsr_dense_mm = None  # type: ignore[assignment]
+    sampled_addmm = None  # type: ignore[assignment]
+    _scaled_dot_product_attention = None  # type: ignore[assignment]
+    _scatter_mm2 = None  # type: ignore[assignment]
+    _scatter_mm6 = None  # type: ignore[assignment]
+    _bsr_strided_addmm_kernel = None  # type: ignore[assignment]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/sparse/_triton_ops_meta.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/sparse/_triton_ops_meta.py
new file mode 100644
index 0000000000000000000000000000000000000000..a97d9c502dc349533ed778c2e4bbbcf1891ded1c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/sparse/_triton_ops_meta.py
@@ -0,0 +1,7387 @@
+# mypy: allow-untyped-defs
+"""Provides optimal triton kernel parameters.
+
+Aim
+---
+
+The usage of optimal triton kernel parameters may increase the
+performance of operations several times. For example, for large tensor
+shapes, the usage of a bsr tensor as mat1 argument in addmm-based
+operations typically outperforms the corresponding operation with
+strided-only inputs when the blocked representation of a tensor
+provides a better alignement with memory access than what the strided
+representation would provide.
+
+Pre-computed kernel parameters
+------------------------------
+
+This script finds and stores the optimal triton kernel parameters for
+a specific set of shape configurations. For instance, the set of shape
+configurations of the bsr_dense_addmm kernel is defined as
+
+  input, out: M x N strided tensor
+  mat1: M x K bsr tensor with blocksize (BM, BK) and given sparsity
+  mat2: M x N strided tensor
+  dtype = float16, bfloat16, float32
+  sparsity = 0.5
+  M = 256, 512, ..., 16384
+  K = M
+  N = 256, 512, ..., 131072
+  BM = 16, 32, ..., 128
+  BK = BM
+  alpha = 1
+  beta = 0, 1
+  GPUs: NVIDIA A100-SXM4-80GB
+
+Approximations
+--------------
+
+It is practically infeasible to pre-compute optimal kernel parameter
+for all possible shape configurations as well as for all existing
+GPUs. Therefore, we'll assume that the pre-computed optimal parameters
+are good enough approximations when
+1) the used GPU is any of NVIDIA A100 Tensor Core GPUs,
+2) the actual sparsity of mat1 is different from sparsity value 0.5.
+
+If a particular shape configuration does not fall in the set of
+pre-computed kernel parameters, or it does not match with the listed
+approximations above, or the used GPU device is not a NVIDIA A100 GPU,
+then a reference set of triton kernel parameters will be used when
+executing operations. The reference kernel parameters are defined in
+torch/sparse/_triton_ops.py, see bsr_dense_addmm_meta function, for
+instance.
+
+Computing optimal kernel parameters
+-----------------------------------
+
+If the approximations listed above are unacceptable, e.g. when one
+seeks a maximal performance possible, the optimal kernel parameters
+for a particular GPU can be computed by simply running this script in
+the pytorch developement tree::
+
+  cd /path/to/pytorch
+  python setup.py develop
+  python torch/sparse/_triton_ops_meta.py
+
+This will compute the optimal kernel parameters for the GPU device
+available in the host system for all shape configurations listed in
+"Pre-computed kernel parameters" above. The results will be stored in
+the database of kernel parameters. Currently, this database is defined
+as this module (see "BEGIN GENERATED DATA" comment below) that will be
+modified when the script is run. Create a pytorch PR with the
+corresponding modifications in this file to make the computed optimal
+kernel parameters available for other users as pre-computed kernel
+parameters.
+
+Moreover, one can compute the optimal kernel parameters for a specific
+set of shape configurations and specific sparsity patterns. For that,
+use tuning functions provided by this module:
+
+  tune_bsr_dense_addmm(input, mat1, mat2, beta=1, alpha=1, out=None, verbose=False, store=False) -> meta
+
+The tuning functions return a dictionary of optimal kernel parameters
+that can be passed to the corresponding operation, e.g.
+
+  bsr_dense_addmm(..., meta=meta)
+
+Or, when store==True, the optimal kernel parameters will be stored in
+the database of pre-computed kernel parameters in runtime so that all
+addmm-based operations such as torch.addmm, torch.mm,
+torch.nn.functional.linear will benefit from using the computed
+optimal set of kernel parameters.
+
+Note that running tune_bsr_dense_addmm can take several minutes. So,
+use it wisely, e.g. by implementing persisten storage of optimized
+kernel parameters. See the source code of get_meta and
+tune_bsr_dense_addmm to learn how to register a custom set of optimal
+kernel parameters for addmm-based operations.
+
+"""
+__all__ = ["get_meta", "tune_bsr_dense_addmm", "tune__int_bsr_dense_addmm"]
+
+import inspect
+import itertools
+import re
+import warnings
+from typing import Any, Dict
+
+import torch
+from torch.hub import tqdm
+from torch.testing import make_tensor
+
+
+def get_meta(op, key, device_name=None, version=(0, torch.float16, 0.5), exact=False):
+    """Return triton kernel meta parameters of the specified op and its inputs key.
+
+    Parameters
+    ----------
+    op (str): The name of an operation that implementation uses meta parameters.
+    key (tuple): A tuple of op input parameters, e.g. shapes, etc.
+    device_name (optional, str): The name of a device for which op
+      parameters are provided.
+    version (optional, hashable): Specifies the version of parameters.
+    exact (optional, bool): When True, the returned data (if
+      available) corresponds exactly to the specified device_name and
+      version information. Otherwise, if the corresponding data is not
+      available but there exists a data set that is computed for a
+      similar GPU device, then this data set will be returned.
+
+    Returns
+    -------
+    result (dict): The requested mapping of parameter names and
+      values, or None when no data is available. If the input `key`
+      contains `"*"`, the result will be a dictionary of keys and
+      mappings that match with the given `key`.
+    """
+    if device_name is None:
+        device_name = torch.cuda.get_device_name()
+
+    op_data = _operation_device_version_data.get((op, device_name, version))
+    if op_data is None and not exact:
+        # A lack of op data could be due to using a (slightly)
+        # different GPU model compared to a model for which optimal
+        # meta parameters have been computed. In the following we'll
+        # assume that there is a set of GPU models that all have
+        # a similar set of optimal meta parameters.
+        if re.match(r"NVIDIA A100[^\d]", device_name) is not None:
+            device_name = "NVIDIA A100-SXM4-80GB"
+        else:
+            return
+        op_data = _operation_device_version_data.get((op, device_name, version))
+    if op_data is None:
+        return
+
+    matching_data = {}
+    if "*" in key:
+        for op_key in op_data:
+            if [None for k1, k2 in zip(op_key, key) if k2 != "*" and k1 != k2]:
+                continue
+            matching_data[op_key] = op_data[op_key]
+    else:
+        values = op_data.get(key)
+        if values is not None:
+            matching_data[key] = values
+    matching_meta = {}
+    for op_key, values in matching_data.items():
+        if op == "scatter_mm":
+            names = (
+                "GROUP_SIZE",
+                "SPLIT_N",
+                "TILE_M",
+                "TILE_N",
+                "num_stages",
+                "num_warps",
+            )
+            meta = dict(zip(names, values))
+        elif op in {"bsr_dense_addmm", "_int_bsr_dense_addmm"}:
+            meta = dict(
+                zip(("GROUP_SIZE_ROW", "SPLIT_N", "num_stages", "num_warps"), values)
+            )
+        else:
+            raise NotImplementedError(f"names for {op=}")
+        if "*" not in key:
+            return meta
+
+        matching_meta[op_key] = meta
+
+    if "*" in key:
+        return matching_meta
+
+
+def update(op, device_name, version, key, value):
+    """Update the db of op parameters."""
+    # avoid storing possible optimization failures:
+    assert value, (op, device_name, version, key, value)
+    if (op, device_name, version) in _operation_device_version_data:
+        if _operation_device_version_data[op, device_name, version].get(key) == value:
+            return
+        _operation_device_version_data[op, device_name, version][key] = value
+    else:
+        _operation_device_version_data[op, device_name, version] = {key: value}
+
+
+def dump():
+    """Store the current runtime db state to the module file."""
+    current_file = inspect.getfile(dump)
+    f = open(current_file)
+    current_content = f.read()
+    f.close()
+    begin_data_str = "# BEGIN GENERATED DATA\n"
+    begin_data_index = current_content.find(begin_data_str)
+    end_data_index = current_content.find("    # END GENERATED DATA\n")
+    if begin_data_index == -1 or end_data_index == -1:
+        warnings.warn(
+            f"{current_file} cannot be updated:"
+            " BEGIN/END GENERATED DATA comment blocks appear to be corrupted"
+        )
+        return
+
+    def sort_key(key):
+        op, device_name, version = key
+        version = tuple(
+            (str(item) if isinstance(item, torch.dtype) else item) for item in version
+        )
+        return (op, device_name, version)
+
+    part1 = current_content[: begin_data_index + len(begin_data_str)]
+    part2 = current_content[end_data_index:]
+    data_part = []
+    for op_key in sorted(_operation_device_version_data, key=sort_key):
+        data_part.append("    " + repr(op_key).replace("'", '"') + ": {")
+        op_data = _operation_device_version_data[op_key]
+        for key in sorted(op_data):
+            data_part.append(f"        {key}: {op_data[key]},")
+        data_part.append("    },")
+    new_content = part1 + "\n".join(data_part) + "\n" + part2
+    if current_content != new_content:
+        f = open(current_file, "w")
+        f.write(new_content)
+        f.close()
+
+
+def minimize(
+    target_func,
+    initial_parameters,
+    reference_parameters,
+    step_func,
+    max_step=2,
+    verbose=False,
+    all_values=None,
+):
+    """Find a dict of parameters that minimizes the target function using
+    the initial dict of parameters and a step function that progresses
+    a specified parameter in a dict of parameters.
+
+    Parameters
+    ----------
+    target_func (callable): a functional with the signature
+      ``target_func(parameters: dict) -> float``
+    initial_parameters (dict): a set of parameters used as an initial
+      value to the minimization process.
+    reference_parameters (dict): a set of parameters used as an
+      reference value with respect to which the speed up is computed.
+    step_func (callable): a functional with the signature
+      ``step_func(parameter_name:str, parameter_value:int, direction:int, parameters:dict) -> int``
+      that increments or decrements (when ``direction`` is positive or
+      negative, respectively) the parameter with given name and value.
+      When return value is equal to ``parameter_value``, it means that
+      no step along the given direction can be made.
+
+    Returns
+    -------
+    parameters (dict): a set of parameters that minimizes the target
+      function.
+    speedup_incr (float): a speedup change given in percentage.
+    timing (float): the value of the target function at the parameters.
+    sensitivity_message (str): a message containing sensitivity.
+      information of parameters around the target function minimizer.
+    """
+
+    def to_key(parameters):
+        return tuple(parameters[k] for k in sorted(parameters))
+
+    def from_key(key, parameters):
+        return dict(zip(sorted(parameters), key))
+
+    if all_values is None:
+        all_values = {}
+
+    directions = list(range(-max_step, max_step + 1))
+    names = sorted(initial_parameters)
+    all_directions = []
+    for d_tuple in itertools.product(*((directions,) * len(names))):
+        dist = sum(map(abs, d_tuple))
+        if dist > 0 and dist <= max_step:
+            all_directions.append((dist, d_tuple))
+    all_directions.sort()
+
+    try:
+        reference_target = target_func(reference_parameters)
+    except Exception as msg:
+        if verbose and "out of resource" not in str(msg):
+            print(f"{reference_parameters=} lead to failure: {msg}.")
+        reference_target = None
+
+    if reference_target is not None:
+        all_values[to_key(reference_parameters)] = reference_target
+
+    parameters = initial_parameters
+    try:
+        initial_target = target_func(parameters)
+    except Exception as msg:
+        if reference_target is None:
+            if verbose:
+                print(
+                    f"{initial_parameters=} lead to failure: {msg}. Optimization failed!"
+                )
+            return {}, -1, -1, f"{msg}"
+        if verbose and "out of resource" not in str(msg):
+            print(
+                f"{initial_parameters=} lead to failure: {msg}. Using reference parameters instead of initial parameters."
+            )
+        parameters = reference_parameters
+        initial_target = reference_target
+
+    if reference_target is None:
+        if verbose:
+            print("Using initial parameters instead of reference parameters.")
+        reference_target = initial_target
+
+    initial_key = to_key(parameters)
+    minimal_target = all_values[initial_key] = initial_target
+    pbar = tqdm(
+        total=len(all_directions),
+        desc="Tuning...",
+        disable=not verbose,
+        ncols=75,
+    )
+    while True:
+        for i, (_, d_tuple) in enumerate(all_directions):
+            pbar.update(1)
+            next_parameters = parameters.copy()
+            for name, direction in zip(names, d_tuple):
+                value = next_parameters[name]
+                if direction == 0:
+                    continue
+                next_value = step_func(name, value, direction, parameters)
+                if next_value == value:
+                    break
+                next_parameters[name] = next_value
+            else:
+                next_key = to_key(next_parameters)
+                if next_key in all_values:
+                    continue
+                try:
+                    next_target = target_func(next_parameters)
+                except Exception as msg:
+                    all_values[next_key] = str(msg)
+                    if verbose and "out of resource" not in str(msg):
+                        print(f"{next_parameters=} lead to failure: {msg}. Skipping.")
+                    continue
+                all_values[next_key] = next_target
+
+                if next_target < minimal_target:
+                    minimal_target = next_target
+                    parameters = next_parameters
+                    pbar.total += i + 1
+                    break
+        else:
+            # ensure stable minimizer:
+            minimizer_keys = {
+                k
+                for k, v in all_values.items()
+                if isinstance(v, float) and abs(1 - v / minimal_target) < 0.001
+            }
+            minimizer_key = (
+                initial_key if initial_key in minimizer_keys else min(minimizer_keys)
+            )
+            minimizer_target = all_values[minimizer_key]
+            parameters = from_key(minimizer_key, parameters)
+            speedup_incr = (1 - minimal_target / reference_target) * 100
+            if speedup_incr < 0:
+                if verbose:
+                    print(
+                        f"{speedup_incr=} is negative. Rerunning minimize with reference parameters as initial parameters."
+                    )
+                return minimize(
+                    target_func,
+                    reference_parameters,
+                    reference_parameters,
+                    step_func,
+                    max_step=max_step,
+                    verbose=verbose,
+                    all_values=all_values,
+                )
+            sensitivity = []
+            for name in parameters:
+                value = parameters[name]
+                rel_diffs = []
+                for direction in range(-max_step, max_step + 1):
+                    if direction == 0:
+                        continue
+                    next_value = step_func(name, value, direction, parameters)
+                    if next_value == value:
+                        rel_diffs.append(0)
+                        continue
+                    next_parameters = parameters.copy()
+                    next_parameters[name] = next_value
+                    next_key = to_key(next_parameters)
+                    next_target = all_values.get(next_key)
+                    if next_target is None or isinstance(next_target, str):
+                        rel_diffs.append(0)
+                        continue
+                    rel_diff = (next_target / minimal_target - 1) * 100
+                    rel_diffs.append(rel_diff)
+                sensitivity.append((max(rel_diffs), rel_diffs, name))
+
+            sensitivity_message = [f"timing0={initial_target:.3f}"]
+            for _, rel_diffs, name in sorted(sensitivity, reverse=True):
+                left_diffs = "|".join(
+                    [f"{rel_diff:.1f}" for rel_diff in rel_diffs[:max_step]]
+                )
+                right_diffs = "|".join(
+                    [f"{rel_diff:.1f}" for rel_diff in rel_diffs[max_step:]]
+                )
+                sensitivity_message.append(
+                    f"{name}={parameters[name]} ({left_diffs}...{right_diffs} %)"
+                )
+            sensitivity_message = ", ".join(sensitivity_message)
+            return parameters, speedup_incr, minimal_target, sensitivity_message
+
+
+def create_blocked_tensor(B, M, N, blocksize, sparsity, dtype, device):
+    assert (
+        sparsity <= 1.0 and sparsity >= 0.0
+    ), "sparsity should be a value between 0 and 1"
+    assert M % blocksize[0] == 0
+    assert N % blocksize[1] == 0
+    shape = (B, M // blocksize[0], N // blocksize[1])[int(B == 0) :]
+    A = torch.bernoulli(
+        torch.full(shape, 1 - sparsity, dtype=torch.float32, device=device)
+    ).to(dtype)
+    expected_nnz = int((1 - sparsity) * M * N / (blocksize[0] * blocksize[1]))
+    nonzero_indices = A.flatten().nonzero()
+    actual_nnz = nonzero_indices.shape[0]
+    if actual_nnz > expected_nnz:
+        selected_nonzeros = torch.randperm(actual_nnz)[: actual_nnz - expected_nnz]
+        A.flatten()[nonzero_indices[selected_nonzeros]] = 0
+    elif actual_nnz < expected_nnz:
+        zero_indices = (A == 0).flatten().nonzero()
+        selected_zeros = torch.randperm(zero_indices.shape[0])[
+            : expected_nnz - actual_nnz
+        ]
+        A.flatten()[zero_indices[selected_zeros]] = 1
+    A = torch.repeat_interleave(A, blocksize[0], dim=-2)
+    A = torch.repeat_interleave(A, blocksize[1], dim=-1)
+    return A
+
+
+def optimize_scatter_mm(
+    m, k, n, bm, bk, dtype=torch.float16, device="cuda", sparsity=0.5, force=False
+):
+    import triton
+
+    from torch.sparse._triton_ops import bsr_scatter_mm, bsr_scatter_mm_indices_data
+
+    key = (m, k, n, bm, bk)
+
+    version = (0, dtype, sparsity)
+    device_name = torch.cuda.get_device_name()
+
+    reference_meta = dict(
+        GROUP_SIZE=1,
+        TILE_M=16,
+        TILE_N=16,
+        SPLIT_N=n // 16,
+        num_stages=1,
+        num_warps=1,
+    )
+
+    initial_meta = get_meta(
+        "scatter_mm", key, device_name=device_name, version=version, exact=True
+    )
+    if initial_meta is None:
+        initial_meta = get_meta(
+            "bsr_dense_addmm",
+            key,
+            device_name=device_name,
+            version=(0, dtype, 0.5),
+            exact=True,
+        )
+        if initial_meta is None:
+            initial_meta = reference_meta
+    elif not force:
+        return
+
+    torch.manual_seed(0)
+    bsr = create_blocked_tensor(
+        0, m, k, (bm, bk), sparsity, dtype, device
+    ).to_sparse_bsr((bm, bk))
+    dense = make_tensor(k, n, dtype=dtype, device=device)
+
+    def bench(meta, bsr=bsr, dense=dense):
+        indices_data = bsr_scatter_mm_indices_data(
+            bsr, dense, indices_format="bsr_strided_mm_compressed", **meta
+        )
+
+        def test_func():
+            return bsr_scatter_mm(bsr, dense, indices_data=indices_data)
+
+        ms_min = triton.testing.do_bench(
+            test_func, warmup=500, rep=100, fast_flush=False
+        )
+
+        return ms_min
+
+    def step_meta_parameter(name, value, direction, meta, m=m, n=n, k=k, bm=bm, bk=bk):
+        # return next value in positive or negative direction, or
+        # input value if the step will result an invalid
+        # value. The input value is assumed to be valid.
+
+        is_log = name in {"SPLIT_N", "TILE_M", "TILE_N", "num_warps"}
+        min_value = dict(
+            SPLIT_N=1, TILE_M=16, TILE_N=16, num_warps=1, num_stages=1, GROUP_SIZE=1
+        )[name]
+        max_value = dict(
+            SPLIT_N=n // meta["TILE_N"], TILE_M=bm, TILE_N=n // meta["SPLIT_N"]
+        ).get(name)
+        value_step = dict(
+            SPLIT_N=2, TILE_M=2, TILE_N=2, num_warps=2, num_stages=1, GROUP_SIZE=1
+        )[name]
+        if is_log:
+            next_value = (
+                value * value_step**direction
+                if direction > 0
+                else value // (value_step ** abs(direction))
+            )
+        else:
+            next_value = value + value_step * direction
+        if min_value is not None:
+            next_value = max(next_value, min_value)
+        if max_value is not None:
+            next_value = min(next_value, max_value)
+        if name == "SPLIT_N" and n % next_value != 0:
+            return value
+        # Hard-skip parameter combinations that break CUDA state for pytorch:
+        if (dtype, name, next_value, m, n, k, bm, bk) in {
+            (torch.float32, "num_warps", 32, 256, 256, 256, 16, 16),
+            (torch.float32, "num_warps", 16, 256, 256, 256, 32, 32),
+            (torch.float32, "num_warps", 16, 256, 256, 256, 64, 64),
+            (torch.float32, "num_warps", 16, 256, 256, 256, 128, 128),
+            (torch.float32, "num_warps", 16, 512, 512, 256, 128, 128),
+        } and re.match(r"NVIDIA A100[^\d]", device_name) is not None:
+            return value
+        return next_value
+
+    meta, speedup, timing, sensitivity_message = minimize(
+        bench, initial_meta, reference_meta, step_meta_parameter
+    )
+    if initial_meta is not reference_meta and initial_meta == meta and not force:
+        return
+    print(f"{meta=} {speedup=:.1f} % {timing=:.3f} ms")
+    if speedup < 0:
+        return
+    device_name = torch.cuda.get_device_name()
+
+    update(
+        "scatter_mm", device_name, version, key, tuple(meta[k] for k in sorted(meta))
+    )
+
+
+def tune__int_bsr_dense_addmm(
+    input,
+    bsr,
+    dense,
+    *,
+    beta=1,
+    alpha=1,
+    out=None,
+    store=False,
+    verbose=False,
+    force=False,
+):
+    return tune_bsr_dense_addmm(
+        input,
+        bsr,
+        dense,
+        beta=beta,
+        alpha=alpha,
+        out=out,
+        store=store,
+        verbose=verbose,
+        force=force,
+        opname="_int_bsr_dense_addmm",
+    )
+
+
+def tune_bsr_dense_addmm(
+    input,
+    bsr,
+    dense,
+    *,
+    beta=1,
+    alpha=1,
+    out=None,
+    store=False,
+    verbose=False,
+    force=False,
+    opname=None,
+):
+    """Tune bsr_dense_addmm kernel parameters against the given inputs.
+
+    When store is True, the tuning results will be stored in the
+    database of kernel parameters.
+    """
+    import triton
+
+    if opname is None:
+        opname = "bsr_dense_addmm"
+
+    if opname == "_int_bsr_dense_addmm":
+        from torch.sparse._triton_ops import _int_bsr_dense_addmm as bsr_dense_addmm
+    else:
+        from torch.sparse._triton_ops import bsr_dense_addmm
+
+    N = dense.shape[-1]
+    values = bsr.values()
+    crow_indices = bsr.crow_indices()
+    batch_ndim = crow_indices.dim() - 1
+    M, K = bsr.shape[batch_ndim : batch_ndim + 2]
+    BM, BK = values.shape[batch_ndim + 1 : batch_ndim + 3]
+
+    # Reference parameters is a set of parameters that leads to a
+    # successful kernel call and the corresponding timing is used as a
+    # reference for computing speedups. Avoid changing the reference
+    # parameters when possible.
+    reference_meta = dict(
+        GROUP_SIZE_ROW=1, num_stages=1, num_warps=4, SPLIT_N=max(N // BM, 1)
+    )
+
+    # Compute the key of parameters:
+    sparsity = round(1 - bsr._nnz() * BM * BK / (M * K), 2)
+    dtype = bsr.dtype
+    version = (0, dtype, sparsity)
+    key = (M, K, N, BM, BK, beta == 0, beta == 1, alpha == 1)
+
+    # For tuning, for an initial state, use parameters from the
+    # database if available, otherwise, use the reference parameters.
+    initial_meta = get_meta(opname, key, version=version, exact=True)
+    if initial_meta is None:
+        may_skip_update = False
+        initial_meta = get_meta(opname, key, version=(0, dtype, 0.5), exact=True)
+        if initial_meta is None:
+            initial_meta = reference_meta
+    elif not force:
+        return initial_meta
+    else:
+        may_skip_update = True
+
+    # The target function that is minimized in the tuning process:
+    def bench(meta, input=input, bsr=bsr, dense=dense, alpha=alpha, out=out):
+        def test_func():
+            return bsr_dense_addmm(
+                input, bsr, dense, beta=beta, alpha=alpha, meta=meta, out=out
+            )
+
+        return triton.testing.do_bench(test_func, warmup=500, rep=100, fast_flush=False)
+
+    # The step function that increments a specified meta parameter:
+    def step_meta_parameter(name, value, direction, meta, M=M, N=N, K=K, BM=BM, BK=BK):
+        # return next value in positive or negative direction, or
+        # input value if the step will result an invalid
+        # value. The input value is assumed to be valid.
+        is_log = name in {"SPLIT_N", "num_warps"}
+        min_value = dict(SPLIT_N=1, num_warps=1, num_stages=1, GROUP_SIZE_ROW=1)[name]
+        max_value = dict(SPLIT_N=max(N // BM, 1)).get(name)
+        value_step = dict(SPLIT_N=2, num_warps=2, num_stages=1, GROUP_SIZE_ROW=1)[name]
+        if is_log:
+            next_value = (
+                value * value_step**direction
+                if direction > 0
+                else value // (value_step ** abs(direction))
+            )
+        else:
+            next_value = value + value_step * direction
+        if min_value is not None:
+            next_value = max(next_value, min_value)
+        if max_value is not None:
+            next_value = min(next_value, max_value)
+        if name == "SPLIT_N" and N % next_value != 0:
+            return value
+        return next_value
+
+    # Tune:
+    meta, speedup, timing, sensitivity_message = minimize(
+        bench,
+        initial_meta,
+        reference_meta,
+        step_meta_parameter,
+        max_step=2,
+        verbose=verbose,
+    )
+    if verbose:
+        print(f"-> {sensitivity_message}, {speedup=:.1f} %, {timing=:.3f} ms")
+
+    if store and not (
+        may_skip_update and meta == initial_meta and initial_meta is not reference_meta
+    ):
+        device_name = torch.cuda.get_device_name()
+        update(
+            opname,
+            device_name,
+            version,
+            key,
+            tuple(meta[k] for k in sorted(meta)),
+        )
+
+    return meta
+
+
+def optimize_bsr_dense_addmm(
+    m,
+    k,
+    n,
+    bm,
+    bk,
+    beta=1,
+    alpha=1,
+    dtype=torch.float16,
+    device="cuda",
+    sparsity=0.5,
+    force=False,
+    verbose=False,
+    opname=None,
+):
+    torch.manual_seed(0)
+    bsr = create_blocked_tensor(
+        0, m, k, (bm, bk), sparsity, dtype, device
+    ).to_sparse_bsr((bm, bk))
+    dense = make_tensor(k, n, dtype=dtype, device=device)
+    input = make_tensor(m, n, dtype=dtype, device=device)
+    tune_bsr_dense_addmm(
+        input,
+        bsr,
+        dense,
+        beta=beta,
+        alpha=alpha,
+        store=True,
+        force=force,
+        verbose=verbose,
+        opname=opname,
+    )
+
+
+def main(op="scatter_mm", force=False, dtype=torch.float16, verbose=True):
+    import itertools
+
+    sizes_lst = [
+        256,
+        512,
+        1024,
+        2048,
+        4096,
+        8192,
+        16384,
+        32768,
+        65536,
+        131072,
+        50432,
+    ]
+    sizes3_lst = [3 * sz for sz in [64, 128] + sizes_lst if sz <= 2048]
+    shapes_lst = [(sz, sz) for sz in sizes_lst[:-4] + sizes3_lst]
+    shapes_lst.extend([(3072, 768), (768, 3072)])
+    if dtype is torch.int8:
+        # triton does not support smaller blocks than 32
+        blocksize_lst = [(32, 32), (64, 64), (128, 128), (256, 256)]
+    else:
+        blocksize_lst = [(16, 16), (32, 32), (64, 64), (128, 128)]
+    sparsity_lst = [0.5, 0.7, 0.3][:1]
+    for sparsity in sparsity_lst:
+        print(f"{op, dtype, sparsity=}")
+        try:
+            for (M, K), N, (BM, BK) in itertools.product(
+                shapes_lst, sizes_lst, blocksize_lst
+            ):
+                if not (BM <= M and BK <= K and M % BM == 0 and K % BK == 0):
+                    continue
+                if op == "scatter_mm":
+                    optimize_scatter_mm(
+                        M, K, N, BM, BK, force=force, sparsity=sparsity, dtype=dtype
+                    )
+                elif op in {"bsr_dense_addmm", "_int_bsr_dense_addmm"}:
+                    if M == K and N == 50432:
+                        continue
+                    print(f"{M, K, N, (BM, BK)=}")
+                    for alpha, beta in [(1, 1), (1, 0)]:
+                        optimize_bsr_dense_addmm(
+                            M,
+                            K,
+                            N,
+                            BM,
+                            BK,
+                            beta=beta,
+                            alpha=alpha,
+                            force=force,
+                            sparsity=sparsity,
+                            dtype=dtype,
+                            verbose=verbose,
+                            opname=op,
+                        )
+                else:
+                    raise NotImplementedError(op)
+        except KeyboardInterrupt:
+            break
+        except Exception as msg:
+            dump()
+            raise
+    dump()
+
+    if 0:
+        # Check performance dependence on sparsity and apply
+        # adjustments when differences are noticable (more than 10%).
+        #
+        # When using NVIDIA A100 GPU, the performance dependence on
+        # sparsity is insignificant (0 % ... 10 %) for majority of
+        # shapes/blocksizes combinations. However, for a very few
+        # specific size combinations, the effect of sparsity on
+        # performance can be up to 20 %.
+        for (M, K), N, (BM, BK) in itertools.product(
+            shapes_lst, sizes_lst, blocksize_lst
+        ):
+            meta_lst: list = []
+            key = (M, K, N, BM, BK)
+            for sparsity1 in sparsity_lst:
+                torch.manual_seed(0)
+                bsr = create_blocked_tensor(
+                    0, M, K, (BM, BK), sparsity1, dtype, device="cuda"
+                ).to_sparse_bsr((BM, BK))
+                dense = make_tensor(K, N, dtype=dtype, device="cuda")
+                meta_lst = []
+                for sparsity in sparsity_lst:
+                    meta = get_meta(op, key, version=(0, dtype, sparsity), exact=True)
+                    if meta is None:
+                        continue
+
+                    def bench(meta, bsr=bsr, dense=dense):
+                        import triton
+
+                        if op == "scatter_mm":
+                            from torch.sparse._triton_ops import (
+                                bsr_scatter_mm,
+                                bsr_scatter_mm_indices_data,
+                            )
+
+                            indices_data = bsr_scatter_mm_indices_data(
+                                bsr,
+                                dense,
+                                indices_format="bsr_strided_mm_compressed",
+                                **meta,
+                            )
+
+                            def test_func():
+                                return bsr_scatter_mm(
+                                    bsr, dense, indices_data=indices_data
+                                )
+
+                        else:
+                            raise NotImplementedError(op)
+
+                        ms_min = triton.testing.do_bench(
+                            test_func, warmup=500, rep=100, fast_flush=False
+                        )
+
+                        return ms_min
+
+                    meta_lst.append(
+                        (bench(meta), sparsity, tuple(meta[k] for k in sorted(meta)))
+                    )
+                if not meta_lst:
+                    continue
+                meta_lst = sorted(meta_lst)
+                index = next(
+                    i for i, item in enumerate(meta_lst) if item[1] == sparsity1
+                )
+                if meta_lst[0][2] == meta_lst[index][2]:
+                    continue
+                speeddiff = (1 - meta_lst[index][0] / meta_lst[0][0]) * 100
+                if abs(speeddiff) < 10:
+                    continue
+
+                print(sparsity1, index, key, meta_lst, speeddiff)
+
+                if index > 0:
+                    device_name = torch.cuda.get_device_name()
+                    meta = get_meta(
+                        op, key, version=(0, dtype, meta_lst[0][1]), exact=True
+                    )
+                    update(
+                        op,
+                        device_name,
+                        (0, dtype, sparsity1),
+                        key,
+                        tuple(meta[k] for k in sorted(meta)),
+                    )
+                    print("update")
+                    dump()
+
+
+_operation_device_version_data: Dict[Any, Dict] = {
+    # Warning: the data in between the BEGIN/END DATA comment lines
+    # below is generated. It can be updated either manually or via
+    # calling dump function defined above.
+    #
+    # Legend [op: key -> data]:
+    #   scatter_mm : M, K, N, Ms, Ks -> GROUP_SIZE, SPLIT_N, TILE_M, TILE_N, num_stages, num_warps
+    #   bsr_dense_addmm : M, K, N, Ms, Ks, beta==0, beta==1, alpha==1  -> GROUP_SIZE_ROW, SPLIT_N, num_stages, num_warps
+    #
+    # BEGIN GENERATED DATA
+    ("_int_bsr_dense_addmm", "NVIDIA A100-SXM4-80GB", (0, torch.int8, 0.5)): {
+        (192, 192, 256, 32, 32, False, True, True): (2, 8, 1, 4),
+        (192, 192, 256, 32, 32, True, False, True): (2, 8, 5, 4),
+        (192, 192, 512, 32, 32, False, True, True): (1, 16, 1, 4),
+        (192, 192, 512, 32, 32, True, False, True): (1, 16, 5, 4),
+        (192, 192, 1024, 32, 32, False, True, True): (1, 32, 1, 4),
+        (192, 192, 1024, 32, 32, True, False, True): (4, 32, 4, 4),
+        (192, 192, 2048, 32, 32, False, True, True): (2, 64, 1, 4),
+        (192, 192, 2048, 32, 32, True, False, True): (3, 16, 5, 4),
+        (192, 192, 4096, 32, 32, False, True, True): (1, 128, 1, 4),
+        (192, 192, 4096, 32, 32, True, False, True): (1, 128, 1, 4),
+        (192, 192, 8192, 32, 32, False, True, True): (1, 256, 1, 4),
+        (192, 192, 8192, 32, 32, True, False, True): (1, 64, 3, 4),
+        (192, 192, 16384, 32, 32, False, True, True): (2, 512, 1, 4),
+        (192, 192, 16384, 32, 32, True, False, True): (5, 128, 1, 4),
+        (192, 192, 32768, 32, 32, False, True, True): (1, 1024, 1, 4),
+        (192, 192, 32768, 32, 32, True, False, True): (1, 256, 1, 4),
+        (192, 192, 65536, 32, 32, False, True, True): (1, 1024, 1, 8),
+        (192, 192, 65536, 32, 32, True, False, True): (1, 512, 1, 4),
+        (192, 192, 131072, 32, 32, False, True, True): (1, 2048, 1, 8),
+        (192, 192, 131072, 32, 32, True, False, True): (2, 512, 1, 4),
+        (256, 256, 256, 32, 32, False, True, True): (4, 8, 1, 4),
+        (256, 256, 256, 32, 32, True, False, True): (1, 8, 6, 4),
+        (256, 256, 256, 64, 64, False, True, True): (1, 4, 1, 16),
+        (256, 256, 256, 64, 64, True, False, True): (1, 4, 4, 4),
+        (256, 256, 256, 128, 128, False, True, True): (3, 2, 1, 16),
+        (256, 256, 256, 128, 128, True, False, True): (1, 2, 1, 4),
+        (256, 256, 512, 32, 32, False, True, True): (2, 16, 1, 4),
+        (256, 256, 512, 32, 32, True, False, True): (2, 16, 4, 4),
+        (256, 256, 512, 64, 64, False, True, True): (7, 8, 1, 16),
+        (256, 256, 512, 64, 64, True, False, True): (3, 8, 3, 4),
+        (256, 256, 512, 128, 128, False, True, True): (1, 4, 1, 32),
+        (256, 256, 512, 128, 128, True, False, True): (1, 4, 1, 4),
+        (256, 256, 1024, 32, 32, False, True, True): (1, 32, 1, 4),
+        (256, 256, 1024, 32, 32, True, False, True): (1, 8, 6, 4),
+        (256, 256, 1024, 64, 64, False, True, True): (2, 16, 1, 16),
+        (256, 256, 1024, 64, 64, True, False, True): (1, 16, 5, 4),
+        (256, 256, 1024, 128, 128, False, True, True): (4, 8, 1, 32),
+        (256, 256, 1024, 128, 128, True, False, True): (1, 8, 2, 4),
+        (256, 256, 2048, 32, 32, False, True, True): (1, 64, 1, 4),
+        (256, 256, 2048, 32, 32, True, False, True): (2, 32, 3, 2),
+        (256, 256, 2048, 64, 64, False, True, True): (2, 32, 1, 16),
+        (256, 256, 2048, 64, 64, True, False, True): (1, 16, 3, 4),
+        (256, 256, 2048, 128, 128, False, True, True): (1, 16, 1, 32),
+        (256, 256, 2048, 128, 128, True, False, True): (1, 16, 2, 4),
+        (256, 256, 4096, 32, 32, False, True, True): (2, 128, 1, 4),
+        (256, 256, 4096, 32, 32, True, False, True): (1, 32, 3, 2),
+        (256, 256, 4096, 64, 64, False, True, True): (2, 64, 1, 8),
+        (256, 256, 4096, 64, 64, True, False, True): (1, 64, 3, 2),
+        (256, 256, 4096, 128, 128, False, True, True): (2, 32, 1, 32),
+        (256, 256, 4096, 128, 128, True, False, True): (3, 32, 2, 8),
+        (256, 256, 8192, 32, 32, False, True, True): (1, 256, 1, 4),
+        (256, 256, 8192, 32, 32, True, False, True): (1, 64, 1, 4),
+        (256, 256, 8192, 64, 64, False, True, True): (1, 128, 1, 8),
+        (256, 256, 8192, 64, 64, True, False, True): (2, 128, 1, 4),
+        (256, 256, 8192, 128, 128, False, True, True): (4, 64, 1, 32),
+        (256, 256, 8192, 128, 128, True, False, True): (3, 64, 1, 4),
+        (256, 256, 16384, 32, 32, False, True, True): (1, 256, 1, 8),
+        (256, 256, 16384, 32, 32, True, False, True): (3, 128, 1, 4),
+        (256, 256, 16384, 64, 64, False, True, True): (2, 256, 1, 8),
+        (256, 256, 16384, 64, 64, True, False, True): (2, 256, 1, 4),
+        (256, 256, 16384, 128, 128, False, True, True): (2, 128, 1, 32),
+        (256, 256, 16384, 128, 128, True, False, True): (4, 128, 2, 4),
+        (256, 256, 32768, 32, 32, False, True, True): (2, 512, 1, 8),
+        (256, 256, 32768, 32, 32, True, False, True): (1, 256, 1, 4),
+        (256, 256, 32768, 64, 64, False, True, True): (1, 512, 1, 8),
+        (256, 256, 32768, 64, 64, True, False, True): (1, 512, 1, 4),
+        (256, 256, 32768, 128, 128, False, True, True): (2, 256, 1, 32),
+        (256, 256, 32768, 128, 128, True, False, True): (1, 256, 2, 4),
+        (256, 256, 65536, 32, 32, False, True, True): (1, 1024, 1, 8),
+        (256, 256, 65536, 32, 32, True, False, True): (1, 512, 1, 4),
+        (256, 256, 65536, 64, 64, False, True, True): (1, 1024, 1, 8),
+        (256, 256, 65536, 64, 64, True, False, True): (1, 512, 1, 4),
+        (256, 256, 65536, 128, 128, False, True, True): (2, 512, 1, 16),
+        (256, 256, 65536, 128, 128, True, False, True): (1, 512, 1, 4),
+        (256, 256, 131072, 32, 32, False, True, True): (1, 2048, 1, 8),
+        (256, 256, 131072, 32, 32, True, False, True): (2, 1024, 1, 4),
+        (256, 256, 131072, 64, 64, False, True, True): (1, 2048, 1, 8),
+        (256, 256, 131072, 64, 64, True, False, True): (2, 512, 1, 4),
+        (256, 256, 131072, 128, 128, False, True, True): (2, 1024, 1, 16),
+        (256, 256, 131072, 128, 128, True, False, True): (4, 1024, 1, 4),
+        (384, 384, 256, 32, 32, False, True, True): (1, 8, 1, 4),
+        (384, 384, 256, 32, 32, True, False, True): (5, 8, 5, 4),
+        (384, 384, 256, 64, 64, False, True, True): (2, 4, 1, 16),
+        (384, 384, 256, 64, 64, True, False, True): (1, 4, 5, 4),
+        (384, 384, 512, 32, 32, False, True, True): (2, 16, 1, 4),
+        (384, 384, 512, 32, 32, True, False, True): (1, 16, 4, 4),
+        (384, 384, 512, 64, 64, False, True, True): (3, 8, 1, 16),
+        (384, 384, 512, 64, 64, True, False, True): (3, 8, 3, 4),
+        (384, 384, 1024, 32, 32, False, True, True): (2, 32, 1, 4),
+        (384, 384, 1024, 32, 32, True, False, True): (1, 8, 6, 4),
+        (384, 384, 1024, 64, 64, False, True, True): (2, 16, 1, 16),
+        (384, 384, 1024, 64, 64, True, False, True): (1, 16, 5, 4),
+        (384, 384, 2048, 32, 32, False, True, True): (1, 64, 1, 4),
+        (384, 384, 2048, 32, 32, True, False, True): (3, 16, 4, 4),
+        (384, 384, 2048, 64, 64, False, True, True): (2, 32, 1, 16),
+        (384, 384, 2048, 64, 64, True, False, True): (1, 16, 4, 4),
+        (384, 384, 4096, 32, 32, False, True, True): (4, 64, 1, 8),
+        (384, 384, 4096, 32, 32, True, False, True): (4, 32, 1, 4),
+        (384, 384, 4096, 64, 64, False, True, True): (1, 64, 1, 8),
+        (384, 384, 4096, 64, 64, True, False, True): (1, 64, 1, 4),
+        (384, 384, 8192, 32, 32, False, True, True): (1, 128, 1, 8),
+        (384, 384, 8192, 32, 32, True, False, True): (3, 64, 1, 1),
+        (384, 384, 8192, 64, 64, False, True, True): (2, 128, 1, 8),
+        (384, 384, 8192, 64, 64, True, False, True): (1, 64, 2, 2),
+        (384, 384, 16384, 32, 32, False, True, True): (1, 256, 1, 8),
+        (384, 384, 16384, 32, 32, True, False, True): (1, 128, 1, 4),
+        (384, 384, 16384, 64, 64, False, True, True): (2, 256, 1, 8),
+        (384, 384, 16384, 64, 64, True, False, True): (2, 128, 1, 4),
+        (384, 384, 32768, 32, 32, False, True, True): (1, 512, 1, 8),
+        (384, 384, 32768, 32, 32, True, False, True): (1, 256, 1, 4),
+        (384, 384, 32768, 64, 64, False, True, True): (1, 512, 1, 8),
+        (384, 384, 32768, 64, 64, True, False, True): (1, 256, 3, 2),
+        (384, 384, 65536, 32, 32, False, True, True): (1, 1024, 1, 8),
+        (384, 384, 65536, 32, 32, True, False, True): (1, 512, 3, 2),
+        (384, 384, 65536, 64, 64, False, True, True): (2, 1024, 1, 8),
+        (384, 384, 65536, 64, 64, True, False, True): (3, 256, 3, 4),
+        (384, 384, 131072, 32, 32, False, True, True): (1, 2048, 1, 8),
+        (384, 384, 131072, 32, 32, True, False, True): (1, 1024, 3, 2),
+        (384, 384, 131072, 64, 64, False, True, True): (1, 2048, 1, 8),
+        (384, 384, 131072, 64, 64, True, False, True): (2, 512, 3, 4),
+        (512, 512, 256, 32, 32, False, True, True): (1, 8, 1, 4),
+        (512, 512, 256, 32, 32, True, False, True): (4, 8, 4, 4),
+        (512, 512, 256, 64, 64, False, True, True): (3, 4, 1, 16),
+        (512, 512, 256, 64, 64, True, False, True): (2, 4, 5, 4),
+        (512, 512, 256, 128, 128, False, True, True): (4, 2, 1, 16),
+        (512, 512, 256, 128, 128, True, False, True): (1, 2, 3, 4),
+        (512, 512, 256, 256, 256, False, True, True): (1, 1, 1, 32),
+        (512, 512, 256, 256, 256, True, False, True): (2, 1, 1, 32),
+        (512, 512, 512, 32, 32, False, True, True): (3, 16, 1, 4),
+        (512, 512, 512, 32, 32, True, False, True): (1, 8, 4, 2),
+        (512, 512, 512, 64, 64, False, True, True): (2, 8, 1, 16),
+        (512, 512, 512, 64, 64, True, False, True): (2, 8, 5, 4),
+        (512, 512, 512, 128, 128, False, True, True): (3, 4, 1, 16),
+        (512, 512, 512, 128, 128, True, False, True): (1, 4, 3, 4),
+        (512, 512, 512, 256, 256, False, True, True): (1, 2, 1, 32),
+        (512, 512, 512, 256, 256, True, False, True): (2, 2, 1, 32),
+        (512, 512, 1024, 32, 32, False, True, True): (2, 32, 1, 4),
+        (512, 512, 1024, 32, 32, True, False, True): (4, 16, 3, 2),
+        (512, 512, 1024, 64, 64, False, True, True): (4, 16, 1, 16),
+        (512, 512, 1024, 64, 64, True, False, True): (1, 8, 4, 4),
+        (512, 512, 1024, 128, 128, False, True, True): (1, 8, 1, 32),
+        (512, 512, 1024, 128, 128, True, False, True): (1, 8, 3, 4),
+        (512, 512, 1024, 256, 256, False, True, True): (4, 4, 1, 32),
+        (512, 512, 1024, 256, 256, True, False, True): (2, 4, 1, 32),
+        (512, 512, 2048, 32, 32, False, True, True): (3, 32, 1, 8),
+        (512, 512, 2048, 32, 32, True, False, True): (1, 16, 3, 4),
+        (512, 512, 2048, 64, 64, False, True, True): (1, 32, 1, 8),
+        (512, 512, 2048, 64, 64, True, False, True): (1, 32, 3, 2),
+        (512, 512, 2048, 128, 128, False, True, True): (4, 16, 1, 32),
+        (512, 512, 2048, 128, 128, True, False, True): (1, 16, 3, 4),
+        (512, 512, 2048, 256, 256, False, True, True): (1, 8, 1, 32),
+        (512, 512, 2048, 256, 256, True, False, True): (3, 8, 1, 32),
+        (512, 512, 4096, 32, 32, False, True, True): (1, 64, 1, 8),
+        (512, 512, 4096, 32, 32, True, False, True): (5, 32, 1, 4),
+        (512, 512, 4096, 64, 64, False, True, True): (1, 64, 1, 8),
+        (512, 512, 4096, 64, 64, True, False, True): (1, 64, 1, 4),
+        (512, 512, 4096, 128, 128, False, True, True): (5, 32, 1, 32),
+        (512, 512, 4096, 128, 128, True, False, True): (2, 32, 3, 4),
+        (512, 512, 4096, 256, 256, False, True, True): (1, 16, 1, 32),
+        (512, 512, 4096, 256, 256, True, False, True): (3, 16, 1, 32),
+        (512, 512, 8192, 32, 32, False, True, True): (3, 128, 1, 8),
+        (512, 512, 8192, 32, 32, True, False, True): (3, 64, 1, 4),
+        (512, 512, 8192, 64, 64, False, True, True): (4, 128, 1, 8),
+        (512, 512, 8192, 64, 64, True, False, True): (1, 64, 3, 2),
+        (512, 512, 8192, 128, 128, False, True, True): (5, 64, 1, 32),
+        (512, 512, 8192, 128, 128, True, False, True): (1, 64, 2, 4),
+        (512, 512, 8192, 256, 256, False, True, True): (1, 32, 1, 32),
+        (512, 512, 8192, 256, 256, True, False, True): (1, 32, 1, 32),
+        (512, 512, 16384, 32, 32, False, True, True): (1, 256, 1, 8),
+        (512, 512, 16384, 32, 32, True, False, True): (2, 128, 1, 4),
+        (512, 512, 16384, 64, 64, False, True, True): (2, 256, 1, 8),
+        (512, 512, 16384, 64, 64, True, False, True): (1, 128, 3, 2),
+        (512, 512, 16384, 128, 128, False, True, True): (4, 128, 1, 16),
+        (512, 512, 16384, 128, 128, True, False, True): (2, 128, 1, 4),
+        (512, 512, 16384, 256, 256, False, True, True): (1, 64, 1, 32),
+        (512, 512, 16384, 256, 256, True, False, True): (2, 64, 1, 32),
+        (512, 512, 32768, 32, 32, False, True, True): (1, 512, 1, 8),
+        (512, 512, 32768, 32, 32, True, False, True): (2, 256, 1, 4),
+        (512, 512, 32768, 64, 64, False, True, True): (1, 512, 1, 8),
+        (512, 512, 32768, 64, 64, True, False, True): (1, 256, 3, 2),
+        (512, 512, 32768, 128, 128, False, True, True): (4, 256, 1, 16),
+        (512, 512, 32768, 128, 128, True, False, True): (2, 256, 1, 4),
+        (512, 512, 32768, 256, 256, False, True, True): (1, 128, 1, 32),
+        (512, 512, 32768, 256, 256, True, False, True): (2, 128, 1, 32),
+        (512, 512, 65536, 32, 32, False, True, True): (1, 1024, 1, 8),
+        (512, 512, 65536, 32, 32, True, False, True): (2, 512, 1, 2),
+        (512, 512, 65536, 64, 64, False, True, True): (1, 1024, 1, 8),
+        (512, 512, 65536, 64, 64, True, False, True): (1, 512, 3, 2),
+        (512, 512, 65536, 128, 128, False, True, True): (4, 512, 1, 16),
+        (512, 512, 65536, 128, 128, True, False, True): (1, 512, 1, 4),
+        (512, 512, 65536, 256, 256, False, True, True): (1, 256, 1, 32),
+        (512, 512, 65536, 256, 256, True, False, True): (1, 256, 1, 32),
+        (512, 512, 131072, 32, 32, False, True, True): (1, 2048, 1, 8),
+        (512, 512, 131072, 32, 32, True, False, True): (1, 1024, 3, 2),
+        (512, 512, 131072, 64, 64, False, True, True): (1, 2048, 1, 8),
+        (512, 512, 131072, 64, 64, True, False, True): (1, 1024, 3, 2),
+        (512, 512, 131072, 128, 128, False, True, True): (4, 1024, 1, 16),
+        (512, 512, 131072, 128, 128, True, False, True): (1, 1024, 1, 4),
+        (512, 512, 131072, 256, 256, False, True, True): (1, 512, 1, 32),
+        (512, 512, 131072, 256, 256, True, False, True): (2, 512, 1, 32),
+        (768, 768, 256, 32, 32, False, True, True): (1, 8, 1, 4),
+        (768, 768, 256, 32, 32, True, False, True): (2, 8, 4, 4),
+        (768, 768, 256, 64, 64, False, True, True): (3, 4, 1, 16),
+        (768, 768, 256, 64, 64, True, False, True): (2, 4, 4, 4),
+        (768, 768, 256, 128, 128, False, True, True): (1, 2, 1, 8),
+        (768, 768, 256, 128, 128, True, False, True): (1, 2, 3, 4),
+        (768, 768, 512, 32, 32, False, True, True): (1, 16, 1, 4),
+        (768, 768, 512, 32, 32, True, False, True): (1, 4, 5, 4),
+        (768, 768, 512, 64, 64, False, True, True): (1, 8, 3, 32),
+        (768, 768, 512, 64, 64, True, False, True): (4, 8, 4, 4),
+        (768, 768, 512, 128, 128, False, True, True): (4, 4, 1, 16),
+        (768, 768, 512, 128, 128, True, False, True): (4, 4, 3, 4),
+        (768, 768, 1024, 32, 32, False, True, True): (1, 16, 1, 8),
+        (768, 768, 1024, 32, 32, True, False, True): (1, 8, 3, 4),
+        (768, 768, 1024, 64, 64, False, True, True): (3, 16, 1, 16),
+        (768, 768, 1024, 64, 64, True, False, True): (1, 8, 4, 4),
+        (768, 768, 1024, 128, 128, False, True, True): (3, 8, 1, 32),
+        (768, 768, 1024, 128, 128, True, False, True): (1, 8, 3, 4),
+        (768, 768, 2048, 32, 32, False, True, True): (2, 32, 1, 8),
+        (768, 768, 2048, 32, 32, True, False, True): (3, 16, 1, 4),
+        (768, 768, 2048, 64, 64, False, True, True): (1, 32, 1, 8),
+        (768, 768, 2048, 64, 64, True, False, True): (4, 8, 3, 4),
+        (768, 768, 2048, 128, 128, False, True, True): (1, 16, 1, 32),
+        (768, 768, 2048, 128, 128, True, False, True): (1, 16, 3, 4),
+        (768, 768, 4096, 32, 32, False, True, True): (1, 64, 1, 8),
+        (768, 768, 4096, 32, 32, True, False, True): (1, 32, 1, 1),
+        (768, 768, 4096, 64, 64, False, True, True): (2, 64, 1, 8),
+        (768, 768, 4096, 64, 64, True, False, True): (1, 32, 2, 2),
+        (768, 768, 4096, 128, 128, False, True, True): (1, 32, 1, 32),
+        (768, 768, 4096, 128, 128, True, False, True): (6, 32, 1, 4),
+        (768, 768, 8192, 32, 32, False, True, True): (1, 128, 1, 8),
+        (768, 768, 8192, 32, 32, True, False, True): (1, 64, 1, 4),
+        (768, 768, 8192, 64, 64, False, True, True): (1, 128, 1, 8),
+        (768, 768, 8192, 64, 64, True, False, True): (4, 32, 3, 4),
+        (768, 768, 8192, 128, 128, False, True, True): (2, 64, 1, 16),
+        (768, 768, 8192, 128, 128, True, False, True): (2, 64, 3, 4),
+        (768, 768, 16384, 32, 32, False, True, True): (1, 256, 1, 8),
+        (768, 768, 16384, 32, 32, True, False, True): (1, 128, 1, 4),
+        (768, 768, 16384, 64, 64, False, True, True): (1, 256, 1, 8),
+        (768, 768, 16384, 64, 64, True, False, True): (1, 128, 3, 2),
+        (768, 768, 16384, 128, 128, False, True, True): (2, 128, 1, 16),
+        (768, 768, 16384, 128, 128, True, False, True): (2, 128, 1, 4),
+        (768, 768, 32768, 32, 32, False, True, True): (1, 512, 1, 8),
+        (768, 768, 32768, 32, 32, True, False, True): (1, 256, 3, 2),
+        (768, 768, 32768, 64, 64, False, True, True): (2, 512, 1, 8),
+        (768, 768, 32768, 64, 64, True, False, True): (1, 256, 3, 2),
+        (768, 768, 32768, 128, 128, False, True, True): (2, 256, 1, 16),
+        (768, 768, 32768, 128, 128, True, False, True): (3, 256, 1, 4),
+        (768, 768, 65536, 32, 32, False, True, True): (1, 1024, 1, 8),
+        (768, 768, 65536, 32, 32, True, False, True): (1, 512, 3, 2),
+        (768, 768, 65536, 64, 64, False, True, True): (2, 512, 1, 4),
+        (768, 768, 65536, 64, 64, True, False, True): (1, 512, 3, 2),
+        (768, 768, 65536, 128, 128, False, True, True): (2, 512, 1, 16),
+        (768, 768, 65536, 128, 128, True, False, True): (2, 512, 1, 4),
+        (768, 768, 131072, 32, 32, False, True, True): (1, 2048, 1, 8),
+        (768, 768, 131072, 32, 32, True, False, True): (1, 1024, 3, 2),
+        (768, 768, 131072, 64, 64, False, True, True): (2, 1024, 1, 4),
+        (768, 768, 131072, 64, 64, True, False, True): (2, 1024, 3, 2),
+        (768, 768, 131072, 128, 128, False, True, True): (2, 1024, 1, 16),
+        (768, 768, 131072, 128, 128, True, False, True): (2, 1024, 1, 4),
+        (768, 3072, 256, 32, 32, False, True, True): (3, 8, 4, 8),
+        (768, 3072, 256, 32, 32, True, False, True): (3, 8, 5, 4),
+        (768, 3072, 256, 64, 64, False, True, True): (1, 4, 4, 16),
+        (768, 3072, 256, 64, 64, True, False, True): (1, 4, 4, 4),
+        (768, 3072, 256, 128, 128, False, True, True): (2, 2, 1, 8),
+        (768, 3072, 256, 128, 128, True, False, True): (2, 2, 4, 4),
+        (768, 3072, 256, 256, 256, False, True, True): (1, 1, 1, 32),
+        (768, 3072, 256, 256, 256, True, False, True): (1, 1, 1, 32),
+        (768, 3072, 512, 32, 32, False, True, True): (1, 16, 1, 4),
+        (768, 3072, 512, 32, 32, True, False, True): (2, 4, 4, 4),
+        (768, 3072, 512, 64, 64, False, True, True): (3, 8, 4, 16),
+        (768, 3072, 512, 64, 64, True, False, True): (1, 8, 4, 4),
+        (768, 3072, 512, 128, 128, False, True, True): (2, 4, 1, 8),
+        (768, 3072, 512, 128, 128, True, False, True): (4, 4, 3, 4),
+        (768, 3072, 512, 256, 256, False, True, True): (1, 2, 1, 32),
+        (768, 3072, 512, 256, 256, True, False, True): (1, 2, 1, 32),
+        (768, 3072, 1024, 32, 32, False, True, True): (1, 16, 1, 8),
+        (768, 3072, 1024, 32, 32, True, False, True): (3, 8, 3, 4),
+        (768, 3072, 1024, 64, 64, False, True, True): (2, 16, 1, 16),
+        (768, 3072, 1024, 64, 64, True, False, True): (1, 8, 3, 4),
+        (768, 3072, 1024, 128, 128, False, True, True): (1, 8, 1, 8),
+        (768, 3072, 1024, 128, 128, True, False, True): (3, 8, 4, 4),
+        (768, 3072, 1024, 256, 256, False, True, True): (1, 4, 1, 32),
+        (768, 3072, 1024, 256, 256, True, False, True): (4, 4, 1, 32),
+        (768, 3072, 2048, 32, 32, False, True, True): (3, 32, 1, 8),
+        (768, 3072, 2048, 32, 32, True, False, True): (4, 8, 3, 4),
+        (768, 3072, 2048, 64, 64, False, True, True): (5, 16, 1, 16),
+        (768, 3072, 2048, 64, 64, True, False, True): (6, 8, 3, 4),
+        (768, 3072, 2048, 128, 128, False, True, True): (2, 16, 1, 16),
+        (768, 3072, 2048, 128, 128, True, False, True): (1, 16, 4, 4),
+        (768, 3072, 2048, 256, 256, False, True, True): (1, 8, 1, 32),
+        (768, 3072, 2048, 256, 256, True, False, True): (1, 8, 1, 32),
+        (768, 3072, 4096, 32, 32, False, True, True): (1, 64, 1, 8),
+        (768, 3072, 4096, 32, 32, True, False, True): (1, 32, 3, 4),
+        (768, 3072, 4096, 64, 64, False, True, True): (1, 64, 1, 8),
+        (768, 3072, 4096, 64, 64, True, False, True): (2, 16, 3, 4),
+        (768, 3072, 4096, 128, 128, False, True, True): (1, 32, 1, 8),
+        (768, 3072, 4096, 128, 128, True, False, True): (2, 32, 2, 4),
+        (768, 3072, 4096, 256, 256, False, True, True): (1, 16, 1, 32),
+        (768, 3072, 4096, 256, 256, True, False, True): (1, 16, 1, 32),
+        (768, 3072, 8192, 32, 32, False, True, True): (1, 128, 1, 8),
+        (768, 3072, 8192, 32, 32, True, False, True): (1, 64, 1, 4),
+        (768, 3072, 8192, 64, 64, False, True, True): (1, 128, 1, 8),
+        (768, 3072, 8192, 64, 64, True, False, True): (2, 32, 3, 4),
+        (768, 3072, 8192, 128, 128, False, True, True): (2, 64, 1, 16),
+        (768, 3072, 8192, 128, 128, True, False, True): (2, 64, 3, 4),
+        (768, 3072, 8192, 256, 256, False, True, True): (1, 32, 1, 32),
+        (768, 3072, 8192, 256, 256, True, False, True): (1, 32, 1, 32),
+        (768, 3072, 16384, 32, 32, False, True, True): (1, 256, 1, 8),
+        (768, 3072, 16384, 32, 32, True, False, True): (1, 128, 1, 4),
+        (768, 3072, 16384, 64, 64, False, True, True): (1, 256, 1, 8),
+        (768, 3072, 16384, 64, 64, True, False, True): (2, 64, 3, 4),
+        (768, 3072, 16384, 128, 128, False, True, True): (2, 128, 1, 16),
+        (768, 3072, 16384, 128, 128, True, False, True): (2, 128, 3, 4),
+        (768, 3072, 16384, 256, 256, False, True, True): (1, 64, 1, 32),
+        (768, 3072, 16384, 256, 256, True, False, True): (1, 64, 1, 32),
+        (768, 3072, 32768, 32, 32, False, True, True): (1, 512, 1, 8),
+        (768, 3072, 32768, 32, 32, True, False, True): (1, 256, 3, 2),
+        (768, 3072, 32768, 64, 64, False, True, True): (1, 512, 1, 8),
+        (768, 3072, 32768, 64, 64, True, False, True): (3, 128, 3, 4),
+        (768, 3072, 32768, 128, 128, False, True, True): (2, 256, 1, 16),
+        (768, 3072, 32768, 128, 128, True, False, True): (2, 256, 3, 4),
+        (768, 3072, 32768, 256, 256, False, True, True): (1, 128, 1, 32),
+        (768, 3072, 32768, 256, 256, True, False, True): (1, 128, 1, 32),
+        (768, 3072, 50432, 32, 32, False, True, True): (1, 788, 1, 8),
+        (768, 3072, 50432, 32, 32, True, False, True): (1, 394, 3, 2),
+        (768, 3072, 50432, 64, 64, False, True, True): (1, 788, 1, 8),
+        (768, 3072, 50432, 64, 64, True, False, True): (2, 197, 3, 4),
+        (768, 3072, 50432, 128, 128, False, True, True): (2, 394, 1, 16),
+        (768, 3072, 50432, 128, 128, True, False, True): (2, 394, 3, 4),
+        (768, 3072, 50432, 256, 256, False, True, True): (1, 197, 1, 32),
+        (768, 3072, 50432, 256, 256, True, False, True): (1, 197, 1, 32),
+        (768, 3072, 65536, 32, 32, False, True, True): (1, 1024, 1, 8),
+        (768, 3072, 65536, 32, 32, True, False, True): (1, 512, 3, 2),
+        (768, 3072, 65536, 64, 64, False, True, True): (1, 1024, 1, 8),
+        (768, 3072, 65536, 64, 64, True, False, True): (2, 256, 3, 4),
+        (768, 3072, 65536, 128, 128, False, True, True): (2, 512, 1, 16),
+        (768, 3072, 65536, 128, 128, True, False, True): (2, 512, 3, 4),
+        (768, 3072, 65536, 256, 256, False, True, True): (1, 256, 1, 32),
+        (768, 3072, 65536, 256, 256, True, False, True): (1, 256, 1, 32),
+        (768, 3072, 131072, 32, 32, False, True, True): (1, 2048, 1, 8),
+        (768, 3072, 131072, 32, 32, True, False, True): (1, 1024, 3, 2),
+        (768, 3072, 131072, 64, 64, False, True, True): (1, 2048, 1, 8),
+        (768, 3072, 131072, 64, 64, True, False, True): (2, 512, 3, 4),
+        (768, 3072, 131072, 128, 128, False, True, True): (2, 1024, 1, 16),
+        (768, 3072, 131072, 128, 128, True, False, True): (1, 1024, 3, 4),
+        (768, 3072, 131072, 256, 256, False, True, True): (1, 512, 1, 32),
+        (768, 3072, 131072, 256, 256, True, False, True): (1, 512, 1, 32),
+        (1024, 1024, 256, 32, 32, False, True, True): (1, 8, 1, 4),
+        (1024, 1024, 256, 32, 32, True, False, True): (1, 8, 5, 4),
+        (1024, 1024, 256, 64, 64, False, True, True): (1, 4, 1, 16),
+        (1024, 1024, 256, 64, 64, True, False, True): (4, 4, 4, 4),
+        (1024, 1024, 256, 128, 128, False, True, True): (1, 2, 1, 8),
+        (1024, 1024, 256, 128, 128, True, False, True): (1, 2, 3, 8),
+        (1024, 1024, 256, 256, 256, False, True, True): (1, 1, 1, 32),
+        (1024, 1024, 256, 256, 256, True, False, True): (1, 1, 1, 32),
+        (1024, 1024, 512, 32, 32, False, True, True): (5, 16, 1, 4),
+        (1024, 1024, 512, 32, 32, True, False, True): (2, 8, 4, 2),
+        (1024, 1024, 512, 64, 64, False, True, True): (4, 8, 1, 16),
+        (1024, 1024, 512, 64, 64, True, False, True): (1, 4, 3, 4),
+        (1024, 1024, 512, 128, 128, False, True, True): (3, 4, 1, 16),
+        (1024, 1024, 512, 128, 128, True, False, True): (1, 4, 2, 4),
+        (1024, 1024, 512, 256, 256, False, True, True): (1, 2, 1, 32),
+        (1024, 1024, 512, 256, 256, True, False, True): (1, 2, 1, 32),
+        (1024, 1024, 1024, 32, 32, False, True, True): (1, 16, 1, 8),
+        (1024, 1024, 1024, 32, 32, True, False, True): (1, 8, 3, 4),
+        (1024, 1024, 1024, 64, 64, False, True, True): (3, 16, 1, 8),
+        (1024, 1024, 1024, 64, 64, True, False, True): (1, 16, 3, 2),
+        (1024, 1024, 1024, 128, 128, False, True, True): (1, 8, 1, 16),
+        (1024, 1024, 1024, 128, 128, True, False, True): (2, 8, 3, 8),
+        (1024, 1024, 1024, 256, 256, False, True, True): (1, 4, 1, 32),
+        (1024, 1024, 1024, 256, 256, True, False, True): (2, 4, 1, 32),
+        (1024, 1024, 2048, 32, 32, False, True, True): (2, 32, 1, 8),
+        (1024, 1024, 2048, 32, 32, True, False, True): (3, 16, 1, 4),
+        (1024, 1024, 2048, 64, 64, False, True, True): (1, 32, 1, 8),
+        (1024, 1024, 2048, 64, 64, True, False, True): (3, 32, 1, 4),
+        (1024, 1024, 2048, 128, 128, False, True, True): (4, 16, 1, 16),
+        (1024, 1024, 2048, 128, 128, True, False, True): (1, 16, 3, 4),
+        (1024, 1024, 2048, 256, 256, False, True, True): (1, 8, 1, 32),
+        (1024, 1024, 2048, 256, 256, True, False, True): (1, 8, 1, 32),
+        (1024, 1024, 4096, 32, 32, False, True, True): (4, 64, 1, 8),
+        (1024, 1024, 4096, 32, 32, True, False, True): (3, 32, 1, 4),
+        (1024, 1024, 4096, 64, 64, False, True, True): (3, 64, 1, 8),
+        (1024, 1024, 4096, 64, 64, True, False, True): (1, 32, 3, 2),
+        (1024, 1024, 4096, 128, 128, False, True, True): (4, 32, 1, 16),
+        (1024, 1024, 4096, 128, 128, True, False, True): (2, 32, 2, 4),
+        (1024, 1024, 4096, 256, 256, False, True, True): (1, 16, 1, 32),
+        (1024, 1024, 4096, 256, 256, True, False, True): (7, 16, 1, 32),
+        (1024, 1024, 8192, 32, 32, False, True, True): (1, 128, 1, 8),
+        (1024, 1024, 8192, 32, 32, True, False, True): (4, 64, 1, 4),
+        (1024, 1024, 8192, 64, 64, False, True, True): (2, 128, 1, 8),
+        (1024, 1024, 8192, 64, 64, True, False, True): (3, 32, 3, 4),
+        (1024, 1024, 8192, 128, 128, False, True, True): (4, 64, 1, 16),
+        (1024, 1024, 8192, 128, 128, True, False, True): (2, 64, 2, 4),
+        (1024, 1024, 8192, 256, 256, False, True, True): (1, 32, 1, 32),
+        (1024, 1024, 8192, 256, 256, True, False, True): (1, 32, 1, 32),
+        (1024, 1024, 16384, 32, 32, False, True, True): (1, 256, 1, 8),
+        (1024, 1024, 16384, 32, 32, True, False, True): (1, 128, 1, 4),
+        (1024, 1024, 16384, 64, 64, False, True, True): (1, 256, 1, 8),
+        (1024, 1024, 16384, 64, 64, True, False, True): (4, 64, 3, 4),
+        (1024, 1024, 16384, 128, 128, False, True, True): (4, 128, 1, 16),
+        (1024, 1024, 16384, 128, 128, True, False, True): (1, 128, 3, 4),
+        (1024, 1024, 16384, 256, 256, False, True, True): (1, 64, 1, 32),
+        (1024, 1024, 16384, 256, 256, True, False, True): (1, 64, 1, 32),
+        (1024, 1024, 32768, 32, 32, False, True, True): (1, 512, 1, 8),
+        (1024, 1024, 32768, 32, 32, True, False, True): (1, 256, 3, 2),
+        (1024, 1024, 32768, 64, 64, False, True, True): (1, 256, 1, 4),
+        (1024, 1024, 32768, 64, 64, True, False, True): (4, 128, 3, 4),
+        (1024, 1024, 32768, 128, 128, False, True, True): (4, 256, 1, 16),
+        (1024, 1024, 32768, 128, 128, True, False, True): (2, 256, 3, 4),
+        (1024, 1024, 32768, 256, 256, False, True, True): (1, 128, 1, 32),
+        (1024, 1024, 32768, 256, 256, True, False, True): (2, 128, 1, 32),
+        (1024, 1024, 65536, 32, 32, False, True, True): (1, 1024, 1, 8),
+        (1024, 1024, 65536, 32, 32, True, False, True): (1, 512, 3, 2),
+        (1024, 1024, 65536, 64, 64, False, True, True): (1, 512, 1, 4),
+        (1024, 1024, 65536, 64, 64, True, False, True): (2, 256, 3, 4),
+        (1024, 1024, 65536, 128, 128, False, True, True): (4, 512, 1, 16),
+        (1024, 1024, 65536, 128, 128, True, False, True): (4, 512, 3, 4),
+        (1024, 1024, 65536, 256, 256, False, True, True): (1, 256, 1, 32),
+        (1024, 1024, 65536, 256, 256, True, False, True): (1, 256, 1, 32),
+        (1024, 1024, 131072, 32, 32, False, True, True): (1, 2048, 1, 8),
+        (1024, 1024, 131072, 32, 32, True, False, True): (1, 1024, 3, 2),
+        (1024, 1024, 131072, 64, 64, False, True, True): (2, 1024, 1, 4),
+        (1024, 1024, 131072, 64, 64, True, False, True): (2, 512, 3, 4),
+        (1024, 1024, 131072, 128, 128, False, True, True): (4, 1024, 1, 16),
+        (1024, 1024, 131072, 128, 128, True, False, True): (1, 1024, 3, 4),
+        (1024, 1024, 131072, 256, 256, False, True, True): (1, 512, 1, 32),
+        (1024, 1024, 131072, 256, 256, True, False, True): (1, 512, 1, 32),
+        (1536, 1536, 256, 32, 32, False, True, True): (1, 8, 1, 4),
+        (1536, 1536, 256, 32, 32, True, False, True): (2, 8, 1, 8),
+        (1536, 1536, 256, 64, 64, False, True, True): (4, 4, 1, 16),
+        (1536, 1536, 256, 64, 64, True, False, True): (1, 4, 4, 4),
+        (1536, 1536, 256, 128, 128, False, True, True): (2, 2, 1, 16),
+        (1536, 1536, 256, 128, 128, True, False, True): (2, 2, 3, 4),
+        (1536, 1536, 256, 256, 256, False, True, True): (1, 1, 1, 32),
+        (1536, 1536, 256, 256, 256, True, False, True): (1, 1, 1, 32),
+        (1536, 1536, 512, 32, 32, False, True, True): (1, 8, 1, 8),
+        (1536, 1536, 512, 32, 32, True, False, True): (3, 4, 4, 4),
+        (1536, 1536, 512, 64, 64, False, True, True): (3, 8, 1, 16),
+        (1536, 1536, 512, 64, 64, True, False, True): (1, 4, 3, 4),
+        (1536, 1536, 512, 128, 128, False, True, True): (1, 4, 1, 16),
+        (1536, 1536, 512, 128, 128, True, False, True): (2, 4, 4, 4),
+        (1536, 1536, 512, 256, 256, False, True, True): (1, 2, 1, 32),
+        (1536, 1536, 512, 256, 256, True, False, True): (1, 2, 1, 32),
+        (1536, 1536, 1024, 32, 32, False, True, True): (4, 16, 1, 8),
+        (1536, 1536, 1024, 32, 32, True, False, True): (2, 8, 1, 4),
+        (1536, 1536, 1024, 64, 64, False, True, True): (2, 16, 1, 16),
+        (1536, 1536, 1024, 64, 64, True, False, True): (2, 4, 3, 4),
+        (1536, 1536, 1024, 128, 128, False, True, True): (3, 8, 1, 32),
+        (1536, 1536, 1024, 128, 128, True, False, True): (4, 8, 3, 4),
+        (1536, 1536, 1024, 256, 256, False, True, True): (1, 4, 1, 32),
+        (1536, 1536, 1024, 256, 256, True, False, True): (1, 4, 1, 32),
+        (1536, 1536, 2048, 32, 32, False, True, True): (1, 32, 1, 8),
+        (1536, 1536, 2048, 32, 32, True, False, True): (1, 16, 1, 4),
+        (1536, 1536, 2048, 64, 64, False, True, True): (1, 32, 1, 8),
+        (1536, 1536, 2048, 64, 64, True, False, True): (1, 16, 2, 2),
+        (1536, 1536, 2048, 128, 128, False, True, True): (2, 16, 1, 16),
+        (1536, 1536, 2048, 128, 128, True, False, True): (4, 16, 2, 4),
+        (1536, 1536, 2048, 256, 256, False, True, True): (1, 8, 1, 32),
+        (1536, 1536, 2048, 256, 256, True, False, True): (1, 8, 1, 32),
+        (1536, 1536, 4096, 32, 32, False, True, True): (1, 64, 1, 8),
+        (1536, 1536, 4096, 32, 32, True, False, True): (1, 32, 1, 4),
+        (1536, 1536, 4096, 64, 64, False, True, True): (3, 64, 1, 8),
+        (1536, 1536, 4096, 64, 64, True, False, True): (1, 32, 3, 2),
+        (1536, 1536, 4096, 128, 128, False, True, True): (1, 32, 1, 8),
+        (1536, 1536, 4096, 128, 128, True, False, True): (2, 32, 2, 4),
+        (1536, 1536, 4096, 256, 256, False, True, True): (1, 16, 1, 32),
+        (1536, 1536, 4096, 256, 256, True, False, True): (2, 16, 1, 32),
+        (1536, 1536, 8192, 32, 32, False, True, True): (1, 128, 1, 8),
+        (1536, 1536, 8192, 32, 32, True, False, True): (1, 64, 1, 4),
+        (1536, 1536, 8192, 64, 64, False, True, True): (3, 128, 1, 8),
+        (1536, 1536, 8192, 64, 64, True, False, True): (1, 64, 3, 2),
+        (1536, 1536, 8192, 128, 128, False, True, True): (1, 64, 1, 8),
+        (1536, 1536, 8192, 128, 128, True, False, True): (1, 64, 2, 4),
+        (1536, 1536, 8192, 256, 256, False, True, True): (1, 32, 1, 32),
+        (1536, 1536, 8192, 256, 256, True, False, True): (2, 32, 1, 32),
+        (1536, 1536, 16384, 32, 32, False, True, True): (1, 256, 1, 8),
+        (1536, 1536, 16384, 32, 32, True, False, True): (1, 128, 3, 2),
+        (1536, 1536, 16384, 64, 64, False, True, True): (2, 128, 1, 4),
+        (1536, 1536, 16384, 64, 64, True, False, True): (2, 64, 3, 4),
+        (1536, 1536, 16384, 128, 128, False, True, True): (1, 128, 1, 8),
+        (1536, 1536, 16384, 128, 128, True, False, True): (2, 128, 2, 4),
+        (1536, 1536, 16384, 256, 256, False, True, True): (1, 64, 1, 32),
+        (1536, 1536, 16384, 256, 256, True, False, True): (2, 64, 1, 32),
+        (1536, 1536, 32768, 32, 32, False, True, True): (1, 512, 1, 8),
+        (1536, 1536, 32768, 32, 32, True, False, True): (1, 256, 3, 2),
+        (1536, 1536, 32768, 64, 64, False, True, True): (1, 256, 1, 4),
+        (1536, 1536, 32768, 64, 64, True, False, True): (3, 128, 3, 4),
+        (1536, 1536, 32768, 128, 128, False, True, True): (1, 256, 1, 8),
+        (1536, 1536, 32768, 128, 128, True, False, True): (1, 256, 2, 4),
+        (1536, 1536, 32768, 256, 256, False, True, True): (1, 128, 1, 32),
+        (1536, 1536, 32768, 256, 256, True, False, True): (2, 128, 1, 32),
+        (1536, 1536, 65536, 32, 32, False, True, True): (1, 1024, 1, 8),
+        (1536, 1536, 65536, 32, 32, True, False, True): (1, 512, 3, 2),
+        (1536, 1536, 65536, 64, 64, False, True, True): (1, 512, 1, 4),
+        (1536, 1536, 65536, 64, 64, True, False, True): (1, 512, 3, 2),
+        (1536, 1536, 65536, 128, 128, False, True, True): (1, 512, 1, 8),
+        (1536, 1536, 65536, 128, 128, True, False, True): (1, 512, 3, 4),
+        (1536, 1536, 65536, 256, 256, False, True, True): (1, 256, 1, 32),
+        (1536, 1536, 65536, 256, 256, True, False, True): (2, 256, 1, 32),
+        (1536, 1536, 131072, 32, 32, False, True, True): (1, 2048, 1, 8),
+        (1536, 1536, 131072, 32, 32, True, False, True): (1, 1024, 3, 2),
+        (1536, 1536, 131072, 64, 64, False, True, True): (3, 1024, 1, 4),
+        (1536, 1536, 131072, 64, 64, True, False, True): (3, 512, 3, 4),
+        (1536, 1536, 131072, 128, 128, False, True, True): (1, 1024, 1, 8),
+        (1536, 1536, 131072, 128, 128, True, False, True): (1, 1024, 3, 4),
+        (1536, 1536, 131072, 256, 256, False, True, True): (1, 512, 1, 32),
+        (1536, 1536, 131072, 256, 256, True, False, True): (2, 512, 1, 32),
+        (2048, 2048, 256, 32, 32, False, True, True): (3, 8, 1, 4),
+        (2048, 2048, 256, 32, 32, True, False, True): (1, 4, 4, 2),
+        (2048, 2048, 256, 64, 64, False, True, True): (2, 4, 1, 16),
+        (2048, 2048, 256, 64, 64, True, False, True): (1, 2, 3, 4),
+        (2048, 2048, 256, 128, 128, False, True, True): (1, 2, 1, 8),
+        (2048, 2048, 256, 128, 128, True, False, True): (1, 2, 4, 4),
+        (2048, 2048, 256, 256, 256, False, True, True): (1, 1, 1, 32),
+        (2048, 2048, 256, 256, 256, True, False, True): (1, 1, 1, 32),
+        (2048, 2048, 512, 32, 32, False, True, True): (3, 8, 1, 8),
+        (2048, 2048, 512, 32, 32, True, False, True): (4, 4, 3, 2),
+        (2048, 2048, 512, 64, 64, False, True, True): (1, 8, 1, 8),
+        (2048, 2048, 512, 64, 64, True, False, True): (1, 8, 3, 4),
+        (2048, 2048, 512, 128, 128, False, True, True): (1, 4, 1, 8),
+        (2048, 2048, 512, 128, 128, True, False, True): (1, 4, 4, 4),
+        (2048, 2048, 512, 256, 256, False, True, True): (1, 2, 1, 32),
+        (2048, 2048, 512, 256, 256, True, False, True): (2, 2, 1, 32),
+        (2048, 2048, 1024, 32, 32, False, True, True): (1, 16, 1, 8),
+        (2048, 2048, 1024, 32, 32, True, False, True): (3, 8, 1, 4),
+        (2048, 2048, 1024, 64, 64, False, True, True): (4, 16, 1, 8),
+        (2048, 2048, 1024, 64, 64, True, False, True): (1, 8, 3, 2),
+        (2048, 2048, 1024, 128, 128, False, True, True): (4, 8, 1, 16),
+        (2048, 2048, 1024, 128, 128, True, False, True): (2, 8, 2, 4),
+        (2048, 2048, 1024, 256, 256, False, True, True): (1, 4, 1, 32),
+        (2048, 2048, 1024, 256, 256, True, False, True): (3, 4, 1, 32),
+        (2048, 2048, 2048, 32, 32, False, True, True): (1, 32, 1, 8),
+        (2048, 2048, 2048, 32, 32, True, False, True): (1, 16, 1, 4),
+        (2048, 2048, 2048, 64, 64, False, True, True): (1, 32, 1, 8),
+        (2048, 2048, 2048, 64, 64, True, False, True): (1, 16, 3, 2),
+        (2048, 2048, 2048, 128, 128, False, True, True): (4, 16, 1, 16),
+        (2048, 2048, 2048, 128, 128, True, False, True): (2, 16, 2, 4),
+        (2048, 2048, 2048, 256, 256, False, True, True): (1, 8, 1, 32),
+        (2048, 2048, 2048, 256, 256, True, False, True): (1, 8, 1, 32),
+        (2048, 2048, 4096, 32, 32, False, True, True): (1, 64, 1, 8),
+        (2048, 2048, 4096, 32, 32, True, False, True): (1, 32, 1, 4),
+        (2048, 2048, 4096, 64, 64, False, True, True): (4, 64, 1, 8),
+        (2048, 2048, 4096, 64, 64, True, False, True): (2, 16, 3, 4),
+        (2048, 2048, 4096, 128, 128, False, True, True): (4, 32, 1, 8),
+        (2048, 2048, 4096, 128, 128, True, False, True): (1, 32, 2, 4),
+        (2048, 2048, 4096, 256, 256, False, True, True): (1, 16, 1, 32),
+        (2048, 2048, 4096, 256, 256, True, False, True): (4, 16, 1, 32),
+        (2048, 2048, 8192, 32, 32, False, True, True): (1, 128, 1, 8),
+        (2048, 2048, 8192, 32, 32, True, False, True): (1, 64, 1, 4),
+        (2048, 2048, 8192, 64, 64, False, True, True): (2, 64, 1, 4),
+        (2048, 2048, 8192, 64, 64, True, False, True): (2, 32, 3, 4),
+        (2048, 2048, 8192, 128, 128, False, True, True): (4, 64, 1, 8),
+        (2048, 2048, 8192, 128, 128, True, False, True): (2, 64, 2, 4),
+        (2048, 2048, 8192, 256, 256, False, True, True): (1, 32, 1, 32),
+        (2048, 2048, 8192, 256, 256, True, False, True): (4, 32, 1, 32),
+        (2048, 2048, 16384, 32, 32, False, True, True): (1, 256, 1, 8),
+        (2048, 2048, 16384, 32, 32, True, False, True): (1, 128, 3, 2),
+        (2048, 2048, 16384, 64, 64, False, True, True): (2, 128, 1, 4),
+        (2048, 2048, 16384, 64, 64, True, False, True): (2, 64, 3, 4),
+        (2048, 2048, 16384, 128, 128, False, True, True): (1, 128, 1, 8),
+        (2048, 2048, 16384, 128, 128, True, False, True): (2, 128, 2, 4),
+        (2048, 2048, 16384, 256, 256, False, True, True): (1, 64, 1, 32),
+        (2048, 2048, 16384, 256, 256, True, False, True): (4, 64, 1, 32),
+        (2048, 2048, 32768, 32, 32, False, True, True): (1, 512, 1, 8),
+        (2048, 2048, 32768, 32, 32, True, False, True): (1, 256, 3, 2),
+        (2048, 2048, 32768, 64, 64, False, True, True): (2, 256, 1, 4),
+        (2048, 2048, 32768, 64, 64, True, False, True): (2, 128, 3, 4),
+        (2048, 2048, 32768, 128, 128, False, True, True): (1, 256, 1, 8),
+        (2048, 2048, 32768, 128, 128, True, False, True): (2, 256, 2, 4),
+        (2048, 2048, 32768, 256, 256, False, True, True): (1, 128, 1, 32),
+        (2048, 2048, 32768, 256, 256, True, False, True): (4, 128, 1, 32),
+        (2048, 2048, 65536, 32, 32, False, True, True): (1, 1024, 1, 8),
+        (2048, 2048, 65536, 32, 32, True, False, True): (1, 512, 3, 2),
+        (2048, 2048, 65536, 64, 64, False, True, True): (1, 512, 1, 4),
+        (2048, 2048, 65536, 64, 64, True, False, True): (2, 256, 3, 4),
+        (2048, 2048, 65536, 128, 128, False, True, True): (1, 512, 1, 8),
+        (2048, 2048, 65536, 128, 128, True, False, True): (1, 512, 2, 4),
+        (2048, 2048, 65536, 256, 256, False, True, True): (1, 256, 1, 32),
+        (2048, 2048, 65536, 256, 256, True, False, True): (4, 256, 1, 32),
+        (2048, 2048, 131072, 32, 32, False, True, True): (1, 2048, 1, 8),
+        (2048, 2048, 131072, 32, 32, True, False, True): (1, 1024, 3, 2),
+        (2048, 2048, 131072, 64, 64, False, True, True): (1, 1024, 1, 4),
+        (2048, 2048, 131072, 64, 64, True, False, True): (2, 512, 3, 4),
+        (2048, 2048, 131072, 128, 128, False, True, True): (1, 1024, 1, 8),
+        (2048, 2048, 131072, 128, 128, True, False, True): (1, 1024, 3, 4),
+        (2048, 2048, 131072, 256, 256, False, True, True): (1, 512, 1, 32),
+        (2048, 2048, 131072, 256, 256, True, False, True): (4, 512, 1, 32),
+        (3072, 768, 256, 32, 32, False, True, True): (5, 4, 1, 8),
+        (3072, 768, 256, 32, 32, True, False, True): (2, 2, 4, 4),
+        (3072, 768, 256, 64, 64, False, True, True): (1, 4, 1, 16),
+        (3072, 768, 256, 64, 64, True, False, True): (2, 2, 3, 4),
+        (3072, 768, 256, 128, 128, False, True, True): (5, 2, 1, 16),
+        (3072, 768, 256, 128, 128, True, False, True): (1, 2, 5, 4),
+        (3072, 768, 256, 256, 256, False, True, True): (1, 1, 1, 32),
+        (3072, 768, 256, 256, 256, True, False, True): (1, 1, 1, 32),
+        (3072, 768, 512, 32, 32, False, True, True): (1, 8, 1, 8),
+        (3072, 768, 512, 32, 32, True, False, True): (5, 4, 1, 4),
+        (3072, 768, 512, 64, 64, False, True, True): (1, 8, 1, 8),
+        (3072, 768, 512, 64, 64, True, False, True): (3, 2, 3, 4),
+        (3072, 768, 512, 128, 128, False, True, True): (3, 4, 1, 32),
+        (3072, 768, 512, 128, 128, True, False, True): (2, 4, 3, 4),
+        (3072, 768, 512, 256, 256, False, True, True): (1, 2, 1, 32),
+        (3072, 768, 512, 256, 256, True, False, True): (2, 2, 1, 32),
+        (3072, 768, 1024, 32, 32, False, True, True): (2, 16, 1, 8),
+        (3072, 768, 1024, 32, 32, True, False, True): (3, 8, 1, 4),
+        (3072, 768, 1024, 64, 64, False, True, True): (4, 16, 1, 8),
+        (3072, 768, 1024, 64, 64, True, False, True): (1, 8, 3, 2),
+        (3072, 768, 1024, 128, 128, False, True, True): (2, 8, 1, 32),
+        (3072, 768, 1024, 128, 128, True, False, True): (3, 8, 2, 4),
+        (3072, 768, 1024, 256, 256, False, True, True): (1, 4, 1, 32),
+        (3072, 768, 1024, 256, 256, True, False, True): (4, 4, 1, 32),
+        (3072, 768, 2048, 32, 32, False, True, True): (1, 32, 1, 8),
+        (3072, 768, 2048, 32, 32, True, False, True): (1, 16, 1, 4),
+        (3072, 768, 2048, 64, 64, False, True, True): (2, 32, 1, 8),
+        (3072, 768, 2048, 64, 64, True, False, True): (2, 8, 3, 4),
+        (3072, 768, 2048, 128, 128, False, True, True): (2, 16, 1, 16),
+        (3072, 768, 2048, 128, 128, True, False, True): (2, 16, 1, 4),
+        (3072, 768, 2048, 256, 256, False, True, True): (1, 8, 1, 32),
+        (3072, 768, 2048, 256, 256, True, False, True): (2, 8, 1, 32),
+        (3072, 768, 4096, 32, 32, False, True, True): (1, 64, 1, 8),
+        (3072, 768, 4096, 32, 32, True, False, True): (1, 32, 1, 2),
+        (3072, 768, 4096, 64, 64, False, True, True): (2, 64, 1, 8),
+        (3072, 768, 4096, 64, 64, True, False, True): (2, 32, 2, 2),
+        (3072, 768, 4096, 128, 128, False, True, True): (1, 32, 1, 8),
+        (3072, 768, 4096, 128, 128, True, False, True): (2, 32, 2, 4),
+        (3072, 768, 4096, 256, 256, False, True, True): (1, 16, 1, 32),
+        (3072, 768, 4096, 256, 256, True, False, True): (4, 16, 1, 32),
+        (3072, 768, 8192, 32, 32, False, True, True): (1, 128, 1, 8),
+        (3072, 768, 8192, 32, 32, True, False, True): (3, 64, 1, 2),
+        (3072, 768, 8192, 64, 64, False, True, True): (1, 128, 1, 8),
+        (3072, 768, 8192, 64, 64, True, False, True): (2, 64, 2, 2),
+        (3072, 768, 8192, 128, 128, False, True, True): (1, 64, 1, 8),
+        (3072, 768, 8192, 128, 128, True, False, True): (2, 64, 2, 4),
+        (3072, 768, 8192, 256, 256, False, True, True): (1, 32, 1, 32),
+        (3072, 768, 8192, 256, 256, True, False, True): (4, 32, 1, 32),
+        (3072, 768, 16384, 32, 32, False, True, True): (1, 256, 1, 8),
+        (3072, 768, 16384, 32, 32, True, False, True): (1, 128, 1, 2),
+        (3072, 768, 16384, 64, 64, False, True, True): (2, 128, 1, 4),
+        (3072, 768, 16384, 64, 64, True, False, True): (1, 128, 2, 2),
+        (3072, 768, 16384, 128, 128, False, True, True): (1, 128, 1, 8),
+        (3072, 768, 16384, 128, 128, True, False, True): (1, 128, 1, 4),
+        (3072, 768, 16384, 256, 256, False, True, True): (1, 64, 1, 32),
+        (3072, 768, 16384, 256, 256, True, False, True): (4, 64, 1, 32),
+        (3072, 768, 32768, 32, 32, False, True, True): (1, 512, 1, 8),
+        (3072, 768, 32768, 32, 32, True, False, True): (1, 256, 1, 2),
+        (3072, 768, 32768, 64, 64, False, True, True): (1, 256, 1, 4),
+        (3072, 768, 32768, 64, 64, True, False, True): (2, 256, 2, 2),
+        (3072, 768, 32768, 128, 128, False, True, True): (1, 256, 1, 8),
+        (3072, 768, 32768, 128, 128, True, False, True): (2, 256, 1, 4),
+        (3072, 768, 32768, 256, 256, False, True, True): (1, 128, 1, 32),
+        (3072, 768, 32768, 256, 256, True, False, True): (4, 128, 1, 32),
+        (3072, 768, 50432, 32, 32, False, True, True): (1, 788, 1, 8),
+        (3072, 768, 50432, 32, 32, True, False, True): (1, 394, 1, 2),
+        (3072, 768, 50432, 64, 64, False, True, True): (2, 394, 1, 4),
+        (3072, 768, 50432, 64, 64, True, False, True): (2, 394, 2, 2),
+        (3072, 768, 50432, 128, 128, False, True, True): (1, 394, 1, 8),
+        (3072, 768, 50432, 128, 128, True, False, True): (2, 394, 1, 4),
+        (3072, 768, 50432, 256, 256, False, True, True): (1, 197, 1, 32),
+        (3072, 768, 50432, 256, 256, True, False, True): (1, 197, 1, 32),
+        (3072, 768, 65536, 32, 32, False, True, True): (1, 1024, 1, 8),
+        (3072, 768, 65536, 32, 32, True, False, True): (1, 512, 1, 2),
+        (3072, 768, 65536, 64, 64, False, True, True): (1, 512, 1, 4),
+        (3072, 768, 65536, 64, 64, True, False, True): (2, 512, 2, 2),
+        (3072, 768, 65536, 128, 128, False, True, True): (1, 512, 1, 8),
+        (3072, 768, 65536, 128, 128, True, False, True): (2, 512, 1, 4),
+        (3072, 768, 65536, 256, 256, False, True, True): (1, 256, 1, 32),
+        (3072, 768, 65536, 256, 256, True, False, True): (4, 256, 1, 32),
+        (3072, 768, 131072, 32, 32, False, True, True): (1, 2048, 1, 8),
+        (3072, 768, 131072, 32, 32, True, False, True): (1, 1024, 1, 2),
+        (3072, 768, 131072, 64, 64, False, True, True): (2, 1024, 1, 4),
+        (3072, 768, 131072, 64, 64, True, False, True): (2, 1024, 2, 2),
+        (3072, 768, 131072, 128, 128, False, True, True): (1, 1024, 1, 8),
+        (3072, 768, 131072, 128, 128, True, False, True): (2, 1024, 1, 4),
+        (3072, 768, 131072, 256, 256, False, True, True): (1, 512, 1, 32),
+        (3072, 768, 131072, 256, 256, True, False, True): (4, 512, 1, 32),
+        (3072, 3072, 256, 32, 32, False, True, True): (1, 4, 1, 8),
+        (3072, 3072, 256, 32, 32, True, False, True): (2, 2, 5, 4),
+        (3072, 3072, 256, 64, 64, False, True, True): (2, 4, 1, 16),
+        (3072, 3072, 256, 64, 64, True, False, True): (3, 2, 3, 4),
+        (3072, 3072, 256, 128, 128, False, True, True): (1, 2, 1, 8),
+        (3072, 3072, 256, 128, 128, True, False, True): (1, 2, 5, 4),
+        (3072, 3072, 256, 256, 256, False, True, True): (1, 1, 1, 32),
+        (3072, 3072, 256, 256, 256, True, False, True): (1, 1, 1, 32),
+        (3072, 3072, 512, 32, 32, False, True, True): (1, 8, 1, 8),
+        (3072, 3072, 512, 32, 32, True, False, True): (3, 2, 3, 4),
+        (3072, 3072, 512, 64, 64, False, True, True): (1, 8, 1, 8),
+        (3072, 3072, 512, 64, 64, True, False, True): (3, 2, 3, 4),
+        (3072, 3072, 512, 128, 128, False, True, True): (2, 4, 1, 8),
+        (3072, 3072, 512, 128, 128, True, False, True): (2, 4, 4, 4),
+        (3072, 3072, 512, 256, 256, False, True, True): (1, 2, 1, 32),
+        (3072, 3072, 512, 256, 256, True, False, True): (1, 2, 1, 32),
+        (3072, 3072, 1024, 32, 32, False, True, True): (1, 16, 1, 8),
+        (3072, 3072, 1024, 32, 32, True, False, True): (3, 8, 3, 4),
+        (3072, 3072, 1024, 64, 64, False, True, True): (2, 16, 1, 8),
+        (3072, 3072, 1024, 64, 64, True, False, True): (2, 4, 3, 4),
+        (3072, 3072, 1024, 128, 128, False, True, True): (1, 8, 1, 8),
+        (3072, 3072, 1024, 128, 128, True, False, True): (3, 8, 2, 4),
+        (3072, 3072, 1024, 256, 256, False, True, True): (1, 4, 1, 32),
+        (3072, 3072, 1024, 256, 256, True, False, True): (3, 4, 1, 32),
+        (3072, 3072, 2048, 32, 32, False, True, True): (1, 32, 1, 8),
+        (3072, 3072, 2048, 32, 32, True, False, True): (1, 16, 1, 4),
+        (3072, 3072, 2048, 64, 64, False, True, True): (1, 32, 1, 8),
+        (3072, 3072, 2048, 64, 64, True, False, True): (1, 16, 3, 2),
+        (3072, 3072, 2048, 128, 128, False, True, True): (1, 16, 1, 8),
+        (3072, 3072, 2048, 128, 128, True, False, True): (2, 16, 2, 4),
+        (3072, 3072, 2048, 256, 256, False, True, True): (1, 8, 1, 32),
+        (3072, 3072, 2048, 256, 256, True, False, True): (3, 8, 1, 32),
+        (3072, 3072, 4096, 32, 32, False, True, True): (1, 64, 1, 8),
+        (3072, 3072, 4096, 32, 32, True, False, True): (1, 32, 1, 4),
+        (3072, 3072, 4096, 64, 64, False, True, True): (1, 64, 1, 8),
+        (3072, 3072, 4096, 64, 64, True, False, True): (3, 16, 3, 4),
+        (3072, 3072, 4096, 128, 128, False, True, True): (1, 32, 1, 8),
+        (3072, 3072, 4096, 128, 128, True, False, True): (2, 32, 2, 4),
+        (3072, 3072, 4096, 256, 256, False, True, True): (1, 16, 1, 32),
+        (3072, 3072, 4096, 256, 256, True, False, True): (2, 16, 1, 32),
+        (3072, 3072, 8192, 32, 32, False, True, True): (1, 128, 1, 8),
+        (3072, 3072, 8192, 32, 32, True, False, True): (1, 64, 1, 2),
+        (3072, 3072, 8192, 64, 64, False, True, True): (1, 64, 1, 4),
+        (3072, 3072, 8192, 64, 64, True, False, True): (1, 64, 3, 2),
+        (3072, 3072, 8192, 128, 128, False, True, True): (1, 64, 1, 8),
+        (3072, 3072, 8192, 128, 128, True, False, True): (2, 64, 2, 4),
+        (3072, 3072, 8192, 256, 256, False, True, True): (1, 32, 1, 32),
+        (3072, 3072, 8192, 256, 256, True, False, True): (4, 32, 1, 32),
+        (3072, 3072, 16384, 32, 32, False, True, True): (1, 256, 1, 8),
+        (3072, 3072, 16384, 32, 32, True, False, True): (1, 128, 3, 2),
+        (3072, 3072, 16384, 64, 64, False, True, True): (1, 128, 1, 4),
+        (3072, 3072, 16384, 64, 64, True, False, True): (2, 64, 3, 4),
+        (3072, 3072, 16384, 128, 128, False, True, True): (1, 128, 1, 8),
+        (3072, 3072, 16384, 128, 128, True, False, True): (1, 128, 2, 4),
+        (3072, 3072, 16384, 256, 256, False, True, True): (1, 64, 1, 32),
+        (3072, 3072, 16384, 256, 256, True, False, True): (4, 64, 1, 32),
+        (3072, 3072, 32768, 32, 32, False, True, True): (1, 512, 1, 8),
+        (3072, 3072, 32768, 32, 32, True, False, True): (1, 256, 3, 2),
+        (3072, 3072, 32768, 64, 64, False, True, True): (1, 256, 1, 4),
+        (3072, 3072, 32768, 64, 64, True, False, True): (1, 256, 3, 2),
+        (3072, 3072, 32768, 128, 128, False, True, True): (1, 256, 1, 8),
+        (3072, 3072, 32768, 128, 128, True, False, True): (1, 256, 2, 4),
+        (3072, 3072, 32768, 256, 256, False, True, True): (1, 128, 1, 32),
+        (3072, 3072, 32768, 256, 256, True, False, True): (4, 128, 1, 32),
+        (3072, 3072, 65536, 32, 32, False, True, True): (1, 1024, 1, 8),
+        (3072, 3072, 65536, 32, 32, True, False, True): (1, 512, 3, 2),
+        (3072, 3072, 65536, 64, 64, False, True, True): (1, 512, 1, 4),
+        (3072, 3072, 65536, 64, 64, True, False, True): (2, 256, 3, 4),
+        (3072, 3072, 65536, 128, 128, False, True, True): (1, 512, 1, 8),
+        (3072, 3072, 65536, 128, 128, True, False, True): (1, 512, 3, 4),
+        (3072, 3072, 65536, 256, 256, False, True, True): (1, 256, 1, 32),
+        (3072, 3072, 65536, 256, 256, True, False, True): (4, 256, 1, 32),
+        (3072, 3072, 131072, 32, 32, False, True, True): (1, 2048, 1, 8),
+        (3072, 3072, 131072, 32, 32, True, False, True): (1, 1024, 3, 2),
+        (3072, 3072, 131072, 64, 64, False, True, True): (1, 1024, 1, 4),
+        (3072, 3072, 131072, 64, 64, True, False, True): (1, 1024, 3, 2),
+        (3072, 3072, 131072, 128, 128, False, True, True): (1, 1024, 1, 8),
+        (3072, 3072, 131072, 128, 128, True, False, True): (1, 1024, 3, 4),
+        (3072, 3072, 131072, 256, 256, False, True, True): (1, 512, 1, 32),
+        (3072, 3072, 131072, 256, 256, True, False, True): (4, 512, 1, 32),
+        (4096, 4096, 256, 32, 32, False, True, True): (1, 4, 1, 8),
+        (4096, 4096, 256, 32, 32, True, False, True): (5, 2, 3, 4),
+        (4096, 4096, 256, 64, 64, False, True, True): (3, 4, 1, 8),
+        (4096, 4096, 256, 64, 64, True, False, True): (3, 4, 3, 2),
+        (4096, 4096, 256, 128, 128, False, True, True): (1, 2, 1, 8),
+        (4096, 4096, 256, 128, 128, True, False, True): (2, 2, 4, 4),
+        (4096, 4096, 256, 256, 256, False, True, True): (1, 1, 1, 32),
+        (4096, 4096, 256, 256, 256, True, False, True): (1, 1, 1, 32),
+        (4096, 4096, 512, 32, 32, False, True, True): (1, 8, 1, 8),
+        (4096, 4096, 512, 32, 32, True, False, True): (1, 4, 1, 4),
+        (4096, 4096, 512, 64, 64, False, True, True): (1, 8, 1, 8),
+        (4096, 4096, 512, 64, 64, True, False, True): (3, 4, 2, 2),
+        (4096, 4096, 512, 128, 128, False, True, True): (2, 4, 1, 8),
+        (4096, 4096, 512, 128, 128, True, False, True): (2, 4, 2, 4),
+        (4096, 4096, 512, 256, 256, False, True, True): (2, 2, 1, 32),
+        (4096, 4096, 512, 256, 256, True, False, True): (2, 2, 1, 32),
+        (4096, 4096, 1024, 32, 32, False, True, True): (4, 16, 1, 8),
+        (4096, 4096, 1024, 32, 32, True, False, True): (1, 8, 1, 4),
+        (4096, 4096, 1024, 64, 64, False, True, True): (1, 16, 1, 8),
+        (4096, 4096, 1024, 64, 64, True, False, True): (4, 4, 3, 4),
+        (4096, 4096, 1024, 128, 128, False, True, True): (2, 8, 1, 8),
+        (4096, 4096, 1024, 128, 128, True, False, True): (1, 8, 3, 4),
+        (4096, 4096, 1024, 256, 256, False, True, True): (1, 4, 1, 32),
+        (4096, 4096, 1024, 256, 256, True, False, True): (6, 4, 1, 32),
+        (4096, 4096, 2048, 32, 32, False, True, True): (1, 32, 1, 8),
+        (4096, 4096, 2048, 32, 32, True, False, True): (1, 16, 1, 4),
+        (4096, 4096, 2048, 64, 64, False, True, True): (4, 32, 1, 8),
+        (4096, 4096, 2048, 64, 64, True, False, True): (4, 8, 3, 4),
+        (4096, 4096, 2048, 128, 128, False, True, True): (2, 16, 1, 8),
+        (4096, 4096, 2048, 128, 128, True, False, True): (1, 16, 3, 4),
+        (4096, 4096, 2048, 256, 256, False, True, True): (1, 8, 1, 32),
+        (4096, 4096, 2048, 256, 256, True, False, True): (4, 8, 1, 32),
+        (4096, 4096, 4096, 32, 32, False, True, True): (1, 64, 1, 8),
+        (4096, 4096, 4096, 32, 32, True, False, True): (1, 32, 1, 4),
+        (4096, 4096, 4096, 64, 64, False, True, True): (1, 64, 1, 8),
+        (4096, 4096, 4096, 64, 64, True, False, True): (1, 32, 3, 2),
+        (4096, 4096, 4096, 128, 128, False, True, True): (1, 32, 1, 8),
+        (4096, 4096, 4096, 128, 128, True, False, True): (2, 32, 3, 4),
+        (4096, 4096, 4096, 256, 256, False, True, True): (1, 16, 1, 32),
+        (4096, 4096, 4096, 256, 256, True, False, True): (4, 16, 1, 32),
+        (4096, 4096, 8192, 32, 32, False, True, True): (1, 128, 1, 8),
+        (4096, 4096, 8192, 32, 32, True, False, True): (1, 64, 1, 4),
+        (4096, 4096, 8192, 64, 64, False, True, True): (1, 128, 1, 8),
+        (4096, 4096, 8192, 64, 64, True, False, True): (1, 64, 3, 2),
+        (4096, 4096, 8192, 128, 128, False, True, True): (1, 64, 1, 8),
+        (4096, 4096, 8192, 128, 128, True, False, True): (1, 64, 3, 4),
+        (4096, 4096, 8192, 256, 256, False, True, True): (1, 32, 1, 32),
+        (4096, 4096, 8192, 256, 256, True, False, True): (4, 32, 1, 32),
+        (4096, 4096, 16384, 32, 32, False, True, True): (1, 256, 1, 8),
+        (4096, 4096, 16384, 32, 32, True, False, True): (1, 128, 3, 2),
+        (4096, 4096, 16384, 64, 64, False, True, True): (1, 128, 1, 4),
+        (4096, 4096, 16384, 64, 64, True, False, True): (4, 64, 3, 4),
+        (4096, 4096, 16384, 128, 128, False, True, True): (1, 128, 1, 8),
+        (4096, 4096, 16384, 128, 128, True, False, True): (1, 128, 3, 4),
+        (4096, 4096, 16384, 256, 256, False, True, True): (1, 64, 1, 32),
+        (4096, 4096, 16384, 256, 256, True, False, True): (4, 64, 1, 32),
+        (4096, 4096, 32768, 32, 32, False, True, True): (1, 512, 1, 8),
+        (4096, 4096, 32768, 32, 32, True, False, True): (1, 256, 3, 2),
+        (4096, 4096, 32768, 64, 64, False, True, True): (1, 256, 1, 4),
+        (4096, 4096, 32768, 64, 64, True, False, True): (1, 256, 3, 2),
+        (4096, 4096, 32768, 128, 128, False, True, True): (1, 256, 1, 8),
+        (4096, 4096, 32768, 128, 128, True, False, True): (1, 256, 3, 4),
+        (4096, 4096, 32768, 256, 256, False, True, True): (1, 128, 1, 32),
+        (4096, 4096, 32768, 256, 256, True, False, True): (4, 128, 1, 32),
+        (4096, 4096, 65536, 32, 32, False, True, True): (1, 1024, 1, 8),
+        (4096, 4096, 65536, 32, 32, True, False, True): (1, 512, 3, 2),
+        (4096, 4096, 65536, 64, 64, False, True, True): (1, 512, 1, 4),
+        (4096, 4096, 65536, 64, 64, True, False, True): (4, 256, 3, 4),
+        (4096, 4096, 65536, 128, 128, False, True, True): (1, 512, 1, 8),
+        (4096, 4096, 65536, 128, 128, True, False, True): (1, 512, 3, 4),
+        (4096, 4096, 65536, 256, 256, False, True, True): (1, 256, 1, 32),
+        (4096, 4096, 65536, 256, 256, True, False, True): (4, 256, 1, 32),
+        (4096, 4096, 131072, 32, 32, False, True, True): (1, 2048, 1, 8),
+        (4096, 4096, 131072, 32, 32, True, False, True): (1, 1024, 3, 2),
+        (4096, 4096, 131072, 64, 64, False, True, True): (1, 2048, 1, 8),
+        (4096, 4096, 131072, 64, 64, True, False, True): (1, 1024, 3, 2),
+        (4096, 4096, 131072, 128, 128, False, True, True): (1, 1024, 1, 8),
+        (4096, 4096, 131072, 128, 128, True, False, True): (1, 1024, 3, 4),
+        (4096, 4096, 131072, 256, 256, False, True, True): (1, 512, 1, 32),
+        (4096, 4096, 131072, 256, 256, True, False, True): (4, 512, 1, 32),
+        (6144, 6144, 256, 32, 32, False, True, True): (2, 4, 1, 8),
+        (6144, 6144, 256, 32, 32, True, False, True): (2, 1, 4, 4),
+        (6144, 6144, 256, 64, 64, False, True, True): (1, 4, 1, 8),
+        (6144, 6144, 256, 64, 64, True, False, True): (5, 1, 3, 4),
+        (6144, 6144, 256, 128, 128, False, True, True): (1, 2, 1, 8),
+        (6144, 6144, 256, 128, 128, True, False, True): (1, 2, 3, 4),
+        (6144, 6144, 256, 256, 256, False, True, True): (1, 1, 1, 32),
+        (6144, 6144, 256, 256, 256, True, False, True): (1, 1, 1, 32),
+        (6144, 6144, 512, 32, 32, False, True, True): (1, 8, 1, 8),
+        (6144, 6144, 512, 32, 32, True, False, True): (1, 4, 4, 2),
+        (6144, 6144, 512, 64, 64, False, True, True): (2, 8, 1, 8),
+        (6144, 6144, 512, 64, 64, True, False, True): (2, 2, 3, 4),
+        (6144, 6144, 512, 128, 128, False, True, True): (3, 4, 1, 8),
+        (6144, 6144, 512, 128, 128, True, False, True): (2, 4, 3, 4),
+        (6144, 6144, 512, 256, 256, False, True, True): (1, 2, 1, 32),
+        (6144, 6144, 512, 256, 256, True, False, True): (2, 2, 1, 32),
+        (6144, 6144, 1024, 32, 32, False, True, True): (1, 16, 1, 8),
+        (6144, 6144, 1024, 32, 32, True, False, True): (1, 8, 1, 4),
+        (6144, 6144, 1024, 64, 64, False, True, True): (1, 16, 1, 8),
+        (6144, 6144, 1024, 64, 64, True, False, True): (4, 4, 3, 4),
+        (6144, 6144, 1024, 128, 128, False, True, True): (1, 8, 1, 8),
+        (6144, 6144, 1024, 128, 128, True, False, True): (3, 8, 3, 4),
+        (6144, 6144, 1024, 256, 256, False, True, True): (1, 4, 1, 32),
+        (6144, 6144, 1024, 256, 256, True, False, True): (1, 4, 1, 32),
+        (6144, 6144, 2048, 32, 32, False, True, True): (1, 32, 1, 8),
+        (6144, 6144, 2048, 32, 32, True, False, True): (1, 16, 1, 4),
+        (6144, 6144, 2048, 64, 64, False, True, True): (1, 32, 1, 8),
+        (6144, 6144, 2048, 64, 64, True, False, True): (4, 8, 3, 4),
+        (6144, 6144, 2048, 128, 128, False, True, True): (1, 16, 1, 8),
+        (6144, 6144, 2048, 128, 128, True, False, True): (3, 16, 3, 4),
+        (6144, 6144, 2048, 256, 256, False, True, True): (1, 8, 1, 32),
+        (6144, 6144, 2048, 256, 256, True, False, True): (4, 8, 1, 32),
+        (6144, 6144, 4096, 32, 32, False, True, True): (1, 64, 1, 8),
+        (6144, 6144, 4096, 32, 32, True, False, True): (1, 32, 1, 4),
+        (6144, 6144, 4096, 64, 64, False, True, True): (1, 64, 1, 8),
+        (6144, 6144, 4096, 64, 64, True, False, True): (4, 16, 3, 4),
+        (6144, 6144, 4096, 128, 128, False, True, True): (1, 32, 1, 8),
+        (6144, 6144, 4096, 128, 128, True, False, True): (4, 32, 3, 4),
+        (6144, 6144, 4096, 256, 256, False, True, True): (1, 16, 1, 32),
+        (6144, 6144, 4096, 256, 256, True, False, True): (4, 16, 1, 32),
+        (6144, 6144, 8192, 32, 32, False, True, True): (1, 128, 1, 8),
+        (6144, 6144, 8192, 32, 32, True, False, True): (1, 64, 1, 4),
+        (6144, 6144, 8192, 64, 64, False, True, True): (1, 128, 1, 8),
+        (6144, 6144, 8192, 64, 64, True, False, True): (4, 32, 3, 4),
+        (6144, 6144, 8192, 128, 128, False, True, True): (1, 64, 1, 8),
+        (6144, 6144, 8192, 128, 128, True, False, True): (1, 64, 3, 4),
+        (6144, 6144, 8192, 256, 256, False, True, True): (1, 32, 1, 32),
+        (6144, 6144, 8192, 256, 256, True, False, True): (4, 32, 1, 32),
+        (6144, 6144, 16384, 32, 32, False, True, True): (1, 256, 1, 8),
+        (6144, 6144, 16384, 32, 32, True, False, True): (1, 128, 1, 4),
+        (6144, 6144, 16384, 64, 64, False, True, True): (1, 256, 1, 8),
+        (6144, 6144, 16384, 64, 64, True, False, True): (4, 64, 3, 4),
+        (6144, 6144, 16384, 128, 128, False, True, True): (1, 128, 1, 8),
+        (6144, 6144, 16384, 128, 128, True, False, True): (4, 128, 3, 4),
+        (6144, 6144, 16384, 256, 256, False, True, True): (1, 64, 1, 32),
+        (6144, 6144, 16384, 256, 256, True, False, True): (4, 64, 1, 32),
+        (6144, 6144, 32768, 32, 32, False, True, True): (1, 512, 1, 8),
+        (6144, 6144, 32768, 32, 32, True, False, True): (1, 256, 1, 4),
+        (6144, 6144, 32768, 64, 64, False, True, True): (1, 512, 1, 8),
+        (6144, 6144, 32768, 64, 64, True, False, True): (4, 128, 3, 4),
+        (6144, 6144, 32768, 128, 128, False, True, True): (1, 256, 1, 8),
+        (6144, 6144, 32768, 128, 128, True, False, True): (1, 256, 3, 4),
+        (6144, 6144, 32768, 256, 256, False, True, True): (1, 128, 1, 32),
+        (6144, 6144, 32768, 256, 256, True, False, True): (4, 128, 1, 32),
+        (6144, 6144, 65536, 32, 32, False, True, True): (1, 1024, 1, 8),
+        (6144, 6144, 65536, 32, 32, True, False, True): (1, 512, 1, 4),
+        (6144, 6144, 65536, 64, 64, False, True, True): (1, 1024, 1, 8),
+        (6144, 6144, 65536, 64, 64, True, False, True): (4, 256, 3, 4),
+        (6144, 6144, 65536, 128, 128, False, True, True): (1, 512, 1, 8),
+        (6144, 6144, 65536, 128, 128, True, False, True): (1, 512, 3, 4),
+        (6144, 6144, 65536, 256, 256, False, True, True): (1, 256, 1, 32),
+        (6144, 6144, 65536, 256, 256, True, False, True): (4, 256, 1, 32),
+        (6144, 6144, 131072, 32, 32, False, True, True): (1, 2048, 1, 8),
+        (6144, 6144, 131072, 32, 32, True, False, True): (1, 1024, 1, 4),
+        (6144, 6144, 131072, 64, 64, False, True, True): (1, 2048, 1, 8),
+        (6144, 6144, 131072, 64, 64, True, False, True): (4, 512, 3, 4),
+        (6144, 6144, 131072, 128, 128, False, True, True): (1, 1024, 1, 8),
+        (6144, 6144, 131072, 128, 128, True, False, True): (1, 1024, 3, 4),
+        (6144, 6144, 131072, 256, 256, False, True, True): (1, 512, 1, 32),
+        (6144, 6144, 131072, 256, 256, True, False, True): (4, 512, 1, 32),
+        (8192, 8192, 256, 32, 32, False, True, True): (1, 4, 1, 8),
+        (8192, 8192, 256, 32, 32, True, False, True): (3, 2, 3, 4),
+        (8192, 8192, 256, 64, 64, False, True, True): (1, 4, 1, 4),
+        (8192, 8192, 256, 64, 64, True, False, True): (1, 4, 1, 4),
+        (8192, 8192, 256, 128, 128, False, True, True): (1, 2, 1, 8),
+        (8192, 8192, 256, 128, 128, True, False, True): (2, 2, 3, 4),
+        (8192, 8192, 256, 256, 256, False, True, True): (1, 1, 1, 32),
+        (8192, 8192, 256, 256, 256, True, False, True): (1, 1, 1, 32),
+        (8192, 8192, 512, 32, 32, False, True, True): (4, 8, 1, 8),
+        (8192, 8192, 512, 32, 32, True, False, True): (2, 4, 4, 2),
+        (8192, 8192, 512, 64, 64, False, True, True): (4, 4, 1, 4),
+        (8192, 8192, 512, 64, 64, True, False, True): (3, 2, 3, 4),
+        (8192, 8192, 512, 128, 128, False, True, True): (1, 4, 1, 8),
+        (8192, 8192, 512, 128, 128, True, False, True): (1, 4, 3, 4),
+        (8192, 8192, 512, 256, 256, False, True, True): (1, 2, 1, 32),
+        (8192, 8192, 512, 256, 256, True, False, True): (1, 2, 1, 32),
+        (8192, 8192, 1024, 32, 32, False, True, True): (4, 16, 1, 8),
+        (8192, 8192, 1024, 32, 32, True, False, True): (1, 8, 3, 2),
+        (8192, 8192, 1024, 64, 64, False, True, True): (4, 8, 1, 4),
+        (8192, 8192, 1024, 64, 64, True, False, True): (4, 4, 3, 4),
+        (8192, 8192, 1024, 128, 128, False, True, True): (1, 8, 1, 8),
+        (8192, 8192, 1024, 128, 128, True, False, True): (1, 8, 3, 4),
+        (8192, 8192, 1024, 256, 256, False, True, True): (1, 4, 1, 32),
+        (8192, 8192, 1024, 256, 256, True, False, True): (4, 4, 1, 32),
+        (8192, 8192, 2048, 32, 32, False, True, True): (4, 32, 1, 8),
+        (8192, 8192, 2048, 32, 32, True, False, True): (1, 16, 3, 2),
+        (8192, 8192, 2048, 64, 64, False, True, True): (4, 32, 1, 8),
+        (8192, 8192, 2048, 64, 64, True, False, True): (4, 8, 3, 4),
+        (8192, 8192, 2048, 128, 128, False, True, True): (4, 16, 1, 8),
+        (8192, 8192, 2048, 128, 128, True, False, True): (4, 16, 3, 4),
+        (8192, 8192, 2048, 256, 256, False, True, True): (1, 8, 1, 32),
+        (8192, 8192, 2048, 256, 256, True, False, True): (4, 8, 1, 32),
+        (8192, 8192, 4096, 32, 32, False, True, True): (4, 64, 1, 8),
+        (8192, 8192, 4096, 32, 32, True, False, True): (2, 32, 3, 2),
+        (8192, 8192, 4096, 64, 64, False, True, True): (4, 64, 1, 8),
+        (8192, 8192, 4096, 64, 64, True, False, True): (4, 16, 3, 4),
+        (8192, 8192, 4096, 128, 128, False, True, True): (4, 32, 1, 8),
+        (8192, 8192, 4096, 128, 128, True, False, True): (4, 32, 3, 4),
+        (8192, 8192, 4096, 256, 256, False, True, True): (1, 16, 1, 32),
+        (8192, 8192, 4096, 256, 256, True, False, True): (2, 16, 1, 32),
+        (8192, 8192, 8192, 32, 32, False, True, True): (4, 128, 1, 8),
+        (8192, 8192, 8192, 32, 32, True, False, True): (1, 64, 3, 2),
+        (8192, 8192, 8192, 64, 64, False, True, True): (4, 64, 1, 4),
+        (8192, 8192, 8192, 64, 64, True, False, True): (4, 32, 3, 4),
+        (8192, 8192, 8192, 128, 128, False, True, True): (4, 64, 1, 16),
+        (8192, 8192, 8192, 128, 128, True, False, True): (4, 64, 3, 4),
+        (8192, 8192, 8192, 256, 256, False, True, True): (1, 32, 1, 32),
+        (8192, 8192, 8192, 256, 256, True, False, True): (4, 32, 1, 32),
+        (8192, 8192, 16384, 32, 32, False, True, True): (4, 256, 1, 8),
+        (8192, 8192, 16384, 32, 32, True, False, True): (4, 128, 3, 2),
+        (8192, 8192, 16384, 64, 64, False, True, True): (4, 128, 1, 4),
+        (8192, 8192, 16384, 64, 64, True, False, True): (4, 64, 3, 4),
+        (8192, 8192, 16384, 128, 128, False, True, True): (4, 128, 1, 16),
+        (8192, 8192, 16384, 128, 128, True, False, True): (4, 128, 3, 4),
+        (8192, 8192, 16384, 256, 256, False, True, True): (1, 64, 1, 32),
+        (8192, 8192, 16384, 256, 256, True, False, True): (4, 64, 1, 32),
+        (8192, 8192, 32768, 32, 32, False, True, True): (4, 512, 1, 8),
+        (8192, 8192, 32768, 32, 32, True, False, True): (2, 256, 3, 2),
+        (8192, 8192, 32768, 64, 64, False, True, True): (4, 256, 1, 4),
+        (8192, 8192, 32768, 64, 64, True, False, True): (4, 128, 3, 4),
+        (8192, 8192, 32768, 128, 128, False, True, True): (4, 256, 1, 16),
+        (8192, 8192, 32768, 128, 128, True, False, True): (4, 256, 3, 4),
+        (8192, 8192, 32768, 256, 256, False, True, True): (1, 128, 1, 32),
+        (8192, 8192, 32768, 256, 256, True, False, True): (4, 128, 1, 32),
+        (8192, 8192, 65536, 32, 32, False, True, True): (4, 1024, 1, 8),
+        (8192, 8192, 65536, 32, 32, True, False, True): (4, 512, 3, 2),
+        (8192, 8192, 65536, 64, 64, False, True, True): (4, 512, 1, 4),
+        (8192, 8192, 65536, 64, 64, True, False, True): (4, 256, 3, 4),
+        (8192, 8192, 65536, 128, 128, False, True, True): (4, 512, 1, 16),
+        (8192, 8192, 65536, 128, 128, True, False, True): (4, 512, 3, 4),
+        (8192, 8192, 65536, 256, 256, False, True, True): (1, 256, 1, 32),
+        (8192, 8192, 65536, 256, 256, True, False, True): (4, 256, 1, 32),
+        (8192, 8192, 131072, 32, 32, False, True, True): (4, 2048, 1, 8),
+        (8192, 8192, 131072, 32, 32, True, False, True): (4, 1024, 3, 2),
+        (8192, 8192, 131072, 64, 64, False, True, True): (4, 1024, 1, 4),
+        (8192, 8192, 131072, 64, 64, True, False, True): (4, 512, 3, 4),
+        (8192, 8192, 131072, 128, 128, False, True, True): (4, 1024, 1, 16),
+        (8192, 8192, 131072, 128, 128, True, False, True): (4, 1024, 3, 4),
+        (8192, 8192, 131072, 256, 256, False, True, True): (1, 512, 1, 32),
+        (8192, 8192, 131072, 256, 256, True, False, True): (4, 512, 1, 32),
+        (16384, 16384, 256, 32, 32, False, True, True): (4, 4, 1, 8),
+        (16384, 16384, 256, 32, 32, True, False, True): (2, 2, 4, 2),
+        (16384, 16384, 256, 64, 64, False, True, True): (2, 2, 1, 4),
+        (16384, 16384, 256, 64, 64, True, False, True): (5, 1, 3, 4),
+        (16384, 16384, 256, 128, 128, False, True, True): (6, 2, 1, 8),
+        (16384, 16384, 256, 128, 128, True, False, True): (6, 2, 3, 4),
+        (16384, 16384, 256, 256, 256, False, True, True): (1, 1, 1, 32),
+        (16384, 16384, 256, 256, 256, True, False, True): (1, 1, 1, 32),
+        (16384, 16384, 512, 32, 32, False, True, True): (4, 8, 1, 8),
+        (16384, 16384, 512, 32, 32, True, False, True): (1, 4, 4, 2),
+        (16384, 16384, 512, 64, 64, False, True, True): (4, 4, 1, 4),
+        (16384, 16384, 512, 64, 64, True, False, True): (2, 2, 3, 4),
+        (16384, 16384, 512, 128, 128, False, True, True): (4, 4, 1, 8),
+        (16384, 16384, 512, 128, 128, True, False, True): (4, 4, 3, 4),
+        (16384, 16384, 512, 256, 256, False, True, True): (1, 2, 1, 32),
+        (16384, 16384, 512, 256, 256, True, False, True): (2, 2, 1, 32),
+        (16384, 16384, 1024, 32, 32, False, True, True): (4, 16, 1, 8),
+        (16384, 16384, 1024, 32, 32, True, False, True): (1, 8, 3, 2),
+        (16384, 16384, 1024, 64, 64, False, True, True): (4, 8, 1, 4),
+        (16384, 16384, 1024, 64, 64, True, False, True): (4, 4, 3, 4),
+        (16384, 16384, 1024, 128, 128, False, True, True): (4, 4, 1, 8),
+        (16384, 16384, 1024, 128, 128, True, False, True): (4, 8, 3, 4),
+        (16384, 16384, 1024, 256, 256, False, True, True): (1, 4, 1, 32),
+        (16384, 16384, 1024, 256, 256, True, False, True): (4, 4, 1, 32),
+        (16384, 16384, 2048, 32, 32, False, True, True): (4, 32, 1, 8),
+        (16384, 16384, 2048, 32, 32, True, False, True): (2, 16, 3, 2),
+        (16384, 16384, 2048, 64, 64, False, True, True): (4, 16, 1, 4),
+        (16384, 16384, 2048, 64, 64, True, False, True): (4, 8, 3, 4),
+        (16384, 16384, 2048, 128, 128, False, True, True): (4, 16, 1, 8),
+        (16384, 16384, 2048, 128, 128, True, False, True): (4, 16, 3, 4),
+        (16384, 16384, 2048, 256, 256, False, True, True): (1, 8, 1, 32),
+        (16384, 16384, 2048, 256, 256, True, False, True): (4, 8, 1, 32),
+        (16384, 16384, 4096, 32, 32, False, True, True): (4, 64, 1, 8),
+        (16384, 16384, 4096, 32, 32, True, False, True): (2, 32, 3, 2),
+        (16384, 16384, 4096, 64, 64, False, True, True): (2, 32, 1, 4),
+        (16384, 16384, 4096, 64, 64, True, False, True): (4, 16, 3, 4),
+        (16384, 16384, 4096, 128, 128, False, True, True): (4, 32, 1, 8),
+        (16384, 16384, 4096, 128, 128, True, False, True): (4, 32, 3, 4),
+        (16384, 16384, 4096, 256, 256, False, True, True): (1, 16, 1, 32),
+        (16384, 16384, 4096, 256, 256, True, False, True): (4, 16, 1, 32),
+        (16384, 16384, 8192, 32, 32, False, True, True): (4, 128, 1, 8),
+        (16384, 16384, 8192, 32, 32, True, False, True): (2, 64, 3, 2),
+        (16384, 16384, 8192, 64, 64, False, True, True): (4, 64, 1, 4),
+        (16384, 16384, 8192, 64, 64, True, False, True): (4, 32, 3, 4),
+        (16384, 16384, 8192, 128, 128, False, True, True): (4, 64, 1, 16),
+        (16384, 16384, 8192, 128, 128, True, False, True): (4, 64, 3, 4),
+        (16384, 16384, 8192, 256, 256, False, True, True): (1, 32, 1, 32),
+        (16384, 16384, 8192, 256, 256, True, False, True): (4, 32, 1, 32),
+        (16384, 16384, 16384, 32, 32, False, True, True): (4, 256, 1, 8),
+        (16384, 16384, 16384, 32, 32, True, False, True): (2, 128, 3, 2),
+        (16384, 16384, 16384, 64, 64, False, True, True): (4, 128, 1, 4),
+        (16384, 16384, 16384, 64, 64, True, False, True): (4, 64, 3, 4),
+        (16384, 16384, 16384, 128, 128, False, True, True): (1, 64, 1, 8),
+        (16384, 16384, 16384, 128, 128, True, False, True): (4, 128, 3, 4),
+        (16384, 16384, 16384, 256, 256, False, True, True): (1, 64, 1, 32),
+        (16384, 16384, 16384, 256, 256, True, False, True): (4, 64, 1, 32),
+        (16384, 16384, 32768, 32, 32, False, True, True): (4, 512, 1, 8),
+        (16384, 16384, 32768, 32, 32, True, False, True): (1, 256, 3, 2),
+        (16384, 16384, 32768, 64, 64, False, True, True): (4, 256, 1, 4),
+        (16384, 16384, 32768, 64, 64, True, False, True): (4, 128, 3, 4),
+        (16384, 16384, 32768, 128, 128, False, True, True): (4, 256, 1, 16),
+        (16384, 16384, 32768, 128, 128, True, False, True): (4, 256, 3, 4),
+        (16384, 16384, 32768, 256, 256, False, True, True): (1, 128, 1, 32),
+        (16384, 16384, 32768, 256, 256, True, False, True): (4, 128, 1, 32),
+        (16384, 16384, 65536, 32, 32, False, True, True): (4, 1024, 1, 8),
+        (16384, 16384, 65536, 32, 32, True, False, True): (1, 512, 3, 2),
+        (16384, 16384, 65536, 64, 64, False, True, True): (2, 512, 1, 4),
+        (16384, 16384, 65536, 64, 64, True, False, True): (4, 256, 3, 4),
+        (16384, 16384, 65536, 128, 128, False, True, True): (4, 512, 1, 16),
+        (16384, 16384, 65536, 128, 128, True, False, True): (4, 512, 3, 4),
+        (16384, 16384, 65536, 256, 256, False, True, True): (1, 256, 1, 32),
+        (16384, 16384, 65536, 256, 256, True, False, True): (4, 256, 1, 32),
+        (16384, 16384, 131072, 32, 32, False, True, True): (4, 1024, 1, 8),
+        (16384, 16384, 131072, 32, 32, True, False, True): (4, 512, 3, 4),
+        (16384, 16384, 131072, 64, 64, False, True, True): (4, 1024, 1, 4),
+        (16384, 16384, 131072, 64, 64, True, False, True): (4, 1024, 3, 2),
+        (16384, 16384, 131072, 128, 128, False, True, True): (2, 1024, 3, 8),
+        (16384, 16384, 131072, 128, 128, True, False, True): (4, 1024, 3, 4),
+        (16384, 16384, 131072, 256, 256, False, True, True): (4, 512, 1, 32),
+        (16384, 16384, 131072, 256, 256, True, False, True): (4, 512, 1, 32),
+    },
+    ("_int_bsr_dense_addmm", "NVIDIA A100-SXM4-80GB", (0, torch.int8, 0.56)): {
+        (192, 192, 256, 64, 64, False, True, True): (3, 4, 3, 32),
+        (192, 192, 256, 64, 64, True, False, True): (1, 4, 3, 4),
+        (192, 192, 512, 64, 64, False, True, True): (1, 8, 1, 16),
+        (192, 192, 512, 64, 64, True, False, True): (1, 8, 5, 4),
+        (192, 192, 1024, 64, 64, False, True, True): (4, 16, 1, 16),
+        (192, 192, 1024, 64, 64, True, False, True): (3, 16, 3, 4),
+        (192, 192, 2048, 64, 64, False, True, True): (5, 32, 1, 8),
+        (192, 192, 2048, 64, 64, True, False, True): (2, 32, 4, 4),
+        (192, 192, 4096, 64, 64, False, True, True): (4, 64, 1, 16),
+        (192, 192, 4096, 64, 64, True, False, True): (1, 32, 4, 4),
+        (192, 192, 8192, 64, 64, False, True, True): (2, 128, 1, 8),
+        (192, 192, 8192, 64, 64, True, False, True): (3, 64, 1, 4),
+        (192, 192, 16384, 64, 64, False, True, True): (2, 256, 1, 8),
+        (192, 192, 16384, 64, 64, True, False, True): (1, 128, 3, 2),
+        (192, 192, 32768, 64, 64, False, True, True): (2, 512, 1, 8),
+        (192, 192, 32768, 64, 64, True, False, True): (3, 128, 1, 4),
+        (192, 192, 65536, 64, 64, False, True, True): (3, 1024, 1, 8),
+        (192, 192, 65536, 64, 64, True, False, True): (1, 512, 3, 4),
+        (192, 192, 131072, 64, 64, False, True, True): (1, 2048, 1, 8),
+        (192, 192, 131072, 64, 64, True, False, True): (1, 512, 1, 4),
+        (384, 384, 256, 128, 128, False, True, True): (4, 2, 1, 16),
+        (384, 384, 256, 128, 128, True, False, True): (1, 2, 3, 4),
+        (384, 384, 512, 128, 128, False, True, True): (2, 4, 1, 16),
+        (384, 384, 512, 128, 128, True, False, True): (2, 4, 3, 4),
+        (384, 384, 1024, 128, 128, False, True, True): (3, 8, 1, 32),
+        (384, 384, 1024, 128, 128, True, False, True): (3, 8, 3, 4),
+        (384, 384, 2048, 128, 128, False, True, True): (3, 16, 1, 32),
+        (384, 384, 2048, 128, 128, True, False, True): (2, 16, 3, 4),
+        (384, 384, 4096, 128, 128, False, True, True): (3, 32, 1, 32),
+        (384, 384, 4096, 128, 128, True, False, True): (3, 32, 3, 4),
+        (384, 384, 8192, 128, 128, False, True, True): (2, 64, 1, 32),
+        (384, 384, 8192, 128, 128, True, False, True): (4, 64, 1, 4),
+        (384, 384, 16384, 128, 128, False, True, True): (2, 128, 1, 32),
+        (384, 384, 16384, 128, 128, True, False, True): (2, 128, 1, 4),
+        (384, 384, 32768, 128, 128, False, True, True): (3, 256, 1, 16),
+        (384, 384, 32768, 128, 128, True, False, True): (1, 256, 1, 4),
+        (384, 384, 65536, 128, 128, False, True, True): (4, 512, 1, 16),
+        (384, 384, 65536, 128, 128, True, False, True): (1, 512, 1, 4),
+        (384, 384, 131072, 128, 128, False, True, True): (4, 1024, 1, 16),
+        (384, 384, 131072, 128, 128, True, False, True): (1, 1024, 1, 4),
+        (768, 768, 256, 256, 256, False, True, True): (1, 1, 1, 32),
+        (768, 768, 256, 256, 256, True, False, True): (3, 1, 1, 32),
+        (768, 768, 512, 256, 256, False, True, True): (1, 2, 1, 32),
+        (768, 768, 512, 256, 256, True, False, True): (1, 2, 1, 32),
+        (768, 768, 1024, 256, 256, False, True, True): (1, 4, 1, 32),
+        (768, 768, 1024, 256, 256, True, False, True): (2, 4, 1, 32),
+        (768, 768, 2048, 256, 256, False, True, True): (1, 8, 1, 32),
+        (768, 768, 2048, 256, 256, True, False, True): (2, 8, 1, 32),
+        (768, 768, 4096, 256, 256, False, True, True): (1, 16, 1, 32),
+        (768, 768, 4096, 256, 256, True, False, True): (1, 16, 1, 32),
+        (768, 768, 8192, 256, 256, False, True, True): (1, 32, 1, 32),
+        (768, 768, 8192, 256, 256, True, False, True): (2, 32, 1, 32),
+        (768, 768, 16384, 256, 256, False, True, True): (1, 64, 1, 32),
+        (768, 768, 16384, 256, 256, True, False, True): (7, 64, 1, 32),
+        (768, 768, 32768, 256, 256, False, True, True): (1, 128, 1, 32),
+        (768, 768, 32768, 256, 256, True, False, True): (1, 128, 1, 32),
+        (768, 768, 65536, 256, 256, False, True, True): (1, 256, 1, 32),
+        (768, 768, 65536, 256, 256, True, False, True): (1, 256, 1, 32),
+        (768, 768, 131072, 256, 256, False, True, True): (1, 512, 1, 32),
+        (768, 768, 131072, 256, 256, True, False, True): (1, 512, 1, 32),
+    },
+    ("_int_bsr_dense_addmm", "NVIDIA A100-SXM4-80GB", (0, torch.int8, 1.0)): {
+        (256, 256, 256, 256, 256, False, True, True): (2, 1, 1, 4),
+        (256, 256, 256, 256, 256, True, False, True): (2, 1, 2, 1),
+        (256, 256, 512, 256, 256, False, True, True): (2, 1, 1, 2),
+        (256, 256, 512, 256, 256, True, False, True): (2, 2, 2, 8),
+        (256, 256, 1024, 256, 256, False, True, True): (1, 4, 1, 4),
+        (256, 256, 1024, 256, 256, True, False, True): (1, 2, 2, 4),
+        (256, 256, 2048, 256, 256, False, True, True): (1, 4, 1, 2),
+        (256, 256, 2048, 256, 256, True, False, True): (1, 8, 1, 2),
+        (256, 256, 4096, 256, 256, False, True, True): (1, 16, 1, 4),
+        (256, 256, 4096, 256, 256, True, False, True): (1, 16, 1, 2),
+        (256, 256, 8192, 256, 256, False, True, True): (1, 16, 3, 4),
+        (256, 256, 8192, 256, 256, True, False, True): (1, 8, 1, 4),
+        (256, 256, 16384, 256, 256, False, True, True): (2, 16, 1, 8),
+        (256, 256, 16384, 256, 256, True, False, True): (1, 32, 1, 2),
+        (256, 256, 32768, 256, 256, False, True, True): (1, 128, 1, 8),
+        (256, 256, 32768, 256, 256, True, False, True): (1, 128, 1, 4),
+        (256, 256, 65536, 256, 256, False, True, True): (1, 4, 1, 1),
+        (256, 256, 65536, 256, 256, True, False, True): (1, 128, 1, 4),
+        (256, 256, 131072, 256, 256, False, True, True): (1, 512, 1, 4),
+        (256, 256, 131072, 256, 256, True, False, True): (1, 512, 1, 2),
+    },
+    ("bsr_dense_addmm", "NVIDIA A100-SXM4-80GB", (0, torch.bfloat16, 0.5)): {
+        (16, 16, 16, 16, 16, False, False, False): (2, 1, 1, 2),
+        (16, 16, 16, 16, 16, False, False, True): (1, 1, 1, 4),
+        (16, 16, 16, 16, 16, False, True, False): (1, 1, 3, 16),
+        (16, 16, 16, 16, 16, False, True, True): (1, 1, 1, 8),
+        (16, 16, 16, 16, 16, True, False, False): (2, 1, 1, 8),
+        (16, 16, 16, 16, 16, True, False, True): (1, 1, 1, 8),
+        (16, 16, 32, 16, 16, False, False, False): (1, 2, 1, 8),
+        (16, 16, 32, 16, 16, False, False, True): (1, 2, 2, 4),
+        (16, 16, 32, 16, 16, False, True, False): (1, 1, 2, 4),
+        (16, 16, 32, 16, 16, False, True, True): (1, 1, 2, 4),
+        (16, 16, 32, 16, 16, True, False, False): (1, 1, 2, 4),
+        (16, 16, 32, 16, 16, True, False, True): (2, 2, 1, 2),
+        (16, 16, 64, 16, 16, False, False, False): (1, 4, 2, 4),
+        (16, 16, 64, 16, 16, False, False, True): (1, 2, 1, 2),
+        (16, 16, 64, 16, 16, False, True, False): (2, 1, 1, 2),
+        (16, 16, 64, 16, 16, False, True, True): (1, 4, 1, 8),
+        (16, 16, 64, 16, 16, True, False, False): (1, 4, 1, 1),
+        (16, 16, 64, 16, 16, True, False, True): (1, 4, 2, 4),
+        (16, 32, 16, 16, 16, False, False, False): (1, 1, 2, 2),
+        (16, 32, 16, 16, 16, False, False, True): (1, 1, 1, 4),
+        (16, 32, 16, 16, 16, False, True, False): (1, 1, 1, 2),
+        (16, 32, 16, 16, 16, False, True, True): (1, 1, 1, 1),
+        (16, 32, 16, 16, 16, True, False, False): (1, 1, 1, 2),
+        (16, 32, 16, 16, 16, True, False, True): (2, 1, 1, 2),
+        (16, 32, 16, 16, 32, False, False, False): (1, 1, 1, 4),
+        (16, 32, 16, 16, 32, False, False, True): (1, 1, 1, 8),
+        (16, 32, 16, 16, 32, False, True, False): (1, 1, 1, 8),
+        (16, 32, 16, 16, 32, False, True, True): (1, 1, 2, 4),
+        (16, 32, 16, 16, 32, True, False, False): (1, 1, 1, 2),
+        (16, 32, 16, 16, 32, True, False, True): (1, 1, 1, 1),
+        (16, 32, 32, 16, 16, False, False, False): (2, 2, 1, 4),
+        (16, 32, 32, 16, 16, False, False, True): (2, 2, 1, 2),
+        (16, 32, 32, 16, 16, False, True, False): (1, 1, 2, 8),
+        (16, 32, 32, 16, 16, False, True, True): (1, 2, 1, 1),
+        (16, 32, 32, 16, 16, True, False, False): (1, 1, 1, 8),
+        (16, 32, 32, 16, 16, True, False, True): (1, 2, 1, 4),
+        (16, 32, 32, 16, 32, False, False, False): (1, 1, 2, 8),
+        (16, 32, 32, 16, 32, False, False, True): (2, 1, 1, 8),
+        (16, 32, 32, 16, 32, False, True, False): (1, 1, 1, 4),
+        (16, 32, 32, 16, 32, False, True, True): (1, 1, 1, 4),
+        (16, 32, 32, 16, 32, True, False, False): (1, 2, 1, 8),
+        (16, 32, 32, 16, 32, True, False, True): (1, 1, 1, 4),
+        (16, 32, 64, 16, 16, False, False, False): (1, 4, 3, 8),
+        (16, 32, 64, 16, 16, False, False, True): (1, 4, 1, 4),
+        (16, 32, 64, 16, 16, False, True, False): (1, 4, 1, 4),
+        (16, 32, 64, 16, 16, False, True, True): (2, 4, 1, 4),
+        (16, 32, 64, 16, 16, True, False, False): (1, 2, 1, 4),
+        (16, 32, 64, 16, 16, True, False, True): (1, 2, 1, 4),
+        (16, 32, 64, 16, 32, False, False, False): (1, 4, 1, 8),
+        (16, 32, 64, 16, 32, False, False, True): (1, 4, 1, 4),
+        (16, 32, 64, 16, 32, False, True, False): (1, 4, 1, 2),
+        (16, 32, 64, 16, 32, False, True, True): (1, 2, 1, 4),
+        (16, 32, 64, 16, 32, True, False, False): (1, 2, 1, 4),
+        (16, 32, 64, 16, 32, True, False, True): (1, 2, 1, 2),
+        (16, 64, 16, 16, 32, False, False, False): (1, 1, 1, 2),
+        (16, 64, 16, 16, 32, False, False, True): (1, 1, 2, 2),
+        (16, 64, 16, 16, 32, False, True, False): (1, 1, 2, 8),
+        (16, 64, 16, 16, 32, False, True, True): (1, 1, 1, 4),
+        (16, 64, 16, 16, 32, True, False, False): (1, 1, 1, 8),
+        (16, 64, 16, 16, 32, True, False, True): (1, 1, 1, 4),
+        (16, 64, 32, 16, 32, False, False, False): (1, 2, 1, 2),
+        (16, 64, 32, 16, 32, False, False, True): (1, 2, 1, 4),
+        (16, 64, 32, 16, 32, False, True, False): (1, 2, 1, 4),
+        (16, 64, 32, 16, 32, False, True, True): (2, 2, 1, 4),
+        (16, 64, 32, 16, 32, True, False, False): (1, 2, 1, 4),
+        (16, 64, 32, 16, 32, True, False, True): (1, 2, 1, 8),
+        (16, 64, 64, 16, 32, False, False, False): (1, 2, 1, 4),
+        (16, 64, 64, 16, 32, False, False, True): (1, 4, 2, 2),
+        (16, 64, 64, 16, 32, False, True, False): (1, 1, 1, 4),
+        (16, 64, 64, 16, 32, False, True, True): (1, 4, 1, 2),
+        (16, 64, 64, 16, 32, True, False, False): (1, 2, 1, 4),
+        (16, 64, 64, 16, 32, True, False, True): (1, 4, 1, 4),
+        (32, 16, 16, 16, 16, False, False, False): (1, 1, 1, 8),
+        (32, 16, 16, 16, 16, False, False, True): (1, 1, 2, 4),
+        (32, 16, 16, 16, 16, False, True, False): (1, 1, 1, 4),
+        (32, 16, 16, 16, 16, False, True, True): (1, 1, 2, 4),
+        (32, 16, 16, 16, 16, True, False, False): (1, 1, 1, 2),
+        (32, 16, 16, 16, 16, True, False, True): (1, 1, 1, 4),
+        (32, 16, 32, 16, 16, False, False, False): (1, 1, 1, 4),
+        (32, 16, 32, 16, 16, False, False, True): (2, 2, 1, 4),
+        (32, 16, 32, 16, 16, False, True, False): (1, 2, 2, 2),
+        (32, 16, 32, 16, 16, False, True, True): (2, 2, 1, 4),
+        (32, 16, 32, 16, 16, True, False, False): (1, 2, 2, 8),
+        (32, 16, 32, 16, 16, True, False, True): (1, 2, 1, 2),
+        (32, 16, 64, 16, 16, False, False, False): (1, 4, 1, 4),
+        (32, 16, 64, 16, 16, False, False, True): (1, 4, 2, 4),
+        (32, 16, 64, 16, 16, False, True, False): (1, 2, 2, 2),
+        (32, 16, 64, 16, 16, False, True, True): (3, 4, 1, 4),
+        (32, 16, 64, 16, 16, True, False, False): (1, 2, 1, 2),
+        (32, 16, 64, 16, 16, True, False, True): (1, 2, 1, 4),
+        (32, 32, 16, 16, 16, False, False, False): (1, 1, 3, 4),
+        (32, 32, 16, 16, 16, False, False, True): (1, 1, 1, 4),
+        (32, 32, 16, 16, 16, False, True, False): (1, 1, 1, 2),
+        (32, 32, 16, 16, 16, False, True, True): (1, 1, 1, 4),
+        (32, 32, 16, 16, 16, True, False, False): (1, 1, 1, 4),
+        (32, 32, 16, 16, 16, True, False, True): (1, 1, 2, 2),
+        (32, 32, 16, 16, 32, False, False, False): (2, 1, 1, 4),
+        (32, 32, 16, 16, 32, False, False, True): (1, 1, 1, 4),
+        (32, 32, 16, 16, 32, False, True, False): (1, 1, 1, 4),
+        (32, 32, 16, 16, 32, False, True, True): (3, 1, 2, 4),
+        (32, 32, 16, 16, 32, True, False, False): (1, 1, 1, 4),
+        (32, 32, 16, 16, 32, True, False, True): (1, 1, 1, 4),
+        (32, 32, 16, 32, 32, False, False, False): (1, 1, 1, 8),
+        (32, 32, 16, 32, 32, False, False, True): (1, 1, 1, 4),
+        (32, 32, 16, 32, 32, False, True, False): (1, 1, 2, 1),
+        (32, 32, 16, 32, 32, False, True, True): (2, 1, 2, 2),
+        (32, 32, 16, 32, 32, True, False, False): (1, 1, 1, 8),
+        (32, 32, 16, 32, 32, True, False, True): (2, 1, 3, 4),
+        (32, 32, 32, 16, 16, False, False, False): (1, 2, 1, 4),
+        (32, 32, 32, 16, 16, False, False, True): (2, 2, 1, 4),
+        (32, 32, 32, 16, 16, False, True, False): (1, 1, 1, 8),
+        (32, 32, 32, 16, 16, False, True, True): (2, 2, 1, 4),
+        (32, 32, 32, 16, 16, True, False, False): (1, 1, 1, 4),
+        (32, 32, 32, 16, 16, True, False, True): (2, 2, 2, 4),
+        (32, 32, 32, 16, 32, False, False, False): (2, 2, 1, 8),
+        (32, 32, 32, 16, 32, False, False, True): (1, 2, 1, 2),
+        (32, 32, 32, 16, 32, False, True, False): (1, 2, 1, 4),
+        (32, 32, 32, 16, 32, False, True, True): (1, 2, 1, 4),
+        (32, 32, 32, 16, 32, True, False, False): (1, 2, 1, 4),
+        (32, 32, 32, 16, 32, True, False, True): (1, 2, 1, 2),
+        (32, 32, 32, 32, 32, False, False, False): (1, 1, 3, 8),
+        (32, 32, 32, 32, 32, False, False, True): (1, 1, 1, 8),
+        (32, 32, 32, 32, 32, False, True, False): (2, 1, 3, 4),
+        (32, 32, 32, 32, 32, False, True, True): (2, 1, 1, 2),
+        (32, 32, 32, 32, 32, True, False, False): (1, 1, 1, 2),
+        (32, 32, 32, 32, 32, True, False, True): (4, 1, 1, 1),
+        (32, 32, 64, 16, 16, False, False, False): (1, 4, 1, 4),
+        (32, 32, 64, 16, 16, False, False, True): (1, 4, 1, 4),
+        (32, 32, 64, 16, 16, False, True, False): (1, 2, 1, 8),
+        (32, 32, 64, 16, 16, False, True, True): (1, 4, 1, 2),
+        (32, 32, 64, 16, 16, True, False, False): (2, 4, 1, 2),
+        (32, 32, 64, 16, 16, True, False, True): (1, 4, 1, 2),
+        (32, 32, 64, 16, 32, False, False, False): (1, 2, 1, 8),
+        (32, 32, 64, 16, 32, False, False, True): (1, 4, 2, 2),
+        (32, 32, 64, 16, 32, False, True, False): (1, 2, 1, 4),
+        (32, 32, 64, 16, 32, False, True, True): (1, 4, 1, 4),
+        (32, 32, 64, 16, 32, True, False, False): (1, 4, 2, 2),
+        (32, 32, 64, 16, 32, True, False, True): (3, 4, 2, 2),
+        (32, 32, 64, 32, 32, False, False, False): (2, 2, 1, 4),
+        (32, 32, 64, 32, 32, False, False, True): (1, 2, 1, 4),
+        (32, 32, 64, 32, 32, False, True, False): (1, 1, 1, 8),
+        (32, 32, 64, 32, 32, False, True, True): (1, 1, 1, 4),
+        (32, 32, 64, 32, 32, True, False, False): (1, 2, 1, 2),
+        (32, 32, 64, 32, 32, True, False, True): (3, 2, 1, 8),
+        (32, 64, 16, 16, 32, False, False, False): (1, 1, 2, 2),
+        (32, 64, 16, 16, 32, False, False, True): (1, 1, 1, 4),
+        (32, 64, 16, 16, 32, False, True, False): (1, 1, 2, 4),
+        (32, 64, 16, 16, 32, False, True, True): (1, 1, 1, 4),
+        (32, 64, 16, 16, 32, True, False, False): (1, 1, 1, 2),
+        (32, 64, 16, 16, 32, True, False, True): (2, 1, 2, 2),
+        (32, 64, 16, 32, 32, False, False, False): (1, 1, 1, 1),
+        (32, 64, 16, 32, 32, False, False, True): (2, 1, 1, 4),
+        (32, 64, 16, 32, 32, False, True, False): (1, 1, 1, 1),
+        (32, 64, 16, 32, 32, False, True, True): (1, 1, 2, 2),
+        (32, 64, 16, 32, 32, True, False, False): (1, 1, 2, 4),
+        (32, 64, 16, 32, 32, True, False, True): (1, 1, 1, 4),
+        (32, 64, 32, 16, 32, False, False, False): (2, 2, 1, 4),
+        (32, 64, 32, 16, 32, False, False, True): (1, 2, 1, 4),
+        (32, 64, 32, 16, 32, False, True, False): (1, 1, 1, 4),
+        (32, 64, 32, 16, 32, False, True, True): (2, 2, 3, 4),
+        (32, 64, 32, 16, 32, True, False, False): (1, 1, 1, 2),
+        (32, 64, 32, 16, 32, True, False, True): (1, 2, 1, 2),
+        (32, 64, 32, 32, 32, False, False, False): (1, 1, 1, 2),
+        (32, 64, 32, 32, 32, False, False, True): (2, 1, 1, 4),
+        (32, 64, 32, 32, 32, False, True, False): (1, 1, 1, 8),
+        (32, 64, 32, 32, 32, False, True, True): (1, 1, 2, 4),
+        (32, 64, 32, 32, 32, True, False, False): (2, 1, 1, 4),
+        (32, 64, 32, 32, 32, True, False, True): (1, 1, 2, 4),
+        (32, 64, 64, 16, 32, False, False, False): (1, 4, 1, 4),
+        (32, 64, 64, 16, 32, False, False, True): (1, 4, 2, 4),
+        (32, 64, 64, 16, 32, False, True, False): (1, 4, 2, 2),
+        (32, 64, 64, 16, 32, False, True, True): (1, 4, 1, 4),
+        (32, 64, 64, 16, 32, True, False, False): (1, 4, 1, 8),
+        (32, 64, 64, 16, 32, True, False, True): (1, 4, 2, 1),
+        (32, 64, 64, 32, 32, False, False, False): (1, 1, 1, 4),
+        (32, 64, 64, 32, 32, False, False, True): (2, 2, 1, 4),
+        (32, 64, 64, 32, 32, False, True, False): (1, 1, 1, 4),
+        (32, 64, 64, 32, 32, False, True, True): (2, 2, 1, 4),
+        (32, 64, 64, 32, 32, True, False, False): (1, 2, 2, 4),
+        (32, 64, 64, 32, 32, True, False, True): (2, 2, 3, 4),
+        (64, 32, 16, 32, 32, False, False, False): (1, 1, 1, 4),
+        (64, 32, 16, 32, 32, False, False, True): (1, 1, 1, 4),
+        (64, 32, 16, 32, 32, False, True, False): (1, 1, 1, 8),
+        (64, 32, 16, 32, 32, False, True, True): (1, 1, 1, 4),
+        (64, 32, 16, 32, 32, True, False, False): (1, 1, 1, 16),
+        (64, 32, 16, 32, 32, True, False, True): (2, 1, 1, 4),
+        (64, 32, 32, 32, 32, False, False, False): (1, 1, 3, 4),
+        (64, 32, 32, 32, 32, False, False, True): (2, 1, 1, 4),
+        (64, 32, 32, 32, 32, False, True, False): (1, 1, 2, 4),
+        (64, 32, 32, 32, 32, False, True, True): (2, 1, 1, 4),
+        (64, 32, 32, 32, 32, True, False, False): (2, 1, 1, 16),
+        (64, 32, 32, 32, 32, True, False, True): (2, 1, 1, 4),
+        (64, 32, 64, 32, 32, False, False, False): (1, 2, 1, 4),
+        (64, 32, 64, 32, 32, False, False, True): (2, 2, 1, 4),
+        (64, 32, 64, 32, 32, False, True, False): (1, 1, 1, 4),
+        (64, 32, 64, 32, 32, False, True, True): (2, 2, 1, 4),
+        (64, 32, 64, 32, 32, True, False, False): (1, 2, 1, 8),
+        (64, 32, 64, 32, 32, True, False, True): (2, 2, 3, 4),
+        (64, 64, 16, 32, 32, False, False, False): (1, 1, 2, 16),
+        (64, 64, 16, 32, 32, False, False, True): (1, 1, 3, 4),
+        (64, 64, 16, 32, 32, False, True, False): (1, 1, 1, 2),
+        (64, 64, 16, 32, 32, False, True, True): (2, 1, 1, 4),
+        (64, 64, 16, 32, 32, True, False, False): (2, 1, 3, 2),
+        (64, 64, 16, 32, 32, True, False, True): (1, 1, 2, 4),
+        (64, 64, 32, 32, 32, False, False, False): (1, 1, 1, 8),
+        (64, 64, 32, 32, 32, False, False, True): (2, 1, 2, 4),
+        (64, 64, 32, 32, 32, False, True, False): (2, 1, 1, 4),
+        (64, 64, 32, 32, 32, False, True, True): (1, 1, 2, 4),
+        (64, 64, 32, 32, 32, True, False, False): (2, 1, 1, 4),
+        (64, 64, 32, 32, 32, True, False, True): (1, 1, 2, 4),
+        (64, 64, 64, 32, 32, False, False, False): (1, 2, 2, 4),
+        (64, 64, 64, 32, 32, False, False, True): (1, 2, 2, 2),
+        (64, 64, 64, 32, 32, False, True, False): (1, 2, 1, 2),
+        (64, 64, 64, 32, 32, False, True, True): (1, 2, 1, 4),
+        (64, 64, 64, 32, 32, True, False, False): (1, 2, 1, 4),
+        (64, 64, 64, 32, 32, True, False, True): (1, 2, 1, 4),
+        (192, 192, 256, 16, 16, False, True, True): (1, 8, 5, 4),
+        (192, 192, 256, 16, 16, True, False, True): (2, 8, 5, 2),
+        (192, 192, 256, 32, 32, False, True, True): (1, 8, 6, 4),
+        (192, 192, 256, 32, 32, True, False, True): (3, 8, 5, 2),
+        (192, 192, 512, 16, 16, False, True, True): (1, 16, 5, 2),
+        (192, 192, 512, 16, 16, True, False, True): (1, 8, 4, 2),
+        (192, 192, 512, 32, 32, False, True, True): (2, 16, 5, 4),
+        (192, 192, 512, 32, 32, True, False, True): (2, 8, 5, 2),
+        (192, 192, 1024, 16, 16, False, True, True): (1, 16, 3, 4),
+        (192, 192, 1024, 16, 16, True, False, True): (1, 16, 6, 2),
+        (192, 192, 1024, 32, 32, False, True, True): (1, 32, 3, 4),
+        (192, 192, 1024, 32, 32, True, False, True): (1, 16, 4, 2),
+        (192, 192, 2048, 16, 16, False, True, True): (1, 32, 1, 4),
+        (192, 192, 2048, 16, 16, True, False, True): (4, 32, 4, 2),
+        (192, 192, 2048, 32, 32, False, True, True): (1, 16, 3, 8),
+        (192, 192, 2048, 32, 32, True, False, True): (2, 32, 4, 2),
+        (192, 192, 4096, 16, 16, False, True, True): (2, 64, 1, 4),
+        (192, 192, 4096, 16, 16, True, False, True): (1, 32, 3, 2),
+        (192, 192, 4096, 32, 32, False, True, True): (1, 64, 1, 8),
+        (192, 192, 4096, 32, 32, True, False, True): (2, 32, 4, 4),
+        (192, 192, 8192, 16, 16, False, True, True): (1, 64, 1, 4),
+        (192, 192, 8192, 16, 16, True, False, True): (2, 32, 3, 1),
+        (192, 192, 8192, 32, 32, False, True, True): (3, 128, 1, 4),
+        (192, 192, 8192, 32, 32, True, False, True): (1, 64, 3, 4),
+        (192, 192, 16384, 16, 16, False, True, True): (1, 128, 1, 4),
+        (192, 192, 16384, 16, 16, True, False, True): (4, 64, 3, 1),
+        (192, 192, 16384, 32, 32, False, True, True): (1, 128, 1, 4),
+        (192, 192, 16384, 32, 32, True, False, True): (1, 64, 3, 4),
+        (192, 192, 32768, 16, 16, False, True, True): (2, 256, 1, 2),
+        (192, 192, 32768, 16, 16, True, False, True): (1, 128, 3, 4),
+        (192, 192, 32768, 32, 32, False, True, True): (2, 256, 1, 4),
+        (192, 192, 32768, 32, 32, True, False, True): (4, 128, 3, 4),
+        (192, 192, 65536, 16, 16, False, True, True): (2, 512, 1, 2),
+        (192, 192, 65536, 16, 16, True, False, True): (2, 256, 3, 2),
+        (192, 192, 65536, 32, 32, False, True, True): (2, 512, 1, 4),
+        (192, 192, 65536, 32, 32, True, False, True): (1, 256, 3, 4),
+        (192, 192, 131072, 16, 16, False, True, True): (4, 1024, 1, 2),
+        (192, 192, 131072, 16, 16, True, False, True): (3, 512, 3, 2),
+        (192, 192, 131072, 32, 32, False, True, True): (1, 1024, 1, 2),
+        (192, 192, 131072, 32, 32, True, False, True): (1, 512, 3, 4),
+        (256, 256, 256, 16, 16, False, True, True): (4, 8, 5, 1),
+        (256, 256, 256, 16, 16, True, False, True): (2, 8, 4, 2),
+        (256, 256, 256, 32, 32, False, True, True): (2, 8, 5, 2),
+        (256, 256, 256, 32, 32, True, False, True): (1, 8, 5, 4),
+        (256, 256, 256, 64, 64, False, True, True): (2, 4, 4, 4),
+        (256, 256, 256, 64, 64, True, False, True): (1, 4, 3, 4),
+        (256, 256, 256, 128, 128, False, True, True): (4, 2, 2, 8),
+        (256, 256, 256, 128, 128, True, False, True): (1, 2, 2, 8),
+        (256, 256, 512, 16, 16, False, True, True): (1, 16, 5, 1),
+        (256, 256, 512, 16, 16, True, False, True): (3, 16, 3, 2),
+        (256, 256, 512, 32, 32, False, True, True): (2, 8, 5, 2),
+        (256, 256, 512, 32, 32, True, False, True): (1, 16, 4, 4),
+        (256, 256, 512, 64, 64, False, True, True): (1, 8, 4, 4),
+        (256, 256, 512, 64, 64, True, False, True): (3, 8, 3, 4),
+        (256, 256, 512, 128, 128, False, True, True): (1, 4, 2, 8),
+        (256, 256, 512, 128, 128, True, False, True): (1, 4, 2, 8),
+        (256, 256, 1024, 16, 16, False, True, True): (1, 16, 5, 4),
+        (256, 256, 1024, 16, 16, True, False, True): (5, 16, 4, 2),
+        (256, 256, 1024, 32, 32, False, True, True): (1, 32, 5, 2),
+        (256, 256, 1024, 32, 32, True, False, True): (2, 16, 5, 2),
+        (256, 256, 1024, 64, 64, False, True, True): (1, 16, 4, 4),
+        (256, 256, 1024, 64, 64, True, False, True): (1, 16, 4, 4),
+        (256, 256, 1024, 128, 128, False, True, True): (1, 8, 2, 8),
+        (256, 256, 1024, 128, 128, True, False, True): (1, 8, 2, 8),
+        (256, 256, 2048, 16, 16, False, True, True): (1, 16, 4, 4),
+        (256, 256, 2048, 16, 16, True, False, True): (2, 32, 5, 1),
+        (256, 256, 2048, 32, 32, False, True, True): (1, 64, 4, 1),
+        (256, 256, 2048, 32, 32, True, False, True): (2, 32, 4, 2),
+        (256, 256, 2048, 64, 64, False, True, True): (8, 16, 5, 4),
+        (256, 256, 2048, 64, 64, True, False, True): (1, 16, 4, 4),
+        (256, 256, 2048, 128, 128, False, True, True): (2, 16, 2, 8),
+        (256, 256, 2048, 128, 128, True, False, True): (1, 16, 2, 8),
+        (256, 256, 4096, 16, 16, False, True, True): (1, 64, 1, 4),
+        (256, 256, 4096, 16, 16, True, False, True): (1, 16, 3, 2),
+        (256, 256, 4096, 32, 32, False, True, True): (6, 32, 3, 2),
+        (256, 256, 4096, 32, 32, True, False, True): (4, 32, 4, 2),
+        (256, 256, 4096, 64, 64, False, True, True): (6, 64, 3, 4),
+        (256, 256, 4096, 64, 64, True, False, True): (2, 64, 3, 4),
+        (256, 256, 4096, 128, 128, False, True, True): (1, 32, 2, 8),
+        (256, 256, 4096, 128, 128, True, False, True): (1, 32, 2, 8),
+        (256, 256, 8192, 16, 16, False, True, True): (2, 32, 3, 4),
+        (256, 256, 8192, 16, 16, True, False, True): (4, 64, 3, 2),
+        (256, 256, 8192, 32, 32, False, True, True): (1, 64, 3, 4),
+        (256, 256, 8192, 32, 32, True, False, True): (3, 128, 1, 2),
+        (256, 256, 8192, 64, 64, False, True, True): (9, 128, 1, 4),
+        (256, 256, 8192, 64, 64, True, False, True): (8, 128, 1, 4),
+        (256, 256, 8192, 128, 128, False, True, True): (7, 64, 1, 4),
+        (256, 256, 8192, 128, 128, True, False, True): (1, 32, 1, 16),
+        (256, 256, 16384, 16, 16, False, True, True): (3, 128, 3, 2),
+        (256, 256, 16384, 16, 16, True, False, True): (5, 64, 3, 2),
+        (256, 256, 16384, 32, 32, False, True, True): (3, 128, 3, 2),
+        (256, 256, 16384, 32, 32, True, False, True): (1, 128, 3, 2),
+        (256, 256, 16384, 64, 64, False, True, True): (3, 128, 1, 4),
+        (256, 256, 16384, 64, 64, True, False, True): (2, 128, 1, 4),
+        (256, 256, 16384, 128, 128, False, True, True): (7, 128, 1, 4),
+        (256, 256, 16384, 128, 128, True, False, True): (1, 128, 2, 8),
+        (256, 256, 32768, 16, 16, False, True, True): (2, 128, 3, 2),
+        (256, 256, 32768, 16, 16, True, False, True): (1, 128, 3, 2),
+        (256, 256, 32768, 32, 32, False, True, True): (1, 256, 3, 4),
+        (256, 256, 32768, 32, 32, True, False, True): (3, 256, 3, 2),
+        (256, 256, 32768, 64, 64, False, True, True): (1, 256, 1, 4),
+        (256, 256, 32768, 64, 64, True, False, True): (3, 256, 1, 4),
+        (256, 256, 32768, 128, 128, False, True, True): (9, 256, 1, 4),
+        (256, 256, 32768, 128, 128, True, False, True): (2, 256, 1, 4),
+        (256, 256, 65536, 16, 16, False, True, True): (1, 256, 3, 2),
+        (256, 256, 65536, 16, 16, True, False, True): (1, 256, 3, 2),
+        (256, 256, 65536, 32, 32, False, True, True): (2, 512, 3, 2),
+        (256, 256, 65536, 32, 32, True, False, True): (2, 512, 3, 2),
+        (256, 256, 65536, 64, 64, False, True, True): (2, 512, 1, 4),
+        (256, 256, 65536, 64, 64, True, False, True): (1, 512, 1, 4),
+        (256, 256, 65536, 128, 128, False, True, True): (7, 512, 1, 4),
+        (256, 256, 65536, 128, 128, True, False, True): (2, 512, 1, 4),
+        (256, 256, 131072, 16, 16, False, True, True): (1, 512, 3, 2),
+        (256, 256, 131072, 16, 16, True, False, True): (1, 512, 3, 2),
+        (256, 256, 131072, 32, 32, False, True, True): (1, 1024, 3, 2),
+        (256, 256, 131072, 32, 32, True, False, True): (1, 1024, 3, 2),
+        (256, 256, 131072, 64, 64, False, True, True): (1, 1024, 1, 4),
+        (256, 256, 131072, 64, 64, True, False, True): (1, 1024, 1, 4),
+        (256, 256, 131072, 128, 128, False, True, True): (3, 1024, 1, 4),
+        (256, 256, 131072, 128, 128, True, False, True): (1, 1024, 1, 4),
+        (384, 384, 256, 16, 16, False, True, True): (1, 8, 5, 2),
+        (384, 384, 256, 16, 16, True, False, True): (3, 4, 5, 2),
+        (384, 384, 256, 32, 32, False, True, True): (2, 8, 4, 4),
+        (384, 384, 256, 32, 32, True, False, True): (1, 4, 6, 2),
+        (384, 384, 256, 64, 64, False, True, True): (2, 4, 4, 4),
+        (384, 384, 256, 64, 64, True, False, True): (2, 4, 4, 4),
+        (384, 384, 512, 16, 16, False, True, True): (1, 8, 4, 2),
+        (384, 384, 512, 16, 16, True, False, True): (1, 4, 5, 4),
+        (384, 384, 512, 32, 32, False, True, True): (1, 8, 4, 4),
+        (384, 384, 512, 32, 32, True, False, True): (3, 8, 5, 2),
+        (384, 384, 512, 64, 64, False, True, True): (3, 8, 3, 4),
+        (384, 384, 512, 64, 64, True, False, True): (5, 8, 5, 4),
+        (384, 384, 1024, 16, 16, False, True, True): (3, 16, 4, 2),
+        (384, 384, 1024, 16, 16, True, False, True): (1, 8, 4, 4),
+        (384, 384, 1024, 32, 32, False, True, True): (6, 32, 3, 2),
+        (384, 384, 1024, 32, 32, True, False, True): (3, 8, 4, 4),
+        (384, 384, 1024, 64, 64, False, True, True): (3, 16, 3, 4),
+        (384, 384, 1024, 64, 64, True, False, True): (2, 16, 4, 4),
+        (384, 384, 2048, 16, 16, False, True, True): (1, 32, 1, 4),
+        (384, 384, 2048, 16, 16, True, False, True): (1, 16, 5, 2),
+        (384, 384, 2048, 32, 32, False, True, True): (1, 32, 1, 8),
+        (384, 384, 2048, 32, 32, True, False, True): (1, 8, 4, 4),
+        (384, 384, 2048, 64, 64, False, True, True): (4, 16, 3, 4),
+        (384, 384, 2048, 64, 64, True, False, True): (1, 16, 3, 8),
+        (384, 384, 4096, 16, 16, False, True, True): (5, 32, 1, 4),
+        (384, 384, 4096, 16, 16, True, False, True): (6, 32, 3, 2),
+        (384, 384, 4096, 32, 32, False, True, True): (1, 32, 1, 8),
+        (384, 384, 4096, 32, 32, True, False, True): (1, 16, 3, 4),
+        (384, 384, 4096, 64, 64, False, True, True): (1, 64, 1, 4),
+        (384, 384, 4096, 64, 64, True, False, True): (2, 32, 3, 4),
+        (384, 384, 8192, 16, 16, False, True, True): (2, 64, 1, 4),
+        (384, 384, 8192, 16, 16, True, False, True): (3, 32, 3, 2),
+        (384, 384, 8192, 32, 32, False, True, True): (5, 64, 1, 8),
+        (384, 384, 8192, 32, 32, True, False, True): (1, 32, 3, 2),
+        (384, 384, 8192, 64, 64, False, True, True): (1, 128, 1, 4),
+        (384, 384, 8192, 64, 64, True, False, True): (3, 64, 3, 4),
+        (384, 384, 16384, 16, 16, False, True, True): (1, 128, 1, 2),
+        (384, 384, 16384, 16, 16, True, False, True): (4, 128, 3, 2),
+        (384, 384, 16384, 32, 32, False, True, True): (3, 128, 1, 4),
+        (384, 384, 16384, 32, 32, True, False, True): (1, 128, 3, 2),
+        (384, 384, 16384, 64, 64, False, True, True): (3, 256, 1, 4),
+        (384, 384, 16384, 64, 64, True, False, True): (2, 128, 3, 4),
+        (384, 384, 32768, 16, 16, False, True, True): (1, 256, 1, 2),
+        (384, 384, 32768, 16, 16, True, False, True): (1, 128, 3, 4),
+        (384, 384, 32768, 32, 32, False, True, True): (1, 256, 1, 2),
+        (384, 384, 32768, 32, 32, True, False, True): (1, 128, 3, 4),
+        (384, 384, 32768, 64, 64, False, True, True): (2, 256, 1, 4),
+        (384, 384, 32768, 64, 64, True, False, True): (1, 256, 3, 4),
+        (384, 384, 65536, 16, 16, False, True, True): (4, 512, 1, 2),
+        (384, 384, 65536, 16, 16, True, False, True): (1, 256, 3, 4),
+        (384, 384, 65536, 32, 32, False, True, True): (1, 512, 1, 2),
+        (384, 384, 65536, 32, 32, True, False, True): (1, 256, 3, 4),
+        (384, 384, 65536, 64, 64, False, True, True): (3, 512, 1, 4),
+        (384, 384, 65536, 64, 64, True, False, True): (3, 256, 3, 4),
+        (384, 384, 131072, 16, 16, False, True, True): (1, 512, 1, 1),
+        (384, 384, 131072, 16, 16, True, False, True): (1, 512, 3, 4),
+        (384, 384, 131072, 32, 32, False, True, True): (1, 512, 1, 4),
+        (384, 384, 131072, 32, 32, True, False, True): (1, 512, 3, 4),
+        (384, 384, 131072, 64, 64, False, True, True): (3, 1024, 1, 4),
+        (384, 384, 131072, 64, 64, True, False, True): (3, 512, 3, 4),
+        (512, 512, 256, 16, 16, False, True, True): (2, 4, 5, 4),
+        (512, 512, 256, 16, 16, True, False, True): (3, 4, 5, 4),
+        (512, 512, 256, 32, 32, False, True, True): (1, 4, 5, 2),
+        (512, 512, 256, 32, 32, True, False, True): (4, 8, 5, 1),
+        (512, 512, 256, 64, 64, False, True, True): (4, 4, 5, 4),
+        (512, 512, 256, 64, 64, True, False, True): (5, 4, 5, 4),
+        (512, 512, 256, 128, 128, False, True, True): (3, 2, 2, 8),
+        (512, 512, 256, 128, 128, True, False, True): (2, 2, 2, 8),
+        (512, 512, 512, 16, 16, False, True, True): (1, 8, 5, 4),
+        (512, 512, 512, 16, 16, True, False, True): (4, 8, 5, 2),
+        (512, 512, 512, 32, 32, False, True, True): (1, 16, 4, 1),
+        (512, 512, 512, 32, 32, True, False, True): (1, 8, 5, 2),
+        (512, 512, 512, 64, 64, False, True, True): (4, 8, 5, 4),
+        (512, 512, 512, 64, 64, True, False, True): (2, 8, 5, 4),
+        (512, 512, 512, 128, 128, False, True, True): (2, 4, 2, 8),
+        (512, 512, 512, 128, 128, True, False, True): (1, 4, 2, 8),
+        (512, 512, 1024, 16, 16, False, True, True): (2, 8, 4, 4),
+        (512, 512, 1024, 16, 16, True, False, True): (1, 8, 4, 4),
+        (512, 512, 1024, 32, 32, False, True, True): (3, 16, 4, 2),
+        (512, 512, 1024, 32, 32, True, False, True): (1, 16, 5, 2),
+        (512, 512, 1024, 64, 64, False, True, True): (2, 8, 3, 4),
+        (512, 512, 1024, 64, 64, True, False, True): (2, 16, 3, 4),
+        (512, 512, 1024, 128, 128, False, True, True): (2, 8, 2, 8),
+        (512, 512, 1024, 128, 128, True, False, True): (3, 8, 2, 8),
+        (512, 512, 2048, 16, 16, False, True, True): (4, 16, 3, 2),
+        (512, 512, 2048, 16, 16, True, False, True): (1, 16, 4, 2),
+        (512, 512, 2048, 32, 32, False, True, True): (3, 32, 3, 2),
+        (512, 512, 2048, 32, 32, True, False, True): (2, 32, 3, 2),
+        (512, 512, 2048, 64, 64, False, True, True): (6, 32, 3, 2),
+        (512, 512, 2048, 64, 64, True, False, True): (1, 32, 3, 2),
+        (512, 512, 2048, 128, 128, False, True, True): (4, 16, 2, 8),
+        (512, 512, 2048, 128, 128, True, False, True): (1, 16, 2, 8),
+        (512, 512, 4096, 16, 16, False, True, True): (1, 16, 3, 2),
+        (512, 512, 4096, 16, 16, True, False, True): (4, 32, 3, 2),
+        (512, 512, 4096, 32, 32, False, True, True): (3, 32, 3, 2),
+        (512, 512, 4096, 32, 32, True, False, True): (2, 32, 3, 2),
+        (512, 512, 4096, 64, 64, False, True, True): (1, 32, 3, 4),
+        (512, 512, 4096, 64, 64, True, False, True): (1, 64, 3, 4),
+        (512, 512, 4096, 128, 128, False, True, True): (4, 32, 1, 4),
+        (512, 512, 4096, 128, 128, True, False, True): (4, 32, 2, 8),
+        (512, 512, 8192, 16, 16, False, True, True): (8, 64, 3, 2),
+        (512, 512, 8192, 16, 16, True, False, True): (4, 64, 3, 2),
+        (512, 512, 8192, 32, 32, False, True, True): (3, 64, 3, 2),
+        (512, 512, 8192, 32, 32, True, False, True): (3, 64, 3, 2),
+        (512, 512, 8192, 64, 64, False, True, True): (1, 64, 3, 4),
+        (512, 512, 8192, 64, 64, True, False, True): (7, 64, 3, 4),
+        (512, 512, 8192, 128, 128, False, True, True): (1, 64, 1, 4),
+        (512, 512, 8192, 128, 128, True, False, True): (4, 64, 2, 8),
+        (512, 512, 16384, 16, 16, False, True, True): (1, 64, 3, 2),
+        (512, 512, 16384, 16, 16, True, False, True): (1, 128, 3, 2),
+        (512, 512, 16384, 32, 32, False, True, True): (3, 128, 3, 2),
+        (512, 512, 16384, 32, 32, True, False, True): (1, 128, 3, 2),
+        (512, 512, 16384, 64, 64, False, True, True): (4, 64, 2, 4),
+        (512, 512, 16384, 64, 64, True, False, True): (2, 64, 2, 4),
+        (512, 512, 16384, 128, 128, False, True, True): (4, 128, 1, 4),
+        (512, 512, 16384, 128, 128, True, False, True): (2, 128, 1, 4),
+        (512, 512, 32768, 16, 16, False, True, True): (1, 128, 3, 2),
+        (512, 512, 32768, 16, 16, True, False, True): (1, 128, 3, 2),
+        (512, 512, 32768, 32, 32, False, True, True): (1, 256, 3, 2),
+        (512, 512, 32768, 32, 32, True, False, True): (1, 256, 3, 2),
+        (512, 512, 32768, 64, 64, False, True, True): (1, 256, 3, 4),
+        (512, 512, 32768, 64, 64, True, False, True): (2, 256, 3, 4),
+        (512, 512, 32768, 128, 128, False, True, True): (5, 256, 1, 4),
+        (512, 512, 32768, 128, 128, True, False, True): (4, 256, 1, 4),
+        (512, 512, 65536, 16, 16, False, True, True): (1, 256, 3, 2),
+        (512, 512, 65536, 16, 16, True, False, True): (1, 256, 3, 1),
+        (512, 512, 65536, 32, 32, False, True, True): (1, 512, 3, 2),
+        (512, 512, 65536, 32, 32, True, False, True): (1, 512, 3, 2),
+        (512, 512, 65536, 64, 64, False, True, True): (4, 256, 2, 4),
+        (512, 512, 65536, 64, 64, True, False, True): (2, 512, 3, 4),
+        (512, 512, 65536, 128, 128, False, True, True): (6, 512, 1, 4),
+        (512, 512, 65536, 128, 128, True, False, True): (4, 512, 1, 4),
+        (512, 512, 131072, 16, 16, False, True, True): (1, 512, 3, 2),
+        (512, 512, 131072, 16, 16, True, False, True): (1, 512, 3, 1),
+        (512, 512, 131072, 32, 32, False, True, True): (1, 1024, 3, 2),
+        (512, 512, 131072, 32, 32, True, False, True): (1, 1024, 3, 2),
+        (512, 512, 131072, 64, 64, False, True, True): (4, 512, 2, 4),
+        (512, 512, 131072, 64, 64, True, False, True): (4, 1024, 3, 4),
+        (512, 512, 131072, 128, 128, False, True, True): (6, 1024, 1, 4),
+        (512, 512, 131072, 128, 128, True, False, True): (4, 1024, 1, 4),
+        (768, 768, 256, 16, 16, False, True, True): (1, 8, 4, 1),
+        (768, 768, 256, 16, 16, True, False, True): (3, 2, 6, 4),
+        (768, 768, 256, 32, 32, False, True, True): (3, 8, 3, 4),
+        (768, 768, 256, 32, 32, True, False, True): (1, 4, 4, 2),
+        (768, 768, 256, 64, 64, False, True, True): (2, 4, 3, 4),
+        (768, 768, 256, 64, 64, True, False, True): (1, 4, 4, 4),
+        (768, 768, 256, 128, 128, False, True, True): (2, 2, 3, 8),
+        (768, 768, 256, 128, 128, True, False, True): (4, 2, 3, 8),
+        (768, 768, 512, 16, 16, False, True, True): (4, 8, 4, 2),
+        (768, 768, 512, 16, 16, True, False, True): (4, 8, 6, 2),
+        (768, 768, 512, 32, 32, False, True, True): (1, 8, 4, 4),
+        (768, 768, 512, 32, 32, True, False, True): (3, 8, 4, 2),
+        (768, 768, 512, 64, 64, False, True, True): (1, 8, 3, 4),
+        (768, 768, 512, 64, 64, True, False, True): (1, 8, 4, 4),
+        (768, 768, 512, 128, 128, False, True, True): (1, 4, 3, 8),
+        (768, 768, 512, 128, 128, True, False, True): (4, 4, 3, 8),
+        (768, 768, 1024, 16, 16, False, True, True): (3, 16, 1, 4),
+        (768, 768, 1024, 16, 16, True, False, True): (1, 8, 5, 2),
+        (768, 768, 1024, 32, 32, False, True, True): (3, 16, 1, 8),
+        (768, 768, 1024, 32, 32, True, False, True): (1, 16, 3, 2),
+        (768, 768, 1024, 64, 64, False, True, True): (1, 8, 3, 4),
+        (768, 768, 1024, 64, 64, True, False, True): (2, 8, 3, 8),
+        (768, 768, 1024, 128, 128, False, True, True): (1, 8, 3, 8),
+        (768, 768, 1024, 128, 128, True, False, True): (1, 8, 3, 8),
+        (768, 768, 2048, 16, 16, False, True, True): (2, 16, 1, 2),
+        (768, 768, 2048, 16, 16, True, False, True): (1, 16, 3, 2),
+        (768, 768, 2048, 32, 32, False, True, True): (5, 32, 1, 4),
+        (768, 768, 2048, 32, 32, True, False, True): (3, 8, 3, 4),
+        (768, 768, 2048, 64, 64, False, True, True): (1, 16, 1, 8),
+        (768, 768, 2048, 64, 64, True, False, True): (3, 16, 3, 4),
+        (768, 768, 2048, 128, 128, False, True, True): (2, 16, 3, 8),
+        (768, 768, 2048, 128, 128, True, False, True): (1, 16, 3, 8),
+        (768, 768, 4096, 16, 16, False, True, True): (3, 32, 1, 4),
+        (768, 768, 4096, 16, 16, True, False, True): (2, 32, 3, 1),
+        (768, 768, 4096, 32, 32, False, True, True): (2, 64, 1, 4),
+        (768, 768, 4096, 32, 32, True, False, True): (1, 16, 4, 4),
+        (768, 768, 4096, 64, 64, False, True, True): (3, 64, 3, 4),
+        (768, 768, 4096, 64, 64, True, False, True): (2, 16, 3, 4),
+        (768, 768, 4096, 128, 128, False, True, True): (1, 32, 3, 8),
+        (768, 768, 4096, 128, 128, True, False, True): (4, 32, 3, 8),
+        (768, 768, 8192, 16, 16, False, True, True): (1, 64, 1, 2),
+        (768, 768, 8192, 16, 16, True, False, True): (4, 64, 3, 2),
+        (768, 768, 8192, 32, 32, False, True, True): (1, 64, 1, 8),
+        (768, 768, 8192, 32, 32, True, False, True): (2, 32, 3, 4),
+        (768, 768, 8192, 64, 64, False, True, True): (4, 64, 3, 4),
+        (768, 768, 8192, 64, 64, True, False, True): (2, 32, 3, 4),
+        (768, 768, 8192, 128, 128, False, True, True): (2, 64, 3, 8),
+        (768, 768, 8192, 128, 128, True, False, True): (1, 64, 3, 8),
+        (768, 768, 16384, 16, 16, False, True, True): (1, 128, 1, 2),
+        (768, 768, 16384, 16, 16, True, False, True): (1, 64, 4, 4),
+        (768, 768, 16384, 32, 32, False, True, True): (1, 128, 1, 8),
+        (768, 768, 16384, 32, 32, True, False, True): (1, 64, 3, 4),
+        (768, 768, 16384, 64, 64, False, True, True): (4, 128, 3, 4),
+        (768, 768, 16384, 64, 64, True, False, True): (1, 64, 3, 4),
+        (768, 768, 16384, 128, 128, False, True, True): (3, 128, 1, 4),
+        (768, 768, 16384, 128, 128, True, False, True): (3, 128, 2, 4),
+        (768, 768, 32768, 16, 16, False, True, True): (1, 256, 1, 2),
+        (768, 768, 32768, 16, 16, True, False, True): (1, 128, 4, 4),
+        (768, 768, 32768, 32, 32, False, True, True): (1, 128, 1, 2),
+        (768, 768, 32768, 32, 32, True, False, True): (1, 128, 3, 4),
+        (768, 768, 32768, 64, 64, False, True, True): (1, 256, 1, 4),
+        (768, 768, 32768, 64, 64, True, False, True): (2, 128, 3, 4),
+        (768, 768, 32768, 128, 128, False, True, True): (3, 256, 1, 4),
+        (768, 768, 32768, 128, 128, True, False, True): (2, 256, 2, 4),
+        (768, 768, 65536, 16, 16, False, True, True): (4, 512, 1, 2),
+        (768, 768, 65536, 16, 16, True, False, True): (1, 256, 4, 4),
+        (768, 768, 65536, 32, 32, False, True, True): (1, 256, 1, 2),
+        (768, 768, 65536, 32, 32, True, False, True): (1, 256, 3, 4),
+        (768, 768, 65536, 64, 64, False, True, True): (3, 512, 1, 4),
+        (768, 768, 65536, 64, 64, True, False, True): (2, 256, 3, 4),
+        (768, 768, 65536, 128, 128, False, True, True): (3, 512, 1, 4),
+        (768, 768, 65536, 128, 128, True, False, True): (2, 512, 2, 4),
+        (768, 768, 131072, 16, 16, False, True, True): (4, 1024, 1, 2),
+        (768, 768, 131072, 16, 16, True, False, True): (1, 512, 4, 1),
+        (768, 768, 131072, 32, 32, False, True, True): (1, 512, 1, 2),
+        (768, 768, 131072, 32, 32, True, False, True): (1, 512, 3, 4),
+        (768, 768, 131072, 64, 64, False, True, True): (1, 1024, 1, 4),
+        (768, 768, 131072, 64, 64, True, False, True): (2, 512, 3, 4),
+        (768, 768, 131072, 128, 128, False, True, True): (3, 1024, 1, 4),
+        (768, 768, 131072, 128, 128, True, False, True): (1, 1024, 2, 4),
+        (768, 3072, 256, 16, 16, False, True, True): (3, 8, 6, 1),
+        (768, 3072, 256, 16, 16, True, False, True): (1, 4, 6, 2),
+        (768, 3072, 256, 32, 32, False, True, True): (1, 8, 4, 4),
+        (768, 3072, 256, 32, 32, True, False, True): (3, 4, 6, 4),
+        (768, 3072, 256, 64, 64, False, True, True): (2, 4, 3, 4),
+        (768, 3072, 256, 64, 64, True, False, True): (1, 4, 4, 4),
+        (768, 3072, 256, 128, 128, False, True, True): (2, 2, 3, 8),
+        (768, 3072, 256, 128, 128, True, False, True): (1, 2, 3, 8),
+        (768, 3072, 512, 16, 16, False, True, True): (1, 8, 4, 2),
+        (768, 3072, 512, 16, 16, True, False, True): (1, 8, 5, 2),
+        (768, 3072, 512, 32, 32, False, True, True): (1, 16, 3, 2),
+        (768, 3072, 512, 32, 32, True, False, True): (1, 8, 5, 2),
+        (768, 3072, 512, 64, 64, False, True, True): (1, 8, 3, 4),
+        (768, 3072, 512, 64, 64, True, False, True): (3, 8, 4, 4),
+        (768, 3072, 512, 128, 128, False, True, True): (1, 4, 3, 8),
+        (768, 3072, 512, 128, 128, True, False, True): (2, 4, 3, 8),
+        (768, 3072, 1024, 16, 16, False, True, True): (1, 16, 1, 4),
+        (768, 3072, 1024, 16, 16, True, False, True): (5, 4, 4, 4),
+        (768, 3072, 1024, 32, 32, False, True, True): (3, 8, 3, 4),
+        (768, 3072, 1024, 32, 32, True, False, True): (1, 8, 4, 4),
+        (768, 3072, 1024, 64, 64, False, True, True): (2, 16, 3, 4),
+        (768, 3072, 1024, 64, 64, True, False, True): (2, 16, 4, 4),
+        (768, 3072, 1024, 128, 128, False, True, True): (1, 8, 3, 8),
+        (768, 3072, 1024, 128, 128, True, False, True): (5, 8, 3, 8),
+        (768, 3072, 2048, 16, 16, False, True, True): (3, 16, 1, 2),
+        (768, 3072, 2048, 16, 16, True, False, True): (1, 8, 3, 4),
+        (768, 3072, 2048, 32, 32, False, True, True): (4, 16, 1, 8),
+        (768, 3072, 2048, 32, 32, True, False, True): (3, 8, 3, 4),
+        (768, 3072, 2048, 64, 64, False, True, True): (2, 16, 3, 4),
+        (768, 3072, 2048, 64, 64, True, False, True): (2, 16, 3, 4),
+        (768, 3072, 2048, 128, 128, False, True, True): (3, 16, 3, 8),
+        (768, 3072, 2048, 128, 128, True, False, True): (4, 16, 3, 8),
+        (768, 3072, 4096, 16, 16, False, True, True): (1, 32, 1, 4),
+        (768, 3072, 4096, 16, 16, True, False, True): (1, 16, 3, 1),
+        (768, 3072, 4096, 32, 32, False, True, True): (3, 32, 1, 8),
+        (768, 3072, 4096, 32, 32, True, False, True): (3, 16, 4, 4),
+        (768, 3072, 4096, 64, 64, False, True, True): (2, 32, 3, 4),
+        (768, 3072, 4096, 64, 64, True, False, True): (2, 16, 3, 4),
+        (768, 3072, 4096, 128, 128, False, True, True): (5, 32, 1, 4),
+        (768, 3072, 4096, 128, 128, True, False, True): (9, 32, 3, 8),
+        (768, 3072, 8192, 16, 16, False, True, True): (1, 32, 1, 4),
+        (768, 3072, 8192, 16, 16, True, False, True): (4, 64, 4, 2),
+        (768, 3072, 8192, 32, 32, False, True, True): (1, 64, 1, 8),
+        (768, 3072, 8192, 32, 32, True, False, True): (2, 64, 4, 2),
+        (768, 3072, 8192, 64, 64, False, True, True): (1, 64, 3, 4),
+        (768, 3072, 8192, 64, 64, True, False, True): (2, 32, 3, 4),
+        (768, 3072, 8192, 128, 128, False, True, True): (2, 64, 3, 8),
+        (768, 3072, 8192, 128, 128, True, False, True): (2, 64, 3, 8),
+        (768, 3072, 16384, 16, 16, False, True, True): (1, 64, 1, 4),
+        (768, 3072, 16384, 16, 16, True, False, True): (1, 64, 4, 1),
+        (768, 3072, 16384, 32, 32, False, True, True): (1, 128, 1, 8),
+        (768, 3072, 16384, 32, 32, True, False, True): (1, 64, 3, 4),
+        (768, 3072, 16384, 64, 64, False, True, True): (1, 128, 3, 4),
+        (768, 3072, 16384, 64, 64, True, False, True): (4, 64, 3, 4),
+        (768, 3072, 16384, 128, 128, False, True, True): (2, 128, 3, 8),
+        (768, 3072, 16384, 128, 128, True, False, True): (2, 128, 3, 8),
+        (768, 3072, 32768, 16, 16, False, True, True): (1, 128, 1, 4),
+        (768, 3072, 32768, 16, 16, True, False, True): (1, 128, 4, 1),
+        (768, 3072, 32768, 32, 32, False, True, True): (1, 256, 1, 8),
+        (768, 3072, 32768, 32, 32, True, False, True): (1, 128, 3, 4),
+        (768, 3072, 32768, 64, 64, False, True, True): (1, 256, 3, 4),
+        (768, 3072, 32768, 64, 64, True, False, True): (1, 128, 3, 4),
+        (768, 3072, 32768, 128, 128, False, True, True): (3, 256, 1, 4),
+        (768, 3072, 32768, 128, 128, True, False, True): (2, 256, 3, 8),
+        (768, 3072, 50432, 16, 16, False, True, True): (1, 197, 1, 4),
+        (768, 3072, 50432, 16, 16, True, False, True): (4, 197, 4, 4),
+        (768, 3072, 50432, 32, 32, False, True, True): (1, 197, 1, 4),
+        (768, 3072, 50432, 32, 32, True, False, True): (4, 197, 3, 4),
+        (768, 3072, 50432, 64, 64, False, True, True): (1, 394, 3, 4),
+        (768, 3072, 50432, 64, 64, True, False, True): (3, 197, 3, 4),
+        (768, 3072, 50432, 128, 128, False, True, True): (3, 394, 1, 4),
+        (768, 3072, 50432, 128, 128, True, False, True): (1, 394, 3, 8),
+        (768, 3072, 65536, 16, 16, False, True, True): (1, 256, 1, 4),
+        (768, 3072, 65536, 16, 16, True, False, True): (5, 256, 4, 1),
+        (768, 3072, 65536, 32, 32, False, True, True): (1, 256, 1, 4),
+        (768, 3072, 65536, 32, 32, True, False, True): (3, 256, 3, 4),
+        (768, 3072, 65536, 64, 64, False, True, True): (2, 512, 3, 4),
+        (768, 3072, 65536, 64, 64, True, False, True): (3, 256, 3, 4),
+        (768, 3072, 65536, 128, 128, False, True, True): (3, 512, 1, 4),
+        (768, 3072, 65536, 128, 128, True, False, True): (2, 512, 3, 8),
+        (768, 3072, 131072, 16, 16, False, True, True): (1, 512, 1, 4),
+        (768, 3072, 131072, 16, 16, True, False, True): (5, 512, 4, 1),
+        (768, 3072, 131072, 32, 32, False, True, True): (1, 512, 1, 4),
+        (768, 3072, 131072, 32, 32, True, False, True): (4, 512, 3, 4),
+        (768, 3072, 131072, 64, 64, False, True, True): (1, 1024, 3, 4),
+        (768, 3072, 131072, 64, 64, True, False, True): (1, 512, 3, 4),
+        (768, 3072, 131072, 128, 128, False, True, True): (3, 1024, 1, 4),
+        (768, 3072, 131072, 128, 128, True, False, True): (1, 1024, 3, 8),
+        (1024, 1024, 256, 16, 16, False, True, True): (1, 4, 5, 4),
+        (1024, 1024, 256, 16, 16, True, False, True): (3, 4, 4, 4),
+        (1024, 1024, 256, 32, 32, False, True, True): (4, 4, 5, 2),
+        (1024, 1024, 256, 32, 32, True, False, True): (3, 4, 5, 2),
+        (1024, 1024, 256, 64, 64, False, True, True): (1, 4, 5, 4),
+        (1024, 1024, 256, 64, 64, True, False, True): (1, 4, 5, 4),
+        (1024, 1024, 256, 128, 128, False, True, True): (1, 2, 2, 8),
+        (1024, 1024, 256, 128, 128, True, False, True): (2, 2, 2, 8),
+        (1024, 1024, 512, 16, 16, False, True, True): (3, 4, 4, 4),
+        (1024, 1024, 512, 16, 16, True, False, True): (4, 8, 5, 2),
+        (1024, 1024, 512, 32, 32, False, True, True): (1, 8, 4, 2),
+        (1024, 1024, 512, 32, 32, True, False, True): (1, 8, 4, 2),
+        (1024, 1024, 512, 64, 64, False, True, True): (4, 8, 4, 4),
+        (1024, 1024, 512, 64, 64, True, False, True): (2, 8, 3, 4),
+        (1024, 1024, 512, 128, 128, False, True, True): (2, 4, 2, 8),
+        (1024, 1024, 512, 128, 128, True, False, True): (1, 4, 2, 8),
+        (1024, 1024, 1024, 16, 16, False, True, True): (3, 8, 4, 4),
+        (1024, 1024, 1024, 16, 16, True, False, True): (4, 8, 4, 2),
+        (1024, 1024, 1024, 32, 32, False, True, True): (1, 16, 3, 2),
+        (1024, 1024, 1024, 32, 32, True, False, True): (1, 16, 3, 2),
+        (1024, 1024, 1024, 64, 64, False, True, True): (1, 16, 3, 4),
+        (1024, 1024, 1024, 64, 64, True, False, True): (3, 16, 3, 2),
+        (1024, 1024, 1024, 128, 128, False, True, True): (1, 8, 2, 8),
+        (1024, 1024, 1024, 128, 128, True, False, True): (2, 8, 2, 8),
+        (1024, 1024, 2048, 16, 16, False, True, True): (3, 8, 3, 4),
+        (1024, 1024, 2048, 16, 16, True, False, True): (3, 8, 3, 2),
+        (1024, 1024, 2048, 32, 32, False, True, True): (5, 16, 3, 4),
+        (1024, 1024, 2048, 32, 32, True, False, True): (1, 16, 3, 2),
+        (1024, 1024, 2048, 64, 64, False, True, True): (6, 16, 4, 4),
+        (1024, 1024, 2048, 64, 64, True, False, True): (5, 16, 3, 4),
+        (1024, 1024, 2048, 128, 128, False, True, True): (4, 16, 2, 8),
+        (1024, 1024, 2048, 128, 128, True, False, True): (4, 16, 2, 8),
+        (1024, 1024, 4096, 16, 16, False, True, True): (8, 32, 3, 2),
+        (1024, 1024, 4096, 16, 16, True, False, True): (4, 32, 3, 2),
+        (1024, 1024, 4096, 32, 32, False, True, True): (2, 32, 3, 4),
+        (1024, 1024, 4096, 32, 32, True, False, True): (3, 32, 3, 2),
+        (1024, 1024, 4096, 64, 64, False, True, True): (3, 32, 3, 4),
+        (1024, 1024, 4096, 64, 64, True, False, True): (1, 32, 3, 4),
+        (1024, 1024, 4096, 128, 128, False, True, True): (4, 32, 2, 8),
+        (1024, 1024, 4096, 128, 128, True, False, True): (1, 32, 2, 8),
+        (1024, 1024, 8192, 16, 16, False, True, True): (4, 64, 3, 2),
+        (1024, 1024, 8192, 16, 16, True, False, True): (4, 64, 3, 2),
+        (1024, 1024, 8192, 32, 32, False, True, True): (8, 64, 3, 4),
+        (1024, 1024, 8192, 32, 32, True, False, True): (4, 32, 3, 4),
+        (1024, 1024, 8192, 64, 64, False, True, True): (4, 64, 3, 4),
+        (1024, 1024, 8192, 64, 64, True, False, True): (2, 64, 3, 4),
+        (1024, 1024, 8192, 128, 128, False, True, True): (4, 64, 2, 8),
+        (1024, 1024, 8192, 128, 128, True, False, True): (4, 64, 1, 4),
+        (1024, 1024, 16384, 16, 16, False, True, True): (1, 64, 3, 2),
+        (1024, 1024, 16384, 16, 16, True, False, True): (1, 64, 3, 2),
+        (1024, 1024, 16384, 32, 32, False, True, True): (1, 128, 3, 2),
+        (1024, 1024, 16384, 32, 32, True, False, True): (1, 64, 3, 4),
+        (1024, 1024, 16384, 64, 64, False, True, True): (1, 128, 3, 4),
+        (1024, 1024, 16384, 64, 64, True, False, True): (1, 128, 3, 4),
+        (1024, 1024, 16384, 128, 128, False, True, True): (2, 128, 1, 4),
+        (1024, 1024, 16384, 128, 128, True, False, True): (4, 128, 1, 4),
+        (1024, 1024, 32768, 16, 16, False, True, True): (1, 128, 3, 2),
+        (1024, 1024, 32768, 16, 16, True, False, True): (1, 128, 3, 2),
+        (1024, 1024, 32768, 32, 32, False, True, True): (1, 256, 3, 2),
+        (1024, 1024, 32768, 32, 32, True, False, True): (1, 128, 3, 4),
+        (1024, 1024, 32768, 64, 64, False, True, True): (2, 128, 2, 4),
+        (1024, 1024, 32768, 64, 64, True, False, True): (1, 256, 3, 4),
+        (1024, 1024, 32768, 128, 128, False, True, True): (2, 256, 1, 4),
+        (1024, 1024, 32768, 128, 128, True, False, True): (4, 256, 1, 4),
+        (1024, 1024, 65536, 16, 16, False, True, True): (1, 256, 3, 4),
+        (1024, 1024, 65536, 16, 16, True, False, True): (1, 256, 3, 4),
+        (1024, 1024, 65536, 32, 32, False, True, True): (9, 256, 3, 4),
+        (1024, 1024, 65536, 32, 32, True, False, True): (7, 256, 3, 4),
+        (1024, 1024, 65536, 64, 64, False, True, True): (2, 256, 2, 4),
+        (1024, 1024, 65536, 64, 64, True, False, True): (2, 512, 3, 4),
+        (1024, 1024, 65536, 128, 128, False, True, True): (2, 512, 1, 4),
+        (1024, 1024, 65536, 128, 128, True, False, True): (4, 512, 1, 4),
+        (1024, 1024, 131072, 16, 16, False, True, True): (11, 512, 3, 2),
+        (1024, 1024, 131072, 16, 16, True, False, True): (11, 512, 3, 2),
+        (1024, 1024, 131072, 32, 32, False, True, True): (4, 512, 3, 4),
+        (1024, 1024, 131072, 32, 32, True, False, True): (6, 512, 3, 4),
+        (1024, 1024, 131072, 64, 64, False, True, True): (2, 512, 2, 4),
+        (1024, 1024, 131072, 64, 64, True, False, True): (2, 1024, 3, 4),
+        (1024, 1024, 131072, 128, 128, False, True, True): (4, 1024, 1, 4),
+        (1024, 1024, 131072, 128, 128, True, False, True): (4, 1024, 1, 4),
+        (1536, 1536, 256, 16, 16, False, True, True): (1, 4, 6, 2),
+        (1536, 1536, 256, 16, 16, True, False, True): (3, 4, 5, 2),
+        (1536, 1536, 256, 32, 32, False, True, True): (2, 4, 3, 4),
+        (1536, 1536, 256, 32, 32, True, False, True): (1, 4, 5, 2),
+        (1536, 1536, 256, 64, 64, False, True, True): (2, 4, 3, 4),
+        (1536, 1536, 256, 64, 64, True, False, True): (1, 4, 4, 4),
+        (1536, 1536, 256, 128, 128, False, True, True): (3, 2, 3, 8),
+        (1536, 1536, 256, 128, 128, True, False, True): (6, 2, 3, 8),
+        (1536, 1536, 512, 16, 16, False, True, True): (1, 8, 1, 4),
+        (1536, 1536, 512, 16, 16, True, False, True): (3, 4, 5, 2),
+        (1536, 1536, 512, 32, 32, False, True, True): (1, 8, 1, 8),
+        (1536, 1536, 512, 32, 32, True, False, True): (1, 4, 4, 4),
+        (1536, 1536, 512, 64, 64, False, True, True): (3, 8, 5, 4),
+        (1536, 1536, 512, 64, 64, True, False, True): (3, 8, 3, 4),
+        (1536, 1536, 512, 128, 128, False, True, True): (2, 4, 3, 8),
+        (1536, 1536, 512, 128, 128, True, False, True): (3, 4, 3, 8),
+        (1536, 1536, 1024, 16, 16, False, True, True): (1, 8, 1, 2),
+        (1536, 1536, 1024, 16, 16, True, False, True): (2, 8, 4, 2),
+        (1536, 1536, 1024, 32, 32, False, True, True): (8, 16, 1, 4),
+        (1536, 1536, 1024, 32, 32, True, False, True): (3, 8, 4, 2),
+        (1536, 1536, 1024, 64, 64, False, True, True): (1, 16, 3, 4),
+        (1536, 1536, 1024, 64, 64, True, False, True): (3, 8, 3, 4),
+        (1536, 1536, 1024, 128, 128, False, True, True): (3, 8, 3, 8),
+        (1536, 1536, 1024, 128, 128, True, False, True): (3, 8, 3, 8),
+        (1536, 1536, 2048, 16, 16, False, True, True): (1, 16, 1, 4),
+        (1536, 1536, 2048, 16, 16, True, False, True): (1, 8, 3, 1),
+        (1536, 1536, 2048, 32, 32, False, True, True): (3, 16, 1, 8),
+        (1536, 1536, 2048, 32, 32, True, False, True): (3, 8, 4, 4),
+        (1536, 1536, 2048, 64, 64, False, True, True): (1, 16, 3, 4),
+        (1536, 1536, 2048, 64, 64, True, False, True): (3, 8, 3, 4),
+        (1536, 1536, 2048, 128, 128, False, True, True): (4, 16, 1, 4),
+        (1536, 1536, 2048, 128, 128, True, False, True): (6, 16, 3, 8),
+        (1536, 1536, 4096, 16, 16, False, True, True): (1, 32, 1, 2),
+        (1536, 1536, 4096, 16, 16, True, False, True): (4, 32, 4, 2),
+        (1536, 1536, 4096, 32, 32, False, True, True): (1, 32, 1, 8),
+        (1536, 1536, 4096, 32, 32, True, False, True): (5, 32, 4, 2),
+        (1536, 1536, 4096, 64, 64, False, True, True): (2, 32, 3, 4),
+        (1536, 1536, 4096, 64, 64, True, False, True): (2, 16, 3, 4),
+        (1536, 1536, 4096, 128, 128, False, True, True): (4, 32, 3, 8),
+        (1536, 1536, 4096, 128, 128, True, False, True): (4, 32, 3, 8),
+        (1536, 1536, 8192, 16, 16, False, True, True): (1, 64, 1, 2),
+        (1536, 1536, 8192, 16, 16, True, False, True): (4, 64, 4, 2),
+        (1536, 1536, 8192, 32, 32, False, True, True): (2, 64, 1, 8),
+        (1536, 1536, 8192, 32, 32, True, False, True): (2, 32, 3, 4),
+        (1536, 1536, 8192, 64, 64, False, True, True): (1, 64, 3, 4),
+        (1536, 1536, 8192, 64, 64, True, False, True): (2, 32, 3, 4),
+        (1536, 1536, 8192, 128, 128, False, True, True): (4, 64, 3, 8),
+        (1536, 1536, 8192, 128, 128, True, False, True): (1, 64, 3, 8),
+        (1536, 1536, 16384, 16, 16, False, True, True): (1, 128, 1, 2),
+        (1536, 1536, 16384, 16, 16, True, False, True): (1, 64, 4, 4),
+        (1536, 1536, 16384, 32, 32, False, True, True): (1, 64, 1, 2),
+        (1536, 1536, 16384, 32, 32, True, False, True): (1, 64, 3, 4),
+        (1536, 1536, 16384, 64, 64, False, True, True): (1, 128, 3, 4),
+        (1536, 1536, 16384, 64, 64, True, False, True): (1, 64, 3, 4),
+        (1536, 1536, 16384, 128, 128, False, True, True): (1, 128, 1, 4),
+        (1536, 1536, 16384, 128, 128, True, False, True): (1, 128, 2, 4),
+        (1536, 1536, 32768, 16, 16, False, True, True): (1, 256, 1, 2),
+        (1536, 1536, 32768, 16, 16, True, False, True): (1, 128, 3, 2),
+        (1536, 1536, 32768, 32, 32, False, True, True): (1, 128, 1, 2),
+        (1536, 1536, 32768, 32, 32, True, False, True): (1, 128, 3, 4),
+        (1536, 1536, 32768, 64, 64, False, True, True): (1, 256, 3, 4),
+        (1536, 1536, 32768, 64, 64, True, False, True): (1, 128, 3, 4),
+        (1536, 1536, 32768, 128, 128, False, True, True): (1, 256, 1, 4),
+        (1536, 1536, 32768, 128, 128, True, False, True): (2, 256, 2, 4),
+        (1536, 1536, 65536, 16, 16, False, True, True): (2, 512, 1, 2),
+        (1536, 1536, 65536, 16, 16, True, False, True): (1, 256, 4, 4),
+        (1536, 1536, 65536, 32, 32, False, True, True): (1, 256, 1, 2),
+        (1536, 1536, 65536, 32, 32, True, False, True): (1, 256, 3, 4),
+        (1536, 1536, 65536, 64, 64, False, True, True): (1, 512, 3, 4),
+        (1536, 1536, 65536, 64, 64, True, False, True): (3, 256, 3, 4),
+        (1536, 1536, 65536, 128, 128, False, True, True): (3, 512, 1, 4),
+        (1536, 1536, 65536, 128, 128, True, False, True): (4, 512, 2, 4),
+        (1536, 1536, 131072, 16, 16, False, True, True): (2, 1024, 1, 2),
+        (1536, 1536, 131072, 16, 16, True, False, True): (9, 512, 4, 4),
+        (1536, 1536, 131072, 32, 32, False, True, True): (1, 512, 1, 2),
+        (1536, 1536, 131072, 32, 32, True, False, True): (5, 512, 3, 4),
+        (1536, 1536, 131072, 64, 64, False, True, True): (1, 1024, 3, 4),
+        (1536, 1536, 131072, 64, 64, True, False, True): (2, 512, 3, 4),
+        (1536, 1536, 131072, 128, 128, False, True, True): (3, 1024, 1, 4),
+        (1536, 1536, 131072, 128, 128, True, False, True): (1, 1024, 2, 4),
+        (2048, 2048, 256, 16, 16, False, True, True): (1, 4, 5, 2),
+        (2048, 2048, 256, 16, 16, True, False, True): (4, 4, 5, 2),
+        (2048, 2048, 256, 32, 32, False, True, True): (3, 4, 6, 2),
+        (2048, 2048, 256, 32, 32, True, False, True): (2, 4, 5, 2),
+        (2048, 2048, 256, 64, 64, False, True, True): (2, 4, 4, 4),
+        (2048, 2048, 256, 64, 64, True, False, True): (2, 4, 3, 4),
+        (2048, 2048, 256, 128, 128, False, True, True): (3, 2, 2, 8),
+        (2048, 2048, 256, 128, 128, True, False, True): (3, 2, 2, 8),
+        (2048, 2048, 512, 16, 16, False, True, True): (3, 4, 4, 4),
+        (2048, 2048, 512, 16, 16, True, False, True): (1, 4, 4, 4),
+        (2048, 2048, 512, 32, 32, False, True, True): (1, 4, 3, 4),
+        (2048, 2048, 512, 32, 32, True, False, True): (1, 4, 4, 2),
+        (2048, 2048, 512, 64, 64, False, True, True): (1, 8, 3, 4),
+        (2048, 2048, 512, 64, 64, True, False, True): (1, 8, 3, 4),
+        (2048, 2048, 512, 128, 128, False, True, True): (3, 4, 2, 8),
+        (2048, 2048, 512, 128, 128, True, False, True): (2, 4, 2, 8),
+        (2048, 2048, 1024, 16, 16, False, True, True): (3, 4, 3, 4),
+        (2048, 2048, 1024, 16, 16, True, False, True): (4, 8, 3, 2),
+        (2048, 2048, 1024, 32, 32, False, True, True): (3, 8, 3, 4),
+        (2048, 2048, 1024, 32, 32, True, False, True): (1, 8, 3, 2),
+        (2048, 2048, 1024, 64, 64, False, True, True): (1, 8, 3, 4),
+        (2048, 2048, 1024, 64, 64, True, False, True): (1, 8, 3, 4),
+        (2048, 2048, 1024, 128, 128, False, True, True): (4, 8, 1, 4),
+        (2048, 2048, 1024, 128, 128, True, False, True): (2, 8, 1, 4),
+        (2048, 2048, 2048, 16, 16, False, True, True): (4, 16, 3, 2),
+        (2048, 2048, 2048, 16, 16, True, False, True): (4, 16, 3, 2),
+        (2048, 2048, 2048, 32, 32, False, True, True): (1, 16, 3, 2),
+        (2048, 2048, 2048, 32, 32, True, False, True): (1, 16, 3, 2),
+        (2048, 2048, 2048, 64, 64, False, True, True): (4, 16, 3, 4),
+        (2048, 2048, 2048, 64, 64, True, False, True): (4, 16, 3, 4),
+        (2048, 2048, 2048, 128, 128, False, True, True): (6, 16, 2, 8),
+        (2048, 2048, 2048, 128, 128, True, False, True): (3, 16, 1, 4),
+        (2048, 2048, 4096, 16, 16, False, True, True): (4, 32, 4, 2),
+        (2048, 2048, 4096, 16, 16, True, False, True): (4, 32, 3, 2),
+        (2048, 2048, 4096, 32, 32, False, True, True): (4, 16, 3, 8),
+        (2048, 2048, 4096, 32, 32, True, False, True): (4, 16, 3, 8),
+        (2048, 2048, 4096, 64, 64, False, True, True): (1, 32, 3, 4),
+        (2048, 2048, 4096, 64, 64, True, False, True): (3, 32, 3, 4),
+        (2048, 2048, 4096, 128, 128, False, True, True): (2, 32, 1, 4),
+        (2048, 2048, 4096, 128, 128, True, False, True): (2, 32, 1, 4),
+        (2048, 2048, 8192, 16, 16, False, True, True): (4, 64, 4, 2),
+        (2048, 2048, 8192, 16, 16, True, False, True): (4, 64, 4, 2),
+        (2048, 2048, 8192, 32, 32, False, True, True): (4, 32, 4, 8),
+        (2048, 2048, 8192, 32, 32, True, False, True): (4, 32, 3, 8),
+        (2048, 2048, 8192, 64, 64, False, True, True): (4, 64, 3, 4),
+        (2048, 2048, 8192, 64, 64, True, False, True): (4, 64, 3, 4),
+        (2048, 2048, 8192, 128, 128, False, True, True): (2, 64, 1, 4),
+        (2048, 2048, 8192, 128, 128, True, False, True): (2, 64, 1, 4),
+        (2048, 2048, 16384, 16, 16, False, True, True): (4, 64, 3, 2),
+        (2048, 2048, 16384, 16, 16, True, False, True): (1, 64, 3, 2),
+        (2048, 2048, 16384, 32, 32, False, True, True): (4, 64, 3, 4),
+        (2048, 2048, 16384, 32, 32, True, False, True): (4, 64, 3, 4),
+        (2048, 2048, 16384, 64, 64, False, True, True): (4, 128, 3, 4),
+        (2048, 2048, 16384, 64, 64, True, False, True): (4, 128, 3, 4),
+        (2048, 2048, 16384, 128, 128, False, True, True): (2, 128, 1, 4),
+        (2048, 2048, 16384, 128, 128, True, False, True): (2, 128, 1, 4),
+        (2048, 2048, 32768, 16, 16, False, True, True): (8, 128, 3, 2),
+        (2048, 2048, 32768, 16, 16, True, False, True): (8, 128, 3, 4),
+        (2048, 2048, 32768, 32, 32, False, True, True): (8, 128, 3, 4),
+        (2048, 2048, 32768, 32, 32, True, False, True): (8, 128, 3, 4),
+        (2048, 2048, 32768, 64, 64, False, True, True): (1, 128, 2, 4),
+        (2048, 2048, 32768, 64, 64, True, False, True): (8, 256, 3, 4),
+        (2048, 2048, 32768, 128, 128, False, True, True): (2, 256, 1, 4),
+        (2048, 2048, 32768, 128, 128, True, False, True): (2, 256, 1, 4),
+        (2048, 2048, 65536, 16, 16, False, True, True): (9, 256, 4, 4),
+        (2048, 2048, 65536, 16, 16, True, False, True): (7, 256, 4, 4),
+        (2048, 2048, 65536, 32, 32, False, True, True): (7, 256, 3, 4),
+        (2048, 2048, 65536, 32, 32, True, False, True): (3, 256, 3, 4),
+        (2048, 2048, 65536, 64, 64, False, True, True): (2, 256, 2, 4),
+        (2048, 2048, 65536, 64, 64, True, False, True): (6, 512, 3, 4),
+        (2048, 2048, 65536, 128, 128, False, True, True): (2, 512, 1, 4),
+        (2048, 2048, 65536, 128, 128, True, False, True): (2, 512, 1, 4),
+        (2048, 2048, 131072, 16, 16, False, True, True): (9, 512, 4, 4),
+        (2048, 2048, 131072, 16, 16, True, False, True): (9, 512, 4, 4),
+        (2048, 2048, 131072, 32, 32, False, True, True): (7, 512, 4, 4),
+        (2048, 2048, 131072, 32, 32, True, False, True): (3, 512, 3, 4),
+        (2048, 2048, 131072, 64, 64, False, True, True): (2, 512, 2, 4),
+        (2048, 2048, 131072, 64, 64, True, False, True): (4, 1024, 3, 4),
+        (2048, 2048, 131072, 128, 128, False, True, True): (1, 1024, 1, 4),
+        (2048, 2048, 131072, 128, 128, True, False, True): (2, 1024, 1, 4),
+        (3072, 768, 256, 16, 16, False, True, True): (6, 4, 1, 4),
+        (3072, 768, 256, 16, 16, True, False, True): (3, 1, 4, 4),
+        (3072, 768, 256, 32, 32, False, True, True): (6, 8, 1, 2),
+        (3072, 768, 256, 32, 32, True, False, True): (1, 2, 4, 4),
+        (3072, 768, 256, 64, 64, False, True, True): (1, 4, 4, 4),
+        (3072, 768, 256, 64, 64, True, False, True): (4, 2, 4, 4),
+        (3072, 768, 256, 128, 128, False, True, True): (1, 2, 3, 8),
+        (3072, 768, 256, 128, 128, True, False, True): (1, 2, 3, 8),
+        (3072, 768, 512, 16, 16, False, True, True): (2, 4, 1, 4),
+        (3072, 768, 512, 16, 16, True, False, True): (1, 4, 4, 1),
+        (3072, 768, 512, 32, 32, False, True, True): (3, 8, 1, 4),
+        (3072, 768, 512, 32, 32, True, False, True): (1, 2, 3, 4),
+        (3072, 768, 512, 64, 64, False, True, True): (1, 8, 1, 4),
+        (3072, 768, 512, 64, 64, True, False, True): (4, 4, 3, 4),
+        (3072, 768, 512, 128, 128, False, True, True): (1, 4, 3, 8),
+        (3072, 768, 512, 128, 128, True, False, True): (1, 4, 3, 8),
+        (3072, 768, 1024, 16, 16, False, True, True): (1, 8, 1, 4),
+        (3072, 768, 1024, 16, 16, True, False, True): (3, 4, 3, 1),
+        (3072, 768, 1024, 32, 32, False, True, True): (1, 8, 1, 8),
+        (3072, 768, 1024, 32, 32, True, False, True): (1, 4, 4, 4),
+        (3072, 768, 1024, 64, 64, False, True, True): (1, 16, 3, 4),
+        (3072, 768, 1024, 64, 64, True, False, True): (1, 4, 3, 4),
+        (3072, 768, 1024, 128, 128, False, True, True): (1, 8, 3, 8),
+        (3072, 768, 1024, 128, 128, True, False, True): (2, 8, 3, 8),
+        (3072, 768, 2048, 16, 16, False, True, True): (3, 8, 1, 4),
+        (3072, 768, 2048, 16, 16, True, False, True): (2, 8, 3, 4),
+        (3072, 768, 2048, 32, 32, False, True, True): (3, 16, 1, 8),
+        (3072, 768, 2048, 32, 32, True, False, True): (3, 8, 3, 4),
+        (3072, 768, 2048, 64, 64, False, True, True): (1, 16, 1, 4),
+        (3072, 768, 2048, 64, 64, True, False, True): (1, 16, 3, 4),
+        (3072, 768, 2048, 128, 128, False, True, True): (1, 16, 3, 8),
+        (3072, 768, 2048, 128, 128, True, False, True): (2, 16, 2, 4),
+        (3072, 768, 4096, 16, 16, False, True, True): (1, 16, 1, 4),
+        (3072, 768, 4096, 16, 16, True, False, True): (4, 32, 4, 2),
+        (3072, 768, 4096, 32, 32, False, True, True): (2, 32, 1, 8),
+        (3072, 768, 4096, 32, 32, True, False, True): (7, 16, 3, 4),
+        (3072, 768, 4096, 64, 64, False, True, True): (2, 32, 1, 4),
+        (3072, 768, 4096, 64, 64, True, False, True): (2, 16, 2, 4),
+        (3072, 768, 4096, 128, 128, False, True, True): (1, 32, 3, 8),
+        (3072, 768, 4096, 128, 128, True, False, True): (3, 32, 2, 4),
+        (3072, 768, 8192, 16, 16, False, True, True): (2, 32, 1, 4),
+        (3072, 768, 8192, 16, 16, True, False, True): (4, 64, 4, 2),
+        (3072, 768, 8192, 32, 32, False, True, True): (4, 32, 1, 4),
+        (3072, 768, 8192, 32, 32, True, False, True): (4, 32, 3, 4),
+        (3072, 768, 8192, 64, 64, False, True, True): (2, 64, 1, 4),
+        (3072, 768, 8192, 64, 64, True, False, True): (4, 32, 2, 4),
+        (3072, 768, 8192, 128, 128, False, True, True): (3, 64, 1, 4),
+        (3072, 768, 8192, 128, 128, True, False, True): (6, 64, 2, 4),
+        (3072, 768, 16384, 16, 16, False, True, True): (1, 64, 1, 4),
+        (3072, 768, 16384, 16, 16, True, False, True): (1, 64, 1, 1),
+        (3072, 768, 16384, 32, 32, False, True, True): (1, 64, 1, 4),
+        (3072, 768, 16384, 32, 32, True, False, True): (4, 64, 3, 4),
+        (3072, 768, 16384, 64, 64, False, True, True): (4, 128, 1, 4),
+        (3072, 768, 16384, 64, 64, True, False, True): (4, 64, 2, 4),
+        (3072, 768, 16384, 128, 128, False, True, True): (3, 128, 1, 4),
+        (3072, 768, 16384, 128, 128, True, False, True): (4, 128, 2, 4),
+        (3072, 768, 32768, 16, 16, False, True, True): (1, 128, 1, 4),
+        (3072, 768, 32768, 16, 16, True, False, True): (8, 128, 4, 1),
+        (3072, 768, 32768, 32, 32, False, True, True): (1, 128, 1, 4),
+        (3072, 768, 32768, 32, 32, True, False, True): (8, 128, 3, 4),
+        (3072, 768, 32768, 64, 64, False, True, True): (1, 256, 1, 4),
+        (3072, 768, 32768, 64, 64, True, False, True): (1, 128, 2, 4),
+        (3072, 768, 32768, 128, 128, False, True, True): (3, 256, 1, 4),
+        (3072, 768, 32768, 128, 128, True, False, True): (8, 256, 2, 4),
+        (3072, 768, 50432, 16, 16, False, True, True): (1, 197, 1, 4),
+        (3072, 768, 50432, 16, 16, True, False, True): (7, 197, 4, 1),
+        (3072, 768, 50432, 32, 32, False, True, True): (1, 197, 1, 4),
+        (3072, 768, 50432, 32, 32, True, False, True): (4, 197, 3, 4),
+        (3072, 768, 50432, 64, 64, False, True, True): (1, 394, 1, 4),
+        (3072, 768, 50432, 64, 64, True, False, True): (3, 197, 2, 4),
+        (3072, 768, 50432, 128, 128, False, True, True): (3, 394, 1, 4),
+        (3072, 768, 50432, 128, 128, True, False, True): (8, 394, 2, 4),
+        (3072, 768, 65536, 16, 16, False, True, True): (1, 256, 1, 4),
+        (3072, 768, 65536, 16, 16, True, False, True): (15, 256, 4, 1),
+        (3072, 768, 65536, 32, 32, False, True, True): (1, 256, 1, 4),
+        (3072, 768, 65536, 32, 32, True, False, True): (15, 256, 3, 4),
+        (3072, 768, 65536, 64, 64, False, True, True): (1, 512, 1, 4),
+        (3072, 768, 65536, 64, 64, True, False, True): (2, 256, 2, 4),
+        (3072, 768, 65536, 128, 128, False, True, True): (3, 512, 1, 4),
+        (3072, 768, 65536, 128, 128, True, False, True): (3, 512, 2, 4),
+        (3072, 768, 131072, 16, 16, False, True, True): (1, 512, 1, 4),
+        (3072, 768, 131072, 16, 16, True, False, True): (15, 512, 4, 1),
+        (3072, 768, 131072, 32, 32, False, True, True): (1, 512, 1, 4),
+        (3072, 768, 131072, 32, 32, True, False, True): (9, 512, 3, 4),
+        (3072, 768, 131072, 64, 64, False, True, True): (1, 1024, 1, 4),
+        (3072, 768, 131072, 64, 64, True, False, True): (3, 512, 2, 4),
+        (3072, 768, 131072, 128, 128, False, True, True): (3, 1024, 1, 4),
+        (3072, 768, 131072, 128, 128, True, False, True): (1, 1024, 2, 4),
+        (3072, 3072, 256, 16, 16, False, True, True): (5, 4, 1, 4),
+        (3072, 3072, 256, 16, 16, True, False, True): (1, 2, 5, 2),
+        (3072, 3072, 256, 32, 32, False, True, True): (5, 4, 1, 8),
+        (3072, 3072, 256, 32, 32, True, False, True): (1, 4, 4, 2),
+        (3072, 3072, 256, 64, 64, False, True, True): (2, 4, 4, 4),
+        (3072, 3072, 256, 64, 64, True, False, True): (2, 4, 4, 4),
+        (3072, 3072, 256, 128, 128, False, True, True): (1, 2, 3, 8),
+        (3072, 3072, 256, 128, 128, True, False, True): (1, 2, 3, 8),
+        (3072, 3072, 512, 16, 16, False, True, True): (5, 4, 1, 2),
+        (3072, 3072, 512, 16, 16, True, False, True): (1, 2, 3, 4),
+        (3072, 3072, 512, 32, 32, False, True, True): (3, 8, 1, 4),
+        (3072, 3072, 512, 32, 32, True, False, True): (1, 4, 4, 2),
+        (3072, 3072, 512, 64, 64, False, True, True): (1, 8, 2, 2),
+        (3072, 3072, 512, 64, 64, True, False, True): (2, 4, 3, 4),
+        (3072, 3072, 512, 128, 128, False, True, True): (2, 4, 3, 8),
+        (3072, 3072, 512, 128, 128, True, False, True): (1, 4, 3, 8),
+        (3072, 3072, 1024, 16, 16, False, True, True): (1, 8, 1, 4),
+        (3072, 3072, 1024, 16, 16, True, False, True): (2, 8, 3, 1),
+        (3072, 3072, 1024, 32, 32, False, True, True): (1, 16, 1, 4),
+        (3072, 3072, 1024, 32, 32, True, False, True): (1, 4, 4, 4),
+        (3072, 3072, 1024, 64, 64, False, True, True): (1, 8, 3, 4),
+        (3072, 3072, 1024, 64, 64, True, False, True): (2, 4, 3, 4),
+        (3072, 3072, 1024, 128, 128, False, True, True): (1, 8, 1, 4),
+        (3072, 3072, 1024, 128, 128, True, False, True): (2, 8, 3, 8),
+        (3072, 3072, 2048, 16, 16, False, True, True): (1, 16, 1, 2),
+        (3072, 3072, 2048, 16, 16, True, False, True): (2, 16, 4, 2),
+        (3072, 3072, 2048, 32, 32, False, True, True): (1, 16, 1, 8),
+        (3072, 3072, 2048, 32, 32, True, False, True): (3, 8, 4, 4),
+        (3072, 3072, 2048, 64, 64, False, True, True): (3, 16, 3, 4),
+        (3072, 3072, 2048, 64, 64, True, False, True): (3, 8, 3, 4),
+        (3072, 3072, 2048, 128, 128, False, True, True): (1, 16, 3, 8),
+        (3072, 3072, 2048, 128, 128, True, False, True): (5, 16, 3, 8),
+        (3072, 3072, 4096, 16, 16, False, True, True): (1, 32, 1, 2),
+        (3072, 3072, 4096, 16, 16, True, False, True): (4, 32, 4, 2),
+        (3072, 3072, 4096, 32, 32, False, True, True): (1, 32, 1, 8),
+        (3072, 3072, 4096, 32, 32, True, False, True): (3, 16, 3, 4),
+        (3072, 3072, 4096, 64, 64, False, True, True): (1, 32, 3, 4),
+        (3072, 3072, 4096, 64, 64, True, False, True): (3, 16, 3, 4),
+        (3072, 3072, 4096, 128, 128, False, True, True): (3, 32, 3, 8),
+        (3072, 3072, 4096, 128, 128, True, False, True): (3, 32, 3, 8),
+        (3072, 3072, 8192, 16, 16, False, True, True): (1, 64, 1, 2),
+        (3072, 3072, 8192, 16, 16, True, False, True): (4, 64, 4, 2),
+        (3072, 3072, 8192, 32, 32, False, True, True): (1, 64, 1, 8),
+        (3072, 3072, 8192, 32, 32, True, False, True): (6, 32, 3, 4),
+        (3072, 3072, 8192, 64, 64, False, True, True): (1, 64, 3, 4),
+        (3072, 3072, 8192, 64, 64, True, False, True): (2, 32, 3, 4),
+        (3072, 3072, 8192, 128, 128, False, True, True): (2, 64, 3, 8),
+        (3072, 3072, 8192, 128, 128, True, False, True): (1, 64, 3, 8),
+        (3072, 3072, 16384, 16, 16, False, True, True): (1, 128, 1, 2),
+        (3072, 3072, 16384, 16, 16, True, False, True): (4, 128, 4, 2),
+        (3072, 3072, 16384, 32, 32, False, True, True): (1, 64, 1, 2),
+        (3072, 3072, 16384, 32, 32, True, False, True): (4, 64, 3, 4),
+        (3072, 3072, 16384, 64, 64, False, True, True): (1, 128, 3, 4),
+        (3072, 3072, 16384, 64, 64, True, False, True): (4, 64, 3, 4),
+        (3072, 3072, 16384, 128, 128, False, True, True): (1, 128, 1, 4),
+        (3072, 3072, 16384, 128, 128, True, False, True): (1, 128, 3, 8),
+        (3072, 3072, 32768, 16, 16, False, True, True): (1, 256, 1, 2),
+        (3072, 3072, 32768, 16, 16, True, False, True): (8, 128, 4, 4),
+        (3072, 3072, 32768, 32, 32, False, True, True): (1, 256, 1, 8),
+        (3072, 3072, 32768, 32, 32, True, False, True): (5, 128, 3, 4),
+        (3072, 3072, 32768, 64, 64, False, True, True): (1, 256, 3, 4),
+        (3072, 3072, 32768, 64, 64, True, False, True): (3, 128, 3, 4),
+        (3072, 3072, 32768, 128, 128, False, True, True): (1, 256, 1, 4),
+        (3072, 3072, 32768, 128, 128, True, False, True): (3, 256, 2, 4),
+        (3072, 3072, 65536, 16, 16, False, True, True): (1, 512, 1, 2),
+        (3072, 3072, 65536, 16, 16, True, False, True): (7, 256, 4, 4),
+        (3072, 3072, 65536, 32, 32, False, True, True): (1, 256, 1, 2),
+        (3072, 3072, 65536, 32, 32, True, False, True): (5, 256, 3, 4),
+        (3072, 3072, 65536, 64, 64, False, True, True): (1, 512, 3, 4),
+        (3072, 3072, 65536, 64, 64, True, False, True): (3, 256, 3, 4),
+        (3072, 3072, 65536, 128, 128, False, True, True): (1, 512, 1, 4),
+        (3072, 3072, 65536, 128, 128, True, False, True): (3, 512, 2, 4),
+        (3072, 3072, 131072, 16, 16, False, True, True): (1, 1024, 1, 2),
+        (3072, 3072, 131072, 16, 16, True, False, True): (5, 512, 4, 4),
+        (3072, 3072, 131072, 32, 32, False, True, True): (1, 512, 1, 2),
+        (3072, 3072, 131072, 32, 32, True, False, True): (5, 512, 3, 4),
+        (3072, 3072, 131072, 64, 64, False, True, True): (1, 1024, 3, 4),
+        (3072, 3072, 131072, 64, 64, True, False, True): (3, 512, 3, 4),
+        (3072, 3072, 131072, 128, 128, False, True, True): (1, 1024, 1, 4),
+        (3072, 3072, 131072, 128, 128, True, False, True): (6, 1024, 2, 4),
+        (4096, 4096, 256, 16, 16, False, True, True): (2, 2, 5, 4),
+        (4096, 4096, 256, 16, 16, True, False, True): (2, 2, 4, 2),
+        (4096, 4096, 256, 32, 32, False, True, True): (1, 2, 4, 4),
+        (4096, 4096, 256, 32, 32, True, False, True): (3, 2, 4, 2),
+        (4096, 4096, 256, 64, 64, False, True, True): (3, 4, 3, 4),
+        (4096, 4096, 256, 64, 64, True, False, True): (1, 4, 3, 2),
+        (4096, 4096, 256, 128, 128, False, True, True): (1, 2, 2, 8),
+        (4096, 4096, 256, 128, 128, True, False, True): (1, 2, 2, 8),
+        (4096, 4096, 512, 16, 16, False, True, True): (4, 2, 3, 4),
+        (4096, 4096, 512, 16, 16, True, False, True): (1, 2, 3, 4),
+        (4096, 4096, 512, 32, 32, False, True, True): (1, 4, 3, 4),
+        (4096, 4096, 512, 32, 32, True, False, True): (3, 4, 3, 2),
+        (4096, 4096, 512, 64, 64, False, True, True): (4, 4, 4, 4),
+        (4096, 4096, 512, 64, 64, True, False, True): (3, 4, 3, 4),
+        (4096, 4096, 512, 128, 128, False, True, True): (2, 4, 2, 8),
+        (4096, 4096, 512, 128, 128, True, False, True): (2, 4, 1, 4),
+        (4096, 4096, 1024, 16, 16, False, True, True): (2, 8, 3, 2),
+        (4096, 4096, 1024, 16, 16, True, False, True): (2, 8, 3, 2),
+        (4096, 4096, 1024, 32, 32, False, True, True): (1, 8, 3, 4),
+        (4096, 4096, 1024, 32, 32, True, False, True): (1, 8, 3, 2),
+        (4096, 4096, 1024, 64, 64, False, True, True): (1, 8, 3, 4),
+        (4096, 4096, 1024, 64, 64, True, False, True): (1, 8, 3, 4),
+        (4096, 4096, 1024, 128, 128, False, True, True): (4, 8, 1, 4),
+        (4096, 4096, 1024, 128, 128, True, False, True): (2, 8, 2, 8),
+        (4096, 4096, 2048, 16, 16, False, True, True): (2, 8, 4, 4),
+        (4096, 4096, 2048, 16, 16, True, False, True): (2, 8, 4, 4),
+        (4096, 4096, 2048, 32, 32, False, True, True): (4, 8, 3, 8),
+        (4096, 4096, 2048, 32, 32, True, False, True): (4, 8, 4, 8),
+        (4096, 4096, 2048, 64, 64, False, True, True): (4, 16, 3, 4),
+        (4096, 4096, 2048, 64, 64, True, False, True): (4, 16, 3, 4),
+        (4096, 4096, 2048, 128, 128, False, True, True): (1, 16, 1, 4),
+        (4096, 4096, 2048, 128, 128, True, False, True): (4, 16, 1, 4),
+        (4096, 4096, 4096, 16, 16, False, True, True): (4, 32, 4, 4),
+        (4096, 4096, 4096, 16, 16, True, False, True): (2, 32, 4, 4),
+        (4096, 4096, 4096, 32, 32, False, True, True): (4, 16, 4, 8),
+        (4096, 4096, 4096, 32, 32, True, False, True): (4, 16, 4, 8),
+        (4096, 4096, 4096, 64, 64, False, True, True): (4, 32, 3, 4),
+        (4096, 4096, 4096, 64, 64, True, False, True): (2, 32, 3, 4),
+        (4096, 4096, 4096, 128, 128, False, True, True): (2, 32, 1, 4),
+        (4096, 4096, 4096, 128, 128, True, False, True): (2, 32, 1, 4),
+        (4096, 4096, 8192, 16, 16, False, True, True): (4, 64, 4, 2),
+        (4096, 4096, 8192, 16, 16, True, False, True): (4, 64, 4, 2),
+        (4096, 4096, 8192, 32, 32, False, True, True): (4, 32, 4, 8),
+        (4096, 4096, 8192, 32, 32, True, False, True): (4, 32, 4, 8),
+        (4096, 4096, 8192, 64, 64, False, True, True): (4, 64, 3, 4),
+        (4096, 4096, 8192, 64, 64, True, False, True): (4, 64, 3, 4),
+        (4096, 4096, 8192, 128, 128, False, True, True): (1, 64, 1, 4),
+        (4096, 4096, 8192, 128, 128, True, False, True): (1, 64, 1, 4),
+        (4096, 4096, 16384, 16, 16, False, True, True): (4, 64, 4, 4),
+        (4096, 4096, 16384, 16, 16, True, False, True): (4, 64, 4, 4),
+        (4096, 4096, 16384, 32, 32, False, True, True): (4, 64, 4, 8),
+        (4096, 4096, 16384, 32, 32, True, False, True): (4, 64, 4, 8),
+        (4096, 4096, 16384, 64, 64, False, True, True): (4, 128, 3, 4),
+        (4096, 4096, 16384, 64, 64, True, False, True): (4, 128, 3, 4),
+        (4096, 4096, 16384, 128, 128, False, True, True): (1, 128, 1, 4),
+        (4096, 4096, 16384, 128, 128, True, False, True): (1, 128, 1, 4),
+        (4096, 4096, 32768, 16, 16, False, True, True): (8, 128, 4, 4),
+        (4096, 4096, 32768, 16, 16, True, False, True): (5, 128, 4, 4),
+        (4096, 4096, 32768, 32, 32, False, True, True): (5, 128, 4, 4),
+        (4096, 4096, 32768, 32, 32, True, False, True): (3, 128, 4, 8),
+        (4096, 4096, 32768, 64, 64, False, True, True): (3, 256, 3, 4),
+        (4096, 4096, 32768, 64, 64, True, False, True): (2, 256, 3, 4),
+        (4096, 4096, 32768, 128, 128, False, True, True): (1, 256, 1, 4),
+        (4096, 4096, 32768, 128, 128, True, False, True): (1, 256, 1, 4),
+        (4096, 4096, 65536, 16, 16, False, True, True): (5, 256, 4, 4),
+        (4096, 4096, 65536, 16, 16, True, False, True): (5, 256, 4, 4),
+        (4096, 4096, 65536, 32, 32, False, True, True): (4, 256, 4, 8),
+        (4096, 4096, 65536, 32, 32, True, False, True): (4, 256, 4, 8),
+        (4096, 4096, 65536, 64, 64, False, True, True): (1, 512, 3, 4),
+        (4096, 4096, 65536, 64, 64, True, False, True): (3, 512, 3, 4),
+        (4096, 4096, 65536, 128, 128, False, True, True): (1, 512, 1, 4),
+        (4096, 4096, 65536, 128, 128, True, False, True): (1, 512, 1, 4),
+        (4096, 4096, 131072, 16, 16, False, True, True): (5, 512, 4, 4),
+        (4096, 4096, 131072, 16, 16, True, False, True): (5, 512, 4, 4),
+        (4096, 4096, 131072, 32, 32, False, True, True): (4, 512, 4, 4),
+        (4096, 4096, 131072, 32, 32, True, False, True): (2, 512, 3, 4),
+        (4096, 4096, 131072, 64, 64, False, True, True): (1, 1024, 3, 4),
+        (4096, 4096, 131072, 64, 64, True, False, True): (3, 1024, 3, 4),
+        (4096, 4096, 131072, 128, 128, False, True, True): (1, 1024, 1, 4),
+        (4096, 4096, 131072, 128, 128, True, False, True): (1, 1024, 1, 4),
+        (6144, 6144, 256, 16, 16, False, True, True): (1, 2, 1, 4),
+        (6144, 6144, 256, 16, 16, True, False, True): (3, 1, 4, 4),
+        (6144, 6144, 256, 32, 32, False, True, True): (3, 2, 1, 8),
+        (6144, 6144, 256, 32, 32, True, False, True): (1, 1, 4, 4),
+        (6144, 6144, 256, 64, 64, False, True, True): (4, 2, 3, 4),
+        (6144, 6144, 256, 64, 64, True, False, True): (3, 2, 4, 4),
+        (6144, 6144, 256, 128, 128, False, True, True): (2, 2, 3, 8),
+        (6144, 6144, 256, 128, 128, True, False, True): (1, 2, 3, 8),
+        (6144, 6144, 512, 16, 16, False, True, True): (4, 4, 1, 4),
+        (6144, 6144, 512, 16, 16, True, False, True): (3, 2, 3, 1),
+        (6144, 6144, 512, 32, 32, False, True, True): (1, 8, 1, 4),
+        (6144, 6144, 512, 32, 32, True, False, True): (1, 2, 3, 2),
+        (6144, 6144, 512, 64, 64, False, True, True): (2, 4, 3, 4),
+        (6144, 6144, 512, 64, 64, True, False, True): (2, 2, 3, 4),
+        (6144, 6144, 512, 128, 128, False, True, True): (1, 4, 3, 8),
+        (6144, 6144, 512, 128, 128, True, False, True): (1, 4, 3, 8),
+        (6144, 6144, 1024, 16, 16, False, True, True): (1, 8, 1, 2),
+        (6144, 6144, 1024, 16, 16, True, False, True): (4, 8, 4, 4),
+        (6144, 6144, 1024, 32, 32, False, True, True): (1, 8, 4, 2),
+        (6144, 6144, 1024, 32, 32, True, False, True): (1, 8, 4, 2),
+        (6144, 6144, 1024, 64, 64, False, True, True): (4, 8, 3, 4),
+        (6144, 6144, 1024, 64, 64, True, False, True): (1, 4, 3, 4),
+        (6144, 6144, 1024, 128, 128, False, True, True): (2, 8, 3, 8),
+        (6144, 6144, 1024, 128, 128, True, False, True): (1, 8, 3, 8),
+        (6144, 6144, 2048, 16, 16, False, True, True): (4, 4, 1, 4),
+        (6144, 6144, 2048, 16, 16, True, False, True): (2, 8, 4, 4),
+        (6144, 6144, 2048, 32, 32, False, True, True): (1, 16, 4, 2),
+        (6144, 6144, 2048, 32, 32, True, False, True): (4, 8, 4, 8),
+        (6144, 6144, 2048, 64, 64, False, True, True): (4, 16, 3, 4),
+        (6144, 6144, 2048, 64, 64, True, False, True): (2, 8, 3, 4),
+        (6144, 6144, 2048, 128, 128, False, True, True): (1, 16, 3, 8),
+        (6144, 6144, 2048, 128, 128, True, False, True): (4, 16, 3, 8),
+        (6144, 6144, 4096, 16, 16, False, True, True): (4, 8, 1, 4),
+        (6144, 6144, 4096, 16, 16, True, False, True): (4, 32, 4, 2),
+        (6144, 6144, 4096, 32, 32, False, True, True): (4, 16, 1, 2),
+        (6144, 6144, 4096, 32, 32, True, False, True): (2, 8, 3, 8),
+        (6144, 6144, 4096, 64, 64, False, True, True): (4, 32, 3, 4),
+        (6144, 6144, 4096, 64, 64, True, False, True): (4, 16, 3, 4),
+        (6144, 6144, 4096, 128, 128, False, True, True): (4, 32, 3, 8),
+        (6144, 6144, 4096, 128, 128, True, False, True): (4, 32, 3, 8),
+        (6144, 6144, 8192, 16, 16, False, True, True): (2, 16, 1, 2),
+        (6144, 6144, 8192, 16, 16, True, False, True): (4, 64, 4, 2),
+        (6144, 6144, 8192, 32, 32, False, True, True): (4, 32, 1, 2),
+        (6144, 6144, 8192, 32, 32, True, False, True): (4, 32, 4, 8),
+        (6144, 6144, 8192, 64, 64, False, True, True): (4, 64, 3, 4),
+        (6144, 6144, 8192, 64, 64, True, False, True): (4, 32, 3, 4),
+        (6144, 6144, 8192, 128, 128, False, True, True): (4, 64, 3, 8),
+        (6144, 6144, 8192, 128, 128, True, False, True): (4, 64, 3, 8),
+        (6144, 6144, 16384, 16, 16, False, True, True): (2, 32, 1, 2),
+        (6144, 6144, 16384, 16, 16, True, False, True): (4, 64, 4, 4),
+        (6144, 6144, 16384, 32, 32, False, True, True): (4, 64, 1, 2),
+        (6144, 6144, 16384, 32, 32, True, False, True): (4, 64, 3, 2),
+        (6144, 6144, 16384, 64, 64, False, True, True): (4, 128, 3, 4),
+        (6144, 6144, 16384, 64, 64, True, False, True): (2, 32, 3, 8),
+        (6144, 6144, 16384, 128, 128, False, True, True): (4, 128, 3, 8),
+        (6144, 6144, 16384, 128, 128, True, False, True): (4, 128, 3, 8),
+        (6144, 6144, 32768, 16, 16, False, True, True): (2, 64, 1, 2),
+        (6144, 6144, 32768, 16, 16, True, False, True): (3, 128, 4, 4),
+        (6144, 6144, 32768, 32, 32, False, True, True): (4, 128, 1, 2),
+        (6144, 6144, 32768, 32, 32, True, False, True): (3, 128, 3, 4),
+        (6144, 6144, 32768, 64, 64, False, True, True): (4, 256, 3, 4),
+        (6144, 6144, 32768, 64, 64, True, False, True): (2, 64, 3, 8),
+        (6144, 6144, 32768, 128, 128, False, True, True): (4, 256, 3, 8),
+        (6144, 6144, 32768, 128, 128, True, False, True): (4, 256, 3, 8),
+        (6144, 6144, 65536, 16, 16, False, True, True): (2, 128, 1, 2),
+        (6144, 6144, 65536, 16, 16, True, False, True): (4, 256, 4, 4),
+        (6144, 6144, 65536, 32, 32, False, True, True): (4, 256, 1, 2),
+        (6144, 6144, 65536, 32, 32, True, False, True): (4, 256, 3, 4),
+        (6144, 6144, 65536, 64, 64, False, True, True): (4, 512, 3, 4),
+        (6144, 6144, 65536, 64, 64, True, False, True): (2, 128, 3, 8),
+        (6144, 6144, 65536, 128, 128, False, True, True): (4, 512, 3, 8),
+        (6144, 6144, 65536, 128, 128, True, False, True): (4, 512, 3, 8),
+        (6144, 6144, 131072, 16, 16, False, True, True): (2, 256, 1, 2),
+        (6144, 6144, 131072, 16, 16, True, False, True): (5, 512, 4, 1),
+        (6144, 6144, 131072, 32, 32, False, True, True): (4, 512, 1, 2),
+        (6144, 6144, 131072, 32, 32, True, False, True): (4, 512, 3, 2),
+        (6144, 6144, 131072, 64, 64, False, True, True): (4, 1024, 3, 4),
+        (6144, 6144, 131072, 64, 64, True, False, True): (2, 256, 3, 8),
+        (6144, 6144, 131072, 128, 128, False, True, True): (4, 1024, 3, 8),
+        (6144, 6144, 131072, 128, 128, True, False, True): (4, 1024, 3, 8),
+        (8192, 8192, 256, 16, 16, False, True, True): (1, 1, 3, 4),
+        (8192, 8192, 256, 16, 16, True, False, True): (4, 1, 3, 4),
+        (8192, 8192, 256, 32, 32, False, True, True): (1, 2, 3, 4),
+        (8192, 8192, 256, 32, 32, True, False, True): (1, 2, 3, 4),
+        (8192, 8192, 256, 64, 64, False, True, True): (6, 2, 3, 8),
+        (8192, 8192, 256, 64, 64, True, False, True): (4, 2, 3, 8),
+        (8192, 8192, 256, 128, 128, False, True, True): (1, 2, 1, 4),
+        (8192, 8192, 256, 128, 128, True, False, True): (1, 2, 1, 4),
+        (8192, 8192, 512, 16, 16, False, True, True): (4, 4, 3, 2),
+        (8192, 8192, 512, 16, 16, True, False, True): (4, 4, 3, 4),
+        (8192, 8192, 512, 32, 32, False, True, True): (1, 4, 3, 4),
+        (8192, 8192, 512, 32, 32, True, False, True): (3, 4, 3, 2),
+        (8192, 8192, 512, 64, 64, False, True, True): (1, 4, 3, 4),
+        (8192, 8192, 512, 64, 64, True, False, True): (1, 4, 3, 4),
+        (8192, 8192, 512, 128, 128, False, True, True): (4, 4, 2, 8),
+        (8192, 8192, 512, 128, 128, True, False, True): (4, 4, 2, 8),
+        (8192, 8192, 1024, 16, 16, False, True, True): (4, 8, 4, 4),
+        (8192, 8192, 1024, 16, 16, True, False, True): (2, 8, 4, 4),
+        (8192, 8192, 1024, 32, 32, False, True, True): (2, 4, 4, 8),
+        (8192, 8192, 1024, 32, 32, True, False, True): (1, 4, 3, 4),
+        (8192, 8192, 1024, 64, 64, False, True, True): (4, 8, 3, 4),
+        (8192, 8192, 1024, 64, 64, True, False, True): (2, 8, 3, 4),
+        (8192, 8192, 1024, 128, 128, False, True, True): (4, 8, 1, 4),
+        (8192, 8192, 1024, 128, 128, True, False, True): (4, 8, 1, 4),
+        (8192, 8192, 2048, 16, 16, False, True, True): (2, 8, 4, 4),
+        (8192, 8192, 2048, 16, 16, True, False, True): (2, 8, 4, 4),
+        (8192, 8192, 2048, 32, 32, False, True, True): (2, 8, 4, 8),
+        (8192, 8192, 2048, 32, 32, True, False, True): (2, 8, 4, 8),
+        (8192, 8192, 2048, 64, 64, False, True, True): (4, 8, 2, 4),
+        (8192, 8192, 2048, 64, 64, True, False, True): (4, 16, 3, 4),
+        (8192, 8192, 2048, 128, 128, False, True, True): (4, 16, 1, 4),
+        (8192, 8192, 2048, 128, 128, True, False, True): (4, 16, 1, 4),
+        (8192, 8192, 4096, 16, 16, False, True, True): (4, 16, 4, 4),
+        (8192, 8192, 4096, 16, 16, True, False, True): (4, 32, 4, 2),
+        (8192, 8192, 4096, 32, 32, False, True, True): (2, 16, 4, 8),
+        (8192, 8192, 4096, 32, 32, True, False, True): (2, 16, 4, 8),
+        (8192, 8192, 4096, 64, 64, False, True, True): (4, 32, 3, 4),
+        (8192, 8192, 4096, 64, 64, True, False, True): (4, 16, 2, 4),
+        (8192, 8192, 4096, 128, 128, False, True, True): (4, 32, 1, 4),
+        (8192, 8192, 4096, 128, 128, True, False, True): (4, 32, 1, 4),
+        (8192, 8192, 8192, 16, 16, False, True, True): (4, 64, 4, 2),
+        (8192, 8192, 8192, 16, 16, True, False, True): (4, 64, 4, 2),
+        (8192, 8192, 8192, 32, 32, False, True, True): (2, 32, 4, 8),
+        (8192, 8192, 8192, 32, 32, True, False, True): (2, 32, 4, 8),
+        (8192, 8192, 8192, 64, 64, False, True, True): (4, 32, 3, 8),
+        (8192, 8192, 8192, 64, 64, True, False, True): (4, 32, 2, 4),
+        (8192, 8192, 8192, 128, 128, False, True, True): (4, 64, 1, 4),
+        (8192, 8192, 8192, 128, 128, True, False, True): (4, 64, 1, 4),
+        (8192, 8192, 16384, 16, 16, False, True, True): (4, 64, 4, 4),
+        (8192, 8192, 16384, 16, 16, True, False, True): (4, 64, 4, 4),
+        (8192, 8192, 16384, 32, 32, False, True, True): (4, 64, 3, 4),
+        (8192, 8192, 16384, 32, 32, True, False, True): (4, 64, 4, 8),
+        (8192, 8192, 16384, 64, 64, False, True, True): (4, 64, 2, 4),
+        (8192, 8192, 16384, 64, 64, True, False, True): (4, 64, 2, 4),
+        (8192, 8192, 16384, 128, 128, False, True, True): (4, 128, 1, 4),
+        (8192, 8192, 16384, 128, 128, True, False, True): (4, 128, 1, 4),
+        (8192, 8192, 32768, 16, 16, False, True, True): (3, 128, 4, 4),
+        (8192, 8192, 32768, 16, 16, True, False, True): (3, 128, 4, 4),
+        (8192, 8192, 32768, 32, 32, False, True, True): (2, 128, 4, 8),
+        (8192, 8192, 32768, 32, 32, True, False, True): (2, 128, 4, 8),
+        (8192, 8192, 32768, 64, 64, False, True, True): (2, 128, 2, 4),
+        (8192, 8192, 32768, 64, 64, True, False, True): (2, 128, 2, 4),
+        (8192, 8192, 32768, 128, 128, False, True, True): (4, 256, 1, 4),
+        (8192, 8192, 32768, 128, 128, True, False, True): (4, 256, 1, 4),
+        (8192, 8192, 65536, 16, 16, False, True, True): (3, 256, 4, 4),
+        (8192, 8192, 65536, 16, 16, True, False, True): (3, 256, 4, 4),
+        (8192, 8192, 65536, 32, 32, False, True, True): (2, 256, 3, 4),
+        (8192, 8192, 65536, 32, 32, True, False, True): (2, 256, 3, 4),
+        (8192, 8192, 65536, 64, 64, False, True, True): (2, 256, 2, 4),
+        (8192, 8192, 65536, 64, 64, True, False, True): (2, 256, 3, 8),
+        (8192, 8192, 65536, 128, 128, False, True, True): (4, 512, 1, 4),
+        (8192, 8192, 65536, 128, 128, True, False, True): (4, 512, 1, 4),
+        (8192, 8192, 131072, 16, 16, False, True, True): (3, 512, 4, 4),
+        (8192, 8192, 131072, 16, 16, True, False, True): (3, 512, 4, 4),
+        (8192, 8192, 131072, 32, 32, False, True, True): (2, 512, 4, 4),
+        (8192, 8192, 131072, 32, 32, True, False, True): (2, 512, 3, 4),
+        (8192, 8192, 131072, 64, 64, False, True, True): (4, 512, 2, 4),
+        (8192, 8192, 131072, 64, 64, True, False, True): (2, 512, 2, 4),
+        (8192, 8192, 131072, 128, 128, False, True, True): (4, 1024, 1, 4),
+        (8192, 8192, 131072, 128, 128, True, False, True): (4, 1024, 1, 4),
+        (16384, 16384, 256, 16, 16, False, True, True): (2, 2, 6, 4),
+        (16384, 16384, 256, 16, 16, True, False, True): (2, 2, 6, 4),
+        (16384, 16384, 256, 32, 32, False, True, True): (4, 2, 3, 2),
+        (16384, 16384, 256, 32, 32, True, False, True): (4, 2, 3, 2),
+        (16384, 16384, 256, 64, 64, False, True, True): (2, 2, 4, 4),
+        (16384, 16384, 256, 64, 64, True, False, True): (4, 2, 3, 8),
+        (16384, 16384, 256, 128, 128, False, True, True): (4, 2, 2, 8),
+        (16384, 16384, 256, 128, 128, True, False, True): (4, 2, 2, 8),
+        (16384, 16384, 512, 16, 16, False, True, True): (1, 2, 4, 4),
+        (16384, 16384, 512, 16, 16, True, False, True): (1, 2, 4, 4),
+        (16384, 16384, 512, 32, 32, False, True, True): (2, 2, 4, 8),
+        (16384, 16384, 512, 32, 32, True, False, True): (2, 2, 4, 8),
+        (16384, 16384, 512, 64, 64, False, True, True): (4, 4, 3, 4),
+        (16384, 16384, 512, 64, 64, True, False, True): (4, 4, 3, 4),
+        (16384, 16384, 512, 128, 128, False, True, True): (4, 4, 2, 8),
+        (16384, 16384, 512, 128, 128, True, False, True): (4, 4, 2, 8),
+        (16384, 16384, 1024, 16, 16, False, True, True): (3, 4, 4, 4),
+        (16384, 16384, 1024, 16, 16, True, False, True): (2, 8, 4, 4),
+        (16384, 16384, 1024, 32, 32, False, True, True): (2, 4, 4, 8),
+        (16384, 16384, 1024, 32, 32, True, False, True): (1, 4, 4, 8),
+        (16384, 16384, 1024, 64, 64, False, True, True): (2, 8, 3, 4),
+        (16384, 16384, 1024, 64, 64, True, False, True): (2, 8, 3, 4),
+        (16384, 16384, 1024, 128, 128, False, True, True): (4, 8, 1, 4),
+        (16384, 16384, 1024, 128, 128, True, False, True): (4, 8, 1, 4),
+        (16384, 16384, 2048, 16, 16, False, True, True): (2, 8, 4, 4),
+        (16384, 16384, 2048, 16, 16, True, False, True): (2, 8, 4, 4),
+        (16384, 16384, 2048, 32, 32, False, True, True): (1, 8, 4, 8),
+        (16384, 16384, 2048, 32, 32, True, False, True): (2, 8, 4, 8),
+        (16384, 16384, 2048, 64, 64, False, True, True): (2, 8, 2, 4),
+        (16384, 16384, 2048, 64, 64, True, False, True): (2, 8, 2, 4),
+        (16384, 16384, 2048, 128, 128, False, True, True): (4, 16, 1, 4),
+        (16384, 16384, 2048, 128, 128, True, False, True): (4, 16, 1, 4),
+        (16384, 16384, 4096, 16, 16, False, True, True): (2, 16, 4, 4),
+        (16384, 16384, 4096, 16, 16, True, False, True): (2, 16, 4, 4),
+        (16384, 16384, 4096, 32, 32, False, True, True): (1, 8, 3, 8),
+        (16384, 16384, 4096, 32, 32, True, False, True): (2, 16, 3, 4),
+        (16384, 16384, 4096, 64, 64, False, True, True): (2, 16, 2, 4),
+        (16384, 16384, 4096, 64, 64, True, False, True): (2, 16, 2, 4),
+        (16384, 16384, 4096, 128, 128, False, True, True): (4, 32, 1, 4),
+        (16384, 16384, 4096, 128, 128, True, False, True): (4, 32, 1, 4),
+        (16384, 16384, 8192, 16, 16, False, True, True): (4, 64, 4, 2),
+        (16384, 16384, 8192, 16, 16, True, False, True): (4, 64, 4, 2),
+        (16384, 16384, 8192, 32, 32, False, True, True): (2, 32, 4, 8),
+        (16384, 16384, 8192, 32, 32, True, False, True): (2, 32, 3, 4),
+        (16384, 16384, 8192, 64, 64, False, True, True): (2, 32, 4, 8),
+        (16384, 16384, 8192, 64, 64, True, False, True): (2, 32, 3, 8),
+        (16384, 16384, 8192, 128, 128, False, True, True): (4, 64, 1, 4),
+        (16384, 16384, 8192, 128, 128, True, False, True): (4, 64, 1, 4),
+        (16384, 16384, 16384, 16, 16, False, True, True): (1, 64, 4, 4),
+        (16384, 16384, 16384, 16, 16, True, False, True): (1, 64, 4, 4),
+        (16384, 16384, 16384, 32, 32, False, True, True): (1, 64, 3, 8),
+        (16384, 16384, 16384, 32, 32, True, False, True): (1, 64, 3, 4),
+        (16384, 16384, 16384, 64, 64, False, True, True): (1, 64, 2, 4),
+        (16384, 16384, 16384, 64, 64, True, False, True): (1, 64, 4, 8),
+        (16384, 16384, 16384, 128, 128, False, True, True): (4, 128, 1, 4),
+        (16384, 16384, 16384, 128, 128, True, False, True): (4, 128, 1, 4),
+        (16384, 16384, 32768, 16, 16, False, True, True): (1, 128, 4, 4),
+        (16384, 16384, 32768, 16, 16, True, False, True): (1, 128, 4, 4),
+        (16384, 16384, 32768, 32, 32, False, True, True): (1, 128, 4, 2),
+        (16384, 16384, 32768, 32, 32, True, False, True): (1, 128, 3, 8),
+        (16384, 16384, 32768, 64, 64, False, True, True): (2, 128, 2, 4),
+        (16384, 16384, 32768, 64, 64, True, False, True): (1, 128, 3, 8),
+        (16384, 16384, 32768, 128, 128, False, True, True): (4, 256, 1, 4),
+        (16384, 16384, 32768, 128, 128, True, False, True): (4, 256, 1, 4),
+        (16384, 16384, 65536, 16, 16, False, True, True): (1, 256, 4, 4),
+        (16384, 16384, 65536, 16, 16, True, False, True): (1, 256, 4, 4),
+        (16384, 16384, 65536, 32, 32, False, True, True): (1, 256, 3, 4),
+        (16384, 16384, 65536, 32, 32, True, False, True): (1, 256, 3, 4),
+        (16384, 16384, 65536, 64, 64, False, True, True): (1, 256, 2, 4),
+        (16384, 16384, 65536, 64, 64, True, False, True): (2, 256, 2, 4),
+        (16384, 16384, 65536, 128, 128, False, True, True): (4, 512, 1, 4),
+        (16384, 16384, 65536, 128, 128, True, False, True): (4, 512, 1, 4),
+        (16384, 16384, 131072, 16, 16, False, True, True): (2, 512, 4, 4),
+        (16384, 16384, 131072, 16, 16, True, False, True): (1, 512, 4, 4),
+        (16384, 16384, 131072, 32, 32, False, True, True): (1, 512, 4, 8),
+        (16384, 16384, 131072, 32, 32, True, False, True): (1, 512, 3, 4),
+        (16384, 16384, 131072, 64, 64, False, True, True): (2, 512, 2, 4),
+        (16384, 16384, 131072, 64, 64, True, False, True): (1, 512, 2, 4),
+        (16384, 16384, 131072, 128, 128, False, True, True): (4, 1024, 1, 4),
+        (16384, 16384, 131072, 128, 128, True, False, True): (4, 1024, 1, 4),
+    },
+    ("bsr_dense_addmm", "NVIDIA A100-SXM4-80GB", (0, torch.bfloat16, 0.56)): {
+        (192, 192, 256, 64, 64, False, True, True): (3, 4, 3, 4),
+        (192, 192, 256, 64, 64, True, False, True): (1, 4, 4, 4),
+        (192, 192, 512, 64, 64, False, True, True): (2, 8, 3, 4),
+        (192, 192, 512, 64, 64, True, False, True): (2, 8, 3, 4),
+        (192, 192, 1024, 64, 64, False, True, True): (1, 16, 3, 4),
+        (192, 192, 1024, 64, 64, True, False, True): (1, 16, 5, 4),
+        (192, 192, 2048, 64, 64, False, True, True): (3, 32, 3, 4),
+        (192, 192, 2048, 64, 64, True, False, True): (5, 32, 3, 4),
+        (192, 192, 4096, 64, 64, False, True, True): (1, 64, 4, 4),
+        (192, 192, 4096, 64, 64, True, False, True): (2, 32, 3, 4),
+        (192, 192, 8192, 64, 64, False, True, True): (1, 128, 2, 4),
+        (192, 192, 8192, 64, 64, True, False, True): (1, 64, 3, 4),
+        (192, 192, 16384, 64, 64, False, True, True): (1, 256, 1, 4),
+        (192, 192, 16384, 64, 64, True, False, True): (1, 64, 3, 4),
+        (192, 192, 32768, 64, 64, False, True, True): (2, 512, 1, 2),
+        (192, 192, 32768, 64, 64, True, False, True): (2, 256, 2, 4),
+        (192, 192, 65536, 64, 64, False, True, True): (3, 512, 1, 4),
+        (192, 192, 65536, 64, 64, True, False, True): (1, 512, 2, 4),
+        (192, 192, 131072, 64, 64, False, True, True): (5, 1024, 1, 4),
+        (192, 192, 131072, 64, 64, True, False, True): (4, 512, 2, 4),
+        (384, 384, 256, 128, 128, False, True, True): (3, 2, 3, 8),
+        (384, 384, 256, 128, 128, True, False, True): (1, 2, 3, 8),
+        (384, 384, 512, 128, 128, False, True, True): (4, 4, 3, 8),
+        (384, 384, 512, 128, 128, True, False, True): (3, 4, 3, 8),
+        (384, 384, 1024, 128, 128, False, True, True): (1, 8, 3, 8),
+        (384, 384, 1024, 128, 128, True, False, True): (2, 8, 3, 8),
+        (384, 384, 2048, 128, 128, False, True, True): (5, 16, 3, 8),
+        (384, 384, 2048, 128, 128, True, False, True): (5, 16, 3, 8),
+        (384, 384, 4096, 128, 128, False, True, True): (3, 32, 3, 8),
+        (384, 384, 4096, 128, 128, True, False, True): (6, 32, 3, 8),
+        (384, 384, 8192, 128, 128, False, True, True): (2, 64, 3, 8),
+        (384, 384, 8192, 128, 128, True, False, True): (4, 32, 2, 8),
+        (384, 384, 16384, 128, 128, False, True, True): (2, 128, 3, 8),
+        (384, 384, 16384, 128, 128, True, False, True): (5, 128, 2, 4),
+        (384, 384, 32768, 128, 128, False, True, True): (2, 256, 3, 8),
+        (384, 384, 32768, 128, 128, True, False, True): (3, 256, 2, 4),
+        (384, 384, 65536, 128, 128, False, True, True): (3, 512, 1, 4),
+        (384, 384, 65536, 128, 128, True, False, True): (1, 512, 2, 4),
+        (384, 384, 131072, 128, 128, False, True, True): (3, 1024, 1, 4),
+        (384, 384, 131072, 128, 128, True, False, True): (1, 1024, 2, 4),
+    },
+    ("bsr_dense_addmm", "NVIDIA A100-SXM4-80GB", (0, torch.float16, 0.5)): {
+        (16, 16, 16, 16, 16, False, False, False): (1, 1, 1, 1),
+        (16, 16, 16, 16, 16, False, False, True): (1, 1, 2, 2),
+        (16, 16, 16, 16, 16, False, True, False): (1, 1, 1, 1),
+        (16, 16, 16, 16, 16, False, True, True): (1, 1, 1, 8),
+        (16, 16, 16, 16, 16, True, False, False): (3, 1, 3, 4),
+        (16, 16, 16, 16, 16, True, False, True): (1, 1, 2, 1),
+        (16, 16, 32, 16, 16, False, False, False): (1, 2, 1, 8),
+        (16, 16, 32, 16, 16, False, False, True): (1, 2, 1, 2),
+        (16, 16, 32, 16, 16, False, True, False): (2, 1, 1, 4),
+        (16, 16, 32, 16, 16, False, True, True): (1, 2, 1, 4),
+        (16, 16, 32, 16, 16, True, False, False): (1, 1, 1, 4),
+        (16, 16, 32, 16, 16, True, False, True): (1, 2, 1, 2),
+        (16, 16, 64, 16, 16, False, False, False): (1, 4, 1, 1),
+        (16, 16, 64, 16, 16, False, False, True): (1, 2, 2, 4),
+        (16, 16, 64, 16, 16, False, True, False): (1, 4, 1, 4),
+        (16, 16, 64, 16, 16, False, True, True): (1, 2, 1, 4),
+        (16, 16, 64, 16, 16, True, False, False): (1, 4, 1, 2),
+        (16, 16, 64, 16, 16, True, False, True): (1, 1, 1, 2),
+        (16, 32, 16, 16, 16, False, False, False): (1, 1, 2, 4),
+        (16, 32, 16, 16, 16, False, False, True): (1, 1, 1, 4),
+        (16, 32, 16, 16, 16, False, True, False): (1, 1, 1, 2),
+        (16, 32, 16, 16, 16, False, True, True): (1, 1, 1, 2),
+        (16, 32, 16, 16, 16, True, False, False): (1, 1, 2, 16),
+        (16, 32, 16, 16, 16, True, False, True): (1, 1, 1, 4),
+        (16, 32, 16, 16, 32, False, False, False): (2, 1, 1, 8),
+        (16, 32, 16, 16, 32, False, False, True): (2, 1, 1, 8),
+        (16, 32, 16, 16, 32, False, True, False): (1, 1, 2, 1),
+        (16, 32, 16, 16, 32, False, True, True): (1, 1, 1, 4),
+        (16, 32, 16, 16, 32, True, False, False): (2, 1, 1, 8),
+        (16, 32, 16, 16, 32, True, False, True): (1, 1, 2, 4),
+        (16, 32, 32, 16, 16, False, False, False): (1, 1, 1, 16),
+        (16, 32, 32, 16, 16, False, False, True): (1, 2, 1, 2),
+        (16, 32, 32, 16, 16, False, True, False): (1, 2, 1, 8),
+        (16, 32, 32, 16, 16, False, True, True): (3, 2, 1, 4),
+        (16, 32, 32, 16, 16, True, False, False): (1, 2, 1, 4),
+        (16, 32, 32, 16, 16, True, False, True): (1, 2, 1, 2),
+        (16, 32, 32, 16, 32, False, False, False): (1, 2, 1, 2),
+        (16, 32, 32, 16, 32, False, False, True): (1, 1, 1, 4),
+        (16, 32, 32, 16, 32, False, True, False): (1, 1, 2, 4),
+        (16, 32, 32, 16, 32, False, True, True): (1, 2, 1, 2),
+        (16, 32, 32, 16, 32, True, False, False): (1, 2, 1, 2),
+        (16, 32, 32, 16, 32, True, False, True): (1, 2, 1, 16),
+        (16, 32, 64, 16, 16, False, False, False): (1, 4, 1, 4),
+        (16, 32, 64, 16, 16, False, False, True): (2, 4, 1, 4),
+        (16, 32, 64, 16, 16, False, True, False): (1, 4, 1, 4),
+        (16, 32, 64, 16, 16, False, True, True): (1, 4, 1, 4),
+        (16, 32, 64, 16, 16, True, False, False): (3, 4, 1, 2),
+        (16, 32, 64, 16, 16, True, False, True): (1, 4, 1, 1),
+        (16, 32, 64, 16, 32, False, False, False): (1, 4, 1, 16),
+        (16, 32, 64, 16, 32, False, False, True): (1, 2, 1, 2),
+        (16, 32, 64, 16, 32, False, True, False): (1, 4, 2, 2),
+        (16, 32, 64, 16, 32, False, True, True): (1, 4, 1, 8),
+        (16, 32, 64, 16, 32, True, False, False): (1, 4, 1, 8),
+        (16, 32, 64, 16, 32, True, False, True): (1, 2, 1, 4),
+        (16, 64, 16, 16, 32, False, False, False): (1, 1, 1, 2),
+        (16, 64, 16, 16, 32, False, False, True): (1, 1, 1, 4),
+        (16, 64, 16, 16, 32, False, True, False): (2, 1, 2, 4),
+        (16, 64, 16, 16, 32, False, True, True): (1, 1, 1, 4),
+        (16, 64, 16, 16, 32, True, False, False): (1, 1, 1, 4),
+        (16, 64, 16, 16, 32, True, False, True): (1, 1, 1, 4),
+        (16, 64, 32, 16, 32, False, False, False): (1, 2, 1, 2),
+        (16, 64, 32, 16, 32, False, False, True): (1, 1, 1, 4),
+        (16, 64, 32, 16, 32, False, True, False): (1, 1, 1, 4),
+        (16, 64, 32, 16, 32, False, True, True): (1, 2, 3, 2),
+        (16, 64, 32, 16, 32, True, False, False): (1, 1, 1, 4),
+        (16, 64, 32, 16, 32, True, False, True): (1, 1, 2, 4),
+        (16, 64, 64, 16, 32, False, False, False): (1, 4, 1, 8),
+        (16, 64, 64, 16, 32, False, False, True): (1, 4, 1, 4),
+        (16, 64, 64, 16, 32, False, True, False): (1, 4, 1, 1),
+        (16, 64, 64, 16, 32, False, True, True): (2, 4, 1, 4),
+        (16, 64, 64, 16, 32, True, False, False): (1, 4, 1, 4),
+        (16, 64, 64, 16, 32, True, False, True): (1, 4, 1, 4),
+        (32, 16, 16, 16, 16, False, False, False): (2, 1, 2, 4),
+        (32, 16, 16, 16, 16, False, False, True): (2, 1, 1, 2),
+        (32, 16, 16, 16, 16, False, True, False): (1, 1, 2, 4),
+        (32, 16, 16, 16, 16, False, True, True): (1, 1, 1, 2),
+        (32, 16, 16, 16, 16, True, False, False): (1, 1, 1, 4),
+        (32, 16, 16, 16, 16, True, False, True): (2, 1, 1, 2),
+        (32, 16, 32, 16, 16, False, False, False): (1, 1, 1, 4),
+        (32, 16, 32, 16, 16, False, False, True): (1, 1, 1, 4),
+        (32, 16, 32, 16, 16, False, True, False): (1, 2, 1, 4),
+        (32, 16, 32, 16, 16, False, True, True): (2, 2, 1, 4),
+        (32, 16, 32, 16, 16, True, False, False): (2, 1, 1, 4),
+        (32, 16, 32, 16, 16, True, False, True): (2, 2, 1, 2),
+        (32, 16, 64, 16, 16, False, False, False): (1, 4, 1, 2),
+        (32, 16, 64, 16, 16, False, False, True): (1, 4, 1, 4),
+        (32, 16, 64, 16, 16, False, True, False): (1, 2, 1, 4),
+        (32, 16, 64, 16, 16, False, True, True): (1, 4, 1, 2),
+        (32, 16, 64, 16, 16, True, False, False): (1, 4, 2, 8),
+        (32, 16, 64, 16, 16, True, False, True): (1, 4, 1, 1),
+        (32, 32, 16, 16, 16, False, False, False): (1, 1, 1, 4),
+        (32, 32, 16, 16, 16, False, False, True): (2, 1, 1, 4),
+        (32, 32, 16, 16, 16, False, True, False): (1, 1, 2, 4),
+        (32, 32, 16, 16, 16, False, True, True): (1, 1, 2, 2),
+        (32, 32, 16, 16, 16, True, False, False): (1, 1, 1, 8),
+        (32, 32, 16, 16, 16, True, False, True): (1, 1, 1, 4),
+        (32, 32, 16, 16, 32, False, False, False): (1, 1, 3, 2),
+        (32, 32, 16, 16, 32, False, False, True): (2, 1, 1, 4),
+        (32, 32, 16, 16, 32, False, True, False): (3, 1, 1, 4),
+        (32, 32, 16, 16, 32, False, True, True): (1, 1, 1, 4),
+        (32, 32, 16, 16, 32, True, False, False): (2, 1, 1, 8),
+        (32, 32, 16, 16, 32, True, False, True): (1, 1, 3, 2),
+        (32, 32, 16, 32, 32, False, False, False): (1, 1, 1, 2),
+        (32, 32, 16, 32, 32, False, False, True): (2, 1, 1, 8),
+        (32, 32, 16, 32, 32, False, True, False): (1, 1, 1, 2),
+        (32, 32, 16, 32, 32, False, True, True): (1, 1, 1, 8),
+        (32, 32, 16, 32, 32, True, False, False): (1, 1, 2, 4),
+        (32, 32, 16, 32, 32, True, False, True): (1, 1, 1, 2),
+        (32, 32, 32, 16, 16, False, False, False): (1, 1, 1, 4),
+        (32, 32, 32, 16, 16, False, False, True): (1, 2, 1, 4),
+        (32, 32, 32, 16, 16, False, True, False): (1, 2, 1, 4),
+        (32, 32, 32, 16, 16, False, True, True): (1, 2, 1, 2),
+        (32, 32, 32, 16, 16, True, False, False): (1, 2, 1, 4),
+        (32, 32, 32, 16, 16, True, False, True): (1, 2, 1, 4),
+        (32, 32, 32, 16, 32, False, False, False): (1, 2, 1, 4),
+        (32, 32, 32, 16, 32, False, False, True): (1, 2, 1, 2),
+        (32, 32, 32, 16, 32, False, True, False): (1, 2, 1, 4),
+        (32, 32, 32, 16, 32, False, True, True): (1, 2, 1, 2),
+        (32, 32, 32, 16, 32, True, False, False): (1, 2, 1, 1),
+        (32, 32, 32, 16, 32, True, False, True): (1, 2, 1, 2),
+        (32, 32, 32, 32, 32, False, False, False): (1, 1, 1, 4),
+        (32, 32, 32, 32, 32, False, False, True): (2, 1, 1, 4),
+        (32, 32, 32, 32, 32, False, True, False): (1, 1, 1, 8),
+        (32, 32, 32, 32, 32, False, True, True): (1, 1, 1, 8),
+        (32, 32, 32, 32, 32, True, False, False): (1, 1, 3, 4),
+        (32, 32, 32, 32, 32, True, False, True): (1, 1, 1, 8),
+        (32, 32, 64, 16, 16, False, False, False): (1, 4, 1, 4),
+        (32, 32, 64, 16, 16, False, False, True): (1, 4, 1, 2),
+        (32, 32, 64, 16, 16, False, True, False): (1, 1, 1, 4),
+        (32, 32, 64, 16, 16, False, True, True): (1, 4, 1, 4),
+        (32, 32, 64, 16, 16, True, False, False): (1, 4, 1, 8),
+        (32, 32, 64, 16, 16, True, False, True): (1, 4, 1, 2),
+        (32, 32, 64, 16, 32, False, False, False): (1, 1, 1, 4),
+        (32, 32, 64, 16, 32, False, False, True): (1, 4, 1, 4),
+        (32, 32, 64, 16, 32, False, True, False): (1, 1, 1, 4),
+        (32, 32, 64, 16, 32, False, True, True): (1, 4, 1, 4),
+        (32, 32, 64, 16, 32, True, False, False): (2, 2, 1, 8),
+        (32, 32, 64, 16, 32, True, False, True): (1, 2, 1, 2),
+        (32, 32, 64, 32, 32, False, False, False): (1, 2, 1, 4),
+        (32, 32, 64, 32, 32, False, False, True): (1, 2, 1, 1),
+        (32, 32, 64, 32, 32, False, True, False): (1, 2, 2, 8),
+        (32, 32, 64, 32, 32, False, True, True): (1, 1, 1, 4),
+        (32, 32, 64, 32, 32, True, False, False): (1, 2, 1, 4),
+        (32, 32, 64, 32, 32, True, False, True): (2, 2, 1, 4),
+        (32, 64, 16, 16, 32, False, False, False): (1, 1, 1, 8),
+        (32, 64, 16, 16, 32, False, False, True): (1, 1, 1, 4),
+        (32, 64, 16, 16, 32, False, True, False): (2, 1, 1, 4),
+        (32, 64, 16, 16, 32, False, True, True): (1, 1, 1, 4),
+        (32, 64, 16, 16, 32, True, False, False): (1, 1, 2, 4),
+        (32, 64, 16, 16, 32, True, False, True): (1, 1, 2, 2),
+        (32, 64, 16, 32, 32, False, False, False): (1, 1, 1, 8),
+        (32, 64, 16, 32, 32, False, False, True): (2, 1, 1, 4),
+        (32, 64, 16, 32, 32, False, True, False): (1, 1, 1, 4),
+        (32, 64, 16, 32, 32, False, True, True): (1, 1, 2, 2),
+        (32, 64, 16, 32, 32, True, False, False): (1, 1, 1, 2),
+        (32, 64, 16, 32, 32, True, False, True): (2, 1, 2, 4),
+        (32, 64, 32, 16, 32, False, False, False): (1, 1, 1, 4),
+        (32, 64, 32, 16, 32, False, False, True): (1, 2, 1, 2),
+        (32, 64, 32, 16, 32, False, True, False): (1, 2, 3, 4),
+        (32, 64, 32, 16, 32, False, True, True): (2, 2, 1, 4),
+        (32, 64, 32, 16, 32, True, False, False): (1, 1, 1, 4),
+        (32, 64, 32, 16, 32, True, False, True): (1, 2, 2, 1),
+        (32, 64, 32, 32, 32, False, False, False): (1, 1, 1, 8),
+        (32, 64, 32, 32, 32, False, False, True): (1, 1, 1, 4),
+        (32, 64, 32, 32, 32, False, True, False): (1, 1, 2, 4),
+        (32, 64, 32, 32, 32, False, True, True): (1, 1, 1, 4),
+        (32, 64, 32, 32, 32, True, False, False): (2, 1, 1, 2),
+        (32, 64, 32, 32, 32, True, False, True): (1, 1, 1, 4),
+        (32, 64, 64, 16, 32, False, False, False): (1, 4, 2, 1),
+        (32, 64, 64, 16, 32, False, False, True): (3, 4, 1, 4),
+        (32, 64, 64, 16, 32, False, True, False): (1, 1, 1, 8),
+        (32, 64, 64, 16, 32, False, True, True): (1, 4, 1, 4),
+        (32, 64, 64, 16, 32, True, False, False): (1, 4, 1, 4),
+        (32, 64, 64, 16, 32, True, False, True): (2, 2, 3, 4),
+        (32, 64, 64, 32, 32, False, False, False): (1, 2, 1, 4),
+        (32, 64, 64, 32, 32, False, False, True): (1, 2, 1, 4),
+        (32, 64, 64, 32, 32, False, True, False): (1, 2, 2, 8),
+        (32, 64, 64, 32, 32, False, True, True): (1, 2, 1, 4),
+        (32, 64, 64, 32, 32, True, False, False): (1, 2, 2, 4),
+        (32, 64, 64, 32, 32, True, False, True): (1, 2, 1, 4),
+        (64, 32, 16, 32, 32, False, False, False): (1, 1, 1, 1),
+        (64, 32, 16, 32, 32, False, False, True): (1, 1, 2, 4),
+        (64, 32, 16, 32, 32, False, True, False): (2, 1, 1, 8),
+        (64, 32, 16, 32, 32, False, True, True): (1, 1, 1, 4),
+        (64, 32, 16, 32, 32, True, False, False): (2, 1, 1, 2),
+        (64, 32, 16, 32, 32, True, False, True): (1, 1, 1, 4),
+        (64, 32, 32, 32, 32, False, False, False): (3, 1, 1, 4),
+        (64, 32, 32, 32, 32, False, False, True): (1, 1, 1, 4),
+        (64, 32, 32, 32, 32, False, True, False): (1, 1, 1, 8),
+        (64, 32, 32, 32, 32, False, True, True): (1, 1, 1, 2),
+        (64, 32, 32, 32, 32, True, False, False): (1, 1, 1, 2),
+        (64, 32, 32, 32, 32, True, False, True): (1, 1, 1, 4),
+        (64, 32, 64, 32, 32, False, False, False): (1, 2, 1, 2),
+        (64, 32, 64, 32, 32, False, False, True): (3, 2, 1, 4),
+        (64, 32, 64, 32, 32, False, True, False): (1, 1, 1, 1),
+        (64, 32, 64, 32, 32, False, True, True): (1, 2, 1, 4),
+        (64, 32, 64, 32, 32, True, False, False): (1, 1, 3, 4),
+        (64, 32, 64, 32, 32, True, False, True): (1, 2, 2, 4),
+        (64, 64, 16, 32, 32, False, False, False): (1, 1, 2, 2),
+        (64, 64, 16, 32, 32, False, False, True): (1, 1, 3, 2),
+        (64, 64, 16, 32, 32, False, True, False): (1, 1, 1, 8),
+        (64, 64, 16, 32, 32, False, True, True): (1, 1, 2, 4),
+        (64, 64, 16, 32, 32, True, False, False): (1, 1, 2, 4),
+        (64, 64, 16, 32, 32, True, False, True): (2, 1, 2, 4),
+        (64, 64, 32, 32, 32, False, False, False): (1, 1, 2, 8),
+        (64, 64, 32, 32, 32, False, False, True): (1, 1, 2, 4),
+        (64, 64, 32, 32, 32, False, True, False): (1, 1, 1, 4),
+        (64, 64, 32, 32, 32, False, True, True): (1, 1, 1, 4),
+        (64, 64, 32, 32, 32, True, False, False): (1, 1, 1, 4),
+        (64, 64, 32, 32, 32, True, False, True): (2, 1, 2, 4),
+        (64, 64, 64, 32, 32, False, False, False): (1, 2, 1, 4),
+        (64, 64, 64, 32, 32, False, False, True): (1, 2, 1, 4),
+        (64, 64, 64, 32, 32, False, True, False): (1, 2, 1, 4),
+        (64, 64, 64, 32, 32, False, True, True): (3, 2, 1, 4),
+        (64, 64, 64, 32, 32, True, False, False): (1, 2, 1, 8),
+        (64, 64, 64, 32, 32, True, False, True): (1, 2, 3, 4),
+        (192, 192, 256, 16, 16, False, True, True): (1, 8, 4, 2),
+        (192, 192, 256, 16, 16, True, False, True): (1, 4, 4, 4),
+        (192, 192, 256, 32, 32, False, True, True): (2, 8, 5, 4),
+        (192, 192, 256, 32, 32, True, False, True): (2, 8, 5, 1),
+        (192, 192, 512, 16, 16, False, True, True): (3, 8, 4, 4),
+        (192, 192, 512, 16, 16, True, False, True): (5, 8, 5, 4),
+        (192, 192, 512, 32, 32, False, True, True): (1, 16, 5, 4),
+        (192, 192, 512, 32, 32, True, False, True): (1, 8, 6, 2),
+        (192, 192, 1024, 16, 16, False, True, True): (1, 16, 4, 4),
+        (192, 192, 1024, 16, 16, True, False, True): (3, 16, 5, 2),
+        (192, 192, 1024, 32, 32, False, True, True): (3, 16, 4, 4),
+        (192, 192, 1024, 32, 32, True, False, True): (1, 16, 5, 4),
+        (192, 192, 2048, 16, 16, False, True, True): (2, 16, 3, 4),
+        (192, 192, 2048, 16, 16, True, False, True): (1, 16, 4, 4),
+        (192, 192, 2048, 32, 32, False, True, True): (1, 32, 3, 4),
+        (192, 192, 2048, 32, 32, True, False, True): (3, 16, 4, 4),
+        (192, 192, 4096, 16, 16, False, True, True): (1, 64, 1, 4),
+        (192, 192, 4096, 16, 16, True, False, True): (1, 16, 3, 4),
+        (192, 192, 4096, 32, 32, False, True, True): (1, 128, 1, 4),
+        (192, 192, 4096, 32, 32, True, False, True): (2, 32, 4, 2),
+        (192, 192, 8192, 16, 16, False, True, True): (1, 64, 1, 4),
+        (192, 192, 8192, 16, 16, True, False, True): (2, 64, 3, 2),
+        (192, 192, 8192, 32, 32, False, True, True): (1, 128, 1, 4),
+        (192, 192, 8192, 32, 32, True, False, True): (4, 32, 3, 4),
+        (192, 192, 16384, 16, 16, False, True, True): (1, 128, 1, 4),
+        (192, 192, 16384, 16, 16, True, False, True): (1, 64, 3, 2),
+        (192, 192, 16384, 32, 32, False, True, True): (1, 128, 1, 4),
+        (192, 192, 16384, 32, 32, True, False, True): (1, 64, 3, 4),
+        (192, 192, 32768, 16, 16, False, True, True): (2, 256, 1, 2),
+        (192, 192, 32768, 16, 16, True, False, True): (1, 128, 3, 2),
+        (192, 192, 32768, 32, 32, False, True, True): (2, 256, 1, 4),
+        (192, 192, 32768, 32, 32, True, False, True): (1, 256, 3, 2),
+        (192, 192, 65536, 16, 16, False, True, True): (2, 512, 1, 2),
+        (192, 192, 65536, 16, 16, True, False, True): (1, 256, 3, 2),
+        (192, 192, 65536, 32, 32, False, True, True): (2, 512, 1, 4),
+        (192, 192, 65536, 32, 32, True, False, True): (2, 256, 3, 4),
+        (192, 192, 131072, 16, 16, False, True, True): (4, 1024, 1, 2),
+        (192, 192, 131072, 16, 16, True, False, True): (3, 512, 3, 2),
+        (192, 192, 131072, 32, 32, False, True, True): (1, 1024, 1, 4),
+        (192, 192, 131072, 32, 32, True, False, True): (3, 512, 3, 4),
+        (256, 256, 256, 16, 16, False, True, True): (4, 8, 6, 2),
+        (256, 256, 256, 16, 16, True, False, True): (5, 16, 5, 1),
+        (256, 256, 256, 32, 32, False, True, True): (1, 8, 7, 4),
+        (256, 256, 256, 32, 32, True, False, True): (1, 8, 5, 4),
+        (256, 256, 256, 64, 64, False, True, True): (1, 4, 5, 4),
+        (256, 256, 256, 64, 64, True, False, True): (2, 4, 3, 4),
+        (256, 256, 256, 128, 128, False, True, True): (1, 2, 2, 8),
+        (256, 256, 256, 128, 128, True, False, True): (1, 2, 2, 8),
+        (256, 256, 512, 16, 16, False, True, True): (4, 8, 4, 4),
+        (256, 256, 512, 16, 16, True, False, True): (4, 8, 6, 2),
+        (256, 256, 512, 32, 32, False, True, True): (3, 8, 5, 4),
+        (256, 256, 512, 32, 32, True, False, True): (2, 8, 5, 4),
+        (256, 256, 512, 64, 64, False, True, True): (2, 8, 4, 4),
+        (256, 256, 512, 64, 64, True, False, True): (1, 8, 7, 4),
+        (256, 256, 512, 128, 128, False, True, True): (2, 4, 2, 8),
+        (256, 256, 512, 128, 128, True, False, True): (5, 4, 2, 8),
+        (256, 256, 1024, 16, 16, False, True, True): (1, 8, 4, 4),
+        (256, 256, 1024, 16, 16, True, False, True): (1, 16, 4, 2),
+        (256, 256, 1024, 32, 32, False, True, True): (5, 32, 5, 1),
+        (256, 256, 1024, 32, 32, True, False, True): (1, 16, 4, 2),
+        (256, 256, 1024, 64, 64, False, True, True): (1, 16, 4, 4),
+        (256, 256, 1024, 64, 64, True, False, True): (2, 16, 3, 4),
+        (256, 256, 1024, 128, 128, False, True, True): (9, 8, 2, 8),
+        (256, 256, 1024, 128, 128, True, False, True): (1, 8, 2, 8),
+        (256, 256, 2048, 16, 16, False, True, True): (6, 32, 5, 2),
+        (256, 256, 2048, 16, 16, True, False, True): (2, 32, 4, 2),
+        (256, 256, 2048, 32, 32, False, True, True): (1, 32, 3, 2),
+        (256, 256, 2048, 32, 32, True, False, True): (1, 32, 3, 2),
+        (256, 256, 2048, 64, 64, False, True, True): (2, 32, 4, 4),
+        (256, 256, 2048, 64, 64, True, False, True): (2, 16, 4, 4),
+        (256, 256, 2048, 128, 128, False, True, True): (3, 16, 2, 8),
+        (256, 256, 2048, 128, 128, True, False, True): (4, 16, 2, 8),
+        (256, 256, 4096, 16, 16, False, True, True): (1, 32, 3, 4),
+        (256, 256, 4096, 16, 16, True, False, True): (3, 16, 3, 2),
+        (256, 256, 4096, 32, 32, False, True, True): (3, 32, 3, 2),
+        (256, 256, 4096, 32, 32, True, False, True): (1, 32, 3, 2),
+        (256, 256, 4096, 64, 64, False, True, True): (2, 32, 3, 4),
+        (256, 256, 4096, 64, 64, True, False, True): (2, 32, 3, 4),
+        (256, 256, 4096, 128, 128, False, True, True): (5, 32, 2, 8),
+        (256, 256, 4096, 128, 128, True, False, True): (1, 32, 2, 8),
+        (256, 256, 8192, 16, 16, False, True, True): (8, 32, 3, 4),
+        (256, 256, 8192, 16, 16, True, False, True): (1, 32, 3, 2),
+        (256, 256, 8192, 32, 32, False, True, True): (3, 64, 3, 4),
+        (256, 256, 8192, 32, 32, True, False, True): (2, 128, 1, 2),
+        (256, 256, 8192, 64, 64, False, True, True): (7, 128, 1, 4),
+        (256, 256, 8192, 64, 64, True, False, True): (4, 128, 1, 4),
+        (256, 256, 8192, 128, 128, False, True, True): (2, 64, 1, 4),
+        (256, 256, 8192, 128, 128, True, False, True): (4, 64, 1, 4),
+        (256, 256, 16384, 16, 16, False, True, True): (4, 128, 3, 2),
+        (256, 256, 16384, 16, 16, True, False, True): (5, 64, 3, 2),
+        (256, 256, 16384, 32, 32, False, True, True): (5, 128, 3, 2),
+        (256, 256, 16384, 32, 32, True, False, True): (5, 128, 3, 2),
+        (256, 256, 16384, 64, 64, False, True, True): (1, 256, 1, 4),
+        (256, 256, 16384, 64, 64, True, False, True): (5, 128, 3, 4),
+        (256, 256, 16384, 128, 128, False, True, True): (11, 128, 2, 8),
+        (256, 256, 16384, 128, 128, True, False, True): (3, 128, 1, 4),
+        (256, 256, 32768, 16, 16, False, True, True): (1, 128, 3, 4),
+        (256, 256, 32768, 16, 16, True, False, True): (2, 128, 3, 2),
+        (256, 256, 32768, 32, 32, False, True, True): (4, 256, 3, 2),
+        (256, 256, 32768, 32, 32, True, False, True): (1, 256, 3, 2),
+        (256, 256, 32768, 64, 64, False, True, True): (2, 256, 1, 4),
+        (256, 256, 32768, 64, 64, True, False, True): (2, 256, 1, 4),
+        (256, 256, 32768, 128, 128, False, True, True): (3, 256, 1, 4),
+        (256, 256, 32768, 128, 128, True, False, True): (2, 256, 1, 4),
+        (256, 256, 50432, 16, 16, False, True, True): (4, 197, 1, 4),
+        (256, 256, 50432, 16, 16, True, False, True): (4, 197, 3, 2),
+        (256, 256, 50432, 32, 32, False, True, True): (1, 394, 1, 2),
+        (256, 256, 50432, 32, 32, True, False, True): (4, 197, 3, 4),
+        (256, 256, 50432, 64, 64, False, True, True): (6, 394, 1, 4),
+        (256, 256, 50432, 64, 64, True, False, True): (4, 394, 2, 4),
+        (256, 256, 50432, 128, 128, False, True, True): (3, 394, 1, 4),
+        (256, 256, 50432, 128, 128, True, False, True): (1, 394, 2, 4),
+        (256, 256, 65536, 16, 16, False, True, True): (1, 256, 3, 2),
+        (256, 256, 65536, 16, 16, True, False, True): (1, 256, 3, 2),
+        (256, 256, 65536, 32, 32, False, True, True): (1, 512, 3, 2),
+        (256, 256, 65536, 32, 32, True, False, True): (4, 512, 3, 2),
+        (256, 256, 65536, 64, 64, False, True, True): (2, 512, 1, 4),
+        (256, 256, 65536, 64, 64, True, False, True): (5, 512, 1, 4),
+        (256, 256, 65536, 128, 128, False, True, True): (3, 512, 1, 4),
+        (256, 256, 65536, 128, 128, True, False, True): (1, 512, 1, 4),
+        (256, 256, 131072, 16, 16, False, True, True): (1, 512, 3, 1),
+        (256, 256, 131072, 16, 16, True, False, True): (1, 512, 3, 2),
+        (256, 256, 131072, 32, 32, False, True, True): (2, 1024, 3, 2),
+        (256, 256, 131072, 32, 32, True, False, True): (1, 1024, 3, 2),
+        (256, 256, 131072, 64, 64, False, True, True): (1, 1024, 1, 4),
+        (256, 256, 131072, 64, 64, True, False, True): (1, 1024, 1, 4),
+        (256, 256, 131072, 128, 128, False, True, True): (7, 1024, 1, 4),
+        (256, 256, 131072, 128, 128, True, False, True): (1, 1024, 1, 4),
+        (384, 384, 256, 16, 16, False, True, True): (3, 16, 4, 1),
+        (384, 384, 256, 16, 16, True, False, True): (2, 4, 6, 2),
+        (384, 384, 256, 32, 32, False, True, True): (1, 8, 4, 4),
+        (384, 384, 256, 32, 32, True, False, True): (1, 4, 5, 2),
+        (384, 384, 256, 64, 64, False, True, True): (3, 4, 3, 4),
+        (384, 384, 256, 64, 64, True, False, True): (4, 4, 5, 4),
+        (384, 384, 512, 16, 16, False, True, True): (1, 16, 4, 1),
+        (384, 384, 512, 16, 16, True, False, True): (1, 8, 5, 2),
+        (384, 384, 512, 32, 32, False, True, True): (4, 16, 4, 2),
+        (384, 384, 512, 32, 32, True, False, True): (1, 8, 5, 2),
+        (384, 384, 512, 64, 64, False, True, True): (2, 8, 3, 4),
+        (384, 384, 512, 64, 64, True, False, True): (1, 8, 4, 4),
+        (384, 384, 1024, 16, 16, False, True, True): (1, 16, 4, 2),
+        (384, 384, 1024, 16, 16, True, False, True): (7, 8, 5, 2),
+        (384, 384, 1024, 32, 32, False, True, True): (2, 16, 3, 4),
+        (384, 384, 1024, 32, 32, True, False, True): (1, 16, 4, 2),
+        (384, 384, 1024, 64, 64, False, True, True): (6, 16, 3, 4),
+        (384, 384, 1024, 64, 64, True, False, True): (4, 16, 4, 4),
+        (384, 384, 2048, 16, 16, False, True, True): (1, 32, 1, 4),
+        (384, 384, 2048, 16, 16, True, False, True): (1, 16, 3, 2),
+        (384, 384, 2048, 32, 32, False, True, True): (1, 32, 1, 8),
+        (384, 384, 2048, 32, 32, True, False, True): (1, 8, 4, 4),
+        (384, 384, 2048, 64, 64, False, True, True): (2, 32, 1, 8),
+        (384, 384, 2048, 64, 64, True, False, True): (3, 16, 3, 4),
+        (384, 384, 4096, 16, 16, False, True, True): (5, 32, 1, 4),
+        (384, 384, 4096, 16, 16, True, False, True): (1, 32, 3, 2),
+        (384, 384, 4096, 32, 32, False, True, True): (1, 32, 1, 8),
+        (384, 384, 4096, 32, 32, True, False, True): (2, 16, 4, 4),
+        (384, 384, 4096, 64, 64, False, True, True): (1, 64, 1, 4),
+        (384, 384, 4096, 64, 64, True, False, True): (2, 32, 3, 4),
+        (384, 384, 8192, 16, 16, False, True, True): (2, 64, 1, 4),
+        (384, 384, 8192, 16, 16, True, False, True): (3, 32, 3, 2),
+        (384, 384, 8192, 32, 32, False, True, True): (4, 128, 1, 4),
+        (384, 384, 8192, 32, 32, True, False, True): (1, 32, 3, 2),
+        (384, 384, 8192, 64, 64, False, True, True): (1, 128, 1, 4),
+        (384, 384, 8192, 64, 64, True, False, True): (1, 64, 3, 4),
+        (384, 384, 16384, 16, 16, False, True, True): (1, 128, 1, 2),
+        (384, 384, 16384, 16, 16, True, False, True): (1, 64, 3, 2),
+        (384, 384, 16384, 32, 32, False, True, True): (1, 128, 1, 4),
+        (384, 384, 16384, 32, 32, True, False, True): (1, 64, 3, 4),
+        (384, 384, 16384, 64, 64, False, True, True): (5, 128, 3, 4),
+        (384, 384, 16384, 64, 64, True, False, True): (1, 128, 3, 4),
+        (384, 384, 32768, 16, 16, False, True, True): (2, 256, 1, 2),
+        (384, 384, 32768, 16, 16, True, False, True): (1, 128, 3, 4),
+        (384, 384, 32768, 32, 32, False, True, True): (1, 256, 1, 2),
+        (384, 384, 32768, 32, 32, True, False, True): (2, 128, 3, 4),
+        (384, 384, 32768, 64, 64, False, True, True): (3, 256, 1, 4),
+        (384, 384, 32768, 64, 64, True, False, True): (2, 256, 3, 4),
+        (384, 384, 65536, 16, 16, False, True, True): (2, 128, 1, 4),
+        (384, 384, 65536, 16, 16, True, False, True): (1, 256, 3, 4),
+        (384, 384, 65536, 32, 32, False, True, True): (1, 512, 1, 2),
+        (384, 384, 65536, 32, 32, True, False, True): (1, 256, 3, 4),
+        (384, 384, 65536, 64, 64, False, True, True): (3, 512, 1, 4),
+        (384, 384, 65536, 64, 64, True, False, True): (3, 256, 3, 4),
+        (384, 384, 131072, 16, 16, False, True, True): (2, 256, 1, 2),
+        (384, 384, 131072, 16, 16, True, False, True): (1, 512, 3, 4),
+        (384, 384, 131072, 32, 32, False, True, True): (1, 512, 1, 2),
+        (384, 384, 131072, 32, 32, True, False, True): (1, 512, 3, 4),
+        (384, 384, 131072, 64, 64, False, True, True): (3, 1024, 1, 4),
+        (384, 384, 131072, 64, 64, True, False, True): (3, 512, 3, 4),
+        (512, 512, 256, 16, 16, False, True, True): (1, 8, 5, 1),
+        (512, 512, 256, 16, 16, True, False, True): (2, 16, 5, 1),
+        (512, 512, 256, 32, 32, False, True, True): (2, 8, 5, 2),
+        (512, 512, 256, 32, 32, True, False, True): (4, 4, 5, 2),
+        (512, 512, 256, 64, 64, False, True, True): (1, 4, 5, 4),
+        (512, 512, 256, 64, 64, True, False, True): (3, 4, 5, 4),
+        (512, 512, 256, 128, 128, False, True, True): (1, 2, 2, 8),
+        (512, 512, 256, 128, 128, True, False, True): (1, 2, 2, 8),
+        (512, 512, 512, 16, 16, False, True, True): (1, 8, 4, 4),
+        (512, 512, 512, 16, 16, True, False, True): (4, 16, 5, 1),
+        (512, 512, 512, 32, 32, False, True, True): (4, 8, 5, 2),
+        (512, 512, 512, 32, 32, True, False, True): (7, 16, 4, 1),
+        (512, 512, 512, 64, 64, False, True, True): (3, 8, 5, 4),
+        (512, 512, 512, 64, 64, True, False, True): (1, 8, 4, 4),
+        (512, 512, 512, 128, 128, False, True, True): (4, 4, 2, 8),
+        (512, 512, 512, 128, 128, True, False, True): (4, 4, 2, 8),
+        (512, 512, 1024, 16, 16, False, True, True): (2, 8, 4, 4),
+        (512, 512, 1024, 16, 16, True, False, True): (2, 16, 4, 2),
+        (512, 512, 1024, 32, 32, False, True, True): (3, 16, 4, 2),
+        (512, 512, 1024, 32, 32, True, False, True): (3, 16, 3, 2),
+        (512, 512, 1024, 64, 64, False, True, True): (5, 8, 5, 4),
+        (512, 512, 1024, 64, 64, True, False, True): (4, 16, 3, 4),
+        (512, 512, 1024, 128, 128, False, True, True): (6, 8, 2, 8),
+        (512, 512, 1024, 128, 128, True, False, True): (4, 8, 2, 8),
+        (512, 512, 2048, 16, 16, False, True, True): (2, 16, 3, 4),
+        (512, 512, 2048, 16, 16, True, False, True): (1, 16, 4, 2),
+        (512, 512, 2048, 32, 32, False, True, True): (2, 32, 3, 2),
+        (512, 512, 2048, 32, 32, True, False, True): (2, 32, 3, 2),
+        (512, 512, 2048, 64, 64, False, True, True): (1, 32, 3, 4),
+        (512, 512, 2048, 64, 64, True, False, True): (1, 32, 3, 2),
+        (512, 512, 2048, 128, 128, False, True, True): (3, 16, 2, 8),
+        (512, 512, 2048, 128, 128, True, False, True): (1, 16, 2, 8),
+        (512, 512, 4096, 16, 16, False, True, True): (4, 32, 3, 2),
+        (512, 512, 4096, 16, 16, True, False, True): (1, 32, 3, 2),
+        (512, 512, 4096, 32, 32, False, True, True): (3, 32, 3, 2),
+        (512, 512, 4096, 32, 32, True, False, True): (3, 32, 3, 2),
+        (512, 512, 4096, 64, 64, False, True, True): (1, 32, 3, 4),
+        (512, 512, 4096, 64, 64, True, False, True): (1, 64, 1, 4),
+        (512, 512, 4096, 128, 128, False, True, True): (7, 32, 2, 8),
+        (512, 512, 4096, 128, 128, True, False, True): (1, 32, 2, 8),
+        (512, 512, 8192, 16, 16, False, True, True): (4, 64, 3, 2),
+        (512, 512, 8192, 16, 16, True, False, True): (1, 64, 3, 2),
+        (512, 512, 8192, 32, 32, False, True, True): (3, 64, 3, 2),
+        (512, 512, 8192, 32, 32, True, False, True): (1, 64, 3, 2),
+        (512, 512, 8192, 64, 64, False, True, True): (1, 64, 3, 4),
+        (512, 512, 8192, 64, 64, True, False, True): (1, 64, 3, 4),
+        (512, 512, 8192, 128, 128, False, True, True): (7, 64, 2, 8),
+        (512, 512, 8192, 128, 128, True, False, True): (1, 64, 1, 4),
+        (512, 512, 16384, 16, 16, False, True, True): (1, 128, 3, 2),
+        (512, 512, 16384, 16, 16, True, False, True): (1, 64, 3, 2),
+        (512, 512, 16384, 32, 32, False, True, True): (1, 128, 3, 2),
+        (512, 512, 16384, 32, 32, True, False, True): (1, 128, 3, 2),
+        (512, 512, 16384, 64, 64, False, True, True): (1, 128, 3, 4),
+        (512, 512, 16384, 64, 64, True, False, True): (4, 128, 3, 4),
+        (512, 512, 16384, 128, 128, False, True, True): (5, 128, 2, 8),
+        (512, 512, 16384, 128, 128, True, False, True): (2, 128, 1, 4),
+        (512, 512, 32768, 16, 16, False, True, True): (1, 128, 3, 4),
+        (512, 512, 32768, 16, 16, True, False, True): (1, 128, 3, 2),
+        (512, 512, 32768, 32, 32, False, True, True): (1, 256, 3, 2),
+        (512, 512, 32768, 32, 32, True, False, True): (1, 256, 3, 2),
+        (512, 512, 32768, 64, 64, False, True, True): (1, 256, 3, 4),
+        (512, 512, 32768, 64, 64, True, False, True): (1, 256, 3, 4),
+        (512, 512, 32768, 128, 128, False, True, True): (5, 256, 1, 4),
+        (512, 512, 32768, 128, 128, True, False, True): (1, 256, 1, 4),
+        (512, 512, 50432, 16, 16, False, True, True): (4, 197, 1, 4),
+        (512, 512, 50432, 16, 16, True, False, True): (4, 197, 3, 2),
+        (512, 512, 50432, 32, 32, False, True, True): (2, 197, 1, 4),
+        (512, 512, 50432, 32, 32, True, False, True): (4, 197, 3, 4),
+        (512, 512, 50432, 64, 64, False, True, True): (2, 394, 1, 4),
+        (512, 512, 50432, 64, 64, True, False, True): (4, 197, 2, 4),
+        (512, 512, 50432, 128, 128, False, True, True): (5, 394, 1, 4),
+        (512, 512, 50432, 128, 128, True, False, True): (6, 394, 2, 4),
+        (512, 512, 65536, 16, 16, False, True, True): (1, 256, 3, 2),
+        (512, 512, 65536, 16, 16, True, False, True): (1, 256, 3, 1),
+        (512, 512, 65536, 32, 32, False, True, True): (1, 512, 3, 2),
+        (512, 512, 65536, 32, 32, True, False, True): (1, 512, 3, 2),
+        (512, 512, 65536, 64, 64, False, True, True): (2, 256, 2, 4),
+        (512, 512, 65536, 64, 64, True, False, True): (1, 512, 3, 4),
+        (512, 512, 65536, 128, 128, False, True, True): (7, 512, 1, 4),
+        (512, 512, 65536, 128, 128, True, False, True): (5, 512, 1, 4),
+        (512, 512, 131072, 16, 16, False, True, True): (1, 512, 3, 1),
+        (512, 512, 131072, 16, 16, True, False, True): (1, 512, 3, 1),
+        (512, 512, 131072, 32, 32, False, True, True): (1, 1024, 3, 2),
+        (512, 512, 131072, 32, 32, True, False, True): (1, 1024, 3, 2),
+        (512, 512, 131072, 64, 64, False, True, True): (4, 512, 2, 4),
+        (512, 512, 131072, 64, 64, True, False, True): (2, 512, 2, 4),
+        (512, 512, 131072, 128, 128, False, True, True): (5, 1024, 1, 4),
+        (512, 512, 131072, 128, 128, True, False, True): (4, 1024, 1, 4),
+        (768, 768, 256, 16, 16, False, True, True): (1, 8, 4, 1),
+        (768, 768, 256, 16, 16, True, False, True): (3, 2, 5, 2),
+        (768, 768, 256, 32, 32, False, True, True): (1, 8, 4, 2),
+        (768, 768, 256, 32, 32, True, False, True): (2, 4, 6, 2),
+        (768, 768, 256, 64, 64, False, True, True): (3, 4, 3, 4),
+        (768, 768, 256, 64, 64, True, False, True): (2, 4, 4, 4),
+        (768, 768, 256, 128, 128, False, True, True): (1, 2, 3, 8),
+        (768, 768, 256, 128, 128, True, False, True): (2, 2, 3, 8),
+        (768, 768, 512, 16, 16, False, True, True): (1, 8, 4, 2),
+        (768, 768, 512, 16, 16, True, False, True): (2, 8, 5, 2),
+        (768, 768, 512, 32, 32, False, True, True): (1, 16, 1, 4),
+        (768, 768, 512, 32, 32, True, False, True): (3, 8, 5, 2),
+        (768, 768, 512, 64, 64, False, True, True): (4, 8, 3, 4),
+        (768, 768, 512, 64, 64, True, False, True): (2, 8, 4, 4),
+        (768, 768, 512, 128, 128, False, True, True): (1, 4, 3, 8),
+        (768, 768, 512, 128, 128, True, False, True): (3, 4, 3, 8),
+        (768, 768, 1024, 16, 16, False, True, True): (1, 16, 1, 4),
+        (768, 768, 1024, 16, 16, True, False, True): (1, 8, 5, 2),
+        (768, 768, 1024, 32, 32, False, True, True): (1, 16, 1, 8),
+        (768, 768, 1024, 32, 32, True, False, True): (1, 4, 4, 4),
+        (768, 768, 1024, 64, 64, False, True, True): (2, 16, 1, 8),
+        (768, 768, 1024, 64, 64, True, False, True): (1, 8, 3, 8),
+        (768, 768, 1024, 128, 128, False, True, True): (1, 8, 3, 8),
+        (768, 768, 1024, 128, 128, True, False, True): (3, 8, 3, 8),
+        (768, 768, 2048, 16, 16, False, True, True): (6, 16, 1, 2),
+        (768, 768, 2048, 16, 16, True, False, True): (2, 16, 4, 2),
+        (768, 768, 2048, 32, 32, False, True, True): (3, 32, 1, 4),
+        (768, 768, 2048, 32, 32, True, False, True): (6, 8, 3, 4),
+        (768, 768, 2048, 64, 64, False, True, True): (2, 32, 2, 2),
+        (768, 768, 2048, 64, 64, True, False, True): (1, 16, 4, 4),
+        (768, 768, 2048, 128, 128, False, True, True): (2, 16, 3, 8),
+        (768, 768, 2048, 128, 128, True, False, True): (4, 16, 3, 8),
+        (768, 768, 4096, 16, 16, False, True, True): (1, 32, 1, 4),
+        (768, 768, 4096, 16, 16, True, False, True): (2, 16, 3, 2),
+        (768, 768, 4096, 32, 32, False, True, True): (3, 32, 1, 8),
+        (768, 768, 4096, 32, 32, True, False, True): (1, 16, 4, 4),
+        (768, 768, 4096, 64, 64, False, True, True): (1, 64, 2, 4),
+        (768, 768, 4096, 64, 64, True, False, True): (1, 8, 3, 8),
+        (768, 768, 4096, 128, 128, False, True, True): (1, 32, 3, 8),
+        (768, 768, 4096, 128, 128, True, False, True): (2, 32, 3, 8),
+        (768, 768, 8192, 16, 16, False, True, True): (1, 64, 1, 2),
+        (768, 768, 8192, 16, 16, True, False, True): (2, 64, 3, 2),
+        (768, 768, 8192, 32, 32, False, True, True): (2, 64, 1, 8),
+        (768, 768, 8192, 32, 32, True, False, True): (2, 32, 3, 4),
+        (768, 768, 8192, 64, 64, False, True, True): (4, 64, 3, 4),
+        (768, 768, 8192, 64, 64, True, False, True): (1, 64, 3, 4),
+        (768, 768, 8192, 128, 128, False, True, True): (4, 64, 3, 8),
+        (768, 768, 8192, 128, 128, True, False, True): (2, 64, 3, 8),
+        (768, 768, 16384, 16, 16, False, True, True): (4, 128, 1, 2),
+        (768, 768, 16384, 16, 16, True, False, True): (1, 64, 3, 4),
+        (768, 768, 16384, 32, 32, False, True, True): (1, 128, 1, 8),
+        (768, 768, 16384, 32, 32, True, False, True): (1, 64, 3, 4),
+        (768, 768, 16384, 64, 64, False, True, True): (1, 128, 3, 4),
+        (768, 768, 16384, 64, 64, True, False, True): (1, 128, 3, 4),
+        (768, 768, 16384, 128, 128, False, True, True): (3, 128, 1, 4),
+        (768, 768, 16384, 128, 128, True, False, True): (1, 128, 2, 4),
+        (768, 768, 32768, 16, 16, False, True, True): (2, 256, 1, 2),
+        (768, 768, 32768, 16, 16, True, False, True): (1, 128, 4, 4),
+        (768, 768, 32768, 32, 32, False, True, True): (1, 128, 1, 2),
+        (768, 768, 32768, 32, 32, True, False, True): (1, 128, 3, 4),
+        (768, 768, 32768, 64, 64, False, True, True): (1, 256, 1, 4),
+        (768, 768, 32768, 64, 64, True, False, True): (1, 128, 3, 4),
+        (768, 768, 32768, 128, 128, False, True, True): (3, 256, 1, 4),
+        (768, 768, 32768, 128, 128, True, False, True): (3, 256, 2, 4),
+        (768, 768, 65536, 16, 16, False, True, True): (4, 512, 1, 2),
+        (768, 768, 65536, 16, 16, True, False, True): (1, 256, 4, 4),
+        (768, 768, 65536, 32, 32, False, True, True): (1, 256, 1, 2),
+        (768, 768, 65536, 32, 32, True, False, True): (1, 256, 3, 4),
+        (768, 768, 65536, 64, 64, False, True, True): (1, 512, 1, 4),
+        (768, 768, 65536, 64, 64, True, False, True): (1, 256, 3, 4),
+        (768, 768, 65536, 128, 128, False, True, True): (3, 512, 1, 4),
+        (768, 768, 65536, 128, 128, True, False, True): (2, 512, 2, 4),
+        (768, 768, 131072, 16, 16, False, True, True): (1, 512, 1, 1),
+        (768, 768, 131072, 16, 16, True, False, True): (1, 512, 4, 4),
+        (768, 768, 131072, 32, 32, False, True, True): (1, 512, 1, 2),
+        (768, 768, 131072, 32, 32, True, False, True): (1, 512, 3, 4),
+        (768, 768, 131072, 64, 64, False, True, True): (1, 1024, 1, 4),
+        (768, 768, 131072, 64, 64, True, False, True): (3, 512, 3, 4),
+        (768, 768, 131072, 128, 128, False, True, True): (3, 1024, 1, 4),
+        (768, 768, 131072, 128, 128, True, False, True): (1, 1024, 2, 4),
+        (768, 3072, 256, 16, 16, False, True, True): (1, 8, 5, 2),
+        (768, 3072, 256, 16, 16, True, False, True): (3, 4, 7, 2),
+        (768, 3072, 256, 32, 32, False, True, True): (1, 8, 4, 2),
+        (768, 3072, 256, 32, 32, True, False, True): (1, 4, 5, 4),
+        (768, 3072, 256, 64, 64, False, True, True): (1, 4, 3, 4),
+        (768, 3072, 256, 64, 64, True, False, True): (1, 4, 5, 4),
+        (768, 3072, 256, 128, 128, False, True, True): (2, 2, 3, 8),
+        (768, 3072, 256, 128, 128, True, False, True): (2, 2, 3, 8),
+        (768, 3072, 512, 16, 16, False, True, True): (1, 8, 5, 2),
+        (768, 3072, 512, 16, 16, True, False, True): (1, 8, 5, 2),
+        (768, 3072, 512, 32, 32, False, True, True): (3, 8, 3, 4),
+        (768, 3072, 512, 32, 32, True, False, True): (1, 8, 7, 4),
+        (768, 3072, 512, 64, 64, False, True, True): (3, 8, 3, 4),
+        (768, 3072, 512, 64, 64, True, False, True): (3, 8, 5, 4),
+        (768, 3072, 512, 128, 128, False, True, True): (1, 4, 3, 8),
+        (768, 3072, 512, 128, 128, True, False, True): (1, 4, 3, 8),
+        (768, 3072, 1024, 16, 16, False, True, True): (4, 16, 1, 4),
+        (768, 3072, 1024, 16, 16, True, False, True): (2, 8, 5, 2),
+        (768, 3072, 1024, 32, 32, False, True, True): (1, 16, 6, 2),
+        (768, 3072, 1024, 32, 32, True, False, True): (1, 8, 4, 4),
+        (768, 3072, 1024, 64, 64, False, True, True): (2, 16, 4, 4),
+        (768, 3072, 1024, 64, 64, True, False, True): (2, 16, 4, 4),
+        (768, 3072, 1024, 128, 128, False, True, True): (1, 8, 3, 8),
+        (768, 3072, 1024, 128, 128, True, False, True): (3, 8, 3, 8),
+        (768, 3072, 2048, 16, 16, False, True, True): (1, 16, 1, 2),
+        (768, 3072, 2048, 16, 16, True, False, True): (1, 16, 5, 2),
+        (768, 3072, 2048, 32, 32, False, True, True): (4, 16, 1, 8),
+        (768, 3072, 2048, 32, 32, True, False, True): (2, 8, 3, 4),
+        (768, 3072, 2048, 64, 64, False, True, True): (2, 16, 3, 4),
+        (768, 3072, 2048, 64, 64, True, False, True): (2, 16, 3, 4),
+        (768, 3072, 2048, 128, 128, False, True, True): (3, 16, 3, 8),
+        (768, 3072, 2048, 128, 128, True, False, True): (1, 16, 3, 8),
+        (768, 3072, 4096, 16, 16, False, True, True): (1, 32, 1, 4),
+        (768, 3072, 4096, 16, 16, True, False, True): (1, 16, 3, 1),
+        (768, 3072, 4096, 32, 32, False, True, True): (3, 32, 1, 8),
+        (768, 3072, 4096, 32, 32, True, False, True): (2, 16, 3, 8),
+        (768, 3072, 4096, 64, 64, False, True, True): (2, 32, 3, 4),
+        (768, 3072, 4096, 64, 64, True, False, True): (2, 16, 3, 4),
+        (768, 3072, 4096, 128, 128, False, True, True): (5, 32, 1, 4),
+        (768, 3072, 4096, 128, 128, True, False, True): (4, 32, 3, 8),
+        (768, 3072, 8192, 16, 16, False, True, True): (1, 32, 1, 4),
+        (768, 3072, 8192, 16, 16, True, False, True): (4, 64, 4, 2),
+        (768, 3072, 8192, 32, 32, False, True, True): (1, 64, 1, 8),
+        (768, 3072, 8192, 32, 32, True, False, True): (2, 32, 3, 8),
+        (768, 3072, 8192, 64, 64, False, True, True): (2, 64, 3, 4),
+        (768, 3072, 8192, 64, 64, True, False, True): (2, 32, 3, 4),
+        (768, 3072, 8192, 128, 128, False, True, True): (1, 64, 3, 8),
+        (768, 3072, 8192, 128, 128, True, False, True): (2, 64, 3, 8),
+        (768, 3072, 16384, 16, 16, False, True, True): (1, 64, 1, 4),
+        (768, 3072, 16384, 16, 16, True, False, True): (1, 64, 4, 1),
+        (768, 3072, 16384, 32, 32, False, True, True): (1, 128, 1, 8),
+        (768, 3072, 16384, 32, 32, True, False, True): (1, 64, 3, 4),
+        (768, 3072, 16384, 64, 64, False, True, True): (1, 128, 3, 4),
+        (768, 3072, 16384, 64, 64, True, False, True): (1, 64, 3, 4),
+        (768, 3072, 16384, 128, 128, False, True, True): (2, 128, 3, 8),
+        (768, 3072, 16384, 128, 128, True, False, True): (1, 128, 3, 8),
+        (768, 3072, 32768, 16, 16, False, True, True): (1, 128, 1, 4),
+        (768, 3072, 32768, 16, 16, True, False, True): (1, 128, 4, 1),
+        (768, 3072, 32768, 32, 32, False, True, True): (1, 256, 1, 8),
+        (768, 3072, 32768, 32, 32, True, False, True): (1, 128, 3, 4),
+        (768, 3072, 32768, 64, 64, False, True, True): (1, 256, 3, 4),
+        (768, 3072, 32768, 64, 64, True, False, True): (1, 128, 3, 4),
+        (768, 3072, 32768, 128, 128, False, True, True): (3, 256, 1, 4),
+        (768, 3072, 32768, 128, 128, True, False, True): (5, 256, 3, 8),
+        (768, 3072, 50432, 16, 16, False, True, True): (1, 197, 1, 4),
+        (768, 3072, 50432, 16, 16, True, False, True): (4, 197, 4, 1),
+        (768, 3072, 50432, 32, 32, False, True, True): (2, 197, 1, 4),
+        (768, 3072, 50432, 32, 32, True, False, True): (4, 197, 3, 4),
+        (768, 3072, 50432, 64, 64, False, True, True): (1, 394, 3, 4),
+        (768, 3072, 50432, 64, 64, True, False, True): (1, 197, 3, 4),
+        (768, 3072, 50432, 128, 128, False, True, True): (3, 394, 1, 4),
+        (768, 3072, 50432, 128, 128, True, False, True): (3, 394, 2, 4),
+        (768, 3072, 65536, 16, 16, False, True, True): (1, 256, 1, 4),
+        (768, 3072, 65536, 16, 16, True, False, True): (5, 256, 4, 1),
+        (768, 3072, 65536, 32, 32, False, True, True): (2, 256, 1, 4),
+        (768, 3072, 65536, 32, 32, True, False, True): (3, 256, 3, 4),
+        (768, 3072, 65536, 64, 64, False, True, True): (1, 512, 3, 4),
+        (768, 3072, 65536, 64, 64, True, False, True): (1, 256, 3, 4),
+        (768, 3072, 65536, 128, 128, False, True, True): (3, 512, 1, 4),
+        (768, 3072, 65536, 128, 128, True, False, True): (2, 512, 3, 8),
+        (768, 3072, 131072, 16, 16, False, True, True): (1, 512, 1, 4),
+        (768, 3072, 131072, 16, 16, True, False, True): (5, 512, 4, 1),
+        (768, 3072, 131072, 32, 32, False, True, True): (2, 512, 1, 4),
+        (768, 3072, 131072, 32, 32, True, False, True): (2, 512, 3, 4),
+        (768, 3072, 131072, 64, 64, False, True, True): (1, 1024, 3, 4),
+        (768, 3072, 131072, 64, 64, True, False, True): (2, 512, 3, 4),
+        (768, 3072, 131072, 128, 128, False, True, True): (3, 1024, 1, 4),
+        (768, 3072, 131072, 128, 128, True, False, True): (2, 1024, 3, 8),
+        (1024, 1024, 256, 16, 16, False, True, True): (3, 4, 5, 4),
+        (1024, 1024, 256, 16, 16, True, False, True): (3, 4, 5, 4),
+        (1024, 1024, 256, 32, 32, False, True, True): (2, 4, 6, 2),
+        (1024, 1024, 256, 32, 32, True, False, True): (2, 4, 6, 2),
+        (1024, 1024, 256, 64, 64, False, True, True): (1, 4, 4, 4),
+        (1024, 1024, 256, 64, 64, True, False, True): (2, 4, 6, 4),
+        (1024, 1024, 256, 128, 128, False, True, True): (1, 2, 2, 8),
+        (1024, 1024, 256, 128, 128, True, False, True): (1, 2, 2, 8),
+        (1024, 1024, 512, 16, 16, False, True, True): (3, 4, 5, 4),
+        (1024, 1024, 512, 16, 16, True, False, True): (3, 8, 4, 2),
+        (1024, 1024, 512, 32, 32, False, True, True): (1, 8, 4, 2),
+        (1024, 1024, 512, 32, 32, True, False, True): (1, 8, 4, 2),
+        (1024, 1024, 512, 64, 64, False, True, True): (2, 8, 3, 4),
+        (1024, 1024, 512, 64, 64, True, False, True): (1, 4, 4, 4),
+        (1024, 1024, 512, 128, 128, False, True, True): (7, 4, 2, 8),
+        (1024, 1024, 512, 128, 128, True, False, True): (1, 4, 2, 8),
+        (1024, 1024, 1024, 16, 16, False, True, True): (4, 8, 4, 2),
+        (1024, 1024, 1024, 16, 16, True, False, True): (3, 8, 5, 2),
+        (1024, 1024, 1024, 32, 32, False, True, True): (1, 8, 4, 4),
+        (1024, 1024, 1024, 32, 32, True, False, True): (1, 8, 4, 2),
+        (1024, 1024, 1024, 64, 64, False, True, True): (1, 16, 3, 4),
+        (1024, 1024, 1024, 64, 64, True, False, True): (3, 16, 3, 4),
+        (1024, 1024, 1024, 128, 128, False, True, True): (6, 8, 2, 8),
+        (1024, 1024, 1024, 128, 128, True, False, True): (4, 8, 2, 8),
+        (1024, 1024, 2048, 16, 16, False, True, True): (3, 8, 3, 4),
+        (1024, 1024, 2048, 16, 16, True, False, True): (3, 8, 3, 4),
+        (1024, 1024, 2048, 32, 32, False, True, True): (1, 16, 3, 4),
+        (1024, 1024, 2048, 32, 32, True, False, True): (1, 16, 3, 2),
+        (1024, 1024, 2048, 64, 64, False, True, True): (5, 16, 3, 4),
+        (1024, 1024, 2048, 64, 64, True, False, True): (5, 16, 3, 4),
+        (1024, 1024, 2048, 128, 128, False, True, True): (3, 16, 2, 8),
+        (1024, 1024, 2048, 128, 128, True, False, True): (4, 16, 2, 16),
+        (1024, 1024, 4096, 16, 16, False, True, True): (4, 32, 3, 2),
+        (1024, 1024, 4096, 16, 16, True, False, True): (8, 32, 3, 2),
+        (1024, 1024, 4096, 32, 32, False, True, True): (9, 32, 3, 2),
+        (1024, 1024, 4096, 32, 32, True, False, True): (1, 32, 3, 2),
+        (1024, 1024, 4096, 64, 64, False, True, True): (6, 32, 3, 4),
+        (1024, 1024, 4096, 64, 64, True, False, True): (1, 32, 3, 4),
+        (1024, 1024, 4096, 128, 128, False, True, True): (4, 32, 2, 8),
+        (1024, 1024, 4096, 128, 128, True, False, True): (4, 32, 1, 4),
+        (1024, 1024, 8192, 16, 16, False, True, True): (4, 64, 3, 2),
+        (1024, 1024, 8192, 16, 16, True, False, True): (4, 64, 3, 2),
+        (1024, 1024, 8192, 32, 32, False, True, True): (8, 64, 3, 2),
+        (1024, 1024, 8192, 32, 32, True, False, True): (6, 64, 3, 2),
+        (1024, 1024, 8192, 64, 64, False, True, True): (2, 64, 3, 4),
+        (1024, 1024, 8192, 64, 64, True, False, True): (2, 64, 3, 4),
+        (1024, 1024, 8192, 128, 128, False, True, True): (3, 64, 1, 4),
+        (1024, 1024, 8192, 128, 128, True, False, True): (2, 64, 1, 4),
+        (1024, 1024, 16384, 16, 16, False, True, True): (1, 64, 3, 4),
+        (1024, 1024, 16384, 16, 16, True, False, True): (1, 64, 3, 2),
+        (1024, 1024, 16384, 32, 32, False, True, True): (1, 128, 3, 4),
+        (1024, 1024, 16384, 32, 32, True, False, True): (1, 64, 3, 4),
+        (1024, 1024, 16384, 64, 64, False, True, True): (1, 128, 3, 4),
+        (1024, 1024, 16384, 64, 64, True, False, True): (1, 128, 3, 4),
+        (1024, 1024, 16384, 128, 128, False, True, True): (11, 128, 1, 4),
+        (1024, 1024, 16384, 128, 128, True, False, True): (4, 128, 1, 4),
+        (1024, 1024, 32768, 16, 16, False, True, True): (1, 128, 3, 4),
+        (1024, 1024, 32768, 16, 16, True, False, True): (1, 128, 3, 1),
+        (1024, 1024, 32768, 32, 32, False, True, True): (1, 256, 3, 2),
+        (1024, 1024, 32768, 32, 32, True, False, True): (1, 128, 3, 4),
+        (1024, 1024, 32768, 64, 64, False, True, True): (2, 128, 2, 4),
+        (1024, 1024, 32768, 64, 64, True, False, True): (1, 256, 3, 4),
+        (1024, 1024, 32768, 128, 128, False, True, True): (7, 256, 1, 4),
+        (1024, 1024, 32768, 128, 128, True, False, True): (4, 256, 1, 4),
+        (1024, 1024, 50432, 16, 16, False, True, True): (1, 197, 1, 4),
+        (1024, 1024, 50432, 16, 16, True, False, True): (4, 197, 3, 4),
+        (1024, 1024, 50432, 32, 32, False, True, True): (2, 197, 1, 4),
+        (1024, 1024, 50432, 32, 32, True, False, True): (1, 197, 3, 4),
+        (1024, 1024, 50432, 64, 64, False, True, True): (2, 394, 1, 4),
+        (1024, 1024, 50432, 64, 64, True, False, True): (1, 197, 2, 4),
+        (1024, 1024, 50432, 128, 128, False, True, True): (3, 394, 1, 4),
+        (1024, 1024, 50432, 128, 128, True, False, True): (2, 394, 2, 4),
+        (1024, 1024, 65536, 16, 16, False, True, True): (1, 256, 3, 4),
+        (1024, 1024, 65536, 16, 16, True, False, True): (1, 256, 3, 1),
+        (1024, 1024, 65536, 32, 32, False, True, True): (1, 512, 3, 2),
+        (1024, 1024, 65536, 32, 32, True, False, True): (1, 256, 3, 4),
+        (1024, 1024, 65536, 64, 64, False, True, True): (2, 256, 2, 4),
+        (1024, 1024, 65536, 64, 64, True, False, True): (1, 512, 3, 4),
+        (1024, 1024, 65536, 128, 128, False, True, True): (10, 512, 1, 4),
+        (1024, 1024, 65536, 128, 128, True, False, True): (4, 512, 1, 4),
+        (1024, 1024, 131072, 16, 16, False, True, True): (11, 512, 3, 2),
+        (1024, 1024, 131072, 16, 16, True, False, True): (11, 512, 3, 2),
+        (1024, 1024, 131072, 32, 32, False, True, True): (7, 1024, 3, 2),
+        (1024, 1024, 131072, 32, 32, True, False, True): (6, 512, 3, 4),
+        (1024, 1024, 131072, 64, 64, False, True, True): (1, 512, 2, 4),
+        (1024, 1024, 131072, 64, 64, True, False, True): (4, 1024, 3, 4),
+        (1024, 1024, 131072, 128, 128, False, True, True): (12, 1024, 1, 4),
+        (1024, 1024, 131072, 128, 128, True, False, True): (4, 1024, 1, 4),
+        (1536, 1536, 256, 16, 16, False, True, True): (5, 4, 4, 2),
+        (1536, 1536, 256, 16, 16, True, False, True): (3, 4, 5, 2),
+        (1536, 1536, 256, 32, 32, False, True, True): (2, 4, 4, 4),
+        (1536, 1536, 256, 32, 32, True, False, True): (1, 4, 6, 2),
+        (1536, 1536, 256, 64, 64, False, True, True): (5, 4, 4, 4),
+        (1536, 1536, 256, 64, 64, True, False, True): (2, 4, 4, 4),
+        (1536, 1536, 256, 128, 128, False, True, True): (1, 2, 3, 8),
+        (1536, 1536, 256, 128, 128, True, False, True): (2, 2, 3, 8),
+        (1536, 1536, 512, 16, 16, False, True, True): (1, 8, 1, 4),
+        (1536, 1536, 512, 16, 16, True, False, True): (3, 4, 4, 2),
+        (1536, 1536, 512, 32, 32, False, True, True): (1, 8, 1, 8),
+        (1536, 1536, 512, 32, 32, True, False, True): (1, 4, 4, 4),
+        (1536, 1536, 512, 64, 64, False, True, True): (3, 8, 3, 4),
+        (1536, 1536, 512, 64, 64, True, False, True): (5, 8, 3, 4),
+        (1536, 1536, 512, 128, 128, False, True, True): (3, 4, 3, 8),
+        (1536, 1536, 512, 128, 128, True, False, True): (1, 4, 3, 8),
+        (1536, 1536, 1024, 16, 16, False, True, True): (6, 8, 1, 2),
+        (1536, 1536, 1024, 16, 16, True, False, True): (2, 8, 5, 2),
+        (1536, 1536, 1024, 32, 32, False, True, True): (6, 8, 1, 8),
+        (1536, 1536, 1024, 32, 32, True, False, True): (2, 4, 3, 4),
+        (1536, 1536, 1024, 64, 64, False, True, True): (1, 16, 3, 4),
+        (1536, 1536, 1024, 64, 64, True, False, True): (3, 8, 3, 4),
+        (1536, 1536, 1024, 128, 128, False, True, True): (3, 8, 3, 8),
+        (1536, 1536, 1024, 128, 128, True, False, True): (3, 8, 3, 8),
+        (1536, 1536, 2048, 16, 16, False, True, True): (1, 16, 1, 4),
+        (1536, 1536, 2048, 16, 16, True, False, True): (1, 8, 3, 1),
+        (1536, 1536, 2048, 32, 32, False, True, True): (1, 16, 1, 8),
+        (1536, 1536, 2048, 32, 32, True, False, True): (4, 8, 3, 2),
+        (1536, 1536, 2048, 64, 64, False, True, True): (1, 16, 3, 4),
+        (1536, 1536, 2048, 64, 64, True, False, True): (3, 8, 3, 4),
+        (1536, 1536, 2048, 128, 128, False, True, True): (6, 16, 1, 4),
+        (1536, 1536, 2048, 128, 128, True, False, True): (4, 16, 3, 8),
+        (1536, 1536, 4096, 16, 16, False, True, True): (1, 32, 1, 2),
+        (1536, 1536, 4096, 16, 16, True, False, True): (4, 32, 4, 2),
+        (1536, 1536, 4096, 32, 32, False, True, True): (1, 32, 1, 8),
+        (1536, 1536, 4096, 32, 32, True, False, True): (3, 16, 3, 4),
+        (1536, 1536, 4096, 64, 64, False, True, True): (1, 32, 3, 4),
+        (1536, 1536, 4096, 64, 64, True, False, True): (1, 16, 3, 4),
+        (1536, 1536, 4096, 128, 128, False, True, True): (4, 32, 3, 8),
+        (1536, 1536, 4096, 128, 128, True, False, True): (2, 32, 3, 8),
+        (1536, 1536, 8192, 16, 16, False, True, True): (2, 64, 1, 2),
+        (1536, 1536, 8192, 16, 16, True, False, True): (4, 64, 4, 2),
+        (1536, 1536, 8192, 32, 32, False, True, True): (1, 64, 1, 8),
+        (1536, 1536, 8192, 32, 32, True, False, True): (12, 32, 3, 4),
+        (1536, 1536, 8192, 64, 64, False, True, True): (2, 64, 3, 4),
+        (1536, 1536, 8192, 64, 64, True, False, True): (2, 32, 3, 4),
+        (1536, 1536, 8192, 128, 128, False, True, True): (3, 64, 1, 4),
+        (1536, 1536, 8192, 128, 128, True, False, True): (4, 64, 3, 8),
+        (1536, 1536, 16384, 16, 16, False, True, True): (1, 128, 1, 2),
+        (1536, 1536, 16384, 16, 16, True, False, True): (1, 64, 4, 4),
+        (1536, 1536, 16384, 32, 32, False, True, True): (1, 64, 1, 2),
+        (1536, 1536, 16384, 32, 32, True, False, True): (1, 64, 3, 4),
+        (1536, 1536, 16384, 64, 64, False, True, True): (1, 128, 3, 4),
+        (1536, 1536, 16384, 64, 64, True, False, True): (1, 64, 3, 4),
+        (1536, 1536, 16384, 128, 128, False, True, True): (3, 128, 1, 4),
+        (1536, 1536, 16384, 128, 128, True, False, True): (1, 128, 2, 4),
+        (1536, 1536, 32768, 16, 16, False, True, True): (1, 256, 1, 2),
+        (1536, 1536, 32768, 16, 16, True, False, True): (1, 128, 3, 2),
+        (1536, 1536, 32768, 32, 32, False, True, True): (1, 128, 1, 2),
+        (1536, 1536, 32768, 32, 32, True, False, True): (1, 128, 3, 4),
+        (1536, 1536, 32768, 64, 64, False, True, True): (3, 256, 3, 4),
+        (1536, 1536, 32768, 64, 64, True, False, True): (1, 128, 3, 4),
+        (1536, 1536, 32768, 128, 128, False, True, True): (3, 256, 1, 4),
+        (1536, 1536, 32768, 128, 128, True, False, True): (1, 256, 2, 4),
+        (1536, 1536, 65536, 16, 16, False, True, True): (4, 512, 1, 2),
+        (1536, 1536, 65536, 16, 16, True, False, True): (1, 256, 4, 4),
+        (1536, 1536, 65536, 32, 32, False, True, True): (1, 256, 1, 2),
+        (1536, 1536, 65536, 32, 32, True, False, True): (1, 256, 3, 4),
+        (1536, 1536, 65536, 64, 64, False, True, True): (2, 512, 3, 4),
+        (1536, 1536, 65536, 64, 64, True, False, True): (1, 256, 3, 4),
+        (1536, 1536, 65536, 128, 128, False, True, True): (3, 512, 1, 4),
+        (1536, 1536, 65536, 128, 128, True, False, True): (2, 512, 2, 4),
+        (1536, 1536, 131072, 16, 16, False, True, True): (2, 1024, 1, 2),
+        (1536, 1536, 131072, 16, 16, True, False, True): (9, 512, 4, 4),
+        (1536, 1536, 131072, 32, 32, False, True, True): (1, 512, 1, 2),
+        (1536, 1536, 131072, 32, 32, True, False, True): (9, 512, 3, 4),
+        (1536, 1536, 131072, 64, 64, False, True, True): (1, 1024, 3, 4),
+        (1536, 1536, 131072, 64, 64, True, False, True): (1, 512, 3, 4),
+        (1536, 1536, 131072, 128, 128, False, True, True): (3, 1024, 1, 4),
+        (1536, 1536, 131072, 128, 128, True, False, True): (1, 1024, 2, 4),
+        (2048, 2048, 256, 16, 16, False, True, True): (4, 4, 6, 2),
+        (2048, 2048, 256, 16, 16, True, False, True): (2, 8, 4, 1),
+        (2048, 2048, 256, 32, 32, False, True, True): (3, 4, 4, 2),
+        (2048, 2048, 256, 32, 32, True, False, True): (1, 4, 5, 2),
+        (2048, 2048, 256, 64, 64, False, True, True): (2, 4, 4, 4),
+        (2048, 2048, 256, 64, 64, True, False, True): (2, 4, 4, 4),
+        (2048, 2048, 256, 128, 128, False, True, True): (3, 2, 2, 8),
+        (2048, 2048, 256, 128, 128, True, False, True): (5, 2, 2, 8),
+        (2048, 2048, 512, 16, 16, False, True, True): (5, 4, 4, 4),
+        (2048, 2048, 512, 16, 16, True, False, True): (2, 4, 4, 2),
+        (2048, 2048, 512, 32, 32, False, True, True): (1, 4, 3, 4),
+        (2048, 2048, 512, 32, 32, True, False, True): (3, 4, 4, 2),
+        (2048, 2048, 512, 64, 64, False, True, True): (1, 8, 3, 4),
+        (2048, 2048, 512, 64, 64, True, False, True): (1, 8, 3, 2),
+        (2048, 2048, 512, 128, 128, False, True, True): (3, 4, 2, 8),
+        (2048, 2048, 512, 128, 128, True, False, True): (2, 4, 2, 8),
+        (2048, 2048, 1024, 16, 16, False, True, True): (3, 4, 3, 4),
+        (2048, 2048, 1024, 16, 16, True, False, True): (2, 8, 3, 2),
+        (2048, 2048, 1024, 32, 32, False, True, True): (3, 8, 3, 4),
+        (2048, 2048, 1024, 32, 32, True, False, True): (1, 8, 3, 2),
+        (2048, 2048, 1024, 64, 64, False, True, True): (1, 8, 3, 4),
+        (2048, 2048, 1024, 64, 64, True, False, True): (1, 8, 3, 4),
+        (2048, 2048, 1024, 128, 128, False, True, True): (4, 8, 2, 8),
+        (2048, 2048, 1024, 128, 128, True, False, True): (4, 8, 1, 4),
+        (2048, 2048, 2048, 16, 16, False, True, True): (4, 16, 3, 2),
+        (2048, 2048, 2048, 16, 16, True, False, True): (2, 16, 3, 2),
+        (2048, 2048, 2048, 32, 32, False, True, True): (1, 16, 3, 4),
+        (2048, 2048, 2048, 32, 32, True, False, True): (1, 16, 3, 2),
+        (2048, 2048, 2048, 64, 64, False, True, True): (1, 16, 3, 4),
+        (2048, 2048, 2048, 64, 64, True, False, True): (1, 16, 3, 4),
+        (2048, 2048, 2048, 128, 128, False, True, True): (6, 16, 2, 8),
+        (2048, 2048, 2048, 128, 128, True, False, True): (5, 16, 1, 4),
+        (2048, 2048, 4096, 16, 16, False, True, True): (4, 32, 4, 2),
+        (2048, 2048, 4096, 16, 16, True, False, True): (4, 32, 3, 2),
+        (2048, 2048, 4096, 32, 32, False, True, True): (4, 16, 3, 8),
+        (2048, 2048, 4096, 32, 32, True, False, True): (4, 16, 3, 4),
+        (2048, 2048, 4096, 64, 64, False, True, True): (4, 32, 3, 4),
+        (2048, 2048, 4096, 64, 64, True, False, True): (4, 32, 3, 4),
+        (2048, 2048, 4096, 128, 128, False, True, True): (4, 32, 2, 8),
+        (2048, 2048, 4096, 128, 128, True, False, True): (2, 32, 1, 4),
+        (2048, 2048, 8192, 16, 16, False, True, True): (4, 64, 4, 2),
+        (2048, 2048, 8192, 16, 16, True, False, True): (4, 64, 4, 2),
+        (2048, 2048, 8192, 32, 32, False, True, True): (4, 32, 3, 8),
+        (2048, 2048, 8192, 32, 32, True, False, True): (4, 32, 4, 8),
+        (2048, 2048, 8192, 64, 64, False, True, True): (2, 64, 3, 4),
+        (2048, 2048, 8192, 64, 64, True, False, True): (4, 64, 3, 4),
+        (2048, 2048, 8192, 128, 128, False, True, True): (3, 64, 1, 4),
+        (2048, 2048, 8192, 128, 128, True, False, True): (2, 64, 1, 4),
+        (2048, 2048, 16384, 16, 16, False, True, True): (4, 64, 3, 4),
+        (2048, 2048, 16384, 16, 16, True, False, True): (1, 64, 3, 4),
+        (2048, 2048, 16384, 32, 32, False, True, True): (4, 64, 3, 4),
+        (2048, 2048, 16384, 32, 32, True, False, True): (4, 64, 3, 4),
+        (2048, 2048, 16384, 64, 64, False, True, True): (4, 128, 3, 4),
+        (2048, 2048, 16384, 64, 64, True, False, True): (4, 128, 3, 4),
+        (2048, 2048, 16384, 128, 128, False, True, True): (3, 128, 1, 4),
+        (2048, 2048, 16384, 128, 128, True, False, True): (2, 128, 1, 4),
+        (2048, 2048, 32768, 16, 16, False, True, True): (8, 128, 3, 2),
+        (2048, 2048, 32768, 16, 16, True, False, True): (8, 128, 3, 4),
+        (2048, 2048, 32768, 32, 32, False, True, True): (8, 128, 3, 4),
+        (2048, 2048, 32768, 32, 32, True, False, True): (8, 128, 3, 4),
+        (2048, 2048, 32768, 64, 64, False, True, True): (8, 256, 3, 4),
+        (2048, 2048, 32768, 64, 64, True, False, True): (8, 256, 3, 4),
+        (2048, 2048, 32768, 128, 128, False, True, True): (3, 256, 1, 4),
+        (2048, 2048, 32768, 128, 128, True, False, True): (1, 256, 1, 4),
+        (2048, 2048, 50432, 16, 16, False, True, True): (1, 197, 1, 4),
+        (2048, 2048, 50432, 16, 16, True, False, True): (4, 197, 4, 1),
+        (2048, 2048, 50432, 32, 32, False, True, True): (2, 197, 1, 4),
+        (2048, 2048, 50432, 32, 32, True, False, True): (4, 197, 3, 4),
+        (2048, 2048, 50432, 64, 64, False, True, True): (2, 394, 3, 4),
+        (2048, 2048, 50432, 64, 64, True, False, True): (4, 197, 2, 4),
+        (2048, 2048, 50432, 128, 128, False, True, True): (3, 394, 1, 4),
+        (2048, 2048, 50432, 128, 128, True, False, True): (4, 394, 2, 4),
+        (2048, 2048, 65536, 16, 16, False, True, True): (9, 256, 3, 2),
+        (2048, 2048, 65536, 16, 16, True, False, True): (9, 256, 4, 4),
+        (2048, 2048, 65536, 32, 32, False, True, True): (7, 256, 3, 4),
+        (2048, 2048, 65536, 32, 32, True, False, True): (7, 256, 3, 4),
+        (2048, 2048, 65536, 64, 64, False, True, True): (2, 256, 2, 4),
+        (2048, 2048, 65536, 64, 64, True, False, True): (9, 512, 3, 4),
+        (2048, 2048, 65536, 128, 128, False, True, True): (5, 512, 1, 4),
+        (2048, 2048, 65536, 128, 128, True, False, True): (1, 512, 1, 4),
+        (2048, 2048, 131072, 16, 16, False, True, True): (9, 512, 3, 2),
+        (2048, 2048, 131072, 16, 16, True, False, True): (9, 512, 4, 4),
+        (2048, 2048, 131072, 32, 32, False, True, True): (7, 512, 3, 4),
+        (2048, 2048, 131072, 32, 32, True, False, True): (3, 512, 3, 4),
+        (2048, 2048, 131072, 64, 64, False, True, True): (1, 512, 2, 4),
+        (2048, 2048, 131072, 64, 64, True, False, True): (2, 1024, 3, 4),
+        (2048, 2048, 131072, 128, 128, False, True, True): (3, 1024, 1, 4),
+        (2048, 2048, 131072, 128, 128, True, False, True): (1, 1024, 1, 4),
+        (3072, 768, 256, 16, 16, False, True, True): (6, 4, 1, 4),
+        (3072, 768, 256, 16, 16, True, False, True): (2, 1, 5, 2),
+        (3072, 768, 256, 32, 32, False, True, True): (1, 4, 1, 8),
+        (3072, 768, 256, 32, 32, True, False, True): (4, 2, 4, 4),
+        (3072, 768, 256, 64, 64, False, True, True): (1, 2, 3, 4),
+        (3072, 768, 256, 64, 64, True, False, True): (3, 4, 3, 4),
+        (3072, 768, 256, 128, 128, False, True, True): (1, 2, 3, 8),
+        (3072, 768, 256, 128, 128, True, False, True): (3, 2, 3, 8),
+        (3072, 768, 512, 16, 16, False, True, True): (1, 4, 1, 4),
+        (3072, 768, 512, 16, 16, True, False, True): (3, 4, 4, 1),
+        (3072, 768, 512, 32, 32, False, True, True): (5, 8, 1, 4),
+        (3072, 768, 512, 32, 32, True, False, True): (3, 4, 4, 2),
+        (3072, 768, 512, 64, 64, False, True, True): (1, 8, 1, 4),
+        (3072, 768, 512, 64, 64, True, False, True): (1, 4, 3, 4),
+        (3072, 768, 512, 128, 128, False, True, True): (3, 4, 3, 8),
+        (3072, 768, 512, 128, 128, True, False, True): (1, 4, 3, 8),
+        (3072, 768, 1024, 16, 16, False, True, True): (1, 8, 1, 4),
+        (3072, 768, 1024, 16, 16, True, False, True): (3, 4, 3, 1),
+        (3072, 768, 1024, 32, 32, False, True, True): (1, 16, 1, 4),
+        (3072, 768, 1024, 32, 32, True, False, True): (1, 4, 3, 8),
+        (3072, 768, 1024, 64, 64, False, True, True): (8, 16, 3, 2),
+        (3072, 768, 1024, 64, 64, True, False, True): (1, 4, 3, 4),
+        (3072, 768, 1024, 128, 128, False, True, True): (2, 8, 3, 8),
+        (3072, 768, 1024, 128, 128, True, False, True): (3, 8, 2, 4),
+        (3072, 768, 2048, 16, 16, False, True, True): (1, 8, 1, 4),
+        (3072, 768, 2048, 16, 16, True, False, True): (6, 8, 4, 4),
+        (3072, 768, 2048, 32, 32, False, True, True): (1, 16, 1, 8),
+        (3072, 768, 2048, 32, 32, True, False, True): (6, 8, 3, 4),
+        (3072, 768, 2048, 64, 64, False, True, True): (8, 16, 3, 4),
+        (3072, 768, 2048, 64, 64, True, False, True): (3, 16, 3, 4),
+        (3072, 768, 2048, 128, 128, False, True, True): (1, 16, 3, 8),
+        (3072, 768, 2048, 128, 128, True, False, True): (2, 16, 2, 4),
+        (3072, 768, 4096, 16, 16, False, True, True): (1, 16, 1, 4),
+        (3072, 768, 4096, 16, 16, True, False, True): (4, 32, 4, 2),
+        (3072, 768, 4096, 32, 32, False, True, True): (1, 32, 1, 8),
+        (3072, 768, 4096, 32, 32, True, False, True): (4, 16, 3, 4),
+        (3072, 768, 4096, 64, 64, False, True, True): (2, 32, 1, 4),
+        (3072, 768, 4096, 64, 64, True, False, True): (2, 16, 2, 4),
+        (3072, 768, 4096, 128, 128, False, True, True): (2, 32, 1, 16),
+        (3072, 768, 4096, 128, 128, True, False, True): (3, 32, 2, 4),
+        (3072, 768, 8192, 16, 16, False, True, True): (2, 32, 1, 4),
+        (3072, 768, 8192, 16, 16, True, False, True): (4, 64, 4, 2),
+        (3072, 768, 8192, 32, 32, False, True, True): (2, 32, 1, 4),
+        (3072, 768, 8192, 32, 32, True, False, True): (6, 32, 3, 4),
+        (3072, 768, 8192, 64, 64, False, True, True): (2, 64, 1, 4),
+        (3072, 768, 8192, 64, 64, True, False, True): (2, 32, 2, 4),
+        (3072, 768, 8192, 128, 128, False, True, True): (3, 64, 1, 4),
+        (3072, 768, 8192, 128, 128, True, False, True): (2, 64, 2, 4),
+        (3072, 768, 16384, 16, 16, False, True, True): (1, 64, 1, 4),
+        (3072, 768, 16384, 16, 16, True, False, True): (1, 64, 1, 1),
+        (3072, 768, 16384, 32, 32, False, True, True): (2, 64, 1, 4),
+        (3072, 768, 16384, 32, 32, True, False, True): (4, 64, 3, 4),
+        (3072, 768, 16384, 64, 64, False, True, True): (2, 128, 1, 4),
+        (3072, 768, 16384, 64, 64, True, False, True): (4, 64, 2, 4),
+        (3072, 768, 16384, 128, 128, False, True, True): (3, 128, 1, 4),
+        (3072, 768, 16384, 128, 128, True, False, True): (1, 128, 2, 4),
+        (3072, 768, 32768, 16, 16, False, True, True): (1, 128, 1, 4),
+        (3072, 768, 32768, 16, 16, True, False, True): (8, 256, 3, 2),
+        (3072, 768, 32768, 32, 32, False, True, True): (2, 128, 1, 4),
+        (3072, 768, 32768, 32, 32, True, False, True): (8, 128, 3, 4),
+        (3072, 768, 32768, 64, 64, False, True, True): (1, 256, 1, 4),
+        (3072, 768, 32768, 64, 64, True, False, True): (8, 128, 2, 4),
+        (3072, 768, 32768, 128, 128, False, True, True): (3, 256, 1, 4),
+        (3072, 768, 32768, 128, 128, True, False, True): (3, 256, 2, 4),
+        (3072, 768, 50432, 16, 16, False, True, True): (1, 197, 1, 4),
+        (3072, 768, 50432, 16, 16, True, False, True): (7, 197, 4, 1),
+        (3072, 768, 50432, 32, 32, False, True, True): (2, 197, 1, 4),
+        (3072, 768, 50432, 32, 32, True, False, True): (10, 197, 3, 4),
+        (3072, 768, 50432, 64, 64, False, True, True): (1, 394, 1, 4),
+        (3072, 768, 50432, 64, 64, True, False, True): (3, 197, 2, 4),
+        (3072, 768, 50432, 128, 128, False, True, True): (3, 394, 1, 4),
+        (3072, 768, 50432, 128, 128, True, False, True): (2, 394, 2, 4),
+        (3072, 768, 65536, 16, 16, False, True, True): (1, 256, 1, 4),
+        (3072, 768, 65536, 16, 16, True, False, True): (15, 256, 4, 1),
+        (3072, 768, 65536, 32, 32, False, True, True): (2, 256, 1, 4),
+        (3072, 768, 65536, 32, 32, True, False, True): (10, 256, 3, 4),
+        (3072, 768, 65536, 64, 64, False, True, True): (1, 512, 1, 4),
+        (3072, 768, 65536, 64, 64, True, False, True): (3, 256, 2, 4),
+        (3072, 768, 65536, 128, 128, False, True, True): (3, 512, 1, 4),
+        (3072, 768, 65536, 128, 128, True, False, True): (3, 512, 2, 4),
+        (3072, 768, 131072, 16, 16, False, True, True): (1, 512, 1, 4),
+        (3072, 768, 131072, 16, 16, True, False, True): (15, 512, 4, 1),
+        (3072, 768, 131072, 32, 32, False, True, True): (2, 512, 1, 4),
+        (3072, 768, 131072, 32, 32, True, False, True): (9, 512, 3, 4),
+        (3072, 768, 131072, 64, 64, False, True, True): (1, 1024, 1, 4),
+        (3072, 768, 131072, 64, 64, True, False, True): (3, 512, 2, 4),
+        (3072, 768, 131072, 128, 128, False, True, True): (3, 1024, 1, 4),
+        (3072, 768, 131072, 128, 128, True, False, True): (3, 1024, 2, 4),
+        (3072, 3072, 256, 16, 16, False, True, True): (5, 4, 1, 4),
+        (3072, 3072, 256, 16, 16, True, False, True): (1, 2, 5, 2),
+        (3072, 3072, 256, 32, 32, False, True, True): (1, 4, 1, 8),
+        (3072, 3072, 256, 32, 32, True, False, True): (3, 4, 4, 2),
+        (3072, 3072, 256, 64, 64, False, True, True): (2, 4, 3, 4),
+        (3072, 3072, 256, 64, 64, True, False, True): (3, 4, 4, 4),
+        (3072, 3072, 256, 128, 128, False, True, True): (1, 2, 3, 8),
+        (3072, 3072, 256, 128, 128, True, False, True): (1, 2, 3, 8),
+        (3072, 3072, 512, 16, 16, False, True, True): (5, 4, 1, 2),
+        (3072, 3072, 512, 16, 16, True, False, True): (1, 2, 4, 4),
+        (3072, 3072, 512, 32, 32, False, True, True): (3, 8, 1, 4),
+        (3072, 3072, 512, 32, 32, True, False, True): (4, 2, 3, 4),
+        (3072, 3072, 512, 64, 64, False, True, True): (1, 8, 2, 2),
+        (3072, 3072, 512, 64, 64, True, False, True): (2, 4, 3, 4),
+        (3072, 3072, 512, 128, 128, False, True, True): (1, 4, 3, 8),
+        (3072, 3072, 512, 128, 128, True, False, True): (4, 4, 3, 8),
+        (3072, 3072, 1024, 16, 16, False, True, True): (1, 8, 1, 4),
+        (3072, 3072, 1024, 16, 16, True, False, True): (4, 8, 5, 2),
+        (3072, 3072, 1024, 32, 32, False, True, True): (1, 8, 1, 8),
+        (3072, 3072, 1024, 32, 32, True, False, True): (1, 4, 4, 4),
+        (3072, 3072, 1024, 64, 64, False, True, True): (3, 8, 3, 4),
+        (3072, 3072, 1024, 64, 64, True, False, True): (2, 4, 3, 4),
+        (3072, 3072, 1024, 128, 128, False, True, True): (3, 8, 1, 4),
+        (3072, 3072, 1024, 128, 128, True, False, True): (1, 8, 3, 8),
+        (3072, 3072, 2048, 16, 16, False, True, True): (1, 16, 1, 2),
+        (3072, 3072, 2048, 16, 16, True, False, True): (4, 16, 4, 2),
+        (3072, 3072, 2048, 32, 32, False, True, True): (1, 16, 1, 8),
+        (3072, 3072, 2048, 32, 32, True, False, True): (3, 8, 4, 4),
+        (3072, 3072, 2048, 64, 64, False, True, True): (3, 16, 3, 4),
+        (3072, 3072, 2048, 64, 64, True, False, True): (3, 8, 3, 4),
+        (3072, 3072, 2048, 128, 128, False, True, True): (4, 16, 3, 8),
+        (3072, 3072, 2048, 128, 128, True, False, True): (3, 16, 3, 8),
+        (3072, 3072, 4096, 16, 16, False, True, True): (1, 32, 1, 2),
+        (3072, 3072, 4096, 16, 16, True, False, True): (4, 32, 4, 2),
+        (3072, 3072, 4096, 32, 32, False, True, True): (1, 32, 1, 8),
+        (3072, 3072, 4096, 32, 32, True, False, True): (3, 16, 3, 4),
+        (3072, 3072, 4096, 64, 64, False, True, True): (1, 32, 3, 4),
+        (3072, 3072, 4096, 64, 64, True, False, True): (3, 16, 3, 4),
+        (3072, 3072, 4096, 128, 128, False, True, True): (1, 32, 3, 8),
+        (3072, 3072, 4096, 128, 128, True, False, True): (3, 32, 3, 8),
+        (3072, 3072, 8192, 16, 16, False, True, True): (1, 64, 1, 2),
+        (3072, 3072, 8192, 16, 16, True, False, True): (4, 64, 4, 2),
+        (3072, 3072, 8192, 32, 32, False, True, True): (1, 64, 1, 8),
+        (3072, 3072, 8192, 32, 32, True, False, True): (8, 32, 3, 4),
+        (3072, 3072, 8192, 64, 64, False, True, True): (3, 64, 3, 4),
+        (3072, 3072, 8192, 64, 64, True, False, True): (2, 32, 3, 4),
+        (3072, 3072, 8192, 128, 128, False, True, True): (2, 64, 3, 8),
+        (3072, 3072, 8192, 128, 128, True, False, True): (1, 64, 3, 8),
+        (3072, 3072, 16384, 16, 16, False, True, True): (1, 128, 1, 2),
+        (3072, 3072, 16384, 16, 16, True, False, True): (4, 128, 4, 2),
+        (3072, 3072, 16384, 32, 32, False, True, True): (1, 64, 1, 2),
+        (3072, 3072, 16384, 32, 32, True, False, True): (4, 64, 3, 4),
+        (3072, 3072, 16384, 64, 64, False, True, True): (1, 128, 3, 4),
+        (3072, 3072, 16384, 64, 64, True, False, True): (4, 64, 3, 4),
+        (3072, 3072, 16384, 128, 128, False, True, True): (3, 128, 1, 4),
+        (3072, 3072, 16384, 128, 128, True, False, True): (1, 128, 3, 8),
+        (3072, 3072, 32768, 16, 16, False, True, True): (1, 256, 1, 2),
+        (3072, 3072, 32768, 16, 16, True, False, True): (8, 128, 4, 4),
+        (3072, 3072, 32768, 32, 32, False, True, True): (1, 256, 1, 8),
+        (3072, 3072, 32768, 32, 32, True, False, True): (5, 128, 3, 4),
+        (3072, 3072, 32768, 64, 64, False, True, True): (1, 256, 3, 4),
+        (3072, 3072, 32768, 64, 64, True, False, True): (1, 128, 3, 4),
+        (3072, 3072, 32768, 128, 128, False, True, True): (3, 256, 1, 4),
+        (3072, 3072, 32768, 128, 128, True, False, True): (3, 256, 2, 4),
+        (3072, 3072, 65536, 16, 16, False, True, True): (1, 512, 1, 2),
+        (3072, 3072, 65536, 16, 16, True, False, True): (7, 256, 4, 4),
+        (3072, 3072, 65536, 32, 32, False, True, True): (1, 256, 1, 2),
+        (3072, 3072, 65536, 32, 32, True, False, True): (5, 256, 3, 4),
+        (3072, 3072, 65536, 64, 64, False, True, True): (1, 512, 3, 4),
+        (3072, 3072, 65536, 64, 64, True, False, True): (3, 256, 3, 4),
+        (3072, 3072, 65536, 128, 128, False, True, True): (3, 512, 1, 4),
+        (3072, 3072, 65536, 128, 128, True, False, True): (3, 512, 2, 4),
+        (3072, 3072, 131072, 16, 16, False, True, True): (1, 1024, 1, 2),
+        (3072, 3072, 131072, 16, 16, True, False, True): (5, 512, 4, 4),
+        (3072, 3072, 131072, 32, 32, False, True, True): (1, 512, 1, 2),
+        (3072, 3072, 131072, 32, 32, True, False, True): (3, 512, 3, 4),
+        (3072, 3072, 131072, 64, 64, False, True, True): (1, 1024, 3, 4),
+        (3072, 3072, 131072, 64, 64, True, False, True): (3, 512, 3, 4),
+        (3072, 3072, 131072, 128, 128, False, True, True): (3, 1024, 1, 4),
+        (3072, 3072, 131072, 128, 128, True, False, True): (1, 1024, 2, 4),
+        (4096, 4096, 256, 16, 16, False, True, True): (2, 2, 6, 4),
+        (4096, 4096, 256, 16, 16, True, False, True): (2, 2, 5, 4),
+        (4096, 4096, 256, 32, 32, False, True, True): (7, 2, 4, 4),
+        (4096, 4096, 256, 32, 32, True, False, True): (1, 2, 4, 4),
+        (4096, 4096, 256, 64, 64, False, True, True): (3, 4, 3, 4),
+        (4096, 4096, 256, 64, 64, True, False, True): (3, 4, 3, 4),
+        (4096, 4096, 256, 128, 128, False, True, True): (1, 2, 2, 8),
+        (4096, 4096, 256, 128, 128, True, False, True): (1, 2, 2, 8),
+        (4096, 4096, 512, 16, 16, False, True, True): (4, 2, 3, 4),
+        (4096, 4096, 512, 16, 16, True, False, True): (2, 4, 3, 2),
+        (4096, 4096, 512, 32, 32, False, True, True): (3, 4, 3, 4),
+        (4096, 4096, 512, 32, 32, True, False, True): (3, 4, 3, 2),
+        (4096, 4096, 512, 64, 64, False, True, True): (3, 4, 3, 4),
+        (4096, 4096, 512, 64, 64, True, False, True): (3, 4, 3, 4),
+        (4096, 4096, 512, 128, 128, False, True, True): (2, 4, 2, 8),
+        (4096, 4096, 512, 128, 128, True, False, True): (2, 4, 1, 4),
+        (4096, 4096, 1024, 16, 16, False, True, True): (2, 8, 3, 2),
+        (4096, 4096, 1024, 16, 16, True, False, True): (2, 8, 3, 2),
+        (4096, 4096, 1024, 32, 32, False, True, True): (3, 8, 3, 4),
+        (4096, 4096, 1024, 32, 32, True, False, True): (1, 8, 3, 2),
+        (4096, 4096, 1024, 64, 64, False, True, True): (1, 8, 3, 4),
+        (4096, 4096, 1024, 64, 64, True, False, True): (1, 8, 3, 4),
+        (4096, 4096, 1024, 128, 128, False, True, True): (2, 8, 2, 8),
+        (4096, 4096, 1024, 128, 128, True, False, True): (2, 8, 2, 8),
+        (4096, 4096, 2048, 16, 16, False, True, True): (2, 8, 4, 4),
+        (4096, 4096, 2048, 16, 16, True, False, True): (2, 8, 4, 4),
+        (4096, 4096, 2048, 32, 32, False, True, True): (4, 8, 4, 8),
+        (4096, 4096, 2048, 32, 32, True, False, True): (4, 8, 4, 8),
+        (4096, 4096, 2048, 64, 64, False, True, True): (1, 16, 3, 4),
+        (4096, 4096, 2048, 64, 64, True, False, True): (4, 16, 3, 4),
+        (4096, 4096, 2048, 128, 128, False, True, True): (2, 16, 2, 8),
+        (4096, 4096, 2048, 128, 128, True, False, True): (4, 16, 1, 4),
+        (4096, 4096, 4096, 16, 16, False, True, True): (4, 32, 4, 4),
+        (4096, 4096, 4096, 16, 16, True, False, True): (4, 32, 4, 2),
+        (4096, 4096, 4096, 32, 32, False, True, True): (4, 16, 4, 8),
+        (4096, 4096, 4096, 32, 32, True, False, True): (4, 16, 3, 8),
+        (4096, 4096, 4096, 64, 64, False, True, True): (1, 32, 3, 4),
+        (4096, 4096, 4096, 64, 64, True, False, True): (1, 32, 3, 4),
+        (4096, 4096, 4096, 128, 128, False, True, True): (3, 32, 1, 4),
+        (4096, 4096, 4096, 128, 128, True, False, True): (2, 32, 1, 4),
+        (4096, 4096, 8192, 16, 16, False, True, True): (4, 64, 4, 2),
+        (4096, 4096, 8192, 16, 16, True, False, True): (4, 64, 4, 2),
+        (4096, 4096, 8192, 32, 32, False, True, True): (4, 32, 4, 8),
+        (4096, 4096, 8192, 32, 32, True, False, True): (4, 32, 4, 8),
+        (4096, 4096, 8192, 64, 64, False, True, True): (2, 64, 3, 4),
+        (4096, 4096, 8192, 64, 64, True, False, True): (2, 64, 3, 4),
+        (4096, 4096, 8192, 128, 128, False, True, True): (3, 64, 1, 4),
+        (4096, 4096, 8192, 128, 128, True, False, True): (1, 64, 1, 4),
+        (4096, 4096, 16384, 16, 16, False, True, True): (4, 64, 3, 4),
+        (4096, 4096, 16384, 16, 16, True, False, True): (4, 64, 4, 4),
+        (4096, 4096, 16384, 32, 32, False, True, True): (4, 64, 4, 8),
+        (4096, 4096, 16384, 32, 32, True, False, True): (4, 64, 4, 8),
+        (4096, 4096, 16384, 64, 64, False, True, True): (1, 64, 2, 4),
+        (4096, 4096, 16384, 64, 64, True, False, True): (1, 64, 3, 8),
+        (4096, 4096, 16384, 128, 128, False, True, True): (3, 128, 1, 4),
+        (4096, 4096, 16384, 128, 128, True, False, True): (1, 128, 1, 4),
+        (4096, 4096, 32768, 16, 16, False, True, True): (8, 128, 3, 2),
+        (4096, 4096, 32768, 16, 16, True, False, True): (5, 128, 4, 4),
+        (4096, 4096, 32768, 32, 32, False, True, True): (3, 128, 4, 4),
+        (4096, 4096, 32768, 32, 32, True, False, True): (3, 128, 4, 8),
+        (4096, 4096, 32768, 64, 64, False, True, True): (1, 128, 2, 4),
+        (4096, 4096, 32768, 64, 64, True, False, True): (3, 256, 3, 4),
+        (4096, 4096, 32768, 128, 128, False, True, True): (3, 256, 1, 4),
+        (4096, 4096, 32768, 128, 128, True, False, True): (1, 256, 1, 4),
+        (4096, 4096, 50432, 16, 16, False, True, True): (1, 197, 1, 4),
+        (4096, 4096, 50432, 16, 16, True, False, True): (4, 197, 4, 1),
+        (4096, 4096, 50432, 32, 32, False, True, True): (1, 197, 1, 4),
+        (4096, 4096, 50432, 32, 32, True, False, True): (2, 197, 3, 4),
+        (4096, 4096, 50432, 64, 64, False, True, True): (1, 394, 3, 4),
+        (4096, 4096, 50432, 64, 64, True, False, True): (1, 197, 2, 4),
+        (4096, 4096, 50432, 128, 128, False, True, True): (3, 394, 1, 4),
+        (4096, 4096, 50432, 128, 128, True, False, True): (1, 394, 2, 4),
+        (4096, 4096, 65536, 16, 16, False, True, True): (5, 256, 4, 4),
+        (4096, 4096, 65536, 16, 16, True, False, True): (5, 256, 4, 4),
+        (4096, 4096, 65536, 32, 32, False, True, True): (4, 256, 4, 8),
+        (4096, 4096, 65536, 32, 32, True, False, True): (4, 256, 3, 8),
+        (4096, 4096, 65536, 64, 64, False, True, True): (1, 256, 2, 4),
+        (4096, 4096, 65536, 64, 64, True, False, True): (1, 512, 3, 4),
+        (4096, 4096, 65536, 128, 128, False, True, True): (3, 512, 1, 4),
+        (4096, 4096, 65536, 128, 128, True, False, True): (1, 512, 1, 4),
+        (4096, 4096, 131072, 16, 16, False, True, True): (4, 512, 3, 4),
+        (4096, 4096, 131072, 16, 16, True, False, True): (5, 512, 4, 4),
+        (4096, 4096, 131072, 32, 32, False, True, True): (1, 512, 4, 8),
+        (4096, 4096, 131072, 32, 32, True, False, True): (4, 512, 4, 8),
+        (4096, 4096, 131072, 64, 64, False, True, True): (1, 512, 2, 4),
+        (4096, 4096, 131072, 64, 64, True, False, True): (1, 512, 2, 4),
+        (4096, 4096, 131072, 128, 128, False, True, True): (3, 1024, 1, 4),
+        (4096, 4096, 131072, 128, 128, True, False, True): (1, 1024, 1, 4),
+        (6144, 6144, 256, 16, 16, False, True, True): (1, 2, 1, 4),
+        (6144, 6144, 256, 16, 16, True, False, True): (1, 1, 4, 4),
+        (6144, 6144, 256, 32, 32, False, True, True): (3, 2, 1, 8),
+        (6144, 6144, 256, 32, 32, True, False, True): (2, 1, 3, 4),
+        (6144, 6144, 256, 64, 64, False, True, True): (2, 2, 3, 4),
+        (6144, 6144, 256, 64, 64, True, False, True): (6, 2, 4, 4),
+        (6144, 6144, 256, 128, 128, False, True, True): (2, 2, 3, 8),
+        (6144, 6144, 256, 128, 128, True, False, True): (1, 2, 3, 8),
+        (6144, 6144, 512, 16, 16, False, True, True): (4, 4, 1, 4),
+        (6144, 6144, 512, 16, 16, True, False, True): (3, 2, 3, 1),
+        (6144, 6144, 512, 32, 32, False, True, True): (1, 8, 1, 4),
+        (6144, 6144, 512, 32, 32, True, False, True): (2, 2, 3, 8),
+        (6144, 6144, 512, 64, 64, False, True, True): (4, 4, 3, 4),
+        (6144, 6144, 512, 64, 64, True, False, True): (6, 2, 3, 4),
+        (6144, 6144, 512, 128, 128, False, True, True): (3, 4, 1, 4),
+        (6144, 6144, 512, 128, 128, True, False, True): (4, 4, 3, 8),
+        (6144, 6144, 1024, 16, 16, False, True, True): (1, 8, 1, 2),
+        (6144, 6144, 1024, 16, 16, True, False, True): (4, 8, 4, 2),
+        (6144, 6144, 1024, 32, 32, False, True, True): (1, 8, 4, 2),
+        (6144, 6144, 1024, 32, 32, True, False, True): (1, 8, 4, 2),
+        (6144, 6144, 1024, 64, 64, False, True, True): (4, 8, 3, 4),
+        (6144, 6144, 1024, 64, 64, True, False, True): (1, 4, 3, 4),
+        (6144, 6144, 1024, 128, 128, False, True, True): (3, 8, 1, 4),
+        (6144, 6144, 1024, 128, 128, True, False, True): (1, 8, 3, 8),
+        (6144, 6144, 2048, 16, 16, False, True, True): (4, 4, 1, 4),
+        (6144, 6144, 2048, 16, 16, True, False, True): (2, 8, 4, 4),
+        (6144, 6144, 2048, 32, 32, False, True, True): (4, 8, 3, 4),
+        (6144, 6144, 2048, 32, 32, True, False, True): (2, 8, 3, 4),
+        (6144, 6144, 2048, 64, 64, False, True, True): (4, 16, 3, 4),
+        (6144, 6144, 2048, 64, 64, True, False, True): (2, 8, 3, 4),
+        (6144, 6144, 2048, 128, 128, False, True, True): (3, 16, 1, 4),
+        (6144, 6144, 2048, 128, 128, True, False, True): (4, 16, 3, 8),
+        (6144, 6144, 4096, 16, 16, False, True, True): (4, 8, 1, 4),
+        (6144, 6144, 4096, 16, 16, True, False, True): (4, 32, 4, 2),
+        (6144, 6144, 4096, 32, 32, False, True, True): (4, 16, 1, 2),
+        (6144, 6144, 4096, 32, 32, True, False, True): (2, 8, 3, 8),
+        (6144, 6144, 4096, 64, 64, False, True, True): (4, 32, 3, 4),
+        (6144, 6144, 4096, 64, 64, True, False, True): (4, 16, 3, 4),
+        (6144, 6144, 4096, 128, 128, False, True, True): (6, 32, 1, 4),
+        (6144, 6144, 4096, 128, 128, True, False, True): (4, 32, 3, 8),
+        (6144, 6144, 8192, 16, 16, False, True, True): (2, 16, 1, 2),
+        (6144, 6144, 8192, 16, 16, True, False, True): (4, 64, 4, 2),
+        (6144, 6144, 8192, 32, 32, False, True, True): (4, 32, 1, 2),
+        (6144, 6144, 8192, 32, 32, True, False, True): (4, 32, 3, 4),
+        (6144, 6144, 8192, 64, 64, False, True, True): (4, 64, 3, 4),
+        (6144, 6144, 8192, 64, 64, True, False, True): (4, 32, 3, 4),
+        (6144, 6144, 8192, 128, 128, False, True, True): (6, 64, 1, 4),
+        (6144, 6144, 8192, 128, 128, True, False, True): (4, 64, 3, 8),
+        (6144, 6144, 16384, 16, 16, False, True, True): (2, 32, 1, 2),
+        (6144, 6144, 16384, 16, 16, True, False, True): (4, 64, 4, 4),
+        (6144, 6144, 16384, 32, 32, False, True, True): (4, 64, 1, 2),
+        (6144, 6144, 16384, 32, 32, True, False, True): (4, 64, 3, 4),
+        (6144, 6144, 16384, 64, 64, False, True, True): (4, 128, 3, 4),
+        (6144, 6144, 16384, 64, 64, True, False, True): (1, 32, 3, 8),
+        (6144, 6144, 16384, 128, 128, False, True, True): (4, 128, 1, 4),
+        (6144, 6144, 16384, 128, 128, True, False, True): (4, 128, 3, 8),
+        (6144, 6144, 32768, 16, 16, False, True, True): (2, 64, 1, 2),
+        (6144, 6144, 32768, 16, 16, True, False, True): (5, 128, 4, 1),
+        (6144, 6144, 32768, 32, 32, False, True, True): (4, 128, 1, 2),
+        (6144, 6144, 32768, 32, 32, True, False, True): (3, 128, 3, 4),
+        (6144, 6144, 32768, 64, 64, False, True, True): (4, 256, 3, 4),
+        (6144, 6144, 32768, 64, 64, True, False, True): (2, 64, 3, 8),
+        (6144, 6144, 32768, 128, 128, False, True, True): (8, 256, 1, 4),
+        (6144, 6144, 32768, 128, 128, True, False, True): (4, 256, 3, 8),
+        (6144, 6144, 65536, 16, 16, False, True, True): (2, 128, 1, 2),
+        (6144, 6144, 65536, 16, 16, True, False, True): (5, 256, 4, 1),
+        (6144, 6144, 65536, 32, 32, False, True, True): (4, 256, 1, 2),
+        (6144, 6144, 65536, 32, 32, True, False, True): (2, 256, 3, 4),
+        (6144, 6144, 65536, 64, 64, False, True, True): (4, 512, 3, 4),
+        (6144, 6144, 65536, 64, 64, True, False, True): (1, 128, 3, 8),
+        (6144, 6144, 65536, 128, 128, False, True, True): (4, 512, 1, 4),
+        (6144, 6144, 65536, 128, 128, True, False, True): (4, 512, 3, 8),
+        (6144, 6144, 131072, 16, 16, False, True, True): (2, 256, 1, 2),
+        (6144, 6144, 131072, 16, 16, True, False, True): (3, 512, 4, 4),
+        (6144, 6144, 131072, 32, 32, False, True, True): (4, 512, 1, 2),
+        (6144, 6144, 131072, 32, 32, True, False, True): (4, 512, 3, 4),
+        (6144, 6144, 131072, 64, 64, False, True, True): (4, 1024, 3, 4),
+        (6144, 6144, 131072, 64, 64, True, False, True): (2, 256, 3, 8),
+        (6144, 6144, 131072, 128, 128, False, True, True): (4, 1024, 1, 4),
+        (6144, 6144, 131072, 128, 128, True, False, True): (4, 1024, 3, 8),
+        (8192, 8192, 256, 16, 16, False, True, True): (2, 2, 6, 4),
+        (8192, 8192, 256, 16, 16, True, False, True): (2, 4, 2, 2),
+        (8192, 8192, 256, 32, 32, False, True, True): (4, 2, 3, 4),
+        (8192, 8192, 256, 32, 32, True, False, True): (4, 2, 3, 4),
+        (8192, 8192, 256, 64, 64, False, True, True): (2, 2, 3, 8),
+        (8192, 8192, 256, 64, 64, True, False, True): (6, 2, 3, 8),
+        (8192, 8192, 256, 128, 128, False, True, True): (3, 2, 1, 4),
+        (8192, 8192, 256, 128, 128, True, False, True): (1, 2, 1, 4),
+        (8192, 8192, 512, 16, 16, False, True, True): (4, 4, 3, 2),
+        (8192, 8192, 512, 16, 16, True, False, True): (4, 4, 3, 4),
+        (8192, 8192, 512, 32, 32, False, True, True): (1, 4, 3, 4),
+        (8192, 8192, 512, 32, 32, True, False, True): (5, 4, 3, 2),
+        (8192, 8192, 512, 64, 64, False, True, True): (1, 4, 3, 4),
+        (8192, 8192, 512, 64, 64, True, False, True): (2, 2, 3, 8),
+        (8192, 8192, 512, 128, 128, False, True, True): (4, 4, 2, 8),
+        (8192, 8192, 512, 128, 128, True, False, True): (4, 4, 2, 8),
+        (8192, 8192, 1024, 16, 16, False, True, True): (4, 8, 4, 4),
+        (8192, 8192, 1024, 16, 16, True, False, True): (4, 8, 4, 4),
+        (8192, 8192, 1024, 32, 32, False, True, True): (2, 4, 4, 8),
+        (8192, 8192, 1024, 32, 32, True, False, True): (1, 4, 3, 4),
+        (8192, 8192, 1024, 64, 64, False, True, True): (4, 8, 3, 4),
+        (8192, 8192, 1024, 64, 64, True, False, True): (2, 8, 3, 4),
+        (8192, 8192, 1024, 128, 128, False, True, True): (4, 8, 2, 8),
+        (8192, 8192, 1024, 128, 128, True, False, True): (4, 8, 1, 4),
+        (8192, 8192, 2048, 16, 16, False, True, True): (2, 8, 4, 4),
+        (8192, 8192, 2048, 16, 16, True, False, True): (2, 8, 4, 4),
+        (8192, 8192, 2048, 32, 32, False, True, True): (2, 8, 4, 8),
+        (8192, 8192, 2048, 32, 32, True, False, True): (2, 8, 4, 8),
+        (8192, 8192, 2048, 64, 64, False, True, True): (4, 8, 2, 4),
+        (8192, 8192, 2048, 64, 64, True, False, True): (4, 16, 3, 4),
+        (8192, 8192, 2048, 128, 128, False, True, True): (6, 16, 1, 4),
+        (8192, 8192, 2048, 128, 128, True, False, True): (4, 16, 1, 4),
+        (8192, 8192, 4096, 16, 16, False, True, True): (4, 32, 4, 2),
+        (8192, 8192, 4096, 16, 16, True, False, True): (4, 32, 4, 2),
+        (8192, 8192, 4096, 32, 32, False, True, True): (2, 16, 4, 8),
+        (8192, 8192, 4096, 32, 32, True, False, True): (4, 16, 4, 8),
+        (8192, 8192, 4096, 64, 64, False, True, True): (4, 16, 2, 4),
+        (8192, 8192, 4096, 64, 64, True, False, True): (4, 16, 2, 4),
+        (8192, 8192, 4096, 128, 128, False, True, True): (6, 32, 1, 4),
+        (8192, 8192, 4096, 128, 128, True, False, True): (4, 32, 1, 4),
+        (8192, 8192, 8192, 16, 16, False, True, True): (4, 64, 4, 2),
+        (8192, 8192, 8192, 16, 16, True, False, True): (4, 64, 4, 2),
+        (8192, 8192, 8192, 32, 32, False, True, True): (2, 32, 4, 8),
+        (8192, 8192, 8192, 32, 32, True, False, True): (2, 32, 4, 8),
+        (8192, 8192, 8192, 64, 64, False, True, True): (2, 32, 2, 4),
+        (8192, 8192, 8192, 64, 64, True, False, True): (4, 32, 2, 4),
+        (8192, 8192, 8192, 128, 128, False, True, True): (6, 64, 1, 4),
+        (8192, 8192, 8192, 128, 128, True, False, True): (4, 64, 1, 4),
+        (8192, 8192, 16384, 16, 16, False, True, True): (4, 64, 3, 4),
+        (8192, 8192, 16384, 16, 16, True, False, True): (4, 64, 4, 4),
+        (8192, 8192, 16384, 32, 32, False, True, True): (4, 64, 4, 8),
+        (8192, 8192, 16384, 32, 32, True, False, True): (4, 64, 4, 8),
+        (8192, 8192, 16384, 64, 64, False, True, True): (4, 64, 2, 4),
+        (8192, 8192, 16384, 64, 64, True, False, True): (4, 64, 3, 8),
+        (8192, 8192, 16384, 128, 128, False, True, True): (6, 128, 1, 4),
+        (8192, 8192, 16384, 128, 128, True, False, True): (4, 128, 1, 4),
+        (8192, 8192, 32768, 16, 16, False, True, True): (3, 128, 4, 4),
+        (8192, 8192, 32768, 16, 16, True, False, True): (3, 128, 4, 4),
+        (8192, 8192, 32768, 32, 32, False, True, True): (2, 128, 4, 8),
+        (8192, 8192, 32768, 32, 32, True, False, True): (2, 128, 4, 8),
+        (8192, 8192, 32768, 64, 64, False, True, True): (2, 128, 2, 4),
+        (8192, 8192, 32768, 64, 64, True, False, True): (2, 128, 3, 8),
+        (8192, 8192, 32768, 128, 128, False, True, True): (6, 256, 1, 4),
+        (8192, 8192, 32768, 128, 128, True, False, True): (4, 256, 1, 4),
+        (8192, 8192, 50432, 16, 16, False, True, True): (1, 197, 1, 1),
+        (8192, 8192, 50432, 16, 16, True, False, True): (3, 197, 4, 1),
+        (8192, 8192, 50432, 32, 32, False, True, True): (2, 197, 1, 4),
+        (8192, 8192, 50432, 32, 32, True, False, True): (2, 197, 3, 4),
+        (8192, 8192, 50432, 64, 64, False, True, True): (2, 394, 3, 4),
+        (8192, 8192, 65536, 16, 16, False, True, True): (3, 256, 4, 4),
+        (8192, 8192, 65536, 16, 16, True, False, True): (4, 256, 4, 4),
+        (8192, 8192, 65536, 32, 32, False, True, True): (2, 256, 4, 8),
+        (8192, 8192, 65536, 32, 32, True, False, True): (2, 256, 3, 8),
+        (8192, 8192, 65536, 64, 64, False, True, True): (2, 256, 2, 4),
+        (8192, 8192, 65536, 64, 64, True, False, True): (4, 256, 3, 8),
+        (8192, 8192, 65536, 128, 128, False, True, True): (6, 512, 1, 4),
+        (8192, 8192, 65536, 128, 128, True, False, True): (4, 512, 1, 4),
+        (8192, 8192, 131072, 16, 16, False, True, True): (4, 512, 4, 4),
+        (8192, 8192, 131072, 16, 16, True, False, True): (3, 512, 4, 4),
+        (8192, 8192, 131072, 32, 32, False, True, True): (2, 512, 4, 8),
+        (8192, 8192, 131072, 32, 32, True, False, True): (2, 512, 4, 8),
+        (8192, 8192, 131072, 64, 64, False, True, True): (2, 512, 2, 4),
+        (8192, 8192, 131072, 64, 64, True, False, True): (2, 512, 2, 4),
+        (8192, 8192, 131072, 128, 128, False, True, True): (4, 1024, 1, 4),
+        (8192, 8192, 131072, 128, 128, True, False, True): (4, 1024, 1, 4),
+        (12288, 12288, 256, 16, 16, False, True, True): (4, 2, 1, 4),
+        (12288, 12288, 256, 16, 16, True, False, True): (1, 1, 3, 1),
+        (12288, 12288, 256, 32, 32, False, True, True): (4, 4, 1, 4),
+        (12288, 12288, 256, 32, 32, True, False, True): (2, 1, 3, 2),
+        (12288, 12288, 256, 64, 64, False, True, True): (4, 2, 3, 4),
+        (12288, 12288, 256, 64, 64, True, False, True): (3, 1, 3, 4),
+        (12288, 12288, 256, 128, 128, False, True, True): (6, 2, 1, 4),
+        (12288, 12288, 256, 128, 128, True, False, True): (4, 2, 3, 8),
+        (12288, 12288, 512, 16, 16, False, True, True): (4, 4, 1, 2),
+        (12288, 12288, 512, 16, 16, True, False, True): (4, 4, 4, 2),
+        (12288, 12288, 512, 32, 32, False, True, True): (4, 4, 4, 2),
+        (12288, 12288, 512, 32, 32, True, False, True): (2, 2, 3, 8),
+        (12288, 12288, 512, 64, 64, False, True, True): (4, 4, 3, 4),
+        (12288, 12288, 512, 64, 64, True, False, True): (8, 2, 3, 4),
+        (12288, 12288, 512, 128, 128, False, True, True): (4, 4, 3, 8),
+        (12288, 12288, 512, 128, 128, True, False, True): (4, 4, 3, 8),
+        (12288, 12288, 1024, 16, 16, False, True, True): (4, 8, 1, 2),
+        (12288, 12288, 1024, 16, 16, True, False, True): (2, 4, 4, 4),
+        (12288, 12288, 1024, 32, 32, False, True, True): (4, 4, 3, 4),
+        (12288, 12288, 1024, 32, 32, True, False, True): (1, 4, 3, 4),
+        (12288, 12288, 1024, 64, 64, False, True, True): (4, 8, 3, 4),
+        (12288, 12288, 1024, 64, 64, True, False, True): (2, 4, 3, 4),
+        (12288, 12288, 1024, 128, 128, False, True, True): (4, 8, 3, 8),
+        (12288, 12288, 1024, 128, 128, True, False, True): (4, 8, 3, 8),
+        (12288, 12288, 2048, 16, 16, False, True, True): (2, 4, 1, 4),
+        (12288, 12288, 2048, 16, 16, True, False, True): (2, 8, 4, 4),
+        (12288, 12288, 2048, 32, 32, False, True, True): (4, 8, 1, 2),
+        (12288, 12288, 2048, 32, 32, True, False, True): (2, 8, 4, 8),
+        (12288, 12288, 2048, 64, 64, False, True, True): (4, 16, 3, 4),
+        (12288, 12288, 2048, 64, 64, True, False, True): (2, 8, 3, 4),
+        (12288, 12288, 2048, 128, 128, False, True, True): (4, 16, 3, 8),
+        (12288, 12288, 2048, 128, 128, True, False, True): (4, 16, 3, 8),
+        (12288, 12288, 4096, 16, 16, False, True, True): (2, 8, 1, 4),
+        (12288, 12288, 4096, 16, 16, True, False, True): (2, 16, 4, 4),
+        (12288, 12288, 4096, 32, 32, False, True, True): (2, 16, 1, 2),
+        (12288, 12288, 4096, 32, 32, True, False, True): (2, 16, 3, 4),
+        (12288, 12288, 4096, 64, 64, False, True, True): (4, 32, 3, 4),
+        (12288, 12288, 4096, 64, 64, True, False, True): (2, 16, 3, 4),
+        (12288, 12288, 4096, 128, 128, False, True, True): (4, 32, 1, 4),
+        (12288, 12288, 4096, 128, 128, True, False, True): (4, 32, 3, 8),
+        (12288, 12288, 8192, 16, 16, False, True, True): (2, 32, 1, 1),
+        (12288, 12288, 8192, 16, 16, True, False, True): (4, 64, 4, 2),
+        (12288, 12288, 8192, 32, 32, False, True, True): (2, 32, 1, 2),
+        (12288, 12288, 8192, 32, 32, True, False, True): (2, 32, 3, 2),
+        (12288, 12288, 8192, 64, 64, False, True, True): (4, 64, 3, 4),
+        (12288, 12288, 8192, 64, 64, True, False, True): (2, 32, 3, 4),
+        (12288, 12288, 8192, 128, 128, False, True, True): (4, 64, 3, 8),
+        (12288, 12288, 8192, 128, 128, True, False, True): (2, 64, 3, 8),
+        (12288, 12288, 16384, 16, 16, False, True, True): (4, 128, 1, 2),
+        (12288, 12288, 16384, 16, 16, True, False, True): (4, 128, 4, 2),
+        (12288, 12288, 16384, 32, 32, False, True, True): (2, 64, 1, 2),
+        (12288, 12288, 16384, 32, 32, True, False, True): (2, 64, 3, 4),
+        (12288, 12288, 16384, 64, 64, False, True, True): (4, 128, 3, 4),
+        (12288, 12288, 16384, 64, 64, True, False, True): (2, 64, 3, 4),
+        (12288, 12288, 16384, 128, 128, False, True, True): (4, 128, 1, 4),
+        (12288, 12288, 16384, 128, 128, True, False, True): (4, 128, 3, 8),
+        (12288, 12288, 32768, 16, 16, False, True, True): (2, 128, 1, 1),
+        (12288, 12288, 32768, 16, 16, True, False, True): (3, 128, 4, 1),
+        (12288, 12288, 32768, 32, 32, False, True, True): (2, 128, 1, 2),
+        (12288, 12288, 32768, 32, 32, True, False, True): (2, 128, 3, 2),
+        (12288, 12288, 32768, 64, 64, False, True, True): (4, 256, 3, 4),
+        (12288, 12288, 32768, 64, 64, True, False, True): (1, 64, 3, 8),
+        (12288, 12288, 32768, 128, 128, False, True, True): (4, 256, 3, 8),
+        (12288, 12288, 32768, 128, 128, True, False, True): (4, 256, 3, 8),
+        (12288, 12288, 65536, 16, 16, False, True, True): (4, 512, 1, 2),
+        (12288, 12288, 65536, 16, 16, True, False, True): (3, 256, 4, 1),
+        (12288, 12288, 65536, 32, 32, False, True, True): (2, 256, 1, 2),
+        (12288, 12288, 65536, 32, 32, True, False, True): (2, 256, 3, 2),
+        (12288, 12288, 65536, 64, 64, False, True, True): (4, 512, 3, 4),
+        (12288, 12288, 65536, 64, 64, True, False, True): (2, 256, 3, 4),
+        (12288, 12288, 65536, 128, 128, False, True, True): (4, 512, 1, 4),
+        (12288, 12288, 65536, 128, 128, True, False, True): (4, 512, 3, 8),
+        (12288, 12288, 131072, 16, 16, False, True, True): (2, 512, 1, 1),
+        (12288, 12288, 131072, 16, 16, True, False, True): (2, 512, 4, 4),
+        (12288, 12288, 131072, 32, 32, False, True, True): (2, 512, 1, 2),
+        (12288, 12288, 131072, 32, 32, True, False, True): (2, 512, 3, 4),
+        (12288, 12288, 131072, 64, 64, False, True, True): (4, 1024, 3, 4),
+        (12288, 12288, 131072, 64, 64, True, False, True): (2, 512, 3, 4),
+        (12288, 12288, 131072, 128, 128, False, True, True): (4, 1024, 3, 8),
+        (12288, 12288, 131072, 128, 128, True, False, True): (4, 1024, 3, 8),
+        (16384, 16384, 256, 16, 16, False, True, True): (2, 2, 3, 2),
+        (16384, 16384, 256, 16, 16, True, False, True): (2, 2, 6, 4),
+        (16384, 16384, 256, 32, 32, False, True, True): (4, 2, 3, 4),
+        (16384, 16384, 256, 32, 32, True, False, True): (4, 2, 3, 2),
+        (16384, 16384, 256, 64, 64, False, True, True): (2, 2, 5, 4),
+        (16384, 16384, 256, 64, 64, True, False, True): (2, 2, 3, 8),
+        (16384, 16384, 256, 128, 128, False, True, True): (4, 2, 2, 8),
+        (16384, 16384, 256, 128, 128, True, False, True): (2, 2, 1, 4),
+        (16384, 16384, 512, 16, 16, False, True, True): (1, 2, 4, 4),
+        (16384, 16384, 512, 16, 16, True, False, True): (1, 2, 4, 4),
+        (16384, 16384, 512, 32, 32, False, True, True): (2, 2, 3, 8),
+        (16384, 16384, 512, 32, 32, True, False, True): (2, 2, 4, 8),
+        (16384, 16384, 512, 64, 64, False, True, True): (4, 4, 3, 4),
+        (16384, 16384, 512, 64, 64, True, False, True): (2, 4, 3, 4),
+        (16384, 16384, 512, 128, 128, False, True, True): (4, 4, 2, 8),
+        (16384, 16384, 512, 128, 128, True, False, True): (4, 4, 2, 8),
+        (16384, 16384, 1024, 16, 16, False, True, True): (4, 8, 4, 4),
+        (16384, 16384, 1024, 16, 16, True, False, True): (2, 4, 4, 4),
+        (16384, 16384, 1024, 32, 32, False, True, True): (2, 4, 4, 8),
+        (16384, 16384, 1024, 32, 32, True, False, True): (2, 4, 4, 8),
+        (16384, 16384, 1024, 64, 64, False, True, True): (4, 4, 2, 4),
+        (16384, 16384, 1024, 64, 64, True, False, True): (2, 4, 2, 4),
+        (16384, 16384, 1024, 128, 128, False, True, True): (6, 8, 1, 4),
+        (16384, 16384, 1024, 128, 128, True, False, True): (4, 8, 1, 4),
+        (16384, 16384, 2048, 16, 16, False, True, True): (2, 8, 4, 4),
+        (16384, 16384, 2048, 16, 16, True, False, True): (2, 8, 4, 4),
+        (16384, 16384, 2048, 32, 32, False, True, True): (2, 8, 4, 8),
+        (16384, 16384, 2048, 32, 32, True, False, True): (2, 8, 4, 8),
+        (16384, 16384, 2048, 64, 64, False, True, True): (2, 8, 2, 4),
+        (16384, 16384, 2048, 64, 64, True, False, True): (2, 8, 2, 4),
+        (16384, 16384, 2048, 128, 128, False, True, True): (4, 16, 2, 8),
+        (16384, 16384, 2048, 128, 128, True, False, True): (4, 16, 1, 4),
+        (16384, 16384, 4096, 16, 16, False, True, True): (2, 16, 4, 4),
+        (16384, 16384, 4096, 16, 16, True, False, True): (2, 16, 4, 4),
+        (16384, 16384, 4096, 32, 32, False, True, True): (1, 16, 4, 8),
+        (16384, 16384, 4096, 32, 32, True, False, True): (2, 16, 3, 4),
+        (16384, 16384, 4096, 64, 64, False, True, True): (1, 16, 2, 4),
+        (16384, 16384, 4096, 64, 64, True, False, True): (2, 16, 2, 4),
+        (16384, 16384, 4096, 128, 128, False, True, True): (4, 32, 2, 8),
+        (16384, 16384, 4096, 128, 128, True, False, True): (4, 32, 1, 4),
+        (16384, 16384, 8192, 16, 16, False, True, True): (2, 64, 4, 2),
+        (16384, 16384, 8192, 16, 16, True, False, True): (2, 64, 4, 2),
+        (16384, 16384, 8192, 32, 32, False, True, True): (2, 32, 4, 8),
+        (16384, 16384, 8192, 32, 32, True, False, True): (2, 32, 4, 8),
+        (16384, 16384, 8192, 64, 64, False, True, True): (2, 32, 2, 4),
+        (16384, 16384, 8192, 64, 64, True, False, True): (2, 32, 4, 8),
+        (16384, 16384, 8192, 128, 128, False, True, True): (4, 64, 2, 8),
+        (16384, 16384, 8192, 128, 128, True, False, True): (4, 64, 1, 4),
+        (16384, 16384, 16384, 16, 16, False, True, True): (1, 64, 4, 4),
+        (16384, 16384, 16384, 16, 16, True, False, True): (1, 64, 4, 4),
+        (16384, 16384, 16384, 32, 32, False, True, True): (1, 64, 4, 8),
+        (16384, 16384, 16384, 32, 32, True, False, True): (1, 64, 4, 8),
+        (16384, 16384, 16384, 64, 64, False, True, True): (1, 64, 2, 4),
+        (16384, 16384, 16384, 64, 64, True, False, True): (1, 64, 3, 8),
+        (16384, 16384, 16384, 128, 128, False, True, True): (4, 128, 1, 4),
+        (16384, 16384, 16384, 128, 128, True, False, True): (4, 128, 1, 4),
+        (16384, 16384, 32768, 16, 16, False, True, True): (1, 128, 4, 4),
+        (16384, 16384, 32768, 16, 16, True, False, True): (1, 128, 4, 4),
+        (16384, 16384, 32768, 32, 32, False, True, True): (1, 128, 3, 4),
+        (16384, 16384, 32768, 32, 32, True, False, True): (1, 128, 3, 8),
+        (16384, 16384, 32768, 64, 64, False, True, True): (2, 128, 2, 4),
+        (16384, 16384, 32768, 64, 64, True, False, True): (1, 128, 4, 8),
+        (16384, 16384, 32768, 128, 128, False, True, True): (4, 256, 2, 8),
+        (16384, 16384, 32768, 128, 128, True, False, True): (4, 256, 1, 4),
+        (16384, 16384, 65536, 16, 16, False, True, True): (1, 256, 3, 4),
+        (16384, 16384, 65536, 16, 16, True, False, True): (1, 256, 4, 4),
+        (16384, 16384, 65536, 32, 32, False, True, True): (1, 256, 4, 8),
+        (16384, 16384, 65536, 32, 32, True, False, True): (1, 256, 3, 4),
+        (16384, 16384, 65536, 64, 64, False, True, True): (2, 256, 2, 4),
+        (16384, 16384, 65536, 64, 64, True, False, True): (1, 256, 3, 8),
+        (16384, 16384, 65536, 128, 128, False, True, True): (4, 512, 2, 8),
+        (16384, 16384, 65536, 128, 128, True, False, True): (4, 512, 1, 4),
+        (16384, 16384, 131072, 16, 16, False, True, True): (1, 512, 4, 4),
+        (16384, 16384, 131072, 16, 16, True, False, True): (1, 512, 3, 2),
+        (16384, 16384, 131072, 32, 32, False, True, True): (1, 512, 4, 8),
+        (16384, 16384, 131072, 32, 32, True, False, True): (1, 512, 3, 2),
+        (16384, 16384, 131072, 64, 64, False, True, True): (1, 512, 2, 4),
+        (16384, 16384, 131072, 64, 64, True, False, True): (1, 512, 2, 4),
+        (16384, 16384, 131072, 128, 128, False, True, True): (4, 1024, 1, 4),
+        (16384, 16384, 131072, 128, 128, True, False, True): (4, 1024, 1, 4),
+        (24576, 24576, 256, 16, 16, False, True, True): (6, 2, 1, 2),
+        (24576, 24576, 256, 16, 16, True, False, True): (2, 2, 5, 4),
+        (24576, 24576, 256, 32, 32, False, True, True): (4, 4, 1, 4),
+        (24576, 24576, 256, 32, 32, True, False, True): (2, 2, 4, 2),
+        (24576, 24576, 256, 64, 64, False, True, True): (2, 2, 3, 4),
+        (24576, 24576, 256, 64, 64, True, False, True): (1, 1, 3, 4),
+        (24576, 24576, 256, 128, 128, False, True, True): (6, 2, 1, 4),
+        (24576, 24576, 256, 128, 128, True, False, True): (2, 2, 3, 8),
+        (24576, 24576, 512, 16, 16, False, True, True): (4, 4, 1, 2),
+        (24576, 24576, 512, 16, 16, True, False, True): (2, 2, 4, 4),
+        (24576, 24576, 512, 32, 32, False, True, True): (1, 2, 3, 4),
+        (24576, 24576, 512, 32, 32, True, False, True): (1, 2, 3, 4),
+        (24576, 24576, 512, 64, 64, False, True, True): (4, 4, 3, 4),
+        (24576, 24576, 512, 64, 64, True, False, True): (1, 2, 3, 4),
+        (24576, 24576, 512, 128, 128, False, True, True): (4, 4, 3, 8),
+        (24576, 24576, 512, 128, 128, True, False, True): (4, 4, 3, 8),
+        (24576, 24576, 1024, 16, 16, False, True, True): (2, 8, 1, 2),
+        (24576, 24576, 1024, 16, 16, True, False, True): (2, 4, 4, 4),
+        (24576, 24576, 1024, 32, 32, False, True, True): (2, 4, 1, 2),
+        (24576, 24576, 1024, 32, 32, True, False, True): (1, 4, 3, 4),
+        (24576, 24576, 1024, 64, 64, False, True, True): (4, 8, 3, 4),
+        (24576, 24576, 1024, 64, 64, True, False, True): (1, 4, 3, 4),
+        (24576, 24576, 1024, 128, 128, False, True, True): (4, 8, 3, 8),
+        (24576, 24576, 1024, 128, 128, True, False, True): (4, 8, 3, 8),
+        (24576, 24576, 2048, 16, 16, False, True, True): (1, 4, 1, 4),
+        (24576, 24576, 2048, 16, 16, True, False, True): (1, 8, 4, 4),
+        (24576, 24576, 2048, 32, 32, False, True, True): (2, 8, 1, 2),
+        (24576, 24576, 2048, 32, 32, True, False, True): (1, 8, 3, 4),
+        (24576, 24576, 2048, 64, 64, False, True, True): (4, 16, 3, 4),
+        (24576, 24576, 2048, 64, 64, True, False, True): (1, 4, 3, 8),
+        (24576, 24576, 2048, 128, 128, False, True, True): (4, 16, 3, 8),
+        (24576, 24576, 2048, 128, 128, True, False, True): (2, 16, 3, 8),
+        (24576, 24576, 4096, 16, 16, False, True, True): (2, 32, 1, 2),
+        (24576, 24576, 4096, 16, 16, True, False, True): (1, 16, 4, 4),
+        (24576, 24576, 4096, 32, 32, False, True, True): (1, 16, 1, 2),
+        (24576, 24576, 4096, 32, 32, True, False, True): (1, 16, 3, 4),
+        (24576, 24576, 4096, 64, 64, False, True, True): (4, 32, 3, 4),
+        (24576, 24576, 4096, 64, 64, True, False, True): (1, 8, 3, 8),
+        (24576, 24576, 4096, 128, 128, False, True, True): (4, 32, 3, 8),
+        (24576, 24576, 4096, 128, 128, True, False, True): (2, 32, 3, 8),
+        (24576, 24576, 8192, 16, 16, False, True, True): (1, 32, 1, 1),
+        (24576, 24576, 8192, 16, 16, True, False, True): (2, 64, 4, 2),
+        (24576, 24576, 8192, 32, 32, False, True, True): (1, 32, 1, 2),
+        (24576, 24576, 8192, 32, 32, True, False, True): (1, 32, 3, 4),
+        (24576, 24576, 8192, 64, 64, False, True, True): (4, 64, 3, 4),
+        (24576, 24576, 8192, 64, 64, True, False, True): (1, 32, 3, 4),
+        (24576, 24576, 8192, 128, 128, False, True, True): (4, 64, 3, 8),
+        (24576, 24576, 8192, 128, 128, True, False, True): (4, 64, 3, 8),
+        (24576, 24576, 16384, 16, 16, False, True, True): (2, 128, 1, 2),
+        (24576, 24576, 16384, 16, 16, True, False, True): (1, 64, 4, 4),
+        (24576, 24576, 16384, 32, 32, False, True, True): (1, 64, 1, 2),
+        (24576, 24576, 16384, 32, 32, True, False, True): (1, 64, 3, 2),
+        (24576, 24576, 16384, 64, 64, False, True, True): (2, 128, 3, 4),
+        (24576, 24576, 16384, 64, 64, True, False, True): (1, 32, 3, 8),
+        (24576, 24576, 16384, 128, 128, False, True, True): (4, 128, 3, 8),
+        (24576, 24576, 16384, 128, 128, True, False, True): (4, 128, 3, 8),
+        (24576, 24576, 32768, 16, 16, False, True, True): (1, 128, 1, 1),
+        (24576, 24576, 32768, 16, 16, True, False, True): (1, 128, 4, 4),
+        (24576, 24576, 32768, 32, 32, False, True, True): (1, 128, 1, 2),
+        (24576, 24576, 32768, 32, 32, True, False, True): (1, 128, 3, 4),
+        (24576, 24576, 32768, 64, 64, False, True, True): (2, 256, 3, 4),
+        (24576, 24576, 32768, 64, 64, True, False, True): (1, 128, 3, 4),
+        (24576, 24576, 32768, 128, 128, False, True, True): (4, 256, 3, 8),
+        (24576, 24576, 32768, 128, 128, True, False, True): (2, 256, 3, 8),
+        (24576, 24576, 65536, 16, 16, False, True, True): (2, 512, 1, 2),
+        (24576, 24576, 65536, 16, 16, True, False, True): (1, 256, 4, 4),
+        (32768, 32768, 256, 16, 16, False, True, True): (4, 2, 1, 2),
+    },
+    ("bsr_dense_addmm", "NVIDIA A100-SXM4-80GB", (0, torch.float16, 0.56)): {
+        (192, 192, 256, 64, 64, False, True, True): (1, 4, 3, 4),
+        (192, 192, 256, 64, 64, True, False, True): (1, 4, 3, 4),
+        (192, 192, 512, 64, 64, False, True, True): (1, 8, 5, 4),
+        (192, 192, 512, 64, 64, True, False, True): (1, 8, 3, 4),
+        (192, 192, 1024, 64, 64, False, True, True): (1, 16, 3, 2),
+        (192, 192, 1024, 64, 64, True, False, True): (1, 16, 3, 4),
+        (192, 192, 2048, 64, 64, False, True, True): (1, 32, 5, 4),
+        (192, 192, 2048, 64, 64, True, False, True): (4, 32, 5, 4),
+        (192, 192, 4096, 64, 64, False, True, True): (1, 64, 1, 8),
+        (192, 192, 4096, 64, 64, True, False, True): (1, 32, 3, 4),
+        (192, 192, 8192, 64, 64, False, True, True): (4, 128, 1, 4),
+        (192, 192, 8192, 64, 64, True, False, True): (3, 64, 3, 4),
+        (192, 192, 16384, 64, 64, False, True, True): (1, 256, 1, 4),
+        (192, 192, 16384, 64, 64, True, False, True): (3, 64, 2, 4),
+        (192, 192, 32768, 64, 64, False, True, True): (1, 512, 1, 2),
+        (192, 192, 32768, 64, 64, True, False, True): (2, 256, 2, 4),
+        (192, 192, 65536, 64, 64, False, True, True): (1, 512, 1, 4),
+        (192, 192, 65536, 64, 64, True, False, True): (2, 512, 2, 4),
+        (192, 192, 131072, 64, 64, False, True, True): (1, 1024, 1, 4),
+        (192, 192, 131072, 64, 64, True, False, True): (1, 512, 3, 4),
+        (384, 384, 256, 128, 128, False, True, True): (3, 2, 3, 8),
+        (384, 384, 256, 128, 128, True, False, True): (5, 2, 3, 8),
+        (384, 384, 512, 128, 128, False, True, True): (4, 4, 3, 8),
+        (384, 384, 512, 128, 128, True, False, True): (1, 4, 3, 8),
+        (384, 384, 1024, 128, 128, False, True, True): (1, 8, 3, 8),
+        (384, 384, 1024, 128, 128, True, False, True): (1, 8, 2, 8),
+        (384, 384, 2048, 128, 128, False, True, True): (3, 16, 3, 8),
+        (384, 384, 2048, 128, 128, True, False, True): (1, 16, 3, 8),
+        (384, 384, 4096, 128, 128, False, True, True): (3, 32, 3, 8),
+        (384, 384, 4096, 128, 128, True, False, True): (3, 32, 3, 8),
+        (384, 384, 8192, 128, 128, False, True, True): (2, 64, 3, 8),
+        (384, 384, 8192, 128, 128, True, False, True): (2, 64, 2, 4),
+        (384, 384, 16384, 128, 128, False, True, True): (1, 128, 2, 8),
+        (384, 384, 16384, 128, 128, True, False, True): (3, 128, 2, 4),
+        (384, 384, 32768, 128, 128, False, True, True): (2, 256, 3, 8),
+        (384, 384, 32768, 128, 128, True, False, True): (1, 256, 2, 4),
+        (384, 384, 65536, 128, 128, False, True, True): (7, 512, 1, 4),
+        (384, 384, 65536, 128, 128, True, False, True): (3, 512, 2, 4),
+        (384, 384, 131072, 128, 128, False, True, True): (5, 1024, 1, 4),
+        (384, 384, 131072, 128, 128, True, False, True): (1, 1024, 2, 4),
+    },
+    ("bsr_dense_addmm", "NVIDIA A100-SXM4-80GB", (0, torch.float32, 0.5)): {
+        (16, 16, 16, 16, 16, False, False, False): (2, 1, 1, 16),
+        (16, 16, 16, 16, 16, False, False, True): (1, 1, 2, 4),
+        (16, 16, 16, 16, 16, False, True, False): (1, 1, 2, 16),
+        (16, 16, 16, 16, 16, False, True, True): (2, 1, 2, 8),
+        (16, 16, 16, 16, 16, True, False, False): (1, 1, 1, 2),
+        (16, 16, 16, 16, 16, True, False, True): (2, 1, 1, 4),
+        (16, 16, 32, 16, 16, False, False, False): (1, 1, 1, 2),
+        (16, 16, 32, 16, 16, False, False, True): (1, 1, 2, 8),
+        (16, 16, 32, 16, 16, False, True, False): (1, 2, 1, 4),
+        (16, 16, 32, 16, 16, False, True, True): (1, 2, 2, 4),
+        (16, 16, 32, 16, 16, True, False, False): (1, 1, 2, 4),
+        (16, 16, 32, 16, 16, True, False, True): (1, 2, 2, 4),
+        (16, 16, 64, 16, 16, False, False, False): (1, 4, 1, 4),
+        (16, 16, 64, 16, 16, False, False, True): (2, 2, 1, 4),
+        (16, 16, 64, 16, 16, False, True, False): (1, 4, 1, 4),
+        (16, 16, 64, 16, 16, False, True, True): (1, 4, 1, 8),
+        (16, 16, 64, 16, 16, True, False, False): (1, 2, 1, 4),
+        (16, 16, 64, 16, 16, True, False, True): (1, 4, 2, 8),
+        (16, 32, 16, 16, 16, False, False, False): (1, 1, 2, 8),
+        (16, 32, 16, 16, 16, False, False, True): (2, 1, 1, 4),
+        (16, 32, 16, 16, 16, False, True, False): (1, 1, 1, 4),
+        (16, 32, 16, 16, 16, False, True, True): (1, 1, 1, 4),
+        (16, 32, 16, 16, 16, True, False, False): (1, 1, 1, 4),
+        (16, 32, 16, 16, 16, True, False, True): (1, 1, 2, 8),
+        (16, 32, 16, 16, 32, False, False, False): (1, 1, 2, 4),
+        (16, 32, 16, 16, 32, False, False, True): (2, 1, 2, 2),
+        (16, 32, 16, 16, 32, False, True, False): (1, 1, 1, 8),
+        (16, 32, 16, 16, 32, False, True, True): (1, 1, 1, 2),
+        (16, 32, 16, 16, 32, True, False, False): (3, 1, 1, 4),
+        (16, 32, 16, 16, 32, True, False, True): (1, 1, 1, 4),
+        (16, 32, 32, 16, 16, False, False, False): (1, 2, 1, 4),
+        (16, 32, 32, 16, 16, False, False, True): (2, 2, 1, 4),
+        (16, 32, 32, 16, 16, False, True, False): (1, 2, 1, 2),
+        (16, 32, 32, 16, 16, False, True, True): (1, 2, 1, 4),
+        (16, 32, 32, 16, 16, True, False, False): (1, 2, 1, 4),
+        (16, 32, 32, 16, 16, True, False, True): (1, 2, 1, 4),
+        (16, 32, 32, 16, 32, False, False, False): (1, 1, 2, 4),
+        (16, 32, 32, 16, 32, False, False, True): (1, 2, 1, 4),
+        (16, 32, 32, 16, 32, False, True, False): (1, 2, 2, 8),
+        (16, 32, 32, 16, 32, False, True, True): (1, 2, 1, 1),
+        (16, 32, 32, 16, 32, True, False, False): (1, 2, 1, 2),
+        (16, 32, 32, 16, 32, True, False, True): (1, 2, 1, 4),
+        (16, 32, 64, 16, 16, False, False, False): (1, 2, 1, 4),
+        (16, 32, 64, 16, 16, False, False, True): (2, 4, 1, 4),
+        (16, 32, 64, 16, 16, False, True, False): (1, 4, 2, 4),
+        (16, 32, 64, 16, 16, False, True, True): (1, 4, 1, 4),
+        (16, 32, 64, 16, 16, True, False, False): (1, 2, 2, 8),
+        (16, 32, 64, 16, 16, True, False, True): (1, 4, 1, 2),
+        (16, 32, 64, 16, 32, False, False, False): (1, 4, 1, 4),
+        (16, 32, 64, 16, 32, False, False, True): (1, 4, 3, 4),
+        (16, 32, 64, 16, 32, False, True, False): (1, 2, 1, 4),
+        (16, 32, 64, 16, 32, False, True, True): (1, 4, 1, 4),
+        (16, 32, 64, 16, 32, True, False, False): (1, 2, 1, 8),
+        (16, 32, 64, 16, 32, True, False, True): (1, 2, 1, 4),
+        (16, 64, 16, 16, 32, False, False, False): (1, 1, 1, 2),
+        (16, 64, 16, 16, 32, False, False, True): (1, 1, 1, 8),
+        (16, 64, 16, 16, 32, False, True, False): (1, 1, 1, 8),
+        (16, 64, 16, 16, 32, False, True, True): (1, 1, 1, 4),
+        (16, 64, 16, 16, 32, True, False, False): (1, 1, 1, 8),
+        (16, 64, 16, 16, 32, True, False, True): (1, 1, 1, 4),
+        (16, 64, 32, 16, 32, False, False, False): (1, 2, 1, 4),
+        (16, 64, 32, 16, 32, False, False, True): (1, 1, 1, 4),
+        (16, 64, 32, 16, 32, False, True, False): (1, 2, 1, 1),
+        (16, 64, 32, 16, 32, False, True, True): (1, 2, 1, 8),
+        (16, 64, 32, 16, 32, True, False, False): (2, 2, 1, 4),
+        (16, 64, 32, 16, 32, True, False, True): (2, 2, 1, 4),
+        (16, 64, 64, 16, 32, False, False, False): (1, 2, 1, 4),
+        (16, 64, 64, 16, 32, False, False, True): (1, 4, 1, 4),
+        (16, 64, 64, 16, 32, False, True, False): (1, 4, 1, 4),
+        (16, 64, 64, 16, 32, False, True, True): (1, 4, 1, 4),
+        (16, 64, 64, 16, 32, True, False, False): (1, 4, 1, 2),
+        (16, 64, 64, 16, 32, True, False, True): (3, 4, 1, 4),
+        (32, 16, 16, 16, 16, False, False, False): (1, 1, 2, 4),
+        (32, 16, 16, 16, 16, False, False, True): (1, 1, 1, 2),
+        (32, 16, 16, 16, 16, False, True, False): (1, 1, 2, 4),
+        (32, 16, 16, 16, 16, False, True, True): (1, 1, 2, 4),
+        (32, 16, 16, 16, 16, True, False, False): (1, 1, 3, 8),
+        (32, 16, 16, 16, 16, True, False, True): (1, 1, 2, 4),
+        (32, 16, 32, 16, 16, False, False, False): (1, 2, 1, 4),
+        (32, 16, 32, 16, 16, False, False, True): (1, 2, 3, 4),
+        (32, 16, 32, 16, 16, False, True, False): (1, 1, 1, 8),
+        (32, 16, 32, 16, 16, False, True, True): (1, 2, 1, 4),
+        (32, 16, 32, 16, 16, True, False, False): (1, 1, 1, 2),
+        (32, 16, 32, 16, 16, True, False, True): (1, 1, 1, 4),
+        (32, 16, 64, 16, 16, False, False, False): (1, 4, 1, 4),
+        (32, 16, 64, 16, 16, False, False, True): (3, 4, 1, 4),
+        (32, 16, 64, 16, 16, False, True, False): (1, 4, 1, 1),
+        (32, 16, 64, 16, 16, False, True, True): (1, 4, 1, 4),
+        (32, 16, 64, 16, 16, True, False, False): (1, 4, 1, 4),
+        (32, 16, 64, 16, 16, True, False, True): (1, 4, 1, 4),
+        (32, 32, 16, 16, 16, False, False, False): (1, 1, 1, 2),
+        (32, 32, 16, 16, 16, False, False, True): (2, 1, 1, 4),
+        (32, 32, 16, 16, 16, False, True, False): (1, 1, 1, 2),
+        (32, 32, 16, 16, 16, False, True, True): (2, 1, 1, 4),
+        (32, 32, 16, 16, 16, True, False, False): (3, 1, 2, 4),
+        (32, 32, 16, 16, 16, True, False, True): (1, 1, 2, 4),
+        (32, 32, 16, 16, 32, False, False, False): (2, 1, 1, 2),
+        (32, 32, 16, 16, 32, False, False, True): (1, 1, 1, 4),
+        (32, 32, 16, 16, 32, False, True, False): (1, 1, 1, 4),
+        (32, 32, 16, 16, 32, False, True, True): (1, 1, 1, 8),
+        (32, 32, 16, 16, 32, True, False, False): (1, 1, 1, 8),
+        (32, 32, 16, 16, 32, True, False, True): (1, 1, 1, 4),
+        (32, 32, 16, 32, 32, False, False, False): (2, 1, 1, 4),
+        (32, 32, 16, 32, 32, False, False, True): (1, 1, 2, 4),
+        (32, 32, 16, 32, 32, False, True, False): (2, 1, 1, 1),
+        (32, 32, 16, 32, 32, False, True, True): (2, 1, 2, 4),
+        (32, 32, 16, 32, 32, True, False, False): (1, 1, 1, 8),
+        (32, 32, 16, 32, 32, True, False, True): (1, 1, 1, 4),
+        (32, 32, 32, 16, 16, False, False, False): (1, 1, 1, 4),
+        (32, 32, 32, 16, 16, False, False, True): (1, 2, 1, 2),
+        (32, 32, 32, 16, 16, False, True, False): (2, 2, 1, 4),
+        (32, 32, 32, 16, 16, False, True, True): (1, 2, 2, 4),
+        (32, 32, 32, 16, 16, True, False, False): (1, 2, 1, 4),
+        (32, 32, 32, 16, 16, True, False, True): (2, 2, 1, 4),
+        (32, 32, 32, 16, 32, False, False, False): (1, 2, 1, 4),
+        (32, 32, 32, 16, 32, False, False, True): (1, 2, 1, 4),
+        (32, 32, 32, 16, 32, False, True, False): (1, 2, 1, 4),
+        (32, 32, 32, 16, 32, False, True, True): (1, 2, 1, 4),
+        (32, 32, 32, 16, 32, True, False, False): (2, 1, 1, 2),
+        (32, 32, 32, 16, 32, True, False, True): (2, 2, 2, 4),
+        (32, 32, 32, 32, 32, False, False, False): (1, 1, 1, 4),
+        (32, 32, 32, 32, 32, False, False, True): (1, 1, 1, 2),
+        (32, 32, 32, 32, 32, False, True, False): (1, 1, 1, 4),
+        (32, 32, 32, 32, 32, False, True, True): (1, 1, 2, 2),
+        (32, 32, 32, 32, 32, True, False, False): (1, 1, 1, 2),
+        (32, 32, 32, 32, 32, True, False, True): (1, 1, 2, 1),
+        (32, 32, 64, 16, 16, False, False, False): (2, 4, 1, 4),
+        (32, 32, 64, 16, 16, False, False, True): (1, 4, 2, 4),
+        (32, 32, 64, 16, 16, False, True, False): (1, 4, 1, 4),
+        (32, 32, 64, 16, 16, False, True, True): (1, 4, 1, 4),
+        (32, 32, 64, 16, 16, True, False, False): (1, 2, 1, 4),
+        (32, 32, 64, 16, 16, True, False, True): (2, 4, 1, 4),
+        (32, 32, 64, 16, 32, False, False, False): (1, 4, 1, 8),
+        (32, 32, 64, 16, 32, False, False, True): (1, 4, 1, 4),
+        (32, 32, 64, 16, 32, False, True, False): (1, 4, 1, 4),
+        (32, 32, 64, 16, 32, False, True, True): (2, 4, 1, 4),
+        (32, 32, 64, 16, 32, True, False, False): (1, 2, 2, 4),
+        (32, 32, 64, 16, 32, True, False, True): (2, 4, 1, 4),
+        (32, 32, 64, 32, 32, False, False, False): (2, 2, 1, 4),
+        (32, 32, 64, 32, 32, False, False, True): (1, 1, 1, 4),
+        (32, 32, 64, 32, 32, False, True, False): (1, 1, 1, 8),
+        (32, 32, 64, 32, 32, False, True, True): (2, 1, 1, 4),
+        (32, 32, 64, 32, 32, True, False, False): (1, 1, 1, 4),
+        (32, 32, 64, 32, 32, True, False, True): (1, 2, 1, 1),
+        (32, 64, 16, 16, 32, False, False, False): (1, 1, 2, 2),
+        (32, 64, 16, 16, 32, False, False, True): (2, 1, 1, 4),
+        (32, 64, 16, 16, 32, False, True, False): (1, 1, 1, 8),
+        (32, 64, 16, 16, 32, False, True, True): (1, 1, 3, 4),
+        (32, 64, 16, 16, 32, True, False, False): (1, 1, 1, 2),
+        (32, 64, 16, 16, 32, True, False, True): (1, 1, 2, 4),
+        (32, 64, 16, 32, 32, False, False, False): (1, 1, 1, 2),
+        (32, 64, 16, 32, 32, False, False, True): (1, 1, 3, 4),
+        (32, 64, 16, 32, 32, False, True, False): (1, 1, 2, 4),
+        (32, 64, 16, 32, 32, False, True, True): (1, 1, 1, 8),
+        (32, 64, 16, 32, 32, True, False, False): (1, 1, 2, 4),
+        (32, 64, 16, 32, 32, True, False, True): (1, 1, 1, 8),
+        (32, 64, 32, 16, 32, False, False, False): (1, 2, 1, 4),
+        (32, 64, 32, 16, 32, False, False, True): (1, 2, 3, 4),
+        (32, 64, 32, 16, 32, False, True, False): (1, 2, 1, 8),
+        (32, 64, 32, 16, 32, False, True, True): (3, 2, 1, 4),
+        (32, 64, 32, 16, 32, True, False, False): (1, 1, 1, 8),
+        (32, 64, 32, 16, 32, True, False, True): (1, 2, 1, 4),
+        (32, 64, 32, 32, 32, False, False, False): (1, 1, 1, 1),
+        (32, 64, 32, 32, 32, False, False, True): (1, 1, 1, 4),
+        (32, 64, 32, 32, 32, False, True, False): (1, 1, 1, 4),
+        (32, 64, 32, 32, 32, False, True, True): (1, 1, 1, 4),
+        (32, 64, 32, 32, 32, True, False, False): (1, 1, 1, 4),
+        (32, 64, 32, 32, 32, True, False, True): (1, 1, 2, 8),
+        (32, 64, 64, 16, 32, False, False, False): (2, 4, 1, 4),
+        (32, 64, 64, 16, 32, False, False, True): (1, 4, 1, 4),
+        (32, 64, 64, 16, 32, False, True, False): (1, 4, 1, 4),
+        (32, 64, 64, 16, 32, False, True, True): (2, 4, 1, 4),
+        (32, 64, 64, 16, 32, True, False, False): (1, 4, 1, 4),
+        (32, 64, 64, 16, 32, True, False, True): (1, 4, 1, 4),
+        (32, 64, 64, 32, 32, False, False, False): (2, 2, 1, 4),
+        (32, 64, 64, 32, 32, False, False, True): (1, 2, 1, 8),
+        (32, 64, 64, 32, 32, False, True, False): (1, 2, 1, 4),
+        (32, 64, 64, 32, 32, False, True, True): (1, 2, 1, 4),
+        (32, 64, 64, 32, 32, True, False, False): (2, 2, 1, 4),
+        (32, 64, 64, 32, 32, True, False, True): (1, 2, 3, 8),
+        (64, 32, 16, 32, 32, False, False, False): (1, 1, 1, 4),
+        (64, 32, 16, 32, 32, False, False, True): (3, 1, 2, 4),
+        (64, 32, 16, 32, 32, False, True, False): (2, 1, 1, 2),
+        (64, 32, 16, 32, 32, False, True, True): (1, 1, 1, 8),
+        (64, 32, 16, 32, 32, True, False, False): (1, 1, 1, 2),
+        (64, 32, 16, 32, 32, True, False, True): (1, 1, 1, 4),
+        (64, 32, 32, 32, 32, False, False, False): (1, 1, 1, 4),
+        (64, 32, 32, 32, 32, False, False, True): (1, 1, 2, 8),
+        (64, 32, 32, 32, 32, False, True, False): (1, 1, 1, 8),
+        (64, 32, 32, 32, 32, False, True, True): (1, 1, 1, 4),
+        (64, 32, 32, 32, 32, True, False, False): (1, 1, 2, 4),
+        (64, 32, 32, 32, 32, True, False, True): (1, 1, 3, 8),
+        (64, 32, 64, 32, 32, False, False, False): (1, 2, 1, 4),
+        (64, 32, 64, 32, 32, False, False, True): (2, 2, 1, 4),
+        (64, 32, 64, 32, 32, False, True, False): (1, 1, 1, 4),
+        (64, 32, 64, 32, 32, False, True, True): (1, 2, 1, 8),
+        (64, 32, 64, 32, 32, True, False, False): (2, 2, 1, 4),
+        (64, 32, 64, 32, 32, True, False, True): (1, 2, 1, 8),
+        (64, 64, 16, 32, 32, False, False, False): (1, 1, 2, 8),
+        (64, 64, 16, 32, 32, False, False, True): (2, 1, 2, 4),
+        (64, 64, 16, 32, 32, False, True, False): (1, 1, 1, 2),
+        (64, 64, 16, 32, 32, False, True, True): (1, 1, 2, 4),
+        (64, 64, 16, 32, 32, True, False, False): (1, 1, 1, 2),
+        (64, 64, 16, 32, 32, True, False, True): (1, 1, 2, 4),
+        (64, 64, 32, 32, 32, False, False, False): (1, 1, 1, 4),
+        (64, 64, 32, 32, 32, False, False, True): (2, 1, 1, 4),
+        (64, 64, 32, 32, 32, False, True, False): (1, 1, 1, 8),
+        (64, 64, 32, 32, 32, False, True, True): (2, 1, 1, 4),
+        (64, 64, 32, 32, 32, True, False, False): (1, 1, 1, 4),
+        (64, 64, 32, 32, 32, True, False, True): (1, 1, 1, 8),
+        (64, 64, 64, 32, 32, False, False, False): (2, 2, 1, 4),
+        (64, 64, 64, 32, 32, False, False, True): (1, 2, 1, 4),
+        (64, 64, 64, 32, 32, False, True, False): (1, 2, 1, 4),
+        (64, 64, 64, 32, 32, False, True, True): (2, 2, 1, 4),
+        (64, 64, 64, 32, 32, True, False, False): (1, 1, 1, 8),
+        (64, 64, 64, 32, 32, True, False, True): (1, 2, 2, 4),
+        (192, 192, 256, 16, 16, False, True, True): (1, 16, 3, 2),
+        (192, 192, 256, 16, 16, True, False, True): (1, 8, 5, 4),
+        (192, 192, 256, 32, 32, False, True, True): (2, 8, 4, 4),
+        (192, 192, 256, 32, 32, True, False, True): (1, 8, 5, 4),
+        (192, 192, 512, 16, 16, False, True, True): (2, 16, 3, 4),
+        (192, 192, 512, 16, 16, True, False, True): (1, 16, 5, 4),
+        (192, 192, 512, 32, 32, False, True, True): (1, 16, 3, 4),
+        (192, 192, 512, 32, 32, True, False, True): (2, 16, 3, 4),
+        (192, 192, 1024, 16, 16, False, True, True): (3, 16, 3, 4),
+        (192, 192, 1024, 16, 16, True, False, True): (2, 8, 3, 4),
+        (192, 192, 1024, 32, 32, False, True, True): (3, 32, 1, 4),
+        (192, 192, 1024, 32, 32, True, False, True): (3, 16, 3, 4),
+        (192, 192, 2048, 16, 16, False, True, True): (1, 32, 3, 4),
+        (192, 192, 2048, 16, 16, True, False, True): (2, 16, 3, 4),
+        (192, 192, 2048, 32, 32, False, True, True): (1, 64, 1, 4),
+        (192, 192, 2048, 32, 32, True, False, True): (1, 64, 2, 4),
+        (192, 192, 4096, 16, 16, False, True, True): (1, 64, 2, 4),
+        (192, 192, 4096, 16, 16, True, False, True): (1, 32, 3, 4),
+        (192, 192, 4096, 32, 32, False, True, True): (3, 128, 2, 4),
+        (192, 192, 4096, 32, 32, True, False, True): (1, 128, 2, 4),
+        (192, 192, 8192, 16, 16, False, True, True): (2, 64, 3, 4),
+        (192, 192, 8192, 16, 16, True, False, True): (1, 64, 3, 4),
+        (192, 192, 8192, 32, 32, False, True, True): (3, 128, 3, 4),
+        (192, 192, 8192, 32, 32, True, False, True): (1, 128, 2, 4),
+        (192, 192, 16384, 16, 16, False, True, True): (1, 256, 3, 2),
+        (192, 192, 16384, 16, 16, True, False, True): (1, 256, 3, 2),
+        (192, 192, 16384, 32, 32, False, True, True): (2, 256, 3, 4),
+        (192, 192, 16384, 32, 32, True, False, True): (2, 256, 3, 4),
+        (192, 192, 32768, 16, 16, False, True, True): (2, 512, 3, 2),
+        (192, 192, 32768, 16, 16, True, False, True): (1, 128, 3, 4),
+        (192, 192, 32768, 32, 32, False, True, True): (2, 512, 3, 4),
+        (192, 192, 32768, 32, 32, True, False, True): (2, 512, 3, 4),
+        (192, 192, 65536, 16, 16, False, True, True): (2, 1024, 3, 2),
+        (192, 192, 65536, 16, 16, True, False, True): (1, 256, 3, 4),
+        (192, 192, 65536, 32, 32, False, True, True): (2, 1024, 3, 4),
+        (192, 192, 65536, 32, 32, True, False, True): (2, 1024, 3, 4),
+        (192, 192, 131072, 16, 16, False, True, True): (2, 512, 3, 4),
+        (192, 192, 131072, 16, 16, True, False, True): (1, 512, 3, 4),
+        (192, 192, 131072, 32, 32, False, True, True): (2, 1024, 3, 4),
+        (192, 192, 131072, 32, 32, True, False, True): (2, 1024, 3, 4),
+        (256, 256, 256, 16, 16, False, True, True): (1, 16, 3, 4),
+        (256, 256, 256, 16, 16, True, False, True): (2, 16, 1, 4),
+        (256, 256, 256, 32, 32, False, True, True): (1, 8, 4, 8),
+        (256, 256, 256, 32, 32, True, False, True): (4, 8, 4, 4),
+        (256, 256, 256, 64, 64, False, True, True): (1, 4, 4, 8),
+        (256, 256, 256, 64, 64, True, False, True): (1, 4, 3, 8),
+        (256, 256, 256, 128, 128, False, True, True): (7, 2, 1, 32),
+        (256, 256, 256, 128, 128, True, False, True): (3, 2, 1, 32),
+        (256, 256, 512, 16, 16, False, True, True): (1, 16, 5, 4),
+        (256, 256, 512, 16, 16, True, False, True): (1, 16, 3, 2),
+        (256, 256, 512, 32, 32, False, True, True): (4, 16, 4, 4),
+        (256, 256, 512, 32, 32, True, False, True): (4, 16, 3, 4),
+        (256, 256, 512, 64, 64, False, True, True): (1, 8, 3, 8),
+        (256, 256, 512, 64, 64, True, False, True): (1, 8, 3, 8),
+        (256, 256, 512, 128, 128, False, True, True): (1, 4, 1, 32),
+        (256, 256, 512, 128, 128, True, False, True): (3, 4, 1, 32),
+        (256, 256, 1024, 16, 16, False, True, True): (3, 32, 5, 2),
+        (256, 256, 1024, 16, 16, True, False, True): (2, 32, 5, 2),
+        (256, 256, 1024, 32, 32, False, True, True): (1, 32, 4, 4),
+        (256, 256, 1024, 32, 32, True, False, True): (1, 32, 5, 4),
+        (256, 256, 1024, 64, 64, False, True, True): (4, 16, 3, 8),
+        (256, 256, 1024, 64, 64, True, False, True): (1, 16, 3, 8),
+        (256, 256, 1024, 128, 128, False, True, True): (1, 8, 1, 32),
+        (256, 256, 1024, 128, 128, True, False, True): (3, 8, 1, 32),
+        (256, 256, 2048, 16, 16, False, True, True): (3, 32, 3, 4),
+        (256, 256, 2048, 16, 16, True, False, True): (1, 64, 3, 2),
+        (256, 256, 2048, 32, 32, False, True, True): (1, 64, 3, 4),
+        (256, 256, 2048, 32, 32, True, False, True): (1, 64, 3, 4),
+        (256, 256, 2048, 64, 64, False, True, True): (2, 32, 1, 8),
+        (256, 256, 2048, 64, 64, True, False, True): (2, 32, 1, 8),
+        (256, 256, 2048, 128, 128, False, True, True): (4, 16, 1, 32),
+        (256, 256, 2048, 128, 128, True, False, True): (4, 16, 1, 32),
+        (256, 256, 4096, 16, 16, False, True, True): (1, 32, 2, 4),
+        (256, 256, 4096, 16, 16, True, False, True): (1, 32, 3, 4),
+        (256, 256, 4096, 32, 32, False, True, True): (1, 128, 2, 4),
+        (256, 256, 4096, 32, 32, True, False, True): (1, 128, 2, 4),
+        (256, 256, 4096, 64, 64, False, True, True): (2, 64, 4, 8),
+        (256, 256, 4096, 64, 64, True, False, True): (3, 64, 2, 8),
+        (256, 256, 4096, 128, 128, False, True, True): (3, 32, 1, 32),
+        (256, 256, 4096, 128, 128, True, False, True): (2, 32, 1, 32),
+        (256, 256, 8192, 16, 16, False, True, True): (1, 64, 3, 4),
+        (256, 256, 8192, 16, 16, True, False, True): (2, 128, 3, 2),
+        (256, 256, 8192, 32, 32, False, True, True): (3, 128, 3, 4),
+        (256, 256, 8192, 32, 32, True, False, True): (1, 128, 3, 4),
+        (256, 256, 8192, 64, 64, False, True, True): (3, 128, 1, 4),
+        (256, 256, 8192, 64, 64, True, False, True): (4, 128, 2, 8),
+        (256, 256, 8192, 128, 128, False, True, True): (6, 64, 1, 32),
+        (256, 256, 8192, 128, 128, True, False, True): (2, 64, 1, 32),
+        (256, 256, 16384, 16, 16, False, True, True): (4, 128, 3, 4),
+        (256, 256, 16384, 16, 16, True, False, True): (3, 128, 3, 4),
+        (256, 256, 16384, 32, 32, False, True, True): (4, 256, 3, 4),
+        (256, 256, 16384, 32, 32, True, False, True): (2, 256, 3, 4),
+        (256, 256, 16384, 64, 64, False, True, True): (3, 256, 1, 4),
+        (256, 256, 16384, 64, 64, True, False, True): (2, 256, 2, 4),
+        (256, 256, 16384, 128, 128, False, True, True): (1, 128, 1, 32),
+        (256, 256, 16384, 128, 128, True, False, True): (3, 128, 1, 32),
+        (256, 256, 32768, 16, 16, False, True, True): (1, 256, 3, 4),
+        (256, 256, 32768, 16, 16, True, False, True): (2, 128, 3, 4),
+        (256, 256, 32768, 32, 32, False, True, True): (2, 512, 3, 4),
+        (256, 256, 32768, 32, 32, True, False, True): (4, 512, 3, 4),
+        (256, 256, 32768, 64, 64, False, True, True): (1, 512, 1, 8),
+        (256, 256, 32768, 64, 64, True, False, True): (1, 512, 2, 4),
+        (256, 256, 32768, 128, 128, False, True, True): (1, 256, 1, 32),
+        (256, 256, 32768, 128, 128, True, False, True): (1, 256, 1, 32),
+        (256, 256, 65536, 16, 16, False, True, True): (2, 512, 3, 4),
+        (256, 256, 65536, 16, 16, True, False, True): (1, 256, 3, 4),
+        (256, 256, 65536, 32, 32, False, True, True): (1, 1024, 3, 4),
+        (256, 256, 65536, 32, 32, True, False, True): (2, 1024, 3, 4),
+        (256, 256, 65536, 64, 64, False, True, True): (1, 1024, 2, 4),
+        (256, 256, 65536, 64, 64, True, False, True): (1, 1024, 2, 4),
+        (256, 256, 65536, 128, 128, False, True, True): (1, 512, 1, 32),
+        (256, 256, 65536, 128, 128, True, False, True): (2, 512, 1, 32),
+        (256, 256, 131072, 16, 16, False, True, True): (1, 1024, 3, 4),
+        (256, 256, 131072, 16, 16, True, False, True): (1, 512, 3, 4),
+        (256, 256, 131072, 32, 32, False, True, True): (1, 2048, 3, 4),
+        (256, 256, 131072, 32, 32, True, False, True): (1, 2048, 3, 4),
+        (256, 256, 131072, 64, 64, False, True, True): (1, 2048, 1, 8),
+        (256, 256, 131072, 64, 64, True, False, True): (1, 2048, 2, 4),
+        (256, 256, 131072, 128, 128, False, True, True): (1, 1024, 1, 32),
+        (256, 256, 131072, 128, 128, True, False, True): (4, 1024, 1, 32),
+        (384, 384, 256, 16, 16, False, True, True): (1, 8, 3, 4),
+        (384, 384, 256, 16, 16, True, False, True): (1, 8, 3, 4),
+        (384, 384, 256, 32, 32, False, True, True): (2, 8, 3, 8),
+        (384, 384, 256, 32, 32, True, False, True): (1, 8, 3, 4),
+        (384, 384, 256, 64, 64, False, True, True): (1, 4, 4, 8),
+        (384, 384, 256, 64, 64, True, False, True): (2, 4, 3, 8),
+        (384, 384, 512, 16, 16, False, True, True): (3, 16, 3, 2),
+        (384, 384, 512, 16, 16, True, False, True): (3, 16, 3, 2),
+        (384, 384, 512, 32, 32, False, True, True): (2, 8, 3, 4),
+        (384, 384, 512, 32, 32, True, False, True): (1, 8, 3, 4),
+        (384, 384, 512, 64, 64, False, True, True): (2, 8, 3, 8),
+        (384, 384, 512, 64, 64, True, False, True): (2, 8, 4, 8),
+        (384, 384, 1024, 16, 16, False, True, True): (3, 16, 3, 2),
+        (384, 384, 1024, 16, 16, True, False, True): (4, 32, 3, 2),
+        (384, 384, 1024, 32, 32, False, True, True): (1, 32, 3, 4),
+        (384, 384, 1024, 32, 32, True, False, True): (2, 16, 3, 4),
+        (384, 384, 1024, 64, 64, False, True, True): (2, 16, 3, 8),
+        (384, 384, 1024, 64, 64, True, False, True): (4, 16, 4, 8),
+        (384, 384, 2048, 16, 16, False, True, True): (3, 16, 3, 4),
+        (384, 384, 2048, 16, 16, True, False, True): (1, 32, 3, 4),
+        (384, 384, 2048, 32, 32, False, True, True): (3, 64, 2, 4),
+        (384, 384, 2048, 32, 32, True, False, True): (1, 64, 3, 4),
+        (384, 384, 2048, 64, 64, False, True, True): (4, 32, 4, 8),
+        (384, 384, 2048, 64, 64, True, False, True): (5, 32, 4, 8),
+        (384, 384, 4096, 16, 16, False, True, True): (1, 32, 3, 4),
+        (384, 384, 4096, 16, 16, True, False, True): (3, 32, 3, 4),
+        (384, 384, 4096, 32, 32, False, True, True): (2, 64, 3, 4),
+        (384, 384, 4096, 32, 32, True, False, True): (2, 64, 3, 4),
+        (384, 384, 4096, 64, 64, False, True, True): (2, 64, 3, 8),
+        (384, 384, 4096, 64, 64, True, False, True): (2, 64, 3, 8),
+        (384, 384, 8192, 16, 16, False, True, True): (1, 128, 3, 2),
+        (384, 384, 8192, 16, 16, True, False, True): (1, 128, 3, 2),
+        (384, 384, 8192, 32, 32, False, True, True): (1, 128, 3, 4),
+        (384, 384, 8192, 32, 32, True, False, True): (1, 128, 3, 4),
+        (384, 384, 8192, 64, 64, False, True, True): (3, 128, 3, 4),
+        (384, 384, 8192, 64, 64, True, False, True): (2, 128, 3, 4),
+        (384, 384, 16384, 16, 16, False, True, True): (1, 256, 3, 2),
+        (384, 384, 16384, 16, 16, True, False, True): (1, 64, 3, 4),
+        (384, 384, 16384, 32, 32, False, True, True): (2, 256, 3, 4),
+        (384, 384, 16384, 32, 32, True, False, True): (4, 256, 3, 4),
+        (384, 384, 16384, 64, 64, False, True, True): (2, 256, 3, 4),
+        (384, 384, 16384, 64, 64, True, False, True): (1, 256, 3, 4),
+        (384, 384, 32768, 16, 16, False, True, True): (1, 128, 3, 4),
+        (384, 384, 32768, 16, 16, True, False, True): (1, 128, 3, 4),
+        (384, 384, 32768, 32, 32, False, True, True): (1, 512, 3, 4),
+        (384, 384, 32768, 32, 32, True, False, True): (1, 512, 2, 4),
+        (384, 384, 32768, 64, 64, False, True, True): (1, 512, 3, 4),
+        (384, 384, 32768, 64, 64, True, False, True): (1, 512, 3, 4),
+        (384, 384, 65536, 16, 16, False, True, True): (1, 256, 3, 4),
+        (384, 384, 65536, 16, 16, True, False, True): (1, 256, 3, 4),
+        (384, 384, 65536, 32, 32, False, True, True): (1, 1024, 3, 4),
+        (384, 384, 65536, 32, 32, True, False, True): (1, 512, 3, 4),
+        (384, 384, 65536, 64, 64, False, True, True): (1, 1024, 3, 4),
+        (384, 384, 65536, 64, 64, True, False, True): (1, 1024, 3, 4),
+        (384, 384, 131072, 16, 16, False, True, True): (1, 512, 3, 4),
+        (384, 384, 131072, 16, 16, True, False, True): (1, 512, 3, 4),
+        (384, 384, 131072, 32, 32, False, True, True): (1, 1024, 3, 4),
+        (384, 384, 131072, 32, 32, True, False, True): (1, 1024, 3, 4),
+        (384, 384, 131072, 64, 64, False, True, True): (1, 2048, 3, 4),
+        (384, 384, 131072, 64, 64, True, False, True): (1, 2048, 3, 4),
+        (512, 512, 256, 16, 16, False, True, True): (1, 8, 4, 4),
+        (512, 512, 256, 16, 16, True, False, True): (1, 8, 3, 2),
+        (512, 512, 256, 32, 32, False, True, True): (4, 8, 3, 4),
+        (512, 512, 256, 32, 32, True, False, True): (4, 8, 3, 4),
+        (512, 512, 256, 64, 64, False, True, True): (3, 4, 3, 8),
+        (512, 512, 256, 64, 64, True, False, True): (5, 4, 3, 8),
+        (512, 512, 256, 128, 128, False, True, True): (1, 2, 1, 32),
+        (512, 512, 256, 128, 128, True, False, True): (3, 2, 1, 32),
+        (512, 512, 512, 16, 16, False, True, True): (2, 16, 3, 2),
+        (512, 512, 512, 16, 16, True, False, True): (1, 8, 4, 4),
+        (512, 512, 512, 32, 32, False, True, True): (3, 16, 3, 4),
+        (512, 512, 512, 32, 32, True, False, True): (5, 16, 2, 4),
+        (512, 512, 512, 64, 64, False, True, True): (1, 8, 3, 8),
+        (512, 512, 512, 64, 64, True, False, True): (3, 8, 3, 8),
+        (512, 512, 512, 128, 128, False, True, True): (1, 4, 1, 32),
+        (512, 512, 512, 128, 128, True, False, True): (3, 4, 1, 16),
+        (512, 512, 1024, 16, 16, False, True, True): (1, 16, 3, 4),
+        (512, 512, 1024, 16, 16, True, False, True): (3, 16, 3, 4),
+        (512, 512, 1024, 32, 32, False, True, True): (3, 32, 3, 4),
+        (512, 512, 1024, 32, 32, True, False, True): (3, 32, 2, 4),
+        (512, 512, 1024, 64, 64, False, True, True): (1, 16, 3, 8),
+        (512, 512, 1024, 64, 64, True, False, True): (4, 16, 3, 8),
+        (512, 512, 1024, 128, 128, False, True, True): (4, 8, 1, 32),
+        (512, 512, 1024, 128, 128, True, False, True): (4, 8, 1, 32),
+        (512, 512, 2048, 16, 16, False, True, True): (5, 16, 3, 4),
+        (512, 512, 2048, 16, 16, True, False, True): (5, 16, 3, 4),
+        (512, 512, 2048, 32, 32, False, True, True): (1, 32, 3, 4),
+        (512, 512, 2048, 32, 32, True, False, True): (1, 32, 4, 4),
+        (512, 512, 2048, 64, 64, False, True, True): (4, 32, 3, 8),
+        (512, 512, 2048, 64, 64, True, False, True): (4, 32, 3, 8),
+        (512, 512, 2048, 128, 128, False, True, True): (3, 16, 1, 32),
+        (512, 512, 2048, 128, 128, True, False, True): (3, 16, 1, 32),
+        (512, 512, 4096, 16, 16, False, True, True): (4, 32, 3, 4),
+        (512, 512, 4096, 16, 16, True, False, True): (4, 64, 3, 2),
+        (512, 512, 4096, 32, 32, False, True, True): (3, 64, 3, 4),
+        (512, 512, 4096, 32, 32, True, False, True): (3, 64, 3, 4),
+        (512, 512, 4096, 64, 64, False, True, True): (4, 64, 2, 4),
+        (512, 512, 4096, 64, 64, True, False, True): (1, 64, 2, 4),
+        (512, 512, 4096, 128, 128, False, True, True): (1, 32, 1, 32),
+        (512, 512, 4096, 128, 128, True, False, True): (1, 32, 1, 32),
+        (512, 512, 8192, 16, 16, False, True, True): (1, 64, 3, 4),
+        (512, 512, 8192, 16, 16, True, False, True): (4, 64, 3, 4),
+        (512, 512, 8192, 32, 32, False, True, True): (2, 128, 3, 4),
+        (512, 512, 8192, 32, 32, True, False, True): (3, 128, 3, 4),
+        (512, 512, 8192, 64, 64, False, True, True): (1, 128, 2, 4),
+        (512, 512, 8192, 64, 64, True, False, True): (1, 128, 2, 4),
+        (512, 512, 8192, 128, 128, False, True, True): (6, 64, 1, 32),
+        (512, 512, 8192, 128, 128, True, False, True): (4, 64, 1, 32),
+        (512, 512, 16384, 16, 16, False, True, True): (1, 128, 3, 4),
+        (512, 512, 16384, 16, 16, True, False, True): (1, 64, 3, 4),
+        (512, 512, 16384, 32, 32, False, True, True): (1, 256, 3, 4),
+        (512, 512, 16384, 32, 32, True, False, True): (4, 256, 3, 4),
+        (512, 512, 16384, 64, 64, False, True, True): (1, 256, 2, 4),
+        (512, 512, 16384, 64, 64, True, False, True): (1, 256, 2, 4),
+        (512, 512, 16384, 128, 128, False, True, True): (1, 128, 1, 32),
+        (512, 512, 16384, 128, 128, True, False, True): (2, 128, 1, 32),
+        (512, 512, 32768, 16, 16, False, True, True): (1, 256, 3, 4),
+        (512, 512, 32768, 16, 16, True, False, True): (1, 128, 3, 4),
+        (512, 512, 32768, 32, 32, False, True, True): (1, 512, 3, 4),
+        (512, 512, 32768, 32, 32, True, False, True): (1, 512, 3, 4),
+        (512, 512, 32768, 64, 64, False, True, True): (1, 512, 2, 4),
+        (512, 512, 32768, 64, 64, True, False, True): (2, 512, 2, 4),
+        (512, 512, 32768, 128, 128, False, True, True): (1, 256, 1, 32),
+        (512, 512, 32768, 128, 128, True, False, True): (2, 256, 1, 32),
+        (512, 512, 65536, 16, 16, False, True, True): (1, 512, 3, 4),
+        (512, 512, 65536, 16, 16, True, False, True): (1, 256, 3, 4),
+        (512, 512, 65536, 32, 32, False, True, True): (1, 1024, 3, 4),
+        (512, 512, 65536, 32, 32, True, False, True): (1, 1024, 3, 4),
+        (512, 512, 65536, 64, 64, False, True, True): (1, 1024, 2, 4),
+        (512, 512, 65536, 64, 64, True, False, True): (1, 1024, 2, 4),
+        (512, 512, 65536, 128, 128, False, True, True): (1, 512, 1, 32),
+        (512, 512, 65536, 128, 128, True, False, True): (4, 512, 1, 32),
+        (512, 512, 131072, 16, 16, False, True, True): (1, 512, 3, 4),
+        (512, 512, 131072, 16, 16, True, False, True): (1, 512, 3, 4),
+        (512, 512, 131072, 32, 32, False, True, True): (1, 2048, 3, 4),
+        (512, 512, 131072, 32, 32, True, False, True): (1, 2048, 3, 4),
+        (512, 512, 131072, 64, 64, False, True, True): (1, 2048, 2, 4),
+        (512, 512, 131072, 64, 64, True, False, True): (1, 2048, 2, 4),
+        (512, 512, 131072, 128, 128, False, True, True): (1, 1024, 1, 32),
+        (512, 512, 131072, 128, 128, True, False, True): (2, 1024, 1, 32),
+        (768, 768, 256, 16, 16, False, True, True): (1, 4, 5, 4),
+        (768, 768, 256, 16, 16, True, False, True): (3, 8, 3, 2),
+        (768, 768, 256, 32, 32, False, True, True): (2, 4, 3, 4),
+        (768, 768, 256, 32, 32, True, False, True): (3, 8, 4, 4),
+        (768, 768, 256, 64, 64, False, True, True): (1, 4, 4, 8),
+        (768, 768, 256, 64, 64, True, False, True): (3, 4, 3, 8),
+        (768, 768, 256, 128, 128, False, True, True): (3, 2, 1, 32),
+        (768, 768, 256, 128, 128, True, False, True): (2, 2, 2, 32),
+        (768, 768, 512, 16, 16, False, True, True): (2, 4, 5, 4),
+        (768, 768, 512, 16, 16, True, False, True): (2, 4, 4, 4),
+        (768, 768, 512, 32, 32, False, True, True): (1, 8, 3, 4),
+        (768, 768, 512, 32, 32, True, False, True): (3, 8, 4, 4),
+        (768, 768, 512, 64, 64, False, True, True): (2, 8, 3, 8),
+        (768, 768, 512, 64, 64, True, False, True): (5, 8, 3, 8),
+        (768, 768, 512, 128, 128, False, True, True): (2, 4, 1, 32),
+        (768, 768, 512, 128, 128, True, False, True): (2, 4, 2, 32),
+        (768, 768, 1024, 16, 16, False, True, True): (2, 16, 4, 2),
+        (768, 768, 1024, 16, 16, True, False, True): (4, 32, 3, 1),
+        (768, 768, 1024, 32, 32, False, True, True): (1, 32, 2, 4),
+        (768, 768, 1024, 32, 32, True, False, True): (1, 16, 5, 4),
+        (768, 768, 1024, 64, 64, False, True, True): (2, 16, 3, 8),
+        (768, 768, 1024, 64, 64, True, False, True): (2, 16, 3, 8),
+        (768, 768, 1024, 128, 128, False, True, True): (1, 8, 2, 32),
+        (768, 768, 1024, 128, 128, True, False, True): (1, 8, 1, 32),
+        (768, 768, 2048, 16, 16, False, True, True): (1, 16, 3, 4),
+        (768, 768, 2048, 16, 16, True, False, True): (1, 16, 3, 4),
+        (768, 768, 2048, 32, 32, False, True, True): (1, 32, 3, 4),
+        (768, 768, 2048, 32, 32, True, False, True): (5, 32, 3, 4),
+        (768, 768, 2048, 64, 64, False, True, True): (1, 32, 3, 8),
+        (768, 768, 2048, 64, 64, True, False, True): (1, 32, 3, 4),
+        (768, 768, 2048, 128, 128, False, True, True): (3, 16, 1, 32),
+        (768, 768, 2048, 128, 128, True, False, True): (4, 16, 1, 32),
+        (768, 768, 4096, 16, 16, False, True, True): (1, 64, 3, 2),
+        (768, 768, 4096, 16, 16, True, False, True): (3, 64, 3, 2),
+        (768, 768, 4096, 32, 32, False, True, True): (1, 64, 3, 4),
+        (768, 768, 4096, 32, 32, True, False, True): (1, 64, 3, 4),
+        (768, 768, 4096, 64, 64, False, True, True): (4, 64, 3, 4),
+        (768, 768, 4096, 64, 64, True, False, True): (4, 64, 3, 4),
+        (768, 768, 4096, 128, 128, False, True, True): (1, 32, 1, 32),
+        (768, 768, 4096, 128, 128, True, False, True): (1, 32, 2, 32),
+        (768, 768, 8192, 16, 16, False, True, True): (1, 128, 3, 2),
+        (768, 768, 8192, 16, 16, True, False, True): (2, 32, 3, 4),
+        (768, 768, 8192, 32, 32, False, True, True): (2, 128, 3, 4),
+        (768, 768, 8192, 32, 32, True, False, True): (1, 128, 2, 4),
+        (768, 768, 8192, 64, 64, False, True, True): (1, 128, 3, 4),
+        (768, 768, 8192, 64, 64, True, False, True): (2, 128, 3, 4),
+        (768, 768, 8192, 128, 128, False, True, True): (1, 64, 1, 32),
+        (768, 768, 8192, 128, 128, True, False, True): (2, 64, 1, 32),
+        (768, 768, 16384, 16, 16, False, True, True): (3, 64, 3, 4),
+        (768, 768, 16384, 16, 16, True, False, True): (1, 64, 3, 4),
+        (768, 768, 16384, 32, 32, False, True, True): (2, 256, 3, 4),
+        (768, 768, 16384, 32, 32, True, False, True): (4, 256, 2, 4),
+        (768, 768, 16384, 64, 64, False, True, True): (1, 256, 3, 4),
+        (768, 768, 16384, 64, 64, True, False, True): (1, 256, 3, 4),
+        (768, 768, 16384, 128, 128, False, True, True): (1, 128, 1, 32),
+        (768, 768, 16384, 128, 128, True, False, True): (2, 128, 1, 32),
+        (768, 768, 32768, 16, 16, False, True, True): (1, 128, 3, 4),
+        (768, 768, 32768, 16, 16, True, False, True): (2, 128, 3, 4),
+        (768, 768, 32768, 32, 32, False, True, True): (2, 256, 3, 4),
+        (768, 768, 32768, 32, 32, True, False, True): (1, 256, 3, 4),
+        (768, 768, 32768, 64, 64, False, True, True): (1, 512, 3, 4),
+        (768, 768, 32768, 64, 64, True, False, True): (1, 512, 3, 4),
+        (768, 768, 32768, 128, 128, False, True, True): (1, 256, 1, 32),
+        (768, 768, 32768, 128, 128, True, False, True): (1, 256, 1, 32),
+        (768, 768, 50432, 16, 16, False, True, True): (1, 197, 3, 4),
+        (768, 768, 50432, 32, 32, False, True, True): (1, 394, 3, 4),
+        (768, 768, 50432, 64, 64, False, True, True): (1, 788, 3, 4),
+        (768, 768, 50432, 128, 128, False, True, True): (3, 394, 1, 32),
+        (768, 768, 65536, 16, 16, False, True, True): (1, 256, 3, 4),
+        (768, 768, 65536, 16, 16, True, False, True): (1, 256, 3, 4),
+        (768, 768, 65536, 32, 32, False, True, True): (1, 512, 3, 4),
+        (768, 768, 65536, 32, 32, True, False, True): (1, 512, 3, 4),
+        (768, 768, 65536, 64, 64, False, True, True): (1, 1024, 3, 4),
+        (768, 768, 65536, 64, 64, True, False, True): (1, 1024, 3, 4),
+        (768, 768, 65536, 128, 128, False, True, True): (1, 512, 1, 32),
+        (768, 768, 65536, 128, 128, True, False, True): (1, 512, 1, 32),
+        (768, 768, 131072, 16, 16, False, True, True): (1, 512, 3, 4),
+        (768, 768, 131072, 16, 16, True, False, True): (1, 512, 3, 4),
+        (768, 768, 131072, 32, 32, False, True, True): (1, 1024, 3, 4),
+        (768, 768, 131072, 32, 32, True, False, True): (1, 1024, 3, 4),
+        (768, 768, 131072, 64, 64, False, True, True): (1, 2048, 3, 4),
+        (768, 768, 131072, 64, 64, True, False, True): (1, 2048, 3, 4),
+        (768, 768, 131072, 128, 128, False, True, True): (1, 1024, 1, 32),
+        (768, 768, 131072, 128, 128, True, False, True): (1, 1024, 1, 32),
+        (768, 3072, 256, 16, 16, False, True, True): (1, 2, 4, 4),
+        (768, 3072, 256, 16, 16, True, False, True): (1, 4, 3, 4),
+        (768, 3072, 256, 32, 32, False, True, True): (1, 4, 3, 4),
+        (768, 3072, 256, 32, 32, True, False, True): (3, 4, 3, 4),
+        (768, 3072, 256, 64, 64, False, True, True): (1, 4, 3, 8),
+        (768, 3072, 256, 64, 64, True, False, True): (1, 4, 3, 8),
+        (768, 3072, 256, 128, 128, False, True, True): (2, 2, 2, 32),
+        (768, 3072, 256, 128, 128, True, False, True): (2, 2, 1, 32),
+        (768, 3072, 512, 16, 16, False, True, True): (2, 4, 3, 4),
+        (768, 3072, 512, 16, 16, True, False, True): (1, 8, 3, 2),
+        (768, 3072, 512, 32, 32, False, True, True): (3, 8, 4, 4),
+        (768, 3072, 512, 32, 32, True, False, True): (3, 8, 3, 4),
+        (768, 3072, 512, 64, 64, False, True, True): (1, 8, 4, 8),
+        (768, 3072, 512, 64, 64, True, False, True): (1, 8, 3, 8),
+        (768, 3072, 512, 128, 128, False, True, True): (1, 4, 2, 32),
+        (768, 3072, 512, 128, 128, True, False, True): (1, 4, 1, 32),
+        (768, 3072, 1024, 16, 16, False, True, True): (4, 16, 3, 2),
+        (768, 3072, 1024, 16, 16, True, False, True): (4, 16, 3, 2),
+        (768, 3072, 1024, 32, 32, False, True, True): (4, 16, 5, 4),
+        (768, 3072, 1024, 32, 32, True, False, True): (4, 16, 5, 4),
+        (768, 3072, 1024, 64, 64, False, True, True): (2, 16, 3, 8),
+        (768, 3072, 1024, 64, 64, True, False, True): (2, 16, 3, 8),
+        (768, 3072, 1024, 128, 128, False, True, True): (1, 8, 1, 32),
+        (768, 3072, 1024, 128, 128, True, False, True): (1, 8, 1, 32),
+        (768, 3072, 2048, 16, 16, False, True, True): (2, 16, 3, 4),
+        (768, 3072, 2048, 16, 16, True, False, True): (2, 16, 3, 4),
+        (768, 3072, 2048, 32, 32, False, True, True): (4, 32, 5, 4),
+        (768, 3072, 2048, 32, 32, True, False, True): (2, 32, 3, 4),
+        (768, 3072, 2048, 64, 64, False, True, True): (2, 32, 3, 8),
+        (768, 3072, 2048, 64, 64, True, False, True): (2, 32, 3, 8),
+        (768, 3072, 2048, 128, 128, False, True, True): (1, 16, 1, 32),
+        (768, 3072, 2048, 128, 128, True, False, True): (2, 16, 1, 32),
+        (768, 3072, 4096, 16, 16, False, True, True): (1, 32, 5, 4),
+        (768, 3072, 4096, 16, 16, True, False, True): (3, 64, 3, 2),
+        (768, 3072, 4096, 32, 32, False, True, True): (5, 64, 3, 4),
+        (768, 3072, 4096, 32, 32, True, False, True): (5, 64, 3, 4),
+        (768, 3072, 4096, 64, 64, False, True, True): (1, 64, 3, 8),
+        (768, 3072, 4096, 64, 64, True, False, True): (5, 64, 3, 4),
+        (768, 3072, 4096, 128, 128, False, True, True): (1, 32, 1, 32),
+        (768, 3072, 4096, 128, 128, True, False, True): (1, 32, 1, 32),
+        (768, 3072, 8192, 16, 16, False, True, True): (1, 128, 3, 2),
+        (768, 3072, 8192, 16, 16, True, False, True): (1, 128, 3, 2),
+        (768, 3072, 8192, 32, 32, False, True, True): (1, 128, 3, 4),
+        (768, 3072, 8192, 32, 32, True, False, True): (1, 64, 3, 4),
+        (768, 3072, 8192, 64, 64, False, True, True): (3, 128, 3, 4),
+        (768, 3072, 8192, 64, 64, True, False, True): (3, 128, 3, 4),
+        (768, 3072, 8192, 128, 128, False, True, True): (4, 64, 2, 32),
+        (768, 3072, 8192, 128, 128, True, False, True): (2, 64, 1, 32),
+        (768, 3072, 16384, 16, 16, False, True, True): (1, 256, 2, 2),
+        (768, 3072, 16384, 16, 16, True, False, True): (1, 64, 3, 4),
+        (768, 3072, 16384, 32, 32, False, True, True): (8, 128, 3, 4),
+        (768, 3072, 16384, 32, 32, True, False, True): (1, 128, 3, 4),
+        (768, 3072, 16384, 64, 64, False, True, True): (1, 256, 3, 4),
+        (768, 3072, 16384, 64, 64, True, False, True): (3, 256, 3, 4),
+        (768, 3072, 16384, 128, 128, False, True, True): (3, 128, 1, 32),
+        (768, 3072, 16384, 128, 128, True, False, True): (2, 128, 2, 32),
+        (768, 3072, 32768, 16, 16, False, True, True): (1, 512, 3, 1),
+        (768, 3072, 32768, 16, 16, True, False, True): (1, 128, 3, 4),
+        (768, 3072, 32768, 32, 32, False, True, True): (1, 256, 3, 4),
+        (768, 3072, 32768, 32, 32, True, False, True): (1, 256, 3, 4),
+        (768, 3072, 32768, 64, 64, False, True, True): (2, 512, 3, 4),
+        (768, 3072, 32768, 64, 64, True, False, True): (1, 512, 3, 4),
+        (768, 3072, 32768, 128, 128, False, True, True): (1, 256, 1, 32),
+        (768, 3072, 32768, 128, 128, True, False, True): (2, 256, 2, 32),
+        (768, 3072, 50432, 16, 16, False, True, True): (1, 197, 3, 4),
+        (768, 3072, 50432, 16, 16, True, False, True): (1, 197, 3, 4),
+        (768, 3072, 50432, 32, 32, False, True, True): (1, 788, 2, 4),
+        (768, 3072, 50432, 32, 32, True, False, True): (1, 394, 3, 4),
+        (768, 3072, 50432, 64, 64, False, True, True): (1, 788, 3, 4),
+        (768, 3072, 50432, 64, 64, True, False, True): (2, 788, 3, 4),
+        (768, 3072, 50432, 128, 128, False, True, True): (1, 394, 1, 32),
+        (768, 3072, 50432, 128, 128, True, False, True): (2, 394, 2, 32),
+        (768, 3072, 65536, 16, 16, False, True, True): (1, 1024, 3, 1),
+        (768, 3072, 65536, 16, 16, True, False, True): (1, 256, 3, 4),
+        (768, 3072, 65536, 32, 32, False, True, True): (1, 512, 3, 4),
+        (768, 3072, 65536, 32, 32, True, False, True): (1, 512, 3, 4),
+        (768, 3072, 65536, 64, 64, False, True, True): (2, 1024, 3, 4),
+        (768, 3072, 65536, 64, 64, True, False, True): (5, 1024, 3, 4),
+        (768, 3072, 65536, 128, 128, False, True, True): (1, 512, 1, 32),
+        (768, 3072, 65536, 128, 128, True, False, True): (2, 512, 2, 32),
+        (768, 3072, 131072, 16, 16, False, True, True): (1, 2048, 3, 1),
+        (768, 3072, 131072, 16, 16, True, False, True): (1, 512, 3, 4),
+        (768, 3072, 131072, 32, 32, False, True, True): (1, 1024, 3, 4),
+        (768, 3072, 131072, 32, 32, True, False, True): (1, 1024, 3, 4),
+        (768, 3072, 131072, 64, 64, False, True, True): (1, 2048, 3, 4),
+        (768, 3072, 131072, 64, 64, True, False, True): (2, 2048, 3, 4),
+        (768, 3072, 131072, 128, 128, False, True, True): (1, 1024, 1, 32),
+        (768, 3072, 131072, 128, 128, True, False, True): (1, 1024, 2, 32),
+        (1024, 1024, 256, 16, 16, False, True, True): (4, 8, 3, 2),
+        (1024, 1024, 256, 16, 16, True, False, True): (2, 8, 3, 2),
+        (1024, 1024, 256, 32, 32, False, True, True): (1, 8, 3, 4),
+        (1024, 1024, 256, 32, 32, True, False, True): (1, 8, 3, 4),
+        (1024, 1024, 256, 64, 64, False, True, True): (1, 4, 3, 8),
+        (1024, 1024, 256, 64, 64, True, False, True): (2, 4, 3, 8),
+        (1024, 1024, 256, 128, 128, False, True, True): (3, 2, 1, 32),
+        (1024, 1024, 256, 128, 128, True, False, True): (5, 2, 1, 32),
+        (1024, 1024, 512, 16, 16, False, True, True): (3, 8, 3, 4),
+        (1024, 1024, 512, 16, 16, True, False, True): (3, 8, 3, 4),
+        (1024, 1024, 512, 32, 32, False, True, True): (1, 16, 3, 4),
+        (1024, 1024, 512, 32, 32, True, False, True): (3, 16, 3, 4),
+        (1024, 1024, 512, 64, 64, False, True, True): (6, 8, 3, 8),
+        (1024, 1024, 512, 64, 64, True, False, True): (8, 8, 3, 8),
+        (1024, 1024, 512, 128, 128, False, True, True): (1, 4, 1, 32),
+        (1024, 1024, 512, 128, 128, True, False, True): (1, 4, 1, 32),
+        (1024, 1024, 1024, 16, 16, False, True, True): (4, 8, 3, 4),
+        (1024, 1024, 1024, 16, 16, True, False, True): (1, 8, 3, 4),
+        (1024, 1024, 1024, 32, 32, False, True, True): (4, 16, 4, 4),
+        (1024, 1024, 1024, 32, 32, True, False, True): (5, 16, 3, 4),
+        (1024, 1024, 1024, 64, 64, False, True, True): (6, 16, 3, 8),
+        (1024, 1024, 1024, 64, 64, True, False, True): (3, 16, 2, 4),
+        (1024, 1024, 1024, 128, 128, False, True, True): (1, 8, 1, 32),
+        (1024, 1024, 1024, 128, 128, True, False, True): (2, 8, 1, 32),
+        (1024, 1024, 2048, 16, 16, False, True, True): (4, 16, 3, 4),
+        (1024, 1024, 2048, 16, 16, True, False, True): (1, 16, 3, 4),
+        (1024, 1024, 2048, 32, 32, False, True, True): (1, 32, 3, 4),
+        (1024, 1024, 2048, 32, 32, True, False, True): (2, 32, 3, 4),
+        (1024, 1024, 2048, 64, 64, False, True, True): (4, 32, 2, 4),
+        (1024, 1024, 2048, 64, 64, True, False, True): (8, 32, 2, 4),
+        (1024, 1024, 2048, 128, 128, False, True, True): (1, 16, 1, 32),
+        (1024, 1024, 2048, 128, 128, True, False, True): (1, 16, 1, 32),
+        (1024, 1024, 4096, 16, 16, False, True, True): (4, 32, 3, 4),
+        (1024, 1024, 4096, 16, 16, True, False, True): (1, 64, 3, 2),
+        (1024, 1024, 4096, 32, 32, False, True, True): (1, 64, 3, 4),
+        (1024, 1024, 4096, 32, 32, True, False, True): (1, 64, 3, 4),
+        (1024, 1024, 4096, 64, 64, False, True, True): (2, 64, 2, 4),
+        (1024, 1024, 4096, 64, 64, True, False, True): (2, 64, 2, 4),
+        (1024, 1024, 4096, 128, 128, False, True, True): (1, 32, 1, 32),
+        (1024, 1024, 4096, 128, 128, True, False, True): (4, 32, 1, 32),
+        (1024, 1024, 8192, 16, 16, False, True, True): (1, 128, 3, 1),
+        (1024, 1024, 8192, 16, 16, True, False, True): (1, 128, 3, 1),
+        (1024, 1024, 8192, 32, 32, False, True, True): (1, 128, 3, 4),
+        (1024, 1024, 8192, 32, 32, True, False, True): (1, 128, 3, 4),
+        (1024, 1024, 8192, 64, 64, False, True, True): (2, 128, 2, 4),
+        (1024, 1024, 8192, 64, 64, True, False, True): (2, 128, 2, 4),
+        (1024, 1024, 8192, 128, 128, False, True, True): (1, 64, 1, 32),
+        (1024, 1024, 8192, 128, 128, True, False, True): (4, 64, 1, 32),
+        (1024, 1024, 16384, 16, 16, False, True, True): (1, 128, 2, 4),
+        (1024, 1024, 16384, 16, 16, True, False, True): (4, 256, 3, 1),
+        (1024, 1024, 16384, 32, 32, False, True, True): (1, 256, 3, 4),
+        (1024, 1024, 16384, 32, 32, True, False, True): (1, 256, 3, 4),
+        (1024, 1024, 16384, 64, 64, False, True, True): (1, 256, 2, 4),
+        (1024, 1024, 16384, 64, 64, True, False, True): (1, 256, 2, 4),
+        (1024, 1024, 16384, 128, 128, False, True, True): (1, 128, 1, 32),
+        (1024, 1024, 16384, 128, 128, True, False, True): (4, 128, 1, 32),
+        (1024, 1024, 32768, 16, 16, False, True, True): (1, 256, 2, 4),
+        (1024, 1024, 32768, 16, 16, True, False, True): (4, 512, 3, 1),
+        (1024, 1024, 32768, 32, 32, False, True, True): (1, 512, 3, 4),
+        (1024, 1024, 32768, 32, 32, True, False, True): (1, 512, 3, 4),
+        (1024, 1024, 32768, 64, 64, False, True, True): (1, 512, 2, 4),
+        (1024, 1024, 32768, 64, 64, True, False, True): (1, 512, 2, 4),
+        (1024, 1024, 32768, 128, 128, False, True, True): (1, 256, 1, 32),
+        (1024, 1024, 32768, 128, 128, True, False, True): (1, 256, 1, 32),
+        (1024, 1024, 65536, 16, 16, False, True, True): (1, 512, 2, 4),
+        (1024, 1024, 65536, 16, 16, True, False, True): (1, 1024, 3, 1),
+        (1024, 1024, 65536, 32, 32, False, True, True): (1, 1024, 3, 4),
+        (1024, 1024, 65536, 32, 32, True, False, True): (1, 512, 3, 4),
+        (1024, 1024, 65536, 64, 64, False, True, True): (1, 1024, 2, 4),
+        (1024, 1024, 65536, 64, 64, True, False, True): (1, 1024, 2, 4),
+        (1024, 1024, 65536, 128, 128, False, True, True): (1, 512, 1, 32),
+        (1024, 1024, 65536, 128, 128, True, False, True): (1, 512, 1, 32),
+        (1024, 1024, 131072, 16, 16, False, True, True): (4, 2048, 3, 1),
+        (1024, 1024, 131072, 16, 16, True, False, True): (4, 2048, 3, 1),
+        (1024, 1024, 131072, 32, 32, False, True, True): (1, 2048, 3, 4),
+        (1024, 1024, 131072, 32, 32, True, False, True): (1, 1024, 3, 4),
+        (1024, 1024, 131072, 64, 64, False, True, True): (1, 2048, 2, 4),
+        (1024, 1024, 131072, 64, 64, True, False, True): (1, 2048, 2, 4),
+        (1024, 1024, 131072, 128, 128, False, True, True): (1, 1024, 1, 32),
+        (1024, 1024, 131072, 128, 128, True, False, True): (1, 1024, 1, 32),
+        (1536, 1536, 256, 16, 16, False, True, True): (5, 4, 3, 2),
+        (1536, 1536, 256, 16, 16, True, False, True): (2, 2, 3, 4),
+        (1536, 1536, 256, 32, 32, False, True, True): (1, 8, 2, 4),
+        (1536, 1536, 256, 32, 32, True, False, True): (2, 4, 3, 4),
+        (1536, 1536, 256, 64, 64, False, True, True): (1, 4, 3, 8),
+        (1536, 1536, 256, 64, 64, True, False, True): (2, 4, 3, 8),
+        (1536, 1536, 256, 128, 128, False, True, True): (1, 2, 1, 32),
+        (1536, 1536, 256, 128, 128, True, False, True): (2, 2, 2, 32),
+        (1536, 1536, 512, 16, 16, False, True, True): (1, 8, 3, 2),
+        (1536, 1536, 512, 16, 16, True, False, True): (1, 8, 3, 2),
+        (1536, 1536, 512, 32, 32, False, True, True): (1, 16, 3, 4),
+        (1536, 1536, 512, 32, 32, True, False, True): (1, 16, 3, 4),
+        (1536, 1536, 512, 64, 64, False, True, True): (3, 8, 3, 8),
+        (1536, 1536, 512, 64, 64, True, False, True): (3, 8, 3, 8),
+        (1536, 1536, 512, 128, 128, False, True, True): (1, 4, 1, 32),
+        (1536, 1536, 512, 128, 128, True, False, True): (2, 4, 2, 32),
+        (1536, 1536, 1024, 16, 16, False, True, True): (2, 8, 3, 4),
+        (1536, 1536, 1024, 16, 16, True, False, True): (2, 8, 3, 4),
+        (1536, 1536, 1024, 32, 32, False, True, True): (1, 16, 3, 4),
+        (1536, 1536, 1024, 32, 32, True, False, True): (1, 16, 3, 4),
+        (1536, 1536, 1024, 64, 64, False, True, True): (2, 16, 3, 8),
+        (1536, 1536, 1024, 64, 64, True, False, True): (2, 16, 3, 8),
+        (1536, 1536, 1024, 128, 128, False, True, True): (3, 8, 1, 32),
+        (1536, 1536, 1024, 128, 128, True, False, True): (1, 8, 2, 32),
+        (1536, 1536, 2048, 16, 16, False, True, True): (1, 32, 3, 2),
+        (1536, 1536, 2048, 16, 16, True, False, True): (1, 32, 3, 2),
+        (1536, 1536, 2048, 32, 32, False, True, True): (3, 32, 2, 4),
+        (1536, 1536, 2048, 32, 32, True, False, True): (4, 32, 3, 4),
+        (1536, 1536, 2048, 64, 64, False, True, True): (1, 32, 3, 4),
+        (1536, 1536, 2048, 64, 64, True, False, True): (1, 32, 3, 4),
+        (1536, 1536, 2048, 128, 128, False, True, True): (1, 16, 1, 32),
+        (1536, 1536, 2048, 128, 128, True, False, True): (2, 16, 1, 32),
+        (1536, 1536, 4096, 16, 16, False, True, True): (1, 64, 3, 2),
+        (1536, 1536, 4096, 16, 16, True, False, True): (1, 16, 3, 4),
+        (1536, 1536, 4096, 32, 32, False, True, True): (1, 64, 2, 4),
+        (1536, 1536, 4096, 32, 32, True, False, True): (1, 64, 2, 4),
+        (1536, 1536, 4096, 64, 64, False, True, True): (1, 64, 3, 4),
+        (1536, 1536, 4096, 64, 64, True, False, True): (1, 64, 3, 4),
+        (1536, 1536, 4096, 128, 128, False, True, True): (1, 32, 1, 32),
+        (1536, 1536, 4096, 128, 128, True, False, True): (4, 32, 2, 32),
+        (1536, 1536, 8192, 16, 16, False, True, True): (1, 32, 3, 4),
+        (1536, 1536, 8192, 16, 16, True, False, True): (5, 32, 3, 4),
+        (1536, 1536, 8192, 32, 32, False, True, True): (1, 128, 2, 4),
+        (1536, 1536, 8192, 32, 32, True, False, True): (1, 128, 2, 4),
+        (1536, 1536, 8192, 64, 64, False, True, True): (1, 128, 3, 4),
+        (1536, 1536, 8192, 64, 64, True, False, True): (1, 128, 3, 4),
+        (1536, 1536, 8192, 128, 128, False, True, True): (1, 64, 1, 32),
+        (1536, 1536, 8192, 128, 128, True, False, True): (4, 64, 2, 32),
+        (1536, 1536, 16384, 16, 16, False, True, True): (1, 64, 3, 4),
+        (1536, 1536, 16384, 16, 16, True, False, True): (1, 64, 3, 4),
+        (1536, 1536, 16384, 32, 32, False, True, True): (1, 256, 2, 4),
+        (1536, 1536, 16384, 32, 32, True, False, True): (1, 128, 3, 4),
+        (1536, 1536, 16384, 64, 64, False, True, True): (1, 256, 3, 4),
+        (1536, 1536, 16384, 64, 64, True, False, True): (3, 256, 3, 4),
+        (1536, 1536, 16384, 128, 128, False, True, True): (1, 128, 1, 32),
+        (1536, 1536, 16384, 128, 128, True, False, True): (4, 128, 2, 32),
+        (1536, 1536, 32768, 16, 16, False, True, True): (1, 128, 3, 4),
+        (1536, 1536, 32768, 16, 16, True, False, True): (1, 128, 3, 4),
+        (1536, 1536, 32768, 32, 32, False, True, True): (1, 256, 3, 4),
+        (1536, 1536, 32768, 32, 32, True, False, True): (1, 256, 3, 4),
+        (1536, 1536, 32768, 64, 64, False, True, True): (1, 512, 3, 4),
+        (1536, 1536, 32768, 64, 64, True, False, True): (1, 512, 3, 4),
+        (1536, 1536, 32768, 128, 128, False, True, True): (1, 256, 1, 32),
+        (1536, 1536, 32768, 128, 128, True, False, True): (4, 256, 2, 32),
+        (1536, 1536, 65536, 16, 16, False, True, True): (5, 256, 3, 4),
+        (1536, 1536, 65536, 16, 16, True, False, True): (1, 256, 3, 4),
+        (1536, 1536, 65536, 32, 32, False, True, True): (1, 512, 3, 4),
+        (1536, 1536, 65536, 32, 32, True, False, True): (1, 512, 3, 4),
+        (1536, 1536, 65536, 64, 64, False, True, True): (1, 1024, 3, 4),
+        (1536, 1536, 65536, 64, 64, True, False, True): (1, 1024, 3, 4),
+        (1536, 1536, 65536, 128, 128, False, True, True): (1, 512, 1, 32),
+        (1536, 1536, 65536, 128, 128, True, False, True): (4, 512, 2, 32),
+        (1536, 1536, 131072, 16, 16, False, True, True): (3, 512, 3, 4),
+        (1536, 1536, 131072, 16, 16, True, False, True): (1, 512, 3, 4),
+        (1536, 1536, 131072, 32, 32, False, True, True): (1, 1024, 3, 4),
+        (1536, 1536, 131072, 32, 32, True, False, True): (1, 1024, 3, 4),
+        (1536, 1536, 131072, 64, 64, False, True, True): (1, 2048, 3, 4),
+        (1536, 1536, 131072, 64, 64, True, False, True): (1, 2048, 3, 4),
+        (1536, 1536, 131072, 128, 128, False, True, True): (1, 1024, 1, 32),
+        (1536, 1536, 131072, 128, 128, True, False, True): (4, 1024, 2, 32),
+        (2048, 2048, 256, 16, 16, False, True, True): (1, 4, 3, 4),
+        (2048, 2048, 256, 16, 16, True, False, True): (1, 4, 3, 4),
+        (2048, 2048, 256, 32, 32, False, True, True): (3, 8, 3, 4),
+        (2048, 2048, 256, 32, 32, True, False, True): (3, 8, 3, 4),
+        (2048, 2048, 256, 64, 64, False, True, True): (4, 4, 4, 8),
+        (2048, 2048, 256, 64, 64, True, False, True): (8, 4, 4, 8),
+        (2048, 2048, 256, 128, 128, False, True, True): (3, 2, 1, 32),
+        (2048, 2048, 256, 128, 128, True, False, True): (3, 2, 1, 32),
+        (2048, 2048, 512, 16, 16, False, True, True): (4, 8, 3, 2),
+        (2048, 2048, 512, 16, 16, True, False, True): (4, 8, 3, 2),
+        (2048, 2048, 512, 32, 32, False, True, True): (3, 8, 3, 4),
+        (2048, 2048, 512, 32, 32, True, False, True): (1, 16, 2, 4),
+        (2048, 2048, 512, 64, 64, False, True, True): (4, 8, 2, 4),
+        (2048, 2048, 512, 64, 64, True, False, True): (4, 8, 2, 4),
+        (2048, 2048, 512, 128, 128, False, True, True): (1, 4, 1, 32),
+        (2048, 2048, 512, 128, 128, True, False, True): (4, 4, 1, 32),
+        (2048, 2048, 1024, 16, 16, False, True, True): (4, 8, 3, 4),
+        (2048, 2048, 1024, 16, 16, True, False, True): (4, 8, 3, 4),
+        (2048, 2048, 1024, 32, 32, False, True, True): (4, 16, 3, 4),
+        (2048, 2048, 1024, 32, 32, True, False, True): (1, 16, 3, 4),
+        (2048, 2048, 1024, 64, 64, False, True, True): (2, 16, 2, 4),
+        (2048, 2048, 1024, 64, 64, True, False, True): (2, 16, 2, 4),
+        (2048, 2048, 1024, 128, 128, False, True, True): (8, 8, 1, 32),
+        (2048, 2048, 1024, 128, 128, True, False, True): (4, 8, 1, 32),
+        (2048, 2048, 2048, 16, 16, False, True, True): (4, 32, 3, 1),
+        (2048, 2048, 2048, 16, 16, True, False, True): (3, 32, 3, 2),
+        (2048, 2048, 2048, 32, 32, False, True, True): (1, 32, 3, 4),
+        (2048, 2048, 2048, 32, 32, True, False, True): (1, 32, 3, 4),
+        (2048, 2048, 2048, 64, 64, False, True, True): (2, 32, 2, 4),
+        (2048, 2048, 2048, 64, 64, True, False, True): (2, 32, 2, 4),
+        (2048, 2048, 2048, 128, 128, False, True, True): (6, 16, 1, 32),
+        (2048, 2048, 2048, 128, 128, True, False, True): (4, 16, 1, 32),
+        (2048, 2048, 4096, 16, 16, False, True, True): (4, 64, 3, 1),
+        (2048, 2048, 4096, 16, 16, True, False, True): (1, 64, 3, 1),
+        (2048, 2048, 4096, 32, 32, False, True, True): (1, 64, 3, 4),
+        (2048, 2048, 4096, 32, 32, True, False, True): (4, 64, 3, 4),
+        (2048, 2048, 4096, 64, 64, False, True, True): (2, 64, 2, 4),
+        (2048, 2048, 4096, 64, 64, True, False, True): (2, 64, 2, 4),
+        (2048, 2048, 4096, 128, 128, False, True, True): (4, 32, 1, 32),
+        (2048, 2048, 4096, 128, 128, True, False, True): (4, 32, 1, 32),
+        (2048, 2048, 8192, 16, 16, False, True, True): (4, 128, 3, 1),
+        (2048, 2048, 8192, 16, 16, True, False, True): (1, 128, 3, 1),
+        (2048, 2048, 8192, 32, 32, False, True, True): (4, 128, 3, 4),
+        (2048, 2048, 8192, 32, 32, True, False, True): (4, 64, 3, 4),
+        (2048, 2048, 8192, 64, 64, False, True, True): (1, 128, 2, 4),
+        (2048, 2048, 8192, 64, 64, True, False, True): (2, 128, 2, 4),
+        (2048, 2048, 8192, 128, 128, False, True, True): (1, 64, 1, 32),
+        (2048, 2048, 8192, 128, 128, True, False, True): (4, 64, 1, 32),
+        (2048, 2048, 16384, 16, 16, False, True, True): (4, 256, 3, 1),
+        (2048, 2048, 16384, 16, 16, True, False, True): (1, 256, 3, 1),
+        (2048, 2048, 16384, 32, 32, False, True, True): (1, 256, 3, 4),
+        (2048, 2048, 16384, 32, 32, True, False, True): (1, 128, 3, 4),
+        (2048, 2048, 16384, 64, 64, False, True, True): (1, 256, 2, 4),
+        (2048, 2048, 16384, 64, 64, True, False, True): (1, 256, 2, 4),
+        (2048, 2048, 16384, 128, 128, False, True, True): (1, 128, 1, 32),
+        (2048, 2048, 16384, 128, 128, True, False, True): (4, 128, 1, 32),
+        (2048, 2048, 32768, 16, 16, False, True, True): (8, 512, 3, 1),
+        (2048, 2048, 32768, 16, 16, True, False, True): (1, 512, 3, 1),
+        (2048, 2048, 32768, 32, 32, False, True, True): (1, 512, 3, 4),
+        (2048, 2048, 32768, 32, 32, True, False, True): (1, 256, 3, 4),
+        (2048, 2048, 32768, 64, 64, False, True, True): (1, 512, 2, 4),
+        (2048, 2048, 32768, 64, 64, True, False, True): (1, 512, 2, 4),
+        (2048, 2048, 32768, 128, 128, False, True, True): (1, 256, 1, 32),
+        (2048, 2048, 32768, 128, 128, True, False, True): (4, 256, 1, 32),
+        (2048, 2048, 65536, 16, 16, False, True, True): (4, 1024, 3, 1),
+        (2048, 2048, 65536, 16, 16, True, False, True): (1, 1024, 3, 1),
+        (2048, 2048, 65536, 32, 32, False, True, True): (1, 1024, 3, 4),
+        (2048, 2048, 65536, 32, 32, True, False, True): (1, 512, 3, 4),
+        (2048, 2048, 65536, 64, 64, False, True, True): (1, 1024, 2, 4),
+        (2048, 2048, 65536, 64, 64, True, False, True): (1, 1024, 2, 4),
+        (2048, 2048, 65536, 128, 128, False, True, True): (1, 512, 1, 32),
+        (2048, 2048, 65536, 128, 128, True, False, True): (4, 512, 1, 32),
+        (2048, 2048, 131072, 16, 16, False, True, True): (4, 2048, 3, 1),
+        (2048, 2048, 131072, 16, 16, True, False, True): (1, 2048, 3, 1),
+        (2048, 2048, 131072, 32, 32, False, True, True): (1, 2048, 3, 4),
+        (2048, 2048, 131072, 32, 32, True, False, True): (1, 1024, 3, 4),
+        (2048, 2048, 131072, 64, 64, False, True, True): (1, 2048, 2, 4),
+        (2048, 2048, 131072, 64, 64, True, False, True): (1, 2048, 2, 4),
+        (2048, 2048, 131072, 128, 128, False, True, True): (1, 1024, 1, 32),
+        (2048, 2048, 131072, 128, 128, True, False, True): (4, 1024, 1, 32),
+        (3072, 768, 256, 16, 16, False, True, True): (4, 4, 3, 2),
+        (3072, 768, 256, 16, 16, True, False, True): (1, 2, 6, 4),
+        (3072, 768, 256, 32, 32, False, True, True): (1, 4, 6, 4),
+        (3072, 768, 256, 32, 32, True, False, True): (5, 4, 3, 4),
+        (3072, 768, 256, 64, 64, False, True, True): (4, 4, 3, 8),
+        (3072, 768, 256, 64, 64, True, False, True): (4, 4, 3, 8),
+        (3072, 768, 256, 128, 128, False, True, True): (1, 2, 1, 32),
+        (3072, 768, 256, 128, 128, True, False, True): (5, 2, 1, 32),
+        (3072, 768, 512, 16, 16, False, True, True): (4, 4, 3, 4),
+        (3072, 768, 512, 16, 16, True, False, True): (1, 4, 3, 4),
+        (3072, 768, 512, 32, 32, False, True, True): (3, 8, 3, 4),
+        (3072, 768, 512, 32, 32, True, False, True): (3, 8, 3, 4),
+        (3072, 768, 512, 64, 64, False, True, True): (2, 8, 3, 8),
+        (3072, 768, 512, 64, 64, True, False, True): (2, 8, 3, 8),
+        (3072, 768, 512, 128, 128, False, True, True): (1, 4, 2, 32),
+        (3072, 768, 512, 128, 128, True, False, True): (1, 4, 1, 32),
+        (3072, 768, 1024, 16, 16, False, True, True): (1, 16, 3, 2),
+        (3072, 768, 1024, 16, 16, True, False, True): (3, 16, 3, 2),
+        (3072, 768, 1024, 32, 32, False, True, True): (1, 16, 3, 4),
+        (3072, 768, 1024, 32, 32, True, False, True): (3, 16, 3, 4),
+        (3072, 768, 1024, 64, 64, False, True, True): (4, 16, 3, 8),
+        (3072, 768, 1024, 64, 64, True, False, True): (4, 16, 3, 4),
+        (3072, 768, 1024, 128, 128, False, True, True): (5, 8, 1, 32),
+        (3072, 768, 1024, 128, 128, True, False, True): (5, 8, 1, 32),
+        (3072, 768, 2048, 16, 16, False, True, True): (4, 32, 3, 2),
+        (3072, 768, 2048, 16, 16, True, False, True): (1, 32, 3, 2),
+        (3072, 768, 2048, 32, 32, False, True, True): (1, 32, 3, 4),
+        (3072, 768, 2048, 32, 32, True, False, True): (1, 32, 2, 4),
+        (3072, 768, 2048, 64, 64, False, True, True): (2, 32, 3, 4),
+        (3072, 768, 2048, 64, 64, True, False, True): (4, 32, 3, 4),
+        (3072, 768, 2048, 128, 128, False, True, True): (1, 16, 1, 32),
+        (3072, 768, 2048, 128, 128, True, False, True): (1, 16, 1, 32),
+        (3072, 768, 4096, 16, 16, False, True, True): (3, 64, 3, 2),
+        (3072, 768, 4096, 16, 16, True, False, True): (1, 64, 3, 2),
+        (3072, 768, 4096, 32, 32, False, True, True): (1, 64, 3, 4),
+        (3072, 768, 4096, 32, 32, True, False, True): (1, 32, 3, 4),
+        (3072, 768, 4096, 64, 64, False, True, True): (2, 64, 3, 4),
+        (3072, 768, 4096, 64, 64, True, False, True): (2, 64, 3, 4),
+        (3072, 768, 4096, 128, 128, False, True, True): (1, 32, 1, 32),
+        (3072, 768, 4096, 128, 128, True, False, True): (1, 32, 1, 32),
+        (3072, 768, 8192, 16, 16, False, True, True): (4, 128, 3, 1),
+        (3072, 768, 8192, 16, 16, True, False, True): (1, 32, 3, 4),
+        (3072, 768, 8192, 32, 32, False, True, True): (1, 64, 3, 4),
+        (3072, 768, 8192, 32, 32, True, False, True): (1, 64, 3, 4),
+        (3072, 768, 8192, 64, 64, False, True, True): (2, 128, 3, 4),
+        (3072, 768, 8192, 64, 64, True, False, True): (2, 128, 3, 4),
+        (3072, 768, 8192, 128, 128, False, True, True): (1, 64, 1, 32),
+        (3072, 768, 8192, 128, 128, True, False, True): (1, 64, 1, 32),
+        (3072, 768, 16384, 16, 16, False, True, True): (4, 256, 3, 1),
+        (3072, 768, 16384, 16, 16, True, False, True): (1, 64, 3, 4),
+        (3072, 768, 16384, 32, 32, False, True, True): (1, 128, 3, 4),
+        (3072, 768, 16384, 32, 32, True, False, True): (1, 128, 3, 4),
+        (3072, 768, 16384, 64, 64, False, True, True): (2, 256, 3, 4),
+        (3072, 768, 16384, 64, 64, True, False, True): (2, 256, 3, 4),
+        (3072, 768, 16384, 128, 128, False, True, True): (1, 128, 1, 32),
+        (3072, 768, 16384, 128, 128, True, False, True): (1, 128, 1, 32),
+        (3072, 768, 32768, 16, 16, False, True, True): (4, 512, 3, 1),
+        (3072, 768, 32768, 16, 16, True, False, True): (1, 128, 3, 4),
+        (3072, 768, 32768, 32, 32, False, True, True): (1, 256, 3, 4),
+        (3072, 768, 32768, 32, 32, True, False, True): (1, 256, 3, 4),
+        (3072, 768, 32768, 64, 64, False, True, True): (2, 512, 3, 4),
+        (3072, 768, 32768, 64, 64, True, False, True): (2, 512, 3, 4),
+        (3072, 768, 32768, 128, 128, False, True, True): (1, 256, 1, 32),
+        (3072, 768, 32768, 128, 128, True, False, True): (1, 256, 1, 32),
+        (3072, 768, 50432, 16, 16, False, True, True): (4, 788, 3, 1),
+        (3072, 768, 50432, 16, 16, True, False, True): (1, 197, 3, 4),
+        (3072, 768, 50432, 32, 32, False, True, True): (1, 394, 3, 4),
+        (3072, 768, 50432, 32, 32, True, False, True): (1, 394, 3, 4),
+        (3072, 768, 50432, 64, 64, False, True, True): (1, 788, 3, 4),
+        (3072, 768, 50432, 64, 64, True, False, True): (2, 788, 3, 4),
+        (3072, 768, 50432, 128, 128, False, True, True): (1, 394, 1, 32),
+        (3072, 768, 50432, 128, 128, True, False, True): (1, 394, 1, 32),
+        (3072, 768, 65536, 16, 16, False, True, True): (4, 1024, 3, 1),
+        (3072, 768, 65536, 16, 16, True, False, True): (1, 256, 3, 4),
+        (3072, 768, 65536, 32, 32, False, True, True): (1, 512, 3, 4),
+        (3072, 768, 65536, 32, 32, True, False, True): (1, 512, 3, 4),
+        (3072, 768, 65536, 64, 64, False, True, True): (2, 1024, 3, 4),
+        (3072, 768, 65536, 64, 64, True, False, True): (2, 1024, 3, 4),
+        (3072, 768, 65536, 128, 128, False, True, True): (1, 512, 1, 32),
+        (3072, 768, 65536, 128, 128, True, False, True): (1, 512, 1, 32),
+        (3072, 768, 131072, 16, 16, False, True, True): (4, 2048, 3, 1),
+        (3072, 768, 131072, 16, 16, True, False, True): (1, 512, 3, 4),
+        (3072, 768, 131072, 32, 32, False, True, True): (1, 1024, 3, 4),
+        (3072, 768, 131072, 32, 32, True, False, True): (1, 1024, 3, 4),
+        (3072, 768, 131072, 64, 64, False, True, True): (2, 2048, 3, 4),
+        (3072, 768, 131072, 64, 64, True, False, True): (2, 2048, 3, 4),
+        (3072, 768, 131072, 128, 128, False, True, True): (1, 1024, 1, 32),
+        (3072, 768, 131072, 128, 128, True, False, True): (1, 1024, 1, 32),
+        (3072, 3072, 256, 16, 16, False, True, True): (1, 4, 5, 2),
+        (3072, 3072, 256, 16, 16, True, False, True): (1, 4, 3, 2),
+        (3072, 3072, 256, 32, 32, False, True, True): (1, 4, 4, 4),
+        (3072, 3072, 256, 32, 32, True, False, True): (1, 4, 3, 4),
+        (3072, 3072, 256, 64, 64, False, True, True): (2, 4, 3, 8),
+        (3072, 3072, 256, 64, 64, True, False, True): (2, 4, 3, 8),
+        (3072, 3072, 256, 128, 128, False, True, True): (6, 2, 1, 32),
+        (3072, 3072, 256, 128, 128, True, False, True): (8, 2, 2, 32),
+        (3072, 3072, 512, 16, 16, False, True, True): (2, 4, 3, 4),
+        (3072, 3072, 512, 16, 16, True, False, True): (2, 4, 3, 4),
+        (3072, 3072, 512, 32, 32, False, True, True): (2, 8, 3, 4),
+        (3072, 3072, 512, 32, 32, True, False, True): (2, 8, 3, 4),
+        (3072, 3072, 512, 64, 64, False, True, True): (2, 8, 3, 8),
+        (3072, 3072, 512, 64, 64, True, False, True): (2, 8, 3, 8),
+        (3072, 3072, 512, 128, 128, False, True, True): (5, 4, 1, 32),
+        (3072, 3072, 512, 128, 128, True, False, True): (5, 4, 2, 32),
+        (3072, 3072, 1024, 16, 16, False, True, True): (1, 16, 3, 2),
+        (3072, 3072, 1024, 16, 16, True, False, True): (1, 16, 3, 2),
+        (3072, 3072, 1024, 32, 32, False, True, True): (2, 16, 3, 4),
+        (3072, 3072, 1024, 32, 32, True, False, True): (1, 16, 3, 4),
+        (3072, 3072, 1024, 64, 64, False, True, True): (1, 16, 3, 4),
+        (3072, 3072, 1024, 64, 64, True, False, True): (1, 16, 3, 4),
+        (3072, 3072, 1024, 128, 128, False, True, True): (1, 8, 1, 32),
+        (3072, 3072, 1024, 128, 128, True, False, True): (3, 8, 2, 32),
+        (3072, 3072, 2048, 16, 16, False, True, True): (1, 32, 3, 2),
+        (3072, 3072, 2048, 16, 16, True, False, True): (1, 16, 2, 4),
+        (3072, 3072, 2048, 32, 32, False, True, True): (1, 32, 2, 4),
+        (3072, 3072, 2048, 32, 32, True, False, True): (1, 32, 3, 4),
+        (3072, 3072, 2048, 64, 64, False, True, True): (1, 32, 3, 4),
+        (3072, 3072, 2048, 64, 64, True, False, True): (1, 32, 3, 4),
+        (3072, 3072, 2048, 128, 128, False, True, True): (1, 16, 1, 32),
+        (3072, 3072, 2048, 128, 128, True, False, True): (4, 16, 2, 32),
+        (3072, 3072, 4096, 16, 16, False, True, True): (2, 16, 3, 4),
+        (3072, 3072, 4096, 16, 16, True, False, True): (2, 16, 3, 4),
+        (3072, 3072, 4096, 32, 32, False, True, True): (1, 64, 2, 4),
+        (3072, 3072, 4096, 32, 32, True, False, True): (1, 32, 3, 4),
+        (3072, 3072, 4096, 64, 64, False, True, True): (1, 64, 3, 4),
+        (3072, 3072, 4096, 64, 64, True, False, True): (1, 64, 3, 4),
+        (3072, 3072, 4096, 128, 128, False, True, True): (1, 32, 1, 32),
+        (3072, 3072, 4096, 128, 128, True, False, True): (2, 32, 2, 32),
+        (3072, 3072, 8192, 16, 16, False, True, True): (2, 32, 3, 4),
+        (3072, 3072, 8192, 16, 16, True, False, True): (2, 32, 3, 4),
+        (3072, 3072, 8192, 32, 32, False, True, True): (1, 64, 3, 4),
+        (3072, 3072, 8192, 32, 32, True, False, True): (1, 64, 3, 4),
+        (3072, 3072, 8192, 64, 64, False, True, True): (1, 128, 3, 4),
+        (3072, 3072, 8192, 64, 64, True, False, True): (1, 128, 3, 4),
+        (3072, 3072, 8192, 128, 128, False, True, True): (1, 64, 1, 32),
+        (3072, 3072, 8192, 128, 128, True, False, True): (4, 64, 2, 32),
+        (3072, 3072, 16384, 16, 16, False, True, True): (2, 64, 3, 4),
+        (3072, 3072, 16384, 16, 16, True, False, True): (1, 64, 3, 4),
+        (3072, 3072, 16384, 32, 32, False, True, True): (1, 128, 3, 4),
+        (3072, 3072, 16384, 32, 32, True, False, True): (1, 128, 3, 4),
+        (3072, 3072, 16384, 64, 64, False, True, True): (1, 256, 3, 4),
+        (3072, 3072, 16384, 64, 64, True, False, True): (1, 256, 3, 4),
+        (3072, 3072, 16384, 128, 128, False, True, True): (1, 128, 1, 32),
+        (3072, 3072, 16384, 128, 128, True, False, True): (4, 128, 2, 32),
+        (3072, 3072, 32768, 16, 16, False, True, True): (3, 128, 3, 4),
+        (3072, 3072, 32768, 16, 16, True, False, True): (1, 128, 3, 4),
+        (3072, 3072, 32768, 32, 32, False, True, True): (1, 256, 3, 4),
+        (3072, 3072, 32768, 32, 32, True, False, True): (1, 256, 3, 4),
+        (3072, 3072, 32768, 64, 64, False, True, True): (1, 512, 3, 4),
+        (3072, 3072, 32768, 64, 64, True, False, True): (1, 512, 3, 4),
+        (3072, 3072, 32768, 128, 128, False, True, True): (1, 256, 1, 32),
+        (3072, 3072, 32768, 128, 128, True, False, True): (4, 256, 2, 32),
+        (3072, 3072, 65536, 16, 16, False, True, True): (5, 256, 3, 4),
+        (3072, 3072, 65536, 16, 16, True, False, True): (2, 256, 3, 4),
+        (3072, 3072, 65536, 32, 32, False, True, True): (1, 512, 3, 4),
+        (3072, 3072, 65536, 32, 32, True, False, True): (1, 512, 3, 4),
+        (3072, 3072, 65536, 64, 64, False, True, True): (1, 1024, 3, 4),
+        (3072, 3072, 65536, 64, 64, True, False, True): (1, 1024, 3, 4),
+        (3072, 3072, 65536, 128, 128, False, True, True): (1, 512, 1, 32),
+        (3072, 3072, 65536, 128, 128, True, False, True): (4, 512, 2, 32),
+        (3072, 3072, 131072, 16, 16, False, True, True): (5, 512, 3, 4),
+        (3072, 3072, 131072, 16, 16, True, False, True): (1, 512, 3, 4),
+        (3072, 3072, 131072, 32, 32, False, True, True): (1, 1024, 3, 4),
+        (3072, 3072, 131072, 32, 32, True, False, True): (1, 1024, 3, 4),
+        (3072, 3072, 131072, 64, 64, False, True, True): (1, 2048, 3, 4),
+        (3072, 3072, 131072, 64, 64, True, False, True): (1, 2048, 3, 4),
+        (3072, 3072, 131072, 128, 128, False, True, True): (1, 1024, 1, 32),
+        (3072, 3072, 131072, 128, 128, True, False, True): (4, 1024, 2, 32),
+        (4096, 4096, 256, 16, 16, False, True, True): (1, 4, 3, 2),
+        (4096, 4096, 256, 16, 16, True, False, True): (1, 2, 3, 4),
+        (4096, 4096, 256, 32, 32, False, True, True): (4, 4, 4, 4),
+        (4096, 4096, 256, 32, 32, True, False, True): (4, 4, 4, 4),
+        (4096, 4096, 256, 64, 64, False, True, True): (1, 4, 3, 8),
+        (4096, 4096, 256, 64, 64, True, False, True): (4, 4, 2, 4),
+        (4096, 4096, 256, 128, 128, False, True, True): (1, 2, 1, 32),
+        (4096, 4096, 256, 128, 128, True, False, True): (3, 2, 1, 32),
+        (4096, 4096, 512, 16, 16, False, True, True): (1, 4, 3, 4),
+        (4096, 4096, 512, 16, 16, True, False, True): (5, 8, 3, 2),
+        (4096, 4096, 512, 32, 32, False, True, True): (4, 8, 3, 4),
+        (4096, 4096, 512, 32, 32, True, False, True): (4, 8, 3, 4),
+        (4096, 4096, 512, 64, 64, False, True, True): (1, 8, 2, 4),
+        (4096, 4096, 512, 64, 64, True, False, True): (1, 8, 2, 4),
+        (4096, 4096, 512, 128, 128, False, True, True): (4, 4, 1, 32),
+        (4096, 4096, 512, 128, 128, True, False, True): (4, 4, 1, 32),
+        (4096, 4096, 1024, 16, 16, False, True, True): (1, 8, 3, 4),
+        (4096, 4096, 1024, 16, 16, True, False, True): (1, 8, 3, 4),
+        (4096, 4096, 1024, 32, 32, False, True, True): (1, 16, 3, 4),
+        (4096, 4096, 1024, 32, 32, True, False, True): (1, 16, 3, 4),
+        (4096, 4096, 1024, 64, 64, False, True, True): (4, 16, 2, 4),
+        (4096, 4096, 1024, 64, 64, True, False, True): (4, 16, 2, 4),
+        (4096, 4096, 1024, 128, 128, False, True, True): (4, 8, 1, 32),
+        (4096, 4096, 1024, 128, 128, True, False, True): (4, 8, 1, 32),
+        (4096, 4096, 2048, 16, 16, False, True, True): (1, 32, 3, 1),
+        (4096, 4096, 2048, 16, 16, True, False, True): (6, 8, 3, 4),
+        (4096, 4096, 2048, 32, 32, False, True, True): (1, 32, 3, 4),
+        (4096, 4096, 2048, 32, 32, True, False, True): (1, 32, 3, 4),
+        (4096, 4096, 2048, 64, 64, False, True, True): (4, 32, 2, 4),
+        (4096, 4096, 2048, 64, 64, True, False, True): (4, 32, 2, 4),
+        (4096, 4096, 2048, 128, 128, False, True, True): (4, 16, 1, 32),
+        (4096, 4096, 2048, 128, 128, True, False, True): (4, 16, 1, 32),
+        (4096, 4096, 4096, 16, 16, False, True, True): (1, 16, 3, 4),
+        (4096, 4096, 4096, 16, 16, True, False, True): (1, 64, 3, 1),
+        (4096, 4096, 4096, 32, 32, False, True, True): (1, 64, 3, 4),
+        (4096, 4096, 4096, 32, 32, True, False, True): (1, 32, 3, 4),
+        (4096, 4096, 4096, 64, 64, False, True, True): (4, 64, 2, 4),
+        (4096, 4096, 4096, 64, 64, True, False, True): (4, 64, 2, 4),
+        (4096, 4096, 4096, 128, 128, False, True, True): (4, 32, 1, 32),
+        (4096, 4096, 4096, 128, 128, True, False, True): (4, 32, 1, 32),
+        (4096, 4096, 8192, 16, 16, False, True, True): (4, 128, 3, 1),
+        (4096, 4096, 8192, 16, 16, True, False, True): (1, 128, 3, 1),
+        (4096, 4096, 8192, 32, 32, False, True, True): (1, 128, 3, 4),
+        (4096, 4096, 8192, 32, 32, True, False, True): (1, 64, 3, 4),
+        (4096, 4096, 8192, 64, 64, False, True, True): (4, 128, 2, 4),
+        (4096, 4096, 8192, 64, 64, True, False, True): (4, 128, 2, 4),
+        (4096, 4096, 8192, 128, 128, False, True, True): (4, 64, 1, 32),
+        (4096, 4096, 8192, 128, 128, True, False, True): (4, 64, 1, 32),
+        (4096, 4096, 16384, 16, 16, False, True, True): (1, 64, 3, 4),
+        (4096, 4096, 16384, 16, 16, True, False, True): (1, 256, 3, 1),
+        (4096, 4096, 16384, 32, 32, False, True, True): (1, 256, 3, 4),
+        (4096, 4096, 16384, 32, 32, True, False, True): (1, 128, 3, 4),
+        (4096, 4096, 16384, 64, 64, False, True, True): (4, 256, 2, 4),
+        (4096, 4096, 16384, 64, 64, True, False, True): (4, 256, 2, 4),
+        (4096, 4096, 16384, 128, 128, False, True, True): (4, 128, 1, 32),
+        (4096, 4096, 16384, 128, 128, True, False, True): (4, 128, 1, 32),
+        (4096, 4096, 32768, 16, 16, False, True, True): (1, 128, 3, 4),
+        (4096, 4096, 32768, 16, 16, True, False, True): (1, 512, 3, 1),
+        (4096, 4096, 32768, 32, 32, False, True, True): (1, 512, 3, 4),
+        (4096, 4096, 32768, 32, 32, True, False, True): (1, 256, 3, 4),
+        (4096, 4096, 32768, 64, 64, False, True, True): (4, 512, 2, 4),
+        (4096, 4096, 32768, 64, 64, True, False, True): (4, 512, 2, 4),
+        (4096, 4096, 32768, 128, 128, False, True, True): (4, 256, 1, 32),
+        (4096, 4096, 32768, 128, 128, True, False, True): (4, 256, 1, 32),
+        (4096, 4096, 65536, 16, 16, False, True, True): (1, 256, 3, 4),
+        (4096, 4096, 65536, 16, 16, True, False, True): (1, 1024, 3, 1),
+        (4096, 4096, 65536, 32, 32, False, True, True): (1, 1024, 3, 4),
+        (4096, 4096, 65536, 32, 32, True, False, True): (1, 512, 3, 4),
+        (4096, 4096, 65536, 64, 64, False, True, True): (4, 1024, 2, 4),
+        (4096, 4096, 65536, 64, 64, True, False, True): (2, 1024, 2, 4),
+        (4096, 4096, 65536, 128, 128, False, True, True): (4, 512, 1, 32),
+        (4096, 4096, 65536, 128, 128, True, False, True): (4, 512, 1, 32),
+        (4096, 4096, 131072, 16, 16, False, True, True): (2, 2048, 3, 1),
+        (4096, 4096, 131072, 16, 16, True, False, True): (1, 2048, 3, 1),
+        (4096, 4096, 131072, 32, 32, False, True, True): (2, 2048, 3, 4),
+        (4096, 4096, 131072, 32, 32, True, False, True): (1, 1024, 3, 4),
+        (4096, 4096, 131072, 64, 64, False, True, True): (2, 2048, 2, 4),
+        (4096, 4096, 131072, 64, 64, True, False, True): (2, 2048, 2, 4),
+        (4096, 4096, 131072, 128, 128, False, True, True): (4, 1024, 1, 32),
+        (4096, 4096, 131072, 128, 128, True, False, True): (4, 1024, 1, 32),
+        (6144, 6144, 256, 16, 16, False, True, True): (2, 2, 3, 4),
+        (6144, 6144, 256, 16, 16, True, False, True): (2, 2, 3, 4),
+        (6144, 6144, 256, 32, 32, False, True, True): (2, 4, 3, 4),
+        (6144, 6144, 256, 32, 32, True, False, True): (2, 4, 3, 4),
+        (6144, 6144, 256, 64, 64, False, True, True): (1, 4, 3, 4),
+        (6144, 6144, 256, 64, 64, True, False, True): (1, 4, 3, 4),
+        (6144, 6144, 256, 128, 128, False, True, True): (1, 2, 1, 32),
+        (6144, 6144, 256, 128, 128, True, False, True): (5, 2, 2, 32),
+        (6144, 6144, 512, 16, 16, False, True, True): (4, 8, 3, 2),
+        (6144, 6144, 512, 16, 16, True, False, True): (4, 8, 3, 2),
+        (6144, 6144, 512, 32, 32, False, True, True): (2, 8, 3, 4),
+        (6144, 6144, 512, 32, 32, True, False, True): (2, 8, 3, 4),
+        (6144, 6144, 512, 64, 64, False, True, True): (1, 8, 3, 4),
+        (6144, 6144, 512, 64, 64, True, False, True): (1, 8, 3, 4),
+        (6144, 6144, 512, 128, 128, False, True, True): (1, 4, 1, 32),
+        (6144, 6144, 512, 128, 128, True, False, True): (4, 4, 2, 32),
+        (6144, 6144, 1024, 16, 16, False, True, True): (4, 16, 3, 2),
+        (6144, 6144, 1024, 16, 16, True, False, True): (4, 4, 3, 4),
+        (6144, 6144, 1024, 32, 32, False, True, True): (1, 16, 3, 4),
+        (6144, 6144, 1024, 32, 32, True, False, True): (1, 16, 3, 4),
+        (6144, 6144, 1024, 64, 64, False, True, True): (1, 16, 3, 4),
+        (6144, 6144, 1024, 64, 64, True, False, True): (1, 16, 3, 4),
+        (6144, 6144, 1024, 128, 128, False, True, True): (1, 8, 1, 32),
+        (6144, 6144, 1024, 128, 128, True, False, True): (4, 8, 2, 32),
+        (6144, 6144, 2048, 16, 16, False, True, True): (1, 8, 3, 4),
+        (6144, 6144, 2048, 16, 16, True, False, True): (4, 8, 3, 4),
+        (6144, 6144, 2048, 32, 32, False, True, True): (1, 16, 3, 4),
+        (6144, 6144, 2048, 32, 32, True, False, True): (1, 16, 3, 4),
+        (6144, 6144, 2048, 64, 64, False, True, True): (1, 32, 3, 4),
+        (6144, 6144, 2048, 64, 64, True, False, True): (3, 32, 3, 4),
+        (6144, 6144, 2048, 128, 128, False, True, True): (1, 16, 1, 32),
+        (6144, 6144, 2048, 128, 128, True, False, True): (1, 16, 2, 32),
+        (6144, 6144, 4096, 16, 16, False, True, True): (3, 16, 3, 4),
+        (6144, 6144, 4096, 16, 16, True, False, True): (4, 16, 3, 4),
+        (6144, 6144, 4096, 32, 32, False, True, True): (1, 32, 3, 4),
+        (6144, 6144, 4096, 32, 32, True, False, True): (1, 32, 3, 4),
+        (6144, 6144, 4096, 64, 64, False, True, True): (1, 64, 3, 4),
+        (6144, 6144, 4096, 64, 64, True, False, True): (1, 64, 3, 4),
+        (6144, 6144, 4096, 128, 128, False, True, True): (1, 32, 1, 32),
+        (6144, 6144, 4096, 128, 128, True, False, True): (4, 32, 2, 32),
+        (6144, 6144, 8192, 16, 16, False, True, True): (1, 32, 3, 4),
+        (6144, 6144, 8192, 16, 16, True, False, True): (4, 32, 3, 4),
+        (6144, 6144, 8192, 32, 32, False, True, True): (1, 64, 3, 4),
+        (6144, 6144, 8192, 32, 32, True, False, True): (1, 64, 3, 4),
+        (6144, 6144, 8192, 64, 64, False, True, True): (1, 128, 3, 4),
+        (6144, 6144, 8192, 64, 64, True, False, True): (1, 128, 3, 4),
+        (6144, 6144, 8192, 128, 128, False, True, True): (1, 64, 1, 32),
+        (6144, 6144, 8192, 128, 128, True, False, True): (4, 64, 2, 32),
+        (6144, 6144, 16384, 16, 16, False, True, True): (1, 64, 3, 4),
+        (6144, 6144, 16384, 16, 16, True, False, True): (4, 64, 3, 4),
+        (6144, 6144, 16384, 32, 32, False, True, True): (1, 128, 3, 4),
+        (6144, 6144, 16384, 32, 32, True, False, True): (1, 128, 3, 4),
+        (6144, 6144, 16384, 64, 64, False, True, True): (1, 256, 3, 4),
+        (6144, 6144, 16384, 64, 64, True, False, True): (1, 256, 3, 4),
+        (6144, 6144, 16384, 128, 128, False, True, True): (1, 128, 1, 32),
+        (6144, 6144, 16384, 128, 128, True, False, True): (4, 128, 2, 32),
+        (6144, 6144, 32768, 16, 16, False, True, True): (1, 128, 3, 4),
+        (6144, 6144, 32768, 16, 16, True, False, True): (4, 128, 3, 4),
+        (6144, 6144, 32768, 32, 32, False, True, True): (1, 256, 3, 4),
+        (6144, 6144, 32768, 32, 32, True, False, True): (1, 256, 3, 4),
+        (6144, 6144, 32768, 64, 64, False, True, True): (1, 512, 3, 4),
+        (6144, 6144, 32768, 64, 64, True, False, True): (1, 512, 3, 4),
+        (6144, 6144, 32768, 128, 128, False, True, True): (1, 256, 1, 32),
+        (6144, 6144, 32768, 128, 128, True, False, True): (4, 256, 2, 32),
+        (6144, 6144, 65536, 16, 16, False, True, True): (1, 256, 3, 4),
+        (6144, 6144, 65536, 16, 16, True, False, True): (2, 256, 3, 4),
+        (6144, 6144, 65536, 32, 32, False, True, True): (1, 512, 3, 4),
+        (6144, 6144, 65536, 32, 32, True, False, True): (1, 512, 3, 4),
+        (6144, 6144, 65536, 64, 64, False, True, True): (1, 1024, 3, 4),
+        (6144, 6144, 65536, 64, 64, True, False, True): (1, 1024, 3, 4),
+        (6144, 6144, 65536, 128, 128, False, True, True): (1, 512, 1, 32),
+        (6144, 6144, 65536, 128, 128, True, False, True): (4, 512, 2, 32),
+        (6144, 6144, 131072, 16, 16, False, True, True): (1, 512, 3, 4),
+        (6144, 6144, 131072, 16, 16, True, False, True): (2, 512, 3, 4),
+        (6144, 6144, 131072, 32, 32, False, True, True): (1, 1024, 3, 4),
+        (6144, 6144, 131072, 32, 32, True, False, True): (1, 1024, 3, 4),
+        (6144, 6144, 131072, 64, 64, False, True, True): (1, 2048, 3, 4),
+        (6144, 6144, 131072, 64, 64, True, False, True): (1, 2048, 3, 4),
+        (6144, 6144, 131072, 128, 128, False, True, True): (1, 1024, 1, 32),
+        (6144, 6144, 131072, 128, 128, True, False, True): (4, 1024, 2, 32),
+        (8192, 8192, 256, 16, 16, False, True, True): (2, 2, 4, 4),
+        (8192, 8192, 256, 16, 16, True, False, True): (1, 1, 3, 4),
+        (8192, 8192, 256, 32, 32, False, True, True): (2, 4, 3, 4),
+        (8192, 8192, 256, 32, 32, True, False, True): (2, 4, 3, 4),
+        (8192, 8192, 256, 64, 64, False, True, True): (4, 4, 2, 4),
+        (8192, 8192, 256, 64, 64, True, False, True): (4, 4, 2, 4),
+        (8192, 8192, 256, 128, 128, False, True, True): (1, 2, 1, 32),
+        (8192, 8192, 256, 128, 128, True, False, True): (4, 2, 1, 32),
+        (8192, 8192, 512, 16, 16, False, True, True): (1, 4, 3, 4),
+        (8192, 8192, 512, 16, 16, True, False, True): (3, 4, 3, 4),
+        (8192, 8192, 512, 32, 32, False, True, True): (1, 8, 3, 4),
+        (8192, 8192, 512, 32, 32, True, False, True): (6, 8, 3, 4),
+        (8192, 8192, 512, 64, 64, False, True, True): (4, 8, 2, 4),
+        (8192, 8192, 512, 64, 64, True, False, True): (4, 8, 2, 4),
+        (8192, 8192, 512, 128, 128, False, True, True): (4, 4, 1, 32),
+        (8192, 8192, 512, 128, 128, True, False, True): (4, 4, 1, 32),
+        (8192, 8192, 1024, 16, 16, False, True, True): (1, 4, 3, 4),
+        (8192, 8192, 1024, 16, 16, True, False, True): (1, 32, 3, 1),
+        (8192, 8192, 1024, 32, 32, False, True, True): (1, 16, 3, 4),
+        (8192, 8192, 1024, 32, 32, True, False, True): (1, 16, 3, 4),
+        (8192, 8192, 1024, 64, 64, False, True, True): (4, 16, 2, 4),
+        (8192, 8192, 1024, 64, 64, True, False, True): (4, 16, 2, 4),
+        (8192, 8192, 1024, 128, 128, False, True, True): (4, 8, 1, 32),
+        (8192, 8192, 1024, 128, 128, True, False, True): (4, 8, 1, 32),
+        (8192, 8192, 2048, 16, 16, False, True, True): (4, 8, 3, 4),
+        (8192, 8192, 2048, 16, 16, True, False, True): (1, 32, 3, 1),
+        (8192, 8192, 2048, 32, 32, False, True, True): (1, 32, 3, 4),
+        (8192, 8192, 2048, 32, 32, True, False, True): (1, 16, 4, 4),
+        (8192, 8192, 2048, 64, 64, False, True, True): (4, 32, 2, 4),
+        (8192, 8192, 2048, 64, 64, True, False, True): (4, 32, 2, 4),
+        (8192, 8192, 2048, 128, 128, False, True, True): (4, 16, 1, 32),
+        (8192, 8192, 2048, 128, 128, True, False, True): (4, 16, 1, 32),
+        (8192, 8192, 4096, 16, 16, False, True, True): (3, 16, 3, 4),
+        (8192, 8192, 4096, 16, 16, True, False, True): (2, 64, 3, 1),
+        (8192, 8192, 4096, 32, 32, False, True, True): (1, 64, 3, 4),
+        (8192, 8192, 4096, 32, 32, True, False, True): (1, 32, 3, 4),
+        (8192, 8192, 4096, 64, 64, False, True, True): (4, 64, 2, 4),
+        (8192, 8192, 4096, 64, 64, True, False, True): (2, 64, 2, 4),
+        (8192, 8192, 4096, 128, 128, False, True, True): (4, 32, 1, 32),
+        (8192, 8192, 4096, 128, 128, True, False, True): (4, 32, 1, 32),
+        (8192, 8192, 8192, 16, 16, False, True, True): (2, 128, 3, 1),
+        (8192, 8192, 8192, 16, 16, True, False, True): (2, 128, 3, 1),
+        (8192, 8192, 8192, 32, 32, False, True, True): (1, 128, 3, 4),
+        (8192, 8192, 8192, 32, 32, True, False, True): (1, 64, 3, 4),
+        (8192, 8192, 8192, 64, 64, False, True, True): (4, 128, 2, 4),
+        (8192, 8192, 8192, 64, 64, True, False, True): (2, 128, 2, 4),
+        (8192, 8192, 8192, 128, 128, False, True, True): (4, 64, 1, 32),
+        (8192, 8192, 8192, 128, 128, True, False, True): (4, 64, 1, 32),
+        (8192, 8192, 16384, 16, 16, False, True, True): (1, 64, 3, 4),
+        (8192, 8192, 16384, 16, 16, True, False, True): (1, 256, 3, 1),
+        (8192, 8192, 16384, 32, 32, False, True, True): (1, 256, 3, 4),
+        (8192, 8192, 16384, 32, 32, True, False, True): (1, 128, 3, 4),
+        (8192, 8192, 16384, 64, 64, False, True, True): (2, 256, 2, 4),
+        (8192, 8192, 16384, 64, 64, True, False, True): (2, 256, 2, 4),
+        (8192, 8192, 16384, 128, 128, False, True, True): (4, 128, 1, 32),
+        (8192, 8192, 16384, 128, 128, True, False, True): (4, 128, 1, 32),
+        (8192, 8192, 32768, 16, 16, False, True, True): (1, 512, 3, 1),
+        (8192, 8192, 32768, 16, 16, True, False, True): (1, 512, 3, 1),
+        (8192, 8192, 32768, 32, 32, False, True, True): (1, 512, 3, 4),
+        (8192, 8192, 32768, 32, 32, True, False, True): (1, 256, 3, 4),
+        (8192, 8192, 32768, 64, 64, False, True, True): (2, 512, 2, 4),
+        (8192, 8192, 32768, 64, 64, True, False, True): (2, 512, 2, 4),
+        (8192, 8192, 32768, 128, 128, False, True, True): (4, 256, 1, 32),
+        (8192, 8192, 32768, 128, 128, True, False, True): (4, 256, 1, 32),
+        (8192, 8192, 65536, 16, 16, False, True, True): (1, 256, 3, 4),
+        (8192, 8192, 65536, 16, 16, True, False, True): (1, 1024, 3, 1),
+        (8192, 8192, 65536, 32, 32, False, True, True): (1, 1024, 3, 4),
+        (8192, 8192, 65536, 32, 32, True, False, True): (1, 512, 3, 4),
+        (8192, 8192, 65536, 64, 64, False, True, True): (4, 1024, 2, 4),
+        (8192, 8192, 65536, 64, 64, True, False, True): (2, 1024, 2, 4),
+        (8192, 8192, 65536, 128, 128, False, True, True): (4, 512, 1, 32),
+        (8192, 8192, 65536, 128, 128, True, False, True): (4, 512, 1, 32),
+        (8192, 8192, 131072, 16, 16, False, True, True): (1, 2048, 3, 1),
+        (8192, 8192, 131072, 16, 16, True, False, True): (2, 2048, 3, 1),
+        (8192, 8192, 131072, 32, 32, False, True, True): (4, 2048, 3, 4),
+        (8192, 8192, 131072, 32, 32, True, False, True): (1, 1024, 3, 4),
+        (8192, 8192, 131072, 64, 64, False, True, True): (2, 2048, 2, 4),
+        (8192, 8192, 131072, 64, 64, True, False, True): (2, 2048, 2, 4),
+        (8192, 8192, 131072, 128, 128, False, True, True): (4, 1024, 1, 32),
+        (8192, 8192, 131072, 128, 128, True, False, True): (4, 1024, 1, 32),
+        (16384, 16384, 256, 16, 16, False, True, True): (1, 2, 3, 4),
+        (16384, 16384, 256, 16, 16, True, False, True): (1, 2, 3, 4),
+        (16384, 16384, 256, 32, 32, False, True, True): (1, 4, 3, 4),
+        (16384, 16384, 256, 32, 32, True, False, True): (1, 4, 3, 4),
+        (16384, 16384, 256, 64, 64, False, True, True): (2, 4, 2, 4),
+        (16384, 16384, 256, 64, 64, True, False, True): (2, 4, 2, 4),
+        (16384, 16384, 256, 128, 128, False, True, True): (2, 2, 1, 32),
+        (16384, 16384, 256, 128, 128, True, False, True): (2, 2, 1, 32),
+        (16384, 16384, 512, 16, 16, False, True, True): (1, 2, 3, 4),
+        (16384, 16384, 512, 16, 16, True, False, True): (5, 2, 3, 4),
+        (16384, 16384, 512, 32, 32, False, True, True): (1, 8, 3, 4),
+        (16384, 16384, 512, 32, 32, True, False, True): (1, 4, 3, 4),
+        (16384, 16384, 512, 64, 64, False, True, True): (4, 8, 2, 4),
+        (16384, 16384, 512, 64, 64, True, False, True): (4, 8, 2, 4),
+        (16384, 16384, 512, 128, 128, False, True, True): (4, 4, 1, 32),
+        (16384, 16384, 512, 128, 128, True, False, True): (4, 4, 1, 32),
+        (16384, 16384, 1024, 16, 16, False, True, True): (1, 4, 3, 4),
+        (16384, 16384, 1024, 16, 16, True, False, True): (2, 16, 3, 1),
+        (16384, 16384, 1024, 32, 32, False, True, True): (1, 16, 3, 4),
+        (16384, 16384, 1024, 32, 32, True, False, True): (1, 8, 3, 4),
+        (16384, 16384, 1024, 64, 64, False, True, True): (4, 16, 2, 4),
+        (16384, 16384, 1024, 64, 64, True, False, True): (4, 16, 2, 4),
+        (16384, 16384, 1024, 128, 128, False, True, True): (4, 8, 1, 32),
+        (16384, 16384, 1024, 128, 128, True, False, True): (4, 8, 1, 32),
+        (16384, 16384, 2048, 16, 16, False, True, True): (1, 8, 3, 4),
+        (16384, 16384, 2048, 16, 16, True, False, True): (2, 32, 3, 1),
+        (16384, 16384, 2048, 32, 32, False, True, True): (1, 32, 3, 4),
+        (16384, 16384, 2048, 32, 32, True, False, True): (1, 16, 3, 4),
+        (16384, 16384, 2048, 64, 64, False, True, True): (4, 32, 2, 4),
+        (16384, 16384, 2048, 64, 64, True, False, True): (2, 32, 2, 4),
+        (16384, 16384, 2048, 128, 128, False, True, True): (4, 16, 1, 32),
+        (16384, 16384, 2048, 128, 128, True, False, True): (4, 16, 1, 32),
+        (16384, 16384, 4096, 16, 16, False, True, True): (1, 16, 3, 4),
+        (16384, 16384, 4096, 16, 16, True, False, True): (2, 64, 3, 1),
+        (16384, 16384, 4096, 32, 32, False, True, True): (1, 64, 3, 4),
+        (16384, 16384, 4096, 32, 32, True, False, True): (1, 32, 3, 4),
+        (16384, 16384, 4096, 64, 64, False, True, True): (4, 64, 2, 4),
+        (16384, 16384, 4096, 64, 64, True, False, True): (2, 64, 2, 4),
+        (16384, 16384, 4096, 128, 128, False, True, True): (4, 32, 1, 32),
+        (16384, 16384, 4096, 128, 128, True, False, True): (4, 32, 1, 32),
+        (16384, 16384, 8192, 16, 16, False, True, True): (1, 128, 3, 1),
+        (16384, 16384, 8192, 16, 16, True, False, True): (2, 128, 3, 1),
+        (16384, 16384, 8192, 32, 32, False, True, True): (1, 128, 3, 4),
+        (16384, 16384, 8192, 32, 32, True, False, True): (1, 64, 3, 4),
+        (16384, 16384, 8192, 64, 64, False, True, True): (2, 128, 2, 4),
+        (16384, 16384, 8192, 64, 64, True, False, True): (2, 128, 2, 4),
+        (16384, 16384, 8192, 128, 128, False, True, True): (4, 64, 1, 32),
+        (16384, 16384, 8192, 128, 128, True, False, True): (4, 64, 1, 32),
+        (16384, 16384, 16384, 16, 16, False, True, True): (1, 64, 3, 4),
+        (16384, 16384, 16384, 16, 16, True, False, True): (2, 256, 3, 1),
+        (16384, 16384, 16384, 32, 32, False, True, True): (1, 256, 3, 4),
+        (16384, 16384, 16384, 32, 32, True, False, True): (1, 128, 3, 4),
+        (16384, 16384, 16384, 64, 64, False, True, True): (2, 256, 2, 4),
+        (16384, 16384, 16384, 64, 64, True, False, True): (2, 256, 2, 4),
+        (16384, 16384, 16384, 128, 128, False, True, True): (4, 128, 1, 32),
+        (16384, 16384, 16384, 128, 128, True, False, True): (4, 128, 1, 32),
+        (16384, 16384, 32768, 16, 16, False, True, True): (1, 512, 3, 1),
+        (16384, 16384, 32768, 16, 16, True, False, True): (1, 128, 3, 4),
+        (16384, 16384, 32768, 32, 32, False, True, True): (2, 512, 3, 4),
+        (16384, 16384, 32768, 32, 32, True, False, True): (1, 256, 4, 4),
+        (16384, 16384, 32768, 64, 64, False, True, True): (2, 512, 2, 4),
+        (16384, 16384, 32768, 64, 64, True, False, True): (2, 512, 2, 4),
+        (16384, 16384, 32768, 128, 128, False, True, True): (4, 256, 1, 32),
+        (16384, 16384, 32768, 128, 128, True, False, True): (4, 256, 1, 32),
+        (16384, 16384, 65536, 16, 16, False, True, True): (1, 256, 3, 4),
+        (16384, 16384, 65536, 16, 16, True, False, True): (1, 1024, 3, 1),
+        (16384, 16384, 65536, 32, 32, False, True, True): (1, 1024, 3, 4),
+        (16384, 16384, 65536, 32, 32, True, False, True): (1, 512, 4, 4),
+        (16384, 16384, 65536, 64, 64, False, True, True): (2, 1024, 2, 4),
+        (16384, 16384, 65536, 64, 64, True, False, True): (2, 1024, 2, 4),
+        (16384, 16384, 65536, 128, 128, False, True, True): (4, 512, 1, 32),
+        (16384, 16384, 65536, 128, 128, True, False, True): (4, 512, 1, 32),
+        (16384, 16384, 131072, 16, 16, False, True, True): (1, 1024, 4, 4),
+        (16384, 16384, 131072, 16, 16, True, False, True): (2, 2048, 3, 1),
+        (16384, 16384, 131072, 32, 32, False, True, True): (1, 1024, 2, 4),
+        (16384, 16384, 131072, 32, 32, True, False, True): (1, 1024, 2, 4),
+        (16384, 16384, 131072, 64, 64, False, True, True): (4, 2048, 2, 4),
+        (16384, 16384, 131072, 64, 64, True, False, True): (2, 2048, 2, 4),
+        (16384, 16384, 131072, 128, 128, False, True, True): (4, 1024, 1, 32),
+        (16384, 16384, 131072, 128, 128, True, False, True): (4, 1024, 1, 32),
+    },
+    ("bsr_dense_addmm", "NVIDIA A100-SXM4-80GB", (0, torch.float32, 0.56)): {
+        (192, 192, 256, 64, 64, False, True, True): (1, 4, 3, 8),
+        (192, 192, 256, 64, 64, True, False, True): (1, 4, 3, 8),
+        (192, 192, 512, 64, 64, False, True, True): (2, 8, 3, 8),
+        (192, 192, 512, 64, 64, True, False, True): (5, 8, 3, 8),
+        (192, 192, 1024, 64, 64, False, True, True): (2, 16, 4, 8),
+        (192, 192, 1024, 64, 64, True, False, True): (1, 16, 3, 8),
+        (192, 192, 2048, 64, 64, False, True, True): (3, 32, 3, 8),
+        (192, 192, 2048, 64, 64, True, False, True): (5, 32, 5, 8),
+        (192, 192, 4096, 64, 64, False, True, True): (3, 64, 2, 8),
+        (192, 192, 4096, 64, 64, True, False, True): (1, 64, 3, 8),
+        (192, 192, 8192, 64, 64, False, True, True): (3, 128, 3, 8),
+        (192, 192, 8192, 64, 64, True, False, True): (6, 128, 3, 4),
+        (192, 192, 16384, 64, 64, False, True, True): (1, 256, 1, 8),
+        (192, 192, 16384, 64, 64, True, False, True): (1, 256, 3, 4),
+        (192, 192, 32768, 64, 64, False, True, True): (1, 512, 1, 8),
+        (192, 192, 32768, 64, 64, True, False, True): (1, 512, 3, 4),
+        (192, 192, 65536, 64, 64, False, True, True): (1, 1024, 1, 8),
+        (192, 192, 65536, 64, 64, True, False, True): (1, 1024, 3, 4),
+        (192, 192, 131072, 64, 64, False, True, True): (1, 2048, 1, 8),
+        (192, 192, 131072, 64, 64, True, False, True): (3, 2048, 1, 4),
+        (384, 384, 256, 128, 128, False, True, True): (1, 2, 1, 32),
+        (384, 384, 256, 128, 128, True, False, True): (1, 2, 1, 32),
+        (384, 384, 512, 128, 128, False, True, True): (1, 4, 1, 32),
+        (384, 384, 512, 128, 128, True, False, True): (2, 4, 1, 32),
+        (384, 384, 1024, 128, 128, False, True, True): (1, 8, 1, 32),
+        (384, 384, 1024, 128, 128, True, False, True): (4, 8, 1, 32),
+        (384, 384, 2048, 128, 128, False, True, True): (1, 16, 1, 32),
+        (384, 384, 2048, 128, 128, True, False, True): (1, 16, 1, 32),
+        (384, 384, 4096, 128, 128, False, True, True): (1, 32, 1, 32),
+        (384, 384, 4096, 128, 128, True, False, True): (2, 32, 2, 32),
+        (384, 384, 8192, 128, 128, False, True, True): (1, 64, 1, 32),
+        (384, 384, 8192, 128, 128, True, False, True): (1, 64, 2, 32),
+        (384, 384, 16384, 128, 128, False, True, True): (1, 128, 1, 32),
+        (384, 384, 16384, 128, 128, True, False, True): (4, 128, 1, 32),
+        (384, 384, 32768, 128, 128, False, True, True): (3, 256, 1, 32),
+        (384, 384, 32768, 128, 128, True, False, True): (3, 256, 1, 32),
+        (384, 384, 65536, 128, 128, False, True, True): (3, 512, 1, 32),
+        (384, 384, 65536, 128, 128, True, False, True): (3, 512, 1, 32),
+        (384, 384, 131072, 128, 128, False, True, True): (1, 1024, 1, 32),
+        (384, 384, 131072, 128, 128, True, False, True): (3, 1024, 1, 32),
+    },
+    ("scatter_mm", "NVIDIA A100-SXM4-80GB", (0, torch.bfloat16, 0.5)): {
+        (256, 256, 256, 16, 16): (1, 1, 16, 16, 1, 2),
+        (256, 256, 256, 32, 32): (1, 1, 16, 16, 1, 4),
+        (256, 256, 256, 64, 64): (1, 1, 16, 16, 1, 1),
+        (256, 256, 256, 128, 128): (2, 4, 16, 64, 1, 4),
+        (256, 256, 512, 16, 16): (1, 1, 16, 16, 1, 4),
+        (256, 256, 512, 32, 32): (1, 1, 16, 32, 1, 4),
+        (256, 256, 512, 64, 64): (1, 1, 16, 32, 1, 1),
+        (256, 256, 512, 128, 128): (1, 1, 32, 32, 1, 4),
+        (256, 256, 1024, 16, 16): (1, 1, 16, 16, 1, 4),
+        (256, 256, 1024, 32, 32): (1, 2, 16, 32, 1, 1),
+        (256, 256, 1024, 64, 64): (1, 1, 32, 32, 1, 2),
+        (256, 256, 1024, 128, 128): (1, 1, 32, 64, 1, 4),
+        (256, 256, 2048, 16, 16): (1, 1, 16, 64, 1, 8),
+        (256, 256, 2048, 32, 32): (2, 1, 32, 64, 1, 2),
+        (256, 256, 2048, 64, 64): (1, 1, 32, 32, 1, 1),
+        (256, 256, 2048, 128, 128): (1, 1, 64, 64, 1, 4),
+        (256, 256, 4096, 16, 16): (1, 1, 16, 64, 1, 1),
+        (256, 256, 4096, 32, 32): (2, 2, 32, 64, 1, 2),
+        (256, 256, 4096, 64, 64): (1, 1, 32, 128, 1, 4),
+        (256, 256, 4096, 128, 128): (1, 1, 64, 64, 1, 4),
+        (256, 256, 8192, 16, 16): (1, 2, 16, 64, 1, 2),
+        (256, 256, 8192, 32, 32): (1, 1, 32, 64, 1, 2),
+        (256, 256, 8192, 64, 64): (1, 1, 32, 64, 1, 2),
+        (256, 256, 8192, 128, 128): (1, 1, 64, 64, 1, 4),
+        (256, 256, 16384, 16, 16): (1, 1, 16, 64, 1, 2),
+        (256, 256, 16384, 32, 32): (1, 1, 32, 64, 1, 2),
+        (256, 256, 16384, 64, 64): (1, 1, 64, 64, 1, 2),
+        (256, 256, 16384, 128, 128): (2, 16, 64, 64, 1, 4),
+        (256, 256, 32768, 16, 16): (1, 1, 16, 128, 1, 2),
+        (256, 256, 32768, 32, 32): (1, 1, 32, 64, 1, 2),
+        (256, 256, 32768, 64, 64): (1, 1, 64, 64, 1, 2),
+        (256, 256, 32768, 128, 128): (2, 32, 64, 64, 1, 4),
+        (256, 256, 65536, 16, 16): (1, 1, 16, 64, 1, 1),
+        (256, 256, 65536, 32, 32): (1, 1, 32, 64, 1, 2),
+        (256, 256, 65536, 64, 64): (1, 1, 64, 32, 1, 1),
+        (256, 256, 65536, 128, 128): (2, 32, 64, 64, 1, 4),
+        (256, 256, 131072, 16, 16): (1, 1, 16, 64, 1, 1),
+        (256, 256, 131072, 32, 32): (1, 1, 32, 64, 1, 2),
+        (256, 256, 131072, 64, 64): (4, 1, 64, 32, 1, 1),
+        (256, 256, 131072, 128, 128): (2, 64, 64, 64, 1, 4),
+        (512, 512, 256, 16, 16): (1, 1, 16, 16, 1, 2),
+        (512, 512, 256, 32, 32): (1, 1, 16, 32, 1, 1),
+        (512, 512, 256, 64, 64): (1, 2, 16, 32, 1, 1),
+        (512, 512, 256, 128, 128): (2, 16, 64, 16, 2, 4),
+        (512, 512, 512, 16, 16): (1, 1, 16, 16, 1, 4),
+        (512, 512, 512, 32, 32): (1, 1, 16, 32, 1, 1),
+        (512, 512, 512, 64, 64): (1, 1, 32, 32, 1, 2),
+        (512, 512, 512, 128, 128): (2, 8, 32, 64, 1, 4),
+        (512, 512, 1024, 16, 16): (1, 1, 16, 64, 1, 8),
+        (512, 512, 1024, 32, 32): (1, 1, 32, 32, 3, 1),
+        (512, 512, 1024, 64, 64): (1, 4, 32, 64, 1, 2),
+        (512, 512, 1024, 128, 128): (1, 4, 64, 64, 1, 4),
+        (512, 512, 2048, 16, 16): (1, 1, 16, 64, 1, 2),
+        (512, 512, 2048, 32, 32): (1, 1, 32, 64, 1, 2),
+        (512, 512, 2048, 64, 64): (1, 1, 64, 64, 3, 4),
+        (512, 512, 2048, 128, 128): (1, 1, 64, 64, 1, 4),
+        (512, 512, 4096, 16, 16): (1, 1, 16, 64, 1, 2),
+        (512, 512, 4096, 32, 32): (2, 64, 32, 64, 1, 2),
+        (512, 512, 4096, 64, 64): (1, 1, 64, 64, 3, 4),
+        (512, 512, 4096, 128, 128): (1, 1, 64, 64, 1, 4),
+        (512, 512, 8192, 16, 16): (1, 2, 16, 128, 1, 2),
+        (512, 512, 8192, 32, 32): (1, 1, 32, 64, 1, 2),
+        (512, 512, 8192, 64, 64): (1, 1, 64, 64, 1, 2),
+        (512, 512, 8192, 128, 128): (1, 1, 64, 64, 1, 4),
+        (512, 512, 16384, 16, 16): (1, 2, 16, 128, 1, 2),
+        (512, 512, 16384, 32, 32): (1, 1, 32, 64, 1, 2),
+        (512, 512, 16384, 64, 64): (1, 1, 64, 64, 3, 2),
+        (512, 512, 16384, 128, 128): (2, 1, 64, 64, 1, 4),
+        (512, 512, 32768, 16, 16): (1, 2, 16, 128, 1, 2),
+        (512, 512, 32768, 32, 32): (1, 1, 32, 64, 1, 2),
+        (512, 512, 32768, 64, 64): (1, 1, 64, 64, 3, 4),
+        (512, 512, 32768, 128, 128): (2, 1, 64, 64, 1, 4),
+        (512, 512, 65536, 16, 16): (1, 2, 16, 128, 1, 2),
+        (512, 512, 65536, 32, 32): (1, 1, 32, 64, 1, 2),
+        (512, 512, 65536, 64, 64): (1, 1, 64, 64, 3, 4),
+        (512, 512, 65536, 128, 128): (2, 1, 64, 64, 1, 4),
+        (512, 512, 131072, 16, 16): (1, 1, 16, 64, 1, 1),
+        (512, 512, 131072, 32, 32): (1, 1, 32, 64, 1, 2),
+        (512, 512, 131072, 64, 64): (1, 1, 64, 64, 3, 4),
+        (512, 512, 131072, 128, 128): (2, 4, 64, 64, 1, 4),
+        (1024, 1024, 256, 16, 16): (1, 1, 16, 16, 1, 4),
+        (1024, 1024, 256, 32, 32): (2, 16, 32, 16, 3, 4),
+        (1024, 1024, 256, 64, 64): (1, 4, 32, 32, 1, 2),
+        (1024, 1024, 256, 128, 128): (1, 4, 128, 16, 3, 16),
+        (1024, 1024, 512, 16, 16): (1, 1, 16, 64, 1, 2),
+        (1024, 1024, 512, 32, 32): (2, 2, 32, 64, 1, 2),
+        (1024, 1024, 512, 64, 64): (2, 8, 64, 64, 3, 4),
+        (1024, 1024, 512, 128, 128): (1, 4, 64, 64, 1, 8),
+        (1024, 1024, 1024, 16, 16): (1, 1, 16, 64, 1, 2),
+        (1024, 1024, 1024, 32, 32): (1, 1, 32, 64, 1, 2),
+        (1024, 1024, 1024, 64, 64): (1, 8, 64, 64, 3, 4),
+        (1024, 1024, 1024, 128, 128): (1, 8, 64, 64, 1, 4),
+        (1024, 1024, 2048, 16, 16): (1, 2, 16, 64, 1, 2),
+        (1024, 1024, 2048, 32, 32): (1, 1, 32, 64, 1, 2),
+        (1024, 1024, 2048, 64, 64): (2, 16, 64, 64, 2, 2),
+        (1024, 1024, 2048, 128, 128): (2, 32, 64, 64, 1, 4),
+        (1024, 1024, 4096, 16, 16): (2, 16, 16, 128, 1, 2),
+        (1024, 1024, 4096, 32, 32): (1, 16, 32, 64, 3, 2),
+        (1024, 1024, 4096, 64, 64): (1, 1, 64, 64, 3, 4),
+        (1024, 1024, 4096, 128, 128): (2, 64, 128, 64, 1, 4),
+        (1024, 1024, 8192, 16, 16): (2, 16, 16, 128, 1, 2),
+        (1024, 1024, 8192, 32, 32): (1, 16, 32, 64, 3, 2),
+        (1024, 1024, 8192, 64, 64): (1, 1, 64, 64, 3, 4),
+        (1024, 1024, 8192, 128, 128): (2, 1, 64, 64, 1, 4),
+        (1024, 1024, 16384, 16, 16): (1, 2, 16, 128, 1, 2),
+        (1024, 1024, 16384, 32, 32): (1, 16, 32, 64, 3, 2),
+        (1024, 1024, 16384, 64, 64): (1, 1, 64, 64, 3, 4),
+        (1024, 1024, 16384, 128, 128): (2, 16, 128, 64, 1, 4),
+        (1024, 1024, 32768, 16, 16): (1, 1, 16, 128, 1, 2),
+        (1024, 1024, 32768, 32, 32): (1, 1, 32, 128, 1, 2),
+        (1024, 1024, 32768, 64, 64): (1, 32, 64, 32, 2, 1),
+        (1024, 1024, 32768, 128, 128): (2, 8, 128, 64, 1, 4),
+        (1024, 1024, 65536, 16, 16): (3, 2, 16, 128, 1, 2),
+        (1024, 1024, 65536, 32, 32): (1, 1, 32, 128, 1, 2),
+        (1024, 1024, 65536, 64, 64): (2, 4, 64, 32, 2, 1),
+        (1024, 1024, 65536, 128, 128): (2, 8, 128, 64, 1, 4),
+        (1024, 1024, 131072, 16, 16): (2, 1, 16, 128, 1, 2),
+        (1024, 1024, 131072, 32, 32): (1, 1, 32, 128, 1, 2),
+        (1024, 1024, 131072, 64, 64): (1, 4, 64, 32, 2, 1),
+        (1024, 1024, 131072, 128, 128): (4, 1, 128, 64, 1, 4),
+        (2048, 2048, 256, 16, 16): (1, 1, 16, 64, 1, 8),
+        (2048, 2048, 256, 32, 32): (1, 1, 32, 32, 3, 1),
+        (2048, 2048, 256, 64, 64): (1, 1, 32, 32, 2, 1),
+        (2048, 2048, 256, 128, 128): (1, 4, 64, 64, 1, 8),
+        (2048, 2048, 512, 16, 16): (1, 2, 16, 64, 1, 2),
+        (2048, 2048, 512, 32, 32): (1, 2, 32, 64, 1, 4),
+        (2048, 2048, 512, 64, 64): (1, 4, 64, 64, 1, 8),
+        (2048, 2048, 512, 128, 128): (1, 4, 64, 64, 1, 4),
+        (2048, 2048, 1024, 16, 16): (1, 2, 16, 128, 1, 2),
+        (2048, 2048, 1024, 32, 32): (1, 1, 32, 64, 1, 2),
+        (2048, 2048, 1024, 64, 64): (1, 8, 64, 64, 1, 4),
+        (2048, 2048, 1024, 128, 128): (1, 8, 128, 64, 1, 4),
+        (2048, 2048, 2048, 16, 16): (3, 4, 16, 128, 1, 2),
+        (2048, 2048, 2048, 32, 32): (1, 16, 32, 64, 5, 2),
+        (2048, 2048, 2048, 64, 64): (1, 1, 64, 64, 3, 4),
+        (2048, 2048, 2048, 128, 128): (1, 8, 128, 64, 1, 4),
+        (2048, 2048, 4096, 16, 16): (1, 2, 16, 128, 1, 2),
+        (2048, 2048, 4096, 32, 32): (1, 8, 32, 64, 3, 2),
+        (2048, 2048, 4096, 64, 64): (1, 1, 64, 64, 3, 4),
+        (2048, 2048, 4096, 128, 128): (1, 8, 128, 64, 1, 4),
+        (2048, 2048, 8192, 16, 16): (2, 4, 16, 128, 1, 2),
+        (2048, 2048, 8192, 32, 32): (1, 4, 32, 128, 3, 2),
+        (2048, 2048, 8192, 64, 64): (1, 8, 64, 64, 3, 2),
+        (2048, 2048, 8192, 128, 128): (1, 8, 128, 64, 1, 4),
+        (2048, 2048, 16384, 16, 16): (1, 2, 16, 128, 1, 2),
+        (2048, 2048, 16384, 32, 32): (1, 4, 32, 128, 3, 2),
+        (2048, 2048, 16384, 64, 64): (1, 8, 64, 64, 3, 2),
+        (2048, 2048, 16384, 128, 128): (1, 4, 128, 64, 1, 4),
+        (2048, 2048, 32768, 16, 16): (3, 2, 16, 128, 1, 2),
+        (2048, 2048, 32768, 32, 32): (1, 1, 32, 128, 3, 2),
+        (2048, 2048, 32768, 64, 64): (1, 1, 64, 64, 3, 2),
+        (2048, 2048, 32768, 128, 128): (1, 4, 128, 64, 1, 4),
+        (2048, 2048, 65536, 16, 16): (1, 2, 16, 128, 1, 2),
+        (2048, 2048, 65536, 32, 32): (1, 4, 32, 128, 1, 2),
+        (2048, 2048, 65536, 64, 64): (1, 1, 64, 64, 3, 2),
+        (2048, 2048, 65536, 128, 128): (1, 2, 128, 64, 1, 4),
+        (2048, 2048, 131072, 16, 16): (4, 2, 16, 128, 1, 2),
+        (2048, 2048, 131072, 32, 32): (1, 1, 32, 128, 3, 2),
+        (2048, 2048, 131072, 64, 64): (1, 1, 64, 64, 3, 2),
+        (2048, 2048, 131072, 128, 128): (1, 2, 128, 64, 1, 4),
+        (4096, 4096, 256, 16, 16): (1, 1, 16, 64, 1, 2),
+        (4096, 4096, 256, 32, 32): (1, 1, 32, 64, 3, 4),
+        (4096, 4096, 256, 64, 64): (1, 1, 64, 64, 3, 4),
+        (4096, 4096, 256, 128, 128): (3, 4, 128, 32, 1, 4),
+        (4096, 4096, 512, 16, 16): (1, 2, 16, 128, 1, 2),
+        (4096, 4096, 512, 32, 32): (1, 2, 32, 64, 3, 2),
+        (4096, 4096, 512, 64, 64): (1, 4, 64, 64, 1, 4),
+        (4096, 4096, 512, 128, 128): (1, 4, 128, 64, 1, 4),
+        (4096, 4096, 1024, 16, 16): (1, 2, 16, 128, 1, 2),
+        (4096, 4096, 1024, 32, 32): (1, 8, 32, 64, 3, 2),
+        (4096, 4096, 1024, 64, 64): (1, 4, 64, 64, 1, 4),
+        (4096, 4096, 1024, 128, 128): (2, 4, 128, 64, 1, 4),
+        (4096, 4096, 2048, 16, 16): (1, 1, 16, 128, 1, 2),
+        (4096, 4096, 2048, 32, 32): (1, 4, 32, 128, 1, 4),
+        (4096, 4096, 2048, 64, 64): (1, 1, 64, 64, 3, 4),
+        (4096, 4096, 2048, 128, 128): (1, 16, 128, 64, 1, 4),
+        (4096, 4096, 4096, 16, 16): (1, 1, 16, 64, 3, 1),
+        (4096, 4096, 4096, 32, 32): (1, 4, 32, 64, 3, 2),
+        (4096, 4096, 4096, 64, 64): (1, 1, 64, 64, 3, 4),
+        (4096, 4096, 4096, 128, 128): (5, 1, 128, 64, 1, 4),
+        (4096, 4096, 8192, 16, 16): (1, 1, 16, 128, 1, 2),
+        (4096, 4096, 8192, 32, 32): (1, 1, 32, 128, 3, 2),
+        (4096, 4096, 8192, 64, 64): (1, 1, 64, 64, 3, 4),
+        (4096, 4096, 8192, 128, 128): (2, 1, 128, 64, 1, 4),
+        (4096, 4096, 16384, 16, 16): (1, 1, 16, 128, 1, 2),
+        (4096, 4096, 16384, 32, 32): (1, 1, 32, 128, 3, 2),
+        (4096, 4096, 16384, 64, 64): (1, 1, 64, 64, 4, 4),
+        (4096, 4096, 16384, 128, 128): (2, 1, 128, 64, 1, 4),
+        (4096, 4096, 32768, 16, 16): (3, 1, 16, 128, 1, 2),
+        (4096, 4096, 32768, 32, 32): (1, 1, 32, 128, 3, 2),
+        (4096, 4096, 32768, 64, 64): (1, 1, 64, 64, 3, 4),
+        (4096, 4096, 32768, 128, 128): (2, 1, 128, 64, 1, 4),
+        (4096, 4096, 65536, 16, 16): (2, 2, 16, 128, 1, 2),
+        (4096, 4096, 65536, 32, 32): (1, 1, 32, 128, 4, 2),
+        (4096, 4096, 65536, 64, 64): (1, 1, 64, 64, 4, 4),
+        (4096, 4096, 65536, 128, 128): (2, 1, 128, 64, 1, 4),
+        (4096, 4096, 131072, 16, 16): (2, 1, 16, 128, 1, 2),
+        (4096, 4096, 131072, 32, 32): (1, 1, 32, 128, 3, 2),
+        (4096, 4096, 131072, 64, 64): (1, 1, 64, 64, 3, 4),
+        (4096, 4096, 131072, 128, 128): (2, 1, 128, 64, 1, 4),
+        (8192, 8192, 256, 16, 16): (1, 2, 16, 64, 1, 2),
+        (8192, 8192, 256, 32, 32): (1, 1, 32, 64, 1, 2),
+        (8192, 8192, 256, 64, 64): (1, 2, 64, 64, 1, 4),
+        (8192, 8192, 256, 128, 128): (3, 16, 128, 16, 1, 2),
+        (8192, 8192, 512, 16, 16): (1, 2, 16, 128, 1, 2),
+        (8192, 8192, 512, 32, 32): (1, 4, 32, 64, 3, 2),
+        (8192, 8192, 512, 64, 64): (2, 8, 64, 64, 4, 4),
+        (8192, 8192, 512, 128, 128): (1, 8, 128, 64, 1, 4),
+        (8192, 8192, 1024, 16, 16): (4, 2, 16, 128, 1, 2),
+        (8192, 8192, 1024, 32, 32): (1, 8, 32, 128, 1, 2),
+        (8192, 8192, 1024, 64, 64): (1, 16, 64, 64, 3, 2),
+        (8192, 8192, 1024, 128, 128): (2, 16, 128, 64, 2, 4),
+        (8192, 8192, 2048, 16, 16): (2, 1, 16, 64, 4, 1),
+        (8192, 8192, 2048, 32, 32): (1, 16, 32, 64, 5, 2),
+        (8192, 8192, 2048, 64, 64): (1, 16, 64, 64, 3, 2),
+        (8192, 8192, 2048, 128, 128): (2, 16, 128, 64, 2, 4),
+        (8192, 8192, 4096, 16, 16): (1, 1, 16, 64, 4, 1),
+        (8192, 8192, 4096, 32, 32): (1, 16, 32, 64, 5, 2),
+        (8192, 8192, 4096, 64, 64): (1, 16, 64, 64, 3, 2),
+        (8192, 8192, 4096, 128, 128): (2, 64, 128, 64, 2, 4),
+        (8192, 8192, 8192, 16, 16): (1, 1, 16, 64, 4, 1),
+        (8192, 8192, 8192, 32, 32): (1, 8, 32, 128, 5, 4),
+        (8192, 8192, 8192, 64, 64): (1, 8, 64, 64, 3, 2),
+        (8192, 8192, 8192, 128, 128): (2, 8, 128, 64, 1, 4),
+        (8192, 8192, 16384, 16, 16): (1, 1, 16, 64, 4, 1),
+        (8192, 8192, 16384, 32, 32): (1, 8, 32, 64, 5, 2),
+        (8192, 8192, 16384, 64, 64): (1, 8, 64, 64, 3, 2),
+        (8192, 8192, 16384, 128, 128): (1, 8, 128, 64, 1, 4),
+        (8192, 8192, 32768, 16, 16): (1, 1, 16, 64, 4, 1),
+        (8192, 8192, 32768, 32, 32): (1, 8, 32, 64, 5, 2),
+        (8192, 8192, 32768, 64, 64): (3, 8, 64, 64, 3, 2),
+        (8192, 8192, 32768, 128, 128): (2, 8, 128, 64, 1, 4),
+        (8192, 8192, 65536, 16, 16): (1, 1, 16, 64, 4, 1),
+        (8192, 8192, 65536, 32, 32): (5, 4, 32, 64, 3, 2),
+        (8192, 8192, 65536, 64, 64): (1, 8, 64, 64, 3, 2),
+        (8192, 8192, 65536, 128, 128): (2, 8, 128, 64, 1, 4),
+        (8192, 8192, 131072, 16, 16): (2, 1, 16, 64, 4, 1),
+        (8192, 8192, 131072, 32, 32): (1, 4, 32, 64, 5, 2),
+        (8192, 8192, 131072, 64, 64): (1, 4, 64, 128, 3, 4),
+        (8192, 8192, 131072, 128, 128): (2, 8, 128, 64, 1, 4),
+        (16384, 16384, 256, 16, 16): (1, 2, 16, 128, 1, 2),
+        (16384, 16384, 256, 32, 32): (1, 4, 32, 64, 3, 2),
+        (16384, 16384, 256, 64, 64): (2, 4, 64, 64, 4, 4),
+        (16384, 16384, 256, 128, 128): (1, 4, 128, 64, 1, 16),
+        (16384, 16384, 512, 16, 16): (1, 2, 16, 128, 3, 2),
+        (16384, 16384, 512, 32, 32): (1, 4, 32, 128, 5, 4),
+        (16384, 16384, 512, 64, 64): (1, 8, 64, 64, 3, 2),
+        (16384, 16384, 512, 128, 128): (2, 8, 128, 64, 1, 4),
+        (16384, 16384, 1024, 16, 16): (1, 2, 16, 128, 1, 2),
+        (16384, 16384, 1024, 32, 32): (1, 8, 32, 64, 5, 2),
+        (16384, 16384, 1024, 64, 64): (1, 16, 64, 64, 3, 2),
+        (16384, 16384, 1024, 128, 128): (5, 16, 128, 64, 2, 4),
+        (16384, 16384, 2048, 16, 16): (1, 2, 16, 128, 1, 2),
+        (16384, 16384, 2048, 32, 32): (1, 8, 32, 64, 5, 2),
+        (16384, 16384, 2048, 64, 64): (1, 16, 64, 64, 3, 2),
+        (16384, 16384, 2048, 128, 128): (4, 32, 128, 64, 2, 4),
+        (16384, 16384, 4096, 16, 16): (3, 2, 16, 128, 1, 2),
+        (16384, 16384, 4096, 32, 32): (1, 4, 32, 64, 5, 2),
+        (16384, 16384, 4096, 64, 64): (2, 16, 64, 64, 3, 2),
+        (16384, 16384, 4096, 128, 128): (3, 32, 128, 64, 2, 4),
+        (16384, 16384, 8192, 16, 16): (1, 2, 16, 128, 1, 2),
+        (16384, 16384, 8192, 32, 32): (1, 4, 32, 64, 5, 2),
+        (16384, 16384, 8192, 64, 64): (4, 8, 64, 64, 3, 2),
+        (16384, 16384, 8192, 128, 128): (5, 8, 128, 64, 1, 4),
+        (16384, 16384, 16384, 16, 16): (1, 2, 16, 128, 1, 2),
+        (16384, 16384, 16384, 32, 32): (1, 4, 32, 64, 5, 2),
+        (16384, 16384, 16384, 64, 64): (2, 4, 64, 128, 3, 4),
+        (16384, 16384, 16384, 128, 128): (4, 8, 128, 64, 1, 4),
+        (16384, 16384, 32768, 16, 16): (4, 2, 16, 128, 1, 2),
+        (16384, 16384, 32768, 32, 32): (1, 4, 32, 64, 5, 2),
+        (16384, 16384, 32768, 64, 64): (1, 8, 64, 64, 3, 2),
+        (16384, 16384, 32768, 128, 128): (2, 512, 128, 64, 2, 4),
+        (16384, 16384, 65536, 16, 16): (3, 2, 16, 128, 1, 2),
+        (16384, 16384, 65536, 32, 32): (1, 4, 32, 64, 5, 2),
+        (16384, 16384, 65536, 64, 64): (1, 4, 64, 128, 3, 4),
+        (16384, 16384, 65536, 128, 128): (2, 1024, 128, 64, 2, 4),
+        (16384, 16384, 131072, 16, 16): (1, 2, 16, 128, 1, 2),
+        (16384, 16384, 131072, 32, 32): (1, 4, 32, 64, 5, 2),
+        (16384, 16384, 131072, 64, 64): (3, 4, 64, 128, 3, 4),
+        (16384, 16384, 131072, 128, 128): (4, 2048, 128, 64, 2, 4),
+    },
+    ("scatter_mm", "NVIDIA A100-SXM4-80GB", (0, torch.float16, 0.5)): {
+        (256, 256, 256, 16, 16): (5, 4, 16, 16, 1, 4),
+        (256, 256, 256, 32, 32): (5, 2, 32, 16, 1, 4),
+        (256, 256, 256, 64, 64): (4, 1, 32, 32, 1, 8),
+        (256, 256, 256, 128, 128): (2, 1, 32, 32, 1, 4),
+        (256, 256, 512, 16, 16): (2, 2, 16, 32, 1, 4),
+        (256, 256, 512, 32, 32): (4, 8, 32, 32, 1, 8),
+        (256, 256, 512, 64, 64): (4, 8, 32, 64, 1, 4),
+        (256, 256, 512, 128, 128): (4, 8, 32, 64, 1, 4),
+        (256, 256, 1024, 16, 16): (4, 2, 16, 64, 1, 2),
+        (256, 256, 1024, 32, 32): (4, 16, 32, 64, 1, 2),
+        (256, 256, 1024, 64, 64): (4, 16, 32, 64, 1, 4),
+        (256, 256, 1024, 128, 128): (4, 16, 64, 64, 1, 8),
+        (256, 256, 2048, 16, 16): (2, 16, 16, 64, 1, 8),
+        (256, 256, 2048, 32, 32): (4, 16, 32, 64, 1, 2),
+        (256, 256, 2048, 64, 64): (4, 16, 32, 64, 1, 4),
+        (256, 256, 2048, 128, 128): (4, 16, 64, 64, 1, 4),
+        (256, 256, 4096, 16, 16): (4, 32, 16, 64, 1, 1),
+        (256, 256, 4096, 32, 32): (2, 64, 32, 64, 1, 2),
+        (256, 256, 4096, 64, 64): (4, 64, 64, 64, 1, 4),
+        (256, 256, 4096, 128, 128): (4, 32, 64, 64, 1, 4),
+        (256, 256, 8192, 16, 16): (4, 64, 16, 64, 1, 1),
+        (256, 256, 8192, 32, 32): (4, 128, 32, 64, 1, 2),
+        (256, 256, 8192, 64, 64): (4, 64, 64, 64, 1, 4),
+        (256, 256, 8192, 128, 128): (4, 64, 64, 64, 1, 4),
+        (256, 256, 16384, 16, 16): (4, 128, 16, 64, 1, 1),
+        (256, 256, 16384, 32, 32): (2, 128, 32, 64, 1, 2),
+        (256, 256, 16384, 64, 64): (4, 32, 32, 128, 1, 4),
+        (256, 256, 16384, 128, 128): (4, 16, 64, 64, 1, 4),
+        (256, 256, 32768, 16, 16): (4, 64, 16, 64, 1, 1),
+        (256, 256, 32768, 32, 32): (2, 256, 32, 64, 1, 2),
+        (256, 256, 32768, 64, 64): (4, 32, 32, 128, 1, 4),
+        (256, 256, 32768, 128, 128): (4, 32, 64, 64, 1, 4),
+        (256, 256, 65536, 16, 16): (4, 128, 16, 64, 1, 1),
+        (256, 256, 65536, 32, 32): (4, 1, 32, 64, 1, 2),
+        (256, 256, 65536, 64, 64): (2, 1, 64, 64, 1, 2),
+        (256, 256, 65536, 128, 128): (4, 32, 64, 64, 1, 4),
+        (256, 256, 131072, 16, 16): (4, 64, 16, 64, 1, 1),
+        (256, 256, 131072, 32, 32): (2, 1, 32, 64, 1, 2),
+        (256, 256, 131072, 64, 64): (4, 32, 32, 128, 1, 4),
+        (256, 256, 131072, 128, 128): (4, 32, 64, 64, 1, 4),
+        (512, 512, 256, 16, 16): (4, 16, 16, 16, 1, 4),
+        (512, 512, 256, 32, 32): (2, 4, 32, 16, 1, 4),
+        (512, 512, 256, 64, 64): (2, 16, 64, 16, 3, 8),
+        (512, 512, 256, 128, 128): (4, 16, 64, 16, 1, 4),
+        (512, 512, 512, 16, 16): (1, 1, 16, 64, 1, 8),
+        (512, 512, 512, 32, 32): (2, 4, 16, 32, 1, 1),
+        (512, 512, 512, 64, 64): (2, 1, 32, 32, 1, 2),
+        (512, 512, 512, 128, 128): (4, 8, 32, 64, 1, 4),
+        (512, 512, 1024, 16, 16): (2, 8, 16, 64, 1, 8),
+        (512, 512, 1024, 32, 32): (4, 16, 32, 64, 1, 2),
+        (512, 512, 1024, 64, 64): (4, 16, 64, 64, 1, 4),
+        (512, 512, 1024, 128, 128): (2, 8, 64, 64, 1, 4),
+        (512, 512, 2048, 16, 16): (4, 16, 16, 64, 1, 4),
+        (512, 512, 2048, 32, 32): (4, 16, 32, 64, 1, 2),
+        (512, 512, 2048, 64, 64): (4, 16, 64, 64, 1, 8),
+        (512, 512, 2048, 128, 128): (4, 16, 64, 64, 1, 4),
+        (512, 512, 4096, 16, 16): (4, 32, 16, 128, 1, 2),
+        (512, 512, 4096, 32, 32): (4, 32, 32, 64, 1, 2),
+        (512, 512, 4096, 64, 64): (4, 32, 64, 64, 1, 4),
+        (512, 512, 4096, 128, 128): (4, 32, 64, 64, 1, 4),
+        (512, 512, 8192, 16, 16): (2, 32, 16, 128, 1, 2),
+        (512, 512, 8192, 32, 32): (4, 64, 32, 64, 1, 2),
+        (512, 512, 8192, 64, 64): (4, 128, 64, 64, 1, 2),
+        (512, 512, 8192, 128, 128): (4, 64, 64, 64, 1, 4),
+        (512, 512, 16384, 16, 16): (4, 32, 16, 64, 1, 1),
+        (512, 512, 16384, 32, 32): (4, 64, 32, 64, 1, 2),
+        (512, 512, 16384, 64, 64): (4, 16, 64, 64, 1, 4),
+        (512, 512, 16384, 128, 128): (4, 32, 64, 64, 1, 4),
+        (512, 512, 32768, 16, 16): (7, 16, 16, 128, 1, 2),
+        (512, 512, 32768, 32, 32): (4, 64, 32, 64, 1, 2),
+        (512, 512, 32768, 64, 64): (2, 32, 64, 64, 3, 2),
+        (512, 512, 32768, 128, 128): (2, 32, 64, 64, 1, 4),
+        (512, 512, 65536, 16, 16): (2, 32, 16, 64, 1, 1),
+        (512, 512, 65536, 32, 32): (4, 64, 32, 64, 1, 2),
+        (512, 512, 65536, 64, 64): (3, 32, 64, 64, 3, 2),
+        (512, 512, 65536, 128, 128): (4, 16, 64, 64, 1, 4),
+        (512, 512, 131072, 16, 16): (3, 32, 16, 128, 1, 2),
+        (512, 512, 131072, 32, 32): (4, 64, 32, 64, 1, 2),
+        (512, 512, 131072, 64, 64): (2, 32, 64, 64, 3, 2),
+        (512, 512, 131072, 128, 128): (3, 1, 64, 64, 1, 4),
+        (1024, 1024, 256, 16, 16): (4, 16, 16, 16, 1, 4),
+        (1024, 1024, 256, 32, 32): (4, 16, 32, 16, 1, 4),
+        (1024, 1024, 256, 64, 64): (4, 4, 64, 32, 1, 16),
+        (1024, 1024, 256, 128, 128): (4, 16, 64, 16, 1, 8),
+        (1024, 1024, 512, 16, 16): (2, 8, 16, 64, 1, 8),
+        (1024, 1024, 512, 32, 32): (3, 2, 32, 64, 1, 2),
+        (1024, 1024, 512, 64, 64): (4, 8, 32, 64, 1, 8),
+        (1024, 1024, 512, 128, 128): (4, 8, 64, 64, 1, 8),
+        (1024, 1024, 1024, 16, 16): (2, 2, 16, 64, 1, 2),
+        (1024, 1024, 1024, 32, 32): (2, 8, 32, 64, 1, 2),
+        (1024, 1024, 1024, 64, 64): (2, 8, 32, 128, 1, 4),
+        (1024, 1024, 1024, 128, 128): (2, 8, 64, 64, 1, 4),
+        (1024, 1024, 2048, 16, 16): (2, 16, 16, 128, 3, 2),
+        (1024, 1024, 2048, 32, 32): (4, 32, 32, 64, 1, 2),
+        (1024, 1024, 2048, 64, 64): (4, 16, 64, 64, 1, 4),
+        (1024, 1024, 2048, 128, 128): (4, 32, 64, 64, 1, 4),
+        (1024, 1024, 4096, 16, 16): (4, 16, 16, 128, 1, 2),
+        (1024, 1024, 4096, 32, 32): (3, 32, 32, 64, 1, 2),
+        (1024, 1024, 4096, 64, 64): (4, 32, 64, 64, 1, 4),
+        (1024, 1024, 4096, 128, 128): (4, 32, 64, 64, 1, 4),
+        (1024, 1024, 8192, 16, 16): (5, 16, 16, 128, 1, 2),
+        (1024, 1024, 8192, 32, 32): (2, 32, 32, 64, 3, 2),
+        (1024, 1024, 8192, 64, 64): (1, 16, 64, 64, 3, 2),
+        (1024, 1024, 8192, 128, 128): (4, 32, 64, 64, 1, 4),
+        (1024, 1024, 16384, 16, 16): (4, 16, 16, 128, 1, 2),
+        (1024, 1024, 16384, 32, 32): (1, 32, 32, 64, 3, 2),
+        (1024, 1024, 16384, 64, 64): (4, 16, 64, 64, 3, 2),
+        (1024, 1024, 16384, 128, 128): (4, 32, 128, 64, 1, 4),
+        (1024, 1024, 32768, 16, 16): (3, 16, 16, 128, 1, 2),
+        (1024, 1024, 32768, 32, 32): (1, 8, 32, 64, 3, 2),
+        (1024, 1024, 32768, 64, 64): (4, 16, 64, 64, 3, 2),
+        (1024, 1024, 32768, 128, 128): (4, 8, 128, 64, 2, 4),
+        (1024, 1024, 65536, 16, 16): (1, 2, 16, 128, 1, 2),
+        (1024, 1024, 65536, 32, 32): (2, 4, 32, 64, 3, 2),
+        (1024, 1024, 65536, 64, 64): (5, 16, 64, 64, 3, 2),
+        (1024, 1024, 65536, 128, 128): (5, 8, 128, 64, 2, 4),
+        (1024, 1024, 131072, 16, 16): (5, 2, 16, 128, 1, 2),
+        (1024, 1024, 131072, 32, 32): (1, 2, 32, 64, 3, 2),
+        (1024, 1024, 131072, 64, 64): (5, 16, 64, 64, 3, 2),
+        (1024, 1024, 131072, 128, 128): (2, 1, 128, 64, 2, 4),
+        (2048, 2048, 256, 16, 16): (4, 4, 16, 64, 1, 8),
+        (2048, 2048, 256, 32, 32): (4, 8, 32, 32, 1, 8),
+        (2048, 2048, 256, 64, 64): (4, 16, 64, 16, 1, 8),
+        (2048, 2048, 256, 128, 128): (4, 4, 128, 32, 3, 8),
+        (2048, 2048, 512, 16, 16): (2, 2, 16, 64, 1, 2),
+        (2048, 2048, 512, 32, 32): (2, 4, 32, 64, 3, 2),
+        (2048, 2048, 512, 64, 64): (4, 4, 64, 64, 1, 8),
+        (2048, 2048, 512, 128, 128): (4, 8, 64, 64, 1, 4),
+        (2048, 2048, 1024, 16, 16): (1, 8, 16, 64, 1, 2),
+        (2048, 2048, 1024, 32, 32): (2, 16, 32, 64, 3, 2),
+        (2048, 2048, 1024, 64, 64): (4, 8, 64, 64, 1, 4),
+        (2048, 2048, 1024, 128, 128): (4, 8, 128, 64, 1, 4),
+        (2048, 2048, 2048, 16, 16): (5, 4, 16, 128, 1, 2),
+        (2048, 2048, 2048, 32, 32): (1, 16, 32, 64, 3, 2),
+        (2048, 2048, 2048, 64, 64): (2, 8, 64, 64, 1, 4),
+        (2048, 2048, 2048, 128, 128): (2, 8, 128, 64, 1, 4),
+        (2048, 2048, 4096, 16, 16): (4, 2, 16, 128, 1, 2),
+        (2048, 2048, 4096, 32, 32): (2, 16, 32, 64, 3, 2),
+        (2048, 2048, 4096, 64, 64): (2, 8, 64, 64, 3, 2),
+        (2048, 2048, 4096, 128, 128): (4, 8, 128, 64, 1, 4),
+        (2048, 2048, 8192, 16, 16): (5, 4, 16, 128, 1, 2),
+        (2048, 2048, 8192, 32, 32): (2, 8, 32, 64, 3, 2),
+        (2048, 2048, 8192, 64, 64): (4, 8, 64, 64, 3, 2),
+        (2048, 2048, 8192, 128, 128): (4, 8, 128, 64, 1, 4),
+        (2048, 2048, 16384, 16, 16): (3, 2, 16, 128, 1, 2),
+        (2048, 2048, 16384, 32, 32): (2, 4, 32, 128, 3, 2),
+        (2048, 2048, 16384, 64, 64): (4, 8, 64, 64, 3, 2),
+        (2048, 2048, 16384, 128, 128): (4, 4, 128, 64, 1, 4),
+        (2048, 2048, 32768, 16, 16): (3, 2, 16, 128, 1, 2),
+        (2048, 2048, 32768, 32, 32): (3, 4, 32, 128, 3, 2),
+        (2048, 2048, 32768, 64, 64): (6, 4, 64, 64, 3, 2),
+        (2048, 2048, 32768, 128, 128): (3, 4, 128, 64, 1, 4),
+        (2048, 2048, 65536, 16, 16): (6, 2, 16, 128, 1, 2),
+        (2048, 2048, 65536, 32, 32): (1, 2, 32, 128, 1, 2),
+        (2048, 2048, 65536, 64, 64): (5, 4, 64, 64, 3, 2),
+        (2048, 2048, 65536, 128, 128): (5, 1, 128, 64, 2, 4),
+        (2048, 2048, 131072, 16, 16): (3, 2, 16, 128, 1, 2),
+        (2048, 2048, 131072, 32, 32): (2, 1, 32, 128, 3, 2),
+        (2048, 2048, 131072, 64, 64): (4, 1, 64, 64, 3, 2),
+        (2048, 2048, 131072, 128, 128): (3, 1, 128, 64, 2, 4),
+        (4096, 4096, 256, 16, 16): (5, 8, 16, 32, 1, 4),
+        (4096, 4096, 256, 32, 32): (4, 16, 32, 16, 2, 4),
+        (4096, 4096, 256, 64, 64): (2, 1, 64, 64, 3, 4),
+        (4096, 4096, 256, 128, 128): (4, 4, 128, 32, 1, 4),
+        (4096, 4096, 512, 16, 16): (4, 2, 16, 128, 1, 2),
+        (4096, 4096, 512, 32, 32): (4, 8, 32, 64, 1, 2),
+        (4096, 4096, 512, 64, 64): (4, 4, 64, 64, 1, 4),
+        (4096, 4096, 512, 128, 128): (4, 8, 128, 64, 2, 4),
+        (4096, 4096, 1024, 16, 16): (1, 2, 16, 128, 1, 2),
+        (4096, 4096, 1024, 32, 32): (6, 8, 32, 64, 3, 2),
+        (4096, 4096, 1024, 64, 64): (2, 16, 64, 64, 4, 4),
+        (4096, 4096, 1024, 128, 128): (2, 4, 128, 64, 2, 4),
+        (4096, 4096, 2048, 16, 16): (3, 1, 16, 128, 1, 2),
+        (4096, 4096, 2048, 32, 32): (1, 4, 32, 64, 5, 2),
+        (4096, 4096, 2048, 64, 64): (3, 16, 64, 64, 3, 2),
+        (4096, 4096, 2048, 128, 128): (4, 32, 128, 64, 2, 4),
+        (4096, 4096, 4096, 16, 16): (1, 2, 16, 128, 1, 2),
+        (4096, 4096, 4096, 32, 32): (1, 4, 32, 64, 3, 2),
+        (4096, 4096, 4096, 64, 64): (1, 1, 64, 64, 4, 4),
+        (4096, 4096, 4096, 128, 128): (2, 1, 128, 128, 1, 8),
+        (4096, 4096, 8192, 16, 16): (3, 1, 16, 128, 1, 2),
+        (4096, 4096, 8192, 32, 32): (2, 2, 32, 64, 5, 2),
+        (4096, 4096, 8192, 64, 64): (4, 16, 64, 64, 3, 2),
+        (4096, 4096, 8192, 128, 128): (4, 16, 128, 64, 2, 4),
+        (4096, 4096, 16384, 16, 16): (1, 2, 16, 128, 1, 2),
+        (4096, 4096, 16384, 32, 32): (4, 2, 32, 64, 5, 2),
+        (4096, 4096, 16384, 64, 64): (4, 16, 64, 64, 3, 2),
+        (4096, 4096, 16384, 128, 128): (4, 16, 128, 64, 2, 4),
+        (4096, 4096, 32768, 16, 16): (3, 1, 16, 128, 1, 2),
+        (4096, 4096, 32768, 32, 32): (3, 1, 32, 128, 1, 4),
+        (4096, 4096, 32768, 64, 64): (3, 1, 64, 64, 3, 4),
+        (4096, 4096, 32768, 128, 128): (5, 16, 128, 64, 2, 4),
+        (4096, 4096, 65536, 16, 16): (5, 1, 16, 128, 1, 2),
+        (4096, 4096, 65536, 32, 32): (5, 1, 32, 128, 1, 4),
+        (4096, 4096, 65536, 64, 64): (1, 1, 64, 64, 3, 4),
+        (4096, 4096, 65536, 128, 128): (3, 16, 128, 64, 2, 4),
+        (4096, 4096, 131072, 16, 16): (3, 1, 16, 128, 1, 2),
+        (4096, 4096, 131072, 32, 32): (3, 1, 32, 128, 3, 2),
+        (4096, 4096, 131072, 64, 64): (2, 1, 64, 64, 3, 4),
+        (4096, 4096, 131072, 128, 128): (1, 1, 128, 64, 1, 4),
+        (8192, 8192, 256, 16, 16): (4, 16, 16, 16, 1, 4),
+        (8192, 8192, 256, 32, 32): (1, 16, 32, 16, 4, 4),
+        (8192, 8192, 256, 64, 64): (4, 16, 64, 16, 3, 8),
+        (8192, 8192, 256, 128, 128): (4, 16, 128, 16, 1, 2),
+        (8192, 8192, 512, 16, 16): (2, 8, 16, 64, 1, 4),
+        (8192, 8192, 512, 32, 32): (4, 8, 32, 64, 3, 2),
+        (8192, 8192, 512, 64, 64): (2, 8, 64, 64, 4, 4),
+        (8192, 8192, 512, 128, 128): (4, 8, 128, 64, 2, 4),
+        (8192, 8192, 1024, 16, 16): (4, 16, 16, 64, 1, 8),
+        (8192, 8192, 1024, 32, 32): (2, 8, 32, 64, 5, 2),
+        (8192, 8192, 1024, 64, 64): (1, 16, 64, 64, 3, 2),
+        (8192, 8192, 1024, 128, 128): (5, 16, 128, 64, 2, 4),
+        (8192, 8192, 2048, 16, 16): (7, 2, 16, 128, 1, 2),
+        (8192, 8192, 2048, 32, 32): (1, 16, 32, 64, 5, 2),
+        (8192, 8192, 2048, 64, 64): (4, 16, 64, 64, 3, 2),
+        (8192, 8192, 2048, 128, 128): (6, 16, 128, 64, 2, 4),
+        (8192, 8192, 4096, 16, 16): (4, 2, 16, 128, 1, 2),
+        (8192, 8192, 4096, 32, 32): (2, 8, 32, 64, 5, 2),
+        (8192, 8192, 4096, 64, 64): (3, 16, 64, 64, 3, 2),
+        (8192, 8192, 4096, 128, 128): (3, 64, 128, 64, 2, 4),
+        (8192, 8192, 8192, 16, 16): (4, 2, 16, 128, 1, 2),
+        (8192, 8192, 8192, 32, 32): (1, 4, 32, 128, 5, 4),
+        (8192, 8192, 8192, 64, 64): (4, 4, 64, 64, 1, 4),
+        (8192, 8192, 8192, 128, 128): (2, 2, 128, 128, 3, 8),
+        (8192, 8192, 16384, 16, 16): (1, 2, 16, 128, 1, 2),
+        (8192, 8192, 16384, 32, 32): (4, 8, 32, 64, 5, 2),
+        (8192, 8192, 16384, 64, 64): (5, 8, 64, 64, 3, 2),
+        (8192, 8192, 16384, 128, 128): (3, 16, 128, 64, 2, 4),
+        (8192, 8192, 32768, 16, 16): (7, 2, 16, 128, 1, 2),
+        (8192, 8192, 32768, 32, 32): (3, 4, 32, 64, 3, 2),
+        (8192, 8192, 32768, 64, 64): (2, 8, 64, 64, 3, 2),
+        (8192, 8192, 32768, 128, 128): (6, 16, 128, 64, 2, 4),
+        (8192, 8192, 65536, 16, 16): (9, 2, 16, 128, 1, 2),
+        (8192, 8192, 65536, 32, 32): (7, 4, 32, 64, 5, 2),
+        (8192, 8192, 65536, 64, 64): (4, 8, 64, 64, 3, 2),
+        (8192, 8192, 65536, 128, 128): (3, 16, 128, 64, 2, 4),
+        (8192, 8192, 131072, 16, 16): (9, 2, 16, 128, 1, 2),
+        (8192, 8192, 131072, 32, 32): (1, 8, 32, 64, 5, 2),
+        (8192, 8192, 131072, 64, 64): (1, 8, 64, 64, 3, 2),
+        (8192, 8192, 131072, 128, 128): (4, 16, 128, 64, 2, 4),
+        (16384, 16384, 256, 16, 16): (5, 16, 16, 16, 1, 4),
+        (16384, 16384, 256, 32, 32): (4, 16, 32, 16, 4, 4),
+        (16384, 16384, 256, 64, 64): (4, 16, 64, 16, 3, 8),
+        (16384, 16384, 256, 128, 128): (4, 16, 128, 16, 1, 2),
+        (16384, 16384, 512, 16, 16): (2, 8, 16, 64, 1, 4),
+        (16384, 16384, 512, 32, 32): (1, 4, 32, 64, 5, 2),
+        (16384, 16384, 512, 64, 64): (4, 8, 64, 64, 1, 4),
+        (16384, 16384, 512, 128, 128): (3, 8, 128, 64, 2, 4),
+        (16384, 16384, 1024, 16, 16): (4, 2, 16, 128, 1, 2),
+        (16384, 16384, 1024, 32, 32): (4, 8, 32, 64, 5, 2),
+        (16384, 16384, 1024, 64, 64): (6, 16, 64, 64, 3, 2),
+        (16384, 16384, 1024, 128, 128): (3, 16, 128, 64, 2, 4),
+        (16384, 16384, 2048, 16, 16): (3, 2, 16, 128, 1, 2),
+        (16384, 16384, 2048, 32, 32): (1, 8, 32, 64, 5, 2),
+        (16384, 16384, 2048, 64, 64): (5, 16, 64, 64, 3, 2),
+        (16384, 16384, 2048, 128, 128): (2, 32, 128, 64, 2, 4),
+        (16384, 16384, 4096, 16, 16): (2, 2, 16, 128, 1, 2),
+        (16384, 16384, 4096, 32, 32): (1, 4, 32, 64, 3, 2),
+        (16384, 16384, 4096, 64, 64): (2, 8, 64, 64, 3, 2),
+        (16384, 16384, 4096, 128, 128): (3, 16, 128, 64, 2, 4),
+        (16384, 16384, 8192, 16, 16): (3, 2, 16, 128, 1, 2),
+        (16384, 16384, 8192, 32, 32): (2, 4, 32, 64, 5, 2),
+        (16384, 16384, 8192, 64, 64): (4, 8, 64, 64, 3, 2),
+        (16384, 16384, 8192, 128, 128): (8, 32, 128, 64, 2, 4),
+        (16384, 16384, 16384, 16, 16): (1, 2, 16, 256, 1, 4),
+        (16384, 16384, 16384, 32, 32): (1, 4, 32, 128, 3, 4),
+        (16384, 16384, 16384, 64, 64): (5, 4, 64, 64, 1, 4),
+        (16384, 16384, 16384, 128, 128): (4, 8, 128, 64, 2, 4),
+        (16384, 16384, 32768, 16, 16): (2, 2, 16, 128, 1, 2),
+        (16384, 16384, 32768, 32, 32): (1, 4, 32, 64, 3, 2),
+        (16384, 16384, 32768, 64, 64): (5, 4, 64, 64, 1, 4),
+        (16384, 16384, 32768, 128, 128): (5, 8, 128, 64, 2, 4),
+        (16384, 16384, 65536, 16, 16): (8, 2, 16, 128, 1, 2),
+        (16384, 16384, 65536, 32, 32): (6, 4, 32, 64, 5, 2),
+        (16384, 16384, 65536, 64, 64): (2, 4, 64, 64, 1, 4),
+        (16384, 16384, 65536, 128, 128): (4, 8, 128, 64, 2, 4),
+        (16384, 16384, 131072, 16, 16): (3, 1, 16, 128, 1, 2),
+        (16384, 16384, 131072, 32, 32): (1, 4, 32, 64, 3, 2),
+        (16384, 16384, 131072, 64, 64): (4, 4, 64, 64, 1, 4),
+        (16384, 16384, 131072, 128, 128): (1, 8, 128, 64, 2, 4),
+        (32768, 32768, 256, 16, 16): (4, 16, 16, 16, 1, 4),
+        (32768, 32768, 512, 16, 16): (4, 2, 16, 128, 1, 2),
+        (32768, 32768, 1024, 16, 16): (3, 2, 16, 128, 1, 2),
+        (32768, 32768, 2048, 16, 16): (4, 2, 16, 128, 1, 2),
+        (32768, 32768, 4096, 16, 16): (5, 4, 16, 64, 1, 1),
+        (32768, 32768, 8192, 16, 16): (4, 4, 16, 64, 1, 1),
+        (32768, 32768, 16384, 16, 16): (4, 4, 16, 64, 1, 1),
+        (32768, 32768, 32768, 16, 16): (5, 4, 16, 64, 1, 1),
+    },
+    ("scatter_mm", "NVIDIA A100-SXM4-80GB", (0, torch.float32, 0.5)): {
+        (256, 256, 256, 16, 16): (1, 1, 16, 16, 1, 8),
+        (256, 256, 256, 32, 32): (1, 1, 16, 16, 1, 4),
+        (256, 256, 256, 64, 64): (1, 1, 16, 16, 1, 4),
+        (256, 256, 256, 128, 128): (1, 1, 16, 16, 1, 1),
+        (256, 256, 512, 16, 16): (1, 1, 16, 16, 1, 4),
+        (256, 256, 512, 32, 32): (1, 16, 16, 16, 1, 1),
+        (256, 256, 512, 64, 64): (1, 1, 16, 16, 1, 1),
+        (256, 256, 512, 128, 128): (1, 1, 32, 32, 1, 4),
+        (256, 256, 1024, 16, 16): (1, 1, 16, 32, 1, 2),
+        (256, 256, 1024, 32, 32): (1, 4, 16, 16, 1, 1),
+        (256, 256, 1024, 64, 64): (1, 1, 32, 32, 1, 4),
+        (256, 256, 1024, 128, 128): (1, 1, 32, 32, 1, 4),
+        (256, 256, 2048, 16, 16): (1, 2, 16, 32, 1, 2),
+        (256, 256, 2048, 32, 32): (1, 1, 16, 32, 1, 2),
+        (256, 256, 2048, 64, 64): (2, 1, 16, 32, 1, 2),
+        (256, 256, 2048, 128, 128): (1, 1, 16, 16, 1, 1),
+        (256, 256, 4096, 16, 16): (1, 1, 16, 32, 1, 2),
+        (256, 256, 4096, 32, 32): (1, 1, 16, 32, 1, 2),
+        (256, 256, 4096, 64, 64): (1, 1, 32, 32, 1, 4),
+        (256, 256, 4096, 128, 128): (3, 1, 32, 64, 1, 4),
+        (256, 256, 8192, 16, 16): (1, 32, 16, 64, 1, 2),
+        (256, 256, 8192, 32, 32): (1, 1, 32, 64, 1, 4),
+        (256, 256, 8192, 64, 64): (1, 1, 32, 64, 1, 4),
+        (256, 256, 8192, 128, 128): (2, 1, 64, 32, 1, 4),
+        (256, 256, 16384, 16, 16): (1, 1, 16, 64, 1, 2),
+        (256, 256, 16384, 32, 32): (1, 1, 32, 64, 1, 4),
+        (256, 256, 16384, 64, 64): (1, 128, 64, 64, 1, 4),
+        (256, 256, 16384, 128, 128): (2, 1, 64, 32, 1, 4),
+        (256, 256, 32768, 16, 16): (2, 128, 16, 64, 1, 1),
+        (256, 256, 32768, 32, 32): (1, 1, 32, 64, 1, 4),
+        (256, 256, 32768, 64, 64): (1, 128, 64, 64, 1, 4),
+        (256, 256, 32768, 128, 128): (2, 1, 64, 64, 1, 4),
+        (256, 256, 65536, 16, 16): (1, 1, 16, 64, 1, 2),
+        (256, 256, 65536, 32, 32): (1, 1, 32, 64, 1, 4),
+        (256, 256, 65536, 64, 64): (2, 1, 64, 64, 1, 4),
+        (256, 256, 65536, 128, 128): (1, 1, 128, 32, 1, 4),
+        (256, 256, 131072, 16, 16): (3, 128, 16, 64, 1, 1),
+        (256, 256, 131072, 32, 32): (1, 1, 32, 64, 1, 4),
+        (256, 256, 131072, 64, 64): (2, 1, 64, 64, 1, 4),
+        (256, 256, 131072, 128, 128): (1, 8192, 64, 16, 1, 4),
+        (512, 512, 256, 16, 16): (1, 2, 16, 16, 1, 1),
+        (512, 512, 256, 32, 32): (1, 4, 16, 16, 1, 1),
+        (512, 512, 256, 64, 64): (1, 16, 16, 16, 1, 1),
+        (512, 512, 256, 128, 128): (1, 1, 16, 32, 1, 4),
+        (512, 512, 512, 16, 16): (1, 8, 16, 32, 1, 2),
+        (512, 512, 512, 32, 32): (1, 8, 16, 32, 1, 2),
+        (512, 512, 512, 64, 64): (1, 2, 16, 32, 1, 2),
+        (512, 512, 512, 128, 128): (1, 1, 32, 32, 1, 4),
+        (512, 512, 1024, 16, 16): (1, 1, 16, 32, 1, 2),
+        (512, 512, 1024, 32, 32): (1, 1, 16, 32, 1, 2),
+        (512, 512, 1024, 64, 64): (1, 1, 16, 32, 1, 2),
+        (512, 512, 1024, 128, 128): (1, 1, 64, 32, 1, 4),
+        (512, 512, 2048, 16, 16): (1, 16, 16, 64, 1, 2),
+        (512, 512, 2048, 32, 32): (1, 1, 32, 32, 1, 4),
+        (512, 512, 2048, 64, 64): (1, 1, 32, 32, 1, 4),
+        (512, 512, 2048, 128, 128): (2, 1, 32, 32, 1, 4),
+        (512, 512, 4096, 16, 16): (2, 64, 16, 64, 1, 1),
+        (512, 512, 4096, 32, 32): (1, 64, 32, 64, 1, 4),
+        (512, 512, 4096, 64, 64): (1, 1, 32, 32, 1, 4),
+        (512, 512, 4096, 128, 128): (1, 1, 64, 32, 1, 4),
+        (512, 512, 8192, 16, 16): (2, 64, 16, 64, 1, 1),
+        (512, 512, 8192, 32, 32): (1, 256, 32, 32, 1, 1),
+        (512, 512, 8192, 64, 64): (1, 64, 64, 64, 1, 4),
+        (512, 512, 8192, 128, 128): (2, 1, 64, 32, 1, 8),
+        (512, 512, 16384, 16, 16): (2, 64, 16, 64, 1, 1),
+        (512, 512, 16384, 32, 32): (1, 128, 32, 32, 1, 1),
+        (512, 512, 16384, 64, 64): (1, 64, 64, 64, 1, 4),
+        (512, 512, 16384, 128, 128): (3, 1, 64, 32, 1, 8),
+        (512, 512, 32768, 16, 16): (2, 64, 16, 64, 1, 1),
+        (512, 512, 32768, 32, 32): (1, 128, 32, 32, 1, 1),
+        (512, 512, 32768, 64, 64): (1, 64, 64, 64, 1, 4),
+        (512, 512, 32768, 128, 128): (2, 1, 64, 32, 1, 8),
+        (512, 512, 65536, 16, 16): (2, 32, 16, 64, 1, 1),
+        (512, 512, 65536, 32, 32): (1, 128, 32, 32, 1, 1),
+        (512, 512, 65536, 64, 64): (1, 64, 64, 64, 1, 4),
+        (512, 512, 65536, 128, 128): (2, 1, 64, 32, 1, 8),
+        (512, 512, 131072, 16, 16): (2, 32, 16, 64, 1, 1),
+        (512, 512, 131072, 32, 32): (1, 128, 32, 32, 1, 1),
+        (512, 512, 131072, 64, 64): (3, 64, 64, 64, 1, 4),
+        (512, 512, 131072, 128, 128): (1, 8192, 64, 16, 1, 4),
+        (1024, 1024, 256, 16, 16): (1, 4, 16, 32, 1, 2),
+        (1024, 1024, 256, 32, 32): (1, 4, 16, 32, 1, 2),
+        (1024, 1024, 256, 64, 64): (1, 1, 16, 32, 1, 2),
+        (1024, 1024, 256, 128, 128): (1, 1, 16, 16, 1, 1),
+        (1024, 1024, 512, 16, 16): (1, 8, 16, 32, 1, 2),
+        (1024, 1024, 512, 32, 32): (1, 8, 16, 32, 1, 1),
+        (1024, 1024, 512, 64, 64): (1, 8, 32, 32, 1, 4),
+        (1024, 1024, 512, 128, 128): (2, 1, 32, 32, 1, 4),
+        (1024, 1024, 1024, 16, 16): (1, 16, 16, 32, 1, 2),
+        (1024, 1024, 1024, 32, 32): (1, 16, 32, 64, 1, 4),
+        (1024, 1024, 1024, 64, 64): (1, 16, 32, 64, 1, 4),
+        (1024, 1024, 1024, 128, 128): (1, 1, 32, 32, 1, 4),
+        (1024, 1024, 2048, 16, 16): (2, 32, 16, 64, 1, 1),
+        (1024, 1024, 2048, 32, 32): (1, 32, 32, 64, 1, 4),
+        (1024, 1024, 2048, 64, 64): (1, 32, 64, 64, 1, 4),
+        (1024, 1024, 2048, 128, 128): (1, 1, 32, 64, 1, 4),
+        (1024, 1024, 4096, 16, 16): (2, 16, 16, 64, 1, 1),
+        (1024, 1024, 4096, 32, 32): (1, 64, 32, 32, 1, 1),
+        (1024, 1024, 4096, 64, 64): (1, 64, 64, 64, 1, 4),
+        (1024, 1024, 4096, 128, 128): (2, 64, 64, 32, 1, 8),
+        (1024, 1024, 8192, 16, 16): (2, 16, 16, 64, 1, 1),
+        (1024, 1024, 8192, 32, 32): (1, 64, 32, 32, 1, 1),
+        (1024, 1024, 8192, 64, 64): (1, 64, 64, 64, 1, 4),
+        (1024, 1024, 8192, 128, 128): (4, 1, 32, 64, 1, 4),
+        (1024, 1024, 16384, 16, 16): (2, 16, 16, 64, 1, 1),
+        (1024, 1024, 16384, 32, 32): (1, 64, 32, 32, 1, 1),
+        (1024, 1024, 16384, 64, 64): (1, 32, 64, 64, 1, 4),
+        (1024, 1024, 16384, 128, 128): (2, 64, 64, 32, 1, 4),
+        (1024, 1024, 32768, 16, 16): (2, 16, 16, 64, 1, 1),
+        (1024, 1024, 32768, 32, 32): (1, 64, 32, 32, 1, 1),
+        (1024, 1024, 32768, 64, 64): (1, 32, 64, 64, 1, 4),
+        (1024, 1024, 32768, 128, 128): (4, 1, 32, 64, 1, 4),
+        (1024, 1024, 65536, 16, 16): (2, 16, 16, 64, 1, 1),
+        (1024, 1024, 65536, 32, 32): (1, 32, 32, 32, 1, 1),
+        (1024, 1024, 65536, 64, 64): (2, 32, 64, 64, 1, 4),
+        (1024, 1024, 65536, 128, 128): (4, 1, 64, 32, 1, 4),
+        (1024, 1024, 131072, 16, 16): (2, 16, 16, 64, 1, 1),
+        (1024, 1024, 131072, 32, 32): (1, 32, 32, 32, 1, 1),
+        (1024, 1024, 131072, 64, 64): (1, 16, 64, 64, 1, 4),
+        (1024, 1024, 131072, 128, 128): (1, 8192, 64, 16, 1, 4),
+        (2048, 2048, 256, 16, 16): (1, 4, 16, 32, 1, 2),
+        (2048, 2048, 256, 32, 32): (1, 8, 16, 32, 1, 1),
+        (2048, 2048, 256, 64, 64): (1, 8, 32, 32, 1, 4),
+        (2048, 2048, 256, 128, 128): (1, 4, 64, 64, 1, 8),
+        (2048, 2048, 512, 16, 16): (2, 8, 16, 32, 1, 2),
+        (2048, 2048, 512, 32, 32): (2, 8, 32, 64, 1, 4),
+        (2048, 2048, 512, 64, 64): (2, 4, 64, 64, 1, 4),
+        (2048, 2048, 512, 128, 128): (1, 8, 32, 64, 1, 4),
+        (2048, 2048, 1024, 16, 16): (2, 16, 16, 64, 3, 1),
+        (2048, 2048, 1024, 32, 32): (1, 32, 32, 32, 1, 1),
+        (2048, 2048, 1024, 64, 64): (1, 16, 64, 64, 1, 4),
+        (2048, 2048, 1024, 128, 128): (2, 4, 64, 64, 1, 8),
+        (2048, 2048, 2048, 16, 16): (2, 16, 16, 64, 1, 1),
+        (2048, 2048, 2048, 32, 32): (1, 32, 32, 32, 1, 1),
+        (2048, 2048, 2048, 64, 64): (1, 16, 64, 64, 1, 4),
+        (2048, 2048, 2048, 128, 128): (2, 32, 32, 64, 1, 4),
+        (2048, 2048, 4096, 16, 16): (3, 2, 16, 64, 1, 1),
+        (2048, 2048, 4096, 32, 32): (3, 4, 32, 32, 1, 1),
+        (2048, 2048, 4096, 64, 64): (1, 16, 64, 64, 1, 4),
+        (2048, 2048, 4096, 128, 128): (2, 32, 64, 32, 1, 4),
+        (2048, 2048, 8192, 16, 16): (3, 4, 16, 64, 1, 1),
+        (2048, 2048, 8192, 32, 32): (2, 4, 32, 32, 1, 1),
+        (2048, 2048, 8192, 64, 64): (2, 32, 64, 32, 1, 2),
+        (2048, 2048, 8192, 128, 128): (4, 1, 32, 64, 1, 4),
+        (2048, 2048, 16384, 16, 16): (3, 4, 16, 64, 1, 1),
+        (2048, 2048, 16384, 32, 32): (1, 4, 32, 32, 1, 1),
+        (2048, 2048, 16384, 64, 64): (2, 8, 64, 32, 1, 2),
+        (2048, 2048, 16384, 128, 128): (2, 8, 64, 32, 1, 4),
+        (2048, 2048, 32768, 16, 16): (2, 4, 16, 64, 1, 1),
+        (2048, 2048, 32768, 32, 32): (2, 8, 32, 32, 1, 1),
+        (2048, 2048, 32768, 64, 64): (1, 16, 64, 32, 1, 2),
+        (2048, 2048, 32768, 128, 128): (4, 1, 32, 64, 1, 4),
+        (2048, 2048, 65536, 16, 16): (3, 4, 16, 64, 1, 1),
+        (2048, 2048, 65536, 32, 32): (1, 8, 32, 32, 1, 1),
+        (2048, 2048, 65536, 64, 64): (1, 8, 64, 32, 1, 2),
+        (2048, 2048, 65536, 128, 128): (4, 1, 64, 32, 1, 4),
+        (2048, 2048, 131072, 16, 16): (2, 4, 16, 64, 1, 1),
+        (2048, 2048, 131072, 32, 32): (1, 8, 32, 32, 1, 1),
+        (2048, 2048, 131072, 64, 64): (3, 1, 64, 32, 1, 2),
+        (2048, 2048, 131072, 128, 128): (1, 8192, 128, 16, 1, 8),
+        (4096, 4096, 256, 16, 16): (2, 4, 16, 32, 1, 2),
+        (4096, 4096, 256, 32, 32): (1, 4, 32, 64, 1, 4),
+        (4096, 4096, 256, 64, 64): (1, 4, 64, 64, 1, 4),
+        (4096, 4096, 256, 128, 128): (1, 4, 32, 64, 1, 4),
+        (4096, 4096, 512, 16, 16): (2, 8, 16, 64, 3, 1),
+        (4096, 4096, 512, 32, 32): (2, 16, 32, 32, 1, 1),
+        (4096, 4096, 512, 64, 64): (1, 8, 64, 64, 1, 4),
+        (4096, 4096, 512, 128, 128): (1, 8, 32, 64, 1, 4),
+        (4096, 4096, 1024, 16, 16): (1, 8, 16, 64, 3, 1),
+        (4096, 4096, 1024, 32, 32): (1, 16, 32, 32, 1, 1),
+        (4096, 4096, 1024, 64, 64): (1, 16, 64, 32, 1, 2),
+        (4096, 4096, 1024, 128, 128): (1, 16, 32, 64, 1, 4),
+        (4096, 4096, 2048, 16, 16): (1, 16, 16, 64, 3, 1),
+        (4096, 4096, 2048, 32, 32): (1, 16, 32, 32, 1, 1),
+        (4096, 4096, 2048, 64, 64): (3, 16, 64, 32, 1, 2),
+        (4096, 4096, 2048, 128, 128): (4, 8, 32, 64, 1, 4),
+        (4096, 4096, 4096, 16, 16): (1, 8, 16, 64, 3, 1),
+        (4096, 4096, 4096, 32, 32): (1, 1, 32, 32, 1, 1),
+        (4096, 4096, 4096, 64, 64): (2, 16, 64, 32, 1, 2),
+        (4096, 4096, 4096, 128, 128): (4, 8, 32, 64, 1, 4),
+        (4096, 4096, 8192, 16, 16): (1, 8, 16, 64, 3, 1),
+        (4096, 4096, 8192, 32, 32): (2, 1, 32, 32, 1, 1),
+        (4096, 4096, 8192, 64, 64): (1, 16, 64, 32, 1, 2),
+        (4096, 4096, 8192, 128, 128): (2, 1, 32, 64, 1, 4),
+        (4096, 4096, 16384, 16, 16): (1, 8, 16, 64, 3, 1),
+        (4096, 4096, 16384, 32, 32): (1, 1, 32, 32, 1, 1),
+        (4096, 4096, 16384, 64, 64): (2, 8, 64, 32, 1, 2),
+        (4096, 4096, 16384, 128, 128): (2, 1, 32, 64, 1, 4),
+        (4096, 4096, 32768, 16, 16): (1, 8, 16, 64, 3, 1),
+        (4096, 4096, 32768, 32, 32): (1, 1, 32, 32, 1, 1),
+        (4096, 4096, 32768, 64, 64): (1, 8, 64, 32, 1, 2),
+        (4096, 4096, 32768, 128, 128): (2, 1, 32, 64, 1, 4),
+        (4096, 4096, 65536, 16, 16): (1, 8, 16, 64, 3, 1),
+        (4096, 4096, 65536, 32, 32): (3, 1, 32, 32, 1, 1),
+        (4096, 4096, 65536, 64, 64): (3, 4, 64, 32, 1, 2),
+        (4096, 4096, 65536, 128, 128): (2, 1, 32, 64, 1, 4),
+        (4096, 4096, 131072, 16, 16): (1, 8, 16, 64, 3, 1),
+        (4096, 4096, 131072, 32, 32): (1, 1, 32, 32, 1, 1),
+        (4096, 4096, 131072, 64, 64): (2, 8, 64, 32, 1, 2),
+        (4096, 4096, 131072, 128, 128): (1, 8192, 128, 16, 1, 8),
+        (8192, 8192, 256, 16, 16): (2, 4, 16, 64, 3, 1),
+        (8192, 8192, 256, 32, 32): (1, 8, 32, 32, 1, 1),
+        (8192, 8192, 256, 64, 64): (1, 4, 64, 64, 1, 4),
+        (8192, 8192, 256, 128, 128): (1, 4, 32, 64, 1, 4),
+        (8192, 8192, 512, 16, 16): (1, 4, 16, 64, 3, 1),
+        (8192, 8192, 512, 32, 32): (1, 16, 32, 32, 1, 1),
+        (8192, 8192, 512, 64, 64): (2, 4, 64, 64, 1, 4),
+        (8192, 8192, 512, 128, 128): (2, 1, 32, 64, 1, 4),
+        (8192, 8192, 1024, 16, 16): (3, 8, 16, 64, 3, 1),
+        (8192, 8192, 1024, 32, 32): (1, 16, 32, 32, 1, 1),
+        (8192, 8192, 1024, 64, 64): (1, 8, 64, 32, 1, 2),
+        (8192, 8192, 1024, 128, 128): (2, 4, 32, 64, 1, 4),
+        (8192, 8192, 2048, 16, 16): (1, 8, 16, 64, 3, 1),
+        (8192, 8192, 2048, 32, 32): (1, 16, 32, 32, 1, 1),
+        (8192, 8192, 2048, 64, 64): (2, 8, 64, 32, 1, 2),
+        (8192, 8192, 2048, 128, 128): (4, 1, 32, 64, 1, 4),
+        (8192, 8192, 4096, 16, 16): (1, 8, 16, 64, 3, 1),
+        (8192, 8192, 4096, 32, 32): (1, 16, 32, 32, 1, 1),
+        (8192, 8192, 4096, 64, 64): (1, 4, 64, 32, 1, 2),
+        (8192, 8192, 4096, 128, 128): (3, 1, 32, 64, 1, 4),
+        (8192, 8192, 8192, 16, 16): (1, 8, 16, 64, 3, 1),
+        (8192, 8192, 8192, 32, 32): (1, 8, 32, 32, 1, 1),
+        (8192, 8192, 8192, 64, 64): (1, 8, 64, 32, 1, 2),
+        (8192, 8192, 8192, 128, 128): (4, 1, 32, 64, 1, 4),
+        (8192, 8192, 16384, 16, 16): (3, 4, 16, 64, 3, 1),
+        (8192, 8192, 16384, 32, 32): (1, 8, 32, 32, 1, 1),
+        (8192, 8192, 16384, 64, 64): (2, 2, 64, 32, 1, 2),
+        (8192, 8192, 16384, 128, 128): (7, 1, 32, 64, 1, 4),
+        (8192, 8192, 32768, 16, 16): (1, 4, 16, 64, 3, 1),
+        (8192, 8192, 32768, 32, 32): (1, 8, 32, 32, 1, 1),
+        (8192, 8192, 32768, 64, 64): (3, 2, 64, 32, 1, 2),
+        (8192, 8192, 32768, 128, 128): (6, 1, 32, 64, 1, 4),
+        (8192, 8192, 65536, 16, 16): (1, 4, 16, 64, 3, 1),
+        (8192, 8192, 65536, 32, 32): (4, 8, 32, 32, 1, 1),
+        (8192, 8192, 65536, 64, 64): (1, 2, 64, 32, 1, 2),
+        (8192, 8192, 65536, 128, 128): (4, 1, 32, 64, 1, 4),
+        (8192, 8192, 131072, 16, 16): (1, 4, 16, 64, 3, 1),
+        (8192, 8192, 131072, 32, 32): (1, 8, 32, 32, 1, 1),
+        (8192, 8192, 131072, 64, 64): (5, 4, 64, 32, 1, 2),
+        (8192, 8192, 131072, 128, 128): (1, 4096, 128, 16, 1, 8),
+        (16384, 16384, 256, 16, 16): (1, 4, 16, 64, 3, 1),
+        (16384, 16384, 256, 32, 32): (1, 8, 32, 32, 1, 1),
+        (16384, 16384, 256, 64, 64): (1, 4, 64, 32, 1, 2),
+        (16384, 16384, 256, 128, 128): (1, 4, 32, 64, 1, 4),
+        (16384, 16384, 512, 16, 16): (1, 8, 16, 64, 3, 1),
+        (16384, 16384, 512, 32, 32): (1, 16, 32, 32, 1, 1),
+        (16384, 16384, 512, 64, 64): (1, 4, 64, 32, 1, 2),
+        (16384, 16384, 512, 128, 128): (3, 1, 32, 64, 1, 4),
+        (16384, 16384, 1024, 16, 16): (1, 8, 16, 64, 3, 1),
+        (16384, 16384, 1024, 32, 32): (1, 16, 32, 32, 1, 1),
+        (16384, 16384, 1024, 64, 64): (2, 4, 64, 32, 1, 2),
+        (16384, 16384, 1024, 128, 128): (1, 2, 32, 64, 1, 4),
+        (16384, 16384, 2048, 16, 16): (1, 4, 16, 64, 3, 1),
+        (16384, 16384, 2048, 32, 32): (1, 16, 32, 32, 1, 1),
+        (16384, 16384, 2048, 64, 64): (3, 4, 64, 32, 1, 2),
+        (16384, 16384, 2048, 128, 128): (2, 1, 32, 64, 1, 4),
+        (16384, 16384, 4096, 16, 16): (4, 8, 16, 64, 3, 1),
+        (16384, 16384, 4096, 32, 32): (5, 16, 32, 32, 1, 1),
+        (16384, 16384, 4096, 64, 64): (3, 2, 64, 32, 1, 2),
+        (16384, 16384, 4096, 128, 128): (2, 1, 32, 64, 1, 4),
+        (16384, 16384, 8192, 16, 16): (1, 4, 16, 64, 3, 1),
+        (16384, 16384, 8192, 32, 32): (1, 4, 32, 32, 1, 1),
+        (16384, 16384, 8192, 64, 64): (1, 2, 64, 32, 1, 2),
+        (16384, 16384, 8192, 128, 128): (2, 1, 32, 64, 1, 4),
+        (16384, 16384, 16384, 16, 16): (1, 8, 16, 64, 3, 1),
+        (16384, 16384, 16384, 32, 32): (1, 4, 32, 32, 1, 1),
+        (16384, 16384, 16384, 64, 64): (1, 2, 64, 32, 1, 2),
+        (16384, 16384, 16384, 128, 128): (3, 1, 32, 64, 1, 4),
+        (16384, 16384, 32768, 16, 16): (1, 4, 16, 64, 3, 1),
+        (16384, 16384, 32768, 32, 32): (1, 2, 32, 32, 1, 1),
+        (16384, 16384, 32768, 64, 64): (3, 2, 64, 32, 1, 2),
+        (16384, 16384, 32768, 128, 128): (3, 1, 32, 64, 1, 4),
+        (16384, 16384, 65536, 16, 16): (1, 8, 16, 64, 3, 1),
+        (16384, 16384, 65536, 32, 32): (1, 4, 32, 32, 1, 1),
+        (16384, 16384, 65536, 64, 64): (4, 4, 64, 32, 1, 2),
+        (16384, 16384, 65536, 128, 128): (5, 1, 32, 64, 1, 4),
+        (16384, 16384, 131072, 16, 16): (1, 2, 16, 64, 3, 1),
+        (16384, 16384, 131072, 32, 32): (1, 4, 32, 32, 1, 1),
+        (16384, 16384, 131072, 64, 64): (1, 2, 64, 32, 1, 2),
+        (16384, 16384, 131072, 128, 128): (1, 4096, 128, 16, 1, 8),
+    },
+    # END GENERATED DATA
+}
+
+if __name__ == "__main__":
+    for dtype in [torch.int8]:
+        for op in ["_int_bsr_dense_addmm"]:
+            main(op=op, force=False, dtype=dtype)
+    for dtype in [torch.float16, torch.bfloat16, torch.float32]:
+        for op in ["bsr_dense_addmm"]:
+            main(op=op, force=False, dtype=dtype)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/sparse/semi_structured.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/sparse/semi_structured.py
new file mode 100644
index 0000000000000000000000000000000000000000..0017a10e6771bb96f6c808ec9ece2e40dead4671
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/sparse/semi_structured.py
@@ -0,0 +1,648 @@
+# mypy: allow-untyped-defs
+import warnings
+from collections import namedtuple
+from typing import Any, Callable, Dict, List, Optional, Tuple
+
+import torch
+from torch.sparse._semi_structured_conversions import (
+    sparse_semi_structured_from_dense_cutlass,
+    sparse_semi_structured_to_dense_cutlass,
+)
+from torch.sparse._semi_structured_ops import (
+    fallback_dispatcher,
+    semi_sparse_addmm,
+    semi_sparse_detach,
+    semi_sparse_indices,
+    semi_sparse_linear,
+    semi_sparse_mm,
+    semi_sparse_t,
+    semi_sparse_values,
+    semi_sparse_view,
+)
+
+
+__all__ = [
+    "SparseSemiStructuredTensor",
+    "SparseSemiStructuredTensorCUTLASS",
+    "SparseSemiStructuredTensorCUSPARSELT",
+    "to_sparse_semi_structured",
+]
+
+_SEMI_STRUCTURED_SPARSE_CONFIG = namedtuple(
+    "_SEMI_STRUCTURED_SPARSE_CONFIG",
+    "sparse_min_rows sparse_min_cols dense_min_rows dense_min_cols",
+)
+
+
+class SparseSemiStructuredTensor(torch.Tensor):
+    """
+    This class implementes semi-structured sparsity as a Tensor subclass.
+
+    Semi-structured sparsity describes a sparsity pattern where n in every 2n elements are sparse,
+    depending on the datatype. It is also referred to as 2:4 sparsity or fine-grained
+    structured sparsity.
+
+    There are two backends available for semi_structred sparsity, either cuSPARSELt or CUTLASS.
+    This class is meant to serve as a base class for both implementations. SparseSemiStructuredCUTLASS
+    and SparseSemiStructuredCUSPARSELT both inherit from this class and define three backend-specific items.
+    Note that as such, this class cannot be insantiated directly.
+
+    -`_DTYPE_SHAPE_CONSTRAINTS` - A dictionary holding backend specific dense/sparse min shape constraints
+    - `def from_dense()` - backend specific compression routines
+    - `def _mm()` - backend specifc mm op (either torch._cslt_sparse_mm or torch._sparse_semi_structured_(mm|addmm))
+    """
+
+    _DEFAULT_ALG_ID: int = 0
+    _DTYPE_SHAPE_CONSTRAINTS: Dict[torch.dtype, _SEMI_STRUCTURED_SPARSE_CONFIG]
+    _FORCE_CUTLASS: bool = True
+    _FUSE_TRANSPOSE: bool = False
+    _PROTOTYPE_WARNING_SHOWN: bool = False
+
+    BACKEND: str
+    SPARSE_DISPATCH: Dict[Callable, Callable]
+
+    packed: Optional[torch.Tensor]
+    meta: Optional[torch.Tensor]
+    packed_t: Optional[torch.Tensor]
+    meta_t: Optional[torch.Tensor]
+    compressed_swizzled_bitmask: Optional[torch.Tensor]
+    fuse_transpose_cusparselt: bool
+    alg_id_cusparselt: int
+
+    __slots__ = ["packed", "meta", "packed_t", "meta_t", "compressed_swizzled_bitmask"]
+
+    @staticmethod
+    def __new__(  # noqa: PYI034
+        cls,
+        shape: torch.Size,
+        packed: Optional[torch.Tensor],
+        meta: Optional[torch.Tensor],
+        packed_t: Optional[torch.Tensor],
+        meta_t: Optional[torch.Tensor],
+        compressed_swizzled_bitmask: Optional[torch.Tensor],
+        fuse_transpose_cusparselt: bool = False,
+        alg_id_cusparselt: int = 0,
+        requires_grad: bool = False,
+    ):
+        """
+        Create a new instance of the tensor subclass from the compressed sparse representation.
+
+        We have the option to create the subclass with the compressed representations of both X and X', for training.
+        For inference, we only need a single representation (either X or X'), while the corresponding other set will be None.
+
+        Depending on the backend selected, certain fields will be set to None. (CUSPARSELT vs CUTLASS)
+
+        Args:
+            shape: The shape of the original dense tensor
+            packed: The compressed representation of the original dense tensor
+            meta: The metadata of the original dense tensor, if it is stored separately
+            packed_t: The compressed representation of the transposed original dense tensor
+            meta_t: The metadata of the transposed original dense tensor, if it is stored separately
+            compressed_swizzled_bitmask: The masks used by the CUTLASS backend to determine which threads should
+                                         participate in the computation. Used for pointwise ops.
+            fuse_transpose_cusparselt: When running with cuSPARSELt, we have the option to fuse a transposition
+                                       with a matmul, which is useful in the case of 2:4 sparse training.
+            alg_id_cusparselt: The algorithm id to use when using cuSPARSELT, will have effect on performance
+
+        Returns:
+            torch.Tensor: A torch.Tensor wrapper subclass.
+
+        Raises:
+            ValueError: If all of the tensor arguments are None.
+        """
+        if not cls._PROTOTYPE_WARNING_SHOWN:
+            warnings.warn(
+                (
+                    "The PyTorch API of SparseSemiStructuredTensor is in prototype stage "
+                    "and will change in the near future. Please open a Github issue "
+                    "for features requests and see our documentation on the torch.sparse "
+                    "module for further information about the project."
+                ),
+                UserWarning,
+            )
+            cls._PROTOTYPE_WARNING_SHOWN = True
+
+            # Because this only runs onces, we also load the dispatch table here as well.
+            # We can't define the dispatch table explicitly because of torch.ops import errors, so we do this instead
+            # But this is useful since it allows users to overload the dispatch table for debugging / testing.
+            cls._load_dispatch_table()
+
+            # we can also register the classes with dynamo when the warning is shown.
+            torch._dynamo.allow_in_graph(cls)
+
+        if packed is not None:
+            previous_tensor = packed
+        elif packed_t is not None:
+            previous_tensor = packed_t
+        else:
+            raise ValueError("At least one of packed or packed_t must be provided")
+
+        kwargs = {
+            "device": previous_tensor.device,
+            "dtype": previous_tensor.dtype,
+            "layout": previous_tensor.layout,
+            "requires_grad": requires_grad,
+        }
+        tensor = torch.Tensor._make_wrapper_subclass(cls, shape, **kwargs)  # type: ignore[attr-defined]
+
+        tensor.packed = packed
+        tensor.meta = meta
+        tensor.packed_t = packed_t
+        tensor.meta_t = meta_t
+        tensor.compressed_swizzled_bitmask = compressed_swizzled_bitmask
+        tensor.fuse_transpose_cusparselt = fuse_transpose_cusparselt
+        tensor.alg_id_cusparselt = alg_id_cusparselt
+        return tensor
+
+    def __repr__(self) -> str:  # type: ignore[override]
+        assert hasattr(self, "shape")
+        return f"{self.__class__.__name__}(shape={self.shape})"
+
+    def __tensor_flatten__(
+        self,
+    ) -> Tuple[List[str], Tuple[torch.Size, bool, int, bool]]:
+        inner_tensors = list(
+            filter(lambda x: getattr(self, x) is not None, self.__slots__)
+        )
+        tensor_meta = (
+            self.shape,
+            self.fuse_transpose_cusparselt,
+            self.alg_id_cusparselt,
+            self.requires_grad,
+        )
+        return inner_tensors, tensor_meta
+
+    @classmethod
+    def __tensor_unflatten__(
+        cls,
+        inner_tensors,
+        tensor_meta: Tuple[torch.Size, bool, int, bool],
+        outer_size,
+        outer_stride,
+    ) -> torch.Tensor:
+        shape, fuse_transpose_cusparselt, alg_id_cusparselt, requires_grad = tensor_meta
+        return cls(
+            shape=shape,
+            packed=inner_tensors.get("packed", None),
+            meta=inner_tensors.get("meta", None),
+            packed_t=inner_tensors.get("packed_t", None),
+            meta_t=inner_tensors.get("meta_t", None),
+            compressed_swizzled_bitmask=inner_tensors.get(
+                "compressed_swizzled_bitmask", None
+            ),
+            fuse_transpose_cusparselt=fuse_transpose_cusparselt,
+            alg_id_cusparselt=alg_id_cusparselt,
+            requires_grad=requires_grad,
+        )
+
+    __torch_function__ = torch._C._disabled_torch_function_impl
+
+    @classmethod
+    def __torch_dispatch__(cls, func, types, args, kwargs) -> Any:
+        if func._overloadpacket not in cls.SPARSE_DISPATCH:
+            raise NotImplementedError(
+                f"{cls.__name__} only supports a specific set of operations, "
+                f"can't perform requested op ({func.__name__})"
+            )
+        return cls.SPARSE_DISPATCH[func._overloadpacket](func, types, args, kwargs)
+
+    @classmethod
+    def _load_dispatch_table(cls, custom_dispatch_table=None) -> None:
+        """
+        Loads the op overload sparse dispatch table for the current class.
+        """
+        if getattr(cls, "SPARSE_DISPATCH", None) is None:
+            cls.SPARSE_DISPATCH = {
+                torch.ops.aten.values: semi_sparse_values,
+                torch.ops.aten.indices: semi_sparse_indices,
+                torch.ops.aten.is_same_size: fallback_dispatcher,
+                torch.ops.aten.detach_: fallback_dispatcher,
+                torch.ops.aten.detach: semi_sparse_detach,
+                torch.ops.aten.t: semi_sparse_t,
+                torch.ops.aten.view: semi_sparse_view,
+                torch.ops.aten.mm: semi_sparse_mm,
+                torch.ops.aten.matmul: semi_sparse_mm,
+                torch.ops.aten.addmm: semi_sparse_addmm,
+                torch.ops.aten.linear: semi_sparse_linear,
+                torch.ops.aten._to_copy: fallback_dispatcher,
+            }
+            if custom_dispatch_table is not None:
+                cls.SPARSE_DISPATCH.update(custom_dispatch_table)
+
+    @classmethod
+    def _validate_device_dim_dtype_shape(cls, original_tensor: torch.Tensor) -> None:
+        """
+        Assert that the given tensor is valid for semi-structured sparse compression.
+        """
+        # check device
+        if not original_tensor.is_cuda:
+            raise RuntimeError(
+                f"Error original_tensor.device= {original_tensor.device} is not supported! "
+                "Only CUDA tensors are currently supported."
+            )
+
+        # check dim
+        if original_tensor.dim() != 2:
+            raise RuntimeError(
+                f"Error original_tensor.dim = {original_tensor.dim()} is not supported! "
+                "Only 2d tensors are currently supported."
+            )
+
+        # check contiguous
+        if not original_tensor.is_contiguous():
+            raise RuntimeError(
+                "Error original_tensor is not contiguous!"
+                "Only contiguous tensors are currently supported."
+            )
+
+        # check dtype
+        if original_tensor.dtype not in cls._DTYPE_SHAPE_CONSTRAINTS:
+            raise RuntimeError(
+                f"Error original_tensor.dtype {original_tensor.dtype} is not a supported dtype! "
+                "dtype must be one of: {cls._DTYPE_SHAPE_CONSTRAINTS}"
+            )
+
+        # check shape
+        m, n = original_tensor.shape
+        min_rows = cls._DTYPE_SHAPE_CONSTRAINTS[original_tensor.dtype].sparse_min_rows
+        min_cols = cls._DTYPE_SHAPE_CONSTRAINTS[original_tensor.dtype].sparse_min_cols
+        if m < min_rows or m % min_rows or n < min_cols or n % min_cols:
+            # TODO in the future we can add in padding to support sparse dimensions that aren't perfect multiples
+            raise RuntimeError(
+                f"Error original_tensor.shape {original_tensor.shape} is not supported! "
+                f"Both dimensions must be larger or equal than and a multiple of ({min_rows}, {min_cols})"
+            )
+
+    @classmethod
+    def _pad_dense_input(cls, dense_input: torch.Tensor) -> torch.Tensor:
+        """
+        Calculates padding for dense tensor and pads tensor if necessary.
+        If padding is not required, this function returns the original tensor.
+        """
+        # only 2d matmul
+        assert dense_input.dim() == 2
+
+        # check shape
+        m, n = dense_input.shape
+        min_rows = cls._DTYPE_SHAPE_CONSTRAINTS[dense_input.dtype].dense_min_rows
+        min_cols = cls._DTYPE_SHAPE_CONSTRAINTS[dense_input.dtype].dense_min_cols
+
+        # calculate padding
+        to_pad_m = -m % min_rows if m < min_rows or m % min_rows else 0
+        to_pad_n = -n % min_cols if n < min_cols or n % min_rows else 0
+        if to_pad_m or to_pad_n:
+            return torch.nn.functional.pad(dense_input, (0, to_pad_n, 0, to_pad_m))
+        else:
+            return dense_input
+
+    def to_dense(self):
+        col = self.shape[-1]
+        return torch.mm(self, torch.eye(col, dtype=self.dtype, device=self.device))
+
+    @classmethod
+    def from_dense(cls, original_tensor: torch.Tensor) -> "SparseSemiStructuredTensor":
+        raise NotImplementedError
+
+    def _mm(
+        self,
+        B: torch.Tensor,
+        *,
+        bias: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        raise NotImplementedError
+
+
+def to_sparse_semi_structured(
+    original_tensor: torch.Tensor,
+    transposed: bool = False,
+) -> SparseSemiStructuredTensor:
+    """
+    This function converts a dense tensor into a sparse semi-structured tensor.
+    It will return a SparseSemiStructuredTensor, a subclass of torch.Tensor.
+
+    This function will check to ensure the dense tensor has the right dtype, size, dims, and device.
+    We currently only support semi-structured sparse tensors for 2d CUDA tensors.
+    Additionally, your tensor must be a positive multiple of the mininum sparse block size, given in
+    `_DTYPE_TO_SHAPE_CONSTRAINTS` for each dtype (float32, float16, bfloat16, int8).
+
+    Args:
+        original_tensor (Tensor): the dense tensor to convert
+        transposed (bool, optional): deprecated arg to be removed in another release. Do not use.
+    Returns:
+        SparseSemiStructuredTensor: A sparse semi-structured tensor created from the given original_tensor
+    Raises:
+        None
+    Example:
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
+        >>> A = torch.Tensor([0, 0, 1, 1]).tile((128, 32)).half().cuda()
+        tensor([[0., 0., 1.,  ..., 0., 1., 1.],
+                [0., 0., 1.,  ..., 0., 1., 1.],
+                [0., 0., 1.,  ..., 0., 1., 1.],
+                ...,
+                [0., 0., 1.,  ..., 0., 1., 1.],
+                [0., 0., 1.,  ..., 0., 1., 1.],
+                [0., 0., 1.,  ..., 0., 1., 1.]], device='cuda:0', dtype=torch.float16)
+        >>> A_sparse = to_sparse_semi_structured(A)
+        SparseSemiStructuredTensor(shape=torch.Size([128, 128]))
+        >>> A_sparse.values()
+        tensor([[1., 1., 1.,  ..., 1., 1., 1.],
+                [1., 1., 1.,  ..., 1., 1., 1.],
+                [1., 1., 1.,  ..., 1., 1., 1.],
+                ...,
+                [1., 1., 1.,  ..., 1., 1., 1.],
+                [1., 1., 1.,  ..., 1., 1., 1.],
+                [1., 1., 1.,  ..., 1., 1., 1.]], device='cuda:0', dtype=torch.float16),
+        >>> A_sparse.indices()
+        tensor([[-4370, -4370, -4370,  ..., -4370, -4370, -4370],
+                [-4370, -4370, -4370,  ..., -4370, -4370, -4370],
+                [-4370, -4370, -4370,  ..., -4370, -4370, -4370],
+                ...,
+                [-4370, -4370, -4370,  ..., -4370, -4370, -4370],
+                [-4370, -4370, -4370,  ..., -4370, -4370, -4370],
+                [-4370, -4370, -4370,  ..., -4370, -4370, -4370]], device='cuda:0', dtype=torch.int16))
+    """
+    if transposed:
+        warnings.warn(
+            "Setting transpose from `to_sparse_semi_structured` is deprecated "
+            "and will be removed in a future release. "
+            "`SparseSemiStructuredTensor` only support contiguous input tensors.",
+            FutureWarning,
+            stacklevel=2,
+        )
+
+    # set from _FORCE_CUTLASS flag
+    SPARSE_SUBCLASS = (
+        torch.sparse.SparseSemiStructuredTensorCUTLASS
+        if SparseSemiStructuredTensor._FORCE_CUTLASS
+        else torch.sparse.SparseSemiStructuredTensorCUSPARSELT
+    )
+
+    return SPARSE_SUBCLASS.from_dense(original_tensor)
+
+
+class SparseSemiStructuredTensorCUTLASS(SparseSemiStructuredTensor):
+    """
+    This class implements semi-structured sparsity for the CUTLASS backend.
+
+
+    In this implementation, the specified elements and metadata are stored seprately,
+    in packed and meta respectively.
+
+    When _FORCE_CUTLASS is set, or when cuSPARSELt is not available, this subclass calls into _sparse_semi_structured_(mm|addmm) and
+    sparse_semi_structured_from_dense for conversion to the compressed format.
+    """
+
+    BACKEND = "cutlass"
+    _DTYPE_SHAPE_CONSTRAINTS = {
+        torch.int8: _SEMI_STRUCTURED_SPARSE_CONFIG(16, 128, 16, 16),
+        torch.float16: _SEMI_STRUCTURED_SPARSE_CONFIG(32, 64, 8, 8),
+        torch.bfloat16: _SEMI_STRUCTURED_SPARSE_CONFIG(32, 64, 8, 8),
+        torch.float32: _SEMI_STRUCTURED_SPARSE_CONFIG(32, 32, 4, 4),
+    }
+
+    @classmethod
+    def from_dense(
+        cls, original_tensor: torch.Tensor
+    ) -> "SparseSemiStructuredTensorCUTLASS":
+        cls._validate_device_dim_dtype_shape(original_tensor)
+        (
+            sparse_tensor_cutlass,
+            meta_tensor_cutlass,
+        ) = sparse_semi_structured_from_dense_cutlass(original_tensor)
+        return cls(
+            original_tensor.shape,
+            packed=sparse_tensor_cutlass,
+            meta=meta_tensor_cutlass,
+            packed_t=None,
+            meta_t=None,
+            compressed_swizzled_bitmask=None,
+            requires_grad=original_tensor.requires_grad,
+        )
+
+    def to_dense(self):
+        assert self.meta is not None and self.packed is not None
+        return (
+            sparse_semi_structured_to_dense_cutlass(
+                self.packed,
+                self.meta,
+            )
+            if self.meta.ndim == 2
+            else super().to_dense()
+        )
+
+    @classmethod
+    def prune_dense_static_sort(
+        cls, original_tensor: torch.Tensor, algorithm=""
+    ) -> "SparseSemiStructuredTensor":
+        """
+        This function takes in a unpruned dense tensor and runs a (branchless) static sort across a 4x4 tile.
+
+        It greedily picks the largest values in the tile, upholding the 2:4 sparsity constraint across both rows and columns.
+        The algorithm used to prune the matrix is implemented in `_sparse_semi_structured_tile`.
+
+        Then it creates the packed and meta tensors for the compressed sparse representation of the pruned dense tensor.
+        It also calculates the packed_t and meta_t tensors for the compressed sparse representation of the transposed
+        pruned dense tensor.
+        Since we cannot transpose the compressed representations, we store both for the fw/bw pass respectively.
+
+        Finally, this function also computes a compressed swizzled bitmask that encodes the sparsity pattern
+        This can be used in the backward pass to mask the gradients.
+
+        [9 1 7 4]                       [9 0 7 0]
+        [1 2 3 0]                       [0 2 0 0]
+        [8 3 5 4] -> prune 4x4 tile  -> [8 0 0 4] -> pack to CUTLASS semi-structured -> packed
+        [1 2 6 2]                       [0 0 6 2]                                    -> metadata
+
+                                                  -> pack to transposed CUTLASS      -> packed_t
+                                                     semi-structured representation  -> metadata_t
+
+                                                  -> compute swizzled bitmask        -> compressed_swizzled_bitmask
+
+
+        The equivalent PyTorch code to create the same five outputs from the dense tensor can be found below:
+        ```
+        from torch.sparse import SparseSemiStructuredTensorCUTLASS
+        from torch.sparse._semi_structured_conversions import _sparse_semi_structured_tile, _compute_compressed_swizzled_bitmask
+
+        pruned = _sparse_semi_structured_tile(dense)
+        packed_cutlass, meta_cutlass = sparse_semi_structured_from_dense_cutlass(pruned)
+        packed_t_cutlass, meta_t_cutlass = sparse_semi_structured_from_dense_cutlass(pruned.t().contiguous())
+        bitmask = _compute_compressed_swizzled_bitmask(pruned)
+
+        SparseSemiStructuredTensorCUTLASS(dense.shape, packed_cutlass, meta_cutlass, packed_t_cutlass, meta_t_cutlass, bitmask)
+        ```
+        """
+        # We can either pack to the CUTLASS or cuSPARSELt representation, depending on the use_cutlass flag.
+        (
+            packed,
+            meta,
+            packed_t,
+            meta_t,
+            compressed_swizzled_bitmask,
+        ) = torch._sparse_semi_structured_tile(
+            original_tensor, algorithm=algorithm, use_cutlass=True
+        )
+
+        return cls(
+            original_tensor.shape,
+            packed=packed,
+            meta=meta,
+            packed_t=packed_t,
+            meta_t=meta_t,
+            compressed_swizzled_bitmask=compressed_swizzled_bitmask,
+            requires_grad=False,
+        )
+
+    def _mm(
+        self, B: torch.Tensor, *, bias: Optional[torch.Tensor] = None, **kwargs
+    ) -> torch.Tensor:
+        if isinstance(B, SparseSemiStructuredTensor):
+            raise ValueError(
+                "`SparseSemiStructuredTensor @ SparseSemiStructuredTensor` is not supported by the hardware"
+            )
+        cls_name = self.__class__.__name__
+        if self.ndim != 2 or B.ndim != 2:
+            raise NotImplementedError(
+                f"`{cls_name}` matmul: Broadcasting is not implemented"
+            )
+        if self.packed is None or self.meta is None:
+            raise NotImplementedError(
+                f"`{cls_name}` matmul: operation is not supported"
+            )
+        else:
+            if bias is None:
+                res = torch._sparse_semi_structured_mm(self.packed, self.meta, B)
+            else:
+                res = torch._sparse_semi_structured_addmm(
+                    bias, self.packed, self.meta, B
+                )
+            return res[: self.shape[0]]
+
+
+class SparseSemiStructuredTensorCUSPARSELT(SparseSemiStructuredTensor):
+    """
+    The cuSPARSELt backend expects the specified elements and the metadata to be stored in a single tensor:
+    packed = [ specified elements of original tensor | metadata ]
+    For an original tensor of size (m, k) we expect the first m * k // 2 elements to be the kept elements
+    The rest of the tensor is metadata. Since there is only one tensor, we only use the packed and packed_t
+    attributes respectively.
+
+    cuSPARSELt also supports transposition fusion, which is necessary for performant 2:4 sparse training, as well
+    as specifying alg_id, a config that affects the performance of the matmul depending on matmul sizes.
+    """
+
+    BACKEND = "cusparselt"
+    _DTYPE_SHAPE_CONSTRAINTS = {
+        torch.int8: _SEMI_STRUCTURED_SPARSE_CONFIG(32, 32, 16, 16),
+        torch.float16: _SEMI_STRUCTURED_SPARSE_CONFIG(16, 16, 8, 8),
+        torch.bfloat16: _SEMI_STRUCTURED_SPARSE_CONFIG(16, 16, 8, 8),
+    }
+
+    @classmethod
+    def from_dense(
+        cls, original_tensor: torch.Tensor
+    ) -> "SparseSemiStructuredTensorCUSPARSELT":
+        cls._validate_device_dim_dtype_shape(original_tensor)
+        return cls(
+            shape=original_tensor.shape,
+            packed=torch._cslt_compress(original_tensor),
+            meta=None,
+            packed_t=None,
+            meta_t=None,
+            compressed_swizzled_bitmask=None,
+            fuse_transpose_cusparselt=SparseSemiStructuredTensor._FUSE_TRANSPOSE,
+            alg_id_cusparselt=SparseSemiStructuredTensor._DEFAULT_ALG_ID,
+            requires_grad=original_tensor.requires_grad,
+        )
+
+    @classmethod
+    def prune_dense_static_sort(
+        cls, original_tensor: torch.Tensor, algorithm=""
+    ) -> "SparseSemiStructuredTensor":
+        """
+        This function does the same thing as described in SparseSemiStructuredCUTLASS, but uses the cuSPASRELt metadata
+        layout and sparse matmul.
+
+        The only functional difference is that cuSPARSELt stores `metadata` and `packed` together into a single tensor.
+
+        [9 1 7 4]                       [9 0 7 0]
+        [1 2 3 0]                       [0 2 0 0]
+        [8 3 5 4] -> prune 4x4 tile  -> [8 0 0 4] -> pack to cuSPARSELT semi-structured -> packed
+        [1 2 6 2]                       [0 0 6 2]
+
+                                                  -> pack to transposed cuSPARSELt      -> packed_t
+                                                     semi-structured representation
+
+                                                  -> compute swizzled bitmask           -> compressed_swizzled_bitmask
+
+
+        The equivalent PyTorch code to create the same three outputs from the dense tensor can be found below:
+        ```
+        from torch.sparse import SparseSemiStructuredTensorCUSPARSELT
+        from torch.sparse._semi_structured_conversions import _sparse_semi_structured_tile, _compute_compressed_swizzled_bitmask
+
+        pruned = _sparse_semi_structured_tile(dense)
+        packed_cusparselt = torch._cslt_compress(pruned)
+        packed_t_cusparselt = torch._cslt_compress(pruned.t().contiguous())
+        bitmask = _compute_compressed_swizzled_bitmask(pruned)
+
+        SparseSemiStructuredTensorCUSPARSELT(dense.shape, packed_cutlass, None, packed_t_cutlass, None, bitmask)
+        ```
+        """
+        (
+            packed,
+            meta,
+            packed_t,
+            meta_t,
+            compressed_swizzled_bitmask,
+        ) = torch._sparse_semi_structured_tile(
+            original_tensor, algorithm=algorithm, use_cutlass=False
+        )
+
+        return cls(
+            original_tensor.shape,
+            packed=packed,
+            meta=meta,
+            packed_t=packed_t,
+            meta_t=meta_t,
+            compressed_swizzled_bitmask=compressed_swizzled_bitmask,
+            requires_grad=False,
+        )
+
+    def _mm(
+        self, B: torch.Tensor, *, bias: Optional[torch.Tensor] = None, **kwargs
+    ) -> torch.Tensor:
+        if isinstance(B, SparseSemiStructuredTensor):
+            raise ValueError(
+                "`SparseSemiStructuredTensor @ SparseSemiStructuredTensor` is not supported by the hardware"
+            )
+        if self.ndim != 2 or B.ndim != 2:
+            raise NotImplementedError(
+                f"`{self.__class__.__name__}` matmul: Broadcasting is not implemented"
+            )
+        if B.dtype != self.dtype:
+            raise NotImplementedError(
+                f"`{self.__class__.__name__}` matmul: trying to do `A={tuple(self.shape)} @ B={tuple(B.shape)}`, "
+                f"with A.dtype={self.dtype} and B.dtype={B.dtype}. "
+                "This operation is only supported when A and B have the same data type."
+            )
+        if bias is not None and bias.dtype != self.dtype:
+            raise NotImplementedError(
+                f"`{self.__class__.__name__}` matmul: trying to do `A={tuple(self.shape)} @ B={tuple(B.shape)} + C`, "
+                "with A.dtype=B.dtype={self.dtype} and C.dtype={B.dtype}. "
+                "This operation is only supported when A, B and C have the same data type."
+            )
+        if self.packed is None:
+            raise NotImplementedError(
+                f"`{self.__class__.__name__}` matmul: operation is not supported"
+            )
+        else:
+            res = torch._cslt_sparse_mm(
+                self.packed,
+                B,
+                bias=bias,
+                transpose_result=self.fuse_transpose_cusparselt,
+                alg_id=self.alg_id_cusparselt,
+            )
+            return res.t() if self.fuse_transpose_cusparselt else res
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/special/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/special/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..07e104c4090ef8456f8acf5f2548bbbc3532a2d3
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/special/__init__.py
@@ -0,0 +1,1283 @@
+import torch
+from torch._C import _add_docstr, _special  # type: ignore[attr-defined]
+from torch._torch_docs import common_args, multi_dim_common
+
+__all__ = [
+    'airy_ai',
+    'bessel_j0',
+    'bessel_j1',
+    'bessel_y0',
+    'bessel_y1',
+    'chebyshev_polynomial_t',
+    'chebyshev_polynomial_u',
+    'chebyshev_polynomial_v',
+    'chebyshev_polynomial_w',
+    'digamma',
+    'entr',
+    'erf',
+    'erfc',
+    'erfcx',
+    'erfinv',
+    'exp2',
+    'expit',
+    'expm1',
+    'gammainc',
+    'gammaincc',
+    'gammaln',
+    'hermite_polynomial_h',
+    'hermite_polynomial_he',
+    'i0',
+    'i0e',
+    'i1',
+    'i1e',
+    'laguerre_polynomial_l',
+    'legendre_polynomial_p',
+    'log1p',
+    'log_ndtr',
+    'log_softmax',
+    'logit',
+    'logsumexp',
+    'modified_bessel_i0',
+    'modified_bessel_i1',
+    'modified_bessel_k0',
+    'modified_bessel_k1',
+    'multigammaln',
+    'ndtr',
+    'ndtri',
+    'polygamma',
+    'psi',
+    'round',
+    'shifted_chebyshev_polynomial_t',
+    'shifted_chebyshev_polynomial_u',
+    'shifted_chebyshev_polynomial_v',
+    'shifted_chebyshev_polynomial_w',
+    'scaled_modified_bessel_k0',
+    'scaled_modified_bessel_k1',
+    'sinc',
+    'softmax',
+    'spherical_bessel_j0',
+    'xlog1py',
+    'xlogy',
+    'zeta',
+]
+
+Tensor = torch.Tensor
+
+entr = _add_docstr(_special.special_entr,
+                   r"""
+entr(input, *, out=None) -> Tensor
+Computes the entropy on :attr:`input` (as defined below), elementwise.
+
+.. math::
+    \begin{align}
+    \text{entr(x)} = \begin{cases}
+        -x * \ln(x)  & x > 0 \\
+        0 &  x = 0.0 \\
+        -\infty & x < 0
+    \end{cases}
+    \end{align}
+""" + """
+
+Args:
+   input (Tensor): the input tensor.
+
+Keyword args:
+    out (Tensor, optional): the output tensor.
+
+Example::
+    >>> a = torch.arange(-0.5, 1, 0.5)
+    >>> a
+    tensor([-0.5000,  0.0000,  0.5000])
+    >>> torch.special.entr(a)
+    tensor([  -inf, 0.0000, 0.3466])
+""")
+
+psi = _add_docstr(_special.special_psi,
+                  r"""
+psi(input, *, out=None) -> Tensor
+
+Alias for :func:`torch.special.digamma`.
+""")
+
+digamma = _add_docstr(_special.special_digamma,
+                      r"""
+digamma(input, *, out=None) -> Tensor
+
+Computes the logarithmic derivative of the gamma function on `input`.
+
+.. math::
+    \digamma(x) = \frac{d}{dx} \ln\left(\Gamma\left(x\right)\right) = \frac{\Gamma'(x)}{\Gamma(x)}
+""" + r"""
+Args:
+    input (Tensor): the tensor to compute the digamma function on
+
+Keyword args:
+    {out}
+
+.. note::  This function is similar to SciPy's `scipy.special.digamma`.
+
+.. note::  From PyTorch 1.8 onwards, the digamma function returns `-Inf` for `0`.
+           Previously it returned `NaN` for `0`.
+
+Example::
+
+    >>> a = torch.tensor([1, 0.5])
+    >>> torch.special.digamma(a)
+    tensor([-0.5772, -1.9635])
+
+""".format(**common_args))
+
+gammaln = _add_docstr(_special.special_gammaln,
+                      r"""
+gammaln(input, *, out=None) -> Tensor
+
+Computes the natural logarithm of the absolute value of the gamma function on :attr:`input`.
+
+.. math::
+    \text{out}_{i} = \ln \Gamma(|\text{input}_{i}|)
+""" + """
+Args:
+    {input}
+
+Keyword args:
+    {out}
+
+Example::
+
+    >>> a = torch.arange(0.5, 2, 0.5)
+    >>> torch.special.gammaln(a)
+    tensor([ 0.5724,  0.0000, -0.1208])
+
+""".format(**common_args))
+
+polygamma = _add_docstr(_special.special_polygamma,
+                        r"""
+polygamma(n, input, *, out=None) -> Tensor
+
+Computes the :math:`n^{th}` derivative of the digamma function on :attr:`input`.
+:math:`n \geq 0` is called the order of the polygamma function.
+
+.. math::
+    \psi^{(n)}(x) = \frac{d^{(n)}}{dx^{(n)}} \psi(x)
+
+.. note::
+    This function is implemented only for nonnegative integers :math:`n \geq 0`.
+""" + """
+Args:
+    n (int): the order of the polygamma function
+    {input}
+
+Keyword args:
+    {out}
+
+Example::
+    >>> a = torch.tensor([1, 0.5])
+    >>> torch.special.polygamma(1, a)
+    tensor([1.64493, 4.9348])
+    >>> torch.special.polygamma(2, a)
+    tensor([ -2.4041, -16.8288])
+    >>> torch.special.polygamma(3, a)
+    tensor([ 6.4939, 97.4091])
+    >>> torch.special.polygamma(4, a)
+    tensor([ -24.8863, -771.4742])
+""".format(**common_args))
+
+erf = _add_docstr(_special.special_erf,
+                  r"""
+erf(input, *, out=None) -> Tensor
+
+Computes the error function of :attr:`input`. The error function is defined as follows:
+
+.. math::
+    \mathrm{erf}(x) = \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+
+Example::
+
+    >>> torch.special.erf(torch.tensor([0, -1., 10.]))
+    tensor([ 0.0000, -0.8427,  1.0000])
+""".format(**common_args))
+
+erfc = _add_docstr(_special.special_erfc,
+                   r"""
+erfc(input, *, out=None) -> Tensor
+
+Computes the complementary error function of :attr:`input`.
+The complementary error function is defined as follows:
+
+.. math::
+    \mathrm{erfc}(x) = 1 - \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+
+Example::
+
+    >>> torch.special.erfc(torch.tensor([0, -1., 10.]))
+    tensor([ 1.0000, 1.8427,  0.0000])
+""".format(**common_args))
+
+erfcx = _add_docstr(_special.special_erfcx,
+                    r"""
+erfcx(input, *, out=None) -> Tensor
+
+Computes the scaled complementary error function for each element of :attr:`input`.
+The scaled complementary error function is defined as follows:
+
+.. math::
+    \mathrm{erfcx}(x) = e^{x^2} \mathrm{erfc}(x)
+""" + r"""
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+
+Example::
+
+    >>> torch.special.erfcx(torch.tensor([0, -1., 10.]))
+    tensor([ 1.0000, 5.0090, 0.0561])
+""".format(**common_args))
+
+erfinv = _add_docstr(_special.special_erfinv,
+                     r"""
+erfinv(input, *, out=None) -> Tensor
+
+Computes the inverse error function of :attr:`input`.
+The inverse error function is defined in the range :math:`(-1, 1)` as:
+
+.. math::
+    \mathrm{erfinv}(\mathrm{erf}(x)) = x
+""" + r"""
+
+Args:
+    {input}
+
+Keyword args:
+    {out}
+
+Example::
+
+    >>> torch.special.erfinv(torch.tensor([0, 0.5, -1.]))
+    tensor([ 0.0000,  0.4769,    -inf])
+""".format(**common_args))
+
+logit = _add_docstr(_special.special_logit,
+                    r"""
+logit(input, eps=None, *, out=None) -> Tensor
+
+Returns a new tensor with the logit of the elements of :attr:`input`.
+:attr:`input` is clamped to [eps, 1 - eps] when eps is not None.
+When eps is None and :attr:`input` < 0 or :attr:`input` > 1, the function will yields NaN.
+
+.. math::
+    \begin{align}
+    y_{i} &= \ln(\frac{z_{i}}{1 - z_{i}}) \\
+    z_{i} &= \begin{cases}
+        x_{i} & \text{if eps is None} \\
+        \text{eps} & \text{if } x_{i} < \text{eps} \\
+        x_{i} & \text{if } \text{eps} \leq x_{i} \leq 1 - \text{eps} \\
+        1 - \text{eps} & \text{if } x_{i} > 1 - \text{eps}
+    \end{cases}
+    \end{align}
+""" + r"""
+Args:
+    {input}
+    eps (float, optional): the epsilon for input clamp bound. Default: ``None``
+
+Keyword args:
+    {out}
+
+Example::
+
+    >>> a = torch.rand(5)
+    >>> a
+    tensor([0.2796, 0.9331, 0.6486, 0.1523, 0.6516])
+    >>> torch.special.logit(a, eps=1e-6)
+    tensor([-0.9466,  2.6352,  0.6131, -1.7169,  0.6261])
+""".format(**common_args))
+
+logsumexp = _add_docstr(_special.special_logsumexp,
+                        r"""
+logsumexp(input, dim, keepdim=False, *, out=None)
+
+Alias for :func:`torch.logsumexp`.
+""".format(**multi_dim_common))
+
+expit = _add_docstr(_special.special_expit,
+                    r"""
+expit(input, *, out=None) -> Tensor
+
+Computes the expit (also known as the logistic sigmoid function) of the elements of :attr:`input`.
+
+.. math::
+    \text{out}_{i} = \frac{1}{1 + e^{-\text{input}_{i}}}
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+
+Example::
+
+    >>> t = torch.randn(4)
+    >>> t
+    tensor([ 0.9213,  1.0887, -0.8858, -1.7683])
+    >>> torch.special.expit(t)
+    tensor([ 0.7153,  0.7481,  0.2920,  0.1458])
+""".format(**common_args))
+
+exp2 = _add_docstr(_special.special_exp2,
+                   r"""
+exp2(input, *, out=None) -> Tensor
+
+Computes the base two exponential function of :attr:`input`.
+
+.. math::
+    y_{i} = 2^{x_{i}}
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+
+Example::
+
+    >>> torch.special.exp2(torch.tensor([0, math.log2(2.), 3, 4]))
+    tensor([ 1.,  2.,  8., 16.])
+""".format(**common_args))
+
+expm1 = _add_docstr(_special.special_expm1,
+                    r"""
+expm1(input, *, out=None) -> Tensor
+
+Computes the exponential of the elements minus 1
+of :attr:`input`.
+
+.. math::
+    y_{i} = e^{x_{i}} - 1
+
+.. note:: This function provides greater precision than exp(x) - 1 for small values of x.
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+
+Example::
+
+    >>> torch.special.expm1(torch.tensor([0, math.log(2.)]))
+    tensor([ 0.,  1.])
+""".format(**common_args))
+
+xlog1py = _add_docstr(_special.special_xlog1py,
+                      r"""
+xlog1py(input, other, *, out=None) -> Tensor
+
+Computes ``input * log1p(other)`` with the following cases.
+
+.. math::
+    \text{out}_{i} = \begin{cases}
+        \text{NaN} & \text{if } \text{other}_{i} = \text{NaN} \\
+        0 & \text{if } \text{input}_{i} = 0.0 \text{ and } \text{other}_{i} != \text{NaN} \\
+        \text{input}_{i} * \text{log1p}(\text{other}_{i})& \text{otherwise}
+    \end{cases}
+
+Similar to SciPy's `scipy.special.xlog1py`.
+
+""" + r"""
+
+Args:
+    input (Number or Tensor) : Multiplier
+    other (Number or Tensor) : Argument
+
+.. note:: At least one of :attr:`input` or :attr:`other` must be a tensor.
+
+Keyword args:
+    {out}
+
+Example::
+
+    >>> x = torch.zeros(5,)
+    >>> y = torch.tensor([-1, 0, 1, float('inf'), float('nan')])
+    >>> torch.special.xlog1py(x, y)
+    tensor([0., 0., 0., 0., nan])
+    >>> x = torch.tensor([1, 2, 3])
+    >>> y = torch.tensor([3, 2, 1])
+    >>> torch.special.xlog1py(x, y)
+    tensor([1.3863, 2.1972, 2.0794])
+    >>> torch.special.xlog1py(x, 4)
+    tensor([1.6094, 3.2189, 4.8283])
+    >>> torch.special.xlog1py(2, y)
+    tensor([2.7726, 2.1972, 1.3863])
+""".format(**common_args))
+
+xlogy = _add_docstr(_special.special_xlogy,
+                    r"""
+xlogy(input, other, *, out=None) -> Tensor
+
+Computes ``input * log(other)`` with the following cases.
+
+.. math::
+    \text{out}_{i} = \begin{cases}
+        \text{NaN} & \text{if } \text{other}_{i} = \text{NaN} \\
+        0 & \text{if } \text{input}_{i} = 0.0 \\
+        \text{input}_{i} * \log{(\text{other}_{i})} & \text{otherwise}
+    \end{cases}
+
+Similar to SciPy's `scipy.special.xlogy`.
+
+""" + r"""
+
+Args:
+    input (Number or Tensor) : Multiplier
+    other (Number or Tensor) : Argument
+
+.. note:: At least one of :attr:`input` or :attr:`other` must be a tensor.
+
+Keyword args:
+    {out}
+
+Example::
+
+    >>> x = torch.zeros(5,)
+    >>> y = torch.tensor([-1, 0, 1, float('inf'), float('nan')])
+    >>> torch.special.xlogy(x, y)
+    tensor([0., 0., 0., 0., nan])
+    >>> x = torch.tensor([1, 2, 3])
+    >>> y = torch.tensor([3, 2, 1])
+    >>> torch.special.xlogy(x, y)
+    tensor([1.0986, 1.3863, 0.0000])
+    >>> torch.special.xlogy(x, 4)
+    tensor([1.3863, 2.7726, 4.1589])
+    >>> torch.special.xlogy(2, y)
+    tensor([2.1972, 1.3863, 0.0000])
+""".format(**common_args))
+
+i0 = _add_docstr(_special.special_i0,
+                 r"""
+i0(input, *, out=None) -> Tensor
+
+Computes the zeroth order modified Bessel function of the first kind for each element of :attr:`input`.
+
+.. math::
+    \text{out}_{i} = I_0(\text{input}_{i}) = \sum_{k=0}^{\infty} \frac{(\text{input}_{i}^2/4)^k}{(k!)^2}
+
+""" + r"""
+Args:
+    input (Tensor): the input tensor
+
+Keyword args:
+    {out}
+
+Example::
+
+    >>> torch.i0(torch.arange(5, dtype=torch.float32))
+    tensor([ 1.0000,  1.2661,  2.2796,  4.8808, 11.3019])
+
+""".format(**common_args))
+
+i0e = _add_docstr(_special.special_i0e,
+                  r"""
+i0e(input, *, out=None) -> Tensor
+Computes the exponentially scaled zeroth order modified Bessel function of the first kind (as defined below)
+for each element of :attr:`input`.
+
+.. math::
+    \text{out}_{i} = \exp(-|x|) * i0(x) = \exp(-|x|) * \sum_{k=0}^{\infty} \frac{(\text{input}_{i}^2/4)^k}{(k!)^2}
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+
+Example::
+    >>> torch.special.i0e(torch.arange(5, dtype=torch.float32))
+    tensor([1.0000, 0.4658, 0.3085, 0.2430, 0.2070])
+""".format(**common_args))
+
+i1 = _add_docstr(_special.special_i1,
+                 r"""
+i1(input, *, out=None) -> Tensor
+Computes the first order modified Bessel function of the first kind (as defined below)
+for each element of :attr:`input`.
+
+.. math::
+    \text{out}_{i} = \frac{(\text{input}_{i})}{2} * \sum_{k=0}^{\infty} \frac{(\text{input}_{i}^2/4)^k}{(k!) * (k+1)!}
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+
+Example::
+    >>> torch.special.i1(torch.arange(5, dtype=torch.float32))
+    tensor([0.0000, 0.5652, 1.5906, 3.9534, 9.7595])
+""".format(**common_args))
+
+i1e = _add_docstr(_special.special_i1e,
+                  r"""
+i1e(input, *, out=None) -> Tensor
+Computes the exponentially scaled first order modified Bessel function of the first kind (as defined below)
+for each element of :attr:`input`.
+
+.. math::
+    \text{out}_{i} = \exp(-|x|) * i1(x) =
+        \exp(-|x|) * \frac{(\text{input}_{i})}{2} * \sum_{k=0}^{\infty} \frac{(\text{input}_{i}^2/4)^k}{(k!) * (k+1)!}
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+
+Example::
+    >>> torch.special.i1e(torch.arange(5, dtype=torch.float32))
+    tensor([0.0000, 0.2079, 0.2153, 0.1968, 0.1788])
+""".format(**common_args))
+
+ndtr = _add_docstr(_special.special_ndtr,
+                   r"""
+ndtr(input, *, out=None) -> Tensor
+Computes the area under the standard Gaussian probability density function,
+integrated from minus infinity to :attr:`input`, elementwise.
+
+.. math::
+    \text{ndtr}(x) = \frac{1}{\sqrt{2 \pi}}\int_{-\infty}^{x} e^{-\frac{1}{2}t^2} dt
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+
+Example::
+    >>> torch.special.ndtr(torch.tensor([-3., -2, -1, 0, 1, 2, 3]))
+    tensor([0.0013, 0.0228, 0.1587, 0.5000, 0.8413, 0.9772, 0.9987])
+""".format(**common_args))
+
+ndtri = _add_docstr(_special.special_ndtri,
+                    r"""
+ndtri(input, *, out=None) -> Tensor
+Computes the argument, x, for which the area under the Gaussian probability density function
+(integrated from minus infinity to x) is equal to :attr:`input`, elementwise.
+
+.. math::
+    \text{ndtri}(p) = \sqrt{2}\text{erf}^{-1}(2p - 1)
+
+.. note::
+    Also known as quantile function for Normal Distribution.
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+
+Example::
+    >>> torch.special.ndtri(torch.tensor([0, 0.25, 0.5, 0.75, 1]))
+    tensor([   -inf, -0.6745,  0.0000,  0.6745,     inf])
+""".format(**common_args))
+
+log_ndtr = _add_docstr(_special.special_log_ndtr,
+                       r"""
+log_ndtr(input, *, out=None) -> Tensor
+Computes the log of the area under the standard Gaussian probability density function,
+integrated from minus infinity to :attr:`input`, elementwise.
+
+.. math::
+    \text{log\_ndtr}(x) = \log\left(\frac{1}{\sqrt{2 \pi}}\int_{-\infty}^{x} e^{-\frac{1}{2}t^2} dt \right)
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+
+Example::
+    >>> torch.special.log_ndtr(torch.tensor([-3., -2, -1, 0, 1, 2, 3]))
+    tensor([-6.6077 -3.7832 -1.841  -0.6931 -0.1728 -0.023  -0.0014])
+""".format(**common_args))
+
+log1p = _add_docstr(_special.special_log1p,
+                    r"""
+log1p(input, *, out=None) -> Tensor
+
+Alias for :func:`torch.log1p`.
+""")
+
+sinc = _add_docstr(_special.special_sinc,
+                   r"""
+sinc(input, *, out=None) -> Tensor
+
+Computes the normalized sinc of :attr:`input.`
+
+.. math::
+    \text{out}_{i} =
+    \begin{cases}
+      1, & \text{if}\ \text{input}_{i}=0 \\
+      \sin(\pi \text{input}_{i}) / (\pi \text{input}_{i}), & \text{otherwise}
+    \end{cases}
+""" + r"""
+
+Args:
+    {input}
+
+Keyword args:
+    {out}
+
+Example::
+    >>> t = torch.randn(4)
+    >>> t
+    tensor([ 0.2252, -0.2948,  1.0267, -1.1566])
+    >>> torch.special.sinc(t)
+    tensor([ 0.9186,  0.8631, -0.0259, -0.1300])
+""".format(**common_args))
+
+round = _add_docstr(_special.special_round,
+                    r"""
+round(input, *, out=None) -> Tensor
+
+Alias for :func:`torch.round`.
+""")
+
+softmax = _add_docstr(_special.special_softmax,
+                      r"""
+softmax(input, dim, *, dtype=None) -> Tensor
+
+Computes the softmax function.
+
+Softmax is defined as:
+
+:math:`\text{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}`
+
+It is applied to all slices along dim, and will re-scale them so that the elements
+lie in the range `[0, 1]` and sum to 1.
+
+Args:
+    input (Tensor): input
+    dim (int): A dimension along which softmax will be computed.
+    dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
+        If specified, the input tensor is cast to :attr:`dtype` before the operation
+        is performed. This is useful for preventing data type overflows. Default: None.
+
+Examples::
+    >>> t = torch.ones(2, 2)
+    >>> torch.special.softmax(t, 0)
+    tensor([[0.5000, 0.5000],
+            [0.5000, 0.5000]])
+
+""")
+
+log_softmax = _add_docstr(_special.special_log_softmax,
+                          r"""
+log_softmax(input, dim, *, dtype=None) -> Tensor
+
+Computes softmax followed by a logarithm.
+
+While mathematically equivalent to log(softmax(x)), doing these two
+operations separately is slower and numerically unstable. This function
+is computed as:
+
+.. math::
+    \text{log\_softmax}(x_{i}) = \log\left(\frac{\exp(x_i) }{ \sum_j \exp(x_j)} \right)
+""" + r"""
+
+Args:
+    input (Tensor): input
+    dim (int): A dimension along which log_softmax will be computed.
+    dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
+        If specified, the input tensor is cast to :attr:`dtype` before the operation
+        is performed. This is useful for preventing data type overflows. Default: None.
+
+Example::
+    >>> t = torch.ones(2, 2)
+    >>> torch.special.log_softmax(t, 0)
+    tensor([[-0.6931, -0.6931],
+            [-0.6931, -0.6931]])
+""")
+
+zeta = _add_docstr(_special.special_zeta,
+                   r"""
+zeta(input, other, *, out=None) -> Tensor
+
+Computes the Hurwitz zeta function, elementwise.
+
+.. math::
+    \zeta(x, q) = \sum_{k=0}^{\infty} \frac{1}{(k + q)^x}
+
+""" + r"""
+Args:
+    input (Tensor): the input tensor corresponding to `x`.
+    other (Tensor): the input tensor corresponding to `q`.
+
+.. note::
+    The Riemann zeta function corresponds to the case when `q = 1`
+
+Keyword args:
+    {out}
+
+Example::
+    >>> x = torch.tensor([2., 4.])
+    >>> torch.special.zeta(x, 1)
+    tensor([1.6449, 1.0823])
+    >>> torch.special.zeta(x, torch.tensor([1., 2.]))
+    tensor([1.6449, 0.0823])
+    >>> torch.special.zeta(2, torch.tensor([1., 2.]))
+    tensor([1.6449, 0.6449])
+""".format(**common_args))
+
+multigammaln = _add_docstr(_special.special_multigammaln,
+                           r"""
+multigammaln(input, p, *, out=None) -> Tensor
+
+Computes the `multivariate log-gamma function
+`_ with dimension
+:math:`p` element-wise, given by
+
+.. math::
+    \log(\Gamma_{p}(a)) = C + \displaystyle \sum_{i=1}^{p} \log\left(\Gamma\left(a - \frac{i - 1}{2}\right)\right)
+
+where :math:`C = \log(\pi) \cdot \frac{p (p - 1)}{4}` and :math:`\Gamma(-)` is the Gamma function.
+
+All elements must be greater than :math:`\frac{p - 1}{2}`, otherwise the behavior is undefiend.
+""" + """
+
+Args:
+    input (Tensor): the tensor to compute the multivariate log-gamma function
+    p (int): the number of dimensions
+
+Keyword args:
+    {out}
+
+Example::
+
+    >>> a = torch.empty(2, 3).uniform_(1, 2)
+    >>> a
+    tensor([[1.6835, 1.8474, 1.1929],
+            [1.0475, 1.7162, 1.4180]])
+    >>> torch.special.multigammaln(a, 2)
+    tensor([[0.3928, 0.4007, 0.7586],
+            [1.0311, 0.3901, 0.5049]])
+""".format(**common_args))
+
+gammainc = _add_docstr(_special.special_gammainc,
+                       r"""
+gammainc(input, other, *, out=None) -> Tensor
+
+Computes the regularized lower incomplete gamma function:
+
+.. math::
+    \text{out}_{i} = \frac{1}{\Gamma(\text{input}_i)} \int_0^{\text{other}_i} t^{\text{input}_i-1} e^{-t} dt
+
+where both :math:`\text{input}_i` and :math:`\text{other}_i` are weakly positive
+and at least one is strictly positive.
+If both are zero or either is negative then :math:`\text{out}_i=\text{nan}`.
+:math:`\Gamma(\cdot)` in the equation above is the gamma function,
+
+.. math::
+    \Gamma(\text{input}_i) = \int_0^\infty t^{(\text{input}_i-1)} e^{-t} dt.
+
+See :func:`torch.special.gammaincc` and :func:`torch.special.gammaln` for related functions.
+
+Supports :ref:`broadcasting to a common shape `
+and float inputs.
+
+.. note::
+    The backward pass with respect to :attr:`input` is not yet supported.
+    Please open an issue on PyTorch's Github to request it.
+
+""" + r"""
+Args:
+    input (Tensor): the first non-negative input tensor
+    other (Tensor): the second non-negative input tensor
+
+Keyword args:
+    {out}
+
+Example::
+
+    >>> a1 = torch.tensor([4.0])
+    >>> a2 = torch.tensor([3.0, 4.0, 5.0])
+    >>> a = torch.special.gammaincc(a1, a2)
+    tensor([0.3528, 0.5665, 0.7350])
+    tensor([0.3528, 0.5665, 0.7350])
+    >>> b = torch.special.gammainc(a1, a2) + torch.special.gammaincc(a1, a2)
+    tensor([1., 1., 1.])
+
+""".format(**common_args))
+
+gammaincc = _add_docstr(_special.special_gammaincc,
+                        r"""
+gammaincc(input, other, *, out=None) -> Tensor
+
+Computes the regularized upper incomplete gamma function:
+
+.. math::
+    \text{out}_{i} = \frac{1}{\Gamma(\text{input}_i)} \int_{\text{other}_i}^{\infty} t^{\text{input}_i-1} e^{-t} dt
+
+where both :math:`\text{input}_i` and :math:`\text{other}_i` are weakly positive
+and at least one is strictly positive.
+If both are zero or either is negative then :math:`\text{out}_i=\text{nan}`.
+:math:`\Gamma(\cdot)` in the equation above is the gamma function,
+
+.. math::
+    \Gamma(\text{input}_i) = \int_0^\infty t^{(\text{input}_i-1)} e^{-t} dt.
+
+See :func:`torch.special.gammainc` and :func:`torch.special.gammaln` for related functions.
+
+Supports :ref:`broadcasting to a common shape `
+and float inputs.
+
+.. note::
+    The backward pass with respect to :attr:`input` is not yet supported.
+    Please open an issue on PyTorch's Github to request it.
+
+""" + r"""
+Args:
+    input (Tensor): the first non-negative input tensor
+    other (Tensor): the second non-negative input tensor
+
+Keyword args:
+    {out}
+
+Example::
+
+    >>> a1 = torch.tensor([4.0])
+    >>> a2 = torch.tensor([3.0, 4.0, 5.0])
+    >>> a = torch.special.gammaincc(a1, a2)
+    tensor([0.6472, 0.4335, 0.2650])
+    >>> b = torch.special.gammainc(a1, a2) + torch.special.gammaincc(a1, a2)
+    tensor([1., 1., 1.])
+
+""".format(**common_args))
+
+airy_ai = _add_docstr(_special.special_airy_ai,
+                      r"""
+airy_ai(input, *, out=None) -> Tensor
+
+Airy function :math:`\text{Ai}\left(\text{input}\right)`.
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+bessel_j0 = _add_docstr(_special.special_bessel_j0,
+                        r"""
+bessel_j0(input, *, out=None) -> Tensor
+
+Bessel function of the first kind of order :math:`0`.
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+bessel_j1 = _add_docstr(_special.special_bessel_j1,
+                        r"""
+bessel_j1(input, *, out=None) -> Tensor
+
+Bessel function of the first kind of order :math:`1`.
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+bessel_y0 = _add_docstr(_special.special_bessel_y0,
+                        r"""
+bessel_y0(input, *, out=None) -> Tensor
+
+Bessel function of the second kind of order :math:`0`.
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+bessel_y1 = _add_docstr(_special.special_bessel_y1,
+                        r"""
+bessel_y1(input, *, out=None) -> Tensor
+
+Bessel function of the second kind of order :math:`1`.
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+chebyshev_polynomial_t = _add_docstr(_special.special_chebyshev_polynomial_t,
+                                     r"""
+chebyshev_polynomial_t(input, n, *, out=None) -> Tensor
+
+Chebyshev polynomial of the first kind :math:`T_{n}(\text{input})`.
+
+If :math:`n = 0`, :math:`1` is returned. If :math:`n = 1`, :math:`\text{input}`
+is returned. If :math:`n < 6` or :math:`|\text{input}| > 1` the recursion:
+
+.. math::
+    T_{n + 1}(\text{input}) = 2 \times \text{input} \times T_{n}(\text{input}) - T_{n - 1}(\text{input})
+
+is evaluated. Otherwise, the explicit trigonometric formula:
+
+.. math::
+    T_{n}(\text{input}) = \text{cos}(n \times \text{arccos}(x))
+
+is evaluated.
+
+""" + r"""
+Args:
+    {input}
+    n (Tensor): Degree of the polynomial.
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+chebyshev_polynomial_u = _add_docstr(_special.special_chebyshev_polynomial_u,
+                                     r"""
+chebyshev_polynomial_t(input, n, *, out=None) -> Tensor
+
+Chebyshev polynomial of the second kind :math:`U_{n}(\text{input})`.
+
+If :math:`n = 0`, :math:`1` is returned. If :math:`n = 1`,
+:math:`2 \times \text{input}` is returned. If :math:`n < 6` or
+:math:`|\text{input}| > 1`, the recursion:
+
+.. math::
+    T_{n + 1}(\text{input}) = 2 \times \text{input} \times T_{n}(\text{input}) - T_{n - 1}(\text{input})
+
+is evaluated. Otherwise, the explicit trigonometric formula:
+
+.. math::
+    \frac{\text{sin}((n + 1) \times \text{arccos}(\text{input}))}{\text{sin}(\text{arccos}(\text{input}))}
+
+is evaluated.
+
+""" + r"""
+Args:
+    {input}
+    n (Tensor): Degree of the polynomial.
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+chebyshev_polynomial_v = _add_docstr(_special.special_chebyshev_polynomial_v,
+                                     r"""
+chebyshev_polynomial_v(input, n, *, out=None) -> Tensor
+
+Chebyshev polynomial of the third kind :math:`V_{n}^{\ast}(\text{input})`.
+
+""" + r"""
+Args:
+    {input}
+    n (Tensor): Degree of the polynomial.
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+chebyshev_polynomial_w = _add_docstr(_special.special_chebyshev_polynomial_w,
+                                     r"""
+chebyshev_polynomial_w(input, n, *, out=None) -> Tensor
+
+Chebyshev polynomial of the fourth kind :math:`W_{n}^{\ast}(\text{input})`.
+
+""" + r"""
+Args:
+    {input}
+    n (Tensor): Degree of the polynomial.
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+hermite_polynomial_h = _add_docstr(_special.special_hermite_polynomial_h,
+                                   r"""
+hermite_polynomial_h(input, n, *, out=None) -> Tensor
+
+Physicist's Hermite polynomial :math:`H_{n}(\text{input})`.
+
+If :math:`n = 0`, :math:`1` is returned. If :math:`n = 1`, :math:`\text{input}`
+is returned. Otherwise, the recursion:
+
+.. math::
+    H_{n + 1}(\text{input}) = 2 \times \text{input} \times H_{n}(\text{input}) - H_{n - 1}(\text{input})
+
+is evaluated.
+
+""" + r"""
+Args:
+    {input}
+    n (Tensor): Degree of the polynomial.
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+hermite_polynomial_he = _add_docstr(_special.special_hermite_polynomial_he,
+                                    r"""
+hermite_polynomial_he(input, n, *, out=None) -> Tensor
+
+Probabilist's Hermite polynomial :math:`He_{n}(\text{input})`.
+
+If :math:`n = 0`, :math:`1` is returned. If :math:`n = 1`, :math:`\text{input}`
+is returned. Otherwise, the recursion:
+
+.. math::
+    He_{n + 1}(\text{input}) = 2 \times \text{input} \times He_{n}(\text{input}) - He_{n - 1}(\text{input})
+
+is evaluated.
+
+""" + r"""
+Args:
+    {input}
+    n (Tensor): Degree of the polynomial.
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+laguerre_polynomial_l = _add_docstr(_special.special_laguerre_polynomial_l,
+                                    r"""
+laguerre_polynomial_l(input, n, *, out=None) -> Tensor
+
+Laguerre polynomial :math:`L_{n}(\text{input})`.
+
+If :math:`n = 0`, :math:`1` is returned. If :math:`n = 1`, :math:`\text{input}`
+is returned. Otherwise, the recursion:
+
+.. math::
+    L_{n + 1}(\text{input}) = 2 \times \text{input} \times L_{n}(\text{input}) - L_{n - 1}(\text{input})
+
+is evaluated.
+
+""" + r"""
+Args:
+    {input}
+    n (Tensor): Degree of the polynomial.
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+legendre_polynomial_p = _add_docstr(_special.special_legendre_polynomial_p,
+                                    r"""
+legendre_polynomial_p(input, n, *, out=None) -> Tensor
+
+Legendre polynomial :math:`P_{n}(\text{input})`.
+
+If :math:`n = 0`, :math:`1` is returned. If :math:`n = 1`, :math:`\text{input}`
+is returned. Otherwise, the recursion:
+
+.. math::
+    P_{n + 1}(\text{input}) = 2 \times \text{input} \times P_{n}(\text{input}) - P_{n - 1}(\text{input})
+
+is evaluated.
+
+""" + r"""
+Args:
+    {input}
+    n (Tensor): Degree of the polynomial.
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+modified_bessel_i0 = _add_docstr(_special.special_modified_bessel_i0,
+                                 r"""
+modified_bessel_i0(input, *, out=None) -> Tensor
+
+Modified Bessel function of the first kind of order :math:`0`.
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+modified_bessel_i1 = _add_docstr(_special.special_modified_bessel_i1,
+                                 r"""
+modified_bessel_i1(input, *, out=None) -> Tensor
+
+Modified Bessel function of the first kind of order :math:`1`.
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+modified_bessel_k0 = _add_docstr(_special.special_modified_bessel_k0,
+                                 r"""
+modified_bessel_k0(input, *, out=None) -> Tensor
+
+Modified Bessel function of the second kind of order :math:`0`.
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+modified_bessel_k1 = _add_docstr(_special.special_modified_bessel_k1,
+                                 r"""
+modified_bessel_k1(input, *, out=None) -> Tensor
+
+Modified Bessel function of the second kind of order :math:`1`.
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+scaled_modified_bessel_k0 = _add_docstr(_special.special_scaled_modified_bessel_k0,
+                                        r"""
+scaled_modified_bessel_k0(input, *, out=None) -> Tensor
+
+Scaled modified Bessel function of the second kind of order :math:`0`.
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+scaled_modified_bessel_k1 = _add_docstr(_special.special_scaled_modified_bessel_k1,
+                                        r"""
+scaled_modified_bessel_k1(input, *, out=None) -> Tensor
+
+Scaled modified Bessel function of the second kind of order :math:`1`.
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+shifted_chebyshev_polynomial_t = _add_docstr(_special.special_shifted_chebyshev_polynomial_t,
+                                             r"""
+shifted_chebyshev_polynomial_t(input, n, *, out=None) -> Tensor
+
+Chebyshev polynomial of the first kind :math:`T_{n}^{\ast}(\text{input})`.
+
+""" + r"""
+Args:
+    {input}
+    n (Tensor): Degree of the polynomial.
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+shifted_chebyshev_polynomial_u = _add_docstr(_special.special_shifted_chebyshev_polynomial_u,
+                                             r"""
+shifted_chebyshev_polynomial_u(input, n, *, out=None) -> Tensor
+
+Chebyshev polynomial of the second kind :math:`U_{n}^{\ast}(\text{input})`.
+
+""" + r"""
+Args:
+    {input}
+    n (Tensor): Degree of the polynomial.
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+shifted_chebyshev_polynomial_v = _add_docstr(_special.special_shifted_chebyshev_polynomial_v,
+                                             r"""
+shifted_chebyshev_polynomial_v(input, n, *, out=None) -> Tensor
+
+Chebyshev polynomial of the third kind :math:`V_{n}^{\ast}(\text{input})`.
+
+""" + r"""
+Args:
+    {input}
+    n (Tensor): Degree of the polynomial.
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+shifted_chebyshev_polynomial_w = _add_docstr(_special.special_shifted_chebyshev_polynomial_w,
+                                             r"""
+shifted_chebyshev_polynomial_w(input, n, *, out=None) -> Tensor
+
+Chebyshev polynomial of the fourth kind :math:`W_{n}^{\ast}(\text{input})`.
+
+""" + r"""
+Args:
+    {input}
+    n (Tensor): Degree of the polynomial.
+
+Keyword args:
+    {out}
+""".format(**common_args))
+
+spherical_bessel_j0 = _add_docstr(_special.special_spherical_bessel_j0,
+                                  r"""
+spherical_bessel_j0(input, *, out=None) -> Tensor
+
+Spherical Bessel function of the first kind of order :math:`0`.
+
+""" + r"""
+Args:
+    {input}
+
+Keyword args:
+    {out}
+""".format(**common_args))
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/special/__pycache__/__init__.cpython-311.pyc b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/special/__pycache__/__init__.cpython-311.pyc
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/test/verify_api_visibility b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/test/verify_api_visibility
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/testing/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/testing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..de042277c7c8fd67084cdf70026c1779755a164e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/testing/__init__.py
@@ -0,0 +1,5 @@
+from torch._C import FileCheck as FileCheck
+
+from . import _utils
+from ._comparison import assert_allclose, assert_close as assert_close
+from ._creation import make_tensor as make_tensor
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/testing/_comparison.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/testing/_comparison.py
new file mode 100644
index 0000000000000000000000000000000000000000..668d6bff0d378ee5e3bb4b638b700a3801c17a3f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/testing/_comparison.py
@@ -0,0 +1,1580 @@
+# mypy: allow-untyped-defs
+import abc
+import cmath
+import collections.abc
+import contextlib
+from typing import (
+    Any,
+    Callable,
+    Collection,
+    Dict,
+    List,
+    NoReturn,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
+    Union,
+)
+from typing_extensions import deprecated
+
+import torch
+
+
+try:
+    import numpy as np
+
+    HAS_NUMPY = True
+except ModuleNotFoundError:
+    HAS_NUMPY = False
+    np = None  # type: ignore[assignment]
+
+
+class ErrorMeta(Exception):
+    """Internal testing exception that makes that carries error metadata."""
+
+    def __init__(
+        self, type: Type[Exception], msg: str, *, id: Tuple[Any, ...] = ()
+    ) -> None:
+        super().__init__(
+            "If you are a user and see this message during normal operation "
+            "please file an issue at https://github.com/pytorch/pytorch/issues. "
+            "If you are a developer and working on the comparison functions, please `raise ErrorMeta.to_error()` "
+            "for user facing errors."
+        )
+        self.type = type
+        self.msg = msg
+        self.id = id
+
+    def to_error(
+        self, msg: Optional[Union[str, Callable[[str], str]]] = None
+    ) -> Exception:
+        if not isinstance(msg, str):
+            generated_msg = self.msg
+            if self.id:
+                generated_msg += f"\n\nThe failure occurred for item {''.join(str([item]) for item in self.id)}"
+
+            msg = msg(generated_msg) if callable(msg) else generated_msg
+
+        return self.type(msg)
+
+
+# Some analysis of tolerance by logging tests from test_torch.py can be found in
+# https://github.com/pytorch/pytorch/pull/32538.
+# {dtype: (rtol, atol)}
+_DTYPE_PRECISIONS = {
+    torch.float16: (0.001, 1e-5),
+    torch.bfloat16: (0.016, 1e-5),
+    torch.float32: (1.3e-6, 1e-5),
+    torch.float64: (1e-7, 1e-7),
+    torch.complex32: (0.001, 1e-5),
+    torch.complex64: (1.3e-6, 1e-5),
+    torch.complex128: (1e-7, 1e-7),
+}
+# The default tolerances of torch.float32 are used for quantized dtypes, because quantized tensors are compared in
+# their dequantized and floating point representation. For more details see `TensorLikePair._compare_quantized_values`
+_DTYPE_PRECISIONS.update(
+    dict.fromkeys(
+        (torch.quint8, torch.quint2x4, torch.quint4x2, torch.qint8, torch.qint32),
+        _DTYPE_PRECISIONS[torch.float32],
+    )
+)
+
+
+def default_tolerances(
+    *inputs: Union[torch.Tensor, torch.dtype],
+    dtype_precisions: Optional[Dict[torch.dtype, Tuple[float, float]]] = None,
+) -> Tuple[float, float]:
+    """Returns the default absolute and relative testing tolerances for a set of inputs based on the dtype.
+
+    See :func:`assert_close` for a table of the default tolerance for each dtype.
+
+    Returns:
+        (Tuple[float, float]): Loosest tolerances of all input dtypes.
+    """
+    dtypes = []
+    for input in inputs:
+        if isinstance(input, torch.Tensor):
+            dtypes.append(input.dtype)
+        elif isinstance(input, torch.dtype):
+            dtypes.append(input)
+        else:
+            raise TypeError(
+                f"Expected a torch.Tensor or a torch.dtype, but got {type(input)} instead."
+            )
+    dtype_precisions = dtype_precisions or _DTYPE_PRECISIONS
+    rtols, atols = zip(*[dtype_precisions.get(dtype, (0.0, 0.0)) for dtype in dtypes])
+    return max(rtols), max(atols)
+
+
+def get_tolerances(
+    *inputs: Union[torch.Tensor, torch.dtype],
+    rtol: Optional[float],
+    atol: Optional[float],
+    id: Tuple[Any, ...] = (),
+) -> Tuple[float, float]:
+    """Gets absolute and relative to be used for numeric comparisons.
+
+    If both ``rtol`` and ``atol`` are specified, this is a no-op. If both are not specified, the return value of
+    :func:`default_tolerances` is used.
+
+    Raises:
+        ErrorMeta: With :class:`ValueError`, if only ``rtol`` or ``atol`` is specified.
+
+    Returns:
+        (Tuple[float, float]): Valid absolute and relative tolerances.
+    """
+    if (rtol is None) ^ (atol is None):
+        # We require both tolerance to be omitted or specified, because specifying only one might lead to surprising
+        # results. Imagine setting atol=0.0 and the tensors still match because rtol>0.0.
+        raise ErrorMeta(
+            ValueError,
+            f"Both 'rtol' and 'atol' must be either specified or omitted, "
+            f"but got no {'rtol' if rtol is None else 'atol'}.",
+            id=id,
+        )
+    elif rtol is not None and atol is not None:
+        return rtol, atol
+    else:
+        return default_tolerances(*inputs)
+
+
+def _make_mismatch_msg(
+    *,
+    default_identifier: str,
+    identifier: Optional[Union[str, Callable[[str], str]]] = None,
+    extra: Optional[str] = None,
+    abs_diff: float,
+    abs_diff_idx: Optional[Union[int, Tuple[int, ...]]] = None,
+    atol: float,
+    rel_diff: float,
+    rel_diff_idx: Optional[Union[int, Tuple[int, ...]]] = None,
+    rtol: float,
+) -> str:
+    """Makes a mismatch error message for numeric values.
+
+    Args:
+        default_identifier (str): Default description of the compared values, e.g. "Tensor-likes".
+        identifier (Optional[Union[str, Callable[[str], str]]]): Optional identifier that overrides
+            ``default_identifier``. Can be passed as callable in which case it will be called with
+            ``default_identifier`` to create the description at runtime.
+        extra (Optional[str]): Extra information to be placed after the message header and the mismatch statistics.
+        abs_diff (float): Absolute difference.
+        abs_diff_idx (Optional[Union[int, Tuple[int, ...]]]): Optional index of the absolute difference.
+        atol (float): Allowed absolute tolerance. Will only be added to mismatch statistics if it or ``rtol`` are
+            ``> 0``.
+        rel_diff (float): Relative difference.
+        rel_diff_idx (Optional[Union[int, Tuple[int, ...]]]): Optional index of the relative difference.
+        rtol (float): Allowed relative tolerance. Will only be added to mismatch statistics if it or ``atol`` are
+            ``> 0``.
+    """
+    equality = rtol == 0 and atol == 0
+
+    def make_diff_msg(
+        *,
+        type: str,
+        diff: float,
+        idx: Optional[Union[int, Tuple[int, ...]]],
+        tol: float,
+    ) -> str:
+        if idx is None:
+            msg = f"{type.title()} difference: {diff}"
+        else:
+            msg = f"Greatest {type} difference: {diff} at index {idx}"
+        if not equality:
+            msg += f" (up to {tol} allowed)"
+        return msg + "\n"
+
+    if identifier is None:
+        identifier = default_identifier
+    elif callable(identifier):
+        identifier = identifier(default_identifier)
+
+    msg = f"{identifier} are not {'equal' if equality else 'close'}!\n\n"
+
+    if extra:
+        msg += f"{extra.strip()}\n"
+
+    msg += make_diff_msg(type="absolute", diff=abs_diff, idx=abs_diff_idx, tol=atol)
+    msg += make_diff_msg(type="relative", diff=rel_diff, idx=rel_diff_idx, tol=rtol)
+
+    return msg.strip()
+
+
+def make_scalar_mismatch_msg(
+    actual: Union[bool, int, float, complex],
+    expected: Union[bool, int, float, complex],
+    *,
+    rtol: float,
+    atol: float,
+    identifier: Optional[Union[str, Callable[[str], str]]] = None,
+) -> str:
+    """Makes a mismatch error message for scalars.
+
+    Args:
+        actual (Union[bool, int, float, complex]): Actual scalar.
+        expected (Union[bool, int, float, complex]): Expected scalar.
+        rtol (float): Relative tolerance.
+        atol (float): Absolute tolerance.
+        identifier (Optional[Union[str, Callable[[str], str]]]): Optional description for the scalars. Can be passed
+            as callable in which case it will be called by the default value to create the description at runtime.
+            Defaults to "Scalars".
+    """
+    abs_diff = abs(actual - expected)
+    rel_diff = float("inf") if expected == 0 else abs_diff / abs(expected)
+    return _make_mismatch_msg(
+        default_identifier="Scalars",
+        identifier=identifier,
+        extra=f"Expected {expected} but got {actual}.",
+        abs_diff=abs_diff,
+        atol=atol,
+        rel_diff=rel_diff,
+        rtol=rtol,
+    )
+
+
+def make_tensor_mismatch_msg(
+    actual: torch.Tensor,
+    expected: torch.Tensor,
+    matches: torch.Tensor,
+    *,
+    rtol: float,
+    atol: float,
+    identifier: Optional[Union[str, Callable[[str], str]]] = None,
+):
+    """Makes a mismatch error message for tensors.
+
+    Args:
+        actual (torch.Tensor): Actual tensor.
+        expected (torch.Tensor): Expected tensor.
+        matches (torch.Tensor): Boolean mask of the same shape as ``actual`` and ``expected`` that indicates the
+            location of matches.
+        rtol (float): Relative tolerance.
+        atol (float): Absolute tolerance.
+        identifier (Optional[Union[str, Callable[[str], str]]]): Optional description for the tensors. Can be passed
+            as callable in which case it will be called by the default value to create the description at runtime.
+            Defaults to "Tensor-likes".
+    """
+
+    def unravel_flat_index(flat_index: int) -> Tuple[int, ...]:
+        if not matches.shape:
+            return ()
+
+        inverse_index = []
+        for size in matches.shape[::-1]:
+            div, mod = divmod(flat_index, size)
+            flat_index = div
+            inverse_index.append(mod)
+
+        return tuple(inverse_index[::-1])
+
+    number_of_elements = matches.numel()
+    total_mismatches = number_of_elements - int(torch.sum(matches))
+    extra = (
+        f"Mismatched elements: {total_mismatches} / {number_of_elements} "
+        f"({total_mismatches / number_of_elements:.1%})"
+    )
+
+    actual_flat = actual.flatten()
+    expected_flat = expected.flatten()
+    matches_flat = matches.flatten()
+
+    if not actual.dtype.is_floating_point and not actual.dtype.is_complex:
+        # TODO: Instead of always upcasting to int64, it would be sufficient to cast to the next higher dtype to avoid
+        #  overflow
+        actual_flat = actual_flat.to(torch.int64)
+        expected_flat = expected_flat.to(torch.int64)
+
+    abs_diff = torch.abs(actual_flat - expected_flat)
+    # Ensure that only mismatches are used for the max_abs_diff computation
+    abs_diff[matches_flat] = 0
+    max_abs_diff, max_abs_diff_flat_idx = torch.max(abs_diff, 0)
+
+    rel_diff = abs_diff / torch.abs(expected_flat)
+    # Ensure that only mismatches are used for the max_rel_diff computation
+    rel_diff[matches_flat] = 0
+    max_rel_diff, max_rel_diff_flat_idx = torch.max(rel_diff, 0)
+    return _make_mismatch_msg(
+        default_identifier="Tensor-likes",
+        identifier=identifier,
+        extra=extra,
+        abs_diff=max_abs_diff.item(),
+        abs_diff_idx=unravel_flat_index(int(max_abs_diff_flat_idx)),
+        atol=atol,
+        rel_diff=max_rel_diff.item(),
+        rel_diff_idx=unravel_flat_index(int(max_rel_diff_flat_idx)),
+        rtol=rtol,
+    )
+
+
+class UnsupportedInputs(Exception):  # noqa: B903
+    """Exception to be raised during the construction of a :class:`Pair` in case it doesn't support the inputs."""
+
+
+class Pair(abc.ABC):
+    """ABC for all comparison pairs to be used in conjunction with :func:`assert_equal`.
+
+    Each subclass needs to overwrite :meth:`Pair.compare` that performs the actual comparison.
+
+    Each pair receives **all** options, so select the ones applicable for the subclass and forward the rest to the
+    super class. Raising an :class:`UnsupportedInputs` during constructions indicates that the pair is not able to
+    handle the inputs and the next pair type will be tried.
+
+    All other errors should be raised as :class:`ErrorMeta`. After the instantiation, :meth:`Pair._make_error_meta` can
+    be used to automatically handle overwriting the message with a user supplied one and id handling.
+    """
+
+    def __init__(
+        self,
+        actual: Any,
+        expected: Any,
+        *,
+        id: Tuple[Any, ...] = (),
+        **unknown_parameters: Any,
+    ) -> None:
+        self.actual = actual
+        self.expected = expected
+        self.id = id
+        self._unknown_parameters = unknown_parameters
+
+    @staticmethod
+    def _inputs_not_supported() -> NoReturn:
+        raise UnsupportedInputs
+
+    @staticmethod
+    def _check_inputs_isinstance(*inputs: Any, cls: Union[Type, Tuple[Type, ...]]):
+        """Checks if all inputs are instances of a given class and raise :class:`UnsupportedInputs` otherwise."""
+        if not all(isinstance(input, cls) for input in inputs):
+            Pair._inputs_not_supported()
+
+    def _fail(
+        self, type: Type[Exception], msg: str, *, id: Tuple[Any, ...] = ()
+    ) -> NoReturn:
+        """Raises an :class:`ErrorMeta` from a given exception type and message and the stored id.
+
+        .. warning::
+
+            If you use this before the ``super().__init__(...)`` call in the constructor, you have to pass the ``id``
+            explicitly.
+        """
+        raise ErrorMeta(type, msg, id=self.id if not id and hasattr(self, "id") else id)
+
+    @abc.abstractmethod
+    def compare(self) -> None:
+        """Compares the inputs and raises an :class`ErrorMeta` in case they mismatch."""
+
+    def extra_repr(self) -> Sequence[Union[str, Tuple[str, Any]]]:
+        """Returns extra information that will be included in the representation.
+
+        Should be overwritten by all subclasses that use additional options. The representation of the object will only
+        be surfaced in case we encounter an unexpected error and thus should help debug the issue. Can be a sequence of
+        key-value-pairs or attribute names.
+        """
+        return []
+
+    def __repr__(self) -> str:
+        head = f"{type(self).__name__}("
+        tail = ")"
+        body = [
+            f"    {name}={value!s},"
+            for name, value in [
+                ("id", self.id),
+                ("actual", self.actual),
+                ("expected", self.expected),
+                *[
+                    (extra, getattr(self, extra)) if isinstance(extra, str) else extra
+                    for extra in self.extra_repr()
+                ],
+            ]
+        ]
+        return "\n".join((head, *body, *tail))
+
+
+class ObjectPair(Pair):
+    """Pair for any type of inputs that will be compared with the `==` operator.
+
+    .. note::
+
+        Since this will instantiate for any kind of inputs, it should only be used as fallback after all other pairs
+        couldn't handle the inputs.
+
+    """
+
+    def compare(self) -> None:
+        try:
+            equal = self.actual == self.expected
+        except Exception as error:
+            # We are not using `self._raise_error_meta` here since we need the exception chaining
+            raise ErrorMeta(
+                ValueError,
+                f"{self.actual} == {self.expected} failed with:\n{error}.",
+                id=self.id,
+            ) from error
+
+        if not equal:
+            self._fail(AssertionError, f"{self.actual} != {self.expected}")
+
+
+class NonePair(Pair):
+    """Pair for ``None`` inputs."""
+
+    def __init__(self, actual: Any, expected: Any, **other_parameters: Any) -> None:
+        if not (actual is None or expected is None):
+            self._inputs_not_supported()
+
+        super().__init__(actual, expected, **other_parameters)
+
+    def compare(self) -> None:
+        if not (self.actual is None and self.expected is None):
+            self._fail(
+                AssertionError, f"None mismatch: {self.actual} is not {self.expected}"
+            )
+
+
+class BooleanPair(Pair):
+    """Pair for :class:`bool` inputs.
+
+    .. note::
+
+        If ``numpy`` is available, also handles :class:`numpy.bool_` inputs.
+
+    """
+
+    def __init__(
+        self,
+        actual: Any,
+        expected: Any,
+        *,
+        id: Tuple[Any, ...],
+        **other_parameters: Any,
+    ) -> None:
+        actual, expected = self._process_inputs(actual, expected, id=id)
+        super().__init__(actual, expected, **other_parameters)
+
+    @property
+    def _supported_types(self) -> Tuple[Type, ...]:
+        cls: List[Type] = [bool]
+        if HAS_NUMPY:
+            cls.append(np.bool_)
+        return tuple(cls)
+
+    def _process_inputs(
+        self, actual: Any, expected: Any, *, id: Tuple[Any, ...]
+    ) -> Tuple[bool, bool]:
+        self._check_inputs_isinstance(actual, expected, cls=self._supported_types)
+        actual, expected = (
+            self._to_bool(bool_like, id=id) for bool_like in (actual, expected)
+        )
+        return actual, expected
+
+    def _to_bool(self, bool_like: Any, *, id: Tuple[Any, ...]) -> bool:
+        if isinstance(bool_like, bool):
+            return bool_like
+        elif isinstance(bool_like, np.bool_):
+            return bool_like.item()
+        else:
+            raise ErrorMeta(
+                TypeError, f"Unknown boolean type {type(bool_like)}.", id=id
+            )
+
+    def compare(self) -> None:
+        if self.actual is not self.expected:
+            self._fail(
+                AssertionError,
+                f"Booleans mismatch: {self.actual} is not {self.expected}",
+            )
+
+
+class NumberPair(Pair):
+    """Pair for Python number (:class:`int`, :class:`float`, and :class:`complex`) inputs.
+
+    .. note::
+
+        If ``numpy`` is available, also handles :class:`numpy.number` inputs.
+
+    Kwargs:
+        rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default
+            values based on the type are selected with the below table.
+        atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default
+            values based on the type are selected with the below table.
+        equal_nan (bool): If ``True``, two ``NaN`` values are considered equal. Defaults to ``False``.
+        check_dtype (bool): If ``True``, the type of the inputs will be checked for equality. Defaults to ``False``.
+
+    The following table displays correspondence between Python number type and the ``torch.dtype``'s. See
+    :func:`assert_close` for the corresponding tolerances.
+
+    +------------------+-------------------------------+
+    | ``type``         | corresponding ``torch.dtype`` |
+    +==================+===============================+
+    | :class:`int`     | :attr:`~torch.int64`          |
+    +------------------+-------------------------------+
+    | :class:`float`   | :attr:`~torch.float64`        |
+    +------------------+-------------------------------+
+    | :class:`complex` | :attr:`~torch.complex64`      |
+    +------------------+-------------------------------+
+    """
+
+    _TYPE_TO_DTYPE = {
+        int: torch.int64,
+        float: torch.float64,
+        complex: torch.complex128,
+    }
+    _NUMBER_TYPES = tuple(_TYPE_TO_DTYPE.keys())
+
+    def __init__(
+        self,
+        actual: Any,
+        expected: Any,
+        *,
+        id: Tuple[Any, ...] = (),
+        rtol: Optional[float] = None,
+        atol: Optional[float] = None,
+        equal_nan: bool = False,
+        check_dtype: bool = False,
+        **other_parameters: Any,
+    ) -> None:
+        actual, expected = self._process_inputs(actual, expected, id=id)
+        super().__init__(actual, expected, id=id, **other_parameters)
+
+        self.rtol, self.atol = get_tolerances(
+            *[self._TYPE_TO_DTYPE[type(input)] for input in (actual, expected)],
+            rtol=rtol,
+            atol=atol,
+            id=id,
+        )
+        self.equal_nan = equal_nan
+        self.check_dtype = check_dtype
+
+    @property
+    def _supported_types(self) -> Tuple[Type, ...]:
+        cls = list(self._NUMBER_TYPES)
+        if HAS_NUMPY:
+            cls.append(np.number)
+        return tuple(cls)
+
+    def _process_inputs(
+        self, actual: Any, expected: Any, *, id: Tuple[Any, ...]
+    ) -> Tuple[Union[int, float, complex], Union[int, float, complex]]:
+        self._check_inputs_isinstance(actual, expected, cls=self._supported_types)
+        actual, expected = (
+            self._to_number(number_like, id=id) for number_like in (actual, expected)
+        )
+        return actual, expected
+
+    def _to_number(
+        self, number_like: Any, *, id: Tuple[Any, ...]
+    ) -> Union[int, float, complex]:
+        if HAS_NUMPY and isinstance(number_like, np.number):
+            return number_like.item()
+        elif isinstance(number_like, self._NUMBER_TYPES):
+            return number_like  # type: ignore[return-value]
+        else:
+            raise ErrorMeta(
+                TypeError, f"Unknown number type {type(number_like)}.", id=id
+            )
+
+    def compare(self) -> None:
+        if self.check_dtype and type(self.actual) is not type(self.expected):
+            self._fail(
+                AssertionError,
+                f"The (d)types do not match: {type(self.actual)} != {type(self.expected)}.",
+            )
+
+        if self.actual == self.expected:
+            return
+
+        if self.equal_nan and cmath.isnan(self.actual) and cmath.isnan(self.expected):
+            return
+
+        abs_diff = abs(self.actual - self.expected)
+        tolerance = self.atol + self.rtol * abs(self.expected)
+
+        if cmath.isfinite(abs_diff) and abs_diff <= tolerance:
+            return
+
+        self._fail(
+            AssertionError,
+            make_scalar_mismatch_msg(
+                self.actual, self.expected, rtol=self.rtol, atol=self.atol
+            ),
+        )
+
+    def extra_repr(self) -> Sequence[str]:
+        return (
+            "rtol",
+            "atol",
+            "equal_nan",
+            "check_dtype",
+        )
+
+
+class TensorLikePair(Pair):
+    """Pair for :class:`torch.Tensor`-like inputs.
+
+    Kwargs:
+        allow_subclasses (bool):
+        rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default
+            values based on the type are selected. See :func:assert_close: for details.
+        atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default
+            values based on the type are selected. See :func:assert_close: for details.
+        equal_nan (bool): If ``True``, two ``NaN`` values are considered equal. Defaults to ``False``.
+        check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same
+            :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different
+            :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared.
+        check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this
+            check is disabled, tensors with different ``dtype``'s are promoted  to a common ``dtype`` (according to
+            :func:`torch.promote_types`) before being compared.
+        check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this
+            check is disabled, tensors with different ``layout``'s are converted to strided tensors before being
+            compared.
+        check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride.
+    """
+
+    def __init__(
+        self,
+        actual: Any,
+        expected: Any,
+        *,
+        id: Tuple[Any, ...] = (),
+        allow_subclasses: bool = True,
+        rtol: Optional[float] = None,
+        atol: Optional[float] = None,
+        equal_nan: bool = False,
+        check_device: bool = True,
+        check_dtype: bool = True,
+        check_layout: bool = True,
+        check_stride: bool = False,
+        **other_parameters: Any,
+    ):
+        actual, expected = self._process_inputs(
+            actual, expected, id=id, allow_subclasses=allow_subclasses
+        )
+        super().__init__(actual, expected, id=id, **other_parameters)
+
+        self.rtol, self.atol = get_tolerances(
+            actual, expected, rtol=rtol, atol=atol, id=self.id
+        )
+        self.equal_nan = equal_nan
+        self.check_device = check_device
+        self.check_dtype = check_dtype
+        self.check_layout = check_layout
+        self.check_stride = check_stride
+
+    def _process_inputs(
+        self, actual: Any, expected: Any, *, id: Tuple[Any, ...], allow_subclasses: bool
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        directly_related = isinstance(actual, type(expected)) or isinstance(
+            expected, type(actual)
+        )
+        if not directly_related:
+            self._inputs_not_supported()
+
+        if not allow_subclasses and type(actual) is not type(expected):
+            self._inputs_not_supported()
+
+        actual, expected = (self._to_tensor(input) for input in (actual, expected))
+        for tensor in (actual, expected):
+            self._check_supported(tensor, id=id)
+        return actual, expected
+
+    def _to_tensor(self, tensor_like: Any) -> torch.Tensor:
+        if isinstance(tensor_like, torch.Tensor):
+            return tensor_like
+
+        try:
+            return torch.as_tensor(tensor_like)
+        except Exception:
+            self._inputs_not_supported()
+
+    def _check_supported(self, tensor: torch.Tensor, *, id: Tuple[Any, ...]) -> None:
+        if tensor.layout not in {
+            torch.strided,
+            torch.jagged,
+            torch.sparse_coo,
+            torch.sparse_csr,
+            torch.sparse_csc,
+            torch.sparse_bsr,
+            torch.sparse_bsc,
+        }:
+            raise ErrorMeta(
+                ValueError, f"Unsupported tensor layout {tensor.layout}", id=id
+            )
+
+    def compare(self) -> None:
+        actual, expected = self.actual, self.expected
+
+        self._compare_attributes(actual, expected)
+        if any(input.device.type == "meta" for input in (actual, expected)):
+            return
+
+        actual, expected = self._equalize_attributes(actual, expected)
+        self._compare_values(actual, expected)
+
+    def _compare_attributes(
+        self,
+        actual: torch.Tensor,
+        expected: torch.Tensor,
+    ) -> None:
+        """Checks if the attributes of two tensors match.
+
+        Always checks
+
+        - the :attr:`~torch.Tensor.shape`,
+        - whether both inputs are quantized or not,
+        - and if they use the same quantization scheme.
+
+        Checks for
+
+        - :attr:`~torch.Tensor.layout`,
+        - :meth:`~torch.Tensor.stride`,
+        - :attr:`~torch.Tensor.device`, and
+        - :attr:`~torch.Tensor.dtype`
+
+        are optional and can be disabled through the corresponding ``check_*`` flag during construction of the pair.
+        """
+
+        def raise_mismatch_error(
+            attribute_name: str, actual_value: Any, expected_value: Any
+        ) -> NoReturn:
+            self._fail(
+                AssertionError,
+                f"The values for attribute '{attribute_name}' do not match: {actual_value} != {expected_value}.",
+            )
+
+        if actual.shape != expected.shape:
+            raise_mismatch_error("shape", actual.shape, expected.shape)
+
+        if actual.is_quantized != expected.is_quantized:
+            raise_mismatch_error(
+                "is_quantized", actual.is_quantized, expected.is_quantized
+            )
+        elif actual.is_quantized and actual.qscheme() != expected.qscheme():
+            raise_mismatch_error("qscheme()", actual.qscheme(), expected.qscheme())
+
+        if actual.layout != expected.layout:
+            if self.check_layout:
+                raise_mismatch_error("layout", actual.layout, expected.layout)
+        elif (
+            actual.layout == torch.strided
+            and self.check_stride
+            and actual.stride() != expected.stride()
+        ):
+            raise_mismatch_error("stride()", actual.stride(), expected.stride())
+
+        if self.check_device and actual.device != expected.device:
+            raise_mismatch_error("device", actual.device, expected.device)
+
+        if self.check_dtype and actual.dtype != expected.dtype:
+            raise_mismatch_error("dtype", actual.dtype, expected.dtype)
+
+    def _equalize_attributes(
+        self, actual: torch.Tensor, expected: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Equalizes some attributes of two tensors for value comparison.
+
+        If ``actual`` and ``expected`` are ...
+
+        - ... not on the same :attr:`~torch.Tensor.device`, they are moved CPU memory.
+        - ... not of the same ``dtype``, they are promoted  to a common ``dtype`` (according to
+            :func:`torch.promote_types`).
+        - ... not of the same ``layout``, they are converted to strided tensors.
+
+        Args:
+            actual (Tensor): Actual tensor.
+            expected (Tensor): Expected tensor.
+
+        Returns:
+            (Tuple[Tensor, Tensor]): Equalized tensors.
+        """
+        # The comparison logic uses operators currently not supported by the MPS backends.
+        #  See https://github.com/pytorch/pytorch/issues/77144 for details.
+        # TODO: Remove this conversion as soon as all operations are supported natively by the MPS backend
+        if actual.is_mps or expected.is_mps:  # type: ignore[attr-defined]
+            actual = actual.cpu()
+            expected = expected.cpu()
+
+        if actual.device != expected.device:
+            actual = actual.cpu()
+            expected = expected.cpu()
+
+        if actual.dtype != expected.dtype:
+            actual_dtype = actual.dtype
+            expected_dtype = expected.dtype
+            # For uint64, this is not sound in general, which is why promote_types doesn't
+            # allow it, but for easy testing, we're unlikely to get confused
+            # by large uint64 overflowing into negative int64
+            if actual_dtype in [torch.uint64, torch.uint32, torch.uint16]:
+                actual_dtype = torch.int64
+            if expected_dtype in [torch.uint64, torch.uint32, torch.uint16]:
+                expected_dtype = torch.int64
+            dtype = torch.promote_types(actual_dtype, expected_dtype)
+            actual = actual.to(dtype)
+            expected = expected.to(dtype)
+
+        if actual.layout != expected.layout:
+            # These checks are needed, since Tensor.to_dense() fails on tensors that are already strided
+            actual = actual.to_dense() if actual.layout != torch.strided else actual
+            expected = (
+                expected.to_dense() if expected.layout != torch.strided else expected
+            )
+
+        return actual, expected
+
+    def _compare_values(self, actual: torch.Tensor, expected: torch.Tensor) -> None:
+        if actual.is_quantized:
+            compare_fn = self._compare_quantized_values
+        elif actual.is_sparse:
+            compare_fn = self._compare_sparse_coo_values
+        elif actual.layout in {
+            torch.sparse_csr,
+            torch.sparse_csc,
+            torch.sparse_bsr,
+            torch.sparse_bsc,
+        }:
+            compare_fn = self._compare_sparse_compressed_values
+        elif actual.layout == torch.jagged:
+            actual, expected = actual.values(), expected.values()
+            compare_fn = self._compare_regular_values_close
+        else:
+            compare_fn = self._compare_regular_values_close
+
+        compare_fn(
+            actual, expected, rtol=self.rtol, atol=self.atol, equal_nan=self.equal_nan
+        )
+
+    def _compare_quantized_values(
+        self,
+        actual: torch.Tensor,
+        expected: torch.Tensor,
+        *,
+        rtol: float,
+        atol: float,
+        equal_nan: bool,
+    ) -> None:
+        """Compares quantized tensors by comparing the :meth:`~torch.Tensor.dequantize`'d variants for closeness.
+
+        .. note::
+
+            A detailed discussion about why only the dequantized variant is checked for closeness rather than checking
+            the individual quantization parameters for closeness and the integer representation for equality can be
+            found in https://github.com/pytorch/pytorch/issues/68548.
+        """
+        return self._compare_regular_values_close(
+            actual.dequantize(),
+            expected.dequantize(),
+            rtol=rtol,
+            atol=atol,
+            equal_nan=equal_nan,
+            identifier=lambda default_identifier: f"Quantized {default_identifier.lower()}",
+        )
+
+    def _compare_sparse_coo_values(
+        self,
+        actual: torch.Tensor,
+        expected: torch.Tensor,
+        *,
+        rtol: float,
+        atol: float,
+        equal_nan: bool,
+    ) -> None:
+        """Compares sparse COO tensors by comparing
+
+        - the number of sparse dimensions,
+        - the number of non-zero elements (nnz) for equality,
+        - the indices for equality, and
+        - the values for closeness.
+        """
+        if actual.sparse_dim() != expected.sparse_dim():
+            self._fail(
+                AssertionError,
+                (
+                    f"The number of sparse dimensions in sparse COO tensors does not match: "
+                    f"{actual.sparse_dim()} != {expected.sparse_dim()}"
+                ),
+            )
+
+        if actual._nnz() != expected._nnz():
+            self._fail(
+                AssertionError,
+                (
+                    f"The number of specified values in sparse COO tensors does not match: "
+                    f"{actual._nnz()} != {expected._nnz()}"
+                ),
+            )
+
+        self._compare_regular_values_equal(
+            actual._indices(),
+            expected._indices(),
+            identifier="Sparse COO indices",
+        )
+        self._compare_regular_values_close(
+            actual._values(),
+            expected._values(),
+            rtol=rtol,
+            atol=atol,
+            equal_nan=equal_nan,
+            identifier="Sparse COO values",
+        )
+
+    def _compare_sparse_compressed_values(
+        self,
+        actual: torch.Tensor,
+        expected: torch.Tensor,
+        *,
+        rtol: float,
+        atol: float,
+        equal_nan: bool,
+    ) -> None:
+        """Compares sparse compressed tensors by comparing
+
+        - the number of non-zero elements (nnz) for equality,
+        - the plain indices for equality,
+        - the compressed indices for equality, and
+        - the values for closeness.
+        """
+        format_name, compressed_indices_method, plain_indices_method = {
+            torch.sparse_csr: (
+                "CSR",
+                torch.Tensor.crow_indices,
+                torch.Tensor.col_indices,
+            ),
+            torch.sparse_csc: (
+                "CSC",
+                torch.Tensor.ccol_indices,
+                torch.Tensor.row_indices,
+            ),
+            torch.sparse_bsr: (
+                "BSR",
+                torch.Tensor.crow_indices,
+                torch.Tensor.col_indices,
+            ),
+            torch.sparse_bsc: (
+                "BSC",
+                torch.Tensor.ccol_indices,
+                torch.Tensor.row_indices,
+            ),
+        }[actual.layout]
+
+        if actual._nnz() != expected._nnz():
+            self._fail(
+                AssertionError,
+                (
+                    f"The number of specified values in sparse {format_name} tensors does not match: "
+                    f"{actual._nnz()} != {expected._nnz()}"
+                ),
+            )
+
+        # Compressed and plain indices in the CSR / CSC / BSR / BSC sparse formates can be `torch.int32` _or_
+        # `torch.int64`. While the same dtype is enforced for the compressed and plain indices of a single tensor, it
+        # can be different between two tensors. Thus, we need to convert them to the same dtype, or the comparison will
+        # fail.
+        actual_compressed_indices = compressed_indices_method(actual)
+        expected_compressed_indices = compressed_indices_method(expected)
+        indices_dtype = torch.promote_types(
+            actual_compressed_indices.dtype, expected_compressed_indices.dtype
+        )
+
+        self._compare_regular_values_equal(
+            actual_compressed_indices.to(indices_dtype),
+            expected_compressed_indices.to(indices_dtype),
+            identifier=f"Sparse {format_name} {compressed_indices_method.__name__}",
+        )
+        self._compare_regular_values_equal(
+            plain_indices_method(actual).to(indices_dtype),
+            plain_indices_method(expected).to(indices_dtype),
+            identifier=f"Sparse {format_name} {plain_indices_method.__name__}",
+        )
+        self._compare_regular_values_close(
+            actual.values(),
+            expected.values(),
+            rtol=rtol,
+            atol=atol,
+            equal_nan=equal_nan,
+            identifier=f"Sparse {format_name} values",
+        )
+
+    def _compare_regular_values_equal(
+        self,
+        actual: torch.Tensor,
+        expected: torch.Tensor,
+        *,
+        equal_nan: bool = False,
+        identifier: Optional[Union[str, Callable[[str], str]]] = None,
+    ) -> None:
+        """Checks if the values of two tensors are equal."""
+        self._compare_regular_values_close(
+            actual, expected, rtol=0, atol=0, equal_nan=equal_nan, identifier=identifier
+        )
+
+    def _compare_regular_values_close(
+        self,
+        actual: torch.Tensor,
+        expected: torch.Tensor,
+        *,
+        rtol: float,
+        atol: float,
+        equal_nan: bool,
+        identifier: Optional[Union[str, Callable[[str], str]]] = None,
+    ) -> None:
+        """Checks if the values of two tensors are close up to a desired tolerance."""
+        matches = torch.isclose(
+            actual, expected, rtol=rtol, atol=atol, equal_nan=equal_nan
+        )
+        if torch.all(matches):
+            return
+
+        if actual.shape == torch.Size([]):
+            msg = make_scalar_mismatch_msg(
+                actual.item(),
+                expected.item(),
+                rtol=rtol,
+                atol=atol,
+                identifier=identifier,
+            )
+        else:
+            msg = make_tensor_mismatch_msg(
+                actual, expected, matches, rtol=rtol, atol=atol, identifier=identifier
+            )
+        self._fail(AssertionError, msg)
+
+    def extra_repr(self) -> Sequence[str]:
+        return (
+            "rtol",
+            "atol",
+            "equal_nan",
+            "check_device",
+            "check_dtype",
+            "check_layout",
+            "check_stride",
+        )
+
+
+def originate_pairs(
+    actual: Any,
+    expected: Any,
+    *,
+    pair_types: Sequence[Type[Pair]],
+    sequence_types: Tuple[Type, ...] = (collections.abc.Sequence,),
+    mapping_types: Tuple[Type, ...] = (collections.abc.Mapping,),
+    id: Tuple[Any, ...] = (),
+    **options: Any,
+) -> List[Pair]:
+    """Originates pairs from the individual inputs.
+
+    ``actual`` and ``expected`` can be possibly nested :class:`~collections.abc.Sequence`'s or
+    :class:`~collections.abc.Mapping`'s. In this case the pairs are originated by recursing through them.
+
+    Args:
+        actual (Any): Actual input.
+        expected (Any): Expected input.
+        pair_types (Sequence[Type[Pair]]): Sequence of pair types that will be tried to construct with the inputs.
+            First successful pair will be used.
+        sequence_types (Tuple[Type, ...]): Optional types treated as sequences that will be checked elementwise.
+        mapping_types (Tuple[Type, ...]): Optional types treated as mappings that will be checked elementwise.
+        id (Tuple[Any, ...]): Optional id of a pair that will be included in an error message.
+        **options (Any): Options passed to each pair during construction.
+
+    Raises:
+        ErrorMeta: With :class`AssertionError`, if the inputs are :class:`~collections.abc.Sequence`'s, but their
+            length does not match.
+        ErrorMeta: With :class`AssertionError`, if the inputs are :class:`~collections.abc.Mapping`'s, but their set of
+            keys do not match.
+        ErrorMeta: With :class`TypeError`, if no pair is able to handle the inputs.
+        ErrorMeta: With any expected exception that happens during the construction of a pair.
+
+    Returns:
+        (List[Pair]): Originated pairs.
+    """
+    # We explicitly exclude str's here since they are self-referential and would cause an infinite recursion loop:
+    # "a" == "a"[0][0]...
+    if (
+        isinstance(actual, sequence_types)
+        and not isinstance(actual, str)
+        and isinstance(expected, sequence_types)
+        and not isinstance(expected, str)
+    ):
+        actual_len = len(actual)
+        expected_len = len(expected)
+        if actual_len != expected_len:
+            raise ErrorMeta(
+                AssertionError,
+                f"The length of the sequences mismatch: {actual_len} != {expected_len}",
+                id=id,
+            )
+
+        pairs = []
+        for idx in range(actual_len):
+            pairs.extend(
+                originate_pairs(
+                    actual[idx],
+                    expected[idx],
+                    pair_types=pair_types,
+                    sequence_types=sequence_types,
+                    mapping_types=mapping_types,
+                    id=(*id, idx),
+                    **options,
+                )
+            )
+        return pairs
+
+    elif isinstance(actual, mapping_types) and isinstance(expected, mapping_types):
+        actual_keys = set(actual.keys())
+        expected_keys = set(expected.keys())
+        if actual_keys != expected_keys:
+            missing_keys = expected_keys - actual_keys
+            additional_keys = actual_keys - expected_keys
+            raise ErrorMeta(
+                AssertionError,
+                (
+                    f"The keys of the mappings do not match:\n"
+                    f"Missing keys in the actual mapping: {sorted(missing_keys)}\n"
+                    f"Additional keys in the actual mapping: {sorted(additional_keys)}"
+                ),
+                id=id,
+            )
+
+        keys: Collection = actual_keys
+        # Since the origination aborts after the first failure, we try to be deterministic
+        with contextlib.suppress(Exception):
+            keys = sorted(keys)
+
+        pairs = []
+        for key in keys:
+            pairs.extend(
+                originate_pairs(
+                    actual[key],
+                    expected[key],
+                    pair_types=pair_types,
+                    sequence_types=sequence_types,
+                    mapping_types=mapping_types,
+                    id=(*id, key),
+                    **options,
+                )
+            )
+        return pairs
+
+    else:
+        for pair_type in pair_types:
+            try:
+                return [pair_type(actual, expected, id=id, **options)]
+            # Raising an `UnsupportedInputs` during origination indicates that the pair type is not able to handle the
+            # inputs. Thus, we try the next pair type.
+            except UnsupportedInputs:
+                continue
+            # Raising an `ErrorMeta` during origination is the orderly way to abort and so we simply re-raise it. This
+            # is only in a separate branch, because the one below would also except it.
+            except ErrorMeta:
+                raise
+            # Raising any other exception during origination is unexpected and will give some extra information about
+            # what happened. If applicable, the exception should be expected in the future.
+            except Exception as error:
+                raise RuntimeError(
+                    f"Originating a {pair_type.__name__}() at item {''.join(str([item]) for item in id)} with\n\n"
+                    f"{type(actual).__name__}(): {actual}\n\n"
+                    f"and\n\n"
+                    f"{type(expected).__name__}(): {expected}\n\n"
+                    f"resulted in the unexpected exception above. "
+                    f"If you are a user and see this message during normal operation "
+                    "please file an issue at https://github.com/pytorch/pytorch/issues. "
+                    "If you are a developer and working on the comparison functions, "
+                    "please except the previous error and raise an expressive `ErrorMeta` instead."
+                ) from error
+        else:
+            raise ErrorMeta(
+                TypeError,
+                f"No comparison pair was able to handle inputs of type {type(actual)} and {type(expected)}.",
+                id=id,
+            )
+
+
+def not_close_error_metas(
+    actual: Any,
+    expected: Any,
+    *,
+    pair_types: Sequence[Type[Pair]] = (ObjectPair,),
+    sequence_types: Tuple[Type, ...] = (collections.abc.Sequence,),
+    mapping_types: Tuple[Type, ...] = (collections.abc.Mapping,),
+    **options: Any,
+) -> List[ErrorMeta]:
+    """Asserts that inputs are equal.
+
+    ``actual`` and ``expected`` can be possibly nested :class:`~collections.abc.Sequence`'s or
+    :class:`~collections.abc.Mapping`'s. In this case the comparison happens elementwise by recursing through them.
+
+    Args:
+        actual (Any): Actual input.
+        expected (Any): Expected input.
+        pair_types (Sequence[Type[Pair]]): Sequence of :class:`Pair` types that will be tried to construct with the
+            inputs. First successful pair will be used. Defaults to only using :class:`ObjectPair`.
+        sequence_types (Tuple[Type, ...]): Optional types treated as sequences that will be checked elementwise.
+        mapping_types (Tuple[Type, ...]): Optional types treated as mappings that will be checked elementwise.
+        **options (Any): Options passed to each pair during construction.
+    """
+    # Hide this function from `pytest`'s traceback
+    __tracebackhide__ = True
+
+    try:
+        pairs = originate_pairs(
+            actual,
+            expected,
+            pair_types=pair_types,
+            sequence_types=sequence_types,
+            mapping_types=mapping_types,
+            **options,
+        )
+    except ErrorMeta as error_meta:
+        # Explicitly raising from None to hide the internal traceback
+        raise error_meta.to_error() from None  # noqa: RSE102
+
+    error_metas: List[ErrorMeta] = []
+    for pair in pairs:
+        try:
+            pair.compare()
+        except ErrorMeta as error_meta:
+            error_metas.append(error_meta)
+        # Raising any exception besides `ErrorMeta` while comparing is unexpected and will give some extra information
+        # about what happened. If applicable, the exception should be expected in the future.
+        except Exception as error:
+            raise RuntimeError(
+                f"Comparing\n\n"
+                f"{pair}\n\n"
+                f"resulted in the unexpected exception above. "
+                f"If you are a user and see this message during normal operation "
+                "please file an issue at https://github.com/pytorch/pytorch/issues. "
+                "If you are a developer and working on the comparison functions, "
+                "please except the previous error and raise an expressive `ErrorMeta` instead."
+            ) from error
+
+    # [ErrorMeta Cycles]
+    # ErrorMeta objects in this list capture
+    # tracebacks that refer to the frame of this function.
+    # The local variable `error_metas` refers to the error meta
+    # objects, creating a reference cycle. Frames in the traceback
+    # would not get freed until cycle collection, leaking cuda memory in tests.
+    # We break the cycle by removing the reference to the error_meta objects
+    # from this frame as it returns.
+    error_metas = [error_metas]
+    return error_metas.pop()
+
+
+def assert_close(
+    actual: Any,
+    expected: Any,
+    *,
+    allow_subclasses: bool = True,
+    rtol: Optional[float] = None,
+    atol: Optional[float] = None,
+    equal_nan: bool = False,
+    check_device: bool = True,
+    check_dtype: bool = True,
+    check_layout: bool = True,
+    check_stride: bool = False,
+    msg: Optional[Union[str, Callable[[str], str]]] = None,
+):
+    r"""Asserts that ``actual`` and ``expected`` are close.
+
+    If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if
+
+    .. math::
+
+        \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert
+
+    Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are
+    only considered equal to each other if ``equal_nan`` is ``True``.
+
+    In addition, they are only considered close if they have the same
+
+    - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``),
+    - ``dtype`` (if ``check_dtype`` is ``True``),
+    - ``layout`` (if ``check_layout`` is ``True``), and
+    - stride (if ``check_stride`` is ``True``).
+
+    If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed.
+
+    If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are
+    checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR,
+    or ``ccol_indices``  and ``row_indices`` for CSC and BSC layouts, respectively,
+    are always checked for equality whereas the values are checked for closeness according to the definition above.
+
+    If ``actual`` and ``expected`` are quantized, they are considered close if they have the same
+    :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the
+    definition above.
+
+    ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which
+    :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types
+    have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s
+    or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all
+    their elements are considered close according to the above definition.
+
+    .. note::
+
+        Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e.
+        :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus,
+        Python scalars of different types can be checked, but require ``check_dtype=False``.
+
+    Args:
+        actual (Any): Actual input.
+        expected (Any): Expected input.
+        allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types
+            are allowed. Otherwise type equality is required.
+        rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default
+            values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
+        atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default
+            values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
+        equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal.
+        check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same
+            :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different
+            :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared.
+        check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this
+            check is disabled, tensors with different ``dtype``'s are promoted  to a common ``dtype`` (according to
+            :func:`torch.promote_types`) before being compared.
+        check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this
+            check is disabled, tensors with different ``layout``'s are converted to strided tensors before being
+            compared.
+        check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride.
+        msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during
+            the comparison. Can also passed as callable in which case it will be called with the generated message and
+            should return the new message.
+
+    Raises:
+        ValueError: If no :class:`torch.Tensor` can be constructed from an input.
+        ValueError: If only ``rtol`` or ``atol`` is specified.
+        AssertionError: If corresponding inputs are not Python scalars and are not directly related.
+        AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have
+            different types.
+        AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match.
+        AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match.
+        AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`.
+        AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same
+            :attr:`~torch.Tensor.layout`.
+        AssertionError: If only one of corresponding tensors is quantized.
+        AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s.
+        AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same
+            :attr:`~torch.Tensor.device`.
+        AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``.
+        AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride.
+        AssertionError: If the values of corresponding tensors are not close according to the definition above.
+
+    The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching
+    ``dtype``'s, the maximum of both tolerances is used.
+
+    +---------------------------+------------+----------+
+    | ``dtype``                 | ``rtol``   | ``atol`` |
+    +===========================+============+==========+
+    | :attr:`~torch.float16`    | ``1e-3``   | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.bfloat16`   | ``1.6e-2`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.float32`    | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.float64`    | ``1e-7``   | ``1e-7`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.complex32`  | ``1e-3``   | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.complex64`  | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.complex128` | ``1e-7``   | ``1e-7`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.quint8`     | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.quint2x4`   | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.quint4x2`   | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.qint8`      | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.qint32`     | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | other                     | ``0.0``    | ``0.0``  |
+    +---------------------------+------------+----------+
+
+    .. note::
+
+        :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged
+        to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might
+        define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default:
+
+        >>> import functools
+        >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0)
+        >>> assert_equal(1e-9, 1e-10)
+        Traceback (most recent call last):
+        ...
+        AssertionError: Scalars are not equal!
+        
+        Expected 1e-10 but got 1e-09.
+        Absolute difference: 9.000000000000001e-10
+        Relative difference: 9.0
+
+    Examples:
+        >>> # tensor to tensor comparison
+        >>> expected = torch.tensor([1e0, 1e-1, 1e-2])
+        >>> actual = torch.acos(torch.cos(expected))
+        >>> torch.testing.assert_close(actual, expected)
+
+        >>> # scalar to scalar comparison
+        >>> import math
+        >>> expected = math.sqrt(2.0)
+        >>> actual = 2.0 / math.sqrt(2.0)
+        >>> torch.testing.assert_close(actual, expected)
+
+        >>> # numpy array to numpy array comparison
+        >>> import numpy as np
+        >>> expected = np.array([1e0, 1e-1, 1e-2])
+        >>> actual = np.arccos(np.cos(expected))
+        >>> torch.testing.assert_close(actual, expected)
+
+        >>> # sequence to sequence comparison
+        >>> import numpy as np
+        >>> # The types of the sequences do not have to match. They only have to have the same
+        >>> # length and their elements have to match.
+        >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)]
+        >>> actual = tuple(expected)
+        >>> torch.testing.assert_close(actual, expected)
+
+        >>> # mapping to mapping comparison
+        >>> from collections import OrderedDict
+        >>> import numpy as np
+        >>> foo = torch.tensor(1.0)
+        >>> bar = 2.0
+        >>> baz = np.array(3.0)
+        >>> # The types and a possible ordering of mappings do not have to match. They only
+        >>> # have to have the same set of keys and their elements have to match.
+        >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)])
+        >>> actual = {"baz": baz, "bar": bar, "foo": foo}
+        >>> torch.testing.assert_close(actual, expected)
+
+        >>> expected = torch.tensor([1.0, 2.0, 3.0])
+        >>> actual = expected.clone()
+        >>> # By default, directly related instances can be compared
+        >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected)
+        >>> # This check can be made more strict with allow_subclasses=False
+        >>> torch.testing.assert_close(
+        ...     torch.nn.Parameter(actual), expected, allow_subclasses=False
+        ... )
+        Traceback (most recent call last):
+        ...
+        TypeError: No comparison pair was able to handle inputs of type
+         and .
+        >>> # If the inputs are not directly related, they are never considered close
+        >>> torch.testing.assert_close(actual.numpy(), expected)
+        Traceback (most recent call last):
+        ...
+        TypeError: No comparison pair was able to handle inputs of type 
+        and .
+        >>> # Exceptions to these rules are Python scalars. They can be checked regardless of
+        >>> # their type if check_dtype=False.
+        >>> torch.testing.assert_close(1.0, 1, check_dtype=False)
+
+        >>> # NaN != NaN by default.
+        >>> expected = torch.tensor(float("Nan"))
+        >>> actual = expected.clone()
+        >>> torch.testing.assert_close(actual, expected)
+        Traceback (most recent call last):
+        ...
+        AssertionError: Scalars are not close!
+        
+        Expected nan but got nan.
+        Absolute difference: nan (up to 1e-05 allowed)
+        Relative difference: nan (up to 1.3e-06 allowed)
+        >>> torch.testing.assert_close(actual, expected, equal_nan=True)
+
+        >>> expected = torch.tensor([1.0, 2.0, 3.0])
+        >>> actual = torch.tensor([1.0, 4.0, 5.0])
+        >>> # The default error message can be overwritten.
+        >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!")
+        Traceback (most recent call last):
+        ...
+        AssertionError: Argh, the tensors are not close!
+        >>> # If msg is a callable, it can be used to augment the generated message with
+        >>> # extra information
+        >>> torch.testing.assert_close(
+        ...     actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter"
+        ... )
+        Traceback (most recent call last):
+        ...
+        AssertionError: Header
+        
+        Tensor-likes are not close!
+        
+        Mismatched elements: 2 / 3 (66.7%)
+        Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed)
+        Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed)
+        
+        Footer
+    """
+    # Hide this function from `pytest`'s traceback
+    __tracebackhide__ = True
+
+    error_metas = not_close_error_metas(
+        actual,
+        expected,
+        pair_types=(
+            NonePair,
+            BooleanPair,
+            NumberPair,
+            TensorLikePair,
+        ),
+        allow_subclasses=allow_subclasses,
+        rtol=rtol,
+        atol=atol,
+        equal_nan=equal_nan,
+        check_device=check_device,
+        check_dtype=check_dtype,
+        check_layout=check_layout,
+        check_stride=check_stride,
+        msg=msg,
+    )
+
+    if error_metas:
+        # TODO: compose all metas into one AssertionError
+        raise error_metas[0].to_error(msg)
+
+
+@deprecated(
+    "`torch.testing.assert_allclose()` is deprecated since 1.12 and will be removed in a future release. "
+    "Please use `torch.testing.assert_close()` instead. "
+    "You can find detailed upgrade instructions in https://github.com/pytorch/pytorch/issues/61844.",
+    category=FutureWarning,
+)
+def assert_allclose(
+    actual: Any,
+    expected: Any,
+    rtol: Optional[float] = None,
+    atol: Optional[float] = None,
+    equal_nan: bool = True,
+    msg: str = "",
+) -> None:
+    """
+    .. warning::
+
+       :func:`torch.testing.assert_allclose` is deprecated since ``1.12`` and will be removed in a future release.
+       Please use :func:`torch.testing.assert_close` instead. You can find detailed upgrade instructions
+       `here `_.
+    """
+    if not isinstance(actual, torch.Tensor):
+        actual = torch.tensor(actual)
+    if not isinstance(expected, torch.Tensor):
+        expected = torch.tensor(expected, dtype=actual.dtype)
+
+    if rtol is None and atol is None:
+        rtol, atol = default_tolerances(
+            actual,
+            expected,
+            dtype_precisions={
+                torch.float16: (1e-3, 1e-3),
+                torch.float32: (1e-4, 1e-5),
+                torch.float64: (1e-5, 1e-8),
+            },
+        )
+
+    torch.testing.assert_close(
+        actual,
+        expected,
+        rtol=rtol,
+        atol=atol,
+        equal_nan=equal_nan,
+        check_device=True,
+        check_dtype=False,
+        check_stride=False,
+        msg=msg or None,
+    )
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/testing/_creation.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/testing/_creation.py
new file mode 100644
index 0000000000000000000000000000000000000000..9de3bd09882e7b03fa276413e467868192073034
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/testing/_creation.py
@@ -0,0 +1,269 @@
+"""
+This module contains tensor creation utilities.
+"""
+
+import collections.abc
+import math
+import warnings
+from typing import cast, List, Optional, Tuple, Union
+
+import torch
+
+
+_INTEGRAL_TYPES = [
+    torch.uint8,
+    torch.int8,
+    torch.int16,
+    torch.int32,
+    torch.int64,
+    torch.uint16,
+    torch.uint32,
+    torch.uint64,
+]
+_FLOATING_TYPES = [torch.float16, torch.bfloat16, torch.float32, torch.float64]
+_FLOATING_8BIT_TYPES = [
+    torch.float8_e4m3fn,
+    torch.float8_e5m2,
+    torch.float8_e4m3fnuz,
+    torch.float8_e5m2fnuz,
+]
+_COMPLEX_TYPES = [torch.complex32, torch.complex64, torch.complex128]
+_BOOLEAN_OR_INTEGRAL_TYPES = [torch.bool, *_INTEGRAL_TYPES]
+_FLOATING_OR_COMPLEX_TYPES = [*_FLOATING_TYPES, *_COMPLEX_TYPES]
+
+
+def _uniform_random_(t: torch.Tensor, low: float, high: float) -> torch.Tensor:
+    # uniform_ requires to-from <= std::numeric_limits::max()
+    # Work around this by scaling the range before and after the PRNG
+    if high - low >= torch.finfo(t.dtype).max:
+        return t.uniform_(low / 2, high / 2).mul_(2)
+    else:
+        return t.uniform_(low, high)
+
+
+def make_tensor(
+    *shape: Union[int, torch.Size, List[int], Tuple[int, ...]],
+    dtype: torch.dtype,
+    device: Union[str, torch.device],
+    low: Optional[float] = None,
+    high: Optional[float] = None,
+    requires_grad: bool = False,
+    noncontiguous: bool = False,
+    exclude_zero: bool = False,
+    memory_format: Optional[torch.memory_format] = None,
+) -> torch.Tensor:
+    r"""Creates a tensor with the given :attr:`shape`, :attr:`device`, and :attr:`dtype`, and filled with
+    values uniformly drawn from ``[low, high)``.
+
+    If :attr:`low` or :attr:`high` are specified and are outside the range of the :attr:`dtype`'s representable
+    finite values then they are clamped to the lowest or highest representable finite value, respectively.
+    If ``None``, then the following table describes the default values for :attr:`low` and :attr:`high`,
+    which depend on :attr:`dtype`.
+
+    +---------------------------+------------+----------+
+    | ``dtype``                 | ``low``    | ``high`` |
+    +===========================+============+==========+
+    | boolean type              | ``0``      | ``2``    |
+    +---------------------------+------------+----------+
+    | unsigned integral type    | ``0``      | ``10``   |
+    +---------------------------+------------+----------+
+    | signed integral types     | ``-9``     | ``10``   |
+    +---------------------------+------------+----------+
+    | floating types            | ``-9``     | ``9``    |
+    +---------------------------+------------+----------+
+    | complex types             | ``-9``     | ``9``    |
+    +---------------------------+------------+----------+
+
+    Args:
+        shape (Tuple[int, ...]): Single integer or a sequence of integers defining the shape of the output tensor.
+        dtype (:class:`torch.dtype`): The data type of the returned tensor.
+        device (Union[str, torch.device]): The device of the returned tensor.
+        low (Optional[Number]): Sets the lower limit (inclusive) of the given range. If a number is provided it is
+            clamped to the least representable finite value of the given dtype. When ``None`` (default),
+            this value is determined based on the :attr:`dtype` (see the table above). Default: ``None``.
+        high (Optional[Number]): Sets the upper limit (exclusive) of the given range. If a number is provided it is
+            clamped to the greatest representable finite value of the given dtype. When ``None`` (default) this value
+            is determined based on the :attr:`dtype` (see the table above). Default: ``None``.
+
+            .. deprecated:: 2.1
+
+                Passing ``low==high`` to :func:`~torch.testing.make_tensor` for floating or complex types is deprecated
+                since 2.1 and will be removed in 2.3. Use :func:`torch.full` instead.
+
+        requires_grad (Optional[bool]): If autograd should record operations on the returned tensor. Default: ``False``.
+        noncontiguous (Optional[bool]): If `True`, the returned tensor will be noncontiguous. This argument is
+            ignored if the constructed tensor has fewer than two elements. Mutually exclusive with ``memory_format``.
+        exclude_zero (Optional[bool]): If ``True`` then zeros are replaced with the dtype's small positive value
+            depending on the :attr:`dtype`. For bool and integer types zero is replaced with one. For floating
+            point types it is replaced with the dtype's smallest positive normal number (the "tiny" value of the
+            :attr:`dtype`'s :func:`~torch.finfo` object), and for complex types it is replaced with a complex number
+            whose real and imaginary parts are both the smallest positive normal number representable by the complex
+            type. Default ``False``.
+        memory_format (Optional[torch.memory_format]): The memory format of the returned tensor. Mutually exclusive
+            with ``noncontiguous``.
+
+    Raises:
+        ValueError: If ``requires_grad=True`` is passed for integral `dtype`
+        ValueError: If ``low >= high``.
+        ValueError: If either :attr:`low` or :attr:`high` is ``nan``.
+        ValueError: If both :attr:`noncontiguous` and :attr:`memory_format` are passed.
+        TypeError: If :attr:`dtype` isn't supported by this function.
+
+    Examples:
+        >>> # xdoctest: +SKIP
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
+        >>> from torch.testing import make_tensor
+        >>> # Creates a float tensor with values in [-1, 1)
+        >>> make_tensor((3,), device='cpu', dtype=torch.float32, low=-1, high=1)
+        >>> # xdoctest: +SKIP
+        tensor([ 0.1205, 0.2282, -0.6380])
+        >>> # Creates a bool tensor on CUDA
+        >>> make_tensor((2, 2), device='cuda', dtype=torch.bool)
+        tensor([[False, False],
+                [False, True]], device='cuda:0')
+    """
+
+    def modify_low_high(
+        low: Optional[float],
+        high: Optional[float],
+        *,
+        lowest_inclusive: float,
+        highest_exclusive: float,
+        default_low: float,
+        default_high: float,
+    ) -> Tuple[float, float]:
+        """
+        Modifies (and raises ValueError when appropriate) low and high values given by the user (input_low, input_high)
+        if required.
+        """
+
+        def clamp(a: float, l: float, h: float) -> float:
+            return min(max(a, l), h)
+
+        low = low if low is not None else default_low
+        high = high if high is not None else default_high
+
+        if any(isinstance(value, float) and math.isnan(value) for value in [low, high]):
+            raise ValueError(
+                f"`low` and `high` cannot be NaN, but got {low=} and {high=}"
+            )
+        elif low == high and dtype in _FLOATING_OR_COMPLEX_TYPES:
+            warnings.warn(
+                "Passing `low==high` to `torch.testing.make_tensor` for floating or complex types "
+                "is deprecated since 2.1 and will be removed in 2.3. "
+                "Use `torch.full(...)` instead.",
+                FutureWarning,
+                stacklevel=3,
+            )
+        elif low >= high:
+            raise ValueError(f"`low` must be less than `high`, but got {low} >= {high}")
+        elif high < lowest_inclusive or low >= highest_exclusive:
+            raise ValueError(
+                f"The value interval specified by `low` and `high` is [{low}, {high}), "
+                f"but {dtype} only supports [{lowest_inclusive}, {highest_exclusive})"
+            )
+
+        low = clamp(low, lowest_inclusive, highest_exclusive)
+        high = clamp(high, lowest_inclusive, highest_exclusive)
+
+        if dtype in _BOOLEAN_OR_INTEGRAL_TYPES:
+            # 1. `low` is ceiled to avoid creating values smaller than `low` and thus outside the specified interval
+            # 2. Following the same reasoning as for 1., `high` should be floored. However, the higher bound of
+            #    `torch.randint` is exclusive, and thus we need to ceil here as well.
+            return math.ceil(low), math.ceil(high)
+
+        return low, high
+
+    if len(shape) == 1 and isinstance(shape[0], collections.abc.Sequence):
+        shape = shape[0]  # type: ignore[assignment]
+    shape = cast(Tuple[int, ...], tuple(shape))
+
+    if noncontiguous and memory_format is not None:
+        raise ValueError(
+            f"The parameters `noncontiguous` and `memory_format` are mutually exclusive, "
+            f"but got {noncontiguous=} and {memory_format=}"
+        )
+
+    if requires_grad and dtype in _BOOLEAN_OR_INTEGRAL_TYPES:
+        raise ValueError(
+            f"`requires_grad=True` is not supported for boolean and integral dtypes, but got {dtype=}"
+        )
+
+    if dtype is torch.bool:
+        low, high = cast(
+            Tuple[int, int],
+            modify_low_high(
+                low,
+                high,
+                lowest_inclusive=0,
+                highest_exclusive=2,
+                default_low=0,
+                default_high=2,
+            ),
+        )
+        result = torch.randint(low, high, shape, device=device, dtype=dtype)
+    elif dtype in _BOOLEAN_OR_INTEGRAL_TYPES:
+        low, high = cast(
+            Tuple[int, int],
+            modify_low_high(
+                low,
+                high,
+                lowest_inclusive=torch.iinfo(dtype).min,
+                highest_exclusive=torch.iinfo(dtype).max
+                # In theory, `highest_exclusive` should always be the maximum value + 1. However, `torch.randint`
+                # internally converts the bounds to an int64 and would overflow. In other words: `torch.randint` cannot
+                # sample 2**63 - 1, i.e. the maximum value of `torch.int64` and we need to account for that here.
+                + (1 if dtype is not torch.int64 else 0),
+                # This is incorrect for `torch.uint8`, but since we clamp to `lowest`, i.e. 0 for `torch.uint8`,
+                # _after_ we use the default value, we don't need to special case it here
+                default_low=-9,
+                default_high=10,
+            ),
+        )
+        result = torch.randint(low, high, shape, device=device, dtype=dtype)
+    elif dtype in _FLOATING_OR_COMPLEX_TYPES:
+        low, high = modify_low_high(
+            low,
+            high,
+            lowest_inclusive=torch.finfo(dtype).min,
+            highest_exclusive=torch.finfo(dtype).max,
+            default_low=-9,
+            default_high=9,
+        )
+        result = torch.empty(shape, device=device, dtype=dtype)
+        _uniform_random_(
+            torch.view_as_real(result) if dtype in _COMPLEX_TYPES else result, low, high
+        )
+    elif dtype in _FLOATING_8BIT_TYPES:
+        low, high = modify_low_high(
+            low,
+            high,
+            lowest_inclusive=torch.finfo(dtype).min,
+            highest_exclusive=torch.finfo(dtype).max,
+            default_low=-9,
+            default_high=9,
+        )
+        result = torch.empty(shape, device=device, dtype=torch.float32)
+        _uniform_random_(result, low, high)
+        result = result.to(dtype)
+    else:
+        raise TypeError(
+            f"The requested dtype '{dtype}' is not supported by torch.testing.make_tensor()."
+            " To request support, file an issue at: https://github.com/pytorch/pytorch/issues"
+        )
+
+    if noncontiguous and result.numel() > 1:
+        result = torch.repeat_interleave(result, 2, dim=-1)
+        result = result[..., ::2]
+    elif memory_format is not None:
+        result = result.clone(memory_format=memory_format)
+
+    if exclude_zero:
+        result[result == 0] = (
+            1 if dtype in _BOOLEAN_OR_INTEGRAL_TYPES else torch.finfo(dtype).tiny
+        )
+
+    if dtype in _FLOATING_OR_COMPLEX_TYPES:
+        result.requires_grad = requires_grad
+
+    return result
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/testing/_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/testing/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b4b7c3796e344c552fec79ade5a8a914f516466
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/testing/_utils.py
@@ -0,0 +1,52 @@
+# mypy: allow-untyped-defs
+import contextlib
+
+import torch
+
+
+# Common testing utilities for use in public testing APIs.
+# NB: these should all be importable without optional dependencies
+# (like numpy and expecttest).
+
+
+def wrapper_set_seed(op, *args, **kwargs):
+    """Wrapper to set seed manually for some functions like dropout
+    See: https://github.com/pytorch/pytorch/pull/62315#issuecomment-896143189 for more details.
+    """
+    with freeze_rng_state():
+        torch.manual_seed(42)
+        output = op(*args, **kwargs)
+
+        if isinstance(output, torch.Tensor) and output.device.type == "lazy":
+            # We need to call mark step inside freeze_rng_state so that numerics
+            # match eager execution
+            torch._lazy.mark_step()  # type: ignore[attr-defined]
+
+        return output
+
+
+@contextlib.contextmanager
+def freeze_rng_state():
+    # no_dispatch needed for test_composite_compliance
+    # Some OpInfos use freeze_rng_state for rng determinism, but
+    # test_composite_compliance overrides dispatch for all torch functions
+    # which we need to disable to get and set rng state
+    with torch.utils._mode_utils.no_dispatch(), torch._C._DisableFuncTorch():
+        rng_state = torch.get_rng_state()
+        if torch.cuda.is_available():
+            cuda_rng_state = torch.cuda.get_rng_state()
+    try:
+        yield
+    finally:
+        # Modes are not happy with torch.cuda.set_rng_state
+        # because it clones the state (which could produce a Tensor Subclass)
+        # and then grabs the new tensor's data pointer in generator.set_state.
+        #
+        # In the long run torch.cuda.set_rng_state should probably be
+        # an operator.
+        #
+        # NB: Mode disable is to avoid running cross-ref tests on thes seeding
+        with torch.utils._mode_utils.no_dispatch(), torch._C._DisableFuncTorch():
+            if torch.cuda.is_available():
+                torch.cuda.set_rng_state(cuda_rng_state)  # type: ignore[possibly-undefined]
+            torch.set_rng_state(rng_state)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..23188bba9b8003e9dc44d099ac1ab6099ffff199
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/__init__.py
@@ -0,0 +1,113 @@
+# mypy: allow-untyped-defs
+
+import copyreg
+import os.path as _osp
+import weakref
+
+import torch
+from torch.utils import (
+    backcompat as backcompat,
+    collect_env as collect_env,
+    data as data,
+    deterministic as deterministic,
+    hooks as hooks,
+)
+from torch.utils.backend_registration import (
+    generate_methods_for_privateuse1_backend,
+    rename_privateuse1_backend,
+)
+from torch.utils.cpp_backtrace import get_cpp_backtrace
+from torch.utils.throughput_benchmark import ThroughputBenchmark
+
+
+def set_module(obj, mod):
+    """
+    Set the module attribute on a python object for a given object for nicer printing
+    """
+    if not isinstance(mod, str):
+        raise TypeError("The mod argument should be a string")
+    obj.__module__ = mod
+
+
+if torch._running_with_deploy():
+    # not valid inside torch_deploy interpreter, no paths exists for frozen modules
+    cmake_prefix_path = None
+else:
+    cmake_prefix_path = _osp.join(
+        _osp.dirname(_osp.dirname(__file__)), "share", "cmake"
+    )
+
+
+def swap_tensors(t1, t2):
+    """
+    This function swaps the content of the two Tensor objects.
+    At a high level, this will make t1 have the content of t2 while preserving
+    its identity.
+
+    This will not work if t1 and t2 have different slots.
+    """
+    # Ensure there are no weakrefs
+    if weakref.getweakrefs(t1):
+        raise RuntimeError("Cannot swap t1 because it has weakref associated with it")
+    if weakref.getweakrefs(t2):
+        raise RuntimeError("Cannot swap t2 because it has weakref associated with it")
+    t1_slots = set(copyreg._slotnames(t1.__class__))  # type: ignore[attr-defined]
+    t2_slots = set(copyreg._slotnames(t2.__class__))  # type: ignore[attr-defined]
+    if t1_slots != t2_slots:
+        raise RuntimeError("Cannot swap t1 and t2 if they have different slots")
+
+    def swap_attr(name):
+        tmp = getattr(t1, name)
+        setattr(t1, name, (getattr(t2, name)))
+        setattr(t2, name, tmp)
+
+    def error_pre_hook(grad_outputs):
+        raise RuntimeError(
+            "Trying to execute AccumulateGrad node that was poisoned by swap_tensors "
+            "this can happen when you try to run backward on a tensor that was swapped. "
+            "For a module m with `torch.__future__.set_swap_module_params_on_conversion(True)` "
+            "you should not change the device or dtype of the module (e.g. `m.cpu()` or `m.half()`) "
+            "between running forward and backward. To resolve this, please only change the "
+            "device/dtype before running forward (or after both forward and backward)."
+        )
+
+    def check_use_count(t, name="t1"):
+        use_count = t._use_count()
+        error_str = (
+            f"Expected use_count of {name} to be 1 or 2 with an AccumulateGrad node but got {use_count} "
+            f"make sure you are not holding references to the tensor in other places."
+        )
+        if use_count > 1:
+            if use_count == 2 and t.is_leaf:
+                accum_grad_node = torch.autograd.graph.get_gradient_edge(t).node
+                # Make sure that the accumulate_grad node was not lazy_init-ed by get_gradient_edge
+                if t._use_count() == 2:
+                    accum_grad_node.register_prehook(error_pre_hook)
+                else:
+                    raise RuntimeError(error_str)
+            else:
+                raise RuntimeError(error_str)
+
+    check_use_count(t1, "t1")
+    check_use_count(t2, "t2")
+
+    # Swap the types
+    # Note that this will fail if there are mismatched slots
+    swap_attr("__class__")
+
+    # Swap the dynamic attributes
+    swap_attr("__dict__")
+
+    # Swap the slots
+    for slot in t1_slots:
+        if hasattr(t1, slot) and hasattr(t2, slot):
+            swap_attr(slot)
+        elif hasattr(t1, slot):
+            setattr(t2, slot, (getattr(t1, slot)))
+            delattr(t1, slot)
+        elif hasattr(t2, slot):
+            setattr(t1, slot, (getattr(t2, slot)))
+            delattr(t2, slot)
+
+    # Swap the at::Tensor they point to
+    torch._C._swap_tensor_impl(t1, t2)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_backport_slots.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_backport_slots.py
new file mode 100644
index 0000000000000000000000000000000000000000..dcafb32877f34ca51aa8748b2c23b15e487c9b26
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_backport_slots.py
@@ -0,0 +1,114 @@
+# This code is backported from python 3.10 dataclasses. Once 3.10 becomes the
+# minimum supported we should use dataclass(slots=True) instead.
+
+from __future__ import annotations
+
+import dataclasses
+import itertools
+from typing import Generator, List, Type, TYPE_CHECKING, TypeVar
+
+
+if TYPE_CHECKING:
+    from _typeshed import DataclassInstance
+
+
+__all__ = ["dataclass_slots"]
+
+_T = TypeVar("_T", bound="DataclassInstance")
+
+
+def dataclass_slots(cls: Type[_T]) -> Type[DataclassInstance]:
+    assert dataclasses.is_dataclass(cls), "Can only be used on dataclasses."
+
+    def _get_slots(cls: Type[DataclassInstance]) -> Generator[str, None, None]:
+        slots = cls.__dict__.get("__slots__")
+        # `__dictoffset__` and `__weakrefoffset__` can tell us whether
+        # the base type has dict/weakref slots, in a way that works correctly
+        # for both Python classes and C extension types. Extension types
+        # don't use `__slots__` for slot creation
+        if slots is None:
+            slots = []
+            if getattr(cls, "__weakrefoffset__", -1) != 0:
+                slots.append("__weakref__")
+            if getattr(cls, "__dictrefoffset__", -1) != 0:
+                slots.append("__dict__")
+            yield from slots
+        elif isinstance(slots, str):
+            yield slots
+        # Slots may be any iterable, but we cannot handle an iterator
+        # because it will already be (partially) consumed.
+        elif not hasattr(cls, "__next__"):
+            yield from slots
+        else:
+            raise TypeError(f"Slots of '{cls.__name__}' cannot be determined")
+
+    def _add_slots(
+        cls: Type[DataclassInstance], is_frozen: bool, weakref_slot: bool
+    ) -> Type[DataclassInstance]:
+        # Need to create a new class, since we can't set __slots__
+        #  after a class has been created.
+
+        # Make sure __slots__ isn't already set.
+        if "__slots__" in cls.__dict__:
+            raise TypeError(f"{cls.__name__} already specifies __slots__")
+
+        # Create a new dict for our new class.
+        cls_dict = dict(cls.__dict__)
+        field_names = tuple(f.name for f in dataclasses.fields(cls))
+        # Make sure slots don't overlap with those in base classes.
+        inherited_slots = set(
+            itertools.chain.from_iterable(map(_get_slots, cls.__mro__[1:-1]))
+        )
+        # The slots for our class.  Remove slots from our base classes.  Add
+        # '__weakref__' if weakref_slot was given, unless it is already present.
+        cls_dict["__slots__"] = tuple(
+            itertools.filterfalse(
+                inherited_slots.__contains__,
+                itertools.chain(
+                    # gh-93521: '__weakref__' also needs to be filtered out if
+                    # already present in inherited_slots
+                    field_names,
+                    ("__weakref__",) if weakref_slot else (),
+                ),
+            ),
+        )
+
+        for field_name in field_names:
+            # Remove our attributes, if present. They'll still be
+            #  available in _MARKER.
+            cls_dict.pop(field_name, None)
+
+        # Remove __dict__ itself.
+        cls_dict.pop("__dict__", None)
+
+        # Clear existing `__weakref__` descriptor, it belongs to a previous type:
+        cls_dict.pop("__weakref__", None)  # gh-102069
+
+        # And finally create the class.
+        qualname = getattr(cls, "__qualname__", None)
+        cls = type(cls.__name__, cls.__bases__, cls_dict)
+        if qualname is not None:
+            cls.__qualname__ = qualname
+
+        def _dataclass_getstate(self: _T) -> object:
+            fields = dataclasses.fields(self)
+            return [getattr(self, f.name) for f in fields]
+
+        def _dataclass_setstate(self: _T, state: List[object]) -> None:
+            fields = dataclasses.fields(self)
+            for field, value in zip(fields, state):
+                # use setattr because dataclass may be frozen
+                object.__setattr__(self, field.name, value)
+
+        if is_frozen:
+            # Need this for pickling frozen classes with slots.
+            if "__getstate__" not in cls_dict:
+                cls.__getstate__ = _dataclass_getstate  # type: ignore[method-assign, assignment]
+            if "__setstate__" not in cls_dict:
+                cls.__setstate__ = _dataclass_setstate  # type: ignore[attr-defined]
+
+        return cls
+
+    params = getattr(cls, dataclasses._PARAMS)  # type: ignore[attr-defined]
+    weakref_slot = getattr(params, "weakref_slot", False)
+    return _add_slots(cls, params.frozen, weakref_slot)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_config_module.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_config_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..8aa9d4063f0183cc6e8a03b5d3b808959d03ad6c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_config_module.py
@@ -0,0 +1,392 @@
+import contextlib
+import copy
+import hashlib
+import inspect
+import io
+import pickle
+import tokenize
+import unittest
+import warnings
+from types import FunctionType, ModuleType
+from typing import Any, Callable, Dict, NoReturn, Optional, Set, Union
+from typing_extensions import deprecated
+from unittest import mock
+
+
+# Types saved/loaded in configs
+CONFIG_TYPES = (int, float, bool, type(None), str, list, set, tuple, dict)
+
+
+def install_config_module(module: ModuleType) -> None:
+    """
+    Converts a module-level config into a `ConfigModule()`.
+
+    See _config_typing.pyi for instructions on how to get the converted module to typecheck.
+    """
+
+    class ConfigModuleInstance(ConfigModule):
+        _bypass_keys = set({"_is_dirty", "_hash_digest"})
+
+    def visit(
+        source: Union[ModuleType, type],
+        dest: Union[ModuleType, SubConfigProxy],
+        prefix: str,
+    ) -> None:
+        """Walk the module structure and move everything to module._config"""
+        for key, value in list(source.__dict__.items()):
+            if (
+                key.startswith("__")
+                or isinstance(value, (ModuleType, FunctionType))
+                or (hasattr(value, "__module__") and value.__module__ == "typing")
+            ):
+                continue
+
+            name = f"{prefix}{key}"
+            if isinstance(value, CONFIG_TYPES):
+                config[name] = value
+                default[name] = value
+                if dest is module:
+                    delattr(module, key)
+            elif isinstance(value, type):
+                assert value.__module__ == module.__name__
+                # a subconfig with `class Blah:` syntax
+                proxy = SubConfigProxy(module, f"{name}.")
+                visit(value, proxy, f"{name}.")
+                if dest is module:
+                    setattr(dest, key, proxy)
+                else:
+                    dest.__dict__[key] = proxy
+            else:
+                raise AssertionError(f"Unhandled config {key}={value} ({type(value)})")
+
+    config: Dict[str, Any] = {}
+    default: Dict[str, Any] = {}
+
+    compile_ignored_keys = get_assignments_with_compile_ignored_comments(module)
+
+    visit(module, module, "")
+    module._config = config  # type: ignore[attr-defined]
+    module._default = default  # type: ignore[attr-defined]
+    module._allowed_keys = set(config.keys())  # type: ignore[attr-defined]
+    module._compile_ignored_keys = compile_ignored_keys  # type: ignore[attr-defined]
+    module.__class__ = ConfigModuleInstance
+    module._is_dirty = True  # type: ignore[attr-defined]
+    module._hash_digest = None  # type: ignore[attr-defined]
+
+
+COMPILE_IGNORED_MARKER = "@compile_ignored"
+
+
+# Gets all the keys (i.e. assignments) with a @compile_ignored comment
+def get_assignments_with_compile_ignored_comments(module: ModuleType) -> Set[str]:
+    source_code = inspect.getsource(module)
+    assignments = set()
+
+    # Tokenize the source code to retrieve comments
+    tokens = tokenize.tokenize(io.BytesIO(source_code.encode("utf-8")).readline)
+    current_comment = "", -1
+    prev_name = ""
+
+    for token in tokens:
+        if token.type == tokenize.COMMENT:
+            prev_name = ""
+            maybe_current = token.string.strip()
+            if COMPILE_IGNORED_MARKER in maybe_current:
+                assert current_comment == (
+                    "",
+                    -1,
+                ), f"unconsumed {COMPILE_IGNORED_MARKER}"
+                current_comment = maybe_current, token.start[0]
+        elif token.type == tokenize.NAME:
+            # Only accept the first name token, to handle if you have
+            # something like foo: Bar = ...
+            if not prev_name:
+                prev_name = token.string
+        elif token.type == tokenize.OP and token.string == "=":
+            # Check if the current assignment follows a comment
+            # with COMPILE_IGNORED_MARKER
+            if (
+                COMPILE_IGNORED_MARKER in current_comment[0]
+                and current_comment[1] == token.start[0] - 1
+            ):
+                assignments.add(prev_name)
+                current_comment = "", -1  # reset
+            prev_name = ""
+    assert current_comment == ("", -1), f"unconsumed {COMPILE_IGNORED_MARKER}"
+    return assignments
+
+
+class ConfigModule(ModuleType):
+    # NOTE: This should be kept in sync with _config_typing.pyi.
+
+    # The default values of the configuration settings.  This can be used to
+    # determine if the config has been changed or not.
+    _default: Dict[str, Any]
+    # The actual configuration settings.  E.g., torch._dynamo.config.debug
+    # would live as "debug" in the key, and torch._inductor.config.triton.cudagraphs
+    # maps as "triton.cudagraphs"
+    _config: Dict[str, Any]
+    _allowed_keys: Set[str]
+    _bypass_keys: Set[str]
+    _compile_ignored_keys: Set[str]
+    _is_dirty: bool
+    _hash_digest: Optional[bytes]
+
+    def __init__(self) -> None:
+        raise NotImplementedError(
+            f"use {__name__}.install_config_module(sys.modules[__name__])"
+        )
+
+    def __setattr__(self, name: str, value: object) -> None:
+        if name in self._bypass_keys:
+            super().__setattr__(name, value)
+        elif name not in self._allowed_keys:
+            raise AttributeError(f"{self.__name__}.{name} does not exist")
+        else:
+            self._config[name] = value
+
+    def __getattr__(self, name: str) -> Any:
+        try:
+            return self._config[name]
+        except KeyError as e:
+            # make hasattr() work properly
+            raise AttributeError(f"{self.__name__}.{name} does not exist") from e
+
+    def __delattr__(self, name: str) -> None:
+        # must support delete because unittest.mock.patch deletes
+        # then recreate things
+        del self._config[name]
+
+    def save_config(self) -> bytes:
+        """Convert config to a pickled blob"""
+        config = dict(self._config)
+        for key in config.get("_save_config_ignore", ()):
+            config.pop(key)
+        return pickle.dumps(config, protocol=2)
+
+    def save_config_portable(self) -> Dict[str, Any]:
+        """Convert config to portable format"""
+        config: Dict[str, Any] = {}
+        for key in sorted(self._config):
+            if key.startswith("_"):
+                continue
+            if any(
+                key.startswith(e) for e in self._config["_cache_config_ignore_prefix"]
+            ):
+                continue
+            config[key] = self._config[key]
+        return config
+
+    def codegen_config(self) -> str:
+        """Convert config to Python statements that replicate current config.
+        This does NOT include config settings that are at default values.
+        """
+        lines = []
+        mod = self.__name__
+        for k, v in self._config.items():
+            if k in self._config.get("_save_config_ignore", ()):
+                if v != self._default[k]:
+                    warnings.warn(f"Skipping serialization of {k} value {v}")
+                continue
+            if v == self._default[k]:
+                continue
+            lines.append(f"{mod}.{k} = {v!r}")
+        return "\n".join(lines)
+
+    def get_hash(self) -> bytes:
+        """Hashes the configs that are not compile_ignored"""
+        if self._is_dirty or self._hash_digest is None:
+            dict_to_hash = {
+                k: v
+                for k, v in self._config.items()
+                if k not in self._compile_ignored_keys
+            }
+            string_to_hash = repr(sorted(dict_to_hash.items()))
+            self._hash_digest = hashlib.md5(string_to_hash.encode("utf-8")).digest()
+            self._is_dirty = False
+        return self._hash_digest
+
+    @deprecated(
+        "`config.to_dict()` has been deprecated. It may no longer change the underlying config."
+        " use `config.shallow_copy_dict()` or `config.get_config_copy()` instead",
+        category=FutureWarning,
+    )
+    def to_dict(self) -> Dict[str, Any]:
+        return self.shallow_copy_dict()
+
+    def shallow_copy_dict(self) -> Dict[str, Any]:
+        return {**self._config}
+
+    def load_config(self, maybe_pickled_config: Union[bytes, Dict[str, Any]]) -> None:
+        """Restore from a prior call to save_config() or shallow_copy_dict()"""
+        if not isinstance(maybe_pickled_config, dict):
+            config = pickle.loads(maybe_pickled_config)
+        else:
+            config = maybe_pickled_config
+        self._config.update(config)
+
+    def get_config_copy(self) -> Dict[str, Any]:
+        return copy.deepcopy(self._config)
+
+    def patch(
+        self,
+        arg1: Optional[Union[str, Dict[str, Any]]] = None,
+        arg2: Any = None,
+        **kwargs: Dict[str, Any],
+    ) -> "ContextDecorator":
+        """
+        Decorator and/or context manager to make temporary changes to a config.
+
+        As a decorator:
+
+            @config.patch("name", val)
+            @config.patch(name1=val1, name2=val2)
+            @config.patch({"name1": val1, "name2", val2})
+            def foo(...):
+                ...
+
+        As a context manager:
+
+            with config.patch("name", val):
+                ...
+        """
+        changes: Dict[str, Any]
+        if arg1 is not None:
+            if arg2 is not None:
+                assert isinstance(arg1, str)
+                # patch("key", True) syntax
+                changes = {arg1: arg2}
+            else:
+                assert isinstance(arg1, dict)
+                # patch({"key": True}) syntax
+                changes = arg1
+            assert not kwargs
+        else:
+            # patch(key=True) syntax
+            changes = kwargs
+            assert arg2 is None
+        assert isinstance(changes, dict), f"expected `dict` got {type(changes)}"
+        prior: Dict[str, Any] = {}
+        config = self
+        dirty = False
+
+        class ConfigPatch(ContextDecorator):
+            def __enter__(self) -> None:
+                assert not prior
+                nonlocal dirty
+                for key in changes.keys():
+                    # KeyError on invalid entry
+                    prior[key] = config._config[key]
+                    dirty = key not in config._compile_ignored_keys
+                config._config.update(changes)
+                config._is_dirty = dirty
+
+            def __exit__(self, exc_type, exc_val, exc_tb):  # type: ignore[no-untyped-def]
+                nonlocal dirty
+                config._config.update(prior)
+                config._is_dirty = dirty
+                prior.clear()
+
+        return ConfigPatch()
+
+    def _make_closure_patcher(self, **changes: Dict[str, Any]) -> Any:
+        """
+        A lower-overhead version of patch() for things on the critical path.
+
+        Usage:
+
+            # do this off the critical path
+            change_fn = config.make_closure_patcher(foo=True)
+
+            ...
+
+            revert = change_fn()
+            try:
+              ...
+            finally:
+                revert()
+
+        """
+        config = self._config
+
+        def change() -> Callable[[], None]:
+            prior = {k: config[k] for k in changes}
+            config.update(changes)
+
+            def revert() -> None:
+                config.update(prior)
+
+            return revert
+
+        return change
+
+
+class ContextDecorator(contextlib.ContextDecorator):
+    """
+    Same as contextlib.ContextDecorator, but with support for
+    `unittest.TestCase`
+    """
+
+    def __enter__(self) -> None:
+        raise NotImplementedError("NYI")
+
+    def __exit__(self, exc_type, exc_val, exc_tb) -> NoReturn:  # type: ignore[no-untyped-def]
+        raise NotImplementedError("NYI")
+
+    def __call__(self, func: Callable[[Any], Any]) -> Any:
+        if isinstance(func, type) and issubclass(func, unittest.TestCase):
+
+            class _TestCase(func):  # type: ignore[valid-type, misc]
+                @classmethod
+                def setUpClass(cls) -> None:
+                    self.__enter__()
+                    try:
+                        super().setUpClass()
+                    except Exception:
+                        self.__exit__(None, None, None)
+                        raise
+
+                @classmethod
+                def tearDownClass(cls) -> None:
+                    try:
+                        super().tearDownClass()
+                    finally:
+                        self.__exit__(None, None, None)
+
+            _TestCase.__name__ = func.__name__
+            _TestCase.__qualname__ = func.__qualname__
+            _TestCase.__module__ = func.__module__
+
+            return _TestCase
+
+        return super().__call__(func)
+
+
+class SubConfigProxy:
+    """
+    Shim to redirect to main config.
+    `config.triton.cudagraphs` maps to _config["triton.cudagraphs"]
+    """
+
+    def __init__(self, config: object, prefix: str):
+        # `super().__setattr__` to bypass custom `__setattr__`
+        super().__setattr__("_config", config)
+        super().__setattr__("_prefix", prefix)
+
+    def __setattr__(self, name: str, value: object) -> None:
+        return self._config.__setattr__(self._prefix + name, value)
+
+    def __getattr__(self, name: str) -> Any:
+        return self._config.__getattr__(self._prefix + name)
+
+    def __delattr__(self, name: str) -> None:
+        return self._config.__delattr__(self._prefix + name)
+
+
+def patch_object(obj: object, name: str, value: object) -> object:
+    """
+    Workaround `mock.patch.object` issue with ConfigModule
+    """
+    if isinstance(obj, ConfigModule):
+        return obj.patch(name, value)
+    return mock.patch.object(obj, name, value)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_content_store.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_content_store.py
new file mode 100644
index 0000000000000000000000000000000000000000..deb3ba9008c06a46f1c3c43c7ea8ae34e19f73e8
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_content_store.py
@@ -0,0 +1,238 @@
+# mypy: allow-untyped-defs
+# This module provides a FAST (on GPU) content addressable store for storages
+# (and tensors on top of them) with VERY WEAK portability guarantees (e.g.,
+# don't expect CPU/CUDA to address to the same hash, don't expect it to be
+# portable across devices) that is NOT cryptographically secure.  In return,
+# we are able to hash 40G of tensor data on GPU in less than a second,
+# compared to running SHA-1 in CPU which would a minute or so.  The primary
+# use case is for efficiently snapshotting intermediate tensor data for
+# offline debugging, but it's been put in this module in case you think of
+# another use case for it.  The hash function could be replaced with a
+# straight reimplementation of SHA-1, which would give us much stronger
+# portability guarantees.
+#
+# WARNING: THERE IS NO BC/FC GUARANTEE FOR THIS FORMAT!  If you need to format
+# shift the result, consider packing it into a single torch.save object
+# with traditional view sharing.
+#
+# Because of the weak portability guarantees, you can only write to the
+# content store from a single process; we don't provide any capability
+# of "reopening" a content store to add more things to it.  But we don't
+# assume that you can keep all of the tensors you want to add to the store
+# in memory at once, because you probably can't!  Nor do we assume that
+# you know a priori whether or not two storages can be deduplicated or not.
+#
+# Note: only storages are content-addressed; tensors are name addressed
+#
+# Note: our padding strategy means that [1, 0] and [1] int16 tensors would
+# map to the same (padded) storage.  We think this will be immaterial for most
+# users.
+
+import ctypes
+import functools
+import hashlib
+import os.path
+import struct
+from collections import defaultdict
+from typing import Dict, Optional, Set
+
+import torch
+import torch._prims as prims
+import torch._utils
+import torch.nn.functional as F
+from torch._C import default_generator
+from torch.multiprocessing.reductions import StorageWeakRef
+
+
+def lazy_compile(**compile_kwargs):
+    """Lazily wrap a function with torch.compile on the first call
+
+    This avoids eagerly importing dynamo.
+    """
+
+    def decorate_fn(fn):
+        @functools.wraps(fn)
+        def compile_hook(*args, **kwargs):
+            compiled_fn = torch.compile(fn, **compile_kwargs)
+            globals()[fn.__name__] = functools.wraps(fn)(compiled_fn)
+            return compiled_fn(*args, **kwargs)
+
+        return compile_hook
+
+    return decorate_fn
+
+
+# Use of torch.compile is mandatory for (1) good memory usage
+# and (2) xor_sum implementation.  This is our first instance of
+# using PT2 to implement a kernel in PyTorch; if we get AOT capabilities
+# it would be good to apply it here.
+@lazy_compile(dynamic=True)
+def hash_storage_kernel(x):
+    # The randint calls are carefully written to hit things we
+    # have lowerings for in inductor.  Lack of unsigned 32-bit integer
+    # is a pain.
+    a = torch.randint(
+        -(2**31), 2**31, x.shape, device=x.device, dtype=torch.int32
+    ).abs()
+    a = ((a % (2**31 - 1)) + 1).long()
+    b = (
+        torch.randint(-(2**31), 2**31, x.shape, device=x.device, dtype=torch.int32)
+        .abs()
+        .long()
+    )
+    # This is a standard shift-multiply universal hash family
+    # plus xor sum hash, using Philox to generate random numbers.
+    # Our Philox RNG is not deterministic across devices so
+    # don't use this for stable hashing.
+    #
+    # This assumes fixed length so you're also obligated to bucket
+    # by the length of tensor as well
+    return prims.xor_sum((a * x + b).int(), [0])
+
+
+# Returns a hex digest of the data in the storage.  Guaranteed to be
+# SHA-1 if stable_hash=True, otherwise it will consistent for a single
+# process run but not necessarily across processes.
+def hash_storage(storage: torch.UntypedStorage, *, stable_hash: bool = False) -> str:
+    import torch._dynamo
+    from torch._dynamo.utils import is_compile_supported
+
+    device_type = storage.device.type
+    if stable_hash or not is_compile_supported(device_type):
+        cpu_storage = storage.cpu()
+        # TODO: make storage support buffer protocol so this isn't
+        # necessary
+        buf = (ctypes.c_byte * cpu_storage.nbytes()).from_address(
+            cpu_storage.data_ptr()
+        )
+        sha1 = hashlib.sha1()
+        sha1.update(buf)
+        return sha1.hexdigest()
+
+    # TODO: factor this into a random utility
+    if device_type == "cpu":
+        generator = default_generator
+    elif device_type == "cuda":
+        import torch.cuda
+
+        generator = torch.cuda.default_generators[storage.device.index]
+    else:
+        raise AssertionError(f"unhandled device type {device_type}")
+    state = generator.get_state()
+    try:
+        generator.manual_seed(0)
+        x = torch.empty(0, dtype=torch.uint8, device=storage.device).set_(storage)  # type: ignore[call-overload]
+        # The dtype-casting view cannot be compiled, and so the
+        # padding/reshaping also needs to be done externally even
+        # though it could be profitably fused
+        pad = -x.numel() % 4
+        if pad > 0:
+            x = F.pad(x, (0, pad), "constant", 0)
+        x = x.view(torch.int32)
+        # We run the 32-bit hash five times with differing parameters to
+        # reduce chance of collision
+        ITER = 5
+        cs = [hash_storage_kernel(x).item() for _ in range(ITER)]
+        return struct.pack(">" + "i" * ITER, *cs).hex()
+    finally:
+        generator.set_state(state)
+
+
+class ContentStoreWriter:
+    # Structure:
+    #   storages/
+    #     00/
+    #       0000..00
+    #   tensors/
+    #     name
+    def __init__(self, loc: str, stable_hash: bool = False) -> None:
+        self.loc: str = loc
+        self.seen_storage_hashes: Set[str] = set()
+        self.stable_hash = stable_hash
+
+    # TODO: offer some sort of non-blocking API to speed things up
+    def write_storage(self, storage: torch.UntypedStorage) -> str:
+        h = hash_storage(storage, stable_hash=self.stable_hash)
+        if h in self.seen_storage_hashes:
+            return h
+        # TODO: consider not using torch.save for this; we don't actually
+        # need any metadata for the storage
+        subfolder = os.path.join(self.loc, "storages")
+        os.makedirs(subfolder, exist_ok=True)
+        target = os.path.join(subfolder, h)
+        if os.path.exists(target):
+            return h
+        torch.save(storage, target)
+        self.seen_storage_hashes.add(h)
+        return h
+
+    def compute_tensor_metadata(self, t: torch.Tensor, h=None):
+        if h is None:
+            h = hash_storage(t.untyped_storage(), stable_hash=self.stable_hash)
+        return (
+            t.dtype,
+            h,
+            t.storage_offset(),
+            tuple(t.shape),
+            t.stride(),
+            torch._utils.get_tensor_metadata(t),
+        )
+
+    def write_tensor(self, name: str, t: torch.Tensor) -> None:
+        storage = t.untyped_storage()
+        h = self.write_storage(storage)
+        # TODO: Support more advanced snapshotting of requires_grad/grad/etc
+        d, f = os.path.split(name)
+        payload = self.compute_tensor_metadata(t, h=h)
+        subfolder = os.path.join(self.loc, "tensors", d)
+        os.makedirs(subfolder, exist_ok=True)
+        torch.save(payload, os.path.join(subfolder, f))
+
+
+class ContentStoreReader:
+    def __init__(self, loc: str, *, cache=True) -> None:
+        self.loc = loc
+        self.storage_cache: Optional[
+            Dict[Optional[torch.device], Dict[str, StorageWeakRef]]
+        ] = None
+        if cache:
+            self.storage_cache = defaultdict(dict)
+
+    def read_storage(self, h: str, *, device=None) -> torch.UntypedStorage:
+        if device is not None:
+            device = torch.device(device)
+        ws = (
+            self.storage_cache[device].get(h)
+            if self.storage_cache is not None
+            else None
+        )
+        s: Optional[torch.UntypedStorage]
+        if ws is not None:
+            s = torch.UntypedStorage._new_with_weak_ptr(ws.cdata)
+            if s is not None:
+                return s
+        s = torch.load(
+            os.path.join(self.loc, "storages", h),
+            weights_only=True,
+            map_location=device,
+        )._untyped_storage
+        assert s is not None
+        if self.storage_cache is not None:
+            self.storage_cache[device][h] = StorageWeakRef(s)
+        return s
+
+    def read_tensor_metadata(self, name: str):
+        fn = os.path.join(self.loc, "tensors", name)
+        if not os.path.exists(fn):
+            raise FileNotFoundError(fn)
+        return torch.load(fn, weights_only=True)
+
+    def read_tensor(self, name: str, *, device=None) -> torch.Tensor:
+        dtype, h, storage_offset, size, stride, metadata = self.read_tensor_metadata(
+            name
+        )
+        storage = self.read_storage(h, device=device)
+        t = torch.tensor([], dtype=dtype, device=storage.device)
+        t.set_(storage, storage_offset, size, stride)
+        torch._utils.set_tensor_metadata(t, metadata)
+        return t
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_contextlib.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_contextlib.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f1b991438c0783ff3b1d7e7ab50bc628cb5cf2c
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_contextlib.py
@@ -0,0 +1,157 @@
+# mypy: allow-untyped-defs
+# Extra utilities for working with context managers that should have been
+# in the standard library but are not
+
+import functools
+import inspect
+import warnings
+import sys
+from typing import Any, Callable, TypeVar, cast
+
+# Used for annotating the decorator usage of _DecoratorContextManager (e.g.,
+# 'no_grad' and 'enable_grad').
+# See https://mypy.readthedocs.io/en/latest/generics.html#declaring-decorators
+FuncType = Callable[..., Any]
+F = TypeVar('F', bound=FuncType)
+
+
+def _wrap_generator(ctx_factory, func):
+    """
+    Wrap each generator invocation with the context manager factory.
+
+    The input should be a function that returns a context manager,
+    not a context manager itself, to handle one-shot context managers.
+    """
+    @functools.wraps(func)
+    def generator_context(*args, **kwargs):
+        gen = func(*args, **kwargs)
+
+        # Generators are suspended and unsuspended at `yield`, hence we
+        # make sure the grad mode is properly set every time the execution
+        # flow returns into the wrapped generator and restored when it
+        # returns through our `yield` to our caller (see PR #49017).
+        try:
+            # Issuing `None` to a generator fires it up
+            with ctx_factory():
+                response = gen.send(None)
+
+            while True:
+                try:
+                    # Forward the response to our caller and get its next request
+                    request = yield response
+
+                except GeneratorExit:
+                    # Inform the still active generator about its imminent closure
+                    with ctx_factory():
+                        gen.close()
+                    raise
+
+                except BaseException:
+                    # Propagate the exception thrown at us by the caller
+                    with ctx_factory():
+                        response = gen.throw(*sys.exc_info())
+
+                else:
+                    # Pass the last request to the generator and get its response
+                    with ctx_factory():
+                        response = gen.send(request)
+
+        # We let the exceptions raised above by the generator's `.throw` or
+        # `.send` methods bubble up to our caller, except for StopIteration
+        except StopIteration as e:
+            # The generator informed us that it is done: take whatever its
+            # returned value (if any) was and indicate that we're done too
+            # by returning it (see docs for python's return-statement).
+            return e.value
+
+    return generator_context
+
+
+def context_decorator(ctx, func):
+    """
+    Like contextlib.ContextDecorator.
+
+    But with the following differences:
+    1. Is done by wrapping, rather than inheritance, so it works with context
+       managers that are implemented from C and thus cannot easily inherit from
+       Python classes
+    2. Wraps generators in the intuitive way (c.f. https://bugs.python.org/issue37743)
+    3. Errors out if you try to wrap a class, because it is ambiguous whether
+       or not you intended to wrap only the constructor
+
+    The input argument can either be a context manager (in which case it must
+    be a multi-shot context manager that can be directly invoked multiple times)
+    or a callable that produces a context manager.
+    """
+    assert not (callable(ctx) and hasattr(ctx, '__enter__')), (
+        f"Passed in {ctx} is both callable and also a valid context manager "
+        "(has __enter__), making it ambiguous which interface to use.  If you "
+        "intended to pass a context manager factory, rewrite your call as "
+        "context_decorator(lambda: ctx()); if you intended to pass a context "
+        "manager directly, rewrite your call as context_decorator(lambda: ctx)"
+    )
+
+    if not callable(ctx):
+        def ctx_factory():
+            return ctx
+    else:
+        ctx_factory = ctx
+
+    if inspect.isclass(func):
+        raise RuntimeError(
+            "Cannot decorate classes; it is ambiguous whether or not only the "
+            "constructor or all methods should have the context manager applied; "
+            "additionally, decorating a class at definition-site will prevent "
+            "use of the identifier as a conventional type.  "
+            "To specify which methods to decorate, decorate each of them "
+            "individually."
+        )
+
+    if inspect.isgeneratorfunction(func):
+        return _wrap_generator(ctx_factory, func)
+
+    @functools.wraps(func)
+    def decorate_context(*args, **kwargs):
+        with ctx_factory():
+            return func(*args, **kwargs)
+
+    return decorate_context
+
+
+class _DecoratorContextManager:
+    """Allow a context manager to be used as a decorator."""
+
+    def __call__(self, orig_func: F) -> F:
+        if inspect.isclass(orig_func):
+            warnings.warn(
+                "Decorating classes is deprecated and will be disabled in "
+                "future versions. You should only decorate functions or methods. "
+                "To preserve the current behavior of class decoration, you can "
+                "directly decorate the `__init__` method and nothing else.",
+                FutureWarning,
+                stacklevel=2,
+            )
+            func = cast(F, lambda *args, **kwargs: orig_func(*args, **kwargs))
+        else:
+            func = orig_func
+
+        return cast(F, context_decorator(self.clone, func))
+
+    def __enter__(self) -> None:
+        raise NotImplementedError
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        raise NotImplementedError
+
+    def clone(self):
+        # override this method if your children class takes __init__ parameters
+        return self.__class__()
+
+
+class _NoParamDecoratorContextManager(_DecoratorContextManager):
+    """Allow a context manager to be used as a decorator without parentheses."""
+
+    def __new__(cls, orig_func=None):
+        if orig_func is None:
+            return super().__new__(cls)
+        return cls()(orig_func)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_cpp_extension_versioner.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_cpp_extension_versioner.py
new file mode 100644
index 0000000000000000000000000000000000000000..0686e826007d2466faae3b49814f4e8dcf2db3d2
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_cpp_extension_versioner.py
@@ -0,0 +1,59 @@
+# mypy: allow-untyped-defs
+import collections
+
+
+Entry = collections.namedtuple('Entry', 'version, hash')
+
+
+def update_hash(seed, value):
+    # Good old boost::hash_combine
+    # https://www.boost.org/doc/libs/1_35_0/doc/html/boost/hash_combine_id241013.html
+    return seed ^ (hash(value) + 0x9e3779b9 + (seed << 6) + (seed >> 2))
+
+
+def hash_source_files(hash_value, source_files):
+    for filename in source_files:
+        with open(filename) as file:
+            hash_value = update_hash(hash_value, file.read())
+    return hash_value
+
+
+def hash_build_arguments(hash_value, build_arguments):
+    for group in build_arguments:
+        if group:
+            for argument in group:
+                hash_value = update_hash(hash_value, argument)
+    return hash_value
+
+
+class ExtensionVersioner:
+    def __init__(self):
+        self.entries = {}
+
+    def get_version(self, name):
+        entry = self.entries.get(name)
+        return None if entry is None else entry.version
+
+    def bump_version_if_changed(self,
+                                name,
+                                source_files,
+                                build_arguments,
+                                build_directory,
+                                with_cuda,
+                                is_python_module,
+                                is_standalone):
+        hash_value = 0
+        hash_value = hash_source_files(hash_value, source_files)
+        hash_value = hash_build_arguments(hash_value, build_arguments)
+        hash_value = update_hash(hash_value, build_directory)
+        hash_value = update_hash(hash_value, with_cuda)
+        hash_value = update_hash(hash_value, is_python_module)
+        hash_value = update_hash(hash_value, is_standalone)
+
+        entry = self.entries.get(name)
+        if entry is None:
+            self.entries[name] = entry = Entry(0, hash_value)
+        elif hash_value != entry.hash:
+            self.entries[name] = entry = Entry(entry.version + 1, hash_value)
+
+        return entry.version
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_cxx_pytree.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_cxx_pytree.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b7cdc8a7e2569146baba2570de1c1b587983b05
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_cxx_pytree.py
@@ -0,0 +1,1007 @@
+"""
+Contains utility functions for working with nested python data structures.
+
+A *pytree* is Python nested data structure. It is a tree in the sense that
+nodes are Python collections (e.g., list, tuple, dict) and the leaves are
+Python values. Furthermore, a pytree should not contain reference cycles.
+
+pytrees are useful for working with nested collections of Tensors. For example,
+one can use `tree_map` to map a function over all Tensors inside some nested
+collection of Tensors and `tree_leaves` to get a flat list of all Tensors
+inside some nested collection. pytrees are helpful for implementing nested
+collection support for PyTorch APIs.
+"""
+
+import functools
+import sys
+import types
+from typing import (
+    Any,
+    Callable,
+    Iterable,
+    List,
+    Optional,
+    overload,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+)
+from typing_extensions import deprecated
+
+import optree
+from optree import PyTreeSpec  # direct import for type annotations
+
+import torch.utils._pytree as _pytree
+from torch.utils._pytree import KeyEntry
+
+
+__all__ = [
+    "PyTree",
+    "Context",
+    "FlattenFunc",
+    "UnflattenFunc",
+    "DumpableContext",
+    "ToDumpableContextFn",
+    "FromDumpableContextFn",
+    "TreeSpec",
+    "LeafSpec",
+    "keystr",
+    "key_get",
+    "register_pytree_node",
+    "tree_flatten",
+    "tree_flatten_with_path",
+    "tree_unflatten",
+    "tree_iter",
+    "tree_leaves",
+    "tree_leaves_with_path",
+    "tree_structure",
+    "tree_map",
+    "tree_map_with_path",
+    "tree_map_",
+    "tree_map_only",
+    "tree_map_only_",
+    "tree_all",
+    "tree_any",
+    "tree_all_only",
+    "tree_any_only",
+    "treespec_dumps",
+    "treespec_loads",
+    "treespec_pprint",
+]
+
+
+T = TypeVar("T")
+S = TypeVar("S")
+U = TypeVar("U")
+R = TypeVar("R")
+
+
+Context = Any
+PyTree = Any
+TreeSpec = PyTreeSpec
+FlattenFunc = Callable[[PyTree], Tuple[List[Any], Context]]
+UnflattenFunc = Callable[[Iterable[Any], Context], PyTree]
+OpTreeUnflattenFunc = Callable[[Context, Iterable[Any]], PyTree]
+DumpableContext = Any  # Any json dumpable text
+ToDumpableContextFn = Callable[[Context], DumpableContext]
+FromDumpableContextFn = Callable[[DumpableContext], Context]
+KeyPath = Tuple[KeyEntry, ...]
+FlattenWithKeysFunc = Callable[[PyTree], Tuple[List[Tuple[KeyEntry, Any]], Any]]
+
+
+def _reverse_args(func: UnflattenFunc) -> OpTreeUnflattenFunc:
+    @functools.wraps(func)
+    def wrapped(*args: Any, **kwargs: Any) -> Any:
+        return func(*reversed(args), **kwargs)
+
+    return wrapped
+
+
+def register_pytree_node(
+    cls: Type[Any],
+    flatten_fn: FlattenFunc,
+    unflatten_fn: UnflattenFunc,
+    *,
+    serialized_type_name: Optional[str] = None,
+    to_dumpable_context: Optional[ToDumpableContextFn] = None,
+    from_dumpable_context: Optional[FromDumpableContextFn] = None,
+    flatten_with_keys_fn: Optional[FlattenWithKeysFunc] = None,
+) -> None:
+    """Register a container-like type as pytree node.
+
+    Args:
+        cls (type): A Python type to treat as an internal pytree node.
+        flatten_fn (callable): A function to be used during flattening, taking an instance of
+            ``cls`` and returning a pair, with (1) an iterable for the children to be flattened
+            recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be
+            passed to the ``unflatten_fn``.
+        unflatten_fn (callable): A function taking two arguments: the auxiliary data that was
+            returned by ``flatten_fn`` and stored in the treespec, and the unflattened children.
+            The function should return an instance of ``cls``.
+        serialized_type_name (str, optional): A keyword argument used to specify the fully
+            qualified name used when serializing the tree spec.
+        to_dumpable_context (callable, optional): An optional keyword argument to custom specify how
+            to convert the context of the pytree to a custom json dumpable representation. This is
+            used for json serialization, which is being used in :mod:`torch.export` right now.
+        from_dumpable_context (callable, optional): An optional keyword argument to custom specify
+            how to convert the custom json dumpable representation of the context back to the
+            original context. This is used for json deserialization, which is being used in
+            :mod:`torch.export` right now.
+
+    Example::
+
+        >>> # xdoctest: +SKIP
+        >>> # Registry a Python type with lambda functions
+        >>> register_pytree_node(
+        ...     set,
+        ...     lambda s: (sorted(s), None, None),
+        ...     lambda children, _: set(children),
+        ... )
+    """
+    if flatten_with_keys_fn is not None:
+        raise NotImplementedError("KeyPaths are not yet supported in cxx_pytree.")
+
+    _private_register_pytree_node(
+        cls,
+        flatten_fn,
+        unflatten_fn,
+        serialized_type_name=serialized_type_name,
+        to_dumpable_context=to_dumpable_context,
+        from_dumpable_context=from_dumpable_context,
+    )
+
+    from . import _pytree as python
+
+    python._private_register_pytree_node(
+        cls,
+        flatten_fn,
+        unflatten_fn,
+        serialized_type_name=serialized_type_name,
+        to_dumpable_context=to_dumpable_context,
+        from_dumpable_context=from_dumpable_context,
+    )
+
+
+@deprecated(
+    "`torch.utils._cxx_pytree._register_pytree_node` is deprecated. "
+    "Please use `torch.utils._cxx_pytree.register_pytree_node` instead.",
+    category=FutureWarning,
+)
+def _register_pytree_node(
+    cls: Type[Any],
+    flatten_fn: FlattenFunc,
+    unflatten_fn: UnflattenFunc,
+    *,
+    serialized_type_name: Optional[str] = None,
+    to_dumpable_context: Optional[ToDumpableContextFn] = None,
+    from_dumpable_context: Optional[FromDumpableContextFn] = None,
+) -> None:
+    """Register a container-like type as pytree node for the C++ pytree only.
+
+    The ``namespace`` argument is used to avoid collisions that occur when different libraries
+    register the same Python type with different behaviors. It is recommended to add a unique prefix
+    to the namespace to avoid conflicts with other libraries. Namespaces can also be used to specify
+    the same class in different namespaces for different use cases.
+
+    .. warning::
+        For safety reasons, a ``namespace`` must be specified while registering a custom type. It is
+        used to isolate the behavior of flattening and unflattening a pytree node type. This is to
+        prevent accidental collisions between different libraries that may register the same type.
+
+    Args:
+        cls (type): A Python type to treat as an internal pytree node.
+        flatten_fn (callable): A function to be used during flattening, taking an instance of
+            ``cls`` and returning a pair, with (1) an iterable for the children to be flattened
+            recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be
+            passed to the ``unflatten_fn``.
+        unflatten_fn (callable): A function taking two arguments: the auxiliary data that was
+            returned by ``flatten_fn`` and stored in the treespec, and the unflattened children.
+            The function should return an instance of ``cls``.
+        serialized_type_name (str, optional): A keyword argument used to specify the fully
+            qualified name used when serializing the tree spec.
+        to_dumpable_context (callable, optional): An optional keyword argument to custom specify how
+            to convert the context of the pytree to a custom json dumpable representation. This is
+            used for json serialization, which is being used in :mod:`torch.export` right now.
+        from_dumpable_context (callable, optional): An optional keyword argument to custom specify
+            how to convert the custom json dumpable representation of the context back to the
+            original context. This is used for json deserialization, which is being used in
+            :mod:`torch.export` right now.
+    """
+
+    _private_register_pytree_node(
+        cls,
+        flatten_fn,
+        unflatten_fn,
+        serialized_type_name=serialized_type_name,
+        to_dumpable_context=to_dumpable_context,
+        from_dumpable_context=from_dumpable_context,
+    )
+
+
+def _private_register_pytree_node(
+    cls: Type[Any],
+    flatten_fn: FlattenFunc,
+    unflatten_fn: UnflattenFunc,
+    *,
+    serialized_type_name: Optional[str] = None,
+    to_dumpable_context: Optional[ToDumpableContextFn] = None,
+    from_dumpable_context: Optional[FromDumpableContextFn] = None,
+) -> None:
+    """This is an internal function that is used to register a pytree node type
+    for the C++ pytree only. End-users should use :func:`register_pytree_node`
+    instead.
+    """
+    # TODO(XuehaiPan): remove this condition when we make Python pytree out-of-box support
+    # PyStructSequence types
+    if not optree.is_structseq_class(cls):
+        optree.register_pytree_node(
+            cls,
+            flatten_fn,
+            _reverse_args(unflatten_fn),
+            namespace="torch",
+        )
+
+
+def tree_flatten(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> Tuple[List[Any], TreeSpec]:
+    """Flatten a pytree.
+
+    See also :func:`tree_unflatten`.
+
+    The flattening order (i.e., the order of elements in the output list) is deterministic,
+    corresponding to a left-to-right depth-first tree traversal.
+
+    >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5}
+    >>> tree_flatten(tree)
+    ([1, 2, 3, 4, None, 5], PyTreeSpec({'a': *, 'b': (*, [*, *]), 'c': *, 'd': *}, NoneIsLeaf))
+    >>> tree_flatten(1)
+    ([1], PyTreeSpec(*, NoneIsLeaf))
+    >>> tree_flatten(None)
+    ([None], PyTreeSpec(*, NoneIsLeaf))
+
+    For unordered dictionaries, :class:`dict` and :class:`collections.defaultdict`, the order is
+    dependent on the **sorted** keys in the dictionary. Please use :class:`collections.OrderedDict`
+    if you want to keep the keys in the insertion order.
+
+    >>> from collections import OrderedDict
+    >>> tree = OrderedDict([('b', (2, [3, 4])), ('a', 1), ('c', None), ('d', 5)])
+    >>> tree_flatten(tree)
+    ([2, 3, 4, 1, None, 5], PyTreeSpec(OrderedDict({'b': (*, [*, *]), 'a': *, 'c': *, 'd': *}), NoneIsLeaf))
+
+    Args:
+        tree (pytree): A pytree to flatten.
+        is_leaf (callable, optional): An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns:
+        A pair ``(leaves, treespec)`` where the first element is a list of leaf values and the
+        second element is a treespec representing the structure of the pytree.
+    """
+    return optree.tree_flatten(  # type: ignore[return-value]
+        tree,
+        is_leaf=is_leaf,
+        none_is_leaf=True,
+        namespace="torch",
+    )
+
+
+def tree_unflatten(leaves: Iterable[Any], treespec: TreeSpec) -> PyTree:
+    """Reconstruct a pytree from the treespec and the leaves.
+
+    The inverse of :func:`tree_flatten`.
+
+    >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5}
+    >>> leaves, treespec = tree_flatten(tree)
+    >>> tree == tree_unflatten(leaves, treespec)
+    True
+
+    Args:
+        leaves (iterable): The list of leaves to use for reconstruction. The list must match the
+            number of leaves of the treespec.
+        treespec (TreeSpec): The treespec to reconstruct.
+
+    Returns:
+        The reconstructed pytree, containing the ``leaves`` placed in the structure described by
+        ``treespec``.
+    """
+    if not isinstance(treespec, TreeSpec):
+        raise TypeError(
+            f"tree_unflatten(values, spec): Expected `spec` to be instance of "
+            f"TreeSpec but got item of type {type(treespec)}."
+        )
+    return optree.tree_unflatten(treespec, leaves)  # type: ignore[arg-type]
+
+
+def tree_iter(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> Iterable[Any]:
+    """Get an iterator over the leaves of a pytree.
+
+    See also :func:`tree_flatten`.
+
+    >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5}
+    >>> list(tree_iter(tree))
+    [1, 2, 3, 4, None, 5]
+    >>> list(tree_iter(1))
+    [1]
+    >>> list(tree_iter(None))
+    [None]
+
+    Args:
+        tree (pytree): A pytree to flatten.
+        is_leaf (callable, optional): An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns:
+        An iterator over the leaf values.
+    """
+    return optree.tree_iter(
+        tree,
+        is_leaf=is_leaf,
+        none_is_leaf=True,
+        namespace="torch",
+    )
+
+
+def tree_leaves(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> List[Any]:
+    """Get the leaves of a pytree.
+
+    See also :func:`tree_flatten`.
+
+    >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5}
+    >>> tree_leaves(tree)
+    [1, 2, 3, 4, None, 5]
+    >>> tree_leaves(1)
+    [1]
+    >>> tree_leaves(None)
+    [None]
+
+    Args:
+        tree (pytree): A pytree to flatten.
+        is_leaf (callable, optional): An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns:
+        A list of leaf values.
+    """
+    return optree.tree_leaves(
+        tree,
+        is_leaf=is_leaf,
+        none_is_leaf=True,
+        namespace="torch",
+    )
+
+
+def tree_structure(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> TreeSpec:
+    """Get the treespec for a pytree.
+
+    See also :func:`tree_flatten`.
+
+    >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5}
+    >>> tree_structure(tree)
+    PyTreeSpec({'a': *, 'b': (*, [*, *]), 'c': *, 'd': *}, NoneIsLeaf)
+    >>> tree_structure(1)
+    PyTreeSpec(*, NoneIsLeaf)
+    >>> tree_structure(None)
+    PyTreeSpec(*, NoneIsLeaf)
+
+    Args:
+        tree (pytree): A pytree to flatten.
+        is_leaf (callable, optional): An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns:
+        A treespec object representing the structure of the pytree.
+    """
+    return optree.tree_structure(  # type: ignore[return-value]
+        tree,
+        is_leaf=is_leaf,
+        none_is_leaf=True,
+        namespace="torch",
+    )
+
+
+def tree_map(
+    func: Callable[..., Any],
+    tree: PyTree,
+    *rests: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    """Map a multi-input function over pytree args to produce a new pytree.
+
+    See also :func:`tree_map_`.
+
+    >>> tree_map(lambda x: x + 1, {'x': 7, 'y': (42, 64)})
+    {'x': 8, 'y': (43, 65)}
+    >>> tree_map(lambda x: x is None, {'x': 7, 'y': (42, 64), 'z': None})
+    {'x': False, 'y': (False, False), 'z': True}
+
+    If multiple inputs are given, the structure of the tree is taken from the first input;
+    subsequent inputs need only have ``tree`` as a prefix:
+
+    >>> tree_map(lambda x, y: [x] + y, [5, 6], [[7, 9], [1, 2]])
+    [[5, 7, 9], [6, 1, 2]]
+
+    Args:
+        func (callable): A function that takes ``1 + len(rests)`` arguments, to be applied at the
+            corresponding leaves of the pytrees.
+        tree (pytree): A pytree to be mapped over, with each leaf providing the first positional
+            argument to function ``func``.
+        rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as
+            ``tree`` or has ``tree`` as a prefix.
+        is_leaf (callable, optional): An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns:
+        A new pytree with the same structure as ``tree`` but with the value at each leaf given by
+        ``func(x, *xs)`` where ``x`` is the value at the corresponding leaf in ``tree`` and ``xs``
+        is the tuple of values at corresponding nodes in ``rests``.
+    """
+    return optree.tree_map(
+        func,
+        tree,
+        *rests,
+        is_leaf=is_leaf,
+        none_is_leaf=True,
+        namespace="torch",
+    )
+
+
+def tree_map_(
+    func: Callable[..., Any],
+    tree: PyTree,
+    *rests: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    """Like :func:`tree_map`, but do an inplace call on each leaf and return the original tree.
+
+    See also :func:`tree_map`.
+
+    Args:
+        func (callable): A function that takes ``1 + len(rests)`` arguments, to be applied at the
+            corresponding leaves of the pytrees.
+        tree (pytree): A pytree to be mapped over, with each leaf providing the first positional
+            argument to function ``func``.
+        rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as
+            ``tree`` or has ``tree`` as a prefix.
+        is_leaf (callable, optional): An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns:
+        The original ``tree`` with the value at each leaf is given by the side-effect of function
+        ``func(x, *xs)`` (not the return value) where ``x`` is the value at the corresponding leaf
+        in ``tree`` and ``xs`` is the tuple of values at values at corresponding nodes in ``rests``.
+    """
+    return optree.tree_map_(
+        func,
+        tree,
+        *rests,
+        is_leaf=is_leaf,
+        none_is_leaf=True,
+        namespace="torch",
+    )
+
+
+Type2 = Tuple[Type[T], Type[S]]
+Type3 = Tuple[Type[T], Type[S], Type[U]]
+if sys.version_info >= (3, 10):
+    TypeAny = Union[Type[Any], Tuple[Type[Any], ...], types.UnionType]
+else:
+    TypeAny = Union[Type[Any], Tuple[Type[Any], ...]]
+
+Fn2 = Callable[[Union[T, S]], R]
+Fn3 = Callable[[Union[T, S, U]], R]
+Fn = Callable[[T], R]
+FnAny = Callable[[Any], R]
+
+MapOnlyFn = Callable[[T], Callable[[Any], Any]]
+
+
+# These specializations help with type inference on the lambda passed to this
+# function
+@overload
+def map_only(__type_or_types_or_pred: Type2[T, S]) -> MapOnlyFn[Fn2[T, S, Any]]:
+    ...
+
+
+@overload
+def map_only(__type_or_types_or_pred: Type3[T, S, U]) -> MapOnlyFn[Fn3[T, S, U, Any]]:
+    ...
+
+
+@overload
+def map_only(__type_or_types_or_pred: Type[T]) -> MapOnlyFn[Fn[T, Any]]:
+    ...
+
+
+# This specialization is needed for the implementations below that call
+@overload
+def map_only(__type_or_types_or_pred: TypeAny) -> MapOnlyFn[FnAny[Any]]:
+    ...
+
+
+@overload
+def map_only(__type_or_types_or_pred: Callable[[Any], bool]) -> MapOnlyFn[FnAny[Any]]:
+    ...
+
+
+def map_only(
+    __type_or_types_or_pred: Union[TypeAny, Callable[[Any], bool]]
+) -> MapOnlyFn[FnAny[Any]]:
+    """
+    Suppose you are writing a tree_map over tensors, leaving everything
+    else unchanged.  Ordinarily you would have to write:
+
+        def go(t):
+            if isinstance(t, Tensor):
+                return ...
+            else:
+                return t
+
+    With this function, you only need to write:
+
+        @map_only(Tensor)
+        def go(t):
+            return ...
+
+    You can also directly use 'tree_map_only'
+    """
+    if isinstance(__type_or_types_or_pred, (type, tuple)) or (
+        sys.version_info >= (3, 10)
+        and isinstance(__type_or_types_or_pred, types.UnionType)
+    ):
+
+        def pred(x: Any) -> bool:
+            return isinstance(x, __type_or_types_or_pred)  # type: ignore[arg-type]
+
+    elif callable(__type_or_types_or_pred):
+        pred = __type_or_types_or_pred  # type: ignore[assignment]
+    else:
+        raise TypeError("Argument must be a type, a tuple of types, or a callable.")
+
+    def wrapper(func: Callable[[T], Any]) -> Callable[[Any], Any]:
+        @functools.wraps(func)
+        def wrapped(x: T) -> Any:
+            if pred(x):
+                return func(x)
+            return x
+
+        return wrapped
+
+    return wrapper
+
+
+@overload
+def tree_map_only(
+    __type_or_types_or_pred: Type[T],
+    func: Fn[T, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only(
+    __type_or_types_or_pred: Type2[T, S],
+    func: Fn2[T, S, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only(
+    __type_or_types_or_pred: Type3[T, S, U],
+    func: Fn3[T, S, U, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only(
+    __type_or_types_or_pred: Callable[[Any], bool],
+    func: FnAny[Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+def tree_map_only(
+    __type_or_types_or_pred: Union[TypeAny, Callable[[Any], bool]],
+    func: FnAny[Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    return tree_map(map_only(__type_or_types_or_pred)(func), tree, is_leaf=is_leaf)
+
+
+@overload
+def tree_map_only_(
+    __type_or_types_or_pred: Type[T],
+    func: Fn[T, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only_(
+    __type_or_types_or_pred: Type2[T, S],
+    func: Fn2[T, S, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only_(
+    __type_or_types_or_pred: Type3[T, S, U],
+    func: Fn3[T, S, U, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only_(
+    __type_or_types_or_pred: Callable[[Any], bool],
+    func: FnAny[Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+def tree_map_only_(
+    __type_or_types_or_pred: Union[TypeAny, Callable[[Any], bool]],
+    func: FnAny[Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    return tree_map_(map_only(__type_or_types_or_pred)(func), tree, is_leaf=is_leaf)
+
+
+def tree_all(
+    pred: Callable[[Any], bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    flat_args = tree_iter(tree, is_leaf=is_leaf)
+    return all(map(pred, flat_args))
+
+
+def tree_any(
+    pred: Callable[[Any], bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    flat_args = tree_iter(tree, is_leaf=is_leaf)
+    return any(map(pred, flat_args))
+
+
+@overload
+def tree_all_only(
+    __type_or_types: Type[T],
+    pred: Fn[T, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+@overload
+def tree_all_only(
+    __type_or_types: Type2[T, S],
+    pred: Fn2[T, S, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+@overload
+def tree_all_only(
+    __type_or_types: Type3[T, S, U],
+    pred: Fn3[T, S, U, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+def tree_all_only(
+    __type_or_types: TypeAny,
+    pred: FnAny[bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    flat_args = tree_iter(tree, is_leaf=is_leaf)
+    return all(pred(x) for x in flat_args if isinstance(x, __type_or_types))
+
+
+@overload
+def tree_any_only(
+    __type_or_types: Type[T],
+    pred: Fn[T, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+@overload
+def tree_any_only(
+    __type_or_types: Type2[T, S],
+    pred: Fn2[T, S, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+@overload
+def tree_any_only(
+    __type_or_types: Type3[T, S, U],
+    pred: Fn3[T, S, U, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+def tree_any_only(
+    __type_or_types: TypeAny,
+    pred: FnAny[bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    flat_args = tree_iter(tree, is_leaf=is_leaf)
+    return any(pred(x) for x in flat_args if isinstance(x, __type_or_types))
+
+
+def broadcast_prefix(
+    prefix_tree: PyTree,
+    full_tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> List[Any]:
+    """Return a list of broadcasted leaves in ``prefix_tree`` to match the number of leaves in ``full_tree``.
+
+    If a ``prefix_tree`` is a prefix of a ``full_tree``, this means the ``full_tree`` can be
+    constructed by replacing the leaves of ``prefix_tree`` with appropriate **subtrees**.
+
+    This function returns a list of leaves with the same size as ``full_tree``. The leaves are
+    replicated from ``prefix_tree``. The number of replicas is determined by the corresponding
+    subtree in ``full_tree``.
+
+    >>> broadcast_prefix(1, [1, 2, 3])
+    [1, 1, 1]
+    >>> broadcast_prefix([1, 2, 3], [1, 2, 3])
+    [1, 2, 3]
+    >>> broadcast_prefix([1, 2, 3], [1, 2, 3, 4])
+    Traceback (most recent call last):
+        ...
+    ValueError: list arity mismatch; expected: 3, got: 4; list: [1, 2, 3, 4].
+    >>> broadcast_prefix([1, 2, 3], [1, 2, (3, 4)])
+    [1, 2, 3, 3]
+    >>> broadcast_prefix([1, 2, 3], [1, 2, {'a': 3, 'b': 4, 'c': (None, 5)}])
+    [1, 2, 3, 3, 3, 3]
+
+    Args:
+        prefix_tree (pytree): A pytree with the same structure as a prefix of ``full_tree``.
+        full_tree (pytree): A pytree with the same structure as a suffix of ``prefix_tree``.
+        is_leaf (callable, optional): An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns:
+        A list of leaves in ``prefix_tree`` broadcasted to match the number of leaves in ``full_tree``.
+    """
+    return optree.broadcast_prefix(
+        prefix_tree,
+        full_tree,
+        is_leaf=is_leaf,
+        none_is_leaf=True,
+        namespace="torch",
+    )
+
+
+# Broadcasts a pytree to the provided TreeSpec and returns the flattened
+# values. If this is not possible, then this function returns None.
+#
+# For example, given pytree=0 and spec=TreeSpec(list, None, [LeafSpec(), LeafSpec()]),
+# would return [0, 0]. This is useful for part of the vmap implementation:
+# a user can pass in vmap(fn, in_dims)(*inputs). `in_dims` should be
+# broadcastable to the tree structure of `inputs` and we use
+# _broadcast_to_and_flatten to check this.
+def _broadcast_to_and_flatten(
+    tree: PyTree,
+    treespec: TreeSpec,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> Optional[List[Any]]:
+    assert isinstance(treespec, TreeSpec)
+    full_tree = tree_unflatten([0] * treespec.num_leaves, treespec)
+    try:
+        return broadcast_prefix(tree, full_tree, is_leaf=is_leaf)
+    except ValueError:
+        return None
+
+
+def treespec_dumps(treespec: TreeSpec, protocol: Optional[int] = None) -> str:
+    """Serialize a treespec to a JSON string."""
+    if not isinstance(treespec, TreeSpec):
+        raise TypeError(
+            f"treespec_dumps(spec): Expected `spec` to be instance of "
+            f"TreeSpec but got item of type {type(treespec)}."
+        )
+    from ._pytree import (
+        tree_structure as _tree_structure,
+        treespec_dumps as _treespec_dumps,
+    )
+
+    orig_treespec = _tree_structure(tree_unflatten([0] * treespec.num_leaves, treespec))
+    return _treespec_dumps(orig_treespec, protocol=protocol)
+
+
+def treespec_loads(serialized: str) -> TreeSpec:
+    """Deserialize a treespec from a JSON string."""
+    from ._pytree import (
+        tree_unflatten as _tree_unflatten,
+        treespec_loads as _treespec_loads,
+    )
+
+    orig_treespec = _treespec_loads(serialized)
+    dummy_tree = _tree_unflatten([0] * orig_treespec.num_leaves, orig_treespec)
+    treespec = tree_structure(dummy_tree)
+    return treespec
+
+
+class _DummyLeaf:
+    def __repr__(self) -> str:
+        return "*"
+
+
+def treespec_pprint(treespec: TreeSpec) -> str:
+    dummy_tree = tree_unflatten(
+        [_DummyLeaf() for _ in range(treespec.num_leaves)],
+        treespec,
+    )
+    return repr(dummy_tree)
+
+
+class LeafSpecMeta(type(TreeSpec)):  # type: ignore[misc]
+    def __instancecheck__(self, instance: object) -> bool:
+        return isinstance(instance, TreeSpec) and instance.is_leaf()
+
+
+class LeafSpec(TreeSpec, metaclass=LeafSpecMeta):
+    def __new__(cls) -> "LeafSpec":
+        return optree.treespec_leaf(none_is_leaf=True)  # type: ignore[return-value]
+
+
+def tree_flatten_with_path(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> Tuple[List[Tuple[KeyPath, Any]], TreeSpec]:
+    """Flattens a pytree like :func:`tree_flatten`, but also returns each leaf's key path.
+
+    Args:
+        tree: a pytree to flatten. If it contains a custom type, that type must be
+            registered with an appropriate `tree_flatten_with_path_fn` when registered
+            with :func:`register_pytree_node`.
+        is_leaf: An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+    Returns:
+        A tuple where the first element is a list of (key path, leaf) pairs, and the
+        second element is a :class:`TreeSpec` representing the structure of the flattened
+        tree.
+    """
+    raise NotImplementedError("KeyPaths are not yet supported in cxx_pytree.")
+
+
+def tree_leaves_with_path(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> List[Tuple[KeyPath, Any]]:
+    """Gets the leaves of a pytree like ``tree_leaves`` and returns each leaf's key path.
+
+    Args:
+        tree: a pytree. If it contains a custom type, that type must be
+            registered with an appropriate `tree_flatten_with_path_fn` when registered
+            with :func:`register_pytree_node`.
+        is_leaf: An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+    Returns:
+        A list of (key path, leaf) pairs.
+    """
+    raise NotImplementedError("KeyPaths are not yet supported in cxx_pytree.")
+
+
+def tree_map_with_path(
+    func: Callable[..., Any],
+    tree: PyTree,
+    *rests: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    """Like :func:`tree_map`, but the provided callable takes an additional key path argument.
+
+    Args:
+        func: A function that takes ``2 + len(rests)`` arguments, to be applied at the
+            corresponding leaves of the pytrees. The first positional argument
+            to ``func`` is the key path of the leaf in question. The second
+            positional argument is the value of the leaf.
+        tree: A pytree to be mapped over, with each leaf providing the first positional
+            argument to function ``func``.
+        rests: A tuple of pytrees, each of which has the same structure as
+            ``tree`` or has ``tree`` as a prefix.
+        is_leaf: An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns
+        A new pytree with the same structure as ``tree`` but with the value at each leaf given by
+        ``func(keypath, x, *xs)`` where ``keypath`` is the key path at the
+        corresponding leaf in ``tree``, ``x`` is the value at that leaf, and
+        ``xs`` is the tuple of values at corresponding nodes in ``rests``.
+    """
+    raise NotImplementedError("KeyPaths are not yet supported in cxx_pytree.")
+
+
+def keystr(kp: KeyPath) -> str:
+    """Given a key path, return a pretty-printed representation."""
+    raise NotImplementedError("KeyPaths are not yet supported in cxx_pytree.")
+
+
+def key_get(obj: Any, kp: KeyPath) -> Any:
+    """Given an object and a key path, return the value at the key path."""
+    raise NotImplementedError("KeyPaths are not yet supported in cxx_pytree.")
+
+
+_pytree._cxx_pytree_imported = True
+for args, kwargs in _pytree._cxx_pytree_pending_imports:
+    _private_register_pytree_node(*args, **kwargs)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_device.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_device.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9e885256ec61b0c56ed424dc78f4029fd860c93
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_device.py
@@ -0,0 +1,119 @@
+# mypy: allow-untyped-defs
+from typing import Optional
+import torch
+from torch.overrides import TorchFunctionMode, _pop_mode, _push_mode
+from torch.utils._contextlib import context_decorator
+from torch._C import _len_torch_function_stack
+import functools
+
+CURRENT_DEVICE: Optional[torch.device] = None
+
+@functools.lru_cache(1)
+def _device_constructors():
+    return {
+        # standard ones
+        torch.empty,
+        torch.empty_permuted,
+        torch.empty_strided,
+        torch.empty_quantized,
+        torch.ones,
+        torch.arange,
+        torch.bartlett_window,
+        torch.blackman_window,
+        torch.eye,
+        torch.fft.fftfreq,
+        torch.fft.rfftfreq,
+        torch.full,
+        torch.fill,
+        torch.hamming_window,
+        torch.hann_window,
+        torch.kaiser_window,
+        torch.linspace,
+        torch.logspace,
+        torch.nested.nested_tensor,
+        # This function doesn't actually take a device argument
+        # torch.normal,
+        torch.ones,
+        torch.rand,
+        torch.randn,
+        torch.randint,
+        torch.randperm,
+        torch.range,
+        torch.sparse_coo_tensor,
+        torch.sparse_compressed_tensor,
+        torch.sparse_csr_tensor,
+        torch.sparse_csc_tensor,
+        torch.sparse_bsr_tensor,
+        torch.sparse_bsc_tensor,
+        torch.tril_indices,
+        torch.triu_indices,
+        torch.vander,
+        torch.zeros,
+        torch.asarray,
+        # weird ones
+        torch.tensor,
+        torch.as_tensor,
+        torch.scalar_tensor,
+        torch.asarray,
+    }
+
+# NB: This is directly called from C++ in torch/csrc/Device.cpp
+class DeviceContext(TorchFunctionMode):
+    def __init__(self, device):
+        self.device = torch.device(device)
+
+    def __enter__(self):
+        global CURRENT_DEVICE
+        self.old_device = CURRENT_DEVICE
+        CURRENT_DEVICE = self.device
+        # We need to put the device at the bottom of the stack
+        # If we set default device within a function mode context
+        # exiting that context mode will pop the device function mode off
+        # of the stack incorrectly
+        cur_stack = []
+        for _ in range(_len_torch_function_stack()):
+            cur_stack.append(_pop_mode())
+
+        _push_mode(self)
+
+        for mode in reversed(cur_stack):
+            _push_mode(mode)
+
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        global CURRENT_DEVICE
+        CURRENT_DEVICE = self.old_device
+        cur_stack = []
+        # Invariant: there should only be one DeviceContext on the stack at any time
+        # (At the bottom), pop all mdoes until we hit the bottom, assert it's a DeviceContext
+        # or else someone else has popped it!
+        for _ in range(_len_torch_function_stack() - 1):
+            mode = _pop_mode()
+            assert not isinstance(mode, DeviceContext)
+            cur_stack.append(mode)
+
+        if _len_torch_function_stack() > 0:
+            mode = _pop_mode()
+            assert isinstance(mode, DeviceContext)
+
+        for mode in reversed(cur_stack):
+            _push_mode(mode)
+
+    def __torch_function__(self, func, types, args=(), kwargs=None):
+        kwargs = kwargs or {}
+        if func in _device_constructors() and kwargs.get('device') is None:
+            kwargs['device'] = self.device
+        return func(*args, **kwargs)
+
+# NB: This is directly called from C++ in torch/csrc/Device.cpp
+def device_decorator(device, func):
+    return context_decorator(lambda: device, func)
+
+def set_device(device):
+    """
+    Set the default device inside of the wrapped function by decorating it with this function.
+
+    If you would like to use this as a context manager, use device as a
+    context manager directly, e.g., ``with torch.device(device)``.
+    """
+    return lambda func: device_decorator(torch.device(device), func)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_exposed_in.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_exposed_in.py
new file mode 100644
index 0000000000000000000000000000000000000000..54faf279ecfc66a4ea70604359b94ce91fba4030
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_exposed_in.py
@@ -0,0 +1,15 @@
+# mypy: allow-untyped-defs
+# Allows one to expose an API in a private submodule publicly as per the definition
+# in PyTorch's public api policy.
+#
+# It is a temporary solution while we figure out if it should be the long-term solution
+# or if we should amend PyTorch's public api policy. The concern is that this approach
+# may not be very robust because it's not clear what __module__ is used for.
+# However, both numpy and jax overwrite the __module__ attribute of their APIs
+# without problem, so it seems fine.
+def exposed_in(module):
+    def wrapper(fn):
+        fn.__module__ = module
+        return fn
+
+    return wrapper
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_foreach_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_foreach_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3100d41b6c0f55dcff986a18e3bb0c2d417e111
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_foreach_utils.py
@@ -0,0 +1,44 @@
+from typing import List, Dict, Tuple, Optional
+
+import torch
+from torch import Tensor
+from torch.autograd.grad_mode import no_grad
+from typing_extensions import TypeAlias
+
+def _get_foreach_kernels_supported_devices() -> List[str]:
+    r"""Return the device type list that supports foreach kernels."""
+    return ["cuda", "xpu", torch._C._get_privateuse1_backend_name()]
+
+def _get_fused_kernels_supported_devices() -> List[str]:
+    r"""Return the device type list that supports fused kernels in optimizer."""
+    return ["mps", "cuda", "xpu", "cpu", torch._C._get_privateuse1_backend_name()]
+
+TensorListList: TypeAlias = List[List[Optional[Tensor]]]
+Indices: TypeAlias = List[int]
+_foreach_supported_types = [torch.Tensor]
+
+
+# This util function splits tensors into groups by device and dtype, which is useful before sending
+# tensors off to a foreach implementation, which requires tensors to be on one device and dtype.
+# If tensorlistlist contains more than one tensorlist, the following assumptions are made BUT NOT verified:
+#   - tensorlists CAN be None
+#   - all tensors in the first specified list cannot be None
+#   - given an index i, all specified tensorlist[i]s match in dtype and device
+# with_indices (bool, optional): whether to track previous indices as the last list per dictionary entry.
+#   It comes in handy if there are Nones or literals in the tensorlists that are getting scattered out.
+#   Whereas mutating a tensor in the resulting split-up tensorlists WILL propagate changes back to the
+#   original input tensorlists, changing up Nones/literals WILL NOT propagate, and manual propagation
+#   may be necessary. Check out torch/optim/sgd.py for an example.
+@no_grad()
+def _group_tensors_by_device_and_dtype(
+    tensorlistlist: TensorListList,
+    with_indices: bool = False,
+) -> Dict[Tuple[torch.device, torch.dtype], Tuple[TensorListList, Indices]]:
+    return torch._C._group_tensors_by_device_and_dtype(tensorlistlist, with_indices)
+
+def _device_has_foreach_support(device: torch.device) -> bool:
+    return device.type in (_get_foreach_kernels_supported_devices() + ["cpu"]) and not torch.jit.is_scripting()
+
+
+def _has_foreach_support(tensors: List[Tensor], device: torch.device) -> bool:
+    return _device_has_foreach_support(device) and all(t is None or type(t) in _foreach_supported_types for t in tensors)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_freeze.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_freeze.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2d6cea7f22843d51ee93a8664f9cbe46024cb29
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_freeze.py
@@ -0,0 +1,291 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+"""
+Freeze Python packages.
+
+Freezing makes it possible to ship arbitrary Python modules as part of a C++
+library. The Python source of the module is compiled to bytecode and written
+to `.c` files, to be imported by Python's built-in FrozenImporter.
+
+In a normal Python installation, FrozenImporter is only used to bootstrap the
+initialization of the import machinery. Python's importers are defined in
+Python (see `_bootstrap.py` and `_bootstrap_external.py`) but need to be
+retrieved before any importers are available. Freezing the module bytecode
+resolves this circular dependency.
+
+This script will freeze the Python standard library. It produces two things:
+- Bytecode files: A set of `.c` that define C variables containing Python bytecode.
+- Main file: A `main.c` file listing all of these modules in the right form to be
+  consumed by FrozenImporter.
+
+The library that wishes to these modules make them available to the local
+Python instance by extending `PyImport_FrozenModules` appropriately (see
+https://docs.python.org/3/c-api/import.html#c.PyImport_FrozenModules).
+"""
+
+import argparse
+import functools
+import itertools
+import marshal
+import os
+import types
+from dataclasses import dataclass
+from pathlib import Path
+from typing import List
+
+
+PATH_MARKER = ""
+MAIN_INCLUDES = """#include 
+
+"""
+
+MAIN_PREFIX_TEMPLATE = """
+// Compiled standard library modules. These should be appended to the existing
+// `PyImport_FrozenModules` that ships with CPython.
+struct _frozen {}[] = {{
+"""
+
+FAKE_PREFIX = MAIN_PREFIX_TEMPLATE.format("_PyImport_FrozenModules")
+
+MAIN_SUFFIX = """\
+    {0, 0, 0} /* sentinel */
+};
+"""
+
+# Exclude some standard library modules to:
+# 1. Slim down the final frozen lib.
+# 2. Remove functionality we don't want to support.
+DENY_LIST = [
+    # Interface to unix databases
+    "dbm",
+    # ncurses bindings (terminal interfaces)
+    "curses",
+    # Tcl/Tk GUI
+    "tkinter",
+    "tkinter",
+    # Tests for the standard library
+    "test",
+    "tests",
+    "idle_test",
+    "__phello__.foo.py",
+    # importlib frozen modules. These are already baked into CPython.
+    "_bootstrap.py",
+    "_bootstrap_external.py",
+]
+
+NUM_BYTECODE_FILES = 5
+
+
+def indent_msg(fn):
+    @functools.wraps(fn)
+    def wrapper(*args, **kwargs):
+        args[0].indent += 1
+        ret = fn(*args, **kwargs)
+        args[0].indent -= 1
+        return ret
+
+    return wrapper
+
+
+@dataclass
+class FrozenModule:
+    # The fully qualified module name, e.g. 'foo.bar.baz'
+    module_name: str
+    # The name of the C variable that holds the bytecode, e.g. 'M_foo__bar__baz'
+    c_name: str
+    # The size of the C variable. Negative if this module is a package.
+    size: int
+    # The frozen bytecode
+    bytecode: bytes
+
+
+class Freezer:
+    def __init__(self, verbose: bool):
+        self.frozen_modules: List[FrozenModule] = []
+        self.indent: int = 0
+        self.verbose: bool = verbose
+
+    def msg(self, path: Path, code: str):
+        if not self.verbose:
+            return
+        # P: package dir
+        # F: python file
+        # S: skipped (not a package dir)
+        # X: skipped (deny-listed)
+        # N: skipped (not a python file)
+        for i in range(self.indent):
+            print("    ", end="")
+        print(f"{code} {path}")
+
+    def write_bytecode(self, install_root):
+        """
+        Write the `.c` files containing the frozen bytecode.
+
+        Shared frozen modules evenly across the files.
+        """
+        bytecode_file_names = [f"bytecode_{i}.c" for i in range(NUM_BYTECODE_FILES)]
+        bytecode_files = [
+            open(os.path.join(install_root, name), "w") for name in bytecode_file_names
+        ]
+        it = itertools.cycle(bytecode_files)
+        for m in self.frozen_modules:
+            self.write_frozen(m, next(it))
+
+        for f in bytecode_files:
+            f.close()
+
+    def write_main(self, install_root, oss, symbol_name):
+        """Write the `main.c` file containing a table enumerating all the frozen modules."""
+        with open(os.path.join(install_root, "main.c"), "w") as outfp:
+            outfp.write(MAIN_INCLUDES)
+            for m in self.frozen_modules:
+                outfp.write(f"extern unsigned char {m.c_name}[];\n")
+
+            outfp.write(MAIN_PREFIX_TEMPLATE.format(symbol_name))
+            for m in self.frozen_modules:
+                outfp.write(f'\t{{"{m.module_name}", {m.c_name}, {m.size}}},\n')
+            outfp.write(MAIN_SUFFIX)
+            if oss:
+                outfp.write(FAKE_PREFIX)
+                outfp.write(MAIN_SUFFIX)
+
+    def write_frozen(self, m: FrozenModule, outfp):
+        """Write a single frozen module's bytecode out to a C variable."""
+        outfp.write(f"unsigned char {m.c_name}[] = {{")
+        for i in range(0, len(m.bytecode), 16):
+            outfp.write("\n\t")
+            for c in bytes(m.bytecode[i : i + 16]):
+                outfp.write("%d," % c)
+        outfp.write("\n};\n")
+
+    def compile_path(self, path: Path, top_package_path: Path):
+        """Entry point for compiling a Path object."""
+        if path.is_dir():
+            self.compile_package(path, top_package_path)
+        else:
+            self.compile_file(path, top_package_path)
+
+    @indent_msg
+    def compile_package(self, path: Path, top_package_path: Path):
+        """Compile all the files within a Python package dir."""
+        assert path.is_dir()
+        if path.name in DENY_LIST:
+            self.msg(path, "X")
+            return
+
+        # Python packages are directories that have __init__.py in them.
+        is_package_dir = any(child.name == "__init__.py" for child in path.iterdir())
+        if not is_package_dir:
+            self.msg(path, "S")
+            return
+
+        self.msg(path, "P")
+        # Recursively compile all children in this dir
+        for child in path.iterdir():
+            self.compile_path(child, top_package_path)
+
+    def get_module_qualname(self, file_path: Path, top_package_path: Path) -> List[str]:
+        # `path` looks like 'Lib/foo/bar/baz.py'
+
+        # chop off 'Lib/' to get something that represents a Python module hierarchy.
+        # e.g. 'foo/bar/baz.py', which maps to 'foo.bar.baz'
+        normalized_path = file_path.relative_to(top_package_path.parent)
+
+        if normalized_path.name == "__init__.py":
+            # Special handling for `__init__.py`. In this case, this file
+            # specifies that the containing directory should be treated as a package.
+            # For 'foo/bar/baz/__init__.py':
+            # - The module name is 'baz'
+            module_basename = normalized_path.parent.name
+            # - The parent is foo.bar (need to shave off the 'baz')
+            module_parent = normalized_path.parent.parent.parts
+        else:
+            module_basename = normalized_path.stem
+            module_parent = normalized_path.parent.parts
+        return list(module_parent) + [module_basename]
+
+    def compile_string(self, file_content: str) -> types.CodeType:
+        # instead of passing in the real build time path to 'compile', we
+        # pass in a marker instead. This prevents the build time path being
+        # leaked to runtime. That path may not be available at runtime.
+        # Setting the path to a mark make sure it's a hard error rather
+        # than a flaky error when inspect module tries to retrieve python source
+        # code during torchscripting.
+        path_marker = PATH_MARKER
+        return compile(file_content, path_marker, "exec")
+
+    @indent_msg
+    def compile_file(self, path: Path, top_package_path: Path):
+        """
+        Compile a Python source file to frozen bytecode.
+
+        Append the result to `self.frozen_modules`.
+        """
+        assert path.is_file()
+        if path.suffix != ".py":
+            self.msg(path, "N")
+            return
+
+        if path.name in DENY_LIST:
+            self.msg(path, "X")
+            return
+
+        self.msg(path, "F")
+        module_qualname = self.get_module_qualname(path, top_package_path)
+        module_mangled_name = "__".join(module_qualname)
+        c_name = "M_" + module_mangled_name
+
+        with open(path) as src_file:
+            co = self.compile_string(src_file.read())
+
+        bytecode = marshal.dumps(co)
+        size = len(bytecode)
+        if path.name == "__init__.py":
+            # Python packages are signified by negative size.
+            size = -size
+        self.frozen_modules.append(
+            FrozenModule(".".join(module_qualname), c_name, size, bytecode)
+        )
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Compile py source")
+    parser.add_argument("paths", nargs="*", help="Paths to freeze.")
+    parser.add_argument("--verbose", action="store_true", help="Print debug logs")
+    parser.add_argument(
+        "--install-dir", "--install_dir", help="Root directory for all output files"
+    )
+    parser.add_argument(
+        "--oss",
+        action="store_true",
+        help="If it's OSS build, add a fake _PyImport_FrozenModules",
+    )
+    parser.add_argument(
+        "--symbol-name",
+        "--symbol_name",
+        help="The name of the frozen module array symbol to generate",
+        default="_PyImport_FrozenModules_torch",
+    )
+
+    args = parser.parse_args()
+
+    f = Freezer(args.verbose)
+
+    for p in args.paths:
+        path = Path(p)
+        if path.is_dir() and not Path.exists(path / "__init__.py"):
+            # this 'top level path p' is a standard directory containing modules,
+            # not a module itself
+            # each 'mod' could be a dir containing __init__.py or .py file
+            # NB: sorted to make sure this is deterministic
+            for mod in sorted(path.glob("*")):
+                f.compile_path(mod, mod)
+        else:
+            f.compile_path(path, path)
+
+    f.write_bytecode(args.install_dir)
+    f.write_main(args.install_dir, args.oss, args.symbol_name)
+
+
+if __name__ == "__main__":
+    main()  # pragma: no cover
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_get_clean_triton.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_get_clean_triton.py
new file mode 100644
index 0000000000000000000000000000000000000000..0367980509c2c43d8645959e707ceb3530fa8777
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_get_clean_triton.py
@@ -0,0 +1,152 @@
+# mypy: allow-untyped-defs
+import argparse
+import os
+import re
+from pathlib import Path
+from typing import Dict, List
+
+
+def remove_triton_function_declaration(source_code: str) -> str:
+    remove_head = re.sub(r"(\n.+\s\'\'\'\n)", "\n", source_code)
+    remove_tail = re.sub(r"(\'\'\'\,.+)", "\n", remove_head)
+    return remove_tail
+
+
+def remove_async_compile(source_code: str) -> str:
+    remove_top_level = str.replace(source_code, "async_compile = AsyncCompile()", "")
+    remove_compile = str.replace(remove_top_level, "async_compile.wait(globals())", "")
+    remove_del = str.replace(remove_compile, "del async_compile", "")
+    return remove_del
+
+
+def rename_kernels(source_code: str) -> str:
+    pattern = r"(\w+)\s*=\s*async_compile\.triton\('triton_',\s"
+    triton_kernel_decl = "def triton_"
+    matches = [
+        (match.end(), match.group(1))
+        for match in re.finditer(pattern, source_code, re.DOTALL)
+    ]
+
+    # Starting from the last match to avoid issues with shifting indices after replacements
+    for end_index, captured_string in reversed(matches):
+        # Find the index of the next "B" after the current match
+        index_of_B = source_code.find(triton_kernel_decl, end_index)
+        if index_of_B != -1:
+            # Replace the triton_kernel_decl with the captured string
+            source_code = (
+                source_code[:index_of_B]
+                + f"def {captured_string}"
+                + source_code[index_of_B + len(triton_kernel_decl) :]
+            )
+        else:
+            # If triton_kernel_decl is not found after the current match, continue to the next
+            continue
+
+    return source_code
+
+
+def merge_params(original_params: List[str], new_params: List[str]) -> List[str]:
+    assert len(new_params) >= len(original_params)
+    for idx in range(len(new_params)):
+        if new_params[idx] == "T":
+            new_params[idx] = original_params[idx]
+    return new_params
+
+
+def add_launch_params(original: str, kernel_to_params: Dict[str, str]) -> str:
+    # Regex to match the function call in the original string
+    pattern = r"(\w+)\.run\((.*), grid=(.*\)), [^)]*\)"
+
+    def replace(match) -> str:
+        # Extract parts from the regex match
+        func_name = match.group(1)
+        params = match.group(2)
+        grid = match.group(3)
+        new_params = kernel_to_params[func_name]
+        new_params = merge_params(params.split(", "), new_params.split(", "))
+
+        # Format the new function call
+        new_string = f"{func_name}[{grid}]({', '.join(new_params)})"
+        return new_string
+
+    transformed = re.sub(pattern, replace, original)
+
+    remove_inductor_wrappers = re.sub(
+        r"@triton_heuristics[^@]*@triton.jit",
+        r"@triton.jit",
+        transformed,
+        flags=re.DOTALL,
+    )
+
+    return remove_inductor_wrappers
+
+
+def process_file(input_filename: str, output_filename: str) -> str:
+    with open(input_filename) as file:
+        source_code = file.read()
+
+    transformed_code = source_code
+    if "def triton_(" in source_code:
+        raise RuntimeError(
+            "Need to run original Pytorch code generating kernels with TORCHINDUCTOR_UNIQUE_KERNEL_NAMES=1"
+        )
+    # transformed_code = rename_kernels(transformed_code)
+    transformed_code = remove_triton_function_declaration(transformed_code)
+    transformed_code = remove_async_compile(transformed_code)
+
+    launch_params_filename = f"{input_filename}.launch_params"
+    if not os.path.exists(launch_params_filename):
+        raise RuntimeError(
+            f"Missing {launch_params_filename}. Run `TORCHINDUCTOR_DUMP_LAUNCH_PARAMS=1 python {input_filename} first."
+        )
+
+    with open(launch_params_filename) as f:
+        launch_params_meta = f.readlines()
+
+    split_params = [i.split("|") for i in launch_params_meta]
+    strip_params = [[a.strip(), b.strip()] for a, b in split_params]
+    kernel_to_args: Dict[str, str] = dict(strip_params)
+    transformed_code = add_launch_params(transformed_code, kernel_to_args)
+
+    with open(output_filename, "w") as file:
+        file.write(transformed_code)
+    return transformed_code
+
+
+def get_clean_triton(
+    input_path: Path, output_path: Path = Path("triton_only_repro.py")
+):
+    """Run experiments and output results to file
+
+    Args:
+        input_path (Optional[Path]): Path to inductor generated output codede
+        output_path (Optional[Path]): Path to write out the new python file
+    """
+    return process_file(str(input_path), str(output_path))
+
+
+if __name__ == "__main__":
+    """Sample usage:
+    # Running sweep
+    python inputcode.py
+    """
+    parser = argparse.ArgumentParser(
+        description="Clean Inductor generated code to remove Inductor dependencies"
+    )
+
+    # Add the arguments
+    parser.add_argument(
+        "input_path", type=Path, help="Path to inductor generated output code"
+    )
+    parser.add_argument(
+        "--output_path",
+        type=Path,
+        default=Path("triton_only_repro.py"),
+        help="Path to write out the clean triton output",
+    )
+
+    # Parse the arguments
+    args = parser.parse_args()
+
+    # Call the function with parsed arguments
+    result = get_clean_triton(args.input_path, args.output_path)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_import_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_import_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1102fa8a019dadee99bfa579a4fe643dc00d1a41
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_import_utils.py
@@ -0,0 +1,43 @@
+# mypy: allow-untyped-defs
+import functools
+import importlib.util
+
+import torch
+
+
+def _check_module_exists(name: str) -> bool:
+    r"""Returns if a top-level module with :attr:`name` exists *without**
+    importing it. This is generally safer than try-catch block around a
+    `import X`. It avoids third party libraries breaking assumptions of some of
+    our tests, e.g., setting multiprocessing start method when imported
+    (see librosa/#747, torchvision/#544).
+    """
+    try:
+        spec = importlib.util.find_spec(name)
+        return spec is not None
+    except ImportError:
+        return False
+
+
+@functools.lru_cache
+def dill_available():
+    return (
+        _check_module_exists("dill")
+        # dill fails to import under torchdeploy
+        and not torch._running_with_deploy()
+    )
+
+
+@functools.lru_cache
+def import_dill():
+    if not dill_available():
+        return None
+
+    import dill
+
+    # XXX: By default, dill writes the Pickler dispatch table to inject its
+    # own logic there. This globally affects the behavior of the standard library
+    # pickler for any user who transitively depends on this module!
+    # Undo this extension to avoid altering the behavior of the pickler globally.
+    dill.extend(use_dill=False)
+    return dill
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_mode_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_mode_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..91c0e07b3d9345094e532beb4b94c1ff3c4b26a6
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_mode_utils.py
@@ -0,0 +1,11 @@
+# mypy: allow-untyped-defs
+import torch
+from typing import TypeVar
+
+T = TypeVar('T')
+
+# returns if all are the same mode
+def all_same_mode(modes):
+    return all(tuple(mode == modes[0] for mode in modes))
+
+no_dispatch = torch._C._DisableTorchDispatch
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_ordered_set.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_ordered_set.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e076deca79b8321f01c4d6cccc1720b70f0b7fa
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_ordered_set.py
@@ -0,0 +1,180 @@
+from __future__ import annotations
+
+from collections.abc import MutableSet, Set as AbstractSet
+from typing import (
+    Any,
+    cast,
+    Dict,
+    Generic,
+    Iterable,
+    Iterator,
+    List,
+    Optional,
+    Tuple,
+    Type,
+    TypeVar,
+)
+
+
+T = TypeVar("T")
+T_co = TypeVar("T_co", covariant=True)
+
+__all__ = ["OrderedSet"]
+
+
+# Using Generic[T] bc py38 does not support type parameterized MutableSet
+class OrderedSet(MutableSet, Generic[T]):
+    """
+    Insertion ordered set, similar to OrderedDict.
+    """
+
+    __slots__ = ("_dict",)
+
+    def __init__(self, iterable: Optional[Iterable[T]] = None):
+        self._dict = dict.fromkeys(iterable, None) if iterable is not None else {}
+
+    @staticmethod
+    def _from_dict(dict_inp: Dict[T, None]) -> OrderedSet[T]:
+        s: OrderedSet[T] = OrderedSet()
+        s._dict = dict_inp
+        return s
+
+    #
+    # Required overriden abstract methods
+    #
+    def __contains__(self, elem: object) -> bool:
+        return elem in self._dict
+
+    def __iter__(self) -> Iterator[T]:
+        return iter(self._dict)
+
+    def __len__(self) -> int:
+        return len(self._dict)
+
+    def add(self, elem: T) -> None:
+        self._dict[elem] = None
+
+    def discard(self, elem: T) -> None:
+        self._dict.pop(elem, None)
+
+    def clear(self) -> None:
+        # overridden because MutableSet impl is slow
+        self._dict.clear()
+
+    # Unimplemented set() methods in _collections_abc.MutableSet
+
+    @classmethod
+    def _wrap_iter_in_set(cls, other: Any) -> Any:
+        """
+        Wrap non-Set Iterables in OrderedSets
+
+        Some of the magic methods are more strict on input types than
+        the public apis, so we need to wrap inputs in sets.
+        """
+
+        if not isinstance(other, AbstractSet) and isinstance(other, Iterable):
+            return cls(other)
+        else:
+            return other
+
+    def pop(self) -> T:
+        if not self:
+            raise KeyError("pop from an empty set")
+        return self._dict.popitem()[0]
+
+    def copy(self) -> OrderedSet[T]:
+        return OrderedSet._from_dict(self._dict.copy())
+
+    def difference(self, *others: Iterable[T]) -> OrderedSet[T]:
+        res = self.copy()
+        res.difference_update(*others)
+        return res
+
+    def difference_update(self, *others: Iterable[T]) -> None:
+        for other in others:
+            self -= other  # type: ignore[operator, arg-type]
+
+    def update(self, *others: Iterable[T]) -> None:
+        for other in others:
+            self |= other  # type: ignore[operator, arg-type]
+
+    def intersection(self, *others: Iterable[T]) -> OrderedSet[T]:
+        res = self.copy()
+        for other in others:
+            if other is not self:
+                res &= other  # type: ignore[operator, arg-type]
+        return res
+
+    def intersection_update(self, *others: Iterable[T]) -> None:
+        for other in others:
+            self &= other  # type: ignore[operator, arg-type]
+
+    def issubset(self, other: Iterable[T]) -> bool:
+        return self <= self._wrap_iter_in_set(other)
+
+    def issuperset(self, other: Iterable[T]) -> bool:
+        return self >= self._wrap_iter_in_set(other)
+
+    def symmetric_difference(self, other: Iterable[T]) -> OrderedSet[T]:
+        return self ^ other  # type: ignore[operator, arg-type]
+
+    def symmetric_difference_update(self, other: Iterable[T]) -> None:
+        self ^= other  # type: ignore[operator, arg-type]
+
+    def union(self, *others: Iterable[T]) -> OrderedSet[T]:
+        res = self.copy()
+        for other in others:
+            if other is self:
+                continue
+            res |= other  # type: ignore[operator, arg-type]
+        return res
+
+    # Specify here for correct type inference, otherwise would
+    # return AbstractSet[T]
+    def __sub__(self, other: AbstractSet[T_co]) -> OrderedSet[T]:
+        # following cpython set impl optimization
+        if isinstance(other, OrderedSet) and (len(self) * 4) > len(other):
+            out = self.copy()
+            out -= other
+            return out
+        return cast(OrderedSet[T], super().__sub__(other))
+
+    def __ior__(self, other: Iterable[T]) -> OrderedSet[T]:  # type: ignore[misc, override]   # noqa: PYI034
+        if isinstance(other, OrderedSet):
+            self._dict.update(other._dict)
+            return self
+        return super().__ior__(other)  # type: ignore[arg-type]
+
+    def __eq__(self, other: AbstractSet[T]) -> bool:  # type: ignore[misc, override]
+        if isinstance(other, OrderedSet):
+            return self._dict == other._dict
+        return super().__eq__(other)  # type: ignore[arg-type]
+
+    def __ne__(self, other: AbstractSet[T]) -> bool:  # type: ignore[misc, override]
+        if isinstance(other, OrderedSet):
+            return self._dict != other._dict
+        return super().__ne__(other)  # type: ignore[arg-type]
+
+    def __or__(self, other: AbstractSet[T_co]) -> OrderedSet[T]:
+        return cast(OrderedSet[T], super().__or__(other))
+
+    def __and__(self, other: AbstractSet[T_co]) -> OrderedSet[T]:
+        # MutableSet impl will iterate over other, iter over smaller of two sets
+        if isinstance(other, OrderedSet) and len(self) < len(other):
+            return other & self
+        return cast(OrderedSet[T], super().__and__(other))
+
+    def __xor__(self, other: AbstractSet[T_co]) -> OrderedSet[T]:
+        return cast(OrderedSet[T], super().__xor__(other))
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}({list(self)})"
+
+    def __getstate__(self) -> List[T]:
+        return list(self._dict.keys())
+
+    def __setstate__(self, state: List[T]) -> None:
+        self._dict = dict.fromkeys(state, None)
+
+    def __reduce__(self) -> Tuple[Type[OrderedSet[T]], Tuple[List[T]]]:
+        return (OrderedSet, (list(self),))
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_python_dispatch.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_python_dispatch.py
new file mode 100644
index 0000000000000000000000000000000000000000..70c65a690717337e15af7d755377e64660c41c0e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_python_dispatch.py
@@ -0,0 +1,701 @@
+# mypy: allow-untyped-defs
+import contextlib
+
+import warnings
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Set, Union, Protocol, Tuple, Sequence, overload, Deque, Type
+from typing_extensions import TypeGuard
+from collections import deque
+
+import torch
+import torchgen
+import torchgen.model
+from torch._C import (
+    _get_dispatch_stack_at,
+    _len_torch_dispatch_stack,
+    _pop_torch_dispatch_stack,
+    _push_on_torch_dispatch_stack,
+    DispatchKey,
+)
+
+
+# TODO: Limitations and things about enable_torch_dispatch_mode we should fix before exposing it:
+# - We need a better user-facing api for _DisableTorchDispatch that
+#   is able to selectively disable __torch_dispatch__ of a particular class.
+# - It doesn't work with the tensor constructors (torch.tensor, torch.Tensor)
+# - Better name (see https://github.com/pytorch/pytorch/pull/63496#discussion_r694091694)
+
+_is_in_torch_dispatch_mode = False
+_is_in_non_infra_torch_dispatch_mode = False
+
+def is_in_torch_dispatch_mode(include_infra_modes=True) -> bool:
+    return _is_in_torch_dispatch_mode if include_infra_modes else _is_in_non_infra_torch_dispatch_mode
+
+
+class TorchDispatchMode:
+    """
+    A ``TorchDispatchMode`` allows you to override the meaning of all
+    ``__torch_dispatch__`` overrideable functions within a dynamic scope,
+    without having to actually create a tensor subclass or manually
+    monkey-patch functions in the PyTorch API.  Some common situations
+    where you should use a mode:
+
+        * You want to override the meaning of factory functions, or other
+          functions that do not otherwise take a tensor as an argument
+          (these cannot be overridden with tensor subclasses).
+
+        * You want to override the behavior of all functions without needing
+          to wrap your inputs in tensor subclasses; e.g., if you are just
+          interested in logging intermediate computations.
+
+        * You want to control the order of execution of various tensor
+          subclasses explicitly, rather than implicitly via the return of
+          ``NotImplemented``.
+
+    Independent subclasses of :class:`TorchDispatchMode` are compositional:
+    modes can be pushed onto a stack using ``with MyMode():``.
+    When you call functions in the PyTorch API inside your
+    ``__torch_dispatch__`` implementation, by default, they will forward on to
+    the next mode on the mode stack.  If you want recursively call back into
+    your current ``__torch_dispatch__`` implementation, either explicitly
+    invoke ``self.__torch_dispatch__(...)``, or use the context manager
+    ``__torch_dispatch__(self)`` to make PyTorch
+    API self-referential (beware of infinite loops, in this case!)
+    """
+
+    def __init__(self, _dispatch_key=None):
+        if _dispatch_key is not None:
+            assert isinstance(_dispatch_key, torch._C.DispatchKey)
+            self.__dict__["_dispatch_key"] = _dispatch_key
+
+        self.old_dispatch_mode_flags: Deque[bool] = deque()
+        self.old_non_infra_dispatch_mode_flags: Deque[bool] = deque()
+
+    def _lazy_init_old_dispatch_mode_flags(self):
+        if not hasattr(self, "old_dispatch_mode_flags"):
+            self.old_dispatch_mode_flags: Deque[bool] = deque()  # type: ignore[no-redef]
+
+        if not hasattr(self, "old_non_infra_dispatch_mode_flags"):
+            self.old_non_infra_dispatch_mode_flags: Deque[bool] = deque()  # type: ignore[no-redef]
+
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        raise NotImplementedError
+
+    def __enter__(self):
+        global _is_in_torch_dispatch_mode
+        global _is_in_non_infra_torch_dispatch_mode
+        # Previously, there wasn't any state in this class' constructor
+        # super calls were added to existing modes, but for any new modes
+        # this will replicate the previous behavior of not strictly needing
+        # to call super().__init__()
+        self._lazy_init_old_dispatch_mode_flags()
+        self.old_dispatch_mode_flags.append(_is_in_torch_dispatch_mode)
+        _is_in_torch_dispatch_mode = True
+        self.old_non_infra_dispatch_mode_flags.append(_is_in_non_infra_torch_dispatch_mode)
+        _is_in_non_infra_torch_dispatch_mode = _is_in_non_infra_torch_dispatch_mode or not self.is_infra_mode()
+        _push_mode(self)
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        mb_dk_or_mode_key = self.__dict__.get("_dispatch_key", None)
+        if mb_dk_or_mode_key is None:
+            # Today, mode keys are not used at all in the per-dispatch-key-mode logic (for pre-dispatch)
+            # We should probably revisit this.
+            mb_dk_or_mode_key = self.__dict__.get("_mode_key", None)
+        global _is_in_torch_dispatch_mode
+        _is_in_torch_dispatch_mode = self.old_dispatch_mode_flags.pop()
+        global _is_in_non_infra_torch_dispatch_mode
+        _is_in_non_infra_torch_dispatch_mode = self.old_non_infra_dispatch_mode_flags.pop()
+        _pop_mode(mb_dk_or_mode_key)
+
+    @classmethod
+    def push(cls, *args, **kwargs):
+        warnings.warn(
+            "`Mode.push()` is no longer necessary and can be replaced with just `with Mode()`"
+        )
+        instance = cls(*args, **kwargs)
+        return instance
+
+    @classmethod
+    def is_infra_mode(cls):
+        return False
+
+
+
+def _get_current_dispatch_mode():
+    stack_len = _len_torch_dispatch_stack()
+    # Return a user mode on the stack if there are any
+    if stack_len > 0:
+        return _get_dispatch_stack_at(stack_len - 1)
+    return None
+
+
+def _detect_infra_mode(key):
+    assert key in [torch._C._TorchDispatchModeKey.FUNCTIONAL, torch._C._TorchDispatchModeKey.PROXY]
+    from torch._ops import _get_dispatch_mode_pre_dispatch
+
+    pre_dispatch_mode = _get_dispatch_mode_pre_dispatch(
+        key
+    )
+    post_dispatch_mode = torch._C._get_dispatch_mode(
+        key
+    )
+
+    assert (pre_dispatch_mode is None) or (
+        post_dispatch_mode is None
+    )
+
+    if pre_dispatch_mode is None:
+        return post_dispatch_mode
+
+    return pre_dispatch_mode
+
+
+def _unset_infra_mode(key):
+    from torch._ops import _get_dispatch_mode_pre_dispatch, unset_mode_pre_dispatch
+
+    pre_dispatch_mode = _get_dispatch_mode_pre_dispatch(key)
+    post_dispatch_mode = torch._C._get_dispatch_mode(key)
+    if pre_dispatch_mode and post_dispatch_mode:
+        raise AssertionError(
+            "Can't have active infra mode on both pre and post dispatch mode stack"
+        )
+
+    if pre_dispatch_mode:
+        mode = unset_mode_pre_dispatch(key)
+        return mode
+    if post_dispatch_mode:
+        return torch._C._unset_dispatch_mode(key)
+
+
+def _disable_infra_mode(key):
+    assert key in (
+        torch._C._TorchDispatchModeKey.FUNCTIONAL,
+        torch._C._TorchDispatchModeKey.PROXY,
+    )
+    mode_unset = _unset_infra_mode(key)
+    try:
+        yield mode_unset
+    finally:
+        if mode_unset is not None:
+            _push_mode(mode_unset)
+
+
+def _get_current_dispatch_mode_stack():
+    stack_len = _len_torch_dispatch_stack()
+    return [_get_dispatch_stack_at(i) for i in range(stack_len)]
+
+
+def _push_mode(mode: TorchDispatchMode):
+    k = mode._dispatch_key if hasattr(mode, "_dispatch_key") else None
+    assert k is None or k == torch._C.DispatchKey.PreDispatch
+    if k is None:
+        _push_on_torch_dispatch_stack(mode)
+        return
+
+    from torch._ops import _set_mode_pre_dispatch, get_cached_ops
+
+    # See Note [Not Caching Per-Dispatch-Key Mode Handlers]
+    # Clear the cache of every op that has been used so far, for this particular key.
+    ks = torch._C._functionality_to_backend_keys(k)
+    for op in get_cached_ops():
+        for key in ks:
+            op._uncache_dispatch(key)
+    _set_mode_pre_dispatch(mode)
+
+
+def _pop_mode(k: Optional[Union[DispatchKey, torch._C._TorchDispatchModeKey]] = None):
+    if k == torch._C.DispatchKey.PreDispatch:  # type: ignore[attr-defined]
+        from torch._ops import _pop_mode_from_pre_dispatch
+
+        return _pop_mode_from_pre_dispatch()
+
+    if k is None or isinstance(k, torch._C._TorchDispatchModeKey):
+        return _pop_torch_dispatch_stack(k)
+
+
+@contextlib.contextmanager
+def _pop_mode_temporarily(k: Optional[DispatchKey] = None):
+    old = _pop_mode(k)
+    try:
+        yield old
+    finally:
+        _push_mode(old)
+
+
+@contextlib.contextmanager
+def _disable_current_modes():
+    from torch._ops import (
+        _len_torch_dispatch_stack_pre_dispatch,
+        _pop_mode_from_pre_dispatch,
+    )
+    from torch._subclasses.functional_tensor import FunctionalTensorMode
+    from torch.fx.experimental.proxy_tensor import ProxyTorchDispatchMode
+    from torch._subclasses.schema_check_mode import SchemaCheckMode
+
+    mode_len_pre_dispatch = _len_torch_dispatch_stack_pre_dispatch()
+    old_pre_dispatch_modes = [
+        _pop_mode_from_pre_dispatch() for _ in range(mode_len_pre_dispatch)
+    ]
+
+    has_proxy_mode_in_pre_dispatch = False
+    has_functional_mode_in_pre_dispatch = False
+    has_schema_check_mode_in_pre_dispatch = False
+
+    for i in old_pre_dispatch_modes:
+        if isinstance(i, ProxyTorchDispatchMode):
+            has_proxy_mode_in_pre_dispatch = True
+        if isinstance(i, FunctionalTensorMode):
+            has_functional_mode_in_pre_dispatch = True
+        if isinstance(i, SchemaCheckMode):
+            has_schema_check_mode_in_pre_dispatch = True
+
+    mode_len = _len_torch_dispatch_stack()
+    old_modes = [_pop_mode() for _ in range(mode_len)]
+
+    for old in old_modes:
+        if (
+            isinstance(old, FunctionalTensorMode)
+            and has_functional_mode_in_pre_dispatch
+        ):
+            raise AssertionError(
+                "Can't have FunctionalMode available both in PreDispatch and Python Key"
+            )
+        if isinstance(old, ProxyTorchDispatchMode) and has_proxy_mode_in_pre_dispatch:
+            raise AssertionError(
+                "Can't have ProxyTorchDispatchMode available both in PreDispatch and Python Key"
+            )
+        if (
+            isinstance(old, SchemaCheckMode)
+            and has_schema_check_mode_in_pre_dispatch
+        ):
+            raise AssertionError(
+                "Can't have SchemaCheckMode available both in PreDispatch and Python Key"
+            )
+
+    # Manually disable proxy and fake modes, if any are active
+    try:
+        yield old_pre_dispatch_modes + old_modes
+    finally:
+        for mode in reversed(old_modes):
+            _push_mode(mode)
+        for mode in reversed(old_pre_dispatch_modes):
+            _push_mode(mode)
+
+
+class BaseTorchDispatchMode(TorchDispatchMode):
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        if kwargs is None:
+            kwargs = {}
+        return func(*args, **kwargs)
+
+
+# Subtypes which have __tensor_flatten__ and __tensor_unflatten__.
+class TensorWithFlatten(Protocol):
+    def __tensor_flatten__(self) -> Tuple[Sequence[str], object]:
+        ...
+
+    @staticmethod
+    def __tensor_unflatten__(inner_tensors: int, flatten_spec: int, outer_size: int, outer_stride: int) -> torch.Tensor:
+        ...
+
+    # It would be really nice to be able to say that the return of
+    # is_traceable_wrapper_subclass() is Intersection[torch.Tensor,
+    # TensorWithFlatten] - but that doesn't exist.
+
+    shape: torch._C.Size
+
+    @overload
+    def stride(self, dim: None = None) -> Tuple[int, ...]:
+        ...
+
+    @overload
+    def stride(self, dim: int) -> int:
+        ...
+
+    def dim(self) -> int:
+        ...
+
+    @overload
+    def to(
+            self,
+            dtype: torch.types._dtype,
+            non_blocking: bool = False,
+            copy: bool = False,
+            *,
+            memory_format: Optional[torch.memory_format] = None
+    ) -> torch.Tensor:
+        ...
+
+    @overload
+    def to(
+            self,
+            device: Optional["torch._prims_common.DeviceLikeType"] = None,
+            dtype: Optional[torch.types._dtype] = None,
+            non_blocking: bool = False,
+            copy: bool = False,
+            *,
+            memory_format: Optional[torch.memory_format] = None
+    ) -> torch.Tensor:
+        ...
+
+    @overload
+    def to(
+            self,
+            other: torch.Tensor,
+            non_blocking: bool = False,
+            copy: bool = False,
+            *,
+            memory_format: Optional[torch.memory_format] = None
+    ) -> torch.Tensor:
+        ...
+
+
+
+
+def is_traceable_wrapper_subclass(t: object) -> TypeGuard[TensorWithFlatten]:
+    """
+    Returns whether or not a tensor subclass that implements __torch_dispatch__
+    is 'traceable' with torch.compile.
+    In order for a tensor subclass to support TorchDispatchMode-style tracing in PT2,
+    It must implement two magic methods: __tensor_flatten__ and __tensor_unflatten__.
+    It is also expected to obey some restrictions around traceability and aliasing:
+        * The subclass's __torch_dispatch__() implementation should desugar into pytorch
+            dispatcher operations that can be traced into a graph.
+        * The subclass should use return_and_correct_aliasing(). This is needed today to make
+            sure that torch.compile does the right thing in a few cases around input mutation
+            and output aliasing.
+
+    Expected magic method signatures:
+        attrs, ctx = t.__tensor_flatten__()
+            attrs: list of attribute name strings for inner tensors
+            ctx: dict containing any other subclass-specific metadata needed for unflattening
+
+        t = MySubClass.__tensor_unflatten__(inner_tensors, ctx, outer_size, outer_stride)
+            inner_tensors: dict mapping attribute name -> tensor for each inner tensor
+            ctx: dict with subclass metadata in the form that __tensor_flatten__() produces
+            outer_size: expected (possibly symbolic) size that the returned subclass
+                instance should have. Note that this arg is useful for certain subclasses
+                that require the shape info to be constructed. In most cases, this arg can be
+                safely ignored.
+            outer_stride: expected (possibly symbolic) stride that the returned subclass
+                instance should have. Note that this arg is useful for certain subclasses
+                that require the stride info to be constructed. In most cases, this arg can be
+                safely ignored.
+    """
+    is_subclass = isinstance(t, torch.Tensor) and type(t) != torch.Tensor
+    return (
+        is_subclass
+        and hasattr(t, "__tensor_flatten__")
+        and hasattr(t, "__tensor_unflatten__")
+    )
+
+def is_traceable_wrapper_subclass_type(t: Type) -> TypeGuard[Type[TensorWithFlatten]]:
+    """Same as above, but takes a type argument instead of an instance."""
+    return (issubclass(t, torch.Tensor) and t != torch.Tensor
+            and hasattr(t, "__tensor_flatten__") and hasattr(t, "__tensor_unflatten__"))
+
+
+def transform_subclass(t, callback, outer_size=None, outer_stride=None):
+    """
+    Given a traceable, wrapper tensor subclass ``t`` that implements
+    ``__torch_dispatch__`` and holds some inner tensors,
+    and a callback of type ``Callable[[str, torch.Tensor], torch.Tensor]``,
+    `transform_subclass` will construct a fresh instance of the wrapper tensor subclass.
+    It will do so by grabbing each inner tensor attribute from the wrapper,
+    passing them into ``callback`` to get a transformed tensor,
+    and putting each transformed tensor into the fresh tensor subclass instance.
+
+    Note: this function will not handle ensuring that the fresh subclass
+    gets the same (autograd, and aliasing) metadata as the original tensor.
+    This is generally handled in other subsystems like AOTAutograd.
+    """
+    outer_size = outer_size if outer_size is not None else t.size()
+    outer_stride = outer_stride if outer_stride is not None else t.stride()
+
+    attrs, ctx = t.__tensor_flatten__()
+    transformed_tensors_dict = {}
+    for attr in attrs:
+        transformed_tensors_dict[attr] = callback(attr, getattr(t, attr))
+    sub = type(t).__tensor_unflatten__(
+        transformed_tensors_dict, ctx, outer_size, outer_stride
+    )
+
+    # NB: Purposefully guard here to simplify the inner / outer symbols.
+    # Using sym_eq() for symbolic comparison can result in an expression that's too
+    # difficult to guard on, so we use == here.
+    assert sub.shape == outer_size, (
+        f"Expected return value from {type(t)}__tensor_unflatten__() to have "
+        f"shape equal to {outer_size}, but got: {sub.shape}"
+    )
+    assert sub.stride() == outer_stride, (
+        f"Expected return value from {type(t)}__tensor_unflatten__() to have "
+        f"stride equal to {outer_stride}, but got: {sub.stride()}"
+    )
+
+    return sub
+
+
+def _correct_storage_aliasing(func, schema_info, args, outs):
+    """
+    Given: an OpOverload, a SchemaInfo (cached information from torchgen about schema),
+    and the inputs/outputs to the OpOverload,
+    this function checks to see if func is a view operator
+    (by checking if any of the outputs in the op's schema
+     are immutable aliases of inputs).
+    If so, this function manually aliases the storage of the output tensor
+    with its corresponding input tensor alias.
+    It does this by unsafely overwriting the storage field of the output tensor
+    to be the same storage as the input.
+    """
+    assert isinstance(func, torch._ops.OpOverload)
+    assert isinstance(args, tuple)
+    assert isinstance(outs, (list, tuple))
+    flat_outs = torch.utils._pytree.tree_leaves(outs)
+
+    def alias_non_inplace_storage(arg, ret):
+        # This is hopefully a reasonable assert:
+        # subclasses that rely on this API for output aliasing
+        # should always return wrapper tensor subclasses for us to manually alias.
+        # in theory if a subclass that needs this API wants to sometimes return
+        # plain tensors, we could remove the assert and just not perform the aliasing,
+        # but it seems safer to learn more about this case first.
+        if is_traceable_wrapper_subclass(arg) or is_traceable_wrapper_subclass(ret):
+            ret_list = ret if isinstance(ret, list) else [ret]
+            for r in ret_list:
+                assert type(arg) == type(
+                    r
+                ), f"""Called {str(func)} with input of type {type(arg)}
+and output of type {type(ret)}. But expected types to match."""
+        # Need to call a non-dispatcher helper, because we explicitly do **not**
+        # want our subclass to intercept the set_() call.
+        # instead, our subclass should directly have its storage swapped out.
+        # we **explicitly** don't want to reset the sizes on ret, if the storage implies a size change.
+        # Why?
+        # The purpose of this API is *not* to change the size/strides of our output- we assume it's already correct.
+        # We just want to "fix up" the storage aliasing, without modifying or output's metadata.
+        # Example: out = inp.expand(inp.shape[0], inp.shape[0])
+        #     This requires swapping the storage of out to be the same as inp,
+        #     but we do *not* want it to change the sizes/strides that were compute for out.
+
+        if isinstance(ret, list):
+            for r in ret:
+                torch._functionalize_unsafe_set(r, arg)
+        else:
+            assert isinstance(ret, torch.Tensor), f"type: {type(ret)}"
+            torch._functionalize_unsafe_set(ret, arg)
+
+    def is_read_only_alias_match(arg, ret):
+        shared_aliases = arg.alias_set & ret.alias_set
+        return len(shared_aliases) > 0 and not arg.is_write
+
+    num_args = len(func._schema.arguments)
+    num_returns = len(func._schema.returns)
+    for arg_idx in range(num_args):
+        for return_idx in range(num_returns):
+            if is_read_only_alias_match(
+                schema_info.args[arg_idx], schema_info.outs[return_idx]
+            ):
+                alias_non_inplace_storage(args[arg_idx], outs[return_idx])
+
+
+# This abstracts over the fact that in return_and_correct_aliasing,
+# we sometimes use torchgen schema parsing (for aten ops, since torchscript's schema parsing is sometimes buggy),
+# and sometimes use torchscript schema parsing (for custom ops, for which torchgen parsing is untested).
+@dataclass
+class AliasInfo:
+    alias_set: Set[str]
+    is_write: bool
+    name: Optional[str]
+
+
+@dataclass
+class SchemaInfo:
+    args: List[AliasInfo]
+    outs: List[AliasInfo]
+
+
+# Can't import torch._ops.OpOverload due to circular reference
+parsed_schema_map: Dict[Any, SchemaInfo] = {}
+
+
+# Given an OpOverload, returns schema information on it.
+# This is cached for efficiency, since it can involve running torchgen
+def get_alias_info(func) -> SchemaInfo:
+    if func in parsed_schema_map:
+        return parsed_schema_map[func]
+    # For ATen ops: use torchgen (since torchscript parser doesn't handle alias annotations
+    # properly for some ops that output tensorlists)
+    if func.namespace == "aten":
+        torchgen_schema_str = str(func._schema)
+        assert torchgen_schema_str.startswith("aten::")
+        # remove the aten:: namespace, which is added by the torchscript parser,
+        # and torchgen doesn't know how to handle
+        torchgen_schema_str = torchgen_schema_str[6:]
+        import re
+
+        # the torchscript parser ends up converting int[2]=1 into int[2]=[1, 1],
+        # which torchgen chokes on.
+        torchgen_schema_str = re.sub(r"=\[[0, ]+\]", "=0", torchgen_schema_str)
+        torchgen_schema_str = re.sub(r"=\[[1, ]+\]", "=1", torchgen_schema_str)
+        # for aten::rot90
+        torchgen_schema_str = torchgen_schema_str.replace("=[0, 1]", "=[0,1]")
+        torchgen_schema = torchgen.model.FunctionSchema.parse(torchgen_schema_str)
+        arg_schemas = [
+            AliasInfo(
+                alias_set=(
+                    set() if a.annotation is None else set(a.annotation.alias_set)
+                ),
+                is_write=a.annotation is not None and a.annotation.is_write,
+                name=a.name,
+            )
+            for a in torchgen_schema.arguments.flat_all
+        ]
+        out_schemas = [
+            AliasInfo(
+                alias_set=(
+                    set() if a.annotation is None else set(a.annotation.alias_set)
+                ),
+                is_write=a.annotation is not None and a.annotation.is_write,
+                name=a.name,
+            )
+            for a in torchgen_schema.returns
+        ]
+    else:
+        # For non-aten ops, torchgen is untested so we rely on torchscript schema parsing
+        arg_schemas = [
+            AliasInfo(
+                alias_set=(
+                    set() if a.alias_info is None else set(a.alias_info.before_set)
+                ),
+                is_write=a.alias_info is not None and a.alias_info.is_write,
+                name=a.name,
+            )
+            for a in func._schema.arguments
+        ]
+        out_schemas = [
+            AliasInfo(
+                alias_set=(
+                    set() if a.alias_info is None else set(a.alias_info.before_set)
+                ),
+                is_write=a.alias_info is not None and a.alias_info.is_write,
+                name=a.name,
+            )
+            for a in func._schema.returns
+        ]
+    schema_info = SchemaInfo(args=arg_schemas, outs=out_schemas)
+    parsed_schema_map[func] = schema_info
+    return schema_info
+
+
+def return_and_correct_aliasing(func, args, kwargs, out):
+    """
+    This function should be used by wrapper tensor ``__torch_dispatch__`` subclasses
+    that would like to work with torch.compile. It ensures that the subclass
+    properly implements the aliasing behavior of every op,
+    which is needed for correctness in AOTAutograd.
+    This function will handle:
+
+        * When we see a view op, we will alias the storages of any
+          input and output tensor subclasses
+
+        * When we see an inplace or out= op, we will directly
+          return the corresponding input tensor, instead of returning
+          a (potentially) fresh output tensor.
+    """
+
+    # Caching here because torchgen parsing is definitely not fast, and this function is called
+    # once for every op in the graph during functionalization.
+    schema_info = get_alias_info(func)
+
+    def get_write_alias(x):
+        if len(x.alias_set) == 0:
+            return None
+        alias_set = list(x.alias_set)
+        # torchscript allows for complicated alias sets, but our dispatcher ops only really involve simple aliasing
+        assert len(alias_set) == 1
+        if x.is_write:
+            return alias_set[0]
+        return None
+
+    def get_arg_from_alias(output_alias, schema_info, args, kwargs):
+        new_args, new_kwargs = torch.fx.operator_schemas.normalize_function(  # type: ignore[misc]
+            func, args=args, kwargs=kwargs
+        )
+
+        arg_indices = [
+            i for i, a in enumerate(schema_info.args) if output_alias in a.alias_set
+        ]
+        # For any dispatcher op with an output alias, we expect it to map to exactly one alias in the schema's input arguments.
+        assert len(arg_indices) == 1
+        idx = arg_indices[0]
+        arg_info = schema_info.args[idx]
+        if arg_info.name is not None and arg_info.name in new_kwargs:
+            return new_kwargs[arg_info.name]
+        return new_args[idx]
+
+    # Fix up the storages of any outs so that they point to the same storage as the input,
+    # if func is a view op.
+    _correct_storage_aliasing(
+        func, schema_info, args, (out,) if not isinstance(out, tuple) else out
+    )
+
+    # For inplace_view ops in particular, we'll try hard to make sure that the wrapper subclass's
+    # metadata is set correctly.
+    if torch.Tag.inplace_view in func.tags:
+        # no_dispatch() to make sure that we secretly change the metadata on the wrapper,
+        # but don't end up dispatching the op anywhere else.
+        mutated_args = [
+            x
+            for i, x in enumerate(args)
+            if get_write_alias(schema_info.args[i]) is not None
+        ]
+        # Assumption: we have a very small number of inplace_view ops that follow a strict schema:
+        # there is only a single argument that gets its metadata mutated.
+        assert len(mutated_args) == 1
+        # This check exists because we generally *do* want to update the metadata of any wrapper subclasses,
+        # but FunctionalTensor is special: it overrides all size/stride calls to plumb to the inner tensor.
+        # so we don't actually need to update the metadata (and attempting to do so causes errors)
+        from torch._subclasses.functional_tensor import FunctionalTensor
+
+        if not isinstance(mutated_args[0], FunctionalTensor):
+            with torch.utils._mode_utils.no_dispatch():
+                # See Note: [Fake Tensor Dispatch Keys]
+                # we're borrowing the way it modifies dispatch key TLS.
+                meta_in_tls = torch._C._meta_in_tls_dispatch_include()
+                torch._C._set_meta_in_tls_dispatch_include(True)
+                try:
+                    func(*args, **kwargs)
+                finally:
+                    torch._C._set_meta_in_tls_dispatch_include(meta_in_tls)
+
+    # Next: we need to make sure to return inputs directly, if the output is a mutable alias (e.g. add_()).
+
+    # simple case: none of our outputs have mutable aliases, so we can return the output as-is
+    if not any(get_write_alias(r) is not None for r in schema_info.outs):
+        return out
+
+    # simplifying assumption: we don't have **any** ops with return types like "-> (Tensor(a!), Tensor)"
+    if not all(get_write_alias(r) is not None for r in schema_info.outs):
+        raise RuntimeError("Unsupported schema: " + str(func._schema))
+
+    if len(func._schema.returns) == 1:
+        return get_arg_from_alias(
+            get_write_alias(schema_info.outs[0]), schema_info, args, kwargs
+        )
+
+    # In the multi-return case, all aten ops return a tuple / list, so cast accordingly.
+    outs_to_return = type(out)(
+        [
+            (
+                get_arg_from_alias(
+                    get_write_alias(schema_info.outs[i]), schema_info, args, kwargs
+                )
+                if get_write_alias(r) is not None
+                else o
+            )
+            for ((i, r), o) in zip(enumerate(schema_info.outs), out)
+        ]
+    )
+    return outs_to_return
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_pytree.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_pytree.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1b836594a8fd03fc6cb62f51e687056460df092
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_pytree.py
@@ -0,0 +1,1620 @@
+"""
+Contains utility functions for working with nested python data structures.
+
+A *pytree* is Python nested data structure. It is a tree in the sense that
+nodes are Python collections (e.g., list, tuple, dict) and the leaves are
+Python values. Furthermore, a pytree should not contain reference cycles.
+
+pytrees are useful for working with nested collections of Tensors. For example,
+one can use `tree_map` to map a function over all Tensors inside some nested
+collection of Tensors and `tree_leaves` to get a flat list of all Tensors
+inside some nested collection. pytrees are helpful for implementing nested
+collection support for PyTorch APIs.
+
+This pytree implementation is not very performant due to Python overhead
+To improve the performance we can move parts of the implementation to C++.
+"""
+
+import dataclasses
+import functools
+import importlib
+import json
+import sys
+import threading
+import types
+import warnings
+from collections import defaultdict, deque, namedtuple, OrderedDict
+from typing import (
+    Any,
+    Callable,
+    cast,
+    DefaultDict,
+    Deque,
+    Dict,
+    FrozenSet,
+    Generic,
+    Hashable,
+    Iterable,
+    List,
+    Mapping,
+    NamedTuple,
+    Optional,
+    OrderedDict as GenericOrderedDict,
+    overload,
+    Protocol,
+    Sequence,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+)
+from typing_extensions import deprecated
+
+
+__all__ = [
+    "PyTree",
+    "Context",
+    "FlattenFunc",
+    "UnflattenFunc",
+    "DumpableContext",
+    "ToDumpableContextFn",
+    "FromDumpableContextFn",
+    "TreeSpec",
+    "LeafSpec",
+    "keystr",
+    "key_get",
+    "register_pytree_node",
+    "tree_flatten",
+    "tree_flatten_with_path",
+    "tree_unflatten",
+    "tree_iter",
+    "tree_leaves",
+    "tree_leaves_with_path",
+    "tree_structure",
+    "tree_map",
+    "tree_map_with_path",
+    "tree_map_",
+    "tree_map_only",
+    "tree_map_only_",
+    "tree_all",
+    "tree_any",
+    "tree_all_only",
+    "tree_any_only",
+    "treespec_dumps",
+    "treespec_loads",
+    "treespec_pprint",
+]
+
+
+T = TypeVar("T")
+S = TypeVar("S")
+U = TypeVar("U")
+R = TypeVar("R")
+
+
+DEFAULT_TREESPEC_SERIALIZATION_PROTOCOL = 1
+NO_SERIALIZED_TYPE_NAME_FOUND = "NO_SERIALIZED_TYPE_NAME_FOUND"
+
+
+class KeyEntry(Protocol):
+    def __hash__(self) -> int:
+        ...
+
+    def __eq__(self, other: object) -> bool:
+        ...
+
+    def __str__(self) -> str:
+        ...
+
+    def get(self, parent: Any) -> Any:
+        ...
+
+
+Context = Any
+PyTree = Any
+FlattenFunc = Callable[[PyTree], Tuple[List[Any], Context]]
+UnflattenFunc = Callable[[Iterable[Any], Context], PyTree]
+DumpableContext = Any  # Any json dumpable text
+ToDumpableContextFn = Callable[[Context], DumpableContext]
+FromDumpableContextFn = Callable[[DumpableContext], Context]
+ToStrFunc = Callable[["TreeSpec", List[str]], str]
+MaybeFromStrFunc = Callable[[str], Optional[Tuple[Any, Context, str]]]
+KeyPath = Tuple[KeyEntry, ...]
+FlattenWithKeysFunc = Callable[[PyTree], Tuple[List[Tuple[KeyEntry, Any]], Any]]
+
+
+# A NodeDef holds two callables:
+# - flatten_fn should take the collection and return a flat list of values.
+#   It can also return some context that is used in reconstructing the
+#   collection.
+# - unflatten_fn should take a flat list of values and some context
+#   (returned by flatten_fn). It returns the collection by reconstructing
+#   it from the list and the context.
+# - flatten_with_keys_fn, which is a callable that takes a
+#   pytree and returns a list of (keypath, value) pairs and a context.
+class NodeDef(NamedTuple):
+    type: Type[Any]
+    flatten_fn: FlattenFunc
+    unflatten_fn: UnflattenFunc
+    flatten_with_keys_fn: Optional[FlattenWithKeysFunc]
+
+
+_NODE_REGISTRY_LOCK = threading.Lock()
+SUPPORTED_NODES: Dict[Type[Any], NodeDef] = {}
+
+
+# _SerializeNodeDef holds the following:
+# - typ: the type of the node (e.g., "Dict", "List", etc)
+# - serialized_type_name: the fully qualified name of the type, e.g. "collections.OrderedDict"
+# - to_dumpable_context takes a TreeSpec, and returns a serialized string format of the
+#   context, and the version number
+# - from_dumpable_context takes in a string representation of the context, and the
+#   version, and returns the deserialized context
+class _SerializeNodeDef(NamedTuple):
+    typ: Type[Any]
+    serialized_type_name: str
+    to_dumpable_context: Optional[ToDumpableContextFn]
+    from_dumpable_context: Optional[FromDumpableContextFn]
+
+
+SUPPORTED_SERIALIZED_TYPES: Dict[Type[Any], _SerializeNodeDef] = {}
+SERIALIZED_TYPE_TO_PYTHON_TYPE: Dict[str, Type[Any]] = {}
+
+# NB: we try really hard to not import _cxx_pytree (which depends on optree)
+# as much as possible. This is for isolation: a user who is not using C++ pytree
+# shouldn't pay for it, and it helps makes things like cpython upgrades easier.
+_cxx_pytree_exists = importlib.util.find_spec("optree")  # type: ignore[attr-defined]
+_cxx_pytree_imported = False
+_cxx_pytree_pending_imports: List[Any] = []
+
+
+def register_pytree_node(
+    cls: Type[Any],
+    flatten_fn: FlattenFunc,
+    unflatten_fn: UnflattenFunc,
+    *,
+    serialized_type_name: Optional[str] = None,
+    to_dumpable_context: Optional[ToDumpableContextFn] = None,
+    from_dumpable_context: Optional[FromDumpableContextFn] = None,
+    flatten_with_keys_fn: Optional[FlattenWithKeysFunc] = None,
+) -> None:
+    """Register a container-like type as pytree node.
+
+    Args:
+        cls: the type to register
+        flatten_fn: A callable that takes a pytree and returns a flattened
+            representation of the pytree and additional context to represent the
+            flattened pytree.
+        unflatten_fn: A callable that takes a flattened version of the pytree,
+            additional context, and returns an unflattened pytree.
+        serialized_type_name: A keyword argument used to specify the fully qualified
+            name used when serializing the tree spec.
+        to_dumpable_context: An optional keyword argument to custom specify how
+            to convert the context of the pytree to a custom json dumpable
+            representation. This is used for json serialization, which is being
+            used in torch.export right now.
+        from_dumpable_context: An optional keyword argument to custom specify how
+            to convert the custom json dumpable representation of the context
+            back to the original context. This is used for json deserialization,
+            which is being used in torch.export right now.
+        flatten_with_keys_fn: An optional keyword argument to specify how to
+            access each pytree leaf's keypath when flattening and tree-mapping.
+            Like ``flatten_fn``, but in place of a List[leaf], it should return
+            a List[(keypath, leaf)].
+    """
+    with _NODE_REGISTRY_LOCK:
+        if cls in SUPPORTED_NODES:
+            raise ValueError(f"{cls} is already registered as pytree node.")
+
+    _private_register_pytree_node(
+        cls,
+        flatten_fn,
+        unflatten_fn,
+        serialized_type_name=serialized_type_name,
+        to_dumpable_context=to_dumpable_context,
+        from_dumpable_context=from_dumpable_context,
+        flatten_with_keys_fn=flatten_with_keys_fn,
+    )
+
+    if not _cxx_pytree_exists:
+        return
+
+    if _cxx_pytree_imported:
+        from . import _cxx_pytree as cxx
+
+        cxx._private_register_pytree_node(
+            cls,
+            flatten_fn,
+            unflatten_fn,
+            serialized_type_name=serialized_type_name,
+            to_dumpable_context=to_dumpable_context,
+            from_dumpable_context=from_dumpable_context,
+        )
+    else:
+        args = (cls, flatten_fn, unflatten_fn)
+        kwargs = {
+            "serialized_type_name": serialized_type_name,
+            "to_dumpable_context": to_dumpable_context,
+            "from_dumpable_context": from_dumpable_context,
+        }
+        _cxx_pytree_pending_imports.append((args, kwargs))
+
+
+def _register_namedtuple(
+    cls: Type[Any],
+    *,
+    serialized_type_name: str,
+) -> None:
+    """
+    Registers a namedtuple as a valid pytree node. By default namedtuples are
+    valid pytree nodes, but they are not serializable. This API provides the
+    argument `serialized_type_name` which allows these namedtuples to be
+    serialized.
+
+    Args:
+        cls: the dataclass type to register
+        serialized_type_name: The serialized name for the dataclass. This is
+        required if you want to serialize the pytree TreeSpec containing this
+        namedtuple.
+    """
+    _private_register_pytree_node(
+        cls,
+        _namedtuple_flatten,
+        _namedtuple_unflatten,
+        serialized_type_name=serialized_type_name,
+        to_dumpable_context=_namedtuple_serialize,
+        from_dumpable_context=_namedtuple_deserialize,
+        flatten_with_keys_fn=_namedtuple_flatten_with_keys,
+    )
+
+
+@deprecated(
+    "`torch.utils._pytree._register_pytree_node` is deprecated. "
+    "Please use `torch.utils._pytree.register_pytree_node` instead.",
+    category=FutureWarning,
+)
+def _register_pytree_node(
+    cls: Type[Any],
+    flatten_fn: FlattenFunc,
+    unflatten_fn: UnflattenFunc,
+    to_str_fn: Optional[ToStrFunc] = None,  # deprecated
+    maybe_from_str_fn: Optional[MaybeFromStrFunc] = None,  # deprecated
+    *,
+    serialized_type_name: Optional[str] = None,
+    to_dumpable_context: Optional[ToDumpableContextFn] = None,
+    from_dumpable_context: Optional[FromDumpableContextFn] = None,
+    flatten_with_keys_fn: Optional[FlattenWithKeysFunc] = None,
+) -> None:
+    """Register a container-like type as pytree node for the Python pytree only.
+
+    Args:
+        cls: the type to register
+        flatten_fn: A callable that takes a pytree and returns a flattened
+            representation of the pytree and additional context to represent the
+            flattened pytree.
+        unflatten_fn: A callable that takes a flattened version of the pytree,
+            additional context, and returns an unflattened pytree.
+        serialized_type_name: A keyword argument used to specify the fully qualified
+            name used when serializing the tree spec.
+        to_dumpable_context: An optional keyword argument to custom specify how
+            to convert the context of the pytree to a custom json dumpable
+            representation. This is used for json serialization, which is being
+            used in torch.export right now.
+        from_dumpable_context: An optional keyword argument to custom specify how
+            to convert the custom json dumpable representation of the context
+            back to the original context. This is used for json deserialization,
+            which is being used in torch.export right now.
+        flatten_with_keys_fn: An optional keyword argument to specify how to
+            access each pytree leaf's keypath when flattening and tree-mapping.
+            Like ``flatten_fn``, but in place of a List[leaf], it should return
+            a List[(keypath, leaf)].
+    """
+    if to_str_fn is not None or maybe_from_str_fn is not None:
+        warnings.warn(
+            "`to_str_fn` and `maybe_from_str_fn` is deprecated. "
+            "Please use `to_dumpable_context` and `from_dumpable_context` instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+
+    _private_register_pytree_node(
+        cls,
+        flatten_fn,
+        unflatten_fn,
+        serialized_type_name=serialized_type_name,
+        to_dumpable_context=to_dumpable_context,
+        from_dumpable_context=from_dumpable_context,
+        flatten_with_keys_fn=flatten_with_keys_fn,
+    )
+
+
+def _private_register_pytree_node(
+    cls: Type[Any],
+    flatten_fn: FlattenFunc,
+    unflatten_fn: UnflattenFunc,
+    *,
+    serialized_type_name: Optional[str] = None,
+    to_dumpable_context: Optional[ToDumpableContextFn] = None,
+    from_dumpable_context: Optional[FromDumpableContextFn] = None,
+    flatten_with_keys_fn: Optional[FlattenWithKeysFunc] = None,
+) -> None:
+    """This is an internal function that is used to register a pytree node type
+    for the Python pytree only. End-users should use :func:`register_pytree_node`
+    instead.
+    """
+    with _NODE_REGISTRY_LOCK:
+        if cls in SUPPORTED_NODES:
+            # TODO: change this warning to an error after OSS/internal stabilize
+            warnings.warn(
+                f"{cls} is already registered as pytree node. "
+                "Overwriting the previous registration.",
+            )
+
+        node_def = NodeDef(cls, flatten_fn, unflatten_fn, flatten_with_keys_fn)
+        SUPPORTED_NODES[cls] = node_def
+
+        if (to_dumpable_context is None) ^ (from_dumpable_context is None):
+            raise ValueError(
+                f"Both to_dumpable_context and from_dumpable_context for {cls} must "
+                "be None or registered."
+            )
+
+        if serialized_type_name is None:
+            serialized_type_name = NO_SERIALIZED_TYPE_NAME_FOUND
+
+        serialize_node_def = _SerializeNodeDef(
+            cls,
+            serialized_type_name,
+            to_dumpable_context,
+            from_dumpable_context,
+        )
+        SUPPORTED_SERIALIZED_TYPES[cls] = serialize_node_def
+        SERIALIZED_TYPE_TO_PYTHON_TYPE[serialized_type_name] = cls
+
+
+@dataclasses.dataclass(frozen=True)
+class SequenceKey(Generic[T]):
+    idx: int
+
+    def __str__(self) -> str:
+        return f"[{self.idx!r}]"
+
+    def get(self, sequence: Sequence[T]) -> T:
+        return sequence[self.idx]
+
+
+K = TypeVar("K", bound=Hashable)
+
+
+@dataclasses.dataclass(frozen=True)
+class MappingKey(Generic[K, T]):
+    key: K
+
+    def __str__(self) -> str:
+        return f"[{self.key!r}]"
+
+    def get(self, mapping: Mapping[K, T]) -> T:
+        return mapping[self.key]
+
+
+@dataclasses.dataclass(frozen=True)
+class GetAttrKey:
+    name: str
+
+    def __str__(self) -> str:
+        return f".{self.name}"
+
+    def get(self, obj: Any) -> Any:
+        return getattr(obj, self.name)
+
+
+def _tuple_flatten(d: Tuple[Any, ...]) -> Tuple[List[Any], Context]:
+    return list(d), None
+
+
+def _tuple_flatten_with_keys(
+    d: Tuple[Any, ...]
+) -> Tuple[List[Tuple[KeyEntry, Any]], Context]:
+    values, context = _tuple_flatten(d)
+    return [(SequenceKey(i), v) for i, v in enumerate(values)], context
+
+
+def _tuple_unflatten(values: Iterable[Any], context: Context) -> Tuple[Any, ...]:
+    return tuple(values)
+
+
+def _list_flatten(d: List[Any]) -> Tuple[List[Any], Context]:
+    return d, None
+
+
+def _list_flatten_with_keys(d: List[Any]) -> Tuple[List[Tuple[KeyEntry, Any]], Context]:
+    values, context = _list_flatten(d)
+    return [(SequenceKey(i), v) for i, v in enumerate(values)], context
+
+
+def _list_unflatten(values: Iterable[Any], context: Context) -> List[Any]:
+    return list(values)
+
+
+def _dict_flatten(d: Dict[Any, Any]) -> Tuple[List[Any], Context]:
+    return list(d.values()), list(d.keys())
+
+
+def _dict_flatten_with_keys(
+    d: Dict[Any, Any]
+) -> Tuple[List[Tuple[KeyEntry, Any]], Context]:
+    values, context = _dict_flatten(d)
+    return [(MappingKey(k), v) for k, v in zip(context, values)], context
+
+
+def _dict_unflatten(values: Iterable[Any], context: Context) -> Dict[Any, Any]:
+    return dict(zip(context, values))
+
+
+def _namedtuple_flatten(d: NamedTuple) -> Tuple[List[Any], Context]:
+    return list(d), type(d)
+
+
+def _namedtuple_flatten_with_keys(
+    d: NamedTuple,
+) -> Tuple[List[Tuple[KeyEntry, Any]], Context]:
+    values, context = _namedtuple_flatten(d)
+    return (
+        [(GetAttrKey(field), v) for field, v in zip(context._fields, values)],
+        context,
+    )
+
+
+def _namedtuple_unflatten(values: Iterable[Any], context: Context) -> NamedTuple:
+    return cast(NamedTuple, context(*values))
+
+
+def _namedtuple_serialize(context: Context) -> DumpableContext:
+    if context not in SUPPORTED_SERIALIZED_TYPES:
+        raise NotImplementedError(
+            f"Can't serialize TreeSpec of namedtuple class {context} because we "
+            "didn't register a serializated_type_name. Please register using "
+            "`_register_namedtuple`."
+        )
+
+    serialize_node_def = SUPPORTED_SERIALIZED_TYPES[context]
+    serialized_type_name = serialize_node_def.serialized_type_name
+
+    if serialized_type_name == NO_SERIALIZED_TYPE_NAME_FOUND:
+        raise NotImplementedError(
+            f"Can't serialize TreeSpec of namedtuple class {context} because we "
+            "couldn't find a serializated_type_name. Please register using "
+            "`_register_namedtuple`."
+        )
+    return serialized_type_name
+
+
+def _namedtuple_deserialize(dumpable_context: DumpableContext) -> Context:
+    if dumpable_context not in SERIALIZED_TYPE_TO_PYTHON_TYPE:
+        raise NotImplementedError(
+            f"Can't deserialize TreeSpec of namedtuple class {dumpable_context} "
+            "because we couldn't find a serializated name."
+        )
+
+    typ = SERIALIZED_TYPE_TO_PYTHON_TYPE[dumpable_context]
+    return typ
+
+
+def _ordereddict_flatten(d: GenericOrderedDict[Any, Any]) -> Tuple[List[Any], Context]:
+    return list(d.values()), list(d.keys())
+
+
+def _ordereddict_flatten_with_keys(
+    d: GenericOrderedDict[Any, Any]
+) -> Tuple[List[Tuple[KeyEntry, Any]], Context]:
+    values, context = _ordereddict_flatten(d)
+    return [(MappingKey(k), v) for k, v in zip(context, values)], context
+
+
+def _ordereddict_unflatten(
+    values: Iterable[Any],
+    context: Context,
+) -> GenericOrderedDict[Any, Any]:
+    return OrderedDict((key, value) for key, value in zip(context, values))
+
+
+_odict_flatten = _ordereddict_flatten
+_odict_unflatten = _ordereddict_unflatten
+
+
+def _defaultdict_flatten(d: DefaultDict[Any, Any]) -> Tuple[List[Any], Context]:
+    values, dict_context = _dict_flatten(d)
+    return values, [d.default_factory, dict_context]
+
+
+def _defaultdict_flatten_with_keys(
+    d: DefaultDict[Any, Any]
+) -> Tuple[List[Tuple[KeyEntry, Any]], Context]:
+    values, context = _defaultdict_flatten(d)
+    _, dict_context = context
+    return [(MappingKey(k), v) for k, v in zip(dict_context, values)], context
+
+
+def _defaultdict_unflatten(
+    values: Iterable[Any],
+    context: Context,
+) -> DefaultDict[Any, Any]:
+    default_factory, dict_context = context
+    return defaultdict(default_factory, _dict_unflatten(values, dict_context))
+
+
+def _defaultdict_serialize(context: Context) -> DumpableContext:
+    default_factory, dict_context = context
+    json_defaultdict = {
+        "default_factory_module": default_factory.__module__,
+        "default_factory_name": default_factory.__qualname__,
+        "dict_context": dict_context,
+    }
+    return json_defaultdict
+
+
+def _defaultdict_deserialize(dumpable_context: DumpableContext) -> Context:
+    assert isinstance(dumpable_context, dict)
+    assert set(dumpable_context) == {
+        "default_factory_module",
+        "default_factory_name",
+        "dict_context",
+    }
+
+    default_factory_module = dumpable_context["default_factory_module"]
+    default_factory_name = dumpable_context["default_factory_name"]
+    assert isinstance(default_factory_module, str)
+    assert isinstance(default_factory_name, str)
+    module = importlib.import_module(default_factory_module)
+    default_factory = getattr(module, default_factory_name)
+
+    dict_context = dumpable_context["dict_context"]
+    return [default_factory, dict_context]
+
+
+def _deque_flatten(d: Deque[Any]) -> Tuple[List[Any], Context]:
+    return list(d), d.maxlen
+
+
+def _deque_flatten_with_keys(
+    d: Deque[Any],
+) -> Tuple[List[Tuple[KeyEntry, Any]], Context]:
+    values, context = _deque_flatten(d)
+    return [(SequenceKey(i), v) for i, v in enumerate(values)], context
+
+
+def _deque_unflatten(values: Iterable[Any], context: Context) -> Deque[Any]:
+    return deque(values, maxlen=context)
+
+
+_private_register_pytree_node(
+    tuple,
+    _tuple_flatten,
+    _tuple_unflatten,
+    serialized_type_name="builtins.tuple",
+    flatten_with_keys_fn=_tuple_flatten_with_keys,
+)
+_private_register_pytree_node(
+    list,
+    _list_flatten,
+    _list_unflatten,
+    serialized_type_name="builtins.list",
+    flatten_with_keys_fn=_list_flatten_with_keys,
+)
+_private_register_pytree_node(
+    dict,
+    _dict_flatten,
+    _dict_unflatten,
+    serialized_type_name="builtins.dict",
+    flatten_with_keys_fn=_dict_flatten_with_keys,
+)
+_private_register_pytree_node(
+    namedtuple,  # type: ignore[arg-type]
+    _namedtuple_flatten,
+    _namedtuple_unflatten,
+    serialized_type_name="collections.namedtuple",
+    to_dumpable_context=_namedtuple_serialize,
+    from_dumpable_context=_namedtuple_deserialize,
+    flatten_with_keys_fn=_namedtuple_flatten_with_keys,
+)
+_private_register_pytree_node(
+    OrderedDict,
+    _ordereddict_flatten,
+    _ordereddict_unflatten,
+    serialized_type_name="collections.OrderedDict",
+    flatten_with_keys_fn=_ordereddict_flatten_with_keys,
+)
+_private_register_pytree_node(
+    defaultdict,
+    _defaultdict_flatten,
+    _defaultdict_unflatten,
+    serialized_type_name="collections.defaultdict",
+    to_dumpable_context=_defaultdict_serialize,
+    from_dumpable_context=_defaultdict_deserialize,
+    flatten_with_keys_fn=_defaultdict_flatten_with_keys,
+)
+_private_register_pytree_node(
+    deque,
+    _deque_flatten,
+    _deque_unflatten,
+    serialized_type_name="collections.deque",
+    flatten_with_keys_fn=_deque_flatten_with_keys,
+)
+
+
+STANDARD_DICT_TYPES: FrozenSet[type] = frozenset(
+    {dict, OrderedDict, defaultdict},
+)
+BUILTIN_TYPES: FrozenSet[type] = frozenset(
+    {tuple, list, dict, namedtuple, OrderedDict, defaultdict, deque},  # type: ignore[arg-type]
+)
+
+
+# h/t https://stackoverflow.com/questions/2166818/how-to-check-if-an-object-is-an-instance-of-a-namedtuple
+def _is_namedtuple_instance(tree: Any) -> bool:
+    typ = type(tree)
+    bases = typ.__bases__
+    if len(bases) != 1 or bases[0] != tuple:
+        return False
+    fields = getattr(typ, "_fields", None)
+    if not isinstance(fields, tuple):
+        return False
+    return all(type(entry) == str for entry in fields)
+
+
+def _get_node_type(tree: Any) -> Any:
+    if _is_namedtuple_instance(tree):
+        return namedtuple
+    return type(tree)
+
+
+# A leaf is defined as anything that is not a Node.
+def _is_leaf(tree: PyTree, is_leaf: Optional[Callable[[PyTree], bool]] = None) -> bool:
+    return (is_leaf is not None and is_leaf(tree)) or _get_node_type(
+        tree
+    ) not in SUPPORTED_NODES
+
+
+# A TreeSpec represents the structure of a pytree. It holds:
+# "type": the type of root Node of the pytree
+# context: some context that is useful in unflattening the pytree
+# children_specs: specs for each child of the root Node
+# num_leaves: the number of leaves
+@dataclasses.dataclass(init=True, frozen=True, eq=True, repr=False)
+class TreeSpec:
+    type: Any
+    context: Context
+    children_specs: List["TreeSpec"]
+
+    num_nodes: int = dataclasses.field(init=False)
+    num_leaves: int = dataclasses.field(init=False)
+    num_children: int = dataclasses.field(init=False)
+
+    def __post_init__(self) -> None:
+        num_nodes = sum((spec.num_nodes for spec in self.children_specs), start=1)
+        num_leaves = sum(spec.num_leaves for spec in self.children_specs)
+        num_children = len(self.children_specs)
+        object.__setattr__(self, "num_nodes", num_nodes)
+        object.__setattr__(self, "num_leaves", num_leaves)
+        object.__setattr__(self, "num_children", num_children)
+
+    def __repr__(self, indent: int = 0) -> str:
+        repr_prefix: str = f"TreeSpec({self.type.__name__}, {self.context}, ["
+        children_specs_str: str = ""
+        if self.num_children > 0:
+            indent += 2
+            children_specs_str += self.children_specs[0].__repr__(indent)
+            children_specs_str += "," if self.num_children > 1 else ""
+            children_specs_str += ",".join(
+                [
+                    "\n" + " " * indent + child.__repr__(indent)
+                    for child in self.children_specs[1:]
+                ]
+            )
+        repr_suffix: str = f"{children_specs_str}])"
+        return repr_prefix + repr_suffix
+
+    def is_leaf(self) -> bool:
+        return self.num_nodes == 1 and self.num_leaves == 1
+
+    def _flatten_up_to_helper(self, tree: PyTree, subtrees: List[PyTree]) -> None:
+        if self.is_leaf():
+            subtrees.append(tree)
+            return
+
+        node_type = _get_node_type(tree)
+        if self.type not in BUILTIN_TYPES:
+            # Always require custom node types to match exactly
+            if node_type != self.type:
+                raise ValueError(
+                    f"Type mismatch; "
+                    f"expected {self.type!r}, but got {node_type!r}.",
+                )
+            flatten_fn = SUPPORTED_NODES[node_type].flatten_fn
+            child_pytrees, context = flatten_fn(tree)
+            if len(child_pytrees) != self.num_children:
+                raise ValueError(
+                    f"Node arity mismatch; "
+                    f"expected {self.num_children}, but got {len(child_pytrees)}.",
+                )
+            if context != self.context:
+                raise ValueError(
+                    f"Node context mismatch for custom node type {self.type!r}.",
+                )
+        else:
+            # For builtin dictionary types, we allow some flexibility
+            # Otherwise, we require exact matches
+            both_standard_dict = (
+                self.type in STANDARD_DICT_TYPES and node_type in STANDARD_DICT_TYPES
+            )
+            if node_type != self.type and not both_standard_dict:
+                raise ValueError(
+                    f"Node type mismatch; "
+                    f"expected {self.type!r}, but got {node_type!r}.",
+                )
+            if len(tree) != self.num_children:
+                raise ValueError(
+                    f"Node arity mismatch; "
+                    f"expected {self.num_children}, but got {len(tree)}.",
+                )
+
+            if both_standard_dict:  # dictionary types are compatible with each other
+                dict_context = (
+                    self.context
+                    if self.type is not defaultdict
+                    # ignore mismatch of `default_factory` for defaultdict
+                    else self.context[1]
+                )
+                expected_keys = dict_context
+                got_key_set = set(tree)
+                expected_key_set = set(expected_keys)
+                if got_key_set != expected_key_set:
+                    missing_keys = expected_key_set.difference(got_key_set)
+                    extra_keys = got_key_set.difference(expected_key_set)
+                    message = ""
+                    if missing_keys:
+                        message += f"; missing key(s): {missing_keys}"
+                    if extra_keys:
+                        message += f"; extra key(s): {extra_keys}"
+                    raise ValueError(f"Node keys mismatch{message}.")
+                child_pytrees = [tree[key] for key in expected_keys]
+            else:
+                flatten_fn = SUPPORTED_NODES[node_type].flatten_fn
+                child_pytrees, context = flatten_fn(tree)
+                if (
+                    context != self.context
+                    and self.type is not deque  # ignore mismatch of `maxlen` for deque
+                ):
+                    raise ValueError(
+                        f"Node context mismatch for node type {self.type!r}; "
+                        f"expected {self.context!r}, but got {context!r}.",  # namedtuple type mismatch
+                    )
+
+        for child_pytree, child_spec in zip(child_pytrees, self.children_specs):
+            child_spec._flatten_up_to_helper(child_pytree, subtrees)
+
+    def flatten_up_to(self, tree: PyTree) -> List[PyTree]:
+        subtrees: List[PyTree] = []
+        self._flatten_up_to_helper(tree, subtrees)
+        return subtrees
+
+    def unflatten(self, leaves: Iterable[Any]) -> PyTree:
+        if not isinstance(leaves, (list, tuple)):
+            leaves = list(leaves)
+        if len(leaves) != self.num_leaves:
+            raise ValueError(
+                f"treespec.unflatten(leaves): `leaves` has length {len(leaves)} "
+                f"but the spec refers to a pytree that holds {self.num_leaves} "
+                f"items ({self}).",
+            )
+        if self.is_leaf():
+            return leaves[0]
+
+        unflatten_fn = SUPPORTED_NODES[self.type].unflatten_fn
+
+        # Recursively unflatten the children
+        start = 0
+        end = 0
+        child_pytrees = []
+        for child_spec in self.children_specs:
+            end += child_spec.num_leaves
+            child_pytrees.append(child_spec.unflatten(leaves[start:end]))
+            start = end
+
+        return unflatten_fn(child_pytrees, self.context)
+
+
+class LeafSpec(TreeSpec):
+    def __init__(self) -> None:
+        super().__init__(None, None, [])
+
+    def __post_init__(self) -> None:
+        object.__setattr__(self, "num_nodes", 1)
+        object.__setattr__(self, "num_leaves", 1)
+        object.__setattr__(self, "num_children", 0)
+
+    def __repr__(self, indent: int = 0) -> str:
+        return "*"
+
+
+# All leaves are equivalent, so represent with a single object to save on
+# object construction time
+_LEAF_SPEC = LeafSpec()
+
+
+def _tree_flatten_helper(
+    tree: PyTree,
+    leaves: List[Any],
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> TreeSpec:
+    if _is_leaf(tree, is_leaf=is_leaf):
+        leaves.append(tree)
+        return _LEAF_SPEC
+
+    node_type = _get_node_type(tree)
+    flatten_fn = SUPPORTED_NODES[node_type].flatten_fn
+    child_pytrees, context = flatten_fn(tree)
+
+    # Recursively flatten the children
+    children_specs = [
+        _tree_flatten_helper(child, leaves, is_leaf=is_leaf) for child in child_pytrees
+    ]
+
+    return TreeSpec(node_type, context, children_specs)
+
+
+def tree_flatten(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> Tuple[List[Any], TreeSpec]:
+    """Flattens a pytree into a list of values and a TreeSpec that can be used
+    to reconstruct the pytree.
+    """
+    leaves: List[Any] = []
+    spec = _tree_flatten_helper(tree, leaves, is_leaf=is_leaf)
+    return leaves, spec
+
+
+def tree_unflatten(leaves: Iterable[Any], treespec: TreeSpec) -> PyTree:
+    """Given a list of values and a TreeSpec, builds a pytree.
+    This is the inverse operation of `tree_flatten`.
+    """
+    if not isinstance(treespec, TreeSpec):
+        raise TypeError(
+            f"tree_unflatten(leaves, treespec): Expected `treespec` to be "
+            f"instance of TreeSpec but got item of type {type(treespec)}.",
+        )
+    return treespec.unflatten(leaves)
+
+
+def tree_iter(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> Iterable[Any]:
+    """Get an iterator over the leaves of a pytree."""
+    if _is_leaf(tree, is_leaf=is_leaf):
+        yield tree
+    else:
+        node_type = _get_node_type(tree)
+        flatten_fn = SUPPORTED_NODES[node_type].flatten_fn
+        child_pytrees, _ = flatten_fn(tree)
+
+        # Recursively flatten the children
+        for child in child_pytrees:
+            yield from tree_iter(child, is_leaf=is_leaf)
+
+
+def tree_leaves(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> List[Any]:
+    """Get a list of leaves of a pytree."""
+    return list(tree_iter(tree, is_leaf=is_leaf))
+
+
+def tree_structure(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> TreeSpec:
+    """Get the TreeSpec for a pytree."""
+    return tree_flatten(tree, is_leaf=is_leaf)[1]
+
+
+def tree_map(
+    func: Callable[..., Any],
+    tree: PyTree,
+    *rests: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    """Map a multi-input function over pytree args to produce a new pytree.
+
+    See also :func:`tree_map_`.
+
+    >>> tree_map(lambda x: x + 1, {'x': 7, 'y': (42, 64)})
+    {'x': 8, 'y': (43, 65)}
+    >>> tree_map(lambda x: x is None, {'x': 7, 'y': (42, 64), 'z': None})
+    {'x': False, 'y': (False, False), 'z': True}
+
+    If multiple inputs are given, the structure of the tree is taken from the first input;
+    subsequent inputs need only have ``tree`` as a prefix:
+
+    >>> tree_map(lambda x, y: [x] + y, [5, 6], [[7, 9], [1, 2]])
+    [[5, 7, 9], [6, 1, 2]]
+
+    Args:
+        func (callable): A function that takes ``1 + len(rests)`` arguments, to be applied at the
+            corresponding leaves of the pytrees.
+        tree (pytree): A pytree to be mapped over, with each leaf providing the first positional
+            argument to function ``func``.
+        rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as
+            ``tree`` or has ``tree`` as a prefix.
+        is_leaf (callable, optional): An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns:
+        A new pytree with the same structure as ``tree`` but with the value at each leaf given by
+        ``func(x, *xs)`` where ``x`` is the value at the corresponding leaf in ``tree`` and ``xs``
+        is the tuple of values at corresponding nodes in ``rests``.
+    """
+    leaves, treespec = tree_flatten(tree, is_leaf=is_leaf)
+    flat_args = [leaves] + [treespec.flatten_up_to(r) for r in rests]
+    return treespec.unflatten(map(func, *flat_args))
+
+
+def tree_map_(
+    func: Callable[..., Any],
+    tree: PyTree,
+    *rests: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    """Like :func:`tree_map`, but do an inplace call on each leaf and return the original tree.
+
+    See also :func:`tree_map`.
+
+    Args:
+        func (callable): A function that takes ``1 + len(rests)`` arguments, to be applied at the
+            corresponding leaves of the pytrees.
+        tree (pytree): A pytree to be mapped over, with each leaf providing the first positional
+            argument to function ``func``.
+        rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as
+            ``tree`` or has ``tree`` as a prefix.
+        is_leaf (callable, optional): An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns:
+        The original ``tree`` with the value at each leaf is given by the side-effect of function
+        ``func(x, *xs)`` (not the return value) where ``x`` is the value at the corresponding leaf
+        in ``tree`` and ``xs`` is the tuple of values at values at corresponding nodes in ``rests``.
+    """
+    leaves, treespec = tree_flatten(tree, is_leaf=is_leaf)
+    flat_args = [leaves] + [treespec.flatten_up_to(r) for r in rests]
+    tuple(map(func, *flat_args))  # consume and exhaust the iterable
+    return tree
+
+
+Type2 = Tuple[Type[T], Type[S]]
+Type3 = Tuple[Type[T], Type[S], Type[U]]
+if sys.version_info >= (3, 10):
+    TypeAny = Union[Type[Any], Tuple[Type[Any], ...], types.UnionType]
+else:
+    TypeAny = Union[Type[Any], Tuple[Type[Any], ...]]
+
+Fn2 = Callable[[Union[T, S]], R]
+Fn3 = Callable[[Union[T, S, U]], R]
+Fn = Callable[[T], R]
+FnAny = Callable[[Any], R]
+
+MapOnlyFn = Callable[[T], Callable[[Any], Any]]
+
+
+# These specializations help with type inference on the lambda passed to this
+# function
+@overload
+def map_only(__type_or_types_or_pred: Type2[T, S]) -> MapOnlyFn[Fn2[T, S, Any]]:
+    ...
+
+
+@overload
+def map_only(__type_or_types_or_pred: Type3[T, S, U]) -> MapOnlyFn[Fn3[T, S, U, Any]]:
+    ...
+
+
+@overload
+def map_only(__type_or_types_or_pred: Type[T]) -> MapOnlyFn[Fn[T, Any]]:
+    ...
+
+
+# This specialization is needed for the implementations below that call
+@overload
+def map_only(__type_or_types_or_pred: TypeAny) -> MapOnlyFn[FnAny[Any]]:
+    ...
+
+
+@overload
+def map_only(__type_or_types_or_pred: Callable[[Any], bool]) -> MapOnlyFn[FnAny[Any]]:
+    ...
+
+
+def map_only(
+    __type_or_types_or_pred: Union[TypeAny, Callable[[Any], bool]]
+) -> MapOnlyFn[FnAny[Any]]:
+    """
+    Suppose you are writing a tree_map over tensors, leaving everything
+    else unchanged.  Ordinarily you would have to write:
+
+        def go(t):
+            if isinstance(t, Tensor):
+                return ...
+            else:
+                return t
+
+    With this function, you only need to write:
+
+        @map_only(Tensor)
+        def go(t):
+            return ...
+
+    You can also directly use 'tree_map_only'
+    """
+    if isinstance(__type_or_types_or_pred, (type, tuple)) or (
+        sys.version_info >= (3, 10)
+        and isinstance(__type_or_types_or_pred, types.UnionType)
+    ):
+
+        def pred(x: Any) -> bool:
+            return isinstance(x, __type_or_types_or_pred)  # type: ignore[arg-type]
+
+    elif callable(__type_or_types_or_pred):
+        pred = __type_or_types_or_pred  # type: ignore[assignment]
+    else:
+        raise TypeError("Argument must be a type, a tuple of types, or a callable.")
+
+    def wrapper(func: Callable[[T], Any]) -> Callable[[Any], Any]:
+        @functools.wraps(func)
+        def wrapped(x: T) -> Any:
+            if pred(x):
+                return func(x)
+            return x
+
+        return wrapped
+
+    return wrapper
+
+
+@overload
+def tree_map_only(
+    __type_or_types_or_pred: Type[T],
+    func: Fn[T, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only(
+    __type_or_types_or_pred: Type2[T, S],
+    func: Fn2[T, S, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only(
+    __type_or_types_or_pred: Type3[T, S, U],
+    func: Fn3[T, S, U, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only(
+    __type_or_types_or_pred: Callable[[Any], bool],
+    func: FnAny[Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+def tree_map_only(
+    __type_or_types_or_pred: Union[TypeAny, Callable[[Any], bool]],
+    func: FnAny[Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    return tree_map(map_only(__type_or_types_or_pred)(func), tree, is_leaf=is_leaf)
+
+
+@overload
+def tree_map_only_(
+    __type_or_types_or_pred: Type[T],
+    func: Fn[T, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only_(
+    __type_or_types_or_pred: Type2[T, S],
+    func: Fn2[T, S, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only_(
+    __type_or_types_or_pred: Type3[T, S, U],
+    func: Fn3[T, S, U, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only_(
+    __type_or_types_or_pred: Callable[[Any], bool],
+    func: FnAny[Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+def tree_map_only_(
+    __type_or_types_or_pred: Union[TypeAny, Callable[[Any], bool]],
+    func: FnAny[Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    return tree_map_(map_only(__type_or_types_or_pred)(func), tree, is_leaf=is_leaf)
+
+
+def tree_all(
+    pred: Callable[[Any], bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    flat_args = tree_iter(tree, is_leaf=is_leaf)
+    return all(map(pred, flat_args))
+
+
+def tree_any(
+    pred: Callable[[Any], bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    flat_args = tree_iter(tree, is_leaf=is_leaf)
+    return any(map(pred, flat_args))
+
+
+@overload
+def tree_all_only(
+    __type_or_types: Type[T],
+    pred: Fn[T, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+@overload
+def tree_all_only(
+    __type_or_types: Type2[T, S],
+    pred: Fn2[T, S, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+@overload
+def tree_all_only(
+    __type_or_types: Type3[T, S, U],
+    pred: Fn3[T, S, U, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+def tree_all_only(
+    __type_or_types: TypeAny,
+    pred: FnAny[bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    flat_args = tree_iter(tree, is_leaf=is_leaf)
+    return all(pred(x) for x in flat_args if isinstance(x, __type_or_types))
+
+
+@overload
+def tree_any_only(
+    __type_or_types: Type[T],
+    pred: Fn[T, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+@overload
+def tree_any_only(
+    __type_or_types: Type2[T, S],
+    pred: Fn2[T, S, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+@overload
+def tree_any_only(
+    __type_or_types: Type3[T, S, U],
+    pred: Fn3[T, S, U, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+def tree_any_only(
+    __type_or_types: TypeAny,
+    pred: FnAny[bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    flat_args = tree_iter(tree, is_leaf=is_leaf)
+    return any(pred(x) for x in flat_args if isinstance(x, __type_or_types))
+
+
+# Broadcasts a pytree to the provided TreeSpec and returns the flattened
+# values. If this is not possible, then this function returns None.
+#
+# For example, given pytree=0 and spec=TreeSpec(list, None, [LeafSpec(), LeafSpec()]),
+# would return [0, 0]. This is useful for part of the vmap implementation:
+# a user can pass in vmap(fn, in_dims)(*inputs). `in_dims` should be
+# broadcastable to the tree structure of `inputs` and we use
+# _broadcast_to_and_flatten to check this.
+def _broadcast_to_and_flatten(
+    tree: PyTree,
+    treespec: TreeSpec,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> Optional[List[Any]]:
+    assert isinstance(treespec, TreeSpec)
+
+    if _is_leaf(tree, is_leaf=is_leaf):
+        return [tree] * treespec.num_leaves
+    if treespec.is_leaf():
+        return None
+    node_type = _get_node_type(tree)
+    if node_type != treespec.type:
+        return None
+
+    flatten_fn = SUPPORTED_NODES[node_type].flatten_fn
+    child_pytrees, ctx = flatten_fn(tree)
+
+    # Check if the Node is different from the spec
+    if len(child_pytrees) != treespec.num_children or ctx != treespec.context:
+        return None
+
+    # Recursively flatten the children
+    result: List[Any] = []
+    for child, child_spec in zip(child_pytrees, treespec.children_specs):
+        flat = _broadcast_to_and_flatten(child, child_spec, is_leaf=is_leaf)
+        if flat is not None:
+            result += flat
+        else:
+            return None
+
+    return result
+
+
+@dataclasses.dataclass
+class _TreeSpecSchema:
+    """
+    _TreeSpecSchema is the schema used to serialize the TreeSpec
+    It contains the following fields:
+    - type: A string name of the type. null for the case of a LeafSpec.
+    - context: Any format which is json dumpable
+    - children_spec: A list of children serialized specs.
+    """
+
+    type: Optional[str]
+    context: DumpableContext
+    children_spec: List["_TreeSpecSchema"]
+
+
+class _ProtocolFn(NamedTuple):
+    treespec_to_json: Callable[[TreeSpec], DumpableContext]
+    json_to_treespec: Callable[[DumpableContext], TreeSpec]
+
+
+_SUPPORTED_PROTOCOLS: Dict[int, _ProtocolFn] = {}
+
+
+def _treespec_to_json(treespec: TreeSpec) -> _TreeSpecSchema:
+    if treespec.is_leaf():
+        return _TreeSpecSchema(None, None, [])
+
+    if treespec.type not in SUPPORTED_SERIALIZED_TYPES:
+        raise NotImplementedError(
+            f"Serializing {treespec.type} in pytree is not registered.",
+        )
+
+    serialize_node_def = SUPPORTED_SERIALIZED_TYPES[treespec.type]
+
+    serialized_type_name = serialize_node_def.serialized_type_name
+
+    if serialized_type_name == NO_SERIALIZED_TYPE_NAME_FOUND:
+        raise NotImplementedError(
+            f"No registered serialization name for {treespec.type} found. "
+            "Please update your _register_pytree_node call with a `serialized_type_name` kwarg."
+        )
+
+    if serialize_node_def.to_dumpable_context is None:
+        try:
+            serialized_context = json.dumps(treespec.context)
+        except TypeError as e:
+            raise TypeError(
+                "Unable to serialize context. "
+                "Please make the context json dump-able, or register a "
+                "custom serializer using _register_pytree_node."
+            ) from e
+    else:
+        serialized_context = serialize_node_def.to_dumpable_context(treespec.context)
+
+    child_schemas = [_treespec_to_json(child) for child in treespec.children_specs]
+
+    return _TreeSpecSchema(serialized_type_name, serialized_context, child_schemas)
+
+
+def _json_to_treespec(json_schema: DumpableContext) -> TreeSpec:
+    if (
+        json_schema["type"] is None
+        and json_schema["context"] is None
+        and len(json_schema["children_spec"]) == 0
+    ):
+        return _LEAF_SPEC
+
+    if json_schema["type"] not in SERIALIZED_TYPE_TO_PYTHON_TYPE:
+        raise NotImplementedError(
+            f'Deserializing {json_schema["type"]} in pytree is not registered.',
+        )
+
+    typ = SERIALIZED_TYPE_TO_PYTHON_TYPE[json_schema["type"]]
+    serialize_node_def = SUPPORTED_SERIALIZED_TYPES[typ]
+
+    if serialize_node_def.from_dumpable_context is None:
+        try:
+            context = json.loads(json_schema["context"])
+        except TypeError as ex:
+            raise TypeError(
+                "Unable to deserialize context. "
+                "Please make the context json load-able, or register a "
+                "custom serializer using _register_pytree_node.",
+            ) from ex
+    else:
+        context = serialize_node_def.from_dumpable_context(json_schema["context"])
+
+    children_specs = []
+    for child_string in json_schema["children_spec"]:
+        children_specs.append(_json_to_treespec(child_string))
+
+    return TreeSpec(typ, context, children_specs)
+
+
+_SUPPORTED_PROTOCOLS[1] = _ProtocolFn(_treespec_to_json, _json_to_treespec)
+
+
+def treespec_dumps(treespec: TreeSpec, protocol: Optional[int] = None) -> str:
+    if not isinstance(treespec, TreeSpec):
+        raise TypeError(
+            f"treespec_dumps(treespec, protocol): Expected `treespec` to be instance of "
+            f"TreeSpec but got item of type {type(treespec)}.",
+        )
+
+    if protocol is None:
+        protocol = DEFAULT_TREESPEC_SERIALIZATION_PROTOCOL
+
+    if protocol in _SUPPORTED_PROTOCOLS:
+        json_spec = _SUPPORTED_PROTOCOLS[protocol].treespec_to_json(treespec)
+    else:
+        raise ValueError(
+            f"Unknown protocol {protocol}. "
+            f"Available protocols: {list(_SUPPORTED_PROTOCOLS.keys())}",
+        )
+
+    str_spec = json.dumps((protocol, dataclasses.asdict(json_spec)))
+    return str_spec
+
+
+def treespec_loads(serialized: str) -> TreeSpec:
+    protocol, json_schema = json.loads(serialized)
+
+    if protocol in _SUPPORTED_PROTOCOLS:
+        return _SUPPORTED_PROTOCOLS[protocol].json_to_treespec(json_schema)
+    raise ValueError(
+        f"Unknown protocol {protocol}. "
+        f"Available protocols: {list(_SUPPORTED_PROTOCOLS.keys())}",
+    )
+
+
+class _DummyLeaf:
+    def __repr__(self) -> str:
+        return "*"
+
+
+def treespec_pprint(treespec: TreeSpec) -> str:
+    dummy_tree = tree_unflatten(
+        [_DummyLeaf() for _ in range(treespec.num_leaves)],
+        treespec,
+    )
+    return repr(dummy_tree)
+
+
+# TODO(angelayi): remove this function after OSS/internal stabilize
+@deprecated(
+    "`pytree_to_str` is deprecated. Please use `treespec_dumps` instead.",
+    category=FutureWarning,
+)
+def pytree_to_str(treespec: TreeSpec) -> str:
+    return treespec_dumps(treespec)
+
+
+# TODO(angelayi): remove this function after OSS/internal stabilize
+@deprecated(
+    "`str_to_pytree` is deprecated. Please use `treespec_loads` instead.",
+    category=FutureWarning,
+)
+def str_to_pytree(json: str) -> TreeSpec:
+    return treespec_loads(json)
+
+
+def arg_tree_leaves(*args: PyTree, **kwargs: PyTree) -> List[Any]:
+    """Get a flat list of arguments to this function
+
+    A slightly faster version of tree_leaves((args, kwargs))
+    """
+    leaves: List[Any] = []
+    for a in args:
+        leaves.extend(tree_iter(a))
+    for a in kwargs.values():
+        leaves.extend(tree_iter(a))
+    return leaves
+
+
+def tree_flatten_with_path(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> Tuple[List[Tuple[KeyPath, Any]], TreeSpec]:
+    """Flattens a pytree like :func:`tree_flatten`, but also returns each leaf's key path.
+
+    Args:
+        tree: a pytree to flatten. If it contains a custom type, that type must be
+            registered with an appropriate `tree_flatten_with_path_fn` when registered
+            with :func:`register_pytree_node`.
+        is_leaf: An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+    Returns:
+        A tuple where the first element is a list of (key path, leaf) pairs, and the
+        second element is a :class:`TreeSpec` representing the structure of the flattened
+        tree.
+    """
+    _, treespec = tree_flatten(tree, is_leaf)
+    return list(_generate_key_paths((), tree, is_leaf)), treespec
+
+
+def tree_leaves_with_path(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> List[Tuple[KeyPath, Any]]:
+    """Gets the leaves of a pytree like ``tree_leaves`` and returns each leaf's key path.
+
+    Args:
+        tree: a pytree. If it contains a custom type, that type must be
+            registered with an appropriate `tree_flatten_with_path_fn` when registered
+            with :func:`register_pytree_node`.
+        is_leaf: An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+    Returns:
+        A list of (key path, leaf) pairs.
+    """
+    return list(_generate_key_paths((), tree, is_leaf))
+
+
+def _generate_key_paths(
+    key_path: KeyPath,
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> Iterable[Tuple[KeyPath, Any]]:
+    if is_leaf and is_leaf(tree):
+        yield key_path, tree
+        return
+
+    node_type = _get_node_type(tree)
+    handler = SUPPORTED_NODES.get(node_type)
+    if not handler:
+        # This is a leaf
+        yield key_path, tree
+        return
+
+    flatten_with_keys = handler.flatten_with_keys_fn
+    if flatten_with_keys:
+        key_children, _ = flatten_with_keys(tree)
+        for k, c in key_children:
+            yield from _generate_key_paths((*key_path, k), c, is_leaf)
+    else:
+        # We registered this pytree but didn't add a flatten_with_keys_fn, complain.
+        raise ValueError(
+            f"Did not find a flatten_with_keys_fn for type: {node_type}. "
+            "Please pass a flatten_with_keys_fn argument to register_pytree_node."
+        )
+
+
+def tree_map_with_path(
+    func: Callable[..., Any],
+    tree: PyTree,
+    *rests: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    """Like :func:`tree_map`, but the provided callable takes an additional key path argument.
+
+    Args:
+        func: A function that takes ``2 + len(rests)`` arguments, to be applied at the
+            corresponding leaves of the pytrees. The first positional argument
+            to ``func`` is the key path of the leaf in question. The second
+            positional argument is the value of the leaf.
+        tree: A pytree to be mapped over, with each leaf providing the first positional
+            argument to function ``func``.
+        rests: A tuple of pytrees, each of which has the same structure as
+            ``tree`` or has ``tree`` as a prefix.
+        is_leaf: An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns
+        A new pytree with the same structure as ``tree`` but with the value at each leaf given by
+        ``func(keypath, x, *xs)`` where ``keypath`` is the key path at the
+        corresponding leaf in ``tree``, ``x`` is the value at that leaf, and
+        ``xs`` is the tuple of values at corresponding nodes in ``rests``.
+    """
+    keypath_leaves, treespec = tree_flatten_with_path(tree, is_leaf)
+    keypath_leaves = list(zip(*keypath_leaves))
+    all_keypath_leaves = keypath_leaves + [treespec.flatten_up_to(r) for r in rests]
+    return treespec.unflatten(func(*xs) for xs in zip(*all_keypath_leaves))
+
+
+def keystr(kp: KeyPath) -> str:
+    """Given a key path, return a pretty-printed representation."""
+    return "".join([str(k) for k in kp])
+
+
+def key_get(obj: Any, kp: KeyPath) -> Any:
+    """Given an object and a key path, return the value at the key path."""
+    for k in kp:
+        obj = k.get(obj)
+    return obj
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_stats.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_stats.py
new file mode 100644
index 0000000000000000000000000000000000000000..c11cbd5df270a1224041eb80ff69cf39799106ab
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_stats.py
@@ -0,0 +1,22 @@
+# mypy: allow-untyped-defs
+# NOTE! PLEASE KEEP THIS FILE *FREE* OF TORCH DEPS! IT SHOULD BE IMPORTABLE ANYWHERE.
+# IF YOU FEEL AN OVERWHELMING URGE TO ADD A TORCH DEP, MAKE A TRAMPOLINE FILE A LA torch._dynamo.utils
+# AND SCRUB AWAY TORCH NOTIONS THERE.
+import collections
+import functools
+from typing import OrderedDict
+
+simple_call_counter: OrderedDict[str, int] = collections.OrderedDict()
+
+def count_label(label):
+    prev = simple_call_counter.setdefault(label, 0)
+    simple_call_counter[label] = prev + 1
+
+def count(fn):
+    @functools.wraps(fn)
+    def wrapper(*args, **kwargs):
+        if fn.__qualname__ not in simple_call_counter:
+            simple_call_counter[fn.__qualname__] = 0
+        simple_call_counter[fn.__qualname__] = simple_call_counter[fn.__qualname__] + 1
+        return fn(*args, **kwargs)
+    return wrapper
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_thunk.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_thunk.py
new file mode 100644
index 0000000000000000000000000000000000000000..08cf6efc96fcffef154e8421905f570d1fdcda66
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_thunk.py
@@ -0,0 +1,28 @@
+from typing import Callable, Generic, Optional, TypeVar
+
+
+R = TypeVar("R")
+
+
+class Thunk(Generic[R]):
+    """
+    A simple lazy evaluation implementation that lets you delay
+    execution of a function.  It properly handles releasing the
+    function once it is forced.
+    """
+
+    f: Optional[Callable[[], R]]
+    r: Optional[R]
+
+    __slots__ = ["f", "r"]
+
+    def __init__(self, f: Callable[[], R]):
+        self.f = f
+        self.r = None
+
+    def force(self) -> R:
+        if self.f is None:
+            return self.r  # type: ignore[return-value]
+        self.r = self.f()
+        self.f = None
+        return self.r
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_traceback.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_traceback.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa3944d4170861553d7bc87daad33de39b1a5f1b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_traceback.py
@@ -0,0 +1,255 @@
+# mypy: allow-untyped-defs
+from types import TracebackType
+from typing import List, Optional
+import tempfile
+import traceback
+import contextlib
+import inspect
+import os.path
+
+# This file contains utilities for ensuring dynamically compile()'d
+# code fragments display their line numbers in backtraces.
+#
+# The constraints:
+#
+# - We don't have control over the user exception printer (in particular,
+#   we cannot assume the linecache trick will work, c.f.
+#   https://stackoverflow.com/q/50515651/23845 )
+#
+# - We don't want to create temporary files every time we compile()
+#   some code; file creation should happen lazily only at exception
+#   time.  Arguably, you *should* be willing to write out your
+#   generated Python code to file system, but in some situations
+#   (esp. library code) it would violate user expectation to write
+#   to the file system, so we try to avoid it.  In particular, we'd
+#   like to keep the files around, so users can open up the files
+#   mentioned in the trace; if the file is invisible, we want to
+#   avoid clogging up the filesystem.
+#
+#   If this is not a constraint for you, there is a substantially simpler
+#   way to implement the functionality in this PR: instead of using
+#   eval/exec directly, just always write a Python file to filesystem
+#   and compile that.
+#
+# - You have control over a context where the compiled code will get
+#   executed, so that we can interpose while the stack is unwinding
+#   (otherwise, we have no way to interpose on the exception printing
+#   process.)
+#
+# There are two things you have to do to make use of the utilities here:
+#
+# - When you compile your source code, you must save its string source
+#   in its f_globals under the magic name "__compile_source__"
+#
+# - Before running the compiled code, enter the
+#   report_compile_source_on_error() context manager.
+
+@contextlib.contextmanager
+def report_compile_source_on_error():
+    try:
+        yield
+    except Exception as exc:
+        tb = exc.__traceback__
+
+        # Walk the traceback, looking for frames that have
+        # source attached
+        stack = []
+        while tb is not None:
+            filename = tb.tb_frame.f_code.co_filename
+            source = tb.tb_frame.f_globals.get("__compile_source__")
+
+            if filename == "" and source is not None:
+                # What black magic are we doing here?  Intuitively, what
+                # we would like to do is overwrite the co_filename on any
+                # frames that were generated from exec/eval so that they
+                # point to a temporary file that has the actual line
+                # information, so Python's default error printer can print
+                # useful line information on it.
+                #
+                # Writing out the temporary file is easy.  But overwriting
+                # co_filename is not!  You can't modify the code object
+                # associated with a frame.  You can, however, reconstruct
+                # a traceback with entirely new frames from scratch, so that's
+                # what we do.  But there's another problem, which is how to
+                # make the frame?
+                #
+                # The black magic is we make a frankenstein frame and code
+                # object which resembles the original frame/code enough so
+                # that it will print properly under traceback and the default
+                # error printer, but IT IS NOT THE ORIGINAL FRAME (you
+                # couldn't, e.g., execute its code with different variables
+                # and expect it to work.)
+
+                # Don't delete the temporary file so the user can inspect it
+                # TODO: This creates a temporary file for every frame, but we
+                # technically only need one per distinct __compile_source__
+                with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix=".py") as f:
+                    f.write(source)
+                # Create a frame.  Python doesn't let you construct
+                # FrameType directly, so just make one with compile
+                frame = tb.tb_frame
+                code = compile('__inspect_currentframe()', f.name, 'eval')
+                code = code.replace(co_name=frame.f_code.co_name)
+                # Python 3.11 only
+                if hasattr(frame.f_code, 'co_linetable'):
+                    # We can't copy ALL of the metadata over, because you
+                    # can cause Python to segfault this way.  What exactly
+                    # do we need?  We need enough information for
+                    # traceback to be able to print the exception
+                    # correctly.  Code reading Lib/traceback.py reveals
+                    # that traceback calls code.co_positions() in order to
+                    # get the augmented line/col numbers.  Objects/codeobject.c,
+                    # specifically _PyCode_InitAddressRange, reveals that
+                    # this iterator is initialized from co_linetable and
+                    # co_firstfileno.  So copy these we must!
+                    code = code.replace(  # type: ignore[call-arg]
+                        co_linetable=frame.f_code.co_linetable,  # type: ignore[attr-defined]
+                        co_firstlineno=frame.f_code.co_firstlineno,  # type: ignore[attr-defined]
+                    )
+                fake_frame = eval(
+                    code,
+                    frame.f_globals,
+                    {
+                        **frame.f_locals,
+                        '__inspect_currentframe': inspect.currentframe
+                    }
+                )
+                fake_tb = TracebackType(
+                    None, fake_frame, tb.tb_lasti, tb.tb_lineno
+                )
+                stack.append(fake_tb)
+            else:
+                stack.append(tb)
+
+            tb = tb.tb_next
+
+        # Reconstruct the linked list
+        tb_next = None
+        for tb in reversed(stack):
+            tb.tb_next = tb_next
+            tb_next = tb
+
+        raise exc.with_traceback(tb_next)  # noqa: B904
+
+def shorten_filename(fn, *, base=None):
+    """Shorten a source filepath, with the assumption that torch/ subdirectories don't need to be shown to user."""
+    if base is None:
+        base = os.path.dirname(os.path.dirname(__file__))
+    # Truncate torch/foo.py to foo.py
+    try:
+        prefix = os.path.commonpath([fn, base])
+    except ValueError:
+        return fn
+    else:
+        return fn[len(prefix) + 1:]
+
+def format_frame(frame, *, base=None, line=False):
+    """
+    Format a FrameSummary in a short way, without printing full absolute path or code.
+
+    The idea is the result fits on a single line.
+    """
+    extra_line = ""
+    if line:
+        extra_line = f"{frame.line}  # "
+    return f"{extra_line}{shorten_filename(frame.filename, base=base)}:{frame.lineno} in {frame.name}"
+
+def format_traceback_short(tb):
+    """Format a TracebackType in a short way, printing only the inner-most frame."""
+    return format_frame(traceback.extract_tb(tb)[-1])
+
+class CapturedTraceback:
+    __slots__ = ['tb', 'skip']
+
+    def __init__(self, tb, skip=0):
+        self.tb = tb
+        self.skip = skip
+
+    def cleanup(self):
+        self.tb = None
+
+    def summary(self):
+        import torch._C._profiler
+
+        if self.tb is None:
+            # TODO: Maybe indicate that the traceback was elided?
+            return traceback.StackSummary()
+
+        return _extract_symbolized_tb(
+            torch._C._profiler.symbolize_tracebacks([self.tb])[0],
+            self.skip
+        )
+
+    def __getstate__(self):
+        return (None, {
+            'tb': None,  # TB is not pickleable
+            'skip': self.skip,
+        })
+
+    @staticmethod
+    def extract(*, script=False, cpp=False, skip=0):
+        """
+        Like traceback.extract_stack(), but faster (approximately 20x faster); it
+        is fast enough that you can unconditionally log stacks this way as part of
+        normal execution.  It returns a torch._C._profiler.CapturedTraceback
+        object that must be formatted specially with format_captured_tb.
+
+        By default, this only reports Python backtraces (like extract_stack).  You
+        can set the script/cpp kwargs to also turn on TorchScript/C++ trace
+        reporting.
+        """
+        import torch._C._profiler
+
+        if script or cpp:
+            assert skip == 0, "skip with script/cpp NYI"
+
+        return CapturedTraceback(
+            torch._C._profiler.gather_traceback(python=True, script=script, cpp=cpp),
+            # Elide extract() frame if we don't have script/cpp frames.  If
+            # we do have those frames, it doesn't work so force zero.
+            0 if script or cpp else skip + 1
+        )
+
+    def format(self):
+        """
+        Formats a single torch._C._profiler.CapturedTraceback into a list of
+        strings equivalent to the output of traceback.format_list.  Note that if
+        pass it CapturedTraceback with C++ traces,  it is better not to use this
+        function and use the batch formatting API format_captured_tbs to amortize
+        the cost of symbolization
+        """
+        return traceback.format_list(self.summary())
+
+    @staticmethod
+    def format_all(tbs):
+        """
+        Bulk version of CapturedTraceback.format.  Returns a list of list of strings.
+        """
+        import torch._C._profiler
+
+        # Directly populate tracebacks that already have cached summaries
+        rs: List[Optional[List[str]]] = []
+        delayed_idxs = []
+        for i, tb in enumerate(tbs):
+            if tb.tb is None:
+                rs.append([])
+            else:
+                rs.append(None)
+                delayed_idxs.append(i)
+
+        stbs = torch._C._profiler.symbolize_tracebacks([tbs[i].tb for i in delayed_idxs])
+        for i, stb in zip(delayed_idxs, stbs):
+            rs[i] = traceback.format_list(tbs[i].summary())
+
+        return rs
+
+
+def _extract_symbolized_tb(tb, skip):
+    """
+    Given a symbolized traceback from symbolize_tracebacks, return a StackSummary object of
+    pre-processed stack trace entries.
+    """
+    stack = traceback.StackSummary()
+    for f in reversed(tb[skip:]):
+        stack.append(traceback.FrameSummary(f['filename'], f['line'], f['name']))
+    return stack
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_triton.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_triton.py
new file mode 100644
index 0000000000000000000000000000000000000000..92b42320c3c66898dd88a259c1ccfe59705dcb9e
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_triton.py
@@ -0,0 +1,77 @@
+# mypy: allow-untyped-defs
+import functools
+import hashlib
+
+
+@functools.lru_cache(None)
+def has_triton_package() -> bool:
+    try:
+        from triton.compiler.compiler import triton_key
+
+        return triton_key is not None
+    except ImportError:
+        return False
+    except RuntimeError:
+        return False
+
+
+@functools.lru_cache(None)
+def has_triton() -> bool:
+    from torch._dynamo.device_interface import get_interface_for_device
+
+    def cuda_extra_check(device_interface):
+        return device_interface.Worker.get_device_properties().major >= 7
+
+    def _return_true(device_interface):
+        return True
+
+    triton_supported_devices = {"cuda": cuda_extra_check, "xpu": _return_true}
+
+    def is_device_compatible_with_triton():
+        for device, extra_check in triton_supported_devices.items():
+            device_interface = get_interface_for_device(device)
+            if device_interface.is_available() and extra_check(device_interface):
+                return True
+        return False
+
+    return is_device_compatible_with_triton() and has_triton_package()
+
+
+@functools.lru_cache(None)
+def triton_backend():
+    from triton.compiler.compiler import make_backend
+    from triton.runtime.driver import driver
+
+    target = driver.active.get_current_target()
+    return make_backend(target)
+
+
+@functools.lru_cache(None)
+def triton_hash_with_backend():
+    from triton.compiler.compiler import triton_key
+
+    backend = triton_backend()
+    key = f"{triton_key()}-{backend.hash()}"
+
+    # Hash is upper case so that it can't contain any Python keywords.
+    return hashlib.sha256(key.encode("utf-8")).hexdigest().upper()
+
+
+def dtype_to_string(dtype):
+    if dtype.name.startswith("fp"):
+        suffix = "float" + dtype.name[2:]
+    elif dtype.name.startswith("bf"):
+        suffix = "bfloat" + dtype.name[2:]
+    else:
+        suffix = dtype.name
+    return "triton.language." + suffix
+
+
+def patch_triton_dtype_repr():
+    import triton
+
+    # Hack to get triton dtype repr to produce an evaluatable expression
+    # triton.language.float32 emits triton.language.fp32 which does not
+    # exist
+    # REMOVE when https://github.com/openai/triton/pull/3342 lands
+    triton.language.dtype.__repr__ = lambda self: dtype_to_string(self)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_typing_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_typing_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffb6b383e4e6b9506baa803ab5ac6613dfc9b387
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_typing_utils.py
@@ -0,0 +1,14 @@
+"""Miscellaneous utilities to aid with typing."""
+
+from typing import Optional, TypeVar
+
+
+# Helper to turn Optional[T] into T when we know None either isn't
+# possible or should trigger an exception.
+T = TypeVar("T")
+
+
+def not_none(obj: Optional[T]) -> T:
+    if obj is None:
+        raise TypeError("Invariant encountered: value was None when it should not be")
+    return obj
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_zip.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_zip.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7dd6445fabe98209d3294e933a3f1baca71bd64
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/_zip.py
@@ -0,0 +1,86 @@
+# mypy: allow-untyped-defs
+import argparse
+import glob
+import os
+from pathlib import Path
+from zipfile import ZipFile
+
+# Exclude some standard library modules to:
+# 1. Slim down the final zipped file size
+# 2. Remove functionality we don't want to support.
+DENY_LIST = [
+    # Interface to unix databases
+    "dbm",
+    # ncurses bindings (terminal interfaces)
+    "curses",
+    # Tcl/Tk GUI
+    "tkinter",
+    "tkinter",
+    # Tests for the standard library
+    "test",
+    "tests",
+    "idle_test",
+    "__phello__.foo.py",
+    # importlib frozen modules. These are already baked into CPython.
+    "_bootstrap.py",
+    "_bootstrap_external.py",
+]
+
+strip_file_dir = ""
+
+
+def remove_prefix(text, prefix):
+    if text.startswith(prefix):
+        return text[len(prefix) :]
+    return text
+
+
+def write_to_zip(file_path, strip_file_path, zf, prepend_str=""):
+    stripped_file_path = prepend_str + remove_prefix(file_path, strip_file_dir + "/")
+    path = Path(stripped_file_path)
+    if path.name in DENY_LIST:
+        return
+    zf.write(file_path, stripped_file_path)
+
+
+def main() -> None:
+    global strip_file_dir
+    parser = argparse.ArgumentParser(description="Zip py source")
+    parser.add_argument("paths", nargs="*", help="Paths to zip.")
+    parser.add_argument(
+        "--install-dir", "--install_dir", help="Root directory for all output files"
+    )
+    parser.add_argument(
+        "--strip-dir",
+        "--strip_dir",
+        help="The absolute directory we want to remove from zip",
+    )
+    parser.add_argument(
+        "--prepend-str",
+        "--prepend_str",
+        help="A string to prepend onto all paths of a file in the zip",
+        default="",
+    )
+    parser.add_argument("--zip-name", "--zip_name", help="Output zip name")
+
+    args = parser.parse_args()
+
+    zip_file_name = args.install_dir + "/" + args.zip_name
+    strip_file_dir = args.strip_dir
+    prepend_str = args.prepend_str
+    zf = ZipFile(zip_file_name, mode="w")
+
+    for p in sorted(args.paths):
+        if os.path.isdir(p):
+            files = glob.glob(p + "/**/*.py", recursive=True)
+            for file_path in sorted(files):
+                # strip the absolute path
+                write_to_zip(
+                    file_path, strip_file_dir + "/", zf, prepend_str=prepend_str
+                )
+        else:
+            write_to_zip(p, strip_file_dir + "/", zf, prepend_str=prepend_str)
+
+
+if __name__ == "__main__":
+    main()  # pragma: no cover
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/backend_registration.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/backend_registration.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f3444116f3a118541d650670f35d8542f40427b
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/backend_registration.py
@@ -0,0 +1,382 @@
+# mypy: allow-untyped-defs
+import torch
+from torch.overrides import (
+    handle_torch_function,
+    has_torch_function_unary,
+)
+from torch._C import _rename_privateuse1_backend, _get_privateuse1_backend_name
+from typing import List, Optional, Union
+
+__all__ = ["rename_privateuse1_backend", "generate_methods_for_privateuse1_backend"]
+
+# TODO: Should use `torch._C._get_privateuse1_backend_name()` to get
+# renamed-backend name for `privateuse1`, but the func will cause an
+# error with torch.jit.script, so we use the global variable named
+# `_privateuse1_backend_name`.
+_privateuse1_backend_name = "privateuseone"
+
+def rename_privateuse1_backend(backend_name: str) -> None:
+    r"""
+    Rename the privateuse1 backend device to make it more convenient to use as a device name within PyTorch APIs.
+
+    The steps are:
+
+    (1) (In C++) implement kernels for various torch operations, and register them
+        to the PrivateUse1 dispatch key.
+    (2) (In python) call torch.utils.rename_privateuse1_backend("foo")
+
+    You can now use "foo" as an ordinary device string in python.
+
+    Note: this API can only be called once per process. Attempting to change
+    the external backend after it's already been set will result in an error.
+
+    Note(AMP): If you want to support AMP on your device, you can register a custom backend module.
+    The backend must register a custom backend module with ``torch._register_device_module("foo", BackendModule)``.
+    BackendModule needs to have the following API's:
+
+    (1) ``get_amp_supported_dtype() -> List[torch.dtype]``
+        get the supported dtypes on your "foo" device in AMP, maybe the "foo" device supports one more dtype.
+
+    Note(random): If you want to support to set seed for your device, BackendModule needs to have the following API's:
+
+    (1) ``_is_in_bad_fork() -> bool``
+        Return ``True`` if now it is in bad_fork, else return ``False``.
+
+    (2) ``manual_seed_all(seed int) -> None``
+        Sets the seed for generating random numbers for your devices.
+
+    (3) ``device_count() -> int``
+        Returns the number of "foo"s available.
+
+    (4) ``get_rng_state(device: Union[int, str, torch.device] = 'foo') -> Tensor``
+        Returns a list of ByteTensor representing the random number states of all devices.
+
+    (5) ``set_rng_state(new_state: Tensor, device: Union[int, str, torch.device] = 'foo') -> None``
+        Sets the random number generator state of the specified "foo" device.
+
+    And there are some common funcs:
+
+    (1) ``is_available() -> bool``
+        Returns a bool indicating if "foo" is currently available.
+
+    (2) ``current_device() -> int``
+        Returns the index of a currently selected device.
+
+    For more details, see https://pytorch.org/tutorials/advanced/extend_dispatcher.html#get-a-dispatch-key-for-your-backend
+    For an existing example, see https://github.com/bdhirsh/pytorch_open_registration_example
+
+    Example::
+
+        >>> # xdoctest: +SKIP("failing")
+        >>> torch.utils.rename_privateuse1_backend("foo")
+        # This will work, assuming that you've implemented the right C++ kernels
+        # to implement torch.ones.
+        >>> a = torch.ones(2, device="foo")
+
+    """
+    _rename_privateuse1_backend(backend_name)
+    global _privateuse1_backend_name
+    _privateuse1_backend_name = backend_name
+
+def _check_register_once(module, attr):
+    if hasattr(module, attr):
+        raise RuntimeError(f"The custom device module of {module} has already been registered with {attr}")
+
+
+def _normalization_device(custom_backend_name: str, device: Optional[Union[int, str, torch.device]] = None) -> int:
+    def _get_current_device_index():
+        _get_device_index = "current_device"
+        if hasattr(torch, custom_backend_name) and \
+                hasattr(getattr(torch, custom_backend_name), _get_device_index):
+            return getattr(getattr(torch, custom_backend_name), _get_device_index)()
+        else:
+            # The default device index is 0.
+            return 0
+
+    if device is None:
+        return _get_current_device_index()
+    # if isinstance(device, str), this means that the parameter passed in is in the string format "foo:0"
+    # convert str object to torch.device object, and then process it uniformly
+    elif isinstance(device, str):
+        device = torch.device(device)
+
+    # variable devcie can only be torch.device type or int type
+    if isinstance(device, torch.device):
+        if device.type != custom_backend_name:
+            raise RuntimeError(f"Invalid device, must be {custom_backend_name} device")
+        elif device.index is None:
+            device_idx = _get_current_device_index()
+        else:
+            device_idx = device.index
+    # if isinstance(device, int), we can take the index number directly
+    else:
+        device_idx = device
+    return device_idx
+
+
+def _generate_tensor_methods_for_privateuse1_backend(custom_backend_name: str) -> None:
+    @property  # type: ignore[misc]
+    def wrap_tensor_backend(self: torch.Tensor) -> bool:
+        if has_torch_function_unary(self):
+            # TODO mypy doesn't support @property, see: https://github.com/python/mypy/issues/6185
+            return handle_torch_function(wrap_tensor_backend.__get__, (self,), self)  # type: ignore[attr-defined]
+        return self.device.type == custom_backend_name
+
+    _check_register_once(torch.Tensor, f'is_{custom_backend_name}')
+    wrap_tensor_backend.fget.__name__ = f'is_{custom_backend_name}'  # type: ignore[attr-defined]
+    setattr(torch.Tensor, f'is_{custom_backend_name}', wrap_tensor_backend)
+
+    def wrap_tensor_to(self: torch.Tensor, device: Optional[Union[int, torch.device]] = None, non_blocking=False,
+                       **kwargs) -> torch.Tensor:
+        r"""Perform Tensor device conversion. Call the to operator implementation.
+
+        .. note::
+            If the ``self`` Tensor already
+            has the correct :class:`torch.device`, then ``self`` is returned.
+            Otherwise, the returned tensor is a copy of ``self`` with the desired :class:`torch.device`.
+
+        Args:
+            device (int, optional): if specified, all parameters will be copied to that device
+            non_blocking (bool): If ``True`` and the source is in pinned memory,
+                the copy will be asynchronous with respect to the host. Otherwise,
+                the argument has no effect.
+            **kwargs (dict): For compatibility, may contain the key ``memory_format`` argument.
+        """
+        if has_torch_function_unary(self):
+            return handle_torch_function(wrap_tensor_to, (self,), self, device=device, non_blocking=False, **kwargs)
+        device_idx = _normalization_device(custom_backend_name, device)
+        return self.to(device=torch.device(f'{custom_backend_name}:{device_idx}'), non_blocking=non_blocking, **kwargs)
+
+    _check_register_once(torch.Tensor, custom_backend_name)
+    wrap_tensor_to.__name__ = custom_backend_name
+    setattr(torch.Tensor, custom_backend_name, wrap_tensor_to)
+
+
+def _generate_module_methods_for_privateuse1_backend(custom_backend_name: str) -> None:
+    # Generate Module attributes and methods depends on Tensor methods,
+    # so we need to check whether Tensor methods is already registered.
+    if not hasattr(torch.Tensor, custom_backend_name):
+        raise RuntimeError(
+            f"Can not automatically generate {custom_backend_name}() method for torch.nn.Module."
+            f"Because torch.Tensor doesn't has the method {custom_backend_name}()."
+            f"For this error, you can try setting for_tensor=True.")
+
+    def wrap_module_to(self: torch.nn.modules.module.T,
+                       device: Optional[Union[int, torch.device]] = None) -> torch.nn.modules.module.T:
+        r"""Move all model parameters and buffers to the custom device.
+
+        This also makes associated parameters and buffers different objects. So
+        it should be called before constructing optimizer if the module will
+        live on device while being optimized.
+
+        .. note::
+            This method modifies the module in-place.
+
+        Args:
+            device (int, optional): if specified, all parameters will be copied to that device
+        """
+        return self._apply(lambda t: getattr(t, custom_backend_name)(device))
+
+    _check_register_once(torch.nn.Module, custom_backend_name)
+    setattr(torch.nn.Module, custom_backend_name, wrap_module_to)
+
+def _generate_packed_sequence_methods_for_privateuse1_backend(custom_backend_name: str) -> None:
+    # Generate PackedSequence Module attributes and methods depends on Tensor methods,
+    # so we need to check whether Tensor methods is already registered.
+    if not hasattr(torch.Tensor, f'is_{custom_backend_name}') or \
+       not hasattr(torch.Tensor, custom_backend_name):
+        raise RuntimeError(
+            f"Can not automatically generate is_{custom_backend_name}() or "
+            f"{custom_backend_name}() method for torch.nn.utils.rnn.PackedSequence."
+            f"Because torch.Tensor doesn't has the method is_{custom_backend_name}()"
+            f"or {custom_backend_name}()."
+            f"For this error, you can try setting for_tensor=True.")
+
+    @property  # type: ignore[misc]
+    def wrap_tensor_backend(self: torch.nn.utils.rnn.PackedSequence) -> bool:
+        return self.data.device.type == custom_backend_name
+
+    _check_register_once(torch.nn.utils.rnn.PackedSequence, f'is_{custom_backend_name}')
+    setattr(torch.nn.utils.rnn.PackedSequence, f'is_{custom_backend_name}', wrap_tensor_backend)
+
+    def wrap_module_to(self: torch.nn.utils.rnn.PackedSequence,
+                       *args, **kwargs) -> torch.nn.utils.rnn.PackedSequence:
+        r"""Move all model parameters and buffers to the custom device.
+
+        This also makes associated parameters and buffers different objects. So
+        it should be called before constructing optimizer if the module will
+        live on device while being optimized.
+
+        .. note::
+            This method modifies the module in-place.
+
+        Args:
+            device (int, optional): if specified, all parameters will be copied to that device
+        """
+        ex = torch.tensor((), dtype=self.data.dtype, device=self.data.device).to(*args, **kwargs)
+        if ex.device.type == custom_backend_name:
+            return self.to(*args, **kwargs)
+        kwargs.update({'device': custom_backend_name})
+        return self.to(*args, **kwargs)
+
+    _check_register_once(torch.nn.utils.rnn.PackedSequence, custom_backend_name)
+    setattr(torch.nn.utils.rnn.PackedSequence, custom_backend_name, wrap_module_to)
+
+def _generate_storage_methods_for_privateuse1_backend(custom_backend_name: str,
+                                                      unsupported_dtype: Optional[List[torch.dtype]] = None) -> None:
+    # Attribute is registered in the _StorageBase class
+    # and UntypedStorage obtains through inheritance.
+    @property  # type: ignore[misc]
+    def wrap_storage_backend(self: torch.storage._StorageBase) -> bool:
+        r"""Return the internal :class:`torch.UntypedStorage`."""
+        return self.device.type == custom_backend_name
+
+    _check_register_once(torch.storage._StorageBase, f'is_{custom_backend_name}')
+    setattr(torch.storage._StorageBase, f'is_{custom_backend_name}', wrap_storage_backend)
+
+    def wrap_storage_to(self, device=None, non_blocking=False):
+        r"""Return a copy of this object in custom device memory.
+
+        If this object is already in device memory and on the correct device, then
+        no copy is performed and the original object is returned.
+
+        Args:
+            device (int): The destination device id. Defaults to the current device.
+            non_blocking (bool): If ``True`` and the source is in pinned memory,
+            the copy will be asynchronous with respect to the host. Otherwise,
+            the argument has no effect.
+        """
+        # There should be a judgment related to storage device and a judgment related to storage type,
+        # but it depends on the extended function, so this part is temporarily omitted in the automatic generation.
+        device_idx = _normalization_device(custom_backend_name, device)
+
+        if getattr(self, f'is_{custom_backend_name}'):
+            # storage has already on expected device.
+            if self.get_device() == device_idx:
+                return self
+        # For sparse storage, custom need to extend the implementation by themselves.
+        if self.is_sparse:
+            raise RuntimeError(f"Can not support a sparse storage move to {custom_backend_name} backend")
+        # create untyped_storage and copy data
+        untyped_storage = torch.UntypedStorage(
+            self.size(), device=torch.device(f'{custom_backend_name}:{device_idx}')
+        )
+        untyped_storage.copy_(self, non_blocking)
+        return untyped_storage
+
+    _check_register_once(torch.storage._StorageBase, custom_backend_name)
+    setattr(torch.storage._StorageBase, custom_backend_name, wrap_storage_to)
+
+    # Register the corresponding attribute for the TypedStorage class.
+    # When the TypedStorage class is removed, the registration is also removed.
+
+    @property  # type: ignore[misc]
+    def wrap_typed_storage_backend(self: torch.storage.TypedStorage) -> bool:
+        torch.storage._warn_typed_storage_removal()
+        return self._untyped_storage.device.type == custom_backend_name
+
+    _check_register_once(torch.TypedStorage, f'is_{custom_backend_name}')
+    setattr(torch.storage.TypedStorage, f'is_{custom_backend_name}', wrap_typed_storage_backend)
+
+    def wrap_typed_storage_to(self: torch.storage.TypedStorage,
+                              device=None, non_blocking=False, **kwargs) -> torch.storage.TypedStorage:
+        torch.storage._warn_typed_storage_removal()
+        if unsupported_dtype and self.dtype in unsupported_dtype:
+            raise RuntimeError(f"Cannot create {custom_backend_name} storage "
+                               f"as {self.dtype} dtype is not supported by this backend")
+        custom_backend_storage: torch.UntypedStorage = getattr(
+            self._untyped_storage, custom_backend_name)(device, non_blocking, **kwargs)
+        return self._new_wrapped_storage(custom_backend_storage)
+
+    _check_register_once(torch.TypedStorage, custom_backend_name)
+    setattr(torch.TypedStorage, custom_backend_name, wrap_typed_storage_to)
+
+
+def generate_methods_for_privateuse1_backend(for_tensor: bool = True, for_module: bool = True,
+                                             for_packed_sequence: bool = True,
+                                             for_storage: bool = False,
+                                             unsupported_dtype: Optional[List[torch.dtype]] = None) -> None:
+    r"""
+    Automatically generate attributes and methods for the custom backend after rename privateuse1 backend.
+
+    In the default scenario, storage-related methods will not be generated automatically.
+
+    When you implement kernels for various torch operations, and register them to the PrivateUse1 dispatch key.
+    And call the function torch.rename_privateuse1_backend("foo") to rename your backend name.
+    At this point, you can easily register specific methods and attributes by calling this function.
+    Just like torch.Tensor.foo(), torch.Tensor.is_foo, torch.Storage.foo(), torch.Storage.is_foo.
+
+    Note: We recommend you use generic functions (check devices are equal or to(device=)).
+    We provide these methods for convenience only and they will be "monkey patched" onto the objects
+    and so will not be properly typed. For Storage methods generate, if you need to support sparse data storage,
+    you need to extend the implementation yourself.
+
+    Args:
+        for_tensor (bool): whether register related methods for torch.Tensor class.
+        for_module (bool): whether register related methods for torch.nn.Module class.
+        for_storage (bool): whether register related methods for torch.Storage class.
+        unsupported_dtype (List[torch.dtype]): takes effect only when the storage method needs to be generated,
+            indicating that the storage does not support the torch.dtype type.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("failing")
+        >>> torch.utils.rename_privateuse1_backend("foo")
+        >>> torch.utils.generate_methods_for_privateuse1_backend()
+        # Then automatically generate backend-related attributes and methods.
+        >>> a = torch.tensor(2).foo()
+        >>> a.is_foo
+        >>> hasattr(torch.nn.Module, 'foo')
+    """
+    custom_backend_name = _get_privateuse1_backend_name()
+
+    if for_tensor:
+        _generate_tensor_methods_for_privateuse1_backend(custom_backend_name)
+
+    if for_module:
+        _generate_module_methods_for_privateuse1_backend(custom_backend_name)
+
+    if for_storage:
+        _generate_storage_methods_for_privateuse1_backend(custom_backend_name, unsupported_dtype)
+
+    if for_packed_sequence:
+        _generate_packed_sequence_methods_for_privateuse1_backend(custom_backend_name)
+
+def _get_custom_mod_func(func_name: str):
+    r"""
+    Return the func named `func_name` defined in custom device module. If not defined,
+    return `None`. And the func is registered with `torch.utils.rename_privateuse1_backend('foo')`
+    and `torch._register_device_module('foo', BackendModule)`.
+    If the custom device module or the func is not defined, it will give warning or error message.
+    Args:
+        func_name (str): return the callable func named func_name defined in custom device module.
+    Example::
+        class DummyfooModule:
+            @staticmethod
+            def is_available():
+                return True
+            @staticmethod
+            def func_name(*args, **kwargs):
+                ....
+        torch.utils.rename_privateuse1_backend("foo")
+        torch._register_device_module("foo", DummyfooModule)
+        foo_is_available_func = torch.utils.backend_registration._get_custom_mod_func("is_available")
+        if foo_is_available_func:
+            foo_is_available = foo_is_available_func()
+        func_ = torch.utils.backend_registration._get_custom_mod_func("func_name")
+        if func_:
+            result = func_(*args, **kwargs)
+    Attention: This function is not meant to be used directly by users, which is why
+    it is marked as private. It is a convenience function for backend implementers to
+    more easily call the hooks into their backend extensions.
+    """
+    assert isinstance(func_name, str), f"func_name must be `str`, but got `{type(func_name)}`."
+    backend_name = _get_privateuse1_backend_name()
+    custom_device_mod = getattr(torch, backend_name, None)  # type: ignore[arg-type]
+    function = getattr(custom_device_mod, func_name, None)  # type: ignore[arg-type]
+    if custom_device_mod is None or function is None:
+        message = f'Try to call torch.{backend_name}.{func_name}. The backend must register a custom backend '
+        message += f"module with `torch._register_device_module('{backend_name}', BackendModule)`. And "
+        message += f"BackendModule needs to have the following API's:\n `{func_name}(*args, **kwargs)`. \n"
+        raise RuntimeError(message)
+    return function
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/bundled_inputs.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/bundled_inputs.py
new file mode 100644
index 0000000000000000000000000000000000000000..21fa4e50396da2f79e26963a00f2a79dd3c9d521
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/bundled_inputs.py
@@ -0,0 +1,469 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+from typing import Any, TypeVar, Optional, Tuple, List, NamedTuple, Union, Sequence, Dict, Callable
+import textwrap
+import torch
+from torch._C import TupleType, ListType
+from torch.jit._recursive import wrap_cpp_module
+
+
+T = TypeVar("T")
+
+MAX_RAW_TENSOR_SIZE = 16
+
+class InflatableArg(NamedTuple):
+    """Helper type for bundled inputs.
+
+    'value' is the compressed/deflated input that is stored in the model. Value
+    must be of the same type as the argument to the function that it is a deflated
+    input for.
+
+    'fmt' is a formatable code string that is executed to inflate the compressed data into
+    the appropriate input. It can use 'value' as an input to the format str. It must result
+    in a value of the same type as 'value'.
+
+    'fmt_fn' is a formatable function code string that is executed to inflate the compressed
+    data into the appropriate input. It must result in a value of the same type as 'value'.
+    The function name should be the formatable part of the string.
+
+    Note: Only top level InflatableArgs can be inflated. i.e. you cannot place
+    an inflatable arg inside of some other structure. You should instead create
+    an inflatable arg such that the fmt code string returns the full structure
+    of your input.
+    """
+
+    value: Any
+    fmt: str = "{}"
+    fmt_fn: str = ""
+
+
+def bundle_inputs(
+        model: torch.jit.ScriptModule,
+        inputs: Union[Optional[Sequence[Tuple[Any, ...]]], Dict[Callable, Optional[Sequence[Tuple[Any, ...]]]]],
+        info: Optional[Union[List[str], Dict[Callable, List[str]]]] = None,
+        *,
+        _receive_inflate_expr: Optional[List[str]] = None,
+) -> torch.jit.ScriptModule:
+    """Create and return a copy of the specified model with inputs attached.
+
+    The original model is not mutated or changed in any way.
+
+    Models with bundled inputs can be invoked in a uniform manner by
+    benchmarking and code coverage tools.
+
+    If inputs is passed in as a list then the inputs will be bundled for 'forward'.
+    If inputs is instead passed in as a map then all the methods specified in the map
+    will have their corresponding inputs bundled. Info should match watchever type is
+    chosen for the inputs.
+
+    The returned model will support the following methods:
+
+        `get_all_bundled_inputs_for_() -> List[Tuple[Any, ...]]`
+            Returns a list of tuples suitable for passing to the model like
+            `for inp in model.get_all_bundled_inputs_for_foo(): model.foo(*inp)`
+
+        `get_bundled_inputs_functions_and_info() -> Dict[str, Dict[str: List[str]]]`
+            Returns a dictionary mapping function names to a metadata dictionary.
+            This nested dictionary maps preset strings like:
+                'get_inputs_function_name' -> the name of a function attribute in this model that can be
+                    run to get back a list of inputs corresponding to that function.
+                'info' -> the user provided extra information about the bundled inputs
+
+    If forward has bundled inputs then these following functions will also be defined on the returned module:
+
+        `get_all_bundled_inputs() -> List[Tuple[Any, ...]]`
+            Returns a list of tuples suitable for passing to the model like
+            `for inp in model.get_all_bundled_inputs(): model(*inp)`
+
+        `get_num_bundled_inputs() -> int`
+            Equivalent to `len(model.get_all_bundled_inputs())`,
+            but slightly easier to call from C++.
+
+    Inputs can be specified in one of two ways:
+
+      - The model can define `_generate_bundled_inputs_for_`.
+        If the user chooses this method inputs[] should map to None
+
+      - The `inputs` argument to this function can be a dictionary mapping functions to a
+        list of inputs, of the same form that will be returned by get_all_bundled_inputs_for_.
+        Alternatively if only bundling inputs for forward the map can be omitted and a singular list of inputs
+        can be provided instead.
+
+        The type of the inputs is List[Tuple[Any, ...]]. The outer list corresponds with a
+        list of inputs, the inner tuple is the list of args that together make up one input.
+        For inputs of functions that take one arg, this will be a tuple of length one. The Any, ...
+        is the actual data that makes up the args, e.g. a tensor.
+
+    Info is an optional parameter that maps functions to a list of strings providing extra information about that
+    function's bundled inputs. Alternatively if only bundling inputs for forward the map can be omitted and
+    a singular list of information can be provided instead. This could be descriptions, expected outputs, etc.
+        - Ex: info={model.forward : ['man eating icecream', 'an airplane', 'a dog']}
+
+    This function will attempt to optimize arguments so that (e.g.)
+    arguments like `torch.zeros(1000)` will be represented compactly.
+    Only top-level arguments will be optimized.
+    Tensors in lists or tuples will not.
+    """
+    if not isinstance(model, torch.jit.ScriptModule):
+        raise Exception("Only ScriptModule is supported.")  # noqa: TRY002
+
+    ignored_methods, ignored_attrs = _get_bundled_inputs_attributes_and_methods(model)
+    clone = torch._C._hack_do_not_use_clone_module_with_class(  # type: ignore[attr-defined]
+        model._c,
+        ignored_methods,
+        ignored_attrs,
+    )
+
+    # The above cloning function returns a torch._C.scriptmodule and we need a torch.jit.scriptmodule.
+    # Fortunately theres a function in _recursive that does exactly that conversion.
+    cloned_module = wrap_cpp_module(clone)
+    if isinstance(inputs, dict):
+        assert isinstance(info, dict) or info is None
+        augment_many_model_functions_with_bundled_inputs(cloned_module, inputs, _receive_inflate_expr, info)
+    else:
+        assert isinstance(info, list) or info is None
+        augment_model_with_bundled_inputs(cloned_module, inputs, _receive_inflate_expr, info)
+    return cloned_module
+
+def augment_model_with_bundled_inputs(
+        model: torch.jit.ScriptModule,
+        inputs: Optional[Sequence[Tuple[Any, ...]]] = None,
+        _receive_inflate_expr: Optional[List[str]] = None,  # For debugging.
+        info: Optional[List[str]] = None,  # Optional argument to provide info about forward or its inputs
+        skip_size_check=False,
+) -> None:
+    """Add bundled sample inputs to a model for the forward function.
+
+    Models with bundled inputs can be invoked in a uniform manner by
+    benchmarking and code coverage tools.
+
+    Augmented models will support the following methods:
+
+        `get_all_bundled_inputs() -> List[Tuple[Any, ...]]`
+            Returns a list of tuples suitable for passing to the model like
+            `for inp in model.get_all_bundled_inputs(): model(*inp)`
+
+        `get_num_bundled_inputs() -> int`
+            Equivalent to `len(model.get_all_bundled_inputs())`,
+            but slightly easier to call from C++.
+
+        `get_bundled_inputs_functions_and_info() -> Dict[str, Dict[str: List[str]]]`
+            Returns a dictionary mapping function names to a metadata dictionary.
+            This nested dictionary maps preset strings like:
+                'get_inputs_function_name' -> the name of a function attribute in this model that can be
+                    run to get back a list of inputs corresponding to that function.
+                'info' -> the user provided extra information about the bundled inputs
+
+    Inputs can be specified in one of two ways:
+
+      - The model can define `_generate_bundled_inputs_for_forward`.
+        If the user chooses this method inputs should be None
+
+      - `inputs` is a list of inputs of form List[Tuple[Any, ...]]. A list of tuples where the elements
+        of each tuple are the args that make up one input.
+    """
+    if not isinstance(model, torch.jit.ScriptModule):
+        raise Exception("Only ScriptModule is supported.")  # noqa: TRY002
+
+    forward: Callable = model.forward
+
+    # Sometimes forward won't have a name attached so just in case
+    if not hasattr(forward, "__name__"):
+        forward.__name__ = 'forward'
+    augment_many_model_functions_with_bundled_inputs(
+        model,
+        inputs={forward : inputs},
+        _receive_inflate_expr=_receive_inflate_expr,
+        info={forward : info} if info else None,
+        skip_size_check=skip_size_check,
+    )
+
+
+def augment_many_model_functions_with_bundled_inputs(
+        model: torch.jit.ScriptModule,
+        inputs: Dict[Callable, Optional[Sequence[Tuple[Any, ...]]]],
+        _receive_inflate_expr: Optional[List[str]] = None,  # For debugging.
+        info: Optional[Dict[Callable, List[str]]] = None,  # Optional argument to provide info about the function or its inputs
+        skip_size_check=False,
+) -> None:
+    """Add bundled sample inputs to a model for an arbitrary list of public functions.
+
+    Models with bundled inputs can be invoked in a uniform manner by
+    benchmarking and code coverage tools.
+
+    Augmented models will support the following methods:
+
+        `get_all_bundled_inputs_for_() -> List[Tuple[Any, ...]]`
+            Returns a list of tuples suitable for passing to the model like
+            `for inp in model.get_all_bundled_inputs_for_foo(): model.foo(*inp)`
+
+        `get_bundled_inputs_functions_and_info() -> Dict[str, Dict[str: List[str]]]`
+            Returns a dictionary mapping function names to a metadata dictionary.
+            This nested dictionary maps preset strings like:
+                'get_inputs_function_name' -> the name of a function attribute in this model that can be
+                    run to get back a list of inputs corresponding to that function.
+                'info' -> the user provided extra information about the bundled inputs
+
+    If forward has bundled inputs then these following functions are also defined:
+
+        `get_all_bundled_inputs() -> List[Tuple[Any, ...]]`
+            Returns a list of tuples suitable for passing to the model like
+            `for inp in model.get_all_bundled_inputs(): model(*inp)`
+
+        `get_num_bundled_inputs() -> int`
+            Equivalent to `len(model.get_all_bundled_inputs())`,
+            but slightly easier to call from C++.
+
+    Inputs can be specified in one of two ways:
+
+      - The model can define `_generate_bundled_inputs_for_`.
+        If the user chooses this method inputs[] should map to None
+
+      - The `inputs` argument to this function can be a dictionary mapping functions to a
+        list of inputs, of the same form that will be returned by get_all_bundled_inputs_for_.
+        The type of the inputs is List[Tuple[Any, ...]]. The outer list corresponds with a
+        list of inputs, the inner tuple is the list of args that together make up one input.
+        For inputs of functions that take one arg, this will be a tuple of length one. The Any, ...
+        is the actual data that makes up the args, e.g. a tensor.
+
+    Info is an optional parameter that maps functions to a list of strings providing extra information about that
+    function's bundled inputs. This could be descriptions, expected outputs, etc.
+        - Ex: info={model.forward : ['man eating icecream', 'an airplane', 'a dog']}
+
+    This function will attempt to optimize arguments so that (e.g.)
+    arguments like `torch.zeros(1000)` will be represented compactly.
+    Only top-level arguments will be optimized.
+    Tensors in lists or tuples will not.
+    """
+    if not isinstance(model, torch.jit.ScriptModule):
+        raise Exception("Only ScriptModule is supported.")  # noqa: TRY002
+
+    if not inputs:
+        raise Exception("Please provide inputs for at least 1 function")  # noqa: TRY002
+
+    if hasattr(model, "get_all_bundled_inputs") or hasattr(model, "get_bundled_inputs_functions_and_info"):
+        raise Exception(  # noqa: TRY002
+            "Models can only be augmented with bundled inputs once. "
+            "This Model seems to have already been augmented with "
+            "bundled inputs. Please start afresh with one that "
+            "doesn't have bundled inputs.",
+        )
+
+    get_bundled_inputs_functions_and_info_template = ""
+
+    for function, input_list in inputs.items():
+        if hasattr(function, "__name__"):
+            function_name = function.__name__
+        else:
+            if hasattr(function, "name"):
+                function_name = function.name  # type: ignore[attr-defined]
+            else:
+                raise Exception(  # noqa: TRY002
+                    'At least one of your functions has no attribute name please ensure all have one. m.foo.name = "foo"')
+
+
+        if input_list is not None and not isinstance(input_list, Sequence):
+            raise TypeError(f"Error inputs for function {function_name} is not a Sequence")
+
+        function_arg_types = [arg.type for arg in function.schema.arguments[1:]]  # type: ignore[attr-defined]
+        deflated_inputs_type: ListType = ListType(TupleType(function_arg_types))
+        model._c._register_attribute(f"_bundled_inputs_deflated_{function_name}", deflated_inputs_type, [])
+
+        if hasattr(model, "_generate_bundled_inputs_for_" + function_name):
+            if input_list is not None:
+                raise Exception(  # noqa: TRY002
+                    f"inputs[{function_name}] is not None, but _generate_bundled_inputs_for_{function_name} is already defined"
+                )
+            # Model author already defined _generate_bundled_inputs_for_.
+        elif input_list is None or len(input_list) == 0:
+            raise Exception(  # noqa: TRY002
+                f"inputs for {function_name} must be specified if "
+                f"_generate_bundled_inputs_for_{function_name} is not already defined"
+            )
+        else:
+            # Iterate over the inputs and args in each input.
+            # Accumulate `deflated_inputs` as (possibly) compressed values
+            # and `parts` to be joined into the expression that unpacks them.
+            deflated_inputs = []
+            parts = []
+            for inp_idx, args in enumerate(input_list):
+                if not isinstance(args, Tuple) and not isinstance(args, List):  # type: ignore[arg-type]
+                    raise TypeError(
+                        f"Error bundled input for function {function_name} idx: {inp_idx} is not a Tuple or a List"
+                    )
+                deflated_args = []
+                parts.append("(")
+                for arg_idx, arg in enumerate(args):
+                    inflate_helper_fn_name = _get_inflate_helper_fn_name(arg_idx, inp_idx, function_name)
+                    deflated, inflater, helper_definition = _inflate_expr(
+                        arg,
+                        f"deflated[{inp_idx}][{arg_idx}]",
+                        inflate_helper_fn_name,
+                        skip_size_check=skip_size_check,
+                    )
+                    deflated_args.append(deflated)
+                    parts.append(f"    {inflater},")
+                    if helper_definition:
+                        model.define(textwrap.dedent(helper_definition))
+                deflated_inputs.append(tuple(deflated_args))
+                parts.append("),")
+            parts.append("")
+            expr = "\n".join(parts)
+
+            # Back-channel return this expr for debugging.
+            if _receive_inflate_expr is not None:
+                _receive_inflate_expr.append(expr)
+            setattr(model, f"_bundled_inputs_deflated_{function_name}", deflated_inputs)
+            definition = textwrap.dedent("""
+                def _generate_bundled_inputs_for_{name}(self):
+                    deflated = self._bundled_inputs_deflated_{name}
+                    return [
+                {expr}
+                    ]
+                """).format(expr=expr, name=function_name)
+            model.define(definition)
+
+        # Define get_all_bundled_inputs_for_ that caches the generated inputs.
+        model.define(textwrap.dedent("""
+            def get_all_bundled_inputs_for_{name}(self):
+                all_inputs = self._generate_bundled_inputs_for_{name}()
+                assert all_inputs is not None
+                return all_inputs
+            """).format(name=function_name))
+
+        # Add to the high level helper methods
+        inputs_info = repr(info[function]) if info and function in info else '[]'
+        get_bundled_inputs_functions_and_info_template += f"""
+            temp_dict : Dict[str,List[str]] = {{}}
+            info: List[str] = {inputs_info}
+
+            temp_dict['info'] = info
+            temp_dict['get_inputs_function_name'] = ['get_all_bundled_inputs_for_{function_name}']
+            all_inputs['{function_name}'] = temp_dict
+            """
+
+        # To ensure backwards compatibility and a streamlined api for forward these wrappers are provided
+        if function_name == 'forward':
+            model.define(textwrap.dedent("""
+                def get_all_bundled_inputs(self):
+                    return self.get_all_bundled_inputs_for_forward()
+                """))
+            model.define(textwrap.dedent("""
+                def get_num_bundled_inputs(self):
+                    return len(self.get_all_bundled_inputs_for_forward())
+                """))
+
+    # Define some high level helper methods that act on all bundled inputs
+    model.define(textwrap.dedent(f"""
+        def get_bundled_inputs_functions_and_info(self):
+            all_inputs : Dict[str, Dict[str,List[str]]] = {{}}
+            {get_bundled_inputs_functions_and_info_template}
+            return all_inputs
+        """))
+
+def _inflate_expr(
+    arg: T, ref: str, inflate_helper_fn_name: str, skip_size_check: bool = False
+) -> Tuple[Union[T, torch.Tensor], str, Optional[str]]:
+    # Allow custom inflation expressions any object.
+    # For example, calling custom image-decoding ops.
+    # Or just use "{}" as the format string to ignore size limits.
+    if isinstance(arg, InflatableArg):
+        if arg.fmt_fn:
+            if arg.fmt not in ["{}", ""]:
+                raise Exception(  # noqa: TRY002
+                    f"Bundled input argument at position '{ref}' has "
+                    f"both arg.fmt_fn => \n{arg.fmt_fn} "
+                    f"\n and arg.fmt  => {arg.fmt}. "
+                    "Please choose `arg.fmt` if the deflater is straightforward or "
+                    "`arg.fmt_fn` if you need a function."
+                )
+
+            helper_definition = arg.fmt_fn.format(inflate_helper_fn_name)
+            expr = f"self.{inflate_helper_fn_name}({ref})"
+
+            return arg.value, expr, helper_definition
+        else:
+            return arg.value, arg.fmt.format(ref), None
+
+    if isinstance(arg, torch.Tensor):
+        # Small-storage tensors can just be saved directly.
+        if arg._typed_storage().size() <= MAX_RAW_TENSOR_SIZE or skip_size_check:
+            return arg, ref, None
+        # Small contiguous tensors can be cloned to have small storage.
+        # TODO: Should we do this even for non-contiguous tensors?
+        if arg.is_contiguous() and arg.numel() <= MAX_RAW_TENSOR_SIZE:
+            return arg.clone(), ref, None
+        # Example inputs commonly come from torch.zeros, torch.ones, or torch.full.
+        # These can be represented compactly.
+        for fmt in [torch.contiguous_format, torch.channels_last]:
+            if arg.is_contiguous(memory_format=fmt) and (arg == arg.flatten()[0]).all().item():
+                return (arg.flatten()[0].clone().expand(*arg.size()),
+                        f"{ref}.contiguous(memory_format={fmt})", None)
+        # Prevent big tensors from being bundled by default.
+        # TODO: Provide more useful diagnostics.
+        raise Exception(  # noqa: TRY002
+            f"Bundled input argument at position '{ref}' is "
+            f"a tensor with storage size {arg._typed_storage().size()}. "
+            f"You probably don't want to bundle this as an input. "
+        )
+    else:
+        return arg, ref, None
+
+def _get_bundled_inputs_attributes_and_methods(script_module: torch.jit.ScriptModule) -> Tuple[List[str], List[str]]:
+    methods: List[str] = []
+    attributes: List[str] = []
+
+    # Has bundled inputs for forward
+    if hasattr(script_module, 'get_all_bundled_inputs'):
+        methods.append('get_all_bundled_inputs')
+        methods.append('get_num_bundled_inputs')
+        methods.append('run_on_bundled_input')
+
+    if hasattr(script_module, 'get_bundled_inputs_functions_and_info'):
+        methods.append('get_bundled_inputs_functions_and_info')
+        all_info = script_module.get_bundled_inputs_functions_and_info()
+        for function_name in all_info:
+            methods.append("get_all_bundled_inputs_for_" + function_name)
+            methods.append("_generate_bundled_inputs_for_" + function_name)
+            attributes.append("_bundled_inputs_deflated_" + function_name)
+
+            bundled_inputs_fn = getattr(
+                script_module,
+                f"get_all_bundled_inputs_for_{function_name}"
+            )
+            num_bundled_inputs: int = len(bundled_inputs_fn())
+
+            # Check inflate helper functions for each function, argument and bundled input
+            func = getattr(script_module, function_name)
+            for arg_idx in range(len(func.schema.arguments) - 1):
+                for input_idx in range(num_bundled_inputs):
+                    helper_fn_name = _get_inflate_helper_fn_name(
+                        arg_idx=arg_idx,
+                        input_idx=input_idx,
+                        function_name=function_name
+                    )
+                    # if the arg has an InflatableArg with fmt_fn, add the helper function name
+                    if hasattr(script_module, helper_fn_name):
+                        methods.append(helper_fn_name)
+
+    return (methods, attributes)
+
+
+def _get_inflate_helper_fn_name(
+    arg_idx: int,
+    input_idx: int,
+    function_name: str,
+) -> str:
+    return f"_inflate_helper_for_{function_name}_input_{input_idx}_arg_{arg_idx}"
+
+
+
+def bundle_randn(*size, dtype=None):
+    """Generate a tensor that will be inflated with torch.randn."""
+    stub = torch.zeros(1, dtype=dtype).expand(*size)
+    return InflatableArg(value=stub, fmt="torch.randn_like({})")
+
+
+def bundle_large_tensor(t):
+    """Wrap a tensor to allow bundling regardless of size."""
+    return InflatableArg(value=t, fmt="{}")
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/checkpoint.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..94a8744e5c47a2c150b4c859ae22d966124162b5
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/checkpoint.py
@@ -0,0 +1,1549 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import contextlib
+import platform
+import uuid
+import warnings
+import weakref
+from collections import defaultdict
+from typing import *  # noqa: F403
+import enum
+from weakref import ReferenceType
+
+import torch
+import torch.fx.traceback as fx_traceback
+from torch._functorch._aot_autograd.functional_utils import is_fun
+from torch.utils._pytree import tree_map
+from torch.testing._internal.logging_tensor import capture_logs, LoggingTensorMode
+from torch.utils._python_dispatch import TorchDispatchMode
+
+__all__ = [
+    "checkpoint",
+    "checkpoint_sequential",
+    "CheckpointError",
+    "CheckpointFunction",
+    "check_backward_validity",
+    "detach_variable",
+    "get_device_states",
+    "set_device_states",
+    "noop_context_fn",
+    "set_checkpoint_early_stop",
+    "DefaultDeviceType",
+    "set_checkpoint_debug_enabled",
+    "CheckpointPolicy",
+    "SelectiveCheckpointContext",
+    "create_selective_checkpoint_contexts",
+    "SAC_IGNORED_OPS",
+]
+
+_DEFAULT_DETERMINISM_MODE = "default"
+
+_checkpoint_debug_enabled: Optional[bool] = None
+
+
+@contextlib.contextmanager
+def set_checkpoint_debug_enabled(enabled: Optional[bool]):
+    """
+    Context manager that sets whether checkpoint should print additional debug
+    information when running. See the ``debug`` flag for
+    :func:`~torch.utils.checkpoint.checkpoint` for more information. Note that
+    when set, this context manager overrides the value of ``debug`` passed to
+    checkpoint. To defer to the local setting, pass ``None`` to this context.
+
+    Args:
+        enabled (bool): Whether checkpoint should print debug information.
+            Default is 'None'.
+    """
+    global _checkpoint_debug_enabled
+    try:
+        prev = _checkpoint_debug_enabled
+        _checkpoint_debug_enabled = enabled
+        yield
+    finally:
+        _checkpoint_debug_enabled = prev
+
+
+def detach_variable(inputs: Tuple[Any, ...]) -> Tuple[torch.Tensor, ...]:
+    if isinstance(inputs, tuple):
+        out = []
+        for inp in inputs:
+            if not isinstance(inp, torch.Tensor):
+                out.append(inp)
+                continue
+
+            x = inp.detach()
+            x.requires_grad = inp.requires_grad
+            out.append(x)
+        return tuple(out)
+    else:
+        raise RuntimeError(
+            "Only tuple of tensors is supported. Got Unsupported input type: ",
+            type(inputs).__name__,
+        )
+
+
+def check_backward_validity(inputs: Iterable[Any]) -> None:
+    if not any(inp.requires_grad for inp in inputs if isinstance(inp, torch.Tensor)):
+        warnings.warn(
+            "None of the inputs have requires_grad=True. Gradients will be None"
+        )
+
+
+def _get_device_module(device="cuda"):
+    if device == "meta":
+        return torch.device("meta")
+    device_module = getattr(torch, device)
+    return device_module
+
+
+class DefaultDeviceType:
+    r"""
+    A class that manages the default device type for checkpointing.
+
+    If no non-CPU tensors are present, the default device type will
+    be used. The default value is 'cuda'. The device type is used in
+    the checkpointing process when determining which device states
+    to save and restore for recomputation.
+    """
+
+    _default_device_type = "cuda"
+
+    @staticmethod
+    def set_device_type(device: str = "cuda"):
+        """
+        Set the default device type for checkpointing.
+
+        Args:
+            device (str): The device type to be set as default. Default is 'cuda'.
+        """
+        DefaultDeviceType._default_device_type = device
+
+    @staticmethod
+    def get_device_type() -> str:
+        """
+        Get the current default device type for checkpointing.
+
+        Returns:
+            str: The current default device type.
+        """
+        return DefaultDeviceType._default_device_type
+
+
+def _infer_device_type(*args):
+    device_types = []
+
+    def add_device_types(arg):
+        nonlocal device_types
+        if isinstance(arg, torch.Tensor) and not arg.device.type == "cpu":
+            device_types.append(arg.device.type)
+    tree_map(add_device_types, args)
+
+    device_types_set = set(device_types)
+    if len(device_types_set) > 1:
+        warnings.warn(
+            "Tensor arguments, excluding CPU tensors, are detected on at least two types of devices. "
+            "Device state will only be saved for devices of a single device type, and the remaining "
+            "devices will be ignored. Consequently, if any checkpointed functions involve randomness, "
+            "this may result in incorrect gradients. (Note that if CUDA devices are among the devices "
+            "detected, it will be prioritized; otherwise, the first device encountered will be selected.)"
+            f"\nDevice types: {sorted(device_types_set)} first device type: {device_types[0]}"
+        )
+    if len(device_types) == 0:
+        return DefaultDeviceType.get_device_type()
+    elif "cuda" in device_types_set:
+        return "cuda"
+    else:
+        return device_types[0]
+
+
+# We can't know if the run_fn will internally move some args to different devices,
+# which would require logic to preserve rng states for those devices as well.
+# We could paranoically stash and restore ALL the rng states for all visible devices,
+# but that seems very wasteful for most cases.  Compromise:  Stash the RNG state for
+# the device of all Tensor args.
+#
+# To consider:  maybe get_device_states and set_device_states should reside in torch/random.py?
+def get_device_states(*args) -> Tuple[List[int], List[torch.Tensor]]:
+    # This will not error out if "arg" is a CPU tensor or a non-tensor type because
+    # the conditionals short-circuit.
+    fwd_device_ids = []
+
+    def add_device_ids(arg):
+        nonlocal fwd_device_ids
+        if isinstance(arg, torch.Tensor) and arg.device.type not in {"cpu", "meta"}:
+            fwd_device_ids.append(arg.get_device())
+    tree_map(add_device_ids, args)
+
+    fwd_device_states = []
+    device_module = _get_device_module(_infer_device_type(*args))
+    for device_id in fwd_device_ids:
+        with device_module.device(device_id):
+            fwd_device_states.append(device_module.get_rng_state())
+
+    return fwd_device_ids, fwd_device_states
+
+
+def set_device_states(devices, states, *, device_type=None) -> None:
+    """Sets random number generator states for the specified devices.
+
+    Args:
+        devices: Device ids to set states for.
+        states: States to set.
+        device_type: ``device_type`` of the devices to set states for. Default
+            is the device returned by a call to ``DefaultDeviceType.get_device_type()``,
+            which is ``cuda`` if not changed by calling ``DefaultDeviceType::set_device_type()``.
+    """
+    if device_type is None:
+        device_type = DefaultDeviceType.get_device_type()
+    if device_type == "meta":
+        return
+    device_module = _get_device_module(device_type)
+    for device, state in zip(devices, states):
+        with device_module.device(device):
+            device_module.set_rng_state(state)
+
+
+def _get_autocast_kwargs(device_type="cuda"):
+    if torch.amp.is_autocast_available(device_type):
+        device_autocast_kwargs = {
+            "enabled": torch.is_autocast_enabled(device_type),
+            "dtype": torch.get_autocast_dtype(device_type),
+            "cache_enabled": torch.is_autocast_cache_enabled(),
+        }
+    else:
+        device_autocast_kwargs = None
+
+    cpu_autocast_kwargs = {
+        "enabled": torch.is_autocast_enabled('cpu'),
+        "dtype": torch.get_autocast_dtype('cpu'),
+        "cache_enabled": torch.is_autocast_cache_enabled(),
+    }
+
+    return device_autocast_kwargs, cpu_autocast_kwargs
+
+
+class CheckpointFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, run_function, preserve_rng_state, *args):
+        check_backward_validity(args)
+        ctx.run_function = run_function
+        ctx.preserve_rng_state = preserve_rng_state
+        # Accommodates the (remote) possibility that autocast is enabled for cpu AND gpu.
+        ctx.device_type = _infer_device_type(*args)
+        ctx.device_autocast_kwargs, ctx.cpu_autocast_kwargs = _get_autocast_kwargs(
+            ctx.device_type
+        )
+        if preserve_rng_state:
+            ctx.fwd_cpu_state = torch.get_rng_state()
+            # Don't eagerly initialize the cuda context by accident.
+            # (If the user intends that the context is initialized later, within their
+            # run_function, we SHOULD actually stash the cuda state here.  Unfortunately,
+            # we have no way to anticipate this will happen before we run the function.)
+            ctx.had_device_in_fwd = False
+            device_module = _get_device_module(ctx.device_type)
+            if getattr(device_module, "_initialized", False):
+                ctx.had_device_in_fwd = True
+                ctx.fwd_devices, ctx.fwd_device_states = get_device_states(*args)
+
+        # Save non-tensor inputs in ctx, keep a placeholder None for tensors
+        # to be filled out during the backward.
+        ctx.inputs = []
+        ctx.tensor_indices = []
+        tensor_inputs = []
+        for i, arg in enumerate(args):
+            if torch.is_tensor(arg):
+                tensor_inputs.append(arg)
+                ctx.tensor_indices.append(i)
+                ctx.inputs.append(None)
+            else:
+                ctx.inputs.append(arg)
+
+        ctx.save_for_backward(*tensor_inputs)
+
+        with torch.no_grad():
+            outputs = run_function(*args)
+        return outputs
+
+    @staticmethod
+    def backward(ctx, *args):
+        if not torch.autograd._is_checkpoint_valid():
+            raise RuntimeError(
+                "When use_reentrant=True, torch.utils.checkpoint is incompatible"
+                " with .grad() or passing an `inputs` parameter to .backward()."
+                " To resolve this error, you can either set use_reentrant=False,"
+                " or call .backward() without passing the `inputs` argument."
+            )
+        # Copy the list to avoid modifying original list.
+        inputs = list(ctx.inputs)
+        tensor_indices = ctx.tensor_indices
+        tensors = ctx.saved_tensors
+
+        # Fill in inputs with appropriate saved tensors.
+        for i, idx in enumerate(tensor_indices):
+            inputs[idx] = tensors[i]
+
+        # Stash the surrounding rng state, and mimic the state that was
+        # present at this time during forward.  Restore the surrounding state
+        # when we're done.
+        rng_devices = []
+        if ctx.preserve_rng_state and ctx.had_device_in_fwd:
+            rng_devices = ctx.fwd_devices
+        with torch.random.fork_rng(
+            devices=rng_devices, enabled=ctx.preserve_rng_state, device_type=ctx.device_type
+        ):
+            if ctx.preserve_rng_state:
+                torch.set_rng_state(ctx.fwd_cpu_state)
+                if ctx.had_device_in_fwd:
+                    set_device_states(ctx.fwd_devices, ctx.fwd_device_states, device_type=ctx.device_type)
+            detached_inputs = detach_variable(tuple(inputs))
+
+            device_autocast_ctx = torch.amp.autocast(
+                device_type=ctx.device_type, **ctx.device_autocast_kwargs
+            ) if torch.amp.is_autocast_available(ctx.device_type) else contextlib.nullcontext()
+            with torch.enable_grad(), device_autocast_ctx, torch.amp.autocast("cpu", **ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
+                outputs = ctx.run_function(*detached_inputs)
+
+        if isinstance(outputs, torch.Tensor):
+            outputs = (outputs,)
+
+        # run backward() with only tensor that requires grad
+        outputs_with_grad = []
+        args_with_grad = []
+        for i in range(len(outputs)):
+            if torch.is_tensor(outputs[i]) and outputs[i].requires_grad:
+                outputs_with_grad.append(outputs[i])
+                args_with_grad.append(args[i])
+        if len(outputs_with_grad) == 0:
+            raise RuntimeError(
+                "none of output has requires_grad=True,"
+                " this checkpoint() is not necessary"
+            )
+        torch.autograd.backward(outputs_with_grad, args_with_grad)
+        grads = tuple(
+            inp.grad if isinstance(inp, torch.Tensor) else None
+            for inp in detached_inputs
+        )
+
+        return (None, None) + grads
+
+
+def noop_context_fn():
+    return contextlib.nullcontext(), contextlib.nullcontext()
+
+# TorchDynamo does not step inside utils.checkpoint function.  The flow
+# looks likes this
+#  1) TorchDynamo tries to wrap utils.checkpoint in a HigherOrderOp by
+#     speculatively checking if the forward function is safe to trace.
+#  2) If yes, then Dynamo-generated Fx graph has the wrapped higher
+#     order op. As a result, TorchDynamo does not look inside utils.checkpoint.
+#  3) If not, then TorchDynamo falls back to eager by performing a graph
+#     break. And here, the following disable wrapper ensures that
+#     TorchDynamo does not trigger again on the frames created by
+#     utils.checkpoint innards.
+@torch._disable_dynamo
+def checkpoint(
+    function,
+    *args,
+    use_reentrant: Optional[bool] = None,
+    context_fn: Callable[[], Tuple[ContextManager, ContextManager]] = noop_context_fn,
+    determinism_check: str = _DEFAULT_DETERMINISM_MODE,
+    debug: bool = False,
+    **kwargs
+):
+    r"""Checkpoint a model or part of the model.
+
+    Activation checkpointing is a technique that trades compute for memory.
+    Instead of keeping tensors needed for backward alive until they are used in
+    gradient computation during backward, forward computation in checkpointed
+    regions omits saving tensors for backward and recomputes them during the
+    backward pass. Activation checkpointing can be applied to any part of a
+    model.
+
+    There are currently two checkpointing implementations available, determined
+    by the :attr:`use_reentrant` parameter. It is recommended that you use
+    ``use_reentrant=False``. Please refer the note below for a discussion of
+    their differences.
+
+    .. warning::
+
+        If the :attr:`function` invocation during the backward pass differs
+        from the forward pass, e.g., due to a global variable, the checkpointed
+        version may not be equivalent, potentially causing an
+        error being raised or leading to silently incorrect gradients.
+
+    .. warning::
+
+        The ``use_reentrant`` parameter should be passed explicitly. In version
+        2.4 we will raise an exception if ``use_reentrant`` is not passed.
+        If you are using the ``use_reentrant=True`` variant, please refer to the
+        note below for important considerations and potential limitations.
+
+    .. note::
+
+        The reentrant variant of checkpoint (``use_reentrant=True``) and
+        the non-reentrant variant of checkpoint (``use_reentrant=False``)
+        differ in the following ways:
+
+        * Non-reentrant checkpoint stops recomputation as soon as all needed
+          intermediate activations have been recomputed. This feature is enabled
+          by default, but can be disabled with :func:`set_checkpoint_early_stop`.
+          Reentrant checkpoint always recomputes :attr:`function` in its
+          entirety during the backward pass.
+
+        * The reentrant variant does not record the autograd graph during the
+          forward pass, as it runs with the forward pass under
+          :func:`torch.no_grad`. The non-reentrant version does record the
+          autograd graph, allowing one to perform backward on the graph within
+          checkpointed regions.
+
+        * The reentrant checkpoint only supports the
+          :func:`torch.autograd.backward` API for the backward pass without its
+          `inputs` argument, while the non-reentrant version supports all ways
+          of performing the backward pass.
+
+        * At least one input and output must have ``requires_grad=True`` for the
+          reentrant variant. If this condition is unmet, the checkpointed part
+          of the model will not have gradients. The non-reentrant version does
+          not have this requirement.
+
+        * The reentrant version does not consider tensors in nested structures
+          (e.g., custom objects, lists, dicts, etc) as participating in
+          autograd, while the non-reentrant version does.
+
+        * The reentrant checkpoint does not support checkpointed regions with
+          detached tensors from the computational graph, whereas the
+          non-reentrant version does. For the reentrant variant, if the
+          checkpointed segment contains tensors detached using ``detach()`` or
+          with :func:`torch.no_grad`, the backward pass will raise an error.
+          This is because ``checkpoint`` makes all the outputs require gradients
+          and this causes issues when a tensor is defined to have no gradient in
+          the model. To avoid this, detach the tensors outside of the
+          ``checkpoint`` function.
+
+    Args:
+        function: describes what to run in the forward pass of the model or
+            part of the model. It should also know how to handle the inputs
+            passed as the tuple. For example, in LSTM, if user passes
+            ``(activation, hidden)``, :attr:`function` should correctly use the
+            first input as ``activation`` and the second input as ``hidden``
+        preserve_rng_state(bool, optional):  Omit stashing and restoring
+            the RNG state during each checkpoint. Note that under torch.compile,
+            this flag doesn't take effect and we always preserve RNG state.
+            Default: ``True``
+        use_reentrant(bool):
+            specify whether to use the activation checkpoint variant that
+            requires reentrant autograd. This parameter should be passed
+            explicitly. In version 2.5 we will raise an exception if
+            ``use_reentrant`` is not passed. If ``use_reentrant=False``,
+            ``checkpoint`` will use an implementation that does not require
+            reentrant autograd. This allows ``checkpoint`` to support additional
+            functionality, such as working as expected with
+            ``torch.autograd.grad`` and support for keyword arguments input into
+            the checkpointed function.
+        context_fn(Callable, optional): A callable returning a tuple of two
+            context managers. The function and its recomputation will be run
+            under the first and second context managers respectively.
+            This argument is only supported if ``use_reentrant=False``.
+        determinism_check(str, optional): A string specifying the determinism
+            check to perform. By default it is set to ``"default"`` which
+            compares the shapes, dtypes, and devices of the recomputed tensors
+            against those the saved tensors. To turn off this check, specify
+            ``"none"``. Currently these are the only two supported values.
+            Please open an issue if you would like to see more determinism
+            checks. This argument is only supported if ``use_reentrant=False``,
+            if ``use_reentrant=True``, the determinism check is always disabled.
+        debug(bool, optional): If ``True``, error messages will also include
+            a trace of the operators ran during the original forward computation
+            as well as the recomputation. This argument is only supported if
+            ``use_reentrant=False``.
+        args: tuple containing inputs to the :attr:`function`
+
+    Returns:
+        Output of running :attr:`function` on :attr:`*args`
+    """
+    if use_reentrant is None:
+        warnings.warn(
+            "torch.utils.checkpoint: the use_reentrant parameter should be "
+            "passed explicitly. In version 2.5 we will raise an exception "
+            "if use_reentrant is not passed. use_reentrant=False is "
+            "recommended, but if you need to preserve the current default "
+            "behavior, you can pass use_reentrant=True. Refer to docs for more "
+            "details on the differences between the two variants.",
+            stacklevel=2
+        )
+        use_reentrant = True
+
+    # Hack to mix *args with **kwargs in a python 2.7-compliant way
+    preserve = kwargs.pop("preserve_rng_state", True)
+    if kwargs and use_reentrant:
+        raise ValueError(
+            "Unexpected keyword arguments: " + ",".join(arg for arg in kwargs)
+        )
+
+    if use_reentrant:
+        if context_fn is not noop_context_fn or debug is not False:
+            raise ValueError(
+                "Passing `context_fn` or `debug` is only supported when "
+                "use_reentrant=False."
+            )
+        return CheckpointFunction.apply(function, preserve, *args)
+    else:
+        gen = _checkpoint_without_reentrant_generator(
+            function, preserve, context_fn, determinism_check, debug, *args, **kwargs
+        )
+        # Runs pre-forward logic
+        next(gen)
+        ret = function(*args, **kwargs)
+        # Runs post-forward logic
+        try:
+            next(gen)
+        except StopIteration:
+            return ret
+
+
+def checkpoint_sequential(functions, segments, input, use_reentrant=None, **kwargs):
+    r"""Checkpoint a sequential model to save memory.
+
+    Sequential models execute a list of modules/functions in order
+    (sequentially). Therefore, we can divide such a model in various segments
+    and checkpoint each segment. All segments except the last will not store
+    the intermediate activations. The inputs of each checkpointed segment will
+    be saved for re-running the segment in the backward pass.
+
+    .. warning::
+        The ``use_reentrant`` parameter should be passed explicitly. In version
+        2.4 we will raise an exception if ``use_reentrant`` is not passed.
+        If you are using the ``use_reentrant=True` variant, please see
+        :func:`~torch.utils.checkpoint.checkpoint` for
+        the important considerations and limitations of this variant. It is
+        recommended that you use ``use_reentrant=False``.
+
+    .. warning:
+        Since PyTorch 1.4, it allows only one Tensor as the input and
+        intermediate outputs, just like :class:`torch.nn.Sequential`.
+
+    Args:
+        functions: A :class:`torch.nn.Sequential` or the list of modules or
+            functions (comprising the model) to run sequentially.
+        segments: Number of chunks to create in the model
+        input: A Tensor that is input to :attr:`functions`
+        preserve_rng_state(bool, optional):  Omit stashing and restoring
+            the RNG state during each checkpoint.
+            Default: ``True``
+        use_reentrant(bool):
+            specify whether to use the activation checkpoint variant that
+            requires reentrant autograd. This parameter should be passed
+            explicitly. In version 2.5 we will raise an exception if
+            ``use_reentrant`` is not passed. If ``use_reentrant=False``,
+            ``checkpoint`` will use an implementation that does not require
+            reentrant autograd. This allows ``checkpoint`` to support additional
+            functionality, such as working as expected with
+            ``torch.autograd.grad`` and support for keyword arguments input into
+            the checkpointed function.
+
+    Returns:
+        Output of running :attr:`functions` sequentially on :attr:`*inputs`
+
+    Example:
+        >>> # xdoctest: +SKIP("stub")
+        >>> model = nn.Sequential(...)
+        >>> input_var = checkpoint_sequential(model, chunks, input_var)
+    """
+    if use_reentrant is None:
+        warnings.warn(
+            "torch.utils.checkpoint.checkpoint_sequential: the use_reentrant "
+            "parameter should be passed explicitly. "
+            "In version 2.5 we will raise an exception if use_reentrant "
+            "is not passed. use_reentrant=False is "
+            "recommended, but if you need to preserve the current default "
+            "behavior, you can pass use_reentrant=True. Refer to docs for more "
+            "details on the differences between the two variants."
+        )
+        use_reentrant = True
+
+    # Hack for keyword-only parameter in a python 2.7-compliant way
+    preserve = kwargs.pop("preserve_rng_state", True)
+    if kwargs:
+        raise ValueError(
+            "Unexpected keyword arguments: " + ",".join(arg for arg in kwargs)
+        )
+
+    def run_function(start, end, functions):
+        def forward(input):
+            for j in range(start, end + 1):
+                input = functions[j](input)
+            return input
+
+        return forward
+
+    if isinstance(functions, torch.nn.Sequential):
+        functions = list(functions.children())
+
+    segment_size = len(functions) // segments
+    # the last chunk has to be non-volatile
+    end = -1
+    for start in range(0, segment_size * (segments - 1), segment_size):
+        end = start + segment_size - 1
+        input = checkpoint(
+            run_function(start, end, functions),
+            input,
+            use_reentrant=use_reentrant,
+            preserve_rng_state=preserve,
+        )
+    return run_function(end + 1, len(functions) - 1, functions)(input)
+
+
+def _internal_assert(cond):
+    if not cond:
+        raise AssertionError(
+            "Something went unexpectedly wrong in activation checkpoint. "
+            "Please report this bug by filing an issue to PyTorch."
+        )
+
+
+# NOTE [ Nestable Checkpoint ]
+#
+# The semantics of nested checkpoint can be defined by two basic rules.
+# Following the two rules leads to an important implication that is central
+# to motivating the design.
+#
+# Rule 1. Saved tensors are managed by inner-most checkpoint only and hidden
+#         from any outer layers of checkpoint.
+#
+# Rule 2. The inputs of inner checkpoints are treated as tensors saved to its
+#         parent checkpoint.
+#
+# Implication: To recompute any given saved tensor, we need to recompute all of
+#              the checkpoints wrapping it.
+#
+# Why is this implied? To unpack a saved tensor X during backward we need to
+# recompute the inner-most checkpoint (#1), and in order to recompute that
+# checkpoint I need to have its inputs, which are managed by that checkpoint's
+# parent (#2), which thus also needs to be recomputed first. Continue this line
+# of reasoning and we realize that in order to unpack X, all checkpoints that
+# were active at the time X was saved need to be recomputed. (unless we have
+# already done so in that backward for some other saved tensor).
+#
+# In practice, we use a noop autograd Function to save inputs as saved tensors.
+# During unpack calling ctx.saved_tensor triggers the parent checkpoint to
+# recompute.
+#
+# Rule 3. We should start recomputation as if there are no checkpoints currently
+#         active. Checkpoints encountered during recomputation are still
+#         respected.
+#
+# When we start recomputation, we push the saved variable hook meant for
+# recomputation on the stack. See examples in Rule 6 for more context.
+#
+#                                  * * * *
+#
+# Beyond the basic semantics specific to nested checkpoint, we impose several
+# more constraints that may apply to checkpointing in general.
+#
+# Rule 4. Lifetime of recomputed tensors
+#
+#         Recomputed tensors are considered specific to particular invocations
+#         of backward and are always cleared immediately as they are unpacked
+#         Particularly, we require this to happen even if retain_graph=True.
+#
+# [ Implementation details of Rule 4 ]
+#
+# If we were okay with recomputed tensors staying alive after backward is run
+# with retain_graph=True, we would store recomputed variables as the values of a
+# WeakKeyDictionary and pack strong references to the keys, so that as we
+# backward, those packed keys would be cleared as long as retain_graph=False.
+# Clearing the packed key clears the corresponding entry in the WKD.
+#
+# If we wish recomputed variables to be immediately cleared as we unpack them in
+# the retain_graph=True case, we cannot rely on the packed keys to be cleared by
+# backward automatically. Instead of packing the strong reference to the key
+# directly, we pack a container object, which we manually clear as we unpack.
+#
+# An important detail is that if a second backward happens, the second
+# recomputation needs to reset the container with a newly created key.
+#
+# Rule 5. Stop recomputation as soon as we've recomputed the saved tensors we
+#         know we need.
+#
+# [ Implementation details of Rule 5 ]
+#
+# During recomputation, raise an exception if the number of recomputed tensors
+# matches the number of tensors that we expected to recompute. We wrap the
+# recomputation call with a try-catch to catch this specific exception. See
+# Rule #6 below for some examples.
+#
+# Rule 6. We support doing backward inside checkpoint context
+#
+# [ retain_graph is True]
+#
+# def fn(x):
+#   y = x.sin()
+#   z = y.cos()
+#   gx, = torch.autograd.grad(z, x, retains_grad=True)
+#   return gx, z
+#
+# out = checkpoint(fn)(inp)
+# out.backward()
+#
+# Because z is saved by cos while checkpoint is enabled, it would not be
+# actually saved, and so the .grad() call inside must trigger a recomputation.
+#
+# During recomputation the "inner pack hook" has two responsibilities:
+#
+# 1) As usual, populating the WeakKeyDictionary storing recomputed tensors
+# 2) Pack the actual tensor (detached) so that one may perform backward on the
+#    recomputed graph. The tensors saved to this graph will live until the end
+#    of recomputation, or die earlier if someone performs backward with
+#    retain_graph=False.
+#
+# More generally performing backward on the recomputed graph occurs in the
+# following cases:
+# - If backward is performed inside forward,
+#   - During the original forward IF early-stop is disabled
+#   - During the original backward
+# - If there are multiple .grad()/.backward() calls, we would perform backward
+#   on the recomputed graph even if early-stop is enabled (see the example below)
+#
+# [ retain_graph is False ]
+#
+# The example below shows what happens if during recomputation we find that some
+# of the tensors we are trying to recompute have already been cleared.
+#
+# Spoiler: we don't do anything special, we just skip over them!
+#
+# def fn(x):
+#   y = x.sin()                           # (1)
+#   z = y.cos()                           # (2)
+#   gx, = torch.autograd.grad(z, x)       # (3)
+#   return x.cos() * gx                   # (4)
+#
+# out = checkpoint(fn)(inp)
+# out.backward()                          # (5)
+#
+# 1, 2. Don't save x and y since we are inside a checkpoint.
+# 3. Trigger a recompute of fn since x and y weren't saved.
+#    And depending on whether early stop is enabled, either stop at (2) or
+#    continue running the function.
+#    Because we are running backward with retain_graph=False, we clear x and y's
+#    holders.
+# 4. Don't save x since we are inside a checkpoint.
+# 5. Calling backward triggers another recompute of fn. During recompute, we see
+#    that x and y have already been cleared in the original graph as indicated
+#    by holder=None. We skip over them. We still save x at (4) (since its holder
+#    is still alive.)
+
+_enable_checkpoint_early_stop = True
+
+
+@contextlib.contextmanager
+def set_checkpoint_early_stop(enable: bool):
+    """Context manager that sets whether checkpoint should stop recomputation early.
+
+    By default, non-reentrant checkpoint stops recomputation as soon as it
+    has computed all needed Tensors. This context manager can be used to disable
+    that feature if it is problematic for your specific application.
+
+    This context manager only needs to be active when forward is run. It does
+    not need to be active during backward.
+
+    Example::
+
+    >>> # xdoctest: +SKIP(failing)
+    >>> message = "saved tensors default hooks are disabled"
+    >>> with set_checkpoint_early_stop(False):
+    ...     # Any checkpoint under this context manager will respect this
+    ...     # context manager, even if its backward is performed outside.
+    ...     out = checkpoint(fn, inputs)
+    ...
+    >>> out.backward()
+    """
+    global _enable_checkpoint_early_stop
+    try:
+        prev = _enable_checkpoint_early_stop
+        _enable_checkpoint_early_stop = enable
+        yield
+    finally:
+        _enable_checkpoint_early_stop = prev
+
+
+class _Handle:
+    pass
+
+
+class _Holder:
+    def __init__(self):
+        self.handles: Dict[int, Optional[_Handle]] = {}
+
+
+class _NoopSaveInputs(torch.autograd.Function):
+    @staticmethod
+    def forward(*args):
+        return torch.empty((0,))
+
+    @staticmethod
+    def setup_context(ctx: Any, inputs: Tuple[Any, ...], output: Any) -> None:
+        # Only tensors can be saved with ctx.save_for_backward, everything else
+        # is captured by get_args, which is saved directly on ctx
+        tensor_indices, tensors = zip(
+            *[(i, o) for i, o in enumerate(inputs) if isinstance(o, torch.Tensor)]
+        )
+        idx2saved_idx = {b: a for a, b in enumerate(tensor_indices)}
+        # args but with tensors replaced with None as placeholders
+        args = [None if isinstance(o, torch.Tensor) else o for o in inputs]
+
+        def get_args(saved_tensors):
+            # restore the placeholders with the original tensors grabbed from
+            # ctx.saved_tensors (which may be saved on a parent checkpoint if
+            # this checkpoint is nested, and that would trigger a recursive
+            # unpack!)
+            ret = [
+                saved_tensors[idx2saved_idx[i]] if i in tensor_indices else o
+                for i, o in enumerate(args)
+            ]
+            # grab the tail since we also saved the dummy to avoid having to explicitly
+            # handle the case where there are no tensor inputs
+            return ret[1:]
+
+        ctx.get_args = get_args
+        ctx.save_for_backward(*tensors)
+
+    @staticmethod
+    def backward(ctx, *grad_outputs):
+        raise AssertionError("Did not expect to backward on this graph")
+
+
+class _CheckpointFrame:
+    def __init__(self, recompute_fn, early_stop, unpack_error_cb, metadata_fn):
+        self.recompute_fn = recompute_fn
+        self.input_saver = None
+        self.weak_holders: List[ReferenceType] = []
+        # We store this as a weakkeydictionary so that in the case of a partial
+        # backward, the entries in the dict are cleared alongside the Holder
+        # which will be removed when the SavedVariable is cleared.
+        self.recomputed: DefaultDict[
+            int, weakref.WeakKeyDictionary[_Handle, torch.Tensor]
+        ] = defaultdict(weakref.WeakKeyDictionary)
+        # We need both recomp_counter and recomputed since they can diverge
+        # https://github.com/pytorch/pytorch/pull/90105#discussion_r1135889885
+        self.recomp_counter: DefaultDict[int, int] = defaultdict(int)
+        self.is_recomputed: DefaultDict[int, bool] = defaultdict(bool)
+
+        # See Rule 5
+        self.early_stop = early_stop
+
+        # Debugging
+        self.metadata_fn = metadata_fn
+        self.unpack_error_cb = unpack_error_cb
+        self.x_metadatas = []
+        self.forward_completed = False
+        self.ignore_saved_mismatch = False
+
+    def check_recomputed_tensors_match(self, gid):
+        if self.ignore_saved_mismatch:
+            # TODO: we can probably make this check stricter by checking that
+            #       the metadata of the first tensors still match.
+            return
+        # NOTE [ Error handling for checkpoint ]
+        #
+        # At a high level, we need to check that the tensors saved
+        # during original forward matches tensors saved during recompute
+        # This means handling 3 cases:
+        #
+        # 1. During recompute, more tensors were saved.
+        #
+        #    Usually this is hidden due to the StopRecomputationError
+        #    but if early stop is not enabled, or we would have errored
+        #    anyway because there aren't enough weak_holders. But we
+        #    do want to have a nice error. See the _recomputation_hook
+        #    for details.
+        if not len(self.weak_holders) == self.recomp_counter[gid]:
+            # 2. During recompute, fewer tensors were saved
+            #
+            # We know that everytime we save something do original forward
+            # we append to weak_holder, and every time we save a tensor
+            # during recompute we increment recompute_counter.
+            raise CheckpointError(
+                "torch.utils.checkpoint: A different number of tensors was saved "
+                "during the original forward and recomputation.\n"
+                f"Number of tensors saved during forward: {len(self.weak_holders)}\n"
+                f"Number of tensors saved during recomputation: {self.recomp_counter[gid]}"
+            )
+
+        # 3. During recompute, the same tensors were saved, but they
+        #    have different metadata
+        nb_meta_different = []
+        for idx, weak_holder in enumerate(self.weak_holders):
+            holder = weak_holder()
+            if holder is None:
+                continue
+            # We've seen all holders since we iterate over them in order
+            # For every holder that is still alive now, it must've been
+            # alive when we saw it during recompute, therefore, the
+            # gid must be set.
+            _internal_assert(gid in holder.handles)
+            # We know this is the first unpack, so it couldn't have been set
+            # to None yet.
+            _internal_assert(holder.handles[gid] is not None)
+            # We always set these together in the recomputation hook
+            _internal_assert(holder.handles[gid] in self.recomputed[gid])
+            # see pack hook, x_metadata is 1:1 with weak_holders.
+            x_meta = self.x_metadatas[idx]
+            recomputed_x = self.recomputed[gid][holder.handles[gid]]
+            if x_meta != self.metadata_fn(recomputed_x):
+                nb_meta_different.append((idx, x_meta, self.metadata_fn(recomputed_x)))
+
+        if len(nb_meta_different) > 0:
+            mismatched_tensors = ""
+            for idx, x_meta, recomputed_meta in nb_meta_different:
+                mismatched_tensors += (
+                    f"tensor at position {idx}:\n"
+                    f"saved metadata: {x_meta}\n"
+                    f"recomputed metadata: {recomputed_meta}\n"
+                )
+            raise CheckpointError(
+                "torch.utils.checkpoint: Recomputed values for the following tensors "
+                "have different metadata than during the forward pass.\n"
+                f"{mismatched_tensors}"
+            )
+
+
+_checkpoint_error_template = """ \
+An error happened while unpacking tensors; dumping logs of latest computation
+because you passed `debug=True` to `torch.utils.checkpoint.checkpoint()`.
+Scroll all the way down for guidance on how to navigate these logs.
+
++~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~+
+|        1. Stack traces of the operators that ran in the original forward     |
++------------------------------------------------------------------------------+
+
+{forward_traces}
++~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~+
+|        2. Stack traces of the operators that ran during recomputation        |
++------------------------------------------------------------------------------+
+
+{recompute_traces}
++~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~+
+|       3. Log of operators in the original forward and recomputation          |
++------------------------------------------------------------------------------+
+(Scroll up to correlate stack traces with each operation listed below. This
+ helps identify their source in the code.)
+
+IMPORTANT: Differences in "detach" calls between the original forward and the
+           recomputation are expected. They are introduced by the checkpointing
+           mechanism and can be ignored.
+
+Operations executed during the original forward:
+
+{forward_ops}
+
+Operations executed during recomputation:
+
+{recompute_ops}
+
++------------------------------------------------------------------------------+
+ ERROR: Detected non-determinism while running activation checkpointing
+
+ You are seeing this error because you passed `debug=True` to checkpoint and
+ tensors to be saved during the original forward and differ between those saved
+ during recomputation. This can happen if different operators were ran in the
+ original forward and in the recomputation.
+
+ To identify where the mismatch may be coming from, you can do the following:
+
+ 1) Compare the operators ran during original forward and recomputation to
+    see where they differ. These operators are printed above in the order they
+    were executed.
+
+ 2) Review the stack trace for each operator to locate its invocation source.
+    Each operator's stack trace is printed in their execution order.
+
+ Note that the logs can be quite long. Here's how they are structured:
+ (Tip: you can Ctrl-f for these headers)
+
+ 1. Stack traces of the operators that ran in the original forward
+ 2. Stack traces of the operators that ran during recomputation
+ 3. Log of operators in the original forward and recomputation
+ 4. Error message                                             <--- You are here
+--------------------------------------------------------------------------------
+"""
+
+class CheckpointError(RuntimeError):
+    pass
+
+
+def _get_debug_context_and_cb() -> Tuple[Callable[[], Any], Callable[[CheckpointError], None]]:
+    # This function returns the context_fn and error_cb to be used by the
+    # checkpointing mechanism. error_cb is invoked when an error is detected
+    # during unpack.
+
+    # record_context_cpp is not support on non-linux non-x86_64 platforms
+    cpp_tb = platform.machine() == 'x86_64' and platform.system() == 'Linux'
+
+    class CaptureLogs:
+        def __init__(self):
+            self.logs = None
+            self.tbs = None
+
+        def get_context_manager(self):
+            @contextlib.contextmanager
+            def logging_mode():
+                with LoggingTensorMode(), \
+                     capture_logs(True, python_tb=True, script_tb=True, cpp_tb=cpp_tb) as logs_and_tb:
+                    self.logs, self.tbs = logs_and_tb
+                    yield logs_and_tb
+            return logging_mode()
+
+    capture_logs_fwd = CaptureLogs()
+    capture_logs_recompute = CaptureLogs()
+
+    def unpack_error_cb(e: CheckpointError):
+        def get_str_tb(label, capture_logs):
+            out = ""
+            total_len = len(capture_logs.logs)
+            for i, (log, tb) in enumerate(zip(capture_logs.logs, capture_logs.tbs)):
+                out += f"{log}   ({i + 1} of {total_len} in {label})\n\n"
+                found_torch_dispatch = False
+                for line in tb:
+                    # Start printing stack trace only after __torch_dispatch__ is found
+                    is_torch_dispatch = line['name'] == '__torch_dispatch__'
+                    if not found_torch_dispatch and not is_torch_dispatch:
+                        continue
+                    elif is_torch_dispatch:
+                        found_torch_dispatch = True
+                        continue
+                    out += f"{line['filename']}:{line['line']}:{line['name']}\n"
+                out += "\n\n"
+            return out
+        assert capture_logs_fwd.logs is not None
+        assert capture_logs_recompute.logs is not None
+        raise CheckpointError(
+            _checkpoint_error_template.format(
+                forward_traces=get_str_tb("original", capture_logs_fwd),
+                recompute_traces=get_str_tb("recompute", capture_logs_recompute),
+                forward_ops="\n".join(capture_logs_fwd.logs),
+                recompute_ops="\n".join(capture_logs_recompute.logs)
+            )
+        ) from e
+
+    def context_fn():
+        return capture_logs_fwd.get_context_manager(), capture_logs_recompute.get_context_manager()
+
+    return context_fn, unpack_error_cb
+
+def _default_meta_extractor(x: torch.Tensor) -> Dict[str, Any]:
+    # These properties are fast to check, easy to understand
+    return {
+        "shape": x.shape,
+        "dtype": x.dtype,
+        "device": x.device
+    }
+
+_allowed_determinism_checks_to_fns: Dict[str, Callable[[torch.Tensor], Any]] = {
+    _DEFAULT_DETERMINISM_MODE: _default_meta_extractor,
+    "none": lambda _: None,
+}
+
+# See Rule 5
+class _StopRecomputationError(Exception):
+    pass
+
+
+class _recomputation_hook(torch.autograd.graph.saved_tensors_hooks):
+    def __init__(self, target_frame_ref: ReferenceType, gid: int):
+        def pack_hook(x):
+            x = x.detach() if x.requires_grad else x
+            target_frame = target_frame_ref()
+            assert target_frame is not None  # appease mypy
+            recomp_idx = target_frame.recomp_counter[gid]
+            target_frame.recomp_counter[gid] += 1
+
+            if recomp_idx >= len(target_frame.weak_holders):
+                assert not target_frame.early_stop
+                if not target_frame.forward_completed:
+                    # We run into this case when early stop is not enabled and do
+                    # grad within checkpoint.
+                    # We need to set this flag, so we don't error out later when
+                    # we check if the number of tensors saved during forward and
+                    # recomputation match.
+                    target_frame.ignore_saved_mismatch = True
+                    return x
+                raise CheckpointError(
+                    "torch.utils.checkpoint: trying to save more tensors during "
+                    "recomputation than during the original forward pass."
+                )
+
+            holder = target_frame.weak_holders[recomp_idx]()
+
+            # This holder may have been cleared because someone may have called
+            # backward within forward. If so, we don't need to save.
+            if holder is not None:
+                _internal_assert(holder.handles.get(gid, None) is None)
+                holder.handles[gid] = _Handle()
+                target_frame.recomputed[gid][holder.handles[gid]] = x
+
+            if target_frame.early_stop and target_frame.recomp_counter[gid] == len(
+                target_frame.weak_holders
+            ):
+                raise _StopRecomputationError
+            # See Rule 6: [ retain_graph is True ] above
+            return x
+
+        def unpack_hook(x):
+            # See Rule 6: [ retain_graph is True ] above for an example of when
+            # the graph created during recomputation could be backwarded.
+            return x
+
+        super().__init__(pack_hook, unpack_hook)
+
+
+class _checkpoint_hook(torch.autograd.graph.saved_tensors_hooks):
+    def __init__(self, frame):
+        def pack_hook(x):
+            # See Rule 4 above
+            holder = _Holder()
+            frame.weak_holders.append(weakref.ref(holder))
+            # Save metadata to detect non-determinism
+            if frame.metadata_fn is not None:
+                with torch.no_grad():
+                    frame.x_metadatas.append(frame.metadata_fn(x))
+            return holder
+
+        def unpack_hook(holder):
+            gid = torch._C._current_graph_task_id()
+            if gid == -1:
+                # generate a temporary id if we trigger unpack outside of a backward call
+                gid = int(uuid.uuid4())
+
+            if not frame.is_recomputed[gid]:
+                ctx = frame.input_saver.grad_fn
+                args = ctx.get_args(ctx.saved_tensors)
+
+                try:
+                    with _recomputation_hook(
+                        weakref.ref(frame), gid
+                    ), torch.autograd.enable_grad():
+                        frame.recompute_fn(*args)
+                except _StopRecomputationError:
+                    pass
+                frame.is_recomputed[gid] = True
+                frame.check_recomputed_tensors_match(gid)
+
+            _internal_assert(gid in holder.handles)
+
+            if holder.handles[gid] is None:
+                raise CheckpointError(
+                    "torch.utils.checkpoint: Unpack is being triggered for a tensor that was already "
+                    "unpacked once. If you are calling ctx.saved_tensors in backward, make sure to do "
+                    "so only once. Otherwise please open an issue with details on your use case."
+                )
+            _internal_assert(holder.handles[gid] in frame.recomputed[gid])
+            ret = frame.recomputed[gid][holder.handles[gid]]
+            holder.handles[gid] = None
+            return ret
+
+        if frame.unpack_error_cb is not None:
+            def unpack_hook_with_error_cb(holder):
+                try:
+                    return unpack_hook(holder)
+                except CheckpointError as e:
+                    frame.unpack_error_cb(e)
+            super().__init__(pack_hook, unpack_hook_with_error_cb)
+        else:
+            super().__init__(pack_hook, unpack_hook)
+
+
+def _is_compiling(func, args, kwargs):
+    # Check if we are under AOTAutograd tracing
+    # There should probably be a better way to do this...
+    # TODO: unify _is_compiling across all compile stacks
+    for arg in args:
+        if isinstance(arg, torch.Tensor) and is_fun(arg):
+            return True
+    return False
+
+
+class _VersionWrapper:
+    # Check that cached tensors are not mutated.
+    def __init__(self, val):
+        self.val: Union[torch.Tensor, Any] = val
+        self.version: Optional[int] = val._version if isinstance(val, torch.Tensor) else None
+
+    def get_val(self, allow_cache_entry_mutation):
+        if self.version is not None and not allow_cache_entry_mutation:
+            if self.val._version != self.version:
+                # Can we give user a stack trace of where the mutation happened?
+                raise RuntimeError(
+                    "Tensor cached during selective activation checkpoint has been mutated"
+                )
+        return self.val
+
+
+def _maybe_detach(x, any_ret_has_alias_info):
+    # We detach for two separate reasons:
+    # - For view ops, we need to ensure that when the tensor is returned from
+    #   CachedDispatchMode, as_view sees that the AutogradMeta is nullptr
+    # - Avoid reference cycles
+    # For case 1, it is not enough to check whether x has differentiable dtype
+    # because non-differentiable dtype can have non-nullptr AutogradMeta, e.g.
+    # when the tensor is a view.
+    if isinstance(x, torch.Tensor) and (x.is_floating_point() or x.is_complex() or any_ret_has_alias_info):
+        with torch._C._SetExcludeDispatchKeyGuard(torch._C.DispatchKey.ADInplaceOrView, False):
+            # Ensure that view performed beneath autograd properly propagates
+            # version counter. TODO: Use reentrant_dispatch instead of
+            # manually manipulating dispatch keys. Using reentrant_dispatch
+            # would respect inference_mode, though that is not relevant for
+            # this case.
+            x = x.detach()
+    return x
+
+
+class SelectiveCheckpointContext:
+    """
+    Context passed to policy function during selective checkpointing.
+
+    This class is used to pass relevant metadata to the policy function during
+    selective checkpointing. The metadata includes whether the current invocation
+    of the policy function is during recomputation or not.
+
+    Example:
+        >>> # xdoctest: +SKIP(stub)
+        >>>
+        >>> def policy_fn(ctx, op, *args, **kwargs):
+        >>>    print(ctx.is_recompute)
+        >>>
+        >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
+        >>>
+        >>> out = torch.utils.checkpoint.checkpoint(
+        >>>     fn, x, y,
+        >>>     use_reentrant=False,
+        >>>     context_fn=context_fn,
+        >>> )
+    """
+    def __init__(self, *, is_recompute):
+        self.is_recompute = is_recompute
+
+
+class CheckpointPolicy(enum.Enum):
+    """
+    Enum for specifying the policy for checkpointing during backpropagation.
+
+    The following policies are supported:
+
+    - ``{MUST,PREFER}_SAVE``: The operation's output will be saved during the forward
+      pass and will not be recomputed during the backward pass
+    - ``{MUST,PREFER}_RECOMPUTE``: The operation's output will not be saved during the
+      forward pass and will be recomputed during the backward pass
+
+    Use ``MUST_*`` over ``PREFER_*`` to indicate that the policy should not be overridden
+    by other subsystems like `torch.compile`.
+
+    .. note::
+        A policy function that always returns ``PREFER_RECOMPUTE`` is
+        equivalent to vanilla checkpointing.
+
+        A policy function that returns ``PREFER_SAVE`` every op is
+        NOT equivalent to not using checkpointing. Using such a policy would
+        save additional tensors not limited to ones that are actually needed for
+        gradient computation.
+    """
+    MUST_SAVE = 0
+    PREFER_SAVE = 1
+    MUST_RECOMPUTE = 2
+    PREFER_RECOMPUTE = 3
+
+
+def _policy_from_bool(b):
+    # For backward compatability
+    return CheckpointPolicy.MUST_SAVE if b else CheckpointPolicy.PREFER_RECOMPUTE
+
+
+SAC_IGNORED_OPS = {
+    # AC inserts different number of detach during forward and recompute.
+    torch.ops.aten.detach.default,
+    # AC's determinism check invokes additional metadata ops during forward.
+    # With subclasses involved, these metadata ops become dispatchable, this
+    # can result in incorrectness if these ops are selected cached.
+    torch.ops.prim.device.default,
+} | set(torch._subclasses.functional_tensor.FunctionalTensor.metadata_fns)
+
+
+class _CachingTorchDispatchMode(TorchDispatchMode):
+    # Used together with _CachedTorchDispatchMode to implement SAC.
+    def __init__(self, policy_fn, storage):
+        self.policy_fn = policy_fn
+        self.storage = storage
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        if func in SAC_IGNORED_OPS:
+            return func(*args, **kwargs)
+
+        kwargs = {} if kwargs is None else kwargs
+        policy = self.policy_fn(SelectiveCheckpointContext(is_recompute=False),
+                                func, *args, **kwargs)
+        if isinstance(policy, bool):
+            policy = _policy_from_bool(policy)
+
+        is_compiling = _is_compiling(func, args, kwargs)
+
+        if is_compiling:
+            # Overwrite each node's "recompute" tag to add in the user annotation.
+            fx_traceback.current_meta["recompute"] = policy
+
+        out = func(*args, **kwargs)
+
+        any_ret_has_alias_info = any(ret.alias_info is not None for ret in func._schema.returns)
+
+        if policy in (CheckpointPolicy.MUST_SAVE, CheckpointPolicy.PREFER_SAVE) or is_compiling:
+            self.storage[func].append(tree_map(lambda x: _VersionWrapper(_maybe_detach(x, any_ret_has_alias_info)), out))
+        return out
+
+class _CachedTorchDispatchMode(TorchDispatchMode):
+    # Used together with _CachedTorchDispatchMode to implement SAC.
+    def __init__(self, policy_fn, storage, allow_cache_entry_mutation):
+        self.policy_fn = policy_fn
+        self.storage = storage
+        self.allow_cache_entry_mutation = allow_cache_entry_mutation
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        if func in SAC_IGNORED_OPS:
+            return func(*args, **kwargs)
+
+        kwargs = {} if kwargs is None else kwargs
+        policy = self.policy_fn(SelectiveCheckpointContext(is_recompute=True),
+                                func, *args, **kwargs)
+        if isinstance(policy, bool):
+            policy = _policy_from_bool(policy)
+
+        is_compiling = _is_compiling(func, args, kwargs)
+
+        if policy in (CheckpointPolicy.MUST_SAVE, CheckpointPolicy.PREFER_SAVE) or is_compiling:
+            storage = self.storage.get(func)
+            if storage is None:
+                raise RuntimeError(f"{func} encountered during backward, but not found in storage")
+            if len(storage) == 0:
+                raise RuntimeError(
+                    "Trying to backward an extra time. You are only allowed to backward once "
+                    "on any region computed under selective activation checkpoint."
+                )
+            out = tree_map(lambda x: x.get_val(self.allow_cache_entry_mutation), storage.pop(0))
+        else:
+            out = func(*args, **kwargs)
+        return out
+
+
+def create_selective_checkpoint_contexts(policy_fn_or_list, allow_cache_entry_mutation=False):
+    """
+    Helper to avoid recomputing certain ops during activation checkpointing.
+
+    Use this with `torch.utils.checkpoint.checkpoint` to control which
+    operations are recomputed during the backward pass.
+
+    Args:
+        policy_fn_or_list (Callable or List):
+          - If a policy function is provided, it should accept a
+            :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and
+            kwargs to the op, and return a :class:`CheckpointPolicy` enum value
+            indicating whether the execution of the op should be recomputed or not.
+          - If a list of operations is provided, it is equivalent to a policy
+            returning `CheckpointPolicy.MUST_SAVE` for the specified
+            operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other
+            operations.
+        allow_cache_entry_mutation (bool, optional): By default, an error is
+            raised if any tensors cached by selective activation checkpoint are
+            mutated in order to ensure correctness. If set to `True`, this check
+            is disabled.
+    Returns:
+        A tuple of two context managers.
+
+    Example:
+        >>> # xdoctest: +REQUIRES(LINUX)
+        >>> import functools
+        >>>
+        >>> x = torch.rand(10, 10, requires_grad=True)
+        >>> y = torch.rand(10, 10, requires_grad=True)
+        >>>
+        >>> ops_to_save = [
+        >>>    torch.ops.aten.mm.default,
+        >>> ]
+        >>>
+        >>> def policy_fn(ctx, op, *args, **kwargs):
+        >>>    if op in ops_to_save:
+        >>>        return CheckpointPolicy.MUST_SAVE
+        >>>    else:
+        >>>        return CheckpointPolicy.PREFER_RECOMPUTE
+        >>>
+        >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
+        >>>
+        >>> # or equivalently
+        >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save)
+        >>>
+        >>> def fn(x, y):
+        >>>     return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y
+        >>>
+        >>> out = torch.utils.checkpoint.checkpoint(
+        >>>     fn, x, y,
+        >>>     use_reentrant=False,
+        >>>     context_fn=context_fn,
+        >>> )
+    """
+    # NB: If grad_mode is disabled, checkpoint would not run forward under
+    #     context_fn anyway, so proceed as usual.
+    if isinstance(policy_fn_or_list, list):
+        for op in policy_fn_or_list:
+            if not isinstance(op, torch._ops.OpOverload):
+                _extra_msg = (
+                    "Please update the OpOverloadPacket to a specific OpOverload."
+                    "For example, if you have `torch.ops.aten.mm`, change it to `torch.ops.aten.mm.default`."
+                ) if isinstance(op, torch._ops.OpOverloadPacket) else ""
+                raise ValueError(
+                    f"Expected op in `op_list` to be an OpOverload but got: {op} "
+                    f"of type {type(op)}. {_extra_msg}"
+                )
+
+        def policy_fn(ctx, op, *args, **kwargs):
+            if op in policy_fn_or_list:
+                return CheckpointPolicy.MUST_SAVE
+            else:
+                return CheckpointPolicy.PREFER_RECOMPUTE
+    elif callable(policy_fn_or_list):
+        policy_fn = policy_fn_or_list
+    else:
+        raise TypeError("policy_fn_or_list must be either a function or a list of ops.")
+
+    storage: Dict[Any, List[Any]] = defaultdict(list)
+    return (
+        _CachingTorchDispatchMode(policy_fn, storage),
+        _CachedTorchDispatchMode(policy_fn, storage, allow_cache_entry_mutation),
+    )
+
+# NB: this helper wraps fn before calling checkpoint_impl. kwargs and
+#     saving/restoring of global state is handled here.
+
+def _checkpoint_without_reentrant_generator(
+    fn,
+    preserve_rng_state=True,
+    context_fn: Callable[[], Tuple[ContextManager, ContextManager]] = noop_context_fn,
+    determinism_check: str = _DEFAULT_DETERMINISM_MODE,
+    debug: bool = False,
+    *args,
+    **kwargs
+):
+    """Checkpointing without reentrant autograd.
+
+    Args:
+        function: describes what to run in the forward pass of the model or
+            part of the model. It should also know how to handle the inputs
+            passed as the tuple. For example, in LSTM, if user passes
+            ``(activation, hidden)``, :attr:`function` should correctly use the
+            first input as ``activation`` and the second input as ``hidden``
+        preserve_rng_state(bool, optional):  Omit stashing and restoring
+            the RNG state during each checkpoint.
+            Default: ``True``
+        context_fn(Callable, optional): A callable returning a tuple of two
+            context managers. The function and its recomputation will be run
+            under the first and second context managers respectively.
+        determinism_check(str, optional): A string specifying the determinism
+            check to perform. By default it is set to ``"default"`` which
+            compares the shapes, dtypes, and devices of the recomputed tensors
+            against those the saved tensors. To turn off this check, specify
+            ``"none"``. Currently these are the only two supported values.
+            Please open an issue if you would like to see more determinism
+            checks.
+        debug(bool, optional): If ``True``, error messages will also include
+            a trace of the operators ran during the original forward computation
+            as well as the recomputation.
+        *args: Arguments to pass in to the given ``function``.
+        **kwargs: Keyword arguments to pass into the given ``function``.
+    """
+    unpack_error_cb = None
+
+    if _checkpoint_debug_enabled if _checkpoint_debug_enabled is not None else debug:
+        if context_fn != noop_context_fn:
+            raise ValueError(
+                "debug=True is incompatible with non-default context_fn"
+            )
+        context_fn, unpack_error_cb = _get_debug_context_and_cb()
+
+    if determinism_check in _allowed_determinism_checks_to_fns:
+        metadata_fn = _allowed_determinism_checks_to_fns[determinism_check]
+    else:
+        raise ValueError(
+            f"determinism_check should be one of {list(_allowed_determinism_checks_to_fns.keys())}, "
+            f"but got {determinism_check}"
+        )
+
+    device_type = _infer_device_type(*args)
+    device_module = _get_device_module(device_type)
+    forward_context, recompute_context = context_fn()
+    if _is_compiling(fn, args, kwargs) and context_fn != noop_context_fn:
+        assert (
+            isinstance(forward_context, TorchDispatchMode) and
+            isinstance(recompute_context, TorchDispatchMode)
+        ), \
+            "In torch.compile mode, `context_fn` arg passed to `torch.utils.checkpoint` " + \
+            "must generate a tuple of two `TorchDispatchMode`s."
+    # Accommodates the (remote) possibility that autocast is enabled for cpu AND gpu.
+    device_autocast_kwargs, cpu_autocast_kwargs = _get_autocast_kwargs(device_type=device_type)
+
+    if preserve_rng_state:
+        fwd_cpu_state = torch.get_rng_state()
+        # Don't eagerly initialize the cuda context by accident.
+        # (If the user intends that the context is initialized later, within their
+        # run_function, we SHOULD actually stash the cuda state here.  Unfortunately,
+        # we have no way to anticipate this will happen before we run the function.
+        # If they do so, we raise an error.)
+        had_device_in_fwd = False
+        if getattr(device_module, "_initialized", False):
+            had_device_in_fwd = True
+            fwd_devices, fwd_device_states = get_device_states(*args)
+
+    def recompute_fn(*inputs):
+        kwargs, *args = inputs
+        # This will be called later during recomputation. This wrapping enables
+        # the necessary global state to be captured.
+        rng_devices = []
+        if preserve_rng_state and had_device_in_fwd:
+            rng_devices = fwd_devices
+        with torch.random.fork_rng(
+            devices=rng_devices, enabled=preserve_rng_state, device_type=device_type
+        ):
+            if preserve_rng_state:
+                torch.set_rng_state(fwd_cpu_state)
+                if had_device_in_fwd:
+                    set_device_states(fwd_devices, fwd_device_states, device_type=device_type)
+
+            device_autocast_ctx = torch.amp.autocast(
+                device_type=device_type, **device_autocast_kwargs
+            ) if torch.amp.is_autocast_available(device_type) else contextlib.nullcontext()
+            with device_autocast_ctx, torch.amp.autocast("cpu", **cpu_autocast_kwargs), recompute_context:  # type: ignore[attr-defined]
+                fn(*args, **kwargs)
+
+    new_frame = _CheckpointFrame(
+        recompute_fn,
+        _enable_checkpoint_early_stop,
+        unpack_error_cb,
+        metadata_fn
+    )
+    dummy = torch.empty((0,), requires_grad=True)
+    new_frame.input_saver = _NoopSaveInputs.apply(dummy, kwargs, *args)
+
+    # When ambient grad_mode is False
+    if new_frame.input_saver.grad_fn is None:
+        yield
+        return
+
+    with _checkpoint_hook(new_frame), forward_context:
+        yield
+    new_frame.forward_completed = True
+
+    if getattr(device_module, "_initialized", False) and \
+       preserve_rng_state and not had_device_in_fwd:  # type: ignore[possibly-undefined]
+        # Device was not initialized before running the forward, so we didn't
+        # stash the device state.
+        raise RuntimeError(
+            "PyTorch's device state was initialized in the forward pass "
+            "of a Checkpoint, which is not allowed. Please open an issue "
+            "if you need this feature."
+        )
+
+    return
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/collect_env.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/collect_env.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed0e02c4c1b93c6333b9db080602ac0136f97a2f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/collect_env.py
@@ -0,0 +1,651 @@
+# mypy: allow-untyped-defs
+
+# Unlike the rest of the PyTorch this file must be python2 compliant.
+# This script outputs relevant system environment info
+# Run it with `python collect_env.py` or `python -m torch.utils.collect_env`
+import datetime
+import locale
+import re
+import subprocess
+import sys
+import os
+from collections import namedtuple
+
+
+try:
+    import torch
+    TORCH_AVAILABLE = True
+except (ImportError, NameError, AttributeError, OSError):
+    TORCH_AVAILABLE = False
+
+# System Environment Information
+SystemEnv = namedtuple('SystemEnv', [
+    'torch_version',
+    'is_debug_build',
+    'cuda_compiled_version',
+    'gcc_version',
+    'clang_version',
+    'cmake_version',
+    'os',
+    'libc_version',
+    'python_version',
+    'python_platform',
+    'is_cuda_available',
+    'cuda_runtime_version',
+    'cuda_module_loading',
+    'nvidia_driver_version',
+    'nvidia_gpu_models',
+    'cudnn_version',
+    'pip_version',  # 'pip' or 'pip3'
+    'pip_packages',
+    'conda_packages',
+    'hip_compiled_version',
+    'hip_runtime_version',
+    'miopen_runtime_version',
+    'caching_allocator_config',
+    'is_xnnpack_available',
+    'cpu_info',
+])
+
+DEFAULT_CONDA_PATTERNS = {
+    "torch",
+    "numpy",
+    "cudatoolkit",
+    "soumith",
+    "mkl",
+    "magma",
+    "triton",
+    "optree",
+}
+
+DEFAULT_PIP_PATTERNS = {
+    "torch",
+    "numpy",
+    "mypy",
+    "flake8",
+    "triton",
+    "optree",
+    "onnx",
+}
+
+
+def run(command):
+    """Return (return-code, stdout, stderr)."""
+    shell = True if type(command) is str else False
+    p = subprocess.Popen(command, stdout=subprocess.PIPE,
+                         stderr=subprocess.PIPE, shell=shell)
+    raw_output, raw_err = p.communicate()
+    rc = p.returncode
+    if get_platform() == 'win32':
+        enc = 'oem'
+    else:
+        enc = locale.getpreferredencoding()
+    output = raw_output.decode(enc)
+    err = raw_err.decode(enc)
+    return rc, output.strip(), err.strip()
+
+
+def run_and_read_all(run_lambda, command):
+    """Run command using run_lambda; reads and returns entire output if rc is 0."""
+    rc, out, _ = run_lambda(command)
+    if rc != 0:
+        return None
+    return out
+
+
+def run_and_parse_first_match(run_lambda, command, regex):
+    """Run command using run_lambda, returns the first regex match if it exists."""
+    rc, out, _ = run_lambda(command)
+    if rc != 0:
+        return None
+    match = re.search(regex, out)
+    if match is None:
+        return None
+    return match.group(1)
+
+def run_and_return_first_line(run_lambda, command):
+    """Run command using run_lambda and returns first line if output is not empty."""
+    rc, out, _ = run_lambda(command)
+    if rc != 0:
+        return None
+    return out.split('\n')[0]
+
+
+def get_conda_packages(run_lambda, patterns=None):
+    if patterns is None:
+        patterns = DEFAULT_CONDA_PATTERNS
+    conda = os.environ.get('CONDA_EXE', 'conda')
+    out = run_and_read_all(run_lambda, "{} list".format(conda))
+    if out is None:
+        return out
+
+    return "\n".join(
+        line
+        for line in out.splitlines()
+        if not line.startswith("#")
+        and any(name in line for name in patterns)
+    )
+
+def get_gcc_version(run_lambda):
+    return run_and_parse_first_match(run_lambda, 'gcc --version', r'gcc (.*)')
+
+def get_clang_version(run_lambda):
+    return run_and_parse_first_match(run_lambda, 'clang --version', r'clang version (.*)')
+
+
+def get_cmake_version(run_lambda):
+    return run_and_parse_first_match(run_lambda, 'cmake --version', r'cmake (.*)')
+
+
+def get_nvidia_driver_version(run_lambda):
+    if get_platform() == 'darwin':
+        cmd = 'kextstat | grep -i cuda'
+        return run_and_parse_first_match(run_lambda, cmd,
+                                         r'com[.]nvidia[.]CUDA [(](.*?)[)]')
+    smi = get_nvidia_smi()
+    return run_and_parse_first_match(run_lambda, smi, r'Driver Version: (.*?) ')
+
+
+def get_gpu_info(run_lambda):
+    if get_platform() == 'darwin' or (TORCH_AVAILABLE and hasattr(torch.version, 'hip') and torch.version.hip is not None):
+        if TORCH_AVAILABLE and torch.cuda.is_available():
+            if torch.version.hip is not None:
+                prop = torch.cuda.get_device_properties(0)
+                if hasattr(prop, "gcnArchName"):
+                    gcnArch = " ({})".format(prop.gcnArchName)
+                else:
+                    gcnArch = "NoGCNArchNameOnOldPyTorch"
+            else:
+                gcnArch = ""
+            return torch.cuda.get_device_name(None) + gcnArch
+        return None
+    smi = get_nvidia_smi()
+    uuid_regex = re.compile(r' \(UUID: .+?\)')
+    rc, out, _ = run_lambda(smi + ' -L')
+    if rc != 0:
+        return None
+    # Anonymize GPUs by removing their UUID
+    return re.sub(uuid_regex, '', out)
+
+
+def get_running_cuda_version(run_lambda):
+    return run_and_parse_first_match(run_lambda, 'nvcc --version', r'release .+ V(.*)')
+
+
+def get_cudnn_version(run_lambda):
+    """Return a list of libcudnn.so; it's hard to tell which one is being used."""
+    if get_platform() == 'win32':
+        system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows')
+        cuda_path = os.environ.get('CUDA_PATH', "%CUDA_PATH%")
+        where_cmd = os.path.join(system_root, 'System32', 'where')
+        cudnn_cmd = '{} /R "{}\\bin" cudnn*.dll'.format(where_cmd, cuda_path)
+    elif get_platform() == 'darwin':
+        # CUDA libraries and drivers can be found in /usr/local/cuda/. See
+        # https://docs.nvidia.com/cuda/cuda-installation-guide-mac-os-x/index.html#install
+        # https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installmac
+        # Use CUDNN_LIBRARY when cudnn library is installed elsewhere.
+        cudnn_cmd = 'ls /usr/local/cuda/lib/libcudnn*'
+    else:
+        cudnn_cmd = 'ldconfig -p | grep libcudnn | rev | cut -d" " -f1 | rev'
+    rc, out, _ = run_lambda(cudnn_cmd)
+    # find will return 1 if there are permission errors or if not found
+    if len(out) == 0 or (rc != 1 and rc != 0):
+        l = os.environ.get('CUDNN_LIBRARY')
+        if l is not None and os.path.isfile(l):
+            return os.path.realpath(l)
+        return None
+    files_set = set()
+    for fn in out.split('\n'):
+        fn = os.path.realpath(fn)  # eliminate symbolic links
+        if os.path.isfile(fn):
+            files_set.add(fn)
+    if not files_set:
+        return None
+    # Alphabetize the result because the order is non-deterministic otherwise
+    files = sorted(files_set)
+    if len(files) == 1:
+        return files[0]
+    result = '\n'.join(files)
+    return 'Probably one of the following:\n{}'.format(result)
+
+
+def get_nvidia_smi():
+    # Note: nvidia-smi is currently available only on Windows and Linux
+    smi = 'nvidia-smi'
+    if get_platform() == 'win32':
+        system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows')
+        program_files_root = os.environ.get('PROGRAMFILES', 'C:\\Program Files')
+        legacy_path = os.path.join(program_files_root, 'NVIDIA Corporation', 'NVSMI', smi)
+        new_path = os.path.join(system_root, 'System32', smi)
+        smis = [new_path, legacy_path]
+        for candidate_smi in smis:
+            if os.path.exists(candidate_smi):
+                smi = '"{}"'.format(candidate_smi)
+                break
+    return smi
+
+
+# example outputs of CPU infos
+#  * linux
+#    Architecture:            x86_64
+#      CPU op-mode(s):        32-bit, 64-bit
+#      Address sizes:         46 bits physical, 48 bits virtual
+#      Byte Order:            Little Endian
+#    CPU(s):                  128
+#      On-line CPU(s) list:   0-127
+#    Vendor ID:               GenuineIntel
+#      Model name:            Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz
+#        CPU family:          6
+#        Model:               106
+#        Thread(s) per core:  2
+#        Core(s) per socket:  32
+#        Socket(s):           2
+#        Stepping:            6
+#        BogoMIPS:            5799.78
+#        Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr
+#                             sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl
+#                             xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16
+#                             pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand
+#                             hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced
+#                             fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap
+#                             avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1
+#                             xsaves wbnoinvd ida arat avx512vbmi pku ospke avx512_vbmi2 gfni vaes vpclmulqdq
+#                             avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear flush_l1d arch_capabilities
+#    Virtualization features:
+#      Hypervisor vendor:     KVM
+#      Virtualization type:   full
+#    Caches (sum of all):
+#      L1d:                   3 MiB (64 instances)
+#      L1i:                   2 MiB (64 instances)
+#      L2:                    80 MiB (64 instances)
+#      L3:                    108 MiB (2 instances)
+#    NUMA:
+#      NUMA node(s):          2
+#      NUMA node0 CPU(s):     0-31,64-95
+#      NUMA node1 CPU(s):     32-63,96-127
+#    Vulnerabilities:
+#      Itlb multihit:         Not affected
+#      L1tf:                  Not affected
+#      Mds:                   Not affected
+#      Meltdown:              Not affected
+#      Mmio stale data:       Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
+#      Retbleed:              Not affected
+#      Spec store bypass:     Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+#      Spectre v1:            Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+#      Spectre v2:            Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
+#      Srbds:                 Not affected
+#      Tsx async abort:       Not affected
+#  * win32
+#    Architecture=9
+#    CurrentClockSpeed=2900
+#    DeviceID=CPU0
+#    Family=179
+#    L2CacheSize=40960
+#    L2CacheSpeed=
+#    Manufacturer=GenuineIntel
+#    MaxClockSpeed=2900
+#    Name=Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz
+#    ProcessorType=3
+#    Revision=27142
+#
+#    Architecture=9
+#    CurrentClockSpeed=2900
+#    DeviceID=CPU1
+#    Family=179
+#    L2CacheSize=40960
+#    L2CacheSpeed=
+#    Manufacturer=GenuineIntel
+#    MaxClockSpeed=2900
+#    Name=Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz
+#    ProcessorType=3
+#    Revision=27142
+
+def get_cpu_info(run_lambda):
+    rc, out, err = 0, '', ''
+    if get_platform() == 'linux':
+        rc, out, err = run_lambda('lscpu')
+    elif get_platform() == 'win32':
+        rc, out, err = run_lambda('wmic cpu get Name,Manufacturer,Family,Architecture,ProcessorType,DeviceID, \
+        CurrentClockSpeed,MaxClockSpeed,L2CacheSize,L2CacheSpeed,Revision /VALUE')
+    elif get_platform() == 'darwin':
+        rc, out, err = run_lambda("sysctl -n machdep.cpu.brand_string")
+    cpu_info = 'None'
+    if rc == 0:
+        cpu_info = out
+    else:
+        cpu_info = err
+    return cpu_info
+
+
+def get_platform():
+    if sys.platform.startswith('linux'):
+        return 'linux'
+    elif sys.platform.startswith('win32'):
+        return 'win32'
+    elif sys.platform.startswith('cygwin'):
+        return 'cygwin'
+    elif sys.platform.startswith('darwin'):
+        return 'darwin'
+    else:
+        return sys.platform
+
+
+def get_mac_version(run_lambda):
+    return run_and_parse_first_match(run_lambda, 'sw_vers -productVersion', r'(.*)')
+
+
+def get_windows_version(run_lambda):
+    system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows')
+    wmic_cmd = os.path.join(system_root, 'System32', 'Wbem', 'wmic')
+    findstr_cmd = os.path.join(system_root, 'System32', 'findstr')
+    return run_and_read_all(run_lambda, '{} os get Caption | {} /v Caption'.format(wmic_cmd, findstr_cmd))
+
+
+def get_lsb_version(run_lambda):
+    return run_and_parse_first_match(run_lambda, 'lsb_release -a', r'Description:\t(.*)')
+
+
+def check_release_file(run_lambda):
+    return run_and_parse_first_match(run_lambda, 'cat /etc/*-release',
+                                     r'PRETTY_NAME="(.*)"')
+
+
+def get_os(run_lambda):
+    from platform import machine
+    platform = get_platform()
+
+    if platform == 'win32' or platform == 'cygwin':
+        return get_windows_version(run_lambda)
+
+    if platform == 'darwin':
+        version = get_mac_version(run_lambda)
+        if version is None:
+            return None
+        return 'macOS {} ({})'.format(version, machine())
+
+    if platform == 'linux':
+        # Ubuntu/Debian based
+        desc = get_lsb_version(run_lambda)
+        if desc is not None:
+            return '{} ({})'.format(desc, machine())
+
+        # Try reading /etc/*-release
+        desc = check_release_file(run_lambda)
+        if desc is not None:
+            return '{} ({})'.format(desc, machine())
+
+        return '{} ({})'.format(platform, machine())
+
+    # Unknown platform
+    return platform
+
+
+def get_python_platform():
+    import platform
+    return platform.platform()
+
+
+def get_libc_version():
+    import platform
+    if get_platform() != 'linux':
+        return 'N/A'
+    return '-'.join(platform.libc_ver())
+
+
+def get_pip_packages(run_lambda, patterns=None):
+    """Return `pip list` output. Note: will also find conda-installed pytorch and numpy packages."""
+    if patterns is None:
+        patterns = DEFAULT_PIP_PATTERNS
+
+    # People generally have `pip` as `pip` or `pip3`
+    # But here it is invoked as `python -mpip`
+    def run_with_pip(pip):
+        out = run_and_read_all(run_lambda, pip + ["list", "--format=freeze"])
+        return "\n".join(
+            line
+            for line in out.splitlines()
+            if any(name in line for name in patterns)
+        )
+
+    pip_version = 'pip3' if sys.version[0] == '3' else 'pip'
+    out = run_with_pip([sys.executable, '-mpip'])
+
+    return pip_version, out
+
+
+def get_cachingallocator_config():
+    ca_config = os.environ.get('PYTORCH_CUDA_ALLOC_CONF', '')
+    return ca_config
+
+
+def get_cuda_module_loading_config():
+    if TORCH_AVAILABLE and torch.cuda.is_available():
+        torch.cuda.init()
+        config = os.environ.get('CUDA_MODULE_LOADING', '')
+        return config
+    else:
+        return "N/A"
+
+
+def is_xnnpack_available():
+    if TORCH_AVAILABLE:
+        import torch.backends.xnnpack
+        return str(torch.backends.xnnpack.enabled)  # type: ignore[attr-defined]
+    else:
+        return "N/A"
+
+def get_env_info():
+    """
+    Collects environment information to aid in debugging.
+
+    The returned environment information contains details on torch version, is debug build
+    or not, cuda compiled version, gcc version, clang version, cmake version, operating
+    system, libc version, python version, python platform, CUDA availability, CUDA
+    runtime version, CUDA module loading config, GPU model and configuration, Nvidia
+    driver version, cuDNN version, pip version and versions of relevant pip and
+    conda packages, HIP runtime version, MIOpen runtime version,
+    Caching allocator config, XNNPACK availability and CPU information.
+
+    Returns:
+        SystemEnv (namedtuple): A tuple containining various environment details
+            and system information.
+    """
+    run_lambda = run
+    pip_version, pip_list_output = get_pip_packages(run_lambda)
+
+    if TORCH_AVAILABLE:
+        version_str = torch.__version__
+        debug_mode_str = str(torch.version.debug)
+        cuda_available_str = str(torch.cuda.is_available())
+        cuda_version_str = torch.version.cuda
+        if not hasattr(torch.version, 'hip') or torch.version.hip is None:  # cuda version
+            hip_compiled_version = hip_runtime_version = miopen_runtime_version = 'N/A'
+        else:  # HIP version
+            def get_version_or_na(cfg, prefix):
+                _lst = [s.rsplit(None, 1)[-1] for s in cfg if prefix in s]
+                return _lst[0] if _lst else 'N/A'
+
+            cfg = torch._C._show_config().split('\n')
+            hip_runtime_version = get_version_or_na(cfg, 'HIP Runtime')
+            miopen_runtime_version = get_version_or_na(cfg, 'MIOpen')
+            cuda_version_str = 'N/A'
+            hip_compiled_version = torch.version.hip
+    else:
+        version_str = debug_mode_str = cuda_available_str = cuda_version_str = 'N/A'
+        hip_compiled_version = hip_runtime_version = miopen_runtime_version = 'N/A'
+
+    sys_version = sys.version.replace("\n", " ")
+
+    conda_packages = get_conda_packages(run_lambda)
+
+    return SystemEnv(
+        torch_version=version_str,
+        is_debug_build=debug_mode_str,
+        python_version='{} ({}-bit runtime)'.format(sys_version, sys.maxsize.bit_length() + 1),
+        python_platform=get_python_platform(),
+        is_cuda_available=cuda_available_str,
+        cuda_compiled_version=cuda_version_str,
+        cuda_runtime_version=get_running_cuda_version(run_lambda),
+        cuda_module_loading=get_cuda_module_loading_config(),
+        nvidia_gpu_models=get_gpu_info(run_lambda),
+        nvidia_driver_version=get_nvidia_driver_version(run_lambda),
+        cudnn_version=get_cudnn_version(run_lambda),
+        hip_compiled_version=hip_compiled_version,
+        hip_runtime_version=hip_runtime_version,
+        miopen_runtime_version=miopen_runtime_version,
+        pip_version=pip_version,
+        pip_packages=pip_list_output,
+        conda_packages=conda_packages,
+        os=get_os(run_lambda),
+        libc_version=get_libc_version(),
+        gcc_version=get_gcc_version(run_lambda),
+        clang_version=get_clang_version(run_lambda),
+        cmake_version=get_cmake_version(run_lambda),
+        caching_allocator_config=get_cachingallocator_config(),
+        is_xnnpack_available=is_xnnpack_available(),
+        cpu_info=get_cpu_info(run_lambda),
+    )
+
+env_info_fmt = """
+PyTorch version: {torch_version}
+Is debug build: {is_debug_build}
+CUDA used to build PyTorch: {cuda_compiled_version}
+ROCM used to build PyTorch: {hip_compiled_version}
+
+OS: {os}
+GCC version: {gcc_version}
+Clang version: {clang_version}
+CMake version: {cmake_version}
+Libc version: {libc_version}
+
+Python version: {python_version}
+Python platform: {python_platform}
+Is CUDA available: {is_cuda_available}
+CUDA runtime version: {cuda_runtime_version}
+CUDA_MODULE_LOADING set to: {cuda_module_loading}
+GPU models and configuration: {nvidia_gpu_models}
+Nvidia driver version: {nvidia_driver_version}
+cuDNN version: {cudnn_version}
+HIP runtime version: {hip_runtime_version}
+MIOpen runtime version: {miopen_runtime_version}
+Is XNNPACK available: {is_xnnpack_available}
+
+CPU:
+{cpu_info}
+
+Versions of relevant libraries:
+{pip_packages}
+{conda_packages}
+""".strip()
+
+
+def pretty_str(envinfo):
+    def replace_nones(dct, replacement='Could not collect'):
+        for key in dct.keys():
+            if dct[key] is not None:
+                continue
+            dct[key] = replacement
+        return dct
+
+    def replace_bools(dct, true='Yes', false='No'):
+        for key in dct.keys():
+            if dct[key] is True:
+                dct[key] = true
+            elif dct[key] is False:
+                dct[key] = false
+        return dct
+
+    def prepend(text, tag='[prepend]'):
+        lines = text.split('\n')
+        updated_lines = [tag + line for line in lines]
+        return '\n'.join(updated_lines)
+
+    def replace_if_empty(text, replacement='No relevant packages'):
+        if text is not None and len(text) == 0:
+            return replacement
+        return text
+
+    def maybe_start_on_next_line(string):
+        # If `string` is multiline, prepend a \n to it.
+        if string is not None and len(string.split('\n')) > 1:
+            return '\n{}\n'.format(string)
+        return string
+
+    mutable_dict = envinfo._asdict()
+
+    # If nvidia_gpu_models is multiline, start on the next line
+    mutable_dict['nvidia_gpu_models'] = \
+        maybe_start_on_next_line(envinfo.nvidia_gpu_models)
+
+    # If the machine doesn't have CUDA, report some fields as 'No CUDA'
+    dynamic_cuda_fields = [
+        'cuda_runtime_version',
+        'nvidia_gpu_models',
+        'nvidia_driver_version',
+    ]
+    all_cuda_fields = dynamic_cuda_fields + ['cudnn_version']
+    all_dynamic_cuda_fields_missing = all(
+        mutable_dict[field] is None for field in dynamic_cuda_fields)
+    if TORCH_AVAILABLE and not torch.cuda.is_available() and all_dynamic_cuda_fields_missing:
+        for field in all_cuda_fields:
+            mutable_dict[field] = 'No CUDA'
+        if envinfo.cuda_compiled_version is None:
+            mutable_dict['cuda_compiled_version'] = 'None'
+
+    # Replace True with Yes, False with No
+    mutable_dict = replace_bools(mutable_dict)
+
+    # Replace all None objects with 'Could not collect'
+    mutable_dict = replace_nones(mutable_dict)
+
+    # If either of these are '', replace with 'No relevant packages'
+    mutable_dict['pip_packages'] = replace_if_empty(mutable_dict['pip_packages'])
+    mutable_dict['conda_packages'] = replace_if_empty(mutable_dict['conda_packages'])
+
+    # Tag conda and pip packages with a prefix
+    # If they were previously None, they'll show up as ie '[conda] Could not collect'
+    if mutable_dict['pip_packages']:
+        mutable_dict['pip_packages'] = prepend(mutable_dict['pip_packages'],
+                                               '[{}] '.format(envinfo.pip_version))
+    if mutable_dict['conda_packages']:
+        mutable_dict['conda_packages'] = prepend(mutable_dict['conda_packages'],
+                                                 '[conda] ')
+    mutable_dict['cpu_info'] = envinfo.cpu_info
+    return env_info_fmt.format(**mutable_dict)
+
+
+def get_pretty_env_info():
+    """
+    Returns a pretty string of environment information.
+
+    This function retrieves environment information by calling the `get_env_info` function
+    and then formats the information into a human-readable string. The retrieved environment
+    information is listed in the document of `get_env_info`.
+    This function is used in `python collect_env.py` that should be executed when reporting a bug.
+
+    Returns:
+        str: A pretty string of the environment information.
+    """
+    return pretty_str(get_env_info())
+
+
+def main():
+    print("Collecting environment information...")
+    output = get_pretty_env_info()
+    print(output)
+
+    if TORCH_AVAILABLE and hasattr(torch, 'utils') and hasattr(torch.utils, '_crash_handler'):
+        minidump_dir = torch.utils._crash_handler.DEFAULT_MINIDUMP_DIR
+        if sys.platform == "linux" and os.path.exists(minidump_dir):
+            dumps = [os.path.join(minidump_dir, dump) for dump in os.listdir(minidump_dir)]
+            latest = max(dumps, key=os.path.getctime)
+            ctime = os.path.getctime(latest)
+            creation_time = datetime.datetime.fromtimestamp(ctime).strftime('%Y-%m-%d %H:%M:%S')
+            msg = "\n*** Detected a minidump at {} created on {}, ".format(latest, creation_time) + \
+                  "if this is related to your bug please include it when you file a report ***"
+            print(msg, file=sys.stderr)
+
+
+
+if __name__ == '__main__':
+    main()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/cpp_backtrace.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/cpp_backtrace.py
new file mode 100644
index 0000000000000000000000000000000000000000..af4a7fcb63e263038255359c946a6a0d4a21dbd0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/cpp_backtrace.py
@@ -0,0 +1,12 @@
+# mypy: allow-untyped-defs
+from torch._C import _get_cpp_backtrace
+
+def get_cpp_backtrace(frames_to_skip=0, maximum_number_of_frames=64) -> str:
+    r"""
+    Return a string containing the C++ stack trace of the current thread.
+
+    Args:
+        frames_to_skip (int): the number of frames to skip from the top of the stack
+        maximum_number_of_frames (int): the maximum number of frames to return
+    """
+    return _get_cpp_backtrace(frames_to_skip, maximum_number_of_frames)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/cpp_extension.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/cpp_extension.py
new file mode 100644
index 0000000000000000000000000000000000000000..aaa45ea4c909a337c5d2428607ee2376e6877322
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/cpp_extension.py
@@ -0,0 +1,2425 @@
+# mypy: allow-untyped-defs
+import copy
+import glob
+import importlib
+import importlib.abc
+import os
+import re
+import shlex
+import shutil
+import setuptools
+import subprocess
+import sys
+import sysconfig
+import warnings
+import collections
+from pathlib import Path
+import errno
+
+import torch
+import torch._appdirs
+from .file_baton import FileBaton
+from ._cpp_extension_versioner import ExtensionVersioner
+from .hipify import hipify_python
+from .hipify.hipify_python import GeneratedFileCleaner
+from typing import Dict, List, Optional, Union, Tuple
+from torch.torch_version import TorchVersion, Version
+
+from setuptools.command.build_ext import build_ext
+
+IS_WINDOWS = sys.platform == 'win32'
+IS_MACOS = sys.platform.startswith('darwin')
+IS_LINUX = sys.platform.startswith('linux')
+LIB_EXT = '.pyd' if IS_WINDOWS else '.so'
+EXEC_EXT = '.exe' if IS_WINDOWS else ''
+CLIB_PREFIX = '' if IS_WINDOWS else 'lib'
+CLIB_EXT = '.dll' if IS_WINDOWS else '.so'
+SHARED_FLAG = '/DLL' if IS_WINDOWS else '-shared'
+
+_HERE = os.path.abspath(__file__)
+_TORCH_PATH = os.path.dirname(os.path.dirname(_HERE))
+TORCH_LIB_PATH = os.path.join(_TORCH_PATH, 'lib')
+
+
+SUBPROCESS_DECODE_ARGS = ('oem',) if IS_WINDOWS else ()
+MINIMUM_GCC_VERSION = (5, 0, 0)
+MINIMUM_MSVC_VERSION = (19, 0, 24215)
+
+VersionRange = Tuple[Tuple[int, ...], Tuple[int, ...]]
+VersionMap = Dict[str, VersionRange]
+# The following values were taken from the following GitHub gist that
+# summarizes the minimum valid major versions of g++/clang++ for each supported
+# CUDA version: https://gist.github.com/ax3l/9489132
+# Or from include/crt/host_config.h in the CUDA SDK
+# The second value is the exclusive(!) upper bound, i.e. min <= version < max
+CUDA_GCC_VERSIONS: VersionMap = {
+    '11.0': (MINIMUM_GCC_VERSION, (10, 0)),
+    '11.1': (MINIMUM_GCC_VERSION, (11, 0)),
+    '11.2': (MINIMUM_GCC_VERSION, (11, 0)),
+    '11.3': (MINIMUM_GCC_VERSION, (11, 0)),
+    '11.4': ((6, 0, 0), (12, 0)),
+    '11.5': ((6, 0, 0), (12, 0)),
+    '11.6': ((6, 0, 0), (12, 0)),
+    '11.7': ((6, 0, 0), (12, 0)),
+}
+
+MINIMUM_CLANG_VERSION = (3, 3, 0)
+CUDA_CLANG_VERSIONS: VersionMap = {
+    '11.1': (MINIMUM_CLANG_VERSION, (11, 0)),
+    '11.2': (MINIMUM_CLANG_VERSION, (12, 0)),
+    '11.3': (MINIMUM_CLANG_VERSION, (12, 0)),
+    '11.4': (MINIMUM_CLANG_VERSION, (13, 0)),
+    '11.5': (MINIMUM_CLANG_VERSION, (13, 0)),
+    '11.6': (MINIMUM_CLANG_VERSION, (14, 0)),
+    '11.7': (MINIMUM_CLANG_VERSION, (14, 0)),
+}
+
+__all__ = ["get_default_build_root", "check_compiler_ok_for_platform", "get_compiler_abi_compatibility_and_version", "BuildExtension",
+           "CppExtension", "CUDAExtension", "include_paths", "library_paths", "load", "load_inline", "is_ninja_available",
+           "verify_ninja_availability", "remove_extension_h_precompiler_headers", "get_cxx_compiler", "check_compiler_is_gcc"]
+# Taken directly from python stdlib < 3.9
+# See https://github.com/pytorch/pytorch/issues/48617
+def _nt_quote_args(args: Optional[List[str]]) -> List[str]:
+    """Quote command-line arguments for DOS/Windows conventions.
+
+    Just wraps every argument which contains blanks in double quotes, and
+    returns a new argument list.
+    """
+    # Cover None-type
+    if not args:
+        return []
+    return [f'"{arg}"' if ' ' in arg else arg for arg in args]
+
+def _find_cuda_home() -> Optional[str]:
+    """Find the CUDA install path."""
+    # Guess #1
+    cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH')
+    if cuda_home is None:
+        # Guess #2
+        nvcc_path = shutil.which("nvcc")
+        if nvcc_path is not None:
+            cuda_home = os.path.dirname(os.path.dirname(nvcc_path))
+        else:
+            # Guess #3
+            if IS_WINDOWS:
+                cuda_homes = glob.glob(
+                    'C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v*.*')
+                if len(cuda_homes) == 0:
+                    cuda_home = ''
+                else:
+                    cuda_home = cuda_homes[0]
+            else:
+                cuda_home = '/usr/local/cuda'
+            if not os.path.exists(cuda_home):
+                cuda_home = None
+    if cuda_home and not torch.cuda.is_available():
+        print(f"No CUDA runtime is found, using CUDA_HOME='{cuda_home}'",
+              file=sys.stderr)
+    return cuda_home
+
+def _find_rocm_home() -> Optional[str]:
+    """Find the ROCm install path."""
+    # Guess #1
+    rocm_home = os.environ.get('ROCM_HOME') or os.environ.get('ROCM_PATH')
+    if rocm_home is None:
+        # Guess #2
+        hipcc_path = shutil.which('hipcc')
+        if hipcc_path is not None:
+            rocm_home = os.path.dirname(os.path.dirname(
+                os.path.realpath(hipcc_path)))
+            # can be either /hip/bin/hipcc or /bin/hipcc
+            if os.path.basename(rocm_home) == 'hip':
+                rocm_home = os.path.dirname(rocm_home)
+        else:
+            # Guess #3
+            fallback_path = '/opt/rocm'
+            if os.path.exists(fallback_path):
+                rocm_home = fallback_path
+    if rocm_home and torch.version.hip is None:
+        print(f"No ROCm runtime is found, using ROCM_HOME='{rocm_home}'",
+              file=sys.stderr)
+    return rocm_home
+
+
+def _join_rocm_home(*paths) -> str:
+    """
+    Join paths with ROCM_HOME, or raises an error if it ROCM_HOME is not set.
+
+    This is basically a lazy way of raising an error for missing $ROCM_HOME
+    only once we need to get any ROCm-specific path.
+    """
+    if ROCM_HOME is None:
+        raise OSError('ROCM_HOME environment variable is not set. '
+                      'Please set it to your ROCm install root.')
+    elif IS_WINDOWS:
+        raise OSError('Building PyTorch extensions using '
+                      'ROCm and Windows is not supported.')
+    return os.path.join(ROCM_HOME, *paths)
+
+
+ABI_INCOMPATIBILITY_WARNING = '''
+
+                               !! WARNING !!
+
+!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+Your compiler ({}) may be ABI-incompatible with PyTorch!
+Please use a compiler that is ABI-compatible with GCC 5.0 and above.
+See https://gcc.gnu.org/onlinedocs/libstdc++/manual/abi.html.
+
+See https://gist.github.com/goldsborough/d466f43e8ffc948ff92de7486c5216d6
+for instructions on how to install GCC 5 or higher.
+!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+
+                              !! WARNING !!
+'''
+WRONG_COMPILER_WARNING = '''
+
+                               !! WARNING !!
+
+!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+Your compiler ({user_compiler}) is not compatible with the compiler Pytorch was
+built with for this platform, which is {pytorch_compiler} on {platform}. Please
+use {pytorch_compiler} to to compile your extension. Alternatively, you may
+compile PyTorch from source using {user_compiler}, and then you can also use
+{user_compiler} to compile your extension.
+
+See https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md for help
+with compiling PyTorch from source.
+!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+
+                              !! WARNING !!
+'''
+CUDA_MISMATCH_MESSAGE = '''
+The detected CUDA version ({0}) mismatches the version that was used to compile
+PyTorch ({1}). Please make sure to use the same CUDA versions.
+'''
+CUDA_MISMATCH_WARN = "The detected CUDA version ({0}) has a minor version mismatch with the version that was used to compile PyTorch ({1}). Most likely this shouldn't be a problem."
+CUDA_NOT_FOUND_MESSAGE = '''
+CUDA was not found on the system, please set the CUDA_HOME or the CUDA_PATH
+environment variable or add NVCC to your system PATH. The extension compilation will fail.
+'''
+ROCM_HOME = _find_rocm_home()
+HIP_HOME = _join_rocm_home('hip') if ROCM_HOME else None
+IS_HIP_EXTENSION = True if ((ROCM_HOME is not None) and (torch.version.hip is not None)) else False
+ROCM_VERSION = None
+if torch.version.hip is not None:
+    ROCM_VERSION = tuple(int(v) for v in torch.version.hip.split('.')[:2])
+
+CUDA_HOME = _find_cuda_home() if torch.cuda._is_compiled() else None
+CUDNN_HOME = os.environ.get('CUDNN_HOME') or os.environ.get('CUDNN_PATH')
+# PyTorch releases have the version pattern major.minor.patch, whereas when
+# PyTorch is built from source, we append the git commit hash, which gives
+# it the below pattern.
+BUILT_FROM_SOURCE_VERSION_PATTERN = re.compile(r'\d+\.\d+\.\d+\w+\+\w+')
+
+COMMON_MSVC_FLAGS = ['/MD', '/wd4819', '/wd4251', '/wd4244', '/wd4267', '/wd4275', '/wd4018', '/wd4190', '/wd4624', '/wd4067', '/wd4068', '/EHsc']
+
+MSVC_IGNORE_CUDAFE_WARNINGS = [
+    'base_class_has_different_dll_interface',
+    'field_without_dll_interface',
+    'dll_interface_conflict_none_assumed',
+    'dll_interface_conflict_dllexport_assumed'
+]
+
+COMMON_NVCC_FLAGS = [
+    '-D__CUDA_NO_HALF_OPERATORS__',
+    '-D__CUDA_NO_HALF_CONVERSIONS__',
+    '-D__CUDA_NO_BFLOAT16_CONVERSIONS__',
+    '-D__CUDA_NO_HALF2_OPERATORS__',
+    '--expt-relaxed-constexpr'
+]
+
+COMMON_HIP_FLAGS = [
+    '-fPIC',
+    '-D__HIP_PLATFORM_AMD__=1',
+    '-DUSE_ROCM=1',
+    '-DHIPBLAS_V2',
+]
+
+COMMON_HIPCC_FLAGS = [
+    '-DCUDA_HAS_FP16=1',
+    '-D__HIP_NO_HALF_OPERATORS__=1',
+    '-D__HIP_NO_HALF_CONVERSIONS__=1',
+]
+
+JIT_EXTENSION_VERSIONER = ExtensionVersioner()
+
+PLAT_TO_VCVARS = {
+    'win32' : 'x86',
+    'win-amd64' : 'x86_amd64',
+}
+
+def get_cxx_compiler():
+    if IS_WINDOWS:
+        compiler = os.environ.get('CXX', 'cl')
+    else:
+        compiler = os.environ.get('CXX', 'c++')
+    return compiler
+
+def _is_binary_build() -> bool:
+    return not BUILT_FROM_SOURCE_VERSION_PATTERN.match(torch.version.__version__)
+
+
+def _accepted_compilers_for_platform() -> List[str]:
+    # gnu-c++ and gnu-cc are the conda gcc compilers
+    return ['clang++', 'clang'] if IS_MACOS else ['g++', 'gcc', 'gnu-c++', 'gnu-cc', 'clang++', 'clang']
+
+def _maybe_write(filename, new_content):
+    r'''
+    Equivalent to writing the content into the file but will not touch the file
+    if it already had the right content (to avoid triggering recompile).
+    '''
+    if os.path.exists(filename):
+        with open(filename) as f:
+            content = f.read()
+
+        if content == new_content:
+            # The file already contains the right thing!
+            return
+
+    with open(filename, 'w') as source_file:
+        source_file.write(new_content)
+
+def get_default_build_root() -> str:
+    """
+    Return the path to the root folder under which extensions will built.
+
+    For each extension module built, there will be one folder underneath the
+    folder returned by this function. For example, if ``p`` is the path
+    returned by this function and ``ext`` the name of an extension, the build
+    folder for the extension will be ``p/ext``.
+
+    This directory is **user-specific** so that multiple users on the same
+    machine won't meet permission issues.
+    """
+    return os.path.realpath(torch._appdirs.user_cache_dir(appname='torch_extensions'))
+
+
+def check_compiler_ok_for_platform(compiler: str) -> bool:
+    """
+    Verify that the compiler is the expected one for the current platform.
+
+    Args:
+        compiler (str): The compiler executable to check.
+
+    Returns:
+        True if the compiler is gcc/g++ on Linux or clang/clang++ on macOS,
+        and always True for Windows.
+    """
+    if IS_WINDOWS:
+        return True
+    compiler_path = shutil.which(compiler)
+    if compiler_path is None:
+        return False
+    # Use os.path.realpath to resolve any symlinks, in particular from 'c++' to e.g. 'g++'.
+    compiler_path = os.path.realpath(compiler_path)
+    # Check the compiler name
+    if any(name in compiler_path for name in _accepted_compilers_for_platform()):
+        return True
+    # If compiler wrapper is used try to infer the actual compiler by invoking it with -v flag
+    env = os.environ.copy()
+    env['LC_ALL'] = 'C'  # Don't localize output
+    version_string = subprocess.check_output([compiler, '-v'], stderr=subprocess.STDOUT, env=env).decode(*SUBPROCESS_DECODE_ARGS)
+    if IS_LINUX:
+        # Check for 'gcc' or 'g++' for sccache wrapper
+        pattern = re.compile("^COLLECT_GCC=(.*)$", re.MULTILINE)
+        results = re.findall(pattern, version_string)
+        if len(results) != 1:
+            # Clang is also a supported compiler on Linux
+            # Though on Ubuntu it's sometimes called "Ubuntu clang version"
+            return 'clang version' in version_string
+        compiler_path = os.path.realpath(results[0].strip())
+        # On RHEL/CentOS c++ is a gcc compiler wrapper
+        if os.path.basename(compiler_path) == 'c++' and 'gcc version' in version_string:
+            return True
+        return any(name in compiler_path for name in _accepted_compilers_for_platform())
+    if IS_MACOS:
+        # Check for 'clang' or 'clang++'
+        return version_string.startswith("Apple clang")
+    return False
+
+
+def get_compiler_abi_compatibility_and_version(compiler) -> Tuple[bool, TorchVersion]:
+    """
+    Determine if the given compiler is ABI-compatible with PyTorch alongside its version.
+
+    Args:
+        compiler (str): The compiler executable name to check (e.g. ``g++``).
+            Must be executable in a shell process.
+
+    Returns:
+        A tuple that contains a boolean that defines if the compiler is (likely) ABI-incompatible with PyTorch,
+        followed by a `TorchVersion` string that contains the compiler version separated by dots.
+    """
+    if not _is_binary_build():
+        return (True, TorchVersion('0.0.0'))
+    if os.environ.get('TORCH_DONT_CHECK_COMPILER_ABI') in ['ON', '1', 'YES', 'TRUE', 'Y']:
+        return (True, TorchVersion('0.0.0'))
+
+    # First check if the compiler is one of the expected ones for the particular platform.
+    if not check_compiler_ok_for_platform(compiler):
+        warnings.warn(WRONG_COMPILER_WARNING.format(
+            user_compiler=compiler,
+            pytorch_compiler=_accepted_compilers_for_platform()[0],
+            platform=sys.platform))
+        return (False, TorchVersion('0.0.0'))
+
+    if IS_MACOS:
+        # There is no particular minimum version we need for clang, so we're good here.
+        return (True, TorchVersion('0.0.0'))
+    try:
+        if IS_LINUX:
+            minimum_required_version = MINIMUM_GCC_VERSION
+            versionstr = subprocess.check_output([compiler, '-dumpfullversion', '-dumpversion'])
+            version = versionstr.decode(*SUBPROCESS_DECODE_ARGS).strip().split('.')
+        else:
+            minimum_required_version = MINIMUM_MSVC_VERSION
+            compiler_info = subprocess.check_output(compiler, stderr=subprocess.STDOUT)
+            match = re.search(r'(\d+)\.(\d+)\.(\d+)', compiler_info.decode(*SUBPROCESS_DECODE_ARGS).strip())
+            version = ['0', '0', '0'] if match is None else list(match.groups())
+    except Exception:
+        _, error, _ = sys.exc_info()
+        warnings.warn(f'Error checking compiler version for {compiler}: {error}')
+        return (False, TorchVersion('0.0.0'))
+
+    if tuple(map(int, version)) >= minimum_required_version:
+        return (True, TorchVersion('.'.join(version)))
+
+    compiler = f'{compiler} {".".join(version)}'
+    warnings.warn(ABI_INCOMPATIBILITY_WARNING.format(compiler))
+
+    return (False, TorchVersion('.'.join(version)))
+
+
+def _check_cuda_version(compiler_name: str, compiler_version: TorchVersion) -> None:
+    if not CUDA_HOME:
+        raise RuntimeError(CUDA_NOT_FOUND_MESSAGE)
+
+    nvcc = os.path.join(CUDA_HOME, 'bin', 'nvcc')
+    cuda_version_str = subprocess.check_output([nvcc, '--version']).strip().decode(*SUBPROCESS_DECODE_ARGS)
+    cuda_version = re.search(r'release (\d+[.]\d+)', cuda_version_str)
+    if cuda_version is None:
+        return
+
+    cuda_str_version = cuda_version.group(1)
+    cuda_ver = Version(cuda_str_version)
+    if torch.version.cuda is None:
+        return
+
+    torch_cuda_version = Version(torch.version.cuda)
+    if cuda_ver != torch_cuda_version:
+        # major/minor attributes are only available in setuptools>=49.4.0
+        if getattr(cuda_ver, "major", None) is None:
+            raise ValueError("setuptools>=49.4.0 is required")
+        if cuda_ver.major != torch_cuda_version.major:
+            raise RuntimeError(CUDA_MISMATCH_MESSAGE.format(cuda_str_version, torch.version.cuda))
+        warnings.warn(CUDA_MISMATCH_WARN.format(cuda_str_version, torch.version.cuda))
+
+    if not (sys.platform.startswith('linux') and
+            os.environ.get('TORCH_DONT_CHECK_COMPILER_ABI') not in ['ON', '1', 'YES', 'TRUE', 'Y'] and
+            _is_binary_build()):
+        return
+
+    cuda_compiler_bounds: VersionMap = CUDA_CLANG_VERSIONS if compiler_name.startswith('clang') else CUDA_GCC_VERSIONS
+
+    if cuda_str_version not in cuda_compiler_bounds:
+        warnings.warn(f'There are no {compiler_name} version bounds defined for CUDA version {cuda_str_version}')
+    else:
+        min_compiler_version, max_excl_compiler_version = cuda_compiler_bounds[cuda_str_version]
+        # Special case for 11.4.0, which has lower compiler bounds than 11.4.1
+        if "V11.4.48" in cuda_version_str and cuda_compiler_bounds == CUDA_GCC_VERSIONS:
+            max_excl_compiler_version = (11, 0)
+        min_compiler_version_str = '.'.join(map(str, min_compiler_version))
+        max_excl_compiler_version_str = '.'.join(map(str, max_excl_compiler_version))
+
+        version_bound_str = f'>={min_compiler_version_str}, <{max_excl_compiler_version_str}'
+
+        if compiler_version < TorchVersion(min_compiler_version_str):
+            raise RuntimeError(
+                f'The current installed version of {compiler_name} ({compiler_version}) is less '
+                f'than the minimum required version by CUDA {cuda_str_version} ({min_compiler_version_str}). '
+                f'Please make sure to use an adequate version of {compiler_name} ({version_bound_str}).'
+            )
+        if compiler_version >= TorchVersion(max_excl_compiler_version_str):
+            raise RuntimeError(
+                f'The current installed version of {compiler_name} ({compiler_version}) is greater '
+                f'than the maximum required version by CUDA {cuda_str_version}. '
+                f'Please make sure to use an adequate version of {compiler_name} ({version_bound_str}).'
+            )
+
+
+class BuildExtension(build_ext):
+    """
+    A custom :mod:`setuptools` build extension .
+
+    This :class:`setuptools.build_ext` subclass takes care of passing the
+    minimum required compiler flags (e.g. ``-std=c++17``) as well as mixed
+    C++/CUDA compilation (and support for CUDA files in general).
+
+    When using :class:`BuildExtension`, it is allowed to supply a dictionary
+    for ``extra_compile_args`` (rather than the usual list) that maps from
+    languages (``cxx`` or ``nvcc``) to a list of additional compiler flags to
+    supply to the compiler. This makes it possible to supply different flags to
+    the C++ and CUDA compiler during mixed compilation.
+
+    ``use_ninja`` (bool): If ``use_ninja`` is ``True`` (default), then we
+    attempt to build using the Ninja backend. Ninja greatly speeds up
+    compilation compared to the standard ``setuptools.build_ext``.
+    Fallbacks to the standard distutils backend if Ninja is not available.
+
+    .. note::
+        By default, the Ninja backend uses #CPUS + 2 workers to build the
+        extension. This may use up too many resources on some systems. One
+        can control the number of workers by setting the `MAX_JOBS` environment
+        variable to a non-negative number.
+    """
+
+    @classmethod
+    def with_options(cls, **options):
+        """Return a subclass with alternative constructor that extends any original keyword arguments to the original constructor with the given options."""
+        class cls_with_options(cls):  # type: ignore[misc, valid-type]
+            def __init__(self, *args, **kwargs):
+                kwargs.update(options)
+                super().__init__(*args, **kwargs)
+
+        return cls_with_options
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        self.no_python_abi_suffix = kwargs.get("no_python_abi_suffix", False)
+
+        self.use_ninja = kwargs.get('use_ninja', True)
+        if self.use_ninja:
+            # Test if we can use ninja. Fallback otherwise.
+            msg = ('Attempted to use ninja as the BuildExtension backend but '
+                   '{}. Falling back to using the slow distutils backend.')
+            if not is_ninja_available():
+                warnings.warn(msg.format('we could not find ninja.'))
+                self.use_ninja = False
+
+    def finalize_options(self) -> None:
+        super().finalize_options()
+        if self.use_ninja:
+            self.force = True
+
+    def build_extensions(self) -> None:
+        compiler_name, compiler_version = self._check_abi()
+
+        cuda_ext = False
+        extension_iter = iter(self.extensions)
+        extension = next(extension_iter, None)
+        while not cuda_ext and extension:
+            for source in extension.sources:
+                _, ext = os.path.splitext(source)
+                if ext == '.cu':
+                    cuda_ext = True
+                    break
+            extension = next(extension_iter, None)
+
+        if cuda_ext and not IS_HIP_EXTENSION:
+            _check_cuda_version(compiler_name, compiler_version)
+
+        for extension in self.extensions:
+            # Ensure at least an empty list of flags for 'cxx' and 'nvcc' when
+            # extra_compile_args is a dict. Otherwise, default torch flags do
+            # not get passed. Necessary when only one of 'cxx' and 'nvcc' is
+            # passed to extra_compile_args in CUDAExtension, i.e.
+            #   CUDAExtension(..., extra_compile_args={'cxx': [...]})
+            # or
+            #   CUDAExtension(..., extra_compile_args={'nvcc': [...]})
+            if isinstance(extension.extra_compile_args, dict):
+                for ext in ['cxx', 'nvcc']:
+                    if ext not in extension.extra_compile_args:
+                        extension.extra_compile_args[ext] = []
+
+            self._add_compile_flag(extension, '-DTORCH_API_INCLUDE_EXTENSION_H')
+            # See note [Pybind11 ABI constants]
+            for name in ["COMPILER_TYPE", "STDLIB", "BUILD_ABI"]:
+                val = getattr(torch._C, f"_PYBIND11_{name}")
+                if val is not None and not IS_WINDOWS:
+                    self._add_compile_flag(extension, f'-DPYBIND11_{name}="{val}"')
+            self._define_torch_extension_name(extension)
+            self._add_gnu_cpp_abi_flag(extension)
+
+            if 'nvcc_dlink' in extension.extra_compile_args:
+                assert self.use_ninja, f"With dlink=True, ninja is required to build cuda extension {extension.name}."
+
+        # Register .cu, .cuh, .hip, and .mm as valid source extensions.
+        self.compiler.src_extensions += ['.cu', '.cuh', '.hip']
+        if torch.backends.mps.is_built():
+            self.compiler.src_extensions += ['.mm']
+        # Save the original _compile method for later.
+        if self.compiler.compiler_type == 'msvc':
+            self.compiler._cpp_extensions += ['.cu', '.cuh']
+            original_compile = self.compiler.compile
+            original_spawn = self.compiler.spawn
+        else:
+            original_compile = self.compiler._compile
+
+        def append_std17_if_no_std_present(cflags) -> None:
+            # NVCC does not allow multiple -std to be passed, so we avoid
+            # overriding the option if the user explicitly passed it.
+            cpp_format_prefix = '/{}:' if self.compiler.compiler_type == 'msvc' else '-{}='
+            cpp_flag_prefix = cpp_format_prefix.format('std')
+            cpp_flag = cpp_flag_prefix + 'c++17'
+            if not any(flag.startswith(cpp_flag_prefix) for flag in cflags):
+                cflags.append(cpp_flag)
+
+        def unix_cuda_flags(cflags):
+            cflags = (COMMON_NVCC_FLAGS +
+                      ['--compiler-options', "'-fPIC'"] +
+                      cflags + _get_cuda_arch_flags(cflags))
+
+            # NVCC does not allow multiple -ccbin/--compiler-bindir to be passed, so we avoid
+            # overriding the option if the user explicitly passed it.
+            _ccbin = os.getenv("CC")
+            if (
+                _ccbin is not None
+                and not any(flag.startswith(('-ccbin', '--compiler-bindir')) for flag in cflags)
+            ):
+                cflags.extend(['-ccbin', _ccbin])
+
+            return cflags
+
+        def convert_to_absolute_paths_inplace(paths):
+            # Helper function. See Note [Absolute include_dirs]
+            if paths is not None:
+                for i in range(len(paths)):
+                    if not os.path.isabs(paths[i]):
+                        paths[i] = os.path.abspath(paths[i])
+
+        def unix_wrap_single_compile(obj, src, ext, cc_args, extra_postargs, pp_opts) -> None:
+            # Copy before we make any modifications.
+            cflags = copy.deepcopy(extra_postargs)
+            try:
+                original_compiler = self.compiler.compiler_so
+                if _is_cuda_file(src):
+                    nvcc = [_join_rocm_home('bin', 'hipcc') if IS_HIP_EXTENSION else _join_cuda_home('bin', 'nvcc')]
+                    self.compiler.set_executable('compiler_so', nvcc)
+                    if isinstance(cflags, dict):
+                        cflags = cflags['nvcc']
+                    if IS_HIP_EXTENSION:
+                        cflags = COMMON_HIPCC_FLAGS + cflags + _get_rocm_arch_flags(cflags)
+                    else:
+                        cflags = unix_cuda_flags(cflags)
+                elif isinstance(cflags, dict):
+                    cflags = cflags['cxx']
+                if IS_HIP_EXTENSION:
+                    cflags = COMMON_HIP_FLAGS + cflags
+                append_std17_if_no_std_present(cflags)
+
+                original_compile(obj, src, ext, cc_args, cflags, pp_opts)
+            finally:
+                # Put the original compiler back in place.
+                self.compiler.set_executable('compiler_so', original_compiler)
+
+        def unix_wrap_ninja_compile(sources,
+                                    output_dir=None,
+                                    macros=None,
+                                    include_dirs=None,
+                                    debug=0,
+                                    extra_preargs=None,
+                                    extra_postargs=None,
+                                    depends=None):
+            r"""Compiles sources by outputting a ninja file and running it."""
+            # NB: I copied some lines from self.compiler (which is an instance
+            # of distutils.UnixCCompiler). See the following link.
+            # https://github.com/python/cpython/blob/f03a8f8d5001963ad5b5b28dbd95497e9cc15596/Lib/distutils/ccompiler.py#L564-L567
+            # This can be fragile, but a lot of other repos also do this
+            # (see https://github.com/search?q=_setup_compile&type=Code)
+            # so it is probably OK; we'll also get CI signal if/when
+            # we update our python version (which is when distutils can be
+            # upgraded)
+
+            # Use absolute path for output_dir so that the object file paths
+            # (`objects`) get generated with absolute paths.
+            output_dir = os.path.abspath(output_dir)
+
+            # See Note [Absolute include_dirs]
+            convert_to_absolute_paths_inplace(self.compiler.include_dirs)
+
+            _, objects, extra_postargs, pp_opts, _ = \
+                self.compiler._setup_compile(output_dir, macros,
+                                             include_dirs, sources,
+                                             depends, extra_postargs)
+            common_cflags = self.compiler._get_cc_args(pp_opts, debug, extra_preargs)
+            extra_cc_cflags = self.compiler.compiler_so[1:]
+            with_cuda = any(map(_is_cuda_file, sources))
+
+            # extra_postargs can be either:
+            # - a dict mapping cxx/nvcc to extra flags
+            # - a list of extra flags.
+            if isinstance(extra_postargs, dict):
+                post_cflags = extra_postargs['cxx']
+            else:
+                post_cflags = list(extra_postargs)
+            if IS_HIP_EXTENSION:
+                post_cflags = COMMON_HIP_FLAGS + post_cflags
+            append_std17_if_no_std_present(post_cflags)
+
+            cuda_post_cflags = None
+            cuda_cflags = None
+            if with_cuda:
+                cuda_cflags = common_cflags
+                if isinstance(extra_postargs, dict):
+                    cuda_post_cflags = extra_postargs['nvcc']
+                else:
+                    cuda_post_cflags = list(extra_postargs)
+                if IS_HIP_EXTENSION:
+                    cuda_post_cflags = cuda_post_cflags + _get_rocm_arch_flags(cuda_post_cflags)
+                    cuda_post_cflags = COMMON_HIP_FLAGS + COMMON_HIPCC_FLAGS + cuda_post_cflags
+                else:
+                    cuda_post_cflags = unix_cuda_flags(cuda_post_cflags)
+                append_std17_if_no_std_present(cuda_post_cflags)
+                cuda_cflags = [shlex.quote(f) for f in cuda_cflags]
+                cuda_post_cflags = [shlex.quote(f) for f in cuda_post_cflags]
+
+            if isinstance(extra_postargs, dict) and 'nvcc_dlink' in extra_postargs:
+                cuda_dlink_post_cflags = unix_cuda_flags(extra_postargs['nvcc_dlink'])
+            else:
+                cuda_dlink_post_cflags = None
+            _write_ninja_file_and_compile_objects(
+                sources=sources,
+                objects=objects,
+                cflags=[shlex.quote(f) for f in extra_cc_cflags + common_cflags],
+                post_cflags=[shlex.quote(f) for f in post_cflags],
+                cuda_cflags=cuda_cflags,
+                cuda_post_cflags=cuda_post_cflags,
+                cuda_dlink_post_cflags=cuda_dlink_post_cflags,
+                build_directory=output_dir,
+                verbose=True,
+                with_cuda=with_cuda)
+
+            # Return *all* object filenames, not just the ones we just built.
+            return objects
+
+        def win_cuda_flags(cflags):
+            return (COMMON_NVCC_FLAGS +
+                    cflags + _get_cuda_arch_flags(cflags))
+
+        def win_wrap_single_compile(sources,
+                                    output_dir=None,
+                                    macros=None,
+                                    include_dirs=None,
+                                    debug=0,
+                                    extra_preargs=None,
+                                    extra_postargs=None,
+                                    depends=None):
+
+            self.cflags = copy.deepcopy(extra_postargs)
+            extra_postargs = None
+
+            def spawn(cmd):
+                # Using regex to match src, obj and include files
+                src_regex = re.compile('/T(p|c)(.*)')
+                src_list = [
+                    m.group(2) for m in (src_regex.match(elem) for elem in cmd)
+                    if m
+                ]
+
+                obj_regex = re.compile('/Fo(.*)')
+                obj_list = [
+                    m.group(1) for m in (obj_regex.match(elem) for elem in cmd)
+                    if m
+                ]
+
+                include_regex = re.compile(r'((\-|\/)I.*)')
+                include_list = [
+                    m.group(1)
+                    for m in (include_regex.match(elem) for elem in cmd) if m
+                ]
+
+                if len(src_list) >= 1 and len(obj_list) >= 1:
+                    src = src_list[0]
+                    obj = obj_list[0]
+                    if _is_cuda_file(src):
+                        nvcc = _join_cuda_home('bin', 'nvcc')
+                        if isinstance(self.cflags, dict):
+                            cflags = self.cflags['nvcc']
+                        elif isinstance(self.cflags, list):
+                            cflags = self.cflags
+                        else:
+                            cflags = []
+
+                        cflags = win_cuda_flags(cflags) + ['-std=c++17', '--use-local-env']
+                        for flag in COMMON_MSVC_FLAGS:
+                            cflags = ['-Xcompiler', flag] + cflags
+                        for ignore_warning in MSVC_IGNORE_CUDAFE_WARNINGS:
+                            cflags = ['-Xcudafe', '--diag_suppress=' + ignore_warning] + cflags
+                        cmd = [nvcc, '-c', src, '-o', obj] + include_list + cflags
+                    elif isinstance(self.cflags, dict):
+                        cflags = COMMON_MSVC_FLAGS + self.cflags['cxx']
+                        append_std17_if_no_std_present(cflags)
+                        cmd += cflags
+                    elif isinstance(self.cflags, list):
+                        cflags = COMMON_MSVC_FLAGS + self.cflags
+                        append_std17_if_no_std_present(cflags)
+                        cmd += cflags
+
+                return original_spawn(cmd)
+
+            try:
+                self.compiler.spawn = spawn
+                return original_compile(sources, output_dir, macros,
+                                        include_dirs, debug, extra_preargs,
+                                        extra_postargs, depends)
+            finally:
+                self.compiler.spawn = original_spawn
+
+        def win_wrap_ninja_compile(sources,
+                                   output_dir=None,
+                                   macros=None,
+                                   include_dirs=None,
+                                   debug=0,
+                                   extra_preargs=None,
+                                   extra_postargs=None,
+                                   depends=None):
+
+            if not self.compiler.initialized:
+                self.compiler.initialize()
+            output_dir = os.path.abspath(output_dir)
+
+            # Note [Absolute include_dirs]
+            # Convert relative path in self.compiler.include_dirs to absolute path if any,
+            # For ninja build, the build location is not local, the build happens
+            # in a in script created build folder, relative path lost their correctness.
+            # To be consistent with jit extension, we allow user to enter relative include_dirs
+            # in setuptools.setup, and we convert the relative path to absolute path here
+            convert_to_absolute_paths_inplace(self.compiler.include_dirs)
+
+            _, objects, extra_postargs, pp_opts, _ = \
+                self.compiler._setup_compile(output_dir, macros,
+                                             include_dirs, sources,
+                                             depends, extra_postargs)
+            common_cflags = extra_preargs or []
+            cflags = []
+            if debug:
+                cflags.extend(self.compiler.compile_options_debug)
+            else:
+                cflags.extend(self.compiler.compile_options)
+            common_cflags.extend(COMMON_MSVC_FLAGS)
+            cflags = cflags + common_cflags + pp_opts
+            with_cuda = any(map(_is_cuda_file, sources))
+
+            # extra_postargs can be either:
+            # - a dict mapping cxx/nvcc to extra flags
+            # - a list of extra flags.
+            if isinstance(extra_postargs, dict):
+                post_cflags = extra_postargs['cxx']
+            else:
+                post_cflags = list(extra_postargs)
+            append_std17_if_no_std_present(post_cflags)
+
+            cuda_post_cflags = None
+            cuda_cflags = None
+            if with_cuda:
+                cuda_cflags = ['-std=c++17', '--use-local-env']
+                for common_cflag in common_cflags:
+                    cuda_cflags.append('-Xcompiler')
+                    cuda_cflags.append(common_cflag)
+                for ignore_warning in MSVC_IGNORE_CUDAFE_WARNINGS:
+                    cuda_cflags.append('-Xcudafe')
+                    cuda_cflags.append('--diag_suppress=' + ignore_warning)
+                cuda_cflags.extend(pp_opts)
+                if isinstance(extra_postargs, dict):
+                    cuda_post_cflags = extra_postargs['nvcc']
+                else:
+                    cuda_post_cflags = list(extra_postargs)
+                cuda_post_cflags = win_cuda_flags(cuda_post_cflags)
+
+            cflags = _nt_quote_args(cflags)
+            post_cflags = _nt_quote_args(post_cflags)
+            if with_cuda:
+                cuda_cflags = _nt_quote_args(cuda_cflags)
+                cuda_post_cflags = _nt_quote_args(cuda_post_cflags)
+            if isinstance(extra_postargs, dict) and 'nvcc_dlink' in extra_postargs:
+                cuda_dlink_post_cflags = win_cuda_flags(extra_postargs['nvcc_dlink'])
+            else:
+                cuda_dlink_post_cflags = None
+
+            _write_ninja_file_and_compile_objects(
+                sources=sources,
+                objects=objects,
+                cflags=cflags,
+                post_cflags=post_cflags,
+                cuda_cflags=cuda_cflags,
+                cuda_post_cflags=cuda_post_cflags,
+                cuda_dlink_post_cflags=cuda_dlink_post_cflags,
+                build_directory=output_dir,
+                verbose=True,
+                with_cuda=with_cuda)
+
+            # Return *all* object filenames, not just the ones we just built.
+            return objects
+
+        # Monkey-patch the _compile or compile method.
+        # https://github.com/python/cpython/blob/dc0284ee8f7a270b6005467f26d8e5773d76e959/Lib/distutils/ccompiler.py#L511
+        if self.compiler.compiler_type == 'msvc':
+            if self.use_ninja:
+                self.compiler.compile = win_wrap_ninja_compile
+            else:
+                self.compiler.compile = win_wrap_single_compile
+        else:
+            if self.use_ninja:
+                self.compiler.compile = unix_wrap_ninja_compile
+            else:
+                self.compiler._compile = unix_wrap_single_compile
+
+        build_ext.build_extensions(self)
+
+    def get_ext_filename(self, ext_name):
+        # Get the original shared library name. For Python 3, this name will be
+        # suffixed with ".so", where  will be something like
+        # cpython-37m-x86_64-linux-gnu.
+        ext_filename = super().get_ext_filename(ext_name)
+        # If `no_python_abi_suffix` is `True`, we omit the Python 3 ABI
+        # component. This makes building shared libraries with setuptools that
+        # aren't Python modules nicer.
+        if self.no_python_abi_suffix:
+            # The parts will be e.g. ["my_extension", "cpython-37m-x86_64-linux-gnu", "so"].
+            ext_filename_parts = ext_filename.split('.')
+            # Omit the second to last element.
+            without_abi = ext_filename_parts[:-2] + ext_filename_parts[-1:]
+            ext_filename = '.'.join(without_abi)
+        return ext_filename
+
+    def _check_abi(self) -> Tuple[str, TorchVersion]:
+        # On some platforms, like Windows, compiler_cxx is not available.
+        if hasattr(self.compiler, 'compiler_cxx'):
+            compiler = self.compiler.compiler_cxx[0]
+        else:
+            compiler = get_cxx_compiler()
+        _, version = get_compiler_abi_compatibility_and_version(compiler)
+        # Warn user if VC env is activated but `DISTUILS_USE_SDK` is not set.
+        if IS_WINDOWS and 'VSCMD_ARG_TGT_ARCH' in os.environ and 'DISTUTILS_USE_SDK' not in os.environ:
+            msg = ('It seems that the VC environment is activated but DISTUTILS_USE_SDK is not set.'
+                   'This may lead to multiple activations of the VC env.'
+                   'Please set `DISTUTILS_USE_SDK=1` and try again.')
+            raise UserWarning(msg)
+        return compiler, version
+
+    def _add_compile_flag(self, extension, flag):
+        extension.extra_compile_args = copy.deepcopy(extension.extra_compile_args)
+        if isinstance(extension.extra_compile_args, dict):
+            for args in extension.extra_compile_args.values():
+                args.append(flag)
+        else:
+            extension.extra_compile_args.append(flag)
+
+    def _define_torch_extension_name(self, extension):
+        # pybind11 doesn't support dots in the names
+        # so in order to support extensions in the packages
+        # like torch._C, we take the last part of the string
+        # as the library name
+        names = extension.name.split('.')
+        name = names[-1]
+        define = f'-DTORCH_EXTENSION_NAME={name}'
+        self._add_compile_flag(extension, define)
+
+    def _add_gnu_cpp_abi_flag(self, extension):
+        # use the same CXX ABI as what PyTorch was compiled with
+        self._add_compile_flag(extension, '-D_GLIBCXX_USE_CXX11_ABI=' + str(int(torch._C._GLIBCXX_USE_CXX11_ABI)))
+
+
+def CppExtension(name, sources, *args, **kwargs):
+    """
+    Create a :class:`setuptools.Extension` for C++.
+
+    Convenience method that creates a :class:`setuptools.Extension` with the
+    bare minimum (but often sufficient) arguments to build a C++ extension.
+
+    All arguments are forwarded to the :class:`setuptools.Extension`
+    constructor. Full list arguments can be found at
+    https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
+        >>> from setuptools import setup
+        >>> from torch.utils.cpp_extension import BuildExtension, CppExtension
+        >>> setup(
+        ...     name='extension',
+        ...     ext_modules=[
+        ...         CppExtension(
+        ...             name='extension',
+        ...             sources=['extension.cpp'],
+        ...             extra_compile_args=['-g'],
+        ...             extra_link_args=['-Wl,--no-as-needed', '-lm'])
+        ...     ],
+        ...     cmdclass={
+        ...         'build_ext': BuildExtension
+        ...     })
+    """
+    include_dirs = kwargs.get('include_dirs', [])
+    include_dirs += include_paths()
+    kwargs['include_dirs'] = include_dirs
+
+    library_dirs = kwargs.get('library_dirs', [])
+    library_dirs += library_paths()
+    kwargs['library_dirs'] = library_dirs
+
+    libraries = kwargs.get('libraries', [])
+    libraries.append('c10')
+    libraries.append('torch')
+    libraries.append('torch_cpu')
+    libraries.append('torch_python')
+    if IS_WINDOWS:
+        libraries.append("sleef")
+
+    kwargs['libraries'] = libraries
+
+    kwargs['language'] = 'c++'
+    return setuptools.Extension(name, sources, *args, **kwargs)
+
+
+def CUDAExtension(name, sources, *args, **kwargs):
+    """
+    Create a :class:`setuptools.Extension` for CUDA/C++.
+
+    Convenience method that creates a :class:`setuptools.Extension` with the
+    bare minimum (but often sufficient) arguments to build a CUDA/C++
+    extension. This includes the CUDA include path, library path and runtime
+    library.
+
+    All arguments are forwarded to the :class:`setuptools.Extension`
+    constructor. Full list arguments can be found at
+    https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
+        >>> from setuptools import setup
+        >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension
+        >>> setup(
+        ...     name='cuda_extension',
+        ...     ext_modules=[
+        ...         CUDAExtension(
+        ...                 name='cuda_extension',
+        ...                 sources=['extension.cpp', 'extension_kernel.cu'],
+        ...                 extra_compile_args={'cxx': ['-g'],
+        ...                                     'nvcc': ['-O2']},
+        ...                 extra_link_args=['-Wl,--no-as-needed', '-lcuda'])
+        ...     ],
+        ...     cmdclass={
+        ...         'build_ext': BuildExtension
+        ...     })
+
+    Compute capabilities:
+
+    By default the extension will be compiled to run on all archs of the cards visible during the
+    building process of the extension, plus PTX. If down the road a new card is installed the
+    extension may need to be recompiled. If a visible card has a compute capability (CC) that's
+    newer than the newest version for which your nvcc can build fully-compiled binaries, Pytorch
+    will make nvcc fall back to building kernels with the newest version of PTX your nvcc does
+    support (see below for details on PTX).
+
+    You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which
+    CCs you want the extension to support:
+
+    ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py``
+    ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py``
+
+    The +PTX option causes extension kernel binaries to include PTX instructions for the specified
+    CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >=
+    the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with
+    CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to
+    provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on
+    those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better
+    off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6,
+    "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but
+    "8.0 8.6" would be better.
+
+    Note that while it's possible to include all supported archs, the more archs get included the
+    slower the building process will be, as it will build a separate kernel image for each arch.
+
+    Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows.
+    To workaround the issue, move python binding logic to pure C++ file.
+
+    Example use:
+        #include 
+        at::Tensor SigmoidAlphaBlendForwardCuda(....)
+
+    Instead of:
+        #include 
+        torch::Tensor SigmoidAlphaBlendForwardCuda(...)
+
+    Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460
+    Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48
+
+    Relocatable device code linking:
+
+    If you want to reference device symbols across compilation units (across object files),
+    the object files need to be built with `relocatable device code` (-rdc=true or -dc).
+    An exception to this rule is "dynamic parallelism" (nested kernel launches)  which is not used a lot anymore.
+    `Relocatable device code` is less optimized so it needs to be used only on object files that need it.
+    Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step
+    help reduce the protentional perf degradation of `-rdc`.
+    Note that it needs to be used at both steps to be useful.
+
+    If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step.
+    There is also a case where `-dlink` is used without `-rdc`:
+    when an extension is linked against a static lib containing rdc-compiled objects
+    like the [NVSHMEM library](https://developer.nvidia.com/nvshmem).
+
+    Note: Ninja is required to build a CUDA Extension with RDC linking.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
+        >>> CUDAExtension(
+        ...        name='cuda_extension',
+        ...        sources=['extension.cpp', 'extension_kernel.cu'],
+        ...        dlink=True,
+        ...        dlink_libraries=["dlink_lib"],
+        ...        extra_compile_args={'cxx': ['-g'],
+        ...                            'nvcc': ['-O2', '-rdc=true']})
+    """
+    library_dirs = kwargs.get('library_dirs', [])
+    library_dirs += library_paths(cuda=True)
+    kwargs['library_dirs'] = library_dirs
+
+    libraries = kwargs.get('libraries', [])
+    libraries.append('c10')
+    libraries.append('torch')
+    libraries.append('torch_cpu')
+    libraries.append('torch_python')
+    if IS_HIP_EXTENSION:
+        libraries.append('amdhip64')
+        libraries.append('c10_hip')
+        libraries.append('torch_hip')
+    else:
+        libraries.append('cudart')
+        libraries.append('c10_cuda')
+        libraries.append('torch_cuda')
+    kwargs['libraries'] = libraries
+
+    include_dirs = kwargs.get('include_dirs', [])
+
+    if IS_HIP_EXTENSION:
+        build_dir = os.getcwd()
+        hipify_result = hipify_python.hipify(
+            project_directory=build_dir,
+            output_directory=build_dir,
+            header_include_dirs=include_dirs,
+            includes=[os.path.join(build_dir, '*')],  # limit scope to build_dir only
+            extra_files=[os.path.abspath(s) for s in sources],
+            show_detailed=True,
+            is_pytorch_extension=True,
+            hipify_extra_files_only=True,  # don't hipify everything in includes path
+        )
+
+        hipified_sources = set()
+        for source in sources:
+            s_abs = os.path.abspath(source)
+            hipified_s_abs = (hipify_result[s_abs].hipified_path if (s_abs in hipify_result and
+                              hipify_result[s_abs].hipified_path is not None) else s_abs)
+            # setup() arguments must *always* be /-separated paths relative to the setup.py directory,
+            # *never* absolute paths
+            hipified_sources.add(os.path.relpath(hipified_s_abs, build_dir))
+
+        sources = list(hipified_sources)
+
+    include_dirs += include_paths(cuda=True)
+    kwargs['include_dirs'] = include_dirs
+
+    kwargs['language'] = 'c++'
+
+    dlink_libraries = kwargs.get('dlink_libraries', [])
+    dlink = kwargs.get('dlink', False) or dlink_libraries
+    if dlink:
+        extra_compile_args = kwargs.get('extra_compile_args', {})
+
+        extra_compile_args_dlink = extra_compile_args.get('nvcc_dlink', [])
+        extra_compile_args_dlink += ['-dlink']
+        extra_compile_args_dlink += [f'-L{x}' for x in library_dirs]
+        extra_compile_args_dlink += [f'-l{x}' for x in dlink_libraries]
+
+        if (torch.version.cuda is not None) and TorchVersion(torch.version.cuda) >= '11.2':
+            extra_compile_args_dlink += ['-dlto']   # Device Link Time Optimization started from cuda 11.2
+
+        extra_compile_args['nvcc_dlink'] = extra_compile_args_dlink
+
+        kwargs['extra_compile_args'] = extra_compile_args
+
+    return setuptools.Extension(name, sources, *args, **kwargs)
+
+
+def include_paths(cuda: bool = False) -> List[str]:
+    """
+    Get the include paths required to build a C++ or CUDA extension.
+
+    Args:
+        cuda: If `True`, includes CUDA-specific include paths.
+
+    Returns:
+        A list of include path strings.
+    """
+    lib_include = os.path.join(_TORCH_PATH, 'include')
+    paths = [
+        lib_include,
+        # Remove this once torch/torch.h is officially no longer supported for C++ extensions.
+        os.path.join(lib_include, 'torch', 'csrc', 'api', 'include'),
+        # Some internal (old) Torch headers don't properly prefix their includes,
+        # so we need to pass -Itorch/lib/include/TH as well.
+        os.path.join(lib_include, 'TH'),
+        os.path.join(lib_include, 'THC')
+    ]
+    if cuda and IS_HIP_EXTENSION:
+        paths.append(os.path.join(lib_include, 'THH'))
+        paths.append(_join_rocm_home('include'))
+    elif cuda:
+        cuda_home_include = _join_cuda_home('include')
+        # if we have the Debian/Ubuntu packages for cuda, we get /usr as cuda home.
+        # but gcc doesn't like having /usr/include passed explicitly
+        if cuda_home_include != '/usr/include':
+            paths.append(cuda_home_include)
+
+        # Support CUDA_INC_PATH env variable supported by CMake files
+        if (cuda_inc_path := os.environ.get("CUDA_INC_PATH", None)) and \
+                cuda_inc_path != '/usr/include':
+            paths.append(cuda_inc_path)
+        if CUDNN_HOME is not None:
+            paths.append(os.path.join(CUDNN_HOME, 'include'))
+    return paths
+
+
+def library_paths(cuda: bool = False) -> List[str]:
+    """
+    Get the library paths required to build a C++ or CUDA extension.
+
+    Args:
+        cuda: If `True`, includes CUDA-specific library paths.
+
+    Returns:
+        A list of library path strings.
+    """
+    # We need to link against libtorch.so
+    paths = [TORCH_LIB_PATH]
+
+    if cuda and IS_HIP_EXTENSION:
+        lib_dir = 'lib'
+        paths.append(_join_rocm_home(lib_dir))
+        if HIP_HOME is not None:
+            paths.append(os.path.join(HIP_HOME, 'lib'))
+    elif cuda:
+        if IS_WINDOWS:
+            lib_dir = os.path.join('lib', 'x64')
+        else:
+            lib_dir = 'lib64'
+            if (not os.path.exists(_join_cuda_home(lib_dir)) and
+                    os.path.exists(_join_cuda_home('lib'))):
+                # 64-bit CUDA may be installed in 'lib' (see e.g. gh-16955)
+                # Note that it's also possible both don't exist (see
+                # _find_cuda_home) - in that case we stay with 'lib64'.
+                lib_dir = 'lib'
+
+        paths.append(_join_cuda_home(lib_dir))
+        if CUDNN_HOME is not None:
+            paths.append(os.path.join(CUDNN_HOME, lib_dir))
+    return paths
+
+
+def load(name,
+         sources: Union[str, List[str]],
+         extra_cflags=None,
+         extra_cuda_cflags=None,
+         extra_ldflags=None,
+         extra_include_paths=None,
+         build_directory=None,
+         verbose=False,
+         with_cuda: Optional[bool] = None,
+         is_python_module=True,
+         is_standalone=False,
+         keep_intermediates=True):
+    """
+    Load a PyTorch C++ extension just-in-time (JIT).
+
+    To load an extension, a Ninja build file is emitted, which is used to
+    compile the given sources into a dynamic library. This library is
+    subsequently loaded into the current Python process as a module and
+    returned from this function, ready for use.
+
+    By default, the directory to which the build file is emitted and the
+    resulting library compiled to is ``/torch_extensions/``, where
+    ```` is the temporary folder on the current platform and ````
+    the name of the extension. This location can be overridden in two ways.
+    First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it
+    replaces ``/torch_extensions`` and all extensions will be compiled
+    into subfolders of this directory. Second, if the ``build_directory``
+    argument to this function is supplied, it overrides the entire path, i.e.
+    the library will be compiled into that folder directly.
+
+    To compile the sources, the default system compiler (``c++``) is used,
+    which can be overridden by setting the ``CXX`` environment variable. To pass
+    additional arguments to the compilation process, ``extra_cflags`` or
+    ``extra_ldflags`` can be provided. For example, to compile your extension
+    with optimizations, pass ``extra_cflags=['-O3']``. You can also use
+    ``extra_cflags`` to pass further include directories.
+
+    CUDA support with mixed compilation is provided. Simply pass CUDA source
+    files (``.cu`` or ``.cuh``) along with other sources. Such files will be
+    detected and compiled with nvcc rather than the C++ compiler. This includes
+    passing the CUDA lib64 directory as a library directory, and linking
+    ``cudart``. You can pass additional flags to nvcc via
+    ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various
+    heuristics for finding the CUDA install directory are used, which usually
+    work fine. If not, setting the ``CUDA_HOME`` environment variable is the
+    safest option.
+
+    Args:
+        name: The name of the extension to build. This MUST be the same as the
+            name of the pybind11 module!
+        sources: A list of relative or absolute paths to C++ source files.
+        extra_cflags: optional list of compiler flags to forward to the build.
+        extra_cuda_cflags: optional list of compiler flags to forward to nvcc
+            when building CUDA sources.
+        extra_ldflags: optional list of linker flags to forward to the build.
+        extra_include_paths: optional list of include directories to forward
+            to the build.
+        build_directory: optional path to use as build workspace.
+        verbose: If ``True``, turns on verbose logging of load steps.
+        with_cuda: Determines whether CUDA headers and libraries are added to
+            the build. If set to ``None`` (default), this value is
+            automatically determined based on the existence of ``.cu`` or
+            ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers
+            and libraries to be included.
+        is_python_module: If ``True`` (default), imports the produced shared
+            library as a Python module. If ``False``, behavior depends on
+            ``is_standalone``.
+        is_standalone: If ``False`` (default) loads the constructed extension
+            into the process as a plain dynamic library. If ``True``, build a
+            standalone executable.
+
+    Returns:
+        If ``is_python_module`` is ``True``:
+            Returns the loaded PyTorch extension as a Python module.
+
+        If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``:
+            Returns nothing. (The shared library is loaded into the process as
+            a side effect.)
+
+        If ``is_standalone`` is ``True``.
+            Return the path to the executable. (On Windows, TORCH_LIB_PATH is
+            added to the PATH environment variable as a side effect.)
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> from torch.utils.cpp_extension import load
+        >>> module = load(
+        ...     name='extension',
+        ...     sources=['extension.cpp', 'extension_kernel.cu'],
+        ...     extra_cflags=['-O2'],
+        ...     verbose=True)
+    """
+    return _jit_compile(
+        name,
+        [sources] if isinstance(sources, str) else sources,
+        extra_cflags,
+        extra_cuda_cflags,
+        extra_ldflags,
+        extra_include_paths,
+        build_directory or _get_build_directory(name, verbose),
+        verbose,
+        with_cuda,
+        is_python_module,
+        is_standalone,
+        keep_intermediates=keep_intermediates)
+
+def _get_pybind11_abi_build_flags():
+    # Note [Pybind11 ABI constants]
+    #
+    # Pybind11 before 2.4 used to build an ABI strings using the following pattern:
+    # f"__pybind11_internals_v{PYBIND11_INTERNALS_VERSION}{PYBIND11_INTERNALS_KIND}{PYBIND11_BUILD_TYPE}__"
+    # Since 2.4 compier type, stdlib and build abi parameters are also encoded like this:
+    # f"__pybind11_internals_v{PYBIND11_INTERNALS_VERSION}{PYBIND11_INTERNALS_KIND}{PYBIND11_COMPILER_TYPE}{PYBIND11_STDLIB}{PYBIND11_BUILD_ABI}{PYBIND11_BUILD_TYPE}__"
+    #
+    # This was done in order to further narrow down the chances of compiler ABI incompatibility
+    # that can cause a hard to debug segfaults.
+    # For PyTorch extensions we want to relax those restrictions and pass compiler, stdlib and abi properties
+    # captured during PyTorch native library compilation in torch/csrc/Module.cpp
+
+    abi_cflags = []
+    for pname in ["COMPILER_TYPE", "STDLIB", "BUILD_ABI"]:
+        pval = getattr(torch._C, f"_PYBIND11_{pname}")
+        if pval is not None and not IS_WINDOWS:
+            abi_cflags.append(f'-DPYBIND11_{pname}=\\"{pval}\\"')
+    return abi_cflags
+
+def _get_glibcxx_abi_build_flags():
+    glibcxx_abi_cflags = ['-D_GLIBCXX_USE_CXX11_ABI=' + str(int(torch._C._GLIBCXX_USE_CXX11_ABI))]
+    return glibcxx_abi_cflags
+
+def check_compiler_is_gcc(compiler):
+    if not IS_LINUX:
+        return False
+
+    env = os.environ.copy()
+    env['LC_ALL'] = 'C'  # Don't localize output
+    try:
+        version_string = subprocess.check_output([compiler, '-v'], stderr=subprocess.STDOUT, env=env).decode(*SUBPROCESS_DECODE_ARGS)
+    except Exception as e:
+        try:
+            version_string = subprocess.check_output([compiler, '--version'], stderr=subprocess.STDOUT, env=env).decode(*SUBPROCESS_DECODE_ARGS)
+        except Exception as e:
+            return False
+    # Check for 'gcc' or 'g++' for sccache wrapper
+    pattern = re.compile("^COLLECT_GCC=(.*)$", re.MULTILINE)
+    results = re.findall(pattern, version_string)
+    if len(results) != 1:
+        return False
+    compiler_path = os.path.realpath(results[0].strip())
+    # On RHEL/CentOS c++ is a gcc compiler wrapper
+    if os.path.basename(compiler_path) == 'c++' and 'gcc version' in version_string:
+        return True
+    return False
+
+def _check_and_build_extension_h_precompiler_headers(
+        extra_cflags,
+        extra_include_paths,
+        is_standalone=False):
+    r'''
+    Precompiled Headers(PCH) can pre-build the same headers and reduce build time for pytorch load_inline modules.
+    GCC offical manual: https://gcc.gnu.org/onlinedocs/gcc-4.0.4/gcc/Precompiled-Headers.html
+    PCH only works when built pch file(header.h.gch) and build target have the same build parameters. So, We need
+    add a signature file to record PCH file parameters. If the build parameters(signature) changed, it should rebuild
+    PCH file.
+
+    Note:
+    1. Windows and MacOS have different PCH mechanism. We only support Linux currently.
+    2. It only works on GCC/G++.
+    '''
+    if not IS_LINUX:
+        return
+
+    compiler = get_cxx_compiler()
+
+    b_is_gcc = check_compiler_is_gcc(compiler)
+    if b_is_gcc is False:
+        return
+
+    head_file = os.path.join(_TORCH_PATH, 'include', 'torch', 'extension.h')
+    head_file_pch = os.path.join(_TORCH_PATH, 'include', 'torch', 'extension.h.gch')
+    head_file_signature = os.path.join(_TORCH_PATH, 'include', 'torch', 'extension.h.sign')
+
+    def listToString(s):
+        # initialize an empty string
+        string = ""
+        if s is None:
+            return string
+
+        # traverse in the string
+        for element in s:
+            string += (element + ' ')
+        # return string
+        return string
+
+    def format_precompiler_header_cmd(compiler, head_file, head_file_pch, common_cflags, torch_include_dirs, extra_cflags, extra_include_paths):
+        return re.sub(
+            r"[ \n]+",
+            " ",
+            f"""
+                {compiler} -x c++-header {head_file} -o {head_file_pch} {torch_include_dirs} {extra_include_paths} {extra_cflags} {common_cflags}
+            """,
+        ).strip()
+
+    def command_to_signature(cmd):
+        signature = cmd.replace(' ', '_')
+        return signature
+
+    def check_pch_signature_in_file(file_path, signature):
+        b_exist = os.path.isfile(file_path)
+        if b_exist is False:
+            return False
+
+        with open(file_path) as file:
+            # read all content of a file
+            content = file.read()
+            # check if string present in a file
+            return signature == content
+
+    def _create_if_not_exist(path_dir):
+        if not os.path.exists(path_dir):
+            try:
+                Path(path_dir).mkdir(parents=True, exist_ok=True)
+            except OSError as exc:  # Guard against race condition
+                if exc.errno != errno.EEXIST:
+                    raise RuntimeError(f"Fail to create path {path_dir}") from exc
+
+    def write_pch_signature_to_file(file_path, pch_sign):
+        _create_if_not_exist(os.path.dirname(file_path))
+        with open(file_path, "w") as f:
+            f.write(pch_sign)
+            f.close()
+
+    def build_precompile_header(pch_cmd):
+        try:
+            subprocess.check_output(pch_cmd, shell=True, stderr=subprocess.STDOUT)
+        except subprocess.CalledProcessError as e:
+            raise RuntimeError(f"Compile PreCompile Header fail, command: {pch_cmd}") from e
+
+    extra_cflags_str = listToString(extra_cflags)
+    extra_include_paths_str = " ".join(
+        [f"-I{include}" for include in extra_include_paths] if extra_include_paths else []
+    )
+
+    lib_include = os.path.join(_TORCH_PATH, 'include')
+    torch_include_dirs = [
+        f"-I {lib_include}",
+        # Python.h
+        "-I {}".format(sysconfig.get_path("include")),
+        # torch/all.h
+        "-I {}".format(os.path.join(lib_include, 'torch', 'csrc', 'api', 'include')),
+    ]
+
+    torch_include_dirs_str = listToString(torch_include_dirs)
+
+    common_cflags = []
+    if not is_standalone:
+        common_cflags += ['-DTORCH_API_INCLUDE_EXTENSION_H']
+
+    common_cflags += ['-std=c++17', '-fPIC']
+    common_cflags += [f"{x}" for x in _get_pybind11_abi_build_flags()]
+    common_cflags += [f"{x}" for x in _get_glibcxx_abi_build_flags()]
+    common_cflags_str = listToString(common_cflags)
+
+    pch_cmd = format_precompiler_header_cmd(compiler, head_file, head_file_pch, common_cflags_str, torch_include_dirs_str, extra_cflags_str, extra_include_paths_str)
+    pch_sign = command_to_signature(pch_cmd)
+
+    if os.path.isfile(head_file_pch) is not True:
+        build_precompile_header(pch_cmd)
+        write_pch_signature_to_file(head_file_signature, pch_sign)
+    else:
+        b_same_sign = check_pch_signature_in_file(head_file_signature, pch_sign)
+        if b_same_sign is False:
+            build_precompile_header(pch_cmd)
+            write_pch_signature_to_file(head_file_signature, pch_sign)
+
+def remove_extension_h_precompiler_headers():
+    def _remove_if_file_exists(path_file):
+        if os.path.exists(path_file):
+            os.remove(path_file)
+
+    head_file_pch = os.path.join(_TORCH_PATH, 'include', 'torch', 'extension.h.gch')
+    head_file_signature = os.path.join(_TORCH_PATH, 'include', 'torch', 'extension.h.sign')
+
+    _remove_if_file_exists(head_file_pch)
+    _remove_if_file_exists(head_file_signature)
+
+def load_inline(name,
+                cpp_sources,
+                cuda_sources=None,
+                functions=None,
+                extra_cflags=None,
+                extra_cuda_cflags=None,
+                extra_ldflags=None,
+                extra_include_paths=None,
+                build_directory=None,
+                verbose=False,
+                with_cuda=None,
+                is_python_module=True,
+                with_pytorch_error_handling=True,
+                keep_intermediates=True,
+                use_pch=False):
+    r'''
+    Load a PyTorch C++ extension just-in-time (JIT) from string sources.
+
+    This function behaves exactly like :func:`load`, but takes its sources as
+    strings rather than filenames. These strings are stored to files in the
+    build directory, after which the behavior of :func:`load_inline` is
+    identical to :func:`load`.
+
+    See `the
+    tests `_
+    for good examples of using this function.
+
+    Sources may omit two required parts of a typical non-inline C++ extension:
+    the necessary header includes, as well as the (pybind11) binding code. More
+    precisely, strings passed to ``cpp_sources`` are first concatenated into a
+    single ``.cpp`` file. This file is then prepended with ``#include
+    ``.
+
+    Furthermore, if the ``functions`` argument is supplied, bindings will be
+    automatically generated for each function specified. ``functions`` can
+    either be a list of function names, or a dictionary mapping from function
+    names to docstrings. If a list is given, the name of each function is used
+    as its docstring.
+
+    The sources in ``cuda_sources`` are concatenated into a separate ``.cu``
+    file and  prepended with ``torch/types.h``, ``cuda.h`` and
+    ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled
+    separately, but ultimately linked into a single library. Note that no
+    bindings are generated for functions in ``cuda_sources`` per  se. To bind
+    to a CUDA kernel, you must create a C++ function that calls it, and either
+    declare or define this C++ function in one of the ``cpp_sources`` (and
+    include its name in ``functions``).
+
+    See :func:`load` for a description of arguments omitted below.
+
+    Args:
+        cpp_sources: A string, or list of strings, containing C++ source code.
+        cuda_sources: A string, or list of strings, containing CUDA source code.
+        functions: A list of function names for which to generate function
+            bindings. If a dictionary is given, it should map function names to
+            docstrings (which are otherwise just the function names).
+        with_cuda: Determines whether CUDA headers and libraries are added to
+            the build. If set to ``None`` (default), this value is
+            automatically determined based on whether ``cuda_sources`` is
+            provided. Set it to ``True`` to force CUDA headers
+            and libraries to be included.
+        with_pytorch_error_handling: Determines whether pytorch error and
+            warning macros are handled by pytorch instead of pybind. To do
+            this, each function ``foo`` is called via an intermediary ``_safe_foo``
+            function. This redirection might cause issues in obscure cases
+            of cpp. This flag should be set to ``False`` when this redirect
+            causes issues.
+
+    Example:
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
+        >>> from torch.utils.cpp_extension import load_inline
+        >>> source = """
+        at::Tensor sin_add(at::Tensor x, at::Tensor y) {
+          return x.sin() + y.sin();
+        }
+        """
+        >>> module = load_inline(name='inline_extension',
+        ...                      cpp_sources=[source],
+        ...                      functions=['sin_add'])
+
+    .. note::
+        By default, the Ninja backend uses #CPUS + 2 workers to build the
+        extension. This may use up too many resources on some systems. One
+        can control the number of workers by setting the `MAX_JOBS` environment
+        variable to a non-negative number.
+    '''
+    build_directory = build_directory or _get_build_directory(name, verbose)
+
+    if isinstance(cpp_sources, str):
+        cpp_sources = [cpp_sources]
+    cuda_sources = cuda_sources or []
+    if isinstance(cuda_sources, str):
+        cuda_sources = [cuda_sources]
+
+    cpp_sources.insert(0, '#include ')
+
+    if use_pch is True:
+        # Using PreCompile Header('torch/extension.h') to reduce compile time.
+        _check_and_build_extension_h_precompiler_headers(extra_cflags, extra_include_paths)
+    else:
+        remove_extension_h_precompiler_headers()
+
+    # If `functions` is supplied, we create the pybind11 bindings for the user.
+    # Here, `functions` is (or becomes, after some processing) a map from
+    # function names to function docstrings.
+    if functions is not None:
+        module_def = []
+        module_def.append('PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {')
+        if isinstance(functions, str):
+            functions = [functions]
+        if isinstance(functions, list):
+            # Make the function docstring the same as the function name.
+            functions = {f: f for f in functions}
+        elif not isinstance(functions, dict):
+            raise ValueError(f"Expected 'functions' to be a list or dict, but was {type(functions)}")
+        for function_name, docstring in functions.items():
+            if with_pytorch_error_handling:
+                module_def.append(f'm.def("{function_name}", torch::wrap_pybind_function({function_name}), "{docstring}");')
+            else:
+                module_def.append(f'm.def("{function_name}", {function_name}, "{docstring}");')
+        module_def.append('}')
+        cpp_sources += module_def
+
+    cpp_source_path = os.path.join(build_directory, 'main.cpp')
+    _maybe_write(cpp_source_path, "\n".join(cpp_sources))
+
+    sources = [cpp_source_path]
+
+    if cuda_sources:
+        cuda_sources.insert(0, '#include ')
+        cuda_sources.insert(1, '#include ')
+        cuda_sources.insert(2, '#include ')
+
+        cuda_source_path = os.path.join(build_directory, 'cuda.cu')
+        _maybe_write(cuda_source_path, "\n".join(cuda_sources))
+
+        sources.append(cuda_source_path)
+
+    return _jit_compile(
+        name,
+        sources,
+        extra_cflags,
+        extra_cuda_cflags,
+        extra_ldflags,
+        extra_include_paths,
+        build_directory,
+        verbose,
+        with_cuda,
+        is_python_module,
+        is_standalone=False,
+        keep_intermediates=keep_intermediates)
+
+
+def _jit_compile(name,
+                 sources,
+                 extra_cflags,
+                 extra_cuda_cflags,
+                 extra_ldflags,
+                 extra_include_paths,
+                 build_directory: str,
+                 verbose: bool,
+                 with_cuda: Optional[bool],
+                 is_python_module,
+                 is_standalone,
+                 keep_intermediates=True) -> None:
+    if is_python_module and is_standalone:
+        raise ValueError("`is_python_module` and `is_standalone` are mutually exclusive.")
+
+    if with_cuda is None:
+        with_cuda = any(map(_is_cuda_file, sources))
+    with_cudnn = any('cudnn' in f for f in extra_ldflags or [])
+    old_version = JIT_EXTENSION_VERSIONER.get_version(name)
+    version = JIT_EXTENSION_VERSIONER.bump_version_if_changed(
+        name,
+        sources,
+        build_arguments=[extra_cflags, extra_cuda_cflags, extra_ldflags, extra_include_paths],
+        build_directory=build_directory,
+        with_cuda=with_cuda,
+        is_python_module=is_python_module,
+        is_standalone=is_standalone,
+    )
+    if version > 0:
+        if version != old_version and verbose:
+            print(f'The input conditions for extension module {name} have changed. ' +
+                  f'Bumping to version {version} and re-building as {name}_v{version}...',
+                  file=sys.stderr)
+        name = f'{name}_v{version}'
+
+    baton = FileBaton(os.path.join(build_directory, 'lock'))
+    if baton.try_acquire():
+        try:
+            if version != old_version:
+                with GeneratedFileCleaner(keep_intermediates=keep_intermediates) as clean_ctx:
+                    if IS_HIP_EXTENSION and (with_cuda or with_cudnn):
+                        hipify_result = hipify_python.hipify(
+                            project_directory=build_directory,
+                            output_directory=build_directory,
+                            header_include_dirs=(extra_include_paths if extra_include_paths is not None else []),
+                            extra_files=[os.path.abspath(s) for s in sources],
+                            ignores=[_join_rocm_home('*'), os.path.join(_TORCH_PATH, '*')],  # no need to hipify ROCm or PyTorch headers
+                            show_detailed=verbose,
+                            show_progress=verbose,
+                            is_pytorch_extension=True,
+                            clean_ctx=clean_ctx
+                        )
+
+                        hipified_sources = set()
+                        for source in sources:
+                            s_abs = os.path.abspath(source)
+                            hipified_sources.add(hipify_result[s_abs].hipified_path if s_abs in hipify_result else s_abs)
+
+                        sources = list(hipified_sources)
+
+                    _write_ninja_file_and_build_library(
+                        name=name,
+                        sources=sources,
+                        extra_cflags=extra_cflags or [],
+                        extra_cuda_cflags=extra_cuda_cflags or [],
+                        extra_ldflags=extra_ldflags or [],
+                        extra_include_paths=extra_include_paths or [],
+                        build_directory=build_directory,
+                        verbose=verbose,
+                        with_cuda=with_cuda,
+                        is_standalone=is_standalone)
+            elif verbose:
+                print('No modifications detected for re-loaded extension '
+                      f'module {name}, skipping build step...', file=sys.stderr)
+        finally:
+            baton.release()
+    else:
+        baton.wait()
+
+    if verbose:
+        print(f'Loading extension module {name}...', file=sys.stderr)
+
+    if is_standalone:
+        return _get_exec_path(name, build_directory)
+
+    return _import_module_from_library(name, build_directory, is_python_module)
+
+
+def _write_ninja_file_and_compile_objects(
+        sources: List[str],
+        objects,
+        cflags,
+        post_cflags,
+        cuda_cflags,
+        cuda_post_cflags,
+        cuda_dlink_post_cflags,
+        build_directory: str,
+        verbose: bool,
+        with_cuda: Optional[bool]) -> None:
+    verify_ninja_availability()
+
+    compiler = get_cxx_compiler()
+
+    get_compiler_abi_compatibility_and_version(compiler)
+    if with_cuda is None:
+        with_cuda = any(map(_is_cuda_file, sources))
+    build_file_path = os.path.join(build_directory, 'build.ninja')
+    if verbose:
+        print(f'Emitting ninja build file {build_file_path}...', file=sys.stderr)
+    _write_ninja_file(
+        path=build_file_path,
+        cflags=cflags,
+        post_cflags=post_cflags,
+        cuda_cflags=cuda_cflags,
+        cuda_post_cflags=cuda_post_cflags,
+        cuda_dlink_post_cflags=cuda_dlink_post_cflags,
+        sources=sources,
+        objects=objects,
+        ldflags=None,
+        library_target=None,
+        with_cuda=with_cuda)
+    if verbose:
+        print('Compiling objects...', file=sys.stderr)
+    _run_ninja_build(
+        build_directory,
+        verbose,
+        # It would be better if we could tell users the name of the extension
+        # that failed to build but there isn't a good way to get it here.
+        error_prefix='Error compiling objects for extension')
+
+
+def _write_ninja_file_and_build_library(
+        name,
+        sources: List[str],
+        extra_cflags,
+        extra_cuda_cflags,
+        extra_ldflags,
+        extra_include_paths,
+        build_directory: str,
+        verbose: bool,
+        with_cuda: Optional[bool],
+        is_standalone: bool = False) -> None:
+    verify_ninja_availability()
+
+    compiler = get_cxx_compiler()
+
+    get_compiler_abi_compatibility_and_version(compiler)
+    if with_cuda is None:
+        with_cuda = any(map(_is_cuda_file, sources))
+    extra_ldflags = _prepare_ldflags(
+        extra_ldflags or [],
+        with_cuda,
+        verbose,
+        is_standalone)
+    build_file_path = os.path.join(build_directory, 'build.ninja')
+    if verbose:
+        print(f'Emitting ninja build file {build_file_path}...', file=sys.stderr)
+    # NOTE: Emitting a new ninja build file does not cause re-compilation if
+    # the sources did not change, so it's ok to re-emit (and it's fast).
+    _write_ninja_file_to_build_library(
+        path=build_file_path,
+        name=name,
+        sources=sources,
+        extra_cflags=extra_cflags or [],
+        extra_cuda_cflags=extra_cuda_cflags or [],
+        extra_ldflags=extra_ldflags or [],
+        extra_include_paths=extra_include_paths or [],
+        with_cuda=with_cuda,
+        is_standalone=is_standalone)
+
+    if verbose:
+        print(f'Building extension module {name}...', file=sys.stderr)
+    _run_ninja_build(
+        build_directory,
+        verbose,
+        error_prefix=f"Error building extension '{name}'")
+
+
+def is_ninja_available():
+    """Return ``True`` if the `ninja `_ build system is available on the system, ``False`` otherwise."""
+    try:
+        subprocess.check_output('ninja --version'.split())
+    except Exception:
+        return False
+    else:
+        return True
+
+
+def verify_ninja_availability():
+    """Raise ``RuntimeError`` if `ninja `_ build system is not available on the system, does nothing otherwise."""
+    if not is_ninja_available():
+        raise RuntimeError("Ninja is required to load C++ extensions")
+
+
+def _prepare_ldflags(extra_ldflags, with_cuda, verbose, is_standalone):
+    if IS_WINDOWS:
+        python_lib_path = os.path.join(sys.base_exec_prefix, 'libs')
+
+        extra_ldflags.append('c10.lib')
+        if with_cuda:
+            extra_ldflags.append('c10_cuda.lib')
+        extra_ldflags.append('torch_cpu.lib')
+        if with_cuda:
+            extra_ldflags.append('torch_cuda.lib')
+            # /INCLUDE is used to ensure torch_cuda is linked against in a project that relies on it.
+            # Related issue: https://github.com/pytorch/pytorch/issues/31611
+            extra_ldflags.append('-INCLUDE:?warp_size@cuda@at@@YAHXZ')
+        extra_ldflags.append('torch.lib')
+        extra_ldflags.append(f'/LIBPATH:{TORCH_LIB_PATH}')
+        if not is_standalone:
+            extra_ldflags.append('torch_python.lib')
+            extra_ldflags.append(f'/LIBPATH:{python_lib_path}')
+
+    else:
+        extra_ldflags.append(f'-L{TORCH_LIB_PATH}')
+        extra_ldflags.append('-lc10')
+        if with_cuda:
+            extra_ldflags.append('-lc10_hip' if IS_HIP_EXTENSION else '-lc10_cuda')
+        extra_ldflags.append('-ltorch_cpu')
+        if with_cuda:
+            extra_ldflags.append('-ltorch_hip' if IS_HIP_EXTENSION else '-ltorch_cuda')
+        extra_ldflags.append('-ltorch')
+        if not is_standalone:
+            extra_ldflags.append('-ltorch_python')
+
+        if is_standalone:
+            extra_ldflags.append(f"-Wl,-rpath,{TORCH_LIB_PATH}")
+
+    if with_cuda:
+        if verbose:
+            print('Detected CUDA files, patching ldflags', file=sys.stderr)
+        if IS_WINDOWS:
+            extra_ldflags.append(f'/LIBPATH:{_join_cuda_home("lib", "x64")}')
+            extra_ldflags.append('cudart.lib')
+            if CUDNN_HOME is not None:
+                extra_ldflags.append(f'/LIBPATH:{os.path.join(CUDNN_HOME, "lib", "x64")}')
+        elif not IS_HIP_EXTENSION:
+            extra_lib_dir = "lib64"
+            if (not os.path.exists(_join_cuda_home(extra_lib_dir)) and
+                    os.path.exists(_join_cuda_home("lib"))):
+                # 64-bit CUDA may be installed in "lib"
+                # Note that it's also possible both don't exist (see _find_cuda_home) - in that case we stay with "lib64"
+                extra_lib_dir = "lib"
+            extra_ldflags.append(f'-L{_join_cuda_home(extra_lib_dir)}')
+            extra_ldflags.append('-lcudart')
+            if CUDNN_HOME is not None:
+                extra_ldflags.append(f'-L{os.path.join(CUDNN_HOME, "lib64")}')
+        elif IS_HIP_EXTENSION:
+            extra_ldflags.append(f'-L{_join_rocm_home("lib")}')
+            extra_ldflags.append('-lamdhip64')
+    return extra_ldflags
+
+
+def _get_cuda_arch_flags(cflags: Optional[List[str]] = None) -> List[str]:
+    """
+    Determine CUDA arch flags to use.
+
+    For an arch, say "6.1", the added compile flag will be
+    ``-gencode=arch=compute_61,code=sm_61``.
+    For an added "+PTX", an additional
+    ``-gencode=arch=compute_xx,code=compute_xx`` is added.
+
+    See select_compute_arch.cmake for corresponding named and supported arches
+    when building with CMake.
+    """
+    # If cflags is given, there may already be user-provided arch flags in it
+    # (from `extra_compile_args`)
+    if cflags is not None:
+        for flag in cflags:
+            if 'TORCH_EXTENSION_NAME' in flag:
+                continue
+            if 'arch' in flag:
+                return []
+
+    # Note: keep combined names ("arch1+arch2") above single names, otherwise
+    # string replacement may not do the right thing
+    named_arches = collections.OrderedDict([
+        ('Kepler+Tesla', '3.7'),
+        ('Kepler', '3.5+PTX'),
+        ('Maxwell+Tegra', '5.3'),
+        ('Maxwell', '5.0;5.2+PTX'),
+        ('Pascal', '6.0;6.1+PTX'),
+        ('Volta+Tegra', '7.2'),
+        ('Volta', '7.0+PTX'),
+        ('Turing', '7.5+PTX'),
+        ('Ampere+Tegra', '8.7'),
+        ('Ampere', '8.0;8.6+PTX'),
+        ('Ada', '8.9+PTX'),
+        ('Hopper', '9.0+PTX'),
+    ])
+
+    supported_arches = ['3.5', '3.7', '5.0', '5.2', '5.3', '6.0', '6.1', '6.2',
+                        '7.0', '7.2', '7.5', '8.0', '8.6', '8.7', '8.9', '9.0', '9.0a']
+    valid_arch_strings = supported_arches + [s + "+PTX" for s in supported_arches]
+
+    # The default is sm_30 for CUDA 9.x and 10.x
+    # First check for an env var (same as used by the main setup.py)
+    # Can be one or more architectures, e.g. "6.1" or "3.5;5.2;6.0;6.1;7.0+PTX"
+    # See cmake/Modules_CUDA_fix/upstream/FindCUDA/select_compute_arch.cmake
+    _arch_list = os.environ.get('TORCH_CUDA_ARCH_LIST', None)
+
+    # If not given, determine what's best for the GPU / CUDA version that can be found
+    if not _arch_list:
+        warnings.warn(
+            "TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. \n"
+            "If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'].")
+        arch_list = []
+        # the assumption is that the extension should run on any of the currently visible cards,
+        # which could be of different types - therefore all archs for visible cards should be included
+        for i in range(torch.cuda.device_count()):
+            capability = torch.cuda.get_device_capability(i)
+            supported_sm = [int(arch.split('_')[1])
+                            for arch in torch.cuda.get_arch_list() if 'sm_' in arch]
+            max_supported_sm = max((sm // 10, sm % 10) for sm in supported_sm)
+            # Capability of the device may be higher than what's supported by the user's
+            # NVCC, causing compilation error. User's NVCC is expected to match the one
+            # used to build pytorch, so we use the maximum supported capability of pytorch
+            # to clamp the capability.
+            capability = min(max_supported_sm, capability)
+            arch = f'{capability[0]}.{capability[1]}'
+            if arch not in arch_list:
+                arch_list.append(arch)
+        arch_list = sorted(arch_list)
+        arch_list[-1] += '+PTX'
+    else:
+        # Deal with lists that are ' ' separated (only deal with ';' after)
+        _arch_list = _arch_list.replace(' ', ';')
+        # Expand named arches
+        for named_arch, archval in named_arches.items():
+            _arch_list = _arch_list.replace(named_arch, archval)
+
+        arch_list = _arch_list.split(';')
+
+    flags = []
+    for arch in arch_list:
+        if arch not in valid_arch_strings:
+            raise ValueError(f"Unknown CUDA arch ({arch}) or GPU not supported")
+        else:
+            num = arch[0] + arch[2:].split("+")[0]
+            flags.append(f'-gencode=arch=compute_{num},code=sm_{num}')
+            if arch.endswith('+PTX'):
+                flags.append(f'-gencode=arch=compute_{num},code=compute_{num}')
+
+    return sorted(set(flags))
+
+
+def _get_rocm_arch_flags(cflags: Optional[List[str]] = None) -> List[str]:
+    # If cflags is given, there may already be user-provided arch flags in it
+    # (from `extra_compile_args`)
+    if cflags is not None:
+        for flag in cflags:
+            if 'amdgpu-target' in flag or 'offload-arch' in flag:
+                return ['-fno-gpu-rdc']
+    # Use same defaults as used for building PyTorch
+    # Allow env var to override, just like during initial cmake build.
+    _archs = os.environ.get('PYTORCH_ROCM_ARCH', None)
+    if not _archs:
+        archFlags = torch._C._cuda_getArchFlags()
+        if archFlags:
+            archs = archFlags.split()
+        else:
+            archs = []
+    else:
+        archs = _archs.replace(' ', ';').split(';')
+    flags = [f'--offload-arch={arch}' for arch in archs]
+    flags += ['-fno-gpu-rdc']
+    return flags
+
+def _get_build_directory(name: str, verbose: bool) -> str:
+    root_extensions_directory = os.environ.get('TORCH_EXTENSIONS_DIR')
+    if root_extensions_directory is None:
+        root_extensions_directory = get_default_build_root()
+        cu_str = ('cpu' if torch.version.cuda is None else
+                  f'cu{torch.version.cuda.replace(".", "")}')  # type: ignore[attr-defined]
+        python_version = f'py{sys.version_info.major}{sys.version_info.minor}'
+        build_folder = f'{python_version}_{cu_str}'
+
+        root_extensions_directory = os.path.join(
+            root_extensions_directory, build_folder)
+
+    if verbose:
+        print(f'Using {root_extensions_directory} as PyTorch extensions root...', file=sys.stderr)
+
+    build_directory = os.path.join(root_extensions_directory, name)
+    if not os.path.exists(build_directory):
+        if verbose:
+            print(f'Creating extension directory {build_directory}...', file=sys.stderr)
+        # This is like mkdir -p, i.e. will also create parent directories.
+        os.makedirs(build_directory, exist_ok=True)
+
+    return build_directory
+
+
+def _get_num_workers(verbose: bool) -> Optional[int]:
+    max_jobs = os.environ.get('MAX_JOBS')
+    if max_jobs is not None and max_jobs.isdigit():
+        if verbose:
+            print(f'Using envvar MAX_JOBS ({max_jobs}) as the number of workers...',
+                  file=sys.stderr)
+        return int(max_jobs)
+    if verbose:
+        print('Allowing ninja to set a default number of workers... '
+              '(overridable by setting the environment variable MAX_JOBS=N)',
+              file=sys.stderr)
+    return None
+
+
+def _run_ninja_build(build_directory: str, verbose: bool, error_prefix: str) -> None:
+    command = ['ninja', '-v']
+    num_workers = _get_num_workers(verbose)
+    if num_workers is not None:
+        command.extend(['-j', str(num_workers)])
+    env = os.environ.copy()
+    # Try to activate the vc env for the users
+    if IS_WINDOWS and 'VSCMD_ARG_TGT_ARCH' not in env:
+        from setuptools import distutils
+
+        plat_name = distutils.util.get_platform()
+        plat_spec = PLAT_TO_VCVARS[plat_name]
+
+        vc_env = distutils._msvccompiler._get_vc_env(plat_spec)
+        vc_env = {k.upper(): v for k, v in vc_env.items()}
+        for k, v in env.items():
+            uk = k.upper()
+            if uk not in vc_env:
+                vc_env[uk] = v
+        env = vc_env
+    try:
+        sys.stdout.flush()
+        sys.stderr.flush()
+        # Warning: don't pass stdout=None to subprocess.run to get output.
+        # subprocess.run assumes that sys.__stdout__ has not been modified and
+        # attempts to write to it by default.  However, when we call _run_ninja_build
+        # from ahead-of-time cpp extensions, the following happens:
+        # 1) If the stdout encoding is not utf-8, setuptools detachs __stdout__.
+        #    https://github.com/pypa/setuptools/blob/7e97def47723303fafabe48b22168bbc11bb4821/setuptools/dist.py#L1110
+        #    (it probably shouldn't do this)
+        # 2) subprocess.run (on POSIX, with no stdout override) relies on
+        #    __stdout__ not being detached:
+        #    https://github.com/python/cpython/blob/c352e6c7446c894b13643f538db312092b351789/Lib/subprocess.py#L1214
+        # To work around this, we pass in the fileno directly and hope that
+        # it is valid.
+        stdout_fileno = 1
+        subprocess.run(
+            command,
+            stdout=stdout_fileno if verbose else subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            cwd=build_directory,
+            check=True,
+            env=env)
+    except subprocess.CalledProcessError as e:
+        # Python 2 and 3 compatible way of getting the error object.
+        _, error, _ = sys.exc_info()
+        # error.output contains the stdout and stderr of the build attempt.
+        message = error_prefix
+        # `error` is a CalledProcessError (which has an `output`) attribute, but
+        # mypy thinks it's Optional[BaseException] and doesn't narrow
+        if hasattr(error, 'output') and error.output:  # type: ignore[union-attr]
+            message += f": {error.output.decode(*SUBPROCESS_DECODE_ARGS)}"  # type: ignore[union-attr]
+        raise RuntimeError(message) from e
+
+
+def _get_exec_path(module_name, path):
+    if IS_WINDOWS and TORCH_LIB_PATH not in os.getenv('PATH', '').split(';'):
+        torch_lib_in_path = any(
+            os.path.exists(p) and os.path.samefile(p, TORCH_LIB_PATH)
+            for p in os.getenv('PATH', '').split(';')
+        )
+        if not torch_lib_in_path:
+            os.environ['PATH'] = f"{TORCH_LIB_PATH};{os.getenv('PATH', '')}"
+    return os.path.join(path, f'{module_name}{EXEC_EXT}')
+
+
+def _import_module_from_library(module_name, path, is_python_module):
+    filepath = os.path.join(path, f"{module_name}{LIB_EXT}")
+    if is_python_module:
+        # https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path
+        spec = importlib.util.spec_from_file_location(module_name, filepath)
+        assert spec is not None
+        module = importlib.util.module_from_spec(spec)
+        assert isinstance(spec.loader, importlib.abc.Loader)
+        spec.loader.exec_module(module)
+        return module
+    else:
+        torch.ops.load_library(filepath)
+        return filepath
+
+
+def _write_ninja_file_to_build_library(path,
+                                       name,
+                                       sources,
+                                       extra_cflags,
+                                       extra_cuda_cflags,
+                                       extra_ldflags,
+                                       extra_include_paths,
+                                       with_cuda,
+                                       is_standalone) -> None:
+    extra_cflags = [flag.strip() for flag in extra_cflags]
+    extra_cuda_cflags = [flag.strip() for flag in extra_cuda_cflags]
+    extra_ldflags = [flag.strip() for flag in extra_ldflags]
+    extra_include_paths = [flag.strip() for flag in extra_include_paths]
+
+    # Turn into absolute paths so we can emit them into the ninja build
+    # file wherever it is.
+    user_includes = [os.path.abspath(file) for file in extra_include_paths]
+
+    # include_paths() gives us the location of torch/extension.h
+    system_includes = include_paths(with_cuda)
+    # sysconfig.get_path('include') gives us the location of Python.h
+    # Explicitly specify 'posix_prefix' scheme on non-Windows platforms to workaround error on some MacOS
+    # installations where default `get_path` points to non-existing `/Library/Python/M.m/include` folder
+    python_include_path = sysconfig.get_path('include', scheme='nt' if IS_WINDOWS else 'posix_prefix')
+    if python_include_path is not None:
+        system_includes.append(python_include_path)
+
+    common_cflags = []
+    if not is_standalone:
+        common_cflags.append(f'-DTORCH_EXTENSION_NAME={name}')
+        common_cflags.append('-DTORCH_API_INCLUDE_EXTENSION_H')
+
+    common_cflags += [f"{x}" for x in _get_pybind11_abi_build_flags()]
+
+    # Windows does not understand `-isystem` and quotes flags later.
+    if IS_WINDOWS:
+        common_cflags += [f'-I{include}' for include in user_includes + system_includes]
+    else:
+        common_cflags += [f'-I{shlex.quote(include)}' for include in user_includes]
+        common_cflags += [f'-isystem {shlex.quote(include)}' for include in system_includes]
+
+    common_cflags += [f"{x}" for x in _get_glibcxx_abi_build_flags()]
+
+    if IS_WINDOWS:
+        cflags = common_cflags + COMMON_MSVC_FLAGS + ['/std:c++17'] + extra_cflags
+        cflags = _nt_quote_args(cflags)
+    else:
+        cflags = common_cflags + ['-fPIC', '-std=c++17'] + extra_cflags
+
+    if with_cuda and IS_HIP_EXTENSION:
+        cuda_flags = ['-DWITH_HIP'] + cflags + COMMON_HIP_FLAGS + COMMON_HIPCC_FLAGS
+        cuda_flags += extra_cuda_cflags
+        cuda_flags += _get_rocm_arch_flags(cuda_flags)
+    elif with_cuda:
+        cuda_flags = common_cflags + COMMON_NVCC_FLAGS + _get_cuda_arch_flags()
+        if IS_WINDOWS:
+            for flag in COMMON_MSVC_FLAGS:
+                cuda_flags = ['-Xcompiler', flag] + cuda_flags
+            for ignore_warning in MSVC_IGNORE_CUDAFE_WARNINGS:
+                cuda_flags = ['-Xcudafe', '--diag_suppress=' + ignore_warning] + cuda_flags
+            cuda_flags = cuda_flags + ['-std=c++17']
+            cuda_flags = _nt_quote_args(cuda_flags)
+            cuda_flags += _nt_quote_args(extra_cuda_cflags)
+        else:
+            cuda_flags += ['--compiler-options', "'-fPIC'"]
+            cuda_flags += extra_cuda_cflags
+            if not any(flag.startswith('-std=') for flag in cuda_flags):
+                cuda_flags.append('-std=c++17')
+            cc_env = os.getenv("CC")
+            if cc_env is not None:
+                cuda_flags = ['-ccbin', cc_env] + cuda_flags
+    else:
+        cuda_flags = None
+
+    def object_file_path(source_file: str) -> str:
+        # '/path/to/file.cpp' -> 'file'
+        file_name = os.path.splitext(os.path.basename(source_file))[0]
+        if _is_cuda_file(source_file) and with_cuda:
+            # Use a different object filename in case a C++ and CUDA file have
+            # the same filename but different extension (.cpp vs. .cu).
+            target = f'{file_name}.cuda.o'
+        else:
+            target = f'{file_name}.o'
+        return target
+
+    objects = [object_file_path(src) for src in sources]
+    ldflags = ([] if is_standalone else [SHARED_FLAG]) + extra_ldflags
+
+    # The darwin linker needs explicit consent to ignore unresolved symbols.
+    if IS_MACOS:
+        ldflags.append('-undefined dynamic_lookup')
+    elif IS_WINDOWS:
+        ldflags = _nt_quote_args(ldflags)
+
+    ext = EXEC_EXT if is_standalone else LIB_EXT
+    library_target = f'{name}{ext}'
+
+    _write_ninja_file(
+        path=path,
+        cflags=cflags,
+        post_cflags=None,
+        cuda_cflags=cuda_flags,
+        cuda_post_cflags=None,
+        cuda_dlink_post_cflags=None,
+        sources=sources,
+        objects=objects,
+        ldflags=ldflags,
+        library_target=library_target,
+        with_cuda=with_cuda)
+
+
+def _write_ninja_file(path,
+                      cflags,
+                      post_cflags,
+                      cuda_cflags,
+                      cuda_post_cflags,
+                      cuda_dlink_post_cflags,
+                      sources,
+                      objects,
+                      ldflags,
+                      library_target,
+                      with_cuda) -> None:
+    r"""Write a ninja file that does the desired compiling and linking.
+
+    `path`: Where to write this file
+    `cflags`: list of flags to pass to $cxx. Can be None.
+    `post_cflags`: list of flags to append to the $cxx invocation. Can be None.
+    `cuda_cflags`: list of flags to pass to $nvcc. Can be None.
+    `cuda_postflags`: list of flags to append to the $nvcc invocation. Can be None.
+    `sources`: list of paths to source files
+    `objects`: list of desired paths to objects, one per source.
+    `ldflags`: list of flags to pass to linker. Can be None.
+    `library_target`: Name of the output library. Can be None; in that case,
+                      we do no linking.
+    `with_cuda`: If we should be compiling with CUDA.
+    """
+    def sanitize_flags(flags):
+        if flags is None:
+            return []
+        else:
+            return [flag.strip() for flag in flags]
+
+    cflags = sanitize_flags(cflags)
+    post_cflags = sanitize_flags(post_cflags)
+    cuda_cflags = sanitize_flags(cuda_cflags)
+    cuda_post_cflags = sanitize_flags(cuda_post_cflags)
+    cuda_dlink_post_cflags = sanitize_flags(cuda_dlink_post_cflags)
+    ldflags = sanitize_flags(ldflags)
+
+    # Sanity checks...
+    assert len(sources) == len(objects)
+    assert len(sources) > 0
+
+    compiler = get_cxx_compiler()
+
+    # Version 1.3 is required for the `deps` directive.
+    config = ['ninja_required_version = 1.3']
+    config.append(f'cxx = {compiler}')
+    if with_cuda or cuda_dlink_post_cflags:
+        if "PYTORCH_NVCC" in os.environ:
+            nvcc = os.getenv("PYTORCH_NVCC")    # user can set nvcc compiler with ccache using the environment variable here
+        else:
+            if IS_HIP_EXTENSION:
+                nvcc = _join_rocm_home('bin', 'hipcc')
+            else:
+                nvcc = _join_cuda_home('bin', 'nvcc')
+        config.append(f'nvcc = {nvcc}')
+
+    if IS_HIP_EXTENSION:
+        post_cflags = COMMON_HIP_FLAGS + post_cflags
+    flags = [f'cflags = {" ".join(cflags)}']
+    flags.append(f'post_cflags = {" ".join(post_cflags)}')
+    if with_cuda:
+        flags.append(f'cuda_cflags = {" ".join(cuda_cflags)}')
+        flags.append(f'cuda_post_cflags = {" ".join(cuda_post_cflags)}')
+    flags.append(f'cuda_dlink_post_cflags = {" ".join(cuda_dlink_post_cflags)}')
+    flags.append(f'ldflags = {" ".join(ldflags)}')
+
+    # Turn into absolute paths so we can emit them into the ninja build
+    # file wherever it is.
+    sources = [os.path.abspath(file) for file in sources]
+
+    # See https://ninja-build.org/build.ninja.html for reference.
+    compile_rule = ['rule compile']
+    if IS_WINDOWS:
+        compile_rule.append(
+            '  command = cl /showIncludes $cflags -c $in /Fo$out $post_cflags')
+        compile_rule.append('  deps = msvc')
+    else:
+        compile_rule.append(
+            '  command = $cxx -MMD -MF $out.d $cflags -c $in -o $out $post_cflags')
+        compile_rule.append('  depfile = $out.d')
+        compile_rule.append('  deps = gcc')
+
+    if with_cuda:
+        cuda_compile_rule = ['rule cuda_compile']
+        nvcc_gendeps = ''
+        # --generate-dependencies-with-compile is not supported by ROCm
+        # Nvcc flag `--generate-dependencies-with-compile` is not supported by sccache, which may increase build time.
+        if torch.version.cuda is not None and os.getenv('TORCH_EXTENSION_SKIP_NVCC_GEN_DEPENDENCIES', '0') != '1':
+            cuda_compile_rule.append('  depfile = $out.d')
+            cuda_compile_rule.append('  deps = gcc')
+            # Note: non-system deps with nvcc are only supported
+            # on Linux so use --generate-dependencies-with-compile
+            # to make this work on Windows too.
+            nvcc_gendeps = '--generate-dependencies-with-compile --dependency-output $out.d'
+        cuda_compile_rule.append(
+            f'  command = $nvcc {nvcc_gendeps} $cuda_cflags -c $in -o $out $cuda_post_cflags')
+
+    # Emit one build rule per source to enable incremental build.
+    build = []
+    for source_file, object_file in zip(sources, objects):
+        is_cuda_source = _is_cuda_file(source_file) and with_cuda
+        rule = 'cuda_compile' if is_cuda_source else 'compile'
+        if IS_WINDOWS:
+            source_file = source_file.replace(':', '$:')
+            object_file = object_file.replace(':', '$:')
+        source_file = source_file.replace(" ", "$ ")
+        object_file = object_file.replace(" ", "$ ")
+        build.append(f'build {object_file}: {rule} {source_file}')
+
+    if cuda_dlink_post_cflags:
+        devlink_out = os.path.join(os.path.dirname(objects[0]), 'dlink.o')
+        devlink_rule = ['rule cuda_devlink']
+        devlink_rule.append('  command = $nvcc $in -o $out $cuda_dlink_post_cflags')
+        devlink = [f'build {devlink_out}: cuda_devlink {" ".join(objects)}']
+        objects += [devlink_out]
+    else:
+        devlink_rule, devlink = [], []
+
+    if library_target is not None:
+        link_rule = ['rule link']
+        if IS_WINDOWS:
+            cl_paths = subprocess.check_output(['where',
+                                                'cl']).decode(*SUBPROCESS_DECODE_ARGS).split('\r\n')
+            if len(cl_paths) >= 1:
+                cl_path = os.path.dirname(cl_paths[0]).replace(':', '$:')
+            else:
+                raise RuntimeError("MSVC is required to load C++ extensions")
+            link_rule.append(f'  command = "{cl_path}/link.exe" $in /nologo $ldflags /out:$out')
+        else:
+            link_rule.append('  command = $cxx $in $ldflags -o $out')
+
+        link = [f'build {library_target}: link {" ".join(objects)}']
+
+        default = [f'default {library_target}']
+    else:
+        link_rule, link, default = [], [], []
+
+    # 'Blocks' should be separated by newlines, for visual benefit.
+    blocks = [config, flags, compile_rule]
+    if with_cuda:
+        blocks.append(cuda_compile_rule)  # type: ignore[possibly-undefined]
+    blocks += [devlink_rule, link_rule, build, devlink, link, default]
+    content = "\n\n".join("\n".join(b) for b in blocks)
+    # Ninja requires a new lines at the end of the .ninja file
+    content += "\n"
+    _maybe_write(path, content)
+
+def _join_cuda_home(*paths) -> str:
+    """
+    Join paths with CUDA_HOME, or raises an error if it CUDA_HOME is not set.
+
+    This is basically a lazy way of raising an error for missing $CUDA_HOME
+    only once we need to get any CUDA-specific path.
+    """
+    if CUDA_HOME is None:
+        raise OSError('CUDA_HOME environment variable is not set. '
+                      'Please set it to your CUDA install root.')
+    return os.path.join(CUDA_HOME, *paths)
+
+
+def _is_cuda_file(path: str) -> bool:
+    valid_ext = ['.cu', '.cuh']
+    if IS_HIP_EXTENSION:
+        valid_ext.append('.hip')
+    return os.path.splitext(path)[1] in valid_ext
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/deterministic.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/deterministic.py
new file mode 100644
index 0000000000000000000000000000000000000000..a055c43be531a5c65c4f29f6c8165104e98e5ca0
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/deterministic.py
@@ -0,0 +1,22 @@
+# mypy: allow-untyped-defs
+import sys
+import types
+
+import torch
+
+
+class _Deterministic(types.ModuleType):
+    @property
+    def fill_uninitialized_memory(self):
+        """
+        Whether to fill uninitialized memory with a known value when
+        :meth:`torch.use_deterministic_algorithms()` is set to ``True``.
+        """
+        return torch._C._get_deterministic_fill_uninitialized_memory()
+
+    @fill_uninitialized_memory.setter
+    def fill_uninitialized_memory(self, mode):
+        return torch._C._set_deterministic_fill_uninitialized_memory(mode)
+
+
+sys.modules[__name__].__class__ = _Deterministic
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/dlpack.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/dlpack.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bfa4b9f85bd6fc8bb8524926210b1e931e2bd50
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/dlpack.py
@@ -0,0 +1,121 @@
+from typing import Any
+
+import torch
+import enum
+
+from torch._C import _from_dlpack
+from torch._C import _to_dlpack as to_dlpack
+
+
+class DLDeviceType(enum.IntEnum):
+    # Enums as in DLPack specification (aten/src/ATen/dlpack.h)
+    kDLCPU = 1,
+    kDLGPU = 2,
+    kDLCPUPinned = 3,
+    kDLOpenCL = 4,
+    kDLVulkan = 7,
+    kDLMetal = 8,
+    kDLVPI = 9,
+    kDLROCM = 10,
+    kDLExtDev = 12,
+    kDLOneAPI = 14,
+
+
+torch._C._add_docstr(to_dlpack, r"""to_dlpack(tensor) -> PyCapsule
+
+Returns an opaque object (a "DLPack capsule") representing the tensor.
+
+.. note::
+  ``to_dlpack`` is a legacy DLPack interface. The capsule it returns
+  cannot be used for anything in Python other than use it as input to
+  ``from_dlpack``. The more idiomatic use of DLPack is to call
+  ``from_dlpack`` directly on the tensor object - this works when that
+  object has a ``__dlpack__`` method, which PyTorch and most other
+  libraries indeed have now.
+
+.. warning::
+  Only call ``from_dlpack`` once per capsule produced with ``to_dlpack``.
+  Behavior when a capsule is consumed multiple times is undefined.
+
+Args:
+    tensor: a tensor to be exported
+
+The DLPack capsule shares the tensor's memory.
+""")
+
+
+# TODO: add a typing.Protocol to be able to tell Mypy that only objects with
+# __dlpack__ and __dlpack_device__ methods are accepted.
+def from_dlpack(ext_tensor: Any) -> 'torch.Tensor':
+    """from_dlpack(ext_tensor) -> Tensor
+
+    Converts a tensor from an external library into a ``torch.Tensor``.
+
+    The returned PyTorch tensor will share the memory with the input tensor
+    (which may have come from another library). Note that in-place operations
+    will therefore also affect the data of the input tensor. This may lead to
+    unexpected issues (e.g., other libraries may have read-only flags or
+    immutable data structures), so the user should only do this if they know
+    for sure that this is fine.
+
+    Args:
+        ext_tensor (object with ``__dlpack__`` attribute, or a DLPack capsule):
+            The tensor or DLPack capsule to convert.
+
+            If ``ext_tensor`` is a tensor (or ndarray) object, it must support
+            the ``__dlpack__`` protocol (i.e., have a ``ext_tensor.__dlpack__``
+            method). Otherwise ``ext_tensor`` may be a DLPack capsule, which is
+            an opaque ``PyCapsule`` instance, typically produced by a
+            ``to_dlpack`` function or method.
+
+    Examples::
+
+        >>> import torch.utils.dlpack
+        >>> t = torch.arange(4)
+
+        # Convert a tensor directly (supported in PyTorch >= 1.10)
+        >>> t2 = torch.from_dlpack(t)
+        >>> t2[:2] = -1  # show that memory is shared
+        >>> t2
+        tensor([-1, -1,  2,  3])
+        >>> t
+        tensor([-1, -1,  2,  3])
+
+        # The old-style DLPack usage, with an intermediate capsule object
+        >>> capsule = torch.utils.dlpack.to_dlpack(t)
+        >>> capsule
+        
+        >>> t3 = torch.from_dlpack(capsule)
+        >>> t3
+        tensor([-1, -1,  2,  3])
+        >>> t3[0] = -9  # now we're sharing memory between 3 tensors
+        >>> t3
+        tensor([-9, -1,  2,  3])
+        >>> t2
+        tensor([-9, -1,  2,  3])
+        >>> t
+        tensor([-9, -1,  2,  3])
+
+    """
+    if hasattr(ext_tensor, '__dlpack__'):
+        device = ext_tensor.__dlpack_device__()
+        # device is either CUDA or ROCm, we need to pass the current
+        # stream
+        if device[0] in (DLDeviceType.kDLGPU, DLDeviceType.kDLROCM):
+            stream = torch.cuda.current_stream(f'cuda:{device[1]}')
+            # cuda_stream is the pointer to the stream and it is a public
+            # attribute, but it is not documented
+            # The array API specify that the default legacy stream must be passed
+            # with a value of 1 for CUDA
+            # https://data-apis.org/array-api/latest/API_specification/array_object.html?dlpack-self-stream-none#dlpack-self-stream-none
+            is_cuda = device[0] == DLDeviceType.kDLGPU
+            # Since pytorch is not using PTDS by default, lets directly pass
+            # the legacy stream
+            stream_ptr = 1 if is_cuda and stream.cuda_stream == 0 else stream.cuda_stream
+            dlpack = ext_tensor.__dlpack__(stream=stream_ptr)
+        else:
+            dlpack = ext_tensor.__dlpack__()
+    else:
+        # Old versions just call the converter
+        dlpack = ext_tensor
+    return _from_dlpack(dlpack)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/file_baton.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/file_baton.py
new file mode 100644
index 0000000000000000000000000000000000000000..77ee5091b3f727a8ec56bf6dde10f7fbc2e3352f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/file_baton.py
@@ -0,0 +1,50 @@
+# mypy: allow-untyped-defs
+import os
+import time
+
+
+class FileBaton:
+    """A primitive, file-based synchronization utility."""
+
+    def __init__(self, lock_file_path, wait_seconds=0.1):
+        """
+        Create a new :class:`FileBaton`.
+
+        Args:
+            lock_file_path: The path to the file used for locking.
+            wait_seconds: The seconds to periodically sleep (spin) when
+                calling ``wait()``.
+        """
+        self.lock_file_path = lock_file_path
+        self.wait_seconds = wait_seconds
+        self.fd = None
+
+    def try_acquire(self):
+        """
+        Try to atomically create a file under exclusive access.
+
+        Returns:
+            True if the file could be created, else False.
+        """
+        try:
+            self.fd = os.open(self.lock_file_path, os.O_CREAT | os.O_EXCL)
+            return True
+        except FileExistsError:
+            return False
+
+    def wait(self):
+        """
+        Periodically sleeps for a certain amount until the baton is released.
+
+        The amount of time slept depends on the ``wait_seconds`` parameter
+        passed to the constructor.
+        """
+        while os.path.exists(self.lock_file_path):
+            time.sleep(self.wait_seconds)
+
+    def release(self):
+        """Release the baton and removes its file."""
+        if self.fd is not None:
+            os.close(self.fd)
+
+        os.remove(self.lock_file_path)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/flop_counter.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/flop_counter.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6b2195fdf694fc92daee1f3905b7a768c2e1384
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/flop_counter.py
@@ -0,0 +1,735 @@
+# mypy: allow-untyped-defs
+# mypy: allow-untyped-decorators
+import torch
+from torch.utils._pytree import tree_map, tree_flatten, tree_unflatten
+from .module_tracker import ModuleTracker
+from typing import List, Any, Dict, Optional, Union, Tuple, Iterator
+from collections import defaultdict
+from torch.utils._python_dispatch import TorchDispatchMode
+from math import prod
+from functools import wraps
+import warnings
+
+
+
+__all__ = ["FlopCounterMode", "register_flop_formula"]
+
+aten = torch.ops.aten
+
+def get_shape(i):
+    if isinstance(i, torch.Tensor):
+        return i.shape
+    return i
+
+flop_registry: Dict[Any, Any] = {}
+
+def shape_wrapper(f):
+    @wraps(f)
+    def nf(*args, out_val=None, **kwargs):
+        args, kwargs, out_shape = tree_map(get_shape, (args, kwargs, out_val))
+        return f(*args, out_shape=out_shape, **kwargs)
+    return nf
+
+def register_flop_formula(targets, get_raw=False):
+    def register_fun(flop_formula):
+        if not get_raw:
+            flop_formula = shape_wrapper(flop_formula)
+
+        def register(target):
+            if not isinstance(target, torch._ops.OpOverloadPacket):
+                raise ValueError(
+                    f"register_flop_formula(targets): expected each target to be "
+                    f"OpOverloadPacket (i.e. torch.ops.mylib.foo), got "
+                    f"{target} which is of type {type(target)}")
+            if target in flop_registry:
+                raise RuntimeError(f"duplicate registrations for {target}")
+            flop_registry[target] = flop_formula
+
+        # To handle allowing multiple aten_ops at once
+        torch.utils._pytree.tree_map_(register, targets)
+
+        return flop_formula
+
+    return register_fun
+
+@register_flop_formula(aten.mm)
+def mm_flop(a_shape, b_shape, *args, out_shape=None, **kwargs) -> int:
+    """Count flops for matmul."""
+    # Inputs should be a list of length 2.
+    # Inputs contains the shapes of two matrices.
+    m, k = a_shape
+    k2, n = b_shape
+    assert k == k2
+    # NB(chilli): Should be 2 * k - 1 technically for FLOPs.
+    return m * n * 2 * k
+
+@register_flop_formula(aten.addmm)
+def addmm_flop(self_shape, a_shape, b_shape, out_shape=None, **kwargs) -> int:
+    """Count flops for addmm."""
+    return mm_flop(a_shape, b_shape)
+
+@register_flop_formula(aten.bmm)
+def bmm_flop(a_shape, b_shape, out_shape=None, **kwargs) -> int:
+    """Count flops for the bmm operation."""
+    # Inputs should be a list of length 2.
+    # Inputs contains the shapes of two tensor.
+    b, m, k = a_shape
+    b2, k2, n = b_shape
+    assert b == b2
+    assert k == k2
+    # NB(chilli): Should be 2 * k - 1 technically for FLOPs.
+    flop = b * m * n * 2 * k
+    return flop
+
+@register_flop_formula(aten.baddbmm)
+def baddbmm_flop(self_shape, a_shape, b_shape, out_shape=None, **kwargs) -> int:
+    """Count flops for the baddbmm operation."""
+    # Inputs should be a list of length 3.
+    # Inputs contains the shapes of three tensors.
+    return bmm_flop(a_shape, b_shape)
+
+
+def conv_flop_count(
+    x_shape: List[int],
+    w_shape: List[int],
+    out_shape: List[int],
+    transposed: bool = False,
+) -> int:
+    """Count flops for convolution.
+
+    Note only multiplication is
+    counted. Computation for bias are ignored.
+    Flops for a transposed convolution are calculated as
+    flops = (x_shape[2:] * prod(w_shape) * batch_size).
+    Args:
+        x_shape (list(int)): The input shape before convolution.
+        w_shape (list(int)): The filter shape.
+        out_shape (list(int)): The output shape after convolution.
+        transposed (bool): is the convolution transposed
+    Returns:
+        int: the number of flops
+    """
+
+    batch_size = x_shape[0]
+    conv_shape = (x_shape if transposed else out_shape)[2:]
+    c_out, c_in, *filter_size = w_shape
+
+    """
+    General idea here is that for a regular conv, for each point in the output
+    spatial dimension we convolve the filter with something (hence
+    `prod(conv_shape) * prod(filter_size)` ops). Then, this gets multiplied by
+    1. batch_size, 2. the cross product of input and weight channels.
+
+    For the transpose, it's not each point in the *output* spatial dimension but
+    each point in the *input* spatial dimension.
+    """
+    # NB(chilli): I don't think this properly accounts for padding :think:
+    # NB(chilli): Should be 2 * c_in - 1 technically for FLOPs.
+    flop = prod(conv_shape) * prod(filter_size) * batch_size * c_out * c_in * 2
+    return flop
+
+@register_flop_formula([aten.convolution, aten._convolution])
+def conv_flop(x_shape, w_shape, _bias, _stride, _padding, _dilation, transposed, *args, out_shape=None, **kwargs) -> int:
+    """Count flops for convolution."""
+    return conv_flop_count(x_shape, w_shape, out_shape, transposed=transposed)
+
+
+@register_flop_formula(aten.convolution_backward)
+def conv_backward_flop(
+        grad_out_shape,
+        x_shape,
+        w_shape,
+        _bias,
+        _stride,
+        _padding,
+        _dilation,
+        transposed,
+        _output_padding,
+        _groups,
+        output_mask,
+        out_shape) -> int:
+
+    def t(shape):
+        return [shape[1], shape[0]] + list(shape[2:])
+    flop_count = 0
+
+    """
+    Let's say we have a regular 1D conv
+    {A, B, C} [inp]
+    {i, j} [weight]
+    => (conv)
+    {Ai + Bj, Bi + Cj} [out]
+
+    And as a reminder, the transposed conv of the above is
+    => {Ai, Aj + Bi, Bj + Ci, Cj} [transposed conv out]
+
+    For the backwards of conv, we now have
+    {D, E} [grad_out]
+    {A, B, C} [inp]
+    {i, j} [weight]
+
+    # grad_inp as conv_transpose(grad_out, weight)
+    Let's first compute grad_inp. To do so, we can simply look at all the
+    multiplications that each element of inp is involved in. For example, A is
+    only involved in the first element of the output (and thus only depends upon
+    D in grad_out), and C is only involved in the last element of the output
+    (and thus only depends upon E in grad_out)
+
+    {Di, Dj + Ei, Ej} [grad_inp]
+
+    Note that this corresponds to the below conv_transpose. This gives us the
+    output_mask[0] branch, which is grad_inp.
+
+    {D, E} [inp (grad_out)]
+    {i, j} [weight]
+    => (conv_transpose)
+    {Di, Dj + Ei, Ej} [out (grad_inp)]
+
+    I leave the fact that grad_inp for a transposed conv is just conv(grad_out,
+    weight) as an exercise for the reader.
+
+    # grad_weight as conv(inp, grad_out)
+    To compute grad_weight, we again look at the terms in the output, which as
+    a reminder is:
+    => {Ai + Bj, Bi + Cj} [out]
+    => {D, E} [grad_out]
+    If we manually compute the gradient for the weights, we see it's
+    {AD + BE, BD + CE} [grad_weight]
+
+    This corresponds to the below conv
+    {A, B, C} [inp]
+    {D, E} [weight (grad_out)]
+    => (conv)
+    {AD + BE, BD + CE} [out (grad_weight)]
+
+    # grad_weight of transposed conv as conv(grad_out, inp)
+    As a reminder, the terms of the output of a transposed conv are:
+    => {Ai, Aj + Bi, Bj + Ci, Cj} [transposed conv out]
+    => {D, E, F, G} [grad_out]
+
+    Manually computing the gradient for the weights, we see it's
+    {AD + BE + CF, AE + BF + CG} [grad_weight]
+
+    This corresponds to the below conv
+    {D, E, F, G} [inp (grad_out)]
+    {A, B, C} [weight (inp)]
+    => (conv)
+    {AD + BE + CF, AE + BF + CG} [out (grad_weight)]
+
+    For the full backwards formula, there are also some details involving
+    transpose of the batch/channel dimensions and groups, but I skip those for
+    the sake of brevity (and they're pretty similar to matmul backwards)
+
+    Check [conv backwards decomposition as conv forwards]
+    """
+    # grad_inp as conv_transpose(grad_out, weight)
+    if output_mask[0]:
+        grad_input_shape = get_shape(out_shape[0])
+        flop_count += conv_flop_count(grad_out_shape, w_shape, grad_input_shape, not transposed)
+
+    if output_mask[1]:
+        grad_weight_shape = get_shape(out_shape[1])
+        if transposed:
+            # grad_weight of transposed conv as conv(grad_out, inp)
+            flop_count += conv_flop_count(t(grad_out_shape), t(x_shape), t(grad_weight_shape), transposed=False)
+        else:
+            # grad_weight as conv(inp, grad_out)
+            flop_count += conv_flop_count(t(x_shape), t(grad_out_shape), t(grad_weight_shape), transposed=False)
+
+    return flop_count
+
+def sdpa_flop_count(query_shape, key_shape, value_shape):
+    """
+    Count flops for self-attention.
+
+    NB: We can assume that value_shape == key_shape
+    """
+    b, h, s_q, d_q = query_shape
+    _b2, _h2, s_k, _d2 = key_shape
+    _b3, _h3, _s3, d_v = value_shape
+    assert b == _b2 == _b3 and h == _h2 == _h3 and d_q == _d2 and s_k == _s3 and d_q == _d2
+    total_flops = 0
+    # q: [b, h, s_q, d_q] @ k: [b, h, d_q, s_k] -> scores: [b, h, s_q, s_k]
+    total_flops += bmm_flop((b * h, s_q, d_q), (b * h, d_q, s_k))
+    # scores: [b, h, s_q, s_k] @ v: [b, h, s_k, d_v] -> out: [b, h, s_q, d_v]
+    total_flops += bmm_flop((b * h, s_q, s_k), (b * h, s_k, d_v))
+    return total_flops
+
+
+@register_flop_formula([aten._scaled_dot_product_efficient_attention,
+                        aten._scaled_dot_product_flash_attention,
+                        aten._scaled_dot_product_cudnn_attention])
+def sdpa_flop(query_shape, key_shape, value_shape, *args, out_shape=None, **kwargs) -> int:
+    """Count flops for self-attention."""
+    # NB: We aren't accounting for causal attention here
+    return sdpa_flop_count(query_shape, key_shape, value_shape)
+
+
+def _offsets_to_lengths(offsets, max_len):
+    """
+    If the offsets tensor is fake, then we don't know the actual lengths.
+    In that case, we can just assume the worst case; each batch has max length.
+    """
+    from torch._subclasses.fake_tensor import FakeTensor
+    from torch._subclasses.functional_tensor import FunctionalTensor
+    if not isinstance(offsets, (FakeTensor, FunctionalTensor)):
+        return offsets.diff().tolist()
+    return [max_len] * (offsets.size(0) - 1)
+
+
+def _unpack_flash_attention_nested_shapes(
+    *,
+    query,
+    key,
+    value,
+    grad_out=None,
+    cum_seq_q,
+    cum_seq_k,
+    max_q,
+    max_k,
+) -> Iterator[Tuple[Tuple[int, ...], Tuple[int, ...], Tuple[int, ...], Optional[Tuple[int, ...]]]]:
+    """
+    Given inputs to a flash_attention_(forward|backward) kernel, this will handle behavior for
+    NestedTensor inputs by effectively unbinding the NestedTensor and yielding the shapes for
+    each batch element.
+
+    In the case that this isn't a NestedTensor kernel, then it just yields the original shapes.
+    """
+    if cum_seq_q is not None:
+        # This means we should be dealing with a Nested Jagged Tensor query.
+        # The inputs will have shape                  (sum(sequence len), heads, dimension)
+        # In comparison, non-Nested inputs have shape (batch, heads, sequence len, dimension)
+        # To deal with this, we convert to a shape of (batch, heads, max_seq_len, dimension)
+        # So the flops calculation in this case is an overestimate of the actual flops.
+        assert len(key.shape) == 3
+        assert len(value.shape) == 3
+        assert grad_out is None or grad_out.shape == query.shape
+        _, h_q, d_q = query.shape
+        _, h_k, d_k = key.shape
+        _, h_v, d_v = value.shape
+        assert cum_seq_q is not None
+        assert cum_seq_k is not None
+        assert cum_seq_q.shape == cum_seq_k.shape
+        seq_q_lengths = _offsets_to_lengths(cum_seq_q, max_q)
+        seq_k_lengths = _offsets_to_lengths(cum_seq_k, max_k)
+        for (seq_q_len, seq_k_len) in zip(seq_q_lengths, seq_k_lengths):
+            new_query_shape = (1, h_q, seq_q_len, d_q)
+            new_key_shape = (1, h_k, seq_k_len, d_k)
+            new_value_shape = (1, h_v, seq_k_len, d_v)
+            new_grad_out_shape = new_query_shape if grad_out is not None else None
+            yield new_query_shape, new_key_shape, new_value_shape, new_grad_out_shape
+        return
+
+    yield query.shape, key.shape, value.shape, grad_out.shape if grad_out is not None else None
+
+
+def _unpack_efficient_attention_nested_shapes(
+    *,
+    query,
+    key,
+    value,
+    grad_out=None,
+    cu_seqlens_q,
+    cu_seqlens_k,
+    max_seqlen_q,
+    max_seqlen_k,
+) -> Iterator[Tuple[Tuple[int, ...], Tuple[int, ...], Tuple[int, ...], Optional[Tuple[int, ...]]]]:
+    """
+    Given inputs to a efficient_attention_(forward|backward) kernel, this will handle behavior for
+    NestedTensor inputs by effectively unbinding the NestedTensor and yielding the shapes for
+    each batch element.
+
+    In the case that this isn't a NestedTensor kernel, then it just yields the original shapes.
+    """
+    if cu_seqlens_q is not None:
+        # Unlike flash_attention_forward, we get a 4D tensor instead of a 3D tensor for efficient attention.
+        #
+        # This means we should be dealing with a Nested Jagged Tensor query.
+        # The inputs will have shape                  (sum(sequence len), heads, dimension)
+        # In comparison, non-Nested inputs have shape (batch, heads, sequence len, dimension)
+        # To deal with this, we convert to a shape of (batch, heads, max_seq_len, dimension)
+        # So the flops calculation in this case is an overestimate of the actual flops.
+        assert len(key.shape) == 4
+        assert len(value.shape) == 4
+        assert grad_out is None or grad_out.shape == query.shape
+        _, _, h_q, d_q = query.shape
+        _, _, h_k, d_k = key.shape
+        _, _, h_v, d_v = value.shape
+        assert cu_seqlens_q is not None
+        assert cu_seqlens_k is not None
+        assert cu_seqlens_q.shape == cu_seqlens_k.shape
+        seqlens_q = _offsets_to_lengths(cu_seqlens_q, max_seqlen_q)
+        seqlens_k = _offsets_to_lengths(cu_seqlens_k, max_seqlen_k)
+        for len_q, len_k in zip(seqlens_q, seqlens_k):
+            new_query_shape = (1, h_q, len_q, d_q)
+            new_key_shape = (1, h_k, len_k, d_k)
+            new_value_shape = (1, h_v, len_k, d_v)
+            new_grad_out_shape = new_query_shape if grad_out is not None else None
+            yield new_query_shape, new_key_shape, new_value_shape, new_grad_out_shape
+        return
+
+    yield query.shape, key.shape, value.shape, grad_out.shape if grad_out is not None else None
+
+
+@register_flop_formula(aten._flash_attention_forward, get_raw=True)
+def _flash_attention_forward_flop(
+    query,
+    key,
+    value,
+    cum_seq_q,
+    cum_seq_k,
+    max_q,
+    max_k,
+    *args,
+    out_shape=None,
+    **kwargs
+) -> int:
+    """Count flops for self-attention."""
+    # NB: We aren't accounting for causal attention here
+    # in case this is a nested tensor, we unpack the individual batch elements
+    # and then sum the flops per batch element
+    sizes = _unpack_flash_attention_nested_shapes(
+        query=query,
+        key=key,
+        value=value,
+        cum_seq_q=cum_seq_q,
+        cum_seq_k=cum_seq_k,
+        max_q=max_q,
+        max_k=max_k,
+    )
+    return sum(
+        sdpa_flop_count(query_shape, key_shape, value_shape)
+        for query_shape, key_shape, value_shape, _ in sizes
+    )
+
+
+@register_flop_formula(aten._efficient_attention_forward, get_raw=True)
+def _efficient_attention_forward_flop(
+    query,
+    key,
+    value,
+    bias,
+    cu_seqlens_q,
+    cu_seqlens_k,
+    max_seqlen_q,
+    max_seqlen_k,
+    *args,
+    **kwargs
+) -> int:
+    """Count flops for self-attention."""
+    # NB: We aren't accounting for causal attention here
+    # in case this is a nested tensor, we unpack the individual batch elements
+    # and then sum the flops per batch element
+    sizes = _unpack_efficient_attention_nested_shapes(
+        query=query,
+        key=key,
+        value=value,
+        cu_seqlens_q=cu_seqlens_q,
+        cu_seqlens_k=cu_seqlens_k,
+        max_seqlen_q=max_seqlen_q,
+        max_seqlen_k=max_seqlen_k,
+    )
+    return sum(
+        sdpa_flop_count(query_shape, key_shape, value_shape)
+        for query_shape, key_shape, value_shape, _ in sizes
+    )
+
+
+def sdpa_backward_flop_count(grad_out_shape, query_shape, key_shape, value_shape):
+    total_flops = 0
+    b, h, s_q, d_q = query_shape
+    _b2, _h2, s_k, _d2 = key_shape
+    _b3, _h3, _s3, d_v = value_shape
+    _b4, _h4, _s4, _d4 = grad_out_shape
+    assert b == _b2 == _b3 == _b4 and h == _h2 == _h3 == _h4 and d_q == _d2
+    assert d_v == _d4 and s_k == _s3 and s_q == _s4
+    total_flops = 0
+    # Step 1: We recompute the scores matrix.
+    # q: [b, h, s_q, d_q] @ k: [b, h, d_q, s_k] -> scores: [b, h, s_q, s_k]
+    total_flops += bmm_flop((b * h, s_q, d_q), (b * h, d_q, s_k))
+
+    # Step 2: We propagate the gradients through the score @ v operation.
+    # gradOut: [b, h, s_q, d_v] @ v: [b, h, d_v, s_k] -> gradScores: [b, h, s_q, s_k]
+    total_flops += bmm_flop((b * h, s_q, d_v), (b * h, d_v, s_k))
+    # scores: [b, h, s_k, s_q] @ gradOut: [b, h, s_q, d_v] -> gradV: [b, h, s_k, d_v]
+    total_flops += bmm_flop((b * h, s_k, s_q), (b * h, s_q, d_v))
+
+    # Step 3: We propagate th gradients through the k @ v operation
+    # gradScores: [b, h, s_q, s_k] @ k: [b, h, s_k, d_q] -> gradQ: [b, h, s_q, d_q]
+    total_flops += bmm_flop((b * h, s_q, s_k), (b * h, s_k, d_q))
+    # q: [b, h, d_q, s_q] @ gradScores: [b, h, s_q, s_k] -> gradK: [b, h, d_q, s_k]
+    total_flops += bmm_flop((b * h, d_q, s_q), (b * h, s_q, s_k))
+    return total_flops
+
+
+@register_flop_formula([aten._scaled_dot_product_efficient_attention_backward,
+                        aten._scaled_dot_product_flash_attention_backward,
+                        aten._scaled_dot_product_cudnn_attention_backward])
+def sdpa_backward_flop(grad_out_shape, query_shape, key_shape, value_shape, *args, out_shape=None, **kwargs) -> int:
+    """Count flops for self-attention backward."""
+    return sdpa_backward_flop_count(grad_out_shape, query_shape, key_shape, value_shape)
+
+@register_flop_formula(aten._flash_attention_backward, get_raw=True)
+def _flash_attention_backward_flop(
+    grad_out,
+    query,
+    key,
+    value,
+    out,  # named _out_shape to avoid kwarg collision with out_shape created in wrapper
+    logsumexp,
+    cum_seq_q,
+    cum_seq_k,
+    max_q,
+    max_k,
+    *args,
+    **kwargs,
+) -> int:
+    # in case this is a nested tensor, we unpack the individual batch elements
+    # and then sum the flops per batch element
+    shapes = _unpack_flash_attention_nested_shapes(
+        query=query,
+        key=key,
+        value=value,
+        grad_out=grad_out,
+        cum_seq_q=cum_seq_q,
+        cum_seq_k=cum_seq_k,
+        max_q=max_q,
+        max_k=max_k,
+    )
+    return sum(
+        sdpa_backward_flop_count(grad_out_shape, query_shape, key_shape, value_shape)
+        for query_shape, key_shape, value_shape, grad_out_shape in shapes
+    )
+
+
+@register_flop_formula(aten._efficient_attention_backward, get_raw=True)
+def _efficient_attention_backward_flop(
+    grad_out,
+    query,
+    key,
+    value,
+    bias,
+    out,  # named _out to avoid kwarg collision with out created in wrapper
+    cu_seqlens_q,
+    cu_seqlens_k,
+    max_seqlen_q,
+    max_seqlen_k,
+    *args,
+    **kwargs,
+) -> int:
+    # in case this is a nested tensor, we unpack the individual batch elements
+    # and then sum the flops per batch element
+    shapes = _unpack_efficient_attention_nested_shapes(
+        query=query,
+        key=key,
+        value=value,
+        grad_out=grad_out,
+        cu_seqlens_q=cu_seqlens_q,
+        cu_seqlens_k=cu_seqlens_k,
+        max_seqlen_q=max_seqlen_q,
+        max_seqlen_k=max_seqlen_k,
+    )
+    return sum(
+        sdpa_backward_flop_count(grad_out_shape, query_shape, key_shape, value_shape)
+        for query_shape, key_shape, value_shape, grad_out_shape in shapes
+    )
+
+
+flop_registry = {
+    aten.mm: mm_flop,
+    aten.addmm: addmm_flop,
+    aten.bmm: bmm_flop,
+    aten.baddbmm: baddbmm_flop,
+    aten.convolution: conv_flop,
+    aten._convolution: conv_flop,
+    aten.convolution_backward: conv_backward_flop,
+    aten._scaled_dot_product_efficient_attention: sdpa_flop,
+    aten._scaled_dot_product_flash_attention: sdpa_flop,
+    aten._scaled_dot_product_cudnn_attention: sdpa_flop,
+    aten._scaled_dot_product_efficient_attention_backward: sdpa_backward_flop,
+    aten._scaled_dot_product_flash_attention_backward: sdpa_backward_flop,
+    aten._scaled_dot_product_cudnn_attention_backward: sdpa_backward_flop,
+    aten._flash_attention_forward: _flash_attention_forward_flop,
+    aten._efficient_attention_forward: _efficient_attention_forward_flop,
+    aten._flash_attention_backward: _flash_attention_backward_flop,
+    aten._efficient_attention_backward: _efficient_attention_backward_flop,
+}
+
+def normalize_tuple(x):
+    if not isinstance(x, tuple):
+        return (x,)
+    return x
+
+
+# Define the suffixes for different orders of magnitude
+suffixes = ["", "K", "M", "B", "T"]
+# Thanks BingChat!
+def get_suffix_str(number):
+    # Find the index of the appropriate suffix based on the number of digits
+    # with some additional overflow.
+    # i.e. 1.01B should be displayed as 1001M, not 1.001B
+    index = max(0, min(len(suffixes) - 1, (len(str(number)) - 2) // 3))
+    return suffixes[index]
+
+def convert_num_with_suffix(number, suffix):
+    index = suffixes.index(suffix)
+    # Divide the number by 1000^index and format it to two decimal places
+    value = f"{number / 1000 ** index:.3f}"
+    # Return the value and the suffix as a string
+    return value + suffixes[index]
+
+def convert_to_percent_str(num, denom):
+    if denom == 0:
+        return "0%"
+    return f"{num / denom:.2%}"
+
+def _pytreeify_preserve_structure(f):
+    @wraps(f)
+    def nf(args):
+        flat_args, spec = tree_flatten(args)
+        out = f(*flat_args)
+        return tree_unflatten(out, spec)
+
+    return nf
+
+
+class FlopCounterMode(TorchDispatchMode):
+    """
+    ``FlopCounterMode`` is a context manager that counts the number of flops within its context.
+
+    It does this using a ``TorchDispatchMode``.
+
+    It also supports hierarchical output by passing a module (or list of
+    modules) to FlopCounterMode on construction. If you do not need hierarchical
+    output, you do not need to use it with a module.
+
+    Example usage
+
+    .. code-block:: python
+
+        mod = ...
+        with FlopCounterMode(mod) as flop_counter:
+            mod.sum().backward()
+
+    """
+
+    def __init__(
+            self,
+            mods: Optional[Union[torch.nn.Module, List[torch.nn.Module]]] = None,
+            depth: int = 2,
+            display: bool = True,
+            custom_mapping: Optional[Dict[Any, Any]] = None):
+        super().__init__()
+        self.flop_counts: Dict[str, Dict[Any, int]] = defaultdict(lambda: defaultdict(int))
+        self.depth = depth
+        self.display = display
+        if custom_mapping is None:
+            custom_mapping = {}
+        if mods is not None:
+            warnings.warn("mods argument is not needed anymore, you can stop passing it", stacklevel=2)
+        self.flop_registry = {
+            **flop_registry,
+            **{k: v if getattr(v, "_get_raw", False) else shape_wrapper(v) for k, v in custom_mapping.items()}
+        }
+        self.mod_tracker = ModuleTracker()
+
+    def get_total_flops(self) -> int:
+        return sum(self.flop_counts['Global'].values())
+
+    def get_flop_counts(self) -> Dict[str, Dict[Any, int]]:
+        """Return the flop counts as a dictionary of dictionaries.
+
+        The outer
+        dictionary is keyed by module name, and the inner dictionary is keyed by
+        operation name.
+
+        Returns:
+            Dict[str, Dict[Any, int]]: The flop counts as a dictionary.
+        """
+        return {k: dict(v) for k, v in self.flop_counts.items()}
+
+    def get_table(self, depth=None):
+        if depth is None:
+            depth = self.depth
+        if depth is None:
+            depth = 999999
+
+        import tabulate
+        tabulate.PRESERVE_WHITESPACE = True
+        header = ["Module", "FLOP", "% Total"]
+        values = []
+        global_flops = self.get_total_flops()
+        global_suffix = get_suffix_str(global_flops)
+        is_global_subsumed = False
+
+        def process_mod(mod_name, depth):
+            nonlocal is_global_subsumed
+
+            total_flops = sum(self.flop_counts[mod_name].values())
+
+            is_global_subsumed |= total_flops >= global_flops
+
+            padding = " " * depth
+            values = []
+            values.append([
+                padding + mod_name,
+                convert_num_with_suffix(total_flops, global_suffix),
+                convert_to_percent_str(total_flops, global_flops)
+            ])
+            for k, v in self.flop_counts[mod_name].items():
+                values.append([
+                    padding + " - " + str(k),
+                    convert_num_with_suffix(v, global_suffix),
+                    convert_to_percent_str(v, global_flops)
+                ])
+            return values
+
+        for mod in sorted(self.flop_counts.keys()):
+            if mod == 'Global':
+                continue
+            mod_depth = mod.count(".") + 1
+            if mod_depth > depth:
+                continue
+
+            cur_values = process_mod(mod, mod_depth - 1)
+            values.extend(cur_values)
+
+        # We do a bit of messing around here to only output the "Global" value
+        # if there are any FLOPs in there that aren't already fully contained by
+        # a module.
+        if 'Global' in self.flop_counts and not is_global_subsumed:
+            for idx in range(len(values)):
+                values[idx][0] = " " + values[idx][0]
+
+            values = process_mod('Global', 0) + values
+
+        if len(values) == 0:
+            values = [["Global", "0", "0%"]]
+
+        return tabulate.tabulate(values, headers=header, colalign=("left", "right", "right"))
+
+    def __enter__(self):
+        self.flop_counts.clear()
+        self.mod_tracker.__enter__()
+        super().__enter__()
+        return self
+
+    def __exit__(self, *args):
+        super().__exit__(*args)
+        self.mod_tracker.__exit__()
+        if self.display:
+            print(self.get_table(self.depth))
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        kwargs = kwargs if kwargs else {}
+        out = func(*args, **kwargs)
+        return self._count_flops(func._overloadpacket, out, args, kwargs)
+
+    def _count_flops(self, func_packet, out, args, kwargs):
+        if func_packet in self.flop_registry:
+            flop_count_func = self.flop_registry[func_packet]
+            flop_count = flop_count_func(*args, **kwargs, out_val=out)  # type: ignore[operator]
+            for par in set(self.mod_tracker.parents):
+                self.flop_counts[par][func_packet] += flop_count
+
+        return out
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/hooks.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee828034bdf6ebb315140e0b6cea817c846189fe
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/hooks.py
@@ -0,0 +1,253 @@
+# mypy: allow-untyped-defs
+import torch
+from collections import OrderedDict
+import weakref
+import warnings
+from typing import Any, Tuple
+
+__all__ = ["RemovableHandle", "unserializable_hook", "warn_if_has_hooks", "BackwardHook"]
+
+class RemovableHandle:
+    r"""
+    A handle which provides the capability to remove a hook.
+
+    Args:
+        hooks_dict (dict): A dictionary of hooks, indexed by hook ``id``.
+        extra_dict (Union[dict, List[dict]]): An additional dictionary or list of
+            dictionaries whose keys will be deleted when the same keys are
+            removed from ``hooks_dict``.
+    """
+
+    id: int
+    next_id: int = 0
+
+    def __init__(self, hooks_dict: Any, *, extra_dict: Any = None) -> None:
+        self.hooks_dict_ref = weakref.ref(hooks_dict)
+        self.id = RemovableHandle.next_id
+        RemovableHandle.next_id += 1
+
+        self.extra_dict_ref: Tuple = ()
+        if isinstance(extra_dict, dict):
+            self.extra_dict_ref = (weakref.ref(extra_dict),)
+        elif isinstance(extra_dict, list):
+            self.extra_dict_ref = tuple(weakref.ref(d) for d in extra_dict)
+
+    def remove(self) -> None:
+        hooks_dict = self.hooks_dict_ref()
+        if hooks_dict is not None and self.id in hooks_dict:
+            del hooks_dict[self.id]
+
+        for ref in self.extra_dict_ref:
+            extra_dict = ref()
+            if extra_dict is not None and self.id in extra_dict:
+                del extra_dict[self.id]
+
+    def __getstate__(self):
+        if self.extra_dict_ref is None:
+            return (self.hooks_dict_ref(), self.id)
+        else:
+            return (self.hooks_dict_ref(), self.id, tuple(ref() for ref in self.extra_dict_ref))
+
+    def __setstate__(self, state) -> None:
+        if state[0] is None:
+            # create a dead reference
+            self.hooks_dict_ref = weakref.ref(OrderedDict())
+        else:
+            self.hooks_dict_ref = weakref.ref(state[0])
+        self.id = state[1]
+        RemovableHandle.next_id = max(RemovableHandle.next_id, self.id + 1)
+
+        if len(state) < 3 or state[2] is None:
+            self.extra_dict_ref = ()
+        else:
+            self.extra_dict_ref = tuple(weakref.ref(d) for d in state[2])
+
+    def __enter__(self) -> "RemovableHandle":
+        return self
+
+    def __exit__(self, type: Any, value: Any, tb: Any) -> None:
+        self.remove()
+
+
+def unserializable_hook(f):
+    """
+    Mark a function as an unserializable hook with this decorator.
+
+    This suppresses warnings that would otherwise arise if you attempt
+    to serialize a tensor that has a hook.
+    """
+    f.__torch_unserializable__ = True
+    return f
+
+
+def warn_if_has_hooks(tensor):
+    if tensor._backward_hooks:
+        for k in tensor._backward_hooks:
+            hook = tensor._backward_hooks[k]
+            if not hasattr(hook, "__torch_unserializable__"):
+                warnings.warn(f"backward hook {repr(hook)} on tensor will not be "
+                              "serialized.  If this is expected, you can "
+                              "decorate the function with @torch.utils.hooks.unserializable_hook "
+                              "to suppress this warning")
+
+class BackwardHook:
+    """
+    A wrapper class to implement nn.Module backward hooks.
+
+    It handles:
+      - Ignoring non-Tensor inputs and replacing them by None before calling the user hook
+      - Generating the proper Node to capture a set of Tensor's gradients
+      - Linking the gradients captures for the outputs with the gradients captured for the input
+      - Calling the user hook once both output and input gradients are available
+    """
+
+    def __init__(self, module, user_hooks, user_pre_hooks):
+        self.user_hooks = user_hooks
+        self.user_pre_hooks = user_pre_hooks
+        self.module = module
+
+        self.grad_outputs = None
+        self.n_outputs = -1
+        self.output_tensors_index = None
+        self.n_inputs = -1
+        self.input_tensors_index = None
+
+    def _pack_with_none(self, indices, values, size):
+        res = [None] * size
+        for idx, val in zip(indices, values):
+            res[idx] = val
+
+        return tuple(res)
+
+    def _unpack_none(self, indices, values):
+        res = []
+        for idx in indices:
+            res.append(values[idx])
+
+        return tuple(res)
+
+    def _set_user_hook(self, grad_fn):
+        def hook(grad_input, _):
+            if self.grad_outputs is None:
+                # This happens because the gradient in your nn.Module flows to
+                # the Module's input without " passing through the Module's
+                # output, e.g. when you're doing double backward.
+                return
+            res = self._pack_with_none(self.input_tensors_index, grad_input, self.n_inputs)
+
+            for hook in self.user_hooks:
+                out = hook(self.module, res, self.grad_outputs)
+
+                if out is None:
+                    continue
+
+                if len(out) != len(res):
+                    raise RuntimeError("Backward hook returned an invalid number of grad_input, "
+                                       f"got {len(out)}, but expected {len(res)}")
+
+                res = out
+
+            self.grad_outputs = None
+
+            return self._unpack_none(self.input_tensors_index, res)
+
+        grad_fn.register_hook(hook)
+
+    def _apply_on_tensors(self, fn, args):
+        # Can be used to apply the given function to the tensors contained in the
+        # args. Will return updated args and the tensors indices
+        tensors_idx = []
+        tensors = []
+
+        requires_grad = False
+        for i, arg in enumerate(args):
+            if isinstance(arg, torch.Tensor):
+                tensors_idx.append(i)
+                tensors.append(arg)
+                requires_grad |= arg.requires_grad
+
+        if not (requires_grad and torch.is_grad_enabled()):
+            return args, None
+
+        new_tensors = torch.nn.modules._functions.BackwardHookFunction.apply(*tensors)
+        if len(new_tensors) == 0:
+            raise RuntimeError("Cannot set Module backward hook for a Module with no input Tensors.")
+
+        grad_fns = [t.grad_fn for t in new_tensors if t.grad_fn is not None and t.grad_fn.name() == "BackwardHookFunctionBackward"]
+        if len(grad_fns) == 0:
+            raise RuntimeError("Error while setting up backward hooks. Please open "
+                               "an issue with a code sample to reproduce this.")
+
+        fn(grad_fns[0])
+
+        arg_list = list(args)
+        for idx, val in zip(tensors_idx, new_tensors):
+            arg_list[idx] = val
+
+        if type(args) is tuple:
+            out = tuple(arg_list)
+        else:
+            out = type(args)(*arg_list)
+        return out, tensors_idx
+
+    def setup_input_hook(self, args):
+        def fn(grad_fn):
+            self._set_user_hook(grad_fn)
+
+        res, input_idx = self._apply_on_tensors(fn, args)
+        self.n_inputs = len(args)
+        self.input_tensors_index = input_idx
+        return res
+
+    def setup_output_hook(self, args):
+        def fn(grad_fn):
+            def hook(_, grad_output):
+                self.grad_outputs = self._pack_with_none(self.output_tensors_index,
+                                                         grad_output,
+                                                         self.n_outputs)
+
+                if self.user_pre_hooks:
+                    expected_len = len(self.grad_outputs)
+                    for user_pre_hook in self.user_pre_hooks:
+                        hook_grad_outputs = user_pre_hook(self.module, self.grad_outputs)
+                        if hook_grad_outputs is None:
+                            continue
+
+                        actual_len = len(hook_grad_outputs)
+                        if actual_len != expected_len:
+                            raise RuntimeError("Backward pre hook returned an invalid number of grad_output, "
+                                               f"got {actual_len}, but expected {expected_len}")
+                        self.grad_outputs = hook_grad_outputs
+
+                # We need to be able to clear self.grad_outputs but also return it
+                local_grad_outputs = self.grad_outputs
+
+                # Special case if no input required gradients, this hook should call the user
+                # hook directly
+                if self.input_tensors_index is None:
+                    grad_inputs = self._pack_with_none([], [], self.n_inputs)
+                    for user_hook in self.user_hooks:
+                        res = user_hook(self.module, grad_inputs, self.grad_outputs)
+                        if res is not None and not (isinstance(res, tuple) and all(el is None for el in res)):
+                            raise RuntimeError("Backward hook for Modules where no input requires "
+                                               "gradient should always return None or None for all gradients.")
+                    self.grad_outputs = None
+
+                if local_grad_outputs is not None:
+                    assert self.output_tensors_index is not None  # mypy
+                    return tuple(local_grad_outputs[i] for i in self.output_tensors_index)
+
+            grad_fn.register_hook(hook)
+
+        is_tuple = True
+        if not isinstance(args, tuple):
+            args = (args,)
+            is_tuple = False
+
+        res, output_idx = self._apply_on_tensors(fn, args)
+        self.n_outputs = len(args)
+        self.output_tensors_index = output_idx
+
+        if not is_tuple:
+            res = res[0]
+        return res
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/mkldnn.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/mkldnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..06ca96d2de9a9729b8250d5c333d74d2352af861
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/mkldnn.py
@@ -0,0 +1,234 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+class MkldnnLinear(torch.jit.ScriptModule):
+    def __init__(self, dense_module, dtype):
+        super().__init__()
+        self.register_buffer('weight', dense_module.weight.to_mkldnn(dtype))
+        if dense_module.bias is not None:
+            # Bias can be fp32 or bf16 for OneDNN bf16 path, but for good accuracy,
+            # we use fp32 dtype.
+            self.register_buffer('bias', dense_module.bias.to_mkldnn())
+        else:
+            # TODO: Remove this once ScriptModule supports registering None buffer
+            self.register_buffer(
+                'bias',
+                torch.zeros([dense_module.weight.size(0)], dtype=torch.float).to_mkldnn())
+
+    @torch.jit.script_method
+    def __getstate__(self):
+        return (self.weight.to_dense(), self.bias.to_dense(), self.training)
+
+    @torch.jit.script_method
+    def __setstate__(self, state):
+        self.weight = state[0].to_mkldnn()
+        self.bias = state[1].to_mkldnn()
+        self.training = state[2]
+
+    @torch.jit.script_method
+    def forward(self, x):
+        x_mkldnn = x if x.is_mkldnn else x.to_mkldnn()
+        y_mkldnn = torch._C._nn.mkldnn_linear(x_mkldnn, self.weight, self.bias)
+        y = y_mkldnn if x.is_mkldnn else y_mkldnn.to_dense()
+        return y
+
+
+class _MkldnnConvNd(torch.jit.ScriptModule):
+    """Common base of MkldnnConv1d and MkldnnConv2d."""
+
+    __constants__ = ['stride', 'padding', 'dilation', 'groups']
+
+    def __init__(self, dense_module):
+        super().__init__()
+
+        self.stride = dense_module.stride
+        self.padding = dense_module.padding
+        self.dilation = dense_module.dilation
+        self.groups = dense_module.groups
+
+        if dense_module.bias is not None:
+            self.register_buffer('bias', dense_module.bias.to_mkldnn())
+        else:
+            # Bias can be fp32 or bf16 for OneDNN bf16 path, but for good accuracy,
+            # we use fp32 dtype.
+            # TODO: Remove this once ScriptModule supports registering None buffer
+            self.register_buffer(
+                'bias',
+                torch.zeros([dense_module.weight.size(0)], dtype=torch.float).to_mkldnn())
+
+    @torch.jit.script_method
+    def __getstate__(self):
+        return (self.weight.to_dense(), self.bias.to_dense(), self.training)
+
+    @torch.jit.script_method
+    def forward(self, x):
+        return torch.mkldnn_convolution(
+            x,
+            self.weight,
+            self.bias,
+            self.padding,
+            self.stride,
+            self.dilation,
+            self.groups)
+
+
+class MkldnnConv1d(_MkldnnConvNd):
+    def __init__(self, dense_module, dtype):
+        super().__init__(dense_module)
+
+        self.register_buffer('weight', dense_module.weight.to_mkldnn(dtype))
+
+    @torch.jit.script_method
+    def __setstate__(self, state):
+        self.weight = state[0].to_mkldnn()
+        self.bias = state[1].to_mkldnn()
+        self.training = state[2]
+
+
+class MkldnnConv2d(_MkldnnConvNd):
+    def __init__(self, dense_module, dtype):
+        super().__init__(dense_module)
+
+        self.register_buffer('weight', torch._C._nn.mkldnn_reorder_conv2d_weight(
+            dense_module.weight.to_mkldnn(dtype),
+            self.padding,
+            self.stride,
+            self.dilation,
+            self.groups))
+
+    @torch.jit.script_method
+    def __setstate__(self, state):
+        self.weight = torch._C._nn.mkldnn_reorder_conv2d_weight(
+            state[0].to_mkldnn(),
+            self.padding,
+            self.stride,
+            self.dilation,
+            self.groups)
+        self.bias = state[1].to_mkldnn()
+        self.training = state[2]
+
+class MkldnnConv3d(_MkldnnConvNd):
+    def __init__(self, dense_module, dtype):
+        super().__init__(dense_module)
+
+        self.register_buffer('weight', torch._C._nn.mkldnn_reorder_conv3d_weight(
+            dense_module.weight.to_mkldnn(dtype),
+            self.padding,
+            self.stride,
+            self.dilation,
+            self.groups))
+
+    @torch.jit.script_method
+    def __setstate__(self, state):
+        self.weight = torch._C._nn.mkldnn_reorder_conv3d_weight(
+            state[0].to_mkldnn(),
+            self.padding,
+            self.stride,
+            self.dilation,
+            self.groups)
+        self.bias = state[1].to_mkldnn()
+        self.training = state[2]
+
+
+class MkldnnBatchNorm(torch.jit.ScriptModule):
+    __constants__ = ['exponential_average_factor', 'eps']
+
+    def __init__(self, dense_module):
+        super().__init__()
+
+        assert not dense_module.training
+        assert dense_module.track_running_stats
+        assert dense_module.affine
+
+        if dense_module.momentum is None:
+            self.exponential_average_factor = 0.0
+        else:
+            self.exponential_average_factor = dense_module.momentum
+        self.eps = dense_module.eps
+
+        self.register_buffer('weight', dense_module.weight.to_mkldnn())
+        self.register_buffer('bias', dense_module.bias.to_mkldnn())
+        self.register_buffer('running_mean', dense_module.running_mean.to_mkldnn())
+        self.register_buffer('running_var', dense_module.running_var.to_mkldnn())
+
+    @torch.jit.script_method
+    def __getstate__(self):
+        weight = self.weight.to_dense()
+        bias = self.bias.to_dense()
+        running_mean = self.running_mean.to_dense()
+        running_var = self.running_var.to_dense()
+        return (weight, bias, running_mean, running_var, self.training)
+
+    @torch.jit.script_method
+    def __setstate__(self, state):
+        self.weight = state[0].to_mkldnn()
+        self.bias = state[1].to_mkldnn()
+        self.running_mean = state[2].to_mkldnn()
+        self.running_var = state[3].to_mkldnn()
+        self.training = state[4]
+
+    @torch.jit.script_method
+    def forward(self, x):
+        return torch.batch_norm(
+            x,
+            self.weight,
+            self.bias,
+            self.running_mean,
+            self.running_var,
+            False,  # training
+            self.exponential_average_factor,
+            self.eps,
+            False,  # cuda_enabled
+        )
+
+class MkldnnPrelu(torch.jit.ScriptModule):
+    def __init__(self, dense_module, dtype):
+        super().__init__()
+        self.register_buffer('weight', dense_module.weight.to_mkldnn(dtype))
+
+    @torch.jit.script_method
+    def __getstate__(self):
+        return (self.weight.to_dense(), self.training)
+
+    @torch.jit.script_method
+    def __setstate__(self, state):
+        self.weight = state[0].to_mkldnn()
+        self.training = state[1]
+
+    @torch.jit.script_method
+    def forward(self, x):
+        x_mkldnn = x if x.is_mkldnn else x.to_mkldnn()
+        y_mkldnn = torch.prelu(x_mkldnn, self.weight)
+        y = y_mkldnn if x.is_mkldnn else y_mkldnn.to_dense()
+        return y
+
+def to_mkldnn(module, dtype=torch.float):
+    assert dtype in [torch.float, torch.bfloat16, torch.half], \
+        "MKLDNN only support float, bfloat16, and half path now"
+
+    def m_fn(m, d):
+        if isinstance(m, torch.nn.Linear):
+            return MkldnnLinear(m, d)
+        elif isinstance(m, torch.nn.Conv1d):
+            return MkldnnConv1d(m, d)
+        elif isinstance(m, torch.nn.Conv2d):
+            return MkldnnConv2d(m, d)
+        elif isinstance(m, torch.nn.Conv3d):
+            return MkldnnConv3d(m, d)
+        elif isinstance(m, (torch.nn.BatchNorm2d, torch.nn.BatchNorm3d)):
+            # For batchnorm bf16 path, OneDNN requires weight and bias need fp32 dtype.
+            # so it doesn't need dtype argument.
+            return MkldnnBatchNorm(m)
+        elif isinstance(m, torch.nn.PReLU):
+            return MkldnnPrelu(m, d)
+        else:
+            return m
+
+    def m_fn_rec(m, d):
+        new_m = m_fn(m, d)
+        for name, sub_m in m.named_children():
+            setattr(new_m, name, m_fn_rec(sub_m, d))
+        return new_m
+
+    return m_fn_rec(module, dtype)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/mobile_optimizer.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/mobile_optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d2230da8ae1453342a7c5d58614b5d1de05808f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/mobile_optimizer.py
@@ -0,0 +1,138 @@
+# mypy: allow-untyped-defs
+"""This module contains utility method for mobile model optimization and lint."""
+
+import torch
+from enum import Enum
+from torch._C import _MobileOptimizerType as MobileOptimizerType
+from typing import Optional, Set, List, AnyStr
+
+class LintCode(Enum):
+    BUNDLED_INPUT = 1
+    REQUIRES_GRAD = 2
+    DROPOUT = 3
+    BATCHNORM = 4
+
+def optimize_for_mobile(
+        script_module: torch.jit.ScriptModule,
+        optimization_blocklist: Optional[Set[MobileOptimizerType]] = None,
+        preserved_methods: Optional[List[AnyStr]] = None,
+        backend: str = 'CPU') -> torch.jit.RecursiveScriptModule:
+    """
+    Optimize a torch script module for mobile deployment.
+
+    Args:
+        script_module: An instance of torch script module with type of ScriptModule.
+        optimization_blocklist: A set with type of MobileOptimizerType. When set is not passed,
+            optimization method will run all the optimizer pass; otherwise, optimizer
+            method will run the optimization pass that is not included inside optimization_blocklist.
+        preserved_methods: A list of methods that needed to be preserved when freeze_module pass is invoked
+        backend: Device type to use for running the result model ('CPU'(default), 'Vulkan' or 'Metal').
+    Returns:
+        A new optimized torch script module
+    """
+    if not isinstance(script_module, torch.jit.ScriptModule):
+        raise TypeError(
+            f'Got {type(script_module)}, but ScriptModule is expected.')
+
+    if optimization_blocklist is None:
+        optimization_blocklist = set()
+
+    if preserved_methods is None:
+        preserved_methods = []
+
+    # Convert potential byte arrays into strings (if there is any) to pass type checking
+    # Here we use a new name as assigning it back to preserved_methods will invoke
+    # mypy errors (i.e. List[AnyStr] = List[str])
+    preserved_methods_str: List[str] = [str(method) for method in preserved_methods]
+
+    bundled_inputs_attributes = _get_bundled_inputs_preserved_attributes(script_module, preserved_methods_str)
+    if all(hasattr(script_module, method) for method in bundled_inputs_attributes):
+        preserved_methods_str = list(set(preserved_methods_str + bundled_inputs_attributes))
+
+    non_exist_methods = []
+    for method in preserved_methods_str:
+        if not hasattr(script_module, method):
+            non_exist_methods.append(method)
+    if non_exist_methods:
+        raise AttributeError(
+            f"The following methods to preserve do not exist in script_module: {', '.join(non_exist_methods)}")
+
+    backend = backend.lower()
+    if backend == 'cpu':
+        optimized_cpp_module = torch._C._jit_pass_optimize_for_mobile(
+            script_module._c,
+            optimization_blocklist,
+            preserved_methods_str)
+    elif backend == 'vulkan':
+        optimized_cpp_module = torch._C._jit_pass_vulkan_optimize_for_mobile(
+            script_module._c,
+            optimization_blocklist,
+            preserved_methods_str)
+    elif backend == 'metal':
+        optimized_cpp_module = torch._C._jit_pass_metal_optimize_for_mobile(script_module._c, preserved_methods_str)
+    else:
+        raise TypeError("Unknown backend, must be one of 'CPU', 'Vulkan' or 'Metal'")
+
+    return torch.jit._recursive.wrap_cpp_module(optimized_cpp_module)
+
+
+def generate_mobile_module_lints(script_module: torch.jit.ScriptModule):
+    """
+    Generate a list of lints for a given torch script module.
+
+    Args:
+        script_module: An instance of torch script module with type of ScriptModule.
+
+    Returns:
+        lint_map: A list of dictionary that contains modules lints
+    """
+    if not isinstance(script_module, torch.jit.ScriptModule):
+        raise TypeError(
+            f'Got {type(script_module)}, but ScriptModule is expected.')
+
+    lint_list = []
+
+    if not hasattr(script_module, "_generate_bundled_inputs_for_forward"):
+        lint_list.append({"name": LintCode.BUNDLED_INPUT.name, "message": "No bundled input for forward, please add bundled inputs "
+                          "before saving the module using torch.utils.bundled_inputs.augment_model_with_bundled_inputs."})
+
+    for name, param in script_module.named_parameters():
+        if param.requires_grad:
+            lint_list.append({"name": LintCode.REQUIRES_GRAD.name, "message": f"Param {name} requires grad, "
+                             "please set torch.no_grad() to reduce memory usage and improve computation speed during "
+                              "inference phase."})
+
+    op_names = torch.jit.export_opnames(script_module)
+    for op_name in op_names:
+        if "dropout" in op_name:
+            lint_list.append({"name": LintCode.DROPOUT.name,
+                              "message": f"Operator {op_name} exists, remember to call eval() before "
+                              "saving the module.and call torch.utils.mobile_optimizer.optimize_for_mobile to drop dropout "
+                              "operator."})
+        if "batch_norm" in op_name:
+            lint_list.append({"name": LintCode.BATCHNORM.name,
+                              "message": f"Operator {op_name} exists, remember to call eval() before "
+                              "saving the module and call torch.utils.mobile_optimizer.optimize_for_mobile to drop batch_norm "
+                              "operator."})
+
+    return lint_list
+
+def _get_bundled_inputs_preserved_attributes(script_module: torch.jit.ScriptModule, preserved_methods: List[str]) -> List[str]:
+
+    bundled_inputs_attributes = []
+    # Has bundled inputs for forward
+    if hasattr(script_module, 'get_all_bundled_inputs'):
+        bundled_inputs_attributes.append('get_all_bundled_inputs')
+        bundled_inputs_attributes.append('get_num_bundled_inputs')
+
+    # Bundled inputs in module after the change that introduced bundled inputs for multiple functions
+    if hasattr(script_module, 'get_bundled_inputs_functions_and_info'):
+        bundled_inputs_attributes.append('get_bundled_inputs_functions_and_info')
+        all_info = script_module.get_bundled_inputs_functions_and_info()
+        for function_name in all_info:
+            if function_name not in preserved_methods:
+                bundled_inputs_attributes.append(function_name)
+            bundled_inputs_attributes.append("get_all_bundled_inputs_for_" + function_name)
+            bundled_inputs_attributes.append("_bundled_inputs_deflated_" + function_name)
+
+    return bundled_inputs_attributes
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/model_zoo.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/model_zoo.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0c6004e23ea806a2c83e12cd2998e0279e0b16f
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/model_zoo.py
@@ -0,0 +1,2 @@
+# torchvision imports tqdm from here.
+from torch.hub import tqdm, load_state_dict_from_url as load_url  # noqa: F401
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/module_tracker.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/module_tracker.py
new file mode 100644
index 0000000000000000000000000000000000000000..63a6b817c42462b48efeb86a6f714d7e4ed13906
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/module_tracker.py
@@ -0,0 +1,147 @@
+# mypy: allow-untyped-defs
+import logging
+import weakref
+from typing import Set
+
+import torch
+from torch.autograd.graph import register_multi_grad_hook
+from torch.nn.modules.module import (
+    register_module_forward_hook,
+    register_module_forward_pre_hook,
+)
+from torch.utils._pytree import tree_flatten
+
+
+logger = logging.getLogger(__name__)
+
+
+__all__ = ["ModuleTracker"]
+
+
+class ModuleTracker:
+    """
+    ``ModuleTracker`` is a context manager that tracks the nn.Module hierarchy during execution
+    so that other system can query which Module is currently being executed (or its backward is being
+    executed).
+
+    You can access the ``parents`` attribute on this context manager to get the set of all the
+    Modules currently being executed via their fqn (fully qualified name, also used as the key within
+    the state_dict).
+    You can access the ``is_bw`` attribute to know if you are currently running in backward or not.
+
+    Note that ``parents`` is never empty and always contains the "Global" key. The ``is_bw`` flag
+    will remain ``True`` after the forward until another Module is executed. If you need it to be
+    more accurate, please submit an issue requesting this. Adding a map from fqn to the module instance
+    is possible but not done yet, please submit an issue requesting this if you need it.
+
+    Example usage
+
+    .. code-block:: python
+
+        mod = torch.nn.Linear(2, 2)
+
+        with ModuleTracker() as tracker:
+            # Access anything during the forward pass
+            def my_linear(m1, m2, bias):
+                print(f"Current modules: {tracker.parents}")
+                return torch.mm(m1, m2.t()) + bias
+            torch.nn.functional.linear = my_linear
+
+            mod(torch.rand(2, 2))
+
+    """
+
+    parents: Set[str]
+    """
+    A Set containing the fqn for each module currently running their forward
+    """
+
+    def __init__(self) -> None:
+        self.parents = {"Global"}
+        self._known_modules: weakref.WeakKeyDictionary = weakref.WeakKeyDictionary()
+        self._seen_modules: weakref.WeakSet = weakref.WeakSet()
+        self._has_callback = False
+
+    def _maybe_set_engine_callback(self):
+        # This assumes no concurrent calls to backward
+        if self._has_callback:
+            return
+
+        def callback():
+            self.parents = {"Global"}
+            self._has_callback = False
+
+        torch.autograd.Variable._execution_engine.queue_callback(callback)
+        self._has_callback = True
+
+    @property
+    def is_bw(self):
+        """
+        A boolean marking if this is currently running during the backward pass or not
+        """
+        return torch._C._current_graph_task_id() != -1
+
+    def _get_mod_name(self, mod):
+        if mod not in self._known_modules:
+            self._known_modules[mod] = type(mod).__name__
+        mod_name = self._known_modules[mod]
+        if mod not in self._seen_modules:
+            for name, submod in mod.named_children():
+                self._known_modules[submod] = f"{mod_name}.{name}"
+                self._get_mod_name(submod)
+            self._seen_modules.add(mod)
+        return mod_name
+
+    def _get_append_fn(self, name, is_bw):
+        def fn(*args):
+            if is_bw:
+                self._maybe_set_engine_callback()
+            if name in self.parents:
+                logger.info(
+                    "The module hierarchy tracking seems to be broken as this Module was already entered. %s during %s",
+                    name,
+                    "backward" if is_bw else "forward",
+                )
+            self.parents.add(name)
+
+        return fn
+
+    def _get_pop_fn(self, name, is_bw):
+        def fn(*args):
+            if name in self.parents:
+                self.parents.remove(name)
+            else:
+                logger.info(
+                    "The Module hierarchy tracking is confused as we're exiting a Module that was never entered. %s during %s",
+                    name,
+                    "backward" if is_bw else "forward",
+                )
+
+        return fn
+
+    def _fw_pre_hook(self, mod, input):
+        name = self._get_mod_name(mod)
+        self._get_append_fn(name, False)()
+
+        args, _ = tree_flatten(input)
+        tensors = [a for a in args if isinstance(a, torch.Tensor) and a.requires_grad]
+        if tensors:
+            register_multi_grad_hook(tensors, self._get_pop_fn(name, True))
+
+    def _fw_post_hook(self, mod, input, output):
+        name = self._get_mod_name(mod)
+        self._get_pop_fn(name, False)()
+
+        args, _ = tree_flatten(output)
+        tensors = [a for a in args if isinstance(a, torch.Tensor) and a.requires_grad]
+        if tensors:
+            register_multi_grad_hook(tensors, self._get_append_fn(name, True))
+
+    def __enter__(self):
+        self._fw_pre_handle = register_module_forward_pre_hook(self._fw_pre_hook)
+        self._fw_post_handle = register_module_forward_hook(self._fw_post_hook)
+        return self
+
+    def __exit__(self, *args):
+        self._fw_pre_handle.remove()
+        self._fw_post_handle.remove()
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/show_pickle.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/show_pickle.py
new file mode 100644
index 0000000000000000000000000000000000000000..66549fac267326899db82c843e6cde2835c0257d
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/show_pickle.py
@@ -0,0 +1,151 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+import sys
+import pickle
+import struct
+import pprint
+import zipfile
+import fnmatch
+from typing import Any, IO, BinaryIO, Union
+
+__all__ = ["FakeObject", "FakeClass", "DumpUnpickler", "main"]
+
+class FakeObject:
+    def __init__(self, module, name, args):
+        self.module = module
+        self.name = name
+        self.args = args
+        # NOTE: We don't distinguish between state never set and state set to None.
+        self.state = None
+
+    def __repr__(self):
+        state_str = "" if self.state is None else f"(state={self.state!r})"
+        return f"{self.module}.{self.name}{self.args!r}{state_str}"
+
+    def __setstate__(self, state):
+        self.state = state
+
+    @staticmethod
+    def pp_format(printer, obj, stream, indent, allowance, context, level):
+        if not obj.args and obj.state is None:
+            stream.write(repr(obj))
+            return
+        if obj.state is None:
+            stream.write(f"{obj.module}.{obj.name}")
+            printer._format(obj.args, stream, indent + 1, allowance + 1, context, level)
+            return
+        if not obj.args:
+            stream.write(f"{obj.module}.{obj.name}()(state=\n")
+            indent += printer._indent_per_level
+            stream.write(" " * indent)
+            printer._format(obj.state, stream, indent, allowance + 1, context, level + 1)
+            stream.write(")")
+            return
+        raise Exception("Need to implement")  # noqa: TRY002
+
+
+class FakeClass:
+    def __init__(self, module, name):
+        self.module = module
+        self.name = name
+        self.__new__ = self.fake_new  # type: ignore[assignment]
+
+    def __repr__(self):
+        return f"{self.module}.{self.name}"
+
+    def __call__(self, *args):
+        return FakeObject(self.module, self.name, args)
+
+    def fake_new(self, *args):
+        return FakeObject(self.module, self.name, args[1:])
+
+
+class DumpUnpickler(pickle._Unpickler):  # type: ignore[name-defined]
+    def __init__(
+            self,
+            file,
+            *,
+            catch_invalid_utf8=False,
+            **kwargs):
+        super().__init__(file, **kwargs)
+        self.catch_invalid_utf8 = catch_invalid_utf8
+
+    def find_class(self, module, name):
+        return FakeClass(module, name)
+
+    def persistent_load(self, pid):
+        return FakeObject("pers", "obj", (pid,))
+
+    dispatch = dict(pickle._Unpickler.dispatch)  # type: ignore[attr-defined]
+
+    # Custom objects in TorchScript are able to return invalid UTF-8 strings
+    # from their pickle (__getstate__) functions.  Install a custom loader
+    # for strings that catches the decode exception and replaces it with
+    # a sentinel object.
+    def load_binunicode(self):
+        strlen, = struct.unpack(" sys.maxsize:
+            raise Exception("String too long.")  # noqa: TRY002
+        str_bytes = self.read(strlen)  # type: ignore[attr-defined]
+        obj: Any
+        try:
+            obj = str(str_bytes, "utf-8", "surrogatepass")
+        except UnicodeDecodeError as exn:
+            if not self.catch_invalid_utf8:
+                raise
+            obj = FakeObject("builtin", "UnicodeDecodeError", (str(exn),))
+        self.append(obj)  # type: ignore[attr-defined]
+    dispatch[pickle.BINUNICODE[0]] = load_binunicode  # type: ignore[assignment]
+
+    @classmethod
+    def dump(cls, in_stream, out_stream):
+        value = cls(in_stream).load()
+        pprint.pprint(value, stream=out_stream)
+        return value
+
+
+def main(argv, output_stream=None):
+    if len(argv) != 2:
+        # Don't spam stderr if not using stdout.
+        if output_stream is not None:
+            raise Exception("Pass argv of length 2.")  # noqa: TRY002
+        sys.stderr.write("usage: show_pickle PICKLE_FILE\n")
+        sys.stderr.write("  PICKLE_FILE can be any of:\n")
+        sys.stderr.write("    path to a pickle file\n")
+        sys.stderr.write("    file.zip@member.pkl\n")
+        sys.stderr.write("    file.zip@*/pattern.*\n")
+        sys.stderr.write("      (shell glob pattern for members)\n")
+        sys.stderr.write("      (only first match will be shown)\n")
+        return 2
+
+    fname = argv[1]
+    handle: Union[IO[bytes], BinaryIO]
+    if "@" not in fname:
+        with open(fname, "rb") as handle:
+            DumpUnpickler.dump(handle, output_stream)
+    else:
+        zfname, mname = fname.split("@", 1)
+        with zipfile.ZipFile(zfname) as zf:
+            if "*" not in mname:
+                with zf.open(mname) as handle:
+                    DumpUnpickler.dump(handle, output_stream)
+            else:
+                found = False
+                for info in zf.infolist():
+                    if fnmatch.fnmatch(info.filename, mname):
+                        with zf.open(info) as handle:
+                            DumpUnpickler.dump(handle, output_stream)
+                        found = True
+                        break
+                if not found:
+                    raise Exception(f"Could not find member matching {mname} in {zfname}")  # noqa: TRY002
+
+
+if __name__ == "__main__":
+    # This hack works on every version of Python I've tested.
+    # I've tested on the following versions:
+    #   3.7.4
+    if True:
+        pprint.PrettyPrinter._dispatch[FakeObject.__repr__] = FakeObject.pp_format  # type: ignore[attr-defined]
+
+    sys.exit(main(sys.argv))
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/throughput_benchmark.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/throughput_benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..2778b37b5a78dfc51d0b6211d7eff320678ee752
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/throughput_benchmark.py
@@ -0,0 +1,160 @@
+# mypy: allow-untyped-defs
+
+import torch._C
+
+
+def format_time(time_us=None, time_ms=None, time_s=None):
+    """Define time formatting."""
+    assert sum([time_us is not None, time_ms is not None, time_s is not None]) == 1
+
+    US_IN_SECOND = 1e6
+    US_IN_MS = 1e3
+
+    if time_us is None:
+        if time_ms is not None:
+            time_us = time_ms * US_IN_MS
+        elif time_s is not None:
+            time_us = time_s * US_IN_SECOND
+        else:
+            raise AssertionError("Shouldn't reach here :)")
+
+    if time_us >= US_IN_SECOND:
+        return f'{time_us / US_IN_SECOND:.3f}s'
+    if time_us >= US_IN_MS:
+        return f'{time_us / US_IN_MS:.3f}ms'
+    return f'{time_us:.3f}us'
+
+
+class ExecutionStats:
+    def __init__(self, c_stats, benchmark_config):
+        self._c_stats = c_stats
+        self.benchmark_config = benchmark_config
+
+    @property
+    def latency_avg_ms(self):
+        return self._c_stats.latency_avg_ms
+
+    @property
+    def num_iters(self):
+        return self._c_stats.num_iters
+
+    @property
+    def iters_per_second(self):
+        """Return total number of iterations per second across all calling threads."""
+        return self.num_iters / self.total_time_seconds
+
+    @property
+    def total_time_seconds(self):
+        return self.num_iters * (
+            self.latency_avg_ms / 1000.0) / self.benchmark_config.num_calling_threads
+
+    def __str__(self):
+        return '\n'.join([
+            "Average latency per example: " + format_time(time_ms=self.latency_avg_ms),
+            f"Total number of iterations: {self.num_iters}",
+            f"Total number of iterations per second (across all threads): {self.iters_per_second:.2f}",
+            "Total time: " + format_time(time_s=self.total_time_seconds)
+        ])
+
+
+class ThroughputBenchmark:
+    """
+    This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark.
+
+    This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible
+    for executing a PyTorch module (nn.Module or ScriptModule) under an inference
+    server like load. It can emulate multiple calling threads to a single module
+    provided. In the future we plan to enhance this component to support inter and
+    intra-op parallelism as well as multiple models running in a single process.
+
+    Please note that even though nn.Module is supported, it might incur an overhead
+    from the need to hold GIL every time we execute Python code or pass around
+    inputs as Python objects. As soon as you have a ScriptModule version of your
+    model for inference deployment it is better to switch to using it in this
+    benchmark.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("undefined vars")
+        >>> from torch.utils import ThroughputBenchmark
+        >>> bench = ThroughputBenchmark(my_module)
+        >>> # Pre-populate benchmark's data set with the inputs
+        >>> for input in inputs:
+        ...     # Both args and kwargs work, same as any PyTorch Module / ScriptModule
+        ...     bench.add_input(input[0], x2=input[1])
+        >>> # Inputs supplied above are randomly used during the execution
+        >>> stats = bench.benchmark(
+        ...     num_calling_threads=4,
+        ...     num_warmup_iters = 100,
+        ...     num_iters = 1000,
+        ... )
+        >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms))
+        >>> print("Number of iterations: {}".format(stats.num_iters))
+    """
+
+    def __init__(self, module):
+        if isinstance(module, torch.jit.ScriptModule):
+            self._benchmark = torch._C.ThroughputBenchmark(module._c)
+        else:
+            self._benchmark = torch._C.ThroughputBenchmark(module)
+
+    def run_once(self, *args, **kwargs):
+        """
+        Given input id (input_idx) run benchmark once and return prediction.
+
+        This is useful for testing that benchmark actually runs the module you
+        want it to run. input_idx here is an index into inputs array populated
+        by calling add_input() method.
+        """
+        return self._benchmark.run_once(*args, **kwargs)
+
+    def add_input(self, *args, **kwargs):
+        """
+        Store a single input to a module into the benchmark memory and keep it there.
+
+        During the benchmark execution every thread is going to pick up a
+        random input from the all the inputs ever supplied to the benchmark via
+        this function.
+        """
+        self._benchmark.add_input(*args, **kwargs)
+
+    def benchmark(
+            self,
+            num_calling_threads=1,
+            num_warmup_iters=10,
+            num_iters=100,
+            profiler_output_path=""):
+        """
+        Run a benchmark on the module.
+
+        Args:
+            num_warmup_iters (int): Warmup iters are used to make sure we run a module
+                a few times before actually measuring things. This way we avoid cold
+                caches and any other similar problems. This is the number of warmup
+                iterations for each of the thread in separate
+
+            num_iters (int): Number of iterations the benchmark should run with.
+                This number is separate from the warmup iterations. Also the number is
+                shared across all the threads. Once the num_iters iterations across all
+                the threads is reached, we will stop execution. Though total number of
+                iterations might be slightly larger. Which is reported as
+                stats.num_iters where stats is the result of this function
+
+            profiler_output_path (str): Location to save Autograd Profiler trace.
+                If not empty, Autograd Profiler will be enabled for the main benchmark
+                execution (but not the warmup phase). The full trace will be saved
+                into the file path provided by this argument
+
+
+        This function returns BenchmarkExecutionStats object which is defined via pybind11.
+        It currently has two fields:
+            - num_iters - number of actual iterations the benchmark have made
+            - avg_latency_ms - average time it took to infer on one input example in milliseconds
+        """
+        config = torch._C.BenchmarkConfig()
+        config.num_calling_threads = num_calling_threads
+        config.num_warmup_iters = num_warmup_iters
+        config.num_iters = num_iters
+        config.profiler_output_path = profiler_output_path
+        c_stats = self._benchmark.benchmark(config)
+        return ExecutionStats(c_stats, config)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/weak.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/weak.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc272a7f26375ada658ea3ef99351306e362e3c9
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/utils/weak.py
@@ -0,0 +1,322 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import weakref
+from weakref import ref
+from _weakrefset import _IterationGuard  # type: ignore[attr-defined]
+from collections.abc import MutableMapping, Mapping
+from torch import Tensor
+import collections.abc as _collections_abc
+
+
+WeakRef = ref
+
+
+__all__ = ['TensorWeakRef', 'WeakIdRef', 'WeakIdKeyDictionary', 'WeakTensorKeyDictionary']
+
+
+# This file defines a variant of WeakKeyDictionary that overrides the hashing
+# behavior of the key to use object identity, rather than the builtin
+# __eq__/__hash__ functions.  This is useful for Tensor weak keys, as their
+# __eq__ implementation return a Tensor (elementwise equality), which means
+# you can't use them directly with the WeakKeyDictionary in standard library.
+#
+# Our implementation strategy is to create a wrapper weak key object, which we
+# use as a key in a stock Python dictionary.  This is similar to how weakref
+# implements WeakKeyDictionary, but instead of using weakref.ref as the
+# wrapper, we use a custom wrapper that has different __eq__ and __hash__
+# behavior.  Note that we subsequently store this weak key directly in an
+# ORDINARY dictionary, since the newly constructed WeakIdKey's only use would
+# be a dictionary so it would have no strong references.  Ensuring that
+# only live WeakIdKeys are in the map is handled by putting finalizers on the
+# original key object.
+
+
+# It is simpler to implement this with composition, but if we want to
+# directly reuse the callback mechanism on weakref, we need the weakref
+# and the key to be exactly the same object.  Reusing the callback mechanism
+# minimizes the divergence between our implementation and Lib/weakref.py
+#
+# NB: Prefer using this when working with weakrefs of Tensors; e.g., do
+# WeakIdRef(tensor) rather than weakref.ref(tensor); it handles a number of
+# easy to get wrong cases transparently for you.
+class WeakIdRef(weakref.ref):
+    __slots__ = ['_id']
+
+    def __init__(self, key, callback=None):
+        # Unlike stock weakref, which preserves hash semantics of the
+        # original object but lazily defers hash calls until the first
+        # time the user attempts to hash the weakref, we can eagerly
+        # cache the id of the key as we know this is definitely the hash
+        # method
+        self._id = id(key)
+        super().__init__(key, callback)  # type: ignore[call-arg]
+
+    def __call__(self):
+        r = super().__call__()
+        # Special logic for Tensor PyObject resurrection
+        if hasattr(r, '_fix_weakref'):
+            r._fix_weakref()  # type: ignore[union-attr]
+        return r
+
+    def __hash__(self):
+        return self._id
+
+    def __eq__(self, other):
+        # An attractive but wrong alternate implementation is to only test if
+        # the stored _ids match.  This can lead to an ABA problem if you have:
+        #
+        #   a1 = A()
+        #   w1 = WeakIdRef(a1)
+        #   del a1
+        #   a2 = A()  # suppose it gets the same ID as a1
+        #   w2 = WeakIdRef(a2)
+        #   print(w1 == w2)
+        #
+        # This should be False, as a1 and a2 are unrelated (and a1 is
+        # dead anyway)
+        a = self()
+        b = other()
+        if a is not None and b is not None:
+            return a is b
+        return self is other
+
+# This is the same as WeakIdRef but equality is checked using hash() rather than id.
+# This will be equivalent to the one above except for classes where hash is not their id.
+class _WeakHashRef(weakref.ref):
+    __slots__ = ['_id']
+
+    def __init__(self, key, callback=None):
+        # Unlike stock weakref, which preserves hash semantics of the
+        # original object but lazily defers hash calls until the first
+        # time the user attempts to hash the weakref, we can eagerly
+        # cache the id of the key as we know this is definitely the hash
+        # method
+        self._id = hash(key)
+        super().__init__(key, callback)  # type: ignore[call-arg]
+
+    def __call__(self):
+        r = super().__call__()
+        # Special logic for Tensor PyObject resurrection
+        if hasattr(r, '_fix_weakref'):
+            r._fix_weakref()  # type: ignore[union-attr]
+        return r
+
+    def __hash__(self):
+        return self._id
+
+    def __eq__(self, other):
+        # Use hash equality to determine ref equality.
+        # ScriptObject implements __hash__ to return the wrapped IValue's id, so
+        # this is equivalent to doing an identity comparison.
+        a = self()
+        b = other()
+        if a is not None and b is not None:
+            return hash(a) == hash(b)
+        return self is other
+
+# This is directly adapted from cpython/Lib/weakref.py
+class WeakIdKeyDictionary(MutableMapping):
+    def __init__(self, dict=None, ref_type=WeakIdRef):  # CHANGED
+        self.data = {}
+
+        self.ref_type = ref_type  # CHANGED
+
+        def remove(k, selfref=ref(self)):
+            self = selfref()
+            if self is not None:
+                if self._iterating:
+                    self._pending_removals.append(k)
+                else:
+                    try:
+                        del self.data[k]
+                    except KeyError:
+                        pass
+        self._remove = remove
+        # A list of dead weakrefs (keys to be removed)
+        self._pending_removals = []
+        self._iterating = set()
+        self._dirty_len = False
+        if dict is not None:
+            self.update(dict)
+
+    def _commit_removals(self):
+        # NOTE: We don't need to call this method before mutating the dict,
+        # because a dead weakref never compares equal to a live weakref,
+        # even if they happened to refer to equal objects.
+        # However, it means keys may already have been removed.
+        pop = self._pending_removals.pop
+        d = self.data
+        while True:
+            try:
+                key = pop()
+            except IndexError:
+                return
+
+            try:
+                del d[key]
+            except KeyError:
+                pass
+
+    def _scrub_removals(self):
+        d = self.data
+        self._pending_removals = [k for k in self._pending_removals if k in d]
+        self._dirty_len = False
+
+    def __delitem__(self, key):
+        self._dirty_len = True
+        del self.data[self.ref_type(key)]  # CHANGED
+
+    def __getitem__(self, key):
+        return self.data[self.ref_type(key)]  # CHANGED
+
+    def __len__(self):
+        if self._dirty_len and self._pending_removals:
+            # self._pending_removals may still contain keys which were
+            # explicitly removed, we have to scrub them (see issue #21173).
+            self._scrub_removals()
+        return len(self.data) - len(self._pending_removals)
+
+    def __repr__(self):
+        return f"<{self.__class__.__name__} at {id(self):#x}>"
+
+    def __setitem__(self, key, value):
+        self.data[self.ref_type(key, self._remove)] = value  # CHANGED
+
+    def copy(self):
+        new = WeakIdKeyDictionary()
+        with _IterationGuard(self):
+            for key, value in self.data.items():
+                o = key()
+                if o is not None:
+                    new[o] = value
+        return new
+
+    __copy__ = copy
+
+    def __deepcopy__(self, memo):
+        from copy import deepcopy
+        new = self.__class__()
+        with _IterationGuard(self):
+            for key, value in self.data.items():
+                o = key()
+                if o is not None:
+                    new[o] = deepcopy(value, memo)
+        return new
+
+    def get(self, key, default=None):
+        return self.data.get(self.ref_type(key), default)  # CHANGED
+
+    def __contains__(self, key):
+        try:
+            wr = self.ref_type(key)  # CHANGED
+        except TypeError:
+            return False
+        return wr in self.data
+
+    def items(self):
+        with _IterationGuard(self):
+            for wr, value in self.data.items():
+                key = wr()
+                if key is not None:
+                    yield key, value
+
+    def keys(self):
+        with _IterationGuard(self):
+            for wr in self.data:
+                obj = wr()
+                if obj is not None:
+                    yield obj
+
+    __iter__ = keys
+
+    def values(self):
+        with _IterationGuard(self):
+            for wr, value in self.data.items():
+                if wr() is not None:
+                    yield value
+
+    def keyrefs(self):
+        """Return a list of weak references to the keys.
+
+        The references are not guaranteed to be 'live' at the time
+        they are used, so the result of calling the references needs
+        to be checked before being used.  This can be used to avoid
+        creating references that will cause the garbage collector to
+        keep the keys around longer than needed.
+
+        """
+        return list(self.data)
+
+    def popitem(self):
+        self._dirty_len = True
+        while True:
+            key, value = self.data.popitem()
+            o = key()
+            if o is not None:
+                return o, value
+
+    def pop(self, key, *args):
+        self._dirty_len = True
+        return self.data.pop(self.ref_type(key), *args)  # CHANGED
+
+    def setdefault(self, key, default=None):
+        return self.data.setdefault(self.ref_type(key, self._remove), default)  # CHANGED
+
+    def update(self, dict=None, **kwargs):
+        d = self.data
+        if dict is not None:
+            if not hasattr(dict, "items"):
+                dict = type({})(dict)
+            for key, value in dict.items():
+                d[self.ref_type(key, self._remove)] = value  # CHANGED
+        if len(kwargs):
+            self.update(kwargs)
+
+    def __ior__(self, other):
+        self.update(other)
+        return self
+
+    def __or__(self, other):
+        if isinstance(other, _collections_abc.Mapping):
+            c = self.copy()
+            c.update(other)
+            return c
+        return NotImplemented
+
+    def __ror__(self, other):
+        if isinstance(other, _collections_abc.Mapping):
+            c = self.__class__()
+            c.update(other)
+            c.update(self)
+            return c
+        return NotImplemented
+
+    # Default Mapping equality will tests keys for equality, but
+    # we want to test ids for equality
+    def __eq__(self, other):
+        if not isinstance(other, Mapping):
+            return NotImplemented
+        return {id(k): v for k, v in self.items()} == {id(k): v for k, v in other.items()}
+
+# Convenience alias
+WeakTensorKeyDictionary = WeakIdKeyDictionary
+
+
+class TensorWeakRef:
+    """Wrapper around a weak ref of a Tensor that handles the _fix_weakref() call required when unwrapping a Tensor weakref."""
+
+    ref: WeakRef[Tensor]
+
+    def __init__(self, tensor: Tensor):
+        assert isinstance(tensor, Tensor)
+        self.ref = weakref.ref(tensor)
+
+    def __call__(self):
+        out = self.ref()
+        if out is None:
+            return out
+        assert isinstance(out, Tensor)
+        # TODO, add _fix_weakref type binding
+        out._fix_weakref()  # type: ignore[attr-defined]
+        return out
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/xpu/__init__.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/xpu/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a51924f89d21fed5840c2443d2ebcffd4f559e32
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/xpu/__init__.py
@@ -0,0 +1,504 @@
+# mypy: allow-untyped-defs
+r"""
+This package introduces support for the XPU backend, specifically tailored for
+Intel GPU optimization.
+
+This package is lazily initialized, so you can always import it, and use
+:func:`is_available()` to determine if your system supports XPU.
+"""
+import threading
+import traceback
+from functools import lru_cache
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch._C
+from torch import device as _device
+from torch._utils import _dummy_type, _LazySeedTracker
+
+from ._utils import _get_device_index
+from .streams import Event, Stream
+
+
+_initialized = False
+_tls = threading.local()
+_initialization_lock = threading.Lock()
+_queued_calls: List[
+    Tuple[Callable[[], None], List[str]]
+] = []  # don't invoke these until initialization occurs
+_is_in_bad_fork = getattr(torch._C, "_xpu_isInBadFork", lambda: False)
+_device_t = Union[_device, str, int, None]
+_lazy_seed_tracker = _LazySeedTracker()
+default_generators: Tuple[torch._C.Generator] = ()  # type: ignore[assignment]
+
+
+def _is_compiled() -> bool:
+    r"""Return true if compile with XPU support."""
+    return torch._C._has_xpu
+
+
+if _is_compiled():
+    _XpuDeviceProperties = torch._C._XpuDeviceProperties
+    _exchange_device = torch._C._xpu_exchangeDevice
+    _maybe_exchange_device = torch._C._xpu_maybeExchangeDevice
+else:
+    # Define dummy if PyTorch was compiled without XPU
+    _XpuDeviceProperties = _dummy_type("_XpuDeviceProperties")  # type: ignore[assignment, misc]
+
+    def _exchange_device(device: int) -> int:
+        raise NotImplementedError("PyTorch was compiled without XPU support")
+
+    def _maybe_exchange_device(device: int) -> int:
+        raise NotImplementedError("PyTorch was compiled without XPU support")
+
+
+@lru_cache(maxsize=1)
+def device_count() -> int:
+    r"""Return the number of XPU device available."""
+    if not _is_compiled():
+        return 0
+    return torch._C._xpu_getDeviceCount()
+
+
+def is_available() -> bool:
+    r"""Return a bool indicating if XPU is currently available."""
+    # This function nerver throws.
+    return device_count() > 0
+
+
+def is_bf16_supported():
+    r"""Return a bool indicating if the current XPU device supports dtype bfloat16."""
+    return True
+
+
+def is_initialized():
+    r"""Return whether PyTorch's XPU state has been initialized."""
+    return _initialized and not _is_in_bad_fork()
+
+
+def _lazy_call(callable, **kwargs):
+    if is_initialized():
+        callable()
+    else:
+        global _lazy_seed_tracker
+        if kwargs.get("seed_all", False):
+            _lazy_seed_tracker.queue_seed_all(callable, traceback.format_stack())
+        elif kwargs.get("seed", False):
+            _lazy_seed_tracker.queue_seed(callable, traceback.format_stack())
+        else:
+            # Don't store the actual traceback to avoid memory cycle
+            _queued_calls.append((callable, traceback.format_stack()))
+
+
+def init():
+    r"""Initialize PyTorch's XPU state.
+    This is a Python API about lazy initialization that avoids initializing
+    XPU until the first time it is accessed. Does nothing if the XPU state is
+    already initialized.
+    """
+    _lazy_init()
+
+
+def _lazy_init():
+    global _initialized, _queued_calls
+    if is_initialized() or hasattr(_tls, "is_initializing"):
+        return
+    with _initialization_lock:
+        # This test was was protected via GIL. Double-check whether XPU has
+        # already been initialized.
+        if is_initialized():
+            return
+        # Stop promptly upon encountering a bad fork error.
+        if _is_in_bad_fork():
+            raise RuntimeError(
+                "Cannot re-initialize XPU in forked subprocess. To use XPU with "
+                "multiprocessing, you must use the 'spawn' start method"
+            )
+        if not _is_compiled():
+            raise AssertionError("Torch not compiled with XPU enabled")
+        # This function inits XPU backend and detects bad fork processing.
+        torch._C._xpu_init()
+        # Some of the queued calls may reentrantly call _lazy_init(); We need to
+        # just return without initializing in that case.
+        _tls.is_initializing = True
+
+        for calls in _lazy_seed_tracker.get_calls():
+            if calls:
+                _queued_calls.append(calls)
+
+        try:
+            for queued_call, orig_traceback in _queued_calls:
+                try:
+                    queued_call()
+                except Exception as e:
+                    msg = (
+                        f"XPU call failed lazily at initialization with error: {str(e)}\n\n"
+                        f"XPU call was originally invoked at:\n\n{''.join(orig_traceback)}"
+                    )
+                    raise Exception(msg) from e  # noqa: TRY002
+        finally:
+            delattr(_tls, "is_initializing")
+        _initialized = True
+
+
+class _DeviceGuard:
+    def __init__(self, index: int):
+        self.idx = index
+        self.prev_idx = -1
+
+    def __enter__(self):
+        self.prev_idx = torch.xpu._exchange_device(self.idx)
+
+    def __exit__(self, type: Any, value: Any, traceback: Any):
+        self.idx = torch.xpu._maybe_exchange_device(self.prev_idx)
+        return False
+
+
+class device:
+    r"""Context-manager that changes the selected device.
+
+    Args:
+        device (torch.device or int or str): device index to select. It's a no-op if
+            this argument is a negative integer or ``None``.
+    """
+
+    def __init__(self, device: Any):
+        self.idx = _get_device_index(device, optional=True)
+        self.prev_idx = -1
+
+    def __enter__(self):
+        self.prev_idx = torch.xpu._exchange_device(self.idx)
+
+    def __exit__(self, type: Any, value: Any, traceback: Any):
+        self.idx = torch.xpu._maybe_exchange_device(self.prev_idx)
+        return False
+
+
+class device_of(device):
+    r"""Context-manager that changes the current device to that of given object.
+
+    You can use both tensors and storages as arguments. If a given object is
+    not allocated on a XPU, this is a no-op.
+
+    Args:
+        obj (Tensor or Storage): object allocated on the selected device.
+    """
+
+    def __init__(self, obj):
+        idx = obj.get_device() if obj.is_xpu else -1
+        super().__init__(idx)
+
+
+def set_device(device: _device_t) -> None:
+    r"""Set the current device.
+
+    Args:
+        device (torch.device or int or str): selected device. This function is a
+            no-op if this argument is negative.
+    """
+    _lazy_init()
+    device = _get_device_index(device)
+    if device >= 0:
+        torch._C._xpu_setDevice(device)
+
+
+def get_device_name(device: Optional[_device_t] = None) -> str:
+    r"""Get the name of a device.
+
+    Args:
+        device (torch.device or int or str, optional): device for which to
+            return the name. This function is a no-op if this argument is a
+            negative integer. It uses the current device, given by :func:`~torch.xpu.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    Returns:
+        str: the name of the device
+    """
+    return get_device_properties(device).name
+
+
+@lru_cache(None)
+def get_device_capability(device: Optional[_device_t] = None) -> Dict[str, Any]:
+    r"""Get the xpu capability of a device.
+
+    Args:
+        device (torch.device or int or str, optional): device for which to
+            return the device capability. This function is a no-op if this
+            argument is a negative integer. It uses the current device, given by
+            :func:`~torch.xpu.current_device`, if :attr:`device` is ``None``
+            (default).
+
+    Returns:
+        Dict[str, Any]: the xpu capability dictionary of the device
+    """
+    props = get_device_properties(device)
+    return {
+        prop: getattr(props, prop) for prop in dir(props) if not prop.startswith("__")
+    }
+
+
+def get_device_properties(device: Optional[_device_t] = None) -> _XpuDeviceProperties:
+    r"""Get the properties of a device.
+
+    Args:
+        device (torch.device or int or str): device for which to return the
+            properties of the device.
+
+    Returns:
+        _XpuDeviceProperties: the properties of the device
+    """
+    _lazy_init()
+    device = _get_device_index(device, optional=True)
+    if device < 0 or device >= device_count():
+        raise AssertionError("Invalid device index")
+    return _get_device_properties(device)  # type: ignore[name-defined]  # noqa: F821
+
+
+def current_device() -> int:
+    r"""Return the index of a currently selected device."""
+    _lazy_init()
+    return torch._C._xpu_getDevice()
+
+
+def _get_device(device: Union[int, str, torch.device]) -> torch.device:
+    r"""Return the torch.device type object from the passed in device.
+
+    Args:
+        device (torch.device or int or str): selected device.
+    """
+    if isinstance(device, str):
+        device = torch.device(device)
+    elif isinstance(device, int):
+        device = torch.device("xpu", device)
+    return device
+
+
+class StreamContext:
+    r"""Context-manager that selects a given stream.
+
+    All XPU kernels queued within its context will be enqueued on a selected
+    stream.
+
+    Args:
+        Stream (Stream): selected stream. This manager is a no-op if it's
+            ``None``.
+    .. note:: Streams are per-device.
+    """
+    cur_stream: Optional["torch.xpu.Stream"]
+
+    def __init__(self, stream: Optional["torch.xpu.Stream"]):
+        self.stream = stream
+        self.idx = _get_device_index(None, True)
+        if self.idx is None:
+            self.idx = -1
+
+    def __enter__(self):
+        cur_stream = self.stream
+        if cur_stream is None or self.idx == -1:
+            return
+        self.src_prev_stream = torch.xpu.current_stream(None)
+
+        # If the stream is not on the current device, then set the current stream on the device
+        if self.src_prev_stream.device != cur_stream.device:
+            with device(cur_stream.device):
+                self.dst_prev_stream = torch.xpu.current_stream(cur_stream.device)
+        torch.xpu.set_stream(cur_stream)
+
+    def __exit__(self, type: Any, value: Any, traceback: Any):
+        cur_stream = self.stream
+        if cur_stream is None or self.idx == -1:
+            return
+
+        # Reset the stream on the original device and destination device
+        if self.src_prev_stream.device != cur_stream.device:
+            torch.xpu.set_stream(self.dst_prev_stream)
+        torch.xpu.set_stream(self.src_prev_stream)
+
+
+def stream(stream: Optional["torch.xpu.Stream"]) -> StreamContext:
+    r"""Wrap around the Context-manager StreamContext that selects a given stream.
+
+    Arguments:
+        stream (Stream): selected stream. This manager is a no-op if it's ``None``.
+    """
+    return StreamContext(stream)
+
+
+def _set_stream_by_id(stream_id, device_index, device_type):
+    r"""set stream specified by the stream id, device index and device type
+
+    Args: stream_id (int): not visible to the user, used to assigned to the specific stream.
+          device_index (int): selected device index.
+          device_type (int): selected device type.
+    """
+    torch._C._xpu_setStream(
+        stream_id=stream_id,
+        device_index=device_index,
+        device_type=device_type,
+    )
+
+
+def set_stream(stream: Stream):
+    r"""Set the current stream.This is a wrapper API to set the stream.
+        Usage of this function is discouraged in favor of the ``stream``
+        context manager.
+
+    Args:
+        stream (Stream): selected stream. This function is a no-op
+            if this argument is ``None``.
+    """
+    if stream is None:
+        return
+    _lazy_init()
+    _set_stream_by_id(
+        stream_id=stream.stream_id,
+        device_index=stream.device_index,
+        device_type=stream.device_type,
+    )
+
+
+def current_stream(device: Optional[_device_t] = None) -> Stream:
+    r"""Return the currently selected :class:`Stream` for a given device.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            the currently selected :class:`Stream` for the current device, given
+            by :func:`~torch.xpu.current_device`, if :attr:`device` is ``None``
+            (default).
+    """
+    _lazy_init()
+    streamdata = torch._C._xpu_getCurrentStream(
+        _get_device_index(device, optional=True)
+    )
+    return Stream(
+        stream_id=streamdata[0], device_index=streamdata[1], device_type=streamdata[2]
+    )
+
+
+def synchronize(device: _device_t = None) -> None:
+    r"""Wait for all kernels in all streams on a XPU device to complete.
+
+    Args:
+        device (torch.device or int, optional): device for which to synchronize.
+            It uses the current device, given by :func:`~torch.xpu.current_device`,
+            if :attr:`device` is ``None`` (default).
+    """
+    _lazy_init()
+    device = _get_device_index(device, optional=True)
+    return torch._C._xpu_synchronize(device)
+
+
+def _get_generator(device: torch.device) -> torch._C.Generator:
+    r"""Return the XPU Generator object for the given device.
+
+    Args:
+        device (torch.device): selected device.
+    """
+    idx = device.index
+    if idx is None:
+        idx = current_device()
+    return torch.xpu.default_generators[idx]
+
+
+def _set_rng_state_offset(
+    offset: int, device: Union[int, str, torch.device] = "xpu"
+) -> None:
+    r"""Set the random number generator state offset of the specified GPU.
+
+    Args:
+        offset (int): The desired offset
+        device (torch.device or int, optional): The device to set the RNG state.
+            Default: ``'xpu'`` (i.e., ``torch.device('xpu')``, the current XPU device).
+    """
+    final_device = _get_device(device)
+
+    def cb():
+        default_generator = _get_generator(final_device)
+        default_generator.set_offset(offset)
+
+    _lazy_call(cb)
+
+
+def _get_rng_state_offset(device: Union[int, str, torch.device] = "xpu") -> int:
+    r"""Return the random number generator state offset of the specified GPU.
+
+    Args:
+        device (torch.device or int, optional): The device to return the RNG state offset of.
+            Default: ``'xpu'`` (i.e., ``torch.device('xpu')``, the current XPU device).
+
+    .. warning::
+        This function eagerly initializes XPU.
+    """
+    _lazy_init()
+    final_device = _get_device(device)
+    default_generator = _get_generator(final_device)
+    return default_generator.get_offset()
+
+
+# import here to avoid circular import
+from .memory import (
+    empty_cache,
+    max_memory_allocated,
+    max_memory_reserved,
+    memory_allocated,
+    memory_reserved,
+    memory_stats,
+    memory_stats_as_nested_dict,
+    reset_accumulated_memory_stats,
+    reset_peak_memory_stats,
+)
+from .random import (
+    get_rng_state,
+    get_rng_state_all,
+    initial_seed,
+    manual_seed,
+    manual_seed_all,
+    seed,
+    seed_all,
+    set_rng_state,
+    set_rng_state_all,
+)
+
+
+__all__ = [
+    "Event",
+    "Stream",
+    "StreamContext",
+    "current_device",
+    "current_stream",
+    "default_generators",
+    "device",
+    "device_of",
+    "device_count",
+    "empty_cache",
+    "get_device_capability",
+    "get_device_name",
+    "get_device_properties",
+    "get_rng_state",
+    "get_rng_state_all",
+    "get_stream",
+    "init",
+    "initial_seed",
+    "is_available",
+    "is_bf16_supported",
+    "is_initialized",
+    "manual_seed",
+    "manual_seed_all",
+    "max_memory_allocated",
+    "max_memory_reserved",
+    "memory_allocated",
+    "memory_reserved",
+    "memory_stats",
+    "memory_stats_as_nested_dict",
+    "reset_accumulated_memory_stats",
+    "reset_peak_memory_stats",
+    "seed",
+    "seed_all",
+    "set_device",
+    "set_rng_state",
+    "set_rng_state_all",
+    "set_stream",
+    "stream",
+    "streams",
+    "synchronize",
+]
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diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/xpu/_gpu_trace.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/xpu/_gpu_trace.py
new file mode 100644
index 0000000000000000000000000000000000000000..0407abbf24959346ed78572e4be183c877d8a855
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/xpu/_gpu_trace.py
@@ -0,0 +1,75 @@
+from typing import Callable
+
+from torch._utils import CallbackRegistry
+
+
+EventCreationCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "XPU event creation"
+)
+EventDeletionCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "XPU event deletion"
+)
+EventRecordCallbacks: "CallbackRegistry[int, int]" = CallbackRegistry(
+    "XPU event record"
+)
+EventWaitCallbacks: "CallbackRegistry[int, int]" = CallbackRegistry(
+    "XPU event wait"
+)
+MemoryAllocationCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "XPU memory allocation"
+)
+MemoryDeallocationCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "XPU memory deallocation"
+)
+StreamCreationCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "XPU stream creation"
+)
+DeviceSynchronizationCallbacks: "CallbackRegistry[[]]" = CallbackRegistry(
+    "XPU device synchronization"
+)
+StreamSynchronizationCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "XPU stream synchronization"
+)
+EventSynchronizationCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "XPU event synchronization"
+)
+
+
+def register_callback_for_event_creation(cb: Callable[[int], None]) -> None:
+    EventCreationCallbacks.add_callback(cb)
+
+
+def register_callback_for_event_deletion(cb: Callable[[int], None]) -> None:
+    EventDeletionCallbacks.add_callback(cb)
+
+
+def register_callback_for_event_record(cb: Callable[[int, int], None]) -> None:
+    EventRecordCallbacks.add_callback(cb)
+
+
+def register_callback_for_event_wait(cb: Callable[[int, int], None]) -> None:
+    EventWaitCallbacks.add_callback(cb)
+
+
+def register_callback_for_memory_allocation(cb: Callable[[int], None]) -> None:
+    MemoryAllocationCallbacks.add_callback(cb)
+
+
+def register_callback_for_memory_deallocation(cb: Callable[[int], None]) -> None:
+    MemoryDeallocationCallbacks.add_callback(cb)
+
+
+def register_callback_for_stream_creation(cb: Callable[[int], None]) -> None:
+    StreamCreationCallbacks.add_callback(cb)
+
+
+def register_callback_for_device_synchronization(cb: Callable[[], None]) -> None:
+    DeviceSynchronizationCallbacks.add_callback(cb)
+
+
+def register_callback_for_stream_synchronization(cb: Callable[[int], None]) -> None:
+    StreamSynchronizationCallbacks.add_callback(cb)
+
+
+def register_callback_for_event_synchronization(cb: Callable[[int], None]) -> None:
+    EventSynchronizationCallbacks.add_callback(cb)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/xpu/_utils.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/xpu/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f738267459a2791a4a33ca4bec74800a58f0b9a
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/xpu/_utils.py
@@ -0,0 +1,39 @@
+from typing import Any
+
+import torch
+
+# The _get_device_index has been moved to torch.utils._get_device_index
+from torch._utils import _get_device_index as _torch_get_device_index
+
+
+def _get_device_index(
+    device: Any, optional: bool = False, allow_cpu: bool = False
+) -> int:
+    r"""Get the device index from :attr:`device`, which can be a torch.device
+    object, a Python integer, or ``None``.
+
+    If :attr:`device` is a torch.device object, returns the device index if it
+    is a XPU device. Note that for a XPU device without a specified index,
+    i.e., ``torch.device('xpu')``, this will return the current default XPU
+    device if :attr:`optional` is ``True``. If :attr:`allow_cpu` is ``True``,
+    CPU devices will be accepted and ``-1`` will be returned in this case.
+
+    If :attr:`device` is a Python integer, it is returned as is.
+
+    If :attr:`device` is ``None``, this will return the current default XPU
+    device if :attr:`optional` is ``True``.
+    """
+    if isinstance(device, int):
+        return device
+    if isinstance(device, str):
+        device = torch.device(device)
+    if isinstance(device, torch.device):
+        if allow_cpu:
+            if device.type not in ["xpu", "cpu"]:
+                raise ValueError(f"Expected a xpu or cpu device, but got: {device}")
+        elif device.type != "xpu":
+            raise ValueError(f"Expected a xpu device, but got: {device}")
+    if not torch.jit.is_scripting():
+        if isinstance(device, torch.xpu.device):
+            return device.idx
+    return _torch_get_device_index(device, optional, allow_cpu)
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/xpu/memory.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/xpu/memory.py
new file mode 100644
index 0000000000000000000000000000000000000000..32e7f6c2ce315fde764fbce4f5727e3f7ed2e7cd
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/xpu/memory.py
@@ -0,0 +1,191 @@
+import collections
+from typing import Any, Dict, Union
+
+import torch
+from torch.types import Device
+
+from . import _get_device_index, is_initialized
+
+
+_device_t = Union[Device, str, int, None]
+
+
+def empty_cache() -> None:
+    r"""Release all unoccupied cached memory currently held by the caching
+    allocator so that those can be used in other XPU application.
+
+    .. note::
+        :func:`~torch.xpu.empty_cache` doesn't increase the amount of XPU
+        memory available for PyTorch. However, it may help reduce fragmentation
+        of XPU memory in certain cases.
+    """
+    if is_initialized():
+        torch._C._xpu_emptyCache()
+
+
+def reset_peak_memory_stats(device: _device_t = None) -> None:
+    r"""Reset the "peak" stats tracked by the XPU memory allocator.
+
+    See :func:`~torch.xpu.memory_stats` for details. Peak stats correspond to the
+    `"peak"` key in each individual stat dict.
+
+    Args:
+        device (torch.device or int or str, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.xpu.current_device`,
+            if :attr:`device` is ``None`` (default).
+    """
+    device = _get_device_index(device, optional=True)
+    return torch._C._xpu_resetPeakMemoryStats(device)
+
+
+def reset_accumulated_memory_stats(device: _device_t = None) -> None:
+    r"""Reset the "accumulated" (historical) stats tracked by the XPU memory allocator.
+
+    See :func:`~torch.xpu.memory_stats` for details. Accumulated stats correspond to
+    the `"allocated"` and `"freed"` keys in each individual stat dict.
+
+    Args:
+        device (torch.device or int or str, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.xpu.current_device`,
+            if :attr:`device` is ``None`` (default).
+    """
+    device = _get_device_index(device, optional=True)
+    return torch._C._xpu_resetAccumulatedMemoryStats(device)
+
+
+def memory_stats_as_nested_dict(device: _device_t = None) -> Dict[str, Any]:
+    r"""Return the result of :func:`~torch.xpu.memory_stats` as a nested dictionary."""
+    if not is_initialized():
+        return {}
+    device = _get_device_index(device, optional=True)
+    return torch._C._xpu_memoryStats(device)
+
+
+def memory_stats(device: _device_t = None) -> Dict[str, Any]:
+    r"""Return a dictionary of XPU memory allocator statistics for a given device.
+
+    The return value of this function is a dictionary of statistics, each of
+    which is a non-negative integer.
+
+    Core statistics:
+
+    - ``"allocated_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      amount of allocated memory.
+    - ``"reserved_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      amount of reserved memory.
+    - ``"active_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      amount of active memory.
+    - ``"requested_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      memory requested by client code, compare this with allocated_bytes to check if
+      allocation rounding adds too much overhead.
+
+    For these core statistics, values are broken down as follows.
+
+    Pool type:
+
+    - ``all``: combined statistics across all memory pools.
+    - ``large_pool``: statistics for the large allocation pool (for size >= 1MB allocations).
+    - ``small_pool``: statistics for the small allocation pool (for size < 1MB allocations).
+
+    Metric type:
+
+    - ``current``: current value of this metric.
+    - ``peak``: maximum value of this metric.
+    - ``allocated``: historical total increase in this metric.
+    - ``freed``: historical total decrease in this metric.
+
+    Args:
+        device (torch.device or int or str, optional): selected device. Returns
+            statistics for the current device, given by :func:`~torch.xpu.current_device`,
+            if :attr:`device` is ``None`` (default).
+    """
+    result = []
+
+    def _recurse_add_to_result(prefix: str, obj: Any) -> None:
+        if isinstance(obj, dict):
+            if len(prefix) > 0:
+                prefix += "."
+            for k, v in obj.items():
+                _recurse_add_to_result(prefix + k, v)
+        else:
+            result.append((prefix, obj))
+
+    stats = memory_stats_as_nested_dict(device=device)
+    _recurse_add_to_result("", stats)
+    result.sort()
+
+    return collections.OrderedDict(result)
+
+
+def memory_allocated(device: _device_t = None) -> int:
+    r"""Return the current GPU memory occupied by tensors in bytes for a given device.
+
+    Args:
+        device (torch.device or int or str, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.xpu.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    .. note::
+        This is likely less than the amount shown in `xpu-smi` since some
+        unused memory can be held by the caching allocator and some context
+        needs to be created on GPU.
+    """
+    return memory_stats(device=device).get("allocated_bytes.all.current", 0)
+
+
+def max_memory_allocated(device: _device_t = None) -> int:
+    r"""Return the maximum GPU memory occupied by tensors in bytes for a given device.
+
+    By default, this returns the peak allocated memory since the beginning of
+    this program. :func:`~torch.xpu.reset_peak_memory_stats` can be used to
+    reset the starting point in tracking this metric. For example, these two
+    functions can measure the peak allocated memory usage of each iteration in a
+    training loop.
+
+    Args:
+        device (torch.device or int or str, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.xpu.current_device`,
+            if :attr:`device` is ``None`` (default).
+    """
+    return memory_stats(device=device).get("allocated_bytes.all.peak", 0)
+
+
+def memory_reserved(device: _device_t = None) -> int:
+    r"""Return the current GPU memory managed by the caching allocator in bytes for a given device.
+
+    Args:
+        device (torch.device or int or str, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.xpu.current_device`,
+            if :attr:`device` is ``None`` (default).
+    """
+    return memory_stats(device=device).get("reserved_bytes.all.current", 0)
+
+
+def max_memory_reserved(device: _device_t = None) -> int:
+    r"""Return the maximum GPU memory managed by the caching allocator in bytes for a given device.
+
+    By default, this returns the peak cached memory since the beginning of this
+    program. :func:`~torch.xpu.reset_peak_memory_stats` can be used to reset
+    the starting point in tracking this metric. For example, these two functions
+    can measure the peak cached memory amount of each iteration in a training
+    loop.
+
+    Args:
+        device (torch.device or int or str, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.xpu.current_device`,
+            if :attr:`device` is ``None`` (default).
+    """
+    return memory_stats(device=device).get("reserved_bytes.all.peak", 0)
+
+
+__all__ = [
+    "empty_cache",
+    "max_memory_allocated",
+    "max_memory_reserved",
+    "memory_allocated",
+    "memory_reserved",
+    "memory_stats",
+    "memory_stats_as_nested_dict",
+    "reset_accumulated_memory_stats",
+    "reset_peak_memory_stats",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/xpu/random.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/xpu/random.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8631ddc1850c933908a5f626da90dedee03eb39
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/xpu/random.py
@@ -0,0 +1,178 @@
+# mypy: allow-untyped-defs
+from typing import Iterable, List, Union
+
+import torch
+from torch import Tensor
+
+from . import _lazy_call, _lazy_init, current_device, device_count
+
+
+def get_rng_state(device: Union[int, str, torch.device] = "xpu") -> Tensor:
+    r"""Return the random number generator state of the specified GPU as a ByteTensor.
+
+    Args:
+        device (torch.device or int, optional): The device to return the RNG state of.
+            Default: ``'xpu'`` (i.e., ``torch.device('xpu')``, the current XPU device).
+
+    .. warning::
+        This function eagerly initializes XPU.
+    """
+    _lazy_init()
+    if isinstance(device, str):
+        device = torch.device(device)
+    elif isinstance(device, int):
+        device = torch.device("xpu", device)
+    idx = device.index
+    if idx is None:
+        idx = current_device()
+    default_generator = torch.xpu.default_generators[idx]
+    return default_generator.get_state()
+
+
+def get_rng_state_all() -> List[Tensor]:
+    r"""Return a list of ByteTensor representing the random number states of all devices."""
+    results = []
+    for i in range(device_count()):
+        results.append(get_rng_state(i))
+    return results
+
+
+def set_rng_state(
+    new_state: Tensor, device: Union[int, str, torch.device] = "xpu"
+) -> None:
+    r"""Set the random number generator state of the specified GPU.
+
+    Args:
+        new_state (torch.ByteTensor): The desired state
+        device (torch.device or int, optional): The device to set the RNG state.
+            Default: ``'xpu'`` (i.e., ``torch.device('xpu')``, the current XPU device).
+    """
+    with torch._C._DisableFuncTorch():
+        new_state_copy = new_state.clone(memory_format=torch.contiguous_format)
+    if isinstance(device, str):
+        device = torch.device(device)
+    elif isinstance(device, int):
+        device = torch.device("xpu", device)
+
+    def cb():
+        idx = device.index
+        if idx is None:
+            idx = current_device()
+        default_generator = torch.xpu.default_generators[idx]
+        default_generator.set_state(new_state_copy)
+
+    _lazy_call(cb)
+
+
+def set_rng_state_all(new_states: Iterable[Tensor]) -> None:
+    r"""Set the random number generator state of all devices.
+
+    Args:
+        new_states (Iterable of torch.ByteTensor): The desired state for each device.
+    """
+    for i, state in enumerate(new_states):
+        set_rng_state(state, i)
+
+
+def manual_seed(seed: int) -> None:
+    r"""Set the seed for generating random numbers for the current GPU.
+
+    It's safe to call this function if XPU is not available; in that case, it is silently ignored.
+
+    Args:
+        seed (int): The desired seed.
+
+    .. warning::
+        If you are working with a multi-GPU model, this function is insufficient
+        to get determinism.  To seed all GPUs, use :func:`manual_seed_all`.
+    """
+    seed = int(seed)
+
+    def cb():
+        idx = current_device()
+        default_generator = torch.xpu.default_generators[idx]
+        default_generator.manual_seed(seed)
+
+    _lazy_call(cb, seed=True)
+
+
+def manual_seed_all(seed: int) -> None:
+    r"""Set the seed for generating random numbers on all GPUs.
+
+    It's safe to call this function if XPU is not available; in that case, it is silently ignored.
+
+    Args:
+        seed (int): The desired seed.
+    """
+    seed = int(seed)
+
+    def cb():
+        for i in range(device_count()):
+            default_generator = torch.xpu.default_generators[i]
+            default_generator.manual_seed(seed)
+
+    _lazy_call(cb, seed_all=True)
+
+
+def seed() -> None:
+    r"""Set the seed for generating random numbers to a random number for the current GPU.
+
+    It's safe to call this function if XPU is not available; in that case, it is silently ignored.
+
+    .. warning::
+        If you are working with a multi-GPU model, this function will only initialize
+        the seed on one GPU.  To initialize all GPUs, use :func:`seed_all`.
+    """
+
+    def cb():
+        idx = current_device()
+        default_generator = torch.xpu.default_generators[idx]
+        default_generator.seed()
+
+    _lazy_call(cb)
+
+
+def seed_all() -> None:
+    r"""Set the seed for generating random numbers to a random number on all GPUs.
+
+    It's safe to call this function if XPU is not available; in that case, it is silently ignored.
+    """
+
+    def cb():
+        random_seed = 0
+        seeded = False
+        for i in range(device_count()):
+            default_generator = torch.xpu.default_generators[i]
+            if not seeded:
+                default_generator.seed()
+                random_seed = default_generator.initial_seed()
+                seeded = True
+            else:
+                default_generator.manual_seed(random_seed)
+
+    _lazy_call(cb)
+
+
+def initial_seed() -> int:
+    r"""Return the current random seed of the current GPU.
+
+    .. warning::
+        This function eagerly initializes XPU.
+    """
+    _lazy_init()
+    idx = current_device()
+    default_generator = torch.xpu.default_generators[idx]
+    return default_generator.initial_seed()
+
+
+__all__ = [
+    "get_rng_state",
+    "get_rng_state_all",
+    "set_rng_state",
+    "set_rng_state_all",
+    "manual_seed",
+    "manual_seed_all",
+    "seed",
+    "seed_all",
+    "initial_seed",
+]
diff --git a/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/xpu/streams.py b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/xpu/streams.py
new file mode 100644
index 0000000000000000000000000000000000000000..19a7cda162f459db2239c2aae375b410b4934809
--- /dev/null
+++ b/data/conda_envs/evalclaw/lib/python3.11/site-packages/torch/xpu/streams.py
@@ -0,0 +1,171 @@
+# mypy: allow-untyped-defs
+import ctypes
+
+import torch
+from torch._streambase import _EventBase, _StreamBase
+
+from .._utils import _dummy_type
+
+
+if not hasattr(torch._C, "_XpuStreamBase"):
+    # Define dummy base classes
+    torch._C.__dict__["_XpuStreamBase"] = _dummy_type("_XpuStreamBase")
+    torch._C.__dict__["_XpuEventBase"] = _dummy_type("_XpuEventBase")
+
+
+class Stream(torch._C._XpuStreamBase, _StreamBase):
+    r"""Wrapper around a XPU stream.
+
+    A XPU stream is a linear sequence of execution that belongs to a specific
+    device, independent from other streams.
+
+    Args:
+        device(torch.device or int, optional): a device on which to allocate
+            the stream. If :attr:`device` is ``None`` (default) or a negative
+            integer, this will use the current device.
+        priority(int, optional): priority of the stream, should be 0 or
+            negative, where negative numbers indicate higher priority. By default,
+            streams have priority 0.
+    """
+
+    def __new__(cls, device=None, priority=0, **kwargs):
+        # setting device manager is expensive, so we avoid it unless necessary
+        if device is None or ("stream_id" in kwargs and "device_index" in kwargs):
+            return super().__new__(cls, priority=priority, **kwargs)
+        else:
+            with torch.xpu.device(device):
+                return super().__new__(cls, priority=priority, **kwargs)
+
+    def wait_event(self, event) -> None:
+        r"""Make all future work submitted to the stream wait for an event.
+
+        Args:
+            event (torch.xpu.Event): an event to wait for.
+        """
+        event.wait(self)
+
+    def wait_stream(self, stream) -> None:
+        r"""Synchronize with another stream.
+
+        All future work submitted to this stream will wait until all kernels
+        submitted to a given stream at the time of call complete.
+
+        Args:
+            stream (Stream): a stream to synchronize.
+        """
+        self.wait_event(stream.record_event())
+
+    def record_event(self, event=None):
+        r"""Record an event.
+
+        Args:
+            event (torch.xpu.Event, optional): event to record. If not given, a new one
+                will be allocated.
+
+        Returns:
+            Recorded event.
+        """
+        if event is None:
+            event = Event()
+        event.record(self)
+        return event
+
+    def query(self) -> bool:
+        r"""Check if all the work submitted has been completed.
+
+        Returns:
+            A boolean indicating if all kernels in this stream are completed.
+        """
+        return super().query()
+
+    def synchronize(self) -> None:
+        r"""Wait for all the kernels in this stream to complete."""
+        super().synchronize()
+
+    @property
+    def _as_parameter_(self):
+        return ctypes.c_void_p(self.sycl_queue)
+
+    def __eq__(self, o):
+        if isinstance(o, Stream):
+            return super().__eq__(o)
+        return False
+
+    def __hash__(self):
+        return hash((self.sycl_queue, self.device))
+
+    def __repr__(self):
+        return f"torch.xpu.Stream(device={self.device} sycl_queue={self.sycl_queue:#x})"
+
+
+class Event(torch._C._XpuEventBase, _EventBase):
+    r"""Wrapper around a XPU event.
+
+    XPU events are synchronization markers that can be used to monitor the
+    device's progress, and to synchronize XPU streams.
+
+    The underlying XPU events are lazily initialized when the event is first
+    recorded. After creation, only streams on the same device may record the
+    event. However, streams on any device can wait on the event.
+
+    Args:
+        enable_timing (bool, optional): indicates if the event should measure time
+            (default: ``False``)
+    """
+
+    def __new__(cls, enable_timing=False):
+        return super().__new__(cls, enable_timing=enable_timing)
+
+    def record(self, stream=None) -> None:
+        r"""Record the event in a given stream.
+
+        Uses ``torch.xpu.current_stream()`` if no stream is specified. The
+        stream's device must match the event's device.
+        """
+        if stream is None:
+            stream = torch.xpu.current_stream()
+        super().record(stream)
+
+    def wait(self, stream=None) -> None:
+        r"""Make all future work submitted to the given stream wait for this event.
+
+        Use ``torch.xpu.current_stream()`` if no stream is specified.
+        """
+        if stream is None:
+            stream = torch.xpu.current_stream()
+        super().wait(stream)
+
+    def query(self) -> bool:
+        r"""Check if all work currently captured by event has completed.
+
+        Returns:
+            A boolean indicating if all work currently captured by event has
+            completed.
+        """
+        return super().query()
+
+    def elapsed_time(self, end_event):
+        r"""Return the time elapsed.
+
+        Time reported in milliseconds after the event was recorded and
+        before the end_event was recorded.
+        """
+        return super().elapsed_time(end_event)
+
+    def synchronize(self) -> None:
+        r"""Wait for the event to complete.
+
+        Waits until the completion of all work currently captured in this event.
+        This prevents the CPU thread from proceeding until the event completes.
+        """
+        super().synchronize()
+
+    @property
+    def _as_parameter_(self):
+        return ctypes.c_void_p(self.sycl_event)
+
+    def __repr__(self):
+        if self.sycl_event:
+            return f"torch.xpu.Event(sycl_event={self.sycl_event:#x})"
+        else:
+            return "torch.xpu.Event(uninitialized)"